From c671daf20bfbe037385de10453019483ba7e125e Mon Sep 17 00:00:00 2001
From: Dong Jun <121388798+Chan-Dong-Jun@users.noreply.github.com>
Date: Tue, 9 Jul 2024 11:22:49 +0800
Subject: [PATCH 1/9] init

---
 mesa/datacollection_with_caching.py |  285 +++++
 mesa/example.ipynb                  | 1734 +++++++++++++++++++++++++++
 2 files changed, 2019 insertions(+)
 create mode 100644 mesa/datacollection_with_caching.py
 create mode 100644 mesa/example.ipynb

diff --git a/mesa/datacollection_with_caching.py b/mesa/datacollection_with_caching.py
new file mode 100644
index 00000000000..712482af8e2
--- /dev/null
+++ b/mesa/datacollection_with_caching.py
@@ -0,0 +1,285 @@
+"""
+Mesa Data Collection Module
+===========================
+
+DataCollector is meant to provide a simple, standard way to collect data
+generated by a Mesa model. It collects three types of data: model-level data,
+agent-level data, and tables.
+
+A DataCollector is instantiated with two dictionaries of reporter names and
+associated variable names or functions for each, one for model-level data and
+one for agent-level data; a third dictionary provides table names and columns.
+Variable names are converted into functions which retrieve attributes of that
+name.
+
+When the collect() method is called, each model-level function is called, with
+the model as the argument, and the results associated with the relevant
+variable. Then the agent-level functions are called on each agent.
+
+Additionally, other objects can write directly to tables by passing in an
+appropriate dictionary object for a table row.
+
+The DataCollector then stores the data it collects in dictionaries:
+    * model_vars maps each reporter to a list of its values
+    * tables maps each table to a dictionary, with each column as a key with a
+      list as its value.
+    * _agent_records maps each model step to a list of each agents id
+      and its values.
+
+Finally, DataCollector can create a pandas DataFrame from each collection.
+
+The default DataCollector here makes several assumptions:
+    * The model has an agent list called agents
+    * For collecting agent-level variables, agents must have a unique_id
+"""
+
+import contextlib
+import itertools
+import types
+from copy import deepcopy
+from functools import partial
+
+with contextlib.suppress(ImportError):
+    import pandas as pd
+
+
+class DataCollector:
+    """Class for collecting data generated by a Mesa model.
+
+    A DataCollector is instantiated with dictionaries of names of model- and
+    agent-level variables to collect, associated with attribute names or
+    functions which actually collect them. When the collect(...) method is
+    called, it collects these attributes and executes these functions one by
+    one and stores the results.
+    """
+
+    def __init__(
+        self,
+        model_reporters=None,
+        agent_reporters=None,
+        tables=None,
+    ):
+        """
+        Instantiate a DataCollector with lists of model and agent reporters.
+        Both model_reporters and agent_reporters accept a dictionary mapping a
+        variable name to either an attribute name, a function, a method of a class/instance,
+        or a function with parameters placed in a list.
+
+        Model reporters can take four types of arguments:
+        1. Lambda function:
+           {"agent_count": lambda m: len(m.agents)}
+        2. Method of a class/instance:
+           {"agent_count": self.get_agent_count} # self here is a class instance
+           {"agent_count": Model.get_agent_count} # Model here is a class
+        3. Class attributes of a model:
+           {"model_attribute": "model_attribute"}
+        4. Functions with parameters that have been placed in a list:
+           {"Model_Function": [function, [param_1, param_2]]}
+
+        Agent reporters can similarly take:
+        1. Attribute name (string) referring to agent's attribute:
+           {"energy": "energy"}
+        2. Lambda function:
+           {"energy": lambda a: a.energy}
+        3. Method of an agent class/instance:
+           {"agent_action": self.do_action} # self here is an agent class instance
+           {"agent_action": Agent.do_action} # Agent here is a class
+        4. Functions with parameters placed in a list:
+           {"Agent_Function": [function, [param_1, param_2]]}
+
+        The tables arg accepts a dictionary mapping names of tables to lists of
+        columns. For example, if we want to allow agents to write their age
+        when they are destroyed (to keep track of lifespans), it might look
+        like:
+           {"Lifespan": ["unique_id", "age"]}
+
+        Args:
+            model_reporters: Dictionary of reporter names and attributes/funcs/methods.
+            agent_reporters: Dictionary of reporter names and attributes/funcs/methods.
+            tables: Dictionary of table names to lists of column names.
+
+        Notes:
+            - If you want to pickle your model you must not use lambda functions.
+            - If your model includes a large number of agents, it is recommended to
+              use attribute names for the agent reporter, as it will be faster.
+        """
+        self.model_reporters = {}
+        self.agent_reporters = {}
+
+        self.model_vars = {}
+        self._agent_records = {}
+        self.tables = {}
+
+        if model_reporters is not None:
+            for name, reporter in model_reporters.items():
+                self._new_model_reporter(name, reporter)
+
+        if agent_reporters is not None:
+            for name, reporter in agent_reporters.items():
+                self._new_agent_reporter(name, reporter)
+
+        if tables is not None:
+            for name, columns in tables.items():
+                self._new_table(name, columns)
+
+    def _new_model_reporter(self, name, reporter):
+        """Add a new model-level reporter to collect.
+
+        Args:
+            name: Name of the model-level variable to collect.
+            reporter: Attribute string, or function object that returns the
+                      variable when given a model instance.
+        """
+        self.model_reporters[name] = reporter
+        self.model_vars[name] = []
+
+    def _new_agent_reporter(self, name, reporter):
+        """Add a new agent-level reporter to collect.
+
+        Args:
+            name: Name of the agent-level variable to collect.
+            reporter: Attribute string, function object, method of a class/instance, or
+                      function with parameters placed in a list that returns the
+                      variable when given an agent instance.
+        """
+        # Check if the reporter is an attribute string
+        if isinstance(reporter, str):
+            attribute_name = reporter
+
+            def attr_reporter(agent):
+                return getattr(agent, attribute_name, None)
+
+            reporter = attr_reporter
+
+        # Check if the reporter is a function with arguments placed in a list
+        elif isinstance(reporter, list):
+            func, params = reporter[0], reporter[1]
+
+            def func_with_params(agent):
+                return func(agent, *params)
+
+            reporter = func_with_params
+
+        # For other types (like lambda functions, method of a class/instance),
+        # it's already suitable to be used as a reporter directly.
+
+        self.agent_reporters[name] = reporter
+
+    def _new_table(self, table_name, table_columns):
+        """Add a new table that objects can write to.
+
+        Args:
+            table_name: Name of the new table.
+            table_columns: List of columns to add to the table.
+        """
+        new_table = {column: [] for column in table_columns}
+        self.tables[table_name] = new_table
+
+    def _record_agents(self, model):
+        """Record agents data in a mapping of functions and agents."""
+        rep_funcs = self.agent_reporters.values()
+
+        def get_reports(agent):
+            _prefix = (agent.model._steps, agent.unique_id)
+            reports = tuple(rep(agent) for rep in rep_funcs)
+            return _prefix + reports
+
+        agent_records = map(
+            get_reports,
+            model.schedule.agents
+            if hasattr(model, "schedule") and model.schedule is not None
+            else model.agents,
+        )
+        return agent_records
+
+    def collect(self, model):
+        """Collect all the data for the given model object."""
+        if self.model_reporters:
+            for var, reporter in self.model_reporters.items():
+                # Check if lambda or partial function
+                if isinstance(reporter, types.LambdaType | partial):
+                    # Use deepcopy to store a copy of the data,
+                    # preventing references from being updated across steps.
+                    self.model_vars[var].append(deepcopy(reporter(model)))
+                # Check if model attribute
+                elif isinstance(reporter, str):
+                    self.model_vars[var].append(
+                        deepcopy(getattr(model, reporter, None))
+                    )
+                # Check if function with arguments
+                elif isinstance(reporter, list):
+                    self.model_vars[var].append(deepcopy(reporter[0](*reporter[1])))
+                # Assume it's a callable otherwise (e.g., method)
+                # TODO: Check if method of a class explicitly
+                else:
+                    self.model_vars[var].append(deepcopy(reporter()))
+
+        if self.agent_reporters:
+            agent_records = self._record_agents(model)
+            self._agent_records[model._steps] = list(agent_records)
+
+    def add_table_row(self, table_name, row, ignore_missing=False):
+        """Add a row dictionary to a specific table.
+
+        Args:
+            table_name: Name of the table to append a row to.
+            row: A dictionary of the form {column_name: value...}
+            ignore_missing: If True, fill any missing columns with Nones;
+                            if False, throw an error if any columns are missing
+        """
+        if table_name not in self.tables:
+            raise Exception("Table does not exist.")
+
+        for column in self.tables[table_name]:
+            if column in row:
+                self.tables[table_name][column].append(row[column])
+            elif ignore_missing:
+                self.tables[table_name][column].append(None)
+            else:
+                raise Exception("Could not insert row with missing column")
+
+    def get_model_vars_dataframe(self):
+        """Create a pandas DataFrame from the model variables.
+
+        The DataFrame has one column for each model variable, and the index is
+        (implicitly) the model tick.
+        """
+        # Check if self.model_reporters dictionary is empty, if so raise warning
+        if not self.model_reporters:
+            raise UserWarning(
+                "No model reporters have been defined in the DataCollector, returning empty DataFrame."
+            )
+
+        return pd.DataFrame(self.model_vars)
+
+    def get_agent_vars_dataframe(self):
+        """Create a pandas DataFrame from the agent variables.
+
+        The DataFrame has one column for each variable, with two additional
+        columns for tick and agent_id.
+        """
+        # Check if self.agent_reporters dictionary is empty, if so raise warning
+        if not self.agent_reporters:
+            raise UserWarning(
+                "No agent reporters have been defined in the DataCollector, returning empty DataFrame."
+            )
+
+        all_records = itertools.chain.from_iterable(self._agent_records.values())
+        rep_names = list(self.agent_reporters)
+
+        df = pd.DataFrame.from_records(
+            data=all_records,
+            columns=["Step", "AgentID", *rep_names],
+            index=["Step", "AgentID"],
+        )
+        return df
+
+    def get_table_dataframe(self, table_name):
+        """Create a pandas DataFrame from a particular table.
+
+        Args:
+            table_name: The name of the table to convert.
+        """
+        if table_name not in self.tables:
+            raise Exception("No such table.")
+        return pd.DataFrame(self.tables[table_name])
diff --git a/mesa/example.ipynb b/mesa/example.ipynb
new file mode 100644
index 00000000000..7040d566e60
--- /dev/null
+++ b/mesa/example.ipynb
@@ -0,0 +1,1734 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "daa66e6c-9441-4029-86a3-d038b111d5b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/mnt/c/wsl/github/mesa-forked/mesa\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pwd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4694ef1e959aaa8c",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-07-05T09:10:20.918337Z",
+     "start_time": "2024-07-05T09:10:20.912659Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import datacollection_with_caching"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "57b8f8c18d56265c",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-07-05T09:11:18.795826Z",
+     "start_time": "2024-07-05T09:11:18.791176Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import mesa\n",
+    "\n",
+    "# Data visualization tools.\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Has multi-dimensional arrays and matrices. Has a large collection of\n",
+    "# mathematical functions to operate on these arrays.\n",
+    "import numpy as np\n",
+    "\n",
+    "# Data manipulation and analysis.\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e0f2c44e3fe344a3",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-07-05T09:10:24.818999Z",
+     "start_time": "2024-07-05T09:10:24.807828Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def compute_gini(model):\n",
+    "    agent_wealths = [agent.wealth for agent in model.schedule.agents]\n",
+    "    x = sorted(agent_wealths)\n",
+    "    N = model.num_agents\n",
+    "    B = sum(xi * (N - i) for i, xi in enumerate(x)) / (N * sum(x))\n",
+    "    return 1 + (1 / N) - 2 * B\n",
+    "\n",
+    "\n",
+    "class MoneyAgent(mesa.Agent):\n",
+    "    \"\"\"An agent with fixed initial wealth.\"\"\"\n",
+    "\n",
+    "    def __init__(self, unique_id, model):\n",
+    "        super().__init__(unique_id, model)\n",
+    "        self.wealth = 1\n",
+    "\n",
+    "    def move(self):\n",
+    "        possible_steps = self.model.grid.get_neighborhood(\n",
+    "            self.pos, moore=True, include_center=False\n",
+    "        )\n",
+    "        new_position = self.random.choice(possible_steps)\n",
+    "        self.model.grid.move_agent(self, new_position)\n",
+    "\n",
+    "    def give_money(self):\n",
+    "        cellmates = self.model.grid.get_cell_list_contents([self.pos])\n",
+    "        cellmates.pop(\n",
+    "            cellmates.index(self)\n",
+    "        )  # Ensure agent is not giving money to itself\n",
+    "        if len(cellmates) > 1:\n",
+    "            other = self.random.choice(cellmates)\n",
+    "            other.wealth += 1\n",
+    "            self.wealth -= 1\n",
+    "            if other == self:\n",
+    "                print(\"I JUST GAVE MONEY TO MYSELF HEHEHE!\")\n",
+    "\n",
+    "    def step(self):\n",
+    "        self.move()\n",
+    "        if self.wealth > 0:\n",
+    "            self.give_money()\n",
+    "\n",
+    "\n",
+    "class MoneyModel(mesa.Model):\n",
+    "    \"\"\"A model with some number of agents.\"\"\"\n",
+    "\n",
+    "    def __init__(self, N, width, height):\n",
+    "        super().__init__()\n",
+    "        self.num_agents = N\n",
+    "        self.grid = mesa.space.MultiGrid(width, height, True)\n",
+    "        self.schedule = mesa.time.RandomActivation(self)\n",
+    "\n",
+    "        # Create agents\n",
+    "        for i in range(self.num_agents):\n",
+    "            a = MoneyAgent(i, self)\n",
+    "            self.schedule.add(a)\n",
+    "            # Add the agent to a random grid cell\n",
+    "            x = self.random.randrange(self.grid.width)\n",
+    "            y = self.random.randrange(self.grid.height)\n",
+    "            self.grid.place_agent(a, (x, y))\n",
+    "\n",
+    "        self.datacollector = datacollection_with_caching.DataCollector(\n",
+    "            model_reporters={\"Gini\": compute_gini}, agent_reporters={\"Wealth\": \"wealth\"}\n",
+    "        )\n",
+    "\n",
+    "    def step(self):\n",
+    "        self.datacollector.collect(self)\n",
+    "        self.schedule.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "52a4ee6e154b2ea4",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-07-05T09:10:25.609759Z",
+     "start_time": "2024-07-05T09:10:24.975940Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model._steps=0\n",
+      "model._steps=1\n",
+      "model._steps=2\n",
+      "model._steps=3\n",
+      "model._steps=4\n",
+      "model._steps=5\n",
+      "model._steps=6\n",
+      "model._steps=7\n",
+      "model._steps=8\n",
+      "model._steps=9\n",
+      "model._steps=10\n",
+      "model._steps=11\n",
+      "model._steps=12\n",
+      "model._steps=13\n",
+      "model._steps=14\n",
+      "model._steps=15\n",
+      "model._steps=16\n",
+      "model._steps=17\n",
+      "model._steps=18\n",
+      "model._steps=19\n",
+      "model._steps=20\n",
+      "model._steps=21\n",
+      "model._steps=22\n",
+      "model._steps=23\n",
+      "model._steps=24\n",
+      "model._steps=25\n",
+      "model._steps=26\n",
+      "model._steps=27\n",
+      "model._steps=28\n",
+      "model._steps=29\n",
+      "model._steps=30\n",
+      "model._steps=31\n",
+      "model._steps=32\n",
+      "model._steps=33\n",
+      "model._steps=34\n",
+      "model._steps=35\n",
+      "model._steps=36\n",
+      "model._steps=37\n",
+      "model._steps=38\n",
+      "model._steps=39\n",
+      "model._steps=40\n",
+      "model._steps=41\n",
+      "model._steps=42\n",
+      "model._steps=43\n",
+      "model._steps=44\n",
+      "model._steps=45\n",
+      "model._steps=46\n",
+      "model._steps=47\n",
+      "model._steps=48\n",
+      "model._steps=49\n",
+      "model._steps=50\n",
+      "model._steps=51\n",
+      "model._steps=52\n",
+      "model._steps=53\n",
+      "model._steps=54\n",
+      "model._steps=55\n",
+      "model._steps=56\n",
+      "model._steps=57\n",
+      "model._steps=58\n",
+      "model._steps=59\n",
+      "model._steps=60\n",
+      "model._steps=61\n",
+      "model._steps=62\n",
+      "model._steps=63\n",
+      "model._steps=64\n",
+      "model._steps=65\n",
+      "model._steps=66\n",
+      "model._steps=67\n",
+      "model._steps=68\n",
+      "model._steps=69\n",
+      "model._steps=70\n",
+      "model._steps=71\n",
+      "model._steps=72\n",
+      "model._steps=73\n",
+      "model._steps=74\n",
+      "model._steps=75\n",
+      "model._steps=76\n",
+      "model._steps=77\n",
+      "model._steps=78\n",
+      "model._steps=79\n",
+      "model._steps=80\n",
+      "model._steps=81\n",
+      "model._steps=82\n",
+      "model._steps=83\n",
+      "model._steps=84\n",
+      "model._steps=85\n",
+      "model._steps=86\n",
+      "model._steps=87\n",
+      "model._steps=88\n",
+      "model._steps=89\n",
+      "model._steps=90\n",
+      "model._steps=91\n",
+      "model._steps=92\n",
+      "model._steps=93\n",
+      "model._steps=94\n",
+      "model._steps=95\n",
+      "model._steps=96\n",
+      "model._steps=97\n",
+      "model._steps=98\n",
+      "model._steps=99\n",
+      "model._steps=100\n",
+      "Saving model to output_dir/model_data_1.parquet\n",
+      "model_file='output_dir/model_data_1.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_1.parquet'\n",
+      "model._steps=101\n",
+      "model._steps=102\n",
+      "model._steps=103\n",
+      "model._steps=104\n",
+      "model._steps=105\n",
+      "model._steps=106\n",
+      "model._steps=107\n",
+      "model._steps=108\n",
+      "model._steps=109\n",
+      "model._steps=110\n",
+      "model._steps=111\n",
+      "model._steps=112\n",
+      "model._steps=113\n",
+      "model._steps=114\n",
+      "model._steps=115\n",
+      "model._steps=116\n",
+      "model._steps=117\n",
+      "model._steps=118\n",
+      "model._steps=119\n",
+      "model._steps=120\n",
+      "model._steps=121\n",
+      "model._steps=122\n",
+      "model._steps=123\n",
+      "model._steps=124\n",
+      "model._steps=125\n",
+      "model._steps=126\n",
+      "model._steps=127\n",
+      "model._steps=128\n",
+      "model._steps=129\n",
+      "model._steps=130\n",
+      "model._steps=131\n",
+      "model._steps=132\n",
+      "model._steps=133\n",
+      "model._steps=134\n",
+      "model._steps=135\n",
+      "model._steps=136\n",
+      "model._steps=137\n",
+      "model._steps=138\n",
+      "model._steps=139\n",
+      "model._steps=140\n",
+      "model._steps=141\n",
+      "model._steps=142\n",
+      "model._steps=143\n",
+      "model._steps=144\n",
+      "model._steps=145\n",
+      "model._steps=146\n",
+      "model._steps=147\n",
+      "model._steps=148\n",
+      "model._steps=149\n",
+      "model._steps=150\n",
+      "model._steps=151\n",
+      "model._steps=152\n",
+      "model._steps=153\n",
+      "model._steps=154\n",
+      "model._steps=155\n",
+      "model._steps=156\n",
+      "model._steps=157\n",
+      "model._steps=158\n",
+      "model._steps=159\n",
+      "model._steps=160\n",
+      "model._steps=161\n",
+      "model._steps=162\n",
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+      "model._steps=164\n",
+      "model._steps=165\n",
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+      "model._steps=167\n",
+      "model._steps=168\n",
+      "model._steps=169\n",
+      "model._steps=170\n",
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+      "model._steps=172\n",
+      "model._steps=173\n",
+      "model._steps=174\n",
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+      "model_file='output_dir/model_data_2.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_2.parquet'\n",
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+      "Saving model to output_dir/model_data_3.parquet\n",
+      "model_file='output_dir/model_data_3.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_3.parquet'\n",
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+      "model._steps=760\n",
+      "model._steps=761\n",
+      "model._steps=762\n",
+      "model._steps=763\n",
+      "model._steps=764\n",
+      "model._steps=765\n",
+      "model._steps=766\n",
+      "model._steps=767\n",
+      "model._steps=768\n",
+      "model._steps=769\n",
+      "model._steps=770\n",
+      "model._steps=771\n",
+      "model._steps=772\n",
+      "model._steps=773\n",
+      "model._steps=774\n",
+      "model._steps=775\n",
+      "model._steps=776\n",
+      "model._steps=777\n",
+      "model._steps=778\n",
+      "model._steps=779\n",
+      "model._steps=780\n",
+      "model._steps=781\n",
+      "model._steps=782\n",
+      "model._steps=783\n",
+      "model._steps=784\n",
+      "model._steps=785\n",
+      "model._steps=786\n",
+      "model._steps=787\n",
+      "model._steps=788\n",
+      "model._steps=789\n",
+      "model._steps=790\n",
+      "model._steps=791\n",
+      "model._steps=792\n",
+      "model._steps=793\n",
+      "model._steps=794\n",
+      "model._steps=795\n",
+      "model._steps=796\n",
+      "model._steps=797\n",
+      "model._steps=798\n",
+      "model._steps=799\n",
+      "model._steps=800\n",
+      "Saving model to output_dir/model_data_8.parquet\n",
+      "model_file='output_dir/model_data_8.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_8.parquet'\n",
+      "model._steps=801\n",
+      "model._steps=802\n",
+      "model._steps=803\n",
+      "model._steps=804\n",
+      "model._steps=805\n",
+      "model._steps=806\n",
+      "model._steps=807\n",
+      "model._steps=808\n",
+      "model._steps=809\n",
+      "model._steps=810\n",
+      "model._steps=811\n",
+      "model._steps=812\n",
+      "model._steps=813\n",
+      "model._steps=814\n",
+      "model._steps=815\n",
+      "model._steps=816\n",
+      "model._steps=817\n",
+      "model._steps=818\n",
+      "model._steps=819\n",
+      "model._steps=820\n",
+      "model._steps=821\n",
+      "model._steps=822\n",
+      "model._steps=823\n",
+      "model._steps=824\n",
+      "model._steps=825\n",
+      "model._steps=826\n",
+      "model._steps=827\n",
+      "model._steps=828\n",
+      "model._steps=829\n",
+      "model._steps=830\n",
+      "model._steps=831\n",
+      "model._steps=832\n",
+      "model._steps=833\n",
+      "model._steps=834\n",
+      "model._steps=835\n",
+      "model._steps=836\n",
+      "model._steps=837\n",
+      "model._steps=838\n",
+      "model._steps=839\n",
+      "model._steps=840\n",
+      "model._steps=841\n",
+      "model._steps=842\n",
+      "model._steps=843\n",
+      "model._steps=844\n",
+      "model._steps=845\n",
+      "model._steps=846\n",
+      "model._steps=847\n",
+      "model._steps=848\n",
+      "model._steps=849\n",
+      "model._steps=850\n",
+      "model._steps=851\n",
+      "model._steps=852\n",
+      "model._steps=853\n",
+      "model._steps=854\n",
+      "model._steps=855\n",
+      "model._steps=856\n",
+      "model._steps=857\n",
+      "model._steps=858\n",
+      "model._steps=859\n",
+      "model._steps=860\n",
+      "model._steps=861\n",
+      "model._steps=862\n",
+      "model._steps=863\n",
+      "model._steps=864\n",
+      "model._steps=865\n",
+      "model._steps=866\n",
+      "model._steps=867\n",
+      "model._steps=868\n",
+      "model._steps=869\n",
+      "model._steps=870\n",
+      "model._steps=871\n",
+      "model._steps=872\n",
+      "model._steps=873\n",
+      "model._steps=874\n",
+      "model._steps=875\n",
+      "model._steps=876\n",
+      "model._steps=877\n",
+      "model._steps=878\n",
+      "model._steps=879\n",
+      "model._steps=880\n",
+      "model._steps=881\n",
+      "model._steps=882\n",
+      "model._steps=883\n",
+      "model._steps=884\n",
+      "model._steps=885\n",
+      "model._steps=886\n",
+      "model._steps=887\n",
+      "model._steps=888\n",
+      "model._steps=889\n",
+      "model._steps=890\n",
+      "model._steps=891\n",
+      "model._steps=892\n",
+      "model._steps=893\n",
+      "model._steps=894\n",
+      "model._steps=895\n",
+      "model._steps=896\n",
+      "model._steps=897\n",
+      "model._steps=898\n",
+      "model._steps=899\n",
+      "model._steps=900\n",
+      "Saving model to output_dir/model_data_9.parquet\n",
+      "model_file='output_dir/model_data_9.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_9.parquet'\n",
+      "model._steps=901\n",
+      "model._steps=902\n",
+      "model._steps=903\n",
+      "model._steps=904\n",
+      "model._steps=905\n",
+      "model._steps=906\n",
+      "model._steps=907\n",
+      "model._steps=908\n",
+      "model._steps=909\n",
+      "model._steps=910\n",
+      "model._steps=911\n",
+      "model._steps=912\n",
+      "model._steps=913\n",
+      "model._steps=914\n",
+      "model._steps=915\n",
+      "model._steps=916\n",
+      "model._steps=917\n",
+      "model._steps=918\n",
+      "model._steps=919\n",
+      "model._steps=920\n",
+      "model._steps=921\n",
+      "model._steps=922\n",
+      "model._steps=923\n",
+      "model._steps=924\n",
+      "model._steps=925\n",
+      "model._steps=926\n",
+      "model._steps=927\n",
+      "model._steps=928\n",
+      "model._steps=929\n",
+      "model._steps=930\n",
+      "model._steps=931\n",
+      "model._steps=932\n",
+      "model._steps=933\n",
+      "model._steps=934\n",
+      "model._steps=935\n",
+      "model._steps=936\n",
+      "model._steps=937\n",
+      "model._steps=938\n",
+      "model._steps=939\n",
+      "model._steps=940\n",
+      "model._steps=941\n",
+      "model._steps=942\n",
+      "model._steps=943\n",
+      "model._steps=944\n",
+      "model._steps=945\n",
+      "model._steps=946\n",
+      "model._steps=947\n",
+      "model._steps=948\n",
+      "model._steps=949\n",
+      "model._steps=950\n",
+      "model._steps=951\n",
+      "model._steps=952\n",
+      "model._steps=953\n",
+      "model._steps=954\n",
+      "model._steps=955\n",
+      "model._steps=956\n",
+      "model._steps=957\n",
+      "model._steps=958\n",
+      "model._steps=959\n",
+      "model._steps=960\n",
+      "model._steps=961\n",
+      "model._steps=962\n",
+      "model._steps=963\n",
+      "model._steps=964\n",
+      "model._steps=965\n",
+      "model._steps=966\n",
+      "model._steps=967\n",
+      "model._steps=968\n",
+      "model._steps=969\n",
+      "model._steps=970\n",
+      "model._steps=971\n",
+      "model._steps=972\n",
+      "model._steps=973\n",
+      "model._steps=974\n",
+      "model._steps=975\n",
+      "model._steps=976\n",
+      "model._steps=977\n",
+      "model._steps=978\n",
+      "model._steps=979\n",
+      "model._steps=980\n",
+      "model._steps=981\n",
+      "model._steps=982\n",
+      "model._steps=983\n",
+      "model._steps=984\n",
+      "model._steps=985\n",
+      "model._steps=986\n",
+      "model._steps=987\n",
+      "model._steps=988\n",
+      "model._steps=989\n",
+      "model._steps=990\n",
+      "model._steps=991\n",
+      "model._steps=992\n",
+      "model._steps=993\n",
+      "model._steps=994\n",
+      "model._steps=995\n",
+      "model._steps=996\n",
+      "model._steps=997\n",
+      "model._steps=998\n",
+      "model._steps=999\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = MoneyModel(100, 10, 10)\n",
+    "for i in range(1000):\n",
+    "    model.step()\n",
+    "model.datacollector.cache_remaining_data(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "25bf27a3-2605-4b0d-be6d-68ff33bdb1fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__class__',\n",
+       " '__delattr__',\n",
+       " '__dict__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
+       " '__module__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " '__weakref__',\n",
+       " '_agent_records',\n",
+       " '_new_agent_reporter',\n",
+       " '_new_model_reporter',\n",
+       " '_new_table',\n",
+       " '_record_agents',\n",
+       " '_save_to_parquet',\n",
+       " 'add_table_row',\n",
+       " 'agent_reporters',\n",
+       " 'cache_interval',\n",
+       " 'cache_remaining_data',\n",
+       " 'collect',\n",
+       " 'get_agent_vars_dataframe',\n",
+       " 'get_model_vars_cache_dataframe',\n",
+       " 'get_model_vars_dataframe',\n",
+       " 'get_table_dataframe',\n",
+       " 'model_reporters',\n",
+       " 'model_vars',\n",
+       " 'model_vars_cache',\n",
+       " 'output_dir',\n",
+       " 'tables']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(model.datacollector)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "40a5975d590ee1fc",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-07-05T09:10:58.193832Z",
+     "start_time": "2024-07-05T09:10:58.024329Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gini = model.datacollector.get_model_vars_dataframe()\n",
+    "# Plot the Gini coefficient over time\n",
+    "g = sns.lineplot(data=gini)\n",
+    "g.set(title=\"Gini Coefficient over Time\", ylabel=\"Gini Coefficient\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "80f1723e2db59897",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-07-05T09:10:27.300815Z",
+     "start_time": "2024-07-05T09:10:27.230279Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Wealth</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Step</th>\n",
+       "      <th>AgentID</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">901</th>\n",
+       "      <th>62</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Wealth\n",
+       "Step AgentID        \n",
+       "901  62            6\n",
+       "     81            0\n",
+       "     3             0\n",
+       "     73            0\n",
+       "     47            2"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_wealth = model.datacollector.get_agent_vars_dataframe()\n",
+    "agent_wealth.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4adad84db4bc82",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       Gini\n",
+      "0    0.0000\n",
+      "1    0.3238\n",
+      "2    0.4170\n",
+      "3    0.4370\n",
+      "4    0.5410\n",
+      "..      ...\n",
+      "96   0.6002\n",
+      "97   0.5882\n",
+      "98   0.5748\n",
+      "99   0.5758\n",
+      "100  0.6174\n",
+      "\n",
+      "[101 rows x 1 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pyarrow.parquet as pq\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Read a single Parquet file\n",
+    "table = pq.read_table('output_dir/model_data_1.parquet')\n",
+    "\n",
+    "# Convert to a pandas DataFrame\n",
+    "df = table.to_pandas()\n",
+    "\n",
+    "# Display the DataFrame\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ee5ec296-f096-445d-90a8-af9a85d42ef6",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       Gini\n",
+      "0    0.0000\n",
+      "1    0.3238\n",
+      "2    0.4170\n",
+      "3    0.4370\n",
+      "4    0.5410\n",
+      "..      ...\n",
+      "896  0.7260\n",
+      "897  0.7300\n",
+      "898  0.7176\n",
+      "899  0.7136\n",
+      "900  0.7324\n",
+      "\n",
+      "[901 rows x 1 columns]\n",
+      "       Wealth\n",
+      "0           1\n",
+      "1           1\n",
+      "2           1\n",
+      "3           1\n",
+      "4           1\n",
+      "...       ...\n",
+      "90095       3\n",
+      "90096       0\n",
+      "90097       0\n",
+      "90098       0\n",
+      "90099       4\n",
+      "\n",
+      "[90100 rows x 1 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pyarrow.parquet as pq\n",
+    "import pandas as pd\n",
+    "import glob\n",
+    "\n",
+    "# Get a list of all Parquet files\n",
+    "model_files = glob.glob('output_dir/model_data_*.parquet')\n",
+    "agent_files = glob.glob('output_dir/agent_data_*.parquet')\n",
+    "\n",
+    "# Initialize lists to hold dataframes\n",
+    "model_dfs = []\n",
+    "agent_dfs = []\n",
+    "\n",
+    "# Read and append each file to the list\n",
+    "for model_file in model_files:\n",
+    "    table = pq.read_table(model_file)\n",
+    "    df = table.to_pandas()\n",
+    "    model_dfs.append(df)\n",
+    "\n",
+    "for agent_file in agent_files:\n",
+    "    table = pq.read_table(agent_file)\n",
+    "    df = table.to_pandas()\n",
+    "    agent_dfs.append(df)\n",
+    "\n",
+    "# Concatenate all DataFrames\n",
+    "model_df = pd.concat(model_dfs, ignore_index=True)\n",
+    "agent_df = pd.concat(agent_dfs, ignore_index=True)\n",
+    "\n",
+    "# Display the combined DataFrames\n",
+    "print(model_df)\n",
+    "print(agent_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2ecca3e2-18c9-4dd7-b5da-98a9841a3714",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = sns.lineplot(data=model_df)\n",
+    "g.set(title=\"Gini Coefficient over Time\", ylabel=\"Gini Coefficient\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6e53d7b9-9220-4d9f-afa0-f74a7e2a53e6",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Gini\n",
+      "0  0.0000\n",
+      "1  0.3238\n",
+      "2  0.4170\n",
+      "3  0.4370\n",
+      "4  0.5410\n",
+      "     Gini\n",
+      "0  0.6254\n",
+      "1  0.6132\n",
+      "2  0.5976\n",
+      "3  0.6208\n",
+      "4  0.6502\n",
+      "     Gini\n",
+      "0  0.7184\n",
+      "1  0.7054\n",
+      "2  0.7100\n",
+      "3  0.6866\n",
+      "4  0.7066\n",
+      "     Gini\n",
+      "0  0.6212\n",
+      "1  0.6128\n",
+      "2  0.6254\n",
+      "3  0.6222\n",
+      "4  0.6308\n",
+      "     Gini\n",
+      "0  0.6406\n",
+      "1  0.6270\n",
+      "2  0.6264\n",
+      "3  0.6092\n",
+      "4  0.5822\n",
+      "     Gini\n",
+      "0  0.6996\n",
+      "1  0.6792\n",
+      "2  0.6850\n",
+      "3  0.6680\n",
+      "4  0.6586\n",
+      "     Gini\n",
+      "0  0.6458\n",
+      "1  0.6474\n",
+      "2  0.6430\n",
+      "3  0.6266\n",
+      "4  0.6288\n",
+      "     Gini\n",
+      "0  0.7006\n",
+      "1  0.7406\n",
+      "2  0.7164\n",
+      "3  0.6830\n",
+      "4  0.6928\n",
+      "     Gini\n",
+      "0  0.6780\n",
+      "1  0.6488\n",
+      "2  0.6318\n",
+      "3  0.6368\n",
+      "4  0.6390\n",
+      "              Wealth\n",
+      "Step AgentID        \n",
+      "0    0             1\n",
+      "     1             1\n",
+      "     2             1\n",
+      "     3             1\n",
+      "     4             1\n",
+      "              Wealth\n",
+      "Step AgentID        \n",
+      "101  82            1\n",
+      "     70            0\n",
+      "     30            1\n",
+      "     50            0\n",
+      "     76            3\n",
+      "              Wealth\n",
+      "Step AgentID        \n",
+      "201  64            1\n",
+      "     72            1\n",
+      "     75            1\n",
+      "     53            0\n",
+      "     67            4\n",
+      "              Wealth\n",
+      "Step AgentID        \n",
+      "301  28            4\n",
+      "     33            0\n",
+      "     62            0\n",
+      "     83            0\n",
+      "     31            2\n",
+      "              Wealth\n",
+      "Step AgentID        \n",
+      "401  93            2\n",
+      "     96            0\n",
+      "     55            3\n",
+      "     14            1\n",
+      "     51            2\n",
+      "              Wealth\n",
+      "Step AgentID        \n",
+      "501  1             0\n",
+      "     62            0\n",
+      "     34            1\n",
+      "     30            0\n",
+      "     37            0\n",
+      "              Wealth\n",
+      "Step AgentID        \n",
+      "601  59            4\n",
+      "     5             0\n",
+      "     22            2\n",
+      "     94            1\n",
+      "     19            0\n",
+      "              Wealth\n",
+      "Step AgentID        \n",
+      "701  24            1\n",
+      "     88            0\n",
+      "     75            0\n",
+      "     55            0\n",
+      "     77            0\n",
+      "              Wealth\n",
+      "Step AgentID        \n",
+      "801  47            0\n",
+      "     90            0\n",
+      "     9             0\n",
+      "     30            3\n",
+      "     0             1\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pyarrow.parquet as pq\n",
+    "\n",
+    "def process_parquet_file(file_path):\n",
+    "    # Read a single Parquet file\n",
+    "    table = pq.read_table(file_path)\n",
+    "    \n",
+    "    # Convert to a pandas DataFrame\n",
+    "    df = table.to_pandas()\n",
+    "    \n",
+    "    # Process the DataFrame (e.g., analyze, transform, etc.)\n",
+    "    # For example, just print the first few rows\n",
+    "    print(df.head())\n",
+    "    \n",
+    "    # Optionally, return or save the processed DataFrame\n",
+    "    return df\n",
+    "\n",
+    "# Get a list of all Parquet files\n",
+    "model_files = glob.glob('output_dir/model_data_*.parquet')\n",
+    "agent_files = glob.glob('output_dir/agent_data_*.parquet')\n",
+    "\n",
+    "# Process each file incrementally\n",
+    "for model_file in model_files:\n",
+    "    process_parquet_file(model_file)\n",
+    "\n",
+    "for agent_file in agent_files:\n",
+    "    process_parquet_file(agent_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e734a650-4c8e-484e-b8f7-b687e2ab988a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['output_dir/model_data_1.parquet',\n",
+       " 'output_dir/model_data_2.parquet',\n",
+       " 'output_dir/model_data_3.parquet',\n",
+       " 'output_dir/model_data_4.parquet',\n",
+       " 'output_dir/model_data_5.parquet',\n",
+       " 'output_dir/model_data_6.parquet',\n",
+       " 'output_dir/model_data_7.parquet',\n",
+       " 'output_dir/model_data_8.parquet',\n",
+       " 'output_dir/model_data_9.parquet']"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34615c6d-3312-4be3-a9e4-ad8bd02d6fa4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From e1b943b67d52f13309645c04be2d6815c43055ec Mon Sep 17 00:00:00 2001
From: Dong Jun <121388798+Chan-Dong-Jun@users.noreply.github.com>
Date: Tue, 9 Jul 2024 11:31:51 +0800
Subject: [PATCH 2/9] add cached-dir to gitignore

---
 .gitignore | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.gitignore b/.gitignore
index 78b8b417a8f..5e7523ce2f3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -88,3 +88,6 @@ dmypy.json
 # JS dependencies
 mesa/visualization/templates/external/
 mesa/visualization/templates/js/external/
+
+# output results default dir
+mesa/output_dir/
\ No newline at end of file

From 67e8bf0c419d6a00d4d2c54e0bc76a689615ecd8 Mon Sep 17 00:00:00 2001
From: Chan-Dong-Jun <dongjuncoding@gmail.com>
Date: Tue, 9 Jul 2024 11:37:00 +0800
Subject: [PATCH 3/9] add caching skeleton code

---
 mesa/datacollection_with_caching.py | 76 +++++++++++++++++++++++++++++
 1 file changed, 76 insertions(+)

diff --git a/mesa/datacollection_with_caching.py b/mesa/datacollection_with_caching.py
index 712482af8e2..3e093f18da4 100644
--- a/mesa/datacollection_with_caching.py
+++ b/mesa/datacollection_with_caching.py
@@ -33,11 +33,15 @@
     * For collecting agent-level variables, agents must have a unique_id
 """
 
+import os
 import contextlib
 import itertools
 import types
 from copy import deepcopy
 from functools import partial
+import pyarrow as pa
+import pyarrow.parquet as pq
+
 
 with contextlib.suppress(ImportError):
     import pandas as pd
@@ -58,6 +62,7 @@ def __init__(
         model_reporters=None,
         agent_reporters=None,
         tables=None,
+        output_dir='output_dir',
     ):
         """
         Instantiate a DataCollector with lists of model and agent reporters.
@@ -107,9 +112,19 @@ def __init__(
         self.agent_reporters = {}
 
         self.model_vars = {}
+        self.model_vars_cache = {}
         self._agent_records = {}
         self.tables = {}
 
+        self.cache_interval = 100
+        self.output_dir = output_dir
+
+        if not output_dir:
+            raise ValueError('output_dir cannot be None')
+        if not os.path.exists(output_dir):
+            print("Creating output directory {}".format(output_dir))
+            os.makedirs(output_dir)
+
         if model_reporters is not None:
             for name, reporter in model_reporters.items():
                 self._new_model_reporter(name, reporter)
@@ -132,6 +147,7 @@ def _new_model_reporter(self, name, reporter):
         """
         self.model_reporters[name] = reporter
         self.model_vars[name] = []
+        self.model_vars_cache[name] = []
 
     def _new_agent_reporter(self, name, reporter):
         """Add a new agent-level reporter to collect.
@@ -201,23 +217,33 @@ def collect(self, model):
                     # Use deepcopy to store a copy of the data,
                     # preventing references from being updated across steps.
                     self.model_vars[var].append(deepcopy(reporter(model)))
+                    self.model_vars_cache[var].append(deepcopy(reporter(model)))
                 # Check if model attribute
                 elif isinstance(reporter, str):
                     self.model_vars[var].append(
                         deepcopy(getattr(model, reporter, None))
                     )
+                    self.model_vars_cache[var].append(
+                        deepcopy(getattr(model, reporter, None))
+                    )
                 # Check if function with arguments
                 elif isinstance(reporter, list):
                     self.model_vars[var].append(deepcopy(reporter[0](*reporter[1])))
+                    self.model_vars_cache[var].append(deepcopy(reporter[0](*reporter[1])))
                 # Assume it's a callable otherwise (e.g., method)
                 # TODO: Check if method of a class explicitly
                 else:
                     self.model_vars[var].append(deepcopy(reporter()))
+                    self.model_vars_cache[var].append(deepcopy(reporter()))
 
         if self.agent_reporters:
             agent_records = self._record_agents(model)
             self._agent_records[model._steps] = list(agent_records)
 
+        print(f"{model._steps=}")
+        if model._steps % self.cache_interval == 0 and model._steps != 0:
+            self._save_to_parquet(model)
+
     def add_table_row(self, table_name, row, ignore_missing=False):
         """Add a row dictionary to a specific table.
 
@@ -252,6 +278,20 @@ def get_model_vars_dataframe(self):
 
         return pd.DataFrame(self.model_vars)
 
+    def get_model_vars_cache_dataframe(self):
+        """Create a pandas DataFrame from the model variables.
+
+        The DataFrame has one column for each model variable, and the index is
+        (implicitly) the model tick.
+        """
+        # Check if self.model_reporters dictionary is empty, if so raise warning
+        if not self.model_reporters:
+            raise UserWarning(
+                "No model reporters have been defined in the DataCollector, returning empty DataFrame."
+            )
+
+        return pd.DataFrame(self.model_vars_cache)
+
     def get_agent_vars_dataframe(self):
         """Create a pandas DataFrame from the agent variables.
 
@@ -283,3 +323,39 @@ def get_table_dataframe(self, table_name):
         if table_name not in self.tables:
             raise Exception("No such table.")
         return pd.DataFrame(self.tables[table_name])
+
+    def _save_to_parquet(self, model):
+        """Save the current cache of data to a Parquet file and clear the cache."""
+        model_df = self.get_model_vars_cache_dataframe()
+        agent_df = self.get_agent_vars_dataframe()
+
+        model_file = f"{self.output_dir}/model_data_{model._steps // self.cache_interval}.parquet"
+        agent_file = f"{self.output_dir}/agent_data_{model._steps // self.cache_interval}.parquet"
+        print(f"Saving model to {model_file}")
+        print(f"{model_file=}")
+        absolute_path = os.path.abspath(model_file)
+        print(f"{absolute_path=}")
+        if os.path.exists(absolute_path):
+            raise FileExistsError(f"A directory with the name {model_file} already exists.")
+        if os.path.exists(model_file):
+            raise FileExistsError(f"A directory with the name {model_file} already exists.")
+        if os.path.exists(agent_file):
+            raise FileExistsError(f"A directory with the name {agent_file} already exists.")
+
+        if not model_df.empty:
+            model_table = pa.Table.from_pandas(model_df)
+            pq.write_table(model_table, model_file)
+
+        if not agent_df.empty:
+            agent_table = pa.Table.from_pandas(agent_df)
+            pq.write_table(agent_table, agent_file)
+
+        # Clear the cache
+        self.model_vars_cache = {var: [] for var in self.model_vars_cache}
+        self._agent_records = {}
+
+    def cache_remaining_data(self, model):
+        """Cache the remaining data in model_vars_cache and _agent_records."""
+        if not self.model_vars_cache:
+            print("No model vars cached yet.")
+        self._save_to_parquet(model)
\ No newline at end of file

From 20db0168cdac6af3e9c8bb8a7d91c53353883f29 Mon Sep 17 00:00:00 2001
From: Chan-Dong-Jun <dongjuncoding@gmail.com>
Date: Fri, 12 Jul 2024 16:57:10 +0800
Subject: [PATCH 4/9] -

---
 mesa/example.ipynb | 280 ++++++++++++++++++++++-----------------------
 1 file changed, 140 insertions(+), 140 deletions(-)

diff --git a/mesa/example.ipynb b/mesa/example.ipynb
index 7040d566e60..06a09cfb2c7 100644
--- a/mesa/example.ipynb
+++ b/mesa/example.ipynb
@@ -1179,7 +1179,10 @@
       "model._steps=996\n",
       "model._steps=997\n",
       "model._steps=998\n",
-      "model._steps=999\n"
+      "model._steps=999\n",
+      "Saving model to output_dir/model_data_10.parquet\n",
+      "model_file='output_dir/model_data_10.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_10.parquet'\n"
      ]
     }
    ],
@@ -1269,7 +1272,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1317,49 +1320,32 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>Wealth</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>Step</th>\n",
        "      <th>AgentID</th>\n",
-       "      <th></th>\n",
+       "      <th>Wealth</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">901</th>\n",
-       "      <th>62</th>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>81</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>73</th>\n",
-       "      <td>0</td>\n",
+       "      <th>Step</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>2</td>\n",
+       "      <th>AgentID</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "              Wealth\n",
-       "Step AgentID        \n",
-       "901  62            6\n",
-       "     81            0\n",
-       "     3             0\n",
-       "     73            0\n",
-       "     47            2"
+       "        Step AgentID Wealth\n",
+       "Step     NaN     NaN    NaN\n",
+       "AgentID  NaN     NaN    NaN"
       ]
      },
      "execution_count": 8,
@@ -1384,16 +1370,16 @@
      "text": [
       "       Gini\n",
       "0    0.0000\n",
-      "1    0.3238\n",
-      "2    0.4170\n",
-      "3    0.4370\n",
-      "4    0.5410\n",
+      "1    0.3146\n",
+      "2    0.3576\n",
+      "3    0.4404\n",
+      "4    0.4484\n",
       "..      ...\n",
-      "96   0.6002\n",
-      "97   0.5882\n",
-      "98   0.5748\n",
-      "99   0.5758\n",
-      "100  0.6174\n",
+      "96   0.6668\n",
+      "97   0.6820\n",
+      "98   0.6826\n",
+      "99   0.6416\n",
+      "100  0.6364\n",
       "\n",
       "[101 rows x 1 columns]\n"
      ]
@@ -1427,18 +1413,18 @@
      "text": [
       "       Gini\n",
       "0    0.0000\n",
-      "1    0.3238\n",
-      "2    0.4170\n",
-      "3    0.4370\n",
-      "4    0.5410\n",
+      "1    0.3146\n",
+      "2    0.3576\n",
+      "3    0.4404\n",
+      "4    0.4484\n",
       "..      ...\n",
-      "896  0.7260\n",
-      "897  0.7300\n",
-      "898  0.7176\n",
-      "899  0.7136\n",
-      "900  0.7324\n",
+      "995  0.6322\n",
+      "996  0.6274\n",
+      "997  0.6276\n",
+      "998  0.6088\n",
+      "999  0.6256\n",
       "\n",
-      "[901 rows x 1 columns]\n",
+      "[1000 rows x 1 columns]\n",
       "       Wealth\n",
       "0           1\n",
       "1           1\n",
@@ -1446,13 +1432,13 @@
       "3           1\n",
       "4           1\n",
       "...       ...\n",
-      "90095       3\n",
-      "90096       0\n",
-      "90097       0\n",
-      "90098       0\n",
-      "90099       4\n",
+      "99995       1\n",
+      "99996       0\n",
+      "99997       0\n",
+      "99998       2\n",
+      "99999       0\n",
       "\n",
-      "[90100 rows x 1 columns]\n"
+      "[100000 rows x 1 columns]\n"
      ]
     }
    ],
@@ -1497,7 +1483,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1525,58 +1511,64 @@
      "text": [
       "     Gini\n",
       "0  0.0000\n",
-      "1  0.3238\n",
-      "2  0.4170\n",
-      "3  0.4370\n",
-      "4  0.5410\n",
+      "1  0.3146\n",
+      "2  0.3576\n",
+      "3  0.4404\n",
+      "4  0.4484\n",
       "     Gini\n",
-      "0  0.6254\n",
-      "1  0.6132\n",
-      "2  0.5976\n",
-      "3  0.6208\n",
-      "4  0.6502\n",
+      "0  0.6464\n",
+      "1  0.6660\n",
+      "2  0.6868\n",
+      "3  0.6882\n",
+      "4  0.6638\n",
       "     Gini\n",
-      "0  0.7184\n",
-      "1  0.7054\n",
-      "2  0.7100\n",
-      "3  0.6866\n",
-      "4  0.7066\n",
+      "0  0.6682\n",
+      "1  0.6486\n",
+      "2  0.6362\n",
+      "3  0.6306\n",
+      "4  0.6478\n",
       "     Gini\n",
-      "0  0.6212\n",
-      "1  0.6128\n",
-      "2  0.6254\n",
-      "3  0.6222\n",
-      "4  0.6308\n",
+      "0  0.6858\n",
+      "1  0.6680\n",
+      "2  0.6784\n",
+      "3  0.6580\n",
+      "4  0.6282\n",
       "     Gini\n",
-      "0  0.6406\n",
-      "1  0.6270\n",
-      "2  0.6264\n",
-      "3  0.6092\n",
-      "4  0.5822\n",
+      "0  0.6598\n",
+      "1  0.6522\n",
+      "2  0.6406\n",
+      "3  0.6294\n",
+      "4  0.5926\n",
       "     Gini\n",
-      "0  0.6996\n",
-      "1  0.6792\n",
-      "2  0.6850\n",
-      "3  0.6680\n",
-      "4  0.6586\n",
+      "0  0.6316\n",
+      "1  0.6408\n",
+      "2  0.6316\n",
+      "3  0.6290\n",
+      "4  0.6290\n",
       "     Gini\n",
-      "0  0.6458\n",
-      "1  0.6474\n",
-      "2  0.6430\n",
-      "3  0.6266\n",
-      "4  0.6288\n",
+      "0  0.6842\n",
+      "1  0.6890\n",
+      "2  0.6466\n",
+      "3  0.6742\n",
+      "4  0.6860\n",
       "     Gini\n",
-      "0  0.7006\n",
-      "1  0.7406\n",
-      "2  0.7164\n",
-      "3  0.6830\n",
-      "4  0.6928\n",
+      "0  0.6142\n",
+      "1  0.6048\n",
+      "2  0.6356\n",
+      "3  0.6490\n",
+      "4  0.6292\n",
       "     Gini\n",
-      "0  0.6780\n",
-      "1  0.6488\n",
-      "2  0.6318\n",
-      "3  0.6368\n",
-      "4  0.6390\n",
+      "0  0.6488\n",
+      "1  0.6858\n",
+      "2  0.6940\n",
+      "3  0.6962\n",
+      "4  0.6824\n",
+      "     Gini\n",
+      "0  0.6966\n",
+      "1  0.6894\n",
+      "2  0.7008\n",
+      "3  0.6778\n",
+      "4  0.6778\n",
       "              Wealth\n",
       "Step AgentID        \n",
       "0    0             1\n",
@@ -1586,60 +1578,67 @@
       "     4             1\n",
       "              Wealth\n",
       "Step AgentID        \n",
-      "101  82            1\n",
-      "     70            0\n",
-      "     30            1\n",
-      "     50            0\n",
-      "     76            3\n",
+      "901  82            2\n",
+      "     29            0\n",
+      "     53            0\n",
+      "     40            2\n",
+      "     87            0\n",
       "              Wealth\n",
       "Step AgentID        \n",
-      "201  64            1\n",
-      "     72            1\n",
-      "     75            1\n",
-      "     53            0\n",
-      "     67            4\n",
+      "101  39            0\n",
+      "     67            0\n",
+      "     0             0\n",
+      "     5             2\n",
+      "     29            0\n",
       "              Wealth\n",
       "Step AgentID        \n",
-      "301  28            4\n",
-      "     33            0\n",
-      "     62            0\n",
-      "     83            0\n",
-      "     31            2\n",
+      "201  56            4\n",
+      "     1             4\n",
+      "     22            1\n",
+      "     2             0\n",
+      "     69            2\n",
+      "              Wealth\n",
+      "Step AgentID        \n",
+      "301  7             1\n",
+      "     70            0\n",
+      "     13            3\n",
+      "     35            0\n",
+      "     20            1\n",
       "              Wealth\n",
       "Step AgentID        \n",
-      "401  93            2\n",
-      "     96            0\n",
-      "     55            3\n",
-      "     14            1\n",
-      "     51            2\n",
+      "401  65            2\n",
+      "     18            3\n",
+      "     68            0\n",
+      "     60            0\n",
+      "     42            0\n",
       "              Wealth\n",
       "Step AgentID        \n",
-      "501  1             0\n",
-      "     62            0\n",
-      "     34            1\n",
-      "     30            0\n",
-      "     37            0\n",
+      "501  45            1\n",
+      "     60            6\n",
+      "     72            0\n",
+      "     79            1\n",
+      "     99            1\n",
       "              Wealth\n",
       "Step AgentID        \n",
-      "601  59            4\n",
+      "601  21            1\n",
+      "     95            4\n",
+      "     39            1\n",
       "     5             0\n",
-      "     22            2\n",
-      "     94            1\n",
-      "     19            0\n",
+      "     76            2\n",
       "              Wealth\n",
       "Step AgentID        \n",
-      "701  24            1\n",
-      "     88            0\n",
-      "     75            0\n",
-      "     55            0\n",
-      "     77            0\n",
+      "701  73            0\n",
+      "     13            1\n",
+      "     49            0\n",
+      "     94            0\n",
+      "     44            0\n",
       "              Wealth\n",
       "Step AgentID        \n",
-      "801  47            0\n",
-      "     90            0\n",
-      "     9             0\n",
-      "     30            3\n",
-      "     0             1\n"
+      "801  56            0\n",
+      "     31            1\n",
+      "     45            0\n",
+      "     81            0\n",
+      "     83            1\n"
      ]
     }
    ],
@@ -1682,6 +1681,7 @@
      "data": {
       "text/plain": [
        "['output_dir/model_data_1.parquet',\n",
+       " 'output_dir/model_data_10.parquet',\n",
        " 'output_dir/model_data_2.parquet',\n",
        " 'output_dir/model_data_3.parquet',\n",
        " 'output_dir/model_data_4.parquet',\n",

From 46fda253a8238cce9c2ac11e0d012c7ff40fba63 Mon Sep 17 00:00:00 2001
From: Chan-Dong-Jun <dongjuncoding@gmail.com>
Date: Tue, 16 Jul 2024 13:31:47 +0800
Subject: [PATCH 5/9] add cacheable model wrapper

---
 mesa/cacheable_model.py              |   156 +
 mesa/cacheable_wrapper_example.ipynb | 13370 +++++++++++++++++++++++++
 mesa/example.ipynb                   |   236 +-
 3 files changed, 13645 insertions(+), 117 deletions(-)
 create mode 100644 mesa/cacheable_model.py
 create mode 100644 mesa/cacheable_wrapper_example.ipynb

diff --git a/mesa/cacheable_model.py b/mesa/cacheable_model.py
new file mode 100644
index 00000000000..4f958d2b4fd
--- /dev/null
+++ b/mesa/cacheable_model.py
@@ -0,0 +1,156 @@
+import os
+import contextlib
+import itertools
+import types
+from copy import deepcopy
+from functools import partial
+from typing import Any
+
+import pyarrow as pa
+import pyarrow.parquet as pq
+from pathlib import Path
+from enum import Enum
+from mesa import Model
+
+with contextlib.suppress(ImportError):
+    import pandas as pd
+
+
+class CacheState(Enum):
+    """When using 'RECORD', with every simulation step the actual simulation will be performed and the model state
+    written to the cache (also called simulation mode).
+    When using 'REPLAY', with every step the model state will be read from the cache (also called replay mode)."""
+
+    RECORD = (1,)
+    REPLAY = 2
+
+
+class CacheableModel:
+    """Class that takes a model and writes its steps to a cache file or reads them from a cache file."""
+
+    def __init__(
+        self,
+        model: Model,
+        cache_file_path: str | Path,
+        cache_state: CacheState,
+        total_steps: int,
+        cache_step_rate: int = 1,
+    ) -> None:
+        """Create a new caching wrapper around an existing mesa model instance.
+
+        Attributes:
+            model: mesa model
+            cache_file_path: cache file to write to or read from
+            cache_state: whether to replay by reading from the cache or simulate and write to the cache
+            cache_step_rate: only every n-th step is cached. If it is 1, every step is cached. If it is 2,
+            only every second step is cached and so on. Increasing 'cache_step_rate' will reduce cache size and
+            increase replay performance by skipping the steps inbetween every n-th step.
+        """
+
+        self.model = model
+        self.cache_file_path = Path(cache_file_path)
+        self._cache_state = cache_state
+        self._cache_step_rate = cache_step_rate
+        self._total_steps = total_steps
+        # self.cache: list[Any] = []
+        self.step_count: int = 0
+        self.run_finished = False
+
+        # temporary dicts to be flushed
+        self.model_vars_cache = {}
+
+
+        self._agent_records = {}
+
+        self._cache_interval = 100
+        self.output_dir = 'output_dir'
+
+    def get_agent_vars_dataframe(self):
+        """Create a pandas DataFrame from the agent variables.
+
+        The DataFrame has one column for each variable, with two additional
+        columns for tick and agent_id.
+        """
+        # Check if self.agent_reporters dictionary is empty, if so raise warning
+        if not self.model.datacollector.agent_reporters:
+            raise UserWarning(
+                "No agent reporters have been defined in the DataCollector, returning empty DataFrame."
+            )
+
+        all_records = itertools.chain.from_iterable(self._agent_records.values())
+        rep_names = list(self.model.datacollector.agent_reporters)
+
+        df = pd.DataFrame.from_records(
+            data=all_records,
+            columns=["Step", "AgentID", *rep_names],
+            index=["Step", "AgentID"],
+        )
+        return df
+
+    def get_model_vars_dataframe(self):
+        """Create a pandas DataFrame from the model variables.
+
+        The DataFrame has one column for each model variable, and the index is
+        (implicitly) the model tick.
+        """
+        # Check if self.model_reporters dictionary is empty, if so raise warning
+        if not self.model.datacollector.model_reporters:
+            raise UserWarning(
+                "No model reporters have been defined in the DataCollector, returning empty DataFrame."
+            )
+
+        print(f" TEST {self.model._steps=}")
+        print(f" TEST {self._cache_interval=}")
+        print(pd.DataFrame(self.model.datacollector.model_vars)[self.model._steps-self._cache_interval:self.model._steps])
+        return pd.DataFrame(self.model.datacollector.model_vars)[self.model._steps-self._cache_interval:self.model._steps]
+
+    # def get_table_dataframe(self, table_name):
+    #     """Create a pandas DataFrame from a particular table.
+    #
+    #     Args:
+    #         table_name: The name of the table to convert.
+    #     """
+    #     if table_name not in self.tables:
+    #         raise Exception("No such table.")
+    #     return pd.DataFrame(self.tables[table_name])
+
+    def _save_to_parquet(self, model):
+        """Save the current cache of data to a Parquet file and clear the cache."""
+        model_df = self.get_model_vars_dataframe()
+        agent_df = self.get_agent_vars_dataframe()
+        padding = len(str(self._total_steps)) - 1
+        print(padding)
+        model_file = f"{self.output_dir}/model_data_{self.model._steps // self._cache_interval:0{padding}}.parquet"
+        agent_file = f"{self.output_dir}/agent_data_{self.model._steps // self._cache_interval:0{padding}}.parquet"
+
+        print(f"{model_file=}")
+        absolute_path = os.path.abspath(model_file)
+        print(f"{absolute_path=}")
+        if os.path.exists(absolute_path):
+            raise FileExistsError(f"A directory with the name {model_file} already exists.")
+        if os.path.exists(model_file):
+            raise FileExistsError(f"A directory with the name {model_file} already exists.")
+        if os.path.exists(agent_file):
+            raise FileExistsError(f"A directory with the name {agent_file} already exists.")
+
+        if not model_df.empty:
+            print(f"Saving model to {model_file}")
+            model_table = pa.Table.from_pandas(model_df)
+            pq.write_table(model_table, model_file)
+
+        if not agent_df.empty:
+            print(f"Saving agent to {agent_file}")
+            agent_table = pa.Table.from_pandas(agent_df)
+            pq.write_table(agent_table, agent_file)
+
+        # Clear the cache
+
+
+    def cache(self):
+        """Custom collect method to extend the original collect behavior."""
+        # Implement your custom logic here
+        # For example, let's say we want to write collected data to a cache file every `cache_step_rate` steps
+        if self.model._steps % self._cache_interval == 0:
+            print("CALLED")
+            print(f"{self.model._steps=}")
+            self._save_to_parquet(self.model)
diff --git a/mesa/cacheable_wrapper_example.ipynb b/mesa/cacheable_wrapper_example.ipynb
new file mode 100644
index 00000000000..3239129baef
--- /dev/null
+++ b/mesa/cacheable_wrapper_example.ipynb
@@ -0,0 +1,13370 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "initial_id",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import mesa\n",
+    "\n",
+    "# Data visualization tools.\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Has multi-dimensional arrays and matrices. Has a large collection of\n",
+    "# mathematical functions to operate on these arrays.\n",
+    "import numpy as np\n",
+    "\n",
+    "# Data manipulation and analysis.\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c25f0179-d5ea-47d1-9e4e-c460030d5f73",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cacheable_model import CacheableModel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7ecda9c7-ec67-4322-b351-99dc5d3a5a67",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cacheable_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d29660ed-cd5d-45a1-af20-cab8fdceeb7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_gini(model):\n",
+    "    agent_wealths = [agent.wealth for agent in model.schedule.agents]\n",
+    "    x = sorted(agent_wealths)\n",
+    "    N = model.num_agents\n",
+    "    B = sum(xi * (N - i) for i, xi in enumerate(x)) / (N * sum(x))\n",
+    "    return 1 + (1 / N) - 2 * B\n",
+    "\n",
+    "\n",
+    "class MoneyAgent(mesa.Agent):\n",
+    "    \"\"\"An agent with fixed initial wealth.\"\"\"\n",
+    "\n",
+    "    def __init__(self, unique_id, model):\n",
+    "        super().__init__(unique_id, model)\n",
+    "        self.wealth = 1\n",
+    "\n",
+    "    def move(self):\n",
+    "        possible_steps = self.model.grid.get_neighborhood(\n",
+    "            self.pos, moore=True, include_center=False\n",
+    "        )\n",
+    "        new_position = self.random.choice(possible_steps)\n",
+    "        self.model.grid.move_agent(self, new_position)\n",
+    "\n",
+    "    def give_money(self):\n",
+    "        cellmates = self.model.grid.get_cell_list_contents([self.pos])\n",
+    "        cellmates.pop(\n",
+    "            cellmates.index(self)\n",
+    "        )  # Ensure agent is not giving money to itself\n",
+    "        if len(cellmates) > 1:\n",
+    "            other = self.random.choice(cellmates)\n",
+    "            other.wealth += 1\n",
+    "            self.wealth -= 1\n",
+    "            if other == self:\n",
+    "                print(\"I JUST GAVE MONEY TO MYSELF HEHEHE!\")\n",
+    "\n",
+    "    def step(self):\n",
+    "        self.move()\n",
+    "        if self.wealth > 0:\n",
+    "            self.give_money()\n",
+    "\n",
+    "\n",
+    "class MoneyModel(mesa.Model):\n",
+    "    \"\"\"A model with some number of agents.\"\"\"\n",
+    "\n",
+    "    def __init__(self, N, width, height):\n",
+    "        super().__init__()\n",
+    "        self.num_agents = N\n",
+    "        self.grid = mesa.space.MultiGrid(width, height, True)\n",
+    "        self.schedule = mesa.time.RandomActivation(self)\n",
+    "\n",
+    "        # Create agents\n",
+    "        for i in range(self.num_agents):\n",
+    "            a = MoneyAgent(i, self)\n",
+    "            self.schedule.add(a)\n",
+    "            # Add the agent to a random grid cell\n",
+    "            x = self.random.randrange(self.grid.width)\n",
+    "            y = self.random.randrange(self.grid.height)\n",
+    "            self.grid.place_agent(a, (x, y))\n",
+    "\n",
+    "        self.datacollector = mesa.DataCollector(\n",
+    "            model_reporters={\"Gini\": compute_gini}, agent_reporters={\"Wealth\": \"wealth\"}\n",
+    "        )\n",
+    "\n",
+    "    def step(self):\n",
+    "        self.datacollector.collect(self)\n",
+    "        self.schedule.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "657a3190-c26a-492c-abe6-be53b6e8a6d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = MoneyModel(100, 10, 10)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b699ed8e-0cd0-452f-8719-6c6e0956f1a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cacheable_model = CacheableModel(model, \"a\", 10000, None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d237ce1-d933-44fc-8818-2f373c4e710c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "dde8f54d-34ed-429d-99f7-b17c1b2cd5a5",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n",
+      "10\n",
+      "11\n",
+      "12\n",
+      "13\n",
+      "14\n",
+      "15\n",
+      "16\n",
+      "17\n",
+      "18\n",
+      "19\n",
+      "20\n",
+      "21\n",
+      "22\n",
+      "23\n",
+      "24\n",
+      "25\n",
+      "26\n",
+      "27\n",
+      "28\n",
+      "29\n",
+      "30\n",
+      "31\n",
+      "32\n",
+      "33\n",
+      "34\n",
+      "35\n",
+      "36\n",
+      "37\n",
+      "38\n",
+      "39\n",
+      "40\n",
+      "41\n",
+      "42\n",
+      "43\n",
+      "44\n",
+      "45\n",
+      "46\n",
+      "47\n",
+      "48\n",
+      "49\n",
+      "50\n",
+      "51\n",
+      "52\n",
+      "53\n",
+      "54\n",
+      "55\n",
+      "56\n",
+      "57\n",
+      "58\n",
+      "59\n",
+      "60\n",
+      "61\n",
+      "62\n",
+      "63\n",
+      "64\n",
+      "65\n",
+      "66\n",
+      "67\n",
+      "68\n",
+      "69\n",
+      "70\n",
+      "71\n",
+      "72\n",
+      "73\n",
+      "74\n",
+      "75\n",
+      "76\n",
+      "77\n",
+      "78\n",
+      "79\n",
+      "80\n",
+      "81\n",
+      "82\n",
+      "83\n",
+      "84\n",
+      "85\n",
+      "86\n",
+      "87\n",
+      "88\n",
+      "89\n",
+      "90\n",
+      "91\n",
+      "92\n",
+      "93\n",
+      "94\n",
+      "95\n",
+      "96\n",
+      "97\n",
+      "98\n",
+      "99\n",
+      "CALLED\n",
+      "self.model._steps=100\n",
+      " TEST self.model._steps=100\n",
+      " TEST self._cache_interval=100\n",
+      "      Gini\n",
+      "0   0.0000\n",
+      "1   0.2822\n",
+      "2   0.3666\n",
+      "3   0.4510\n",
+      "4   0.5058\n",
+      "..     ...\n",
+      "95  0.6584\n",
+      "96  0.6582\n",
+      "97  0.6362\n",
+      "98  0.6440\n",
+      "99  0.6602\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_001.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_001.parquet'\n",
+      "Saving model to output_dir/model_data_001.parquet\n",
+      "Saving agent to output_dir/agent_data_001.parquet\n",
+      "100\n",
+      "101\n",
+      "102\n",
+      "103\n",
+      "104\n",
+      "105\n",
+      "106\n",
+      "107\n",
+      "108\n",
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+      "198\n",
+      "199\n",
+      "CALLED\n",
+      "self.model._steps=200\n",
+      " TEST self.model._steps=200\n",
+      " TEST self._cache_interval=100\n",
+      "       Gini\n",
+      "100  0.6794\n",
+      "101  0.6842\n",
+      "102  0.6756\n",
+      "103  0.6532\n",
+      "104  0.6452\n",
+      "..      ...\n",
+      "195  0.6662\n",
+      "196  0.6802\n",
+      "197  0.6730\n",
+      "198  0.6922\n",
+      "199  0.6740\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_002.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_002.parquet'\n",
+      "Saving model to output_dir/model_data_002.parquet\n",
+      "Saving agent to output_dir/agent_data_002.parquet\n",
+      "200\n",
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+      "298\n",
+      "299\n",
+      "CALLED\n",
+      "self.model._steps=300\n",
+      " TEST self.model._steps=300\n",
+      " TEST self._cache_interval=100\n",
+      "       Gini\n",
+      "200  0.6700\n",
+      "201  0.7024\n",
+      "202  0.7048\n",
+      "203  0.7060\n",
+      "204  0.7140\n",
+      "..      ...\n",
+      "295  0.6354\n",
+      "296  0.6508\n",
+      "297  0.6608\n",
+      "298  0.6710\n",
+      "299  0.6758\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_003.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_003.parquet'\n",
+      "Saving model to output_dir/model_data_003.parquet\n",
+      "Saving agent to output_dir/agent_data_003.parquet\n",
+      "300\n",
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+      "395\n",
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+      "397\n",
+      "398\n",
+      "399\n",
+      "CALLED\n",
+      "self.model._steps=400\n",
+      " TEST self.model._steps=400\n",
+      " TEST self._cache_interval=100\n",
+      "       Gini\n",
+      "300  0.6624\n",
+      "301  0.6698\n",
+      "302  0.6680\n",
+      "303  0.6694\n",
+      "304  0.6552\n",
+      "..      ...\n",
+      "395  0.7076\n",
+      "396  0.7040\n",
+      "397  0.6994\n",
+      "398  0.6720\n",
+      "399  0.6754\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_004.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_004.parquet'\n",
+      "Saving model to output_dir/model_data_004.parquet\n",
+      "Saving agent to output_dir/agent_data_004.parquet\n",
+      "400\n",
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+      "495\n",
+      "496\n",
+      "497\n",
+      "498\n",
+      "499\n",
+      "CALLED\n",
+      "self.model._steps=500\n",
+      " TEST self.model._steps=500\n",
+      " TEST self._cache_interval=100\n",
+      "       Gini\n",
+      "400  0.6564\n",
+      "401  0.6750\n",
+      "402  0.6640\n",
+      "403  0.6606\n",
+      "404  0.6750\n",
+      "..      ...\n",
+      "495  0.6998\n",
+      "496  0.7218\n",
+      "497  0.7408\n",
+      "498  0.7520\n",
+      "499  0.7272\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_005.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_005.parquet'\n",
+      "Saving model to output_dir/model_data_005.parquet\n",
+      "Saving agent to output_dir/agent_data_005.parquet\n",
+      "500\n",
+      "501\n",
+      "502\n",
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+      "593\n",
+      "594\n",
+      "595\n",
+      "596\n",
+      "597\n",
+      "598\n",
+      "599\n",
+      "CALLED\n",
+      "self.model._steps=600\n",
+      " TEST self.model._steps=600\n",
+      " TEST self._cache_interval=100\n",
+      "       Gini\n",
+      "500  0.7098\n",
+      "501  0.7074\n",
+      "502  0.6860\n",
+      "503  0.6830\n",
+      "504  0.6782\n",
+      "..      ...\n",
+      "595  0.5814\n",
+      "596  0.6096\n",
+      "597  0.5986\n",
+      "598  0.6060\n",
+      "599  0.5644\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_006.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_006.parquet'\n",
+      "Saving model to output_dir/model_data_006.parquet\n",
+      "Saving agent to output_dir/agent_data_006.parquet\n",
+      "600\n",
+      "601\n",
+      "602\n",
+      "603\n",
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+      "667\n",
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+      "670\n",
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+      "673\n",
+      "674\n",
+      "675\n",
+      "676\n",
+      "677\n",
+      "678\n",
+      "679\n",
+      "680\n",
+      "681\n",
+      "682\n",
+      "683\n",
+      "684\n",
+      "685\n",
+      "686\n",
+      "687\n",
+      "688\n",
+      "689\n",
+      "690\n",
+      "691\n",
+      "692\n",
+      "693\n",
+      "694\n",
+      "695\n",
+      "696\n",
+      "697\n",
+      "698\n",
+      "699\n",
+      "CALLED\n",
+      "self.model._steps=700\n",
+      " TEST self.model._steps=700\n",
+      " TEST self._cache_interval=100\n",
+      "       Gini\n",
+      "600  0.5644\n",
+      "601  0.5670\n",
+      "602  0.5526\n",
+      "603  0.5812\n",
+      "604  0.5790\n",
+      "..      ...\n",
+      "695  0.5914\n",
+      "696  0.5914\n",
+      "697  0.5842\n",
+      "698  0.5660\n",
+      "699  0.5610\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_007.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_007.parquet'\n",
+      "Saving model to output_dir/model_data_007.parquet\n",
+      "Saving agent to output_dir/agent_data_007.parquet\n",
+      "700\n",
+      "701\n",
+      "702\n",
+      "703\n",
+      "704\n",
+      "705\n",
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+      "732\n",
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+      "735\n",
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+      "753\n",
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+      "759\n",
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+      "762\n",
+      "763\n",
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+      "765\n",
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+      "768\n",
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+      "771\n",
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+      "773\n",
+      "774\n",
+      "775\n",
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+      "777\n",
+      "778\n",
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+      "780\n",
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+      "782\n",
+      "783\n",
+      "784\n",
+      "785\n",
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+      "789\n",
+      "790\n",
+      "791\n",
+      "792\n",
+      "793\n",
+      "794\n",
+      "795\n",
+      "796\n",
+      "797\n",
+      "798\n",
+      "799\n",
+      "CALLED\n",
+      "self.model._steps=800\n",
+      " TEST self.model._steps=800\n",
+      " TEST self._cache_interval=100\n",
+      "       Gini\n",
+      "700  0.5854\n",
+      "701  0.5978\n",
+      "702  0.6238\n",
+      "703  0.6210\n",
+      "704  0.6024\n",
+      "..      ...\n",
+      "795  0.6382\n",
+      "796  0.6890\n",
+      "797  0.6552\n",
+      "798  0.6240\n",
+      "799  0.6294\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_008.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_008.parquet'\n",
+      "Saving model to output_dir/model_data_008.parquet\n",
+      "Saving agent to output_dir/agent_data_008.parquet\n",
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+      "897\n",
+      "898\n",
+      "899\n",
+      "CALLED\n",
+      "self.model._steps=900\n",
+      " TEST self.model._steps=900\n",
+      " TEST self._cache_interval=100\n",
+      "       Gini\n",
+      "800  0.6286\n",
+      "801  0.6666\n",
+      "802  0.6810\n",
+      "803  0.6466\n",
+      "804  0.6530\n",
+      "..      ...\n",
+      "895  0.6044\n",
+      "896  0.6332\n",
+      "897  0.6382\n",
+      "898  0.6346\n",
+      "899  0.6394\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_009.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_009.parquet'\n",
+      "Saving model to output_dir/model_data_009.parquet\n",
+      "Saving agent to output_dir/agent_data_009.parquet\n",
+      "900\n",
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+      "995\n",
+      "996\n",
+      "997\n",
+      "998\n",
+      "999\n",
+      "CALLED\n",
+      "self.model._steps=1000\n",
+      " TEST self.model._steps=1000\n",
+      " TEST self._cache_interval=100\n",
+      "       Gini\n",
+      "900  0.6104\n",
+      "901  0.6256\n",
+      "902  0.6256\n",
+      "903  0.6492\n",
+      "904  0.6398\n",
+      "..      ...\n",
+      "995  0.6574\n",
+      "996  0.6876\n",
+      "997  0.6758\n",
+      "998  0.6832\n",
+      "999  0.6808\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_010.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_010.parquet'\n",
+      "Saving model to output_dir/model_data_010.parquet\n",
+      "Saving agent to output_dir/agent_data_010.parquet\n",
+      "1000\n",
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+      "1094\n",
+      "1095\n",
+      "1096\n",
+      "1097\n",
+      "1098\n",
+      "1099\n",
+      "CALLED\n",
+      "self.model._steps=1100\n",
+      " TEST self.model._steps=1100\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "1000  0.6886\n",
+      "1001  0.6966\n",
+      "1002  0.7010\n",
+      "1003  0.6996\n",
+      "1004  0.7024\n",
+      "...      ...\n",
+      "1095  0.6654\n",
+      "1096  0.6440\n",
+      "1097  0.6194\n",
+      "1098  0.6104\n",
+      "1099  0.6164\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_011.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_011.parquet'\n",
+      "Saving model to output_dir/model_data_011.parquet\n",
+      "Saving agent to output_dir/agent_data_011.parquet\n",
+      "1100\n",
+      "1101\n",
+      "1102\n",
+      "1103\n",
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+      "1194\n",
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+      "1196\n",
+      "1197\n",
+      "1198\n",
+      "1199\n",
+      "CALLED\n",
+      "self.model._steps=1200\n",
+      " TEST self.model._steps=1200\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "1100  0.6172\n",
+      "1101  0.6234\n",
+      "1102  0.6470\n",
+      "1103  0.6720\n",
+      "1104  0.6834\n",
+      "...      ...\n",
+      "1195  0.7018\n",
+      "1196  0.7106\n",
+      "1197  0.7160\n",
+      "1198  0.7260\n",
+      "1199  0.7386\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_012.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_012.parquet'\n",
+      "Saving model to output_dir/model_data_012.parquet\n",
+      "Saving agent to output_dir/agent_data_012.parquet\n",
+      "1200\n",
+      "1201\n",
+      "1202\n",
+      "1203\n",
+      "1204\n",
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+      "1285\n",
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+      "1288\n",
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+      "1290\n",
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+      "1292\n",
+      "1293\n",
+      "1294\n",
+      "1295\n",
+      "1296\n",
+      "1297\n",
+      "1298\n",
+      "1299\n",
+      "CALLED\n",
+      "self.model._steps=1300\n",
+      " TEST self.model._steps=1300\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "1200  0.7374\n",
+      "1201  0.7374\n",
+      "1202  0.7254\n",
+      "1203  0.7120\n",
+      "1204  0.7244\n",
+      "...      ...\n",
+      "1295  0.7200\n",
+      "1296  0.7142\n",
+      "1297  0.7236\n",
+      "1298  0.7348\n",
+      "1299  0.7336\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_013.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_013.parquet'\n",
+      "Saving model to output_dir/model_data_013.parquet\n",
+      "Saving agent to output_dir/agent_data_013.parquet\n",
+      "1300\n",
+      "1301\n",
+      "1302\n",
+      "1303\n",
+      "1304\n",
+      "1305\n",
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+      "1381\n",
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+      "1384\n",
+      "1385\n",
+      "1386\n",
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+      "1388\n",
+      "1389\n",
+      "1390\n",
+      "1391\n",
+      "1392\n",
+      "1393\n",
+      "1394\n",
+      "1395\n",
+      "1396\n",
+      "1397\n",
+      "1398\n",
+      "1399\n",
+      "CALLED\n",
+      "self.model._steps=1400\n",
+      " TEST self.model._steps=1400\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "1300  0.7340\n",
+      "1301  0.7028\n",
+      "1302  0.7090\n",
+      "1303  0.7110\n",
+      "1304  0.7042\n",
+      "...      ...\n",
+      "1395  0.6168\n",
+      "1396  0.6126\n",
+      "1397  0.6136\n",
+      "1398  0.6016\n",
+      "1399  0.6364\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_014.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_014.parquet'\n",
+      "Saving model to output_dir/model_data_014.parquet\n",
+      "Saving agent to output_dir/agent_data_014.parquet\n",
+      "1400\n",
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+      "1494\n",
+      "1495\n",
+      "1496\n",
+      "1497\n",
+      "1498\n",
+      "1499\n",
+      "CALLED\n",
+      "self.model._steps=1500\n",
+      " TEST self.model._steps=1500\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "1400  0.6408\n",
+      "1401  0.6330\n",
+      "1402  0.6278\n",
+      "1403  0.6626\n",
+      "1404  0.6406\n",
+      "...      ...\n",
+      "1495  0.6386\n",
+      "1496  0.6352\n",
+      "1497  0.6134\n",
+      "1498  0.6234\n",
+      "1499  0.6340\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_015.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_015.parquet'\n",
+      "Saving model to output_dir/model_data_015.parquet\n",
+      "Saving agent to output_dir/agent_data_015.parquet\n",
+      "1500\n",
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+      "1594\n",
+      "1595\n",
+      "1596\n",
+      "1597\n",
+      "1598\n",
+      "1599\n",
+      "CALLED\n",
+      "self.model._steps=1600\n",
+      " TEST self.model._steps=1600\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "1500  0.6612\n",
+      "1501  0.6702\n",
+      "1502  0.6478\n",
+      "1503  0.6638\n",
+      "1504  0.6648\n",
+      "...      ...\n",
+      "1595  0.7150\n",
+      "1596  0.7262\n",
+      "1597  0.7246\n",
+      "1598  0.6906\n",
+      "1599  0.6838\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_016.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_016.parquet'\n",
+      "Saving model to output_dir/model_data_016.parquet\n",
+      "Saving agent to output_dir/agent_data_016.parquet\n",
+      "1600\n",
+      "1601\n",
+      "1602\n",
+      "1603\n",
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+      "1694\n",
+      "1695\n",
+      "1696\n",
+      "1697\n",
+      "1698\n",
+      "1699\n",
+      "CALLED\n",
+      "self.model._steps=1700\n",
+      " TEST self.model._steps=1700\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "1600  0.6952\n",
+      "1601  0.7148\n",
+      "1602  0.6994\n",
+      "1603  0.6886\n",
+      "1604  0.7010\n",
+      "...      ...\n",
+      "1695  0.6130\n",
+      "1696  0.6006\n",
+      "1697  0.6050\n",
+      "1698  0.6002\n",
+      "1699  0.5850\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_017.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_017.parquet'\n",
+      "Saving model to output_dir/model_data_017.parquet\n",
+      "Saving agent to output_dir/agent_data_017.parquet\n",
+      "1700\n",
+      "1701\n",
+      "1702\n",
+      "1703\n",
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+      "1773\n",
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+      "1780\n",
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+      "1784\n",
+      "1785\n",
+      "1786\n",
+      "1787\n",
+      "1788\n",
+      "1789\n",
+      "1790\n",
+      "1791\n",
+      "1792\n",
+      "1793\n",
+      "1794\n",
+      "1795\n",
+      "1796\n",
+      "1797\n",
+      "1798\n",
+      "1799\n",
+      "CALLED\n",
+      "self.model._steps=1800\n",
+      " TEST self.model._steps=1800\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "1700  0.5704\n",
+      "1701  0.5840\n",
+      "1702  0.5956\n",
+      "1703  0.6024\n",
+      "1704  0.6298\n",
+      "...      ...\n",
+      "1795  0.6470\n",
+      "1796  0.6504\n",
+      "1797  0.6308\n",
+      "1798  0.6372\n",
+      "1799  0.6220\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_018.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_018.parquet'\n",
+      "Saving model to output_dir/model_data_018.parquet\n",
+      "Saving agent to output_dir/agent_data_018.parquet\n",
+      "1800\n",
+      "1801\n",
+      "1802\n",
+      "1803\n",
+      "1804\n",
+      "1805\n",
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+      "1807\n",
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+      "1843\n",
+      "1844\n",
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+      "1849\n",
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+      "1851\n",
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+      "1853\n",
+      "1854\n",
+      "1855\n",
+      "1856\n",
+      "1857\n",
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+      "1859\n",
+      "1860\n",
+      "1861\n",
+      "1862\n",
+      "1863\n",
+      "1864\n",
+      "1865\n",
+      "1866\n",
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+      "1868\n",
+      "1869\n",
+      "1870\n",
+      "1871\n",
+      "1872\n",
+      "1873\n",
+      "1874\n",
+      "1875\n",
+      "1876\n",
+      "1877\n",
+      "1878\n",
+      "1879\n",
+      "1880\n",
+      "1881\n",
+      "1882\n",
+      "1883\n",
+      "1884\n",
+      "1885\n",
+      "1886\n",
+      "1887\n",
+      "1888\n",
+      "1889\n",
+      "1890\n",
+      "1891\n",
+      "1892\n",
+      "1893\n",
+      "1894\n",
+      "1895\n",
+      "1896\n",
+      "1897\n",
+      "1898\n",
+      "1899\n",
+      "CALLED\n",
+      "self.model._steps=1900\n",
+      " TEST self.model._steps=1900\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "1800  0.6428\n",
+      "1801  0.6518\n",
+      "1802  0.6422\n",
+      "1803  0.6568\n",
+      "1804  0.6676\n",
+      "...      ...\n",
+      "1895  0.6824\n",
+      "1896  0.6732\n",
+      "1897  0.6770\n",
+      "1898  0.6472\n",
+      "1899  0.6642\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_019.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_019.parquet'\n",
+      "Saving model to output_dir/model_data_019.parquet\n",
+      "Saving agent to output_dir/agent_data_019.parquet\n",
+      "1900\n",
+      "1901\n",
+      "1902\n",
+      "1903\n",
+      "1904\n",
+      "1905\n",
+      "1906\n",
+      "1907\n",
+      "1908\n",
+      "1909\n",
+      "1910\n",
+      "1911\n",
+      "1912\n",
+      "1913\n",
+      "1914\n",
+      "1915\n",
+      "1916\n",
+      "1917\n",
+      "1918\n",
+      "1919\n",
+      "1920\n",
+      "1921\n",
+      "1922\n",
+      "1923\n",
+      "1924\n",
+      "1925\n",
+      "1926\n",
+      "1927\n",
+      "1928\n",
+      "1929\n",
+      "1930\n",
+      "1931\n",
+      "1932\n",
+      "1933\n",
+      "1934\n",
+      "1935\n",
+      "1936\n",
+      "1937\n",
+      "1938\n",
+      "1939\n",
+      "1940\n",
+      "1941\n",
+      "1942\n",
+      "1943\n",
+      "1944\n",
+      "1945\n",
+      "1946\n",
+      "1947\n",
+      "1948\n",
+      "1949\n",
+      "1950\n",
+      "1951\n",
+      "1952\n",
+      "1953\n",
+      "1954\n",
+      "1955\n",
+      "1956\n",
+      "1957\n",
+      "1958\n",
+      "1959\n",
+      "1960\n",
+      "1961\n",
+      "1962\n",
+      "1963\n",
+      "1964\n",
+      "1965\n",
+      "1966\n",
+      "1967\n",
+      "1968\n",
+      "1969\n",
+      "1970\n",
+      "1971\n",
+      "1972\n",
+      "1973\n",
+      "1974\n",
+      "1975\n",
+      "1976\n",
+      "1977\n",
+      "1978\n",
+      "1979\n",
+      "1980\n",
+      "1981\n",
+      "1982\n",
+      "1983\n",
+      "1984\n",
+      "1985\n",
+      "1986\n",
+      "1987\n",
+      "1988\n",
+      "1989\n",
+      "1990\n",
+      "1991\n",
+      "1992\n",
+      "1993\n",
+      "1994\n",
+      "1995\n",
+      "1996\n",
+      "1997\n",
+      "1998\n",
+      "1999\n",
+      "CALLED\n",
+      "self.model._steps=2000\n",
+      " TEST self.model._steps=2000\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "1900  0.6662\n",
+      "1901  0.6552\n",
+      "1902  0.6858\n",
+      "1903  0.6734\n",
+      "1904  0.6736\n",
+      "...      ...\n",
+      "1995  0.6336\n",
+      "1996  0.6394\n",
+      "1997  0.6560\n",
+      "1998  0.6730\n",
+      "1999  0.6548\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_020.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_020.parquet'\n",
+      "Saving model to output_dir/model_data_020.parquet\n",
+      "Saving agent to output_dir/agent_data_020.parquet\n",
+      "2000\n",
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+      "2096\n",
+      "2097\n",
+      "2098\n",
+      "2099\n",
+      "CALLED\n",
+      "self.model._steps=2100\n",
+      " TEST self.model._steps=2100\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "2000  0.6300\n",
+      "2001  0.6346\n",
+      "2002  0.6306\n",
+      "2003  0.6200\n",
+      "2004  0.6350\n",
+      "...      ...\n",
+      "2095  0.5842\n",
+      "2096  0.6182\n",
+      "2097  0.6242\n",
+      "2098  0.6362\n",
+      "2099  0.6582\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_021.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_021.parquet'\n",
+      "Saving model to output_dir/model_data_021.parquet\n",
+      "Saving agent to output_dir/agent_data_021.parquet\n",
+      "2100\n",
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+      "2194\n",
+      "2195\n",
+      "2196\n",
+      "2197\n",
+      "2198\n",
+      "2199\n",
+      "CALLED\n",
+      "self.model._steps=2200\n",
+      " TEST self.model._steps=2200\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "2100  0.6650\n",
+      "2101  0.6848\n",
+      "2102  0.6912\n",
+      "2103  0.6896\n",
+      "2104  0.6706\n",
+      "...      ...\n",
+      "2195  0.7040\n",
+      "2196  0.6770\n",
+      "2197  0.6816\n",
+      "2198  0.7012\n",
+      "2199  0.6818\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_022.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_022.parquet'\n",
+      "Saving model to output_dir/model_data_022.parquet\n",
+      "Saving agent to output_dir/agent_data_022.parquet\n",
+      "2200\n",
+      "2201\n",
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+      "2294\n",
+      "2295\n",
+      "2296\n",
+      "2297\n",
+      "2298\n",
+      "2299\n",
+      "CALLED\n",
+      "self.model._steps=2300\n",
+      " TEST self.model._steps=2300\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "2200  0.6824\n",
+      "2201  0.6906\n",
+      "2202  0.7024\n",
+      "2203  0.6690\n",
+      "2204  0.6562\n",
+      "...      ...\n",
+      "2295  0.7012\n",
+      "2296  0.6888\n",
+      "2297  0.6826\n",
+      "2298  0.6680\n",
+      "2299  0.6778\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_023.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_023.parquet'\n",
+      "Saving model to output_dir/model_data_023.parquet\n",
+      "Saving agent to output_dir/agent_data_023.parquet\n",
+      "2300\n",
+      "2301\n",
+      "2302\n",
+      "2303\n",
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+      "2388\n",
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+      "2393\n",
+      "2394\n",
+      "2395\n",
+      "2396\n",
+      "2397\n",
+      "2398\n",
+      "2399\n",
+      "CALLED\n",
+      "self.model._steps=2400\n",
+      " TEST self.model._steps=2400\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "2300  0.6866\n",
+      "2301  0.7058\n",
+      "2302  0.7228\n",
+      "2303  0.6972\n",
+      "2304  0.7260\n",
+      "...      ...\n",
+      "2395  0.6832\n",
+      "2396  0.6752\n",
+      "2397  0.6798\n",
+      "2398  0.6688\n",
+      "2399  0.6850\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_024.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_024.parquet'\n",
+      "Saving model to output_dir/model_data_024.parquet\n",
+      "Saving agent to output_dir/agent_data_024.parquet\n",
+      "2400\n",
+      "2401\n",
+      "2402\n",
+      "2403\n",
+      "2404\n",
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+      "2478\n",
+      "2479\n",
+      "2480\n",
+      "2481\n",
+      "2482\n",
+      "2483\n",
+      "2484\n",
+      "2485\n",
+      "2486\n",
+      "2487\n",
+      "2488\n",
+      "2489\n",
+      "2490\n",
+      "2491\n",
+      "2492\n",
+      "2493\n",
+      "2494\n",
+      "2495\n",
+      "2496\n",
+      "2497\n",
+      "2498\n",
+      "2499\n",
+      "CALLED\n",
+      "self.model._steps=2500\n",
+      " TEST self.model._steps=2500\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "2400  0.6994\n",
+      "2401  0.6884\n",
+      "2402  0.6870\n",
+      "2403  0.6612\n",
+      "2404  0.6716\n",
+      "...      ...\n",
+      "2495  0.6496\n",
+      "2496  0.6534\n",
+      "2497  0.6500\n",
+      "2498  0.6460\n",
+      "2499  0.6532\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_025.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_025.parquet'\n",
+      "Saving model to output_dir/model_data_025.parquet\n",
+      "Saving agent to output_dir/agent_data_025.parquet\n",
+      "2500\n",
+      "2501\n",
+      "2502\n",
+      "2503\n",
+      "2504\n",
+      "2505\n",
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+      "2562\n",
+      "2563\n",
+      "2564\n",
+      "2565\n",
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+      "2573\n",
+      "2574\n",
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+      "2576\n",
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+      "2578\n",
+      "2579\n",
+      "2580\n",
+      "2581\n",
+      "2582\n",
+      "2583\n",
+      "2584\n",
+      "2585\n",
+      "2586\n",
+      "2587\n",
+      "2588\n",
+      "2589\n",
+      "2590\n",
+      "2591\n",
+      "2592\n",
+      "2593\n",
+      "2594\n",
+      "2595\n",
+      "2596\n",
+      "2597\n",
+      "2598\n",
+      "2599\n",
+      "CALLED\n",
+      "self.model._steps=2600\n",
+      " TEST self.model._steps=2600\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "2500  0.6828\n",
+      "2501  0.6718\n",
+      "2502  0.6546\n",
+      "2503  0.6384\n",
+      "2504  0.6320\n",
+      "...      ...\n",
+      "2595  0.6142\n",
+      "2596  0.6306\n",
+      "2597  0.6378\n",
+      "2598  0.6732\n",
+      "2599  0.6952\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_026.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_026.parquet'\n",
+      "Saving model to output_dir/model_data_026.parquet\n",
+      "Saving agent to output_dir/agent_data_026.parquet\n",
+      "2600\n",
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+      "2696\n",
+      "2697\n",
+      "2698\n",
+      "2699\n",
+      "CALLED\n",
+      "self.model._steps=2700\n",
+      " TEST self.model._steps=2700\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "2600  0.6610\n",
+      "2601  0.6734\n",
+      "2602  0.6920\n",
+      "2603  0.6636\n",
+      "2604  0.6586\n",
+      "...      ...\n",
+      "2695  0.6574\n",
+      "2696  0.6408\n",
+      "2697  0.6620\n",
+      "2698  0.6498\n",
+      "2699  0.6674\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_027.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_027.parquet'\n",
+      "Saving model to output_dir/model_data_027.parquet\n",
+      "Saving agent to output_dir/agent_data_027.parquet\n",
+      "2700\n",
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+      "2794\n",
+      "2795\n",
+      "2796\n",
+      "2797\n",
+      "2798\n",
+      "2799\n",
+      "CALLED\n",
+      "self.model._steps=2800\n",
+      " TEST self.model._steps=2800\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "2700  0.6478\n",
+      "2701  0.6354\n",
+      "2702  0.6686\n",
+      "2703  0.6860\n",
+      "2704  0.6950\n",
+      "...      ...\n",
+      "2795  0.6406\n",
+      "2796  0.6630\n",
+      "2797  0.6570\n",
+      "2798  0.6592\n",
+      "2799  0.6754\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_028.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_028.parquet'\n",
+      "Saving model to output_dir/model_data_028.parquet\n",
+      "Saving agent to output_dir/agent_data_028.parquet\n",
+      "2800\n",
+      "2801\n",
+      "2802\n",
+      "2803\n",
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+      "2884\n",
+      "2885\n",
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+      "2888\n",
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+      "2892\n",
+      "2893\n",
+      "2894\n",
+      "2895\n",
+      "2896\n",
+      "2897\n",
+      "2898\n",
+      "2899\n",
+      "CALLED\n",
+      "self.model._steps=2900\n",
+      " TEST self.model._steps=2900\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "2800  0.6906\n",
+      "2801  0.6980\n",
+      "2802  0.6872\n",
+      "2803  0.6568\n",
+      "2804  0.6436\n",
+      "...      ...\n",
+      "2895  0.6928\n",
+      "2896  0.7038\n",
+      "2897  0.6912\n",
+      "2898  0.6860\n",
+      "2899  0.6788\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_029.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_029.parquet'\n",
+      "Saving model to output_dir/model_data_029.parquet\n",
+      "Saving agent to output_dir/agent_data_029.parquet\n",
+      "2900\n",
+      "2901\n",
+      "2902\n",
+      "2903\n",
+      "2904\n",
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+      "2974\n",
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+      "2978\n",
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+      "2980\n",
+      "2981\n",
+      "2982\n",
+      "2983\n",
+      "2984\n",
+      "2985\n",
+      "2986\n",
+      "2987\n",
+      "2988\n",
+      "2989\n",
+      "2990\n",
+      "2991\n",
+      "2992\n",
+      "2993\n",
+      "2994\n",
+      "2995\n",
+      "2996\n",
+      "2997\n",
+      "2998\n",
+      "2999\n",
+      "CALLED\n",
+      "self.model._steps=3000\n",
+      " TEST self.model._steps=3000\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "2900  0.6914\n",
+      "2901  0.6850\n",
+      "2902  0.7054\n",
+      "2903  0.6760\n",
+      "2904  0.6714\n",
+      "...      ...\n",
+      "2995  0.6808\n",
+      "2996  0.6906\n",
+      "2997  0.7006\n",
+      "2998  0.7008\n",
+      "2999  0.6908\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_030.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_030.parquet'\n",
+      "Saving model to output_dir/model_data_030.parquet\n",
+      "Saving agent to output_dir/agent_data_030.parquet\n",
+      "3000\n",
+      "3001\n",
+      "3002\n",
+      "3003\n",
+      "3004\n",
+      "3005\n",
+      "3006\n",
+      "3007\n",
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+      "3009\n",
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+      "3012\n",
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+      "3034\n",
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+      "3042\n",
+      "3043\n",
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+      "3045\n",
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+      "3049\n",
+      "3050\n",
+      "3051\n",
+      "3052\n",
+      "3053\n",
+      "3054\n",
+      "3055\n",
+      "3056\n",
+      "3057\n",
+      "3058\n",
+      "3059\n",
+      "3060\n",
+      "3061\n",
+      "3062\n",
+      "3063\n",
+      "3064\n",
+      "3065\n",
+      "3066\n",
+      "3067\n",
+      "3068\n",
+      "3069\n",
+      "3070\n",
+      "3071\n",
+      "3072\n",
+      "3073\n",
+      "3074\n",
+      "3075\n",
+      "3076\n",
+      "3077\n",
+      "3078\n",
+      "3079\n",
+      "3080\n",
+      "3081\n",
+      "3082\n",
+      "3083\n",
+      "3084\n",
+      "3085\n",
+      "3086\n",
+      "3087\n",
+      "3088\n",
+      "3089\n",
+      "3090\n",
+      "3091\n",
+      "3092\n",
+      "3093\n",
+      "3094\n",
+      "3095\n",
+      "3096\n",
+      "3097\n",
+      "3098\n",
+      "3099\n",
+      "CALLED\n",
+      "self.model._steps=3100\n",
+      " TEST self.model._steps=3100\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "3000  0.6942\n",
+      "3001  0.6854\n",
+      "3002  0.6828\n",
+      "3003  0.6758\n",
+      "3004  0.6922\n",
+      "...      ...\n",
+      "3095  0.6512\n",
+      "3096  0.6520\n",
+      "3097  0.6322\n",
+      "3098  0.6354\n",
+      "3099  0.6106\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_031.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_031.parquet'\n",
+      "Saving model to output_dir/model_data_031.parquet\n",
+      "Saving agent to output_dir/agent_data_031.parquet\n",
+      "3100\n",
+      "3101\n",
+      "3102\n",
+      "3103\n",
+      "3104\n",
+      "3105\n",
+      "3106\n",
+      "3107\n",
+      "3108\n",
+      "3109\n",
+      "3110\n",
+      "3111\n",
+      "3112\n",
+      "3113\n",
+      "3114\n",
+      "3115\n",
+      "3116\n",
+      "3117\n",
+      "3118\n",
+      "3119\n",
+      "3120\n",
+      "3121\n",
+      "3122\n",
+      "3123\n",
+      "3124\n",
+      "3125\n",
+      "3126\n",
+      "3127\n",
+      "3128\n",
+      "3129\n",
+      "3130\n",
+      "3131\n",
+      "3132\n",
+      "3133\n",
+      "3134\n",
+      "3135\n",
+      "3136\n",
+      "3137\n",
+      "3138\n",
+      "3139\n",
+      "3140\n",
+      "3141\n",
+      "3142\n",
+      "3143\n",
+      "3144\n",
+      "3145\n",
+      "3146\n",
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+      "3148\n",
+      "3149\n",
+      "3150\n",
+      "3151\n",
+      "3152\n",
+      "3153\n",
+      "3154\n",
+      "3155\n",
+      "3156\n",
+      "3157\n",
+      "3158\n",
+      "3159\n",
+      "3160\n",
+      "3161\n",
+      "3162\n",
+      "3163\n",
+      "3164\n",
+      "3165\n",
+      "3166\n",
+      "3167\n",
+      "3168\n",
+      "3169\n",
+      "3170\n",
+      "3171\n",
+      "3172\n",
+      "3173\n",
+      "3174\n",
+      "3175\n",
+      "3176\n",
+      "3177\n",
+      "3178\n",
+      "3179\n",
+      "3180\n",
+      "3181\n",
+      "3182\n",
+      "3183\n",
+      "3184\n",
+      "3185\n",
+      "3186\n",
+      "3187\n",
+      "3188\n",
+      "3189\n",
+      "3190\n",
+      "3191\n",
+      "3192\n",
+      "3193\n",
+      "3194\n",
+      "3195\n",
+      "3196\n",
+      "3197\n",
+      "3198\n",
+      "3199\n",
+      "CALLED\n",
+      "self.model._steps=3200\n",
+      " TEST self.model._steps=3200\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "3100  0.6394\n",
+      "3101  0.6518\n",
+      "3102  0.6254\n",
+      "3103  0.6022\n",
+      "3104  0.5750\n",
+      "...      ...\n",
+      "3195  0.6678\n",
+      "3196  0.6514\n",
+      "3197  0.6456\n",
+      "3198  0.6450\n",
+      "3199  0.6520\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_032.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_032.parquet'\n",
+      "Saving model to output_dir/model_data_032.parquet\n",
+      "Saving agent to output_dir/agent_data_032.parquet\n",
+      "3200\n",
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+      "3295\n",
+      "3296\n",
+      "3297\n",
+      "3298\n",
+      "3299\n",
+      "CALLED\n",
+      "self.model._steps=3300\n",
+      " TEST self.model._steps=3300\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "3200  0.6600\n",
+      "3201  0.6952\n",
+      "3202  0.6746\n",
+      "3203  0.6792\n",
+      "3204  0.6746\n",
+      "...      ...\n",
+      "3295  0.6034\n",
+      "3296  0.6086\n",
+      "3297  0.6050\n",
+      "3298  0.6384\n",
+      "3299  0.6246\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_033.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_033.parquet'\n",
+      "Saving model to output_dir/model_data_033.parquet\n",
+      "Saving agent to output_dir/agent_data_033.parquet\n",
+      "3300\n",
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+      "3394\n",
+      "3395\n",
+      "3396\n",
+      "3397\n",
+      "3398\n",
+      "3399\n",
+      "CALLED\n",
+      "self.model._steps=3400\n",
+      " TEST self.model._steps=3400\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "3300  0.6344\n",
+      "3301  0.6618\n",
+      "3302  0.6624\n",
+      "3303  0.6802\n",
+      "3304  0.6522\n",
+      "...      ...\n",
+      "3395  0.7030\n",
+      "3396  0.7020\n",
+      "3397  0.7192\n",
+      "3398  0.7042\n",
+      "3399  0.6716\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_034.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_034.parquet'\n",
+      "Saving model to output_dir/model_data_034.parquet\n",
+      "Saving agent to output_dir/agent_data_034.parquet\n",
+      "3400\n",
+      "3401\n",
+      "3402\n",
+      "3403\n",
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+      "3480\n",
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+      "3482\n",
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+      "3484\n",
+      "3485\n",
+      "3486\n",
+      "3487\n",
+      "3488\n",
+      "3489\n",
+      "3490\n",
+      "3491\n",
+      "3492\n",
+      "3493\n",
+      "3494\n",
+      "3495\n",
+      "3496\n",
+      "3497\n",
+      "3498\n",
+      "3499\n",
+      "CALLED\n",
+      "self.model._steps=3500\n",
+      " TEST self.model._steps=3500\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "3400  0.6644\n",
+      "3401  0.6556\n",
+      "3402  0.6176\n",
+      "3403  0.6558\n",
+      "3404  0.6584\n",
+      "...      ...\n",
+      "3495  0.6556\n",
+      "3496  0.6556\n",
+      "3497  0.6476\n",
+      "3498  0.6894\n",
+      "3499  0.7044\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_035.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_035.parquet'\n",
+      "Saving model to output_dir/model_data_035.parquet\n",
+      "Saving agent to output_dir/agent_data_035.parquet\n",
+      "3500\n",
+      "3501\n",
+      "3502\n",
+      "3503\n",
+      "3504\n",
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+      "3555\n",
+      "3556\n",
+      "3557\n",
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+      "3563\n",
+      "3564\n",
+      "3565\n",
+      "3566\n",
+      "3567\n",
+      "3568\n",
+      "3569\n",
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+      "3571\n",
+      "3572\n",
+      "3573\n",
+      "3574\n",
+      "3575\n",
+      "3576\n",
+      "3577\n",
+      "3578\n",
+      "3579\n",
+      "3580\n",
+      "3581\n",
+      "3582\n",
+      "3583\n",
+      "3584\n",
+      "3585\n",
+      "3586\n",
+      "3587\n",
+      "3588\n",
+      "3589\n",
+      "3590\n",
+      "3591\n",
+      "3592\n",
+      "3593\n",
+      "3594\n",
+      "3595\n",
+      "3596\n",
+      "3597\n",
+      "3598\n",
+      "3599\n",
+      "CALLED\n",
+      "self.model._steps=3600\n",
+      " TEST self.model._steps=3600\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "3500  0.6840\n",
+      "3501  0.6700\n",
+      "3502  0.6556\n",
+      "3503  0.6634\n",
+      "3504  0.6734\n",
+      "...      ...\n",
+      "3595  0.6882\n",
+      "3596  0.6588\n",
+      "3597  0.6494\n",
+      "3598  0.6612\n",
+      "3599  0.6488\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_036.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_036.parquet'\n",
+      "Saving model to output_dir/model_data_036.parquet\n",
+      "Saving agent to output_dir/agent_data_036.parquet\n",
+      "3600\n",
+      "3601\n",
+      "3602\n",
+      "3603\n",
+      "3604\n",
+      "3605\n",
+      "3606\n",
+      "3607\n",
+      "3608\n",
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+      "3623\n",
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+      "3626\n",
+      "3627\n",
+      "3628\n",
+      "3629\n",
+      "3630\n",
+      "3631\n",
+      "3632\n",
+      "3633\n",
+      "3634\n",
+      "3635\n",
+      "3636\n",
+      "3637\n",
+      "3638\n",
+      "3639\n",
+      "3640\n",
+      "3641\n",
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+      "3643\n",
+      "3644\n",
+      "3645\n",
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+      "3648\n",
+      "3649\n",
+      "3650\n",
+      "3651\n",
+      "3652\n",
+      "3653\n",
+      "3654\n",
+      "3655\n",
+      "3656\n",
+      "3657\n",
+      "3658\n",
+      "3659\n",
+      "3660\n",
+      "3661\n",
+      "3662\n",
+      "3663\n",
+      "3664\n",
+      "3665\n",
+      "3666\n",
+      "3667\n",
+      "3668\n",
+      "3669\n",
+      "3670\n",
+      "3671\n",
+      "3672\n",
+      "3673\n",
+      "3674\n",
+      "3675\n",
+      "3676\n",
+      "3677\n",
+      "3678\n",
+      "3679\n",
+      "3680\n",
+      "3681\n",
+      "3682\n",
+      "3683\n",
+      "3684\n",
+      "3685\n",
+      "3686\n",
+      "3687\n",
+      "3688\n",
+      "3689\n",
+      "3690\n",
+      "3691\n",
+      "3692\n",
+      "3693\n",
+      "3694\n",
+      "3695\n",
+      "3696\n",
+      "3697\n",
+      "3698\n",
+      "3699\n",
+      "CALLED\n",
+      "self.model._steps=3700\n",
+      " TEST self.model._steps=3700\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "3600  0.6346\n",
+      "3601  0.6464\n",
+      "3602  0.6606\n",
+      "3603  0.6786\n",
+      "3604  0.6776\n",
+      "...      ...\n",
+      "3695  0.6574\n",
+      "3696  0.6392\n",
+      "3697  0.6462\n",
+      "3698  0.6488\n",
+      "3699  0.6564\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_037.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_037.parquet'\n",
+      "Saving model to output_dir/model_data_037.parquet\n",
+      "Saving agent to output_dir/agent_data_037.parquet\n",
+      "3700\n",
+      "3701\n",
+      "3702\n",
+      "3703\n",
+      "3704\n",
+      "3705\n",
+      "3706\n",
+      "3707\n",
+      "3708\n",
+      "3709\n",
+      "3710\n",
+      "3711\n",
+      "3712\n",
+      "3713\n",
+      "3714\n",
+      "3715\n",
+      "3716\n",
+      "3717\n",
+      "3718\n",
+      "3719\n",
+      "3720\n",
+      "3721\n",
+      "3722\n",
+      "3723\n",
+      "3724\n",
+      "3725\n",
+      "3726\n",
+      "3727\n",
+      "3728\n",
+      "3729\n",
+      "3730\n",
+      "3731\n",
+      "3732\n",
+      "3733\n",
+      "3734\n",
+      "3735\n",
+      "3736\n",
+      "3737\n",
+      "3738\n",
+      "3739\n",
+      "3740\n",
+      "3741\n",
+      "3742\n",
+      "3743\n",
+      "3744\n",
+      "3745\n",
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+      "3747\n",
+      "3748\n",
+      "3749\n",
+      "3750\n",
+      "3751\n",
+      "3752\n",
+      "3753\n",
+      "3754\n",
+      "3755\n",
+      "3756\n",
+      "3757\n",
+      "3758\n",
+      "3759\n",
+      "3760\n",
+      "3761\n",
+      "3762\n",
+      "3763\n",
+      "3764\n",
+      "3765\n",
+      "3766\n",
+      "3767\n",
+      "3768\n",
+      "3769\n",
+      "3770\n",
+      "3771\n",
+      "3772\n",
+      "3773\n",
+      "3774\n",
+      "3775\n",
+      "3776\n",
+      "3777\n",
+      "3778\n",
+      "3779\n",
+      "3780\n",
+      "3781\n",
+      "3782\n",
+      "3783\n",
+      "3784\n",
+      "3785\n",
+      "3786\n",
+      "3787\n",
+      "3788\n",
+      "3789\n",
+      "3790\n",
+      "3791\n",
+      "3792\n",
+      "3793\n",
+      "3794\n",
+      "3795\n",
+      "3796\n",
+      "3797\n",
+      "3798\n",
+      "3799\n",
+      "CALLED\n",
+      "self.model._steps=3800\n",
+      " TEST self.model._steps=3800\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "3700  0.6678\n",
+      "3701  0.6698\n",
+      "3702  0.6574\n",
+      "3703  0.6756\n",
+      "3704  0.6732\n",
+      "...      ...\n",
+      "3795  0.6104\n",
+      "3796  0.6070\n",
+      "3797  0.6162\n",
+      "3798  0.6340\n",
+      "3799  0.6198\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_038.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_038.parquet'\n",
+      "Saving model to output_dir/model_data_038.parquet\n",
+      "Saving agent to output_dir/agent_data_038.parquet\n",
+      "3800\n",
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+      "3895\n",
+      "3896\n",
+      "3897\n",
+      "3898\n",
+      "3899\n",
+      "CALLED\n",
+      "self.model._steps=3900\n",
+      " TEST self.model._steps=3900\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "3800  0.6248\n",
+      "3801  0.6336\n",
+      "3802  0.6418\n",
+      "3803  0.6578\n",
+      "3804  0.6586\n",
+      "...      ...\n",
+      "3895  0.6710\n",
+      "3896  0.6606\n",
+      "3897  0.6676\n",
+      "3898  0.6784\n",
+      "3899  0.6920\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_039.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_039.parquet'\n",
+      "Saving model to output_dir/model_data_039.parquet\n",
+      "Saving agent to output_dir/agent_data_039.parquet\n",
+      "3900\n",
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+      "3994\n",
+      "3995\n",
+      "3996\n",
+      "3997\n",
+      "3998\n",
+      "3999\n",
+      "CALLED\n",
+      "self.model._steps=4000\n",
+      " TEST self.model._steps=4000\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "3900  0.6756\n",
+      "3901  0.6754\n",
+      "3902  0.6668\n",
+      "3903  0.6708\n",
+      "3904  0.6940\n",
+      "...      ...\n",
+      "3995  0.6720\n",
+      "3996  0.6460\n",
+      "3997  0.6556\n",
+      "3998  0.6400\n",
+      "3999  0.6360\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_040.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_040.parquet'\n",
+      "Saving model to output_dir/model_data_040.parquet\n",
+      "Saving agent to output_dir/agent_data_040.parquet\n",
+      "4000\n",
+      "4001\n",
+      "4002\n",
+      "4003\n",
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+      "4085\n",
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+      "4088\n",
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+      "4092\n",
+      "4093\n",
+      "4094\n",
+      "4095\n",
+      "4096\n",
+      "4097\n",
+      "4098\n",
+      "4099\n",
+      "CALLED\n",
+      "self.model._steps=4100\n",
+      " TEST self.model._steps=4100\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "4000  0.5930\n",
+      "4001  0.5802\n",
+      "4002  0.5750\n",
+      "4003  0.6004\n",
+      "4004  0.5798\n",
+      "...      ...\n",
+      "4095  0.6906\n",
+      "4096  0.6782\n",
+      "4097  0.6606\n",
+      "4098  0.6482\n",
+      "4099  0.6362\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_041.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_041.parquet'\n",
+      "Saving model to output_dir/model_data_041.parquet\n",
+      "Saving agent to output_dir/agent_data_041.parquet\n",
+      "4100\n",
+      "4101\n",
+      "4102\n",
+      "4103\n",
+      "4104\n",
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+      "4153\n",
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+      "4170\n",
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+      "4180\n",
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+      "4182\n",
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+      "4184\n",
+      "4185\n",
+      "4186\n",
+      "4187\n",
+      "4188\n",
+      "4189\n",
+      "4190\n",
+      "4191\n",
+      "4192\n",
+      "4193\n",
+      "4194\n",
+      "4195\n",
+      "4196\n",
+      "4197\n",
+      "4198\n",
+      "4199\n",
+      "CALLED\n",
+      "self.model._steps=4200\n",
+      " TEST self.model._steps=4200\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "4100  0.6638\n",
+      "4101  0.6520\n",
+      "4102  0.6346\n",
+      "4103  0.6352\n",
+      "4104  0.6618\n",
+      "...      ...\n",
+      "4195  0.6758\n",
+      "4196  0.7008\n",
+      "4197  0.6850\n",
+      "4198  0.6952\n",
+      "4199  0.6928\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_042.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_042.parquet'\n",
+      "Saving model to output_dir/model_data_042.parquet\n",
+      "Saving agent to output_dir/agent_data_042.parquet\n",
+      "4200\n",
+      "4201\n",
+      "4202\n",
+      "4203\n",
+      "4204\n",
+      "4205\n",
+      "4206\n",
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+      "4208\n",
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+      "4223\n",
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+      "4225\n",
+      "4226\n",
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+      "4228\n",
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+      "4230\n",
+      "4231\n",
+      "4232\n",
+      "4233\n",
+      "4234\n",
+      "4235\n",
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+      "4237\n",
+      "4238\n",
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+      "4240\n",
+      "4241\n",
+      "4242\n",
+      "4243\n",
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+      "4245\n",
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+      "4249\n",
+      "4250\n",
+      "4251\n",
+      "4252\n",
+      "4253\n",
+      "4254\n",
+      "4255\n",
+      "4256\n",
+      "4257\n",
+      "4258\n",
+      "4259\n",
+      "4260\n",
+      "4261\n",
+      "4262\n",
+      "4263\n",
+      "4264\n",
+      "4265\n",
+      "4266\n",
+      "4267\n",
+      "4268\n",
+      "4269\n",
+      "4270\n",
+      "4271\n",
+      "4272\n",
+      "4273\n",
+      "4274\n",
+      "4275\n",
+      "4276\n",
+      "4277\n",
+      "4278\n",
+      "4279\n",
+      "4280\n",
+      "4281\n",
+      "4282\n",
+      "4283\n",
+      "4284\n",
+      "4285\n",
+      "4286\n",
+      "4287\n",
+      "4288\n",
+      "4289\n",
+      "4290\n",
+      "4291\n",
+      "4292\n",
+      "4293\n",
+      "4294\n",
+      "4295\n",
+      "4296\n",
+      "4297\n",
+      "4298\n",
+      "4299\n",
+      "CALLED\n",
+      "self.model._steps=4300\n",
+      " TEST self.model._steps=4300\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "4200  0.7056\n",
+      "4201  0.7012\n",
+      "4202  0.6968\n",
+      "4203  0.6604\n",
+      "4204  0.6212\n",
+      "...      ...\n",
+      "4295  0.6988\n",
+      "4296  0.6814\n",
+      "4297  0.6724\n",
+      "4298  0.6732\n",
+      "4299  0.6884\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_043.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_043.parquet'\n",
+      "Saving model to output_dir/model_data_043.parquet\n",
+      "Saving agent to output_dir/agent_data_043.parquet\n",
+      "4300\n",
+      "4301\n",
+      "4302\n",
+      "4303\n",
+      "4304\n",
+      "4305\n",
+      "4306\n",
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+      "4392\n",
+      "4393\n",
+      "4394\n",
+      "4395\n",
+      "4396\n",
+      "4397\n",
+      "4398\n",
+      "4399\n",
+      "CALLED\n",
+      "self.model._steps=4400\n",
+      " TEST self.model._steps=4400\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "4300  0.6724\n",
+      "4301  0.6880\n",
+      "4302  0.6988\n",
+      "4303  0.6800\n",
+      "4304  0.6872\n",
+      "...      ...\n",
+      "4395  0.7256\n",
+      "4396  0.7320\n",
+      "4397  0.7132\n",
+      "4398  0.7356\n",
+      "4399  0.7414\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_044.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_044.parquet'\n",
+      "Saving model to output_dir/model_data_044.parquet\n",
+      "Saving agent to output_dir/agent_data_044.parquet\n",
+      "4400\n",
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+      "4496\n",
+      "4497\n",
+      "4498\n",
+      "4499\n",
+      "CALLED\n",
+      "self.model._steps=4500\n",
+      " TEST self.model._steps=4500\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "4400  0.7330\n",
+      "4401  0.7446\n",
+      "4402  0.7126\n",
+      "4403  0.7188\n",
+      "4404  0.7308\n",
+      "...      ...\n",
+      "4495  0.6998\n",
+      "4496  0.6968\n",
+      "4497  0.6790\n",
+      "4498  0.6564\n",
+      "4499  0.6662\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_045.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_045.parquet'\n",
+      "Saving model to output_dir/model_data_045.parquet\n",
+      "Saving agent to output_dir/agent_data_045.parquet\n",
+      "4500\n",
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+      "4594\n",
+      "4595\n",
+      "4596\n",
+      "4597\n",
+      "4598\n",
+      "4599\n",
+      "CALLED\n",
+      "self.model._steps=4600\n",
+      " TEST self.model._steps=4600\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "4500  0.6500\n",
+      "4501  0.6568\n",
+      "4502  0.6662\n",
+      "4503  0.6720\n",
+      "4504  0.6622\n",
+      "...      ...\n",
+      "4595  0.7024\n",
+      "4596  0.6896\n",
+      "4597  0.7136\n",
+      "4598  0.7180\n",
+      "4599  0.7112\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_046.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_046.parquet'\n",
+      "Saving model to output_dir/model_data_046.parquet\n",
+      "Saving agent to output_dir/agent_data_046.parquet\n",
+      "4600\n",
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+      "4692\n",
+      "4693\n",
+      "4694\n",
+      "4695\n",
+      "4696\n",
+      "4697\n",
+      "4698\n",
+      "4699\n",
+      "CALLED\n",
+      "self.model._steps=4700\n",
+      " TEST self.model._steps=4700\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "4600  0.6964\n",
+      "4601  0.6790\n",
+      "4602  0.6760\n",
+      "4603  0.6958\n",
+      "4604  0.6650\n",
+      "...      ...\n",
+      "4695  0.6366\n",
+      "4696  0.6312\n",
+      "4697  0.6948\n",
+      "4698  0.6788\n",
+      "4699  0.6832\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_047.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_047.parquet'\n",
+      "Saving model to output_dir/model_data_047.parquet\n",
+      "Saving agent to output_dir/agent_data_047.parquet\n",
+      "4700\n",
+      "4701\n",
+      "4702\n",
+      "4703\n",
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+      "4773\n",
+      "4774\n",
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+      "4776\n",
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+      "4778\n",
+      "4779\n",
+      "4780\n",
+      "4781\n",
+      "4782\n",
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+      "4784\n",
+      "4785\n",
+      "4786\n",
+      "4787\n",
+      "4788\n",
+      "4789\n",
+      "4790\n",
+      "4791\n",
+      "4792\n",
+      "4793\n",
+      "4794\n",
+      "4795\n",
+      "4796\n",
+      "4797\n",
+      "4798\n",
+      "4799\n",
+      "CALLED\n",
+      "self.model._steps=4800\n",
+      " TEST self.model._steps=4800\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "4700  0.6968\n",
+      "4701  0.7030\n",
+      "4702  0.7016\n",
+      "4703  0.7108\n",
+      "4704  0.7126\n",
+      "...      ...\n",
+      "4795  0.6664\n",
+      "4796  0.6556\n",
+      "4797  0.6322\n",
+      "4798  0.6430\n",
+      "4799  0.6480\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_048.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_048.parquet'\n",
+      "Saving model to output_dir/model_data_048.parquet\n",
+      "Saving agent to output_dir/agent_data_048.parquet\n",
+      "4800\n",
+      "4801\n",
+      "4802\n",
+      "4803\n",
+      "4804\n",
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+      "4854\n",
+      "4855\n",
+      "4856\n",
+      "4857\n",
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+      "4861\n",
+      "4862\n",
+      "4863\n",
+      "4864\n",
+      "4865\n",
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+      "4868\n",
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+      "4871\n",
+      "4872\n",
+      "4873\n",
+      "4874\n",
+      "4875\n",
+      "4876\n",
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+      "4878\n",
+      "4879\n",
+      "4880\n",
+      "4881\n",
+      "4882\n",
+      "4883\n",
+      "4884\n",
+      "4885\n",
+      "4886\n",
+      "4887\n",
+      "4888\n",
+      "4889\n",
+      "4890\n",
+      "4891\n",
+      "4892\n",
+      "4893\n",
+      "4894\n",
+      "4895\n",
+      "4896\n",
+      "4897\n",
+      "4898\n",
+      "4899\n",
+      "CALLED\n",
+      "self.model._steps=4900\n",
+      " TEST self.model._steps=4900\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "4800  0.6212\n",
+      "4801  0.6648\n",
+      "4802  0.6418\n",
+      "4803  0.6202\n",
+      "4804  0.6282\n",
+      "...      ...\n",
+      "4895  0.6880\n",
+      "4896  0.6904\n",
+      "4897  0.6694\n",
+      "4898  0.6674\n",
+      "4899  0.6632\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_049.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_049.parquet'\n",
+      "Saving model to output_dir/model_data_049.parquet\n",
+      "Saving agent to output_dir/agent_data_049.parquet\n",
+      "4900\n",
+      "4901\n",
+      "4902\n",
+      "4903\n",
+      "4904\n",
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+      "4993\n",
+      "4994\n",
+      "4995\n",
+      "4996\n",
+      "4997\n",
+      "4998\n",
+      "4999\n",
+      "CALLED\n",
+      "self.model._steps=5000\n",
+      " TEST self.model._steps=5000\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "4900  0.6716\n",
+      "4901  0.6754\n",
+      "4902  0.6870\n",
+      "4903  0.6812\n",
+      "4904  0.6874\n",
+      "...      ...\n",
+      "4995  0.6382\n",
+      "4996  0.6416\n",
+      "4997  0.6322\n",
+      "4998  0.6220\n",
+      "4999  0.6396\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_050.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_050.parquet'\n",
+      "Saving model to output_dir/model_data_050.parquet\n",
+      "Saving agent to output_dir/agent_data_050.parquet\n",
+      "5000\n",
+      "5001\n",
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+      "5094\n",
+      "5095\n",
+      "5096\n",
+      "5097\n",
+      "5098\n",
+      "5099\n",
+      "CALLED\n",
+      "self.model._steps=5100\n",
+      " TEST self.model._steps=5100\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "5000  0.6246\n",
+      "5001  0.6210\n",
+      "5002  0.6736\n",
+      "5003  0.6626\n",
+      "5004  0.6456\n",
+      "...      ...\n",
+      "5095  0.6524\n",
+      "5096  0.6610\n",
+      "5097  0.6748\n",
+      "5098  0.6804\n",
+      "5099  0.6846\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_051.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_051.parquet'\n",
+      "Saving model to output_dir/model_data_051.parquet\n",
+      "Saving agent to output_dir/agent_data_051.parquet\n",
+      "5100\n",
+      "5101\n",
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+      "5194\n",
+      "5195\n",
+      "5196\n",
+      "5197\n",
+      "5198\n",
+      "5199\n",
+      "CALLED\n",
+      "self.model._steps=5200\n",
+      " TEST self.model._steps=5200\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "5100  0.6938\n",
+      "5101  0.6982\n",
+      "5102  0.7010\n",
+      "5103  0.6944\n",
+      "5104  0.6960\n",
+      "...      ...\n",
+      "5195  0.6748\n",
+      "5196  0.7096\n",
+      "5197  0.7074\n",
+      "5198  0.6946\n",
+      "5199  0.6784\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_052.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_052.parquet'\n",
+      "Saving model to output_dir/model_data_052.parquet\n",
+      "Saving agent to output_dir/agent_data_052.parquet\n",
+      "5200\n",
+      "5201\n",
+      "5202\n",
+      "5203\n",
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+      "5280\n",
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+      "5282\n",
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+      "5284\n",
+      "5285\n",
+      "5286\n",
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+      "5288\n",
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+      "5290\n",
+      "5291\n",
+      "5292\n",
+      "5293\n",
+      "5294\n",
+      "5295\n",
+      "5296\n",
+      "5297\n",
+      "5298\n",
+      "5299\n",
+      "CALLED\n",
+      "self.model._steps=5300\n",
+      " TEST self.model._steps=5300\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "5200  0.6756\n",
+      "5201  0.6502\n",
+      "5202  0.6784\n",
+      "5203  0.6718\n",
+      "5204  0.6522\n",
+      "...      ...\n",
+      "5295  0.6344\n",
+      "5296  0.6598\n",
+      "5297  0.6662\n",
+      "5298  0.6868\n",
+      "5299  0.6816\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_053.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_053.parquet'\n",
+      "Saving model to output_dir/model_data_053.parquet\n",
+      "Saving agent to output_dir/agent_data_053.parquet\n",
+      "5300\n",
+      "5301\n",
+      "5302\n",
+      "5303\n",
+      "5304\n",
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+      "5360\n",
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+      "5362\n",
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+      "5365\n",
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+      "5368\n",
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+      "5374\n",
+      "5375\n",
+      "5376\n",
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+      "5380\n",
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+      "5382\n",
+      "5383\n",
+      "5384\n",
+      "5385\n",
+      "5386\n",
+      "5387\n",
+      "5388\n",
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+      "5390\n",
+      "5391\n",
+      "5392\n",
+      "5393\n",
+      "5394\n",
+      "5395\n",
+      "5396\n",
+      "5397\n",
+      "5398\n",
+      "5399\n",
+      "CALLED\n",
+      "self.model._steps=5400\n",
+      " TEST self.model._steps=5400\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "5300  0.6626\n",
+      "5301  0.6426\n",
+      "5302  0.6606\n",
+      "5303  0.6736\n",
+      "5304  0.6600\n",
+      "...      ...\n",
+      "5395  0.6588\n",
+      "5396  0.6626\n",
+      "5397  0.6586\n",
+      "5398  0.6400\n",
+      "5399  0.6606\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_054.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_054.parquet'\n",
+      "Saving model to output_dir/model_data_054.parquet\n",
+      "Saving agent to output_dir/agent_data_054.parquet\n",
+      "5400\n",
+      "5401\n",
+      "5402\n",
+      "5403\n",
+      "5404\n",
+      "5405\n",
+      "5406\n",
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+      "5451\n",
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+      "5453\n",
+      "5454\n",
+      "5455\n",
+      "5456\n",
+      "5457\n",
+      "5458\n",
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+      "5460\n",
+      "5461\n",
+      "5462\n",
+      "5463\n",
+      "5464\n",
+      "5465\n",
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+      "5467\n",
+      "5468\n",
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+      "5471\n",
+      "5472\n",
+      "5473\n",
+      "5474\n",
+      "5475\n",
+      "5476\n",
+      "5477\n",
+      "5478\n",
+      "5479\n",
+      "5480\n",
+      "5481\n",
+      "5482\n",
+      "5483\n",
+      "5484\n",
+      "5485\n",
+      "5486\n",
+      "5487\n",
+      "5488\n",
+      "5489\n",
+      "5490\n",
+      "5491\n",
+      "5492\n",
+      "5493\n",
+      "5494\n",
+      "5495\n",
+      "5496\n",
+      "5497\n",
+      "5498\n",
+      "5499\n",
+      "CALLED\n",
+      "self.model._steps=5500\n",
+      " TEST self.model._steps=5500\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "5400  0.6546\n",
+      "5401  0.6418\n",
+      "5402  0.6224\n",
+      "5403  0.6440\n",
+      "5404  0.6510\n",
+      "...      ...\n",
+      "5495  0.6696\n",
+      "5496  0.6654\n",
+      "5497  0.6728\n",
+      "5498  0.6728\n",
+      "5499  0.6632\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_055.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_055.parquet'\n",
+      "Saving model to output_dir/model_data_055.parquet\n",
+      "Saving agent to output_dir/agent_data_055.parquet\n",
+      "5500\n",
+      "5501\n",
+      "5502\n",
+      "5503\n",
+      "5504\n",
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+      "5596\n",
+      "5597\n",
+      "5598\n",
+      "5599\n",
+      "CALLED\n",
+      "self.model._steps=5600\n",
+      " TEST self.model._steps=5600\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "5500  0.6564\n",
+      "5501  0.6546\n",
+      "5502  0.6722\n",
+      "5503  0.6564\n",
+      "5504  0.6656\n",
+      "...      ...\n",
+      "5595  0.6404\n",
+      "5596  0.6396\n",
+      "5597  0.6376\n",
+      "5598  0.6480\n",
+      "5599  0.6518\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_056.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_056.parquet'\n",
+      "Saving model to output_dir/model_data_056.parquet\n",
+      "Saving agent to output_dir/agent_data_056.parquet\n",
+      "5600\n",
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+      "5696\n",
+      "5697\n",
+      "5698\n",
+      "5699\n",
+      "CALLED\n",
+      "self.model._steps=5700\n",
+      " TEST self.model._steps=5700\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "5600  0.6502\n",
+      "5601  0.6484\n",
+      "5602  0.6394\n",
+      "5603  0.6448\n",
+      "5604  0.6208\n",
+      "...      ...\n",
+      "5695  0.6908\n",
+      "5696  0.7294\n",
+      "5697  0.7138\n",
+      "5698  0.7230\n",
+      "5699  0.7194\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_057.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_057.parquet'\n",
+      "Saving model to output_dir/model_data_057.parquet\n",
+      "Saving agent to output_dir/agent_data_057.parquet\n",
+      "5700\n",
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+      "5794\n",
+      "5795\n",
+      "5796\n",
+      "5797\n",
+      "5798\n",
+      "5799\n",
+      "CALLED\n",
+      "self.model._steps=5800\n",
+      " TEST self.model._steps=5800\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "5700  0.7168\n",
+      "5701  0.7240\n",
+      "5702  0.7228\n",
+      "5703  0.7160\n",
+      "5704  0.7184\n",
+      "...      ...\n",
+      "5795  0.6828\n",
+      "5796  0.6898\n",
+      "5797  0.7010\n",
+      "5798  0.7120\n",
+      "5799  0.7136\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_058.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_058.parquet'\n",
+      "Saving model to output_dir/model_data_058.parquet\n",
+      "Saving agent to output_dir/agent_data_058.parquet\n",
+      "5800\n",
+      "5801\n",
+      "5802\n",
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+      "5885\n",
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+      "5888\n",
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+      "5890\n",
+      "5891\n",
+      "5892\n",
+      "5893\n",
+      "5894\n",
+      "5895\n",
+      "5896\n",
+      "5897\n",
+      "5898\n",
+      "5899\n",
+      "CALLED\n",
+      "self.model._steps=5900\n",
+      " TEST self.model._steps=5900\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "5800  0.6898\n",
+      "5801  0.6930\n",
+      "5802  0.6824\n",
+      "5803  0.6764\n",
+      "5804  0.6460\n",
+      "...      ...\n",
+      "5895  0.6548\n",
+      "5896  0.6816\n",
+      "5897  0.6702\n",
+      "5898  0.6754\n",
+      "5899  0.6860\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_059.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_059.parquet'\n",
+      "Saving model to output_dir/model_data_059.parquet\n",
+      "Saving agent to output_dir/agent_data_059.parquet\n",
+      "5900\n",
+      "5901\n",
+      "5902\n",
+      "5903\n",
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+      "5956\n",
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+      "5962\n",
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+      "5982\n",
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+      "5985\n",
+      "5986\n",
+      "5987\n",
+      "5988\n",
+      "5989\n",
+      "5990\n",
+      "5991\n",
+      "5992\n",
+      "5993\n",
+      "5994\n",
+      "5995\n",
+      "5996\n",
+      "5997\n",
+      "5998\n",
+      "5999\n",
+      "CALLED\n",
+      "self.model._steps=6000\n",
+      " TEST self.model._steps=6000\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "5900  0.6962\n",
+      "5901  0.6814\n",
+      "5902  0.6622\n",
+      "5903  0.6738\n",
+      "5904  0.6670\n",
+      "...      ...\n",
+      "5995  0.5982\n",
+      "5996  0.5974\n",
+      "5997  0.5904\n",
+      "5998  0.5750\n",
+      "5999  0.5794\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_060.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_060.parquet'\n",
+      "Saving model to output_dir/model_data_060.parquet\n",
+      "Saving agent to output_dir/agent_data_060.parquet\n",
+      "6000\n",
+      "6001\n",
+      "6002\n",
+      "6003\n",
+      "6004\n",
+      "6005\n",
+      "6006\n",
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+      "6057\n",
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+      "6060\n",
+      "6061\n",
+      "6062\n",
+      "6063\n",
+      "6064\n",
+      "6065\n",
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+      "6068\n",
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+      "6073\n",
+      "6074\n",
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+      "6076\n",
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+      "6078\n",
+      "6079\n",
+      "6080\n",
+      "6081\n",
+      "6082\n",
+      "6083\n",
+      "6084\n",
+      "6085\n",
+      "6086\n",
+      "6087\n",
+      "6088\n",
+      "6089\n",
+      "6090\n",
+      "6091\n",
+      "6092\n",
+      "6093\n",
+      "6094\n",
+      "6095\n",
+      "6096\n",
+      "6097\n",
+      "6098\n",
+      "6099\n",
+      "CALLED\n",
+      "self.model._steps=6100\n",
+      " TEST self.model._steps=6100\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "6000  0.5854\n",
+      "6001  0.5958\n",
+      "6002  0.6226\n",
+      "6003  0.6268\n",
+      "6004  0.6618\n",
+      "...      ...\n",
+      "6095  0.6460\n",
+      "6096  0.6372\n",
+      "6097  0.6520\n",
+      "6098  0.6554\n",
+      "6099  0.6380\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_061.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_061.parquet'\n",
+      "Saving model to output_dir/model_data_061.parquet\n",
+      "Saving agent to output_dir/agent_data_061.parquet\n",
+      "6100\n",
+      "6101\n",
+      "6102\n",
+      "6103\n",
+      "6104\n",
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+      "6194\n",
+      "6195\n",
+      "6196\n",
+      "6197\n",
+      "6198\n",
+      "6199\n",
+      "CALLED\n",
+      "self.model._steps=6200\n",
+      " TEST self.model._steps=6200\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "6100  0.6122\n",
+      "6101  0.6106\n",
+      "6102  0.6094\n",
+      "6103  0.6184\n",
+      "6104  0.6390\n",
+      "...      ...\n",
+      "6195  0.6700\n",
+      "6196  0.6602\n",
+      "6197  0.6812\n",
+      "6198  0.6620\n",
+      "6199  0.6818\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_062.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_062.parquet'\n",
+      "Saving model to output_dir/model_data_062.parquet\n",
+      "Saving agent to output_dir/agent_data_062.parquet\n",
+      "6200\n",
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+      "6294\n",
+      "6295\n",
+      "6296\n",
+      "6297\n",
+      "6298\n",
+      "6299\n",
+      "CALLED\n",
+      "self.model._steps=6300\n",
+      " TEST self.model._steps=6300\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "6200  0.7074\n",
+      "6201  0.6912\n",
+      "6202  0.6990\n",
+      "6203  0.7122\n",
+      "6204  0.7230\n",
+      "...      ...\n",
+      "6295  0.6710\n",
+      "6296  0.6800\n",
+      "6297  0.6578\n",
+      "6298  0.6654\n",
+      "6299  0.6560\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_063.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_063.parquet'\n",
+      "Saving model to output_dir/model_data_063.parquet\n",
+      "Saving agent to output_dir/agent_data_063.parquet\n",
+      "6300\n",
+      "6301\n",
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+      "6394\n",
+      "6395\n",
+      "6396\n",
+      "6397\n",
+      "6398\n",
+      "6399\n",
+      "CALLED\n",
+      "self.model._steps=6400\n",
+      " TEST self.model._steps=6400\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "6300  0.6668\n",
+      "6301  0.6226\n",
+      "6302  0.6426\n",
+      "6303  0.6436\n",
+      "6304  0.6544\n",
+      "...      ...\n",
+      "6395  0.6688\n",
+      "6396  0.6844\n",
+      "6397  0.6896\n",
+      "6398  0.6938\n",
+      "6399  0.7022\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_064.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_064.parquet'\n",
+      "Saving model to output_dir/model_data_064.parquet\n",
+      "Saving agent to output_dir/agent_data_064.parquet\n",
+      "6400\n",
+      "6401\n",
+      "6402\n",
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+      "6485\n",
+      "6486\n",
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+      "6488\n",
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+      "6490\n",
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+      "6492\n",
+      "6493\n",
+      "6494\n",
+      "6495\n",
+      "6496\n",
+      "6497\n",
+      "6498\n",
+      "6499\n",
+      "CALLED\n",
+      "self.model._steps=6500\n",
+      " TEST self.model._steps=6500\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "6400  0.7002\n",
+      "6401  0.6872\n",
+      "6402  0.6718\n",
+      "6403  0.7072\n",
+      "6404  0.6848\n",
+      "...      ...\n",
+      "6495  0.7172\n",
+      "6496  0.6978\n",
+      "6497  0.7108\n",
+      "6498  0.7166\n",
+      "6499  0.6990\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_065.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_065.parquet'\n",
+      "Saving model to output_dir/model_data_065.parquet\n",
+      "Saving agent to output_dir/agent_data_065.parquet\n",
+      "6500\n",
+      "6501\n",
+      "6502\n",
+      "6503\n",
+      "6504\n",
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+      "6573\n",
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+      "6580\n",
+      "6581\n",
+      "6582\n",
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+      "6584\n",
+      "6585\n",
+      "6586\n",
+      "6587\n",
+      "6588\n",
+      "6589\n",
+      "6590\n",
+      "6591\n",
+      "6592\n",
+      "6593\n",
+      "6594\n",
+      "6595\n",
+      "6596\n",
+      "6597\n",
+      "6598\n",
+      "6599\n",
+      "CALLED\n",
+      "self.model._steps=6600\n",
+      " TEST self.model._steps=6600\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "6500  0.6888\n",
+      "6501  0.7066\n",
+      "6502  0.7068\n",
+      "6503  0.6898\n",
+      "6504  0.6928\n",
+      "...      ...\n",
+      "6595  0.6804\n",
+      "6596  0.6648\n",
+      "6597  0.6424\n",
+      "6598  0.6346\n",
+      "6599  0.6264\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_066.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_066.parquet'\n",
+      "Saving model to output_dir/model_data_066.parquet\n",
+      "Saving agent to output_dir/agent_data_066.parquet\n",
+      "6600\n",
+      "6601\n",
+      "6602\n",
+      "6603\n",
+      "6604\n",
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+      "6654\n",
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+      "6656\n",
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+      "6661\n",
+      "6662\n",
+      "6663\n",
+      "6664\n",
+      "6665\n",
+      "6666\n",
+      "6667\n",
+      "6668\n",
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+      "6671\n",
+      "6672\n",
+      "6673\n",
+      "6674\n",
+      "6675\n",
+      "6676\n",
+      "6677\n",
+      "6678\n",
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+      "6680\n",
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+      "6682\n",
+      "6683\n",
+      "6684\n",
+      "6685\n",
+      "6686\n",
+      "6687\n",
+      "6688\n",
+      "6689\n",
+      "6690\n",
+      "6691\n",
+      "6692\n",
+      "6693\n",
+      "6694\n",
+      "6695\n",
+      "6696\n",
+      "6697\n",
+      "6698\n",
+      "6699\n",
+      "CALLED\n",
+      "self.model._steps=6700\n",
+      " TEST self.model._steps=6700\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "6600  0.6150\n",
+      "6601  0.6304\n",
+      "6602  0.6556\n",
+      "6603  0.6400\n",
+      "6604  0.6178\n",
+      "...      ...\n",
+      "6695  0.7218\n",
+      "6696  0.7260\n",
+      "6697  0.7100\n",
+      "6698  0.7012\n",
+      "6699  0.7178\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_067.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_067.parquet'\n",
+      "Saving model to output_dir/model_data_067.parquet\n",
+      "Saving agent to output_dir/agent_data_067.parquet\n",
+      "6700\n",
+      "6701\n",
+      "6702\n",
+      "6703\n",
+      "6704\n",
+      "6705\n",
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+      "6796\n",
+      "6797\n",
+      "6798\n",
+      "6799\n",
+      "CALLED\n",
+      "self.model._steps=6800\n",
+      " TEST self.model._steps=6800\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "6700  0.7140\n",
+      "6701  0.6918\n",
+      "6702  0.6902\n",
+      "6703  0.6968\n",
+      "6704  0.7136\n",
+      "...      ...\n",
+      "6795  0.5904\n",
+      "6796  0.5826\n",
+      "6797  0.5538\n",
+      "6798  0.5822\n",
+      "6799  0.6018\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_068.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_068.parquet'\n",
+      "Saving model to output_dir/model_data_068.parquet\n",
+      "Saving agent to output_dir/agent_data_068.parquet\n",
+      "6800\n",
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+      "6896\n",
+      "6897\n",
+      "6898\n",
+      "6899\n",
+      "CALLED\n",
+      "self.model._steps=6900\n",
+      " TEST self.model._steps=6900\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "6800  0.6018\n",
+      "6801  0.6132\n",
+      "6802  0.6478\n",
+      "6803  0.6440\n",
+      "6804  0.6480\n",
+      "...      ...\n",
+      "6895  0.6738\n",
+      "6896  0.6890\n",
+      "6897  0.6738\n",
+      "6898  0.6594\n",
+      "6899  0.6536\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_069.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_069.parquet'\n",
+      "Saving model to output_dir/model_data_069.parquet\n",
+      "Saving agent to output_dir/agent_data_069.parquet\n",
+      "6900\n",
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+      "6994\n",
+      "6995\n",
+      "6996\n",
+      "6997\n",
+      "6998\n",
+      "6999\n",
+      "CALLED\n",
+      "self.model._steps=7000\n",
+      " TEST self.model._steps=7000\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "6900  0.6618\n",
+      "6901  0.6596\n",
+      "6902  0.6514\n",
+      "6903  0.6430\n",
+      "6904  0.6200\n",
+      "...      ...\n",
+      "6995  0.6768\n",
+      "6996  0.6620\n",
+      "6997  0.6800\n",
+      "6998  0.6548\n",
+      "6999  0.6348\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_070.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_070.parquet'\n",
+      "Saving model to output_dir/model_data_070.parquet\n",
+      "Saving agent to output_dir/agent_data_070.parquet\n",
+      "7000\n",
+      "7001\n",
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+      "7080\n",
+      "7081\n",
+      "7082\n",
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+      "7084\n",
+      "7085\n",
+      "7086\n",
+      "7087\n",
+      "7088\n",
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+      "7090\n",
+      "7091\n",
+      "7092\n",
+      "7093\n",
+      "7094\n",
+      "7095\n",
+      "7096\n",
+      "7097\n",
+      "7098\n",
+      "7099\n",
+      "CALLED\n",
+      "self.model._steps=7100\n",
+      " TEST self.model._steps=7100\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "7000  0.6408\n",
+      "7001  0.6480\n",
+      "7002  0.6576\n",
+      "7003  0.6788\n",
+      "7004  0.6646\n",
+      "...      ...\n",
+      "7095  0.6596\n",
+      "7096  0.6640\n",
+      "7097  0.6632\n",
+      "7098  0.6574\n",
+      "7099  0.6560\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_071.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_071.parquet'\n",
+      "Saving model to output_dir/model_data_071.parquet\n",
+      "Saving agent to output_dir/agent_data_071.parquet\n",
+      "7100\n",
+      "7101\n",
+      "7102\n",
+      "7103\n",
+      "7104\n",
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+      "7151\n",
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+      "7162\n",
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+      "7168\n",
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+      "7173\n",
+      "7174\n",
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+      "7176\n",
+      "7177\n",
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+      "7179\n",
+      "7180\n",
+      "7181\n",
+      "7182\n",
+      "7183\n",
+      "7184\n",
+      "7185\n",
+      "7186\n",
+      "7187\n",
+      "7188\n",
+      "7189\n",
+      "7190\n",
+      "7191\n",
+      "7192\n",
+      "7193\n",
+      "7194\n",
+      "7195\n",
+      "7196\n",
+      "7197\n",
+      "7198\n",
+      "7199\n",
+      "CALLED\n",
+      "self.model._steps=7200\n",
+      " TEST self.model._steps=7200\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "7100  0.6498\n",
+      "7101  0.7024\n",
+      "7102  0.6968\n",
+      "7103  0.7072\n",
+      "7104  0.7078\n",
+      "...      ...\n",
+      "7195  0.5950\n",
+      "7196  0.5954\n",
+      "7197  0.5890\n",
+      "7198  0.6056\n",
+      "7199  0.6184\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_072.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_072.parquet'\n",
+      "Saving model to output_dir/model_data_072.parquet\n",
+      "Saving agent to output_dir/agent_data_072.parquet\n",
+      "7200\n",
+      "7201\n",
+      "7202\n",
+      "7203\n",
+      "7204\n",
+      "7205\n",
+      "7206\n",
+      "7207\n",
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+      "7209\n",
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+      "7226\n",
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+      "7232\n",
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+      "7243\n",
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+      "7250\n",
+      "7251\n",
+      "7252\n",
+      "7253\n",
+      "7254\n",
+      "7255\n",
+      "7256\n",
+      "7257\n",
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+      "7259\n",
+      "7260\n",
+      "7261\n",
+      "7262\n",
+      "7263\n",
+      "7264\n",
+      "7265\n",
+      "7266\n",
+      "7267\n",
+      "7268\n",
+      "7269\n",
+      "7270\n",
+      "7271\n",
+      "7272\n",
+      "7273\n",
+      "7274\n",
+      "7275\n",
+      "7276\n",
+      "7277\n",
+      "7278\n",
+      "7279\n",
+      "7280\n",
+      "7281\n",
+      "7282\n",
+      "7283\n",
+      "7284\n",
+      "7285\n",
+      "7286\n",
+      "7287\n",
+      "7288\n",
+      "7289\n",
+      "7290\n",
+      "7291\n",
+      "7292\n",
+      "7293\n",
+      "7294\n",
+      "7295\n",
+      "7296\n",
+      "7297\n",
+      "7298\n",
+      "7299\n",
+      "CALLED\n",
+      "self.model._steps=7300\n",
+      " TEST self.model._steps=7300\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "7200  0.6282\n",
+      "7201  0.6050\n",
+      "7202  0.6012\n",
+      "7203  0.6304\n",
+      "7204  0.6386\n",
+      "...      ...\n",
+      "7295  0.7196\n",
+      "7296  0.7090\n",
+      "7297  0.7040\n",
+      "7298  0.7220\n",
+      "7299  0.7098\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_073.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_073.parquet'\n",
+      "Saving model to output_dir/model_data_073.parquet\n",
+      "Saving agent to output_dir/agent_data_073.parquet\n",
+      "7300\n",
+      "7301\n",
+      "7302\n",
+      "7303\n",
+      "7304\n",
+      "7305\n",
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+      "7355\n",
+      "7356\n",
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+      "7397\n",
+      "7398\n",
+      "7399\n",
+      "CALLED\n",
+      "self.model._steps=7400\n",
+      " TEST self.model._steps=7400\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "7300  0.7098\n",
+      "7301  0.7130\n",
+      "7302  0.7068\n",
+      "7303  0.7118\n",
+      "7304  0.6830\n",
+      "...      ...\n",
+      "7395  0.7224\n",
+      "7396  0.7202\n",
+      "7397  0.7058\n",
+      "7398  0.7010\n",
+      "7399  0.7196\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_074.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_074.parquet'\n",
+      "Saving model to output_dir/model_data_074.parquet\n",
+      "Saving agent to output_dir/agent_data_074.parquet\n",
+      "7400\n",
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+      "7495\n",
+      "7496\n",
+      "7497\n",
+      "7498\n",
+      "7499\n",
+      "CALLED\n",
+      "self.model._steps=7500\n",
+      " TEST self.model._steps=7500\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "7400  0.7164\n",
+      "7401  0.7264\n",
+      "7402  0.7132\n",
+      "7403  0.6930\n",
+      "7404  0.6944\n",
+      "...      ...\n",
+      "7495  0.6570\n",
+      "7496  0.6382\n",
+      "7497  0.6440\n",
+      "7498  0.6264\n",
+      "7499  0.6110\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_075.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_075.parquet'\n",
+      "Saving model to output_dir/model_data_075.parquet\n",
+      "Saving agent to output_dir/agent_data_075.parquet\n",
+      "7500\n",
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+      "7594\n",
+      "7595\n",
+      "7596\n",
+      "7597\n",
+      "7598\n",
+      "7599\n",
+      "CALLED\n",
+      "self.model._steps=7600\n",
+      " TEST self.model._steps=7600\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "7500  0.6100\n",
+      "7501  0.6186\n",
+      "7502  0.6224\n",
+      "7503  0.6370\n",
+      "7504  0.6460\n",
+      "...      ...\n",
+      "7595  0.6688\n",
+      "7596  0.6596\n",
+      "7597  0.6746\n",
+      "7598  0.6608\n",
+      "7599  0.6634\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_076.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_076.parquet'\n",
+      "Saving model to output_dir/model_data_076.parquet\n",
+      "Saving agent to output_dir/agent_data_076.parquet\n",
+      "7600\n",
+      "7601\n",
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+      "7685\n",
+      "7686\n",
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+      "7688\n",
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+      "7692\n",
+      "7693\n",
+      "7694\n",
+      "7695\n",
+      "7696\n",
+      "7697\n",
+      "7698\n",
+      "7699\n",
+      "CALLED\n",
+      "self.model._steps=7700\n",
+      " TEST self.model._steps=7700\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "7600  0.6758\n",
+      "7601  0.6774\n",
+      "7602  0.6970\n",
+      "7603  0.6934\n",
+      "7604  0.6738\n",
+      "...      ...\n",
+      "7695  0.6820\n",
+      "7696  0.6854\n",
+      "7697  0.6614\n",
+      "7698  0.6598\n",
+      "7699  0.6828\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_077.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_077.parquet'\n",
+      "Saving model to output_dir/model_data_077.parquet\n",
+      "Saving agent to output_dir/agent_data_077.parquet\n",
+      "7700\n",
+      "7701\n",
+      "7702\n",
+      "7703\n",
+      "7704\n",
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+      "7757\n",
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+      "7780\n",
+      "7781\n",
+      "7782\n",
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+      "7784\n",
+      "7785\n",
+      "7786\n",
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+      "7788\n",
+      "7789\n",
+      "7790\n",
+      "7791\n",
+      "7792\n",
+      "7793\n",
+      "7794\n",
+      "7795\n",
+      "7796\n",
+      "7797\n",
+      "7798\n",
+      "7799\n",
+      "CALLED\n",
+      "self.model._steps=7800\n",
+      " TEST self.model._steps=7800\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "7700  0.6920\n",
+      "7701  0.7154\n",
+      "7702  0.7136\n",
+      "7703  0.6998\n",
+      "7704  0.7004\n",
+      "...      ...\n",
+      "7795  0.6688\n",
+      "7796  0.6688\n",
+      "7797  0.6500\n",
+      "7798  0.6186\n",
+      "7799  0.6290\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_078.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_078.parquet'\n",
+      "Saving model to output_dir/model_data_078.parquet\n",
+      "Saving agent to output_dir/agent_data_078.parquet\n",
+      "7800\n",
+      "7801\n",
+      "7802\n",
+      "7803\n",
+      "7804\n",
+      "7805\n",
+      "7806\n",
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+      "7851\n",
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+      "7854\n",
+      "7855\n",
+      "7856\n",
+      "7857\n",
+      "7858\n",
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+      "7860\n",
+      "7861\n",
+      "7862\n",
+      "7863\n",
+      "7864\n",
+      "7865\n",
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+      "7868\n",
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+      "7871\n",
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+      "7873\n",
+      "7874\n",
+      "7875\n",
+      "7876\n",
+      "7877\n",
+      "7878\n",
+      "7879\n",
+      "7880\n",
+      "7881\n",
+      "7882\n",
+      "7883\n",
+      "7884\n",
+      "7885\n",
+      "7886\n",
+      "7887\n",
+      "7888\n",
+      "7889\n",
+      "7890\n",
+      "7891\n",
+      "7892\n",
+      "7893\n",
+      "7894\n",
+      "7895\n",
+      "7896\n",
+      "7897\n",
+      "7898\n",
+      "7899\n",
+      "CALLED\n",
+      "self.model._steps=7900\n",
+      " TEST self.model._steps=7900\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "7800  0.6536\n",
+      "7801  0.6282\n",
+      "7802  0.5970\n",
+      "7803  0.5968\n",
+      "7804  0.5590\n",
+      "...      ...\n",
+      "7895  0.6542\n",
+      "7896  0.6522\n",
+      "7897  0.6520\n",
+      "7898  0.6602\n",
+      "7899  0.6742\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_079.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_079.parquet'\n",
+      "Saving model to output_dir/model_data_079.parquet\n",
+      "Saving agent to output_dir/agent_data_079.parquet\n",
+      "7900\n",
+      "7901\n",
+      "7902\n",
+      "7903\n",
+      "7904\n",
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+      "7996\n",
+      "7997\n",
+      "7998\n",
+      "7999\n",
+      "CALLED\n",
+      "self.model._steps=8000\n",
+      " TEST self.model._steps=8000\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "7900  0.6750\n",
+      "7901  0.6750\n",
+      "7902  0.6586\n",
+      "7903  0.6552\n",
+      "7904  0.6562\n",
+      "...      ...\n",
+      "7995  0.6274\n",
+      "7996  0.6490\n",
+      "7997  0.6490\n",
+      "7998  0.6466\n",
+      "7999  0.6676\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_080.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_080.parquet'\n",
+      "Saving model to output_dir/model_data_080.parquet\n",
+      "Saving agent to output_dir/agent_data_080.parquet\n",
+      "8000\n",
+      "8001\n",
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+      "8096\n",
+      "8097\n",
+      "8098\n",
+      "8099\n",
+      "CALLED\n",
+      "self.model._steps=8100\n",
+      " TEST self.model._steps=8100\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "8000  0.6536\n",
+      "8001  0.6876\n",
+      "8002  0.6596\n",
+      "8003  0.6678\n",
+      "8004  0.6604\n",
+      "...      ...\n",
+      "8095  0.6826\n",
+      "8096  0.6794\n",
+      "8097  0.6702\n",
+      "8098  0.6896\n",
+      "8099  0.6846\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_081.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_081.parquet'\n",
+      "Saving model to output_dir/model_data_081.parquet\n",
+      "Saving agent to output_dir/agent_data_081.parquet\n",
+      "8100\n",
+      "8101\n",
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+      "8194\n",
+      "8195\n",
+      "8196\n",
+      "8197\n",
+      "8198\n",
+      "8199\n",
+      "CALLED\n",
+      "self.model._steps=8200\n",
+      " TEST self.model._steps=8200\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "8100  0.6596\n",
+      "8101  0.6556\n",
+      "8102  0.6390\n",
+      "8103  0.6512\n",
+      "8104  0.6536\n",
+      "...      ...\n",
+      "8195  0.6192\n",
+      "8196  0.5868\n",
+      "8197  0.5528\n",
+      "8198  0.5546\n",
+      "8199  0.5764\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_082.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_082.parquet'\n",
+      "Saving model to output_dir/model_data_082.parquet\n",
+      "Saving agent to output_dir/agent_data_082.parquet\n",
+      "8200\n",
+      "8201\n",
+      "8202\n",
+      "8203\n",
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+      "8280\n",
+      "8281\n",
+      "8282\n",
+      "8283\n",
+      "8284\n",
+      "8285\n",
+      "8286\n",
+      "8287\n",
+      "8288\n",
+      "8289\n",
+      "8290\n",
+      "8291\n",
+      "8292\n",
+      "8293\n",
+      "8294\n",
+      "8295\n",
+      "8296\n",
+      "8297\n",
+      "8298\n",
+      "8299\n",
+      "CALLED\n",
+      "self.model._steps=8300\n",
+      " TEST self.model._steps=8300\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "8200  0.5874\n",
+      "8201  0.6296\n",
+      "8202  0.6404\n",
+      "8203  0.6348\n",
+      "8204  0.6300\n",
+      "...      ...\n",
+      "8295  0.6170\n",
+      "8296  0.6200\n",
+      "8297  0.6168\n",
+      "8298  0.6098\n",
+      "8299  0.6362\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_083.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_083.parquet'\n",
+      "Saving model to output_dir/model_data_083.parquet\n",
+      "Saving agent to output_dir/agent_data_083.parquet\n",
+      "8300\n",
+      "8301\n",
+      "8302\n",
+      "8303\n",
+      "8304\n",
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+      "8350\n",
+      "8351\n",
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+      "8353\n",
+      "8354\n",
+      "8355\n",
+      "8356\n",
+      "8357\n",
+      "8358\n",
+      "8359\n",
+      "8360\n",
+      "8361\n",
+      "8362\n",
+      "8363\n",
+      "8364\n",
+      "8365\n",
+      "8366\n",
+      "8367\n",
+      "8368\n",
+      "8369\n",
+      "8370\n",
+      "8371\n",
+      "8372\n",
+      "8373\n",
+      "8374\n",
+      "8375\n",
+      "8376\n",
+      "8377\n",
+      "8378\n",
+      "8379\n",
+      "8380\n",
+      "8381\n",
+      "8382\n",
+      "8383\n",
+      "8384\n",
+      "8385\n",
+      "8386\n",
+      "8387\n",
+      "8388\n",
+      "8389\n",
+      "8390\n",
+      "8391\n",
+      "8392\n",
+      "8393\n",
+      "8394\n",
+      "8395\n",
+      "8396\n",
+      "8397\n",
+      "8398\n",
+      "8399\n",
+      "CALLED\n",
+      "self.model._steps=8400\n",
+      " TEST self.model._steps=8400\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "8300  0.6248\n",
+      "8301  0.6196\n",
+      "8302  0.5904\n",
+      "8303  0.5808\n",
+      "8304  0.6252\n",
+      "...      ...\n",
+      "8395  0.6416\n",
+      "8396  0.6250\n",
+      "8397  0.6210\n",
+      "8398  0.6002\n",
+      "8399  0.6116\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_084.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_084.parquet'\n",
+      "Saving model to output_dir/model_data_084.parquet\n",
+      "Saving agent to output_dir/agent_data_084.parquet\n",
+      "8400\n",
+      "8401\n",
+      "8402\n",
+      "8403\n",
+      "8404\n",
+      "8405\n",
+      "8406\n",
+      "8407\n",
+      "8408\n",
+      "8409\n",
+      "8410\n",
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+      "8420\n",
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+      "8423\n",
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+      "8428\n",
+      "8429\n",
+      "8430\n",
+      "8431\n",
+      "8432\n",
+      "8433\n",
+      "8434\n",
+      "8435\n",
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+      "8437\n",
+      "8438\n",
+      "8439\n",
+      "8440\n",
+      "8441\n",
+      "8442\n",
+      "8443\n",
+      "8444\n",
+      "8445\n",
+      "8446\n",
+      "8447\n",
+      "8448\n",
+      "8449\n",
+      "8450\n",
+      "8451\n",
+      "8452\n",
+      "8453\n",
+      "8454\n",
+      "8455\n",
+      "8456\n",
+      "8457\n",
+      "8458\n",
+      "8459\n",
+      "8460\n",
+      "8461\n",
+      "8462\n",
+      "8463\n",
+      "8464\n",
+      "8465\n",
+      "8466\n",
+      "8467\n",
+      "8468\n",
+      "8469\n",
+      "8470\n",
+      "8471\n",
+      "8472\n",
+      "8473\n",
+      "8474\n",
+      "8475\n",
+      "8476\n",
+      "8477\n",
+      "8478\n",
+      "8479\n",
+      "8480\n",
+      "8481\n",
+      "8482\n",
+      "8483\n",
+      "8484\n",
+      "8485\n",
+      "8486\n",
+      "8487\n",
+      "8488\n",
+      "8489\n",
+      "8490\n",
+      "8491\n",
+      "8492\n",
+      "8493\n",
+      "8494\n",
+      "8495\n",
+      "8496\n",
+      "8497\n",
+      "8498\n",
+      "8499\n",
+      "CALLED\n",
+      "self.model._steps=8500\n",
+      " TEST self.model._steps=8500\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "8400  0.6312\n",
+      "8401  0.6326\n",
+      "8402  0.6460\n",
+      "8403  0.6402\n",
+      "8404  0.6592\n",
+      "...      ...\n",
+      "8495  0.6840\n",
+      "8496  0.6944\n",
+      "8497  0.6580\n",
+      "8498  0.6248\n",
+      "8499  0.6256\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_085.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_085.parquet'\n",
+      "Saving model to output_dir/model_data_085.parquet\n",
+      "Saving agent to output_dir/agent_data_085.parquet\n",
+      "8500\n",
+      "8501\n",
+      "8502\n",
+      "8503\n",
+      "8504\n",
+      "8505\n",
+      "8506\n",
+      "8507\n",
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+      "8598\n",
+      "8599\n",
+      "CALLED\n",
+      "self.model._steps=8600\n",
+      " TEST self.model._steps=8600\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "8500  0.5962\n",
+      "8501  0.5966\n",
+      "8502  0.5870\n",
+      "8503  0.6094\n",
+      "8504  0.6174\n",
+      "...      ...\n",
+      "8595  0.7202\n",
+      "8596  0.7282\n",
+      "8597  0.7224\n",
+      "8598  0.6968\n",
+      "8599  0.6676\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_086.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_086.parquet'\n",
+      "Saving model to output_dir/model_data_086.parquet\n",
+      "Saving agent to output_dir/agent_data_086.parquet\n",
+      "8600\n",
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+      "8696\n",
+      "8697\n",
+      "8698\n",
+      "8699\n",
+      "CALLED\n",
+      "self.model._steps=8700\n",
+      " TEST self.model._steps=8700\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "8600  0.6634\n",
+      "8601  0.6824\n",
+      "8602  0.7022\n",
+      "8603  0.6984\n",
+      "8604  0.6728\n",
+      "...      ...\n",
+      "8695  0.6688\n",
+      "8696  0.6624\n",
+      "8697  0.6290\n",
+      "8698  0.6286\n",
+      "8699  0.6720\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_087.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_087.parquet'\n",
+      "Saving model to output_dir/model_data_087.parquet\n",
+      "Saving agent to output_dir/agent_data_087.parquet\n",
+      "8700\n",
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+      "8794\n",
+      "8795\n",
+      "8796\n",
+      "8797\n",
+      "8798\n",
+      "8799\n",
+      "CALLED\n",
+      "self.model._steps=8800\n",
+      " TEST self.model._steps=8800\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "8700  0.6530\n",
+      "8701  0.6516\n",
+      "8702  0.6486\n",
+      "8703  0.6506\n",
+      "8704  0.6426\n",
+      "...      ...\n",
+      "8795  0.6002\n",
+      "8796  0.6142\n",
+      "8797  0.6314\n",
+      "8798  0.6416\n",
+      "8799  0.6268\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_088.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_088.parquet'\n",
+      "Saving model to output_dir/model_data_088.parquet\n",
+      "Saving agent to output_dir/agent_data_088.parquet\n",
+      "8800\n",
+      "8801\n",
+      "8802\n",
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+      "8885\n",
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+      "8888\n",
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+      "8892\n",
+      "8893\n",
+      "8894\n",
+      "8895\n",
+      "8896\n",
+      "8897\n",
+      "8898\n",
+      "8899\n",
+      "CALLED\n",
+      "self.model._steps=8900\n",
+      " TEST self.model._steps=8900\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "8800  0.6426\n",
+      "8801  0.6366\n",
+      "8802  0.6264\n",
+      "8803  0.6396\n",
+      "8804  0.6566\n",
+      "...      ...\n",
+      "8895  0.6882\n",
+      "8896  0.6882\n",
+      "8897  0.6818\n",
+      "8898  0.6820\n",
+      "8899  0.6970\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_089.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_089.parquet'\n",
+      "Saving model to output_dir/model_data_089.parquet\n",
+      "Saving agent to output_dir/agent_data_089.parquet\n",
+      "8900\n",
+      "8901\n",
+      "8902\n",
+      "8903\n",
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+      "8980\n",
+      "8981\n",
+      "8982\n",
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+      "8984\n",
+      "8985\n",
+      "8986\n",
+      "8987\n",
+      "8988\n",
+      "8989\n",
+      "8990\n",
+      "8991\n",
+      "8992\n",
+      "8993\n",
+      "8994\n",
+      "8995\n",
+      "8996\n",
+      "8997\n",
+      "8998\n",
+      "8999\n",
+      "CALLED\n",
+      "self.model._steps=9000\n",
+      " TEST self.model._steps=9000\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "8900  0.7008\n",
+      "8901  0.6852\n",
+      "8902  0.6920\n",
+      "8903  0.6604\n",
+      "8904  0.6550\n",
+      "...      ...\n",
+      "8995  0.7144\n",
+      "8996  0.6980\n",
+      "8997  0.6754\n",
+      "8998  0.6324\n",
+      "8999  0.6424\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_090.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_090.parquet'\n",
+      "Saving model to output_dir/model_data_090.parquet\n",
+      "Saving agent to output_dir/agent_data_090.parquet\n",
+      "9000\n",
+      "9001\n",
+      "9002\n",
+      "9003\n",
+      "9004\n",
+      "9005\n",
+      "9006\n",
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+      "9053\n",
+      "9054\n",
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+      "9056\n",
+      "9057\n",
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+      "9060\n",
+      "9061\n",
+      "9062\n",
+      "9063\n",
+      "9064\n",
+      "9065\n",
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+      "9068\n",
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+      "9073\n",
+      "9074\n",
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+      "9078\n",
+      "9079\n",
+      "9080\n",
+      "9081\n",
+      "9082\n",
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+      "9084\n",
+      "9085\n",
+      "9086\n",
+      "9087\n",
+      "9088\n",
+      "9089\n",
+      "9090\n",
+      "9091\n",
+      "9092\n",
+      "9093\n",
+      "9094\n",
+      "9095\n",
+      "9096\n",
+      "9097\n",
+      "9098\n",
+      "9099\n",
+      "CALLED\n",
+      "self.model._steps=9100\n",
+      " TEST self.model._steps=9100\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "9000  0.6600\n",
+      "9001  0.6660\n",
+      "9002  0.6482\n",
+      "9003  0.6292\n",
+      "9004  0.6260\n",
+      "...      ...\n",
+      "9095  0.6864\n",
+      "9096  0.6864\n",
+      "9097  0.6562\n",
+      "9098  0.6756\n",
+      "9099  0.6722\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_091.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_091.parquet'\n",
+      "Saving model to output_dir/model_data_091.parquet\n",
+      "Saving agent to output_dir/agent_data_091.parquet\n",
+      "9100\n",
+      "9101\n",
+      "9102\n",
+      "9103\n",
+      "9104\n",
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+      "9131\n",
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+      "9133\n",
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+      "9196\n",
+      "9197\n",
+      "9198\n",
+      "9199\n",
+      "CALLED\n",
+      "self.model._steps=9200\n",
+      " TEST self.model._steps=9200\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "9100  0.6948\n",
+      "9101  0.6942\n",
+      "9102  0.6880\n",
+      "9103  0.6748\n",
+      "9104  0.6588\n",
+      "...      ...\n",
+      "9195  0.6270\n",
+      "9196  0.6274\n",
+      "9197  0.6492\n",
+      "9198  0.6668\n",
+      "9199  0.6688\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_092.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_092.parquet'\n",
+      "Saving model to output_dir/model_data_092.parquet\n",
+      "Saving agent to output_dir/agent_data_092.parquet\n",
+      "9200\n",
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+      "9294\n",
+      "9295\n",
+      "9296\n",
+      "9297\n",
+      "9298\n",
+      "9299\n",
+      "CALLED\n",
+      "self.model._steps=9300\n",
+      " TEST self.model._steps=9300\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "9200  0.6650\n",
+      "9201  0.6328\n",
+      "9202  0.6276\n",
+      "9203  0.6102\n",
+      "9204  0.6698\n",
+      "...      ...\n",
+      "9295  0.6362\n",
+      "9296  0.5956\n",
+      "9297  0.6454\n",
+      "9298  0.6164\n",
+      "9299  0.6040\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_093.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_093.parquet'\n",
+      "Saving model to output_dir/model_data_093.parquet\n",
+      "Saving agent to output_dir/agent_data_093.parquet\n",
+      "9300\n",
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+      "9394\n",
+      "9395\n",
+      "9396\n",
+      "9397\n",
+      "9398\n",
+      "9399\n",
+      "CALLED\n",
+      "self.model._steps=9400\n",
+      " TEST self.model._steps=9400\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "9300  0.5816\n",
+      "9301  0.6006\n",
+      "9302  0.6044\n",
+      "9303  0.6112\n",
+      "9304  0.6036\n",
+      "...      ...\n",
+      "9395  0.6742\n",
+      "9396  0.6826\n",
+      "9397  0.6758\n",
+      "9398  0.6590\n",
+      "9399  0.6568\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_094.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_094.parquet'\n",
+      "Saving model to output_dir/model_data_094.parquet\n",
+      "Saving agent to output_dir/agent_data_094.parquet\n",
+      "9400\n",
+      "9401\n",
+      "9402\n",
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+      "9480\n",
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+      "9482\n",
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+      "9484\n",
+      "9485\n",
+      "9486\n",
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+      "9488\n",
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+      "9490\n",
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+      "9492\n",
+      "9493\n",
+      "9494\n",
+      "9495\n",
+      "9496\n",
+      "9497\n",
+      "9498\n",
+      "9499\n",
+      "CALLED\n",
+      "self.model._steps=9500\n",
+      " TEST self.model._steps=9500\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "9400  0.6706\n",
+      "9401  0.6598\n",
+      "9402  0.6360\n",
+      "9403  0.6558\n",
+      "9404  0.6356\n",
+      "...      ...\n",
+      "9495  0.6264\n",
+      "9496  0.6366\n",
+      "9497  0.6178\n",
+      "9498  0.6306\n",
+      "9499  0.6230\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_095.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_095.parquet'\n",
+      "Saving model to output_dir/model_data_095.parquet\n",
+      "Saving agent to output_dir/agent_data_095.parquet\n",
+      "9500\n",
+      "9501\n",
+      "9502\n",
+      "9503\n",
+      "9504\n",
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+      "9562\n",
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+      "9573\n",
+      "9574\n",
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+      "9576\n",
+      "9577\n",
+      "9578\n",
+      "9579\n",
+      "9580\n",
+      "9581\n",
+      "9582\n",
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+      "9584\n",
+      "9585\n",
+      "9586\n",
+      "9587\n",
+      "9588\n",
+      "9589\n",
+      "9590\n",
+      "9591\n",
+      "9592\n",
+      "9593\n",
+      "9594\n",
+      "9595\n",
+      "9596\n",
+      "9597\n",
+      "9598\n",
+      "9599\n",
+      "CALLED\n",
+      "self.model._steps=9600\n",
+      " TEST self.model._steps=9600\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "9500  0.6226\n",
+      "9501  0.6344\n",
+      "9502  0.6200\n",
+      "9503  0.5742\n",
+      "9504  0.5810\n",
+      "...      ...\n",
+      "9595  0.5926\n",
+      "9596  0.6168\n",
+      "9597  0.6368\n",
+      "9598  0.5948\n",
+      "9599  0.5980\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_096.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_096.parquet'\n",
+      "Saving model to output_dir/model_data_096.parquet\n",
+      "Saving agent to output_dir/agent_data_096.parquet\n",
+      "9600\n",
+      "9601\n",
+      "9602\n",
+      "9603\n",
+      "9604\n",
+      "9605\n",
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+      "9654\n",
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+      "9656\n",
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+      "9659\n",
+      "9660\n",
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+      "9662\n",
+      "9663\n",
+      "9664\n",
+      "9665\n",
+      "9666\n",
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+      "9668\n",
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+      "9673\n",
+      "9674\n",
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+      "9676\n",
+      "9677\n",
+      "9678\n",
+      "9679\n",
+      "9680\n",
+      "9681\n",
+      "9682\n",
+      "9683\n",
+      "9684\n",
+      "9685\n",
+      "9686\n",
+      "9687\n",
+      "9688\n",
+      "9689\n",
+      "9690\n",
+      "9691\n",
+      "9692\n",
+      "9693\n",
+      "9694\n",
+      "9695\n",
+      "9696\n",
+      "9697\n",
+      "9698\n",
+      "9699\n",
+      "CALLED\n",
+      "self.model._steps=9700\n",
+      " TEST self.model._steps=9700\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "9600  0.5968\n",
+      "9601  0.6164\n",
+      "9602  0.6104\n",
+      "9603  0.5840\n",
+      "9604  0.6014\n",
+      "...      ...\n",
+      "9695  0.6778\n",
+      "9696  0.6622\n",
+      "9697  0.6408\n",
+      "9698  0.6512\n",
+      "9699  0.6408\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_097.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_097.parquet'\n",
+      "Saving model to output_dir/model_data_097.parquet\n",
+      "Saving agent to output_dir/agent_data_097.parquet\n",
+      "9700\n",
+      "9701\n",
+      "9702\n",
+      "9703\n",
+      "9704\n",
+      "9705\n",
+      "9706\n",
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+      "9725\n",
+      "9726\n",
+      "9727\n",
+      "9728\n",
+      "9729\n",
+      "9730\n",
+      "9731\n",
+      "9732\n",
+      "9733\n",
+      "9734\n",
+      "9735\n",
+      "9736\n",
+      "9737\n",
+      "9738\n",
+      "9739\n",
+      "9740\n",
+      "9741\n",
+      "9742\n",
+      "9743\n",
+      "9744\n",
+      "9745\n",
+      "9746\n",
+      "9747\n",
+      "9748\n",
+      "9749\n",
+      "9750\n",
+      "9751\n",
+      "9752\n",
+      "9753\n",
+      "9754\n",
+      "9755\n",
+      "9756\n",
+      "9757\n",
+      "9758\n",
+      "9759\n",
+      "9760\n",
+      "9761\n",
+      "9762\n",
+      "9763\n",
+      "9764\n",
+      "9765\n",
+      "9766\n",
+      "9767\n",
+      "9768\n",
+      "9769\n",
+      "9770\n",
+      "9771\n",
+      "9772\n",
+      "9773\n",
+      "9774\n",
+      "9775\n",
+      "9776\n",
+      "9777\n",
+      "9778\n",
+      "9779\n",
+      "9780\n",
+      "9781\n",
+      "9782\n",
+      "9783\n",
+      "9784\n",
+      "9785\n",
+      "9786\n",
+      "9787\n",
+      "9788\n",
+      "9789\n",
+      "9790\n",
+      "9791\n",
+      "9792\n",
+      "9793\n",
+      "9794\n",
+      "9795\n",
+      "9796\n",
+      "9797\n",
+      "9798\n",
+      "9799\n",
+      "CALLED\n",
+      "self.model._steps=9800\n",
+      " TEST self.model._steps=9800\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "9700  0.6264\n",
+      "9701  0.6406\n",
+      "9702  0.6062\n",
+      "9703  0.5840\n",
+      "9704  0.6138\n",
+      "...      ...\n",
+      "9795  0.6252\n",
+      "9796  0.6192\n",
+      "9797  0.6358\n",
+      "9798  0.6650\n",
+      "9799  0.6634\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_098.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_098.parquet'\n",
+      "Saving model to output_dir/model_data_098.parquet\n",
+      "Saving agent to output_dir/agent_data_098.parquet\n",
+      "9800\n",
+      "9801\n",
+      "9802\n",
+      "9803\n",
+      "9804\n",
+      "9805\n",
+      "9806\n",
+      "9807\n",
+      "9808\n",
+      "9809\n",
+      "9810\n",
+      "9811\n",
+      "9812\n",
+      "9813\n",
+      "9814\n",
+      "9815\n",
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+      "9818\n",
+      "9819\n",
+      "9820\n",
+      "9821\n",
+      "9822\n",
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+      "9829\n",
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+      "9832\n",
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+      "9834\n",
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+      "9836\n",
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+      "9839\n",
+      "9840\n",
+      "9841\n",
+      "9842\n",
+      "9843\n",
+      "9844\n",
+      "9845\n",
+      "9846\n",
+      "9847\n",
+      "9848\n",
+      "9849\n",
+      "9850\n",
+      "9851\n",
+      "9852\n",
+      "9853\n",
+      "9854\n",
+      "9855\n",
+      "9856\n",
+      "9857\n",
+      "9858\n",
+      "9859\n",
+      "9860\n",
+      "9861\n",
+      "9862\n",
+      "9863\n",
+      "9864\n",
+      "9865\n",
+      "9866\n",
+      "9867\n",
+      "9868\n",
+      "9869\n",
+      "9870\n",
+      "9871\n",
+      "9872\n",
+      "9873\n",
+      "9874\n",
+      "9875\n",
+      "9876\n",
+      "9877\n",
+      "9878\n",
+      "9879\n",
+      "9880\n",
+      "9881\n",
+      "9882\n",
+      "9883\n",
+      "9884\n",
+      "9885\n",
+      "9886\n",
+      "9887\n",
+      "9888\n",
+      "9889\n",
+      "9890\n",
+      "9891\n",
+      "9892\n",
+      "9893\n",
+      "9894\n",
+      "9895\n",
+      "9896\n",
+      "9897\n",
+      "9898\n",
+      "9899\n",
+      "CALLED\n",
+      "self.model._steps=9900\n",
+      " TEST self.model._steps=9900\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "9800  0.6592\n",
+      "9801  0.6786\n",
+      "9802  0.6664\n",
+      "9803  0.6486\n",
+      "9804  0.6418\n",
+      "...      ...\n",
+      "9895  0.6196\n",
+      "9896  0.6306\n",
+      "9897  0.6260\n",
+      "9898  0.6456\n",
+      "9899  0.6434\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_099.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_099.parquet'\n",
+      "Saving model to output_dir/model_data_099.parquet\n",
+      "Saving agent to output_dir/agent_data_099.parquet\n",
+      "9900\n",
+      "9901\n",
+      "9902\n",
+      "9903\n",
+      "9904\n",
+      "9905\n",
+      "9906\n",
+      "9907\n",
+      "9908\n",
+      "9909\n",
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+      "9939\n",
+      "9940\n",
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+      "9942\n",
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+      "9945\n",
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+      "9949\n",
+      "9950\n",
+      "9951\n",
+      "9952\n",
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+      "9954\n",
+      "9955\n",
+      "9956\n",
+      "9957\n",
+      "9958\n",
+      "9959\n",
+      "9960\n",
+      "9961\n",
+      "9962\n",
+      "9963\n",
+      "9964\n",
+      "9965\n",
+      "9966\n",
+      "9967\n",
+      "9968\n",
+      "9969\n",
+      "9970\n",
+      "9971\n",
+      "9972\n",
+      "9973\n",
+      "9974\n",
+      "9975\n",
+      "9976\n",
+      "9977\n",
+      "9978\n",
+      "9979\n",
+      "9980\n",
+      "9981\n",
+      "9982\n",
+      "9983\n",
+      "9984\n",
+      "9985\n",
+      "9986\n",
+      "9987\n",
+      "9988\n",
+      "9989\n",
+      "9990\n",
+      "9991\n",
+      "9992\n",
+      "9993\n",
+      "9994\n",
+      "9995\n",
+      "9996\n",
+      "9997\n",
+      "9998\n",
+      "9999\n",
+      "CALLED\n",
+      "self.model._steps=10000\n",
+      " TEST self.model._steps=10000\n",
+      " TEST self._cache_interval=100\n",
+      "        Gini\n",
+      "9900  0.6436\n",
+      "9901  0.6410\n",
+      "9902  0.6306\n",
+      "9903  0.6366\n",
+      "9904  0.6398\n",
+      "...      ...\n",
+      "9995  0.6358\n",
+      "9996  0.6332\n",
+      "9997  0.6128\n",
+      "9998  0.6314\n",
+      "9999  0.6778\n",
+      "\n",
+      "[100 rows x 1 columns]\n",
+      "3\n",
+      "model_file='output_dir/model_data_100.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_100.parquet'\n",
+      "Saving model to output_dir/model_data_100.parquet\n",
+      "Saving agent to output_dir/agent_data_100.parquet\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(10000):\n",
+    "    print(i)\n",
+    "    cacheable_model.model.step()\n",
+    "    cacheable_model.cache()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "248ea838-5c71-4838-b6ad-fdcfdc3a1c1e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "gini = model.datacollector.get_model_vars_dataframe()\n",
+    "# Plot the Gini coefficient over time\n",
+    "g = sns.lineplot(data=gini)\n",
+    "g.set(title=\"Gini Coefficient over Time\", ylabel=\"Gini Coefficient\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9cf0a6c4-a976-4835-82e2-706f49b175d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Gini</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.2822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.3666</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.4510</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.5058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9995</th>\n",
+       "      <td>0.6358</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9996</th>\n",
+       "      <td>0.6332</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9997</th>\n",
+       "      <td>0.6128</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9998</th>\n",
+       "      <td>0.6314</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999</th>\n",
+       "      <td>0.6778</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Gini\n",
+       "0     0.0000\n",
+       "1     0.2822\n",
+       "2     0.3666\n",
+       "3     0.4510\n",
+       "4     0.5058\n",
+       "...      ...\n",
+       "9995  0.6358\n",
+       "9996  0.6332\n",
+       "9997  0.6128\n",
+       "9998  0.6314\n",
+       "9999  0.6778\n",
+       "\n",
+       "[10000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gini"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c9515417-7c5f-45f8-8ffa-2f466288ea1f",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        Gini\n",
+      "0     0.0000\n",
+      "1     0.2822\n",
+      "2     0.3666\n",
+      "3     0.4510\n",
+      "4     0.5058\n",
+      "...      ...\n",
+      "9995  0.6358\n",
+      "9996  0.6332\n",
+      "9997  0.6128\n",
+      "9998  0.6314\n",
+      "9999  0.6778\n",
+      "\n",
+      "[10000 rows x 1 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pyarrow.parquet as pq\n",
+    "import pandas as pd\n",
+    "import glob\n",
+    "\n",
+    "# Get a list of all Parquet files\n",
+    "model_files = glob.glob('output_dir/model_data_*.parquet')\n",
+    "agent_files = glob.glob('output_dir/agent_data_*.parquet')\n",
+    "\n",
+    "# Initialize lists to hold dataframes\n",
+    "model_dfs = []\n",
+    "agent_dfs = []\n",
+    "\n",
+    "# Read and append each file to the list\n",
+    "for model_file in model_files:\n",
+    "    table = pq.read_table(model_file)\n",
+    "    df = table.to_pandas()\n",
+    "    model_dfs.append(df)\n",
+    "\n",
+    "for agent_file in agent_files:\n",
+    "    table = pq.read_table(agent_file)\n",
+    "    df = table.to_pandas()\n",
+    "    agent_dfs.append(df)\n",
+    "\n",
+    "# Concatenate all DataFrames\n",
+    "model_df = pd.concat(model_dfs, ignore_index=True)\n",
+    "agent_df = pd.concat(agent_dfs, ignore_index=True)\n",
+    "\n",
+    "# Display the combined DataFrames\n",
+    "print(model_df)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c6c73b47-b23c-4347-9951-b721ea5ed5da",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model_df\n",
+    "g = sns.lineplot(data=model_df)\n",
+    "g.set(title=\"Gini Coefficient over Time\", ylabel=\"Gini Coefficient\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "21cfeacd-dcfa-485f-848e-88cb619addfc",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Gini</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.2822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.3666</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.4510</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.5058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9995</th>\n",
+       "      <td>0.6358</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9996</th>\n",
+       "      <td>0.6332</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9997</th>\n",
+       "      <td>0.6128</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9998</th>\n",
+       "      <td>0.6314</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999</th>\n",
+       "      <td>0.6778</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Gini\n",
+       "0     0.0000\n",
+       "1     0.2822\n",
+       "2     0.3666\n",
+       "3     0.4510\n",
+       "4     0.5058\n",
+       "...      ...\n",
+       "9995  0.6358\n",
+       "9996  0.6332\n",
+       "9997  0.6128\n",
+       "9998  0.6314\n",
+       "9999  0.6778\n",
+       "\n",
+       "[10000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "421bd10b-33a6-470e-bcc1-0fc0d840062d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Gini</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.2822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.3666</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.4510</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
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diff --git a/mesa/example.ipynb b/mesa/example.ipynb
index 06a09cfb2c7..84f02283e0b 100644
--- a/mesa/example.ipynb
+++ b/mesa/example.ipynb
@@ -1197,7 +1197,9 @@
    "cell_type": "code",
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    "id": "25bf27a3-2605-4b0d-be6d-68ff33bdb1fd",
-   "metadata": {},
+   "metadata": {
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+   },
    "outputs": [
     {
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@@ -1272,7 +1274,7 @@
    "outputs": [
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1370,16 +1372,16 @@
      "text": [
       "       Gini\n",
       "0    0.0000\n",
-      "1    0.3146\n",
-      "2    0.3576\n",
-      "3    0.4404\n",
-      "4    0.4484\n",
+      "1    0.3306\n",
+      "2    0.4212\n",
+      "3    0.4780\n",
+      "4    0.5296\n",
       "..      ...\n",
-      "96   0.6668\n",
-      "97   0.6820\n",
-      "98   0.6826\n",
-      "99   0.6416\n",
-      "100  0.6364\n",
+      "96   0.6400\n",
+      "97   0.6526\n",
+      "98   0.6444\n",
+      "99   0.6492\n",
+      "100  0.6492\n",
       "\n",
       "[101 rows x 1 columns]\n"
      ]
@@ -1413,16 +1415,16 @@
      "text": [
       "       Gini\n",
       "0    0.0000\n",
-      "1    0.3146\n",
-      "2    0.3576\n",
-      "3    0.4404\n",
-      "4    0.4484\n",
+      "1    0.3306\n",
+      "2    0.4212\n",
+      "3    0.4780\n",
+      "4    0.5296\n",
       "..      ...\n",
-      "995  0.6322\n",
-      "996  0.6274\n",
-      "997  0.6276\n",
-      "998  0.6088\n",
-      "999  0.6256\n",
+      "995  0.6524\n",
+      "996  0.6772\n",
+      "997  0.6878\n",
+      "998  0.6702\n",
+      "999  0.6804\n",
       "\n",
       "[1000 rows x 1 columns]\n",
       "       Wealth\n",
@@ -1433,9 +1435,9 @@
       "4           1\n",
       "...       ...\n",
       "99995       1\n",
-      "99996       0\n",
+      "99996       1\n",
       "99997       0\n",
-      "99998       2\n",
+      "99998       1\n",
       "99999       0\n",
       "\n",
       "[100000 rows x 1 columns]\n"
@@ -1483,7 +1485,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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BDREREdUrDW7MjTsEQUBZWRksFovWTan1/Pz8YDAYtG4GERGRhMGNQklJCS5cuICCggKtm1In6HQ6tGzZEkFBQVo3hYiICACDGxtWqxUnTpyAwWBAixYtYDKZWOjPCUEQkJ6ejrNnz6JDhw7M4BARUa3A4EampKQEVqsVMTExCAwM1Lo5dUKzZs1w8uRJlJaWMrghIqJagQOKVbgq60yVmNkiIqLahldxIiIiqlcY3BAREVG9wuCmgdHpdPj222/d3n/ZsmUIDQ31WXuIiIi8jcFNPZKSkoLJkyejffv28Pf3R2RkJAYOHIh3331Xmtp+4cIF3HjjjW4/56hRo3D48GFfNZmIiMjrOFuqnjh+/DgGDhyI0NBQvPbaa+jevTvMZjP27duH999/H9HR0bjlllsQFRXl0fMGBAQgICDAR60mIqp9jqTm4s+jGWjTLAhZBSW4tVe01k0iDzG4cUEQBBSWalOpOMDP4PZspMceewxGoxHbt29Ho0aNpO1t27bFrbfeCkEQAJR3S33zzTcYMWIETp48iTZt2uDrr7/GggULsHXrVnTo0AGLFy9GfHw8gPJuqSeffBJZWVlef39ERLXR06v2YO/ZbOnfcW2aIirEX8MWkacY3LhQWGpBlxm/aPLa//53KAJNrv9EFy9exK+//orXXnvNJrCRcxYkvfDCC3jjjTfQoUMHvPDCC7jnnntw9OhRGI38eBBRwyMPbADgUkEJg5s6hmNu6oGjR49CEAR06tTJZnt4eDiCgoIQFBSE5557zuHjp0yZgmHDhqFjx46YNWsWTp06haNHj/q62UREdUKRRtl7qjremrsQ4GfAv/8dqtlrV8e2bdtgtVoxevRoFBcXO9yvR48e0v83b94cAJCWlobOnTtX6/WJiOqa/OIyu21aDU2gqmNw44JOp3Ora0hL7du3h06nw6FDh2y2t23bFgBcDgj28/OT/l/svrJarV5uJRFR7ffD3vN225i5qXvYLVUPNG3aFEOGDMHChQuRn5+vdXOIiOqk81mFeO7rfXbbi0p5s1fXMLipJ9555x2UlZWhX79+WLFiBQ4cOIBDhw7hs88+w8GDB7moJRGRC/+cz5H+399PD5Ox/BJZWKKeudlzJgtL/zwhzUal2qN297eQ29q1a4ddu3bhtddew7Rp03D27FmYzWZ06dIFU6ZMwWOPPaZ1E4mIarXxn2yX/r9FSADaRwTh139TUVRmH9xkF5bi1kV/AgC6NA9GXNumXm3LhkNp2HAoHc/fdJkUZJH7GNzUI82bN8eCBQuwYMECh/vI7zBiY2Pt7jhCQ0Nttt1///24//77vd5WR4rLLDAbmWUiopqlzM4IAAJMBtXfAcC2E5nS/5+9VIg4L7fn/qV/AwAig/3x6DXtvPzs9R/DQao1tp3IRPeZv+KDjce1bgoRNTBZhSV228QZq2oDilOyC2WPLfVZu/acyfLZc9dnDG6o1pi2ei9KLFa8+tMBrZtCRA1MtkqA4i8FN1as2X8Bj3+5CwUl5VPFL2QXSftl5jsutVFdGXm+e+76jN1SVGvo3VxqgojI27ILbIMbQRCk4Kaw1IIJn+0EALRpGojE6zshxSa4sc/6eMtFHz53fcbMDdUaBn3dDW4uZBdiyqo9OJSSq3VTiKgKDqfaf3cDK8bciNkaADibVd4dlS7LqFzM810AUswaO1XCzI0KTutznzePldFQd4ObWxb+ifTcYuw6fQm392mJ3q1CMaBduNbNogZg4+F0AMDVHZtp3JK66++TmZj+3T822wQATYNMAIAMWfBSaik/5+XIurGyCkthtQrQe+kGzWqtPK/yalQ1zNzIiJV6CwoKNG5J3VFSUv6l90YdHYO+8uNYaqlbRbPSc8vv4o6l52POL4cw/dv9GreIGoKCkjI89PF2jFmyTQpyyHMf/qE+iaFZkBlA5fcbAMoqzk25RZXZnG0nMnHzgk2wWL0TihSXVZ7/rLzZrhJmbmQMBgNCQ0ORlpYGAAgMDHS6mnZDZ7VakZ6ejsDAQK+sIC6/6blUUIKIxnV3Fd5j6awUTb6XmV+CkoqL7a7TWTbZmx2nLiEy2IyWYYFaNa/OaFoRxMg9O7QzmjW2D25+3p+C2Kk/2u3/74UcnMksQGx4o2q3R76WVR27z6s1GNwoREVFAYAU4JBzer0erVq18koQKF+wLreoDBGNq/2UNaLMwdmn1GKFn4HJUfKdnMLK74y80Nzh1FyMfPcvAMDJ2cNqvF11wdG0PMz77TAeH9weoQGV6+u9MqIbhnSJRGSwP85eKs/iy4MbZ0q8FInIgxuOuakaBjcKOp0OzZs3R0REBEpLfVe7oL4wmUzQ66t+AT+RkQ9BENC2WRDyZGne6978HdtfTEC4yh1VbeNoxeBnVu3B/Lt713BrqCHJKao8RxWXWpFXXIYF647ghz0XNGxV3fDIp9txLD0fvx9Kx92Xx0jbxcAGAJo2Kj//uBu05BZ555ohLxqYX1IGQRDYi+AhBjcOGAwGrsfkYyVlVlz7xgYAwMGXb7ALEr7ecRaPDKr9lTkdrTvz7e7zePOuXnV6FhjVXmUWK05frBwfWFxmwVfbz+C9323Hj/DCqE7sOs4rLkN+xWyo0XGtpMAGKF9fyqjXoczNsTTyTFp1yIsGWoXyMTjitPTaoMxixX8+2IrgACM+HHu51s1RxZw5aUaeRr+YX2K38u65rELlQ2olR5kbADiSxqnh5LmVf5/B89/sczpAdcJnO/Ds13ulfxeVWnE60/47U+Ag+G7o5Os15RWXH6O2zYJs9tHpdGhkdj8HkFPFzE1abhFufOsPLP3zBAD7c0pt+BvKhw0cTMnFtpOZ+O1Ams00+dqEwQ1pxmKpPHEXl1rsFqeTF8mqzZydeC7ls2uTPPfs13vxxdbT+Gmf4+6l3w7YjgvMyCvGkoqLo5z8okSV/OXBTUVQEmS2z44EOQlu/P1sL6E5iirHa/anYOfpSy7b8t7vx3HgQg5m/d+/AOyzwb7+G6blFmHN/hSHwfSnW06h68xf8OPe8s9jak7ludmXNX6qg8ENaabUWpmpyS4shXLGo/wLVJsp77JahFSmtXlhIU/Ja0edzrQvS5FdUKp6t/y7g6ngefwMqjLLunnWHyo/dmpZGmfBzcZnrrX5d45s3ODTK/dgwmc7MPajbS7rgSnPId7K3BSXWTD6wy1467cjDvdZkHwE/V9NxoTPdmDl9jOq+4ilLSZ+UV6l+ZSsO7S2VlBmcEOaKZNlbtSi/9ScurGmivIua9at3RDftikASH35RO6S1zhRrndUUFKGq+esR5cZv7j9fAxu1CmzLkDlQplyQf6Og5uIYH9MvLZyXOCnm0/hvo+24kxmAb7eeRYAkFtc5nIsjlmWRSous9gt1Hkhu2pd9D/tu4A/j17EvN8Oq/4+I68Yb66t/N26g+7NEj51sbLUhS/X1aqOWhHcLFq0CLGxsfD390dcXBy2bdvmcN9rrrkGOp3O7mfYME539KWMvGL88k+Kw2nPVSEPbtQWh8vML6kT1aKVwU3/Nk3QyCyWbde+r5zqFnlAo1yz6NylQtUFHp1hcKNOLZAZ2N6+qrjafnJPJXRE/9gmAICUnCL8cSQDT63YbbPPmUvuF4ZNyym2C27UMnjuKCxxfr5WdqM1MhlwPqvQZqA6ADRtZJL+/6Xv/8Hh1Dzp3xnsllK3YsUKJCYmYubMmdi5cyd69uyJoUOHOqwzs3r1aly4cEH62b9/PwwGA+68884abrlvZeQVY/vJzBp9zee+2ovhCzapprzvWrwZj3y6A59uOeW115N3S6mlNkssVhxLz8O+s9lee013fLf7HFb+rZ6eVVNQcSJqHuKPdU8PQkiAHwJN5Xd7r685iPm/Ha5zFZdJO/ILjvK7KO/2ED14ZRunz5en8pj67nxWIf635qDTjIeyC+qKtk1UZySdyHBekNNo0GNYj+Y227afsh1nc/aS88yLfNHOf87n2N0wncyoftV8tRvFXMVnI8BkwA3zN+LqOettbjjlx2rZXyex+fhF6d+X2C2lbu7cuRg/fjzGjRuHLl26YPHixQgMDMSSJUtU92/SpAmioqKkn7Vr1yIwMLDOBzeX8ktwVDazZviCTbhj8Wb8caTmSqqv2H4G+85l44utp+1+d7ziCy4OKPMGR91SU67vKP1/wtyNGL5wk92dhK8UllgwefluPPv1Xqx3M0VbWHEB6tI8WJptIZ4MLhWUYv5vR/D1jrMet6Wo1IKJX+zEi9/uQ3EZM0ANhTwzU6yYQahWR2WQYk2pt+7uZfPvqnSN7jiViQFJyfh8q/duZlxJyy2yWVOpOh79bAfe3XAMDyzb7nAf5bE1G11PtZaPp5t6Y2fp/4MDnM+oSs8twrYTmYid+iM6vPCTXcCUJfub//JPCgoVbfti2ynsOOX5za68AoC8u1OknN315bYzUgCdfCBV2m50Us7C2WxRLWka3JSUlGDHjh1ISEiQtun1eiQkJGDz5s1uPcdHH32Eu+++G40aqZe8Li4uRk5Ojs1PbTRk3kYkzN2IY+nl6b4LFTOFvtl1rsbb8sqPB/Dp5pOqv/NmJ5E8m3HgQvnfJTo0AJMGd0BksG3xvkMqK/b6gnwQ89YT7p1MLlXcdYUEVlY5bWSyPVH+c979z11abhHm/3YYK/4+gx/3XsBnW07j7xOuZ1xQ/SC/4MiLx5VZrFjy50m7/QMUn7X4tk3xW+LV0r/Fac6eGPnuZpzPLsKbv6qP1fCmlOwi/H44Hf1fTcbU1XtdP8ANeyqyveJ5RY1ydqZ83Ivcm3f1RM+YUPz4xJUYN7A8S3ZZ82BMkNXgCvb3U32s6PVfDuGu98qvaaUWAS9+u8/m9/KA9nBqrhQwtKlYyqGo1IqR726uVje9Wj0uZeZG7oQsW+RsfavaGtxoWsQvIyMDFosFkZGRNtsjIyNx8OBBl4/ftm0b9u/fj48++sjhPklJSZg1a1a12+pLgiBIKcA/DqejnazWQk31ZyrvmKZ/9w/ui4/16WvKC2OJaU6xtk1YoMlmQHEjlSmavpAiC25cVRsVBAGXCkqltGyTwMp+6UBFyttVn7vFKuCt3w4ju7AU/5zPsUtrp+V6PnNs9c6zOJqWh2eGdmIRtzokq0A9czN84Z+qF2vlRTnAZEBEsD9G9mmJr3eerVa3lLcq7jpyJrMAV72+Xvr3yu1n8ewNnT2qTJ5VUILCUguahwS4tX9aThH2nM3GccX6byYHwc0VbZviu4kDAQCdo4LRo2UIurcMsdknOMB5cKMMIpSTJeSBx4mMfCkb3CmysU2Wx9NifiWybE1BqQVhdu1y/PeV33w6GzuozIDVFpp3S1XHRx99hO7du6N///4O95k2bRqys7OlnzNn3B9LUVPkI+mVF6GNh9NrZFCtWnlxtcHD3myLs3EoYbJAofyFq/96m45k4JtdzruHUm2CG+cXhdk/H0Sfl9dK2bUw2aA7ZeZmw6F0/OykZsm+c9l4e91RfLz5lF1gA1StlkTiyj14Z8MxbHMzA0W1g3wQsbw70lEWQtmdIo73Emu2VKccga+ra284ZN/16+nq5r3+uxbxSeuQVVB53BxN37ZaBfR/LRnjP7HvrnKnW8qg1yGubVPpGItcZW6UGitmYMmzSAUlFiz76yQAoH2EbVFBTycoyPcvVOmedDaL66NNJ6ShEs5ed9lfJ2t8XKQ7NA1uwsPDYTAYkJqaarM9NTVVWsDSkfz8fCxfvhwPPvig0/3MZjOCg4NtfmobeRVbtZkQ8i6ZS/kleOu3I16v3qsWfRdVRP3yIMdX3VJKQ7vaZvPU+os9de9HW/HUij04np7ncJ802R2Vq7vW9zaWl7lPq1hUr4ksuIkKsV/R/NHPdzp8Lld312qzyZyRB6HKGTdUu8mzteLn3lmlYvmUZrNRLwUk4riv2jxbSi2g2HL8Ii5kF2LDoTSXN1Pyc4h8Bo880/v6moPS7KO95xxfhB1lbtwh/+67I1Bx81OkCB5KK8YjKqehe1oNWN5lpBagZFYEhOFB6u2fvHw3MvKKXX6Ghi/c5FG7aoKmwY3JZELfvn2RnJwsbbNarUhOTkZ8fLzTx65atQrFxcW49957fd1Mn3tMdtETux/kXzR5dD3hsx2Y99thu6mG1aU2YFVMlcozGI7WUXKXIAjSxVY+oFj0+sgeAGDXJVbdAbXybjdnRafkAY2zzI3aSVeebbq6QzO73zt6HACUWJy/P0+7J0t8FJCSb2QXlOJCdiHe/PUQ3t94TNouBjfOxjXIAwT5RVO8MHoa3Mg/ozr4NnOjV8kMHU7NwzVzNuD+pX/jjyMZTh/v6Dsqn93zzoZjmPV//wCwrc+i5GjMjTuaNfZsgV/lzYz491XW3lFOQ/c0cyPP1qg9dl1FlWu1KfBA+VjBz7w4Q7Ymad4tlZiYiA8++AAff/wxDhw4gEcffRT5+fkYN24cAGDMmDGYNm2a3eM++ugjjBgxAk2bNq3pJnud/It4Ma+8tou8r1SeVhYHuXqzq+HspQLcuuhPAOUnR/ELJd7tyEfyH0zJxZkq1lwAgAXrjqLPy2vx27+pKLPaZmP6t2mCuypW5zXodZg/qpf0u+pmbuSP9zM4/tjLTwDOgpsP/jhuty1UNqA4rJEJr4/sgSeu62Czj6MsSkmZ8xDkooeFsuRBaB0oFdSgCYKAPq+Ud60sWHcU8iRNccV3sEARoPz31q7S/8svyvKbIrFrxp1uqa93nMXLP/wLQRC8kiV1l1om4khqrtSGv12Uw5Bnui9kF0rnLGU30crt5d3RyinZb97ZU/p/s0pRP090bWHfK3Clg6AhLbfYJogU19VbeE8fm/38/fToHNVY+rfHwU2pvFvK9rFWq4DDFb0GU67v5LCkwFduzvT893xOrSp5oXlwM2rUKLzxxhuYMWMGevXqhd27d2PNmjXSIOPTp0/jwgXbsQqHDh3Cpk2bXHZJ1RXykeh5xWV2419yVU5O4ih6bxi7ZJs0O8ts1EuzL8QvxjnFCcHZDARX5lZUw3zok+1S6lX0+OD2Nv8e0TsarZoEAqj+oDX5SdTZtMaCUnlw47hbSq1uhfKEetflMUgc0tFm2/ks9YHBamOeAOB/I7sDsC+25Uq+7ETmSdaruMyCtDqy7EV9kZpT7LDbSfxcKLMv98a1xrxRPbHxmWttBpga9ZWn9EYm9zM3T6/ag482ncCfRy9WOzvrCbUbCPln127snYL8ezF5+W7cMH8jAPsuJvH4nlUM7O8kCxzMTm563HF3/1Z22+QzqgDgvfv6wmTU40J2EfZWjFMRBEE61/aMCUWELAvUyGzEh2P7Sf8+lua4S12NPBjKLynDv+dz8M/58tctKLVINz7NGpsx/eYuaNfM/roinuseGNgGr9/RA+MGxgKwH9d009t/YOrX+5QP14zmwQ0ATJo0CadOnUJxcTG2bt2KuLg46XcbNmzAsmXLbPbv1KkTBEHAkCFDarilvpEvm6qZX1xmd+e04WAaJny6Q5pKCAAtw9ybGeDMjlOXsO1EplTDBihPcYuZG/EkdyrTNpVbVGbFqYv5Ho/7+VcxHVrsluoVE4p1Tw/CVSpdOd2iy++GqtstdTClctyS02mNbmZu1C4AykGCouUPXyH9f3qefeCw72w2dqoMIm4fEYSYiuBOXrxt/7lsfLXjrNPxCPI7/fziMuQUlaLMYoUgCHj2qz144Rv1k9Dt7/yF/q8l29RcIt9ydrMgBvXKO3a9XofberdEq6aBNhdyWWwjdUu5Ghgvl19SZhPgl1R8ZnzFVVbJVSZAOUbxZEU9rBLFOdTPUH5Dk55rmzkNkc1yqs6YGwC4N64VZg7v4vD5b+8TjaFdoxDXprya8ZGKQEV+vvf309sEDUFmI1qGBaJnxeysp1ftwY5TmcjIK8bHf510Wa1afp5KyS7CTW//gWFvb0JRqUXqGjPodVL27+tHByA6VP3aEhboh7v6xWDGzV3w61NX22TWReKSE7WBplPBqZw8q5BfbLHLUqxWqXXjLPvgjnUHU/HAsu0w6nU23RZmP7303OLdxClFAb3MvGJc+8YGWAVg30vXI8hsdGuq8eTlu2z+LfaDm416qfidkjieoKiamZvRH26V/l+ZMZKT/y3ySspgtQp24wIu5ZdglUqq1tEMjSvaNkV826bYfPyi3YVmz5ksqUtQTq8Dfn3yahxIKb/wye9Qb15QPnivddNAXF5R9l1Jfvd78mIB4l5NRufmjTHvrl5Sin7aTZfZtVmsx/PD3gt4MqExyD1r9l/A1zvPYc4dPRDqItug9K+z4KbiwucsCJDPaNLLvodiJlFZqE1JXurf389gc0G0WAVsOJSOaztHOH0OTxWXWWA2GlxmlVwFZo4u7srgRsxoKTOkEbJ6WtVdKkWn02HcwDZo1SQQD368Hf+Ja2VT3E/8ron/FcfDKI+/fBCxuK+8ltHi34+jsMSCTUczsOPUJbx9T2+HbZJ3S52UjTcqKLFIx15+/g4NNOHZGzph8vLdds8lZgh1Oh06Rjb2eJJDTasVmZuGzGoVbL5UecVlbmUpHHVjuOufc+Un1DJFOlytWyo91/ZDfCw9XxoX0P2lXzH9u/1uvaZyfRRxlpGzAn3iHUV1MjfKuz9nM08KFGNV8lTGBDz3tXqhMbUVhUViVkc5Vmftv6lqu6NpkBl6vc7uAiX/W4gzuz7bcgoPLPsb+cVlmLZ6L5J+PmATpH23+zwKSy3YdToLD3z8t7TdWbebnnVxPDLhs51Y+28q5jtZfdkRZ8FNicVqd45wRh7oiGPAjqfnY+/ZLIePkQcYJoPebl2jccv+Vj6kWtYfTEOXGb9g7q+HXM4SdDVjUS1ws1gFu/NFUZkFVquAkortJqMeS++/3GYwtrdmFV53WST+mjoYr9zaDY1lXdViRlw8v4p/U/HGzajXwc+gyNxUnDfk4wRNRj02HS0faP39nvN2r5+WUyQFd/LPjbxLvMxilQJm5Q2OyUH3nHKwsycZQS0wuNFIqcWKgpIyuyqZ57IKcSjFdZeA/M5k6/GL+PWfFI9e39Edj8morxxQXGKBIAh2+yq7oz7bYr9cg9zBlBxkF5Q6LMzlLNioDG5cB3NFpRbsOJVp93zK8THOFv9UdjeNW/o3flEc219VAhKzUe80rS2e5Pafy8HuM1nSdkd3ruJCdWJxsKJSK9745ZDNchx6HXAyIx8vfrsf6w6mYfWuc/hy2xm89/txRb2eyr+fvHCZsxoXPi5vUm9V5W5W2V2rVGKxur2EgkEWlMrHq9yy0D47CJRnhG57p/J38vEfvrD/XDbGLfsbFquAt9cdtRtPKHYfiVxdQNXG4pWUWaWbP/FCLQjlY0zE8+aCe3rbZaOczaL0VIvQAOj1OpuuarE7XOr2rzjO4n/F7fKbJHHclDxoLXby9zmWnof+ryXjng+2ALDNCv15tHLmWXGZVTr3KAukOjqPKaftX9GmabVmmPla7W1ZPTfqvc3oNWut6gBTtQJTSiWy+hej3t+Chz/dYXNBc8VRcKPX6aT0Y2GpBeOW/Y11FWssiV/U8x6MtTmalocb5v+Ba95Yb1eQSuTsrlT8oi1Yd9RpEAQAj3+5CyPf3Yz3N9pmR04qpn+WOnieY+l5dgX0dpy6hEc+3eH0dQHY3KGpkc/EkA/YdTRQWLzrbiw70S1cfxSLf6+cJpxfYpGW6wBsg5j95yovmI4CQ+Vdsfxut7ZXNE7LKcLO07VvSQpPu4sz80tcLsxYXGq1CUo/fyjO4b7yjJt89h6gPrbny22ncSaz8vtcqpIlamw2em3NJ/nnF7CfEh0ZbFsfylWXmtqYnOKyyiBm9WMDpL9JXlHlZA35BVysqXX/gFg33oFn5BkX8RCK0/XFGynxv+aK8658gLiYVZl4beVkC/nfS+n/KjI5OyrOYwU2Ewsqj1WpRR7c2GZuHM0mVc4mCwn0w/YXE9DWi5NbvInBjUZ2ns5CicVqlxUAAHfOI8VlVhSVWmwCGmX3kTOOghuLVZDuIM5kFmLDocpMgVjLQTl7CgD+OJKOG+ZvxO4zWUjPLUbSzwdwMCVHSrlfKih1OH3dWdAir++y9cRFh/sBlV08H1dU9xQp7/4sVvWL/aJ1R50+vzOOBhOL5HeYfkbxblLAluPq70m8Y1OO95EXKisoKbMp1S9fWfijTSdctvm/P/yLgbPXIS23COezCnEsrfICWstjG1z35u+4/Z2/vBbglFmsduM0qsKg9+yU+u4G15+5WxZtwpI/y/+er9/Rw2FNkvLXr/zDKcv03/jWH7jxrT9silgqM5UPffw3LihuXnKLy2zGrFWHsoyEMnPZXFH8Urnm0j/ns23+5urBjVW6kJuNelm9n1LpbyyfGbXoP33w59TBTo+rN4jnuYCK77YYeBSWllVsL29TsOxcIgYel8c2QfLTgwDYd+PLz5/yLqaiUotdCQFRibNuKQfZGLVlHxr7+2FQJ/WaXlpjcKMB+V3QhezyE4mnFS4PpuSi8/Q1+G53ZZ+rq7scOUfBjU5XXm8GAJb/bdvd1KyiW0ltavp9H23DwZRcPPTx3xgy73e89/txLFp/zOa9iulXT1KZ8iyRmFYVBMHpnaSf0fbKrBxD4GhAcXXqXDgaTKzWhr+OZqDUYsVP+1JwPls92yYflDrj5i6q+xSUWHBJVnJeHui4Y+/ZbJzLKkT/V5MxYPY63PT2H9LvtF4vJqeoFBeddPGIn8E1+z3rjlUjCAJuWfgnBr+5AcVlFhSWWDy6UZDzJHOTllOED/5wHISK9U1OXSyQ/rZdmjuvsO5quYQDF3KQuHKP9G/lZ77UIkjjOOSZn83HL3ql0vGBC7YX5n2KisHKzI04Dmb7yUxcP28jhr29Cbe/8xdOi7OiVL7LxaWVgarZaJBuPLYcz5RuDuQXcKNB73CGkDc56pYS/7ahAeXfefnkCnk7HbVRnoGVZ1w7T1/j8PxSIuuWcje4cXTejmtTO2vNMbjRgHycTUrFh0/tzr9JI5PLBeT+t6ZygVFPLm6OgpuwQBOuu6w8TXtB8cUIV1ThVKuJkJFXIrVjx8lM1UCoddNAt9v5ZEJlnZjM/PLnHf/JDlw393cpYFi47gi6v/SLtJ+f4u5Z2Ue972y2atdagF/l3yBW0caiUgtuXfSnwynUroIbeZfPB3+cQNJPB/HFNseVP5s0qrywPHBlG7w03D7AKShWZG48rIXjzFvJR1SzijWl16xf0feV31wOKPWkK9aRjLwS/HshB2cvFeLghVzcsfgvDJid7FH3q0it4q4jU76qHJiuFhT1igm12+bqJkj5NH1bK5dKtB2DpjbL+69j5dlEefE4ADiR7rz7zJXzWYUuJ0IoL6xicPOzIogVM8KuuqVMRj0CK77XL35bOfGhutO+PXF5bPnf4M6+5QVKld1Sl8TgpiKYHNa9OQD74+/vZ1A9z2QXluLf8zlIyS5yux5WSZlVyoorP1OOAnRHC3aGBTrvktcKgxsNyFPB4owhs1GPF4ddZrPfqyO62a1B4sxRDwo8OQqEmjYyOSwl3kwRaE25vhNimji+42nbLEh1CmvrprZBkbPAIL5dUylzkV1Y/mX87UAqTmTkY3PFSfiNXw/bdD0p+4yV08gXrj+KAbPXYemfJzDpi53SAGMxOLi2UzOb4l5A+ayEPWey8PlW9cHTyjVglJ5SFPNb8ucJp4Frk0a2v1OuMg6Uj7nJKpRlbgq9u4aUO2ONfMFiFaSu2SMuPtPeCG5Oy+o4rf03Ff+cz0GpRcB+J+sQycmziJ5kbuQLRKp9FmJVxjK4DG4Ur//5Q3EY1qO5zTZ5htdZNkY5Ru6YkzXZ3OFOsNhSkZ3IKiyFxSrAaFAe1/JjXloRxEwY1A5RFVmfvOIyaRaov5/ebpYPULPBzecPXYG/pg6WVhK3z9yUf2/FAeBRIf7Y9vx1+OrRAXbPJb8eRFZMY/9m1znc9PYfGDRnvdQT4EpJmRXpFUv9KM/3jsoaOQpulKUPakuVYgY3GpAP8hJPzmajwe5DJp+55I65aw+7NfCv1GKV1rBSah4agEYmg2oKUtm+dhFB6B4d4vB1SsqsqsGNPCui1wErH3G+jph4Qj+RUYCbF1R2nTi6C1SeuBzN/pj1f//ih70X8Ms/qcjIK5YKUF3fNcouPe6qm8JV5qZrixDc2quFzTZnKwmP6G27r1qQW1BSJt31AZ53S9VW8i68olILXvvpAFbvPIuXvv8HRxTjDarafSQnr+P024HKmXDuToeXf748WUW7Z0Vmpn1EECwqV5TIYNvvWyOTweEFRhQTZptx9Pcz4I6+LW22yccWOZuNFKu4CXG24Kw7nHWbzx/VC5Ov64B741vbbBcE4FJBiV02VjzmpdKsKJ3UxSaf0t3IbFRdnNPRdGdfMBn1aCEL2iqngpcf+0tScFN5PogI9lc9p8j//mKBQHH2ZXGZ1e0b3BKLVSolEdHY9lzn6AriqFtKOXC9OqvQexODGw3IT95idWKTUW/34THJas64/dxu1INJyS5SHbTcrlkjPHxVW+h0OtU7SXnmxmTUI7ZpIwzt6nj19uIyi+qdofyO9KOxl6OLyposciEVX54DF3JsZgGVWqx242kA2N3lqe0jl1VYgh/3Vi7x4e+nR/MQ2ztItRoY13epXLncnUua8q77U5UF6e7o2xJ7Zlxv9/riAGO5ldvP4rCsbIC8CvO1nZphkmyGhdrru2Iy6LH1+EWvzZRxl/zv9fmW03h/43EkrtyDZX+dxO3v/mWzb7aT6ezu2iSbIisfLO/OdwmwDW7cDYg+2XwSeyouSlOu76R6jJUXHbOTwGbZuMtxfZdITFcZn6V2kRSzKJ4EN8eq2S3lrNv0pu7N8dSQjoho7I8PxvTDPf1jpN9dzCuxy8aKg+fFMTd+hsrzpzil299PX75d48yNUqCszk1xmQWL1pfPIPN341wvz0KJwY0YpADlS3m4o6TMKvUaRChuWpXT8UWOjpm8CjNQe+rfMLipYUWlFuxSqXNiNurt7jD8DPaZG1erz7pT7EttXaTLY8OQ/PQ1CKu4AIYH2V4IQwP9bAKt7tEhMBn1aOegsjBQfiehNuBzaNcoxDYNhFGvQ4dIx48XKb88opIyq+oXadfpLBxKycWHfxyvWEzPeZrUahVs7rgjG/vbBZrK2UeBJgPeH1O55ovanbeSq+wOUD41PESlD1stc2OxCqrdNm3DG2HpuP52Yzb6tAq129eZEosVo97fgs+2Vm9V4AMXcpD08wGk5RTh+z3npXFmjqTJsjHymkBA+YlTPjskq6D6XXHy+h/yMWKPf7kLT63Y7XL5AXk3szsp+aJSC2Z894/07wCTwaaY5vM3dcaqCfF2mb1SJ7O5rukUgffH9FM9P6h9dsQZR47GNAX4GWyq9wKV3VL7z2Xj6ZV73O4CEWU7ySzKL5xDukQi6fYe6FHRjXMiI8/uhkVczFc83n6y8+fFirEk4vdNLdulZXAjZoVPZxbghW8qxwEpu+TUyM+34nlRvl6Wu0vilFisUtZT+Znp0jwYt/WORsJltnWAHE35Vh5fb479qw4GNzXs6ZV78KxsIKHIUeZG/qVeNSEeq1X6YeXcWfROrUtKWetAWbfl28cG2hQF6xhZPiallZPBwQdTcu0GJet05eN6fn1qEDZPuw4tw1wPLg51Etw4SnUPnb8Rr/x4AKPe2+LyDtxitS1UGN+uqd2YGyVl0Kms9KzGUQVjeQbI0bT4QJXMjSNqtTwA2GWDHOmtCIIWbzimvqMbft53ATe+9Qfe+/04+r+WjCe+3IWhFYsbOnLjW5Vdj2onSnkAUWYV8HbykWoFOY7uNAWhfDxDmouuL3nmpqTMisz8EizfdtrheBZloT9/o94mc/Pw1e1weWwTdIgMsvmcVXXKrVrWr6AiY5zuYEZa8xB/u4vWhewiLFp/FDcv2ISvd57F5C93u92GolILvtltX00XcNwt3SGi/Du460yWXXef2AUrBTfyzE3Fe3IW3JgNnmXEval9RBD0uvL3IF9x+5Ze0S4fO3N4V1zVIRwfjOmHLi3Kg78clc/vczd0dvo8RaVW6XOoDGJ1Oh3mjeqF1++oXDH9hZsuc1r76lrZZ9OTWbu+xOCmhv2474LqdrNRb5d2Nhn0Nh+oni1DpcyKI+5kbtT2UV545VmacQNjERveyKZbQ4z2g/39PJra3chUvo6Jyah3mYUSOcp4FJVaXKZAT2cWoMjFMbEIld1Ojwwq75Yb0K4pkm7vrjoYEbA/YbqzuKC/g+N0V78YJN3eHdGhAZh6o/pJKdDs+GQcGuiHl2/tKv1bPCbKVH6UooaII/0V61VdqsZYnse/3GW3zZM7O7UAQVmUcO7aw6o3DO5wZ2kDV2MIlv5ZmdUrtVjx2Oc7MHX1PsxwsCyJvHYTUP5dU8v8+fsZ8PeLCXhndB8M694cL9/azWk7HFELqsXjqlazCij/rCi/19mFpZjzyyHp3zsqsj95xWX4+K+TWLLphE3guelIBiZ8ugPpucWY9X//SN1wSmLpCSVxQPN7vx/HF4qB/GL7nY25EQf5q52ftMzc+PsZ7Lr8AvwMDjPUclEh/vj0wTgM6RKJK53U5VGO2VNKzSmSbsgcTWyQ31Q6W2wYAJbcf7mUaXNW+bwmceHMWsJktB/EazLarvPiZ9DBz8UdR4EbZdrVTtaNFKlr+b+HVEwNbyrrqpJ3W3lS8M2T2V8iRyei/BKLW1MfXU1pLi6z4FLFCbFJRXZKp9Phnv6t8PWOs3ZViwHYjYVyp06GozETQf5GJHSJxD39Wzl8bHiQGSaDXnUQtb/RgHv6t8L0iq4OMQugPG5Rwe4FN8rB1NUpxx8S4OdRWXt31hBT6/qRDwR2xys//AuTUW9T+dURZ8HYmcwCfLntjPTvYosVW46XF6tcvfMc3ryzp90db4YiE+TvZ3CYsQsyG3FT9+a4qXtz1d+7Q1leHyg/TxSVWqSsVJNGJptxZeXBjfPvqtjmN389hKV/ngRQnjWcMKgdAODej8oL/61x8v1zNmFCPlBVuS6deF4sKasccyOWchCzxWLGSuvZUmoigs04LqtMXZUZRo4Kh+p05eeL7tEh2HcuG50iG9sV/hOHJjRpZHJYkVg+887VrZtOp5OWjGHmpoFyFAiYjfZTFk0Gvc06SDqdzmVZfHe6pdzL3FT+O7Lijl/eLVXV2QbOFpd0xNGJKLeozK3Ba+IK2Y4msuQXlyFTnLGgyIw56m4ST8ofP9Aft/WOxuPXdXDZDkdZIHcCvpAAP6x+TL1LssxqhVH29xCzAMq/kbuZG3f2cydTBTi+K3TEnRlfapWEPRnzfCG7EB9uOoF3NhyTxmc4bZOT4EaZWVIWP7wiKRmL1ttWIbbvlnIc3HiDv0qQkldskcY+BZoMNlVxgfJuKXcCgL+OZUiBDWA7fskVk0GP/3t8oMPfO/terNmfgqJSixTs+xkqM8EHU8onHYgXf7X378msNl9QBhSe1EcSOQoMW4QEwM+gxzuj++D+AbH46P5+dvuIY3OUg4mVbu3VAsH+Rozs09LpfkDlOnju1trxNQY3NcjZrBOTyoBik1GPF2/ugp4xoTYXNrUZEaKqdkspu37kXxxxYKP8ZOdqSqqj51K7i3TFUSC1+PdjmPjFTrefx1FglV9skbpemihqNjhKx4qByqCOzTBvVC+n07qlxzi4E3Z3un83B9PulQGYxUHmxp02ArZBrPI5AeBoWi56v7wWC9e5XgFbOU3UlUtujJ2p7jIJ8vWMHJVEkFMbCOsoGFFmkFJzim26cgBIgbTI36R3a0B6Ven1OrvvUEFJmZSRCgs02d2ZR4cGutXd/J8PbJdlUNbCUnNn35a494pW2D9rKNpHOB7bpjZWSFRmFfDaTwekQdYmo166UIvfZfHz7sm5qqYoPz+L/tPH4+dwNJNW7JKKaRKIl27pipZhlX/LThVjJcX17dS+63LzR/XC3y8muDWEQDzeamOAtMDgpgblFpdJBZLmj+pl87vyMTf23VJ9WoXhu4kD0adVZaXRB69sgycT1DMFBW50Iah1XSmDG/lFXZ7+HDcwFt2jQ5xOAVcKDqh8/PEqTCc1GvQuV6n299Nj3qieTvdxdLLcdSZL6pYKa2R7MS5zsFSDO/3j9m1UPxl5cvKdcXMXtFVUhrYo2ij+7ZRTOo0GHdY8eRWWP3wF7r2isgvsRNJNNs+pdscuH7D7/sbjyCooxRu/HnbZ3gg3u8JEl/Jd3/UlzP1ddbvFKuCvYxkux8jIu5lcDRYGyrMTcmk5Rej3ylq8+O0+JP180MGjHFNmV/39DBjUsXxAplrVb29Q/k3zisuk49TIbLAL4js3b4xGZiNmDu/icnCqXHpeMbYcvygt4aDmnrhWeGVEd5eZIbVxZvLlJz7ZfMpmQLGyO/WailW/lUHaB2PsMxk1TXlDMkQ2qcBdjm6W2obbz0D9a+pgfD9pIPpVVEsWuyBdZdJ1Op3L7kmReJ6vLZkbjrmpQeIdoNo0S5NRb3fxddQXCtiPixAVujXmxj4AUnYfyO8s5BffmcO7wlPB/n5S/QV3MktqXN3XjuoX4/ROD3A87VU+0FF5J+MoczP6itaq251xtHaVJ8HNA1e2wQNXtsHctYfxdnJ55qRUsRCoo8yNn0En3Sn/+k9lhkGn02Hd09fgx70XHC6NkZFXgqYVnxH5zK2CkjKnM7lK3KwVI3Inc+Ooq/DDP44j6eeDuLJ9OD5zsnK2vOsrzY0Kxyu3n8XLI7qhoNiCsEYmrNpxFpcKSvHZFvVq1WrScoqkQM8uuDEaMPeuXvhy22nc3sf1jJmqUPZmFxRbbFaFVp4TxDv8cQPbQBAEvP7LQYeVa+XScopx9/tbnO7T1M16S2rfZ/mNEiAbUGzU2RU9FAfGy79fTwxuX6VAwtsGtgt3uJCwu/xN6ucT5TI5ANA0yIymQWapnpcY1Fclk+5IZeamdgQ3zNzUkM3HLmLF9vKTYWig/QyjMotg18fsLC3sKPBxJ3gQV6GVjwFRBkvuTG0WyU96B1++AftnDbX5vaulCTx9DTWN/f1crluTLzs2V3dUn1arLHQ37abLVPdr1cT99bFEju6AHI3FcSZxSEe0DCsfxHxle9v3Iv7plF0RRlmVV7VM2LAezdEtOkT1cycfJyIvVCdfSVyNo0VK391wDKu2n7HbrhyP4glxaYxNRzPw1IrdDvfL8jBzAwAPfbwdA/+3DsfS86rUzfHs15WzuZTZVT+DDk0amTDx2vZuT9f3lLK4YH5JGfJLKhdOlI+hmjm8i80dvU6nc7vr1J0lQJq6OQ5L7cKrzJjKi/gpn1d8vDxgHuVk0H5NemRQ22o/h8lBRttZ8KjM1FRlDKQjlWNu2C3VYAiCgPGfbJcqUUYG+9sFJ5fyS6DT6dBY9mFzlrlRdjmEePDBEu/SOkdVpniVdz0Wq/vjGsQF3nS68rukRiaDzZdO/j6eki2E6U0BJvvplc44Wl1ZOS5lUMdm2DV9CFooBtlWrVuq+pkbuS/HX4FJ17bH7JHdVX+vzNzIayY5G1Cp1l0gDzoy8yv//1xWgdSlp8bR+Jj/rTmIZ77aazdLRDmTyBPy4/vNrnOq3VMnM/IxZVXlqthicOPq7/nHkQwUlFjwxi+HPPpuiGwqH8uC7AcGtnE5ScAblC+RW1QmjT1qZDLajPkZN7CN3eMNbrbR1c1VsL/RbmamI2oZQZPiBkHeLaW8qIuPv7aie2pY9+Y1svq3O/z9DLihomu/KjdKgOOg09kgfuXwA3f/Fu5g5qYBKrMKNrMqmofYBzfiIEN5dVpPRvQ3r7j4Xsx3fXEQ2yKvYKscG9HCg5PAgnvK63B8N7F85oNOp7O5YMsXE3ziOtdTb6uisMSCbtEheGd0H/zfpCtV95l4bTvodcDLI7rhievaY9zAWLt91GYthDUyYYWi0Jg3x9w4C2KdiWkSiClDOzk8mSmfV/5vZ7Mz1IIb+RpOmbJunce/3IXeL6/F2n/Vp2K7GvyrXExRLCpXldl4yvd7IbsIaTlFNgP5hy/YZLOPGLS5u7Jxem6xtDq90vVOujsuZBfhfFYhVu88K33/Xh7RDTNUVnv3BeVfO6ewFHkVNzlB/kaXs81y3VwvyNnsxejQAOyYPsTtYE6tW0o+Xkqnkwc3OruZjuL58/LYJvhz6mC8fU9vt163pswe2R1PD+mIz510obqSrxJMNg1ynLlRBjeeFAd1pbaNuWFwUwOUd6dFpRa7C4jYFeDuRVPZby8GN67S+hfzirGvYrXjm7o3R5vwRugZE2o3FfThq9vizr4tsfT+y122pVXTQCwa3Qc9WoZK2+TpTnmQ5qu7VPGCcVP35ujeMgSPXN3WboT/sO4t8O9/b8B9V7RGoMmImcO72tz5vHV3L4fPH9Mk0OauryoBiTy4cVaAy1uU3Us2wY2TP4M8sBCzcvLCc/LBxWK3U9JPB1Sfq9hFV6F80UqgMoh66ZaumDm8C0wGPQa2b+r0OUTK78SqHWfQ/7VkKVPz7a5zdhdpcSq4fGXj6zpHYN6ontgy7Tq7u+rCUgsW/25fsVmvq6zarSavuAzDF2xC4so9+LUiEPRkUdzqUnYPZheWSl1IQWaj2+NgqmNt4tUefW/ks4Fu7tEcUcH+uH9A5Vg3vU4nZQ1DAkxOsxDRoQGaT/9WCg004fHrOiCmipkbpVUT4vHNYwOcHmPlEIGq1B1zRMzc1Ja1pTiguAaUltmeWLpHh9jdmT5fMbajXbMg/HM+B64oF4NsXnHhVVY/VTqUmouSMiuiQwNweWwY1j51tWr9nECTEXPudD77yJkAB5kbX9DrgLEDYm22TbvpMjx6TTv0+u9aaZvJqLfLnpTJuhhudVH+vCqFtuTkFYqVa+X4gtGgR6DJIHUVyF+zpywQVZIH3h0jG+NgSq5N0KxWS8lRVspV5ubkxXxcjfIxQ1kFJUiv+PyGB5lwfdcojI5rDQEClm87g/WH0rDhULrD51JOQX3v9+MAgNW7zmHOnT3xpMo4HLFUf/MQf+yWDQG6rXd5XY/QQD+clo37dPTdtAr2g13tXkvRfefNC4srykKMF/NLpOOj1+mw4J7eeOarvQ5nYXqDUe/ZDYHJqEdMkwBk5pXgjTt7wt/PYLN2ksUqSH/ziGBzjXTv1VadoxrjckVlcTXKMTaejK10RRxzk1VQAkEQNP97MHNTA+QDXadc3xETrmlnE12vePgKtK4YLzJzeBe0jwjCHX2dF01SXkzEjI+rzI14sQkN9INOp4PRoPfJHY08oDF4eFLzxA1do7B75vVoo7KoW2igyWadJLWBso4GvKqZXHHiv6Kt65OIGnmFYl8WbZOTl1D3k/0dhnSJxJt39sQvT15t9xh5YCoutin/XKkFefK7bPngVFezpfacKc8ivrPhKHr9d600c03Muon1n8YOiEVfWTmEXjGhdpk2Z3399364VXW7GHDIyx3Ix4140v0Y5eFg4JrM3Dj7vLUJD0SHyMb4duJAXNMpQnWfYdWokCxytNq0M8mJ12D7i0Ok853aEiYBfgabsYoNkTi5wBXl38Cb42Oah/jDoNchv8SC8y4Wx60JDG5qQKlsIcNJgzsg0GS0uTuWL1LZNMiMtU9djTdcZE1G9I62mdIoXtzF+gW5RaU4nJprV0m2VDa7wJfkdwTejp30OuD2PtF4YGAbzLq1q9PidPJ1kqr7nkfHtcaqCfGYP6pqfffyE3NVKzx7Klh2cZZnbnQ6HUb2bam6QKjRoMdfUwdj03PXShdseT+6WjZGvFD/vO8C+r3ym1QbxtEinqI9Z7MAAK+vsS10pzaOSD4bpn1EEK7vEmVTJNBZlmjz8Yuq28V1quQz2eSzmYI9CG6Gdo3EiF4tcFWHcJvAxdH0eq2Kyyk/eyNd3EgBwP/u6IG1T11tV2PJE1W5kzcZ9TaBs9rnSJ61cVVxt755YGAbBPgZpMy/K8og152ii+7y9zNI3dgDZ6/D8YpV5LXC4KYGVC7upt4toZzy6M5JwN/PgA/G9MPqxwbg9ZE9cHWH8tR+TmEprFYBN771B66ft9Fm3Rug8gLg67VV5Glwb+YoGvsbcfDlGzH3rl6YMbyLw3o/Ivn79MZ7vjy2idvLGCgZDXq8NLwLplzf0eFigd4mzzx40j3YIjTAprKpPPuour5VxYX60c934mJ+CR5ctr1834rPm6MxHY6WW1CriCpfz8zfr/yi9+dzg3FjN9uCks8M7eTxGBL5TCt5rahANwOQkAA/mI0GzL+7Nz59MM6my6m3bOC+XE12S8nJJy1c1znCrUGlQWYjOkQ29miwaEwT789MCjIb7bI08vPqotF9oNNBdbJAfTRjeBfsmXk92jazL9ynpnerMOk8+J+4Vri3CvW6nOnWorKK+uTlu7363J5q2Lm8GmCxClIBO3lKUH5n5+oC7UyfVmHo0ypMGgdhFcpnnIgLo/11LAP/iaus7SBO4/V15kA+Jui5Gzrj75OZePjq6td2MBv1HgUp8voeWi+WBwD3V0yzLSq14EJ2ERIu821BMfksuKreOQO2WRHVzI3iQl1YakGpxSrt26SRSVrQUE45dgwAGpuNqlkNeeZGzLQ0MhsRq+iSbNUkEPHtmuKHioJl7pC/njxr5E4dkGaNzfhorG3VW3kXaN/WYfh2t33FXq2Cm8ZmozRw25NZkYBnK7pvmHIt2j3/k0fP74pOp8PPT16FK/+3XtomLtQJlN987Jo+pEqzGesqT85rQWYjdk0fApNR75Ps/cRr22NFRf0qV2PQfE37s30998SXu3DXe5sBwGZxQz+DHn9WpP69kZ7299NLwZP8IiKexPKKy/DuhmPSytG+vtDLFxBsHxGEnS8OwWPXVH8auKdBmXw8kdpjxe48ZR0bX/P3M+ClW7riyg7emzW18pF4dIpsjOUPXyFtSxzSEY39jegWrV7XxxWzLLjJKy7D+oNpqtNP/fQ6u0UTn/hyV2XmxkH6u6giCJJzNJVVnrmRBw/K1LpZZeC4UmdFd5y/nwGfPtgfA9o1xWu3VdYNcrR+j9zfLyTYzBQEAD9Z+/o5GOjp6dIU1fHGnT2h1wGL7+1j8548vQCpdWPe3jsaG5+51m67r2YnNW1k+/dWdpWFBpo0H8xamzUyG302LEE+G8ub08yrgpkbH/txX+Xdo/Li6s2CUjqdDsH+friYX4KU7MoZBeLsgnc3HJWKCAJVG9znCWXXRVVWvZUL8DOgsNSCK9q5Ny1YJD/Bqr3nj8b2w6L1x/DYte3sflfX9G/TBL88ZTtAuE14I2x6bnCVB6/KMzdPLt+F3w6kqe5XVGbBmCXbbLb9vD9FOuaOuonKrAIOXLCdgeTopCjPMsirziqXMvH3M7is+tyssRkHU3Klf5uNelzVoRmu6mBb7bmqRc7kwX07B10GyvILvnRH35a4uUdz+PsZsGTTSWm7pxVqF97TBwvWHYXJqJO6vNuEN0IrB+OKfCHAVP73Lao4xlpfRKmSfIiFu4UffYWZGx9SrgLu64BCHPx4Pqsyc5NW0SW28bDtXbXRx91STwwuz9LIu8Sq44cnrsTk6zrgpVs8W9tK3i2ldjfXtlkQ3ryrp8MLUH0QEuBX5UydSTbmxlFgAwA/7UtRnZEjDmB3lgG5ZeGfNv92tK/8bvN0ZmV9HOWSGf5+BofB3F39WuKqDuEYrfhcmh3sH+Diwulolop8cVqTUY+7L4+x26emswtiNkt+fF2tx6YUG94Ib97VE1e0rbzJcLRmmi81kdUl0qp7j+zJB+b7cJKsWxjy+lCmYhFAX89QEoOb//7wr7StxGKF1SqgV0yoVLwPgHdH+ap44roOuKZzhM0As+po1ywITw3xfOmGWla3q84Rs43yTERV+Hnwh3CWdWkZFoCzlwpt1tNSBjLFZRaH3VKJQzohKsTfrgtNbYox4Dhz88VDcSgus6JHS/XPt7Lr7vlhl+HKDuH4YONx7DmbbdctVpPkwUBV1xaSD/jWYtZXcICfNN3Ym+sjkffo7Opi1yx+KnwoRTGA0ufBjYM0d4nFCkERzfi6zorRoEcfWV0SrSgXDCTPiJkbtUrDLUL83a5n4Umm0FkX2urHBuDPoxm4SVZ3Rbkgaf82TbD3bLbyoQCAwIq0uXIgs6MLtPzzE+xvlIrGDXBRYVr5/Qr298PNPVrg2k4RWPz7MQztGuXgkb4n78YJquKq0BGNK8cLieOfHrumHd7ZYFu92ajXebVQnEj+92LmpnbS+tTLbikfUlYk9fPxIN5BDla6nvr1XiQruhQsrpbZrieqO9anoVObLSVaN+UazBvlXhVrT1Y+d5YJiGjsj9t6t7QJaOTdIrf3iYbZaFAt2AhUdsMop+I72r9YVoTwkwfj0DMmFCtkA7ZdUY41amQ24unrO6FbtHcymlUhDwaqOl5Fbar+szd0xm29bat8y5e18Cb52A5HfzvSltY3lpp/KhYtWoTY2Fj4+/sjLi4O27Ztc7p/VlYWJk6ciObNm8NsNqNjx4746SfvTjf0hlKL1WYNHgAw+XjMzZj4WNXt3+4+bzcNt6Yq5Gqtvxslyckxs8FxoOHvZ0BogHsXr/sHtEFEY7Pdwn2OntcT8oubGPQ4eg5xgHljfz+8JFu00lGxvhJZBeteMaH4buJAxMnGmziSOKQjDHod3r23r+s3UMO80S0lzxLL1xJ6aXhX3D8gFt9PKl9EN9TNBUk9Fdm4eiUOyHe6VwTurqrs+5qm3VIrVqxAYmIiFi9ejLi4OMyfPx9Dhw7FoUOHEBFhXwa8pKQEQ4YMQUREBL766itER0fj1KlTCA0NrfnGO2GxCrhmzgabdVAA33dLmYx63NWvJVZuP+ty34YS3HRvGYJVE+K9OjOtIXE1WFQ5K+7mHs1xZftwLP/7DHZXLKUAlN/pb33+OhxLz0PC3I1On9PTmV0mm+BGr/ocI/u0xJh424JlgbILu6NFL0f0aoEF647gWgfLEjjyxHUdMP6qtm5NJa9p8myNsoCou+QBRX6xbLmKQD+bQf+hPqo3U5PT6MkzKx65AqczC9A5qmrlJ7xF0+Bm7ty5GD9+PMaNGwcAWLx4MX788UcsWbIEU6dOtdt/yZIlyMzMxF9//QU/v/IvTWxsbE022S0pOUV2gQ3g++AGcD8NbG0g3VIA3FpQjtS5qiskX0TTbNTj+ZsuQ4vQAOw5m2UT3ADlF8R2zYIwrEdzFJdaHM6+8rQ+inLMDWC7IOrO6UPsZlQBtrMZ1X4PlNfn2fZ8QpVmOtbGwAaw7SKsTrG7sfGt8f2e87inv/1MMFHvVqHYfupSlV/DkTv6tsTi3485HNBN2gk0GTUPbAANg5uSkhLs2LED06ZNk7bp9XokJCRg8+bNqo/5/vvvER8fj4kTJ+K7775Ds2bN8J///AfPPfccDA7S58XFxSgurlz0LyfH9Yrb1eXo3FwTwY27J6uGkrmh6tHrdU4Hhcprz2x/MUFaJ62kTH1/nU6HRf/pAwCInfqj6j55xWWq2x1RG3MhHyPkKHAZ3rMFftx3Add1dp6VqQ2Vrb2pd6swGPU63NKrBZp7uNin3Kxbu2HG8K5Og9HEIZ1QahHslsiorvYRQfhr6mCE+WhMD9V9mgU3GRkZsFgsiIy0LT8fGRmJgwcPqj7m+PHjWLduHUaPHo2ffvoJR48exWOPPYbS0lLMnDlT9TFJSUmYNWuW19vvjKOkiMno+75hBjfkbSajHmUqVYkB4PLYMLwyohs6RATZLAArz5w48sigtnjv9+N223M9XKlYLbgZ0Tsa6w6m4QYnF9VGZiM+fTDOo9eqD/q3aYJ9Lw31SmbJVZYtwGTwuDaVuzxdOoIaljp1S2K1WhEREYH3338fffv2xahRo/DCCy9g8eLFDh8zbdo0ZGdnSz9nzpxxuK+3lFnUA4ea6ZZyL7hpSN1SVD0FssBGufimTqfDvVe0thtk687CoNNuvAyPXmNfGdrTzI3agNLG/n5YOq4/Rl3unSKS9U1t7TIj8hbNgpvw8HAYDAakpqbabE9NTUVUlPrdVvPmzdGxY0ebLqjLLrsMKSkpKCkpUX2M2WxGcHCwzY+vqa2aDPh+sUqAmRvyrc3TrkN4kBkPVCwA6siofo7HYcipfV57KtZpIiLylGbBjclkQt++fZGcnCxts1qtSE5ORnx8vOpjBg4ciKNHj8IqS3kfPnwYzZs3h8lUe/peHaXka6KSpqupueLU6Ok3d3G6H5FInPZ7VYdwNGtsxrbnr8OM4c4/P0aD3uFYFzn5rKaB7Zvi2Rs6YdLg6i+wSkQNm6azpRITEzF27Fj069cP/fv3x/z585Gfny/NnhozZgyio6ORlJQEAHj00UexcOFCTJ48GY8//jiOHDmC1157DU888YSWb8OOo26pxjWwUJ6rzM2zN5QXENOiZDrVTcsfjsf3e87jjr7lBdrcLYzoThEvec2V7tGh1V45XmB3KxFB4+Bm1KhRSE9Px4wZM5CSkoJevXphzZo10iDj06dPQy9bfSsmJga//PILnnrqKfTo0QPR0dGYPHkynnvuOa3egqpSB91SNZG5CVEZcyNfQVev1zGwIY90aRGMLi087851pxdWHvBXdQVuIiIlzdeWmjRpEiZNmqT6uw0bNthti4+Px5YtW3zcqupxNG22JoKbxiqvEWT2Q1Fp+XR4R1klIm8zuJG5CTJXBuPe+H7w001EQB2bLVVXOMrcxLf1fTE5tS4DedEud6boEnmDO91XQf7yRRyrHtxc26l8XbXRca1d7ElEDYHmmZv6qFSRHdky7Tqk5xajfYR6iXdvW3J/PxxPz8crPx4AUF6L4rrOETiekY++rbVfqZsahjv6tsT83444rSIrD2iqk7n5aOzlyC0qU+2WJaKGh8GND5QpMjdRIf6ICqm5tVAGd47E4M7AgnVHkV1YioHtw/HqiG6wCp6XtieqqonXtkfXFiFOFy+Vj7kJrOI6R0B5loiBDRGJGNz4gDJzo5X/m3Qlft5/AaOvaA2dTgcfL0pOZMPPoMeQLpFO95FnbtwZo0NE5A4GNz4gH9ei5fm6VdNAPDLIvgIsUW0hnwpeO24JiKg+4IBiH5DPSFKWqyeiSvKlEy5rXjNj0oio/mPmxgfks6Xahgdp2BKi2u/vFxKQV1yGiMY1Ny6NiOo3Bjc+IB9zs/i+vhq2hKj2a9bYjGaNzVo3g4jqEXZL+YA45uam7lFoE95I49YQERE1LAxufEDM3Bj1PLxEREQ1jVdfHxDr3Pi5s7gOEREReRWvvj4gri3lx8IyRERENY7BjQ+Is6WMDG6IiIhqHIMbHygssQAA/I1VLydPREREVcPgxgcu5pcAAJoEmTRuCRERUcPD4MYHMiuCm6aNGNwQERHVNAY3PiBlbhqxMBkREVFNY3DjA5n5xQCAJszcEBER1TgGNz5wKb8UAIMbIiIiLTC48YGSsvKp4GYjDy8REVFN49XXByxCeRE/g551boiIiGoagxsfsFRUKNbrGNwQERHVNAY3XmatCGwAZm6IiIi0wODGy6xCZXDD2IaIiKjmMbjxMos8uGF0Q0REVOMY3HiZ1Vr5/waOuSEiIqpxDG68TJ654ZgbIiKimsfgxssssgHFTNwQERHVPAY3XibIMzeMboiIiGocgxsvs3AqOBERkaYY3HiZOOZGpwN0zNwQERHVOAY3XibOlmJ1YiIiIm0wuPEyaV0pBjdERESaYHDjZeLyC3oeWSIiIk3UikvwokWLEBsbC39/f8TFxWHbtm0O9122bBl0Op3Nj7+/fw221jkrMzdERESa0jy4WbFiBRITEzFz5kzs3LkTPXv2xNChQ5GWlubwMcHBwbhw4YL0c+rUqRpssXPSiuCcKUVERKQJzYObuXPnYvz48Rg3bhy6dOmCxYsXIzAwEEuWLHH4GJ1Oh6ioKOknMjLS4b7FxcXIycmx+fElMXPDAcVERETa0DS4KSkpwY4dO5CQkCBt0+v1SEhIwObNmx0+Li8vD61bt0ZMTAxuvfVW/PPPPw73TUpKQkhIiPQTExPj1fegZKmYLcUaN0RERNrwOLg5ffq0TRVekSAIOH36tEfPlZGRAYvFYpd5iYyMREpKiupjOnXqhCVLluC7777DZ599BqvVigEDBuDs2bOq+0+bNg3Z2dnSz5kzZzxqo6eYuSEiItKW0dMHtGnTBhcuXEBERITN9szMTLRp0wYWi8VrjVMTHx+P+Ph46d8DBgzAZZddhvfeew8vv/yy3f5msxlms9mnbZITx9wYNO/wIyIiapg8vgQLgqBaeTcvL8/jWUvh4eEwGAxITU212Z6amoqoqCi3nsPPzw+9e/fG0aNHPXptX2HmhoiISFtuZ24SExMBlA/mnT59OgIDA6XfWSwWbN26Fb169fLoxU0mE/r27Yvk5GSMGDECAGC1WpGcnIxJkya59RwWiwX79u3DTTfd5NFr+4o0W4rBDRERkSbcDm527doFoDxzs2/fPphMJul3JpMJPXv2xJQpUzxuQGJiIsaOHYt+/fqhf//+mD9/PvLz8zFu3DgAwJgxYxAdHY2kpCQAwH//+19cccUVaN++PbKysjBnzhycOnUKDz30kMev7QtSnRsOKCYiItKE28HN+vXrAQDjxo3DW2+9heDgYK80YNSoUUhPT8eMGTOQkpKCXr16Yc2aNdIg49OnT0MvK/d76dIljB8/HikpKQgLC0Pfvn3x119/oUuXLl5pT3WJi4IzuCEiItKGTlCb+lSP5eTkICQkBNnZ2V4L0OS2HL+Iu9/fgnbNGiH56Wu8/vxEREQNkSfXb49nS+Xn52P27NlITk5GWloarOIy2BWOHz/u6VPWK1aOuSEiItKUx8HNQw89hN9//x333XcfmjdvrjpzqiGzcMwNERGRpjwObn7++Wf8+OOPGDhwoC/aU+dxthQREZG2PK5zExYWhiZNmviiLfWCwAHFREREmvI4uHn55ZcxY8YMFBQU+KI9dV5l5kbjhhARETVQHndLvfnmmzh27BgiIyMRGxsLPz8/m9/v3LnTa42ri8QxN3pGN0RERJrwOLgRKwmTOnG2lIFjboiIiDThcXAzc+ZMX7Sj3mDmhoiISFtVWrs6KysLH374IaZNm4bMzEwA5d1R586d82rj6iKpQjEzN0RERJrwOHOzd+9eJCQkICQkBCdPnsT48ePRpEkTrF69GqdPn8Ynn3zii3bWGVIRvyqFjURERFRdHl+CExMTcf/99+PIkSPw9/eXtt90003YuHGjVxtXF7HODRERkbY8Dm7+/vtvPPLII3bbo6OjkZKS4pVG1WWsUExERKQtj4Mbs9mMnJwcu+2HDx9Gs2bNvNKouoyzpYiIiLTlcXBzyy234L///S9KS0sBADqdDqdPn8Zzzz2HkSNHer2BdY04oJhrbhEREWnD4+DmzTffRF5eHiIiIlBYWIhBgwahffv2aNy4MV599VVftLFOqeyW0rghREREDZTHs6VCQkKwdu1abNq0CXv37kVeXh769OmDhIQEX7SvzskrKgMANDJ5fGiJiIjIC6p8Bb7yyitx5ZVXerMt9cKZS+VrbrUMC9C4JURERA2TW8HN22+/jYcffhj+/v54++23ne77xBNPeKVhddXZS4UAgJZhgRq3hIiIqGFyK7iZN28eRo8eDX9/f8ybN8/hfjqdrsEHN+eYuSEiItKUW8HNiRMnVP+f7BWVWgEAgWaOuSEiItIC5/R4mYV1boiIiDTlcXAzcuRI/O9//7Pb/vrrr+POO+/0SqPqMlYoJiIi0pbHwc3GjRtx00032W2/8cYbubYUZBWKGdwQERFpwuPgJi8vDyaTyW67n5+f6rIMDQ2L+BEREWnL40tw9+7dsWLFCrvty5cvR5cuXbzSqLqMq4ITERFpy+MpPdOnT8ftt9+OY8eOYfDgwQCA5ORkfPnll1i1apXXG1jXsFuKiIhIWx4HN8OHD8e3336L1157DV999RUCAgLQo0cP/Pbbbxg0aJAv2linlDG4ISIi0lSVirEMGzYMw4YN83Zb6gUrZ0sRERFpisNevYx1boiIiLTlVuamSZMmOHz4MMLDwxEWFgadkwt3Zmam1xpX1wiCgIrYBnpmboiIiDTh9tpSjRs3BgDMnz/fl+2p08TABmDmhoiISCtuBTd79uzBHXfcAbPZjDZt2mDAgAEwGrl2kpJFFt0wc0NERKQNt8bcLFiwAHl5eQCAa6+9tkF3PTkjD26MDG6IiIg04VZwExsbi7fffhu///47BEHA5s2bsXHjRtWfqli0aBFiY2Ph7++PuLg4bNu2za3HLV++HDqdDiNGjKjS63qbWJ0Y4GwpIiIirbjVtzRnzhxMmDABSUlJ0Ol0uO2221T30+l0sFgsHjVgxYoVSExMxOLFixEXF4f58+dj6NChOHToECIiIhw+7uTJk5gyZQquuuoqj17Pl2y6pTjmhoiISBNuZW5GjBiBlJQU5OTkQBAEHDp0CJcuXbL7qUp31dy5czF+/HiMGzcOXbp0weLFixEYGIglS5Y4fIzFYsHo0aMxa9YstG3b1uPX9BWrlZkbIiIirbkV3CQmJiI/Px9BQUFYv3492rRpg5CQENUfT5SUlGDHjh1ISEiobJBej4SEBGzevNnh4/773/8iIiICDz74oMvXKC4uRk5Ojs2Pr8i7pRjbEBERacPjAcWDBw/22oDijIwMWCwWREZG2myPjIxESkqK6mM2bdqEjz76CB988IFbr5GUlGQTfMXExFS73Y5YpUUz4bQWEBEREfmOW2NuxAHF119/vTSgOCwsTHXfq6++2qsNlMvNzcV9992HDz74AOHh4W49Ztq0aUhMTJT+nZOT47MAR1xXyqhn4WciIiKtaDqgODw8HAaDAampqTbbU1NTERUVZbf/sWPHcPLkSQwfPlzaZrVay9+I0YhDhw6hXbt2No8xm80wm81ut6k6xAHFjG2IiIi0o+mAYpPJhL59+yI5OVnaZrVakZycjPj4eLv9O3fujH379mH37t3Szy233IJrr70Wu3fv9mmXkzukRTPZJUVERKQZj8oMywcUe6tCcWJiIsaOHYt+/fqhf//+mD9/PvLz8zFu3DgAwJgxYxAdHY2kpCT4+/ujW7duNo8PDQ0FALvtWqjM3DC4ISIi0orHEcqgQYNw7NgxLF26FMeOHcNbb72FiIgI/Pzzz2jVqhW6du3q0fONGjUK6enpmDFjBlJSUtCrVy+sWbNGGmR8+vRp6OtIP4+UuWFwQ0REpBmdIMjmL7vh999/x4033oiBAwdi48aNOHDgANq2bYvZs2dj+/bt+Oqrr3zVVq/IyclBSEgIsrOzERwc7NXnPpSSi6HzN6JpIxN2TB/i1ecmIiJqyDy5fnucEpk6dSpeeeUVrF27FiaTSdo+ePBgbNmyxfPW1iNlFYObmbkhIiLSjsfBzb59+1RnS0VERCAjI8MrjaqrKmIbBjdEREQa8ji4CQ0NxYULF+y279q1C9HR0V5pVF0lVijmulJERETa8Ti4ufvuu/Hcc88hJSUFOp0OVqsVf/75J6ZMmYIxY8b4oo11hjhbipkbIiIi7Xgc3Lz22mvo3LkzYmJikJeXhy5duuDqq6/GgAED8OKLL/qijXUGZ0sRERFpz+Op4CaTCR988AGmT5+O/fv3Iy8vD71790aHDh180b46xSJbW4qIiIi0UeVKfK1atZIqAnORyHIWri1FRESkuSpdhT/55BN0794dAQEBCAgIQI8ePfDpp596u211DisUExERac/jzM3cuXMxffp0TJo0CQMHDgQAbNq0CRMmTEBGRgaeeuoprzeyrrBIY240bggREVED5nFws2DBArz77rs2M6NuueUWdO3aFS+99FKDDm6sVi6cSUREpDWPcwwXLlzAgAED7LYPGDBAtf5NQ8JuKSIiIu15HNy0b98eK1eutNu+YsWKBj9jqnJAMYMbIiIirXjcLTVr1iyMGjUKGzdulMbc/Pnnn0hOTlYNehqSEkv5+gt+HHRDRESkGY+vwiNHjsTWrVsRHh6Ob7/9Ft9++y3Cw8Oxbds21TWnGpJSS3nmhsENERGRdqpU56Zv37747LPPvN2WOq+UmRsiIiLNuX0VPn/+PKZMmYKcnBy732VnZ+OZZ55BamqqVxtX14jBjdnI4IaIiEgrbl+F586di5ycHAQHB9v9LiQkBLm5uZg7d65XG1fXlJSJmRsOKCYiItKK28HNmjVrnK76PWbMGPzwww9eaVRdxTE3RERE2nP7KnzixAm0atXK4e9btmyJkydPeqNNdZY05obdUkRERJpx+yocEBDgNHg5efIkAgICvNGmOksMbkzM3BAREWnG7atwXFyc08UxP/nkE/Tv398rjaqrKuvccMwNERGRVtyeCj5lyhQMGTIEISEheOaZZxAZGQkASE1Nxeuvv45ly5bh119/9VlD64LSMo65ISIi0prbwc21116LRYsWYfLkyZg3bx6Cg4Oh0+mQnZ0NPz8/LFiwAIMHD/ZlW2s91rkhIiLSnkdF/B555BHcfPPNWLlyJY4ePQpBENCxY0fccccdaNmypa/aWGdIY244oJiIiEgzHlcojo6OxlNPPeWLttR5HHNDRESkPaYYvIh1boiIiLTHq7AXlZZxzA0REZHWeBX2Ita5ISIi0h6vwl4kjbkxcswNERGRVhjceBGnghMREWnPrdlSTZo0weHDhxEeHo6wsDDodI4zE5mZmV5rXF1TVjGg2Khn5oaIiEgrbgU38+bNQ+PGjQEA8+fP92V76jSrUB7cOAv+iIiIyLfcCm7Gjh2r+v9kS6j4L0MbIiIi7VRpcIjVasXhw4exadMmbNy40eanKhYtWoTY2Fj4+/sjLi4O27Ztc7jv6tWr0a9fP4SGhqJRo0bo1auX0wU9a1JF4oaZGyIiIg15XKF4y5Yt+M9//oNTp05BEK/mFXQ6HSwWi0fPt2LFCiQmJmLx4sWIi4vD/PnzMXToUBw6dAgRERF2+zdp0gQvvPACOnfuDJPJhB9++AHjxo1DREQEhg4d6unb8QmGNkRERNrxOHMzYcIE9OvXD/v370dmZiYuXbok/VRlMPHcuXMxfvx4jBs3Dl26dMHixYsRGBiIJUuWqO5/zTXX4LbbbsNll12Gdu3aYfLkyejRowc2bdrk8Wt7m+B6FyIiIvIxjzM3R44cwVdffYX27dtX+8VLSkqwY8cOTJs2Tdqm1+uRkJCAzZs3u3y8IAhYt24dDh06hP/973+q+xQXF6O4uFj6d05OTrXb7aRBAAD2ShEREWnH48xNXFwcjh496pUXz8jIgMViQWRkpM32yMhIpKSkOHxcdnY2goKCYDKZMGzYMCxYsABDhgxR3TcpKQkhISHST0xMjFfarkYaUMzghoiISDMeZ24ef/xxPP3000hJSUH37t3h5+dn8/sePXp4rXGONG7cGLt370ZeXh6Sk5ORmJiItm3b4pprrrHbd9q0aUhMTJT+nZOT47MARxpQzFE3REREmvE4uBk5ciQA4IEHHpC26XQ6CILg8YDi8PBwGAwGpKam2mxPTU1FVFSUw8fp9XqpW6xXr144cOAAkpKSVIMbs9kMs9nsdpuqQ4AU3RAREZFGPA5uTpw44bUXN5lM6Nu3L5KTkzFixAgA5dPMk5OTMWnSJLefx2q12oyrISIioobL4+CmdevWXm1AYmIixo4di379+qF///6YP38+8vPzMW7cOADAmDFjEB0djaSkJADlY2j69euHdu3aobi4GD/99BM+/fRTvPvuu15tV1UITNwQERFpzq3g5vvvv8eNN94IPz8/fP/99073veWWWzxqwKhRo5Ceno4ZM2YgJSUFvXr1wpo1a6RBxqdPn4ZeXznuOT8/H4899hjOnj2LgIAAdO7cGZ999hlGjRrl0ev6Aov4ERERaU8nKCvxqdDr9UhJSUFERIRNoGH3ZFUo4lfTcnJyEBISguzsbAQHB3v1uW986w8cuJCDTx7oj6s7NvPqcxMRETVknly/3crcWK1W1f8nWwLr3BAREWmuSmtLkXOcCk5ERKQdtwcUFxYWIjk5GTfffDOA8vox8hlKBoMBL7/8Mvz9/b3fSiIiIiI3uR3cfPzxx/jxxx+l4GbhwoXo2rUrAgICAAAHDx5EixYt8NRTT/mmpXVA5YBibdtBRETUkLndLfX555/j4Ycfttn2xRdfYP369Vi/fj3mzJmDlStXer2BdYlYxI+xDRERkXbcDm6OHj2K7t27S//29/e3mTnVv39//Pvvv95tXR0jzTtjdENERKQZt7ulsrKybMbYpKen2/yeVYJlC2cyuiEiItKM25mbli1bYv/+/Q5/v3fvXrRs2dIrjaqrOBWciIhIe24HNzfddBNmzJiBoqIiu98VFhZi1qxZGDZsmFcbR0REROQpt7ulnn/+eaxcuRKdOnXCpEmT0LFjRwDAoUOHsHDhQpSVleH555/3WUPrAg65ISIi0p7bwU1kZCT++usvPProo5g6daqsC0aHIUOG4J133pHWg2qwuLYUERGR5jxaFbxNmzZYs2YNMjMzcfToUQBA+/bt0aRJE580rq6RMjeMbYiIiDTjUXAjatKkCfr37+/tttR5UjZL43YQERE1ZFxbyouYuSEiItIegxsiIiKqVxjceJFUoZgdU0RERJphcONF0tpSjG2IiIg0w+DGi6RVwbVtBhERUYPG4MaLBNa5ISIi0hyDGx9gaENERKQdBjdERERUrzC48SKuCk5ERKQ9BjdeVLlwJqMbIiIirTC48aLKAcXatoOIiKghY3DjRYKUuyEiIiKtMLjxIoGxDRERkeYY3PgAu6WIiIi0w+DGizigmIiISHsMbryIA4qJiIi0x+DGq1jnhoiISGsMbryocuFMRjdERERaYXDjRZwsRUREpD0GNz7AbikiIiLt1IrgZtGiRYiNjYW/vz/i4uKwbds2h/t+8MEHuOqqqxAWFoawsDAkJCQ43b8mSWtLadwOIiKihkzz4GbFihVITEzEzJkzsXPnTvTs2RNDhw5FWlqa6v4bNmzAPffcg/Xr12Pz5s2IiYnB9ddfj3PnztVwy+1JU8EZ3RAREWlGJwja1tWNi4vD5ZdfjoULFwIArFYrYmJi8Pjjj2Pq1KkuH2+xWBAWFoaFCxdizJgxLvfPyclBSEgIsrOzERwcXO32y/Wc9SuyC0vxW+IgtI8I8upzExERNWSeXL81zdyUlJRgx44dSEhIkLbp9XokJCRg8+bNbj1HQUEBSktL0aRJE9XfFxcXIycnx+bHV6RuKWZuiIiINKNpcJORkQGLxYLIyEib7ZGRkUhJSXHrOZ577jm0aNHCJkCSS0pKQkhIiPQTExNT7XY7wtlSRERE2tN8zE11zJ49G8uXL8c333wDf39/1X2mTZuG7Oxs6efMmTM+bxcTN0RERNoxavni4eHhMBgMSE1NtdmempqKqKgop4994403MHv2bPz222/o0aOHw/3MZjPMZrNX2uuStPwCwxsiIiKtaJq5MZlM6Nu3L5KTk6VtVqsVycnJiI+Pd/i4119/HS+//DLWrFmDfv361URT3VK5cCYRERFpRdPMDQAkJiZi7Nix6NevH/r374/58+cjPz8f48aNAwCMGTMG0dHRSEpKAgD873//w4wZM/DFF18gNjZWGpsTFBSEoCBtZyhxQDEREZH2NA9uRo0ahfT0dMyYMQMpKSno1asX1qxZIw0yPn36NPT6ygTTu+++i5KSEtxxxx02zzNz5ky89NJLNdl0O5WZG0Y3REREWtG8zk1N82Wdm8umr0FhqQUbn7kWrZoGevW5iYiIGrI6U+emvmK3FBERkXYY3HiRwEo3REREmmNw40WCNBVc23YQERE1ZAxuvKhy4UxGN0RERFphcONNYuZG21YQERE1aAxuvIhjboiIiLTH4MYH2CtFRESkHQY3XiQNKGbHFBERkWYY3HhR5YBiTZtBRETUoDG48SJpbSmN20FERNSQMbjxIg4nJiIi0h6DGy8SKlfOJCIiIo0wuPEBDigmIiLSDoMbH+CAYiIiIu0wuPESQagcccPYhoiISDsMbrxEFttwbSkiIiINMbjxEs6UIiIiqh0Y3HgJu6WIiIhqBwY3PsBeKSIiIu0wuPESebcUp4ITERFph8GNlwi20Q0RERFphMGNlwiy3A27pYiIiLTD4MZLBE6XIiIiqhUY3PgAEzdERETaYXDjAyziR0REpB0GN15iU6FYu2YQERE1eAxuvIQDiomIiGoHBjdeYpu5YXRDRESkFQY3XsLJUkRERLUDgxsvsVlbiokbIiIizTC48RJmboiIiGoHBjc+wMwNERGRdhjceAkHFBMREdUOmgc3ixYtQmxsLPz9/REXF4dt27Y53Peff/7ByJEjERsbC51Oh/nz59dcQ12RBzeMbYiIiDSjaXCzYsUKJCYmYubMmdi5cyd69uyJoUOHIi0tTXX/goICtG3bFrNnz0ZUVFQNt9Y5gaNuiIiIagVNg5u5c+di/PjxGDduHLp06YLFixcjMDAQS5YsUd3/8ssvx5w5c3D33XfDbDbXcGudY4ViIiKi2kGz4KakpAQ7duxAQkJCZWP0eiQkJGDz5s1ee53i4mLk5OTY/PiCPG/DtaWIiIi0o1lwk5GRAYvFgsjISJvtkZGRSElJ8drrJCUlISQkRPqJiYnx2nM7wtCGiIhIO5oPKPa1adOmITs7W/o5c+aMT16HRfyIiIhqB6NWLxweHg6DwYDU1FSb7ampqV4dLGw2m2tkfA6HExMREdUOmmVuTCYT+vbti+TkZGmb1WpFcnIy4uPjtWpWldkMKGbqhoiISDOaZW4AIDExEWPHjkW/fv3Qv39/zJ8/H/n5+Rg3bhwAYMyYMYiOjkZSUhKA8kHI//77r/T/586dw+7duxEUFIT27dtr9j4ATgUnIiKqLTQNbkaNGoX09HTMmDEDKSkp6NWrF9asWSMNMj59+jT0+srk0vnz59G7d2/p32+88QbeeOMNDBo0CBs2bKjp5tuqiG2YtCEiItKWTpCPhG0AcnJyEBISguzsbAQHB3vtedNyitD/tWTodcDxpGFee14iIiLy7Ppd72dL1RQxQuR4GyIiIm0xuPGShpX/IiIiqr0Y3HiJOKCYeRsiIiJtMbjxEoEDiomIiGoFBjdeIo25Ye6GiIhIUwxuvI2xDRERkaYY3HiJOKOesQ0REZG2GNx4CWdLERER1Q4MbryMA4qJiIi0xeDGS6TZUuyYIiIi0hSDGy+R6twwtiEiItIUgxsvY2xDRESkLQY3XlJZxI/hDRERkZYY3HgJJ0sRERHVDgxuvIR1boiIiGoHBjdeImVuGN0QERFpisGNl1ROBSciIiItMbjxMg4oJiIi0haDG69hnRsiIqLagMGNl3BtKSIiotqBwY2XiLENEzdERETaYnDjJSziR0REVDswuPESaW0pjdtBRETU0DG48TImboiIiLTF4MZLOKCYiIiodmBw4yUCSxQTERHVCgxuvERgnRsiIqJagcGNl3D5BSIiotqBwY2XMXNDRESkLQY3XqZj7oaIiEhTDG68hLOliIiIagcGN17CAcVERES1A4MbL+GAYiIiotqBwY2XSAtnMnVDRESkqVoR3CxatAixsbHw9/dHXFwctm3b5nT/VatWoXPnzvD390f37t3x008/1VBLHRM46IaIiKhW0Dy4WbFiBRITEzFz5kzs3LkTPXv2xNChQ5GWlqa6/19//YV77rkHDz74IHbt2oURI0ZgxIgR2L9/fw23XB0TN0RERNrSCRqnHOLi4nD55Zdj4cKFAACr1YqYmBg8/vjjmDp1qt3+o0aNQn5+Pn744Qdp2xVXXIFevXph8eLFdvsXFxejuLhY+ndOTg5iYmKQnZ2N4OBgr72Pnacv4fZ3/kLLsABsem6w156XiIiIyq/fISEhbl2/Nc3clJSUYMeOHUhISJC26fV6JCQkYPPmzaqP2bx5s83+ADB06FCH+yclJSEkJET6iYmJ8d4bkJEGFDNzQ0REpClNg5uMjAxYLBZERkbabI+MjERKSorqY1JSUjzaf9q0acjOzpZ+zpw5453GK0SF+GPite1w3xWtffL8RERE5B6j1g3wNbPZDLPZ7PPXiQ4NwDNDO/v8dYiIiMg5TTM34eHhMBgMSE1NtdmempqKqKgo1cdERUV5tD8RERE1LJoGNyaTCX379kVycrK0zWq1Ijk5GfHx8aqPiY+Pt9kfANauXetwfyIiImpYNO+WSkxMxNixY9GvXz/0798f8+fPR35+PsaNGwcAGDNmDKKjo5GUlAQAmDx5MgYNGoQ333wTw4YNw/Lly7F9+3a8//77Wr4NIiIiqiU0D25GjRqF9PR0zJgxAykpKejVqxfWrFkjDRo+ffo09PrKBNOAAQPwxRdf4MUXX8Tzzz+PDh064Ntvv0W3bt20egtERERUi2he56ameTJPnoiIiGqHOlPnhoiIiMjbGNwQERFRvcLghoiIiOoVBjdERERUrzC4ISIionqFwQ0RERHVKwxuiIiIqF5hcENERET1iuYVimuaWLMwJydH45YQERGRu8Trtju1hxtccJObmwsAiImJ0bglRERE5Knc3FyEhIQ43afBLb9gtVpx/vx5NG7cGDqdzqvPnZOTg5iYGJw5c4ZLO/gQj3PN4HGuOTzWNYPHuWb46jgLgoDc3Fy0aNHCZs1JNQ0uc6PX69GyZUufvkZwcDC/ODWAx7lm8DjXHB7rmsHjXDN8cZxdZWxEHFBMRERE9QqDGyIiIqpXGNx4kdlsxsyZM2E2m7VuSr3G41wzeJxrDo91zeBxrhm14Tg3uAHFREREVL8xc0NERET1CoMbIiIiqlcY3BAREVG9wuCGiIiI6hUGN0RERFSvMLjxkkWLFiE2Nhb+/v6Ii4vDtm3btG5SnZKUlITLL78cjRs3RkREBEaMGIFDhw7Z7FNUVISJEyeiadOmCAoKwsiRI5Gammqzz+nTpzFs2DAEBgYiIiICzzzzDMrKymryrdQps2fPhk6nw5NPPilt43H2jnPnzuHee+9F06ZNERAQgO7du2P79u3S7wVBwIwZM9C8eXMEBAQgISEBR44csXmOzMxMjB49GsHBwQgNDcWDDz6IvLy8mn4rtZrFYsH06dPRpk0bBAQEoF27dnj55ZdtFlfksfbcxo0bMXz4cLRo0QI6nQ7ffvutze+9dUz37t2Lq666Cv7+/oiJicHrr7/unTcgULUtX75cMJlMwpIlS4R//vlHGD9+vBAaGiqkpqZq3bQ6Y+jQocLSpUuF/fv3C7t37xZuuukmoVWrVkJeXp60z4QJE4SYmBghOTlZ2L59u3DFFVcIAwYMkH5fVlYmdOvWTUhISBB27dol/PTTT0J4eLgwbdo0Ld5Srbdt2zYhNjZW6NGjhzB58mRpO49z9WVmZgqtW7cW7r//fmHr1q3C8ePHhV9++UU4evSotM/s2bOFkJAQ4dtvvxX27Nkj3HLLLUKbNm2EwsJCaZ8bbrhB6Nmzp7Blyxbhjz/+ENq3by/cc889WrylWuvVV18VmjZtKvzwww/CiRMnhFWrVglBQUHCW2+9Je3DY+25n376SXjhhReE1atXCwCEb775xub33jim2dnZQmRkpDB69Ghh//79wpdffikEBAQI7733XrXbz+DGC/r37y9MnDhR+rfFYhFatGghJCUladiqui0tLU0AIPz++++CIAhCVlaW4OfnJ6xatUra58CBAwIAYfPmzYIglH8Z9Xq9kJKSIu3z7rvvCsHBwUJxcXHNvoFaLjc3V+jQoYOwdu1aYdCgQVJww+PsHc8995xw5ZVXOvy91WoVoqKihDlz5kjbsrKyBLPZLHz55ZeCIAjCv//+KwAQ/v77b2mfn3/+WdDpdMK5c+d81/g6ZtiwYcIDDzxgs+32228XRo8eLQgCj7U3KIMbbx3Td955RwgLC7M5bzz33HNCp06dqt1mdktVU0lJCXbs2IGEhARpm16vR0JCAjZv3qxhy+q27OxsAECTJk0AADt27EBpaanNce7cuTNatWolHefNmzeje/fuiIyMlPYZOnQocnJy8M8//9Rg62u/iRMnYtiwYTbHE+Bx9pbvv/8e/fr1w5133omIiAj07t0bH3zwgfT7EydOICUlxeY4h4SEIC4uzuY4h4aGol+/ftI+CQkJ0Ov12Lp1a829mVpuwIABSE5OxuHDhwEAe/bswaZNm3DjjTcC4LH2BW8d082bN+Pqq6+GyWSS9hk6dCgOHTqES5cuVauNDW5VcG/LyMiAxWKxOdEDQGRkJA4ePKhRq+o2q9WKJ598EgMHDkS3bt0AACkpKTCZTAgNDbXZNzIyEikpKdI+an8H8XdUbvny5di5cyf+/vtvu9/xOHvH8ePH8e677yIxMRHPP/88/v77bzzxxBMwmUwYO3asdJzUjqP8OEdERNj83mg0okmTJjzOMlOnTkVOTg46d+4Mg8EAi8WCV199FaNHjwYAHmsf8NYxTUlJQZs2beyeQ/xdWFhYldvI4IZqnYkTJ2L//v3YtGmT1k2pd86cOYPJkydj7dq18Pf317o59ZbVakW/fv3w2muvAQB69+6N/fv3Y/HixRg7dqzGratfVq5cic8//xxffPEFunbtit27d+PJJ59EixYteKwbMHZLVVN4eDgMBoPdbJLU1FRERUVp1Kq6a9KkSfjhhx+wfv16tGzZUtoeFRWFkpISZGVl2ewvP85RUVGqfwfxd1Te7ZSWloY+ffrAaDTCaDTi999/x9tvvw2j0YjIyEgeZy9o3rw5unTpYrPtsssuw+nTpwFUHidn542oqCikpaXZ/L6srAyZmZk8zjLPPPMMpk6dirvvvhvdu3fHfffdh6eeegpJSUkAeKx9wVvH1JfnEgY31WQymdC3b18kJydL26xWK5KTkxEfH69hy+oWQRAwadIkfPPNN1i3bp1dqrJv377w8/OzOc6HDh3C6dOnpeMcHx+Pffv22Xyh1q5di+DgYLsLTUN13XXXYd++fdi9e7f0069fP4wePVr6fx7n6hs4cKBdKYPDhw+jdevWAIA2bdogKirK5jjn5ORg69atNsc5KysLO3bskPZZt24drFYr4uLiauBd1A0FBQXQ620vZQaDAVarFQCPtS9465jGx8dj48aNKC0tlfZZu3YtOnXqVK0uKQCcCu4Ny5cvF8xms7Bs2TLh33//FR5++GEhNDTUZjYJOffoo48KISEhwoYNG4QLFy5IPwUFBdI+EyZMEFq1aiWsW7dO2L59uxAfHy/Ex8dLvxenKF9//fXC7t27hTVr1gjNmjXjFGUX5LOlBIHH2Ru2bdsmGI1G4dVXXxWOHDkifP7550JgYKDw2WefSfvMnj1bCA0NFb777jth7969wq233qo6lbZ3797C1q1bhU2bNgkdOnRo0NOT1YwdO1aIjo6WpoKvXr1aCA8PF5599llpHx5rz+Xm5gq7du0Sdu3aJQAQ5s6dK+zatUs4deqUIAjeOaZZWVlCZGSkcN999wn79+8Xli9fLgQGBnIqeG2yYMECoVWrVoLJZBL69+8vbNmyResm1SkAVH+WLl0q7VNYWCg89thjQlhYmBAYGCjcdtttwoULF2ye5+TJk8KNN94oBAQECOHh4cLTTz8tlJaW1vC7qVuUwQ2Ps3f83//9n9CtWzfBbDYLnTt3Ft5//32b31utVmH69OlCZGSkYDabheuuu044dOiQzT4XL14U7rnnHiEoKEgIDg4Wxo0bJ+Tm5tbk26j1cnJyhMmTJwutWrUS/P39hbZt2wovvPCCzfRiHmvPrV+/XvWcPHbsWEEQvHdM9+zZI1x55ZWC2WwWoqOjhdmzZ3ul/TpBkJVxJCIiIqrjOOaGiIiI6hUGN0RERFSvMLghIiKieoXBDREREdUrDG6IiIioXmFwQ0RERPUKgxsiIiKqVxjcEBERUb3C4IaIiIjqFQY3REREVK8wuCEiIqJ65f8BOfQaxuAdleIAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1511,64 +1513,64 @@
      "text": [
       "     Gini\n",
       "0  0.0000\n",
-      "1  0.3146\n",
-      "2  0.3576\n",
-      "3  0.4404\n",
-      "4  0.4484\n",
+      "1  0.3306\n",
+      "2  0.4212\n",
+      "3  0.4780\n",
+      "4  0.5296\n",
       "     Gini\n",
-      "0  0.6464\n",
-      "1  0.6660\n",
-      "2  0.6868\n",
-      "3  0.6882\n",
-      "4  0.6638\n",
+      "0  0.6946\n",
+      "1  0.6848\n",
+      "2  0.6424\n",
+      "3  0.6594\n",
+      "4  0.6154\n",
       "     Gini\n",
-      "0  0.6682\n",
-      "1  0.6486\n",
-      "2  0.6362\n",
-      "3  0.6306\n",
-      "4  0.6478\n",
+      "0  0.6788\n",
+      "1  0.6416\n",
+      "2  0.6414\n",
+      "3  0.6464\n",
+      "4  0.6328\n",
       "     Gini\n",
-      "0  0.6858\n",
-      "1  0.6680\n",
-      "2  0.6784\n",
-      "3  0.6580\n",
-      "4  0.6282\n",
+      "0  0.6142\n",
+      "1  0.6306\n",
+      "2  0.6226\n",
+      "3  0.6378\n",
+      "4  0.6510\n",
       "     Gini\n",
-      "0  0.6598\n",
-      "1  0.6522\n",
-      "2  0.6406\n",
-      "3  0.6294\n",
-      "4  0.5926\n",
+      "0  0.6512\n",
+      "1  0.6582\n",
+      "2  0.6444\n",
+      "3  0.6696\n",
+      "4  0.6732\n",
       "     Gini\n",
-      "0  0.6316\n",
-      "1  0.6408\n",
-      "2  0.6316\n",
-      "3  0.6290\n",
-      "4  0.6290\n",
+      "0  0.7136\n",
+      "1  0.7310\n",
+      "2  0.7370\n",
+      "3  0.7268\n",
+      "4  0.7102\n",
       "     Gini\n",
-      "0  0.6842\n",
-      "1  0.6890\n",
-      "2  0.6466\n",
-      "3  0.6742\n",
-      "4  0.6860\n",
+      "0  0.6758\n",
+      "1  0.6930\n",
+      "2  0.6886\n",
+      "3  0.7034\n",
+      "4  0.6858\n",
       "     Gini\n",
-      "0  0.6142\n",
-      "1  0.6048\n",
-      "2  0.6356\n",
-      "3  0.6490\n",
-      "4  0.6292\n",
+      "0  0.6150\n",
+      "1  0.6206\n",
+      "2  0.6342\n",
+      "3  0.6640\n",
+      "4  0.6834\n",
       "     Gini\n",
-      "0  0.6488\n",
-      "1  0.6858\n",
-      "2  0.6940\n",
-      "3  0.6962\n",
-      "4  0.6824\n",
+      "0  0.7000\n",
+      "1  0.7150\n",
+      "2  0.7220\n",
+      "3  0.7152\n",
+      "4  0.7160\n",
       "     Gini\n",
-      "0  0.6966\n",
-      "1  0.6894\n",
-      "2  0.7008\n",
-      "3  0.6778\n",
-      "4  0.6778\n",
+      "0  0.6556\n",
+      "1  0.6782\n",
+      "2  0.6824\n",
+      "3  0.6520\n",
+      "4  0.6560\n",
       "              Wealth\n",
       "Step AgentID        \n",
       "0    0             1\n",
@@ -1578,67 +1580,67 @@
       "     4             1\n",
       "              Wealth\n",
       "Step AgentID        \n",
-      "901  82            2\n",
-      "     29            0\n",
-      "     53            0\n",
-      "     40            2\n",
-      "     87            0\n",
+      "901  27            0\n",
+      "     6             0\n",
+      "     71            0\n",
+      "     68            0\n",
+      "     94            1\n",
       "              Wealth\n",
       "Step AgentID        \n",
-      "101  39            0\n",
-      "     67            0\n",
-      "     0             0\n",
-      "     5             2\n",
-      "     29            0\n",
+      "101  5             0\n",
+      "     27            0\n",
+      "     51            0\n",
+      "     81            2\n",
+      "     63            2\n",
       "              Wealth\n",
       "Step AgentID        \n",
-      "201  56            4\n",
-      "     1             4\n",
-      "     22            1\n",
-      "     2             0\n",
-      "     69            2\n",
+      "201  96            1\n",
+      "     81            4\n",
+      "     46            1\n",
+      "     55            3\n",
+      "     80            1\n",
       "              Wealth\n",
       "Step AgentID        \n",
-      "301  7             1\n",
-      "     70            0\n",
-      "     13            3\n",
-      "     35            0\n",
-      "     20            1\n",
+      "301  66            1\n",
+      "     71            0\n",
+      "     91            2\n",
+      "     45            0\n",
+      "     50            2\n",
       "              Wealth\n",
       "Step AgentID        \n",
-      "401  65            2\n",
-      "     18            3\n",
-      "     68            0\n",
-      "     60            0\n",
-      "     42            0\n",
+      "401  70            0\n",
+      "     35            4\n",
+      "     27            2\n",
+      "     41            1\n",
+      "     0             0\n",
       "              Wealth\n",
       "Step AgentID        \n",
-      "501  45            1\n",
-      "     60            6\n",
-      "     72            0\n",
-      "     79            1\n",
-      "     99            1\n",
+      "501  68            1\n",
+      "     48            0\n",
+      "     19            1\n",
+      "     66            0\n",
+      "     81            0\n",
       "              Wealth\n",
       "Step AgentID        \n",
-      "601  21            1\n",
-      "     95            4\n",
-      "     39            1\n",
-      "     5             0\n",
-      "     76            2\n",
+      "601  50            3\n",
+      "     92            1\n",
+      "     40            2\n",
+      "     52            0\n",
+      "     57            0\n",
       "              Wealth\n",
       "Step AgentID        \n",
-      "701  73            0\n",
-      "     13            1\n",
-      "     49            0\n",
-      "     94            0\n",
-      "     44            0\n",
+      "701  46            6\n",
+      "     16            3\n",
+      "     63            0\n",
+      "     98            0\n",
+      "     15            0\n",
       "              Wealth\n",
       "Step AgentID        \n",
-      "801  56            0\n",
-      "     31            1\n",
-      "     45            0\n",
-      "     81            0\n",
-      "     83            1\n"
+      "801  18            1\n",
+      "     78            1\n",
+      "     87            0\n",
+      "     79            0\n",
+      "     11            1\n"
      ]
     }
    ],

From 1bc139cd7993911c3759d5d5e5fb23e229224c43 Mon Sep 17 00:00:00 2001
From: Chan-Dong-Jun <dongjuncoding@gmail.com>
Date: Tue, 16 Jul 2024 15:25:56 +0800
Subject: [PATCH 6/9] add save_special_results method

---
 mesa/cacheable_model.py              |    76 +-
 mesa/cacheable_wrapper_example.ipynb | 19971 ++++++++++++++++---------
 2 files changed, 13226 insertions(+), 6821 deletions(-)

diff --git a/mesa/cacheable_model.py b/mesa/cacheable_model.py
index 4f958d2b4fd..f4d3b47be63 100644
--- a/mesa/cacheable_model.py
+++ b/mesa/cacheable_model.py
@@ -4,7 +4,7 @@
 import types
 from copy import deepcopy
 from functools import partial
-from typing import Any
+from typing import Any, Callable
 
 import pyarrow as pa
 import pyarrow.parquet as pq
@@ -32,9 +32,10 @@ def __init__(
         self,
         model: Model,
         cache_file_path: str | Path,
-        cache_state: CacheState,
+        # cache_state: CacheState,
         total_steps: int,
-        cache_step_rate: int = 1,
+        # cache_step_rate: int = 1,
+        condition_function=None,
     ) -> None:
         """Create a new caching wrapper around an existing mesa model instance.
 
@@ -49,8 +50,8 @@ def __init__(
 
         self.model = model
         self.cache_file_path = Path(cache_file_path)
-        self._cache_state = cache_state
-        self._cache_step_rate = cache_step_rate
+        # self._cache_state = cache_state
+        # self._cache_step_rate = cache_step_rate
         self._total_steps = total_steps
         # self.cache: list[Any] = []
         self.step_count: int = 0
@@ -63,8 +64,10 @@ def __init__(
         self._agent_records = {}
 
         self._cache_interval = 100
-        self.output_dir = 'output_dir'
+        # self.output_dir = 'output_dir'
 
+        self._last_cached_step = 0 # inclusive since it is the bottom bound of slicing
+        self.condition_function = condition_function
     def get_agent_vars_dataframe(self):
         """Create a pandas DataFrame from the agent variables.
 
@@ -101,8 +104,8 @@ def get_model_vars_dataframe(self):
 
         print(f" TEST {self.model._steps=}")
         print(f" TEST {self._cache_interval=}")
-        print(pd.DataFrame(self.model.datacollector.model_vars)[self.model._steps-self._cache_interval:self.model._steps])
-        return pd.DataFrame(self.model.datacollector.model_vars)[self.model._steps-self._cache_interval:self.model._steps]
+        print(pd.DataFrame(self.model.datacollector.model_vars)[self._last_cached_step:self.model._steps])
+        return pd.DataFrame(self.model.datacollector.model_vars)[self._last_cached_step:self.model._steps]
 
     # def get_table_dataframe(self, table_name):
     #     """Create a pandas DataFrame from a particular table.
@@ -120,8 +123,12 @@ def _save_to_parquet(self, model):
         agent_df = self.get_agent_vars_dataframe()
         padding = len(str(self._total_steps)) - 1
         print(padding)
-        model_file = f"{self.output_dir}/model_data_{self.model._steps // self._cache_interval:0{padding}}.parquet"
-        agent_file = f"{self.output_dir}/agent_data_{self.model._steps // self._cache_interval:0{padding}}.parquet"
+
+        # ceiling function
+        model_file = f"{self.cache_file_path}/model_data_{-(self.model._steps // -self._cache_interval):0{padding}}.parquet"
+        agent_file = f"{self.cache_file_path}/agent_data_{-(self.model._steps // -self._cache_interval):0{padding}}.parquet"
+
+        self.cache_file_path.mkdir(parents=True, exist_ok=True)
 
         print(f"{model_file=}")
         absolute_path = os.path.abspath(model_file)
@@ -154,3 +161,52 @@ def cache(self):
             print("CALLED")
             print(f"{self.model._steps=}")
             self._save_to_parquet(self.model)
+            self._last_cached_step = self.model._steps
+        elif self.model._steps == self._total_steps:
+            print("FINAL CALLED")
+            print(f"{self.model._steps=}")
+            self._save_to_parquet(self.model)
+            self._last_cached_step = self.model._steps
+
+        if self.condition_function and self.save_special_results(self.condition_function):
+            # print("CHECK SPECIAL RESULTS CONDITION")
+            pass
+
+    def save_special_results(self, condition_function: Callable[[dict], bool]):
+
+        model_vars = self.model.datacollector.model_vars
+        # print(f"{model_vars=}")
+        print(f"{model_vars.get('Gini', 0)[-1]=}")
+        self.cache_file_path.mkdir(parents=True, exist_ok=True)
+        # if condition_function(model_vars):
+        #     special_results_df = pd.DataFrame(model_vars)
+        #     special_results_file = f"{self.cache_file_path}/special_results.parquet"
+        #
+        #
+        #     if not special_results_df.empty:
+        #         print(f"Condition met. Saving special results to {special_results_file}")
+        #         special_results_table = pa.Table.from_pandas(special_results_df)
+        #         pq.write_table(special_results_table, special_results_file)
+        #     else:
+        #         print("Condition met but no data to save.")
+
+        current_step = self.model._steps
+        special_results_file = f"{self.cache_file_path}/special_results.parquet"
+        if condition_function(model_vars):
+            step_data = {key: [value[-1]] for key, value in model_vars.items()}
+            step_data['Step'] = current_step
+            special_results_df = pd.DataFrame(step_data)
+
+            # Append the current step data to the Parquet file
+            if os.path.exists(special_results_file):
+                existing_data = pq.read_table(special_results_file).to_pandas()
+                combined_data = pd.concat([existing_data, special_results_df], ignore_index=True)
+                special_results_table = pa.Table.from_pandas(combined_data)
+            else:
+                special_results_table = pa.Table.from_pandas(special_results_df)
+
+            pq.write_table(special_results_table, special_results_file)
+
+            print(f"Condition met. Appended special results for step {current_step} to {special_results_file}")
+        else:
+            print(f"Condition not met at step {current_step}. No data to save.")
diff --git a/mesa/cacheable_wrapper_example.ipynb b/mesa/cacheable_wrapper_example.ipynb
index 3239129baef..0d646d87e17 100644
--- a/mesa/cacheable_wrapper_example.ipynb
+++ b/mesa/cacheable_wrapper_example.ipynb
@@ -23,6 +23,31 @@
   {
    "cell_type": "code",
    "execution_count": 2,
+   "id": "79ad4023-eeec-417d-bba6-488789992889",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Directory test_path and its contents have been deleted.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import shutil\n",
+    "\n",
+    "def delete_directory(directory_path):\n",
+    "    shutil.rmtree(directory_path)\n",
+    "    print(f\"Directory {directory_path} and its contents have been deleted.\")\n",
+    "\n",
+    "directory_to_delete = \"test_path\"\n",
+    "delete_directory(directory_to_delete)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
    "id": "c25f0179-d5ea-47d1-9e4e-c460030d5f73",
    "metadata": {},
    "outputs": [],
@@ -32,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "7ecda9c7-ec67-4322-b351-99dc5d3a5a67",
    "metadata": {},
    "outputs": [],
@@ -42,7 +67,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "d29660ed-cd5d-45a1-af20-cab8fdceeb7a",
    "metadata": {},
    "outputs": [],
@@ -116,7 +141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "657a3190-c26a-492c-abe6-be53b6e8a6d8",
    "metadata": {},
    "outputs": [],
@@ -126,12 +151,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
+   "id": "165b9f97-1e7b-40d5-8d6f-b705d0e45bd1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def condition_function(model_vars):\n",
+    "        return model_vars.get('Gini', 0)[-1] > 0.7"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
    "id": "b699ed8e-0cd0-452f-8719-6c6e0956f1a6",
    "metadata": {},
    "outputs": [],
    "source": [
-    "cacheable_model = CacheableModel(model, \"a\", 10000, None)"
+    "cacheable_model = CacheableModel(model, \"test_path\", 5555, condition_function)"
    ]
   },
   {
@@ -144,7 +180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "id": "dde8f54d-34ed-429d-99f7-b17c1b2cd5a5",
    "metadata": {
     "scrolled": true
@@ -155,104 +191,302 @@
      "output_type": "stream",
      "text": [
       "0\n",
+      "model_vars.get('Gini', 0)[-1]=0.0\n",
+      "Condition not met at step 1. No data to save.\n",
       "1\n",
+      "model_vars.get('Gini', 0)[-1]=0.32720000000000005\n",
+      "Condition not met at step 2. No data to save.\n",
       "2\n",
+      "model_vars.get('Gini', 0)[-1]=0.4626\n",
+      "Condition not met at step 3. No data to save.\n",
       "3\n",
+      "model_vars.get('Gini', 0)[-1]=0.4626\n",
+      "Condition not met at step 4. No data to save.\n",
       "4\n",
+      "model_vars.get('Gini', 0)[-1]=0.508\n",
+      "Condition not met at step 5. No data to save.\n",
       "5\n",
+      "model_vars.get('Gini', 0)[-1]=0.5302\n",
+      "Condition not met at step 6. No data to save.\n",
       "6\n",
+      "model_vars.get('Gini', 0)[-1]=0.5518000000000001\n",
+      "Condition not met at step 7. No data to save.\n",
       "7\n",
+      "model_vars.get('Gini', 0)[-1]=0.5973999999999999\n",
+      "Condition not met at step 8. No data to save.\n",
       "8\n",
+      "model_vars.get('Gini', 0)[-1]=0.6082000000000001\n",
+      "Condition not met at step 9. No data to save.\n",
       "9\n",
+      "model_vars.get('Gini', 0)[-1]=0.6000000000000001\n",
+      "Condition not met at step 10. No data to save.\n",
       "10\n",
+      "model_vars.get('Gini', 0)[-1]=0.6046\n",
+      "Condition not met at step 11. No data to save.\n",
       "11\n",
+      "model_vars.get('Gini', 0)[-1]=0.6093999999999999\n",
+      "Condition not met at step 12. No data to save.\n",
       "12\n",
+      "model_vars.get('Gini', 0)[-1]=0.618\n",
+      "Condition not met at step 13. No data to save.\n",
       "13\n",
+      "model_vars.get('Gini', 0)[-1]=0.6096\n",
+      "Condition not met at step 14. No data to save.\n",
       "14\n",
+      "model_vars.get('Gini', 0)[-1]=0.6315999999999999\n",
+      "Condition not met at step 15. No data to save.\n",
       "15\n",
+      "model_vars.get('Gini', 0)[-1]=0.6044\n",
+      "Condition not met at step 16. No data to save.\n",
       "16\n",
+      "model_vars.get('Gini', 0)[-1]=0.6078\n",
+      "Condition not met at step 17. No data to save.\n",
       "17\n",
+      "model_vars.get('Gini', 0)[-1]=0.6018\n",
+      "Condition not met at step 18. No data to save.\n",
       "18\n",
+      "model_vars.get('Gini', 0)[-1]=0.6058\n",
+      "Condition not met at step 19. No data to save.\n",
       "19\n",
+      "model_vars.get('Gini', 0)[-1]=0.616\n",
+      "Condition not met at step 20. No data to save.\n",
       "20\n",
+      "model_vars.get('Gini', 0)[-1]=0.5754\n",
+      "Condition not met at step 21. No data to save.\n",
       "21\n",
+      "model_vars.get('Gini', 0)[-1]=0.5886\n",
+      "Condition not met at step 22. No data to save.\n",
       "22\n",
+      "model_vars.get('Gini', 0)[-1]=0.6106\n",
+      "Condition not met at step 23. No data to save.\n",
       "23\n",
+      "model_vars.get('Gini', 0)[-1]=0.6156\n",
+      "Condition not met at step 24. No data to save.\n",
       "24\n",
+      "model_vars.get('Gini', 0)[-1]=0.6334\n",
+      "Condition not met at step 25. No data to save.\n",
       "25\n",
+      "model_vars.get('Gini', 0)[-1]=0.6562\n",
+      "Condition not met at step 26. No data to save.\n",
       "26\n",
+      "model_vars.get('Gini', 0)[-1]=0.6592\n",
+      "Condition not met at step 27. No data to save.\n",
       "27\n",
+      "model_vars.get('Gini', 0)[-1]=0.6652\n",
+      "Condition not met at step 28. No data to save.\n",
       "28\n",
+      "model_vars.get('Gini', 0)[-1]=0.6408\n",
+      "Condition not met at step 29. No data to save.\n",
       "29\n",
+      "model_vars.get('Gini', 0)[-1]=0.653\n",
+      "Condition not met at step 30. No data to save.\n",
       "30\n",
+      "model_vars.get('Gini', 0)[-1]=0.6264000000000001\n",
+      "Condition not met at step 31. No data to save.\n",
       "31\n",
+      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
+      "Condition not met at step 32. No data to save.\n",
       "32\n",
+      "model_vars.get('Gini', 0)[-1]=0.6662\n",
+      "Condition not met at step 33. No data to save.\n",
       "33\n",
+      "model_vars.get('Gini', 0)[-1]=0.6664\n",
+      "Condition not met at step 34. No data to save.\n",
       "34\n",
+      "model_vars.get('Gini', 0)[-1]=0.6804\n",
+      "Condition not met at step 35. No data to save.\n",
       "35\n",
+      "model_vars.get('Gini', 0)[-1]=0.6636\n",
+      "Condition not met at step 36. No data to save.\n",
       "36\n",
+      "model_vars.get('Gini', 0)[-1]=0.6568\n",
+      "Condition not met at step 37. No data to save.\n",
       "37\n",
+      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
+      "Condition not met at step 38. No data to save.\n",
       "38\n",
+      "model_vars.get('Gini', 0)[-1]=0.6892\n",
+      "Condition not met at step 39. No data to save.\n",
       "39\n",
+      "model_vars.get('Gini', 0)[-1]=0.6948000000000001\n",
+      "Condition not met at step 40. No data to save.\n",
       "40\n",
+      "model_vars.get('Gini', 0)[-1]=0.6894\n",
+      "Condition not met at step 41. No data to save.\n",
       "41\n",
+      "model_vars.get('Gini', 0)[-1]=0.7046\n",
+      "Condition met. Appended special results for step 42 to test_path/special_results.parquet\n",
       "42\n",
+      "model_vars.get('Gini', 0)[-1]=0.7010000000000001\n",
+      "Condition met. Appended special results for step 43 to test_path/special_results.parquet\n",
       "43\n",
+      "model_vars.get('Gini', 0)[-1]=0.7056\n",
+      "Condition met. Appended special results for step 44 to test_path/special_results.parquet\n",
       "44\n",
+      "model_vars.get('Gini', 0)[-1]=0.7203999999999999\n",
+      "Condition met. Appended special results for step 45 to test_path/special_results.parquet\n",
       "45\n",
+      "model_vars.get('Gini', 0)[-1]=0.7092\n",
+      "Condition met. Appended special results for step 46 to test_path/special_results.parquet\n",
       "46\n",
+      "model_vars.get('Gini', 0)[-1]=0.7028000000000001\n",
+      "Condition met. Appended special results for step 47 to test_path/special_results.parquet\n",
       "47\n",
+      "model_vars.get('Gini', 0)[-1]=0.7166\n",
+      "Condition met. Appended special results for step 48 to test_path/special_results.parquet\n",
       "48\n",
+      "model_vars.get('Gini', 0)[-1]=0.7146\n",
+      "Condition met. Appended special results for step 49 to test_path/special_results.parquet\n",
       "49\n",
+      "model_vars.get('Gini', 0)[-1]=0.6961999999999999\n",
+      "Condition not met at step 50. No data to save.\n",
       "50\n",
+      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
+      "Condition not met at step 51. No data to save.\n",
       "51\n",
+      "model_vars.get('Gini', 0)[-1]=0.6504000000000001\n",
+      "Condition not met at step 52. No data to save.\n",
       "52\n",
+      "model_vars.get('Gini', 0)[-1]=0.647\n",
+      "Condition not met at step 53. No data to save.\n",
       "53\n",
+      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
+      "Condition not met at step 54. No data to save.\n",
       "54\n",
+      "model_vars.get('Gini', 0)[-1]=0.6734\n",
+      "Condition not met at step 55. No data to save.\n",
       "55\n",
+      "model_vars.get('Gini', 0)[-1]=0.6914\n",
+      "Condition not met at step 56. No data to save.\n",
       "56\n",
+      "model_vars.get('Gini', 0)[-1]=0.6819999999999999\n",
+      "Condition not met at step 57. No data to save.\n",
       "57\n",
+      "model_vars.get('Gini', 0)[-1]=0.7058\n",
+      "Condition met. Appended special results for step 58 to test_path/special_results.parquet\n",
       "58\n",
+      "model_vars.get('Gini', 0)[-1]=0.7056\n",
+      "Condition met. Appended special results for step 59 to test_path/special_results.parquet\n",
       "59\n",
+      "model_vars.get('Gini', 0)[-1]=0.7250000000000001\n",
+      "Condition met. Appended special results for step 60 to test_path/special_results.parquet\n",
       "60\n",
+      "model_vars.get('Gini', 0)[-1]=0.7012\n",
+      "Condition met. Appended special results for step 61 to test_path/special_results.parquet\n",
       "61\n",
+      "model_vars.get('Gini', 0)[-1]=0.7021999999999999\n",
+      "Condition met. Appended special results for step 62 to test_path/special_results.parquet\n",
       "62\n",
+      "model_vars.get('Gini', 0)[-1]=0.7088\n",
+      "Condition met. Appended special results for step 63 to test_path/special_results.parquet\n",
       "63\n",
+      "model_vars.get('Gini', 0)[-1]=0.6976\n",
+      "Condition not met at step 64. No data to save.\n",
       "64\n",
+      "model_vars.get('Gini', 0)[-1]=0.7183999999999999\n",
+      "Condition met. Appended special results for step 65 to test_path/special_results.parquet\n",
       "65\n",
+      "model_vars.get('Gini', 0)[-1]=0.7010000000000001\n",
+      "Condition met. Appended special results for step 66 to test_path/special_results.parquet\n",
       "66\n",
+      "model_vars.get('Gini', 0)[-1]=0.7012\n",
+      "Condition met. Appended special results for step 67 to test_path/special_results.parquet\n",
       "67\n",
+      "model_vars.get('Gini', 0)[-1]=0.698\n",
+      "Condition not met at step 68. No data to save.\n",
       "68\n",
+      "model_vars.get('Gini', 0)[-1]=0.7028000000000001\n",
+      "Condition met. Appended special results for step 69 to test_path/special_results.parquet\n",
       "69\n",
+      "model_vars.get('Gini', 0)[-1]=0.7414000000000001\n",
+      "Condition met. Appended special results for step 70 to test_path/special_results.parquet\n",
       "70\n",
+      "model_vars.get('Gini', 0)[-1]=0.73\n",
+      "Condition met. Appended special results for step 71 to test_path/special_results.parquet\n",
       "71\n",
+      "model_vars.get('Gini', 0)[-1]=0.7041999999999999\n",
+      "Condition met. Appended special results for step 72 to test_path/special_results.parquet\n",
       "72\n",
+      "model_vars.get('Gini', 0)[-1]=0.6754\n",
+      "Condition not met at step 73. No data to save.\n",
       "73\n",
+      "model_vars.get('Gini', 0)[-1]=0.6956\n",
+      "Condition not met at step 74. No data to save.\n",
       "74\n",
+      "model_vars.get('Gini', 0)[-1]=0.6762\n",
+      "Condition not met at step 75. No data to save.\n",
       "75\n",
+      "model_vars.get('Gini', 0)[-1]=0.6848000000000001\n",
+      "Condition not met at step 76. No data to save.\n",
       "76\n",
+      "model_vars.get('Gini', 0)[-1]=0.6848000000000001\n",
+      "Condition not met at step 77. No data to save.\n",
       "77\n",
+      "model_vars.get('Gini', 0)[-1]=0.6952\n",
+      "Condition not met at step 78. No data to save.\n",
       "78\n",
+      "model_vars.get('Gini', 0)[-1]=0.6904\n",
+      "Condition not met at step 79. No data to save.\n",
       "79\n",
+      "model_vars.get('Gini', 0)[-1]=0.7098\n",
+      "Condition met. Appended special results for step 80 to test_path/special_results.parquet\n",
       "80\n",
+      "model_vars.get('Gini', 0)[-1]=0.7174\n",
+      "Condition met. Appended special results for step 81 to test_path/special_results.parquet\n",
       "81\n",
+      "model_vars.get('Gini', 0)[-1]=0.7203999999999999\n",
+      "Condition met. Appended special results for step 82 to test_path/special_results.parquet\n",
       "82\n",
+      "model_vars.get('Gini', 0)[-1]=0.7144\n",
+      "Condition met. Appended special results for step 83 to test_path/special_results.parquet\n",
       "83\n",
+      "model_vars.get('Gini', 0)[-1]=0.712\n",
+      "Condition met. Appended special results for step 84 to test_path/special_results.parquet\n",
       "84\n",
+      "model_vars.get('Gini', 0)[-1]=0.7004\n",
+      "Condition met. Appended special results for step 85 to test_path/special_results.parquet\n",
       "85\n",
+      "model_vars.get('Gini', 0)[-1]=0.6952\n",
+      "Condition not met at step 86. No data to save.\n",
       "86\n",
+      "model_vars.get('Gini', 0)[-1]=0.7016\n",
+      "Condition met. Appended special results for step 87 to test_path/special_results.parquet\n",
       "87\n",
+      "model_vars.get('Gini', 0)[-1]=0.6692\n",
+      "Condition not met at step 88. No data to save.\n",
       "88\n",
+      "model_vars.get('Gini', 0)[-1]=0.6692\n",
+      "Condition not met at step 89. No data to save.\n",
       "89\n",
+      "model_vars.get('Gini', 0)[-1]=0.679\n",
+      "Condition not met at step 90. No data to save.\n",
       "90\n",
+      "model_vars.get('Gini', 0)[-1]=0.6826\n",
+      "Condition not met at step 91. No data to save.\n",
       "91\n",
+      "model_vars.get('Gini', 0)[-1]=0.6794\n",
+      "Condition not met at step 92. No data to save.\n",
       "92\n",
+      "model_vars.get('Gini', 0)[-1]=0.6890000000000001\n",
+      "Condition not met at step 93. No data to save.\n",
       "93\n",
+      "model_vars.get('Gini', 0)[-1]=0.6974\n",
+      "Condition not met at step 94. No data to save.\n",
       "94\n",
+      "model_vars.get('Gini', 0)[-1]=0.6828000000000001\n",
+      "Condition not met at step 95. No data to save.\n",
       "95\n",
+      "model_vars.get('Gini', 0)[-1]=0.6754\n",
+      "Condition not met at step 96. No data to save.\n",
       "96\n",
+      "model_vars.get('Gini', 0)[-1]=0.6764\n",
+      "Condition not met at step 97. No data to save.\n",
       "97\n",
+      "model_vars.get('Gini', 0)[-1]=0.6834\n",
+      "Condition not met at step 98. No data to save.\n",
       "98\n",
+      "model_vars.get('Gini', 0)[-1]=0.6582\n",
+      "Condition not met at step 99. No data to save.\n",
       "99\n",
       "CALLED\n",
       "self.model._steps=100\n",
@@ -260,12367 +494,17864 @@
       " TEST self._cache_interval=100\n",
       "      Gini\n",
       "0   0.0000\n",
-      "1   0.2822\n",
-      "2   0.3666\n",
-      "3   0.4510\n",
-      "4   0.5058\n",
+      "1   0.3272\n",
+      "2   0.4626\n",
+      "3   0.4626\n",
+      "4   0.5080\n",
       "..     ...\n",
-      "95  0.6584\n",
-      "96  0.6582\n",
-      "97  0.6362\n",
-      "98  0.6440\n",
-      "99  0.6602\n",
+      "95  0.6754\n",
+      "96  0.6764\n",
+      "97  0.6834\n",
+      "98  0.6582\n",
+      "99  0.6464\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_001.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_001.parquet'\n",
-      "Saving model to output_dir/model_data_001.parquet\n",
-      "Saving agent to output_dir/agent_data_001.parquet\n",
+      "model_file='test_path/model_data_001.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_001.parquet'\n",
+      "Saving model to test_path/model_data_001.parquet\n",
+      "Saving agent to test_path/agent_data_001.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6464000000000001\n",
+      "Condition not met at step 100. No data to save.\n",
       "100\n",
+      "model_vars.get('Gini', 0)[-1]=0.665\n",
+      "Condition not met at step 101. No data to save.\n",
       "101\n",
+      "model_vars.get('Gini', 0)[-1]=0.6506000000000001\n",
+      "Condition not met at step 102. No data to save.\n",
       "102\n",
+      "model_vars.get('Gini', 0)[-1]=0.6734\n",
+      "Condition not met at step 103. No data to save.\n",
       "103\n",
+      "model_vars.get('Gini', 0)[-1]=0.6656\n",
+      "Condition not met at step 104. No data to save.\n",
       "104\n",
+      "model_vars.get('Gini', 0)[-1]=0.645\n",
+      "Condition not met at step 105. No data to save.\n",
       "105\n",
+      "model_vars.get('Gini', 0)[-1]=0.6514\n",
+      "Condition not met at step 106. No data to save.\n",
       "106\n",
+      "model_vars.get('Gini', 0)[-1]=0.6352\n",
+      "Condition not met at step 107. No data to save.\n",
       "107\n",
+      "model_vars.get('Gini', 0)[-1]=0.6412\n",
+      "Condition not met at step 108. No data to save.\n",
       "108\n",
+      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
+      "Condition not met at step 109. No data to save.\n",
       "109\n",
+      "model_vars.get('Gini', 0)[-1]=0.662\n",
+      "Condition not met at step 110. No data to save.\n",
       "110\n",
+      "model_vars.get('Gini', 0)[-1]=0.6388\n",
+      "Condition not met at step 111. No data to save.\n",
       "111\n",
+      "model_vars.get('Gini', 0)[-1]=0.628\n",
+      "Condition not met at step 112. No data to save.\n",
       "112\n",
+      "model_vars.get('Gini', 0)[-1]=0.6182000000000001\n",
+      "Condition not met at step 113. No data to save.\n",
       "113\n",
+      "model_vars.get('Gini', 0)[-1]=0.647\n",
+      "Condition not met at step 114. No data to save.\n",
       "114\n",
+      "model_vars.get('Gini', 0)[-1]=0.6702\n",
+      "Condition not met at step 115. No data to save.\n",
       "115\n",
+      "model_vars.get('Gini', 0)[-1]=0.6414\n",
+      "Condition not met at step 116. No data to save.\n",
       "116\n",
+      "model_vars.get('Gini', 0)[-1]=0.655\n",
+      "Condition not met at step 117. No data to save.\n",
       "117\n",
+      "model_vars.get('Gini', 0)[-1]=0.6696\n",
+      "Condition not met at step 118. No data to save.\n",
       "118\n",
+      "model_vars.get('Gini', 0)[-1]=0.6576\n",
+      "Condition not met at step 119. No data to save.\n",
       "119\n",
+      "model_vars.get('Gini', 0)[-1]=0.654\n",
+      "Condition not met at step 120. No data to save.\n",
       "120\n",
+      "model_vars.get('Gini', 0)[-1]=0.6482\n",
+      "Condition not met at step 121. No data to save.\n",
       "121\n",
+      "model_vars.get('Gini', 0)[-1]=0.6516\n",
+      "Condition not met at step 122. No data to save.\n",
       "122\n",
+      "model_vars.get('Gini', 0)[-1]=0.656\n",
+      "Condition not met at step 123. No data to save.\n",
       "123\n",
+      "model_vars.get('Gini', 0)[-1]=0.6736\n",
+      "Condition not met at step 124. No data to save.\n",
       "124\n",
+      "model_vars.get('Gini', 0)[-1]=0.6736\n",
+      "Condition not met at step 125. No data to save.\n",
       "125\n",
+      "model_vars.get('Gini', 0)[-1]=0.6664\n",
+      "Condition not met at step 126. No data to save.\n",
       "126\n",
+      "model_vars.get('Gini', 0)[-1]=0.658\n",
+      "Condition not met at step 127. No data to save.\n",
       "127\n",
+      "model_vars.get('Gini', 0)[-1]=0.6782\n",
+      "Condition not met at step 128. No data to save.\n",
       "128\n",
+      "model_vars.get('Gini', 0)[-1]=0.6742\n",
+      "Condition not met at step 129. No data to save.\n",
       "129\n",
+      "model_vars.get('Gini', 0)[-1]=0.6712\n",
+      "Condition not met at step 130. No data to save.\n",
       "130\n",
+      "model_vars.get('Gini', 0)[-1]=0.6614\n",
+      "Condition not met at step 131. No data to save.\n",
       "131\n",
+      "model_vars.get('Gini', 0)[-1]=0.6626000000000001\n",
+      "Condition not met at step 132. No data to save.\n",
       "132\n",
+      "model_vars.get('Gini', 0)[-1]=0.6512\n",
+      "Condition not met at step 133. No data to save.\n",
       "133\n",
+      "model_vars.get('Gini', 0)[-1]=0.639\n",
+      "Condition not met at step 134. No data to save.\n",
       "134\n",
+      "model_vars.get('Gini', 0)[-1]=0.638\n",
+      "Condition not met at step 135. No data to save.\n",
       "135\n",
+      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
+      "Condition not met at step 136. No data to save.\n",
       "136\n",
+      "model_vars.get('Gini', 0)[-1]=0.6504000000000001\n",
+      "Condition not met at step 137. No data to save.\n",
       "137\n",
+      "model_vars.get('Gini', 0)[-1]=0.6144000000000001\n",
+      "Condition not met at step 138. No data to save.\n",
       "138\n",
+      "model_vars.get('Gini', 0)[-1]=0.5794\n",
+      "Condition not met at step 139. No data to save.\n",
       "139\n",
+      "model_vars.get('Gini', 0)[-1]=0.5993999999999999\n",
+      "Condition not met at step 140. No data to save.\n",
       "140\n",
+      "model_vars.get('Gini', 0)[-1]=0.6272\n",
+      "Condition not met at step 141. No data to save.\n",
       "141\n",
+      "model_vars.get('Gini', 0)[-1]=0.643\n",
+      "Condition not met at step 142. No data to save.\n",
       "142\n",
+      "model_vars.get('Gini', 0)[-1]=0.621\n",
+      "Condition not met at step 143. No data to save.\n",
       "143\n",
+      "model_vars.get('Gini', 0)[-1]=0.6192\n",
+      "Condition not met at step 144. No data to save.\n",
       "144\n",
+      "model_vars.get('Gini', 0)[-1]=0.5778000000000001\n",
+      "Condition not met at step 145. No data to save.\n",
       "145\n",
+      "model_vars.get('Gini', 0)[-1]=0.616\n",
+      "Condition not met at step 146. No data to save.\n",
       "146\n",
+      "model_vars.get('Gini', 0)[-1]=0.6402\n",
+      "Condition not met at step 147. No data to save.\n",
       "147\n",
+      "model_vars.get('Gini', 0)[-1]=0.6315999999999999\n",
+      "Condition not met at step 148. No data to save.\n",
       "148\n",
+      "model_vars.get('Gini', 0)[-1]=0.6113999999999999\n",
+      "Condition not met at step 149. No data to save.\n",
       "149\n",
+      "model_vars.get('Gini', 0)[-1]=0.6378\n",
+      "Condition not met at step 150. No data to save.\n",
       "150\n",
+      "model_vars.get('Gini', 0)[-1]=0.6392\n",
+      "Condition not met at step 151. No data to save.\n",
       "151\n",
+      "model_vars.get('Gini', 0)[-1]=0.6238\n",
+      "Condition not met at step 152. No data to save.\n",
       "152\n",
+      "model_vars.get('Gini', 0)[-1]=0.6294\n",
+      "Condition not met at step 153. No data to save.\n",
       "153\n",
+      "model_vars.get('Gini', 0)[-1]=0.6315999999999999\n",
+      "Condition not met at step 154. No data to save.\n",
       "154\n",
+      "model_vars.get('Gini', 0)[-1]=0.6372\n",
+      "Condition not met at step 155. No data to save.\n",
       "155\n",
+      "model_vars.get('Gini', 0)[-1]=0.6392\n",
+      "Condition not met at step 156. No data to save.\n",
       "156\n",
+      "model_vars.get('Gini', 0)[-1]=0.6232\n",
+      "Condition not met at step 157. No data to save.\n",
       "157\n",
+      "model_vars.get('Gini', 0)[-1]=0.6604\n",
+      "Condition not met at step 158. No data to save.\n",
       "158\n",
+      "model_vars.get('Gini', 0)[-1]=0.6454\n",
+      "Condition not met at step 159. No data to save.\n",
       "159\n",
+      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
+      "Condition not met at step 160. No data to save.\n",
       "160\n",
+      "model_vars.get('Gini', 0)[-1]=0.64\n",
+      "Condition not met at step 161. No data to save.\n",
       "161\n",
+      "model_vars.get('Gini', 0)[-1]=0.6434\n",
+      "Condition not met at step 162. No data to save.\n",
       "162\n",
+      "model_vars.get('Gini', 0)[-1]=0.6512\n",
+      "Condition not met at step 163. No data to save.\n",
       "163\n",
+      "model_vars.get('Gini', 0)[-1]=0.6612\n",
+      "Condition not met at step 164. No data to save.\n",
       "164\n",
+      "model_vars.get('Gini', 0)[-1]=0.6622\n",
+      "Condition not met at step 165. No data to save.\n",
       "165\n",
+      "model_vars.get('Gini', 0)[-1]=0.6572\n",
+      "Condition not met at step 166. No data to save.\n",
       "166\n",
+      "model_vars.get('Gini', 0)[-1]=0.6734\n",
+      "Condition not met at step 167. No data to save.\n",
       "167\n",
+      "model_vars.get('Gini', 0)[-1]=0.654\n",
+      "Condition not met at step 168. No data to save.\n",
       "168\n",
+      "model_vars.get('Gini', 0)[-1]=0.6832\n",
+      "Condition not met at step 169. No data to save.\n",
       "169\n",
+      "model_vars.get('Gini', 0)[-1]=0.6736\n",
+      "Condition not met at step 170. No data to save.\n",
       "170\n",
+      "model_vars.get('Gini', 0)[-1]=0.6346\n",
+      "Condition not met at step 171. No data to save.\n",
       "171\n",
+      "model_vars.get('Gini', 0)[-1]=0.652\n",
+      "Condition not met at step 172. No data to save.\n",
       "172\n",
+      "model_vars.get('Gini', 0)[-1]=0.6556\n",
+      "Condition not met at step 173. No data to save.\n",
       "173\n",
+      "model_vars.get('Gini', 0)[-1]=0.6315999999999999\n",
+      "Condition not met at step 174. No data to save.\n",
       "174\n",
+      "model_vars.get('Gini', 0)[-1]=0.6742\n",
+      "Condition not met at step 175. No data to save.\n",
       "175\n",
+      "model_vars.get('Gini', 0)[-1]=0.6722\n",
+      "Condition not met at step 176. No data to save.\n",
       "176\n",
+      "model_vars.get('Gini', 0)[-1]=0.6746000000000001\n",
+      "Condition not met at step 177. No data to save.\n",
       "177\n",
+      "model_vars.get('Gini', 0)[-1]=0.6876\n",
+      "Condition not met at step 178. No data to save.\n",
       "178\n",
+      "model_vars.get('Gini', 0)[-1]=0.6776\n",
+      "Condition not met at step 179. No data to save.\n",
       "179\n",
+      "model_vars.get('Gini', 0)[-1]=0.706\n",
+      "Condition met. Appended special results for step 180 to test_path/special_results.parquet\n",
       "180\n",
+      "model_vars.get('Gini', 0)[-1]=0.6544\n",
+      "Condition not met at step 181. No data to save.\n",
       "181\n",
+      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
+      "Condition not met at step 182. No data to save.\n",
       "182\n",
+      "model_vars.get('Gini', 0)[-1]=0.6542\n",
+      "Condition not met at step 183. No data to save.\n",
       "183\n",
+      "model_vars.get('Gini', 0)[-1]=0.6492\n",
+      "Condition not met at step 184. No data to save.\n",
       "184\n",
+      "model_vars.get('Gini', 0)[-1]=0.6374\n",
+      "Condition not met at step 185. No data to save.\n",
       "185\n",
+      "model_vars.get('Gini', 0)[-1]=0.6366\n",
+      "Condition not met at step 186. No data to save.\n",
       "186\n",
+      "model_vars.get('Gini', 0)[-1]=0.6604\n",
+      "Condition not met at step 187. No data to save.\n",
       "187\n",
+      "model_vars.get('Gini', 0)[-1]=0.6444000000000001\n",
+      "Condition not met at step 188. No data to save.\n",
       "188\n",
+      "model_vars.get('Gini', 0)[-1]=0.6562\n",
+      "Condition not met at step 189. No data to save.\n",
       "189\n",
+      "model_vars.get('Gini', 0)[-1]=0.652\n",
+      "Condition not met at step 190. No data to save.\n",
       "190\n",
+      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
+      "Condition not met at step 191. No data to save.\n",
       "191\n",
+      "model_vars.get('Gini', 0)[-1]=0.5978\n",
+      "Condition not met at step 192. No data to save.\n",
       "192\n",
+      "model_vars.get('Gini', 0)[-1]=0.6194\n",
+      "Condition not met at step 193. No data to save.\n",
       "193\n",
+      "model_vars.get('Gini', 0)[-1]=0.6195999999999999\n",
+      "Condition not met at step 194. No data to save.\n",
       "194\n",
+      "model_vars.get('Gini', 0)[-1]=0.6144000000000001\n",
+      "Condition not met at step 195. No data to save.\n",
       "195\n",
+      "model_vars.get('Gini', 0)[-1]=0.6086\n",
+      "Condition not met at step 196. No data to save.\n",
       "196\n",
+      "model_vars.get('Gini', 0)[-1]=0.6013999999999999\n",
+      "Condition not met at step 197. No data to save.\n",
       "197\n",
+      "model_vars.get('Gini', 0)[-1]=0.613\n",
+      "Condition not met at step 198. No data to save.\n",
       "198\n",
+      "model_vars.get('Gini', 0)[-1]=0.6184000000000001\n",
+      "Condition not met at step 199. No data to save.\n",
       "199\n",
       "CALLED\n",
       "self.model._steps=200\n",
       " TEST self.model._steps=200\n",
       " TEST self._cache_interval=100\n",
       "       Gini\n",
-      "100  0.6794\n",
-      "101  0.6842\n",
-      "102  0.6756\n",
-      "103  0.6532\n",
-      "104  0.6452\n",
+      "100  0.6650\n",
+      "101  0.6506\n",
+      "102  0.6734\n",
+      "103  0.6656\n",
+      "104  0.6450\n",
       "..      ...\n",
-      "195  0.6662\n",
-      "196  0.6802\n",
-      "197  0.6730\n",
-      "198  0.6922\n",
-      "199  0.6740\n",
+      "195  0.6086\n",
+      "196  0.6014\n",
+      "197  0.6130\n",
+      "198  0.6184\n",
+      "199  0.6284\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_002.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_002.parquet'\n",
-      "Saving model to output_dir/model_data_002.parquet\n",
-      "Saving agent to output_dir/agent_data_002.parquet\n",
+      "model_file='test_path/model_data_002.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_002.parquet'\n",
+      "Saving model to test_path/model_data_002.parquet\n",
+      "Saving agent to test_path/agent_data_002.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6284000000000001\n",
+      "Condition not met at step 200. No data to save.\n",
       "200\n",
+      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
+      "Condition not met at step 201. No data to save.\n",
       "201\n",
+      "model_vars.get('Gini', 0)[-1]=0.6086\n",
+      "Condition not met at step 202. No data to save.\n",
       "202\n",
+      "model_vars.get('Gini', 0)[-1]=0.625\n",
+      "Condition not met at step 203. No data to save.\n",
       "203\n",
+      "model_vars.get('Gini', 0)[-1]=0.606\n",
+      "Condition not met at step 204. No data to save.\n",
       "204\n",
+      "model_vars.get('Gini', 0)[-1]=0.624\n",
+      "Condition not met at step 205. No data to save.\n",
       "205\n",
+      "model_vars.get('Gini', 0)[-1]=0.6388\n",
+      "Condition not met at step 206. No data to save.\n",
       "206\n",
+      "model_vars.get('Gini', 0)[-1]=0.6158\n",
+      "Condition not met at step 207. No data to save.\n",
       "207\n",
+      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
+      "Condition not met at step 208. No data to save.\n",
       "208\n",
+      "model_vars.get('Gini', 0)[-1]=0.6494\n",
+      "Condition not met at step 209. No data to save.\n",
       "209\n",
+      "model_vars.get('Gini', 0)[-1]=0.6434\n",
+      "Condition not met at step 210. No data to save.\n",
       "210\n",
+      "model_vars.get('Gini', 0)[-1]=0.6358\n",
+      "Condition not met at step 211. No data to save.\n",
       "211\n",
+      "model_vars.get('Gini', 0)[-1]=0.6328\n",
+      "Condition not met at step 212. No data to save.\n",
       "212\n",
+      "model_vars.get('Gini', 0)[-1]=0.646\n",
+      "Condition not met at step 213. No data to save.\n",
       "213\n",
+      "model_vars.get('Gini', 0)[-1]=0.6508\n",
+      "Condition not met at step 214. No data to save.\n",
       "214\n",
+      "model_vars.get('Gini', 0)[-1]=0.6448\n",
+      "Condition not met at step 215. No data to save.\n",
       "215\n",
+      "model_vars.get('Gini', 0)[-1]=0.6456\n",
+      "Condition not met at step 216. No data to save.\n",
       "216\n",
+      "model_vars.get('Gini', 0)[-1]=0.6358\n",
+      "Condition not met at step 217. No data to save.\n",
       "217\n",
+      "model_vars.get('Gini', 0)[-1]=0.642\n",
+      "Condition not met at step 218. No data to save.\n",
       "218\n",
+      "model_vars.get('Gini', 0)[-1]=0.659\n",
+      "Condition not met at step 219. No data to save.\n",
       "219\n",
+      "model_vars.get('Gini', 0)[-1]=0.6632\n",
+      "Condition not met at step 220. No data to save.\n",
       "220\n",
+      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
+      "Condition not met at step 221. No data to save.\n",
       "221\n",
+      "model_vars.get('Gini', 0)[-1]=0.6752\n",
+      "Condition not met at step 222. No data to save.\n",
       "222\n",
+      "model_vars.get('Gini', 0)[-1]=0.6584\n",
+      "Condition not met at step 223. No data to save.\n",
       "223\n",
+      "model_vars.get('Gini', 0)[-1]=0.663\n",
+      "Condition not met at step 224. No data to save.\n",
       "224\n",
+      "model_vars.get('Gini', 0)[-1]=0.6312\n",
+      "Condition not met at step 225. No data to save.\n",
       "225\n",
+      "model_vars.get('Gini', 0)[-1]=0.6215999999999999\n",
+      "Condition not met at step 226. No data to save.\n",
       "226\n",
+      "model_vars.get('Gini', 0)[-1]=0.5973999999999999\n",
+      "Condition not met at step 227. No data to save.\n",
       "227\n",
+      "model_vars.get('Gini', 0)[-1]=0.623\n",
+      "Condition not met at step 228. No data to save.\n",
       "228\n",
+      "model_vars.get('Gini', 0)[-1]=0.6532\n",
+      "Condition not met at step 229. No data to save.\n",
       "229\n",
+      "model_vars.get('Gini', 0)[-1]=0.6502\n",
+      "Condition not met at step 230. No data to save.\n",
       "230\n",
+      "model_vars.get('Gini', 0)[-1]=0.654\n",
+      "Condition not met at step 231. No data to save.\n",
       "231\n",
+      "model_vars.get('Gini', 0)[-1]=0.6608\n",
+      "Condition not met at step 232. No data to save.\n",
       "232\n",
+      "model_vars.get('Gini', 0)[-1]=0.6464000000000001\n",
+      "Condition not met at step 233. No data to save.\n",
       "233\n",
+      "model_vars.get('Gini', 0)[-1]=0.6344000000000001\n",
+      "Condition not met at step 234. No data to save.\n",
       "234\n",
+      "model_vars.get('Gini', 0)[-1]=0.6374\n",
+      "Condition not met at step 235. No data to save.\n",
       "235\n",
+      "model_vars.get('Gini', 0)[-1]=0.621\n",
+      "Condition not met at step 236. No data to save.\n",
       "236\n",
+      "model_vars.get('Gini', 0)[-1]=0.6282000000000001\n",
+      "Condition not met at step 237. No data to save.\n",
       "237\n",
+      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
+      "Condition not met at step 238. No data to save.\n",
       "238\n",
+      "model_vars.get('Gini', 0)[-1]=0.6166\n",
+      "Condition not met at step 239. No data to save.\n",
       "239\n",
+      "model_vars.get('Gini', 0)[-1]=0.6033999999999999\n",
+      "Condition not met at step 240. No data to save.\n",
       "240\n",
+      "model_vars.get('Gini', 0)[-1]=0.6504000000000001\n",
+      "Condition not met at step 241. No data to save.\n",
       "241\n",
+      "model_vars.get('Gini', 0)[-1]=0.5998\n",
+      "Condition not met at step 242. No data to save.\n",
       "242\n",
+      "model_vars.get('Gini', 0)[-1]=0.6292\n",
+      "Condition not met at step 243. No data to save.\n",
       "243\n",
+      "model_vars.get('Gini', 0)[-1]=0.6394\n",
+      "Condition not met at step 244. No data to save.\n",
       "244\n",
+      "model_vars.get('Gini', 0)[-1]=0.643\n",
+      "Condition not met at step 245. No data to save.\n",
       "245\n",
+      "model_vars.get('Gini', 0)[-1]=0.6448\n",
+      "Condition not met at step 246. No data to save.\n",
       "246\n",
+      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
+      "Condition not met at step 247. No data to save.\n",
       "247\n",
+      "model_vars.get('Gini', 0)[-1]=0.6752\n",
+      "Condition not met at step 248. No data to save.\n",
       "248\n",
+      "model_vars.get('Gini', 0)[-1]=0.683\n",
+      "Condition not met at step 249. No data to save.\n",
       "249\n",
+      "model_vars.get('Gini', 0)[-1]=0.679\n",
+      "Condition not met at step 250. No data to save.\n",
       "250\n",
+      "model_vars.get('Gini', 0)[-1]=0.6862\n",
+      "Condition not met at step 251. No data to save.\n",
       "251\n",
+      "model_vars.get('Gini', 0)[-1]=0.6882\n",
+      "Condition not met at step 252. No data to save.\n",
       "252\n",
+      "model_vars.get('Gini', 0)[-1]=0.6888000000000001\n",
+      "Condition not met at step 253. No data to save.\n",
       "253\n",
+      "model_vars.get('Gini', 0)[-1]=0.6978\n",
+      "Condition not met at step 254. No data to save.\n",
       "254\n",
+      "model_vars.get('Gini', 0)[-1]=0.6836\n",
+      "Condition not met at step 255. No data to save.\n",
       "255\n",
+      "model_vars.get('Gini', 0)[-1]=0.6996\n",
+      "Condition not met at step 256. No data to save.\n",
       "256\n",
+      "model_vars.get('Gini', 0)[-1]=0.6984\n",
+      "Condition not met at step 257. No data to save.\n",
       "257\n",
+      "model_vars.get('Gini', 0)[-1]=0.6950000000000001\n",
+      "Condition not met at step 258. No data to save.\n",
       "258\n",
+      "model_vars.get('Gini', 0)[-1]=0.6904\n",
+      "Condition not met at step 259. No data to save.\n",
       "259\n",
+      "model_vars.get('Gini', 0)[-1]=0.6964\n",
+      "Condition not met at step 260. No data to save.\n",
       "260\n",
+      "model_vars.get('Gini', 0)[-1]=0.6602\n",
+      "Condition not met at step 261. No data to save.\n",
       "261\n",
+      "model_vars.get('Gini', 0)[-1]=0.6961999999999999\n",
+      "Condition not met at step 262. No data to save.\n",
       "262\n",
+      "model_vars.get('Gini', 0)[-1]=0.6828000000000001\n",
+      "Condition not met at step 263. No data to save.\n",
       "263\n",
+      "model_vars.get('Gini', 0)[-1]=0.6692\n",
+      "Condition not met at step 264. No data to save.\n",
       "264\n",
+      "model_vars.get('Gini', 0)[-1]=0.7076\n",
+      "Condition met. Appended special results for step 265 to test_path/special_results.parquet\n",
       "265\n",
+      "model_vars.get('Gini', 0)[-1]=0.6912\n",
+      "Condition not met at step 266. No data to save.\n",
       "266\n",
+      "model_vars.get('Gini', 0)[-1]=0.6754\n",
+      "Condition not met at step 267. No data to save.\n",
       "267\n",
+      "model_vars.get('Gini', 0)[-1]=0.6626000000000001\n",
+      "Condition not met at step 268. No data to save.\n",
       "268\n",
+      "model_vars.get('Gini', 0)[-1]=0.6376\n",
+      "Condition not met at step 269. No data to save.\n",
       "269\n",
+      "model_vars.get('Gini', 0)[-1]=0.6392\n",
+      "Condition not met at step 270. No data to save.\n",
       "270\n",
+      "model_vars.get('Gini', 0)[-1]=0.6476\n",
+      "Condition not met at step 271. No data to save.\n",
       "271\n",
+      "model_vars.get('Gini', 0)[-1]=0.6482\n",
+      "Condition not met at step 272. No data to save.\n",
       "272\n",
+      "model_vars.get('Gini', 0)[-1]=0.6666000000000001\n",
+      "Condition not met at step 273. No data to save.\n",
       "273\n",
+      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
+      "Condition not met at step 274. No data to save.\n",
       "274\n",
+      "model_vars.get('Gini', 0)[-1]=0.6338\n",
+      "Condition not met at step 275. No data to save.\n",
       "275\n",
+      "model_vars.get('Gini', 0)[-1]=0.6106\n",
+      "Condition not met at step 276. No data to save.\n",
       "276\n",
+      "model_vars.get('Gini', 0)[-1]=0.6264000000000001\n",
+      "Condition not met at step 277. No data to save.\n",
       "277\n",
+      "model_vars.get('Gini', 0)[-1]=0.627\n",
+      "Condition not met at step 278. No data to save.\n",
       "278\n",
+      "model_vars.get('Gini', 0)[-1]=0.622\n",
+      "Condition not met at step 279. No data to save.\n",
       "279\n",
+      "model_vars.get('Gini', 0)[-1]=0.6416\n",
+      "Condition not met at step 280. No data to save.\n",
       "280\n",
+      "model_vars.get('Gini', 0)[-1]=0.6444000000000001\n",
+      "Condition not met at step 281. No data to save.\n",
       "281\n",
+      "model_vars.get('Gini', 0)[-1]=0.6592\n",
+      "Condition not met at step 282. No data to save.\n",
       "282\n",
+      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
+      "Condition not met at step 283. No data to save.\n",
       "283\n",
+      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
+      "Condition not met at step 284. No data to save.\n",
       "284\n",
+      "model_vars.get('Gini', 0)[-1]=0.6572\n",
+      "Condition not met at step 285. No data to save.\n",
       "285\n",
+      "model_vars.get('Gini', 0)[-1]=0.6497999999999999\n",
+      "Condition not met at step 286. No data to save.\n",
       "286\n",
+      "model_vars.get('Gini', 0)[-1]=0.6722\n",
+      "Condition not met at step 287. No data to save.\n",
       "287\n",
+      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
+      "Condition not met at step 288. No data to save.\n",
       "288\n",
+      "model_vars.get('Gini', 0)[-1]=0.6716\n",
+      "Condition not met at step 289. No data to save.\n",
       "289\n",
+      "model_vars.get('Gini', 0)[-1]=0.6786000000000001\n",
+      "Condition not met at step 290. No data to save.\n",
       "290\n",
+      "model_vars.get('Gini', 0)[-1]=0.6648000000000001\n",
+      "Condition not met at step 291. No data to save.\n",
       "291\n",
+      "model_vars.get('Gini', 0)[-1]=0.6588\n",
+      "Condition not met at step 292. No data to save.\n",
       "292\n",
+      "model_vars.get('Gini', 0)[-1]=0.6696\n",
+      "Condition not met at step 293. No data to save.\n",
       "293\n",
+      "model_vars.get('Gini', 0)[-1]=0.6604\n",
+      "Condition not met at step 294. No data to save.\n",
       "294\n",
+      "model_vars.get('Gini', 0)[-1]=0.64\n",
+      "Condition not met at step 295. No data to save.\n",
       "295\n",
+      "model_vars.get('Gini', 0)[-1]=0.6338\n",
+      "Condition not met at step 296. No data to save.\n",
       "296\n",
+      "model_vars.get('Gini', 0)[-1]=0.642\n",
+      "Condition not met at step 297. No data to save.\n",
       "297\n",
+      "model_vars.get('Gini', 0)[-1]=0.6716\n",
+      "Condition not met at step 298. No data to save.\n",
       "298\n",
+      "model_vars.get('Gini', 0)[-1]=0.6904\n",
+      "Condition not met at step 299. No data to save.\n",
       "299\n",
       "CALLED\n",
       "self.model._steps=300\n",
       " TEST self.model._steps=300\n",
       " TEST self._cache_interval=100\n",
       "       Gini\n",
-      "200  0.6700\n",
-      "201  0.7024\n",
-      "202  0.7048\n",
-      "203  0.7060\n",
-      "204  0.7140\n",
+      "200  0.6256\n",
+      "201  0.6086\n",
+      "202  0.6250\n",
+      "203  0.6060\n",
+      "204  0.6240\n",
       "..      ...\n",
-      "295  0.6354\n",
-      "296  0.6508\n",
-      "297  0.6608\n",
-      "298  0.6710\n",
-      "299  0.6758\n",
+      "295  0.6338\n",
+      "296  0.6420\n",
+      "297  0.6716\n",
+      "298  0.6904\n",
+      "299  0.6808\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_003.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_003.parquet'\n",
-      "Saving model to output_dir/model_data_003.parquet\n",
-      "Saving agent to output_dir/agent_data_003.parquet\n",
+      "model_file='test_path/model_data_003.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_003.parquet'\n",
+      "Saving model to test_path/model_data_003.parquet\n",
+      "Saving agent to test_path/agent_data_003.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
+      "Condition not met at step 300. No data to save.\n",
       "300\n",
+      "model_vars.get('Gini', 0)[-1]=0.6764\n",
+      "Condition not met at step 301. No data to save.\n",
       "301\n",
+      "model_vars.get('Gini', 0)[-1]=0.6888000000000001\n",
+      "Condition not met at step 302. No data to save.\n",
       "302\n",
+      "model_vars.get('Gini', 0)[-1]=0.6702\n",
+      "Condition not met at step 303. No data to save.\n",
       "303\n",
+      "model_vars.get('Gini', 0)[-1]=0.6726000000000001\n",
+      "Condition not met at step 304. No data to save.\n",
       "304\n",
+      "model_vars.get('Gini', 0)[-1]=0.6638\n",
+      "Condition not met at step 305. No data to save.\n",
       "305\n",
+      "model_vars.get('Gini', 0)[-1]=0.664\n",
+      "Condition not met at step 306. No data to save.\n",
       "306\n",
+      "model_vars.get('Gini', 0)[-1]=0.6686000000000001\n",
+      "Condition not met at step 307. No data to save.\n",
       "307\n",
+      "model_vars.get('Gini', 0)[-1]=0.6792\n",
+      "Condition not met at step 308. No data to save.\n",
       "308\n",
+      "model_vars.get('Gini', 0)[-1]=0.6752\n",
+      "Condition not met at step 309. No data to save.\n",
       "309\n",
+      "model_vars.get('Gini', 0)[-1]=0.6416\n",
+      "Condition not met at step 310. No data to save.\n",
       "310\n",
+      "model_vars.get('Gini', 0)[-1]=0.648\n",
+      "Condition not met at step 311. No data to save.\n",
       "311\n",
+      "model_vars.get('Gini', 0)[-1]=0.6332\n",
+      "Condition not met at step 312. No data to save.\n",
       "312\n",
+      "model_vars.get('Gini', 0)[-1]=0.6235999999999999\n",
+      "Condition not met at step 313. No data to save.\n",
       "313\n",
+      "model_vars.get('Gini', 0)[-1]=0.6734\n",
+      "Condition not met at step 314. No data to save.\n",
       "314\n",
+      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
+      "Condition not met at step 315. No data to save.\n",
       "315\n",
+      "model_vars.get('Gini', 0)[-1]=0.612\n",
+      "Condition not met at step 316. No data to save.\n",
       "316\n",
+      "model_vars.get('Gini', 0)[-1]=0.626\n",
+      "Condition not met at step 317. No data to save.\n",
       "317\n",
+      "model_vars.get('Gini', 0)[-1]=0.631\n",
+      "Condition not met at step 318. No data to save.\n",
       "318\n",
+      "model_vars.get('Gini', 0)[-1]=0.632\n",
+      "Condition not met at step 319. No data to save.\n",
       "319\n",
+      "model_vars.get('Gini', 0)[-1]=0.6266\n",
+      "Condition not met at step 320. No data to save.\n",
       "320\n",
+      "model_vars.get('Gini', 0)[-1]=0.6248\n",
+      "Condition not met at step 321. No data to save.\n",
       "321\n",
+      "model_vars.get('Gini', 0)[-1]=0.6656\n",
+      "Condition not met at step 322. No data to save.\n",
       "322\n",
+      "model_vars.get('Gini', 0)[-1]=0.6554\n",
+      "Condition not met at step 323. No data to save.\n",
       "323\n",
+      "model_vars.get('Gini', 0)[-1]=0.6658\n",
+      "Condition not met at step 324. No data to save.\n",
       "324\n",
+      "model_vars.get('Gini', 0)[-1]=0.6474\n",
+      "Condition not met at step 325. No data to save.\n",
       "325\n",
+      "model_vars.get('Gini', 0)[-1]=0.657\n",
+      "Condition not met at step 326. No data to save.\n",
       "326\n",
+      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
+      "Condition not met at step 327. No data to save.\n",
       "327\n",
+      "model_vars.get('Gini', 0)[-1]=0.6614\n",
+      "Condition not met at step 328. No data to save.\n",
       "328\n",
+      "model_vars.get('Gini', 0)[-1]=0.6694\n",
+      "Condition not met at step 329. No data to save.\n",
       "329\n",
+      "model_vars.get('Gini', 0)[-1]=0.6802\n",
+      "Condition not met at step 330. No data to save.\n",
       "330\n",
+      "model_vars.get('Gini', 0)[-1]=0.6506000000000001\n",
+      "Condition not met at step 331. No data to save.\n",
       "331\n",
+      "model_vars.get('Gini', 0)[-1]=0.638\n",
+      "Condition not met at step 332. No data to save.\n",
       "332\n",
+      "model_vars.get('Gini', 0)[-1]=0.6674\n",
+      "Condition not met at step 333. No data to save.\n",
       "333\n",
+      "model_vars.get('Gini', 0)[-1]=0.6734\n",
+      "Condition not met at step 334. No data to save.\n",
       "334\n",
+      "model_vars.get('Gini', 0)[-1]=0.666\n",
+      "Condition not met at step 335. No data to save.\n",
       "335\n",
+      "model_vars.get('Gini', 0)[-1]=0.6736\n",
+      "Condition not met at step 336. No data to save.\n",
       "336\n",
+      "model_vars.get('Gini', 0)[-1]=0.6842\n",
+      "Condition not met at step 337. No data to save.\n",
       "337\n",
+      "model_vars.get('Gini', 0)[-1]=0.6617999999999999\n",
+      "Condition not met at step 338. No data to save.\n",
       "338\n",
+      "model_vars.get('Gini', 0)[-1]=0.651\n",
+      "Condition not met at step 339. No data to save.\n",
       "339\n",
+      "model_vars.get('Gini', 0)[-1]=0.659\n",
+      "Condition not met at step 340. No data to save.\n",
       "340\n",
+      "model_vars.get('Gini', 0)[-1]=0.6654\n",
+      "Condition not met at step 341. No data to save.\n",
       "341\n",
+      "model_vars.get('Gini', 0)[-1]=0.6626000000000001\n",
+      "Condition not met at step 342. No data to save.\n",
       "342\n",
+      "model_vars.get('Gini', 0)[-1]=0.6704\n",
+      "Condition not met at step 343. No data to save.\n",
       "343\n",
+      "model_vars.get('Gini', 0)[-1]=0.6542\n",
+      "Condition not met at step 344. No data to save.\n",
       "344\n",
+      "model_vars.get('Gini', 0)[-1]=0.6584\n",
+      "Condition not met at step 345. No data to save.\n",
       "345\n",
+      "model_vars.get('Gini', 0)[-1]=0.6572\n",
+      "Condition not met at step 346. No data to save.\n",
       "346\n",
+      "model_vars.get('Gini', 0)[-1]=0.6436\n",
+      "Condition not met at step 347. No data to save.\n",
       "347\n",
+      "model_vars.get('Gini', 0)[-1]=0.6626000000000001\n",
+      "Condition not met at step 348. No data to save.\n",
       "348\n",
+      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
+      "Condition not met at step 349. No data to save.\n",
       "349\n",
+      "model_vars.get('Gini', 0)[-1]=0.6806\n",
+      "Condition not met at step 350. No data to save.\n",
       "350\n",
+      "model_vars.get('Gini', 0)[-1]=0.6918\n",
+      "Condition not met at step 351. No data to save.\n",
       "351\n",
+      "model_vars.get('Gini', 0)[-1]=0.6842\n",
+      "Condition not met at step 352. No data to save.\n",
       "352\n",
+      "model_vars.get('Gini', 0)[-1]=0.6508\n",
+      "Condition not met at step 353. No data to save.\n",
       "353\n",
+      "model_vars.get('Gini', 0)[-1]=0.6476\n",
+      "Condition not met at step 354. No data to save.\n",
       "354\n",
+      "model_vars.get('Gini', 0)[-1]=0.645\n",
+      "Condition not met at step 355. No data to save.\n",
       "355\n",
+      "model_vars.get('Gini', 0)[-1]=0.615\n",
+      "Condition not met at step 356. No data to save.\n",
       "356\n",
+      "model_vars.get('Gini', 0)[-1]=0.6254\n",
+      "Condition not met at step 357. No data to save.\n",
       "357\n",
+      "model_vars.get('Gini', 0)[-1]=0.6093999999999999\n",
+      "Condition not met at step 358. No data to save.\n",
       "358\n",
+      "model_vars.get('Gini', 0)[-1]=0.601\n",
+      "Condition not met at step 359. No data to save.\n",
       "359\n",
+      "model_vars.get('Gini', 0)[-1]=0.5958\n",
+      "Condition not met at step 360. No data to save.\n",
       "360\n",
+      "model_vars.get('Gini', 0)[-1]=0.6116\n",
+      "Condition not met at step 361. No data to save.\n",
       "361\n",
+      "model_vars.get('Gini', 0)[-1]=0.6602\n",
+      "Condition not met at step 362. No data to save.\n",
       "362\n",
+      "model_vars.get('Gini', 0)[-1]=0.6948000000000001\n",
+      "Condition not met at step 363. No data to save.\n",
       "363\n",
+      "model_vars.get('Gini', 0)[-1]=0.675\n",
+      "Condition not met at step 364. No data to save.\n",
       "364\n",
+      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
+      "Condition not met at step 365. No data to save.\n",
       "365\n",
+      "model_vars.get('Gini', 0)[-1]=0.7064\n",
+      "Condition met. Appended special results for step 366 to test_path/special_results.parquet\n",
       "366\n",
+      "model_vars.get('Gini', 0)[-1]=0.6936\n",
+      "Condition not met at step 367. No data to save.\n",
       "367\n",
+      "model_vars.get('Gini', 0)[-1]=0.685\n",
+      "Condition not met at step 368. No data to save.\n",
       "368\n",
+      "model_vars.get('Gini', 0)[-1]=0.7116\n",
+      "Condition met. Appended special results for step 369 to test_path/special_results.parquet\n",
       "369\n",
+      "model_vars.get('Gini', 0)[-1]=0.708\n",
+      "Condition met. Appended special results for step 370 to test_path/special_results.parquet\n",
       "370\n",
+      "model_vars.get('Gini', 0)[-1]=0.6938\n",
+      "Condition not met at step 371. No data to save.\n",
       "371\n",
+      "model_vars.get('Gini', 0)[-1]=0.6912\n",
+      "Condition not met at step 372. No data to save.\n",
       "372\n",
+      "model_vars.get('Gini', 0)[-1]=0.6914\n",
+      "Condition not met at step 373. No data to save.\n",
       "373\n",
+      "model_vars.get('Gini', 0)[-1]=0.7058\n",
+      "Condition met. Appended special results for step 374 to test_path/special_results.parquet\n",
       "374\n",
+      "model_vars.get('Gini', 0)[-1]=0.6928000000000001\n",
+      "Condition not met at step 375. No data to save.\n",
       "375\n",
+      "model_vars.get('Gini', 0)[-1]=0.6916\n",
+      "Condition not met at step 376. No data to save.\n",
       "376\n",
+      "model_vars.get('Gini', 0)[-1]=0.6926\n",
+      "Condition not met at step 377. No data to save.\n",
       "377\n",
+      "model_vars.get('Gini', 0)[-1]=0.6916\n",
+      "Condition not met at step 378. No data to save.\n",
       "378\n",
+      "model_vars.get('Gini', 0)[-1]=0.7114\n",
+      "Condition met. Appended special results for step 379 to test_path/special_results.parquet\n",
       "379\n",
+      "model_vars.get('Gini', 0)[-1]=0.7052\n",
+      "Condition met. Appended special results for step 380 to test_path/special_results.parquet\n",
       "380\n",
+      "model_vars.get('Gini', 0)[-1]=0.6968000000000001\n",
+      "Condition not met at step 381. No data to save.\n",
       "381\n",
+      "model_vars.get('Gini', 0)[-1]=0.6576\n",
+      "Condition not met at step 382. No data to save.\n",
       "382\n",
+      "model_vars.get('Gini', 0)[-1]=0.6544\n",
+      "Condition not met at step 383. No data to save.\n",
       "383\n",
+      "model_vars.get('Gini', 0)[-1]=0.6342\n",
+      "Condition not met at step 384. No data to save.\n",
       "384\n",
+      "model_vars.get('Gini', 0)[-1]=0.615\n",
+      "Condition not met at step 385. No data to save.\n",
       "385\n",
+      "model_vars.get('Gini', 0)[-1]=0.6298\n",
+      "Condition not met at step 386. No data to save.\n",
       "386\n",
+      "model_vars.get('Gini', 0)[-1]=0.6224000000000001\n",
+      "Condition not met at step 387. No data to save.\n",
       "387\n",
+      "model_vars.get('Gini', 0)[-1]=0.5982000000000001\n",
+      "Condition not met at step 388. No data to save.\n",
       "388\n",
+      "model_vars.get('Gini', 0)[-1]=0.6122000000000001\n",
+      "Condition not met at step 389. No data to save.\n",
       "389\n",
+      "model_vars.get('Gini', 0)[-1]=0.6398\n",
+      "Condition not met at step 390. No data to save.\n",
       "390\n",
+      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
+      "Condition not met at step 391. No data to save.\n",
       "391\n",
+      "model_vars.get('Gini', 0)[-1]=0.6258\n",
+      "Condition not met at step 392. No data to save.\n",
       "392\n",
+      "model_vars.get('Gini', 0)[-1]=0.6372\n",
+      "Condition not met at step 393. No data to save.\n",
       "393\n",
+      "model_vars.get('Gini', 0)[-1]=0.6828000000000001\n",
+      "Condition not met at step 394. No data to save.\n",
       "394\n",
+      "model_vars.get('Gini', 0)[-1]=0.6734\n",
+      "Condition not met at step 395. No data to save.\n",
       "395\n",
+      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
+      "Condition not met at step 396. No data to save.\n",
       "396\n",
+      "model_vars.get('Gini', 0)[-1]=0.6448\n",
+      "Condition not met at step 397. No data to save.\n",
       "397\n",
+      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
+      "Condition not met at step 398. No data to save.\n",
       "398\n",
+      "model_vars.get('Gini', 0)[-1]=0.6556\n",
+      "Condition not met at step 399. No data to save.\n",
       "399\n",
       "CALLED\n",
       "self.model._steps=400\n",
       " TEST self.model._steps=400\n",
       " TEST self._cache_interval=100\n",
       "       Gini\n",
-      "300  0.6624\n",
-      "301  0.6698\n",
-      "302  0.6680\n",
-      "303  0.6694\n",
-      "304  0.6552\n",
+      "300  0.6764\n",
+      "301  0.6888\n",
+      "302  0.6702\n",
+      "303  0.6726\n",
+      "304  0.6638\n",
       "..      ...\n",
-      "395  0.7076\n",
-      "396  0.7040\n",
-      "397  0.6994\n",
-      "398  0.6720\n",
-      "399  0.6754\n",
+      "395  0.6800\n",
+      "396  0.6448\n",
+      "397  0.6418\n",
+      "398  0.6556\n",
+      "399  0.6790\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_004.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_004.parquet'\n",
-      "Saving model to output_dir/model_data_004.parquet\n",
-      "Saving agent to output_dir/agent_data_004.parquet\n",
+      "model_file='test_path/model_data_004.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_004.parquet'\n",
+      "Saving model to test_path/model_data_004.parquet\n",
+      "Saving agent to test_path/agent_data_004.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.679\n",
+      "Condition not met at step 400. No data to save.\n",
       "400\n",
+      "model_vars.get('Gini', 0)[-1]=0.6890000000000001\n",
+      "Condition not met at step 401. No data to save.\n",
       "401\n",
+      "model_vars.get('Gini', 0)[-1]=0.6862\n",
+      "Condition not met at step 402. No data to save.\n",
       "402\n",
+      "model_vars.get('Gini', 0)[-1]=0.6704\n",
+      "Condition not met at step 403. No data to save.\n",
       "403\n",
+      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
+      "Condition not met at step 404. No data to save.\n",
       "404\n",
+      "model_vars.get('Gini', 0)[-1]=0.6194\n",
+      "Condition not met at step 405. No data to save.\n",
       "405\n",
+      "model_vars.get('Gini', 0)[-1]=0.6532\n",
+      "Condition not met at step 406. No data to save.\n",
       "406\n",
+      "model_vars.get('Gini', 0)[-1]=0.6784\n",
+      "Condition not met at step 407. No data to save.\n",
       "407\n",
+      "model_vars.get('Gini', 0)[-1]=0.6746000000000001\n",
+      "Condition not met at step 408. No data to save.\n",
       "408\n",
+      "model_vars.get('Gini', 0)[-1]=0.6664\n",
+      "Condition not met at step 409. No data to save.\n",
       "409\n",
+      "model_vars.get('Gini', 0)[-1]=0.6726000000000001\n",
+      "Condition not met at step 410. No data to save.\n",
       "410\n",
+      "model_vars.get('Gini', 0)[-1]=0.658\n",
+      "Condition not met at step 411. No data to save.\n",
       "411\n",
+      "model_vars.get('Gini', 0)[-1]=0.627\n",
+      "Condition not met at step 412. No data to save.\n",
       "412\n",
+      "model_vars.get('Gini', 0)[-1]=0.6175999999999999\n",
+      "Condition not met at step 413. No data to save.\n",
       "413\n",
+      "model_vars.get('Gini', 0)[-1]=0.6168\n",
+      "Condition not met at step 414. No data to save.\n",
       "414\n",
+      "model_vars.get('Gini', 0)[-1]=0.6318\n",
+      "Condition not met at step 415. No data to save.\n",
       "415\n",
+      "model_vars.get('Gini', 0)[-1]=0.6318\n",
+      "Condition not met at step 416. No data to save.\n",
       "416\n",
+      "model_vars.get('Gini', 0)[-1]=0.6422\n",
+      "Condition not met at step 417. No data to save.\n",
       "417\n",
+      "model_vars.get('Gini', 0)[-1]=0.609\n",
+      "Condition not met at step 418. No data to save.\n",
       "418\n",
+      "model_vars.get('Gini', 0)[-1]=0.5594\n",
+      "Condition not met at step 419. No data to save.\n",
       "419\n",
+      "model_vars.get('Gini', 0)[-1]=0.577\n",
+      "Condition not met at step 420. No data to save.\n",
       "420\n",
+      "model_vars.get('Gini', 0)[-1]=0.5862\n",
+      "Condition not met at step 421. No data to save.\n",
       "421\n",
+      "model_vars.get('Gini', 0)[-1]=0.5842\n",
+      "Condition not met at step 422. No data to save.\n",
       "422\n",
+      "model_vars.get('Gini', 0)[-1]=0.5722\n",
+      "Condition not met at step 423. No data to save.\n",
       "423\n",
+      "model_vars.get('Gini', 0)[-1]=0.5756\n",
+      "Condition not met at step 424. No data to save.\n",
       "424\n",
+      "model_vars.get('Gini', 0)[-1]=0.625\n",
+      "Condition not met at step 425. No data to save.\n",
       "425\n",
+      "model_vars.get('Gini', 0)[-1]=0.6556\n",
+      "Condition not met at step 426. No data to save.\n",
       "426\n",
+      "model_vars.get('Gini', 0)[-1]=0.62\n",
+      "Condition not met at step 427. No data to save.\n",
       "427\n",
+      "model_vars.get('Gini', 0)[-1]=0.6142000000000001\n",
+      "Condition not met at step 428. No data to save.\n",
       "428\n",
+      "model_vars.get('Gini', 0)[-1]=0.6086\n",
+      "Condition not met at step 429. No data to save.\n",
       "429\n",
+      "model_vars.get('Gini', 0)[-1]=0.622\n",
+      "Condition not met at step 430. No data to save.\n",
       "430\n",
+      "model_vars.get('Gini', 0)[-1]=0.664\n",
+      "Condition not met at step 431. No data to save.\n",
       "431\n",
+      "model_vars.get('Gini', 0)[-1]=0.6468\n",
+      "Condition not met at step 432. No data to save.\n",
       "432\n",
+      "model_vars.get('Gini', 0)[-1]=0.6398\n",
+      "Condition not met at step 433. No data to save.\n",
       "433\n",
+      "model_vars.get('Gini', 0)[-1]=0.6448\n",
+      "Condition not met at step 434. No data to save.\n",
       "434\n",
+      "model_vars.get('Gini', 0)[-1]=0.663\n",
+      "Condition not met at step 435. No data to save.\n",
       "435\n",
+      "model_vars.get('Gini', 0)[-1]=0.655\n",
+      "Condition not met at step 436. No data to save.\n",
       "436\n",
+      "model_vars.get('Gini', 0)[-1]=0.6812\n",
+      "Condition not met at step 437. No data to save.\n",
       "437\n",
+      "model_vars.get('Gini', 0)[-1]=0.6436\n",
+      "Condition not met at step 438. No data to save.\n",
       "438\n",
+      "model_vars.get('Gini', 0)[-1]=0.6594\n",
+      "Condition not met at step 439. No data to save.\n",
       "439\n",
+      "model_vars.get('Gini', 0)[-1]=0.6616\n",
+      "Condition not met at step 440. No data to save.\n",
       "440\n",
+      "model_vars.get('Gini', 0)[-1]=0.675\n",
+      "Condition not met at step 441. No data to save.\n",
       "441\n",
+      "model_vars.get('Gini', 0)[-1]=0.6794\n",
+      "Condition not met at step 442. No data to save.\n",
       "442\n",
+      "model_vars.get('Gini', 0)[-1]=0.6924\n",
+      "Condition not met at step 443. No data to save.\n",
       "443\n",
+      "model_vars.get('Gini', 0)[-1]=0.7192000000000001\n",
+      "Condition met. Appended special results for step 444 to test_path/special_results.parquet\n",
       "444\n",
+      "model_vars.get('Gini', 0)[-1]=0.7183999999999999\n",
+      "Condition met. Appended special results for step 445 to test_path/special_results.parquet\n",
       "445\n",
+      "model_vars.get('Gini', 0)[-1]=0.7298\n",
+      "Condition met. Appended special results for step 446 to test_path/special_results.parquet\n",
       "446\n",
+      "model_vars.get('Gini', 0)[-1]=0.7202\n",
+      "Condition met. Appended special results for step 447 to test_path/special_results.parquet\n",
       "447\n",
+      "model_vars.get('Gini', 0)[-1]=0.6868000000000001\n",
+      "Condition not met at step 448. No data to save.\n",
       "448\n",
+      "model_vars.get('Gini', 0)[-1]=0.6944\n",
+      "Condition not met at step 449. No data to save.\n",
       "449\n",
+      "model_vars.get('Gini', 0)[-1]=0.6902\n",
+      "Condition not met at step 450. No data to save.\n",
       "450\n",
+      "model_vars.get('Gini', 0)[-1]=0.6698\n",
+      "Condition not met at step 451. No data to save.\n",
       "451\n",
+      "model_vars.get('Gini', 0)[-1]=0.6742\n",
+      "Condition not met at step 452. No data to save.\n",
       "452\n",
+      "model_vars.get('Gini', 0)[-1]=0.6699999999999999\n",
+      "Condition not met at step 453. No data to save.\n",
       "453\n",
+      "model_vars.get('Gini', 0)[-1]=0.7101999999999999\n",
+      "Condition met. Appended special results for step 454 to test_path/special_results.parquet\n",
       "454\n",
+      "model_vars.get('Gini', 0)[-1]=0.7146\n",
+      "Condition met. Appended special results for step 455 to test_path/special_results.parquet\n",
       "455\n",
+      "model_vars.get('Gini', 0)[-1]=0.6864\n",
+      "Condition not met at step 456. No data to save.\n",
       "456\n",
+      "model_vars.get('Gini', 0)[-1]=0.6948000000000001\n",
+      "Condition not met at step 457. No data to save.\n",
       "457\n",
+      "model_vars.get('Gini', 0)[-1]=0.6744\n",
+      "Condition not met at step 458. No data to save.\n",
       "458\n",
+      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
+      "Condition not met at step 459. No data to save.\n",
       "459\n",
+      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
+      "Condition not met at step 460. No data to save.\n",
       "460\n",
+      "model_vars.get('Gini', 0)[-1]=0.6928000000000001\n",
+      "Condition not met at step 461. No data to save.\n",
       "461\n",
+      "model_vars.get('Gini', 0)[-1]=0.7168\n",
+      "Condition met. Appended special results for step 462 to test_path/special_results.parquet\n",
       "462\n",
+      "model_vars.get('Gini', 0)[-1]=0.7326\n",
+      "Condition met. Appended special results for step 463 to test_path/special_results.parquet\n",
       "463\n",
+      "model_vars.get('Gini', 0)[-1]=0.7286\n",
+      "Condition met. Appended special results for step 464 to test_path/special_results.parquet\n",
       "464\n",
+      "model_vars.get('Gini', 0)[-1]=0.7270000000000001\n",
+      "Condition met. Appended special results for step 465 to test_path/special_results.parquet\n",
       "465\n",
+      "model_vars.get('Gini', 0)[-1]=0.7182\n",
+      "Condition met. Appended special results for step 466 to test_path/special_results.parquet\n",
       "466\n",
+      "model_vars.get('Gini', 0)[-1]=0.7226\n",
+      "Condition met. Appended special results for step 467 to test_path/special_results.parquet\n",
       "467\n",
+      "model_vars.get('Gini', 0)[-1]=0.7472000000000001\n",
+      "Condition met. Appended special results for step 468 to test_path/special_results.parquet\n",
       "468\n",
+      "model_vars.get('Gini', 0)[-1]=0.7326\n",
+      "Condition met. Appended special results for step 469 to test_path/special_results.parquet\n",
       "469\n",
+      "model_vars.get('Gini', 0)[-1]=0.746\n",
+      "Condition met. Appended special results for step 470 to test_path/special_results.parquet\n",
       "470\n",
+      "model_vars.get('Gini', 0)[-1]=0.7574000000000001\n",
+      "Condition met. Appended special results for step 471 to test_path/special_results.parquet\n",
       "471\n",
+      "model_vars.get('Gini', 0)[-1]=0.7532000000000001\n",
+      "Condition met. Appended special results for step 472 to test_path/special_results.parquet\n",
       "472\n",
+      "model_vars.get('Gini', 0)[-1]=0.745\n",
+      "Condition met. Appended special results for step 473 to test_path/special_results.parquet\n",
       "473\n",
+      "model_vars.get('Gini', 0)[-1]=0.7472000000000001\n",
+      "Condition met. Appended special results for step 474 to test_path/special_results.parquet\n",
       "474\n",
+      "model_vars.get('Gini', 0)[-1]=0.7332000000000001\n",
+      "Condition met. Appended special results for step 475 to test_path/special_results.parquet\n",
       "475\n",
+      "model_vars.get('Gini', 0)[-1]=0.7425999999999999\n",
+      "Condition met. Appended special results for step 476 to test_path/special_results.parquet\n",
       "476\n",
+      "model_vars.get('Gini', 0)[-1]=0.7256\n",
+      "Condition met. Appended special results for step 477 to test_path/special_results.parquet\n",
       "477\n",
+      "model_vars.get('Gini', 0)[-1]=0.7432000000000001\n",
+      "Condition met. Appended special results for step 478 to test_path/special_results.parquet\n",
       "478\n",
+      "model_vars.get('Gini', 0)[-1]=0.7074\n",
+      "Condition met. Appended special results for step 479 to test_path/special_results.parquet\n",
       "479\n",
+      "model_vars.get('Gini', 0)[-1]=0.6832\n",
+      "Condition not met at step 480. No data to save.\n",
       "480\n",
+      "model_vars.get('Gini', 0)[-1]=0.6674\n",
+      "Condition not met at step 481. No data to save.\n",
       "481\n",
+      "model_vars.get('Gini', 0)[-1]=0.643\n",
+      "Condition not met at step 482. No data to save.\n",
       "482\n",
+      "model_vars.get('Gini', 0)[-1]=0.6738\n",
+      "Condition not met at step 483. No data to save.\n",
       "483\n",
+      "model_vars.get('Gini', 0)[-1]=0.673\n",
+      "Condition not met at step 484. No data to save.\n",
       "484\n",
+      "model_vars.get('Gini', 0)[-1]=0.6772\n",
+      "Condition not met at step 485. No data to save.\n",
       "485\n",
+      "model_vars.get('Gini', 0)[-1]=0.6716\n",
+      "Condition not met at step 486. No data to save.\n",
       "486\n",
+      "model_vars.get('Gini', 0)[-1]=0.6698\n",
+      "Condition not met at step 487. No data to save.\n",
       "487\n",
+      "model_vars.get('Gini', 0)[-1]=0.6488\n",
+      "Condition not met at step 488. No data to save.\n",
       "488\n",
+      "model_vars.get('Gini', 0)[-1]=0.6354\n",
+      "Condition not met at step 489. No data to save.\n",
       "489\n",
+      "model_vars.get('Gini', 0)[-1]=0.652\n",
+      "Condition not met at step 490. No data to save.\n",
       "490\n",
+      "model_vars.get('Gini', 0)[-1]=0.6594\n",
+      "Condition not met at step 491. No data to save.\n",
       "491\n",
+      "model_vars.get('Gini', 0)[-1]=0.6786000000000001\n",
+      "Condition not met at step 492. No data to save.\n",
       "492\n",
+      "model_vars.get('Gini', 0)[-1]=0.6874\n",
+      "Condition not met at step 493. No data to save.\n",
       "493\n",
+      "model_vars.get('Gini', 0)[-1]=0.7092\n",
+      "Condition met. Appended special results for step 494 to test_path/special_results.parquet\n",
       "494\n",
+      "model_vars.get('Gini', 0)[-1]=0.6984\n",
+      "Condition not met at step 495. No data to save.\n",
       "495\n",
+      "model_vars.get('Gini', 0)[-1]=0.7084\n",
+      "Condition met. Appended special results for step 496 to test_path/special_results.parquet\n",
       "496\n",
+      "model_vars.get('Gini', 0)[-1]=0.6936\n",
+      "Condition not met at step 497. No data to save.\n",
       "497\n",
+      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
+      "Condition not met at step 498. No data to save.\n",
       "498\n",
+      "model_vars.get('Gini', 0)[-1]=0.6916\n",
+      "Condition not met at step 499. No data to save.\n",
       "499\n",
       "CALLED\n",
       "self.model._steps=500\n",
       " TEST self.model._steps=500\n",
       " TEST self._cache_interval=100\n",
       "       Gini\n",
-      "400  0.6564\n",
-      "401  0.6750\n",
-      "402  0.6640\n",
-      "403  0.6606\n",
-      "404  0.6750\n",
+      "400  0.6890\n",
+      "401  0.6862\n",
+      "402  0.6704\n",
+      "403  0.6486\n",
+      "404  0.6194\n",
       "..      ...\n",
-      "495  0.6998\n",
-      "496  0.7218\n",
-      "497  0.7408\n",
-      "498  0.7520\n",
-      "499  0.7272\n",
+      "495  0.7084\n",
+      "496  0.6936\n",
+      "497  0.6788\n",
+      "498  0.6916\n",
+      "499  0.6602\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_005.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_005.parquet'\n",
-      "Saving model to output_dir/model_data_005.parquet\n",
-      "Saving agent to output_dir/agent_data_005.parquet\n",
+      "model_file='test_path/model_data_005.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_005.parquet'\n",
+      "Saving model to test_path/model_data_005.parquet\n",
+      "Saving agent to test_path/agent_data_005.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6602\n",
+      "Condition not met at step 500. No data to save.\n",
       "500\n",
+      "model_vars.get('Gini', 0)[-1]=0.661\n",
+      "Condition not met at step 501. No data to save.\n",
       "501\n",
+      "model_vars.get('Gini', 0)[-1]=0.6544\n",
+      "Condition not met at step 502. No data to save.\n",
       "502\n",
+      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
+      "Condition not met at step 503. No data to save.\n",
       "503\n",
+      "model_vars.get('Gini', 0)[-1]=0.6584\n",
+      "Condition not met at step 504. No data to save.\n",
       "504\n",
+      "model_vars.get('Gini', 0)[-1]=0.6759999999999999\n",
+      "Condition not met at step 505. No data to save.\n",
       "505\n",
+      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
+      "Condition not met at step 506. No data to save.\n",
       "506\n",
+      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
+      "Condition not met at step 507. No data to save.\n",
       "507\n",
+      "model_vars.get('Gini', 0)[-1]=0.6482\n",
+      "Condition not met at step 508. No data to save.\n",
       "508\n",
+      "model_vars.get('Gini', 0)[-1]=0.6288\n",
+      "Condition not met at step 509. No data to save.\n",
       "509\n",
+      "model_vars.get('Gini', 0)[-1]=0.616\n",
+      "Condition not met at step 510. No data to save.\n",
       "510\n",
+      "model_vars.get('Gini', 0)[-1]=0.65\n",
+      "Condition not met at step 511. No data to save.\n",
       "511\n",
+      "model_vars.get('Gini', 0)[-1]=0.6388\n",
+      "Condition not met at step 512. No data to save.\n",
       "512\n",
+      "model_vars.get('Gini', 0)[-1]=0.6442\n",
+      "Condition not met at step 513. No data to save.\n",
       "513\n",
+      "model_vars.get('Gini', 0)[-1]=0.671\n",
+      "Condition not met at step 514. No data to save.\n",
       "514\n",
+      "model_vars.get('Gini', 0)[-1]=0.6472\n",
+      "Condition not met at step 515. No data to save.\n",
       "515\n",
+      "model_vars.get('Gini', 0)[-1]=0.6568\n",
+      "Condition not met at step 516. No data to save.\n",
       "516\n",
+      "model_vars.get('Gini', 0)[-1]=0.6782\n",
+      "Condition not met at step 517. No data to save.\n",
       "517\n",
+      "model_vars.get('Gini', 0)[-1]=0.6848000000000001\n",
+      "Condition not met at step 518. No data to save.\n",
       "518\n",
+      "model_vars.get('Gini', 0)[-1]=0.6950000000000001\n",
+      "Condition not met at step 519. No data to save.\n",
       "519\n",
+      "model_vars.get('Gini', 0)[-1]=0.6712\n",
+      "Condition not met at step 520. No data to save.\n",
       "520\n",
+      "model_vars.get('Gini', 0)[-1]=0.6762\n",
+      "Condition not met at step 521. No data to save.\n",
       "521\n",
+      "model_vars.get('Gini', 0)[-1]=0.6866\n",
+      "Condition not met at step 522. No data to save.\n",
       "522\n",
+      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
+      "Condition not met at step 523. No data to save.\n",
       "523\n",
+      "model_vars.get('Gini', 0)[-1]=0.6882\n",
+      "Condition not met at step 524. No data to save.\n",
       "524\n",
+      "model_vars.get('Gini', 0)[-1]=0.7050000000000001\n",
+      "Condition met. Appended special results for step 525 to test_path/special_results.parquet\n",
       "525\n",
+      "model_vars.get('Gini', 0)[-1]=0.6818\n",
+      "Condition not met at step 526. No data to save.\n",
       "526\n",
+      "model_vars.get('Gini', 0)[-1]=0.6712\n",
+      "Condition not met at step 527. No data to save.\n",
       "527\n",
+      "model_vars.get('Gini', 0)[-1]=0.6862\n",
+      "Condition not met at step 528. No data to save.\n",
       "528\n",
+      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
+      "Condition not met at step 529. No data to save.\n",
       "529\n",
+      "model_vars.get('Gini', 0)[-1]=0.6702\n",
+      "Condition not met at step 530. No data to save.\n",
       "530\n",
+      "model_vars.get('Gini', 0)[-1]=0.6778\n",
+      "Condition not met at step 531. No data to save.\n",
       "531\n",
+      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
+      "Condition not met at step 532. No data to save.\n",
       "532\n",
+      "model_vars.get('Gini', 0)[-1]=0.6776\n",
+      "Condition not met at step 533. No data to save.\n",
       "533\n",
+      "model_vars.get('Gini', 0)[-1]=0.6868000000000001\n",
+      "Condition not met at step 534. No data to save.\n",
       "534\n",
+      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
+      "Condition not met at step 535. No data to save.\n",
       "535\n",
+      "model_vars.get('Gini', 0)[-1]=0.6712\n",
+      "Condition not met at step 536. No data to save.\n",
       "536\n",
+      "model_vars.get('Gini', 0)[-1]=0.658\n",
+      "Condition not met at step 537. No data to save.\n",
       "537\n",
+      "model_vars.get('Gini', 0)[-1]=0.6636\n",
+      "Condition not met at step 538. No data to save.\n",
       "538\n",
+      "model_vars.get('Gini', 0)[-1]=0.6468\n",
+      "Condition not met at step 539. No data to save.\n",
       "539\n",
+      "model_vars.get('Gini', 0)[-1]=0.6268\n",
+      "Condition not met at step 540. No data to save.\n",
       "540\n",
+      "model_vars.get('Gini', 0)[-1]=0.6328\n",
+      "Condition not met at step 541. No data to save.\n",
       "541\n",
+      "model_vars.get('Gini', 0)[-1]=0.649\n",
+      "Condition not met at step 542. No data to save.\n",
       "542\n",
+      "model_vars.get('Gini', 0)[-1]=0.6516\n",
+      "Condition not met at step 543. No data to save.\n",
       "543\n",
+      "model_vars.get('Gini', 0)[-1]=0.6614\n",
+      "Condition not met at step 544. No data to save.\n",
       "544\n",
+      "model_vars.get('Gini', 0)[-1]=0.6992\n",
+      "Condition not met at step 545. No data to save.\n",
       "545\n",
+      "model_vars.get('Gini', 0)[-1]=0.6924\n",
+      "Condition not met at step 546. No data to save.\n",
       "546\n",
+      "model_vars.get('Gini', 0)[-1]=0.6998\n",
+      "Condition not met at step 547. No data to save.\n",
       "547\n",
+      "model_vars.get('Gini', 0)[-1]=0.7034\n",
+      "Condition met. Appended special results for step 548 to test_path/special_results.parquet\n",
       "548\n",
+      "model_vars.get('Gini', 0)[-1]=0.687\n",
+      "Condition not met at step 549. No data to save.\n",
       "549\n",
+      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
+      "Condition not met at step 550. No data to save.\n",
       "550\n",
+      "model_vars.get('Gini', 0)[-1]=0.687\n",
+      "Condition not met at step 551. No data to save.\n",
       "551\n",
+      "model_vars.get('Gini', 0)[-1]=0.669\n",
+      "Condition not met at step 552. No data to save.\n",
       "552\n",
+      "model_vars.get('Gini', 0)[-1]=0.6796\n",
+      "Condition not met at step 553. No data to save.\n",
       "553\n",
+      "model_vars.get('Gini', 0)[-1]=0.683\n",
+      "Condition not met at step 554. No data to save.\n",
       "554\n",
+      "model_vars.get('Gini', 0)[-1]=0.6608\n",
+      "Condition not met at step 555. No data to save.\n",
       "555\n",
+      "model_vars.get('Gini', 0)[-1]=0.6758\n",
+      "Condition not met at step 556. No data to save.\n",
       "556\n",
+      "model_vars.get('Gini', 0)[-1]=0.6824\n",
+      "Condition not met at step 557. No data to save.\n",
       "557\n",
+      "model_vars.get('Gini', 0)[-1]=0.6804\n",
+      "Condition not met at step 558. No data to save.\n",
       "558\n",
+      "model_vars.get('Gini', 0)[-1]=0.6682\n",
+      "Condition not met at step 559. No data to save.\n",
       "559\n",
+      "model_vars.get('Gini', 0)[-1]=0.643\n",
+      "Condition not met at step 560. No data to save.\n",
       "560\n",
+      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
+      "Condition not met at step 561. No data to save.\n",
       "561\n",
+      "model_vars.get('Gini', 0)[-1]=0.609\n",
+      "Condition not met at step 562. No data to save.\n",
       "562\n",
+      "model_vars.get('Gini', 0)[-1]=0.6064\n",
+      "Condition not met at step 563. No data to save.\n",
       "563\n",
+      "model_vars.get('Gini', 0)[-1]=0.647\n",
+      "Condition not met at step 564. No data to save.\n",
       "564\n",
+      "model_vars.get('Gini', 0)[-1]=0.66\n",
+      "Condition not met at step 565. No data to save.\n",
       "565\n",
+      "model_vars.get('Gini', 0)[-1]=0.634\n",
+      "Condition not met at step 566. No data to save.\n",
       "566\n",
+      "model_vars.get('Gini', 0)[-1]=0.652\n",
+      "Condition not met at step 567. No data to save.\n",
       "567\n",
+      "model_vars.get('Gini', 0)[-1]=0.6492\n",
+      "Condition not met at step 568. No data to save.\n",
       "568\n",
+      "model_vars.get('Gini', 0)[-1]=0.656\n",
+      "Condition not met at step 569. No data to save.\n",
       "569\n",
+      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
+      "Condition not met at step 570. No data to save.\n",
       "570\n",
+      "model_vars.get('Gini', 0)[-1]=0.6354\n",
+      "Condition not met at step 571. No data to save.\n",
       "571\n",
+      "model_vars.get('Gini', 0)[-1]=0.621\n",
+      "Condition not met at step 572. No data to save.\n",
       "572\n",
+      "model_vars.get('Gini', 0)[-1]=0.6318\n",
+      "Condition not met at step 573. No data to save.\n",
       "573\n",
+      "model_vars.get('Gini', 0)[-1]=0.6298\n",
+      "Condition not met at step 574. No data to save.\n",
       "574\n",
+      "model_vars.get('Gini', 0)[-1]=0.629\n",
+      "Condition not met at step 575. No data to save.\n",
       "575\n",
+      "model_vars.get('Gini', 0)[-1]=0.6408\n",
+      "Condition not met at step 576. No data to save.\n",
       "576\n",
+      "model_vars.get('Gini', 0)[-1]=0.6058\n",
+      "Condition not met at step 577. No data to save.\n",
       "577\n",
+      "model_vars.get('Gini', 0)[-1]=0.607\n",
+      "Condition not met at step 578. No data to save.\n",
       "578\n",
+      "model_vars.get('Gini', 0)[-1]=0.6268\n",
+      "Condition not met at step 579. No data to save.\n",
       "579\n",
+      "model_vars.get('Gini', 0)[-1]=0.632\n",
+      "Condition not met at step 580. No data to save.\n",
       "580\n",
+      "model_vars.get('Gini', 0)[-1]=0.64\n",
+      "Condition not met at step 581. No data to save.\n",
       "581\n",
+      "model_vars.get('Gini', 0)[-1]=0.6298\n",
+      "Condition not met at step 582. No data to save.\n",
       "582\n",
+      "model_vars.get('Gini', 0)[-1]=0.6053999999999999\n",
+      "Condition not met at step 583. No data to save.\n",
       "583\n",
+      "model_vars.get('Gini', 0)[-1]=0.5938\n",
+      "Condition not met at step 584. No data to save.\n",
       "584\n",
+      "model_vars.get('Gini', 0)[-1]=0.5976\n",
+      "Condition not met at step 585. No data to save.\n",
       "585\n",
+      "model_vars.get('Gini', 0)[-1]=0.6462\n",
+      "Condition not met at step 586. No data to save.\n",
       "586\n",
+      "model_vars.get('Gini', 0)[-1]=0.637\n",
+      "Condition not met at step 587. No data to save.\n",
       "587\n",
+      "model_vars.get('Gini', 0)[-1]=0.6202000000000001\n",
+      "Condition not met at step 588. No data to save.\n",
       "588\n",
+      "model_vars.get('Gini', 0)[-1]=0.6142000000000001\n",
+      "Condition not met at step 589. No data to save.\n",
       "589\n",
+      "model_vars.get('Gini', 0)[-1]=0.6113999999999999\n",
+      "Condition not met at step 590. No data to save.\n",
       "590\n",
+      "model_vars.get('Gini', 0)[-1]=0.6384000000000001\n",
+      "Condition not met at step 591. No data to save.\n",
       "591\n",
+      "model_vars.get('Gini', 0)[-1]=0.6437999999999999\n",
+      "Condition not met at step 592. No data to save.\n",
       "592\n",
+      "model_vars.get('Gini', 0)[-1]=0.6342\n",
+      "Condition not met at step 593. No data to save.\n",
       "593\n",
+      "model_vars.get('Gini', 0)[-1]=0.6436\n",
+      "Condition not met at step 594. No data to save.\n",
       "594\n",
+      "model_vars.get('Gini', 0)[-1]=0.6608\n",
+      "Condition not met at step 595. No data to save.\n",
       "595\n",
+      "model_vars.get('Gini', 0)[-1]=0.654\n",
+      "Condition not met at step 596. No data to save.\n",
       "596\n",
+      "model_vars.get('Gini', 0)[-1]=0.6926\n",
+      "Condition not met at step 597. No data to save.\n",
       "597\n",
+      "model_vars.get('Gini', 0)[-1]=0.6836\n",
+      "Condition not met at step 598. No data to save.\n",
       "598\n",
+      "model_vars.get('Gini', 0)[-1]=0.7032\n",
+      "Condition met. Appended special results for step 599 to test_path/special_results.parquet\n",
       "599\n",
       "CALLED\n",
       "self.model._steps=600\n",
       " TEST self.model._steps=600\n",
       " TEST self._cache_interval=100\n",
       "       Gini\n",
-      "500  0.7098\n",
-      "501  0.7074\n",
-      "502  0.6860\n",
-      "503  0.6830\n",
-      "504  0.6782\n",
+      "500  0.6610\n",
+      "501  0.6544\n",
+      "502  0.6406\n",
+      "503  0.6584\n",
+      "504  0.6760\n",
       "..      ...\n",
-      "595  0.5814\n",
-      "596  0.6096\n",
-      "597  0.5986\n",
-      "598  0.6060\n",
-      "599  0.5644\n",
+      "595  0.6540\n",
+      "596  0.6926\n",
+      "597  0.6836\n",
+      "598  0.7032\n",
+      "599  0.6522\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_006.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_006.parquet'\n",
-      "Saving model to output_dir/model_data_006.parquet\n",
-      "Saving agent to output_dir/agent_data_006.parquet\n",
+      "model_file='test_path/model_data_006.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_006.parquet'\n",
+      "Saving model to test_path/model_data_006.parquet\n",
+      "Saving agent to test_path/agent_data_006.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6522\n",
+      "Condition not met at step 600. No data to save.\n",
       "600\n",
+      "model_vars.get('Gini', 0)[-1]=0.6476\n",
+      "Condition not met at step 601. No data to save.\n",
       "601\n",
+      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
+      "Condition not met at step 602. No data to save.\n",
       "602\n",
+      "model_vars.get('Gini', 0)[-1]=0.6836\n",
+      "Condition not met at step 603. No data to save.\n",
       "603\n",
+      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
+      "Condition not met at step 604. No data to save.\n",
       "604\n",
+      "model_vars.get('Gini', 0)[-1]=0.6448\n",
+      "Condition not met at step 605. No data to save.\n",
       "605\n",
+      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
+      "Condition not met at step 606. No data to save.\n",
       "606\n",
+      "model_vars.get('Gini', 0)[-1]=0.6536\n",
+      "Condition not met at step 607. No data to save.\n",
       "607\n",
+      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
+      "Condition not met at step 608. No data to save.\n",
       "608\n",
+      "model_vars.get('Gini', 0)[-1]=0.635\n",
+      "Condition not met at step 609. No data to save.\n",
       "609\n",
+      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
+      "Condition not met at step 610. No data to save.\n",
       "610\n",
+      "model_vars.get('Gini', 0)[-1]=0.645\n",
+      "Condition not met at step 611. No data to save.\n",
       "611\n",
+      "model_vars.get('Gini', 0)[-1]=0.6666000000000001\n",
+      "Condition not met at step 612. No data to save.\n",
       "612\n",
+      "model_vars.get('Gini', 0)[-1]=0.665\n",
+      "Condition not met at step 613. No data to save.\n",
       "613\n",
+      "model_vars.get('Gini', 0)[-1]=0.6888000000000001\n",
+      "Condition not met at step 614. No data to save.\n",
       "614\n",
+      "model_vars.get('Gini', 0)[-1]=0.6536\n",
+      "Condition not met at step 615. No data to save.\n",
       "615\n",
+      "model_vars.get('Gini', 0)[-1]=0.6576\n",
+      "Condition not met at step 616. No data to save.\n",
       "616\n",
+      "model_vars.get('Gini', 0)[-1]=0.6522\n",
+      "Condition not met at step 617. No data to save.\n",
       "617\n",
+      "model_vars.get('Gini', 0)[-1]=0.6612\n",
+      "Condition not met at step 618. No data to save.\n",
       "618\n",
+      "model_vars.get('Gini', 0)[-1]=0.6806\n",
+      "Condition not met at step 619. No data to save.\n",
       "619\n",
+      "model_vars.get('Gini', 0)[-1]=0.6744\n",
+      "Condition not met at step 620. No data to save.\n",
       "620\n",
+      "model_vars.get('Gini', 0)[-1]=0.671\n",
+      "Condition not met at step 621. No data to save.\n",
       "621\n",
+      "model_vars.get('Gini', 0)[-1]=0.6612\n",
+      "Condition not met at step 622. No data to save.\n",
       "622\n",
+      "model_vars.get('Gini', 0)[-1]=0.6528\n",
+      "Condition not met at step 623. No data to save.\n",
       "623\n",
+      "model_vars.get('Gini', 0)[-1]=0.6642\n",
+      "Condition not met at step 624. No data to save.\n",
       "624\n",
+      "model_vars.get('Gini', 0)[-1]=0.6858\n",
+      "Condition not met at step 625. No data to save.\n",
       "625\n",
+      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
+      "Condition not met at step 626. No data to save.\n",
       "626\n",
+      "model_vars.get('Gini', 0)[-1]=0.6472\n",
+      "Condition not met at step 627. No data to save.\n",
       "627\n",
+      "model_vars.get('Gini', 0)[-1]=0.6378\n",
+      "Condition not met at step 628. No data to save.\n",
       "628\n",
+      "model_vars.get('Gini', 0)[-1]=0.6232\n",
+      "Condition not met at step 629. No data to save.\n",
       "629\n",
+      "model_vars.get('Gini', 0)[-1]=0.6232\n",
+      "Condition not met at step 630. No data to save.\n",
       "630\n",
+      "model_vars.get('Gini', 0)[-1]=0.6514\n",
+      "Condition not met at step 631. No data to save.\n",
       "631\n",
+      "model_vars.get('Gini', 0)[-1]=0.6528\n",
+      "Condition not met at step 632. No data to save.\n",
       "632\n",
+      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
+      "Condition not met at step 633. No data to save.\n",
       "633\n",
+      "model_vars.get('Gini', 0)[-1]=0.6264000000000001\n",
+      "Condition not met at step 634. No data to save.\n",
       "634\n",
+      "model_vars.get('Gini', 0)[-1]=0.6582\n",
+      "Condition not met at step 635. No data to save.\n",
       "635\n",
+      "model_vars.get('Gini', 0)[-1]=0.6492\n",
+      "Condition not met at step 636. No data to save.\n",
       "636\n",
+      "model_vars.get('Gini', 0)[-1]=0.6492\n",
+      "Condition not met at step 637. No data to save.\n",
       "637\n",
+      "model_vars.get('Gini', 0)[-1]=0.6497999999999999\n",
+      "Condition not met at step 638. No data to save.\n",
       "638\n",
+      "model_vars.get('Gini', 0)[-1]=0.6404000000000001\n",
+      "Condition not met at step 639. No data to save.\n",
       "639\n",
+      "model_vars.get('Gini', 0)[-1]=0.647\n",
+      "Condition not met at step 640. No data to save.\n",
       "640\n",
+      "model_vars.get('Gini', 0)[-1]=0.6754\n",
+      "Condition not met at step 641. No data to save.\n",
       "641\n",
+      "model_vars.get('Gini', 0)[-1]=0.658\n",
+      "Condition not met at step 642. No data to save.\n",
       "642\n",
+      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
+      "Condition not met at step 643. No data to save.\n",
       "643\n",
+      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
+      "Condition not met at step 644. No data to save.\n",
       "644\n",
+      "model_vars.get('Gini', 0)[-1]=0.6504000000000001\n",
+      "Condition not met at step 645. No data to save.\n",
       "645\n",
+      "model_vars.get('Gini', 0)[-1]=0.662\n",
+      "Condition not met at step 646. No data to save.\n",
       "646\n",
+      "model_vars.get('Gini', 0)[-1]=0.6676\n",
+      "Condition not met at step 647. No data to save.\n",
       "647\n",
+      "model_vars.get('Gini', 0)[-1]=0.665\n",
+      "Condition not met at step 648. No data to save.\n",
       "648\n",
+      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
+      "Condition not met at step 649. No data to save.\n",
       "649\n",
+      "model_vars.get('Gini', 0)[-1]=0.6536\n",
+      "Condition not met at step 650. No data to save.\n",
       "650\n",
+      "model_vars.get('Gini', 0)[-1]=0.6454\n",
+      "Condition not met at step 651. No data to save.\n",
       "651\n",
+      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
+      "Condition not met at step 652. No data to save.\n",
       "652\n",
+      "model_vars.get('Gini', 0)[-1]=0.6612\n",
+      "Condition not met at step 653. No data to save.\n",
       "653\n",
+      "model_vars.get('Gini', 0)[-1]=0.6446000000000001\n",
+      "Condition not met at step 654. No data to save.\n",
       "654\n",
+      "model_vars.get('Gini', 0)[-1]=0.6446000000000001\n",
+      "Condition not met at step 655. No data to save.\n",
       "655\n",
+      "model_vars.get('Gini', 0)[-1]=0.64\n",
+      "Condition not met at step 656. No data to save.\n",
       "656\n",
+      "model_vars.get('Gini', 0)[-1]=0.6414\n",
+      "Condition not met at step 657. No data to save.\n",
       "657\n",
+      "model_vars.get('Gini', 0)[-1]=0.6444000000000001\n",
+      "Condition not met at step 658. No data to save.\n",
       "658\n",
+      "model_vars.get('Gini', 0)[-1]=0.634\n",
+      "Condition not met at step 659. No data to save.\n",
       "659\n",
+      "model_vars.get('Gini', 0)[-1]=0.6532\n",
+      "Condition not met at step 660. No data to save.\n",
       "660\n",
+      "model_vars.get('Gini', 0)[-1]=0.6384000000000001\n",
+      "Condition not met at step 661. No data to save.\n",
       "661\n",
+      "model_vars.get('Gini', 0)[-1]=0.6434\n",
+      "Condition not met at step 662. No data to save.\n",
       "662\n",
+      "model_vars.get('Gini', 0)[-1]=0.6048\n",
+      "Condition not met at step 663. No data to save.\n",
       "663\n",
+      "model_vars.get('Gini', 0)[-1]=0.6148\n",
+      "Condition not met at step 664. No data to save.\n",
       "664\n",
+      "model_vars.get('Gini', 0)[-1]=0.6214\n",
+      "Condition not met at step 665. No data to save.\n",
       "665\n",
+      "model_vars.get('Gini', 0)[-1]=0.6235999999999999\n",
+      "Condition not met at step 666. No data to save.\n",
       "666\n",
+      "model_vars.get('Gini', 0)[-1]=0.6355999999999999\n",
+      "Condition not met at step 667. No data to save.\n",
       "667\n",
+      "model_vars.get('Gini', 0)[-1]=0.6372\n",
+      "Condition not met at step 668. No data to save.\n",
       "668\n",
+      "model_vars.get('Gini', 0)[-1]=0.618\n",
+      "Condition not met at step 669. No data to save.\n",
       "669\n",
+      "model_vars.get('Gini', 0)[-1]=0.6335999999999999\n",
+      "Condition not met at step 670. No data to save.\n",
       "670\n",
+      "model_vars.get('Gini', 0)[-1]=0.6454\n",
+      "Condition not met at step 671. No data to save.\n",
       "671\n",
+      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
+      "Condition not met at step 672. No data to save.\n",
       "672\n",
+      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
+      "Condition not met at step 673. No data to save.\n",
       "673\n",
+      "model_vars.get('Gini', 0)[-1]=0.6616\n",
+      "Condition not met at step 674. No data to save.\n",
       "674\n",
+      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
+      "Condition not met at step 675. No data to save.\n",
       "675\n",
+      "model_vars.get('Gini', 0)[-1]=0.6584\n",
+      "Condition not met at step 676. No data to save.\n",
       "676\n",
+      "model_vars.get('Gini', 0)[-1]=0.6306\n",
+      "Condition not met at step 677. No data to save.\n",
       "677\n",
+      "model_vars.get('Gini', 0)[-1]=0.6862\n",
+      "Condition not met at step 678. No data to save.\n",
       "678\n",
+      "model_vars.get('Gini', 0)[-1]=0.7\n",
+      "Condition not met at step 679. No data to save.\n",
       "679\n",
+      "model_vars.get('Gini', 0)[-1]=0.683\n",
+      "Condition not met at step 680. No data to save.\n",
       "680\n",
+      "model_vars.get('Gini', 0)[-1]=0.6779999999999999\n",
+      "Condition not met at step 681. No data to save.\n",
       "681\n",
+      "model_vars.get('Gini', 0)[-1]=0.6908000000000001\n",
+      "Condition not met at step 682. No data to save.\n",
       "682\n",
+      "model_vars.get('Gini', 0)[-1]=0.6822\n",
+      "Condition not met at step 683. No data to save.\n",
       "683\n",
+      "model_vars.get('Gini', 0)[-1]=0.6796\n",
+      "Condition not met at step 684. No data to save.\n",
       "684\n",
+      "model_vars.get('Gini', 0)[-1]=0.6616\n",
+      "Condition not met at step 685. No data to save.\n",
       "685\n",
+      "model_vars.get('Gini', 0)[-1]=0.6548\n",
+      "Condition not met at step 686. No data to save.\n",
       "686\n",
+      "model_vars.get('Gini', 0)[-1]=0.637\n",
+      "Condition not met at step 687. No data to save.\n",
       "687\n",
+      "model_vars.get('Gini', 0)[-1]=0.6494\n",
+      "Condition not met at step 688. No data to save.\n",
       "688\n",
+      "model_vars.get('Gini', 0)[-1]=0.6342\n",
+      "Condition not met at step 689. No data to save.\n",
       "689\n",
+      "model_vars.get('Gini', 0)[-1]=0.652\n",
+      "Condition not met at step 690. No data to save.\n",
       "690\n",
+      "model_vars.get('Gini', 0)[-1]=0.6308\n",
+      "Condition not met at step 691. No data to save.\n",
       "691\n",
+      "model_vars.get('Gini', 0)[-1]=0.6472\n",
+      "Condition not met at step 692. No data to save.\n",
       "692\n",
+      "model_vars.get('Gini', 0)[-1]=0.6468\n",
+      "Condition not met at step 693. No data to save.\n",
       "693\n",
+      "model_vars.get('Gini', 0)[-1]=0.6532\n",
+      "Condition not met at step 694. No data to save.\n",
       "694\n",
+      "model_vars.get('Gini', 0)[-1]=0.6448\n",
+      "Condition not met at step 695. No data to save.\n",
       "695\n",
+      "model_vars.get('Gini', 0)[-1]=0.6056\n",
+      "Condition not met at step 696. No data to save.\n",
       "696\n",
+      "model_vars.get('Gini', 0)[-1]=0.6456\n",
+      "Condition not met at step 697. No data to save.\n",
       "697\n",
+      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
+      "Condition not met at step 698. No data to save.\n",
       "698\n",
+      "model_vars.get('Gini', 0)[-1]=0.6537999999999999\n",
+      "Condition not met at step 699. No data to save.\n",
       "699\n",
       "CALLED\n",
       "self.model._steps=700\n",
       " TEST self.model._steps=700\n",
       " TEST self._cache_interval=100\n",
       "       Gini\n",
-      "600  0.5644\n",
-      "601  0.5670\n",
-      "602  0.5526\n",
-      "603  0.5812\n",
-      "604  0.5790\n",
+      "600  0.6476\n",
+      "601  0.6418\n",
+      "602  0.6836\n",
+      "603  0.6526\n",
+      "604  0.6448\n",
       "..      ...\n",
-      "695  0.5914\n",
-      "696  0.5914\n",
-      "697  0.5842\n",
-      "698  0.5660\n",
-      "699  0.5610\n",
+      "695  0.6056\n",
+      "696  0.6456\n",
+      "697  0.6458\n",
+      "698  0.6538\n",
+      "699  0.6572\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_007.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_007.parquet'\n",
-      "Saving model to output_dir/model_data_007.parquet\n",
-      "Saving agent to output_dir/agent_data_007.parquet\n",
+      "model_file='test_path/model_data_007.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_007.parquet'\n",
+      "Saving model to test_path/model_data_007.parquet\n",
+      "Saving agent to test_path/agent_data_007.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6572\n",
+      "Condition not met at step 700. No data to save.\n",
       "700\n",
+      "model_vars.get('Gini', 0)[-1]=0.65\n",
+      "Condition not met at step 701. No data to save.\n",
       "701\n",
+      "model_vars.get('Gini', 0)[-1]=0.65\n",
+      "Condition not met at step 702. No data to save.\n",
       "702\n",
+      "model_vars.get('Gini', 0)[-1]=0.6402\n",
+      "Condition not met at step 703. No data to save.\n",
       "703\n",
+      "model_vars.get('Gini', 0)[-1]=0.6274\n",
+      "Condition not met at step 704. No data to save.\n",
       "704\n",
+      "model_vars.get('Gini', 0)[-1]=0.6596\n",
+      "Condition not met at step 705. No data to save.\n",
       "705\n",
+      "model_vars.get('Gini', 0)[-1]=0.6474\n",
+      "Condition not met at step 706. No data to save.\n",
       "706\n",
+      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
+      "Condition not met at step 707. No data to save.\n",
       "707\n",
+      "model_vars.get('Gini', 0)[-1]=0.6106\n",
+      "Condition not met at step 708. No data to save.\n",
       "708\n",
+      "model_vars.get('Gini', 0)[-1]=0.6174\n",
+      "Condition not met at step 709. No data to save.\n",
       "709\n",
+      "model_vars.get('Gini', 0)[-1]=0.6226\n",
+      "Condition not met at step 710. No data to save.\n",
       "710\n",
+      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
+      "Condition not met at step 711. No data to save.\n",
       "711\n",
+      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
+      "Condition not met at step 712. No data to save.\n",
       "712\n",
+      "model_vars.get('Gini', 0)[-1]=0.6388\n",
+      "Condition not met at step 713. No data to save.\n",
       "713\n",
+      "model_vars.get('Gini', 0)[-1]=0.633\n",
+      "Condition not met at step 714. No data to save.\n",
       "714\n",
+      "model_vars.get('Gini', 0)[-1]=0.6736\n",
+      "Condition not met at step 715. No data to save.\n",
       "715\n",
+      "model_vars.get('Gini', 0)[-1]=0.6718\n",
+      "Condition not met at step 716. No data to save.\n",
       "716\n",
+      "model_vars.get('Gini', 0)[-1]=0.6744\n",
+      "Condition not met at step 717. No data to save.\n",
       "717\n",
+      "model_vars.get('Gini', 0)[-1]=0.6616\n",
+      "Condition not met at step 718. No data to save.\n",
       "718\n",
+      "model_vars.get('Gini', 0)[-1]=0.6288\n",
+      "Condition not met at step 719. No data to save.\n",
       "719\n",
+      "model_vars.get('Gini', 0)[-1]=0.6284000000000001\n",
+      "Condition not met at step 720. No data to save.\n",
       "720\n",
+      "model_vars.get('Gini', 0)[-1]=0.6414\n",
+      "Condition not met at step 721. No data to save.\n",
       "721\n",
+      "model_vars.get('Gini', 0)[-1]=0.6264000000000001\n",
+      "Condition not met at step 722. No data to save.\n",
       "722\n",
+      "model_vars.get('Gini', 0)[-1]=0.6235999999999999\n",
+      "Condition not met at step 723. No data to save.\n",
       "723\n",
+      "model_vars.get('Gini', 0)[-1]=0.6224000000000001\n",
+      "Condition not met at step 724. No data to save.\n",
       "724\n",
+      "model_vars.get('Gini', 0)[-1]=0.6355999999999999\n",
+      "Condition not met at step 725. No data to save.\n",
       "725\n",
+      "model_vars.get('Gini', 0)[-1]=0.6194\n",
+      "Condition not met at step 726. No data to save.\n",
       "726\n",
+      "model_vars.get('Gini', 0)[-1]=0.6288\n",
+      "Condition not met at step 727. No data to save.\n",
       "727\n",
+      "model_vars.get('Gini', 0)[-1]=0.6048\n",
+      "Condition not met at step 728. No data to save.\n",
       "728\n",
+      "model_vars.get('Gini', 0)[-1]=0.6000000000000001\n",
+      "Condition not met at step 729. No data to save.\n",
       "729\n",
+      "model_vars.get('Gini', 0)[-1]=0.6204000000000001\n",
+      "Condition not met at step 730. No data to save.\n",
       "730\n",
+      "model_vars.get('Gini', 0)[-1]=0.6148\n",
+      "Condition not met at step 731. No data to save.\n",
       "731\n",
+      "model_vars.get('Gini', 0)[-1]=0.6312\n",
+      "Condition not met at step 732. No data to save.\n",
       "732\n",
+      "model_vars.get('Gini', 0)[-1]=0.6264000000000001\n",
+      "Condition not met at step 733. No data to save.\n",
       "733\n",
+      "model_vars.get('Gini', 0)[-1]=0.6096\n",
+      "Condition not met at step 734. No data to save.\n",
       "734\n",
+      "model_vars.get('Gini', 0)[-1]=0.6344000000000001\n",
+      "Condition not met at step 735. No data to save.\n",
       "735\n",
+      "model_vars.get('Gini', 0)[-1]=0.6516\n",
+      "Condition not met at step 736. No data to save.\n",
       "736\n",
+      "model_vars.get('Gini', 0)[-1]=0.6352\n",
+      "Condition not met at step 737. No data to save.\n",
       "737\n",
+      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
+      "Condition not met at step 738. No data to save.\n",
       "738\n",
+      "model_vars.get('Gini', 0)[-1]=0.665\n",
+      "Condition not met at step 739. No data to save.\n",
       "739\n",
+      "model_vars.get('Gini', 0)[-1]=0.6358\n",
+      "Condition not met at step 740. No data to save.\n",
       "740\n",
+      "model_vars.get('Gini', 0)[-1]=0.6175999999999999\n",
+      "Condition not met at step 741. No data to save.\n",
       "741\n",
+      "model_vars.get('Gini', 0)[-1]=0.6408\n",
+      "Condition not met at step 742. No data to save.\n",
       "742\n",
+      "model_vars.get('Gini', 0)[-1]=0.6512\n",
+      "Condition not met at step 743. No data to save.\n",
       "743\n",
+      "model_vars.get('Gini', 0)[-1]=0.6759999999999999\n",
+      "Condition not met at step 744. No data to save.\n",
       "744\n",
+      "model_vars.get('Gini', 0)[-1]=0.6614\n",
+      "Condition not met at step 745. No data to save.\n",
       "745\n",
+      "model_vars.get('Gini', 0)[-1]=0.6432\n",
+      "Condition not met at step 746. No data to save.\n",
       "746\n",
+      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
+      "Condition not met at step 747. No data to save.\n",
       "747\n",
+      "model_vars.get('Gini', 0)[-1]=0.6452\n",
+      "Condition not met at step 748. No data to save.\n",
       "748\n",
+      "model_vars.get('Gini', 0)[-1]=0.6568\n",
+      "Condition not met at step 749. No data to save.\n",
       "749\n",
+      "model_vars.get('Gini', 0)[-1]=0.652\n",
+      "Condition not met at step 750. No data to save.\n",
       "750\n",
+      "model_vars.get('Gini', 0)[-1]=0.6542\n",
+      "Condition not met at step 751. No data to save.\n",
       "751\n",
+      "model_vars.get('Gini', 0)[-1]=0.6508\n",
+      "Condition not met at step 752. No data to save.\n",
       "752\n",
+      "model_vars.get('Gini', 0)[-1]=0.666\n",
+      "Condition not met at step 753. No data to save.\n",
       "753\n",
+      "model_vars.get('Gini', 0)[-1]=0.656\n",
+      "Condition not met at step 754. No data to save.\n",
       "754\n",
+      "model_vars.get('Gini', 0)[-1]=0.666\n",
+      "Condition not met at step 755. No data to save.\n",
       "755\n",
+      "model_vars.get('Gini', 0)[-1]=0.6832\n",
+      "Condition not met at step 756. No data to save.\n",
       "756\n",
+      "model_vars.get('Gini', 0)[-1]=0.6764\n",
+      "Condition not met at step 757. No data to save.\n",
       "757\n",
+      "model_vars.get('Gini', 0)[-1]=0.657\n",
+      "Condition not met at step 758. No data to save.\n",
       "758\n",
+      "model_vars.get('Gini', 0)[-1]=0.6624\n",
+      "Condition not met at step 759. No data to save.\n",
       "759\n",
+      "model_vars.get('Gini', 0)[-1]=0.6586000000000001\n",
+      "Condition not met at step 760. No data to save.\n",
       "760\n",
+      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
+      "Condition not met at step 761. No data to save.\n",
       "761\n",
+      "model_vars.get('Gini', 0)[-1]=0.5982000000000001\n",
+      "Condition not met at step 762. No data to save.\n",
       "762\n",
+      "model_vars.get('Gini', 0)[-1]=0.643\n",
+      "Condition not met at step 763. No data to save.\n",
       "763\n",
+      "model_vars.get('Gini', 0)[-1]=0.6308\n",
+      "Condition not met at step 764. No data to save.\n",
       "764\n",
+      "model_vars.get('Gini', 0)[-1]=0.6452\n",
+      "Condition not met at step 765. No data to save.\n",
       "765\n",
+      "model_vars.get('Gini', 0)[-1]=0.6608\n",
+      "Condition not met at step 766. No data to save.\n",
       "766\n",
+      "model_vars.get('Gini', 0)[-1]=0.6594\n",
+      "Condition not met at step 767. No data to save.\n",
       "767\n",
+      "model_vars.get('Gini', 0)[-1]=0.661\n",
+      "Condition not met at step 768. No data to save.\n",
       "768\n",
+      "model_vars.get('Gini', 0)[-1]=0.6394\n",
+      "Condition not met at step 769. No data to save.\n",
       "769\n",
+      "model_vars.get('Gini', 0)[-1]=0.6577999999999999\n",
+      "Condition not met at step 770. No data to save.\n",
       "770\n",
+      "model_vars.get('Gini', 0)[-1]=0.6718\n",
+      "Condition not met at step 771. No data to save.\n",
       "771\n",
+      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
+      "Condition not met at step 772. No data to save.\n",
       "772\n",
+      "model_vars.get('Gini', 0)[-1]=0.6328\n",
+      "Condition not met at step 773. No data to save.\n",
       "773\n",
+      "model_vars.get('Gini', 0)[-1]=0.6454\n",
+      "Condition not met at step 774. No data to save.\n",
       "774\n",
+      "model_vars.get('Gini', 0)[-1]=0.6468\n",
+      "Condition not met at step 775. No data to save.\n",
       "775\n",
+      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
+      "Condition not met at step 776. No data to save.\n",
       "776\n",
+      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
+      "Condition not met at step 777. No data to save.\n",
       "777\n",
+      "model_vars.get('Gini', 0)[-1]=0.6608\n",
+      "Condition not met at step 778. No data to save.\n",
       "778\n",
+      "model_vars.get('Gini', 0)[-1]=0.6776\n",
+      "Condition not met at step 779. No data to save.\n",
       "779\n",
+      "model_vars.get('Gini', 0)[-1]=0.6314\n",
+      "Condition not met at step 780. No data to save.\n",
       "780\n",
+      "model_vars.get('Gini', 0)[-1]=0.6358\n",
+      "Condition not met at step 781. No data to save.\n",
       "781\n",
+      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
+      "Condition not met at step 782. No data to save.\n",
       "782\n",
+      "model_vars.get('Gini', 0)[-1]=0.658\n",
+      "Condition not met at step 783. No data to save.\n",
       "783\n",
+      "model_vars.get('Gini', 0)[-1]=0.6396\n",
+      "Condition not met at step 784. No data to save.\n",
       "784\n",
+      "model_vars.get('Gini', 0)[-1]=0.641\n",
+      "Condition not met at step 785. No data to save.\n",
       "785\n",
+      "model_vars.get('Gini', 0)[-1]=0.6294\n",
+      "Condition not met at step 786. No data to save.\n",
       "786\n",
+      "model_vars.get('Gini', 0)[-1]=0.6762\n",
+      "Condition not met at step 787. No data to save.\n",
       "787\n",
+      "model_vars.get('Gini', 0)[-1]=0.6896\n",
+      "Condition not met at step 788. No data to save.\n",
       "788\n",
+      "model_vars.get('Gini', 0)[-1]=0.6872\n",
+      "Condition not met at step 789. No data to save.\n",
       "789\n",
+      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
+      "Condition not met at step 790. No data to save.\n",
       "790\n",
+      "model_vars.get('Gini', 0)[-1]=0.645\n",
+      "Condition not met at step 791. No data to save.\n",
       "791\n",
+      "model_vars.get('Gini', 0)[-1]=0.6342\n",
+      "Condition not met at step 792. No data to save.\n",
       "792\n",
+      "model_vars.get('Gini', 0)[-1]=0.635\n",
+      "Condition not met at step 793. No data to save.\n",
       "793\n",
+      "model_vars.get('Gini', 0)[-1]=0.6186\n",
+      "Condition not met at step 794. No data to save.\n",
       "794\n",
+      "model_vars.get('Gini', 0)[-1]=0.6136\n",
+      "Condition not met at step 795. No data to save.\n",
       "795\n",
+      "model_vars.get('Gini', 0)[-1]=0.6366\n",
+      "Condition not met at step 796. No data to save.\n",
       "796\n",
+      "model_vars.get('Gini', 0)[-1]=0.613\n",
+      "Condition not met at step 797. No data to save.\n",
       "797\n",
+      "model_vars.get('Gini', 0)[-1]=0.6186\n",
+      "Condition not met at step 798. No data to save.\n",
       "798\n",
+      "model_vars.get('Gini', 0)[-1]=0.6078\n",
+      "Condition not met at step 799. No data to save.\n",
       "799\n",
       "CALLED\n",
       "self.model._steps=800\n",
       " TEST self.model._steps=800\n",
       " TEST self._cache_interval=100\n",
       "       Gini\n",
-      "700  0.5854\n",
-      "701  0.5978\n",
-      "702  0.6238\n",
-      "703  0.6210\n",
-      "704  0.6024\n",
+      "700  0.6500\n",
+      "701  0.6500\n",
+      "702  0.6402\n",
+      "703  0.6274\n",
+      "704  0.6596\n",
       "..      ...\n",
-      "795  0.6382\n",
-      "796  0.6890\n",
-      "797  0.6552\n",
-      "798  0.6240\n",
-      "799  0.6294\n",
+      "795  0.6366\n",
+      "796  0.6130\n",
+      "797  0.6186\n",
+      "798  0.6078\n",
+      "799  0.6270\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_008.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_008.parquet'\n",
-      "Saving model to output_dir/model_data_008.parquet\n",
-      "Saving agent to output_dir/agent_data_008.parquet\n",
+      "model_file='test_path/model_data_008.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_008.parquet'\n",
+      "Saving model to test_path/model_data_008.parquet\n",
+      "Saving agent to test_path/agent_data_008.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.627\n",
+      "Condition not met at step 800. No data to save.\n",
       "800\n",
+      "model_vars.get('Gini', 0)[-1]=0.6175999999999999\n",
+      "Condition not met at step 801. No data to save.\n",
       "801\n",
+      "model_vars.get('Gini', 0)[-1]=0.6202000000000001\n",
+      "Condition not met at step 802. No data to save.\n",
       "802\n",
+      "model_vars.get('Gini', 0)[-1]=0.621\n",
+      "Condition not met at step 803. No data to save.\n",
       "803\n",
+      "model_vars.get('Gini', 0)[-1]=0.6514\n",
+      "Condition not met at step 804. No data to save.\n",
       "804\n",
+      "model_vars.get('Gini', 0)[-1]=0.6577999999999999\n",
+      "Condition not met at step 805. No data to save.\n",
       "805\n",
+      "model_vars.get('Gini', 0)[-1]=0.6252\n",
+      "Condition not met at step 806. No data to save.\n",
       "806\n",
+      "model_vars.get('Gini', 0)[-1]=0.6388\n",
+      "Condition not met at step 807. No data to save.\n",
       "807\n",
+      "model_vars.get('Gini', 0)[-1]=0.6212\n",
+      "Condition not met at step 808. No data to save.\n",
       "808\n",
+      "model_vars.get('Gini', 0)[-1]=0.6156\n",
+      "Condition not met at step 809. No data to save.\n",
       "809\n",
+      "model_vars.get('Gini', 0)[-1]=0.6416\n",
+      "Condition not met at step 810. No data to save.\n",
       "810\n",
+      "model_vars.get('Gini', 0)[-1]=0.635\n",
+      "Condition not met at step 811. No data to save.\n",
       "811\n",
+      "model_vars.get('Gini', 0)[-1]=0.6376\n",
+      "Condition not met at step 812. No data to save.\n",
       "812\n",
+      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
+      "Condition not met at step 813. No data to save.\n",
       "813\n",
+      "model_vars.get('Gini', 0)[-1]=0.644\n",
+      "Condition not met at step 814. No data to save.\n",
       "814\n",
+      "model_vars.get('Gini', 0)[-1]=0.653\n",
+      "Condition not met at step 815. No data to save.\n",
       "815\n",
+      "model_vars.get('Gini', 0)[-1]=0.6452\n",
+      "Condition not met at step 816. No data to save.\n",
       "816\n",
+      "model_vars.get('Gini', 0)[-1]=0.6344000000000001\n",
+      "Condition not met at step 817. No data to save.\n",
       "817\n",
+      "model_vars.get('Gini', 0)[-1]=0.6434\n",
+      "Condition not met at step 818. No data to save.\n",
       "818\n",
+      "model_vars.get('Gini', 0)[-1]=0.6394\n",
+      "Condition not met at step 819. No data to save.\n",
       "819\n",
+      "model_vars.get('Gini', 0)[-1]=0.6576\n",
+      "Condition not met at step 820. No data to save.\n",
       "820\n",
+      "model_vars.get('Gini', 0)[-1]=0.6632\n",
+      "Condition not met at step 821. No data to save.\n",
       "821\n",
+      "model_vars.get('Gini', 0)[-1]=0.6686000000000001\n",
+      "Condition not met at step 822. No data to save.\n",
       "822\n",
+      "model_vars.get('Gini', 0)[-1]=0.6522\n",
+      "Condition not met at step 823. No data to save.\n",
       "823\n",
+      "model_vars.get('Gini', 0)[-1]=0.6388\n",
+      "Condition not met at step 824. No data to save.\n",
       "824\n",
+      "model_vars.get('Gini', 0)[-1]=0.6318\n",
+      "Condition not met at step 825. No data to save.\n",
       "825\n",
+      "model_vars.get('Gini', 0)[-1]=0.5884\n",
+      "Condition not met at step 826. No data to save.\n",
       "826\n",
+      "model_vars.get('Gini', 0)[-1]=0.5722\n",
+      "Condition not met at step 827. No data to save.\n",
       "827\n",
+      "model_vars.get('Gini', 0)[-1]=0.5920000000000001\n",
+      "Condition not met at step 828. No data to save.\n",
       "828\n",
+      "model_vars.get('Gini', 0)[-1]=0.6338\n",
+      "Condition not met at step 829. No data to save.\n",
       "829\n",
+      "model_vars.get('Gini', 0)[-1]=0.6382\n",
+      "Condition not met at step 830. No data to save.\n",
       "830\n",
+      "model_vars.get('Gini', 0)[-1]=0.621\n",
+      "Condition not met at step 831. No data to save.\n",
       "831\n",
+      "model_vars.get('Gini', 0)[-1]=0.64\n",
+      "Condition not met at step 832. No data to save.\n",
       "832\n",
+      "model_vars.get('Gini', 0)[-1]=0.642\n",
+      "Condition not met at step 833. No data to save.\n",
       "833\n",
+      "model_vars.get('Gini', 0)[-1]=0.663\n",
+      "Condition not met at step 834. No data to save.\n",
       "834\n",
+      "model_vars.get('Gini', 0)[-1]=0.6632\n",
+      "Condition not met at step 835. No data to save.\n",
       "835\n",
+      "model_vars.get('Gini', 0)[-1]=0.687\n",
+      "Condition not met at step 836. No data to save.\n",
       "836\n",
+      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
+      "Condition not met at step 837. No data to save.\n",
       "837\n",
+      "model_vars.get('Gini', 0)[-1]=0.6504000000000001\n",
+      "Condition not met at step 838. No data to save.\n",
       "838\n",
+      "model_vars.get('Gini', 0)[-1]=0.6134\n",
+      "Condition not met at step 839. No data to save.\n",
       "839\n",
+      "model_vars.get('Gini', 0)[-1]=0.5918\n",
+      "Condition not met at step 840. No data to save.\n",
       "840\n",
+      "model_vars.get('Gini', 0)[-1]=0.5864\n",
+      "Condition not met at step 841. No data to save.\n",
       "841\n",
+      "model_vars.get('Gini', 0)[-1]=0.5804\n",
+      "Condition not met at step 842. No data to save.\n",
       "842\n",
+      "model_vars.get('Gini', 0)[-1]=0.6076\n",
+      "Condition not met at step 843. No data to save.\n",
       "843\n",
+      "model_vars.get('Gini', 0)[-1]=0.5880000000000001\n",
+      "Condition not met at step 844. No data to save.\n",
       "844\n",
+      "model_vars.get('Gini', 0)[-1]=0.5884\n",
+      "Condition not met at step 845. No data to save.\n",
       "845\n",
+      "model_vars.get('Gini', 0)[-1]=0.5984\n",
+      "Condition not met at step 846. No data to save.\n",
       "846\n",
+      "model_vars.get('Gini', 0)[-1]=0.6104\n",
+      "Condition not met at step 847. No data to save.\n",
       "847\n",
+      "model_vars.get('Gini', 0)[-1]=0.6335999999999999\n",
+      "Condition not met at step 848. No data to save.\n",
       "848\n",
+      "model_vars.get('Gini', 0)[-1]=0.6314\n",
+      "Condition not met at step 849. No data to save.\n",
       "849\n",
+      "model_vars.get('Gini', 0)[-1]=0.6355999999999999\n",
+      "Condition not met at step 850. No data to save.\n",
       "850\n",
+      "model_vars.get('Gini', 0)[-1]=0.6388\n",
+      "Condition not met at step 851. No data to save.\n",
       "851\n",
+      "model_vars.get('Gini', 0)[-1]=0.6292\n",
+      "Condition not met at step 852. No data to save.\n",
       "852\n",
+      "model_vars.get('Gini', 0)[-1]=0.643\n",
+      "Condition not met at step 853. No data to save.\n",
       "853\n",
+      "model_vars.get('Gini', 0)[-1]=0.6166\n",
+      "Condition not met at step 854. No data to save.\n",
       "854\n",
+      "model_vars.get('Gini', 0)[-1]=0.627\n",
+      "Condition not met at step 855. No data to save.\n",
       "855\n",
+      "model_vars.get('Gini', 0)[-1]=0.627\n",
+      "Condition not met at step 856. No data to save.\n",
       "856\n",
+      "model_vars.get('Gini', 0)[-1]=0.6136\n",
+      "Condition not met at step 857. No data to save.\n",
       "857\n",
+      "model_vars.get('Gini', 0)[-1]=0.6295999999999999\n",
+      "Condition not met at step 858. No data to save.\n",
       "858\n",
+      "model_vars.get('Gini', 0)[-1]=0.635\n",
+      "Condition not met at step 859. No data to save.\n",
       "859\n",
+      "model_vars.get('Gini', 0)[-1]=0.634\n",
+      "Condition not met at step 860. No data to save.\n",
       "860\n",
+      "model_vars.get('Gini', 0)[-1]=0.6148\n",
+      "Condition not met at step 861. No data to save.\n",
       "861\n",
+      "model_vars.get('Gini', 0)[-1]=0.656\n",
+      "Condition not met at step 862. No data to save.\n",
       "862\n",
+      "model_vars.get('Gini', 0)[-1]=0.6834\n",
+      "Condition not met at step 863. No data to save.\n",
       "863\n",
+      "model_vars.get('Gini', 0)[-1]=0.6604\n",
+      "Condition not met at step 864. No data to save.\n",
       "864\n",
+      "model_vars.get('Gini', 0)[-1]=0.634\n",
+      "Condition not met at step 865. No data to save.\n",
       "865\n",
+      "model_vars.get('Gini', 0)[-1]=0.6386000000000001\n",
+      "Condition not met at step 866. No data to save.\n",
       "866\n",
+      "model_vars.get('Gini', 0)[-1]=0.6378\n",
+      "Condition not met at step 867. No data to save.\n",
       "867\n",
+      "model_vars.get('Gini', 0)[-1]=0.6517999999999999\n",
+      "Condition not met at step 868. No data to save.\n",
       "868\n",
+      "model_vars.get('Gini', 0)[-1]=0.6614\n",
+      "Condition not met at step 869. No data to save.\n",
       "869\n",
+      "model_vars.get('Gini', 0)[-1]=0.6678\n",
+      "Condition not met at step 870. No data to save.\n",
       "870\n",
+      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
+      "Condition not met at step 871. No data to save.\n",
       "871\n",
+      "model_vars.get('Gini', 0)[-1]=0.6852\n",
+      "Condition not met at step 872. No data to save.\n",
       "872\n",
+      "model_vars.get('Gini', 0)[-1]=0.6878\n",
+      "Condition not met at step 873. No data to save.\n",
       "873\n",
+      "model_vars.get('Gini', 0)[-1]=0.6548\n",
+      "Condition not met at step 874. No data to save.\n",
       "874\n",
+      "model_vars.get('Gini', 0)[-1]=0.6798\n",
+      "Condition not met at step 875. No data to save.\n",
       "875\n",
+      "model_vars.get('Gini', 0)[-1]=0.651\n",
+      "Condition not met at step 876. No data to save.\n",
       "876\n",
+      "model_vars.get('Gini', 0)[-1]=0.6536\n",
+      "Condition not met at step 877. No data to save.\n",
       "877\n",
+      "model_vars.get('Gini', 0)[-1]=0.6488\n",
+      "Condition not met at step 878. No data to save.\n",
       "878\n",
+      "model_vars.get('Gini', 0)[-1]=0.6235999999999999\n",
+      "Condition not met at step 879. No data to save.\n",
       "879\n",
+      "model_vars.get('Gini', 0)[-1]=0.6352\n",
+      "Condition not met at step 880. No data to save.\n",
       "880\n",
+      "model_vars.get('Gini', 0)[-1]=0.6497999999999999\n",
+      "Condition not met at step 881. No data to save.\n",
       "881\n",
+      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
+      "Condition not met at step 882. No data to save.\n",
       "882\n",
+      "model_vars.get('Gini', 0)[-1]=0.6352\n",
+      "Condition not met at step 883. No data to save.\n",
       "883\n",
+      "model_vars.get('Gini', 0)[-1]=0.6152\n",
+      "Condition not met at step 884. No data to save.\n",
       "884\n",
+      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
+      "Condition not met at step 885. No data to save.\n",
       "885\n",
+      "model_vars.get('Gini', 0)[-1]=0.644\n",
+      "Condition not met at step 886. No data to save.\n",
       "886\n",
+      "model_vars.get('Gini', 0)[-1]=0.6352\n",
+      "Condition not met at step 887. No data to save.\n",
       "887\n",
+      "model_vars.get('Gini', 0)[-1]=0.6314\n",
+      "Condition not met at step 888. No data to save.\n",
       "888\n",
+      "model_vars.get('Gini', 0)[-1]=0.6244000000000001\n",
+      "Condition not met at step 889. No data to save.\n",
       "889\n",
+      "model_vars.get('Gini', 0)[-1]=0.6082000000000001\n",
+      "Condition not met at step 890. No data to save.\n",
       "890\n",
+      "model_vars.get('Gini', 0)[-1]=0.5820000000000001\n",
+      "Condition not met at step 891. No data to save.\n",
       "891\n",
+      "model_vars.get('Gini', 0)[-1]=0.6096\n",
+      "Condition not met at step 892. No data to save.\n",
       "892\n",
+      "model_vars.get('Gini', 0)[-1]=0.6436\n",
+      "Condition not met at step 893. No data to save.\n",
       "893\n",
+      "model_vars.get('Gini', 0)[-1]=0.6812\n",
+      "Condition not met at step 894. No data to save.\n",
       "894\n",
+      "model_vars.get('Gini', 0)[-1]=0.655\n",
+      "Condition not met at step 895. No data to save.\n",
       "895\n",
+      "model_vars.get('Gini', 0)[-1]=0.6924\n",
+      "Condition not met at step 896. No data to save.\n",
       "896\n",
+      "model_vars.get('Gini', 0)[-1]=0.667\n",
+      "Condition not met at step 897. No data to save.\n",
       "897\n",
+      "model_vars.get('Gini', 0)[-1]=0.7064\n",
+      "Condition met. Appended special results for step 898 to test_path/special_results.parquet\n",
       "898\n",
+      "model_vars.get('Gini', 0)[-1]=0.7068\n",
+      "Condition met. Appended special results for step 899 to test_path/special_results.parquet\n",
       "899\n",
       "CALLED\n",
       "self.model._steps=900\n",
       " TEST self.model._steps=900\n",
       " TEST self._cache_interval=100\n",
       "       Gini\n",
-      "800  0.6286\n",
-      "801  0.6666\n",
-      "802  0.6810\n",
-      "803  0.6466\n",
-      "804  0.6530\n",
+      "800  0.6176\n",
+      "801  0.6202\n",
+      "802  0.6210\n",
+      "803  0.6514\n",
+      "804  0.6578\n",
       "..      ...\n",
-      "895  0.6044\n",
-      "896  0.6332\n",
-      "897  0.6382\n",
-      "898  0.6346\n",
-      "899  0.6394\n",
+      "895  0.6924\n",
+      "896  0.6670\n",
+      "897  0.7064\n",
+      "898  0.7068\n",
+      "899  0.6792\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_009.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_009.parquet'\n",
-      "Saving model to output_dir/model_data_009.parquet\n",
-      "Saving agent to output_dir/agent_data_009.parquet\n",
+      "model_file='test_path/model_data_009.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_009.parquet'\n",
+      "Saving model to test_path/model_data_009.parquet\n",
+      "Saving agent to test_path/agent_data_009.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6792\n",
+      "Condition not met at step 900. No data to save.\n",
       "900\n",
+      "model_vars.get('Gini', 0)[-1]=0.7038\n",
+      "Condition met. Appended special results for step 901 to test_path/special_results.parquet\n",
       "901\n",
+      "model_vars.get('Gini', 0)[-1]=0.6784\n",
+      "Condition not met at step 902. No data to save.\n",
       "902\n",
+      "model_vars.get('Gini', 0)[-1]=0.6872\n",
+      "Condition not met at step 903. No data to save.\n",
       "903\n",
+      "model_vars.get('Gini', 0)[-1]=0.6596\n",
+      "Condition not met at step 904. No data to save.\n",
       "904\n",
+      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
+      "Condition not met at step 905. No data to save.\n",
       "905\n",
+      "model_vars.get('Gini', 0)[-1]=0.63\n",
+      "Condition not met at step 906. No data to save.\n",
       "906\n",
+      "model_vars.get('Gini', 0)[-1]=0.5976\n",
+      "Condition not met at step 907. No data to save.\n",
       "907\n",
+      "model_vars.get('Gini', 0)[-1]=0.6124\n",
+      "Condition not met at step 908. No data to save.\n",
       "908\n",
+      "model_vars.get('Gini', 0)[-1]=0.6004\n",
+      "Condition not met at step 909. No data to save.\n",
       "909\n",
+      "model_vars.get('Gini', 0)[-1]=0.6488\n",
+      "Condition not met at step 910. No data to save.\n",
       "910\n",
+      "model_vars.get('Gini', 0)[-1]=0.6362\n",
+      "Condition not met at step 911. No data to save.\n",
       "911\n",
+      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
+      "Condition not met at step 912. No data to save.\n",
       "912\n",
+      "model_vars.get('Gini', 0)[-1]=0.6454\n",
+      "Condition not met at step 913. No data to save.\n",
       "913\n",
+      "model_vars.get('Gini', 0)[-1]=0.6432\n",
+      "Condition not met at step 914. No data to save.\n",
       "914\n",
+      "model_vars.get('Gini', 0)[-1]=0.6564\n",
+      "Condition not met at step 915. No data to save.\n",
       "915\n",
+      "model_vars.get('Gini', 0)[-1]=0.6175999999999999\n",
+      "Condition not met at step 916. No data to save.\n",
       "916\n",
+      "model_vars.get('Gini', 0)[-1]=0.6274\n",
+      "Condition not met at step 917. No data to save.\n",
       "917\n",
+      "model_vars.get('Gini', 0)[-1]=0.6076\n",
+      "Condition not met at step 918. No data to save.\n",
       "918\n",
+      "model_vars.get('Gini', 0)[-1]=0.6013999999999999\n",
+      "Condition not met at step 919. No data to save.\n",
       "919\n",
+      "model_vars.get('Gini', 0)[-1]=0.6088\n",
+      "Condition not met at step 920. No data to save.\n",
       "920\n",
+      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
+      "Condition not met at step 921. No data to save.\n",
       "921\n",
+      "model_vars.get('Gini', 0)[-1]=0.6544\n",
+      "Condition not met at step 922. No data to save.\n",
       "922\n",
+      "model_vars.get('Gini', 0)[-1]=0.6542\n",
+      "Condition not met at step 923. No data to save.\n",
       "923\n",
+      "model_vars.get('Gini', 0)[-1]=0.6792\n",
+      "Condition not met at step 924. No data to save.\n",
       "924\n",
+      "model_vars.get('Gini', 0)[-1]=0.6818\n",
+      "Condition not met at step 925. No data to save.\n",
       "925\n",
+      "model_vars.get('Gini', 0)[-1]=0.6898\n",
+      "Condition not met at step 926. No data to save.\n",
       "926\n",
+      "model_vars.get('Gini', 0)[-1]=0.6928000000000001\n",
+      "Condition not met at step 927. No data to save.\n",
       "927\n",
+      "model_vars.get('Gini', 0)[-1]=0.698\n",
+      "Condition not met at step 928. No data to save.\n",
       "928\n",
+      "model_vars.get('Gini', 0)[-1]=0.7078\n",
+      "Condition met. Appended special results for step 929 to test_path/special_results.parquet\n",
       "929\n",
+      "model_vars.get('Gini', 0)[-1]=0.6846\n",
+      "Condition not met at step 930. No data to save.\n",
       "930\n",
+      "model_vars.get('Gini', 0)[-1]=0.6884\n",
+      "Condition not met at step 931. No data to save.\n",
       "931\n",
+      "model_vars.get('Gini', 0)[-1]=0.7004\n",
+      "Condition met. Appended special results for step 932 to test_path/special_results.parquet\n",
       "932\n",
+      "model_vars.get('Gini', 0)[-1]=0.7048\n",
+      "Condition met. Appended special results for step 933 to test_path/special_results.parquet\n",
       "933\n",
+      "model_vars.get('Gini', 0)[-1]=0.7021999999999999\n",
+      "Condition met. Appended special results for step 934 to test_path/special_results.parquet\n",
       "934\n",
+      "model_vars.get('Gini', 0)[-1]=0.6952\n",
+      "Condition not met at step 935. No data to save.\n",
       "935\n",
+      "model_vars.get('Gini', 0)[-1]=0.6924\n",
+      "Condition not met at step 936. No data to save.\n",
       "936\n",
+      "model_vars.get('Gini', 0)[-1]=0.6862\n",
+      "Condition not met at step 937. No data to save.\n",
       "937\n",
+      "model_vars.get('Gini', 0)[-1]=0.6624\n",
+      "Condition not met at step 938. No data to save.\n",
       "938\n",
+      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
+      "Condition not met at step 939. No data to save.\n",
       "939\n",
+      "model_vars.get('Gini', 0)[-1]=0.6858\n",
+      "Condition not met at step 940. No data to save.\n",
       "940\n",
+      "model_vars.get('Gini', 0)[-1]=0.6946\n",
+      "Condition not met at step 941. No data to save.\n",
       "941\n",
+      "model_vars.get('Gini', 0)[-1]=0.6874\n",
+      "Condition not met at step 942. No data to save.\n",
       "942\n",
+      "model_vars.get('Gini', 0)[-1]=0.6804\n",
+      "Condition not met at step 943. No data to save.\n",
       "943\n",
+      "model_vars.get('Gini', 0)[-1]=0.6582\n",
+      "Condition not met at step 944. No data to save.\n",
       "944\n",
+      "model_vars.get('Gini', 0)[-1]=0.6636\n",
+      "Condition not met at step 945. No data to save.\n",
       "945\n",
+      "model_vars.get('Gini', 0)[-1]=0.6708000000000001\n",
+      "Condition not met at step 946. No data to save.\n",
       "946\n",
+      "model_vars.get('Gini', 0)[-1]=0.6464000000000001\n",
+      "Condition not met at step 947. No data to save.\n",
       "947\n",
+      "model_vars.get('Gini', 0)[-1]=0.6434\n",
+      "Condition not met at step 948. No data to save.\n",
       "948\n",
+      "model_vars.get('Gini', 0)[-1]=0.6712\n",
+      "Condition not met at step 949. No data to save.\n",
       "949\n",
+      "model_vars.get('Gini', 0)[-1]=0.6544\n",
+      "Condition not met at step 950. No data to save.\n",
       "950\n",
+      "model_vars.get('Gini', 0)[-1]=0.6428\n",
+      "Condition not met at step 951. No data to save.\n",
       "951\n",
+      "model_vars.get('Gini', 0)[-1]=0.646\n",
+      "Condition not met at step 952. No data to save.\n",
       "952\n",
+      "model_vars.get('Gini', 0)[-1]=0.6404000000000001\n",
+      "Condition not met at step 953. No data to save.\n",
       "953\n",
+      "model_vars.get('Gini', 0)[-1]=0.6536\n",
+      "Condition not met at step 954. No data to save.\n",
       "954\n",
+      "model_vars.get('Gini', 0)[-1]=0.6442\n",
+      "Condition not met at step 955. No data to save.\n",
       "955\n",
+      "model_vars.get('Gini', 0)[-1]=0.6294\n",
+      "Condition not met at step 956. No data to save.\n",
       "956\n",
+      "model_vars.get('Gini', 0)[-1]=0.6374\n",
+      "Condition not met at step 957. No data to save.\n",
       "957\n",
+      "model_vars.get('Gini', 0)[-1]=0.6362\n",
+      "Condition not met at step 958. No data to save.\n",
       "958\n",
+      "model_vars.get('Gini', 0)[-1]=0.6492\n",
+      "Condition not met at step 959. No data to save.\n",
       "959\n",
+      "model_vars.get('Gini', 0)[-1]=0.642\n",
+      "Condition not met at step 960. No data to save.\n",
       "960\n",
+      "model_vars.get('Gini', 0)[-1]=0.6468\n",
+      "Condition not met at step 961. No data to save.\n",
       "961\n",
+      "model_vars.get('Gini', 0)[-1]=0.652\n",
+      "Condition not met at step 962. No data to save.\n",
       "962\n",
+      "model_vars.get('Gini', 0)[-1]=0.6394\n",
+      "Condition not met at step 963. No data to save.\n",
       "963\n",
+      "model_vars.get('Gini', 0)[-1]=0.656\n",
+      "Condition not met at step 964. No data to save.\n",
       "964\n",
+      "model_vars.get('Gini', 0)[-1]=0.6362\n",
+      "Condition not met at step 965. No data to save.\n",
       "965\n",
+      "model_vars.get('Gini', 0)[-1]=0.6452\n",
+      "Condition not met at step 966. No data to save.\n",
       "966\n",
+      "model_vars.get('Gini', 0)[-1]=0.6402\n",
+      "Condition not met at step 967. No data to save.\n",
       "967\n",
+      "model_vars.get('Gini', 0)[-1]=0.6528\n",
+      "Condition not met at step 968. No data to save.\n",
       "968\n",
+      "model_vars.get('Gini', 0)[-1]=0.6358\n",
+      "Condition not met at step 969. No data to save.\n",
       "969\n",
+      "model_vars.get('Gini', 0)[-1]=0.6416\n",
+      "Condition not met at step 970. No data to save.\n",
       "970\n",
+      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
+      "Condition not met at step 971. No data to save.\n",
       "971\n",
+      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
+      "Condition not met at step 972. No data to save.\n",
       "972\n",
+      "model_vars.get('Gini', 0)[-1]=0.6234\n",
+      "Condition not met at step 973. No data to save.\n",
       "973\n",
+      "model_vars.get('Gini', 0)[-1]=0.5932\n",
+      "Condition not met at step 974. No data to save.\n",
       "974\n",
+      "model_vars.get('Gini', 0)[-1]=0.6332\n",
+      "Condition not met at step 975. No data to save.\n",
       "975\n",
+      "model_vars.get('Gini', 0)[-1]=0.6226\n",
+      "Condition not met at step 976. No data to save.\n",
       "976\n",
+      "model_vars.get('Gini', 0)[-1]=0.6388\n",
+      "Condition not met at step 977. No data to save.\n",
       "977\n",
+      "model_vars.get('Gini', 0)[-1]=0.6634\n",
+      "Condition not met at step 978. No data to save.\n",
       "978\n",
+      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
+      "Condition not met at step 979. No data to save.\n",
       "979\n",
+      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
+      "Condition not met at step 980. No data to save.\n",
       "980\n",
+      "model_vars.get('Gini', 0)[-1]=0.6702\n",
+      "Condition not met at step 981. No data to save.\n",
       "981\n",
+      "model_vars.get('Gini', 0)[-1]=0.6582\n",
+      "Condition not met at step 982. No data to save.\n",
       "982\n",
+      "model_vars.get('Gini', 0)[-1]=0.6294\n",
+      "Condition not met at step 983. No data to save.\n",
       "983\n",
+      "model_vars.get('Gini', 0)[-1]=0.6582\n",
+      "Condition not met at step 984. No data to save.\n",
       "984\n",
+      "model_vars.get('Gini', 0)[-1]=0.6754\n",
+      "Condition not met at step 985. No data to save.\n",
       "985\n",
+      "model_vars.get('Gini', 0)[-1]=0.6562\n",
+      "Condition not met at step 986. No data to save.\n",
       "986\n",
+      "model_vars.get('Gini', 0)[-1]=0.646\n",
+      "Condition not met at step 987. No data to save.\n",
       "987\n",
+      "model_vars.get('Gini', 0)[-1]=0.6862\n",
+      "Condition not met at step 988. No data to save.\n",
       "988\n",
+      "model_vars.get('Gini', 0)[-1]=0.7024\n",
+      "Condition met. Appended special results for step 989 to test_path/special_results.parquet\n",
       "989\n",
+      "model_vars.get('Gini', 0)[-1]=0.7170000000000001\n",
+      "Condition met. Appended special results for step 990 to test_path/special_results.parquet\n",
       "990\n",
+      "model_vars.get('Gini', 0)[-1]=0.6974\n",
+      "Condition not met at step 991. No data to save.\n",
       "991\n",
+      "model_vars.get('Gini', 0)[-1]=0.6816\n",
+      "Condition not met at step 992. No data to save.\n",
       "992\n",
+      "model_vars.get('Gini', 0)[-1]=0.7086\n",
+      "Condition met. Appended special results for step 993 to test_path/special_results.parquet\n",
       "993\n",
+      "model_vars.get('Gini', 0)[-1]=0.6996\n",
+      "Condition not met at step 994. No data to save.\n",
       "994\n",
+      "model_vars.get('Gini', 0)[-1]=0.677\n",
+      "Condition not met at step 995. No data to save.\n",
       "995\n",
+      "model_vars.get('Gini', 0)[-1]=0.6921999999999999\n",
+      "Condition not met at step 996. No data to save.\n",
       "996\n",
+      "model_vars.get('Gini', 0)[-1]=0.685\n",
+      "Condition not met at step 997. No data to save.\n",
       "997\n",
+      "model_vars.get('Gini', 0)[-1]=0.6806\n",
+      "Condition not met at step 998. No data to save.\n",
       "998\n",
+      "model_vars.get('Gini', 0)[-1]=0.651\n",
+      "Condition not met at step 999. No data to save.\n",
       "999\n",
       "CALLED\n",
       "self.model._steps=1000\n",
       " TEST self.model._steps=1000\n",
       " TEST self._cache_interval=100\n",
       "       Gini\n",
-      "900  0.6104\n",
-      "901  0.6256\n",
-      "902  0.6256\n",
-      "903  0.6492\n",
-      "904  0.6398\n",
+      "900  0.7038\n",
+      "901  0.6784\n",
+      "902  0.6872\n",
+      "903  0.6596\n",
+      "904  0.6486\n",
       "..      ...\n",
-      "995  0.6574\n",
-      "996  0.6876\n",
-      "997  0.6758\n",
-      "998  0.6832\n",
-      "999  0.6808\n",
+      "995  0.6922\n",
+      "996  0.6850\n",
+      "997  0.6806\n",
+      "998  0.6510\n",
+      "999  0.6530\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_010.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_010.parquet'\n",
-      "Saving model to output_dir/model_data_010.parquet\n",
-      "Saving agent to output_dir/agent_data_010.parquet\n",
+      "model_file='test_path/model_data_010.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_010.parquet'\n",
+      "Saving model to test_path/model_data_010.parquet\n",
+      "Saving agent to test_path/agent_data_010.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.653\n",
+      "Condition not met at step 1000. No data to save.\n",
       "1000\n",
+      "model_vars.get('Gini', 0)[-1]=0.6574\n",
+      "Condition not met at step 1001. No data to save.\n",
       "1001\n",
+      "model_vars.get('Gini', 0)[-1]=0.6482\n",
+      "Condition not met at step 1002. No data to save.\n",
       "1002\n",
+      "model_vars.get('Gini', 0)[-1]=0.6254\n",
+      "Condition not met at step 1003. No data to save.\n",
       "1003\n",
+      "model_vars.get('Gini', 0)[-1]=0.6315999999999999\n",
+      "Condition not met at step 1004. No data to save.\n",
       "1004\n",
+      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
+      "Condition not met at step 1005. No data to save.\n",
       "1005\n",
+      "model_vars.get('Gini', 0)[-1]=0.6548\n",
+      "Condition not met at step 1006. No data to save.\n",
       "1006\n",
+      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
+      "Condition not met at step 1007. No data to save.\n",
       "1007\n",
+      "model_vars.get('Gini', 0)[-1]=0.6348\n",
+      "Condition not met at step 1008. No data to save.\n",
       "1008\n",
+      "model_vars.get('Gini', 0)[-1]=0.6442\n",
+      "Condition not met at step 1009. No data to save.\n",
       "1009\n",
+      "model_vars.get('Gini', 0)[-1]=0.6184000000000001\n",
+      "Condition not met at step 1010. No data to save.\n",
       "1010\n",
+      "model_vars.get('Gini', 0)[-1]=0.5946\n",
+      "Condition not met at step 1011. No data to save.\n",
       "1011\n",
+      "model_vars.get('Gini', 0)[-1]=0.6018\n",
+      "Condition not met at step 1012. No data to save.\n",
       "1012\n",
+      "model_vars.get('Gini', 0)[-1]=0.6093999999999999\n",
+      "Condition not met at step 1013. No data to save.\n",
       "1013\n",
+      "model_vars.get('Gini', 0)[-1]=0.615\n",
+      "Condition not met at step 1014. No data to save.\n",
       "1014\n",
+      "model_vars.get('Gini', 0)[-1]=0.648\n",
+      "Condition not met at step 1015. No data to save.\n",
       "1015\n",
+      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
+      "Condition not met at step 1016. No data to save.\n",
       "1016\n",
+      "model_vars.get('Gini', 0)[-1]=0.6432\n",
+      "Condition not met at step 1017. No data to save.\n",
       "1017\n",
+      "model_vars.get('Gini', 0)[-1]=0.6542\n",
+      "Condition not met at step 1018. No data to save.\n",
       "1018\n",
+      "model_vars.get('Gini', 0)[-1]=0.6806\n",
+      "Condition not met at step 1019. No data to save.\n",
       "1019\n",
+      "model_vars.get('Gini', 0)[-1]=0.6582\n",
+      "Condition not met at step 1020. No data to save.\n",
       "1020\n",
+      "model_vars.get('Gini', 0)[-1]=0.6416\n",
+      "Condition not met at step 1021. No data to save.\n",
       "1021\n",
+      "model_vars.get('Gini', 0)[-1]=0.6614\n",
+      "Condition not met at step 1022. No data to save.\n",
       "1022\n",
+      "model_vars.get('Gini', 0)[-1]=0.6324000000000001\n",
+      "Condition not met at step 1023. No data to save.\n",
       "1023\n",
+      "model_vars.get('Gini', 0)[-1]=0.6522\n",
+      "Condition not met at step 1024. No data to save.\n",
       "1024\n",
+      "model_vars.get('Gini', 0)[-1]=0.6528\n",
+      "Condition not met at step 1025. No data to save.\n",
       "1025\n",
+      "model_vars.get('Gini', 0)[-1]=0.6748000000000001\n",
+      "Condition not met at step 1026. No data to save.\n",
       "1026\n",
+      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
+      "Condition not met at step 1027. No data to save.\n",
       "1027\n",
+      "model_vars.get('Gini', 0)[-1]=0.6522\n",
+      "Condition not met at step 1028. No data to save.\n",
       "1028\n",
+      "model_vars.get('Gini', 0)[-1]=0.6612\n",
+      "Condition not met at step 1029. No data to save.\n",
       "1029\n",
+      "model_vars.get('Gini', 0)[-1]=0.6732\n",
+      "Condition not met at step 1030. No data to save.\n",
       "1030\n",
+      "model_vars.get('Gini', 0)[-1]=0.6594\n",
+      "Condition not met at step 1031. No data to save.\n",
       "1031\n",
+      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
+      "Condition not met at step 1032. No data to save.\n",
       "1032\n",
+      "model_vars.get('Gini', 0)[-1]=0.6452\n",
+      "Condition not met at step 1033. No data to save.\n",
       "1033\n",
+      "model_vars.get('Gini', 0)[-1]=0.6404000000000001\n",
+      "Condition not met at step 1034. No data to save.\n",
       "1034\n",
+      "model_vars.get('Gini', 0)[-1]=0.6532\n",
+      "Condition not met at step 1035. No data to save.\n",
       "1035\n",
+      "model_vars.get('Gini', 0)[-1]=0.6586000000000001\n",
+      "Condition not met at step 1036. No data to save.\n",
       "1036\n",
+      "model_vars.get('Gini', 0)[-1]=0.6646000000000001\n",
+      "Condition not met at step 1037. No data to save.\n",
       "1037\n",
+      "model_vars.get('Gini', 0)[-1]=0.6644\n",
+      "Condition not met at step 1038. No data to save.\n",
       "1038\n",
+      "model_vars.get('Gini', 0)[-1]=0.6859999999999999\n",
+      "Condition not met at step 1039. No data to save.\n",
       "1039\n",
+      "model_vars.get('Gini', 0)[-1]=0.6806\n",
+      "Condition not met at step 1040. No data to save.\n",
       "1040\n",
+      "model_vars.get('Gini', 0)[-1]=0.6856\n",
+      "Condition not met at step 1041. No data to save.\n",
       "1041\n",
+      "model_vars.get('Gini', 0)[-1]=0.6779999999999999\n",
+      "Condition not met at step 1042. No data to save.\n",
       "1042\n",
+      "model_vars.get('Gini', 0)[-1]=0.6708000000000001\n",
+      "Condition not met at step 1043. No data to save.\n",
       "1043\n",
+      "model_vars.get('Gini', 0)[-1]=0.6594\n",
+      "Condition not met at step 1044. No data to save.\n",
       "1044\n",
+      "model_vars.get('Gini', 0)[-1]=0.6534\n",
+      "Condition not met at step 1045. No data to save.\n",
       "1045\n",
+      "model_vars.get('Gini', 0)[-1]=0.6577999999999999\n",
+      "Condition not met at step 1046. No data to save.\n",
       "1046\n",
+      "model_vars.get('Gini', 0)[-1]=0.6384000000000001\n",
+      "Condition not met at step 1047. No data to save.\n",
       "1047\n",
+      "model_vars.get('Gini', 0)[-1]=0.6474\n",
+      "Condition not met at step 1048. No data to save.\n",
       "1048\n",
+      "model_vars.get('Gini', 0)[-1]=0.6304000000000001\n",
+      "Condition not met at step 1049. No data to save.\n",
       "1049\n",
+      "model_vars.get('Gini', 0)[-1]=0.6574\n",
+      "Condition not met at step 1050. No data to save.\n",
       "1050\n",
+      "model_vars.get('Gini', 0)[-1]=0.6352\n",
+      "Condition not met at step 1051. No data to save.\n",
       "1051\n",
+      "model_vars.get('Gini', 0)[-1]=0.6496\n",
+      "Condition not met at step 1052. No data to save.\n",
       "1052\n",
+      "model_vars.get('Gini', 0)[-1]=0.6616\n",
+      "Condition not met at step 1053. No data to save.\n",
       "1053\n",
+      "model_vars.get('Gini', 0)[-1]=0.6166\n",
+      "Condition not met at step 1054. No data to save.\n",
       "1054\n",
+      "model_vars.get('Gini', 0)[-1]=0.6148\n",
+      "Condition not met at step 1055. No data to save.\n",
       "1055\n",
+      "model_vars.get('Gini', 0)[-1]=0.6577999999999999\n",
+      "Condition not met at step 1056. No data to save.\n",
       "1056\n",
+      "model_vars.get('Gini', 0)[-1]=0.6506000000000001\n",
+      "Condition not met at step 1057. No data to save.\n",
       "1057\n",
+      "model_vars.get('Gini', 0)[-1]=0.6488\n",
+      "Condition not met at step 1058. No data to save.\n",
       "1058\n",
+      "model_vars.get('Gini', 0)[-1]=0.6617999999999999\n",
+      "Condition not met at step 1059. No data to save.\n",
       "1059\n",
+      "model_vars.get('Gini', 0)[-1]=0.6548\n",
+      "Condition not met at step 1060. No data to save.\n",
       "1060\n",
+      "model_vars.get('Gini', 0)[-1]=0.6572\n",
+      "Condition not met at step 1061. No data to save.\n",
       "1061\n",
+      "model_vars.get('Gini', 0)[-1]=0.6348\n",
+      "Condition not met at step 1062. No data to save.\n",
       "1062\n",
+      "model_vars.get('Gini', 0)[-1]=0.6534\n",
+      "Condition not met at step 1063. No data to save.\n",
       "1063\n",
+      "model_vars.get('Gini', 0)[-1]=0.65\n",
+      "Condition not met at step 1064. No data to save.\n",
       "1064\n",
+      "model_vars.get('Gini', 0)[-1]=0.6426000000000001\n",
+      "Condition not met at step 1065. No data to save.\n",
       "1065\n",
+      "model_vars.get('Gini', 0)[-1]=0.6242000000000001\n",
+      "Condition not met at step 1066. No data to save.\n",
       "1066\n",
+      "model_vars.get('Gini', 0)[-1]=0.6188\n",
+      "Condition not met at step 1067. No data to save.\n",
       "1067\n",
+      "model_vars.get('Gini', 0)[-1]=0.6068\n",
+      "Condition not met at step 1068. No data to save.\n",
       "1068\n",
+      "model_vars.get('Gini', 0)[-1]=0.6228\n",
+      "Condition not met at step 1069. No data to save.\n",
       "1069\n",
+      "model_vars.get('Gini', 0)[-1]=0.6204000000000001\n",
+      "Condition not met at step 1070. No data to save.\n",
       "1070\n",
+      "model_vars.get('Gini', 0)[-1]=0.6266\n",
+      "Condition not met at step 1071. No data to save.\n",
       "1071\n",
+      "model_vars.get('Gini', 0)[-1]=0.6437999999999999\n",
+      "Condition not met at step 1072. No data to save.\n",
       "1072\n",
+      "model_vars.get('Gini', 0)[-1]=0.6315999999999999\n",
+      "Condition not met at step 1073. No data to save.\n",
       "1073\n",
+      "model_vars.get('Gini', 0)[-1]=0.638\n",
+      "Condition not met at step 1074. No data to save.\n",
       "1074\n",
+      "model_vars.get('Gini', 0)[-1]=0.622\n",
+      "Condition not met at step 1075. No data to save.\n",
       "1075\n",
+      "model_vars.get('Gini', 0)[-1]=0.639\n",
+      "Condition not met at step 1076. No data to save.\n",
       "1076\n",
+      "model_vars.get('Gini', 0)[-1]=0.6634\n",
+      "Condition not met at step 1077. No data to save.\n",
       "1077\n",
+      "model_vars.get('Gini', 0)[-1]=0.658\n",
+      "Condition not met at step 1078. No data to save.\n",
       "1078\n",
+      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
+      "Condition not met at step 1079. No data to save.\n",
       "1079\n",
+      "model_vars.get('Gini', 0)[-1]=0.6378\n",
+      "Condition not met at step 1080. No data to save.\n",
       "1080\n",
+      "model_vars.get('Gini', 0)[-1]=0.6386000000000001\n",
+      "Condition not met at step 1081. No data to save.\n",
       "1081\n",
+      "model_vars.get('Gini', 0)[-1]=0.647\n",
+      "Condition not met at step 1082. No data to save.\n",
       "1082\n",
+      "model_vars.get('Gini', 0)[-1]=0.6662\n",
+      "Condition not met at step 1083. No data to save.\n",
       "1083\n",
+      "model_vars.get('Gini', 0)[-1]=0.6548\n",
+      "Condition not met at step 1084. No data to save.\n",
       "1084\n",
+      "model_vars.get('Gini', 0)[-1]=0.6502\n",
+      "Condition not met at step 1085. No data to save.\n",
       "1085\n",
+      "model_vars.get('Gini', 0)[-1]=0.6386000000000001\n",
+      "Condition not met at step 1086. No data to save.\n",
       "1086\n",
+      "model_vars.get('Gini', 0)[-1]=0.6528\n",
+      "Condition not met at step 1087. No data to save.\n",
       "1087\n",
+      "model_vars.get('Gini', 0)[-1]=0.6512\n",
+      "Condition not met at step 1088. No data to save.\n",
       "1088\n",
+      "model_vars.get('Gini', 0)[-1]=0.6594\n",
+      "Condition not met at step 1089. No data to save.\n",
       "1089\n",
+      "model_vars.get('Gini', 0)[-1]=0.6914\n",
+      "Condition not met at step 1090. No data to save.\n",
       "1090\n",
+      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
+      "Condition not met at step 1091. No data to save.\n",
       "1091\n",
+      "model_vars.get('Gini', 0)[-1]=0.6568\n",
+      "Condition not met at step 1092. No data to save.\n",
       "1092\n",
+      "model_vars.get('Gini', 0)[-1]=0.6602\n",
+      "Condition not met at step 1093. No data to save.\n",
       "1093\n",
+      "model_vars.get('Gini', 0)[-1]=0.6834\n",
+      "Condition not met at step 1094. No data to save.\n",
       "1094\n",
+      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
+      "Condition not met at step 1095. No data to save.\n",
       "1095\n",
+      "model_vars.get('Gini', 0)[-1]=0.6819999999999999\n",
+      "Condition not met at step 1096. No data to save.\n",
       "1096\n",
+      "model_vars.get('Gini', 0)[-1]=0.6684\n",
+      "Condition not met at step 1097. No data to save.\n",
       "1097\n",
+      "model_vars.get('Gini', 0)[-1]=0.6692\n",
+      "Condition not met at step 1098. No data to save.\n",
       "1098\n",
+      "model_vars.get('Gini', 0)[-1]=0.6672\n",
+      "Condition not met at step 1099. No data to save.\n",
       "1099\n",
       "CALLED\n",
       "self.model._steps=1100\n",
       " TEST self.model._steps=1100\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "1000  0.6886\n",
-      "1001  0.6966\n",
-      "1002  0.7010\n",
-      "1003  0.6996\n",
-      "1004  0.7024\n",
+      "1000  0.6574\n",
+      "1001  0.6482\n",
+      "1002  0.6254\n",
+      "1003  0.6316\n",
+      "1004  0.6486\n",
       "...      ...\n",
-      "1095  0.6654\n",
-      "1096  0.6440\n",
-      "1097  0.6194\n",
-      "1098  0.6104\n",
-      "1099  0.6164\n",
+      "1095  0.6820\n",
+      "1096  0.6684\n",
+      "1097  0.6692\n",
+      "1098  0.6672\n",
+      "1099  0.6878\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_011.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_011.parquet'\n",
-      "Saving model to output_dir/model_data_011.parquet\n",
-      "Saving agent to output_dir/agent_data_011.parquet\n",
+      "model_file='test_path/model_data_011.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_011.parquet'\n",
+      "Saving model to test_path/model_data_011.parquet\n",
+      "Saving agent to test_path/agent_data_011.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6878\n",
+      "Condition not met at step 1100. No data to save.\n",
       "1100\n",
+      "model_vars.get('Gini', 0)[-1]=0.6744\n",
+      "Condition not met at step 1101. No data to save.\n",
       "1101\n",
+      "model_vars.get('Gini', 0)[-1]=0.6722\n",
+      "Condition not met at step 1102. No data to save.\n",
       "1102\n",
+      "model_vars.get('Gini', 0)[-1]=0.6566000000000001\n",
+      "Condition not met at step 1103. No data to save.\n",
       "1103\n",
+      "model_vars.get('Gini', 0)[-1]=0.6402\n",
+      "Condition not met at step 1104. No data to save.\n",
       "1104\n",
+      "model_vars.get('Gini', 0)[-1]=0.6434\n",
+      "Condition not met at step 1105. No data to save.\n",
       "1105\n",
+      "model_vars.get('Gini', 0)[-1]=0.6126\n",
+      "Condition not met at step 1106. No data to save.\n",
       "1106\n",
+      "model_vars.get('Gini', 0)[-1]=0.608\n",
+      "Condition not met at step 1107. No data to save.\n",
       "1107\n",
+      "model_vars.get('Gini', 0)[-1]=0.6312\n",
+      "Condition not met at step 1108. No data to save.\n",
       "1108\n",
+      "model_vars.get('Gini', 0)[-1]=0.6536\n",
+      "Condition not met at step 1109. No data to save.\n",
       "1109\n",
+      "model_vars.get('Gini', 0)[-1]=0.6208\n",
+      "Condition not met at step 1110. No data to save.\n",
       "1110\n",
+      "model_vars.get('Gini', 0)[-1]=0.646\n",
+      "Condition not met at step 1111. No data to save.\n",
       "1111\n",
+      "model_vars.get('Gini', 0)[-1]=0.6534\n",
+      "Condition not met at step 1112. No data to save.\n",
       "1112\n",
+      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
+      "Condition not met at step 1113. No data to save.\n",
       "1113\n",
+      "model_vars.get('Gini', 0)[-1]=0.6544\n",
+      "Condition not met at step 1114. No data to save.\n",
       "1114\n",
+      "model_vars.get('Gini', 0)[-1]=0.6517999999999999\n",
+      "Condition not met at step 1115. No data to save.\n",
       "1115\n",
+      "model_vars.get('Gini', 0)[-1]=0.6774\n",
+      "Condition not met at step 1116. No data to save.\n",
       "1116\n",
+      "model_vars.get('Gini', 0)[-1]=0.6966\n",
+      "Condition not met at step 1117. No data to save.\n",
       "1117\n",
+      "model_vars.get('Gini', 0)[-1]=0.6898\n",
+      "Condition not met at step 1118. No data to save.\n",
       "1118\n",
+      "model_vars.get('Gini', 0)[-1]=0.6686000000000001\n",
+      "Condition not met at step 1119. No data to save.\n",
       "1119\n",
+      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
+      "Condition not met at step 1120. No data to save.\n",
       "1120\n",
+      "model_vars.get('Gini', 0)[-1]=0.6676\n",
+      "Condition not met at step 1121. No data to save.\n",
       "1121\n",
+      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
+      "Condition not met at step 1122. No data to save.\n",
       "1122\n",
+      "model_vars.get('Gini', 0)[-1]=0.665\n",
+      "Condition not met at step 1123. No data to save.\n",
       "1123\n",
+      "model_vars.get('Gini', 0)[-1]=0.6568\n",
+      "Condition not met at step 1124. No data to save.\n",
       "1124\n",
+      "model_vars.get('Gini', 0)[-1]=0.6616\n",
+      "Condition not met at step 1125. No data to save.\n",
       "1125\n",
+      "model_vars.get('Gini', 0)[-1]=0.6577999999999999\n",
+      "Condition not met at step 1126. No data to save.\n",
       "1126\n",
+      "model_vars.get('Gini', 0)[-1]=0.644\n",
+      "Condition not met at step 1127. No data to save.\n",
       "1127\n",
+      "model_vars.get('Gini', 0)[-1]=0.6604\n",
+      "Condition not met at step 1128. No data to save.\n",
       "1128\n",
+      "model_vars.get('Gini', 0)[-1]=0.6468\n",
+      "Condition not met at step 1129. No data to save.\n",
       "1129\n",
+      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
+      "Condition not met at step 1130. No data to save.\n",
       "1130\n",
+      "model_vars.get('Gini', 0)[-1]=0.6684\n",
+      "Condition not met at step 1131. No data to save.\n",
       "1131\n",
+      "model_vars.get('Gini', 0)[-1]=0.6414\n",
+      "Condition not met at step 1132. No data to save.\n",
       "1132\n",
+      "model_vars.get('Gini', 0)[-1]=0.6076\n",
+      "Condition not met at step 1133. No data to save.\n",
       "1133\n",
+      "model_vars.get('Gini', 0)[-1]=0.606\n",
+      "Condition not met at step 1134. No data to save.\n",
       "1134\n",
+      "model_vars.get('Gini', 0)[-1]=0.6234\n",
+      "Condition not met at step 1135. No data to save.\n",
       "1135\n",
+      "model_vars.get('Gini', 0)[-1]=0.6232\n",
+      "Condition not met at step 1136. No data to save.\n",
       "1136\n",
+      "model_vars.get('Gini', 0)[-1]=0.6326\n",
+      "Condition not met at step 1137. No data to save.\n",
       "1137\n",
+      "model_vars.get('Gini', 0)[-1]=0.627\n",
+      "Condition not met at step 1138. No data to save.\n",
       "1138\n",
+      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
+      "Condition not met at step 1139. No data to save.\n",
       "1139\n",
+      "model_vars.get('Gini', 0)[-1]=0.6642\n",
+      "Condition not met at step 1140. No data to save.\n",
       "1140\n",
+      "model_vars.get('Gini', 0)[-1]=0.654\n",
+      "Condition not met at step 1141. No data to save.\n",
       "1141\n",
+      "model_vars.get('Gini', 0)[-1]=0.6348\n",
+      "Condition not met at step 1142. No data to save.\n",
       "1142\n",
+      "model_vars.get('Gini', 0)[-1]=0.6292\n",
+      "Condition not met at step 1143. No data to save.\n",
       "1143\n",
+      "model_vars.get('Gini', 0)[-1]=0.6098\n",
+      "Condition not met at step 1144. No data to save.\n",
       "1144\n",
+      "model_vars.get('Gini', 0)[-1]=0.6166\n",
+      "Condition not met at step 1145. No data to save.\n",
       "1145\n",
+      "model_vars.get('Gini', 0)[-1]=0.6166\n",
+      "Condition not met at step 1146. No data to save.\n",
       "1146\n",
+      "model_vars.get('Gini', 0)[-1]=0.6066\n",
+      "Condition not met at step 1147. No data to save.\n",
       "1147\n",
+      "model_vars.get('Gini', 0)[-1]=0.6228\n",
+      "Condition not met at step 1148. No data to save.\n",
       "1148\n",
+      "model_vars.get('Gini', 0)[-1]=0.6204000000000001\n",
+      "Condition not met at step 1149. No data to save.\n",
       "1149\n",
+      "model_vars.get('Gini', 0)[-1]=0.6268\n",
+      "Condition not met at step 1150. No data to save.\n",
       "1150\n",
+      "model_vars.get('Gini', 0)[-1]=0.641\n",
+      "Condition not met at step 1151. No data to save.\n",
       "1151\n",
+      "model_vars.get('Gini', 0)[-1]=0.6652\n",
+      "Condition not met at step 1152. No data to save.\n",
       "1152\n",
+      "model_vars.get('Gini', 0)[-1]=0.6686000000000001\n",
+      "Condition not met at step 1153. No data to save.\n",
       "1153\n",
+      "model_vars.get('Gini', 0)[-1]=0.6738\n",
+      "Condition not met at step 1154. No data to save.\n",
       "1154\n",
+      "model_vars.get('Gini', 0)[-1]=0.6798\n",
+      "Condition not met at step 1155. No data to save.\n",
       "1155\n",
+      "model_vars.get('Gini', 0)[-1]=0.685\n",
+      "Condition not met at step 1156. No data to save.\n",
       "1156\n",
+      "model_vars.get('Gini', 0)[-1]=0.6826\n",
+      "Condition not met at step 1157. No data to save.\n",
       "1157\n",
+      "model_vars.get('Gini', 0)[-1]=0.6802\n",
+      "Condition not met at step 1158. No data to save.\n",
       "1158\n",
+      "model_vars.get('Gini', 0)[-1]=0.7026\n",
+      "Condition met. Appended special results for step 1159 to test_path/special_results.parquet\n",
       "1159\n",
+      "model_vars.get('Gini', 0)[-1]=0.6964\n",
+      "Condition not met at step 1160. No data to save.\n",
       "1160\n",
+      "model_vars.get('Gini', 0)[-1]=0.7001999999999999\n",
+      "Condition met. Appended special results for step 1161 to test_path/special_results.parquet\n",
       "1161\n",
+      "model_vars.get('Gini', 0)[-1]=0.6906\n",
+      "Condition not met at step 1162. No data to save.\n",
       "1162\n",
+      "model_vars.get('Gini', 0)[-1]=0.6854\n",
+      "Condition not met at step 1163. No data to save.\n",
       "1163\n",
+      "model_vars.get('Gini', 0)[-1]=0.6968000000000001\n",
+      "Condition not met at step 1164. No data to save.\n",
       "1164\n",
+      "model_vars.get('Gini', 0)[-1]=0.6842\n",
+      "Condition not met at step 1165. No data to save.\n",
       "1165\n",
+      "model_vars.get('Gini', 0)[-1]=0.6819999999999999\n",
+      "Condition not met at step 1166. No data to save.\n",
       "1166\n",
+      "model_vars.get('Gini', 0)[-1]=0.7068\n",
+      "Condition met. Appended special results for step 1167 to test_path/special_results.parquet\n",
       "1167\n",
+      "model_vars.get('Gini', 0)[-1]=0.6978\n",
+      "Condition not met at step 1168. No data to save.\n",
       "1168\n",
+      "model_vars.get('Gini', 0)[-1]=0.6718\n",
+      "Condition not met at step 1169. No data to save.\n",
       "1169\n",
+      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
+      "Condition not met at step 1170. No data to save.\n",
       "1170\n",
+      "model_vars.get('Gini', 0)[-1]=0.6638\n",
+      "Condition not met at step 1171. No data to save.\n",
       "1171\n",
+      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
+      "Condition not met at step 1172. No data to save.\n",
       "1172\n",
+      "model_vars.get('Gini', 0)[-1]=0.6824\n",
+      "Condition not met at step 1173. No data to save.\n",
       "1173\n",
+      "model_vars.get('Gini', 0)[-1]=0.6794\n",
+      "Condition not met at step 1174. No data to save.\n",
       "1174\n",
+      "model_vars.get('Gini', 0)[-1]=0.6828000000000001\n",
+      "Condition not met at step 1175. No data to save.\n",
       "1175\n",
+      "model_vars.get('Gini', 0)[-1]=0.6772\n",
+      "Condition not met at step 1176. No data to save.\n",
       "1176\n",
+      "model_vars.get('Gini', 0)[-1]=0.6796\n",
+      "Condition not met at step 1177. No data to save.\n",
       "1177\n",
+      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
+      "Condition not met at step 1178. No data to save.\n",
       "1178\n",
+      "model_vars.get('Gini', 0)[-1]=0.6662\n",
+      "Condition not met at step 1179. No data to save.\n",
       "1179\n",
+      "model_vars.get('Gini', 0)[-1]=0.6762\n",
+      "Condition not met at step 1180. No data to save.\n",
       "1180\n",
+      "model_vars.get('Gini', 0)[-1]=0.6906\n",
+      "Condition not met at step 1181. No data to save.\n",
       "1181\n",
+      "model_vars.get('Gini', 0)[-1]=0.6702\n",
+      "Condition not met at step 1182. No data to save.\n",
       "1182\n",
+      "model_vars.get('Gini', 0)[-1]=0.669\n",
+      "Condition not met at step 1183. No data to save.\n",
       "1183\n",
+      "model_vars.get('Gini', 0)[-1]=0.6374\n",
+      "Condition not met at step 1184. No data to save.\n",
       "1184\n",
+      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
+      "Condition not met at step 1185. No data to save.\n",
       "1185\n",
+      "model_vars.get('Gini', 0)[-1]=0.6778\n",
+      "Condition not met at step 1186. No data to save.\n",
       "1186\n",
+      "model_vars.get('Gini', 0)[-1]=0.6826\n",
+      "Condition not met at step 1187. No data to save.\n",
       "1187\n",
+      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
+      "Condition not met at step 1188. No data to save.\n",
       "1188\n",
+      "model_vars.get('Gini', 0)[-1]=0.6738\n",
+      "Condition not met at step 1189. No data to save.\n",
       "1189\n",
+      "model_vars.get('Gini', 0)[-1]=0.6759999999999999\n",
+      "Condition not met at step 1190. No data to save.\n",
       "1190\n",
+      "model_vars.get('Gini', 0)[-1]=0.6862\n",
+      "Condition not met at step 1191. No data to save.\n",
       "1191\n",
+      "model_vars.get('Gini', 0)[-1]=0.6921999999999999\n",
+      "Condition not met at step 1192. No data to save.\n",
       "1192\n",
+      "model_vars.get('Gini', 0)[-1]=0.7166\n",
+      "Condition met. Appended special results for step 1193 to test_path/special_results.parquet\n",
       "1193\n",
+      "model_vars.get('Gini', 0)[-1]=0.6864\n",
+      "Condition not met at step 1194. No data to save.\n",
       "1194\n",
+      "model_vars.get('Gini', 0)[-1]=0.6646000000000001\n",
+      "Condition not met at step 1195. No data to save.\n",
       "1195\n",
+      "model_vars.get('Gini', 0)[-1]=0.6562\n",
+      "Condition not met at step 1196. No data to save.\n",
       "1196\n",
+      "model_vars.get('Gini', 0)[-1]=0.6602\n",
+      "Condition not met at step 1197. No data to save.\n",
       "1197\n",
+      "model_vars.get('Gini', 0)[-1]=0.642\n",
+      "Condition not met at step 1198. No data to save.\n",
       "1198\n",
+      "model_vars.get('Gini', 0)[-1]=0.6554\n",
+      "Condition not met at step 1199. No data to save.\n",
       "1199\n",
       "CALLED\n",
       "self.model._steps=1200\n",
       " TEST self.model._steps=1200\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "1100  0.6172\n",
-      "1101  0.6234\n",
-      "1102  0.6470\n",
-      "1103  0.6720\n",
-      "1104  0.6834\n",
+      "1100  0.6744\n",
+      "1101  0.6722\n",
+      "1102  0.6566\n",
+      "1103  0.6402\n",
+      "1104  0.6434\n",
       "...      ...\n",
-      "1195  0.7018\n",
-      "1196  0.7106\n",
-      "1197  0.7160\n",
-      "1198  0.7260\n",
-      "1199  0.7386\n",
+      "1195  0.6562\n",
+      "1196  0.6602\n",
+      "1197  0.6420\n",
+      "1198  0.6554\n",
+      "1199  0.6302\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_012.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_012.parquet'\n",
-      "Saving model to output_dir/model_data_012.parquet\n",
-      "Saving agent to output_dir/agent_data_012.parquet\n",
+      "model_file='test_path/model_data_012.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_012.parquet'\n",
+      "Saving model to test_path/model_data_012.parquet\n",
+      "Saving agent to test_path/agent_data_012.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6302\n",
+      "Condition not met at step 1200. No data to save.\n",
       "1200\n",
+      "model_vars.get('Gini', 0)[-1]=0.6602\n",
+      "Condition not met at step 1201. No data to save.\n",
       "1201\n",
+      "model_vars.get('Gini', 0)[-1]=0.6497999999999999\n",
+      "Condition not met at step 1202. No data to save.\n",
       "1202\n",
+      "model_vars.get('Gini', 0)[-1]=0.645\n",
+      "Condition not met at step 1203. No data to save.\n",
       "1203\n",
+      "model_vars.get('Gini', 0)[-1]=0.6416\n",
+      "Condition not met at step 1204. No data to save.\n",
       "1204\n",
+      "model_vars.get('Gini', 0)[-1]=0.6194\n",
+      "Condition not met at step 1205. No data to save.\n",
       "1205\n",
+      "model_vars.get('Gini', 0)[-1]=0.666\n",
+      "Condition not met at step 1206. No data to save.\n",
       "1206\n",
+      "model_vars.get('Gini', 0)[-1]=0.6542\n",
+      "Condition not met at step 1207. No data to save.\n",
       "1207\n",
+      "model_vars.get('Gini', 0)[-1]=0.6446000000000001\n",
+      "Condition not met at step 1208. No data to save.\n",
       "1208\n",
+      "model_vars.get('Gini', 0)[-1]=0.6412\n",
+      "Condition not met at step 1209. No data to save.\n",
       "1209\n",
+      "model_vars.get('Gini', 0)[-1]=0.639\n",
+      "Condition not met at step 1210. No data to save.\n",
       "1210\n",
+      "model_vars.get('Gini', 0)[-1]=0.6414\n",
+      "Condition not met at step 1211. No data to save.\n",
       "1211\n",
+      "model_vars.get('Gini', 0)[-1]=0.6536\n",
+      "Condition not met at step 1212. No data to save.\n",
       "1212\n",
+      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
+      "Condition not met at step 1213. No data to save.\n",
       "1213\n",
+      "model_vars.get('Gini', 0)[-1]=0.6242000000000001\n",
+      "Condition not met at step 1214. No data to save.\n",
       "1214\n",
+      "model_vars.get('Gini', 0)[-1]=0.6098\n",
+      "Condition not met at step 1215. No data to save.\n",
       "1215\n",
+      "model_vars.get('Gini', 0)[-1]=0.6242000000000001\n",
+      "Condition not met at step 1216. No data to save.\n",
       "1216\n",
+      "model_vars.get('Gini', 0)[-1]=0.6266\n",
+      "Condition not met at step 1217. No data to save.\n",
       "1217\n",
+      "model_vars.get('Gini', 0)[-1]=0.6315999999999999\n",
+      "Condition not met at step 1218. No data to save.\n",
       "1218\n",
+      "model_vars.get('Gini', 0)[-1]=0.6258\n",
+      "Condition not met at step 1219. No data to save.\n",
       "1219\n",
+      "model_vars.get('Gini', 0)[-1]=0.6228\n",
+      "Condition not met at step 1220. No data to save.\n",
       "1220\n",
+      "model_vars.get('Gini', 0)[-1]=0.6124\n",
+      "Condition not met at step 1221. No data to save.\n",
       "1221\n",
+      "model_vars.get('Gini', 0)[-1]=0.657\n",
+      "Condition not met at step 1222. No data to save.\n",
       "1222\n",
+      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
+      "Condition not met at step 1223. No data to save.\n",
       "1223\n",
+      "model_vars.get('Gini', 0)[-1]=0.6722\n",
+      "Condition not met at step 1224. No data to save.\n",
       "1224\n",
+      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
+      "Condition not met at step 1225. No data to save.\n",
       "1225\n",
+      "model_vars.get('Gini', 0)[-1]=0.6456\n",
+      "Condition not met at step 1226. No data to save.\n",
       "1226\n",
+      "model_vars.get('Gini', 0)[-1]=0.625\n",
+      "Condition not met at step 1227. No data to save.\n",
       "1227\n",
+      "model_vars.get('Gini', 0)[-1]=0.6116\n",
+      "Condition not met at step 1228. No data to save.\n",
       "1228\n",
+      "model_vars.get('Gini', 0)[-1]=0.6154\n",
+      "Condition not met at step 1229. No data to save.\n",
       "1229\n",
+      "model_vars.get('Gini', 0)[-1]=0.624\n",
+      "Condition not met at step 1230. No data to save.\n",
       "1230\n",
+      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
+      "Condition not met at step 1231. No data to save.\n",
       "1231\n",
+      "model_vars.get('Gini', 0)[-1]=0.6392\n",
+      "Condition not met at step 1232. No data to save.\n",
       "1232\n",
+      "model_vars.get('Gini', 0)[-1]=0.6398\n",
+      "Condition not met at step 1233. No data to save.\n",
       "1233\n",
+      "model_vars.get('Gini', 0)[-1]=0.658\n",
+      "Condition not met at step 1234. No data to save.\n",
       "1234\n",
+      "model_vars.get('Gini', 0)[-1]=0.6352\n",
+      "Condition not met at step 1235. No data to save.\n",
       "1235\n",
+      "model_vars.get('Gini', 0)[-1]=0.656\n",
+      "Condition not met at step 1236. No data to save.\n",
       "1236\n",
+      "model_vars.get('Gini', 0)[-1]=0.6572\n",
+      "Condition not met at step 1237. No data to save.\n",
       "1237\n",
+      "model_vars.get('Gini', 0)[-1]=0.6472\n",
+      "Condition not met at step 1238. No data to save.\n",
       "1238\n",
+      "model_vars.get('Gini', 0)[-1]=0.6318\n",
+      "Condition not met at step 1239. No data to save.\n",
       "1239\n",
+      "model_vars.get('Gini', 0)[-1]=0.6374\n",
+      "Condition not met at step 1240. No data to save.\n",
       "1240\n",
+      "model_vars.get('Gini', 0)[-1]=0.65\n",
+      "Condition not met at step 1241. No data to save.\n",
       "1241\n",
+      "model_vars.get('Gini', 0)[-1]=0.6464000000000001\n",
+      "Condition not met at step 1242. No data to save.\n",
       "1242\n",
+      "model_vars.get('Gini', 0)[-1]=0.6616\n",
+      "Condition not met at step 1243. No data to save.\n",
       "1243\n",
+      "model_vars.get('Gini', 0)[-1]=0.637\n",
+      "Condition not met at step 1244. No data to save.\n",
       "1244\n",
+      "model_vars.get('Gini', 0)[-1]=0.6536\n",
+      "Condition not met at step 1245. No data to save.\n",
       "1245\n",
+      "model_vars.get('Gini', 0)[-1]=0.647\n",
+      "Condition not met at step 1246. No data to save.\n",
       "1246\n",
+      "model_vars.get('Gini', 0)[-1]=0.647\n",
+      "Condition not met at step 1247. No data to save.\n",
       "1247\n",
+      "model_vars.get('Gini', 0)[-1]=0.6437999999999999\n",
+      "Condition not met at step 1248. No data to save.\n",
       "1248\n",
+      "model_vars.get('Gini', 0)[-1]=0.6537999999999999\n",
+      "Condition not met at step 1249. No data to save.\n",
       "1249\n",
+      "model_vars.get('Gini', 0)[-1]=0.6537999999999999\n",
+      "Condition not met at step 1250. No data to save.\n",
       "1250\n",
+      "model_vars.get('Gini', 0)[-1]=0.6544\n",
+      "Condition not met at step 1251. No data to save.\n",
       "1251\n",
+      "model_vars.get('Gini', 0)[-1]=0.6798\n",
+      "Condition not met at step 1252. No data to save.\n",
       "1252\n",
+      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
+      "Condition not met at step 1253. No data to save.\n",
       "1253\n",
+      "model_vars.get('Gini', 0)[-1]=0.6672\n",
+      "Condition not met at step 1254. No data to save.\n",
       "1254\n",
+      "model_vars.get('Gini', 0)[-1]=0.692\n",
+      "Condition not met at step 1255. No data to save.\n",
       "1255\n",
+      "model_vars.get('Gini', 0)[-1]=0.6984\n",
+      "Condition not met at step 1256. No data to save.\n",
       "1256\n",
+      "model_vars.get('Gini', 0)[-1]=0.6874\n",
+      "Condition not met at step 1257. No data to save.\n",
       "1257\n",
+      "model_vars.get('Gini', 0)[-1]=0.7146\n",
+      "Condition met. Appended special results for step 1258 to test_path/special_results.parquet\n",
       "1258\n",
+      "model_vars.get('Gini', 0)[-1]=0.7041999999999999\n",
+      "Condition met. Appended special results for step 1259 to test_path/special_results.parquet\n",
       "1259\n",
+      "model_vars.get('Gini', 0)[-1]=0.6888000000000001\n",
+      "Condition not met at step 1260. No data to save.\n",
       "1260\n",
+      "model_vars.get('Gini', 0)[-1]=0.658\n",
+      "Condition not met at step 1261. No data to save.\n",
       "1261\n",
+      "model_vars.get('Gini', 0)[-1]=0.6422\n",
+      "Condition not met at step 1262. No data to save.\n",
       "1262\n",
+      "model_vars.get('Gini', 0)[-1]=0.6582\n",
+      "Condition not met at step 1263. No data to save.\n",
       "1263\n",
+      "model_vars.get('Gini', 0)[-1]=0.6442\n",
+      "Condition not met at step 1264. No data to save.\n",
       "1264\n",
+      "model_vars.get('Gini', 0)[-1]=0.6552\n",
+      "Condition not met at step 1265. No data to save.\n",
       "1265\n",
+      "model_vars.get('Gini', 0)[-1]=0.6588\n",
+      "Condition not met at step 1266. No data to save.\n",
       "1266\n",
+      "model_vars.get('Gini', 0)[-1]=0.658\n",
+      "Condition not met at step 1267. No data to save.\n",
       "1267\n",
+      "model_vars.get('Gini', 0)[-1]=0.6232\n",
+      "Condition not met at step 1268. No data to save.\n",
       "1268\n",
+      "model_vars.get('Gini', 0)[-1]=0.633\n",
+      "Condition not met at step 1269. No data to save.\n",
       "1269\n",
+      "model_vars.get('Gini', 0)[-1]=0.6246\n",
+      "Condition not met at step 1270. No data to save.\n",
       "1270\n",
+      "model_vars.get('Gini', 0)[-1]=0.635\n",
+      "Condition not met at step 1271. No data to save.\n",
       "1271\n",
+      "model_vars.get('Gini', 0)[-1]=0.6508\n",
+      "Condition not met at step 1272. No data to save.\n",
       "1272\n",
+      "model_vars.get('Gini', 0)[-1]=0.6258\n",
+      "Condition not met at step 1273. No data to save.\n",
       "1273\n",
+      "model_vars.get('Gini', 0)[-1]=0.6308\n",
+      "Condition not met at step 1274. No data to save.\n",
       "1274\n",
+      "model_vars.get('Gini', 0)[-1]=0.6278\n",
+      "Condition not met at step 1275. No data to save.\n",
       "1275\n",
+      "model_vars.get('Gini', 0)[-1]=0.6264000000000001\n",
+      "Condition not met at step 1276. No data to save.\n",
       "1276\n",
+      "model_vars.get('Gini', 0)[-1]=0.6232\n",
+      "Condition not met at step 1277. No data to save.\n",
       "1277\n",
+      "model_vars.get('Gini', 0)[-1]=0.6704\n",
+      "Condition not met at step 1278. No data to save.\n",
       "1278\n",
+      "model_vars.get('Gini', 0)[-1]=0.6542\n",
+      "Condition not met at step 1279. No data to save.\n",
       "1279\n",
+      "model_vars.get('Gini', 0)[-1]=0.6508\n",
+      "Condition not met at step 1280. No data to save.\n",
       "1280\n",
+      "model_vars.get('Gini', 0)[-1]=0.6328\n",
+      "Condition not met at step 1281. No data to save.\n",
       "1281\n",
+      "model_vars.get('Gini', 0)[-1]=0.604\n",
+      "Condition not met at step 1282. No data to save.\n",
       "1282\n",
+      "model_vars.get('Gini', 0)[-1]=0.5860000000000001\n",
+      "Condition not met at step 1283. No data to save.\n",
       "1283\n",
+      "model_vars.get('Gini', 0)[-1]=0.6142000000000001\n",
+      "Condition not met at step 1284. No data to save.\n",
       "1284\n",
+      "model_vars.get('Gini', 0)[-1]=0.5940000000000001\n",
+      "Condition not met at step 1285. No data to save.\n",
       "1285\n",
+      "model_vars.get('Gini', 0)[-1]=0.6122000000000001\n",
+      "Condition not met at step 1286. No data to save.\n",
       "1286\n",
+      "model_vars.get('Gini', 0)[-1]=0.6116\n",
+      "Condition not met at step 1287. No data to save.\n",
       "1287\n",
+      "model_vars.get('Gini', 0)[-1]=0.6116\n",
+      "Condition not met at step 1288. No data to save.\n",
       "1288\n",
+      "model_vars.get('Gini', 0)[-1]=0.6116\n",
+      "Condition not met at step 1289. No data to save.\n",
       "1289\n",
+      "model_vars.get('Gini', 0)[-1]=0.6234\n",
+      "Condition not met at step 1290. No data to save.\n",
       "1290\n",
+      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
+      "Condition not met at step 1291. No data to save.\n",
       "1291\n",
+      "model_vars.get('Gini', 0)[-1]=0.6834\n",
+      "Condition not met at step 1292. No data to save.\n",
       "1292\n",
+      "model_vars.get('Gini', 0)[-1]=0.6752\n",
+      "Condition not met at step 1293. No data to save.\n",
       "1293\n",
+      "model_vars.get('Gini', 0)[-1]=0.6516\n",
+      "Condition not met at step 1294. No data to save.\n",
       "1294\n",
+      "model_vars.get('Gini', 0)[-1]=0.6616\n",
+      "Condition not met at step 1295. No data to save.\n",
       "1295\n",
+      "model_vars.get('Gini', 0)[-1]=0.6175999999999999\n",
+      "Condition not met at step 1296. No data to save.\n",
       "1296\n",
+      "model_vars.get('Gini', 0)[-1]=0.6446000000000001\n",
+      "Condition not met at step 1297. No data to save.\n",
       "1297\n",
+      "model_vars.get('Gini', 0)[-1]=0.6462\n",
+      "Condition not met at step 1298. No data to save.\n",
       "1298\n",
+      "model_vars.get('Gini', 0)[-1]=0.6282000000000001\n",
+      "Condition not met at step 1299. No data to save.\n",
       "1299\n",
       "CALLED\n",
       "self.model._steps=1300\n",
       " TEST self.model._steps=1300\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "1200  0.7374\n",
-      "1201  0.7374\n",
-      "1202  0.7254\n",
-      "1203  0.7120\n",
-      "1204  0.7244\n",
+      "1200  0.6602\n",
+      "1201  0.6498\n",
+      "1202  0.6450\n",
+      "1203  0.6416\n",
+      "1204  0.6194\n",
       "...      ...\n",
-      "1295  0.7200\n",
-      "1296  0.7142\n",
-      "1297  0.7236\n",
-      "1298  0.7348\n",
-      "1299  0.7336\n",
+      "1295  0.6176\n",
+      "1296  0.6446\n",
+      "1297  0.6462\n",
+      "1298  0.6282\n",
+      "1299  0.6144\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_013.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_013.parquet'\n",
-      "Saving model to output_dir/model_data_013.parquet\n",
-      "Saving agent to output_dir/agent_data_013.parquet\n",
+      "model_file='test_path/model_data_013.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_013.parquet'\n",
+      "Saving model to test_path/model_data_013.parquet\n",
+      "Saving agent to test_path/agent_data_013.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6144000000000001\n",
+      "Condition not met at step 1300. No data to save.\n",
       "1300\n",
+      "model_vars.get('Gini', 0)[-1]=0.5794\n",
+      "Condition not met at step 1301. No data to save.\n",
       "1301\n",
+      "model_vars.get('Gini', 0)[-1]=0.5938\n",
+      "Condition not met at step 1302. No data to save.\n",
       "1302\n",
+      "model_vars.get('Gini', 0)[-1]=0.6288\n",
+      "Condition not met at step 1303. No data to save.\n",
       "1303\n",
+      "model_vars.get('Gini', 0)[-1]=0.6124\n",
+      "Condition not met at step 1304. No data to save.\n",
       "1304\n",
+      "model_vars.get('Gini', 0)[-1]=0.6046\n",
+      "Condition not met at step 1305. No data to save.\n",
       "1305\n",
+      "model_vars.get('Gini', 0)[-1]=0.6224000000000001\n",
+      "Condition not met at step 1306. No data to save.\n",
       "1306\n",
+      "model_vars.get('Gini', 0)[-1]=0.6072\n",
+      "Condition not met at step 1307. No data to save.\n",
       "1307\n",
+      "model_vars.get('Gini', 0)[-1]=0.6264000000000001\n",
+      "Condition not met at step 1308. No data to save.\n",
       "1308\n",
+      "model_vars.get('Gini', 0)[-1]=0.6178\n",
+      "Condition not met at step 1309. No data to save.\n",
       "1309\n",
+      "model_vars.get('Gini', 0)[-1]=0.6332\n",
+      "Condition not met at step 1310. No data to save.\n",
       "1310\n",
+      "model_vars.get('Gini', 0)[-1]=0.6362\n",
+      "Condition not met at step 1311. No data to save.\n",
       "1311\n",
+      "model_vars.get('Gini', 0)[-1]=0.6315999999999999\n",
+      "Condition not met at step 1312. No data to save.\n",
       "1312\n",
+      "model_vars.get('Gini', 0)[-1]=0.644\n",
+      "Condition not met at step 1313. No data to save.\n",
       "1313\n",
+      "model_vars.get('Gini', 0)[-1]=0.6168\n",
+      "Condition not met at step 1314. No data to save.\n",
       "1314\n",
+      "model_vars.get('Gini', 0)[-1]=0.6272\n",
+      "Condition not met at step 1315. No data to save.\n",
       "1315\n",
+      "model_vars.get('Gini', 0)[-1]=0.6184000000000001\n",
+      "Condition not met at step 1316. No data to save.\n",
       "1316\n",
+      "model_vars.get('Gini', 0)[-1]=0.625\n",
+      "Condition not met at step 1317. No data to save.\n",
       "1317\n",
+      "model_vars.get('Gini', 0)[-1]=0.6268\n",
+      "Condition not met at step 1318. No data to save.\n",
       "1318\n",
+      "model_vars.get('Gini', 0)[-1]=0.6338\n",
+      "Condition not met at step 1319. No data to save.\n",
       "1319\n",
+      "model_vars.get('Gini', 0)[-1]=0.6304000000000001\n",
+      "Condition not met at step 1320. No data to save.\n",
       "1320\n",
+      "model_vars.get('Gini', 0)[-1]=0.618\n",
+      "Condition not met at step 1321. No data to save.\n",
       "1321\n",
+      "model_vars.get('Gini', 0)[-1]=0.6532\n",
+      "Condition not met at step 1322. No data to save.\n",
       "1322\n",
+      "model_vars.get('Gini', 0)[-1]=0.6532\n",
+      "Condition not met at step 1323. No data to save.\n",
       "1323\n",
+      "model_vars.get('Gini', 0)[-1]=0.6638\n",
+      "Condition not met at step 1324. No data to save.\n",
       "1324\n",
+      "model_vars.get('Gini', 0)[-1]=0.6908000000000001\n",
+      "Condition not met at step 1325. No data to save.\n",
       "1325\n",
+      "model_vars.get('Gini', 0)[-1]=0.7034\n",
+      "Condition met. Appended special results for step 1326 to test_path/special_results.parquet\n",
       "1326\n",
+      "model_vars.get('Gini', 0)[-1]=0.7008000000000001\n",
+      "Condition met. Appended special results for step 1327 to test_path/special_results.parquet\n",
       "1327\n",
+      "model_vars.get('Gini', 0)[-1]=0.6802\n",
+      "Condition not met at step 1328. No data to save.\n",
       "1328\n",
+      "model_vars.get('Gini', 0)[-1]=0.6796\n",
+      "Condition not met at step 1329. No data to save.\n",
       "1329\n",
+      "model_vars.get('Gini', 0)[-1]=0.6974\n",
+      "Condition not met at step 1330. No data to save.\n",
       "1330\n",
+      "model_vars.get('Gini', 0)[-1]=0.6928000000000001\n",
+      "Condition not met at step 1331. No data to save.\n",
       "1331\n",
+      "model_vars.get('Gini', 0)[-1]=0.6844\n",
+      "Condition not met at step 1332. No data to save.\n",
       "1332\n",
+      "model_vars.get('Gini', 0)[-1]=0.6822\n",
+      "Condition not met at step 1333. No data to save.\n",
       "1333\n",
+      "model_vars.get('Gini', 0)[-1]=0.696\n",
+      "Condition not met at step 1334. No data to save.\n",
       "1334\n",
+      "model_vars.get('Gini', 0)[-1]=0.7034\n",
+      "Condition met. Appended special results for step 1335 to test_path/special_results.parquet\n",
       "1335\n",
+      "model_vars.get('Gini', 0)[-1]=0.673\n",
+      "Condition not met at step 1336. No data to save.\n",
       "1336\n",
+      "model_vars.get('Gini', 0)[-1]=0.657\n",
+      "Condition not met at step 1337. No data to save.\n",
       "1337\n",
+      "model_vars.get('Gini', 0)[-1]=0.6882\n",
+      "Condition not met at step 1338. No data to save.\n",
       "1338\n",
+      "model_vars.get('Gini', 0)[-1]=0.6930000000000001\n",
+      "Condition not met at step 1339. No data to save.\n",
       "1339\n",
+      "model_vars.get('Gini', 0)[-1]=0.6842\n",
+      "Condition not met at step 1340. No data to save.\n",
       "1340\n",
+      "model_vars.get('Gini', 0)[-1]=0.681\n",
+      "Condition not met at step 1341. No data to save.\n",
       "1341\n",
+      "model_vars.get('Gini', 0)[-1]=0.7061999999999999\n",
+      "Condition met. Appended special results for step 1342 to test_path/special_results.parquet\n",
       "1342\n",
+      "model_vars.get('Gini', 0)[-1]=0.7112\n",
+      "Condition met. Appended special results for step 1343 to test_path/special_results.parquet\n",
       "1343\n",
+      "model_vars.get('Gini', 0)[-1]=0.6936\n",
+      "Condition not met at step 1344. No data to save.\n",
       "1344\n",
+      "model_vars.get('Gini', 0)[-1]=0.6544\n",
+      "Condition not met at step 1345. No data to save.\n",
       "1345\n",
+      "model_vars.get('Gini', 0)[-1]=0.673\n",
+      "Condition not met at step 1346. No data to save.\n",
       "1346\n",
+      "model_vars.get('Gini', 0)[-1]=0.6972\n",
+      "Condition not met at step 1347. No data to save.\n",
       "1347\n",
+      "model_vars.get('Gini', 0)[-1]=0.6756\n",
+      "Condition not met at step 1348. No data to save.\n",
       "1348\n",
+      "model_vars.get('Gini', 0)[-1]=0.6638\n",
+      "Condition not met at step 1349. No data to save.\n",
       "1349\n",
+      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
+      "Condition not met at step 1350. No data to save.\n",
       "1350\n",
+      "model_vars.get('Gini', 0)[-1]=0.6548\n",
+      "Condition not met at step 1351. No data to save.\n",
       "1351\n",
+      "model_vars.get('Gini', 0)[-1]=0.65\n",
+      "Condition not met at step 1352. No data to save.\n",
       "1352\n",
+      "model_vars.get('Gini', 0)[-1]=0.6632\n",
+      "Condition not met at step 1353. No data to save.\n",
       "1353\n",
+      "model_vars.get('Gini', 0)[-1]=0.6612\n",
+      "Condition not met at step 1354. No data to save.\n",
       "1354\n",
+      "model_vars.get('Gini', 0)[-1]=0.6592\n",
+      "Condition not met at step 1355. No data to save.\n",
       "1355\n",
+      "model_vars.get('Gini', 0)[-1]=0.6806\n",
+      "Condition not met at step 1356. No data to save.\n",
       "1356\n",
+      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
+      "Condition not met at step 1357. No data to save.\n",
       "1357\n",
+      "model_vars.get('Gini', 0)[-1]=0.6712\n",
+      "Condition not met at step 1358. No data to save.\n",
       "1358\n",
+      "model_vars.get('Gini', 0)[-1]=0.6662\n",
+      "Condition not met at step 1359. No data to save.\n",
       "1359\n",
+      "model_vars.get('Gini', 0)[-1]=0.6878\n",
+      "Condition not met at step 1360. No data to save.\n",
       "1360\n",
+      "model_vars.get('Gini', 0)[-1]=0.6786000000000001\n",
+      "Condition not met at step 1361. No data to save.\n",
       "1361\n",
+      "model_vars.get('Gini', 0)[-1]=0.6764\n",
+      "Condition not met at step 1362. No data to save.\n",
       "1362\n",
+      "model_vars.get('Gini', 0)[-1]=0.685\n",
+      "Condition not met at step 1363. No data to save.\n",
       "1363\n",
+      "model_vars.get('Gini', 0)[-1]=0.69\n",
+      "Condition not met at step 1364. No data to save.\n",
       "1364\n",
+      "model_vars.get('Gini', 0)[-1]=0.6756\n",
+      "Condition not met at step 1365. No data to save.\n",
       "1365\n",
+      "model_vars.get('Gini', 0)[-1]=0.6786000000000001\n",
+      "Condition not met at step 1366. No data to save.\n",
       "1366\n",
+      "model_vars.get('Gini', 0)[-1]=0.6702\n",
+      "Condition not met at step 1367. No data to save.\n",
       "1367\n",
+      "model_vars.get('Gini', 0)[-1]=0.6936\n",
+      "Condition not met at step 1368. No data to save.\n",
       "1368\n",
+      "model_vars.get('Gini', 0)[-1]=0.6902\n",
+      "Condition not met at step 1369. No data to save.\n",
       "1369\n",
+      "model_vars.get('Gini', 0)[-1]=0.6908000000000001\n",
+      "Condition not met at step 1370. No data to save.\n",
       "1370\n",
+      "model_vars.get('Gini', 0)[-1]=0.6954\n",
+      "Condition not met at step 1371. No data to save.\n",
       "1371\n",
+      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
+      "Condition not met at step 1372. No data to save.\n",
       "1372\n",
+      "model_vars.get('Gini', 0)[-1]=0.6772\n",
+      "Condition not met at step 1373. No data to save.\n",
       "1373\n",
+      "model_vars.get('Gini', 0)[-1]=0.6836\n",
+      "Condition not met at step 1374. No data to save.\n",
       "1374\n",
+      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
+      "Condition not met at step 1375. No data to save.\n",
       "1375\n",
+      "model_vars.get('Gini', 0)[-1]=0.681\n",
+      "Condition not met at step 1376. No data to save.\n",
       "1376\n",
+      "model_vars.get('Gini', 0)[-1]=0.6888000000000001\n",
+      "Condition not met at step 1377. No data to save.\n",
       "1377\n",
+      "model_vars.get('Gini', 0)[-1]=0.6906\n",
+      "Condition not met at step 1378. No data to save.\n",
       "1378\n",
+      "model_vars.get('Gini', 0)[-1]=0.7372000000000001\n",
+      "Condition met. Appended special results for step 1379 to test_path/special_results.parquet\n",
       "1379\n",
+      "model_vars.get('Gini', 0)[-1]=0.7106\n",
+      "Condition met. Appended special results for step 1380 to test_path/special_results.parquet\n",
       "1380\n",
+      "model_vars.get('Gini', 0)[-1]=0.7096\n",
+      "Condition met. Appended special results for step 1381 to test_path/special_results.parquet\n",
       "1381\n",
+      "model_vars.get('Gini', 0)[-1]=0.685\n",
+      "Condition not met at step 1382. No data to save.\n",
       "1382\n",
+      "model_vars.get('Gini', 0)[-1]=0.6634\n",
+      "Condition not met at step 1383. No data to save.\n",
       "1383\n",
+      "model_vars.get('Gini', 0)[-1]=0.6334\n",
+      "Condition not met at step 1384. No data to save.\n",
       "1384\n",
+      "model_vars.get('Gini', 0)[-1]=0.6624\n",
+      "Condition not met at step 1385. No data to save.\n",
       "1385\n",
+      "model_vars.get('Gini', 0)[-1]=0.6612\n",
+      "Condition not met at step 1386. No data to save.\n",
       "1386\n",
+      "model_vars.get('Gini', 0)[-1]=0.6886\n",
+      "Condition not met at step 1387. No data to save.\n",
       "1387\n",
+      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
+      "Condition not met at step 1388. No data to save.\n",
       "1388\n",
+      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
+      "Condition not met at step 1389. No data to save.\n",
       "1389\n",
+      "model_vars.get('Gini', 0)[-1]=0.6592\n",
+      "Condition not met at step 1390. No data to save.\n",
       "1390\n",
+      "model_vars.get('Gini', 0)[-1]=0.6604\n",
+      "Condition not met at step 1391. No data to save.\n",
       "1391\n",
+      "model_vars.get('Gini', 0)[-1]=0.6798\n",
+      "Condition not met at step 1392. No data to save.\n",
       "1392\n",
+      "model_vars.get('Gini', 0)[-1]=0.6682\n",
+      "Condition not met at step 1393. No data to save.\n",
       "1393\n",
+      "model_vars.get('Gini', 0)[-1]=0.645\n",
+      "Condition not met at step 1394. No data to save.\n",
       "1394\n",
+      "model_vars.get('Gini', 0)[-1]=0.6268\n",
+      "Condition not met at step 1395. No data to save.\n",
       "1395\n",
+      "model_vars.get('Gini', 0)[-1]=0.6232\n",
+      "Condition not met at step 1396. No data to save.\n",
       "1396\n",
+      "model_vars.get('Gini', 0)[-1]=0.607\n",
+      "Condition not met at step 1397. No data to save.\n",
       "1397\n",
+      "model_vars.get('Gini', 0)[-1]=0.6194\n",
+      "Condition not met at step 1398. No data to save.\n",
       "1398\n",
+      "model_vars.get('Gini', 0)[-1]=0.6392\n",
+      "Condition not met at step 1399. No data to save.\n",
       "1399\n",
       "CALLED\n",
       "self.model._steps=1400\n",
       " TEST self.model._steps=1400\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "1300  0.7340\n",
-      "1301  0.7028\n",
-      "1302  0.7090\n",
-      "1303  0.7110\n",
-      "1304  0.7042\n",
+      "1300  0.5794\n",
+      "1301  0.5938\n",
+      "1302  0.6288\n",
+      "1303  0.6124\n",
+      "1304  0.6046\n",
       "...      ...\n",
-      "1395  0.6168\n",
-      "1396  0.6126\n",
-      "1397  0.6136\n",
-      "1398  0.6016\n",
-      "1399  0.6364\n",
+      "1395  0.6232\n",
+      "1396  0.6070\n",
+      "1397  0.6194\n",
+      "1398  0.6392\n",
+      "1399  0.6406\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_014.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_014.parquet'\n",
-      "Saving model to output_dir/model_data_014.parquet\n",
-      "Saving agent to output_dir/agent_data_014.parquet\n",
+      "model_file='test_path/model_data_014.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_014.parquet'\n",
+      "Saving model to test_path/model_data_014.parquet\n",
+      "Saving agent to test_path/agent_data_014.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
+      "Condition not met at step 1400. No data to save.\n",
       "1400\n",
+      "model_vars.get('Gini', 0)[-1]=0.6554\n",
+      "Condition not met at step 1401. No data to save.\n",
       "1401\n",
+      "model_vars.get('Gini', 0)[-1]=0.6462\n",
+      "Condition not met at step 1402. No data to save.\n",
       "1402\n",
+      "model_vars.get('Gini', 0)[-1]=0.6474\n",
+      "Condition not met at step 1403. No data to save.\n",
       "1403\n",
+      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
+      "Condition not met at step 1404. No data to save.\n",
       "1404\n",
+      "model_vars.get('Gini', 0)[-1]=0.6426000000000001\n",
+      "Condition not met at step 1405. No data to save.\n",
       "1405\n",
+      "model_vars.get('Gini', 0)[-1]=0.655\n",
+      "Condition not met at step 1406. No data to save.\n",
       "1406\n",
+      "model_vars.get('Gini', 0)[-1]=0.6588\n",
+      "Condition not met at step 1407. No data to save.\n",
       "1407\n",
+      "model_vars.get('Gini', 0)[-1]=0.641\n",
+      "Condition not met at step 1408. No data to save.\n",
       "1408\n",
+      "model_vars.get('Gini', 0)[-1]=0.6302\n",
+      "Condition not met at step 1409. No data to save.\n",
       "1409\n",
+      "model_vars.get('Gini', 0)[-1]=0.6482\n",
+      "Condition not met at step 1410. No data to save.\n",
       "1410\n",
+      "model_vars.get('Gini', 0)[-1]=0.652\n",
+      "Condition not met at step 1411. No data to save.\n",
       "1411\n",
+      "model_vars.get('Gini', 0)[-1]=0.6284000000000001\n",
+      "Condition not met at step 1412. No data to save.\n",
       "1412\n",
+      "model_vars.get('Gini', 0)[-1]=0.6544\n",
+      "Condition not met at step 1413. No data to save.\n",
       "1413\n",
+      "model_vars.get('Gini', 0)[-1]=0.6616\n",
+      "Condition not met at step 1414. No data to save.\n",
       "1414\n",
+      "model_vars.get('Gini', 0)[-1]=0.6426000000000001\n",
+      "Condition not met at step 1415. No data to save.\n",
       "1415\n",
+      "model_vars.get('Gini', 0)[-1]=0.675\n",
+      "Condition not met at step 1416. No data to save.\n",
       "1416\n",
+      "model_vars.get('Gini', 0)[-1]=0.6842\n",
+      "Condition not met at step 1417. No data to save.\n",
       "1417\n",
+      "model_vars.get('Gini', 0)[-1]=0.6704\n",
+      "Condition not met at step 1418. No data to save.\n",
       "1418\n",
+      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
+      "Condition not met at step 1419. No data to save.\n",
       "1419\n",
+      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
+      "Condition not met at step 1420. No data to save.\n",
       "1420\n",
+      "model_vars.get('Gini', 0)[-1]=0.647\n",
+      "Condition not met at step 1421. No data to save.\n",
       "1421\n",
+      "model_vars.get('Gini', 0)[-1]=0.6426000000000001\n",
+      "Condition not met at step 1422. No data to save.\n",
       "1422\n",
+      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
+      "Condition not met at step 1423. No data to save.\n",
       "1423\n",
+      "model_vars.get('Gini', 0)[-1]=0.6378\n",
+      "Condition not met at step 1424. No data to save.\n",
       "1424\n",
+      "model_vars.get('Gini', 0)[-1]=0.6544\n",
+      "Condition not met at step 1425. No data to save.\n",
       "1425\n",
+      "model_vars.get('Gini', 0)[-1]=0.6416\n",
+      "Condition not met at step 1426. No data to save.\n",
       "1426\n",
+      "model_vars.get('Gini', 0)[-1]=0.6166\n",
+      "Condition not met at step 1427. No data to save.\n",
       "1427\n",
+      "model_vars.get('Gini', 0)[-1]=0.603\n",
+      "Condition not met at step 1428. No data to save.\n",
       "1428\n",
+      "model_vars.get('Gini', 0)[-1]=0.5776\n",
+      "Condition not met at step 1429. No data to save.\n",
       "1429\n",
+      "model_vars.get('Gini', 0)[-1]=0.5660000000000001\n",
+      "Condition not met at step 1430. No data to save.\n",
       "1430\n",
+      "model_vars.get('Gini', 0)[-1]=0.5811999999999999\n",
+      "Condition not met at step 1431. No data to save.\n",
       "1431\n",
+      "model_vars.get('Gini', 0)[-1]=0.6016\n",
+      "Condition not met at step 1432. No data to save.\n",
       "1432\n",
+      "model_vars.get('Gini', 0)[-1]=0.5882000000000001\n",
+      "Condition not met at step 1433. No data to save.\n",
       "1433\n",
+      "model_vars.get('Gini', 0)[-1]=0.6082000000000001\n",
+      "Condition not met at step 1434. No data to save.\n",
       "1434\n",
+      "model_vars.get('Gini', 0)[-1]=0.615\n",
+      "Condition not met at step 1435. No data to save.\n",
       "1435\n",
+      "model_vars.get('Gini', 0)[-1]=0.6208\n",
+      "Condition not met at step 1436. No data to save.\n",
       "1436\n",
+      "model_vars.get('Gini', 0)[-1]=0.5973999999999999\n",
+      "Condition not met at step 1437. No data to save.\n",
       "1437\n",
+      "model_vars.get('Gini', 0)[-1]=0.5718000000000001\n",
+      "Condition not met at step 1438. No data to save.\n",
       "1438\n",
+      "model_vars.get('Gini', 0)[-1]=0.5786\n",
+      "Condition not met at step 1439. No data to save.\n",
       "1439\n",
+      "model_vars.get('Gini', 0)[-1]=0.5778000000000001\n",
+      "Condition not met at step 1440. No data to save.\n",
       "1440\n",
+      "model_vars.get('Gini', 0)[-1]=0.6166\n",
+      "Condition not met at step 1441. No data to save.\n",
       "1441\n",
+      "model_vars.get('Gini', 0)[-1]=0.623\n",
+      "Condition not met at step 1442. No data to save.\n",
       "1442\n",
+      "model_vars.get('Gini', 0)[-1]=0.6274\n",
+      "Condition not met at step 1443. No data to save.\n",
       "1443\n",
+      "model_vars.get('Gini', 0)[-1]=0.6076\n",
+      "Condition not met at step 1444. No data to save.\n",
       "1444\n",
+      "model_vars.get('Gini', 0)[-1]=0.6306\n",
+      "Condition not met at step 1445. No data to save.\n",
       "1445\n",
+      "model_vars.get('Gini', 0)[-1]=0.6186\n",
+      "Condition not met at step 1446. No data to save.\n",
       "1446\n",
+      "model_vars.get('Gini', 0)[-1]=0.6192\n",
+      "Condition not met at step 1447. No data to save.\n",
       "1447\n",
+      "model_vars.get('Gini', 0)[-1]=0.613\n",
+      "Condition not met at step 1448. No data to save.\n",
       "1448\n",
+      "model_vars.get('Gini', 0)[-1]=0.6226\n",
+      "Condition not met at step 1449. No data to save.\n",
       "1449\n",
+      "model_vars.get('Gini', 0)[-1]=0.6268\n",
+      "Condition not met at step 1450. No data to save.\n",
       "1450\n",
+      "model_vars.get('Gini', 0)[-1]=0.6446000000000001\n",
+      "Condition not met at step 1451. No data to save.\n",
       "1451\n",
+      "model_vars.get('Gini', 0)[-1]=0.6564\n",
+      "Condition not met at step 1452. No data to save.\n",
       "1452\n",
+      "model_vars.get('Gini', 0)[-1]=0.6658\n",
+      "Condition not met at step 1453. No data to save.\n",
       "1453\n",
+      "model_vars.get('Gini', 0)[-1]=0.6504000000000001\n",
+      "Condition not met at step 1454. No data to save.\n",
       "1454\n",
+      "model_vars.get('Gini', 0)[-1]=0.6694\n",
+      "Condition not met at step 1455. No data to save.\n",
       "1455\n",
+      "model_vars.get('Gini', 0)[-1]=0.6348\n",
+      "Condition not met at step 1456. No data to save.\n",
       "1456\n",
+      "model_vars.get('Gini', 0)[-1]=0.6202000000000001\n",
+      "Condition not met at step 1457. No data to save.\n",
       "1457\n",
+      "model_vars.get('Gini', 0)[-1]=0.6058\n",
+      "Condition not met at step 1458. No data to save.\n",
       "1458\n",
+      "model_vars.get('Gini', 0)[-1]=0.6038\n",
+      "Condition not met at step 1459. No data to save.\n",
       "1459\n",
+      "model_vars.get('Gini', 0)[-1]=0.5986\n",
+      "Condition not met at step 1460. No data to save.\n",
       "1460\n",
+      "model_vars.get('Gini', 0)[-1]=0.611\n",
+      "Condition not met at step 1461. No data to save.\n",
       "1461\n",
+      "model_vars.get('Gini', 0)[-1]=0.6294\n",
+      "Condition not met at step 1462. No data to save.\n",
       "1462\n",
+      "model_vars.get('Gini', 0)[-1]=0.5918\n",
+      "Condition not met at step 1463. No data to save.\n",
       "1463\n",
+      "model_vars.get('Gini', 0)[-1]=0.5814\n",
+      "Condition not met at step 1464. No data to save.\n",
       "1464\n",
+      "model_vars.get('Gini', 0)[-1]=0.5913999999999999\n",
+      "Condition not met at step 1465. No data to save.\n",
       "1465\n",
+      "model_vars.get('Gini', 0)[-1]=0.587\n",
+      "Condition not met at step 1466. No data to save.\n",
       "1466\n",
+      "model_vars.get('Gini', 0)[-1]=0.595\n",
+      "Condition not met at step 1467. No data to save.\n",
       "1467\n",
+      "model_vars.get('Gini', 0)[-1]=0.5740000000000001\n",
+      "Condition not met at step 1468. No data to save.\n",
       "1468\n",
+      "model_vars.get('Gini', 0)[-1]=0.5834\n",
+      "Condition not met at step 1469. No data to save.\n",
       "1469\n",
+      "model_vars.get('Gini', 0)[-1]=0.5626\n",
+      "Condition not met at step 1470. No data to save.\n",
       "1470\n",
+      "model_vars.get('Gini', 0)[-1]=0.5886\n",
+      "Condition not met at step 1471. No data to save.\n",
       "1471\n",
+      "model_vars.get('Gini', 0)[-1]=0.6156\n",
+      "Condition not met at step 1472. No data to save.\n",
       "1472\n",
+      "model_vars.get('Gini', 0)[-1]=0.5844\n",
+      "Condition not met at step 1473. No data to save.\n",
       "1473\n",
+      "model_vars.get('Gini', 0)[-1]=0.6013999999999999\n",
+      "Condition not met at step 1474. No data to save.\n",
       "1474\n",
+      "model_vars.get('Gini', 0)[-1]=0.587\n",
+      "Condition not met at step 1475. No data to save.\n",
       "1475\n",
+      "model_vars.get('Gini', 0)[-1]=0.5913999999999999\n",
+      "Condition not met at step 1476. No data to save.\n",
       "1476\n",
+      "model_vars.get('Gini', 0)[-1]=0.603\n",
+      "Condition not met at step 1477. No data to save.\n",
       "1477\n",
+      "model_vars.get('Gini', 0)[-1]=0.6076\n",
+      "Condition not met at step 1478. No data to save.\n",
       "1478\n",
+      "model_vars.get('Gini', 0)[-1]=0.619\n",
+      "Condition not met at step 1479. No data to save.\n",
       "1479\n",
+      "model_vars.get('Gini', 0)[-1]=0.6574\n",
+      "Condition not met at step 1480. No data to save.\n",
       "1480\n",
+      "model_vars.get('Gini', 0)[-1]=0.6506000000000001\n",
+      "Condition not met at step 1481. No data to save.\n",
       "1481\n",
+      "model_vars.get('Gini', 0)[-1]=0.641\n",
+      "Condition not met at step 1482. No data to save.\n",
       "1482\n",
+      "model_vars.get('Gini', 0)[-1]=0.6464000000000001\n",
+      "Condition not met at step 1483. No data to save.\n",
       "1483\n",
+      "model_vars.get('Gini', 0)[-1]=0.6344000000000001\n",
+      "Condition not met at step 1484. No data to save.\n",
       "1484\n",
+      "model_vars.get('Gini', 0)[-1]=0.6617999999999999\n",
+      "Condition not met at step 1485. No data to save.\n",
       "1485\n",
+      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
+      "Condition not met at step 1486. No data to save.\n",
       "1486\n",
+      "model_vars.get('Gini', 0)[-1]=0.6588\n",
+      "Condition not met at step 1487. No data to save.\n",
       "1487\n",
+      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
+      "Condition not met at step 1488. No data to save.\n",
       "1488\n",
+      "model_vars.get('Gini', 0)[-1]=0.629\n",
+      "Condition not met at step 1489. No data to save.\n",
       "1489\n",
+      "model_vars.get('Gini', 0)[-1]=0.611\n",
+      "Condition not met at step 1490. No data to save.\n",
       "1490\n",
+      "model_vars.get('Gini', 0)[-1]=0.6172\n",
+      "Condition not met at step 1491. No data to save.\n",
       "1491\n",
+      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
+      "Condition not met at step 1492. No data to save.\n",
       "1492\n",
+      "model_vars.get('Gini', 0)[-1]=0.6266\n",
+      "Condition not met at step 1493. No data to save.\n",
       "1493\n",
+      "model_vars.get('Gini', 0)[-1]=0.6232\n",
+      "Condition not met at step 1494. No data to save.\n",
       "1494\n",
+      "model_vars.get('Gini', 0)[-1]=0.6038\n",
+      "Condition not met at step 1495. No data to save.\n",
       "1495\n",
+      "model_vars.get('Gini', 0)[-1]=0.599\n",
+      "Condition not met at step 1496. No data to save.\n",
       "1496\n",
+      "model_vars.get('Gini', 0)[-1]=0.5984\n",
+      "Condition not met at step 1497. No data to save.\n",
       "1497\n",
+      "model_vars.get('Gini', 0)[-1]=0.5858\n",
+      "Condition not met at step 1498. No data to save.\n",
       "1498\n",
+      "model_vars.get('Gini', 0)[-1]=0.5858\n",
+      "Condition not met at step 1499. No data to save.\n",
       "1499\n",
       "CALLED\n",
       "self.model._steps=1500\n",
       " TEST self.model._steps=1500\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "1400  0.6408\n",
-      "1401  0.6330\n",
-      "1402  0.6278\n",
-      "1403  0.6626\n",
-      "1404  0.6406\n",
+      "1400  0.6554\n",
+      "1401  0.6462\n",
+      "1402  0.6474\n",
+      "1403  0.6526\n",
+      "1404  0.6426\n",
       "...      ...\n",
-      "1495  0.6386\n",
-      "1496  0.6352\n",
-      "1497  0.6134\n",
-      "1498  0.6234\n",
-      "1499  0.6340\n",
+      "1495  0.5990\n",
+      "1496  0.5984\n",
+      "1497  0.5858\n",
+      "1498  0.5858\n",
+      "1499  0.5856\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_015.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_015.parquet'\n",
-      "Saving model to output_dir/model_data_015.parquet\n",
-      "Saving agent to output_dir/agent_data_015.parquet\n",
+      "model_file='test_path/model_data_015.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_015.parquet'\n",
+      "Saving model to test_path/model_data_015.parquet\n",
+      "Saving agent to test_path/agent_data_015.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.5856\n",
+      "Condition not met at step 1500. No data to save.\n",
       "1500\n",
+      "model_vars.get('Gini', 0)[-1]=0.6012\n",
+      "Condition not met at step 1501. No data to save.\n",
       "1501\n",
+      "model_vars.get('Gini', 0)[-1]=0.6192\n",
+      "Condition not met at step 1502. No data to save.\n",
       "1502\n",
+      "model_vars.get('Gini', 0)[-1]=0.6602\n",
+      "Condition not met at step 1503. No data to save.\n",
       "1503\n",
+      "model_vars.get('Gini', 0)[-1]=0.6812\n",
+      "Condition not met at step 1504. No data to save.\n",
       "1504\n",
+      "model_vars.get('Gini', 0)[-1]=0.6632\n",
+      "Condition not met at step 1505. No data to save.\n",
       "1505\n",
+      "model_vars.get('Gini', 0)[-1]=0.6684\n",
+      "Condition not met at step 1506. No data to save.\n",
       "1506\n",
+      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
+      "Condition not met at step 1507. No data to save.\n",
       "1507\n",
+      "model_vars.get('Gini', 0)[-1]=0.6568\n",
+      "Condition not met at step 1508. No data to save.\n",
       "1508\n",
+      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
+      "Condition not met at step 1509. No data to save.\n",
       "1509\n",
+      "model_vars.get('Gini', 0)[-1]=0.6842\n",
+      "Condition not met at step 1510. No data to save.\n",
       "1510\n",
+      "model_vars.get('Gini', 0)[-1]=0.6990000000000001\n",
+      "Condition not met at step 1511. No data to save.\n",
       "1511\n",
+      "model_vars.get('Gini', 0)[-1]=0.6926\n",
+      "Condition not met at step 1512. No data to save.\n",
       "1512\n",
+      "model_vars.get('Gini', 0)[-1]=0.6972\n",
+      "Condition not met at step 1513. No data to save.\n",
       "1513\n",
+      "model_vars.get('Gini', 0)[-1]=0.7066\n",
+      "Condition met. Appended special results for step 1514 to test_path/special_results.parquet\n",
       "1514\n",
+      "model_vars.get('Gini', 0)[-1]=0.6984\n",
+      "Condition not met at step 1515. No data to save.\n",
       "1515\n",
+      "model_vars.get('Gini', 0)[-1]=0.658\n",
+      "Condition not met at step 1516. No data to save.\n",
       "1516\n",
+      "model_vars.get('Gini', 0)[-1]=0.6446000000000001\n",
+      "Condition not met at step 1517. No data to save.\n",
       "1517\n",
+      "model_vars.get('Gini', 0)[-1]=0.6378\n",
+      "Condition not met at step 1518. No data to save.\n",
       "1518\n",
+      "model_vars.get('Gini', 0)[-1]=0.64\n",
+      "Condition not met at step 1519. No data to save.\n",
       "1519\n",
+      "model_vars.get('Gini', 0)[-1]=0.6288\n",
+      "Condition not met at step 1520. No data to save.\n",
       "1520\n",
+      "model_vars.get('Gini', 0)[-1]=0.6548\n",
+      "Condition not met at step 1521. No data to save.\n",
       "1521\n",
+      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
+      "Condition not met at step 1522. No data to save.\n",
       "1522\n",
+      "model_vars.get('Gini', 0)[-1]=0.634\n",
+      "Condition not met at step 1523. No data to save.\n",
       "1523\n",
+      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
+      "Condition not met at step 1524. No data to save.\n",
       "1524\n",
+      "model_vars.get('Gini', 0)[-1]=0.6666000000000001\n",
+      "Condition not met at step 1525. No data to save.\n",
       "1525\n",
+      "model_vars.get('Gini', 0)[-1]=0.6474\n",
+      "Condition not met at step 1526. No data to save.\n",
       "1526\n",
+      "model_vars.get('Gini', 0)[-1]=0.643\n",
+      "Condition not met at step 1527. No data to save.\n",
       "1527\n",
+      "model_vars.get('Gini', 0)[-1]=0.6096\n",
+      "Condition not met at step 1528. No data to save.\n",
       "1528\n",
+      "model_vars.get('Gini', 0)[-1]=0.666\n",
+      "Condition not met at step 1529. No data to save.\n",
       "1529\n",
+      "model_vars.get('Gini', 0)[-1]=0.6436\n",
+      "Condition not met at step 1530. No data to save.\n",
       "1530\n",
+      "model_vars.get('Gini', 0)[-1]=0.6194\n",
+      "Condition not met at step 1531. No data to save.\n",
       "1531\n",
+      "model_vars.get('Gini', 0)[-1]=0.626\n",
+      "Condition not met at step 1532. No data to save.\n",
       "1532\n",
+      "model_vars.get('Gini', 0)[-1]=0.6146\n",
+      "Condition not met at step 1533. No data to save.\n",
       "1533\n",
+      "model_vars.get('Gini', 0)[-1]=0.6258\n",
+      "Condition not met at step 1534. No data to save.\n",
       "1534\n",
+      "model_vars.get('Gini', 0)[-1]=0.6254\n",
+      "Condition not met at step 1535. No data to save.\n",
       "1535\n",
+      "model_vars.get('Gini', 0)[-1]=0.652\n",
+      "Condition not met at step 1536. No data to save.\n",
       "1536\n",
+      "model_vars.get('Gini', 0)[-1]=0.6386000000000001\n",
+      "Condition not met at step 1537. No data to save.\n",
       "1537\n",
+      "model_vars.get('Gini', 0)[-1]=0.6142000000000001\n",
+      "Condition not met at step 1538. No data to save.\n",
       "1538\n",
+      "model_vars.get('Gini', 0)[-1]=0.6202000000000001\n",
+      "Condition not met at step 1539. No data to save.\n",
       "1539\n",
+      "model_vars.get('Gini', 0)[-1]=0.6164000000000001\n",
+      "Condition not met at step 1540. No data to save.\n",
       "1540\n",
+      "model_vars.get('Gini', 0)[-1]=0.603\n",
+      "Condition not met at step 1541. No data to save.\n",
       "1541\n",
+      "model_vars.get('Gini', 0)[-1]=0.6182000000000001\n",
+      "Condition not met at step 1542. No data to save.\n",
       "1542\n",
+      "model_vars.get('Gini', 0)[-1]=0.5884\n",
+      "Condition not met at step 1543. No data to save.\n",
       "1543\n",
+      "model_vars.get('Gini', 0)[-1]=0.6033999999999999\n",
+      "Condition not met at step 1544. No data to save.\n",
       "1544\n",
+      "model_vars.get('Gini', 0)[-1]=0.6348\n",
+      "Condition not met at step 1545. No data to save.\n",
       "1545\n",
+      "model_vars.get('Gini', 0)[-1]=0.6376\n",
+      "Condition not met at step 1546. No data to save.\n",
       "1546\n",
+      "model_vars.get('Gini', 0)[-1]=0.6306\n",
+      "Condition not met at step 1547. No data to save.\n",
       "1547\n",
+      "model_vars.get('Gini', 0)[-1]=0.6306\n",
+      "Condition not met at step 1548. No data to save.\n",
       "1548\n",
+      "model_vars.get('Gini', 0)[-1]=0.6033999999999999\n",
+      "Condition not met at step 1549. No data to save.\n",
       "1549\n",
+      "model_vars.get('Gini', 0)[-1]=0.5868\n",
+      "Condition not met at step 1550. No data to save.\n",
       "1550\n",
+      "model_vars.get('Gini', 0)[-1]=0.6136\n",
+      "Condition not met at step 1551. No data to save.\n",
       "1551\n",
+      "model_vars.get('Gini', 0)[-1]=0.6294\n",
+      "Condition not met at step 1552. No data to save.\n",
       "1552\n",
+      "model_vars.get('Gini', 0)[-1]=0.6272\n",
+      "Condition not met at step 1553. No data to save.\n",
       "1553\n",
+      "model_vars.get('Gini', 0)[-1]=0.6113999999999999\n",
+      "Condition not met at step 1554. No data to save.\n",
       "1554\n",
+      "model_vars.get('Gini', 0)[-1]=0.6454\n",
+      "Condition not met at step 1555. No data to save.\n",
       "1555\n",
+      "model_vars.get('Gini', 0)[-1]=0.647\n",
+      "Condition not met at step 1556. No data to save.\n",
       "1556\n",
+      "model_vars.get('Gini', 0)[-1]=0.6508\n",
+      "Condition not met at step 1557. No data to save.\n",
       "1557\n",
+      "model_vars.get('Gini', 0)[-1]=0.6528\n",
+      "Condition not met at step 1558. No data to save.\n",
       "1558\n",
+      "model_vars.get('Gini', 0)[-1]=0.6658\n",
+      "Condition not met at step 1559. No data to save.\n",
       "1559\n",
+      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
+      "Condition not met at step 1560. No data to save.\n",
       "1560\n",
+      "model_vars.get('Gini', 0)[-1]=0.6602\n",
+      "Condition not met at step 1561. No data to save.\n",
       "1561\n",
+      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
+      "Condition not met at step 1562. No data to save.\n",
       "1562\n",
+      "model_vars.get('Gini', 0)[-1]=0.6716\n",
+      "Condition not met at step 1563. No data to save.\n",
       "1563\n",
+      "model_vars.get('Gini', 0)[-1]=0.6776\n",
+      "Condition not met at step 1564. No data to save.\n",
       "1564\n",
+      "model_vars.get('Gini', 0)[-1]=0.6614\n",
+      "Condition not met at step 1565. No data to save.\n",
       "1565\n",
+      "model_vars.get('Gini', 0)[-1]=0.662\n",
+      "Condition not met at step 1566. No data to save.\n",
       "1566\n",
+      "model_vars.get('Gini', 0)[-1]=0.663\n",
+      "Condition not met at step 1567. No data to save.\n",
       "1567\n",
+      "model_vars.get('Gini', 0)[-1]=0.6694\n",
+      "Condition not met at step 1568. No data to save.\n",
       "1568\n",
+      "model_vars.get('Gini', 0)[-1]=0.6514\n",
+      "Condition not met at step 1569. No data to save.\n",
       "1569\n",
+      "model_vars.get('Gini', 0)[-1]=0.6482\n",
+      "Condition not met at step 1570. No data to save.\n",
       "1570\n",
+      "model_vars.get('Gini', 0)[-1]=0.6682\n",
+      "Condition not met at step 1571. No data to save.\n",
       "1571\n",
+      "model_vars.get('Gini', 0)[-1]=0.6548\n",
+      "Condition not met at step 1572. No data to save.\n",
       "1572\n",
+      "model_vars.get('Gini', 0)[-1]=0.6544\n",
+      "Condition not met at step 1573. No data to save.\n",
       "1573\n",
+      "model_vars.get('Gini', 0)[-1]=0.6616\n",
+      "Condition not met at step 1574. No data to save.\n",
       "1574\n",
+      "model_vars.get('Gini', 0)[-1]=0.6468\n",
+      "Condition not met at step 1575. No data to save.\n",
       "1575\n",
+      "model_vars.get('Gini', 0)[-1]=0.6315999999999999\n",
+      "Condition not met at step 1576. No data to save.\n",
       "1576\n",
+      "model_vars.get('Gini', 0)[-1]=0.6226\n",
+      "Condition not met at step 1577. No data to save.\n",
       "1577\n",
+      "model_vars.get('Gini', 0)[-1]=0.5968\n",
+      "Condition not met at step 1578. No data to save.\n",
       "1578\n",
+      "model_vars.get('Gini', 0)[-1]=0.6002000000000001\n",
+      "Condition not met at step 1579. No data to save.\n",
       "1579\n",
+      "model_vars.get('Gini', 0)[-1]=0.6446000000000001\n",
+      "Condition not met at step 1580. No data to save.\n",
       "1580\n",
+      "model_vars.get('Gini', 0)[-1]=0.6474\n",
+      "Condition not met at step 1581. No data to save.\n",
       "1581\n",
+      "model_vars.get('Gini', 0)[-1]=0.6662\n",
+      "Condition not met at step 1582. No data to save.\n",
       "1582\n",
+      "model_vars.get('Gini', 0)[-1]=0.6544\n",
+      "Condition not met at step 1583. No data to save.\n",
       "1583\n",
+      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
+      "Condition not met at step 1584. No data to save.\n",
       "1584\n",
+      "model_vars.get('Gini', 0)[-1]=0.5976\n",
+      "Condition not met at step 1585. No data to save.\n",
       "1585\n",
+      "model_vars.get('Gini', 0)[-1]=0.6282000000000001\n",
+      "Condition not met at step 1586. No data to save.\n",
       "1586\n",
+      "model_vars.get('Gini', 0)[-1]=0.6678\n",
+      "Condition not met at step 1587. No data to save.\n",
       "1587\n",
+      "model_vars.get('Gini', 0)[-1]=0.7068\n",
+      "Condition met. Appended special results for step 1588 to test_path/special_results.parquet\n",
       "1588\n",
+      "model_vars.get('Gini', 0)[-1]=0.7034\n",
+      "Condition met. Appended special results for step 1589 to test_path/special_results.parquet\n",
       "1589\n",
+      "model_vars.get('Gini', 0)[-1]=0.6828000000000001\n",
+      "Condition not met at step 1590. No data to save.\n",
       "1590\n",
+      "model_vars.get('Gini', 0)[-1]=0.6824\n",
+      "Condition not met at step 1591. No data to save.\n",
       "1591\n",
+      "model_vars.get('Gini', 0)[-1]=0.6674\n",
+      "Condition not met at step 1592. No data to save.\n",
       "1592\n",
+      "model_vars.get('Gini', 0)[-1]=0.663\n",
+      "Condition not met at step 1593. No data to save.\n",
       "1593\n",
+      "model_vars.get('Gini', 0)[-1]=0.659\n",
+      "Condition not met at step 1594. No data to save.\n",
       "1594\n",
+      "model_vars.get('Gini', 0)[-1]=0.6608\n",
+      "Condition not met at step 1595. No data to save.\n",
       "1595\n",
+      "model_vars.get('Gini', 0)[-1]=0.6454\n",
+      "Condition not met at step 1596. No data to save.\n",
       "1596\n",
+      "model_vars.get('Gini', 0)[-1]=0.6582\n",
+      "Condition not met at step 1597. No data to save.\n",
       "1597\n",
+      "model_vars.get('Gini', 0)[-1]=0.6726000000000001\n",
+      "Condition not met at step 1598. No data to save.\n",
       "1598\n",
+      "model_vars.get('Gini', 0)[-1]=0.6318\n",
+      "Condition not met at step 1599. No data to save.\n",
       "1599\n",
       "CALLED\n",
       "self.model._steps=1600\n",
       " TEST self.model._steps=1600\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "1500  0.6612\n",
-      "1501  0.6702\n",
-      "1502  0.6478\n",
-      "1503  0.6638\n",
-      "1504  0.6648\n",
+      "1500  0.6012\n",
+      "1501  0.6192\n",
+      "1502  0.6602\n",
+      "1503  0.6812\n",
+      "1504  0.6632\n",
       "...      ...\n",
-      "1595  0.7150\n",
-      "1596  0.7262\n",
-      "1597  0.7246\n",
-      "1598  0.6906\n",
-      "1599  0.6838\n",
+      "1595  0.6454\n",
+      "1596  0.6582\n",
+      "1597  0.6726\n",
+      "1598  0.6318\n",
+      "1599  0.6750\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_016.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_016.parquet'\n",
-      "Saving model to output_dir/model_data_016.parquet\n",
-      "Saving agent to output_dir/agent_data_016.parquet\n",
+      "model_file='test_path/model_data_016.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_016.parquet'\n",
+      "Saving model to test_path/model_data_016.parquet\n",
+      "Saving agent to test_path/agent_data_016.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.675\n",
+      "Condition not met at step 1600. No data to save.\n",
       "1600\n",
+      "model_vars.get('Gini', 0)[-1]=0.6614\n",
+      "Condition not met at step 1601. No data to save.\n",
       "1601\n",
+      "model_vars.get('Gini', 0)[-1]=0.6834\n",
+      "Condition not met at step 1602. No data to save.\n",
       "1602\n",
+      "model_vars.get('Gini', 0)[-1]=0.688\n",
+      "Condition not met at step 1603. No data to save.\n",
       "1603\n",
+      "model_vars.get('Gini', 0)[-1]=0.6752\n",
+      "Condition not met at step 1604. No data to save.\n",
       "1604\n",
+      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
+      "Condition not met at step 1605. No data to save.\n",
       "1605\n",
+      "model_vars.get('Gini', 0)[-1]=0.66\n",
+      "Condition not met at step 1606. No data to save.\n",
       "1606\n",
+      "model_vars.get('Gini', 0)[-1]=0.65\n",
+      "Condition not met at step 1607. No data to save.\n",
       "1607\n",
+      "model_vars.get('Gini', 0)[-1]=0.671\n",
+      "Condition not met at step 1608. No data to save.\n",
       "1608\n",
+      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
+      "Condition not met at step 1609. No data to save.\n",
       "1609\n",
+      "model_vars.get('Gini', 0)[-1]=0.6286\n",
+      "Condition not met at step 1610. No data to save.\n",
       "1610\n",
+      "model_vars.get('Gini', 0)[-1]=0.6138\n",
+      "Condition not met at step 1611. No data to save.\n",
       "1611\n",
+      "model_vars.get('Gini', 0)[-1]=0.6412\n",
+      "Condition not met at step 1612. No data to save.\n",
       "1612\n",
+      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
+      "Condition not met at step 1613. No data to save.\n",
       "1613\n",
+      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
+      "Condition not met at step 1614. No data to save.\n",
       "1614\n",
+      "model_vars.get('Gini', 0)[-1]=0.656\n",
+      "Condition not met at step 1615. No data to save.\n",
       "1615\n",
+      "model_vars.get('Gini', 0)[-1]=0.6934\n",
+      "Condition not met at step 1616. No data to save.\n",
       "1616\n",
+      "model_vars.get('Gini', 0)[-1]=0.6798\n",
+      "Condition not met at step 1617. No data to save.\n",
       "1617\n",
+      "model_vars.get('Gini', 0)[-1]=0.6654\n",
+      "Condition not met at step 1618. No data to save.\n",
       "1618\n",
+      "model_vars.get('Gini', 0)[-1]=0.6652\n",
+      "Condition not met at step 1619. No data to save.\n",
       "1619\n",
+      "model_vars.get('Gini', 0)[-1]=0.667\n",
+      "Condition not met at step 1620. No data to save.\n",
       "1620\n",
+      "model_vars.get('Gini', 0)[-1]=0.6964\n",
+      "Condition not met at step 1621. No data to save.\n",
       "1621\n",
+      "model_vars.get('Gini', 0)[-1]=0.6974\n",
+      "Condition not met at step 1622. No data to save.\n",
       "1622\n",
+      "model_vars.get('Gini', 0)[-1]=0.6888000000000001\n",
+      "Condition not met at step 1623. No data to save.\n",
       "1623\n",
+      "model_vars.get('Gini', 0)[-1]=0.6824\n",
+      "Condition not met at step 1624. No data to save.\n",
       "1624\n",
+      "model_vars.get('Gini', 0)[-1]=0.6846\n",
+      "Condition not met at step 1625. No data to save.\n",
       "1625\n",
+      "model_vars.get('Gini', 0)[-1]=0.6882\n",
+      "Condition not met at step 1626. No data to save.\n",
       "1626\n",
+      "model_vars.get('Gini', 0)[-1]=0.685\n",
+      "Condition not met at step 1627. No data to save.\n",
       "1627\n",
+      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
+      "Condition not met at step 1628. No data to save.\n",
       "1628\n",
+      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
+      "Condition not met at step 1629. No data to save.\n",
       "1629\n",
+      "model_vars.get('Gini', 0)[-1]=0.6704\n",
+      "Condition not met at step 1630. No data to save.\n",
       "1630\n",
+      "model_vars.get('Gini', 0)[-1]=0.679\n",
+      "Condition not met at step 1631. No data to save.\n",
       "1631\n",
+      "model_vars.get('Gini', 0)[-1]=0.6732\n",
+      "Condition not met at step 1632. No data to save.\n",
       "1632\n",
+      "model_vars.get('Gini', 0)[-1]=0.665\n",
+      "Condition not met at step 1633. No data to save.\n",
       "1633\n",
+      "model_vars.get('Gini', 0)[-1]=0.6476\n",
+      "Condition not met at step 1634. No data to save.\n",
       "1634\n",
+      "model_vars.get('Gini', 0)[-1]=0.635\n",
+      "Condition not met at step 1635. No data to save.\n",
       "1635\n",
+      "model_vars.get('Gini', 0)[-1]=0.6324000000000001\n",
+      "Condition not met at step 1636. No data to save.\n",
       "1636\n",
+      "model_vars.get('Gini', 0)[-1]=0.6496\n",
+      "Condition not met at step 1637. No data to save.\n",
       "1637\n",
+      "model_vars.get('Gini', 0)[-1]=0.622\n",
+      "Condition not met at step 1638. No data to save.\n",
       "1638\n",
+      "model_vars.get('Gini', 0)[-1]=0.6184000000000001\n",
+      "Condition not met at step 1639. No data to save.\n",
       "1639\n",
+      "model_vars.get('Gini', 0)[-1]=0.6158\n",
+      "Condition not met at step 1640. No data to save.\n",
       "1640\n",
+      "model_vars.get('Gini', 0)[-1]=0.6416\n",
+      "Condition not met at step 1641. No data to save.\n",
       "1641\n",
+      "model_vars.get('Gini', 0)[-1]=0.6732\n",
+      "Condition not met at step 1642. No data to save.\n",
       "1642\n",
+      "model_vars.get('Gini', 0)[-1]=0.6672\n",
+      "Condition not met at step 1643. No data to save.\n",
       "1643\n",
+      "model_vars.get('Gini', 0)[-1]=0.6648000000000001\n",
+      "Condition not met at step 1644. No data to save.\n",
       "1644\n",
+      "model_vars.get('Gini', 0)[-1]=0.6704\n",
+      "Condition not met at step 1645. No data to save.\n",
       "1645\n",
+      "model_vars.get('Gini', 0)[-1]=0.664\n",
+      "Condition not met at step 1646. No data to save.\n",
       "1646\n",
+      "model_vars.get('Gini', 0)[-1]=0.6632\n",
+      "Condition not met at step 1647. No data to save.\n",
       "1647\n",
+      "model_vars.get('Gini', 0)[-1]=0.6882\n",
+      "Condition not met at step 1648. No data to save.\n",
       "1648\n",
+      "model_vars.get('Gini', 0)[-1]=0.6696\n",
+      "Condition not met at step 1649. No data to save.\n",
       "1649\n",
+      "model_vars.get('Gini', 0)[-1]=0.6592\n",
+      "Condition not met at step 1650. No data to save.\n",
       "1650\n",
+      "model_vars.get('Gini', 0)[-1]=0.6492\n",
+      "Condition not met at step 1651. No data to save.\n",
       "1651\n",
+      "model_vars.get('Gini', 0)[-1]=0.6768000000000001\n",
+      "Condition not met at step 1652. No data to save.\n",
       "1652\n",
+      "model_vars.get('Gini', 0)[-1]=0.6742\n",
+      "Condition not met at step 1653. No data to save.\n",
       "1653\n",
+      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
+      "Condition not met at step 1654. No data to save.\n",
       "1654\n",
+      "model_vars.get('Gini', 0)[-1]=0.652\n",
+      "Condition not met at step 1655. No data to save.\n",
       "1655\n",
+      "model_vars.get('Gini', 0)[-1]=0.6312\n",
+      "Condition not met at step 1656. No data to save.\n",
       "1656\n",
+      "model_vars.get('Gini', 0)[-1]=0.6342\n",
+      "Condition not met at step 1657. No data to save.\n",
       "1657\n",
+      "model_vars.get('Gini', 0)[-1]=0.6184000000000001\n",
+      "Condition not met at step 1658. No data to save.\n",
       "1658\n",
+      "model_vars.get('Gini', 0)[-1]=0.6192\n",
+      "Condition not met at step 1659. No data to save.\n",
       "1659\n",
+      "model_vars.get('Gini', 0)[-1]=0.6246\n",
+      "Condition not met at step 1660. No data to save.\n",
       "1660\n",
+      "model_vars.get('Gini', 0)[-1]=0.6254\n",
+      "Condition not met at step 1661. No data to save.\n",
       "1661\n",
+      "model_vars.get('Gini', 0)[-1]=0.6062000000000001\n",
+      "Condition not met at step 1662. No data to save.\n",
       "1662\n",
+      "model_vars.get('Gini', 0)[-1]=0.6062000000000001\n",
+      "Condition not met at step 1663. No data to save.\n",
       "1663\n",
+      "model_vars.get('Gini', 0)[-1]=0.6084\n",
+      "Condition not met at step 1664. No data to save.\n",
       "1664\n",
+      "model_vars.get('Gini', 0)[-1]=0.6152\n",
+      "Condition not met at step 1665. No data to save.\n",
       "1665\n",
+      "model_vars.get('Gini', 0)[-1]=0.6152\n",
+      "Condition not met at step 1666. No data to save.\n",
       "1666\n",
+      "model_vars.get('Gini', 0)[-1]=0.6398\n",
+      "Condition not met at step 1667. No data to save.\n",
       "1667\n",
+      "model_vars.get('Gini', 0)[-1]=0.6384000000000001\n",
+      "Condition not met at step 1668. No data to save.\n",
       "1668\n",
+      "model_vars.get('Gini', 0)[-1]=0.626\n",
+      "Condition not met at step 1669. No data to save.\n",
       "1669\n",
+      "model_vars.get('Gini', 0)[-1]=0.6298\n",
+      "Condition not met at step 1670. No data to save.\n",
       "1670\n",
+      "model_vars.get('Gini', 0)[-1]=0.5984\n",
+      "Condition not met at step 1671. No data to save.\n",
       "1671\n",
+      "model_vars.get('Gini', 0)[-1]=0.577\n",
+      "Condition not met at step 1672. No data to save.\n",
       "1672\n",
+      "model_vars.get('Gini', 0)[-1]=0.5791999999999999\n",
+      "Condition not met at step 1673. No data to save.\n",
       "1673\n",
+      "model_vars.get('Gini', 0)[-1]=0.599\n",
+      "Condition not met at step 1674. No data to save.\n",
       "1674\n",
+      "model_vars.get('Gini', 0)[-1]=0.5682\n",
+      "Condition not met at step 1675. No data to save.\n",
       "1675\n",
+      "model_vars.get('Gini', 0)[-1]=0.5718000000000001\n",
+      "Condition not met at step 1676. No data to save.\n",
       "1676\n",
+      "model_vars.get('Gini', 0)[-1]=0.575\n",
+      "Condition not met at step 1677. No data to save.\n",
       "1677\n",
+      "model_vars.get('Gini', 0)[-1]=0.6136\n",
+      "Condition not met at step 1678. No data to save.\n",
       "1678\n",
+      "model_vars.get('Gini', 0)[-1]=0.63\n",
+      "Condition not met at step 1679. No data to save.\n",
       "1679\n",
+      "model_vars.get('Gini', 0)[-1]=0.6302\n",
+      "Condition not met at step 1680. No data to save.\n",
       "1680\n",
+      "model_vars.get('Gini', 0)[-1]=0.6168\n",
+      "Condition not met at step 1681. No data to save.\n",
       "1681\n",
+      "model_vars.get('Gini', 0)[-1]=0.636\n",
+      "Condition not met at step 1682. No data to save.\n",
       "1682\n",
+      "model_vars.get('Gini', 0)[-1]=0.6386000000000001\n",
+      "Condition not met at step 1683. No data to save.\n",
       "1683\n",
+      "model_vars.get('Gini', 0)[-1]=0.633\n",
+      "Condition not met at step 1684. No data to save.\n",
       "1684\n",
+      "model_vars.get('Gini', 0)[-1]=0.6548\n",
+      "Condition not met at step 1685. No data to save.\n",
       "1685\n",
+      "model_vars.get('Gini', 0)[-1]=0.6496\n",
+      "Condition not met at step 1686. No data to save.\n",
       "1686\n",
+      "model_vars.get('Gini', 0)[-1]=0.6648000000000001\n",
+      "Condition not met at step 1687. No data to save.\n",
       "1687\n",
+      "model_vars.get('Gini', 0)[-1]=0.6762\n",
+      "Condition not met at step 1688. No data to save.\n",
       "1688\n",
+      "model_vars.get('Gini', 0)[-1]=0.658\n",
+      "Condition not met at step 1689. No data to save.\n",
       "1689\n",
+      "model_vars.get('Gini', 0)[-1]=0.6646000000000001\n",
+      "Condition not met at step 1690. No data to save.\n",
       "1690\n",
+      "model_vars.get('Gini', 0)[-1]=0.6384000000000001\n",
+      "Condition not met at step 1691. No data to save.\n",
       "1691\n",
+      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
+      "Condition not met at step 1692. No data to save.\n",
       "1692\n",
+      "model_vars.get('Gini', 0)[-1]=0.644\n",
+      "Condition not met at step 1693. No data to save.\n",
       "1693\n",
+      "model_vars.get('Gini', 0)[-1]=0.6704\n",
+      "Condition not met at step 1694. No data to save.\n",
       "1694\n",
+      "model_vars.get('Gini', 0)[-1]=0.6846\n",
+      "Condition not met at step 1695. No data to save.\n",
       "1695\n",
+      "model_vars.get('Gini', 0)[-1]=0.6726000000000001\n",
+      "Condition not met at step 1696. No data to save.\n",
       "1696\n",
+      "model_vars.get('Gini', 0)[-1]=0.6748000000000001\n",
+      "Condition not met at step 1697. No data to save.\n",
       "1697\n",
+      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
+      "Condition not met at step 1698. No data to save.\n",
       "1698\n",
+      "model_vars.get('Gini', 0)[-1]=0.6786000000000001\n",
+      "Condition not met at step 1699. No data to save.\n",
       "1699\n",
       "CALLED\n",
       "self.model._steps=1700\n",
       " TEST self.model._steps=1700\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "1600  0.6952\n",
-      "1601  0.7148\n",
-      "1602  0.6994\n",
-      "1603  0.6886\n",
-      "1604  0.7010\n",
+      "1600  0.6614\n",
+      "1601  0.6834\n",
+      "1602  0.6880\n",
+      "1603  0.6752\n",
+      "1604  0.6766\n",
       "...      ...\n",
-      "1695  0.6130\n",
-      "1696  0.6006\n",
-      "1697  0.6050\n",
-      "1698  0.6002\n",
-      "1699  0.5850\n",
+      "1695  0.6726\n",
+      "1696  0.6748\n",
+      "1697  0.6706\n",
+      "1698  0.6786\n",
+      "1699  0.6730\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_017.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_017.parquet'\n",
-      "Saving model to output_dir/model_data_017.parquet\n",
-      "Saving agent to output_dir/agent_data_017.parquet\n",
+      "model_file='test_path/model_data_017.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_017.parquet'\n",
+      "Saving model to test_path/model_data_017.parquet\n",
+      "Saving agent to test_path/agent_data_017.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.673\n",
+      "Condition not met at step 1700. No data to save.\n",
       "1700\n",
+      "model_vars.get('Gini', 0)[-1]=0.6658\n",
+      "Condition not met at step 1701. No data to save.\n",
       "1701\n",
+      "model_vars.get('Gini', 0)[-1]=0.654\n",
+      "Condition not met at step 1702. No data to save.\n",
       "1702\n",
+      "model_vars.get('Gini', 0)[-1]=0.653\n",
+      "Condition not met at step 1703. No data to save.\n",
       "1703\n",
+      "model_vars.get('Gini', 0)[-1]=0.6748000000000001\n",
+      "Condition not met at step 1704. No data to save.\n",
       "1704\n",
+      "model_vars.get('Gini', 0)[-1]=0.6738\n",
+      "Condition not met at step 1705. No data to save.\n",
       "1705\n",
+      "model_vars.get('Gini', 0)[-1]=0.6776\n",
+      "Condition not met at step 1706. No data to save.\n",
       "1706\n",
+      "model_vars.get('Gini', 0)[-1]=0.6872\n",
+      "Condition not met at step 1707. No data to save.\n",
       "1707\n",
+      "model_vars.get('Gini', 0)[-1]=0.6938\n",
+      "Condition not met at step 1708. No data to save.\n",
       "1708\n",
+      "model_vars.get('Gini', 0)[-1]=0.675\n",
+      "Condition not met at step 1709. No data to save.\n",
       "1709\n",
+      "model_vars.get('Gini', 0)[-1]=0.6738\n",
+      "Condition not met at step 1710. No data to save.\n",
       "1710\n",
+      "model_vars.get('Gini', 0)[-1]=0.6759999999999999\n",
+      "Condition not met at step 1711. No data to save.\n",
       "1711\n",
+      "model_vars.get('Gini', 0)[-1]=0.6704\n",
+      "Condition not met at step 1712. No data to save.\n",
       "1712\n",
+      "model_vars.get('Gini', 0)[-1]=0.6564\n",
+      "Condition not met at step 1713. No data to save.\n",
       "1713\n",
+      "model_vars.get('Gini', 0)[-1]=0.6652\n",
+      "Condition not met at step 1714. No data to save.\n",
       "1714\n",
+      "model_vars.get('Gini', 0)[-1]=0.66\n",
+      "Condition not met at step 1715. No data to save.\n",
       "1715\n",
+      "model_vars.get('Gini', 0)[-1]=0.6468\n",
+      "Condition not met at step 1716. No data to save.\n",
       "1716\n",
+      "model_vars.get('Gini', 0)[-1]=0.6632\n",
+      "Condition not met at step 1717. No data to save.\n",
       "1717\n",
+      "model_vars.get('Gini', 0)[-1]=0.6376\n",
+      "Condition not met at step 1718. No data to save.\n",
       "1718\n",
+      "model_vars.get('Gini', 0)[-1]=0.673\n",
+      "Condition not met at step 1719. No data to save.\n",
       "1719\n",
+      "model_vars.get('Gini', 0)[-1]=0.677\n",
+      "Condition not met at step 1720. No data to save.\n",
       "1720\n",
+      "model_vars.get('Gini', 0)[-1]=0.6956\n",
+      "Condition not met at step 1721. No data to save.\n",
       "1721\n",
+      "model_vars.get('Gini', 0)[-1]=0.7052\n",
+      "Condition met. Appended special results for step 1722 to test_path/special_results.parquet\n",
       "1722\n",
+      "model_vars.get('Gini', 0)[-1]=0.7094\n",
+      "Condition met. Appended special results for step 1723 to test_path/special_results.parquet\n",
       "1723\n",
+      "model_vars.get('Gini', 0)[-1]=0.6944\n",
+      "Condition not met at step 1724. No data to save.\n",
       "1724\n",
+      "model_vars.get('Gini', 0)[-1]=0.6921999999999999\n",
+      "Condition not met at step 1725. No data to save.\n",
       "1725\n",
+      "model_vars.get('Gini', 0)[-1]=0.6744\n",
+      "Condition not met at step 1726. No data to save.\n",
       "1726\n",
+      "model_vars.get('Gini', 0)[-1]=0.6948000000000001\n",
+      "Condition not met at step 1727. No data to save.\n",
       "1727\n",
+      "model_vars.get('Gini', 0)[-1]=0.6832\n",
+      "Condition not met at step 1728. No data to save.\n",
       "1728\n",
+      "model_vars.get('Gini', 0)[-1]=0.6512\n",
+      "Condition not met at step 1729. No data to save.\n",
       "1729\n",
+      "model_vars.get('Gini', 0)[-1]=0.675\n",
+      "Condition not met at step 1730. No data to save.\n",
       "1730\n",
+      "model_vars.get('Gini', 0)[-1]=0.696\n",
+      "Condition not met at step 1731. No data to save.\n",
       "1731\n",
+      "model_vars.get('Gini', 0)[-1]=0.7028000000000001\n",
+      "Condition met. Appended special results for step 1732 to test_path/special_results.parquet\n",
       "1732\n",
+      "model_vars.get('Gini', 0)[-1]=0.7132000000000001\n",
+      "Condition met. Appended special results for step 1733 to test_path/special_results.parquet\n",
       "1733\n",
+      "model_vars.get('Gini', 0)[-1]=0.7076\n",
+      "Condition met. Appended special results for step 1734 to test_path/special_results.parquet\n",
       "1734\n",
+      "model_vars.get('Gini', 0)[-1]=0.7094\n",
+      "Condition met. Appended special results for step 1735 to test_path/special_results.parquet\n",
       "1735\n",
+      "model_vars.get('Gini', 0)[-1]=0.7203999999999999\n",
+      "Condition met. Appended special results for step 1736 to test_path/special_results.parquet\n",
       "1736\n",
+      "model_vars.get('Gini', 0)[-1]=0.7030000000000001\n",
+      "Condition met. Appended special results for step 1737 to test_path/special_results.parquet\n",
       "1737\n",
+      "model_vars.get('Gini', 0)[-1]=0.7156\n",
+      "Condition met. Appended special results for step 1738 to test_path/special_results.parquet\n",
       "1738\n",
+      "model_vars.get('Gini', 0)[-1]=0.7142\n",
+      "Condition met. Appended special results for step 1739 to test_path/special_results.parquet\n",
       "1739\n",
+      "model_vars.get('Gini', 0)[-1]=0.742\n",
+      "Condition met. Appended special results for step 1740 to test_path/special_results.parquet\n",
       "1740\n",
+      "model_vars.get('Gini', 0)[-1]=0.7408\n",
+      "Condition met. Appended special results for step 1741 to test_path/special_results.parquet\n",
       "1741\n",
+      "model_vars.get('Gini', 0)[-1]=0.74\n",
+      "Condition met. Appended special results for step 1742 to test_path/special_results.parquet\n",
       "1742\n",
+      "model_vars.get('Gini', 0)[-1]=0.738\n",
+      "Condition met. Appended special results for step 1743 to test_path/special_results.parquet\n",
       "1743\n",
+      "model_vars.get('Gini', 0)[-1]=0.7328\n",
+      "Condition met. Appended special results for step 1744 to test_path/special_results.parquet\n",
       "1744\n",
+      "model_vars.get('Gini', 0)[-1]=0.742\n",
+      "Condition met. Appended special results for step 1745 to test_path/special_results.parquet\n",
       "1745\n",
+      "model_vars.get('Gini', 0)[-1]=0.7408\n",
+      "Condition met. Appended special results for step 1746 to test_path/special_results.parquet\n",
       "1746\n",
+      "model_vars.get('Gini', 0)[-1]=0.7438\n",
+      "Condition met. Appended special results for step 1747 to test_path/special_results.parquet\n",
       "1747\n",
+      "model_vars.get('Gini', 0)[-1]=0.7214\n",
+      "Condition met. Appended special results for step 1748 to test_path/special_results.parquet\n",
       "1748\n",
+      "model_vars.get('Gini', 0)[-1]=0.7130000000000001\n",
+      "Condition met. Appended special results for step 1749 to test_path/special_results.parquet\n",
       "1749\n",
+      "model_vars.get('Gini', 0)[-1]=0.7108\n",
+      "Condition met. Appended special results for step 1750 to test_path/special_results.parquet\n",
       "1750\n",
+      "model_vars.get('Gini', 0)[-1]=0.6878\n",
+      "Condition not met at step 1751. No data to save.\n",
       "1751\n",
+      "model_vars.get('Gini', 0)[-1]=0.7034\n",
+      "Condition met. Appended special results for step 1752 to test_path/special_results.parquet\n",
       "1752\n",
+      "model_vars.get('Gini', 0)[-1]=0.7076\n",
+      "Condition met. Appended special results for step 1753 to test_path/special_results.parquet\n",
       "1753\n",
+      "model_vars.get('Gini', 0)[-1]=0.7178\n",
+      "Condition met. Appended special results for step 1754 to test_path/special_results.parquet\n",
       "1754\n",
+      "model_vars.get('Gini', 0)[-1]=0.7081999999999999\n",
+      "Condition met. Appended special results for step 1755 to test_path/special_results.parquet\n",
       "1755\n",
+      "model_vars.get('Gini', 0)[-1]=0.7154\n",
+      "Condition met. Appended special results for step 1756 to test_path/special_results.parquet\n",
       "1756\n",
+      "model_vars.get('Gini', 0)[-1]=0.7352000000000001\n",
+      "Condition met. Appended special results for step 1757 to test_path/special_results.parquet\n",
       "1757\n",
+      "model_vars.get('Gini', 0)[-1]=0.7372000000000001\n",
+      "Condition met. Appended special results for step 1758 to test_path/special_results.parquet\n",
       "1758\n",
+      "model_vars.get('Gini', 0)[-1]=0.7452000000000001\n",
+      "Condition met. Appended special results for step 1759 to test_path/special_results.parquet\n",
       "1759\n",
+      "model_vars.get('Gini', 0)[-1]=0.76\n",
+      "Condition met. Appended special results for step 1760 to test_path/special_results.parquet\n",
       "1760\n",
+      "model_vars.get('Gini', 0)[-1]=0.7445999999999999\n",
+      "Condition met. Appended special results for step 1761 to test_path/special_results.parquet\n",
       "1761\n",
+      "model_vars.get('Gini', 0)[-1]=0.7404\n",
+      "Condition met. Appended special results for step 1762 to test_path/special_results.parquet\n",
       "1762\n",
+      "model_vars.get('Gini', 0)[-1]=0.7124\n",
+      "Condition met. Appended special results for step 1763 to test_path/special_results.parquet\n",
       "1763\n",
+      "model_vars.get('Gini', 0)[-1]=0.7163999999999999\n",
+      "Condition met. Appended special results for step 1764 to test_path/special_results.parquet\n",
       "1764\n",
+      "model_vars.get('Gini', 0)[-1]=0.7056\n",
+      "Condition met. Appended special results for step 1765 to test_path/special_results.parquet\n",
       "1765\n",
+      "model_vars.get('Gini', 0)[-1]=0.6884\n",
+      "Condition not met at step 1766. No data to save.\n",
       "1766\n",
+      "model_vars.get('Gini', 0)[-1]=0.6699999999999999\n",
+      "Condition not met at step 1767. No data to save.\n",
       "1767\n",
+      "model_vars.get('Gini', 0)[-1]=0.6868000000000001\n",
+      "Condition not met at step 1768. No data to save.\n",
       "1768\n",
+      "model_vars.get('Gini', 0)[-1]=0.7194\n",
+      "Condition met. Appended special results for step 1769 to test_path/special_results.parquet\n",
       "1769\n",
+      "model_vars.get('Gini', 0)[-1]=0.6924\n",
+      "Condition not met at step 1770. No data to save.\n",
       "1770\n",
+      "model_vars.get('Gini', 0)[-1]=0.6804\n",
+      "Condition not met at step 1771. No data to save.\n",
       "1771\n",
+      "model_vars.get('Gini', 0)[-1]=0.6918\n",
+      "Condition not met at step 1772. No data to save.\n",
       "1772\n",
+      "model_vars.get('Gini', 0)[-1]=0.6844\n",
+      "Condition not met at step 1773. No data to save.\n",
       "1773\n",
+      "model_vars.get('Gini', 0)[-1]=0.6872\n",
+      "Condition not met at step 1774. No data to save.\n",
       "1774\n",
+      "model_vars.get('Gini', 0)[-1]=0.669\n",
+      "Condition not met at step 1775. No data to save.\n",
       "1775\n",
+      "model_vars.get('Gini', 0)[-1]=0.671\n",
+      "Condition not met at step 1776. No data to save.\n",
       "1776\n",
+      "model_vars.get('Gini', 0)[-1]=0.6712\n",
+      "Condition not met at step 1777. No data to save.\n",
       "1777\n",
+      "model_vars.get('Gini', 0)[-1]=0.6422\n",
+      "Condition not met at step 1778. No data to save.\n",
       "1778\n",
+      "model_vars.get('Gini', 0)[-1]=0.646\n",
+      "Condition not met at step 1779. No data to save.\n",
       "1779\n",
+      "model_vars.get('Gini', 0)[-1]=0.63\n",
+      "Condition not met at step 1780. No data to save.\n",
       "1780\n",
+      "model_vars.get('Gini', 0)[-1]=0.6332\n",
+      "Condition not met at step 1781. No data to save.\n",
       "1781\n",
+      "model_vars.get('Gini', 0)[-1]=0.6468\n",
+      "Condition not met at step 1782. No data to save.\n",
       "1782\n",
+      "model_vars.get('Gini', 0)[-1]=0.6262000000000001\n",
+      "Condition not met at step 1783. No data to save.\n",
       "1783\n",
+      "model_vars.get('Gini', 0)[-1]=0.6474\n",
+      "Condition not met at step 1784. No data to save.\n",
       "1784\n",
+      "model_vars.get('Gini', 0)[-1]=0.622\n",
+      "Condition not met at step 1785. No data to save.\n",
       "1785\n",
+      "model_vars.get('Gini', 0)[-1]=0.6326\n",
+      "Condition not met at step 1786. No data to save.\n",
       "1786\n",
+      "model_vars.get('Gini', 0)[-1]=0.6198\n",
+      "Condition not met at step 1787. No data to save.\n",
       "1787\n",
+      "model_vars.get('Gini', 0)[-1]=0.6020000000000001\n",
+      "Condition not met at step 1788. No data to save.\n",
       "1788\n",
+      "model_vars.get('Gini', 0)[-1]=0.5938\n",
+      "Condition not met at step 1789. No data to save.\n",
       "1789\n",
+      "model_vars.get('Gini', 0)[-1]=0.5960000000000001\n",
+      "Condition not met at step 1790. No data to save.\n",
       "1790\n",
+      "model_vars.get('Gini', 0)[-1]=0.5912\n",
+      "Condition not met at step 1791. No data to save.\n",
       "1791\n",
+      "model_vars.get('Gini', 0)[-1]=0.6234\n",
+      "Condition not met at step 1792. No data to save.\n",
       "1792\n",
+      "model_vars.get('Gini', 0)[-1]=0.5998\n",
+      "Condition not met at step 1793. No data to save.\n",
       "1793\n",
+      "model_vars.get('Gini', 0)[-1]=0.6116\n",
+      "Condition not met at step 1794. No data to save.\n",
       "1794\n",
+      "model_vars.get('Gini', 0)[-1]=0.6232\n",
+      "Condition not met at step 1795. No data to save.\n",
       "1795\n",
+      "model_vars.get('Gini', 0)[-1]=0.6408\n",
+      "Condition not met at step 1796. No data to save.\n",
       "1796\n",
+      "model_vars.get('Gini', 0)[-1]=0.6602\n",
+      "Condition not met at step 1797. No data to save.\n",
       "1797\n",
+      "model_vars.get('Gini', 0)[-1]=0.6676\n",
+      "Condition not met at step 1798. No data to save.\n",
       "1798\n",
+      "model_vars.get('Gini', 0)[-1]=0.661\n",
+      "Condition not met at step 1799. No data to save.\n",
       "1799\n",
       "CALLED\n",
       "self.model._steps=1800\n",
       " TEST self.model._steps=1800\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "1700  0.5704\n",
-      "1701  0.5840\n",
-      "1702  0.5956\n",
-      "1703  0.6024\n",
-      "1704  0.6298\n",
+      "1700  0.6658\n",
+      "1701  0.6540\n",
+      "1702  0.6530\n",
+      "1703  0.6748\n",
+      "1704  0.6738\n",
       "...      ...\n",
-      "1795  0.6470\n",
-      "1796  0.6504\n",
-      "1797  0.6308\n",
-      "1798  0.6372\n",
-      "1799  0.6220\n",
+      "1795  0.6408\n",
+      "1796  0.6602\n",
+      "1797  0.6676\n",
+      "1798  0.6610\n",
+      "1799  0.6514\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_018.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_018.parquet'\n",
-      "Saving model to output_dir/model_data_018.parquet\n",
-      "Saving agent to output_dir/agent_data_018.parquet\n",
+      "model_file='test_path/model_data_018.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_018.parquet'\n",
+      "Saving model to test_path/model_data_018.parquet\n",
+      "Saving agent to test_path/agent_data_018.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6514\n",
+      "Condition not met at step 1800. No data to save.\n",
       "1800\n",
+      "model_vars.get('Gini', 0)[-1]=0.6714\n",
+      "Condition not met at step 1801. No data to save.\n",
       "1801\n",
+      "model_vars.get('Gini', 0)[-1]=0.6408\n",
+      "Condition not met at step 1802. No data to save.\n",
       "1802\n",
+      "model_vars.get('Gini', 0)[-1]=0.6295999999999999\n",
+      "Condition not met at step 1803. No data to save.\n",
       "1803\n",
+      "model_vars.get('Gini', 0)[-1]=0.6528\n",
+      "Condition not met at step 1804. No data to save.\n",
       "1804\n",
+      "model_vars.get('Gini', 0)[-1]=0.6374\n",
+      "Condition not met at step 1805. No data to save.\n",
       "1805\n",
+      "model_vars.get('Gini', 0)[-1]=0.657\n",
+      "Condition not met at step 1806. No data to save.\n",
       "1806\n",
+      "model_vars.get('Gini', 0)[-1]=0.6584\n",
+      "Condition not met at step 1807. No data to save.\n",
       "1807\n",
+      "model_vars.get('Gini', 0)[-1]=0.6542\n",
+      "Condition not met at step 1808. No data to save.\n",
       "1808\n",
+      "model_vars.get('Gini', 0)[-1]=0.623\n",
+      "Condition not met at step 1809. No data to save.\n",
       "1809\n",
+      "model_vars.get('Gini', 0)[-1]=0.6402\n",
+      "Condition not met at step 1810. No data to save.\n",
       "1810\n",
+      "model_vars.get('Gini', 0)[-1]=0.6434\n",
+      "Condition not met at step 1811. No data to save.\n",
       "1811\n",
+      "model_vars.get('Gini', 0)[-1]=0.6464000000000001\n",
+      "Condition not met at step 1812. No data to save.\n",
       "1812\n",
+      "model_vars.get('Gini', 0)[-1]=0.6517999999999999\n",
+      "Condition not met at step 1813. No data to save.\n",
       "1813\n",
+      "model_vars.get('Gini', 0)[-1]=0.6284000000000001\n",
+      "Condition not met at step 1814. No data to save.\n",
       "1814\n",
+      "model_vars.get('Gini', 0)[-1]=0.6152\n",
+      "Condition not met at step 1815. No data to save.\n",
       "1815\n",
+      "model_vars.get('Gini', 0)[-1]=0.6016\n",
+      "Condition not met at step 1816. No data to save.\n",
       "1816\n",
+      "model_vars.get('Gini', 0)[-1]=0.6106\n",
+      "Condition not met at step 1817. No data to save.\n",
       "1817\n",
+      "model_vars.get('Gini', 0)[-1]=0.6106\n",
+      "Condition not met at step 1818. No data to save.\n",
       "1818\n",
+      "model_vars.get('Gini', 0)[-1]=0.6248\n",
+      "Condition not met at step 1819. No data to save.\n",
       "1819\n",
+      "model_vars.get('Gini', 0)[-1]=0.6335999999999999\n",
+      "Condition not met at step 1820. No data to save.\n",
       "1820\n",
+      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
+      "Condition not met at step 1821. No data to save.\n",
       "1821\n",
+      "model_vars.get('Gini', 0)[-1]=0.6138\n",
+      "Condition not met at step 1822. No data to save.\n",
       "1822\n",
+      "model_vars.get('Gini', 0)[-1]=0.6024\n",
+      "Condition not met at step 1823. No data to save.\n",
       "1823\n",
+      "model_vars.get('Gini', 0)[-1]=0.585\n",
+      "Condition not met at step 1824. No data to save.\n",
       "1824\n",
+      "model_vars.get('Gini', 0)[-1]=0.618\n",
+      "Condition not met at step 1825. No data to save.\n",
       "1825\n",
+      "model_vars.get('Gini', 0)[-1]=0.6016\n",
+      "Condition not met at step 1826. No data to save.\n",
       "1826\n",
+      "model_vars.get('Gini', 0)[-1]=0.5840000000000001\n",
+      "Condition not met at step 1827. No data to save.\n",
       "1827\n",
+      "model_vars.get('Gini', 0)[-1]=0.5882000000000001\n",
+      "Condition not met at step 1828. No data to save.\n",
       "1828\n",
+      "model_vars.get('Gini', 0)[-1]=0.6020000000000001\n",
+      "Condition not met at step 1829. No data to save.\n",
       "1829\n",
+      "model_vars.get('Gini', 0)[-1]=0.6342\n",
+      "Condition not met at step 1830. No data to save.\n",
       "1830\n",
+      "model_vars.get('Gini', 0)[-1]=0.647\n",
+      "Condition not met at step 1831. No data to save.\n",
       "1831\n",
+      "model_vars.get('Gini', 0)[-1]=0.6304000000000001\n",
+      "Condition not met at step 1832. No data to save.\n",
       "1832\n",
+      "model_vars.get('Gini', 0)[-1]=0.628\n",
+      "Condition not met at step 1833. No data to save.\n",
       "1833\n",
+      "model_vars.get('Gini', 0)[-1]=0.615\n",
+      "Condition not met at step 1834. No data to save.\n",
       "1834\n",
+      "model_vars.get('Gini', 0)[-1]=0.6048\n",
+      "Condition not met at step 1835. No data to save.\n",
       "1835\n",
+      "model_vars.get('Gini', 0)[-1]=0.637\n",
+      "Condition not met at step 1836. No data to save.\n",
       "1836\n",
+      "model_vars.get('Gini', 0)[-1]=0.6282000000000001\n",
+      "Condition not met at step 1837. No data to save.\n",
       "1837\n",
+      "model_vars.get('Gini', 0)[-1]=0.6652\n",
+      "Condition not met at step 1838. No data to save.\n",
       "1838\n",
+      "model_vars.get('Gini', 0)[-1]=0.6215999999999999\n",
+      "Condition not met at step 1839. No data to save.\n",
       "1839\n",
+      "model_vars.get('Gini', 0)[-1]=0.6068\n",
+      "Condition not met at step 1840. No data to save.\n",
       "1840\n",
+      "model_vars.get('Gini', 0)[-1]=0.6248\n",
+      "Condition not met at step 1841. No data to save.\n",
       "1841\n",
+      "model_vars.get('Gini', 0)[-1]=0.6275999999999999\n",
+      "Condition not met at step 1842. No data to save.\n",
       "1842\n",
+      "model_vars.get('Gini', 0)[-1]=0.6302\n",
+      "Condition not met at step 1843. No data to save.\n",
       "1843\n",
+      "model_vars.get('Gini', 0)[-1]=0.6664\n",
+      "Condition not met at step 1844. No data to save.\n",
       "1844\n",
+      "model_vars.get('Gini', 0)[-1]=0.656\n",
+      "Condition not met at step 1845. No data to save.\n",
       "1845\n",
+      "model_vars.get('Gini', 0)[-1]=0.6274\n",
+      "Condition not met at step 1846. No data to save.\n",
       "1846\n",
+      "model_vars.get('Gini', 0)[-1]=0.609\n",
+      "Condition not met at step 1847. No data to save.\n",
       "1847\n",
+      "model_vars.get('Gini', 0)[-1]=0.64\n",
+      "Condition not met at step 1848. No data to save.\n",
       "1848\n",
+      "model_vars.get('Gini', 0)[-1]=0.623\n",
+      "Condition not met at step 1849. No data to save.\n",
       "1849\n",
+      "model_vars.get('Gini', 0)[-1]=0.6494\n",
+      "Condition not met at step 1850. No data to save.\n",
       "1850\n",
+      "model_vars.get('Gini', 0)[-1]=0.6616\n",
+      "Condition not met at step 1851. No data to save.\n",
       "1851\n",
+      "model_vars.get('Gini', 0)[-1]=0.6474\n",
+      "Condition not met at step 1852. No data to save.\n",
       "1852\n",
+      "model_vars.get('Gini', 0)[-1]=0.6542\n",
+      "Condition not met at step 1853. No data to save.\n",
       "1853\n",
+      "model_vars.get('Gini', 0)[-1]=0.644\n",
+      "Condition not met at step 1854. No data to save.\n",
       "1854\n",
+      "model_vars.get('Gini', 0)[-1]=0.6756\n",
+      "Condition not met at step 1855. No data to save.\n",
       "1855\n",
+      "model_vars.get('Gini', 0)[-1]=0.6772\n",
+      "Condition not met at step 1856. No data to save.\n",
       "1856\n",
+      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
+      "Condition not met at step 1857. No data to save.\n",
       "1857\n",
+      "model_vars.get('Gini', 0)[-1]=0.6888000000000001\n",
+      "Condition not met at step 1858. No data to save.\n",
       "1858\n",
+      "model_vars.get('Gini', 0)[-1]=0.6952\n",
+      "Condition not met at step 1859. No data to save.\n",
       "1859\n",
+      "model_vars.get('Gini', 0)[-1]=0.6854\n",
+      "Condition not met at step 1860. No data to save.\n",
       "1860\n",
+      "model_vars.get('Gini', 0)[-1]=0.657\n",
+      "Condition not met at step 1861. No data to save.\n",
       "1861\n",
+      "model_vars.get('Gini', 0)[-1]=0.6378\n",
+      "Condition not met at step 1862. No data to save.\n",
       "1862\n",
+      "model_vars.get('Gini', 0)[-1]=0.6198\n",
+      "Condition not met at step 1863. No data to save.\n",
       "1863\n",
+      "model_vars.get('Gini', 0)[-1]=0.609\n",
+      "Condition not met at step 1864. No data to save.\n",
       "1864\n",
+      "model_vars.get('Gini', 0)[-1]=0.6134\n",
+      "Condition not met at step 1865. No data to save.\n",
       "1865\n",
+      "model_vars.get('Gini', 0)[-1]=0.6072\n",
+      "Condition not met at step 1866. No data to save.\n",
       "1866\n",
+      "model_vars.get('Gini', 0)[-1]=0.6234\n",
+      "Condition not met at step 1867. No data to save.\n",
       "1867\n",
+      "model_vars.get('Gini', 0)[-1]=0.6038\n",
+      "Condition not met at step 1868. No data to save.\n",
       "1868\n",
+      "model_vars.get('Gini', 0)[-1]=0.6226\n",
+      "Condition not met at step 1869. No data to save.\n",
       "1869\n",
+      "model_vars.get('Gini', 0)[-1]=0.5726\n",
+      "Condition not met at step 1870. No data to save.\n",
       "1870\n",
+      "model_vars.get('Gini', 0)[-1]=0.5686\n",
+      "Condition not met at step 1871. No data to save.\n",
       "1871\n",
+      "model_vars.get('Gini', 0)[-1]=0.5932\n",
+      "Condition not met at step 1872. No data to save.\n",
       "1872\n",
+      "model_vars.get('Gini', 0)[-1]=0.5734\n",
+      "Condition not met at step 1873. No data to save.\n",
       "1873\n",
+      "model_vars.get('Gini', 0)[-1]=0.5418000000000001\n",
+      "Condition not met at step 1874. No data to save.\n",
       "1874\n",
+      "model_vars.get('Gini', 0)[-1]=0.5686\n",
+      "Condition not met at step 1875. No data to save.\n",
       "1875\n",
+      "model_vars.get('Gini', 0)[-1]=0.5726\n",
+      "Condition not met at step 1876. No data to save.\n",
       "1876\n",
+      "model_vars.get('Gini', 0)[-1]=0.5926\n",
+      "Condition not met at step 1877. No data to save.\n",
       "1877\n",
+      "model_vars.get('Gini', 0)[-1]=0.5704\n",
+      "Condition not met at step 1878. No data to save.\n",
       "1878\n",
+      "model_vars.get('Gini', 0)[-1]=0.6214\n",
+      "Condition not met at step 1879. No data to save.\n",
       "1879\n",
+      "model_vars.get('Gini', 0)[-1]=0.6644\n",
+      "Condition not met at step 1880. No data to save.\n",
       "1880\n",
+      "model_vars.get('Gini', 0)[-1]=0.666\n",
+      "Condition not met at step 1881. No data to save.\n",
       "1881\n",
+      "model_vars.get('Gini', 0)[-1]=0.6468\n",
+      "Condition not met at step 1882. No data to save.\n",
       "1882\n",
+      "model_vars.get('Gini', 0)[-1]=0.6698\n",
+      "Condition not met at step 1883. No data to save.\n",
       "1883\n",
+      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
+      "Condition not met at step 1884. No data to save.\n",
       "1884\n",
+      "model_vars.get('Gini', 0)[-1]=0.6724\n",
+      "Condition not met at step 1885. No data to save.\n",
       "1885\n",
+      "model_vars.get('Gini', 0)[-1]=0.6864\n",
+      "Condition not met at step 1886. No data to save.\n",
       "1886\n",
+      "model_vars.get('Gini', 0)[-1]=0.6921999999999999\n",
+      "Condition not met at step 1887. No data to save.\n",
       "1887\n",
+      "model_vars.get('Gini', 0)[-1]=0.6926\n",
+      "Condition not met at step 1888. No data to save.\n",
       "1888\n",
+      "model_vars.get('Gini', 0)[-1]=0.6916\n",
+      "Condition not met at step 1889. No data to save.\n",
       "1889\n",
+      "model_vars.get('Gini', 0)[-1]=0.6918\n",
+      "Condition not met at step 1890. No data to save.\n",
       "1890\n",
+      "model_vars.get('Gini', 0)[-1]=0.6948000000000001\n",
+      "Condition not met at step 1891. No data to save.\n",
       "1891\n",
+      "model_vars.get('Gini', 0)[-1]=0.6998\n",
+      "Condition not met at step 1892. No data to save.\n",
       "1892\n",
+      "model_vars.get('Gini', 0)[-1]=0.704\n",
+      "Condition met. Appended special results for step 1893 to test_path/special_results.parquet\n",
       "1893\n",
+      "model_vars.get('Gini', 0)[-1]=0.702\n",
+      "Condition met. Appended special results for step 1894 to test_path/special_results.parquet\n",
       "1894\n",
+      "model_vars.get('Gini', 0)[-1]=0.692\n",
+      "Condition not met at step 1895. No data to save.\n",
       "1895\n",
+      "model_vars.get('Gini', 0)[-1]=0.6882\n",
+      "Condition not met at step 1896. No data to save.\n",
       "1896\n",
+      "model_vars.get('Gini', 0)[-1]=0.6914\n",
+      "Condition not met at step 1897. No data to save.\n",
       "1897\n",
+      "model_vars.get('Gini', 0)[-1]=0.6844\n",
+      "Condition not met at step 1898. No data to save.\n",
       "1898\n",
+      "model_vars.get('Gini', 0)[-1]=0.6692\n",
+      "Condition not met at step 1899. No data to save.\n",
       "1899\n",
       "CALLED\n",
       "self.model._steps=1900\n",
       " TEST self.model._steps=1900\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "1800  0.6428\n",
-      "1801  0.6518\n",
-      "1802  0.6422\n",
-      "1803  0.6568\n",
-      "1804  0.6676\n",
+      "1800  0.6714\n",
+      "1801  0.6408\n",
+      "1802  0.6296\n",
+      "1803  0.6528\n",
+      "1804  0.6374\n",
       "...      ...\n",
-      "1895  0.6824\n",
-      "1896  0.6732\n",
-      "1897  0.6770\n",
-      "1898  0.6472\n",
-      "1899  0.6642\n",
+      "1895  0.6882\n",
+      "1896  0.6914\n",
+      "1897  0.6844\n",
+      "1898  0.6692\n",
+      "1899  0.6628\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_019.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_019.parquet'\n",
-      "Saving model to output_dir/model_data_019.parquet\n",
-      "Saving agent to output_dir/agent_data_019.parquet\n",
+      "model_file='test_path/model_data_019.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_019.parquet'\n",
+      "Saving model to test_path/model_data_019.parquet\n",
+      "Saving agent to test_path/agent_data_019.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
+      "Condition not met at step 1900. No data to save.\n",
       "1900\n",
+      "model_vars.get('Gini', 0)[-1]=0.657\n",
+      "Condition not met at step 1901. No data to save.\n",
       "1901\n",
+      "model_vars.get('Gini', 0)[-1]=0.6568\n",
+      "Condition not met at step 1902. No data to save.\n",
       "1902\n",
+      "model_vars.get('Gini', 0)[-1]=0.6496\n",
+      "Condition not met at step 1903. No data to save.\n",
       "1903\n",
+      "model_vars.get('Gini', 0)[-1]=0.6517999999999999\n",
+      "Condition not met at step 1904. No data to save.\n",
       "1904\n",
+      "model_vars.get('Gini', 0)[-1]=0.6396\n",
+      "Condition not met at step 1905. No data to save.\n",
       "1905\n",
+      "model_vars.get('Gini', 0)[-1]=0.644\n",
+      "Condition not met at step 1906. No data to save.\n",
       "1906\n",
+      "model_vars.get('Gini', 0)[-1]=0.6366\n",
+      "Condition not met at step 1907. No data to save.\n",
       "1907\n",
+      "model_vars.get('Gini', 0)[-1]=0.631\n",
+      "Condition not met at step 1908. No data to save.\n",
       "1908\n",
+      "model_vars.get('Gini', 0)[-1]=0.636\n",
+      "Condition not met at step 1909. No data to save.\n",
       "1909\n",
+      "model_vars.get('Gini', 0)[-1]=0.6268\n",
+      "Condition not met at step 1910. No data to save.\n",
       "1910\n",
+      "model_vars.get('Gini', 0)[-1]=0.6426000000000001\n",
+      "Condition not met at step 1911. No data to save.\n",
       "1911\n",
+      "model_vars.get('Gini', 0)[-1]=0.6314\n",
+      "Condition not met at step 1912. No data to save.\n",
       "1912\n",
+      "model_vars.get('Gini', 0)[-1]=0.6376\n",
+      "Condition not met at step 1913. No data to save.\n",
       "1913\n",
+      "model_vars.get('Gini', 0)[-1]=0.634\n",
+      "Condition not met at step 1914. No data to save.\n",
       "1914\n",
+      "model_vars.get('Gini', 0)[-1]=0.6452\n",
+      "Condition not met at step 1915. No data to save.\n",
       "1915\n",
+      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
+      "Condition not met at step 1916. No data to save.\n",
       "1916\n",
+      "model_vars.get('Gini', 0)[-1]=0.6572\n",
+      "Condition not met at step 1917. No data to save.\n",
       "1917\n",
+      "model_vars.get('Gini', 0)[-1]=0.6516\n",
+      "Condition not met at step 1918. No data to save.\n",
       "1918\n",
+      "model_vars.get('Gini', 0)[-1]=0.665\n",
+      "Condition not met at step 1919. No data to save.\n",
       "1919\n",
+      "model_vars.get('Gini', 0)[-1]=0.636\n",
+      "Condition not met at step 1920. No data to save.\n",
       "1920\n",
+      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
+      "Condition not met at step 1921. No data to save.\n",
       "1921\n",
+      "model_vars.get('Gini', 0)[-1]=0.6746000000000001\n",
+      "Condition not met at step 1922. No data to save.\n",
       "1922\n",
+      "model_vars.get('Gini', 0)[-1]=0.6844\n",
+      "Condition not met at step 1923. No data to save.\n",
       "1923\n",
+      "model_vars.get('Gini', 0)[-1]=0.7070000000000001\n",
+      "Condition met. Appended special results for step 1924 to test_path/special_results.parquet\n",
       "1924\n",
+      "model_vars.get('Gini', 0)[-1]=0.6584\n",
+      "Condition not met at step 1925. No data to save.\n",
       "1925\n",
+      "model_vars.get('Gini', 0)[-1]=0.6422\n",
+      "Condition not met at step 1926. No data to save.\n",
       "1926\n",
+      "model_vars.get('Gini', 0)[-1]=0.6468\n",
+      "Condition not met at step 1927. No data to save.\n",
       "1927\n",
+      "model_vars.get('Gini', 0)[-1]=0.6376\n",
+      "Condition not met at step 1928. No data to save.\n",
       "1928\n",
+      "model_vars.get('Gini', 0)[-1]=0.6338\n",
+      "Condition not met at step 1929. No data to save.\n",
       "1929\n",
+      "model_vars.get('Gini', 0)[-1]=0.6436\n",
+      "Condition not met at step 1930. No data to save.\n",
       "1930\n",
+      "model_vars.get('Gini', 0)[-1]=0.6494\n",
+      "Condition not met at step 1931. No data to save.\n",
       "1931\n",
+      "model_vars.get('Gini', 0)[-1]=0.6734\n",
+      "Condition not met at step 1932. No data to save.\n",
       "1932\n",
+      "model_vars.get('Gini', 0)[-1]=0.6694\n",
+      "Condition not met at step 1933. No data to save.\n",
       "1933\n",
+      "model_vars.get('Gini', 0)[-1]=0.669\n",
+      "Condition not met at step 1934. No data to save.\n",
       "1934\n",
+      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
+      "Condition not met at step 1935. No data to save.\n",
       "1935\n",
+      "model_vars.get('Gini', 0)[-1]=0.5906\n",
+      "Condition not met at step 1936. No data to save.\n",
       "1936\n",
+      "model_vars.get('Gini', 0)[-1]=0.6254\n",
+      "Condition not met at step 1937. No data to save.\n",
       "1937\n",
+      "model_vars.get('Gini', 0)[-1]=0.651\n",
+      "Condition not met at step 1938. No data to save.\n",
       "1938\n",
+      "model_vars.get('Gini', 0)[-1]=0.6488\n",
+      "Condition not met at step 1939. No data to save.\n",
       "1939\n",
+      "model_vars.get('Gini', 0)[-1]=0.641\n",
+      "Condition not met at step 1940. No data to save.\n",
       "1940\n",
+      "model_vars.get('Gini', 0)[-1]=0.6098\n",
+      "Condition not met at step 1941. No data to save.\n",
       "1941\n",
+      "model_vars.get('Gini', 0)[-1]=0.6322\n",
+      "Condition not met at step 1942. No data to save.\n",
       "1942\n",
+      "model_vars.get('Gini', 0)[-1]=0.6372\n",
+      "Condition not met at step 1943. No data to save.\n",
       "1943\n",
+      "model_vars.get('Gini', 0)[-1]=0.6295999999999999\n",
+      "Condition not met at step 1944. No data to save.\n",
       "1944\n",
+      "model_vars.get('Gini', 0)[-1]=0.632\n",
+      "Condition not met at step 1945. No data to save.\n",
       "1945\n",
+      "model_vars.get('Gini', 0)[-1]=0.6052\n",
+      "Condition not met at step 1946. No data to save.\n",
       "1946\n",
+      "model_vars.get('Gini', 0)[-1]=0.6004\n",
+      "Condition not met at step 1947. No data to save.\n",
       "1947\n",
+      "model_vars.get('Gini', 0)[-1]=0.5936\n",
+      "Condition not met at step 1948. No data to save.\n",
       "1948\n",
+      "model_vars.get('Gini', 0)[-1]=0.6152\n",
+      "Condition not met at step 1949. No data to save.\n",
       "1949\n",
+      "model_vars.get('Gini', 0)[-1]=0.6136\n",
+      "Condition not met at step 1950. No data to save.\n",
       "1950\n",
+      "model_vars.get('Gini', 0)[-1]=0.6312\n",
+      "Condition not met at step 1951. No data to save.\n",
       "1951\n",
+      "model_vars.get('Gini', 0)[-1]=0.6046\n",
+      "Condition not met at step 1952. No data to save.\n",
       "1952\n",
+      "model_vars.get('Gini', 0)[-1]=0.6056\n",
+      "Condition not met at step 1953. No data to save.\n",
       "1953\n",
+      "model_vars.get('Gini', 0)[-1]=0.5814\n",
+      "Condition not met at step 1954. No data to save.\n",
       "1954\n",
+      "model_vars.get('Gini', 0)[-1]=0.5766\n",
+      "Condition not met at step 1955. No data to save.\n",
       "1955\n",
+      "model_vars.get('Gini', 0)[-1]=0.5962000000000001\n",
+      "Condition not met at step 1956. No data to save.\n",
       "1956\n",
+      "model_vars.get('Gini', 0)[-1]=0.5972\n",
+      "Condition not met at step 1957. No data to save.\n",
       "1957\n",
+      "model_vars.get('Gini', 0)[-1]=0.5842\n",
+      "Condition not met at step 1958. No data to save.\n",
       "1958\n",
+      "model_vars.get('Gini', 0)[-1]=0.6084\n",
+      "Condition not met at step 1959. No data to save.\n",
       "1959\n",
+      "model_vars.get('Gini', 0)[-1]=0.5702\n",
+      "Condition not met at step 1960. No data to save.\n",
       "1960\n",
+      "model_vars.get('Gini', 0)[-1]=0.5776\n",
+      "Condition not met at step 1961. No data to save.\n",
       "1961\n",
+      "model_vars.get('Gini', 0)[-1]=0.6112\n",
+      "Condition not met at step 1962. No data to save.\n",
       "1962\n",
+      "model_vars.get('Gini', 0)[-1]=0.6192\n",
+      "Condition not met at step 1963. No data to save.\n",
       "1963\n",
+      "model_vars.get('Gini', 0)[-1]=0.6116\n",
+      "Condition not met at step 1964. No data to save.\n",
       "1964\n",
+      "model_vars.get('Gini', 0)[-1]=0.6446000000000001\n",
+      "Condition not met at step 1965. No data to save.\n",
       "1965\n",
+      "model_vars.get('Gini', 0)[-1]=0.6454\n",
+      "Condition not met at step 1966. No data to save.\n",
       "1966\n",
+      "model_vars.get('Gini', 0)[-1]=0.654\n",
+      "Condition not met at step 1967. No data to save.\n",
       "1967\n",
+      "model_vars.get('Gini', 0)[-1]=0.6396\n",
+      "Condition not met at step 1968. No data to save.\n",
       "1968\n",
+      "model_vars.get('Gini', 0)[-1]=0.645\n",
+      "Condition not met at step 1969. No data to save.\n",
       "1969\n",
+      "model_vars.get('Gini', 0)[-1]=0.6138\n",
+      "Condition not met at step 1970. No data to save.\n",
       "1970\n",
+      "model_vars.get('Gini', 0)[-1]=0.6326\n",
+      "Condition not met at step 1971. No data to save.\n",
       "1971\n",
+      "model_vars.get('Gini', 0)[-1]=0.6286\n",
+      "Condition not met at step 1972. No data to save.\n",
       "1972\n",
+      "model_vars.get('Gini', 0)[-1]=0.6244000000000001\n",
+      "Condition not met at step 1973. No data to save.\n",
       "1973\n",
+      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
+      "Condition not met at step 1974. No data to save.\n",
       "1974\n",
+      "model_vars.get('Gini', 0)[-1]=0.6522\n",
+      "Condition not met at step 1975. No data to save.\n",
       "1975\n",
+      "model_vars.get('Gini', 0)[-1]=0.6328\n",
+      "Condition not met at step 1976. No data to save.\n",
       "1976\n",
+      "model_vars.get('Gini', 0)[-1]=0.6304000000000001\n",
+      "Condition not met at step 1977. No data to save.\n",
       "1977\n",
+      "model_vars.get('Gini', 0)[-1]=0.6275999999999999\n",
+      "Condition not met at step 1978. No data to save.\n",
       "1978\n",
+      "model_vars.get('Gini', 0)[-1]=0.6456\n",
+      "Condition not met at step 1979. No data to save.\n",
       "1979\n",
+      "model_vars.get('Gini', 0)[-1]=0.6376\n",
+      "Condition not met at step 1980. No data to save.\n",
       "1980\n",
+      "model_vars.get('Gini', 0)[-1]=0.6437999999999999\n",
+      "Condition not met at step 1981. No data to save.\n",
       "1981\n",
+      "model_vars.get('Gini', 0)[-1]=0.6394\n",
+      "Condition not met at step 1982. No data to save.\n",
       "1982\n",
+      "model_vars.get('Gini', 0)[-1]=0.6836\n",
+      "Condition not met at step 1983. No data to save.\n",
       "1983\n",
+      "model_vars.get('Gini', 0)[-1]=0.6842\n",
+      "Condition not met at step 1984. No data to save.\n",
       "1984\n",
+      "model_vars.get('Gini', 0)[-1]=0.6702\n",
+      "Condition not met at step 1985. No data to save.\n",
       "1985\n",
+      "model_vars.get('Gini', 0)[-1]=0.6534\n",
+      "Condition not met at step 1986. No data to save.\n",
       "1986\n",
+      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
+      "Condition not met at step 1987. No data to save.\n",
       "1987\n",
+      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
+      "Condition not met at step 1988. No data to save.\n",
       "1988\n",
+      "model_vars.get('Gini', 0)[-1]=0.622\n",
+      "Condition not met at step 1989. No data to save.\n",
       "1989\n",
+      "model_vars.get('Gini', 0)[-1]=0.6482\n",
+      "Condition not met at step 1990. No data to save.\n",
       "1990\n",
+      "model_vars.get('Gini', 0)[-1]=0.6682\n",
+      "Condition not met at step 1991. No data to save.\n",
       "1991\n",
+      "model_vars.get('Gini', 0)[-1]=0.6536\n",
+      "Condition not met at step 1992. No data to save.\n",
       "1992\n",
+      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
+      "Condition not met at step 1993. No data to save.\n",
       "1993\n",
+      "model_vars.get('Gini', 0)[-1]=0.6252\n",
+      "Condition not met at step 1994. No data to save.\n",
       "1994\n",
+      "model_vars.get('Gini', 0)[-1]=0.6278\n",
+      "Condition not met at step 1995. No data to save.\n",
       "1995\n",
+      "model_vars.get('Gini', 0)[-1]=0.6664\n",
+      "Condition not met at step 1996. No data to save.\n",
       "1996\n",
+      "model_vars.get('Gini', 0)[-1]=0.6378\n",
+      "Condition not met at step 1997. No data to save.\n",
       "1997\n",
+      "model_vars.get('Gini', 0)[-1]=0.6748000000000001\n",
+      "Condition not met at step 1998. No data to save.\n",
       "1998\n",
+      "model_vars.get('Gini', 0)[-1]=0.6704\n",
+      "Condition not met at step 1999. No data to save.\n",
       "1999\n",
       "CALLED\n",
       "self.model._steps=2000\n",
       " TEST self.model._steps=2000\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "1900  0.6662\n",
-      "1901  0.6552\n",
-      "1902  0.6858\n",
-      "1903  0.6734\n",
-      "1904  0.6736\n",
+      "1900  0.6570\n",
+      "1901  0.6568\n",
+      "1902  0.6496\n",
+      "1903  0.6518\n",
+      "1904  0.6396\n",
       "...      ...\n",
-      "1995  0.6336\n",
-      "1996  0.6394\n",
-      "1997  0.6560\n",
-      "1998  0.6730\n",
-      "1999  0.6548\n",
+      "1995  0.6664\n",
+      "1996  0.6378\n",
+      "1997  0.6748\n",
+      "1998  0.6704\n",
+      "1999  0.7110\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_020.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_020.parquet'\n",
-      "Saving model to output_dir/model_data_020.parquet\n",
-      "Saving agent to output_dir/agent_data_020.parquet\n",
+      "model_file='test_path/model_data_020.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_020.parquet'\n",
+      "Saving model to test_path/model_data_020.parquet\n",
+      "Saving agent to test_path/agent_data_020.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.7110000000000001\n",
+      "Condition met. Appended special results for step 2000 to test_path/special_results.parquet\n",
       "2000\n",
+      "model_vars.get('Gini', 0)[-1]=0.6892\n",
+      "Condition not met at step 2001. No data to save.\n",
       "2001\n",
+      "model_vars.get('Gini', 0)[-1]=0.7076\n",
+      "Condition met. Appended special results for step 2002 to test_path/special_results.parquet\n",
       "2002\n",
+      "model_vars.get('Gini', 0)[-1]=0.7010000000000001\n",
+      "Condition met. Appended special results for step 2003 to test_path/special_results.parquet\n",
       "2003\n",
+      "model_vars.get('Gini', 0)[-1]=0.6986\n",
+      "Condition not met at step 2004. No data to save.\n",
       "2004\n",
+      "model_vars.get('Gini', 0)[-1]=0.7021999999999999\n",
+      "Condition met. Appended special results for step 2005 to test_path/special_results.parquet\n",
       "2005\n",
+      "model_vars.get('Gini', 0)[-1]=0.7068\n",
+      "Condition met. Appended special results for step 2006 to test_path/special_results.parquet\n",
       "2006\n",
+      "model_vars.get('Gini', 0)[-1]=0.6988000000000001\n",
+      "Condition not met at step 2007. No data to save.\n",
       "2007\n",
+      "model_vars.get('Gini', 0)[-1]=0.6988000000000001\n",
+      "Condition not met at step 2008. No data to save.\n",
       "2008\n",
+      "model_vars.get('Gini', 0)[-1]=0.6856\n",
+      "Condition not met at step 2009. No data to save.\n",
       "2009\n",
+      "model_vars.get('Gini', 0)[-1]=0.7126\n",
+      "Condition met. Appended special results for step 2010 to test_path/special_results.parquet\n",
       "2010\n",
+      "model_vars.get('Gini', 0)[-1]=0.6981999999999999\n",
+      "Condition not met at step 2011. No data to save.\n",
       "2011\n",
+      "model_vars.get('Gini', 0)[-1]=0.7150000000000001\n",
+      "Condition met. Appended special results for step 2012 to test_path/special_results.parquet\n",
       "2012\n",
+      "model_vars.get('Gini', 0)[-1]=0.7144\n",
+      "Condition met. Appended special results for step 2013 to test_path/special_results.parquet\n",
       "2013\n",
+      "model_vars.get('Gini', 0)[-1]=0.7178\n",
+      "Condition met. Appended special results for step 2014 to test_path/special_results.parquet\n",
       "2014\n",
+      "model_vars.get('Gini', 0)[-1]=0.7114\n",
+      "Condition met. Appended special results for step 2015 to test_path/special_results.parquet\n",
       "2015\n",
+      "model_vars.get('Gini', 0)[-1]=0.7121999999999999\n",
+      "Condition met. Appended special results for step 2016 to test_path/special_results.parquet\n",
       "2016\n",
+      "model_vars.get('Gini', 0)[-1]=0.687\n",
+      "Condition not met at step 2017. No data to save.\n",
       "2017\n",
+      "model_vars.get('Gini', 0)[-1]=0.6961999999999999\n",
+      "Condition not met at step 2018. No data to save.\n",
       "2018\n",
+      "model_vars.get('Gini', 0)[-1]=0.7056\n",
+      "Condition met. Appended special results for step 2019 to test_path/special_results.parquet\n",
       "2019\n",
+      "model_vars.get('Gini', 0)[-1]=0.6990000000000001\n",
+      "Condition not met at step 2020. No data to save.\n",
       "2020\n",
+      "model_vars.get('Gini', 0)[-1]=0.7134\n",
+      "Condition met. Appended special results for step 2021 to test_path/special_results.parquet\n",
       "2021\n",
+      "model_vars.get('Gini', 0)[-1]=0.7090000000000001\n",
+      "Condition met. Appended special results for step 2022 to test_path/special_results.parquet\n",
       "2022\n",
+      "model_vars.get('Gini', 0)[-1]=0.7234\n",
+      "Condition met. Appended special results for step 2023 to test_path/special_results.parquet\n",
       "2023\n",
+      "model_vars.get('Gini', 0)[-1]=0.7030000000000001\n",
+      "Condition met. Appended special results for step 2024 to test_path/special_results.parquet\n",
       "2024\n",
+      "model_vars.get('Gini', 0)[-1]=0.7176\n",
+      "Condition met. Appended special results for step 2025 to test_path/special_results.parquet\n",
       "2025\n",
+      "model_vars.get('Gini', 0)[-1]=0.714\n",
+      "Condition met. Appended special results for step 2026 to test_path/special_results.parquet\n",
       "2026\n",
+      "model_vars.get('Gini', 0)[-1]=0.71\n",
+      "Condition met. Appended special results for step 2027 to test_path/special_results.parquet\n",
       "2027\n",
+      "model_vars.get('Gini', 0)[-1]=0.6696\n",
+      "Condition not met at step 2028. No data to save.\n",
       "2028\n",
+      "model_vars.get('Gini', 0)[-1]=0.6732\n",
+      "Condition not met at step 2029. No data to save.\n",
       "2029\n",
+      "model_vars.get('Gini', 0)[-1]=0.6632\n",
+      "Condition not met at step 2030. No data to save.\n",
       "2030\n",
+      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
+      "Condition not met at step 2031. No data to save.\n",
       "2031\n",
+      "model_vars.get('Gini', 0)[-1]=0.6676\n",
+      "Condition not met at step 2032. No data to save.\n",
       "2032\n",
+      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
+      "Condition not met at step 2033. No data to save.\n",
       "2033\n",
+      "model_vars.get('Gini', 0)[-1]=0.6696\n",
+      "Condition not met at step 2034. No data to save.\n",
       "2034\n",
+      "model_vars.get('Gini', 0)[-1]=0.6428\n",
+      "Condition not met at step 2035. No data to save.\n",
       "2035\n",
+      "model_vars.get('Gini', 0)[-1]=0.6362\n",
+      "Condition not met at step 2036. No data to save.\n",
       "2036\n",
+      "model_vars.get('Gini', 0)[-1]=0.6308\n",
+      "Condition not met at step 2037. No data to save.\n",
       "2037\n",
+      "model_vars.get('Gini', 0)[-1]=0.605\n",
+      "Condition not met at step 2038. No data to save.\n",
       "2038\n",
+      "model_vars.get('Gini', 0)[-1]=0.6476\n",
+      "Condition not met at step 2039. No data to save.\n",
       "2039\n",
+      "model_vars.get('Gini', 0)[-1]=0.6452\n",
+      "Condition not met at step 2040. No data to save.\n",
       "2040\n",
+      "model_vars.get('Gini', 0)[-1]=0.6616\n",
+      "Condition not met at step 2041. No data to save.\n",
       "2041\n",
+      "model_vars.get('Gini', 0)[-1]=0.6476\n",
+      "Condition not met at step 2042. No data to save.\n",
       "2042\n",
+      "model_vars.get('Gini', 0)[-1]=0.6432\n",
+      "Condition not met at step 2043. No data to save.\n",
       "2043\n",
+      "model_vars.get('Gini', 0)[-1]=0.6556\n",
+      "Condition not met at step 2044. No data to save.\n",
       "2044\n",
+      "model_vars.get('Gini', 0)[-1]=0.6684\n",
+      "Condition not met at step 2045. No data to save.\n",
       "2045\n",
+      "model_vars.get('Gini', 0)[-1]=0.6812\n",
+      "Condition not met at step 2046. No data to save.\n",
       "2046\n",
+      "model_vars.get('Gini', 0)[-1]=0.702\n",
+      "Condition met. Appended special results for step 2047 to test_path/special_results.parquet\n",
       "2047\n",
+      "model_vars.get('Gini', 0)[-1]=0.7064\n",
+      "Condition met. Appended special results for step 2048 to test_path/special_results.parquet\n",
       "2048\n",
+      "model_vars.get('Gini', 0)[-1]=0.7070000000000001\n",
+      "Condition met. Appended special results for step 2049 to test_path/special_results.parquet\n",
       "2049\n",
+      "model_vars.get('Gini', 0)[-1]=0.6758\n",
+      "Condition not met at step 2050. No data to save.\n",
       "2050\n",
+      "model_vars.get('Gini', 0)[-1]=0.6826\n",
+      "Condition not met at step 2051. No data to save.\n",
       "2051\n",
+      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
+      "Condition not met at step 2052. No data to save.\n",
       "2052\n",
+      "model_vars.get('Gini', 0)[-1]=0.6642\n",
+      "Condition not met at step 2053. No data to save.\n",
       "2053\n",
+      "model_vars.get('Gini', 0)[-1]=0.6482\n",
+      "Condition not met at step 2054. No data to save.\n",
       "2054\n",
+      "model_vars.get('Gini', 0)[-1]=0.637\n",
+      "Condition not met at step 2055. No data to save.\n",
       "2055\n",
+      "model_vars.get('Gini', 0)[-1]=0.645\n",
+      "Condition not met at step 2056. No data to save.\n",
       "2056\n",
+      "model_vars.get('Gini', 0)[-1]=0.6574\n",
+      "Condition not met at step 2057. No data to save.\n",
       "2057\n",
+      "model_vars.get('Gini', 0)[-1]=0.6284000000000001\n",
+      "Condition not met at step 2058. No data to save.\n",
       "2058\n",
+      "model_vars.get('Gini', 0)[-1]=0.677\n",
+      "Condition not met at step 2059. No data to save.\n",
       "2059\n",
+      "model_vars.get('Gini', 0)[-1]=0.6859999999999999\n",
+      "Condition not met at step 2060. No data to save.\n",
       "2060\n",
+      "model_vars.get('Gini', 0)[-1]=0.6517999999999999\n",
+      "Condition not met at step 2061. No data to save.\n",
       "2061\n",
+      "model_vars.get('Gini', 0)[-1]=0.6448\n",
+      "Condition not met at step 2062. No data to save.\n",
       "2062\n",
+      "model_vars.get('Gini', 0)[-1]=0.642\n",
+      "Condition not met at step 2063. No data to save.\n",
       "2063\n",
+      "model_vars.get('Gini', 0)[-1]=0.6548\n",
+      "Condition not met at step 2064. No data to save.\n",
       "2064\n",
+      "model_vars.get('Gini', 0)[-1]=0.6144000000000001\n",
+      "Condition not met at step 2065. No data to save.\n",
       "2065\n",
+      "model_vars.get('Gini', 0)[-1]=0.6724\n",
+      "Condition not met at step 2066. No data to save.\n",
       "2066\n",
+      "model_vars.get('Gini', 0)[-1]=0.6662\n",
+      "Condition not met at step 2067. No data to save.\n",
       "2067\n",
+      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
+      "Condition not met at step 2068. No data to save.\n",
       "2068\n",
+      "model_vars.get('Gini', 0)[-1]=0.6784\n",
+      "Condition not met at step 2069. No data to save.\n",
       "2069\n",
+      "model_vars.get('Gini', 0)[-1]=0.6902\n",
+      "Condition not met at step 2070. No data to save.\n",
       "2070\n",
+      "model_vars.get('Gini', 0)[-1]=0.6990000000000001\n",
+      "Condition not met at step 2071. No data to save.\n",
       "2071\n",
+      "model_vars.get('Gini', 0)[-1]=0.706\n",
+      "Condition met. Appended special results for step 2072 to test_path/special_results.parquet\n",
       "2072\n",
+      "model_vars.get('Gini', 0)[-1]=0.7048\n",
+      "Condition met. Appended special results for step 2073 to test_path/special_results.parquet\n",
       "2073\n",
+      "model_vars.get('Gini', 0)[-1]=0.7016\n",
+      "Condition met. Appended special results for step 2074 to test_path/special_results.parquet\n",
       "2074\n",
+      "model_vars.get('Gini', 0)[-1]=0.7030000000000001\n",
+      "Condition met. Appended special results for step 2075 to test_path/special_results.parquet\n",
       "2075\n",
+      "model_vars.get('Gini', 0)[-1]=0.6981999999999999\n",
+      "Condition not met at step 2076. No data to save.\n",
       "2076\n",
+      "model_vars.get('Gini', 0)[-1]=0.6702\n",
+      "Condition not met at step 2077. No data to save.\n",
       "2077\n",
+      "model_vars.get('Gini', 0)[-1]=0.6508\n",
+      "Condition not met at step 2078. No data to save.\n",
       "2078\n",
+      "model_vars.get('Gini', 0)[-1]=0.6444000000000001\n",
+      "Condition not met at step 2079. No data to save.\n",
       "2079\n",
+      "model_vars.get('Gini', 0)[-1]=0.6262000000000001\n",
+      "Condition not met at step 2080. No data to save.\n",
       "2080\n",
+      "model_vars.get('Gini', 0)[-1]=0.6496\n",
+      "Condition not met at step 2081. No data to save.\n",
       "2081\n",
+      "model_vars.get('Gini', 0)[-1]=0.6358\n",
+      "Condition not met at step 2082. No data to save.\n",
       "2082\n",
+      "model_vars.get('Gini', 0)[-1]=0.6268\n",
+      "Condition not met at step 2083. No data to save.\n",
       "2083\n",
+      "model_vars.get('Gini', 0)[-1]=0.6113999999999999\n",
+      "Condition not met at step 2084. No data to save.\n",
       "2084\n",
+      "model_vars.get('Gini', 0)[-1]=0.637\n",
+      "Condition not met at step 2085. No data to save.\n",
       "2085\n",
+      "model_vars.get('Gini', 0)[-1]=0.6224000000000001\n",
+      "Condition not met at step 2086. No data to save.\n",
       "2086\n",
+      "model_vars.get('Gini', 0)[-1]=0.6324000000000001\n",
+      "Condition not met at step 2087. No data to save.\n",
       "2087\n",
+      "model_vars.get('Gini', 0)[-1]=0.6328\n",
+      "Condition not met at step 2088. No data to save.\n",
       "2088\n",
+      "model_vars.get('Gini', 0)[-1]=0.656\n",
+      "Condition not met at step 2089. No data to save.\n",
       "2089\n",
+      "model_vars.get('Gini', 0)[-1]=0.652\n",
+      "Condition not met at step 2090. No data to save.\n",
       "2090\n",
+      "model_vars.get('Gini', 0)[-1]=0.638\n",
+      "Condition not met at step 2091. No data to save.\n",
       "2091\n",
+      "model_vars.get('Gini', 0)[-1]=0.6275999999999999\n",
+      "Condition not met at step 2092. No data to save.\n",
       "2092\n",
+      "model_vars.get('Gini', 0)[-1]=0.6502\n",
+      "Condition not met at step 2093. No data to save.\n",
       "2093\n",
+      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
+      "Condition not met at step 2094. No data to save.\n",
       "2094\n",
+      "model_vars.get('Gini', 0)[-1]=0.632\n",
+      "Condition not met at step 2095. No data to save.\n",
       "2095\n",
+      "model_vars.get('Gini', 0)[-1]=0.6136\n",
+      "Condition not met at step 2096. No data to save.\n",
       "2096\n",
+      "model_vars.get('Gini', 0)[-1]=0.6152\n",
+      "Condition not met at step 2097. No data to save.\n",
       "2097\n",
+      "model_vars.get('Gini', 0)[-1]=0.6226\n",
+      "Condition not met at step 2098. No data to save.\n",
       "2098\n",
+      "model_vars.get('Gini', 0)[-1]=0.616\n",
+      "Condition not met at step 2099. No data to save.\n",
       "2099\n",
       "CALLED\n",
       "self.model._steps=2100\n",
       " TEST self.model._steps=2100\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "2000  0.6300\n",
-      "2001  0.6346\n",
-      "2002  0.6306\n",
-      "2003  0.6200\n",
-      "2004  0.6350\n",
+      "2000  0.6892\n",
+      "2001  0.7076\n",
+      "2002  0.7010\n",
+      "2003  0.6986\n",
+      "2004  0.7022\n",
       "...      ...\n",
-      "2095  0.5842\n",
-      "2096  0.6182\n",
-      "2097  0.6242\n",
-      "2098  0.6362\n",
-      "2099  0.6582\n",
+      "2095  0.6136\n",
+      "2096  0.6152\n",
+      "2097  0.6226\n",
+      "2098  0.6160\n",
+      "2099  0.6212\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_021.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_021.parquet'\n",
-      "Saving model to output_dir/model_data_021.parquet\n",
-      "Saving agent to output_dir/agent_data_021.parquet\n",
+      "model_file='test_path/model_data_021.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_021.parquet'\n",
+      "Saving model to test_path/model_data_021.parquet\n",
+      "Saving agent to test_path/agent_data_021.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6212\n",
+      "Condition not met at step 2100. No data to save.\n",
       "2100\n",
+      "model_vars.get('Gini', 0)[-1]=0.629\n",
+      "Condition not met at step 2101. No data to save.\n",
       "2101\n",
+      "model_vars.get('Gini', 0)[-1]=0.6408\n",
+      "Condition not met at step 2102. No data to save.\n",
       "2102\n",
+      "model_vars.get('Gini', 0)[-1]=0.6448\n",
+      "Condition not met at step 2103. No data to save.\n",
       "2103\n",
+      "model_vars.get('Gini', 0)[-1]=0.654\n",
+      "Condition not met at step 2104. No data to save.\n",
       "2104\n",
+      "model_vars.get('Gini', 0)[-1]=0.615\n",
+      "Condition not met at step 2105. No data to save.\n",
       "2105\n",
+      "model_vars.get('Gini', 0)[-1]=0.6308\n",
+      "Condition not met at step 2106. No data to save.\n",
       "2106\n",
+      "model_vars.get('Gini', 0)[-1]=0.6348\n",
+      "Condition not met at step 2107. No data to save.\n",
       "2107\n",
+      "model_vars.get('Gini', 0)[-1]=0.6274\n",
+      "Condition not met at step 2108. No data to save.\n",
       "2108\n",
+      "model_vars.get('Gini', 0)[-1]=0.656\n",
+      "Condition not met at step 2109. No data to save.\n",
       "2109\n",
+      "model_vars.get('Gini', 0)[-1]=0.708\n",
+      "Condition met. Appended special results for step 2110 to test_path/special_results.parquet\n",
       "2110\n",
+      "model_vars.get('Gini', 0)[-1]=0.6916\n",
+      "Condition not met at step 2111. No data to save.\n",
       "2111\n",
+      "model_vars.get('Gini', 0)[-1]=0.675\n",
+      "Condition not met at step 2112. No data to save.\n",
       "2112\n",
+      "model_vars.get('Gini', 0)[-1]=0.6488\n",
+      "Condition not met at step 2113. No data to save.\n",
       "2113\n",
+      "model_vars.get('Gini', 0)[-1]=0.6568\n",
+      "Condition not met at step 2114. No data to save.\n",
       "2114\n",
+      "model_vars.get('Gini', 0)[-1]=0.635\n",
+      "Condition not met at step 2115. No data to save.\n",
       "2115\n",
+      "model_vars.get('Gini', 0)[-1]=0.6148\n",
+      "Condition not met at step 2116. No data to save.\n",
       "2116\n",
+      "model_vars.get('Gini', 0)[-1]=0.6426000000000001\n",
+      "Condition not met at step 2117. No data to save.\n",
       "2117\n",
+      "model_vars.get('Gini', 0)[-1]=0.6492\n",
+      "Condition not met at step 2118. No data to save.\n",
       "2118\n",
+      "model_vars.get('Gini', 0)[-1]=0.6622\n",
+      "Condition not met at step 2119. No data to save.\n",
       "2119\n",
+      "model_vars.get('Gini', 0)[-1]=0.658\n",
+      "Condition not met at step 2120. No data to save.\n",
       "2120\n",
+      "model_vars.get('Gini', 0)[-1]=0.6076\n",
+      "Condition not met at step 2121. No data to save.\n",
       "2121\n",
+      "model_vars.get('Gini', 0)[-1]=0.6224000000000001\n",
+      "Condition not met at step 2122. No data to save.\n",
       "2122\n",
+      "model_vars.get('Gini', 0)[-1]=0.634\n",
+      "Condition not met at step 2123. No data to save.\n",
       "2123\n",
+      "model_vars.get('Gini', 0)[-1]=0.5848\n",
+      "Condition not met at step 2124. No data to save.\n",
       "2124\n",
+      "model_vars.get('Gini', 0)[-1]=0.6104\n",
+      "Condition not met at step 2125. No data to save.\n",
       "2125\n",
+      "model_vars.get('Gini', 0)[-1]=0.6208\n",
+      "Condition not met at step 2126. No data to save.\n",
       "2126\n",
+      "model_vars.get('Gini', 0)[-1]=0.6372\n",
+      "Condition not met at step 2127. No data to save.\n",
       "2127\n",
+      "model_vars.get('Gini', 0)[-1]=0.6448\n",
+      "Condition not met at step 2128. No data to save.\n",
       "2128\n",
+      "model_vars.get('Gini', 0)[-1]=0.6378\n",
+      "Condition not met at step 2129. No data to save.\n",
       "2129\n",
+      "model_vars.get('Gini', 0)[-1]=0.6554\n",
+      "Condition not met at step 2130. No data to save.\n",
       "2130\n",
+      "model_vars.get('Gini', 0)[-1]=0.6584\n",
+      "Condition not met at step 2131. No data to save.\n",
       "2131\n",
+      "model_vars.get('Gini', 0)[-1]=0.6652\n",
+      "Condition not met at step 2132. No data to save.\n",
       "2132\n",
+      "model_vars.get('Gini', 0)[-1]=0.6612\n",
+      "Condition not met at step 2133. No data to save.\n",
       "2133\n",
+      "model_vars.get('Gini', 0)[-1]=0.6542\n",
+      "Condition not met at step 2134. No data to save.\n",
       "2134\n",
+      "model_vars.get('Gini', 0)[-1]=0.6508\n",
+      "Condition not met at step 2135. No data to save.\n",
       "2135\n",
+      "model_vars.get('Gini', 0)[-1]=0.6554\n",
+      "Condition not met at step 2136. No data to save.\n",
       "2136\n",
+      "model_vars.get('Gini', 0)[-1]=0.6772\n",
+      "Condition not met at step 2137. No data to save.\n",
       "2137\n",
+      "model_vars.get('Gini', 0)[-1]=0.6644\n",
+      "Condition not met at step 2138. No data to save.\n",
       "2138\n",
+      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
+      "Condition not met at step 2139. No data to save.\n",
       "2139\n",
+      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
+      "Condition not met at step 2140. No data to save.\n",
       "2140\n",
+      "model_vars.get('Gini', 0)[-1]=0.7074\n",
+      "Condition met. Appended special results for step 2141 to test_path/special_results.parquet\n",
       "2141\n",
+      "model_vars.get('Gini', 0)[-1]=0.6986\n",
+      "Condition not met at step 2142. No data to save.\n",
       "2142\n",
+      "model_vars.get('Gini', 0)[-1]=0.706\n",
+      "Condition met. Appended special results for step 2143 to test_path/special_results.parquet\n",
       "2143\n",
+      "model_vars.get('Gini', 0)[-1]=0.7008000000000001\n",
+      "Condition met. Appended special results for step 2144 to test_path/special_results.parquet\n",
       "2144\n",
+      "model_vars.get('Gini', 0)[-1]=0.7223999999999999\n",
+      "Condition met. Appended special results for step 2145 to test_path/special_results.parquet\n",
       "2145\n",
+      "model_vars.get('Gini', 0)[-1]=0.7094\n",
+      "Condition met. Appended special results for step 2146 to test_path/special_results.parquet\n",
       "2146\n",
+      "model_vars.get('Gini', 0)[-1]=0.7001999999999999\n",
+      "Condition met. Appended special results for step 2147 to test_path/special_results.parquet\n",
       "2147\n",
+      "model_vars.get('Gini', 0)[-1]=0.688\n",
+      "Condition not met at step 2148. No data to save.\n",
       "2148\n",
+      "model_vars.get('Gini', 0)[-1]=0.6804\n",
+      "Condition not met at step 2149. No data to save.\n",
       "2149\n",
+      "model_vars.get('Gini', 0)[-1]=0.6552\n",
+      "Condition not met at step 2150. No data to save.\n",
       "2150\n",
+      "model_vars.get('Gini', 0)[-1]=0.6554\n",
+      "Condition not met at step 2151. No data to save.\n",
       "2151\n",
+      "model_vars.get('Gini', 0)[-1]=0.6148\n",
+      "Condition not met at step 2152. No data to save.\n",
       "2152\n",
+      "model_vars.get('Gini', 0)[-1]=0.6362\n",
+      "Condition not met at step 2153. No data to save.\n",
       "2153\n",
+      "model_vars.get('Gini', 0)[-1]=0.6494\n",
+      "Condition not met at step 2154. No data to save.\n",
       "2154\n",
+      "model_vars.get('Gini', 0)[-1]=0.6574\n",
+      "Condition not met at step 2155. No data to save.\n",
       "2155\n",
+      "model_vars.get('Gini', 0)[-1]=0.6588\n",
+      "Condition not met at step 2156. No data to save.\n",
       "2156\n",
+      "model_vars.get('Gini', 0)[-1]=0.677\n",
+      "Condition not met at step 2157. No data to save.\n",
       "2157\n",
+      "model_vars.get('Gini', 0)[-1]=0.7086\n",
+      "Condition met. Appended special results for step 2158 to test_path/special_results.parquet\n",
       "2158\n",
+      "model_vars.get('Gini', 0)[-1]=0.7323999999999999\n",
+      "Condition met. Appended special results for step 2159 to test_path/special_results.parquet\n",
       "2159\n",
+      "model_vars.get('Gini', 0)[-1]=0.7282\n",
+      "Condition met. Appended special results for step 2160 to test_path/special_results.parquet\n",
       "2160\n",
+      "model_vars.get('Gini', 0)[-1]=0.7236\n",
+      "Condition met. Appended special results for step 2161 to test_path/special_results.parquet\n",
       "2161\n",
+      "model_vars.get('Gini', 0)[-1]=0.7046\n",
+      "Condition met. Appended special results for step 2162 to test_path/special_results.parquet\n",
       "2162\n",
+      "model_vars.get('Gini', 0)[-1]=0.6804\n",
+      "Condition not met at step 2163. No data to save.\n",
       "2163\n",
+      "model_vars.get('Gini', 0)[-1]=0.6752\n",
+      "Condition not met at step 2164. No data to save.\n",
       "2164\n",
+      "model_vars.get('Gini', 0)[-1]=0.6664\n",
+      "Condition not met at step 2165. No data to save.\n",
       "2165\n",
+      "model_vars.get('Gini', 0)[-1]=0.6512\n",
+      "Condition not met at step 2166. No data to save.\n",
       "2166\n",
+      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
+      "Condition not met at step 2167. No data to save.\n",
       "2167\n",
+      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
+      "Condition not met at step 2168. No data to save.\n",
       "2168\n",
+      "model_vars.get('Gini', 0)[-1]=0.6918\n",
+      "Condition not met at step 2169. No data to save.\n",
       "2169\n",
+      "model_vars.get('Gini', 0)[-1]=0.6794\n",
+      "Condition not met at step 2170. No data to save.\n",
       "2170\n",
+      "model_vars.get('Gini', 0)[-1]=0.6742\n",
+      "Condition not met at step 2171. No data to save.\n",
       "2171\n",
+      "model_vars.get('Gini', 0)[-1]=0.6652\n",
+      "Condition not met at step 2172. No data to save.\n",
       "2172\n",
+      "model_vars.get('Gini', 0)[-1]=0.6582\n",
+      "Condition not met at step 2173. No data to save.\n",
       "2173\n",
+      "model_vars.get('Gini', 0)[-1]=0.66\n",
+      "Condition not met at step 2174. No data to save.\n",
       "2174\n",
+      "model_vars.get('Gini', 0)[-1]=0.6752\n",
+      "Condition not met at step 2175. No data to save.\n",
       "2175\n",
+      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
+      "Condition not met at step 2176. No data to save.\n",
       "2176\n",
+      "model_vars.get('Gini', 0)[-1]=0.6532\n",
+      "Condition not met at step 2177. No data to save.\n",
       "2177\n",
+      "model_vars.get('Gini', 0)[-1]=0.6672\n",
+      "Condition not met at step 2178. No data to save.\n",
       "2178\n",
+      "model_vars.get('Gini', 0)[-1]=0.6382\n",
+      "Condition not met at step 2179. No data to save.\n",
       "2179\n",
+      "model_vars.get('Gini', 0)[-1]=0.637\n",
+      "Condition not met at step 2180. No data to save.\n",
       "2180\n",
+      "model_vars.get('Gini', 0)[-1]=0.6332\n",
+      "Condition not met at step 2181. No data to save.\n",
       "2181\n",
+      "model_vars.get('Gini', 0)[-1]=0.6344000000000001\n",
+      "Condition not met at step 2182. No data to save.\n",
       "2182\n",
+      "model_vars.get('Gini', 0)[-1]=0.625\n",
+      "Condition not met at step 2183. No data to save.\n",
       "2183\n",
+      "model_vars.get('Gini', 0)[-1]=0.6258\n",
+      "Condition not met at step 2184. No data to save.\n",
       "2184\n",
+      "model_vars.get('Gini', 0)[-1]=0.6398\n",
+      "Condition not met at step 2185. No data to save.\n",
       "2185\n",
+      "model_vars.get('Gini', 0)[-1]=0.6275999999999999\n",
+      "Condition not met at step 2186. No data to save.\n",
       "2186\n",
+      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
+      "Condition not met at step 2187. No data to save.\n",
       "2187\n",
+      "model_vars.get('Gini', 0)[-1]=0.669\n",
+      "Condition not met at step 2188. No data to save.\n",
       "2188\n",
+      "model_vars.get('Gini', 0)[-1]=0.6676\n",
+      "Condition not met at step 2189. No data to save.\n",
       "2189\n",
+      "model_vars.get('Gini', 0)[-1]=0.6454\n",
+      "Condition not met at step 2190. No data to save.\n",
       "2190\n",
+      "model_vars.get('Gini', 0)[-1]=0.6416\n",
+      "Condition not met at step 2191. No data to save.\n",
       "2191\n",
+      "model_vars.get('Gini', 0)[-1]=0.64\n",
+      "Condition not met at step 2192. No data to save.\n",
       "2192\n",
+      "model_vars.get('Gini', 0)[-1]=0.6696\n",
+      "Condition not met at step 2193. No data to save.\n",
       "2193\n",
+      "model_vars.get('Gini', 0)[-1]=0.6572\n",
+      "Condition not met at step 2194. No data to save.\n",
       "2194\n",
+      "model_vars.get('Gini', 0)[-1]=0.6812\n",
+      "Condition not met at step 2195. No data to save.\n",
       "2195\n",
+      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
+      "Condition not met at step 2196. No data to save.\n",
       "2196\n",
+      "model_vars.get('Gini', 0)[-1]=0.687\n",
+      "Condition not met at step 2197. No data to save.\n",
       "2197\n",
+      "model_vars.get('Gini', 0)[-1]=0.7068\n",
+      "Condition met. Appended special results for step 2198 to test_path/special_results.parquet\n",
       "2198\n",
+      "model_vars.get('Gini', 0)[-1]=0.6941999999999999\n",
+      "Condition not met at step 2199. No data to save.\n",
       "2199\n",
       "CALLED\n",
       "self.model._steps=2200\n",
       " TEST self.model._steps=2200\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "2100  0.6650\n",
-      "2101  0.6848\n",
-      "2102  0.6912\n",
-      "2103  0.6896\n",
-      "2104  0.6706\n",
+      "2100  0.6290\n",
+      "2101  0.6408\n",
+      "2102  0.6448\n",
+      "2103  0.6540\n",
+      "2104  0.6150\n",
       "...      ...\n",
-      "2195  0.7040\n",
-      "2196  0.6770\n",
-      "2197  0.6816\n",
-      "2198  0.7012\n",
-      "2199  0.6818\n",
+      "2195  0.6788\n",
+      "2196  0.6870\n",
+      "2197  0.7068\n",
+      "2198  0.6942\n",
+      "2199  0.6918\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_022.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_022.parquet'\n",
-      "Saving model to output_dir/model_data_022.parquet\n",
-      "Saving agent to output_dir/agent_data_022.parquet\n",
+      "model_file='test_path/model_data_022.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_022.parquet'\n",
+      "Saving model to test_path/model_data_022.parquet\n",
+      "Saving agent to test_path/agent_data_022.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6918\n",
+      "Condition not met at step 2200. No data to save.\n",
       "2200\n",
+      "model_vars.get('Gini', 0)[-1]=0.6854\n",
+      "Condition not met at step 2201. No data to save.\n",
       "2201\n",
+      "model_vars.get('Gini', 0)[-1]=0.687\n",
+      "Condition not met at step 2202. No data to save.\n",
       "2202\n",
+      "model_vars.get('Gini', 0)[-1]=0.6954\n",
+      "Condition not met at step 2203. No data to save.\n",
       "2203\n",
+      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
+      "Condition not met at step 2204. No data to save.\n",
       "2204\n",
+      "model_vars.get('Gini', 0)[-1]=0.671\n",
+      "Condition not met at step 2205. No data to save.\n",
       "2205\n",
+      "model_vars.get('Gini', 0)[-1]=0.6496\n",
+      "Condition not met at step 2206. No data to save.\n",
       "2206\n",
+      "model_vars.get('Gini', 0)[-1]=0.669\n",
+      "Condition not met at step 2207. No data to save.\n",
       "2207\n",
+      "model_vars.get('Gini', 0)[-1]=0.6826\n",
+      "Condition not met at step 2208. No data to save.\n",
       "2208\n",
+      "model_vars.get('Gini', 0)[-1]=0.6734\n",
+      "Condition not met at step 2209. No data to save.\n",
       "2209\n",
+      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
+      "Condition not met at step 2210. No data to save.\n",
       "2210\n",
+      "model_vars.get('Gini', 0)[-1]=0.6664\n",
+      "Condition not met at step 2211. No data to save.\n",
       "2211\n",
+      "model_vars.get('Gini', 0)[-1]=0.6596\n",
+      "Condition not met at step 2212. No data to save.\n",
       "2212\n",
+      "model_vars.get('Gini', 0)[-1]=0.637\n",
+      "Condition not met at step 2213. No data to save.\n",
       "2213\n",
+      "model_vars.get('Gini', 0)[-1]=0.6204000000000001\n",
+      "Condition not met at step 2214. No data to save.\n",
       "2214\n",
+      "model_vars.get('Gini', 0)[-1]=0.6355999999999999\n",
+      "Condition not met at step 2215. No data to save.\n",
       "2215\n",
+      "model_vars.get('Gini', 0)[-1]=0.5978\n",
+      "Condition not met at step 2216. No data to save.\n",
       "2216\n",
+      "model_vars.get('Gini', 0)[-1]=0.5908\n",
+      "Condition not met at step 2217. No data to save.\n",
       "2217\n",
+      "model_vars.get('Gini', 0)[-1]=0.6088\n",
+      "Condition not met at step 2218. No data to save.\n",
       "2218\n",
+      "model_vars.get('Gini', 0)[-1]=0.6388\n",
+      "Condition not met at step 2219. No data to save.\n",
       "2219\n",
+      "model_vars.get('Gini', 0)[-1]=0.6186\n",
+      "Condition not met at step 2220. No data to save.\n",
       "2220\n",
+      "model_vars.get('Gini', 0)[-1]=0.5946\n",
+      "Condition not met at step 2221. No data to save.\n",
       "2221\n",
+      "model_vars.get('Gini', 0)[-1]=0.5992\n",
+      "Condition not met at step 2222. No data to save.\n",
       "2222\n",
+      "model_vars.get('Gini', 0)[-1]=0.5892\n",
+      "Condition not met at step 2223. No data to save.\n",
       "2223\n",
+      "model_vars.get('Gini', 0)[-1]=0.5938\n",
+      "Condition not met at step 2224. No data to save.\n",
       "2224\n",
+      "model_vars.get('Gini', 0)[-1]=0.6174\n",
+      "Condition not met at step 2225. No data to save.\n",
       "2225\n",
+      "model_vars.get('Gini', 0)[-1]=0.6474\n",
+      "Condition not met at step 2226. No data to save.\n",
       "2226\n",
+      "model_vars.get('Gini', 0)[-1]=0.6632\n",
+      "Condition not met at step 2227. No data to save.\n",
       "2227\n",
+      "model_vars.get('Gini', 0)[-1]=0.657\n",
+      "Condition not met at step 2228. No data to save.\n",
       "2228\n",
+      "model_vars.get('Gini', 0)[-1]=0.6514\n",
+      "Condition not met at step 2229. No data to save.\n",
       "2229\n",
+      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
+      "Condition not met at step 2230. No data to save.\n",
       "2230\n",
+      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
+      "Condition not met at step 2231. No data to save.\n",
       "2231\n",
+      "model_vars.get('Gini', 0)[-1]=0.6876\n",
+      "Condition not met at step 2232. No data to save.\n",
       "2232\n",
+      "model_vars.get('Gini', 0)[-1]=0.7026\n",
+      "Condition met. Appended special results for step 2233 to test_path/special_results.parquet\n",
       "2233\n",
+      "model_vars.get('Gini', 0)[-1]=0.6966\n",
+      "Condition not met at step 2234. No data to save.\n",
       "2234\n",
+      "model_vars.get('Gini', 0)[-1]=0.6666000000000001\n",
+      "Condition not met at step 2235. No data to save.\n",
       "2235\n",
+      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
+      "Condition not met at step 2236. No data to save.\n",
       "2236\n",
+      "model_vars.get('Gini', 0)[-1]=0.6696\n",
+      "Condition not met at step 2237. No data to save.\n",
       "2237\n",
+      "model_vars.get('Gini', 0)[-1]=0.6702\n",
+      "Condition not met at step 2238. No data to save.\n",
       "2238\n",
+      "model_vars.get('Gini', 0)[-1]=0.6776\n",
+      "Condition not met at step 2239. No data to save.\n",
       "2239\n",
+      "model_vars.get('Gini', 0)[-1]=0.6654\n",
+      "Condition not met at step 2240. No data to save.\n",
       "2240\n",
+      "model_vars.get('Gini', 0)[-1]=0.6608\n",
+      "Condition not met at step 2241. No data to save.\n",
       "2241\n",
+      "model_vars.get('Gini', 0)[-1]=0.6512\n",
+      "Condition not met at step 2242. No data to save.\n",
       "2242\n",
+      "model_vars.get('Gini', 0)[-1]=0.6574\n",
+      "Condition not met at step 2243. No data to save.\n",
       "2243\n",
+      "model_vars.get('Gini', 0)[-1]=0.6872\n",
+      "Condition not met at step 2244. No data to save.\n",
       "2244\n",
+      "model_vars.get('Gini', 0)[-1]=0.6814\n",
+      "Condition not met at step 2245. No data to save.\n",
       "2245\n",
+      "model_vars.get('Gini', 0)[-1]=0.6626000000000001\n",
+      "Condition not met at step 2246. No data to save.\n",
       "2246\n",
+      "model_vars.get('Gini', 0)[-1]=0.6884\n",
+      "Condition not met at step 2247. No data to save.\n",
       "2247\n",
+      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
+      "Condition not met at step 2248. No data to save.\n",
       "2248\n",
+      "model_vars.get('Gini', 0)[-1]=0.6656\n",
+      "Condition not met at step 2249. No data to save.\n",
       "2249\n",
+      "model_vars.get('Gini', 0)[-1]=0.6844\n",
+      "Condition not met at step 2250. No data to save.\n",
       "2250\n",
+      "model_vars.get('Gini', 0)[-1]=0.6886\n",
+      "Condition not met at step 2251. No data to save.\n",
       "2251\n",
+      "model_vars.get('Gini', 0)[-1]=0.6874\n",
+      "Condition not met at step 2252. No data to save.\n",
       "2252\n",
+      "model_vars.get('Gini', 0)[-1]=0.671\n",
+      "Condition not met at step 2253. No data to save.\n",
       "2253\n",
+      "model_vars.get('Gini', 0)[-1]=0.661\n",
+      "Condition not met at step 2254. No data to save.\n",
       "2254\n",
+      "model_vars.get('Gini', 0)[-1]=0.6656\n",
+      "Condition not met at step 2255. No data to save.\n",
       "2255\n",
+      "model_vars.get('Gini', 0)[-1]=0.704\n",
+      "Condition met. Appended special results for step 2256 to test_path/special_results.parquet\n",
       "2256\n",
+      "model_vars.get('Gini', 0)[-1]=0.7061999999999999\n",
+      "Condition met. Appended special results for step 2257 to test_path/special_results.parquet\n",
       "2257\n",
+      "model_vars.get('Gini', 0)[-1]=0.685\n",
+      "Condition not met at step 2258. No data to save.\n",
       "2258\n",
+      "model_vars.get('Gini', 0)[-1]=0.7094\n",
+      "Condition met. Appended special results for step 2259 to test_path/special_results.parquet\n",
       "2259\n",
+      "model_vars.get('Gini', 0)[-1]=0.7018\n",
+      "Condition met. Appended special results for step 2260 to test_path/special_results.parquet\n",
       "2260\n",
+      "model_vars.get('Gini', 0)[-1]=0.6978\n",
+      "Condition not met at step 2261. No data to save.\n",
       "2261\n",
+      "model_vars.get('Gini', 0)[-1]=0.7030000000000001\n",
+      "Condition met. Appended special results for step 2262 to test_path/special_results.parquet\n",
       "2262\n",
+      "model_vars.get('Gini', 0)[-1]=0.698\n",
+      "Condition not met at step 2263. No data to save.\n",
       "2263\n",
+      "model_vars.get('Gini', 0)[-1]=0.6859999999999999\n",
+      "Condition not met at step 2264. No data to save.\n",
       "2264\n",
+      "model_vars.get('Gini', 0)[-1]=0.6819999999999999\n",
+      "Condition not met at step 2265. No data to save.\n",
       "2265\n",
+      "model_vars.get('Gini', 0)[-1]=0.6874\n",
+      "Condition not met at step 2266. No data to save.\n",
       "2266\n",
+      "model_vars.get('Gini', 0)[-1]=0.6902\n",
+      "Condition not met at step 2267. No data to save.\n",
       "2267\n",
+      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
+      "Condition not met at step 2268. No data to save.\n",
       "2268\n",
+      "model_vars.get('Gini', 0)[-1]=0.6656\n",
+      "Condition not met at step 2269. No data to save.\n",
       "2269\n",
+      "model_vars.get('Gini', 0)[-1]=0.6679999999999999\n",
+      "Condition not met at step 2270. No data to save.\n",
       "2270\n",
+      "model_vars.get('Gini', 0)[-1]=0.6608\n",
+      "Condition not met at step 2271. No data to save.\n",
       "2271\n",
+      "model_vars.get('Gini', 0)[-1]=0.6496\n",
+      "Condition not met at step 2272. No data to save.\n",
       "2272\n",
+      "model_vars.get('Gini', 0)[-1]=0.6584\n",
+      "Condition not met at step 2273. No data to save.\n",
       "2273\n",
+      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
+      "Condition not met at step 2274. No data to save.\n",
       "2274\n",
+      "model_vars.get('Gini', 0)[-1]=0.673\n",
+      "Condition not met at step 2275. No data to save.\n",
       "2275\n",
+      "model_vars.get('Gini', 0)[-1]=0.6932\n",
+      "Condition not met at step 2276. No data to save.\n",
       "2276\n",
+      "model_vars.get('Gini', 0)[-1]=0.6961999999999999\n",
+      "Condition not met at step 2277. No data to save.\n",
       "2277\n",
+      "model_vars.get('Gini', 0)[-1]=0.6762\n",
+      "Condition not met at step 2278. No data to save.\n",
       "2278\n",
+      "model_vars.get('Gini', 0)[-1]=0.6864\n",
+      "Condition not met at step 2279. No data to save.\n",
       "2279\n",
+      "model_vars.get('Gini', 0)[-1]=0.6990000000000001\n",
+      "Condition not met at step 2280. No data to save.\n",
       "2280\n",
+      "model_vars.get('Gini', 0)[-1]=0.6816\n",
+      "Condition not met at step 2281. No data to save.\n",
       "2281\n",
+      "model_vars.get('Gini', 0)[-1]=0.6914\n",
+      "Condition not met at step 2282. No data to save.\n",
       "2282\n",
+      "model_vars.get('Gini', 0)[-1]=0.7028000000000001\n",
+      "Condition met. Appended special results for step 2283 to test_path/special_results.parquet\n",
       "2283\n",
+      "model_vars.get('Gini', 0)[-1]=0.6964\n",
+      "Condition not met at step 2284. No data to save.\n",
       "2284\n",
+      "model_vars.get('Gini', 0)[-1]=0.698\n",
+      "Condition not met at step 2285. No data to save.\n",
       "2285\n",
+      "model_vars.get('Gini', 0)[-1]=0.6981999999999999\n",
+      "Condition not met at step 2286. No data to save.\n",
       "2286\n",
+      "model_vars.get('Gini', 0)[-1]=0.6930000000000001\n",
+      "Condition not met at step 2287. No data to save.\n",
       "2287\n",
+      "model_vars.get('Gini', 0)[-1]=0.7006\n",
+      "Condition met. Appended special results for step 2288 to test_path/special_results.parquet\n",
       "2288\n",
+      "model_vars.get('Gini', 0)[-1]=0.6914\n",
+      "Condition not met at step 2289. No data to save.\n",
       "2289\n",
+      "model_vars.get('Gini', 0)[-1]=0.6914\n",
+      "Condition not met at step 2290. No data to save.\n",
       "2290\n",
+      "model_vars.get('Gini', 0)[-1]=0.664\n",
+      "Condition not met at step 2291. No data to save.\n",
       "2291\n",
+      "model_vars.get('Gini', 0)[-1]=0.6548\n",
+      "Condition not met at step 2292. No data to save.\n",
       "2292\n",
+      "model_vars.get('Gini', 0)[-1]=0.641\n",
+      "Condition not met at step 2293. No data to save.\n",
       "2293\n",
+      "model_vars.get('Gini', 0)[-1]=0.6446000000000001\n",
+      "Condition not met at step 2294. No data to save.\n",
       "2294\n",
+      "model_vars.get('Gini', 0)[-1]=0.635\n",
+      "Condition not met at step 2295. No data to save.\n",
       "2295\n",
+      "model_vars.get('Gini', 0)[-1]=0.6338\n",
+      "Condition not met at step 2296. No data to save.\n",
       "2296\n",
+      "model_vars.get('Gini', 0)[-1]=0.6366\n",
+      "Condition not met at step 2297. No data to save.\n",
       "2297\n",
+      "model_vars.get('Gini', 0)[-1]=0.6246\n",
+      "Condition not met at step 2298. No data to save.\n",
       "2298\n",
+      "model_vars.get('Gini', 0)[-1]=0.6198\n",
+      "Condition not met at step 2299. No data to save.\n",
       "2299\n",
       "CALLED\n",
       "self.model._steps=2300\n",
       " TEST self.model._steps=2300\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "2200  0.6824\n",
-      "2201  0.6906\n",
-      "2202  0.7024\n",
-      "2203  0.6690\n",
-      "2204  0.6562\n",
+      "2200  0.6854\n",
+      "2201  0.6870\n",
+      "2202  0.6954\n",
+      "2203  0.6740\n",
+      "2204  0.6710\n",
       "...      ...\n",
-      "2295  0.7012\n",
-      "2296  0.6888\n",
-      "2297  0.6826\n",
-      "2298  0.6680\n",
-      "2299  0.6778\n",
+      "2295  0.6338\n",
+      "2296  0.6366\n",
+      "2297  0.6246\n",
+      "2298  0.6198\n",
+      "2299  0.6274\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_023.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_023.parquet'\n",
-      "Saving model to output_dir/model_data_023.parquet\n",
-      "Saving agent to output_dir/agent_data_023.parquet\n",
+      "model_file='test_path/model_data_023.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_023.parquet'\n",
+      "Saving model to test_path/model_data_023.parquet\n",
+      "Saving agent to test_path/agent_data_023.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6274\n",
+      "Condition not met at step 2300. No data to save.\n",
       "2300\n",
+      "model_vars.get('Gini', 0)[-1]=0.6312\n",
+      "Condition not met at step 2301. No data to save.\n",
       "2301\n",
+      "model_vars.get('Gini', 0)[-1]=0.6636\n",
+      "Condition not met at step 2302. No data to save.\n",
       "2302\n",
+      "model_vars.get('Gini', 0)[-1]=0.6456\n",
+      "Condition not met at step 2303. No data to save.\n",
       "2303\n",
+      "model_vars.get('Gini', 0)[-1]=0.6352\n",
+      "Condition not met at step 2304. No data to save.\n",
       "2304\n",
+      "model_vars.get('Gini', 0)[-1]=0.6234\n",
+      "Condition not met at step 2305. No data to save.\n",
       "2305\n",
+      "model_vars.get('Gini', 0)[-1]=0.5984\n",
+      "Condition not met at step 2306. No data to save.\n",
       "2306\n",
+      "model_vars.get('Gini', 0)[-1]=0.5906\n",
+      "Condition not met at step 2307. No data to save.\n",
       "2307\n",
+      "model_vars.get('Gini', 0)[-1]=0.626\n",
+      "Condition not met at step 2308. No data to save.\n",
       "2308\n",
+      "model_vars.get('Gini', 0)[-1]=0.6342\n",
+      "Condition not met at step 2309. No data to save.\n",
       "2309\n",
+      "model_vars.get('Gini', 0)[-1]=0.6432\n",
+      "Condition not met at step 2310. No data to save.\n",
       "2310\n",
+      "model_vars.get('Gini', 0)[-1]=0.6372\n",
+      "Condition not met at step 2311. No data to save.\n",
       "2311\n",
+      "model_vars.get('Gini', 0)[-1]=0.6666000000000001\n",
+      "Condition not met at step 2312. No data to save.\n",
       "2312\n",
+      "model_vars.get('Gini', 0)[-1]=0.656\n",
+      "Condition not met at step 2313. No data to save.\n",
       "2313\n",
+      "model_vars.get('Gini', 0)[-1]=0.6497999999999999\n",
+      "Condition not met at step 2314. No data to save.\n",
       "2314\n",
+      "model_vars.get('Gini', 0)[-1]=0.6472\n",
+      "Condition not met at step 2315. No data to save.\n",
       "2315\n",
+      "model_vars.get('Gini', 0)[-1]=0.641\n",
+      "Condition not met at step 2316. No data to save.\n",
       "2316\n",
+      "model_vars.get('Gini', 0)[-1]=0.65\n",
+      "Condition not met at step 2317. No data to save.\n",
       "2317\n",
+      "model_vars.get('Gini', 0)[-1]=0.646\n",
+      "Condition not met at step 2318. No data to save.\n",
       "2318\n",
+      "model_vars.get('Gini', 0)[-1]=0.6452\n",
+      "Condition not met at step 2319. No data to save.\n",
       "2319\n",
+      "model_vars.get('Gini', 0)[-1]=0.6516\n",
+      "Condition not met at step 2320. No data to save.\n",
       "2320\n",
+      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
+      "Condition not met at step 2321. No data to save.\n",
       "2321\n",
+      "model_vars.get('Gini', 0)[-1]=0.6594\n",
+      "Condition not met at step 2322. No data to save.\n",
       "2322\n",
+      "model_vars.get('Gini', 0)[-1]=0.6368\n",
+      "Condition not met at step 2323. No data to save.\n",
       "2323\n",
+      "model_vars.get('Gini', 0)[-1]=0.6508\n",
+      "Condition not met at step 2324. No data to save.\n",
       "2324\n",
+      "model_vars.get('Gini', 0)[-1]=0.6252\n",
+      "Condition not met at step 2325. No data to save.\n",
       "2325\n",
+      "model_vars.get('Gini', 0)[-1]=0.6175999999999999\n",
+      "Condition not met at step 2326. No data to save.\n",
       "2326\n",
+      "model_vars.get('Gini', 0)[-1]=0.6312\n",
+      "Condition not met at step 2327. No data to save.\n",
       "2327\n",
+      "model_vars.get('Gini', 0)[-1]=0.637\n",
+      "Condition not met at step 2328. No data to save.\n",
       "2328\n",
+      "model_vars.get('Gini', 0)[-1]=0.6234\n",
+      "Condition not met at step 2329. No data to save.\n",
       "2329\n",
+      "model_vars.get('Gini', 0)[-1]=0.6212\n",
+      "Condition not met at step 2330. No data to save.\n",
       "2330\n",
+      "model_vars.get('Gini', 0)[-1]=0.6388\n",
+      "Condition not met at step 2331. No data to save.\n",
       "2331\n",
+      "model_vars.get('Gini', 0)[-1]=0.641\n",
+      "Condition not met at step 2332. No data to save.\n",
       "2332\n",
+      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
+      "Condition not met at step 2333. No data to save.\n",
       "2333\n",
+      "model_vars.get('Gini', 0)[-1]=0.6859999999999999\n",
+      "Condition not met at step 2334. No data to save.\n",
       "2334\n",
+      "model_vars.get('Gini', 0)[-1]=0.6456\n",
+      "Condition not met at step 2335. No data to save.\n",
       "2335\n",
+      "model_vars.get('Gini', 0)[-1]=0.6404000000000001\n",
+      "Condition not met at step 2336. No data to save.\n",
       "2336\n",
+      "model_vars.get('Gini', 0)[-1]=0.6512\n",
+      "Condition not met at step 2337. No data to save.\n",
       "2337\n",
+      "model_vars.get('Gini', 0)[-1]=0.6616\n",
+      "Condition not met at step 2338. No data to save.\n",
       "2338\n",
+      "model_vars.get('Gini', 0)[-1]=0.655\n",
+      "Condition not met at step 2339. No data to save.\n",
       "2339\n",
+      "model_vars.get('Gini', 0)[-1]=0.6394\n",
+      "Condition not met at step 2340. No data to save.\n",
       "2340\n",
+      "model_vars.get('Gini', 0)[-1]=0.6378\n",
+      "Condition not met at step 2341. No data to save.\n",
       "2341\n",
+      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
+      "Condition not met at step 2342. No data to save.\n",
       "2342\n",
+      "model_vars.get('Gini', 0)[-1]=0.6464000000000001\n",
+      "Condition not met at step 2343. No data to save.\n",
       "2343\n",
+      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
+      "Condition not met at step 2344. No data to save.\n",
       "2344\n",
+      "model_vars.get('Gini', 0)[-1]=0.6468\n",
+      "Condition not met at step 2345. No data to save.\n",
       "2345\n",
+      "model_vars.get('Gini', 0)[-1]=0.6502\n",
+      "Condition not met at step 2346. No data to save.\n",
       "2346\n",
+      "model_vars.get('Gini', 0)[-1]=0.6358\n",
+      "Condition not met at step 2347. No data to save.\n",
       "2347\n",
+      "model_vars.get('Gini', 0)[-1]=0.6134\n",
+      "Condition not met at step 2348. No data to save.\n",
       "2348\n",
+      "model_vars.get('Gini', 0)[-1]=0.6026\n",
+      "Condition not met at step 2349. No data to save.\n",
       "2349\n",
+      "model_vars.get('Gini', 0)[-1]=0.5978\n",
+      "Condition not met at step 2350. No data to save.\n",
       "2350\n",
+      "model_vars.get('Gini', 0)[-1]=0.6235999999999999\n",
+      "Condition not met at step 2351. No data to save.\n",
       "2351\n",
+      "model_vars.get('Gini', 0)[-1]=0.6152\n",
+      "Condition not met at step 2352. No data to save.\n",
       "2352\n",
+      "model_vars.get('Gini', 0)[-1]=0.6052\n",
+      "Condition not met at step 2353. No data to save.\n",
       "2353\n",
+      "model_vars.get('Gini', 0)[-1]=0.6168\n",
+      "Condition not met at step 2354. No data to save.\n",
       "2354\n",
+      "model_vars.get('Gini', 0)[-1]=0.6000000000000001\n",
+      "Condition not met at step 2355. No data to save.\n",
       "2355\n",
+      "model_vars.get('Gini', 0)[-1]=0.6142000000000001\n",
+      "Condition not met at step 2356. No data to save.\n",
       "2356\n",
+      "model_vars.get('Gini', 0)[-1]=0.6154\n",
+      "Condition not met at step 2357. No data to save.\n",
       "2357\n",
+      "model_vars.get('Gini', 0)[-1]=0.6528\n",
+      "Condition not met at step 2358. No data to save.\n",
       "2358\n",
+      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
+      "Condition not met at step 2359. No data to save.\n",
       "2359\n",
+      "model_vars.get('Gini', 0)[-1]=0.6724\n",
+      "Condition not met at step 2360. No data to save.\n",
       "2360\n",
+      "model_vars.get('Gini', 0)[-1]=0.6522\n",
+      "Condition not met at step 2361. No data to save.\n",
       "2361\n",
+      "model_vars.get('Gini', 0)[-1]=0.659\n",
+      "Condition not met at step 2362. No data to save.\n",
       "2362\n",
+      "model_vars.get('Gini', 0)[-1]=0.647\n",
+      "Condition not met at step 2363. No data to save.\n",
       "2363\n",
+      "model_vars.get('Gini', 0)[-1]=0.654\n",
+      "Condition not met at step 2364. No data to save.\n",
       "2364\n",
+      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
+      "Condition not met at step 2365. No data to save.\n",
       "2365\n",
+      "model_vars.get('Gini', 0)[-1]=0.6134\n",
+      "Condition not met at step 2366. No data to save.\n",
       "2366\n",
+      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
+      "Condition not met at step 2367. No data to save.\n",
       "2367\n",
+      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
+      "Condition not met at step 2368. No data to save.\n",
       "2368\n",
+      "model_vars.get('Gini', 0)[-1]=0.6342\n",
+      "Condition not met at step 2369. No data to save.\n",
       "2369\n",
+      "model_vars.get('Gini', 0)[-1]=0.6422\n",
+      "Condition not met at step 2370. No data to save.\n",
       "2370\n",
+      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
+      "Condition not met at step 2371. No data to save.\n",
       "2371\n",
+      "model_vars.get('Gini', 0)[-1]=0.6682\n",
+      "Condition not met at step 2372. No data to save.\n",
       "2372\n",
+      "model_vars.get('Gini', 0)[-1]=0.6544\n",
+      "Condition not met at step 2373. No data to save.\n",
       "2373\n",
+      "model_vars.get('Gini', 0)[-1]=0.6582\n",
+      "Condition not met at step 2374. No data to save.\n",
       "2374\n",
+      "model_vars.get('Gini', 0)[-1]=0.6614\n",
+      "Condition not met at step 2375. No data to save.\n",
       "2375\n",
+      "model_vars.get('Gini', 0)[-1]=0.658\n",
+      "Condition not met at step 2376. No data to save.\n",
       "2376\n",
+      "model_vars.get('Gini', 0)[-1]=0.6758\n",
+      "Condition not met at step 2377. No data to save.\n",
       "2377\n",
+      "model_vars.get('Gini', 0)[-1]=0.6666000000000001\n",
+      "Condition not met at step 2378. No data to save.\n",
       "2378\n",
+      "model_vars.get('Gini', 0)[-1]=0.7024\n",
+      "Condition met. Appended special results for step 2379 to test_path/special_results.parquet\n",
       "2379\n",
+      "model_vars.get('Gini', 0)[-1]=0.7014\n",
+      "Condition met. Appended special results for step 2380 to test_path/special_results.parquet\n",
       "2380\n",
+      "model_vars.get('Gini', 0)[-1]=0.6948000000000001\n",
+      "Condition not met at step 2381. No data to save.\n",
       "2381\n",
+      "model_vars.get('Gini', 0)[-1]=0.6754\n",
+      "Condition not met at step 2382. No data to save.\n",
       "2382\n",
+      "model_vars.get('Gini', 0)[-1]=0.6786000000000001\n",
+      "Condition not met at step 2383. No data to save.\n",
       "2383\n",
+      "model_vars.get('Gini', 0)[-1]=0.6772\n",
+      "Condition not met at step 2384. No data to save.\n",
       "2384\n",
+      "model_vars.get('Gini', 0)[-1]=0.6990000000000001\n",
+      "Condition not met at step 2385. No data to save.\n",
       "2385\n",
+      "model_vars.get('Gini', 0)[-1]=0.6812\n",
+      "Condition not met at step 2386. No data to save.\n",
       "2386\n",
+      "model_vars.get('Gini', 0)[-1]=0.6862\n",
+      "Condition not met at step 2387. No data to save.\n",
       "2387\n",
+      "model_vars.get('Gini', 0)[-1]=0.6941999999999999\n",
+      "Condition not met at step 2388. No data to save.\n",
       "2388\n",
+      "model_vars.get('Gini', 0)[-1]=0.6936\n",
+      "Condition not met at step 2389. No data to save.\n",
       "2389\n",
+      "model_vars.get('Gini', 0)[-1]=0.6822\n",
+      "Condition not met at step 2390. No data to save.\n",
       "2390\n",
+      "model_vars.get('Gini', 0)[-1]=0.681\n",
+      "Condition not met at step 2391. No data to save.\n",
       "2391\n",
+      "model_vars.get('Gini', 0)[-1]=0.6898\n",
+      "Condition not met at step 2392. No data to save.\n",
       "2392\n",
+      "model_vars.get('Gini', 0)[-1]=0.6796\n",
+      "Condition not met at step 2393. No data to save.\n",
       "2393\n",
+      "model_vars.get('Gini', 0)[-1]=0.6584\n",
+      "Condition not met at step 2394. No data to save.\n",
       "2394\n",
+      "model_vars.get('Gini', 0)[-1]=0.6859999999999999\n",
+      "Condition not met at step 2395. No data to save.\n",
       "2395\n",
+      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
+      "Condition not met at step 2396. No data to save.\n",
       "2396\n",
+      "model_vars.get('Gini', 0)[-1]=0.6616\n",
+      "Condition not met at step 2397. No data to save.\n",
       "2397\n",
+      "model_vars.get('Gini', 0)[-1]=0.6836\n",
+      "Condition not met at step 2398. No data to save.\n",
       "2398\n",
+      "model_vars.get('Gini', 0)[-1]=0.6586000000000001\n",
+      "Condition not met at step 2399. No data to save.\n",
       "2399\n",
       "CALLED\n",
       "self.model._steps=2400\n",
       " TEST self.model._steps=2400\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "2300  0.6866\n",
-      "2301  0.7058\n",
-      "2302  0.7228\n",
-      "2303  0.6972\n",
-      "2304  0.7260\n",
+      "2300  0.6312\n",
+      "2301  0.6636\n",
+      "2302  0.6456\n",
+      "2303  0.6352\n",
+      "2304  0.6234\n",
       "...      ...\n",
-      "2395  0.6832\n",
-      "2396  0.6752\n",
-      "2397  0.6798\n",
-      "2398  0.6688\n",
-      "2399  0.6850\n",
+      "2395  0.6728\n",
+      "2396  0.6616\n",
+      "2397  0.6836\n",
+      "2398  0.6586\n",
+      "2399  0.6722\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_024.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_024.parquet'\n",
-      "Saving model to output_dir/model_data_024.parquet\n",
-      "Saving agent to output_dir/agent_data_024.parquet\n",
+      "model_file='test_path/model_data_024.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_024.parquet'\n",
+      "Saving model to test_path/model_data_024.parquet\n",
+      "Saving agent to test_path/agent_data_024.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6722\n",
+      "Condition not met at step 2400. No data to save.\n",
       "2400\n",
+      "model_vars.get('Gini', 0)[-1]=0.6744\n",
+      "Condition not met at step 2401. No data to save.\n",
       "2401\n",
+      "model_vars.get('Gini', 0)[-1]=0.6704\n",
+      "Condition not met at step 2402. No data to save.\n",
       "2402\n",
+      "model_vars.get('Gini', 0)[-1]=0.6748000000000001\n",
+      "Condition not met at step 2403. No data to save.\n",
       "2403\n",
+      "model_vars.get('Gini', 0)[-1]=0.6946\n",
+      "Condition not met at step 2404. No data to save.\n",
       "2404\n",
+      "model_vars.get('Gini', 0)[-1]=0.6826\n",
+      "Condition not met at step 2405. No data to save.\n",
       "2405\n",
+      "model_vars.get('Gini', 0)[-1]=0.6976\n",
+      "Condition not met at step 2406. No data to save.\n",
       "2406\n",
+      "model_vars.get('Gini', 0)[-1]=0.7116\n",
+      "Condition met. Appended special results for step 2407 to test_path/special_results.parquet\n",
       "2407\n",
+      "model_vars.get('Gini', 0)[-1]=0.6924\n",
+      "Condition not met at step 2408. No data to save.\n",
       "2408\n",
+      "model_vars.get('Gini', 0)[-1]=0.6624\n",
+      "Condition not met at step 2409. No data to save.\n",
       "2409\n",
+      "model_vars.get('Gini', 0)[-1]=0.6602\n",
+      "Condition not met at step 2410. No data to save.\n",
       "2410\n",
+      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
+      "Condition not met at step 2411. No data to save.\n",
       "2411\n",
+      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
+      "Condition not met at step 2412. No data to save.\n",
       "2412\n",
+      "model_vars.get('Gini', 0)[-1]=0.6764\n",
+      "Condition not met at step 2413. No data to save.\n",
       "2413\n",
+      "model_vars.get('Gini', 0)[-1]=0.6752\n",
+      "Condition not met at step 2414. No data to save.\n",
       "2414\n",
+      "model_vars.get('Gini', 0)[-1]=0.6884\n",
+      "Condition not met at step 2415. No data to save.\n",
       "2415\n",
+      "model_vars.get('Gini', 0)[-1]=0.7001999999999999\n",
+      "Condition met. Appended special results for step 2416 to test_path/special_results.parquet\n",
       "2416\n",
+      "model_vars.get('Gini', 0)[-1]=0.6978\n",
+      "Condition not met at step 2417. No data to save.\n",
       "2417\n",
+      "model_vars.get('Gini', 0)[-1]=0.6826\n",
+      "Condition not met at step 2418. No data to save.\n",
       "2418\n",
+      "model_vars.get('Gini', 0)[-1]=0.6792\n",
+      "Condition not met at step 2419. No data to save.\n",
       "2419\n",
+      "model_vars.get('Gini', 0)[-1]=0.6746000000000001\n",
+      "Condition not met at step 2420. No data to save.\n",
       "2420\n",
+      "model_vars.get('Gini', 0)[-1]=0.6826\n",
+      "Condition not met at step 2421. No data to save.\n",
       "2421\n",
+      "model_vars.get('Gini', 0)[-1]=0.6768000000000001\n",
+      "Condition not met at step 2422. No data to save.\n",
       "2422\n",
+      "model_vars.get('Gini', 0)[-1]=0.6772\n",
+      "Condition not met at step 2423. No data to save.\n",
       "2423\n",
+      "model_vars.get('Gini', 0)[-1]=0.6858\n",
+      "Condition not met at step 2424. No data to save.\n",
       "2424\n",
+      "model_vars.get('Gini', 0)[-1]=0.6566000000000001\n",
+      "Condition not met at step 2425. No data to save.\n",
       "2425\n",
+      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
+      "Condition not met at step 2426. No data to save.\n",
       "2426\n",
+      "model_vars.get('Gini', 0)[-1]=0.649\n",
+      "Condition not met at step 2427. No data to save.\n",
       "2427\n",
+      "model_vars.get('Gini', 0)[-1]=0.6366\n",
+      "Condition not met at step 2428. No data to save.\n",
       "2428\n",
+      "model_vars.get('Gini', 0)[-1]=0.6448\n",
+      "Condition not met at step 2429. No data to save.\n",
       "2429\n",
+      "model_vars.get('Gini', 0)[-1]=0.6806\n",
+      "Condition not met at step 2430. No data to save.\n",
       "2430\n",
+      "model_vars.get('Gini', 0)[-1]=0.6654\n",
+      "Condition not met at step 2431. No data to save.\n",
       "2431\n",
+      "model_vars.get('Gini', 0)[-1]=0.6742\n",
+      "Condition not met at step 2432. No data to save.\n",
       "2432\n",
+      "model_vars.get('Gini', 0)[-1]=0.655\n",
+      "Condition not met at step 2433. No data to save.\n",
       "2433\n",
+      "model_vars.get('Gini', 0)[-1]=0.658\n",
+      "Condition not met at step 2434. No data to save.\n",
       "2434\n",
+      "model_vars.get('Gini', 0)[-1]=0.6422\n",
+      "Condition not met at step 2435. No data to save.\n",
       "2435\n",
+      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
+      "Condition not met at step 2436. No data to save.\n",
       "2436\n",
+      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
+      "Condition not met at step 2437. No data to save.\n",
       "2437\n",
+      "model_vars.get('Gini', 0)[-1]=0.6678\n",
+      "Condition not met at step 2438. No data to save.\n",
       "2438\n",
+      "model_vars.get('Gini', 0)[-1]=0.6758\n",
+      "Condition not met at step 2439. No data to save.\n",
       "2439\n",
+      "model_vars.get('Gini', 0)[-1]=0.6544\n",
+      "Condition not met at step 2440. No data to save.\n",
       "2440\n",
+      "model_vars.get('Gini', 0)[-1]=0.6582\n",
+      "Condition not met at step 2441. No data to save.\n",
       "2441\n",
+      "model_vars.get('Gini', 0)[-1]=0.658\n",
+      "Condition not met at step 2442. No data to save.\n",
       "2442\n",
+      "model_vars.get('Gini', 0)[-1]=0.6504000000000001\n",
+      "Condition not met at step 2443. No data to save.\n",
       "2443\n",
+      "model_vars.get('Gini', 0)[-1]=0.64\n",
+      "Condition not met at step 2444. No data to save.\n",
       "2444\n",
+      "model_vars.get('Gini', 0)[-1]=0.643\n",
+      "Condition not met at step 2445. No data to save.\n",
       "2445\n",
+      "model_vars.get('Gini', 0)[-1]=0.6482\n",
+      "Condition not met at step 2446. No data to save.\n",
       "2446\n",
+      "model_vars.get('Gini', 0)[-1]=0.6638\n",
+      "Condition not met at step 2447. No data to save.\n",
       "2447\n",
+      "model_vars.get('Gini', 0)[-1]=0.6335999999999999\n",
+      "Condition not met at step 2448. No data to save.\n",
       "2448\n",
+      "model_vars.get('Gini', 0)[-1]=0.627\n",
+      "Condition not met at step 2449. No data to save.\n",
       "2449\n",
+      "model_vars.get('Gini', 0)[-1]=0.619\n",
+      "Condition not met at step 2450. No data to save.\n",
       "2450\n",
+      "model_vars.get('Gini', 0)[-1]=0.625\n",
+      "Condition not met at step 2451. No data to save.\n",
       "2451\n",
+      "model_vars.get('Gini', 0)[-1]=0.6268\n",
+      "Condition not met at step 2452. No data to save.\n",
       "2452\n",
+      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
+      "Condition not met at step 2453. No data to save.\n",
       "2453\n",
+      "model_vars.get('Gini', 0)[-1]=0.623\n",
+      "Condition not met at step 2454. No data to save.\n",
       "2454\n",
+      "model_vars.get('Gini', 0)[-1]=0.6078\n",
+      "Condition not met at step 2455. No data to save.\n",
       "2455\n",
+      "model_vars.get('Gini', 0)[-1]=0.6262000000000001\n",
+      "Condition not met at step 2456. No data to save.\n",
       "2456\n",
+      "model_vars.get('Gini', 0)[-1]=0.6138\n",
+      "Condition not met at step 2457. No data to save.\n",
       "2457\n",
+      "model_vars.get('Gini', 0)[-1]=0.5862\n",
+      "Condition not met at step 2458. No data to save.\n",
       "2458\n",
+      "model_vars.get('Gini', 0)[-1]=0.5738000000000001\n",
+      "Condition not met at step 2459. No data to save.\n",
       "2459\n",
+      "model_vars.get('Gini', 0)[-1]=0.5794\n",
+      "Condition not met at step 2460. No data to save.\n",
       "2460\n",
+      "model_vars.get('Gini', 0)[-1]=0.5984\n",
+      "Condition not met at step 2461. No data to save.\n",
       "2461\n",
+      "model_vars.get('Gini', 0)[-1]=0.5884\n",
+      "Condition not met at step 2462. No data to save.\n",
       "2462\n",
+      "model_vars.get('Gini', 0)[-1]=0.5574\n",
+      "Condition not met at step 2463. No data to save.\n",
       "2463\n",
+      "model_vars.get('Gini', 0)[-1]=0.5602\n",
+      "Condition not met at step 2464. No data to save.\n",
       "2464\n",
+      "model_vars.get('Gini', 0)[-1]=0.565\n",
+      "Condition not met at step 2465. No data to save.\n",
       "2465\n",
+      "model_vars.get('Gini', 0)[-1]=0.5922000000000001\n",
+      "Condition not met at step 2466. No data to save.\n",
       "2466\n",
+      "model_vars.get('Gini', 0)[-1]=0.6106\n",
+      "Condition not met at step 2467. No data to save.\n",
       "2467\n",
+      "model_vars.get('Gini', 0)[-1]=0.627\n",
+      "Condition not met at step 2468. No data to save.\n",
       "2468\n",
+      "model_vars.get('Gini', 0)[-1]=0.6348\n",
+      "Condition not met at step 2469. No data to save.\n",
       "2469\n",
+      "model_vars.get('Gini', 0)[-1]=0.6032\n",
+      "Condition not met at step 2470. No data to save.\n",
       "2470\n",
+      "model_vars.get('Gini', 0)[-1]=0.5920000000000001\n",
+      "Condition not met at step 2471. No data to save.\n",
       "2471\n",
+      "model_vars.get('Gini', 0)[-1]=0.5682\n",
+      "Condition not met at step 2472. No data to save.\n",
       "2472\n",
+      "model_vars.get('Gini', 0)[-1]=0.5996\n",
+      "Condition not met at step 2473. No data to save.\n",
       "2473\n",
+      "model_vars.get('Gini', 0)[-1]=0.6058\n",
+      "Condition not met at step 2474. No data to save.\n",
       "2474\n",
+      "model_vars.get('Gini', 0)[-1]=0.5972\n",
+      "Condition not met at step 2475. No data to save.\n",
       "2475\n",
+      "model_vars.get('Gini', 0)[-1]=0.6274\n",
+      "Condition not met at step 2476. No data to save.\n",
       "2476\n",
+      "model_vars.get('Gini', 0)[-1]=0.6298\n",
+      "Condition not met at step 2477. No data to save.\n",
       "2477\n",
+      "model_vars.get('Gini', 0)[-1]=0.6246\n",
+      "Condition not met at step 2478. No data to save.\n",
       "2478\n",
+      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
+      "Condition not met at step 2479. No data to save.\n",
       "2479\n",
+      "model_vars.get('Gini', 0)[-1]=0.6262000000000001\n",
+      "Condition not met at step 2480. No data to save.\n",
       "2480\n",
+      "model_vars.get('Gini', 0)[-1]=0.6304000000000001\n",
+      "Condition not met at step 2481. No data to save.\n",
       "2481\n",
+      "model_vars.get('Gini', 0)[-1]=0.6754\n",
+      "Condition not met at step 2482. No data to save.\n",
       "2482\n",
+      "model_vars.get('Gini', 0)[-1]=0.6534\n",
+      "Condition not met at step 2483. No data to save.\n",
       "2483\n",
+      "model_vars.get('Gini', 0)[-1]=0.6744\n",
+      "Condition not met at step 2484. No data to save.\n",
       "2484\n",
+      "model_vars.get('Gini', 0)[-1]=0.661\n",
+      "Condition not met at step 2485. No data to save.\n",
       "2485\n",
+      "model_vars.get('Gini', 0)[-1]=0.6282000000000001\n",
+      "Condition not met at step 2486. No data to save.\n",
       "2486\n",
+      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
+      "Condition not met at step 2487. No data to save.\n",
       "2487\n",
+      "model_vars.get('Gini', 0)[-1]=0.6408\n",
+      "Condition not met at step 2488. No data to save.\n",
       "2488\n",
+      "model_vars.get('Gini', 0)[-1]=0.6166\n",
+      "Condition not met at step 2489. No data to save.\n",
       "2489\n",
+      "model_vars.get('Gini', 0)[-1]=0.643\n",
+      "Condition not met at step 2490. No data to save.\n",
       "2490\n",
+      "model_vars.get('Gini', 0)[-1]=0.617\n",
+      "Condition not met at step 2491. No data to save.\n",
       "2491\n",
+      "model_vars.get('Gini', 0)[-1]=0.6194\n",
+      "Condition not met at step 2492. No data to save.\n",
       "2492\n",
+      "model_vars.get('Gini', 0)[-1]=0.6024\n",
+      "Condition not met at step 2493. No data to save.\n",
       "2493\n",
+      "model_vars.get('Gini', 0)[-1]=0.606\n",
+      "Condition not met at step 2494. No data to save.\n",
       "2494\n",
+      "model_vars.get('Gini', 0)[-1]=0.6264000000000001\n",
+      "Condition not met at step 2495. No data to save.\n",
       "2495\n",
+      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
+      "Condition not met at step 2496. No data to save.\n",
       "2496\n",
+      "model_vars.get('Gini', 0)[-1]=0.6262000000000001\n",
+      "Condition not met at step 2497. No data to save.\n",
       "2497\n",
+      "model_vars.get('Gini', 0)[-1]=0.6274\n",
+      "Condition not met at step 2498. No data to save.\n",
       "2498\n",
+      "model_vars.get('Gini', 0)[-1]=0.6262000000000001\n",
+      "Condition not met at step 2499. No data to save.\n",
       "2499\n",
       "CALLED\n",
       "self.model._steps=2500\n",
       " TEST self.model._steps=2500\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "2400  0.6994\n",
-      "2401  0.6884\n",
-      "2402  0.6870\n",
-      "2403  0.6612\n",
-      "2404  0.6716\n",
+      "2400  0.6744\n",
+      "2401  0.6704\n",
+      "2402  0.6748\n",
+      "2403  0.6946\n",
+      "2404  0.6826\n",
       "...      ...\n",
-      "2495  0.6496\n",
-      "2496  0.6534\n",
-      "2497  0.6500\n",
-      "2498  0.6460\n",
-      "2499  0.6532\n",
+      "2495  0.6406\n",
+      "2496  0.6262\n",
+      "2497  0.6274\n",
+      "2498  0.6262\n",
+      "2499  0.6760\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_025.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_025.parquet'\n",
-      "Saving model to output_dir/model_data_025.parquet\n",
-      "Saving agent to output_dir/agent_data_025.parquet\n",
+      "model_file='test_path/model_data_025.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_025.parquet'\n",
+      "Saving model to test_path/model_data_025.parquet\n",
+      "Saving agent to test_path/agent_data_025.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6759999999999999\n",
+      "Condition not met at step 2500. No data to save.\n",
       "2500\n",
+      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
+      "Condition not met at step 2501. No data to save.\n",
       "2501\n",
+      "model_vars.get('Gini', 0)[-1]=0.657\n",
+      "Condition not met at step 2502. No data to save.\n",
       "2502\n",
+      "model_vars.get('Gini', 0)[-1]=0.6582\n",
+      "Condition not met at step 2503. No data to save.\n",
       "2503\n",
+      "model_vars.get('Gini', 0)[-1]=0.653\n",
+      "Condition not met at step 2504. No data to save.\n",
       "2504\n",
+      "model_vars.get('Gini', 0)[-1]=0.6582\n",
+      "Condition not met at step 2505. No data to save.\n",
       "2505\n",
+      "model_vars.get('Gini', 0)[-1]=0.6554\n",
+      "Condition not met at step 2506. No data to save.\n",
       "2506\n",
+      "model_vars.get('Gini', 0)[-1]=0.6358\n",
+      "Condition not met at step 2507. No data to save.\n",
       "2507\n",
+      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
+      "Condition not met at step 2508. No data to save.\n",
       "2508\n",
+      "model_vars.get('Gini', 0)[-1]=0.6946\n",
+      "Condition not met at step 2509. No data to save.\n",
       "2509\n",
+      "model_vars.get('Gini', 0)[-1]=0.6736\n",
+      "Condition not met at step 2510. No data to save.\n",
       "2510\n",
+      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
+      "Condition not met at step 2511. No data to save.\n",
       "2511\n",
+      "model_vars.get('Gini', 0)[-1]=0.6588\n",
+      "Condition not met at step 2512. No data to save.\n",
       "2512\n",
+      "model_vars.get('Gini', 0)[-1]=0.6718\n",
+      "Condition not met at step 2513. No data to save.\n",
       "2513\n",
+      "model_vars.get('Gini', 0)[-1]=0.6536\n",
+      "Condition not met at step 2514. No data to save.\n",
       "2514\n",
+      "model_vars.get('Gini', 0)[-1]=0.6306\n",
+      "Condition not met at step 2515. No data to save.\n",
       "2515\n",
+      "model_vars.get('Gini', 0)[-1]=0.6462\n",
+      "Condition not met at step 2516. No data to save.\n",
       "2516\n",
+      "model_vars.get('Gini', 0)[-1]=0.6642\n",
+      "Condition not met at step 2517. No data to save.\n",
       "2517\n",
+      "model_vars.get('Gini', 0)[-1]=0.6858\n",
+      "Condition not met at step 2518. No data to save.\n",
       "2518\n",
+      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
+      "Condition not met at step 2519. No data to save.\n",
       "2519\n",
+      "model_vars.get('Gini', 0)[-1]=0.6702\n",
+      "Condition not met at step 2520. No data to save.\n",
       "2520\n",
+      "model_vars.get('Gini', 0)[-1]=0.6819999999999999\n",
+      "Condition not met at step 2521. No data to save.\n",
       "2521\n",
+      "model_vars.get('Gini', 0)[-1]=0.671\n",
+      "Condition not met at step 2522. No data to save.\n",
       "2522\n",
+      "model_vars.get('Gini', 0)[-1]=0.6542\n",
+      "Condition not met at step 2523. No data to save.\n",
       "2523\n",
+      "model_vars.get('Gini', 0)[-1]=0.6692\n",
+      "Condition not met at step 2524. No data to save.\n",
       "2524\n",
+      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
+      "Condition not met at step 2525. No data to save.\n",
       "2525\n",
+      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
+      "Condition not met at step 2526. No data to save.\n",
       "2526\n",
+      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
+      "Condition not met at step 2527. No data to save.\n",
       "2527\n",
+      "model_vars.get('Gini', 0)[-1]=0.612\n",
+      "Condition not met at step 2528. No data to save.\n",
       "2528\n",
+      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
+      "Condition not met at step 2529. No data to save.\n",
       "2529\n",
+      "model_vars.get('Gini', 0)[-1]=0.673\n",
+      "Condition not met at step 2530. No data to save.\n",
       "2530\n",
+      "model_vars.get('Gini', 0)[-1]=0.6474\n",
+      "Condition not met at step 2531. No data to save.\n",
       "2531\n",
+      "model_vars.get('Gini', 0)[-1]=0.667\n",
+      "Condition not met at step 2532. No data to save.\n",
       "2532\n",
+      "model_vars.get('Gini', 0)[-1]=0.6444000000000001\n",
+      "Condition not met at step 2533. No data to save.\n",
       "2533\n",
+      "model_vars.get('Gini', 0)[-1]=0.6654\n",
+      "Condition not met at step 2534. No data to save.\n",
       "2534\n",
+      "model_vars.get('Gini', 0)[-1]=0.6536\n",
+      "Condition not met at step 2535. No data to save.\n",
       "2535\n",
+      "model_vars.get('Gini', 0)[-1]=0.6516\n",
+      "Condition not met at step 2536. No data to save.\n",
       "2536\n",
+      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
+      "Condition not met at step 2537. No data to save.\n",
       "2537\n",
+      "model_vars.get('Gini', 0)[-1]=0.6482\n",
+      "Condition not met at step 2538. No data to save.\n",
       "2538\n",
+      "model_vars.get('Gini', 0)[-1]=0.6722\n",
+      "Condition not met at step 2539. No data to save.\n",
       "2539\n",
+      "model_vars.get('Gini', 0)[-1]=0.6852\n",
+      "Condition not met at step 2540. No data to save.\n",
       "2540\n",
+      "model_vars.get('Gini', 0)[-1]=0.6898\n",
+      "Condition not met at step 2541. No data to save.\n",
       "2541\n",
+      "model_vars.get('Gini', 0)[-1]=0.7252000000000001\n",
+      "Condition met. Appended special results for step 2542 to test_path/special_results.parquet\n",
       "2542\n",
+      "model_vars.get('Gini', 0)[-1]=0.7106\n",
+      "Condition met. Appended special results for step 2543 to test_path/special_results.parquet\n",
       "2543\n",
+      "model_vars.get('Gini', 0)[-1]=0.6898\n",
+      "Condition not met at step 2544. No data to save.\n",
       "2544\n",
+      "model_vars.get('Gini', 0)[-1]=0.687\n",
+      "Condition not met at step 2545. No data to save.\n",
       "2545\n",
+      "model_vars.get('Gini', 0)[-1]=0.6872\n",
+      "Condition not met at step 2546. No data to save.\n",
       "2546\n",
+      "model_vars.get('Gini', 0)[-1]=0.6656\n",
+      "Condition not met at step 2547. No data to save.\n",
       "2547\n",
+      "model_vars.get('Gini', 0)[-1]=0.6462\n",
+      "Condition not met at step 2548. No data to save.\n",
       "2548\n",
+      "model_vars.get('Gini', 0)[-1]=0.6588\n",
+      "Condition not met at step 2549. No data to save.\n",
       "2549\n",
+      "model_vars.get('Gini', 0)[-1]=0.6437999999999999\n",
+      "Condition not met at step 2550. No data to save.\n",
       "2550\n",
+      "model_vars.get('Gini', 0)[-1]=0.6378\n",
+      "Condition not met at step 2551. No data to save.\n",
       "2551\n",
+      "model_vars.get('Gini', 0)[-1]=0.6674\n",
+      "Condition not met at step 2552. No data to save.\n",
       "2552\n",
+      "model_vars.get('Gini', 0)[-1]=0.6868000000000001\n",
+      "Condition not met at step 2553. No data to save.\n",
       "2553\n",
+      "model_vars.get('Gini', 0)[-1]=0.7188\n",
+      "Condition met. Appended special results for step 2554 to test_path/special_results.parquet\n",
       "2554\n",
+      "model_vars.get('Gini', 0)[-1]=0.6972\n",
+      "Condition not met at step 2555. No data to save.\n",
       "2555\n",
+      "model_vars.get('Gini', 0)[-1]=0.6814\n",
+      "Condition not met at step 2556. No data to save.\n",
       "2556\n",
+      "model_vars.get('Gini', 0)[-1]=0.6981999999999999\n",
+      "Condition not met at step 2557. No data to save.\n",
       "2557\n",
+      "model_vars.get('Gini', 0)[-1]=0.6834\n",
+      "Condition not met at step 2558. No data to save.\n",
       "2558\n",
+      "model_vars.get('Gini', 0)[-1]=0.673\n",
+      "Condition not met at step 2559. No data to save.\n",
       "2559\n",
+      "model_vars.get('Gini', 0)[-1]=0.647\n",
+      "Condition not met at step 2560. No data to save.\n",
       "2560\n",
+      "model_vars.get('Gini', 0)[-1]=0.664\n",
+      "Condition not met at step 2561. No data to save.\n",
       "2561\n",
+      "model_vars.get('Gini', 0)[-1]=0.69\n",
+      "Condition not met at step 2562. No data to save.\n",
       "2562\n",
+      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
+      "Condition not met at step 2563. No data to save.\n",
       "2563\n",
+      "model_vars.get('Gini', 0)[-1]=0.646\n",
+      "Condition not met at step 2564. No data to save.\n",
       "2564\n",
+      "model_vars.get('Gini', 0)[-1]=0.6748000000000001\n",
+      "Condition not met at step 2565. No data to save.\n",
       "2565\n",
+      "model_vars.get('Gini', 0)[-1]=0.6662\n",
+      "Condition not met at step 2566. No data to save.\n",
       "2566\n",
+      "model_vars.get('Gini', 0)[-1]=0.6804\n",
+      "Condition not met at step 2567. No data to save.\n",
       "2567\n",
+      "model_vars.get('Gini', 0)[-1]=0.6778\n",
+      "Condition not met at step 2568. No data to save.\n",
       "2568\n",
+      "model_vars.get('Gini', 0)[-1]=0.6852\n",
+      "Condition not met at step 2569. No data to save.\n",
       "2569\n",
+      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
+      "Condition not met at step 2570. No data to save.\n",
       "2570\n",
+      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
+      "Condition not met at step 2571. No data to save.\n",
       "2571\n",
+      "model_vars.get('Gini', 0)[-1]=0.6764\n",
+      "Condition not met at step 2572. No data to save.\n",
       "2572\n",
+      "model_vars.get('Gini', 0)[-1]=0.669\n",
+      "Condition not met at step 2573. No data to save.\n",
       "2573\n",
+      "model_vars.get('Gini', 0)[-1]=0.6634\n",
+      "Condition not met at step 2574. No data to save.\n",
       "2574\n",
+      "model_vars.get('Gini', 0)[-1]=0.657\n",
+      "Condition not met at step 2575. No data to save.\n",
       "2575\n",
+      "model_vars.get('Gini', 0)[-1]=0.6684\n",
+      "Condition not met at step 2576. No data to save.\n",
       "2576\n",
+      "model_vars.get('Gini', 0)[-1]=0.6577999999999999\n",
+      "Condition not met at step 2577. No data to save.\n",
       "2577\n",
+      "model_vars.get('Gini', 0)[-1]=0.634\n",
+      "Condition not met at step 2578. No data to save.\n",
       "2578\n",
+      "model_vars.get('Gini', 0)[-1]=0.6414\n",
+      "Condition not met at step 2579. No data to save.\n",
       "2579\n",
+      "model_vars.get('Gini', 0)[-1]=0.6392\n",
+      "Condition not met at step 2580. No data to save.\n",
       "2580\n",
+      "model_vars.get('Gini', 0)[-1]=0.6268\n",
+      "Condition not met at step 2581. No data to save.\n",
       "2581\n",
+      "model_vars.get('Gini', 0)[-1]=0.613\n",
+      "Condition not met at step 2582. No data to save.\n",
       "2582\n",
+      "model_vars.get('Gini', 0)[-1]=0.637\n",
+      "Condition not met at step 2583. No data to save.\n",
       "2583\n",
+      "model_vars.get('Gini', 0)[-1]=0.6462\n",
+      "Condition not met at step 2584. No data to save.\n",
       "2584\n",
+      "model_vars.get('Gini', 0)[-1]=0.6658\n",
+      "Condition not met at step 2585. No data to save.\n",
       "2585\n",
+      "model_vars.get('Gini', 0)[-1]=0.6782\n",
+      "Condition not met at step 2586. No data to save.\n",
       "2586\n",
+      "model_vars.get('Gini', 0)[-1]=0.6804\n",
+      "Condition not met at step 2587. No data to save.\n",
       "2587\n",
+      "model_vars.get('Gini', 0)[-1]=0.6704\n",
+      "Condition not met at step 2588. No data to save.\n",
       "2588\n",
+      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
+      "Condition not met at step 2589. No data to save.\n",
       "2589\n",
+      "model_vars.get('Gini', 0)[-1]=0.6704\n",
+      "Condition not met at step 2590. No data to save.\n",
       "2590\n",
+      "model_vars.get('Gini', 0)[-1]=0.6506000000000001\n",
+      "Condition not met at step 2591. No data to save.\n",
       "2591\n",
+      "model_vars.get('Gini', 0)[-1]=0.667\n",
+      "Condition not met at step 2592. No data to save.\n",
       "2592\n",
+      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
+      "Condition not met at step 2593. No data to save.\n",
       "2593\n",
+      "model_vars.get('Gini', 0)[-1]=0.6222000000000001\n",
+      "Condition not met at step 2594. No data to save.\n",
       "2594\n",
+      "model_vars.get('Gini', 0)[-1]=0.6374\n",
+      "Condition not met at step 2595. No data to save.\n",
       "2595\n",
+      "model_vars.get('Gini', 0)[-1]=0.6859999999999999\n",
+      "Condition not met at step 2596. No data to save.\n",
       "2596\n",
+      "model_vars.get('Gini', 0)[-1]=0.7074\n",
+      "Condition met. Appended special results for step 2597 to test_path/special_results.parquet\n",
       "2597\n",
+      "model_vars.get('Gini', 0)[-1]=0.6884\n",
+      "Condition not met at step 2598. No data to save.\n",
       "2598\n",
+      "model_vars.get('Gini', 0)[-1]=0.6714\n",
+      "Condition not met at step 2599. No data to save.\n",
       "2599\n",
       "CALLED\n",
       "self.model._steps=2600\n",
       " TEST self.model._steps=2600\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "2500  0.6828\n",
-      "2501  0.6718\n",
-      "2502  0.6546\n",
-      "2503  0.6384\n",
-      "2504  0.6320\n",
+      "2500  0.6766\n",
+      "2501  0.6570\n",
+      "2502  0.6582\n",
+      "2503  0.6530\n",
+      "2504  0.6582\n",
       "...      ...\n",
-      "2595  0.6142\n",
-      "2596  0.6306\n",
-      "2597  0.6378\n",
-      "2598  0.6732\n",
-      "2599  0.6952\n",
+      "2595  0.6860\n",
+      "2596  0.7074\n",
+      "2597  0.6884\n",
+      "2598  0.6714\n",
+      "2599  0.6552\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_026.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_026.parquet'\n",
-      "Saving model to output_dir/model_data_026.parquet\n",
-      "Saving agent to output_dir/agent_data_026.parquet\n",
+      "model_file='test_path/model_data_026.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_026.parquet'\n",
+      "Saving model to test_path/model_data_026.parquet\n",
+      "Saving agent to test_path/agent_data_026.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6552\n",
+      "Condition not met at step 2600. No data to save.\n",
       "2600\n",
+      "model_vars.get('Gini', 0)[-1]=0.6246\n",
+      "Condition not met at step 2601. No data to save.\n",
       "2601\n",
+      "model_vars.get('Gini', 0)[-1]=0.6338\n",
+      "Condition not met at step 2602. No data to save.\n",
       "2602\n",
+      "model_vars.get('Gini', 0)[-1]=0.605\n",
+      "Condition not met at step 2603. No data to save.\n",
       "2603\n",
+      "model_vars.get('Gini', 0)[-1]=0.6116\n",
+      "Condition not met at step 2604. No data to save.\n",
       "2604\n",
+      "model_vars.get('Gini', 0)[-1]=0.6238\n",
+      "Condition not met at step 2605. No data to save.\n",
       "2605\n",
+      "model_vars.get('Gini', 0)[-1]=0.634\n",
+      "Condition not met at step 2606. No data to save.\n",
       "2606\n",
+      "model_vars.get('Gini', 0)[-1]=0.629\n",
+      "Condition not met at step 2607. No data to save.\n",
       "2607\n",
+      "model_vars.get('Gini', 0)[-1]=0.6058\n",
+      "Condition not met at step 2608. No data to save.\n",
       "2608\n",
+      "model_vars.get('Gini', 0)[-1]=0.6258\n",
+      "Condition not met at step 2609. No data to save.\n",
       "2609\n",
+      "model_vars.get('Gini', 0)[-1]=0.5802\n",
+      "Condition not met at step 2610. No data to save.\n",
       "2610\n",
+      "model_vars.get('Gini', 0)[-1]=0.6072\n",
+      "Condition not met at step 2611. No data to save.\n",
       "2611\n",
+      "model_vars.get('Gini', 0)[-1]=0.605\n",
+      "Condition not met at step 2612. No data to save.\n",
       "2612\n",
+      "model_vars.get('Gini', 0)[-1]=0.6392\n",
+      "Condition not met at step 2613. No data to save.\n",
       "2613\n",
+      "model_vars.get('Gini', 0)[-1]=0.6414\n",
+      "Condition not met at step 2614. No data to save.\n",
       "2614\n",
+      "model_vars.get('Gini', 0)[-1]=0.6338\n",
+      "Condition not met at step 2615. No data to save.\n",
       "2615\n",
+      "model_vars.get('Gini', 0)[-1]=0.5788\n",
+      "Condition not met at step 2616. No data to save.\n",
       "2616\n",
+      "model_vars.get('Gini', 0)[-1]=0.623\n",
+      "Condition not met at step 2617. No data to save.\n",
       "2617\n",
+      "model_vars.get('Gini', 0)[-1]=0.5900000000000001\n",
+      "Condition not met at step 2618. No data to save.\n",
       "2618\n",
+      "model_vars.get('Gini', 0)[-1]=0.611\n",
+      "Condition not met at step 2619. No data to save.\n",
       "2619\n",
+      "model_vars.get('Gini', 0)[-1]=0.6142000000000001\n",
+      "Condition not met at step 2620. No data to save.\n",
       "2620\n",
+      "model_vars.get('Gini', 0)[-1]=0.6226\n",
+      "Condition not met at step 2621. No data to save.\n",
       "2621\n",
+      "model_vars.get('Gini', 0)[-1]=0.6262000000000001\n",
+      "Condition not met at step 2622. No data to save.\n",
       "2622\n",
+      "model_vars.get('Gini', 0)[-1]=0.623\n",
+      "Condition not met at step 2623. No data to save.\n",
       "2623\n",
+      "model_vars.get('Gini', 0)[-1]=0.6582\n",
+      "Condition not met at step 2624. No data to save.\n",
       "2624\n",
+      "model_vars.get('Gini', 0)[-1]=0.6736\n",
+      "Condition not met at step 2625. No data to save.\n",
       "2625\n",
+      "model_vars.get('Gini', 0)[-1]=0.6924\n",
+      "Condition not met at step 2626. No data to save.\n",
       "2626\n",
+      "model_vars.get('Gini', 0)[-1]=0.664\n",
+      "Condition not met at step 2627. No data to save.\n",
       "2627\n",
+      "model_vars.get('Gini', 0)[-1]=0.6616\n",
+      "Condition not met at step 2628. No data to save.\n",
       "2628\n",
+      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
+      "Condition not met at step 2629. No data to save.\n",
       "2629\n",
+      "model_vars.get('Gini', 0)[-1]=0.6686000000000001\n",
+      "Condition not met at step 2630. No data to save.\n",
       "2630\n",
+      "model_vars.get('Gini', 0)[-1]=0.6572\n",
+      "Condition not met at step 2631. No data to save.\n",
       "2631\n",
+      "model_vars.get('Gini', 0)[-1]=0.6658\n",
+      "Condition not met at step 2632. No data to save.\n",
       "2632\n",
+      "model_vars.get('Gini', 0)[-1]=0.6468\n",
+      "Condition not met at step 2633. No data to save.\n",
       "2633\n",
+      "model_vars.get('Gini', 0)[-1]=0.6175999999999999\n",
+      "Condition not met at step 2634. No data to save.\n",
       "2634\n",
+      "model_vars.get('Gini', 0)[-1]=0.631\n",
+      "Condition not met at step 2635. No data to save.\n",
       "2635\n",
+      "model_vars.get('Gini', 0)[-1]=0.6576\n",
+      "Condition not met at step 2636. No data to save.\n",
       "2636\n",
+      "model_vars.get('Gini', 0)[-1]=0.6456\n",
+      "Condition not met at step 2637. No data to save.\n",
       "2637\n",
+      "model_vars.get('Gini', 0)[-1]=0.6456\n",
+      "Condition not met at step 2638. No data to save.\n",
       "2638\n",
+      "model_vars.get('Gini', 0)[-1]=0.6508\n",
+      "Condition not met at step 2639. No data to save.\n",
       "2639\n",
+      "model_vars.get('Gini', 0)[-1]=0.6422\n",
+      "Condition not met at step 2640. No data to save.\n",
       "2640\n",
+      "model_vars.get('Gini', 0)[-1]=0.613\n",
+      "Condition not met at step 2641. No data to save.\n",
       "2641\n",
+      "model_vars.get('Gini', 0)[-1]=0.6246\n",
+      "Condition not met at step 2642. No data to save.\n",
       "2642\n",
+      "model_vars.get('Gini', 0)[-1]=0.6238\n",
+      "Condition not met at step 2643. No data to save.\n",
       "2643\n",
+      "model_vars.get('Gini', 0)[-1]=0.6093999999999999\n",
+      "Condition not met at step 2644. No data to save.\n",
       "2644\n",
+      "model_vars.get('Gini', 0)[-1]=0.6306\n",
+      "Condition not met at step 2645. No data to save.\n",
       "2645\n",
+      "model_vars.get('Gini', 0)[-1]=0.673\n",
+      "Condition not met at step 2646. No data to save.\n",
       "2646\n",
+      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
+      "Condition not met at step 2647. No data to save.\n",
       "2647\n",
+      "model_vars.get('Gini', 0)[-1]=0.6398\n",
+      "Condition not met at step 2648. No data to save.\n",
       "2648\n",
+      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
+      "Condition not met at step 2649. No data to save.\n",
       "2649\n",
+      "model_vars.get('Gini', 0)[-1]=0.6614\n",
+      "Condition not met at step 2650. No data to save.\n",
       "2650\n",
+      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
+      "Condition not met at step 2651. No data to save.\n",
       "2651\n",
+      "model_vars.get('Gini', 0)[-1]=0.652\n",
+      "Condition not met at step 2652. No data to save.\n",
       "2652\n",
+      "model_vars.get('Gini', 0)[-1]=0.634\n",
+      "Condition not met at step 2653. No data to save.\n",
       "2653\n",
+      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
+      "Condition not met at step 2654. No data to save.\n",
       "2654\n",
+      "model_vars.get('Gini', 0)[-1]=0.6335999999999999\n",
+      "Condition not met at step 2655. No data to save.\n",
       "2655\n",
+      "model_vars.get('Gini', 0)[-1]=0.6824\n",
+      "Condition not met at step 2656. No data to save.\n",
       "2656\n",
+      "model_vars.get('Gini', 0)[-1]=0.6786000000000001\n",
+      "Condition not met at step 2657. No data to save.\n",
       "2657\n",
+      "model_vars.get('Gini', 0)[-1]=0.6824\n",
+      "Condition not met at step 2658. No data to save.\n",
       "2658\n",
+      "model_vars.get('Gini', 0)[-1]=0.6694\n",
+      "Condition not met at step 2659. No data to save.\n",
       "2659\n",
+      "model_vars.get('Gini', 0)[-1]=0.634\n",
+      "Condition not met at step 2660. No data to save.\n",
       "2660\n",
+      "model_vars.get('Gini', 0)[-1]=0.6194\n",
+      "Condition not met at step 2661. No data to save.\n",
       "2661\n",
+      "model_vars.get('Gini', 0)[-1]=0.63\n",
+      "Condition not met at step 2662. No data to save.\n",
       "2662\n",
+      "model_vars.get('Gini', 0)[-1]=0.6188\n",
+      "Condition not met at step 2663. No data to save.\n",
       "2663\n",
+      "model_vars.get('Gini', 0)[-1]=0.6315999999999999\n",
+      "Condition not met at step 2664. No data to save.\n",
       "2664\n",
+      "model_vars.get('Gini', 0)[-1]=0.6182000000000001\n",
+      "Condition not met at step 2665. No data to save.\n",
       "2665\n",
+      "model_vars.get('Gini', 0)[-1]=0.5933999999999999\n",
+      "Condition not met at step 2666. No data to save.\n",
       "2666\n",
+      "model_vars.get('Gini', 0)[-1]=0.611\n",
+      "Condition not met at step 2667. No data to save.\n",
       "2667\n",
+      "model_vars.get('Gini', 0)[-1]=0.593\n",
+      "Condition not met at step 2668. No data to save.\n",
       "2668\n",
+      "model_vars.get('Gini', 0)[-1]=0.622\n",
+      "Condition not met at step 2669. No data to save.\n",
       "2669\n",
+      "model_vars.get('Gini', 0)[-1]=0.624\n",
+      "Condition not met at step 2670. No data to save.\n",
       "2670\n",
+      "model_vars.get('Gini', 0)[-1]=0.629\n",
+      "Condition not met at step 2671. No data to save.\n",
       "2671\n",
+      "model_vars.get('Gini', 0)[-1]=0.6113999999999999\n",
+      "Condition not met at step 2672. No data to save.\n",
       "2672\n",
+      "model_vars.get('Gini', 0)[-1]=0.6442\n",
+      "Condition not met at step 2673. No data to save.\n",
       "2673\n",
+      "model_vars.get('Gini', 0)[-1]=0.6138\n",
+      "Condition not met at step 2674. No data to save.\n",
       "2674\n",
+      "model_vars.get('Gini', 0)[-1]=0.6128\n",
+      "Condition not met at step 2675. No data to save.\n",
       "2675\n",
+      "model_vars.get('Gini', 0)[-1]=0.6472\n",
+      "Condition not met at step 2676. No data to save.\n",
       "2676\n",
+      "model_vars.get('Gini', 0)[-1]=0.6577999999999999\n",
+      "Condition not met at step 2677. No data to save.\n",
       "2677\n",
+      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
+      "Condition not met at step 2678. No data to save.\n",
       "2678\n",
+      "model_vars.get('Gini', 0)[-1]=0.6172\n",
+      "Condition not met at step 2679. No data to save.\n",
       "2679\n",
+      "model_vars.get('Gini', 0)[-1]=0.6232\n",
+      "Condition not met at step 2680. No data to save.\n",
       "2680\n",
+      "model_vars.get('Gini', 0)[-1]=0.634\n",
+      "Condition not met at step 2681. No data to save.\n",
       "2681\n",
+      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
+      "Condition not met at step 2682. No data to save.\n",
       "2682\n",
+      "model_vars.get('Gini', 0)[-1]=0.6482\n",
+      "Condition not met at step 2683. No data to save.\n",
       "2683\n",
+      "model_vars.get('Gini', 0)[-1]=0.666\n",
+      "Condition not met at step 2684. No data to save.\n",
       "2684\n",
+      "model_vars.get('Gini', 0)[-1]=0.6576\n",
+      "Condition not met at step 2685. No data to save.\n",
       "2685\n",
+      "model_vars.get('Gini', 0)[-1]=0.6626000000000001\n",
+      "Condition not met at step 2686. No data to save.\n",
       "2686\n",
+      "model_vars.get('Gini', 0)[-1]=0.651\n",
+      "Condition not met at step 2687. No data to save.\n",
       "2687\n",
+      "model_vars.get('Gini', 0)[-1]=0.6426000000000001\n",
+      "Condition not met at step 2688. No data to save.\n",
       "2688\n",
+      "model_vars.get('Gini', 0)[-1]=0.6272\n",
+      "Condition not met at step 2689. No data to save.\n",
       "2689\n",
+      "model_vars.get('Gini', 0)[-1]=0.6318\n",
+      "Condition not met at step 2690. No data to save.\n",
       "2690\n",
+      "model_vars.get('Gini', 0)[-1]=0.6318\n",
+      "Condition not met at step 2691. No data to save.\n",
       "2691\n",
+      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
+      "Condition not met at step 2692. No data to save.\n",
       "2692\n",
+      "model_vars.get('Gini', 0)[-1]=0.6298\n",
+      "Condition not met at step 2693. No data to save.\n",
       "2693\n",
+      "model_vars.get('Gini', 0)[-1]=0.603\n",
+      "Condition not met at step 2694. No data to save.\n",
       "2694\n",
+      "model_vars.get('Gini', 0)[-1]=0.5956\n",
+      "Condition not met at step 2695. No data to save.\n",
       "2695\n",
+      "model_vars.get('Gini', 0)[-1]=0.5864\n",
+      "Condition not met at step 2696. No data to save.\n",
       "2696\n",
+      "model_vars.get('Gini', 0)[-1]=0.6312\n",
+      "Condition not met at step 2697. No data to save.\n",
       "2697\n",
+      "model_vars.get('Gini', 0)[-1]=0.6082000000000001\n",
+      "Condition not met at step 2698. No data to save.\n",
       "2698\n",
+      "model_vars.get('Gini', 0)[-1]=0.5956\n",
+      "Condition not met at step 2699. No data to save.\n",
       "2699\n",
       "CALLED\n",
       "self.model._steps=2700\n",
       " TEST self.model._steps=2700\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "2600  0.6610\n",
-      "2601  0.6734\n",
-      "2602  0.6920\n",
-      "2603  0.6636\n",
-      "2604  0.6586\n",
+      "2600  0.6246\n",
+      "2601  0.6338\n",
+      "2602  0.6050\n",
+      "2603  0.6116\n",
+      "2604  0.6238\n",
       "...      ...\n",
-      "2695  0.6574\n",
-      "2696  0.6408\n",
-      "2697  0.6620\n",
-      "2698  0.6498\n",
-      "2699  0.6674\n",
+      "2695  0.5864\n",
+      "2696  0.6312\n",
+      "2697  0.6082\n",
+      "2698  0.5956\n",
+      "2699  0.6114\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_027.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_027.parquet'\n",
-      "Saving model to output_dir/model_data_027.parquet\n",
-      "Saving agent to output_dir/agent_data_027.parquet\n",
+      "model_file='test_path/model_data_027.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_027.parquet'\n",
+      "Saving model to test_path/model_data_027.parquet\n",
+      "Saving agent to test_path/agent_data_027.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6113999999999999\n",
+      "Condition not met at step 2700. No data to save.\n",
       "2700\n",
+      "model_vars.get('Gini', 0)[-1]=0.6156\n",
+      "Condition not met at step 2701. No data to save.\n",
       "2701\n",
+      "model_vars.get('Gini', 0)[-1]=0.608\n",
+      "Condition not met at step 2702. No data to save.\n",
       "2702\n",
+      "model_vars.get('Gini', 0)[-1]=0.6272\n",
+      "Condition not met at step 2703. No data to save.\n",
       "2703\n",
+      "model_vars.get('Gini', 0)[-1]=0.6298\n",
+      "Condition not met at step 2704. No data to save.\n",
       "2704\n",
+      "model_vars.get('Gini', 0)[-1]=0.6332\n",
+      "Condition not met at step 2705. No data to save.\n",
       "2705\n",
+      "model_vars.get('Gini', 0)[-1]=0.6108\n",
+      "Condition not met at step 2706. No data to save.\n",
       "2706\n",
+      "model_vars.get('Gini', 0)[-1]=0.6298\n",
+      "Condition not met at step 2707. No data to save.\n",
       "2707\n",
+      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
+      "Condition not met at step 2708. No data to save.\n",
       "2708\n",
+      "model_vars.get('Gini', 0)[-1]=0.62\n",
+      "Condition not met at step 2709. No data to save.\n",
       "2709\n",
+      "model_vars.get('Gini', 0)[-1]=0.6304000000000001\n",
+      "Condition not met at step 2710. No data to save.\n",
       "2710\n",
+      "model_vars.get('Gini', 0)[-1]=0.6402\n",
+      "Condition not met at step 2711. No data to save.\n",
       "2711\n",
+      "model_vars.get('Gini', 0)[-1]=0.6448\n",
+      "Condition not met at step 2712. No data to save.\n",
       "2712\n",
+      "model_vars.get('Gini', 0)[-1]=0.6724\n",
+      "Condition not met at step 2713. No data to save.\n",
       "2713\n",
+      "model_vars.get('Gini', 0)[-1]=0.6776\n",
+      "Condition not met at step 2714. No data to save.\n",
       "2714\n",
+      "model_vars.get('Gini', 0)[-1]=0.652\n",
+      "Condition not met at step 2715. No data to save.\n",
       "2715\n",
+      "model_vars.get('Gini', 0)[-1]=0.675\n",
+      "Condition not met at step 2716. No data to save.\n",
       "2716\n",
+      "model_vars.get('Gini', 0)[-1]=0.6594\n",
+      "Condition not met at step 2717. No data to save.\n",
       "2717\n",
+      "model_vars.get('Gini', 0)[-1]=0.6496\n",
+      "Condition not met at step 2718. No data to save.\n",
       "2718\n",
+      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
+      "Condition not met at step 2719. No data to save.\n",
       "2719\n",
+      "model_vars.get('Gini', 0)[-1]=0.6482\n",
+      "Condition not met at step 2720. No data to save.\n",
       "2720\n",
+      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
+      "Condition not met at step 2721. No data to save.\n",
       "2721\n",
+      "model_vars.get('Gini', 0)[-1]=0.647\n",
+      "Condition not met at step 2722. No data to save.\n",
       "2722\n",
+      "model_vars.get('Gini', 0)[-1]=0.6568\n",
+      "Condition not met at step 2723. No data to save.\n",
       "2723\n",
+      "model_vars.get('Gini', 0)[-1]=0.6624\n",
+      "Condition not met at step 2724. No data to save.\n",
       "2724\n",
+      "model_vars.get('Gini', 0)[-1]=0.6496\n",
+      "Condition not met at step 2725. No data to save.\n",
       "2725\n",
+      "model_vars.get('Gini', 0)[-1]=0.647\n",
+      "Condition not met at step 2726. No data to save.\n",
       "2726\n",
+      "model_vars.get('Gini', 0)[-1]=0.6586000000000001\n",
+      "Condition not met at step 2727. No data to save.\n",
       "2727\n",
+      "model_vars.get('Gini', 0)[-1]=0.6335999999999999\n",
+      "Condition not met at step 2728. No data to save.\n",
       "2728\n",
+      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
+      "Condition not met at step 2729. No data to save.\n",
       "2729\n",
+      "model_vars.get('Gini', 0)[-1]=0.6548\n",
+      "Condition not met at step 2730. No data to save.\n",
       "2730\n",
+      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
+      "Condition not met at step 2731. No data to save.\n",
       "2731\n",
+      "model_vars.get('Gini', 0)[-1]=0.6514\n",
+      "Condition not met at step 2732. No data to save.\n",
       "2732\n",
+      "model_vars.get('Gini', 0)[-1]=0.6286\n",
+      "Condition not met at step 2733. No data to save.\n",
       "2733\n",
+      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
+      "Condition not met at step 2734. No data to save.\n",
       "2734\n",
+      "model_vars.get('Gini', 0)[-1]=0.6488\n",
+      "Condition not met at step 2735. No data to save.\n",
       "2735\n",
+      "model_vars.get('Gini', 0)[-1]=0.6268\n",
+      "Condition not met at step 2736. No data to save.\n",
       "2736\n",
+      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
+      "Condition not met at step 2737. No data to save.\n",
       "2737\n",
+      "model_vars.get('Gini', 0)[-1]=0.6778\n",
+      "Condition not met at step 2738. No data to save.\n",
       "2738\n",
+      "model_vars.get('Gini', 0)[-1]=0.6856\n",
+      "Condition not met at step 2739. No data to save.\n",
       "2739\n",
+      "model_vars.get('Gini', 0)[-1]=0.694\n",
+      "Condition not met at step 2740. No data to save.\n",
       "2740\n",
+      "model_vars.get('Gini', 0)[-1]=0.7\n",
+      "Condition not met at step 2741. No data to save.\n",
       "2741\n",
+      "model_vars.get('Gini', 0)[-1]=0.675\n",
+      "Condition not met at step 2742. No data to save.\n",
       "2742\n",
+      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
+      "Condition not met at step 2743. No data to save.\n",
       "2743\n",
+      "model_vars.get('Gini', 0)[-1]=0.6941999999999999\n",
+      "Condition not met at step 2744. No data to save.\n",
       "2744\n",
+      "model_vars.get('Gini', 0)[-1]=0.6994\n",
+      "Condition not met at step 2745. No data to save.\n",
       "2745\n",
+      "model_vars.get('Gini', 0)[-1]=0.6812\n",
+      "Condition not met at step 2746. No data to save.\n",
       "2746\n",
+      "model_vars.get('Gini', 0)[-1]=0.646\n",
+      "Condition not met at step 2747. No data to save.\n",
       "2747\n",
+      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
+      "Condition not met at step 2748. No data to save.\n",
       "2748\n",
+      "model_vars.get('Gini', 0)[-1]=0.6312\n",
+      "Condition not met at step 2749. No data to save.\n",
       "2749\n",
+      "model_vars.get('Gini', 0)[-1]=0.6214\n",
+      "Condition not met at step 2750. No data to save.\n",
       "2750\n",
+      "model_vars.get('Gini', 0)[-1]=0.66\n",
+      "Condition not met at step 2751. No data to save.\n",
       "2751\n",
+      "model_vars.get('Gini', 0)[-1]=0.643\n",
+      "Condition not met at step 2752. No data to save.\n",
       "2752\n",
+      "model_vars.get('Gini', 0)[-1]=0.6556\n",
+      "Condition not met at step 2753. No data to save.\n",
       "2753\n",
+      "model_vars.get('Gini', 0)[-1]=0.667\n",
+      "Condition not met at step 2754. No data to save.\n",
       "2754\n",
+      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
+      "Condition not met at step 2755. No data to save.\n",
       "2755\n",
+      "model_vars.get('Gini', 0)[-1]=0.6352\n",
+      "Condition not met at step 2756. No data to save.\n",
       "2756\n",
+      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
+      "Condition not met at step 2757. No data to save.\n",
       "2757\n",
+      "model_vars.get('Gini', 0)[-1]=0.6462\n",
+      "Condition not met at step 2758. No data to save.\n",
       "2758\n",
+      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
+      "Condition not met at step 2759. No data to save.\n",
       "2759\n",
+      "model_vars.get('Gini', 0)[-1]=0.6352\n",
+      "Condition not met at step 2760. No data to save.\n",
       "2760\n",
+      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
+      "Condition not met at step 2761. No data to save.\n",
       "2761\n",
+      "model_vars.get('Gini', 0)[-1]=0.6226\n",
+      "Condition not met at step 2762. No data to save.\n",
       "2762\n",
+      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
+      "Condition not met at step 2763. No data to save.\n",
       "2763\n",
+      "model_vars.get('Gini', 0)[-1]=0.6232\n",
+      "Condition not met at step 2764. No data to save.\n",
       "2764\n",
+      "model_vars.get('Gini', 0)[-1]=0.6154\n",
+      "Condition not met at step 2765. No data to save.\n",
       "2765\n",
+      "model_vars.get('Gini', 0)[-1]=0.5968\n",
+      "Condition not met at step 2766. No data to save.\n",
       "2766\n",
+      "model_vars.get('Gini', 0)[-1]=0.6044\n",
+      "Condition not met at step 2767. No data to save.\n",
       "2767\n",
+      "model_vars.get('Gini', 0)[-1]=0.6128\n",
+      "Condition not met at step 2768. No data to save.\n",
       "2768\n",
+      "model_vars.get('Gini', 0)[-1]=0.6275999999999999\n",
+      "Condition not met at step 2769. No data to save.\n",
       "2769\n",
+      "model_vars.get('Gini', 0)[-1]=0.6282000000000001\n",
+      "Condition not met at step 2770. No data to save.\n",
       "2770\n",
+      "model_vars.get('Gini', 0)[-1]=0.6352\n",
+      "Condition not met at step 2771. No data to save.\n",
       "2771\n",
+      "model_vars.get('Gini', 0)[-1]=0.604\n",
+      "Condition not met at step 2772. No data to save.\n",
       "2772\n",
+      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
+      "Condition not met at step 2773. No data to save.\n",
       "2773\n",
+      "model_vars.get('Gini', 0)[-1]=0.643\n",
+      "Condition not met at step 2774. No data to save.\n",
       "2774\n",
+      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
+      "Condition not met at step 2775. No data to save.\n",
       "2775\n",
+      "model_vars.get('Gini', 0)[-1]=0.6308\n",
+      "Condition not met at step 2776. No data to save.\n",
       "2776\n",
+      "model_vars.get('Gini', 0)[-1]=0.6306\n",
+      "Condition not met at step 2777. No data to save.\n",
       "2777\n",
+      "model_vars.get('Gini', 0)[-1]=0.6252\n",
+      "Condition not met at step 2778. No data to save.\n",
       "2778\n",
+      "model_vars.get('Gini', 0)[-1]=0.6306\n",
+      "Condition not met at step 2779. No data to save.\n",
       "2779\n",
+      "model_vars.get('Gini', 0)[-1]=0.656\n",
+      "Condition not met at step 2780. No data to save.\n",
       "2780\n",
+      "model_vars.get('Gini', 0)[-1]=0.66\n",
+      "Condition not met at step 2781. No data to save.\n",
       "2781\n",
+      "model_vars.get('Gini', 0)[-1]=0.6838\n",
+      "Condition not met at step 2782. No data to save.\n",
       "2782\n",
+      "model_vars.get('Gini', 0)[-1]=0.6772\n",
+      "Condition not met at step 2783. No data to save.\n",
       "2783\n",
+      "model_vars.get('Gini', 0)[-1]=0.6932\n",
+      "Condition not met at step 2784. No data to save.\n",
       "2784\n",
+      "model_vars.get('Gini', 0)[-1]=0.6782\n",
+      "Condition not met at step 2785. No data to save.\n",
       "2785\n",
+      "model_vars.get('Gini', 0)[-1]=0.6678\n",
+      "Condition not met at step 2786. No data to save.\n",
       "2786\n",
+      "model_vars.get('Gini', 0)[-1]=0.6464000000000001\n",
+      "Condition not met at step 2787. No data to save.\n",
       "2787\n",
+      "model_vars.get('Gini', 0)[-1]=0.6442\n",
+      "Condition not met at step 2788. No data to save.\n",
       "2788\n",
+      "model_vars.get('Gini', 0)[-1]=0.6352\n",
+      "Condition not met at step 2789. No data to save.\n",
       "2789\n",
+      "model_vars.get('Gini', 0)[-1]=0.6384000000000001\n",
+      "Condition not met at step 2790. No data to save.\n",
       "2790\n",
+      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
+      "Condition not met at step 2791. No data to save.\n",
       "2791\n",
+      "model_vars.get('Gini', 0)[-1]=0.6312\n",
+      "Condition not met at step 2792. No data to save.\n",
       "2792\n",
+      "model_vars.get('Gini', 0)[-1]=0.6215999999999999\n",
+      "Condition not met at step 2793. No data to save.\n",
       "2793\n",
+      "model_vars.get('Gini', 0)[-1]=0.6228\n",
+      "Condition not met at step 2794. No data to save.\n",
       "2794\n",
+      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
+      "Condition not met at step 2795. No data to save.\n",
       "2795\n",
+      "model_vars.get('Gini', 0)[-1]=0.6548\n",
+      "Condition not met at step 2796. No data to save.\n",
       "2796\n",
+      "model_vars.get('Gini', 0)[-1]=0.6616\n",
+      "Condition not met at step 2797. No data to save.\n",
       "2797\n",
+      "model_vars.get('Gini', 0)[-1]=0.6782\n",
+      "Condition not met at step 2798. No data to save.\n",
       "2798\n",
+      "model_vars.get('Gini', 0)[-1]=0.6828000000000001\n",
+      "Condition not met at step 2799. No data to save.\n",
       "2799\n",
       "CALLED\n",
       "self.model._steps=2800\n",
       " TEST self.model._steps=2800\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "2700  0.6478\n",
-      "2701  0.6354\n",
-      "2702  0.6686\n",
-      "2703  0.6860\n",
-      "2704  0.6950\n",
+      "2700  0.6156\n",
+      "2701  0.6080\n",
+      "2702  0.6272\n",
+      "2703  0.6298\n",
+      "2704  0.6332\n",
       "...      ...\n",
-      "2795  0.6406\n",
-      "2796  0.6630\n",
-      "2797  0.6570\n",
-      "2798  0.6592\n",
-      "2799  0.6754\n",
+      "2795  0.6548\n",
+      "2796  0.6616\n",
+      "2797  0.6782\n",
+      "2798  0.6828\n",
+      "2799  0.6620\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_028.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_028.parquet'\n",
-      "Saving model to output_dir/model_data_028.parquet\n",
-      "Saving agent to output_dir/agent_data_028.parquet\n",
+      "model_file='test_path/model_data_028.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_028.parquet'\n",
+      "Saving model to test_path/model_data_028.parquet\n",
+      "Saving agent to test_path/agent_data_028.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.662\n",
+      "Condition not met at step 2800. No data to save.\n",
       "2800\n",
+      "model_vars.get('Gini', 0)[-1]=0.6664\n",
+      "Condition not met at step 2801. No data to save.\n",
       "2801\n",
+      "model_vars.get('Gini', 0)[-1]=0.6636\n",
+      "Condition not met at step 2802. No data to save.\n",
       "2802\n",
+      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
+      "Condition not met at step 2803. No data to save.\n",
       "2803\n",
+      "model_vars.get('Gini', 0)[-1]=0.6434\n",
+      "Condition not met at step 2804. No data to save.\n",
       "2804\n",
+      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
+      "Condition not met at step 2805. No data to save.\n",
       "2805\n",
+      "model_vars.get('Gini', 0)[-1]=0.6436\n",
+      "Condition not met at step 2806. No data to save.\n",
       "2806\n",
+      "model_vars.get('Gini', 0)[-1]=0.6742\n",
+      "Condition not met at step 2807. No data to save.\n",
       "2807\n",
+      "model_vars.get('Gini', 0)[-1]=0.6322\n",
+      "Condition not met at step 2808. No data to save.\n",
       "2808\n",
+      "model_vars.get('Gini', 0)[-1]=0.677\n",
+      "Condition not met at step 2809. No data to save.\n",
       "2809\n",
+      "model_vars.get('Gini', 0)[-1]=0.6676\n",
+      "Condition not met at step 2810. No data to save.\n",
       "2810\n",
+      "model_vars.get('Gini', 0)[-1]=0.6756\n",
+      "Condition not met at step 2811. No data to save.\n",
       "2811\n",
+      "model_vars.get('Gini', 0)[-1]=0.6574\n",
+      "Condition not met at step 2812. No data to save.\n",
       "2812\n",
+      "model_vars.get('Gini', 0)[-1]=0.6506000000000001\n",
+      "Condition not met at step 2813. No data to save.\n",
       "2813\n",
+      "model_vars.get('Gini', 0)[-1]=0.6434\n",
+      "Condition not met at step 2814. No data to save.\n",
       "2814\n",
+      "model_vars.get('Gini', 0)[-1]=0.6508\n",
+      "Condition not met at step 2815. No data to save.\n",
       "2815\n",
+      "model_vars.get('Gini', 0)[-1]=0.637\n",
+      "Condition not met at step 2816. No data to save.\n",
       "2816\n",
+      "model_vars.get('Gini', 0)[-1]=0.63\n",
+      "Condition not met at step 2817. No data to save.\n",
       "2817\n",
+      "model_vars.get('Gini', 0)[-1]=0.6224000000000001\n",
+      "Condition not met at step 2818. No data to save.\n",
       "2818\n",
+      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
+      "Condition not met at step 2819. No data to save.\n",
       "2819\n",
+      "model_vars.get('Gini', 0)[-1]=0.6242000000000001\n",
+      "Condition not met at step 2820. No data to save.\n",
       "2820\n",
+      "model_vars.get('Gini', 0)[-1]=0.6372\n",
+      "Condition not met at step 2821. No data to save.\n",
       "2821\n",
+      "model_vars.get('Gini', 0)[-1]=0.6368\n",
+      "Condition not met at step 2822. No data to save.\n",
       "2822\n",
+      "model_vars.get('Gini', 0)[-1]=0.6262000000000001\n",
+      "Condition not met at step 2823. No data to save.\n",
       "2823\n",
+      "model_vars.get('Gini', 0)[-1]=0.6096\n",
+      "Condition not met at step 2824. No data to save.\n",
       "2824\n",
+      "model_vars.get('Gini', 0)[-1]=0.6166\n",
+      "Condition not met at step 2825. No data to save.\n",
       "2825\n",
+      "model_vars.get('Gini', 0)[-1]=0.6588\n",
+      "Condition not met at step 2826. No data to save.\n",
       "2826\n",
+      "model_vars.get('Gini', 0)[-1]=0.6704\n",
+      "Condition not met at step 2827. No data to save.\n",
       "2827\n",
+      "model_vars.get('Gini', 0)[-1]=0.675\n",
+      "Condition not met at step 2828. No data to save.\n",
       "2828\n",
+      "model_vars.get('Gini', 0)[-1]=0.6794\n",
+      "Condition not met at step 2829. No data to save.\n",
       "2829\n",
+      "model_vars.get('Gini', 0)[-1]=0.6756\n",
+      "Condition not met at step 2830. No data to save.\n",
       "2830\n",
+      "model_vars.get('Gini', 0)[-1]=0.6724\n",
+      "Condition not met at step 2831. No data to save.\n",
       "2831\n",
+      "model_vars.get('Gini', 0)[-1]=0.6719999999999999\n",
+      "Condition not met at step 2832. No data to save.\n",
       "2832\n",
+      "model_vars.get('Gini', 0)[-1]=0.663\n",
+      "Condition not met at step 2833. No data to save.\n",
       "2833\n",
+      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
+      "Condition not met at step 2834. No data to save.\n",
       "2834\n",
+      "model_vars.get('Gini', 0)[-1]=0.7024\n",
+      "Condition met. Appended special results for step 2835 to test_path/special_results.parquet\n",
       "2835\n",
+      "model_vars.get('Gini', 0)[-1]=0.7196\n",
+      "Condition met. Appended special results for step 2836 to test_path/special_results.parquet\n",
       "2836\n",
+      "model_vars.get('Gini', 0)[-1]=0.7096\n",
+      "Condition met. Appended special results for step 2837 to test_path/special_results.parquet\n",
       "2837\n",
+      "model_vars.get('Gini', 0)[-1]=0.6798\n",
+      "Condition not met at step 2838. No data to save.\n",
       "2838\n",
+      "model_vars.get('Gini', 0)[-1]=0.6764\n",
+      "Condition not met at step 2839. No data to save.\n",
       "2839\n",
+      "model_vars.get('Gini', 0)[-1]=0.6746000000000001\n",
+      "Condition not met at step 2840. No data to save.\n",
       "2840\n",
+      "model_vars.get('Gini', 0)[-1]=0.6699999999999999\n",
+      "Condition not met at step 2841. No data to save.\n",
       "2841\n",
+      "model_vars.get('Gini', 0)[-1]=0.6836\n",
+      "Condition not met at step 2842. No data to save.\n",
       "2842\n",
+      "model_vars.get('Gini', 0)[-1]=0.634\n",
+      "Condition not met at step 2843. No data to save.\n",
       "2843\n",
+      "model_vars.get('Gini', 0)[-1]=0.6748000000000001\n",
+      "Condition not met at step 2844. No data to save.\n",
       "2844\n",
+      "model_vars.get('Gini', 0)[-1]=0.6754\n",
+      "Condition not met at step 2845. No data to save.\n",
       "2845\n",
+      "model_vars.get('Gini', 0)[-1]=0.7021999999999999\n",
+      "Condition met. Appended special results for step 2846 to test_path/special_results.parquet\n",
       "2846\n",
+      "model_vars.get('Gini', 0)[-1]=0.6986\n",
+      "Condition not met at step 2847. No data to save.\n",
       "2847\n",
+      "model_vars.get('Gini', 0)[-1]=0.7334\n",
+      "Condition met. Appended special results for step 2848 to test_path/special_results.parquet\n",
       "2848\n",
+      "model_vars.get('Gini', 0)[-1]=0.7252000000000001\n",
+      "Condition met. Appended special results for step 2849 to test_path/special_results.parquet\n",
       "2849\n",
+      "model_vars.get('Gini', 0)[-1]=0.7298\n",
+      "Condition met. Appended special results for step 2850 to test_path/special_results.parquet\n",
       "2850\n",
+      "model_vars.get('Gini', 0)[-1]=0.7306\n",
+      "Condition met. Appended special results for step 2851 to test_path/special_results.parquet\n",
       "2851\n",
+      "model_vars.get('Gini', 0)[-1]=0.7090000000000001\n",
+      "Condition met. Appended special results for step 2852 to test_path/special_results.parquet\n",
       "2852\n",
+      "model_vars.get('Gini', 0)[-1]=0.6904\n",
+      "Condition not met at step 2853. No data to save.\n",
       "2853\n",
+      "model_vars.get('Gini', 0)[-1]=0.7041999999999999\n",
+      "Condition met. Appended special results for step 2854 to test_path/special_results.parquet\n",
       "2854\n",
+      "model_vars.get('Gini', 0)[-1]=0.696\n",
+      "Condition not met at step 2855. No data to save.\n",
       "2855\n",
+      "model_vars.get('Gini', 0)[-1]=0.6798\n",
+      "Condition not met at step 2856. No data to save.\n",
       "2856\n",
+      "model_vars.get('Gini', 0)[-1]=0.63\n",
+      "Condition not met at step 2857. No data to save.\n",
       "2857\n",
+      "model_vars.get('Gini', 0)[-1]=0.6338\n",
+      "Condition not met at step 2858. No data to save.\n",
       "2858\n",
+      "model_vars.get('Gini', 0)[-1]=0.6195999999999999\n",
+      "Condition not met at step 2859. No data to save.\n",
       "2859\n",
+      "model_vars.get('Gini', 0)[-1]=0.6158\n",
+      "Condition not met at step 2860. No data to save.\n",
       "2860\n",
+      "model_vars.get('Gini', 0)[-1]=0.5882000000000001\n",
+      "Condition not met at step 2861. No data to save.\n",
       "2861\n",
+      "model_vars.get('Gini', 0)[-1]=0.6242000000000001\n",
+      "Condition not met at step 2862. No data to save.\n",
       "2862\n",
+      "model_vars.get('Gini', 0)[-1]=0.6508\n",
+      "Condition not met at step 2863. No data to save.\n",
       "2863\n",
+      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
+      "Condition not met at step 2864. No data to save.\n",
       "2864\n",
+      "model_vars.get('Gini', 0)[-1]=0.6534\n",
+      "Condition not met at step 2865. No data to save.\n",
       "2865\n",
+      "model_vars.get('Gini', 0)[-1]=0.6454\n",
+      "Condition not met at step 2866. No data to save.\n",
       "2866\n",
+      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
+      "Condition not met at step 2867. No data to save.\n",
       "2867\n",
+      "model_vars.get('Gini', 0)[-1]=0.6348\n",
+      "Condition not met at step 2868. No data to save.\n",
       "2868\n",
+      "model_vars.get('Gini', 0)[-1]=0.6368\n",
+      "Condition not met at step 2869. No data to save.\n",
       "2869\n",
+      "model_vars.get('Gini', 0)[-1]=0.6508\n",
+      "Condition not met at step 2870. No data to save.\n",
       "2870\n",
+      "model_vars.get('Gini', 0)[-1]=0.6254\n",
+      "Condition not met at step 2871. No data to save.\n",
       "2871\n",
+      "model_vars.get('Gini', 0)[-1]=0.637\n",
+      "Condition not met at step 2872. No data to save.\n",
       "2872\n",
+      "model_vars.get('Gini', 0)[-1]=0.6636\n",
+      "Condition not met at step 2873. No data to save.\n",
       "2873\n",
+      "model_vars.get('Gini', 0)[-1]=0.6328\n",
+      "Condition not met at step 2874. No data to save.\n",
       "2874\n",
+      "model_vars.get('Gini', 0)[-1]=0.6696\n",
+      "Condition not met at step 2875. No data to save.\n",
       "2875\n",
+      "model_vars.get('Gini', 0)[-1]=0.6692\n",
+      "Condition not met at step 2876. No data to save.\n",
       "2876\n",
+      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
+      "Condition not met at step 2877. No data to save.\n",
       "2877\n",
+      "model_vars.get('Gini', 0)[-1]=0.6294\n",
+      "Condition not met at step 2878. No data to save.\n",
       "2878\n",
+      "model_vars.get('Gini', 0)[-1]=0.6416\n",
+      "Condition not met at step 2879. No data to save.\n",
       "2879\n",
+      "model_vars.get('Gini', 0)[-1]=0.6366\n",
+      "Condition not met at step 2880. No data to save.\n",
       "2880\n",
+      "model_vars.get('Gini', 0)[-1]=0.6428\n",
+      "Condition not met at step 2881. No data to save.\n",
       "2881\n",
+      "model_vars.get('Gini', 0)[-1]=0.6584\n",
+      "Condition not met at step 2882. No data to save.\n",
       "2882\n",
+      "model_vars.get('Gini', 0)[-1]=0.654\n",
+      "Condition not met at step 2883. No data to save.\n",
       "2883\n",
+      "model_vars.get('Gini', 0)[-1]=0.632\n",
+      "Condition not met at step 2884. No data to save.\n",
       "2884\n",
+      "model_vars.get('Gini', 0)[-1]=0.6376\n",
+      "Condition not met at step 2885. No data to save.\n",
       "2885\n",
+      "model_vars.get('Gini', 0)[-1]=0.6658\n",
+      "Condition not met at step 2886. No data to save.\n",
       "2886\n",
+      "model_vars.get('Gini', 0)[-1]=0.6784\n",
+      "Condition not met at step 2887. No data to save.\n",
       "2887\n",
+      "model_vars.get('Gini', 0)[-1]=0.6784\n",
+      "Condition not met at step 2888. No data to save.\n",
       "2888\n",
+      "model_vars.get('Gini', 0)[-1]=0.6634\n",
+      "Condition not met at step 2889. No data to save.\n",
       "2889\n",
+      "model_vars.get('Gini', 0)[-1]=0.6806\n",
+      "Condition not met at step 2890. No data to save.\n",
       "2890\n",
+      "model_vars.get('Gini', 0)[-1]=0.7061999999999999\n",
+      "Condition met. Appended special results for step 2891 to test_path/special_results.parquet\n",
       "2891\n",
+      "model_vars.get('Gini', 0)[-1]=0.7072\n",
+      "Condition met. Appended special results for step 2892 to test_path/special_results.parquet\n",
       "2892\n",
+      "model_vars.get('Gini', 0)[-1]=0.7030000000000001\n",
+      "Condition met. Appended special results for step 2893 to test_path/special_results.parquet\n",
       "2893\n",
+      "model_vars.get('Gini', 0)[-1]=0.694\n",
+      "Condition not met at step 2894. No data to save.\n",
       "2894\n",
+      "model_vars.get('Gini', 0)[-1]=0.6992\n",
+      "Condition not met at step 2895. No data to save.\n",
       "2895\n",
+      "model_vars.get('Gini', 0)[-1]=0.702\n",
+      "Condition met. Appended special results for step 2896 to test_path/special_results.parquet\n",
       "2896\n",
+      "model_vars.get('Gini', 0)[-1]=0.706\n",
+      "Condition met. Appended special results for step 2897 to test_path/special_results.parquet\n",
       "2897\n",
+      "model_vars.get('Gini', 0)[-1]=0.7158\n",
+      "Condition met. Appended special results for step 2898 to test_path/special_results.parquet\n",
       "2898\n",
+      "model_vars.get('Gini', 0)[-1]=0.7270000000000001\n",
+      "Condition met. Appended special results for step 2899 to test_path/special_results.parquet\n",
       "2899\n",
       "CALLED\n",
       "self.model._steps=2900\n",
       " TEST self.model._steps=2900\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "2800  0.6906\n",
-      "2801  0.6980\n",
-      "2802  0.6872\n",
-      "2803  0.6568\n",
-      "2804  0.6436\n",
+      "2800  0.6664\n",
+      "2801  0.6636\n",
+      "2802  0.6406\n",
+      "2803  0.6434\n",
+      "2804  0.6484\n",
       "...      ...\n",
-      "2895  0.6928\n",
-      "2896  0.7038\n",
-      "2897  0.6912\n",
-      "2898  0.6860\n",
-      "2899  0.6788\n",
+      "2895  0.7020\n",
+      "2896  0.7060\n",
+      "2897  0.7158\n",
+      "2898  0.7270\n",
+      "2899  0.7020\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_029.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_029.parquet'\n",
-      "Saving model to output_dir/model_data_029.parquet\n",
-      "Saving agent to output_dir/agent_data_029.parquet\n",
+      "model_file='test_path/model_data_029.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_029.parquet'\n",
+      "Saving model to test_path/model_data_029.parquet\n",
+      "Saving agent to test_path/agent_data_029.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.702\n",
+      "Condition met. Appended special results for step 2900 to test_path/special_results.parquet\n",
       "2900\n",
+      "model_vars.get('Gini', 0)[-1]=0.675\n",
+      "Condition not met at step 2901. No data to save.\n",
       "2901\n",
+      "model_vars.get('Gini', 0)[-1]=0.6644\n",
+      "Condition not met at step 2902. No data to save.\n",
       "2902\n",
+      "model_vars.get('Gini', 0)[-1]=0.6452\n",
+      "Condition not met at step 2903. No data to save.\n",
       "2903\n",
+      "model_vars.get('Gini', 0)[-1]=0.647\n",
+      "Condition not met at step 2904. No data to save.\n",
       "2904\n",
+      "model_vars.get('Gini', 0)[-1]=0.6428\n",
+      "Condition not met at step 2905. No data to save.\n",
       "2905\n",
+      "model_vars.get('Gini', 0)[-1]=0.658\n",
+      "Condition not met at step 2906. No data to save.\n",
       "2906\n",
+      "model_vars.get('Gini', 0)[-1]=0.6476\n",
+      "Condition not met at step 2907. No data to save.\n",
       "2907\n",
+      "model_vars.get('Gini', 0)[-1]=0.6472\n",
+      "Condition not met at step 2908. No data to save.\n",
       "2908\n",
+      "model_vars.get('Gini', 0)[-1]=0.6446000000000001\n",
+      "Condition not met at step 2909. No data to save.\n",
       "2909\n",
+      "model_vars.get('Gini', 0)[-1]=0.647\n",
+      "Condition not met at step 2910. No data to save.\n",
       "2910\n",
+      "model_vars.get('Gini', 0)[-1]=0.6275999999999999\n",
+      "Condition not met at step 2911. No data to save.\n",
       "2911\n",
+      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
+      "Condition not met at step 2912. No data to save.\n",
       "2912\n",
+      "model_vars.get('Gini', 0)[-1]=0.647\n",
+      "Condition not met at step 2913. No data to save.\n",
       "2913\n",
+      "model_vars.get('Gini', 0)[-1]=0.6452\n",
+      "Condition not met at step 2914. No data to save.\n",
       "2914\n",
+      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
+      "Condition not met at step 2915. No data to save.\n",
       "2915\n",
+      "model_vars.get('Gini', 0)[-1]=0.6716\n",
+      "Condition not met at step 2916. No data to save.\n",
       "2916\n",
+      "model_vars.get('Gini', 0)[-1]=0.6416\n",
+      "Condition not met at step 2917. No data to save.\n",
       "2917\n",
+      "model_vars.get('Gini', 0)[-1]=0.6414\n",
+      "Condition not met at step 2918. No data to save.\n",
       "2918\n",
+      "model_vars.get('Gini', 0)[-1]=0.6128\n",
+      "Condition not met at step 2919. No data to save.\n",
       "2919\n",
+      "model_vars.get('Gini', 0)[-1]=0.6376\n",
+      "Condition not met at step 2920. No data to save.\n",
       "2920\n",
+      "model_vars.get('Gini', 0)[-1]=0.647\n",
+      "Condition not met at step 2921. No data to save.\n",
       "2921\n",
+      "model_vars.get('Gini', 0)[-1]=0.6646000000000001\n",
+      "Condition not met at step 2922. No data to save.\n",
       "2922\n",
+      "model_vars.get('Gini', 0)[-1]=0.659\n",
+      "Condition not met at step 2923. No data to save.\n",
       "2923\n",
+      "model_vars.get('Gini', 0)[-1]=0.6644\n",
+      "Condition not met at step 2924. No data to save.\n",
       "2924\n",
+      "model_vars.get('Gini', 0)[-1]=0.6537999999999999\n",
+      "Condition not met at step 2925. No data to save.\n",
       "2925\n",
+      "model_vars.get('Gini', 0)[-1]=0.6496\n",
+      "Condition not met at step 2926. No data to save.\n",
       "2926\n",
+      "model_vars.get('Gini', 0)[-1]=0.6496\n",
+      "Condition not met at step 2927. No data to save.\n",
       "2927\n",
+      "model_vars.get('Gini', 0)[-1]=0.6516\n",
+      "Condition not met at step 2928. No data to save.\n",
       "2928\n",
+      "model_vars.get('Gini', 0)[-1]=0.6568\n",
+      "Condition not met at step 2929. No data to save.\n",
       "2929\n",
+      "model_vars.get('Gini', 0)[-1]=0.6492\n",
+      "Condition not met at step 2930. No data to save.\n",
       "2930\n",
+      "model_vars.get('Gini', 0)[-1]=0.652\n",
+      "Condition not met at step 2931. No data to save.\n",
       "2931\n",
+      "model_vars.get('Gini', 0)[-1]=0.665\n",
+      "Condition not met at step 2932. No data to save.\n",
       "2932\n",
+      "model_vars.get('Gini', 0)[-1]=0.6506000000000001\n",
+      "Condition not met at step 2933. No data to save.\n",
       "2933\n",
+      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
+      "Condition not met at step 2934. No data to save.\n",
       "2934\n",
+      "model_vars.get('Gini', 0)[-1]=0.6494\n",
+      "Condition not met at step 2935. No data to save.\n",
       "2935\n",
+      "model_vars.get('Gini', 0)[-1]=0.6416\n",
+      "Condition not met at step 2936. No data to save.\n",
       "2936\n",
+      "model_vars.get('Gini', 0)[-1]=0.6008\n",
+      "Condition not met at step 2937. No data to save.\n",
       "2937\n",
+      "model_vars.get('Gini', 0)[-1]=0.6248\n",
+      "Condition not met at step 2938. No data to save.\n",
       "2938\n",
+      "model_vars.get('Gini', 0)[-1]=0.63\n",
+      "Condition not met at step 2939. No data to save.\n",
       "2939\n",
+      "model_vars.get('Gini', 0)[-1]=0.6376\n",
+      "Condition not met at step 2940. No data to save.\n",
       "2940\n",
+      "model_vars.get('Gini', 0)[-1]=0.6416\n",
+      "Condition not met at step 2941. No data to save.\n",
       "2941\n",
+      "model_vars.get('Gini', 0)[-1]=0.6416\n",
+      "Condition not met at step 2942. No data to save.\n",
       "2942\n",
+      "model_vars.get('Gini', 0)[-1]=0.6696\n",
+      "Condition not met at step 2943. No data to save.\n",
       "2943\n",
+      "model_vars.get('Gini', 0)[-1]=0.6652\n",
+      "Condition not met at step 2944. No data to save.\n",
       "2944\n",
+      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
+      "Condition not met at step 2945. No data to save.\n",
       "2945\n",
+      "model_vars.get('Gini', 0)[-1]=0.6566000000000001\n",
+      "Condition not met at step 2946. No data to save.\n",
       "2946\n",
+      "model_vars.get('Gini', 0)[-1]=0.648\n",
+      "Condition not met at step 2947. No data to save.\n",
       "2947\n",
+      "model_vars.get('Gini', 0)[-1]=0.63\n",
+      "Condition not met at step 2948. No data to save.\n",
       "2948\n",
+      "model_vars.get('Gini', 0)[-1]=0.6274\n",
+      "Condition not met at step 2949. No data to save.\n",
       "2949\n",
+      "model_vars.get('Gini', 0)[-1]=0.6302\n",
+      "Condition not met at step 2950. No data to save.\n",
       "2950\n",
+      "model_vars.get('Gini', 0)[-1]=0.6174\n",
+      "Condition not met at step 2951. No data to save.\n",
       "2951\n",
+      "model_vars.get('Gini', 0)[-1]=0.585\n",
+      "Condition not met at step 2952. No data to save.\n",
       "2952\n",
+      "model_vars.get('Gini', 0)[-1]=0.5836\n",
+      "Condition not met at step 2953. No data to save.\n",
       "2953\n",
+      "model_vars.get('Gini', 0)[-1]=0.6000000000000001\n",
+      "Condition not met at step 2954. No data to save.\n",
       "2954\n",
+      "model_vars.get('Gini', 0)[-1]=0.5758000000000001\n",
+      "Condition not met at step 2955. No data to save.\n",
       "2955\n",
+      "model_vars.get('Gini', 0)[-1]=0.5896\n",
+      "Condition not met at step 2956. No data to save.\n",
       "2956\n",
+      "model_vars.get('Gini', 0)[-1]=0.5978\n",
+      "Condition not met at step 2957. No data to save.\n",
       "2957\n",
+      "model_vars.get('Gini', 0)[-1]=0.5906\n",
+      "Condition not met at step 2958. No data to save.\n",
       "2958\n",
+      "model_vars.get('Gini', 0)[-1]=0.5892\n",
+      "Condition not met at step 2959. No data to save.\n",
       "2959\n",
+      "model_vars.get('Gini', 0)[-1]=0.615\n",
+      "Condition not met at step 2960. No data to save.\n",
       "2960\n",
+      "model_vars.get('Gini', 0)[-1]=0.6274\n",
+      "Condition not met at step 2961. No data to save.\n",
       "2961\n",
+      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
+      "Condition not met at step 2962. No data to save.\n",
       "2962\n",
+      "model_vars.get('Gini', 0)[-1]=0.6454\n",
+      "Condition not met at step 2963. No data to save.\n",
       "2963\n",
+      "model_vars.get('Gini', 0)[-1]=0.6334\n",
+      "Condition not met at step 2964. No data to save.\n",
       "2964\n",
+      "model_vars.get('Gini', 0)[-1]=0.6308\n",
+      "Condition not met at step 2965. No data to save.\n",
       "2965\n",
+      "model_vars.get('Gini', 0)[-1]=0.6304000000000001\n",
+      "Condition not met at step 2966. No data to save.\n",
       "2966\n",
+      "model_vars.get('Gini', 0)[-1]=0.616\n",
+      "Condition not met at step 2967. No data to save.\n",
       "2967\n",
+      "model_vars.get('Gini', 0)[-1]=0.6324000000000001\n",
+      "Condition not met at step 2968. No data to save.\n",
       "2968\n",
+      "model_vars.get('Gini', 0)[-1]=0.6295999999999999\n",
+      "Condition not met at step 2969. No data to save.\n",
       "2969\n",
+      "model_vars.get('Gini', 0)[-1]=0.6617999999999999\n",
+      "Condition not met at step 2970. No data to save.\n",
       "2970\n",
+      "model_vars.get('Gini', 0)[-1]=0.6828000000000001\n",
+      "Condition not met at step 2971. No data to save.\n",
       "2971\n",
+      "model_vars.get('Gini', 0)[-1]=0.658\n",
+      "Condition not met at step 2972. No data to save.\n",
       "2972\n",
+      "model_vars.get('Gini', 0)[-1]=0.6422\n",
+      "Condition not met at step 2973. No data to save.\n",
       "2973\n",
+      "model_vars.get('Gini', 0)[-1]=0.6664\n",
+      "Condition not met at step 2974. No data to save.\n",
       "2974\n",
+      "model_vars.get('Gini', 0)[-1]=0.6582\n",
+      "Condition not met at step 2975. No data to save.\n",
       "2975\n",
+      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
+      "Condition not met at step 2976. No data to save.\n",
       "2976\n",
+      "model_vars.get('Gini', 0)[-1]=0.6892\n",
+      "Condition not met at step 2977. No data to save.\n",
       "2977\n",
+      "model_vars.get('Gini', 0)[-1]=0.6754\n",
+      "Condition not met at step 2978. No data to save.\n",
       "2978\n",
+      "model_vars.get('Gini', 0)[-1]=0.6839999999999999\n",
+      "Condition not met at step 2979. No data to save.\n",
       "2979\n",
+      "model_vars.get('Gini', 0)[-1]=0.6932\n",
+      "Condition not met at step 2980. No data to save.\n",
       "2980\n",
+      "model_vars.get('Gini', 0)[-1]=0.692\n",
+      "Condition not met at step 2981. No data to save.\n",
       "2981\n",
+      "model_vars.get('Gini', 0)[-1]=0.6912\n",
+      "Condition not met at step 2982. No data to save.\n",
       "2982\n",
+      "model_vars.get('Gini', 0)[-1]=0.6772\n",
+      "Condition not met at step 2983. No data to save.\n",
       "2983\n",
+      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
+      "Condition not met at step 2984. No data to save.\n",
       "2984\n",
+      "model_vars.get('Gini', 0)[-1]=0.6816\n",
+      "Condition not met at step 2985. No data to save.\n",
       "2985\n",
+      "model_vars.get('Gini', 0)[-1]=0.702\n",
+      "Condition met. Appended special results for step 2986 to test_path/special_results.parquet\n",
       "2986\n",
+      "model_vars.get('Gini', 0)[-1]=0.6946\n",
+      "Condition not met at step 2987. No data to save.\n",
       "2987\n",
+      "model_vars.get('Gini', 0)[-1]=0.6950000000000001\n",
+      "Condition not met at step 2988. No data to save.\n",
       "2988\n",
+      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
+      "Condition not met at step 2989. No data to save.\n",
       "2989\n",
+      "model_vars.get('Gini', 0)[-1]=0.6644\n",
+      "Condition not met at step 2990. No data to save.\n",
       "2990\n",
+      "model_vars.get('Gini', 0)[-1]=0.6694\n",
+      "Condition not met at step 2991. No data to save.\n",
       "2991\n",
+      "model_vars.get('Gini', 0)[-1]=0.6934\n",
+      "Condition not met at step 2992. No data to save.\n",
       "2992\n",
+      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
+      "Condition not met at step 2993. No data to save.\n",
       "2993\n",
+      "model_vars.get('Gini', 0)[-1]=0.6930000000000001\n",
+      "Condition not met at step 2994. No data to save.\n",
       "2994\n",
+      "model_vars.get('Gini', 0)[-1]=0.6892\n",
+      "Condition not met at step 2995. No data to save.\n",
       "2995\n",
+      "model_vars.get('Gini', 0)[-1]=0.6776\n",
+      "Condition not met at step 2996. No data to save.\n",
       "2996\n",
+      "model_vars.get('Gini', 0)[-1]=0.6758\n",
+      "Condition not met at step 2997. No data to save.\n",
       "2997\n",
+      "model_vars.get('Gini', 0)[-1]=0.6648000000000001\n",
+      "Condition not met at step 2998. No data to save.\n",
       "2998\n",
+      "model_vars.get('Gini', 0)[-1]=0.6634\n",
+      "Condition not met at step 2999. No data to save.\n",
       "2999\n",
       "CALLED\n",
       "self.model._steps=3000\n",
       " TEST self.model._steps=3000\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "2900  0.6914\n",
-      "2901  0.6850\n",
-      "2902  0.7054\n",
-      "2903  0.6760\n",
-      "2904  0.6714\n",
+      "2900  0.6750\n",
+      "2901  0.6644\n",
+      "2902  0.6452\n",
+      "2903  0.6470\n",
+      "2904  0.6428\n",
       "...      ...\n",
-      "2995  0.6808\n",
-      "2996  0.6906\n",
-      "2997  0.7006\n",
-      "2998  0.7008\n",
-      "2999  0.6908\n",
+      "2995  0.6776\n",
+      "2996  0.6758\n",
+      "2997  0.6648\n",
+      "2998  0.6634\n",
+      "2999  0.6394\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_030.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_030.parquet'\n",
-      "Saving model to output_dir/model_data_030.parquet\n",
-      "Saving agent to output_dir/agent_data_030.parquet\n",
+      "model_file='test_path/model_data_030.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_030.parquet'\n",
+      "Saving model to test_path/model_data_030.parquet\n",
+      "Saving agent to test_path/agent_data_030.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6394\n",
+      "Condition not met at step 3000. No data to save.\n",
       "3000\n",
+      "model_vars.get('Gini', 0)[-1]=0.6186\n",
+      "Condition not met at step 3001. No data to save.\n",
       "3001\n",
+      "model_vars.get('Gini', 0)[-1]=0.6182000000000001\n",
+      "Condition not met at step 3002. No data to save.\n",
       "3002\n",
+      "model_vars.get('Gini', 0)[-1]=0.6096\n",
+      "Condition not met at step 3003. No data to save.\n",
       "3003\n",
+      "model_vars.get('Gini', 0)[-1]=0.628\n",
+      "Condition not met at step 3004. No data to save.\n",
       "3004\n",
+      "model_vars.get('Gini', 0)[-1]=0.6492\n",
+      "Condition not met at step 3005. No data to save.\n",
       "3005\n",
+      "model_vars.get('Gini', 0)[-1]=0.6532\n",
+      "Condition not met at step 3006. No data to save.\n",
       "3006\n",
+      "model_vars.get('Gini', 0)[-1]=0.6194\n",
+      "Condition not met at step 3007. No data to save.\n",
       "3007\n",
+      "model_vars.get('Gini', 0)[-1]=0.637\n",
+      "Condition not met at step 3008. No data to save.\n",
       "3008\n",
+      "model_vars.get('Gini', 0)[-1]=0.6592\n",
+      "Condition not met at step 3009. No data to save.\n",
       "3009\n",
+      "model_vars.get('Gini', 0)[-1]=0.6782\n",
+      "Condition not met at step 3010. No data to save.\n",
       "3010\n",
+      "model_vars.get('Gini', 0)[-1]=0.6970000000000001\n",
+      "Condition not met at step 3011. No data to save.\n",
       "3011\n",
+      "model_vars.get('Gini', 0)[-1]=0.6848000000000001\n",
+      "Condition not met at step 3012. No data to save.\n",
       "3012\n",
+      "model_vars.get('Gini', 0)[-1]=0.6482\n",
+      "Condition not met at step 3013. No data to save.\n",
       "3013\n",
+      "model_vars.get('Gini', 0)[-1]=0.6358\n",
+      "Condition not met at step 3014. No data to save.\n",
       "3014\n",
+      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
+      "Condition not met at step 3015. No data to save.\n",
       "3015\n",
+      "model_vars.get('Gini', 0)[-1]=0.6482\n",
+      "Condition not met at step 3016. No data to save.\n",
       "3016\n",
+      "model_vars.get('Gini', 0)[-1]=0.6376\n",
+      "Condition not met at step 3017. No data to save.\n",
       "3017\n",
+      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
+      "Condition not met at step 3018. No data to save.\n",
       "3018\n",
+      "model_vars.get('Gini', 0)[-1]=0.6758\n",
+      "Condition not met at step 3019. No data to save.\n",
       "3019\n",
+      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
+      "Condition not met at step 3020. No data to save.\n",
       "3020\n",
+      "model_vars.get('Gini', 0)[-1]=0.6679999999999999\n",
+      "Condition not met at step 3021. No data to save.\n",
       "3021\n",
+      "model_vars.get('Gini', 0)[-1]=0.6762\n",
+      "Condition not met at step 3022. No data to save.\n",
       "3022\n",
+      "model_vars.get('Gini', 0)[-1]=0.6348\n",
+      "Condition not met at step 3023. No data to save.\n",
       "3023\n",
+      "model_vars.get('Gini', 0)[-1]=0.6378\n",
+      "Condition not met at step 3024. No data to save.\n",
       "3024\n",
+      "model_vars.get('Gini', 0)[-1]=0.613\n",
+      "Condition not met at step 3025. No data to save.\n",
       "3025\n",
+      "model_vars.get('Gini', 0)[-1]=0.6118\n",
+      "Condition not met at step 3026. No data to save.\n",
       "3026\n",
+      "model_vars.get('Gini', 0)[-1]=0.6166\n",
+      "Condition not met at step 3027. No data to save.\n",
       "3027\n",
+      "model_vars.get('Gini', 0)[-1]=0.6496\n",
+      "Condition not met at step 3028. No data to save.\n",
       "3028\n",
+      "model_vars.get('Gini', 0)[-1]=0.6306\n",
+      "Condition not met at step 3029. No data to save.\n",
       "3029\n",
+      "model_vars.get('Gini', 0)[-1]=0.6306\n",
+      "Condition not met at step 3030. No data to save.\n",
       "3030\n",
+      "model_vars.get('Gini', 0)[-1]=0.6298\n",
+      "Condition not met at step 3031. No data to save.\n",
       "3031\n",
+      "model_vars.get('Gini', 0)[-1]=0.6342\n",
+      "Condition not met at step 3032. No data to save.\n",
       "3032\n",
+      "model_vars.get('Gini', 0)[-1]=0.6335999999999999\n",
+      "Condition not met at step 3033. No data to save.\n",
       "3033\n",
+      "model_vars.get('Gini', 0)[-1]=0.647\n",
+      "Condition not met at step 3034. No data to save.\n",
       "3034\n",
+      "model_vars.get('Gini', 0)[-1]=0.624\n",
+      "Condition not met at step 3035. No data to save.\n",
       "3035\n",
+      "model_vars.get('Gini', 0)[-1]=0.6468\n",
+      "Condition not met at step 3036. No data to save.\n",
       "3036\n",
+      "model_vars.get('Gini', 0)[-1]=0.6328\n",
+      "Condition not met at step 3037. No data to save.\n",
       "3037\n",
+      "model_vars.get('Gini', 0)[-1]=0.6402\n",
+      "Condition not met at step 3038. No data to save.\n",
       "3038\n",
+      "model_vars.get('Gini', 0)[-1]=0.6182000000000001\n",
+      "Condition not met at step 3039. No data to save.\n",
       "3039\n",
+      "model_vars.get('Gini', 0)[-1]=0.6038\n",
+      "Condition not met at step 3040. No data to save.\n",
       "3040\n",
+      "model_vars.get('Gini', 0)[-1]=0.642\n",
+      "Condition not met at step 3041. No data to save.\n",
       "3041\n",
+      "model_vars.get('Gini', 0)[-1]=0.644\n",
+      "Condition not met at step 3042. No data to save.\n",
       "3042\n",
+      "model_vars.get('Gini', 0)[-1]=0.65\n",
+      "Condition not met at step 3043. No data to save.\n",
       "3043\n",
+      "model_vars.get('Gini', 0)[-1]=0.6626000000000001\n",
+      "Condition not met at step 3044. No data to save.\n",
       "3044\n",
+      "model_vars.get('Gini', 0)[-1]=0.6772\n",
+      "Condition not met at step 3045. No data to save.\n",
       "3045\n",
+      "model_vars.get('Gini', 0)[-1]=0.6436\n",
+      "Condition not met at step 3046. No data to save.\n",
       "3046\n",
+      "model_vars.get('Gini', 0)[-1]=0.664\n",
+      "Condition not met at step 3047. No data to save.\n",
       "3047\n",
+      "model_vars.get('Gini', 0)[-1]=0.6852\n",
+      "Condition not met at step 3048. No data to save.\n",
       "3048\n",
+      "model_vars.get('Gini', 0)[-1]=0.6894\n",
+      "Condition not met at step 3049. No data to save.\n",
       "3049\n",
+      "model_vars.get('Gini', 0)[-1]=0.661\n",
+      "Condition not met at step 3050. No data to save.\n",
       "3050\n",
+      "model_vars.get('Gini', 0)[-1]=0.6396\n",
+      "Condition not met at step 3051. No data to save.\n",
       "3051\n",
+      "model_vars.get('Gini', 0)[-1]=0.651\n",
+      "Condition not met at step 3052. No data to save.\n",
       "3052\n",
+      "model_vars.get('Gini', 0)[-1]=0.6582\n",
+      "Condition not met at step 3053. No data to save.\n",
       "3053\n",
+      "model_vars.get('Gini', 0)[-1]=0.6679999999999999\n",
+      "Condition not met at step 3054. No data to save.\n",
       "3054\n",
+      "model_vars.get('Gini', 0)[-1]=0.6952\n",
+      "Condition not met at step 3055. No data to save.\n",
       "3055\n",
+      "model_vars.get('Gini', 0)[-1]=0.7092\n",
+      "Condition met. Appended special results for step 3056 to test_path/special_results.parquet\n",
       "3056\n",
+      "model_vars.get('Gini', 0)[-1]=0.7092\n",
+      "Condition met. Appended special results for step 3057 to test_path/special_results.parquet\n",
       "3057\n",
+      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
+      "Condition not met at step 3058. No data to save.\n",
       "3058\n",
+      "model_vars.get('Gini', 0)[-1]=0.6838\n",
+      "Condition not met at step 3059. No data to save.\n",
       "3059\n",
+      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
+      "Condition not met at step 3060. No data to save.\n",
       "3060\n",
+      "model_vars.get('Gini', 0)[-1]=0.69\n",
+      "Condition not met at step 3061. No data to save.\n",
       "3061\n",
+      "model_vars.get('Gini', 0)[-1]=0.6632\n",
+      "Condition not met at step 3062. No data to save.\n",
       "3062\n",
+      "model_vars.get('Gini', 0)[-1]=0.6804\n",
+      "Condition not met at step 3063. No data to save.\n",
       "3063\n",
+      "model_vars.get('Gini', 0)[-1]=0.6686000000000001\n",
+      "Condition not met at step 3064. No data to save.\n",
       "3064\n",
+      "model_vars.get('Gini', 0)[-1]=0.6648000000000001\n",
+      "Condition not met at step 3065. No data to save.\n",
       "3065\n",
+      "model_vars.get('Gini', 0)[-1]=0.667\n",
+      "Condition not met at step 3066. No data to save.\n",
       "3066\n",
+      "model_vars.get('Gini', 0)[-1]=0.7046\n",
+      "Condition met. Appended special results for step 3067 to test_path/special_results.parquet\n",
       "3067\n",
+      "model_vars.get('Gini', 0)[-1]=0.6890000000000001\n",
+      "Condition not met at step 3068. No data to save.\n",
       "3068\n",
+      "model_vars.get('Gini', 0)[-1]=0.6718\n",
+      "Condition not met at step 3069. No data to save.\n",
       "3069\n",
+      "model_vars.get('Gini', 0)[-1]=0.6774\n",
+      "Condition not met at step 3070. No data to save.\n",
       "3070\n",
+      "model_vars.get('Gini', 0)[-1]=0.6646000000000001\n",
+      "Condition not met at step 3071. No data to save.\n",
       "3071\n",
+      "model_vars.get('Gini', 0)[-1]=0.6612\n",
+      "Condition not met at step 3072. No data to save.\n",
       "3072\n",
+      "model_vars.get('Gini', 0)[-1]=0.6646000000000001\n",
+      "Condition not met at step 3073. No data to save.\n",
       "3073\n",
+      "model_vars.get('Gini', 0)[-1]=0.6694\n",
+      "Condition not met at step 3074. No data to save.\n",
       "3074\n",
+      "model_vars.get('Gini', 0)[-1]=0.6792\n",
+      "Condition not met at step 3075. No data to save.\n",
       "3075\n",
+      "model_vars.get('Gini', 0)[-1]=0.7008000000000001\n",
+      "Condition met. Appended special results for step 3076 to test_path/special_results.parquet\n",
       "3076\n",
+      "model_vars.get('Gini', 0)[-1]=0.7064\n",
+      "Condition met. Appended special results for step 3077 to test_path/special_results.parquet\n",
       "3077\n",
+      "model_vars.get('Gini', 0)[-1]=0.6836\n",
+      "Condition not met at step 3078. No data to save.\n",
       "3078\n",
+      "model_vars.get('Gini', 0)[-1]=0.7021999999999999\n",
+      "Condition met. Appended special results for step 3079 to test_path/special_results.parquet\n",
       "3079\n",
+      "model_vars.get('Gini', 0)[-1]=0.6930000000000001\n",
+      "Condition not met at step 3080. No data to save.\n",
       "3080\n",
+      "model_vars.get('Gini', 0)[-1]=0.669\n",
+      "Condition not met at step 3081. No data to save.\n",
       "3081\n",
+      "model_vars.get('Gini', 0)[-1]=0.7218\n",
+      "Condition met. Appended special results for step 3082 to test_path/special_results.parquet\n",
       "3082\n",
+      "model_vars.get('Gini', 0)[-1]=0.7006\n",
+      "Condition met. Appended special results for step 3083 to test_path/special_results.parquet\n",
       "3083\n",
+      "model_vars.get('Gini', 0)[-1]=0.6984\n",
+      "Condition not met at step 3084. No data to save.\n",
       "3084\n",
+      "model_vars.get('Gini', 0)[-1]=0.6822\n",
+      "Condition not met at step 3085. No data to save.\n",
       "3085\n",
+      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
+      "Condition not met at step 3086. No data to save.\n",
       "3086\n",
+      "model_vars.get('Gini', 0)[-1]=0.6584\n",
+      "Condition not met at step 3087. No data to save.\n",
       "3087\n",
+      "model_vars.get('Gini', 0)[-1]=0.652\n",
+      "Condition not met at step 3088. No data to save.\n",
       "3088\n",
+      "model_vars.get('Gini', 0)[-1]=0.652\n",
+      "Condition not met at step 3089. No data to save.\n",
       "3089\n",
+      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
+      "Condition not met at step 3090. No data to save.\n",
       "3090\n",
+      "model_vars.get('Gini', 0)[-1]=0.6295999999999999\n",
+      "Condition not met at step 3091. No data to save.\n",
       "3091\n",
+      "model_vars.get('Gini', 0)[-1]=0.656\n",
+      "Condition not met at step 3092. No data to save.\n",
       "3092\n",
+      "model_vars.get('Gini', 0)[-1]=0.6408\n",
+      "Condition not met at step 3093. No data to save.\n",
       "3093\n",
+      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
+      "Condition not met at step 3094. No data to save.\n",
       "3094\n",
+      "model_vars.get('Gini', 0)[-1]=0.6142000000000001\n",
+      "Condition not met at step 3095. No data to save.\n",
       "3095\n",
+      "model_vars.get('Gini', 0)[-1]=0.6224000000000001\n",
+      "Condition not met at step 3096. No data to save.\n",
       "3096\n",
+      "model_vars.get('Gini', 0)[-1]=0.5896\n",
+      "Condition not met at step 3097. No data to save.\n",
       "3097\n",
+      "model_vars.get('Gini', 0)[-1]=0.6138\n",
+      "Condition not met at step 3098. No data to save.\n",
       "3098\n",
+      "model_vars.get('Gini', 0)[-1]=0.6548\n",
+      "Condition not met at step 3099. No data to save.\n",
       "3099\n",
       "CALLED\n",
       "self.model._steps=3100\n",
       " TEST self.model._steps=3100\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "3000  0.6942\n",
-      "3001  0.6854\n",
-      "3002  0.6828\n",
-      "3003  0.6758\n",
-      "3004  0.6922\n",
+      "3000  0.6186\n",
+      "3001  0.6182\n",
+      "3002  0.6096\n",
+      "3003  0.6280\n",
+      "3004  0.6492\n",
       "...      ...\n",
-      "3095  0.6512\n",
-      "3096  0.6520\n",
-      "3097  0.6322\n",
-      "3098  0.6354\n",
-      "3099  0.6106\n",
+      "3095  0.6224\n",
+      "3096  0.5896\n",
+      "3097  0.6138\n",
+      "3098  0.6548\n",
+      "3099  0.6348\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_031.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_031.parquet'\n",
-      "Saving model to output_dir/model_data_031.parquet\n",
-      "Saving agent to output_dir/agent_data_031.parquet\n",
+      "model_file='test_path/model_data_031.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_031.parquet'\n",
+      "Saving model to test_path/model_data_031.parquet\n",
+      "Saving agent to test_path/agent_data_031.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6348\n",
+      "Condition not met at step 3100. No data to save.\n",
       "3100\n",
+      "model_vars.get('Gini', 0)[-1]=0.6175999999999999\n",
+      "Condition not met at step 3101. No data to save.\n",
       "3101\n",
+      "model_vars.get('Gini', 0)[-1]=0.6246\n",
+      "Condition not met at step 3102. No data to save.\n",
       "3102\n",
+      "model_vars.get('Gini', 0)[-1]=0.6066\n",
+      "Condition not met at step 3103. No data to save.\n",
       "3103\n",
+      "model_vars.get('Gini', 0)[-1]=0.6264000000000001\n",
+      "Condition not met at step 3104. No data to save.\n",
       "3104\n",
+      "model_vars.get('Gini', 0)[-1]=0.6182000000000001\n",
+      "Condition not met at step 3105. No data to save.\n",
       "3105\n",
+      "model_vars.get('Gini', 0)[-1]=0.5920000000000001\n",
+      "Condition not met at step 3106. No data to save.\n",
       "3106\n",
+      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
+      "Condition not met at step 3107. No data to save.\n",
       "3107\n",
+      "model_vars.get('Gini', 0)[-1]=0.6664\n",
+      "Condition not met at step 3108. No data to save.\n",
       "3108\n",
+      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
+      "Condition not met at step 3109. No data to save.\n",
       "3109\n",
+      "model_vars.get('Gini', 0)[-1]=0.5996\n",
+      "Condition not met at step 3110. No data to save.\n",
       "3110\n",
+      "model_vars.get('Gini', 0)[-1]=0.6442\n",
+      "Condition not met at step 3111. No data to save.\n",
       "3111\n",
+      "model_vars.get('Gini', 0)[-1]=0.6248\n",
+      "Condition not met at step 3112. No data to save.\n",
       "3112\n",
+      "model_vars.get('Gini', 0)[-1]=0.6497999999999999\n",
+      "Condition not met at step 3113. No data to save.\n",
       "3113\n",
+      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
+      "Condition not met at step 3114. No data to save.\n",
       "3114\n",
+      "model_vars.get('Gini', 0)[-1]=0.641\n",
+      "Condition not met at step 3115. No data to save.\n",
       "3115\n",
+      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
+      "Condition not met at step 3116. No data to save.\n",
       "3116\n",
+      "model_vars.get('Gini', 0)[-1]=0.6542\n",
+      "Condition not met at step 3117. No data to save.\n",
       "3117\n",
+      "model_vars.get('Gini', 0)[-1]=0.6664\n",
+      "Condition not met at step 3118. No data to save.\n",
       "3118\n",
+      "model_vars.get('Gini', 0)[-1]=0.6886\n",
+      "Condition not met at step 3119. No data to save.\n",
       "3119\n",
+      "model_vars.get('Gini', 0)[-1]=0.6914\n",
+      "Condition not met at step 3120. No data to save.\n",
       "3120\n",
+      "model_vars.get('Gini', 0)[-1]=0.702\n",
+      "Condition met. Appended special results for step 3121 to test_path/special_results.parquet\n",
       "3121\n",
+      "model_vars.get('Gini', 0)[-1]=0.7016\n",
+      "Condition met. Appended special results for step 3122 to test_path/special_results.parquet\n",
       "3122\n",
+      "model_vars.get('Gini', 0)[-1]=0.6844\n",
+      "Condition not met at step 3123. No data to save.\n",
       "3123\n",
+      "model_vars.get('Gini', 0)[-1]=0.6948000000000001\n",
+      "Condition not met at step 3124. No data to save.\n",
       "3124\n",
+      "model_vars.get('Gini', 0)[-1]=0.6832\n",
+      "Condition not met at step 3125. No data to save.\n",
       "3125\n",
+      "model_vars.get('Gini', 0)[-1]=0.6832\n",
+      "Condition not met at step 3126. No data to save.\n",
       "3126\n",
+      "model_vars.get('Gini', 0)[-1]=0.694\n",
+      "Condition not met at step 3127. No data to save.\n",
       "3127\n",
+      "model_vars.get('Gini', 0)[-1]=0.6832\n",
+      "Condition not met at step 3128. No data to save.\n",
       "3128\n",
+      "model_vars.get('Gini', 0)[-1]=0.6586000000000001\n",
+      "Condition not met at step 3129. No data to save.\n",
       "3129\n",
+      "model_vars.get('Gini', 0)[-1]=0.6776\n",
+      "Condition not met at step 3130. No data to save.\n",
       "3130\n",
+      "model_vars.get('Gini', 0)[-1]=0.6448\n",
+      "Condition not met at step 3131. No data to save.\n",
       "3131\n",
+      "model_vars.get('Gini', 0)[-1]=0.6816\n",
+      "Condition not met at step 3132. No data to save.\n",
       "3132\n",
+      "model_vars.get('Gini', 0)[-1]=0.6528\n",
+      "Condition not met at step 3133. No data to save.\n",
       "3133\n",
+      "model_vars.get('Gini', 0)[-1]=0.6774\n",
+      "Condition not met at step 3134. No data to save.\n",
       "3134\n",
+      "model_vars.get('Gini', 0)[-1]=0.6612\n",
+      "Condition not met at step 3135. No data to save.\n",
       "3135\n",
+      "model_vars.get('Gini', 0)[-1]=0.6866\n",
+      "Condition not met at step 3136. No data to save.\n",
       "3136\n",
+      "model_vars.get('Gini', 0)[-1]=0.6778\n",
+      "Condition not met at step 3137. No data to save.\n",
       "3137\n",
+      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
+      "Condition not met at step 3138. No data to save.\n",
       "3138\n",
+      "model_vars.get('Gini', 0)[-1]=0.6824\n",
+      "Condition not met at step 3139. No data to save.\n",
       "3139\n",
+      "model_vars.get('Gini', 0)[-1]=0.6892\n",
+      "Condition not met at step 3140. No data to save.\n",
       "3140\n",
+      "model_vars.get('Gini', 0)[-1]=0.6894\n",
+      "Condition not met at step 3141. No data to save.\n",
       "3141\n",
+      "model_vars.get('Gini', 0)[-1]=0.6838\n",
+      "Condition not met at step 3142. No data to save.\n",
       "3142\n",
+      "model_vars.get('Gini', 0)[-1]=0.6752\n",
+      "Condition not met at step 3143. No data to save.\n",
       "3143\n",
+      "model_vars.get('Gini', 0)[-1]=0.6719999999999999\n",
+      "Condition not met at step 3144. No data to save.\n",
       "3144\n",
+      "model_vars.get('Gini', 0)[-1]=0.6878\n",
+      "Condition not met at step 3145. No data to save.\n",
       "3145\n",
+      "model_vars.get('Gini', 0)[-1]=0.7064\n",
+      "Condition met. Appended special results for step 3146 to test_path/special_results.parquet\n",
       "3146\n",
+      "model_vars.get('Gini', 0)[-1]=0.7048\n",
+      "Condition met. Appended special results for step 3147 to test_path/special_results.parquet\n",
       "3147\n",
+      "model_vars.get('Gini', 0)[-1]=0.6966\n",
+      "Condition not met at step 3148. No data to save.\n",
       "3148\n",
+      "model_vars.get('Gini', 0)[-1]=0.7048\n",
+      "Condition met. Appended special results for step 3149 to test_path/special_results.parquet\n",
       "3149\n",
+      "model_vars.get('Gini', 0)[-1]=0.7010000000000001\n",
+      "Condition met. Appended special results for step 3150 to test_path/special_results.parquet\n",
       "3150\n",
+      "model_vars.get('Gini', 0)[-1]=0.7004\n",
+      "Condition met. Appended special results for step 3151 to test_path/special_results.parquet\n",
       "3151\n",
+      "model_vars.get('Gini', 0)[-1]=0.6839999999999999\n",
+      "Condition not met at step 3152. No data to save.\n",
       "3152\n",
+      "model_vars.get('Gini', 0)[-1]=0.6616\n",
+      "Condition not met at step 3153. No data to save.\n",
       "3153\n",
+      "model_vars.get('Gini', 0)[-1]=0.654\n",
+      "Condition not met at step 3154. No data to save.\n",
       "3154\n",
+      "model_vars.get('Gini', 0)[-1]=0.6222000000000001\n",
+      "Condition not met at step 3155. No data to save.\n",
       "3155\n",
+      "model_vars.get('Gini', 0)[-1]=0.6352\n",
+      "Condition not met at step 3156. No data to save.\n",
       "3156\n",
+      "model_vars.get('Gini', 0)[-1]=0.6504000000000001\n",
+      "Condition not met at step 3157. No data to save.\n",
       "3157\n",
+      "model_vars.get('Gini', 0)[-1]=0.662\n",
+      "Condition not met at step 3158. No data to save.\n",
       "3158\n",
+      "model_vars.get('Gini', 0)[-1]=0.6784\n",
+      "Condition not met at step 3159. No data to save.\n",
       "3159\n",
+      "model_vars.get('Gini', 0)[-1]=0.6654\n",
+      "Condition not met at step 3160. No data to save.\n",
       "3160\n",
+      "model_vars.get('Gini', 0)[-1]=0.6676\n",
+      "Condition not met at step 3161. No data to save.\n",
       "3161\n",
+      "model_vars.get('Gini', 0)[-1]=0.6414\n",
+      "Condition not met at step 3162. No data to save.\n",
       "3162\n",
+      "model_vars.get('Gini', 0)[-1]=0.6502\n",
+      "Condition not met at step 3163. No data to save.\n",
       "3163\n",
+      "model_vars.get('Gini', 0)[-1]=0.6614\n",
+      "Condition not met at step 3164. No data to save.\n",
       "3164\n",
+      "model_vars.get('Gini', 0)[-1]=0.6604\n",
+      "Condition not met at step 3165. No data to save.\n",
       "3165\n",
+      "model_vars.get('Gini', 0)[-1]=0.6412\n",
+      "Condition not met at step 3166. No data to save.\n",
       "3166\n",
+      "model_vars.get('Gini', 0)[-1]=0.66\n",
+      "Condition not met at step 3167. No data to save.\n",
       "3167\n",
+      "model_vars.get('Gini', 0)[-1]=0.6818\n",
+      "Condition not met at step 3168. No data to save.\n",
       "3168\n",
+      "model_vars.get('Gini', 0)[-1]=0.6844\n",
+      "Condition not met at step 3169. No data to save.\n",
       "3169\n",
+      "model_vars.get('Gini', 0)[-1]=0.6914\n",
+      "Condition not met at step 3170. No data to save.\n",
       "3170\n",
+      "model_vars.get('Gini', 0)[-1]=0.6734\n",
+      "Condition not met at step 3171. No data to save.\n",
       "3171\n",
+      "model_vars.get('Gini', 0)[-1]=0.6776\n",
+      "Condition not met at step 3172. No data to save.\n",
       "3172\n",
+      "model_vars.get('Gini', 0)[-1]=0.6839999999999999\n",
+      "Condition not met at step 3173. No data to save.\n",
       "3173\n",
+      "model_vars.get('Gini', 0)[-1]=0.6716\n",
+      "Condition not met at step 3174. No data to save.\n",
       "3174\n",
+      "model_vars.get('Gini', 0)[-1]=0.6812\n",
+      "Condition not met at step 3175. No data to save.\n",
       "3175\n",
+      "model_vars.get('Gini', 0)[-1]=0.6836\n",
+      "Condition not met at step 3176. No data to save.\n",
       "3176\n",
+      "model_vars.get('Gini', 0)[-1]=0.6712\n",
+      "Condition not met at step 3177. No data to save.\n",
       "3177\n",
+      "model_vars.get('Gini', 0)[-1]=0.6604\n",
+      "Condition not met at step 3178. No data to save.\n",
       "3178\n",
+      "model_vars.get('Gini', 0)[-1]=0.6584\n",
+      "Condition not met at step 3179. No data to save.\n",
       "3179\n",
+      "model_vars.get('Gini', 0)[-1]=0.6432\n",
+      "Condition not met at step 3180. No data to save.\n",
       "3180\n",
+      "model_vars.get('Gini', 0)[-1]=0.6335999999999999\n",
+      "Condition not met at step 3181. No data to save.\n",
       "3181\n",
+      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
+      "Condition not met at step 3182. No data to save.\n",
       "3182\n",
+      "model_vars.get('Gini', 0)[-1]=0.6326\n",
+      "Condition not met at step 3183. No data to save.\n",
       "3183\n",
+      "model_vars.get('Gini', 0)[-1]=0.6382\n",
+      "Condition not met at step 3184. No data to save.\n",
       "3184\n",
+      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
+      "Condition not met at step 3185. No data to save.\n",
       "3185\n",
+      "model_vars.get('Gini', 0)[-1]=0.6274\n",
+      "Condition not met at step 3186. No data to save.\n",
       "3186\n",
+      "model_vars.get('Gini', 0)[-1]=0.632\n",
+      "Condition not met at step 3187. No data to save.\n",
       "3187\n",
+      "model_vars.get('Gini', 0)[-1]=0.6624\n",
+      "Condition not met at step 3188. No data to save.\n",
       "3188\n",
+      "model_vars.get('Gini', 0)[-1]=0.6482\n",
+      "Condition not met at step 3189. No data to save.\n",
       "3189\n",
+      "model_vars.get('Gini', 0)[-1]=0.6384000000000001\n",
+      "Condition not met at step 3190. No data to save.\n",
       "3190\n",
+      "model_vars.get('Gini', 0)[-1]=0.6437999999999999\n",
+      "Condition not met at step 3191. No data to save.\n",
       "3191\n",
+      "model_vars.get('Gini', 0)[-1]=0.6326\n",
+      "Condition not met at step 3192. No data to save.\n",
       "3192\n",
+      "model_vars.get('Gini', 0)[-1]=0.6448\n",
+      "Condition not met at step 3193. No data to save.\n",
       "3193\n",
+      "model_vars.get('Gini', 0)[-1]=0.6592\n",
+      "Condition not met at step 3194. No data to save.\n",
       "3194\n",
+      "model_vars.get('Gini', 0)[-1]=0.6306\n",
+      "Condition not met at step 3195. No data to save.\n",
       "3195\n",
+      "model_vars.get('Gini', 0)[-1]=0.6355999999999999\n",
+      "Condition not met at step 3196. No data to save.\n",
       "3196\n",
+      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
+      "Condition not met at step 3197. No data to save.\n",
       "3197\n",
+      "model_vars.get('Gini', 0)[-1]=0.6416\n",
+      "Condition not met at step 3198. No data to save.\n",
       "3198\n",
+      "model_vars.get('Gini', 0)[-1]=0.6512\n",
+      "Condition not met at step 3199. No data to save.\n",
       "3199\n",
       "CALLED\n",
       "self.model._steps=3200\n",
       " TEST self.model._steps=3200\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "3100  0.6394\n",
-      "3101  0.6518\n",
-      "3102  0.6254\n",
-      "3103  0.6022\n",
-      "3104  0.5750\n",
+      "3100  0.6176\n",
+      "3101  0.6246\n",
+      "3102  0.6066\n",
+      "3103  0.6264\n",
+      "3104  0.6182\n",
       "...      ...\n",
-      "3195  0.6678\n",
-      "3196  0.6514\n",
-      "3197  0.6456\n",
-      "3198  0.6450\n",
-      "3199  0.6520\n",
+      "3195  0.6356\n",
+      "3196  0.6256\n",
+      "3197  0.6416\n",
+      "3198  0.6512\n",
+      "3199  0.5912\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_032.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_032.parquet'\n",
-      "Saving model to output_dir/model_data_032.parquet\n",
-      "Saving agent to output_dir/agent_data_032.parquet\n",
+      "model_file='test_path/model_data_032.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_032.parquet'\n",
+      "Saving model to test_path/model_data_032.parquet\n",
+      "Saving agent to test_path/agent_data_032.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.5912\n",
+      "Condition not met at step 3200. No data to save.\n",
       "3200\n",
+      "model_vars.get('Gini', 0)[-1]=0.628\n",
+      "Condition not met at step 3201. No data to save.\n",
       "3201\n",
+      "model_vars.get('Gini', 0)[-1]=0.637\n",
+      "Condition not met at step 3202. No data to save.\n",
       "3202\n",
+      "model_vars.get('Gini', 0)[-1]=0.6604\n",
+      "Condition not met at step 3203. No data to save.\n",
       "3203\n",
+      "model_vars.get('Gini', 0)[-1]=0.634\n",
+      "Condition not met at step 3204. No data to save.\n",
       "3204\n",
+      "model_vars.get('Gini', 0)[-1]=0.6162000000000001\n",
+      "Condition not met at step 3205. No data to save.\n",
       "3205\n",
+      "model_vars.get('Gini', 0)[-1]=0.616\n",
+      "Condition not met at step 3206. No data to save.\n",
       "3206\n",
+      "model_vars.get('Gini', 0)[-1]=0.622\n",
+      "Condition not met at step 3207. No data to save.\n",
       "3207\n",
+      "model_vars.get('Gini', 0)[-1]=0.6462\n",
+      "Condition not met at step 3208. No data to save.\n",
       "3208\n",
+      "model_vars.get('Gini', 0)[-1]=0.6496\n",
+      "Condition not met at step 3209. No data to save.\n",
       "3209\n",
+      "model_vars.get('Gini', 0)[-1]=0.6572\n",
+      "Condition not met at step 3210. No data to save.\n",
       "3210\n",
+      "model_vars.get('Gini', 0)[-1]=0.6204000000000001\n",
+      "Condition not met at step 3211. No data to save.\n",
       "3211\n",
+      "model_vars.get('Gini', 0)[-1]=0.6224000000000001\n",
+      "Condition not met at step 3212. No data to save.\n",
       "3212\n",
+      "model_vars.get('Gini', 0)[-1]=0.6734\n",
+      "Condition not met at step 3213. No data to save.\n",
       "3213\n",
+      "model_vars.get('Gini', 0)[-1]=0.6396\n",
+      "Condition not met at step 3214. No data to save.\n",
       "3214\n",
+      "model_vars.get('Gini', 0)[-1]=0.6304000000000001\n",
+      "Condition not met at step 3215. No data to save.\n",
       "3215\n",
+      "model_vars.get('Gini', 0)[-1]=0.6174\n",
+      "Condition not met at step 3216. No data to save.\n",
       "3216\n",
+      "model_vars.get('Gini', 0)[-1]=0.6502\n",
+      "Condition not met at step 3217. No data to save.\n",
       "3217\n",
+      "model_vars.get('Gini', 0)[-1]=0.655\n",
+      "Condition not met at step 3218. No data to save.\n",
       "3218\n",
+      "model_vars.get('Gini', 0)[-1]=0.6644\n",
+      "Condition not met at step 3219. No data to save.\n",
       "3219\n",
+      "model_vars.get('Gini', 0)[-1]=0.6614\n",
+      "Condition not met at step 3220. No data to save.\n",
       "3220\n",
+      "model_vars.get('Gini', 0)[-1]=0.6812\n",
+      "Condition not met at step 3221. No data to save.\n",
       "3221\n",
+      "model_vars.get('Gini', 0)[-1]=0.6798\n",
+      "Condition not met at step 3222. No data to save.\n",
       "3222\n",
+      "model_vars.get('Gini', 0)[-1]=0.6981999999999999\n",
+      "Condition not met at step 3223. No data to save.\n",
       "3223\n",
+      "model_vars.get('Gini', 0)[-1]=0.7018\n",
+      "Condition met. Appended special results for step 3224 to test_path/special_results.parquet\n",
       "3224\n",
+      "model_vars.get('Gini', 0)[-1]=0.6738\n",
+      "Condition not met at step 3225. No data to save.\n",
       "3225\n",
+      "model_vars.get('Gini', 0)[-1]=0.6476\n",
+      "Condition not met at step 3226. No data to save.\n",
       "3226\n",
+      "model_vars.get('Gini', 0)[-1]=0.631\n",
+      "Condition not met at step 3227. No data to save.\n",
       "3227\n",
+      "model_vars.get('Gini', 0)[-1]=0.6416\n",
+      "Condition not met at step 3228. No data to save.\n",
       "3228\n",
+      "model_vars.get('Gini', 0)[-1]=0.6374\n",
+      "Condition not met at step 3229. No data to save.\n",
       "3229\n",
+      "model_vars.get('Gini', 0)[-1]=0.6358\n",
+      "Condition not met at step 3230. No data to save.\n",
       "3230\n",
+      "model_vars.get('Gini', 0)[-1]=0.6366\n",
+      "Condition not met at step 3231. No data to save.\n",
       "3231\n",
+      "model_vars.get('Gini', 0)[-1]=0.6448\n",
+      "Condition not met at step 3232. No data to save.\n",
       "3232\n",
+      "model_vars.get('Gini', 0)[-1]=0.6634\n",
+      "Condition not met at step 3233. No data to save.\n",
       "3233\n",
+      "model_vars.get('Gini', 0)[-1]=0.662\n",
+      "Condition not met at step 3234. No data to save.\n",
       "3234\n",
+      "model_vars.get('Gini', 0)[-1]=0.6352\n",
+      "Condition not met at step 3235. No data to save.\n",
       "3235\n",
+      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
+      "Condition not met at step 3236. No data to save.\n",
       "3236\n",
+      "model_vars.get('Gini', 0)[-1]=0.6742\n",
+      "Condition not met at step 3237. No data to save.\n",
       "3237\n",
+      "model_vars.get('Gini', 0)[-1]=0.6774\n",
+      "Condition not met at step 3238. No data to save.\n",
       "3238\n",
+      "model_vars.get('Gini', 0)[-1]=0.6822\n",
+      "Condition not met at step 3239. No data to save.\n",
       "3239\n",
+      "model_vars.get('Gini', 0)[-1]=0.6914\n",
+      "Condition not met at step 3240. No data to save.\n",
       "3240\n",
+      "model_vars.get('Gini', 0)[-1]=0.6846\n",
+      "Condition not met at step 3241. No data to save.\n",
       "3241\n",
+      "model_vars.get('Gini', 0)[-1]=0.6961999999999999\n",
+      "Condition not met at step 3242. No data to save.\n",
       "3242\n",
+      "model_vars.get('Gini', 0)[-1]=0.6882\n",
+      "Condition not met at step 3243. No data to save.\n",
       "3243\n",
+      "model_vars.get('Gini', 0)[-1]=0.7142\n",
+      "Condition met. Appended special results for step 3244 to test_path/special_results.parquet\n",
       "3244\n",
+      "model_vars.get('Gini', 0)[-1]=0.7188\n",
+      "Condition met. Appended special results for step 3245 to test_path/special_results.parquet\n",
       "3245\n",
+      "model_vars.get('Gini', 0)[-1]=0.7242\n",
+      "Condition met. Appended special results for step 3246 to test_path/special_results.parquet\n",
       "3246\n",
+      "model_vars.get('Gini', 0)[-1]=0.7218\n",
+      "Condition met. Appended special results for step 3247 to test_path/special_results.parquet\n",
       "3247\n",
+      "model_vars.get('Gini', 0)[-1]=0.7343999999999999\n",
+      "Condition met. Appended special results for step 3248 to test_path/special_results.parquet\n",
       "3248\n",
+      "model_vars.get('Gini', 0)[-1]=0.7328\n",
+      "Condition met. Appended special results for step 3249 to test_path/special_results.parquet\n",
       "3249\n",
+      "model_vars.get('Gini', 0)[-1]=0.738\n",
+      "Condition met. Appended special results for step 3250 to test_path/special_results.parquet\n",
       "3250\n",
+      "model_vars.get('Gini', 0)[-1]=0.7242\n",
+      "Condition met. Appended special results for step 3251 to test_path/special_results.parquet\n",
       "3251\n",
+      "model_vars.get('Gini', 0)[-1]=0.7058\n",
+      "Condition met. Appended special results for step 3252 to test_path/special_results.parquet\n",
       "3252\n",
+      "model_vars.get('Gini', 0)[-1]=0.7094\n",
+      "Condition met. Appended special results for step 3253 to test_path/special_results.parquet\n",
       "3253\n",
+      "model_vars.get('Gini', 0)[-1]=0.7094\n",
+      "Condition met. Appended special results for step 3254 to test_path/special_results.parquet\n",
       "3254\n",
+      "model_vars.get('Gini', 0)[-1]=0.7114\n",
+      "Condition met. Appended special results for step 3255 to test_path/special_results.parquet\n",
       "3255\n",
+      "model_vars.get('Gini', 0)[-1]=0.7146\n",
+      "Condition met. Appended special results for step 3256 to test_path/special_results.parquet\n",
       "3256\n",
+      "model_vars.get('Gini', 0)[-1]=0.7322\n",
+      "Condition met. Appended special results for step 3257 to test_path/special_results.parquet\n",
       "3257\n",
+      "model_vars.get('Gini', 0)[-1]=0.739\n",
+      "Condition met. Appended special results for step 3258 to test_path/special_results.parquet\n",
       "3258\n",
+      "model_vars.get('Gini', 0)[-1]=0.7348\n",
+      "Condition met. Appended special results for step 3259 to test_path/special_results.parquet\n",
       "3259\n",
+      "model_vars.get('Gini', 0)[-1]=0.7112\n",
+      "Condition met. Appended special results for step 3260 to test_path/special_results.parquet\n",
       "3260\n",
+      "model_vars.get('Gini', 0)[-1]=0.7021999999999999\n",
+      "Condition met. Appended special results for step 3261 to test_path/special_results.parquet\n",
       "3261\n",
+      "model_vars.get('Gini', 0)[-1]=0.7074\n",
+      "Condition met. Appended special results for step 3262 to test_path/special_results.parquet\n",
       "3262\n",
+      "model_vars.get('Gini', 0)[-1]=0.6854\n",
+      "Condition not met at step 3263. No data to save.\n",
       "3263\n",
+      "model_vars.get('Gini', 0)[-1]=0.6846\n",
+      "Condition not met at step 3264. No data to save.\n",
       "3264\n",
+      "model_vars.get('Gini', 0)[-1]=0.6826\n",
+      "Condition not met at step 3265. No data to save.\n",
       "3265\n",
+      "model_vars.get('Gini', 0)[-1]=0.6910000000000001\n",
+      "Condition not met at step 3266. No data to save.\n",
       "3266\n",
+      "model_vars.get('Gini', 0)[-1]=0.6422\n",
+      "Condition not met at step 3267. No data to save.\n",
       "3267\n",
+      "model_vars.get('Gini', 0)[-1]=0.6266\n",
+      "Condition not met at step 3268. No data to save.\n",
       "3268\n",
+      "model_vars.get('Gini', 0)[-1]=0.6248\n",
+      "Condition not met at step 3269. No data to save.\n",
       "3269\n",
+      "model_vars.get('Gini', 0)[-1]=0.6342\n",
+      "Condition not met at step 3270. No data to save.\n",
       "3270\n",
+      "model_vars.get('Gini', 0)[-1]=0.6372\n",
+      "Condition not met at step 3271. No data to save.\n",
       "3271\n",
+      "model_vars.get('Gini', 0)[-1]=0.6502\n",
+      "Condition not met at step 3272. No data to save.\n",
       "3272\n",
+      "model_vars.get('Gini', 0)[-1]=0.6768000000000001\n",
+      "Condition not met at step 3273. No data to save.\n",
       "3273\n",
+      "model_vars.get('Gini', 0)[-1]=0.662\n",
+      "Condition not met at step 3274. No data to save.\n",
       "3274\n",
+      "model_vars.get('Gini', 0)[-1]=0.6686000000000001\n",
+      "Condition not met at step 3275. No data to save.\n",
       "3275\n",
+      "model_vars.get('Gini', 0)[-1]=0.651\n",
+      "Condition not met at step 3276. No data to save.\n",
       "3276\n",
+      "model_vars.get('Gini', 0)[-1]=0.6412\n",
+      "Condition not met at step 3277. No data to save.\n",
       "3277\n",
+      "model_vars.get('Gini', 0)[-1]=0.6654\n",
+      "Condition not met at step 3278. No data to save.\n",
       "3278\n",
+      "model_vars.get('Gini', 0)[-1]=0.6734\n",
+      "Condition not met at step 3279. No data to save.\n",
       "3279\n",
+      "model_vars.get('Gini', 0)[-1]=0.6642\n",
+      "Condition not met at step 3280. No data to save.\n",
       "3280\n",
+      "model_vars.get('Gini', 0)[-1]=0.653\n",
+      "Condition not met at step 3281. No data to save.\n",
       "3281\n",
+      "model_vars.get('Gini', 0)[-1]=0.6912\n",
+      "Condition not met at step 3282. No data to save.\n",
       "3282\n",
+      "model_vars.get('Gini', 0)[-1]=0.6868000000000001\n",
+      "Condition not met at step 3283. No data to save.\n",
       "3283\n",
+      "model_vars.get('Gini', 0)[-1]=0.673\n",
+      "Condition not met at step 3284. No data to save.\n",
       "3284\n",
+      "model_vars.get('Gini', 0)[-1]=0.6686000000000001\n",
+      "Condition not met at step 3285. No data to save.\n",
       "3285\n",
+      "model_vars.get('Gini', 0)[-1]=0.6698\n",
+      "Condition not met at step 3286. No data to save.\n",
       "3286\n",
+      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
+      "Condition not met at step 3287. No data to save.\n",
       "3287\n",
+      "model_vars.get('Gini', 0)[-1]=0.6414\n",
+      "Condition not met at step 3288. No data to save.\n",
       "3288\n",
+      "model_vars.get('Gini', 0)[-1]=0.6819999999999999\n",
+      "Condition not met at step 3289. No data to save.\n",
       "3289\n",
+      "model_vars.get('Gini', 0)[-1]=0.6872\n",
+      "Condition not met at step 3290. No data to save.\n",
       "3290\n",
+      "model_vars.get('Gini', 0)[-1]=0.6848000000000001\n",
+      "Condition not met at step 3291. No data to save.\n",
       "3291\n",
+      "model_vars.get('Gini', 0)[-1]=0.7008000000000001\n",
+      "Condition met. Appended special results for step 3292 to test_path/special_results.parquet\n",
       "3292\n",
+      "model_vars.get('Gini', 0)[-1]=0.7072\n",
+      "Condition met. Appended special results for step 3293 to test_path/special_results.parquet\n",
       "3293\n",
+      "model_vars.get('Gini', 0)[-1]=0.712\n",
+      "Condition met. Appended special results for step 3294 to test_path/special_results.parquet\n",
       "3294\n",
+      "model_vars.get('Gini', 0)[-1]=0.6944\n",
+      "Condition not met at step 3295. No data to save.\n",
       "3295\n",
+      "model_vars.get('Gini', 0)[-1]=0.6514\n",
+      "Condition not met at step 3296. No data to save.\n",
       "3296\n",
+      "model_vars.get('Gini', 0)[-1]=0.6617999999999999\n",
+      "Condition not met at step 3297. No data to save.\n",
       "3297\n",
+      "model_vars.get('Gini', 0)[-1]=0.6632\n",
+      "Condition not met at step 3298. No data to save.\n",
       "3298\n",
+      "model_vars.get('Gini', 0)[-1]=0.6654\n",
+      "Condition not met at step 3299. No data to save.\n",
       "3299\n",
       "CALLED\n",
       "self.model._steps=3300\n",
       " TEST self.model._steps=3300\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "3200  0.6600\n",
-      "3201  0.6952\n",
-      "3202  0.6746\n",
-      "3203  0.6792\n",
-      "3204  0.6746\n",
+      "3200  0.6280\n",
+      "3201  0.6370\n",
+      "3202  0.6604\n",
+      "3203  0.6340\n",
+      "3204  0.6162\n",
       "...      ...\n",
-      "3295  0.6034\n",
-      "3296  0.6086\n",
-      "3297  0.6050\n",
-      "3298  0.6384\n",
-      "3299  0.6246\n",
+      "3295  0.6514\n",
+      "3296  0.6618\n",
+      "3297  0.6632\n",
+      "3298  0.6654\n",
+      "3299  0.6692\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_033.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_033.parquet'\n",
-      "Saving model to output_dir/model_data_033.parquet\n",
-      "Saving agent to output_dir/agent_data_033.parquet\n",
+      "model_file='test_path/model_data_033.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_033.parquet'\n",
+      "Saving model to test_path/model_data_033.parquet\n",
+      "Saving agent to test_path/agent_data_033.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6692\n",
+      "Condition not met at step 3300. No data to save.\n",
       "3300\n",
+      "model_vars.get('Gini', 0)[-1]=0.6642\n",
+      "Condition not met at step 3301. No data to save.\n",
       "3301\n",
+      "model_vars.get('Gini', 0)[-1]=0.7061999999999999\n",
+      "Condition met. Appended special results for step 3302 to test_path/special_results.parquet\n",
       "3302\n",
+      "model_vars.get('Gini', 0)[-1]=0.728\n",
+      "Condition met. Appended special results for step 3303 to test_path/special_results.parquet\n",
       "3303\n",
+      "model_vars.get('Gini', 0)[-1]=0.718\n",
+      "Condition met. Appended special results for step 3304 to test_path/special_results.parquet\n",
       "3304\n",
+      "model_vars.get('Gini', 0)[-1]=0.7168\n",
+      "Condition met. Appended special results for step 3305 to test_path/special_results.parquet\n",
       "3305\n",
+      "model_vars.get('Gini', 0)[-1]=0.6819999999999999\n",
+      "Condition not met at step 3306. No data to save.\n",
       "3306\n",
+      "model_vars.get('Gini', 0)[-1]=0.7044\n",
+      "Condition met. Appended special results for step 3307 to test_path/special_results.parquet\n",
       "3307\n",
+      "model_vars.get('Gini', 0)[-1]=0.6886\n",
+      "Condition not met at step 3308. No data to save.\n",
       "3308\n",
+      "model_vars.get('Gini', 0)[-1]=0.7078\n",
+      "Condition met. Appended special results for step 3309 to test_path/special_results.parquet\n",
       "3309\n",
+      "model_vars.get('Gini', 0)[-1]=0.7156\n",
+      "Condition met. Appended special results for step 3310 to test_path/special_results.parquet\n",
       "3310\n",
+      "model_vars.get('Gini', 0)[-1]=0.733\n",
+      "Condition met. Appended special results for step 3311 to test_path/special_results.parquet\n",
       "3311\n",
+      "model_vars.get('Gini', 0)[-1]=0.718\n",
+      "Condition met. Appended special results for step 3312 to test_path/special_results.parquet\n",
       "3312\n",
+      "model_vars.get('Gini', 0)[-1]=0.7298\n",
+      "Condition met. Appended special results for step 3313 to test_path/special_results.parquet\n",
       "3313\n",
+      "model_vars.get('Gini', 0)[-1]=0.7468\n",
+      "Condition met. Appended special results for step 3314 to test_path/special_results.parquet\n",
       "3314\n",
+      "model_vars.get('Gini', 0)[-1]=0.6944\n",
+      "Condition not met at step 3315. No data to save.\n",
       "3315\n",
+      "model_vars.get('Gini', 0)[-1]=0.6904\n",
+      "Condition not met at step 3316. No data to save.\n",
       "3316\n",
+      "model_vars.get('Gini', 0)[-1]=0.6908000000000001\n",
+      "Condition not met at step 3317. No data to save.\n",
       "3317\n",
+      "model_vars.get('Gini', 0)[-1]=0.6941999999999999\n",
+      "Condition not met at step 3318. No data to save.\n",
       "3318\n",
+      "model_vars.get('Gini', 0)[-1]=0.7128\n",
+      "Condition met. Appended special results for step 3319 to test_path/special_results.parquet\n",
       "3319\n",
+      "model_vars.get('Gini', 0)[-1]=0.7021999999999999\n",
+      "Condition met. Appended special results for step 3320 to test_path/special_results.parquet\n",
       "3320\n",
+      "model_vars.get('Gini', 0)[-1]=0.6914\n",
+      "Condition not met at step 3321. No data to save.\n",
       "3321\n",
+      "model_vars.get('Gini', 0)[-1]=0.6704\n",
+      "Condition not met at step 3322. No data to save.\n",
       "3322\n",
+      "model_vars.get('Gini', 0)[-1]=0.6734\n",
+      "Condition not met at step 3323. No data to save.\n",
       "3323\n",
+      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
+      "Condition not met at step 3324. No data to save.\n",
       "3324\n",
+      "model_vars.get('Gini', 0)[-1]=0.669\n",
+      "Condition not met at step 3325. No data to save.\n",
       "3325\n",
+      "model_vars.get('Gini', 0)[-1]=0.6694\n",
+      "Condition not met at step 3326. No data to save.\n",
       "3326\n",
+      "model_vars.get('Gini', 0)[-1]=0.6822\n",
+      "Condition not met at step 3327. No data to save.\n",
       "3327\n",
+      "model_vars.get('Gini', 0)[-1]=0.6964\n",
+      "Condition not met at step 3328. No data to save.\n",
       "3328\n",
+      "model_vars.get('Gini', 0)[-1]=0.6846\n",
+      "Condition not met at step 3329. No data to save.\n",
       "3329\n",
+      "model_vars.get('Gini', 0)[-1]=0.6874\n",
+      "Condition not met at step 3330. No data to save.\n",
       "3330\n",
+      "model_vars.get('Gini', 0)[-1]=0.6934\n",
+      "Condition not met at step 3331. No data to save.\n",
       "3331\n",
+      "model_vars.get('Gini', 0)[-1]=0.6846\n",
+      "Condition not met at step 3332. No data to save.\n",
       "3332\n",
+      "model_vars.get('Gini', 0)[-1]=0.7048\n",
+      "Condition met. Appended special results for step 3333 to test_path/special_results.parquet\n",
       "3333\n",
+      "model_vars.get('Gini', 0)[-1]=0.7064\n",
+      "Condition met. Appended special results for step 3334 to test_path/special_results.parquet\n",
       "3334\n",
+      "model_vars.get('Gini', 0)[-1]=0.7076\n",
+      "Condition met. Appended special results for step 3335 to test_path/special_results.parquet\n",
       "3335\n",
+      "model_vars.get('Gini', 0)[-1]=0.6938\n",
+      "Condition not met at step 3336. No data to save.\n",
       "3336\n",
+      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
+      "Condition not met at step 3337. No data to save.\n",
       "3337\n",
+      "model_vars.get('Gini', 0)[-1]=0.6996\n",
+      "Condition not met at step 3338. No data to save.\n",
       "3338\n",
+      "model_vars.get('Gini', 0)[-1]=0.6798\n",
+      "Condition not met at step 3339. No data to save.\n",
       "3339\n",
+      "model_vars.get('Gini', 0)[-1]=0.6656\n",
+      "Condition not met at step 3340. No data to save.\n",
       "3340\n",
+      "model_vars.get('Gini', 0)[-1]=0.6428\n",
+      "Condition not met at step 3341. No data to save.\n",
       "3341\n",
+      "model_vars.get('Gini', 0)[-1]=0.6602\n",
+      "Condition not met at step 3342. No data to save.\n",
       "3342\n",
+      "model_vars.get('Gini', 0)[-1]=0.6588\n",
+      "Condition not met at step 3343. No data to save.\n",
       "3343\n",
+      "model_vars.get('Gini', 0)[-1]=0.637\n",
+      "Condition not met at step 3344. No data to save.\n",
       "3344\n",
+      "model_vars.get('Gini', 0)[-1]=0.6456\n",
+      "Condition not met at step 3345. No data to save.\n",
       "3345\n",
+      "model_vars.get('Gini', 0)[-1]=0.6662\n",
+      "Condition not met at step 3346. No data to save.\n",
       "3346\n",
+      "model_vars.get('Gini', 0)[-1]=0.7030000000000001\n",
+      "Condition met. Appended special results for step 3347 to test_path/special_results.parquet\n",
       "3347\n",
+      "model_vars.get('Gini', 0)[-1]=0.6898\n",
+      "Condition not met at step 3348. No data to save.\n",
       "3348\n",
+      "model_vars.get('Gini', 0)[-1]=0.7010000000000001\n",
+      "Condition met. Appended special results for step 3349 to test_path/special_results.parquet\n",
       "3349\n",
+      "model_vars.get('Gini', 0)[-1]=0.6990000000000001\n",
+      "Condition not met at step 3350. No data to save.\n",
       "3350\n",
+      "model_vars.get('Gini', 0)[-1]=0.6948000000000001\n",
+      "Condition not met at step 3351. No data to save.\n",
       "3351\n",
+      "model_vars.get('Gini', 0)[-1]=0.6948000000000001\n",
+      "Condition not met at step 3352. No data to save.\n",
       "3352\n",
+      "model_vars.get('Gini', 0)[-1]=0.688\n",
+      "Condition not met at step 3353. No data to save.\n",
       "3353\n",
+      "model_vars.get('Gini', 0)[-1]=0.7112\n",
+      "Condition met. Appended special results for step 3354 to test_path/special_results.parquet\n",
       "3354\n",
+      "model_vars.get('Gini', 0)[-1]=0.7068\n",
+      "Condition met. Appended special results for step 3355 to test_path/special_results.parquet\n",
       "3355\n",
+      "model_vars.get('Gini', 0)[-1]=0.7190000000000001\n",
+      "Condition met. Appended special results for step 3356 to test_path/special_results.parquet\n",
       "3356\n",
+      "model_vars.get('Gini', 0)[-1]=0.7028000000000001\n",
+      "Condition met. Appended special results for step 3357 to test_path/special_results.parquet\n",
       "3357\n",
+      "model_vars.get('Gini', 0)[-1]=0.6882\n",
+      "Condition not met at step 3358. No data to save.\n",
       "3358\n",
+      "model_vars.get('Gini', 0)[-1]=0.6686000000000001\n",
+      "Condition not met at step 3359. No data to save.\n",
       "3359\n",
+      "model_vars.get('Gini', 0)[-1]=0.6738\n",
+      "Condition not met at step 3360. No data to save.\n",
       "3360\n",
+      "model_vars.get('Gini', 0)[-1]=0.6758\n",
+      "Condition not met at step 3361. No data to save.\n",
       "3361\n",
+      "model_vars.get('Gini', 0)[-1]=0.6588\n",
+      "Condition not met at step 3362. No data to save.\n",
       "3362\n",
+      "model_vars.get('Gini', 0)[-1]=0.6892\n",
+      "Condition not met at step 3363. No data to save.\n",
       "3363\n",
+      "model_vars.get('Gini', 0)[-1]=0.6936\n",
+      "Condition not met at step 3364. No data to save.\n",
       "3364\n",
+      "model_vars.get('Gini', 0)[-1]=0.6876\n",
+      "Condition not met at step 3365. No data to save.\n",
       "3365\n",
+      "model_vars.get('Gini', 0)[-1]=0.666\n",
+      "Condition not met at step 3366. No data to save.\n",
       "3366\n",
+      "model_vars.get('Gini', 0)[-1]=0.6854\n",
+      "Condition not met at step 3367. No data to save.\n",
       "3367\n",
+      "model_vars.get('Gini', 0)[-1]=0.6644\n",
+      "Condition not met at step 3368. No data to save.\n",
       "3368\n",
+      "model_vars.get('Gini', 0)[-1]=0.6676\n",
+      "Condition not met at step 3369. No data to save.\n",
       "3369\n",
+      "model_vars.get('Gini', 0)[-1]=0.656\n",
+      "Condition not met at step 3370. No data to save.\n",
       "3370\n",
+      "model_vars.get('Gini', 0)[-1]=0.6454\n",
+      "Condition not met at step 3371. No data to save.\n",
       "3371\n",
+      "model_vars.get('Gini', 0)[-1]=0.6502\n",
+      "Condition not met at step 3372. No data to save.\n",
       "3372\n",
+      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
+      "Condition not met at step 3373. No data to save.\n",
       "3373\n",
+      "model_vars.get('Gini', 0)[-1]=0.6718\n",
+      "Condition not met at step 3374. No data to save.\n",
       "3374\n",
+      "model_vars.get('Gini', 0)[-1]=0.6556\n",
+      "Condition not met at step 3375. No data to save.\n",
       "3375\n",
+      "model_vars.get('Gini', 0)[-1]=0.6778\n",
+      "Condition not met at step 3376. No data to save.\n",
       "3376\n",
+      "model_vars.get('Gini', 0)[-1]=0.6532\n",
+      "Condition not met at step 3377. No data to save.\n",
       "3377\n",
+      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
+      "Condition not met at step 3378. No data to save.\n",
       "3378\n",
+      "model_vars.get('Gini', 0)[-1]=0.6704\n",
+      "Condition not met at step 3379. No data to save.\n",
       "3379\n",
+      "model_vars.get('Gini', 0)[-1]=0.6918\n",
+      "Condition not met at step 3380. No data to save.\n",
       "3380\n",
+      "model_vars.get('Gini', 0)[-1]=0.6948000000000001\n",
+      "Condition not met at step 3381. No data to save.\n",
       "3381\n",
+      "model_vars.get('Gini', 0)[-1]=0.679\n",
+      "Condition not met at step 3382. No data to save.\n",
       "3382\n",
+      "model_vars.get('Gini', 0)[-1]=0.6624\n",
+      "Condition not met at step 3383. No data to save.\n",
       "3383\n",
+      "model_vars.get('Gini', 0)[-1]=0.665\n",
+      "Condition not met at step 3384. No data to save.\n",
       "3384\n",
+      "model_vars.get('Gini', 0)[-1]=0.6284000000000001\n",
+      "Condition not met at step 3385. No data to save.\n",
       "3385\n",
+      "model_vars.get('Gini', 0)[-1]=0.622\n",
+      "Condition not met at step 3386. No data to save.\n",
       "3386\n",
+      "model_vars.get('Gini', 0)[-1]=0.6194\n",
+      "Condition not met at step 3387. No data to save.\n",
       "3387\n",
+      "model_vars.get('Gini', 0)[-1]=0.5702\n",
+      "Condition not met at step 3388. No data to save.\n",
       "3388\n",
+      "model_vars.get('Gini', 0)[-1]=0.5546\n",
+      "Condition not met at step 3389. No data to save.\n",
       "3389\n",
+      "model_vars.get('Gini', 0)[-1]=0.5831999999999999\n",
+      "Condition not met at step 3390. No data to save.\n",
       "3390\n",
+      "model_vars.get('Gini', 0)[-1]=0.6022000000000001\n",
+      "Condition not met at step 3391. No data to save.\n",
       "3391\n",
+      "model_vars.get('Gini', 0)[-1]=0.5968\n",
+      "Condition not met at step 3392. No data to save.\n",
       "3392\n",
+      "model_vars.get('Gini', 0)[-1]=0.599\n",
+      "Condition not met at step 3393. No data to save.\n",
       "3393\n",
+      "model_vars.get('Gini', 0)[-1]=0.6036\n",
+      "Condition not met at step 3394. No data to save.\n",
       "3394\n",
+      "model_vars.get('Gini', 0)[-1]=0.5862\n",
+      "Condition not met at step 3395. No data to save.\n",
       "3395\n",
+      "model_vars.get('Gini', 0)[-1]=0.5936\n",
+      "Condition not met at step 3396. No data to save.\n",
       "3396\n",
+      "model_vars.get('Gini', 0)[-1]=0.6238\n",
+      "Condition not met at step 3397. No data to save.\n",
       "3397\n",
+      "model_vars.get('Gini', 0)[-1]=0.6232\n",
+      "Condition not met at step 3398. No data to save.\n",
       "3398\n",
+      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
+      "Condition not met at step 3399. No data to save.\n",
       "3399\n",
       "CALLED\n",
       "self.model._steps=3400\n",
       " TEST self.model._steps=3400\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "3300  0.6344\n",
-      "3301  0.6618\n",
-      "3302  0.6624\n",
-      "3303  0.6802\n",
-      "3304  0.6522\n",
+      "3300  0.6642\n",
+      "3301  0.7062\n",
+      "3302  0.7280\n",
+      "3303  0.7180\n",
+      "3304  0.7168\n",
       "...      ...\n",
-      "3395  0.7030\n",
-      "3396  0.7020\n",
-      "3397  0.7192\n",
-      "3398  0.7042\n",
-      "3399  0.6716\n",
+      "3395  0.5936\n",
+      "3396  0.6238\n",
+      "3397  0.6232\n",
+      "3398  0.6546\n",
+      "3399  0.6640\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_034.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_034.parquet'\n",
-      "Saving model to output_dir/model_data_034.parquet\n",
-      "Saving agent to output_dir/agent_data_034.parquet\n",
+      "model_file='test_path/model_data_034.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_034.parquet'\n",
+      "Saving model to test_path/model_data_034.parquet\n",
+      "Saving agent to test_path/agent_data_034.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.664\n",
+      "Condition not met at step 3400. No data to save.\n",
       "3400\n",
+      "model_vars.get('Gini', 0)[-1]=0.6626000000000001\n",
+      "Condition not met at step 3401. No data to save.\n",
       "3401\n",
+      "model_vars.get('Gini', 0)[-1]=0.6537999999999999\n",
+      "Condition not met at step 3402. No data to save.\n",
       "3402\n",
+      "model_vars.get('Gini', 0)[-1]=0.6572\n",
+      "Condition not met at step 3403. No data to save.\n",
       "3403\n",
+      "model_vars.get('Gini', 0)[-1]=0.6492\n",
+      "Condition not met at step 3404. No data to save.\n",
       "3404\n",
+      "model_vars.get('Gini', 0)[-1]=0.6386000000000001\n",
+      "Condition not met at step 3405. No data to save.\n",
       "3405\n",
+      "model_vars.get('Gini', 0)[-1]=0.6226\n",
+      "Condition not met at step 3406. No data to save.\n",
       "3406\n",
+      "model_vars.get('Gini', 0)[-1]=0.5988\n",
+      "Condition not met at step 3407. No data to save.\n",
       "3407\n",
+      "model_vars.get('Gini', 0)[-1]=0.5926\n",
+      "Condition not met at step 3408. No data to save.\n",
       "3408\n",
+      "model_vars.get('Gini', 0)[-1]=0.5906\n",
+      "Condition not met at step 3409. No data to save.\n",
       "3409\n",
+      "model_vars.get('Gini', 0)[-1]=0.5724\n",
+      "Condition not met at step 3410. No data to save.\n",
       "3410\n",
+      "model_vars.get('Gini', 0)[-1]=0.6158\n",
+      "Condition not met at step 3411. No data to save.\n",
       "3411\n",
+      "model_vars.get('Gini', 0)[-1]=0.6414\n",
+      "Condition not met at step 3412. No data to save.\n",
       "3412\n",
+      "model_vars.get('Gini', 0)[-1]=0.6494\n",
+      "Condition not met at step 3413. No data to save.\n",
       "3413\n",
+      "model_vars.get('Gini', 0)[-1]=0.669\n",
+      "Condition not met at step 3414. No data to save.\n",
       "3414\n",
+      "model_vars.get('Gini', 0)[-1]=0.6352\n",
+      "Condition not met at step 3415. No data to save.\n",
       "3415\n",
+      "model_vars.get('Gini', 0)[-1]=0.665\n",
+      "Condition not met at step 3416. No data to save.\n",
       "3416\n",
+      "model_vars.get('Gini', 0)[-1]=0.6908000000000001\n",
+      "Condition not met at step 3417. No data to save.\n",
       "3417\n",
+      "model_vars.get('Gini', 0)[-1]=0.6762\n",
+      "Condition not met at step 3418. No data to save.\n",
       "3418\n",
+      "model_vars.get('Gini', 0)[-1]=0.6754\n",
+      "Condition not met at step 3419. No data to save.\n",
       "3419\n",
+      "model_vars.get('Gini', 0)[-1]=0.7172000000000001\n",
+      "Condition met. Appended special results for step 3420 to test_path/special_results.parquet\n",
       "3420\n",
+      "model_vars.get('Gini', 0)[-1]=0.714\n",
+      "Condition met. Appended special results for step 3421 to test_path/special_results.parquet\n",
       "3421\n",
+      "model_vars.get('Gini', 0)[-1]=0.7056\n",
+      "Condition met. Appended special results for step 3422 to test_path/special_results.parquet\n",
       "3422\n",
+      "model_vars.get('Gini', 0)[-1]=0.7222\n",
+      "Condition met. Appended special results for step 3423 to test_path/special_results.parquet\n",
       "3423\n",
+      "model_vars.get('Gini', 0)[-1]=0.704\n",
+      "Condition met. Appended special results for step 3424 to test_path/special_results.parquet\n",
       "3424\n",
+      "model_vars.get('Gini', 0)[-1]=0.7238\n",
+      "Condition met. Appended special results for step 3425 to test_path/special_results.parquet\n",
       "3425\n",
+      "model_vars.get('Gini', 0)[-1]=0.741\n",
+      "Condition met. Appended special results for step 3426 to test_path/special_results.parquet\n",
       "3426\n",
+      "model_vars.get('Gini', 0)[-1]=0.7366\n",
+      "Condition met. Appended special results for step 3427 to test_path/special_results.parquet\n",
       "3427\n",
+      "model_vars.get('Gini', 0)[-1]=0.7206\n",
+      "Condition met. Appended special results for step 3428 to test_path/special_results.parquet\n",
       "3428\n",
+      "model_vars.get('Gini', 0)[-1]=0.712\n",
+      "Condition met. Appended special results for step 3429 to test_path/special_results.parquet\n",
       "3429\n",
+      "model_vars.get('Gini', 0)[-1]=0.6616\n",
+      "Condition not met at step 3430. No data to save.\n",
       "3430\n",
+      "model_vars.get('Gini', 0)[-1]=0.6634\n",
+      "Condition not met at step 3431. No data to save.\n",
       "3431\n",
+      "model_vars.get('Gini', 0)[-1]=0.6712\n",
+      "Condition not met at step 3432. No data to save.\n",
       "3432\n",
+      "model_vars.get('Gini', 0)[-1]=0.7052\n",
+      "Condition met. Appended special results for step 3433 to test_path/special_results.parquet\n",
       "3433\n",
+      "model_vars.get('Gini', 0)[-1]=0.6934\n",
+      "Condition not met at step 3434. No data to save.\n",
       "3434\n",
+      "model_vars.get('Gini', 0)[-1]=0.7070000000000001\n",
+      "Condition met. Appended special results for step 3435 to test_path/special_results.parquet\n",
       "3435\n",
+      "model_vars.get('Gini', 0)[-1]=0.6981999999999999\n",
+      "Condition not met at step 3436. No data to save.\n",
       "3436\n",
+      "model_vars.get('Gini', 0)[-1]=0.7098\n",
+      "Condition met. Appended special results for step 3437 to test_path/special_results.parquet\n",
       "3437\n",
+      "model_vars.get('Gini', 0)[-1]=0.712\n",
+      "Condition met. Appended special results for step 3438 to test_path/special_results.parquet\n",
       "3438\n",
+      "model_vars.get('Gini', 0)[-1]=0.739\n",
+      "Condition met. Appended special results for step 3439 to test_path/special_results.parquet\n",
       "3439\n",
+      "model_vars.get('Gini', 0)[-1]=0.7592\n",
+      "Condition met. Appended special results for step 3440 to test_path/special_results.parquet\n",
       "3440\n",
+      "model_vars.get('Gini', 0)[-1]=0.7752\n",
+      "Condition met. Appended special results for step 3441 to test_path/special_results.parquet\n",
       "3441\n",
+      "model_vars.get('Gini', 0)[-1]=0.7854\n",
+      "Condition met. Appended special results for step 3442 to test_path/special_results.parquet\n",
       "3442\n",
+      "model_vars.get('Gini', 0)[-1]=0.7886\n",
+      "Condition met. Appended special results for step 3443 to test_path/special_results.parquet\n",
       "3443\n",
+      "model_vars.get('Gini', 0)[-1]=0.7818\n",
+      "Condition met. Appended special results for step 3444 to test_path/special_results.parquet\n",
       "3444\n",
+      "model_vars.get('Gini', 0)[-1]=0.766\n",
+      "Condition met. Appended special results for step 3445 to test_path/special_results.parquet\n",
       "3445\n",
+      "model_vars.get('Gini', 0)[-1]=0.7464\n",
+      "Condition met. Appended special results for step 3446 to test_path/special_results.parquet\n",
       "3446\n",
+      "model_vars.get('Gini', 0)[-1]=0.747\n",
+      "Condition met. Appended special results for step 3447 to test_path/special_results.parquet\n",
       "3447\n",
+      "model_vars.get('Gini', 0)[-1]=0.7323999999999999\n",
+      "Condition met. Appended special results for step 3448 to test_path/special_results.parquet\n",
       "3448\n",
+      "model_vars.get('Gini', 0)[-1]=0.74\n",
+      "Condition met. Appended special results for step 3449 to test_path/special_results.parquet\n",
       "3449\n",
+      "model_vars.get('Gini', 0)[-1]=0.7296\n",
+      "Condition met. Appended special results for step 3450 to test_path/special_results.parquet\n",
       "3450\n",
+      "model_vars.get('Gini', 0)[-1]=0.722\n",
+      "Condition met. Appended special results for step 3451 to test_path/special_results.parquet\n",
       "3451\n",
+      "model_vars.get('Gini', 0)[-1]=0.7150000000000001\n",
+      "Condition met. Appended special results for step 3452 to test_path/special_results.parquet\n",
       "3452\n",
+      "model_vars.get('Gini', 0)[-1]=0.708\n",
+      "Condition met. Appended special results for step 3453 to test_path/special_results.parquet\n",
       "3453\n",
+      "model_vars.get('Gini', 0)[-1]=0.7070000000000001\n",
+      "Condition met. Appended special results for step 3454 to test_path/special_results.parquet\n",
       "3454\n",
+      "model_vars.get('Gini', 0)[-1]=0.6778\n",
+      "Condition not met at step 3455. No data to save.\n",
       "3455\n",
+      "model_vars.get('Gini', 0)[-1]=0.677\n",
+      "Condition not met at step 3456. No data to save.\n",
       "3456\n",
+      "model_vars.get('Gini', 0)[-1]=0.6646000000000001\n",
+      "Condition not met at step 3457. No data to save.\n",
       "3457\n",
+      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
+      "Condition not met at step 3458. No data to save.\n",
       "3458\n",
+      "model_vars.get('Gini', 0)[-1]=0.6868000000000001\n",
+      "Condition not met at step 3459. No data to save.\n",
       "3459\n",
+      "model_vars.get('Gini', 0)[-1]=0.6961999999999999\n",
+      "Condition not met at step 3460. No data to save.\n",
       "3460\n",
+      "model_vars.get('Gini', 0)[-1]=0.6718\n",
+      "Condition not met at step 3461. No data to save.\n",
       "3461\n",
+      "model_vars.get('Gini', 0)[-1]=0.6312\n",
+      "Condition not met at step 3462. No data to save.\n",
       "3462\n",
+      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
+      "Condition not met at step 3463. No data to save.\n",
       "3463\n",
+      "model_vars.get('Gini', 0)[-1]=0.658\n",
+      "Condition not met at step 3464. No data to save.\n",
       "3464\n",
+      "model_vars.get('Gini', 0)[-1]=0.6872\n",
+      "Condition not met at step 3465. No data to save.\n",
       "3465\n",
+      "model_vars.get('Gini', 0)[-1]=0.6896\n",
+      "Condition not met at step 3466. No data to save.\n",
       "3466\n",
+      "model_vars.get('Gini', 0)[-1]=0.7096\n",
+      "Condition met. Appended special results for step 3467 to test_path/special_results.parquet\n",
       "3467\n",
+      "model_vars.get('Gini', 0)[-1]=0.6986\n",
+      "Condition not met at step 3468. No data to save.\n",
       "3468\n",
+      "model_vars.get('Gini', 0)[-1]=0.6708000000000001\n",
+      "Condition not met at step 3469. No data to save.\n",
       "3469\n",
+      "model_vars.get('Gini', 0)[-1]=0.6946\n",
+      "Condition not met at step 3470. No data to save.\n",
       "3470\n",
+      "model_vars.get('Gini', 0)[-1]=0.6826\n",
+      "Condition not met at step 3471. No data to save.\n",
       "3471\n",
+      "model_vars.get('Gini', 0)[-1]=0.6758\n",
+      "Condition not met at step 3472. No data to save.\n",
       "3472\n",
+      "model_vars.get('Gini', 0)[-1]=0.6632\n",
+      "Condition not met at step 3473. No data to save.\n",
       "3473\n",
+      "model_vars.get('Gini', 0)[-1]=0.6537999999999999\n",
+      "Condition not met at step 3474. No data to save.\n",
       "3474\n",
+      "model_vars.get('Gini', 0)[-1]=0.6548\n",
+      "Condition not met at step 3475. No data to save.\n",
       "3475\n",
+      "model_vars.get('Gini', 0)[-1]=0.6754\n",
+      "Condition not met at step 3476. No data to save.\n",
       "3476\n",
+      "model_vars.get('Gini', 0)[-1]=0.6834\n",
+      "Condition not met at step 3477. No data to save.\n",
       "3477\n",
+      "model_vars.get('Gini', 0)[-1]=0.6814\n",
+      "Condition not met at step 3478. No data to save.\n",
       "3478\n",
+      "model_vars.get('Gini', 0)[-1]=0.6832\n",
+      "Condition not met at step 3479. No data to save.\n",
       "3479\n",
+      "model_vars.get('Gini', 0)[-1]=0.7004\n",
+      "Condition met. Appended special results for step 3480 to test_path/special_results.parquet\n",
       "3480\n",
+      "model_vars.get('Gini', 0)[-1]=0.6819999999999999\n",
+      "Condition not met at step 3481. No data to save.\n",
       "3481\n",
+      "model_vars.get('Gini', 0)[-1]=0.6782\n",
+      "Condition not met at step 3482. No data to save.\n",
       "3482\n",
+      "model_vars.get('Gini', 0)[-1]=0.6556\n",
+      "Condition not met at step 3483. No data to save.\n",
       "3483\n",
+      "model_vars.get('Gini', 0)[-1]=0.6712\n",
+      "Condition not met at step 3484. No data to save.\n",
       "3484\n",
+      "model_vars.get('Gini', 0)[-1]=0.6768000000000001\n",
+      "Condition not met at step 3485. No data to save.\n",
       "3485\n",
+      "model_vars.get('Gini', 0)[-1]=0.6824\n",
+      "Condition not met at step 3486. No data to save.\n",
       "3486\n",
+      "model_vars.get('Gini', 0)[-1]=0.6828000000000001\n",
+      "Condition not met at step 3487. No data to save.\n",
       "3487\n",
+      "model_vars.get('Gini', 0)[-1]=0.679\n",
+      "Condition not met at step 3488. No data to save.\n",
       "3488\n",
+      "model_vars.get('Gini', 0)[-1]=0.6762\n",
+      "Condition not met at step 3489. No data to save.\n",
       "3489\n",
+      "model_vars.get('Gini', 0)[-1]=0.6699999999999999\n",
+      "Condition not met at step 3490. No data to save.\n",
       "3490\n",
+      "model_vars.get('Gini', 0)[-1]=0.6846\n",
+      "Condition not met at step 3491. No data to save.\n",
       "3491\n",
+      "model_vars.get('Gini', 0)[-1]=0.6794\n",
+      "Condition not met at step 3492. No data to save.\n",
       "3492\n",
+      "model_vars.get('Gini', 0)[-1]=0.6762\n",
+      "Condition not met at step 3493. No data to save.\n",
       "3493\n",
+      "model_vars.get('Gini', 0)[-1]=0.692\n",
+      "Condition not met at step 3494. No data to save.\n",
       "3494\n",
+      "model_vars.get('Gini', 0)[-1]=0.673\n",
+      "Condition not met at step 3495. No data to save.\n",
       "3495\n",
+      "model_vars.get('Gini', 0)[-1]=0.6898\n",
+      "Condition not met at step 3496. No data to save.\n",
       "3496\n",
+      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
+      "Condition not met at step 3497. No data to save.\n",
       "3497\n",
+      "model_vars.get('Gini', 0)[-1]=0.6576\n",
+      "Condition not met at step 3498. No data to save.\n",
       "3498\n",
+      "model_vars.get('Gini', 0)[-1]=0.6604\n",
+      "Condition not met at step 3499. No data to save.\n",
       "3499\n",
       "CALLED\n",
       "self.model._steps=3500\n",
       " TEST self.model._steps=3500\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "3400  0.6644\n",
-      "3401  0.6556\n",
-      "3402  0.6176\n",
-      "3403  0.6558\n",
-      "3404  0.6584\n",
+      "3400  0.6626\n",
+      "3401  0.6538\n",
+      "3402  0.6572\n",
+      "3403  0.6492\n",
+      "3404  0.6386\n",
       "...      ...\n",
-      "3495  0.6556\n",
-      "3496  0.6556\n",
-      "3497  0.6476\n",
-      "3498  0.6894\n",
-      "3499  0.7044\n",
+      "3495  0.6898\n",
+      "3496  0.6606\n",
+      "3497  0.6576\n",
+      "3498  0.6604\n",
+      "3499  0.6790\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_035.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_035.parquet'\n",
-      "Saving model to output_dir/model_data_035.parquet\n",
-      "Saving agent to output_dir/agent_data_035.parquet\n",
+      "model_file='test_path/model_data_035.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_035.parquet'\n",
+      "Saving model to test_path/model_data_035.parquet\n",
+      "Saving agent to test_path/agent_data_035.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.679\n",
+      "Condition not met at step 3500. No data to save.\n",
       "3500\n",
+      "model_vars.get('Gini', 0)[-1]=0.7090000000000001\n",
+      "Condition met. Appended special results for step 3501 to test_path/special_results.parquet\n",
       "3501\n",
+      "model_vars.get('Gini', 0)[-1]=0.7061999999999999\n",
+      "Condition met. Appended special results for step 3502 to test_path/special_results.parquet\n",
       "3502\n",
+      "model_vars.get('Gini', 0)[-1]=0.712\n",
+      "Condition met. Appended special results for step 3503 to test_path/special_results.parquet\n",
       "3503\n",
+      "model_vars.get('Gini', 0)[-1]=0.6938\n",
+      "Condition not met at step 3504. No data to save.\n",
       "3504\n",
+      "model_vars.get('Gini', 0)[-1]=0.7054\n",
+      "Condition met. Appended special results for step 3505 to test_path/special_results.parquet\n",
       "3505\n",
+      "model_vars.get('Gini', 0)[-1]=0.6921999999999999\n",
+      "Condition not met at step 3506. No data to save.\n",
       "3506\n",
+      "model_vars.get('Gini', 0)[-1]=0.6912\n",
+      "Condition not met at step 3507. No data to save.\n",
       "3507\n",
+      "model_vars.get('Gini', 0)[-1]=0.6868000000000001\n",
+      "Condition not met at step 3508. No data to save.\n",
       "3508\n",
+      "model_vars.get('Gini', 0)[-1]=0.6748000000000001\n",
+      "Condition not met at step 3509. No data to save.\n",
       "3509\n",
+      "model_vars.get('Gini', 0)[-1]=0.6448\n",
+      "Condition not met at step 3510. No data to save.\n",
       "3510\n",
+      "model_vars.get('Gini', 0)[-1]=0.646\n",
+      "Condition not met at step 3511. No data to save.\n",
       "3511\n",
+      "model_vars.get('Gini', 0)[-1]=0.6716\n",
+      "Condition not met at step 3512. No data to save.\n",
       "3512\n",
+      "model_vars.get('Gini', 0)[-1]=0.649\n",
+      "Condition not met at step 3513. No data to save.\n",
       "3513\n",
+      "model_vars.get('Gini', 0)[-1]=0.667\n",
+      "Condition not met at step 3514. No data to save.\n",
       "3514\n",
+      "model_vars.get('Gini', 0)[-1]=0.6646000000000001\n",
+      "Condition not met at step 3515. No data to save.\n",
       "3515\n",
+      "model_vars.get('Gini', 0)[-1]=0.677\n",
+      "Condition not met at step 3516. No data to save.\n",
       "3516\n",
+      "model_vars.get('Gini', 0)[-1]=0.6684\n",
+      "Condition not met at step 3517. No data to save.\n",
       "3517\n",
+      "model_vars.get('Gini', 0)[-1]=0.6596\n",
+      "Condition not met at step 3518. No data to save.\n",
       "3518\n",
+      "model_vars.get('Gini', 0)[-1]=0.6794\n",
+      "Condition not met at step 3519. No data to save.\n",
       "3519\n",
+      "model_vars.get('Gini', 0)[-1]=0.6537999999999999\n",
+      "Condition not met at step 3520. No data to save.\n",
       "3520\n",
+      "model_vars.get('Gini', 0)[-1]=0.675\n",
+      "Condition not met at step 3521. No data to save.\n",
       "3521\n",
+      "model_vars.get('Gini', 0)[-1]=0.7012\n",
+      "Condition met. Appended special results for step 3522 to test_path/special_results.parquet\n",
       "3522\n",
+      "model_vars.get('Gini', 0)[-1]=0.7021999999999999\n",
+      "Condition met. Appended special results for step 3523 to test_path/special_results.parquet\n",
       "3523\n",
+      "model_vars.get('Gini', 0)[-1]=0.7101999999999999\n",
+      "Condition met. Appended special results for step 3524 to test_path/special_results.parquet\n",
       "3524\n",
+      "model_vars.get('Gini', 0)[-1]=0.6906\n",
+      "Condition not met at step 3525. No data to save.\n",
       "3525\n",
+      "model_vars.get('Gini', 0)[-1]=0.6958\n",
+      "Condition not met at step 3526. No data to save.\n",
       "3526\n",
+      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
+      "Condition not met at step 3527. No data to save.\n",
       "3527\n",
+      "model_vars.get('Gini', 0)[-1]=0.6812\n",
+      "Condition not met at step 3528. No data to save.\n",
       "3528\n",
+      "model_vars.get('Gini', 0)[-1]=0.6378\n",
+      "Condition not met at step 3529. No data to save.\n",
       "3529\n",
+      "model_vars.get('Gini', 0)[-1]=0.652\n",
+      "Condition not met at step 3530. No data to save.\n",
       "3530\n",
+      "model_vars.get('Gini', 0)[-1]=0.6168\n",
+      "Condition not met at step 3531. No data to save.\n",
       "3531\n",
+      "model_vars.get('Gini', 0)[-1]=0.621\n",
+      "Condition not met at step 3532. No data to save.\n",
       "3532\n",
+      "model_vars.get('Gini', 0)[-1]=0.6174\n",
+      "Condition not met at step 3533. No data to save.\n",
       "3533\n",
+      "model_vars.get('Gini', 0)[-1]=0.6246\n",
+      "Condition not met at step 3534. No data to save.\n",
       "3534\n",
+      "model_vars.get('Gini', 0)[-1]=0.5993999999999999\n",
+      "Condition not met at step 3535. No data to save.\n",
       "3535\n",
+      "model_vars.get('Gini', 0)[-1]=0.6208\n",
+      "Condition not met at step 3536. No data to save.\n",
       "3536\n",
+      "model_vars.get('Gini', 0)[-1]=0.652\n",
+      "Condition not met at step 3537. No data to save.\n",
       "3537\n",
+      "model_vars.get('Gini', 0)[-1]=0.637\n",
+      "Condition not met at step 3538. No data to save.\n",
       "3538\n",
+      "model_vars.get('Gini', 0)[-1]=0.6534\n",
+      "Condition not met at step 3539. No data to save.\n",
       "3539\n",
+      "model_vars.get('Gini', 0)[-1]=0.6577999999999999\n",
+      "Condition not met at step 3540. No data to save.\n",
       "3540\n",
+      "model_vars.get('Gini', 0)[-1]=0.6396\n",
+      "Condition not met at step 3541. No data to save.\n",
       "3541\n",
+      "model_vars.get('Gini', 0)[-1]=0.6496\n",
+      "Condition not met at step 3542. No data to save.\n",
       "3542\n",
+      "model_vars.get('Gini', 0)[-1]=0.6654\n",
+      "Condition not met at step 3543. No data to save.\n",
       "3543\n",
+      "model_vars.get('Gini', 0)[-1]=0.6604\n",
+      "Condition not met at step 3544. No data to save.\n",
       "3544\n",
+      "model_vars.get('Gini', 0)[-1]=0.6774\n",
+      "Condition not met at step 3545. No data to save.\n",
       "3545\n",
+      "model_vars.get('Gini', 0)[-1]=0.6872\n",
+      "Condition not met at step 3546. No data to save.\n",
       "3546\n",
+      "model_vars.get('Gini', 0)[-1]=0.6930000000000001\n",
+      "Condition not met at step 3547. No data to save.\n",
       "3547\n",
+      "model_vars.get('Gini', 0)[-1]=0.694\n",
+      "Condition not met at step 3548. No data to save.\n",
       "3548\n",
+      "model_vars.get('Gini', 0)[-1]=0.6908000000000001\n",
+      "Condition not met at step 3549. No data to save.\n",
       "3549\n",
+      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
+      "Condition not met at step 3550. No data to save.\n",
       "3550\n",
+      "model_vars.get('Gini', 0)[-1]=0.663\n",
+      "Condition not met at step 3551. No data to save.\n",
       "3551\n",
+      "model_vars.get('Gini', 0)[-1]=0.6686000000000001\n",
+      "Condition not met at step 3552. No data to save.\n",
       "3552\n",
+      "model_vars.get('Gini', 0)[-1]=0.6372\n",
+      "Condition not met at step 3553. No data to save.\n",
       "3553\n",
+      "model_vars.get('Gini', 0)[-1]=0.6682\n",
+      "Condition not met at step 3554. No data to save.\n",
       "3554\n",
+      "model_vars.get('Gini', 0)[-1]=0.6588\n",
+      "Condition not met at step 3555. No data to save.\n",
       "3555\n",
+      "model_vars.get('Gini', 0)[-1]=0.6404000000000001\n",
+      "Condition not met at step 3556. No data to save.\n",
       "3556\n",
+      "model_vars.get('Gini', 0)[-1]=0.6308\n",
+      "Condition not met at step 3557. No data to save.\n",
       "3557\n",
+      "model_vars.get('Gini', 0)[-1]=0.6517999999999999\n",
+      "Condition not met at step 3558. No data to save.\n",
       "3558\n",
+      "model_vars.get('Gini', 0)[-1]=0.6562\n",
+      "Condition not met at step 3559. No data to save.\n",
       "3559\n",
+      "model_vars.get('Gini', 0)[-1]=0.6664\n",
+      "Condition not met at step 3560. No data to save.\n",
       "3560\n",
+      "model_vars.get('Gini', 0)[-1]=0.6517999999999999\n",
+      "Condition not met at step 3561. No data to save.\n",
       "3561\n",
+      "model_vars.get('Gini', 0)[-1]=0.6642\n",
+      "Condition not met at step 3562. No data to save.\n",
       "3562\n",
+      "model_vars.get('Gini', 0)[-1]=0.6658\n",
+      "Condition not met at step 3563. No data to save.\n",
       "3563\n",
+      "model_vars.get('Gini', 0)[-1]=0.6548\n",
+      "Condition not met at step 3564. No data to save.\n",
       "3564\n",
+      "model_vars.get('Gini', 0)[-1]=0.6698\n",
+      "Condition not met at step 3565. No data to save.\n",
       "3565\n",
+      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
+      "Condition not met at step 3566. No data to save.\n",
       "3566\n",
+      "model_vars.get('Gini', 0)[-1]=0.6774\n",
+      "Condition not met at step 3567. No data to save.\n",
       "3567\n",
+      "model_vars.get('Gini', 0)[-1]=0.6926\n",
+      "Condition not met at step 3568. No data to save.\n",
       "3568\n",
+      "model_vars.get('Gini', 0)[-1]=0.7004\n",
+      "Condition met. Appended special results for step 3569 to test_path/special_results.parquet\n",
       "3569\n",
+      "model_vars.get('Gini', 0)[-1]=0.6864\n",
+      "Condition not met at step 3570. No data to save.\n",
       "3570\n",
+      "model_vars.get('Gini', 0)[-1]=0.6912\n",
+      "Condition not met at step 3571. No data to save.\n",
       "3571\n",
+      "model_vars.get('Gini', 0)[-1]=0.6738\n",
+      "Condition not met at step 3572. No data to save.\n",
       "3572\n",
+      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
+      "Condition not met at step 3573. No data to save.\n",
       "3573\n",
+      "model_vars.get('Gini', 0)[-1]=0.6746000000000001\n",
+      "Condition not met at step 3574. No data to save.\n",
       "3574\n",
+      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
+      "Condition not met at step 3575. No data to save.\n",
       "3575\n",
+      "model_vars.get('Gini', 0)[-1]=0.6496\n",
+      "Condition not met at step 3576. No data to save.\n",
       "3576\n",
+      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
+      "Condition not met at step 3577. No data to save.\n",
       "3577\n",
+      "model_vars.get('Gini', 0)[-1]=0.6444000000000001\n",
+      "Condition not met at step 3578. No data to save.\n",
       "3578\n",
+      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
+      "Condition not met at step 3579. No data to save.\n",
       "3579\n",
+      "model_vars.get('Gini', 0)[-1]=0.6692\n",
+      "Condition not met at step 3580. No data to save.\n",
       "3580\n",
+      "model_vars.get('Gini', 0)[-1]=0.6876\n",
+      "Condition not met at step 3581. No data to save.\n",
       "3581\n",
+      "model_vars.get('Gini', 0)[-1]=0.6932\n",
+      "Condition not met at step 3582. No data to save.\n",
       "3582\n",
+      "model_vars.get('Gini', 0)[-1]=0.6836\n",
+      "Condition not met at step 3583. No data to save.\n",
       "3583\n",
+      "model_vars.get('Gini', 0)[-1]=0.687\n",
+      "Condition not met at step 3584. No data to save.\n",
       "3584\n",
+      "model_vars.get('Gini', 0)[-1]=0.6912\n",
+      "Condition not met at step 3585. No data to save.\n",
       "3585\n",
+      "model_vars.get('Gini', 0)[-1]=0.6924\n",
+      "Condition not met at step 3586. No data to save.\n",
       "3586\n",
+      "model_vars.get('Gini', 0)[-1]=0.671\n",
+      "Condition not met at step 3587. No data to save.\n",
       "3587\n",
+      "model_vars.get('Gini', 0)[-1]=0.673\n",
+      "Condition not met at step 3588. No data to save.\n",
       "3588\n",
+      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
+      "Condition not met at step 3589. No data to save.\n",
       "3589\n",
+      "model_vars.get('Gini', 0)[-1]=0.64\n",
+      "Condition not met at step 3590. No data to save.\n",
       "3590\n",
+      "model_vars.get('Gini', 0)[-1]=0.635\n",
+      "Condition not met at step 3591. No data to save.\n",
       "3591\n",
+      "model_vars.get('Gini', 0)[-1]=0.642\n",
+      "Condition not met at step 3592. No data to save.\n",
       "3592\n",
+      "model_vars.get('Gini', 0)[-1]=0.6202000000000001\n",
+      "Condition not met at step 3593. No data to save.\n",
       "3593\n",
+      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
+      "Condition not met at step 3594. No data to save.\n",
       "3594\n",
+      "model_vars.get('Gini', 0)[-1]=0.6454\n",
+      "Condition not met at step 3595. No data to save.\n",
       "3595\n",
+      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
+      "Condition not met at step 3596. No data to save.\n",
       "3596\n",
+      "model_vars.get('Gini', 0)[-1]=0.673\n",
+      "Condition not met at step 3597. No data to save.\n",
       "3597\n",
+      "model_vars.get('Gini', 0)[-1]=0.6938\n",
+      "Condition not met at step 3598. No data to save.\n",
       "3598\n",
+      "model_vars.get('Gini', 0)[-1]=0.677\n",
+      "Condition not met at step 3599. No data to save.\n",
       "3599\n",
       "CALLED\n",
       "self.model._steps=3600\n",
       " TEST self.model._steps=3600\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "3500  0.6840\n",
-      "3501  0.6700\n",
-      "3502  0.6556\n",
-      "3503  0.6634\n",
-      "3504  0.6734\n",
+      "3500  0.7090\n",
+      "3501  0.7062\n",
+      "3502  0.7120\n",
+      "3503  0.6938\n",
+      "3504  0.7054\n",
       "...      ...\n",
-      "3595  0.6882\n",
-      "3596  0.6588\n",
-      "3597  0.6494\n",
-      "3598  0.6612\n",
-      "3599  0.6488\n",
+      "3595  0.6466\n",
+      "3596  0.6730\n",
+      "3597  0.6938\n",
+      "3598  0.6770\n",
+      "3599  0.6718\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_036.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_036.parquet'\n",
-      "Saving model to output_dir/model_data_036.parquet\n",
-      "Saving agent to output_dir/agent_data_036.parquet\n",
+      "model_file='test_path/model_data_036.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_036.parquet'\n",
+      "Saving model to test_path/model_data_036.parquet\n",
+      "Saving agent to test_path/agent_data_036.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6718\n",
+      "Condition not met at step 3600. No data to save.\n",
       "3600\n",
+      "model_vars.get('Gini', 0)[-1]=0.6856\n",
+      "Condition not met at step 3601. No data to save.\n",
       "3601\n",
+      "model_vars.get('Gini', 0)[-1]=0.677\n",
+      "Condition not met at step 3602. No data to save.\n",
       "3602\n",
+      "model_vars.get('Gini', 0)[-1]=0.6754\n",
+      "Condition not met at step 3603. No data to save.\n",
       "3603\n",
+      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
+      "Condition not met at step 3604. No data to save.\n",
       "3604\n",
+      "model_vars.get('Gini', 0)[-1]=0.6642\n",
+      "Condition not met at step 3605. No data to save.\n",
       "3605\n",
+      "model_vars.get('Gini', 0)[-1]=0.6674\n",
+      "Condition not met at step 3606. No data to save.\n",
       "3606\n",
+      "model_vars.get('Gini', 0)[-1]=0.6794\n",
+      "Condition not met at step 3607. No data to save.\n",
       "3607\n",
+      "model_vars.get('Gini', 0)[-1]=0.6832\n",
+      "Condition not met at step 3608. No data to save.\n",
       "3608\n",
+      "model_vars.get('Gini', 0)[-1]=0.667\n",
+      "Condition not met at step 3609. No data to save.\n",
       "3609\n",
+      "model_vars.get('Gini', 0)[-1]=0.6814\n",
+      "Condition not met at step 3610. No data to save.\n",
       "3610\n",
+      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
+      "Condition not met at step 3611. No data to save.\n",
       "3611\n",
+      "model_vars.get('Gini', 0)[-1]=0.6839999999999999\n",
+      "Condition not met at step 3612. No data to save.\n",
       "3612\n",
+      "model_vars.get('Gini', 0)[-1]=0.6828000000000001\n",
+      "Condition not met at step 3613. No data to save.\n",
       "3613\n",
+      "model_vars.get('Gini', 0)[-1]=0.6712\n",
+      "Condition not met at step 3614. No data to save.\n",
       "3614\n",
+      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
+      "Condition not met at step 3615. No data to save.\n",
       "3615\n",
+      "model_vars.get('Gini', 0)[-1]=0.666\n",
+      "Condition not met at step 3616. No data to save.\n",
       "3616\n",
+      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
+      "Condition not met at step 3617. No data to save.\n",
       "3617\n",
+      "model_vars.get('Gini', 0)[-1]=0.6398\n",
+      "Condition not met at step 3618. No data to save.\n",
       "3618\n",
+      "model_vars.get('Gini', 0)[-1]=0.6674\n",
+      "Condition not met at step 3619. No data to save.\n",
       "3619\n",
+      "model_vars.get('Gini', 0)[-1]=0.6622\n",
+      "Condition not met at step 3620. No data to save.\n",
       "3620\n",
+      "model_vars.get('Gini', 0)[-1]=0.6517999999999999\n",
+      "Condition not met at step 3621. No data to save.\n",
       "3621\n",
+      "model_vars.get('Gini', 0)[-1]=0.6352\n",
+      "Condition not met at step 3622. No data to save.\n",
       "3622\n",
+      "model_vars.get('Gini', 0)[-1]=0.6122000000000001\n",
+      "Condition not met at step 3623. No data to save.\n",
       "3623\n",
+      "model_vars.get('Gini', 0)[-1]=0.6384000000000001\n",
+      "Condition not met at step 3624. No data to save.\n",
       "3624\n",
+      "model_vars.get('Gini', 0)[-1]=0.6444000000000001\n",
+      "Condition not met at step 3625. No data to save.\n",
       "3625\n",
+      "model_vars.get('Gini', 0)[-1]=0.6512\n",
+      "Condition not met at step 3626. No data to save.\n",
       "3626\n",
+      "model_vars.get('Gini', 0)[-1]=0.6334\n",
+      "Condition not met at step 3627. No data to save.\n",
       "3627\n",
+      "model_vars.get('Gini', 0)[-1]=0.622\n",
+      "Condition not met at step 3628. No data to save.\n",
       "3628\n",
+      "model_vars.get('Gini', 0)[-1]=0.657\n",
+      "Condition not met at step 3629. No data to save.\n",
       "3629\n",
+      "model_vars.get('Gini', 0)[-1]=0.6544\n",
+      "Condition not met at step 3630. No data to save.\n",
       "3630\n",
+      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
+      "Condition not met at step 3631. No data to save.\n",
       "3631\n",
+      "model_vars.get('Gini', 0)[-1]=0.6312\n",
+      "Condition not met at step 3632. No data to save.\n",
       "3632\n",
+      "model_vars.get('Gini', 0)[-1]=0.6434\n",
+      "Condition not met at step 3633. No data to save.\n",
       "3633\n",
+      "model_vars.get('Gini', 0)[-1]=0.6332\n",
+      "Condition not met at step 3634. No data to save.\n",
       "3634\n",
+      "model_vars.get('Gini', 0)[-1]=0.6338\n",
+      "Condition not met at step 3635. No data to save.\n",
       "3635\n",
+      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
+      "Condition not met at step 3636. No data to save.\n",
       "3636\n",
+      "model_vars.get('Gini', 0)[-1]=0.6536\n",
+      "Condition not met at step 3637. No data to save.\n",
       "3637\n",
+      "model_vars.get('Gini', 0)[-1]=0.6642\n",
+      "Condition not met at step 3638. No data to save.\n",
       "3638\n",
+      "model_vars.get('Gini', 0)[-1]=0.6617999999999999\n",
+      "Condition not met at step 3639. No data to save.\n",
       "3639\n",
+      "model_vars.get('Gini', 0)[-1]=0.6726000000000001\n",
+      "Condition not met at step 3640. No data to save.\n",
       "3640\n",
+      "model_vars.get('Gini', 0)[-1]=0.6708000000000001\n",
+      "Condition not met at step 3641. No data to save.\n",
       "3641\n",
+      "model_vars.get('Gini', 0)[-1]=0.6932\n",
+      "Condition not met at step 3642. No data to save.\n",
       "3642\n",
+      "model_vars.get('Gini', 0)[-1]=0.6998\n",
+      "Condition not met at step 3643. No data to save.\n",
       "3643\n",
+      "model_vars.get('Gini', 0)[-1]=0.6768000000000001\n",
+      "Condition not met at step 3644. No data to save.\n",
       "3644\n",
+      "model_vars.get('Gini', 0)[-1]=0.6792\n",
+      "Condition not met at step 3645. No data to save.\n",
       "3645\n",
+      "model_vars.get('Gini', 0)[-1]=0.6698\n",
+      "Condition not met at step 3646. No data to save.\n",
       "3646\n",
+      "model_vars.get('Gini', 0)[-1]=0.673\n",
+      "Condition not met at step 3647. No data to save.\n",
       "3647\n",
+      "model_vars.get('Gini', 0)[-1]=0.6666000000000001\n",
+      "Condition not met at step 3648. No data to save.\n",
       "3648\n",
+      "model_vars.get('Gini', 0)[-1]=0.658\n",
+      "Condition not met at step 3649. No data to save.\n",
       "3649\n",
+      "model_vars.get('Gini', 0)[-1]=0.6719999999999999\n",
+      "Condition not met at step 3650. No data to save.\n",
       "3650\n",
+      "model_vars.get('Gini', 0)[-1]=0.6972\n",
+      "Condition not met at step 3651. No data to save.\n",
       "3651\n",
+      "model_vars.get('Gini', 0)[-1]=0.6772\n",
+      "Condition not met at step 3652. No data to save.\n",
       "3652\n",
+      "model_vars.get('Gini', 0)[-1]=0.6842\n",
+      "Condition not met at step 3653. No data to save.\n",
       "3653\n",
+      "model_vars.get('Gini', 0)[-1]=0.6872\n",
+      "Condition not met at step 3654. No data to save.\n",
       "3654\n",
+      "model_vars.get('Gini', 0)[-1]=0.6896\n",
+      "Condition not met at step 3655. No data to save.\n",
       "3655\n",
+      "model_vars.get('Gini', 0)[-1]=0.679\n",
+      "Condition not met at step 3656. No data to save.\n",
       "3656\n",
+      "model_vars.get('Gini', 0)[-1]=0.6572\n",
+      "Condition not met at step 3657. No data to save.\n",
       "3657\n",
+      "model_vars.get('Gini', 0)[-1]=0.645\n",
+      "Condition not met at step 3658. No data to save.\n",
       "3658\n",
+      "model_vars.get('Gini', 0)[-1]=0.6355999999999999\n",
+      "Condition not met at step 3659. No data to save.\n",
       "3659\n",
+      "model_vars.get('Gini', 0)[-1]=0.6517999999999999\n",
+      "Condition not met at step 3660. No data to save.\n",
       "3660\n",
+      "model_vars.get('Gini', 0)[-1]=0.6759999999999999\n",
+      "Condition not met at step 3661. No data to save.\n",
       "3661\n",
+      "model_vars.get('Gini', 0)[-1]=0.6864\n",
+      "Condition not met at step 3662. No data to save.\n",
       "3662\n",
+      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
+      "Condition not met at step 3663. No data to save.\n",
       "3663\n",
+      "model_vars.get('Gini', 0)[-1]=0.696\n",
+      "Condition not met at step 3664. No data to save.\n",
       "3664\n",
+      "model_vars.get('Gini', 0)[-1]=0.6682\n",
+      "Condition not met at step 3665. No data to save.\n",
       "3665\n",
+      "model_vars.get('Gini', 0)[-1]=0.679\n",
+      "Condition not met at step 3666. No data to save.\n",
       "3666\n",
+      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
+      "Condition not met at step 3667. No data to save.\n",
       "3667\n",
+      "model_vars.get('Gini', 0)[-1]=0.662\n",
+      "Condition not met at step 3668. No data to save.\n",
       "3668\n",
+      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
+      "Condition not met at step 3669. No data to save.\n",
       "3669\n",
+      "model_vars.get('Gini', 0)[-1]=0.6792\n",
+      "Condition not met at step 3670. No data to save.\n",
       "3670\n",
+      "model_vars.get('Gini', 0)[-1]=0.6254\n",
+      "Condition not met at step 3671. No data to save.\n",
       "3671\n",
+      "model_vars.get('Gini', 0)[-1]=0.6212\n",
+      "Condition not met at step 3672. No data to save.\n",
       "3672\n",
+      "model_vars.get('Gini', 0)[-1]=0.632\n",
+      "Condition not met at step 3673. No data to save.\n",
       "3673\n",
+      "model_vars.get('Gini', 0)[-1]=0.649\n",
+      "Condition not met at step 3674. No data to save.\n",
       "3674\n",
+      "model_vars.get('Gini', 0)[-1]=0.6448\n",
+      "Condition not met at step 3675. No data to save.\n",
       "3675\n",
+      "model_vars.get('Gini', 0)[-1]=0.6318\n",
+      "Condition not met at step 3676. No data to save.\n",
       "3676\n",
+      "model_vars.get('Gini', 0)[-1]=0.6376\n",
+      "Condition not met at step 3677. No data to save.\n",
       "3677\n",
+      "model_vars.get('Gini', 0)[-1]=0.633\n",
+      "Condition not met at step 3678. No data to save.\n",
       "3678\n",
+      "model_vars.get('Gini', 0)[-1]=0.6656\n",
+      "Condition not met at step 3679. No data to save.\n",
       "3679\n",
+      "model_vars.get('Gini', 0)[-1]=0.6878\n",
+      "Condition not met at step 3680. No data to save.\n",
       "3680\n",
+      "model_vars.get('Gini', 0)[-1]=0.6872\n",
+      "Condition not met at step 3681. No data to save.\n",
       "3681\n",
+      "model_vars.get('Gini', 0)[-1]=0.7046\n",
+      "Condition met. Appended special results for step 3682 to test_path/special_results.parquet\n",
       "3682\n",
+      "model_vars.get('Gini', 0)[-1]=0.698\n",
+      "Condition not met at step 3683. No data to save.\n",
       "3683\n",
+      "model_vars.get('Gini', 0)[-1]=0.6752\n",
+      "Condition not met at step 3684. No data to save.\n",
       "3684\n",
+      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
+      "Condition not met at step 3685. No data to save.\n",
       "3685\n",
+      "model_vars.get('Gini', 0)[-1]=0.6902\n",
+      "Condition not met at step 3686. No data to save.\n",
       "3686\n",
+      "model_vars.get('Gini', 0)[-1]=0.6974\n",
+      "Condition not met at step 3687. No data to save.\n",
       "3687\n",
+      "model_vars.get('Gini', 0)[-1]=0.6928000000000001\n",
+      "Condition not met at step 3688. No data to save.\n",
       "3688\n",
+      "model_vars.get('Gini', 0)[-1]=0.6974\n",
+      "Condition not met at step 3689. No data to save.\n",
       "3689\n",
+      "model_vars.get('Gini', 0)[-1]=0.6779999999999999\n",
+      "Condition not met at step 3690. No data to save.\n",
       "3690\n",
+      "model_vars.get('Gini', 0)[-1]=0.6674\n",
+      "Condition not met at step 3691. No data to save.\n",
       "3691\n",
+      "model_vars.get('Gini', 0)[-1]=0.6718\n",
+      "Condition not met at step 3692. No data to save.\n",
       "3692\n",
+      "model_vars.get('Gini', 0)[-1]=0.664\n",
+      "Condition not met at step 3693. No data to save.\n",
       "3693\n",
+      "model_vars.get('Gini', 0)[-1]=0.6644\n",
+      "Condition not met at step 3694. No data to save.\n",
       "3694\n",
+      "model_vars.get('Gini', 0)[-1]=0.6624\n",
+      "Condition not met at step 3695. No data to save.\n",
       "3695\n",
+      "model_vars.get('Gini', 0)[-1]=0.6432\n",
+      "Condition not met at step 3696. No data to save.\n",
       "3696\n",
+      "model_vars.get('Gini', 0)[-1]=0.6644\n",
+      "Condition not met at step 3697. No data to save.\n",
       "3697\n",
+      "model_vars.get('Gini', 0)[-1]=0.6624\n",
+      "Condition not met at step 3698. No data to save.\n",
       "3698\n",
+      "model_vars.get('Gini', 0)[-1]=0.6758\n",
+      "Condition not met at step 3699. No data to save.\n",
       "3699\n",
       "CALLED\n",
       "self.model._steps=3700\n",
       " TEST self.model._steps=3700\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "3600  0.6346\n",
-      "3601  0.6464\n",
-      "3602  0.6606\n",
-      "3603  0.6786\n",
-      "3604  0.6776\n",
+      "3600  0.6856\n",
+      "3601  0.6770\n",
+      "3602  0.6754\n",
+      "3603  0.6688\n",
+      "3604  0.6642\n",
       "...      ...\n",
-      "3695  0.6574\n",
-      "3696  0.6392\n",
-      "3697  0.6462\n",
-      "3698  0.6488\n",
-      "3699  0.6564\n",
+      "3695  0.6432\n",
+      "3696  0.6644\n",
+      "3697  0.6624\n",
+      "3698  0.6758\n",
+      "3699  0.6884\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_037.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_037.parquet'\n",
-      "Saving model to output_dir/model_data_037.parquet\n",
-      "Saving agent to output_dir/agent_data_037.parquet\n",
+      "model_file='test_path/model_data_037.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_037.parquet'\n",
+      "Saving model to test_path/model_data_037.parquet\n",
+      "Saving agent to test_path/agent_data_037.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6884\n",
+      "Condition not met at step 3700. No data to save.\n",
       "3700\n",
+      "model_vars.get('Gini', 0)[-1]=0.6858\n",
+      "Condition not met at step 3701. No data to save.\n",
       "3701\n",
+      "model_vars.get('Gini', 0)[-1]=0.6748000000000001\n",
+      "Condition not met at step 3702. No data to save.\n",
       "3702\n",
+      "model_vars.get('Gini', 0)[-1]=0.6759999999999999\n",
+      "Condition not met at step 3703. No data to save.\n",
       "3703\n",
+      "model_vars.get('Gini', 0)[-1]=0.6604\n",
+      "Condition not met at step 3704. No data to save.\n",
       "3704\n",
+      "model_vars.get('Gini', 0)[-1]=0.6464000000000001\n",
+      "Condition not met at step 3705. No data to save.\n",
       "3705\n",
+      "model_vars.get('Gini', 0)[-1]=0.646\n",
+      "Condition not met at step 3706. No data to save.\n",
       "3706\n",
+      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
+      "Condition not met at step 3707. No data to save.\n",
       "3707\n",
+      "model_vars.get('Gini', 0)[-1]=0.6548\n",
+      "Condition not met at step 3708. No data to save.\n",
       "3708\n",
+      "model_vars.get('Gini', 0)[-1]=0.6514\n",
+      "Condition not met at step 3709. No data to save.\n",
       "3709\n",
+      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
+      "Condition not met at step 3710. No data to save.\n",
       "3710\n",
+      "model_vars.get('Gini', 0)[-1]=0.6662\n",
+      "Condition not met at step 3711. No data to save.\n",
       "3711\n",
+      "model_vars.get('Gini', 0)[-1]=0.6698\n",
+      "Condition not met at step 3712. No data to save.\n",
       "3712\n",
+      "model_vars.get('Gini', 0)[-1]=0.6746000000000001\n",
+      "Condition not met at step 3713. No data to save.\n",
       "3713\n",
+      "model_vars.get('Gini', 0)[-1]=0.6784\n",
+      "Condition not met at step 3714. No data to save.\n",
       "3714\n",
+      "model_vars.get('Gini', 0)[-1]=0.6866\n",
+      "Condition not met at step 3715. No data to save.\n",
       "3715\n",
+      "model_vars.get('Gini', 0)[-1]=0.69\n",
+      "Condition not met at step 3716. No data to save.\n",
       "3716\n",
+      "model_vars.get('Gini', 0)[-1]=0.7001999999999999\n",
+      "Condition met. Appended special results for step 3717 to test_path/special_results.parquet\n",
       "3717\n",
+      "model_vars.get('Gini', 0)[-1]=0.692\n",
+      "Condition not met at step 3718. No data to save.\n",
       "3718\n",
+      "model_vars.get('Gini', 0)[-1]=0.7138\n",
+      "Condition met. Appended special results for step 3719 to test_path/special_results.parquet\n",
       "3719\n",
+      "model_vars.get('Gini', 0)[-1]=0.7203999999999999\n",
+      "Condition met. Appended special results for step 3720 to test_path/special_results.parquet\n",
       "3720\n",
+      "model_vars.get('Gini', 0)[-1]=0.7142\n",
+      "Condition met. Appended special results for step 3721 to test_path/special_results.parquet\n",
       "3721\n",
+      "model_vars.get('Gini', 0)[-1]=0.7108\n",
+      "Condition met. Appended special results for step 3722 to test_path/special_results.parquet\n",
       "3722\n",
+      "model_vars.get('Gini', 0)[-1]=0.7144\n",
+      "Condition met. Appended special results for step 3723 to test_path/special_results.parquet\n",
       "3723\n",
+      "model_vars.get('Gini', 0)[-1]=0.6876\n",
+      "Condition not met at step 3724. No data to save.\n",
       "3724\n",
+      "model_vars.get('Gini', 0)[-1]=0.6626000000000001\n",
+      "Condition not met at step 3725. No data to save.\n",
       "3725\n",
+      "model_vars.get('Gini', 0)[-1]=0.6232\n",
+      "Condition not met at step 3726. No data to save.\n",
       "3726\n",
+      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
+      "Condition not met at step 3727. No data to save.\n",
       "3727\n",
+      "model_vars.get('Gini', 0)[-1]=0.6234\n",
+      "Condition not met at step 3728. No data to save.\n",
       "3728\n",
+      "model_vars.get('Gini', 0)[-1]=0.6656\n",
+      "Condition not met at step 3729. No data to save.\n",
       "3729\n",
+      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
+      "Condition not met at step 3730. No data to save.\n",
       "3730\n",
+      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
+      "Condition not met at step 3731. No data to save.\n",
       "3731\n",
+      "model_vars.get('Gini', 0)[-1]=0.644\n",
+      "Condition not met at step 3732. No data to save.\n",
       "3732\n",
+      "model_vars.get('Gini', 0)[-1]=0.631\n",
+      "Condition not met at step 3733. No data to save.\n",
       "3733\n",
+      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
+      "Condition not met at step 3734. No data to save.\n",
       "3734\n",
+      "model_vars.get('Gini', 0)[-1]=0.6022000000000001\n",
+      "Condition not met at step 3735. No data to save.\n",
       "3735\n",
+      "model_vars.get('Gini', 0)[-1]=0.6078\n",
+      "Condition not met at step 3736. No data to save.\n",
       "3736\n",
+      "model_vars.get('Gini', 0)[-1]=0.6554\n",
+      "Condition not met at step 3737. No data to save.\n",
       "3737\n",
+      "model_vars.get('Gini', 0)[-1]=0.6612\n",
+      "Condition not met at step 3738. No data to save.\n",
       "3738\n",
+      "model_vars.get('Gini', 0)[-1]=0.6552\n",
+      "Condition not met at step 3739. No data to save.\n",
       "3739\n",
+      "model_vars.get('Gini', 0)[-1]=0.6506000000000001\n",
+      "Condition not met at step 3740. No data to save.\n",
       "3740\n",
+      "model_vars.get('Gini', 0)[-1]=0.6376\n",
+      "Condition not met at step 3741. No data to save.\n",
       "3741\n",
+      "model_vars.get('Gini', 0)[-1]=0.6648000000000001\n",
+      "Condition not met at step 3742. No data to save.\n",
       "3742\n",
+      "model_vars.get('Gini', 0)[-1]=0.6652\n",
+      "Condition not met at step 3743. No data to save.\n",
       "3743\n",
+      "model_vars.get('Gini', 0)[-1]=0.6556\n",
+      "Condition not met at step 3744. No data to save.\n",
       "3744\n",
+      "model_vars.get('Gini', 0)[-1]=0.6517999999999999\n",
+      "Condition not met at step 3745. No data to save.\n",
       "3745\n",
+      "model_vars.get('Gini', 0)[-1]=0.664\n",
+      "Condition not met at step 3746. No data to save.\n",
       "3746\n",
+      "model_vars.get('Gini', 0)[-1]=0.629\n",
+      "Condition not met at step 3747. No data to save.\n",
       "3747\n",
+      "model_vars.get('Gini', 0)[-1]=0.6224000000000001\n",
+      "Condition not met at step 3748. No data to save.\n",
       "3748\n",
+      "model_vars.get('Gini', 0)[-1]=0.6497999999999999\n",
+      "Condition not met at step 3749. No data to save.\n",
       "3749\n",
+      "model_vars.get('Gini', 0)[-1]=0.649\n",
+      "Condition not met at step 3750. No data to save.\n",
       "3750\n",
+      "model_vars.get('Gini', 0)[-1]=0.6666000000000001\n",
+      "Condition not met at step 3751. No data to save.\n",
       "3751\n",
+      "model_vars.get('Gini', 0)[-1]=0.6848000000000001\n",
+      "Condition not met at step 3752. No data to save.\n",
       "3752\n",
+      "model_vars.get('Gini', 0)[-1]=0.6644\n",
+      "Condition not met at step 3753. No data to save.\n",
       "3753\n",
+      "model_vars.get('Gini', 0)[-1]=0.6617999999999999\n",
+      "Condition not met at step 3754. No data to save.\n",
       "3754\n",
+      "model_vars.get('Gini', 0)[-1]=0.6664\n",
+      "Condition not met at step 3755. No data to save.\n",
       "3755\n",
+      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
+      "Condition not met at step 3756. No data to save.\n",
       "3756\n",
+      "model_vars.get('Gini', 0)[-1]=0.6888000000000001\n",
+      "Condition not met at step 3757. No data to save.\n",
       "3757\n",
+      "model_vars.get('Gini', 0)[-1]=0.6704\n",
+      "Condition not met at step 3758. No data to save.\n",
       "3758\n",
+      "model_vars.get('Gini', 0)[-1]=0.666\n",
+      "Condition not met at step 3759. No data to save.\n",
       "3759\n",
+      "model_vars.get('Gini', 0)[-1]=0.6472\n",
+      "Condition not met at step 3760. No data to save.\n",
       "3760\n",
+      "model_vars.get('Gini', 0)[-1]=0.6656\n",
+      "Condition not met at step 3761. No data to save.\n",
       "3761\n",
+      "model_vars.get('Gini', 0)[-1]=0.6444000000000001\n",
+      "Condition not met at step 3762. No data to save.\n",
       "3762\n",
+      "model_vars.get('Gini', 0)[-1]=0.6582\n",
+      "Condition not met at step 3763. No data to save.\n",
       "3763\n",
+      "model_vars.get('Gini', 0)[-1]=0.6802\n",
+      "Condition not met at step 3764. No data to save.\n",
       "3764\n",
+      "model_vars.get('Gini', 0)[-1]=0.6828000000000001\n",
+      "Condition not met at step 3765. No data to save.\n",
       "3765\n",
+      "model_vars.get('Gini', 0)[-1]=0.6712\n",
+      "Condition not met at step 3766. No data to save.\n",
       "3766\n",
+      "model_vars.get('Gini', 0)[-1]=0.624\n",
+      "Condition not met at step 3767. No data to save.\n",
       "3767\n",
+      "model_vars.get('Gini', 0)[-1]=0.6274\n",
+      "Condition not met at step 3768. No data to save.\n",
       "3768\n",
+      "model_vars.get('Gini', 0)[-1]=0.6204000000000001\n",
+      "Condition not met at step 3769. No data to save.\n",
       "3769\n",
+      "model_vars.get('Gini', 0)[-1]=0.6284000000000001\n",
+      "Condition not met at step 3770. No data to save.\n",
       "3770\n",
+      "model_vars.get('Gini', 0)[-1]=0.6537999999999999\n",
+      "Condition not met at step 3771. No data to save.\n",
       "3771\n",
+      "model_vars.get('Gini', 0)[-1]=0.6274\n",
+      "Condition not met at step 3772. No data to save.\n",
       "3772\n",
+      "model_vars.get('Gini', 0)[-1]=0.6552\n",
+      "Condition not met at step 3773. No data to save.\n",
       "3773\n",
+      "model_vars.get('Gini', 0)[-1]=0.6444000000000001\n",
+      "Condition not met at step 3774. No data to save.\n",
       "3774\n",
+      "model_vars.get('Gini', 0)[-1]=0.6204000000000001\n",
+      "Condition not met at step 3775. No data to save.\n",
       "3775\n",
+      "model_vars.get('Gini', 0)[-1]=0.6416\n",
+      "Condition not met at step 3776. No data to save.\n",
       "3776\n",
+      "model_vars.get('Gini', 0)[-1]=0.659\n",
+      "Condition not met at step 3777. No data to save.\n",
       "3777\n",
+      "model_vars.get('Gini', 0)[-1]=0.6612\n",
+      "Condition not met at step 3778. No data to save.\n",
       "3778\n",
+      "model_vars.get('Gini', 0)[-1]=0.6658\n",
+      "Condition not met at step 3779. No data to save.\n",
       "3779\n",
+      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
+      "Condition not met at step 3780. No data to save.\n",
       "3780\n",
+      "model_vars.get('Gini', 0)[-1]=0.6582\n",
+      "Condition not met at step 3781. No data to save.\n",
       "3781\n",
+      "model_vars.get('Gini', 0)[-1]=0.6426000000000001\n",
+      "Condition not met at step 3782. No data to save.\n",
       "3782\n",
+      "model_vars.get('Gini', 0)[-1]=0.643\n",
+      "Condition not met at step 3783. No data to save.\n",
       "3783\n",
+      "model_vars.get('Gini', 0)[-1]=0.6168\n",
+      "Condition not met at step 3784. No data to save.\n",
       "3784\n",
+      "model_vars.get('Gini', 0)[-1]=0.656\n",
+      "Condition not met at step 3785. No data to save.\n",
       "3785\n",
+      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
+      "Condition not met at step 3786. No data to save.\n",
       "3786\n",
+      "model_vars.get('Gini', 0)[-1]=0.62\n",
+      "Condition not met at step 3787. No data to save.\n",
       "3787\n",
+      "model_vars.get('Gini', 0)[-1]=0.62\n",
+      "Condition not met at step 3788. No data to save.\n",
       "3788\n",
+      "model_vars.get('Gini', 0)[-1]=0.603\n",
+      "Condition not met at step 3789. No data to save.\n",
       "3789\n",
+      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
+      "Condition not met at step 3790. No data to save.\n",
       "3790\n",
+      "model_vars.get('Gini', 0)[-1]=0.6306\n",
+      "Condition not met at step 3791. No data to save.\n",
       "3791\n",
+      "model_vars.get('Gini', 0)[-1]=0.6532\n",
+      "Condition not met at step 3792. No data to save.\n",
       "3792\n",
+      "model_vars.get('Gini', 0)[-1]=0.6355999999999999\n",
+      "Condition not met at step 3793. No data to save.\n",
       "3793\n",
+      "model_vars.get('Gini', 0)[-1]=0.6278\n",
+      "Condition not met at step 3794. No data to save.\n",
       "3794\n",
+      "model_vars.get('Gini', 0)[-1]=0.6378\n",
+      "Condition not met at step 3795. No data to save.\n",
       "3795\n",
+      "model_vars.get('Gini', 0)[-1]=0.6304000000000001\n",
+      "Condition not met at step 3796. No data to save.\n",
       "3796\n",
+      "model_vars.get('Gini', 0)[-1]=0.624\n",
+      "Condition not met at step 3797. No data to save.\n",
       "3797\n",
+      "model_vars.get('Gini', 0)[-1]=0.618\n",
+      "Condition not met at step 3798. No data to save.\n",
       "3798\n",
+      "model_vars.get('Gini', 0)[-1]=0.5933999999999999\n",
+      "Condition not met at step 3799. No data to save.\n",
       "3799\n",
       "CALLED\n",
       "self.model._steps=3800\n",
       " TEST self.model._steps=3800\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "3700  0.6678\n",
-      "3701  0.6698\n",
-      "3702  0.6574\n",
-      "3703  0.6756\n",
-      "3704  0.6732\n",
+      "3700  0.6858\n",
+      "3701  0.6748\n",
+      "3702  0.6760\n",
+      "3703  0.6604\n",
+      "3704  0.6464\n",
       "...      ...\n",
-      "3795  0.6104\n",
-      "3796  0.6070\n",
-      "3797  0.6162\n",
-      "3798  0.6340\n",
-      "3799  0.6198\n",
+      "3795  0.6304\n",
+      "3796  0.6240\n",
+      "3797  0.6180\n",
+      "3798  0.5934\n",
+      "3799  0.5876\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_038.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_038.parquet'\n",
-      "Saving model to output_dir/model_data_038.parquet\n",
-      "Saving agent to output_dir/agent_data_038.parquet\n",
+      "model_file='test_path/model_data_038.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_038.parquet'\n",
+      "Saving model to test_path/model_data_038.parquet\n",
+      "Saving agent to test_path/agent_data_038.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.5876\n",
+      "Condition not met at step 3800. No data to save.\n",
       "3800\n",
+      "model_vars.get('Gini', 0)[-1]=0.619\n",
+      "Condition not met at step 3801. No data to save.\n",
       "3801\n",
+      "model_vars.get('Gini', 0)[-1]=0.6318\n",
+      "Condition not met at step 3802. No data to save.\n",
       "3802\n",
+      "model_vars.get('Gini', 0)[-1]=0.6033999999999999\n",
+      "Condition not met at step 3803. No data to save.\n",
       "3803\n",
+      "model_vars.get('Gini', 0)[-1]=0.6322\n",
+      "Condition not met at step 3804. No data to save.\n",
       "3804\n",
+      "model_vars.get('Gini', 0)[-1]=0.6234\n",
+      "Condition not met at step 3805. No data to save.\n",
       "3805\n",
+      "model_vars.get('Gini', 0)[-1]=0.6434\n",
+      "Condition not met at step 3806. No data to save.\n",
       "3806\n",
+      "model_vars.get('Gini', 0)[-1]=0.6388\n",
+      "Condition not met at step 3807. No data to save.\n",
       "3807\n",
+      "model_vars.get('Gini', 0)[-1]=0.661\n",
+      "Condition not met at step 3808. No data to save.\n",
       "3808\n",
+      "model_vars.get('Gini', 0)[-1]=0.6646000000000001\n",
+      "Condition not met at step 3809. No data to save.\n",
       "3809\n",
+      "model_vars.get('Gini', 0)[-1]=0.6314\n",
+      "Condition not met at step 3810. No data to save.\n",
       "3810\n",
+      "model_vars.get('Gini', 0)[-1]=0.6295999999999999\n",
+      "Condition not met at step 3811. No data to save.\n",
       "3811\n",
+      "model_vars.get('Gini', 0)[-1]=0.6672\n",
+      "Condition not met at step 3812. No data to save.\n",
       "3812\n",
+      "model_vars.get('Gini', 0)[-1]=0.6402\n",
+      "Condition not met at step 3813. No data to save.\n",
       "3813\n",
+      "model_vars.get('Gini', 0)[-1]=0.6304000000000001\n",
+      "Condition not met at step 3814. No data to save.\n",
       "3814\n",
+      "model_vars.get('Gini', 0)[-1]=0.597\n",
+      "Condition not met at step 3815. No data to save.\n",
       "3815\n",
+      "model_vars.get('Gini', 0)[-1]=0.606\n",
+      "Condition not met at step 3816. No data to save.\n",
       "3816\n",
+      "model_vars.get('Gini', 0)[-1]=0.6452\n",
+      "Condition not met at step 3817. No data to save.\n",
       "3817\n",
+      "model_vars.get('Gini', 0)[-1]=0.6434\n",
+      "Condition not met at step 3818. No data to save.\n",
       "3818\n",
+      "model_vars.get('Gini', 0)[-1]=0.6704\n",
+      "Condition not met at step 3819. No data to save.\n",
       "3819\n",
+      "model_vars.get('Gini', 0)[-1]=0.6574\n",
+      "Condition not met at step 3820. No data to save.\n",
       "3820\n",
+      "model_vars.get('Gini', 0)[-1]=0.6622\n",
+      "Condition not met at step 3821. No data to save.\n",
       "3821\n",
+      "model_vars.get('Gini', 0)[-1]=0.6784\n",
+      "Condition not met at step 3822. No data to save.\n",
       "3822\n",
+      "model_vars.get('Gini', 0)[-1]=0.6866\n",
+      "Condition not met at step 3823. No data to save.\n",
       "3823\n",
+      "model_vars.get('Gini', 0)[-1]=0.6936\n",
+      "Condition not met at step 3824. No data to save.\n",
       "3824\n",
+      "model_vars.get('Gini', 0)[-1]=0.6858\n",
+      "Condition not met at step 3825. No data to save.\n",
       "3825\n",
+      "model_vars.get('Gini', 0)[-1]=0.6596\n",
+      "Condition not met at step 3826. No data to save.\n",
       "3826\n",
+      "model_vars.get('Gini', 0)[-1]=0.7028000000000001\n",
+      "Condition met. Appended special results for step 3827 to test_path/special_results.parquet\n",
       "3827\n",
+      "model_vars.get('Gini', 0)[-1]=0.6834\n",
+      "Condition not met at step 3828. No data to save.\n",
       "3828\n",
+      "model_vars.get('Gini', 0)[-1]=0.6932\n",
+      "Condition not met at step 3829. No data to save.\n",
       "3829\n",
+      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
+      "Condition not met at step 3830. No data to save.\n",
       "3830\n",
+      "model_vars.get('Gini', 0)[-1]=0.643\n",
+      "Condition not met at step 3831. No data to save.\n",
       "3831\n",
+      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
+      "Condition not met at step 3832. No data to save.\n",
       "3832\n",
+      "model_vars.get('Gini', 0)[-1]=0.626\n",
+      "Condition not met at step 3833. No data to save.\n",
       "3833\n",
+      "model_vars.get('Gini', 0)[-1]=0.6258\n",
+      "Condition not met at step 3834. No data to save.\n",
       "3834\n",
+      "model_vars.get('Gini', 0)[-1]=0.6258\n",
+      "Condition not met at step 3835. No data to save.\n",
       "3835\n",
+      "model_vars.get('Gini', 0)[-1]=0.627\n",
+      "Condition not met at step 3836. No data to save.\n",
       "3836\n",
+      "model_vars.get('Gini', 0)[-1]=0.591\n",
+      "Condition not met at step 3837. No data to save.\n",
       "3837\n",
+      "model_vars.get('Gini', 0)[-1]=0.6496\n",
+      "Condition not met at step 3838. No data to save.\n",
       "3838\n",
+      "model_vars.get('Gini', 0)[-1]=0.6537999999999999\n",
+      "Condition not met at step 3839. No data to save.\n",
       "3839\n",
+      "model_vars.get('Gini', 0)[-1]=0.621\n",
+      "Condition not met at step 3840. No data to save.\n",
       "3840\n",
+      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
+      "Condition not met at step 3841. No data to save.\n",
       "3841\n",
+      "model_vars.get('Gini', 0)[-1]=0.683\n",
+      "Condition not met at step 3842. No data to save.\n",
       "3842\n",
+      "model_vars.get('Gini', 0)[-1]=0.6608\n",
+      "Condition not met at step 3843. No data to save.\n",
       "3843\n",
+      "model_vars.get('Gini', 0)[-1]=0.6396\n",
+      "Condition not met at step 3844. No data to save.\n",
       "3844\n",
+      "model_vars.get('Gini', 0)[-1]=0.6392\n",
+      "Condition not met at step 3845. No data to save.\n",
       "3845\n",
+      "model_vars.get('Gini', 0)[-1]=0.629\n",
+      "Condition not met at step 3846. No data to save.\n",
       "3846\n",
+      "model_vars.get('Gini', 0)[-1]=0.646\n",
+      "Condition not met at step 3847. No data to save.\n",
       "3847\n",
+      "model_vars.get('Gini', 0)[-1]=0.6596\n",
+      "Condition not met at step 3848. No data to save.\n",
       "3848\n",
+      "model_vars.get('Gini', 0)[-1]=0.6632\n",
+      "Condition not met at step 3849. No data to save.\n",
       "3849\n",
+      "model_vars.get('Gini', 0)[-1]=0.6712\n",
+      "Condition not met at step 3850. No data to save.\n",
       "3850\n",
+      "model_vars.get('Gini', 0)[-1]=0.6776\n",
+      "Condition not met at step 3851. No data to save.\n",
       "3851\n",
+      "model_vars.get('Gini', 0)[-1]=0.6774\n",
+      "Condition not met at step 3852. No data to save.\n",
       "3852\n",
+      "model_vars.get('Gini', 0)[-1]=0.7074\n",
+      "Condition met. Appended special results for step 3853 to test_path/special_results.parquet\n",
       "3853\n",
+      "model_vars.get('Gini', 0)[-1]=0.6958\n",
+      "Condition not met at step 3854. No data to save.\n",
       "3854\n",
+      "model_vars.get('Gini', 0)[-1]=0.6758\n",
+      "Condition not met at step 3855. No data to save.\n",
       "3855\n",
+      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
+      "Condition not met at step 3856. No data to save.\n",
       "3856\n",
+      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
+      "Condition not met at step 3857. No data to save.\n",
       "3857\n",
+      "model_vars.get('Gini', 0)[-1]=0.6776\n",
+      "Condition not met at step 3858. No data to save.\n",
       "3858\n",
+      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
+      "Condition not met at step 3859. No data to save.\n",
       "3859\n",
+      "model_vars.get('Gini', 0)[-1]=0.6714\n",
+      "Condition not met at step 3860. No data to save.\n",
       "3860\n",
+      "model_vars.get('Gini', 0)[-1]=0.6398\n",
+      "Condition not met at step 3861. No data to save.\n",
       "3861\n",
+      "model_vars.get('Gini', 0)[-1]=0.653\n",
+      "Condition not met at step 3862. No data to save.\n",
       "3862\n",
+      "model_vars.get('Gini', 0)[-1]=0.651\n",
+      "Condition not met at step 3863. No data to save.\n",
       "3863\n",
+      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
+      "Condition not met at step 3864. No data to save.\n",
       "3864\n",
+      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
+      "Condition not met at step 3865. No data to save.\n",
       "3865\n",
+      "model_vars.get('Gini', 0)[-1]=0.6926\n",
+      "Condition not met at step 3866. No data to save.\n",
       "3866\n",
+      "model_vars.get('Gini', 0)[-1]=0.6972\n",
+      "Condition not met at step 3867. No data to save.\n",
       "3867\n",
+      "model_vars.get('Gini', 0)[-1]=0.694\n",
+      "Condition not met at step 3868. No data to save.\n",
       "3868\n",
+      "model_vars.get('Gini', 0)[-1]=0.6819999999999999\n",
+      "Condition not met at step 3869. No data to save.\n",
       "3869\n",
+      "model_vars.get('Gini', 0)[-1]=0.6884\n",
+      "Condition not met at step 3870. No data to save.\n",
       "3870\n",
+      "model_vars.get('Gini', 0)[-1]=0.6866\n",
+      "Condition not met at step 3871. No data to save.\n",
       "3871\n",
+      "model_vars.get('Gini', 0)[-1]=0.6692\n",
+      "Condition not met at step 3872. No data to save.\n",
       "3872\n",
+      "model_vars.get('Gini', 0)[-1]=0.6768000000000001\n",
+      "Condition not met at step 3873. No data to save.\n",
       "3873\n",
+      "model_vars.get('Gini', 0)[-1]=0.6576\n",
+      "Condition not met at step 3874. No data to save.\n",
       "3874\n",
+      "model_vars.get('Gini', 0)[-1]=0.6536\n",
+      "Condition not met at step 3875. No data to save.\n",
       "3875\n",
+      "model_vars.get('Gini', 0)[-1]=0.6492\n",
+      "Condition not met at step 3876. No data to save.\n",
       "3876\n",
+      "model_vars.get('Gini', 0)[-1]=0.6388\n",
+      "Condition not met at step 3877. No data to save.\n",
       "3877\n",
+      "model_vars.get('Gini', 0)[-1]=0.6564\n",
+      "Condition not met at step 3878. No data to save.\n",
       "3878\n",
+      "model_vars.get('Gini', 0)[-1]=0.6506000000000001\n",
+      "Condition not met at step 3879. No data to save.\n",
       "3879\n",
+      "model_vars.get('Gini', 0)[-1]=0.6402\n",
+      "Condition not met at step 3880. No data to save.\n",
       "3880\n",
+      "model_vars.get('Gini', 0)[-1]=0.6502\n",
+      "Condition not met at step 3881. No data to save.\n",
       "3881\n",
+      "model_vars.get('Gini', 0)[-1]=0.6436\n",
+      "Condition not met at step 3882. No data to save.\n",
       "3882\n",
+      "model_vars.get('Gini', 0)[-1]=0.6328\n",
+      "Condition not met at step 3883. No data to save.\n",
       "3883\n",
+      "model_vars.get('Gini', 0)[-1]=0.6302\n",
+      "Condition not met at step 3884. No data to save.\n",
       "3884\n",
+      "model_vars.get('Gini', 0)[-1]=0.6658\n",
+      "Condition not met at step 3885. No data to save.\n",
       "3885\n",
+      "model_vars.get('Gini', 0)[-1]=0.6782\n",
+      "Condition not met at step 3886. No data to save.\n",
       "3886\n",
+      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
+      "Condition not met at step 3887. No data to save.\n",
       "3887\n",
+      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
+      "Condition not met at step 3888. No data to save.\n",
       "3888\n",
+      "model_vars.get('Gini', 0)[-1]=0.6792\n",
+      "Condition not met at step 3889. No data to save.\n",
       "3889\n",
+      "model_vars.get('Gini', 0)[-1]=0.6804\n",
+      "Condition not met at step 3890. No data to save.\n",
       "3890\n",
+      "model_vars.get('Gini', 0)[-1]=0.6856\n",
+      "Condition not met at step 3891. No data to save.\n",
       "3891\n",
+      "model_vars.get('Gini', 0)[-1]=0.6941999999999999\n",
+      "Condition not met at step 3892. No data to save.\n",
       "3892\n",
+      "model_vars.get('Gini', 0)[-1]=0.6742\n",
+      "Condition not met at step 3893. No data to save.\n",
       "3893\n",
+      "model_vars.get('Gini', 0)[-1]=0.635\n",
+      "Condition not met at step 3894. No data to save.\n",
       "3894\n",
+      "model_vars.get('Gini', 0)[-1]=0.6679999999999999\n",
+      "Condition not met at step 3895. No data to save.\n",
       "3895\n",
+      "model_vars.get('Gini', 0)[-1]=0.66\n",
+      "Condition not met at step 3896. No data to save.\n",
       "3896\n",
+      "model_vars.get('Gini', 0)[-1]=0.649\n",
+      "Condition not met at step 3897. No data to save.\n",
       "3897\n",
+      "model_vars.get('Gini', 0)[-1]=0.6235999999999999\n",
+      "Condition not met at step 3898. No data to save.\n",
       "3898\n",
+      "model_vars.get('Gini', 0)[-1]=0.6242000000000001\n",
+      "Condition not met at step 3899. No data to save.\n",
       "3899\n",
       "CALLED\n",
       "self.model._steps=3900\n",
       " TEST self.model._steps=3900\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "3800  0.6248\n",
-      "3801  0.6336\n",
-      "3802  0.6418\n",
-      "3803  0.6578\n",
-      "3804  0.6586\n",
+      "3800  0.6190\n",
+      "3801  0.6318\n",
+      "3802  0.6034\n",
+      "3803  0.6322\n",
+      "3804  0.6234\n",
       "...      ...\n",
-      "3895  0.6710\n",
-      "3896  0.6606\n",
-      "3897  0.6676\n",
-      "3898  0.6784\n",
-      "3899  0.6920\n",
+      "3895  0.6600\n",
+      "3896  0.6490\n",
+      "3897  0.6236\n",
+      "3898  0.6242\n",
+      "3899  0.6214\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_039.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_039.parquet'\n",
-      "Saving model to output_dir/model_data_039.parquet\n",
-      "Saving agent to output_dir/agent_data_039.parquet\n",
+      "model_file='test_path/model_data_039.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_039.parquet'\n",
+      "Saving model to test_path/model_data_039.parquet\n",
+      "Saving agent to test_path/agent_data_039.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6214\n",
+      "Condition not met at step 3900. No data to save.\n",
       "3900\n",
+      "model_vars.get('Gini', 0)[-1]=0.5982000000000001\n",
+      "Condition not met at step 3901. No data to save.\n",
       "3901\n",
+      "model_vars.get('Gini', 0)[-1]=0.579\n",
+      "Condition not met at step 3902. No data to save.\n",
       "3902\n",
+      "model_vars.get('Gini', 0)[-1]=0.5786\n",
+      "Condition not met at step 3903. No data to save.\n",
       "3903\n",
+      "model_vars.get('Gini', 0)[-1]=0.5740000000000001\n",
+      "Condition not met at step 3904. No data to save.\n",
       "3904\n",
+      "model_vars.get('Gini', 0)[-1]=0.5940000000000001\n",
+      "Condition not met at step 3905. No data to save.\n",
       "3905\n",
+      "model_vars.get('Gini', 0)[-1]=0.6138\n",
+      "Condition not met at step 3906. No data to save.\n",
       "3906\n",
+      "model_vars.get('Gini', 0)[-1]=0.6284000000000001\n",
+      "Condition not met at step 3907. No data to save.\n",
       "3907\n",
+      "model_vars.get('Gini', 0)[-1]=0.6378\n",
+      "Condition not met at step 3908. No data to save.\n",
       "3908\n",
+      "model_vars.get('Gini', 0)[-1]=0.6497999999999999\n",
+      "Condition not met at step 3909. No data to save.\n",
       "3909\n",
+      "model_vars.get('Gini', 0)[-1]=0.6262000000000001\n",
+      "Condition not met at step 3910. No data to save.\n",
       "3910\n",
+      "model_vars.get('Gini', 0)[-1]=0.6248\n",
+      "Condition not met at step 3911. No data to save.\n",
       "3911\n",
+      "model_vars.get('Gini', 0)[-1]=0.6052\n",
+      "Condition not met at step 3912. No data to save.\n",
       "3912\n",
+      "model_vars.get('Gini', 0)[-1]=0.6208\n",
+      "Condition not met at step 3913. No data to save.\n",
       "3913\n",
+      "model_vars.get('Gini', 0)[-1]=0.6186\n",
+      "Condition not met at step 3914. No data to save.\n",
       "3914\n",
+      "model_vars.get('Gini', 0)[-1]=0.6113999999999999\n",
+      "Condition not met at step 3915. No data to save.\n",
       "3915\n",
+      "model_vars.get('Gini', 0)[-1]=0.6096\n",
+      "Condition not met at step 3916. No data to save.\n",
       "3916\n",
+      "model_vars.get('Gini', 0)[-1]=0.6234\n",
+      "Condition not met at step 3917. No data to save.\n",
       "3917\n",
+      "model_vars.get('Gini', 0)[-1]=0.628\n",
+      "Condition not met at step 3918. No data to save.\n",
       "3918\n",
+      "model_vars.get('Gini', 0)[-1]=0.6175999999999999\n",
+      "Condition not met at step 3919. No data to save.\n",
       "3919\n",
+      "model_vars.get('Gini', 0)[-1]=0.5806\n",
+      "Condition not met at step 3920. No data to save.\n",
       "3920\n",
+      "model_vars.get('Gini', 0)[-1]=0.635\n",
+      "Condition not met at step 3921. No data to save.\n",
       "3921\n",
+      "model_vars.get('Gini', 0)[-1]=0.6398\n",
+      "Condition not met at step 3922. No data to save.\n",
       "3922\n",
+      "model_vars.get('Gini', 0)[-1]=0.6134\n",
+      "Condition not met at step 3923. No data to save.\n",
       "3923\n",
+      "model_vars.get('Gini', 0)[-1]=0.6166\n",
+      "Condition not met at step 3924. No data to save.\n",
       "3924\n",
+      "model_vars.get('Gini', 0)[-1]=0.6136\n",
+      "Condition not met at step 3925. No data to save.\n",
       "3925\n",
+      "model_vars.get('Gini', 0)[-1]=0.6306\n",
+      "Condition not met at step 3926. No data to save.\n",
       "3926\n",
+      "model_vars.get('Gini', 0)[-1]=0.6212\n",
+      "Condition not met at step 3927. No data to save.\n",
       "3927\n",
+      "model_vars.get('Gini', 0)[-1]=0.6192\n",
+      "Condition not met at step 3928. No data to save.\n",
       "3928\n",
+      "model_vars.get('Gini', 0)[-1]=0.6248\n",
+      "Condition not met at step 3929. No data to save.\n",
       "3929\n",
+      "model_vars.get('Gini', 0)[-1]=0.646\n",
+      "Condition not met at step 3930. No data to save.\n",
       "3930\n",
+      "model_vars.get('Gini', 0)[-1]=0.6368\n",
+      "Condition not met at step 3931. No data to save.\n",
       "3931\n",
+      "model_vars.get('Gini', 0)[-1]=0.647\n",
+      "Condition not met at step 3932. No data to save.\n",
       "3932\n",
+      "model_vars.get('Gini', 0)[-1]=0.641\n",
+      "Condition not met at step 3933. No data to save.\n",
       "3933\n",
+      "model_vars.get('Gini', 0)[-1]=0.621\n",
+      "Condition not met at step 3934. No data to save.\n",
       "3934\n",
+      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
+      "Condition not met at step 3935. No data to save.\n",
       "3935\n",
+      "model_vars.get('Gini', 0)[-1]=0.6564\n",
+      "Condition not met at step 3936. No data to save.\n",
       "3936\n",
+      "model_vars.get('Gini', 0)[-1]=0.6834\n",
+      "Condition not met at step 3937. No data to save.\n",
       "3937\n",
+      "model_vars.get('Gini', 0)[-1]=0.7048\n",
+      "Condition met. Appended special results for step 3938 to test_path/special_results.parquet\n",
       "3938\n",
+      "model_vars.get('Gini', 0)[-1]=0.6878\n",
+      "Condition not met at step 3939. No data to save.\n",
       "3939\n",
+      "model_vars.get('Gini', 0)[-1]=0.7058\n",
+      "Condition met. Appended special results for step 3940 to test_path/special_results.parquet\n",
       "3940\n",
+      "model_vars.get('Gini', 0)[-1]=0.6992\n",
+      "Condition not met at step 3941. No data to save.\n",
       "3941\n",
+      "model_vars.get('Gini', 0)[-1]=0.7038\n",
+      "Condition met. Appended special results for step 3942 to test_path/special_results.parquet\n",
       "3942\n",
+      "model_vars.get('Gini', 0)[-1]=0.6622\n",
+      "Condition not met at step 3943. No data to save.\n",
       "3943\n",
+      "model_vars.get('Gini', 0)[-1]=0.6956\n",
+      "Condition not met at step 3944. No data to save.\n",
       "3944\n",
+      "model_vars.get('Gini', 0)[-1]=0.7098\n",
+      "Condition met. Appended special results for step 3945 to test_path/special_results.parquet\n",
       "3945\n",
+      "model_vars.get('Gini', 0)[-1]=0.7048\n",
+      "Condition met. Appended special results for step 3946 to test_path/special_results.parquet\n",
       "3946\n",
+      "model_vars.get('Gini', 0)[-1]=0.6678\n",
+      "Condition not met at step 3947. No data to save.\n",
       "3947\n",
+      "model_vars.get('Gini', 0)[-1]=0.6806\n",
+      "Condition not met at step 3948. No data to save.\n",
       "3948\n",
+      "model_vars.get('Gini', 0)[-1]=0.658\n",
+      "Condition not met at step 3949. No data to save.\n",
       "3949\n",
+      "model_vars.get('Gini', 0)[-1]=0.6608\n",
+      "Condition not met at step 3950. No data to save.\n",
       "3950\n",
+      "model_vars.get('Gini', 0)[-1]=0.64\n",
+      "Condition not met at step 3951. No data to save.\n",
       "3951\n",
+      "model_vars.get('Gini', 0)[-1]=0.6644\n",
+      "Condition not met at step 3952. No data to save.\n",
       "3952\n",
+      "model_vars.get('Gini', 0)[-1]=0.663\n",
+      "Condition not met at step 3953. No data to save.\n",
       "3953\n",
+      "model_vars.get('Gini', 0)[-1]=0.6774\n",
+      "Condition not met at step 3954. No data to save.\n",
       "3954\n",
+      "model_vars.get('Gini', 0)[-1]=0.6704\n",
+      "Condition not met at step 3955. No data to save.\n",
       "3955\n",
+      "model_vars.get('Gini', 0)[-1]=0.6574\n",
+      "Condition not met at step 3956. No data to save.\n",
       "3956\n",
+      "model_vars.get('Gini', 0)[-1]=0.6534\n",
+      "Condition not met at step 3957. No data to save.\n",
       "3957\n",
+      "model_vars.get('Gini', 0)[-1]=0.6674\n",
+      "Condition not met at step 3958. No data to save.\n",
       "3958\n",
+      "model_vars.get('Gini', 0)[-1]=0.6719999999999999\n",
+      "Condition not met at step 3959. No data to save.\n",
       "3959\n",
+      "model_vars.get('Gini', 0)[-1]=0.6674\n",
+      "Condition not met at step 3960. No data to save.\n",
       "3960\n",
+      "model_vars.get('Gini', 0)[-1]=0.6941999999999999\n",
+      "Condition not met at step 3961. No data to save.\n",
       "3961\n",
+      "model_vars.get('Gini', 0)[-1]=0.6852\n",
+      "Condition not met at step 3962. No data to save.\n",
       "3962\n",
+      "model_vars.get('Gini', 0)[-1]=0.7034\n",
+      "Condition met. Appended special results for step 3963 to test_path/special_results.parquet\n",
       "3963\n",
+      "model_vars.get('Gini', 0)[-1]=0.6884\n",
+      "Condition not met at step 3964. No data to save.\n",
       "3964\n",
+      "model_vars.get('Gini', 0)[-1]=0.6719999999999999\n",
+      "Condition not met at step 3965. No data to save.\n",
       "3965\n",
+      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
+      "Condition not met at step 3966. No data to save.\n",
       "3966\n",
+      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
+      "Condition not met at step 3967. No data to save.\n",
       "3967\n",
+      "model_vars.get('Gini', 0)[-1]=0.6718\n",
+      "Condition not met at step 3968. No data to save.\n",
       "3968\n",
+      "model_vars.get('Gini', 0)[-1]=0.6654\n",
+      "Condition not met at step 3969. No data to save.\n",
       "3969\n",
+      "model_vars.get('Gini', 0)[-1]=0.6572\n",
+      "Condition not met at step 3970. No data to save.\n",
       "3970\n",
+      "model_vars.get('Gini', 0)[-1]=0.6576\n",
+      "Condition not met at step 3971. No data to save.\n",
       "3971\n",
+      "model_vars.get('Gini', 0)[-1]=0.6892\n",
+      "Condition not met at step 3972. No data to save.\n",
       "3972\n",
+      "model_vars.get('Gini', 0)[-1]=0.6946\n",
+      "Condition not met at step 3973. No data to save.\n",
       "3973\n",
+      "model_vars.get('Gini', 0)[-1]=0.6930000000000001\n",
+      "Condition not met at step 3974. No data to save.\n",
       "3974\n",
+      "model_vars.get('Gini', 0)[-1]=0.7041999999999999\n",
+      "Condition met. Appended special results for step 3975 to test_path/special_results.parquet\n",
       "3975\n",
+      "model_vars.get('Gini', 0)[-1]=0.6804\n",
+      "Condition not met at step 3976. No data to save.\n",
       "3976\n",
+      "model_vars.get('Gini', 0)[-1]=0.6824\n",
+      "Condition not met at step 3977. No data to save.\n",
       "3977\n",
+      "model_vars.get('Gini', 0)[-1]=0.6679999999999999\n",
+      "Condition not met at step 3978. No data to save.\n",
       "3978\n",
+      "model_vars.get('Gini', 0)[-1]=0.6474\n",
+      "Condition not met at step 3979. No data to save.\n",
       "3979\n",
+      "model_vars.get('Gini', 0)[-1]=0.6502\n",
+      "Condition not met at step 3980. No data to save.\n",
       "3980\n",
+      "model_vars.get('Gini', 0)[-1]=0.64\n",
+      "Condition not met at step 3981. No data to save.\n",
       "3981\n",
+      "model_vars.get('Gini', 0)[-1]=0.6234\n",
+      "Condition not met at step 3982. No data to save.\n",
       "3982\n",
+      "model_vars.get('Gini', 0)[-1]=0.6046\n",
+      "Condition not met at step 3983. No data to save.\n",
       "3983\n",
+      "model_vars.get('Gini', 0)[-1]=0.6036\n",
+      "Condition not met at step 3984. No data to save.\n",
       "3984\n",
+      "model_vars.get('Gini', 0)[-1]=0.6052\n",
+      "Condition not met at step 3985. No data to save.\n",
       "3985\n",
+      "model_vars.get('Gini', 0)[-1]=0.6004\n",
+      "Condition not met at step 3986. No data to save.\n",
       "3986\n",
+      "model_vars.get('Gini', 0)[-1]=0.6073999999999999\n",
+      "Condition not met at step 3987. No data to save.\n",
       "3987\n",
+      "model_vars.get('Gini', 0)[-1]=0.6138\n",
+      "Condition not met at step 3988. No data to save.\n",
       "3988\n",
+      "model_vars.get('Gini', 0)[-1]=0.6502\n",
+      "Condition not met at step 3989. No data to save.\n",
       "3989\n",
+      "model_vars.get('Gini', 0)[-1]=0.6554\n",
+      "Condition not met at step 3990. No data to save.\n",
       "3990\n",
+      "model_vars.get('Gini', 0)[-1]=0.6778\n",
+      "Condition not met at step 3991. No data to save.\n",
       "3991\n",
+      "model_vars.get('Gini', 0)[-1]=0.69\n",
+      "Condition not met at step 3992. No data to save.\n",
       "3992\n",
+      "model_vars.get('Gini', 0)[-1]=0.679\n",
+      "Condition not met at step 3993. No data to save.\n",
       "3993\n",
+      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
+      "Condition not met at step 3994. No data to save.\n",
       "3994\n",
+      "model_vars.get('Gini', 0)[-1]=0.6594\n",
+      "Condition not met at step 3995. No data to save.\n",
       "3995\n",
+      "model_vars.get('Gini', 0)[-1]=0.6608\n",
+      "Condition not met at step 3996. No data to save.\n",
       "3996\n",
+      "model_vars.get('Gini', 0)[-1]=0.6699999999999999\n",
+      "Condition not met at step 3997. No data to save.\n",
       "3997\n",
+      "model_vars.get('Gini', 0)[-1]=0.6604\n",
+      "Condition not met at step 3998. No data to save.\n",
       "3998\n",
+      "model_vars.get('Gini', 0)[-1]=0.6602\n",
+      "Condition not met at step 3999. No data to save.\n",
       "3999\n",
       "CALLED\n",
       "self.model._steps=4000\n",
       " TEST self.model._steps=4000\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "3900  0.6756\n",
-      "3901  0.6754\n",
-      "3902  0.6668\n",
-      "3903  0.6708\n",
-      "3904  0.6940\n",
+      "3900  0.5982\n",
+      "3901  0.5790\n",
+      "3902  0.5786\n",
+      "3903  0.5740\n",
+      "3904  0.5940\n",
       "...      ...\n",
-      "3995  0.6720\n",
-      "3996  0.6460\n",
-      "3997  0.6556\n",
-      "3998  0.6400\n",
-      "3999  0.6360\n",
+      "3995  0.6608\n",
+      "3996  0.6700\n",
+      "3997  0.6604\n",
+      "3998  0.6602\n",
+      "3999  0.6558\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_040.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_040.parquet'\n",
-      "Saving model to output_dir/model_data_040.parquet\n",
-      "Saving agent to output_dir/agent_data_040.parquet\n",
+      "model_file='test_path/model_data_040.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_040.parquet'\n",
+      "Saving model to test_path/model_data_040.parquet\n",
+      "Saving agent to test_path/agent_data_040.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
+      "Condition not met at step 4000. No data to save.\n",
       "4000\n",
+      "model_vars.get('Gini', 0)[-1]=0.6534\n",
+      "Condition not met at step 4001. No data to save.\n",
       "4001\n",
+      "model_vars.get('Gini', 0)[-1]=0.618\n",
+      "Condition not met at step 4002. No data to save.\n",
       "4002\n",
+      "model_vars.get('Gini', 0)[-1]=0.62\n",
+      "Condition not met at step 4003. No data to save.\n",
       "4003\n",
+      "model_vars.get('Gini', 0)[-1]=0.615\n",
+      "Condition not met at step 4004. No data to save.\n",
       "4004\n",
+      "model_vars.get('Gini', 0)[-1]=0.6076\n",
+      "Condition not met at step 4005. No data to save.\n",
       "4005\n",
+      "model_vars.get('Gini', 0)[-1]=0.6186\n",
+      "Condition not met at step 4006. No data to save.\n",
       "4006\n",
+      "model_vars.get('Gini', 0)[-1]=0.6106\n",
+      "Condition not met at step 4007. No data to save.\n",
       "4007\n",
+      "model_vars.get('Gini', 0)[-1]=0.6113999999999999\n",
+      "Condition not met at step 4008. No data to save.\n",
       "4008\n",
+      "model_vars.get('Gini', 0)[-1]=0.6102000000000001\n",
+      "Condition not met at step 4009. No data to save.\n",
       "4009\n",
+      "model_vars.get('Gini', 0)[-1]=0.6136\n",
+      "Condition not met at step 4010. No data to save.\n",
       "4010\n",
+      "model_vars.get('Gini', 0)[-1]=0.636\n",
+      "Condition not met at step 4011. No data to save.\n",
       "4011\n",
+      "model_vars.get('Gini', 0)[-1]=0.6462\n",
+      "Condition not met at step 4012. No data to save.\n",
       "4012\n",
+      "model_vars.get('Gini', 0)[-1]=0.6698\n",
+      "Condition not met at step 4013. No data to save.\n",
       "4013\n",
+      "model_vars.get('Gini', 0)[-1]=0.6658\n",
+      "Condition not met at step 4014. No data to save.\n",
       "4014\n",
+      "model_vars.get('Gini', 0)[-1]=0.6594\n",
+      "Condition not met at step 4015. No data to save.\n",
       "4015\n",
+      "model_vars.get('Gini', 0)[-1]=0.648\n",
+      "Condition not met at step 4016. No data to save.\n",
       "4016\n",
+      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
+      "Condition not met at step 4017. No data to save.\n",
       "4017\n",
+      "model_vars.get('Gini', 0)[-1]=0.6528\n",
+      "Condition not met at step 4018. No data to save.\n",
       "4018\n",
+      "model_vars.get('Gini', 0)[-1]=0.6592\n",
+      "Condition not met at step 4019. No data to save.\n",
       "4019\n",
+      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
+      "Condition not met at step 4020. No data to save.\n",
       "4020\n",
+      "model_vars.get('Gini', 0)[-1]=0.6382\n",
+      "Condition not met at step 4021. No data to save.\n",
       "4021\n",
+      "model_vars.get('Gini', 0)[-1]=0.6652\n",
+      "Condition not met at step 4022. No data to save.\n",
       "4022\n",
+      "model_vars.get('Gini', 0)[-1]=0.629\n",
+      "Condition not met at step 4023. No data to save.\n",
       "4023\n",
+      "model_vars.get('Gini', 0)[-1]=0.6182000000000001\n",
+      "Condition not met at step 4024. No data to save.\n",
       "4024\n",
+      "model_vars.get('Gini', 0)[-1]=0.6264000000000001\n",
+      "Condition not met at step 4025. No data to save.\n",
       "4025\n",
+      "model_vars.get('Gini', 0)[-1]=0.6442\n",
+      "Condition not met at step 4026. No data to save.\n",
       "4026\n",
+      "model_vars.get('Gini', 0)[-1]=0.657\n",
+      "Condition not met at step 4027. No data to save.\n",
       "4027\n",
+      "model_vars.get('Gini', 0)[-1]=0.6592\n",
+      "Condition not met at step 4028. No data to save.\n",
       "4028\n",
+      "model_vars.get('Gini', 0)[-1]=0.6384000000000001\n",
+      "Condition not met at step 4029. No data to save.\n",
       "4029\n",
+      "model_vars.get('Gini', 0)[-1]=0.6372\n",
+      "Condition not met at step 4030. No data to save.\n",
       "4030\n",
+      "model_vars.get('Gini', 0)[-1]=0.6476\n",
+      "Condition not met at step 4031. No data to save.\n",
       "4031\n",
+      "model_vars.get('Gini', 0)[-1]=0.6446000000000001\n",
+      "Condition not met at step 4032. No data to save.\n",
       "4032\n",
+      "model_vars.get('Gini', 0)[-1]=0.6328\n",
+      "Condition not met at step 4033. No data to save.\n",
       "4033\n",
+      "model_vars.get('Gini', 0)[-1]=0.6234\n",
+      "Condition not met at step 4034. No data to save.\n",
       "4034\n",
+      "model_vars.get('Gini', 0)[-1]=0.641\n",
+      "Condition not met at step 4035. No data to save.\n",
       "4035\n",
+      "model_vars.get('Gini', 0)[-1]=0.6512\n",
+      "Condition not met at step 4036. No data to save.\n",
       "4036\n",
+      "model_vars.get('Gini', 0)[-1]=0.6416\n",
+      "Condition not met at step 4037. No data to save.\n",
       "4037\n",
+      "model_vars.get('Gini', 0)[-1]=0.6208\n",
+      "Condition not met at step 4038. No data to save.\n",
       "4038\n",
+      "model_vars.get('Gini', 0)[-1]=0.65\n",
+      "Condition not met at step 4039. No data to save.\n",
       "4039\n",
+      "model_vars.get('Gini', 0)[-1]=0.6444000000000001\n",
+      "Condition not met at step 4040. No data to save.\n",
       "4040\n",
+      "model_vars.get('Gini', 0)[-1]=0.6554\n",
+      "Condition not met at step 4041. No data to save.\n",
       "4041\n",
+      "model_vars.get('Gini', 0)[-1]=0.683\n",
+      "Condition not met at step 4042. No data to save.\n",
       "4042\n",
+      "model_vars.get('Gini', 0)[-1]=0.6724\n",
+      "Condition not met at step 4043. No data to save.\n",
       "4043\n",
+      "model_vars.get('Gini', 0)[-1]=0.6592\n",
+      "Condition not met at step 4044. No data to save.\n",
       "4044\n",
+      "model_vars.get('Gini', 0)[-1]=0.631\n",
+      "Condition not met at step 4045. No data to save.\n",
       "4045\n",
+      "model_vars.get('Gini', 0)[-1]=0.6186\n",
+      "Condition not met at step 4046. No data to save.\n",
       "4046\n",
+      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
+      "Condition not met at step 4047. No data to save.\n",
       "4047\n",
+      "model_vars.get('Gini', 0)[-1]=0.6378\n",
+      "Condition not met at step 4048. No data to save.\n",
       "4048\n",
+      "model_vars.get('Gini', 0)[-1]=0.663\n",
+      "Condition not met at step 4049. No data to save.\n",
       "4049\n",
+      "model_vars.get('Gini', 0)[-1]=0.6718\n",
+      "Condition not met at step 4050. No data to save.\n",
       "4050\n",
+      "model_vars.get('Gini', 0)[-1]=0.6990000000000001\n",
+      "Condition not met at step 4051. No data to save.\n",
       "4051\n",
+      "model_vars.get('Gini', 0)[-1]=0.6828000000000001\n",
+      "Condition not met at step 4052. No data to save.\n",
       "4052\n",
+      "model_vars.get('Gini', 0)[-1]=0.6722\n",
+      "Condition not met at step 4053. No data to save.\n",
       "4053\n",
+      "model_vars.get('Gini', 0)[-1]=0.659\n",
+      "Condition not met at step 4054. No data to save.\n",
       "4054\n",
+      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
+      "Condition not met at step 4055. No data to save.\n",
       "4055\n",
+      "model_vars.get('Gini', 0)[-1]=0.654\n",
+      "Condition not met at step 4056. No data to save.\n",
       "4056\n",
+      "model_vars.get('Gini', 0)[-1]=0.6468\n",
+      "Condition not met at step 4057. No data to save.\n",
       "4057\n",
+      "model_vars.get('Gini', 0)[-1]=0.6644\n",
+      "Condition not met at step 4058. No data to save.\n",
       "4058\n",
+      "model_vars.get('Gini', 0)[-1]=0.6826\n",
+      "Condition not met at step 4059. No data to save.\n",
       "4059\n",
+      "model_vars.get('Gini', 0)[-1]=0.6596\n",
+      "Condition not met at step 4060. No data to save.\n",
       "4060\n",
+      "model_vars.get('Gini', 0)[-1]=0.6568\n",
+      "Condition not met at step 4061. No data to save.\n",
       "4061\n",
+      "model_vars.get('Gini', 0)[-1]=0.6408\n",
+      "Condition not met at step 4062. No data to save.\n",
       "4062\n",
+      "model_vars.get('Gini', 0)[-1]=0.6692\n",
+      "Condition not met at step 4063. No data to save.\n",
       "4063\n",
+      "model_vars.get('Gini', 0)[-1]=0.677\n",
+      "Condition not met at step 4064. No data to save.\n",
       "4064\n",
+      "model_vars.get('Gini', 0)[-1]=0.6826\n",
+      "Condition not met at step 4065. No data to save.\n",
       "4065\n",
+      "model_vars.get('Gini', 0)[-1]=0.6698\n",
+      "Condition not met at step 4066. No data to save.\n",
       "4066\n",
+      "model_vars.get('Gini', 0)[-1]=0.6642\n",
+      "Condition not met at step 4067. No data to save.\n",
       "4067\n",
+      "model_vars.get('Gini', 0)[-1]=0.6626000000000001\n",
+      "Condition not met at step 4068. No data to save.\n",
       "4068\n",
+      "model_vars.get('Gini', 0)[-1]=0.6724\n",
+      "Condition not met at step 4069. No data to save.\n",
       "4069\n",
+      "model_vars.get('Gini', 0)[-1]=0.6588\n",
+      "Condition not met at step 4070. No data to save.\n",
       "4070\n",
+      "model_vars.get('Gini', 0)[-1]=0.6496\n",
+      "Condition not met at step 4071. No data to save.\n",
       "4071\n",
+      "model_vars.get('Gini', 0)[-1]=0.6726000000000001\n",
+      "Condition not met at step 4072. No data to save.\n",
       "4072\n",
+      "model_vars.get('Gini', 0)[-1]=0.6718\n",
+      "Condition not met at step 4073. No data to save.\n",
       "4073\n",
+      "model_vars.get('Gini', 0)[-1]=0.6878\n",
+      "Condition not met at step 4074. No data to save.\n",
       "4074\n",
+      "model_vars.get('Gini', 0)[-1]=0.6874\n",
+      "Condition not met at step 4075. No data to save.\n",
       "4075\n",
+      "model_vars.get('Gini', 0)[-1]=0.6952\n",
+      "Condition not met at step 4076. No data to save.\n",
       "4076\n",
+      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
+      "Condition not met at step 4077. No data to save.\n",
       "4077\n",
+      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
+      "Condition not met at step 4078. No data to save.\n",
       "4078\n",
+      "model_vars.get('Gini', 0)[-1]=0.6658\n",
+      "Condition not met at step 4079. No data to save.\n",
       "4079\n",
+      "model_vars.get('Gini', 0)[-1]=0.6798\n",
+      "Condition not met at step 4080. No data to save.\n",
       "4080\n",
+      "model_vars.get('Gini', 0)[-1]=0.665\n",
+      "Condition not met at step 4081. No data to save.\n",
       "4081\n",
+      "model_vars.get('Gini', 0)[-1]=0.6872\n",
+      "Condition not met at step 4082. No data to save.\n",
       "4082\n",
+      "model_vars.get('Gini', 0)[-1]=0.6556\n",
+      "Condition not met at step 4083. No data to save.\n",
       "4083\n",
+      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
+      "Condition not met at step 4084. No data to save.\n",
       "4084\n",
+      "model_vars.get('Gini', 0)[-1]=0.6666000000000001\n",
+      "Condition not met at step 4085. No data to save.\n",
       "4085\n",
+      "model_vars.get('Gini', 0)[-1]=0.638\n",
+      "Condition not met at step 4086. No data to save.\n",
       "4086\n",
+      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
+      "Condition not met at step 4087. No data to save.\n",
       "4087\n",
+      "model_vars.get('Gini', 0)[-1]=0.6608\n",
+      "Condition not met at step 4088. No data to save.\n",
       "4088\n",
+      "model_vars.get('Gini', 0)[-1]=0.6941999999999999\n",
+      "Condition not met at step 4089. No data to save.\n",
       "4089\n",
+      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
+      "Condition not met at step 4090. No data to save.\n",
       "4090\n",
+      "model_vars.get('Gini', 0)[-1]=0.6759999999999999\n",
+      "Condition not met at step 4091. No data to save.\n",
       "4091\n",
+      "model_vars.get('Gini', 0)[-1]=0.643\n",
+      "Condition not met at step 4092. No data to save.\n",
       "4092\n",
+      "model_vars.get('Gini', 0)[-1]=0.6414\n",
+      "Condition not met at step 4093. No data to save.\n",
       "4093\n",
+      "model_vars.get('Gini', 0)[-1]=0.6617999999999999\n",
+      "Condition not met at step 4094. No data to save.\n",
       "4094\n",
+      "model_vars.get('Gini', 0)[-1]=0.6534\n",
+      "Condition not met at step 4095. No data to save.\n",
       "4095\n",
+      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
+      "Condition not met at step 4096. No data to save.\n",
       "4096\n",
+      "model_vars.get('Gini', 0)[-1]=0.6422\n",
+      "Condition not met at step 4097. No data to save.\n",
       "4097\n",
+      "model_vars.get('Gini', 0)[-1]=0.6564\n",
+      "Condition not met at step 4098. No data to save.\n",
       "4098\n",
+      "model_vars.get('Gini', 0)[-1]=0.6382\n",
+      "Condition not met at step 4099. No data to save.\n",
       "4099\n",
       "CALLED\n",
       "self.model._steps=4100\n",
       " TEST self.model._steps=4100\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "4000  0.5930\n",
-      "4001  0.5802\n",
-      "4002  0.5750\n",
-      "4003  0.6004\n",
-      "4004  0.5798\n",
+      "4000  0.6534\n",
+      "4001  0.6180\n",
+      "4002  0.6200\n",
+      "4003  0.6150\n",
+      "4004  0.6076\n",
       "...      ...\n",
-      "4095  0.6906\n",
-      "4096  0.6782\n",
-      "4097  0.6606\n",
-      "4098  0.6482\n",
-      "4099  0.6362\n",
+      "4095  0.6546\n",
+      "4096  0.6422\n",
+      "4097  0.6564\n",
+      "4098  0.6382\n",
+      "4099  0.6618\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_041.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_041.parquet'\n",
-      "Saving model to output_dir/model_data_041.parquet\n",
-      "Saving agent to output_dir/agent_data_041.parquet\n",
+      "model_file='test_path/model_data_041.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_041.parquet'\n",
+      "Saving model to test_path/model_data_041.parquet\n",
+      "Saving agent to test_path/agent_data_041.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6617999999999999\n",
+      "Condition not met at step 4100. No data to save.\n",
       "4100\n",
+      "model_vars.get('Gini', 0)[-1]=0.6626000000000001\n",
+      "Condition not met at step 4101. No data to save.\n",
       "4101\n",
+      "model_vars.get('Gini', 0)[-1]=0.669\n",
+      "Condition not met at step 4102. No data to save.\n",
       "4102\n",
+      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
+      "Condition not met at step 4103. No data to save.\n",
       "4103\n",
+      "model_vars.get('Gini', 0)[-1]=0.6602\n",
+      "Condition not met at step 4104. No data to save.\n",
       "4104\n",
+      "model_vars.get('Gini', 0)[-1]=0.6462\n",
+      "Condition not met at step 4105. No data to save.\n",
       "4105\n",
+      "model_vars.get('Gini', 0)[-1]=0.6462\n",
+      "Condition not met at step 4106. No data to save.\n",
       "4106\n",
+      "model_vars.get('Gini', 0)[-1]=0.6536\n",
+      "Condition not met at step 4107. No data to save.\n",
       "4107\n",
+      "model_vars.get('Gini', 0)[-1]=0.6502\n",
+      "Condition not met at step 4108. No data to save.\n",
       "4108\n",
+      "model_vars.get('Gini', 0)[-1]=0.6432\n",
+      "Condition not met at step 4109. No data to save.\n",
       "4109\n",
+      "model_vars.get('Gini', 0)[-1]=0.6315999999999999\n",
+      "Condition not met at step 4110. No data to save.\n",
       "4110\n",
+      "model_vars.get('Gini', 0)[-1]=0.6178\n",
+      "Condition not met at step 4111. No data to save.\n",
       "4111\n",
+      "model_vars.get('Gini', 0)[-1]=0.6444000000000001\n",
+      "Condition not met at step 4112. No data to save.\n",
       "4112\n",
+      "model_vars.get('Gini', 0)[-1]=0.6244000000000001\n",
+      "Condition not met at step 4113. No data to save.\n",
       "4113\n",
+      "model_vars.get('Gini', 0)[-1]=0.6274\n",
+      "Condition not met at step 4114. No data to save.\n",
       "4114\n",
+      "model_vars.get('Gini', 0)[-1]=0.6122000000000001\n",
+      "Condition not met at step 4115. No data to save.\n",
       "4115\n",
+      "model_vars.get('Gini', 0)[-1]=0.5768\n",
+      "Condition not met at step 4116. No data to save.\n",
       "4116\n",
+      "model_vars.get('Gini', 0)[-1]=0.5876\n",
+      "Condition not met at step 4117. No data to save.\n",
       "4117\n",
+      "model_vars.get('Gini', 0)[-1]=0.6058\n",
+      "Condition not met at step 4118. No data to save.\n",
       "4118\n",
+      "model_vars.get('Gini', 0)[-1]=0.6298\n",
+      "Condition not met at step 4119. No data to save.\n",
       "4119\n",
+      "model_vars.get('Gini', 0)[-1]=0.6752\n",
+      "Condition not met at step 4120. No data to save.\n",
       "4120\n",
+      "model_vars.get('Gini', 0)[-1]=0.6956\n",
+      "Condition not met at step 4121. No data to save.\n",
       "4121\n",
+      "model_vars.get('Gini', 0)[-1]=0.6812\n",
+      "Condition not met at step 4122. No data to save.\n",
       "4122\n",
+      "model_vars.get('Gini', 0)[-1]=0.6762\n",
+      "Condition not met at step 4123. No data to save.\n",
       "4123\n",
+      "model_vars.get('Gini', 0)[-1]=0.6762\n",
+      "Condition not met at step 4124. No data to save.\n",
       "4124\n",
+      "model_vars.get('Gini', 0)[-1]=0.644\n",
+      "Condition not met at step 4125. No data to save.\n",
       "4125\n",
+      "model_vars.get('Gini', 0)[-1]=0.6604\n",
+      "Condition not met at step 4126. No data to save.\n",
       "4126\n",
+      "model_vars.get('Gini', 0)[-1]=0.6434\n",
+      "Condition not met at step 4127. No data to save.\n",
       "4127\n",
+      "model_vars.get('Gini', 0)[-1]=0.6188\n",
+      "Condition not met at step 4128. No data to save.\n",
       "4128\n",
+      "model_vars.get('Gini', 0)[-1]=0.6178\n",
+      "Condition not met at step 4129. No data to save.\n",
       "4129\n",
+      "model_vars.get('Gini', 0)[-1]=0.622\n",
+      "Condition not met at step 4130. No data to save.\n",
       "4130\n",
+      "model_vars.get('Gini', 0)[-1]=0.651\n",
+      "Condition not met at step 4131. No data to save.\n",
       "4131\n",
+      "model_vars.get('Gini', 0)[-1]=0.6226\n",
+      "Condition not met at step 4132. No data to save.\n",
       "4132\n",
+      "model_vars.get('Gini', 0)[-1]=0.629\n",
+      "Condition not met at step 4133. No data to save.\n",
       "4133\n",
+      "model_vars.get('Gini', 0)[-1]=0.6136\n",
+      "Condition not met at step 4134. No data to save.\n",
       "4134\n",
+      "model_vars.get('Gini', 0)[-1]=0.6084\n",
+      "Condition not met at step 4135. No data to save.\n",
       "4135\n",
+      "model_vars.get('Gini', 0)[-1]=0.6013999999999999\n",
+      "Condition not met at step 4136. No data to save.\n",
       "4136\n",
+      "model_vars.get('Gini', 0)[-1]=0.6158\n",
+      "Condition not met at step 4137. No data to save.\n",
       "4137\n",
+      "model_vars.get('Gini', 0)[-1]=0.6338\n",
+      "Condition not met at step 4138. No data to save.\n",
       "4138\n",
+      "model_vars.get('Gini', 0)[-1]=0.6052\n",
+      "Condition not met at step 4139. No data to save.\n",
       "4139\n",
+      "model_vars.get('Gini', 0)[-1]=0.5896\n",
+      "Condition not met at step 4140. No data to save.\n",
       "4140\n",
+      "model_vars.get('Gini', 0)[-1]=0.6218\n",
+      "Condition not met at step 4141. No data to save.\n",
       "4141\n",
+      "model_vars.get('Gini', 0)[-1]=0.6286\n",
+      "Condition not met at step 4142. No data to save.\n",
       "4142\n",
+      "model_vars.get('Gini', 0)[-1]=0.615\n",
+      "Condition not met at step 4143. No data to save.\n",
       "4143\n",
+      "model_vars.get('Gini', 0)[-1]=0.658\n",
+      "Condition not met at step 4144. No data to save.\n",
       "4144\n",
+      "model_vars.get('Gini', 0)[-1]=0.6508\n",
+      "Condition not met at step 4145. No data to save.\n",
       "4145\n",
+      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
+      "Condition not met at step 4146. No data to save.\n",
       "4146\n",
+      "model_vars.get('Gini', 0)[-1]=0.6718\n",
+      "Condition not met at step 4147. No data to save.\n",
       "4147\n",
+      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
+      "Condition not met at step 4148. No data to save.\n",
       "4148\n",
+      "model_vars.get('Gini', 0)[-1]=0.6672\n",
+      "Condition not met at step 4149. No data to save.\n",
       "4149\n",
+      "model_vars.get('Gini', 0)[-1]=0.6175999999999999\n",
+      "Condition not met at step 4150. No data to save.\n",
       "4150\n",
+      "model_vars.get('Gini', 0)[-1]=0.643\n",
+      "Condition not met at step 4151. No data to save.\n",
       "4151\n",
+      "model_vars.get('Gini', 0)[-1]=0.6246\n",
+      "Condition not met at step 4152. No data to save.\n",
       "4152\n",
+      "model_vars.get('Gini', 0)[-1]=0.6759999999999999\n",
+      "Condition not met at step 4153. No data to save.\n",
       "4153\n",
+      "model_vars.get('Gini', 0)[-1]=0.6622\n",
+      "Condition not met at step 4154. No data to save.\n",
       "4154\n",
+      "model_vars.get('Gini', 0)[-1]=0.6674\n",
+      "Condition not met at step 4155. No data to save.\n",
       "4155\n",
+      "model_vars.get('Gini', 0)[-1]=0.6612\n",
+      "Condition not met at step 4156. No data to save.\n",
       "4156\n",
+      "model_vars.get('Gini', 0)[-1]=0.6656\n",
+      "Condition not met at step 4157. No data to save.\n",
       "4157\n",
+      "model_vars.get('Gini', 0)[-1]=0.6508\n",
+      "Condition not met at step 4158. No data to save.\n",
       "4158\n",
+      "model_vars.get('Gini', 0)[-1]=0.6382\n",
+      "Condition not met at step 4159. No data to save.\n",
       "4159\n",
+      "model_vars.get('Gini', 0)[-1]=0.6806\n",
+      "Condition not met at step 4160. No data to save.\n",
       "4160\n",
+      "model_vars.get('Gini', 0)[-1]=0.6682\n",
+      "Condition not met at step 4161. No data to save.\n",
       "4161\n",
+      "model_vars.get('Gini', 0)[-1]=0.662\n",
+      "Condition not met at step 4162. No data to save.\n",
       "4162\n",
+      "model_vars.get('Gini', 0)[-1]=0.6412\n",
+      "Condition not met at step 4163. No data to save.\n",
       "4163\n",
+      "model_vars.get('Gini', 0)[-1]=0.6564\n",
+      "Condition not met at step 4164. No data to save.\n",
       "4164\n",
+      "model_vars.get('Gini', 0)[-1]=0.6676\n",
+      "Condition not met at step 4165. No data to save.\n",
       "4165\n",
+      "model_vars.get('Gini', 0)[-1]=0.6594\n",
+      "Condition not met at step 4166. No data to save.\n",
       "4166\n",
+      "model_vars.get('Gini', 0)[-1]=0.6288\n",
+      "Condition not met at step 4167. No data to save.\n",
       "4167\n",
+      "model_vars.get('Gini', 0)[-1]=0.6352\n",
+      "Condition not met at step 4168. No data to save.\n",
       "4168\n",
+      "model_vars.get('Gini', 0)[-1]=0.6286\n",
+      "Condition not met at step 4169. No data to save.\n",
       "4169\n",
+      "model_vars.get('Gini', 0)[-1]=0.66\n",
+      "Condition not met at step 4170. No data to save.\n",
       "4170\n",
+      "model_vars.get('Gini', 0)[-1]=0.6504000000000001\n",
+      "Condition not met at step 4171. No data to save.\n",
       "4171\n",
+      "model_vars.get('Gini', 0)[-1]=0.6679999999999999\n",
+      "Condition not met at step 4172. No data to save.\n",
       "4172\n",
+      "model_vars.get('Gini', 0)[-1]=0.6796\n",
+      "Condition not met at step 4173. No data to save.\n",
       "4173\n",
+      "model_vars.get('Gini', 0)[-1]=0.6822\n",
+      "Condition not met at step 4174. No data to save.\n",
       "4174\n",
+      "model_vars.get('Gini', 0)[-1]=0.6878\n",
+      "Condition not met at step 4175. No data to save.\n",
       "4175\n",
+      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
+      "Condition not met at step 4176. No data to save.\n",
       "4176\n",
+      "model_vars.get('Gini', 0)[-1]=0.6622\n",
+      "Condition not met at step 4177. No data to save.\n",
       "4177\n",
+      "model_vars.get('Gini', 0)[-1]=0.6732\n",
+      "Condition not met at step 4178. No data to save.\n",
       "4178\n",
+      "model_vars.get('Gini', 0)[-1]=0.663\n",
+      "Condition not met at step 4179. No data to save.\n",
       "4179\n",
+      "model_vars.get('Gini', 0)[-1]=0.6718\n",
+      "Condition not met at step 4180. No data to save.\n",
       "4180\n",
+      "model_vars.get('Gini', 0)[-1]=0.6798\n",
+      "Condition not met at step 4181. No data to save.\n",
       "4181\n",
+      "model_vars.get('Gini', 0)[-1]=0.7018\n",
+      "Condition met. Appended special results for step 4182 to test_path/special_results.parquet\n",
       "4182\n",
+      "model_vars.get('Gini', 0)[-1]=0.7068\n",
+      "Condition met. Appended special results for step 4183 to test_path/special_results.parquet\n",
       "4183\n",
+      "model_vars.get('Gini', 0)[-1]=0.7208\n",
+      "Condition met. Appended special results for step 4184 to test_path/special_results.parquet\n",
       "4184\n",
+      "model_vars.get('Gini', 0)[-1]=0.7068\n",
+      "Condition met. Appended special results for step 4185 to test_path/special_results.parquet\n",
       "4185\n",
+      "model_vars.get('Gini', 0)[-1]=0.6924\n",
+      "Condition not met at step 4186. No data to save.\n",
       "4186\n",
+      "model_vars.get('Gini', 0)[-1]=0.6768000000000001\n",
+      "Condition not met at step 4187. No data to save.\n",
       "4187\n",
+      "model_vars.get('Gini', 0)[-1]=0.6738\n",
+      "Condition not met at step 4188. No data to save.\n",
       "4188\n",
+      "model_vars.get('Gini', 0)[-1]=0.6930000000000001\n",
+      "Condition not met at step 4189. No data to save.\n",
       "4189\n",
+      "model_vars.get('Gini', 0)[-1]=0.6854\n",
+      "Condition not met at step 4190. No data to save.\n",
       "4190\n",
+      "model_vars.get('Gini', 0)[-1]=0.6956\n",
+      "Condition not met at step 4191. No data to save.\n",
       "4191\n",
+      "model_vars.get('Gini', 0)[-1]=0.6696\n",
+      "Condition not met at step 4192. No data to save.\n",
       "4192\n",
+      "model_vars.get('Gini', 0)[-1]=0.6778\n",
+      "Condition not met at step 4193. No data to save.\n",
       "4193\n",
+      "model_vars.get('Gini', 0)[-1]=0.7032\n",
+      "Condition met. Appended special results for step 4194 to test_path/special_results.parquet\n",
       "4194\n",
+      "model_vars.get('Gini', 0)[-1]=0.6994\n",
+      "Condition not met at step 4195. No data to save.\n",
       "4195\n",
+      "model_vars.get('Gini', 0)[-1]=0.7121999999999999\n",
+      "Condition met. Appended special results for step 4196 to test_path/special_results.parquet\n",
       "4196\n",
+      "model_vars.get('Gini', 0)[-1]=0.7070000000000001\n",
+      "Condition met. Appended special results for step 4197 to test_path/special_results.parquet\n",
       "4197\n",
+      "model_vars.get('Gini', 0)[-1]=0.729\n",
+      "Condition met. Appended special results for step 4198 to test_path/special_results.parquet\n",
       "4198\n",
+      "model_vars.get('Gini', 0)[-1]=0.7230000000000001\n",
+      "Condition met. Appended special results for step 4199 to test_path/special_results.parquet\n",
       "4199\n",
       "CALLED\n",
       "self.model._steps=4200\n",
       " TEST self.model._steps=4200\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "4100  0.6638\n",
-      "4101  0.6520\n",
-      "4102  0.6346\n",
-      "4103  0.6352\n",
-      "4104  0.6618\n",
+      "4100  0.6626\n",
+      "4101  0.6690\n",
+      "4102  0.6628\n",
+      "4103  0.6602\n",
+      "4104  0.6462\n",
       "...      ...\n",
-      "4195  0.6758\n",
-      "4196  0.7008\n",
-      "4197  0.6850\n",
-      "4198  0.6952\n",
-      "4199  0.6928\n",
+      "4195  0.7122\n",
+      "4196  0.7070\n",
+      "4197  0.7290\n",
+      "4198  0.7230\n",
+      "4199  0.7214\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_042.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_042.parquet'\n",
-      "Saving model to output_dir/model_data_042.parquet\n",
-      "Saving agent to output_dir/agent_data_042.parquet\n",
+      "model_file='test_path/model_data_042.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_042.parquet'\n",
+      "Saving model to test_path/model_data_042.parquet\n",
+      "Saving agent to test_path/agent_data_042.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.7214\n",
+      "Condition met. Appended special results for step 4200 to test_path/special_results.parquet\n",
       "4200\n",
+      "model_vars.get('Gini', 0)[-1]=0.7041999999999999\n",
+      "Condition met. Appended special results for step 4201 to test_path/special_results.parquet\n",
       "4201\n",
+      "model_vars.get('Gini', 0)[-1]=0.7142\n",
+      "Condition met. Appended special results for step 4202 to test_path/special_results.parquet\n",
       "4202\n",
+      "model_vars.get('Gini', 0)[-1]=0.7242\n",
+      "Condition met. Appended special results for step 4203 to test_path/special_results.parquet\n",
       "4203\n",
+      "model_vars.get('Gini', 0)[-1]=0.6921999999999999\n",
+      "Condition not met at step 4204. No data to save.\n",
       "4204\n",
+      "model_vars.get('Gini', 0)[-1]=0.6664\n",
+      "Condition not met at step 4205. No data to save.\n",
       "4205\n",
+      "model_vars.get('Gini', 0)[-1]=0.6894\n",
+      "Condition not met at step 4206. No data to save.\n",
       "4206\n",
+      "model_vars.get('Gini', 0)[-1]=0.6838\n",
+      "Condition not met at step 4207. No data to save.\n",
       "4207\n",
+      "model_vars.get('Gini', 0)[-1]=0.6876\n",
+      "Condition not met at step 4208. No data to save.\n",
       "4208\n",
+      "model_vars.get('Gini', 0)[-1]=0.6552\n",
+      "Condition not met at step 4209. No data to save.\n",
       "4209\n",
+      "model_vars.get('Gini', 0)[-1]=0.6368\n",
+      "Condition not met at step 4210. No data to save.\n",
       "4210\n",
+      "model_vars.get('Gini', 0)[-1]=0.6702\n",
+      "Condition not met at step 4211. No data to save.\n",
       "4211\n",
+      "model_vars.get('Gini', 0)[-1]=0.6676\n",
+      "Condition not met at step 4212. No data to save.\n",
       "4212\n",
+      "model_vars.get('Gini', 0)[-1]=0.6414\n",
+      "Condition not met at step 4213. No data to save.\n",
       "4213\n",
+      "model_vars.get('Gini', 0)[-1]=0.6724\n",
+      "Condition not met at step 4214. No data to save.\n",
       "4214\n",
+      "model_vars.get('Gini', 0)[-1]=0.6636\n",
+      "Condition not met at step 4215. No data to save.\n",
       "4215\n",
+      "model_vars.get('Gini', 0)[-1]=0.6882\n",
+      "Condition not met at step 4216. No data to save.\n",
       "4216\n",
+      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
+      "Condition not met at step 4217. No data to save.\n",
       "4217\n",
+      "model_vars.get('Gini', 0)[-1]=0.6778\n",
+      "Condition not met at step 4218. No data to save.\n",
       "4218\n",
+      "model_vars.get('Gini', 0)[-1]=0.6686000000000001\n",
+      "Condition not met at step 4219. No data to save.\n",
       "4219\n",
+      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
+      "Condition not met at step 4220. No data to save.\n",
       "4220\n",
+      "model_vars.get('Gini', 0)[-1]=0.6182000000000001\n",
+      "Condition not met at step 4221. No data to save.\n",
       "4221\n",
+      "model_vars.get('Gini', 0)[-1]=0.6252\n",
+      "Condition not met at step 4222. No data to save.\n",
       "4222\n",
+      "model_vars.get('Gini', 0)[-1]=0.6508\n",
+      "Condition not met at step 4223. No data to save.\n",
       "4223\n",
+      "model_vars.get('Gini', 0)[-1]=0.6664\n",
+      "Condition not met at step 4224. No data to save.\n",
       "4224\n",
+      "model_vars.get('Gini', 0)[-1]=0.6782\n",
+      "Condition not met at step 4225. No data to save.\n",
       "4225\n",
+      "model_vars.get('Gini', 0)[-1]=0.6698\n",
+      "Condition not met at step 4226. No data to save.\n",
       "4226\n",
+      "model_vars.get('Gini', 0)[-1]=0.6464000000000001\n",
+      "Condition not met at step 4227. No data to save.\n",
       "4227\n",
+      "model_vars.get('Gini', 0)[-1]=0.687\n",
+      "Condition not met at step 4228. No data to save.\n",
       "4228\n",
+      "model_vars.get('Gini', 0)[-1]=0.6798\n",
+      "Condition not met at step 4229. No data to save.\n",
       "4229\n",
+      "model_vars.get('Gini', 0)[-1]=0.6654\n",
+      "Condition not met at step 4230. No data to save.\n",
       "4230\n",
+      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
+      "Condition not met at step 4231. No data to save.\n",
       "4231\n",
+      "model_vars.get('Gini', 0)[-1]=0.6956\n",
+      "Condition not met at step 4232. No data to save.\n",
       "4232\n",
+      "model_vars.get('Gini', 0)[-1]=0.6918\n",
+      "Condition not met at step 4233. No data to save.\n",
       "4233\n",
+      "model_vars.get('Gini', 0)[-1]=0.704\n",
+      "Condition met. Appended special results for step 4234 to test_path/special_results.parquet\n",
       "4234\n",
+      "model_vars.get('Gini', 0)[-1]=0.6992\n",
+      "Condition not met at step 4235. No data to save.\n",
       "4235\n",
+      "model_vars.get('Gini', 0)[-1]=0.6718\n",
+      "Condition not met at step 4236. No data to save.\n",
       "4236\n",
+      "model_vars.get('Gini', 0)[-1]=0.7066\n",
+      "Condition met. Appended special results for step 4237 to test_path/special_results.parquet\n",
       "4237\n",
+      "model_vars.get('Gini', 0)[-1]=0.6904\n",
+      "Condition not met at step 4238. No data to save.\n",
       "4238\n",
+      "model_vars.get('Gini', 0)[-1]=0.6832\n",
+      "Condition not met at step 4239. No data to save.\n",
       "4239\n",
+      "model_vars.get('Gini', 0)[-1]=0.6786000000000001\n",
+      "Condition not met at step 4240. No data to save.\n",
       "4240\n",
+      "model_vars.get('Gini', 0)[-1]=0.6994\n",
+      "Condition not met at step 4241. No data to save.\n",
       "4241\n",
+      "model_vars.get('Gini', 0)[-1]=0.6884\n",
+      "Condition not met at step 4242. No data to save.\n",
       "4242\n",
+      "model_vars.get('Gini', 0)[-1]=0.6582\n",
+      "Condition not met at step 4243. No data to save.\n",
       "4243\n",
+      "model_vars.get('Gini', 0)[-1]=0.6516\n",
+      "Condition not met at step 4244. No data to save.\n",
       "4244\n",
+      "model_vars.get('Gini', 0)[-1]=0.6226\n",
+      "Condition not met at step 4245. No data to save.\n",
       "4245\n",
+      "model_vars.get('Gini', 0)[-1]=0.631\n",
+      "Condition not met at step 4246. No data to save.\n",
       "4246\n",
+      "model_vars.get('Gini', 0)[-1]=0.6604\n",
+      "Condition not met at step 4247. No data to save.\n",
       "4247\n",
+      "model_vars.get('Gini', 0)[-1]=0.663\n",
+      "Condition not met at step 4248. No data to save.\n",
       "4248\n",
+      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
+      "Condition not met at step 4249. No data to save.\n",
       "4249\n",
+      "model_vars.get('Gini', 0)[-1]=0.6522\n",
+      "Condition not met at step 4250. No data to save.\n",
       "4250\n",
+      "model_vars.get('Gini', 0)[-1]=0.6374\n",
+      "Condition not met at step 4251. No data to save.\n",
       "4251\n",
+      "model_vars.get('Gini', 0)[-1]=0.6314\n",
+      "Condition not met at step 4252. No data to save.\n",
       "4252\n",
+      "model_vars.get('Gini', 0)[-1]=0.6334\n",
+      "Condition not met at step 4253. No data to save.\n",
       "4253\n",
+      "model_vars.get('Gini', 0)[-1]=0.6446000000000001\n",
+      "Condition not met at step 4254. No data to save.\n",
       "4254\n",
+      "model_vars.get('Gini', 0)[-1]=0.6298\n",
+      "Condition not met at step 4255. No data to save.\n",
       "4255\n",
+      "model_vars.get('Gini', 0)[-1]=0.6224000000000001\n",
+      "Condition not met at step 4256. No data to save.\n",
       "4256\n",
+      "model_vars.get('Gini', 0)[-1]=0.6234\n",
+      "Condition not met at step 4257. No data to save.\n",
       "4257\n",
+      "model_vars.get('Gini', 0)[-1]=0.6234\n",
+      "Condition not met at step 4258. No data to save.\n",
       "4258\n",
+      "model_vars.get('Gini', 0)[-1]=0.611\n",
+      "Condition not met at step 4259. No data to save.\n",
       "4259\n",
+      "model_vars.get('Gini', 0)[-1]=0.5913999999999999\n",
+      "Condition not met at step 4260. No data to save.\n",
       "4260\n",
+      "model_vars.get('Gini', 0)[-1]=0.612\n",
+      "Condition not met at step 4261. No data to save.\n",
       "4261\n",
+      "model_vars.get('Gini', 0)[-1]=0.646\n",
+      "Condition not met at step 4262. No data to save.\n",
       "4262\n",
+      "model_vars.get('Gini', 0)[-1]=0.616\n",
+      "Condition not met at step 4263. No data to save.\n",
       "4263\n",
+      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
+      "Condition not met at step 4264. No data to save.\n",
       "4264\n",
+      "model_vars.get('Gini', 0)[-1]=0.6536\n",
+      "Condition not met at step 4265. No data to save.\n",
       "4265\n",
+      "model_vars.get('Gini', 0)[-1]=0.6517999999999999\n",
+      "Condition not met at step 4266. No data to save.\n",
       "4266\n",
+      "model_vars.get('Gini', 0)[-1]=0.6284000000000001\n",
+      "Condition not met at step 4267. No data to save.\n",
       "4267\n",
+      "model_vars.get('Gini', 0)[-1]=0.6328\n",
+      "Condition not met at step 4268. No data to save.\n",
       "4268\n",
+      "model_vars.get('Gini', 0)[-1]=0.6198\n",
+      "Condition not met at step 4269. No data to save.\n",
       "4269\n",
+      "model_vars.get('Gini', 0)[-1]=0.6295999999999999\n",
+      "Condition not met at step 4270. No data to save.\n",
       "4270\n",
+      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
+      "Condition not met at step 4271. No data to save.\n",
       "4271\n",
+      "model_vars.get('Gini', 0)[-1]=0.6634\n",
+      "Condition not met at step 4272. No data to save.\n",
       "4272\n",
+      "model_vars.get('Gini', 0)[-1]=0.6656\n",
+      "Condition not met at step 4273. No data to save.\n",
       "4273\n",
+      "model_vars.get('Gini', 0)[-1]=0.659\n",
+      "Condition not met at step 4274. No data to save.\n",
       "4274\n",
+      "model_vars.get('Gini', 0)[-1]=0.6632\n",
+      "Condition not met at step 4275. No data to save.\n",
       "4275\n",
+      "model_vars.get('Gini', 0)[-1]=0.6528\n",
+      "Condition not met at step 4276. No data to save.\n",
       "4276\n",
+      "model_vars.get('Gini', 0)[-1]=0.6517999999999999\n",
+      "Condition not met at step 4277. No data to save.\n",
       "4277\n",
+      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
+      "Condition not met at step 4278. No data to save.\n",
       "4278\n",
+      "model_vars.get('Gini', 0)[-1]=0.6536\n",
+      "Condition not met at step 4279. No data to save.\n",
       "4279\n",
+      "model_vars.get('Gini', 0)[-1]=0.6632\n",
+      "Condition not met at step 4280. No data to save.\n",
       "4280\n",
+      "model_vars.get('Gini', 0)[-1]=0.6566000000000001\n",
+      "Condition not met at step 4281. No data to save.\n",
       "4281\n",
+      "model_vars.get('Gini', 0)[-1]=0.6644\n",
+      "Condition not met at step 4282. No data to save.\n",
       "4282\n",
+      "model_vars.get('Gini', 0)[-1]=0.6582\n",
+      "Condition not met at step 4283. No data to save.\n",
       "4283\n",
+      "model_vars.get('Gini', 0)[-1]=0.654\n",
+      "Condition not met at step 4284. No data to save.\n",
       "4284\n",
+      "model_vars.get('Gini', 0)[-1]=0.6596\n",
+      "Condition not met at step 4285. No data to save.\n",
       "4285\n",
+      "model_vars.get('Gini', 0)[-1]=0.6272\n",
+      "Condition not met at step 4286. No data to save.\n",
       "4286\n",
+      "model_vars.get('Gini', 0)[-1]=0.6372\n",
+      "Condition not met at step 4287. No data to save.\n",
       "4287\n",
+      "model_vars.get('Gini', 0)[-1]=0.673\n",
+      "Condition not met at step 4288. No data to save.\n",
       "4288\n",
+      "model_vars.get('Gini', 0)[-1]=0.6759999999999999\n",
+      "Condition not met at step 4289. No data to save.\n",
       "4289\n",
+      "model_vars.get('Gini', 0)[-1]=0.6858\n",
+      "Condition not met at step 4290. No data to save.\n",
       "4290\n",
+      "model_vars.get('Gini', 0)[-1]=0.6986\n",
+      "Condition not met at step 4291. No data to save.\n",
       "4291\n",
+      "model_vars.get('Gini', 0)[-1]=0.6602\n",
+      "Condition not met at step 4292. No data to save.\n",
       "4292\n",
+      "model_vars.get('Gini', 0)[-1]=0.6676\n",
+      "Condition not met at step 4293. No data to save.\n",
       "4293\n",
+      "model_vars.get('Gini', 0)[-1]=0.6708000000000001\n",
+      "Condition not met at step 4294. No data to save.\n",
       "4294\n",
+      "model_vars.get('Gini', 0)[-1]=0.6468\n",
+      "Condition not met at step 4295. No data to save.\n",
       "4295\n",
+      "model_vars.get('Gini', 0)[-1]=0.6961999999999999\n",
+      "Condition not met at step 4296. No data to save.\n",
       "4296\n",
+      "model_vars.get('Gini', 0)[-1]=0.6776\n",
+      "Condition not met at step 4297. No data to save.\n",
       "4297\n",
+      "model_vars.get('Gini', 0)[-1]=0.6608\n",
+      "Condition not met at step 4298. No data to save.\n",
       "4298\n",
+      "model_vars.get('Gini', 0)[-1]=0.6378\n",
+      "Condition not met at step 4299. No data to save.\n",
       "4299\n",
       "CALLED\n",
       "self.model._steps=4300\n",
       " TEST self.model._steps=4300\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "4200  0.7056\n",
-      "4201  0.7012\n",
-      "4202  0.6968\n",
-      "4203  0.6604\n",
-      "4204  0.6212\n",
+      "4200  0.7042\n",
+      "4201  0.7142\n",
+      "4202  0.7242\n",
+      "4203  0.6922\n",
+      "4204  0.6664\n",
       "...      ...\n",
-      "4295  0.6988\n",
-      "4296  0.6814\n",
-      "4297  0.6724\n",
-      "4298  0.6732\n",
-      "4299  0.6884\n",
+      "4295  0.6962\n",
+      "4296  0.6776\n",
+      "4297  0.6608\n",
+      "4298  0.6378\n",
+      "4299  0.6044\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_043.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_043.parquet'\n",
-      "Saving model to output_dir/model_data_043.parquet\n",
-      "Saving agent to output_dir/agent_data_043.parquet\n",
+      "model_file='test_path/model_data_043.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_043.parquet'\n",
+      "Saving model to test_path/model_data_043.parquet\n",
+      "Saving agent to test_path/agent_data_043.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6044\n",
+      "Condition not met at step 4300. No data to save.\n",
       "4300\n",
+      "model_vars.get('Gini', 0)[-1]=0.6328\n",
+      "Condition not met at step 4301. No data to save.\n",
       "4301\n",
+      "model_vars.get('Gini', 0)[-1]=0.6124\n",
+      "Condition not met at step 4302. No data to save.\n",
       "4302\n",
+      "model_vars.get('Gini', 0)[-1]=0.611\n",
+      "Condition not met at step 4303. No data to save.\n",
       "4303\n",
+      "model_vars.get('Gini', 0)[-1]=0.6012\n",
+      "Condition not met at step 4304. No data to save.\n",
       "4304\n",
+      "model_vars.get('Gini', 0)[-1]=0.5771999999999999\n",
+      "Condition not met at step 4305. No data to save.\n",
       "4305\n",
+      "model_vars.get('Gini', 0)[-1]=0.6146\n",
+      "Condition not met at step 4306. No data to save.\n",
       "4306\n",
+      "model_vars.get('Gini', 0)[-1]=0.6312\n",
+      "Condition not met at step 4307. No data to save.\n",
       "4307\n",
+      "model_vars.get('Gini', 0)[-1]=0.6164000000000001\n",
+      "Condition not met at step 4308. No data to save.\n",
       "4308\n",
+      "model_vars.get('Gini', 0)[-1]=0.6335999999999999\n",
+      "Condition not met at step 4309. No data to save.\n",
       "4309\n",
+      "model_vars.get('Gini', 0)[-1]=0.6642\n",
+      "Condition not met at step 4310. No data to save.\n",
       "4310\n",
+      "model_vars.get('Gini', 0)[-1]=0.6514\n",
+      "Condition not met at step 4311. No data to save.\n",
       "4311\n",
+      "model_vars.get('Gini', 0)[-1]=0.644\n",
+      "Condition not met at step 4312. No data to save.\n",
       "4312\n",
+      "model_vars.get('Gini', 0)[-1]=0.637\n",
+      "Condition not met at step 4313. No data to save.\n",
       "4313\n",
+      "model_vars.get('Gini', 0)[-1]=0.6295999999999999\n",
+      "Condition not met at step 4314. No data to save.\n",
       "4314\n",
+      "model_vars.get('Gini', 0)[-1]=0.6334\n",
+      "Condition not met at step 4315. No data to save.\n",
       "4315\n",
+      "model_vars.get('Gini', 0)[-1]=0.6175999999999999\n",
+      "Condition not met at step 4316. No data to save.\n",
       "4316\n",
+      "model_vars.get('Gini', 0)[-1]=0.6262000000000001\n",
+      "Condition not met at step 4317. No data to save.\n",
       "4317\n",
+      "model_vars.get('Gini', 0)[-1]=0.6226\n",
+      "Condition not met at step 4318. No data to save.\n",
       "4318\n",
+      "model_vars.get('Gini', 0)[-1]=0.6278\n",
+      "Condition not met at step 4319. No data to save.\n",
       "4319\n",
+      "model_vars.get('Gini', 0)[-1]=0.6086\n",
+      "Condition not met at step 4320. No data to save.\n",
       "4320\n",
+      "model_vars.get('Gini', 0)[-1]=0.6116\n",
+      "Condition not met at step 4321. No data to save.\n",
       "4321\n",
+      "model_vars.get('Gini', 0)[-1]=0.6262000000000001\n",
+      "Condition not met at step 4322. No data to save.\n",
       "4322\n",
+      "model_vars.get('Gini', 0)[-1]=0.628\n",
+      "Condition not met at step 4323. No data to save.\n",
       "4323\n",
+      "model_vars.get('Gini', 0)[-1]=0.655\n",
+      "Condition not met at step 4324. No data to save.\n",
       "4324\n",
+      "model_vars.get('Gini', 0)[-1]=0.6368\n",
+      "Condition not met at step 4325. No data to save.\n",
       "4325\n",
+      "model_vars.get('Gini', 0)[-1]=0.6033999999999999\n",
+      "Condition not met at step 4326. No data to save.\n",
       "4326\n",
+      "model_vars.get('Gini', 0)[-1]=0.5978\n",
+      "Condition not met at step 4327. No data to save.\n",
       "4327\n",
+      "model_vars.get('Gini', 0)[-1]=0.5920000000000001\n",
+      "Condition not met at step 4328. No data to save.\n",
       "4328\n",
+      "model_vars.get('Gini', 0)[-1]=0.6142000000000001\n",
+      "Condition not met at step 4329. No data to save.\n",
       "4329\n",
+      "model_vars.get('Gini', 0)[-1]=0.6416\n",
+      "Condition not met at step 4330. No data to save.\n",
       "4330\n",
+      "model_vars.get('Gini', 0)[-1]=0.6532\n",
+      "Condition not met at step 4331. No data to save.\n",
       "4331\n",
+      "model_vars.get('Gini', 0)[-1]=0.671\n",
+      "Condition not met at step 4332. No data to save.\n",
       "4332\n",
+      "model_vars.get('Gini', 0)[-1]=0.6388\n",
+      "Condition not met at step 4333. No data to save.\n",
       "4333\n",
+      "model_vars.get('Gini', 0)[-1]=0.6262000000000001\n",
+      "Condition not met at step 4334. No data to save.\n",
       "4334\n",
+      "model_vars.get('Gini', 0)[-1]=0.6366\n",
+      "Condition not met at step 4335. No data to save.\n",
       "4335\n",
+      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
+      "Condition not met at step 4336. No data to save.\n",
       "4336\n",
+      "model_vars.get('Gini', 0)[-1]=0.6652\n",
+      "Condition not met at step 4337. No data to save.\n",
       "4337\n",
+      "model_vars.get('Gini', 0)[-1]=0.6652\n",
+      "Condition not met at step 4338. No data to save.\n",
       "4338\n",
+      "model_vars.get('Gini', 0)[-1]=0.6586000000000001\n",
+      "Condition not met at step 4339. No data to save.\n",
       "4339\n",
+      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
+      "Condition not met at step 4340. No data to save.\n",
       "4340\n",
+      "model_vars.get('Gini', 0)[-1]=0.6782\n",
+      "Condition not met at step 4341. No data to save.\n",
       "4341\n",
+      "model_vars.get('Gini', 0)[-1]=0.6862\n",
+      "Condition not met at step 4342. No data to save.\n",
       "4342\n",
+      "model_vars.get('Gini', 0)[-1]=0.6816\n",
+      "Condition not met at step 4343. No data to save.\n",
       "4343\n",
+      "model_vars.get('Gini', 0)[-1]=0.692\n",
+      "Condition not met at step 4344. No data to save.\n",
       "4344\n",
+      "model_vars.get('Gini', 0)[-1]=0.6872\n",
+      "Condition not met at step 4345. No data to save.\n",
       "4345\n",
+      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
+      "Condition not met at step 4346. No data to save.\n",
       "4346\n",
+      "model_vars.get('Gini', 0)[-1]=0.6892\n",
+      "Condition not met at step 4347. No data to save.\n",
       "4347\n",
+      "model_vars.get('Gini', 0)[-1]=0.6930000000000001\n",
+      "Condition not met at step 4348. No data to save.\n",
       "4348\n",
+      "model_vars.get('Gini', 0)[-1]=0.7054\n",
+      "Condition met. Appended special results for step 4349 to test_path/special_results.parquet\n",
       "4349\n",
+      "model_vars.get('Gini', 0)[-1]=0.688\n",
+      "Condition not met at step 4350. No data to save.\n",
       "4350\n",
+      "model_vars.get('Gini', 0)[-1]=0.6856\n",
+      "Condition not met at step 4351. No data to save.\n",
       "4351\n",
+      "model_vars.get('Gini', 0)[-1]=0.6746000000000001\n",
+      "Condition not met at step 4352. No data to save.\n",
       "4352\n",
+      "model_vars.get('Gini', 0)[-1]=0.683\n",
+      "Condition not met at step 4353. No data to save.\n",
       "4353\n",
+      "model_vars.get('Gini', 0)[-1]=0.7044\n",
+      "Condition met. Appended special results for step 4354 to test_path/special_results.parquet\n",
       "4354\n",
+      "model_vars.get('Gini', 0)[-1]=0.6981999999999999\n",
+      "Condition not met at step 4355. No data to save.\n",
       "4355\n",
+      "model_vars.get('Gini', 0)[-1]=0.6928000000000001\n",
+      "Condition not met at step 4356. No data to save.\n",
       "4356\n",
+      "model_vars.get('Gini', 0)[-1]=0.6992\n",
+      "Condition not met at step 4357. No data to save.\n",
       "4357\n",
+      "model_vars.get('Gini', 0)[-1]=0.7010000000000001\n",
+      "Condition met. Appended special results for step 4358 to test_path/special_results.parquet\n",
       "4358\n",
+      "model_vars.get('Gini', 0)[-1]=0.7076\n",
+      "Condition met. Appended special results for step 4359 to test_path/special_results.parquet\n",
       "4359\n",
+      "model_vars.get('Gini', 0)[-1]=0.683\n",
+      "Condition not met at step 4360. No data to save.\n",
       "4360\n",
+      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
+      "Condition not met at step 4361. No data to save.\n",
       "4361\n",
+      "model_vars.get('Gini', 0)[-1]=0.688\n",
+      "Condition not met at step 4362. No data to save.\n",
       "4362\n",
+      "model_vars.get('Gini', 0)[-1]=0.6752\n",
+      "Condition not met at step 4363. No data to save.\n",
       "4363\n",
+      "model_vars.get('Gini', 0)[-1]=0.6856\n",
+      "Condition not met at step 4364. No data to save.\n",
       "4364\n",
+      "model_vars.get('Gini', 0)[-1]=0.6972\n",
+      "Condition not met at step 4365. No data to save.\n",
       "4365\n",
+      "model_vars.get('Gini', 0)[-1]=0.6894\n",
+      "Condition not met at step 4366. No data to save.\n",
       "4366\n",
+      "model_vars.get('Gini', 0)[-1]=0.7156\n",
+      "Condition met. Appended special results for step 4367 to test_path/special_results.parquet\n",
       "4367\n",
+      "model_vars.get('Gini', 0)[-1]=0.7108\n",
+      "Condition met. Appended special results for step 4368 to test_path/special_results.parquet\n",
       "4368\n",
+      "model_vars.get('Gini', 0)[-1]=0.6859999999999999\n",
+      "Condition not met at step 4369. No data to save.\n",
       "4369\n",
+      "model_vars.get('Gini', 0)[-1]=0.6448\n",
+      "Condition not met at step 4370. No data to save.\n",
       "4370\n",
+      "model_vars.get('Gini', 0)[-1]=0.6548\n",
+      "Condition not met at step 4371. No data to save.\n",
       "4371\n",
+      "model_vars.get('Gini', 0)[-1]=0.6574\n",
+      "Condition not met at step 4372. No data to save.\n",
       "4372\n",
+      "model_vars.get('Gini', 0)[-1]=0.658\n",
+      "Condition not met at step 4373. No data to save.\n",
       "4373\n",
+      "model_vars.get('Gini', 0)[-1]=0.6792\n",
+      "Condition not met at step 4374. No data to save.\n",
       "4374\n",
+      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
+      "Condition not met at step 4375. No data to save.\n",
       "4375\n",
+      "model_vars.get('Gini', 0)[-1]=0.6386000000000001\n",
+      "Condition not met at step 4376. No data to save.\n",
       "4376\n",
+      "model_vars.get('Gini', 0)[-1]=0.647\n",
+      "Condition not met at step 4377. No data to save.\n",
       "4377\n",
+      "model_vars.get('Gini', 0)[-1]=0.6404000000000001\n",
+      "Condition not met at step 4378. No data to save.\n",
       "4378\n",
+      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
+      "Condition not met at step 4379. No data to save.\n",
       "4379\n",
+      "model_vars.get('Gini', 0)[-1]=0.658\n",
+      "Condition not met at step 4380. No data to save.\n",
       "4380\n",
+      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
+      "Condition not met at step 4381. No data to save.\n",
       "4381\n",
+      "model_vars.get('Gini', 0)[-1]=0.616\n",
+      "Condition not met at step 4382. No data to save.\n",
       "4382\n",
+      "model_vars.get('Gini', 0)[-1]=0.634\n",
+      "Condition not met at step 4383. No data to save.\n",
       "4383\n",
+      "model_vars.get('Gini', 0)[-1]=0.648\n",
+      "Condition not met at step 4384. No data to save.\n",
       "4384\n",
+      "model_vars.get('Gini', 0)[-1]=0.635\n",
+      "Condition not met at step 4385. No data to save.\n",
       "4385\n",
+      "model_vars.get('Gini', 0)[-1]=0.6394\n",
+      "Condition not met at step 4386. No data to save.\n",
       "4386\n",
+      "model_vars.get('Gini', 0)[-1]=0.634\n",
+      "Condition not met at step 4387. No data to save.\n",
       "4387\n",
+      "model_vars.get('Gini', 0)[-1]=0.6228\n",
+      "Condition not met at step 4388. No data to save.\n",
       "4388\n",
+      "model_vars.get('Gini', 0)[-1]=0.6632\n",
+      "Condition not met at step 4389. No data to save.\n",
       "4389\n",
+      "model_vars.get('Gini', 0)[-1]=0.6779999999999999\n",
+      "Condition not met at step 4390. No data to save.\n",
       "4390\n",
+      "model_vars.get('Gini', 0)[-1]=0.6878\n",
+      "Condition not met at step 4391. No data to save.\n",
       "4391\n",
+      "model_vars.get('Gini', 0)[-1]=0.6742\n",
+      "Condition not met at step 4392. No data to save.\n",
       "4392\n",
+      "model_vars.get('Gini', 0)[-1]=0.6699999999999999\n",
+      "Condition not met at step 4393. No data to save.\n",
       "4393\n",
+      "model_vars.get('Gini', 0)[-1]=0.6882\n",
+      "Condition not met at step 4394. No data to save.\n",
       "4394\n",
+      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
+      "Condition not met at step 4395. No data to save.\n",
       "4395\n",
+      "model_vars.get('Gini', 0)[-1]=0.6734\n",
+      "Condition not met at step 4396. No data to save.\n",
       "4396\n",
+      "model_vars.get('Gini', 0)[-1]=0.673\n",
+      "Condition not met at step 4397. No data to save.\n",
       "4397\n",
+      "model_vars.get('Gini', 0)[-1]=0.6712\n",
+      "Condition not met at step 4398. No data to save.\n",
       "4398\n",
+      "model_vars.get('Gini', 0)[-1]=0.6699999999999999\n",
+      "Condition not met at step 4399. No data to save.\n",
       "4399\n",
       "CALLED\n",
       "self.model._steps=4400\n",
       " TEST self.model._steps=4400\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "4300  0.6724\n",
-      "4301  0.6880\n",
-      "4302  0.6988\n",
-      "4303  0.6800\n",
-      "4304  0.6872\n",
+      "4300  0.6328\n",
+      "4301  0.6124\n",
+      "4302  0.6110\n",
+      "4303  0.6012\n",
+      "4304  0.5772\n",
       "...      ...\n",
-      "4395  0.7256\n",
-      "4396  0.7320\n",
-      "4397  0.7132\n",
-      "4398  0.7356\n",
-      "4399  0.7414\n",
+      "4395  0.6734\n",
+      "4396  0.6730\n",
+      "4397  0.6712\n",
+      "4398  0.6700\n",
+      "4399  0.6712\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_044.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_044.parquet'\n",
-      "Saving model to output_dir/model_data_044.parquet\n",
-      "Saving agent to output_dir/agent_data_044.parquet\n",
+      "model_file='test_path/model_data_044.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_044.parquet'\n",
+      "Saving model to test_path/model_data_044.parquet\n",
+      "Saving agent to test_path/agent_data_044.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6712\n",
+      "Condition not met at step 4400. No data to save.\n",
       "4400\n",
+      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
+      "Condition not met at step 4401. No data to save.\n",
       "4401\n",
+      "model_vars.get('Gini', 0)[-1]=0.664\n",
+      "Condition not met at step 4402. No data to save.\n",
       "4402\n",
+      "model_vars.get('Gini', 0)[-1]=0.6932\n",
+      "Condition not met at step 4403. No data to save.\n",
       "4403\n",
+      "model_vars.get('Gini', 0)[-1]=0.702\n",
+      "Condition met. Appended special results for step 4404 to test_path/special_results.parquet\n",
       "4404\n",
+      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
+      "Condition not met at step 4405. No data to save.\n",
       "4405\n",
+      "model_vars.get('Gini', 0)[-1]=0.6832\n",
+      "Condition not met at step 4406. No data to save.\n",
       "4406\n",
+      "model_vars.get('Gini', 0)[-1]=0.6896\n",
+      "Condition not met at step 4407. No data to save.\n",
       "4407\n",
+      "model_vars.get('Gini', 0)[-1]=0.6934\n",
+      "Condition not met at step 4408. No data to save.\n",
       "4408\n",
+      "model_vars.get('Gini', 0)[-1]=0.6848000000000001\n",
+      "Condition not met at step 4409. No data to save.\n",
       "4409\n",
+      "model_vars.get('Gini', 0)[-1]=0.6796\n",
+      "Condition not met at step 4410. No data to save.\n",
       "4410\n",
+      "model_vars.get('Gini', 0)[-1]=0.6792\n",
+      "Condition not met at step 4411. No data to save.\n",
       "4411\n",
+      "model_vars.get('Gini', 0)[-1]=0.6964\n",
+      "Condition not met at step 4412. No data to save.\n",
       "4412\n",
+      "model_vars.get('Gini', 0)[-1]=0.6992\n",
+      "Condition not met at step 4413. No data to save.\n",
       "4413\n",
+      "model_vars.get('Gini', 0)[-1]=0.7024\n",
+      "Condition met. Appended special results for step 4414 to test_path/special_results.parquet\n",
       "4414\n",
+      "model_vars.get('Gini', 0)[-1]=0.6839999999999999\n",
+      "Condition not met at step 4415. No data to save.\n",
       "4415\n",
+      "model_vars.get('Gini', 0)[-1]=0.6648000000000001\n",
+      "Condition not met at step 4416. No data to save.\n",
       "4416\n",
+      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
+      "Condition not met at step 4417. No data to save.\n",
       "4417\n",
+      "model_vars.get('Gini', 0)[-1]=0.6572\n",
+      "Condition not met at step 4418. No data to save.\n",
       "4418\n",
+      "model_vars.get('Gini', 0)[-1]=0.6652\n",
+      "Condition not met at step 4419. No data to save.\n",
       "4419\n",
+      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
+      "Condition not met at step 4420. No data to save.\n",
       "4420\n",
+      "model_vars.get('Gini', 0)[-1]=0.6718\n",
+      "Condition not met at step 4421. No data to save.\n",
       "4421\n",
+      "model_vars.get('Gini', 0)[-1]=0.6568\n",
+      "Condition not met at step 4422. No data to save.\n",
       "4422\n",
+      "model_vars.get('Gini', 0)[-1]=0.6662\n",
+      "Condition not met at step 4423. No data to save.\n",
       "4423\n",
+      "model_vars.get('Gini', 0)[-1]=0.6768000000000001\n",
+      "Condition not met at step 4424. No data to save.\n",
       "4424\n",
+      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
+      "Condition not met at step 4425. No data to save.\n",
       "4425\n",
+      "model_vars.get('Gini', 0)[-1]=0.6588\n",
+      "Condition not met at step 4426. No data to save.\n",
       "4426\n",
+      "model_vars.get('Gini', 0)[-1]=0.646\n",
+      "Condition not met at step 4427. No data to save.\n",
       "4427\n",
+      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
+      "Condition not met at step 4428. No data to save.\n",
       "4428\n",
+      "model_vars.get('Gini', 0)[-1]=0.6392\n",
+      "Condition not met at step 4429. No data to save.\n",
       "4429\n",
+      "model_vars.get('Gini', 0)[-1]=0.638\n",
+      "Condition not met at step 4430. No data to save.\n",
       "4430\n",
+      "model_vars.get('Gini', 0)[-1]=0.634\n",
+      "Condition not met at step 4431. No data to save.\n",
       "4431\n",
+      "model_vars.get('Gini', 0)[-1]=0.6152\n",
+      "Condition not met at step 4432. No data to save.\n",
       "4432\n",
+      "model_vars.get('Gini', 0)[-1]=0.6288\n",
+      "Condition not met at step 4433. No data to save.\n",
       "4433\n",
+      "model_vars.get('Gini', 0)[-1]=0.6284000000000001\n",
+      "Condition not met at step 4434. No data to save.\n",
       "4434\n",
+      "model_vars.get('Gini', 0)[-1]=0.6506000000000001\n",
+      "Condition not met at step 4435. No data to save.\n",
       "4435\n",
+      "model_vars.get('Gini', 0)[-1]=0.6836\n",
+      "Condition not met at step 4436. No data to save.\n",
       "4436\n",
+      "model_vars.get('Gini', 0)[-1]=0.6776\n",
+      "Condition not met at step 4437. No data to save.\n",
       "4437\n",
+      "model_vars.get('Gini', 0)[-1]=0.6762\n",
+      "Condition not met at step 4438. No data to save.\n",
       "4438\n",
+      "model_vars.get('Gini', 0)[-1]=0.694\n",
+      "Condition not met at step 4439. No data to save.\n",
       "4439\n",
+      "model_vars.get('Gini', 0)[-1]=0.6779999999999999\n",
+      "Condition not met at step 4440. No data to save.\n",
       "4440\n",
+      "model_vars.get('Gini', 0)[-1]=0.6772\n",
+      "Condition not met at step 4441. No data to save.\n",
       "4441\n",
+      "model_vars.get('Gini', 0)[-1]=0.6588\n",
+      "Condition not met at step 4442. No data to save.\n",
       "4442\n",
+      "model_vars.get('Gini', 0)[-1]=0.6784\n",
+      "Condition not met at step 4443. No data to save.\n",
       "4443\n",
+      "model_vars.get('Gini', 0)[-1]=0.6724\n",
+      "Condition not met at step 4444. No data to save.\n",
       "4444\n",
+      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
+      "Condition not met at step 4445. No data to save.\n",
       "4445\n",
+      "model_vars.get('Gini', 0)[-1]=0.6494\n",
+      "Condition not met at step 4446. No data to save.\n",
       "4446\n",
+      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
+      "Condition not met at step 4447. No data to save.\n",
       "4447\n",
+      "model_vars.get('Gini', 0)[-1]=0.6918\n",
+      "Condition not met at step 4448. No data to save.\n",
       "4448\n",
+      "model_vars.get('Gini', 0)[-1]=0.7088\n",
+      "Condition met. Appended special results for step 4449 to test_path/special_results.parquet\n",
       "4449\n",
+      "model_vars.get('Gini', 0)[-1]=0.7148\n",
+      "Condition met. Appended special results for step 4450 to test_path/special_results.parquet\n",
       "4450\n",
+      "model_vars.get('Gini', 0)[-1]=0.7328\n",
+      "Condition met. Appended special results for step 4451 to test_path/special_results.parquet\n",
       "4451\n",
+      "model_vars.get('Gini', 0)[-1]=0.735\n",
+      "Condition met. Appended special results for step 4452 to test_path/special_results.parquet\n",
       "4452\n",
+      "model_vars.get('Gini', 0)[-1]=0.7306\n",
+      "Condition met. Appended special results for step 4453 to test_path/special_results.parquet\n",
       "4453\n",
+      "model_vars.get('Gini', 0)[-1]=0.6958\n",
+      "Condition not met at step 4454. No data to save.\n",
       "4454\n",
+      "model_vars.get('Gini', 0)[-1]=0.7004\n",
+      "Condition met. Appended special results for step 4455 to test_path/special_results.parquet\n",
       "4455\n",
+      "model_vars.get('Gini', 0)[-1]=0.7174\n",
+      "Condition met. Appended special results for step 4456 to test_path/special_results.parquet\n",
       "4456\n",
+      "model_vars.get('Gini', 0)[-1]=0.718\n",
+      "Condition met. Appended special results for step 4457 to test_path/special_results.parquet\n",
       "4457\n",
+      "model_vars.get('Gini', 0)[-1]=0.7138\n",
+      "Condition met. Appended special results for step 4458 to test_path/special_results.parquet\n",
       "4458\n",
+      "model_vars.get('Gini', 0)[-1]=0.6984\n",
+      "Condition not met at step 4459. No data to save.\n",
       "4459\n",
+      "model_vars.get('Gini', 0)[-1]=0.7012\n",
+      "Condition met. Appended special results for step 4460 to test_path/special_results.parquet\n",
       "4460\n",
+      "model_vars.get('Gini', 0)[-1]=0.6988000000000001\n",
+      "Condition not met at step 4461. No data to save.\n",
       "4461\n",
+      "model_vars.get('Gini', 0)[-1]=0.7048\n",
+      "Condition met. Appended special results for step 4462 to test_path/special_results.parquet\n",
       "4462\n",
+      "model_vars.get('Gini', 0)[-1]=0.7070000000000001\n",
+      "Condition met. Appended special results for step 4463 to test_path/special_results.parquet\n",
       "4463\n",
+      "model_vars.get('Gini', 0)[-1]=0.7148\n",
+      "Condition met. Appended special results for step 4464 to test_path/special_results.parquet\n",
       "4464\n",
+      "model_vars.get('Gini', 0)[-1]=0.6912\n",
+      "Condition not met at step 4465. No data to save.\n",
       "4465\n",
+      "model_vars.get('Gini', 0)[-1]=0.6844\n",
+      "Condition not met at step 4466. No data to save.\n",
       "4466\n",
+      "model_vars.get('Gini', 0)[-1]=0.6752\n",
+      "Condition not met at step 4467. No data to save.\n",
       "4467\n",
+      "model_vars.get('Gini', 0)[-1]=0.6422\n",
+      "Condition not met at step 4468. No data to save.\n",
       "4468\n",
+      "model_vars.get('Gini', 0)[-1]=0.649\n",
+      "Condition not met at step 4469. No data to save.\n",
       "4469\n",
+      "model_vars.get('Gini', 0)[-1]=0.656\n",
+      "Condition not met at step 4470. No data to save.\n",
       "4470\n",
+      "model_vars.get('Gini', 0)[-1]=0.6334\n",
+      "Condition not met at step 4471. No data to save.\n",
       "4471\n",
+      "model_vars.get('Gini', 0)[-1]=0.6616\n",
+      "Condition not met at step 4472. No data to save.\n",
       "4472\n",
+      "model_vars.get('Gini', 0)[-1]=0.6756\n",
+      "Condition not met at step 4473. No data to save.\n",
       "4473\n",
+      "model_vars.get('Gini', 0)[-1]=0.6719999999999999\n",
+      "Condition not met at step 4474. No data to save.\n",
       "4474\n",
+      "model_vars.get('Gini', 0)[-1]=0.6752\n",
+      "Condition not met at step 4475. No data to save.\n",
       "4475\n",
+      "model_vars.get('Gini', 0)[-1]=0.6746000000000001\n",
+      "Condition not met at step 4476. No data to save.\n",
       "4476\n",
+      "model_vars.get('Gini', 0)[-1]=0.675\n",
+      "Condition not met at step 4477. No data to save.\n",
       "4477\n",
+      "model_vars.get('Gini', 0)[-1]=0.662\n",
+      "Condition not met at step 4478. No data to save.\n",
       "4478\n",
+      "model_vars.get('Gini', 0)[-1]=0.6492\n",
+      "Condition not met at step 4479. No data to save.\n",
       "4479\n",
+      "model_vars.get('Gini', 0)[-1]=0.62\n",
+      "Condition not met at step 4480. No data to save.\n",
       "4480\n",
+      "model_vars.get('Gini', 0)[-1]=0.6056\n",
+      "Condition not met at step 4481. No data to save.\n",
       "4481\n",
+      "model_vars.get('Gini', 0)[-1]=0.6634\n",
+      "Condition not met at step 4482. No data to save.\n",
       "4482\n",
+      "model_vars.get('Gini', 0)[-1]=0.6719999999999999\n",
+      "Condition not met at step 4483. No data to save.\n",
       "4483\n",
+      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
+      "Condition not met at step 4484. No data to save.\n",
       "4484\n",
+      "model_vars.get('Gini', 0)[-1]=0.6224000000000001\n",
+      "Condition not met at step 4485. No data to save.\n",
       "4485\n",
+      "model_vars.get('Gini', 0)[-1]=0.6574\n",
+      "Condition not met at step 4486. No data to save.\n",
       "4486\n",
+      "model_vars.get('Gini', 0)[-1]=0.6272\n",
+      "Condition not met at step 4487. No data to save.\n",
       "4487\n",
+      "model_vars.get('Gini', 0)[-1]=0.648\n",
+      "Condition not met at step 4488. No data to save.\n",
       "4488\n",
+      "model_vars.get('Gini', 0)[-1]=0.6752\n",
+      "Condition not met at step 4489. No data to save.\n",
       "4489\n",
+      "model_vars.get('Gini', 0)[-1]=0.685\n",
+      "Condition not met at step 4490. No data to save.\n",
       "4490\n",
+      "model_vars.get('Gini', 0)[-1]=0.6816\n",
+      "Condition not met at step 4491. No data to save.\n",
       "4491\n",
+      "model_vars.get('Gini', 0)[-1]=0.6882\n",
+      "Condition not met at step 4492. No data to save.\n",
       "4492\n",
+      "model_vars.get('Gini', 0)[-1]=0.6712\n",
+      "Condition not met at step 4493. No data to save.\n",
       "4493\n",
+      "model_vars.get('Gini', 0)[-1]=0.6596\n",
+      "Condition not met at step 4494. No data to save.\n",
       "4494\n",
+      "model_vars.get('Gini', 0)[-1]=0.6434\n",
+      "Condition not met at step 4495. No data to save.\n",
       "4495\n",
+      "model_vars.get('Gini', 0)[-1]=0.6592\n",
+      "Condition not met at step 4496. No data to save.\n",
       "4496\n",
+      "model_vars.get('Gini', 0)[-1]=0.6544\n",
+      "Condition not met at step 4497. No data to save.\n",
       "4497\n",
+      "model_vars.get('Gini', 0)[-1]=0.6496\n",
+      "Condition not met at step 4498. No data to save.\n",
       "4498\n",
+      "model_vars.get('Gini', 0)[-1]=0.6482\n",
+      "Condition not met at step 4499. No data to save.\n",
       "4499\n",
       "CALLED\n",
       "self.model._steps=4500\n",
       " TEST self.model._steps=4500\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "4400  0.7330\n",
-      "4401  0.7446\n",
-      "4402  0.7126\n",
-      "4403  0.7188\n",
-      "4404  0.7308\n",
+      "4400  0.6628\n",
+      "4401  0.6640\n",
+      "4402  0.6932\n",
+      "4403  0.7020\n",
+      "4404  0.6788\n",
       "...      ...\n",
-      "4495  0.6998\n",
-      "4496  0.6968\n",
-      "4497  0.6790\n",
-      "4498  0.6564\n",
-      "4499  0.6662\n",
+      "4495  0.6592\n",
+      "4496  0.6544\n",
+      "4497  0.6496\n",
+      "4498  0.6482\n",
+      "4499  0.6526\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_045.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_045.parquet'\n",
-      "Saving model to output_dir/model_data_045.parquet\n",
-      "Saving agent to output_dir/agent_data_045.parquet\n",
+      "model_file='test_path/model_data_045.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_045.parquet'\n",
+      "Saving model to test_path/model_data_045.parquet\n",
+      "Saving agent to test_path/agent_data_045.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
+      "Condition not met at step 4500. No data to save.\n",
       "4500\n",
+      "model_vars.get('Gini', 0)[-1]=0.6476\n",
+      "Condition not met at step 4501. No data to save.\n",
       "4501\n",
+      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
+      "Condition not met at step 4502. No data to save.\n",
       "4502\n",
+      "model_vars.get('Gini', 0)[-1]=0.6642\n",
+      "Condition not met at step 4503. No data to save.\n",
       "4503\n",
+      "model_vars.get('Gini', 0)[-1]=0.6734\n",
+      "Condition not met at step 4504. No data to save.\n",
       "4504\n",
+      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
+      "Condition not met at step 4505. No data to save.\n",
       "4505\n",
+      "model_vars.get('Gini', 0)[-1]=0.6972\n",
+      "Condition not met at step 4506. No data to save.\n",
       "4506\n",
+      "model_vars.get('Gini', 0)[-1]=0.6856\n",
+      "Condition not met at step 4507. No data to save.\n",
       "4507\n",
+      "model_vars.get('Gini', 0)[-1]=0.6859999999999999\n",
+      "Condition not met at step 4508. No data to save.\n",
       "4508\n",
+      "model_vars.get('Gini', 0)[-1]=0.6934\n",
+      "Condition not met at step 4509. No data to save.\n",
       "4509\n",
+      "model_vars.get('Gini', 0)[-1]=0.6946\n",
+      "Condition not met at step 4510. No data to save.\n",
       "4510\n",
+      "model_vars.get('Gini', 0)[-1]=0.692\n",
+      "Condition not met at step 4511. No data to save.\n",
       "4511\n",
+      "model_vars.get('Gini', 0)[-1]=0.6692\n",
+      "Condition not met at step 4512. No data to save.\n",
       "4512\n",
+      "model_vars.get('Gini', 0)[-1]=0.6494\n",
+      "Condition not met at step 4513. No data to save.\n",
       "4513\n",
+      "model_vars.get('Gini', 0)[-1]=0.6614\n",
+      "Condition not met at step 4514. No data to save.\n",
       "4514\n",
+      "model_vars.get('Gini', 0)[-1]=0.6468\n",
+      "Condition not met at step 4515. No data to save.\n",
       "4515\n",
+      "model_vars.get('Gini', 0)[-1]=0.6722\n",
+      "Condition not met at step 4516. No data to save.\n",
       "4516\n",
+      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
+      "Condition not met at step 4517. No data to save.\n",
       "4517\n",
+      "model_vars.get('Gini', 0)[-1]=0.6736\n",
+      "Condition not met at step 4518. No data to save.\n",
       "4518\n",
+      "model_vars.get('Gini', 0)[-1]=0.6774\n",
+      "Condition not met at step 4519. No data to save.\n",
       "4519\n",
+      "model_vars.get('Gini', 0)[-1]=0.6568\n",
+      "Condition not met at step 4520. No data to save.\n",
       "4520\n",
+      "model_vars.get('Gini', 0)[-1]=0.6734\n",
+      "Condition not met at step 4521. No data to save.\n",
       "4521\n",
+      "model_vars.get('Gini', 0)[-1]=0.6699999999999999\n",
+      "Condition not met at step 4522. No data to save.\n",
       "4522\n",
+      "model_vars.get('Gini', 0)[-1]=0.6428\n",
+      "Condition not met at step 4523. No data to save.\n",
       "4523\n",
+      "model_vars.get('Gini', 0)[-1]=0.6254\n",
+      "Condition not met at step 4524. No data to save.\n",
       "4524\n",
+      "model_vars.get('Gini', 0)[-1]=0.6564\n",
+      "Condition not met at step 4525. No data to save.\n",
       "4525\n",
+      "model_vars.get('Gini', 0)[-1]=0.6844\n",
+      "Condition not met at step 4526. No data to save.\n",
       "4526\n",
+      "model_vars.get('Gini', 0)[-1]=0.6848000000000001\n",
+      "Condition not met at step 4527. No data to save.\n",
       "4527\n",
+      "model_vars.get('Gini', 0)[-1]=0.6914\n",
+      "Condition not met at step 4528. No data to save.\n",
       "4528\n",
+      "model_vars.get('Gini', 0)[-1]=0.6932\n",
+      "Condition not met at step 4529. No data to save.\n",
       "4529\n",
+      "model_vars.get('Gini', 0)[-1]=0.6864\n",
+      "Condition not met at step 4530. No data to save.\n",
       "4530\n",
+      "model_vars.get('Gini', 0)[-1]=0.69\n",
+      "Condition not met at step 4531. No data to save.\n",
       "4531\n",
+      "model_vars.get('Gini', 0)[-1]=0.7010000000000001\n",
+      "Condition met. Appended special results for step 4532 to test_path/special_results.parquet\n",
       "4532\n",
+      "model_vars.get('Gini', 0)[-1]=0.6930000000000001\n",
+      "Condition not met at step 4533. No data to save.\n",
       "4533\n",
+      "model_vars.get('Gini', 0)[-1]=0.6876\n",
+      "Condition not met at step 4534. No data to save.\n",
       "4534\n",
+      "model_vars.get('Gini', 0)[-1]=0.6976\n",
+      "Condition not met at step 4535. No data to save.\n",
       "4535\n",
+      "model_vars.get('Gini', 0)[-1]=0.6832\n",
+      "Condition not met at step 4536. No data to save.\n",
       "4536\n",
+      "model_vars.get('Gini', 0)[-1]=0.7012\n",
+      "Condition met. Appended special results for step 4537 to test_path/special_results.parquet\n",
       "4537\n",
+      "model_vars.get('Gini', 0)[-1]=0.7032\n",
+      "Condition met. Appended special results for step 4538 to test_path/special_results.parquet\n",
       "4538\n",
+      "model_vars.get('Gini', 0)[-1]=0.7068\n",
+      "Condition met. Appended special results for step 4539 to test_path/special_results.parquet\n",
       "4539\n",
+      "model_vars.get('Gini', 0)[-1]=0.7058\n",
+      "Condition met. Appended special results for step 4540 to test_path/special_results.parquet\n",
       "4540\n",
+      "model_vars.get('Gini', 0)[-1]=0.7136\n",
+      "Condition met. Appended special results for step 4541 to test_path/special_results.parquet\n",
       "4541\n",
+      "model_vars.get('Gini', 0)[-1]=0.7108\n",
+      "Condition met. Appended special results for step 4542 to test_path/special_results.parquet\n",
       "4542\n",
+      "model_vars.get('Gini', 0)[-1]=0.7288\n",
+      "Condition met. Appended special results for step 4543 to test_path/special_results.parquet\n",
       "4543\n",
+      "model_vars.get('Gini', 0)[-1]=0.7363999999999999\n",
+      "Condition met. Appended special results for step 4544 to test_path/special_results.parquet\n",
       "4544\n",
+      "model_vars.get('Gini', 0)[-1]=0.742\n",
+      "Condition met. Appended special results for step 4545 to test_path/special_results.parquet\n",
       "4545\n",
+      "model_vars.get('Gini', 0)[-1]=0.7448\n",
+      "Condition met. Appended special results for step 4546 to test_path/special_results.parquet\n",
       "4546\n",
+      "model_vars.get('Gini', 0)[-1]=0.7462\n",
+      "Condition met. Appended special results for step 4547 to test_path/special_results.parquet\n",
       "4547\n",
+      "model_vars.get('Gini', 0)[-1]=0.7472000000000001\n",
+      "Condition met. Appended special results for step 4548 to test_path/special_results.parquet\n",
       "4548\n",
+      "model_vars.get('Gini', 0)[-1]=0.7374\n",
+      "Condition met. Appended special results for step 4549 to test_path/special_results.parquet\n",
       "4549\n",
+      "model_vars.get('Gini', 0)[-1]=0.7256\n",
+      "Condition met. Appended special results for step 4550 to test_path/special_results.parquet\n",
       "4550\n",
+      "model_vars.get('Gini', 0)[-1]=0.7343999999999999\n",
+      "Condition met. Appended special results for step 4551 to test_path/special_results.parquet\n",
       "4551\n",
+      "model_vars.get('Gini', 0)[-1]=0.7392000000000001\n",
+      "Condition met. Appended special results for step 4552 to test_path/special_results.parquet\n",
       "4552\n",
+      "model_vars.get('Gini', 0)[-1]=0.7392000000000001\n",
+      "Condition met. Appended special results for step 4553 to test_path/special_results.parquet\n",
       "4553\n",
+      "model_vars.get('Gini', 0)[-1]=0.7422\n",
+      "Condition met. Appended special results for step 4554 to test_path/special_results.parquet\n",
       "4554\n",
+      "model_vars.get('Gini', 0)[-1]=0.7504\n",
+      "Condition met. Appended special results for step 4555 to test_path/special_results.parquet\n",
       "4555\n",
+      "model_vars.get('Gini', 0)[-1]=0.7488\n",
+      "Condition met. Appended special results for step 4556 to test_path/special_results.parquet\n",
       "4556\n",
+      "model_vars.get('Gini', 0)[-1]=0.7596\n",
+      "Condition met. Appended special results for step 4557 to test_path/special_results.parquet\n",
       "4557\n",
+      "model_vars.get('Gini', 0)[-1]=0.7412000000000001\n",
+      "Condition met. Appended special results for step 4558 to test_path/special_results.parquet\n",
       "4558\n",
+      "model_vars.get('Gini', 0)[-1]=0.7328\n",
+      "Condition met. Appended special results for step 4559 to test_path/special_results.parquet\n",
       "4559\n",
+      "model_vars.get('Gini', 0)[-1]=0.7223999999999999\n",
+      "Condition met. Appended special results for step 4560 to test_path/special_results.parquet\n",
       "4560\n",
+      "model_vars.get('Gini', 0)[-1]=0.7270000000000001\n",
+      "Condition met. Appended special results for step 4561 to test_path/special_results.parquet\n",
       "4561\n",
+      "model_vars.get('Gini', 0)[-1]=0.7163999999999999\n",
+      "Condition met. Appended special results for step 4562 to test_path/special_results.parquet\n",
       "4562\n",
+      "model_vars.get('Gini', 0)[-1]=0.7108\n",
+      "Condition met. Appended special results for step 4563 to test_path/special_results.parquet\n",
       "4563\n",
+      "model_vars.get('Gini', 0)[-1]=0.7178\n",
+      "Condition met. Appended special results for step 4564 to test_path/special_results.parquet\n",
       "4564\n",
+      "model_vars.get('Gini', 0)[-1]=0.7168\n",
+      "Condition met. Appended special results for step 4565 to test_path/special_results.parquet\n",
       "4565\n",
+      "model_vars.get('Gini', 0)[-1]=0.7054\n",
+      "Condition met. Appended special results for step 4566 to test_path/special_results.parquet\n",
       "4566\n",
+      "model_vars.get('Gini', 0)[-1]=0.6782\n",
+      "Condition not met at step 4567. No data to save.\n",
       "4567\n",
+      "model_vars.get('Gini', 0)[-1]=0.6892\n",
+      "Condition not met at step 4568. No data to save.\n",
       "4568\n",
+      "model_vars.get('Gini', 0)[-1]=0.6842\n",
+      "Condition not met at step 4569. No data to save.\n",
       "4569\n",
+      "model_vars.get('Gini', 0)[-1]=0.7094\n",
+      "Condition met. Appended special results for step 4570 to test_path/special_results.parquet\n",
       "4570\n",
+      "model_vars.get('Gini', 0)[-1]=0.6864\n",
+      "Condition not met at step 4571. No data to save.\n",
       "4571\n",
+      "model_vars.get('Gini', 0)[-1]=0.6832\n",
+      "Condition not met at step 4572. No data to save.\n",
       "4572\n",
+      "model_vars.get('Gini', 0)[-1]=0.7064\n",
+      "Condition met. Appended special results for step 4573 to test_path/special_results.parquet\n",
       "4573\n",
+      "model_vars.get('Gini', 0)[-1]=0.6996\n",
+      "Condition not met at step 4574. No data to save.\n",
       "4574\n",
+      "model_vars.get('Gini', 0)[-1]=0.7054\n",
+      "Condition met. Appended special results for step 4575 to test_path/special_results.parquet\n",
       "4575\n",
+      "model_vars.get('Gini', 0)[-1]=0.6778\n",
+      "Condition not met at step 4576. No data to save.\n",
       "4576\n",
+      "model_vars.get('Gini', 0)[-1]=0.6784\n",
+      "Condition not met at step 4577. No data to save.\n",
       "4577\n",
+      "model_vars.get('Gini', 0)[-1]=0.6902\n",
+      "Condition not met at step 4578. No data to save.\n",
       "4578\n",
+      "model_vars.get('Gini', 0)[-1]=0.6859999999999999\n",
+      "Condition not met at step 4579. No data to save.\n",
       "4579\n",
+      "model_vars.get('Gini', 0)[-1]=0.7088\n",
+      "Condition met. Appended special results for step 4580 to test_path/special_results.parquet\n",
       "4580\n",
+      "model_vars.get('Gini', 0)[-1]=0.7048\n",
+      "Condition met. Appended special results for step 4581 to test_path/special_results.parquet\n",
       "4581\n",
+      "model_vars.get('Gini', 0)[-1]=0.6916\n",
+      "Condition not met at step 4582. No data to save.\n",
       "4582\n",
+      "model_vars.get('Gini', 0)[-1]=0.6968000000000001\n",
+      "Condition not met at step 4583. No data to save.\n",
       "4583\n",
+      "model_vars.get('Gini', 0)[-1]=0.708\n",
+      "Condition met. Appended special results for step 4584 to test_path/special_results.parquet\n",
       "4584\n",
+      "model_vars.get('Gini', 0)[-1]=0.692\n",
+      "Condition not met at step 4585. No data to save.\n",
       "4585\n",
+      "model_vars.get('Gini', 0)[-1]=0.7034\n",
+      "Condition met. Appended special results for step 4586 to test_path/special_results.parquet\n",
       "4586\n",
+      "model_vars.get('Gini', 0)[-1]=0.7014\n",
+      "Condition met. Appended special results for step 4587 to test_path/special_results.parquet\n",
       "4587\n",
+      "model_vars.get('Gini', 0)[-1]=0.6872\n",
+      "Condition not met at step 4588. No data to save.\n",
       "4588\n",
+      "model_vars.get('Gini', 0)[-1]=0.6774\n",
+      "Condition not met at step 4589. No data to save.\n",
       "4589\n",
+      "model_vars.get('Gini', 0)[-1]=0.6950000000000001\n",
+      "Condition not met at step 4590. No data to save.\n",
       "4590\n",
+      "model_vars.get('Gini', 0)[-1]=0.6862\n",
+      "Condition not met at step 4591. No data to save.\n",
       "4591\n",
+      "model_vars.get('Gini', 0)[-1]=0.7018\n",
+      "Condition met. Appended special results for step 4592 to test_path/special_results.parquet\n",
       "4592\n",
+      "model_vars.get('Gini', 0)[-1]=0.664\n",
+      "Condition not met at step 4593. No data to save.\n",
       "4593\n",
+      "model_vars.get('Gini', 0)[-1]=0.6890000000000001\n",
+      "Condition not met at step 4594. No data to save.\n",
       "4594\n",
+      "model_vars.get('Gini', 0)[-1]=0.6694\n",
+      "Condition not met at step 4595. No data to save.\n",
       "4595\n",
+      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
+      "Condition not met at step 4596. No data to save.\n",
       "4596\n",
+      "model_vars.get('Gini', 0)[-1]=0.6378\n",
+      "Condition not met at step 4597. No data to save.\n",
       "4597\n",
+      "model_vars.get('Gini', 0)[-1]=0.6602\n",
+      "Condition not met at step 4598. No data to save.\n",
       "4598\n",
+      "model_vars.get('Gini', 0)[-1]=0.6638\n",
+      "Condition not met at step 4599. No data to save.\n",
       "4599\n",
       "CALLED\n",
       "self.model._steps=4600\n",
       " TEST self.model._steps=4600\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "4500  0.6500\n",
-      "4501  0.6568\n",
-      "4502  0.6662\n",
-      "4503  0.6720\n",
-      "4504  0.6622\n",
+      "4500  0.6476\n",
+      "4501  0.6484\n",
+      "4502  0.6642\n",
+      "4503  0.6734\n",
+      "4504  0.6740\n",
       "...      ...\n",
-      "4595  0.7024\n",
-      "4596  0.6896\n",
-      "4597  0.7136\n",
-      "4598  0.7180\n",
-      "4599  0.7112\n",
+      "4595  0.6558\n",
+      "4596  0.6378\n",
+      "4597  0.6602\n",
+      "4598  0.6638\n",
+      "4599  0.6902\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_046.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_046.parquet'\n",
-      "Saving model to output_dir/model_data_046.parquet\n",
-      "Saving agent to output_dir/agent_data_046.parquet\n",
+      "model_file='test_path/model_data_046.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_046.parquet'\n",
+      "Saving model to test_path/model_data_046.parquet\n",
+      "Saving agent to test_path/agent_data_046.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6902\n",
+      "Condition not met at step 4600. No data to save.\n",
       "4600\n",
+      "model_vars.get('Gini', 0)[-1]=0.7034\n",
+      "Condition met. Appended special results for step 4601 to test_path/special_results.parquet\n",
       "4601\n",
+      "model_vars.get('Gini', 0)[-1]=0.7354\n",
+      "Condition met. Appended special results for step 4602 to test_path/special_results.parquet\n",
       "4602\n",
+      "model_vars.get('Gini', 0)[-1]=0.7234\n",
+      "Condition met. Appended special results for step 4603 to test_path/special_results.parquet\n",
       "4603\n",
+      "model_vars.get('Gini', 0)[-1]=0.6974\n",
+      "Condition not met at step 4604. No data to save.\n",
       "4604\n",
+      "model_vars.get('Gini', 0)[-1]=0.6846\n",
+      "Condition not met at step 4605. No data to save.\n",
       "4605\n",
+      "model_vars.get('Gini', 0)[-1]=0.6732\n",
+      "Condition not met at step 4606. No data to save.\n",
       "4606\n",
+      "model_vars.get('Gini', 0)[-1]=0.6626000000000001\n",
+      "Condition not met at step 4607. No data to save.\n",
       "4607\n",
+      "model_vars.get('Gini', 0)[-1]=0.6592\n",
+      "Condition not met at step 4608. No data to save.\n",
       "4608\n",
+      "model_vars.get('Gini', 0)[-1]=0.6802\n",
+      "Condition not met at step 4609. No data to save.\n",
       "4609\n",
+      "model_vars.get('Gini', 0)[-1]=0.6864\n",
+      "Condition not met at step 4610. No data to save.\n",
       "4610\n",
+      "model_vars.get('Gini', 0)[-1]=0.679\n",
+      "Condition not met at step 4611. No data to save.\n",
       "4611\n",
+      "model_vars.get('Gini', 0)[-1]=0.7066\n",
+      "Condition met. Appended special results for step 4612 to test_path/special_results.parquet\n",
       "4612\n",
+      "model_vars.get('Gini', 0)[-1]=0.6932\n",
+      "Condition not met at step 4613. No data to save.\n",
       "4613\n",
+      "model_vars.get('Gini', 0)[-1]=0.7014\n",
+      "Condition met. Appended special results for step 4614 to test_path/special_results.parquet\n",
       "4614\n",
+      "model_vars.get('Gini', 0)[-1]=0.6866\n",
+      "Condition not met at step 4615. No data to save.\n",
       "4615\n",
+      "model_vars.get('Gini', 0)[-1]=0.6802\n",
+      "Condition not met at step 4616. No data to save.\n",
       "4616\n",
+      "model_vars.get('Gini', 0)[-1]=0.6886\n",
+      "Condition not met at step 4617. No data to save.\n",
       "4617\n",
+      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
+      "Condition not met at step 4618. No data to save.\n",
       "4618\n",
+      "model_vars.get('Gini', 0)[-1]=0.6724\n",
+      "Condition not met at step 4619. No data to save.\n",
       "4619\n",
+      "model_vars.get('Gini', 0)[-1]=0.6714\n",
+      "Condition not met at step 4620. No data to save.\n",
       "4620\n",
+      "model_vars.get('Gini', 0)[-1]=0.6886\n",
+      "Condition not met at step 4621. No data to save.\n",
       "4621\n",
+      "model_vars.get('Gini', 0)[-1]=0.687\n",
+      "Condition not met at step 4622. No data to save.\n",
       "4622\n",
+      "model_vars.get('Gini', 0)[-1]=0.6786000000000001\n",
+      "Condition not met at step 4623. No data to save.\n",
       "4623\n",
+      "model_vars.get('Gini', 0)[-1]=0.7052\n",
+      "Condition met. Appended special results for step 4624 to test_path/special_results.parquet\n",
       "4624\n",
+      "model_vars.get('Gini', 0)[-1]=0.6961999999999999\n",
+      "Condition not met at step 4625. No data to save.\n",
       "4625\n",
+      "model_vars.get('Gini', 0)[-1]=0.6832\n",
+      "Condition not met at step 4626. No data to save.\n",
       "4626\n",
+      "model_vars.get('Gini', 0)[-1]=0.6708000000000001\n",
+      "Condition not met at step 4627. No data to save.\n",
       "4627\n",
+      "model_vars.get('Gini', 0)[-1]=0.685\n",
+      "Condition not met at step 4628. No data to save.\n",
       "4628\n",
+      "model_vars.get('Gini', 0)[-1]=0.6576\n",
+      "Condition not met at step 4629. No data to save.\n",
       "4629\n",
+      "model_vars.get('Gini', 0)[-1]=0.6878\n",
+      "Condition not met at step 4630. No data to save.\n",
       "4630\n",
+      "model_vars.get('Gini', 0)[-1]=0.6992\n",
+      "Condition not met at step 4631. No data to save.\n",
       "4631\n",
+      "model_vars.get('Gini', 0)[-1]=0.6916\n",
+      "Condition not met at step 4632. No data to save.\n",
       "4632\n",
+      "model_vars.get('Gini', 0)[-1]=0.6782\n",
+      "Condition not met at step 4633. No data to save.\n",
       "4633\n",
+      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
+      "Condition not met at step 4634. No data to save.\n",
       "4634\n",
+      "model_vars.get('Gini', 0)[-1]=0.7024\n",
+      "Condition met. Appended special results for step 4635 to test_path/special_results.parquet\n",
       "4635\n",
+      "model_vars.get('Gini', 0)[-1]=0.6872\n",
+      "Condition not met at step 4636. No data to save.\n",
       "4636\n",
+      "model_vars.get('Gini', 0)[-1]=0.6814\n",
+      "Condition not met at step 4637. No data to save.\n",
       "4637\n",
+      "model_vars.get('Gini', 0)[-1]=0.6679999999999999\n",
+      "Condition not met at step 4638. No data to save.\n",
       "4638\n",
+      "model_vars.get('Gini', 0)[-1]=0.692\n",
+      "Condition not met at step 4639. No data to save.\n",
       "4639\n",
+      "model_vars.get('Gini', 0)[-1]=0.6762\n",
+      "Condition not met at step 4640. No data to save.\n",
       "4640\n",
+      "model_vars.get('Gini', 0)[-1]=0.6814\n",
+      "Condition not met at step 4641. No data to save.\n",
       "4641\n",
+      "model_vars.get('Gini', 0)[-1]=0.6642\n",
+      "Condition not met at step 4642. No data to save.\n",
       "4642\n",
+      "model_vars.get('Gini', 0)[-1]=0.6886\n",
+      "Condition not met at step 4643. No data to save.\n",
       "4643\n",
+      "model_vars.get('Gini', 0)[-1]=0.6902\n",
+      "Condition not met at step 4644. No data to save.\n",
       "4644\n",
+      "model_vars.get('Gini', 0)[-1]=0.6828000000000001\n",
+      "Condition not met at step 4645. No data to save.\n",
       "4645\n",
+      "model_vars.get('Gini', 0)[-1]=0.6848000000000001\n",
+      "Condition not met at step 4646. No data to save.\n",
       "4646\n",
+      "model_vars.get('Gini', 0)[-1]=0.6852\n",
+      "Condition not met at step 4647. No data to save.\n",
       "4647\n",
+      "model_vars.get('Gini', 0)[-1]=0.704\n",
+      "Condition met. Appended special results for step 4648 to test_path/special_results.parquet\n",
       "4648\n",
+      "model_vars.get('Gini', 0)[-1]=0.6954\n",
+      "Condition not met at step 4649. No data to save.\n",
       "4649\n",
+      "model_vars.get('Gini', 0)[-1]=0.7041999999999999\n",
+      "Condition met. Appended special results for step 4650 to test_path/special_results.parquet\n",
       "4650\n",
+      "model_vars.get('Gini', 0)[-1]=0.6734\n",
+      "Condition not met at step 4651. No data to save.\n",
       "4651\n",
+      "model_vars.get('Gini', 0)[-1]=0.6758\n",
+      "Condition not met at step 4652. No data to save.\n",
       "4652\n",
+      "model_vars.get('Gini', 0)[-1]=0.6956\n",
+      "Condition not met at step 4653. No data to save.\n",
       "4653\n",
+      "model_vars.get('Gini', 0)[-1]=0.6574\n",
+      "Condition not met at step 4654. No data to save.\n",
       "4654\n",
+      "model_vars.get('Gini', 0)[-1]=0.6762\n",
+      "Condition not met at step 4655. No data to save.\n",
       "4655\n",
+      "model_vars.get('Gini', 0)[-1]=0.6652\n",
+      "Condition not met at step 4656. No data to save.\n",
       "4656\n",
+      "model_vars.get('Gini', 0)[-1]=0.6568\n",
+      "Condition not met at step 4657. No data to save.\n",
       "4657\n",
+      "model_vars.get('Gini', 0)[-1]=0.6472\n",
+      "Condition not met at step 4658. No data to save.\n",
       "4658\n",
+      "model_vars.get('Gini', 0)[-1]=0.655\n",
+      "Condition not met at step 4659. No data to save.\n",
       "4659\n",
+      "model_vars.get('Gini', 0)[-1]=0.6634\n",
+      "Condition not met at step 4660. No data to save.\n",
       "4660\n",
+      "model_vars.get('Gini', 0)[-1]=0.6684\n",
+      "Condition not met at step 4661. No data to save.\n",
       "4661\n",
+      "model_vars.get('Gini', 0)[-1]=0.649\n",
+      "Condition not met at step 4662. No data to save.\n",
       "4662\n",
+      "model_vars.get('Gini', 0)[-1]=0.6684\n",
+      "Condition not met at step 4663. No data to save.\n",
       "4663\n",
+      "model_vars.get('Gini', 0)[-1]=0.6584\n",
+      "Condition not met at step 4664. No data to save.\n",
       "4664\n",
+      "model_vars.get('Gini', 0)[-1]=0.6542\n",
+      "Condition not met at step 4665. No data to save.\n",
       "4665\n",
+      "model_vars.get('Gini', 0)[-1]=0.6278\n",
+      "Condition not met at step 4666. No data to save.\n",
       "4666\n",
+      "model_vars.get('Gini', 0)[-1]=0.6246\n",
+      "Condition not met at step 4667. No data to save.\n",
       "4667\n",
+      "model_vars.get('Gini', 0)[-1]=0.6434\n",
+      "Condition not met at step 4668. No data to save.\n",
       "4668\n",
+      "model_vars.get('Gini', 0)[-1]=0.683\n",
+      "Condition not met at step 4669. No data to save.\n",
       "4669\n",
+      "model_vars.get('Gini', 0)[-1]=0.6936\n",
+      "Condition not met at step 4670. No data to save.\n",
       "4670\n",
+      "model_vars.get('Gini', 0)[-1]=0.679\n",
+      "Condition not met at step 4671. No data to save.\n",
       "4671\n",
+      "model_vars.get('Gini', 0)[-1]=0.6512\n",
+      "Condition not met at step 4672. No data to save.\n",
       "4672\n",
+      "model_vars.get('Gini', 0)[-1]=0.6472\n",
+      "Condition not met at step 4673. No data to save.\n",
       "4673\n",
+      "model_vars.get('Gini', 0)[-1]=0.6416\n",
+      "Condition not met at step 4674. No data to save.\n",
       "4674\n",
+      "model_vars.get('Gini', 0)[-1]=0.6302\n",
+      "Condition not met at step 4675. No data to save.\n",
       "4675\n",
+      "model_vars.get('Gini', 0)[-1]=0.6252\n",
+      "Condition not met at step 4676. No data to save.\n",
       "4676\n",
+      "model_vars.get('Gini', 0)[-1]=0.6082000000000001\n",
+      "Condition not met at step 4677. No data to save.\n",
       "4677\n",
+      "model_vars.get('Gini', 0)[-1]=0.658\n",
+      "Condition not met at step 4678. No data to save.\n",
       "4678\n",
+      "model_vars.get('Gini', 0)[-1]=0.6382\n",
+      "Condition not met at step 4679. No data to save.\n",
       "4679\n",
+      "model_vars.get('Gini', 0)[-1]=0.6416\n",
+      "Condition not met at step 4680. No data to save.\n",
       "4680\n",
+      "model_vars.get('Gini', 0)[-1]=0.6175999999999999\n",
+      "Condition not met at step 4681. No data to save.\n",
       "4681\n",
+      "model_vars.get('Gini', 0)[-1]=0.6284000000000001\n",
+      "Condition not met at step 4682. No data to save.\n",
       "4682\n",
+      "model_vars.get('Gini', 0)[-1]=0.6195999999999999\n",
+      "Condition not met at step 4683. No data to save.\n",
       "4683\n",
+      "model_vars.get('Gini', 0)[-1]=0.6324000000000001\n",
+      "Condition not met at step 4684. No data to save.\n",
       "4684\n",
+      "model_vars.get('Gini', 0)[-1]=0.6144000000000001\n",
+      "Condition not met at step 4685. No data to save.\n",
       "4685\n",
+      "model_vars.get('Gini', 0)[-1]=0.6262000000000001\n",
+      "Condition not met at step 4686. No data to save.\n",
       "4686\n",
+      "model_vars.get('Gini', 0)[-1]=0.5698000000000001\n",
+      "Condition not met at step 4687. No data to save.\n",
       "4687\n",
+      "model_vars.get('Gini', 0)[-1]=0.5482\n",
+      "Condition not met at step 4688. No data to save.\n",
       "4688\n",
+      "model_vars.get('Gini', 0)[-1]=0.5798\n",
+      "Condition not met at step 4689. No data to save.\n",
       "4689\n",
+      "model_vars.get('Gini', 0)[-1]=0.607\n",
+      "Condition not met at step 4690. No data to save.\n",
       "4690\n",
+      "model_vars.get('Gini', 0)[-1]=0.5962000000000001\n",
+      "Condition not met at step 4691. No data to save.\n",
       "4691\n",
+      "model_vars.get('Gini', 0)[-1]=0.5928\n",
+      "Condition not met at step 4692. No data to save.\n",
       "4692\n",
+      "model_vars.get('Gini', 0)[-1]=0.5576\n",
+      "Condition not met at step 4693. No data to save.\n",
       "4693\n",
+      "model_vars.get('Gini', 0)[-1]=0.5408\n",
+      "Condition not met at step 4694. No data to save.\n",
       "4694\n",
+      "model_vars.get('Gini', 0)[-1]=0.5696\n",
+      "Condition not met at step 4695. No data to save.\n",
       "4695\n",
+      "model_vars.get('Gini', 0)[-1]=0.5686\n",
+      "Condition not met at step 4696. No data to save.\n",
       "4696\n",
+      "model_vars.get('Gini', 0)[-1]=0.5356000000000001\n",
+      "Condition not met at step 4697. No data to save.\n",
       "4697\n",
+      "model_vars.get('Gini', 0)[-1]=0.5691999999999999\n",
+      "Condition not met at step 4698. No data to save.\n",
       "4698\n",
+      "model_vars.get('Gini', 0)[-1]=0.5406\n",
+      "Condition not met at step 4699. No data to save.\n",
       "4699\n",
       "CALLED\n",
       "self.model._steps=4700\n",
       " TEST self.model._steps=4700\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "4600  0.6964\n",
-      "4601  0.6790\n",
-      "4602  0.6760\n",
-      "4603  0.6958\n",
-      "4604  0.6650\n",
+      "4600  0.7034\n",
+      "4601  0.7354\n",
+      "4602  0.7234\n",
+      "4603  0.6974\n",
+      "4604  0.6846\n",
       "...      ...\n",
-      "4695  0.6366\n",
-      "4696  0.6312\n",
-      "4697  0.6948\n",
-      "4698  0.6788\n",
-      "4699  0.6832\n",
+      "4695  0.5686\n",
+      "4696  0.5356\n",
+      "4697  0.5692\n",
+      "4698  0.5406\n",
+      "4699  0.5570\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_047.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_047.parquet'\n",
-      "Saving model to output_dir/model_data_047.parquet\n",
-      "Saving agent to output_dir/agent_data_047.parquet\n",
+      "model_file='test_path/model_data_047.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_047.parquet'\n",
+      "Saving model to test_path/model_data_047.parquet\n",
+      "Saving agent to test_path/agent_data_047.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.5569999999999999\n",
+      "Condition not met at step 4700. No data to save.\n",
       "4700\n",
+      "model_vars.get('Gini', 0)[-1]=0.5552\n",
+      "Condition not met at step 4701. No data to save.\n",
       "4701\n",
+      "model_vars.get('Gini', 0)[-1]=0.5780000000000001\n",
+      "Condition not met at step 4702. No data to save.\n",
       "4702\n",
+      "model_vars.get('Gini', 0)[-1]=0.5768\n",
+      "Condition not met at step 4703. No data to save.\n",
       "4703\n",
+      "model_vars.get('Gini', 0)[-1]=0.599\n",
+      "Condition not met at step 4704. No data to save.\n",
       "4704\n",
+      "model_vars.get('Gini', 0)[-1]=0.6172\n",
+      "Condition not met at step 4705. No data to save.\n",
       "4705\n",
+      "model_vars.get('Gini', 0)[-1]=0.6384000000000001\n",
+      "Condition not met at step 4706. No data to save.\n",
       "4706\n",
+      "model_vars.get('Gini', 0)[-1]=0.6576\n",
+      "Condition not met at step 4707. No data to save.\n",
       "4707\n",
+      "model_vars.get('Gini', 0)[-1]=0.649\n",
+      "Condition not met at step 4708. No data to save.\n",
       "4708\n",
+      "model_vars.get('Gini', 0)[-1]=0.634\n",
+      "Condition not met at step 4709. No data to save.\n",
       "4709\n",
+      "model_vars.get('Gini', 0)[-1]=0.6382\n",
+      "Condition not met at step 4710. No data to save.\n",
       "4710\n",
+      "model_vars.get('Gini', 0)[-1]=0.651\n",
+      "Condition not met at step 4711. No data to save.\n",
       "4711\n",
+      "model_vars.get('Gini', 0)[-1]=0.6442\n",
+      "Condition not met at step 4712. No data to save.\n",
       "4712\n",
+      "model_vars.get('Gini', 0)[-1]=0.6226\n",
+      "Condition not met at step 4713. No data to save.\n",
       "4713\n",
+      "model_vars.get('Gini', 0)[-1]=0.6288\n",
+      "Condition not met at step 4714. No data to save.\n",
       "4714\n",
+      "model_vars.get('Gini', 0)[-1]=0.6234\n",
+      "Condition not met at step 4715. No data to save.\n",
       "4715\n",
+      "model_vars.get('Gini', 0)[-1]=0.6244000000000001\n",
+      "Condition not met at step 4716. No data to save.\n",
       "4716\n",
+      "model_vars.get('Gini', 0)[-1]=0.6548\n",
+      "Condition not met at step 4717. No data to save.\n",
       "4717\n",
+      "model_vars.get('Gini', 0)[-1]=0.628\n",
+      "Condition not met at step 4718. No data to save.\n",
       "4718\n",
+      "model_vars.get('Gini', 0)[-1]=0.6344000000000001\n",
+      "Condition not met at step 4719. No data to save.\n",
       "4719\n",
+      "model_vars.get('Gini', 0)[-1]=0.64\n",
+      "Condition not met at step 4720. No data to save.\n",
       "4720\n",
+      "model_vars.get('Gini', 0)[-1]=0.593\n",
+      "Condition not met at step 4721. No data to save.\n",
       "4721\n",
+      "model_vars.get('Gini', 0)[-1]=0.6142000000000001\n",
+      "Condition not met at step 4722. No data to save.\n",
       "4722\n",
+      "model_vars.get('Gini', 0)[-1]=0.6392\n",
+      "Condition not met at step 4723. No data to save.\n",
       "4723\n",
+      "model_vars.get('Gini', 0)[-1]=0.6352\n",
+      "Condition not met at step 4724. No data to save.\n",
       "4724\n",
+      "model_vars.get('Gini', 0)[-1]=0.6264000000000001\n",
+      "Condition not met at step 4725. No data to save.\n",
       "4725\n",
+      "model_vars.get('Gini', 0)[-1]=0.6128\n",
+      "Condition not met at step 4726. No data to save.\n",
       "4726\n",
+      "model_vars.get('Gini', 0)[-1]=0.6020000000000001\n",
+      "Condition not met at step 4727. No data to save.\n",
       "4727\n",
+      "model_vars.get('Gini', 0)[-1]=0.6134\n",
+      "Condition not met at step 4728. No data to save.\n",
       "4728\n",
+      "model_vars.get('Gini', 0)[-1]=0.6076\n",
+      "Condition not met at step 4729. No data to save.\n",
       "4729\n",
+      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
+      "Condition not met at step 4730. No data to save.\n",
       "4730\n",
+      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
+      "Condition not met at step 4731. No data to save.\n",
       "4731\n",
+      "model_vars.get('Gini', 0)[-1]=0.6568\n",
+      "Condition not met at step 4732. No data to save.\n",
       "4732\n",
+      "model_vars.get('Gini', 0)[-1]=0.6332\n",
+      "Condition not met at step 4733. No data to save.\n",
       "4733\n",
+      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
+      "Condition not met at step 4734. No data to save.\n",
       "4734\n",
+      "model_vars.get('Gini', 0)[-1]=0.6888000000000001\n",
+      "Condition not met at step 4735. No data to save.\n",
       "4735\n",
+      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
+      "Condition not met at step 4736. No data to save.\n",
       "4736\n",
+      "model_vars.get('Gini', 0)[-1]=0.7058\n",
+      "Condition met. Appended special results for step 4737 to test_path/special_results.parquet\n",
       "4737\n",
+      "model_vars.get('Gini', 0)[-1]=0.7050000000000001\n",
+      "Condition met. Appended special results for step 4738 to test_path/special_results.parquet\n",
       "4738\n",
+      "model_vars.get('Gini', 0)[-1]=0.6996\n",
+      "Condition not met at step 4739. No data to save.\n",
       "4739\n",
+      "model_vars.get('Gini', 0)[-1]=0.6988000000000001\n",
+      "Condition not met at step 4740. No data to save.\n",
       "4740\n",
+      "model_vars.get('Gini', 0)[-1]=0.6944\n",
+      "Condition not met at step 4741. No data to save.\n",
       "4741\n",
+      "model_vars.get('Gini', 0)[-1]=0.6902\n",
+      "Condition not met at step 4742. No data to save.\n",
       "4742\n",
+      "model_vars.get('Gini', 0)[-1]=0.6548\n",
+      "Condition not met at step 4743. No data to save.\n",
       "4743\n",
+      "model_vars.get('Gini', 0)[-1]=0.6508\n",
+      "Condition not met at step 4744. No data to save.\n",
       "4744\n",
+      "model_vars.get('Gini', 0)[-1]=0.6738\n",
+      "Condition not met at step 4745. No data to save.\n",
       "4745\n",
+      "model_vars.get('Gini', 0)[-1]=0.646\n",
+      "Condition not met at step 4746. No data to save.\n",
       "4746\n",
+      "model_vars.get('Gini', 0)[-1]=0.658\n",
+      "Condition not met at step 4747. No data to save.\n",
       "4747\n",
+      "model_vars.get('Gini', 0)[-1]=0.6874\n",
+      "Condition not met at step 4748. No data to save.\n",
       "4748\n",
+      "model_vars.get('Gini', 0)[-1]=0.702\n",
+      "Condition met. Appended special results for step 4749 to test_path/special_results.parquet\n",
       "4749\n",
+      "model_vars.get('Gini', 0)[-1]=0.6988000000000001\n",
+      "Condition not met at step 4750. No data to save.\n",
       "4750\n",
+      "model_vars.get('Gini', 0)[-1]=0.6884\n",
+      "Condition not met at step 4751. No data to save.\n",
       "4751\n",
+      "model_vars.get('Gini', 0)[-1]=0.6658\n",
+      "Condition not met at step 4752. No data to save.\n",
       "4752\n",
+      "model_vars.get('Gini', 0)[-1]=0.6754\n",
+      "Condition not met at step 4753. No data to save.\n",
       "4753\n",
+      "model_vars.get('Gini', 0)[-1]=0.6636\n",
+      "Condition not met at step 4754. No data to save.\n",
       "4754\n",
+      "model_vars.get('Gini', 0)[-1]=0.6302\n",
+      "Condition not met at step 4755. No data to save.\n",
       "4755\n",
+      "model_vars.get('Gini', 0)[-1]=0.6386000000000001\n",
+      "Condition not met at step 4756. No data to save.\n",
       "4756\n",
+      "model_vars.get('Gini', 0)[-1]=0.6726000000000001\n",
+      "Condition not met at step 4757. No data to save.\n",
       "4757\n",
+      "model_vars.get('Gini', 0)[-1]=0.6722\n",
+      "Condition not met at step 4758. No data to save.\n",
       "4758\n",
+      "model_vars.get('Gini', 0)[-1]=0.6708000000000001\n",
+      "Condition not met at step 4759. No data to save.\n",
       "4759\n",
+      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
+      "Condition not met at step 4760. No data to save.\n",
       "4760\n",
+      "model_vars.get('Gini', 0)[-1]=0.6839999999999999\n",
+      "Condition not met at step 4761. No data to save.\n",
       "4761\n",
+      "model_vars.get('Gini', 0)[-1]=0.6704\n",
+      "Condition not met at step 4762. No data to save.\n",
       "4762\n",
+      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
+      "Condition not met at step 4763. No data to save.\n",
       "4763\n",
+      "model_vars.get('Gini', 0)[-1]=0.645\n",
+      "Condition not met at step 4764. No data to save.\n",
       "4764\n",
+      "model_vars.get('Gini', 0)[-1]=0.62\n",
+      "Condition not met at step 4765. No data to save.\n",
       "4765\n",
+      "model_vars.get('Gini', 0)[-1]=0.6266\n",
+      "Condition not met at step 4766. No data to save.\n",
       "4766\n",
+      "model_vars.get('Gini', 0)[-1]=0.6318\n",
+      "Condition not met at step 4767. No data to save.\n",
       "4767\n",
+      "model_vars.get('Gini', 0)[-1]=0.6186\n",
+      "Condition not met at step 4768. No data to save.\n",
       "4768\n",
+      "model_vars.get('Gini', 0)[-1]=0.6315999999999999\n",
+      "Condition not met at step 4769. No data to save.\n",
       "4769\n",
+      "model_vars.get('Gini', 0)[-1]=0.6382\n",
+      "Condition not met at step 4770. No data to save.\n",
       "4770\n",
+      "model_vars.get('Gini', 0)[-1]=0.6508\n",
+      "Condition not met at step 4771. No data to save.\n",
       "4771\n",
+      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
+      "Condition not met at step 4772. No data to save.\n",
       "4772\n",
+      "model_vars.get('Gini', 0)[-1]=0.6544\n",
+      "Condition not met at step 4773. No data to save.\n",
       "4773\n",
+      "model_vars.get('Gini', 0)[-1]=0.6382\n",
+      "Condition not met at step 4774. No data to save.\n",
       "4774\n",
+      "model_vars.get('Gini', 0)[-1]=0.6554\n",
+      "Condition not met at step 4775. No data to save.\n",
       "4775\n",
+      "model_vars.get('Gini', 0)[-1]=0.6512\n",
+      "Condition not met at step 4776. No data to save.\n",
       "4776\n",
+      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
+      "Condition not met at step 4777. No data to save.\n",
       "4777\n",
+      "model_vars.get('Gini', 0)[-1]=0.6624\n",
+      "Condition not met at step 4778. No data to save.\n",
       "4778\n",
+      "model_vars.get('Gini', 0)[-1]=0.6576\n",
+      "Condition not met at step 4779. No data to save.\n",
       "4779\n",
+      "model_vars.get('Gini', 0)[-1]=0.6712\n",
+      "Condition not met at step 4780. No data to save.\n",
       "4780\n",
+      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
+      "Condition not met at step 4781. No data to save.\n",
       "4781\n",
+      "model_vars.get('Gini', 0)[-1]=0.6896\n",
+      "Condition not met at step 4782. No data to save.\n",
       "4782\n",
+      "model_vars.get('Gini', 0)[-1]=0.7004\n",
+      "Condition met. Appended special results for step 4783 to test_path/special_results.parquet\n",
       "4783\n",
+      "model_vars.get('Gini', 0)[-1]=0.6966\n",
+      "Condition not met at step 4784. No data to save.\n",
       "4784\n",
+      "model_vars.get('Gini', 0)[-1]=0.6692\n",
+      "Condition not met at step 4785. No data to save.\n",
       "4785\n",
+      "model_vars.get('Gini', 0)[-1]=0.6586000000000001\n",
+      "Condition not met at step 4786. No data to save.\n",
       "4786\n",
+      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
+      "Condition not met at step 4787. No data to save.\n",
       "4787\n",
+      "model_vars.get('Gini', 0)[-1]=0.6818\n",
+      "Condition not met at step 4788. No data to save.\n",
       "4788\n",
+      "model_vars.get('Gini', 0)[-1]=0.6582\n",
+      "Condition not met at step 4789. No data to save.\n",
       "4789\n",
+      "model_vars.get('Gini', 0)[-1]=0.6448\n",
+      "Condition not met at step 4790. No data to save.\n",
       "4790\n",
+      "model_vars.get('Gini', 0)[-1]=0.6352\n",
+      "Condition not met at step 4791. No data to save.\n",
       "4791\n",
+      "model_vars.get('Gini', 0)[-1]=0.6448\n",
+      "Condition not met at step 4792. No data to save.\n",
       "4792\n",
+      "model_vars.get('Gini', 0)[-1]=0.6522\n",
+      "Condition not met at step 4793. No data to save.\n",
       "4793\n",
+      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
+      "Condition not met at step 4794. No data to save.\n",
       "4794\n",
+      "model_vars.get('Gini', 0)[-1]=0.6462\n",
+      "Condition not met at step 4795. No data to save.\n",
       "4795\n",
+      "model_vars.get('Gini', 0)[-1]=0.662\n",
+      "Condition not met at step 4796. No data to save.\n",
       "4796\n",
+      "model_vars.get('Gini', 0)[-1]=0.6856\n",
+      "Condition not met at step 4797. No data to save.\n",
       "4797\n",
+      "model_vars.get('Gini', 0)[-1]=0.6602\n",
+      "Condition not met at step 4798. No data to save.\n",
       "4798\n",
+      "model_vars.get('Gini', 0)[-1]=0.6508\n",
+      "Condition not met at step 4799. No data to save.\n",
       "4799\n",
       "CALLED\n",
       "self.model._steps=4800\n",
       " TEST self.model._steps=4800\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "4700  0.6968\n",
-      "4701  0.7030\n",
-      "4702  0.7016\n",
-      "4703  0.7108\n",
-      "4704  0.7126\n",
+      "4700  0.5552\n",
+      "4701  0.5780\n",
+      "4702  0.5768\n",
+      "4703  0.5990\n",
+      "4704  0.6172\n",
       "...      ...\n",
-      "4795  0.6664\n",
-      "4796  0.6556\n",
-      "4797  0.6322\n",
-      "4798  0.6430\n",
-      "4799  0.6480\n",
+      "4795  0.6620\n",
+      "4796  0.6856\n",
+      "4797  0.6602\n",
+      "4798  0.6508\n",
+      "4799  0.6674\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_048.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_048.parquet'\n",
-      "Saving model to output_dir/model_data_048.parquet\n",
-      "Saving agent to output_dir/agent_data_048.parquet\n",
+      "model_file='test_path/model_data_048.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_048.parquet'\n",
+      "Saving model to test_path/model_data_048.parquet\n",
+      "Saving agent to test_path/agent_data_048.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6674\n",
+      "Condition not met at step 4800. No data to save.\n",
       "4800\n",
+      "model_vars.get('Gini', 0)[-1]=0.6742\n",
+      "Condition not met at step 4801. No data to save.\n",
       "4801\n",
+      "model_vars.get('Gini', 0)[-1]=0.6914\n",
+      "Condition not met at step 4802. No data to save.\n",
       "4802\n",
+      "model_vars.get('Gini', 0)[-1]=0.6712\n",
+      "Condition not met at step 4803. No data to save.\n",
       "4803\n",
+      "model_vars.get('Gini', 0)[-1]=0.6592\n",
+      "Condition not met at step 4804. No data to save.\n",
       "4804\n",
+      "model_vars.get('Gini', 0)[-1]=0.6118\n",
+      "Condition not met at step 4805. No data to save.\n",
       "4805\n",
+      "model_vars.get('Gini', 0)[-1]=0.6314\n",
+      "Condition not met at step 4806. No data to save.\n",
       "4806\n",
+      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
+      "Condition not met at step 4807. No data to save.\n",
       "4807\n",
+      "model_vars.get('Gini', 0)[-1]=0.6812\n",
+      "Condition not met at step 4808. No data to save.\n",
       "4808\n",
+      "model_vars.get('Gini', 0)[-1]=0.643\n",
+      "Condition not met at step 4809. No data to save.\n",
       "4809\n",
+      "model_vars.get('Gini', 0)[-1]=0.6582\n",
+      "Condition not met at step 4810. No data to save.\n",
       "4810\n",
+      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
+      "Condition not met at step 4811. No data to save.\n",
       "4811\n",
+      "model_vars.get('Gini', 0)[-1]=0.6608\n",
+      "Condition not met at step 4812. No data to save.\n",
       "4812\n",
+      "model_vars.get('Gini', 0)[-1]=0.6512\n",
+      "Condition not met at step 4813. No data to save.\n",
       "4813\n",
+      "model_vars.get('Gini', 0)[-1]=0.6514\n",
+      "Condition not met at step 4814. No data to save.\n",
       "4814\n",
+      "model_vars.get('Gini', 0)[-1]=0.6497999999999999\n",
+      "Condition not met at step 4815. No data to save.\n",
       "4815\n",
+      "model_vars.get('Gini', 0)[-1]=0.6614\n",
+      "Condition not met at step 4816. No data to save.\n",
       "4816\n",
+      "model_vars.get('Gini', 0)[-1]=0.6614\n",
+      "Condition not met at step 4817. No data to save.\n",
       "4817\n",
+      "model_vars.get('Gini', 0)[-1]=0.6517999999999999\n",
+      "Condition not met at step 4818. No data to save.\n",
       "4818\n",
+      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
+      "Condition not met at step 4819. No data to save.\n",
       "4819\n",
+      "model_vars.get('Gini', 0)[-1]=0.6838\n",
+      "Condition not met at step 4820. No data to save.\n",
       "4820\n",
+      "model_vars.get('Gini', 0)[-1]=0.6696\n",
+      "Condition not met at step 4821. No data to save.\n",
       "4821\n",
+      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
+      "Condition not met at step 4822. No data to save.\n",
       "4822\n",
+      "model_vars.get('Gini', 0)[-1]=0.6774\n",
+      "Condition not met at step 4823. No data to save.\n",
       "4823\n",
+      "model_vars.get('Gini', 0)[-1]=0.6968000000000001\n",
+      "Condition not met at step 4824. No data to save.\n",
       "4824\n",
+      "model_vars.get('Gini', 0)[-1]=0.653\n",
+      "Condition not met at step 4825. No data to save.\n",
       "4825\n",
+      "model_vars.get('Gini', 0)[-1]=0.663\n",
+      "Condition not met at step 4826. No data to save.\n",
       "4826\n",
+      "model_vars.get('Gini', 0)[-1]=0.6961999999999999\n",
+      "Condition not met at step 4827. No data to save.\n",
       "4827\n",
+      "model_vars.get('Gini', 0)[-1]=0.6864\n",
+      "Condition not met at step 4828. No data to save.\n",
       "4828\n",
+      "model_vars.get('Gini', 0)[-1]=0.6784\n",
+      "Condition not met at step 4829. No data to save.\n",
       "4829\n",
+      "model_vars.get('Gini', 0)[-1]=0.6918\n",
+      "Condition not met at step 4830. No data to save.\n",
       "4830\n",
+      "model_vars.get('Gini', 0)[-1]=0.6918\n",
+      "Condition not met at step 4831. No data to save.\n",
       "4831\n",
+      "model_vars.get('Gini', 0)[-1]=0.7056\n",
+      "Condition met. Appended special results for step 4832 to test_path/special_results.parquet\n",
       "4832\n",
+      "model_vars.get('Gini', 0)[-1]=0.6938\n",
+      "Condition not met at step 4833. No data to save.\n",
       "4833\n",
+      "model_vars.get('Gini', 0)[-1]=0.6944\n",
+      "Condition not met at step 4834. No data to save.\n",
       "4834\n",
+      "model_vars.get('Gini', 0)[-1]=0.6806\n",
+      "Condition not met at step 4835. No data to save.\n",
       "4835\n",
+      "model_vars.get('Gini', 0)[-1]=0.6726000000000001\n",
+      "Condition not met at step 4836. No data to save.\n",
       "4836\n",
+      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
+      "Condition not met at step 4837. No data to save.\n",
       "4837\n",
+      "model_vars.get('Gini', 0)[-1]=0.6544\n",
+      "Condition not met at step 4838. No data to save.\n",
       "4838\n",
+      "model_vars.get('Gini', 0)[-1]=0.638\n",
+      "Condition not met at step 4839. No data to save.\n",
       "4839\n",
+      "model_vars.get('Gini', 0)[-1]=0.648\n",
+      "Condition not met at step 4840. No data to save.\n",
       "4840\n",
+      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
+      "Condition not met at step 4841. No data to save.\n",
       "4841\n",
+      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
+      "Condition not met at step 4842. No data to save.\n",
       "4842\n",
+      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
+      "Condition not met at step 4843. No data to save.\n",
       "4843\n",
+      "model_vars.get('Gini', 0)[-1]=0.6376\n",
+      "Condition not met at step 4844. No data to save.\n",
       "4844\n",
+      "model_vars.get('Gini', 0)[-1]=0.629\n",
+      "Condition not met at step 4845. No data to save.\n",
       "4845\n",
+      "model_vars.get('Gini', 0)[-1]=0.6468\n",
+      "Condition not met at step 4846. No data to save.\n",
       "4846\n",
+      "model_vars.get('Gini', 0)[-1]=0.6434\n",
+      "Condition not met at step 4847. No data to save.\n",
       "4847\n",
+      "model_vars.get('Gini', 0)[-1]=0.6512\n",
+      "Condition not met at step 4848. No data to save.\n",
       "4848\n",
+      "model_vars.get('Gini', 0)[-1]=0.641\n",
+      "Condition not met at step 4849. No data to save.\n",
       "4849\n",
+      "model_vars.get('Gini', 0)[-1]=0.6132\n",
+      "Condition not met at step 4850. No data to save.\n",
       "4850\n",
+      "model_vars.get('Gini', 0)[-1]=0.6274\n",
+      "Condition not met at step 4851. No data to save.\n",
       "4851\n",
+      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
+      "Condition not met at step 4852. No data to save.\n",
       "4852\n",
+      "model_vars.get('Gini', 0)[-1]=0.6516\n",
+      "Condition not met at step 4853. No data to save.\n",
       "4853\n",
+      "model_vars.get('Gini', 0)[-1]=0.6664\n",
+      "Condition not met at step 4854. No data to save.\n",
       "4854\n",
+      "model_vars.get('Gini', 0)[-1]=0.6614\n",
+      "Condition not met at step 4855. No data to save.\n",
       "4855\n",
+      "model_vars.get('Gini', 0)[-1]=0.6566000000000001\n",
+      "Condition not met at step 4856. No data to save.\n",
       "4856\n",
+      "model_vars.get('Gini', 0)[-1]=0.6512\n",
+      "Condition not met at step 4857. No data to save.\n",
       "4857\n",
+      "model_vars.get('Gini', 0)[-1]=0.6352\n",
+      "Condition not met at step 4858. No data to save.\n",
       "4858\n",
+      "model_vars.get('Gini', 0)[-1]=0.613\n",
+      "Condition not met at step 4859. No data to save.\n",
       "4859\n",
+      "model_vars.get('Gini', 0)[-1]=0.618\n",
+      "Condition not met at step 4860. No data to save.\n",
       "4860\n",
+      "model_vars.get('Gini', 0)[-1]=0.5874\n",
+      "Condition not met at step 4861. No data to save.\n",
       "4861\n",
+      "model_vars.get('Gini', 0)[-1]=0.6016\n",
+      "Condition not met at step 4862. No data to save.\n",
       "4862\n",
+      "model_vars.get('Gini', 0)[-1]=0.6148\n",
+      "Condition not met at step 4863. No data to save.\n",
       "4863\n",
+      "model_vars.get('Gini', 0)[-1]=0.6278\n",
+      "Condition not met at step 4864. No data to save.\n",
       "4864\n",
+      "model_vars.get('Gini', 0)[-1]=0.6368\n",
+      "Condition not met at step 4865. No data to save.\n",
       "4865\n",
+      "model_vars.get('Gini', 0)[-1]=0.6577999999999999\n",
+      "Condition not met at step 4866. No data to save.\n",
       "4866\n",
+      "model_vars.get('Gini', 0)[-1]=0.6638\n",
+      "Condition not met at step 4867. No data to save.\n",
       "4867\n",
+      "model_vars.get('Gini', 0)[-1]=0.657\n",
+      "Condition not met at step 4868. No data to save.\n",
       "4868\n",
+      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
+      "Condition not met at step 4869. No data to save.\n",
       "4869\n",
+      "model_vars.get('Gini', 0)[-1]=0.6522\n",
+      "Condition not met at step 4870. No data to save.\n",
       "4870\n",
+      "model_vars.get('Gini', 0)[-1]=0.6638\n",
+      "Condition not met at step 4871. No data to save.\n",
       "4871\n",
+      "model_vars.get('Gini', 0)[-1]=0.648\n",
+      "Condition not met at step 4872. No data to save.\n",
       "4872\n",
+      "model_vars.get('Gini', 0)[-1]=0.6576\n",
+      "Condition not met at step 4873. No data to save.\n",
       "4873\n",
+      "model_vars.get('Gini', 0)[-1]=0.6812\n",
+      "Condition not met at step 4874. No data to save.\n",
       "4874\n",
+      "model_vars.get('Gini', 0)[-1]=0.6786000000000001\n",
+      "Condition not met at step 4875. No data to save.\n",
       "4875\n",
+      "model_vars.get('Gini', 0)[-1]=0.6674\n",
+      "Condition not met at step 4876. No data to save.\n",
       "4876\n",
+      "model_vars.get('Gini', 0)[-1]=0.6566000000000001\n",
+      "Condition not met at step 4877. No data to save.\n",
       "4877\n",
+      "model_vars.get('Gini', 0)[-1]=0.6574\n",
+      "Condition not met at step 4878. No data to save.\n",
       "4878\n",
+      "model_vars.get('Gini', 0)[-1]=0.6634\n",
+      "Condition not met at step 4879. No data to save.\n",
       "4879\n",
+      "model_vars.get('Gini', 0)[-1]=0.6626000000000001\n",
+      "Condition not met at step 4880. No data to save.\n",
       "4880\n",
+      "model_vars.get('Gini', 0)[-1]=0.652\n",
+      "Condition not met at step 4881. No data to save.\n",
       "4881\n",
+      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
+      "Condition not met at step 4882. No data to save.\n",
       "4882\n",
+      "model_vars.get('Gini', 0)[-1]=0.6338\n",
+      "Condition not met at step 4883. No data to save.\n",
       "4883\n",
+      "model_vars.get('Gini', 0)[-1]=0.6048\n",
+      "Condition not met at step 4884. No data to save.\n",
       "4884\n",
+      "model_vars.get('Gini', 0)[-1]=0.6144000000000001\n",
+      "Condition not met at step 4885. No data to save.\n",
       "4885\n",
+      "model_vars.get('Gini', 0)[-1]=0.6113999999999999\n",
+      "Condition not met at step 4886. No data to save.\n",
       "4886\n",
+      "model_vars.get('Gini', 0)[-1]=0.6138\n",
+      "Condition not met at step 4887. No data to save.\n",
       "4887\n",
+      "model_vars.get('Gini', 0)[-1]=0.6128\n",
+      "Condition not met at step 4888. No data to save.\n",
       "4888\n",
+      "model_vars.get('Gini', 0)[-1]=0.5953999999999999\n",
+      "Condition not met at step 4889. No data to save.\n",
       "4889\n",
+      "model_vars.get('Gini', 0)[-1]=0.5658000000000001\n",
+      "Condition not met at step 4890. No data to save.\n",
       "4890\n",
+      "model_vars.get('Gini', 0)[-1]=0.558\n",
+      "Condition not met at step 4891. No data to save.\n",
       "4891\n",
+      "model_vars.get('Gini', 0)[-1]=0.5694\n",
+      "Condition not met at step 4892. No data to save.\n",
       "4892\n",
+      "model_vars.get('Gini', 0)[-1]=0.601\n",
+      "Condition not met at step 4893. No data to save.\n",
       "4893\n",
+      "model_vars.get('Gini', 0)[-1]=0.618\n",
+      "Condition not met at step 4894. No data to save.\n",
       "4894\n",
+      "model_vars.get('Gini', 0)[-1]=0.611\n",
+      "Condition not met at step 4895. No data to save.\n",
       "4895\n",
+      "model_vars.get('Gini', 0)[-1]=0.6104\n",
+      "Condition not met at step 4896. No data to save.\n",
       "4896\n",
+      "model_vars.get('Gini', 0)[-1]=0.6073999999999999\n",
+      "Condition not met at step 4897. No data to save.\n",
       "4897\n",
+      "model_vars.get('Gini', 0)[-1]=0.6334\n",
+      "Condition not met at step 4898. No data to save.\n",
       "4898\n",
+      "model_vars.get('Gini', 0)[-1]=0.6053999999999999\n",
+      "Condition not met at step 4899. No data to save.\n",
       "4899\n",
       "CALLED\n",
       "self.model._steps=4900\n",
       " TEST self.model._steps=4900\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "4800  0.6212\n",
-      "4801  0.6648\n",
-      "4802  0.6418\n",
-      "4803  0.6202\n",
-      "4804  0.6282\n",
+      "4800  0.6742\n",
+      "4801  0.6914\n",
+      "4802  0.6712\n",
+      "4803  0.6592\n",
+      "4804  0.6118\n",
       "...      ...\n",
-      "4895  0.6880\n",
-      "4896  0.6904\n",
-      "4897  0.6694\n",
-      "4898  0.6674\n",
-      "4899  0.6632\n",
+      "4895  0.6104\n",
+      "4896  0.6074\n",
+      "4897  0.6334\n",
+      "4898  0.6054\n",
+      "4899  0.6306\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_049.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_049.parquet'\n",
-      "Saving model to output_dir/model_data_049.parquet\n",
-      "Saving agent to output_dir/agent_data_049.parquet\n",
+      "model_file='test_path/model_data_049.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_049.parquet'\n",
+      "Saving model to test_path/model_data_049.parquet\n",
+      "Saving agent to test_path/agent_data_049.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6306\n",
+      "Condition not met at step 4900. No data to save.\n",
       "4900\n",
+      "model_vars.get('Gini', 0)[-1]=0.625\n",
+      "Condition not met at step 4901. No data to save.\n",
       "4901\n",
+      "model_vars.get('Gini', 0)[-1]=0.6354\n",
+      "Condition not met at step 4902. No data to save.\n",
       "4902\n",
+      "model_vars.get('Gini', 0)[-1]=0.638\n",
+      "Condition not met at step 4903. No data to save.\n",
       "4903\n",
+      "model_vars.get('Gini', 0)[-1]=0.6444000000000001\n",
+      "Condition not met at step 4904. No data to save.\n",
       "4904\n",
+      "model_vars.get('Gini', 0)[-1]=0.6386000000000001\n",
+      "Condition not met at step 4905. No data to save.\n",
       "4905\n",
+      "model_vars.get('Gini', 0)[-1]=0.6302\n",
+      "Condition not met at step 4906. No data to save.\n",
       "4906\n",
+      "model_vars.get('Gini', 0)[-1]=0.6504000000000001\n",
+      "Condition not met at step 4907. No data to save.\n",
       "4907\n",
+      "model_vars.get('Gini', 0)[-1]=0.628\n",
+      "Condition not met at step 4908. No data to save.\n",
       "4908\n",
+      "model_vars.get('Gini', 0)[-1]=0.6146\n",
+      "Condition not met at step 4909. No data to save.\n",
       "4909\n",
+      "model_vars.get('Gini', 0)[-1]=0.6235999999999999\n",
+      "Condition not met at step 4910. No data to save.\n",
       "4910\n",
+      "model_vars.get('Gini', 0)[-1]=0.612\n",
+      "Condition not met at step 4911. No data to save.\n",
       "4911\n",
+      "model_vars.get('Gini', 0)[-1]=0.6376\n",
+      "Condition not met at step 4912. No data to save.\n",
       "4912\n",
+      "model_vars.get('Gini', 0)[-1]=0.613\n",
+      "Condition not met at step 4913. No data to save.\n",
       "4913\n",
+      "model_vars.get('Gini', 0)[-1]=0.609\n",
+      "Condition not met at step 4914. No data to save.\n",
       "4914\n",
+      "model_vars.get('Gini', 0)[-1]=0.6554\n",
+      "Condition not met at step 4915. No data to save.\n",
       "4915\n",
+      "model_vars.get('Gini', 0)[-1]=0.653\n",
+      "Condition not met at step 4916. No data to save.\n",
       "4916\n",
+      "model_vars.get('Gini', 0)[-1]=0.6266\n",
+      "Condition not met at step 4917. No data to save.\n",
       "4917\n",
+      "model_vars.get('Gini', 0)[-1]=0.6358\n",
+      "Condition not met at step 4918. No data to save.\n",
       "4918\n",
+      "model_vars.get('Gini', 0)[-1]=0.6698\n",
+      "Condition not met at step 4919. No data to save.\n",
       "4919\n",
+      "model_vars.get('Gini', 0)[-1]=0.637\n",
+      "Condition not met at step 4920. No data to save.\n",
       "4920\n",
+      "model_vars.get('Gini', 0)[-1]=0.6497999999999999\n",
+      "Condition not met at step 4921. No data to save.\n",
       "4921\n",
+      "model_vars.get('Gini', 0)[-1]=0.6516\n",
+      "Condition not met at step 4922. No data to save.\n",
       "4922\n",
+      "model_vars.get('Gini', 0)[-1]=0.646\n",
+      "Condition not met at step 4923. No data to save.\n",
       "4923\n",
+      "model_vars.get('Gini', 0)[-1]=0.626\n",
+      "Condition not met at step 4924. No data to save.\n",
       "4924\n",
+      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
+      "Condition not met at step 4925. No data to save.\n",
       "4925\n",
+      "model_vars.get('Gini', 0)[-1]=0.6658\n",
+      "Condition not met at step 4926. No data to save.\n",
       "4926\n",
+      "model_vars.get('Gini', 0)[-1]=0.6718\n",
+      "Condition not met at step 4927. No data to save.\n",
       "4927\n",
+      "model_vars.get('Gini', 0)[-1]=0.6754\n",
+      "Condition not met at step 4928. No data to save.\n",
       "4928\n",
+      "model_vars.get('Gini', 0)[-1]=0.6472\n",
+      "Condition not met at step 4929. No data to save.\n",
       "4929\n",
+      "model_vars.get('Gini', 0)[-1]=0.6298\n",
+      "Condition not met at step 4930. No data to save.\n",
       "4930\n",
+      "model_vars.get('Gini', 0)[-1]=0.61\n",
+      "Condition not met at step 4931. No data to save.\n",
       "4931\n",
+      "model_vars.get('Gini', 0)[-1]=0.6062000000000001\n",
+      "Condition not met at step 4932. No data to save.\n",
       "4932\n",
+      "model_vars.get('Gini', 0)[-1]=0.6158\n",
+      "Condition not met at step 4933. No data to save.\n",
       "4933\n",
+      "model_vars.get('Gini', 0)[-1]=0.6072\n",
+      "Condition not met at step 4934. No data to save.\n",
       "4934\n",
+      "model_vars.get('Gini', 0)[-1]=0.6338\n",
+      "Condition not met at step 4935. No data to save.\n",
       "4935\n",
+      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
+      "Condition not met at step 4936. No data to save.\n",
       "4936\n",
+      "model_vars.get('Gini', 0)[-1]=0.6192\n",
+      "Condition not met at step 4937. No data to save.\n",
       "4937\n",
+      "model_vars.get('Gini', 0)[-1]=0.6398\n",
+      "Condition not met at step 4938. No data to save.\n",
       "4938\n",
+      "model_vars.get('Gini', 0)[-1]=0.6272\n",
+      "Condition not met at step 4939. No data to save.\n",
       "4939\n",
+      "model_vars.get('Gini', 0)[-1]=0.6428\n",
+      "Condition not met at step 4940. No data to save.\n",
       "4940\n",
+      "model_vars.get('Gini', 0)[-1]=0.6392\n",
+      "Condition not met at step 4941. No data to save.\n",
       "4941\n",
+      "model_vars.get('Gini', 0)[-1]=0.6092\n",
+      "Condition not met at step 4942. No data to save.\n",
       "4942\n",
+      "model_vars.get('Gini', 0)[-1]=0.6088\n",
+      "Condition not met at step 4943. No data to save.\n",
       "4943\n",
+      "model_vars.get('Gini', 0)[-1]=0.6128\n",
+      "Condition not met at step 4944. No data to save.\n",
       "4944\n",
+      "model_vars.get('Gini', 0)[-1]=0.64\n",
+      "Condition not met at step 4945. No data to save.\n",
       "4945\n",
+      "model_vars.get('Gini', 0)[-1]=0.6004\n",
+      "Condition not met at step 4946. No data to save.\n",
       "4946\n",
+      "model_vars.get('Gini', 0)[-1]=0.624\n",
+      "Condition not met at step 4947. No data to save.\n",
       "4947\n",
+      "model_vars.get('Gini', 0)[-1]=0.6386000000000001\n",
+      "Condition not met at step 4948. No data to save.\n",
       "4948\n",
+      "model_vars.get('Gini', 0)[-1]=0.6166\n",
+      "Condition not met at step 4949. No data to save.\n",
       "4949\n",
+      "model_vars.get('Gini', 0)[-1]=0.6202000000000001\n",
+      "Condition not met at step 4950. No data to save.\n",
       "4950\n",
+      "model_vars.get('Gini', 0)[-1]=0.6214\n",
+      "Condition not met at step 4951. No data to save.\n",
       "4951\n",
+      "model_vars.get('Gini', 0)[-1]=0.6434\n",
+      "Condition not met at step 4952. No data to save.\n",
       "4952\n",
+      "model_vars.get('Gini', 0)[-1]=0.6252\n",
+      "Condition not met at step 4953. No data to save.\n",
       "4953\n",
+      "model_vars.get('Gini', 0)[-1]=0.6093999999999999\n",
+      "Condition not met at step 4954. No data to save.\n",
       "4954\n",
+      "model_vars.get('Gini', 0)[-1]=0.567\n",
+      "Condition not met at step 4955. No data to save.\n",
       "4955\n",
+      "model_vars.get('Gini', 0)[-1]=0.5834\n",
+      "Condition not met at step 4956. No data to save.\n",
       "4956\n",
+      "model_vars.get('Gini', 0)[-1]=0.605\n",
+      "Condition not met at step 4957. No data to save.\n",
       "4957\n",
+      "model_vars.get('Gini', 0)[-1]=0.5806\n",
+      "Condition not met at step 4958. No data to save.\n",
       "4958\n",
+      "model_vars.get('Gini', 0)[-1]=0.5926\n",
+      "Condition not met at step 4959. No data to save.\n",
       "4959\n",
+      "model_vars.get('Gini', 0)[-1]=0.5884\n",
+      "Condition not met at step 4960. No data to save.\n",
       "4960\n",
+      "model_vars.get('Gini', 0)[-1]=0.603\n",
+      "Condition not met at step 4961. No data to save.\n",
       "4961\n",
+      "model_vars.get('Gini', 0)[-1]=0.5956\n",
+      "Condition not met at step 4962. No data to save.\n",
       "4962\n",
+      "model_vars.get('Gini', 0)[-1]=0.5898\n",
+      "Condition not met at step 4963. No data to save.\n",
       "4963\n",
+      "model_vars.get('Gini', 0)[-1]=0.5986\n",
+      "Condition not met at step 4964. No data to save.\n",
       "4964\n",
+      "model_vars.get('Gini', 0)[-1]=0.5960000000000001\n",
+      "Condition not met at step 4965. No data to save.\n",
       "4965\n",
+      "model_vars.get('Gini', 0)[-1]=0.5906\n",
+      "Condition not met at step 4966. No data to save.\n",
       "4966\n",
+      "model_vars.get('Gini', 0)[-1]=0.617\n",
+      "Condition not met at step 4967. No data to save.\n",
       "4967\n",
+      "model_vars.get('Gini', 0)[-1]=0.6134\n",
+      "Condition not met at step 4968. No data to save.\n",
       "4968\n",
+      "model_vars.get('Gini', 0)[-1]=0.624\n",
+      "Condition not met at step 4969. No data to save.\n",
       "4969\n",
+      "model_vars.get('Gini', 0)[-1]=0.6295999999999999\n",
+      "Condition not met at step 4970. No data to save.\n",
       "4970\n",
+      "model_vars.get('Gini', 0)[-1]=0.6372\n",
+      "Condition not met at step 4971. No data to save.\n",
       "4971\n",
+      "model_vars.get('Gini', 0)[-1]=0.6604\n",
+      "Condition not met at step 4972. No data to save.\n",
       "4972\n",
+      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
+      "Condition not met at step 4973. No data to save.\n",
       "4973\n",
+      "model_vars.get('Gini', 0)[-1]=0.6738\n",
+      "Condition not met at step 4974. No data to save.\n",
       "4974\n",
+      "model_vars.get('Gini', 0)[-1]=0.6566000000000001\n",
+      "Condition not met at step 4975. No data to save.\n",
       "4975\n",
+      "model_vars.get('Gini', 0)[-1]=0.6502\n",
+      "Condition not met at step 4976. No data to save.\n",
       "4976\n",
+      "model_vars.get('Gini', 0)[-1]=0.6832\n",
+      "Condition not met at step 4977. No data to save.\n",
       "4977\n",
+      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
+      "Condition not met at step 4978. No data to save.\n",
       "4978\n",
+      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
+      "Condition not met at step 4979. No data to save.\n",
       "4979\n",
+      "model_vars.get('Gini', 0)[-1]=0.69\n",
+      "Condition not met at step 4980. No data to save.\n",
       "4980\n",
+      "model_vars.get('Gini', 0)[-1]=0.6838\n",
+      "Condition not met at step 4981. No data to save.\n",
       "4981\n",
+      "model_vars.get('Gini', 0)[-1]=0.6588\n",
+      "Condition not met at step 4982. No data to save.\n",
       "4982\n",
+      "model_vars.get('Gini', 0)[-1]=0.639\n",
+      "Condition not met at step 4983. No data to save.\n",
       "4983\n",
+      "model_vars.get('Gini', 0)[-1]=0.6456\n",
+      "Condition not met at step 4984. No data to save.\n",
       "4984\n",
+      "model_vars.get('Gini', 0)[-1]=0.6679999999999999\n",
+      "Condition not met at step 4985. No data to save.\n",
       "4985\n",
+      "model_vars.get('Gini', 0)[-1]=0.6718\n",
+      "Condition not met at step 4986. No data to save.\n",
       "4986\n",
+      "model_vars.get('Gini', 0)[-1]=0.6758\n",
+      "Condition not met at step 4987. No data to save.\n",
       "4987\n",
+      "model_vars.get('Gini', 0)[-1]=0.6576\n",
+      "Condition not met at step 4988. No data to save.\n",
       "4988\n",
+      "model_vars.get('Gini', 0)[-1]=0.692\n",
+      "Condition not met at step 4989. No data to save.\n",
       "4989\n",
+      "model_vars.get('Gini', 0)[-1]=0.6718\n",
+      "Condition not met at step 4990. No data to save.\n",
       "4990\n",
+      "model_vars.get('Gini', 0)[-1]=0.6384000000000001\n",
+      "Condition not met at step 4991. No data to save.\n",
       "4991\n",
+      "model_vars.get('Gini', 0)[-1]=0.6528\n",
+      "Condition not met at step 4992. No data to save.\n",
       "4992\n",
+      "model_vars.get('Gini', 0)[-1]=0.6404000000000001\n",
+      "Condition not met at step 4993. No data to save.\n",
       "4993\n",
+      "model_vars.get('Gini', 0)[-1]=0.6497999999999999\n",
+      "Condition not met at step 4994. No data to save.\n",
       "4994\n",
+      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
+      "Condition not met at step 4995. No data to save.\n",
       "4995\n",
+      "model_vars.get('Gini', 0)[-1]=0.6632\n",
+      "Condition not met at step 4996. No data to save.\n",
       "4996\n",
+      "model_vars.get('Gini', 0)[-1]=0.6708000000000001\n",
+      "Condition not met at step 4997. No data to save.\n",
       "4997\n",
+      "model_vars.get('Gini', 0)[-1]=0.653\n",
+      "Condition not met at step 4998. No data to save.\n",
       "4998\n",
+      "model_vars.get('Gini', 0)[-1]=0.591\n",
+      "Condition not met at step 4999. No data to save.\n",
       "4999\n",
       "CALLED\n",
       "self.model._steps=5000\n",
       " TEST self.model._steps=5000\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "4900  0.6716\n",
-      "4901  0.6754\n",
-      "4902  0.6870\n",
-      "4903  0.6812\n",
-      "4904  0.6874\n",
+      "4900  0.6250\n",
+      "4901  0.6354\n",
+      "4902  0.6380\n",
+      "4903  0.6444\n",
+      "4904  0.6386\n",
       "...      ...\n",
-      "4995  0.6382\n",
-      "4996  0.6416\n",
-      "4997  0.6322\n",
-      "4998  0.6220\n",
-      "4999  0.6396\n",
+      "4995  0.6632\n",
+      "4996  0.6708\n",
+      "4997  0.6530\n",
+      "4998  0.5910\n",
+      "4999  0.6036\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_050.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_050.parquet'\n",
-      "Saving model to output_dir/model_data_050.parquet\n",
-      "Saving agent to output_dir/agent_data_050.parquet\n",
+      "model_file='test_path/model_data_050.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_050.parquet'\n",
+      "Saving model to test_path/model_data_050.parquet\n",
+      "Saving agent to test_path/agent_data_050.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6036\n",
+      "Condition not met at step 5000. No data to save.\n",
       "5000\n",
+      "model_vars.get('Gini', 0)[-1]=0.6046\n",
+      "Condition not met at step 5001. No data to save.\n",
       "5001\n",
+      "model_vars.get('Gini', 0)[-1]=0.6226\n",
+      "Condition not met at step 5002. No data to save.\n",
       "5002\n",
+      "model_vars.get('Gini', 0)[-1]=0.6248\n",
+      "Condition not met at step 5003. No data to save.\n",
       "5003\n",
+      "model_vars.get('Gini', 0)[-1]=0.6382\n",
+      "Condition not met at step 5004. No data to save.\n",
       "5004\n",
+      "model_vars.get('Gini', 0)[-1]=0.6644\n",
+      "Condition not met at step 5005. No data to save.\n",
       "5005\n",
+      "model_vars.get('Gini', 0)[-1]=0.667\n",
+      "Condition not met at step 5006. No data to save.\n",
       "5006\n",
+      "model_vars.get('Gini', 0)[-1]=0.6744\n",
+      "Condition not met at step 5007. No data to save.\n",
       "5007\n",
+      "model_vars.get('Gini', 0)[-1]=0.6832\n",
+      "Condition not met at step 5008. No data to save.\n",
       "5008\n",
+      "model_vars.get('Gini', 0)[-1]=0.7088\n",
+      "Condition met. Appended special results for step 5009 to test_path/special_results.parquet\n",
       "5009\n",
+      "model_vars.get('Gini', 0)[-1]=0.7130000000000001\n",
+      "Condition met. Appended special results for step 5010 to test_path/special_results.parquet\n",
       "5010\n",
+      "model_vars.get('Gini', 0)[-1]=0.728\n",
+      "Condition met. Appended special results for step 5011 to test_path/special_results.parquet\n",
       "5011\n",
+      "model_vars.get('Gini', 0)[-1]=0.726\n",
+      "Condition met. Appended special results for step 5012 to test_path/special_results.parquet\n",
       "5012\n",
+      "model_vars.get('Gini', 0)[-1]=0.7414000000000001\n",
+      "Condition met. Appended special results for step 5013 to test_path/special_results.parquet\n",
       "5013\n",
+      "model_vars.get('Gini', 0)[-1]=0.7216\n",
+      "Condition met. Appended special results for step 5014 to test_path/special_results.parquet\n",
       "5014\n",
+      "model_vars.get('Gini', 0)[-1]=0.6998\n",
+      "Condition not met at step 5015. No data to save.\n",
       "5015\n",
+      "model_vars.get('Gini', 0)[-1]=0.698\n",
+      "Condition not met at step 5016. No data to save.\n",
       "5016\n",
+      "model_vars.get('Gini', 0)[-1]=0.6888000000000001\n",
+      "Condition not met at step 5017. No data to save.\n",
       "5017\n",
+      "model_vars.get('Gini', 0)[-1]=0.6794\n",
+      "Condition not met at step 5018. No data to save.\n",
       "5018\n",
+      "model_vars.get('Gini', 0)[-1]=0.6448\n",
+      "Condition not met at step 5019. No data to save.\n",
       "5019\n",
+      "model_vars.get('Gini', 0)[-1]=0.6408\n",
+      "Condition not met at step 5020. No data to save.\n",
       "5020\n",
+      "model_vars.get('Gini', 0)[-1]=0.646\n",
+      "Condition not met at step 5021. No data to save.\n",
       "5021\n",
+      "model_vars.get('Gini', 0)[-1]=0.6672\n",
+      "Condition not met at step 5022. No data to save.\n",
       "5022\n",
+      "model_vars.get('Gini', 0)[-1]=0.6412\n",
+      "Condition not met at step 5023. No data to save.\n",
       "5023\n",
+      "model_vars.get('Gini', 0)[-1]=0.673\n",
+      "Condition not met at step 5024. No data to save.\n",
       "5024\n",
+      "model_vars.get('Gini', 0)[-1]=0.6374\n",
+      "Condition not met at step 5025. No data to save.\n",
       "5025\n",
+      "model_vars.get('Gini', 0)[-1]=0.6154\n",
+      "Condition not met at step 5026. No data to save.\n",
       "5026\n",
+      "model_vars.get('Gini', 0)[-1]=0.6026\n",
+      "Condition not met at step 5027. No data to save.\n",
       "5027\n",
+      "model_vars.get('Gini', 0)[-1]=0.6168\n",
+      "Condition not met at step 5028. No data to save.\n",
       "5028\n",
+      "model_vars.get('Gini', 0)[-1]=0.6294\n",
+      "Condition not met at step 5029. No data to save.\n",
       "5029\n",
+      "model_vars.get('Gini', 0)[-1]=0.6836\n",
+      "Condition not met at step 5030. No data to save.\n",
       "5030\n",
+      "model_vars.get('Gini', 0)[-1]=0.7094\n",
+      "Condition met. Appended special results for step 5031 to test_path/special_results.parquet\n",
       "5031\n",
+      "model_vars.get('Gini', 0)[-1]=0.7150000000000001\n",
+      "Condition met. Appended special results for step 5032 to test_path/special_results.parquet\n",
       "5032\n",
+      "model_vars.get('Gini', 0)[-1]=0.6961999999999999\n",
+      "Condition not met at step 5033. No data to save.\n",
       "5033\n",
+      "model_vars.get('Gini', 0)[-1]=0.7292000000000001\n",
+      "Condition met. Appended special results for step 5034 to test_path/special_results.parquet\n",
       "5034\n",
+      "model_vars.get('Gini', 0)[-1]=0.7356\n",
+      "Condition met. Appended special results for step 5035 to test_path/special_results.parquet\n",
       "5035\n",
+      "model_vars.get('Gini', 0)[-1]=0.7296\n",
+      "Condition met. Appended special results for step 5036 to test_path/special_results.parquet\n",
       "5036\n",
+      "model_vars.get('Gini', 0)[-1]=0.7088\n",
+      "Condition met. Appended special results for step 5037 to test_path/special_results.parquet\n",
       "5037\n",
+      "model_vars.get('Gini', 0)[-1]=0.6981999999999999\n",
+      "Condition not met at step 5038. No data to save.\n",
       "5038\n",
+      "model_vars.get('Gini', 0)[-1]=0.6981999999999999\n",
+      "Condition not met at step 5039. No data to save.\n",
       "5039\n",
+      "model_vars.get('Gini', 0)[-1]=0.7202\n",
+      "Condition met. Appended special results for step 5040 to test_path/special_results.parquet\n",
       "5040\n",
+      "model_vars.get('Gini', 0)[-1]=0.722\n",
+      "Condition met. Appended special results for step 5041 to test_path/special_results.parquet\n",
       "5041\n",
+      "model_vars.get('Gini', 0)[-1]=0.7006\n",
+      "Condition met. Appended special results for step 5042 to test_path/special_results.parquet\n",
       "5042\n",
+      "model_vars.get('Gini', 0)[-1]=0.6854\n",
+      "Condition not met at step 5043. No data to save.\n",
       "5043\n",
+      "model_vars.get('Gini', 0)[-1]=0.6782\n",
+      "Condition not met at step 5044. No data to save.\n",
       "5044\n",
+      "model_vars.get('Gini', 0)[-1]=0.6678\n",
+      "Condition not met at step 5045. No data to save.\n",
       "5045\n",
+      "model_vars.get('Gini', 0)[-1]=0.6826\n",
+      "Condition not met at step 5046. No data to save.\n",
       "5046\n",
+      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
+      "Condition not met at step 5047. No data to save.\n",
       "5047\n",
+      "model_vars.get('Gini', 0)[-1]=0.6814\n",
+      "Condition not met at step 5048. No data to save.\n",
       "5048\n",
+      "model_vars.get('Gini', 0)[-1]=0.6814\n",
+      "Condition not met at step 5049. No data to save.\n",
       "5049\n",
+      "model_vars.get('Gini', 0)[-1]=0.6762\n",
+      "Condition not met at step 5050. No data to save.\n",
       "5050\n",
+      "model_vars.get('Gini', 0)[-1]=0.6882\n",
+      "Condition not met at step 5051. No data to save.\n",
       "5051\n",
+      "model_vars.get('Gini', 0)[-1]=0.6594\n",
+      "Condition not met at step 5052. No data to save.\n",
       "5052\n",
+      "model_vars.get('Gini', 0)[-1]=0.6658\n",
+      "Condition not met at step 5053. No data to save.\n",
       "5053\n",
+      "model_vars.get('Gini', 0)[-1]=0.6634\n",
+      "Condition not met at step 5054. No data to save.\n",
       "5054\n",
+      "model_vars.get('Gini', 0)[-1]=0.6806\n",
+      "Condition not met at step 5055. No data to save.\n",
       "5055\n",
+      "model_vars.get('Gini', 0)[-1]=0.6941999999999999\n",
+      "Condition not met at step 5056. No data to save.\n",
       "5056\n",
+      "model_vars.get('Gini', 0)[-1]=0.6862\n",
+      "Condition not met at step 5057. No data to save.\n",
       "5057\n",
+      "model_vars.get('Gini', 0)[-1]=0.6778\n",
+      "Condition not met at step 5058. No data to save.\n",
       "5058\n",
+      "model_vars.get('Gini', 0)[-1]=0.6972\n",
+      "Condition not met at step 5059. No data to save.\n",
       "5059\n",
+      "model_vars.get('Gini', 0)[-1]=0.7126\n",
+      "Condition met. Appended special results for step 5060 to test_path/special_results.parquet\n",
       "5060\n",
+      "model_vars.get('Gini', 0)[-1]=0.6848000000000001\n",
+      "Condition not met at step 5061. No data to save.\n",
       "5061\n",
+      "model_vars.get('Gini', 0)[-1]=0.698\n",
+      "Condition not met at step 5062. No data to save.\n",
       "5062\n",
+      "model_vars.get('Gini', 0)[-1]=0.6862\n",
+      "Condition not met at step 5063. No data to save.\n",
       "5063\n",
+      "model_vars.get('Gini', 0)[-1]=0.6514\n",
+      "Condition not met at step 5064. No data to save.\n",
       "5064\n",
+      "model_vars.get('Gini', 0)[-1]=0.6806\n",
+      "Condition not met at step 5065. No data to save.\n",
       "5065\n",
+      "model_vars.get('Gini', 0)[-1]=0.6724\n",
+      "Condition not met at step 5066. No data to save.\n",
       "5066\n",
+      "model_vars.get('Gini', 0)[-1]=0.6896\n",
+      "Condition not met at step 5067. No data to save.\n",
       "5067\n",
+      "model_vars.get('Gini', 0)[-1]=0.7052\n",
+      "Condition met. Appended special results for step 5068 to test_path/special_results.parquet\n",
       "5068\n",
+      "model_vars.get('Gini', 0)[-1]=0.7096\n",
+      "Condition met. Appended special results for step 5069 to test_path/special_results.parquet\n",
       "5069\n",
+      "model_vars.get('Gini', 0)[-1]=0.688\n",
+      "Condition not met at step 5070. No data to save.\n",
       "5070\n",
+      "model_vars.get('Gini', 0)[-1]=0.7048\n",
+      "Condition met. Appended special results for step 5071 to test_path/special_results.parquet\n",
       "5071\n",
+      "model_vars.get('Gini', 0)[-1]=0.6782\n",
+      "Condition not met at step 5072. No data to save.\n",
       "5072\n",
+      "model_vars.get('Gini', 0)[-1]=0.6996\n",
+      "Condition not met at step 5073. No data to save.\n",
       "5073\n",
+      "model_vars.get('Gini', 0)[-1]=0.6924\n",
+      "Condition not met at step 5074. No data to save.\n",
       "5074\n",
+      "model_vars.get('Gini', 0)[-1]=0.677\n",
+      "Condition not met at step 5075. No data to save.\n",
       "5075\n",
+      "model_vars.get('Gini', 0)[-1]=0.6497999999999999\n",
+      "Condition not met at step 5076. No data to save.\n",
       "5076\n",
+      "model_vars.get('Gini', 0)[-1]=0.6834\n",
+      "Condition not met at step 5077. No data to save.\n",
       "5077\n",
+      "model_vars.get('Gini', 0)[-1]=0.6708000000000001\n",
+      "Condition not met at step 5078. No data to save.\n",
       "5078\n",
+      "model_vars.get('Gini', 0)[-1]=0.6986\n",
+      "Condition not met at step 5079. No data to save.\n",
       "5079\n",
+      "model_vars.get('Gini', 0)[-1]=0.7024\n",
+      "Condition met. Appended special results for step 5080 to test_path/special_results.parquet\n",
       "5080\n",
+      "model_vars.get('Gini', 0)[-1]=0.7092\n",
+      "Condition met. Appended special results for step 5081 to test_path/special_results.parquet\n",
       "5081\n",
+      "model_vars.get('Gini', 0)[-1]=0.683\n",
+      "Condition not met at step 5082. No data to save.\n",
       "5082\n",
+      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
+      "Condition not met at step 5083. No data to save.\n",
       "5083\n",
+      "model_vars.get('Gini', 0)[-1]=0.6836\n",
+      "Condition not met at step 5084. No data to save.\n",
       "5084\n",
+      "model_vars.get('Gini', 0)[-1]=0.6742\n",
+      "Condition not met at step 5085. No data to save.\n",
       "5085\n",
+      "model_vars.get('Gini', 0)[-1]=0.6679999999999999\n",
+      "Condition not met at step 5086. No data to save.\n",
       "5086\n",
+      "model_vars.get('Gini', 0)[-1]=0.6508\n",
+      "Condition not met at step 5087. No data to save.\n",
       "5087\n",
+      "model_vars.get('Gini', 0)[-1]=0.654\n",
+      "Condition not met at step 5088. No data to save.\n",
       "5088\n",
+      "model_vars.get('Gini', 0)[-1]=0.647\n",
+      "Condition not met at step 5089. No data to save.\n",
       "5089\n",
+      "model_vars.get('Gini', 0)[-1]=0.6504000000000001\n",
+      "Condition not met at step 5090. No data to save.\n",
       "5090\n",
+      "model_vars.get('Gini', 0)[-1]=0.6542\n",
+      "Condition not met at step 5091. No data to save.\n",
       "5091\n",
+      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
+      "Condition not met at step 5092. No data to save.\n",
       "5092\n",
+      "model_vars.get('Gini', 0)[-1]=0.6678\n",
+      "Condition not met at step 5093. No data to save.\n",
       "5093\n",
+      "model_vars.get('Gini', 0)[-1]=0.6584\n",
+      "Condition not met at step 5094. No data to save.\n",
       "5094\n",
+      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
+      "Condition not met at step 5095. No data to save.\n",
       "5095\n",
+      "model_vars.get('Gini', 0)[-1]=0.6348\n",
+      "Condition not met at step 5096. No data to save.\n",
       "5096\n",
+      "model_vars.get('Gini', 0)[-1]=0.6528\n",
+      "Condition not met at step 5097. No data to save.\n",
       "5097\n",
+      "model_vars.get('Gini', 0)[-1]=0.65\n",
+      "Condition not met at step 5098. No data to save.\n",
       "5098\n",
+      "model_vars.get('Gini', 0)[-1]=0.635\n",
+      "Condition not met at step 5099. No data to save.\n",
       "5099\n",
       "CALLED\n",
       "self.model._steps=5100\n",
       " TEST self.model._steps=5100\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "5000  0.6246\n",
-      "5001  0.6210\n",
-      "5002  0.6736\n",
-      "5003  0.6626\n",
-      "5004  0.6456\n",
+      "5000  0.6046\n",
+      "5001  0.6226\n",
+      "5002  0.6248\n",
+      "5003  0.6382\n",
+      "5004  0.6644\n",
       "...      ...\n",
-      "5095  0.6524\n",
-      "5096  0.6610\n",
-      "5097  0.6748\n",
-      "5098  0.6804\n",
-      "5099  0.6846\n",
+      "5095  0.6348\n",
+      "5096  0.6528\n",
+      "5097  0.6500\n",
+      "5098  0.6350\n",
+      "5099  0.6402\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_051.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_051.parquet'\n",
-      "Saving model to output_dir/model_data_051.parquet\n",
-      "Saving agent to output_dir/agent_data_051.parquet\n",
+      "model_file='test_path/model_data_051.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_051.parquet'\n",
+      "Saving model to test_path/model_data_051.parquet\n",
+      "Saving agent to test_path/agent_data_051.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6402\n",
+      "Condition not met at step 5100. No data to save.\n",
       "5100\n",
+      "model_vars.get('Gini', 0)[-1]=0.658\n",
+      "Condition not met at step 5101. No data to save.\n",
       "5101\n",
+      "model_vars.get('Gini', 0)[-1]=0.6764\n",
+      "Condition not met at step 5102. No data to save.\n",
       "5102\n",
+      "model_vars.get('Gini', 0)[-1]=0.6952\n",
+      "Condition not met at step 5103. No data to save.\n",
       "5103\n",
+      "model_vars.get('Gini', 0)[-1]=0.6768000000000001\n",
+      "Condition not met at step 5104. No data to save.\n",
       "5104\n",
+      "model_vars.get('Gini', 0)[-1]=0.6866\n",
+      "Condition not met at step 5105. No data to save.\n",
       "5105\n",
+      "model_vars.get('Gini', 0)[-1]=0.704\n",
+      "Condition met. Appended special results for step 5106 to test_path/special_results.parquet\n",
       "5106\n",
+      "model_vars.get('Gini', 0)[-1]=0.6932\n",
+      "Condition not met at step 5107. No data to save.\n",
       "5107\n",
+      "model_vars.get('Gini', 0)[-1]=0.6842\n",
+      "Condition not met at step 5108. No data to save.\n",
       "5108\n",
+      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
+      "Condition not met at step 5109. No data to save.\n",
       "5109\n",
+      "model_vars.get('Gini', 0)[-1]=0.669\n",
+      "Condition not met at step 5110. No data to save.\n",
       "5110\n",
+      "model_vars.get('Gini', 0)[-1]=0.6722\n",
+      "Condition not met at step 5111. No data to save.\n",
       "5111\n",
+      "model_vars.get('Gini', 0)[-1]=0.6386000000000001\n",
+      "Condition not met at step 5112. No data to save.\n",
       "5112\n",
+      "model_vars.get('Gini', 0)[-1]=0.643\n",
+      "Condition not met at step 5113. No data to save.\n",
       "5113\n",
+      "model_vars.get('Gini', 0)[-1]=0.651\n",
+      "Condition not met at step 5114. No data to save.\n",
       "5114\n",
+      "model_vars.get('Gini', 0)[-1]=0.6366\n",
+      "Condition not met at step 5115. No data to save.\n",
       "5115\n",
+      "model_vars.get('Gini', 0)[-1]=0.634\n",
+      "Condition not met at step 5116. No data to save.\n",
       "5116\n",
+      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
+      "Condition not met at step 5117. No data to save.\n",
       "5117\n",
+      "model_vars.get('Gini', 0)[-1]=0.6398\n",
+      "Condition not met at step 5118. No data to save.\n",
       "5118\n",
+      "model_vars.get('Gini', 0)[-1]=0.6504000000000001\n",
+      "Condition not met at step 5119. No data to save.\n",
       "5119\n",
+      "model_vars.get('Gini', 0)[-1]=0.66\n",
+      "Condition not met at step 5120. No data to save.\n",
       "5120\n",
+      "model_vars.get('Gini', 0)[-1]=0.6612\n",
+      "Condition not met at step 5121. No data to save.\n",
       "5121\n",
+      "model_vars.get('Gini', 0)[-1]=0.6454\n",
+      "Condition not met at step 5122. No data to save.\n",
       "5122\n",
+      "model_vars.get('Gini', 0)[-1]=0.6448\n",
+      "Condition not met at step 5123. No data to save.\n",
       "5123\n",
+      "model_vars.get('Gini', 0)[-1]=0.6408\n",
+      "Condition not met at step 5124. No data to save.\n",
       "5124\n",
+      "model_vars.get('Gini', 0)[-1]=0.6174\n",
+      "Condition not met at step 5125. No data to save.\n",
       "5125\n",
+      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
+      "Condition not met at step 5126. No data to save.\n",
       "5126\n",
+      "model_vars.get('Gini', 0)[-1]=0.6378\n",
+      "Condition not met at step 5127. No data to save.\n",
       "5127\n",
+      "model_vars.get('Gini', 0)[-1]=0.6482\n",
+      "Condition not met at step 5128. No data to save.\n",
       "5128\n",
+      "model_vars.get('Gini', 0)[-1]=0.6542\n",
+      "Condition not met at step 5129. No data to save.\n",
       "5129\n",
+      "model_vars.get('Gini', 0)[-1]=0.6654\n",
+      "Condition not met at step 5130. No data to save.\n",
       "5130\n",
+      "model_vars.get('Gini', 0)[-1]=0.6762\n",
+      "Condition not met at step 5131. No data to save.\n",
       "5131\n",
+      "model_vars.get('Gini', 0)[-1]=0.688\n",
+      "Condition not met at step 5132. No data to save.\n",
       "5132\n",
+      "model_vars.get('Gini', 0)[-1]=0.6896\n",
+      "Condition not met at step 5133. No data to save.\n",
       "5133\n",
+      "model_vars.get('Gini', 0)[-1]=0.6744\n",
+      "Condition not met at step 5134. No data to save.\n",
       "5134\n",
+      "model_vars.get('Gini', 0)[-1]=0.6838\n",
+      "Condition not met at step 5135. No data to save.\n",
       "5135\n",
+      "model_vars.get('Gini', 0)[-1]=0.6588\n",
+      "Condition not met at step 5136. No data to save.\n",
       "5136\n",
+      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
+      "Condition not met at step 5137. No data to save.\n",
       "5137\n",
+      "model_vars.get('Gini', 0)[-1]=0.6514\n",
+      "Condition not met at step 5138. No data to save.\n",
       "5138\n",
+      "model_vars.get('Gini', 0)[-1]=0.6462\n",
+      "Condition not met at step 5139. No data to save.\n",
       "5139\n",
+      "model_vars.get('Gini', 0)[-1]=0.653\n",
+      "Condition not met at step 5140. No data to save.\n",
       "5140\n",
+      "model_vars.get('Gini', 0)[-1]=0.6574\n",
+      "Condition not met at step 5141. No data to save.\n",
       "5141\n",
+      "model_vars.get('Gini', 0)[-1]=0.6462\n",
+      "Condition not met at step 5142. No data to save.\n",
       "5142\n",
+      "model_vars.get('Gini', 0)[-1]=0.6712\n",
+      "Condition not met at step 5143. No data to save.\n",
       "5143\n",
+      "model_vars.get('Gini', 0)[-1]=0.6902\n",
+      "Condition not met at step 5144. No data to save.\n",
       "5144\n",
+      "model_vars.get('Gini', 0)[-1]=0.6736\n",
+      "Condition not met at step 5145. No data to save.\n",
       "5145\n",
+      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
+      "Condition not met at step 5146. No data to save.\n",
       "5146\n",
+      "model_vars.get('Gini', 0)[-1]=0.6154\n",
+      "Condition not met at step 5147. No data to save.\n",
       "5147\n",
+      "model_vars.get('Gini', 0)[-1]=0.6215999999999999\n",
+      "Condition not met at step 5148. No data to save.\n",
       "5148\n",
+      "model_vars.get('Gini', 0)[-1]=0.6146\n",
+      "Condition not met at step 5149. No data to save.\n",
       "5149\n",
+      "model_vars.get('Gini', 0)[-1]=0.6006\n",
+      "Condition not met at step 5150. No data to save.\n",
       "5150\n",
+      "model_vars.get('Gini', 0)[-1]=0.6246\n",
+      "Condition not met at step 5151. No data to save.\n",
       "5151\n",
+      "model_vars.get('Gini', 0)[-1]=0.6432\n",
+      "Condition not met at step 5152. No data to save.\n",
       "5152\n",
+      "model_vars.get('Gini', 0)[-1]=0.6617999999999999\n",
+      "Condition not met at step 5153. No data to save.\n",
       "5153\n",
+      "model_vars.get('Gini', 0)[-1]=0.6602\n",
+      "Condition not met at step 5154. No data to save.\n",
       "5154\n",
+      "model_vars.get('Gini', 0)[-1]=0.681\n",
+      "Condition not met at step 5155. No data to save.\n",
       "5155\n",
+      "model_vars.get('Gini', 0)[-1]=0.6732\n",
+      "Condition not met at step 5156. No data to save.\n",
       "5156\n",
+      "model_vars.get('Gini', 0)[-1]=0.7098\n",
+      "Condition met. Appended special results for step 5157 to test_path/special_results.parquet\n",
       "5157\n",
+      "model_vars.get('Gini', 0)[-1]=0.7012\n",
+      "Condition met. Appended special results for step 5158 to test_path/special_results.parquet\n",
       "5158\n",
+      "model_vars.get('Gini', 0)[-1]=0.6858\n",
+      "Condition not met at step 5159. No data to save.\n",
       "5159\n",
+      "model_vars.get('Gini', 0)[-1]=0.7116\n",
+      "Condition met. Appended special results for step 5160 to test_path/special_results.parquet\n",
       "5160\n",
+      "model_vars.get('Gini', 0)[-1]=0.7008000000000001\n",
+      "Condition met. Appended special results for step 5161 to test_path/special_results.parquet\n",
       "5161\n",
+      "model_vars.get('Gini', 0)[-1]=0.7214\n",
+      "Condition met. Appended special results for step 5162 to test_path/special_results.parquet\n",
       "5162\n",
+      "model_vars.get('Gini', 0)[-1]=0.7242\n",
+      "Condition met. Appended special results for step 5163 to test_path/special_results.parquet\n",
       "5163\n",
+      "model_vars.get('Gini', 0)[-1]=0.7378\n",
+      "Condition met. Appended special results for step 5164 to test_path/special_results.parquet\n",
       "5164\n",
+      "model_vars.get('Gini', 0)[-1]=0.7388\n",
+      "Condition met. Appended special results for step 5165 to test_path/special_results.parquet\n",
       "5165\n",
+      "model_vars.get('Gini', 0)[-1]=0.7148\n",
+      "Condition met. Appended special results for step 5166 to test_path/special_results.parquet\n",
       "5166\n",
+      "model_vars.get('Gini', 0)[-1]=0.7242\n",
+      "Condition met. Appended special results for step 5167 to test_path/special_results.parquet\n",
       "5167\n",
+      "model_vars.get('Gini', 0)[-1]=0.7406\n",
+      "Condition met. Appended special results for step 5168 to test_path/special_results.parquet\n",
       "5168\n",
+      "model_vars.get('Gini', 0)[-1]=0.7436\n",
+      "Condition met. Appended special results for step 5169 to test_path/special_results.parquet\n",
       "5169\n",
+      "model_vars.get('Gini', 0)[-1]=0.7323999999999999\n",
+      "Condition met. Appended special results for step 5170 to test_path/special_results.parquet\n",
       "5170\n",
+      "model_vars.get('Gini', 0)[-1]=0.7170000000000001\n",
+      "Condition met. Appended special results for step 5171 to test_path/special_results.parquet\n",
       "5171\n",
+      "model_vars.get('Gini', 0)[-1]=0.6859999999999999\n",
+      "Condition not met at step 5172. No data to save.\n",
       "5172\n",
+      "model_vars.get('Gini', 0)[-1]=0.6744\n",
+      "Condition not met at step 5173. No data to save.\n",
       "5173\n",
+      "model_vars.get('Gini', 0)[-1]=0.6756\n",
+      "Condition not met at step 5174. No data to save.\n",
       "5174\n",
+      "model_vars.get('Gini', 0)[-1]=0.6866\n",
+      "Condition not met at step 5175. No data to save.\n",
       "5175\n",
+      "model_vars.get('Gini', 0)[-1]=0.7056\n",
+      "Condition met. Appended special results for step 5176 to test_path/special_results.parquet\n",
       "5176\n",
+      "model_vars.get('Gini', 0)[-1]=0.6950000000000001\n",
+      "Condition not met at step 5177. No data to save.\n",
       "5177\n",
+      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
+      "Condition not met at step 5178. No data to save.\n",
       "5178\n",
+      "model_vars.get('Gini', 0)[-1]=0.6522\n",
+      "Condition not met at step 5179. No data to save.\n",
       "5179\n",
+      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
+      "Condition not met at step 5180. No data to save.\n",
       "5180\n",
+      "model_vars.get('Gini', 0)[-1]=0.661\n",
+      "Condition not met at step 5181. No data to save.\n",
       "5181\n",
+      "model_vars.get('Gini', 0)[-1]=0.653\n",
+      "Condition not met at step 5182. No data to save.\n",
       "5182\n",
+      "model_vars.get('Gini', 0)[-1]=0.6474\n",
+      "Condition not met at step 5183. No data to save.\n",
       "5183\n",
+      "model_vars.get('Gini', 0)[-1]=0.6298\n",
+      "Condition not met at step 5184. No data to save.\n",
       "5184\n",
+      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
+      "Condition not met at step 5185. No data to save.\n",
       "5185\n",
+      "model_vars.get('Gini', 0)[-1]=0.644\n",
+      "Condition not met at step 5186. No data to save.\n",
       "5186\n",
+      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
+      "Condition not met at step 5187. No data to save.\n",
       "5187\n",
+      "model_vars.get('Gini', 0)[-1]=0.6274\n",
+      "Condition not met at step 5188. No data to save.\n",
       "5188\n",
+      "model_vars.get('Gini', 0)[-1]=0.6192\n",
+      "Condition not met at step 5189. No data to save.\n",
       "5189\n",
+      "model_vars.get('Gini', 0)[-1]=0.631\n",
+      "Condition not met at step 5190. No data to save.\n",
       "5190\n",
+      "model_vars.get('Gini', 0)[-1]=0.644\n",
+      "Condition not met at step 5191. No data to save.\n",
       "5191\n",
+      "model_vars.get('Gini', 0)[-1]=0.639\n",
+      "Condition not met at step 5192. No data to save.\n",
       "5192\n",
+      "model_vars.get('Gini', 0)[-1]=0.6362\n",
+      "Condition not met at step 5193. No data to save.\n",
       "5193\n",
+      "model_vars.get('Gini', 0)[-1]=0.6408\n",
+      "Condition not met at step 5194. No data to save.\n",
       "5194\n",
+      "model_vars.get('Gini', 0)[-1]=0.6275999999999999\n",
+      "Condition not met at step 5195. No data to save.\n",
       "5195\n",
+      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
+      "Condition not met at step 5196. No data to save.\n",
       "5196\n",
+      "model_vars.get('Gini', 0)[-1]=0.6548\n",
+      "Condition not met at step 5197. No data to save.\n",
       "5197\n",
+      "model_vars.get('Gini', 0)[-1]=0.6696\n",
+      "Condition not met at step 5198. No data to save.\n",
       "5198\n",
+      "model_vars.get('Gini', 0)[-1]=0.6844\n",
+      "Condition not met at step 5199. No data to save.\n",
       "5199\n",
       "CALLED\n",
       "self.model._steps=5200\n",
       " TEST self.model._steps=5200\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "5100  0.6938\n",
-      "5101  0.6982\n",
-      "5102  0.7010\n",
-      "5103  0.6944\n",
-      "5104  0.6960\n",
+      "5100  0.6580\n",
+      "5101  0.6764\n",
+      "5102  0.6952\n",
+      "5103  0.6768\n",
+      "5104  0.6866\n",
       "...      ...\n",
-      "5195  0.6748\n",
-      "5196  0.7096\n",
-      "5197  0.7074\n",
-      "5198  0.6946\n",
-      "5199  0.6784\n",
+      "5195  0.6478\n",
+      "5196  0.6548\n",
+      "5197  0.6696\n",
+      "5198  0.6844\n",
+      "5199  0.6708\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_052.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_052.parquet'\n",
-      "Saving model to output_dir/model_data_052.parquet\n",
-      "Saving agent to output_dir/agent_data_052.parquet\n",
+      "model_file='test_path/model_data_052.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_052.parquet'\n",
+      "Saving model to test_path/model_data_052.parquet\n",
+      "Saving agent to test_path/agent_data_052.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6708000000000001\n",
+      "Condition not met at step 5200. No data to save.\n",
       "5200\n",
+      "model_vars.get('Gini', 0)[-1]=0.679\n",
+      "Condition not met at step 5201. No data to save.\n",
       "5201\n",
+      "model_vars.get('Gini', 0)[-1]=0.685\n",
+      "Condition not met at step 5202. No data to save.\n",
       "5202\n",
+      "model_vars.get('Gini', 0)[-1]=0.6684\n",
+      "Condition not met at step 5203. No data to save.\n",
       "5203\n",
+      "model_vars.get('Gini', 0)[-1]=0.6638\n",
+      "Condition not met at step 5204. No data to save.\n",
       "5204\n",
+      "model_vars.get('Gini', 0)[-1]=0.653\n",
+      "Condition not met at step 5205. No data to save.\n",
       "5205\n",
+      "model_vars.get('Gini', 0)[-1]=0.6172\n",
+      "Condition not met at step 5206. No data to save.\n",
       "5206\n",
+      "model_vars.get('Gini', 0)[-1]=0.5984\n",
+      "Condition not met at step 5207. No data to save.\n",
       "5207\n",
+      "model_vars.get('Gini', 0)[-1]=0.636\n",
+      "Condition not met at step 5208. No data to save.\n",
       "5208\n",
+      "model_vars.get('Gini', 0)[-1]=0.6298\n",
+      "Condition not met at step 5209. No data to save.\n",
       "5209\n",
+      "model_vars.get('Gini', 0)[-1]=0.6352\n",
+      "Condition not met at step 5210. No data to save.\n",
       "5210\n",
+      "model_vars.get('Gini', 0)[-1]=0.6522\n",
+      "Condition not met at step 5211. No data to save.\n",
       "5211\n",
+      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
+      "Condition not met at step 5212. No data to save.\n",
       "5212\n",
+      "model_vars.get('Gini', 0)[-1]=0.6554\n",
+      "Condition not met at step 5213. No data to save.\n",
       "5213\n",
+      "model_vars.get('Gini', 0)[-1]=0.6596\n",
+      "Condition not met at step 5214. No data to save.\n",
       "5214\n",
+      "model_vars.get('Gini', 0)[-1]=0.6396\n",
+      "Condition not met at step 5215. No data to save.\n",
       "5215\n",
+      "model_vars.get('Gini', 0)[-1]=0.6488\n",
+      "Condition not met at step 5216. No data to save.\n",
       "5216\n",
+      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
+      "Condition not met at step 5217. No data to save.\n",
       "5217\n",
+      "model_vars.get('Gini', 0)[-1]=0.6716\n",
+      "Condition not met at step 5218. No data to save.\n",
       "5218\n",
+      "model_vars.get('Gini', 0)[-1]=0.6508\n",
+      "Condition not met at step 5219. No data to save.\n",
       "5219\n",
+      "model_vars.get('Gini', 0)[-1]=0.6768000000000001\n",
+      "Condition not met at step 5220. No data to save.\n",
       "5220\n",
+      "model_vars.get('Gini', 0)[-1]=0.6699999999999999\n",
+      "Condition not met at step 5221. No data to save.\n",
       "5221\n",
+      "model_vars.get('Gini', 0)[-1]=0.7072\n",
+      "Condition met. Appended special results for step 5222 to test_path/special_results.parquet\n",
       "5222\n",
+      "model_vars.get('Gini', 0)[-1]=0.7183999999999999\n",
+      "Condition met. Appended special results for step 5223 to test_path/special_results.parquet\n",
       "5223\n",
+      "model_vars.get('Gini', 0)[-1]=0.728\n",
+      "Condition met. Appended special results for step 5224 to test_path/special_results.parquet\n",
       "5224\n",
+      "model_vars.get('Gini', 0)[-1]=0.7334\n",
+      "Condition met. Appended special results for step 5225 to test_path/special_results.parquet\n",
       "5225\n",
+      "model_vars.get('Gini', 0)[-1]=0.7302\n",
+      "Condition met. Appended special results for step 5226 to test_path/special_results.parquet\n",
       "5226\n",
+      "model_vars.get('Gini', 0)[-1]=0.6906\n",
+      "Condition not met at step 5227. No data to save.\n",
       "5227\n",
+      "model_vars.get('Gini', 0)[-1]=0.6934\n",
+      "Condition not met at step 5228. No data to save.\n",
       "5228\n",
+      "model_vars.get('Gini', 0)[-1]=0.6856\n",
+      "Condition not met at step 5229. No data to save.\n",
       "5229\n",
+      "model_vars.get('Gini', 0)[-1]=0.6968000000000001\n",
+      "Condition not met at step 5230. No data to save.\n",
       "5230\n",
+      "model_vars.get('Gini', 0)[-1]=0.6832\n",
+      "Condition not met at step 5231. No data to save.\n",
       "5231\n",
+      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
+      "Condition not met at step 5232. No data to save.\n",
       "5232\n",
+      "model_vars.get('Gini', 0)[-1]=0.6699999999999999\n",
+      "Condition not met at step 5233. No data to save.\n",
       "5233\n",
+      "model_vars.get('Gini', 0)[-1]=0.6614\n",
+      "Condition not met at step 5234. No data to save.\n",
       "5234\n",
+      "model_vars.get('Gini', 0)[-1]=0.6636\n",
+      "Condition not met at step 5235. No data to save.\n",
       "5235\n",
+      "model_vars.get('Gini', 0)[-1]=0.6592\n",
+      "Condition not met at step 5236. No data to save.\n",
       "5236\n",
+      "model_vars.get('Gini', 0)[-1]=0.6442\n",
+      "Condition not met at step 5237. No data to save.\n",
       "5237\n",
+      "model_vars.get('Gini', 0)[-1]=0.6648000000000001\n",
+      "Condition not met at step 5238. No data to save.\n",
       "5238\n",
+      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
+      "Condition not met at step 5239. No data to save.\n",
       "5239\n",
+      "model_vars.get('Gini', 0)[-1]=0.646\n",
+      "Condition not met at step 5240. No data to save.\n",
       "5240\n",
+      "model_vars.get('Gini', 0)[-1]=0.6662\n",
+      "Condition not met at step 5241. No data to save.\n",
       "5241\n",
+      "model_vars.get('Gini', 0)[-1]=0.673\n",
+      "Condition not met at step 5242. No data to save.\n",
       "5242\n",
+      "model_vars.get('Gini', 0)[-1]=0.6784\n",
+      "Condition not met at step 5243. No data to save.\n",
       "5243\n",
+      "model_vars.get('Gini', 0)[-1]=0.6856\n",
+      "Condition not met at step 5244. No data to save.\n",
       "5244\n",
+      "model_vars.get('Gini', 0)[-1]=0.6842\n",
+      "Condition not met at step 5245. No data to save.\n",
       "5245\n",
+      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
+      "Condition not met at step 5246. No data to save.\n",
       "5246\n",
+      "model_vars.get('Gini', 0)[-1]=0.6779999999999999\n",
+      "Condition not met at step 5247. No data to save.\n",
       "5247\n",
+      "model_vars.get('Gini', 0)[-1]=0.652\n",
+      "Condition not met at step 5248. No data to save.\n",
       "5248\n",
+      "model_vars.get('Gini', 0)[-1]=0.6568\n",
+      "Condition not met at step 5249. No data to save.\n",
       "5249\n",
+      "model_vars.get('Gini', 0)[-1]=0.665\n",
+      "Condition not met at step 5250. No data to save.\n",
       "5250\n",
+      "model_vars.get('Gini', 0)[-1]=0.6758\n",
+      "Condition not met at step 5251. No data to save.\n",
       "5251\n",
+      "model_vars.get('Gini', 0)[-1]=0.6698\n",
+      "Condition not met at step 5252. No data to save.\n",
       "5252\n",
+      "model_vars.get('Gini', 0)[-1]=0.6335999999999999\n",
+      "Condition not met at step 5253. No data to save.\n",
       "5253\n",
+      "model_vars.get('Gini', 0)[-1]=0.6242000000000001\n",
+      "Condition not met at step 5254. No data to save.\n",
       "5254\n",
+      "model_vars.get('Gini', 0)[-1]=0.606\n",
+      "Condition not met at step 5255. No data to save.\n",
       "5255\n",
+      "model_vars.get('Gini', 0)[-1]=0.6304000000000001\n",
+      "Condition not met at step 5256. No data to save.\n",
       "5256\n",
+      "model_vars.get('Gini', 0)[-1]=0.6315999999999999\n",
+      "Condition not met at step 5257. No data to save.\n",
       "5257\n",
+      "model_vars.get('Gini', 0)[-1]=0.6496\n",
+      "Condition not met at step 5258. No data to save.\n",
       "5258\n",
+      "model_vars.get('Gini', 0)[-1]=0.6214\n",
+      "Condition not met at step 5259. No data to save.\n",
       "5259\n",
+      "model_vars.get('Gini', 0)[-1]=0.6332\n",
+      "Condition not met at step 5260. No data to save.\n",
       "5260\n",
+      "model_vars.get('Gini', 0)[-1]=0.6342\n",
+      "Condition not met at step 5261. No data to save.\n",
       "5261\n",
+      "model_vars.get('Gini', 0)[-1]=0.6182000000000001\n",
+      "Condition not met at step 5262. No data to save.\n",
       "5262\n",
+      "model_vars.get('Gini', 0)[-1]=0.6082000000000001\n",
+      "Condition not met at step 5263. No data to save.\n",
       "5263\n",
+      "model_vars.get('Gini', 0)[-1]=0.607\n",
+      "Condition not met at step 5264. No data to save.\n",
       "5264\n",
+      "model_vars.get('Gini', 0)[-1]=0.5856\n",
+      "Condition not met at step 5265. No data to save.\n",
       "5265\n",
+      "model_vars.get('Gini', 0)[-1]=0.5896\n",
+      "Condition not met at step 5266. No data to save.\n",
       "5266\n",
+      "model_vars.get('Gini', 0)[-1]=0.6066\n",
+      "Condition not met at step 5267. No data to save.\n",
       "5267\n",
+      "model_vars.get('Gini', 0)[-1]=0.6246\n",
+      "Condition not met at step 5268. No data to save.\n",
       "5268\n",
+      "model_vars.get('Gini', 0)[-1]=0.621\n",
+      "Condition not met at step 5269. No data to save.\n",
       "5269\n",
+      "model_vars.get('Gini', 0)[-1]=0.6096\n",
+      "Condition not met at step 5270. No data to save.\n",
       "5270\n",
+      "model_vars.get('Gini', 0)[-1]=0.6038\n",
+      "Condition not met at step 5271. No data to save.\n",
       "5271\n",
+      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
+      "Condition not met at step 5272. No data to save.\n",
       "5272\n",
+      "model_vars.get('Gini', 0)[-1]=0.6532\n",
+      "Condition not met at step 5273. No data to save.\n",
       "5273\n",
+      "model_vars.get('Gini', 0)[-1]=0.621\n",
+      "Condition not met at step 5274. No data to save.\n",
       "5274\n",
+      "model_vars.get('Gini', 0)[-1]=0.6158\n",
+      "Condition not met at step 5275. No data to save.\n",
       "5275\n",
+      "model_vars.get('Gini', 0)[-1]=0.6295999999999999\n",
+      "Condition not met at step 5276. No data to save.\n",
       "5276\n",
+      "model_vars.get('Gini', 0)[-1]=0.5942000000000001\n",
+      "Condition not met at step 5277. No data to save.\n",
       "5277\n",
+      "model_vars.get('Gini', 0)[-1]=0.639\n",
+      "Condition not met at step 5278. No data to save.\n",
       "5278\n",
+      "model_vars.get('Gini', 0)[-1]=0.6058\n",
+      "Condition not met at step 5279. No data to save.\n",
       "5279\n",
+      "model_vars.get('Gini', 0)[-1]=0.6158\n",
+      "Condition not met at step 5280. No data to save.\n",
       "5280\n",
+      "model_vars.get('Gini', 0)[-1]=0.637\n",
+      "Condition not met at step 5281. No data to save.\n",
       "5281\n",
+      "model_vars.get('Gini', 0)[-1]=0.6602\n",
+      "Condition not met at step 5282. No data to save.\n",
       "5282\n",
+      "model_vars.get('Gini', 0)[-1]=0.6514\n",
+      "Condition not met at step 5283. No data to save.\n",
       "5283\n",
+      "model_vars.get('Gini', 0)[-1]=0.644\n",
+      "Condition not met at step 5284. No data to save.\n",
       "5284\n",
+      "model_vars.get('Gini', 0)[-1]=0.6355999999999999\n",
+      "Condition not met at step 5285. No data to save.\n",
       "5285\n",
+      "model_vars.get('Gini', 0)[-1]=0.639\n",
+      "Condition not met at step 5286. No data to save.\n",
       "5286\n",
+      "model_vars.get('Gini', 0)[-1]=0.6566000000000001\n",
+      "Condition not met at step 5287. No data to save.\n",
       "5287\n",
+      "model_vars.get('Gini', 0)[-1]=0.6496\n",
+      "Condition not met at step 5288. No data to save.\n",
       "5288\n",
+      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
+      "Condition not met at step 5289. No data to save.\n",
       "5289\n",
+      "model_vars.get('Gini', 0)[-1]=0.6456\n",
+      "Condition not met at step 5290. No data to save.\n",
       "5290\n",
+      "model_vars.get('Gini', 0)[-1]=0.6624\n",
+      "Condition not met at step 5291. No data to save.\n",
       "5291\n",
+      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
+      "Condition not met at step 5292. No data to save.\n",
       "5292\n",
+      "model_vars.get('Gini', 0)[-1]=0.651\n",
+      "Condition not met at step 5293. No data to save.\n",
       "5293\n",
+      "model_vars.get('Gini', 0)[-1]=0.6686000000000001\n",
+      "Condition not met at step 5294. No data to save.\n",
       "5294\n",
+      "model_vars.get('Gini', 0)[-1]=0.6748000000000001\n",
+      "Condition not met at step 5295. No data to save.\n",
       "5295\n",
+      "model_vars.get('Gini', 0)[-1]=0.647\n",
+      "Condition not met at step 5296. No data to save.\n",
       "5296\n",
+      "model_vars.get('Gini', 0)[-1]=0.6278\n",
+      "Condition not met at step 5297. No data to save.\n",
       "5297\n",
+      "model_vars.get('Gini', 0)[-1]=0.6398\n",
+      "Condition not met at step 5298. No data to save.\n",
       "5298\n",
+      "model_vars.get('Gini', 0)[-1]=0.6326\n",
+      "Condition not met at step 5299. No data to save.\n",
       "5299\n",
       "CALLED\n",
       "self.model._steps=5300\n",
       " TEST self.model._steps=5300\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "5200  0.6756\n",
-      "5201  0.6502\n",
-      "5202  0.6784\n",
-      "5203  0.6718\n",
-      "5204  0.6522\n",
+      "5200  0.6790\n",
+      "5201  0.6850\n",
+      "5202  0.6684\n",
+      "5203  0.6638\n",
+      "5204  0.6530\n",
       "...      ...\n",
-      "5295  0.6344\n",
-      "5296  0.6598\n",
-      "5297  0.6662\n",
-      "5298  0.6868\n",
-      "5299  0.6816\n",
+      "5295  0.6470\n",
+      "5296  0.6278\n",
+      "5297  0.6398\n",
+      "5298  0.6326\n",
+      "5299  0.6286\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_053.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_053.parquet'\n",
-      "Saving model to output_dir/model_data_053.parquet\n",
-      "Saving agent to output_dir/agent_data_053.parquet\n",
+      "model_file='test_path/model_data_053.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_053.parquet'\n",
+      "Saving model to test_path/model_data_053.parquet\n",
+      "Saving agent to test_path/agent_data_053.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6286\n",
+      "Condition not met at step 5300. No data to save.\n",
       "5300\n",
+      "model_vars.get('Gini', 0)[-1]=0.6302\n",
+      "Condition not met at step 5301. No data to save.\n",
       "5301\n",
+      "model_vars.get('Gini', 0)[-1]=0.6332\n",
+      "Condition not met at step 5302. No data to save.\n",
       "5302\n",
+      "model_vars.get('Gini', 0)[-1]=0.646\n",
+      "Condition not met at step 5303. No data to save.\n",
       "5303\n",
+      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
+      "Condition not met at step 5304. No data to save.\n",
       "5304\n",
+      "model_vars.get('Gini', 0)[-1]=0.688\n",
+      "Condition not met at step 5305. No data to save.\n",
       "5305\n",
+      "model_vars.get('Gini', 0)[-1]=0.7038\n",
+      "Condition met. Appended special results for step 5306 to test_path/special_results.parquet\n",
       "5306\n",
+      "model_vars.get('Gini', 0)[-1]=0.7064\n",
+      "Condition met. Appended special results for step 5307 to test_path/special_results.parquet\n",
       "5307\n",
+      "model_vars.get('Gini', 0)[-1]=0.6786000000000001\n",
+      "Condition not met at step 5308. No data to save.\n",
       "5308\n",
+      "model_vars.get('Gini', 0)[-1]=0.6588\n",
+      "Condition not met at step 5309. No data to save.\n",
       "5309\n",
+      "model_vars.get('Gini', 0)[-1]=0.6454\n",
+      "Condition not met at step 5310. No data to save.\n",
       "5310\n",
+      "model_vars.get('Gini', 0)[-1]=0.6544\n",
+      "Condition not met at step 5311. No data to save.\n",
       "5311\n",
+      "model_vars.get('Gini', 0)[-1]=0.664\n",
+      "Condition not met at step 5312. No data to save.\n",
       "5312\n",
+      "model_vars.get('Gini', 0)[-1]=0.6724\n",
+      "Condition not met at step 5313. No data to save.\n",
       "5313\n",
+      "model_vars.get('Gini', 0)[-1]=0.6462\n",
+      "Condition not met at step 5314. No data to save.\n",
       "5314\n",
+      "model_vars.get('Gini', 0)[-1]=0.662\n",
+      "Condition not met at step 5315. No data to save.\n",
       "5315\n",
+      "model_vars.get('Gini', 0)[-1]=0.6335999999999999\n",
+      "Condition not met at step 5316. No data to save.\n",
       "5316\n",
+      "model_vars.get('Gini', 0)[-1]=0.6528\n",
+      "Condition not met at step 5317. No data to save.\n",
       "5317\n",
+      "model_vars.get('Gini', 0)[-1]=0.6738\n",
+      "Condition not met at step 5318. No data to save.\n",
       "5318\n",
+      "model_vars.get('Gini', 0)[-1]=0.6642\n",
+      "Condition not met at step 5319. No data to save.\n",
       "5319\n",
+      "model_vars.get('Gini', 0)[-1]=0.6636\n",
+      "Condition not met at step 5320. No data to save.\n",
       "5320\n",
+      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
+      "Condition not met at step 5321. No data to save.\n",
       "5321\n",
+      "model_vars.get('Gini', 0)[-1]=0.664\n",
+      "Condition not met at step 5322. No data to save.\n",
       "5322\n",
+      "model_vars.get('Gini', 0)[-1]=0.6708000000000001\n",
+      "Condition not met at step 5323. No data to save.\n",
       "5323\n",
+      "model_vars.get('Gini', 0)[-1]=0.6768000000000001\n",
+      "Condition not met at step 5324. No data to save.\n",
       "5324\n",
+      "model_vars.get('Gini', 0)[-1]=0.6968000000000001\n",
+      "Condition not met at step 5325. No data to save.\n",
       "5325\n",
+      "model_vars.get('Gini', 0)[-1]=0.694\n",
+      "Condition not met at step 5326. No data to save.\n",
       "5326\n",
+      "model_vars.get('Gini', 0)[-1]=0.6758\n",
+      "Condition not met at step 5327. No data to save.\n",
       "5327\n",
+      "model_vars.get('Gini', 0)[-1]=0.6712\n",
+      "Condition not met at step 5328. No data to save.\n",
       "5328\n",
+      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
+      "Condition not met at step 5329. No data to save.\n",
       "5329\n",
+      "model_vars.get('Gini', 0)[-1]=0.6948000000000001\n",
+      "Condition not met at step 5330. No data to save.\n",
       "5330\n",
+      "model_vars.get('Gini', 0)[-1]=0.7158\n",
+      "Condition met. Appended special results for step 5331 to test_path/special_results.parquet\n",
       "5331\n",
+      "model_vars.get('Gini', 0)[-1]=0.7174\n",
+      "Condition met. Appended special results for step 5332 to test_path/special_results.parquet\n",
       "5332\n",
+      "model_vars.get('Gini', 0)[-1]=0.7368\n",
+      "Condition met. Appended special results for step 5333 to test_path/special_results.parquet\n",
       "5333\n",
+      "model_vars.get('Gini', 0)[-1]=0.7388\n",
+      "Condition met. Appended special results for step 5334 to test_path/special_results.parquet\n",
       "5334\n",
+      "model_vars.get('Gini', 0)[-1]=0.7276\n",
+      "Condition met. Appended special results for step 5335 to test_path/special_results.parquet\n",
       "5335\n",
+      "model_vars.get('Gini', 0)[-1]=0.722\n",
+      "Condition met. Appended special results for step 5336 to test_path/special_results.parquet\n",
       "5336\n",
+      "model_vars.get('Gini', 0)[-1]=0.7176\n",
+      "Condition met. Appended special results for step 5337 to test_path/special_results.parquet\n",
       "5337\n",
+      "model_vars.get('Gini', 0)[-1]=0.7058\n",
+      "Condition met. Appended special results for step 5338 to test_path/special_results.parquet\n",
       "5338\n",
+      "model_vars.get('Gini', 0)[-1]=0.6838\n",
+      "Condition not met at step 5339. No data to save.\n",
       "5339\n",
+      "model_vars.get('Gini', 0)[-1]=0.6762\n",
+      "Condition not met at step 5340. No data to save.\n",
       "5340\n",
+      "model_vars.get('Gini', 0)[-1]=0.675\n",
+      "Condition not met at step 5341. No data to save.\n",
       "5341\n",
+      "model_vars.get('Gini', 0)[-1]=0.6784\n",
+      "Condition not met at step 5342. No data to save.\n",
       "5342\n",
+      "model_vars.get('Gini', 0)[-1]=0.6644\n",
+      "Condition not met at step 5343. No data to save.\n",
       "5343\n",
+      "model_vars.get('Gini', 0)[-1]=0.6738\n",
+      "Condition not met at step 5344. No data to save.\n",
       "5344\n",
+      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
+      "Condition not met at step 5345. No data to save.\n",
       "5345\n",
+      "model_vars.get('Gini', 0)[-1]=0.6192\n",
+      "Condition not met at step 5346. No data to save.\n",
       "5346\n",
+      "model_vars.get('Gini', 0)[-1]=0.6516\n",
+      "Condition not met at step 5347. No data to save.\n",
       "5347\n",
+      "model_vars.get('Gini', 0)[-1]=0.6468\n",
+      "Condition not met at step 5348. No data to save.\n",
       "5348\n",
+      "model_vars.get('Gini', 0)[-1]=0.6719999999999999\n",
+      "Condition not met at step 5349. No data to save.\n",
       "5349\n",
+      "model_vars.get('Gini', 0)[-1]=0.687\n",
+      "Condition not met at step 5350. No data to save.\n",
       "5350\n",
+      "model_vars.get('Gini', 0)[-1]=0.6826\n",
+      "Condition not met at step 5351. No data to save.\n",
       "5351\n",
+      "model_vars.get('Gini', 0)[-1]=0.6734\n",
+      "Condition not met at step 5352. No data to save.\n",
       "5352\n",
+      "model_vars.get('Gini', 0)[-1]=0.6772\n",
+      "Condition not met at step 5353. No data to save.\n",
       "5353\n",
+      "model_vars.get('Gini', 0)[-1]=0.6658\n",
+      "Condition not met at step 5354. No data to save.\n",
       "5354\n",
+      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
+      "Condition not met at step 5355. No data to save.\n",
       "5355\n",
+      "model_vars.get('Gini', 0)[-1]=0.6818\n",
+      "Condition not met at step 5356. No data to save.\n",
       "5356\n",
+      "model_vars.get('Gini', 0)[-1]=0.6624\n",
+      "Condition not met at step 5357. No data to save.\n",
       "5357\n",
+      "model_vars.get('Gini', 0)[-1]=0.6936\n",
+      "Condition not met at step 5358. No data to save.\n",
       "5358\n",
+      "model_vars.get('Gini', 0)[-1]=0.7016\n",
+      "Condition met. Appended special results for step 5359 to test_path/special_results.parquet\n",
       "5359\n",
+      "model_vars.get('Gini', 0)[-1]=0.6719999999999999\n",
+      "Condition not met at step 5360. No data to save.\n",
       "5360\n",
+      "model_vars.get('Gini', 0)[-1]=0.6694\n",
+      "Condition not met at step 5361. No data to save.\n",
       "5361\n",
+      "model_vars.get('Gini', 0)[-1]=0.665\n",
+      "Condition not met at step 5362. No data to save.\n",
       "5362\n",
+      "model_vars.get('Gini', 0)[-1]=0.687\n",
+      "Condition not met at step 5363. No data to save.\n",
       "5363\n",
+      "model_vars.get('Gini', 0)[-1]=0.6624\n",
+      "Condition not met at step 5364. No data to save.\n",
       "5364\n",
+      "model_vars.get('Gini', 0)[-1]=0.708\n",
+      "Condition met. Appended special results for step 5365 to test_path/special_results.parquet\n",
       "5365\n",
+      "model_vars.get('Gini', 0)[-1]=0.7112\n",
+      "Condition met. Appended special results for step 5366 to test_path/special_results.parquet\n",
       "5366\n",
+      "model_vars.get('Gini', 0)[-1]=0.6892\n",
+      "Condition not met at step 5367. No data to save.\n",
       "5367\n",
+      "model_vars.get('Gini', 0)[-1]=0.649\n",
+      "Condition not met at step 5368. No data to save.\n",
       "5368\n",
+      "model_vars.get('Gini', 0)[-1]=0.6366\n",
+      "Condition not met at step 5369. No data to save.\n",
       "5369\n",
+      "model_vars.get('Gini', 0)[-1]=0.6759999999999999\n",
+      "Condition not met at step 5370. No data to save.\n",
       "5370\n",
+      "model_vars.get('Gini', 0)[-1]=0.6786000000000001\n",
+      "Condition not met at step 5371. No data to save.\n",
       "5371\n",
+      "model_vars.get('Gini', 0)[-1]=0.6552\n",
+      "Condition not met at step 5372. No data to save.\n",
       "5372\n",
+      "model_vars.get('Gini', 0)[-1]=0.611\n",
+      "Condition not met at step 5373. No data to save.\n",
       "5373\n",
+      "model_vars.get('Gini', 0)[-1]=0.6044\n",
+      "Condition not met at step 5374. No data to save.\n",
       "5374\n",
+      "model_vars.get('Gini', 0)[-1]=0.6275999999999999\n",
+      "Condition not met at step 5375. No data to save.\n",
       "5375\n",
+      "model_vars.get('Gini', 0)[-1]=0.6335999999999999\n",
+      "Condition not met at step 5376. No data to save.\n",
       "5376\n",
+      "model_vars.get('Gini', 0)[-1]=0.6562\n",
+      "Condition not met at step 5377. No data to save.\n",
       "5377\n",
+      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
+      "Condition not met at step 5378. No data to save.\n",
       "5378\n",
+      "model_vars.get('Gini', 0)[-1]=0.6961999999999999\n",
+      "Condition not met at step 5379. No data to save.\n",
       "5379\n",
+      "model_vars.get('Gini', 0)[-1]=0.6894\n",
+      "Condition not met at step 5380. No data to save.\n",
       "5380\n",
+      "model_vars.get('Gini', 0)[-1]=0.6784\n",
+      "Condition not met at step 5381. No data to save.\n",
       "5381\n",
+      "model_vars.get('Gini', 0)[-1]=0.6778\n",
+      "Condition not met at step 5382. No data to save.\n",
       "5382\n",
+      "model_vars.get('Gini', 0)[-1]=0.6734\n",
+      "Condition not met at step 5383. No data to save.\n",
       "5383\n",
+      "model_vars.get('Gini', 0)[-1]=0.6981999999999999\n",
+      "Condition not met at step 5384. No data to save.\n",
       "5384\n",
+      "model_vars.get('Gini', 0)[-1]=0.6918\n",
+      "Condition not met at step 5385. No data to save.\n",
       "5385\n",
+      "model_vars.get('Gini', 0)[-1]=0.6804\n",
+      "Condition not met at step 5386. No data to save.\n",
       "5386\n",
+      "model_vars.get('Gini', 0)[-1]=0.6966\n",
+      "Condition not met at step 5387. No data to save.\n",
       "5387\n",
+      "model_vars.get('Gini', 0)[-1]=0.698\n",
+      "Condition not met at step 5388. No data to save.\n",
       "5388\n",
+      "model_vars.get('Gini', 0)[-1]=0.7\n",
+      "Condition not met at step 5389. No data to save.\n",
       "5389\n",
+      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
+      "Condition not met at step 5390. No data to save.\n",
       "5390\n",
+      "model_vars.get('Gini', 0)[-1]=0.6622\n",
+      "Condition not met at step 5391. No data to save.\n",
       "5391\n",
+      "model_vars.get('Gini', 0)[-1]=0.6556\n",
+      "Condition not met at step 5392. No data to save.\n",
       "5392\n",
+      "model_vars.get('Gini', 0)[-1]=0.6328\n",
+      "Condition not met at step 5393. No data to save.\n",
       "5393\n",
+      "model_vars.get('Gini', 0)[-1]=0.6586000000000001\n",
+      "Condition not met at step 5394. No data to save.\n",
       "5394\n",
+      "model_vars.get('Gini', 0)[-1]=0.6548\n",
+      "Condition not met at step 5395. No data to save.\n",
       "5395\n",
+      "model_vars.get('Gini', 0)[-1]=0.6404000000000001\n",
+      "Condition not met at step 5396. No data to save.\n",
       "5396\n",
+      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
+      "Condition not met at step 5397. No data to save.\n",
       "5397\n",
+      "model_vars.get('Gini', 0)[-1]=0.6318\n",
+      "Condition not met at step 5398. No data to save.\n",
       "5398\n",
+      "model_vars.get('Gini', 0)[-1]=0.6246\n",
+      "Condition not met at step 5399. No data to save.\n",
       "5399\n",
       "CALLED\n",
       "self.model._steps=5400\n",
       " TEST self.model._steps=5400\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "5300  0.6626\n",
-      "5301  0.6426\n",
-      "5302  0.6606\n",
-      "5303  0.6736\n",
-      "5304  0.6600\n",
+      "5300  0.6302\n",
+      "5301  0.6332\n",
+      "5302  0.6460\n",
+      "5303  0.6546\n",
+      "5304  0.6880\n",
       "...      ...\n",
-      "5395  0.6588\n",
-      "5396  0.6626\n",
-      "5397  0.6586\n",
-      "5398  0.6400\n",
-      "5399  0.6606\n",
+      "5395  0.6404\n",
+      "5396  0.6364\n",
+      "5397  0.6318\n",
+      "5398  0.6246\n",
+      "5399  0.6476\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_054.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_054.parquet'\n",
-      "Saving model to output_dir/model_data_054.parquet\n",
-      "Saving agent to output_dir/agent_data_054.parquet\n",
+      "model_file='test_path/model_data_054.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_054.parquet'\n",
+      "Saving model to test_path/model_data_054.parquet\n",
+      "Saving agent to test_path/agent_data_054.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6476\n",
+      "Condition not met at step 5400. No data to save.\n",
       "5400\n",
+      "model_vars.get('Gini', 0)[-1]=0.6206\n",
+      "Condition not met at step 5401. No data to save.\n",
       "5401\n",
+      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
+      "Condition not met at step 5402. No data to save.\n",
       "5402\n",
+      "model_vars.get('Gini', 0)[-1]=0.6368\n",
+      "Condition not met at step 5403. No data to save.\n",
       "5403\n",
+      "model_vars.get('Gini', 0)[-1]=0.6778\n",
+      "Condition not met at step 5404. No data to save.\n",
       "5404\n",
+      "model_vars.get('Gini', 0)[-1]=0.667\n",
+      "Condition not met at step 5405. No data to save.\n",
       "5405\n",
+      "model_vars.get('Gini', 0)[-1]=0.6542\n",
+      "Condition not met at step 5406. No data to save.\n",
       "5406\n",
+      "model_vars.get('Gini', 0)[-1]=0.6288\n",
+      "Condition not met at step 5407. No data to save.\n",
       "5407\n",
+      "model_vars.get('Gini', 0)[-1]=0.6532\n",
+      "Condition not met at step 5408. No data to save.\n",
       "5408\n",
+      "model_vars.get('Gini', 0)[-1]=0.6642\n",
+      "Condition not met at step 5409. No data to save.\n",
       "5409\n",
+      "model_vars.get('Gini', 0)[-1]=0.6732\n",
+      "Condition not met at step 5410. No data to save.\n",
       "5410\n",
+      "model_vars.get('Gini', 0)[-1]=0.6934\n",
+      "Condition not met at step 5411. No data to save.\n",
       "5411\n",
+      "model_vars.get('Gini', 0)[-1]=0.6798\n",
+      "Condition not met at step 5412. No data to save.\n",
       "5412\n",
+      "model_vars.get('Gini', 0)[-1]=0.6906\n",
+      "Condition not met at step 5413. No data to save.\n",
       "5413\n",
+      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
+      "Condition not met at step 5414. No data to save.\n",
       "5414\n",
+      "model_vars.get('Gini', 0)[-1]=0.6848000000000001\n",
+      "Condition not met at step 5415. No data to save.\n",
       "5415\n",
+      "model_vars.get('Gini', 0)[-1]=0.6648000000000001\n",
+      "Condition not met at step 5416. No data to save.\n",
       "5416\n",
+      "model_vars.get('Gini', 0)[-1]=0.6636\n",
+      "Condition not met at step 5417. No data to save.\n",
       "5417\n",
+      "model_vars.get('Gini', 0)[-1]=0.6862\n",
+      "Condition not met at step 5418. No data to save.\n",
       "5418\n",
+      "model_vars.get('Gini', 0)[-1]=0.6834\n",
+      "Condition not met at step 5419. No data to save.\n",
       "5419\n",
+      "model_vars.get('Gini', 0)[-1]=0.6758\n",
+      "Condition not met at step 5420. No data to save.\n",
       "5420\n",
+      "model_vars.get('Gini', 0)[-1]=0.6714\n",
+      "Condition not met at step 5421. No data to save.\n",
       "5421\n",
+      "model_vars.get('Gini', 0)[-1]=0.6614\n",
+      "Condition not met at step 5422. No data to save.\n",
       "5422\n",
+      "model_vars.get('Gini', 0)[-1]=0.654\n",
+      "Condition not met at step 5423. No data to save.\n",
       "5423\n",
+      "model_vars.get('Gini', 0)[-1]=0.6642\n",
+      "Condition not met at step 5424. No data to save.\n",
       "5424\n",
+      "model_vars.get('Gini', 0)[-1]=0.6722\n",
+      "Condition not met at step 5425. No data to save.\n",
       "5425\n",
+      "model_vars.get('Gini', 0)[-1]=0.6732\n",
+      "Condition not met at step 5426. No data to save.\n",
       "5426\n",
+      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
+      "Condition not met at step 5427. No data to save.\n",
       "5427\n",
+      "model_vars.get('Gini', 0)[-1]=0.6494\n",
+      "Condition not met at step 5428. No data to save.\n",
       "5428\n",
+      "model_vars.get('Gini', 0)[-1]=0.6678\n",
+      "Condition not met at step 5429. No data to save.\n",
       "5429\n",
+      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
+      "Condition not met at step 5430. No data to save.\n",
       "5430\n",
+      "model_vars.get('Gini', 0)[-1]=0.6274\n",
+      "Condition not met at step 5431. No data to save.\n",
       "5431\n",
+      "model_vars.get('Gini', 0)[-1]=0.629\n",
+      "Condition not met at step 5432. No data to save.\n",
       "5432\n",
+      "model_vars.get('Gini', 0)[-1]=0.6306\n",
+      "Condition not met at step 5433. No data to save.\n",
       "5433\n",
+      "model_vars.get('Gini', 0)[-1]=0.6314\n",
+      "Condition not met at step 5434. No data to save.\n",
       "5434\n",
+      "model_vars.get('Gini', 0)[-1]=0.651\n",
+      "Condition not met at step 5435. No data to save.\n",
       "5435\n",
+      "model_vars.get('Gini', 0)[-1]=0.6614\n",
+      "Condition not met at step 5436. No data to save.\n",
       "5436\n",
+      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
+      "Condition not met at step 5437. No data to save.\n",
       "5437\n",
+      "model_vars.get('Gini', 0)[-1]=0.6348\n",
+      "Condition not met at step 5438. No data to save.\n",
       "5438\n",
+      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
+      "Condition not met at step 5439. No data to save.\n",
       "5439\n",
+      "model_vars.get('Gini', 0)[-1]=0.6576\n",
+      "Condition not met at step 5440. No data to save.\n",
       "5440\n",
+      "model_vars.get('Gini', 0)[-1]=0.662\n",
+      "Condition not met at step 5441. No data to save.\n",
       "5441\n",
+      "model_vars.get('Gini', 0)[-1]=0.6138\n",
+      "Condition not met at step 5442. No data to save.\n",
       "5442\n",
+      "model_vars.get('Gini', 0)[-1]=0.623\n",
+      "Condition not met at step 5443. No data to save.\n",
       "5443\n",
+      "model_vars.get('Gini', 0)[-1]=0.6028\n",
+      "Condition not met at step 5444. No data to save.\n",
       "5444\n",
+      "model_vars.get('Gini', 0)[-1]=0.6124\n",
+      "Condition not met at step 5445. No data to save.\n",
       "5445\n",
+      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
+      "Condition not met at step 5446. No data to save.\n",
       "5446\n",
+      "model_vars.get('Gini', 0)[-1]=0.6562\n",
+      "Condition not met at step 5447. No data to save.\n",
       "5447\n",
+      "model_vars.get('Gini', 0)[-1]=0.6428\n",
+      "Condition not met at step 5448. No data to save.\n",
       "5448\n",
+      "model_vars.get('Gini', 0)[-1]=0.6516\n",
+      "Condition not met at step 5449. No data to save.\n",
       "5449\n",
+      "model_vars.get('Gini', 0)[-1]=0.6398\n",
+      "Condition not met at step 5450. No data to save.\n",
       "5450\n",
+      "model_vars.get('Gini', 0)[-1]=0.643\n",
+      "Condition not met at step 5451. No data to save.\n",
       "5451\n",
+      "model_vars.get('Gini', 0)[-1]=0.6342\n",
+      "Condition not met at step 5452. No data to save.\n",
       "5452\n",
+      "model_vars.get('Gini', 0)[-1]=0.6592\n",
+      "Condition not met at step 5453. No data to save.\n",
       "5453\n",
+      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
+      "Condition not met at step 5454. No data to save.\n",
       "5454\n",
+      "model_vars.get('Gini', 0)[-1]=0.675\n",
+      "Condition not met at step 5455. No data to save.\n",
       "5455\n",
+      "model_vars.get('Gini', 0)[-1]=0.6921999999999999\n",
+      "Condition not met at step 5456. No data to save.\n",
       "5456\n",
+      "model_vars.get('Gini', 0)[-1]=0.7032\n",
+      "Condition met. Appended special results for step 5457 to test_path/special_results.parquet\n",
       "5457\n",
+      "model_vars.get('Gini', 0)[-1]=0.7094\n",
+      "Condition met. Appended special results for step 5458 to test_path/special_results.parquet\n",
       "5458\n",
+      "model_vars.get('Gini', 0)[-1]=0.6970000000000001\n",
+      "Condition not met at step 5459. No data to save.\n",
       "5459\n",
+      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
+      "Condition not met at step 5460. No data to save.\n",
       "5460\n",
+      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
+      "Condition not met at step 5461. No data to save.\n",
       "5461\n",
+      "model_vars.get('Gini', 0)[-1]=0.6759999999999999\n",
+      "Condition not met at step 5462. No data to save.\n",
       "5462\n",
+      "model_vars.get('Gini', 0)[-1]=0.6699999999999999\n",
+      "Condition not met at step 5463. No data to save.\n",
       "5463\n",
+      "model_vars.get('Gini', 0)[-1]=0.6664\n",
+      "Condition not met at step 5464. No data to save.\n",
       "5464\n",
+      "model_vars.get('Gini', 0)[-1]=0.6577999999999999\n",
+      "Condition not met at step 5465. No data to save.\n",
       "5465\n",
+      "model_vars.get('Gini', 0)[-1]=0.6602\n",
+      "Condition not met at step 5466. No data to save.\n",
       "5466\n",
+      "model_vars.get('Gini', 0)[-1]=0.6396\n",
+      "Condition not met at step 5467. No data to save.\n",
       "5467\n",
+      "model_vars.get('Gini', 0)[-1]=0.6266\n",
+      "Condition not met at step 5468. No data to save.\n",
       "5468\n",
+      "model_vars.get('Gini', 0)[-1]=0.6534\n",
+      "Condition not met at step 5469. No data to save.\n",
       "5469\n",
+      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
+      "Condition not met at step 5470. No data to save.\n",
       "5470\n",
+      "model_vars.get('Gini', 0)[-1]=0.6392\n",
+      "Condition not met at step 5471. No data to save.\n",
       "5471\n",
+      "model_vars.get('Gini', 0)[-1]=0.615\n",
+      "Condition not met at step 5472. No data to save.\n",
       "5472\n",
+      "model_vars.get('Gini', 0)[-1]=0.5933999999999999\n",
+      "Condition not met at step 5473. No data to save.\n",
       "5473\n",
+      "model_vars.get('Gini', 0)[-1]=0.5920000000000001\n",
+      "Condition not met at step 5474. No data to save.\n",
       "5474\n",
+      "model_vars.get('Gini', 0)[-1]=0.5774\n",
+      "Condition not met at step 5475. No data to save.\n",
       "5475\n",
+      "model_vars.get('Gini', 0)[-1]=0.5860000000000001\n",
+      "Condition not met at step 5476. No data to save.\n",
       "5476\n",
+      "model_vars.get('Gini', 0)[-1]=0.5938\n",
+      "Condition not met at step 5477. No data to save.\n",
       "5477\n",
+      "model_vars.get('Gini', 0)[-1]=0.6148\n",
+      "Condition not met at step 5478. No data to save.\n",
       "5478\n",
+      "model_vars.get('Gini', 0)[-1]=0.6326\n",
+      "Condition not met at step 5479. No data to save.\n",
       "5479\n",
+      "model_vars.get('Gini', 0)[-1]=0.6408\n",
+      "Condition not met at step 5480. No data to save.\n",
       "5480\n",
+      "model_vars.get('Gini', 0)[-1]=0.6368\n",
+      "Condition not met at step 5481. No data to save.\n",
       "5481\n",
+      "model_vars.get('Gini', 0)[-1]=0.6396\n",
+      "Condition not met at step 5482. No data to save.\n",
       "5482\n",
+      "model_vars.get('Gini', 0)[-1]=0.6768000000000001\n",
+      "Condition not met at step 5483. No data to save.\n",
       "5483\n",
+      "model_vars.get('Gini', 0)[-1]=0.6839999999999999\n",
+      "Condition not met at step 5484. No data to save.\n",
       "5484\n",
+      "model_vars.get('Gini', 0)[-1]=0.6756\n",
+      "Condition not met at step 5485. No data to save.\n",
       "5485\n",
+      "model_vars.get('Gini', 0)[-1]=0.6702\n",
+      "Condition not met at step 5486. No data to save.\n",
       "5486\n",
+      "model_vars.get('Gini', 0)[-1]=0.6738\n",
+      "Condition not met at step 5487. No data to save.\n",
       "5487\n",
+      "model_vars.get('Gini', 0)[-1]=0.6844\n",
+      "Condition not met at step 5488. No data to save.\n",
       "5488\n",
+      "model_vars.get('Gini', 0)[-1]=0.6946\n",
+      "Condition not met at step 5489. No data to save.\n",
       "5489\n",
+      "model_vars.get('Gini', 0)[-1]=0.6634\n",
+      "Condition not met at step 5490. No data to save.\n",
       "5490\n",
+      "model_vars.get('Gini', 0)[-1]=0.6756\n",
+      "Condition not met at step 5491. No data to save.\n",
       "5491\n",
+      "model_vars.get('Gini', 0)[-1]=0.6848000000000001\n",
+      "Condition not met at step 5492. No data to save.\n",
       "5492\n",
+      "model_vars.get('Gini', 0)[-1]=0.6966\n",
+      "Condition not met at step 5493. No data to save.\n",
       "5493\n",
+      "model_vars.get('Gini', 0)[-1]=0.6941999999999999\n",
+      "Condition not met at step 5494. No data to save.\n",
       "5494\n",
+      "model_vars.get('Gini', 0)[-1]=0.6984\n",
+      "Condition not met at step 5495. No data to save.\n",
       "5495\n",
+      "model_vars.get('Gini', 0)[-1]=0.6759999999999999\n",
+      "Condition not met at step 5496. No data to save.\n",
       "5496\n",
+      "model_vars.get('Gini', 0)[-1]=0.675\n",
+      "Condition not met at step 5497. No data to save.\n",
       "5497\n",
+      "model_vars.get('Gini', 0)[-1]=0.6826\n",
+      "Condition not met at step 5498. No data to save.\n",
       "5498\n",
+      "model_vars.get('Gini', 0)[-1]=0.6856\n",
+      "Condition not met at step 5499. No data to save.\n",
       "5499\n",
       "CALLED\n",
       "self.model._steps=5500\n",
       " TEST self.model._steps=5500\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "5400  0.6546\n",
-      "5401  0.6418\n",
-      "5402  0.6224\n",
-      "5403  0.6440\n",
-      "5404  0.6510\n",
+      "5400  0.6206\n",
+      "5401  0.6526\n",
+      "5402  0.6368\n",
+      "5403  0.6778\n",
+      "5404  0.6670\n",
       "...      ...\n",
-      "5495  0.6696\n",
-      "5496  0.6654\n",
-      "5497  0.6728\n",
-      "5498  0.6728\n",
-      "5499  0.6632\n",
+      "5495  0.6760\n",
+      "5496  0.6750\n",
+      "5497  0.6826\n",
+      "5498  0.6856\n",
+      "5499  0.6806\n",
       "\n",
       "[100 rows x 1 columns]\n",
       "3\n",
-      "model_file='output_dir/model_data_055.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_055.parquet'\n",
-      "Saving model to output_dir/model_data_055.parquet\n",
-      "Saving agent to output_dir/agent_data_055.parquet\n",
+      "model_file='test_path/model_data_055.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_055.parquet'\n",
+      "Saving model to test_path/model_data_055.parquet\n",
+      "Saving agent to test_path/agent_data_055.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.6806\n",
+      "Condition not met at step 5500. No data to save.\n",
       "5500\n",
+      "model_vars.get('Gini', 0)[-1]=0.6888000000000001\n",
+      "Condition not met at step 5501. No data to save.\n",
       "5501\n",
+      "model_vars.get('Gini', 0)[-1]=0.6708000000000001\n",
+      "Condition not met at step 5502. No data to save.\n",
       "5502\n",
+      "model_vars.get('Gini', 0)[-1]=0.6882\n",
+      "Condition not met at step 5503. No data to save.\n",
       "5503\n",
+      "model_vars.get('Gini', 0)[-1]=0.6648000000000001\n",
+      "Condition not met at step 5504. No data to save.\n",
       "5504\n",
+      "model_vars.get('Gini', 0)[-1]=0.6682\n",
+      "Condition not met at step 5505. No data to save.\n",
       "5505\n",
+      "model_vars.get('Gini', 0)[-1]=0.657\n",
+      "Condition not met at step 5506. No data to save.\n",
       "5506\n",
+      "model_vars.get('Gini', 0)[-1]=0.6442\n",
+      "Condition not met at step 5507. No data to save.\n",
       "5507\n",
+      "model_vars.get('Gini', 0)[-1]=0.65\n",
+      "Condition not met at step 5508. No data to save.\n",
       "5508\n",
+      "model_vars.get('Gini', 0)[-1]=0.6497999999999999\n",
+      "Condition not met at step 5509. No data to save.\n",
       "5509\n",
+      "model_vars.get('Gini', 0)[-1]=0.6366\n",
+      "Condition not met at step 5510. No data to save.\n",
       "5510\n",
+      "model_vars.get('Gini', 0)[-1]=0.6335999999999999\n",
+      "Condition not met at step 5511. No data to save.\n",
       "5511\n",
+      "model_vars.get('Gini', 0)[-1]=0.6252\n",
+      "Condition not met at step 5512. No data to save.\n",
       "5512\n",
+      "model_vars.get('Gini', 0)[-1]=0.615\n",
+      "Condition not met at step 5513. No data to save.\n",
       "5513\n",
+      "model_vars.get('Gini', 0)[-1]=0.615\n",
+      "Condition not met at step 5514. No data to save.\n",
       "5514\n",
+      "model_vars.get('Gini', 0)[-1]=0.6412\n",
+      "Condition not met at step 5515. No data to save.\n",
       "5515\n",
+      "model_vars.get('Gini', 0)[-1]=0.6214\n",
+      "Condition not met at step 5516. No data to save.\n",
       "5516\n",
+      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
+      "Condition not met at step 5517. No data to save.\n",
       "5517\n",
+      "model_vars.get('Gini', 0)[-1]=0.6322\n",
+      "Condition not met at step 5518. No data to save.\n",
       "5518\n",
+      "model_vars.get('Gini', 0)[-1]=0.656\n",
+      "Condition not met at step 5519. No data to save.\n",
       "5519\n",
+      "model_vars.get('Gini', 0)[-1]=0.6762\n",
+      "Condition not met at step 5520. No data to save.\n",
       "5520\n",
+      "model_vars.get('Gini', 0)[-1]=0.6844\n",
+      "Condition not met at step 5521. No data to save.\n",
       "5521\n",
+      "model_vars.get('Gini', 0)[-1]=0.6694\n",
+      "Condition not met at step 5522. No data to save.\n",
       "5522\n",
+      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
+      "Condition not met at step 5523. No data to save.\n",
       "5523\n",
+      "model_vars.get('Gini', 0)[-1]=0.6752\n",
+      "Condition not met at step 5524. No data to save.\n",
       "5524\n",
+      "model_vars.get('Gini', 0)[-1]=0.6574\n",
+      "Condition not met at step 5525. No data to save.\n",
       "5525\n",
+      "model_vars.get('Gini', 0)[-1]=0.6632\n",
+      "Condition not met at step 5526. No data to save.\n",
       "5526\n",
+      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
+      "Condition not met at step 5527. No data to save.\n",
       "5527\n",
+      "model_vars.get('Gini', 0)[-1]=0.6732\n",
+      "Condition not met at step 5528. No data to save.\n",
       "5528\n",
+      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
+      "Condition not met at step 5529. No data to save.\n",
       "5529\n",
+      "model_vars.get('Gini', 0)[-1]=0.6332\n",
+      "Condition not met at step 5530. No data to save.\n",
       "5530\n",
+      "model_vars.get('Gini', 0)[-1]=0.6516\n",
+      "Condition not met at step 5531. No data to save.\n",
       "5531\n",
+      "model_vars.get('Gini', 0)[-1]=0.6388\n",
+      "Condition not met at step 5532. No data to save.\n",
       "5532\n",
+      "model_vars.get('Gini', 0)[-1]=0.6496\n",
+      "Condition not met at step 5533. No data to save.\n",
       "5533\n",
+      "model_vars.get('Gini', 0)[-1]=0.6562\n",
+      "Condition not met at step 5534. No data to save.\n",
       "5534\n",
+      "model_vars.get('Gini', 0)[-1]=0.6442\n",
+      "Condition not met at step 5535. No data to save.\n",
       "5535\n",
+      "model_vars.get('Gini', 0)[-1]=0.6202000000000001\n",
+      "Condition not met at step 5536. No data to save.\n",
       "5536\n",
+      "model_vars.get('Gini', 0)[-1]=0.621\n",
+      "Condition not met at step 5537. No data to save.\n",
       "5537\n",
+      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
+      "Condition not met at step 5538. No data to save.\n",
       "5538\n",
+      "model_vars.get('Gini', 0)[-1]=0.6644\n",
+      "Condition not met at step 5539. No data to save.\n",
       "5539\n",
+      "model_vars.get('Gini', 0)[-1]=0.6322\n",
+      "Condition not met at step 5540. No data to save.\n",
       "5540\n",
+      "model_vars.get('Gini', 0)[-1]=0.6384000000000001\n",
+      "Condition not met at step 5541. No data to save.\n",
       "5541\n",
+      "model_vars.get('Gini', 0)[-1]=0.6368\n",
+      "Condition not met at step 5542. No data to save.\n",
       "5542\n",
+      "model_vars.get('Gini', 0)[-1]=0.6404000000000001\n",
+      "Condition not met at step 5543. No data to save.\n",
       "5543\n",
+      "model_vars.get('Gini', 0)[-1]=0.6352\n",
+      "Condition not met at step 5544. No data to save.\n",
       "5544\n",
+      "model_vars.get('Gini', 0)[-1]=0.6295999999999999\n",
+      "Condition not met at step 5545. No data to save.\n",
       "5545\n",
+      "model_vars.get('Gini', 0)[-1]=0.6679999999999999\n",
+      "Condition not met at step 5546. No data to save.\n",
       "5546\n",
+      "model_vars.get('Gini', 0)[-1]=0.6882\n",
+      "Condition not met at step 5547. No data to save.\n",
       "5547\n",
+      "model_vars.get('Gini', 0)[-1]=0.6686000000000001\n",
+      "Condition not met at step 5548. No data to save.\n",
       "5548\n",
+      "model_vars.get('Gini', 0)[-1]=0.6722\n",
+      "Condition not met at step 5549. No data to save.\n",
       "5549\n",
+      "model_vars.get('Gini', 0)[-1]=0.6758\n",
+      "Condition not met at step 5550. No data to save.\n",
       "5550\n",
+      "model_vars.get('Gini', 0)[-1]=0.6930000000000001\n",
+      "Condition not met at step 5551. No data to save.\n",
       "5551\n",
+      "model_vars.get('Gini', 0)[-1]=0.6732\n",
+      "Condition not met at step 5552. No data to save.\n",
       "5552\n",
+      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
+      "Condition not met at step 5553. No data to save.\n",
       "5553\n",
+      "model_vars.get('Gini', 0)[-1]=0.6368\n",
+      "Condition not met at step 5554. No data to save.\n",
       "5554\n",
-      "5555\n",
-      "5556\n",
-      "5557\n",
-      "5558\n",
-      "5559\n",
-      "5560\n",
-      "5561\n",
-      "5562\n",
-      "5563\n",
-      "5564\n",
-      "5565\n",
-      "5566\n",
-      "5567\n",
-      "5568\n",
-      "5569\n",
-      "5570\n",
-      "5571\n",
-      "5572\n",
-      "5573\n",
-      "5574\n",
-      "5575\n",
-      "5576\n",
-      "5577\n",
-      "5578\n",
-      "5579\n",
-      "5580\n",
-      "5581\n",
-      "5582\n",
-      "5583\n",
-      "5584\n",
-      "5585\n",
-      "5586\n",
-      "5587\n",
-      "5588\n",
-      "5589\n",
-      "5590\n",
-      "5591\n",
-      "5592\n",
-      "5593\n",
-      "5594\n",
-      "5595\n",
-      "5596\n",
-      "5597\n",
-      "5598\n",
-      "5599\n",
-      "CALLED\n",
-      "self.model._steps=5600\n",
-      " TEST self.model._steps=5600\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "5500  0.6564\n",
-      "5501  0.6546\n",
-      "5502  0.6722\n",
-      "5503  0.6564\n",
-      "5504  0.6656\n",
-      "...      ...\n",
-      "5595  0.6404\n",
-      "5596  0.6396\n",
-      "5597  0.6376\n",
-      "5598  0.6480\n",
-      "5599  0.6518\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_056.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_056.parquet'\n",
-      "Saving model to output_dir/model_data_056.parquet\n",
-      "Saving agent to output_dir/agent_data_056.parquet\n",
-      "5600\n",
-      "5601\n",
-      "5602\n",
-      "5603\n",
-      "5604\n",
-      "5605\n",
-      "5606\n",
-      "5607\n",
-      "5608\n",
-      "5609\n",
-      "5610\n",
-      "5611\n",
-      "5612\n",
-      "5613\n",
-      "5614\n",
-      "5615\n",
-      "5616\n",
-      "5617\n",
-      "5618\n",
-      "5619\n",
-      "5620\n",
-      "5621\n",
-      "5622\n",
-      "5623\n",
-      "5624\n",
-      "5625\n",
-      "5626\n",
-      "5627\n",
-      "5628\n",
-      "5629\n",
-      "5630\n",
-      "5631\n",
-      "5632\n",
-      "5633\n",
-      "5634\n",
-      "5635\n",
-      "5636\n",
-      "5637\n",
-      "5638\n",
-      "5639\n",
-      "5640\n",
-      "5641\n",
-      "5642\n",
-      "5643\n",
-      "5644\n",
-      "5645\n",
-      "5646\n",
-      "5647\n",
-      "5648\n",
-      "5649\n",
-      "5650\n",
-      "5651\n",
-      "5652\n",
-      "5653\n",
-      "5654\n",
-      "5655\n",
-      "5656\n",
-      "5657\n",
-      "5658\n",
-      "5659\n",
-      "5660\n",
-      "5661\n",
-      "5662\n",
-      "5663\n",
-      "5664\n",
-      "5665\n",
-      "5666\n",
-      "5667\n",
-      "5668\n",
-      "5669\n",
-      "5670\n",
-      "5671\n",
-      "5672\n",
-      "5673\n",
-      "5674\n",
-      "5675\n",
-      "5676\n",
-      "5677\n",
-      "5678\n",
-      "5679\n",
-      "5680\n",
-      "5681\n",
-      "5682\n",
-      "5683\n",
-      "5684\n",
-      "5685\n",
-      "5686\n",
-      "5687\n",
-      "5688\n",
-      "5689\n",
-      "5690\n",
-      "5691\n",
-      "5692\n",
-      "5693\n",
-      "5694\n",
-      "5695\n",
-      "5696\n",
-      "5697\n",
-      "5698\n",
-      "5699\n",
-      "CALLED\n",
-      "self.model._steps=5700\n",
-      " TEST self.model._steps=5700\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "5600  0.6502\n",
-      "5601  0.6484\n",
-      "5602  0.6394\n",
-      "5603  0.6448\n",
-      "5604  0.6208\n",
-      "...      ...\n",
-      "5695  0.6908\n",
-      "5696  0.7294\n",
-      "5697  0.7138\n",
-      "5698  0.7230\n",
-      "5699  0.7194\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_057.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_057.parquet'\n",
-      "Saving model to output_dir/model_data_057.parquet\n",
-      "Saving agent to output_dir/agent_data_057.parquet\n",
-      "5700\n",
-      "5701\n",
-      "5702\n",
-      "5703\n",
-      "5704\n",
-      "5705\n",
-      "5706\n",
-      "5707\n",
-      "5708\n",
-      "5709\n",
-      "5710\n",
-      "5711\n",
-      "5712\n",
-      "5713\n",
-      "5714\n",
-      "5715\n",
-      "5716\n",
-      "5717\n",
-      "5718\n",
-      "5719\n",
-      "5720\n",
-      "5721\n",
-      "5722\n",
-      "5723\n",
-      "5724\n",
-      "5725\n",
-      "5726\n",
-      "5727\n",
-      "5728\n",
-      "5729\n",
-      "5730\n",
-      "5731\n",
-      "5732\n",
-      "5733\n",
-      "5734\n",
-      "5735\n",
-      "5736\n",
-      "5737\n",
-      "5738\n",
-      "5739\n",
-      "5740\n",
-      "5741\n",
-      "5742\n",
-      "5743\n",
-      "5744\n",
-      "5745\n",
-      "5746\n",
-      "5747\n",
-      "5748\n",
-      "5749\n",
-      "5750\n",
-      "5751\n",
-      "5752\n",
-      "5753\n",
-      "5754\n",
-      "5755\n",
-      "5756\n",
-      "5757\n",
-      "5758\n",
-      "5759\n",
-      "5760\n",
-      "5761\n",
-      "5762\n",
-      "5763\n",
-      "5764\n",
-      "5765\n",
-      "5766\n",
-      "5767\n",
-      "5768\n",
-      "5769\n",
-      "5770\n",
-      "5771\n",
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-      "5797\n",
-      "5798\n",
-      "5799\n",
-      "CALLED\n",
-      "self.model._steps=5800\n",
-      " TEST self.model._steps=5800\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "5700  0.7168\n",
-      "5701  0.7240\n",
-      "5702  0.7228\n",
-      "5703  0.7160\n",
-      "5704  0.7184\n",
-      "...      ...\n",
-      "5795  0.6828\n",
-      "5796  0.6898\n",
-      "5797  0.7010\n",
-      "5798  0.7120\n",
-      "5799  0.7136\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_058.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_058.parquet'\n",
-      "Saving model to output_dir/model_data_058.parquet\n",
-      "Saving agent to output_dir/agent_data_058.parquet\n",
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-      "5897\n",
-      "5898\n",
-      "5899\n",
-      "CALLED\n",
-      "self.model._steps=5900\n",
-      " TEST self.model._steps=5900\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "5800  0.6898\n",
-      "5801  0.6930\n",
-      "5802  0.6824\n",
-      "5803  0.6764\n",
-      "5804  0.6460\n",
-      "...      ...\n",
-      "5895  0.6548\n",
-      "5896  0.6816\n",
-      "5897  0.6702\n",
-      "5898  0.6754\n",
-      "5899  0.6860\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_059.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_059.parquet'\n",
-      "Saving model to output_dir/model_data_059.parquet\n",
-      "Saving agent to output_dir/agent_data_059.parquet\n",
-      "5900\n",
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-      "5996\n",
-      "5997\n",
-      "5998\n",
-      "5999\n",
-      "CALLED\n",
-      "self.model._steps=6000\n",
-      " TEST self.model._steps=6000\n",
+      "FINAL CALLED\n",
+      "self.model._steps=5555\n",
+      " TEST self.model._steps=5555\n",
       " TEST self._cache_interval=100\n",
       "        Gini\n",
-      "5900  0.6962\n",
-      "5901  0.6814\n",
-      "5902  0.6622\n",
-      "5903  0.6738\n",
-      "5904  0.6670\n",
-      "...      ...\n",
-      "5995  0.5982\n",
-      "5996  0.5974\n",
-      "5997  0.5904\n",
-      "5998  0.5750\n",
-      "5999  0.5794\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_060.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_060.parquet'\n",
-      "Saving model to output_dir/model_data_060.parquet\n",
-      "Saving agent to output_dir/agent_data_060.parquet\n",
-      "6000\n",
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-      "6097\n",
-      "6098\n",
-      "6099\n",
-      "CALLED\n",
-      "self.model._steps=6100\n",
-      " TEST self.model._steps=6100\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "6000  0.5854\n",
-      "6001  0.5958\n",
-      "6002  0.6226\n",
-      "6003  0.6268\n",
-      "6004  0.6618\n",
-      "...      ...\n",
-      "6095  0.6460\n",
-      "6096  0.6372\n",
-      "6097  0.6520\n",
-      "6098  0.6554\n",
-      "6099  0.6380\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_061.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_061.parquet'\n",
-      "Saving model to output_dir/model_data_061.parquet\n",
-      "Saving agent to output_dir/agent_data_061.parquet\n",
-      "6100\n",
-      "6101\n",
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-      "6194\n",
-      "6195\n",
-      "6196\n",
-      "6197\n",
-      "6198\n",
-      "6199\n",
-      "CALLED\n",
-      "self.model._steps=6200\n",
-      " TEST self.model._steps=6200\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "6100  0.6122\n",
-      "6101  0.6106\n",
-      "6102  0.6094\n",
-      "6103  0.6184\n",
-      "6104  0.6390\n",
-      "...      ...\n",
-      "6195  0.6700\n",
-      "6196  0.6602\n",
-      "6197  0.6812\n",
-      "6198  0.6620\n",
-      "6199  0.6818\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_062.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_062.parquet'\n",
-      "Saving model to output_dir/model_data_062.parquet\n",
-      "Saving agent to output_dir/agent_data_062.parquet\n",
-      "6200\n",
-      "6201\n",
-      "6202\n",
-      "6203\n",
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-      "6294\n",
-      "6295\n",
-      "6296\n",
-      "6297\n",
-      "6298\n",
-      "6299\n",
-      "CALLED\n",
-      "self.model._steps=6300\n",
-      " TEST self.model._steps=6300\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "6200  0.7074\n",
-      "6201  0.6912\n",
-      "6202  0.6990\n",
-      "6203  0.7122\n",
-      "6204  0.7230\n",
-      "...      ...\n",
-      "6295  0.6710\n",
-      "6296  0.6800\n",
-      "6297  0.6578\n",
-      "6298  0.6654\n",
-      "6299  0.6560\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_063.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_063.parquet'\n",
-      "Saving model to output_dir/model_data_063.parquet\n",
-      "Saving agent to output_dir/agent_data_063.parquet\n",
-      "6300\n",
-      "6301\n",
-      "6302\n",
-      "6303\n",
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-      "6398\n",
-      "6399\n",
-      "CALLED\n",
-      "self.model._steps=6400\n",
-      " TEST self.model._steps=6400\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "6300  0.6668\n",
-      "6301  0.6226\n",
-      "6302  0.6426\n",
-      "6303  0.6436\n",
-      "6304  0.6544\n",
-      "...      ...\n",
-      "6395  0.6688\n",
-      "6396  0.6844\n",
-      "6397  0.6896\n",
-      "6398  0.6938\n",
-      "6399  0.7022\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_064.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_064.parquet'\n",
-      "Saving model to output_dir/model_data_064.parquet\n",
-      "Saving agent to output_dir/agent_data_064.parquet\n",
-      "6400\n",
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-      "6497\n",
-      "6498\n",
-      "6499\n",
-      "CALLED\n",
-      "self.model._steps=6500\n",
-      " TEST self.model._steps=6500\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "6400  0.7002\n",
-      "6401  0.6872\n",
-      "6402  0.6718\n",
-      "6403  0.7072\n",
-      "6404  0.6848\n",
-      "...      ...\n",
-      "6495  0.7172\n",
-      "6496  0.6978\n",
-      "6497  0.7108\n",
-      "6498  0.7166\n",
-      "6499  0.6990\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_065.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_065.parquet'\n",
-      "Saving model to output_dir/model_data_065.parquet\n",
-      "Saving agent to output_dir/agent_data_065.parquet\n",
-      "6500\n",
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-      "6594\n",
-      "6595\n",
-      "6596\n",
-      "6597\n",
-      "6598\n",
-      "6599\n",
-      "CALLED\n",
-      "self.model._steps=6600\n",
-      " TEST self.model._steps=6600\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "6500  0.6888\n",
-      "6501  0.7066\n",
-      "6502  0.7068\n",
-      "6503  0.6898\n",
-      "6504  0.6928\n",
-      "...      ...\n",
-      "6595  0.6804\n",
-      "6596  0.6648\n",
-      "6597  0.6424\n",
-      "6598  0.6346\n",
-      "6599  0.6264\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_066.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_066.parquet'\n",
-      "Saving model to output_dir/model_data_066.parquet\n",
-      "Saving agent to output_dir/agent_data_066.parquet\n",
-      "6600\n",
-      "6601\n",
-      "6602\n",
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-      "6693\n",
-      "6694\n",
-      "6695\n",
-      "6696\n",
-      "6697\n",
-      "6698\n",
-      "6699\n",
-      "CALLED\n",
-      "self.model._steps=6700\n",
-      " TEST self.model._steps=6700\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "6600  0.6150\n",
-      "6601  0.6304\n",
-      "6602  0.6556\n",
-      "6603  0.6400\n",
-      "6604  0.6178\n",
-      "...      ...\n",
-      "6695  0.7218\n",
-      "6696  0.7260\n",
-      "6697  0.7100\n",
-      "6698  0.7012\n",
-      "6699  0.7178\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_067.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_067.parquet'\n",
-      "Saving model to output_dir/model_data_067.parquet\n",
-      "Saving agent to output_dir/agent_data_067.parquet\n",
-      "6700\n",
-      "6701\n",
-      "6702\n",
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-      "6795\n",
-      "6796\n",
-      "6797\n",
-      "6798\n",
-      "6799\n",
-      "CALLED\n",
-      "self.model._steps=6800\n",
-      " TEST self.model._steps=6800\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "6700  0.7140\n",
-      "6701  0.6918\n",
-      "6702  0.6902\n",
-      "6703  0.6968\n",
-      "6704  0.7136\n",
-      "...      ...\n",
-      "6795  0.5904\n",
-      "6796  0.5826\n",
-      "6797  0.5538\n",
-      "6798  0.5822\n",
-      "6799  0.6018\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
+      "5500  0.6888\n",
+      "5501  0.6708\n",
+      "5502  0.6882\n",
+      "5503  0.6648\n",
+      "5504  0.6682\n",
+      "5505  0.6570\n",
+      "5506  0.6442\n",
+      "5507  0.6500\n",
+      "5508  0.6498\n",
+      "5509  0.6366\n",
+      "5510  0.6336\n",
+      "5511  0.6252\n",
+      "5512  0.6150\n",
+      "5513  0.6150\n",
+      "5514  0.6412\n",
+      "5515  0.6214\n",
+      "5516  0.6256\n",
+      "5517  0.6322\n",
+      "5518  0.6560\n",
+      "5519  0.6762\n",
+      "5520  0.6844\n",
+      "5521  0.6694\n",
+      "5522  0.6668\n",
+      "5523  0.6752\n",
+      "5524  0.6574\n",
+      "5525  0.6632\n",
+      "5526  0.6688\n",
+      "5527  0.6732\n",
+      "5528  0.6484\n",
+      "5529  0.6332\n",
+      "5530  0.6516\n",
+      "5531  0.6388\n",
+      "5532  0.6496\n",
+      "5533  0.6562\n",
+      "5534  0.6442\n",
+      "5535  0.6202\n",
+      "5536  0.6210\n",
+      "5537  0.6486\n",
+      "5538  0.6644\n",
+      "5539  0.6322\n",
+      "5540  0.6384\n",
+      "5541  0.6368\n",
+      "5542  0.6404\n",
+      "5543  0.6352\n",
+      "5544  0.6296\n",
+      "5545  0.6680\n",
+      "5546  0.6882\n",
+      "5547  0.6686\n",
+      "5548  0.6722\n",
+      "5549  0.6758\n",
+      "5550  0.6930\n",
+      "5551  0.6732\n",
+      "5552  0.6606\n",
+      "5553  0.6368\n",
+      "5554  0.6510\n",
       "3\n",
-      "model_file='output_dir/model_data_068.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_068.parquet'\n",
-      "Saving model to output_dir/model_data_068.parquet\n",
-      "Saving agent to output_dir/agent_data_068.parquet\n",
-      "6800\n",
-      "6801\n",
-      "6802\n",
-      "6803\n",
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-      "6894\n",
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-      "6896\n",
-      "6897\n",
-      "6898\n",
-      "6899\n",
-      "CALLED\n",
-      "self.model._steps=6900\n",
-      " TEST self.model._steps=6900\n",
-      " TEST self._cache_interval=100\n",
+      "model_file='test_path/model_data_056.parquet'\n",
+      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_056.parquet'\n",
+      "Saving model to test_path/model_data_056.parquet\n",
+      "Saving agent to test_path/agent_data_056.parquet\n",
+      "model_vars.get('Gini', 0)[-1]=0.651\n",
+      "Condition not met at step 5555. No data to save.\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(5555):\n",
+    "    print(i)\n",
+    "    cacheable_model.model.step()\n",
+    "    cacheable_model.cache()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "248ea838-5c71-4838-b6ad-fdcfdc3a1c1e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "gini = model.datacollector.get_model_vars_dataframe()\n",
+    "# Plot the Gini coefficient over time\n",
+    "g = sns.lineplot(data=gini)\n",
+    "g.set(title=\"Gini Coefficient over Time\", ylabel=\"Gini Coefficient\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "9cf0a6c4-a976-4835-82e2-706f49b175d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Gini</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.3272</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.4626</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.4626</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.5080</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5550</th>\n",
+       "      <td>0.6930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5551</th>\n",
+       "      <td>0.6732</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5552</th>\n",
+       "      <td>0.6606</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5553</th>\n",
+       "      <td>0.6368</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5554</th>\n",
+       "      <td>0.6510</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5555 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Gini\n",
+       "0     0.0000\n",
+       "1     0.3272\n",
+       "2     0.4626\n",
+       "3     0.4626\n",
+       "4     0.5080\n",
+       "...      ...\n",
+       "5550  0.6930\n",
+       "5551  0.6732\n",
+       "5552  0.6606\n",
+       "5553  0.6368\n",
+       "5554  0.6510\n",
+       "\n",
+       "[5555 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gini"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c9515417-7c5f-45f8-8ffa-2f466288ea1f",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "        Gini\n",
-      "6800  0.6018\n",
-      "6801  0.6132\n",
-      "6802  0.6478\n",
-      "6803  0.6440\n",
-      "6804  0.6480\n",
+      "0     0.0000\n",
+      "1     0.3272\n",
+      "2     0.4626\n",
+      "3     0.4626\n",
+      "4     0.5080\n",
       "...      ...\n",
-      "6895  0.6738\n",
-      "6896  0.6890\n",
-      "6897  0.6738\n",
-      "6898  0.6594\n",
-      "6899  0.6536\n",
+      "5550  0.6930\n",
+      "5551  0.6732\n",
+      "5552  0.6606\n",
+      "5553  0.6368\n",
+      "5554  0.6510\n",
       "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_069.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_069.parquet'\n",
-      "Saving model to output_dir/model_data_069.parquet\n",
-      "Saving agent to output_dir/agent_data_069.parquet\n",
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-      "6997\n",
-      "6998\n",
-      "6999\n",
-      "CALLED\n",
-      "self.model._steps=7000\n",
-      " TEST self.model._steps=7000\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "6900  0.6618\n",
-      "6901  0.6596\n",
-      "6902  0.6514\n",
-      "6903  0.6430\n",
-      "6904  0.6200\n",
-      "...      ...\n",
-      "6995  0.6768\n",
-      "6996  0.6620\n",
-      "6997  0.6800\n",
-      "6998  0.6548\n",
-      "6999  0.6348\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_070.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_070.parquet'\n",
-      "Saving model to output_dir/model_data_070.parquet\n",
-      "Saving agent to output_dir/agent_data_070.parquet\n",
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-      "7098\n",
-      "7099\n",
-      "CALLED\n",
-      "self.model._steps=7100\n",
-      " TEST self.model._steps=7100\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "7000  0.6408\n",
-      "7001  0.6480\n",
-      "7002  0.6576\n",
-      "7003  0.6788\n",
-      "7004  0.6646\n",
-      "...      ...\n",
-      "7095  0.6596\n",
-      "7096  0.6640\n",
-      "7097  0.6632\n",
-      "7098  0.6574\n",
-      "7099  0.6560\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_071.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_071.parquet'\n",
-      "Saving model to output_dir/model_data_071.parquet\n",
-      "Saving agent to output_dir/agent_data_071.parquet\n",
-      "7100\n",
-      "7101\n",
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-      "7194\n",
-      "7195\n",
-      "7196\n",
-      "7197\n",
-      "7198\n",
-      "7199\n",
-      "CALLED\n",
-      "self.model._steps=7200\n",
-      " TEST self.model._steps=7200\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "7100  0.6498\n",
-      "7101  0.7024\n",
-      "7102  0.6968\n",
-      "7103  0.7072\n",
-      "7104  0.7078\n",
-      "...      ...\n",
-      "7195  0.5950\n",
-      "7196  0.5954\n",
-      "7197  0.5890\n",
-      "7198  0.6056\n",
-      "7199  0.6184\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_072.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_072.parquet'\n",
-      "Saving model to output_dir/model_data_072.parquet\n",
-      "Saving agent to output_dir/agent_data_072.parquet\n",
-      "7200\n",
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-      "7295\n",
-      "7296\n",
-      "7297\n",
-      "7298\n",
-      "7299\n",
-      "CALLED\n",
-      "self.model._steps=7300\n",
-      " TEST self.model._steps=7300\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "7200  0.6282\n",
-      "7201  0.6050\n",
-      "7202  0.6012\n",
-      "7203  0.6304\n",
-      "7204  0.6386\n",
-      "...      ...\n",
-      "7295  0.7196\n",
-      "7296  0.7090\n",
-      "7297  0.7040\n",
-      "7298  0.7220\n",
-      "7299  0.7098\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_073.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_073.parquet'\n",
-      "Saving model to output_dir/model_data_073.parquet\n",
-      "Saving agent to output_dir/agent_data_073.parquet\n",
-      "7300\n",
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-      "7395\n",
-      "7396\n",
-      "7397\n",
-      "7398\n",
-      "7399\n",
-      "CALLED\n",
-      "self.model._steps=7400\n",
-      " TEST self.model._steps=7400\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "7300  0.7098\n",
-      "7301  0.7130\n",
-      "7302  0.7068\n",
-      "7303  0.7118\n",
-      "7304  0.6830\n",
-      "...      ...\n",
-      "7395  0.7224\n",
-      "7396  0.7202\n",
-      "7397  0.7058\n",
-      "7398  0.7010\n",
-      "7399  0.7196\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_074.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_074.parquet'\n",
-      "Saving model to output_dir/model_data_074.parquet\n",
-      "Saving agent to output_dir/agent_data_074.parquet\n",
-      "7400\n",
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-      "7499\n",
-      "CALLED\n",
-      "self.model._steps=7500\n",
-      " TEST self.model._steps=7500\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "7400  0.7164\n",
-      "7401  0.7264\n",
-      "7402  0.7132\n",
-      "7403  0.6930\n",
-      "7404  0.6944\n",
-      "...      ...\n",
-      "7495  0.6570\n",
-      "7496  0.6382\n",
-      "7497  0.6440\n",
-      "7498  0.6264\n",
-      "7499  0.6110\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_075.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_075.parquet'\n",
-      "Saving model to output_dir/model_data_075.parquet\n",
-      "Saving agent to output_dir/agent_data_075.parquet\n",
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-      "7599\n",
-      "CALLED\n",
-      "self.model._steps=7600\n",
-      " TEST self.model._steps=7600\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "7500  0.6100\n",
-      "7501  0.6186\n",
-      "7502  0.6224\n",
-      "7503  0.6370\n",
-      "7504  0.6460\n",
-      "...      ...\n",
-      "7595  0.6688\n",
-      "7596  0.6596\n",
-      "7597  0.6746\n",
-      "7598  0.6608\n",
-      "7599  0.6634\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_076.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_076.parquet'\n",
-      "Saving model to output_dir/model_data_076.parquet\n",
-      "Saving agent to output_dir/agent_data_076.parquet\n",
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-      "7698\n",
-      "7699\n",
-      "CALLED\n",
-      "self.model._steps=7700\n",
-      " TEST self.model._steps=7700\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "7600  0.6758\n",
-      "7601  0.6774\n",
-      "7602  0.6970\n",
-      "7603  0.6934\n",
-      "7604  0.6738\n",
-      "...      ...\n",
-      "7695  0.6820\n",
-      "7696  0.6854\n",
-      "7697  0.6614\n",
-      "7698  0.6598\n",
-      "7699  0.6828\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_077.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_077.parquet'\n",
-      "Saving model to output_dir/model_data_077.parquet\n",
-      "Saving agent to output_dir/agent_data_077.parquet\n",
-      "7700\n",
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-      "7797\n",
-      "7798\n",
-      "7799\n",
-      "CALLED\n",
-      "self.model._steps=7800\n",
-      " TEST self.model._steps=7800\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "7700  0.6920\n",
-      "7701  0.7154\n",
-      "7702  0.7136\n",
-      "7703  0.6998\n",
-      "7704  0.7004\n",
-      "...      ...\n",
-      "7795  0.6688\n",
-      "7796  0.6688\n",
-      "7797  0.6500\n",
-      "7798  0.6186\n",
-      "7799  0.6290\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_078.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_078.parquet'\n",
-      "Saving model to output_dir/model_data_078.parquet\n",
-      "Saving agent to output_dir/agent_data_078.parquet\n",
-      "7800\n",
-      "7801\n",
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-      "7894\n",
-      "7895\n",
-      "7896\n",
-      "7897\n",
-      "7898\n",
-      "7899\n",
-      "CALLED\n",
-      "self.model._steps=7900\n",
-      " TEST self.model._steps=7900\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "7800  0.6536\n",
-      "7801  0.6282\n",
-      "7802  0.5970\n",
-      "7803  0.5968\n",
-      "7804  0.5590\n",
-      "...      ...\n",
-      "7895  0.6542\n",
-      "7896  0.6522\n",
-      "7897  0.6520\n",
-      "7898  0.6602\n",
-      "7899  0.6742\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_079.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_079.parquet'\n",
-      "Saving model to output_dir/model_data_079.parquet\n",
-      "Saving agent to output_dir/agent_data_079.parquet\n",
-      "7900\n",
-      "7901\n",
-      "7902\n",
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-      "7994\n",
-      "7995\n",
-      "7996\n",
-      "7997\n",
-      "7998\n",
-      "7999\n",
-      "CALLED\n",
-      "self.model._steps=8000\n",
-      " TEST self.model._steps=8000\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "7900  0.6750\n",
-      "7901  0.6750\n",
-      "7902  0.6586\n",
-      "7903  0.6552\n",
-      "7904  0.6562\n",
-      "...      ...\n",
-      "7995  0.6274\n",
-      "7996  0.6490\n",
-      "7997  0.6490\n",
-      "7998  0.6466\n",
-      "7999  0.6676\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_080.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_080.parquet'\n",
-      "Saving model to output_dir/model_data_080.parquet\n",
-      "Saving agent to output_dir/agent_data_080.parquet\n",
-      "8000\n",
-      "8001\n",
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-      "8099\n",
-      "CALLED\n",
-      "self.model._steps=8100\n",
-      " TEST self.model._steps=8100\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "8000  0.6536\n",
-      "8001  0.6876\n",
-      "8002  0.6596\n",
-      "8003  0.6678\n",
-      "8004  0.6604\n",
-      "...      ...\n",
-      "8095  0.6826\n",
-      "8096  0.6794\n",
-      "8097  0.6702\n",
-      "8098  0.6896\n",
-      "8099  0.6846\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_081.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_081.parquet'\n",
-      "Saving model to output_dir/model_data_081.parquet\n",
-      "Saving agent to output_dir/agent_data_081.parquet\n",
-      "8100\n",
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-      "8197\n",
-      "8198\n",
-      "8199\n",
-      "CALLED\n",
-      "self.model._steps=8200\n",
-      " TEST self.model._steps=8200\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "8100  0.6596\n",
-      "8101  0.6556\n",
-      "8102  0.6390\n",
-      "8103  0.6512\n",
-      "8104  0.6536\n",
-      "...      ...\n",
-      "8195  0.6192\n",
-      "8196  0.5868\n",
-      "8197  0.5528\n",
-      "8198  0.5546\n",
-      "8199  0.5764\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_082.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_082.parquet'\n",
-      "Saving model to output_dir/model_data_082.parquet\n",
-      "Saving agent to output_dir/agent_data_082.parquet\n",
-      "8200\n",
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-      "8296\n",
-      "8297\n",
-      "8298\n",
-      "8299\n",
-      "CALLED\n",
-      "self.model._steps=8300\n",
-      " TEST self.model._steps=8300\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "8200  0.5874\n",
-      "8201  0.6296\n",
-      "8202  0.6404\n",
-      "8203  0.6348\n",
-      "8204  0.6300\n",
-      "...      ...\n",
-      "8295  0.6170\n",
-      "8296  0.6200\n",
-      "8297  0.6168\n",
-      "8298  0.6098\n",
-      "8299  0.6362\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_083.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_083.parquet'\n",
-      "Saving model to output_dir/model_data_083.parquet\n",
-      "Saving agent to output_dir/agent_data_083.parquet\n",
-      "8300\n",
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-      "8394\n",
-      "8395\n",
-      "8396\n",
-      "8397\n",
-      "8398\n",
-      "8399\n",
-      "CALLED\n",
-      "self.model._steps=8400\n",
-      " TEST self.model._steps=8400\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "8300  0.6248\n",
-      "8301  0.6196\n",
-      "8302  0.5904\n",
-      "8303  0.5808\n",
-      "8304  0.6252\n",
-      "...      ...\n",
-      "8395  0.6416\n",
-      "8396  0.6250\n",
-      "8397  0.6210\n",
-      "8398  0.6002\n",
-      "8399  0.6116\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_084.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_084.parquet'\n",
-      "Saving model to output_dir/model_data_084.parquet\n",
-      "Saving agent to output_dir/agent_data_084.parquet\n",
-      "8400\n",
-      "8401\n",
-      "8402\n",
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-      "8494\n",
-      "8495\n",
-      "8496\n",
-      "8497\n",
-      "8498\n",
-      "8499\n",
-      "CALLED\n",
-      "self.model._steps=8500\n",
-      " TEST self.model._steps=8500\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "8400  0.6312\n",
-      "8401  0.6326\n",
-      "8402  0.6460\n",
-      "8403  0.6402\n",
-      "8404  0.6592\n",
-      "...      ...\n",
-      "8495  0.6840\n",
-      "8496  0.6944\n",
-      "8497  0.6580\n",
-      "8498  0.6248\n",
-      "8499  0.6256\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_085.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_085.parquet'\n",
-      "Saving model to output_dir/model_data_085.parquet\n",
-      "Saving agent to output_dir/agent_data_085.parquet\n",
-      "8500\n",
-      "8501\n",
-      "8502\n",
-      "8503\n",
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-      "8585\n",
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-      "8593\n",
-      "8594\n",
-      "8595\n",
-      "8596\n",
-      "8597\n",
-      "8598\n",
-      "8599\n",
-      "CALLED\n",
-      "self.model._steps=8600\n",
-      " TEST self.model._steps=8600\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "8500  0.5962\n",
-      "8501  0.5966\n",
-      "8502  0.5870\n",
-      "8503  0.6094\n",
-      "8504  0.6174\n",
-      "...      ...\n",
-      "8595  0.7202\n",
-      "8596  0.7282\n",
-      "8597  0.7224\n",
-      "8598  0.6968\n",
-      "8599  0.6676\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_086.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_086.parquet'\n",
-      "Saving model to output_dir/model_data_086.parquet\n",
-      "Saving agent to output_dir/agent_data_086.parquet\n",
-      "8600\n",
-      "8601\n",
-      "8602\n",
-      "8603\n",
-      "8604\n",
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-      "8699\n",
-      "CALLED\n",
-      "self.model._steps=8700\n",
-      " TEST self.model._steps=8700\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "8600  0.6634\n",
-      "8601  0.6824\n",
-      "8602  0.7022\n",
-      "8603  0.6984\n",
-      "8604  0.6728\n",
-      "...      ...\n",
-      "8695  0.6688\n",
-      "8696  0.6624\n",
-      "8697  0.6290\n",
-      "8698  0.6286\n",
-      "8699  0.6720\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_087.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_087.parquet'\n",
-      "Saving model to output_dir/model_data_087.parquet\n",
-      "Saving agent to output_dir/agent_data_087.parquet\n",
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-      "8798\n",
-      "8799\n",
-      "CALLED\n",
-      "self.model._steps=8800\n",
-      " TEST self.model._steps=8800\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "8700  0.6530\n",
-      "8701  0.6516\n",
-      "8702  0.6486\n",
-      "8703  0.6506\n",
-      "8704  0.6426\n",
-      "...      ...\n",
-      "8795  0.6002\n",
-      "8796  0.6142\n",
-      "8797  0.6314\n",
-      "8798  0.6416\n",
-      "8799  0.6268\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_088.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_088.parquet'\n",
-      "Saving model to output_dir/model_data_088.parquet\n",
-      "Saving agent to output_dir/agent_data_088.parquet\n",
-      "8800\n",
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-      "8898\n",
-      "8899\n",
-      "CALLED\n",
-      "self.model._steps=8900\n",
-      " TEST self.model._steps=8900\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "8800  0.6426\n",
-      "8801  0.6366\n",
-      "8802  0.6264\n",
-      "8803  0.6396\n",
-      "8804  0.6566\n",
-      "...      ...\n",
-      "8895  0.6882\n",
-      "8896  0.6882\n",
-      "8897  0.6818\n",
-      "8898  0.6820\n",
-      "8899  0.6970\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_089.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_089.parquet'\n",
-      "Saving model to output_dir/model_data_089.parquet\n",
-      "Saving agent to output_dir/agent_data_089.parquet\n",
-      "8900\n",
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-      "8995\n",
-      "8996\n",
-      "8997\n",
-      "8998\n",
-      "8999\n",
-      "CALLED\n",
-      "self.model._steps=9000\n",
-      " TEST self.model._steps=9000\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "8900  0.7008\n",
-      "8901  0.6852\n",
-      "8902  0.6920\n",
-      "8903  0.6604\n",
-      "8904  0.6550\n",
-      "...      ...\n",
-      "8995  0.7144\n",
-      "8996  0.6980\n",
-      "8997  0.6754\n",
-      "8998  0.6324\n",
-      "8999  0.6424\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_090.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_090.parquet'\n",
-      "Saving model to output_dir/model_data_090.parquet\n",
-      "Saving agent to output_dir/agent_data_090.parquet\n",
-      "9000\n",
-      "9001\n",
-      "9002\n",
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-      "9094\n",
-      "9095\n",
-      "9096\n",
-      "9097\n",
-      "9098\n",
-      "9099\n",
-      "CALLED\n",
-      "self.model._steps=9100\n",
-      " TEST self.model._steps=9100\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "9000  0.6600\n",
-      "9001  0.6660\n",
-      "9002  0.6482\n",
-      "9003  0.6292\n",
-      "9004  0.6260\n",
-      "...      ...\n",
-      "9095  0.6864\n",
-      "9096  0.6864\n",
-      "9097  0.6562\n",
-      "9098  0.6756\n",
-      "9099  0.6722\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_091.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_091.parquet'\n",
-      "Saving model to output_dir/model_data_091.parquet\n",
-      "Saving agent to output_dir/agent_data_091.parquet\n",
-      "9100\n",
-      "9101\n",
-      "9102\n",
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-      "9194\n",
-      "9195\n",
-      "9196\n",
-      "9197\n",
-      "9198\n",
-      "9199\n",
-      "CALLED\n",
-      "self.model._steps=9200\n",
-      " TEST self.model._steps=9200\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "9100  0.6948\n",
-      "9101  0.6942\n",
-      "9102  0.6880\n",
-      "9103  0.6748\n",
-      "9104  0.6588\n",
-      "...      ...\n",
-      "9195  0.6270\n",
-      "9196  0.6274\n",
-      "9197  0.6492\n",
-      "9198  0.6668\n",
-      "9199  0.6688\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_092.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_092.parquet'\n",
-      "Saving model to output_dir/model_data_092.parquet\n",
-      "Saving agent to output_dir/agent_data_092.parquet\n",
-      "9200\n",
-      "9201\n",
-      "9202\n",
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-      "9298\n",
-      "9299\n",
-      "CALLED\n",
-      "self.model._steps=9300\n",
-      " TEST self.model._steps=9300\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "9200  0.6650\n",
-      "9201  0.6328\n",
-      "9202  0.6276\n",
-      "9203  0.6102\n",
-      "9204  0.6698\n",
-      "...      ...\n",
-      "9295  0.6362\n",
-      "9296  0.5956\n",
-      "9297  0.6454\n",
-      "9298  0.6164\n",
-      "9299  0.6040\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_093.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_093.parquet'\n",
-      "Saving model to output_dir/model_data_093.parquet\n",
-      "Saving agent to output_dir/agent_data_093.parquet\n",
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-      "9398\n",
-      "9399\n",
-      "CALLED\n",
-      "self.model._steps=9400\n",
-      " TEST self.model._steps=9400\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "9300  0.5816\n",
-      "9301  0.6006\n",
-      "9302  0.6044\n",
-      "9303  0.6112\n",
-      "9304  0.6036\n",
-      "...      ...\n",
-      "9395  0.6742\n",
-      "9396  0.6826\n",
-      "9397  0.6758\n",
-      "9398  0.6590\n",
-      "9399  0.6568\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_094.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_094.parquet'\n",
-      "Saving model to output_dir/model_data_094.parquet\n",
-      "Saving agent to output_dir/agent_data_094.parquet\n",
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-      "9497\n",
-      "9498\n",
-      "9499\n",
-      "CALLED\n",
-      "self.model._steps=9500\n",
-      " TEST self.model._steps=9500\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "9400  0.6706\n",
-      "9401  0.6598\n",
-      "9402  0.6360\n",
-      "9403  0.6558\n",
-      "9404  0.6356\n",
-      "...      ...\n",
-      "9495  0.6264\n",
-      "9496  0.6366\n",
-      "9497  0.6178\n",
-      "9498  0.6306\n",
-      "9499  0.6230\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_095.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_095.parquet'\n",
-      "Saving model to output_dir/model_data_095.parquet\n",
-      "Saving agent to output_dir/agent_data_095.parquet\n",
-      "9500\n",
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-      "9596\n",
-      "9597\n",
-      "9598\n",
-      "9599\n",
-      "CALLED\n",
-      "self.model._steps=9600\n",
-      " TEST self.model._steps=9600\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "9500  0.6226\n",
-      "9501  0.6344\n",
-      "9502  0.6200\n",
-      "9503  0.5742\n",
-      "9504  0.5810\n",
-      "...      ...\n",
-      "9595  0.5926\n",
-      "9596  0.6168\n",
-      "9597  0.6368\n",
-      "9598  0.5948\n",
-      "9599  0.5980\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_096.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_096.parquet'\n",
-      "Saving model to output_dir/model_data_096.parquet\n",
-      "Saving agent to output_dir/agent_data_096.parquet\n",
-      "9600\n",
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-      "9694\n",
-      "9695\n",
-      "9696\n",
-      "9697\n",
-      "9698\n",
-      "9699\n",
-      "CALLED\n",
-      "self.model._steps=9700\n",
-      " TEST self.model._steps=9700\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "9600  0.5968\n",
-      "9601  0.6164\n",
-      "9602  0.6104\n",
-      "9603  0.5840\n",
-      "9604  0.6014\n",
-      "...      ...\n",
-      "9695  0.6778\n",
-      "9696  0.6622\n",
-      "9697  0.6408\n",
-      "9698  0.6512\n",
-      "9699  0.6408\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_097.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_097.parquet'\n",
-      "Saving model to output_dir/model_data_097.parquet\n",
-      "Saving agent to output_dir/agent_data_097.parquet\n",
-      "9700\n",
-      "9701\n",
-      "9702\n",
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-      "9780\n",
-      "9781\n",
-      "9782\n",
-      "9783\n",
-      "9784\n",
-      "9785\n",
-      "9786\n",
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-      "9794\n",
-      "9795\n",
-      "9796\n",
-      "9797\n",
-      "9798\n",
-      "9799\n",
-      "CALLED\n",
-      "self.model._steps=9800\n",
-      " TEST self.model._steps=9800\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "9700  0.6264\n",
-      "9701  0.6406\n",
-      "9702  0.6062\n",
-      "9703  0.5840\n",
-      "9704  0.6138\n",
-      "...      ...\n",
-      "9795  0.6252\n",
-      "9796  0.6192\n",
-      "9797  0.6358\n",
-      "9798  0.6650\n",
-      "9799  0.6634\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_098.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_098.parquet'\n",
-      "Saving model to output_dir/model_data_098.parquet\n",
-      "Saving agent to output_dir/agent_data_098.parquet\n",
-      "9800\n",
-      "9801\n",
-      "9802\n",
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-      "9841\n",
-      "9842\n",
-      "9843\n",
-      "9844\n",
-      "9845\n",
-      "9846\n",
-      "9847\n",
-      "9848\n",
-      "9849\n",
-      "9850\n",
-      "9851\n",
-      "9852\n",
-      "9853\n",
-      "9854\n",
-      "9855\n",
-      "9856\n",
-      "9857\n",
-      "9858\n",
-      "9859\n",
-      "9860\n",
-      "9861\n",
-      "9862\n",
-      "9863\n",
-      "9864\n",
-      "9865\n",
-      "9866\n",
-      "9867\n",
-      "9868\n",
-      "9869\n",
-      "9870\n",
-      "9871\n",
-      "9872\n",
-      "9873\n",
-      "9874\n",
-      "9875\n",
-      "9876\n",
-      "9877\n",
-      "9878\n",
-      "9879\n",
-      "9880\n",
-      "9881\n",
-      "9882\n",
-      "9883\n",
-      "9884\n",
-      "9885\n",
-      "9886\n",
-      "9887\n",
-      "9888\n",
-      "9889\n",
-      "9890\n",
-      "9891\n",
-      "9892\n",
-      "9893\n",
-      "9894\n",
-      "9895\n",
-      "9896\n",
-      "9897\n",
-      "9898\n",
-      "9899\n",
-      "CALLED\n",
-      "self.model._steps=9900\n",
-      " TEST self.model._steps=9900\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "9800  0.6592\n",
-      "9801  0.6786\n",
-      "9802  0.6664\n",
-      "9803  0.6486\n",
-      "9804  0.6418\n",
-      "...      ...\n",
-      "9895  0.6196\n",
-      "9896  0.6306\n",
-      "9897  0.6260\n",
-      "9898  0.6456\n",
-      "9899  0.6434\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_099.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_099.parquet'\n",
-      "Saving model to output_dir/model_data_099.parquet\n",
-      "Saving agent to output_dir/agent_data_099.parquet\n",
-      "9900\n",
-      "9901\n",
-      "9902\n",
-      "9903\n",
-      "9904\n",
-      "9905\n",
-      "9906\n",
-      "9907\n",
-      "9908\n",
-      "9909\n",
-      "9910\n",
-      "9911\n",
-      "9912\n",
-      "9913\n",
-      "9914\n",
-      "9915\n",
-      "9916\n",
-      "9917\n",
-      "9918\n",
-      "9919\n",
-      "9920\n",
-      "9921\n",
-      "9922\n",
-      "9923\n",
-      "9924\n",
-      "9925\n",
-      "9926\n",
-      "9927\n",
-      "9928\n",
-      "9929\n",
-      "9930\n",
-      "9931\n",
-      "9932\n",
-      "9933\n",
-      "9934\n",
-      "9935\n",
-      "9936\n",
-      "9937\n",
-      "9938\n",
-      "9939\n",
-      "9940\n",
-      "9941\n",
-      "9942\n",
-      "9943\n",
-      "9944\n",
-      "9945\n",
-      "9946\n",
-      "9947\n",
-      "9948\n",
-      "9949\n",
-      "9950\n",
-      "9951\n",
-      "9952\n",
-      "9953\n",
-      "9954\n",
-      "9955\n",
-      "9956\n",
-      "9957\n",
-      "9958\n",
-      "9959\n",
-      "9960\n",
-      "9961\n",
-      "9962\n",
-      "9963\n",
-      "9964\n",
-      "9965\n",
-      "9966\n",
-      "9967\n",
-      "9968\n",
-      "9969\n",
-      "9970\n",
-      "9971\n",
-      "9972\n",
-      "9973\n",
-      "9974\n",
-      "9975\n",
-      "9976\n",
-      "9977\n",
-      "9978\n",
-      "9979\n",
-      "9980\n",
-      "9981\n",
-      "9982\n",
-      "9983\n",
-      "9984\n",
-      "9985\n",
-      "9986\n",
-      "9987\n",
-      "9988\n",
-      "9989\n",
-      "9990\n",
-      "9991\n",
-      "9992\n",
-      "9993\n",
-      "9994\n",
-      "9995\n",
-      "9996\n",
-      "9997\n",
-      "9998\n",
-      "9999\n",
-      "CALLED\n",
-      "self.model._steps=10000\n",
-      " TEST self.model._steps=10000\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "9900  0.6436\n",
-      "9901  0.6410\n",
-      "9902  0.6306\n",
-      "9903  0.6366\n",
-      "9904  0.6398\n",
-      "...      ...\n",
-      "9995  0.6358\n",
-      "9996  0.6332\n",
-      "9997  0.6128\n",
-      "9998  0.6314\n",
-      "9999  0.6778\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='output_dir/model_data_100.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/output_dir/model_data_100.parquet'\n",
-      "Saving model to output_dir/model_data_100.parquet\n",
-      "Saving agent to output_dir/agent_data_100.parquet\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in range(10000):\n",
-    "    print(i)\n",
-    "    cacheable_model.model.step()\n",
-    "    cacheable_model.cache()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "248ea838-5c71-4838-b6ad-fdcfdc3a1c1e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "gini = model.datacollector.get_model_vars_dataframe()\n",
-    "# Plot the Gini coefficient over time\n",
-    "g = sns.lineplot(data=gini)\n",
-    "g.set(title=\"Gini Coefficient over Time\", ylabel=\"Gini Coefficient\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "9cf0a6c4-a976-4835-82e2-706f49b175d3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Gini</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.0000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.2822</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.3666</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.4510</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.5058</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9995</th>\n",
-       "      <td>0.6358</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9996</th>\n",
-       "      <td>0.6332</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9997</th>\n",
-       "      <td>0.6128</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9998</th>\n",
-       "      <td>0.6314</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9999</th>\n",
-       "      <td>0.6778</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>10000 rows × 1 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        Gini\n",
-       "0     0.0000\n",
-       "1     0.2822\n",
-       "2     0.3666\n",
-       "3     0.4510\n",
-       "4     0.5058\n",
-       "...      ...\n",
-       "9995  0.6358\n",
-       "9996  0.6332\n",
-       "9997  0.6128\n",
-       "9998  0.6314\n",
-       "9999  0.6778\n",
-       "\n",
-       "[10000 rows x 1 columns]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gini"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "c9515417-7c5f-45f8-8ffa-2f466288ea1f",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "        Gini\n",
-      "0     0.0000\n",
-      "1     0.2822\n",
-      "2     0.3666\n",
-      "3     0.4510\n",
-      "4     0.5058\n",
-      "...      ...\n",
-      "9995  0.6358\n",
-      "9996  0.6332\n",
-      "9997  0.6128\n",
-      "9998  0.6314\n",
-      "9999  0.6778\n",
-      "\n",
-      "[10000 rows x 1 columns]\n"
+      "[5555 rows x 1 columns]\n"
      ]
     }
    ],
@@ -12630,8 +18361,8 @@
     "import glob\n",
     "\n",
     "# Get a list of all Parquet files\n",
-    "model_files = glob.glob('output_dir/model_data_*.parquet')\n",
-    "agent_files = glob.glob('output_dir/agent_data_*.parquet')\n",
+    "model_files = glob.glob('test_path/model_data_*.parquet')\n",
+    "agent_files = glob.glob('test_path/agent_data_*.parquet')\n",
     "\n",
     "# Initialize lists to hold dataframes\n",
     "model_dfs = []\n",
@@ -12658,13 +18389,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "id": "c6c73b47-b23c-4347-9951-b721ea5ed5da",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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z585FixYt4Ovri8jISIwfPx63bt2yU2mJiIjI2Tk0uFm6dCmSkpIwbdo07N27F+3atUNiYiKys7NF1//pp58wceJETJs2DUePHsU333yDpUuXYtKkSXYuORERETkrh84KHhcXh06dOmHevHkAyrtpR0ZGYuzYsZg4caLB+mPGjMHRo0eRmpqqWfbqq69i165d2LZtm6R95ufnIzAwEHl5ecy5ISIimxMEAaWlpSgrK3N0UZyep6cn3N3dRV+Tc/92WEJxSUkJ9uzZg+TkZM0yNzc3JCQkYMeOHaLv6dKlC3744QekpaWhc+fOOHPmDNatW4fBgwcb3U9xcTGKi4s1f+fn5yv3IYiIiEwoKSnB5cuXcePGDUcXpUpQqVSoX78+/P39rdqOw4Kb3NxclJWVISwsTGd5WFgYjh07Jvqep556Crm5uejWrZsmEh45cqTJZqmUlBRMnz5d0bITERGZo1arcfbsWbi7u6NevXrw8vLi4LEmCIKAnJwcXLhwAc2aNTNagyNFleoKvnXrVsyYMQOfffYZ4uLicOrUKYwbNw7vvPMOpkyZIvqe5ORkJCUlaf6u6CdPRERkSyUlJZp0Cz8/P0cXp0qoU6cO0tPTcfv27aoZ3ISEhMDd3R1ZWVk6y7OyshAeHi76nilTpmDw4MF44YUXAADR0dEoKirCiBEjMHnyZNHhmL29veHt7a38ByAiIpLA3FQBVEmpmi2HHXEvLy/ExMToJAer1WqkpqYiPj5e9D03btwwOEkqIjsH5kUTERGRE3Fos1RSUhKGDh2K2NhYdO7cGXPnzkVRURGGDRsGABgyZAgiIiKQkpICAOjXrx9mz56NDh06aJqlpkyZgn79+llVfUVERESuw6HBzcCBA5GTk4OpU6ciMzMT7du3x4YNGzRJxhkZGTo1NW+++SZUKhXefPNNXLx4EXXq1EG/fv3w3nvvOeojEBERVVsqlQqrVq1C//79Ja2/ePFivPLKK7h+/bpty+XIcW4cgePcEBGRPdy6dQtnz55Fo0aN4OPj4+jiyJaZmYmUlBSsXbsWFy5cQGBgIJo2bYpnnnkGQ4cOhZ+fHzIzMxEcHCw5t/XmzZsoKChAaGio6OumjlmVGOeGyN4OnL+OvRnXMDQ+Cm5u7I5JRGTMmTNn0LVrVwQFBWHGjBmIjo6Gt7c3Dh06hC+//BIRERF4+OGHjXYAMsbX1xe+vr42KnUlBjdUbfxv/nYAQK0aXvhf+wgHl4aIqiNBEHDztv1HKvb1dJfVE+mll16Ch4cHdu/ejRo1amiWN27cGP/73/80nXi0m6XS09PRqFEj/PLLL/j000+xa9cuNGvWDAsWLNB0FLJXsxSDG6p2TmQVOLoIRFRN3bxdhlZTf7f7fv97OxF+XtJu+VeuXMHGjRsxY8YMncBGm6lAafLkyfjwww/RrFkzTJ48GYMGDcKpU6fg4WG/kIOd76naUYFNUkRExpw6dQqCIKBFixY6y0NCQuDv7w9/f3+88cYbRt8/YcIE9O3bF82bN8f06dNx7tw5nDp1ytbF1sGaGyIiIjvx9XTHf28nOmS/1kpLS4NarcbTTz+tM2ejvrZt22r+v27dugCA7OxstGzZ0uoySMXghqodTu1CRI6iUqkkNw85StOmTaFSqXD8+HGd5Y0bNwYAswnBnp6emv+vaL5Sq9UKl9I0NktRtaBWV454cOHaTQeWhIjIudWuXRu9e/fGvHnzUFRU5OjiWITBDVUL2gHN+as3HFgSIiLn99lnn6G0tBSxsbFYunQpjh49iuPHj+OHH37AsWPHnH5WAOeuGyNSiId7ZVtUi/CaDiwJyXHx+k0M/GIHnu0ShRe6N3Z0cYiqjSZNmmDfvn2YMWMGkpOTceHCBXh7e6NVq1aYMGECXnrpJUcX0SSOUEzVwvmrN9D9gy0AgK+GxKJ3qzAHl4ikePnnfVhz4BIAIH1mXweXhkieqj5CsSMoNUIxm6WoWlBrxfBl6moVz9td3o3bGL90P7adzJW0/vmrN/Da8gO4cM2wubDUzkmI5JxOZRdiz7mrji4GVSEMbqha0A5objlgdNDqZOaGo1i17yKe+WaXpPW7f7AFy/dcQLf3txi8xjGJnM+/6VfRdeZm/PFflt32mTD7Twz4fAcuXmdnAJKGwU0VUFKqxrkrVTNj3Vmw5sZ+zl/lDciVDfkmDRev38QL3+22+77P5vA6SNIwuKkCHl/wD3rM2iq5mp90Zebdwrgl+zV/l9kwzUwQBLz04x68seKgzfbh7ARIO75qBplV0q1S1nyS82NwUwUcuJAHAFi2+7yDS1Ip48oNfPj7cVwpND5KpbN4bcUBHLmUr/nbljfVi9dvYt2hTCzdfd5lmr8uXb+J9YcuSz5u209dMbvO5mNZiH7rd6w9eNna4pGVrhaVIHnlIezLuCZpfTYUylfN+u1YRaljxeCGLPLo59sxb8spjF92wNFFMet0dqHO37asuXF3q7z0F5e6RjJsl5mbMerHvfhl7wUA5c16ci9A+k2Bzy3ejaKSMoz+aa/pN/JOanPv/vYffk7LwCOf/SNpfTkzSytNaq1ghbSzV/HoZ9txtajERiUyrWKk3hs3OLaWVCUl5d+VtePocJybKsSZYv/cwvIT8N+zzt+DQf9ibMuaG50EWGf6whTwz+krSGwTjrZvbURco1pY+mK86HphAd7Iyq+s0Xt/wzF8vvU0tkzoiUYh4jMMA4CbyD3TrYrMlfHXiRzk3byNfu3qOboosp3OlZfH4u6mcljemtznkie+2AEAiH13E86k2H8oAXd3dwQFBSE7OxsA4Ofn59Dg0Nmp1Wrk5OTAz8/P6hnEGdyQ3fzxXxZW7b+IGY9EI9DX0/wbFKLWuyLa68JsyxoiR1mxu7z2ZtfZq7hZUgZfL8Onq5o+njrBzedbTwMAen24VfZYNWIBjzMasjANANChQRDqB/s5uDTyyD3E3h5uKKlitZKOTO8KDw8HAE2AQ6a5ubmhQYMGVgeBDG6qEEEQoFYLcLPzFX/jkUyEBfigXWSQVdup6F0RVtMHU/u1UqBkuj7aeBy5hcWY8Ui0zg/DILix04VOf79Ky7t5G3vOXUX3ZnXg6W6fFuYfdp3T/H9uYTEia5m+kUdNXCu6vF6gDy7l3TK7P/cq9pSbW1hS5YIbueTWpt0oKcXp7CK0iQiw6IZV1fNVVCoV6tati9DQUNy+fdvRxXF6Xl5ecHOz/nrG4KYK+e3gZZzOKcJvY7vp5HbY0tncIoz4fg+AyhFi/zltXa+trALzNzUAKCwuxYHz13F349qSPu+nm08BAJ7r2gjNwiqnWNB/aivTGxguu+AWQmsqM3qodk6ArXsDtZu+EQDwaMcIzH6ivU33VaFleE2cUaA7ro+nYY2P2OGqClX42jffigeQnWevoHW9QLvWUNqL3K8k+q2NKFML+HRQB4ua7bTPi6oU5ny/Ix15N29jzL3NAJQ3UTn7fEympB7NwtJ/z2PmgLaoVcPL0cUxiwnFVczRy/l2nfgxO78yEKno/fPUV5WDs920oEeQ1Gvj01/vwtNf78LXf58xu652U9Oif9J1XtN/8ivTim0+23oKnd9LxRd/npZYKtO0L8TmmqUEQcArS/bhw9+PW7XPlXsvWvV+qQRBQNv6QZq/XzWSTC7l+5V6g6wKzVL6zZxLd5/HU1/twqOfbTf5vvlbTmHlnSRtR5IbrFy/Ia/2oeL4vP3bf/J2dId+8FgVXCsqwZRfj+DDjSeQccU1komf/3Y3Nv6XhffXH3N0USRhcOMgN0vKLM7g154E0tY8PSpPEaWaWaQ+jR84fx0AsGKP+RvAnycq27N/2pWh81qp3s1H+3N8sKE8sEhR6AerffE1V3Fz5FI+Vu+/hHlbTimyb1sToBu4pKWLJ5NLyTUyVhOnX9slt4YyPbdI8vAEStWspWkl1QsA1uwvnwvrtIkarqOX8zHr9+NIktDb8PDFPGw+ZpvRgP/4Lwv7Mq5LXr+wuNTifeUUWDZsRFWsuRn01U7N/5/KKbB4O0rW/gqCgHFL9uH1Fdb1cM3Wqnl35rGqGNw4SOy7m9DxnU24ZiLAOXD+OnrOMhyS3lGMJeKuP+T4sUpulhhPcLytl/xoy4Ri7fu6uR9+SZnlSZmOGl9IypGT0mxlLG+j3dsbseN05Tg5cmoV0s5eRc8PtyLm3T/MrnvoQh46vLMJ3+88Z3Zdc7Rzh5KW7seOM+bH+ZFT+/HQp9vw3OLdOKU3pIESHDHKsFy2zl2zhWOZlQFN8spDFm0j/9ZtdH1/Myatsuz9+jLzb+HX/ZewbPcF3CixPEiteDj9fuc5tH97Iw7dGYfN2TC4cYC0s1dRVFLenHPwovET44XvdiPdwVWaujdr8XVG/Wh8rJKCW7cNqpJtUe9kavyL22r9ZinbXSzVOjU3pvejfRzkPgFduGb/KQ5yC4tld8U1xljtXcGtUjz/7b+av6V+V6Vlak23X335t27j57QMnQeJ8cv2I+/mbUxZfVjS9n8/kolf94s3/2kPrin19yq1QqrgVmUQJHcKlgvXbuCZr3dh63HleunIbRa6fsN87XTGlRsmz3/tXR69nI+DF65L2vfxTMtrTJSk3XNQjuW7L+By3i2DmmhLlSrck2LK6sPIv1WK8cv2K7pdpTC4sbO0s1d1LsKmulQW3hKPrh31IGNqhuZLIhPa7U6/iui3NmLiL4d0Xpfbxm/tx72tV0Oy/vBlTP31MEqtqDkRU1KqxhWtG6i5G/P2U5WJ2XKfTi3tZr5y7wXslFCzIGb7qSuyB1EzxtTNveKlTf9lYdluaTkpwxb/a/S1N1cdRvLKQzpBk5xgUq0W8OL3ezBuyX6dKvkKF00EmoIgIHnlIXy2VbfpMV/rt20sANhyLBvRb23U2pbkIgMA3vjlILadysWzi4wfG7nkfvvvb6jMJ6tT09vg9e92pOOeWVsw5VfjQaZ+M/LD87Yj/5b5mq8956SNuOys9ANJtVrAkUt5BtczS8idkNbU78VZm6YY3NiZ/tOldhW8VGdlDrpliRslpXh43jbM3lR5cTJ1Q31rzRGDZR+nngRQnmD56508BEtIeVrU/7GO+Wkv/jqRc+f9uuueyCrEdzvOScrlkaP3nD/xqNYor+Z+8xlaieFyrw+WXFAOX8xD0rIDePLLnSbXKyouRb9Pt+HjP04avBbsp0wvCe3pMAz2f6dWc7iMJpO/Tcy7tuFIJgBgr1ZuiZxgUnvdApEHjvBA4z3tDlzIw89pGZrcrgrFWvMzGbvR6CfgvvTTXlk3bFM5LrfL1HjhW/lBj9wAS3sW777RdXEyq0Bn2dRfy68bP5qonRDbZa6E/J2qkIwux8LtZ9H3k22YrEAzldyHlH+N5NcBwG0TD72OxODGwRZuP2uw7PqNEry67IDRnkhDFqZZldgnxbJ/z+PghTydeYJMncNiF31t2sOwSLnmaNcumErMrKB/s/rt4GXNwGrG6FcXW5u0eU6vScLcDbRWjconWak325JSNYpLxZPRi8ycE1J72f2cloFDF/Mw548TBq/5e1s3eoQjert4exhe5uTUfJlb94VujYy+Ziy3QXukZpVb5bop645q5njSPydKStUY8Lm0KRLMWXvwMv44Kr+56nKevOZQ7d96bmExes/5C11nbpa1DbHfhpTYvqqMbi3VrDu9Ks3VZl4pLBY973RSDGT+DPU7ZWhz0tiGwY0zeuOXg5p5fIyxdUKpWEBRqlYbvTkpfR0xV7tQrDczsSVJh2npurVmP+1SdmJSc2Xq0qS25HWB8tqa+JRUdHr3D83YQxX+OZ2L1tN+x3trjXe3lXpBu22ibd7a5E5TF0l7knNB1v7ISp3mHlqDlAl3yvJJ6il88dcZzRxP1hzrwuJSnYejhz79GzdLKv8uMhJ0/WeiRg0AdqfLa+rRvi78pjdJ6p5z0qZuEUS+K7FjcywzH49+th3/3Gnutfdgp0rT/oiFxaWS5qq7fqMEMe/+gY7vbDLcnlZtjdyHjCsmOr6YSldwJAY3Tuj3I+ZrEGw9iJ9YLxK12ni1tFhwo500KreN15T/O3AJLd7cgOVaiZyWDCplOHu1sjdeczk32sdMyj2/sKQUV4pKdPI1KqSsK+/K/tXfhjWBlfswvZObJWUoKVUrHqhqUyKZ+/0Nx/DlX9LHJRL7OHIu7ubKrJ+wLoXud1/+/hNZugmw569aljSef+s22kz7Xef9hy/mY/ke88F7n0/+xqls3XKcyi7EoC93YsfpK/BSaCTs7adyMeBz8QRwfWLn7dCFafhuR7rOsucX78bejOt46uvycbi0i1rbwYPOrTt02WzNqj7t/C6pPa4OXywPTm/dNh1wyD1lX/55n9HXlE5UVgqDGyej3fPCFA8FhqfWlpV/y2weR5lgvKXWXPCiXbVvTRdoABh754f22oqDmmXWNpfYgvY1ubRMjT9P5Oj0ftFm7U3f2qfU22Vq9PxwC7rMTDWznnXltPZzns0twudbT2PGOunjEon1zJJTDHNBof6I19rSJEwsq/StQSxXCqgcwducMT/p3shG/bAHO85cwaCvdiLAV97vzNhZ+eedfDgpxI7P5bxbmPrrEZ0eUdo5RusPXdapnZPy+7hdpsZHG4+bzC+RQuw6+tKPezHie9M5ZIIgYMfpK5qgRvvzHJLYQ8xLqwlWP4DX/lNOcK/fBL5b7/gokeBsCwxubEwQBKzYcwGHL+aZDR6KikvxutYN2xQla27+PJGDuBmpGPnDHpPrlanVRi/02vePtLNX0evDrdh2svICpl3dnVtg2eCFpjhJa4cO7Rv5rN+PY+jCNAz8Qry5zdpcFA8J54PYHs5fvYEbJaXILihGVn4xcgtLcEOr+eJ0ju7YKhOW6w4AJrfcakHAySzzXXTzbooHgX9p3RSl7ltsW/ISiiv/XyxQKjbxlDzXSKAhVhZrxh65nHcTszedQHb+LaPb0b5ZmnoY0e/5mKk1SrmxucSOZxbgwjXDnC5jeS9Z+dKmYAFMf1faY/9oP0CN+nGvzvmuX4pdZ64YNMF9t+McPt18Co8vkFajZIyxHC3DmmJdf53MxaCvdiJuRvkDhnaNoNSJSj21BnjVb8bSLpWcn+1TX+les/Rrjh01Q7w5zve462K2ncrV3BCOv/uAyXUdFQF/9Vf59AYb/6tsDku4K9Qg4bDMRLOUtqEL0wySof+7rHUhsUmzh/U/MKVzXbUvyl/cOcbax0H7BmPt9cGSCSZPZhWg95y/EOTniSc7NdAqV6U+H/9tchtqAZAzYPbxzAI8JuHmod80UkE7afvWbTW2ncrF+sPyBpFMXnkI2TJGyzX3UPLhRsPEa3N0kzvL/9h5xvIag6EL03AiqxB/ncjBXXUDTK575FKeyYHhDAI4nSd+w/WzC24hce5fAIBdk+5DWEBl7zFjD2FyRis2Fdxo1yXr32S136f9kbLyb2HgnZy+J2Lr44PH2gEAzuQoM0iipTf7iqEhKordqHZl0rnUTWp/d/qHTXf0dOllPGZmvCBnyaPT5xQ1N/Pnz0dUVBR8fHwQFxeHtDTjvVx69uwJlUpl8K9v3752LLF0J7MqfzBKtk0qNd4IIJ4v4+tlGPdm5t+S9KMQa3ZSGfl/pTj693XkkuFgjOZ+9DqTbJo5rnvOXcML3xqv1tafCuF4ZgFe+PZfHDYxSOTaOyNLX79xGwu05tbSLom5JMZ5m0/hWKbpJFRtP0gcEdhY81fjOpUX/HZvb8Tw73aLzq1lqlbn5zR5g6KZ6y2Va0Fyv87vV4Fz98Sd68z+89ex6T/TOXt9P9lm8nX960GBmVyRs1qdD+JmpOrU/BgLboJl5MCYOvwmvxqt17Lyi5F3Z1Ro7QEwjfU82nYy1+Rvx5S1By0bsV3/uLeqVxmkmjoHjZ3r+tcUQec12cXT8b1evpMzcnhws3TpUiQlJWHatGnYu3cv2rVrh8TERGRni3dTXLlyJS5fvqz5d/jwYbi7u+Pxxx+3c8ml0f5xKxrham2quLQMGw5fxvHMAjyxYIekiSbNEbskTfxFWpOZn5fpmW+VjENu3S7DsEVp+FZvskylXLh2A1N/PYx0M2MLvf1/hr2UtKu3xbojq3W+Q9NBxIDP/5GUvwEAzy/+F09/vRN/HM3WGXdHn7GeMZ+kmm9KqTDnjxN4YO7f2H/+uqRBEdcdzpS0XWO1JRHBvpr/N1VVb+xeILcZ7fUVB9Bz1laL329s32LdcrUDN2uYCraM5eNok9uFWn+uuy1aIyIbC27EjuPHf5wUHdDQdM2Ncfo9wl74rmJcH/F3aS995ptdeOhT00GgMUsl5k3q0659vVZUohOkGqsNmr3pBO5OSUVm3i28vuIAhi2qrBjQP27avylrH46n/Go4rpmzcXiz1OzZszF8+HAMGzYMALBgwQKsXbsWCxcuxMSJEw3Wr1Wrls7fS5YsgZ+fn1MGNzdKSvHhxsrBuxSdBE3r/2euP4ZF29M1f6elX0VkLT8ktg43+v43VhzEjdtlqBvoIzoAmlgOx+U84zU32tWh2jkbYuW1RJlaEL1QLknLwJbjxpMTrR2F+PnFu3E8qwC/H8nErkkJmuXZBbcQ5OulSeC7KDJCM1B+EVepVKLBi/axnLf5JFIebav5+/qNEvxxNBsPtAmXnSydeqzy5mIqeVvJWLv//O3wcnfD2pe7mVxPau6A0QcBiWVWCwLcoDL4zW2VkcgKGD7ZV2xOEARcyruFeoE+GBrfEN/uMF8jJQiVT+c6yZ13PlRIDW9J83JZQ2zsIn1yQptzV4oMOjdoN7caC27Eaubm/HECRy7l4cshsTrLTdfcGH9Rf8DDf2V2YwfKR1AeEh9l9PVPUk/Cz8sdL3RvDKC8p5rUhxB9HlrduwZ+uUNTGwcYv45VPIi8/dsRrDuk++Cg/xPapT3Bq3O2JCnKoTU3JSUl2LNnDxISKm8abm5uSEhIwI4d0pK6vvnmGzz55JOoUUP8qae4uBj5+fk6/2xp15kreHP1IRQWl2Le5lM6g9uZezof/ZPxOZr0ad8kfhEZaVe/m6S24tIyLN19Hv934BK+/Eu8lsfY3D9SfhRiTxnWJszuMjJlgLnBA7eaCHykOH4n+VV7wL9T2YXo/F4qmr+53uz7y9SC0Vwq7WOiP4LziO/2YMLyAxZPuifFg22MB7+WKClTY6JC5Z0pMku7sbmdxFQc2Ut6g85ZeuPRbPfOdzZs8b/oOrN8UsPa/obTCogxNu+Yo5tU5dD/GR+8kGcQwAgQcP1GCSavOoT1RmrqjP0mxB60TNXcmHrNx1O8BtnocBYiy6YaqaG4XabG8cwCzN50Au+uPar5POvMNEmN/nGvzrmdU1CMkd/vwd8nc+CpdRy1AxvA/DmiH9gAhtdc7RpZYw9jJaVqvLXmiM1mobcnhwY3ubm5KCsrQ1hYmM7ysLAwZGaar75OS0vD4cOH8cILLxhdJyUlBYGBgZp/kZGRVpfblIFf7sQPOzMwe+MJg3Er7k4x3c3WXDa9tun/Z7pa0NLROfNu3Mb+89dxq1R8dGRz12FjvSCsvX5b2n1czgBTFWW8dbsMezOuGa1p+2bbWc3/X7x+ExN/OWh0IssyQTA6A7R20fTzsSpyaP7vgOXTVujT7wVjrvnQEkrN56OTgH7HnE0nJFen59+8jQPnr2Of1pQLgPX5XhWnREXQ/HPaeclPwRWrZRfc0qnddLa5eUxdOvSPvwDx2pn31h7Fj7syjNbUidXuArq/c0EQ8OqyA3j3t6PGy2PBoTM28rscfT/5W5NErV0ODzPjAK09dFknv+2tNUew4UgmBn+TZpA3p03sgXGumVo4/bcE+Hpq/n/C8gMQBAF/ncjRGXV69b6LWPxPOp5bvBuvLNlncvvaft1/0WkmKq3g8GYpa3zzzTeIjo5G586dja6TnJyMpKQkzd/5+fk2D3AA4GxuIdwVHotGm3ZPJrFaFmM1L4DpC0K7tzcafxHGn5Qq9mZskj7tnBVLbjCWXv5LZCRxV0xpMfrHvUg9lo03HmhpsE5xaZlOQqq5oeTL1ILoDXnVvgtYo1VbU1Kmxo+7zmHnmauY/UQ7zXIlB9TTfmKsaC6rStxUKsk3s8e/2IEzOUVoEVZT0TKInf9SAy5BKA9sOr9n+iHH0eTGWgadqwTglJmeR8Zq0LRv4ocu5pkdqd1RcaF+zco/p3MR6OspeT6rit/fBa0HDlPzo+knFH+SetLsMAMVNTeZebfw5Jc7dGasv3DtJrYez9FMOJs+s7xDzuW8yofT1fsvYe6THcx+luJSNcYt2a+zHWfg0OAmJCQE7u7uyMrSrQLLyspCeLjpKvOioiIsWbIEb7/9tsn1vL294e0trdpYSQIgaeZaW7ll5MkIsK691dx7j4o8cQOOuwi9L9K8YUxFQl9FzspXeonZJ7IKsPW4vPl4ytSCYRdVtYDxSw8YrDt5VfnMyD2a19Ess9X8ONr5H1WFu5v04KYid+W4Xu2pnM8cIzKEvXiTq7TtqQUBe0TyPiydZuFsbhEig33N1hbIdbtMjX9O5SI0wBtNQ3WDQ7Gi6o+ZI2UcIymk5GdZ0tyt/5YD56+jXWSQVbXLFQ917w+IlrS+WgDUpoZ8119f77ybvcl87lTFWxb8eVonsAEACMAOrab+uX+cwFNxDaDvlgK1XI7i0GYpLy8vxMTEIDW18klGrVYjNTUV8fHxJt+7fPlyFBcX45lnnrF1MS1yo6TM6vZ9a6SlX0VRcanoj9+aOWscMfFh+Y5lLdYw1rYsRv+46AcWc/84YbT5yZgytWDQ5GS+23fleWPJ+DVSqAWhyk0seDK70GgOh1RypgERm0/H2iYNsSBf9iSGZWr8dvASen24FS/ImDldqoJbpXjq611ImP2X2XW3ncwxCBi/3XHOoDlQroMXrhttztUm93J0MqvAoBbkf/O3o7i0DD+ZmJlcqsX/SBvq4PDFPLR4cz0OXJDW3VzORK8VTD1c61+D5v5xEiO+MxzEteWUDbL2mVtYjM+3nka2jEEabcXhXcGTkpLw1Vdf4dtvv8XRo0cxatQoFBUVaXpPDRkyBMnJyQbv++abb9C/f3/Url3b4DVnICew2XPumtn2U1OM3aNaT/sdScsO4ERWgU4CnzXhiT1iG7EASslxfYzvV/dv/R5j209dwXcSesVoG/7dbmw+plvbs+2U8epnoDyPo4KtWjYFODBQtYK5Zgpz9CdcrXDuShE+2HBMM2aPse7UYjdAqUdREMTPY833IDHu+mbbWU3vSGsT5sXU1+pur08/uF+2+wLun2M+CJJj+e7zeHjedkmBm6nrgn7OIwD0nvMXzl81HEn5lz3Sk9VNOZsrbSDAt3/7T1ZQa8lP1dTQHWKb23/+uvyd6Hnw47/x/oZjeN7EmFz24vCcm4EDByInJwdTp05FZmYm2rdvjw0bNmiSjDMyMuCmd4U/fvw4tm3bho0bTeeHVBUDPjc+Fom1Vu27iFX7LuL+VmGaLpbW1NxImX7BnLjGxgPSmeuPifaK+Tf9Gu5tGSbyDuXcvF2mkxukP36HsSkBTPk3/ZpBF1RjeUlibFlz42R5rHYhNrHooQt56DevfEyTz7aexukZfRD77h+i729Qy89wAkSpTQtGjrnc7+HX/ZdsOpda3UAfozWU45fut9l+K7wmcQoawPSxO2ikVkSsB5QlAzGKMTdhZQV7jOpbcd0Re4gpUwsmc3wsVTHy9CELB0BUksODGwAYM2YMxowZI/ra1q1bDZa1aNGiSj51OpL21ApW5dwYWZ6ZJ70aUr+XzvmrNzDmp714MLquTk8CbZ9vPY2IIF88FlNf8n7kOnQxDz0/3Kr5W+qFypasnRDTGEGomjU3tvDjLt3aOFN5Bs3DaqL1tN91lkk9ipfzbho55vK+B7Ug6PSsSVlvvDeRJUw13Vk76a3SklceQpcmtdGwtnUDIErJYVGSqclWFd+Xkd+5sfxIpTi604LDm6XIelJPnzK1gPlbThnM6ipVhwZBRmtuzM0/ok1/E5NWHcKBC3miY5toe3P1YYM2YFven5V6mrNGwa1Sg1l5LaF/Uy0uVeOIkRGKqxv9ANLUKbX9tOHTrtRzcNBXu0SnnyhTl4+dJHWAQ/2xnb740/oRyXVoHY57P9qq7LZt4M3Vh+22r/NXb2Dk96YnGJZCyal4zHFUDa2xMdTsxSlqbsg+Vuw5j1m/Hze/ohHNQ2uavPKbm9Omgn47uf4ss3LYIxfH0brMtL7rsP5ggO2mu0aTrhL0K8dM1Whpz0ItV05BseiEkUv/PY+F2w2by4yRkyRvCe3DYesRk5Xw98lcFNipZ+q4Jfuw18pkacC+M2nLeQC8JjL9haVS1h/Diz2aKLY9uVhz4wKkVv298Yt1o8cKMJ2n8aHEwEnJ2hZb1ty0MjO7sr0o0Ty25F/L5rupDvRbWkyd42IdBawNsMUCGzmTkSqtKj4ubD6WLXmMGWsoEdgAxkdotgU5g0QuttEcfY7A4KYKO5NTiKX/ZtjtKUBtpLdHBf0xRYxRMtfDmuRoc8RGya2Koiaudej+O0UFO3T/5ug3By2XOfGhLU7BAwr0XKlubHUZtMX11WDcGRtytjwpe2FwU4Xd+9GfeOOXQxb14rGEIChzAdmhN0+UNQ9cqUflDapH9tevXT1HF8Ek/dyqd9fKS9C1xT11usgs8/YiNmmus/vrhPI9fyokzP4TpWVqhybgPxFreUeKVfuU6eZe1TC4IckECIq0bW8/dUUz1YG1XKV2hRznT5kzheuzxdxQxuZesgdjM3k7M2vHPzLlbG4Rzl+7iZd+lD6xsdL8vJgeKxeDG5Ls4rWb+DT1lCLbysy76dDpKYiUcroKJN3KkZ1vvJegp3vVC3yUYu3o2Naork1L1mA4SJLtOnsVsQ2VyZ+QMrS70sICvJFl4sJNtpHYOlx04DRX8cdRab0EqwpTuXND4qPwzTbpPbtcxXYzo4rbmqVTQ7z8s/SZvV0Na25Ilut2yu+xhZ3J9zm6CNXO+wOiERbg4+hikEKqYpOVEuw5lo6S1hy45OgiOAxrbkgWe40nYQuOHC2zuvns6Y7Yc+4aHo+JdHRRSEGOHpiNSCrW3CioOgxnXw0+YpXRup5zjMMjpk90XUx5qJXNpo9wdu/2b+PoIliMzwDkChjcKKg63PizRUZYJccI9PV0dBHIiEYh1s11ZG+fDuqAQZ0bAKge1zFyfQxuFMRrAtlTdc1/qAqqWu1H50a17DLCL5G9MLhRkJLzclQF0x9u7egiVGtuVe0OWo1Ute/GTaWqcmUmMoXBjYI+ST3p6CLY1dAuUUh+sKWji1FtVaUn7TYRzpsfZAvOFCh4eZi/zHu4qarU+URkDoMbBRVYMbu1tg8GtFVkO/bgLeHCSbbhyGYpubt2ppu9PTjTx02bZH4IBDc3VbVN/ibXxDuTgpS6NDwYHa7Qlmxv9X7bjqPwbJcoRbbzy6h4RbbjTBwZMHi4ybt0VKdu+LENg52qFkTKsXdTVb8AlFwbgxsFrVRogrKaPlWnF4ytaw8eaKNMoBfi763IdpyJQ2tuZF45qtNt853+bapcMKdSsVmKXAuDmyqgU5QyUx7Ygq0viGor+qWGa42M6+Hueqe62JP2Z093tMu+3WXevKvYvd4qAb6eTlULIqUoKlSNmpv7W4U5ughURbjeFd8FxTSs5egiGGXrJ9Rjl43Pc2OOds2Ghws+lorlSAT52afWT+73burG2bFBkJWlcS7lgYKjS1FJSlFUKuduOny4XT3sndIbc59sL/u9Pp6Ouc01rlO1xjpyNQxuqgAnvuY41UVcn/YMxq4Y3IhO0GynwZbkzg4ttvaCZ2LQrn4gPnqivSJlchZuKhVUVawhTgXnb5aqVcPLouO6cGgnG5TGvM2v9nTIfqkcgxsnpX3z0G6ZeSqugQNKY5ytq7I9ZN5EtZVpHTi5CbBVgS17t8x+op3O3/rdiRvX8Ze1PbHz5IE24fh1TDc0CqkBPy93+YV0Um4q53ogkVIjo1I59yCkgua/8kvZOiJQ2cJI0DxM3u+DlOd6V3wX52yJsZeu37Tp9uUGJTMeidb8//mrlWVzwdhG9CnW0hvUPc3r6PwdXMNL5++6gZX5S7sm3Sd7HjVz99eqkO8hlaqKDoi3RkbPR3uPbxXXyPKmeUd8FQ1quV6TVKu6VWusKhe85FdtCXeZTphztqrj22W2fd6Tkwf8XNdGiGssfhF0xZobMZbmX3vpHeieesHOV0Ni0b1ZCFa+1AVhAT6ygyj9G8wzd+vWQNrytG5s53me3FTOE0y3qx8o+dhelPGgYu+eehXzXlkSNDrikunI/gu9TSRdT3molcXbrVPTuR6szXGSnyBV6NE8xOTrcnup2JqtL+LuMnYgQEATI80lcnNErBHqwIuAJdX2+vq3r2fQlNE8rCa+fz4OHRuU99yTG0Tp1zK9lthSfwVRresFIL5xbXk70/OhXhObrTnTVAYNaksL7JykuKIebldPE0z5eLojqXdzWe935kRpW3j7f+LT4tzbMhQ3SywfaNbZHqzNYXDjQGJD0retH2TyPc42iqitL+LazSHmmLrhumJXcDFKzOgsZRNyd6Mfo+qfNu2MnPe/je2GRcNMJ4SaCybt/YspTyh2HtK6gtu2xLMes3zUdf1aopfvayZpSonqytg1WRAEfLvjnOLbdVY8Qxyo913GB6gzdrFxxPllauyUYD8vo68poUuT2uhj4YjNDWr54dXezTH/KfuM/WJvYueCpbGN7Gk05Obc6J3P+kWfPbCd6GjUKpXKbBNI92Z1jL62fKT4yNSbxt9jcpvWULkBamfOzhVh6+uKm0plcWeIi9cMm8vkPONZM1aWM3hQbyDT2Iamxz0zdmgEADdLyiwuR1WrAWNw46S0n3S1mxrsHT0P6xqFPtF1jb4eY+aHZi2VSoXPno6RtK5YkuvY+5qhb1vj5a/K1CJ3UFPfx8v3NhVd3rhODUzqe5fm72gJvUta1ZOXXKh/2upfKENr+mBaP918gDfvlMlcU6yplztFiedgNZTYXGMJFZznhqqC8QelCffLa97R2a7M69D6w5mY8Ug03unfRva+sgtuGSyTeh1UqQBBLXuXTmPdy90NauvvNtNMa+q7sea81A8on+vayOJt2QODGwcSy4+o+NH2a1tP9D22aJV6tEOE0dcignxFlze5M0DVfXeFKl8gC+kfzYa1/RxSDnvFn6UiwY2/t4fR9ZPubyG6fPOrPRER5IsNr3THm33vwlAJ83kl97nL7DraLAnKK8phrilW+9XvnusssTyyiyOZm0pl0U2kVwvjNVAA8P3z0j6bPmN5WIPjozT/b+1D0wePtTXZg6qkrDzC0E9UB4DHY+qb3LZYpwWp5RUEwNsBg/iJBZRpk+7Di/c0lrUdsYeI2v6ma8tNndtlVlQp6g//4Owt/U5evKrFWCBgjNj1r/Wdk9lXa9yPGl6VN6x7Wyo//Pjsge1lv6fi6eDuRtYleypJ/3i2ccD4Fkr5crD52ipjlym552GFluEBeKF7Y3hKuGoF+HiaDKT0GdTciK6ju1TO03kFdzeVwZg5viJj6Ejp7bNx/D14qWcTzd9SJ3F1U6nMjk31WmILdGmi+9sxd98Ra35rHuaPwXc3NPoeU4cw0NcTK1/qgt/GdpPd+0l/FOYnYiPxYo8mRtfv1rT8s0bW0n3g+PjJ9pj1uOmEb7FeXHJiMR9P42MoJdjx4axOTW9ZDwVRRh7OapsZDsTU7+bJTpGS96/Nz8sdNX10f+/Olv+pj8GNnWlfIMWaUcROmOe6NUJco1p4q18r1A+27MZlKWMPoBWlNPY7UmLI8xAzTyj69J9QTf30pjzUCuvHdceZGX0sKJlpSiRnBvqan0bB2F7s1Q1Zzlg3+mWVcnOSeu3UPt5RIt2+W4TVNHyPhAI0D6uJ4d0bo0mdGkjq3RyhAdJ6walUuk/I+jcFwPI5kibr3Rw3ju+B/iZqXgHT6VEdGwTb5CFAP3Ab3t10jcXmV3vI2n7TUGUGyWsZbr+xWyrOuQ/NBHMVnutW3uyjf6b2NZEmAJhKKJbfnFwhLMDH4Dfr7AnGDG4UFCLSa0M/GfdVrXZuqbcGf28PLH0xHs92beSwLpsdjMz/Y+wmkfpqT6t6SADA2HubWfV+U7zcVbirboCkp48Vekmp5poPlCDl5mushsVe6R5izWLG6F8IpQSAUvM6VCog9dUe+GVUF0QE+Rp8fmsSIYNreCH11Z54+b5mkodh0G+W0p/6Y+P4e9BMJOCScjTDRHoPyvl4+oM12srTcbq1Sca+g4rDJHfE6zkKTdmhUpXXoiktzEQgLHWoCP1argrubioM6GiiKc/E+WBpq1R5TZ1+zar4up2igvFIhwgse1E8md9eHB7czJ8/H1FRUfDx8UFcXBzS0tJMrn/9+nWMHj0adevWhbe3N5o3b45169bZqbSm1RGpLnygtW6mu/YJYqpd3uhTuULRjX6wYmzCxeLSMtH9mqvGjgjyxeOxllWBVpDb3dPwpma4Trem5eMIPaj19DPSRHU6YBhEzB3YwehYEsb2awvGxu6xV3DzjInmEH2GCcXKlUOlAprU8dckU4vlmCgx9lD+rdtGX9OuiXFTAd4e7lp/G44ZJEasJkz/dyb2szN1KFXQDZrkjixtdLsq08GY1AlcxYbDkKKehU2v+lQAHjFT82WJpN7lAdPHFkz0+eMLcZjc5y7R/KQK2uf4rkn36bxmr+uPsXtR7RremDOwPTpbMaq0Ehwa3CxduhRJSUmYNm0a9u7di3bt2iExMRHZ2dmi65eUlKB3795IT0/HihUrcPz4cXz11VeIiFD+5LSM+WYm3eBGzpYM32+pSX1aGgRdxkbwXbHnwp396i6vVcO2XcAB0xdtsact/eMp1lPm++c749g7D+hMY3FXXfGbTQX9+a0C/TwxRCsZ0xak3ITCAnSf4r8eEgtAPGmwYoTX7s10B4nc8Ep3S4uINx5oiZfvk1a7ZttupOa3vWl8D4wzU9a29U03zxTeMj4AmnYehEqlwl11a+KpuAZI6t3cbK2Fsb8Bw55iljR5+mg9JJjqaaPf5GWJDx9vhxe6NTJolhLzQOtwNA01/dszRm66x6DORh60VCrFg4FHO0Qg8E5w18OCmrKuTUMw/J7Gkn8z+jlmRpulIP3BR8r3p++lnk3g5eGGCYmW98JTkvSMQBuYPXs2hg8fjmHDhgEAFixYgLVr12LhwoWYOHGiwfoLFy7E1atX8c8//8DTs/zkiYqKsmeRTZJy4mifd5b0qFAihyuuUW20rFsTuYXF6NWiPKHO2M20sLi85qZBrRr4N/2aZrklF4SIIF9ZQ7ybCuRG92qKmj4emPrrEYPXtk+8FyezCkQvLCqVyiDB0Nz34OnuhpmPRmPiykOir9cN9MH0h1tjxPd7yvdhcmvSSDkztGu2Pnu6IxLu1B6UiXyeipom7c++580Es8mJ5vY/PqEZPkk9qbO8cR3DvBf981bpmhttYl9noJ8nxvduju7NQhBgJJ9pVI8mGPXjXqPlM9WE2aRODXRoEIRAX09NbUvFPGdf/nVa9D3635NYjZP+uSlac2PiYKpUKni4u+Hv13tBLQioG+gLN5XKIMgFyns+vrfuqObvjg2CcDKrEAXFhkGdr6e76HF+zEzPJ23GmrqlkPuQN/3hNujQIBivrzios9wWIbf29xHk54XNr/YwmdRsERMXCFOfSewcC/DxQL5e4D7xwZZ46ce9uCAyxpCx/bz+QEsk9W7uNAOmOqwUJSUl2LNnDxISEioL4+aGhIQE7NixQ/Q9a9asQXx8PEaPHo2wsDC0adMGM2bMQFmZ8YGJiouLkZ+fr/PPVvRPm08GdTBYR7uJI6SG8RtLkJHB8ZR4Am4XGQRvD3dM7tsKXe400xibfTu3sBiAYROI1CfIZncS/7w93PDuIzLHuDCyiyUj7gYgNoBg+TcQEeSLni1CJR8rtZlxMGp4exht/wbKA4b7tWrC7DXYVf7NymYS7eYLsfFvKs477RuSNYFNBe3P+miHCEzr1wpLht9tuJ7BIH7KHSP9LU26UwMh1rspNqqWplno0Fv3a85PACjQusC3FUmyNXVDValUWDmqCxY9aziackWtmb60s1d1/pb7cCRFxeqRtfzQsHYNeHm4YVTPJpKTiI3tr7sCuTvWNJDJ7anj5eGGxNaGg4GqVOXNKJbSnyMNMAxAG9fx12lGkz8vm+FnNbUNUyMUixELRtrWDzI7l5RYzp+zBDaAA4Ob3NxclJWVISxMt9dAWFgYMjMzRd9z5swZrFixAmVlZVi3bh2mTJmCjz76CO+++67R/aSkpCAwMFDzLzLSujwQU/RPnodEstq1b0JPdo7Eq1rzpGg3lYy4pzES7grFbJnz4vyvvfj4OOZ8/kyMySQ4KfksYr4eGotHOkTg1zFd0atFKDpFBUsuk7FdVFSt6ydqWppOcPhSnsnXI4J8NU0WYrkbtohlpHyWRdvTK8ugtVys5qZCv3bl56SxbqbWCPD1xLCujRAaYD7p1ZY1N0O7RGHbG70MBgXUV9PHE29qTSR4pajE5Prm8sxUKpXojaimjyeahxkmzOpPLaL/tT1zdwODm5jY9k2WSsZx1t+Xt4e76P5+Hn63xcMNAJW/oXtbSuuGbSz3Tu4AomLnnAoqeHm44cDU+3Hwrftlbe+1xBaiqQVKXw9EBye9tyk83FSiA+mZ2r/YpcFYMKS/VP+9PVs4zxhnYhzaLCWXWq1GaGgovvzyS7i7uyMmJgYXL17ErFmzMG3aNNH3JCcnIykpSfN3fn6+zQIc/fPG2NPFgan3o7i0DDV9PDH2vmZ45u6G+HZHuk4GvL+3B74eanpOHSV1bBCMXZMSEDVxraT1A3yMJwxq52E0rF0Dc7TG0fHzMn3KPdslCov/SQdgvupZ/ynB0uCmfWSQ2XVq+nji8PREg9mzAcOLgL26YlcMjAbonnumBup6uF091A/2Fe2tYy3TNRt6fyu434dEBrysHywteNOeMbwied4Ya/LdxGqqavt74XJe5ei7+k0G97YMxU+7MvS2I7JtE8UyN5qtqW2rVOLbbhJafsykNjPrd9v+87VeyC0sNlkbaqpcFV7t3RxPfb1L0jaMbafi8wVKTIDWNrpXUySLNFWbO0/MTZ9gSkWTb+M6/jj6zgPwdHdDgYlEd2131Q0QrfExFrMbjiKu+2CnHewP724YZDmaw2puQkJC4O7ujqysLJ3lWVlZCA8Xn0uobt26aN68OdzdK9sv77rrLmRmZqKkRPypy9vbGwEBATr/bEXqzTXQz1Pn6Ta4hhdeSWgu+ceuRBmkEvshdmgQhCkPGX8qfspINTxg+kL8aIcIvPVwa9F1K3IE/kiqHA/DoObGwopuYzOJ6/P39tB5iqz4sd93V3ntY707T+L3yRxoUWzcCimfxdhF6QsTAwCqVCrENKxlMji1lKmKDVs21cm5gevT/r3oxIQi5bUm303s4+s3hxrUjkJl+MAk8Tiue7k7Ph3UAY+Z6jJsUEa9pkMV0LWJYW5ORRkWDeuEe1uGYs2YrpL3AZQPqih2rXv9AfEu2cY+shLNO9bUQAHitSrmzvUa3h461zE5tMe/MjYUhLF54l5JaIbE1uXXpnZayfNSB3DUH09J+2O+amT0c0dyWHDj5eWFmJgYpKamapap1WqkpqYiPl68f3zXrl1x6tQpqLWuCidOnEDdunXh5WX73jvmOMNsMnk3b1v1ZKBvdC/D+YhWvdQV4SLjbbzTvw3SJt0n+loFUz8j/WHStS/k3w7rjENv3a/zFKj/o7Q0sGsTEYjJfe5C3+i6Ri+wYtaM6Yb3B0Qj6U7T4qrRXfH+gGhMNDIM/ZB48a7TnwzqgL9f76UzB1ZTrYBL7rg6XZqEYN5Tlfle3wyNlfV+S5m6phuMc6NQsONrZaKmdhCpnasUJJJ0rH++aXdjtqSLtX6ysP72VSppzcHav7efh9+N75/vjFb1AtCvXT2rR5Gd8Ui0Qc/Eii02D6uJhc92QlsjM7rrr2+O/sNK5fvFl8vtkKG9+Ul9WmJUzyYmB0D87OmOZqeGCBHJW5Nyapu6RlrDz0u8KbH8NQ+E1vTBkemJWPlSZUAqNWDWPj/1g0JnHM/Podk/SUlJ+Oqrr/Dtt9/i6NGjGDVqFIqKijS9p4YMGYLk5GTN+qNGjcLVq1cxbtw4nDhxAmvXrsWMGTMwevRoR30EHUqNIWENdzeVIjVAFeRcHNvVDxTNt5BK/0lWe99ubirU1Ktt0P9RWnP0h9/TGPOf7ihpZOAK4YE+GNipgaYnRFiA7t/6jM2KLPadhQb4YP247vj79V6YO7CD6EVW+yKm/y1pdyGuqFmyNZPNUmb+tpTU8VSM0f/JLngmBp2igvGeSPK7dsLxjEei8bNI4rQxYjecqXq1n4Z5SSL5NSIHLrSmD5aOuBu/je2G+Ca1Tc6SbrKMIssC/TwNHnDkNs9JTTLVDmLCta4jxnann7MkZ/s9mofijQdamqy16BNd1+jUEFsm9AQAjOxpOEaWlEumv7cHfhll+BDfr51lOZMVoiRMCFvD20Pncxs7BuY+hvb34oyjFTs0uBk4cCA+/PBDTJ06Fe3bt8f+/fuxYcMGTZJxRkYGLl++rFk/MjISv//+O/7991+0bdsWL7/8MsaNGyfabby6EgQBk/vehXtbhkqan8gcKafs+nHd8cXgGLNPcIDpJ3b9phhzFwn915WILW0Zn/p6umuqhaUMH39X3QBE1vJDoJ8nZj3eDh881hYPtqlsshUbNLKCLXJqzDH13SrdFfzb5zqjTUQAvrayVkr7CdrLww0PtAnH8pFdRHN2fjtYeS2Kb1LbINg2Rezjdmkagv/eTtT8bTiKs6GjlwtEtx/XuLbV0yhI/U6krjfrsbaoF+gju1MEAPw4PK5yf0bWaRpaEx9JnMoA0C23tedfozu5Wv7eHtg/tbfOa1Jv9DENayGylm4NiFgzmZxaTkuGF7HkWOg/yDtjcOPwhOIxY8ZgzJgxoq9t3brVYFl8fDx27txp41JZxgkqbqAWyqtKF4p0SbWVu+oG4K661ucy6R+/hrVMP4Xo/+gtzbnR3YYy6gX64JJWsihQPnHe/Kc64sK1m/i/A5fw0aYTOq8HiMxBpO2J2EjcLCnD+sPlvQm7NK2NlXsviq7bNNQfPw+/22x3TimkzhNm6vpmmM9h3cWwR/M6Fg2Qpk+7lk1OC45hsGauJ5X4cu0Ee/1txIr0LLRmVmdzpHbPl/rdPR4bafEo5dp5cKb2NyCmPl5dfsDo6288UNlEbKv7r/6wHXJu9AuHdkLvOX9ZtX/t42PJPciSwyJA93xxvtDGCaZfcCUVN9fuzULwy6guDiqDdaxNsDPH1I+gouzLR8ZjzsB2iDYzYqz+Deb5btZn7CvVtPjj8LvxRKxuU5Kflwc83N1EJ3cEypPyYhsGWz0nV4X4JrUVmWBQSm8ywExCsdWlcAaV54aUWhad1yUcAP1VxHoWaq8jp9ZCCsMebZULtPNh7P2Qbm53pman1262dMabsVielVw1tEYoljq5q+4+xXf6Tv/Kptk3H9Idvbq85lI7MJe9W5tjcKOgivviYzH1dcZgsLYdVV4ZrLs5b32tpzIFMcLUj6CiSrVTVC080sF8Lw/tH+XeKb3Rup511fKAcrVvjUJq4IPHjN98xHYT4u+NFaO6mHzaNfb9KplnJVdFQPxgG+OzFft4KTxCqw2Yr5GofL2G3g3V3FulnFdyc397SRwnRgkRwZUPPbZqgjC6WTO7+3dygtGZsrWPqZLNUrYiVqznukaZfo/Ih5E7BpD2w3jFw9VddQNwekYfHJ6eiHv1eoAG+nqiXpAvpvVrhVmPtbXbwKVyOLxZypXszSifnkD/i24bEYj/O3DJLmWw9uas371Q7o/EGnLLrn3hquGtzM3TkjZrR/plVDwuXb+lSLOgMca6nFb4I6kHcgqK0cDEwICvJDTD7vSruHbjtqZ3WVVTP9hXM2K3/txq5i7tUpqTpAQN2pux9e1Eu5lXe19KTAEjh7naZF8vd4T4m+8tq3t8pX+ITePvwe5z1+Dj6YbxS8WbwAJ9PZF3Z8Twn9IydIa0kEPsHOjQQPo1uOL6tezFeDSZJG1CaXc3ldHrvLubSrRmrCJxeZjIIILOgjU3Crp1u7y7T3pukc7yJztHommov9nZp5WgRN5JhbUvdzN4QrWlUpn5BNpBpFJPkzZMadBh6Q1Cv3gxDWvZvGZQf2I+fb5e7iYDG6C8R8/G8T3w7+QEo9MRmPL+gGjZ71HaMBNP0OaeXKUEn1LGGxFMNI1Zq6Zezpd2nK+9LyWnztCmfxNdMTIevVrUwefPmO8YIeVnq11qOQ9DzcJqYlDnBiaPd8qjlednSamZ+Vy01A/20/nerQ0cK74zqWPXAIZd8ENM5OktHxmPMb2a4kljE5E6Edbc2MAlvVE7a/p4Wjxokzkv39sUPVuG4tHP/gFgvPajeZg/rhaVIPlB8zP/rnqpC7LyixVp5jFk/EdX0ZNIKu3fpP7syZbSn0PLWl7ubjojCVcYfHcUfk47jz7R4gNWGqP9/da0ceD5xgMtsWj7WUzuY3oaA3sY2KkBLl67iU82n3JYGbw9jN8QxZJ/tU19qBUCfT1FJ5ac1Kclvv77LCb3vQsb/8sSeXclncBC4RgjyM8LHz/ZHuOW7Dfcrx2adB7tWB9/HM1Gt6blgzLGRtXComGdFdu+m5sKMx+NRlFJGeoGKptbWLuGZeOseXm44cj0RLScsqF8gZUHt6GEruAV2tUPxIELeZpz8ovBMThyMQ89TSTqd4qqhU5Rtawqo70wuKni/Lw90FGr2tJYs0qHyGDMHBAtqW1UrBpUqQuase0sHXE3OjeS96PReZpUqHwDOtbHz2nncfSyMhOsDo5viG+2nTVYHujniW1v9LKqrfqVBNs274zq2QQjezR2mvZ0W9WqDb67ITYcyTQ5srYxuybdh8t5t8w+CATX8DLaVDHiniYY3l3+cbbF1/K/9hGiwU2L8Jo4nVNks/0C5Td6a7v2m/OkBd9xBVMt1tb8Riztsadtxch4rNx3Uad3mDk/Dr8bBy9cR1yj8mAysXW46OSiVRWbpaq4WnrdEI39AMvniXH8TUq7BIue7YSwAG/88Hwc4hrXll0+7QH3lPpsNbw9sH5cd9EZpS3xxJ3k4HDRySTll1n76w228GlRDkedM8kiozzbKh/qnf5tsCv5PouOZ1iAj+TeZKZIPc72/Dq0D/fYeyvni7NVs5Q1jJ0aSp4y990VikBfT9EJPxV7+LPw2MZG1cKMR6J1ronmrmH+3h7o0iREVhNWVcKaGxuw5wVIP9I29lt2hsBGX6+Wodg1KcHi90eF1MAbD7RErRrKz5NkbCZiuVqE18Tfr/cSHaadjGsk0l3elulQ1k5TYC+OyncP0wrOnfFQaeciPdyuHtbc6cCh5OGq6eOJPW8miAYDtj4kI3s0wYI/T2NSH/NpBRXeeri1ZhLi6ojBjU3Y79fvrpcjYqyrsMQR0G1O6WvzKJHhz5UwskcT/PFfFh4xMfeMVEp203aGKT7sQSwYryYf3aRmYVqD29l4X9oBQ60aXljwTEd4e7hLnk7BUVIejdYEN8YmkbSUsc+u1LOjsUM78cGWeCWhmdGpXciQc5+lZJb+Q4R+XkLFbNqW9FCxBbW9uiNZqVYNL2ye0BNj72tmfmU7eqRDBLzc3XB/K/vMF+UoYSKDkQ2Jbwg3Vfns8dVVhwZBDtv3A23q2nVsHTm0A1/tHp7mhjFQjjLRjakadgY28rDmpoqrSKqd+lArfLL5pMGEf98O64y8m7etzs8Y2CkSS/49b/W4N1VtHBlnU9vfG4enJyreq8tZfDM0Fuev3hCdp6xekC+OvfOgy352Z1OVfqrGhqyw5fhP2pSquVG6e//jMfWxfM8FRbdZVTC4qeIqfgvPdWuEZ7tEGeQOuLmpFEk87dAgGLsm3Wdxl0dSjlL5QM7I3Azmjvzs9zQPQYi/l42GSJCmKgUc9vRSzyb4N/2qplbv79d7IbugWJHpR6SwNiRRqcq/225NQxQpj/Z2qysGNzZgzxNKO7ve1kmRYSI9foiqCz8vD+xMvs+uvUsa1vbDuSs3RHtk2bqTQFWKo4L8vLDqpa6avyNr+dl1ShJrv4vdkxNwOe+W1TO767vvrjAs233B7KS8rsh1HwEdyNZPV1MeqhxUzRl7LhC5Kg93N7v2PPzxhTi81LMJvhhcPkqv9rQPPjaqxRp8d0MAqLLTZFgj+k5wESdzzC1r1fb3VjywAYD7W4VhyYi7sfW1Xopv29lVv3DOLmwb3TzfrRE83FTw9XL+ngv6gvzYrEVkzOheTTB/y2kM714+Z0/9YD+8rjUwm4+nO/5+vRfc3FQ2++2//b/WeOPBliZn23ZVXw+NxYo9F/BkJ3nTCzhrLqFKpcLdjWs7uhgOUf3OXhcxVKFB5uwtuU9LXM67iafjGjq6KEROZ8L9LfBIhwg0DjGeK2Lr5haVSnyyxOogLMAHo3s1lf2+6jJEQ1VSPc9gcpjQmj5YMiLe0cUgckoqlQpNQ2s6uhgkE2Mb51O12jSIiIicTBUZvqtaYXBjA4ziiYiqDzZLOR8GNzbA85yIqPpgzY3zYXCjEEbuRETVk1ClRgWqHhjcKEQ7tqnOo0ISEVU7Wtf/kT1sM5kvycPgxgZeuDNGBRERuT7tehuxSV/J/hjcKET75K5Vgyc3EVF1oV1zf19L0/OjkX0wuFGIds4NW6WIiKoP7ZybBrXtN6cVGcfgRiHaNTfMuSEiqj7YW8r5MLixARXrboiIqg32lnU+DG4UwnObiKh64uXf+TC4UYjOOAesuCEiqjZa1w1wdBFIDyfOVAjHuSEiqp5CA3zw12u94O/DW6qz4DdhA4xtiIiqF/aSci6ym6UyMjJEk6cEQUBGRoYihSIiIiKylOzgplGjRsjJyTFYfvXqVTRqZNnIvPPnz0dUVBR8fHwQFxeHtLQ0o+suXrwYKpVK55+Pj49F+1WSbrMU626IiIgcRXZwIwiC6M27sLDQoiBj6dKlSEpKwrRp07B37160a9cOiYmJyM7ONvqegIAAXL58WfPv3LlzsverNO2EYoY2REREjiM55yYpKQlAea3ElClT4OdX2b5YVlaGXbt2oX379rILMHv2bAwfPhzDhg0DACxYsABr167FwoULMXHiRNH3qFQqhIeHy96XvbDihoiIyHEkBzf79u0DUF5zc+jQIXh5eWle8/LyQrt27TBhwgRZOy8pKcGePXuQnJysWebm5oaEhATs2LHD6PsKCwvRsGFDqNVqdOzYETNmzEDr1q1F1y0uLkZxcbHm7/z8fFllJCIioqpFcnCzZcsWAMCwYcPw8ccfIyDA+n79ubm5KCsrQ1iY7kRjYWFhOHbsmOh7WrRogYULF6Jt27bIy8vDhx9+iC5duuDIkSOoX7++wfopKSmYPn261WUlIiKiqkF2zs2iRYsUCWwsFR8fjyFDhqB9+/bo0aMHVq5ciTp16uCLL74QXT85ORl5eXmaf+fPn7dziYmIiMieZI9zU1RUhJkzZyI1NRXZ2dlQq9U6r585c0bytkJCQuDu7o6srCyd5VlZWZJzajw9PdGhQwecOnVK9HVvb294e3tLLpOlOP0CERGRc5Ad3Lzwwgv4888/MXjwYNStW9eqbs9eXl6IiYlBamoq+vfvDwBQq9VITU3FmDFjJG2jrKwMhw4dQp8+fSwuh9I4cSYREZHjyA5u1q9fj7Vr16Jr166KFCApKQlDhw5FbGwsOnfujLlz56KoqEjTe2rIkCGIiIhASkoKAODtt9/G3XffjaZNm+L69euYNWsWzp07hxdeeEGR8hAREVHVJju4CQ4ORq1atRQrwMCBA5GTk4OpU6ciMzMT7du3x4YNGzRJxhkZGXBzq0wNunbtGoYPH47MzEwEBwcjJiYG//zzD1q1aqVYmYiIiKjqUglicymY8MMPP+DXX3/Ft99+qzPWTVWRn5+PwMBA5OXlKZoYXVRcitbTfgcAHH37Afh6uSu2bSIioupOzv1bds3NRx99hNOnTyMsLAxRUVHw9PTUeX3v3r1yN+kSmE9MRETkHGQHNxWJv2QcRygmIiJyHNnBzbRp02xRDiIiIiJFyB7EDwCuX7+Or7/+GsnJybh69SqA8uaoixcvKlo4IiIiIrlk19wcPHgQCQkJCAwMRHp6OoYPH45atWph5cqVyMjIwHfffWeLchIRERFJIrvmJikpCc8++yxOnjwJHx8fzfI+ffrgr7/+UrRwVYnMTmdERERkI7KDm3///RcvvviiwfKIiAhkZmYqUigiIiIiS8kObry9vZGfn2+w/MSJE6hTp44ihSIiIiKylOzg5uGHH8bbb7+N27dvAwBUKhUyMjLwxhtvYMCAAYoXkIiIiEgO2cHNRx99hMLCQoSGhuLmzZvo0aMHmjZtipo1a+K9996zRRmrBGbcEBEROQfZvaUCAwOxadMmbNu2DQcPHkRhYSE6duyIhIQEW5SvSuIgfkRERI4jO7ip0K1bN3Tr1k3JshARERFZTVJw88knn2DEiBHw8fHBJ598YnLdl19+WZGCEREREVlCUnAzZ84cPP300/Dx8cGcOXOMrqdSqRjcEBERkUNJCm7Onj0r+v9UiWP4EREROQeL5pYi01RgRjEREZGjyA5uBgwYgPfff99g+QcffIDHH39ckUIRERERWUp2cPPXX3+hT58+BssffPDBaj23FBERETkH2cFNYWEhvLy8DJZ7enqKTstAREREZE+yg5vo6GgsXbrUYPmSJUvQqlUrRQpVJWklFHMQPyIiIseRPYjflClT8Oijj+L06dO49957AQCpqan4+eefsXz5csULSERERCSH7OCmX79+WL16NWbMmIEVK1bA19cXbdu2xR9//IEePXrYooxEREREklk0/ULfvn3Rt29fpctCREREZDWOc0NEREQuRVLNTa1atXDixAmEhIQgODgYKhMZs1evXlWscFWJoJVRzHxiIiIix5E8t1TNmjUBAHPnzrVleYiIiIisIim4OXDgAB577DF4e3ujUaNG6NKlCzw8LErXISIiIrIpSTk3n376KQoLCwEAvXr1qrZNT0REROT8JFW/REVF4ZNPPsH9998PQRCwY8cOBAcHi657zz33KFrAqoKzghMRETkHScHNrFmzMHLkSKSkpEClUuGRRx4RXU+lUqGsrEzRAlZFphKuiYiIyLYkBTf9+/dH//79UVhYiICAABw/fhyhoaG2LhsRERGRbJJybpKSklBUVAR/f39s2bIFjRo1QmBgoOg/IiIiIkeSnVB87733Kp5QPH/+fERFRcHHxwdxcXFIS0uT9L4lS5ZApVKhf//+ipaHiIiIqi6HJxQvXboUSUlJWLBgAeLi4jB37lwkJiaabfpKT0/HhAkT0L17d1n7sxXtfGJm3BARETmOShDM9/NZvXo1Ro4ciezsbKhUKhh7iyUJxXFxcejUqRPmzZsHAFCr1YiMjMTYsWMxceJE0feUlZXhnnvuwXPPPYe///4b169fx+rVqyXtLz8/H4GBgcjLy0NAQICssppytagEHd/ZBAA4M6MP3NwY4hARESlFzv1bUrNU//79kZmZifz8fAiCgOPHj+PatWsG/+Q2V5WUlGDPnj1ISEioLJCbGxISErBjxw6j73v77bcRGhqK559/3uw+iouLkZ+fr/OPiIiIXJesYYa1E4qVGKE4NzcXZWVlCAsL01keFhaGY8eOib5n27Zt+Oabb7B//35J+0hJScH06dOtLSoRERFVEbJnBe/RowfOnTuHN998E4MGDUJ2djYAYP369Thy5IjiBdRWUFCAwYMH46uvvkJISIik9yQnJyMvL0/z7/z58zYtIxERETmW7ODmzz//RHR0NHbt2oWVK1dqelEdOHAA06ZNk7WtkJAQuLu7IysrS2d5VlYWwsPDDdY/ffo00tPT0a9fP3h4eMDDwwPfffcd1qxZAw8PD5w+fdrgPd7e3ggICND5ZwvaeUgcw4+IiMhxZAc3EydOxLvvvotNmzbBy8tLs/zee+/Fzp07ZW3Ly8sLMTExSE1N1SxTq9VITU1FfHy8wfotW7bEoUOHsH//fs2/hx9+GL169cL+/fsRGRkp9+MQERGRi5GdOHPo0CH89NNPBstDQ0ORm5sruwBJSUkYOnQoYmNj0blzZ8ydOxdFRUUYNmwYAGDIkCGIiIhASkoKfHx80KZNG533BwUFAYDBciIiIqqeZAc3QUFBuHz5Mho1aqSzfN++fYiIiJBdgIEDByInJwdTp05FZmYm2rdvjw0bNmiSjDMyMuDmJruCiYiIiKop2cHNk08+iTfeeAPLly+HSqWCWq3G9u3bMWHCBAwZMsSiQowZMwZjxowRfW3r1q0m37t48WKL9mlLnDiTiIjIcWRXicyYMQMtW7ZEZGQkCgsL0apVK9xzzz3o0qUL3nzzTVuUsUowOxIiERER2YXsmhsvLy989dVXmDJlCg4fPozCwkJ06NABzZo1s0X5iIiIiGSxeCS+Bg0aaHonsRmGiIiInIVFmbrfffcdoqOj4evrC19fX7Rt2xbff/+90mUjIiIikk12zc3s2bMxZcoUjBkzBl27dgVQPiXCyJEjkZubi/HjxyteyKrA/PSjREREZA+yg5tPP/0Un3/+uU7PqIcffhitW7fGW2+9VW2DGyIiInIOspulLl++jC5duhgs79KlCy5fvqxIoYiIiIgsJTu4adq0KZYtW2awfOnSpewxRURERA4nu1lq+vTpGDhwIP766y9Nzs327duRmpoqGvQQERER2ZPsmpsBAwZg165dCAkJwerVq7F69WqEhIQgLS0NjzzyiC3KWCUId4bxY694IiIix7JonJuYmBj88MMPSpeFiIiIyGqSa24uXbqECRMmID8/3+C1vLw8vPbaa8jKylK0cERERERySQ5uZs+ejfz8fAQEBBi8FhgYiIKCAsyePVvRwhERERHJJTm42bBhg8lZv4cMGYLffvtNkUJVZUy5ISIicizJwc3Zs2fRoEEDo6/Xr18f6enpSpSpauIIxURERE5BcnDj6+trMnhJT0+Hr6+vEmUiIiIispjk4CYuLs7k5JjfffcdOnfurEihiIiIiCwluSv4hAkT0Lt3bwQGBuK1115DWFgYACArKwsffPABFi9ejI0bN9qsoERERERSSA5uevXqhfnz52PcuHGYM2cOAgICoFKpkJeXB09PT3z66ae49957bVnWKkHFUfyIiIgcStYgfi+++CIeeughLFu2DKdOnYIgCGjevDkee+wx1K9f31ZlrBKYT0xEROQcZI9QHBERgfHjx9uiLERERERWkz23FBEREZEzY3BDRERELoXBjUKEO0k3TCcmIiJyLAY3RERE5FIY3BAREZFLkdRbqlatWjhx4gRCQkIQHBxsciyXq1evKlY4IiIiIrkkBTdz5sxBzZo1AQBz5861ZXmqPI7hR0RE5FiSgpuhQ4eK/j9VEjiMHxERkVOQPYgfAKjVapw6dQrZ2dlQq9U6r91zzz2KFIyIiIjIErKDm507d+Kpp57CuXPnIAi6tRUqlQplZWWKFY6IiIhILtnBzciRIxEbG4u1a9eibt26nCiSiIiInIrs4ObkyZNYsWIFmjZtaovyVHkqDuNHRETkULLHuYmLi8OpU6cULcT8+fMRFRUFHx8fxMXFIS0tzei6K1euRGxsLIKCglCjRg20b98e33//vaLlsYTAfGIiIiKnILvmZuzYsXj11VeRmZmJ6OhoeHp66rzetm1bWdtbunQpkpKSsGDBAsTFxWHu3LlITEzE8ePHERoaarB+rVq1MHnyZLRs2RJeXl747bffMGzYMISGhiIxMVHuxyEiIiIXoxL0s4LNcHMzrOxRqVQQBMGihOK4uDh06tQJ8+bNA1DeEysyMhJjx47FxIkTJW2jY8eO6Nu3L9555x2D14qLi1FcXKz5Oz8/H5GRkcjLy0NAQICssppy6fpNdJm5GV7ubjjx3oOKbZeIiIjK79+BgYGS7t+ya27Onj1rccH0lZSUYM+ePUhOTtYsc3NzQ0JCAnbs2GH2/YIgYPPmzTh+/Djef/990XVSUlIwffp0xcpMREREzk12cNOwYUPFdp6bm4uysjKEhYXpLA8LC8OxY8eMvi8vLw8REREoLi6Gu7s7PvvsM/Tu3Vt03eTkZCQlJWn+rqi5sRnmExMRETmUpOBmzZo1ePDBB+Hp6Yk1a9aYXPfhhx9WpGCm1KxZE/v370dhYSFSU1ORlJSExo0bo2fPngbrent7w9vb2+ZlYj4xERGRc5AU3PTv3x+ZmZkIDQ1F//79ja4nN+cmJCQE7u7uyMrK0lmelZWF8PBwo+9zc3PTdEVv3749jh49ipSUFNHghoiIiKoXSV3B1Wq1pueSWq02+k9uMrGXlxdiYmKQmpqqs6/U1FTEx8dL3o5ardZJGiYiIqLqy6K5pZSUlJSEoUOHIjY2Fp07d8bcuXNRVFSEYcOGAQCGDBmCiIgIpKSkAChPEI6NjUWTJk1QXFyMdevW4fvvv8fnn3/uyI+hwZQbIiIix5Ic3Ny8eROpqal46KGHAJQn6mrXlri7u+Odd96Bj4+PrAIMHDgQOTk5mDp1KjIzM9G+fXts2LBBk2SckZGh0/28qKgIL730Ei5cuABfX1+0bNkSP/zwAwYOHChrv0qT2aOeiIiIbETyODcLFizA2rVr8X//938AypN6W7duDV9fXwDAsWPH8Prrr2P8+PG2K60C5PSTl+PCtRvo9v4WeHu44fi7HOeGiIhISXLu35KnX/jxxx8xYsQInWU//fQTtmzZgi1btmDWrFlYtmyZZSUmIiIiUojk4ObUqVOIjo7W/O3j46PTXNS5c2f8999/ypaOiIiISCbJOTfXr1/XybHJycnReZ09lsqpmFFMRETkUJJrburXr4/Dhw8bff3gwYOoX7++IoWqiphPTERE5BwkBzd9+vTB1KlTcevWLYPXbt68ienTp6Nv376KFo6IiIhILsnNUpMmTcKyZcvQokULjBkzBs2bNwcAHD9+HPPmzUNpaSkmTZpks4ISERERSSE5uAkLC8M///yDUaNGYeLEiZpxXVQqFXr37o3PPvvMYAJMIiIiInuTNUJxo0aNsGHDBly9ehWnTp0CADRt2hS1atWySeGqIhXHKCYiInIoi6ZfqFWrFjp37qx0WYiIiIisJjmhmIiIiKgqYHBDRERELoXBjcI4iB8REZFjMbghIiIil8LgRiEcoZiIiMg5MLghIiIil8LghoiIiFwKgxuFMZ+YiIjIsRjcKEQAk26IiIicAYMbIiIicikMboiIiMilMLghIiIil8LgRmEqDlFMRETkUAxuFMJB/IiIiJwDgxsiIiJyKQxuiIiIyKUwuFEYM26IiIgci8ENERERuRQGNwphPjEREZFzYHBDRERELoXBDREREbkUBjdKY0YxERGRQzG4ISIiIpfiFMHN/PnzERUVBR8fH8TFxSEtLc3oul999RW6d++O4OBgBAcHIyEhweT69iJwiGIiIiKn4PDgZunSpUhKSsK0adOwd+9etGvXDomJicjOzhZdf+vWrRg0aBC2bNmCHTt2IDIyEvfffz8uXrxo55ITERGRM1IJDq5yiIuLQ6dOnTBv3jwAgFqtRmRkJMaOHYuJEyeafX9ZWRmCg4Mxb948DBkyxOD14uJiFBcXa/7Oz89HZGQk8vLyEBAQoNjnOJNTiHs/+hM1fTxw6K1ExbZLRERE5ffvwMBASfdvh9bclJSUYM+ePUhISNAsc3NzQ0JCAnbs2CFpGzdu3MDt27dRq1Yt0ddTUlIQGBio+RcZGalI2Y1hPjEREZFjOTS4yc3NRVlZGcLCwnSWh4WFITMzU9I23njjDdSrV08nQNKWnJyMvLw8zb/z589bXW4xzLghIiJyDh6OLoA1Zs6ciSVLlmDr1q3w8fERXcfb2xve3t52LhkRERE5ikODm5CQELi7uyMrK0tneVZWFsLDw02+98MPP8TMmTPxxx9/oG3btrYsJhEREVUhDm2W8vLyQkxMDFJTUzXL1Go1UlNTER8fb/R9H3zwAd555x1s2LABsbGx9iiqZCoVs26IiIgcyeHNUklJSRg6dChiY2PRuXNnzJ07F0VFRRg2bBgAYMiQIYiIiEBKSgoA4P3338fUqVPx008/ISoqSpOb4+/vD39/f4d9DiIiInIODg9uBg4ciJycHEydOhWZmZlo3749NmzYoEkyzsjIgJtbZQXT559/jpKSEjz22GM625k2bRreeustexZdB8fwIyIicg4OD24AYMyYMRgzZozoa1u3btX5Oz093fYFIiIioirL4SMUExERESmJwY3CmE9MRETkWAxuiIiIyKUwuFEMM4qJiIicAYMbIiIicikMboiIiMilMLhRGPOJiYiIHIvBDREREbkUBjcK4QjFREREzoHBDREREbkUBjcK46zgREREjsXghoiIiFwKgxuFMOWGiIjIOTC4ISIiIpfC4IaIiIhcCoMbhTGdmIiIyLEY3BAREZFLYXCjEA7iR0RE5BwY3BAREZFLYXBDRERELoXBjcI4QDEREZFjMbghIiIil8LgRiECxygmIiJyCgxuiIiIyKUwuFEck26IiIgcicENERERuRQGN0RERORSGNwohCMUExEROQcGN0RERORSGNwojIP4ERERORaDGyIiInIpDG4UwpwbIiIi5+Dw4Gb+/PmIioqCj48P4uLikJaWZnTdI0eOYMCAAYiKioJKpcLcuXPtV1AiIiKqEhwa3CxduhRJSUmYNm0a9u7di3bt2iExMRHZ2dmi69+4cQONGzfGzJkzER4ebufSSsOUGyIiIsdyaHAze/ZsDB8+HMOGDUOrVq2wYMEC+Pn5YeHChaLrd+rUCbNmzcKTTz4Jb29vSfsoLi5Gfn6+zj8iIiJyXQ4LbkpKSrBnzx4kJCRUFsbNDQkJCdixY4di+0lJSUFgYKDmX2RkpGLbJiIiIufjsOAmNzcXZWVlCAsL01keFhaGzMxMxfaTnJyMvLw8zb/z588rtm1tnBWciIjIOXg4ugC25u3tLbkJi4iIiKo+h9XchISEwN3dHVlZWTrLs7KynDZZWAoO4kdERORYDgtuvLy8EBMTg9TUVM0ytVqN1NRUxMfHO6pYREREVMU5tFkqKSkJQ4cORWxsLDp37oy5c+eiqKgIw4YNAwAMGTIEERERSElJAVCehPzff/9p/v/ixYvYv38//P390bRpU4d9DiIiInIeDg1uBg4ciJycHEydOhWZmZlo3749NmzYoEkyzsjIgJtbZeXSpUuX0KFDB83fH374IT788EP06NEDW7dutXfxdXCEYiIiIufg8ITiMWPGYMyYMaKv6QcsUVFREBhFEBERkQkOn37B1ag4RjEREZFDMbghIiIil8LghoiIiFwKgxsiIiJyKQxuFMZB/IiIiByLwQ0RERG5FAY3RERE5FIY3CiEw+8QERE5BwY3RERE5FIY3CiM+cRERESOxeCGiIiIXAqDGyIiInIpDG4UIoAZxURERM6AwQ0RERG5FAY3ClNxiGIiIiKHYnBDRERELoXBDREREbkUBjcK4QjFREREzoHBDREREbkUBjdERETkUhjcEBERkUthcENEREQuhcGNQphPTERE5BwY3CiMY/gRERE5FoMbIiIicikMboiIiMilMLghIiIil8LgRiEChygmIiJyCgxuFMaEYiIiIsdicENEREQuhcENERERuRQGNwphxg0REZFzcIrgZv78+YiKioKPjw/i4uKQlpZmcv3ly5ejZcuW8PHxQXR0NNatW2enkpqnApNuiIiIHMnhwc3SpUuRlJSEadOmYe/evWjXrh0SExORnZ0tuv4///yDQYMG4fnnn8e+ffvQv39/9O/fH4cPH7ZzyYmIiMgZqQQH92GOi4tDp06dMG/ePACAWq1GZGQkxo4di4kTJxqsP3DgQBQVFeG3337TLLv77rvRvn17LFiwwGD94uJiFBcXa/7Oz89HZGQk8vLyEBAQoNjn2JtxDY9+9g8a1PLDX6/3Umy7REREVH7/DgwMlHT/dmjNTUlJCfbs2YOEhATNMjc3NyQkJGDHjh2i79mxY4fO+gCQmJhodP2UlBQEBgZq/kVGRir3AYiIiMjpODS4yc3NRVlZGcLCwnSWh4WFITMzU/Q9mZmZstZPTk5GXl6e5t/58+eVKbyesAAfjO7VBIPvbmiT7RMREZE0Ho4ugK15e3vD29vb5vuJCPLFa4ktbb4fIiIiMs2hNTchISFwd3dHVlaWzvKsrCyEh4eLvic8PFzW+kRERFS9ODS48fLyQkxMDFJTUzXL1Go1UlNTER8fL/qe+Ph4nfUBYNOmTUbXJyIiourF4c1SSUlJGDp0KGJjY9G5c2fMnTsXRUVFGDZsGABgyJAhiIiIQEpKCgBg3Lhx6NGjBz766CP07dsXS5Yswe7du/Hll1868mMQERGRk3B4cDNw4EDk5ORg6tSpyMzMRPv27bFhwwZN0nBGRgbc3CormLp06YKffvoJb775JiZNmoRmzZph9erVaNOmjaM+AhERETkRh49zY29y+skTERGRc6gy49wQERERKY3BDREREbkUBjdERETkUhjcEBERkUthcENEREQuhcENERERuRQGN0RERORSGNwQERGRS3H4CMX2VjFmYX5+voNLQkRERFJV3LeljD1c7YKbgoICAEBkZKSDS0JERERyFRQUIDAw0OQ61W76BbVajUuXLqFmzZpQqVSKbjs/Px+RkZE4f/48p3awIR5n++Bxtg8eZ/vhsbYPWx1nQRBQUFCAevXq6cw5Kaba1dy4ubmhfv36Nt1HQEAAfzh2wONsHzzO9sHjbD881vZhi+NsrsamAhOKiYiIyKUwuCEiIiKXwuBGQd7e3pg2bRq8vb0dXRSXxuNsHzzO9sHjbD881vbhDMe52iUUExERkWtjzQ0RERG5FAY3RERE5FIY3BAREZFLYXBDRERELoXBDREREbkUBjcKmT9/PqKiouDj44O4uDikpaU5ukhOLSUlBZ06dULNmjURGhqK/v374/jx4zrr3Lp1C6NHj0bt2rXh7++PAQMGICsrS2edjIwM9O3bF35+fggNDcVrr72G0tJSnXW2bt2Kjh07wtvbG02bNsXixYtt/fGc0syZM6FSqfDKK69olvEYK+fixYt45plnULt2bfj6+iI6Ohq7d+/WvC4IAqZOnYq6devC19cXCQkJOHnypM42rl69iqeffhoBAQEICgrC888/j8LCQp11Dh48iO7du8PHxweRkZH44IMP7PL5nEFZWRmmTJmCRo0awdfXF02aNME777yjM5Eij7N8f/31F/r164d69epBpVJh9erVOq/b85guX74cLVu2hI+PD6Kjo7Fu3TrLPpRAVluyZIng5eUlLFy4UDhy5IgwfPhwISgoSMjKynJ00ZxWYmKisGjRIuHw4cPC/v37hT59+ggNGjQQCgsLNeuMHDlSiIyMFFJTU4Xdu3cLd999t9ClSxfN66WlpUKbNm2EhIQEYd++fcK6deuEkJAQITk5WbPOmTNnBD8/PyEpKUn477//hE8//VRwd3cXNmzYYNfP62hpaWlCVFSU0LZtW2HcuHGa5TzGyrh69arQsGFD4dlnnxV27dolnDlzRvj999+FU6dOadaZOXOmEBgYKKxevVo4cOCA8PDDDwuNGjUSbt68qVnngQceENq1ayfs3LlT+Pvvv4WmTZsKgwYN0ryel5cnhIWFCU8//bRw+PBh4eeffxZ8fX2FL774wq6f11Hee+89oXbt2sJvv/0mnD17Vli+fLng7+8vfPzxx5p1eJzlW7dunTB58mRh5cqVAgBh1apVOq/b65hu375dcHd3Fz744APhv//+E958803B09NTOHTokOzPxOBGAZ07dxZGjx6t+busrEyoV6+ekJKS4sBSVS3Z2dkCAOHPP/8UBEEQrl+/Lnh6egrLly/XrHP06FEBgLBjxw5BEMp/kG5ubkJmZqZmnc8//1wICAgQiouLBUEQhNdff11o3bq1zr4GDhwoJCYm2vojOY2CggKhWbNmwqZNm4QePXpoghseY+W88cYbQrdu3Yy+rlarhfDwcGHWrFmaZdevXxe8vb2Fn3/+WRAEQfjvv/8EAMK///6rWWf9+vWCSqUSLl68KAiCIHz22WdCcHCw5thX7LtFixZKfySn1LdvX+G5557TWfboo48KTz/9tCAIPM5K0A9u7HlMn3jiCaFv37465YmLixNefPFF2Z+DzVJWKikpwZ49e5CQkKBZ5ubmhoSEBOzYscOBJata8vLyAAC1atUCAOzZswe3b9/WOa4tW7ZEgwYNNMd1x44diI6ORlhYmGadxMRE5Ofn48iRI5p1tLdRsU51+m5Gjx6Nvn37GhwHHmPlrFmzBrGxsXj88ccRGhqKDh064KuvvtK8fvbsWWRmZuocp8DAQMTFxekc66CgIMTGxmrWSUhIgJubG3bt2qVZ55577oGXl5dmncTERBw/fhzXrl2z9cd0uC5duiA1NRUnTpwAABw4cADbtm3Dgw8+CIDH2RbseUyVvJYwuLFSbm4uysrKdC7+ABAWFobMzEwHlapqUavVeOWVV9C1a1e0adMGAJCZmQkvLy8EBQXprKt9XDMzM0WPe8VrptbJz8/HzZs3bfFxnMqSJUuwd+9epKSkGLzGY6ycM2fO4PPPP0ezZs3w+++/Y9SoUXj55Zfx7bffAqg8VqauE5mZmQgNDdV53cPDA7Vq1ZL1fbiyiRMn4sknn0TLli3h6emJDh064JVXXsHTTz8NgMfZFux5TI2tY8kx95D9DiKFjR49GocPH8a2bdscXRSXcv78eYwbNw6bNm2Cj4+Po4vj0tRqNWJjYzFjxgwAQIcOHXD48GEsWLAAQ4cOdXDpXMeyZcvw448/4qeffkLr1q2xf/9+vPLKK6hXrx6PM+lgzY2VQkJC4O7ubtDDJCsrC+Hh4Q4qVdUxZswY/Pbbb9iyZQvq16+vWR4eHo6SkhJcv35dZ33t4xoeHi563CteM7VOQEAAfH19lf44TmXPnj3Izs5Gx44d4eHhAQ8PD/z555/45JNP4OHhgbCwMB5jhdStWxetWrXSWXbXXXchIyMDQOWxMnWdCA8PR3Z2ts7rpaWluHr1qqzvw5W99tprmtqb6OhoDB48GOPHj9fUTPI4K8+ex9TYOpYccwY3VvLy8kJMTAxSU1M1y9RqNVJTUxEfH+/Akjk3QRAwZswYrFq1Cps3b0ajRo10Xo+JiYGnp6fOcT1+/DgyMjI0xzU+Ph6HDh3S+VFt2rQJAQEBmhtNfHy8zjYq1qkO3819992HQ4cOYf/+/Zp/sbGxePrppzX/z2OsjK5duxoMZXDixAk0bNgQANCoUSOEh4frHKf8/Hzs2rVL51hfv34de/bs0ayzefNmqNVqxMXFadb566+/cPv2bc06mzZtQosWLRAcHGyzz+csbty4ATc33duWu7s71Go1AB5nW7DnMVX0WiI7BZkMLFmyRPD29hYWL14s/Pfff8KIESOEoKAgnR4mpGvUqFFCYGCgsHXrVuHy5cuafzdu3NCsM3LkSKFBgwbC5s2bhd27dwvx8fFCfHy85vWKbsr333+/sH//fmHDhg1CnTp1RLspv/baa8LRo0eF+fPnV7tuytq0e0sJAo+xUtLS0gQPDw/hvffeE06ePCn8+OOPgp+fn/DDDz9o1pk5c6YQFBQk/Prrr8LBgweF//3vf6LdaTt06CDs2rVL2LZtm9CsWTOd7rTXr18XwsLChMGDBwuHDx8WlixZIvj5+blsF2V9Q4cOFSIiIjRdwVeuXCmEhIQIr7/+umYdHmf5CgoKhH379gn79u0TAAizZ88W9u3bJ5w7d04QBPsd0+3btwseHh7Chx9+KBw9elSYNm0au4I72qeffio0aNBA8PLyEjp37izs3LnT0UVyagBE/y1atEizzs2bN4WXXnpJCA4OFvz8/IRHHnlEuHz5ss520tPThQcffFDw9fUVQkJChFdffVW4ffu2zjpbtmwR2rdvL3h5eQmNGzfW2Ud1ox/c8Bgr5//+7/+ENm3aCN7e3kLLli2FL7/8Uud1tVotTJkyRQgLCxO8vb2F++67Tzh+/LjOOleuXBEGDRok+Pv7CwEBAcKwYcOEgoICnXUOHDggdOvWTfD29hYiIiKEmTNn2vyzOYv8/Hxh3LhxQoMGDQQfHx+hcePGwuTJk3W6F/M4y7dlyxbR6/HQoUMFQbDvMV22bJnQvHlzwcvLS2jdurWwdu1aiz6TShC0hnYkIiIiquKYc0NEREQuhcENERERuRQGN0RERORSGNwQERGRS2FwQ0RERC6FwQ0RERG5FAY3RERE5FIY3BAREZFLYXBDRERELoXBDREREbkUBjdERETkUv4fQmpbS+5IZXYAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -12681,7 +18412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "id": "21cfeacd-dcfa-485f-848e-88cb619addfc",
    "metadata": {
     "scrolled": true
@@ -12718,67 +18449,67 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.2822</td>\n",
+       "      <td>0.3272</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.3666</td>\n",
+       "      <td>0.4626</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.4510</td>\n",
+       "      <td>0.4626</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.5058</td>\n",
+       "      <td>0.5080</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9995</th>\n",
-       "      <td>0.6358</td>\n",
+       "      <th>5550</th>\n",
+       "      <td>0.6930</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9996</th>\n",
-       "      <td>0.6332</td>\n",
+       "      <th>5551</th>\n",
+       "      <td>0.6732</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9997</th>\n",
-       "      <td>0.6128</td>\n",
+       "      <th>5552</th>\n",
+       "      <td>0.6606</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9998</th>\n",
-       "      <td>0.6314</td>\n",
+       "      <th>5553</th>\n",
+       "      <td>0.6368</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9999</th>\n",
-       "      <td>0.6778</td>\n",
+       "      <th>5554</th>\n",
+       "      <td>0.6510</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>10000 rows × 1 columns</p>\n",
+       "<p>5555 rows × 1 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "        Gini\n",
        "0     0.0000\n",
-       "1     0.2822\n",
-       "2     0.3666\n",
-       "3     0.4510\n",
-       "4     0.5058\n",
+       "1     0.3272\n",
+       "2     0.4626\n",
+       "3     0.4626\n",
+       "4     0.5080\n",
        "...      ...\n",
-       "9995  0.6358\n",
-       "9996  0.6332\n",
-       "9997  0.6128\n",
-       "9998  0.6314\n",
-       "9999  0.6778\n",
+       "5550  0.6930\n",
+       "5551  0.6732\n",
+       "5552  0.6606\n",
+       "5553  0.6368\n",
+       "5554  0.6510\n",
        "\n",
-       "[10000 rows x 1 columns]"
+       "[5555 rows x 1 columns]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -12789,115 +18520,89 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "421bd10b-33a6-470e-bcc1-0fc0d840062d",
-   "metadata": {},
+   "execution_count": 15,
+   "id": "8c967f5e-4b91-44b8-b3f5-ab6874d0e28f",
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
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-       "      <th>103</th>\n",
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-       "<p>105 rows × 1 columns</p>\n",
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-       "       Gini\n",
-       "0    0.0000\n",
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-       "2    0.3666\n",
-       "3    0.4510\n",
-       "4    0.5058\n",
-       "..      ...\n",
-       "100  0.6794\n",
-       "101  0.6842\n",
-       "102  0.6756\n",
-       "103  0.6532\n",
-       "104  0.6452\n",
-       "\n",
-       "[105 rows x 1 columns]"
+       "['test_path/model_data_001.parquet',\n",
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+       " 'test_path/model_data_032.parquet',\n",
+       " 'test_path/model_data_033.parquet',\n",
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+       " 'test_path/model_data_035.parquet',\n",
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+       " 'test_path/model_data_052.parquet',\n",
+       " 'test_path/model_data_053.parquet',\n",
+       " 'test_path/model_data_054.parquet',\n",
+       " 'test_path/model_data_055.parquet',\n",
+       " 'test_path/model_data_056.parquet']"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "model_df[:105]"
+    "model_files"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "1473d070-d9ca-4c0f-913e-107f9fa48b46",
-   "metadata": {},
+   "execution_count": 16,
+   "id": "ea25fe67-a4fb-432c-8592-2a6b73dc28f0",
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
@@ -12930,79 +18635,79 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.2822</td>\n",
+       "      <td>0.3272</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.3666</td>\n",
+       "      <td>0.4626</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.4510</td>\n",
+       "      <td>0.4626</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.5058</td>\n",
+       "      <td>0.5080</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>0.6582</td>\n",
+       "      <th>5550</th>\n",
+       "      <td>0.6930</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>0.6362</td>\n",
+       "      <th>5551</th>\n",
+       "      <td>0.6732</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>98</th>\n",
-       "      <td>0.6440</td>\n",
+       "      <th>5552</th>\n",
+       "      <td>0.6606</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>99</th>\n",
-       "      <td>0.6602</td>\n",
+       "      <th>5553</th>\n",
+       "      <td>0.6368</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <td>0.6794</td>\n",
+       "      <th>5554</th>\n",
+       "      <td>0.6510</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>101 rows × 1 columns</p>\n",
+       "<p>5555 rows × 1 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "       Gini\n",
-       "0    0.0000\n",
-       "1    0.2822\n",
-       "2    0.3666\n",
-       "3    0.4510\n",
-       "4    0.5058\n",
-       "..      ...\n",
-       "96   0.6582\n",
-       "97   0.6362\n",
-       "98   0.6440\n",
-       "99   0.6602\n",
-       "100  0.6794\n",
+       "        Gini\n",
+       "0     0.0000\n",
+       "1     0.3272\n",
+       "2     0.4626\n",
+       "3     0.4626\n",
+       "4     0.5080\n",
+       "...      ...\n",
+       "5550  0.6930\n",
+       "5551  0.6732\n",
+       "5552  0.6606\n",
+       "5553  0.6368\n",
+       "5554  0.6510\n",
        "\n",
-       "[101 rows x 1 columns]"
+       "[5555 rows x 1 columns]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "gini[:101]"
+    "model_df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "8c967f5e-4b91-44b8-b3f5-ab6874d0e28f",
+   "execution_count": 17,
+   "id": "50f26db2-06e0-49a1-996e-53e71a62ecbd",
    "metadata": {
     "scrolled": true
    },
@@ -13010,124 +18715,24 @@
     {
      "data": {
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-       " 'output_dir/model_data_027.parquet',\n",
-       " 'output_dir/model_data_028.parquet',\n",
-       " 'output_dir/model_data_029.parquet',\n",
-       " 'output_dir/model_data_030.parquet',\n",
-       " 'output_dir/model_data_031.parquet',\n",
-       " 'output_dir/model_data_032.parquet',\n",
-       " 'output_dir/model_data_033.parquet',\n",
-       " 'output_dir/model_data_034.parquet',\n",
-       " 'output_dir/model_data_035.parquet',\n",
-       " 'output_dir/model_data_036.parquet',\n",
-       " 'output_dir/model_data_037.parquet',\n",
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-       " 'output_dir/model_data_047.parquet',\n",
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-       " 'output_dir/model_data_067.parquet',\n",
-       " 'output_dir/model_data_068.parquet',\n",
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-       " 'output_dir/model_data_070.parquet',\n",
-       " 'output_dir/model_data_071.parquet',\n",
-       " 'output_dir/model_data_072.parquet',\n",
-       " 'output_dir/model_data_073.parquet',\n",
-       " 'output_dir/model_data_074.parquet',\n",
-       " 'output_dir/model_data_075.parquet',\n",
-       " 'output_dir/model_data_076.parquet',\n",
-       " 'output_dir/model_data_077.parquet',\n",
-       " 'output_dir/model_data_078.parquet',\n",
-       " 'output_dir/model_data_079.parquet',\n",
-       " 'output_dir/model_data_080.parquet',\n",
-       " 'output_dir/model_data_081.parquet',\n",
-       " 'output_dir/model_data_082.parquet',\n",
-       " 'output_dir/model_data_083.parquet',\n",
-       " 'output_dir/model_data_084.parquet',\n",
-       " 'output_dir/model_data_085.parquet',\n",
-       " 'output_dir/model_data_086.parquet',\n",
-       " 'output_dir/model_data_087.parquet',\n",
-       " 'output_dir/model_data_088.parquet',\n",
-       " 'output_dir/model_data_089.parquet',\n",
-       " 'output_dir/model_data_090.parquet',\n",
-       " 'output_dir/model_data_091.parquet',\n",
-       " 'output_dir/model_data_092.parquet',\n",
-       " 'output_dir/model_data_093.parquet',\n",
-       " 'output_dir/model_data_094.parquet',\n",
-       " 'output_dir/model_data_095.parquet',\n",
-       " 'output_dir/model_data_096.parquet',\n",
-       " 'output_dir/model_data_097.parquet',\n",
-       " 'output_dir/model_data_098.parquet',\n",
-       " 'output_dir/model_data_099.parquet',\n",
-       " 'output_dir/model_data_100.parquet']"
+       "Gini    5555\n",
+       "dtype: int64"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "model_files"
+    "(gini == model_df).sum()\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "ea25fe67-a4fb-432c-8592-2a6b73dc28f0",
-   "metadata": {
-    "scrolled": true
-   },
+   "execution_count": 18,
+   "id": "1ac6d4b7-b74f-41e8-82eb-368d77355d91",
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -13151,199 +18756,943 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gini</th>\n",
+       "      <th>Step</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.0000</td>\n",
+       "      <td>0.7046</td>\n",
+       "      <td>42</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.2822</td>\n",
+       "      <td>0.7010</td>\n",
+       "      <td>43</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.3666</td>\n",
+       "      <td>0.7056</td>\n",
+       "      <td>44</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.4510</td>\n",
+       "      <td>0.7204</td>\n",
+       "      <td>45</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.5058</td>\n",
+       "      <td>0.7092</td>\n",
+       "      <td>46</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9995</th>\n",
-       "      <td>0.6358</td>\n",
+       "      <th>463</th>\n",
+       "      <td>0.7016</td>\n",
+       "      <td>5359</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9996</th>\n",
-       "      <td>0.6332</td>\n",
+       "      <th>464</th>\n",
+       "      <td>0.7080</td>\n",
+       "      <td>5365</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9997</th>\n",
-       "      <td>0.6128</td>\n",
+       "      <th>465</th>\n",
+       "      <td>0.7112</td>\n",
+       "      <td>5366</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9998</th>\n",
-       "      <td>0.6314</td>\n",
+       "      <th>466</th>\n",
+       "      <td>0.7032</td>\n",
+       "      <td>5457</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9999</th>\n",
-       "      <td>0.6778</td>\n",
+       "      <th>467</th>\n",
+       "      <td>0.7094</td>\n",
+       "      <td>5458</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>10000 rows × 1 columns</p>\n",
+       "<p>468 rows × 2 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "        Gini\n",
-       "0     0.0000\n",
-       "1     0.2822\n",
-       "2     0.3666\n",
-       "3     0.4510\n",
-       "4     0.5058\n",
-       "...      ...\n",
-       "9995  0.6358\n",
-       "9996  0.6332\n",
-       "9997  0.6128\n",
-       "9998  0.6314\n",
-       "9999  0.6778\n",
+       "       Gini  Step\n",
+       "0    0.7046    42\n",
+       "1    0.7010    43\n",
+       "2    0.7056    44\n",
+       "3    0.7204    45\n",
+       "4    0.7092    46\n",
+       "..      ...   ...\n",
+       "463  0.7016  5359\n",
+       "464  0.7080  5365\n",
+       "465  0.7112  5366\n",
+       "466  0.7032  5457\n",
+       "467  0.7094  5458\n",
        "\n",
-       "[10000 rows x 1 columns]"
+       "[468 rows x 2 columns]"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "model_df"
+    "table = pq.read_table(\"test_path/special_results.parquet\")\n",
+    "df = table.to_pandas()\n",
+    "df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "50f26db2-06e0-49a1-996e-53e71a62ecbd",
-   "metadata": {
-    "scrolled": true
-   },
+   "execution_count": 19,
+   "id": "e6bc2d5c-d1a5-4316-9aeb-1fdf75b0b8f1",
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/html": [
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-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "        vertical-align: top;\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Gini</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.0000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.2822</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.3666</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.4510</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.5058</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9995</th>\n",
-       "      <td>0.6358</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9996</th>\n",
-       "      <td>0.6332</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9997</th>\n",
-       "      <td>0.6128</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9998</th>\n",
-       "      <td>0.6314</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9999</th>\n",
-       "      <td>0.6778</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>10000 rows × 1 columns</p>\n",
-       "</div>"
-      ],
       "text/plain": [
+       "[      Gini\n",
+       " 0   0.0000\n",
+       " 1   0.3272\n",
+       " 2   0.4626\n",
+       " 3   0.4626\n",
+       " 4   0.5080\n",
+       " ..     ...\n",
+       " 95  0.6754\n",
+       " 96  0.6764\n",
+       " 97  0.6834\n",
+       " 98  0.6582\n",
+       " 99  0.6464\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
        "        Gini\n",
-       "0     0.0000\n",
-       "1     0.2822\n",
-       "2     0.3666\n",
-       "3     0.4510\n",
-       "4     0.5058\n",
-       "...      ...\n",
-       "9995  0.6358\n",
-       "9996  0.6332\n",
-       "9997  0.6128\n",
-       "9998  0.6314\n",
-       "9999  0.6778\n",
-       "\n",
-       "[10000 rows x 1 columns]"
+       " 100  0.6650\n",
+       " 101  0.6506\n",
+       " 102  0.6734\n",
+       " 103  0.6656\n",
+       " 104  0.6450\n",
+       " ..      ...\n",
+       " 195  0.6086\n",
+       " 196  0.6014\n",
+       " 197  0.6130\n",
+       " 198  0.6184\n",
+       " 199  0.6284\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "        Gini\n",
+       " 200  0.6256\n",
+       " 201  0.6086\n",
+       " 202  0.6250\n",
+       " 203  0.6060\n",
+       " 204  0.6240\n",
+       " ..      ...\n",
+       " 295  0.6338\n",
+       " 296  0.6420\n",
+       " 297  0.6716\n",
+       " 298  0.6904\n",
+       " 299  0.6808\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "        Gini\n",
+       " 300  0.6764\n",
+       " 301  0.6888\n",
+       " 302  0.6702\n",
+       " 303  0.6726\n",
+       " 304  0.6638\n",
+       " ..      ...\n",
+       " 395  0.6800\n",
+       " 396  0.6448\n",
+       " 397  0.6418\n",
+       " 398  0.6556\n",
+       " 399  0.6790\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "        Gini\n",
+       " 400  0.6890\n",
+       " 401  0.6862\n",
+       " 402  0.6704\n",
+       " 403  0.6486\n",
+       " 404  0.6194\n",
+       " ..      ...\n",
+       " 495  0.7084\n",
+       " 496  0.6936\n",
+       " 497  0.6788\n",
+       " 498  0.6916\n",
+       " 499  0.6602\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "        Gini\n",
+       " 500  0.6610\n",
+       " 501  0.6544\n",
+       " 502  0.6406\n",
+       " 503  0.6584\n",
+       " 504  0.6760\n",
+       " ..      ...\n",
+       " 595  0.6540\n",
+       " 596  0.6926\n",
+       " 597  0.6836\n",
+       " 598  0.7032\n",
+       " 599  0.6522\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "        Gini\n",
+       " 600  0.6476\n",
+       " 601  0.6418\n",
+       " 602  0.6836\n",
+       " 603  0.6526\n",
+       " 604  0.6448\n",
+       " ..      ...\n",
+       " 695  0.6056\n",
+       " 696  0.6456\n",
+       " 697  0.6458\n",
+       " 698  0.6538\n",
+       " 699  0.6572\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "        Gini\n",
+       " 700  0.6500\n",
+       " 701  0.6500\n",
+       " 702  0.6402\n",
+       " 703  0.6274\n",
+       " 704  0.6596\n",
+       " ..      ...\n",
+       " 795  0.6366\n",
+       " 796  0.6130\n",
+       " 797  0.6186\n",
+       " 798  0.6078\n",
+       " 799  0.6270\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "        Gini\n",
+       " 800  0.6176\n",
+       " 801  0.6202\n",
+       " 802  0.6210\n",
+       " 803  0.6514\n",
+       " 804  0.6578\n",
+       " ..      ...\n",
+       " 895  0.6924\n",
+       " 896  0.6670\n",
+       " 897  0.7064\n",
+       " 898  0.7068\n",
+       " 899  0.6792\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "        Gini\n",
+       " 900  0.7038\n",
+       " 901  0.6784\n",
+       " 902  0.6872\n",
+       " 903  0.6596\n",
+       " 904  0.6486\n",
+       " ..      ...\n",
+       " 995  0.6922\n",
+       " 996  0.6850\n",
+       " 997  0.6806\n",
+       " 998  0.6510\n",
+       " 999  0.6530\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 1000  0.6574\n",
+       " 1001  0.6482\n",
+       " 1002  0.6254\n",
+       " 1003  0.6316\n",
+       " 1004  0.6486\n",
+       " ...      ...\n",
+       " 1095  0.6820\n",
+       " 1096  0.6684\n",
+       " 1097  0.6692\n",
+       " 1098  0.6672\n",
+       " 1099  0.6878\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 1100  0.6744\n",
+       " 1101  0.6722\n",
+       " 1102  0.6566\n",
+       " 1103  0.6402\n",
+       " 1104  0.6434\n",
+       " ...      ...\n",
+       " 1195  0.6562\n",
+       " 1196  0.6602\n",
+       " 1197  0.6420\n",
+       " 1198  0.6554\n",
+       " 1199  0.6302\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 1200  0.6602\n",
+       " 1201  0.6498\n",
+       " 1202  0.6450\n",
+       " 1203  0.6416\n",
+       " 1204  0.6194\n",
+       " ...      ...\n",
+       " 1295  0.6176\n",
+       " 1296  0.6446\n",
+       " 1297  0.6462\n",
+       " 1298  0.6282\n",
+       " 1299  0.6144\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 1300  0.5794\n",
+       " 1301  0.5938\n",
+       " 1302  0.6288\n",
+       " 1303  0.6124\n",
+       " 1304  0.6046\n",
+       " ...      ...\n",
+       " 1395  0.6232\n",
+       " 1396  0.6070\n",
+       " 1397  0.6194\n",
+       " 1398  0.6392\n",
+       " 1399  0.6406\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 1400  0.6554\n",
+       " 1401  0.6462\n",
+       " 1402  0.6474\n",
+       " 1403  0.6526\n",
+       " 1404  0.6426\n",
+       " ...      ...\n",
+       " 1495  0.5990\n",
+       " 1496  0.5984\n",
+       " 1497  0.5858\n",
+       " 1498  0.5858\n",
+       " 1499  0.5856\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 1500  0.6012\n",
+       " 1501  0.6192\n",
+       " 1502  0.6602\n",
+       " 1503  0.6812\n",
+       " 1504  0.6632\n",
+       " ...      ...\n",
+       " 1595  0.6454\n",
+       " 1596  0.6582\n",
+       " 1597  0.6726\n",
+       " 1598  0.6318\n",
+       " 1599  0.6750\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 1600  0.6614\n",
+       " 1601  0.6834\n",
+       " 1602  0.6880\n",
+       " 1603  0.6752\n",
+       " 1604  0.6766\n",
+       " ...      ...\n",
+       " 1695  0.6726\n",
+       " 1696  0.6748\n",
+       " 1697  0.6706\n",
+       " 1698  0.6786\n",
+       " 1699  0.6730\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 1700  0.6658\n",
+       " 1701  0.6540\n",
+       " 1702  0.6530\n",
+       " 1703  0.6748\n",
+       " 1704  0.6738\n",
+       " ...      ...\n",
+       " 1795  0.6408\n",
+       " 1796  0.6602\n",
+       " 1797  0.6676\n",
+       " 1798  0.6610\n",
+       " 1799  0.6514\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 1800  0.6714\n",
+       " 1801  0.6408\n",
+       " 1802  0.6296\n",
+       " 1803  0.6528\n",
+       " 1804  0.6374\n",
+       " ...      ...\n",
+       " 1895  0.6882\n",
+       " 1896  0.6914\n",
+       " 1897  0.6844\n",
+       " 1898  0.6692\n",
+       " 1899  0.6628\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 1900  0.6570\n",
+       " 1901  0.6568\n",
+       " 1902  0.6496\n",
+       " 1903  0.6518\n",
+       " 1904  0.6396\n",
+       " ...      ...\n",
+       " 1995  0.6664\n",
+       " 1996  0.6378\n",
+       " 1997  0.6748\n",
+       " 1998  0.6704\n",
+       " 1999  0.7110\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 2000  0.6892\n",
+       " 2001  0.7076\n",
+       " 2002  0.7010\n",
+       " 2003  0.6986\n",
+       " 2004  0.7022\n",
+       " ...      ...\n",
+       " 2095  0.6136\n",
+       " 2096  0.6152\n",
+       " 2097  0.6226\n",
+       " 2098  0.6160\n",
+       " 2099  0.6212\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 2100  0.6290\n",
+       " 2101  0.6408\n",
+       " 2102  0.6448\n",
+       " 2103  0.6540\n",
+       " 2104  0.6150\n",
+       " ...      ...\n",
+       " 2195  0.6788\n",
+       " 2196  0.6870\n",
+       " 2197  0.7068\n",
+       " 2198  0.6942\n",
+       " 2199  0.6918\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 2200  0.6854\n",
+       " 2201  0.6870\n",
+       " 2202  0.6954\n",
+       " 2203  0.6740\n",
+       " 2204  0.6710\n",
+       " ...      ...\n",
+       " 2295  0.6338\n",
+       " 2296  0.6366\n",
+       " 2297  0.6246\n",
+       " 2298  0.6198\n",
+       " 2299  0.6274\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 2300  0.6312\n",
+       " 2301  0.6636\n",
+       " 2302  0.6456\n",
+       " 2303  0.6352\n",
+       " 2304  0.6234\n",
+       " ...      ...\n",
+       " 2395  0.6728\n",
+       " 2396  0.6616\n",
+       " 2397  0.6836\n",
+       " 2398  0.6586\n",
+       " 2399  0.6722\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 2400  0.6744\n",
+       " 2401  0.6704\n",
+       " 2402  0.6748\n",
+       " 2403  0.6946\n",
+       " 2404  0.6826\n",
+       " ...      ...\n",
+       " 2495  0.6406\n",
+       " 2496  0.6262\n",
+       " 2497  0.6274\n",
+       " 2498  0.6262\n",
+       " 2499  0.6760\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 2500  0.6766\n",
+       " 2501  0.6570\n",
+       " 2502  0.6582\n",
+       " 2503  0.6530\n",
+       " 2504  0.6582\n",
+       " ...      ...\n",
+       " 2595  0.6860\n",
+       " 2596  0.7074\n",
+       " 2597  0.6884\n",
+       " 2598  0.6714\n",
+       " 2599  0.6552\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 2600  0.6246\n",
+       " 2601  0.6338\n",
+       " 2602  0.6050\n",
+       " 2603  0.6116\n",
+       " 2604  0.6238\n",
+       " ...      ...\n",
+       " 2695  0.5864\n",
+       " 2696  0.6312\n",
+       " 2697  0.6082\n",
+       " 2698  0.5956\n",
+       " 2699  0.6114\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 2700  0.6156\n",
+       " 2701  0.6080\n",
+       " 2702  0.6272\n",
+       " 2703  0.6298\n",
+       " 2704  0.6332\n",
+       " ...      ...\n",
+       " 2795  0.6548\n",
+       " 2796  0.6616\n",
+       " 2797  0.6782\n",
+       " 2798  0.6828\n",
+       " 2799  0.6620\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 2800  0.6664\n",
+       " 2801  0.6636\n",
+       " 2802  0.6406\n",
+       " 2803  0.6434\n",
+       " 2804  0.6484\n",
+       " ...      ...\n",
+       " 2895  0.7020\n",
+       " 2896  0.7060\n",
+       " 2897  0.7158\n",
+       " 2898  0.7270\n",
+       " 2899  0.7020\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 2900  0.6750\n",
+       " 2901  0.6644\n",
+       " 2902  0.6452\n",
+       " 2903  0.6470\n",
+       " 2904  0.6428\n",
+       " ...      ...\n",
+       " 2995  0.6776\n",
+       " 2996  0.6758\n",
+       " 2997  0.6648\n",
+       " 2998  0.6634\n",
+       " 2999  0.6394\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 3000  0.6186\n",
+       " 3001  0.6182\n",
+       " 3002  0.6096\n",
+       " 3003  0.6280\n",
+       " 3004  0.6492\n",
+       " ...      ...\n",
+       " 3095  0.6224\n",
+       " 3096  0.5896\n",
+       " 3097  0.6138\n",
+       " 3098  0.6548\n",
+       " 3099  0.6348\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 3100  0.6176\n",
+       " 3101  0.6246\n",
+       " 3102  0.6066\n",
+       " 3103  0.6264\n",
+       " 3104  0.6182\n",
+       " ...      ...\n",
+       " 3195  0.6356\n",
+       " 3196  0.6256\n",
+       " 3197  0.6416\n",
+       " 3198  0.6512\n",
+       " 3199  0.5912\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 3200  0.6280\n",
+       " 3201  0.6370\n",
+       " 3202  0.6604\n",
+       " 3203  0.6340\n",
+       " 3204  0.6162\n",
+       " ...      ...\n",
+       " 3295  0.6514\n",
+       " 3296  0.6618\n",
+       " 3297  0.6632\n",
+       " 3298  0.6654\n",
+       " 3299  0.6692\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 3300  0.6642\n",
+       " 3301  0.7062\n",
+       " 3302  0.7280\n",
+       " 3303  0.7180\n",
+       " 3304  0.7168\n",
+       " ...      ...\n",
+       " 3395  0.5936\n",
+       " 3396  0.6238\n",
+       " 3397  0.6232\n",
+       " 3398  0.6546\n",
+       " 3399  0.6640\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 3400  0.6626\n",
+       " 3401  0.6538\n",
+       " 3402  0.6572\n",
+       " 3403  0.6492\n",
+       " 3404  0.6386\n",
+       " ...      ...\n",
+       " 3495  0.6898\n",
+       " 3496  0.6606\n",
+       " 3497  0.6576\n",
+       " 3498  0.6604\n",
+       " 3499  0.6790\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 3500  0.7090\n",
+       " 3501  0.7062\n",
+       " 3502  0.7120\n",
+       " 3503  0.6938\n",
+       " 3504  0.7054\n",
+       " ...      ...\n",
+       " 3595  0.6466\n",
+       " 3596  0.6730\n",
+       " 3597  0.6938\n",
+       " 3598  0.6770\n",
+       " 3599  0.6718\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 3600  0.6856\n",
+       " 3601  0.6770\n",
+       " 3602  0.6754\n",
+       " 3603  0.6688\n",
+       " 3604  0.6642\n",
+       " ...      ...\n",
+       " 3695  0.6432\n",
+       " 3696  0.6644\n",
+       " 3697  0.6624\n",
+       " 3698  0.6758\n",
+       " 3699  0.6884\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 3700  0.6858\n",
+       " 3701  0.6748\n",
+       " 3702  0.6760\n",
+       " 3703  0.6604\n",
+       " 3704  0.6464\n",
+       " ...      ...\n",
+       " 3795  0.6304\n",
+       " 3796  0.6240\n",
+       " 3797  0.6180\n",
+       " 3798  0.5934\n",
+       " 3799  0.5876\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 3800  0.6190\n",
+       " 3801  0.6318\n",
+       " 3802  0.6034\n",
+       " 3803  0.6322\n",
+       " 3804  0.6234\n",
+       " ...      ...\n",
+       " 3895  0.6600\n",
+       " 3896  0.6490\n",
+       " 3897  0.6236\n",
+       " 3898  0.6242\n",
+       " 3899  0.6214\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 3900  0.5982\n",
+       " 3901  0.5790\n",
+       " 3902  0.5786\n",
+       " 3903  0.5740\n",
+       " 3904  0.5940\n",
+       " ...      ...\n",
+       " 3995  0.6608\n",
+       " 3996  0.6700\n",
+       " 3997  0.6604\n",
+       " 3998  0.6602\n",
+       " 3999  0.6558\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 4000  0.6534\n",
+       " 4001  0.6180\n",
+       " 4002  0.6200\n",
+       " 4003  0.6150\n",
+       " 4004  0.6076\n",
+       " ...      ...\n",
+       " 4095  0.6546\n",
+       " 4096  0.6422\n",
+       " 4097  0.6564\n",
+       " 4098  0.6382\n",
+       " 4099  0.6618\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 4100  0.6626\n",
+       " 4101  0.6690\n",
+       " 4102  0.6628\n",
+       " 4103  0.6602\n",
+       " 4104  0.6462\n",
+       " ...      ...\n",
+       " 4195  0.7122\n",
+       " 4196  0.7070\n",
+       " 4197  0.7290\n",
+       " 4198  0.7230\n",
+       " 4199  0.7214\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 4200  0.7042\n",
+       " 4201  0.7142\n",
+       " 4202  0.7242\n",
+       " 4203  0.6922\n",
+       " 4204  0.6664\n",
+       " ...      ...\n",
+       " 4295  0.6962\n",
+       " 4296  0.6776\n",
+       " 4297  0.6608\n",
+       " 4298  0.6378\n",
+       " 4299  0.6044\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 4300  0.6328\n",
+       " 4301  0.6124\n",
+       " 4302  0.6110\n",
+       " 4303  0.6012\n",
+       " 4304  0.5772\n",
+       " ...      ...\n",
+       " 4395  0.6734\n",
+       " 4396  0.6730\n",
+       " 4397  0.6712\n",
+       " 4398  0.6700\n",
+       " 4399  0.6712\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 4400  0.6628\n",
+       " 4401  0.6640\n",
+       " 4402  0.6932\n",
+       " 4403  0.7020\n",
+       " 4404  0.6788\n",
+       " ...      ...\n",
+       " 4495  0.6592\n",
+       " 4496  0.6544\n",
+       " 4497  0.6496\n",
+       " 4498  0.6482\n",
+       " 4499  0.6526\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 4500  0.6476\n",
+       " 4501  0.6484\n",
+       " 4502  0.6642\n",
+       " 4503  0.6734\n",
+       " 4504  0.6740\n",
+       " ...      ...\n",
+       " 4595  0.6558\n",
+       " 4596  0.6378\n",
+       " 4597  0.6602\n",
+       " 4598  0.6638\n",
+       " 4599  0.6902\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 4600  0.7034\n",
+       " 4601  0.7354\n",
+       " 4602  0.7234\n",
+       " 4603  0.6974\n",
+       " 4604  0.6846\n",
+       " ...      ...\n",
+       " 4695  0.5686\n",
+       " 4696  0.5356\n",
+       " 4697  0.5692\n",
+       " 4698  0.5406\n",
+       " 4699  0.5570\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 4700  0.5552\n",
+       " 4701  0.5780\n",
+       " 4702  0.5768\n",
+       " 4703  0.5990\n",
+       " 4704  0.6172\n",
+       " ...      ...\n",
+       " 4795  0.6620\n",
+       " 4796  0.6856\n",
+       " 4797  0.6602\n",
+       " 4798  0.6508\n",
+       " 4799  0.6674\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 4800  0.6742\n",
+       " 4801  0.6914\n",
+       " 4802  0.6712\n",
+       " 4803  0.6592\n",
+       " 4804  0.6118\n",
+       " ...      ...\n",
+       " 4895  0.6104\n",
+       " 4896  0.6074\n",
+       " 4897  0.6334\n",
+       " 4898  0.6054\n",
+       " 4899  0.6306\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 4900  0.6250\n",
+       " 4901  0.6354\n",
+       " 4902  0.6380\n",
+       " 4903  0.6444\n",
+       " 4904  0.6386\n",
+       " ...      ...\n",
+       " 4995  0.6632\n",
+       " 4996  0.6708\n",
+       " 4997  0.6530\n",
+       " 4998  0.5910\n",
+       " 4999  0.6036\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 5000  0.6046\n",
+       " 5001  0.6226\n",
+       " 5002  0.6248\n",
+       " 5003  0.6382\n",
+       " 5004  0.6644\n",
+       " ...      ...\n",
+       " 5095  0.6348\n",
+       " 5096  0.6528\n",
+       " 5097  0.6500\n",
+       " 5098  0.6350\n",
+       " 5099  0.6402\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 5100  0.6580\n",
+       " 5101  0.6764\n",
+       " 5102  0.6952\n",
+       " 5103  0.6768\n",
+       " 5104  0.6866\n",
+       " ...      ...\n",
+       " 5195  0.6478\n",
+       " 5196  0.6548\n",
+       " 5197  0.6696\n",
+       " 5198  0.6844\n",
+       " 5199  0.6708\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 5200  0.6790\n",
+       " 5201  0.6850\n",
+       " 5202  0.6684\n",
+       " 5203  0.6638\n",
+       " 5204  0.6530\n",
+       " ...      ...\n",
+       " 5295  0.6470\n",
+       " 5296  0.6278\n",
+       " 5297  0.6398\n",
+       " 5298  0.6326\n",
+       " 5299  0.6286\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 5300  0.6302\n",
+       " 5301  0.6332\n",
+       " 5302  0.6460\n",
+       " 5303  0.6546\n",
+       " 5304  0.6880\n",
+       " ...      ...\n",
+       " 5395  0.6404\n",
+       " 5396  0.6364\n",
+       " 5397  0.6318\n",
+       " 5398  0.6246\n",
+       " 5399  0.6476\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 5400  0.6206\n",
+       " 5401  0.6526\n",
+       " 5402  0.6368\n",
+       " 5403  0.6778\n",
+       " 5404  0.6670\n",
+       " ...      ...\n",
+       " 5495  0.6760\n",
+       " 5496  0.6750\n",
+       " 5497  0.6826\n",
+       " 5498  0.6856\n",
+       " 5499  0.6806\n",
+       " \n",
+       " [100 rows x 1 columns],\n",
+       "         Gini\n",
+       " 5500  0.6888\n",
+       " 5501  0.6708\n",
+       " 5502  0.6882\n",
+       " 5503  0.6648\n",
+       " 5504  0.6682\n",
+       " 5505  0.6570\n",
+       " 5506  0.6442\n",
+       " 5507  0.6500\n",
+       " 5508  0.6498\n",
+       " 5509  0.6366\n",
+       " 5510  0.6336\n",
+       " 5511  0.6252\n",
+       " 5512  0.6150\n",
+       " 5513  0.6150\n",
+       " 5514  0.6412\n",
+       " 5515  0.6214\n",
+       " 5516  0.6256\n",
+       " 5517  0.6322\n",
+       " 5518  0.6560\n",
+       " 5519  0.6762\n",
+       " 5520  0.6844\n",
+       " 5521  0.6694\n",
+       " 5522  0.6668\n",
+       " 5523  0.6752\n",
+       " 5524  0.6574\n",
+       " 5525  0.6632\n",
+       " 5526  0.6688\n",
+       " 5527  0.6732\n",
+       " 5528  0.6484\n",
+       " 5529  0.6332\n",
+       " 5530  0.6516\n",
+       " 5531  0.6388\n",
+       " 5532  0.6496\n",
+       " 5533  0.6562\n",
+       " 5534  0.6442\n",
+       " 5535  0.6202\n",
+       " 5536  0.6210\n",
+       " 5537  0.6486\n",
+       " 5538  0.6644\n",
+       " 5539  0.6322\n",
+       " 5540  0.6384\n",
+       " 5541  0.6368\n",
+       " 5542  0.6404\n",
+       " 5543  0.6352\n",
+       " 5544  0.6296\n",
+       " 5545  0.6680\n",
+       " 5546  0.6882\n",
+       " 5547  0.6686\n",
+       " 5548  0.6722\n",
+       " 5549  0.6758\n",
+       " 5550  0.6930\n",
+       " 5551  0.6732\n",
+       " 5552  0.6606\n",
+       " 5553  0.6368\n",
+       " 5554  0.6510]"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "gini"
+    "model_dfs\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1ac6d4b7-b74f-41e8-82eb-368d77355d91",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From c33ac68dfe48d75344d4a3e22165e4ad95a8eaeb Mon Sep 17 00:00:00 2001
From: Chan-Dong-Jun <dongjuncoding@gmail.com>
Date: Sun, 4 Aug 2024 15:21:02 +0800
Subject: [PATCH 7/9] clean up code

---
 mesa/cacheable_model.py | 149 ++++++++++++++++++++++++----------------
 1 file changed, 91 insertions(+), 58 deletions(-)

diff --git a/mesa/cacheable_model.py b/mesa/cacheable_model.py
index f4d3b47be63..f21f3ae0d5e 100644
--- a/mesa/cacheable_model.py
+++ b/mesa/cacheable_model.py
@@ -12,19 +12,12 @@
 from enum import Enum
 from mesa import Model
 
+import glob
+
 with contextlib.suppress(ImportError):
     import pandas as pd
 
 
-class CacheState(Enum):
-    """When using 'RECORD', with every simulation step the actual simulation will be performed and the model state
-    written to the cache (also called simulation mode).
-    When using 'REPLAY', with every step the model state will be read from the cache (also called replay mode)."""
-
-    RECORD = (1,)
-    REPLAY = 2
-
-
 class CacheableModel:
     """Class that takes a model and writes its steps to a cache file or reads them from a cache file."""
 
@@ -32,9 +25,7 @@ def __init__(
         self,
         model: Model,
         cache_file_path: str | Path,
-        # cache_state: CacheState,
         total_steps: int,
-        # cache_step_rate: int = 1,
         condition_function=None,
     ) -> None:
         """Create a new caching wrapper around an existing mesa model instance.
@@ -50,21 +41,15 @@ def __init__(
 
         self.model = model
         self.cache_file_path = Path(cache_file_path)
-        # self._cache_state = cache_state
-        # self._cache_step_rate = cache_step_rate
         self._total_steps = total_steps
-        # self.cache: list[Any] = []
         self.step_count: int = 0
         self.run_finished = False
 
         # temporary dicts to be flushed
         self.model_vars_cache = {}
-
-
         self._agent_records = {}
 
         self._cache_interval = 100
-        # self.output_dir = 'output_dir'
 
         self._last_cached_step = 0 # inclusive since it is the bottom bound of slicing
         self.condition_function = condition_function
@@ -80,7 +65,7 @@ def get_agent_vars_dataframe(self):
                 "No agent reporters have been defined in the DataCollector, returning empty DataFrame."
             )
 
-        all_records = itertools.chain.from_iterable(self._agent_records.values())
+        all_records = itertools.chain.from_iterable(self.model.datacollector._agent_records.values())
         rep_names = list(self.model.datacollector.agent_reporters)
 
         df = pd.DataFrame.from_records(
@@ -88,7 +73,9 @@ def get_agent_vars_dataframe(self):
             columns=["Step", "AgentID", *rep_names],
             index=["Step", "AgentID"],
         )
-        return df
+        sliced_df = df.loc[self._last_cached_step:self.model._steps]
+
+        return sliced_df
 
     def get_model_vars_dataframe(self):
         """Create a pandas DataFrame from the model variables.
@@ -102,27 +89,13 @@ def get_model_vars_dataframe(self):
                 "No model reporters have been defined in the DataCollector, returning empty DataFrame."
             )
 
-        print(f" TEST {self.model._steps=}")
-        print(f" TEST {self._cache_interval=}")
-        print(pd.DataFrame(self.model.datacollector.model_vars)[self._last_cached_step:self.model._steps])
         return pd.DataFrame(self.model.datacollector.model_vars)[self._last_cached_step:self.model._steps]
 
-    # def get_table_dataframe(self, table_name):
-    #     """Create a pandas DataFrame from a particular table.
-    #
-    #     Args:
-    #         table_name: The name of the table to convert.
-    #     """
-    #     if table_name not in self.tables:
-    #         raise Exception("No such table.")
-    #     return pd.DataFrame(self.tables[table_name])
-
     def _save_to_parquet(self, model):
         """Save the current cache of data to a Parquet file and clear the cache."""
         model_df = self.get_model_vars_dataframe()
         agent_df = self.get_agent_vars_dataframe()
         padding = len(str(self._total_steps)) - 1
-        print(padding)
 
         # ceiling function
         model_file = f"{self.cache_file_path}/model_data_{-(self.model._steps // -self._cache_interval):0{padding}}.parquet"
@@ -130,9 +103,7 @@ def _save_to_parquet(self, model):
 
         self.cache_file_path.mkdir(parents=True, exist_ok=True)
 
-        print(f"{model_file=}")
         absolute_path = os.path.abspath(model_file)
-        print(f"{absolute_path=}")
         if os.path.exists(absolute_path):
             raise FileExistsError(f"A directory with the name {model_file} already exists.")
         if os.path.exists(model_file):
@@ -141,54 +112,31 @@ def _save_to_parquet(self, model):
             raise FileExistsError(f"A directory with the name {agent_file} already exists.")
 
         if not model_df.empty:
-            print(f"Saving model to {model_file}")
             model_table = pa.Table.from_pandas(model_df)
             pq.write_table(model_table, model_file)
 
         if not agent_df.empty:
-            print(f"Saving agent to {agent_file}")
             agent_table = pa.Table.from_pandas(agent_df)
             pq.write_table(agent_table, agent_file)
 
-        # Clear the cache
-
-
     def cache(self):
         """Custom collect method to extend the original collect behavior."""
         # Implement your custom logic here
         # For example, let's say we want to write collected data to a cache file every `cache_step_rate` steps
         if self.model._steps % self._cache_interval == 0:
-            print("CALLED")
-            print(f"{self.model._steps=}")
             self._save_to_parquet(self.model)
             self._last_cached_step = self.model._steps
         elif self.model._steps == self._total_steps:
-            print("FINAL CALLED")
-            print(f"{self.model._steps=}")
             self._save_to_parquet(self.model)
             self._last_cached_step = self.model._steps
 
         if self.condition_function and self.save_special_results(self.condition_function):
-            # print("CHECK SPECIAL RESULTS CONDITION")
             pass
 
     def save_special_results(self, condition_function: Callable[[dict], bool]):
 
         model_vars = self.model.datacollector.model_vars
-        # print(f"{model_vars=}")
-        print(f"{model_vars.get('Gini', 0)[-1]=}")
         self.cache_file_path.mkdir(parents=True, exist_ok=True)
-        # if condition_function(model_vars):
-        #     special_results_df = pd.DataFrame(model_vars)
-        #     special_results_file = f"{self.cache_file_path}/special_results.parquet"
-        #
-        #
-        #     if not special_results_df.empty:
-        #         print(f"Condition met. Saving special results to {special_results_file}")
-        #         special_results_table = pa.Table.from_pandas(special_results_df)
-        #         pq.write_table(special_results_table, special_results_file)
-        #     else:
-        #         print("Condition met but no data to save.")
 
         current_step = self.model._steps
         special_results_file = f"{self.cache_file_path}/special_results.parquet"
@@ -210,3 +158,88 @@ def save_special_results(self, condition_function: Callable[[dict], bool]):
             print(f"Condition met. Appended special results for step {current_step} to {special_results_file}")
         else:
             print(f"Condition not met at step {current_step}. No data to save.")
+
+    def read_model_data(self):
+        """Read and combine all model data Parquet files into a single DataFrame."""
+        model_files = glob.glob(f'{self.cache_file_path}/model_data_*.parquet')
+        model_dfs = []
+
+        for model_file in model_files:
+            table = pq.read_table(model_file)
+            df = table.to_pandas()
+            model_dfs.append(df)
+
+        if model_dfs:
+            model_df = pd.concat(model_dfs, ignore_index=True)
+            return model_df
+        else:
+            raise FileNotFoundError("No model data files found.")
+
+    def read_agent_data(self):
+        """Read and combine all agent data Parquet files into a single DataFrame."""
+        agent_files = glob.glob(f'{self.cache_file_path}/agent_data_*.parquet')
+        agent_dfs = []
+
+        for agent_file in agent_files:
+            table = pq.read_table(agent_file)
+            df = table.to_pandas()
+            agent_dfs.append(df)
+
+        if agent_dfs:
+            agent_df = pd.concat(agent_dfs)
+            return agent_df
+        else:
+            raise FileNotFoundError("No agent data files found.")
+
+    def combine_dataframes(self):
+        """Combine and return the model and agent DataFrames."""
+        try:
+            model_df = self.read_model_data()
+            agent_df = self.read_agent_data()
+
+            # Sort agent DataFrame by the multi-index (Step, AgentID) to ensure order
+            agent_df = agent_df.sort_index()
+
+            return model_df, agent_df
+        except FileNotFoundError as e:
+            print(e)
+            return None, None
+
+
+# FOR GRID KIV IGNORE NOW
+import pandas as pd
+from typing import Dict, List, Any
+from random import Random
+from mesa.agent import Agent
+class AgentSerializer:
+    @staticmethod
+    def agent_to_dict(agent: Agent) -> Dict[str, Any]:
+        """Convert an Agent instance to a dictionary."""
+        return {
+            'unique_id': agent.unique_id,
+            'model': str(agent.model),  # Convert model to a string or identifier
+            'pos': str(agent.pos) if agent.pos else None,  # Convert position to a string or identifier
+        }
+
+    @staticmethod
+    def dict_to_agent(agent_dict: Dict[str, Any], model: Any) -> Agent:
+        """Convert a dictionary to an Agent instance."""
+        unique_id = agent_dict['unique_id']
+        pos = agent_dict['pos'] if agent_dict['pos'] != 'None' else None
+        agent = Agent(unique_id=unique_id, model=model)
+        agent.pos = pos
+        return agent
+
+    @staticmethod
+    def save_agents_to_parquet(agents: List[Agent], filename: str) -> None:
+        """Save a list of agents to a Parquet file."""
+        agent_dicts = [AgentSerializer.agent_to_dict(agent) for agent in agents]
+        df = pd.DataFrame(agent_dicts)
+        df.to_parquet(filename)
+
+    @staticmethod
+    def load_agents_from_parquet(filename: str, model: Any) -> List[Agent]:
+        """Load agents from a Parquet file."""
+        df = pd.read_parquet(filename)
+        agents = [AgentSerializer.dict_to_agent(row.to_dict(), model) for _, row in df.iterrows()]
+        return agents

From 753583dc05da6c0fd9b1943d7d7d126fd25417a9 Mon Sep 17 00:00:00 2001
From: Chan-Dong-Jun <dongjuncoding@gmail.com>
Date: Sun, 4 Aug 2024 17:22:59 +0800
Subject: [PATCH 8/9] cache agent.pos

---
 mesa/cacheable_model.py              |     5 +
 mesa/cacheable_wrapper_example.ipynb | 20297 ++++---------------------
 2 files changed, 2919 insertions(+), 17383 deletions(-)

diff --git a/mesa/cacheable_model.py b/mesa/cacheable_model.py
index f21f3ae0d5e..4f3fbc621e9 100644
--- a/mesa/cacheable_model.py
+++ b/mesa/cacheable_model.py
@@ -67,12 +67,17 @@ def get_agent_vars_dataframe(self):
 
         all_records = itertools.chain.from_iterable(self.model.datacollector._agent_records.values())
         rep_names = list(self.model.datacollector.agent_reporters)
+        print(f"{all_records=}")
+        print(f"{rep_names=}")
 
         df = pd.DataFrame.from_records(
             data=all_records,
             columns=["Step", "AgentID", *rep_names],
             index=["Step", "AgentID"],
         )
+
+
+
         sliced_df = df.loc[self._last_cached_step:self.model._steps]
 
         return sliced_df
diff --git a/mesa/cacheable_wrapper_example.ipynb b/mesa/cacheable_wrapper_example.ipynb
index 0d646d87e17..b83baae0372 100644
--- a/mesa/cacheable_wrapper_example.ipynb
+++ b/mesa/cacheable_wrapper_example.ipynb
@@ -3,6 +3,17 @@
   {
    "cell_type": "code",
    "execution_count": 1,
+   "id": "06fcbb7d-c7f1-41ed-92b8-81bc6a6fcb25",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.core.interactiveshell import InteractiveShell\n",
+    "InteractiveShell.ast_node_interactivity = \"all\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
    "id": "initial_id",
    "metadata": {},
    "outputs": [],
@@ -20,9 +31,28 @@
     "import pandas as pd"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "1a5828b7-cd1b-4b6e-a802-414e36bf92b1",
+   "metadata": {},
+   "source": [
+    "# Cache results without solara; not on grid"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
+   "id": "39645a02-3204-465b-b66a-35c6d31bb40e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cacheable_model import CacheableModel\n",
+    "import cacheable_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
    "id": "79ad4023-eeec-417d-bba6-488789992889",
    "metadata": {},
    "outputs": [
@@ -41,28 +71,11 @@
     "    shutil.rmtree(directory_path)\n",
     "    print(f\"Directory {directory_path} and its contents have been deleted.\")\n",
     "\n",
-    "directory_to_delete = \"test_path\"\n",
-    "delete_directory(directory_to_delete)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "c25f0179-d5ea-47d1-9e4e-c460030d5f73",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from cacheable_model import CacheableModel"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "7ecda9c7-ec67-4322-b351-99dc5d3a5a67",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cacheable_model"
+    "try:\n",
+    "    directory = \"test_path\"\n",
+    "    delete_directory(directory)\n",
+    "except:\n",
+    "    pass"
    ]
   },
   {
@@ -146,16 +159,289 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model = MoneyModel(100, 10, 10)\n"
+    "model = MoneyModel(100, 10, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "c7858312-7d3e-4e7a-9eed-5c3f9dc7510a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'_seed': 0.5967757455124764,\n",
+       " 'random': <random.Random at 0x5613fbf8a970>,\n",
+       " '_steps': 1000,\n",
+       " '_time': 1000,\n",
+       " 'running': True,\n",
+       " 'schedule': <mesa.time.RandomActivation at 0x7f77e00c4370>,\n",
+       " 'current_id': 0,\n",
+       " 'agents_': defaultdict(dict,\n",
+       "             {__main__.MoneyAgent: {<__main__.MoneyAgent at 0x7f77e00c45b0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c49a0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c48e0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c4910>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c7df0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c4a30>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c4b20>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c4b50>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c4ca0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c4cd0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c4dc0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c4d30>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c4e20>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c4e50>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c4be0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c4c40>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c4ee0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c4fd0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6d70>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6d40>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c50f0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5030>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5090>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6d10>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6bf0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6c50>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6ce0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6a40>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6aa0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6b60>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6b00>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c4f10>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6a10>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6920>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6800>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c68f0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6890>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6950>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6620>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c66e0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c66b0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6740>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c65f0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6440>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6410>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c63b0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6350>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6320>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c62c0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6260>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c61d0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6020>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6050>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c61a0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6170>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c64d0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6530>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c69e0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6500>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c7f70>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5f60>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c7f40>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5180>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5120>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c7f10>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c7ee0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c6110>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c4a90>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c4a60>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5270>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c52a0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c53f0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5420>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5480>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5510>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5540>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c55d0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5720>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5780>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c56c0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5600>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c57b0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5930>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c58a0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c57e0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5960>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5ae0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5ba0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5b70>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5c00>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5c30>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5c60>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5cc0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5db0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5ea0>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5e10>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5d80>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5a50>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c5a20>: None,\n",
+       "               <__main__.MoneyAgent at 0x7f77e00c59c0>: None},\n",
+       "              mesa.agent.Agent: {<mesa.agent.Agent at 0x7f77c42af040>: None,\n",
+       "               <mesa.agent.Agent at 0x7f77b5ee23e0>: None,\n",
+       "               <mesa.agent.Agent at 0x7f77b5ee28f0>: None,\n",
+       "               <mesa.agent.Agent at 0x7f77b5ee33a0>: None}}),\n",
+       " 'num_agents': 100,\n",
+       " 'grid': <mesa.space.MultiGrid at 0x7f77e00c47f0>,\n",
+       " 'datacollector': <mesa.datacollection.DataCollector at 0x7f77e00c4970>}"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.__dict__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "d389733b-06a9-485e-821d-40cb06d2024c",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "defaultdict(dict,\n",
+       "            {__main__.MoneyAgent: {<__main__.MoneyAgent at 0x7f77e00c45b0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c49a0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c48e0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c4910>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c7df0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c4a30>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c4b20>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c4b50>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c4ca0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c4cd0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c4dc0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c4d30>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c4e20>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c4e50>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c4be0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c4c40>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c4ee0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c4fd0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6d70>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6d40>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c50f0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5030>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5090>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6d10>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6bf0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6c50>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6ce0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6a40>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6aa0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6b60>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6b00>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c4f10>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6a10>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6920>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6800>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c68f0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6890>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6950>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6620>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c66e0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c66b0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6740>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c65f0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6440>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6410>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c63b0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6350>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6320>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c62c0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6260>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c61d0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6020>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6050>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c61a0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6170>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c64d0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6530>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c69e0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6500>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c7f70>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5f60>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c7f40>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5180>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5120>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c7f10>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c7ee0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c6110>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c4a90>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c4a60>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5270>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c52a0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c53f0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5420>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5480>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5510>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5540>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c55d0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5720>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5780>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c56c0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5600>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c57b0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5930>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c58a0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c57e0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5960>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5ae0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5ba0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5b70>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5c00>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5c30>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5c60>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5cc0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5db0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5ea0>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5e10>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5d80>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5a50>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c5a20>: None,\n",
+       "              <__main__.MoneyAgent at 0x7f77e00c59c0>: None},\n",
+       "             mesa.agent.Agent: {<mesa.agent.Agent at 0x7f77c42af040>: None,\n",
+       "              <mesa.agent.Agent at 0x7f77b5ee23e0>: None,\n",
+       "              <mesa.agent.Agent at 0x7f77b5ee28f0>: None,\n",
+       "              <mesa.agent.Agent at 0x7f77b5ee33a0>: None}})"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class '__main__.MoneyAgent'>\n",
+      "<class 'mesa.agent.Agent'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.agents_\n",
+    "for i in model.agents_.keys():\n",
+    "    print(i)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "165b9f97-1e7b-40d5-8d6f-b705d0e45bd1",
+   "id": "bd0f1cd1-8f30-4ec0-b2c4-79d38cfa6a8f",
    "metadata": {},
    "outputs": [],
    "source": [
+    "# condition to cache the results specifically\n",
     "def condition_function(model_vars):\n",
     "        return model_vars.get('Gini', 0)[-1] > 0.7"
    ]
@@ -167,18064 +453,2057 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "cacheable_model = CacheableModel(model, \"test_path\", 5555, condition_function)"
+    "number_of_steps = 1000\n",
+    "cacheable_model = CacheableModel(model, directory, number_of_steps, condition_function)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9d237ce1-d933-44fc-8818-2f373c4e710c",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "dde8f54d-34ed-429d-99f7-b17c1b2cd5a5",
-   "metadata": {
-    "scrolled": true
-   },
+   "id": "44387fbb-62be-4de3-a552-39c0f29635a0",
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "0\n",
-      "model_vars.get('Gini', 0)[-1]=0.0\n",
       "Condition not met at step 1. No data to save.\n",
       "1\n",
-      "model_vars.get('Gini', 0)[-1]=0.32720000000000005\n",
       "Condition not met at step 2. No data to save.\n",
       "2\n",
-      "model_vars.get('Gini', 0)[-1]=0.4626\n",
       "Condition not met at step 3. No data to save.\n",
       "3\n",
-      "model_vars.get('Gini', 0)[-1]=0.4626\n",
       "Condition not met at step 4. No data to save.\n",
       "4\n",
-      "model_vars.get('Gini', 0)[-1]=0.508\n",
       "Condition not met at step 5. No data to save.\n",
       "5\n",
-      "model_vars.get('Gini', 0)[-1]=0.5302\n",
       "Condition not met at step 6. No data to save.\n",
       "6\n",
-      "model_vars.get('Gini', 0)[-1]=0.5518000000000001\n",
       "Condition not met at step 7. No data to save.\n",
       "7\n",
-      "model_vars.get('Gini', 0)[-1]=0.5973999999999999\n",
       "Condition not met at step 8. No data to save.\n",
       "8\n",
-      "model_vars.get('Gini', 0)[-1]=0.6082000000000001\n",
       "Condition not met at step 9. No data to save.\n",
       "9\n",
-      "model_vars.get('Gini', 0)[-1]=0.6000000000000001\n",
       "Condition not met at step 10. No data to save.\n",
       "10\n",
-      "model_vars.get('Gini', 0)[-1]=0.6046\n",
       "Condition not met at step 11. No data to save.\n",
       "11\n",
-      "model_vars.get('Gini', 0)[-1]=0.6093999999999999\n",
       "Condition not met at step 12. No data to save.\n",
       "12\n",
-      "model_vars.get('Gini', 0)[-1]=0.618\n",
       "Condition not met at step 13. No data to save.\n",
       "13\n",
-      "model_vars.get('Gini', 0)[-1]=0.6096\n",
       "Condition not met at step 14. No data to save.\n",
       "14\n",
-      "model_vars.get('Gini', 0)[-1]=0.6315999999999999\n",
       "Condition not met at step 15. No data to save.\n",
       "15\n",
-      "model_vars.get('Gini', 0)[-1]=0.6044\n",
       "Condition not met at step 16. No data to save.\n",
       "16\n",
-      "model_vars.get('Gini', 0)[-1]=0.6078\n",
       "Condition not met at step 17. No data to save.\n",
       "17\n",
-      "model_vars.get('Gini', 0)[-1]=0.6018\n",
       "Condition not met at step 18. No data to save.\n",
       "18\n",
-      "model_vars.get('Gini', 0)[-1]=0.6058\n",
       "Condition not met at step 19. No data to save.\n",
       "19\n",
-      "model_vars.get('Gini', 0)[-1]=0.616\n",
       "Condition not met at step 20. No data to save.\n",
       "20\n",
-      "model_vars.get('Gini', 0)[-1]=0.5754\n",
       "Condition not met at step 21. No data to save.\n",
       "21\n",
-      "model_vars.get('Gini', 0)[-1]=0.5886\n",
       "Condition not met at step 22. No data to save.\n",
       "22\n",
-      "model_vars.get('Gini', 0)[-1]=0.6106\n",
       "Condition not met at step 23. No data to save.\n",
       "23\n",
-      "model_vars.get('Gini', 0)[-1]=0.6156\n",
       "Condition not met at step 24. No data to save.\n",
       "24\n",
-      "model_vars.get('Gini', 0)[-1]=0.6334\n",
       "Condition not met at step 25. No data to save.\n",
       "25\n",
-      "model_vars.get('Gini', 0)[-1]=0.6562\n",
       "Condition not met at step 26. No data to save.\n",
       "26\n",
-      "model_vars.get('Gini', 0)[-1]=0.6592\n",
       "Condition not met at step 27. No data to save.\n",
       "27\n",
-      "model_vars.get('Gini', 0)[-1]=0.6652\n",
       "Condition not met at step 28. No data to save.\n",
       "28\n",
-      "model_vars.get('Gini', 0)[-1]=0.6408\n",
       "Condition not met at step 29. No data to save.\n",
       "29\n",
-      "model_vars.get('Gini', 0)[-1]=0.653\n",
       "Condition not met at step 30. No data to save.\n",
       "30\n",
-      "model_vars.get('Gini', 0)[-1]=0.6264000000000001\n",
       "Condition not met at step 31. No data to save.\n",
       "31\n",
-      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
       "Condition not met at step 32. No data to save.\n",
       "32\n",
-      "model_vars.get('Gini', 0)[-1]=0.6662\n",
       "Condition not met at step 33. No data to save.\n",
       "33\n",
-      "model_vars.get('Gini', 0)[-1]=0.6664\n",
       "Condition not met at step 34. No data to save.\n",
       "34\n",
-      "model_vars.get('Gini', 0)[-1]=0.6804\n",
       "Condition not met at step 35. No data to save.\n",
       "35\n",
-      "model_vars.get('Gini', 0)[-1]=0.6636\n",
       "Condition not met at step 36. No data to save.\n",
       "36\n",
-      "model_vars.get('Gini', 0)[-1]=0.6568\n",
       "Condition not met at step 37. No data to save.\n",
       "37\n",
-      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
       "Condition not met at step 38. No data to save.\n",
       "38\n",
-      "model_vars.get('Gini', 0)[-1]=0.6892\n",
       "Condition not met at step 39. No data to save.\n",
       "39\n",
-      "model_vars.get('Gini', 0)[-1]=0.6948000000000001\n",
       "Condition not met at step 40. No data to save.\n",
       "40\n",
-      "model_vars.get('Gini', 0)[-1]=0.6894\n",
       "Condition not met at step 41. No data to save.\n",
       "41\n",
-      "model_vars.get('Gini', 0)[-1]=0.7046\n",
-      "Condition met. Appended special results for step 42 to test_path/special_results.parquet\n",
+      "Condition not met at step 42. No data to save.\n",
       "42\n",
-      "model_vars.get('Gini', 0)[-1]=0.7010000000000001\n",
-      "Condition met. Appended special results for step 43 to test_path/special_results.parquet\n",
+      "Condition not met at step 43. No data to save.\n",
       "43\n",
-      "model_vars.get('Gini', 0)[-1]=0.7056\n",
-      "Condition met. Appended special results for step 44 to test_path/special_results.parquet\n",
+      "Condition not met at step 44. No data to save.\n",
       "44\n",
-      "model_vars.get('Gini', 0)[-1]=0.7203999999999999\n",
-      "Condition met. Appended special results for step 45 to test_path/special_results.parquet\n",
+      "Condition not met at step 45. No data to save.\n",
       "45\n",
-      "model_vars.get('Gini', 0)[-1]=0.7092\n",
-      "Condition met. Appended special results for step 46 to test_path/special_results.parquet\n",
+      "Condition not met at step 46. No data to save.\n",
       "46\n",
-      "model_vars.get('Gini', 0)[-1]=0.7028000000000001\n",
-      "Condition met. Appended special results for step 47 to test_path/special_results.parquet\n",
+      "Condition not met at step 47. No data to save.\n",
       "47\n",
-      "model_vars.get('Gini', 0)[-1]=0.7166\n",
-      "Condition met. Appended special results for step 48 to test_path/special_results.parquet\n",
+      "Condition not met at step 48. No data to save.\n",
       "48\n",
-      "model_vars.get('Gini', 0)[-1]=0.7146\n",
-      "Condition met. Appended special results for step 49 to test_path/special_results.parquet\n",
+      "Condition not met at step 49. No data to save.\n",
       "49\n",
-      "model_vars.get('Gini', 0)[-1]=0.6961999999999999\n",
       "Condition not met at step 50. No data to save.\n",
       "50\n",
-      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
       "Condition not met at step 51. No data to save.\n",
       "51\n",
-      "model_vars.get('Gini', 0)[-1]=0.6504000000000001\n",
       "Condition not met at step 52. No data to save.\n",
       "52\n",
-      "model_vars.get('Gini', 0)[-1]=0.647\n",
       "Condition not met at step 53. No data to save.\n",
       "53\n",
-      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
       "Condition not met at step 54. No data to save.\n",
       "54\n",
-      "model_vars.get('Gini', 0)[-1]=0.6734\n",
       "Condition not met at step 55. No data to save.\n",
       "55\n",
-      "model_vars.get('Gini', 0)[-1]=0.6914\n",
       "Condition not met at step 56. No data to save.\n",
       "56\n",
-      "model_vars.get('Gini', 0)[-1]=0.6819999999999999\n",
       "Condition not met at step 57. No data to save.\n",
       "57\n",
-      "model_vars.get('Gini', 0)[-1]=0.7058\n",
-      "Condition met. Appended special results for step 58 to test_path/special_results.parquet\n",
+      "Condition not met at step 58. No data to save.\n",
       "58\n",
-      "model_vars.get('Gini', 0)[-1]=0.7056\n",
-      "Condition met. Appended special results for step 59 to test_path/special_results.parquet\n",
+      "Condition not met at step 59. No data to save.\n",
       "59\n",
-      "model_vars.get('Gini', 0)[-1]=0.7250000000000001\n",
-      "Condition met. Appended special results for step 60 to test_path/special_results.parquet\n",
+      "Condition not met at step 60. No data to save.\n",
       "60\n",
-      "model_vars.get('Gini', 0)[-1]=0.7012\n",
-      "Condition met. Appended special results for step 61 to test_path/special_results.parquet\n",
+      "Condition not met at step 61. No data to save.\n",
       "61\n",
-      "model_vars.get('Gini', 0)[-1]=0.7021999999999999\n",
-      "Condition met. Appended special results for step 62 to test_path/special_results.parquet\n",
+      "Condition not met at step 62. No data to save.\n",
       "62\n",
-      "model_vars.get('Gini', 0)[-1]=0.7088\n",
-      "Condition met. Appended special results for step 63 to test_path/special_results.parquet\n",
+      "Condition not met at step 63. No data to save.\n",
       "63\n",
-      "model_vars.get('Gini', 0)[-1]=0.6976\n",
       "Condition not met at step 64. No data to save.\n",
       "64\n",
-      "model_vars.get('Gini', 0)[-1]=0.7183999999999999\n",
-      "Condition met. Appended special results for step 65 to test_path/special_results.parquet\n",
+      "Condition not met at step 65. No data to save.\n",
       "65\n",
-      "model_vars.get('Gini', 0)[-1]=0.7010000000000001\n",
-      "Condition met. Appended special results for step 66 to test_path/special_results.parquet\n",
+      "Condition not met at step 66. No data to save.\n",
       "66\n",
-      "model_vars.get('Gini', 0)[-1]=0.7012\n",
-      "Condition met. Appended special results for step 67 to test_path/special_results.parquet\n",
+      "Condition not met at step 67. No data to save.\n",
       "67\n",
-      "model_vars.get('Gini', 0)[-1]=0.698\n",
       "Condition not met at step 68. No data to save.\n",
       "68\n",
-      "model_vars.get('Gini', 0)[-1]=0.7028000000000001\n",
-      "Condition met. Appended special results for step 69 to test_path/special_results.parquet\n",
+      "Condition not met at step 69. No data to save.\n",
       "69\n",
-      "model_vars.get('Gini', 0)[-1]=0.7414000000000001\n",
-      "Condition met. Appended special results for step 70 to test_path/special_results.parquet\n",
+      "Condition not met at step 70. No data to save.\n",
       "70\n",
-      "model_vars.get('Gini', 0)[-1]=0.73\n",
-      "Condition met. Appended special results for step 71 to test_path/special_results.parquet\n",
+      "Condition not met at step 71. No data to save.\n",
       "71\n",
-      "model_vars.get('Gini', 0)[-1]=0.7041999999999999\n",
-      "Condition met. Appended special results for step 72 to test_path/special_results.parquet\n",
+      "Condition not met at step 72. No data to save.\n",
       "72\n",
-      "model_vars.get('Gini', 0)[-1]=0.6754\n",
       "Condition not met at step 73. No data to save.\n",
       "73\n",
-      "model_vars.get('Gini', 0)[-1]=0.6956\n",
       "Condition not met at step 74. No data to save.\n",
       "74\n",
-      "model_vars.get('Gini', 0)[-1]=0.6762\n",
       "Condition not met at step 75. No data to save.\n",
       "75\n",
-      "model_vars.get('Gini', 0)[-1]=0.6848000000000001\n",
       "Condition not met at step 76. No data to save.\n",
       "76\n",
-      "model_vars.get('Gini', 0)[-1]=0.6848000000000001\n",
       "Condition not met at step 77. No data to save.\n",
       "77\n",
-      "model_vars.get('Gini', 0)[-1]=0.6952\n",
       "Condition not met at step 78. No data to save.\n",
       "78\n",
-      "model_vars.get('Gini', 0)[-1]=0.6904\n",
       "Condition not met at step 79. No data to save.\n",
       "79\n",
-      "model_vars.get('Gini', 0)[-1]=0.7098\n",
-      "Condition met. Appended special results for step 80 to test_path/special_results.parquet\n",
+      "Condition not met at step 80. No data to save.\n",
       "80\n",
-      "model_vars.get('Gini', 0)[-1]=0.7174\n",
-      "Condition met. Appended special results for step 81 to test_path/special_results.parquet\n",
+      "Condition not met at step 81. No data to save.\n",
       "81\n",
-      "model_vars.get('Gini', 0)[-1]=0.7203999999999999\n",
-      "Condition met. Appended special results for step 82 to test_path/special_results.parquet\n",
+      "Condition not met at step 82. No data to save.\n",
       "82\n",
-      "model_vars.get('Gini', 0)[-1]=0.7144\n",
-      "Condition met. Appended special results for step 83 to test_path/special_results.parquet\n",
+      "Condition not met at step 83. No data to save.\n",
       "83\n",
-      "model_vars.get('Gini', 0)[-1]=0.712\n",
-      "Condition met. Appended special results for step 84 to test_path/special_results.parquet\n",
+      "Condition not met at step 84. No data to save.\n",
       "84\n",
-      "model_vars.get('Gini', 0)[-1]=0.7004\n",
-      "Condition met. Appended special results for step 85 to test_path/special_results.parquet\n",
+      "Condition not met at step 85. No data to save.\n",
       "85\n",
-      "model_vars.get('Gini', 0)[-1]=0.6952\n",
       "Condition not met at step 86. No data to save.\n",
       "86\n",
-      "model_vars.get('Gini', 0)[-1]=0.7016\n",
-      "Condition met. Appended special results for step 87 to test_path/special_results.parquet\n",
+      "Condition not met at step 87. No data to save.\n",
       "87\n",
-      "model_vars.get('Gini', 0)[-1]=0.6692\n",
       "Condition not met at step 88. No data to save.\n",
       "88\n",
-      "model_vars.get('Gini', 0)[-1]=0.6692\n",
       "Condition not met at step 89. No data to save.\n",
       "89\n",
-      "model_vars.get('Gini', 0)[-1]=0.679\n",
       "Condition not met at step 90. No data to save.\n",
       "90\n",
-      "model_vars.get('Gini', 0)[-1]=0.6826\n",
       "Condition not met at step 91. No data to save.\n",
       "91\n",
-      "model_vars.get('Gini', 0)[-1]=0.6794\n",
       "Condition not met at step 92. No data to save.\n",
       "92\n",
-      "model_vars.get('Gini', 0)[-1]=0.6890000000000001\n",
       "Condition not met at step 93. No data to save.\n",
       "93\n",
-      "model_vars.get('Gini', 0)[-1]=0.6974\n",
       "Condition not met at step 94. No data to save.\n",
       "94\n",
-      "model_vars.get('Gini', 0)[-1]=0.6828000000000001\n",
-      "Condition not met at step 95. No data to save.\n",
+      "Condition met. Appended special results for step 95 to test_path/special_results.parquet\n",
       "95\n",
-      "model_vars.get('Gini', 0)[-1]=0.6754\n",
       "Condition not met at step 96. No data to save.\n",
       "96\n",
-      "model_vars.get('Gini', 0)[-1]=0.6764\n",
       "Condition not met at step 97. No data to save.\n",
       "97\n",
-      "model_vars.get('Gini', 0)[-1]=0.6834\n",
       "Condition not met at step 98. No data to save.\n",
       "98\n",
-      "model_vars.get('Gini', 0)[-1]=0.6582\n",
       "Condition not met at step 99. No data to save.\n",
       "99\n",
-      "CALLED\n",
-      "self.model._steps=100\n",
-      " TEST self.model._steps=100\n",
-      " TEST self._cache_interval=100\n",
-      "      Gini\n",
-      "0   0.0000\n",
-      "1   0.3272\n",
-      "2   0.4626\n",
-      "3   0.4626\n",
-      "4   0.5080\n",
-      "..     ...\n",
-      "95  0.6754\n",
-      "96  0.6764\n",
-      "97  0.6834\n",
-      "98  0.6582\n",
-      "99  0.6464\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_001.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_001.parquet'\n",
-      "Saving model to test_path/model_data_001.parquet\n",
-      "Saving agent to test_path/agent_data_001.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6464000000000001\n",
+      "all_records=<itertools.chain object at 0x7f77e00c79d0>\n",
+      "rep_names=['Wealth']\n",
       "Condition not met at step 100. No data to save.\n",
       "100\n",
-      "model_vars.get('Gini', 0)[-1]=0.665\n",
       "Condition not met at step 101. No data to save.\n",
       "101\n",
-      "model_vars.get('Gini', 0)[-1]=0.6506000000000001\n",
       "Condition not met at step 102. No data to save.\n",
       "102\n",
-      "model_vars.get('Gini', 0)[-1]=0.6734\n",
       "Condition not met at step 103. No data to save.\n",
       "103\n",
-      "model_vars.get('Gini', 0)[-1]=0.6656\n",
       "Condition not met at step 104. No data to save.\n",
       "104\n",
-      "model_vars.get('Gini', 0)[-1]=0.645\n",
-      "Condition not met at step 105. No data to save.\n",
+      "Condition met. Appended special results for step 105 to test_path/special_results.parquet\n",
       "105\n",
-      "model_vars.get('Gini', 0)[-1]=0.6514\n",
-      "Condition not met at step 106. No data to save.\n",
+      "Condition met. Appended special results for step 106 to test_path/special_results.parquet\n",
       "106\n",
-      "model_vars.get('Gini', 0)[-1]=0.6352\n",
-      "Condition not met at step 107. No data to save.\n",
+      "Condition met. Appended special results for step 107 to test_path/special_results.parquet\n",
       "107\n",
-      "model_vars.get('Gini', 0)[-1]=0.6412\n",
-      "Condition not met at step 108. No data to save.\n",
+      "Condition met. Appended special results for step 108 to test_path/special_results.parquet\n",
       "108\n",
-      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
-      "Condition not met at step 109. No data to save.\n",
+      "Condition met. Appended special results for step 109 to test_path/special_results.parquet\n",
       "109\n",
-      "model_vars.get('Gini', 0)[-1]=0.662\n",
       "Condition not met at step 110. No data to save.\n",
       "110\n",
-      "model_vars.get('Gini', 0)[-1]=0.6388\n",
-      "Condition not met at step 111. No data to save.\n",
+      "Condition met. Appended special results for step 111 to test_path/special_results.parquet\n",
       "111\n",
-      "model_vars.get('Gini', 0)[-1]=0.628\n",
       "Condition not met at step 112. No data to save.\n",
       "112\n",
-      "model_vars.get('Gini', 0)[-1]=0.6182000000000001\n",
       "Condition not met at step 113. No data to save.\n",
       "113\n",
-      "model_vars.get('Gini', 0)[-1]=0.647\n",
       "Condition not met at step 114. No data to save.\n",
       "114\n",
-      "model_vars.get('Gini', 0)[-1]=0.6702\n",
       "Condition not met at step 115. No data to save.\n",
       "115\n",
-      "model_vars.get('Gini', 0)[-1]=0.6414\n",
       "Condition not met at step 116. No data to save.\n",
       "116\n",
-      "model_vars.get('Gini', 0)[-1]=0.655\n",
       "Condition not met at step 117. No data to save.\n",
       "117\n",
-      "model_vars.get('Gini', 0)[-1]=0.6696\n",
       "Condition not met at step 118. No data to save.\n",
       "118\n",
-      "model_vars.get('Gini', 0)[-1]=0.6576\n",
       "Condition not met at step 119. No data to save.\n",
       "119\n",
-      "model_vars.get('Gini', 0)[-1]=0.654\n",
       "Condition not met at step 120. No data to save.\n",
       "120\n",
-      "model_vars.get('Gini', 0)[-1]=0.6482\n",
       "Condition not met at step 121. No data to save.\n",
       "121\n",
-      "model_vars.get('Gini', 0)[-1]=0.6516\n",
       "Condition not met at step 122. No data to save.\n",
       "122\n",
-      "model_vars.get('Gini', 0)[-1]=0.656\n",
       "Condition not met at step 123. No data to save.\n",
       "123\n",
-      "model_vars.get('Gini', 0)[-1]=0.6736\n",
       "Condition not met at step 124. No data to save.\n",
       "124\n",
-      "model_vars.get('Gini', 0)[-1]=0.6736\n",
       "Condition not met at step 125. No data to save.\n",
       "125\n",
-      "model_vars.get('Gini', 0)[-1]=0.6664\n",
       "Condition not met at step 126. No data to save.\n",
       "126\n",
-      "model_vars.get('Gini', 0)[-1]=0.658\n",
       "Condition not met at step 127. No data to save.\n",
       "127\n",
-      "model_vars.get('Gini', 0)[-1]=0.6782\n",
       "Condition not met at step 128. No data to save.\n",
       "128\n",
-      "model_vars.get('Gini', 0)[-1]=0.6742\n",
       "Condition not met at step 129. No data to save.\n",
       "129\n",
-      "model_vars.get('Gini', 0)[-1]=0.6712\n",
       "Condition not met at step 130. No data to save.\n",
       "130\n",
-      "model_vars.get('Gini', 0)[-1]=0.6614\n",
       "Condition not met at step 131. No data to save.\n",
       "131\n",
-      "model_vars.get('Gini', 0)[-1]=0.6626000000000001\n",
       "Condition not met at step 132. No data to save.\n",
       "132\n",
-      "model_vars.get('Gini', 0)[-1]=0.6512\n",
       "Condition not met at step 133. No data to save.\n",
       "133\n",
-      "model_vars.get('Gini', 0)[-1]=0.639\n",
       "Condition not met at step 134. No data to save.\n",
       "134\n",
-      "model_vars.get('Gini', 0)[-1]=0.638\n",
       "Condition not met at step 135. No data to save.\n",
       "135\n",
-      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
       "Condition not met at step 136. No data to save.\n",
       "136\n",
-      "model_vars.get('Gini', 0)[-1]=0.6504000000000001\n",
       "Condition not met at step 137. No data to save.\n",
       "137\n",
-      "model_vars.get('Gini', 0)[-1]=0.6144000000000001\n",
       "Condition not met at step 138. No data to save.\n",
       "138\n",
-      "model_vars.get('Gini', 0)[-1]=0.5794\n",
       "Condition not met at step 139. No data to save.\n",
       "139\n",
-      "model_vars.get('Gini', 0)[-1]=0.5993999999999999\n",
       "Condition not met at step 140. No data to save.\n",
       "140\n",
-      "model_vars.get('Gini', 0)[-1]=0.6272\n",
       "Condition not met at step 141. No data to save.\n",
       "141\n",
-      "model_vars.get('Gini', 0)[-1]=0.643\n",
       "Condition not met at step 142. No data to save.\n",
       "142\n",
-      "model_vars.get('Gini', 0)[-1]=0.621\n",
       "Condition not met at step 143. No data to save.\n",
       "143\n",
-      "model_vars.get('Gini', 0)[-1]=0.6192\n",
       "Condition not met at step 144. No data to save.\n",
       "144\n",
-      "model_vars.get('Gini', 0)[-1]=0.5778000000000001\n",
       "Condition not met at step 145. No data to save.\n",
       "145\n",
-      "model_vars.get('Gini', 0)[-1]=0.616\n",
       "Condition not met at step 146. No data to save.\n",
       "146\n",
-      "model_vars.get('Gini', 0)[-1]=0.6402\n",
       "Condition not met at step 147. No data to save.\n",
       "147\n",
-      "model_vars.get('Gini', 0)[-1]=0.6315999999999999\n",
       "Condition not met at step 148. No data to save.\n",
       "148\n",
-      "model_vars.get('Gini', 0)[-1]=0.6113999999999999\n",
       "Condition not met at step 149. No data to save.\n",
       "149\n",
-      "model_vars.get('Gini', 0)[-1]=0.6378\n",
       "Condition not met at step 150. No data to save.\n",
       "150\n",
-      "model_vars.get('Gini', 0)[-1]=0.6392\n",
       "Condition not met at step 151. No data to save.\n",
       "151\n",
-      "model_vars.get('Gini', 0)[-1]=0.6238\n",
       "Condition not met at step 152. No data to save.\n",
       "152\n",
-      "model_vars.get('Gini', 0)[-1]=0.6294\n",
       "Condition not met at step 153. No data to save.\n",
       "153\n",
-      "model_vars.get('Gini', 0)[-1]=0.6315999999999999\n",
       "Condition not met at step 154. No data to save.\n",
       "154\n",
-      "model_vars.get('Gini', 0)[-1]=0.6372\n",
       "Condition not met at step 155. No data to save.\n",
       "155\n",
-      "model_vars.get('Gini', 0)[-1]=0.6392\n",
       "Condition not met at step 156. No data to save.\n",
       "156\n",
-      "model_vars.get('Gini', 0)[-1]=0.6232\n",
       "Condition not met at step 157. No data to save.\n",
       "157\n",
-      "model_vars.get('Gini', 0)[-1]=0.6604\n",
       "Condition not met at step 158. No data to save.\n",
       "158\n",
-      "model_vars.get('Gini', 0)[-1]=0.6454\n",
       "Condition not met at step 159. No data to save.\n",
       "159\n",
-      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
       "Condition not met at step 160. No data to save.\n",
       "160\n",
-      "model_vars.get('Gini', 0)[-1]=0.64\n",
       "Condition not met at step 161. No data to save.\n",
       "161\n",
-      "model_vars.get('Gini', 0)[-1]=0.6434\n",
       "Condition not met at step 162. No data to save.\n",
       "162\n",
-      "model_vars.get('Gini', 0)[-1]=0.6512\n",
       "Condition not met at step 163. No data to save.\n",
       "163\n",
-      "model_vars.get('Gini', 0)[-1]=0.6612\n",
       "Condition not met at step 164. No data to save.\n",
       "164\n",
-      "model_vars.get('Gini', 0)[-1]=0.6622\n",
       "Condition not met at step 165. No data to save.\n",
       "165\n",
-      "model_vars.get('Gini', 0)[-1]=0.6572\n",
       "Condition not met at step 166. No data to save.\n",
       "166\n",
-      "model_vars.get('Gini', 0)[-1]=0.6734\n",
       "Condition not met at step 167. No data to save.\n",
       "167\n",
-      "model_vars.get('Gini', 0)[-1]=0.654\n",
       "Condition not met at step 168. No data to save.\n",
       "168\n",
-      "model_vars.get('Gini', 0)[-1]=0.6832\n",
       "Condition not met at step 169. No data to save.\n",
       "169\n",
-      "model_vars.get('Gini', 0)[-1]=0.6736\n",
       "Condition not met at step 170. No data to save.\n",
       "170\n",
-      "model_vars.get('Gini', 0)[-1]=0.6346\n",
       "Condition not met at step 171. No data to save.\n",
       "171\n",
-      "model_vars.get('Gini', 0)[-1]=0.652\n",
       "Condition not met at step 172. No data to save.\n",
       "172\n",
-      "model_vars.get('Gini', 0)[-1]=0.6556\n",
       "Condition not met at step 173. No data to save.\n",
       "173\n",
-      "model_vars.get('Gini', 0)[-1]=0.6315999999999999\n",
       "Condition not met at step 174. No data to save.\n",
       "174\n",
-      "model_vars.get('Gini', 0)[-1]=0.6742\n",
       "Condition not met at step 175. No data to save.\n",
       "175\n",
-      "model_vars.get('Gini', 0)[-1]=0.6722\n",
       "Condition not met at step 176. No data to save.\n",
       "176\n",
-      "model_vars.get('Gini', 0)[-1]=0.6746000000000001\n",
       "Condition not met at step 177. No data to save.\n",
       "177\n",
-      "model_vars.get('Gini', 0)[-1]=0.6876\n",
       "Condition not met at step 178. No data to save.\n",
       "178\n",
-      "model_vars.get('Gini', 0)[-1]=0.6776\n",
       "Condition not met at step 179. No data to save.\n",
       "179\n",
-      "model_vars.get('Gini', 0)[-1]=0.706\n",
-      "Condition met. Appended special results for step 180 to test_path/special_results.parquet\n",
+      "Condition not met at step 180. No data to save.\n",
       "180\n",
-      "model_vars.get('Gini', 0)[-1]=0.6544\n",
       "Condition not met at step 181. No data to save.\n",
       "181\n",
-      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
       "Condition not met at step 182. No data to save.\n",
       "182\n",
-      "model_vars.get('Gini', 0)[-1]=0.6542\n",
       "Condition not met at step 183. No data to save.\n",
       "183\n",
-      "model_vars.get('Gini', 0)[-1]=0.6492\n",
       "Condition not met at step 184. No data to save.\n",
       "184\n",
-      "model_vars.get('Gini', 0)[-1]=0.6374\n",
       "Condition not met at step 185. No data to save.\n",
       "185\n",
-      "model_vars.get('Gini', 0)[-1]=0.6366\n",
       "Condition not met at step 186. No data to save.\n",
       "186\n",
-      "model_vars.get('Gini', 0)[-1]=0.6604\n",
       "Condition not met at step 187. No data to save.\n",
       "187\n",
-      "model_vars.get('Gini', 0)[-1]=0.6444000000000001\n",
       "Condition not met at step 188. No data to save.\n",
       "188\n",
-      "model_vars.get('Gini', 0)[-1]=0.6562\n",
       "Condition not met at step 189. No data to save.\n",
       "189\n",
-      "model_vars.get('Gini', 0)[-1]=0.652\n",
       "Condition not met at step 190. No data to save.\n",
       "190\n",
-      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
       "Condition not met at step 191. No data to save.\n",
       "191\n",
-      "model_vars.get('Gini', 0)[-1]=0.5978\n",
       "Condition not met at step 192. No data to save.\n",
       "192\n",
-      "model_vars.get('Gini', 0)[-1]=0.6194\n",
       "Condition not met at step 193. No data to save.\n",
       "193\n",
-      "model_vars.get('Gini', 0)[-1]=0.6195999999999999\n",
       "Condition not met at step 194. No data to save.\n",
       "194\n",
-      "model_vars.get('Gini', 0)[-1]=0.6144000000000001\n",
       "Condition not met at step 195. No data to save.\n",
       "195\n",
-      "model_vars.get('Gini', 0)[-1]=0.6086\n",
       "Condition not met at step 196. No data to save.\n",
       "196\n",
-      "model_vars.get('Gini', 0)[-1]=0.6013999999999999\n",
       "Condition not met at step 197. No data to save.\n",
       "197\n",
-      "model_vars.get('Gini', 0)[-1]=0.613\n",
       "Condition not met at step 198. No data to save.\n",
       "198\n",
-      "model_vars.get('Gini', 0)[-1]=0.6184000000000001\n",
       "Condition not met at step 199. No data to save.\n",
       "199\n",
-      "CALLED\n",
-      "self.model._steps=200\n",
-      " TEST self.model._steps=200\n",
-      " TEST self._cache_interval=100\n",
-      "       Gini\n",
-      "100  0.6650\n",
-      "101  0.6506\n",
-      "102  0.6734\n",
-      "103  0.6656\n",
-      "104  0.6450\n",
-      "..      ...\n",
-      "195  0.6086\n",
-      "196  0.6014\n",
-      "197  0.6130\n",
-      "198  0.6184\n",
-      "199  0.6284\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_002.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_002.parquet'\n",
-      "Saving model to test_path/model_data_002.parquet\n",
-      "Saving agent to test_path/agent_data_002.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6284000000000001\n",
+      "all_records=<itertools.chain object at 0x7f77e00c5360>\n",
+      "rep_names=['Wealth']\n",
       "Condition not met at step 200. No data to save.\n",
       "200\n",
-      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
       "Condition not met at step 201. No data to save.\n",
       "201\n",
-      "model_vars.get('Gini', 0)[-1]=0.6086\n",
       "Condition not met at step 202. No data to save.\n",
       "202\n",
-      "model_vars.get('Gini', 0)[-1]=0.625\n",
       "Condition not met at step 203. No data to save.\n",
       "203\n",
-      "model_vars.get('Gini', 0)[-1]=0.606\n",
       "Condition not met at step 204. No data to save.\n",
       "204\n",
-      "model_vars.get('Gini', 0)[-1]=0.624\n",
       "Condition not met at step 205. No data to save.\n",
       "205\n",
-      "model_vars.get('Gini', 0)[-1]=0.6388\n",
       "Condition not met at step 206. No data to save.\n",
       "206\n",
-      "model_vars.get('Gini', 0)[-1]=0.6158\n",
       "Condition not met at step 207. No data to save.\n",
       "207\n",
-      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
       "Condition not met at step 208. No data to save.\n",
       "208\n",
-      "model_vars.get('Gini', 0)[-1]=0.6494\n",
       "Condition not met at step 209. No data to save.\n",
       "209\n",
-      "model_vars.get('Gini', 0)[-1]=0.6434\n",
       "Condition not met at step 210. No data to save.\n",
       "210\n",
-      "model_vars.get('Gini', 0)[-1]=0.6358\n",
       "Condition not met at step 211. No data to save.\n",
       "211\n",
-      "model_vars.get('Gini', 0)[-1]=0.6328\n",
       "Condition not met at step 212. No data to save.\n",
       "212\n",
-      "model_vars.get('Gini', 0)[-1]=0.646\n",
       "Condition not met at step 213. No data to save.\n",
       "213\n",
-      "model_vars.get('Gini', 0)[-1]=0.6508\n",
       "Condition not met at step 214. No data to save.\n",
       "214\n",
-      "model_vars.get('Gini', 0)[-1]=0.6448\n",
       "Condition not met at step 215. No data to save.\n",
       "215\n",
-      "model_vars.get('Gini', 0)[-1]=0.6456\n",
       "Condition not met at step 216. No data to save.\n",
       "216\n",
-      "model_vars.get('Gini', 0)[-1]=0.6358\n",
       "Condition not met at step 217. No data to save.\n",
       "217\n",
-      "model_vars.get('Gini', 0)[-1]=0.642\n",
       "Condition not met at step 218. No data to save.\n",
       "218\n",
-      "model_vars.get('Gini', 0)[-1]=0.659\n",
       "Condition not met at step 219. No data to save.\n",
       "219\n",
-      "model_vars.get('Gini', 0)[-1]=0.6632\n",
       "Condition not met at step 220. No data to save.\n",
       "220\n",
-      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
       "Condition not met at step 221. No data to save.\n",
       "221\n",
-      "model_vars.get('Gini', 0)[-1]=0.6752\n",
       "Condition not met at step 222. No data to save.\n",
       "222\n",
-      "model_vars.get('Gini', 0)[-1]=0.6584\n",
       "Condition not met at step 223. No data to save.\n",
       "223\n",
-      "model_vars.get('Gini', 0)[-1]=0.663\n",
       "Condition not met at step 224. No data to save.\n",
       "224\n",
-      "model_vars.get('Gini', 0)[-1]=0.6312\n",
       "Condition not met at step 225. No data to save.\n",
       "225\n",
-      "model_vars.get('Gini', 0)[-1]=0.6215999999999999\n",
       "Condition not met at step 226. No data to save.\n",
       "226\n",
-      "model_vars.get('Gini', 0)[-1]=0.5973999999999999\n",
       "Condition not met at step 227. No data to save.\n",
       "227\n",
-      "model_vars.get('Gini', 0)[-1]=0.623\n",
       "Condition not met at step 228. No data to save.\n",
       "228\n",
-      "model_vars.get('Gini', 0)[-1]=0.6532\n",
       "Condition not met at step 229. No data to save.\n",
       "229\n",
-      "model_vars.get('Gini', 0)[-1]=0.6502\n",
       "Condition not met at step 230. No data to save.\n",
       "230\n",
-      "model_vars.get('Gini', 0)[-1]=0.654\n",
       "Condition not met at step 231. No data to save.\n",
       "231\n",
-      "model_vars.get('Gini', 0)[-1]=0.6608\n",
       "Condition not met at step 232. No data to save.\n",
       "232\n",
-      "model_vars.get('Gini', 0)[-1]=0.6464000000000001\n",
       "Condition not met at step 233. No data to save.\n",
       "233\n",
-      "model_vars.get('Gini', 0)[-1]=0.6344000000000001\n",
       "Condition not met at step 234. No data to save.\n",
       "234\n",
-      "model_vars.get('Gini', 0)[-1]=0.6374\n",
-      "Condition not met at step 235. No data to save.\n",
+      "Condition met. Appended special results for step 235 to test_path/special_results.parquet\n",
       "235\n",
-      "model_vars.get('Gini', 0)[-1]=0.621\n",
       "Condition not met at step 236. No data to save.\n",
       "236\n",
-      "model_vars.get('Gini', 0)[-1]=0.6282000000000001\n",
       "Condition not met at step 237. No data to save.\n",
       "237\n",
-      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
       "Condition not met at step 238. No data to save.\n",
       "238\n",
-      "model_vars.get('Gini', 0)[-1]=0.6166\n",
       "Condition not met at step 239. No data to save.\n",
       "239\n",
-      "model_vars.get('Gini', 0)[-1]=0.6033999999999999\n",
       "Condition not met at step 240. No data to save.\n",
       "240\n",
-      "model_vars.get('Gini', 0)[-1]=0.6504000000000001\n",
       "Condition not met at step 241. No data to save.\n",
       "241\n",
-      "model_vars.get('Gini', 0)[-1]=0.5998\n",
       "Condition not met at step 242. No data to save.\n",
       "242\n",
-      "model_vars.get('Gini', 0)[-1]=0.6292\n",
       "Condition not met at step 243. No data to save.\n",
       "243\n",
-      "model_vars.get('Gini', 0)[-1]=0.6394\n",
       "Condition not met at step 244. No data to save.\n",
       "244\n",
-      "model_vars.get('Gini', 0)[-1]=0.643\n",
       "Condition not met at step 245. No data to save.\n",
       "245\n",
-      "model_vars.get('Gini', 0)[-1]=0.6448\n",
       "Condition not met at step 246. No data to save.\n",
       "246\n",
-      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
       "Condition not met at step 247. No data to save.\n",
       "247\n",
-      "model_vars.get('Gini', 0)[-1]=0.6752\n",
       "Condition not met at step 248. No data to save.\n",
       "248\n",
-      "model_vars.get('Gini', 0)[-1]=0.683\n",
       "Condition not met at step 249. No data to save.\n",
       "249\n",
-      "model_vars.get('Gini', 0)[-1]=0.679\n",
       "Condition not met at step 250. No data to save.\n",
       "250\n",
-      "model_vars.get('Gini', 0)[-1]=0.6862\n",
       "Condition not met at step 251. No data to save.\n",
       "251\n",
-      "model_vars.get('Gini', 0)[-1]=0.6882\n",
       "Condition not met at step 252. No data to save.\n",
       "252\n",
-      "model_vars.get('Gini', 0)[-1]=0.6888000000000001\n",
       "Condition not met at step 253. No data to save.\n",
       "253\n",
-      "model_vars.get('Gini', 0)[-1]=0.6978\n",
       "Condition not met at step 254. No data to save.\n",
       "254\n",
-      "model_vars.get('Gini', 0)[-1]=0.6836\n",
       "Condition not met at step 255. No data to save.\n",
       "255\n",
-      "model_vars.get('Gini', 0)[-1]=0.6996\n",
       "Condition not met at step 256. No data to save.\n",
       "256\n",
-      "model_vars.get('Gini', 0)[-1]=0.6984\n",
       "Condition not met at step 257. No data to save.\n",
       "257\n",
-      "model_vars.get('Gini', 0)[-1]=0.6950000000000001\n",
       "Condition not met at step 258. No data to save.\n",
       "258\n",
-      "model_vars.get('Gini', 0)[-1]=0.6904\n",
       "Condition not met at step 259. No data to save.\n",
       "259\n",
-      "model_vars.get('Gini', 0)[-1]=0.6964\n",
       "Condition not met at step 260. No data to save.\n",
       "260\n",
-      "model_vars.get('Gini', 0)[-1]=0.6602\n",
       "Condition not met at step 261. No data to save.\n",
       "261\n",
-      "model_vars.get('Gini', 0)[-1]=0.6961999999999999\n",
       "Condition not met at step 262. No data to save.\n",
       "262\n",
-      "model_vars.get('Gini', 0)[-1]=0.6828000000000001\n",
       "Condition not met at step 263. No data to save.\n",
       "263\n",
-      "model_vars.get('Gini', 0)[-1]=0.6692\n",
       "Condition not met at step 264. No data to save.\n",
       "264\n",
-      "model_vars.get('Gini', 0)[-1]=0.7076\n",
-      "Condition met. Appended special results for step 265 to test_path/special_results.parquet\n",
+      "Condition not met at step 265. No data to save.\n",
       "265\n",
-      "model_vars.get('Gini', 0)[-1]=0.6912\n",
       "Condition not met at step 266. No data to save.\n",
       "266\n",
-      "model_vars.get('Gini', 0)[-1]=0.6754\n",
       "Condition not met at step 267. No data to save.\n",
       "267\n",
-      "model_vars.get('Gini', 0)[-1]=0.6626000000000001\n",
       "Condition not met at step 268. No data to save.\n",
       "268\n",
-      "model_vars.get('Gini', 0)[-1]=0.6376\n",
       "Condition not met at step 269. No data to save.\n",
       "269\n",
-      "model_vars.get('Gini', 0)[-1]=0.6392\n",
       "Condition not met at step 270. No data to save.\n",
       "270\n",
-      "model_vars.get('Gini', 0)[-1]=0.6476\n",
       "Condition not met at step 271. No data to save.\n",
       "271\n",
-      "model_vars.get('Gini', 0)[-1]=0.6482\n",
       "Condition not met at step 272. No data to save.\n",
       "272\n",
-      "model_vars.get('Gini', 0)[-1]=0.6666000000000001\n",
       "Condition not met at step 273. No data to save.\n",
       "273\n",
-      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
       "Condition not met at step 274. No data to save.\n",
       "274\n",
-      "model_vars.get('Gini', 0)[-1]=0.6338\n",
       "Condition not met at step 275. No data to save.\n",
       "275\n",
-      "model_vars.get('Gini', 0)[-1]=0.6106\n",
       "Condition not met at step 276. No data to save.\n",
       "276\n",
-      "model_vars.get('Gini', 0)[-1]=0.6264000000000001\n",
       "Condition not met at step 277. No data to save.\n",
       "277\n",
-      "model_vars.get('Gini', 0)[-1]=0.627\n",
       "Condition not met at step 278. No data to save.\n",
       "278\n",
-      "model_vars.get('Gini', 0)[-1]=0.622\n",
       "Condition not met at step 279. No data to save.\n",
       "279\n",
-      "model_vars.get('Gini', 0)[-1]=0.6416\n",
       "Condition not met at step 280. No data to save.\n",
       "280\n",
-      "model_vars.get('Gini', 0)[-1]=0.6444000000000001\n",
       "Condition not met at step 281. No data to save.\n",
       "281\n",
-      "model_vars.get('Gini', 0)[-1]=0.6592\n",
-      "Condition not met at step 282. No data to save.\n",
+      "Condition met. Appended special results for step 282 to test_path/special_results.parquet\n",
       "282\n",
-      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
       "Condition not met at step 283. No data to save.\n",
       "283\n",
-      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
-      "Condition not met at step 284. No data to save.\n",
+      "Condition met. Appended special results for step 284 to test_path/special_results.parquet\n",
       "284\n",
-      "model_vars.get('Gini', 0)[-1]=0.6572\n",
-      "Condition not met at step 285. No data to save.\n",
+      "Condition met. Appended special results for step 285 to test_path/special_results.parquet\n",
       "285\n",
-      "model_vars.get('Gini', 0)[-1]=0.6497999999999999\n",
-      "Condition not met at step 286. No data to save.\n",
+      "Condition met. Appended special results for step 286 to test_path/special_results.parquet\n",
       "286\n",
-      "model_vars.get('Gini', 0)[-1]=0.6722\n",
       "Condition not met at step 287. No data to save.\n",
       "287\n",
-      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
       "Condition not met at step 288. No data to save.\n",
       "288\n",
-      "model_vars.get('Gini', 0)[-1]=0.6716\n",
-      "Condition not met at step 289. No data to save.\n",
+      "Condition met. Appended special results for step 289 to test_path/special_results.parquet\n",
       "289\n",
-      "model_vars.get('Gini', 0)[-1]=0.6786000000000001\n",
       "Condition not met at step 290. No data to save.\n",
       "290\n",
-      "model_vars.get('Gini', 0)[-1]=0.6648000000000001\n",
       "Condition not met at step 291. No data to save.\n",
       "291\n",
-      "model_vars.get('Gini', 0)[-1]=0.6588\n",
       "Condition not met at step 292. No data to save.\n",
       "292\n",
-      "model_vars.get('Gini', 0)[-1]=0.6696\n",
       "Condition not met at step 293. No data to save.\n",
       "293\n",
-      "model_vars.get('Gini', 0)[-1]=0.6604\n",
       "Condition not met at step 294. No data to save.\n",
       "294\n",
-      "model_vars.get('Gini', 0)[-1]=0.64\n",
       "Condition not met at step 295. No data to save.\n",
       "295\n",
-      "model_vars.get('Gini', 0)[-1]=0.6338\n",
       "Condition not met at step 296. No data to save.\n",
       "296\n",
-      "model_vars.get('Gini', 0)[-1]=0.642\n",
       "Condition not met at step 297. No data to save.\n",
       "297\n",
-      "model_vars.get('Gini', 0)[-1]=0.6716\n",
       "Condition not met at step 298. No data to save.\n",
       "298\n",
-      "model_vars.get('Gini', 0)[-1]=0.6904\n",
       "Condition not met at step 299. No data to save.\n",
       "299\n",
-      "CALLED\n",
-      "self.model._steps=300\n",
-      " TEST self.model._steps=300\n",
-      " TEST self._cache_interval=100\n",
-      "       Gini\n",
-      "200  0.6256\n",
-      "201  0.6086\n",
-      "202  0.6250\n",
-      "203  0.6060\n",
-      "204  0.6240\n",
-      "..      ...\n",
-      "295  0.6338\n",
-      "296  0.6420\n",
-      "297  0.6716\n",
-      "298  0.6904\n",
-      "299  0.6808\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_003.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_003.parquet'\n",
-      "Saving model to test_path/model_data_003.parquet\n",
-      "Saving agent to test_path/agent_data_003.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
+      "all_records=<itertools.chain object at 0x7f77dfe56d10>\n",
+      "rep_names=['Wealth']\n",
       "Condition not met at step 300. No data to save.\n",
       "300\n",
-      "model_vars.get('Gini', 0)[-1]=0.6764\n",
       "Condition not met at step 301. No data to save.\n",
       "301\n",
-      "model_vars.get('Gini', 0)[-1]=0.6888000000000001\n",
       "Condition not met at step 302. No data to save.\n",
       "302\n",
-      "model_vars.get('Gini', 0)[-1]=0.6702\n",
       "Condition not met at step 303. No data to save.\n",
       "303\n",
-      "model_vars.get('Gini', 0)[-1]=0.6726000000000001\n",
       "Condition not met at step 304. No data to save.\n",
       "304\n",
-      "model_vars.get('Gini', 0)[-1]=0.6638\n",
       "Condition not met at step 305. No data to save.\n",
       "305\n",
-      "model_vars.get('Gini', 0)[-1]=0.664\n",
       "Condition not met at step 306. No data to save.\n",
       "306\n",
-      "model_vars.get('Gini', 0)[-1]=0.6686000000000001\n",
       "Condition not met at step 307. No data to save.\n",
       "307\n",
-      "model_vars.get('Gini', 0)[-1]=0.6792\n",
       "Condition not met at step 308. No data to save.\n",
       "308\n",
-      "model_vars.get('Gini', 0)[-1]=0.6752\n",
       "Condition not met at step 309. No data to save.\n",
       "309\n",
-      "model_vars.get('Gini', 0)[-1]=0.6416\n",
       "Condition not met at step 310. No data to save.\n",
       "310\n",
-      "model_vars.get('Gini', 0)[-1]=0.648\n",
       "Condition not met at step 311. No data to save.\n",
       "311\n",
-      "model_vars.get('Gini', 0)[-1]=0.6332\n",
       "Condition not met at step 312. No data to save.\n",
       "312\n",
-      "model_vars.get('Gini', 0)[-1]=0.6235999999999999\n",
       "Condition not met at step 313. No data to save.\n",
       "313\n",
-      "model_vars.get('Gini', 0)[-1]=0.6734\n",
       "Condition not met at step 314. No data to save.\n",
       "314\n",
-      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
       "Condition not met at step 315. No data to save.\n",
       "315\n",
-      "model_vars.get('Gini', 0)[-1]=0.612\n",
       "Condition not met at step 316. No data to save.\n",
       "316\n",
-      "model_vars.get('Gini', 0)[-1]=0.626\n",
       "Condition not met at step 317. No data to save.\n",
       "317\n",
-      "model_vars.get('Gini', 0)[-1]=0.631\n",
-      "Condition not met at step 318. No data to save.\n",
+      "Condition met. Appended special results for step 318 to test_path/special_results.parquet\n",
       "318\n",
-      "model_vars.get('Gini', 0)[-1]=0.632\n",
       "Condition not met at step 319. No data to save.\n",
       "319\n",
-      "model_vars.get('Gini', 0)[-1]=0.6266\n",
       "Condition not met at step 320. No data to save.\n",
       "320\n",
-      "model_vars.get('Gini', 0)[-1]=0.6248\n",
-      "Condition not met at step 321. No data to save.\n",
+      "Condition met. Appended special results for step 321 to test_path/special_results.parquet\n",
       "321\n",
-      "model_vars.get('Gini', 0)[-1]=0.6656\n",
-      "Condition not met at step 322. No data to save.\n",
+      "Condition met. Appended special results for step 322 to test_path/special_results.parquet\n",
       "322\n",
-      "model_vars.get('Gini', 0)[-1]=0.6554\n",
-      "Condition not met at step 323. No data to save.\n",
+      "Condition met. Appended special results for step 323 to test_path/special_results.parquet\n",
       "323\n",
-      "model_vars.get('Gini', 0)[-1]=0.6658\n",
-      "Condition not met at step 324. No data to save.\n",
+      "Condition met. Appended special results for step 324 to test_path/special_results.parquet\n",
       "324\n",
-      "model_vars.get('Gini', 0)[-1]=0.6474\n",
-      "Condition not met at step 325. No data to save.\n",
+      "Condition met. Appended special results for step 325 to test_path/special_results.parquet\n",
       "325\n",
-      "model_vars.get('Gini', 0)[-1]=0.657\n",
-      "Condition not met at step 326. No data to save.\n",
+      "Condition met. Appended special results for step 326 to test_path/special_results.parquet\n",
       "326\n",
-      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
-      "Condition not met at step 327. No data to save.\n",
+      "Condition met. Appended special results for step 327 to test_path/special_results.parquet\n",
       "327\n",
-      "model_vars.get('Gini', 0)[-1]=0.6614\n",
-      "Condition not met at step 328. No data to save.\n",
+      "Condition met. Appended special results for step 328 to test_path/special_results.parquet\n",
       "328\n",
-      "model_vars.get('Gini', 0)[-1]=0.6694\n",
-      "Condition not met at step 329. No data to save.\n",
+      "Condition met. Appended special results for step 329 to test_path/special_results.parquet\n",
       "329\n",
-      "model_vars.get('Gini', 0)[-1]=0.6802\n",
-      "Condition not met at step 330. No data to save.\n",
+      "Condition met. Appended special results for step 330 to test_path/special_results.parquet\n",
       "330\n",
-      "model_vars.get('Gini', 0)[-1]=0.6506000000000001\n",
-      "Condition not met at step 331. No data to save.\n",
+      "Condition met. Appended special results for step 331 to test_path/special_results.parquet\n",
       "331\n",
-      "model_vars.get('Gini', 0)[-1]=0.638\n",
-      "Condition not met at step 332. No data to save.\n",
+      "Condition met. Appended special results for step 332 to test_path/special_results.parquet\n",
       "332\n",
-      "model_vars.get('Gini', 0)[-1]=0.6674\n",
-      "Condition not met at step 333. No data to save.\n",
+      "Condition met. Appended special results for step 333 to test_path/special_results.parquet\n",
       "333\n",
-      "model_vars.get('Gini', 0)[-1]=0.6734\n",
-      "Condition not met at step 334. No data to save.\n",
+      "Condition met. Appended special results for step 334 to test_path/special_results.parquet\n",
       "334\n",
-      "model_vars.get('Gini', 0)[-1]=0.666\n",
-      "Condition not met at step 335. No data to save.\n",
+      "Condition met. Appended special results for step 335 to test_path/special_results.parquet\n",
       "335\n",
-      "model_vars.get('Gini', 0)[-1]=0.6736\n",
-      "Condition not met at step 336. No data to save.\n",
+      "Condition met. Appended special results for step 336 to test_path/special_results.parquet\n",
       "336\n",
-      "model_vars.get('Gini', 0)[-1]=0.6842\n",
-      "Condition not met at step 337. No data to save.\n",
+      "Condition met. Appended special results for step 337 to test_path/special_results.parquet\n",
       "337\n",
-      "model_vars.get('Gini', 0)[-1]=0.6617999999999999\n",
-      "Condition not met at step 338. No data to save.\n",
+      "Condition met. Appended special results for step 338 to test_path/special_results.parquet\n",
       "338\n",
-      "model_vars.get('Gini', 0)[-1]=0.651\n",
-      "Condition not met at step 339. No data to save.\n",
+      "Condition met. Appended special results for step 339 to test_path/special_results.parquet\n",
       "339\n",
-      "model_vars.get('Gini', 0)[-1]=0.659\n",
       "Condition not met at step 340. No data to save.\n",
       "340\n",
-      "model_vars.get('Gini', 0)[-1]=0.6654\n",
       "Condition not met at step 341. No data to save.\n",
       "341\n",
-      "model_vars.get('Gini', 0)[-1]=0.6626000000000001\n",
       "Condition not met at step 342. No data to save.\n",
       "342\n",
-      "model_vars.get('Gini', 0)[-1]=0.6704\n",
       "Condition not met at step 343. No data to save.\n",
       "343\n",
-      "model_vars.get('Gini', 0)[-1]=0.6542\n",
       "Condition not met at step 344. No data to save.\n",
       "344\n",
-      "model_vars.get('Gini', 0)[-1]=0.6584\n",
       "Condition not met at step 345. No data to save.\n",
       "345\n",
-      "model_vars.get('Gini', 0)[-1]=0.6572\n",
       "Condition not met at step 346. No data to save.\n",
       "346\n",
-      "model_vars.get('Gini', 0)[-1]=0.6436\n",
       "Condition not met at step 347. No data to save.\n",
       "347\n",
-      "model_vars.get('Gini', 0)[-1]=0.6626000000000001\n",
       "Condition not met at step 348. No data to save.\n",
       "348\n",
-      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
       "Condition not met at step 349. No data to save.\n",
       "349\n",
-      "model_vars.get('Gini', 0)[-1]=0.6806\n",
       "Condition not met at step 350. No data to save.\n",
       "350\n",
-      "model_vars.get('Gini', 0)[-1]=0.6918\n",
       "Condition not met at step 351. No data to save.\n",
       "351\n",
-      "model_vars.get('Gini', 0)[-1]=0.6842\n",
       "Condition not met at step 352. No data to save.\n",
       "352\n",
-      "model_vars.get('Gini', 0)[-1]=0.6508\n",
       "Condition not met at step 353. No data to save.\n",
       "353\n",
-      "model_vars.get('Gini', 0)[-1]=0.6476\n",
       "Condition not met at step 354. No data to save.\n",
       "354\n",
-      "model_vars.get('Gini', 0)[-1]=0.645\n",
       "Condition not met at step 355. No data to save.\n",
       "355\n",
-      "model_vars.get('Gini', 0)[-1]=0.615\n",
       "Condition not met at step 356. No data to save.\n",
       "356\n",
-      "model_vars.get('Gini', 0)[-1]=0.6254\n",
       "Condition not met at step 357. No data to save.\n",
       "357\n",
-      "model_vars.get('Gini', 0)[-1]=0.6093999999999999\n",
       "Condition not met at step 358. No data to save.\n",
       "358\n",
-      "model_vars.get('Gini', 0)[-1]=0.601\n",
       "Condition not met at step 359. No data to save.\n",
       "359\n",
-      "model_vars.get('Gini', 0)[-1]=0.5958\n",
       "Condition not met at step 360. No data to save.\n",
       "360\n",
-      "model_vars.get('Gini', 0)[-1]=0.6116\n",
       "Condition not met at step 361. No data to save.\n",
       "361\n",
-      "model_vars.get('Gini', 0)[-1]=0.6602\n",
       "Condition not met at step 362. No data to save.\n",
       "362\n",
-      "model_vars.get('Gini', 0)[-1]=0.6948000000000001\n",
       "Condition not met at step 363. No data to save.\n",
       "363\n",
-      "model_vars.get('Gini', 0)[-1]=0.675\n",
       "Condition not met at step 364. No data to save.\n",
       "364\n",
-      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
       "Condition not met at step 365. No data to save.\n",
       "365\n",
-      "model_vars.get('Gini', 0)[-1]=0.7064\n",
-      "Condition met. Appended special results for step 366 to test_path/special_results.parquet\n",
+      "Condition not met at step 366. No data to save.\n",
       "366\n",
-      "model_vars.get('Gini', 0)[-1]=0.6936\n",
       "Condition not met at step 367. No data to save.\n",
       "367\n",
-      "model_vars.get('Gini', 0)[-1]=0.685\n",
       "Condition not met at step 368. No data to save.\n",
       "368\n",
-      "model_vars.get('Gini', 0)[-1]=0.7116\n",
-      "Condition met. Appended special results for step 369 to test_path/special_results.parquet\n",
+      "Condition not met at step 369. No data to save.\n",
       "369\n",
-      "model_vars.get('Gini', 0)[-1]=0.708\n",
-      "Condition met. Appended special results for step 370 to test_path/special_results.parquet\n",
+      "Condition not met at step 370. No data to save.\n",
       "370\n",
-      "model_vars.get('Gini', 0)[-1]=0.6938\n",
       "Condition not met at step 371. No data to save.\n",
       "371\n",
-      "model_vars.get('Gini', 0)[-1]=0.6912\n",
       "Condition not met at step 372. No data to save.\n",
       "372\n",
-      "model_vars.get('Gini', 0)[-1]=0.6914\n",
       "Condition not met at step 373. No data to save.\n",
       "373\n",
-      "model_vars.get('Gini', 0)[-1]=0.7058\n",
-      "Condition met. Appended special results for step 374 to test_path/special_results.parquet\n",
+      "Condition not met at step 374. No data to save.\n",
       "374\n",
-      "model_vars.get('Gini', 0)[-1]=0.6928000000000001\n",
       "Condition not met at step 375. No data to save.\n",
       "375\n",
-      "model_vars.get('Gini', 0)[-1]=0.6916\n",
       "Condition not met at step 376. No data to save.\n",
       "376\n",
-      "model_vars.get('Gini', 0)[-1]=0.6926\n",
       "Condition not met at step 377. No data to save.\n",
       "377\n",
-      "model_vars.get('Gini', 0)[-1]=0.6916\n",
       "Condition not met at step 378. No data to save.\n",
       "378\n",
-      "model_vars.get('Gini', 0)[-1]=0.7114\n",
-      "Condition met. Appended special results for step 379 to test_path/special_results.parquet\n",
+      "Condition not met at step 379. No data to save.\n",
       "379\n",
-      "model_vars.get('Gini', 0)[-1]=0.7052\n",
-      "Condition met. Appended special results for step 380 to test_path/special_results.parquet\n",
+      "Condition not met at step 380. No data to save.\n",
       "380\n",
-      "model_vars.get('Gini', 0)[-1]=0.6968000000000001\n",
       "Condition not met at step 381. No data to save.\n",
       "381\n",
-      "model_vars.get('Gini', 0)[-1]=0.6576\n",
       "Condition not met at step 382. No data to save.\n",
       "382\n",
-      "model_vars.get('Gini', 0)[-1]=0.6544\n",
       "Condition not met at step 383. No data to save.\n",
       "383\n",
-      "model_vars.get('Gini', 0)[-1]=0.6342\n",
       "Condition not met at step 384. No data to save.\n",
       "384\n",
-      "model_vars.get('Gini', 0)[-1]=0.615\n",
       "Condition not met at step 385. No data to save.\n",
       "385\n",
-      "model_vars.get('Gini', 0)[-1]=0.6298\n",
       "Condition not met at step 386. No data to save.\n",
       "386\n",
-      "model_vars.get('Gini', 0)[-1]=0.6224000000000001\n",
       "Condition not met at step 387. No data to save.\n",
       "387\n",
-      "model_vars.get('Gini', 0)[-1]=0.5982000000000001\n",
       "Condition not met at step 388. No data to save.\n",
       "388\n",
-      "model_vars.get('Gini', 0)[-1]=0.6122000000000001\n",
       "Condition not met at step 389. No data to save.\n",
       "389\n",
-      "model_vars.get('Gini', 0)[-1]=0.6398\n",
       "Condition not met at step 390. No data to save.\n",
       "390\n",
-      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
       "Condition not met at step 391. No data to save.\n",
       "391\n",
-      "model_vars.get('Gini', 0)[-1]=0.6258\n",
       "Condition not met at step 392. No data to save.\n",
       "392\n",
-      "model_vars.get('Gini', 0)[-1]=0.6372\n",
       "Condition not met at step 393. No data to save.\n",
       "393\n",
-      "model_vars.get('Gini', 0)[-1]=0.6828000000000001\n",
       "Condition not met at step 394. No data to save.\n",
       "394\n",
-      "model_vars.get('Gini', 0)[-1]=0.6734\n",
       "Condition not met at step 395. No data to save.\n",
       "395\n",
-      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
       "Condition not met at step 396. No data to save.\n",
       "396\n",
-      "model_vars.get('Gini', 0)[-1]=0.6448\n",
       "Condition not met at step 397. No data to save.\n",
       "397\n",
-      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
       "Condition not met at step 398. No data to save.\n",
       "398\n",
-      "model_vars.get('Gini', 0)[-1]=0.6556\n",
       "Condition not met at step 399. No data to save.\n",
       "399\n",
-      "CALLED\n",
-      "self.model._steps=400\n",
-      " TEST self.model._steps=400\n",
-      " TEST self._cache_interval=100\n",
-      "       Gini\n",
-      "300  0.6764\n",
-      "301  0.6888\n",
-      "302  0.6702\n",
-      "303  0.6726\n",
-      "304  0.6638\n",
-      "..      ...\n",
-      "395  0.6800\n",
-      "396  0.6448\n",
-      "397  0.6418\n",
-      "398  0.6556\n",
-      "399  0.6790\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_004.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_004.parquet'\n",
-      "Saving model to test_path/model_data_004.parquet\n",
-      "Saving agent to test_path/agent_data_004.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.679\n",
+      "all_records=<itertools.chain object at 0x7f77dfe56ad0>\n",
+      "rep_names=['Wealth']\n",
       "Condition not met at step 400. No data to save.\n",
       "400\n",
-      "model_vars.get('Gini', 0)[-1]=0.6890000000000001\n",
       "Condition not met at step 401. No data to save.\n",
       "401\n",
-      "model_vars.get('Gini', 0)[-1]=0.6862\n",
       "Condition not met at step 402. No data to save.\n",
       "402\n",
-      "model_vars.get('Gini', 0)[-1]=0.6704\n",
       "Condition not met at step 403. No data to save.\n",
       "403\n",
-      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
       "Condition not met at step 404. No data to save.\n",
       "404\n",
-      "model_vars.get('Gini', 0)[-1]=0.6194\n",
       "Condition not met at step 405. No data to save.\n",
       "405\n",
-      "model_vars.get('Gini', 0)[-1]=0.6532\n",
       "Condition not met at step 406. No data to save.\n",
       "406\n",
-      "model_vars.get('Gini', 0)[-1]=0.6784\n",
       "Condition not met at step 407. No data to save.\n",
       "407\n",
-      "model_vars.get('Gini', 0)[-1]=0.6746000000000001\n",
       "Condition not met at step 408. No data to save.\n",
       "408\n",
-      "model_vars.get('Gini', 0)[-1]=0.6664\n",
       "Condition not met at step 409. No data to save.\n",
       "409\n",
-      "model_vars.get('Gini', 0)[-1]=0.6726000000000001\n",
       "Condition not met at step 410. No data to save.\n",
       "410\n",
-      "model_vars.get('Gini', 0)[-1]=0.658\n",
       "Condition not met at step 411. No data to save.\n",
       "411\n",
-      "model_vars.get('Gini', 0)[-1]=0.627\n",
       "Condition not met at step 412. No data to save.\n",
       "412\n",
-      "model_vars.get('Gini', 0)[-1]=0.6175999999999999\n",
       "Condition not met at step 413. No data to save.\n",
       "413\n",
-      "model_vars.get('Gini', 0)[-1]=0.6168\n",
       "Condition not met at step 414. No data to save.\n",
       "414\n",
-      "model_vars.get('Gini', 0)[-1]=0.6318\n",
       "Condition not met at step 415. No data to save.\n",
       "415\n",
-      "model_vars.get('Gini', 0)[-1]=0.6318\n",
       "Condition not met at step 416. No data to save.\n",
       "416\n",
-      "model_vars.get('Gini', 0)[-1]=0.6422\n",
       "Condition not met at step 417. No data to save.\n",
       "417\n",
-      "model_vars.get('Gini', 0)[-1]=0.609\n",
       "Condition not met at step 418. No data to save.\n",
       "418\n",
-      "model_vars.get('Gini', 0)[-1]=0.5594\n",
       "Condition not met at step 419. No data to save.\n",
       "419\n",
-      "model_vars.get('Gini', 0)[-1]=0.577\n",
       "Condition not met at step 420. No data to save.\n",
       "420\n",
-      "model_vars.get('Gini', 0)[-1]=0.5862\n",
       "Condition not met at step 421. No data to save.\n",
       "421\n",
-      "model_vars.get('Gini', 0)[-1]=0.5842\n",
       "Condition not met at step 422. No data to save.\n",
       "422\n",
-      "model_vars.get('Gini', 0)[-1]=0.5722\n",
       "Condition not met at step 423. No data to save.\n",
       "423\n",
-      "model_vars.get('Gini', 0)[-1]=0.5756\n",
       "Condition not met at step 424. No data to save.\n",
       "424\n",
-      "model_vars.get('Gini', 0)[-1]=0.625\n",
-      "Condition not met at step 425. No data to save.\n",
+      "Condition met. Appended special results for step 425 to test_path/special_results.parquet\n",
       "425\n",
-      "model_vars.get('Gini', 0)[-1]=0.6556\n",
       "Condition not met at step 426. No data to save.\n",
       "426\n",
-      "model_vars.get('Gini', 0)[-1]=0.62\n",
       "Condition not met at step 427. No data to save.\n",
       "427\n",
-      "model_vars.get('Gini', 0)[-1]=0.6142000000000001\n",
       "Condition not met at step 428. No data to save.\n",
       "428\n",
-      "model_vars.get('Gini', 0)[-1]=0.6086\n",
       "Condition not met at step 429. No data to save.\n",
       "429\n",
-      "model_vars.get('Gini', 0)[-1]=0.622\n",
       "Condition not met at step 430. No data to save.\n",
       "430\n",
-      "model_vars.get('Gini', 0)[-1]=0.664\n",
       "Condition not met at step 431. No data to save.\n",
       "431\n",
-      "model_vars.get('Gini', 0)[-1]=0.6468\n",
       "Condition not met at step 432. No data to save.\n",
       "432\n",
-      "model_vars.get('Gini', 0)[-1]=0.6398\n",
       "Condition not met at step 433. No data to save.\n",
       "433\n",
-      "model_vars.get('Gini', 0)[-1]=0.6448\n",
       "Condition not met at step 434. No data to save.\n",
       "434\n",
-      "model_vars.get('Gini', 0)[-1]=0.663\n",
       "Condition not met at step 435. No data to save.\n",
       "435\n",
-      "model_vars.get('Gini', 0)[-1]=0.655\n",
       "Condition not met at step 436. No data to save.\n",
       "436\n",
-      "model_vars.get('Gini', 0)[-1]=0.6812\n",
       "Condition not met at step 437. No data to save.\n",
       "437\n",
-      "model_vars.get('Gini', 0)[-1]=0.6436\n",
       "Condition not met at step 438. No data to save.\n",
       "438\n",
-      "model_vars.get('Gini', 0)[-1]=0.6594\n",
       "Condition not met at step 439. No data to save.\n",
       "439\n",
-      "model_vars.get('Gini', 0)[-1]=0.6616\n",
       "Condition not met at step 440. No data to save.\n",
       "440\n",
-      "model_vars.get('Gini', 0)[-1]=0.675\n",
       "Condition not met at step 441. No data to save.\n",
       "441\n",
-      "model_vars.get('Gini', 0)[-1]=0.6794\n",
       "Condition not met at step 442. No data to save.\n",
       "442\n",
-      "model_vars.get('Gini', 0)[-1]=0.6924\n",
-      "Condition not met at step 443. No data to save.\n",
+      "Condition met. Appended special results for step 443 to test_path/special_results.parquet\n",
       "443\n",
-      "model_vars.get('Gini', 0)[-1]=0.7192000000000001\n",
       "Condition met. Appended special results for step 444 to test_path/special_results.parquet\n",
       "444\n",
-      "model_vars.get('Gini', 0)[-1]=0.7183999999999999\n",
       "Condition met. Appended special results for step 445 to test_path/special_results.parquet\n",
       "445\n",
-      "model_vars.get('Gini', 0)[-1]=0.7298\n",
       "Condition met. Appended special results for step 446 to test_path/special_results.parquet\n",
       "446\n",
-      "model_vars.get('Gini', 0)[-1]=0.7202\n",
       "Condition met. Appended special results for step 447 to test_path/special_results.parquet\n",
       "447\n",
-      "model_vars.get('Gini', 0)[-1]=0.6868000000000001\n",
-      "Condition not met at step 448. No data to save.\n",
+      "Condition met. Appended special results for step 448 to test_path/special_results.parquet\n",
       "448\n",
-      "model_vars.get('Gini', 0)[-1]=0.6944\n",
       "Condition not met at step 449. No data to save.\n",
       "449\n",
-      "model_vars.get('Gini', 0)[-1]=0.6902\n",
       "Condition not met at step 450. No data to save.\n",
       "450\n",
-      "model_vars.get('Gini', 0)[-1]=0.6698\n",
-      "Condition not met at step 451. No data to save.\n",
+      "Condition met. Appended special results for step 451 to test_path/special_results.parquet\n",
       "451\n",
-      "model_vars.get('Gini', 0)[-1]=0.6742\n",
       "Condition not met at step 452. No data to save.\n",
       "452\n",
-      "model_vars.get('Gini', 0)[-1]=0.6699999999999999\n",
       "Condition not met at step 453. No data to save.\n",
       "453\n",
-      "model_vars.get('Gini', 0)[-1]=0.7101999999999999\n",
-      "Condition met. Appended special results for step 454 to test_path/special_results.parquet\n",
+      "Condition not met at step 454. No data to save.\n",
       "454\n",
-      "model_vars.get('Gini', 0)[-1]=0.7146\n",
-      "Condition met. Appended special results for step 455 to test_path/special_results.parquet\n",
+      "Condition not met at step 455. No data to save.\n",
       "455\n",
-      "model_vars.get('Gini', 0)[-1]=0.6864\n",
-      "Condition not met at step 456. No data to save.\n",
+      "Condition met. Appended special results for step 456 to test_path/special_results.parquet\n",
       "456\n",
-      "model_vars.get('Gini', 0)[-1]=0.6948000000000001\n",
-      "Condition not met at step 457. No data to save.\n",
+      "Condition met. Appended special results for step 457 to test_path/special_results.parquet\n",
       "457\n",
-      "model_vars.get('Gini', 0)[-1]=0.6744\n",
       "Condition not met at step 458. No data to save.\n",
       "458\n",
-      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
       "Condition not met at step 459. No data to save.\n",
       "459\n",
-      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
       "Condition not met at step 460. No data to save.\n",
       "460\n",
-      "model_vars.get('Gini', 0)[-1]=0.6928000000000001\n",
-      "Condition not met at step 461. No data to save.\n",
+      "Condition met. Appended special results for step 461 to test_path/special_results.parquet\n",
       "461\n",
-      "model_vars.get('Gini', 0)[-1]=0.7168\n",
       "Condition met. Appended special results for step 462 to test_path/special_results.parquet\n",
       "462\n",
-      "model_vars.get('Gini', 0)[-1]=0.7326\n",
       "Condition met. Appended special results for step 463 to test_path/special_results.parquet\n",
       "463\n",
-      "model_vars.get('Gini', 0)[-1]=0.7286\n",
       "Condition met. Appended special results for step 464 to test_path/special_results.parquet\n",
       "464\n",
-      "model_vars.get('Gini', 0)[-1]=0.7270000000000001\n",
       "Condition met. Appended special results for step 465 to test_path/special_results.parquet\n",
       "465\n",
-      "model_vars.get('Gini', 0)[-1]=0.7182\n",
       "Condition met. Appended special results for step 466 to test_path/special_results.parquet\n",
       "466\n",
-      "model_vars.get('Gini', 0)[-1]=0.7226\n",
       "Condition met. Appended special results for step 467 to test_path/special_results.parquet\n",
       "467\n",
-      "model_vars.get('Gini', 0)[-1]=0.7472000000000001\n",
-      "Condition met. Appended special results for step 468 to test_path/special_results.parquet\n",
+      "Condition not met at step 468. No data to save.\n",
       "468\n",
-      "model_vars.get('Gini', 0)[-1]=0.7326\n",
-      "Condition met. Appended special results for step 469 to test_path/special_results.parquet\n",
+      "Condition not met at step 469. No data to save.\n",
       "469\n",
-      "model_vars.get('Gini', 0)[-1]=0.746\n",
-      "Condition met. Appended special results for step 470 to test_path/special_results.parquet\n",
+      "Condition not met at step 470. No data to save.\n",
       "470\n",
-      "model_vars.get('Gini', 0)[-1]=0.7574000000000001\n",
-      "Condition met. Appended special results for step 471 to test_path/special_results.parquet\n",
+      "Condition not met at step 471. No data to save.\n",
       "471\n",
-      "model_vars.get('Gini', 0)[-1]=0.7532000000000001\n",
-      "Condition met. Appended special results for step 472 to test_path/special_results.parquet\n",
+      "Condition not met at step 472. No data to save.\n",
       "472\n",
-      "model_vars.get('Gini', 0)[-1]=0.745\n",
-      "Condition met. Appended special results for step 473 to test_path/special_results.parquet\n",
+      "Condition not met at step 473. No data to save.\n",
       "473\n",
-      "model_vars.get('Gini', 0)[-1]=0.7472000000000001\n",
-      "Condition met. Appended special results for step 474 to test_path/special_results.parquet\n",
+      "Condition not met at step 474. No data to save.\n",
       "474\n",
-      "model_vars.get('Gini', 0)[-1]=0.7332000000000001\n",
-      "Condition met. Appended special results for step 475 to test_path/special_results.parquet\n",
+      "Condition not met at step 475. No data to save.\n",
       "475\n",
-      "model_vars.get('Gini', 0)[-1]=0.7425999999999999\n",
-      "Condition met. Appended special results for step 476 to test_path/special_results.parquet\n",
+      "Condition not met at step 476. No data to save.\n",
       "476\n",
-      "model_vars.get('Gini', 0)[-1]=0.7256\n",
-      "Condition met. Appended special results for step 477 to test_path/special_results.parquet\n",
+      "Condition not met at step 477. No data to save.\n",
       "477\n",
-      "model_vars.get('Gini', 0)[-1]=0.7432000000000001\n",
-      "Condition met. Appended special results for step 478 to test_path/special_results.parquet\n",
+      "Condition not met at step 478. No data to save.\n",
       "478\n",
-      "model_vars.get('Gini', 0)[-1]=0.7074\n",
-      "Condition met. Appended special results for step 479 to test_path/special_results.parquet\n",
+      "Condition not met at step 479. No data to save.\n",
       "479\n",
-      "model_vars.get('Gini', 0)[-1]=0.6832\n",
       "Condition not met at step 480. No data to save.\n",
       "480\n",
-      "model_vars.get('Gini', 0)[-1]=0.6674\n",
       "Condition not met at step 481. No data to save.\n",
       "481\n",
-      "model_vars.get('Gini', 0)[-1]=0.643\n",
       "Condition not met at step 482. No data to save.\n",
       "482\n",
-      "model_vars.get('Gini', 0)[-1]=0.6738\n",
       "Condition not met at step 483. No data to save.\n",
       "483\n",
-      "model_vars.get('Gini', 0)[-1]=0.673\n",
       "Condition not met at step 484. No data to save.\n",
       "484\n",
-      "model_vars.get('Gini', 0)[-1]=0.6772\n",
       "Condition not met at step 485. No data to save.\n",
       "485\n",
-      "model_vars.get('Gini', 0)[-1]=0.6716\n",
       "Condition not met at step 486. No data to save.\n",
       "486\n",
-      "model_vars.get('Gini', 0)[-1]=0.6698\n",
       "Condition not met at step 487. No data to save.\n",
       "487\n",
-      "model_vars.get('Gini', 0)[-1]=0.6488\n",
       "Condition not met at step 488. No data to save.\n",
       "488\n",
-      "model_vars.get('Gini', 0)[-1]=0.6354\n",
       "Condition not met at step 489. No data to save.\n",
       "489\n",
-      "model_vars.get('Gini', 0)[-1]=0.652\n",
       "Condition not met at step 490. No data to save.\n",
       "490\n",
-      "model_vars.get('Gini', 0)[-1]=0.6594\n",
       "Condition not met at step 491. No data to save.\n",
       "491\n",
-      "model_vars.get('Gini', 0)[-1]=0.6786000000000001\n",
       "Condition not met at step 492. No data to save.\n",
       "492\n",
-      "model_vars.get('Gini', 0)[-1]=0.6874\n",
       "Condition not met at step 493. No data to save.\n",
       "493\n",
-      "model_vars.get('Gini', 0)[-1]=0.7092\n",
-      "Condition met. Appended special results for step 494 to test_path/special_results.parquet\n",
+      "Condition not met at step 494. No data to save.\n",
       "494\n",
-      "model_vars.get('Gini', 0)[-1]=0.6984\n",
       "Condition not met at step 495. No data to save.\n",
       "495\n",
-      "model_vars.get('Gini', 0)[-1]=0.7084\n",
-      "Condition met. Appended special results for step 496 to test_path/special_results.parquet\n",
+      "Condition not met at step 496. No data to save.\n",
       "496\n",
-      "model_vars.get('Gini', 0)[-1]=0.6936\n",
       "Condition not met at step 497. No data to save.\n",
       "497\n",
-      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
       "Condition not met at step 498. No data to save.\n",
       "498\n",
-      "model_vars.get('Gini', 0)[-1]=0.6916\n",
       "Condition not met at step 499. No data to save.\n",
       "499\n",
-      "CALLED\n",
-      "self.model._steps=500\n",
-      " TEST self.model._steps=500\n",
-      " TEST self._cache_interval=100\n",
-      "       Gini\n",
-      "400  0.6890\n",
-      "401  0.6862\n",
-      "402  0.6704\n",
-      "403  0.6486\n",
-      "404  0.6194\n",
-      "..      ...\n",
-      "495  0.7084\n",
-      "496  0.6936\n",
-      "497  0.6788\n",
-      "498  0.6916\n",
-      "499  0.6602\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_005.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_005.parquet'\n",
-      "Saving model to test_path/model_data_005.parquet\n",
-      "Saving agent to test_path/agent_data_005.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6602\n",
+      "all_records=<itertools.chain object at 0x7f77dfe56b00>\n",
+      "rep_names=['Wealth']\n",
       "Condition not met at step 500. No data to save.\n",
       "500\n",
-      "model_vars.get('Gini', 0)[-1]=0.661\n",
       "Condition not met at step 501. No data to save.\n",
       "501\n",
-      "model_vars.get('Gini', 0)[-1]=0.6544\n",
       "Condition not met at step 502. No data to save.\n",
       "502\n",
-      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
       "Condition not met at step 503. No data to save.\n",
       "503\n",
-      "model_vars.get('Gini', 0)[-1]=0.6584\n",
       "Condition not met at step 504. No data to save.\n",
       "504\n",
-      "model_vars.get('Gini', 0)[-1]=0.6759999999999999\n",
       "Condition not met at step 505. No data to save.\n",
       "505\n",
-      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
       "Condition not met at step 506. No data to save.\n",
       "506\n",
-      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
       "Condition not met at step 507. No data to save.\n",
       "507\n",
-      "model_vars.get('Gini', 0)[-1]=0.6482\n",
       "Condition not met at step 508. No data to save.\n",
       "508\n",
-      "model_vars.get('Gini', 0)[-1]=0.6288\n",
       "Condition not met at step 509. No data to save.\n",
       "509\n",
-      "model_vars.get('Gini', 0)[-1]=0.616\n",
       "Condition not met at step 510. No data to save.\n",
       "510\n",
-      "model_vars.get('Gini', 0)[-1]=0.65\n",
       "Condition not met at step 511. No data to save.\n",
       "511\n",
-      "model_vars.get('Gini', 0)[-1]=0.6388\n",
       "Condition not met at step 512. No data to save.\n",
       "512\n",
-      "model_vars.get('Gini', 0)[-1]=0.6442\n",
       "Condition not met at step 513. No data to save.\n",
       "513\n",
-      "model_vars.get('Gini', 0)[-1]=0.671\n",
       "Condition not met at step 514. No data to save.\n",
       "514\n",
-      "model_vars.get('Gini', 0)[-1]=0.6472\n",
       "Condition not met at step 515. No data to save.\n",
       "515\n",
-      "model_vars.get('Gini', 0)[-1]=0.6568\n",
       "Condition not met at step 516. No data to save.\n",
       "516\n",
-      "model_vars.get('Gini', 0)[-1]=0.6782\n",
       "Condition not met at step 517. No data to save.\n",
       "517\n",
-      "model_vars.get('Gini', 0)[-1]=0.6848000000000001\n",
       "Condition not met at step 518. No data to save.\n",
       "518\n",
-      "model_vars.get('Gini', 0)[-1]=0.6950000000000001\n",
       "Condition not met at step 519. No data to save.\n",
       "519\n",
-      "model_vars.get('Gini', 0)[-1]=0.6712\n",
       "Condition not met at step 520. No data to save.\n",
       "520\n",
-      "model_vars.get('Gini', 0)[-1]=0.6762\n",
       "Condition not met at step 521. No data to save.\n",
       "521\n",
-      "model_vars.get('Gini', 0)[-1]=0.6866\n",
       "Condition not met at step 522. No data to save.\n",
       "522\n",
-      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
       "Condition not met at step 523. No data to save.\n",
       "523\n",
-      "model_vars.get('Gini', 0)[-1]=0.6882\n",
       "Condition not met at step 524. No data to save.\n",
       "524\n",
-      "model_vars.get('Gini', 0)[-1]=0.7050000000000001\n",
-      "Condition met. Appended special results for step 525 to test_path/special_results.parquet\n",
+      "Condition not met at step 525. No data to save.\n",
       "525\n",
-      "model_vars.get('Gini', 0)[-1]=0.6818\n",
       "Condition not met at step 526. No data to save.\n",
       "526\n",
-      "model_vars.get('Gini', 0)[-1]=0.6712\n",
       "Condition not met at step 527. No data to save.\n",
       "527\n",
-      "model_vars.get('Gini', 0)[-1]=0.6862\n",
       "Condition not met at step 528. No data to save.\n",
       "528\n",
-      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
-      "Condition not met at step 529. No data to save.\n",
+      "Condition met. Appended special results for step 529 to test_path/special_results.parquet\n",
       "529\n",
-      "model_vars.get('Gini', 0)[-1]=0.6702\n",
-      "Condition not met at step 530. No data to save.\n",
+      "Condition met. Appended special results for step 530 to test_path/special_results.parquet\n",
       "530\n",
-      "model_vars.get('Gini', 0)[-1]=0.6778\n",
-      "Condition not met at step 531. No data to save.\n",
+      "Condition met. Appended special results for step 531 to test_path/special_results.parquet\n",
       "531\n",
-      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
-      "Condition not met at step 532. No data to save.\n",
+      "Condition met. Appended special results for step 532 to test_path/special_results.parquet\n",
       "532\n",
-      "model_vars.get('Gini', 0)[-1]=0.6776\n",
-      "Condition not met at step 533. No data to save.\n",
+      "Condition met. Appended special results for step 533 to test_path/special_results.parquet\n",
       "533\n",
-      "model_vars.get('Gini', 0)[-1]=0.6868000000000001\n",
       "Condition not met at step 534. No data to save.\n",
       "534\n",
-      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
       "Condition not met at step 535. No data to save.\n",
       "535\n",
-      "model_vars.get('Gini', 0)[-1]=0.6712\n",
       "Condition not met at step 536. No data to save.\n",
       "536\n",
-      "model_vars.get('Gini', 0)[-1]=0.658\n",
       "Condition not met at step 537. No data to save.\n",
       "537\n",
-      "model_vars.get('Gini', 0)[-1]=0.6636\n",
       "Condition not met at step 538. No data to save.\n",
       "538\n",
-      "model_vars.get('Gini', 0)[-1]=0.6468\n",
       "Condition not met at step 539. No data to save.\n",
       "539\n",
-      "model_vars.get('Gini', 0)[-1]=0.6268\n",
-      "Condition not met at step 540. No data to save.\n",
+      "Condition met. Appended special results for step 540 to test_path/special_results.parquet\n",
       "540\n",
-      "model_vars.get('Gini', 0)[-1]=0.6328\n",
       "Condition not met at step 541. No data to save.\n",
       "541\n",
-      "model_vars.get('Gini', 0)[-1]=0.649\n",
       "Condition not met at step 542. No data to save.\n",
       "542\n",
-      "model_vars.get('Gini', 0)[-1]=0.6516\n",
       "Condition not met at step 543. No data to save.\n",
       "543\n",
-      "model_vars.get('Gini', 0)[-1]=0.6614\n",
       "Condition not met at step 544. No data to save.\n",
       "544\n",
-      "model_vars.get('Gini', 0)[-1]=0.6992\n",
       "Condition not met at step 545. No data to save.\n",
       "545\n",
-      "model_vars.get('Gini', 0)[-1]=0.6924\n",
       "Condition not met at step 546. No data to save.\n",
       "546\n",
-      "model_vars.get('Gini', 0)[-1]=0.6998\n",
       "Condition not met at step 547. No data to save.\n",
       "547\n",
-      "model_vars.get('Gini', 0)[-1]=0.7034\n",
-      "Condition met. Appended special results for step 548 to test_path/special_results.parquet\n",
+      "Condition not met at step 548. No data to save.\n",
       "548\n",
-      "model_vars.get('Gini', 0)[-1]=0.687\n",
       "Condition not met at step 549. No data to save.\n",
       "549\n",
-      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
       "Condition not met at step 550. No data to save.\n",
       "550\n",
-      "model_vars.get('Gini', 0)[-1]=0.687\n",
       "Condition not met at step 551. No data to save.\n",
       "551\n",
-      "model_vars.get('Gini', 0)[-1]=0.669\n",
       "Condition not met at step 552. No data to save.\n",
       "552\n",
-      "model_vars.get('Gini', 0)[-1]=0.6796\n",
       "Condition not met at step 553. No data to save.\n",
       "553\n",
-      "model_vars.get('Gini', 0)[-1]=0.683\n",
       "Condition not met at step 554. No data to save.\n",
       "554\n",
-      "model_vars.get('Gini', 0)[-1]=0.6608\n",
       "Condition not met at step 555. No data to save.\n",
       "555\n",
-      "model_vars.get('Gini', 0)[-1]=0.6758\n",
       "Condition not met at step 556. No data to save.\n",
       "556\n",
-      "model_vars.get('Gini', 0)[-1]=0.6824\n",
       "Condition not met at step 557. No data to save.\n",
       "557\n",
-      "model_vars.get('Gini', 0)[-1]=0.6804\n",
       "Condition not met at step 558. No data to save.\n",
       "558\n",
-      "model_vars.get('Gini', 0)[-1]=0.6682\n",
       "Condition not met at step 559. No data to save.\n",
       "559\n",
-      "model_vars.get('Gini', 0)[-1]=0.643\n",
       "Condition not met at step 560. No data to save.\n",
       "560\n",
-      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
       "Condition not met at step 561. No data to save.\n",
       "561\n",
-      "model_vars.get('Gini', 0)[-1]=0.609\n",
       "Condition not met at step 562. No data to save.\n",
       "562\n",
-      "model_vars.get('Gini', 0)[-1]=0.6064\n",
       "Condition not met at step 563. No data to save.\n",
       "563\n",
-      "model_vars.get('Gini', 0)[-1]=0.647\n",
       "Condition not met at step 564. No data to save.\n",
       "564\n",
-      "model_vars.get('Gini', 0)[-1]=0.66\n",
       "Condition not met at step 565. No data to save.\n",
       "565\n",
-      "model_vars.get('Gini', 0)[-1]=0.634\n",
       "Condition not met at step 566. No data to save.\n",
       "566\n",
-      "model_vars.get('Gini', 0)[-1]=0.652\n",
       "Condition not met at step 567. No data to save.\n",
       "567\n",
-      "model_vars.get('Gini', 0)[-1]=0.6492\n",
       "Condition not met at step 568. No data to save.\n",
       "568\n",
-      "model_vars.get('Gini', 0)[-1]=0.656\n",
       "Condition not met at step 569. No data to save.\n",
       "569\n",
-      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
       "Condition not met at step 570. No data to save.\n",
       "570\n",
-      "model_vars.get('Gini', 0)[-1]=0.6354\n",
       "Condition not met at step 571. No data to save.\n",
       "571\n",
-      "model_vars.get('Gini', 0)[-1]=0.621\n",
       "Condition not met at step 572. No data to save.\n",
       "572\n",
-      "model_vars.get('Gini', 0)[-1]=0.6318\n",
       "Condition not met at step 573. No data to save.\n",
       "573\n",
-      "model_vars.get('Gini', 0)[-1]=0.6298\n",
       "Condition not met at step 574. No data to save.\n",
       "574\n",
-      "model_vars.get('Gini', 0)[-1]=0.629\n",
       "Condition not met at step 575. No data to save.\n",
       "575\n",
-      "model_vars.get('Gini', 0)[-1]=0.6408\n",
       "Condition not met at step 576. No data to save.\n",
       "576\n",
-      "model_vars.get('Gini', 0)[-1]=0.6058\n",
       "Condition not met at step 577. No data to save.\n",
       "577\n",
-      "model_vars.get('Gini', 0)[-1]=0.607\n",
       "Condition not met at step 578. No data to save.\n",
       "578\n",
-      "model_vars.get('Gini', 0)[-1]=0.6268\n",
       "Condition not met at step 579. No data to save.\n",
       "579\n",
-      "model_vars.get('Gini', 0)[-1]=0.632\n",
       "Condition not met at step 580. No data to save.\n",
       "580\n",
-      "model_vars.get('Gini', 0)[-1]=0.64\n",
       "Condition not met at step 581. No data to save.\n",
       "581\n",
-      "model_vars.get('Gini', 0)[-1]=0.6298\n",
       "Condition not met at step 582. No data to save.\n",
       "582\n",
-      "model_vars.get('Gini', 0)[-1]=0.6053999999999999\n",
       "Condition not met at step 583. No data to save.\n",
       "583\n",
-      "model_vars.get('Gini', 0)[-1]=0.5938\n",
       "Condition not met at step 584. No data to save.\n",
       "584\n",
-      "model_vars.get('Gini', 0)[-1]=0.5976\n",
       "Condition not met at step 585. No data to save.\n",
       "585\n",
-      "model_vars.get('Gini', 0)[-1]=0.6462\n",
       "Condition not met at step 586. No data to save.\n",
       "586\n",
-      "model_vars.get('Gini', 0)[-1]=0.637\n",
       "Condition not met at step 587. No data to save.\n",
       "587\n",
-      "model_vars.get('Gini', 0)[-1]=0.6202000000000001\n",
       "Condition not met at step 588. No data to save.\n",
       "588\n",
-      "model_vars.get('Gini', 0)[-1]=0.6142000000000001\n",
       "Condition not met at step 589. No data to save.\n",
       "589\n",
-      "model_vars.get('Gini', 0)[-1]=0.6113999999999999\n",
       "Condition not met at step 590. No data to save.\n",
       "590\n",
-      "model_vars.get('Gini', 0)[-1]=0.6384000000000001\n",
       "Condition not met at step 591. No data to save.\n",
       "591\n",
-      "model_vars.get('Gini', 0)[-1]=0.6437999999999999\n",
       "Condition not met at step 592. No data to save.\n",
       "592\n",
-      "model_vars.get('Gini', 0)[-1]=0.6342\n",
       "Condition not met at step 593. No data to save.\n",
       "593\n",
-      "model_vars.get('Gini', 0)[-1]=0.6436\n",
       "Condition not met at step 594. No data to save.\n",
       "594\n",
-      "model_vars.get('Gini', 0)[-1]=0.6608\n",
       "Condition not met at step 595. No data to save.\n",
       "595\n",
-      "model_vars.get('Gini', 0)[-1]=0.654\n",
       "Condition not met at step 596. No data to save.\n",
       "596\n",
-      "model_vars.get('Gini', 0)[-1]=0.6926\n",
       "Condition not met at step 597. No data to save.\n",
       "597\n",
-      "model_vars.get('Gini', 0)[-1]=0.6836\n",
       "Condition not met at step 598. No data to save.\n",
       "598\n",
-      "model_vars.get('Gini', 0)[-1]=0.7032\n",
-      "Condition met. Appended special results for step 599 to test_path/special_results.parquet\n",
+      "Condition not met at step 599. No data to save.\n",
       "599\n",
-      "CALLED\n",
-      "self.model._steps=600\n",
-      " TEST self.model._steps=600\n",
-      " TEST self._cache_interval=100\n",
-      "       Gini\n",
-      "500  0.6610\n",
-      "501  0.6544\n",
-      "502  0.6406\n",
-      "503  0.6584\n",
-      "504  0.6760\n",
-      "..      ...\n",
-      "595  0.6540\n",
-      "596  0.6926\n",
-      "597  0.6836\n",
-      "598  0.7032\n",
-      "599  0.6522\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_006.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_006.parquet'\n",
-      "Saving model to test_path/model_data_006.parquet\n",
-      "Saving agent to test_path/agent_data_006.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6522\n",
+      "all_records=<itertools.chain object at 0x7f77dfdbf130>\n",
+      "rep_names=['Wealth']\n",
       "Condition not met at step 600. No data to save.\n",
       "600\n",
-      "model_vars.get('Gini', 0)[-1]=0.6476\n",
       "Condition not met at step 601. No data to save.\n",
       "601\n",
-      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
       "Condition not met at step 602. No data to save.\n",
       "602\n",
-      "model_vars.get('Gini', 0)[-1]=0.6836\n",
       "Condition not met at step 603. No data to save.\n",
       "603\n",
-      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
       "Condition not met at step 604. No data to save.\n",
       "604\n",
-      "model_vars.get('Gini', 0)[-1]=0.6448\n",
       "Condition not met at step 605. No data to save.\n",
       "605\n",
-      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
       "Condition not met at step 606. No data to save.\n",
       "606\n",
-      "model_vars.get('Gini', 0)[-1]=0.6536\n",
       "Condition not met at step 607. No data to save.\n",
       "607\n",
-      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
       "Condition not met at step 608. No data to save.\n",
       "608\n",
-      "model_vars.get('Gini', 0)[-1]=0.635\n",
       "Condition not met at step 609. No data to save.\n",
       "609\n",
-      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
       "Condition not met at step 610. No data to save.\n",
       "610\n",
-      "model_vars.get('Gini', 0)[-1]=0.645\n",
       "Condition not met at step 611. No data to save.\n",
       "611\n",
-      "model_vars.get('Gini', 0)[-1]=0.6666000000000001\n",
       "Condition not met at step 612. No data to save.\n",
       "612\n",
-      "model_vars.get('Gini', 0)[-1]=0.665\n",
       "Condition not met at step 613. No data to save.\n",
       "613\n",
-      "model_vars.get('Gini', 0)[-1]=0.6888000000000001\n",
       "Condition not met at step 614. No data to save.\n",
       "614\n",
-      "model_vars.get('Gini', 0)[-1]=0.6536\n",
       "Condition not met at step 615. No data to save.\n",
       "615\n",
-      "model_vars.get('Gini', 0)[-1]=0.6576\n",
       "Condition not met at step 616. No data to save.\n",
       "616\n",
-      "model_vars.get('Gini', 0)[-1]=0.6522\n",
       "Condition not met at step 617. No data to save.\n",
       "617\n",
-      "model_vars.get('Gini', 0)[-1]=0.6612\n",
       "Condition not met at step 618. No data to save.\n",
       "618\n",
-      "model_vars.get('Gini', 0)[-1]=0.6806\n",
       "Condition not met at step 619. No data to save.\n",
       "619\n",
-      "model_vars.get('Gini', 0)[-1]=0.6744\n",
       "Condition not met at step 620. No data to save.\n",
       "620\n",
-      "model_vars.get('Gini', 0)[-1]=0.671\n",
       "Condition not met at step 621. No data to save.\n",
       "621\n",
-      "model_vars.get('Gini', 0)[-1]=0.6612\n",
       "Condition not met at step 622. No data to save.\n",
       "622\n",
-      "model_vars.get('Gini', 0)[-1]=0.6528\n",
       "Condition not met at step 623. No data to save.\n",
       "623\n",
-      "model_vars.get('Gini', 0)[-1]=0.6642\n",
       "Condition not met at step 624. No data to save.\n",
       "624\n",
-      "model_vars.get('Gini', 0)[-1]=0.6858\n",
       "Condition not met at step 625. No data to save.\n",
       "625\n",
-      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
       "Condition not met at step 626. No data to save.\n",
       "626\n",
-      "model_vars.get('Gini', 0)[-1]=0.6472\n",
       "Condition not met at step 627. No data to save.\n",
       "627\n",
-      "model_vars.get('Gini', 0)[-1]=0.6378\n",
       "Condition not met at step 628. No data to save.\n",
       "628\n",
-      "model_vars.get('Gini', 0)[-1]=0.6232\n",
       "Condition not met at step 629. No data to save.\n",
       "629\n",
-      "model_vars.get('Gini', 0)[-1]=0.6232\n",
       "Condition not met at step 630. No data to save.\n",
       "630\n",
-      "model_vars.get('Gini', 0)[-1]=0.6514\n",
       "Condition not met at step 631. No data to save.\n",
       "631\n",
-      "model_vars.get('Gini', 0)[-1]=0.6528\n",
       "Condition not met at step 632. No data to save.\n",
       "632\n",
-      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
       "Condition not met at step 633. No data to save.\n",
       "633\n",
-      "model_vars.get('Gini', 0)[-1]=0.6264000000000001\n",
       "Condition not met at step 634. No data to save.\n",
       "634\n",
-      "model_vars.get('Gini', 0)[-1]=0.6582\n",
       "Condition not met at step 635. No data to save.\n",
       "635\n",
-      "model_vars.get('Gini', 0)[-1]=0.6492\n",
       "Condition not met at step 636. No data to save.\n",
       "636\n",
-      "model_vars.get('Gini', 0)[-1]=0.6492\n",
       "Condition not met at step 637. No data to save.\n",
       "637\n",
-      "model_vars.get('Gini', 0)[-1]=0.6497999999999999\n",
       "Condition not met at step 638. No data to save.\n",
       "638\n",
-      "model_vars.get('Gini', 0)[-1]=0.6404000000000001\n",
       "Condition not met at step 639. No data to save.\n",
       "639\n",
-      "model_vars.get('Gini', 0)[-1]=0.647\n",
       "Condition not met at step 640. No data to save.\n",
       "640\n",
-      "model_vars.get('Gini', 0)[-1]=0.6754\n",
       "Condition not met at step 641. No data to save.\n",
       "641\n",
-      "model_vars.get('Gini', 0)[-1]=0.658\n",
       "Condition not met at step 642. No data to save.\n",
       "642\n",
-      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
       "Condition not met at step 643. No data to save.\n",
       "643\n",
-      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
       "Condition not met at step 644. No data to save.\n",
       "644\n",
-      "model_vars.get('Gini', 0)[-1]=0.6504000000000001\n",
       "Condition not met at step 645. No data to save.\n",
       "645\n",
-      "model_vars.get('Gini', 0)[-1]=0.662\n",
       "Condition not met at step 646. No data to save.\n",
       "646\n",
-      "model_vars.get('Gini', 0)[-1]=0.6676\n",
       "Condition not met at step 647. No data to save.\n",
       "647\n",
-      "model_vars.get('Gini', 0)[-1]=0.665\n",
       "Condition not met at step 648. No data to save.\n",
       "648\n",
-      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
       "Condition not met at step 649. No data to save.\n",
       "649\n",
-      "model_vars.get('Gini', 0)[-1]=0.6536\n",
       "Condition not met at step 650. No data to save.\n",
       "650\n",
-      "model_vars.get('Gini', 0)[-1]=0.6454\n",
       "Condition not met at step 651. No data to save.\n",
       "651\n",
-      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
       "Condition not met at step 652. No data to save.\n",
       "652\n",
-      "model_vars.get('Gini', 0)[-1]=0.6612\n",
       "Condition not met at step 653. No data to save.\n",
       "653\n",
-      "model_vars.get('Gini', 0)[-1]=0.6446000000000001\n",
       "Condition not met at step 654. No data to save.\n",
       "654\n",
-      "model_vars.get('Gini', 0)[-1]=0.6446000000000001\n",
       "Condition not met at step 655. No data to save.\n",
       "655\n",
-      "model_vars.get('Gini', 0)[-1]=0.64\n",
       "Condition not met at step 656. No data to save.\n",
       "656\n",
-      "model_vars.get('Gini', 0)[-1]=0.6414\n",
       "Condition not met at step 657. No data to save.\n",
       "657\n",
-      "model_vars.get('Gini', 0)[-1]=0.6444000000000001\n",
       "Condition not met at step 658. No data to save.\n",
       "658\n",
-      "model_vars.get('Gini', 0)[-1]=0.634\n",
       "Condition not met at step 659. No data to save.\n",
       "659\n",
-      "model_vars.get('Gini', 0)[-1]=0.6532\n",
-      "Condition not met at step 660. No data to save.\n",
+      "Condition met. Appended special results for step 660 to test_path/special_results.parquet\n",
       "660\n",
-      "model_vars.get('Gini', 0)[-1]=0.6384000000000001\n",
-      "Condition not met at step 661. No data to save.\n",
+      "Condition met. Appended special results for step 661 to test_path/special_results.parquet\n",
       "661\n",
-      "model_vars.get('Gini', 0)[-1]=0.6434\n",
-      "Condition not met at step 662. No data to save.\n",
+      "Condition met. Appended special results for step 662 to test_path/special_results.parquet\n",
       "662\n",
-      "model_vars.get('Gini', 0)[-1]=0.6048\n",
-      "Condition not met at step 663. No data to save.\n",
+      "Condition met. Appended special results for step 663 to test_path/special_results.parquet\n",
       "663\n",
-      "model_vars.get('Gini', 0)[-1]=0.6148\n",
-      "Condition not met at step 664. No data to save.\n",
+      "Condition met. Appended special results for step 664 to test_path/special_results.parquet\n",
       "664\n",
-      "model_vars.get('Gini', 0)[-1]=0.6214\n",
-      "Condition not met at step 665. No data to save.\n",
+      "Condition met. Appended special results for step 665 to test_path/special_results.parquet\n",
       "665\n",
-      "model_vars.get('Gini', 0)[-1]=0.6235999999999999\n",
-      "Condition not met at step 666. No data to save.\n",
+      "Condition met. Appended special results for step 666 to test_path/special_results.parquet\n",
       "666\n",
-      "model_vars.get('Gini', 0)[-1]=0.6355999999999999\n",
-      "Condition not met at step 667. No data to save.\n",
+      "Condition met. Appended special results for step 667 to test_path/special_results.parquet\n",
       "667\n",
-      "model_vars.get('Gini', 0)[-1]=0.6372\n",
-      "Condition not met at step 668. No data to save.\n",
+      "Condition met. Appended special results for step 668 to test_path/special_results.parquet\n",
       "668\n",
-      "model_vars.get('Gini', 0)[-1]=0.618\n",
-      "Condition not met at step 669. No data to save.\n",
+      "Condition met. Appended special results for step 669 to test_path/special_results.parquet\n",
       "669\n",
-      "model_vars.get('Gini', 0)[-1]=0.6335999999999999\n",
-      "Condition not met at step 670. No data to save.\n",
+      "Condition met. Appended special results for step 670 to test_path/special_results.parquet\n",
       "670\n",
-      "model_vars.get('Gini', 0)[-1]=0.6454\n",
-      "Condition not met at step 671. No data to save.\n",
+      "Condition met. Appended special results for step 671 to test_path/special_results.parquet\n",
       "671\n",
-      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
-      "Condition not met at step 672. No data to save.\n",
+      "Condition met. Appended special results for step 672 to test_path/special_results.parquet\n",
       "672\n",
-      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
-      "Condition not met at step 673. No data to save.\n",
+      "Condition met. Appended special results for step 673 to test_path/special_results.parquet\n",
       "673\n",
-      "model_vars.get('Gini', 0)[-1]=0.6616\n",
-      "Condition not met at step 674. No data to save.\n",
+      "Condition met. Appended special results for step 674 to test_path/special_results.parquet\n",
       "674\n",
-      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
-      "Condition not met at step 675. No data to save.\n",
+      "Condition met. Appended special results for step 675 to test_path/special_results.parquet\n",
       "675\n",
-      "model_vars.get('Gini', 0)[-1]=0.6584\n",
-      "Condition not met at step 676. No data to save.\n",
+      "Condition met. Appended special results for step 676 to test_path/special_results.parquet\n",
       "676\n",
-      "model_vars.get('Gini', 0)[-1]=0.6306\n",
-      "Condition not met at step 677. No data to save.\n",
+      "Condition met. Appended special results for step 677 to test_path/special_results.parquet\n",
       "677\n",
-      "model_vars.get('Gini', 0)[-1]=0.6862\n",
-      "Condition not met at step 678. No data to save.\n",
+      "Condition met. Appended special results for step 678 to test_path/special_results.parquet\n",
       "678\n",
-      "model_vars.get('Gini', 0)[-1]=0.7\n",
-      "Condition not met at step 679. No data to save.\n",
+      "Condition met. Appended special results for step 679 to test_path/special_results.parquet\n",
       "679\n",
-      "model_vars.get('Gini', 0)[-1]=0.683\n",
       "Condition not met at step 680. No data to save.\n",
       "680\n",
-      "model_vars.get('Gini', 0)[-1]=0.6779999999999999\n",
       "Condition not met at step 681. No data to save.\n",
       "681\n",
-      "model_vars.get('Gini', 0)[-1]=0.6908000000000001\n",
       "Condition not met at step 682. No data to save.\n",
       "682\n",
-      "model_vars.get('Gini', 0)[-1]=0.6822\n",
       "Condition not met at step 683. No data to save.\n",
       "683\n",
-      "model_vars.get('Gini', 0)[-1]=0.6796\n",
       "Condition not met at step 684. No data to save.\n",
       "684\n",
-      "model_vars.get('Gini', 0)[-1]=0.6616\n",
       "Condition not met at step 685. No data to save.\n",
       "685\n",
-      "model_vars.get('Gini', 0)[-1]=0.6548\n",
       "Condition not met at step 686. No data to save.\n",
       "686\n",
-      "model_vars.get('Gini', 0)[-1]=0.637\n",
       "Condition not met at step 687. No data to save.\n",
       "687\n",
-      "model_vars.get('Gini', 0)[-1]=0.6494\n",
       "Condition not met at step 688. No data to save.\n",
       "688\n",
-      "model_vars.get('Gini', 0)[-1]=0.6342\n",
       "Condition not met at step 689. No data to save.\n",
       "689\n",
-      "model_vars.get('Gini', 0)[-1]=0.652\n",
       "Condition not met at step 690. No data to save.\n",
       "690\n",
-      "model_vars.get('Gini', 0)[-1]=0.6308\n",
       "Condition not met at step 691. No data to save.\n",
       "691\n",
-      "model_vars.get('Gini', 0)[-1]=0.6472\n",
       "Condition not met at step 692. No data to save.\n",
       "692\n",
-      "model_vars.get('Gini', 0)[-1]=0.6468\n",
       "Condition not met at step 693. No data to save.\n",
       "693\n",
-      "model_vars.get('Gini', 0)[-1]=0.6532\n",
       "Condition not met at step 694. No data to save.\n",
       "694\n",
-      "model_vars.get('Gini', 0)[-1]=0.6448\n",
       "Condition not met at step 695. No data to save.\n",
       "695\n",
-      "model_vars.get('Gini', 0)[-1]=0.6056\n",
       "Condition not met at step 696. No data to save.\n",
       "696\n",
-      "model_vars.get('Gini', 0)[-1]=0.6456\n",
       "Condition not met at step 697. No data to save.\n",
       "697\n",
-      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
       "Condition not met at step 698. No data to save.\n",
       "698\n",
-      "model_vars.get('Gini', 0)[-1]=0.6537999999999999\n",
       "Condition not met at step 699. No data to save.\n",
       "699\n",
-      "CALLED\n",
-      "self.model._steps=700\n",
-      " TEST self.model._steps=700\n",
-      " TEST self._cache_interval=100\n",
-      "       Gini\n",
-      "600  0.6476\n",
-      "601  0.6418\n",
-      "602  0.6836\n",
-      "603  0.6526\n",
-      "604  0.6448\n",
-      "..      ...\n",
-      "695  0.6056\n",
-      "696  0.6456\n",
-      "697  0.6458\n",
-      "698  0.6538\n",
-      "699  0.6572\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_007.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_007.parquet'\n",
-      "Saving model to test_path/model_data_007.parquet\n",
-      "Saving agent to test_path/agent_data_007.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6572\n",
+      "all_records=<itertools.chain object at 0x7f77dfe56b00>\n",
+      "rep_names=['Wealth']\n",
       "Condition not met at step 700. No data to save.\n",
       "700\n",
-      "model_vars.get('Gini', 0)[-1]=0.65\n",
-      "Condition not met at step 701. No data to save.\n",
+      "Condition met. Appended special results for step 701 to test_path/special_results.parquet\n",
       "701\n",
-      "model_vars.get('Gini', 0)[-1]=0.65\n",
-      "Condition not met at step 702. No data to save.\n",
+      "Condition met. Appended special results for step 702 to test_path/special_results.parquet\n",
       "702\n",
-      "model_vars.get('Gini', 0)[-1]=0.6402\n",
-      "Condition not met at step 703. No data to save.\n",
+      "Condition met. Appended special results for step 703 to test_path/special_results.parquet\n",
       "703\n",
-      "model_vars.get('Gini', 0)[-1]=0.6274\n",
-      "Condition not met at step 704. No data to save.\n",
+      "Condition met. Appended special results for step 704 to test_path/special_results.parquet\n",
       "704\n",
-      "model_vars.get('Gini', 0)[-1]=0.6596\n",
-      "Condition not met at step 705. No data to save.\n",
+      "Condition met. Appended special results for step 705 to test_path/special_results.parquet\n",
       "705\n",
-      "model_vars.get('Gini', 0)[-1]=0.6474\n",
       "Condition not met at step 706. No data to save.\n",
       "706\n",
-      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
       "Condition not met at step 707. No data to save.\n",
       "707\n",
-      "model_vars.get('Gini', 0)[-1]=0.6106\n",
       "Condition not met at step 708. No data to save.\n",
       "708\n",
-      "model_vars.get('Gini', 0)[-1]=0.6174\n",
       "Condition not met at step 709. No data to save.\n",
       "709\n",
-      "model_vars.get('Gini', 0)[-1]=0.6226\n",
       "Condition not met at step 710. No data to save.\n",
       "710\n",
-      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
       "Condition not met at step 711. No data to save.\n",
       "711\n",
-      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
       "Condition not met at step 712. No data to save.\n",
       "712\n",
-      "model_vars.get('Gini', 0)[-1]=0.6388\n",
       "Condition not met at step 713. No data to save.\n",
       "713\n",
-      "model_vars.get('Gini', 0)[-1]=0.633\n",
       "Condition not met at step 714. No data to save.\n",
       "714\n",
-      "model_vars.get('Gini', 0)[-1]=0.6736\n",
       "Condition not met at step 715. No data to save.\n",
       "715\n",
-      "model_vars.get('Gini', 0)[-1]=0.6718\n",
       "Condition not met at step 716. No data to save.\n",
       "716\n",
-      "model_vars.get('Gini', 0)[-1]=0.6744\n",
       "Condition not met at step 717. No data to save.\n",
       "717\n",
-      "model_vars.get('Gini', 0)[-1]=0.6616\n",
       "Condition not met at step 718. No data to save.\n",
       "718\n",
-      "model_vars.get('Gini', 0)[-1]=0.6288\n",
       "Condition not met at step 719. No data to save.\n",
       "719\n",
-      "model_vars.get('Gini', 0)[-1]=0.6284000000000001\n",
       "Condition not met at step 720. No data to save.\n",
       "720\n",
-      "model_vars.get('Gini', 0)[-1]=0.6414\n",
       "Condition not met at step 721. No data to save.\n",
       "721\n",
-      "model_vars.get('Gini', 0)[-1]=0.6264000000000001\n",
       "Condition not met at step 722. No data to save.\n",
       "722\n",
-      "model_vars.get('Gini', 0)[-1]=0.6235999999999999\n",
       "Condition not met at step 723. No data to save.\n",
       "723\n",
-      "model_vars.get('Gini', 0)[-1]=0.6224000000000001\n",
       "Condition not met at step 724. No data to save.\n",
       "724\n",
-      "model_vars.get('Gini', 0)[-1]=0.6355999999999999\n",
       "Condition not met at step 725. No data to save.\n",
       "725\n",
-      "model_vars.get('Gini', 0)[-1]=0.6194\n",
       "Condition not met at step 726. No data to save.\n",
       "726\n",
-      "model_vars.get('Gini', 0)[-1]=0.6288\n",
       "Condition not met at step 727. No data to save.\n",
       "727\n",
-      "model_vars.get('Gini', 0)[-1]=0.6048\n",
       "Condition not met at step 728. No data to save.\n",
       "728\n",
-      "model_vars.get('Gini', 0)[-1]=0.6000000000000001\n",
       "Condition not met at step 729. No data to save.\n",
       "729\n",
-      "model_vars.get('Gini', 0)[-1]=0.6204000000000001\n",
       "Condition not met at step 730. No data to save.\n",
       "730\n",
-      "model_vars.get('Gini', 0)[-1]=0.6148\n",
       "Condition not met at step 731. No data to save.\n",
       "731\n",
-      "model_vars.get('Gini', 0)[-1]=0.6312\n",
       "Condition not met at step 732. No data to save.\n",
       "732\n",
-      "model_vars.get('Gini', 0)[-1]=0.6264000000000001\n",
       "Condition not met at step 733. No data to save.\n",
       "733\n",
-      "model_vars.get('Gini', 0)[-1]=0.6096\n",
       "Condition not met at step 734. No data to save.\n",
       "734\n",
-      "model_vars.get('Gini', 0)[-1]=0.6344000000000001\n",
       "Condition not met at step 735. No data to save.\n",
       "735\n",
-      "model_vars.get('Gini', 0)[-1]=0.6516\n",
       "Condition not met at step 736. No data to save.\n",
       "736\n",
-      "model_vars.get('Gini', 0)[-1]=0.6352\n",
       "Condition not met at step 737. No data to save.\n",
       "737\n",
-      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
       "Condition not met at step 738. No data to save.\n",
       "738\n",
-      "model_vars.get('Gini', 0)[-1]=0.665\n",
       "Condition not met at step 739. No data to save.\n",
       "739\n",
-      "model_vars.get('Gini', 0)[-1]=0.6358\n",
       "Condition not met at step 740. No data to save.\n",
       "740\n",
-      "model_vars.get('Gini', 0)[-1]=0.6175999999999999\n",
       "Condition not met at step 741. No data to save.\n",
       "741\n",
-      "model_vars.get('Gini', 0)[-1]=0.6408\n",
       "Condition not met at step 742. No data to save.\n",
       "742\n",
-      "model_vars.get('Gini', 0)[-1]=0.6512\n",
       "Condition not met at step 743. No data to save.\n",
       "743\n",
-      "model_vars.get('Gini', 0)[-1]=0.6759999999999999\n",
       "Condition not met at step 744. No data to save.\n",
       "744\n",
-      "model_vars.get('Gini', 0)[-1]=0.6614\n",
       "Condition not met at step 745. No data to save.\n",
       "745\n",
-      "model_vars.get('Gini', 0)[-1]=0.6432\n",
       "Condition not met at step 746. No data to save.\n",
       "746\n",
-      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
       "Condition not met at step 747. No data to save.\n",
       "747\n",
-      "model_vars.get('Gini', 0)[-1]=0.6452\n",
       "Condition not met at step 748. No data to save.\n",
       "748\n",
-      "model_vars.get('Gini', 0)[-1]=0.6568\n",
       "Condition not met at step 749. No data to save.\n",
       "749\n",
-      "model_vars.get('Gini', 0)[-1]=0.652\n",
       "Condition not met at step 750. No data to save.\n",
       "750\n",
-      "model_vars.get('Gini', 0)[-1]=0.6542\n",
       "Condition not met at step 751. No data to save.\n",
       "751\n",
-      "model_vars.get('Gini', 0)[-1]=0.6508\n",
       "Condition not met at step 752. No data to save.\n",
       "752\n",
-      "model_vars.get('Gini', 0)[-1]=0.666\n",
       "Condition not met at step 753. No data to save.\n",
       "753\n",
-      "model_vars.get('Gini', 0)[-1]=0.656\n",
       "Condition not met at step 754. No data to save.\n",
       "754\n",
-      "model_vars.get('Gini', 0)[-1]=0.666\n",
       "Condition not met at step 755. No data to save.\n",
       "755\n",
-      "model_vars.get('Gini', 0)[-1]=0.6832\n",
       "Condition not met at step 756. No data to save.\n",
       "756\n",
-      "model_vars.get('Gini', 0)[-1]=0.6764\n",
       "Condition not met at step 757. No data to save.\n",
       "757\n",
-      "model_vars.get('Gini', 0)[-1]=0.657\n",
       "Condition not met at step 758. No data to save.\n",
       "758\n",
-      "model_vars.get('Gini', 0)[-1]=0.6624\n",
       "Condition not met at step 759. No data to save.\n",
       "759\n",
-      "model_vars.get('Gini', 0)[-1]=0.6586000000000001\n",
       "Condition not met at step 760. No data to save.\n",
       "760\n",
-      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
       "Condition not met at step 761. No data to save.\n",
       "761\n",
-      "model_vars.get('Gini', 0)[-1]=0.5982000000000001\n",
       "Condition not met at step 762. No data to save.\n",
       "762\n",
-      "model_vars.get('Gini', 0)[-1]=0.643\n",
       "Condition not met at step 763. No data to save.\n",
       "763\n",
-      "model_vars.get('Gini', 0)[-1]=0.6308\n",
       "Condition not met at step 764. No data to save.\n",
       "764\n",
-      "model_vars.get('Gini', 0)[-1]=0.6452\n",
       "Condition not met at step 765. No data to save.\n",
       "765\n",
-      "model_vars.get('Gini', 0)[-1]=0.6608\n",
       "Condition not met at step 766. No data to save.\n",
       "766\n",
-      "model_vars.get('Gini', 0)[-1]=0.6594\n",
       "Condition not met at step 767. No data to save.\n",
       "767\n",
-      "model_vars.get('Gini', 0)[-1]=0.661\n",
       "Condition not met at step 768. No data to save.\n",
       "768\n",
-      "model_vars.get('Gini', 0)[-1]=0.6394\n",
       "Condition not met at step 769. No data to save.\n",
       "769\n",
-      "model_vars.get('Gini', 0)[-1]=0.6577999999999999\n",
       "Condition not met at step 770. No data to save.\n",
       "770\n",
-      "model_vars.get('Gini', 0)[-1]=0.6718\n",
       "Condition not met at step 771. No data to save.\n",
       "771\n",
-      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
       "Condition not met at step 772. No data to save.\n",
       "772\n",
-      "model_vars.get('Gini', 0)[-1]=0.6328\n",
       "Condition not met at step 773. No data to save.\n",
       "773\n",
-      "model_vars.get('Gini', 0)[-1]=0.6454\n",
       "Condition not met at step 774. No data to save.\n",
       "774\n",
-      "model_vars.get('Gini', 0)[-1]=0.6468\n",
       "Condition not met at step 775. No data to save.\n",
       "775\n",
-      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
       "Condition not met at step 776. No data to save.\n",
       "776\n",
-      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
       "Condition not met at step 777. No data to save.\n",
       "777\n",
-      "model_vars.get('Gini', 0)[-1]=0.6608\n",
       "Condition not met at step 778. No data to save.\n",
       "778\n",
-      "model_vars.get('Gini', 0)[-1]=0.6776\n",
       "Condition not met at step 779. No data to save.\n",
       "779\n",
-      "model_vars.get('Gini', 0)[-1]=0.6314\n",
       "Condition not met at step 780. No data to save.\n",
       "780\n",
-      "model_vars.get('Gini', 0)[-1]=0.6358\n",
       "Condition not met at step 781. No data to save.\n",
       "781\n",
-      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
       "Condition not met at step 782. No data to save.\n",
       "782\n",
-      "model_vars.get('Gini', 0)[-1]=0.658\n",
       "Condition not met at step 783. No data to save.\n",
       "783\n",
-      "model_vars.get('Gini', 0)[-1]=0.6396\n",
       "Condition not met at step 784. No data to save.\n",
       "784\n",
-      "model_vars.get('Gini', 0)[-1]=0.641\n",
       "Condition not met at step 785. No data to save.\n",
       "785\n",
-      "model_vars.get('Gini', 0)[-1]=0.6294\n",
       "Condition not met at step 786. No data to save.\n",
       "786\n",
-      "model_vars.get('Gini', 0)[-1]=0.6762\n",
       "Condition not met at step 787. No data to save.\n",
       "787\n",
-      "model_vars.get('Gini', 0)[-1]=0.6896\n",
       "Condition not met at step 788. No data to save.\n",
       "788\n",
-      "model_vars.get('Gini', 0)[-1]=0.6872\n",
       "Condition not met at step 789. No data to save.\n",
       "789\n",
-      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
       "Condition not met at step 790. No data to save.\n",
       "790\n",
-      "model_vars.get('Gini', 0)[-1]=0.645\n",
       "Condition not met at step 791. No data to save.\n",
       "791\n",
-      "model_vars.get('Gini', 0)[-1]=0.6342\n",
       "Condition not met at step 792. No data to save.\n",
       "792\n",
-      "model_vars.get('Gini', 0)[-1]=0.635\n",
       "Condition not met at step 793. No data to save.\n",
       "793\n",
-      "model_vars.get('Gini', 0)[-1]=0.6186\n",
       "Condition not met at step 794. No data to save.\n",
       "794\n",
-      "model_vars.get('Gini', 0)[-1]=0.6136\n",
       "Condition not met at step 795. No data to save.\n",
       "795\n",
-      "model_vars.get('Gini', 0)[-1]=0.6366\n",
       "Condition not met at step 796. No data to save.\n",
       "796\n",
-      "model_vars.get('Gini', 0)[-1]=0.613\n",
       "Condition not met at step 797. No data to save.\n",
       "797\n",
-      "model_vars.get('Gini', 0)[-1]=0.6186\n",
       "Condition not met at step 798. No data to save.\n",
       "798\n",
-      "model_vars.get('Gini', 0)[-1]=0.6078\n",
       "Condition not met at step 799. No data to save.\n",
       "799\n",
-      "CALLED\n",
-      "self.model._steps=800\n",
-      " TEST self.model._steps=800\n",
-      " TEST self._cache_interval=100\n",
-      "       Gini\n",
-      "700  0.6500\n",
-      "701  0.6500\n",
-      "702  0.6402\n",
-      "703  0.6274\n",
-      "704  0.6596\n",
-      "..      ...\n",
-      "795  0.6366\n",
-      "796  0.6130\n",
-      "797  0.6186\n",
-      "798  0.6078\n",
-      "799  0.6270\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_008.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_008.parquet'\n",
-      "Saving model to test_path/model_data_008.parquet\n",
-      "Saving agent to test_path/agent_data_008.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.627\n",
+      "all_records=<itertools.chain object at 0x7f77dfe56b00>\n",
+      "rep_names=['Wealth']\n",
       "Condition not met at step 800. No data to save.\n",
       "800\n",
-      "model_vars.get('Gini', 0)[-1]=0.6175999999999999\n",
       "Condition not met at step 801. No data to save.\n",
       "801\n",
-      "model_vars.get('Gini', 0)[-1]=0.6202000000000001\n",
       "Condition not met at step 802. No data to save.\n",
       "802\n",
-      "model_vars.get('Gini', 0)[-1]=0.621\n",
       "Condition not met at step 803. No data to save.\n",
       "803\n",
-      "model_vars.get('Gini', 0)[-1]=0.6514\n",
       "Condition not met at step 804. No data to save.\n",
       "804\n",
-      "model_vars.get('Gini', 0)[-1]=0.6577999999999999\n",
       "Condition not met at step 805. No data to save.\n",
       "805\n",
-      "model_vars.get('Gini', 0)[-1]=0.6252\n",
       "Condition not met at step 806. No data to save.\n",
       "806\n",
-      "model_vars.get('Gini', 0)[-1]=0.6388\n",
       "Condition not met at step 807. No data to save.\n",
       "807\n",
-      "model_vars.get('Gini', 0)[-1]=0.6212\n",
       "Condition not met at step 808. No data to save.\n",
       "808\n",
-      "model_vars.get('Gini', 0)[-1]=0.6156\n",
       "Condition not met at step 809. No data to save.\n",
       "809\n",
-      "model_vars.get('Gini', 0)[-1]=0.6416\n",
       "Condition not met at step 810. No data to save.\n",
       "810\n",
-      "model_vars.get('Gini', 0)[-1]=0.635\n",
       "Condition not met at step 811. No data to save.\n",
       "811\n",
-      "model_vars.get('Gini', 0)[-1]=0.6376\n",
       "Condition not met at step 812. No data to save.\n",
       "812\n",
-      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
       "Condition not met at step 813. No data to save.\n",
       "813\n",
-      "model_vars.get('Gini', 0)[-1]=0.644\n",
       "Condition not met at step 814. No data to save.\n",
       "814\n",
-      "model_vars.get('Gini', 0)[-1]=0.653\n",
       "Condition not met at step 815. No data to save.\n",
       "815\n",
-      "model_vars.get('Gini', 0)[-1]=0.6452\n",
       "Condition not met at step 816. No data to save.\n",
       "816\n",
-      "model_vars.get('Gini', 0)[-1]=0.6344000000000001\n",
       "Condition not met at step 817. No data to save.\n",
       "817\n",
-      "model_vars.get('Gini', 0)[-1]=0.6434\n",
       "Condition not met at step 818. No data to save.\n",
       "818\n",
-      "model_vars.get('Gini', 0)[-1]=0.6394\n",
       "Condition not met at step 819. No data to save.\n",
       "819\n",
-      "model_vars.get('Gini', 0)[-1]=0.6576\n",
       "Condition not met at step 820. No data to save.\n",
       "820\n",
-      "model_vars.get('Gini', 0)[-1]=0.6632\n",
       "Condition not met at step 821. No data to save.\n",
       "821\n",
-      "model_vars.get('Gini', 0)[-1]=0.6686000000000001\n",
       "Condition not met at step 822. No data to save.\n",
       "822\n",
-      "model_vars.get('Gini', 0)[-1]=0.6522\n",
       "Condition not met at step 823. No data to save.\n",
       "823\n",
-      "model_vars.get('Gini', 0)[-1]=0.6388\n",
       "Condition not met at step 824. No data to save.\n",
       "824\n",
-      "model_vars.get('Gini', 0)[-1]=0.6318\n",
       "Condition not met at step 825. No data to save.\n",
       "825\n",
-      "model_vars.get('Gini', 0)[-1]=0.5884\n",
       "Condition not met at step 826. No data to save.\n",
       "826\n",
-      "model_vars.get('Gini', 0)[-1]=0.5722\n",
       "Condition not met at step 827. No data to save.\n",
       "827\n",
-      "model_vars.get('Gini', 0)[-1]=0.5920000000000001\n",
       "Condition not met at step 828. No data to save.\n",
       "828\n",
-      "model_vars.get('Gini', 0)[-1]=0.6338\n",
       "Condition not met at step 829. No data to save.\n",
       "829\n",
-      "model_vars.get('Gini', 0)[-1]=0.6382\n",
       "Condition not met at step 830. No data to save.\n",
       "830\n",
-      "model_vars.get('Gini', 0)[-1]=0.621\n",
       "Condition not met at step 831. No data to save.\n",
       "831\n",
-      "model_vars.get('Gini', 0)[-1]=0.64\n",
       "Condition not met at step 832. No data to save.\n",
       "832\n",
-      "model_vars.get('Gini', 0)[-1]=0.642\n",
       "Condition not met at step 833. No data to save.\n",
       "833\n",
-      "model_vars.get('Gini', 0)[-1]=0.663\n",
       "Condition not met at step 834. No data to save.\n",
       "834\n",
-      "model_vars.get('Gini', 0)[-1]=0.6632\n",
       "Condition not met at step 835. No data to save.\n",
       "835\n",
-      "model_vars.get('Gini', 0)[-1]=0.687\n",
       "Condition not met at step 836. No data to save.\n",
       "836\n",
-      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
       "Condition not met at step 837. No data to save.\n",
       "837\n",
-      "model_vars.get('Gini', 0)[-1]=0.6504000000000001\n",
       "Condition not met at step 838. No data to save.\n",
       "838\n",
-      "model_vars.get('Gini', 0)[-1]=0.6134\n",
       "Condition not met at step 839. No data to save.\n",
       "839\n",
-      "model_vars.get('Gini', 0)[-1]=0.5918\n",
       "Condition not met at step 840. No data to save.\n",
       "840\n",
-      "model_vars.get('Gini', 0)[-1]=0.5864\n",
       "Condition not met at step 841. No data to save.\n",
       "841\n",
-      "model_vars.get('Gini', 0)[-1]=0.5804\n",
-      "Condition not met at step 842. No data to save.\n",
+      "Condition met. Appended special results for step 842 to test_path/special_results.parquet\n",
       "842\n",
-      "model_vars.get('Gini', 0)[-1]=0.6076\n",
-      "Condition not met at step 843. No data to save.\n",
+      "Condition met. Appended special results for step 843 to test_path/special_results.parquet\n",
       "843\n",
-      "model_vars.get('Gini', 0)[-1]=0.5880000000000001\n",
       "Condition not met at step 844. No data to save.\n",
       "844\n",
-      "model_vars.get('Gini', 0)[-1]=0.5884\n",
       "Condition not met at step 845. No data to save.\n",
       "845\n",
-      "model_vars.get('Gini', 0)[-1]=0.5984\n",
       "Condition not met at step 846. No data to save.\n",
       "846\n",
-      "model_vars.get('Gini', 0)[-1]=0.6104\n",
       "Condition not met at step 847. No data to save.\n",
       "847\n",
-      "model_vars.get('Gini', 0)[-1]=0.6335999999999999\n",
-      "Condition not met at step 848. No data to save.\n",
+      "Condition met. Appended special results for step 848 to test_path/special_results.parquet\n",
       "848\n",
-      "model_vars.get('Gini', 0)[-1]=0.6314\n",
       "Condition not met at step 849. No data to save.\n",
       "849\n",
-      "model_vars.get('Gini', 0)[-1]=0.6355999999999999\n",
       "Condition not met at step 850. No data to save.\n",
       "850\n",
-      "model_vars.get('Gini', 0)[-1]=0.6388\n",
       "Condition not met at step 851. No data to save.\n",
       "851\n",
-      "model_vars.get('Gini', 0)[-1]=0.6292\n",
       "Condition not met at step 852. No data to save.\n",
       "852\n",
-      "model_vars.get('Gini', 0)[-1]=0.643\n",
       "Condition not met at step 853. No data to save.\n",
       "853\n",
-      "model_vars.get('Gini', 0)[-1]=0.6166\n",
       "Condition not met at step 854. No data to save.\n",
       "854\n",
-      "model_vars.get('Gini', 0)[-1]=0.627\n",
       "Condition not met at step 855. No data to save.\n",
       "855\n",
-      "model_vars.get('Gini', 0)[-1]=0.627\n",
-      "Condition not met at step 856. No data to save.\n",
+      "Condition met. Appended special results for step 856 to test_path/special_results.parquet\n",
       "856\n",
-      "model_vars.get('Gini', 0)[-1]=0.6136\n",
       "Condition not met at step 857. No data to save.\n",
       "857\n",
-      "model_vars.get('Gini', 0)[-1]=0.6295999999999999\n",
       "Condition not met at step 858. No data to save.\n",
       "858\n",
-      "model_vars.get('Gini', 0)[-1]=0.635\n",
       "Condition not met at step 859. No data to save.\n",
       "859\n",
-      "model_vars.get('Gini', 0)[-1]=0.634\n",
       "Condition not met at step 860. No data to save.\n",
       "860\n",
-      "model_vars.get('Gini', 0)[-1]=0.6148\n",
       "Condition not met at step 861. No data to save.\n",
       "861\n",
-      "model_vars.get('Gini', 0)[-1]=0.656\n",
       "Condition not met at step 862. No data to save.\n",
       "862\n",
-      "model_vars.get('Gini', 0)[-1]=0.6834\n",
       "Condition not met at step 863. No data to save.\n",
       "863\n",
-      "model_vars.get('Gini', 0)[-1]=0.6604\n",
       "Condition not met at step 864. No data to save.\n",
       "864\n",
-      "model_vars.get('Gini', 0)[-1]=0.634\n",
       "Condition not met at step 865. No data to save.\n",
       "865\n",
-      "model_vars.get('Gini', 0)[-1]=0.6386000000000001\n",
       "Condition not met at step 866. No data to save.\n",
       "866\n",
-      "model_vars.get('Gini', 0)[-1]=0.6378\n",
       "Condition not met at step 867. No data to save.\n",
       "867\n",
-      "model_vars.get('Gini', 0)[-1]=0.6517999999999999\n",
       "Condition not met at step 868. No data to save.\n",
       "868\n",
-      "model_vars.get('Gini', 0)[-1]=0.6614\n",
       "Condition not met at step 869. No data to save.\n",
       "869\n",
-      "model_vars.get('Gini', 0)[-1]=0.6678\n",
       "Condition not met at step 870. No data to save.\n",
       "870\n",
-      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
       "Condition not met at step 871. No data to save.\n",
       "871\n",
-      "model_vars.get('Gini', 0)[-1]=0.6852\n",
       "Condition not met at step 872. No data to save.\n",
       "872\n",
-      "model_vars.get('Gini', 0)[-1]=0.6878\n",
       "Condition not met at step 873. No data to save.\n",
       "873\n",
-      "model_vars.get('Gini', 0)[-1]=0.6548\n",
       "Condition not met at step 874. No data to save.\n",
       "874\n",
-      "model_vars.get('Gini', 0)[-1]=0.6798\n",
       "Condition not met at step 875. No data to save.\n",
       "875\n",
-      "model_vars.get('Gini', 0)[-1]=0.651\n",
       "Condition not met at step 876. No data to save.\n",
       "876\n",
-      "model_vars.get('Gini', 0)[-1]=0.6536\n",
       "Condition not met at step 877. No data to save.\n",
       "877\n",
-      "model_vars.get('Gini', 0)[-1]=0.6488\n",
       "Condition not met at step 878. No data to save.\n",
       "878\n",
-      "model_vars.get('Gini', 0)[-1]=0.6235999999999999\n",
       "Condition not met at step 879. No data to save.\n",
       "879\n",
-      "model_vars.get('Gini', 0)[-1]=0.6352\n",
       "Condition not met at step 880. No data to save.\n",
       "880\n",
-      "model_vars.get('Gini', 0)[-1]=0.6497999999999999\n",
       "Condition not met at step 881. No data to save.\n",
       "881\n",
-      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
       "Condition not met at step 882. No data to save.\n",
       "882\n",
-      "model_vars.get('Gini', 0)[-1]=0.6352\n",
       "Condition not met at step 883. No data to save.\n",
       "883\n",
-      "model_vars.get('Gini', 0)[-1]=0.6152\n",
       "Condition not met at step 884. No data to save.\n",
       "884\n",
-      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
       "Condition not met at step 885. No data to save.\n",
       "885\n",
-      "model_vars.get('Gini', 0)[-1]=0.644\n",
       "Condition not met at step 886. No data to save.\n",
       "886\n",
-      "model_vars.get('Gini', 0)[-1]=0.6352\n",
       "Condition not met at step 887. No data to save.\n",
       "887\n",
-      "model_vars.get('Gini', 0)[-1]=0.6314\n",
       "Condition not met at step 888. No data to save.\n",
       "888\n",
-      "model_vars.get('Gini', 0)[-1]=0.6244000000000001\n",
       "Condition not met at step 889. No data to save.\n",
       "889\n",
-      "model_vars.get('Gini', 0)[-1]=0.6082000000000001\n",
       "Condition not met at step 890. No data to save.\n",
       "890\n",
-      "model_vars.get('Gini', 0)[-1]=0.5820000000000001\n",
       "Condition not met at step 891. No data to save.\n",
       "891\n",
-      "model_vars.get('Gini', 0)[-1]=0.6096\n",
       "Condition not met at step 892. No data to save.\n",
       "892\n",
-      "model_vars.get('Gini', 0)[-1]=0.6436\n",
       "Condition not met at step 893. No data to save.\n",
       "893\n",
-      "model_vars.get('Gini', 0)[-1]=0.6812\n",
       "Condition not met at step 894. No data to save.\n",
       "894\n",
-      "model_vars.get('Gini', 0)[-1]=0.655\n",
       "Condition not met at step 895. No data to save.\n",
       "895\n",
-      "model_vars.get('Gini', 0)[-1]=0.6924\n",
       "Condition not met at step 896. No data to save.\n",
       "896\n",
-      "model_vars.get('Gini', 0)[-1]=0.667\n",
       "Condition not met at step 897. No data to save.\n",
       "897\n",
-      "model_vars.get('Gini', 0)[-1]=0.7064\n",
-      "Condition met. Appended special results for step 898 to test_path/special_results.parquet\n",
+      "Condition not met at step 898. No data to save.\n",
       "898\n",
-      "model_vars.get('Gini', 0)[-1]=0.7068\n",
-      "Condition met. Appended special results for step 899 to test_path/special_results.parquet\n",
+      "Condition not met at step 899. No data to save.\n",
       "899\n",
-      "CALLED\n",
-      "self.model._steps=900\n",
-      " TEST self.model._steps=900\n",
-      " TEST self._cache_interval=100\n",
-      "       Gini\n",
-      "800  0.6176\n",
-      "801  0.6202\n",
-      "802  0.6210\n",
-      "803  0.6514\n",
-      "804  0.6578\n",
-      "..      ...\n",
-      "895  0.6924\n",
-      "896  0.6670\n",
-      "897  0.7064\n",
-      "898  0.7068\n",
-      "899  0.6792\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_009.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_009.parquet'\n",
-      "Saving model to test_path/model_data_009.parquet\n",
-      "Saving agent to test_path/agent_data_009.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6792\n",
+      "all_records=<itertools.chain object at 0x7f77dfe56b00>\n",
+      "rep_names=['Wealth']\n",
       "Condition not met at step 900. No data to save.\n",
       "900\n",
-      "model_vars.get('Gini', 0)[-1]=0.7038\n",
-      "Condition met. Appended special results for step 901 to test_path/special_results.parquet\n",
+      "Condition not met at step 901. No data to save.\n",
       "901\n",
-      "model_vars.get('Gini', 0)[-1]=0.6784\n",
       "Condition not met at step 902. No data to save.\n",
       "902\n",
-      "model_vars.get('Gini', 0)[-1]=0.6872\n",
       "Condition not met at step 903. No data to save.\n",
       "903\n",
-      "model_vars.get('Gini', 0)[-1]=0.6596\n",
       "Condition not met at step 904. No data to save.\n",
       "904\n",
-      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
       "Condition not met at step 905. No data to save.\n",
       "905\n",
-      "model_vars.get('Gini', 0)[-1]=0.63\n",
       "Condition not met at step 906. No data to save.\n",
       "906\n",
-      "model_vars.get('Gini', 0)[-1]=0.5976\n",
       "Condition not met at step 907. No data to save.\n",
       "907\n",
-      "model_vars.get('Gini', 0)[-1]=0.6124\n",
       "Condition not met at step 908. No data to save.\n",
       "908\n",
-      "model_vars.get('Gini', 0)[-1]=0.6004\n",
       "Condition not met at step 909. No data to save.\n",
       "909\n",
-      "model_vars.get('Gini', 0)[-1]=0.6488\n",
       "Condition not met at step 910. No data to save.\n",
       "910\n",
-      "model_vars.get('Gini', 0)[-1]=0.6362\n",
       "Condition not met at step 911. No data to save.\n",
       "911\n",
-      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
       "Condition not met at step 912. No data to save.\n",
       "912\n",
-      "model_vars.get('Gini', 0)[-1]=0.6454\n",
       "Condition not met at step 913. No data to save.\n",
       "913\n",
-      "model_vars.get('Gini', 0)[-1]=0.6432\n",
       "Condition not met at step 914. No data to save.\n",
       "914\n",
-      "model_vars.get('Gini', 0)[-1]=0.6564\n",
       "Condition not met at step 915. No data to save.\n",
       "915\n",
-      "model_vars.get('Gini', 0)[-1]=0.6175999999999999\n",
       "Condition not met at step 916. No data to save.\n",
       "916\n",
-      "model_vars.get('Gini', 0)[-1]=0.6274\n",
       "Condition not met at step 917. No data to save.\n",
       "917\n",
-      "model_vars.get('Gini', 0)[-1]=0.6076\n",
       "Condition not met at step 918. No data to save.\n",
       "918\n",
-      "model_vars.get('Gini', 0)[-1]=0.6013999999999999\n",
       "Condition not met at step 919. No data to save.\n",
       "919\n",
-      "model_vars.get('Gini', 0)[-1]=0.6088\n",
       "Condition not met at step 920. No data to save.\n",
       "920\n",
-      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
       "Condition not met at step 921. No data to save.\n",
       "921\n",
-      "model_vars.get('Gini', 0)[-1]=0.6544\n",
       "Condition not met at step 922. No data to save.\n",
       "922\n",
-      "model_vars.get('Gini', 0)[-1]=0.6542\n",
       "Condition not met at step 923. No data to save.\n",
       "923\n",
-      "model_vars.get('Gini', 0)[-1]=0.6792\n",
       "Condition not met at step 924. No data to save.\n",
       "924\n",
-      "model_vars.get('Gini', 0)[-1]=0.6818\n",
       "Condition not met at step 925. No data to save.\n",
       "925\n",
-      "model_vars.get('Gini', 0)[-1]=0.6898\n",
       "Condition not met at step 926. No data to save.\n",
       "926\n",
-      "model_vars.get('Gini', 0)[-1]=0.6928000000000001\n",
       "Condition not met at step 927. No data to save.\n",
       "927\n",
-      "model_vars.get('Gini', 0)[-1]=0.698\n",
       "Condition not met at step 928. No data to save.\n",
       "928\n",
-      "model_vars.get('Gini', 0)[-1]=0.7078\n",
-      "Condition met. Appended special results for step 929 to test_path/special_results.parquet\n",
+      "Condition not met at step 929. No data to save.\n",
       "929\n",
-      "model_vars.get('Gini', 0)[-1]=0.6846\n",
       "Condition not met at step 930. No data to save.\n",
       "930\n",
-      "model_vars.get('Gini', 0)[-1]=0.6884\n",
       "Condition not met at step 931. No data to save.\n",
       "931\n",
-      "model_vars.get('Gini', 0)[-1]=0.7004\n",
-      "Condition met. Appended special results for step 932 to test_path/special_results.parquet\n",
+      "Condition not met at step 932. No data to save.\n",
       "932\n",
-      "model_vars.get('Gini', 0)[-1]=0.7048\n",
-      "Condition met. Appended special results for step 933 to test_path/special_results.parquet\n",
+      "Condition not met at step 933. No data to save.\n",
       "933\n",
-      "model_vars.get('Gini', 0)[-1]=0.7021999999999999\n",
-      "Condition met. Appended special results for step 934 to test_path/special_results.parquet\n",
+      "Condition not met at step 934. No data to save.\n",
       "934\n",
-      "model_vars.get('Gini', 0)[-1]=0.6952\n",
       "Condition not met at step 935. No data to save.\n",
       "935\n",
-      "model_vars.get('Gini', 0)[-1]=0.6924\n",
       "Condition not met at step 936. No data to save.\n",
       "936\n",
-      "model_vars.get('Gini', 0)[-1]=0.6862\n",
       "Condition not met at step 937. No data to save.\n",
       "937\n",
-      "model_vars.get('Gini', 0)[-1]=0.6624\n",
       "Condition not met at step 938. No data to save.\n",
       "938\n",
-      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
       "Condition not met at step 939. No data to save.\n",
       "939\n",
-      "model_vars.get('Gini', 0)[-1]=0.6858\n",
       "Condition not met at step 940. No data to save.\n",
       "940\n",
-      "model_vars.get('Gini', 0)[-1]=0.6946\n",
       "Condition not met at step 941. No data to save.\n",
       "941\n",
-      "model_vars.get('Gini', 0)[-1]=0.6874\n",
       "Condition not met at step 942. No data to save.\n",
       "942\n",
-      "model_vars.get('Gini', 0)[-1]=0.6804\n",
       "Condition not met at step 943. No data to save.\n",
       "943\n",
-      "model_vars.get('Gini', 0)[-1]=0.6582\n",
       "Condition not met at step 944. No data to save.\n",
       "944\n",
-      "model_vars.get('Gini', 0)[-1]=0.6636\n",
       "Condition not met at step 945. No data to save.\n",
       "945\n",
-      "model_vars.get('Gini', 0)[-1]=0.6708000000000001\n",
       "Condition not met at step 946. No data to save.\n",
       "946\n",
-      "model_vars.get('Gini', 0)[-1]=0.6464000000000001\n",
-      "Condition not met at step 947. No data to save.\n",
+      "Condition met. Appended special results for step 947 to test_path/special_results.parquet\n",
       "947\n",
-      "model_vars.get('Gini', 0)[-1]=0.6434\n",
-      "Condition not met at step 948. No data to save.\n",
+      "Condition met. Appended special results for step 948 to test_path/special_results.parquet\n",
       "948\n",
-      "model_vars.get('Gini', 0)[-1]=0.6712\n",
-      "Condition not met at step 949. No data to save.\n",
+      "Condition met. Appended special results for step 949 to test_path/special_results.parquet\n",
       "949\n",
-      "model_vars.get('Gini', 0)[-1]=0.6544\n",
-      "Condition not met at step 950. No data to save.\n",
+      "Condition met. Appended special results for step 950 to test_path/special_results.parquet\n",
       "950\n",
-      "model_vars.get('Gini', 0)[-1]=0.6428\n",
-      "Condition not met at step 951. No data to save.\n",
+      "Condition met. Appended special results for step 951 to test_path/special_results.parquet\n",
       "951\n",
-      "model_vars.get('Gini', 0)[-1]=0.646\n",
       "Condition not met at step 952. No data to save.\n",
       "952\n",
-      "model_vars.get('Gini', 0)[-1]=0.6404000000000001\n",
       "Condition not met at step 953. No data to save.\n",
       "953\n",
-      "model_vars.get('Gini', 0)[-1]=0.6536\n",
       "Condition not met at step 954. No data to save.\n",
       "954\n",
-      "model_vars.get('Gini', 0)[-1]=0.6442\n",
       "Condition not met at step 955. No data to save.\n",
       "955\n",
-      "model_vars.get('Gini', 0)[-1]=0.6294\n",
       "Condition not met at step 956. No data to save.\n",
       "956\n",
-      "model_vars.get('Gini', 0)[-1]=0.6374\n",
       "Condition not met at step 957. No data to save.\n",
       "957\n",
-      "model_vars.get('Gini', 0)[-1]=0.6362\n",
       "Condition not met at step 958. No data to save.\n",
       "958\n",
-      "model_vars.get('Gini', 0)[-1]=0.6492\n",
       "Condition not met at step 959. No data to save.\n",
       "959\n",
-      "model_vars.get('Gini', 0)[-1]=0.642\n",
       "Condition not met at step 960. No data to save.\n",
       "960\n",
-      "model_vars.get('Gini', 0)[-1]=0.6468\n",
       "Condition not met at step 961. No data to save.\n",
       "961\n",
-      "model_vars.get('Gini', 0)[-1]=0.652\n",
       "Condition not met at step 962. No data to save.\n",
       "962\n",
-      "model_vars.get('Gini', 0)[-1]=0.6394\n",
       "Condition not met at step 963. No data to save.\n",
       "963\n",
-      "model_vars.get('Gini', 0)[-1]=0.656\n",
       "Condition not met at step 964. No data to save.\n",
       "964\n",
-      "model_vars.get('Gini', 0)[-1]=0.6362\n",
       "Condition not met at step 965. No data to save.\n",
       "965\n",
-      "model_vars.get('Gini', 0)[-1]=0.6452\n",
       "Condition not met at step 966. No data to save.\n",
       "966\n",
-      "model_vars.get('Gini', 0)[-1]=0.6402\n",
       "Condition not met at step 967. No data to save.\n",
       "967\n",
-      "model_vars.get('Gini', 0)[-1]=0.6528\n",
       "Condition not met at step 968. No data to save.\n",
       "968\n",
-      "model_vars.get('Gini', 0)[-1]=0.6358\n",
       "Condition not met at step 969. No data to save.\n",
       "969\n",
-      "model_vars.get('Gini', 0)[-1]=0.6416\n",
       "Condition not met at step 970. No data to save.\n",
       "970\n",
-      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
       "Condition not met at step 971. No data to save.\n",
       "971\n",
-      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
       "Condition not met at step 972. No data to save.\n",
       "972\n",
-      "model_vars.get('Gini', 0)[-1]=0.6234\n",
       "Condition not met at step 973. No data to save.\n",
       "973\n",
-      "model_vars.get('Gini', 0)[-1]=0.5932\n",
       "Condition not met at step 974. No data to save.\n",
       "974\n",
-      "model_vars.get('Gini', 0)[-1]=0.6332\n",
       "Condition not met at step 975. No data to save.\n",
       "975\n",
-      "model_vars.get('Gini', 0)[-1]=0.6226\n",
       "Condition not met at step 976. No data to save.\n",
       "976\n",
-      "model_vars.get('Gini', 0)[-1]=0.6388\n",
       "Condition not met at step 977. No data to save.\n",
       "977\n",
-      "model_vars.get('Gini', 0)[-1]=0.6634\n",
       "Condition not met at step 978. No data to save.\n",
       "978\n",
-      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
       "Condition not met at step 979. No data to save.\n",
       "979\n",
-      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
       "Condition not met at step 980. No data to save.\n",
       "980\n",
-      "model_vars.get('Gini', 0)[-1]=0.6702\n",
       "Condition not met at step 981. No data to save.\n",
       "981\n",
-      "model_vars.get('Gini', 0)[-1]=0.6582\n",
       "Condition not met at step 982. No data to save.\n",
       "982\n",
-      "model_vars.get('Gini', 0)[-1]=0.6294\n",
       "Condition not met at step 983. No data to save.\n",
       "983\n",
-      "model_vars.get('Gini', 0)[-1]=0.6582\n",
       "Condition not met at step 984. No data to save.\n",
       "984\n",
-      "model_vars.get('Gini', 0)[-1]=0.6754\n",
       "Condition not met at step 985. No data to save.\n",
       "985\n",
-      "model_vars.get('Gini', 0)[-1]=0.6562\n",
       "Condition not met at step 986. No data to save.\n",
       "986\n",
-      "model_vars.get('Gini', 0)[-1]=0.646\n",
       "Condition not met at step 987. No data to save.\n",
       "987\n",
-      "model_vars.get('Gini', 0)[-1]=0.6862\n",
       "Condition not met at step 988. No data to save.\n",
       "988\n",
-      "model_vars.get('Gini', 0)[-1]=0.7024\n",
-      "Condition met. Appended special results for step 989 to test_path/special_results.parquet\n",
+      "Condition not met at step 989. No data to save.\n",
       "989\n",
-      "model_vars.get('Gini', 0)[-1]=0.7170000000000001\n",
-      "Condition met. Appended special results for step 990 to test_path/special_results.parquet\n",
+      "Condition not met at step 990. No data to save.\n",
       "990\n",
-      "model_vars.get('Gini', 0)[-1]=0.6974\n",
       "Condition not met at step 991. No data to save.\n",
       "991\n",
-      "model_vars.get('Gini', 0)[-1]=0.6816\n",
       "Condition not met at step 992. No data to save.\n",
       "992\n",
-      "model_vars.get('Gini', 0)[-1]=0.7086\n",
       "Condition met. Appended special results for step 993 to test_path/special_results.parquet\n",
       "993\n",
-      "model_vars.get('Gini', 0)[-1]=0.6996\n",
       "Condition not met at step 994. No data to save.\n",
       "994\n",
-      "model_vars.get('Gini', 0)[-1]=0.677\n",
       "Condition not met at step 995. No data to save.\n",
       "995\n",
-      "model_vars.get('Gini', 0)[-1]=0.6921999999999999\n",
       "Condition not met at step 996. No data to save.\n",
       "996\n",
-      "model_vars.get('Gini', 0)[-1]=0.685\n",
       "Condition not met at step 997. No data to save.\n",
       "997\n",
-      "model_vars.get('Gini', 0)[-1]=0.6806\n",
       "Condition not met at step 998. No data to save.\n",
       "998\n",
-      "model_vars.get('Gini', 0)[-1]=0.651\n",
       "Condition not met at step 999. No data to save.\n",
       "999\n",
-      "CALLED\n",
-      "self.model._steps=1000\n",
-      " TEST self.model._steps=1000\n",
-      " TEST self._cache_interval=100\n",
-      "       Gini\n",
-      "900  0.7038\n",
-      "901  0.6784\n",
-      "902  0.6872\n",
-      "903  0.6596\n",
-      "904  0.6486\n",
-      "..      ...\n",
-      "995  0.6922\n",
-      "996  0.6850\n",
-      "997  0.6806\n",
-      "998  0.6510\n",
-      "999  0.6530\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_010.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_010.parquet'\n",
-      "Saving model to test_path/model_data_010.parquet\n",
-      "Saving agent to test_path/agent_data_010.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.653\n",
-      "Condition not met at step 1000. No data to save.\n",
-      "1000\n",
-      "model_vars.get('Gini', 0)[-1]=0.6574\n",
-      "Condition not met at step 1001. No data to save.\n",
-      "1001\n",
-      "model_vars.get('Gini', 0)[-1]=0.6482\n",
-      "Condition not met at step 1002. No data to save.\n",
-      "1002\n",
-      "model_vars.get('Gini', 0)[-1]=0.6254\n",
-      "Condition not met at step 1003. No data to save.\n",
-      "1003\n",
-      "model_vars.get('Gini', 0)[-1]=0.6315999999999999\n",
-      "Condition not met at step 1004. No data to save.\n",
-      "1004\n",
-      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
-      "Condition not met at step 1005. No data to save.\n",
-      "1005\n",
-      "model_vars.get('Gini', 0)[-1]=0.6548\n",
-      "Condition not met at step 1006. No data to save.\n",
-      "1006\n",
-      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
-      "Condition not met at step 1007. No data to save.\n",
-      "1007\n",
-      "model_vars.get('Gini', 0)[-1]=0.6348\n",
-      "Condition not met at step 1008. No data to save.\n",
-      "1008\n",
-      "model_vars.get('Gini', 0)[-1]=0.6442\n",
-      "Condition not met at step 1009. No data to save.\n",
-      "1009\n",
-      "model_vars.get('Gini', 0)[-1]=0.6184000000000001\n",
-      "Condition not met at step 1010. No data to save.\n",
-      "1010\n",
-      "model_vars.get('Gini', 0)[-1]=0.5946\n",
-      "Condition not met at step 1011. No data to save.\n",
-      "1011\n",
-      "model_vars.get('Gini', 0)[-1]=0.6018\n",
-      "Condition not met at step 1012. No data to save.\n",
-      "1012\n",
-      "model_vars.get('Gini', 0)[-1]=0.6093999999999999\n",
-      "Condition not met at step 1013. No data to save.\n",
-      "1013\n",
-      "model_vars.get('Gini', 0)[-1]=0.615\n",
-      "Condition not met at step 1014. No data to save.\n",
-      "1014\n",
-      "model_vars.get('Gini', 0)[-1]=0.648\n",
-      "Condition not met at step 1015. No data to save.\n",
-      "1015\n",
-      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
-      "Condition not met at step 1016. No data to save.\n",
-      "1016\n",
-      "model_vars.get('Gini', 0)[-1]=0.6432\n",
-      "Condition not met at step 1017. No data to save.\n",
-      "1017\n",
-      "model_vars.get('Gini', 0)[-1]=0.6542\n",
-      "Condition not met at step 1018. No data to save.\n",
-      "1018\n",
-      "model_vars.get('Gini', 0)[-1]=0.6806\n",
-      "Condition not met at step 1019. No data to save.\n",
-      "1019\n",
-      "model_vars.get('Gini', 0)[-1]=0.6582\n",
-      "Condition not met at step 1020. No data to save.\n",
-      "1020\n",
-      "model_vars.get('Gini', 0)[-1]=0.6416\n",
-      "Condition not met at step 1021. No data to save.\n",
-      "1021\n",
-      "model_vars.get('Gini', 0)[-1]=0.6614\n",
-      "Condition not met at step 1022. No data to save.\n",
-      "1022\n",
-      "model_vars.get('Gini', 0)[-1]=0.6324000000000001\n",
-      "Condition not met at step 1023. No data to save.\n",
-      "1023\n",
-      "model_vars.get('Gini', 0)[-1]=0.6522\n",
-      "Condition not met at step 1024. No data to save.\n",
-      "1024\n",
-      "model_vars.get('Gini', 0)[-1]=0.6528\n",
-      "Condition not met at step 1025. No data to save.\n",
-      "1025\n",
-      "model_vars.get('Gini', 0)[-1]=0.6748000000000001\n",
-      "Condition not met at step 1026. No data to save.\n",
-      "1026\n",
-      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
-      "Condition not met at step 1027. No data to save.\n",
-      "1027\n",
-      "model_vars.get('Gini', 0)[-1]=0.6522\n",
-      "Condition not met at step 1028. No data to save.\n",
-      "1028\n",
-      "model_vars.get('Gini', 0)[-1]=0.6612\n",
-      "Condition not met at step 1029. No data to save.\n",
-      "1029\n",
-      "model_vars.get('Gini', 0)[-1]=0.6732\n",
-      "Condition not met at step 1030. No data to save.\n",
-      "1030\n",
-      "model_vars.get('Gini', 0)[-1]=0.6594\n",
-      "Condition not met at step 1031. No data to save.\n",
-      "1031\n",
-      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
-      "Condition not met at step 1032. No data to save.\n",
-      "1032\n",
-      "model_vars.get('Gini', 0)[-1]=0.6452\n",
-      "Condition not met at step 1033. No data to save.\n",
-      "1033\n",
-      "model_vars.get('Gini', 0)[-1]=0.6404000000000001\n",
-      "Condition not met at step 1034. No data to save.\n",
-      "1034\n",
-      "model_vars.get('Gini', 0)[-1]=0.6532\n",
-      "Condition not met at step 1035. No data to save.\n",
-      "1035\n",
-      "model_vars.get('Gini', 0)[-1]=0.6586000000000001\n",
-      "Condition not met at step 1036. No data to save.\n",
-      "1036\n",
-      "model_vars.get('Gini', 0)[-1]=0.6646000000000001\n",
-      "Condition not met at step 1037. No data to save.\n",
-      "1037\n",
-      "model_vars.get('Gini', 0)[-1]=0.6644\n",
-      "Condition not met at step 1038. No data to save.\n",
-      "1038\n",
-      "model_vars.get('Gini', 0)[-1]=0.6859999999999999\n",
-      "Condition not met at step 1039. No data to save.\n",
-      "1039\n",
-      "model_vars.get('Gini', 0)[-1]=0.6806\n",
-      "Condition not met at step 1040. No data to save.\n",
-      "1040\n",
-      "model_vars.get('Gini', 0)[-1]=0.6856\n",
-      "Condition not met at step 1041. No data to save.\n",
-      "1041\n",
-      "model_vars.get('Gini', 0)[-1]=0.6779999999999999\n",
-      "Condition not met at step 1042. No data to save.\n",
-      "1042\n",
-      "model_vars.get('Gini', 0)[-1]=0.6708000000000001\n",
-      "Condition not met at step 1043. No data to save.\n",
-      "1043\n",
-      "model_vars.get('Gini', 0)[-1]=0.6594\n",
-      "Condition not met at step 1044. No data to save.\n",
-      "1044\n",
-      "model_vars.get('Gini', 0)[-1]=0.6534\n",
-      "Condition not met at step 1045. No data to save.\n",
-      "1045\n",
-      "model_vars.get('Gini', 0)[-1]=0.6577999999999999\n",
-      "Condition not met at step 1046. No data to save.\n",
-      "1046\n",
-      "model_vars.get('Gini', 0)[-1]=0.6384000000000001\n",
-      "Condition not met at step 1047. No data to save.\n",
-      "1047\n",
-      "model_vars.get('Gini', 0)[-1]=0.6474\n",
-      "Condition not met at step 1048. No data to save.\n",
-      "1048\n",
-      "model_vars.get('Gini', 0)[-1]=0.6304000000000001\n",
-      "Condition not met at step 1049. No data to save.\n",
-      "1049\n",
-      "model_vars.get('Gini', 0)[-1]=0.6574\n",
-      "Condition not met at step 1050. No data to save.\n",
-      "1050\n",
-      "model_vars.get('Gini', 0)[-1]=0.6352\n",
-      "Condition not met at step 1051. No data to save.\n",
-      "1051\n",
-      "model_vars.get('Gini', 0)[-1]=0.6496\n",
-      "Condition not met at step 1052. No data to save.\n",
-      "1052\n",
-      "model_vars.get('Gini', 0)[-1]=0.6616\n",
-      "Condition not met at step 1053. No data to save.\n",
-      "1053\n",
-      "model_vars.get('Gini', 0)[-1]=0.6166\n",
-      "Condition not met at step 1054. No data to save.\n",
-      "1054\n",
-      "model_vars.get('Gini', 0)[-1]=0.6148\n",
-      "Condition not met at step 1055. No data to save.\n",
-      "1055\n",
-      "model_vars.get('Gini', 0)[-1]=0.6577999999999999\n",
-      "Condition not met at step 1056. No data to save.\n",
-      "1056\n",
-      "model_vars.get('Gini', 0)[-1]=0.6506000000000001\n",
-      "Condition not met at step 1057. No data to save.\n",
-      "1057\n",
-      "model_vars.get('Gini', 0)[-1]=0.6488\n",
-      "Condition not met at step 1058. No data to save.\n",
-      "1058\n",
-      "model_vars.get('Gini', 0)[-1]=0.6617999999999999\n",
-      "Condition not met at step 1059. No data to save.\n",
-      "1059\n",
-      "model_vars.get('Gini', 0)[-1]=0.6548\n",
-      "Condition not met at step 1060. No data to save.\n",
-      "1060\n",
-      "model_vars.get('Gini', 0)[-1]=0.6572\n",
-      "Condition not met at step 1061. No data to save.\n",
-      "1061\n",
-      "model_vars.get('Gini', 0)[-1]=0.6348\n",
-      "Condition not met at step 1062. No data to save.\n",
-      "1062\n",
-      "model_vars.get('Gini', 0)[-1]=0.6534\n",
-      "Condition not met at step 1063. No data to save.\n",
-      "1063\n",
-      "model_vars.get('Gini', 0)[-1]=0.65\n",
-      "Condition not met at step 1064. No data to save.\n",
-      "1064\n",
-      "model_vars.get('Gini', 0)[-1]=0.6426000000000001\n",
-      "Condition not met at step 1065. No data to save.\n",
-      "1065\n",
-      "model_vars.get('Gini', 0)[-1]=0.6242000000000001\n",
-      "Condition not met at step 1066. No data to save.\n",
-      "1066\n",
-      "model_vars.get('Gini', 0)[-1]=0.6188\n",
-      "Condition not met at step 1067. No data to save.\n",
-      "1067\n",
-      "model_vars.get('Gini', 0)[-1]=0.6068\n",
-      "Condition not met at step 1068. No data to save.\n",
-      "1068\n",
-      "model_vars.get('Gini', 0)[-1]=0.6228\n",
-      "Condition not met at step 1069. No data to save.\n",
-      "1069\n",
-      "model_vars.get('Gini', 0)[-1]=0.6204000000000001\n",
-      "Condition not met at step 1070. No data to save.\n",
-      "1070\n",
-      "model_vars.get('Gini', 0)[-1]=0.6266\n",
-      "Condition not met at step 1071. No data to save.\n",
-      "1071\n",
-      "model_vars.get('Gini', 0)[-1]=0.6437999999999999\n",
-      "Condition not met at step 1072. No data to save.\n",
-      "1072\n",
-      "model_vars.get('Gini', 0)[-1]=0.6315999999999999\n",
-      "Condition not met at step 1073. No data to save.\n",
-      "1073\n",
-      "model_vars.get('Gini', 0)[-1]=0.638\n",
-      "Condition not met at step 1074. No data to save.\n",
-      "1074\n",
-      "model_vars.get('Gini', 0)[-1]=0.622\n",
-      "Condition not met at step 1075. No data to save.\n",
-      "1075\n",
-      "model_vars.get('Gini', 0)[-1]=0.639\n",
-      "Condition not met at step 1076. No data to save.\n",
-      "1076\n",
-      "model_vars.get('Gini', 0)[-1]=0.6634\n",
-      "Condition not met at step 1077. No data to save.\n",
-      "1077\n",
-      "model_vars.get('Gini', 0)[-1]=0.658\n",
-      "Condition not met at step 1078. No data to save.\n",
-      "1078\n",
-      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
-      "Condition not met at step 1079. No data to save.\n",
-      "1079\n",
-      "model_vars.get('Gini', 0)[-1]=0.6378\n",
-      "Condition not met at step 1080. No data to save.\n",
-      "1080\n",
-      "model_vars.get('Gini', 0)[-1]=0.6386000000000001\n",
-      "Condition not met at step 1081. No data to save.\n",
-      "1081\n",
-      "model_vars.get('Gini', 0)[-1]=0.647\n",
-      "Condition not met at step 1082. No data to save.\n",
-      "1082\n",
-      "model_vars.get('Gini', 0)[-1]=0.6662\n",
-      "Condition not met at step 1083. No data to save.\n",
-      "1083\n",
-      "model_vars.get('Gini', 0)[-1]=0.6548\n",
-      "Condition not met at step 1084. No data to save.\n",
-      "1084\n",
-      "model_vars.get('Gini', 0)[-1]=0.6502\n",
-      "Condition not met at step 1085. No data to save.\n",
-      "1085\n",
-      "model_vars.get('Gini', 0)[-1]=0.6386000000000001\n",
-      "Condition not met at step 1086. No data to save.\n",
-      "1086\n",
-      "model_vars.get('Gini', 0)[-1]=0.6528\n",
-      "Condition not met at step 1087. No data to save.\n",
-      "1087\n",
-      "model_vars.get('Gini', 0)[-1]=0.6512\n",
-      "Condition not met at step 1088. No data to save.\n",
-      "1088\n",
-      "model_vars.get('Gini', 0)[-1]=0.6594\n",
-      "Condition not met at step 1089. No data to save.\n",
-      "1089\n",
-      "model_vars.get('Gini', 0)[-1]=0.6914\n",
-      "Condition not met at step 1090. No data to save.\n",
-      "1090\n",
-      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
-      "Condition not met at step 1091. No data to save.\n",
-      "1091\n",
-      "model_vars.get('Gini', 0)[-1]=0.6568\n",
-      "Condition not met at step 1092. No data to save.\n",
-      "1092\n",
-      "model_vars.get('Gini', 0)[-1]=0.6602\n",
-      "Condition not met at step 1093. No data to save.\n",
-      "1093\n",
-      "model_vars.get('Gini', 0)[-1]=0.6834\n",
-      "Condition not met at step 1094. No data to save.\n",
-      "1094\n",
-      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
-      "Condition not met at step 1095. No data to save.\n",
-      "1095\n",
-      "model_vars.get('Gini', 0)[-1]=0.6819999999999999\n",
-      "Condition not met at step 1096. No data to save.\n",
-      "1096\n",
-      "model_vars.get('Gini', 0)[-1]=0.6684\n",
-      "Condition not met at step 1097. No data to save.\n",
-      "1097\n",
-      "model_vars.get('Gini', 0)[-1]=0.6692\n",
-      "Condition not met at step 1098. No data to save.\n",
-      "1098\n",
-      "model_vars.get('Gini', 0)[-1]=0.6672\n",
-      "Condition not met at step 1099. No data to save.\n",
-      "1099\n",
-      "CALLED\n",
-      "self.model._steps=1100\n",
-      " TEST self.model._steps=1100\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "1000  0.6574\n",
-      "1001  0.6482\n",
-      "1002  0.6254\n",
-      "1003  0.6316\n",
-      "1004  0.6486\n",
-      "...      ...\n",
-      "1095  0.6820\n",
-      "1096  0.6684\n",
-      "1097  0.6692\n",
-      "1098  0.6672\n",
-      "1099  0.6878\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_011.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_011.parquet'\n",
-      "Saving model to test_path/model_data_011.parquet\n",
-      "Saving agent to test_path/agent_data_011.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6878\n",
-      "Condition not met at step 1100. No data to save.\n",
-      "1100\n",
-      "model_vars.get('Gini', 0)[-1]=0.6744\n",
-      "Condition not met at step 1101. No data to save.\n",
-      "1101\n",
-      "model_vars.get('Gini', 0)[-1]=0.6722\n",
-      "Condition not met at step 1102. No data to save.\n",
-      "1102\n",
-      "model_vars.get('Gini', 0)[-1]=0.6566000000000001\n",
-      "Condition not met at step 1103. No data to save.\n",
-      "1103\n",
-      "model_vars.get('Gini', 0)[-1]=0.6402\n",
-      "Condition not met at step 1104. No data to save.\n",
-      "1104\n",
-      "model_vars.get('Gini', 0)[-1]=0.6434\n",
-      "Condition not met at step 1105. No data to save.\n",
-      "1105\n",
-      "model_vars.get('Gini', 0)[-1]=0.6126\n",
-      "Condition not met at step 1106. No data to save.\n",
-      "1106\n",
-      "model_vars.get('Gini', 0)[-1]=0.608\n",
-      "Condition not met at step 1107. No data to save.\n",
-      "1107\n",
-      "model_vars.get('Gini', 0)[-1]=0.6312\n",
-      "Condition not met at step 1108. No data to save.\n",
-      "1108\n",
-      "model_vars.get('Gini', 0)[-1]=0.6536\n",
-      "Condition not met at step 1109. No data to save.\n",
-      "1109\n",
-      "model_vars.get('Gini', 0)[-1]=0.6208\n",
-      "Condition not met at step 1110. No data to save.\n",
-      "1110\n",
-      "model_vars.get('Gini', 0)[-1]=0.646\n",
-      "Condition not met at step 1111. No data to save.\n",
-      "1111\n",
-      "model_vars.get('Gini', 0)[-1]=0.6534\n",
-      "Condition not met at step 1112. No data to save.\n",
-      "1112\n",
-      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
-      "Condition not met at step 1113. No data to save.\n",
-      "1113\n",
-      "model_vars.get('Gini', 0)[-1]=0.6544\n",
-      "Condition not met at step 1114. No data to save.\n",
-      "1114\n",
-      "model_vars.get('Gini', 0)[-1]=0.6517999999999999\n",
-      "Condition not met at step 1115. No data to save.\n",
-      "1115\n",
-      "model_vars.get('Gini', 0)[-1]=0.6774\n",
-      "Condition not met at step 1116. No data to save.\n",
-      "1116\n",
-      "model_vars.get('Gini', 0)[-1]=0.6966\n",
-      "Condition not met at step 1117. No data to save.\n",
-      "1117\n",
-      "model_vars.get('Gini', 0)[-1]=0.6898\n",
-      "Condition not met at step 1118. No data to save.\n",
-      "1118\n",
-      "model_vars.get('Gini', 0)[-1]=0.6686000000000001\n",
-      "Condition not met at step 1119. No data to save.\n",
-      "1119\n",
-      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
-      "Condition not met at step 1120. No data to save.\n",
-      "1120\n",
-      "model_vars.get('Gini', 0)[-1]=0.6676\n",
-      "Condition not met at step 1121. No data to save.\n",
-      "1121\n",
-      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
-      "Condition not met at step 1122. No data to save.\n",
-      "1122\n",
-      "model_vars.get('Gini', 0)[-1]=0.665\n",
-      "Condition not met at step 1123. No data to save.\n",
-      "1123\n",
-      "model_vars.get('Gini', 0)[-1]=0.6568\n",
-      "Condition not met at step 1124. No data to save.\n",
-      "1124\n",
-      "model_vars.get('Gini', 0)[-1]=0.6616\n",
-      "Condition not met at step 1125. No data to save.\n",
-      "1125\n",
-      "model_vars.get('Gini', 0)[-1]=0.6577999999999999\n",
-      "Condition not met at step 1126. No data to save.\n",
-      "1126\n",
-      "model_vars.get('Gini', 0)[-1]=0.644\n",
-      "Condition not met at step 1127. No data to save.\n",
-      "1127\n",
-      "model_vars.get('Gini', 0)[-1]=0.6604\n",
-      "Condition not met at step 1128. No data to save.\n",
-      "1128\n",
-      "model_vars.get('Gini', 0)[-1]=0.6468\n",
-      "Condition not met at step 1129. No data to save.\n",
-      "1129\n",
-      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
-      "Condition not met at step 1130. No data to save.\n",
-      "1130\n",
-      "model_vars.get('Gini', 0)[-1]=0.6684\n",
-      "Condition not met at step 1131. No data to save.\n",
-      "1131\n",
-      "model_vars.get('Gini', 0)[-1]=0.6414\n",
-      "Condition not met at step 1132. No data to save.\n",
-      "1132\n",
-      "model_vars.get('Gini', 0)[-1]=0.6076\n",
-      "Condition not met at step 1133. No data to save.\n",
-      "1133\n",
-      "model_vars.get('Gini', 0)[-1]=0.606\n",
-      "Condition not met at step 1134. No data to save.\n",
-      "1134\n",
-      "model_vars.get('Gini', 0)[-1]=0.6234\n",
-      "Condition not met at step 1135. No data to save.\n",
-      "1135\n",
-      "model_vars.get('Gini', 0)[-1]=0.6232\n",
-      "Condition not met at step 1136. No data to save.\n",
-      "1136\n",
-      "model_vars.get('Gini', 0)[-1]=0.6326\n",
-      "Condition not met at step 1137. No data to save.\n",
-      "1137\n",
-      "model_vars.get('Gini', 0)[-1]=0.627\n",
-      "Condition not met at step 1138. No data to save.\n",
-      "1138\n",
-      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
-      "Condition not met at step 1139. No data to save.\n",
-      "1139\n",
-      "model_vars.get('Gini', 0)[-1]=0.6642\n",
-      "Condition not met at step 1140. No data to save.\n",
-      "1140\n",
-      "model_vars.get('Gini', 0)[-1]=0.654\n",
-      "Condition not met at step 1141. No data to save.\n",
-      "1141\n",
-      "model_vars.get('Gini', 0)[-1]=0.6348\n",
-      "Condition not met at step 1142. No data to save.\n",
-      "1142\n",
-      "model_vars.get('Gini', 0)[-1]=0.6292\n",
-      "Condition not met at step 1143. No data to save.\n",
-      "1143\n",
-      "model_vars.get('Gini', 0)[-1]=0.6098\n",
-      "Condition not met at step 1144. No data to save.\n",
-      "1144\n",
-      "model_vars.get('Gini', 0)[-1]=0.6166\n",
-      "Condition not met at step 1145. No data to save.\n",
-      "1145\n",
-      "model_vars.get('Gini', 0)[-1]=0.6166\n",
-      "Condition not met at step 1146. No data to save.\n",
-      "1146\n",
-      "model_vars.get('Gini', 0)[-1]=0.6066\n",
-      "Condition not met at step 1147. No data to save.\n",
-      "1147\n",
-      "model_vars.get('Gini', 0)[-1]=0.6228\n",
-      "Condition not met at step 1148. No data to save.\n",
-      "1148\n",
-      "model_vars.get('Gini', 0)[-1]=0.6204000000000001\n",
-      "Condition not met at step 1149. No data to save.\n",
-      "1149\n",
-      "model_vars.get('Gini', 0)[-1]=0.6268\n",
-      "Condition not met at step 1150. No data to save.\n",
-      "1150\n",
-      "model_vars.get('Gini', 0)[-1]=0.641\n",
-      "Condition not met at step 1151. No data to save.\n",
-      "1151\n",
-      "model_vars.get('Gini', 0)[-1]=0.6652\n",
-      "Condition not met at step 1152. No data to save.\n",
-      "1152\n",
-      "model_vars.get('Gini', 0)[-1]=0.6686000000000001\n",
-      "Condition not met at step 1153. No data to save.\n",
-      "1153\n",
-      "model_vars.get('Gini', 0)[-1]=0.6738\n",
-      "Condition not met at step 1154. No data to save.\n",
-      "1154\n",
-      "model_vars.get('Gini', 0)[-1]=0.6798\n",
-      "Condition not met at step 1155. No data to save.\n",
-      "1155\n",
-      "model_vars.get('Gini', 0)[-1]=0.685\n",
-      "Condition not met at step 1156. No data to save.\n",
-      "1156\n",
-      "model_vars.get('Gini', 0)[-1]=0.6826\n",
-      "Condition not met at step 1157. No data to save.\n",
-      "1157\n",
-      "model_vars.get('Gini', 0)[-1]=0.6802\n",
-      "Condition not met at step 1158. No data to save.\n",
-      "1158\n",
-      "model_vars.get('Gini', 0)[-1]=0.7026\n",
-      "Condition met. Appended special results for step 1159 to test_path/special_results.parquet\n",
-      "1159\n",
-      "model_vars.get('Gini', 0)[-1]=0.6964\n",
-      "Condition not met at step 1160. No data to save.\n",
-      "1160\n",
-      "model_vars.get('Gini', 0)[-1]=0.7001999999999999\n",
-      "Condition met. Appended special results for step 1161 to test_path/special_results.parquet\n",
-      "1161\n",
-      "model_vars.get('Gini', 0)[-1]=0.6906\n",
-      "Condition not met at step 1162. No data to save.\n",
-      "1162\n",
-      "model_vars.get('Gini', 0)[-1]=0.6854\n",
-      "Condition not met at step 1163. No data to save.\n",
-      "1163\n",
-      "model_vars.get('Gini', 0)[-1]=0.6968000000000001\n",
-      "Condition not met at step 1164. No data to save.\n",
-      "1164\n",
-      "model_vars.get('Gini', 0)[-1]=0.6842\n",
-      "Condition not met at step 1165. No data to save.\n",
-      "1165\n",
-      "model_vars.get('Gini', 0)[-1]=0.6819999999999999\n",
-      "Condition not met at step 1166. No data to save.\n",
-      "1166\n",
-      "model_vars.get('Gini', 0)[-1]=0.7068\n",
-      "Condition met. Appended special results for step 1167 to test_path/special_results.parquet\n",
-      "1167\n",
-      "model_vars.get('Gini', 0)[-1]=0.6978\n",
-      "Condition not met at step 1168. No data to save.\n",
-      "1168\n",
-      "model_vars.get('Gini', 0)[-1]=0.6718\n",
-      "Condition not met at step 1169. No data to save.\n",
-      "1169\n",
-      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
-      "Condition not met at step 1170. No data to save.\n",
-      "1170\n",
-      "model_vars.get('Gini', 0)[-1]=0.6638\n",
-      "Condition not met at step 1171. No data to save.\n",
-      "1171\n",
-      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
-      "Condition not met at step 1172. No data to save.\n",
-      "1172\n",
-      "model_vars.get('Gini', 0)[-1]=0.6824\n",
-      "Condition not met at step 1173. No data to save.\n",
-      "1173\n",
-      "model_vars.get('Gini', 0)[-1]=0.6794\n",
-      "Condition not met at step 1174. No data to save.\n",
-      "1174\n",
-      "model_vars.get('Gini', 0)[-1]=0.6828000000000001\n",
-      "Condition not met at step 1175. No data to save.\n",
-      "1175\n",
-      "model_vars.get('Gini', 0)[-1]=0.6772\n",
-      "Condition not met at step 1176. No data to save.\n",
-      "1176\n",
-      "model_vars.get('Gini', 0)[-1]=0.6796\n",
-      "Condition not met at step 1177. No data to save.\n",
-      "1177\n",
-      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
-      "Condition not met at step 1178. No data to save.\n",
-      "1178\n",
-      "model_vars.get('Gini', 0)[-1]=0.6662\n",
-      "Condition not met at step 1179. No data to save.\n",
-      "1179\n",
-      "model_vars.get('Gini', 0)[-1]=0.6762\n",
-      "Condition not met at step 1180. No data to save.\n",
-      "1180\n",
-      "model_vars.get('Gini', 0)[-1]=0.6906\n",
-      "Condition not met at step 1181. No data to save.\n",
-      "1181\n",
-      "model_vars.get('Gini', 0)[-1]=0.6702\n",
-      "Condition not met at step 1182. No data to save.\n",
-      "1182\n",
-      "model_vars.get('Gini', 0)[-1]=0.669\n",
-      "Condition not met at step 1183. No data to save.\n",
-      "1183\n",
-      "model_vars.get('Gini', 0)[-1]=0.6374\n",
-      "Condition not met at step 1184. No data to save.\n",
-      "1184\n",
-      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
-      "Condition not met at step 1185. No data to save.\n",
-      "1185\n",
-      "model_vars.get('Gini', 0)[-1]=0.6778\n",
-      "Condition not met at step 1186. No data to save.\n",
-      "1186\n",
-      "model_vars.get('Gini', 0)[-1]=0.6826\n",
-      "Condition not met at step 1187. No data to save.\n",
-      "1187\n",
-      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
-      "Condition not met at step 1188. No data to save.\n",
-      "1188\n",
-      "model_vars.get('Gini', 0)[-1]=0.6738\n",
-      "Condition not met at step 1189. No data to save.\n",
-      "1189\n",
-      "model_vars.get('Gini', 0)[-1]=0.6759999999999999\n",
-      "Condition not met at step 1190. No data to save.\n",
-      "1190\n",
-      "model_vars.get('Gini', 0)[-1]=0.6862\n",
-      "Condition not met at step 1191. No data to save.\n",
-      "1191\n",
-      "model_vars.get('Gini', 0)[-1]=0.6921999999999999\n",
-      "Condition not met at step 1192. No data to save.\n",
-      "1192\n",
-      "model_vars.get('Gini', 0)[-1]=0.7166\n",
-      "Condition met. Appended special results for step 1193 to test_path/special_results.parquet\n",
-      "1193\n",
-      "model_vars.get('Gini', 0)[-1]=0.6864\n",
-      "Condition not met at step 1194. No data to save.\n",
-      "1194\n",
-      "model_vars.get('Gini', 0)[-1]=0.6646000000000001\n",
-      "Condition not met at step 1195. No data to save.\n",
-      "1195\n",
-      "model_vars.get('Gini', 0)[-1]=0.6562\n",
-      "Condition not met at step 1196. No data to save.\n",
-      "1196\n",
-      "model_vars.get('Gini', 0)[-1]=0.6602\n",
-      "Condition not met at step 1197. No data to save.\n",
-      "1197\n",
-      "model_vars.get('Gini', 0)[-1]=0.642\n",
-      "Condition not met at step 1198. No data to save.\n",
-      "1198\n",
-      "model_vars.get('Gini', 0)[-1]=0.6554\n",
-      "Condition not met at step 1199. No data to save.\n",
-      "1199\n",
-      "CALLED\n",
-      "self.model._steps=1200\n",
-      " TEST self.model._steps=1200\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "1100  0.6744\n",
-      "1101  0.6722\n",
-      "1102  0.6566\n",
-      "1103  0.6402\n",
-      "1104  0.6434\n",
-      "...      ...\n",
-      "1195  0.6562\n",
-      "1196  0.6602\n",
-      "1197  0.6420\n",
-      "1198  0.6554\n",
-      "1199  0.6302\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_012.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_012.parquet'\n",
-      "Saving model to test_path/model_data_012.parquet\n",
-      "Saving agent to test_path/agent_data_012.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6302\n",
-      "Condition not met at step 1200. No data to save.\n",
-      "1200\n",
-      "model_vars.get('Gini', 0)[-1]=0.6602\n",
-      "Condition not met at step 1201. No data to save.\n",
-      "1201\n",
-      "model_vars.get('Gini', 0)[-1]=0.6497999999999999\n",
-      "Condition not met at step 1202. No data to save.\n",
-      "1202\n",
-      "model_vars.get('Gini', 0)[-1]=0.645\n",
-      "Condition not met at step 1203. No data to save.\n",
-      "1203\n",
-      "model_vars.get('Gini', 0)[-1]=0.6416\n",
-      "Condition not met at step 1204. No data to save.\n",
-      "1204\n",
-      "model_vars.get('Gini', 0)[-1]=0.6194\n",
-      "Condition not met at step 1205. No data to save.\n",
-      "1205\n",
-      "model_vars.get('Gini', 0)[-1]=0.666\n",
-      "Condition not met at step 1206. No data to save.\n",
-      "1206\n",
-      "model_vars.get('Gini', 0)[-1]=0.6542\n",
-      "Condition not met at step 1207. No data to save.\n",
-      "1207\n",
-      "model_vars.get('Gini', 0)[-1]=0.6446000000000001\n",
-      "Condition not met at step 1208. No data to save.\n",
-      "1208\n",
-      "model_vars.get('Gini', 0)[-1]=0.6412\n",
-      "Condition not met at step 1209. No data to save.\n",
-      "1209\n",
-      "model_vars.get('Gini', 0)[-1]=0.639\n",
-      "Condition not met at step 1210. No data to save.\n",
-      "1210\n",
-      "model_vars.get('Gini', 0)[-1]=0.6414\n",
-      "Condition not met at step 1211. No data to save.\n",
-      "1211\n",
-      "model_vars.get('Gini', 0)[-1]=0.6536\n",
-      "Condition not met at step 1212. No data to save.\n",
-      "1212\n",
-      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
-      "Condition not met at step 1213. No data to save.\n",
-      "1213\n",
-      "model_vars.get('Gini', 0)[-1]=0.6242000000000001\n",
-      "Condition not met at step 1214. No data to save.\n",
-      "1214\n",
-      "model_vars.get('Gini', 0)[-1]=0.6098\n",
-      "Condition not met at step 1215. No data to save.\n",
-      "1215\n",
-      "model_vars.get('Gini', 0)[-1]=0.6242000000000001\n",
-      "Condition not met at step 1216. No data to save.\n",
-      "1216\n",
-      "model_vars.get('Gini', 0)[-1]=0.6266\n",
-      "Condition not met at step 1217. No data to save.\n",
-      "1217\n",
-      "model_vars.get('Gini', 0)[-1]=0.6315999999999999\n",
-      "Condition not met at step 1218. No data to save.\n",
-      "1218\n",
-      "model_vars.get('Gini', 0)[-1]=0.6258\n",
-      "Condition not met at step 1219. No data to save.\n",
-      "1219\n",
-      "model_vars.get('Gini', 0)[-1]=0.6228\n",
-      "Condition not met at step 1220. No data to save.\n",
-      "1220\n",
-      "model_vars.get('Gini', 0)[-1]=0.6124\n",
-      "Condition not met at step 1221. No data to save.\n",
-      "1221\n",
-      "model_vars.get('Gini', 0)[-1]=0.657\n",
-      "Condition not met at step 1222. No data to save.\n",
-      "1222\n",
-      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
-      "Condition not met at step 1223. No data to save.\n",
-      "1223\n",
-      "model_vars.get('Gini', 0)[-1]=0.6722\n",
-      "Condition not met at step 1224. No data to save.\n",
-      "1224\n",
-      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
-      "Condition not met at step 1225. No data to save.\n",
-      "1225\n",
-      "model_vars.get('Gini', 0)[-1]=0.6456\n",
-      "Condition not met at step 1226. No data to save.\n",
-      "1226\n",
-      "model_vars.get('Gini', 0)[-1]=0.625\n",
-      "Condition not met at step 1227. No data to save.\n",
-      "1227\n",
-      "model_vars.get('Gini', 0)[-1]=0.6116\n",
-      "Condition not met at step 1228. No data to save.\n",
-      "1228\n",
-      "model_vars.get('Gini', 0)[-1]=0.6154\n",
-      "Condition not met at step 1229. No data to save.\n",
-      "1229\n",
-      "model_vars.get('Gini', 0)[-1]=0.624\n",
-      "Condition not met at step 1230. No data to save.\n",
-      "1230\n",
-      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
-      "Condition not met at step 1231. No data to save.\n",
-      "1231\n",
-      "model_vars.get('Gini', 0)[-1]=0.6392\n",
-      "Condition not met at step 1232. No data to save.\n",
-      "1232\n",
-      "model_vars.get('Gini', 0)[-1]=0.6398\n",
-      "Condition not met at step 1233. No data to save.\n",
-      "1233\n",
-      "model_vars.get('Gini', 0)[-1]=0.658\n",
-      "Condition not met at step 1234. No data to save.\n",
-      "1234\n",
-      "model_vars.get('Gini', 0)[-1]=0.6352\n",
-      "Condition not met at step 1235. No data to save.\n",
-      "1235\n",
-      "model_vars.get('Gini', 0)[-1]=0.656\n",
-      "Condition not met at step 1236. No data to save.\n",
-      "1236\n",
-      "model_vars.get('Gini', 0)[-1]=0.6572\n",
-      "Condition not met at step 1237. No data to save.\n",
-      "1237\n",
-      "model_vars.get('Gini', 0)[-1]=0.6472\n",
-      "Condition not met at step 1238. No data to save.\n",
-      "1238\n",
-      "model_vars.get('Gini', 0)[-1]=0.6318\n",
-      "Condition not met at step 1239. No data to save.\n",
-      "1239\n",
-      "model_vars.get('Gini', 0)[-1]=0.6374\n",
-      "Condition not met at step 1240. No data to save.\n",
-      "1240\n",
-      "model_vars.get('Gini', 0)[-1]=0.65\n",
-      "Condition not met at step 1241. No data to save.\n",
-      "1241\n",
-      "model_vars.get('Gini', 0)[-1]=0.6464000000000001\n",
-      "Condition not met at step 1242. No data to save.\n",
-      "1242\n",
-      "model_vars.get('Gini', 0)[-1]=0.6616\n",
-      "Condition not met at step 1243. No data to save.\n",
-      "1243\n",
-      "model_vars.get('Gini', 0)[-1]=0.637\n",
-      "Condition not met at step 1244. No data to save.\n",
-      "1244\n",
-      "model_vars.get('Gini', 0)[-1]=0.6536\n",
-      "Condition not met at step 1245. No data to save.\n",
-      "1245\n",
-      "model_vars.get('Gini', 0)[-1]=0.647\n",
-      "Condition not met at step 1246. No data to save.\n",
-      "1246\n",
-      "model_vars.get('Gini', 0)[-1]=0.647\n",
-      "Condition not met at step 1247. No data to save.\n",
-      "1247\n",
-      "model_vars.get('Gini', 0)[-1]=0.6437999999999999\n",
-      "Condition not met at step 1248. No data to save.\n",
-      "1248\n",
-      "model_vars.get('Gini', 0)[-1]=0.6537999999999999\n",
-      "Condition not met at step 1249. No data to save.\n",
-      "1249\n",
-      "model_vars.get('Gini', 0)[-1]=0.6537999999999999\n",
-      "Condition not met at step 1250. No data to save.\n",
-      "1250\n",
-      "model_vars.get('Gini', 0)[-1]=0.6544\n",
-      "Condition not met at step 1251. No data to save.\n",
-      "1251\n",
-      "model_vars.get('Gini', 0)[-1]=0.6798\n",
-      "Condition not met at step 1252. No data to save.\n",
-      "1252\n",
-      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
-      "Condition not met at step 1253. No data to save.\n",
-      "1253\n",
-      "model_vars.get('Gini', 0)[-1]=0.6672\n",
-      "Condition not met at step 1254. No data to save.\n",
-      "1254\n",
-      "model_vars.get('Gini', 0)[-1]=0.692\n",
-      "Condition not met at step 1255. No data to save.\n",
-      "1255\n",
-      "model_vars.get('Gini', 0)[-1]=0.6984\n",
-      "Condition not met at step 1256. No data to save.\n",
-      "1256\n",
-      "model_vars.get('Gini', 0)[-1]=0.6874\n",
-      "Condition not met at step 1257. No data to save.\n",
-      "1257\n",
-      "model_vars.get('Gini', 0)[-1]=0.7146\n",
-      "Condition met. Appended special results for step 1258 to test_path/special_results.parquet\n",
-      "1258\n",
-      "model_vars.get('Gini', 0)[-1]=0.7041999999999999\n",
-      "Condition met. Appended special results for step 1259 to test_path/special_results.parquet\n",
-      "1259\n",
-      "model_vars.get('Gini', 0)[-1]=0.6888000000000001\n",
-      "Condition not met at step 1260. No data to save.\n",
-      "1260\n",
-      "model_vars.get('Gini', 0)[-1]=0.658\n",
-      "Condition not met at step 1261. No data to save.\n",
-      "1261\n",
-      "model_vars.get('Gini', 0)[-1]=0.6422\n",
-      "Condition not met at step 1262. No data to save.\n",
-      "1262\n",
-      "model_vars.get('Gini', 0)[-1]=0.6582\n",
-      "Condition not met at step 1263. No data to save.\n",
-      "1263\n",
-      "model_vars.get('Gini', 0)[-1]=0.6442\n",
-      "Condition not met at step 1264. No data to save.\n",
-      "1264\n",
-      "model_vars.get('Gini', 0)[-1]=0.6552\n",
-      "Condition not met at step 1265. No data to save.\n",
-      "1265\n",
-      "model_vars.get('Gini', 0)[-1]=0.6588\n",
-      "Condition not met at step 1266. No data to save.\n",
-      "1266\n",
-      "model_vars.get('Gini', 0)[-1]=0.658\n",
-      "Condition not met at step 1267. No data to save.\n",
-      "1267\n",
-      "model_vars.get('Gini', 0)[-1]=0.6232\n",
-      "Condition not met at step 1268. No data to save.\n",
-      "1268\n",
-      "model_vars.get('Gini', 0)[-1]=0.633\n",
-      "Condition not met at step 1269. No data to save.\n",
-      "1269\n",
-      "model_vars.get('Gini', 0)[-1]=0.6246\n",
-      "Condition not met at step 1270. No data to save.\n",
-      "1270\n",
-      "model_vars.get('Gini', 0)[-1]=0.635\n",
-      "Condition not met at step 1271. No data to save.\n",
-      "1271\n",
-      "model_vars.get('Gini', 0)[-1]=0.6508\n",
-      "Condition not met at step 1272. No data to save.\n",
-      "1272\n",
-      "model_vars.get('Gini', 0)[-1]=0.6258\n",
-      "Condition not met at step 1273. No data to save.\n",
-      "1273\n",
-      "model_vars.get('Gini', 0)[-1]=0.6308\n",
-      "Condition not met at step 1274. No data to save.\n",
-      "1274\n",
-      "model_vars.get('Gini', 0)[-1]=0.6278\n",
-      "Condition not met at step 1275. No data to save.\n",
-      "1275\n",
-      "model_vars.get('Gini', 0)[-1]=0.6264000000000001\n",
-      "Condition not met at step 1276. No data to save.\n",
-      "1276\n",
-      "model_vars.get('Gini', 0)[-1]=0.6232\n",
-      "Condition not met at step 1277. No data to save.\n",
-      "1277\n",
-      "model_vars.get('Gini', 0)[-1]=0.6704\n",
-      "Condition not met at step 1278. No data to save.\n",
-      "1278\n",
-      "model_vars.get('Gini', 0)[-1]=0.6542\n",
-      "Condition not met at step 1279. No data to save.\n",
-      "1279\n",
-      "model_vars.get('Gini', 0)[-1]=0.6508\n",
-      "Condition not met at step 1280. No data to save.\n",
-      "1280\n",
-      "model_vars.get('Gini', 0)[-1]=0.6328\n",
-      "Condition not met at step 1281. No data to save.\n",
-      "1281\n",
-      "model_vars.get('Gini', 0)[-1]=0.604\n",
-      "Condition not met at step 1282. No data to save.\n",
-      "1282\n",
-      "model_vars.get('Gini', 0)[-1]=0.5860000000000001\n",
-      "Condition not met at step 1283. No data to save.\n",
-      "1283\n",
-      "model_vars.get('Gini', 0)[-1]=0.6142000000000001\n",
-      "Condition not met at step 1284. No data to save.\n",
-      "1284\n",
-      "model_vars.get('Gini', 0)[-1]=0.5940000000000001\n",
-      "Condition not met at step 1285. No data to save.\n",
-      "1285\n",
-      "model_vars.get('Gini', 0)[-1]=0.6122000000000001\n",
-      "Condition not met at step 1286. No data to save.\n",
-      "1286\n",
-      "model_vars.get('Gini', 0)[-1]=0.6116\n",
-      "Condition not met at step 1287. No data to save.\n",
-      "1287\n",
-      "model_vars.get('Gini', 0)[-1]=0.6116\n",
-      "Condition not met at step 1288. No data to save.\n",
-      "1288\n",
-      "model_vars.get('Gini', 0)[-1]=0.6116\n",
-      "Condition not met at step 1289. No data to save.\n",
-      "1289\n",
-      "model_vars.get('Gini', 0)[-1]=0.6234\n",
-      "Condition not met at step 1290. No data to save.\n",
-      "1290\n",
-      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
-      "Condition not met at step 1291. No data to save.\n",
-      "1291\n",
-      "model_vars.get('Gini', 0)[-1]=0.6834\n",
-      "Condition not met at step 1292. No data to save.\n",
-      "1292\n",
-      "model_vars.get('Gini', 0)[-1]=0.6752\n",
-      "Condition not met at step 1293. No data to save.\n",
-      "1293\n",
-      "model_vars.get('Gini', 0)[-1]=0.6516\n",
-      "Condition not met at step 1294. No data to save.\n",
-      "1294\n",
-      "model_vars.get('Gini', 0)[-1]=0.6616\n",
-      "Condition not met at step 1295. No data to save.\n",
-      "1295\n",
-      "model_vars.get('Gini', 0)[-1]=0.6175999999999999\n",
-      "Condition not met at step 1296. No data to save.\n",
-      "1296\n",
-      "model_vars.get('Gini', 0)[-1]=0.6446000000000001\n",
-      "Condition not met at step 1297. No data to save.\n",
-      "1297\n",
-      "model_vars.get('Gini', 0)[-1]=0.6462\n",
-      "Condition not met at step 1298. No data to save.\n",
-      "1298\n",
-      "model_vars.get('Gini', 0)[-1]=0.6282000000000001\n",
-      "Condition not met at step 1299. No data to save.\n",
-      "1299\n",
-      "CALLED\n",
-      "self.model._steps=1300\n",
-      " TEST self.model._steps=1300\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "1200  0.6602\n",
-      "1201  0.6498\n",
-      "1202  0.6450\n",
-      "1203  0.6416\n",
-      "1204  0.6194\n",
-      "...      ...\n",
-      "1295  0.6176\n",
-      "1296  0.6446\n",
-      "1297  0.6462\n",
-      "1298  0.6282\n",
-      "1299  0.6144\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_013.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_013.parquet'\n",
-      "Saving model to test_path/model_data_013.parquet\n",
-      "Saving agent to test_path/agent_data_013.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6144000000000001\n",
-      "Condition not met at step 1300. No data to save.\n",
-      "1300\n",
-      "model_vars.get('Gini', 0)[-1]=0.5794\n",
-      "Condition not met at step 1301. No data to save.\n",
-      "1301\n",
-      "model_vars.get('Gini', 0)[-1]=0.5938\n",
-      "Condition not met at step 1302. No data to save.\n",
-      "1302\n",
-      "model_vars.get('Gini', 0)[-1]=0.6288\n",
-      "Condition not met at step 1303. No data to save.\n",
-      "1303\n",
-      "model_vars.get('Gini', 0)[-1]=0.6124\n",
-      "Condition not met at step 1304. No data to save.\n",
-      "1304\n",
-      "model_vars.get('Gini', 0)[-1]=0.6046\n",
-      "Condition not met at step 1305. No data to save.\n",
-      "1305\n",
-      "model_vars.get('Gini', 0)[-1]=0.6224000000000001\n",
-      "Condition not met at step 1306. No data to save.\n",
-      "1306\n",
-      "model_vars.get('Gini', 0)[-1]=0.6072\n",
-      "Condition not met at step 1307. No data to save.\n",
-      "1307\n",
-      "model_vars.get('Gini', 0)[-1]=0.6264000000000001\n",
-      "Condition not met at step 1308. No data to save.\n",
-      "1308\n",
-      "model_vars.get('Gini', 0)[-1]=0.6178\n",
-      "Condition not met at step 1309. No data to save.\n",
-      "1309\n",
-      "model_vars.get('Gini', 0)[-1]=0.6332\n",
-      "Condition not met at step 1310. No data to save.\n",
-      "1310\n",
-      "model_vars.get('Gini', 0)[-1]=0.6362\n",
-      "Condition not met at step 1311. No data to save.\n",
-      "1311\n",
-      "model_vars.get('Gini', 0)[-1]=0.6315999999999999\n",
-      "Condition not met at step 1312. No data to save.\n",
-      "1312\n",
-      "model_vars.get('Gini', 0)[-1]=0.644\n",
-      "Condition not met at step 1313. No data to save.\n",
-      "1313\n",
-      "model_vars.get('Gini', 0)[-1]=0.6168\n",
-      "Condition not met at step 1314. No data to save.\n",
-      "1314\n",
-      "model_vars.get('Gini', 0)[-1]=0.6272\n",
-      "Condition not met at step 1315. No data to save.\n",
-      "1315\n",
-      "model_vars.get('Gini', 0)[-1]=0.6184000000000001\n",
-      "Condition not met at step 1316. No data to save.\n",
-      "1316\n",
-      "model_vars.get('Gini', 0)[-1]=0.625\n",
-      "Condition not met at step 1317. No data to save.\n",
-      "1317\n",
-      "model_vars.get('Gini', 0)[-1]=0.6268\n",
-      "Condition not met at step 1318. No data to save.\n",
-      "1318\n",
-      "model_vars.get('Gini', 0)[-1]=0.6338\n",
-      "Condition not met at step 1319. No data to save.\n",
-      "1319\n",
-      "model_vars.get('Gini', 0)[-1]=0.6304000000000001\n",
-      "Condition not met at step 1320. No data to save.\n",
-      "1320\n",
-      "model_vars.get('Gini', 0)[-1]=0.618\n",
-      "Condition not met at step 1321. No data to save.\n",
-      "1321\n",
-      "model_vars.get('Gini', 0)[-1]=0.6532\n",
-      "Condition not met at step 1322. No data to save.\n",
-      "1322\n",
-      "model_vars.get('Gini', 0)[-1]=0.6532\n",
-      "Condition not met at step 1323. No data to save.\n",
-      "1323\n",
-      "model_vars.get('Gini', 0)[-1]=0.6638\n",
-      "Condition not met at step 1324. No data to save.\n",
-      "1324\n",
-      "model_vars.get('Gini', 0)[-1]=0.6908000000000001\n",
-      "Condition not met at step 1325. No data to save.\n",
-      "1325\n",
-      "model_vars.get('Gini', 0)[-1]=0.7034\n",
-      "Condition met. Appended special results for step 1326 to test_path/special_results.parquet\n",
-      "1326\n",
-      "model_vars.get('Gini', 0)[-1]=0.7008000000000001\n",
-      "Condition met. Appended special results for step 1327 to test_path/special_results.parquet\n",
-      "1327\n",
-      "model_vars.get('Gini', 0)[-1]=0.6802\n",
-      "Condition not met at step 1328. No data to save.\n",
-      "1328\n",
-      "model_vars.get('Gini', 0)[-1]=0.6796\n",
-      "Condition not met at step 1329. No data to save.\n",
-      "1329\n",
-      "model_vars.get('Gini', 0)[-1]=0.6974\n",
-      "Condition not met at step 1330. No data to save.\n",
-      "1330\n",
-      "model_vars.get('Gini', 0)[-1]=0.6928000000000001\n",
-      "Condition not met at step 1331. No data to save.\n",
-      "1331\n",
-      "model_vars.get('Gini', 0)[-1]=0.6844\n",
-      "Condition not met at step 1332. No data to save.\n",
-      "1332\n",
-      "model_vars.get('Gini', 0)[-1]=0.6822\n",
-      "Condition not met at step 1333. No data to save.\n",
-      "1333\n",
-      "model_vars.get('Gini', 0)[-1]=0.696\n",
-      "Condition not met at step 1334. No data to save.\n",
-      "1334\n",
-      "model_vars.get('Gini', 0)[-1]=0.7034\n",
-      "Condition met. Appended special results for step 1335 to test_path/special_results.parquet\n",
-      "1335\n",
-      "model_vars.get('Gini', 0)[-1]=0.673\n",
-      "Condition not met at step 1336. No data to save.\n",
-      "1336\n",
-      "model_vars.get('Gini', 0)[-1]=0.657\n",
-      "Condition not met at step 1337. No data to save.\n",
-      "1337\n",
-      "model_vars.get('Gini', 0)[-1]=0.6882\n",
-      "Condition not met at step 1338. No data to save.\n",
-      "1338\n",
-      "model_vars.get('Gini', 0)[-1]=0.6930000000000001\n",
-      "Condition not met at step 1339. No data to save.\n",
-      "1339\n",
-      "model_vars.get('Gini', 0)[-1]=0.6842\n",
-      "Condition not met at step 1340. No data to save.\n",
-      "1340\n",
-      "model_vars.get('Gini', 0)[-1]=0.681\n",
-      "Condition not met at step 1341. No data to save.\n",
-      "1341\n",
-      "model_vars.get('Gini', 0)[-1]=0.7061999999999999\n",
-      "Condition met. Appended special results for step 1342 to test_path/special_results.parquet\n",
-      "1342\n",
-      "model_vars.get('Gini', 0)[-1]=0.7112\n",
-      "Condition met. Appended special results for step 1343 to test_path/special_results.parquet\n",
-      "1343\n",
-      "model_vars.get('Gini', 0)[-1]=0.6936\n",
-      "Condition not met at step 1344. No data to save.\n",
-      "1344\n",
-      "model_vars.get('Gini', 0)[-1]=0.6544\n",
-      "Condition not met at step 1345. No data to save.\n",
-      "1345\n",
-      "model_vars.get('Gini', 0)[-1]=0.673\n",
-      "Condition not met at step 1346. No data to save.\n",
-      "1346\n",
-      "model_vars.get('Gini', 0)[-1]=0.6972\n",
-      "Condition not met at step 1347. No data to save.\n",
-      "1347\n",
-      "model_vars.get('Gini', 0)[-1]=0.6756\n",
-      "Condition not met at step 1348. No data to save.\n",
-      "1348\n",
-      "model_vars.get('Gini', 0)[-1]=0.6638\n",
-      "Condition not met at step 1349. No data to save.\n",
-      "1349\n",
-      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
-      "Condition not met at step 1350. No data to save.\n",
-      "1350\n",
-      "model_vars.get('Gini', 0)[-1]=0.6548\n",
-      "Condition not met at step 1351. No data to save.\n",
-      "1351\n",
-      "model_vars.get('Gini', 0)[-1]=0.65\n",
-      "Condition not met at step 1352. No data to save.\n",
-      "1352\n",
-      "model_vars.get('Gini', 0)[-1]=0.6632\n",
-      "Condition not met at step 1353. No data to save.\n",
-      "1353\n",
-      "model_vars.get('Gini', 0)[-1]=0.6612\n",
-      "Condition not met at step 1354. No data to save.\n",
-      "1354\n",
-      "model_vars.get('Gini', 0)[-1]=0.6592\n",
-      "Condition not met at step 1355. No data to save.\n",
-      "1355\n",
-      "model_vars.get('Gini', 0)[-1]=0.6806\n",
-      "Condition not met at step 1356. No data to save.\n",
-      "1356\n",
-      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
-      "Condition not met at step 1357. No data to save.\n",
-      "1357\n",
-      "model_vars.get('Gini', 0)[-1]=0.6712\n",
-      "Condition not met at step 1358. No data to save.\n",
-      "1358\n",
-      "model_vars.get('Gini', 0)[-1]=0.6662\n",
-      "Condition not met at step 1359. No data to save.\n",
-      "1359\n",
-      "model_vars.get('Gini', 0)[-1]=0.6878\n",
-      "Condition not met at step 1360. No data to save.\n",
-      "1360\n",
-      "model_vars.get('Gini', 0)[-1]=0.6786000000000001\n",
-      "Condition not met at step 1361. No data to save.\n",
-      "1361\n",
-      "model_vars.get('Gini', 0)[-1]=0.6764\n",
-      "Condition not met at step 1362. No data to save.\n",
-      "1362\n",
-      "model_vars.get('Gini', 0)[-1]=0.685\n",
-      "Condition not met at step 1363. No data to save.\n",
-      "1363\n",
-      "model_vars.get('Gini', 0)[-1]=0.69\n",
-      "Condition not met at step 1364. No data to save.\n",
-      "1364\n",
-      "model_vars.get('Gini', 0)[-1]=0.6756\n",
-      "Condition not met at step 1365. No data to save.\n",
-      "1365\n",
-      "model_vars.get('Gini', 0)[-1]=0.6786000000000001\n",
-      "Condition not met at step 1366. No data to save.\n",
-      "1366\n",
-      "model_vars.get('Gini', 0)[-1]=0.6702\n",
-      "Condition not met at step 1367. No data to save.\n",
-      "1367\n",
-      "model_vars.get('Gini', 0)[-1]=0.6936\n",
-      "Condition not met at step 1368. No data to save.\n",
-      "1368\n",
-      "model_vars.get('Gini', 0)[-1]=0.6902\n",
-      "Condition not met at step 1369. No data to save.\n",
-      "1369\n",
-      "model_vars.get('Gini', 0)[-1]=0.6908000000000001\n",
-      "Condition not met at step 1370. No data to save.\n",
-      "1370\n",
-      "model_vars.get('Gini', 0)[-1]=0.6954\n",
-      "Condition not met at step 1371. No data to save.\n",
-      "1371\n",
-      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
-      "Condition not met at step 1372. No data to save.\n",
-      "1372\n",
-      "model_vars.get('Gini', 0)[-1]=0.6772\n",
-      "Condition not met at step 1373. No data to save.\n",
-      "1373\n",
-      "model_vars.get('Gini', 0)[-1]=0.6836\n",
-      "Condition not met at step 1374. No data to save.\n",
-      "1374\n",
-      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
-      "Condition not met at step 1375. No data to save.\n",
-      "1375\n",
-      "model_vars.get('Gini', 0)[-1]=0.681\n",
-      "Condition not met at step 1376. No data to save.\n",
-      "1376\n",
-      "model_vars.get('Gini', 0)[-1]=0.6888000000000001\n",
-      "Condition not met at step 1377. No data to save.\n",
-      "1377\n",
-      "model_vars.get('Gini', 0)[-1]=0.6906\n",
-      "Condition not met at step 1378. No data to save.\n",
-      "1378\n",
-      "model_vars.get('Gini', 0)[-1]=0.7372000000000001\n",
-      "Condition met. Appended special results for step 1379 to test_path/special_results.parquet\n",
-      "1379\n",
-      "model_vars.get('Gini', 0)[-1]=0.7106\n",
-      "Condition met. Appended special results for step 1380 to test_path/special_results.parquet\n",
-      "1380\n",
-      "model_vars.get('Gini', 0)[-1]=0.7096\n",
-      "Condition met. Appended special results for step 1381 to test_path/special_results.parquet\n",
-      "1381\n",
-      "model_vars.get('Gini', 0)[-1]=0.685\n",
-      "Condition not met at step 1382. No data to save.\n",
-      "1382\n",
-      "model_vars.get('Gini', 0)[-1]=0.6634\n",
-      "Condition not met at step 1383. No data to save.\n",
-      "1383\n",
-      "model_vars.get('Gini', 0)[-1]=0.6334\n",
-      "Condition not met at step 1384. No data to save.\n",
-      "1384\n",
-      "model_vars.get('Gini', 0)[-1]=0.6624\n",
-      "Condition not met at step 1385. No data to save.\n",
-      "1385\n",
-      "model_vars.get('Gini', 0)[-1]=0.6612\n",
-      "Condition not met at step 1386. No data to save.\n",
-      "1386\n",
-      "model_vars.get('Gini', 0)[-1]=0.6886\n",
-      "Condition not met at step 1387. No data to save.\n",
-      "1387\n",
-      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
-      "Condition not met at step 1388. No data to save.\n",
-      "1388\n",
-      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
-      "Condition not met at step 1389. No data to save.\n",
-      "1389\n",
-      "model_vars.get('Gini', 0)[-1]=0.6592\n",
-      "Condition not met at step 1390. No data to save.\n",
-      "1390\n",
-      "model_vars.get('Gini', 0)[-1]=0.6604\n",
-      "Condition not met at step 1391. No data to save.\n",
-      "1391\n",
-      "model_vars.get('Gini', 0)[-1]=0.6798\n",
-      "Condition not met at step 1392. No data to save.\n",
-      "1392\n",
-      "model_vars.get('Gini', 0)[-1]=0.6682\n",
-      "Condition not met at step 1393. No data to save.\n",
-      "1393\n",
-      "model_vars.get('Gini', 0)[-1]=0.645\n",
-      "Condition not met at step 1394. No data to save.\n",
-      "1394\n",
-      "model_vars.get('Gini', 0)[-1]=0.6268\n",
-      "Condition not met at step 1395. No data to save.\n",
-      "1395\n",
-      "model_vars.get('Gini', 0)[-1]=0.6232\n",
-      "Condition not met at step 1396. No data to save.\n",
-      "1396\n",
-      "model_vars.get('Gini', 0)[-1]=0.607\n",
-      "Condition not met at step 1397. No data to save.\n",
-      "1397\n",
-      "model_vars.get('Gini', 0)[-1]=0.6194\n",
-      "Condition not met at step 1398. No data to save.\n",
-      "1398\n",
-      "model_vars.get('Gini', 0)[-1]=0.6392\n",
-      "Condition not met at step 1399. No data to save.\n",
-      "1399\n",
-      "CALLED\n",
-      "self.model._steps=1400\n",
-      " TEST self.model._steps=1400\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "1300  0.5794\n",
-      "1301  0.5938\n",
-      "1302  0.6288\n",
-      "1303  0.6124\n",
-      "1304  0.6046\n",
-      "...      ...\n",
-      "1395  0.6232\n",
-      "1396  0.6070\n",
-      "1397  0.6194\n",
-      "1398  0.6392\n",
-      "1399  0.6406\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_014.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_014.parquet'\n",
-      "Saving model to test_path/model_data_014.parquet\n",
-      "Saving agent to test_path/agent_data_014.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
-      "Condition not met at step 1400. No data to save.\n",
-      "1400\n",
-      "model_vars.get('Gini', 0)[-1]=0.6554\n",
-      "Condition not met at step 1401. No data to save.\n",
-      "1401\n",
-      "model_vars.get('Gini', 0)[-1]=0.6462\n",
-      "Condition not met at step 1402. No data to save.\n",
-      "1402\n",
-      "model_vars.get('Gini', 0)[-1]=0.6474\n",
-      "Condition not met at step 1403. No data to save.\n",
-      "1403\n",
-      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
-      "Condition not met at step 1404. No data to save.\n",
-      "1404\n",
-      "model_vars.get('Gini', 0)[-1]=0.6426000000000001\n",
-      "Condition not met at step 1405. No data to save.\n",
-      "1405\n",
-      "model_vars.get('Gini', 0)[-1]=0.655\n",
-      "Condition not met at step 1406. No data to save.\n",
-      "1406\n",
-      "model_vars.get('Gini', 0)[-1]=0.6588\n",
-      "Condition not met at step 1407. No data to save.\n",
-      "1407\n",
-      "model_vars.get('Gini', 0)[-1]=0.641\n",
-      "Condition not met at step 1408. No data to save.\n",
-      "1408\n",
-      "model_vars.get('Gini', 0)[-1]=0.6302\n",
-      "Condition not met at step 1409. No data to save.\n",
-      "1409\n",
-      "model_vars.get('Gini', 0)[-1]=0.6482\n",
-      "Condition not met at step 1410. No data to save.\n",
-      "1410\n",
-      "model_vars.get('Gini', 0)[-1]=0.652\n",
-      "Condition not met at step 1411. No data to save.\n",
-      "1411\n",
-      "model_vars.get('Gini', 0)[-1]=0.6284000000000001\n",
-      "Condition not met at step 1412. No data to save.\n",
-      "1412\n",
-      "model_vars.get('Gini', 0)[-1]=0.6544\n",
-      "Condition not met at step 1413. No data to save.\n",
-      "1413\n",
-      "model_vars.get('Gini', 0)[-1]=0.6616\n",
-      "Condition not met at step 1414. No data to save.\n",
-      "1414\n",
-      "model_vars.get('Gini', 0)[-1]=0.6426000000000001\n",
-      "Condition not met at step 1415. No data to save.\n",
-      "1415\n",
-      "model_vars.get('Gini', 0)[-1]=0.675\n",
-      "Condition not met at step 1416. No data to save.\n",
-      "1416\n",
-      "model_vars.get('Gini', 0)[-1]=0.6842\n",
-      "Condition not met at step 1417. No data to save.\n",
-      "1417\n",
-      "model_vars.get('Gini', 0)[-1]=0.6704\n",
-      "Condition not met at step 1418. No data to save.\n",
-      "1418\n",
-      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
-      "Condition not met at step 1419. No data to save.\n",
-      "1419\n",
-      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
-      "Condition not met at step 1420. No data to save.\n",
-      "1420\n",
-      "model_vars.get('Gini', 0)[-1]=0.647\n",
-      "Condition not met at step 1421. No data to save.\n",
-      "1421\n",
-      "model_vars.get('Gini', 0)[-1]=0.6426000000000001\n",
-      "Condition not met at step 1422. No data to save.\n",
-      "1422\n",
-      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
-      "Condition not met at step 1423. No data to save.\n",
-      "1423\n",
-      "model_vars.get('Gini', 0)[-1]=0.6378\n",
-      "Condition not met at step 1424. No data to save.\n",
-      "1424\n",
-      "model_vars.get('Gini', 0)[-1]=0.6544\n",
-      "Condition not met at step 1425. No data to save.\n",
-      "1425\n",
-      "model_vars.get('Gini', 0)[-1]=0.6416\n",
-      "Condition not met at step 1426. No data to save.\n",
-      "1426\n",
-      "model_vars.get('Gini', 0)[-1]=0.6166\n",
-      "Condition not met at step 1427. No data to save.\n",
-      "1427\n",
-      "model_vars.get('Gini', 0)[-1]=0.603\n",
-      "Condition not met at step 1428. No data to save.\n",
-      "1428\n",
-      "model_vars.get('Gini', 0)[-1]=0.5776\n",
-      "Condition not met at step 1429. No data to save.\n",
-      "1429\n",
-      "model_vars.get('Gini', 0)[-1]=0.5660000000000001\n",
-      "Condition not met at step 1430. No data to save.\n",
-      "1430\n",
-      "model_vars.get('Gini', 0)[-1]=0.5811999999999999\n",
-      "Condition not met at step 1431. No data to save.\n",
-      "1431\n",
-      "model_vars.get('Gini', 0)[-1]=0.6016\n",
-      "Condition not met at step 1432. No data to save.\n",
-      "1432\n",
-      "model_vars.get('Gini', 0)[-1]=0.5882000000000001\n",
-      "Condition not met at step 1433. No data to save.\n",
-      "1433\n",
-      "model_vars.get('Gini', 0)[-1]=0.6082000000000001\n",
-      "Condition not met at step 1434. No data to save.\n",
-      "1434\n",
-      "model_vars.get('Gini', 0)[-1]=0.615\n",
-      "Condition not met at step 1435. No data to save.\n",
-      "1435\n",
-      "model_vars.get('Gini', 0)[-1]=0.6208\n",
-      "Condition not met at step 1436. No data to save.\n",
-      "1436\n",
-      "model_vars.get('Gini', 0)[-1]=0.5973999999999999\n",
-      "Condition not met at step 1437. No data to save.\n",
-      "1437\n",
-      "model_vars.get('Gini', 0)[-1]=0.5718000000000001\n",
-      "Condition not met at step 1438. No data to save.\n",
-      "1438\n",
-      "model_vars.get('Gini', 0)[-1]=0.5786\n",
-      "Condition not met at step 1439. No data to save.\n",
-      "1439\n",
-      "model_vars.get('Gini', 0)[-1]=0.5778000000000001\n",
-      "Condition not met at step 1440. No data to save.\n",
-      "1440\n",
-      "model_vars.get('Gini', 0)[-1]=0.6166\n",
-      "Condition not met at step 1441. No data to save.\n",
-      "1441\n",
-      "model_vars.get('Gini', 0)[-1]=0.623\n",
-      "Condition not met at step 1442. No data to save.\n",
-      "1442\n",
-      "model_vars.get('Gini', 0)[-1]=0.6274\n",
-      "Condition not met at step 1443. No data to save.\n",
-      "1443\n",
-      "model_vars.get('Gini', 0)[-1]=0.6076\n",
-      "Condition not met at step 1444. No data to save.\n",
-      "1444\n",
-      "model_vars.get('Gini', 0)[-1]=0.6306\n",
-      "Condition not met at step 1445. No data to save.\n",
-      "1445\n",
-      "model_vars.get('Gini', 0)[-1]=0.6186\n",
-      "Condition not met at step 1446. No data to save.\n",
-      "1446\n",
-      "model_vars.get('Gini', 0)[-1]=0.6192\n",
-      "Condition not met at step 1447. No data to save.\n",
-      "1447\n",
-      "model_vars.get('Gini', 0)[-1]=0.613\n",
-      "Condition not met at step 1448. No data to save.\n",
-      "1448\n",
-      "model_vars.get('Gini', 0)[-1]=0.6226\n",
-      "Condition not met at step 1449. No data to save.\n",
-      "1449\n",
-      "model_vars.get('Gini', 0)[-1]=0.6268\n",
-      "Condition not met at step 1450. No data to save.\n",
-      "1450\n",
-      "model_vars.get('Gini', 0)[-1]=0.6446000000000001\n",
-      "Condition not met at step 1451. No data to save.\n",
-      "1451\n",
-      "model_vars.get('Gini', 0)[-1]=0.6564\n",
-      "Condition not met at step 1452. No data to save.\n",
-      "1452\n",
-      "model_vars.get('Gini', 0)[-1]=0.6658\n",
-      "Condition not met at step 1453. No data to save.\n",
-      "1453\n",
-      "model_vars.get('Gini', 0)[-1]=0.6504000000000001\n",
-      "Condition not met at step 1454. No data to save.\n",
-      "1454\n",
-      "model_vars.get('Gini', 0)[-1]=0.6694\n",
-      "Condition not met at step 1455. No data to save.\n",
-      "1455\n",
-      "model_vars.get('Gini', 0)[-1]=0.6348\n",
-      "Condition not met at step 1456. No data to save.\n",
-      "1456\n",
-      "model_vars.get('Gini', 0)[-1]=0.6202000000000001\n",
-      "Condition not met at step 1457. No data to save.\n",
-      "1457\n",
-      "model_vars.get('Gini', 0)[-1]=0.6058\n",
-      "Condition not met at step 1458. No data to save.\n",
-      "1458\n",
-      "model_vars.get('Gini', 0)[-1]=0.6038\n",
-      "Condition not met at step 1459. No data to save.\n",
-      "1459\n",
-      "model_vars.get('Gini', 0)[-1]=0.5986\n",
-      "Condition not met at step 1460. No data to save.\n",
-      "1460\n",
-      "model_vars.get('Gini', 0)[-1]=0.611\n",
-      "Condition not met at step 1461. No data to save.\n",
-      "1461\n",
-      "model_vars.get('Gini', 0)[-1]=0.6294\n",
-      "Condition not met at step 1462. No data to save.\n",
-      "1462\n",
-      "model_vars.get('Gini', 0)[-1]=0.5918\n",
-      "Condition not met at step 1463. No data to save.\n",
-      "1463\n",
-      "model_vars.get('Gini', 0)[-1]=0.5814\n",
-      "Condition not met at step 1464. No data to save.\n",
-      "1464\n",
-      "model_vars.get('Gini', 0)[-1]=0.5913999999999999\n",
-      "Condition not met at step 1465. No data to save.\n",
-      "1465\n",
-      "model_vars.get('Gini', 0)[-1]=0.587\n",
-      "Condition not met at step 1466. No data to save.\n",
-      "1466\n",
-      "model_vars.get('Gini', 0)[-1]=0.595\n",
-      "Condition not met at step 1467. No data to save.\n",
-      "1467\n",
-      "model_vars.get('Gini', 0)[-1]=0.5740000000000001\n",
-      "Condition not met at step 1468. No data to save.\n",
-      "1468\n",
-      "model_vars.get('Gini', 0)[-1]=0.5834\n",
-      "Condition not met at step 1469. No data to save.\n",
-      "1469\n",
-      "model_vars.get('Gini', 0)[-1]=0.5626\n",
-      "Condition not met at step 1470. No data to save.\n",
-      "1470\n",
-      "model_vars.get('Gini', 0)[-1]=0.5886\n",
-      "Condition not met at step 1471. No data to save.\n",
-      "1471\n",
-      "model_vars.get('Gini', 0)[-1]=0.6156\n",
-      "Condition not met at step 1472. No data to save.\n",
-      "1472\n",
-      "model_vars.get('Gini', 0)[-1]=0.5844\n",
-      "Condition not met at step 1473. No data to save.\n",
-      "1473\n",
-      "model_vars.get('Gini', 0)[-1]=0.6013999999999999\n",
-      "Condition not met at step 1474. No data to save.\n",
-      "1474\n",
-      "model_vars.get('Gini', 0)[-1]=0.587\n",
-      "Condition not met at step 1475. No data to save.\n",
-      "1475\n",
-      "model_vars.get('Gini', 0)[-1]=0.5913999999999999\n",
-      "Condition not met at step 1476. No data to save.\n",
-      "1476\n",
-      "model_vars.get('Gini', 0)[-1]=0.603\n",
-      "Condition not met at step 1477. No data to save.\n",
-      "1477\n",
-      "model_vars.get('Gini', 0)[-1]=0.6076\n",
-      "Condition not met at step 1478. No data to save.\n",
-      "1478\n",
-      "model_vars.get('Gini', 0)[-1]=0.619\n",
-      "Condition not met at step 1479. No data to save.\n",
-      "1479\n",
-      "model_vars.get('Gini', 0)[-1]=0.6574\n",
-      "Condition not met at step 1480. No data to save.\n",
-      "1480\n",
-      "model_vars.get('Gini', 0)[-1]=0.6506000000000001\n",
-      "Condition not met at step 1481. No data to save.\n",
-      "1481\n",
-      "model_vars.get('Gini', 0)[-1]=0.641\n",
-      "Condition not met at step 1482. No data to save.\n",
-      "1482\n",
-      "model_vars.get('Gini', 0)[-1]=0.6464000000000001\n",
-      "Condition not met at step 1483. No data to save.\n",
-      "1483\n",
-      "model_vars.get('Gini', 0)[-1]=0.6344000000000001\n",
-      "Condition not met at step 1484. No data to save.\n",
-      "1484\n",
-      "model_vars.get('Gini', 0)[-1]=0.6617999999999999\n",
-      "Condition not met at step 1485. No data to save.\n",
-      "1485\n",
-      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
-      "Condition not met at step 1486. No data to save.\n",
-      "1486\n",
-      "model_vars.get('Gini', 0)[-1]=0.6588\n",
-      "Condition not met at step 1487. No data to save.\n",
-      "1487\n",
-      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
-      "Condition not met at step 1488. No data to save.\n",
-      "1488\n",
-      "model_vars.get('Gini', 0)[-1]=0.629\n",
-      "Condition not met at step 1489. No data to save.\n",
-      "1489\n",
-      "model_vars.get('Gini', 0)[-1]=0.611\n",
-      "Condition not met at step 1490. No data to save.\n",
-      "1490\n",
-      "model_vars.get('Gini', 0)[-1]=0.6172\n",
-      "Condition not met at step 1491. No data to save.\n",
-      "1491\n",
-      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
-      "Condition not met at step 1492. No data to save.\n",
-      "1492\n",
-      "model_vars.get('Gini', 0)[-1]=0.6266\n",
-      "Condition not met at step 1493. No data to save.\n",
-      "1493\n",
-      "model_vars.get('Gini', 0)[-1]=0.6232\n",
-      "Condition not met at step 1494. No data to save.\n",
-      "1494\n",
-      "model_vars.get('Gini', 0)[-1]=0.6038\n",
-      "Condition not met at step 1495. No data to save.\n",
-      "1495\n",
-      "model_vars.get('Gini', 0)[-1]=0.599\n",
-      "Condition not met at step 1496. No data to save.\n",
-      "1496\n",
-      "model_vars.get('Gini', 0)[-1]=0.5984\n",
-      "Condition not met at step 1497. No data to save.\n",
-      "1497\n",
-      "model_vars.get('Gini', 0)[-1]=0.5858\n",
-      "Condition not met at step 1498. No data to save.\n",
-      "1498\n",
-      "model_vars.get('Gini', 0)[-1]=0.5858\n",
-      "Condition not met at step 1499. No data to save.\n",
-      "1499\n",
-      "CALLED\n",
-      "self.model._steps=1500\n",
-      " TEST self.model._steps=1500\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "1400  0.6554\n",
-      "1401  0.6462\n",
-      "1402  0.6474\n",
-      "1403  0.6526\n",
-      "1404  0.6426\n",
-      "...      ...\n",
-      "1495  0.5990\n",
-      "1496  0.5984\n",
-      "1497  0.5858\n",
-      "1498  0.5858\n",
-      "1499  0.5856\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_015.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_015.parquet'\n",
-      "Saving model to test_path/model_data_015.parquet\n",
-      "Saving agent to test_path/agent_data_015.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.5856\n",
-      "Condition not met at step 1500. No data to save.\n",
-      "1500\n",
-      "model_vars.get('Gini', 0)[-1]=0.6012\n",
-      "Condition not met at step 1501. No data to save.\n",
-      "1501\n",
-      "model_vars.get('Gini', 0)[-1]=0.6192\n",
-      "Condition not met at step 1502. No data to save.\n",
-      "1502\n",
-      "model_vars.get('Gini', 0)[-1]=0.6602\n",
-      "Condition not met at step 1503. No data to save.\n",
-      "1503\n",
-      "model_vars.get('Gini', 0)[-1]=0.6812\n",
-      "Condition not met at step 1504. No data to save.\n",
-      "1504\n",
-      "model_vars.get('Gini', 0)[-1]=0.6632\n",
-      "Condition not met at step 1505. No data to save.\n",
-      "1505\n",
-      "model_vars.get('Gini', 0)[-1]=0.6684\n",
-      "Condition not met at step 1506. No data to save.\n",
-      "1506\n",
-      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
-      "Condition not met at step 1507. No data to save.\n",
-      "1507\n",
-      "model_vars.get('Gini', 0)[-1]=0.6568\n",
-      "Condition not met at step 1508. No data to save.\n",
-      "1508\n",
-      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
-      "Condition not met at step 1509. No data to save.\n",
-      "1509\n",
-      "model_vars.get('Gini', 0)[-1]=0.6842\n",
-      "Condition not met at step 1510. No data to save.\n",
-      "1510\n",
-      "model_vars.get('Gini', 0)[-1]=0.6990000000000001\n",
-      "Condition not met at step 1511. No data to save.\n",
-      "1511\n",
-      "model_vars.get('Gini', 0)[-1]=0.6926\n",
-      "Condition not met at step 1512. No data to save.\n",
-      "1512\n",
-      "model_vars.get('Gini', 0)[-1]=0.6972\n",
-      "Condition not met at step 1513. No data to save.\n",
-      "1513\n",
-      "model_vars.get('Gini', 0)[-1]=0.7066\n",
-      "Condition met. Appended special results for step 1514 to test_path/special_results.parquet\n",
-      "1514\n",
-      "model_vars.get('Gini', 0)[-1]=0.6984\n",
-      "Condition not met at step 1515. No data to save.\n",
-      "1515\n",
-      "model_vars.get('Gini', 0)[-1]=0.658\n",
-      "Condition not met at step 1516. No data to save.\n",
-      "1516\n",
-      "model_vars.get('Gini', 0)[-1]=0.6446000000000001\n",
-      "Condition not met at step 1517. No data to save.\n",
-      "1517\n",
-      "model_vars.get('Gini', 0)[-1]=0.6378\n",
-      "Condition not met at step 1518. No data to save.\n",
-      "1518\n",
-      "model_vars.get('Gini', 0)[-1]=0.64\n",
-      "Condition not met at step 1519. No data to save.\n",
-      "1519\n",
-      "model_vars.get('Gini', 0)[-1]=0.6288\n",
-      "Condition not met at step 1520. No data to save.\n",
-      "1520\n",
-      "model_vars.get('Gini', 0)[-1]=0.6548\n",
-      "Condition not met at step 1521. No data to save.\n",
-      "1521\n",
-      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
-      "Condition not met at step 1522. No data to save.\n",
-      "1522\n",
-      "model_vars.get('Gini', 0)[-1]=0.634\n",
-      "Condition not met at step 1523. No data to save.\n",
-      "1523\n",
-      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
-      "Condition not met at step 1524. No data to save.\n",
-      "1524\n",
-      "model_vars.get('Gini', 0)[-1]=0.6666000000000001\n",
-      "Condition not met at step 1525. No data to save.\n",
-      "1525\n",
-      "model_vars.get('Gini', 0)[-1]=0.6474\n",
-      "Condition not met at step 1526. No data to save.\n",
-      "1526\n",
-      "model_vars.get('Gini', 0)[-1]=0.643\n",
-      "Condition not met at step 1527. No data to save.\n",
-      "1527\n",
-      "model_vars.get('Gini', 0)[-1]=0.6096\n",
-      "Condition not met at step 1528. No data to save.\n",
-      "1528\n",
-      "model_vars.get('Gini', 0)[-1]=0.666\n",
-      "Condition not met at step 1529. No data to save.\n",
-      "1529\n",
-      "model_vars.get('Gini', 0)[-1]=0.6436\n",
-      "Condition not met at step 1530. No data to save.\n",
-      "1530\n",
-      "model_vars.get('Gini', 0)[-1]=0.6194\n",
-      "Condition not met at step 1531. No data to save.\n",
-      "1531\n",
-      "model_vars.get('Gini', 0)[-1]=0.626\n",
-      "Condition not met at step 1532. No data to save.\n",
-      "1532\n",
-      "model_vars.get('Gini', 0)[-1]=0.6146\n",
-      "Condition not met at step 1533. No data to save.\n",
-      "1533\n",
-      "model_vars.get('Gini', 0)[-1]=0.6258\n",
-      "Condition not met at step 1534. No data to save.\n",
-      "1534\n",
-      "model_vars.get('Gini', 0)[-1]=0.6254\n",
-      "Condition not met at step 1535. No data to save.\n",
-      "1535\n",
-      "model_vars.get('Gini', 0)[-1]=0.652\n",
-      "Condition not met at step 1536. No data to save.\n",
-      "1536\n",
-      "model_vars.get('Gini', 0)[-1]=0.6386000000000001\n",
-      "Condition not met at step 1537. No data to save.\n",
-      "1537\n",
-      "model_vars.get('Gini', 0)[-1]=0.6142000000000001\n",
-      "Condition not met at step 1538. No data to save.\n",
-      "1538\n",
-      "model_vars.get('Gini', 0)[-1]=0.6202000000000001\n",
-      "Condition not met at step 1539. No data to save.\n",
-      "1539\n",
-      "model_vars.get('Gini', 0)[-1]=0.6164000000000001\n",
-      "Condition not met at step 1540. No data to save.\n",
-      "1540\n",
-      "model_vars.get('Gini', 0)[-1]=0.603\n",
-      "Condition not met at step 1541. No data to save.\n",
-      "1541\n",
-      "model_vars.get('Gini', 0)[-1]=0.6182000000000001\n",
-      "Condition not met at step 1542. No data to save.\n",
-      "1542\n",
-      "model_vars.get('Gini', 0)[-1]=0.5884\n",
-      "Condition not met at step 1543. No data to save.\n",
-      "1543\n",
-      "model_vars.get('Gini', 0)[-1]=0.6033999999999999\n",
-      "Condition not met at step 1544. No data to save.\n",
-      "1544\n",
-      "model_vars.get('Gini', 0)[-1]=0.6348\n",
-      "Condition not met at step 1545. No data to save.\n",
-      "1545\n",
-      "model_vars.get('Gini', 0)[-1]=0.6376\n",
-      "Condition not met at step 1546. No data to save.\n",
-      "1546\n",
-      "model_vars.get('Gini', 0)[-1]=0.6306\n",
-      "Condition not met at step 1547. No data to save.\n",
-      "1547\n",
-      "model_vars.get('Gini', 0)[-1]=0.6306\n",
-      "Condition not met at step 1548. No data to save.\n",
-      "1548\n",
-      "model_vars.get('Gini', 0)[-1]=0.6033999999999999\n",
-      "Condition not met at step 1549. No data to save.\n",
-      "1549\n",
-      "model_vars.get('Gini', 0)[-1]=0.5868\n",
-      "Condition not met at step 1550. No data to save.\n",
-      "1550\n",
-      "model_vars.get('Gini', 0)[-1]=0.6136\n",
-      "Condition not met at step 1551. No data to save.\n",
-      "1551\n",
-      "model_vars.get('Gini', 0)[-1]=0.6294\n",
-      "Condition not met at step 1552. No data to save.\n",
-      "1552\n",
-      "model_vars.get('Gini', 0)[-1]=0.6272\n",
-      "Condition not met at step 1553. No data to save.\n",
-      "1553\n",
-      "model_vars.get('Gini', 0)[-1]=0.6113999999999999\n",
-      "Condition not met at step 1554. No data to save.\n",
-      "1554\n",
-      "model_vars.get('Gini', 0)[-1]=0.6454\n",
-      "Condition not met at step 1555. No data to save.\n",
-      "1555\n",
-      "model_vars.get('Gini', 0)[-1]=0.647\n",
-      "Condition not met at step 1556. No data to save.\n",
-      "1556\n",
-      "model_vars.get('Gini', 0)[-1]=0.6508\n",
-      "Condition not met at step 1557. No data to save.\n",
-      "1557\n",
-      "model_vars.get('Gini', 0)[-1]=0.6528\n",
-      "Condition not met at step 1558. No data to save.\n",
-      "1558\n",
-      "model_vars.get('Gini', 0)[-1]=0.6658\n",
-      "Condition not met at step 1559. No data to save.\n",
-      "1559\n",
-      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
-      "Condition not met at step 1560. No data to save.\n",
-      "1560\n",
-      "model_vars.get('Gini', 0)[-1]=0.6602\n",
-      "Condition not met at step 1561. No data to save.\n",
-      "1561\n",
-      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
-      "Condition not met at step 1562. No data to save.\n",
-      "1562\n",
-      "model_vars.get('Gini', 0)[-1]=0.6716\n",
-      "Condition not met at step 1563. No data to save.\n",
-      "1563\n",
-      "model_vars.get('Gini', 0)[-1]=0.6776\n",
-      "Condition not met at step 1564. No data to save.\n",
-      "1564\n",
-      "model_vars.get('Gini', 0)[-1]=0.6614\n",
-      "Condition not met at step 1565. No data to save.\n",
-      "1565\n",
-      "model_vars.get('Gini', 0)[-1]=0.662\n",
-      "Condition not met at step 1566. No data to save.\n",
-      "1566\n",
-      "model_vars.get('Gini', 0)[-1]=0.663\n",
-      "Condition not met at step 1567. No data to save.\n",
-      "1567\n",
-      "model_vars.get('Gini', 0)[-1]=0.6694\n",
-      "Condition not met at step 1568. No data to save.\n",
-      "1568\n",
-      "model_vars.get('Gini', 0)[-1]=0.6514\n",
-      "Condition not met at step 1569. No data to save.\n",
-      "1569\n",
-      "model_vars.get('Gini', 0)[-1]=0.6482\n",
-      "Condition not met at step 1570. No data to save.\n",
-      "1570\n",
-      "model_vars.get('Gini', 0)[-1]=0.6682\n",
-      "Condition not met at step 1571. No data to save.\n",
-      "1571\n",
-      "model_vars.get('Gini', 0)[-1]=0.6548\n",
-      "Condition not met at step 1572. No data to save.\n",
-      "1572\n",
-      "model_vars.get('Gini', 0)[-1]=0.6544\n",
-      "Condition not met at step 1573. No data to save.\n",
-      "1573\n",
-      "model_vars.get('Gini', 0)[-1]=0.6616\n",
-      "Condition not met at step 1574. No data to save.\n",
-      "1574\n",
-      "model_vars.get('Gini', 0)[-1]=0.6468\n",
-      "Condition not met at step 1575. No data to save.\n",
-      "1575\n",
-      "model_vars.get('Gini', 0)[-1]=0.6315999999999999\n",
-      "Condition not met at step 1576. No data to save.\n",
-      "1576\n",
-      "model_vars.get('Gini', 0)[-1]=0.6226\n",
-      "Condition not met at step 1577. No data to save.\n",
-      "1577\n",
-      "model_vars.get('Gini', 0)[-1]=0.5968\n",
-      "Condition not met at step 1578. No data to save.\n",
-      "1578\n",
-      "model_vars.get('Gini', 0)[-1]=0.6002000000000001\n",
-      "Condition not met at step 1579. No data to save.\n",
-      "1579\n",
-      "model_vars.get('Gini', 0)[-1]=0.6446000000000001\n",
-      "Condition not met at step 1580. No data to save.\n",
-      "1580\n",
-      "model_vars.get('Gini', 0)[-1]=0.6474\n",
-      "Condition not met at step 1581. No data to save.\n",
-      "1581\n",
-      "model_vars.get('Gini', 0)[-1]=0.6662\n",
-      "Condition not met at step 1582. No data to save.\n",
-      "1582\n",
-      "model_vars.get('Gini', 0)[-1]=0.6544\n",
-      "Condition not met at step 1583. No data to save.\n",
-      "1583\n",
-      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
-      "Condition not met at step 1584. No data to save.\n",
-      "1584\n",
-      "model_vars.get('Gini', 0)[-1]=0.5976\n",
-      "Condition not met at step 1585. No data to save.\n",
-      "1585\n",
-      "model_vars.get('Gini', 0)[-1]=0.6282000000000001\n",
-      "Condition not met at step 1586. No data to save.\n",
-      "1586\n",
-      "model_vars.get('Gini', 0)[-1]=0.6678\n",
-      "Condition not met at step 1587. No data to save.\n",
-      "1587\n",
-      "model_vars.get('Gini', 0)[-1]=0.7068\n",
-      "Condition met. Appended special results for step 1588 to test_path/special_results.parquet\n",
-      "1588\n",
-      "model_vars.get('Gini', 0)[-1]=0.7034\n",
-      "Condition met. Appended special results for step 1589 to test_path/special_results.parquet\n",
-      "1589\n",
-      "model_vars.get('Gini', 0)[-1]=0.6828000000000001\n",
-      "Condition not met at step 1590. No data to save.\n",
-      "1590\n",
-      "model_vars.get('Gini', 0)[-1]=0.6824\n",
-      "Condition not met at step 1591. No data to save.\n",
-      "1591\n",
-      "model_vars.get('Gini', 0)[-1]=0.6674\n",
-      "Condition not met at step 1592. No data to save.\n",
-      "1592\n",
-      "model_vars.get('Gini', 0)[-1]=0.663\n",
-      "Condition not met at step 1593. No data to save.\n",
-      "1593\n",
-      "model_vars.get('Gini', 0)[-1]=0.659\n",
-      "Condition not met at step 1594. No data to save.\n",
-      "1594\n",
-      "model_vars.get('Gini', 0)[-1]=0.6608\n",
-      "Condition not met at step 1595. No data to save.\n",
-      "1595\n",
-      "model_vars.get('Gini', 0)[-1]=0.6454\n",
-      "Condition not met at step 1596. No data to save.\n",
-      "1596\n",
-      "model_vars.get('Gini', 0)[-1]=0.6582\n",
-      "Condition not met at step 1597. No data to save.\n",
-      "1597\n",
-      "model_vars.get('Gini', 0)[-1]=0.6726000000000001\n",
-      "Condition not met at step 1598. No data to save.\n",
-      "1598\n",
-      "model_vars.get('Gini', 0)[-1]=0.6318\n",
-      "Condition not met at step 1599. No data to save.\n",
-      "1599\n",
-      "CALLED\n",
-      "self.model._steps=1600\n",
-      " TEST self.model._steps=1600\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "1500  0.6012\n",
-      "1501  0.6192\n",
-      "1502  0.6602\n",
-      "1503  0.6812\n",
-      "1504  0.6632\n",
-      "...      ...\n",
-      "1595  0.6454\n",
-      "1596  0.6582\n",
-      "1597  0.6726\n",
-      "1598  0.6318\n",
-      "1599  0.6750\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_016.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_016.parquet'\n",
-      "Saving model to test_path/model_data_016.parquet\n",
-      "Saving agent to test_path/agent_data_016.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.675\n",
-      "Condition not met at step 1600. No data to save.\n",
-      "1600\n",
-      "model_vars.get('Gini', 0)[-1]=0.6614\n",
-      "Condition not met at step 1601. No data to save.\n",
-      "1601\n",
-      "model_vars.get('Gini', 0)[-1]=0.6834\n",
-      "Condition not met at step 1602. No data to save.\n",
-      "1602\n",
-      "model_vars.get('Gini', 0)[-1]=0.688\n",
-      "Condition not met at step 1603. No data to save.\n",
-      "1603\n",
-      "model_vars.get('Gini', 0)[-1]=0.6752\n",
-      "Condition not met at step 1604. No data to save.\n",
-      "1604\n",
-      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
-      "Condition not met at step 1605. No data to save.\n",
-      "1605\n",
-      "model_vars.get('Gini', 0)[-1]=0.66\n",
-      "Condition not met at step 1606. No data to save.\n",
-      "1606\n",
-      "model_vars.get('Gini', 0)[-1]=0.65\n",
-      "Condition not met at step 1607. No data to save.\n",
-      "1607\n",
-      "model_vars.get('Gini', 0)[-1]=0.671\n",
-      "Condition not met at step 1608. No data to save.\n",
-      "1608\n",
-      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
-      "Condition not met at step 1609. No data to save.\n",
-      "1609\n",
-      "model_vars.get('Gini', 0)[-1]=0.6286\n",
-      "Condition not met at step 1610. No data to save.\n",
-      "1610\n",
-      "model_vars.get('Gini', 0)[-1]=0.6138\n",
-      "Condition not met at step 1611. No data to save.\n",
-      "1611\n",
-      "model_vars.get('Gini', 0)[-1]=0.6412\n",
-      "Condition not met at step 1612. No data to save.\n",
-      "1612\n",
-      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
-      "Condition not met at step 1613. No data to save.\n",
-      "1613\n",
-      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
-      "Condition not met at step 1614. No data to save.\n",
-      "1614\n",
-      "model_vars.get('Gini', 0)[-1]=0.656\n",
-      "Condition not met at step 1615. No data to save.\n",
-      "1615\n",
-      "model_vars.get('Gini', 0)[-1]=0.6934\n",
-      "Condition not met at step 1616. No data to save.\n",
-      "1616\n",
-      "model_vars.get('Gini', 0)[-1]=0.6798\n",
-      "Condition not met at step 1617. No data to save.\n",
-      "1617\n",
-      "model_vars.get('Gini', 0)[-1]=0.6654\n",
-      "Condition not met at step 1618. No data to save.\n",
-      "1618\n",
-      "model_vars.get('Gini', 0)[-1]=0.6652\n",
-      "Condition not met at step 1619. No data to save.\n",
-      "1619\n",
-      "model_vars.get('Gini', 0)[-1]=0.667\n",
-      "Condition not met at step 1620. No data to save.\n",
-      "1620\n",
-      "model_vars.get('Gini', 0)[-1]=0.6964\n",
-      "Condition not met at step 1621. No data to save.\n",
-      "1621\n",
-      "model_vars.get('Gini', 0)[-1]=0.6974\n",
-      "Condition not met at step 1622. No data to save.\n",
-      "1622\n",
-      "model_vars.get('Gini', 0)[-1]=0.6888000000000001\n",
-      "Condition not met at step 1623. No data to save.\n",
-      "1623\n",
-      "model_vars.get('Gini', 0)[-1]=0.6824\n",
-      "Condition not met at step 1624. No data to save.\n",
-      "1624\n",
-      "model_vars.get('Gini', 0)[-1]=0.6846\n",
-      "Condition not met at step 1625. No data to save.\n",
-      "1625\n",
-      "model_vars.get('Gini', 0)[-1]=0.6882\n",
-      "Condition not met at step 1626. No data to save.\n",
-      "1626\n",
-      "model_vars.get('Gini', 0)[-1]=0.685\n",
-      "Condition not met at step 1627. No data to save.\n",
-      "1627\n",
-      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
-      "Condition not met at step 1628. No data to save.\n",
-      "1628\n",
-      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
-      "Condition not met at step 1629. No data to save.\n",
-      "1629\n",
-      "model_vars.get('Gini', 0)[-1]=0.6704\n",
-      "Condition not met at step 1630. No data to save.\n",
-      "1630\n",
-      "model_vars.get('Gini', 0)[-1]=0.679\n",
-      "Condition not met at step 1631. No data to save.\n",
-      "1631\n",
-      "model_vars.get('Gini', 0)[-1]=0.6732\n",
-      "Condition not met at step 1632. No data to save.\n",
-      "1632\n",
-      "model_vars.get('Gini', 0)[-1]=0.665\n",
-      "Condition not met at step 1633. No data to save.\n",
-      "1633\n",
-      "model_vars.get('Gini', 0)[-1]=0.6476\n",
-      "Condition not met at step 1634. No data to save.\n",
-      "1634\n",
-      "model_vars.get('Gini', 0)[-1]=0.635\n",
-      "Condition not met at step 1635. No data to save.\n",
-      "1635\n",
-      "model_vars.get('Gini', 0)[-1]=0.6324000000000001\n",
-      "Condition not met at step 1636. No data to save.\n",
-      "1636\n",
-      "model_vars.get('Gini', 0)[-1]=0.6496\n",
-      "Condition not met at step 1637. No data to save.\n",
-      "1637\n",
-      "model_vars.get('Gini', 0)[-1]=0.622\n",
-      "Condition not met at step 1638. No data to save.\n",
-      "1638\n",
-      "model_vars.get('Gini', 0)[-1]=0.6184000000000001\n",
-      "Condition not met at step 1639. No data to save.\n",
-      "1639\n",
-      "model_vars.get('Gini', 0)[-1]=0.6158\n",
-      "Condition not met at step 1640. No data to save.\n",
-      "1640\n",
-      "model_vars.get('Gini', 0)[-1]=0.6416\n",
-      "Condition not met at step 1641. No data to save.\n",
-      "1641\n",
-      "model_vars.get('Gini', 0)[-1]=0.6732\n",
-      "Condition not met at step 1642. No data to save.\n",
-      "1642\n",
-      "model_vars.get('Gini', 0)[-1]=0.6672\n",
-      "Condition not met at step 1643. No data to save.\n",
-      "1643\n",
-      "model_vars.get('Gini', 0)[-1]=0.6648000000000001\n",
-      "Condition not met at step 1644. No data to save.\n",
-      "1644\n",
-      "model_vars.get('Gini', 0)[-1]=0.6704\n",
-      "Condition not met at step 1645. No data to save.\n",
-      "1645\n",
-      "model_vars.get('Gini', 0)[-1]=0.664\n",
-      "Condition not met at step 1646. No data to save.\n",
-      "1646\n",
-      "model_vars.get('Gini', 0)[-1]=0.6632\n",
-      "Condition not met at step 1647. No data to save.\n",
-      "1647\n",
-      "model_vars.get('Gini', 0)[-1]=0.6882\n",
-      "Condition not met at step 1648. No data to save.\n",
-      "1648\n",
-      "model_vars.get('Gini', 0)[-1]=0.6696\n",
-      "Condition not met at step 1649. No data to save.\n",
-      "1649\n",
-      "model_vars.get('Gini', 0)[-1]=0.6592\n",
-      "Condition not met at step 1650. No data to save.\n",
-      "1650\n",
-      "model_vars.get('Gini', 0)[-1]=0.6492\n",
-      "Condition not met at step 1651. No data to save.\n",
-      "1651\n",
-      "model_vars.get('Gini', 0)[-1]=0.6768000000000001\n",
-      "Condition not met at step 1652. No data to save.\n",
-      "1652\n",
-      "model_vars.get('Gini', 0)[-1]=0.6742\n",
-      "Condition not met at step 1653. No data to save.\n",
-      "1653\n",
-      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
-      "Condition not met at step 1654. No data to save.\n",
-      "1654\n",
-      "model_vars.get('Gini', 0)[-1]=0.652\n",
-      "Condition not met at step 1655. No data to save.\n",
-      "1655\n",
-      "model_vars.get('Gini', 0)[-1]=0.6312\n",
-      "Condition not met at step 1656. No data to save.\n",
-      "1656\n",
-      "model_vars.get('Gini', 0)[-1]=0.6342\n",
-      "Condition not met at step 1657. No data to save.\n",
-      "1657\n",
-      "model_vars.get('Gini', 0)[-1]=0.6184000000000001\n",
-      "Condition not met at step 1658. No data to save.\n",
-      "1658\n",
-      "model_vars.get('Gini', 0)[-1]=0.6192\n",
-      "Condition not met at step 1659. No data to save.\n",
-      "1659\n",
-      "model_vars.get('Gini', 0)[-1]=0.6246\n",
-      "Condition not met at step 1660. No data to save.\n",
-      "1660\n",
-      "model_vars.get('Gini', 0)[-1]=0.6254\n",
-      "Condition not met at step 1661. No data to save.\n",
-      "1661\n",
-      "model_vars.get('Gini', 0)[-1]=0.6062000000000001\n",
-      "Condition not met at step 1662. No data to save.\n",
-      "1662\n",
-      "model_vars.get('Gini', 0)[-1]=0.6062000000000001\n",
-      "Condition not met at step 1663. No data to save.\n",
-      "1663\n",
-      "model_vars.get('Gini', 0)[-1]=0.6084\n",
-      "Condition not met at step 1664. No data to save.\n",
-      "1664\n",
-      "model_vars.get('Gini', 0)[-1]=0.6152\n",
-      "Condition not met at step 1665. No data to save.\n",
-      "1665\n",
-      "model_vars.get('Gini', 0)[-1]=0.6152\n",
-      "Condition not met at step 1666. No data to save.\n",
-      "1666\n",
-      "model_vars.get('Gini', 0)[-1]=0.6398\n",
-      "Condition not met at step 1667. No data to save.\n",
-      "1667\n",
-      "model_vars.get('Gini', 0)[-1]=0.6384000000000001\n",
-      "Condition not met at step 1668. No data to save.\n",
-      "1668\n",
-      "model_vars.get('Gini', 0)[-1]=0.626\n",
-      "Condition not met at step 1669. No data to save.\n",
-      "1669\n",
-      "model_vars.get('Gini', 0)[-1]=0.6298\n",
-      "Condition not met at step 1670. No data to save.\n",
-      "1670\n",
-      "model_vars.get('Gini', 0)[-1]=0.5984\n",
-      "Condition not met at step 1671. No data to save.\n",
-      "1671\n",
-      "model_vars.get('Gini', 0)[-1]=0.577\n",
-      "Condition not met at step 1672. No data to save.\n",
-      "1672\n",
-      "model_vars.get('Gini', 0)[-1]=0.5791999999999999\n",
-      "Condition not met at step 1673. No data to save.\n",
-      "1673\n",
-      "model_vars.get('Gini', 0)[-1]=0.599\n",
-      "Condition not met at step 1674. No data to save.\n",
-      "1674\n",
-      "model_vars.get('Gini', 0)[-1]=0.5682\n",
-      "Condition not met at step 1675. No data to save.\n",
-      "1675\n",
-      "model_vars.get('Gini', 0)[-1]=0.5718000000000001\n",
-      "Condition not met at step 1676. No data to save.\n",
-      "1676\n",
-      "model_vars.get('Gini', 0)[-1]=0.575\n",
-      "Condition not met at step 1677. No data to save.\n",
-      "1677\n",
-      "model_vars.get('Gini', 0)[-1]=0.6136\n",
-      "Condition not met at step 1678. No data to save.\n",
-      "1678\n",
-      "model_vars.get('Gini', 0)[-1]=0.63\n",
-      "Condition not met at step 1679. No data to save.\n",
-      "1679\n",
-      "model_vars.get('Gini', 0)[-1]=0.6302\n",
-      "Condition not met at step 1680. No data to save.\n",
-      "1680\n",
-      "model_vars.get('Gini', 0)[-1]=0.6168\n",
-      "Condition not met at step 1681. No data to save.\n",
-      "1681\n",
-      "model_vars.get('Gini', 0)[-1]=0.636\n",
-      "Condition not met at step 1682. No data to save.\n",
-      "1682\n",
-      "model_vars.get('Gini', 0)[-1]=0.6386000000000001\n",
-      "Condition not met at step 1683. No data to save.\n",
-      "1683\n",
-      "model_vars.get('Gini', 0)[-1]=0.633\n",
-      "Condition not met at step 1684. No data to save.\n",
-      "1684\n",
-      "model_vars.get('Gini', 0)[-1]=0.6548\n",
-      "Condition not met at step 1685. No data to save.\n",
-      "1685\n",
-      "model_vars.get('Gini', 0)[-1]=0.6496\n",
-      "Condition not met at step 1686. No data to save.\n",
-      "1686\n",
-      "model_vars.get('Gini', 0)[-1]=0.6648000000000001\n",
-      "Condition not met at step 1687. No data to save.\n",
-      "1687\n",
-      "model_vars.get('Gini', 0)[-1]=0.6762\n",
-      "Condition not met at step 1688. No data to save.\n",
-      "1688\n",
-      "model_vars.get('Gini', 0)[-1]=0.658\n",
-      "Condition not met at step 1689. No data to save.\n",
-      "1689\n",
-      "model_vars.get('Gini', 0)[-1]=0.6646000000000001\n",
-      "Condition not met at step 1690. No data to save.\n",
-      "1690\n",
-      "model_vars.get('Gini', 0)[-1]=0.6384000000000001\n",
-      "Condition not met at step 1691. No data to save.\n",
-      "1691\n",
-      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
-      "Condition not met at step 1692. No data to save.\n",
-      "1692\n",
-      "model_vars.get('Gini', 0)[-1]=0.644\n",
-      "Condition not met at step 1693. No data to save.\n",
-      "1693\n",
-      "model_vars.get('Gini', 0)[-1]=0.6704\n",
-      "Condition not met at step 1694. No data to save.\n",
-      "1694\n",
-      "model_vars.get('Gini', 0)[-1]=0.6846\n",
-      "Condition not met at step 1695. No data to save.\n",
-      "1695\n",
-      "model_vars.get('Gini', 0)[-1]=0.6726000000000001\n",
-      "Condition not met at step 1696. No data to save.\n",
-      "1696\n",
-      "model_vars.get('Gini', 0)[-1]=0.6748000000000001\n",
-      "Condition not met at step 1697. No data to save.\n",
-      "1697\n",
-      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
-      "Condition not met at step 1698. No data to save.\n",
-      "1698\n",
-      "model_vars.get('Gini', 0)[-1]=0.6786000000000001\n",
-      "Condition not met at step 1699. No data to save.\n",
-      "1699\n",
-      "CALLED\n",
-      "self.model._steps=1700\n",
-      " TEST self.model._steps=1700\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "1600  0.6614\n",
-      "1601  0.6834\n",
-      "1602  0.6880\n",
-      "1603  0.6752\n",
-      "1604  0.6766\n",
-      "...      ...\n",
-      "1695  0.6726\n",
-      "1696  0.6748\n",
-      "1697  0.6706\n",
-      "1698  0.6786\n",
-      "1699  0.6730\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_017.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_017.parquet'\n",
-      "Saving model to test_path/model_data_017.parquet\n",
-      "Saving agent to test_path/agent_data_017.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.673\n",
-      "Condition not met at step 1700. No data to save.\n",
-      "1700\n",
-      "model_vars.get('Gini', 0)[-1]=0.6658\n",
-      "Condition not met at step 1701. No data to save.\n",
-      "1701\n",
-      "model_vars.get('Gini', 0)[-1]=0.654\n",
-      "Condition not met at step 1702. No data to save.\n",
-      "1702\n",
-      "model_vars.get('Gini', 0)[-1]=0.653\n",
-      "Condition not met at step 1703. No data to save.\n",
-      "1703\n",
-      "model_vars.get('Gini', 0)[-1]=0.6748000000000001\n",
-      "Condition not met at step 1704. No data to save.\n",
-      "1704\n",
-      "model_vars.get('Gini', 0)[-1]=0.6738\n",
-      "Condition not met at step 1705. No data to save.\n",
-      "1705\n",
-      "model_vars.get('Gini', 0)[-1]=0.6776\n",
-      "Condition not met at step 1706. No data to save.\n",
-      "1706\n",
-      "model_vars.get('Gini', 0)[-1]=0.6872\n",
-      "Condition not met at step 1707. No data to save.\n",
-      "1707\n",
-      "model_vars.get('Gini', 0)[-1]=0.6938\n",
-      "Condition not met at step 1708. No data to save.\n",
-      "1708\n",
-      "model_vars.get('Gini', 0)[-1]=0.675\n",
-      "Condition not met at step 1709. No data to save.\n",
-      "1709\n",
-      "model_vars.get('Gini', 0)[-1]=0.6738\n",
-      "Condition not met at step 1710. No data to save.\n",
-      "1710\n",
-      "model_vars.get('Gini', 0)[-1]=0.6759999999999999\n",
-      "Condition not met at step 1711. No data to save.\n",
-      "1711\n",
-      "model_vars.get('Gini', 0)[-1]=0.6704\n",
-      "Condition not met at step 1712. No data to save.\n",
-      "1712\n",
-      "model_vars.get('Gini', 0)[-1]=0.6564\n",
-      "Condition not met at step 1713. No data to save.\n",
-      "1713\n",
-      "model_vars.get('Gini', 0)[-1]=0.6652\n",
-      "Condition not met at step 1714. No data to save.\n",
-      "1714\n",
-      "model_vars.get('Gini', 0)[-1]=0.66\n",
-      "Condition not met at step 1715. No data to save.\n",
-      "1715\n",
-      "model_vars.get('Gini', 0)[-1]=0.6468\n",
-      "Condition not met at step 1716. No data to save.\n",
-      "1716\n",
-      "model_vars.get('Gini', 0)[-1]=0.6632\n",
-      "Condition not met at step 1717. No data to save.\n",
-      "1717\n",
-      "model_vars.get('Gini', 0)[-1]=0.6376\n",
-      "Condition not met at step 1718. No data to save.\n",
-      "1718\n",
-      "model_vars.get('Gini', 0)[-1]=0.673\n",
-      "Condition not met at step 1719. No data to save.\n",
-      "1719\n",
-      "model_vars.get('Gini', 0)[-1]=0.677\n",
-      "Condition not met at step 1720. No data to save.\n",
-      "1720\n",
-      "model_vars.get('Gini', 0)[-1]=0.6956\n",
-      "Condition not met at step 1721. No data to save.\n",
-      "1721\n",
-      "model_vars.get('Gini', 0)[-1]=0.7052\n",
-      "Condition met. Appended special results for step 1722 to test_path/special_results.parquet\n",
-      "1722\n",
-      "model_vars.get('Gini', 0)[-1]=0.7094\n",
-      "Condition met. Appended special results for step 1723 to test_path/special_results.parquet\n",
-      "1723\n",
-      "model_vars.get('Gini', 0)[-1]=0.6944\n",
-      "Condition not met at step 1724. No data to save.\n",
-      "1724\n",
-      "model_vars.get('Gini', 0)[-1]=0.6921999999999999\n",
-      "Condition not met at step 1725. No data to save.\n",
-      "1725\n",
-      "model_vars.get('Gini', 0)[-1]=0.6744\n",
-      "Condition not met at step 1726. No data to save.\n",
-      "1726\n",
-      "model_vars.get('Gini', 0)[-1]=0.6948000000000001\n",
-      "Condition not met at step 1727. No data to save.\n",
-      "1727\n",
-      "model_vars.get('Gini', 0)[-1]=0.6832\n",
-      "Condition not met at step 1728. No data to save.\n",
-      "1728\n",
-      "model_vars.get('Gini', 0)[-1]=0.6512\n",
-      "Condition not met at step 1729. No data to save.\n",
-      "1729\n",
-      "model_vars.get('Gini', 0)[-1]=0.675\n",
-      "Condition not met at step 1730. No data to save.\n",
-      "1730\n",
-      "model_vars.get('Gini', 0)[-1]=0.696\n",
-      "Condition not met at step 1731. No data to save.\n",
-      "1731\n",
-      "model_vars.get('Gini', 0)[-1]=0.7028000000000001\n",
-      "Condition met. Appended special results for step 1732 to test_path/special_results.parquet\n",
-      "1732\n",
-      "model_vars.get('Gini', 0)[-1]=0.7132000000000001\n",
-      "Condition met. Appended special results for step 1733 to test_path/special_results.parquet\n",
-      "1733\n",
-      "model_vars.get('Gini', 0)[-1]=0.7076\n",
-      "Condition met. Appended special results for step 1734 to test_path/special_results.parquet\n",
-      "1734\n",
-      "model_vars.get('Gini', 0)[-1]=0.7094\n",
-      "Condition met. Appended special results for step 1735 to test_path/special_results.parquet\n",
-      "1735\n",
-      "model_vars.get('Gini', 0)[-1]=0.7203999999999999\n",
-      "Condition met. Appended special results for step 1736 to test_path/special_results.parquet\n",
-      "1736\n",
-      "model_vars.get('Gini', 0)[-1]=0.7030000000000001\n",
-      "Condition met. Appended special results for step 1737 to test_path/special_results.parquet\n",
-      "1737\n",
-      "model_vars.get('Gini', 0)[-1]=0.7156\n",
-      "Condition met. Appended special results for step 1738 to test_path/special_results.parquet\n",
-      "1738\n",
-      "model_vars.get('Gini', 0)[-1]=0.7142\n",
-      "Condition met. Appended special results for step 1739 to test_path/special_results.parquet\n",
-      "1739\n",
-      "model_vars.get('Gini', 0)[-1]=0.742\n",
-      "Condition met. Appended special results for step 1740 to test_path/special_results.parquet\n",
-      "1740\n",
-      "model_vars.get('Gini', 0)[-1]=0.7408\n",
-      "Condition met. Appended special results for step 1741 to test_path/special_results.parquet\n",
-      "1741\n",
-      "model_vars.get('Gini', 0)[-1]=0.74\n",
-      "Condition met. Appended special results for step 1742 to test_path/special_results.parquet\n",
-      "1742\n",
-      "model_vars.get('Gini', 0)[-1]=0.738\n",
-      "Condition met. Appended special results for step 1743 to test_path/special_results.parquet\n",
-      "1743\n",
-      "model_vars.get('Gini', 0)[-1]=0.7328\n",
-      "Condition met. Appended special results for step 1744 to test_path/special_results.parquet\n",
-      "1744\n",
-      "model_vars.get('Gini', 0)[-1]=0.742\n",
-      "Condition met. Appended special results for step 1745 to test_path/special_results.parquet\n",
-      "1745\n",
-      "model_vars.get('Gini', 0)[-1]=0.7408\n",
-      "Condition met. Appended special results for step 1746 to test_path/special_results.parquet\n",
-      "1746\n",
-      "model_vars.get('Gini', 0)[-1]=0.7438\n",
-      "Condition met. Appended special results for step 1747 to test_path/special_results.parquet\n",
-      "1747\n",
-      "model_vars.get('Gini', 0)[-1]=0.7214\n",
-      "Condition met. Appended special results for step 1748 to test_path/special_results.parquet\n",
-      "1748\n",
-      "model_vars.get('Gini', 0)[-1]=0.7130000000000001\n",
-      "Condition met. Appended special results for step 1749 to test_path/special_results.parquet\n",
-      "1749\n",
-      "model_vars.get('Gini', 0)[-1]=0.7108\n",
-      "Condition met. Appended special results for step 1750 to test_path/special_results.parquet\n",
-      "1750\n",
-      "model_vars.get('Gini', 0)[-1]=0.6878\n",
-      "Condition not met at step 1751. No data to save.\n",
-      "1751\n",
-      "model_vars.get('Gini', 0)[-1]=0.7034\n",
-      "Condition met. Appended special results for step 1752 to test_path/special_results.parquet\n",
-      "1752\n",
-      "model_vars.get('Gini', 0)[-1]=0.7076\n",
-      "Condition met. Appended special results for step 1753 to test_path/special_results.parquet\n",
-      "1753\n",
-      "model_vars.get('Gini', 0)[-1]=0.7178\n",
-      "Condition met. Appended special results for step 1754 to test_path/special_results.parquet\n",
-      "1754\n",
-      "model_vars.get('Gini', 0)[-1]=0.7081999999999999\n",
-      "Condition met. Appended special results for step 1755 to test_path/special_results.parquet\n",
-      "1755\n",
-      "model_vars.get('Gini', 0)[-1]=0.7154\n",
-      "Condition met. Appended special results for step 1756 to test_path/special_results.parquet\n",
-      "1756\n",
-      "model_vars.get('Gini', 0)[-1]=0.7352000000000001\n",
-      "Condition met. Appended special results for step 1757 to test_path/special_results.parquet\n",
-      "1757\n",
-      "model_vars.get('Gini', 0)[-1]=0.7372000000000001\n",
-      "Condition met. Appended special results for step 1758 to test_path/special_results.parquet\n",
-      "1758\n",
-      "model_vars.get('Gini', 0)[-1]=0.7452000000000001\n",
-      "Condition met. Appended special results for step 1759 to test_path/special_results.parquet\n",
-      "1759\n",
-      "model_vars.get('Gini', 0)[-1]=0.76\n",
-      "Condition met. Appended special results for step 1760 to test_path/special_results.parquet\n",
-      "1760\n",
-      "model_vars.get('Gini', 0)[-1]=0.7445999999999999\n",
-      "Condition met. Appended special results for step 1761 to test_path/special_results.parquet\n",
-      "1761\n",
-      "model_vars.get('Gini', 0)[-1]=0.7404\n",
-      "Condition met. Appended special results for step 1762 to test_path/special_results.parquet\n",
-      "1762\n",
-      "model_vars.get('Gini', 0)[-1]=0.7124\n",
-      "Condition met. Appended special results for step 1763 to test_path/special_results.parquet\n",
-      "1763\n",
-      "model_vars.get('Gini', 0)[-1]=0.7163999999999999\n",
-      "Condition met. Appended special results for step 1764 to test_path/special_results.parquet\n",
-      "1764\n",
-      "model_vars.get('Gini', 0)[-1]=0.7056\n",
-      "Condition met. Appended special results for step 1765 to test_path/special_results.parquet\n",
-      "1765\n",
-      "model_vars.get('Gini', 0)[-1]=0.6884\n",
-      "Condition not met at step 1766. No data to save.\n",
-      "1766\n",
-      "model_vars.get('Gini', 0)[-1]=0.6699999999999999\n",
-      "Condition not met at step 1767. No data to save.\n",
-      "1767\n",
-      "model_vars.get('Gini', 0)[-1]=0.6868000000000001\n",
-      "Condition not met at step 1768. No data to save.\n",
-      "1768\n",
-      "model_vars.get('Gini', 0)[-1]=0.7194\n",
-      "Condition met. Appended special results for step 1769 to test_path/special_results.parquet\n",
-      "1769\n",
-      "model_vars.get('Gini', 0)[-1]=0.6924\n",
-      "Condition not met at step 1770. No data to save.\n",
-      "1770\n",
-      "model_vars.get('Gini', 0)[-1]=0.6804\n",
-      "Condition not met at step 1771. No data to save.\n",
-      "1771\n",
-      "model_vars.get('Gini', 0)[-1]=0.6918\n",
-      "Condition not met at step 1772. No data to save.\n",
-      "1772\n",
-      "model_vars.get('Gini', 0)[-1]=0.6844\n",
-      "Condition not met at step 1773. No data to save.\n",
-      "1773\n",
-      "model_vars.get('Gini', 0)[-1]=0.6872\n",
-      "Condition not met at step 1774. No data to save.\n",
-      "1774\n",
-      "model_vars.get('Gini', 0)[-1]=0.669\n",
-      "Condition not met at step 1775. No data to save.\n",
-      "1775\n",
-      "model_vars.get('Gini', 0)[-1]=0.671\n",
-      "Condition not met at step 1776. No data to save.\n",
-      "1776\n",
-      "model_vars.get('Gini', 0)[-1]=0.6712\n",
-      "Condition not met at step 1777. No data to save.\n",
-      "1777\n",
-      "model_vars.get('Gini', 0)[-1]=0.6422\n",
-      "Condition not met at step 1778. No data to save.\n",
-      "1778\n",
-      "model_vars.get('Gini', 0)[-1]=0.646\n",
-      "Condition not met at step 1779. No data to save.\n",
-      "1779\n",
-      "model_vars.get('Gini', 0)[-1]=0.63\n",
-      "Condition not met at step 1780. No data to save.\n",
-      "1780\n",
-      "model_vars.get('Gini', 0)[-1]=0.6332\n",
-      "Condition not met at step 1781. No data to save.\n",
-      "1781\n",
-      "model_vars.get('Gini', 0)[-1]=0.6468\n",
-      "Condition not met at step 1782. No data to save.\n",
-      "1782\n",
-      "model_vars.get('Gini', 0)[-1]=0.6262000000000001\n",
-      "Condition not met at step 1783. No data to save.\n",
-      "1783\n",
-      "model_vars.get('Gini', 0)[-1]=0.6474\n",
-      "Condition not met at step 1784. No data to save.\n",
-      "1784\n",
-      "model_vars.get('Gini', 0)[-1]=0.622\n",
-      "Condition not met at step 1785. No data to save.\n",
-      "1785\n",
-      "model_vars.get('Gini', 0)[-1]=0.6326\n",
-      "Condition not met at step 1786. No data to save.\n",
-      "1786\n",
-      "model_vars.get('Gini', 0)[-1]=0.6198\n",
-      "Condition not met at step 1787. No data to save.\n",
-      "1787\n",
-      "model_vars.get('Gini', 0)[-1]=0.6020000000000001\n",
-      "Condition not met at step 1788. No data to save.\n",
-      "1788\n",
-      "model_vars.get('Gini', 0)[-1]=0.5938\n",
-      "Condition not met at step 1789. No data to save.\n",
-      "1789\n",
-      "model_vars.get('Gini', 0)[-1]=0.5960000000000001\n",
-      "Condition not met at step 1790. No data to save.\n",
-      "1790\n",
-      "model_vars.get('Gini', 0)[-1]=0.5912\n",
-      "Condition not met at step 1791. No data to save.\n",
-      "1791\n",
-      "model_vars.get('Gini', 0)[-1]=0.6234\n",
-      "Condition not met at step 1792. No data to save.\n",
-      "1792\n",
-      "model_vars.get('Gini', 0)[-1]=0.5998\n",
-      "Condition not met at step 1793. No data to save.\n",
-      "1793\n",
-      "model_vars.get('Gini', 0)[-1]=0.6116\n",
-      "Condition not met at step 1794. No data to save.\n",
-      "1794\n",
-      "model_vars.get('Gini', 0)[-1]=0.6232\n",
-      "Condition not met at step 1795. No data to save.\n",
-      "1795\n",
-      "model_vars.get('Gini', 0)[-1]=0.6408\n",
-      "Condition not met at step 1796. No data to save.\n",
-      "1796\n",
-      "model_vars.get('Gini', 0)[-1]=0.6602\n",
-      "Condition not met at step 1797. No data to save.\n",
-      "1797\n",
-      "model_vars.get('Gini', 0)[-1]=0.6676\n",
-      "Condition not met at step 1798. No data to save.\n",
-      "1798\n",
-      "model_vars.get('Gini', 0)[-1]=0.661\n",
-      "Condition not met at step 1799. No data to save.\n",
-      "1799\n",
-      "CALLED\n",
-      "self.model._steps=1800\n",
-      " TEST self.model._steps=1800\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "1700  0.6658\n",
-      "1701  0.6540\n",
-      "1702  0.6530\n",
-      "1703  0.6748\n",
-      "1704  0.6738\n",
-      "...      ...\n",
-      "1795  0.6408\n",
-      "1796  0.6602\n",
-      "1797  0.6676\n",
-      "1798  0.6610\n",
-      "1799  0.6514\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_018.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_018.parquet'\n",
-      "Saving model to test_path/model_data_018.parquet\n",
-      "Saving agent to test_path/agent_data_018.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6514\n",
-      "Condition not met at step 1800. No data to save.\n",
-      "1800\n",
-      "model_vars.get('Gini', 0)[-1]=0.6714\n",
-      "Condition not met at step 1801. No data to save.\n",
-      "1801\n",
-      "model_vars.get('Gini', 0)[-1]=0.6408\n",
-      "Condition not met at step 1802. No data to save.\n",
-      "1802\n",
-      "model_vars.get('Gini', 0)[-1]=0.6295999999999999\n",
-      "Condition not met at step 1803. No data to save.\n",
-      "1803\n",
-      "model_vars.get('Gini', 0)[-1]=0.6528\n",
-      "Condition not met at step 1804. No data to save.\n",
-      "1804\n",
-      "model_vars.get('Gini', 0)[-1]=0.6374\n",
-      "Condition not met at step 1805. No data to save.\n",
-      "1805\n",
-      "model_vars.get('Gini', 0)[-1]=0.657\n",
-      "Condition not met at step 1806. No data to save.\n",
-      "1806\n",
-      "model_vars.get('Gini', 0)[-1]=0.6584\n",
-      "Condition not met at step 1807. No data to save.\n",
-      "1807\n",
-      "model_vars.get('Gini', 0)[-1]=0.6542\n",
-      "Condition not met at step 1808. No data to save.\n",
-      "1808\n",
-      "model_vars.get('Gini', 0)[-1]=0.623\n",
-      "Condition not met at step 1809. No data to save.\n",
-      "1809\n",
-      "model_vars.get('Gini', 0)[-1]=0.6402\n",
-      "Condition not met at step 1810. No data to save.\n",
-      "1810\n",
-      "model_vars.get('Gini', 0)[-1]=0.6434\n",
-      "Condition not met at step 1811. No data to save.\n",
-      "1811\n",
-      "model_vars.get('Gini', 0)[-1]=0.6464000000000001\n",
-      "Condition not met at step 1812. No data to save.\n",
-      "1812\n",
-      "model_vars.get('Gini', 0)[-1]=0.6517999999999999\n",
-      "Condition not met at step 1813. No data to save.\n",
-      "1813\n",
-      "model_vars.get('Gini', 0)[-1]=0.6284000000000001\n",
-      "Condition not met at step 1814. No data to save.\n",
-      "1814\n",
-      "model_vars.get('Gini', 0)[-1]=0.6152\n",
-      "Condition not met at step 1815. No data to save.\n",
-      "1815\n",
-      "model_vars.get('Gini', 0)[-1]=0.6016\n",
-      "Condition not met at step 1816. No data to save.\n",
-      "1816\n",
-      "model_vars.get('Gini', 0)[-1]=0.6106\n",
-      "Condition not met at step 1817. No data to save.\n",
-      "1817\n",
-      "model_vars.get('Gini', 0)[-1]=0.6106\n",
-      "Condition not met at step 1818. No data to save.\n",
-      "1818\n",
-      "model_vars.get('Gini', 0)[-1]=0.6248\n",
-      "Condition not met at step 1819. No data to save.\n",
-      "1819\n",
-      "model_vars.get('Gini', 0)[-1]=0.6335999999999999\n",
-      "Condition not met at step 1820. No data to save.\n",
-      "1820\n",
-      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
-      "Condition not met at step 1821. No data to save.\n",
-      "1821\n",
-      "model_vars.get('Gini', 0)[-1]=0.6138\n",
-      "Condition not met at step 1822. No data to save.\n",
-      "1822\n",
-      "model_vars.get('Gini', 0)[-1]=0.6024\n",
-      "Condition not met at step 1823. No data to save.\n",
-      "1823\n",
-      "model_vars.get('Gini', 0)[-1]=0.585\n",
-      "Condition not met at step 1824. No data to save.\n",
-      "1824\n",
-      "model_vars.get('Gini', 0)[-1]=0.618\n",
-      "Condition not met at step 1825. No data to save.\n",
-      "1825\n",
-      "model_vars.get('Gini', 0)[-1]=0.6016\n",
-      "Condition not met at step 1826. No data to save.\n",
-      "1826\n",
-      "model_vars.get('Gini', 0)[-1]=0.5840000000000001\n",
-      "Condition not met at step 1827. No data to save.\n",
-      "1827\n",
-      "model_vars.get('Gini', 0)[-1]=0.5882000000000001\n",
-      "Condition not met at step 1828. No data to save.\n",
-      "1828\n",
-      "model_vars.get('Gini', 0)[-1]=0.6020000000000001\n",
-      "Condition not met at step 1829. No data to save.\n",
-      "1829\n",
-      "model_vars.get('Gini', 0)[-1]=0.6342\n",
-      "Condition not met at step 1830. No data to save.\n",
-      "1830\n",
-      "model_vars.get('Gini', 0)[-1]=0.647\n",
-      "Condition not met at step 1831. No data to save.\n",
-      "1831\n",
-      "model_vars.get('Gini', 0)[-1]=0.6304000000000001\n",
-      "Condition not met at step 1832. No data to save.\n",
-      "1832\n",
-      "model_vars.get('Gini', 0)[-1]=0.628\n",
-      "Condition not met at step 1833. No data to save.\n",
-      "1833\n",
-      "model_vars.get('Gini', 0)[-1]=0.615\n",
-      "Condition not met at step 1834. No data to save.\n",
-      "1834\n",
-      "model_vars.get('Gini', 0)[-1]=0.6048\n",
-      "Condition not met at step 1835. No data to save.\n",
-      "1835\n",
-      "model_vars.get('Gini', 0)[-1]=0.637\n",
-      "Condition not met at step 1836. No data to save.\n",
-      "1836\n",
-      "model_vars.get('Gini', 0)[-1]=0.6282000000000001\n",
-      "Condition not met at step 1837. No data to save.\n",
-      "1837\n",
-      "model_vars.get('Gini', 0)[-1]=0.6652\n",
-      "Condition not met at step 1838. No data to save.\n",
-      "1838\n",
-      "model_vars.get('Gini', 0)[-1]=0.6215999999999999\n",
-      "Condition not met at step 1839. No data to save.\n",
-      "1839\n",
-      "model_vars.get('Gini', 0)[-1]=0.6068\n",
-      "Condition not met at step 1840. No data to save.\n",
-      "1840\n",
-      "model_vars.get('Gini', 0)[-1]=0.6248\n",
-      "Condition not met at step 1841. No data to save.\n",
-      "1841\n",
-      "model_vars.get('Gini', 0)[-1]=0.6275999999999999\n",
-      "Condition not met at step 1842. No data to save.\n",
-      "1842\n",
-      "model_vars.get('Gini', 0)[-1]=0.6302\n",
-      "Condition not met at step 1843. No data to save.\n",
-      "1843\n",
-      "model_vars.get('Gini', 0)[-1]=0.6664\n",
-      "Condition not met at step 1844. No data to save.\n",
-      "1844\n",
-      "model_vars.get('Gini', 0)[-1]=0.656\n",
-      "Condition not met at step 1845. No data to save.\n",
-      "1845\n",
-      "model_vars.get('Gini', 0)[-1]=0.6274\n",
-      "Condition not met at step 1846. No data to save.\n",
-      "1846\n",
-      "model_vars.get('Gini', 0)[-1]=0.609\n",
-      "Condition not met at step 1847. No data to save.\n",
-      "1847\n",
-      "model_vars.get('Gini', 0)[-1]=0.64\n",
-      "Condition not met at step 1848. No data to save.\n",
-      "1848\n",
-      "model_vars.get('Gini', 0)[-1]=0.623\n",
-      "Condition not met at step 1849. No data to save.\n",
-      "1849\n",
-      "model_vars.get('Gini', 0)[-1]=0.6494\n",
-      "Condition not met at step 1850. No data to save.\n",
-      "1850\n",
-      "model_vars.get('Gini', 0)[-1]=0.6616\n",
-      "Condition not met at step 1851. No data to save.\n",
-      "1851\n",
-      "model_vars.get('Gini', 0)[-1]=0.6474\n",
-      "Condition not met at step 1852. No data to save.\n",
-      "1852\n",
-      "model_vars.get('Gini', 0)[-1]=0.6542\n",
-      "Condition not met at step 1853. No data to save.\n",
-      "1853\n",
-      "model_vars.get('Gini', 0)[-1]=0.644\n",
-      "Condition not met at step 1854. No data to save.\n",
-      "1854\n",
-      "model_vars.get('Gini', 0)[-1]=0.6756\n",
-      "Condition not met at step 1855. No data to save.\n",
-      "1855\n",
-      "model_vars.get('Gini', 0)[-1]=0.6772\n",
-      "Condition not met at step 1856. No data to save.\n",
-      "1856\n",
-      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
-      "Condition not met at step 1857. No data to save.\n",
-      "1857\n",
-      "model_vars.get('Gini', 0)[-1]=0.6888000000000001\n",
-      "Condition not met at step 1858. No data to save.\n",
-      "1858\n",
-      "model_vars.get('Gini', 0)[-1]=0.6952\n",
-      "Condition not met at step 1859. No data to save.\n",
-      "1859\n",
-      "model_vars.get('Gini', 0)[-1]=0.6854\n",
-      "Condition not met at step 1860. No data to save.\n",
-      "1860\n",
-      "model_vars.get('Gini', 0)[-1]=0.657\n",
-      "Condition not met at step 1861. No data to save.\n",
-      "1861\n",
-      "model_vars.get('Gini', 0)[-1]=0.6378\n",
-      "Condition not met at step 1862. No data to save.\n",
-      "1862\n",
-      "model_vars.get('Gini', 0)[-1]=0.6198\n",
-      "Condition not met at step 1863. No data to save.\n",
-      "1863\n",
-      "model_vars.get('Gini', 0)[-1]=0.609\n",
-      "Condition not met at step 1864. No data to save.\n",
-      "1864\n",
-      "model_vars.get('Gini', 0)[-1]=0.6134\n",
-      "Condition not met at step 1865. No data to save.\n",
-      "1865\n",
-      "model_vars.get('Gini', 0)[-1]=0.6072\n",
-      "Condition not met at step 1866. No data to save.\n",
-      "1866\n",
-      "model_vars.get('Gini', 0)[-1]=0.6234\n",
-      "Condition not met at step 1867. No data to save.\n",
-      "1867\n",
-      "model_vars.get('Gini', 0)[-1]=0.6038\n",
-      "Condition not met at step 1868. No data to save.\n",
-      "1868\n",
-      "model_vars.get('Gini', 0)[-1]=0.6226\n",
-      "Condition not met at step 1869. No data to save.\n",
-      "1869\n",
-      "model_vars.get('Gini', 0)[-1]=0.5726\n",
-      "Condition not met at step 1870. No data to save.\n",
-      "1870\n",
-      "model_vars.get('Gini', 0)[-1]=0.5686\n",
-      "Condition not met at step 1871. No data to save.\n",
-      "1871\n",
-      "model_vars.get('Gini', 0)[-1]=0.5932\n",
-      "Condition not met at step 1872. No data to save.\n",
-      "1872\n",
-      "model_vars.get('Gini', 0)[-1]=0.5734\n",
-      "Condition not met at step 1873. No data to save.\n",
-      "1873\n",
-      "model_vars.get('Gini', 0)[-1]=0.5418000000000001\n",
-      "Condition not met at step 1874. No data to save.\n",
-      "1874\n",
-      "model_vars.get('Gini', 0)[-1]=0.5686\n",
-      "Condition not met at step 1875. No data to save.\n",
-      "1875\n",
-      "model_vars.get('Gini', 0)[-1]=0.5726\n",
-      "Condition not met at step 1876. No data to save.\n",
-      "1876\n",
-      "model_vars.get('Gini', 0)[-1]=0.5926\n",
-      "Condition not met at step 1877. No data to save.\n",
-      "1877\n",
-      "model_vars.get('Gini', 0)[-1]=0.5704\n",
-      "Condition not met at step 1878. No data to save.\n",
-      "1878\n",
-      "model_vars.get('Gini', 0)[-1]=0.6214\n",
-      "Condition not met at step 1879. No data to save.\n",
-      "1879\n",
-      "model_vars.get('Gini', 0)[-1]=0.6644\n",
-      "Condition not met at step 1880. No data to save.\n",
-      "1880\n",
-      "model_vars.get('Gini', 0)[-1]=0.666\n",
-      "Condition not met at step 1881. No data to save.\n",
-      "1881\n",
-      "model_vars.get('Gini', 0)[-1]=0.6468\n",
-      "Condition not met at step 1882. No data to save.\n",
-      "1882\n",
-      "model_vars.get('Gini', 0)[-1]=0.6698\n",
-      "Condition not met at step 1883. No data to save.\n",
-      "1883\n",
-      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
-      "Condition not met at step 1884. No data to save.\n",
-      "1884\n",
-      "model_vars.get('Gini', 0)[-1]=0.6724\n",
-      "Condition not met at step 1885. No data to save.\n",
-      "1885\n",
-      "model_vars.get('Gini', 0)[-1]=0.6864\n",
-      "Condition not met at step 1886. No data to save.\n",
-      "1886\n",
-      "model_vars.get('Gini', 0)[-1]=0.6921999999999999\n",
-      "Condition not met at step 1887. No data to save.\n",
-      "1887\n",
-      "model_vars.get('Gini', 0)[-1]=0.6926\n",
-      "Condition not met at step 1888. No data to save.\n",
-      "1888\n",
-      "model_vars.get('Gini', 0)[-1]=0.6916\n",
-      "Condition not met at step 1889. No data to save.\n",
-      "1889\n",
-      "model_vars.get('Gini', 0)[-1]=0.6918\n",
-      "Condition not met at step 1890. No data to save.\n",
-      "1890\n",
-      "model_vars.get('Gini', 0)[-1]=0.6948000000000001\n",
-      "Condition not met at step 1891. No data to save.\n",
-      "1891\n",
-      "model_vars.get('Gini', 0)[-1]=0.6998\n",
-      "Condition not met at step 1892. No data to save.\n",
-      "1892\n",
-      "model_vars.get('Gini', 0)[-1]=0.704\n",
-      "Condition met. Appended special results for step 1893 to test_path/special_results.parquet\n",
-      "1893\n",
-      "model_vars.get('Gini', 0)[-1]=0.702\n",
-      "Condition met. Appended special results for step 1894 to test_path/special_results.parquet\n",
-      "1894\n",
-      "model_vars.get('Gini', 0)[-1]=0.692\n",
-      "Condition not met at step 1895. No data to save.\n",
-      "1895\n",
-      "model_vars.get('Gini', 0)[-1]=0.6882\n",
-      "Condition not met at step 1896. No data to save.\n",
-      "1896\n",
-      "model_vars.get('Gini', 0)[-1]=0.6914\n",
-      "Condition not met at step 1897. No data to save.\n",
-      "1897\n",
-      "model_vars.get('Gini', 0)[-1]=0.6844\n",
-      "Condition not met at step 1898. No data to save.\n",
-      "1898\n",
-      "model_vars.get('Gini', 0)[-1]=0.6692\n",
-      "Condition not met at step 1899. No data to save.\n",
-      "1899\n",
-      "CALLED\n",
-      "self.model._steps=1900\n",
-      " TEST self.model._steps=1900\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "1800  0.6714\n",
-      "1801  0.6408\n",
-      "1802  0.6296\n",
-      "1803  0.6528\n",
-      "1804  0.6374\n",
-      "...      ...\n",
-      "1895  0.6882\n",
-      "1896  0.6914\n",
-      "1897  0.6844\n",
-      "1898  0.6692\n",
-      "1899  0.6628\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_019.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_019.parquet'\n",
-      "Saving model to test_path/model_data_019.parquet\n",
-      "Saving agent to test_path/agent_data_019.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
-      "Condition not met at step 1900. No data to save.\n",
-      "1900\n",
-      "model_vars.get('Gini', 0)[-1]=0.657\n",
-      "Condition not met at step 1901. No data to save.\n",
-      "1901\n",
-      "model_vars.get('Gini', 0)[-1]=0.6568\n",
-      "Condition not met at step 1902. No data to save.\n",
-      "1902\n",
-      "model_vars.get('Gini', 0)[-1]=0.6496\n",
-      "Condition not met at step 1903. No data to save.\n",
-      "1903\n",
-      "model_vars.get('Gini', 0)[-1]=0.6517999999999999\n",
-      "Condition not met at step 1904. No data to save.\n",
-      "1904\n",
-      "model_vars.get('Gini', 0)[-1]=0.6396\n",
-      "Condition not met at step 1905. No data to save.\n",
-      "1905\n",
-      "model_vars.get('Gini', 0)[-1]=0.644\n",
-      "Condition not met at step 1906. No data to save.\n",
-      "1906\n",
-      "model_vars.get('Gini', 0)[-1]=0.6366\n",
-      "Condition not met at step 1907. No data to save.\n",
-      "1907\n",
-      "model_vars.get('Gini', 0)[-1]=0.631\n",
-      "Condition not met at step 1908. No data to save.\n",
-      "1908\n",
-      "model_vars.get('Gini', 0)[-1]=0.636\n",
-      "Condition not met at step 1909. No data to save.\n",
-      "1909\n",
-      "model_vars.get('Gini', 0)[-1]=0.6268\n",
-      "Condition not met at step 1910. No data to save.\n",
-      "1910\n",
-      "model_vars.get('Gini', 0)[-1]=0.6426000000000001\n",
-      "Condition not met at step 1911. No data to save.\n",
-      "1911\n",
-      "model_vars.get('Gini', 0)[-1]=0.6314\n",
-      "Condition not met at step 1912. No data to save.\n",
-      "1912\n",
-      "model_vars.get('Gini', 0)[-1]=0.6376\n",
-      "Condition not met at step 1913. No data to save.\n",
-      "1913\n",
-      "model_vars.get('Gini', 0)[-1]=0.634\n",
-      "Condition not met at step 1914. No data to save.\n",
-      "1914\n",
-      "model_vars.get('Gini', 0)[-1]=0.6452\n",
-      "Condition not met at step 1915. No data to save.\n",
-      "1915\n",
-      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
-      "Condition not met at step 1916. No data to save.\n",
-      "1916\n",
-      "model_vars.get('Gini', 0)[-1]=0.6572\n",
-      "Condition not met at step 1917. No data to save.\n",
-      "1917\n",
-      "model_vars.get('Gini', 0)[-1]=0.6516\n",
-      "Condition not met at step 1918. No data to save.\n",
-      "1918\n",
-      "model_vars.get('Gini', 0)[-1]=0.665\n",
-      "Condition not met at step 1919. No data to save.\n",
-      "1919\n",
-      "model_vars.get('Gini', 0)[-1]=0.636\n",
-      "Condition not met at step 1920. No data to save.\n",
-      "1920\n",
-      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
-      "Condition not met at step 1921. No data to save.\n",
-      "1921\n",
-      "model_vars.get('Gini', 0)[-1]=0.6746000000000001\n",
-      "Condition not met at step 1922. No data to save.\n",
-      "1922\n",
-      "model_vars.get('Gini', 0)[-1]=0.6844\n",
-      "Condition not met at step 1923. No data to save.\n",
-      "1923\n",
-      "model_vars.get('Gini', 0)[-1]=0.7070000000000001\n",
-      "Condition met. Appended special results for step 1924 to test_path/special_results.parquet\n",
-      "1924\n",
-      "model_vars.get('Gini', 0)[-1]=0.6584\n",
-      "Condition not met at step 1925. No data to save.\n",
-      "1925\n",
-      "model_vars.get('Gini', 0)[-1]=0.6422\n",
-      "Condition not met at step 1926. No data to save.\n",
-      "1926\n",
-      "model_vars.get('Gini', 0)[-1]=0.6468\n",
-      "Condition not met at step 1927. No data to save.\n",
-      "1927\n",
-      "model_vars.get('Gini', 0)[-1]=0.6376\n",
-      "Condition not met at step 1928. No data to save.\n",
-      "1928\n",
-      "model_vars.get('Gini', 0)[-1]=0.6338\n",
-      "Condition not met at step 1929. No data to save.\n",
-      "1929\n",
-      "model_vars.get('Gini', 0)[-1]=0.6436\n",
-      "Condition not met at step 1930. No data to save.\n",
-      "1930\n",
-      "model_vars.get('Gini', 0)[-1]=0.6494\n",
-      "Condition not met at step 1931. No data to save.\n",
-      "1931\n",
-      "model_vars.get('Gini', 0)[-1]=0.6734\n",
-      "Condition not met at step 1932. No data to save.\n",
-      "1932\n",
-      "model_vars.get('Gini', 0)[-1]=0.6694\n",
-      "Condition not met at step 1933. No data to save.\n",
-      "1933\n",
-      "model_vars.get('Gini', 0)[-1]=0.669\n",
-      "Condition not met at step 1934. No data to save.\n",
-      "1934\n",
-      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
-      "Condition not met at step 1935. No data to save.\n",
-      "1935\n",
-      "model_vars.get('Gini', 0)[-1]=0.5906\n",
-      "Condition not met at step 1936. No data to save.\n",
-      "1936\n",
-      "model_vars.get('Gini', 0)[-1]=0.6254\n",
-      "Condition not met at step 1937. No data to save.\n",
-      "1937\n",
-      "model_vars.get('Gini', 0)[-1]=0.651\n",
-      "Condition not met at step 1938. No data to save.\n",
-      "1938\n",
-      "model_vars.get('Gini', 0)[-1]=0.6488\n",
-      "Condition not met at step 1939. No data to save.\n",
-      "1939\n",
-      "model_vars.get('Gini', 0)[-1]=0.641\n",
-      "Condition not met at step 1940. No data to save.\n",
-      "1940\n",
-      "model_vars.get('Gini', 0)[-1]=0.6098\n",
-      "Condition not met at step 1941. No data to save.\n",
-      "1941\n",
-      "model_vars.get('Gini', 0)[-1]=0.6322\n",
-      "Condition not met at step 1942. No data to save.\n",
-      "1942\n",
-      "model_vars.get('Gini', 0)[-1]=0.6372\n",
-      "Condition not met at step 1943. No data to save.\n",
-      "1943\n",
-      "model_vars.get('Gini', 0)[-1]=0.6295999999999999\n",
-      "Condition not met at step 1944. No data to save.\n",
-      "1944\n",
-      "model_vars.get('Gini', 0)[-1]=0.632\n",
-      "Condition not met at step 1945. No data to save.\n",
-      "1945\n",
-      "model_vars.get('Gini', 0)[-1]=0.6052\n",
-      "Condition not met at step 1946. No data to save.\n",
-      "1946\n",
-      "model_vars.get('Gini', 0)[-1]=0.6004\n",
-      "Condition not met at step 1947. No data to save.\n",
-      "1947\n",
-      "model_vars.get('Gini', 0)[-1]=0.5936\n",
-      "Condition not met at step 1948. No data to save.\n",
-      "1948\n",
-      "model_vars.get('Gini', 0)[-1]=0.6152\n",
-      "Condition not met at step 1949. No data to save.\n",
-      "1949\n",
-      "model_vars.get('Gini', 0)[-1]=0.6136\n",
-      "Condition not met at step 1950. No data to save.\n",
-      "1950\n",
-      "model_vars.get('Gini', 0)[-1]=0.6312\n",
-      "Condition not met at step 1951. No data to save.\n",
-      "1951\n",
-      "model_vars.get('Gini', 0)[-1]=0.6046\n",
-      "Condition not met at step 1952. No data to save.\n",
-      "1952\n",
-      "model_vars.get('Gini', 0)[-1]=0.6056\n",
-      "Condition not met at step 1953. No data to save.\n",
-      "1953\n",
-      "model_vars.get('Gini', 0)[-1]=0.5814\n",
-      "Condition not met at step 1954. No data to save.\n",
-      "1954\n",
-      "model_vars.get('Gini', 0)[-1]=0.5766\n",
-      "Condition not met at step 1955. No data to save.\n",
-      "1955\n",
-      "model_vars.get('Gini', 0)[-1]=0.5962000000000001\n",
-      "Condition not met at step 1956. No data to save.\n",
-      "1956\n",
-      "model_vars.get('Gini', 0)[-1]=0.5972\n",
-      "Condition not met at step 1957. No data to save.\n",
-      "1957\n",
-      "model_vars.get('Gini', 0)[-1]=0.5842\n",
-      "Condition not met at step 1958. No data to save.\n",
-      "1958\n",
-      "model_vars.get('Gini', 0)[-1]=0.6084\n",
-      "Condition not met at step 1959. No data to save.\n",
-      "1959\n",
-      "model_vars.get('Gini', 0)[-1]=0.5702\n",
-      "Condition not met at step 1960. No data to save.\n",
-      "1960\n",
-      "model_vars.get('Gini', 0)[-1]=0.5776\n",
-      "Condition not met at step 1961. No data to save.\n",
-      "1961\n",
-      "model_vars.get('Gini', 0)[-1]=0.6112\n",
-      "Condition not met at step 1962. No data to save.\n",
-      "1962\n",
-      "model_vars.get('Gini', 0)[-1]=0.6192\n",
-      "Condition not met at step 1963. No data to save.\n",
-      "1963\n",
-      "model_vars.get('Gini', 0)[-1]=0.6116\n",
-      "Condition not met at step 1964. No data to save.\n",
-      "1964\n",
-      "model_vars.get('Gini', 0)[-1]=0.6446000000000001\n",
-      "Condition not met at step 1965. No data to save.\n",
-      "1965\n",
-      "model_vars.get('Gini', 0)[-1]=0.6454\n",
-      "Condition not met at step 1966. No data to save.\n",
-      "1966\n",
-      "model_vars.get('Gini', 0)[-1]=0.654\n",
-      "Condition not met at step 1967. No data to save.\n",
-      "1967\n",
-      "model_vars.get('Gini', 0)[-1]=0.6396\n",
-      "Condition not met at step 1968. No data to save.\n",
-      "1968\n",
-      "model_vars.get('Gini', 0)[-1]=0.645\n",
-      "Condition not met at step 1969. No data to save.\n",
-      "1969\n",
-      "model_vars.get('Gini', 0)[-1]=0.6138\n",
-      "Condition not met at step 1970. No data to save.\n",
-      "1970\n",
-      "model_vars.get('Gini', 0)[-1]=0.6326\n",
-      "Condition not met at step 1971. No data to save.\n",
-      "1971\n",
-      "model_vars.get('Gini', 0)[-1]=0.6286\n",
-      "Condition not met at step 1972. No data to save.\n",
-      "1972\n",
-      "model_vars.get('Gini', 0)[-1]=0.6244000000000001\n",
-      "Condition not met at step 1973. No data to save.\n",
-      "1973\n",
-      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
-      "Condition not met at step 1974. No data to save.\n",
-      "1974\n",
-      "model_vars.get('Gini', 0)[-1]=0.6522\n",
-      "Condition not met at step 1975. No data to save.\n",
-      "1975\n",
-      "model_vars.get('Gini', 0)[-1]=0.6328\n",
-      "Condition not met at step 1976. No data to save.\n",
-      "1976\n",
-      "model_vars.get('Gini', 0)[-1]=0.6304000000000001\n",
-      "Condition not met at step 1977. No data to save.\n",
-      "1977\n",
-      "model_vars.get('Gini', 0)[-1]=0.6275999999999999\n",
-      "Condition not met at step 1978. No data to save.\n",
-      "1978\n",
-      "model_vars.get('Gini', 0)[-1]=0.6456\n",
-      "Condition not met at step 1979. No data to save.\n",
-      "1979\n",
-      "model_vars.get('Gini', 0)[-1]=0.6376\n",
-      "Condition not met at step 1980. No data to save.\n",
-      "1980\n",
-      "model_vars.get('Gini', 0)[-1]=0.6437999999999999\n",
-      "Condition not met at step 1981. No data to save.\n",
-      "1981\n",
-      "model_vars.get('Gini', 0)[-1]=0.6394\n",
-      "Condition not met at step 1982. No data to save.\n",
-      "1982\n",
-      "model_vars.get('Gini', 0)[-1]=0.6836\n",
-      "Condition not met at step 1983. No data to save.\n",
-      "1983\n",
-      "model_vars.get('Gini', 0)[-1]=0.6842\n",
-      "Condition not met at step 1984. No data to save.\n",
-      "1984\n",
-      "model_vars.get('Gini', 0)[-1]=0.6702\n",
-      "Condition not met at step 1985. No data to save.\n",
-      "1985\n",
-      "model_vars.get('Gini', 0)[-1]=0.6534\n",
-      "Condition not met at step 1986. No data to save.\n",
-      "1986\n",
-      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
-      "Condition not met at step 1987. No data to save.\n",
-      "1987\n",
-      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
-      "Condition not met at step 1988. No data to save.\n",
-      "1988\n",
-      "model_vars.get('Gini', 0)[-1]=0.622\n",
-      "Condition not met at step 1989. No data to save.\n",
-      "1989\n",
-      "model_vars.get('Gini', 0)[-1]=0.6482\n",
-      "Condition not met at step 1990. No data to save.\n",
-      "1990\n",
-      "model_vars.get('Gini', 0)[-1]=0.6682\n",
-      "Condition not met at step 1991. No data to save.\n",
-      "1991\n",
-      "model_vars.get('Gini', 0)[-1]=0.6536\n",
-      "Condition not met at step 1992. No data to save.\n",
-      "1992\n",
-      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
-      "Condition not met at step 1993. No data to save.\n",
-      "1993\n",
-      "model_vars.get('Gini', 0)[-1]=0.6252\n",
-      "Condition not met at step 1994. No data to save.\n",
-      "1994\n",
-      "model_vars.get('Gini', 0)[-1]=0.6278\n",
-      "Condition not met at step 1995. No data to save.\n",
-      "1995\n",
-      "model_vars.get('Gini', 0)[-1]=0.6664\n",
-      "Condition not met at step 1996. No data to save.\n",
-      "1996\n",
-      "model_vars.get('Gini', 0)[-1]=0.6378\n",
-      "Condition not met at step 1997. No data to save.\n",
-      "1997\n",
-      "model_vars.get('Gini', 0)[-1]=0.6748000000000001\n",
-      "Condition not met at step 1998. No data to save.\n",
-      "1998\n",
-      "model_vars.get('Gini', 0)[-1]=0.6704\n",
-      "Condition not met at step 1999. No data to save.\n",
-      "1999\n",
-      "CALLED\n",
-      "self.model._steps=2000\n",
-      " TEST self.model._steps=2000\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "1900  0.6570\n",
-      "1901  0.6568\n",
-      "1902  0.6496\n",
-      "1903  0.6518\n",
-      "1904  0.6396\n",
-      "...      ...\n",
-      "1995  0.6664\n",
-      "1996  0.6378\n",
-      "1997  0.6748\n",
-      "1998  0.6704\n",
-      "1999  0.7110\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_020.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_020.parquet'\n",
-      "Saving model to test_path/model_data_020.parquet\n",
-      "Saving agent to test_path/agent_data_020.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.7110000000000001\n",
-      "Condition met. Appended special results for step 2000 to test_path/special_results.parquet\n",
-      "2000\n",
-      "model_vars.get('Gini', 0)[-1]=0.6892\n",
-      "Condition not met at step 2001. No data to save.\n",
-      "2001\n",
-      "model_vars.get('Gini', 0)[-1]=0.7076\n",
-      "Condition met. Appended special results for step 2002 to test_path/special_results.parquet\n",
-      "2002\n",
-      "model_vars.get('Gini', 0)[-1]=0.7010000000000001\n",
-      "Condition met. Appended special results for step 2003 to test_path/special_results.parquet\n",
-      "2003\n",
-      "model_vars.get('Gini', 0)[-1]=0.6986\n",
-      "Condition not met at step 2004. No data to save.\n",
-      "2004\n",
-      "model_vars.get('Gini', 0)[-1]=0.7021999999999999\n",
-      "Condition met. Appended special results for step 2005 to test_path/special_results.parquet\n",
-      "2005\n",
-      "model_vars.get('Gini', 0)[-1]=0.7068\n",
-      "Condition met. Appended special results for step 2006 to test_path/special_results.parquet\n",
-      "2006\n",
-      "model_vars.get('Gini', 0)[-1]=0.6988000000000001\n",
-      "Condition not met at step 2007. No data to save.\n",
-      "2007\n",
-      "model_vars.get('Gini', 0)[-1]=0.6988000000000001\n",
-      "Condition not met at step 2008. No data to save.\n",
-      "2008\n",
-      "model_vars.get('Gini', 0)[-1]=0.6856\n",
-      "Condition not met at step 2009. No data to save.\n",
-      "2009\n",
-      "model_vars.get('Gini', 0)[-1]=0.7126\n",
-      "Condition met. Appended special results for step 2010 to test_path/special_results.parquet\n",
-      "2010\n",
-      "model_vars.get('Gini', 0)[-1]=0.6981999999999999\n",
-      "Condition not met at step 2011. No data to save.\n",
-      "2011\n",
-      "model_vars.get('Gini', 0)[-1]=0.7150000000000001\n",
-      "Condition met. Appended special results for step 2012 to test_path/special_results.parquet\n",
-      "2012\n",
-      "model_vars.get('Gini', 0)[-1]=0.7144\n",
-      "Condition met. Appended special results for step 2013 to test_path/special_results.parquet\n",
-      "2013\n",
-      "model_vars.get('Gini', 0)[-1]=0.7178\n",
-      "Condition met. Appended special results for step 2014 to test_path/special_results.parquet\n",
-      "2014\n",
-      "model_vars.get('Gini', 0)[-1]=0.7114\n",
-      "Condition met. Appended special results for step 2015 to test_path/special_results.parquet\n",
-      "2015\n",
-      "model_vars.get('Gini', 0)[-1]=0.7121999999999999\n",
-      "Condition met. Appended special results for step 2016 to test_path/special_results.parquet\n",
-      "2016\n",
-      "model_vars.get('Gini', 0)[-1]=0.687\n",
-      "Condition not met at step 2017. No data to save.\n",
-      "2017\n",
-      "model_vars.get('Gini', 0)[-1]=0.6961999999999999\n",
-      "Condition not met at step 2018. No data to save.\n",
-      "2018\n",
-      "model_vars.get('Gini', 0)[-1]=0.7056\n",
-      "Condition met. Appended special results for step 2019 to test_path/special_results.parquet\n",
-      "2019\n",
-      "model_vars.get('Gini', 0)[-1]=0.6990000000000001\n",
-      "Condition not met at step 2020. No data to save.\n",
-      "2020\n",
-      "model_vars.get('Gini', 0)[-1]=0.7134\n",
-      "Condition met. Appended special results for step 2021 to test_path/special_results.parquet\n",
-      "2021\n",
-      "model_vars.get('Gini', 0)[-1]=0.7090000000000001\n",
-      "Condition met. Appended special results for step 2022 to test_path/special_results.parquet\n",
-      "2022\n",
-      "model_vars.get('Gini', 0)[-1]=0.7234\n",
-      "Condition met. Appended special results for step 2023 to test_path/special_results.parquet\n",
-      "2023\n",
-      "model_vars.get('Gini', 0)[-1]=0.7030000000000001\n",
-      "Condition met. Appended special results for step 2024 to test_path/special_results.parquet\n",
-      "2024\n",
-      "model_vars.get('Gini', 0)[-1]=0.7176\n",
-      "Condition met. Appended special results for step 2025 to test_path/special_results.parquet\n",
-      "2025\n",
-      "model_vars.get('Gini', 0)[-1]=0.714\n",
-      "Condition met. Appended special results for step 2026 to test_path/special_results.parquet\n",
-      "2026\n",
-      "model_vars.get('Gini', 0)[-1]=0.71\n",
-      "Condition met. Appended special results for step 2027 to test_path/special_results.parquet\n",
-      "2027\n",
-      "model_vars.get('Gini', 0)[-1]=0.6696\n",
-      "Condition not met at step 2028. No data to save.\n",
-      "2028\n",
-      "model_vars.get('Gini', 0)[-1]=0.6732\n",
-      "Condition not met at step 2029. No data to save.\n",
-      "2029\n",
-      "model_vars.get('Gini', 0)[-1]=0.6632\n",
-      "Condition not met at step 2030. No data to save.\n",
-      "2030\n",
-      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
-      "Condition not met at step 2031. No data to save.\n",
-      "2031\n",
-      "model_vars.get('Gini', 0)[-1]=0.6676\n",
-      "Condition not met at step 2032. No data to save.\n",
-      "2032\n",
-      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
-      "Condition not met at step 2033. No data to save.\n",
-      "2033\n",
-      "model_vars.get('Gini', 0)[-1]=0.6696\n",
-      "Condition not met at step 2034. No data to save.\n",
-      "2034\n",
-      "model_vars.get('Gini', 0)[-1]=0.6428\n",
-      "Condition not met at step 2035. No data to save.\n",
-      "2035\n",
-      "model_vars.get('Gini', 0)[-1]=0.6362\n",
-      "Condition not met at step 2036. No data to save.\n",
-      "2036\n",
-      "model_vars.get('Gini', 0)[-1]=0.6308\n",
-      "Condition not met at step 2037. No data to save.\n",
-      "2037\n",
-      "model_vars.get('Gini', 0)[-1]=0.605\n",
-      "Condition not met at step 2038. No data to save.\n",
-      "2038\n",
-      "model_vars.get('Gini', 0)[-1]=0.6476\n",
-      "Condition not met at step 2039. No data to save.\n",
-      "2039\n",
-      "model_vars.get('Gini', 0)[-1]=0.6452\n",
-      "Condition not met at step 2040. No data to save.\n",
-      "2040\n",
-      "model_vars.get('Gini', 0)[-1]=0.6616\n",
-      "Condition not met at step 2041. No data to save.\n",
-      "2041\n",
-      "model_vars.get('Gini', 0)[-1]=0.6476\n",
-      "Condition not met at step 2042. No data to save.\n",
-      "2042\n",
-      "model_vars.get('Gini', 0)[-1]=0.6432\n",
-      "Condition not met at step 2043. No data to save.\n",
-      "2043\n",
-      "model_vars.get('Gini', 0)[-1]=0.6556\n",
-      "Condition not met at step 2044. No data to save.\n",
-      "2044\n",
-      "model_vars.get('Gini', 0)[-1]=0.6684\n",
-      "Condition not met at step 2045. No data to save.\n",
-      "2045\n",
-      "model_vars.get('Gini', 0)[-1]=0.6812\n",
-      "Condition not met at step 2046. No data to save.\n",
-      "2046\n",
-      "model_vars.get('Gini', 0)[-1]=0.702\n",
-      "Condition met. Appended special results for step 2047 to test_path/special_results.parquet\n",
-      "2047\n",
-      "model_vars.get('Gini', 0)[-1]=0.7064\n",
-      "Condition met. Appended special results for step 2048 to test_path/special_results.parquet\n",
-      "2048\n",
-      "model_vars.get('Gini', 0)[-1]=0.7070000000000001\n",
-      "Condition met. Appended special results for step 2049 to test_path/special_results.parquet\n",
-      "2049\n",
-      "model_vars.get('Gini', 0)[-1]=0.6758\n",
-      "Condition not met at step 2050. No data to save.\n",
-      "2050\n",
-      "model_vars.get('Gini', 0)[-1]=0.6826\n",
-      "Condition not met at step 2051. No data to save.\n",
-      "2051\n",
-      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
-      "Condition not met at step 2052. No data to save.\n",
-      "2052\n",
-      "model_vars.get('Gini', 0)[-1]=0.6642\n",
-      "Condition not met at step 2053. No data to save.\n",
-      "2053\n",
-      "model_vars.get('Gini', 0)[-1]=0.6482\n",
-      "Condition not met at step 2054. No data to save.\n",
-      "2054\n",
-      "model_vars.get('Gini', 0)[-1]=0.637\n",
-      "Condition not met at step 2055. No data to save.\n",
-      "2055\n",
-      "model_vars.get('Gini', 0)[-1]=0.645\n",
-      "Condition not met at step 2056. No data to save.\n",
-      "2056\n",
-      "model_vars.get('Gini', 0)[-1]=0.6574\n",
-      "Condition not met at step 2057. No data to save.\n",
-      "2057\n",
-      "model_vars.get('Gini', 0)[-1]=0.6284000000000001\n",
-      "Condition not met at step 2058. No data to save.\n",
-      "2058\n",
-      "model_vars.get('Gini', 0)[-1]=0.677\n",
-      "Condition not met at step 2059. No data to save.\n",
-      "2059\n",
-      "model_vars.get('Gini', 0)[-1]=0.6859999999999999\n",
-      "Condition not met at step 2060. No data to save.\n",
-      "2060\n",
-      "model_vars.get('Gini', 0)[-1]=0.6517999999999999\n",
-      "Condition not met at step 2061. No data to save.\n",
-      "2061\n",
-      "model_vars.get('Gini', 0)[-1]=0.6448\n",
-      "Condition not met at step 2062. No data to save.\n",
-      "2062\n",
-      "model_vars.get('Gini', 0)[-1]=0.642\n",
-      "Condition not met at step 2063. No data to save.\n",
-      "2063\n",
-      "model_vars.get('Gini', 0)[-1]=0.6548\n",
-      "Condition not met at step 2064. No data to save.\n",
-      "2064\n",
-      "model_vars.get('Gini', 0)[-1]=0.6144000000000001\n",
-      "Condition not met at step 2065. No data to save.\n",
-      "2065\n",
-      "model_vars.get('Gini', 0)[-1]=0.6724\n",
-      "Condition not met at step 2066. No data to save.\n",
-      "2066\n",
-      "model_vars.get('Gini', 0)[-1]=0.6662\n",
-      "Condition not met at step 2067. No data to save.\n",
-      "2067\n",
-      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
-      "Condition not met at step 2068. No data to save.\n",
-      "2068\n",
-      "model_vars.get('Gini', 0)[-1]=0.6784\n",
-      "Condition not met at step 2069. No data to save.\n",
-      "2069\n",
-      "model_vars.get('Gini', 0)[-1]=0.6902\n",
-      "Condition not met at step 2070. No data to save.\n",
-      "2070\n",
-      "model_vars.get('Gini', 0)[-1]=0.6990000000000001\n",
-      "Condition not met at step 2071. No data to save.\n",
-      "2071\n",
-      "model_vars.get('Gini', 0)[-1]=0.706\n",
-      "Condition met. Appended special results for step 2072 to test_path/special_results.parquet\n",
-      "2072\n",
-      "model_vars.get('Gini', 0)[-1]=0.7048\n",
-      "Condition met. Appended special results for step 2073 to test_path/special_results.parquet\n",
-      "2073\n",
-      "model_vars.get('Gini', 0)[-1]=0.7016\n",
-      "Condition met. Appended special results for step 2074 to test_path/special_results.parquet\n",
-      "2074\n",
-      "model_vars.get('Gini', 0)[-1]=0.7030000000000001\n",
-      "Condition met. Appended special results for step 2075 to test_path/special_results.parquet\n",
-      "2075\n",
-      "model_vars.get('Gini', 0)[-1]=0.6981999999999999\n",
-      "Condition not met at step 2076. No data to save.\n",
-      "2076\n",
-      "model_vars.get('Gini', 0)[-1]=0.6702\n",
-      "Condition not met at step 2077. No data to save.\n",
-      "2077\n",
-      "model_vars.get('Gini', 0)[-1]=0.6508\n",
-      "Condition not met at step 2078. No data to save.\n",
-      "2078\n",
-      "model_vars.get('Gini', 0)[-1]=0.6444000000000001\n",
-      "Condition not met at step 2079. No data to save.\n",
-      "2079\n",
-      "model_vars.get('Gini', 0)[-1]=0.6262000000000001\n",
-      "Condition not met at step 2080. No data to save.\n",
-      "2080\n",
-      "model_vars.get('Gini', 0)[-1]=0.6496\n",
-      "Condition not met at step 2081. No data to save.\n",
-      "2081\n",
-      "model_vars.get('Gini', 0)[-1]=0.6358\n",
-      "Condition not met at step 2082. No data to save.\n",
-      "2082\n",
-      "model_vars.get('Gini', 0)[-1]=0.6268\n",
-      "Condition not met at step 2083. No data to save.\n",
-      "2083\n",
-      "model_vars.get('Gini', 0)[-1]=0.6113999999999999\n",
-      "Condition not met at step 2084. No data to save.\n",
-      "2084\n",
-      "model_vars.get('Gini', 0)[-1]=0.637\n",
-      "Condition not met at step 2085. No data to save.\n",
-      "2085\n",
-      "model_vars.get('Gini', 0)[-1]=0.6224000000000001\n",
-      "Condition not met at step 2086. No data to save.\n",
-      "2086\n",
-      "model_vars.get('Gini', 0)[-1]=0.6324000000000001\n",
-      "Condition not met at step 2087. No data to save.\n",
-      "2087\n",
-      "model_vars.get('Gini', 0)[-1]=0.6328\n",
-      "Condition not met at step 2088. No data to save.\n",
-      "2088\n",
-      "model_vars.get('Gini', 0)[-1]=0.656\n",
-      "Condition not met at step 2089. No data to save.\n",
-      "2089\n",
-      "model_vars.get('Gini', 0)[-1]=0.652\n",
-      "Condition not met at step 2090. No data to save.\n",
-      "2090\n",
-      "model_vars.get('Gini', 0)[-1]=0.638\n",
-      "Condition not met at step 2091. No data to save.\n",
-      "2091\n",
-      "model_vars.get('Gini', 0)[-1]=0.6275999999999999\n",
-      "Condition not met at step 2092. No data to save.\n",
-      "2092\n",
-      "model_vars.get('Gini', 0)[-1]=0.6502\n",
-      "Condition not met at step 2093. No data to save.\n",
-      "2093\n",
-      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
-      "Condition not met at step 2094. No data to save.\n",
-      "2094\n",
-      "model_vars.get('Gini', 0)[-1]=0.632\n",
-      "Condition not met at step 2095. No data to save.\n",
-      "2095\n",
-      "model_vars.get('Gini', 0)[-1]=0.6136\n",
-      "Condition not met at step 2096. No data to save.\n",
-      "2096\n",
-      "model_vars.get('Gini', 0)[-1]=0.6152\n",
-      "Condition not met at step 2097. No data to save.\n",
-      "2097\n",
-      "model_vars.get('Gini', 0)[-1]=0.6226\n",
-      "Condition not met at step 2098. No data to save.\n",
-      "2098\n",
-      "model_vars.get('Gini', 0)[-1]=0.616\n",
-      "Condition not met at step 2099. No data to save.\n",
-      "2099\n",
-      "CALLED\n",
-      "self.model._steps=2100\n",
-      " TEST self.model._steps=2100\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "2000  0.6892\n",
-      "2001  0.7076\n",
-      "2002  0.7010\n",
-      "2003  0.6986\n",
-      "2004  0.7022\n",
-      "...      ...\n",
-      "2095  0.6136\n",
-      "2096  0.6152\n",
-      "2097  0.6226\n",
-      "2098  0.6160\n",
-      "2099  0.6212\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_021.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_021.parquet'\n",
-      "Saving model to test_path/model_data_021.parquet\n",
-      "Saving agent to test_path/agent_data_021.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6212\n",
-      "Condition not met at step 2100. No data to save.\n",
-      "2100\n",
-      "model_vars.get('Gini', 0)[-1]=0.629\n",
-      "Condition not met at step 2101. No data to save.\n",
-      "2101\n",
-      "model_vars.get('Gini', 0)[-1]=0.6408\n",
-      "Condition not met at step 2102. No data to save.\n",
-      "2102\n",
-      "model_vars.get('Gini', 0)[-1]=0.6448\n",
-      "Condition not met at step 2103. No data to save.\n",
-      "2103\n",
-      "model_vars.get('Gini', 0)[-1]=0.654\n",
-      "Condition not met at step 2104. No data to save.\n",
-      "2104\n",
-      "model_vars.get('Gini', 0)[-1]=0.615\n",
-      "Condition not met at step 2105. No data to save.\n",
-      "2105\n",
-      "model_vars.get('Gini', 0)[-1]=0.6308\n",
-      "Condition not met at step 2106. No data to save.\n",
-      "2106\n",
-      "model_vars.get('Gini', 0)[-1]=0.6348\n",
-      "Condition not met at step 2107. No data to save.\n",
-      "2107\n",
-      "model_vars.get('Gini', 0)[-1]=0.6274\n",
-      "Condition not met at step 2108. No data to save.\n",
-      "2108\n",
-      "model_vars.get('Gini', 0)[-1]=0.656\n",
-      "Condition not met at step 2109. No data to save.\n",
-      "2109\n",
-      "model_vars.get('Gini', 0)[-1]=0.708\n",
-      "Condition met. Appended special results for step 2110 to test_path/special_results.parquet\n",
-      "2110\n",
-      "model_vars.get('Gini', 0)[-1]=0.6916\n",
-      "Condition not met at step 2111. No data to save.\n",
-      "2111\n",
-      "model_vars.get('Gini', 0)[-1]=0.675\n",
-      "Condition not met at step 2112. No data to save.\n",
-      "2112\n",
-      "model_vars.get('Gini', 0)[-1]=0.6488\n",
-      "Condition not met at step 2113. No data to save.\n",
-      "2113\n",
-      "model_vars.get('Gini', 0)[-1]=0.6568\n",
-      "Condition not met at step 2114. No data to save.\n",
-      "2114\n",
-      "model_vars.get('Gini', 0)[-1]=0.635\n",
-      "Condition not met at step 2115. No data to save.\n",
-      "2115\n",
-      "model_vars.get('Gini', 0)[-1]=0.6148\n",
-      "Condition not met at step 2116. No data to save.\n",
-      "2116\n",
-      "model_vars.get('Gini', 0)[-1]=0.6426000000000001\n",
-      "Condition not met at step 2117. No data to save.\n",
-      "2117\n",
-      "model_vars.get('Gini', 0)[-1]=0.6492\n",
-      "Condition not met at step 2118. No data to save.\n",
-      "2118\n",
-      "model_vars.get('Gini', 0)[-1]=0.6622\n",
-      "Condition not met at step 2119. No data to save.\n",
-      "2119\n",
-      "model_vars.get('Gini', 0)[-1]=0.658\n",
-      "Condition not met at step 2120. No data to save.\n",
-      "2120\n",
-      "model_vars.get('Gini', 0)[-1]=0.6076\n",
-      "Condition not met at step 2121. No data to save.\n",
-      "2121\n",
-      "model_vars.get('Gini', 0)[-1]=0.6224000000000001\n",
-      "Condition not met at step 2122. No data to save.\n",
-      "2122\n",
-      "model_vars.get('Gini', 0)[-1]=0.634\n",
-      "Condition not met at step 2123. No data to save.\n",
-      "2123\n",
-      "model_vars.get('Gini', 0)[-1]=0.5848\n",
-      "Condition not met at step 2124. No data to save.\n",
-      "2124\n",
-      "model_vars.get('Gini', 0)[-1]=0.6104\n",
-      "Condition not met at step 2125. No data to save.\n",
-      "2125\n",
-      "model_vars.get('Gini', 0)[-1]=0.6208\n",
-      "Condition not met at step 2126. No data to save.\n",
-      "2126\n",
-      "model_vars.get('Gini', 0)[-1]=0.6372\n",
-      "Condition not met at step 2127. No data to save.\n",
-      "2127\n",
-      "model_vars.get('Gini', 0)[-1]=0.6448\n",
-      "Condition not met at step 2128. No data to save.\n",
-      "2128\n",
-      "model_vars.get('Gini', 0)[-1]=0.6378\n",
-      "Condition not met at step 2129. No data to save.\n",
-      "2129\n",
-      "model_vars.get('Gini', 0)[-1]=0.6554\n",
-      "Condition not met at step 2130. No data to save.\n",
-      "2130\n",
-      "model_vars.get('Gini', 0)[-1]=0.6584\n",
-      "Condition not met at step 2131. No data to save.\n",
-      "2131\n",
-      "model_vars.get('Gini', 0)[-1]=0.6652\n",
-      "Condition not met at step 2132. No data to save.\n",
-      "2132\n",
-      "model_vars.get('Gini', 0)[-1]=0.6612\n",
-      "Condition not met at step 2133. No data to save.\n",
-      "2133\n",
-      "model_vars.get('Gini', 0)[-1]=0.6542\n",
-      "Condition not met at step 2134. No data to save.\n",
-      "2134\n",
-      "model_vars.get('Gini', 0)[-1]=0.6508\n",
-      "Condition not met at step 2135. No data to save.\n",
-      "2135\n",
-      "model_vars.get('Gini', 0)[-1]=0.6554\n",
-      "Condition not met at step 2136. No data to save.\n",
-      "2136\n",
-      "model_vars.get('Gini', 0)[-1]=0.6772\n",
-      "Condition not met at step 2137. No data to save.\n",
-      "2137\n",
-      "model_vars.get('Gini', 0)[-1]=0.6644\n",
-      "Condition not met at step 2138. No data to save.\n",
-      "2138\n",
-      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
-      "Condition not met at step 2139. No data to save.\n",
-      "2139\n",
-      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
-      "Condition not met at step 2140. No data to save.\n",
-      "2140\n",
-      "model_vars.get('Gini', 0)[-1]=0.7074\n",
-      "Condition met. Appended special results for step 2141 to test_path/special_results.parquet\n",
-      "2141\n",
-      "model_vars.get('Gini', 0)[-1]=0.6986\n",
-      "Condition not met at step 2142. No data to save.\n",
-      "2142\n",
-      "model_vars.get('Gini', 0)[-1]=0.706\n",
-      "Condition met. Appended special results for step 2143 to test_path/special_results.parquet\n",
-      "2143\n",
-      "model_vars.get('Gini', 0)[-1]=0.7008000000000001\n",
-      "Condition met. Appended special results for step 2144 to test_path/special_results.parquet\n",
-      "2144\n",
-      "model_vars.get('Gini', 0)[-1]=0.7223999999999999\n",
-      "Condition met. Appended special results for step 2145 to test_path/special_results.parquet\n",
-      "2145\n",
-      "model_vars.get('Gini', 0)[-1]=0.7094\n",
-      "Condition met. Appended special results for step 2146 to test_path/special_results.parquet\n",
-      "2146\n",
-      "model_vars.get('Gini', 0)[-1]=0.7001999999999999\n",
-      "Condition met. Appended special results for step 2147 to test_path/special_results.parquet\n",
-      "2147\n",
-      "model_vars.get('Gini', 0)[-1]=0.688\n",
-      "Condition not met at step 2148. No data to save.\n",
-      "2148\n",
-      "model_vars.get('Gini', 0)[-1]=0.6804\n",
-      "Condition not met at step 2149. No data to save.\n",
-      "2149\n",
-      "model_vars.get('Gini', 0)[-1]=0.6552\n",
-      "Condition not met at step 2150. No data to save.\n",
-      "2150\n",
-      "model_vars.get('Gini', 0)[-1]=0.6554\n",
-      "Condition not met at step 2151. No data to save.\n",
-      "2151\n",
-      "model_vars.get('Gini', 0)[-1]=0.6148\n",
-      "Condition not met at step 2152. No data to save.\n",
-      "2152\n",
-      "model_vars.get('Gini', 0)[-1]=0.6362\n",
-      "Condition not met at step 2153. No data to save.\n",
-      "2153\n",
-      "model_vars.get('Gini', 0)[-1]=0.6494\n",
-      "Condition not met at step 2154. No data to save.\n",
-      "2154\n",
-      "model_vars.get('Gini', 0)[-1]=0.6574\n",
-      "Condition not met at step 2155. No data to save.\n",
-      "2155\n",
-      "model_vars.get('Gini', 0)[-1]=0.6588\n",
-      "Condition not met at step 2156. No data to save.\n",
-      "2156\n",
-      "model_vars.get('Gini', 0)[-1]=0.677\n",
-      "Condition not met at step 2157. No data to save.\n",
-      "2157\n",
-      "model_vars.get('Gini', 0)[-1]=0.7086\n",
-      "Condition met. Appended special results for step 2158 to test_path/special_results.parquet\n",
-      "2158\n",
-      "model_vars.get('Gini', 0)[-1]=0.7323999999999999\n",
-      "Condition met. Appended special results for step 2159 to test_path/special_results.parquet\n",
-      "2159\n",
-      "model_vars.get('Gini', 0)[-1]=0.7282\n",
-      "Condition met. Appended special results for step 2160 to test_path/special_results.parquet\n",
-      "2160\n",
-      "model_vars.get('Gini', 0)[-1]=0.7236\n",
-      "Condition met. Appended special results for step 2161 to test_path/special_results.parquet\n",
-      "2161\n",
-      "model_vars.get('Gini', 0)[-1]=0.7046\n",
-      "Condition met. Appended special results for step 2162 to test_path/special_results.parquet\n",
-      "2162\n",
-      "model_vars.get('Gini', 0)[-1]=0.6804\n",
-      "Condition not met at step 2163. No data to save.\n",
-      "2163\n",
-      "model_vars.get('Gini', 0)[-1]=0.6752\n",
-      "Condition not met at step 2164. No data to save.\n",
-      "2164\n",
-      "model_vars.get('Gini', 0)[-1]=0.6664\n",
-      "Condition not met at step 2165. No data to save.\n",
-      "2165\n",
-      "model_vars.get('Gini', 0)[-1]=0.6512\n",
-      "Condition not met at step 2166. No data to save.\n",
-      "2166\n",
-      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
-      "Condition not met at step 2167. No data to save.\n",
-      "2167\n",
-      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
-      "Condition not met at step 2168. No data to save.\n",
-      "2168\n",
-      "model_vars.get('Gini', 0)[-1]=0.6918\n",
-      "Condition not met at step 2169. No data to save.\n",
-      "2169\n",
-      "model_vars.get('Gini', 0)[-1]=0.6794\n",
-      "Condition not met at step 2170. No data to save.\n",
-      "2170\n",
-      "model_vars.get('Gini', 0)[-1]=0.6742\n",
-      "Condition not met at step 2171. No data to save.\n",
-      "2171\n",
-      "model_vars.get('Gini', 0)[-1]=0.6652\n",
-      "Condition not met at step 2172. No data to save.\n",
-      "2172\n",
-      "model_vars.get('Gini', 0)[-1]=0.6582\n",
-      "Condition not met at step 2173. No data to save.\n",
-      "2173\n",
-      "model_vars.get('Gini', 0)[-1]=0.66\n",
-      "Condition not met at step 2174. No data to save.\n",
-      "2174\n",
-      "model_vars.get('Gini', 0)[-1]=0.6752\n",
-      "Condition not met at step 2175. No data to save.\n",
-      "2175\n",
-      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
-      "Condition not met at step 2176. No data to save.\n",
-      "2176\n",
-      "model_vars.get('Gini', 0)[-1]=0.6532\n",
-      "Condition not met at step 2177. No data to save.\n",
-      "2177\n",
-      "model_vars.get('Gini', 0)[-1]=0.6672\n",
-      "Condition not met at step 2178. No data to save.\n",
-      "2178\n",
-      "model_vars.get('Gini', 0)[-1]=0.6382\n",
-      "Condition not met at step 2179. No data to save.\n",
-      "2179\n",
-      "model_vars.get('Gini', 0)[-1]=0.637\n",
-      "Condition not met at step 2180. No data to save.\n",
-      "2180\n",
-      "model_vars.get('Gini', 0)[-1]=0.6332\n",
-      "Condition not met at step 2181. No data to save.\n",
-      "2181\n",
-      "model_vars.get('Gini', 0)[-1]=0.6344000000000001\n",
-      "Condition not met at step 2182. No data to save.\n",
-      "2182\n",
-      "model_vars.get('Gini', 0)[-1]=0.625\n",
-      "Condition not met at step 2183. No data to save.\n",
-      "2183\n",
-      "model_vars.get('Gini', 0)[-1]=0.6258\n",
-      "Condition not met at step 2184. No data to save.\n",
-      "2184\n",
-      "model_vars.get('Gini', 0)[-1]=0.6398\n",
-      "Condition not met at step 2185. No data to save.\n",
-      "2185\n",
-      "model_vars.get('Gini', 0)[-1]=0.6275999999999999\n",
-      "Condition not met at step 2186. No data to save.\n",
-      "2186\n",
-      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
-      "Condition not met at step 2187. No data to save.\n",
-      "2187\n",
-      "model_vars.get('Gini', 0)[-1]=0.669\n",
-      "Condition not met at step 2188. No data to save.\n",
-      "2188\n",
-      "model_vars.get('Gini', 0)[-1]=0.6676\n",
-      "Condition not met at step 2189. No data to save.\n",
-      "2189\n",
-      "model_vars.get('Gini', 0)[-1]=0.6454\n",
-      "Condition not met at step 2190. No data to save.\n",
-      "2190\n",
-      "model_vars.get('Gini', 0)[-1]=0.6416\n",
-      "Condition not met at step 2191. No data to save.\n",
-      "2191\n",
-      "model_vars.get('Gini', 0)[-1]=0.64\n",
-      "Condition not met at step 2192. No data to save.\n",
-      "2192\n",
-      "model_vars.get('Gini', 0)[-1]=0.6696\n",
-      "Condition not met at step 2193. No data to save.\n",
-      "2193\n",
-      "model_vars.get('Gini', 0)[-1]=0.6572\n",
-      "Condition not met at step 2194. No data to save.\n",
-      "2194\n",
-      "model_vars.get('Gini', 0)[-1]=0.6812\n",
-      "Condition not met at step 2195. No data to save.\n",
-      "2195\n",
-      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
-      "Condition not met at step 2196. No data to save.\n",
-      "2196\n",
-      "model_vars.get('Gini', 0)[-1]=0.687\n",
-      "Condition not met at step 2197. No data to save.\n",
-      "2197\n",
-      "model_vars.get('Gini', 0)[-1]=0.7068\n",
-      "Condition met. Appended special results for step 2198 to test_path/special_results.parquet\n",
-      "2198\n",
-      "model_vars.get('Gini', 0)[-1]=0.6941999999999999\n",
-      "Condition not met at step 2199. No data to save.\n",
-      "2199\n",
-      "CALLED\n",
-      "self.model._steps=2200\n",
-      " TEST self.model._steps=2200\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "2100  0.6290\n",
-      "2101  0.6408\n",
-      "2102  0.6448\n",
-      "2103  0.6540\n",
-      "2104  0.6150\n",
-      "...      ...\n",
-      "2195  0.6788\n",
-      "2196  0.6870\n",
-      "2197  0.7068\n",
-      "2198  0.6942\n",
-      "2199  0.6918\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_022.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_022.parquet'\n",
-      "Saving model to test_path/model_data_022.parquet\n",
-      "Saving agent to test_path/agent_data_022.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6918\n",
-      "Condition not met at step 2200. No data to save.\n",
-      "2200\n",
-      "model_vars.get('Gini', 0)[-1]=0.6854\n",
-      "Condition not met at step 2201. No data to save.\n",
-      "2201\n",
-      "model_vars.get('Gini', 0)[-1]=0.687\n",
-      "Condition not met at step 2202. No data to save.\n",
-      "2202\n",
-      "model_vars.get('Gini', 0)[-1]=0.6954\n",
-      "Condition not met at step 2203. No data to save.\n",
-      "2203\n",
-      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
-      "Condition not met at step 2204. No data to save.\n",
-      "2204\n",
-      "model_vars.get('Gini', 0)[-1]=0.671\n",
-      "Condition not met at step 2205. No data to save.\n",
-      "2205\n",
-      "model_vars.get('Gini', 0)[-1]=0.6496\n",
-      "Condition not met at step 2206. No data to save.\n",
-      "2206\n",
-      "model_vars.get('Gini', 0)[-1]=0.669\n",
-      "Condition not met at step 2207. No data to save.\n",
-      "2207\n",
-      "model_vars.get('Gini', 0)[-1]=0.6826\n",
-      "Condition not met at step 2208. No data to save.\n",
-      "2208\n",
-      "model_vars.get('Gini', 0)[-1]=0.6734\n",
-      "Condition not met at step 2209. No data to save.\n",
-      "2209\n",
-      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
-      "Condition not met at step 2210. No data to save.\n",
-      "2210\n",
-      "model_vars.get('Gini', 0)[-1]=0.6664\n",
-      "Condition not met at step 2211. No data to save.\n",
-      "2211\n",
-      "model_vars.get('Gini', 0)[-1]=0.6596\n",
-      "Condition not met at step 2212. No data to save.\n",
-      "2212\n",
-      "model_vars.get('Gini', 0)[-1]=0.637\n",
-      "Condition not met at step 2213. No data to save.\n",
-      "2213\n",
-      "model_vars.get('Gini', 0)[-1]=0.6204000000000001\n",
-      "Condition not met at step 2214. No data to save.\n",
-      "2214\n",
-      "model_vars.get('Gini', 0)[-1]=0.6355999999999999\n",
-      "Condition not met at step 2215. No data to save.\n",
-      "2215\n",
-      "model_vars.get('Gini', 0)[-1]=0.5978\n",
-      "Condition not met at step 2216. No data to save.\n",
-      "2216\n",
-      "model_vars.get('Gini', 0)[-1]=0.5908\n",
-      "Condition not met at step 2217. No data to save.\n",
-      "2217\n",
-      "model_vars.get('Gini', 0)[-1]=0.6088\n",
-      "Condition not met at step 2218. No data to save.\n",
-      "2218\n",
-      "model_vars.get('Gini', 0)[-1]=0.6388\n",
-      "Condition not met at step 2219. No data to save.\n",
-      "2219\n",
-      "model_vars.get('Gini', 0)[-1]=0.6186\n",
-      "Condition not met at step 2220. No data to save.\n",
-      "2220\n",
-      "model_vars.get('Gini', 0)[-1]=0.5946\n",
-      "Condition not met at step 2221. No data to save.\n",
-      "2221\n",
-      "model_vars.get('Gini', 0)[-1]=0.5992\n",
-      "Condition not met at step 2222. No data to save.\n",
-      "2222\n",
-      "model_vars.get('Gini', 0)[-1]=0.5892\n",
-      "Condition not met at step 2223. No data to save.\n",
-      "2223\n",
-      "model_vars.get('Gini', 0)[-1]=0.5938\n",
-      "Condition not met at step 2224. No data to save.\n",
-      "2224\n",
-      "model_vars.get('Gini', 0)[-1]=0.6174\n",
-      "Condition not met at step 2225. No data to save.\n",
-      "2225\n",
-      "model_vars.get('Gini', 0)[-1]=0.6474\n",
-      "Condition not met at step 2226. No data to save.\n",
-      "2226\n",
-      "model_vars.get('Gini', 0)[-1]=0.6632\n",
-      "Condition not met at step 2227. No data to save.\n",
-      "2227\n",
-      "model_vars.get('Gini', 0)[-1]=0.657\n",
-      "Condition not met at step 2228. No data to save.\n",
-      "2228\n",
-      "model_vars.get('Gini', 0)[-1]=0.6514\n",
-      "Condition not met at step 2229. No data to save.\n",
-      "2229\n",
-      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
-      "Condition not met at step 2230. No data to save.\n",
-      "2230\n",
-      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
-      "Condition not met at step 2231. No data to save.\n",
-      "2231\n",
-      "model_vars.get('Gini', 0)[-1]=0.6876\n",
-      "Condition not met at step 2232. No data to save.\n",
-      "2232\n",
-      "model_vars.get('Gini', 0)[-1]=0.7026\n",
-      "Condition met. Appended special results for step 2233 to test_path/special_results.parquet\n",
-      "2233\n",
-      "model_vars.get('Gini', 0)[-1]=0.6966\n",
-      "Condition not met at step 2234. No data to save.\n",
-      "2234\n",
-      "model_vars.get('Gini', 0)[-1]=0.6666000000000001\n",
-      "Condition not met at step 2235. No data to save.\n",
-      "2235\n",
-      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
-      "Condition not met at step 2236. No data to save.\n",
-      "2236\n",
-      "model_vars.get('Gini', 0)[-1]=0.6696\n",
-      "Condition not met at step 2237. No data to save.\n",
-      "2237\n",
-      "model_vars.get('Gini', 0)[-1]=0.6702\n",
-      "Condition not met at step 2238. No data to save.\n",
-      "2238\n",
-      "model_vars.get('Gini', 0)[-1]=0.6776\n",
-      "Condition not met at step 2239. No data to save.\n",
-      "2239\n",
-      "model_vars.get('Gini', 0)[-1]=0.6654\n",
-      "Condition not met at step 2240. No data to save.\n",
-      "2240\n",
-      "model_vars.get('Gini', 0)[-1]=0.6608\n",
-      "Condition not met at step 2241. No data to save.\n",
-      "2241\n",
-      "model_vars.get('Gini', 0)[-1]=0.6512\n",
-      "Condition not met at step 2242. No data to save.\n",
-      "2242\n",
-      "model_vars.get('Gini', 0)[-1]=0.6574\n",
-      "Condition not met at step 2243. No data to save.\n",
-      "2243\n",
-      "model_vars.get('Gini', 0)[-1]=0.6872\n",
-      "Condition not met at step 2244. No data to save.\n",
-      "2244\n",
-      "model_vars.get('Gini', 0)[-1]=0.6814\n",
-      "Condition not met at step 2245. No data to save.\n",
-      "2245\n",
-      "model_vars.get('Gini', 0)[-1]=0.6626000000000001\n",
-      "Condition not met at step 2246. No data to save.\n",
-      "2246\n",
-      "model_vars.get('Gini', 0)[-1]=0.6884\n",
-      "Condition not met at step 2247. No data to save.\n",
-      "2247\n",
-      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
-      "Condition not met at step 2248. No data to save.\n",
-      "2248\n",
-      "model_vars.get('Gini', 0)[-1]=0.6656\n",
-      "Condition not met at step 2249. No data to save.\n",
-      "2249\n",
-      "model_vars.get('Gini', 0)[-1]=0.6844\n",
-      "Condition not met at step 2250. No data to save.\n",
-      "2250\n",
-      "model_vars.get('Gini', 0)[-1]=0.6886\n",
-      "Condition not met at step 2251. No data to save.\n",
-      "2251\n",
-      "model_vars.get('Gini', 0)[-1]=0.6874\n",
-      "Condition not met at step 2252. No data to save.\n",
-      "2252\n",
-      "model_vars.get('Gini', 0)[-1]=0.671\n",
-      "Condition not met at step 2253. No data to save.\n",
-      "2253\n",
-      "model_vars.get('Gini', 0)[-1]=0.661\n",
-      "Condition not met at step 2254. No data to save.\n",
-      "2254\n",
-      "model_vars.get('Gini', 0)[-1]=0.6656\n",
-      "Condition not met at step 2255. No data to save.\n",
-      "2255\n",
-      "model_vars.get('Gini', 0)[-1]=0.704\n",
-      "Condition met. Appended special results for step 2256 to test_path/special_results.parquet\n",
-      "2256\n",
-      "model_vars.get('Gini', 0)[-1]=0.7061999999999999\n",
-      "Condition met. Appended special results for step 2257 to test_path/special_results.parquet\n",
-      "2257\n",
-      "model_vars.get('Gini', 0)[-1]=0.685\n",
-      "Condition not met at step 2258. No data to save.\n",
-      "2258\n",
-      "model_vars.get('Gini', 0)[-1]=0.7094\n",
-      "Condition met. Appended special results for step 2259 to test_path/special_results.parquet\n",
-      "2259\n",
-      "model_vars.get('Gini', 0)[-1]=0.7018\n",
-      "Condition met. Appended special results for step 2260 to test_path/special_results.parquet\n",
-      "2260\n",
-      "model_vars.get('Gini', 0)[-1]=0.6978\n",
-      "Condition not met at step 2261. No data to save.\n",
-      "2261\n",
-      "model_vars.get('Gini', 0)[-1]=0.7030000000000001\n",
-      "Condition met. Appended special results for step 2262 to test_path/special_results.parquet\n",
-      "2262\n",
-      "model_vars.get('Gini', 0)[-1]=0.698\n",
-      "Condition not met at step 2263. No data to save.\n",
-      "2263\n",
-      "model_vars.get('Gini', 0)[-1]=0.6859999999999999\n",
-      "Condition not met at step 2264. No data to save.\n",
-      "2264\n",
-      "model_vars.get('Gini', 0)[-1]=0.6819999999999999\n",
-      "Condition not met at step 2265. No data to save.\n",
-      "2265\n",
-      "model_vars.get('Gini', 0)[-1]=0.6874\n",
-      "Condition not met at step 2266. No data to save.\n",
-      "2266\n",
-      "model_vars.get('Gini', 0)[-1]=0.6902\n",
-      "Condition not met at step 2267. No data to save.\n",
-      "2267\n",
-      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
-      "Condition not met at step 2268. No data to save.\n",
-      "2268\n",
-      "model_vars.get('Gini', 0)[-1]=0.6656\n",
-      "Condition not met at step 2269. No data to save.\n",
-      "2269\n",
-      "model_vars.get('Gini', 0)[-1]=0.6679999999999999\n",
-      "Condition not met at step 2270. No data to save.\n",
-      "2270\n",
-      "model_vars.get('Gini', 0)[-1]=0.6608\n",
-      "Condition not met at step 2271. No data to save.\n",
-      "2271\n",
-      "model_vars.get('Gini', 0)[-1]=0.6496\n",
-      "Condition not met at step 2272. No data to save.\n",
-      "2272\n",
-      "model_vars.get('Gini', 0)[-1]=0.6584\n",
-      "Condition not met at step 2273. No data to save.\n",
-      "2273\n",
-      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
-      "Condition not met at step 2274. No data to save.\n",
-      "2274\n",
-      "model_vars.get('Gini', 0)[-1]=0.673\n",
-      "Condition not met at step 2275. No data to save.\n",
-      "2275\n",
-      "model_vars.get('Gini', 0)[-1]=0.6932\n",
-      "Condition not met at step 2276. No data to save.\n",
-      "2276\n",
-      "model_vars.get('Gini', 0)[-1]=0.6961999999999999\n",
-      "Condition not met at step 2277. No data to save.\n",
-      "2277\n",
-      "model_vars.get('Gini', 0)[-1]=0.6762\n",
-      "Condition not met at step 2278. No data to save.\n",
-      "2278\n",
-      "model_vars.get('Gini', 0)[-1]=0.6864\n",
-      "Condition not met at step 2279. No data to save.\n",
-      "2279\n",
-      "model_vars.get('Gini', 0)[-1]=0.6990000000000001\n",
-      "Condition not met at step 2280. No data to save.\n",
-      "2280\n",
-      "model_vars.get('Gini', 0)[-1]=0.6816\n",
-      "Condition not met at step 2281. No data to save.\n",
-      "2281\n",
-      "model_vars.get('Gini', 0)[-1]=0.6914\n",
-      "Condition not met at step 2282. No data to save.\n",
-      "2282\n",
-      "model_vars.get('Gini', 0)[-1]=0.7028000000000001\n",
-      "Condition met. Appended special results for step 2283 to test_path/special_results.parquet\n",
-      "2283\n",
-      "model_vars.get('Gini', 0)[-1]=0.6964\n",
-      "Condition not met at step 2284. No data to save.\n",
-      "2284\n",
-      "model_vars.get('Gini', 0)[-1]=0.698\n",
-      "Condition not met at step 2285. No data to save.\n",
-      "2285\n",
-      "model_vars.get('Gini', 0)[-1]=0.6981999999999999\n",
-      "Condition not met at step 2286. No data to save.\n",
-      "2286\n",
-      "model_vars.get('Gini', 0)[-1]=0.6930000000000001\n",
-      "Condition not met at step 2287. No data to save.\n",
-      "2287\n",
-      "model_vars.get('Gini', 0)[-1]=0.7006\n",
-      "Condition met. Appended special results for step 2288 to test_path/special_results.parquet\n",
-      "2288\n",
-      "model_vars.get('Gini', 0)[-1]=0.6914\n",
-      "Condition not met at step 2289. No data to save.\n",
-      "2289\n",
-      "model_vars.get('Gini', 0)[-1]=0.6914\n",
-      "Condition not met at step 2290. No data to save.\n",
-      "2290\n",
-      "model_vars.get('Gini', 0)[-1]=0.664\n",
-      "Condition not met at step 2291. No data to save.\n",
-      "2291\n",
-      "model_vars.get('Gini', 0)[-1]=0.6548\n",
-      "Condition not met at step 2292. No data to save.\n",
-      "2292\n",
-      "model_vars.get('Gini', 0)[-1]=0.641\n",
-      "Condition not met at step 2293. No data to save.\n",
-      "2293\n",
-      "model_vars.get('Gini', 0)[-1]=0.6446000000000001\n",
-      "Condition not met at step 2294. No data to save.\n",
-      "2294\n",
-      "model_vars.get('Gini', 0)[-1]=0.635\n",
-      "Condition not met at step 2295. No data to save.\n",
-      "2295\n",
-      "model_vars.get('Gini', 0)[-1]=0.6338\n",
-      "Condition not met at step 2296. No data to save.\n",
-      "2296\n",
-      "model_vars.get('Gini', 0)[-1]=0.6366\n",
-      "Condition not met at step 2297. No data to save.\n",
-      "2297\n",
-      "model_vars.get('Gini', 0)[-1]=0.6246\n",
-      "Condition not met at step 2298. No data to save.\n",
-      "2298\n",
-      "model_vars.get('Gini', 0)[-1]=0.6198\n",
-      "Condition not met at step 2299. No data to save.\n",
-      "2299\n",
-      "CALLED\n",
-      "self.model._steps=2300\n",
-      " TEST self.model._steps=2300\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "2200  0.6854\n",
-      "2201  0.6870\n",
-      "2202  0.6954\n",
-      "2203  0.6740\n",
-      "2204  0.6710\n",
-      "...      ...\n",
-      "2295  0.6338\n",
-      "2296  0.6366\n",
-      "2297  0.6246\n",
-      "2298  0.6198\n",
-      "2299  0.6274\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_023.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_023.parquet'\n",
-      "Saving model to test_path/model_data_023.parquet\n",
-      "Saving agent to test_path/agent_data_023.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6274\n",
-      "Condition not met at step 2300. No data to save.\n",
-      "2300\n",
-      "model_vars.get('Gini', 0)[-1]=0.6312\n",
-      "Condition not met at step 2301. No data to save.\n",
-      "2301\n",
-      "model_vars.get('Gini', 0)[-1]=0.6636\n",
-      "Condition not met at step 2302. No data to save.\n",
-      "2302\n",
-      "model_vars.get('Gini', 0)[-1]=0.6456\n",
-      "Condition not met at step 2303. No data to save.\n",
-      "2303\n",
-      "model_vars.get('Gini', 0)[-1]=0.6352\n",
-      "Condition not met at step 2304. No data to save.\n",
-      "2304\n",
-      "model_vars.get('Gini', 0)[-1]=0.6234\n",
-      "Condition not met at step 2305. No data to save.\n",
-      "2305\n",
-      "model_vars.get('Gini', 0)[-1]=0.5984\n",
-      "Condition not met at step 2306. No data to save.\n",
-      "2306\n",
-      "model_vars.get('Gini', 0)[-1]=0.5906\n",
-      "Condition not met at step 2307. No data to save.\n",
-      "2307\n",
-      "model_vars.get('Gini', 0)[-1]=0.626\n",
-      "Condition not met at step 2308. No data to save.\n",
-      "2308\n",
-      "model_vars.get('Gini', 0)[-1]=0.6342\n",
-      "Condition not met at step 2309. No data to save.\n",
-      "2309\n",
-      "model_vars.get('Gini', 0)[-1]=0.6432\n",
-      "Condition not met at step 2310. No data to save.\n",
-      "2310\n",
-      "model_vars.get('Gini', 0)[-1]=0.6372\n",
-      "Condition not met at step 2311. No data to save.\n",
-      "2311\n",
-      "model_vars.get('Gini', 0)[-1]=0.6666000000000001\n",
-      "Condition not met at step 2312. No data to save.\n",
-      "2312\n",
-      "model_vars.get('Gini', 0)[-1]=0.656\n",
-      "Condition not met at step 2313. No data to save.\n",
-      "2313\n",
-      "model_vars.get('Gini', 0)[-1]=0.6497999999999999\n",
-      "Condition not met at step 2314. No data to save.\n",
-      "2314\n",
-      "model_vars.get('Gini', 0)[-1]=0.6472\n",
-      "Condition not met at step 2315. No data to save.\n",
-      "2315\n",
-      "model_vars.get('Gini', 0)[-1]=0.641\n",
-      "Condition not met at step 2316. No data to save.\n",
-      "2316\n",
-      "model_vars.get('Gini', 0)[-1]=0.65\n",
-      "Condition not met at step 2317. No data to save.\n",
-      "2317\n",
-      "model_vars.get('Gini', 0)[-1]=0.646\n",
-      "Condition not met at step 2318. No data to save.\n",
-      "2318\n",
-      "model_vars.get('Gini', 0)[-1]=0.6452\n",
-      "Condition not met at step 2319. No data to save.\n",
-      "2319\n",
-      "model_vars.get('Gini', 0)[-1]=0.6516\n",
-      "Condition not met at step 2320. No data to save.\n",
-      "2320\n",
-      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
-      "Condition not met at step 2321. No data to save.\n",
-      "2321\n",
-      "model_vars.get('Gini', 0)[-1]=0.6594\n",
-      "Condition not met at step 2322. No data to save.\n",
-      "2322\n",
-      "model_vars.get('Gini', 0)[-1]=0.6368\n",
-      "Condition not met at step 2323. No data to save.\n",
-      "2323\n",
-      "model_vars.get('Gini', 0)[-1]=0.6508\n",
-      "Condition not met at step 2324. No data to save.\n",
-      "2324\n",
-      "model_vars.get('Gini', 0)[-1]=0.6252\n",
-      "Condition not met at step 2325. No data to save.\n",
-      "2325\n",
-      "model_vars.get('Gini', 0)[-1]=0.6175999999999999\n",
-      "Condition not met at step 2326. No data to save.\n",
-      "2326\n",
-      "model_vars.get('Gini', 0)[-1]=0.6312\n",
-      "Condition not met at step 2327. No data to save.\n",
-      "2327\n",
-      "model_vars.get('Gini', 0)[-1]=0.637\n",
-      "Condition not met at step 2328. No data to save.\n",
-      "2328\n",
-      "model_vars.get('Gini', 0)[-1]=0.6234\n",
-      "Condition not met at step 2329. No data to save.\n",
-      "2329\n",
-      "model_vars.get('Gini', 0)[-1]=0.6212\n",
-      "Condition not met at step 2330. No data to save.\n",
-      "2330\n",
-      "model_vars.get('Gini', 0)[-1]=0.6388\n",
-      "Condition not met at step 2331. No data to save.\n",
-      "2331\n",
-      "model_vars.get('Gini', 0)[-1]=0.641\n",
-      "Condition not met at step 2332. No data to save.\n",
-      "2332\n",
-      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
-      "Condition not met at step 2333. No data to save.\n",
-      "2333\n",
-      "model_vars.get('Gini', 0)[-1]=0.6859999999999999\n",
-      "Condition not met at step 2334. No data to save.\n",
-      "2334\n",
-      "model_vars.get('Gini', 0)[-1]=0.6456\n",
-      "Condition not met at step 2335. No data to save.\n",
-      "2335\n",
-      "model_vars.get('Gini', 0)[-1]=0.6404000000000001\n",
-      "Condition not met at step 2336. No data to save.\n",
-      "2336\n",
-      "model_vars.get('Gini', 0)[-1]=0.6512\n",
-      "Condition not met at step 2337. No data to save.\n",
-      "2337\n",
-      "model_vars.get('Gini', 0)[-1]=0.6616\n",
-      "Condition not met at step 2338. No data to save.\n",
-      "2338\n",
-      "model_vars.get('Gini', 0)[-1]=0.655\n",
-      "Condition not met at step 2339. No data to save.\n",
-      "2339\n",
-      "model_vars.get('Gini', 0)[-1]=0.6394\n",
-      "Condition not met at step 2340. No data to save.\n",
-      "2340\n",
-      "model_vars.get('Gini', 0)[-1]=0.6378\n",
-      "Condition not met at step 2341. No data to save.\n",
-      "2341\n",
-      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
-      "Condition not met at step 2342. No data to save.\n",
-      "2342\n",
-      "model_vars.get('Gini', 0)[-1]=0.6464000000000001\n",
-      "Condition not met at step 2343. No data to save.\n",
-      "2343\n",
-      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
-      "Condition not met at step 2344. No data to save.\n",
-      "2344\n",
-      "model_vars.get('Gini', 0)[-1]=0.6468\n",
-      "Condition not met at step 2345. No data to save.\n",
-      "2345\n",
-      "model_vars.get('Gini', 0)[-1]=0.6502\n",
-      "Condition not met at step 2346. No data to save.\n",
-      "2346\n",
-      "model_vars.get('Gini', 0)[-1]=0.6358\n",
-      "Condition not met at step 2347. No data to save.\n",
-      "2347\n",
-      "model_vars.get('Gini', 0)[-1]=0.6134\n",
-      "Condition not met at step 2348. No data to save.\n",
-      "2348\n",
-      "model_vars.get('Gini', 0)[-1]=0.6026\n",
-      "Condition not met at step 2349. No data to save.\n",
-      "2349\n",
-      "model_vars.get('Gini', 0)[-1]=0.5978\n",
-      "Condition not met at step 2350. No data to save.\n",
-      "2350\n",
-      "model_vars.get('Gini', 0)[-1]=0.6235999999999999\n",
-      "Condition not met at step 2351. No data to save.\n",
-      "2351\n",
-      "model_vars.get('Gini', 0)[-1]=0.6152\n",
-      "Condition not met at step 2352. No data to save.\n",
-      "2352\n",
-      "model_vars.get('Gini', 0)[-1]=0.6052\n",
-      "Condition not met at step 2353. No data to save.\n",
-      "2353\n",
-      "model_vars.get('Gini', 0)[-1]=0.6168\n",
-      "Condition not met at step 2354. No data to save.\n",
-      "2354\n",
-      "model_vars.get('Gini', 0)[-1]=0.6000000000000001\n",
-      "Condition not met at step 2355. No data to save.\n",
-      "2355\n",
-      "model_vars.get('Gini', 0)[-1]=0.6142000000000001\n",
-      "Condition not met at step 2356. No data to save.\n",
-      "2356\n",
-      "model_vars.get('Gini', 0)[-1]=0.6154\n",
-      "Condition not met at step 2357. No data to save.\n",
-      "2357\n",
-      "model_vars.get('Gini', 0)[-1]=0.6528\n",
-      "Condition not met at step 2358. No data to save.\n",
-      "2358\n",
-      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
-      "Condition not met at step 2359. No data to save.\n",
-      "2359\n",
-      "model_vars.get('Gini', 0)[-1]=0.6724\n",
-      "Condition not met at step 2360. No data to save.\n",
-      "2360\n",
-      "model_vars.get('Gini', 0)[-1]=0.6522\n",
-      "Condition not met at step 2361. No data to save.\n",
-      "2361\n",
-      "model_vars.get('Gini', 0)[-1]=0.659\n",
-      "Condition not met at step 2362. No data to save.\n",
-      "2362\n",
-      "model_vars.get('Gini', 0)[-1]=0.647\n",
-      "Condition not met at step 2363. No data to save.\n",
-      "2363\n",
-      "model_vars.get('Gini', 0)[-1]=0.654\n",
-      "Condition not met at step 2364. No data to save.\n",
-      "2364\n",
-      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
-      "Condition not met at step 2365. No data to save.\n",
-      "2365\n",
-      "model_vars.get('Gini', 0)[-1]=0.6134\n",
-      "Condition not met at step 2366. No data to save.\n",
-      "2366\n",
-      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
-      "Condition not met at step 2367. No data to save.\n",
-      "2367\n",
-      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
-      "Condition not met at step 2368. No data to save.\n",
-      "2368\n",
-      "model_vars.get('Gini', 0)[-1]=0.6342\n",
-      "Condition not met at step 2369. No data to save.\n",
-      "2369\n",
-      "model_vars.get('Gini', 0)[-1]=0.6422\n",
-      "Condition not met at step 2370. No data to save.\n",
-      "2370\n",
-      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
-      "Condition not met at step 2371. No data to save.\n",
-      "2371\n",
-      "model_vars.get('Gini', 0)[-1]=0.6682\n",
-      "Condition not met at step 2372. No data to save.\n",
-      "2372\n",
-      "model_vars.get('Gini', 0)[-1]=0.6544\n",
-      "Condition not met at step 2373. No data to save.\n",
-      "2373\n",
-      "model_vars.get('Gini', 0)[-1]=0.6582\n",
-      "Condition not met at step 2374. No data to save.\n",
-      "2374\n",
-      "model_vars.get('Gini', 0)[-1]=0.6614\n",
-      "Condition not met at step 2375. No data to save.\n",
-      "2375\n",
-      "model_vars.get('Gini', 0)[-1]=0.658\n",
-      "Condition not met at step 2376. No data to save.\n",
-      "2376\n",
-      "model_vars.get('Gini', 0)[-1]=0.6758\n",
-      "Condition not met at step 2377. No data to save.\n",
-      "2377\n",
-      "model_vars.get('Gini', 0)[-1]=0.6666000000000001\n",
-      "Condition not met at step 2378. No data to save.\n",
-      "2378\n",
-      "model_vars.get('Gini', 0)[-1]=0.7024\n",
-      "Condition met. Appended special results for step 2379 to test_path/special_results.parquet\n",
-      "2379\n",
-      "model_vars.get('Gini', 0)[-1]=0.7014\n",
-      "Condition met. Appended special results for step 2380 to test_path/special_results.parquet\n",
-      "2380\n",
-      "model_vars.get('Gini', 0)[-1]=0.6948000000000001\n",
-      "Condition not met at step 2381. No data to save.\n",
-      "2381\n",
-      "model_vars.get('Gini', 0)[-1]=0.6754\n",
-      "Condition not met at step 2382. No data to save.\n",
-      "2382\n",
-      "model_vars.get('Gini', 0)[-1]=0.6786000000000001\n",
-      "Condition not met at step 2383. No data to save.\n",
-      "2383\n",
-      "model_vars.get('Gini', 0)[-1]=0.6772\n",
-      "Condition not met at step 2384. No data to save.\n",
-      "2384\n",
-      "model_vars.get('Gini', 0)[-1]=0.6990000000000001\n",
-      "Condition not met at step 2385. No data to save.\n",
-      "2385\n",
-      "model_vars.get('Gini', 0)[-1]=0.6812\n",
-      "Condition not met at step 2386. No data to save.\n",
-      "2386\n",
-      "model_vars.get('Gini', 0)[-1]=0.6862\n",
-      "Condition not met at step 2387. No data to save.\n",
-      "2387\n",
-      "model_vars.get('Gini', 0)[-1]=0.6941999999999999\n",
-      "Condition not met at step 2388. No data to save.\n",
-      "2388\n",
-      "model_vars.get('Gini', 0)[-1]=0.6936\n",
-      "Condition not met at step 2389. No data to save.\n",
-      "2389\n",
-      "model_vars.get('Gini', 0)[-1]=0.6822\n",
-      "Condition not met at step 2390. No data to save.\n",
-      "2390\n",
-      "model_vars.get('Gini', 0)[-1]=0.681\n",
-      "Condition not met at step 2391. No data to save.\n",
-      "2391\n",
-      "model_vars.get('Gini', 0)[-1]=0.6898\n",
-      "Condition not met at step 2392. No data to save.\n",
-      "2392\n",
-      "model_vars.get('Gini', 0)[-1]=0.6796\n",
-      "Condition not met at step 2393. No data to save.\n",
-      "2393\n",
-      "model_vars.get('Gini', 0)[-1]=0.6584\n",
-      "Condition not met at step 2394. No data to save.\n",
-      "2394\n",
-      "model_vars.get('Gini', 0)[-1]=0.6859999999999999\n",
-      "Condition not met at step 2395. No data to save.\n",
-      "2395\n",
-      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
-      "Condition not met at step 2396. No data to save.\n",
-      "2396\n",
-      "model_vars.get('Gini', 0)[-1]=0.6616\n",
-      "Condition not met at step 2397. No data to save.\n",
-      "2397\n",
-      "model_vars.get('Gini', 0)[-1]=0.6836\n",
-      "Condition not met at step 2398. No data to save.\n",
-      "2398\n",
-      "model_vars.get('Gini', 0)[-1]=0.6586000000000001\n",
-      "Condition not met at step 2399. No data to save.\n",
-      "2399\n",
-      "CALLED\n",
-      "self.model._steps=2400\n",
-      " TEST self.model._steps=2400\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "2300  0.6312\n",
-      "2301  0.6636\n",
-      "2302  0.6456\n",
-      "2303  0.6352\n",
-      "2304  0.6234\n",
-      "...      ...\n",
-      "2395  0.6728\n",
-      "2396  0.6616\n",
-      "2397  0.6836\n",
-      "2398  0.6586\n",
-      "2399  0.6722\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_024.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_024.parquet'\n",
-      "Saving model to test_path/model_data_024.parquet\n",
-      "Saving agent to test_path/agent_data_024.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6722\n",
-      "Condition not met at step 2400. No data to save.\n",
-      "2400\n",
-      "model_vars.get('Gini', 0)[-1]=0.6744\n",
-      "Condition not met at step 2401. No data to save.\n",
-      "2401\n",
-      "model_vars.get('Gini', 0)[-1]=0.6704\n",
-      "Condition not met at step 2402. No data to save.\n",
-      "2402\n",
-      "model_vars.get('Gini', 0)[-1]=0.6748000000000001\n",
-      "Condition not met at step 2403. No data to save.\n",
-      "2403\n",
-      "model_vars.get('Gini', 0)[-1]=0.6946\n",
-      "Condition not met at step 2404. No data to save.\n",
-      "2404\n",
-      "model_vars.get('Gini', 0)[-1]=0.6826\n",
-      "Condition not met at step 2405. No data to save.\n",
-      "2405\n",
-      "model_vars.get('Gini', 0)[-1]=0.6976\n",
-      "Condition not met at step 2406. No data to save.\n",
-      "2406\n",
-      "model_vars.get('Gini', 0)[-1]=0.7116\n",
-      "Condition met. Appended special results for step 2407 to test_path/special_results.parquet\n",
-      "2407\n",
-      "model_vars.get('Gini', 0)[-1]=0.6924\n",
-      "Condition not met at step 2408. No data to save.\n",
-      "2408\n",
-      "model_vars.get('Gini', 0)[-1]=0.6624\n",
-      "Condition not met at step 2409. No data to save.\n",
-      "2409\n",
-      "model_vars.get('Gini', 0)[-1]=0.6602\n",
-      "Condition not met at step 2410. No data to save.\n",
-      "2410\n",
-      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
-      "Condition not met at step 2411. No data to save.\n",
-      "2411\n",
-      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
-      "Condition not met at step 2412. No data to save.\n",
-      "2412\n",
-      "model_vars.get('Gini', 0)[-1]=0.6764\n",
-      "Condition not met at step 2413. No data to save.\n",
-      "2413\n",
-      "model_vars.get('Gini', 0)[-1]=0.6752\n",
-      "Condition not met at step 2414. No data to save.\n",
-      "2414\n",
-      "model_vars.get('Gini', 0)[-1]=0.6884\n",
-      "Condition not met at step 2415. No data to save.\n",
-      "2415\n",
-      "model_vars.get('Gini', 0)[-1]=0.7001999999999999\n",
-      "Condition met. Appended special results for step 2416 to test_path/special_results.parquet\n",
-      "2416\n",
-      "model_vars.get('Gini', 0)[-1]=0.6978\n",
-      "Condition not met at step 2417. No data to save.\n",
-      "2417\n",
-      "model_vars.get('Gini', 0)[-1]=0.6826\n",
-      "Condition not met at step 2418. No data to save.\n",
-      "2418\n",
-      "model_vars.get('Gini', 0)[-1]=0.6792\n",
-      "Condition not met at step 2419. No data to save.\n",
-      "2419\n",
-      "model_vars.get('Gini', 0)[-1]=0.6746000000000001\n",
-      "Condition not met at step 2420. No data to save.\n",
-      "2420\n",
-      "model_vars.get('Gini', 0)[-1]=0.6826\n",
-      "Condition not met at step 2421. No data to save.\n",
-      "2421\n",
-      "model_vars.get('Gini', 0)[-1]=0.6768000000000001\n",
-      "Condition not met at step 2422. No data to save.\n",
-      "2422\n",
-      "model_vars.get('Gini', 0)[-1]=0.6772\n",
-      "Condition not met at step 2423. No data to save.\n",
-      "2423\n",
-      "model_vars.get('Gini', 0)[-1]=0.6858\n",
-      "Condition not met at step 2424. No data to save.\n",
-      "2424\n",
-      "model_vars.get('Gini', 0)[-1]=0.6566000000000001\n",
-      "Condition not met at step 2425. No data to save.\n",
-      "2425\n",
-      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
-      "Condition not met at step 2426. No data to save.\n",
-      "2426\n",
-      "model_vars.get('Gini', 0)[-1]=0.649\n",
-      "Condition not met at step 2427. No data to save.\n",
-      "2427\n",
-      "model_vars.get('Gini', 0)[-1]=0.6366\n",
-      "Condition not met at step 2428. No data to save.\n",
-      "2428\n",
-      "model_vars.get('Gini', 0)[-1]=0.6448\n",
-      "Condition not met at step 2429. No data to save.\n",
-      "2429\n",
-      "model_vars.get('Gini', 0)[-1]=0.6806\n",
-      "Condition not met at step 2430. No data to save.\n",
-      "2430\n",
-      "model_vars.get('Gini', 0)[-1]=0.6654\n",
-      "Condition not met at step 2431. No data to save.\n",
-      "2431\n",
-      "model_vars.get('Gini', 0)[-1]=0.6742\n",
-      "Condition not met at step 2432. No data to save.\n",
-      "2432\n",
-      "model_vars.get('Gini', 0)[-1]=0.655\n",
-      "Condition not met at step 2433. No data to save.\n",
-      "2433\n",
-      "model_vars.get('Gini', 0)[-1]=0.658\n",
-      "Condition not met at step 2434. No data to save.\n",
-      "2434\n",
-      "model_vars.get('Gini', 0)[-1]=0.6422\n",
-      "Condition not met at step 2435. No data to save.\n",
-      "2435\n",
-      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
-      "Condition not met at step 2436. No data to save.\n",
-      "2436\n",
-      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
-      "Condition not met at step 2437. No data to save.\n",
-      "2437\n",
-      "model_vars.get('Gini', 0)[-1]=0.6678\n",
-      "Condition not met at step 2438. No data to save.\n",
-      "2438\n",
-      "model_vars.get('Gini', 0)[-1]=0.6758\n",
-      "Condition not met at step 2439. No data to save.\n",
-      "2439\n",
-      "model_vars.get('Gini', 0)[-1]=0.6544\n",
-      "Condition not met at step 2440. No data to save.\n",
-      "2440\n",
-      "model_vars.get('Gini', 0)[-1]=0.6582\n",
-      "Condition not met at step 2441. No data to save.\n",
-      "2441\n",
-      "model_vars.get('Gini', 0)[-1]=0.658\n",
-      "Condition not met at step 2442. No data to save.\n",
-      "2442\n",
-      "model_vars.get('Gini', 0)[-1]=0.6504000000000001\n",
-      "Condition not met at step 2443. No data to save.\n",
-      "2443\n",
-      "model_vars.get('Gini', 0)[-1]=0.64\n",
-      "Condition not met at step 2444. No data to save.\n",
-      "2444\n",
-      "model_vars.get('Gini', 0)[-1]=0.643\n",
-      "Condition not met at step 2445. No data to save.\n",
-      "2445\n",
-      "model_vars.get('Gini', 0)[-1]=0.6482\n",
-      "Condition not met at step 2446. No data to save.\n",
-      "2446\n",
-      "model_vars.get('Gini', 0)[-1]=0.6638\n",
-      "Condition not met at step 2447. No data to save.\n",
-      "2447\n",
-      "model_vars.get('Gini', 0)[-1]=0.6335999999999999\n",
-      "Condition not met at step 2448. No data to save.\n",
-      "2448\n",
-      "model_vars.get('Gini', 0)[-1]=0.627\n",
-      "Condition not met at step 2449. No data to save.\n",
-      "2449\n",
-      "model_vars.get('Gini', 0)[-1]=0.619\n",
-      "Condition not met at step 2450. No data to save.\n",
-      "2450\n",
-      "model_vars.get('Gini', 0)[-1]=0.625\n",
-      "Condition not met at step 2451. No data to save.\n",
-      "2451\n",
-      "model_vars.get('Gini', 0)[-1]=0.6268\n",
-      "Condition not met at step 2452. No data to save.\n",
-      "2452\n",
-      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
-      "Condition not met at step 2453. No data to save.\n",
-      "2453\n",
-      "model_vars.get('Gini', 0)[-1]=0.623\n",
-      "Condition not met at step 2454. No data to save.\n",
-      "2454\n",
-      "model_vars.get('Gini', 0)[-1]=0.6078\n",
-      "Condition not met at step 2455. No data to save.\n",
-      "2455\n",
-      "model_vars.get('Gini', 0)[-1]=0.6262000000000001\n",
-      "Condition not met at step 2456. No data to save.\n",
-      "2456\n",
-      "model_vars.get('Gini', 0)[-1]=0.6138\n",
-      "Condition not met at step 2457. No data to save.\n",
-      "2457\n",
-      "model_vars.get('Gini', 0)[-1]=0.5862\n",
-      "Condition not met at step 2458. No data to save.\n",
-      "2458\n",
-      "model_vars.get('Gini', 0)[-1]=0.5738000000000001\n",
-      "Condition not met at step 2459. No data to save.\n",
-      "2459\n",
-      "model_vars.get('Gini', 0)[-1]=0.5794\n",
-      "Condition not met at step 2460. No data to save.\n",
-      "2460\n",
-      "model_vars.get('Gini', 0)[-1]=0.5984\n",
-      "Condition not met at step 2461. No data to save.\n",
-      "2461\n",
-      "model_vars.get('Gini', 0)[-1]=0.5884\n",
-      "Condition not met at step 2462. No data to save.\n",
-      "2462\n",
-      "model_vars.get('Gini', 0)[-1]=0.5574\n",
-      "Condition not met at step 2463. No data to save.\n",
-      "2463\n",
-      "model_vars.get('Gini', 0)[-1]=0.5602\n",
-      "Condition not met at step 2464. No data to save.\n",
-      "2464\n",
-      "model_vars.get('Gini', 0)[-1]=0.565\n",
-      "Condition not met at step 2465. No data to save.\n",
-      "2465\n",
-      "model_vars.get('Gini', 0)[-1]=0.5922000000000001\n",
-      "Condition not met at step 2466. No data to save.\n",
-      "2466\n",
-      "model_vars.get('Gini', 0)[-1]=0.6106\n",
-      "Condition not met at step 2467. No data to save.\n",
-      "2467\n",
-      "model_vars.get('Gini', 0)[-1]=0.627\n",
-      "Condition not met at step 2468. No data to save.\n",
-      "2468\n",
-      "model_vars.get('Gini', 0)[-1]=0.6348\n",
-      "Condition not met at step 2469. No data to save.\n",
-      "2469\n",
-      "model_vars.get('Gini', 0)[-1]=0.6032\n",
-      "Condition not met at step 2470. No data to save.\n",
-      "2470\n",
-      "model_vars.get('Gini', 0)[-1]=0.5920000000000001\n",
-      "Condition not met at step 2471. No data to save.\n",
-      "2471\n",
-      "model_vars.get('Gini', 0)[-1]=0.5682\n",
-      "Condition not met at step 2472. No data to save.\n",
-      "2472\n",
-      "model_vars.get('Gini', 0)[-1]=0.5996\n",
-      "Condition not met at step 2473. No data to save.\n",
-      "2473\n",
-      "model_vars.get('Gini', 0)[-1]=0.6058\n",
-      "Condition not met at step 2474. No data to save.\n",
-      "2474\n",
-      "model_vars.get('Gini', 0)[-1]=0.5972\n",
-      "Condition not met at step 2475. No data to save.\n",
-      "2475\n",
-      "model_vars.get('Gini', 0)[-1]=0.6274\n",
-      "Condition not met at step 2476. No data to save.\n",
-      "2476\n",
-      "model_vars.get('Gini', 0)[-1]=0.6298\n",
-      "Condition not met at step 2477. No data to save.\n",
-      "2477\n",
-      "model_vars.get('Gini', 0)[-1]=0.6246\n",
-      "Condition not met at step 2478. No data to save.\n",
-      "2478\n",
-      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
-      "Condition not met at step 2479. No data to save.\n",
-      "2479\n",
-      "model_vars.get('Gini', 0)[-1]=0.6262000000000001\n",
-      "Condition not met at step 2480. No data to save.\n",
-      "2480\n",
-      "model_vars.get('Gini', 0)[-1]=0.6304000000000001\n",
-      "Condition not met at step 2481. No data to save.\n",
-      "2481\n",
-      "model_vars.get('Gini', 0)[-1]=0.6754\n",
-      "Condition not met at step 2482. No data to save.\n",
-      "2482\n",
-      "model_vars.get('Gini', 0)[-1]=0.6534\n",
-      "Condition not met at step 2483. No data to save.\n",
-      "2483\n",
-      "model_vars.get('Gini', 0)[-1]=0.6744\n",
-      "Condition not met at step 2484. No data to save.\n",
-      "2484\n",
-      "model_vars.get('Gini', 0)[-1]=0.661\n",
-      "Condition not met at step 2485. No data to save.\n",
-      "2485\n",
-      "model_vars.get('Gini', 0)[-1]=0.6282000000000001\n",
-      "Condition not met at step 2486. No data to save.\n",
-      "2486\n",
-      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
-      "Condition not met at step 2487. No data to save.\n",
-      "2487\n",
-      "model_vars.get('Gini', 0)[-1]=0.6408\n",
-      "Condition not met at step 2488. No data to save.\n",
-      "2488\n",
-      "model_vars.get('Gini', 0)[-1]=0.6166\n",
-      "Condition not met at step 2489. No data to save.\n",
-      "2489\n",
-      "model_vars.get('Gini', 0)[-1]=0.643\n",
-      "Condition not met at step 2490. No data to save.\n",
-      "2490\n",
-      "model_vars.get('Gini', 0)[-1]=0.617\n",
-      "Condition not met at step 2491. No data to save.\n",
-      "2491\n",
-      "model_vars.get('Gini', 0)[-1]=0.6194\n",
-      "Condition not met at step 2492. No data to save.\n",
-      "2492\n",
-      "model_vars.get('Gini', 0)[-1]=0.6024\n",
-      "Condition not met at step 2493. No data to save.\n",
-      "2493\n",
-      "model_vars.get('Gini', 0)[-1]=0.606\n",
-      "Condition not met at step 2494. No data to save.\n",
-      "2494\n",
-      "model_vars.get('Gini', 0)[-1]=0.6264000000000001\n",
-      "Condition not met at step 2495. No data to save.\n",
-      "2495\n",
-      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
-      "Condition not met at step 2496. No data to save.\n",
-      "2496\n",
-      "model_vars.get('Gini', 0)[-1]=0.6262000000000001\n",
-      "Condition not met at step 2497. No data to save.\n",
-      "2497\n",
-      "model_vars.get('Gini', 0)[-1]=0.6274\n",
-      "Condition not met at step 2498. No data to save.\n",
-      "2498\n",
-      "model_vars.get('Gini', 0)[-1]=0.6262000000000001\n",
-      "Condition not met at step 2499. No data to save.\n",
-      "2499\n",
-      "CALLED\n",
-      "self.model._steps=2500\n",
-      " TEST self.model._steps=2500\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "2400  0.6744\n",
-      "2401  0.6704\n",
-      "2402  0.6748\n",
-      "2403  0.6946\n",
-      "2404  0.6826\n",
-      "...      ...\n",
-      "2495  0.6406\n",
-      "2496  0.6262\n",
-      "2497  0.6274\n",
-      "2498  0.6262\n",
-      "2499  0.6760\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_025.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_025.parquet'\n",
-      "Saving model to test_path/model_data_025.parquet\n",
-      "Saving agent to test_path/agent_data_025.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6759999999999999\n",
-      "Condition not met at step 2500. No data to save.\n",
-      "2500\n",
-      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
-      "Condition not met at step 2501. No data to save.\n",
-      "2501\n",
-      "model_vars.get('Gini', 0)[-1]=0.657\n",
-      "Condition not met at step 2502. No data to save.\n",
-      "2502\n",
-      "model_vars.get('Gini', 0)[-1]=0.6582\n",
-      "Condition not met at step 2503. No data to save.\n",
-      "2503\n",
-      "model_vars.get('Gini', 0)[-1]=0.653\n",
-      "Condition not met at step 2504. No data to save.\n",
-      "2504\n",
-      "model_vars.get('Gini', 0)[-1]=0.6582\n",
-      "Condition not met at step 2505. No data to save.\n",
-      "2505\n",
-      "model_vars.get('Gini', 0)[-1]=0.6554\n",
-      "Condition not met at step 2506. No data to save.\n",
-      "2506\n",
-      "model_vars.get('Gini', 0)[-1]=0.6358\n",
-      "Condition not met at step 2507. No data to save.\n",
-      "2507\n",
-      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
-      "Condition not met at step 2508. No data to save.\n",
-      "2508\n",
-      "model_vars.get('Gini', 0)[-1]=0.6946\n",
-      "Condition not met at step 2509. No data to save.\n",
-      "2509\n",
-      "model_vars.get('Gini', 0)[-1]=0.6736\n",
-      "Condition not met at step 2510. No data to save.\n",
-      "2510\n",
-      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
-      "Condition not met at step 2511. No data to save.\n",
-      "2511\n",
-      "model_vars.get('Gini', 0)[-1]=0.6588\n",
-      "Condition not met at step 2512. No data to save.\n",
-      "2512\n",
-      "model_vars.get('Gini', 0)[-1]=0.6718\n",
-      "Condition not met at step 2513. No data to save.\n",
-      "2513\n",
-      "model_vars.get('Gini', 0)[-1]=0.6536\n",
-      "Condition not met at step 2514. No data to save.\n",
-      "2514\n",
-      "model_vars.get('Gini', 0)[-1]=0.6306\n",
-      "Condition not met at step 2515. No data to save.\n",
-      "2515\n",
-      "model_vars.get('Gini', 0)[-1]=0.6462\n",
-      "Condition not met at step 2516. No data to save.\n",
-      "2516\n",
-      "model_vars.get('Gini', 0)[-1]=0.6642\n",
-      "Condition not met at step 2517. No data to save.\n",
-      "2517\n",
-      "model_vars.get('Gini', 0)[-1]=0.6858\n",
-      "Condition not met at step 2518. No data to save.\n",
-      "2518\n",
-      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
-      "Condition not met at step 2519. No data to save.\n",
-      "2519\n",
-      "model_vars.get('Gini', 0)[-1]=0.6702\n",
-      "Condition not met at step 2520. No data to save.\n",
-      "2520\n",
-      "model_vars.get('Gini', 0)[-1]=0.6819999999999999\n",
-      "Condition not met at step 2521. No data to save.\n",
-      "2521\n",
-      "model_vars.get('Gini', 0)[-1]=0.671\n",
-      "Condition not met at step 2522. No data to save.\n",
-      "2522\n",
-      "model_vars.get('Gini', 0)[-1]=0.6542\n",
-      "Condition not met at step 2523. No data to save.\n",
-      "2523\n",
-      "model_vars.get('Gini', 0)[-1]=0.6692\n",
-      "Condition not met at step 2524. No data to save.\n",
-      "2524\n",
-      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
-      "Condition not met at step 2525. No data to save.\n",
-      "2525\n",
-      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
-      "Condition not met at step 2526. No data to save.\n",
-      "2526\n",
-      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
-      "Condition not met at step 2527. No data to save.\n",
-      "2527\n",
-      "model_vars.get('Gini', 0)[-1]=0.612\n",
-      "Condition not met at step 2528. No data to save.\n",
-      "2528\n",
-      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
-      "Condition not met at step 2529. No data to save.\n",
-      "2529\n",
-      "model_vars.get('Gini', 0)[-1]=0.673\n",
-      "Condition not met at step 2530. No data to save.\n",
-      "2530\n",
-      "model_vars.get('Gini', 0)[-1]=0.6474\n",
-      "Condition not met at step 2531. No data to save.\n",
-      "2531\n",
-      "model_vars.get('Gini', 0)[-1]=0.667\n",
-      "Condition not met at step 2532. No data to save.\n",
-      "2532\n",
-      "model_vars.get('Gini', 0)[-1]=0.6444000000000001\n",
-      "Condition not met at step 2533. No data to save.\n",
-      "2533\n",
-      "model_vars.get('Gini', 0)[-1]=0.6654\n",
-      "Condition not met at step 2534. No data to save.\n",
-      "2534\n",
-      "model_vars.get('Gini', 0)[-1]=0.6536\n",
-      "Condition not met at step 2535. No data to save.\n",
-      "2535\n",
-      "model_vars.get('Gini', 0)[-1]=0.6516\n",
-      "Condition not met at step 2536. No data to save.\n",
-      "2536\n",
-      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
-      "Condition not met at step 2537. No data to save.\n",
-      "2537\n",
-      "model_vars.get('Gini', 0)[-1]=0.6482\n",
-      "Condition not met at step 2538. No data to save.\n",
-      "2538\n",
-      "model_vars.get('Gini', 0)[-1]=0.6722\n",
-      "Condition not met at step 2539. No data to save.\n",
-      "2539\n",
-      "model_vars.get('Gini', 0)[-1]=0.6852\n",
-      "Condition not met at step 2540. No data to save.\n",
-      "2540\n",
-      "model_vars.get('Gini', 0)[-1]=0.6898\n",
-      "Condition not met at step 2541. No data to save.\n",
-      "2541\n",
-      "model_vars.get('Gini', 0)[-1]=0.7252000000000001\n",
-      "Condition met. Appended special results for step 2542 to test_path/special_results.parquet\n",
-      "2542\n",
-      "model_vars.get('Gini', 0)[-1]=0.7106\n",
-      "Condition met. Appended special results for step 2543 to test_path/special_results.parquet\n",
-      "2543\n",
-      "model_vars.get('Gini', 0)[-1]=0.6898\n",
-      "Condition not met at step 2544. No data to save.\n",
-      "2544\n",
-      "model_vars.get('Gini', 0)[-1]=0.687\n",
-      "Condition not met at step 2545. No data to save.\n",
-      "2545\n",
-      "model_vars.get('Gini', 0)[-1]=0.6872\n",
-      "Condition not met at step 2546. No data to save.\n",
-      "2546\n",
-      "model_vars.get('Gini', 0)[-1]=0.6656\n",
-      "Condition not met at step 2547. No data to save.\n",
-      "2547\n",
-      "model_vars.get('Gini', 0)[-1]=0.6462\n",
-      "Condition not met at step 2548. No data to save.\n",
-      "2548\n",
-      "model_vars.get('Gini', 0)[-1]=0.6588\n",
-      "Condition not met at step 2549. No data to save.\n",
-      "2549\n",
-      "model_vars.get('Gini', 0)[-1]=0.6437999999999999\n",
-      "Condition not met at step 2550. No data to save.\n",
-      "2550\n",
-      "model_vars.get('Gini', 0)[-1]=0.6378\n",
-      "Condition not met at step 2551. No data to save.\n",
-      "2551\n",
-      "model_vars.get('Gini', 0)[-1]=0.6674\n",
-      "Condition not met at step 2552. No data to save.\n",
-      "2552\n",
-      "model_vars.get('Gini', 0)[-1]=0.6868000000000001\n",
-      "Condition not met at step 2553. No data to save.\n",
-      "2553\n",
-      "model_vars.get('Gini', 0)[-1]=0.7188\n",
-      "Condition met. Appended special results for step 2554 to test_path/special_results.parquet\n",
-      "2554\n",
-      "model_vars.get('Gini', 0)[-1]=0.6972\n",
-      "Condition not met at step 2555. No data to save.\n",
-      "2555\n",
-      "model_vars.get('Gini', 0)[-1]=0.6814\n",
-      "Condition not met at step 2556. No data to save.\n",
-      "2556\n",
-      "model_vars.get('Gini', 0)[-1]=0.6981999999999999\n",
-      "Condition not met at step 2557. No data to save.\n",
-      "2557\n",
-      "model_vars.get('Gini', 0)[-1]=0.6834\n",
-      "Condition not met at step 2558. No data to save.\n",
-      "2558\n",
-      "model_vars.get('Gini', 0)[-1]=0.673\n",
-      "Condition not met at step 2559. No data to save.\n",
-      "2559\n",
-      "model_vars.get('Gini', 0)[-1]=0.647\n",
-      "Condition not met at step 2560. No data to save.\n",
-      "2560\n",
-      "model_vars.get('Gini', 0)[-1]=0.664\n",
-      "Condition not met at step 2561. No data to save.\n",
-      "2561\n",
-      "model_vars.get('Gini', 0)[-1]=0.69\n",
-      "Condition not met at step 2562. No data to save.\n",
-      "2562\n",
-      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
-      "Condition not met at step 2563. No data to save.\n",
-      "2563\n",
-      "model_vars.get('Gini', 0)[-1]=0.646\n",
-      "Condition not met at step 2564. No data to save.\n",
-      "2564\n",
-      "model_vars.get('Gini', 0)[-1]=0.6748000000000001\n",
-      "Condition not met at step 2565. No data to save.\n",
-      "2565\n",
-      "model_vars.get('Gini', 0)[-1]=0.6662\n",
-      "Condition not met at step 2566. No data to save.\n",
-      "2566\n",
-      "model_vars.get('Gini', 0)[-1]=0.6804\n",
-      "Condition not met at step 2567. No data to save.\n",
-      "2567\n",
-      "model_vars.get('Gini', 0)[-1]=0.6778\n",
-      "Condition not met at step 2568. No data to save.\n",
-      "2568\n",
-      "model_vars.get('Gini', 0)[-1]=0.6852\n",
-      "Condition not met at step 2569. No data to save.\n",
-      "2569\n",
-      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
-      "Condition not met at step 2570. No data to save.\n",
-      "2570\n",
-      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
-      "Condition not met at step 2571. No data to save.\n",
-      "2571\n",
-      "model_vars.get('Gini', 0)[-1]=0.6764\n",
-      "Condition not met at step 2572. No data to save.\n",
-      "2572\n",
-      "model_vars.get('Gini', 0)[-1]=0.669\n",
-      "Condition not met at step 2573. No data to save.\n",
-      "2573\n",
-      "model_vars.get('Gini', 0)[-1]=0.6634\n",
-      "Condition not met at step 2574. No data to save.\n",
-      "2574\n",
-      "model_vars.get('Gini', 0)[-1]=0.657\n",
-      "Condition not met at step 2575. No data to save.\n",
-      "2575\n",
-      "model_vars.get('Gini', 0)[-1]=0.6684\n",
-      "Condition not met at step 2576. No data to save.\n",
-      "2576\n",
-      "model_vars.get('Gini', 0)[-1]=0.6577999999999999\n",
-      "Condition not met at step 2577. No data to save.\n",
-      "2577\n",
-      "model_vars.get('Gini', 0)[-1]=0.634\n",
-      "Condition not met at step 2578. No data to save.\n",
-      "2578\n",
-      "model_vars.get('Gini', 0)[-1]=0.6414\n",
-      "Condition not met at step 2579. No data to save.\n",
-      "2579\n",
-      "model_vars.get('Gini', 0)[-1]=0.6392\n",
-      "Condition not met at step 2580. No data to save.\n",
-      "2580\n",
-      "model_vars.get('Gini', 0)[-1]=0.6268\n",
-      "Condition not met at step 2581. No data to save.\n",
-      "2581\n",
-      "model_vars.get('Gini', 0)[-1]=0.613\n",
-      "Condition not met at step 2582. No data to save.\n",
-      "2582\n",
-      "model_vars.get('Gini', 0)[-1]=0.637\n",
-      "Condition not met at step 2583. No data to save.\n",
-      "2583\n",
-      "model_vars.get('Gini', 0)[-1]=0.6462\n",
-      "Condition not met at step 2584. No data to save.\n",
-      "2584\n",
-      "model_vars.get('Gini', 0)[-1]=0.6658\n",
-      "Condition not met at step 2585. No data to save.\n",
-      "2585\n",
-      "model_vars.get('Gini', 0)[-1]=0.6782\n",
-      "Condition not met at step 2586. No data to save.\n",
-      "2586\n",
-      "model_vars.get('Gini', 0)[-1]=0.6804\n",
-      "Condition not met at step 2587. No data to save.\n",
-      "2587\n",
-      "model_vars.get('Gini', 0)[-1]=0.6704\n",
-      "Condition not met at step 2588. No data to save.\n",
-      "2588\n",
-      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
-      "Condition not met at step 2589. No data to save.\n",
-      "2589\n",
-      "model_vars.get('Gini', 0)[-1]=0.6704\n",
-      "Condition not met at step 2590. No data to save.\n",
-      "2590\n",
-      "model_vars.get('Gini', 0)[-1]=0.6506000000000001\n",
-      "Condition not met at step 2591. No data to save.\n",
-      "2591\n",
-      "model_vars.get('Gini', 0)[-1]=0.667\n",
-      "Condition not met at step 2592. No data to save.\n",
-      "2592\n",
-      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
-      "Condition not met at step 2593. No data to save.\n",
-      "2593\n",
-      "model_vars.get('Gini', 0)[-1]=0.6222000000000001\n",
-      "Condition not met at step 2594. No data to save.\n",
-      "2594\n",
-      "model_vars.get('Gini', 0)[-1]=0.6374\n",
-      "Condition not met at step 2595. No data to save.\n",
-      "2595\n",
-      "model_vars.get('Gini', 0)[-1]=0.6859999999999999\n",
-      "Condition not met at step 2596. No data to save.\n",
-      "2596\n",
-      "model_vars.get('Gini', 0)[-1]=0.7074\n",
-      "Condition met. Appended special results for step 2597 to test_path/special_results.parquet\n",
-      "2597\n",
-      "model_vars.get('Gini', 0)[-1]=0.6884\n",
-      "Condition not met at step 2598. No data to save.\n",
-      "2598\n",
-      "model_vars.get('Gini', 0)[-1]=0.6714\n",
-      "Condition not met at step 2599. No data to save.\n",
-      "2599\n",
-      "CALLED\n",
-      "self.model._steps=2600\n",
-      " TEST self.model._steps=2600\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "2500  0.6766\n",
-      "2501  0.6570\n",
-      "2502  0.6582\n",
-      "2503  0.6530\n",
-      "2504  0.6582\n",
-      "...      ...\n",
-      "2595  0.6860\n",
-      "2596  0.7074\n",
-      "2597  0.6884\n",
-      "2598  0.6714\n",
-      "2599  0.6552\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_026.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_026.parquet'\n",
-      "Saving model to test_path/model_data_026.parquet\n",
-      "Saving agent to test_path/agent_data_026.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6552\n",
-      "Condition not met at step 2600. No data to save.\n",
-      "2600\n",
-      "model_vars.get('Gini', 0)[-1]=0.6246\n",
-      "Condition not met at step 2601. No data to save.\n",
-      "2601\n",
-      "model_vars.get('Gini', 0)[-1]=0.6338\n",
-      "Condition not met at step 2602. No data to save.\n",
-      "2602\n",
-      "model_vars.get('Gini', 0)[-1]=0.605\n",
-      "Condition not met at step 2603. No data to save.\n",
-      "2603\n",
-      "model_vars.get('Gini', 0)[-1]=0.6116\n",
-      "Condition not met at step 2604. No data to save.\n",
-      "2604\n",
-      "model_vars.get('Gini', 0)[-1]=0.6238\n",
-      "Condition not met at step 2605. No data to save.\n",
-      "2605\n",
-      "model_vars.get('Gini', 0)[-1]=0.634\n",
-      "Condition not met at step 2606. No data to save.\n",
-      "2606\n",
-      "model_vars.get('Gini', 0)[-1]=0.629\n",
-      "Condition not met at step 2607. No data to save.\n",
-      "2607\n",
-      "model_vars.get('Gini', 0)[-1]=0.6058\n",
-      "Condition not met at step 2608. No data to save.\n",
-      "2608\n",
-      "model_vars.get('Gini', 0)[-1]=0.6258\n",
-      "Condition not met at step 2609. No data to save.\n",
-      "2609\n",
-      "model_vars.get('Gini', 0)[-1]=0.5802\n",
-      "Condition not met at step 2610. No data to save.\n",
-      "2610\n",
-      "model_vars.get('Gini', 0)[-1]=0.6072\n",
-      "Condition not met at step 2611. No data to save.\n",
-      "2611\n",
-      "model_vars.get('Gini', 0)[-1]=0.605\n",
-      "Condition not met at step 2612. No data to save.\n",
-      "2612\n",
-      "model_vars.get('Gini', 0)[-1]=0.6392\n",
-      "Condition not met at step 2613. No data to save.\n",
-      "2613\n",
-      "model_vars.get('Gini', 0)[-1]=0.6414\n",
-      "Condition not met at step 2614. No data to save.\n",
-      "2614\n",
-      "model_vars.get('Gini', 0)[-1]=0.6338\n",
-      "Condition not met at step 2615. No data to save.\n",
-      "2615\n",
-      "model_vars.get('Gini', 0)[-1]=0.5788\n",
-      "Condition not met at step 2616. No data to save.\n",
-      "2616\n",
-      "model_vars.get('Gini', 0)[-1]=0.623\n",
-      "Condition not met at step 2617. No data to save.\n",
-      "2617\n",
-      "model_vars.get('Gini', 0)[-1]=0.5900000000000001\n",
-      "Condition not met at step 2618. No data to save.\n",
-      "2618\n",
-      "model_vars.get('Gini', 0)[-1]=0.611\n",
-      "Condition not met at step 2619. No data to save.\n",
-      "2619\n",
-      "model_vars.get('Gini', 0)[-1]=0.6142000000000001\n",
-      "Condition not met at step 2620. No data to save.\n",
-      "2620\n",
-      "model_vars.get('Gini', 0)[-1]=0.6226\n",
-      "Condition not met at step 2621. No data to save.\n",
-      "2621\n",
-      "model_vars.get('Gini', 0)[-1]=0.6262000000000001\n",
-      "Condition not met at step 2622. No data to save.\n",
-      "2622\n",
-      "model_vars.get('Gini', 0)[-1]=0.623\n",
-      "Condition not met at step 2623. No data to save.\n",
-      "2623\n",
-      "model_vars.get('Gini', 0)[-1]=0.6582\n",
-      "Condition not met at step 2624. No data to save.\n",
-      "2624\n",
-      "model_vars.get('Gini', 0)[-1]=0.6736\n",
-      "Condition not met at step 2625. No data to save.\n",
-      "2625\n",
-      "model_vars.get('Gini', 0)[-1]=0.6924\n",
-      "Condition not met at step 2626. No data to save.\n",
-      "2626\n",
-      "model_vars.get('Gini', 0)[-1]=0.664\n",
-      "Condition not met at step 2627. No data to save.\n",
-      "2627\n",
-      "model_vars.get('Gini', 0)[-1]=0.6616\n",
-      "Condition not met at step 2628. No data to save.\n",
-      "2628\n",
-      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
-      "Condition not met at step 2629. No data to save.\n",
-      "2629\n",
-      "model_vars.get('Gini', 0)[-1]=0.6686000000000001\n",
-      "Condition not met at step 2630. No data to save.\n",
-      "2630\n",
-      "model_vars.get('Gini', 0)[-1]=0.6572\n",
-      "Condition not met at step 2631. No data to save.\n",
-      "2631\n",
-      "model_vars.get('Gini', 0)[-1]=0.6658\n",
-      "Condition not met at step 2632. No data to save.\n",
-      "2632\n",
-      "model_vars.get('Gini', 0)[-1]=0.6468\n",
-      "Condition not met at step 2633. No data to save.\n",
-      "2633\n",
-      "model_vars.get('Gini', 0)[-1]=0.6175999999999999\n",
-      "Condition not met at step 2634. No data to save.\n",
-      "2634\n",
-      "model_vars.get('Gini', 0)[-1]=0.631\n",
-      "Condition not met at step 2635. No data to save.\n",
-      "2635\n",
-      "model_vars.get('Gini', 0)[-1]=0.6576\n",
-      "Condition not met at step 2636. No data to save.\n",
-      "2636\n",
-      "model_vars.get('Gini', 0)[-1]=0.6456\n",
-      "Condition not met at step 2637. No data to save.\n",
-      "2637\n",
-      "model_vars.get('Gini', 0)[-1]=0.6456\n",
-      "Condition not met at step 2638. No data to save.\n",
-      "2638\n",
-      "model_vars.get('Gini', 0)[-1]=0.6508\n",
-      "Condition not met at step 2639. No data to save.\n",
-      "2639\n",
-      "model_vars.get('Gini', 0)[-1]=0.6422\n",
-      "Condition not met at step 2640. No data to save.\n",
-      "2640\n",
-      "model_vars.get('Gini', 0)[-1]=0.613\n",
-      "Condition not met at step 2641. No data to save.\n",
-      "2641\n",
-      "model_vars.get('Gini', 0)[-1]=0.6246\n",
-      "Condition not met at step 2642. No data to save.\n",
-      "2642\n",
-      "model_vars.get('Gini', 0)[-1]=0.6238\n",
-      "Condition not met at step 2643. No data to save.\n",
-      "2643\n",
-      "model_vars.get('Gini', 0)[-1]=0.6093999999999999\n",
-      "Condition not met at step 2644. No data to save.\n",
-      "2644\n",
-      "model_vars.get('Gini', 0)[-1]=0.6306\n",
-      "Condition not met at step 2645. No data to save.\n",
-      "2645\n",
-      "model_vars.get('Gini', 0)[-1]=0.673\n",
-      "Condition not met at step 2646. No data to save.\n",
-      "2646\n",
-      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
-      "Condition not met at step 2647. No data to save.\n",
-      "2647\n",
-      "model_vars.get('Gini', 0)[-1]=0.6398\n",
-      "Condition not met at step 2648. No data to save.\n",
-      "2648\n",
-      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
-      "Condition not met at step 2649. No data to save.\n",
-      "2649\n",
-      "model_vars.get('Gini', 0)[-1]=0.6614\n",
-      "Condition not met at step 2650. No data to save.\n",
-      "2650\n",
-      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
-      "Condition not met at step 2651. No data to save.\n",
-      "2651\n",
-      "model_vars.get('Gini', 0)[-1]=0.652\n",
-      "Condition not met at step 2652. No data to save.\n",
-      "2652\n",
-      "model_vars.get('Gini', 0)[-1]=0.634\n",
-      "Condition not met at step 2653. No data to save.\n",
-      "2653\n",
-      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
-      "Condition not met at step 2654. No data to save.\n",
-      "2654\n",
-      "model_vars.get('Gini', 0)[-1]=0.6335999999999999\n",
-      "Condition not met at step 2655. No data to save.\n",
-      "2655\n",
-      "model_vars.get('Gini', 0)[-1]=0.6824\n",
-      "Condition not met at step 2656. No data to save.\n",
-      "2656\n",
-      "model_vars.get('Gini', 0)[-1]=0.6786000000000001\n",
-      "Condition not met at step 2657. No data to save.\n",
-      "2657\n",
-      "model_vars.get('Gini', 0)[-1]=0.6824\n",
-      "Condition not met at step 2658. No data to save.\n",
-      "2658\n",
-      "model_vars.get('Gini', 0)[-1]=0.6694\n",
-      "Condition not met at step 2659. No data to save.\n",
-      "2659\n",
-      "model_vars.get('Gini', 0)[-1]=0.634\n",
-      "Condition not met at step 2660. No data to save.\n",
-      "2660\n",
-      "model_vars.get('Gini', 0)[-1]=0.6194\n",
-      "Condition not met at step 2661. No data to save.\n",
-      "2661\n",
-      "model_vars.get('Gini', 0)[-1]=0.63\n",
-      "Condition not met at step 2662. No data to save.\n",
-      "2662\n",
-      "model_vars.get('Gini', 0)[-1]=0.6188\n",
-      "Condition not met at step 2663. No data to save.\n",
-      "2663\n",
-      "model_vars.get('Gini', 0)[-1]=0.6315999999999999\n",
-      "Condition not met at step 2664. No data to save.\n",
-      "2664\n",
-      "model_vars.get('Gini', 0)[-1]=0.6182000000000001\n",
-      "Condition not met at step 2665. No data to save.\n",
-      "2665\n",
-      "model_vars.get('Gini', 0)[-1]=0.5933999999999999\n",
-      "Condition not met at step 2666. No data to save.\n",
-      "2666\n",
-      "model_vars.get('Gini', 0)[-1]=0.611\n",
-      "Condition not met at step 2667. No data to save.\n",
-      "2667\n",
-      "model_vars.get('Gini', 0)[-1]=0.593\n",
-      "Condition not met at step 2668. No data to save.\n",
-      "2668\n",
-      "model_vars.get('Gini', 0)[-1]=0.622\n",
-      "Condition not met at step 2669. No data to save.\n",
-      "2669\n",
-      "model_vars.get('Gini', 0)[-1]=0.624\n",
-      "Condition not met at step 2670. No data to save.\n",
-      "2670\n",
-      "model_vars.get('Gini', 0)[-1]=0.629\n",
-      "Condition not met at step 2671. No data to save.\n",
-      "2671\n",
-      "model_vars.get('Gini', 0)[-1]=0.6113999999999999\n",
-      "Condition not met at step 2672. No data to save.\n",
-      "2672\n",
-      "model_vars.get('Gini', 0)[-1]=0.6442\n",
-      "Condition not met at step 2673. No data to save.\n",
-      "2673\n",
-      "model_vars.get('Gini', 0)[-1]=0.6138\n",
-      "Condition not met at step 2674. No data to save.\n",
-      "2674\n",
-      "model_vars.get('Gini', 0)[-1]=0.6128\n",
-      "Condition not met at step 2675. No data to save.\n",
-      "2675\n",
-      "model_vars.get('Gini', 0)[-1]=0.6472\n",
-      "Condition not met at step 2676. No data to save.\n",
-      "2676\n",
-      "model_vars.get('Gini', 0)[-1]=0.6577999999999999\n",
-      "Condition not met at step 2677. No data to save.\n",
-      "2677\n",
-      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
-      "Condition not met at step 2678. No data to save.\n",
-      "2678\n",
-      "model_vars.get('Gini', 0)[-1]=0.6172\n",
-      "Condition not met at step 2679. No data to save.\n",
-      "2679\n",
-      "model_vars.get('Gini', 0)[-1]=0.6232\n",
-      "Condition not met at step 2680. No data to save.\n",
-      "2680\n",
-      "model_vars.get('Gini', 0)[-1]=0.634\n",
-      "Condition not met at step 2681. No data to save.\n",
-      "2681\n",
-      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
-      "Condition not met at step 2682. No data to save.\n",
-      "2682\n",
-      "model_vars.get('Gini', 0)[-1]=0.6482\n",
-      "Condition not met at step 2683. No data to save.\n",
-      "2683\n",
-      "model_vars.get('Gini', 0)[-1]=0.666\n",
-      "Condition not met at step 2684. No data to save.\n",
-      "2684\n",
-      "model_vars.get('Gini', 0)[-1]=0.6576\n",
-      "Condition not met at step 2685. No data to save.\n",
-      "2685\n",
-      "model_vars.get('Gini', 0)[-1]=0.6626000000000001\n",
-      "Condition not met at step 2686. No data to save.\n",
-      "2686\n",
-      "model_vars.get('Gini', 0)[-1]=0.651\n",
-      "Condition not met at step 2687. No data to save.\n",
-      "2687\n",
-      "model_vars.get('Gini', 0)[-1]=0.6426000000000001\n",
-      "Condition not met at step 2688. No data to save.\n",
-      "2688\n",
-      "model_vars.get('Gini', 0)[-1]=0.6272\n",
-      "Condition not met at step 2689. No data to save.\n",
-      "2689\n",
-      "model_vars.get('Gini', 0)[-1]=0.6318\n",
-      "Condition not met at step 2690. No data to save.\n",
-      "2690\n",
-      "model_vars.get('Gini', 0)[-1]=0.6318\n",
-      "Condition not met at step 2691. No data to save.\n",
-      "2691\n",
-      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
-      "Condition not met at step 2692. No data to save.\n",
-      "2692\n",
-      "model_vars.get('Gini', 0)[-1]=0.6298\n",
-      "Condition not met at step 2693. No data to save.\n",
-      "2693\n",
-      "model_vars.get('Gini', 0)[-1]=0.603\n",
-      "Condition not met at step 2694. No data to save.\n",
-      "2694\n",
-      "model_vars.get('Gini', 0)[-1]=0.5956\n",
-      "Condition not met at step 2695. No data to save.\n",
-      "2695\n",
-      "model_vars.get('Gini', 0)[-1]=0.5864\n",
-      "Condition not met at step 2696. No data to save.\n",
-      "2696\n",
-      "model_vars.get('Gini', 0)[-1]=0.6312\n",
-      "Condition not met at step 2697. No data to save.\n",
-      "2697\n",
-      "model_vars.get('Gini', 0)[-1]=0.6082000000000001\n",
-      "Condition not met at step 2698. No data to save.\n",
-      "2698\n",
-      "model_vars.get('Gini', 0)[-1]=0.5956\n",
-      "Condition not met at step 2699. No data to save.\n",
-      "2699\n",
-      "CALLED\n",
-      "self.model._steps=2700\n",
-      " TEST self.model._steps=2700\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "2600  0.6246\n",
-      "2601  0.6338\n",
-      "2602  0.6050\n",
-      "2603  0.6116\n",
-      "2604  0.6238\n",
-      "...      ...\n",
-      "2695  0.5864\n",
-      "2696  0.6312\n",
-      "2697  0.6082\n",
-      "2698  0.5956\n",
-      "2699  0.6114\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_027.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_027.parquet'\n",
-      "Saving model to test_path/model_data_027.parquet\n",
-      "Saving agent to test_path/agent_data_027.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6113999999999999\n",
-      "Condition not met at step 2700. No data to save.\n",
-      "2700\n",
-      "model_vars.get('Gini', 0)[-1]=0.6156\n",
-      "Condition not met at step 2701. No data to save.\n",
-      "2701\n",
-      "model_vars.get('Gini', 0)[-1]=0.608\n",
-      "Condition not met at step 2702. No data to save.\n",
-      "2702\n",
-      "model_vars.get('Gini', 0)[-1]=0.6272\n",
-      "Condition not met at step 2703. No data to save.\n",
-      "2703\n",
-      "model_vars.get('Gini', 0)[-1]=0.6298\n",
-      "Condition not met at step 2704. No data to save.\n",
-      "2704\n",
-      "model_vars.get('Gini', 0)[-1]=0.6332\n",
-      "Condition not met at step 2705. No data to save.\n",
-      "2705\n",
-      "model_vars.get('Gini', 0)[-1]=0.6108\n",
-      "Condition not met at step 2706. No data to save.\n",
-      "2706\n",
-      "model_vars.get('Gini', 0)[-1]=0.6298\n",
-      "Condition not met at step 2707. No data to save.\n",
-      "2707\n",
-      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
-      "Condition not met at step 2708. No data to save.\n",
-      "2708\n",
-      "model_vars.get('Gini', 0)[-1]=0.62\n",
-      "Condition not met at step 2709. No data to save.\n",
-      "2709\n",
-      "model_vars.get('Gini', 0)[-1]=0.6304000000000001\n",
-      "Condition not met at step 2710. No data to save.\n",
-      "2710\n",
-      "model_vars.get('Gini', 0)[-1]=0.6402\n",
-      "Condition not met at step 2711. No data to save.\n",
-      "2711\n",
-      "model_vars.get('Gini', 0)[-1]=0.6448\n",
-      "Condition not met at step 2712. No data to save.\n",
-      "2712\n",
-      "model_vars.get('Gini', 0)[-1]=0.6724\n",
-      "Condition not met at step 2713. No data to save.\n",
-      "2713\n",
-      "model_vars.get('Gini', 0)[-1]=0.6776\n",
-      "Condition not met at step 2714. No data to save.\n",
-      "2714\n",
-      "model_vars.get('Gini', 0)[-1]=0.652\n",
-      "Condition not met at step 2715. No data to save.\n",
-      "2715\n",
-      "model_vars.get('Gini', 0)[-1]=0.675\n",
-      "Condition not met at step 2716. No data to save.\n",
-      "2716\n",
-      "model_vars.get('Gini', 0)[-1]=0.6594\n",
-      "Condition not met at step 2717. No data to save.\n",
-      "2717\n",
-      "model_vars.get('Gini', 0)[-1]=0.6496\n",
-      "Condition not met at step 2718. No data to save.\n",
-      "2718\n",
-      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
-      "Condition not met at step 2719. No data to save.\n",
-      "2719\n",
-      "model_vars.get('Gini', 0)[-1]=0.6482\n",
-      "Condition not met at step 2720. No data to save.\n",
-      "2720\n",
-      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
-      "Condition not met at step 2721. No data to save.\n",
-      "2721\n",
-      "model_vars.get('Gini', 0)[-1]=0.647\n",
-      "Condition not met at step 2722. No data to save.\n",
-      "2722\n",
-      "model_vars.get('Gini', 0)[-1]=0.6568\n",
-      "Condition not met at step 2723. No data to save.\n",
-      "2723\n",
-      "model_vars.get('Gini', 0)[-1]=0.6624\n",
-      "Condition not met at step 2724. No data to save.\n",
-      "2724\n",
-      "model_vars.get('Gini', 0)[-1]=0.6496\n",
-      "Condition not met at step 2725. No data to save.\n",
-      "2725\n",
-      "model_vars.get('Gini', 0)[-1]=0.647\n",
-      "Condition not met at step 2726. No data to save.\n",
-      "2726\n",
-      "model_vars.get('Gini', 0)[-1]=0.6586000000000001\n",
-      "Condition not met at step 2727. No data to save.\n",
-      "2727\n",
-      "model_vars.get('Gini', 0)[-1]=0.6335999999999999\n",
-      "Condition not met at step 2728. No data to save.\n",
-      "2728\n",
-      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
-      "Condition not met at step 2729. No data to save.\n",
-      "2729\n",
-      "model_vars.get('Gini', 0)[-1]=0.6548\n",
-      "Condition not met at step 2730. No data to save.\n",
-      "2730\n",
-      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
-      "Condition not met at step 2731. No data to save.\n",
-      "2731\n",
-      "model_vars.get('Gini', 0)[-1]=0.6514\n",
-      "Condition not met at step 2732. No data to save.\n",
-      "2732\n",
-      "model_vars.get('Gini', 0)[-1]=0.6286\n",
-      "Condition not met at step 2733. No data to save.\n",
-      "2733\n",
-      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
-      "Condition not met at step 2734. No data to save.\n",
-      "2734\n",
-      "model_vars.get('Gini', 0)[-1]=0.6488\n",
-      "Condition not met at step 2735. No data to save.\n",
-      "2735\n",
-      "model_vars.get('Gini', 0)[-1]=0.6268\n",
-      "Condition not met at step 2736. No data to save.\n",
-      "2736\n",
-      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
-      "Condition not met at step 2737. No data to save.\n",
-      "2737\n",
-      "model_vars.get('Gini', 0)[-1]=0.6778\n",
-      "Condition not met at step 2738. No data to save.\n",
-      "2738\n",
-      "model_vars.get('Gini', 0)[-1]=0.6856\n",
-      "Condition not met at step 2739. No data to save.\n",
-      "2739\n",
-      "model_vars.get('Gini', 0)[-1]=0.694\n",
-      "Condition not met at step 2740. No data to save.\n",
-      "2740\n",
-      "model_vars.get('Gini', 0)[-1]=0.7\n",
-      "Condition not met at step 2741. No data to save.\n",
-      "2741\n",
-      "model_vars.get('Gini', 0)[-1]=0.675\n",
-      "Condition not met at step 2742. No data to save.\n",
-      "2742\n",
-      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
-      "Condition not met at step 2743. No data to save.\n",
-      "2743\n",
-      "model_vars.get('Gini', 0)[-1]=0.6941999999999999\n",
-      "Condition not met at step 2744. No data to save.\n",
-      "2744\n",
-      "model_vars.get('Gini', 0)[-1]=0.6994\n",
-      "Condition not met at step 2745. No data to save.\n",
-      "2745\n",
-      "model_vars.get('Gini', 0)[-1]=0.6812\n",
-      "Condition not met at step 2746. No data to save.\n",
-      "2746\n",
-      "model_vars.get('Gini', 0)[-1]=0.646\n",
-      "Condition not met at step 2747. No data to save.\n",
-      "2747\n",
-      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
-      "Condition not met at step 2748. No data to save.\n",
-      "2748\n",
-      "model_vars.get('Gini', 0)[-1]=0.6312\n",
-      "Condition not met at step 2749. No data to save.\n",
-      "2749\n",
-      "model_vars.get('Gini', 0)[-1]=0.6214\n",
-      "Condition not met at step 2750. No data to save.\n",
-      "2750\n",
-      "model_vars.get('Gini', 0)[-1]=0.66\n",
-      "Condition not met at step 2751. No data to save.\n",
-      "2751\n",
-      "model_vars.get('Gini', 0)[-1]=0.643\n",
-      "Condition not met at step 2752. No data to save.\n",
-      "2752\n",
-      "model_vars.get('Gini', 0)[-1]=0.6556\n",
-      "Condition not met at step 2753. No data to save.\n",
-      "2753\n",
-      "model_vars.get('Gini', 0)[-1]=0.667\n",
-      "Condition not met at step 2754. No data to save.\n",
-      "2754\n",
-      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
-      "Condition not met at step 2755. No data to save.\n",
-      "2755\n",
-      "model_vars.get('Gini', 0)[-1]=0.6352\n",
-      "Condition not met at step 2756. No data to save.\n",
-      "2756\n",
-      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
-      "Condition not met at step 2757. No data to save.\n",
-      "2757\n",
-      "model_vars.get('Gini', 0)[-1]=0.6462\n",
-      "Condition not met at step 2758. No data to save.\n",
-      "2758\n",
-      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
-      "Condition not met at step 2759. No data to save.\n",
-      "2759\n",
-      "model_vars.get('Gini', 0)[-1]=0.6352\n",
-      "Condition not met at step 2760. No data to save.\n",
-      "2760\n",
-      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
-      "Condition not met at step 2761. No data to save.\n",
-      "2761\n",
-      "model_vars.get('Gini', 0)[-1]=0.6226\n",
-      "Condition not met at step 2762. No data to save.\n",
-      "2762\n",
-      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
-      "Condition not met at step 2763. No data to save.\n",
-      "2763\n",
-      "model_vars.get('Gini', 0)[-1]=0.6232\n",
-      "Condition not met at step 2764. No data to save.\n",
-      "2764\n",
-      "model_vars.get('Gini', 0)[-1]=0.6154\n",
-      "Condition not met at step 2765. No data to save.\n",
-      "2765\n",
-      "model_vars.get('Gini', 0)[-1]=0.5968\n",
-      "Condition not met at step 2766. No data to save.\n",
-      "2766\n",
-      "model_vars.get('Gini', 0)[-1]=0.6044\n",
-      "Condition not met at step 2767. No data to save.\n",
-      "2767\n",
-      "model_vars.get('Gini', 0)[-1]=0.6128\n",
-      "Condition not met at step 2768. No data to save.\n",
-      "2768\n",
-      "model_vars.get('Gini', 0)[-1]=0.6275999999999999\n",
-      "Condition not met at step 2769. No data to save.\n",
-      "2769\n",
-      "model_vars.get('Gini', 0)[-1]=0.6282000000000001\n",
-      "Condition not met at step 2770. No data to save.\n",
-      "2770\n",
-      "model_vars.get('Gini', 0)[-1]=0.6352\n",
-      "Condition not met at step 2771. No data to save.\n",
-      "2771\n",
-      "model_vars.get('Gini', 0)[-1]=0.604\n",
-      "Condition not met at step 2772. No data to save.\n",
-      "2772\n",
-      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
-      "Condition not met at step 2773. No data to save.\n",
-      "2773\n",
-      "model_vars.get('Gini', 0)[-1]=0.643\n",
-      "Condition not met at step 2774. No data to save.\n",
-      "2774\n",
-      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
-      "Condition not met at step 2775. No data to save.\n",
-      "2775\n",
-      "model_vars.get('Gini', 0)[-1]=0.6308\n",
-      "Condition not met at step 2776. No data to save.\n",
-      "2776\n",
-      "model_vars.get('Gini', 0)[-1]=0.6306\n",
-      "Condition not met at step 2777. No data to save.\n",
-      "2777\n",
-      "model_vars.get('Gini', 0)[-1]=0.6252\n",
-      "Condition not met at step 2778. No data to save.\n",
-      "2778\n",
-      "model_vars.get('Gini', 0)[-1]=0.6306\n",
-      "Condition not met at step 2779. No data to save.\n",
-      "2779\n",
-      "model_vars.get('Gini', 0)[-1]=0.656\n",
-      "Condition not met at step 2780. No data to save.\n",
-      "2780\n",
-      "model_vars.get('Gini', 0)[-1]=0.66\n",
-      "Condition not met at step 2781. No data to save.\n",
-      "2781\n",
-      "model_vars.get('Gini', 0)[-1]=0.6838\n",
-      "Condition not met at step 2782. No data to save.\n",
-      "2782\n",
-      "model_vars.get('Gini', 0)[-1]=0.6772\n",
-      "Condition not met at step 2783. No data to save.\n",
-      "2783\n",
-      "model_vars.get('Gini', 0)[-1]=0.6932\n",
-      "Condition not met at step 2784. No data to save.\n",
-      "2784\n",
-      "model_vars.get('Gini', 0)[-1]=0.6782\n",
-      "Condition not met at step 2785. No data to save.\n",
-      "2785\n",
-      "model_vars.get('Gini', 0)[-1]=0.6678\n",
-      "Condition not met at step 2786. No data to save.\n",
-      "2786\n",
-      "model_vars.get('Gini', 0)[-1]=0.6464000000000001\n",
-      "Condition not met at step 2787. No data to save.\n",
-      "2787\n",
-      "model_vars.get('Gini', 0)[-1]=0.6442\n",
-      "Condition not met at step 2788. No data to save.\n",
-      "2788\n",
-      "model_vars.get('Gini', 0)[-1]=0.6352\n",
-      "Condition not met at step 2789. No data to save.\n",
-      "2789\n",
-      "model_vars.get('Gini', 0)[-1]=0.6384000000000001\n",
-      "Condition not met at step 2790. No data to save.\n",
-      "2790\n",
-      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
-      "Condition not met at step 2791. No data to save.\n",
-      "2791\n",
-      "model_vars.get('Gini', 0)[-1]=0.6312\n",
-      "Condition not met at step 2792. No data to save.\n",
-      "2792\n",
-      "model_vars.get('Gini', 0)[-1]=0.6215999999999999\n",
-      "Condition not met at step 2793. No data to save.\n",
-      "2793\n",
-      "model_vars.get('Gini', 0)[-1]=0.6228\n",
-      "Condition not met at step 2794. No data to save.\n",
-      "2794\n",
-      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
-      "Condition not met at step 2795. No data to save.\n",
-      "2795\n",
-      "model_vars.get('Gini', 0)[-1]=0.6548\n",
-      "Condition not met at step 2796. No data to save.\n",
-      "2796\n",
-      "model_vars.get('Gini', 0)[-1]=0.6616\n",
-      "Condition not met at step 2797. No data to save.\n",
-      "2797\n",
-      "model_vars.get('Gini', 0)[-1]=0.6782\n",
-      "Condition not met at step 2798. No data to save.\n",
-      "2798\n",
-      "model_vars.get('Gini', 0)[-1]=0.6828000000000001\n",
-      "Condition not met at step 2799. No data to save.\n",
-      "2799\n",
-      "CALLED\n",
-      "self.model._steps=2800\n",
-      " TEST self.model._steps=2800\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "2700  0.6156\n",
-      "2701  0.6080\n",
-      "2702  0.6272\n",
-      "2703  0.6298\n",
-      "2704  0.6332\n",
-      "...      ...\n",
-      "2795  0.6548\n",
-      "2796  0.6616\n",
-      "2797  0.6782\n",
-      "2798  0.6828\n",
-      "2799  0.6620\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_028.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_028.parquet'\n",
-      "Saving model to test_path/model_data_028.parquet\n",
-      "Saving agent to test_path/agent_data_028.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.662\n",
-      "Condition not met at step 2800. No data to save.\n",
-      "2800\n",
-      "model_vars.get('Gini', 0)[-1]=0.6664\n",
-      "Condition not met at step 2801. No data to save.\n",
-      "2801\n",
-      "model_vars.get('Gini', 0)[-1]=0.6636\n",
-      "Condition not met at step 2802. No data to save.\n",
-      "2802\n",
-      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
-      "Condition not met at step 2803. No data to save.\n",
-      "2803\n",
-      "model_vars.get('Gini', 0)[-1]=0.6434\n",
-      "Condition not met at step 2804. No data to save.\n",
-      "2804\n",
-      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
-      "Condition not met at step 2805. No data to save.\n",
-      "2805\n",
-      "model_vars.get('Gini', 0)[-1]=0.6436\n",
-      "Condition not met at step 2806. No data to save.\n",
-      "2806\n",
-      "model_vars.get('Gini', 0)[-1]=0.6742\n",
-      "Condition not met at step 2807. No data to save.\n",
-      "2807\n",
-      "model_vars.get('Gini', 0)[-1]=0.6322\n",
-      "Condition not met at step 2808. No data to save.\n",
-      "2808\n",
-      "model_vars.get('Gini', 0)[-1]=0.677\n",
-      "Condition not met at step 2809. No data to save.\n",
-      "2809\n",
-      "model_vars.get('Gini', 0)[-1]=0.6676\n",
-      "Condition not met at step 2810. No data to save.\n",
-      "2810\n",
-      "model_vars.get('Gini', 0)[-1]=0.6756\n",
-      "Condition not met at step 2811. No data to save.\n",
-      "2811\n",
-      "model_vars.get('Gini', 0)[-1]=0.6574\n",
-      "Condition not met at step 2812. No data to save.\n",
-      "2812\n",
-      "model_vars.get('Gini', 0)[-1]=0.6506000000000001\n",
-      "Condition not met at step 2813. No data to save.\n",
-      "2813\n",
-      "model_vars.get('Gini', 0)[-1]=0.6434\n",
-      "Condition not met at step 2814. No data to save.\n",
-      "2814\n",
-      "model_vars.get('Gini', 0)[-1]=0.6508\n",
-      "Condition not met at step 2815. No data to save.\n",
-      "2815\n",
-      "model_vars.get('Gini', 0)[-1]=0.637\n",
-      "Condition not met at step 2816. No data to save.\n",
-      "2816\n",
-      "model_vars.get('Gini', 0)[-1]=0.63\n",
-      "Condition not met at step 2817. No data to save.\n",
-      "2817\n",
-      "model_vars.get('Gini', 0)[-1]=0.6224000000000001\n",
-      "Condition not met at step 2818. No data to save.\n",
-      "2818\n",
-      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
-      "Condition not met at step 2819. No data to save.\n",
-      "2819\n",
-      "model_vars.get('Gini', 0)[-1]=0.6242000000000001\n",
-      "Condition not met at step 2820. No data to save.\n",
-      "2820\n",
-      "model_vars.get('Gini', 0)[-1]=0.6372\n",
-      "Condition not met at step 2821. No data to save.\n",
-      "2821\n",
-      "model_vars.get('Gini', 0)[-1]=0.6368\n",
-      "Condition not met at step 2822. No data to save.\n",
-      "2822\n",
-      "model_vars.get('Gini', 0)[-1]=0.6262000000000001\n",
-      "Condition not met at step 2823. No data to save.\n",
-      "2823\n",
-      "model_vars.get('Gini', 0)[-1]=0.6096\n",
-      "Condition not met at step 2824. No data to save.\n",
-      "2824\n",
-      "model_vars.get('Gini', 0)[-1]=0.6166\n",
-      "Condition not met at step 2825. No data to save.\n",
-      "2825\n",
-      "model_vars.get('Gini', 0)[-1]=0.6588\n",
-      "Condition not met at step 2826. No data to save.\n",
-      "2826\n",
-      "model_vars.get('Gini', 0)[-1]=0.6704\n",
-      "Condition not met at step 2827. No data to save.\n",
-      "2827\n",
-      "model_vars.get('Gini', 0)[-1]=0.675\n",
-      "Condition not met at step 2828. No data to save.\n",
-      "2828\n",
-      "model_vars.get('Gini', 0)[-1]=0.6794\n",
-      "Condition not met at step 2829. No data to save.\n",
-      "2829\n",
-      "model_vars.get('Gini', 0)[-1]=0.6756\n",
-      "Condition not met at step 2830. No data to save.\n",
-      "2830\n",
-      "model_vars.get('Gini', 0)[-1]=0.6724\n",
-      "Condition not met at step 2831. No data to save.\n",
-      "2831\n",
-      "model_vars.get('Gini', 0)[-1]=0.6719999999999999\n",
-      "Condition not met at step 2832. No data to save.\n",
-      "2832\n",
-      "model_vars.get('Gini', 0)[-1]=0.663\n",
-      "Condition not met at step 2833. No data to save.\n",
-      "2833\n",
-      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
-      "Condition not met at step 2834. No data to save.\n",
-      "2834\n",
-      "model_vars.get('Gini', 0)[-1]=0.7024\n",
-      "Condition met. Appended special results for step 2835 to test_path/special_results.parquet\n",
-      "2835\n",
-      "model_vars.get('Gini', 0)[-1]=0.7196\n",
-      "Condition met. Appended special results for step 2836 to test_path/special_results.parquet\n",
-      "2836\n",
-      "model_vars.get('Gini', 0)[-1]=0.7096\n",
-      "Condition met. Appended special results for step 2837 to test_path/special_results.parquet\n",
-      "2837\n",
-      "model_vars.get('Gini', 0)[-1]=0.6798\n",
-      "Condition not met at step 2838. No data to save.\n",
-      "2838\n",
-      "model_vars.get('Gini', 0)[-1]=0.6764\n",
-      "Condition not met at step 2839. No data to save.\n",
-      "2839\n",
-      "model_vars.get('Gini', 0)[-1]=0.6746000000000001\n",
-      "Condition not met at step 2840. No data to save.\n",
-      "2840\n",
-      "model_vars.get('Gini', 0)[-1]=0.6699999999999999\n",
-      "Condition not met at step 2841. No data to save.\n",
-      "2841\n",
-      "model_vars.get('Gini', 0)[-1]=0.6836\n",
-      "Condition not met at step 2842. No data to save.\n",
-      "2842\n",
-      "model_vars.get('Gini', 0)[-1]=0.634\n",
-      "Condition not met at step 2843. No data to save.\n",
-      "2843\n",
-      "model_vars.get('Gini', 0)[-1]=0.6748000000000001\n",
-      "Condition not met at step 2844. No data to save.\n",
-      "2844\n",
-      "model_vars.get('Gini', 0)[-1]=0.6754\n",
-      "Condition not met at step 2845. No data to save.\n",
-      "2845\n",
-      "model_vars.get('Gini', 0)[-1]=0.7021999999999999\n",
-      "Condition met. Appended special results for step 2846 to test_path/special_results.parquet\n",
-      "2846\n",
-      "model_vars.get('Gini', 0)[-1]=0.6986\n",
-      "Condition not met at step 2847. No data to save.\n",
-      "2847\n",
-      "model_vars.get('Gini', 0)[-1]=0.7334\n",
-      "Condition met. Appended special results for step 2848 to test_path/special_results.parquet\n",
-      "2848\n",
-      "model_vars.get('Gini', 0)[-1]=0.7252000000000001\n",
-      "Condition met. Appended special results for step 2849 to test_path/special_results.parquet\n",
-      "2849\n",
-      "model_vars.get('Gini', 0)[-1]=0.7298\n",
-      "Condition met. Appended special results for step 2850 to test_path/special_results.parquet\n",
-      "2850\n",
-      "model_vars.get('Gini', 0)[-1]=0.7306\n",
-      "Condition met. Appended special results for step 2851 to test_path/special_results.parquet\n",
-      "2851\n",
-      "model_vars.get('Gini', 0)[-1]=0.7090000000000001\n",
-      "Condition met. Appended special results for step 2852 to test_path/special_results.parquet\n",
-      "2852\n",
-      "model_vars.get('Gini', 0)[-1]=0.6904\n",
-      "Condition not met at step 2853. No data to save.\n",
-      "2853\n",
-      "model_vars.get('Gini', 0)[-1]=0.7041999999999999\n",
-      "Condition met. Appended special results for step 2854 to test_path/special_results.parquet\n",
-      "2854\n",
-      "model_vars.get('Gini', 0)[-1]=0.696\n",
-      "Condition not met at step 2855. No data to save.\n",
-      "2855\n",
-      "model_vars.get('Gini', 0)[-1]=0.6798\n",
-      "Condition not met at step 2856. No data to save.\n",
-      "2856\n",
-      "model_vars.get('Gini', 0)[-1]=0.63\n",
-      "Condition not met at step 2857. No data to save.\n",
-      "2857\n",
-      "model_vars.get('Gini', 0)[-1]=0.6338\n",
-      "Condition not met at step 2858. No data to save.\n",
-      "2858\n",
-      "model_vars.get('Gini', 0)[-1]=0.6195999999999999\n",
-      "Condition not met at step 2859. No data to save.\n",
-      "2859\n",
-      "model_vars.get('Gini', 0)[-1]=0.6158\n",
-      "Condition not met at step 2860. No data to save.\n",
-      "2860\n",
-      "model_vars.get('Gini', 0)[-1]=0.5882000000000001\n",
-      "Condition not met at step 2861. No data to save.\n",
-      "2861\n",
-      "model_vars.get('Gini', 0)[-1]=0.6242000000000001\n",
-      "Condition not met at step 2862. No data to save.\n",
-      "2862\n",
-      "model_vars.get('Gini', 0)[-1]=0.6508\n",
-      "Condition not met at step 2863. No data to save.\n",
-      "2863\n",
-      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
-      "Condition not met at step 2864. No data to save.\n",
-      "2864\n",
-      "model_vars.get('Gini', 0)[-1]=0.6534\n",
-      "Condition not met at step 2865. No data to save.\n",
-      "2865\n",
-      "model_vars.get('Gini', 0)[-1]=0.6454\n",
-      "Condition not met at step 2866. No data to save.\n",
-      "2866\n",
-      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
-      "Condition not met at step 2867. No data to save.\n",
-      "2867\n",
-      "model_vars.get('Gini', 0)[-1]=0.6348\n",
-      "Condition not met at step 2868. No data to save.\n",
-      "2868\n",
-      "model_vars.get('Gini', 0)[-1]=0.6368\n",
-      "Condition not met at step 2869. No data to save.\n",
-      "2869\n",
-      "model_vars.get('Gini', 0)[-1]=0.6508\n",
-      "Condition not met at step 2870. No data to save.\n",
-      "2870\n",
-      "model_vars.get('Gini', 0)[-1]=0.6254\n",
-      "Condition not met at step 2871. No data to save.\n",
-      "2871\n",
-      "model_vars.get('Gini', 0)[-1]=0.637\n",
-      "Condition not met at step 2872. No data to save.\n",
-      "2872\n",
-      "model_vars.get('Gini', 0)[-1]=0.6636\n",
-      "Condition not met at step 2873. No data to save.\n",
-      "2873\n",
-      "model_vars.get('Gini', 0)[-1]=0.6328\n",
-      "Condition not met at step 2874. No data to save.\n",
-      "2874\n",
-      "model_vars.get('Gini', 0)[-1]=0.6696\n",
-      "Condition not met at step 2875. No data to save.\n",
-      "2875\n",
-      "model_vars.get('Gini', 0)[-1]=0.6692\n",
-      "Condition not met at step 2876. No data to save.\n",
-      "2876\n",
-      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
-      "Condition not met at step 2877. No data to save.\n",
-      "2877\n",
-      "model_vars.get('Gini', 0)[-1]=0.6294\n",
-      "Condition not met at step 2878. No data to save.\n",
-      "2878\n",
-      "model_vars.get('Gini', 0)[-1]=0.6416\n",
-      "Condition not met at step 2879. No data to save.\n",
-      "2879\n",
-      "model_vars.get('Gini', 0)[-1]=0.6366\n",
-      "Condition not met at step 2880. No data to save.\n",
-      "2880\n",
-      "model_vars.get('Gini', 0)[-1]=0.6428\n",
-      "Condition not met at step 2881. No data to save.\n",
-      "2881\n",
-      "model_vars.get('Gini', 0)[-1]=0.6584\n",
-      "Condition not met at step 2882. No data to save.\n",
-      "2882\n",
-      "model_vars.get('Gini', 0)[-1]=0.654\n",
-      "Condition not met at step 2883. No data to save.\n",
-      "2883\n",
-      "model_vars.get('Gini', 0)[-1]=0.632\n",
-      "Condition not met at step 2884. No data to save.\n",
-      "2884\n",
-      "model_vars.get('Gini', 0)[-1]=0.6376\n",
-      "Condition not met at step 2885. No data to save.\n",
-      "2885\n",
-      "model_vars.get('Gini', 0)[-1]=0.6658\n",
-      "Condition not met at step 2886. No data to save.\n",
-      "2886\n",
-      "model_vars.get('Gini', 0)[-1]=0.6784\n",
-      "Condition not met at step 2887. No data to save.\n",
-      "2887\n",
-      "model_vars.get('Gini', 0)[-1]=0.6784\n",
-      "Condition not met at step 2888. No data to save.\n",
-      "2888\n",
-      "model_vars.get('Gini', 0)[-1]=0.6634\n",
-      "Condition not met at step 2889. No data to save.\n",
-      "2889\n",
-      "model_vars.get('Gini', 0)[-1]=0.6806\n",
-      "Condition not met at step 2890. No data to save.\n",
-      "2890\n",
-      "model_vars.get('Gini', 0)[-1]=0.7061999999999999\n",
-      "Condition met. Appended special results for step 2891 to test_path/special_results.parquet\n",
-      "2891\n",
-      "model_vars.get('Gini', 0)[-1]=0.7072\n",
-      "Condition met. Appended special results for step 2892 to test_path/special_results.parquet\n",
-      "2892\n",
-      "model_vars.get('Gini', 0)[-1]=0.7030000000000001\n",
-      "Condition met. Appended special results for step 2893 to test_path/special_results.parquet\n",
-      "2893\n",
-      "model_vars.get('Gini', 0)[-1]=0.694\n",
-      "Condition not met at step 2894. No data to save.\n",
-      "2894\n",
-      "model_vars.get('Gini', 0)[-1]=0.6992\n",
-      "Condition not met at step 2895. No data to save.\n",
-      "2895\n",
-      "model_vars.get('Gini', 0)[-1]=0.702\n",
-      "Condition met. Appended special results for step 2896 to test_path/special_results.parquet\n",
-      "2896\n",
-      "model_vars.get('Gini', 0)[-1]=0.706\n",
-      "Condition met. Appended special results for step 2897 to test_path/special_results.parquet\n",
-      "2897\n",
-      "model_vars.get('Gini', 0)[-1]=0.7158\n",
-      "Condition met. Appended special results for step 2898 to test_path/special_results.parquet\n",
-      "2898\n",
-      "model_vars.get('Gini', 0)[-1]=0.7270000000000001\n",
-      "Condition met. Appended special results for step 2899 to test_path/special_results.parquet\n",
-      "2899\n",
-      "CALLED\n",
-      "self.model._steps=2900\n",
-      " TEST self.model._steps=2900\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "2800  0.6664\n",
-      "2801  0.6636\n",
-      "2802  0.6406\n",
-      "2803  0.6434\n",
-      "2804  0.6484\n",
-      "...      ...\n",
-      "2895  0.7020\n",
-      "2896  0.7060\n",
-      "2897  0.7158\n",
-      "2898  0.7270\n",
-      "2899  0.7020\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_029.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_029.parquet'\n",
-      "Saving model to test_path/model_data_029.parquet\n",
-      "Saving agent to test_path/agent_data_029.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.702\n",
-      "Condition met. Appended special results for step 2900 to test_path/special_results.parquet\n",
-      "2900\n",
-      "model_vars.get('Gini', 0)[-1]=0.675\n",
-      "Condition not met at step 2901. No data to save.\n",
-      "2901\n",
-      "model_vars.get('Gini', 0)[-1]=0.6644\n",
-      "Condition not met at step 2902. No data to save.\n",
-      "2902\n",
-      "model_vars.get('Gini', 0)[-1]=0.6452\n",
-      "Condition not met at step 2903. No data to save.\n",
-      "2903\n",
-      "model_vars.get('Gini', 0)[-1]=0.647\n",
-      "Condition not met at step 2904. No data to save.\n",
-      "2904\n",
-      "model_vars.get('Gini', 0)[-1]=0.6428\n",
-      "Condition not met at step 2905. No data to save.\n",
-      "2905\n",
-      "model_vars.get('Gini', 0)[-1]=0.658\n",
-      "Condition not met at step 2906. No data to save.\n",
-      "2906\n",
-      "model_vars.get('Gini', 0)[-1]=0.6476\n",
-      "Condition not met at step 2907. No data to save.\n",
-      "2907\n",
-      "model_vars.get('Gini', 0)[-1]=0.6472\n",
-      "Condition not met at step 2908. No data to save.\n",
-      "2908\n",
-      "model_vars.get('Gini', 0)[-1]=0.6446000000000001\n",
-      "Condition not met at step 2909. No data to save.\n",
-      "2909\n",
-      "model_vars.get('Gini', 0)[-1]=0.647\n",
-      "Condition not met at step 2910. No data to save.\n",
-      "2910\n",
-      "model_vars.get('Gini', 0)[-1]=0.6275999999999999\n",
-      "Condition not met at step 2911. No data to save.\n",
-      "2911\n",
-      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
-      "Condition not met at step 2912. No data to save.\n",
-      "2912\n",
-      "model_vars.get('Gini', 0)[-1]=0.647\n",
-      "Condition not met at step 2913. No data to save.\n",
-      "2913\n",
-      "model_vars.get('Gini', 0)[-1]=0.6452\n",
-      "Condition not met at step 2914. No data to save.\n",
-      "2914\n",
-      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
-      "Condition not met at step 2915. No data to save.\n",
-      "2915\n",
-      "model_vars.get('Gini', 0)[-1]=0.6716\n",
-      "Condition not met at step 2916. No data to save.\n",
-      "2916\n",
-      "model_vars.get('Gini', 0)[-1]=0.6416\n",
-      "Condition not met at step 2917. No data to save.\n",
-      "2917\n",
-      "model_vars.get('Gini', 0)[-1]=0.6414\n",
-      "Condition not met at step 2918. No data to save.\n",
-      "2918\n",
-      "model_vars.get('Gini', 0)[-1]=0.6128\n",
-      "Condition not met at step 2919. No data to save.\n",
-      "2919\n",
-      "model_vars.get('Gini', 0)[-1]=0.6376\n",
-      "Condition not met at step 2920. No data to save.\n",
-      "2920\n",
-      "model_vars.get('Gini', 0)[-1]=0.647\n",
-      "Condition not met at step 2921. No data to save.\n",
-      "2921\n",
-      "model_vars.get('Gini', 0)[-1]=0.6646000000000001\n",
-      "Condition not met at step 2922. No data to save.\n",
-      "2922\n",
-      "model_vars.get('Gini', 0)[-1]=0.659\n",
-      "Condition not met at step 2923. No data to save.\n",
-      "2923\n",
-      "model_vars.get('Gini', 0)[-1]=0.6644\n",
-      "Condition not met at step 2924. No data to save.\n",
-      "2924\n",
-      "model_vars.get('Gini', 0)[-1]=0.6537999999999999\n",
-      "Condition not met at step 2925. No data to save.\n",
-      "2925\n",
-      "model_vars.get('Gini', 0)[-1]=0.6496\n",
-      "Condition not met at step 2926. No data to save.\n",
-      "2926\n",
-      "model_vars.get('Gini', 0)[-1]=0.6496\n",
-      "Condition not met at step 2927. No data to save.\n",
-      "2927\n",
-      "model_vars.get('Gini', 0)[-1]=0.6516\n",
-      "Condition not met at step 2928. No data to save.\n",
-      "2928\n",
-      "model_vars.get('Gini', 0)[-1]=0.6568\n",
-      "Condition not met at step 2929. No data to save.\n",
-      "2929\n",
-      "model_vars.get('Gini', 0)[-1]=0.6492\n",
-      "Condition not met at step 2930. No data to save.\n",
-      "2930\n",
-      "model_vars.get('Gini', 0)[-1]=0.652\n",
-      "Condition not met at step 2931. No data to save.\n",
-      "2931\n",
-      "model_vars.get('Gini', 0)[-1]=0.665\n",
-      "Condition not met at step 2932. No data to save.\n",
-      "2932\n",
-      "model_vars.get('Gini', 0)[-1]=0.6506000000000001\n",
-      "Condition not met at step 2933. No data to save.\n",
-      "2933\n",
-      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
-      "Condition not met at step 2934. No data to save.\n",
-      "2934\n",
-      "model_vars.get('Gini', 0)[-1]=0.6494\n",
-      "Condition not met at step 2935. No data to save.\n",
-      "2935\n",
-      "model_vars.get('Gini', 0)[-1]=0.6416\n",
-      "Condition not met at step 2936. No data to save.\n",
-      "2936\n",
-      "model_vars.get('Gini', 0)[-1]=0.6008\n",
-      "Condition not met at step 2937. No data to save.\n",
-      "2937\n",
-      "model_vars.get('Gini', 0)[-1]=0.6248\n",
-      "Condition not met at step 2938. No data to save.\n",
-      "2938\n",
-      "model_vars.get('Gini', 0)[-1]=0.63\n",
-      "Condition not met at step 2939. No data to save.\n",
-      "2939\n",
-      "model_vars.get('Gini', 0)[-1]=0.6376\n",
-      "Condition not met at step 2940. No data to save.\n",
-      "2940\n",
-      "model_vars.get('Gini', 0)[-1]=0.6416\n",
-      "Condition not met at step 2941. No data to save.\n",
-      "2941\n",
-      "model_vars.get('Gini', 0)[-1]=0.6416\n",
-      "Condition not met at step 2942. No data to save.\n",
-      "2942\n",
-      "model_vars.get('Gini', 0)[-1]=0.6696\n",
-      "Condition not met at step 2943. No data to save.\n",
-      "2943\n",
-      "model_vars.get('Gini', 0)[-1]=0.6652\n",
-      "Condition not met at step 2944. No data to save.\n",
-      "2944\n",
-      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
-      "Condition not met at step 2945. No data to save.\n",
-      "2945\n",
-      "model_vars.get('Gini', 0)[-1]=0.6566000000000001\n",
-      "Condition not met at step 2946. No data to save.\n",
-      "2946\n",
-      "model_vars.get('Gini', 0)[-1]=0.648\n",
-      "Condition not met at step 2947. No data to save.\n",
-      "2947\n",
-      "model_vars.get('Gini', 0)[-1]=0.63\n",
-      "Condition not met at step 2948. No data to save.\n",
-      "2948\n",
-      "model_vars.get('Gini', 0)[-1]=0.6274\n",
-      "Condition not met at step 2949. No data to save.\n",
-      "2949\n",
-      "model_vars.get('Gini', 0)[-1]=0.6302\n",
-      "Condition not met at step 2950. No data to save.\n",
-      "2950\n",
-      "model_vars.get('Gini', 0)[-1]=0.6174\n",
-      "Condition not met at step 2951. No data to save.\n",
-      "2951\n",
-      "model_vars.get('Gini', 0)[-1]=0.585\n",
-      "Condition not met at step 2952. No data to save.\n",
-      "2952\n",
-      "model_vars.get('Gini', 0)[-1]=0.5836\n",
-      "Condition not met at step 2953. No data to save.\n",
-      "2953\n",
-      "model_vars.get('Gini', 0)[-1]=0.6000000000000001\n",
-      "Condition not met at step 2954. No data to save.\n",
-      "2954\n",
-      "model_vars.get('Gini', 0)[-1]=0.5758000000000001\n",
-      "Condition not met at step 2955. No data to save.\n",
-      "2955\n",
-      "model_vars.get('Gini', 0)[-1]=0.5896\n",
-      "Condition not met at step 2956. No data to save.\n",
-      "2956\n",
-      "model_vars.get('Gini', 0)[-1]=0.5978\n",
-      "Condition not met at step 2957. No data to save.\n",
-      "2957\n",
-      "model_vars.get('Gini', 0)[-1]=0.5906\n",
-      "Condition not met at step 2958. No data to save.\n",
-      "2958\n",
-      "model_vars.get('Gini', 0)[-1]=0.5892\n",
-      "Condition not met at step 2959. No data to save.\n",
-      "2959\n",
-      "model_vars.get('Gini', 0)[-1]=0.615\n",
-      "Condition not met at step 2960. No data to save.\n",
-      "2960\n",
-      "model_vars.get('Gini', 0)[-1]=0.6274\n",
-      "Condition not met at step 2961. No data to save.\n",
-      "2961\n",
-      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
-      "Condition not met at step 2962. No data to save.\n",
-      "2962\n",
-      "model_vars.get('Gini', 0)[-1]=0.6454\n",
-      "Condition not met at step 2963. No data to save.\n",
-      "2963\n",
-      "model_vars.get('Gini', 0)[-1]=0.6334\n",
-      "Condition not met at step 2964. No data to save.\n",
-      "2964\n",
-      "model_vars.get('Gini', 0)[-1]=0.6308\n",
-      "Condition not met at step 2965. No data to save.\n",
-      "2965\n",
-      "model_vars.get('Gini', 0)[-1]=0.6304000000000001\n",
-      "Condition not met at step 2966. No data to save.\n",
-      "2966\n",
-      "model_vars.get('Gini', 0)[-1]=0.616\n",
-      "Condition not met at step 2967. No data to save.\n",
-      "2967\n",
-      "model_vars.get('Gini', 0)[-1]=0.6324000000000001\n",
-      "Condition not met at step 2968. No data to save.\n",
-      "2968\n",
-      "model_vars.get('Gini', 0)[-1]=0.6295999999999999\n",
-      "Condition not met at step 2969. No data to save.\n",
-      "2969\n",
-      "model_vars.get('Gini', 0)[-1]=0.6617999999999999\n",
-      "Condition not met at step 2970. No data to save.\n",
-      "2970\n",
-      "model_vars.get('Gini', 0)[-1]=0.6828000000000001\n",
-      "Condition not met at step 2971. No data to save.\n",
-      "2971\n",
-      "model_vars.get('Gini', 0)[-1]=0.658\n",
-      "Condition not met at step 2972. No data to save.\n",
-      "2972\n",
-      "model_vars.get('Gini', 0)[-1]=0.6422\n",
-      "Condition not met at step 2973. No data to save.\n",
-      "2973\n",
-      "model_vars.get('Gini', 0)[-1]=0.6664\n",
-      "Condition not met at step 2974. No data to save.\n",
-      "2974\n",
-      "model_vars.get('Gini', 0)[-1]=0.6582\n",
-      "Condition not met at step 2975. No data to save.\n",
-      "2975\n",
-      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
-      "Condition not met at step 2976. No data to save.\n",
-      "2976\n",
-      "model_vars.get('Gini', 0)[-1]=0.6892\n",
-      "Condition not met at step 2977. No data to save.\n",
-      "2977\n",
-      "model_vars.get('Gini', 0)[-1]=0.6754\n",
-      "Condition not met at step 2978. No data to save.\n",
-      "2978\n",
-      "model_vars.get('Gini', 0)[-1]=0.6839999999999999\n",
-      "Condition not met at step 2979. No data to save.\n",
-      "2979\n",
-      "model_vars.get('Gini', 0)[-1]=0.6932\n",
-      "Condition not met at step 2980. No data to save.\n",
-      "2980\n",
-      "model_vars.get('Gini', 0)[-1]=0.692\n",
-      "Condition not met at step 2981. No data to save.\n",
-      "2981\n",
-      "model_vars.get('Gini', 0)[-1]=0.6912\n",
-      "Condition not met at step 2982. No data to save.\n",
-      "2982\n",
-      "model_vars.get('Gini', 0)[-1]=0.6772\n",
-      "Condition not met at step 2983. No data to save.\n",
-      "2983\n",
-      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
-      "Condition not met at step 2984. No data to save.\n",
-      "2984\n",
-      "model_vars.get('Gini', 0)[-1]=0.6816\n",
-      "Condition not met at step 2985. No data to save.\n",
-      "2985\n",
-      "model_vars.get('Gini', 0)[-1]=0.702\n",
-      "Condition met. Appended special results for step 2986 to test_path/special_results.parquet\n",
-      "2986\n",
-      "model_vars.get('Gini', 0)[-1]=0.6946\n",
-      "Condition not met at step 2987. No data to save.\n",
-      "2987\n",
-      "model_vars.get('Gini', 0)[-1]=0.6950000000000001\n",
-      "Condition not met at step 2988. No data to save.\n",
-      "2988\n",
-      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
-      "Condition not met at step 2989. No data to save.\n",
-      "2989\n",
-      "model_vars.get('Gini', 0)[-1]=0.6644\n",
-      "Condition not met at step 2990. No data to save.\n",
-      "2990\n",
-      "model_vars.get('Gini', 0)[-1]=0.6694\n",
-      "Condition not met at step 2991. No data to save.\n",
-      "2991\n",
-      "model_vars.get('Gini', 0)[-1]=0.6934\n",
-      "Condition not met at step 2992. No data to save.\n",
-      "2992\n",
-      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
-      "Condition not met at step 2993. No data to save.\n",
-      "2993\n",
-      "model_vars.get('Gini', 0)[-1]=0.6930000000000001\n",
-      "Condition not met at step 2994. No data to save.\n",
-      "2994\n",
-      "model_vars.get('Gini', 0)[-1]=0.6892\n",
-      "Condition not met at step 2995. No data to save.\n",
-      "2995\n",
-      "model_vars.get('Gini', 0)[-1]=0.6776\n",
-      "Condition not met at step 2996. No data to save.\n",
-      "2996\n",
-      "model_vars.get('Gini', 0)[-1]=0.6758\n",
-      "Condition not met at step 2997. No data to save.\n",
-      "2997\n",
-      "model_vars.get('Gini', 0)[-1]=0.6648000000000001\n",
-      "Condition not met at step 2998. No data to save.\n",
-      "2998\n",
-      "model_vars.get('Gini', 0)[-1]=0.6634\n",
-      "Condition not met at step 2999. No data to save.\n",
-      "2999\n",
-      "CALLED\n",
-      "self.model._steps=3000\n",
-      " TEST self.model._steps=3000\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "2900  0.6750\n",
-      "2901  0.6644\n",
-      "2902  0.6452\n",
-      "2903  0.6470\n",
-      "2904  0.6428\n",
-      "...      ...\n",
-      "2995  0.6776\n",
-      "2996  0.6758\n",
-      "2997  0.6648\n",
-      "2998  0.6634\n",
-      "2999  0.6394\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_030.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_030.parquet'\n",
-      "Saving model to test_path/model_data_030.parquet\n",
-      "Saving agent to test_path/agent_data_030.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6394\n",
-      "Condition not met at step 3000. No data to save.\n",
-      "3000\n",
-      "model_vars.get('Gini', 0)[-1]=0.6186\n",
-      "Condition not met at step 3001. No data to save.\n",
-      "3001\n",
-      "model_vars.get('Gini', 0)[-1]=0.6182000000000001\n",
-      "Condition not met at step 3002. No data to save.\n",
-      "3002\n",
-      "model_vars.get('Gini', 0)[-1]=0.6096\n",
-      "Condition not met at step 3003. No data to save.\n",
-      "3003\n",
-      "model_vars.get('Gini', 0)[-1]=0.628\n",
-      "Condition not met at step 3004. No data to save.\n",
-      "3004\n",
-      "model_vars.get('Gini', 0)[-1]=0.6492\n",
-      "Condition not met at step 3005. No data to save.\n",
-      "3005\n",
-      "model_vars.get('Gini', 0)[-1]=0.6532\n",
-      "Condition not met at step 3006. No data to save.\n",
-      "3006\n",
-      "model_vars.get('Gini', 0)[-1]=0.6194\n",
-      "Condition not met at step 3007. No data to save.\n",
-      "3007\n",
-      "model_vars.get('Gini', 0)[-1]=0.637\n",
-      "Condition not met at step 3008. No data to save.\n",
-      "3008\n",
-      "model_vars.get('Gini', 0)[-1]=0.6592\n",
-      "Condition not met at step 3009. No data to save.\n",
-      "3009\n",
-      "model_vars.get('Gini', 0)[-1]=0.6782\n",
-      "Condition not met at step 3010. No data to save.\n",
-      "3010\n",
-      "model_vars.get('Gini', 0)[-1]=0.6970000000000001\n",
-      "Condition not met at step 3011. No data to save.\n",
-      "3011\n",
-      "model_vars.get('Gini', 0)[-1]=0.6848000000000001\n",
-      "Condition not met at step 3012. No data to save.\n",
-      "3012\n",
-      "model_vars.get('Gini', 0)[-1]=0.6482\n",
-      "Condition not met at step 3013. No data to save.\n",
-      "3013\n",
-      "model_vars.get('Gini', 0)[-1]=0.6358\n",
-      "Condition not met at step 3014. No data to save.\n",
-      "3014\n",
-      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
-      "Condition not met at step 3015. No data to save.\n",
-      "3015\n",
-      "model_vars.get('Gini', 0)[-1]=0.6482\n",
-      "Condition not met at step 3016. No data to save.\n",
-      "3016\n",
-      "model_vars.get('Gini', 0)[-1]=0.6376\n",
-      "Condition not met at step 3017. No data to save.\n",
-      "3017\n",
-      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
-      "Condition not met at step 3018. No data to save.\n",
-      "3018\n",
-      "model_vars.get('Gini', 0)[-1]=0.6758\n",
-      "Condition not met at step 3019. No data to save.\n",
-      "3019\n",
-      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
-      "Condition not met at step 3020. No data to save.\n",
-      "3020\n",
-      "model_vars.get('Gini', 0)[-1]=0.6679999999999999\n",
-      "Condition not met at step 3021. No data to save.\n",
-      "3021\n",
-      "model_vars.get('Gini', 0)[-1]=0.6762\n",
-      "Condition not met at step 3022. No data to save.\n",
-      "3022\n",
-      "model_vars.get('Gini', 0)[-1]=0.6348\n",
-      "Condition not met at step 3023. No data to save.\n",
-      "3023\n",
-      "model_vars.get('Gini', 0)[-1]=0.6378\n",
-      "Condition not met at step 3024. No data to save.\n",
-      "3024\n",
-      "model_vars.get('Gini', 0)[-1]=0.613\n",
-      "Condition not met at step 3025. No data to save.\n",
-      "3025\n",
-      "model_vars.get('Gini', 0)[-1]=0.6118\n",
-      "Condition not met at step 3026. No data to save.\n",
-      "3026\n",
-      "model_vars.get('Gini', 0)[-1]=0.6166\n",
-      "Condition not met at step 3027. No data to save.\n",
-      "3027\n",
-      "model_vars.get('Gini', 0)[-1]=0.6496\n",
-      "Condition not met at step 3028. No data to save.\n",
-      "3028\n",
-      "model_vars.get('Gini', 0)[-1]=0.6306\n",
-      "Condition not met at step 3029. No data to save.\n",
-      "3029\n",
-      "model_vars.get('Gini', 0)[-1]=0.6306\n",
-      "Condition not met at step 3030. No data to save.\n",
-      "3030\n",
-      "model_vars.get('Gini', 0)[-1]=0.6298\n",
-      "Condition not met at step 3031. No data to save.\n",
-      "3031\n",
-      "model_vars.get('Gini', 0)[-1]=0.6342\n",
-      "Condition not met at step 3032. No data to save.\n",
-      "3032\n",
-      "model_vars.get('Gini', 0)[-1]=0.6335999999999999\n",
-      "Condition not met at step 3033. No data to save.\n",
-      "3033\n",
-      "model_vars.get('Gini', 0)[-1]=0.647\n",
-      "Condition not met at step 3034. No data to save.\n",
-      "3034\n",
-      "model_vars.get('Gini', 0)[-1]=0.624\n",
-      "Condition not met at step 3035. No data to save.\n",
-      "3035\n",
-      "model_vars.get('Gini', 0)[-1]=0.6468\n",
-      "Condition not met at step 3036. No data to save.\n",
-      "3036\n",
-      "model_vars.get('Gini', 0)[-1]=0.6328\n",
-      "Condition not met at step 3037. No data to save.\n",
-      "3037\n",
-      "model_vars.get('Gini', 0)[-1]=0.6402\n",
-      "Condition not met at step 3038. No data to save.\n",
-      "3038\n",
-      "model_vars.get('Gini', 0)[-1]=0.6182000000000001\n",
-      "Condition not met at step 3039. No data to save.\n",
-      "3039\n",
-      "model_vars.get('Gini', 0)[-1]=0.6038\n",
-      "Condition not met at step 3040. No data to save.\n",
-      "3040\n",
-      "model_vars.get('Gini', 0)[-1]=0.642\n",
-      "Condition not met at step 3041. No data to save.\n",
-      "3041\n",
-      "model_vars.get('Gini', 0)[-1]=0.644\n",
-      "Condition not met at step 3042. No data to save.\n",
-      "3042\n",
-      "model_vars.get('Gini', 0)[-1]=0.65\n",
-      "Condition not met at step 3043. No data to save.\n",
-      "3043\n",
-      "model_vars.get('Gini', 0)[-1]=0.6626000000000001\n",
-      "Condition not met at step 3044. No data to save.\n",
-      "3044\n",
-      "model_vars.get('Gini', 0)[-1]=0.6772\n",
-      "Condition not met at step 3045. No data to save.\n",
-      "3045\n",
-      "model_vars.get('Gini', 0)[-1]=0.6436\n",
-      "Condition not met at step 3046. No data to save.\n",
-      "3046\n",
-      "model_vars.get('Gini', 0)[-1]=0.664\n",
-      "Condition not met at step 3047. No data to save.\n",
-      "3047\n",
-      "model_vars.get('Gini', 0)[-1]=0.6852\n",
-      "Condition not met at step 3048. No data to save.\n",
-      "3048\n",
-      "model_vars.get('Gini', 0)[-1]=0.6894\n",
-      "Condition not met at step 3049. No data to save.\n",
-      "3049\n",
-      "model_vars.get('Gini', 0)[-1]=0.661\n",
-      "Condition not met at step 3050. No data to save.\n",
-      "3050\n",
-      "model_vars.get('Gini', 0)[-1]=0.6396\n",
-      "Condition not met at step 3051. No data to save.\n",
-      "3051\n",
-      "model_vars.get('Gini', 0)[-1]=0.651\n",
-      "Condition not met at step 3052. No data to save.\n",
-      "3052\n",
-      "model_vars.get('Gini', 0)[-1]=0.6582\n",
-      "Condition not met at step 3053. No data to save.\n",
-      "3053\n",
-      "model_vars.get('Gini', 0)[-1]=0.6679999999999999\n",
-      "Condition not met at step 3054. No data to save.\n",
-      "3054\n",
-      "model_vars.get('Gini', 0)[-1]=0.6952\n",
-      "Condition not met at step 3055. No data to save.\n",
-      "3055\n",
-      "model_vars.get('Gini', 0)[-1]=0.7092\n",
-      "Condition met. Appended special results for step 3056 to test_path/special_results.parquet\n",
-      "3056\n",
-      "model_vars.get('Gini', 0)[-1]=0.7092\n",
-      "Condition met. Appended special results for step 3057 to test_path/special_results.parquet\n",
-      "3057\n",
-      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
-      "Condition not met at step 3058. No data to save.\n",
-      "3058\n",
-      "model_vars.get('Gini', 0)[-1]=0.6838\n",
-      "Condition not met at step 3059. No data to save.\n",
-      "3059\n",
-      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
-      "Condition not met at step 3060. No data to save.\n",
-      "3060\n",
-      "model_vars.get('Gini', 0)[-1]=0.69\n",
-      "Condition not met at step 3061. No data to save.\n",
-      "3061\n",
-      "model_vars.get('Gini', 0)[-1]=0.6632\n",
-      "Condition not met at step 3062. No data to save.\n",
-      "3062\n",
-      "model_vars.get('Gini', 0)[-1]=0.6804\n",
-      "Condition not met at step 3063. No data to save.\n",
-      "3063\n",
-      "model_vars.get('Gini', 0)[-1]=0.6686000000000001\n",
-      "Condition not met at step 3064. No data to save.\n",
-      "3064\n",
-      "model_vars.get('Gini', 0)[-1]=0.6648000000000001\n",
-      "Condition not met at step 3065. No data to save.\n",
-      "3065\n",
-      "model_vars.get('Gini', 0)[-1]=0.667\n",
-      "Condition not met at step 3066. No data to save.\n",
-      "3066\n",
-      "model_vars.get('Gini', 0)[-1]=0.7046\n",
-      "Condition met. Appended special results for step 3067 to test_path/special_results.parquet\n",
-      "3067\n",
-      "model_vars.get('Gini', 0)[-1]=0.6890000000000001\n",
-      "Condition not met at step 3068. No data to save.\n",
-      "3068\n",
-      "model_vars.get('Gini', 0)[-1]=0.6718\n",
-      "Condition not met at step 3069. No data to save.\n",
-      "3069\n",
-      "model_vars.get('Gini', 0)[-1]=0.6774\n",
-      "Condition not met at step 3070. No data to save.\n",
-      "3070\n",
-      "model_vars.get('Gini', 0)[-1]=0.6646000000000001\n",
-      "Condition not met at step 3071. No data to save.\n",
-      "3071\n",
-      "model_vars.get('Gini', 0)[-1]=0.6612\n",
-      "Condition not met at step 3072. No data to save.\n",
-      "3072\n",
-      "model_vars.get('Gini', 0)[-1]=0.6646000000000001\n",
-      "Condition not met at step 3073. No data to save.\n",
-      "3073\n",
-      "model_vars.get('Gini', 0)[-1]=0.6694\n",
-      "Condition not met at step 3074. No data to save.\n",
-      "3074\n",
-      "model_vars.get('Gini', 0)[-1]=0.6792\n",
-      "Condition not met at step 3075. No data to save.\n",
-      "3075\n",
-      "model_vars.get('Gini', 0)[-1]=0.7008000000000001\n",
-      "Condition met. Appended special results for step 3076 to test_path/special_results.parquet\n",
-      "3076\n",
-      "model_vars.get('Gini', 0)[-1]=0.7064\n",
-      "Condition met. Appended special results for step 3077 to test_path/special_results.parquet\n",
-      "3077\n",
-      "model_vars.get('Gini', 0)[-1]=0.6836\n",
-      "Condition not met at step 3078. No data to save.\n",
-      "3078\n",
-      "model_vars.get('Gini', 0)[-1]=0.7021999999999999\n",
-      "Condition met. Appended special results for step 3079 to test_path/special_results.parquet\n",
-      "3079\n",
-      "model_vars.get('Gini', 0)[-1]=0.6930000000000001\n",
-      "Condition not met at step 3080. No data to save.\n",
-      "3080\n",
-      "model_vars.get('Gini', 0)[-1]=0.669\n",
-      "Condition not met at step 3081. No data to save.\n",
-      "3081\n",
-      "model_vars.get('Gini', 0)[-1]=0.7218\n",
-      "Condition met. Appended special results for step 3082 to test_path/special_results.parquet\n",
-      "3082\n",
-      "model_vars.get('Gini', 0)[-1]=0.7006\n",
-      "Condition met. Appended special results for step 3083 to test_path/special_results.parquet\n",
-      "3083\n",
-      "model_vars.get('Gini', 0)[-1]=0.6984\n",
-      "Condition not met at step 3084. No data to save.\n",
-      "3084\n",
-      "model_vars.get('Gini', 0)[-1]=0.6822\n",
-      "Condition not met at step 3085. No data to save.\n",
-      "3085\n",
-      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
-      "Condition not met at step 3086. No data to save.\n",
-      "3086\n",
-      "model_vars.get('Gini', 0)[-1]=0.6584\n",
-      "Condition not met at step 3087. No data to save.\n",
-      "3087\n",
-      "model_vars.get('Gini', 0)[-1]=0.652\n",
-      "Condition not met at step 3088. No data to save.\n",
-      "3088\n",
-      "model_vars.get('Gini', 0)[-1]=0.652\n",
-      "Condition not met at step 3089. No data to save.\n",
-      "3089\n",
-      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
-      "Condition not met at step 3090. No data to save.\n",
-      "3090\n",
-      "model_vars.get('Gini', 0)[-1]=0.6295999999999999\n",
-      "Condition not met at step 3091. No data to save.\n",
-      "3091\n",
-      "model_vars.get('Gini', 0)[-1]=0.656\n",
-      "Condition not met at step 3092. No data to save.\n",
-      "3092\n",
-      "model_vars.get('Gini', 0)[-1]=0.6408\n",
-      "Condition not met at step 3093. No data to save.\n",
-      "3093\n",
-      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
-      "Condition not met at step 3094. No data to save.\n",
-      "3094\n",
-      "model_vars.get('Gini', 0)[-1]=0.6142000000000001\n",
-      "Condition not met at step 3095. No data to save.\n",
-      "3095\n",
-      "model_vars.get('Gini', 0)[-1]=0.6224000000000001\n",
-      "Condition not met at step 3096. No data to save.\n",
-      "3096\n",
-      "model_vars.get('Gini', 0)[-1]=0.5896\n",
-      "Condition not met at step 3097. No data to save.\n",
-      "3097\n",
-      "model_vars.get('Gini', 0)[-1]=0.6138\n",
-      "Condition not met at step 3098. No data to save.\n",
-      "3098\n",
-      "model_vars.get('Gini', 0)[-1]=0.6548\n",
-      "Condition not met at step 3099. No data to save.\n",
-      "3099\n",
-      "CALLED\n",
-      "self.model._steps=3100\n",
-      " TEST self.model._steps=3100\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "3000  0.6186\n",
-      "3001  0.6182\n",
-      "3002  0.6096\n",
-      "3003  0.6280\n",
-      "3004  0.6492\n",
-      "...      ...\n",
-      "3095  0.6224\n",
-      "3096  0.5896\n",
-      "3097  0.6138\n",
-      "3098  0.6548\n",
-      "3099  0.6348\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_031.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_031.parquet'\n",
-      "Saving model to test_path/model_data_031.parquet\n",
-      "Saving agent to test_path/agent_data_031.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6348\n",
-      "Condition not met at step 3100. No data to save.\n",
-      "3100\n",
-      "model_vars.get('Gini', 0)[-1]=0.6175999999999999\n",
-      "Condition not met at step 3101. No data to save.\n",
-      "3101\n",
-      "model_vars.get('Gini', 0)[-1]=0.6246\n",
-      "Condition not met at step 3102. No data to save.\n",
-      "3102\n",
-      "model_vars.get('Gini', 0)[-1]=0.6066\n",
-      "Condition not met at step 3103. No data to save.\n",
-      "3103\n",
-      "model_vars.get('Gini', 0)[-1]=0.6264000000000001\n",
-      "Condition not met at step 3104. No data to save.\n",
-      "3104\n",
-      "model_vars.get('Gini', 0)[-1]=0.6182000000000001\n",
-      "Condition not met at step 3105. No data to save.\n",
-      "3105\n",
-      "model_vars.get('Gini', 0)[-1]=0.5920000000000001\n",
-      "Condition not met at step 3106. No data to save.\n",
-      "3106\n",
-      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
-      "Condition not met at step 3107. No data to save.\n",
-      "3107\n",
-      "model_vars.get('Gini', 0)[-1]=0.6664\n",
-      "Condition not met at step 3108. No data to save.\n",
-      "3108\n",
-      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
-      "Condition not met at step 3109. No data to save.\n",
-      "3109\n",
-      "model_vars.get('Gini', 0)[-1]=0.5996\n",
-      "Condition not met at step 3110. No data to save.\n",
-      "3110\n",
-      "model_vars.get('Gini', 0)[-1]=0.6442\n",
-      "Condition not met at step 3111. No data to save.\n",
-      "3111\n",
-      "model_vars.get('Gini', 0)[-1]=0.6248\n",
-      "Condition not met at step 3112. No data to save.\n",
-      "3112\n",
-      "model_vars.get('Gini', 0)[-1]=0.6497999999999999\n",
-      "Condition not met at step 3113. No data to save.\n",
-      "3113\n",
-      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
-      "Condition not met at step 3114. No data to save.\n",
-      "3114\n",
-      "model_vars.get('Gini', 0)[-1]=0.641\n",
-      "Condition not met at step 3115. No data to save.\n",
-      "3115\n",
-      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
-      "Condition not met at step 3116. No data to save.\n",
-      "3116\n",
-      "model_vars.get('Gini', 0)[-1]=0.6542\n",
-      "Condition not met at step 3117. No data to save.\n",
-      "3117\n",
-      "model_vars.get('Gini', 0)[-1]=0.6664\n",
-      "Condition not met at step 3118. No data to save.\n",
-      "3118\n",
-      "model_vars.get('Gini', 0)[-1]=0.6886\n",
-      "Condition not met at step 3119. No data to save.\n",
-      "3119\n",
-      "model_vars.get('Gini', 0)[-1]=0.6914\n",
-      "Condition not met at step 3120. No data to save.\n",
-      "3120\n",
-      "model_vars.get('Gini', 0)[-1]=0.702\n",
-      "Condition met. Appended special results for step 3121 to test_path/special_results.parquet\n",
-      "3121\n",
-      "model_vars.get('Gini', 0)[-1]=0.7016\n",
-      "Condition met. Appended special results for step 3122 to test_path/special_results.parquet\n",
-      "3122\n",
-      "model_vars.get('Gini', 0)[-1]=0.6844\n",
-      "Condition not met at step 3123. No data to save.\n",
-      "3123\n",
-      "model_vars.get('Gini', 0)[-1]=0.6948000000000001\n",
-      "Condition not met at step 3124. No data to save.\n",
-      "3124\n",
-      "model_vars.get('Gini', 0)[-1]=0.6832\n",
-      "Condition not met at step 3125. No data to save.\n",
-      "3125\n",
-      "model_vars.get('Gini', 0)[-1]=0.6832\n",
-      "Condition not met at step 3126. No data to save.\n",
-      "3126\n",
-      "model_vars.get('Gini', 0)[-1]=0.694\n",
-      "Condition not met at step 3127. No data to save.\n",
-      "3127\n",
-      "model_vars.get('Gini', 0)[-1]=0.6832\n",
-      "Condition not met at step 3128. No data to save.\n",
-      "3128\n",
-      "model_vars.get('Gini', 0)[-1]=0.6586000000000001\n",
-      "Condition not met at step 3129. No data to save.\n",
-      "3129\n",
-      "model_vars.get('Gini', 0)[-1]=0.6776\n",
-      "Condition not met at step 3130. No data to save.\n",
-      "3130\n",
-      "model_vars.get('Gini', 0)[-1]=0.6448\n",
-      "Condition not met at step 3131. No data to save.\n",
-      "3131\n",
-      "model_vars.get('Gini', 0)[-1]=0.6816\n",
-      "Condition not met at step 3132. No data to save.\n",
-      "3132\n",
-      "model_vars.get('Gini', 0)[-1]=0.6528\n",
-      "Condition not met at step 3133. No data to save.\n",
-      "3133\n",
-      "model_vars.get('Gini', 0)[-1]=0.6774\n",
-      "Condition not met at step 3134. No data to save.\n",
-      "3134\n",
-      "model_vars.get('Gini', 0)[-1]=0.6612\n",
-      "Condition not met at step 3135. No data to save.\n",
-      "3135\n",
-      "model_vars.get('Gini', 0)[-1]=0.6866\n",
-      "Condition not met at step 3136. No data to save.\n",
-      "3136\n",
-      "model_vars.get('Gini', 0)[-1]=0.6778\n",
-      "Condition not met at step 3137. No data to save.\n",
-      "3137\n",
-      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
-      "Condition not met at step 3138. No data to save.\n",
-      "3138\n",
-      "model_vars.get('Gini', 0)[-1]=0.6824\n",
-      "Condition not met at step 3139. No data to save.\n",
-      "3139\n",
-      "model_vars.get('Gini', 0)[-1]=0.6892\n",
-      "Condition not met at step 3140. No data to save.\n",
-      "3140\n",
-      "model_vars.get('Gini', 0)[-1]=0.6894\n",
-      "Condition not met at step 3141. No data to save.\n",
-      "3141\n",
-      "model_vars.get('Gini', 0)[-1]=0.6838\n",
-      "Condition not met at step 3142. No data to save.\n",
-      "3142\n",
-      "model_vars.get('Gini', 0)[-1]=0.6752\n",
-      "Condition not met at step 3143. No data to save.\n",
-      "3143\n",
-      "model_vars.get('Gini', 0)[-1]=0.6719999999999999\n",
-      "Condition not met at step 3144. No data to save.\n",
-      "3144\n",
-      "model_vars.get('Gini', 0)[-1]=0.6878\n",
-      "Condition not met at step 3145. No data to save.\n",
-      "3145\n",
-      "model_vars.get('Gini', 0)[-1]=0.7064\n",
-      "Condition met. Appended special results for step 3146 to test_path/special_results.parquet\n",
-      "3146\n",
-      "model_vars.get('Gini', 0)[-1]=0.7048\n",
-      "Condition met. Appended special results for step 3147 to test_path/special_results.parquet\n",
-      "3147\n",
-      "model_vars.get('Gini', 0)[-1]=0.6966\n",
-      "Condition not met at step 3148. No data to save.\n",
-      "3148\n",
-      "model_vars.get('Gini', 0)[-1]=0.7048\n",
-      "Condition met. Appended special results for step 3149 to test_path/special_results.parquet\n",
-      "3149\n",
-      "model_vars.get('Gini', 0)[-1]=0.7010000000000001\n",
-      "Condition met. Appended special results for step 3150 to test_path/special_results.parquet\n",
-      "3150\n",
-      "model_vars.get('Gini', 0)[-1]=0.7004\n",
-      "Condition met. Appended special results for step 3151 to test_path/special_results.parquet\n",
-      "3151\n",
-      "model_vars.get('Gini', 0)[-1]=0.6839999999999999\n",
-      "Condition not met at step 3152. No data to save.\n",
-      "3152\n",
-      "model_vars.get('Gini', 0)[-1]=0.6616\n",
-      "Condition not met at step 3153. No data to save.\n",
-      "3153\n",
-      "model_vars.get('Gini', 0)[-1]=0.654\n",
-      "Condition not met at step 3154. No data to save.\n",
-      "3154\n",
-      "model_vars.get('Gini', 0)[-1]=0.6222000000000001\n",
-      "Condition not met at step 3155. No data to save.\n",
-      "3155\n",
-      "model_vars.get('Gini', 0)[-1]=0.6352\n",
-      "Condition not met at step 3156. No data to save.\n",
-      "3156\n",
-      "model_vars.get('Gini', 0)[-1]=0.6504000000000001\n",
-      "Condition not met at step 3157. No data to save.\n",
-      "3157\n",
-      "model_vars.get('Gini', 0)[-1]=0.662\n",
-      "Condition not met at step 3158. No data to save.\n",
-      "3158\n",
-      "model_vars.get('Gini', 0)[-1]=0.6784\n",
-      "Condition not met at step 3159. No data to save.\n",
-      "3159\n",
-      "model_vars.get('Gini', 0)[-1]=0.6654\n",
-      "Condition not met at step 3160. No data to save.\n",
-      "3160\n",
-      "model_vars.get('Gini', 0)[-1]=0.6676\n",
-      "Condition not met at step 3161. No data to save.\n",
-      "3161\n",
-      "model_vars.get('Gini', 0)[-1]=0.6414\n",
-      "Condition not met at step 3162. No data to save.\n",
-      "3162\n",
-      "model_vars.get('Gini', 0)[-1]=0.6502\n",
-      "Condition not met at step 3163. No data to save.\n",
-      "3163\n",
-      "model_vars.get('Gini', 0)[-1]=0.6614\n",
-      "Condition not met at step 3164. No data to save.\n",
-      "3164\n",
-      "model_vars.get('Gini', 0)[-1]=0.6604\n",
-      "Condition not met at step 3165. No data to save.\n",
-      "3165\n",
-      "model_vars.get('Gini', 0)[-1]=0.6412\n",
-      "Condition not met at step 3166. No data to save.\n",
-      "3166\n",
-      "model_vars.get('Gini', 0)[-1]=0.66\n",
-      "Condition not met at step 3167. No data to save.\n",
-      "3167\n",
-      "model_vars.get('Gini', 0)[-1]=0.6818\n",
-      "Condition not met at step 3168. No data to save.\n",
-      "3168\n",
-      "model_vars.get('Gini', 0)[-1]=0.6844\n",
-      "Condition not met at step 3169. No data to save.\n",
-      "3169\n",
-      "model_vars.get('Gini', 0)[-1]=0.6914\n",
-      "Condition not met at step 3170. No data to save.\n",
-      "3170\n",
-      "model_vars.get('Gini', 0)[-1]=0.6734\n",
-      "Condition not met at step 3171. No data to save.\n",
-      "3171\n",
-      "model_vars.get('Gini', 0)[-1]=0.6776\n",
-      "Condition not met at step 3172. No data to save.\n",
-      "3172\n",
-      "model_vars.get('Gini', 0)[-1]=0.6839999999999999\n",
-      "Condition not met at step 3173. No data to save.\n",
-      "3173\n",
-      "model_vars.get('Gini', 0)[-1]=0.6716\n",
-      "Condition not met at step 3174. No data to save.\n",
-      "3174\n",
-      "model_vars.get('Gini', 0)[-1]=0.6812\n",
-      "Condition not met at step 3175. No data to save.\n",
-      "3175\n",
-      "model_vars.get('Gini', 0)[-1]=0.6836\n",
-      "Condition not met at step 3176. No data to save.\n",
-      "3176\n",
-      "model_vars.get('Gini', 0)[-1]=0.6712\n",
-      "Condition not met at step 3177. No data to save.\n",
-      "3177\n",
-      "model_vars.get('Gini', 0)[-1]=0.6604\n",
-      "Condition not met at step 3178. No data to save.\n",
-      "3178\n",
-      "model_vars.get('Gini', 0)[-1]=0.6584\n",
-      "Condition not met at step 3179. No data to save.\n",
-      "3179\n",
-      "model_vars.get('Gini', 0)[-1]=0.6432\n",
-      "Condition not met at step 3180. No data to save.\n",
-      "3180\n",
-      "model_vars.get('Gini', 0)[-1]=0.6335999999999999\n",
-      "Condition not met at step 3181. No data to save.\n",
-      "3181\n",
-      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
-      "Condition not met at step 3182. No data to save.\n",
-      "3182\n",
-      "model_vars.get('Gini', 0)[-1]=0.6326\n",
-      "Condition not met at step 3183. No data to save.\n",
-      "3183\n",
-      "model_vars.get('Gini', 0)[-1]=0.6382\n",
-      "Condition not met at step 3184. No data to save.\n",
-      "3184\n",
-      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
-      "Condition not met at step 3185. No data to save.\n",
-      "3185\n",
-      "model_vars.get('Gini', 0)[-1]=0.6274\n",
-      "Condition not met at step 3186. No data to save.\n",
-      "3186\n",
-      "model_vars.get('Gini', 0)[-1]=0.632\n",
-      "Condition not met at step 3187. No data to save.\n",
-      "3187\n",
-      "model_vars.get('Gini', 0)[-1]=0.6624\n",
-      "Condition not met at step 3188. No data to save.\n",
-      "3188\n",
-      "model_vars.get('Gini', 0)[-1]=0.6482\n",
-      "Condition not met at step 3189. No data to save.\n",
-      "3189\n",
-      "model_vars.get('Gini', 0)[-1]=0.6384000000000001\n",
-      "Condition not met at step 3190. No data to save.\n",
-      "3190\n",
-      "model_vars.get('Gini', 0)[-1]=0.6437999999999999\n",
-      "Condition not met at step 3191. No data to save.\n",
-      "3191\n",
-      "model_vars.get('Gini', 0)[-1]=0.6326\n",
-      "Condition not met at step 3192. No data to save.\n",
-      "3192\n",
-      "model_vars.get('Gini', 0)[-1]=0.6448\n",
-      "Condition not met at step 3193. No data to save.\n",
-      "3193\n",
-      "model_vars.get('Gini', 0)[-1]=0.6592\n",
-      "Condition not met at step 3194. No data to save.\n",
-      "3194\n",
-      "model_vars.get('Gini', 0)[-1]=0.6306\n",
-      "Condition not met at step 3195. No data to save.\n",
-      "3195\n",
-      "model_vars.get('Gini', 0)[-1]=0.6355999999999999\n",
-      "Condition not met at step 3196. No data to save.\n",
-      "3196\n",
-      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
-      "Condition not met at step 3197. No data to save.\n",
-      "3197\n",
-      "model_vars.get('Gini', 0)[-1]=0.6416\n",
-      "Condition not met at step 3198. No data to save.\n",
-      "3198\n",
-      "model_vars.get('Gini', 0)[-1]=0.6512\n",
-      "Condition not met at step 3199. No data to save.\n",
-      "3199\n",
-      "CALLED\n",
-      "self.model._steps=3200\n",
-      " TEST self.model._steps=3200\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "3100  0.6176\n",
-      "3101  0.6246\n",
-      "3102  0.6066\n",
-      "3103  0.6264\n",
-      "3104  0.6182\n",
-      "...      ...\n",
-      "3195  0.6356\n",
-      "3196  0.6256\n",
-      "3197  0.6416\n",
-      "3198  0.6512\n",
-      "3199  0.5912\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_032.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_032.parquet'\n",
-      "Saving model to test_path/model_data_032.parquet\n",
-      "Saving agent to test_path/agent_data_032.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.5912\n",
-      "Condition not met at step 3200. No data to save.\n",
-      "3200\n",
-      "model_vars.get('Gini', 0)[-1]=0.628\n",
-      "Condition not met at step 3201. No data to save.\n",
-      "3201\n",
-      "model_vars.get('Gini', 0)[-1]=0.637\n",
-      "Condition not met at step 3202. No data to save.\n",
-      "3202\n",
-      "model_vars.get('Gini', 0)[-1]=0.6604\n",
-      "Condition not met at step 3203. No data to save.\n",
-      "3203\n",
-      "model_vars.get('Gini', 0)[-1]=0.634\n",
-      "Condition not met at step 3204. No data to save.\n",
-      "3204\n",
-      "model_vars.get('Gini', 0)[-1]=0.6162000000000001\n",
-      "Condition not met at step 3205. No data to save.\n",
-      "3205\n",
-      "model_vars.get('Gini', 0)[-1]=0.616\n",
-      "Condition not met at step 3206. No data to save.\n",
-      "3206\n",
-      "model_vars.get('Gini', 0)[-1]=0.622\n",
-      "Condition not met at step 3207. No data to save.\n",
-      "3207\n",
-      "model_vars.get('Gini', 0)[-1]=0.6462\n",
-      "Condition not met at step 3208. No data to save.\n",
-      "3208\n",
-      "model_vars.get('Gini', 0)[-1]=0.6496\n",
-      "Condition not met at step 3209. No data to save.\n",
-      "3209\n",
-      "model_vars.get('Gini', 0)[-1]=0.6572\n",
-      "Condition not met at step 3210. No data to save.\n",
-      "3210\n",
-      "model_vars.get('Gini', 0)[-1]=0.6204000000000001\n",
-      "Condition not met at step 3211. No data to save.\n",
-      "3211\n",
-      "model_vars.get('Gini', 0)[-1]=0.6224000000000001\n",
-      "Condition not met at step 3212. No data to save.\n",
-      "3212\n",
-      "model_vars.get('Gini', 0)[-1]=0.6734\n",
-      "Condition not met at step 3213. No data to save.\n",
-      "3213\n",
-      "model_vars.get('Gini', 0)[-1]=0.6396\n",
-      "Condition not met at step 3214. No data to save.\n",
-      "3214\n",
-      "model_vars.get('Gini', 0)[-1]=0.6304000000000001\n",
-      "Condition not met at step 3215. No data to save.\n",
-      "3215\n",
-      "model_vars.get('Gini', 0)[-1]=0.6174\n",
-      "Condition not met at step 3216. No data to save.\n",
-      "3216\n",
-      "model_vars.get('Gini', 0)[-1]=0.6502\n",
-      "Condition not met at step 3217. No data to save.\n",
-      "3217\n",
-      "model_vars.get('Gini', 0)[-1]=0.655\n",
-      "Condition not met at step 3218. No data to save.\n",
-      "3218\n",
-      "model_vars.get('Gini', 0)[-1]=0.6644\n",
-      "Condition not met at step 3219. No data to save.\n",
-      "3219\n",
-      "model_vars.get('Gini', 0)[-1]=0.6614\n",
-      "Condition not met at step 3220. No data to save.\n",
-      "3220\n",
-      "model_vars.get('Gini', 0)[-1]=0.6812\n",
-      "Condition not met at step 3221. No data to save.\n",
-      "3221\n",
-      "model_vars.get('Gini', 0)[-1]=0.6798\n",
-      "Condition not met at step 3222. No data to save.\n",
-      "3222\n",
-      "model_vars.get('Gini', 0)[-1]=0.6981999999999999\n",
-      "Condition not met at step 3223. No data to save.\n",
-      "3223\n",
-      "model_vars.get('Gini', 0)[-1]=0.7018\n",
-      "Condition met. Appended special results for step 3224 to test_path/special_results.parquet\n",
-      "3224\n",
-      "model_vars.get('Gini', 0)[-1]=0.6738\n",
-      "Condition not met at step 3225. No data to save.\n",
-      "3225\n",
-      "model_vars.get('Gini', 0)[-1]=0.6476\n",
-      "Condition not met at step 3226. No data to save.\n",
-      "3226\n",
-      "model_vars.get('Gini', 0)[-1]=0.631\n",
-      "Condition not met at step 3227. No data to save.\n",
-      "3227\n",
-      "model_vars.get('Gini', 0)[-1]=0.6416\n",
-      "Condition not met at step 3228. No data to save.\n",
-      "3228\n",
-      "model_vars.get('Gini', 0)[-1]=0.6374\n",
-      "Condition not met at step 3229. No data to save.\n",
-      "3229\n",
-      "model_vars.get('Gini', 0)[-1]=0.6358\n",
-      "Condition not met at step 3230. No data to save.\n",
-      "3230\n",
-      "model_vars.get('Gini', 0)[-1]=0.6366\n",
-      "Condition not met at step 3231. No data to save.\n",
-      "3231\n",
-      "model_vars.get('Gini', 0)[-1]=0.6448\n",
-      "Condition not met at step 3232. No data to save.\n",
-      "3232\n",
-      "model_vars.get('Gini', 0)[-1]=0.6634\n",
-      "Condition not met at step 3233. No data to save.\n",
-      "3233\n",
-      "model_vars.get('Gini', 0)[-1]=0.662\n",
-      "Condition not met at step 3234. No data to save.\n",
-      "3234\n",
-      "model_vars.get('Gini', 0)[-1]=0.6352\n",
-      "Condition not met at step 3235. No data to save.\n",
-      "3235\n",
-      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
-      "Condition not met at step 3236. No data to save.\n",
-      "3236\n",
-      "model_vars.get('Gini', 0)[-1]=0.6742\n",
-      "Condition not met at step 3237. No data to save.\n",
-      "3237\n",
-      "model_vars.get('Gini', 0)[-1]=0.6774\n",
-      "Condition not met at step 3238. No data to save.\n",
-      "3238\n",
-      "model_vars.get('Gini', 0)[-1]=0.6822\n",
-      "Condition not met at step 3239. No data to save.\n",
-      "3239\n",
-      "model_vars.get('Gini', 0)[-1]=0.6914\n",
-      "Condition not met at step 3240. No data to save.\n",
-      "3240\n",
-      "model_vars.get('Gini', 0)[-1]=0.6846\n",
-      "Condition not met at step 3241. No data to save.\n",
-      "3241\n",
-      "model_vars.get('Gini', 0)[-1]=0.6961999999999999\n",
-      "Condition not met at step 3242. No data to save.\n",
-      "3242\n",
-      "model_vars.get('Gini', 0)[-1]=0.6882\n",
-      "Condition not met at step 3243. No data to save.\n",
-      "3243\n",
-      "model_vars.get('Gini', 0)[-1]=0.7142\n",
-      "Condition met. Appended special results for step 3244 to test_path/special_results.parquet\n",
-      "3244\n",
-      "model_vars.get('Gini', 0)[-1]=0.7188\n",
-      "Condition met. Appended special results for step 3245 to test_path/special_results.parquet\n",
-      "3245\n",
-      "model_vars.get('Gini', 0)[-1]=0.7242\n",
-      "Condition met. Appended special results for step 3246 to test_path/special_results.parquet\n",
-      "3246\n",
-      "model_vars.get('Gini', 0)[-1]=0.7218\n",
-      "Condition met. Appended special results for step 3247 to test_path/special_results.parquet\n",
-      "3247\n",
-      "model_vars.get('Gini', 0)[-1]=0.7343999999999999\n",
-      "Condition met. Appended special results for step 3248 to test_path/special_results.parquet\n",
-      "3248\n",
-      "model_vars.get('Gini', 0)[-1]=0.7328\n",
-      "Condition met. Appended special results for step 3249 to test_path/special_results.parquet\n",
-      "3249\n",
-      "model_vars.get('Gini', 0)[-1]=0.738\n",
-      "Condition met. Appended special results for step 3250 to test_path/special_results.parquet\n",
-      "3250\n",
-      "model_vars.get('Gini', 0)[-1]=0.7242\n",
-      "Condition met. Appended special results for step 3251 to test_path/special_results.parquet\n",
-      "3251\n",
-      "model_vars.get('Gini', 0)[-1]=0.7058\n",
-      "Condition met. Appended special results for step 3252 to test_path/special_results.parquet\n",
-      "3252\n",
-      "model_vars.get('Gini', 0)[-1]=0.7094\n",
-      "Condition met. Appended special results for step 3253 to test_path/special_results.parquet\n",
-      "3253\n",
-      "model_vars.get('Gini', 0)[-1]=0.7094\n",
-      "Condition met. Appended special results for step 3254 to test_path/special_results.parquet\n",
-      "3254\n",
-      "model_vars.get('Gini', 0)[-1]=0.7114\n",
-      "Condition met. Appended special results for step 3255 to test_path/special_results.parquet\n",
-      "3255\n",
-      "model_vars.get('Gini', 0)[-1]=0.7146\n",
-      "Condition met. Appended special results for step 3256 to test_path/special_results.parquet\n",
-      "3256\n",
-      "model_vars.get('Gini', 0)[-1]=0.7322\n",
-      "Condition met. Appended special results for step 3257 to test_path/special_results.parquet\n",
-      "3257\n",
-      "model_vars.get('Gini', 0)[-1]=0.739\n",
-      "Condition met. Appended special results for step 3258 to test_path/special_results.parquet\n",
-      "3258\n",
-      "model_vars.get('Gini', 0)[-1]=0.7348\n",
-      "Condition met. Appended special results for step 3259 to test_path/special_results.parquet\n",
-      "3259\n",
-      "model_vars.get('Gini', 0)[-1]=0.7112\n",
-      "Condition met. Appended special results for step 3260 to test_path/special_results.parquet\n",
-      "3260\n",
-      "model_vars.get('Gini', 0)[-1]=0.7021999999999999\n",
-      "Condition met. Appended special results for step 3261 to test_path/special_results.parquet\n",
-      "3261\n",
-      "model_vars.get('Gini', 0)[-1]=0.7074\n",
-      "Condition met. Appended special results for step 3262 to test_path/special_results.parquet\n",
-      "3262\n",
-      "model_vars.get('Gini', 0)[-1]=0.6854\n",
-      "Condition not met at step 3263. No data to save.\n",
-      "3263\n",
-      "model_vars.get('Gini', 0)[-1]=0.6846\n",
-      "Condition not met at step 3264. No data to save.\n",
-      "3264\n",
-      "model_vars.get('Gini', 0)[-1]=0.6826\n",
-      "Condition not met at step 3265. No data to save.\n",
-      "3265\n",
-      "model_vars.get('Gini', 0)[-1]=0.6910000000000001\n",
-      "Condition not met at step 3266. No data to save.\n",
-      "3266\n",
-      "model_vars.get('Gini', 0)[-1]=0.6422\n",
-      "Condition not met at step 3267. No data to save.\n",
-      "3267\n",
-      "model_vars.get('Gini', 0)[-1]=0.6266\n",
-      "Condition not met at step 3268. No data to save.\n",
-      "3268\n",
-      "model_vars.get('Gini', 0)[-1]=0.6248\n",
-      "Condition not met at step 3269. No data to save.\n",
-      "3269\n",
-      "model_vars.get('Gini', 0)[-1]=0.6342\n",
-      "Condition not met at step 3270. No data to save.\n",
-      "3270\n",
-      "model_vars.get('Gini', 0)[-1]=0.6372\n",
-      "Condition not met at step 3271. No data to save.\n",
-      "3271\n",
-      "model_vars.get('Gini', 0)[-1]=0.6502\n",
-      "Condition not met at step 3272. No data to save.\n",
-      "3272\n",
-      "model_vars.get('Gini', 0)[-1]=0.6768000000000001\n",
-      "Condition not met at step 3273. No data to save.\n",
-      "3273\n",
-      "model_vars.get('Gini', 0)[-1]=0.662\n",
-      "Condition not met at step 3274. No data to save.\n",
-      "3274\n",
-      "model_vars.get('Gini', 0)[-1]=0.6686000000000001\n",
-      "Condition not met at step 3275. No data to save.\n",
-      "3275\n",
-      "model_vars.get('Gini', 0)[-1]=0.651\n",
-      "Condition not met at step 3276. No data to save.\n",
-      "3276\n",
-      "model_vars.get('Gini', 0)[-1]=0.6412\n",
-      "Condition not met at step 3277. No data to save.\n",
-      "3277\n",
-      "model_vars.get('Gini', 0)[-1]=0.6654\n",
-      "Condition not met at step 3278. No data to save.\n",
-      "3278\n",
-      "model_vars.get('Gini', 0)[-1]=0.6734\n",
-      "Condition not met at step 3279. No data to save.\n",
-      "3279\n",
-      "model_vars.get('Gini', 0)[-1]=0.6642\n",
-      "Condition not met at step 3280. No data to save.\n",
-      "3280\n",
-      "model_vars.get('Gini', 0)[-1]=0.653\n",
-      "Condition not met at step 3281. No data to save.\n",
-      "3281\n",
-      "model_vars.get('Gini', 0)[-1]=0.6912\n",
-      "Condition not met at step 3282. No data to save.\n",
-      "3282\n",
-      "model_vars.get('Gini', 0)[-1]=0.6868000000000001\n",
-      "Condition not met at step 3283. No data to save.\n",
-      "3283\n",
-      "model_vars.get('Gini', 0)[-1]=0.673\n",
-      "Condition not met at step 3284. No data to save.\n",
-      "3284\n",
-      "model_vars.get('Gini', 0)[-1]=0.6686000000000001\n",
-      "Condition not met at step 3285. No data to save.\n",
-      "3285\n",
-      "model_vars.get('Gini', 0)[-1]=0.6698\n",
-      "Condition not met at step 3286. No data to save.\n",
-      "3286\n",
-      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
-      "Condition not met at step 3287. No data to save.\n",
-      "3287\n",
-      "model_vars.get('Gini', 0)[-1]=0.6414\n",
-      "Condition not met at step 3288. No data to save.\n",
-      "3288\n",
-      "model_vars.get('Gini', 0)[-1]=0.6819999999999999\n",
-      "Condition not met at step 3289. No data to save.\n",
-      "3289\n",
-      "model_vars.get('Gini', 0)[-1]=0.6872\n",
-      "Condition not met at step 3290. No data to save.\n",
-      "3290\n",
-      "model_vars.get('Gini', 0)[-1]=0.6848000000000001\n",
-      "Condition not met at step 3291. No data to save.\n",
-      "3291\n",
-      "model_vars.get('Gini', 0)[-1]=0.7008000000000001\n",
-      "Condition met. Appended special results for step 3292 to test_path/special_results.parquet\n",
-      "3292\n",
-      "model_vars.get('Gini', 0)[-1]=0.7072\n",
-      "Condition met. Appended special results for step 3293 to test_path/special_results.parquet\n",
-      "3293\n",
-      "model_vars.get('Gini', 0)[-1]=0.712\n",
-      "Condition met. Appended special results for step 3294 to test_path/special_results.parquet\n",
-      "3294\n",
-      "model_vars.get('Gini', 0)[-1]=0.6944\n",
-      "Condition not met at step 3295. No data to save.\n",
-      "3295\n",
-      "model_vars.get('Gini', 0)[-1]=0.6514\n",
-      "Condition not met at step 3296. No data to save.\n",
-      "3296\n",
-      "model_vars.get('Gini', 0)[-1]=0.6617999999999999\n",
-      "Condition not met at step 3297. No data to save.\n",
-      "3297\n",
-      "model_vars.get('Gini', 0)[-1]=0.6632\n",
-      "Condition not met at step 3298. No data to save.\n",
-      "3298\n",
-      "model_vars.get('Gini', 0)[-1]=0.6654\n",
-      "Condition not met at step 3299. No data to save.\n",
-      "3299\n",
-      "CALLED\n",
-      "self.model._steps=3300\n",
-      " TEST self.model._steps=3300\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "3200  0.6280\n",
-      "3201  0.6370\n",
-      "3202  0.6604\n",
-      "3203  0.6340\n",
-      "3204  0.6162\n",
-      "...      ...\n",
-      "3295  0.6514\n",
-      "3296  0.6618\n",
-      "3297  0.6632\n",
-      "3298  0.6654\n",
-      "3299  0.6692\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_033.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_033.parquet'\n",
-      "Saving model to test_path/model_data_033.parquet\n",
-      "Saving agent to test_path/agent_data_033.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6692\n",
-      "Condition not met at step 3300. No data to save.\n",
-      "3300\n",
-      "model_vars.get('Gini', 0)[-1]=0.6642\n",
-      "Condition not met at step 3301. No data to save.\n",
-      "3301\n",
-      "model_vars.get('Gini', 0)[-1]=0.7061999999999999\n",
-      "Condition met. Appended special results for step 3302 to test_path/special_results.parquet\n",
-      "3302\n",
-      "model_vars.get('Gini', 0)[-1]=0.728\n",
-      "Condition met. Appended special results for step 3303 to test_path/special_results.parquet\n",
-      "3303\n",
-      "model_vars.get('Gini', 0)[-1]=0.718\n",
-      "Condition met. Appended special results for step 3304 to test_path/special_results.parquet\n",
-      "3304\n",
-      "model_vars.get('Gini', 0)[-1]=0.7168\n",
-      "Condition met. Appended special results for step 3305 to test_path/special_results.parquet\n",
-      "3305\n",
-      "model_vars.get('Gini', 0)[-1]=0.6819999999999999\n",
-      "Condition not met at step 3306. No data to save.\n",
-      "3306\n",
-      "model_vars.get('Gini', 0)[-1]=0.7044\n",
-      "Condition met. Appended special results for step 3307 to test_path/special_results.parquet\n",
-      "3307\n",
-      "model_vars.get('Gini', 0)[-1]=0.6886\n",
-      "Condition not met at step 3308. No data to save.\n",
-      "3308\n",
-      "model_vars.get('Gini', 0)[-1]=0.7078\n",
-      "Condition met. Appended special results for step 3309 to test_path/special_results.parquet\n",
-      "3309\n",
-      "model_vars.get('Gini', 0)[-1]=0.7156\n",
-      "Condition met. Appended special results for step 3310 to test_path/special_results.parquet\n",
-      "3310\n",
-      "model_vars.get('Gini', 0)[-1]=0.733\n",
-      "Condition met. Appended special results for step 3311 to test_path/special_results.parquet\n",
-      "3311\n",
-      "model_vars.get('Gini', 0)[-1]=0.718\n",
-      "Condition met. Appended special results for step 3312 to test_path/special_results.parquet\n",
-      "3312\n",
-      "model_vars.get('Gini', 0)[-1]=0.7298\n",
-      "Condition met. Appended special results for step 3313 to test_path/special_results.parquet\n",
-      "3313\n",
-      "model_vars.get('Gini', 0)[-1]=0.7468\n",
-      "Condition met. Appended special results for step 3314 to test_path/special_results.parquet\n",
-      "3314\n",
-      "model_vars.get('Gini', 0)[-1]=0.6944\n",
-      "Condition not met at step 3315. No data to save.\n",
-      "3315\n",
-      "model_vars.get('Gini', 0)[-1]=0.6904\n",
-      "Condition not met at step 3316. No data to save.\n",
-      "3316\n",
-      "model_vars.get('Gini', 0)[-1]=0.6908000000000001\n",
-      "Condition not met at step 3317. No data to save.\n",
-      "3317\n",
-      "model_vars.get('Gini', 0)[-1]=0.6941999999999999\n",
-      "Condition not met at step 3318. No data to save.\n",
-      "3318\n",
-      "model_vars.get('Gini', 0)[-1]=0.7128\n",
-      "Condition met. Appended special results for step 3319 to test_path/special_results.parquet\n",
-      "3319\n",
-      "model_vars.get('Gini', 0)[-1]=0.7021999999999999\n",
-      "Condition met. Appended special results for step 3320 to test_path/special_results.parquet\n",
-      "3320\n",
-      "model_vars.get('Gini', 0)[-1]=0.6914\n",
-      "Condition not met at step 3321. No data to save.\n",
-      "3321\n",
-      "model_vars.get('Gini', 0)[-1]=0.6704\n",
-      "Condition not met at step 3322. No data to save.\n",
-      "3322\n",
-      "model_vars.get('Gini', 0)[-1]=0.6734\n",
-      "Condition not met at step 3323. No data to save.\n",
-      "3323\n",
-      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
-      "Condition not met at step 3324. No data to save.\n",
-      "3324\n",
-      "model_vars.get('Gini', 0)[-1]=0.669\n",
-      "Condition not met at step 3325. No data to save.\n",
-      "3325\n",
-      "model_vars.get('Gini', 0)[-1]=0.6694\n",
-      "Condition not met at step 3326. No data to save.\n",
-      "3326\n",
-      "model_vars.get('Gini', 0)[-1]=0.6822\n",
-      "Condition not met at step 3327. No data to save.\n",
-      "3327\n",
-      "model_vars.get('Gini', 0)[-1]=0.6964\n",
-      "Condition not met at step 3328. No data to save.\n",
-      "3328\n",
-      "model_vars.get('Gini', 0)[-1]=0.6846\n",
-      "Condition not met at step 3329. No data to save.\n",
-      "3329\n",
-      "model_vars.get('Gini', 0)[-1]=0.6874\n",
-      "Condition not met at step 3330. No data to save.\n",
-      "3330\n",
-      "model_vars.get('Gini', 0)[-1]=0.6934\n",
-      "Condition not met at step 3331. No data to save.\n",
-      "3331\n",
-      "model_vars.get('Gini', 0)[-1]=0.6846\n",
-      "Condition not met at step 3332. No data to save.\n",
-      "3332\n",
-      "model_vars.get('Gini', 0)[-1]=0.7048\n",
-      "Condition met. Appended special results for step 3333 to test_path/special_results.parquet\n",
-      "3333\n",
-      "model_vars.get('Gini', 0)[-1]=0.7064\n",
-      "Condition met. Appended special results for step 3334 to test_path/special_results.parquet\n",
-      "3334\n",
-      "model_vars.get('Gini', 0)[-1]=0.7076\n",
-      "Condition met. Appended special results for step 3335 to test_path/special_results.parquet\n",
-      "3335\n",
-      "model_vars.get('Gini', 0)[-1]=0.6938\n",
-      "Condition not met at step 3336. No data to save.\n",
-      "3336\n",
-      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
-      "Condition not met at step 3337. No data to save.\n",
-      "3337\n",
-      "model_vars.get('Gini', 0)[-1]=0.6996\n",
-      "Condition not met at step 3338. No data to save.\n",
-      "3338\n",
-      "model_vars.get('Gini', 0)[-1]=0.6798\n",
-      "Condition not met at step 3339. No data to save.\n",
-      "3339\n",
-      "model_vars.get('Gini', 0)[-1]=0.6656\n",
-      "Condition not met at step 3340. No data to save.\n",
-      "3340\n",
-      "model_vars.get('Gini', 0)[-1]=0.6428\n",
-      "Condition not met at step 3341. No data to save.\n",
-      "3341\n",
-      "model_vars.get('Gini', 0)[-1]=0.6602\n",
-      "Condition not met at step 3342. No data to save.\n",
-      "3342\n",
-      "model_vars.get('Gini', 0)[-1]=0.6588\n",
-      "Condition not met at step 3343. No data to save.\n",
-      "3343\n",
-      "model_vars.get('Gini', 0)[-1]=0.637\n",
-      "Condition not met at step 3344. No data to save.\n",
-      "3344\n",
-      "model_vars.get('Gini', 0)[-1]=0.6456\n",
-      "Condition not met at step 3345. No data to save.\n",
-      "3345\n",
-      "model_vars.get('Gini', 0)[-1]=0.6662\n",
-      "Condition not met at step 3346. No data to save.\n",
-      "3346\n",
-      "model_vars.get('Gini', 0)[-1]=0.7030000000000001\n",
-      "Condition met. Appended special results for step 3347 to test_path/special_results.parquet\n",
-      "3347\n",
-      "model_vars.get('Gini', 0)[-1]=0.6898\n",
-      "Condition not met at step 3348. No data to save.\n",
-      "3348\n",
-      "model_vars.get('Gini', 0)[-1]=0.7010000000000001\n",
-      "Condition met. Appended special results for step 3349 to test_path/special_results.parquet\n",
-      "3349\n",
-      "model_vars.get('Gini', 0)[-1]=0.6990000000000001\n",
-      "Condition not met at step 3350. No data to save.\n",
-      "3350\n",
-      "model_vars.get('Gini', 0)[-1]=0.6948000000000001\n",
-      "Condition not met at step 3351. No data to save.\n",
-      "3351\n",
-      "model_vars.get('Gini', 0)[-1]=0.6948000000000001\n",
-      "Condition not met at step 3352. No data to save.\n",
-      "3352\n",
-      "model_vars.get('Gini', 0)[-1]=0.688\n",
-      "Condition not met at step 3353. No data to save.\n",
-      "3353\n",
-      "model_vars.get('Gini', 0)[-1]=0.7112\n",
-      "Condition met. Appended special results for step 3354 to test_path/special_results.parquet\n",
-      "3354\n",
-      "model_vars.get('Gini', 0)[-1]=0.7068\n",
-      "Condition met. Appended special results for step 3355 to test_path/special_results.parquet\n",
-      "3355\n",
-      "model_vars.get('Gini', 0)[-1]=0.7190000000000001\n",
-      "Condition met. Appended special results for step 3356 to test_path/special_results.parquet\n",
-      "3356\n",
-      "model_vars.get('Gini', 0)[-1]=0.7028000000000001\n",
-      "Condition met. Appended special results for step 3357 to test_path/special_results.parquet\n",
-      "3357\n",
-      "model_vars.get('Gini', 0)[-1]=0.6882\n",
-      "Condition not met at step 3358. No data to save.\n",
-      "3358\n",
-      "model_vars.get('Gini', 0)[-1]=0.6686000000000001\n",
-      "Condition not met at step 3359. No data to save.\n",
-      "3359\n",
-      "model_vars.get('Gini', 0)[-1]=0.6738\n",
-      "Condition not met at step 3360. No data to save.\n",
-      "3360\n",
-      "model_vars.get('Gini', 0)[-1]=0.6758\n",
-      "Condition not met at step 3361. No data to save.\n",
-      "3361\n",
-      "model_vars.get('Gini', 0)[-1]=0.6588\n",
-      "Condition not met at step 3362. No data to save.\n",
-      "3362\n",
-      "model_vars.get('Gini', 0)[-1]=0.6892\n",
-      "Condition not met at step 3363. No data to save.\n",
-      "3363\n",
-      "model_vars.get('Gini', 0)[-1]=0.6936\n",
-      "Condition not met at step 3364. No data to save.\n",
-      "3364\n",
-      "model_vars.get('Gini', 0)[-1]=0.6876\n",
-      "Condition not met at step 3365. No data to save.\n",
-      "3365\n",
-      "model_vars.get('Gini', 0)[-1]=0.666\n",
-      "Condition not met at step 3366. No data to save.\n",
-      "3366\n",
-      "model_vars.get('Gini', 0)[-1]=0.6854\n",
-      "Condition not met at step 3367. No data to save.\n",
-      "3367\n",
-      "model_vars.get('Gini', 0)[-1]=0.6644\n",
-      "Condition not met at step 3368. No data to save.\n",
-      "3368\n",
-      "model_vars.get('Gini', 0)[-1]=0.6676\n",
-      "Condition not met at step 3369. No data to save.\n",
-      "3369\n",
-      "model_vars.get('Gini', 0)[-1]=0.656\n",
-      "Condition not met at step 3370. No data to save.\n",
-      "3370\n",
-      "model_vars.get('Gini', 0)[-1]=0.6454\n",
-      "Condition not met at step 3371. No data to save.\n",
-      "3371\n",
-      "model_vars.get('Gini', 0)[-1]=0.6502\n",
-      "Condition not met at step 3372. No data to save.\n",
-      "3372\n",
-      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
-      "Condition not met at step 3373. No data to save.\n",
-      "3373\n",
-      "model_vars.get('Gini', 0)[-1]=0.6718\n",
-      "Condition not met at step 3374. No data to save.\n",
-      "3374\n",
-      "model_vars.get('Gini', 0)[-1]=0.6556\n",
-      "Condition not met at step 3375. No data to save.\n",
-      "3375\n",
-      "model_vars.get('Gini', 0)[-1]=0.6778\n",
-      "Condition not met at step 3376. No data to save.\n",
-      "3376\n",
-      "model_vars.get('Gini', 0)[-1]=0.6532\n",
-      "Condition not met at step 3377. No data to save.\n",
-      "3377\n",
-      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
-      "Condition not met at step 3378. No data to save.\n",
-      "3378\n",
-      "model_vars.get('Gini', 0)[-1]=0.6704\n",
-      "Condition not met at step 3379. No data to save.\n",
-      "3379\n",
-      "model_vars.get('Gini', 0)[-1]=0.6918\n",
-      "Condition not met at step 3380. No data to save.\n",
-      "3380\n",
-      "model_vars.get('Gini', 0)[-1]=0.6948000000000001\n",
-      "Condition not met at step 3381. No data to save.\n",
-      "3381\n",
-      "model_vars.get('Gini', 0)[-1]=0.679\n",
-      "Condition not met at step 3382. No data to save.\n",
-      "3382\n",
-      "model_vars.get('Gini', 0)[-1]=0.6624\n",
-      "Condition not met at step 3383. No data to save.\n",
-      "3383\n",
-      "model_vars.get('Gini', 0)[-1]=0.665\n",
-      "Condition not met at step 3384. No data to save.\n",
-      "3384\n",
-      "model_vars.get('Gini', 0)[-1]=0.6284000000000001\n",
-      "Condition not met at step 3385. No data to save.\n",
-      "3385\n",
-      "model_vars.get('Gini', 0)[-1]=0.622\n",
-      "Condition not met at step 3386. No data to save.\n",
-      "3386\n",
-      "model_vars.get('Gini', 0)[-1]=0.6194\n",
-      "Condition not met at step 3387. No data to save.\n",
-      "3387\n",
-      "model_vars.get('Gini', 0)[-1]=0.5702\n",
-      "Condition not met at step 3388. No data to save.\n",
-      "3388\n",
-      "model_vars.get('Gini', 0)[-1]=0.5546\n",
-      "Condition not met at step 3389. No data to save.\n",
-      "3389\n",
-      "model_vars.get('Gini', 0)[-1]=0.5831999999999999\n",
-      "Condition not met at step 3390. No data to save.\n",
-      "3390\n",
-      "model_vars.get('Gini', 0)[-1]=0.6022000000000001\n",
-      "Condition not met at step 3391. No data to save.\n",
-      "3391\n",
-      "model_vars.get('Gini', 0)[-1]=0.5968\n",
-      "Condition not met at step 3392. No data to save.\n",
-      "3392\n",
-      "model_vars.get('Gini', 0)[-1]=0.599\n",
-      "Condition not met at step 3393. No data to save.\n",
-      "3393\n",
-      "model_vars.get('Gini', 0)[-1]=0.6036\n",
-      "Condition not met at step 3394. No data to save.\n",
-      "3394\n",
-      "model_vars.get('Gini', 0)[-1]=0.5862\n",
-      "Condition not met at step 3395. No data to save.\n",
-      "3395\n",
-      "model_vars.get('Gini', 0)[-1]=0.5936\n",
-      "Condition not met at step 3396. No data to save.\n",
-      "3396\n",
-      "model_vars.get('Gini', 0)[-1]=0.6238\n",
-      "Condition not met at step 3397. No data to save.\n",
-      "3397\n",
-      "model_vars.get('Gini', 0)[-1]=0.6232\n",
-      "Condition not met at step 3398. No data to save.\n",
-      "3398\n",
-      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
-      "Condition not met at step 3399. No data to save.\n",
-      "3399\n",
-      "CALLED\n",
-      "self.model._steps=3400\n",
-      " TEST self.model._steps=3400\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "3300  0.6642\n",
-      "3301  0.7062\n",
-      "3302  0.7280\n",
-      "3303  0.7180\n",
-      "3304  0.7168\n",
-      "...      ...\n",
-      "3395  0.5936\n",
-      "3396  0.6238\n",
-      "3397  0.6232\n",
-      "3398  0.6546\n",
-      "3399  0.6640\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_034.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_034.parquet'\n",
-      "Saving model to test_path/model_data_034.parquet\n",
-      "Saving agent to test_path/agent_data_034.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.664\n",
-      "Condition not met at step 3400. No data to save.\n",
-      "3400\n",
-      "model_vars.get('Gini', 0)[-1]=0.6626000000000001\n",
-      "Condition not met at step 3401. No data to save.\n",
-      "3401\n",
-      "model_vars.get('Gini', 0)[-1]=0.6537999999999999\n",
-      "Condition not met at step 3402. No data to save.\n",
-      "3402\n",
-      "model_vars.get('Gini', 0)[-1]=0.6572\n",
-      "Condition not met at step 3403. No data to save.\n",
-      "3403\n",
-      "model_vars.get('Gini', 0)[-1]=0.6492\n",
-      "Condition not met at step 3404. No data to save.\n",
-      "3404\n",
-      "model_vars.get('Gini', 0)[-1]=0.6386000000000001\n",
-      "Condition not met at step 3405. No data to save.\n",
-      "3405\n",
-      "model_vars.get('Gini', 0)[-1]=0.6226\n",
-      "Condition not met at step 3406. No data to save.\n",
-      "3406\n",
-      "model_vars.get('Gini', 0)[-1]=0.5988\n",
-      "Condition not met at step 3407. No data to save.\n",
-      "3407\n",
-      "model_vars.get('Gini', 0)[-1]=0.5926\n",
-      "Condition not met at step 3408. No data to save.\n",
-      "3408\n",
-      "model_vars.get('Gini', 0)[-1]=0.5906\n",
-      "Condition not met at step 3409. No data to save.\n",
-      "3409\n",
-      "model_vars.get('Gini', 0)[-1]=0.5724\n",
-      "Condition not met at step 3410. No data to save.\n",
-      "3410\n",
-      "model_vars.get('Gini', 0)[-1]=0.6158\n",
-      "Condition not met at step 3411. No data to save.\n",
-      "3411\n",
-      "model_vars.get('Gini', 0)[-1]=0.6414\n",
-      "Condition not met at step 3412. No data to save.\n",
-      "3412\n",
-      "model_vars.get('Gini', 0)[-1]=0.6494\n",
-      "Condition not met at step 3413. No data to save.\n",
-      "3413\n",
-      "model_vars.get('Gini', 0)[-1]=0.669\n",
-      "Condition not met at step 3414. No data to save.\n",
-      "3414\n",
-      "model_vars.get('Gini', 0)[-1]=0.6352\n",
-      "Condition not met at step 3415. No data to save.\n",
-      "3415\n",
-      "model_vars.get('Gini', 0)[-1]=0.665\n",
-      "Condition not met at step 3416. No data to save.\n",
-      "3416\n",
-      "model_vars.get('Gini', 0)[-1]=0.6908000000000001\n",
-      "Condition not met at step 3417. No data to save.\n",
-      "3417\n",
-      "model_vars.get('Gini', 0)[-1]=0.6762\n",
-      "Condition not met at step 3418. No data to save.\n",
-      "3418\n",
-      "model_vars.get('Gini', 0)[-1]=0.6754\n",
-      "Condition not met at step 3419. No data to save.\n",
-      "3419\n",
-      "model_vars.get('Gini', 0)[-1]=0.7172000000000001\n",
-      "Condition met. Appended special results for step 3420 to test_path/special_results.parquet\n",
-      "3420\n",
-      "model_vars.get('Gini', 0)[-1]=0.714\n",
-      "Condition met. Appended special results for step 3421 to test_path/special_results.parquet\n",
-      "3421\n",
-      "model_vars.get('Gini', 0)[-1]=0.7056\n",
-      "Condition met. Appended special results for step 3422 to test_path/special_results.parquet\n",
-      "3422\n",
-      "model_vars.get('Gini', 0)[-1]=0.7222\n",
-      "Condition met. Appended special results for step 3423 to test_path/special_results.parquet\n",
-      "3423\n",
-      "model_vars.get('Gini', 0)[-1]=0.704\n",
-      "Condition met. Appended special results for step 3424 to test_path/special_results.parquet\n",
-      "3424\n",
-      "model_vars.get('Gini', 0)[-1]=0.7238\n",
-      "Condition met. Appended special results for step 3425 to test_path/special_results.parquet\n",
-      "3425\n",
-      "model_vars.get('Gini', 0)[-1]=0.741\n",
-      "Condition met. Appended special results for step 3426 to test_path/special_results.parquet\n",
-      "3426\n",
-      "model_vars.get('Gini', 0)[-1]=0.7366\n",
-      "Condition met. Appended special results for step 3427 to test_path/special_results.parquet\n",
-      "3427\n",
-      "model_vars.get('Gini', 0)[-1]=0.7206\n",
-      "Condition met. Appended special results for step 3428 to test_path/special_results.parquet\n",
-      "3428\n",
-      "model_vars.get('Gini', 0)[-1]=0.712\n",
-      "Condition met. Appended special results for step 3429 to test_path/special_results.parquet\n",
-      "3429\n",
-      "model_vars.get('Gini', 0)[-1]=0.6616\n",
-      "Condition not met at step 3430. No data to save.\n",
-      "3430\n",
-      "model_vars.get('Gini', 0)[-1]=0.6634\n",
-      "Condition not met at step 3431. No data to save.\n",
-      "3431\n",
-      "model_vars.get('Gini', 0)[-1]=0.6712\n",
-      "Condition not met at step 3432. No data to save.\n",
-      "3432\n",
-      "model_vars.get('Gini', 0)[-1]=0.7052\n",
-      "Condition met. Appended special results for step 3433 to test_path/special_results.parquet\n",
-      "3433\n",
-      "model_vars.get('Gini', 0)[-1]=0.6934\n",
-      "Condition not met at step 3434. No data to save.\n",
-      "3434\n",
-      "model_vars.get('Gini', 0)[-1]=0.7070000000000001\n",
-      "Condition met. Appended special results for step 3435 to test_path/special_results.parquet\n",
-      "3435\n",
-      "model_vars.get('Gini', 0)[-1]=0.6981999999999999\n",
-      "Condition not met at step 3436. No data to save.\n",
-      "3436\n",
-      "model_vars.get('Gini', 0)[-1]=0.7098\n",
-      "Condition met. Appended special results for step 3437 to test_path/special_results.parquet\n",
-      "3437\n",
-      "model_vars.get('Gini', 0)[-1]=0.712\n",
-      "Condition met. Appended special results for step 3438 to test_path/special_results.parquet\n",
-      "3438\n",
-      "model_vars.get('Gini', 0)[-1]=0.739\n",
-      "Condition met. Appended special results for step 3439 to test_path/special_results.parquet\n",
-      "3439\n",
-      "model_vars.get('Gini', 0)[-1]=0.7592\n",
-      "Condition met. Appended special results for step 3440 to test_path/special_results.parquet\n",
-      "3440\n",
-      "model_vars.get('Gini', 0)[-1]=0.7752\n",
-      "Condition met. Appended special results for step 3441 to test_path/special_results.parquet\n",
-      "3441\n",
-      "model_vars.get('Gini', 0)[-1]=0.7854\n",
-      "Condition met. Appended special results for step 3442 to test_path/special_results.parquet\n",
-      "3442\n",
-      "model_vars.get('Gini', 0)[-1]=0.7886\n",
-      "Condition met. Appended special results for step 3443 to test_path/special_results.parquet\n",
-      "3443\n",
-      "model_vars.get('Gini', 0)[-1]=0.7818\n",
-      "Condition met. Appended special results for step 3444 to test_path/special_results.parquet\n",
-      "3444\n",
-      "model_vars.get('Gini', 0)[-1]=0.766\n",
-      "Condition met. Appended special results for step 3445 to test_path/special_results.parquet\n",
-      "3445\n",
-      "model_vars.get('Gini', 0)[-1]=0.7464\n",
-      "Condition met. Appended special results for step 3446 to test_path/special_results.parquet\n",
-      "3446\n",
-      "model_vars.get('Gini', 0)[-1]=0.747\n",
-      "Condition met. Appended special results for step 3447 to test_path/special_results.parquet\n",
-      "3447\n",
-      "model_vars.get('Gini', 0)[-1]=0.7323999999999999\n",
-      "Condition met. Appended special results for step 3448 to test_path/special_results.parquet\n",
-      "3448\n",
-      "model_vars.get('Gini', 0)[-1]=0.74\n",
-      "Condition met. Appended special results for step 3449 to test_path/special_results.parquet\n",
-      "3449\n",
-      "model_vars.get('Gini', 0)[-1]=0.7296\n",
-      "Condition met. Appended special results for step 3450 to test_path/special_results.parquet\n",
-      "3450\n",
-      "model_vars.get('Gini', 0)[-1]=0.722\n",
-      "Condition met. Appended special results for step 3451 to test_path/special_results.parquet\n",
-      "3451\n",
-      "model_vars.get('Gini', 0)[-1]=0.7150000000000001\n",
-      "Condition met. Appended special results for step 3452 to test_path/special_results.parquet\n",
-      "3452\n",
-      "model_vars.get('Gini', 0)[-1]=0.708\n",
-      "Condition met. Appended special results for step 3453 to test_path/special_results.parquet\n",
-      "3453\n",
-      "model_vars.get('Gini', 0)[-1]=0.7070000000000001\n",
-      "Condition met. Appended special results for step 3454 to test_path/special_results.parquet\n",
-      "3454\n",
-      "model_vars.get('Gini', 0)[-1]=0.6778\n",
-      "Condition not met at step 3455. No data to save.\n",
-      "3455\n",
-      "model_vars.get('Gini', 0)[-1]=0.677\n",
-      "Condition not met at step 3456. No data to save.\n",
-      "3456\n",
-      "model_vars.get('Gini', 0)[-1]=0.6646000000000001\n",
-      "Condition not met at step 3457. No data to save.\n",
-      "3457\n",
-      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
-      "Condition not met at step 3458. No data to save.\n",
-      "3458\n",
-      "model_vars.get('Gini', 0)[-1]=0.6868000000000001\n",
-      "Condition not met at step 3459. No data to save.\n",
-      "3459\n",
-      "model_vars.get('Gini', 0)[-1]=0.6961999999999999\n",
-      "Condition not met at step 3460. No data to save.\n",
-      "3460\n",
-      "model_vars.get('Gini', 0)[-1]=0.6718\n",
-      "Condition not met at step 3461. No data to save.\n",
-      "3461\n",
-      "model_vars.get('Gini', 0)[-1]=0.6312\n",
-      "Condition not met at step 3462. No data to save.\n",
-      "3462\n",
-      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
-      "Condition not met at step 3463. No data to save.\n",
-      "3463\n",
-      "model_vars.get('Gini', 0)[-1]=0.658\n",
-      "Condition not met at step 3464. No data to save.\n",
-      "3464\n",
-      "model_vars.get('Gini', 0)[-1]=0.6872\n",
-      "Condition not met at step 3465. No data to save.\n",
-      "3465\n",
-      "model_vars.get('Gini', 0)[-1]=0.6896\n",
-      "Condition not met at step 3466. No data to save.\n",
-      "3466\n",
-      "model_vars.get('Gini', 0)[-1]=0.7096\n",
-      "Condition met. Appended special results for step 3467 to test_path/special_results.parquet\n",
-      "3467\n",
-      "model_vars.get('Gini', 0)[-1]=0.6986\n",
-      "Condition not met at step 3468. No data to save.\n",
-      "3468\n",
-      "model_vars.get('Gini', 0)[-1]=0.6708000000000001\n",
-      "Condition not met at step 3469. No data to save.\n",
-      "3469\n",
-      "model_vars.get('Gini', 0)[-1]=0.6946\n",
-      "Condition not met at step 3470. No data to save.\n",
-      "3470\n",
-      "model_vars.get('Gini', 0)[-1]=0.6826\n",
-      "Condition not met at step 3471. No data to save.\n",
-      "3471\n",
-      "model_vars.get('Gini', 0)[-1]=0.6758\n",
-      "Condition not met at step 3472. No data to save.\n",
-      "3472\n",
-      "model_vars.get('Gini', 0)[-1]=0.6632\n",
-      "Condition not met at step 3473. No data to save.\n",
-      "3473\n",
-      "model_vars.get('Gini', 0)[-1]=0.6537999999999999\n",
-      "Condition not met at step 3474. No data to save.\n",
-      "3474\n",
-      "model_vars.get('Gini', 0)[-1]=0.6548\n",
-      "Condition not met at step 3475. No data to save.\n",
-      "3475\n",
-      "model_vars.get('Gini', 0)[-1]=0.6754\n",
-      "Condition not met at step 3476. No data to save.\n",
-      "3476\n",
-      "model_vars.get('Gini', 0)[-1]=0.6834\n",
-      "Condition not met at step 3477. No data to save.\n",
-      "3477\n",
-      "model_vars.get('Gini', 0)[-1]=0.6814\n",
-      "Condition not met at step 3478. No data to save.\n",
-      "3478\n",
-      "model_vars.get('Gini', 0)[-1]=0.6832\n",
-      "Condition not met at step 3479. No data to save.\n",
-      "3479\n",
-      "model_vars.get('Gini', 0)[-1]=0.7004\n",
-      "Condition met. Appended special results for step 3480 to test_path/special_results.parquet\n",
-      "3480\n",
-      "model_vars.get('Gini', 0)[-1]=0.6819999999999999\n",
-      "Condition not met at step 3481. No data to save.\n",
-      "3481\n",
-      "model_vars.get('Gini', 0)[-1]=0.6782\n",
-      "Condition not met at step 3482. No data to save.\n",
-      "3482\n",
-      "model_vars.get('Gini', 0)[-1]=0.6556\n",
-      "Condition not met at step 3483. No data to save.\n",
-      "3483\n",
-      "model_vars.get('Gini', 0)[-1]=0.6712\n",
-      "Condition not met at step 3484. No data to save.\n",
-      "3484\n",
-      "model_vars.get('Gini', 0)[-1]=0.6768000000000001\n",
-      "Condition not met at step 3485. No data to save.\n",
-      "3485\n",
-      "model_vars.get('Gini', 0)[-1]=0.6824\n",
-      "Condition not met at step 3486. No data to save.\n",
-      "3486\n",
-      "model_vars.get('Gini', 0)[-1]=0.6828000000000001\n",
-      "Condition not met at step 3487. No data to save.\n",
-      "3487\n",
-      "model_vars.get('Gini', 0)[-1]=0.679\n",
-      "Condition not met at step 3488. No data to save.\n",
-      "3488\n",
-      "model_vars.get('Gini', 0)[-1]=0.6762\n",
-      "Condition not met at step 3489. No data to save.\n",
-      "3489\n",
-      "model_vars.get('Gini', 0)[-1]=0.6699999999999999\n",
-      "Condition not met at step 3490. No data to save.\n",
-      "3490\n",
-      "model_vars.get('Gini', 0)[-1]=0.6846\n",
-      "Condition not met at step 3491. No data to save.\n",
-      "3491\n",
-      "model_vars.get('Gini', 0)[-1]=0.6794\n",
-      "Condition not met at step 3492. No data to save.\n",
-      "3492\n",
-      "model_vars.get('Gini', 0)[-1]=0.6762\n",
-      "Condition not met at step 3493. No data to save.\n",
-      "3493\n",
-      "model_vars.get('Gini', 0)[-1]=0.692\n",
-      "Condition not met at step 3494. No data to save.\n",
-      "3494\n",
-      "model_vars.get('Gini', 0)[-1]=0.673\n",
-      "Condition not met at step 3495. No data to save.\n",
-      "3495\n",
-      "model_vars.get('Gini', 0)[-1]=0.6898\n",
-      "Condition not met at step 3496. No data to save.\n",
-      "3496\n",
-      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
-      "Condition not met at step 3497. No data to save.\n",
-      "3497\n",
-      "model_vars.get('Gini', 0)[-1]=0.6576\n",
-      "Condition not met at step 3498. No data to save.\n",
-      "3498\n",
-      "model_vars.get('Gini', 0)[-1]=0.6604\n",
-      "Condition not met at step 3499. No data to save.\n",
-      "3499\n",
-      "CALLED\n",
-      "self.model._steps=3500\n",
-      " TEST self.model._steps=3500\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "3400  0.6626\n",
-      "3401  0.6538\n",
-      "3402  0.6572\n",
-      "3403  0.6492\n",
-      "3404  0.6386\n",
-      "...      ...\n",
-      "3495  0.6898\n",
-      "3496  0.6606\n",
-      "3497  0.6576\n",
-      "3498  0.6604\n",
-      "3499  0.6790\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_035.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_035.parquet'\n",
-      "Saving model to test_path/model_data_035.parquet\n",
-      "Saving agent to test_path/agent_data_035.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.679\n",
-      "Condition not met at step 3500. No data to save.\n",
-      "3500\n",
-      "model_vars.get('Gini', 0)[-1]=0.7090000000000001\n",
-      "Condition met. Appended special results for step 3501 to test_path/special_results.parquet\n",
-      "3501\n",
-      "model_vars.get('Gini', 0)[-1]=0.7061999999999999\n",
-      "Condition met. Appended special results for step 3502 to test_path/special_results.parquet\n",
-      "3502\n",
-      "model_vars.get('Gini', 0)[-1]=0.712\n",
-      "Condition met. Appended special results for step 3503 to test_path/special_results.parquet\n",
-      "3503\n",
-      "model_vars.get('Gini', 0)[-1]=0.6938\n",
-      "Condition not met at step 3504. No data to save.\n",
-      "3504\n",
-      "model_vars.get('Gini', 0)[-1]=0.7054\n",
-      "Condition met. Appended special results for step 3505 to test_path/special_results.parquet\n",
-      "3505\n",
-      "model_vars.get('Gini', 0)[-1]=0.6921999999999999\n",
-      "Condition not met at step 3506. No data to save.\n",
-      "3506\n",
-      "model_vars.get('Gini', 0)[-1]=0.6912\n",
-      "Condition not met at step 3507. No data to save.\n",
-      "3507\n",
-      "model_vars.get('Gini', 0)[-1]=0.6868000000000001\n",
-      "Condition not met at step 3508. No data to save.\n",
-      "3508\n",
-      "model_vars.get('Gini', 0)[-1]=0.6748000000000001\n",
-      "Condition not met at step 3509. No data to save.\n",
-      "3509\n",
-      "model_vars.get('Gini', 0)[-1]=0.6448\n",
-      "Condition not met at step 3510. No data to save.\n",
-      "3510\n",
-      "model_vars.get('Gini', 0)[-1]=0.646\n",
-      "Condition not met at step 3511. No data to save.\n",
-      "3511\n",
-      "model_vars.get('Gini', 0)[-1]=0.6716\n",
-      "Condition not met at step 3512. No data to save.\n",
-      "3512\n",
-      "model_vars.get('Gini', 0)[-1]=0.649\n",
-      "Condition not met at step 3513. No data to save.\n",
-      "3513\n",
-      "model_vars.get('Gini', 0)[-1]=0.667\n",
-      "Condition not met at step 3514. No data to save.\n",
-      "3514\n",
-      "model_vars.get('Gini', 0)[-1]=0.6646000000000001\n",
-      "Condition not met at step 3515. No data to save.\n",
-      "3515\n",
-      "model_vars.get('Gini', 0)[-1]=0.677\n",
-      "Condition not met at step 3516. No data to save.\n",
-      "3516\n",
-      "model_vars.get('Gini', 0)[-1]=0.6684\n",
-      "Condition not met at step 3517. No data to save.\n",
-      "3517\n",
-      "model_vars.get('Gini', 0)[-1]=0.6596\n",
-      "Condition not met at step 3518. No data to save.\n",
-      "3518\n",
-      "model_vars.get('Gini', 0)[-1]=0.6794\n",
-      "Condition not met at step 3519. No data to save.\n",
-      "3519\n",
-      "model_vars.get('Gini', 0)[-1]=0.6537999999999999\n",
-      "Condition not met at step 3520. No data to save.\n",
-      "3520\n",
-      "model_vars.get('Gini', 0)[-1]=0.675\n",
-      "Condition not met at step 3521. No data to save.\n",
-      "3521\n",
-      "model_vars.get('Gini', 0)[-1]=0.7012\n",
-      "Condition met. Appended special results for step 3522 to test_path/special_results.parquet\n",
-      "3522\n",
-      "model_vars.get('Gini', 0)[-1]=0.7021999999999999\n",
-      "Condition met. Appended special results for step 3523 to test_path/special_results.parquet\n",
-      "3523\n",
-      "model_vars.get('Gini', 0)[-1]=0.7101999999999999\n",
-      "Condition met. Appended special results for step 3524 to test_path/special_results.parquet\n",
-      "3524\n",
-      "model_vars.get('Gini', 0)[-1]=0.6906\n",
-      "Condition not met at step 3525. No data to save.\n",
-      "3525\n",
-      "model_vars.get('Gini', 0)[-1]=0.6958\n",
-      "Condition not met at step 3526. No data to save.\n",
-      "3526\n",
-      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
-      "Condition not met at step 3527. No data to save.\n",
-      "3527\n",
-      "model_vars.get('Gini', 0)[-1]=0.6812\n",
-      "Condition not met at step 3528. No data to save.\n",
-      "3528\n",
-      "model_vars.get('Gini', 0)[-1]=0.6378\n",
-      "Condition not met at step 3529. No data to save.\n",
-      "3529\n",
-      "model_vars.get('Gini', 0)[-1]=0.652\n",
-      "Condition not met at step 3530. No data to save.\n",
-      "3530\n",
-      "model_vars.get('Gini', 0)[-1]=0.6168\n",
-      "Condition not met at step 3531. No data to save.\n",
-      "3531\n",
-      "model_vars.get('Gini', 0)[-1]=0.621\n",
-      "Condition not met at step 3532. No data to save.\n",
-      "3532\n",
-      "model_vars.get('Gini', 0)[-1]=0.6174\n",
-      "Condition not met at step 3533. No data to save.\n",
-      "3533\n",
-      "model_vars.get('Gini', 0)[-1]=0.6246\n",
-      "Condition not met at step 3534. No data to save.\n",
-      "3534\n",
-      "model_vars.get('Gini', 0)[-1]=0.5993999999999999\n",
-      "Condition not met at step 3535. No data to save.\n",
-      "3535\n",
-      "model_vars.get('Gini', 0)[-1]=0.6208\n",
-      "Condition not met at step 3536. No data to save.\n",
-      "3536\n",
-      "model_vars.get('Gini', 0)[-1]=0.652\n",
-      "Condition not met at step 3537. No data to save.\n",
-      "3537\n",
-      "model_vars.get('Gini', 0)[-1]=0.637\n",
-      "Condition not met at step 3538. No data to save.\n",
-      "3538\n",
-      "model_vars.get('Gini', 0)[-1]=0.6534\n",
-      "Condition not met at step 3539. No data to save.\n",
-      "3539\n",
-      "model_vars.get('Gini', 0)[-1]=0.6577999999999999\n",
-      "Condition not met at step 3540. No data to save.\n",
-      "3540\n",
-      "model_vars.get('Gini', 0)[-1]=0.6396\n",
-      "Condition not met at step 3541. No data to save.\n",
-      "3541\n",
-      "model_vars.get('Gini', 0)[-1]=0.6496\n",
-      "Condition not met at step 3542. No data to save.\n",
-      "3542\n",
-      "model_vars.get('Gini', 0)[-1]=0.6654\n",
-      "Condition not met at step 3543. No data to save.\n",
-      "3543\n",
-      "model_vars.get('Gini', 0)[-1]=0.6604\n",
-      "Condition not met at step 3544. No data to save.\n",
-      "3544\n",
-      "model_vars.get('Gini', 0)[-1]=0.6774\n",
-      "Condition not met at step 3545. No data to save.\n",
-      "3545\n",
-      "model_vars.get('Gini', 0)[-1]=0.6872\n",
-      "Condition not met at step 3546. No data to save.\n",
-      "3546\n",
-      "model_vars.get('Gini', 0)[-1]=0.6930000000000001\n",
-      "Condition not met at step 3547. No data to save.\n",
-      "3547\n",
-      "model_vars.get('Gini', 0)[-1]=0.694\n",
-      "Condition not met at step 3548. No data to save.\n",
-      "3548\n",
-      "model_vars.get('Gini', 0)[-1]=0.6908000000000001\n",
-      "Condition not met at step 3549. No data to save.\n",
-      "3549\n",
-      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
-      "Condition not met at step 3550. No data to save.\n",
-      "3550\n",
-      "model_vars.get('Gini', 0)[-1]=0.663\n",
-      "Condition not met at step 3551. No data to save.\n",
-      "3551\n",
-      "model_vars.get('Gini', 0)[-1]=0.6686000000000001\n",
-      "Condition not met at step 3552. No data to save.\n",
-      "3552\n",
-      "model_vars.get('Gini', 0)[-1]=0.6372\n",
-      "Condition not met at step 3553. No data to save.\n",
-      "3553\n",
-      "model_vars.get('Gini', 0)[-1]=0.6682\n",
-      "Condition not met at step 3554. No data to save.\n",
-      "3554\n",
-      "model_vars.get('Gini', 0)[-1]=0.6588\n",
-      "Condition not met at step 3555. No data to save.\n",
-      "3555\n",
-      "model_vars.get('Gini', 0)[-1]=0.6404000000000001\n",
-      "Condition not met at step 3556. No data to save.\n",
-      "3556\n",
-      "model_vars.get('Gini', 0)[-1]=0.6308\n",
-      "Condition not met at step 3557. No data to save.\n",
-      "3557\n",
-      "model_vars.get('Gini', 0)[-1]=0.6517999999999999\n",
-      "Condition not met at step 3558. No data to save.\n",
-      "3558\n",
-      "model_vars.get('Gini', 0)[-1]=0.6562\n",
-      "Condition not met at step 3559. No data to save.\n",
-      "3559\n",
-      "model_vars.get('Gini', 0)[-1]=0.6664\n",
-      "Condition not met at step 3560. No data to save.\n",
-      "3560\n",
-      "model_vars.get('Gini', 0)[-1]=0.6517999999999999\n",
-      "Condition not met at step 3561. No data to save.\n",
-      "3561\n",
-      "model_vars.get('Gini', 0)[-1]=0.6642\n",
-      "Condition not met at step 3562. No data to save.\n",
-      "3562\n",
-      "model_vars.get('Gini', 0)[-1]=0.6658\n",
-      "Condition not met at step 3563. No data to save.\n",
-      "3563\n",
-      "model_vars.get('Gini', 0)[-1]=0.6548\n",
-      "Condition not met at step 3564. No data to save.\n",
-      "3564\n",
-      "model_vars.get('Gini', 0)[-1]=0.6698\n",
-      "Condition not met at step 3565. No data to save.\n",
-      "3565\n",
-      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
-      "Condition not met at step 3566. No data to save.\n",
-      "3566\n",
-      "model_vars.get('Gini', 0)[-1]=0.6774\n",
-      "Condition not met at step 3567. No data to save.\n",
-      "3567\n",
-      "model_vars.get('Gini', 0)[-1]=0.6926\n",
-      "Condition not met at step 3568. No data to save.\n",
-      "3568\n",
-      "model_vars.get('Gini', 0)[-1]=0.7004\n",
-      "Condition met. Appended special results for step 3569 to test_path/special_results.parquet\n",
-      "3569\n",
-      "model_vars.get('Gini', 0)[-1]=0.6864\n",
-      "Condition not met at step 3570. No data to save.\n",
-      "3570\n",
-      "model_vars.get('Gini', 0)[-1]=0.6912\n",
-      "Condition not met at step 3571. No data to save.\n",
-      "3571\n",
-      "model_vars.get('Gini', 0)[-1]=0.6738\n",
-      "Condition not met at step 3572. No data to save.\n",
-      "3572\n",
-      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
-      "Condition not met at step 3573. No data to save.\n",
-      "3573\n",
-      "model_vars.get('Gini', 0)[-1]=0.6746000000000001\n",
-      "Condition not met at step 3574. No data to save.\n",
-      "3574\n",
-      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
-      "Condition not met at step 3575. No data to save.\n",
-      "3575\n",
-      "model_vars.get('Gini', 0)[-1]=0.6496\n",
-      "Condition not met at step 3576. No data to save.\n",
-      "3576\n",
-      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
-      "Condition not met at step 3577. No data to save.\n",
-      "3577\n",
-      "model_vars.get('Gini', 0)[-1]=0.6444000000000001\n",
-      "Condition not met at step 3578. No data to save.\n",
-      "3578\n",
-      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
-      "Condition not met at step 3579. No data to save.\n",
-      "3579\n",
-      "model_vars.get('Gini', 0)[-1]=0.6692\n",
-      "Condition not met at step 3580. No data to save.\n",
-      "3580\n",
-      "model_vars.get('Gini', 0)[-1]=0.6876\n",
-      "Condition not met at step 3581. No data to save.\n",
-      "3581\n",
-      "model_vars.get('Gini', 0)[-1]=0.6932\n",
-      "Condition not met at step 3582. No data to save.\n",
-      "3582\n",
-      "model_vars.get('Gini', 0)[-1]=0.6836\n",
-      "Condition not met at step 3583. No data to save.\n",
-      "3583\n",
-      "model_vars.get('Gini', 0)[-1]=0.687\n",
-      "Condition not met at step 3584. No data to save.\n",
-      "3584\n",
-      "model_vars.get('Gini', 0)[-1]=0.6912\n",
-      "Condition not met at step 3585. No data to save.\n",
-      "3585\n",
-      "model_vars.get('Gini', 0)[-1]=0.6924\n",
-      "Condition not met at step 3586. No data to save.\n",
-      "3586\n",
-      "model_vars.get('Gini', 0)[-1]=0.671\n",
-      "Condition not met at step 3587. No data to save.\n",
-      "3587\n",
-      "model_vars.get('Gini', 0)[-1]=0.673\n",
-      "Condition not met at step 3588. No data to save.\n",
-      "3588\n",
-      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
-      "Condition not met at step 3589. No data to save.\n",
-      "3589\n",
-      "model_vars.get('Gini', 0)[-1]=0.64\n",
-      "Condition not met at step 3590. No data to save.\n",
-      "3590\n",
-      "model_vars.get('Gini', 0)[-1]=0.635\n",
-      "Condition not met at step 3591. No data to save.\n",
-      "3591\n",
-      "model_vars.get('Gini', 0)[-1]=0.642\n",
-      "Condition not met at step 3592. No data to save.\n",
-      "3592\n",
-      "model_vars.get('Gini', 0)[-1]=0.6202000000000001\n",
-      "Condition not met at step 3593. No data to save.\n",
-      "3593\n",
-      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
-      "Condition not met at step 3594. No data to save.\n",
-      "3594\n",
-      "model_vars.get('Gini', 0)[-1]=0.6454\n",
-      "Condition not met at step 3595. No data to save.\n",
-      "3595\n",
-      "model_vars.get('Gini', 0)[-1]=0.6466000000000001\n",
-      "Condition not met at step 3596. No data to save.\n",
-      "3596\n",
-      "model_vars.get('Gini', 0)[-1]=0.673\n",
-      "Condition not met at step 3597. No data to save.\n",
-      "3597\n",
-      "model_vars.get('Gini', 0)[-1]=0.6938\n",
-      "Condition not met at step 3598. No data to save.\n",
-      "3598\n",
-      "model_vars.get('Gini', 0)[-1]=0.677\n",
-      "Condition not met at step 3599. No data to save.\n",
-      "3599\n",
-      "CALLED\n",
-      "self.model._steps=3600\n",
-      " TEST self.model._steps=3600\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "3500  0.7090\n",
-      "3501  0.7062\n",
-      "3502  0.7120\n",
-      "3503  0.6938\n",
-      "3504  0.7054\n",
-      "...      ...\n",
-      "3595  0.6466\n",
-      "3596  0.6730\n",
-      "3597  0.6938\n",
-      "3598  0.6770\n",
-      "3599  0.6718\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_036.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_036.parquet'\n",
-      "Saving model to test_path/model_data_036.parquet\n",
-      "Saving agent to test_path/agent_data_036.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6718\n",
-      "Condition not met at step 3600. No data to save.\n",
-      "3600\n",
-      "model_vars.get('Gini', 0)[-1]=0.6856\n",
-      "Condition not met at step 3601. No data to save.\n",
-      "3601\n",
-      "model_vars.get('Gini', 0)[-1]=0.677\n",
-      "Condition not met at step 3602. No data to save.\n",
-      "3602\n",
-      "model_vars.get('Gini', 0)[-1]=0.6754\n",
-      "Condition not met at step 3603. No data to save.\n",
-      "3603\n",
-      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
-      "Condition not met at step 3604. No data to save.\n",
-      "3604\n",
-      "model_vars.get('Gini', 0)[-1]=0.6642\n",
-      "Condition not met at step 3605. No data to save.\n",
-      "3605\n",
-      "model_vars.get('Gini', 0)[-1]=0.6674\n",
-      "Condition not met at step 3606. No data to save.\n",
-      "3606\n",
-      "model_vars.get('Gini', 0)[-1]=0.6794\n",
-      "Condition not met at step 3607. No data to save.\n",
-      "3607\n",
-      "model_vars.get('Gini', 0)[-1]=0.6832\n",
-      "Condition not met at step 3608. No data to save.\n",
-      "3608\n",
-      "model_vars.get('Gini', 0)[-1]=0.667\n",
-      "Condition not met at step 3609. No data to save.\n",
-      "3609\n",
-      "model_vars.get('Gini', 0)[-1]=0.6814\n",
-      "Condition not met at step 3610. No data to save.\n",
-      "3610\n",
-      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
-      "Condition not met at step 3611. No data to save.\n",
-      "3611\n",
-      "model_vars.get('Gini', 0)[-1]=0.6839999999999999\n",
-      "Condition not met at step 3612. No data to save.\n",
-      "3612\n",
-      "model_vars.get('Gini', 0)[-1]=0.6828000000000001\n",
-      "Condition not met at step 3613. No data to save.\n",
-      "3613\n",
-      "model_vars.get('Gini', 0)[-1]=0.6712\n",
-      "Condition not met at step 3614. No data to save.\n",
-      "3614\n",
-      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
-      "Condition not met at step 3615. No data to save.\n",
-      "3615\n",
-      "model_vars.get('Gini', 0)[-1]=0.666\n",
-      "Condition not met at step 3616. No data to save.\n",
-      "3616\n",
-      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
-      "Condition not met at step 3617. No data to save.\n",
-      "3617\n",
-      "model_vars.get('Gini', 0)[-1]=0.6398\n",
-      "Condition not met at step 3618. No data to save.\n",
-      "3618\n",
-      "model_vars.get('Gini', 0)[-1]=0.6674\n",
-      "Condition not met at step 3619. No data to save.\n",
-      "3619\n",
-      "model_vars.get('Gini', 0)[-1]=0.6622\n",
-      "Condition not met at step 3620. No data to save.\n",
-      "3620\n",
-      "model_vars.get('Gini', 0)[-1]=0.6517999999999999\n",
-      "Condition not met at step 3621. No data to save.\n",
-      "3621\n",
-      "model_vars.get('Gini', 0)[-1]=0.6352\n",
-      "Condition not met at step 3622. No data to save.\n",
-      "3622\n",
-      "model_vars.get('Gini', 0)[-1]=0.6122000000000001\n",
-      "Condition not met at step 3623. No data to save.\n",
-      "3623\n",
-      "model_vars.get('Gini', 0)[-1]=0.6384000000000001\n",
-      "Condition not met at step 3624. No data to save.\n",
-      "3624\n",
-      "model_vars.get('Gini', 0)[-1]=0.6444000000000001\n",
-      "Condition not met at step 3625. No data to save.\n",
-      "3625\n",
-      "model_vars.get('Gini', 0)[-1]=0.6512\n",
-      "Condition not met at step 3626. No data to save.\n",
-      "3626\n",
-      "model_vars.get('Gini', 0)[-1]=0.6334\n",
-      "Condition not met at step 3627. No data to save.\n",
-      "3627\n",
-      "model_vars.get('Gini', 0)[-1]=0.622\n",
-      "Condition not met at step 3628. No data to save.\n",
-      "3628\n",
-      "model_vars.get('Gini', 0)[-1]=0.657\n",
-      "Condition not met at step 3629. No data to save.\n",
-      "3629\n",
-      "model_vars.get('Gini', 0)[-1]=0.6544\n",
-      "Condition not met at step 3630. No data to save.\n",
-      "3630\n",
-      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
-      "Condition not met at step 3631. No data to save.\n",
-      "3631\n",
-      "model_vars.get('Gini', 0)[-1]=0.6312\n",
-      "Condition not met at step 3632. No data to save.\n",
-      "3632\n",
-      "model_vars.get('Gini', 0)[-1]=0.6434\n",
-      "Condition not met at step 3633. No data to save.\n",
-      "3633\n",
-      "model_vars.get('Gini', 0)[-1]=0.6332\n",
-      "Condition not met at step 3634. No data to save.\n",
-      "3634\n",
-      "model_vars.get('Gini', 0)[-1]=0.6338\n",
-      "Condition not met at step 3635. No data to save.\n",
-      "3635\n",
-      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
-      "Condition not met at step 3636. No data to save.\n",
-      "3636\n",
-      "model_vars.get('Gini', 0)[-1]=0.6536\n",
-      "Condition not met at step 3637. No data to save.\n",
-      "3637\n",
-      "model_vars.get('Gini', 0)[-1]=0.6642\n",
-      "Condition not met at step 3638. No data to save.\n",
-      "3638\n",
-      "model_vars.get('Gini', 0)[-1]=0.6617999999999999\n",
-      "Condition not met at step 3639. No data to save.\n",
-      "3639\n",
-      "model_vars.get('Gini', 0)[-1]=0.6726000000000001\n",
-      "Condition not met at step 3640. No data to save.\n",
-      "3640\n",
-      "model_vars.get('Gini', 0)[-1]=0.6708000000000001\n",
-      "Condition not met at step 3641. No data to save.\n",
-      "3641\n",
-      "model_vars.get('Gini', 0)[-1]=0.6932\n",
-      "Condition not met at step 3642. No data to save.\n",
-      "3642\n",
-      "model_vars.get('Gini', 0)[-1]=0.6998\n",
-      "Condition not met at step 3643. No data to save.\n",
-      "3643\n",
-      "model_vars.get('Gini', 0)[-1]=0.6768000000000001\n",
-      "Condition not met at step 3644. No data to save.\n",
-      "3644\n",
-      "model_vars.get('Gini', 0)[-1]=0.6792\n",
-      "Condition not met at step 3645. No data to save.\n",
-      "3645\n",
-      "model_vars.get('Gini', 0)[-1]=0.6698\n",
-      "Condition not met at step 3646. No data to save.\n",
-      "3646\n",
-      "model_vars.get('Gini', 0)[-1]=0.673\n",
-      "Condition not met at step 3647. No data to save.\n",
-      "3647\n",
-      "model_vars.get('Gini', 0)[-1]=0.6666000000000001\n",
-      "Condition not met at step 3648. No data to save.\n",
-      "3648\n",
-      "model_vars.get('Gini', 0)[-1]=0.658\n",
-      "Condition not met at step 3649. No data to save.\n",
-      "3649\n",
-      "model_vars.get('Gini', 0)[-1]=0.6719999999999999\n",
-      "Condition not met at step 3650. No data to save.\n",
-      "3650\n",
-      "model_vars.get('Gini', 0)[-1]=0.6972\n",
-      "Condition not met at step 3651. No data to save.\n",
-      "3651\n",
-      "model_vars.get('Gini', 0)[-1]=0.6772\n",
-      "Condition not met at step 3652. No data to save.\n",
-      "3652\n",
-      "model_vars.get('Gini', 0)[-1]=0.6842\n",
-      "Condition not met at step 3653. No data to save.\n",
-      "3653\n",
-      "model_vars.get('Gini', 0)[-1]=0.6872\n",
-      "Condition not met at step 3654. No data to save.\n",
-      "3654\n",
-      "model_vars.get('Gini', 0)[-1]=0.6896\n",
-      "Condition not met at step 3655. No data to save.\n",
-      "3655\n",
-      "model_vars.get('Gini', 0)[-1]=0.679\n",
-      "Condition not met at step 3656. No data to save.\n",
-      "3656\n",
-      "model_vars.get('Gini', 0)[-1]=0.6572\n",
-      "Condition not met at step 3657. No data to save.\n",
-      "3657\n",
-      "model_vars.get('Gini', 0)[-1]=0.645\n",
-      "Condition not met at step 3658. No data to save.\n",
-      "3658\n",
-      "model_vars.get('Gini', 0)[-1]=0.6355999999999999\n",
-      "Condition not met at step 3659. No data to save.\n",
-      "3659\n",
-      "model_vars.get('Gini', 0)[-1]=0.6517999999999999\n",
-      "Condition not met at step 3660. No data to save.\n",
-      "3660\n",
-      "model_vars.get('Gini', 0)[-1]=0.6759999999999999\n",
-      "Condition not met at step 3661. No data to save.\n",
-      "3661\n",
-      "model_vars.get('Gini', 0)[-1]=0.6864\n",
-      "Condition not met at step 3662. No data to save.\n",
-      "3662\n",
-      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
-      "Condition not met at step 3663. No data to save.\n",
-      "3663\n",
-      "model_vars.get('Gini', 0)[-1]=0.696\n",
-      "Condition not met at step 3664. No data to save.\n",
-      "3664\n",
-      "model_vars.get('Gini', 0)[-1]=0.6682\n",
-      "Condition not met at step 3665. No data to save.\n",
-      "3665\n",
-      "model_vars.get('Gini', 0)[-1]=0.679\n",
-      "Condition not met at step 3666. No data to save.\n",
-      "3666\n",
-      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
-      "Condition not met at step 3667. No data to save.\n",
-      "3667\n",
-      "model_vars.get('Gini', 0)[-1]=0.662\n",
-      "Condition not met at step 3668. No data to save.\n",
-      "3668\n",
-      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
-      "Condition not met at step 3669. No data to save.\n",
-      "3669\n",
-      "model_vars.get('Gini', 0)[-1]=0.6792\n",
-      "Condition not met at step 3670. No data to save.\n",
-      "3670\n",
-      "model_vars.get('Gini', 0)[-1]=0.6254\n",
-      "Condition not met at step 3671. No data to save.\n",
-      "3671\n",
-      "model_vars.get('Gini', 0)[-1]=0.6212\n",
-      "Condition not met at step 3672. No data to save.\n",
-      "3672\n",
-      "model_vars.get('Gini', 0)[-1]=0.632\n",
-      "Condition not met at step 3673. No data to save.\n",
-      "3673\n",
-      "model_vars.get('Gini', 0)[-1]=0.649\n",
-      "Condition not met at step 3674. No data to save.\n",
-      "3674\n",
-      "model_vars.get('Gini', 0)[-1]=0.6448\n",
-      "Condition not met at step 3675. No data to save.\n",
-      "3675\n",
-      "model_vars.get('Gini', 0)[-1]=0.6318\n",
-      "Condition not met at step 3676. No data to save.\n",
-      "3676\n",
-      "model_vars.get('Gini', 0)[-1]=0.6376\n",
-      "Condition not met at step 3677. No data to save.\n",
-      "3677\n",
-      "model_vars.get('Gini', 0)[-1]=0.633\n",
-      "Condition not met at step 3678. No data to save.\n",
-      "3678\n",
-      "model_vars.get('Gini', 0)[-1]=0.6656\n",
-      "Condition not met at step 3679. No data to save.\n",
-      "3679\n",
-      "model_vars.get('Gini', 0)[-1]=0.6878\n",
-      "Condition not met at step 3680. No data to save.\n",
-      "3680\n",
-      "model_vars.get('Gini', 0)[-1]=0.6872\n",
-      "Condition not met at step 3681. No data to save.\n",
-      "3681\n",
-      "model_vars.get('Gini', 0)[-1]=0.7046\n",
-      "Condition met. Appended special results for step 3682 to test_path/special_results.parquet\n",
-      "3682\n",
-      "model_vars.get('Gini', 0)[-1]=0.698\n",
-      "Condition not met at step 3683. No data to save.\n",
-      "3683\n",
-      "model_vars.get('Gini', 0)[-1]=0.6752\n",
-      "Condition not met at step 3684. No data to save.\n",
-      "3684\n",
-      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
-      "Condition not met at step 3685. No data to save.\n",
-      "3685\n",
-      "model_vars.get('Gini', 0)[-1]=0.6902\n",
-      "Condition not met at step 3686. No data to save.\n",
-      "3686\n",
-      "model_vars.get('Gini', 0)[-1]=0.6974\n",
-      "Condition not met at step 3687. No data to save.\n",
-      "3687\n",
-      "model_vars.get('Gini', 0)[-1]=0.6928000000000001\n",
-      "Condition not met at step 3688. No data to save.\n",
-      "3688\n",
-      "model_vars.get('Gini', 0)[-1]=0.6974\n",
-      "Condition not met at step 3689. No data to save.\n",
-      "3689\n",
-      "model_vars.get('Gini', 0)[-1]=0.6779999999999999\n",
-      "Condition not met at step 3690. No data to save.\n",
-      "3690\n",
-      "model_vars.get('Gini', 0)[-1]=0.6674\n",
-      "Condition not met at step 3691. No data to save.\n",
-      "3691\n",
-      "model_vars.get('Gini', 0)[-1]=0.6718\n",
-      "Condition not met at step 3692. No data to save.\n",
-      "3692\n",
-      "model_vars.get('Gini', 0)[-1]=0.664\n",
-      "Condition not met at step 3693. No data to save.\n",
-      "3693\n",
-      "model_vars.get('Gini', 0)[-1]=0.6644\n",
-      "Condition not met at step 3694. No data to save.\n",
-      "3694\n",
-      "model_vars.get('Gini', 0)[-1]=0.6624\n",
-      "Condition not met at step 3695. No data to save.\n",
-      "3695\n",
-      "model_vars.get('Gini', 0)[-1]=0.6432\n",
-      "Condition not met at step 3696. No data to save.\n",
-      "3696\n",
-      "model_vars.get('Gini', 0)[-1]=0.6644\n",
-      "Condition not met at step 3697. No data to save.\n",
-      "3697\n",
-      "model_vars.get('Gini', 0)[-1]=0.6624\n",
-      "Condition not met at step 3698. No data to save.\n",
-      "3698\n",
-      "model_vars.get('Gini', 0)[-1]=0.6758\n",
-      "Condition not met at step 3699. No data to save.\n",
-      "3699\n",
-      "CALLED\n",
-      "self.model._steps=3700\n",
-      " TEST self.model._steps=3700\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "3600  0.6856\n",
-      "3601  0.6770\n",
-      "3602  0.6754\n",
-      "3603  0.6688\n",
-      "3604  0.6642\n",
-      "...      ...\n",
-      "3695  0.6432\n",
-      "3696  0.6644\n",
-      "3697  0.6624\n",
-      "3698  0.6758\n",
-      "3699  0.6884\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_037.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_037.parquet'\n",
-      "Saving model to test_path/model_data_037.parquet\n",
-      "Saving agent to test_path/agent_data_037.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6884\n",
-      "Condition not met at step 3700. No data to save.\n",
-      "3700\n",
-      "model_vars.get('Gini', 0)[-1]=0.6858\n",
-      "Condition not met at step 3701. No data to save.\n",
-      "3701\n",
-      "model_vars.get('Gini', 0)[-1]=0.6748000000000001\n",
-      "Condition not met at step 3702. No data to save.\n",
-      "3702\n",
-      "model_vars.get('Gini', 0)[-1]=0.6759999999999999\n",
-      "Condition not met at step 3703. No data to save.\n",
-      "3703\n",
-      "model_vars.get('Gini', 0)[-1]=0.6604\n",
-      "Condition not met at step 3704. No data to save.\n",
-      "3704\n",
-      "model_vars.get('Gini', 0)[-1]=0.6464000000000001\n",
-      "Condition not met at step 3705. No data to save.\n",
-      "3705\n",
-      "model_vars.get('Gini', 0)[-1]=0.646\n",
-      "Condition not met at step 3706. No data to save.\n",
-      "3706\n",
-      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
-      "Condition not met at step 3707. No data to save.\n",
-      "3707\n",
-      "model_vars.get('Gini', 0)[-1]=0.6548\n",
-      "Condition not met at step 3708. No data to save.\n",
-      "3708\n",
-      "model_vars.get('Gini', 0)[-1]=0.6514\n",
-      "Condition not met at step 3709. No data to save.\n",
-      "3709\n",
-      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
-      "Condition not met at step 3710. No data to save.\n",
-      "3710\n",
-      "model_vars.get('Gini', 0)[-1]=0.6662\n",
-      "Condition not met at step 3711. No data to save.\n",
-      "3711\n",
-      "model_vars.get('Gini', 0)[-1]=0.6698\n",
-      "Condition not met at step 3712. No data to save.\n",
-      "3712\n",
-      "model_vars.get('Gini', 0)[-1]=0.6746000000000001\n",
-      "Condition not met at step 3713. No data to save.\n",
-      "3713\n",
-      "model_vars.get('Gini', 0)[-1]=0.6784\n",
-      "Condition not met at step 3714. No data to save.\n",
-      "3714\n",
-      "model_vars.get('Gini', 0)[-1]=0.6866\n",
-      "Condition not met at step 3715. No data to save.\n",
-      "3715\n",
-      "model_vars.get('Gini', 0)[-1]=0.69\n",
-      "Condition not met at step 3716. No data to save.\n",
-      "3716\n",
-      "model_vars.get('Gini', 0)[-1]=0.7001999999999999\n",
-      "Condition met. Appended special results for step 3717 to test_path/special_results.parquet\n",
-      "3717\n",
-      "model_vars.get('Gini', 0)[-1]=0.692\n",
-      "Condition not met at step 3718. No data to save.\n",
-      "3718\n",
-      "model_vars.get('Gini', 0)[-1]=0.7138\n",
-      "Condition met. Appended special results for step 3719 to test_path/special_results.parquet\n",
-      "3719\n",
-      "model_vars.get('Gini', 0)[-1]=0.7203999999999999\n",
-      "Condition met. Appended special results for step 3720 to test_path/special_results.parquet\n",
-      "3720\n",
-      "model_vars.get('Gini', 0)[-1]=0.7142\n",
-      "Condition met. Appended special results for step 3721 to test_path/special_results.parquet\n",
-      "3721\n",
-      "model_vars.get('Gini', 0)[-1]=0.7108\n",
-      "Condition met. Appended special results for step 3722 to test_path/special_results.parquet\n",
-      "3722\n",
-      "model_vars.get('Gini', 0)[-1]=0.7144\n",
-      "Condition met. Appended special results for step 3723 to test_path/special_results.parquet\n",
-      "3723\n",
-      "model_vars.get('Gini', 0)[-1]=0.6876\n",
-      "Condition not met at step 3724. No data to save.\n",
-      "3724\n",
-      "model_vars.get('Gini', 0)[-1]=0.6626000000000001\n",
-      "Condition not met at step 3725. No data to save.\n",
-      "3725\n",
-      "model_vars.get('Gini', 0)[-1]=0.6232\n",
-      "Condition not met at step 3726. No data to save.\n",
-      "3726\n",
-      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
-      "Condition not met at step 3727. No data to save.\n",
-      "3727\n",
-      "model_vars.get('Gini', 0)[-1]=0.6234\n",
-      "Condition not met at step 3728. No data to save.\n",
-      "3728\n",
-      "model_vars.get('Gini', 0)[-1]=0.6656\n",
-      "Condition not met at step 3729. No data to save.\n",
-      "3729\n",
-      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
-      "Condition not met at step 3730. No data to save.\n",
-      "3730\n",
-      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
-      "Condition not met at step 3731. No data to save.\n",
-      "3731\n",
-      "model_vars.get('Gini', 0)[-1]=0.644\n",
-      "Condition not met at step 3732. No data to save.\n",
-      "3732\n",
-      "model_vars.get('Gini', 0)[-1]=0.631\n",
-      "Condition not met at step 3733. No data to save.\n",
-      "3733\n",
-      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
-      "Condition not met at step 3734. No data to save.\n",
-      "3734\n",
-      "model_vars.get('Gini', 0)[-1]=0.6022000000000001\n",
-      "Condition not met at step 3735. No data to save.\n",
-      "3735\n",
-      "model_vars.get('Gini', 0)[-1]=0.6078\n",
-      "Condition not met at step 3736. No data to save.\n",
-      "3736\n",
-      "model_vars.get('Gini', 0)[-1]=0.6554\n",
-      "Condition not met at step 3737. No data to save.\n",
-      "3737\n",
-      "model_vars.get('Gini', 0)[-1]=0.6612\n",
-      "Condition not met at step 3738. No data to save.\n",
-      "3738\n",
-      "model_vars.get('Gini', 0)[-1]=0.6552\n",
-      "Condition not met at step 3739. No data to save.\n",
-      "3739\n",
-      "model_vars.get('Gini', 0)[-1]=0.6506000000000001\n",
-      "Condition not met at step 3740. No data to save.\n",
-      "3740\n",
-      "model_vars.get('Gini', 0)[-1]=0.6376\n",
-      "Condition not met at step 3741. No data to save.\n",
-      "3741\n",
-      "model_vars.get('Gini', 0)[-1]=0.6648000000000001\n",
-      "Condition not met at step 3742. No data to save.\n",
-      "3742\n",
-      "model_vars.get('Gini', 0)[-1]=0.6652\n",
-      "Condition not met at step 3743. No data to save.\n",
-      "3743\n",
-      "model_vars.get('Gini', 0)[-1]=0.6556\n",
-      "Condition not met at step 3744. No data to save.\n",
-      "3744\n",
-      "model_vars.get('Gini', 0)[-1]=0.6517999999999999\n",
-      "Condition not met at step 3745. No data to save.\n",
-      "3745\n",
-      "model_vars.get('Gini', 0)[-1]=0.664\n",
-      "Condition not met at step 3746. No data to save.\n",
-      "3746\n",
-      "model_vars.get('Gini', 0)[-1]=0.629\n",
-      "Condition not met at step 3747. No data to save.\n",
-      "3747\n",
-      "model_vars.get('Gini', 0)[-1]=0.6224000000000001\n",
-      "Condition not met at step 3748. No data to save.\n",
-      "3748\n",
-      "model_vars.get('Gini', 0)[-1]=0.6497999999999999\n",
-      "Condition not met at step 3749. No data to save.\n",
-      "3749\n",
-      "model_vars.get('Gini', 0)[-1]=0.649\n",
-      "Condition not met at step 3750. No data to save.\n",
-      "3750\n",
-      "model_vars.get('Gini', 0)[-1]=0.6666000000000001\n",
-      "Condition not met at step 3751. No data to save.\n",
-      "3751\n",
-      "model_vars.get('Gini', 0)[-1]=0.6848000000000001\n",
-      "Condition not met at step 3752. No data to save.\n",
-      "3752\n",
-      "model_vars.get('Gini', 0)[-1]=0.6644\n",
-      "Condition not met at step 3753. No data to save.\n",
-      "3753\n",
-      "model_vars.get('Gini', 0)[-1]=0.6617999999999999\n",
-      "Condition not met at step 3754. No data to save.\n",
-      "3754\n",
-      "model_vars.get('Gini', 0)[-1]=0.6664\n",
-      "Condition not met at step 3755. No data to save.\n",
-      "3755\n",
-      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
-      "Condition not met at step 3756. No data to save.\n",
-      "3756\n",
-      "model_vars.get('Gini', 0)[-1]=0.6888000000000001\n",
-      "Condition not met at step 3757. No data to save.\n",
-      "3757\n",
-      "model_vars.get('Gini', 0)[-1]=0.6704\n",
-      "Condition not met at step 3758. No data to save.\n",
-      "3758\n",
-      "model_vars.get('Gini', 0)[-1]=0.666\n",
-      "Condition not met at step 3759. No data to save.\n",
-      "3759\n",
-      "model_vars.get('Gini', 0)[-1]=0.6472\n",
-      "Condition not met at step 3760. No data to save.\n",
-      "3760\n",
-      "model_vars.get('Gini', 0)[-1]=0.6656\n",
-      "Condition not met at step 3761. No data to save.\n",
-      "3761\n",
-      "model_vars.get('Gini', 0)[-1]=0.6444000000000001\n",
-      "Condition not met at step 3762. No data to save.\n",
-      "3762\n",
-      "model_vars.get('Gini', 0)[-1]=0.6582\n",
-      "Condition not met at step 3763. No data to save.\n",
-      "3763\n",
-      "model_vars.get('Gini', 0)[-1]=0.6802\n",
-      "Condition not met at step 3764. No data to save.\n",
-      "3764\n",
-      "model_vars.get('Gini', 0)[-1]=0.6828000000000001\n",
-      "Condition not met at step 3765. No data to save.\n",
-      "3765\n",
-      "model_vars.get('Gini', 0)[-1]=0.6712\n",
-      "Condition not met at step 3766. No data to save.\n",
-      "3766\n",
-      "model_vars.get('Gini', 0)[-1]=0.624\n",
-      "Condition not met at step 3767. No data to save.\n",
-      "3767\n",
-      "model_vars.get('Gini', 0)[-1]=0.6274\n",
-      "Condition not met at step 3768. No data to save.\n",
-      "3768\n",
-      "model_vars.get('Gini', 0)[-1]=0.6204000000000001\n",
-      "Condition not met at step 3769. No data to save.\n",
-      "3769\n",
-      "model_vars.get('Gini', 0)[-1]=0.6284000000000001\n",
-      "Condition not met at step 3770. No data to save.\n",
-      "3770\n",
-      "model_vars.get('Gini', 0)[-1]=0.6537999999999999\n",
-      "Condition not met at step 3771. No data to save.\n",
-      "3771\n",
-      "model_vars.get('Gini', 0)[-1]=0.6274\n",
-      "Condition not met at step 3772. No data to save.\n",
-      "3772\n",
-      "model_vars.get('Gini', 0)[-1]=0.6552\n",
-      "Condition not met at step 3773. No data to save.\n",
-      "3773\n",
-      "model_vars.get('Gini', 0)[-1]=0.6444000000000001\n",
-      "Condition not met at step 3774. No data to save.\n",
-      "3774\n",
-      "model_vars.get('Gini', 0)[-1]=0.6204000000000001\n",
-      "Condition not met at step 3775. No data to save.\n",
-      "3775\n",
-      "model_vars.get('Gini', 0)[-1]=0.6416\n",
-      "Condition not met at step 3776. No data to save.\n",
-      "3776\n",
-      "model_vars.get('Gini', 0)[-1]=0.659\n",
-      "Condition not met at step 3777. No data to save.\n",
-      "3777\n",
-      "model_vars.get('Gini', 0)[-1]=0.6612\n",
-      "Condition not met at step 3778. No data to save.\n",
-      "3778\n",
-      "model_vars.get('Gini', 0)[-1]=0.6658\n",
-      "Condition not met at step 3779. No data to save.\n",
-      "3779\n",
-      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
-      "Condition not met at step 3780. No data to save.\n",
-      "3780\n",
-      "model_vars.get('Gini', 0)[-1]=0.6582\n",
-      "Condition not met at step 3781. No data to save.\n",
-      "3781\n",
-      "model_vars.get('Gini', 0)[-1]=0.6426000000000001\n",
-      "Condition not met at step 3782. No data to save.\n",
-      "3782\n",
-      "model_vars.get('Gini', 0)[-1]=0.643\n",
-      "Condition not met at step 3783. No data to save.\n",
-      "3783\n",
-      "model_vars.get('Gini', 0)[-1]=0.6168\n",
-      "Condition not met at step 3784. No data to save.\n",
-      "3784\n",
-      "model_vars.get('Gini', 0)[-1]=0.656\n",
-      "Condition not met at step 3785. No data to save.\n",
-      "3785\n",
-      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
-      "Condition not met at step 3786. No data to save.\n",
-      "3786\n",
-      "model_vars.get('Gini', 0)[-1]=0.62\n",
-      "Condition not met at step 3787. No data to save.\n",
-      "3787\n",
-      "model_vars.get('Gini', 0)[-1]=0.62\n",
-      "Condition not met at step 3788. No data to save.\n",
-      "3788\n",
-      "model_vars.get('Gini', 0)[-1]=0.603\n",
-      "Condition not met at step 3789. No data to save.\n",
-      "3789\n",
-      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
-      "Condition not met at step 3790. No data to save.\n",
-      "3790\n",
-      "model_vars.get('Gini', 0)[-1]=0.6306\n",
-      "Condition not met at step 3791. No data to save.\n",
-      "3791\n",
-      "model_vars.get('Gini', 0)[-1]=0.6532\n",
-      "Condition not met at step 3792. No data to save.\n",
-      "3792\n",
-      "model_vars.get('Gini', 0)[-1]=0.6355999999999999\n",
-      "Condition not met at step 3793. No data to save.\n",
-      "3793\n",
-      "model_vars.get('Gini', 0)[-1]=0.6278\n",
-      "Condition not met at step 3794. No data to save.\n",
-      "3794\n",
-      "model_vars.get('Gini', 0)[-1]=0.6378\n",
-      "Condition not met at step 3795. No data to save.\n",
-      "3795\n",
-      "model_vars.get('Gini', 0)[-1]=0.6304000000000001\n",
-      "Condition not met at step 3796. No data to save.\n",
-      "3796\n",
-      "model_vars.get('Gini', 0)[-1]=0.624\n",
-      "Condition not met at step 3797. No data to save.\n",
-      "3797\n",
-      "model_vars.get('Gini', 0)[-1]=0.618\n",
-      "Condition not met at step 3798. No data to save.\n",
-      "3798\n",
-      "model_vars.get('Gini', 0)[-1]=0.5933999999999999\n",
-      "Condition not met at step 3799. No data to save.\n",
-      "3799\n",
-      "CALLED\n",
-      "self.model._steps=3800\n",
-      " TEST self.model._steps=3800\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "3700  0.6858\n",
-      "3701  0.6748\n",
-      "3702  0.6760\n",
-      "3703  0.6604\n",
-      "3704  0.6464\n",
-      "...      ...\n",
-      "3795  0.6304\n",
-      "3796  0.6240\n",
-      "3797  0.6180\n",
-      "3798  0.5934\n",
-      "3799  0.5876\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_038.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_038.parquet'\n",
-      "Saving model to test_path/model_data_038.parquet\n",
-      "Saving agent to test_path/agent_data_038.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.5876\n",
-      "Condition not met at step 3800. No data to save.\n",
-      "3800\n",
-      "model_vars.get('Gini', 0)[-1]=0.619\n",
-      "Condition not met at step 3801. No data to save.\n",
-      "3801\n",
-      "model_vars.get('Gini', 0)[-1]=0.6318\n",
-      "Condition not met at step 3802. No data to save.\n",
-      "3802\n",
-      "model_vars.get('Gini', 0)[-1]=0.6033999999999999\n",
-      "Condition not met at step 3803. No data to save.\n",
-      "3803\n",
-      "model_vars.get('Gini', 0)[-1]=0.6322\n",
-      "Condition not met at step 3804. No data to save.\n",
-      "3804\n",
-      "model_vars.get('Gini', 0)[-1]=0.6234\n",
-      "Condition not met at step 3805. No data to save.\n",
-      "3805\n",
-      "model_vars.get('Gini', 0)[-1]=0.6434\n",
-      "Condition not met at step 3806. No data to save.\n",
-      "3806\n",
-      "model_vars.get('Gini', 0)[-1]=0.6388\n",
-      "Condition not met at step 3807. No data to save.\n",
-      "3807\n",
-      "model_vars.get('Gini', 0)[-1]=0.661\n",
-      "Condition not met at step 3808. No data to save.\n",
-      "3808\n",
-      "model_vars.get('Gini', 0)[-1]=0.6646000000000001\n",
-      "Condition not met at step 3809. No data to save.\n",
-      "3809\n",
-      "model_vars.get('Gini', 0)[-1]=0.6314\n",
-      "Condition not met at step 3810. No data to save.\n",
-      "3810\n",
-      "model_vars.get('Gini', 0)[-1]=0.6295999999999999\n",
-      "Condition not met at step 3811. No data to save.\n",
-      "3811\n",
-      "model_vars.get('Gini', 0)[-1]=0.6672\n",
-      "Condition not met at step 3812. No data to save.\n",
-      "3812\n",
-      "model_vars.get('Gini', 0)[-1]=0.6402\n",
-      "Condition not met at step 3813. No data to save.\n",
-      "3813\n",
-      "model_vars.get('Gini', 0)[-1]=0.6304000000000001\n",
-      "Condition not met at step 3814. No data to save.\n",
-      "3814\n",
-      "model_vars.get('Gini', 0)[-1]=0.597\n",
-      "Condition not met at step 3815. No data to save.\n",
-      "3815\n",
-      "model_vars.get('Gini', 0)[-1]=0.606\n",
-      "Condition not met at step 3816. No data to save.\n",
-      "3816\n",
-      "model_vars.get('Gini', 0)[-1]=0.6452\n",
-      "Condition not met at step 3817. No data to save.\n",
-      "3817\n",
-      "model_vars.get('Gini', 0)[-1]=0.6434\n",
-      "Condition not met at step 3818. No data to save.\n",
-      "3818\n",
-      "model_vars.get('Gini', 0)[-1]=0.6704\n",
-      "Condition not met at step 3819. No data to save.\n",
-      "3819\n",
-      "model_vars.get('Gini', 0)[-1]=0.6574\n",
-      "Condition not met at step 3820. No data to save.\n",
-      "3820\n",
-      "model_vars.get('Gini', 0)[-1]=0.6622\n",
-      "Condition not met at step 3821. No data to save.\n",
-      "3821\n",
-      "model_vars.get('Gini', 0)[-1]=0.6784\n",
-      "Condition not met at step 3822. No data to save.\n",
-      "3822\n",
-      "model_vars.get('Gini', 0)[-1]=0.6866\n",
-      "Condition not met at step 3823. No data to save.\n",
-      "3823\n",
-      "model_vars.get('Gini', 0)[-1]=0.6936\n",
-      "Condition not met at step 3824. No data to save.\n",
-      "3824\n",
-      "model_vars.get('Gini', 0)[-1]=0.6858\n",
-      "Condition not met at step 3825. No data to save.\n",
-      "3825\n",
-      "model_vars.get('Gini', 0)[-1]=0.6596\n",
-      "Condition not met at step 3826. No data to save.\n",
-      "3826\n",
-      "model_vars.get('Gini', 0)[-1]=0.7028000000000001\n",
-      "Condition met. Appended special results for step 3827 to test_path/special_results.parquet\n",
-      "3827\n",
-      "model_vars.get('Gini', 0)[-1]=0.6834\n",
-      "Condition not met at step 3828. No data to save.\n",
-      "3828\n",
-      "model_vars.get('Gini', 0)[-1]=0.6932\n",
-      "Condition not met at step 3829. No data to save.\n",
-      "3829\n",
-      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
-      "Condition not met at step 3830. No data to save.\n",
-      "3830\n",
-      "model_vars.get('Gini', 0)[-1]=0.643\n",
-      "Condition not met at step 3831. No data to save.\n",
-      "3831\n",
-      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
-      "Condition not met at step 3832. No data to save.\n",
-      "3832\n",
-      "model_vars.get('Gini', 0)[-1]=0.626\n",
-      "Condition not met at step 3833. No data to save.\n",
-      "3833\n",
-      "model_vars.get('Gini', 0)[-1]=0.6258\n",
-      "Condition not met at step 3834. No data to save.\n",
-      "3834\n",
-      "model_vars.get('Gini', 0)[-1]=0.6258\n",
-      "Condition not met at step 3835. No data to save.\n",
-      "3835\n",
-      "model_vars.get('Gini', 0)[-1]=0.627\n",
-      "Condition not met at step 3836. No data to save.\n",
-      "3836\n",
-      "model_vars.get('Gini', 0)[-1]=0.591\n",
-      "Condition not met at step 3837. No data to save.\n",
-      "3837\n",
-      "model_vars.get('Gini', 0)[-1]=0.6496\n",
-      "Condition not met at step 3838. No data to save.\n",
-      "3838\n",
-      "model_vars.get('Gini', 0)[-1]=0.6537999999999999\n",
-      "Condition not met at step 3839. No data to save.\n",
-      "3839\n",
-      "model_vars.get('Gini', 0)[-1]=0.621\n",
-      "Condition not met at step 3840. No data to save.\n",
-      "3840\n",
-      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
-      "Condition not met at step 3841. No data to save.\n",
-      "3841\n",
-      "model_vars.get('Gini', 0)[-1]=0.683\n",
-      "Condition not met at step 3842. No data to save.\n",
-      "3842\n",
-      "model_vars.get('Gini', 0)[-1]=0.6608\n",
-      "Condition not met at step 3843. No data to save.\n",
-      "3843\n",
-      "model_vars.get('Gini', 0)[-1]=0.6396\n",
-      "Condition not met at step 3844. No data to save.\n",
-      "3844\n",
-      "model_vars.get('Gini', 0)[-1]=0.6392\n",
-      "Condition not met at step 3845. No data to save.\n",
-      "3845\n",
-      "model_vars.get('Gini', 0)[-1]=0.629\n",
-      "Condition not met at step 3846. No data to save.\n",
-      "3846\n",
-      "model_vars.get('Gini', 0)[-1]=0.646\n",
-      "Condition not met at step 3847. No data to save.\n",
-      "3847\n",
-      "model_vars.get('Gini', 0)[-1]=0.6596\n",
-      "Condition not met at step 3848. No data to save.\n",
-      "3848\n",
-      "model_vars.get('Gini', 0)[-1]=0.6632\n",
-      "Condition not met at step 3849. No data to save.\n",
-      "3849\n",
-      "model_vars.get('Gini', 0)[-1]=0.6712\n",
-      "Condition not met at step 3850. No data to save.\n",
-      "3850\n",
-      "model_vars.get('Gini', 0)[-1]=0.6776\n",
-      "Condition not met at step 3851. No data to save.\n",
-      "3851\n",
-      "model_vars.get('Gini', 0)[-1]=0.6774\n",
-      "Condition not met at step 3852. No data to save.\n",
-      "3852\n",
-      "model_vars.get('Gini', 0)[-1]=0.7074\n",
-      "Condition met. Appended special results for step 3853 to test_path/special_results.parquet\n",
-      "3853\n",
-      "model_vars.get('Gini', 0)[-1]=0.6958\n",
-      "Condition not met at step 3854. No data to save.\n",
-      "3854\n",
-      "model_vars.get('Gini', 0)[-1]=0.6758\n",
-      "Condition not met at step 3855. No data to save.\n",
-      "3855\n",
-      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
-      "Condition not met at step 3856. No data to save.\n",
-      "3856\n",
-      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
-      "Condition not met at step 3857. No data to save.\n",
-      "3857\n",
-      "model_vars.get('Gini', 0)[-1]=0.6776\n",
-      "Condition not met at step 3858. No data to save.\n",
-      "3858\n",
-      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
-      "Condition not met at step 3859. No data to save.\n",
-      "3859\n",
-      "model_vars.get('Gini', 0)[-1]=0.6714\n",
-      "Condition not met at step 3860. No data to save.\n",
-      "3860\n",
-      "model_vars.get('Gini', 0)[-1]=0.6398\n",
-      "Condition not met at step 3861. No data to save.\n",
-      "3861\n",
-      "model_vars.get('Gini', 0)[-1]=0.653\n",
-      "Condition not met at step 3862. No data to save.\n",
-      "3862\n",
-      "model_vars.get('Gini', 0)[-1]=0.651\n",
-      "Condition not met at step 3863. No data to save.\n",
-      "3863\n",
-      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
-      "Condition not met at step 3864. No data to save.\n",
-      "3864\n",
-      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
-      "Condition not met at step 3865. No data to save.\n",
-      "3865\n",
-      "model_vars.get('Gini', 0)[-1]=0.6926\n",
-      "Condition not met at step 3866. No data to save.\n",
-      "3866\n",
-      "model_vars.get('Gini', 0)[-1]=0.6972\n",
-      "Condition not met at step 3867. No data to save.\n",
-      "3867\n",
-      "model_vars.get('Gini', 0)[-1]=0.694\n",
-      "Condition not met at step 3868. No data to save.\n",
-      "3868\n",
-      "model_vars.get('Gini', 0)[-1]=0.6819999999999999\n",
-      "Condition not met at step 3869. No data to save.\n",
-      "3869\n",
-      "model_vars.get('Gini', 0)[-1]=0.6884\n",
-      "Condition not met at step 3870. No data to save.\n",
-      "3870\n",
-      "model_vars.get('Gini', 0)[-1]=0.6866\n",
-      "Condition not met at step 3871. No data to save.\n",
-      "3871\n",
-      "model_vars.get('Gini', 0)[-1]=0.6692\n",
-      "Condition not met at step 3872. No data to save.\n",
-      "3872\n",
-      "model_vars.get('Gini', 0)[-1]=0.6768000000000001\n",
-      "Condition not met at step 3873. No data to save.\n",
-      "3873\n",
-      "model_vars.get('Gini', 0)[-1]=0.6576\n",
-      "Condition not met at step 3874. No data to save.\n",
-      "3874\n",
-      "model_vars.get('Gini', 0)[-1]=0.6536\n",
-      "Condition not met at step 3875. No data to save.\n",
-      "3875\n",
-      "model_vars.get('Gini', 0)[-1]=0.6492\n",
-      "Condition not met at step 3876. No data to save.\n",
-      "3876\n",
-      "model_vars.get('Gini', 0)[-1]=0.6388\n",
-      "Condition not met at step 3877. No data to save.\n",
-      "3877\n",
-      "model_vars.get('Gini', 0)[-1]=0.6564\n",
-      "Condition not met at step 3878. No data to save.\n",
-      "3878\n",
-      "model_vars.get('Gini', 0)[-1]=0.6506000000000001\n",
-      "Condition not met at step 3879. No data to save.\n",
-      "3879\n",
-      "model_vars.get('Gini', 0)[-1]=0.6402\n",
-      "Condition not met at step 3880. No data to save.\n",
-      "3880\n",
-      "model_vars.get('Gini', 0)[-1]=0.6502\n",
-      "Condition not met at step 3881. No data to save.\n",
-      "3881\n",
-      "model_vars.get('Gini', 0)[-1]=0.6436\n",
-      "Condition not met at step 3882. No data to save.\n",
-      "3882\n",
-      "model_vars.get('Gini', 0)[-1]=0.6328\n",
-      "Condition not met at step 3883. No data to save.\n",
-      "3883\n",
-      "model_vars.get('Gini', 0)[-1]=0.6302\n",
-      "Condition not met at step 3884. No data to save.\n",
-      "3884\n",
-      "model_vars.get('Gini', 0)[-1]=0.6658\n",
-      "Condition not met at step 3885. No data to save.\n",
-      "3885\n",
-      "model_vars.get('Gini', 0)[-1]=0.6782\n",
-      "Condition not met at step 3886. No data to save.\n",
-      "3886\n",
-      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
-      "Condition not met at step 3887. No data to save.\n",
-      "3887\n",
-      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
-      "Condition not met at step 3888. No data to save.\n",
-      "3888\n",
-      "model_vars.get('Gini', 0)[-1]=0.6792\n",
-      "Condition not met at step 3889. No data to save.\n",
-      "3889\n",
-      "model_vars.get('Gini', 0)[-1]=0.6804\n",
-      "Condition not met at step 3890. No data to save.\n",
-      "3890\n",
-      "model_vars.get('Gini', 0)[-1]=0.6856\n",
-      "Condition not met at step 3891. No data to save.\n",
-      "3891\n",
-      "model_vars.get('Gini', 0)[-1]=0.6941999999999999\n",
-      "Condition not met at step 3892. No data to save.\n",
-      "3892\n",
-      "model_vars.get('Gini', 0)[-1]=0.6742\n",
-      "Condition not met at step 3893. No data to save.\n",
-      "3893\n",
-      "model_vars.get('Gini', 0)[-1]=0.635\n",
-      "Condition not met at step 3894. No data to save.\n",
-      "3894\n",
-      "model_vars.get('Gini', 0)[-1]=0.6679999999999999\n",
-      "Condition not met at step 3895. No data to save.\n",
-      "3895\n",
-      "model_vars.get('Gini', 0)[-1]=0.66\n",
-      "Condition not met at step 3896. No data to save.\n",
-      "3896\n",
-      "model_vars.get('Gini', 0)[-1]=0.649\n",
-      "Condition not met at step 3897. No data to save.\n",
-      "3897\n",
-      "model_vars.get('Gini', 0)[-1]=0.6235999999999999\n",
-      "Condition not met at step 3898. No data to save.\n",
-      "3898\n",
-      "model_vars.get('Gini', 0)[-1]=0.6242000000000001\n",
-      "Condition not met at step 3899. No data to save.\n",
-      "3899\n",
-      "CALLED\n",
-      "self.model._steps=3900\n",
-      " TEST self.model._steps=3900\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "3800  0.6190\n",
-      "3801  0.6318\n",
-      "3802  0.6034\n",
-      "3803  0.6322\n",
-      "3804  0.6234\n",
-      "...      ...\n",
-      "3895  0.6600\n",
-      "3896  0.6490\n",
-      "3897  0.6236\n",
-      "3898  0.6242\n",
-      "3899  0.6214\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_039.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_039.parquet'\n",
-      "Saving model to test_path/model_data_039.parquet\n",
-      "Saving agent to test_path/agent_data_039.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6214\n",
-      "Condition not met at step 3900. No data to save.\n",
-      "3900\n",
-      "model_vars.get('Gini', 0)[-1]=0.5982000000000001\n",
-      "Condition not met at step 3901. No data to save.\n",
-      "3901\n",
-      "model_vars.get('Gini', 0)[-1]=0.579\n",
-      "Condition not met at step 3902. No data to save.\n",
-      "3902\n",
-      "model_vars.get('Gini', 0)[-1]=0.5786\n",
-      "Condition not met at step 3903. No data to save.\n",
-      "3903\n",
-      "model_vars.get('Gini', 0)[-1]=0.5740000000000001\n",
-      "Condition not met at step 3904. No data to save.\n",
-      "3904\n",
-      "model_vars.get('Gini', 0)[-1]=0.5940000000000001\n",
-      "Condition not met at step 3905. No data to save.\n",
-      "3905\n",
-      "model_vars.get('Gini', 0)[-1]=0.6138\n",
-      "Condition not met at step 3906. No data to save.\n",
-      "3906\n",
-      "model_vars.get('Gini', 0)[-1]=0.6284000000000001\n",
-      "Condition not met at step 3907. No data to save.\n",
-      "3907\n",
-      "model_vars.get('Gini', 0)[-1]=0.6378\n",
-      "Condition not met at step 3908. No data to save.\n",
-      "3908\n",
-      "model_vars.get('Gini', 0)[-1]=0.6497999999999999\n",
-      "Condition not met at step 3909. No data to save.\n",
-      "3909\n",
-      "model_vars.get('Gini', 0)[-1]=0.6262000000000001\n",
-      "Condition not met at step 3910. No data to save.\n",
-      "3910\n",
-      "model_vars.get('Gini', 0)[-1]=0.6248\n",
-      "Condition not met at step 3911. No data to save.\n",
-      "3911\n",
-      "model_vars.get('Gini', 0)[-1]=0.6052\n",
-      "Condition not met at step 3912. No data to save.\n",
-      "3912\n",
-      "model_vars.get('Gini', 0)[-1]=0.6208\n",
-      "Condition not met at step 3913. No data to save.\n",
-      "3913\n",
-      "model_vars.get('Gini', 0)[-1]=0.6186\n",
-      "Condition not met at step 3914. No data to save.\n",
-      "3914\n",
-      "model_vars.get('Gini', 0)[-1]=0.6113999999999999\n",
-      "Condition not met at step 3915. No data to save.\n",
-      "3915\n",
-      "model_vars.get('Gini', 0)[-1]=0.6096\n",
-      "Condition not met at step 3916. No data to save.\n",
-      "3916\n",
-      "model_vars.get('Gini', 0)[-1]=0.6234\n",
-      "Condition not met at step 3917. No data to save.\n",
-      "3917\n",
-      "model_vars.get('Gini', 0)[-1]=0.628\n",
-      "Condition not met at step 3918. No data to save.\n",
-      "3918\n",
-      "model_vars.get('Gini', 0)[-1]=0.6175999999999999\n",
-      "Condition not met at step 3919. No data to save.\n",
-      "3919\n",
-      "model_vars.get('Gini', 0)[-1]=0.5806\n",
-      "Condition not met at step 3920. No data to save.\n",
-      "3920\n",
-      "model_vars.get('Gini', 0)[-1]=0.635\n",
-      "Condition not met at step 3921. No data to save.\n",
-      "3921\n",
-      "model_vars.get('Gini', 0)[-1]=0.6398\n",
-      "Condition not met at step 3922. No data to save.\n",
-      "3922\n",
-      "model_vars.get('Gini', 0)[-1]=0.6134\n",
-      "Condition not met at step 3923. No data to save.\n",
-      "3923\n",
-      "model_vars.get('Gini', 0)[-1]=0.6166\n",
-      "Condition not met at step 3924. No data to save.\n",
-      "3924\n",
-      "model_vars.get('Gini', 0)[-1]=0.6136\n",
-      "Condition not met at step 3925. No data to save.\n",
-      "3925\n",
-      "model_vars.get('Gini', 0)[-1]=0.6306\n",
-      "Condition not met at step 3926. No data to save.\n",
-      "3926\n",
-      "model_vars.get('Gini', 0)[-1]=0.6212\n",
-      "Condition not met at step 3927. No data to save.\n",
-      "3927\n",
-      "model_vars.get('Gini', 0)[-1]=0.6192\n",
-      "Condition not met at step 3928. No data to save.\n",
-      "3928\n",
-      "model_vars.get('Gini', 0)[-1]=0.6248\n",
-      "Condition not met at step 3929. No data to save.\n",
-      "3929\n",
-      "model_vars.get('Gini', 0)[-1]=0.646\n",
-      "Condition not met at step 3930. No data to save.\n",
-      "3930\n",
-      "model_vars.get('Gini', 0)[-1]=0.6368\n",
-      "Condition not met at step 3931. No data to save.\n",
-      "3931\n",
-      "model_vars.get('Gini', 0)[-1]=0.647\n",
-      "Condition not met at step 3932. No data to save.\n",
-      "3932\n",
-      "model_vars.get('Gini', 0)[-1]=0.641\n",
-      "Condition not met at step 3933. No data to save.\n",
-      "3933\n",
-      "model_vars.get('Gini', 0)[-1]=0.621\n",
-      "Condition not met at step 3934. No data to save.\n",
-      "3934\n",
-      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
-      "Condition not met at step 3935. No data to save.\n",
-      "3935\n",
-      "model_vars.get('Gini', 0)[-1]=0.6564\n",
-      "Condition not met at step 3936. No data to save.\n",
-      "3936\n",
-      "model_vars.get('Gini', 0)[-1]=0.6834\n",
-      "Condition not met at step 3937. No data to save.\n",
-      "3937\n",
-      "model_vars.get('Gini', 0)[-1]=0.7048\n",
-      "Condition met. Appended special results for step 3938 to test_path/special_results.parquet\n",
-      "3938\n",
-      "model_vars.get('Gini', 0)[-1]=0.6878\n",
-      "Condition not met at step 3939. No data to save.\n",
-      "3939\n",
-      "model_vars.get('Gini', 0)[-1]=0.7058\n",
-      "Condition met. Appended special results for step 3940 to test_path/special_results.parquet\n",
-      "3940\n",
-      "model_vars.get('Gini', 0)[-1]=0.6992\n",
-      "Condition not met at step 3941. No data to save.\n",
-      "3941\n",
-      "model_vars.get('Gini', 0)[-1]=0.7038\n",
-      "Condition met. Appended special results for step 3942 to test_path/special_results.parquet\n",
-      "3942\n",
-      "model_vars.get('Gini', 0)[-1]=0.6622\n",
-      "Condition not met at step 3943. No data to save.\n",
-      "3943\n",
-      "model_vars.get('Gini', 0)[-1]=0.6956\n",
-      "Condition not met at step 3944. No data to save.\n",
-      "3944\n",
-      "model_vars.get('Gini', 0)[-1]=0.7098\n",
-      "Condition met. Appended special results for step 3945 to test_path/special_results.parquet\n",
-      "3945\n",
-      "model_vars.get('Gini', 0)[-1]=0.7048\n",
-      "Condition met. Appended special results for step 3946 to test_path/special_results.parquet\n",
-      "3946\n",
-      "model_vars.get('Gini', 0)[-1]=0.6678\n",
-      "Condition not met at step 3947. No data to save.\n",
-      "3947\n",
-      "model_vars.get('Gini', 0)[-1]=0.6806\n",
-      "Condition not met at step 3948. No data to save.\n",
-      "3948\n",
-      "model_vars.get('Gini', 0)[-1]=0.658\n",
-      "Condition not met at step 3949. No data to save.\n",
-      "3949\n",
-      "model_vars.get('Gini', 0)[-1]=0.6608\n",
-      "Condition not met at step 3950. No data to save.\n",
-      "3950\n",
-      "model_vars.get('Gini', 0)[-1]=0.64\n",
-      "Condition not met at step 3951. No data to save.\n",
-      "3951\n",
-      "model_vars.get('Gini', 0)[-1]=0.6644\n",
-      "Condition not met at step 3952. No data to save.\n",
-      "3952\n",
-      "model_vars.get('Gini', 0)[-1]=0.663\n",
-      "Condition not met at step 3953. No data to save.\n",
-      "3953\n",
-      "model_vars.get('Gini', 0)[-1]=0.6774\n",
-      "Condition not met at step 3954. No data to save.\n",
-      "3954\n",
-      "model_vars.get('Gini', 0)[-1]=0.6704\n",
-      "Condition not met at step 3955. No data to save.\n",
-      "3955\n",
-      "model_vars.get('Gini', 0)[-1]=0.6574\n",
-      "Condition not met at step 3956. No data to save.\n",
-      "3956\n",
-      "model_vars.get('Gini', 0)[-1]=0.6534\n",
-      "Condition not met at step 3957. No data to save.\n",
-      "3957\n",
-      "model_vars.get('Gini', 0)[-1]=0.6674\n",
-      "Condition not met at step 3958. No data to save.\n",
-      "3958\n",
-      "model_vars.get('Gini', 0)[-1]=0.6719999999999999\n",
-      "Condition not met at step 3959. No data to save.\n",
-      "3959\n",
-      "model_vars.get('Gini', 0)[-1]=0.6674\n",
-      "Condition not met at step 3960. No data to save.\n",
-      "3960\n",
-      "model_vars.get('Gini', 0)[-1]=0.6941999999999999\n",
-      "Condition not met at step 3961. No data to save.\n",
-      "3961\n",
-      "model_vars.get('Gini', 0)[-1]=0.6852\n",
-      "Condition not met at step 3962. No data to save.\n",
-      "3962\n",
-      "model_vars.get('Gini', 0)[-1]=0.7034\n",
-      "Condition met. Appended special results for step 3963 to test_path/special_results.parquet\n",
-      "3963\n",
-      "model_vars.get('Gini', 0)[-1]=0.6884\n",
-      "Condition not met at step 3964. No data to save.\n",
-      "3964\n",
-      "model_vars.get('Gini', 0)[-1]=0.6719999999999999\n",
-      "Condition not met at step 3965. No data to save.\n",
-      "3965\n",
-      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
-      "Condition not met at step 3966. No data to save.\n",
-      "3966\n",
-      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
-      "Condition not met at step 3967. No data to save.\n",
-      "3967\n",
-      "model_vars.get('Gini', 0)[-1]=0.6718\n",
-      "Condition not met at step 3968. No data to save.\n",
-      "3968\n",
-      "model_vars.get('Gini', 0)[-1]=0.6654\n",
-      "Condition not met at step 3969. No data to save.\n",
-      "3969\n",
-      "model_vars.get('Gini', 0)[-1]=0.6572\n",
-      "Condition not met at step 3970. No data to save.\n",
-      "3970\n",
-      "model_vars.get('Gini', 0)[-1]=0.6576\n",
-      "Condition not met at step 3971. No data to save.\n",
-      "3971\n",
-      "model_vars.get('Gini', 0)[-1]=0.6892\n",
-      "Condition not met at step 3972. No data to save.\n",
-      "3972\n",
-      "model_vars.get('Gini', 0)[-1]=0.6946\n",
-      "Condition not met at step 3973. No data to save.\n",
-      "3973\n",
-      "model_vars.get('Gini', 0)[-1]=0.6930000000000001\n",
-      "Condition not met at step 3974. No data to save.\n",
-      "3974\n",
-      "model_vars.get('Gini', 0)[-1]=0.7041999999999999\n",
-      "Condition met. Appended special results for step 3975 to test_path/special_results.parquet\n",
-      "3975\n",
-      "model_vars.get('Gini', 0)[-1]=0.6804\n",
-      "Condition not met at step 3976. No data to save.\n",
-      "3976\n",
-      "model_vars.get('Gini', 0)[-1]=0.6824\n",
-      "Condition not met at step 3977. No data to save.\n",
-      "3977\n",
-      "model_vars.get('Gini', 0)[-1]=0.6679999999999999\n",
-      "Condition not met at step 3978. No data to save.\n",
-      "3978\n",
-      "model_vars.get('Gini', 0)[-1]=0.6474\n",
-      "Condition not met at step 3979. No data to save.\n",
-      "3979\n",
-      "model_vars.get('Gini', 0)[-1]=0.6502\n",
-      "Condition not met at step 3980. No data to save.\n",
-      "3980\n",
-      "model_vars.get('Gini', 0)[-1]=0.64\n",
-      "Condition not met at step 3981. No data to save.\n",
-      "3981\n",
-      "model_vars.get('Gini', 0)[-1]=0.6234\n",
-      "Condition not met at step 3982. No data to save.\n",
-      "3982\n",
-      "model_vars.get('Gini', 0)[-1]=0.6046\n",
-      "Condition not met at step 3983. No data to save.\n",
-      "3983\n",
-      "model_vars.get('Gini', 0)[-1]=0.6036\n",
-      "Condition not met at step 3984. No data to save.\n",
-      "3984\n",
-      "model_vars.get('Gini', 0)[-1]=0.6052\n",
-      "Condition not met at step 3985. No data to save.\n",
-      "3985\n",
-      "model_vars.get('Gini', 0)[-1]=0.6004\n",
-      "Condition not met at step 3986. No data to save.\n",
-      "3986\n",
-      "model_vars.get('Gini', 0)[-1]=0.6073999999999999\n",
-      "Condition not met at step 3987. No data to save.\n",
-      "3987\n",
-      "model_vars.get('Gini', 0)[-1]=0.6138\n",
-      "Condition not met at step 3988. No data to save.\n",
-      "3988\n",
-      "model_vars.get('Gini', 0)[-1]=0.6502\n",
-      "Condition not met at step 3989. No data to save.\n",
-      "3989\n",
-      "model_vars.get('Gini', 0)[-1]=0.6554\n",
-      "Condition not met at step 3990. No data to save.\n",
-      "3990\n",
-      "model_vars.get('Gini', 0)[-1]=0.6778\n",
-      "Condition not met at step 3991. No data to save.\n",
-      "3991\n",
-      "model_vars.get('Gini', 0)[-1]=0.69\n",
-      "Condition not met at step 3992. No data to save.\n",
-      "3992\n",
-      "model_vars.get('Gini', 0)[-1]=0.679\n",
-      "Condition not met at step 3993. No data to save.\n",
-      "3993\n",
-      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
-      "Condition not met at step 3994. No data to save.\n",
-      "3994\n",
-      "model_vars.get('Gini', 0)[-1]=0.6594\n",
-      "Condition not met at step 3995. No data to save.\n",
-      "3995\n",
-      "model_vars.get('Gini', 0)[-1]=0.6608\n",
-      "Condition not met at step 3996. No data to save.\n",
-      "3996\n",
-      "model_vars.get('Gini', 0)[-1]=0.6699999999999999\n",
-      "Condition not met at step 3997. No data to save.\n",
-      "3997\n",
-      "model_vars.get('Gini', 0)[-1]=0.6604\n",
-      "Condition not met at step 3998. No data to save.\n",
-      "3998\n",
-      "model_vars.get('Gini', 0)[-1]=0.6602\n",
-      "Condition not met at step 3999. No data to save.\n",
-      "3999\n",
-      "CALLED\n",
-      "self.model._steps=4000\n",
-      " TEST self.model._steps=4000\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "3900  0.5982\n",
-      "3901  0.5790\n",
-      "3902  0.5786\n",
-      "3903  0.5740\n",
-      "3904  0.5940\n",
-      "...      ...\n",
-      "3995  0.6608\n",
-      "3996  0.6700\n",
-      "3997  0.6604\n",
-      "3998  0.6602\n",
-      "3999  0.6558\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_040.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_040.parquet'\n",
-      "Saving model to test_path/model_data_040.parquet\n",
-      "Saving agent to test_path/agent_data_040.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
-      "Condition not met at step 4000. No data to save.\n",
-      "4000\n",
-      "model_vars.get('Gini', 0)[-1]=0.6534\n",
-      "Condition not met at step 4001. No data to save.\n",
-      "4001\n",
-      "model_vars.get('Gini', 0)[-1]=0.618\n",
-      "Condition not met at step 4002. No data to save.\n",
-      "4002\n",
-      "model_vars.get('Gini', 0)[-1]=0.62\n",
-      "Condition not met at step 4003. No data to save.\n",
-      "4003\n",
-      "model_vars.get('Gini', 0)[-1]=0.615\n",
-      "Condition not met at step 4004. No data to save.\n",
-      "4004\n",
-      "model_vars.get('Gini', 0)[-1]=0.6076\n",
-      "Condition not met at step 4005. No data to save.\n",
-      "4005\n",
-      "model_vars.get('Gini', 0)[-1]=0.6186\n",
-      "Condition not met at step 4006. No data to save.\n",
-      "4006\n",
-      "model_vars.get('Gini', 0)[-1]=0.6106\n",
-      "Condition not met at step 4007. No data to save.\n",
-      "4007\n",
-      "model_vars.get('Gini', 0)[-1]=0.6113999999999999\n",
-      "Condition not met at step 4008. No data to save.\n",
-      "4008\n",
-      "model_vars.get('Gini', 0)[-1]=0.6102000000000001\n",
-      "Condition not met at step 4009. No data to save.\n",
-      "4009\n",
-      "model_vars.get('Gini', 0)[-1]=0.6136\n",
-      "Condition not met at step 4010. No data to save.\n",
-      "4010\n",
-      "model_vars.get('Gini', 0)[-1]=0.636\n",
-      "Condition not met at step 4011. No data to save.\n",
-      "4011\n",
-      "model_vars.get('Gini', 0)[-1]=0.6462\n",
-      "Condition not met at step 4012. No data to save.\n",
-      "4012\n",
-      "model_vars.get('Gini', 0)[-1]=0.6698\n",
-      "Condition not met at step 4013. No data to save.\n",
-      "4013\n",
-      "model_vars.get('Gini', 0)[-1]=0.6658\n",
-      "Condition not met at step 4014. No data to save.\n",
-      "4014\n",
-      "model_vars.get('Gini', 0)[-1]=0.6594\n",
-      "Condition not met at step 4015. No data to save.\n",
-      "4015\n",
-      "model_vars.get('Gini', 0)[-1]=0.648\n",
-      "Condition not met at step 4016. No data to save.\n",
-      "4016\n",
-      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
-      "Condition not met at step 4017. No data to save.\n",
-      "4017\n",
-      "model_vars.get('Gini', 0)[-1]=0.6528\n",
-      "Condition not met at step 4018. No data to save.\n",
-      "4018\n",
-      "model_vars.get('Gini', 0)[-1]=0.6592\n",
-      "Condition not met at step 4019. No data to save.\n",
-      "4019\n",
-      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
-      "Condition not met at step 4020. No data to save.\n",
-      "4020\n",
-      "model_vars.get('Gini', 0)[-1]=0.6382\n",
-      "Condition not met at step 4021. No data to save.\n",
-      "4021\n",
-      "model_vars.get('Gini', 0)[-1]=0.6652\n",
-      "Condition not met at step 4022. No data to save.\n",
-      "4022\n",
-      "model_vars.get('Gini', 0)[-1]=0.629\n",
-      "Condition not met at step 4023. No data to save.\n",
-      "4023\n",
-      "model_vars.get('Gini', 0)[-1]=0.6182000000000001\n",
-      "Condition not met at step 4024. No data to save.\n",
-      "4024\n",
-      "model_vars.get('Gini', 0)[-1]=0.6264000000000001\n",
-      "Condition not met at step 4025. No data to save.\n",
-      "4025\n",
-      "model_vars.get('Gini', 0)[-1]=0.6442\n",
-      "Condition not met at step 4026. No data to save.\n",
-      "4026\n",
-      "model_vars.get('Gini', 0)[-1]=0.657\n",
-      "Condition not met at step 4027. No data to save.\n",
-      "4027\n",
-      "model_vars.get('Gini', 0)[-1]=0.6592\n",
-      "Condition not met at step 4028. No data to save.\n",
-      "4028\n",
-      "model_vars.get('Gini', 0)[-1]=0.6384000000000001\n",
-      "Condition not met at step 4029. No data to save.\n",
-      "4029\n",
-      "model_vars.get('Gini', 0)[-1]=0.6372\n",
-      "Condition not met at step 4030. No data to save.\n",
-      "4030\n",
-      "model_vars.get('Gini', 0)[-1]=0.6476\n",
-      "Condition not met at step 4031. No data to save.\n",
-      "4031\n",
-      "model_vars.get('Gini', 0)[-1]=0.6446000000000001\n",
-      "Condition not met at step 4032. No data to save.\n",
-      "4032\n",
-      "model_vars.get('Gini', 0)[-1]=0.6328\n",
-      "Condition not met at step 4033. No data to save.\n",
-      "4033\n",
-      "model_vars.get('Gini', 0)[-1]=0.6234\n",
-      "Condition not met at step 4034. No data to save.\n",
-      "4034\n",
-      "model_vars.get('Gini', 0)[-1]=0.641\n",
-      "Condition not met at step 4035. No data to save.\n",
-      "4035\n",
-      "model_vars.get('Gini', 0)[-1]=0.6512\n",
-      "Condition not met at step 4036. No data to save.\n",
-      "4036\n",
-      "model_vars.get('Gini', 0)[-1]=0.6416\n",
-      "Condition not met at step 4037. No data to save.\n",
-      "4037\n",
-      "model_vars.get('Gini', 0)[-1]=0.6208\n",
-      "Condition not met at step 4038. No data to save.\n",
-      "4038\n",
-      "model_vars.get('Gini', 0)[-1]=0.65\n",
-      "Condition not met at step 4039. No data to save.\n",
-      "4039\n",
-      "model_vars.get('Gini', 0)[-1]=0.6444000000000001\n",
-      "Condition not met at step 4040. No data to save.\n",
-      "4040\n",
-      "model_vars.get('Gini', 0)[-1]=0.6554\n",
-      "Condition not met at step 4041. No data to save.\n",
-      "4041\n",
-      "model_vars.get('Gini', 0)[-1]=0.683\n",
-      "Condition not met at step 4042. No data to save.\n",
-      "4042\n",
-      "model_vars.get('Gini', 0)[-1]=0.6724\n",
-      "Condition not met at step 4043. No data to save.\n",
-      "4043\n",
-      "model_vars.get('Gini', 0)[-1]=0.6592\n",
-      "Condition not met at step 4044. No data to save.\n",
-      "4044\n",
-      "model_vars.get('Gini', 0)[-1]=0.631\n",
-      "Condition not met at step 4045. No data to save.\n",
-      "4045\n",
-      "model_vars.get('Gini', 0)[-1]=0.6186\n",
-      "Condition not met at step 4046. No data to save.\n",
-      "4046\n",
-      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
-      "Condition not met at step 4047. No data to save.\n",
-      "4047\n",
-      "model_vars.get('Gini', 0)[-1]=0.6378\n",
-      "Condition not met at step 4048. No data to save.\n",
-      "4048\n",
-      "model_vars.get('Gini', 0)[-1]=0.663\n",
-      "Condition not met at step 4049. No data to save.\n",
-      "4049\n",
-      "model_vars.get('Gini', 0)[-1]=0.6718\n",
-      "Condition not met at step 4050. No data to save.\n",
-      "4050\n",
-      "model_vars.get('Gini', 0)[-1]=0.6990000000000001\n",
-      "Condition not met at step 4051. No data to save.\n",
-      "4051\n",
-      "model_vars.get('Gini', 0)[-1]=0.6828000000000001\n",
-      "Condition not met at step 4052. No data to save.\n",
-      "4052\n",
-      "model_vars.get('Gini', 0)[-1]=0.6722\n",
-      "Condition not met at step 4053. No data to save.\n",
-      "4053\n",
-      "model_vars.get('Gini', 0)[-1]=0.659\n",
-      "Condition not met at step 4054. No data to save.\n",
-      "4054\n",
-      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
-      "Condition not met at step 4055. No data to save.\n",
-      "4055\n",
-      "model_vars.get('Gini', 0)[-1]=0.654\n",
-      "Condition not met at step 4056. No data to save.\n",
-      "4056\n",
-      "model_vars.get('Gini', 0)[-1]=0.6468\n",
-      "Condition not met at step 4057. No data to save.\n",
-      "4057\n",
-      "model_vars.get('Gini', 0)[-1]=0.6644\n",
-      "Condition not met at step 4058. No data to save.\n",
-      "4058\n",
-      "model_vars.get('Gini', 0)[-1]=0.6826\n",
-      "Condition not met at step 4059. No data to save.\n",
-      "4059\n",
-      "model_vars.get('Gini', 0)[-1]=0.6596\n",
-      "Condition not met at step 4060. No data to save.\n",
-      "4060\n",
-      "model_vars.get('Gini', 0)[-1]=0.6568\n",
-      "Condition not met at step 4061. No data to save.\n",
-      "4061\n",
-      "model_vars.get('Gini', 0)[-1]=0.6408\n",
-      "Condition not met at step 4062. No data to save.\n",
-      "4062\n",
-      "model_vars.get('Gini', 0)[-1]=0.6692\n",
-      "Condition not met at step 4063. No data to save.\n",
-      "4063\n",
-      "model_vars.get('Gini', 0)[-1]=0.677\n",
-      "Condition not met at step 4064. No data to save.\n",
-      "4064\n",
-      "model_vars.get('Gini', 0)[-1]=0.6826\n",
-      "Condition not met at step 4065. No data to save.\n",
-      "4065\n",
-      "model_vars.get('Gini', 0)[-1]=0.6698\n",
-      "Condition not met at step 4066. No data to save.\n",
-      "4066\n",
-      "model_vars.get('Gini', 0)[-1]=0.6642\n",
-      "Condition not met at step 4067. No data to save.\n",
-      "4067\n",
-      "model_vars.get('Gini', 0)[-1]=0.6626000000000001\n",
-      "Condition not met at step 4068. No data to save.\n",
-      "4068\n",
-      "model_vars.get('Gini', 0)[-1]=0.6724\n",
-      "Condition not met at step 4069. No data to save.\n",
-      "4069\n",
-      "model_vars.get('Gini', 0)[-1]=0.6588\n",
-      "Condition not met at step 4070. No data to save.\n",
-      "4070\n",
-      "model_vars.get('Gini', 0)[-1]=0.6496\n",
-      "Condition not met at step 4071. No data to save.\n",
-      "4071\n",
-      "model_vars.get('Gini', 0)[-1]=0.6726000000000001\n",
-      "Condition not met at step 4072. No data to save.\n",
-      "4072\n",
-      "model_vars.get('Gini', 0)[-1]=0.6718\n",
-      "Condition not met at step 4073. No data to save.\n",
-      "4073\n",
-      "model_vars.get('Gini', 0)[-1]=0.6878\n",
-      "Condition not met at step 4074. No data to save.\n",
-      "4074\n",
-      "model_vars.get('Gini', 0)[-1]=0.6874\n",
-      "Condition not met at step 4075. No data to save.\n",
-      "4075\n",
-      "model_vars.get('Gini', 0)[-1]=0.6952\n",
-      "Condition not met at step 4076. No data to save.\n",
-      "4076\n",
-      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
-      "Condition not met at step 4077. No data to save.\n",
-      "4077\n",
-      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
-      "Condition not met at step 4078. No data to save.\n",
-      "4078\n",
-      "model_vars.get('Gini', 0)[-1]=0.6658\n",
-      "Condition not met at step 4079. No data to save.\n",
-      "4079\n",
-      "model_vars.get('Gini', 0)[-1]=0.6798\n",
-      "Condition not met at step 4080. No data to save.\n",
-      "4080\n",
-      "model_vars.get('Gini', 0)[-1]=0.665\n",
-      "Condition not met at step 4081. No data to save.\n",
-      "4081\n",
-      "model_vars.get('Gini', 0)[-1]=0.6872\n",
-      "Condition not met at step 4082. No data to save.\n",
-      "4082\n",
-      "model_vars.get('Gini', 0)[-1]=0.6556\n",
-      "Condition not met at step 4083. No data to save.\n",
-      "4083\n",
-      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
-      "Condition not met at step 4084. No data to save.\n",
-      "4084\n",
-      "model_vars.get('Gini', 0)[-1]=0.6666000000000001\n",
-      "Condition not met at step 4085. No data to save.\n",
-      "4085\n",
-      "model_vars.get('Gini', 0)[-1]=0.638\n",
-      "Condition not met at step 4086. No data to save.\n",
-      "4086\n",
-      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
-      "Condition not met at step 4087. No data to save.\n",
-      "4087\n",
-      "model_vars.get('Gini', 0)[-1]=0.6608\n",
-      "Condition not met at step 4088. No data to save.\n",
-      "4088\n",
-      "model_vars.get('Gini', 0)[-1]=0.6941999999999999\n",
-      "Condition not met at step 4089. No data to save.\n",
-      "4089\n",
-      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
-      "Condition not met at step 4090. No data to save.\n",
-      "4090\n",
-      "model_vars.get('Gini', 0)[-1]=0.6759999999999999\n",
-      "Condition not met at step 4091. No data to save.\n",
-      "4091\n",
-      "model_vars.get('Gini', 0)[-1]=0.643\n",
-      "Condition not met at step 4092. No data to save.\n",
-      "4092\n",
-      "model_vars.get('Gini', 0)[-1]=0.6414\n",
-      "Condition not met at step 4093. No data to save.\n",
-      "4093\n",
-      "model_vars.get('Gini', 0)[-1]=0.6617999999999999\n",
-      "Condition not met at step 4094. No data to save.\n",
-      "4094\n",
-      "model_vars.get('Gini', 0)[-1]=0.6534\n",
-      "Condition not met at step 4095. No data to save.\n",
-      "4095\n",
-      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
-      "Condition not met at step 4096. No data to save.\n",
-      "4096\n",
-      "model_vars.get('Gini', 0)[-1]=0.6422\n",
-      "Condition not met at step 4097. No data to save.\n",
-      "4097\n",
-      "model_vars.get('Gini', 0)[-1]=0.6564\n",
-      "Condition not met at step 4098. No data to save.\n",
-      "4098\n",
-      "model_vars.get('Gini', 0)[-1]=0.6382\n",
-      "Condition not met at step 4099. No data to save.\n",
-      "4099\n",
-      "CALLED\n",
-      "self.model._steps=4100\n",
-      " TEST self.model._steps=4100\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "4000  0.6534\n",
-      "4001  0.6180\n",
-      "4002  0.6200\n",
-      "4003  0.6150\n",
-      "4004  0.6076\n",
-      "...      ...\n",
-      "4095  0.6546\n",
-      "4096  0.6422\n",
-      "4097  0.6564\n",
-      "4098  0.6382\n",
-      "4099  0.6618\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_041.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_041.parquet'\n",
-      "Saving model to test_path/model_data_041.parquet\n",
-      "Saving agent to test_path/agent_data_041.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6617999999999999\n",
-      "Condition not met at step 4100. No data to save.\n",
-      "4100\n",
-      "model_vars.get('Gini', 0)[-1]=0.6626000000000001\n",
-      "Condition not met at step 4101. No data to save.\n",
-      "4101\n",
-      "model_vars.get('Gini', 0)[-1]=0.669\n",
-      "Condition not met at step 4102. No data to save.\n",
-      "4102\n",
-      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
-      "Condition not met at step 4103. No data to save.\n",
-      "4103\n",
-      "model_vars.get('Gini', 0)[-1]=0.6602\n",
-      "Condition not met at step 4104. No data to save.\n",
-      "4104\n",
-      "model_vars.get('Gini', 0)[-1]=0.6462\n",
-      "Condition not met at step 4105. No data to save.\n",
-      "4105\n",
-      "model_vars.get('Gini', 0)[-1]=0.6462\n",
-      "Condition not met at step 4106. No data to save.\n",
-      "4106\n",
-      "model_vars.get('Gini', 0)[-1]=0.6536\n",
-      "Condition not met at step 4107. No data to save.\n",
-      "4107\n",
-      "model_vars.get('Gini', 0)[-1]=0.6502\n",
-      "Condition not met at step 4108. No data to save.\n",
-      "4108\n",
-      "model_vars.get('Gini', 0)[-1]=0.6432\n",
-      "Condition not met at step 4109. No data to save.\n",
-      "4109\n",
-      "model_vars.get('Gini', 0)[-1]=0.6315999999999999\n",
-      "Condition not met at step 4110. No data to save.\n",
-      "4110\n",
-      "model_vars.get('Gini', 0)[-1]=0.6178\n",
-      "Condition not met at step 4111. No data to save.\n",
-      "4111\n",
-      "model_vars.get('Gini', 0)[-1]=0.6444000000000001\n",
-      "Condition not met at step 4112. No data to save.\n",
-      "4112\n",
-      "model_vars.get('Gini', 0)[-1]=0.6244000000000001\n",
-      "Condition not met at step 4113. No data to save.\n",
-      "4113\n",
-      "model_vars.get('Gini', 0)[-1]=0.6274\n",
-      "Condition not met at step 4114. No data to save.\n",
-      "4114\n",
-      "model_vars.get('Gini', 0)[-1]=0.6122000000000001\n",
-      "Condition not met at step 4115. No data to save.\n",
-      "4115\n",
-      "model_vars.get('Gini', 0)[-1]=0.5768\n",
-      "Condition not met at step 4116. No data to save.\n",
-      "4116\n",
-      "model_vars.get('Gini', 0)[-1]=0.5876\n",
-      "Condition not met at step 4117. No data to save.\n",
-      "4117\n",
-      "model_vars.get('Gini', 0)[-1]=0.6058\n",
-      "Condition not met at step 4118. No data to save.\n",
-      "4118\n",
-      "model_vars.get('Gini', 0)[-1]=0.6298\n",
-      "Condition not met at step 4119. No data to save.\n",
-      "4119\n",
-      "model_vars.get('Gini', 0)[-1]=0.6752\n",
-      "Condition not met at step 4120. No data to save.\n",
-      "4120\n",
-      "model_vars.get('Gini', 0)[-1]=0.6956\n",
-      "Condition not met at step 4121. No data to save.\n",
-      "4121\n",
-      "model_vars.get('Gini', 0)[-1]=0.6812\n",
-      "Condition not met at step 4122. No data to save.\n",
-      "4122\n",
-      "model_vars.get('Gini', 0)[-1]=0.6762\n",
-      "Condition not met at step 4123. No data to save.\n",
-      "4123\n",
-      "model_vars.get('Gini', 0)[-1]=0.6762\n",
-      "Condition not met at step 4124. No data to save.\n",
-      "4124\n",
-      "model_vars.get('Gini', 0)[-1]=0.644\n",
-      "Condition not met at step 4125. No data to save.\n",
-      "4125\n",
-      "model_vars.get('Gini', 0)[-1]=0.6604\n",
-      "Condition not met at step 4126. No data to save.\n",
-      "4126\n",
-      "model_vars.get('Gini', 0)[-1]=0.6434\n",
-      "Condition not met at step 4127. No data to save.\n",
-      "4127\n",
-      "model_vars.get('Gini', 0)[-1]=0.6188\n",
-      "Condition not met at step 4128. No data to save.\n",
-      "4128\n",
-      "model_vars.get('Gini', 0)[-1]=0.6178\n",
-      "Condition not met at step 4129. No data to save.\n",
-      "4129\n",
-      "model_vars.get('Gini', 0)[-1]=0.622\n",
-      "Condition not met at step 4130. No data to save.\n",
-      "4130\n",
-      "model_vars.get('Gini', 0)[-1]=0.651\n",
-      "Condition not met at step 4131. No data to save.\n",
-      "4131\n",
-      "model_vars.get('Gini', 0)[-1]=0.6226\n",
-      "Condition not met at step 4132. No data to save.\n",
-      "4132\n",
-      "model_vars.get('Gini', 0)[-1]=0.629\n",
-      "Condition not met at step 4133. No data to save.\n",
-      "4133\n",
-      "model_vars.get('Gini', 0)[-1]=0.6136\n",
-      "Condition not met at step 4134. No data to save.\n",
-      "4134\n",
-      "model_vars.get('Gini', 0)[-1]=0.6084\n",
-      "Condition not met at step 4135. No data to save.\n",
-      "4135\n",
-      "model_vars.get('Gini', 0)[-1]=0.6013999999999999\n",
-      "Condition not met at step 4136. No data to save.\n",
-      "4136\n",
-      "model_vars.get('Gini', 0)[-1]=0.6158\n",
-      "Condition not met at step 4137. No data to save.\n",
-      "4137\n",
-      "model_vars.get('Gini', 0)[-1]=0.6338\n",
-      "Condition not met at step 4138. No data to save.\n",
-      "4138\n",
-      "model_vars.get('Gini', 0)[-1]=0.6052\n",
-      "Condition not met at step 4139. No data to save.\n",
-      "4139\n",
-      "model_vars.get('Gini', 0)[-1]=0.5896\n",
-      "Condition not met at step 4140. No data to save.\n",
-      "4140\n",
-      "model_vars.get('Gini', 0)[-1]=0.6218\n",
-      "Condition not met at step 4141. No data to save.\n",
-      "4141\n",
-      "model_vars.get('Gini', 0)[-1]=0.6286\n",
-      "Condition not met at step 4142. No data to save.\n",
-      "4142\n",
-      "model_vars.get('Gini', 0)[-1]=0.615\n",
-      "Condition not met at step 4143. No data to save.\n",
-      "4143\n",
-      "model_vars.get('Gini', 0)[-1]=0.658\n",
-      "Condition not met at step 4144. No data to save.\n",
-      "4144\n",
-      "model_vars.get('Gini', 0)[-1]=0.6508\n",
-      "Condition not met at step 4145. No data to save.\n",
-      "4145\n",
-      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
-      "Condition not met at step 4146. No data to save.\n",
-      "4146\n",
-      "model_vars.get('Gini', 0)[-1]=0.6718\n",
-      "Condition not met at step 4147. No data to save.\n",
-      "4147\n",
-      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
-      "Condition not met at step 4148. No data to save.\n",
-      "4148\n",
-      "model_vars.get('Gini', 0)[-1]=0.6672\n",
-      "Condition not met at step 4149. No data to save.\n",
-      "4149\n",
-      "model_vars.get('Gini', 0)[-1]=0.6175999999999999\n",
-      "Condition not met at step 4150. No data to save.\n",
-      "4150\n",
-      "model_vars.get('Gini', 0)[-1]=0.643\n",
-      "Condition not met at step 4151. No data to save.\n",
-      "4151\n",
-      "model_vars.get('Gini', 0)[-1]=0.6246\n",
-      "Condition not met at step 4152. No data to save.\n",
-      "4152\n",
-      "model_vars.get('Gini', 0)[-1]=0.6759999999999999\n",
-      "Condition not met at step 4153. No data to save.\n",
-      "4153\n",
-      "model_vars.get('Gini', 0)[-1]=0.6622\n",
-      "Condition not met at step 4154. No data to save.\n",
-      "4154\n",
-      "model_vars.get('Gini', 0)[-1]=0.6674\n",
-      "Condition not met at step 4155. No data to save.\n",
-      "4155\n",
-      "model_vars.get('Gini', 0)[-1]=0.6612\n",
-      "Condition not met at step 4156. No data to save.\n",
-      "4156\n",
-      "model_vars.get('Gini', 0)[-1]=0.6656\n",
-      "Condition not met at step 4157. No data to save.\n",
-      "4157\n",
-      "model_vars.get('Gini', 0)[-1]=0.6508\n",
-      "Condition not met at step 4158. No data to save.\n",
-      "4158\n",
-      "model_vars.get('Gini', 0)[-1]=0.6382\n",
-      "Condition not met at step 4159. No data to save.\n",
-      "4159\n",
-      "model_vars.get('Gini', 0)[-1]=0.6806\n",
-      "Condition not met at step 4160. No data to save.\n",
-      "4160\n",
-      "model_vars.get('Gini', 0)[-1]=0.6682\n",
-      "Condition not met at step 4161. No data to save.\n",
-      "4161\n",
-      "model_vars.get('Gini', 0)[-1]=0.662\n",
-      "Condition not met at step 4162. No data to save.\n",
-      "4162\n",
-      "model_vars.get('Gini', 0)[-1]=0.6412\n",
-      "Condition not met at step 4163. No data to save.\n",
-      "4163\n",
-      "model_vars.get('Gini', 0)[-1]=0.6564\n",
-      "Condition not met at step 4164. No data to save.\n",
-      "4164\n",
-      "model_vars.get('Gini', 0)[-1]=0.6676\n",
-      "Condition not met at step 4165. No data to save.\n",
-      "4165\n",
-      "model_vars.get('Gini', 0)[-1]=0.6594\n",
-      "Condition not met at step 4166. No data to save.\n",
-      "4166\n",
-      "model_vars.get('Gini', 0)[-1]=0.6288\n",
-      "Condition not met at step 4167. No data to save.\n",
-      "4167\n",
-      "model_vars.get('Gini', 0)[-1]=0.6352\n",
-      "Condition not met at step 4168. No data to save.\n",
-      "4168\n",
-      "model_vars.get('Gini', 0)[-1]=0.6286\n",
-      "Condition not met at step 4169. No data to save.\n",
-      "4169\n",
-      "model_vars.get('Gini', 0)[-1]=0.66\n",
-      "Condition not met at step 4170. No data to save.\n",
-      "4170\n",
-      "model_vars.get('Gini', 0)[-1]=0.6504000000000001\n",
-      "Condition not met at step 4171. No data to save.\n",
-      "4171\n",
-      "model_vars.get('Gini', 0)[-1]=0.6679999999999999\n",
-      "Condition not met at step 4172. No data to save.\n",
-      "4172\n",
-      "model_vars.get('Gini', 0)[-1]=0.6796\n",
-      "Condition not met at step 4173. No data to save.\n",
-      "4173\n",
-      "model_vars.get('Gini', 0)[-1]=0.6822\n",
-      "Condition not met at step 4174. No data to save.\n",
-      "4174\n",
-      "model_vars.get('Gini', 0)[-1]=0.6878\n",
-      "Condition not met at step 4175. No data to save.\n",
-      "4175\n",
-      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
-      "Condition not met at step 4176. No data to save.\n",
-      "4176\n",
-      "model_vars.get('Gini', 0)[-1]=0.6622\n",
-      "Condition not met at step 4177. No data to save.\n",
-      "4177\n",
-      "model_vars.get('Gini', 0)[-1]=0.6732\n",
-      "Condition not met at step 4178. No data to save.\n",
-      "4178\n",
-      "model_vars.get('Gini', 0)[-1]=0.663\n",
-      "Condition not met at step 4179. No data to save.\n",
-      "4179\n",
-      "model_vars.get('Gini', 0)[-1]=0.6718\n",
-      "Condition not met at step 4180. No data to save.\n",
-      "4180\n",
-      "model_vars.get('Gini', 0)[-1]=0.6798\n",
-      "Condition not met at step 4181. No data to save.\n",
-      "4181\n",
-      "model_vars.get('Gini', 0)[-1]=0.7018\n",
-      "Condition met. Appended special results for step 4182 to test_path/special_results.parquet\n",
-      "4182\n",
-      "model_vars.get('Gini', 0)[-1]=0.7068\n",
-      "Condition met. Appended special results for step 4183 to test_path/special_results.parquet\n",
-      "4183\n",
-      "model_vars.get('Gini', 0)[-1]=0.7208\n",
-      "Condition met. Appended special results for step 4184 to test_path/special_results.parquet\n",
-      "4184\n",
-      "model_vars.get('Gini', 0)[-1]=0.7068\n",
-      "Condition met. Appended special results for step 4185 to test_path/special_results.parquet\n",
-      "4185\n",
-      "model_vars.get('Gini', 0)[-1]=0.6924\n",
-      "Condition not met at step 4186. No data to save.\n",
-      "4186\n",
-      "model_vars.get('Gini', 0)[-1]=0.6768000000000001\n",
-      "Condition not met at step 4187. No data to save.\n",
-      "4187\n",
-      "model_vars.get('Gini', 0)[-1]=0.6738\n",
-      "Condition not met at step 4188. No data to save.\n",
-      "4188\n",
-      "model_vars.get('Gini', 0)[-1]=0.6930000000000001\n",
-      "Condition not met at step 4189. No data to save.\n",
-      "4189\n",
-      "model_vars.get('Gini', 0)[-1]=0.6854\n",
-      "Condition not met at step 4190. No data to save.\n",
-      "4190\n",
-      "model_vars.get('Gini', 0)[-1]=0.6956\n",
-      "Condition not met at step 4191. No data to save.\n",
-      "4191\n",
-      "model_vars.get('Gini', 0)[-1]=0.6696\n",
-      "Condition not met at step 4192. No data to save.\n",
-      "4192\n",
-      "model_vars.get('Gini', 0)[-1]=0.6778\n",
-      "Condition not met at step 4193. No data to save.\n",
-      "4193\n",
-      "model_vars.get('Gini', 0)[-1]=0.7032\n",
-      "Condition met. Appended special results for step 4194 to test_path/special_results.parquet\n",
-      "4194\n",
-      "model_vars.get('Gini', 0)[-1]=0.6994\n",
-      "Condition not met at step 4195. No data to save.\n",
-      "4195\n",
-      "model_vars.get('Gini', 0)[-1]=0.7121999999999999\n",
-      "Condition met. Appended special results for step 4196 to test_path/special_results.parquet\n",
-      "4196\n",
-      "model_vars.get('Gini', 0)[-1]=0.7070000000000001\n",
-      "Condition met. Appended special results for step 4197 to test_path/special_results.parquet\n",
-      "4197\n",
-      "model_vars.get('Gini', 0)[-1]=0.729\n",
-      "Condition met. Appended special results for step 4198 to test_path/special_results.parquet\n",
-      "4198\n",
-      "model_vars.get('Gini', 0)[-1]=0.7230000000000001\n",
-      "Condition met. Appended special results for step 4199 to test_path/special_results.parquet\n",
-      "4199\n",
-      "CALLED\n",
-      "self.model._steps=4200\n",
-      " TEST self.model._steps=4200\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "4100  0.6626\n",
-      "4101  0.6690\n",
-      "4102  0.6628\n",
-      "4103  0.6602\n",
-      "4104  0.6462\n",
-      "...      ...\n",
-      "4195  0.7122\n",
-      "4196  0.7070\n",
-      "4197  0.7290\n",
-      "4198  0.7230\n",
-      "4199  0.7214\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_042.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_042.parquet'\n",
-      "Saving model to test_path/model_data_042.parquet\n",
-      "Saving agent to test_path/agent_data_042.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.7214\n",
-      "Condition met. Appended special results for step 4200 to test_path/special_results.parquet\n",
-      "4200\n",
-      "model_vars.get('Gini', 0)[-1]=0.7041999999999999\n",
-      "Condition met. Appended special results for step 4201 to test_path/special_results.parquet\n",
-      "4201\n",
-      "model_vars.get('Gini', 0)[-1]=0.7142\n",
-      "Condition met. Appended special results for step 4202 to test_path/special_results.parquet\n",
-      "4202\n",
-      "model_vars.get('Gini', 0)[-1]=0.7242\n",
-      "Condition met. Appended special results for step 4203 to test_path/special_results.parquet\n",
-      "4203\n",
-      "model_vars.get('Gini', 0)[-1]=0.6921999999999999\n",
-      "Condition not met at step 4204. No data to save.\n",
-      "4204\n",
-      "model_vars.get('Gini', 0)[-1]=0.6664\n",
-      "Condition not met at step 4205. No data to save.\n",
-      "4205\n",
-      "model_vars.get('Gini', 0)[-1]=0.6894\n",
-      "Condition not met at step 4206. No data to save.\n",
-      "4206\n",
-      "model_vars.get('Gini', 0)[-1]=0.6838\n",
-      "Condition not met at step 4207. No data to save.\n",
-      "4207\n",
-      "model_vars.get('Gini', 0)[-1]=0.6876\n",
-      "Condition not met at step 4208. No data to save.\n",
-      "4208\n",
-      "model_vars.get('Gini', 0)[-1]=0.6552\n",
-      "Condition not met at step 4209. No data to save.\n",
-      "4209\n",
-      "model_vars.get('Gini', 0)[-1]=0.6368\n",
-      "Condition not met at step 4210. No data to save.\n",
-      "4210\n",
-      "model_vars.get('Gini', 0)[-1]=0.6702\n",
-      "Condition not met at step 4211. No data to save.\n",
-      "4211\n",
-      "model_vars.get('Gini', 0)[-1]=0.6676\n",
-      "Condition not met at step 4212. No data to save.\n",
-      "4212\n",
-      "model_vars.get('Gini', 0)[-1]=0.6414\n",
-      "Condition not met at step 4213. No data to save.\n",
-      "4213\n",
-      "model_vars.get('Gini', 0)[-1]=0.6724\n",
-      "Condition not met at step 4214. No data to save.\n",
-      "4214\n",
-      "model_vars.get('Gini', 0)[-1]=0.6636\n",
-      "Condition not met at step 4215. No data to save.\n",
-      "4215\n",
-      "model_vars.get('Gini', 0)[-1]=0.6882\n",
-      "Condition not met at step 4216. No data to save.\n",
-      "4216\n",
-      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
-      "Condition not met at step 4217. No data to save.\n",
-      "4217\n",
-      "model_vars.get('Gini', 0)[-1]=0.6778\n",
-      "Condition not met at step 4218. No data to save.\n",
-      "4218\n",
-      "model_vars.get('Gini', 0)[-1]=0.6686000000000001\n",
-      "Condition not met at step 4219. No data to save.\n",
-      "4219\n",
-      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
-      "Condition not met at step 4220. No data to save.\n",
-      "4220\n",
-      "model_vars.get('Gini', 0)[-1]=0.6182000000000001\n",
-      "Condition not met at step 4221. No data to save.\n",
-      "4221\n",
-      "model_vars.get('Gini', 0)[-1]=0.6252\n",
-      "Condition not met at step 4222. No data to save.\n",
-      "4222\n",
-      "model_vars.get('Gini', 0)[-1]=0.6508\n",
-      "Condition not met at step 4223. No data to save.\n",
-      "4223\n",
-      "model_vars.get('Gini', 0)[-1]=0.6664\n",
-      "Condition not met at step 4224. No data to save.\n",
-      "4224\n",
-      "model_vars.get('Gini', 0)[-1]=0.6782\n",
-      "Condition not met at step 4225. No data to save.\n",
-      "4225\n",
-      "model_vars.get('Gini', 0)[-1]=0.6698\n",
-      "Condition not met at step 4226. No data to save.\n",
-      "4226\n",
-      "model_vars.get('Gini', 0)[-1]=0.6464000000000001\n",
-      "Condition not met at step 4227. No data to save.\n",
-      "4227\n",
-      "model_vars.get('Gini', 0)[-1]=0.687\n",
-      "Condition not met at step 4228. No data to save.\n",
-      "4228\n",
-      "model_vars.get('Gini', 0)[-1]=0.6798\n",
-      "Condition not met at step 4229. No data to save.\n",
-      "4229\n",
-      "model_vars.get('Gini', 0)[-1]=0.6654\n",
-      "Condition not met at step 4230. No data to save.\n",
-      "4230\n",
-      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
-      "Condition not met at step 4231. No data to save.\n",
-      "4231\n",
-      "model_vars.get('Gini', 0)[-1]=0.6956\n",
-      "Condition not met at step 4232. No data to save.\n",
-      "4232\n",
-      "model_vars.get('Gini', 0)[-1]=0.6918\n",
-      "Condition not met at step 4233. No data to save.\n",
-      "4233\n",
-      "model_vars.get('Gini', 0)[-1]=0.704\n",
-      "Condition met. Appended special results for step 4234 to test_path/special_results.parquet\n",
-      "4234\n",
-      "model_vars.get('Gini', 0)[-1]=0.6992\n",
-      "Condition not met at step 4235. No data to save.\n",
-      "4235\n",
-      "model_vars.get('Gini', 0)[-1]=0.6718\n",
-      "Condition not met at step 4236. No data to save.\n",
-      "4236\n",
-      "model_vars.get('Gini', 0)[-1]=0.7066\n",
-      "Condition met. Appended special results for step 4237 to test_path/special_results.parquet\n",
-      "4237\n",
-      "model_vars.get('Gini', 0)[-1]=0.6904\n",
-      "Condition not met at step 4238. No data to save.\n",
-      "4238\n",
-      "model_vars.get('Gini', 0)[-1]=0.6832\n",
-      "Condition not met at step 4239. No data to save.\n",
-      "4239\n",
-      "model_vars.get('Gini', 0)[-1]=0.6786000000000001\n",
-      "Condition not met at step 4240. No data to save.\n",
-      "4240\n",
-      "model_vars.get('Gini', 0)[-1]=0.6994\n",
-      "Condition not met at step 4241. No data to save.\n",
-      "4241\n",
-      "model_vars.get('Gini', 0)[-1]=0.6884\n",
-      "Condition not met at step 4242. No data to save.\n",
-      "4242\n",
-      "model_vars.get('Gini', 0)[-1]=0.6582\n",
-      "Condition not met at step 4243. No data to save.\n",
-      "4243\n",
-      "model_vars.get('Gini', 0)[-1]=0.6516\n",
-      "Condition not met at step 4244. No data to save.\n",
-      "4244\n",
-      "model_vars.get('Gini', 0)[-1]=0.6226\n",
-      "Condition not met at step 4245. No data to save.\n",
-      "4245\n",
-      "model_vars.get('Gini', 0)[-1]=0.631\n",
-      "Condition not met at step 4246. No data to save.\n",
-      "4246\n",
-      "model_vars.get('Gini', 0)[-1]=0.6604\n",
-      "Condition not met at step 4247. No data to save.\n",
-      "4247\n",
-      "model_vars.get('Gini', 0)[-1]=0.663\n",
-      "Condition not met at step 4248. No data to save.\n",
-      "4248\n",
-      "model_vars.get('Gini', 0)[-1]=0.6457999999999999\n",
-      "Condition not met at step 4249. No data to save.\n",
-      "4249\n",
-      "model_vars.get('Gini', 0)[-1]=0.6522\n",
-      "Condition not met at step 4250. No data to save.\n",
-      "4250\n",
-      "model_vars.get('Gini', 0)[-1]=0.6374\n",
-      "Condition not met at step 4251. No data to save.\n",
-      "4251\n",
-      "model_vars.get('Gini', 0)[-1]=0.6314\n",
-      "Condition not met at step 4252. No data to save.\n",
-      "4252\n",
-      "model_vars.get('Gini', 0)[-1]=0.6334\n",
-      "Condition not met at step 4253. No data to save.\n",
-      "4253\n",
-      "model_vars.get('Gini', 0)[-1]=0.6446000000000001\n",
-      "Condition not met at step 4254. No data to save.\n",
-      "4254\n",
-      "model_vars.get('Gini', 0)[-1]=0.6298\n",
-      "Condition not met at step 4255. No data to save.\n",
-      "4255\n",
-      "model_vars.get('Gini', 0)[-1]=0.6224000000000001\n",
-      "Condition not met at step 4256. No data to save.\n",
-      "4256\n",
-      "model_vars.get('Gini', 0)[-1]=0.6234\n",
-      "Condition not met at step 4257. No data to save.\n",
-      "4257\n",
-      "model_vars.get('Gini', 0)[-1]=0.6234\n",
-      "Condition not met at step 4258. No data to save.\n",
-      "4258\n",
-      "model_vars.get('Gini', 0)[-1]=0.611\n",
-      "Condition not met at step 4259. No data to save.\n",
-      "4259\n",
-      "model_vars.get('Gini', 0)[-1]=0.5913999999999999\n",
-      "Condition not met at step 4260. No data to save.\n",
-      "4260\n",
-      "model_vars.get('Gini', 0)[-1]=0.612\n",
-      "Condition not met at step 4261. No data to save.\n",
-      "4261\n",
-      "model_vars.get('Gini', 0)[-1]=0.646\n",
-      "Condition not met at step 4262. No data to save.\n",
-      "4262\n",
-      "model_vars.get('Gini', 0)[-1]=0.616\n",
-      "Condition not met at step 4263. No data to save.\n",
-      "4263\n",
-      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
-      "Condition not met at step 4264. No data to save.\n",
-      "4264\n",
-      "model_vars.get('Gini', 0)[-1]=0.6536\n",
-      "Condition not met at step 4265. No data to save.\n",
-      "4265\n",
-      "model_vars.get('Gini', 0)[-1]=0.6517999999999999\n",
-      "Condition not met at step 4266. No data to save.\n",
-      "4266\n",
-      "model_vars.get('Gini', 0)[-1]=0.6284000000000001\n",
-      "Condition not met at step 4267. No data to save.\n",
-      "4267\n",
-      "model_vars.get('Gini', 0)[-1]=0.6328\n",
-      "Condition not met at step 4268. No data to save.\n",
-      "4268\n",
-      "model_vars.get('Gini', 0)[-1]=0.6198\n",
-      "Condition not met at step 4269. No data to save.\n",
-      "4269\n",
-      "model_vars.get('Gini', 0)[-1]=0.6295999999999999\n",
-      "Condition not met at step 4270. No data to save.\n",
-      "4270\n",
-      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
-      "Condition not met at step 4271. No data to save.\n",
-      "4271\n",
-      "model_vars.get('Gini', 0)[-1]=0.6634\n",
-      "Condition not met at step 4272. No data to save.\n",
-      "4272\n",
-      "model_vars.get('Gini', 0)[-1]=0.6656\n",
-      "Condition not met at step 4273. No data to save.\n",
-      "4273\n",
-      "model_vars.get('Gini', 0)[-1]=0.659\n",
-      "Condition not met at step 4274. No data to save.\n",
-      "4274\n",
-      "model_vars.get('Gini', 0)[-1]=0.6632\n",
-      "Condition not met at step 4275. No data to save.\n",
-      "4275\n",
-      "model_vars.get('Gini', 0)[-1]=0.6528\n",
-      "Condition not met at step 4276. No data to save.\n",
-      "4276\n",
-      "model_vars.get('Gini', 0)[-1]=0.6517999999999999\n",
-      "Condition not met at step 4277. No data to save.\n",
-      "4277\n",
-      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
-      "Condition not met at step 4278. No data to save.\n",
-      "4278\n",
-      "model_vars.get('Gini', 0)[-1]=0.6536\n",
-      "Condition not met at step 4279. No data to save.\n",
-      "4279\n",
-      "model_vars.get('Gini', 0)[-1]=0.6632\n",
-      "Condition not met at step 4280. No data to save.\n",
-      "4280\n",
-      "model_vars.get('Gini', 0)[-1]=0.6566000000000001\n",
-      "Condition not met at step 4281. No data to save.\n",
-      "4281\n",
-      "model_vars.get('Gini', 0)[-1]=0.6644\n",
-      "Condition not met at step 4282. No data to save.\n",
-      "4282\n",
-      "model_vars.get('Gini', 0)[-1]=0.6582\n",
-      "Condition not met at step 4283. No data to save.\n",
-      "4283\n",
-      "model_vars.get('Gini', 0)[-1]=0.654\n",
-      "Condition not met at step 4284. No data to save.\n",
-      "4284\n",
-      "model_vars.get('Gini', 0)[-1]=0.6596\n",
-      "Condition not met at step 4285. No data to save.\n",
-      "4285\n",
-      "model_vars.get('Gini', 0)[-1]=0.6272\n",
-      "Condition not met at step 4286. No data to save.\n",
-      "4286\n",
-      "model_vars.get('Gini', 0)[-1]=0.6372\n",
-      "Condition not met at step 4287. No data to save.\n",
-      "4287\n",
-      "model_vars.get('Gini', 0)[-1]=0.673\n",
-      "Condition not met at step 4288. No data to save.\n",
-      "4288\n",
-      "model_vars.get('Gini', 0)[-1]=0.6759999999999999\n",
-      "Condition not met at step 4289. No data to save.\n",
-      "4289\n",
-      "model_vars.get('Gini', 0)[-1]=0.6858\n",
-      "Condition not met at step 4290. No data to save.\n",
-      "4290\n",
-      "model_vars.get('Gini', 0)[-1]=0.6986\n",
-      "Condition not met at step 4291. No data to save.\n",
-      "4291\n",
-      "model_vars.get('Gini', 0)[-1]=0.6602\n",
-      "Condition not met at step 4292. No data to save.\n",
-      "4292\n",
-      "model_vars.get('Gini', 0)[-1]=0.6676\n",
-      "Condition not met at step 4293. No data to save.\n",
-      "4293\n",
-      "model_vars.get('Gini', 0)[-1]=0.6708000000000001\n",
-      "Condition not met at step 4294. No data to save.\n",
-      "4294\n",
-      "model_vars.get('Gini', 0)[-1]=0.6468\n",
-      "Condition not met at step 4295. No data to save.\n",
-      "4295\n",
-      "model_vars.get('Gini', 0)[-1]=0.6961999999999999\n",
-      "Condition not met at step 4296. No data to save.\n",
-      "4296\n",
-      "model_vars.get('Gini', 0)[-1]=0.6776\n",
-      "Condition not met at step 4297. No data to save.\n",
-      "4297\n",
-      "model_vars.get('Gini', 0)[-1]=0.6608\n",
-      "Condition not met at step 4298. No data to save.\n",
-      "4298\n",
-      "model_vars.get('Gini', 0)[-1]=0.6378\n",
-      "Condition not met at step 4299. No data to save.\n",
-      "4299\n",
-      "CALLED\n",
-      "self.model._steps=4300\n",
-      " TEST self.model._steps=4300\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "4200  0.7042\n",
-      "4201  0.7142\n",
-      "4202  0.7242\n",
-      "4203  0.6922\n",
-      "4204  0.6664\n",
-      "...      ...\n",
-      "4295  0.6962\n",
-      "4296  0.6776\n",
-      "4297  0.6608\n",
-      "4298  0.6378\n",
-      "4299  0.6044\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_043.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_043.parquet'\n",
-      "Saving model to test_path/model_data_043.parquet\n",
-      "Saving agent to test_path/agent_data_043.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6044\n",
-      "Condition not met at step 4300. No data to save.\n",
-      "4300\n",
-      "model_vars.get('Gini', 0)[-1]=0.6328\n",
-      "Condition not met at step 4301. No data to save.\n",
-      "4301\n",
-      "model_vars.get('Gini', 0)[-1]=0.6124\n",
-      "Condition not met at step 4302. No data to save.\n",
-      "4302\n",
-      "model_vars.get('Gini', 0)[-1]=0.611\n",
-      "Condition not met at step 4303. No data to save.\n",
-      "4303\n",
-      "model_vars.get('Gini', 0)[-1]=0.6012\n",
-      "Condition not met at step 4304. No data to save.\n",
-      "4304\n",
-      "model_vars.get('Gini', 0)[-1]=0.5771999999999999\n",
-      "Condition not met at step 4305. No data to save.\n",
-      "4305\n",
-      "model_vars.get('Gini', 0)[-1]=0.6146\n",
-      "Condition not met at step 4306. No data to save.\n",
-      "4306\n",
-      "model_vars.get('Gini', 0)[-1]=0.6312\n",
-      "Condition not met at step 4307. No data to save.\n",
-      "4307\n",
-      "model_vars.get('Gini', 0)[-1]=0.6164000000000001\n",
-      "Condition not met at step 4308. No data to save.\n",
-      "4308\n",
-      "model_vars.get('Gini', 0)[-1]=0.6335999999999999\n",
-      "Condition not met at step 4309. No data to save.\n",
-      "4309\n",
-      "model_vars.get('Gini', 0)[-1]=0.6642\n",
-      "Condition not met at step 4310. No data to save.\n",
-      "4310\n",
-      "model_vars.get('Gini', 0)[-1]=0.6514\n",
-      "Condition not met at step 4311. No data to save.\n",
-      "4311\n",
-      "model_vars.get('Gini', 0)[-1]=0.644\n",
-      "Condition not met at step 4312. No data to save.\n",
-      "4312\n",
-      "model_vars.get('Gini', 0)[-1]=0.637\n",
-      "Condition not met at step 4313. No data to save.\n",
-      "4313\n",
-      "model_vars.get('Gini', 0)[-1]=0.6295999999999999\n",
-      "Condition not met at step 4314. No data to save.\n",
-      "4314\n",
-      "model_vars.get('Gini', 0)[-1]=0.6334\n",
-      "Condition not met at step 4315. No data to save.\n",
-      "4315\n",
-      "model_vars.get('Gini', 0)[-1]=0.6175999999999999\n",
-      "Condition not met at step 4316. No data to save.\n",
-      "4316\n",
-      "model_vars.get('Gini', 0)[-1]=0.6262000000000001\n",
-      "Condition not met at step 4317. No data to save.\n",
-      "4317\n",
-      "model_vars.get('Gini', 0)[-1]=0.6226\n",
-      "Condition not met at step 4318. No data to save.\n",
-      "4318\n",
-      "model_vars.get('Gini', 0)[-1]=0.6278\n",
-      "Condition not met at step 4319. No data to save.\n",
-      "4319\n",
-      "model_vars.get('Gini', 0)[-1]=0.6086\n",
-      "Condition not met at step 4320. No data to save.\n",
-      "4320\n",
-      "model_vars.get('Gini', 0)[-1]=0.6116\n",
-      "Condition not met at step 4321. No data to save.\n",
-      "4321\n",
-      "model_vars.get('Gini', 0)[-1]=0.6262000000000001\n",
-      "Condition not met at step 4322. No data to save.\n",
-      "4322\n",
-      "model_vars.get('Gini', 0)[-1]=0.628\n",
-      "Condition not met at step 4323. No data to save.\n",
-      "4323\n",
-      "model_vars.get('Gini', 0)[-1]=0.655\n",
-      "Condition not met at step 4324. No data to save.\n",
-      "4324\n",
-      "model_vars.get('Gini', 0)[-1]=0.6368\n",
-      "Condition not met at step 4325. No data to save.\n",
-      "4325\n",
-      "model_vars.get('Gini', 0)[-1]=0.6033999999999999\n",
-      "Condition not met at step 4326. No data to save.\n",
-      "4326\n",
-      "model_vars.get('Gini', 0)[-1]=0.5978\n",
-      "Condition not met at step 4327. No data to save.\n",
-      "4327\n",
-      "model_vars.get('Gini', 0)[-1]=0.5920000000000001\n",
-      "Condition not met at step 4328. No data to save.\n",
-      "4328\n",
-      "model_vars.get('Gini', 0)[-1]=0.6142000000000001\n",
-      "Condition not met at step 4329. No data to save.\n",
-      "4329\n",
-      "model_vars.get('Gini', 0)[-1]=0.6416\n",
-      "Condition not met at step 4330. No data to save.\n",
-      "4330\n",
-      "model_vars.get('Gini', 0)[-1]=0.6532\n",
-      "Condition not met at step 4331. No data to save.\n",
-      "4331\n",
-      "model_vars.get('Gini', 0)[-1]=0.671\n",
-      "Condition not met at step 4332. No data to save.\n",
-      "4332\n",
-      "model_vars.get('Gini', 0)[-1]=0.6388\n",
-      "Condition not met at step 4333. No data to save.\n",
-      "4333\n",
-      "model_vars.get('Gini', 0)[-1]=0.6262000000000001\n",
-      "Condition not met at step 4334. No data to save.\n",
-      "4334\n",
-      "model_vars.get('Gini', 0)[-1]=0.6366\n",
-      "Condition not met at step 4335. No data to save.\n",
-      "4335\n",
-      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
-      "Condition not met at step 4336. No data to save.\n",
-      "4336\n",
-      "model_vars.get('Gini', 0)[-1]=0.6652\n",
-      "Condition not met at step 4337. No data to save.\n",
-      "4337\n",
-      "model_vars.get('Gini', 0)[-1]=0.6652\n",
-      "Condition not met at step 4338. No data to save.\n",
-      "4338\n",
-      "model_vars.get('Gini', 0)[-1]=0.6586000000000001\n",
-      "Condition not met at step 4339. No data to save.\n",
-      "4339\n",
-      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
-      "Condition not met at step 4340. No data to save.\n",
-      "4340\n",
-      "model_vars.get('Gini', 0)[-1]=0.6782\n",
-      "Condition not met at step 4341. No data to save.\n",
-      "4341\n",
-      "model_vars.get('Gini', 0)[-1]=0.6862\n",
-      "Condition not met at step 4342. No data to save.\n",
-      "4342\n",
-      "model_vars.get('Gini', 0)[-1]=0.6816\n",
-      "Condition not met at step 4343. No data to save.\n",
-      "4343\n",
-      "model_vars.get('Gini', 0)[-1]=0.692\n",
-      "Condition not met at step 4344. No data to save.\n",
-      "4344\n",
-      "model_vars.get('Gini', 0)[-1]=0.6872\n",
-      "Condition not met at step 4345. No data to save.\n",
-      "4345\n",
-      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
-      "Condition not met at step 4346. No data to save.\n",
-      "4346\n",
-      "model_vars.get('Gini', 0)[-1]=0.6892\n",
-      "Condition not met at step 4347. No data to save.\n",
-      "4347\n",
-      "model_vars.get('Gini', 0)[-1]=0.6930000000000001\n",
-      "Condition not met at step 4348. No data to save.\n",
-      "4348\n",
-      "model_vars.get('Gini', 0)[-1]=0.7054\n",
-      "Condition met. Appended special results for step 4349 to test_path/special_results.parquet\n",
-      "4349\n",
-      "model_vars.get('Gini', 0)[-1]=0.688\n",
-      "Condition not met at step 4350. No data to save.\n",
-      "4350\n",
-      "model_vars.get('Gini', 0)[-1]=0.6856\n",
-      "Condition not met at step 4351. No data to save.\n",
-      "4351\n",
-      "model_vars.get('Gini', 0)[-1]=0.6746000000000001\n",
-      "Condition not met at step 4352. No data to save.\n",
-      "4352\n",
-      "model_vars.get('Gini', 0)[-1]=0.683\n",
-      "Condition not met at step 4353. No data to save.\n",
-      "4353\n",
-      "model_vars.get('Gini', 0)[-1]=0.7044\n",
-      "Condition met. Appended special results for step 4354 to test_path/special_results.parquet\n",
-      "4354\n",
-      "model_vars.get('Gini', 0)[-1]=0.6981999999999999\n",
-      "Condition not met at step 4355. No data to save.\n",
-      "4355\n",
-      "model_vars.get('Gini', 0)[-1]=0.6928000000000001\n",
-      "Condition not met at step 4356. No data to save.\n",
-      "4356\n",
-      "model_vars.get('Gini', 0)[-1]=0.6992\n",
-      "Condition not met at step 4357. No data to save.\n",
-      "4357\n",
-      "model_vars.get('Gini', 0)[-1]=0.7010000000000001\n",
-      "Condition met. Appended special results for step 4358 to test_path/special_results.parquet\n",
-      "4358\n",
-      "model_vars.get('Gini', 0)[-1]=0.7076\n",
-      "Condition met. Appended special results for step 4359 to test_path/special_results.parquet\n",
-      "4359\n",
-      "model_vars.get('Gini', 0)[-1]=0.683\n",
-      "Condition not met at step 4360. No data to save.\n",
-      "4360\n",
-      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
-      "Condition not met at step 4361. No data to save.\n",
-      "4361\n",
-      "model_vars.get('Gini', 0)[-1]=0.688\n",
-      "Condition not met at step 4362. No data to save.\n",
-      "4362\n",
-      "model_vars.get('Gini', 0)[-1]=0.6752\n",
-      "Condition not met at step 4363. No data to save.\n",
-      "4363\n",
-      "model_vars.get('Gini', 0)[-1]=0.6856\n",
-      "Condition not met at step 4364. No data to save.\n",
-      "4364\n",
-      "model_vars.get('Gini', 0)[-1]=0.6972\n",
-      "Condition not met at step 4365. No data to save.\n",
-      "4365\n",
-      "model_vars.get('Gini', 0)[-1]=0.6894\n",
-      "Condition not met at step 4366. No data to save.\n",
-      "4366\n",
-      "model_vars.get('Gini', 0)[-1]=0.7156\n",
-      "Condition met. Appended special results for step 4367 to test_path/special_results.parquet\n",
-      "4367\n",
-      "model_vars.get('Gini', 0)[-1]=0.7108\n",
-      "Condition met. Appended special results for step 4368 to test_path/special_results.parquet\n",
-      "4368\n",
-      "model_vars.get('Gini', 0)[-1]=0.6859999999999999\n",
-      "Condition not met at step 4369. No data to save.\n",
-      "4369\n",
-      "model_vars.get('Gini', 0)[-1]=0.6448\n",
-      "Condition not met at step 4370. No data to save.\n",
-      "4370\n",
-      "model_vars.get('Gini', 0)[-1]=0.6548\n",
-      "Condition not met at step 4371. No data to save.\n",
-      "4371\n",
-      "model_vars.get('Gini', 0)[-1]=0.6574\n",
-      "Condition not met at step 4372. No data to save.\n",
-      "4372\n",
-      "model_vars.get('Gini', 0)[-1]=0.658\n",
-      "Condition not met at step 4373. No data to save.\n",
-      "4373\n",
-      "model_vars.get('Gini', 0)[-1]=0.6792\n",
-      "Condition not met at step 4374. No data to save.\n",
-      "4374\n",
-      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
-      "Condition not met at step 4375. No data to save.\n",
-      "4375\n",
-      "model_vars.get('Gini', 0)[-1]=0.6386000000000001\n",
-      "Condition not met at step 4376. No data to save.\n",
-      "4376\n",
-      "model_vars.get('Gini', 0)[-1]=0.647\n",
-      "Condition not met at step 4377. No data to save.\n",
-      "4377\n",
-      "model_vars.get('Gini', 0)[-1]=0.6404000000000001\n",
-      "Condition not met at step 4378. No data to save.\n",
-      "4378\n",
-      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
-      "Condition not met at step 4379. No data to save.\n",
-      "4379\n",
-      "model_vars.get('Gini', 0)[-1]=0.658\n",
-      "Condition not met at step 4380. No data to save.\n",
-      "4380\n",
-      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
-      "Condition not met at step 4381. No data to save.\n",
-      "4381\n",
-      "model_vars.get('Gini', 0)[-1]=0.616\n",
-      "Condition not met at step 4382. No data to save.\n",
-      "4382\n",
-      "model_vars.get('Gini', 0)[-1]=0.634\n",
-      "Condition not met at step 4383. No data to save.\n",
-      "4383\n",
-      "model_vars.get('Gini', 0)[-1]=0.648\n",
-      "Condition not met at step 4384. No data to save.\n",
-      "4384\n",
-      "model_vars.get('Gini', 0)[-1]=0.635\n",
-      "Condition not met at step 4385. No data to save.\n",
-      "4385\n",
-      "model_vars.get('Gini', 0)[-1]=0.6394\n",
-      "Condition not met at step 4386. No data to save.\n",
-      "4386\n",
-      "model_vars.get('Gini', 0)[-1]=0.634\n",
-      "Condition not met at step 4387. No data to save.\n",
-      "4387\n",
-      "model_vars.get('Gini', 0)[-1]=0.6228\n",
-      "Condition not met at step 4388. No data to save.\n",
-      "4388\n",
-      "model_vars.get('Gini', 0)[-1]=0.6632\n",
-      "Condition not met at step 4389. No data to save.\n",
-      "4389\n",
-      "model_vars.get('Gini', 0)[-1]=0.6779999999999999\n",
-      "Condition not met at step 4390. No data to save.\n",
-      "4390\n",
-      "model_vars.get('Gini', 0)[-1]=0.6878\n",
-      "Condition not met at step 4391. No data to save.\n",
-      "4391\n",
-      "model_vars.get('Gini', 0)[-1]=0.6742\n",
-      "Condition not met at step 4392. No data to save.\n",
-      "4392\n",
-      "model_vars.get('Gini', 0)[-1]=0.6699999999999999\n",
-      "Condition not met at step 4393. No data to save.\n",
-      "4393\n",
-      "model_vars.get('Gini', 0)[-1]=0.6882\n",
-      "Condition not met at step 4394. No data to save.\n",
-      "4394\n",
-      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
-      "Condition not met at step 4395. No data to save.\n",
-      "4395\n",
-      "model_vars.get('Gini', 0)[-1]=0.6734\n",
-      "Condition not met at step 4396. No data to save.\n",
-      "4396\n",
-      "model_vars.get('Gini', 0)[-1]=0.673\n",
-      "Condition not met at step 4397. No data to save.\n",
-      "4397\n",
-      "model_vars.get('Gini', 0)[-1]=0.6712\n",
-      "Condition not met at step 4398. No data to save.\n",
-      "4398\n",
-      "model_vars.get('Gini', 0)[-1]=0.6699999999999999\n",
-      "Condition not met at step 4399. No data to save.\n",
-      "4399\n",
-      "CALLED\n",
-      "self.model._steps=4400\n",
-      " TEST self.model._steps=4400\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "4300  0.6328\n",
-      "4301  0.6124\n",
-      "4302  0.6110\n",
-      "4303  0.6012\n",
-      "4304  0.5772\n",
-      "...      ...\n",
-      "4395  0.6734\n",
-      "4396  0.6730\n",
-      "4397  0.6712\n",
-      "4398  0.6700\n",
-      "4399  0.6712\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_044.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_044.parquet'\n",
-      "Saving model to test_path/model_data_044.parquet\n",
-      "Saving agent to test_path/agent_data_044.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6712\n",
-      "Condition not met at step 4400. No data to save.\n",
-      "4400\n",
-      "model_vars.get('Gini', 0)[-1]=0.6628000000000001\n",
-      "Condition not met at step 4401. No data to save.\n",
-      "4401\n",
-      "model_vars.get('Gini', 0)[-1]=0.664\n",
-      "Condition not met at step 4402. No data to save.\n",
-      "4402\n",
-      "model_vars.get('Gini', 0)[-1]=0.6932\n",
-      "Condition not met at step 4403. No data to save.\n",
-      "4403\n",
-      "model_vars.get('Gini', 0)[-1]=0.702\n",
-      "Condition met. Appended special results for step 4404 to test_path/special_results.parquet\n",
-      "4404\n",
-      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
-      "Condition not met at step 4405. No data to save.\n",
-      "4405\n",
-      "model_vars.get('Gini', 0)[-1]=0.6832\n",
-      "Condition not met at step 4406. No data to save.\n",
-      "4406\n",
-      "model_vars.get('Gini', 0)[-1]=0.6896\n",
-      "Condition not met at step 4407. No data to save.\n",
-      "4407\n",
-      "model_vars.get('Gini', 0)[-1]=0.6934\n",
-      "Condition not met at step 4408. No data to save.\n",
-      "4408\n",
-      "model_vars.get('Gini', 0)[-1]=0.6848000000000001\n",
-      "Condition not met at step 4409. No data to save.\n",
-      "4409\n",
-      "model_vars.get('Gini', 0)[-1]=0.6796\n",
-      "Condition not met at step 4410. No data to save.\n",
-      "4410\n",
-      "model_vars.get('Gini', 0)[-1]=0.6792\n",
-      "Condition not met at step 4411. No data to save.\n",
-      "4411\n",
-      "model_vars.get('Gini', 0)[-1]=0.6964\n",
-      "Condition not met at step 4412. No data to save.\n",
-      "4412\n",
-      "model_vars.get('Gini', 0)[-1]=0.6992\n",
-      "Condition not met at step 4413. No data to save.\n",
-      "4413\n",
-      "model_vars.get('Gini', 0)[-1]=0.7024\n",
-      "Condition met. Appended special results for step 4414 to test_path/special_results.parquet\n",
-      "4414\n",
-      "model_vars.get('Gini', 0)[-1]=0.6839999999999999\n",
-      "Condition not met at step 4415. No data to save.\n",
-      "4415\n",
-      "model_vars.get('Gini', 0)[-1]=0.6648000000000001\n",
-      "Condition not met at step 4416. No data to save.\n",
-      "4416\n",
-      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
-      "Condition not met at step 4417. No data to save.\n",
-      "4417\n",
-      "model_vars.get('Gini', 0)[-1]=0.6572\n",
-      "Condition not met at step 4418. No data to save.\n",
-      "4418\n",
-      "model_vars.get('Gini', 0)[-1]=0.6652\n",
-      "Condition not met at step 4419. No data to save.\n",
-      "4419\n",
-      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
-      "Condition not met at step 4420. No data to save.\n",
-      "4420\n",
-      "model_vars.get('Gini', 0)[-1]=0.6718\n",
-      "Condition not met at step 4421. No data to save.\n",
-      "4421\n",
-      "model_vars.get('Gini', 0)[-1]=0.6568\n",
-      "Condition not met at step 4422. No data to save.\n",
-      "4422\n",
-      "model_vars.get('Gini', 0)[-1]=0.6662\n",
-      "Condition not met at step 4423. No data to save.\n",
-      "4423\n",
-      "model_vars.get('Gini', 0)[-1]=0.6768000000000001\n",
-      "Condition not met at step 4424. No data to save.\n",
-      "4424\n",
-      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
-      "Condition not met at step 4425. No data to save.\n",
-      "4425\n",
-      "model_vars.get('Gini', 0)[-1]=0.6588\n",
-      "Condition not met at step 4426. No data to save.\n",
-      "4426\n",
-      "model_vars.get('Gini', 0)[-1]=0.646\n",
-      "Condition not met at step 4427. No data to save.\n",
-      "4427\n",
-      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
-      "Condition not met at step 4428. No data to save.\n",
-      "4428\n",
-      "model_vars.get('Gini', 0)[-1]=0.6392\n",
-      "Condition not met at step 4429. No data to save.\n",
-      "4429\n",
-      "model_vars.get('Gini', 0)[-1]=0.638\n",
-      "Condition not met at step 4430. No data to save.\n",
-      "4430\n",
-      "model_vars.get('Gini', 0)[-1]=0.634\n",
-      "Condition not met at step 4431. No data to save.\n",
-      "4431\n",
-      "model_vars.get('Gini', 0)[-1]=0.6152\n",
-      "Condition not met at step 4432. No data to save.\n",
-      "4432\n",
-      "model_vars.get('Gini', 0)[-1]=0.6288\n",
-      "Condition not met at step 4433. No data to save.\n",
-      "4433\n",
-      "model_vars.get('Gini', 0)[-1]=0.6284000000000001\n",
-      "Condition not met at step 4434. No data to save.\n",
-      "4434\n",
-      "model_vars.get('Gini', 0)[-1]=0.6506000000000001\n",
-      "Condition not met at step 4435. No data to save.\n",
-      "4435\n",
-      "model_vars.get('Gini', 0)[-1]=0.6836\n",
-      "Condition not met at step 4436. No data to save.\n",
-      "4436\n",
-      "model_vars.get('Gini', 0)[-1]=0.6776\n",
-      "Condition not met at step 4437. No data to save.\n",
-      "4437\n",
-      "model_vars.get('Gini', 0)[-1]=0.6762\n",
-      "Condition not met at step 4438. No data to save.\n",
-      "4438\n",
-      "model_vars.get('Gini', 0)[-1]=0.694\n",
-      "Condition not met at step 4439. No data to save.\n",
-      "4439\n",
-      "model_vars.get('Gini', 0)[-1]=0.6779999999999999\n",
-      "Condition not met at step 4440. No data to save.\n",
-      "4440\n",
-      "model_vars.get('Gini', 0)[-1]=0.6772\n",
-      "Condition not met at step 4441. No data to save.\n",
-      "4441\n",
-      "model_vars.get('Gini', 0)[-1]=0.6588\n",
-      "Condition not met at step 4442. No data to save.\n",
-      "4442\n",
-      "model_vars.get('Gini', 0)[-1]=0.6784\n",
-      "Condition not met at step 4443. No data to save.\n",
-      "4443\n",
-      "model_vars.get('Gini', 0)[-1]=0.6724\n",
-      "Condition not met at step 4444. No data to save.\n",
-      "4444\n",
-      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
-      "Condition not met at step 4445. No data to save.\n",
-      "4445\n",
-      "model_vars.get('Gini', 0)[-1]=0.6494\n",
-      "Condition not met at step 4446. No data to save.\n",
-      "4446\n",
-      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
-      "Condition not met at step 4447. No data to save.\n",
-      "4447\n",
-      "model_vars.get('Gini', 0)[-1]=0.6918\n",
-      "Condition not met at step 4448. No data to save.\n",
-      "4448\n",
-      "model_vars.get('Gini', 0)[-1]=0.7088\n",
-      "Condition met. Appended special results for step 4449 to test_path/special_results.parquet\n",
-      "4449\n",
-      "model_vars.get('Gini', 0)[-1]=0.7148\n",
-      "Condition met. Appended special results for step 4450 to test_path/special_results.parquet\n",
-      "4450\n",
-      "model_vars.get('Gini', 0)[-1]=0.7328\n",
-      "Condition met. Appended special results for step 4451 to test_path/special_results.parquet\n",
-      "4451\n",
-      "model_vars.get('Gini', 0)[-1]=0.735\n",
-      "Condition met. Appended special results for step 4452 to test_path/special_results.parquet\n",
-      "4452\n",
-      "model_vars.get('Gini', 0)[-1]=0.7306\n",
-      "Condition met. Appended special results for step 4453 to test_path/special_results.parquet\n",
-      "4453\n",
-      "model_vars.get('Gini', 0)[-1]=0.6958\n",
-      "Condition not met at step 4454. No data to save.\n",
-      "4454\n",
-      "model_vars.get('Gini', 0)[-1]=0.7004\n",
-      "Condition met. Appended special results for step 4455 to test_path/special_results.parquet\n",
-      "4455\n",
-      "model_vars.get('Gini', 0)[-1]=0.7174\n",
-      "Condition met. Appended special results for step 4456 to test_path/special_results.parquet\n",
-      "4456\n",
-      "model_vars.get('Gini', 0)[-1]=0.718\n",
-      "Condition met. Appended special results for step 4457 to test_path/special_results.parquet\n",
-      "4457\n",
-      "model_vars.get('Gini', 0)[-1]=0.7138\n",
-      "Condition met. Appended special results for step 4458 to test_path/special_results.parquet\n",
-      "4458\n",
-      "model_vars.get('Gini', 0)[-1]=0.6984\n",
-      "Condition not met at step 4459. No data to save.\n",
-      "4459\n",
-      "model_vars.get('Gini', 0)[-1]=0.7012\n",
-      "Condition met. Appended special results for step 4460 to test_path/special_results.parquet\n",
-      "4460\n",
-      "model_vars.get('Gini', 0)[-1]=0.6988000000000001\n",
-      "Condition not met at step 4461. No data to save.\n",
-      "4461\n",
-      "model_vars.get('Gini', 0)[-1]=0.7048\n",
-      "Condition met. Appended special results for step 4462 to test_path/special_results.parquet\n",
-      "4462\n",
-      "model_vars.get('Gini', 0)[-1]=0.7070000000000001\n",
-      "Condition met. Appended special results for step 4463 to test_path/special_results.parquet\n",
-      "4463\n",
-      "model_vars.get('Gini', 0)[-1]=0.7148\n",
-      "Condition met. Appended special results for step 4464 to test_path/special_results.parquet\n",
-      "4464\n",
-      "model_vars.get('Gini', 0)[-1]=0.6912\n",
-      "Condition not met at step 4465. No data to save.\n",
-      "4465\n",
-      "model_vars.get('Gini', 0)[-1]=0.6844\n",
-      "Condition not met at step 4466. No data to save.\n",
-      "4466\n",
-      "model_vars.get('Gini', 0)[-1]=0.6752\n",
-      "Condition not met at step 4467. No data to save.\n",
-      "4467\n",
-      "model_vars.get('Gini', 0)[-1]=0.6422\n",
-      "Condition not met at step 4468. No data to save.\n",
-      "4468\n",
-      "model_vars.get('Gini', 0)[-1]=0.649\n",
-      "Condition not met at step 4469. No data to save.\n",
-      "4469\n",
-      "model_vars.get('Gini', 0)[-1]=0.656\n",
-      "Condition not met at step 4470. No data to save.\n",
-      "4470\n",
-      "model_vars.get('Gini', 0)[-1]=0.6334\n",
-      "Condition not met at step 4471. No data to save.\n",
-      "4471\n",
-      "model_vars.get('Gini', 0)[-1]=0.6616\n",
-      "Condition not met at step 4472. No data to save.\n",
-      "4472\n",
-      "model_vars.get('Gini', 0)[-1]=0.6756\n",
-      "Condition not met at step 4473. No data to save.\n",
-      "4473\n",
-      "model_vars.get('Gini', 0)[-1]=0.6719999999999999\n",
-      "Condition not met at step 4474. No data to save.\n",
-      "4474\n",
-      "model_vars.get('Gini', 0)[-1]=0.6752\n",
-      "Condition not met at step 4475. No data to save.\n",
-      "4475\n",
-      "model_vars.get('Gini', 0)[-1]=0.6746000000000001\n",
-      "Condition not met at step 4476. No data to save.\n",
-      "4476\n",
-      "model_vars.get('Gini', 0)[-1]=0.675\n",
-      "Condition not met at step 4477. No data to save.\n",
-      "4477\n",
-      "model_vars.get('Gini', 0)[-1]=0.662\n",
-      "Condition not met at step 4478. No data to save.\n",
-      "4478\n",
-      "model_vars.get('Gini', 0)[-1]=0.6492\n",
-      "Condition not met at step 4479. No data to save.\n",
-      "4479\n",
-      "model_vars.get('Gini', 0)[-1]=0.62\n",
-      "Condition not met at step 4480. No data to save.\n",
-      "4480\n",
-      "model_vars.get('Gini', 0)[-1]=0.6056\n",
-      "Condition not met at step 4481. No data to save.\n",
-      "4481\n",
-      "model_vars.get('Gini', 0)[-1]=0.6634\n",
-      "Condition not met at step 4482. No data to save.\n",
-      "4482\n",
-      "model_vars.get('Gini', 0)[-1]=0.6719999999999999\n",
-      "Condition not met at step 4483. No data to save.\n",
-      "4483\n",
-      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
-      "Condition not met at step 4484. No data to save.\n",
-      "4484\n",
-      "model_vars.get('Gini', 0)[-1]=0.6224000000000001\n",
-      "Condition not met at step 4485. No data to save.\n",
-      "4485\n",
-      "model_vars.get('Gini', 0)[-1]=0.6574\n",
-      "Condition not met at step 4486. No data to save.\n",
-      "4486\n",
-      "model_vars.get('Gini', 0)[-1]=0.6272\n",
-      "Condition not met at step 4487. No data to save.\n",
-      "4487\n",
-      "model_vars.get('Gini', 0)[-1]=0.648\n",
-      "Condition not met at step 4488. No data to save.\n",
-      "4488\n",
-      "model_vars.get('Gini', 0)[-1]=0.6752\n",
-      "Condition not met at step 4489. No data to save.\n",
-      "4489\n",
-      "model_vars.get('Gini', 0)[-1]=0.685\n",
-      "Condition not met at step 4490. No data to save.\n",
-      "4490\n",
-      "model_vars.get('Gini', 0)[-1]=0.6816\n",
-      "Condition not met at step 4491. No data to save.\n",
-      "4491\n",
-      "model_vars.get('Gini', 0)[-1]=0.6882\n",
-      "Condition not met at step 4492. No data to save.\n",
-      "4492\n",
-      "model_vars.get('Gini', 0)[-1]=0.6712\n",
-      "Condition not met at step 4493. No data to save.\n",
-      "4493\n",
-      "model_vars.get('Gini', 0)[-1]=0.6596\n",
-      "Condition not met at step 4494. No data to save.\n",
-      "4494\n",
-      "model_vars.get('Gini', 0)[-1]=0.6434\n",
-      "Condition not met at step 4495. No data to save.\n",
-      "4495\n",
-      "model_vars.get('Gini', 0)[-1]=0.6592\n",
-      "Condition not met at step 4496. No data to save.\n",
-      "4496\n",
-      "model_vars.get('Gini', 0)[-1]=0.6544\n",
-      "Condition not met at step 4497. No data to save.\n",
-      "4497\n",
-      "model_vars.get('Gini', 0)[-1]=0.6496\n",
-      "Condition not met at step 4498. No data to save.\n",
-      "4498\n",
-      "model_vars.get('Gini', 0)[-1]=0.6482\n",
-      "Condition not met at step 4499. No data to save.\n",
-      "4499\n",
-      "CALLED\n",
-      "self.model._steps=4500\n",
-      " TEST self.model._steps=4500\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "4400  0.6628\n",
-      "4401  0.6640\n",
-      "4402  0.6932\n",
-      "4403  0.7020\n",
-      "4404  0.6788\n",
-      "...      ...\n",
-      "4495  0.6592\n",
-      "4496  0.6544\n",
-      "4497  0.6496\n",
-      "4498  0.6482\n",
-      "4499  0.6526\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_045.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_045.parquet'\n",
-      "Saving model to test_path/model_data_045.parquet\n",
-      "Saving agent to test_path/agent_data_045.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
-      "Condition not met at step 4500. No data to save.\n",
-      "4500\n",
-      "model_vars.get('Gini', 0)[-1]=0.6476\n",
-      "Condition not met at step 4501. No data to save.\n",
-      "4501\n",
-      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
-      "Condition not met at step 4502. No data to save.\n",
-      "4502\n",
-      "model_vars.get('Gini', 0)[-1]=0.6642\n",
-      "Condition not met at step 4503. No data to save.\n",
-      "4503\n",
-      "model_vars.get('Gini', 0)[-1]=0.6734\n",
-      "Condition not met at step 4504. No data to save.\n",
-      "4504\n",
-      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
-      "Condition not met at step 4505. No data to save.\n",
-      "4505\n",
-      "model_vars.get('Gini', 0)[-1]=0.6972\n",
-      "Condition not met at step 4506. No data to save.\n",
-      "4506\n",
-      "model_vars.get('Gini', 0)[-1]=0.6856\n",
-      "Condition not met at step 4507. No data to save.\n",
-      "4507\n",
-      "model_vars.get('Gini', 0)[-1]=0.6859999999999999\n",
-      "Condition not met at step 4508. No data to save.\n",
-      "4508\n",
-      "model_vars.get('Gini', 0)[-1]=0.6934\n",
-      "Condition not met at step 4509. No data to save.\n",
-      "4509\n",
-      "model_vars.get('Gini', 0)[-1]=0.6946\n",
-      "Condition not met at step 4510. No data to save.\n",
-      "4510\n",
-      "model_vars.get('Gini', 0)[-1]=0.692\n",
-      "Condition not met at step 4511. No data to save.\n",
-      "4511\n",
-      "model_vars.get('Gini', 0)[-1]=0.6692\n",
-      "Condition not met at step 4512. No data to save.\n",
-      "4512\n",
-      "model_vars.get('Gini', 0)[-1]=0.6494\n",
-      "Condition not met at step 4513. No data to save.\n",
-      "4513\n",
-      "model_vars.get('Gini', 0)[-1]=0.6614\n",
-      "Condition not met at step 4514. No data to save.\n",
-      "4514\n",
-      "model_vars.get('Gini', 0)[-1]=0.6468\n",
-      "Condition not met at step 4515. No data to save.\n",
-      "4515\n",
-      "model_vars.get('Gini', 0)[-1]=0.6722\n",
-      "Condition not met at step 4516. No data to save.\n",
-      "4516\n",
-      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
-      "Condition not met at step 4517. No data to save.\n",
-      "4517\n",
-      "model_vars.get('Gini', 0)[-1]=0.6736\n",
-      "Condition not met at step 4518. No data to save.\n",
-      "4518\n",
-      "model_vars.get('Gini', 0)[-1]=0.6774\n",
-      "Condition not met at step 4519. No data to save.\n",
-      "4519\n",
-      "model_vars.get('Gini', 0)[-1]=0.6568\n",
-      "Condition not met at step 4520. No data to save.\n",
-      "4520\n",
-      "model_vars.get('Gini', 0)[-1]=0.6734\n",
-      "Condition not met at step 4521. No data to save.\n",
-      "4521\n",
-      "model_vars.get('Gini', 0)[-1]=0.6699999999999999\n",
-      "Condition not met at step 4522. No data to save.\n",
-      "4522\n",
-      "model_vars.get('Gini', 0)[-1]=0.6428\n",
-      "Condition not met at step 4523. No data to save.\n",
-      "4523\n",
-      "model_vars.get('Gini', 0)[-1]=0.6254\n",
-      "Condition not met at step 4524. No data to save.\n",
-      "4524\n",
-      "model_vars.get('Gini', 0)[-1]=0.6564\n",
-      "Condition not met at step 4525. No data to save.\n",
-      "4525\n",
-      "model_vars.get('Gini', 0)[-1]=0.6844\n",
-      "Condition not met at step 4526. No data to save.\n",
-      "4526\n",
-      "model_vars.get('Gini', 0)[-1]=0.6848000000000001\n",
-      "Condition not met at step 4527. No data to save.\n",
-      "4527\n",
-      "model_vars.get('Gini', 0)[-1]=0.6914\n",
-      "Condition not met at step 4528. No data to save.\n",
-      "4528\n",
-      "model_vars.get('Gini', 0)[-1]=0.6932\n",
-      "Condition not met at step 4529. No data to save.\n",
-      "4529\n",
-      "model_vars.get('Gini', 0)[-1]=0.6864\n",
-      "Condition not met at step 4530. No data to save.\n",
-      "4530\n",
-      "model_vars.get('Gini', 0)[-1]=0.69\n",
-      "Condition not met at step 4531. No data to save.\n",
-      "4531\n",
-      "model_vars.get('Gini', 0)[-1]=0.7010000000000001\n",
-      "Condition met. Appended special results for step 4532 to test_path/special_results.parquet\n",
-      "4532\n",
-      "model_vars.get('Gini', 0)[-1]=0.6930000000000001\n",
-      "Condition not met at step 4533. No data to save.\n",
-      "4533\n",
-      "model_vars.get('Gini', 0)[-1]=0.6876\n",
-      "Condition not met at step 4534. No data to save.\n",
-      "4534\n",
-      "model_vars.get('Gini', 0)[-1]=0.6976\n",
-      "Condition not met at step 4535. No data to save.\n",
-      "4535\n",
-      "model_vars.get('Gini', 0)[-1]=0.6832\n",
-      "Condition not met at step 4536. No data to save.\n",
-      "4536\n",
-      "model_vars.get('Gini', 0)[-1]=0.7012\n",
-      "Condition met. Appended special results for step 4537 to test_path/special_results.parquet\n",
-      "4537\n",
-      "model_vars.get('Gini', 0)[-1]=0.7032\n",
-      "Condition met. Appended special results for step 4538 to test_path/special_results.parquet\n",
-      "4538\n",
-      "model_vars.get('Gini', 0)[-1]=0.7068\n",
-      "Condition met. Appended special results for step 4539 to test_path/special_results.parquet\n",
-      "4539\n",
-      "model_vars.get('Gini', 0)[-1]=0.7058\n",
-      "Condition met. Appended special results for step 4540 to test_path/special_results.parquet\n",
-      "4540\n",
-      "model_vars.get('Gini', 0)[-1]=0.7136\n",
-      "Condition met. Appended special results for step 4541 to test_path/special_results.parquet\n",
-      "4541\n",
-      "model_vars.get('Gini', 0)[-1]=0.7108\n",
-      "Condition met. Appended special results for step 4542 to test_path/special_results.parquet\n",
-      "4542\n",
-      "model_vars.get('Gini', 0)[-1]=0.7288\n",
-      "Condition met. Appended special results for step 4543 to test_path/special_results.parquet\n",
-      "4543\n",
-      "model_vars.get('Gini', 0)[-1]=0.7363999999999999\n",
-      "Condition met. Appended special results for step 4544 to test_path/special_results.parquet\n",
-      "4544\n",
-      "model_vars.get('Gini', 0)[-1]=0.742\n",
-      "Condition met. Appended special results for step 4545 to test_path/special_results.parquet\n",
-      "4545\n",
-      "model_vars.get('Gini', 0)[-1]=0.7448\n",
-      "Condition met. Appended special results for step 4546 to test_path/special_results.parquet\n",
-      "4546\n",
-      "model_vars.get('Gini', 0)[-1]=0.7462\n",
-      "Condition met. Appended special results for step 4547 to test_path/special_results.parquet\n",
-      "4547\n",
-      "model_vars.get('Gini', 0)[-1]=0.7472000000000001\n",
-      "Condition met. Appended special results for step 4548 to test_path/special_results.parquet\n",
-      "4548\n",
-      "model_vars.get('Gini', 0)[-1]=0.7374\n",
-      "Condition met. Appended special results for step 4549 to test_path/special_results.parquet\n",
-      "4549\n",
-      "model_vars.get('Gini', 0)[-1]=0.7256\n",
-      "Condition met. Appended special results for step 4550 to test_path/special_results.parquet\n",
-      "4550\n",
-      "model_vars.get('Gini', 0)[-1]=0.7343999999999999\n",
-      "Condition met. Appended special results for step 4551 to test_path/special_results.parquet\n",
-      "4551\n",
-      "model_vars.get('Gini', 0)[-1]=0.7392000000000001\n",
-      "Condition met. Appended special results for step 4552 to test_path/special_results.parquet\n",
-      "4552\n",
-      "model_vars.get('Gini', 0)[-1]=0.7392000000000001\n",
-      "Condition met. Appended special results for step 4553 to test_path/special_results.parquet\n",
-      "4553\n",
-      "model_vars.get('Gini', 0)[-1]=0.7422\n",
-      "Condition met. Appended special results for step 4554 to test_path/special_results.parquet\n",
-      "4554\n",
-      "model_vars.get('Gini', 0)[-1]=0.7504\n",
-      "Condition met. Appended special results for step 4555 to test_path/special_results.parquet\n",
-      "4555\n",
-      "model_vars.get('Gini', 0)[-1]=0.7488\n",
-      "Condition met. Appended special results for step 4556 to test_path/special_results.parquet\n",
-      "4556\n",
-      "model_vars.get('Gini', 0)[-1]=0.7596\n",
-      "Condition met. Appended special results for step 4557 to test_path/special_results.parquet\n",
-      "4557\n",
-      "model_vars.get('Gini', 0)[-1]=0.7412000000000001\n",
-      "Condition met. Appended special results for step 4558 to test_path/special_results.parquet\n",
-      "4558\n",
-      "model_vars.get('Gini', 0)[-1]=0.7328\n",
-      "Condition met. Appended special results for step 4559 to test_path/special_results.parquet\n",
-      "4559\n",
-      "model_vars.get('Gini', 0)[-1]=0.7223999999999999\n",
-      "Condition met. Appended special results for step 4560 to test_path/special_results.parquet\n",
-      "4560\n",
-      "model_vars.get('Gini', 0)[-1]=0.7270000000000001\n",
-      "Condition met. Appended special results for step 4561 to test_path/special_results.parquet\n",
-      "4561\n",
-      "model_vars.get('Gini', 0)[-1]=0.7163999999999999\n",
-      "Condition met. Appended special results for step 4562 to test_path/special_results.parquet\n",
-      "4562\n",
-      "model_vars.get('Gini', 0)[-1]=0.7108\n",
-      "Condition met. Appended special results for step 4563 to test_path/special_results.parquet\n",
-      "4563\n",
-      "model_vars.get('Gini', 0)[-1]=0.7178\n",
-      "Condition met. Appended special results for step 4564 to test_path/special_results.parquet\n",
-      "4564\n",
-      "model_vars.get('Gini', 0)[-1]=0.7168\n",
-      "Condition met. Appended special results for step 4565 to test_path/special_results.parquet\n",
-      "4565\n",
-      "model_vars.get('Gini', 0)[-1]=0.7054\n",
-      "Condition met. Appended special results for step 4566 to test_path/special_results.parquet\n",
-      "4566\n",
-      "model_vars.get('Gini', 0)[-1]=0.6782\n",
-      "Condition not met at step 4567. No data to save.\n",
-      "4567\n",
-      "model_vars.get('Gini', 0)[-1]=0.6892\n",
-      "Condition not met at step 4568. No data to save.\n",
-      "4568\n",
-      "model_vars.get('Gini', 0)[-1]=0.6842\n",
-      "Condition not met at step 4569. No data to save.\n",
-      "4569\n",
-      "model_vars.get('Gini', 0)[-1]=0.7094\n",
-      "Condition met. Appended special results for step 4570 to test_path/special_results.parquet\n",
-      "4570\n",
-      "model_vars.get('Gini', 0)[-1]=0.6864\n",
-      "Condition not met at step 4571. No data to save.\n",
-      "4571\n",
-      "model_vars.get('Gini', 0)[-1]=0.6832\n",
-      "Condition not met at step 4572. No data to save.\n",
-      "4572\n",
-      "model_vars.get('Gini', 0)[-1]=0.7064\n",
-      "Condition met. Appended special results for step 4573 to test_path/special_results.parquet\n",
-      "4573\n",
-      "model_vars.get('Gini', 0)[-1]=0.6996\n",
-      "Condition not met at step 4574. No data to save.\n",
-      "4574\n",
-      "model_vars.get('Gini', 0)[-1]=0.7054\n",
-      "Condition met. Appended special results for step 4575 to test_path/special_results.parquet\n",
-      "4575\n",
-      "model_vars.get('Gini', 0)[-1]=0.6778\n",
-      "Condition not met at step 4576. No data to save.\n",
-      "4576\n",
-      "model_vars.get('Gini', 0)[-1]=0.6784\n",
-      "Condition not met at step 4577. No data to save.\n",
-      "4577\n",
-      "model_vars.get('Gini', 0)[-1]=0.6902\n",
-      "Condition not met at step 4578. No data to save.\n",
-      "4578\n",
-      "model_vars.get('Gini', 0)[-1]=0.6859999999999999\n",
-      "Condition not met at step 4579. No data to save.\n",
-      "4579\n",
-      "model_vars.get('Gini', 0)[-1]=0.7088\n",
-      "Condition met. Appended special results for step 4580 to test_path/special_results.parquet\n",
-      "4580\n",
-      "model_vars.get('Gini', 0)[-1]=0.7048\n",
-      "Condition met. Appended special results for step 4581 to test_path/special_results.parquet\n",
-      "4581\n",
-      "model_vars.get('Gini', 0)[-1]=0.6916\n",
-      "Condition not met at step 4582. No data to save.\n",
-      "4582\n",
-      "model_vars.get('Gini', 0)[-1]=0.6968000000000001\n",
-      "Condition not met at step 4583. No data to save.\n",
-      "4583\n",
-      "model_vars.get('Gini', 0)[-1]=0.708\n",
-      "Condition met. Appended special results for step 4584 to test_path/special_results.parquet\n",
-      "4584\n",
-      "model_vars.get('Gini', 0)[-1]=0.692\n",
-      "Condition not met at step 4585. No data to save.\n",
-      "4585\n",
-      "model_vars.get('Gini', 0)[-1]=0.7034\n",
-      "Condition met. Appended special results for step 4586 to test_path/special_results.parquet\n",
-      "4586\n",
-      "model_vars.get('Gini', 0)[-1]=0.7014\n",
-      "Condition met. Appended special results for step 4587 to test_path/special_results.parquet\n",
-      "4587\n",
-      "model_vars.get('Gini', 0)[-1]=0.6872\n",
-      "Condition not met at step 4588. No data to save.\n",
-      "4588\n",
-      "model_vars.get('Gini', 0)[-1]=0.6774\n",
-      "Condition not met at step 4589. No data to save.\n",
-      "4589\n",
-      "model_vars.get('Gini', 0)[-1]=0.6950000000000001\n",
-      "Condition not met at step 4590. No data to save.\n",
-      "4590\n",
-      "model_vars.get('Gini', 0)[-1]=0.6862\n",
-      "Condition not met at step 4591. No data to save.\n",
-      "4591\n",
-      "model_vars.get('Gini', 0)[-1]=0.7018\n",
-      "Condition met. Appended special results for step 4592 to test_path/special_results.parquet\n",
-      "4592\n",
-      "model_vars.get('Gini', 0)[-1]=0.664\n",
-      "Condition not met at step 4593. No data to save.\n",
-      "4593\n",
-      "model_vars.get('Gini', 0)[-1]=0.6890000000000001\n",
-      "Condition not met at step 4594. No data to save.\n",
-      "4594\n",
-      "model_vars.get('Gini', 0)[-1]=0.6694\n",
-      "Condition not met at step 4595. No data to save.\n",
-      "4595\n",
-      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
-      "Condition not met at step 4596. No data to save.\n",
-      "4596\n",
-      "model_vars.get('Gini', 0)[-1]=0.6378\n",
-      "Condition not met at step 4597. No data to save.\n",
-      "4597\n",
-      "model_vars.get('Gini', 0)[-1]=0.6602\n",
-      "Condition not met at step 4598. No data to save.\n",
-      "4598\n",
-      "model_vars.get('Gini', 0)[-1]=0.6638\n",
-      "Condition not met at step 4599. No data to save.\n",
-      "4599\n",
-      "CALLED\n",
-      "self.model._steps=4600\n",
-      " TEST self.model._steps=4600\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "4500  0.6476\n",
-      "4501  0.6484\n",
-      "4502  0.6642\n",
-      "4503  0.6734\n",
-      "4504  0.6740\n",
-      "...      ...\n",
-      "4595  0.6558\n",
-      "4596  0.6378\n",
-      "4597  0.6602\n",
-      "4598  0.6638\n",
-      "4599  0.6902\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_046.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_046.parquet'\n",
-      "Saving model to test_path/model_data_046.parquet\n",
-      "Saving agent to test_path/agent_data_046.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6902\n",
-      "Condition not met at step 4600. No data to save.\n",
-      "4600\n",
-      "model_vars.get('Gini', 0)[-1]=0.7034\n",
-      "Condition met. Appended special results for step 4601 to test_path/special_results.parquet\n",
-      "4601\n",
-      "model_vars.get('Gini', 0)[-1]=0.7354\n",
-      "Condition met. Appended special results for step 4602 to test_path/special_results.parquet\n",
-      "4602\n",
-      "model_vars.get('Gini', 0)[-1]=0.7234\n",
-      "Condition met. Appended special results for step 4603 to test_path/special_results.parquet\n",
-      "4603\n",
-      "model_vars.get('Gini', 0)[-1]=0.6974\n",
-      "Condition not met at step 4604. No data to save.\n",
-      "4604\n",
-      "model_vars.get('Gini', 0)[-1]=0.6846\n",
-      "Condition not met at step 4605. No data to save.\n",
-      "4605\n",
-      "model_vars.get('Gini', 0)[-1]=0.6732\n",
-      "Condition not met at step 4606. No data to save.\n",
-      "4606\n",
-      "model_vars.get('Gini', 0)[-1]=0.6626000000000001\n",
-      "Condition not met at step 4607. No data to save.\n",
-      "4607\n",
-      "model_vars.get('Gini', 0)[-1]=0.6592\n",
-      "Condition not met at step 4608. No data to save.\n",
-      "4608\n",
-      "model_vars.get('Gini', 0)[-1]=0.6802\n",
-      "Condition not met at step 4609. No data to save.\n",
-      "4609\n",
-      "model_vars.get('Gini', 0)[-1]=0.6864\n",
-      "Condition not met at step 4610. No data to save.\n",
-      "4610\n",
-      "model_vars.get('Gini', 0)[-1]=0.679\n",
-      "Condition not met at step 4611. No data to save.\n",
-      "4611\n",
-      "model_vars.get('Gini', 0)[-1]=0.7066\n",
-      "Condition met. Appended special results for step 4612 to test_path/special_results.parquet\n",
-      "4612\n",
-      "model_vars.get('Gini', 0)[-1]=0.6932\n",
-      "Condition not met at step 4613. No data to save.\n",
-      "4613\n",
-      "model_vars.get('Gini', 0)[-1]=0.7014\n",
-      "Condition met. Appended special results for step 4614 to test_path/special_results.parquet\n",
-      "4614\n",
-      "model_vars.get('Gini', 0)[-1]=0.6866\n",
-      "Condition not met at step 4615. No data to save.\n",
-      "4615\n",
-      "model_vars.get('Gini', 0)[-1]=0.6802\n",
-      "Condition not met at step 4616. No data to save.\n",
-      "4616\n",
-      "model_vars.get('Gini', 0)[-1]=0.6886\n",
-      "Condition not met at step 4617. No data to save.\n",
-      "4617\n",
-      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
-      "Condition not met at step 4618. No data to save.\n",
-      "4618\n",
-      "model_vars.get('Gini', 0)[-1]=0.6724\n",
-      "Condition not met at step 4619. No data to save.\n",
-      "4619\n",
-      "model_vars.get('Gini', 0)[-1]=0.6714\n",
-      "Condition not met at step 4620. No data to save.\n",
-      "4620\n",
-      "model_vars.get('Gini', 0)[-1]=0.6886\n",
-      "Condition not met at step 4621. No data to save.\n",
-      "4621\n",
-      "model_vars.get('Gini', 0)[-1]=0.687\n",
-      "Condition not met at step 4622. No data to save.\n",
-      "4622\n",
-      "model_vars.get('Gini', 0)[-1]=0.6786000000000001\n",
-      "Condition not met at step 4623. No data to save.\n",
-      "4623\n",
-      "model_vars.get('Gini', 0)[-1]=0.7052\n",
-      "Condition met. Appended special results for step 4624 to test_path/special_results.parquet\n",
-      "4624\n",
-      "model_vars.get('Gini', 0)[-1]=0.6961999999999999\n",
-      "Condition not met at step 4625. No data to save.\n",
-      "4625\n",
-      "model_vars.get('Gini', 0)[-1]=0.6832\n",
-      "Condition not met at step 4626. No data to save.\n",
-      "4626\n",
-      "model_vars.get('Gini', 0)[-1]=0.6708000000000001\n",
-      "Condition not met at step 4627. No data to save.\n",
-      "4627\n",
-      "model_vars.get('Gini', 0)[-1]=0.685\n",
-      "Condition not met at step 4628. No data to save.\n",
-      "4628\n",
-      "model_vars.get('Gini', 0)[-1]=0.6576\n",
-      "Condition not met at step 4629. No data to save.\n",
-      "4629\n",
-      "model_vars.get('Gini', 0)[-1]=0.6878\n",
-      "Condition not met at step 4630. No data to save.\n",
-      "4630\n",
-      "model_vars.get('Gini', 0)[-1]=0.6992\n",
-      "Condition not met at step 4631. No data to save.\n",
-      "4631\n",
-      "model_vars.get('Gini', 0)[-1]=0.6916\n",
-      "Condition not met at step 4632. No data to save.\n",
-      "4632\n",
-      "model_vars.get('Gini', 0)[-1]=0.6782\n",
-      "Condition not met at step 4633. No data to save.\n",
-      "4633\n",
-      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
-      "Condition not met at step 4634. No data to save.\n",
-      "4634\n",
-      "model_vars.get('Gini', 0)[-1]=0.7024\n",
-      "Condition met. Appended special results for step 4635 to test_path/special_results.parquet\n",
-      "4635\n",
-      "model_vars.get('Gini', 0)[-1]=0.6872\n",
-      "Condition not met at step 4636. No data to save.\n",
-      "4636\n",
-      "model_vars.get('Gini', 0)[-1]=0.6814\n",
-      "Condition not met at step 4637. No data to save.\n",
-      "4637\n",
-      "model_vars.get('Gini', 0)[-1]=0.6679999999999999\n",
-      "Condition not met at step 4638. No data to save.\n",
-      "4638\n",
-      "model_vars.get('Gini', 0)[-1]=0.692\n",
-      "Condition not met at step 4639. No data to save.\n",
-      "4639\n",
-      "model_vars.get('Gini', 0)[-1]=0.6762\n",
-      "Condition not met at step 4640. No data to save.\n",
-      "4640\n",
-      "model_vars.get('Gini', 0)[-1]=0.6814\n",
-      "Condition not met at step 4641. No data to save.\n",
-      "4641\n",
-      "model_vars.get('Gini', 0)[-1]=0.6642\n",
-      "Condition not met at step 4642. No data to save.\n",
-      "4642\n",
-      "model_vars.get('Gini', 0)[-1]=0.6886\n",
-      "Condition not met at step 4643. No data to save.\n",
-      "4643\n",
-      "model_vars.get('Gini', 0)[-1]=0.6902\n",
-      "Condition not met at step 4644. No data to save.\n",
-      "4644\n",
-      "model_vars.get('Gini', 0)[-1]=0.6828000000000001\n",
-      "Condition not met at step 4645. No data to save.\n",
-      "4645\n",
-      "model_vars.get('Gini', 0)[-1]=0.6848000000000001\n",
-      "Condition not met at step 4646. No data to save.\n",
-      "4646\n",
-      "model_vars.get('Gini', 0)[-1]=0.6852\n",
-      "Condition not met at step 4647. No data to save.\n",
-      "4647\n",
-      "model_vars.get('Gini', 0)[-1]=0.704\n",
-      "Condition met. Appended special results for step 4648 to test_path/special_results.parquet\n",
-      "4648\n",
-      "model_vars.get('Gini', 0)[-1]=0.6954\n",
-      "Condition not met at step 4649. No data to save.\n",
-      "4649\n",
-      "model_vars.get('Gini', 0)[-1]=0.7041999999999999\n",
-      "Condition met. Appended special results for step 4650 to test_path/special_results.parquet\n",
-      "4650\n",
-      "model_vars.get('Gini', 0)[-1]=0.6734\n",
-      "Condition not met at step 4651. No data to save.\n",
-      "4651\n",
-      "model_vars.get('Gini', 0)[-1]=0.6758\n",
-      "Condition not met at step 4652. No data to save.\n",
-      "4652\n",
-      "model_vars.get('Gini', 0)[-1]=0.6956\n",
-      "Condition not met at step 4653. No data to save.\n",
-      "4653\n",
-      "model_vars.get('Gini', 0)[-1]=0.6574\n",
-      "Condition not met at step 4654. No data to save.\n",
-      "4654\n",
-      "model_vars.get('Gini', 0)[-1]=0.6762\n",
-      "Condition not met at step 4655. No data to save.\n",
-      "4655\n",
-      "model_vars.get('Gini', 0)[-1]=0.6652\n",
-      "Condition not met at step 4656. No data to save.\n",
-      "4656\n",
-      "model_vars.get('Gini', 0)[-1]=0.6568\n",
-      "Condition not met at step 4657. No data to save.\n",
-      "4657\n",
-      "model_vars.get('Gini', 0)[-1]=0.6472\n",
-      "Condition not met at step 4658. No data to save.\n",
-      "4658\n",
-      "model_vars.get('Gini', 0)[-1]=0.655\n",
-      "Condition not met at step 4659. No data to save.\n",
-      "4659\n",
-      "model_vars.get('Gini', 0)[-1]=0.6634\n",
-      "Condition not met at step 4660. No data to save.\n",
-      "4660\n",
-      "model_vars.get('Gini', 0)[-1]=0.6684\n",
-      "Condition not met at step 4661. No data to save.\n",
-      "4661\n",
-      "model_vars.get('Gini', 0)[-1]=0.649\n",
-      "Condition not met at step 4662. No data to save.\n",
-      "4662\n",
-      "model_vars.get('Gini', 0)[-1]=0.6684\n",
-      "Condition not met at step 4663. No data to save.\n",
-      "4663\n",
-      "model_vars.get('Gini', 0)[-1]=0.6584\n",
-      "Condition not met at step 4664. No data to save.\n",
-      "4664\n",
-      "model_vars.get('Gini', 0)[-1]=0.6542\n",
-      "Condition not met at step 4665. No data to save.\n",
-      "4665\n",
-      "model_vars.get('Gini', 0)[-1]=0.6278\n",
-      "Condition not met at step 4666. No data to save.\n",
-      "4666\n",
-      "model_vars.get('Gini', 0)[-1]=0.6246\n",
-      "Condition not met at step 4667. No data to save.\n",
-      "4667\n",
-      "model_vars.get('Gini', 0)[-1]=0.6434\n",
-      "Condition not met at step 4668. No data to save.\n",
-      "4668\n",
-      "model_vars.get('Gini', 0)[-1]=0.683\n",
-      "Condition not met at step 4669. No data to save.\n",
-      "4669\n",
-      "model_vars.get('Gini', 0)[-1]=0.6936\n",
-      "Condition not met at step 4670. No data to save.\n",
-      "4670\n",
-      "model_vars.get('Gini', 0)[-1]=0.679\n",
-      "Condition not met at step 4671. No data to save.\n",
-      "4671\n",
-      "model_vars.get('Gini', 0)[-1]=0.6512\n",
-      "Condition not met at step 4672. No data to save.\n",
-      "4672\n",
-      "model_vars.get('Gini', 0)[-1]=0.6472\n",
-      "Condition not met at step 4673. No data to save.\n",
-      "4673\n",
-      "model_vars.get('Gini', 0)[-1]=0.6416\n",
-      "Condition not met at step 4674. No data to save.\n",
-      "4674\n",
-      "model_vars.get('Gini', 0)[-1]=0.6302\n",
-      "Condition not met at step 4675. No data to save.\n",
-      "4675\n",
-      "model_vars.get('Gini', 0)[-1]=0.6252\n",
-      "Condition not met at step 4676. No data to save.\n",
-      "4676\n",
-      "model_vars.get('Gini', 0)[-1]=0.6082000000000001\n",
-      "Condition not met at step 4677. No data to save.\n",
-      "4677\n",
-      "model_vars.get('Gini', 0)[-1]=0.658\n",
-      "Condition not met at step 4678. No data to save.\n",
-      "4678\n",
-      "model_vars.get('Gini', 0)[-1]=0.6382\n",
-      "Condition not met at step 4679. No data to save.\n",
-      "4679\n",
-      "model_vars.get('Gini', 0)[-1]=0.6416\n",
-      "Condition not met at step 4680. No data to save.\n",
-      "4680\n",
-      "model_vars.get('Gini', 0)[-1]=0.6175999999999999\n",
-      "Condition not met at step 4681. No data to save.\n",
-      "4681\n",
-      "model_vars.get('Gini', 0)[-1]=0.6284000000000001\n",
-      "Condition not met at step 4682. No data to save.\n",
-      "4682\n",
-      "model_vars.get('Gini', 0)[-1]=0.6195999999999999\n",
-      "Condition not met at step 4683. No data to save.\n",
-      "4683\n",
-      "model_vars.get('Gini', 0)[-1]=0.6324000000000001\n",
-      "Condition not met at step 4684. No data to save.\n",
-      "4684\n",
-      "model_vars.get('Gini', 0)[-1]=0.6144000000000001\n",
-      "Condition not met at step 4685. No data to save.\n",
-      "4685\n",
-      "model_vars.get('Gini', 0)[-1]=0.6262000000000001\n",
-      "Condition not met at step 4686. No data to save.\n",
-      "4686\n",
-      "model_vars.get('Gini', 0)[-1]=0.5698000000000001\n",
-      "Condition not met at step 4687. No data to save.\n",
-      "4687\n",
-      "model_vars.get('Gini', 0)[-1]=0.5482\n",
-      "Condition not met at step 4688. No data to save.\n",
-      "4688\n",
-      "model_vars.get('Gini', 0)[-1]=0.5798\n",
-      "Condition not met at step 4689. No data to save.\n",
-      "4689\n",
-      "model_vars.get('Gini', 0)[-1]=0.607\n",
-      "Condition not met at step 4690. No data to save.\n",
-      "4690\n",
-      "model_vars.get('Gini', 0)[-1]=0.5962000000000001\n",
-      "Condition not met at step 4691. No data to save.\n",
-      "4691\n",
-      "model_vars.get('Gini', 0)[-1]=0.5928\n",
-      "Condition not met at step 4692. No data to save.\n",
-      "4692\n",
-      "model_vars.get('Gini', 0)[-1]=0.5576\n",
-      "Condition not met at step 4693. No data to save.\n",
-      "4693\n",
-      "model_vars.get('Gini', 0)[-1]=0.5408\n",
-      "Condition not met at step 4694. No data to save.\n",
-      "4694\n",
-      "model_vars.get('Gini', 0)[-1]=0.5696\n",
-      "Condition not met at step 4695. No data to save.\n",
-      "4695\n",
-      "model_vars.get('Gini', 0)[-1]=0.5686\n",
-      "Condition not met at step 4696. No data to save.\n",
-      "4696\n",
-      "model_vars.get('Gini', 0)[-1]=0.5356000000000001\n",
-      "Condition not met at step 4697. No data to save.\n",
-      "4697\n",
-      "model_vars.get('Gini', 0)[-1]=0.5691999999999999\n",
-      "Condition not met at step 4698. No data to save.\n",
-      "4698\n",
-      "model_vars.get('Gini', 0)[-1]=0.5406\n",
-      "Condition not met at step 4699. No data to save.\n",
-      "4699\n",
-      "CALLED\n",
-      "self.model._steps=4700\n",
-      " TEST self.model._steps=4700\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "4600  0.7034\n",
-      "4601  0.7354\n",
-      "4602  0.7234\n",
-      "4603  0.6974\n",
-      "4604  0.6846\n",
-      "...      ...\n",
-      "4695  0.5686\n",
-      "4696  0.5356\n",
-      "4697  0.5692\n",
-      "4698  0.5406\n",
-      "4699  0.5570\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_047.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_047.parquet'\n",
-      "Saving model to test_path/model_data_047.parquet\n",
-      "Saving agent to test_path/agent_data_047.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.5569999999999999\n",
-      "Condition not met at step 4700. No data to save.\n",
-      "4700\n",
-      "model_vars.get('Gini', 0)[-1]=0.5552\n",
-      "Condition not met at step 4701. No data to save.\n",
-      "4701\n",
-      "model_vars.get('Gini', 0)[-1]=0.5780000000000001\n",
-      "Condition not met at step 4702. No data to save.\n",
-      "4702\n",
-      "model_vars.get('Gini', 0)[-1]=0.5768\n",
-      "Condition not met at step 4703. No data to save.\n",
-      "4703\n",
-      "model_vars.get('Gini', 0)[-1]=0.599\n",
-      "Condition not met at step 4704. No data to save.\n",
-      "4704\n",
-      "model_vars.get('Gini', 0)[-1]=0.6172\n",
-      "Condition not met at step 4705. No data to save.\n",
-      "4705\n",
-      "model_vars.get('Gini', 0)[-1]=0.6384000000000001\n",
-      "Condition not met at step 4706. No data to save.\n",
-      "4706\n",
-      "model_vars.get('Gini', 0)[-1]=0.6576\n",
-      "Condition not met at step 4707. No data to save.\n",
-      "4707\n",
-      "model_vars.get('Gini', 0)[-1]=0.649\n",
-      "Condition not met at step 4708. No data to save.\n",
-      "4708\n",
-      "model_vars.get('Gini', 0)[-1]=0.634\n",
-      "Condition not met at step 4709. No data to save.\n",
-      "4709\n",
-      "model_vars.get('Gini', 0)[-1]=0.6382\n",
-      "Condition not met at step 4710. No data to save.\n",
-      "4710\n",
-      "model_vars.get('Gini', 0)[-1]=0.651\n",
-      "Condition not met at step 4711. No data to save.\n",
-      "4711\n",
-      "model_vars.get('Gini', 0)[-1]=0.6442\n",
-      "Condition not met at step 4712. No data to save.\n",
-      "4712\n",
-      "model_vars.get('Gini', 0)[-1]=0.6226\n",
-      "Condition not met at step 4713. No data to save.\n",
-      "4713\n",
-      "model_vars.get('Gini', 0)[-1]=0.6288\n",
-      "Condition not met at step 4714. No data to save.\n",
-      "4714\n",
-      "model_vars.get('Gini', 0)[-1]=0.6234\n",
-      "Condition not met at step 4715. No data to save.\n",
-      "4715\n",
-      "model_vars.get('Gini', 0)[-1]=0.6244000000000001\n",
-      "Condition not met at step 4716. No data to save.\n",
-      "4716\n",
-      "model_vars.get('Gini', 0)[-1]=0.6548\n",
-      "Condition not met at step 4717. No data to save.\n",
-      "4717\n",
-      "model_vars.get('Gini', 0)[-1]=0.628\n",
-      "Condition not met at step 4718. No data to save.\n",
-      "4718\n",
-      "model_vars.get('Gini', 0)[-1]=0.6344000000000001\n",
-      "Condition not met at step 4719. No data to save.\n",
-      "4719\n",
-      "model_vars.get('Gini', 0)[-1]=0.64\n",
-      "Condition not met at step 4720. No data to save.\n",
-      "4720\n",
-      "model_vars.get('Gini', 0)[-1]=0.593\n",
-      "Condition not met at step 4721. No data to save.\n",
-      "4721\n",
-      "model_vars.get('Gini', 0)[-1]=0.6142000000000001\n",
-      "Condition not met at step 4722. No data to save.\n",
-      "4722\n",
-      "model_vars.get('Gini', 0)[-1]=0.6392\n",
-      "Condition not met at step 4723. No data to save.\n",
-      "4723\n",
-      "model_vars.get('Gini', 0)[-1]=0.6352\n",
-      "Condition not met at step 4724. No data to save.\n",
-      "4724\n",
-      "model_vars.get('Gini', 0)[-1]=0.6264000000000001\n",
-      "Condition not met at step 4725. No data to save.\n",
-      "4725\n",
-      "model_vars.get('Gini', 0)[-1]=0.6128\n",
-      "Condition not met at step 4726. No data to save.\n",
-      "4726\n",
-      "model_vars.get('Gini', 0)[-1]=0.6020000000000001\n",
-      "Condition not met at step 4727. No data to save.\n",
-      "4727\n",
-      "model_vars.get('Gini', 0)[-1]=0.6134\n",
-      "Condition not met at step 4728. No data to save.\n",
-      "4728\n",
-      "model_vars.get('Gini', 0)[-1]=0.6076\n",
-      "Condition not met at step 4729. No data to save.\n",
-      "4729\n",
-      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
-      "Condition not met at step 4730. No data to save.\n",
-      "4730\n",
-      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
-      "Condition not met at step 4731. No data to save.\n",
-      "4731\n",
-      "model_vars.get('Gini', 0)[-1]=0.6568\n",
-      "Condition not met at step 4732. No data to save.\n",
-      "4732\n",
-      "model_vars.get('Gini', 0)[-1]=0.6332\n",
-      "Condition not met at step 4733. No data to save.\n",
-      "4733\n",
-      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
-      "Condition not met at step 4734. No data to save.\n",
-      "4734\n",
-      "model_vars.get('Gini', 0)[-1]=0.6888000000000001\n",
-      "Condition not met at step 4735. No data to save.\n",
-      "4735\n",
-      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
-      "Condition not met at step 4736. No data to save.\n",
-      "4736\n",
-      "model_vars.get('Gini', 0)[-1]=0.7058\n",
-      "Condition met. Appended special results for step 4737 to test_path/special_results.parquet\n",
-      "4737\n",
-      "model_vars.get('Gini', 0)[-1]=0.7050000000000001\n",
-      "Condition met. Appended special results for step 4738 to test_path/special_results.parquet\n",
-      "4738\n",
-      "model_vars.get('Gini', 0)[-1]=0.6996\n",
-      "Condition not met at step 4739. No data to save.\n",
-      "4739\n",
-      "model_vars.get('Gini', 0)[-1]=0.6988000000000001\n",
-      "Condition not met at step 4740. No data to save.\n",
-      "4740\n",
-      "model_vars.get('Gini', 0)[-1]=0.6944\n",
-      "Condition not met at step 4741. No data to save.\n",
-      "4741\n",
-      "model_vars.get('Gini', 0)[-1]=0.6902\n",
-      "Condition not met at step 4742. No data to save.\n",
-      "4742\n",
-      "model_vars.get('Gini', 0)[-1]=0.6548\n",
-      "Condition not met at step 4743. No data to save.\n",
-      "4743\n",
-      "model_vars.get('Gini', 0)[-1]=0.6508\n",
-      "Condition not met at step 4744. No data to save.\n",
-      "4744\n",
-      "model_vars.get('Gini', 0)[-1]=0.6738\n",
-      "Condition not met at step 4745. No data to save.\n",
-      "4745\n",
-      "model_vars.get('Gini', 0)[-1]=0.646\n",
-      "Condition not met at step 4746. No data to save.\n",
-      "4746\n",
-      "model_vars.get('Gini', 0)[-1]=0.658\n",
-      "Condition not met at step 4747. No data to save.\n",
-      "4747\n",
-      "model_vars.get('Gini', 0)[-1]=0.6874\n",
-      "Condition not met at step 4748. No data to save.\n",
-      "4748\n",
-      "model_vars.get('Gini', 0)[-1]=0.702\n",
-      "Condition met. Appended special results for step 4749 to test_path/special_results.parquet\n",
-      "4749\n",
-      "model_vars.get('Gini', 0)[-1]=0.6988000000000001\n",
-      "Condition not met at step 4750. No data to save.\n",
-      "4750\n",
-      "model_vars.get('Gini', 0)[-1]=0.6884\n",
-      "Condition not met at step 4751. No data to save.\n",
-      "4751\n",
-      "model_vars.get('Gini', 0)[-1]=0.6658\n",
-      "Condition not met at step 4752. No data to save.\n",
-      "4752\n",
-      "model_vars.get('Gini', 0)[-1]=0.6754\n",
-      "Condition not met at step 4753. No data to save.\n",
-      "4753\n",
-      "model_vars.get('Gini', 0)[-1]=0.6636\n",
-      "Condition not met at step 4754. No data to save.\n",
-      "4754\n",
-      "model_vars.get('Gini', 0)[-1]=0.6302\n",
-      "Condition not met at step 4755. No data to save.\n",
-      "4755\n",
-      "model_vars.get('Gini', 0)[-1]=0.6386000000000001\n",
-      "Condition not met at step 4756. No data to save.\n",
-      "4756\n",
-      "model_vars.get('Gini', 0)[-1]=0.6726000000000001\n",
-      "Condition not met at step 4757. No data to save.\n",
-      "4757\n",
-      "model_vars.get('Gini', 0)[-1]=0.6722\n",
-      "Condition not met at step 4758. No data to save.\n",
-      "4758\n",
-      "model_vars.get('Gini', 0)[-1]=0.6708000000000001\n",
-      "Condition not met at step 4759. No data to save.\n",
-      "4759\n",
-      "model_vars.get('Gini', 0)[-1]=0.6706000000000001\n",
-      "Condition not met at step 4760. No data to save.\n",
-      "4760\n",
-      "model_vars.get('Gini', 0)[-1]=0.6839999999999999\n",
-      "Condition not met at step 4761. No data to save.\n",
-      "4761\n",
-      "model_vars.get('Gini', 0)[-1]=0.6704\n",
-      "Condition not met at step 4762. No data to save.\n",
-      "4762\n",
-      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
-      "Condition not met at step 4763. No data to save.\n",
-      "4763\n",
-      "model_vars.get('Gini', 0)[-1]=0.645\n",
-      "Condition not met at step 4764. No data to save.\n",
-      "4764\n",
-      "model_vars.get('Gini', 0)[-1]=0.62\n",
-      "Condition not met at step 4765. No data to save.\n",
-      "4765\n",
-      "model_vars.get('Gini', 0)[-1]=0.6266\n",
-      "Condition not met at step 4766. No data to save.\n",
-      "4766\n",
-      "model_vars.get('Gini', 0)[-1]=0.6318\n",
-      "Condition not met at step 4767. No data to save.\n",
-      "4767\n",
-      "model_vars.get('Gini', 0)[-1]=0.6186\n",
-      "Condition not met at step 4768. No data to save.\n",
-      "4768\n",
-      "model_vars.get('Gini', 0)[-1]=0.6315999999999999\n",
-      "Condition not met at step 4769. No data to save.\n",
-      "4769\n",
-      "model_vars.get('Gini', 0)[-1]=0.6382\n",
-      "Condition not met at step 4770. No data to save.\n",
-      "4770\n",
-      "model_vars.get('Gini', 0)[-1]=0.6508\n",
-      "Condition not met at step 4771. No data to save.\n",
-      "4771\n",
-      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
-      "Condition not met at step 4772. No data to save.\n",
-      "4772\n",
-      "model_vars.get('Gini', 0)[-1]=0.6544\n",
-      "Condition not met at step 4773. No data to save.\n",
-      "4773\n",
-      "model_vars.get('Gini', 0)[-1]=0.6382\n",
-      "Condition not met at step 4774. No data to save.\n",
-      "4774\n",
-      "model_vars.get('Gini', 0)[-1]=0.6554\n",
-      "Condition not met at step 4775. No data to save.\n",
-      "4775\n",
-      "model_vars.get('Gini', 0)[-1]=0.6512\n",
-      "Condition not met at step 4776. No data to save.\n",
-      "4776\n",
-      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
-      "Condition not met at step 4777. No data to save.\n",
-      "4777\n",
-      "model_vars.get('Gini', 0)[-1]=0.6624\n",
-      "Condition not met at step 4778. No data to save.\n",
-      "4778\n",
-      "model_vars.get('Gini', 0)[-1]=0.6576\n",
-      "Condition not met at step 4779. No data to save.\n",
-      "4779\n",
-      "model_vars.get('Gini', 0)[-1]=0.6712\n",
-      "Condition not met at step 4780. No data to save.\n",
-      "4780\n",
-      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
-      "Condition not met at step 4781. No data to save.\n",
-      "4781\n",
-      "model_vars.get('Gini', 0)[-1]=0.6896\n",
-      "Condition not met at step 4782. No data to save.\n",
-      "4782\n",
-      "model_vars.get('Gini', 0)[-1]=0.7004\n",
-      "Condition met. Appended special results for step 4783 to test_path/special_results.parquet\n",
-      "4783\n",
-      "model_vars.get('Gini', 0)[-1]=0.6966\n",
-      "Condition not met at step 4784. No data to save.\n",
-      "4784\n",
-      "model_vars.get('Gini', 0)[-1]=0.6692\n",
-      "Condition not met at step 4785. No data to save.\n",
-      "4785\n",
-      "model_vars.get('Gini', 0)[-1]=0.6586000000000001\n",
-      "Condition not met at step 4786. No data to save.\n",
-      "4786\n",
-      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
-      "Condition not met at step 4787. No data to save.\n",
-      "4787\n",
-      "model_vars.get('Gini', 0)[-1]=0.6818\n",
-      "Condition not met at step 4788. No data to save.\n",
-      "4788\n",
-      "model_vars.get('Gini', 0)[-1]=0.6582\n",
-      "Condition not met at step 4789. No data to save.\n",
-      "4789\n",
-      "model_vars.get('Gini', 0)[-1]=0.6448\n",
-      "Condition not met at step 4790. No data to save.\n",
-      "4790\n",
-      "model_vars.get('Gini', 0)[-1]=0.6352\n",
-      "Condition not met at step 4791. No data to save.\n",
-      "4791\n",
-      "model_vars.get('Gini', 0)[-1]=0.6448\n",
-      "Condition not met at step 4792. No data to save.\n",
-      "4792\n",
-      "model_vars.get('Gini', 0)[-1]=0.6522\n",
-      "Condition not met at step 4793. No data to save.\n",
-      "4793\n",
-      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
-      "Condition not met at step 4794. No data to save.\n",
-      "4794\n",
-      "model_vars.get('Gini', 0)[-1]=0.6462\n",
-      "Condition not met at step 4795. No data to save.\n",
-      "4795\n",
-      "model_vars.get('Gini', 0)[-1]=0.662\n",
-      "Condition not met at step 4796. No data to save.\n",
-      "4796\n",
-      "model_vars.get('Gini', 0)[-1]=0.6856\n",
-      "Condition not met at step 4797. No data to save.\n",
-      "4797\n",
-      "model_vars.get('Gini', 0)[-1]=0.6602\n",
-      "Condition not met at step 4798. No data to save.\n",
-      "4798\n",
-      "model_vars.get('Gini', 0)[-1]=0.6508\n",
-      "Condition not met at step 4799. No data to save.\n",
-      "4799\n",
-      "CALLED\n",
-      "self.model._steps=4800\n",
-      " TEST self.model._steps=4800\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "4700  0.5552\n",
-      "4701  0.5780\n",
-      "4702  0.5768\n",
-      "4703  0.5990\n",
-      "4704  0.6172\n",
-      "...      ...\n",
-      "4795  0.6620\n",
-      "4796  0.6856\n",
-      "4797  0.6602\n",
-      "4798  0.6508\n",
-      "4799  0.6674\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_048.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_048.parquet'\n",
-      "Saving model to test_path/model_data_048.parquet\n",
-      "Saving agent to test_path/agent_data_048.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6674\n",
-      "Condition not met at step 4800. No data to save.\n",
-      "4800\n",
-      "model_vars.get('Gini', 0)[-1]=0.6742\n",
-      "Condition not met at step 4801. No data to save.\n",
-      "4801\n",
-      "model_vars.get('Gini', 0)[-1]=0.6914\n",
-      "Condition not met at step 4802. No data to save.\n",
-      "4802\n",
-      "model_vars.get('Gini', 0)[-1]=0.6712\n",
-      "Condition not met at step 4803. No data to save.\n",
-      "4803\n",
-      "model_vars.get('Gini', 0)[-1]=0.6592\n",
-      "Condition not met at step 4804. No data to save.\n",
-      "4804\n",
-      "model_vars.get('Gini', 0)[-1]=0.6118\n",
-      "Condition not met at step 4805. No data to save.\n",
-      "4805\n",
-      "model_vars.get('Gini', 0)[-1]=0.6314\n",
-      "Condition not met at step 4806. No data to save.\n",
-      "4806\n",
-      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
-      "Condition not met at step 4807. No data to save.\n",
-      "4807\n",
-      "model_vars.get('Gini', 0)[-1]=0.6812\n",
-      "Condition not met at step 4808. No data to save.\n",
-      "4808\n",
-      "model_vars.get('Gini', 0)[-1]=0.643\n",
-      "Condition not met at step 4809. No data to save.\n",
-      "4809\n",
-      "model_vars.get('Gini', 0)[-1]=0.6582\n",
-      "Condition not met at step 4810. No data to save.\n",
-      "4810\n",
-      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
-      "Condition not met at step 4811. No data to save.\n",
-      "4811\n",
-      "model_vars.get('Gini', 0)[-1]=0.6608\n",
-      "Condition not met at step 4812. No data to save.\n",
-      "4812\n",
-      "model_vars.get('Gini', 0)[-1]=0.6512\n",
-      "Condition not met at step 4813. No data to save.\n",
-      "4813\n",
-      "model_vars.get('Gini', 0)[-1]=0.6514\n",
-      "Condition not met at step 4814. No data to save.\n",
-      "4814\n",
-      "model_vars.get('Gini', 0)[-1]=0.6497999999999999\n",
-      "Condition not met at step 4815. No data to save.\n",
-      "4815\n",
-      "model_vars.get('Gini', 0)[-1]=0.6614\n",
-      "Condition not met at step 4816. No data to save.\n",
-      "4816\n",
-      "model_vars.get('Gini', 0)[-1]=0.6614\n",
-      "Condition not met at step 4817. No data to save.\n",
-      "4817\n",
-      "model_vars.get('Gini', 0)[-1]=0.6517999999999999\n",
-      "Condition not met at step 4818. No data to save.\n",
-      "4818\n",
-      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
-      "Condition not met at step 4819. No data to save.\n",
-      "4819\n",
-      "model_vars.get('Gini', 0)[-1]=0.6838\n",
-      "Condition not met at step 4820. No data to save.\n",
-      "4820\n",
-      "model_vars.get('Gini', 0)[-1]=0.6696\n",
-      "Condition not met at step 4821. No data to save.\n",
-      "4821\n",
-      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
-      "Condition not met at step 4822. No data to save.\n",
-      "4822\n",
-      "model_vars.get('Gini', 0)[-1]=0.6774\n",
-      "Condition not met at step 4823. No data to save.\n",
-      "4823\n",
-      "model_vars.get('Gini', 0)[-1]=0.6968000000000001\n",
-      "Condition not met at step 4824. No data to save.\n",
-      "4824\n",
-      "model_vars.get('Gini', 0)[-1]=0.653\n",
-      "Condition not met at step 4825. No data to save.\n",
-      "4825\n",
-      "model_vars.get('Gini', 0)[-1]=0.663\n",
-      "Condition not met at step 4826. No data to save.\n",
-      "4826\n",
-      "model_vars.get('Gini', 0)[-1]=0.6961999999999999\n",
-      "Condition not met at step 4827. No data to save.\n",
-      "4827\n",
-      "model_vars.get('Gini', 0)[-1]=0.6864\n",
-      "Condition not met at step 4828. No data to save.\n",
-      "4828\n",
-      "model_vars.get('Gini', 0)[-1]=0.6784\n",
-      "Condition not met at step 4829. No data to save.\n",
-      "4829\n",
-      "model_vars.get('Gini', 0)[-1]=0.6918\n",
-      "Condition not met at step 4830. No data to save.\n",
-      "4830\n",
-      "model_vars.get('Gini', 0)[-1]=0.6918\n",
-      "Condition not met at step 4831. No data to save.\n",
-      "4831\n",
-      "model_vars.get('Gini', 0)[-1]=0.7056\n",
-      "Condition met. Appended special results for step 4832 to test_path/special_results.parquet\n",
-      "4832\n",
-      "model_vars.get('Gini', 0)[-1]=0.6938\n",
-      "Condition not met at step 4833. No data to save.\n",
-      "4833\n",
-      "model_vars.get('Gini', 0)[-1]=0.6944\n",
-      "Condition not met at step 4834. No data to save.\n",
-      "4834\n",
-      "model_vars.get('Gini', 0)[-1]=0.6806\n",
-      "Condition not met at step 4835. No data to save.\n",
-      "4835\n",
-      "model_vars.get('Gini', 0)[-1]=0.6726000000000001\n",
-      "Condition not met at step 4836. No data to save.\n",
-      "4836\n",
-      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
-      "Condition not met at step 4837. No data to save.\n",
-      "4837\n",
-      "model_vars.get('Gini', 0)[-1]=0.6544\n",
-      "Condition not met at step 4838. No data to save.\n",
-      "4838\n",
-      "model_vars.get('Gini', 0)[-1]=0.638\n",
-      "Condition not met at step 4839. No data to save.\n",
-      "4839\n",
-      "model_vars.get('Gini', 0)[-1]=0.648\n",
-      "Condition not met at step 4840. No data to save.\n",
-      "4840\n",
-      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
-      "Condition not met at step 4841. No data to save.\n",
-      "4841\n",
-      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
-      "Condition not met at step 4842. No data to save.\n",
-      "4842\n",
-      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
-      "Condition not met at step 4843. No data to save.\n",
-      "4843\n",
-      "model_vars.get('Gini', 0)[-1]=0.6376\n",
-      "Condition not met at step 4844. No data to save.\n",
-      "4844\n",
-      "model_vars.get('Gini', 0)[-1]=0.629\n",
-      "Condition not met at step 4845. No data to save.\n",
-      "4845\n",
-      "model_vars.get('Gini', 0)[-1]=0.6468\n",
-      "Condition not met at step 4846. No data to save.\n",
-      "4846\n",
-      "model_vars.get('Gini', 0)[-1]=0.6434\n",
-      "Condition not met at step 4847. No data to save.\n",
-      "4847\n",
-      "model_vars.get('Gini', 0)[-1]=0.6512\n",
-      "Condition not met at step 4848. No data to save.\n",
-      "4848\n",
-      "model_vars.get('Gini', 0)[-1]=0.641\n",
-      "Condition not met at step 4849. No data to save.\n",
-      "4849\n",
-      "model_vars.get('Gini', 0)[-1]=0.6132\n",
-      "Condition not met at step 4850. No data to save.\n",
-      "4850\n",
-      "model_vars.get('Gini', 0)[-1]=0.6274\n",
-      "Condition not met at step 4851. No data to save.\n",
-      "4851\n",
-      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
-      "Condition not met at step 4852. No data to save.\n",
-      "4852\n",
-      "model_vars.get('Gini', 0)[-1]=0.6516\n",
-      "Condition not met at step 4853. No data to save.\n",
-      "4853\n",
-      "model_vars.get('Gini', 0)[-1]=0.6664\n",
-      "Condition not met at step 4854. No data to save.\n",
-      "4854\n",
-      "model_vars.get('Gini', 0)[-1]=0.6614\n",
-      "Condition not met at step 4855. No data to save.\n",
-      "4855\n",
-      "model_vars.get('Gini', 0)[-1]=0.6566000000000001\n",
-      "Condition not met at step 4856. No data to save.\n",
-      "4856\n",
-      "model_vars.get('Gini', 0)[-1]=0.6512\n",
-      "Condition not met at step 4857. No data to save.\n",
-      "4857\n",
-      "model_vars.get('Gini', 0)[-1]=0.6352\n",
-      "Condition not met at step 4858. No data to save.\n",
-      "4858\n",
-      "model_vars.get('Gini', 0)[-1]=0.613\n",
-      "Condition not met at step 4859. No data to save.\n",
-      "4859\n",
-      "model_vars.get('Gini', 0)[-1]=0.618\n",
-      "Condition not met at step 4860. No data to save.\n",
-      "4860\n",
-      "model_vars.get('Gini', 0)[-1]=0.5874\n",
-      "Condition not met at step 4861. No data to save.\n",
-      "4861\n",
-      "model_vars.get('Gini', 0)[-1]=0.6016\n",
-      "Condition not met at step 4862. No data to save.\n",
-      "4862\n",
-      "model_vars.get('Gini', 0)[-1]=0.6148\n",
-      "Condition not met at step 4863. No data to save.\n",
-      "4863\n",
-      "model_vars.get('Gini', 0)[-1]=0.6278\n",
-      "Condition not met at step 4864. No data to save.\n",
-      "4864\n",
-      "model_vars.get('Gini', 0)[-1]=0.6368\n",
-      "Condition not met at step 4865. No data to save.\n",
-      "4865\n",
-      "model_vars.get('Gini', 0)[-1]=0.6577999999999999\n",
-      "Condition not met at step 4866. No data to save.\n",
-      "4866\n",
-      "model_vars.get('Gini', 0)[-1]=0.6638\n",
-      "Condition not met at step 4867. No data to save.\n",
-      "4867\n",
-      "model_vars.get('Gini', 0)[-1]=0.657\n",
-      "Condition not met at step 4868. No data to save.\n",
-      "4868\n",
-      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
-      "Condition not met at step 4869. No data to save.\n",
-      "4869\n",
-      "model_vars.get('Gini', 0)[-1]=0.6522\n",
-      "Condition not met at step 4870. No data to save.\n",
-      "4870\n",
-      "model_vars.get('Gini', 0)[-1]=0.6638\n",
-      "Condition not met at step 4871. No data to save.\n",
-      "4871\n",
-      "model_vars.get('Gini', 0)[-1]=0.648\n",
-      "Condition not met at step 4872. No data to save.\n",
-      "4872\n",
-      "model_vars.get('Gini', 0)[-1]=0.6576\n",
-      "Condition not met at step 4873. No data to save.\n",
-      "4873\n",
-      "model_vars.get('Gini', 0)[-1]=0.6812\n",
-      "Condition not met at step 4874. No data to save.\n",
-      "4874\n",
-      "model_vars.get('Gini', 0)[-1]=0.6786000000000001\n",
-      "Condition not met at step 4875. No data to save.\n",
-      "4875\n",
-      "model_vars.get('Gini', 0)[-1]=0.6674\n",
-      "Condition not met at step 4876. No data to save.\n",
-      "4876\n",
-      "model_vars.get('Gini', 0)[-1]=0.6566000000000001\n",
-      "Condition not met at step 4877. No data to save.\n",
-      "4877\n",
-      "model_vars.get('Gini', 0)[-1]=0.6574\n",
-      "Condition not met at step 4878. No data to save.\n",
-      "4878\n",
-      "model_vars.get('Gini', 0)[-1]=0.6634\n",
-      "Condition not met at step 4879. No data to save.\n",
-      "4879\n",
-      "model_vars.get('Gini', 0)[-1]=0.6626000000000001\n",
-      "Condition not met at step 4880. No data to save.\n",
-      "4880\n",
-      "model_vars.get('Gini', 0)[-1]=0.652\n",
-      "Condition not met at step 4881. No data to save.\n",
-      "4881\n",
-      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
-      "Condition not met at step 4882. No data to save.\n",
-      "4882\n",
-      "model_vars.get('Gini', 0)[-1]=0.6338\n",
-      "Condition not met at step 4883. No data to save.\n",
-      "4883\n",
-      "model_vars.get('Gini', 0)[-1]=0.6048\n",
-      "Condition not met at step 4884. No data to save.\n",
-      "4884\n",
-      "model_vars.get('Gini', 0)[-1]=0.6144000000000001\n",
-      "Condition not met at step 4885. No data to save.\n",
-      "4885\n",
-      "model_vars.get('Gini', 0)[-1]=0.6113999999999999\n",
-      "Condition not met at step 4886. No data to save.\n",
-      "4886\n",
-      "model_vars.get('Gini', 0)[-1]=0.6138\n",
-      "Condition not met at step 4887. No data to save.\n",
-      "4887\n",
-      "model_vars.get('Gini', 0)[-1]=0.6128\n",
-      "Condition not met at step 4888. No data to save.\n",
-      "4888\n",
-      "model_vars.get('Gini', 0)[-1]=0.5953999999999999\n",
-      "Condition not met at step 4889. No data to save.\n",
-      "4889\n",
-      "model_vars.get('Gini', 0)[-1]=0.5658000000000001\n",
-      "Condition not met at step 4890. No data to save.\n",
-      "4890\n",
-      "model_vars.get('Gini', 0)[-1]=0.558\n",
-      "Condition not met at step 4891. No data to save.\n",
-      "4891\n",
-      "model_vars.get('Gini', 0)[-1]=0.5694\n",
-      "Condition not met at step 4892. No data to save.\n",
-      "4892\n",
-      "model_vars.get('Gini', 0)[-1]=0.601\n",
-      "Condition not met at step 4893. No data to save.\n",
-      "4893\n",
-      "model_vars.get('Gini', 0)[-1]=0.618\n",
-      "Condition not met at step 4894. No data to save.\n",
-      "4894\n",
-      "model_vars.get('Gini', 0)[-1]=0.611\n",
-      "Condition not met at step 4895. No data to save.\n",
-      "4895\n",
-      "model_vars.get('Gini', 0)[-1]=0.6104\n",
-      "Condition not met at step 4896. No data to save.\n",
-      "4896\n",
-      "model_vars.get('Gini', 0)[-1]=0.6073999999999999\n",
-      "Condition not met at step 4897. No data to save.\n",
-      "4897\n",
-      "model_vars.get('Gini', 0)[-1]=0.6334\n",
-      "Condition not met at step 4898. No data to save.\n",
-      "4898\n",
-      "model_vars.get('Gini', 0)[-1]=0.6053999999999999\n",
-      "Condition not met at step 4899. No data to save.\n",
-      "4899\n",
-      "CALLED\n",
-      "self.model._steps=4900\n",
-      " TEST self.model._steps=4900\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "4800  0.6742\n",
-      "4801  0.6914\n",
-      "4802  0.6712\n",
-      "4803  0.6592\n",
-      "4804  0.6118\n",
-      "...      ...\n",
-      "4895  0.6104\n",
-      "4896  0.6074\n",
-      "4897  0.6334\n",
-      "4898  0.6054\n",
-      "4899  0.6306\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_049.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_049.parquet'\n",
-      "Saving model to test_path/model_data_049.parquet\n",
-      "Saving agent to test_path/agent_data_049.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6306\n",
-      "Condition not met at step 4900. No data to save.\n",
-      "4900\n",
-      "model_vars.get('Gini', 0)[-1]=0.625\n",
-      "Condition not met at step 4901. No data to save.\n",
-      "4901\n",
-      "model_vars.get('Gini', 0)[-1]=0.6354\n",
-      "Condition not met at step 4902. No data to save.\n",
-      "4902\n",
-      "model_vars.get('Gini', 0)[-1]=0.638\n",
-      "Condition not met at step 4903. No data to save.\n",
-      "4903\n",
-      "model_vars.get('Gini', 0)[-1]=0.6444000000000001\n",
-      "Condition not met at step 4904. No data to save.\n",
-      "4904\n",
-      "model_vars.get('Gini', 0)[-1]=0.6386000000000001\n",
-      "Condition not met at step 4905. No data to save.\n",
-      "4905\n",
-      "model_vars.get('Gini', 0)[-1]=0.6302\n",
-      "Condition not met at step 4906. No data to save.\n",
-      "4906\n",
-      "model_vars.get('Gini', 0)[-1]=0.6504000000000001\n",
-      "Condition not met at step 4907. No data to save.\n",
-      "4907\n",
-      "model_vars.get('Gini', 0)[-1]=0.628\n",
-      "Condition not met at step 4908. No data to save.\n",
-      "4908\n",
-      "model_vars.get('Gini', 0)[-1]=0.6146\n",
-      "Condition not met at step 4909. No data to save.\n",
-      "4909\n",
-      "model_vars.get('Gini', 0)[-1]=0.6235999999999999\n",
-      "Condition not met at step 4910. No data to save.\n",
-      "4910\n",
-      "model_vars.get('Gini', 0)[-1]=0.612\n",
-      "Condition not met at step 4911. No data to save.\n",
-      "4911\n",
-      "model_vars.get('Gini', 0)[-1]=0.6376\n",
-      "Condition not met at step 4912. No data to save.\n",
-      "4912\n",
-      "model_vars.get('Gini', 0)[-1]=0.613\n",
-      "Condition not met at step 4913. No data to save.\n",
-      "4913\n",
-      "model_vars.get('Gini', 0)[-1]=0.609\n",
-      "Condition not met at step 4914. No data to save.\n",
-      "4914\n",
-      "model_vars.get('Gini', 0)[-1]=0.6554\n",
-      "Condition not met at step 4915. No data to save.\n",
-      "4915\n",
-      "model_vars.get('Gini', 0)[-1]=0.653\n",
-      "Condition not met at step 4916. No data to save.\n",
-      "4916\n",
-      "model_vars.get('Gini', 0)[-1]=0.6266\n",
-      "Condition not met at step 4917. No data to save.\n",
-      "4917\n",
-      "model_vars.get('Gini', 0)[-1]=0.6358\n",
-      "Condition not met at step 4918. No data to save.\n",
-      "4918\n",
-      "model_vars.get('Gini', 0)[-1]=0.6698\n",
-      "Condition not met at step 4919. No data to save.\n",
-      "4919\n",
-      "model_vars.get('Gini', 0)[-1]=0.637\n",
-      "Condition not met at step 4920. No data to save.\n",
-      "4920\n",
-      "model_vars.get('Gini', 0)[-1]=0.6497999999999999\n",
-      "Condition not met at step 4921. No data to save.\n",
-      "4921\n",
-      "model_vars.get('Gini', 0)[-1]=0.6516\n",
-      "Condition not met at step 4922. No data to save.\n",
-      "4922\n",
-      "model_vars.get('Gini', 0)[-1]=0.646\n",
-      "Condition not met at step 4923. No data to save.\n",
-      "4923\n",
-      "model_vars.get('Gini', 0)[-1]=0.626\n",
-      "Condition not met at step 4924. No data to save.\n",
-      "4924\n",
-      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
-      "Condition not met at step 4925. No data to save.\n",
-      "4925\n",
-      "model_vars.get('Gini', 0)[-1]=0.6658\n",
-      "Condition not met at step 4926. No data to save.\n",
-      "4926\n",
-      "model_vars.get('Gini', 0)[-1]=0.6718\n",
-      "Condition not met at step 4927. No data to save.\n",
-      "4927\n",
-      "model_vars.get('Gini', 0)[-1]=0.6754\n",
-      "Condition not met at step 4928. No data to save.\n",
-      "4928\n",
-      "model_vars.get('Gini', 0)[-1]=0.6472\n",
-      "Condition not met at step 4929. No data to save.\n",
-      "4929\n",
-      "model_vars.get('Gini', 0)[-1]=0.6298\n",
-      "Condition not met at step 4930. No data to save.\n",
-      "4930\n",
-      "model_vars.get('Gini', 0)[-1]=0.61\n",
-      "Condition not met at step 4931. No data to save.\n",
-      "4931\n",
-      "model_vars.get('Gini', 0)[-1]=0.6062000000000001\n",
-      "Condition not met at step 4932. No data to save.\n",
-      "4932\n",
-      "model_vars.get('Gini', 0)[-1]=0.6158\n",
-      "Condition not met at step 4933. No data to save.\n",
-      "4933\n",
-      "model_vars.get('Gini', 0)[-1]=0.6072\n",
-      "Condition not met at step 4934. No data to save.\n",
-      "4934\n",
-      "model_vars.get('Gini', 0)[-1]=0.6338\n",
-      "Condition not met at step 4935. No data to save.\n",
-      "4935\n",
-      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
-      "Condition not met at step 4936. No data to save.\n",
-      "4936\n",
-      "model_vars.get('Gini', 0)[-1]=0.6192\n",
-      "Condition not met at step 4937. No data to save.\n",
-      "4937\n",
-      "model_vars.get('Gini', 0)[-1]=0.6398\n",
-      "Condition not met at step 4938. No data to save.\n",
-      "4938\n",
-      "model_vars.get('Gini', 0)[-1]=0.6272\n",
-      "Condition not met at step 4939. No data to save.\n",
-      "4939\n",
-      "model_vars.get('Gini', 0)[-1]=0.6428\n",
-      "Condition not met at step 4940. No data to save.\n",
-      "4940\n",
-      "model_vars.get('Gini', 0)[-1]=0.6392\n",
-      "Condition not met at step 4941. No data to save.\n",
-      "4941\n",
-      "model_vars.get('Gini', 0)[-1]=0.6092\n",
-      "Condition not met at step 4942. No data to save.\n",
-      "4942\n",
-      "model_vars.get('Gini', 0)[-1]=0.6088\n",
-      "Condition not met at step 4943. No data to save.\n",
-      "4943\n",
-      "model_vars.get('Gini', 0)[-1]=0.6128\n",
-      "Condition not met at step 4944. No data to save.\n",
-      "4944\n",
-      "model_vars.get('Gini', 0)[-1]=0.64\n",
-      "Condition not met at step 4945. No data to save.\n",
-      "4945\n",
-      "model_vars.get('Gini', 0)[-1]=0.6004\n",
-      "Condition not met at step 4946. No data to save.\n",
-      "4946\n",
-      "model_vars.get('Gini', 0)[-1]=0.624\n",
-      "Condition not met at step 4947. No data to save.\n",
-      "4947\n",
-      "model_vars.get('Gini', 0)[-1]=0.6386000000000001\n",
-      "Condition not met at step 4948. No data to save.\n",
-      "4948\n",
-      "model_vars.get('Gini', 0)[-1]=0.6166\n",
-      "Condition not met at step 4949. No data to save.\n",
-      "4949\n",
-      "model_vars.get('Gini', 0)[-1]=0.6202000000000001\n",
-      "Condition not met at step 4950. No data to save.\n",
-      "4950\n",
-      "model_vars.get('Gini', 0)[-1]=0.6214\n",
-      "Condition not met at step 4951. No data to save.\n",
-      "4951\n",
-      "model_vars.get('Gini', 0)[-1]=0.6434\n",
-      "Condition not met at step 4952. No data to save.\n",
-      "4952\n",
-      "model_vars.get('Gini', 0)[-1]=0.6252\n",
-      "Condition not met at step 4953. No data to save.\n",
-      "4953\n",
-      "model_vars.get('Gini', 0)[-1]=0.6093999999999999\n",
-      "Condition not met at step 4954. No data to save.\n",
-      "4954\n",
-      "model_vars.get('Gini', 0)[-1]=0.567\n",
-      "Condition not met at step 4955. No data to save.\n",
-      "4955\n",
-      "model_vars.get('Gini', 0)[-1]=0.5834\n",
-      "Condition not met at step 4956. No data to save.\n",
-      "4956\n",
-      "model_vars.get('Gini', 0)[-1]=0.605\n",
-      "Condition not met at step 4957. No data to save.\n",
-      "4957\n",
-      "model_vars.get('Gini', 0)[-1]=0.5806\n",
-      "Condition not met at step 4958. No data to save.\n",
-      "4958\n",
-      "model_vars.get('Gini', 0)[-1]=0.5926\n",
-      "Condition not met at step 4959. No data to save.\n",
-      "4959\n",
-      "model_vars.get('Gini', 0)[-1]=0.5884\n",
-      "Condition not met at step 4960. No data to save.\n",
-      "4960\n",
-      "model_vars.get('Gini', 0)[-1]=0.603\n",
-      "Condition not met at step 4961. No data to save.\n",
-      "4961\n",
-      "model_vars.get('Gini', 0)[-1]=0.5956\n",
-      "Condition not met at step 4962. No data to save.\n",
-      "4962\n",
-      "model_vars.get('Gini', 0)[-1]=0.5898\n",
-      "Condition not met at step 4963. No data to save.\n",
-      "4963\n",
-      "model_vars.get('Gini', 0)[-1]=0.5986\n",
-      "Condition not met at step 4964. No data to save.\n",
-      "4964\n",
-      "model_vars.get('Gini', 0)[-1]=0.5960000000000001\n",
-      "Condition not met at step 4965. No data to save.\n",
-      "4965\n",
-      "model_vars.get('Gini', 0)[-1]=0.5906\n",
-      "Condition not met at step 4966. No data to save.\n",
-      "4966\n",
-      "model_vars.get('Gini', 0)[-1]=0.617\n",
-      "Condition not met at step 4967. No data to save.\n",
-      "4967\n",
-      "model_vars.get('Gini', 0)[-1]=0.6134\n",
-      "Condition not met at step 4968. No data to save.\n",
-      "4968\n",
-      "model_vars.get('Gini', 0)[-1]=0.624\n",
-      "Condition not met at step 4969. No data to save.\n",
-      "4969\n",
-      "model_vars.get('Gini', 0)[-1]=0.6295999999999999\n",
-      "Condition not met at step 4970. No data to save.\n",
-      "4970\n",
-      "model_vars.get('Gini', 0)[-1]=0.6372\n",
-      "Condition not met at step 4971. No data to save.\n",
-      "4971\n",
-      "model_vars.get('Gini', 0)[-1]=0.6604\n",
-      "Condition not met at step 4972. No data to save.\n",
-      "4972\n",
-      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
-      "Condition not met at step 4973. No data to save.\n",
-      "4973\n",
-      "model_vars.get('Gini', 0)[-1]=0.6738\n",
-      "Condition not met at step 4974. No data to save.\n",
-      "4974\n",
-      "model_vars.get('Gini', 0)[-1]=0.6566000000000001\n",
-      "Condition not met at step 4975. No data to save.\n",
-      "4975\n",
-      "model_vars.get('Gini', 0)[-1]=0.6502\n",
-      "Condition not met at step 4976. No data to save.\n",
-      "4976\n",
-      "model_vars.get('Gini', 0)[-1]=0.6832\n",
-      "Condition not met at step 4977. No data to save.\n",
-      "4977\n",
-      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
-      "Condition not met at step 4978. No data to save.\n",
-      "4978\n",
-      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
-      "Condition not met at step 4979. No data to save.\n",
-      "4979\n",
-      "model_vars.get('Gini', 0)[-1]=0.69\n",
-      "Condition not met at step 4980. No data to save.\n",
-      "4980\n",
-      "model_vars.get('Gini', 0)[-1]=0.6838\n",
-      "Condition not met at step 4981. No data to save.\n",
-      "4981\n",
-      "model_vars.get('Gini', 0)[-1]=0.6588\n",
-      "Condition not met at step 4982. No data to save.\n",
-      "4982\n",
-      "model_vars.get('Gini', 0)[-1]=0.639\n",
-      "Condition not met at step 4983. No data to save.\n",
-      "4983\n",
-      "model_vars.get('Gini', 0)[-1]=0.6456\n",
-      "Condition not met at step 4984. No data to save.\n",
-      "4984\n",
-      "model_vars.get('Gini', 0)[-1]=0.6679999999999999\n",
-      "Condition not met at step 4985. No data to save.\n",
-      "4985\n",
-      "model_vars.get('Gini', 0)[-1]=0.6718\n",
-      "Condition not met at step 4986. No data to save.\n",
-      "4986\n",
-      "model_vars.get('Gini', 0)[-1]=0.6758\n",
-      "Condition not met at step 4987. No data to save.\n",
-      "4987\n",
-      "model_vars.get('Gini', 0)[-1]=0.6576\n",
-      "Condition not met at step 4988. No data to save.\n",
-      "4988\n",
-      "model_vars.get('Gini', 0)[-1]=0.692\n",
-      "Condition not met at step 4989. No data to save.\n",
-      "4989\n",
-      "model_vars.get('Gini', 0)[-1]=0.6718\n",
-      "Condition not met at step 4990. No data to save.\n",
-      "4990\n",
-      "model_vars.get('Gini', 0)[-1]=0.6384000000000001\n",
-      "Condition not met at step 4991. No data to save.\n",
-      "4991\n",
-      "model_vars.get('Gini', 0)[-1]=0.6528\n",
-      "Condition not met at step 4992. No data to save.\n",
-      "4992\n",
-      "model_vars.get('Gini', 0)[-1]=0.6404000000000001\n",
-      "Condition not met at step 4993. No data to save.\n",
-      "4993\n",
-      "model_vars.get('Gini', 0)[-1]=0.6497999999999999\n",
-      "Condition not met at step 4994. No data to save.\n",
-      "4994\n",
-      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
-      "Condition not met at step 4995. No data to save.\n",
-      "4995\n",
-      "model_vars.get('Gini', 0)[-1]=0.6632\n",
-      "Condition not met at step 4996. No data to save.\n",
-      "4996\n",
-      "model_vars.get('Gini', 0)[-1]=0.6708000000000001\n",
-      "Condition not met at step 4997. No data to save.\n",
-      "4997\n",
-      "model_vars.get('Gini', 0)[-1]=0.653\n",
-      "Condition not met at step 4998. No data to save.\n",
-      "4998\n",
-      "model_vars.get('Gini', 0)[-1]=0.591\n",
-      "Condition not met at step 4999. No data to save.\n",
-      "4999\n",
-      "CALLED\n",
-      "self.model._steps=5000\n",
-      " TEST self.model._steps=5000\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "4900  0.6250\n",
-      "4901  0.6354\n",
-      "4902  0.6380\n",
-      "4903  0.6444\n",
-      "4904  0.6386\n",
-      "...      ...\n",
-      "4995  0.6632\n",
-      "4996  0.6708\n",
-      "4997  0.6530\n",
-      "4998  0.5910\n",
-      "4999  0.6036\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_050.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_050.parquet'\n",
-      "Saving model to test_path/model_data_050.parquet\n",
-      "Saving agent to test_path/agent_data_050.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6036\n",
-      "Condition not met at step 5000. No data to save.\n",
-      "5000\n",
-      "model_vars.get('Gini', 0)[-1]=0.6046\n",
-      "Condition not met at step 5001. No data to save.\n",
-      "5001\n",
-      "model_vars.get('Gini', 0)[-1]=0.6226\n",
-      "Condition not met at step 5002. No data to save.\n",
-      "5002\n",
-      "model_vars.get('Gini', 0)[-1]=0.6248\n",
-      "Condition not met at step 5003. No data to save.\n",
-      "5003\n",
-      "model_vars.get('Gini', 0)[-1]=0.6382\n",
-      "Condition not met at step 5004. No data to save.\n",
-      "5004\n",
-      "model_vars.get('Gini', 0)[-1]=0.6644\n",
-      "Condition not met at step 5005. No data to save.\n",
-      "5005\n",
-      "model_vars.get('Gini', 0)[-1]=0.667\n",
-      "Condition not met at step 5006. No data to save.\n",
-      "5006\n",
-      "model_vars.get('Gini', 0)[-1]=0.6744\n",
-      "Condition not met at step 5007. No data to save.\n",
-      "5007\n",
-      "model_vars.get('Gini', 0)[-1]=0.6832\n",
-      "Condition not met at step 5008. No data to save.\n",
-      "5008\n",
-      "model_vars.get('Gini', 0)[-1]=0.7088\n",
-      "Condition met. Appended special results for step 5009 to test_path/special_results.parquet\n",
-      "5009\n",
-      "model_vars.get('Gini', 0)[-1]=0.7130000000000001\n",
-      "Condition met. Appended special results for step 5010 to test_path/special_results.parquet\n",
-      "5010\n",
-      "model_vars.get('Gini', 0)[-1]=0.728\n",
-      "Condition met. Appended special results for step 5011 to test_path/special_results.parquet\n",
-      "5011\n",
-      "model_vars.get('Gini', 0)[-1]=0.726\n",
-      "Condition met. Appended special results for step 5012 to test_path/special_results.parquet\n",
-      "5012\n",
-      "model_vars.get('Gini', 0)[-1]=0.7414000000000001\n",
-      "Condition met. Appended special results for step 5013 to test_path/special_results.parquet\n",
-      "5013\n",
-      "model_vars.get('Gini', 0)[-1]=0.7216\n",
-      "Condition met. Appended special results for step 5014 to test_path/special_results.parquet\n",
-      "5014\n",
-      "model_vars.get('Gini', 0)[-1]=0.6998\n",
-      "Condition not met at step 5015. No data to save.\n",
-      "5015\n",
-      "model_vars.get('Gini', 0)[-1]=0.698\n",
-      "Condition not met at step 5016. No data to save.\n",
-      "5016\n",
-      "model_vars.get('Gini', 0)[-1]=0.6888000000000001\n",
-      "Condition not met at step 5017. No data to save.\n",
-      "5017\n",
-      "model_vars.get('Gini', 0)[-1]=0.6794\n",
-      "Condition not met at step 5018. No data to save.\n",
-      "5018\n",
-      "model_vars.get('Gini', 0)[-1]=0.6448\n",
-      "Condition not met at step 5019. No data to save.\n",
-      "5019\n",
-      "model_vars.get('Gini', 0)[-1]=0.6408\n",
-      "Condition not met at step 5020. No data to save.\n",
-      "5020\n",
-      "model_vars.get('Gini', 0)[-1]=0.646\n",
-      "Condition not met at step 5021. No data to save.\n",
-      "5021\n",
-      "model_vars.get('Gini', 0)[-1]=0.6672\n",
-      "Condition not met at step 5022. No data to save.\n",
-      "5022\n",
-      "model_vars.get('Gini', 0)[-1]=0.6412\n",
-      "Condition not met at step 5023. No data to save.\n",
-      "5023\n",
-      "model_vars.get('Gini', 0)[-1]=0.673\n",
-      "Condition not met at step 5024. No data to save.\n",
-      "5024\n",
-      "model_vars.get('Gini', 0)[-1]=0.6374\n",
-      "Condition not met at step 5025. No data to save.\n",
-      "5025\n",
-      "model_vars.get('Gini', 0)[-1]=0.6154\n",
-      "Condition not met at step 5026. No data to save.\n",
-      "5026\n",
-      "model_vars.get('Gini', 0)[-1]=0.6026\n",
-      "Condition not met at step 5027. No data to save.\n",
-      "5027\n",
-      "model_vars.get('Gini', 0)[-1]=0.6168\n",
-      "Condition not met at step 5028. No data to save.\n",
-      "5028\n",
-      "model_vars.get('Gini', 0)[-1]=0.6294\n",
-      "Condition not met at step 5029. No data to save.\n",
-      "5029\n",
-      "model_vars.get('Gini', 0)[-1]=0.6836\n",
-      "Condition not met at step 5030. No data to save.\n",
-      "5030\n",
-      "model_vars.get('Gini', 0)[-1]=0.7094\n",
-      "Condition met. Appended special results for step 5031 to test_path/special_results.parquet\n",
-      "5031\n",
-      "model_vars.get('Gini', 0)[-1]=0.7150000000000001\n",
-      "Condition met. Appended special results for step 5032 to test_path/special_results.parquet\n",
-      "5032\n",
-      "model_vars.get('Gini', 0)[-1]=0.6961999999999999\n",
-      "Condition not met at step 5033. No data to save.\n",
-      "5033\n",
-      "model_vars.get('Gini', 0)[-1]=0.7292000000000001\n",
-      "Condition met. Appended special results for step 5034 to test_path/special_results.parquet\n",
-      "5034\n",
-      "model_vars.get('Gini', 0)[-1]=0.7356\n",
-      "Condition met. Appended special results for step 5035 to test_path/special_results.parquet\n",
-      "5035\n",
-      "model_vars.get('Gini', 0)[-1]=0.7296\n",
-      "Condition met. Appended special results for step 5036 to test_path/special_results.parquet\n",
-      "5036\n",
-      "model_vars.get('Gini', 0)[-1]=0.7088\n",
-      "Condition met. Appended special results for step 5037 to test_path/special_results.parquet\n",
-      "5037\n",
-      "model_vars.get('Gini', 0)[-1]=0.6981999999999999\n",
-      "Condition not met at step 5038. No data to save.\n",
-      "5038\n",
-      "model_vars.get('Gini', 0)[-1]=0.6981999999999999\n",
-      "Condition not met at step 5039. No data to save.\n",
-      "5039\n",
-      "model_vars.get('Gini', 0)[-1]=0.7202\n",
-      "Condition met. Appended special results for step 5040 to test_path/special_results.parquet\n",
-      "5040\n",
-      "model_vars.get('Gini', 0)[-1]=0.722\n",
-      "Condition met. Appended special results for step 5041 to test_path/special_results.parquet\n",
-      "5041\n",
-      "model_vars.get('Gini', 0)[-1]=0.7006\n",
-      "Condition met. Appended special results for step 5042 to test_path/special_results.parquet\n",
-      "5042\n",
-      "model_vars.get('Gini', 0)[-1]=0.6854\n",
-      "Condition not met at step 5043. No data to save.\n",
-      "5043\n",
-      "model_vars.get('Gini', 0)[-1]=0.6782\n",
-      "Condition not met at step 5044. No data to save.\n",
-      "5044\n",
-      "model_vars.get('Gini', 0)[-1]=0.6678\n",
-      "Condition not met at step 5045. No data to save.\n",
-      "5045\n",
-      "model_vars.get('Gini', 0)[-1]=0.6826\n",
-      "Condition not met at step 5046. No data to save.\n",
-      "5046\n",
-      "model_vars.get('Gini', 0)[-1]=0.6766000000000001\n",
-      "Condition not met at step 5047. No data to save.\n",
-      "5047\n",
-      "model_vars.get('Gini', 0)[-1]=0.6814\n",
-      "Condition not met at step 5048. No data to save.\n",
-      "5048\n",
-      "model_vars.get('Gini', 0)[-1]=0.6814\n",
-      "Condition not met at step 5049. No data to save.\n",
-      "5049\n",
-      "model_vars.get('Gini', 0)[-1]=0.6762\n",
-      "Condition not met at step 5050. No data to save.\n",
-      "5050\n",
-      "model_vars.get('Gini', 0)[-1]=0.6882\n",
-      "Condition not met at step 5051. No data to save.\n",
-      "5051\n",
-      "model_vars.get('Gini', 0)[-1]=0.6594\n",
-      "Condition not met at step 5052. No data to save.\n",
-      "5052\n",
-      "model_vars.get('Gini', 0)[-1]=0.6658\n",
-      "Condition not met at step 5053. No data to save.\n",
-      "5053\n",
-      "model_vars.get('Gini', 0)[-1]=0.6634\n",
-      "Condition not met at step 5054. No data to save.\n",
-      "5054\n",
-      "model_vars.get('Gini', 0)[-1]=0.6806\n",
-      "Condition not met at step 5055. No data to save.\n",
-      "5055\n",
-      "model_vars.get('Gini', 0)[-1]=0.6941999999999999\n",
-      "Condition not met at step 5056. No data to save.\n",
-      "5056\n",
-      "model_vars.get('Gini', 0)[-1]=0.6862\n",
-      "Condition not met at step 5057. No data to save.\n",
-      "5057\n",
-      "model_vars.get('Gini', 0)[-1]=0.6778\n",
-      "Condition not met at step 5058. No data to save.\n",
-      "5058\n",
-      "model_vars.get('Gini', 0)[-1]=0.6972\n",
-      "Condition not met at step 5059. No data to save.\n",
-      "5059\n",
-      "model_vars.get('Gini', 0)[-1]=0.7126\n",
-      "Condition met. Appended special results for step 5060 to test_path/special_results.parquet\n",
-      "5060\n",
-      "model_vars.get('Gini', 0)[-1]=0.6848000000000001\n",
-      "Condition not met at step 5061. No data to save.\n",
-      "5061\n",
-      "model_vars.get('Gini', 0)[-1]=0.698\n",
-      "Condition not met at step 5062. No data to save.\n",
-      "5062\n",
-      "model_vars.get('Gini', 0)[-1]=0.6862\n",
-      "Condition not met at step 5063. No data to save.\n",
-      "5063\n",
-      "model_vars.get('Gini', 0)[-1]=0.6514\n",
-      "Condition not met at step 5064. No data to save.\n",
-      "5064\n",
-      "model_vars.get('Gini', 0)[-1]=0.6806\n",
-      "Condition not met at step 5065. No data to save.\n",
-      "5065\n",
-      "model_vars.get('Gini', 0)[-1]=0.6724\n",
-      "Condition not met at step 5066. No data to save.\n",
-      "5066\n",
-      "model_vars.get('Gini', 0)[-1]=0.6896\n",
-      "Condition not met at step 5067. No data to save.\n",
-      "5067\n",
-      "model_vars.get('Gini', 0)[-1]=0.7052\n",
-      "Condition met. Appended special results for step 5068 to test_path/special_results.parquet\n",
-      "5068\n",
-      "model_vars.get('Gini', 0)[-1]=0.7096\n",
-      "Condition met. Appended special results for step 5069 to test_path/special_results.parquet\n",
-      "5069\n",
-      "model_vars.get('Gini', 0)[-1]=0.688\n",
-      "Condition not met at step 5070. No data to save.\n",
-      "5070\n",
-      "model_vars.get('Gini', 0)[-1]=0.7048\n",
-      "Condition met. Appended special results for step 5071 to test_path/special_results.parquet\n",
-      "5071\n",
-      "model_vars.get('Gini', 0)[-1]=0.6782\n",
-      "Condition not met at step 5072. No data to save.\n",
-      "5072\n",
-      "model_vars.get('Gini', 0)[-1]=0.6996\n",
-      "Condition not met at step 5073. No data to save.\n",
-      "5073\n",
-      "model_vars.get('Gini', 0)[-1]=0.6924\n",
-      "Condition not met at step 5074. No data to save.\n",
-      "5074\n",
-      "model_vars.get('Gini', 0)[-1]=0.677\n",
-      "Condition not met at step 5075. No data to save.\n",
-      "5075\n",
-      "model_vars.get('Gini', 0)[-1]=0.6497999999999999\n",
-      "Condition not met at step 5076. No data to save.\n",
-      "5076\n",
-      "model_vars.get('Gini', 0)[-1]=0.6834\n",
-      "Condition not met at step 5077. No data to save.\n",
-      "5077\n",
-      "model_vars.get('Gini', 0)[-1]=0.6708000000000001\n",
-      "Condition not met at step 5078. No data to save.\n",
-      "5078\n",
-      "model_vars.get('Gini', 0)[-1]=0.6986\n",
-      "Condition not met at step 5079. No data to save.\n",
-      "5079\n",
-      "model_vars.get('Gini', 0)[-1]=0.7024\n",
-      "Condition met. Appended special results for step 5080 to test_path/special_results.parquet\n",
-      "5080\n",
-      "model_vars.get('Gini', 0)[-1]=0.7092\n",
-      "Condition met. Appended special results for step 5081 to test_path/special_results.parquet\n",
-      "5081\n",
-      "model_vars.get('Gini', 0)[-1]=0.683\n",
-      "Condition not met at step 5082. No data to save.\n",
-      "5082\n",
-      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
-      "Condition not met at step 5083. No data to save.\n",
-      "5083\n",
-      "model_vars.get('Gini', 0)[-1]=0.6836\n",
-      "Condition not met at step 5084. No data to save.\n",
-      "5084\n",
-      "model_vars.get('Gini', 0)[-1]=0.6742\n",
-      "Condition not met at step 5085. No data to save.\n",
-      "5085\n",
-      "model_vars.get('Gini', 0)[-1]=0.6679999999999999\n",
-      "Condition not met at step 5086. No data to save.\n",
-      "5086\n",
-      "model_vars.get('Gini', 0)[-1]=0.6508\n",
-      "Condition not met at step 5087. No data to save.\n",
-      "5087\n",
-      "model_vars.get('Gini', 0)[-1]=0.654\n",
-      "Condition not met at step 5088. No data to save.\n",
-      "5088\n",
-      "model_vars.get('Gini', 0)[-1]=0.647\n",
-      "Condition not met at step 5089. No data to save.\n",
-      "5089\n",
-      "model_vars.get('Gini', 0)[-1]=0.6504000000000001\n",
-      "Condition not met at step 5090. No data to save.\n",
-      "5090\n",
-      "model_vars.get('Gini', 0)[-1]=0.6542\n",
-      "Condition not met at step 5091. No data to save.\n",
-      "5091\n",
-      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
-      "Condition not met at step 5092. No data to save.\n",
-      "5092\n",
-      "model_vars.get('Gini', 0)[-1]=0.6678\n",
-      "Condition not met at step 5093. No data to save.\n",
-      "5093\n",
-      "model_vars.get('Gini', 0)[-1]=0.6584\n",
-      "Condition not met at step 5094. No data to save.\n",
-      "5094\n",
-      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
-      "Condition not met at step 5095. No data to save.\n",
-      "5095\n",
-      "model_vars.get('Gini', 0)[-1]=0.6348\n",
-      "Condition not met at step 5096. No data to save.\n",
-      "5096\n",
-      "model_vars.get('Gini', 0)[-1]=0.6528\n",
-      "Condition not met at step 5097. No data to save.\n",
-      "5097\n",
-      "model_vars.get('Gini', 0)[-1]=0.65\n",
-      "Condition not met at step 5098. No data to save.\n",
-      "5098\n",
-      "model_vars.get('Gini', 0)[-1]=0.635\n",
-      "Condition not met at step 5099. No data to save.\n",
-      "5099\n",
-      "CALLED\n",
-      "self.model._steps=5100\n",
-      " TEST self.model._steps=5100\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "5000  0.6046\n",
-      "5001  0.6226\n",
-      "5002  0.6248\n",
-      "5003  0.6382\n",
-      "5004  0.6644\n",
-      "...      ...\n",
-      "5095  0.6348\n",
-      "5096  0.6528\n",
-      "5097  0.6500\n",
-      "5098  0.6350\n",
-      "5099  0.6402\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_051.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_051.parquet'\n",
-      "Saving model to test_path/model_data_051.parquet\n",
-      "Saving agent to test_path/agent_data_051.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6402\n",
-      "Condition not met at step 5100. No data to save.\n",
-      "5100\n",
-      "model_vars.get('Gini', 0)[-1]=0.658\n",
-      "Condition not met at step 5101. No data to save.\n",
-      "5101\n",
-      "model_vars.get('Gini', 0)[-1]=0.6764\n",
-      "Condition not met at step 5102. No data to save.\n",
-      "5102\n",
-      "model_vars.get('Gini', 0)[-1]=0.6952\n",
-      "Condition not met at step 5103. No data to save.\n",
-      "5103\n",
-      "model_vars.get('Gini', 0)[-1]=0.6768000000000001\n",
-      "Condition not met at step 5104. No data to save.\n",
-      "5104\n",
-      "model_vars.get('Gini', 0)[-1]=0.6866\n",
-      "Condition not met at step 5105. No data to save.\n",
-      "5105\n",
-      "model_vars.get('Gini', 0)[-1]=0.704\n",
-      "Condition met. Appended special results for step 5106 to test_path/special_results.parquet\n",
-      "5106\n",
-      "model_vars.get('Gini', 0)[-1]=0.6932\n",
-      "Condition not met at step 5107. No data to save.\n",
-      "5107\n",
-      "model_vars.get('Gini', 0)[-1]=0.6842\n",
-      "Condition not met at step 5108. No data to save.\n",
-      "5108\n",
-      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
-      "Condition not met at step 5109. No data to save.\n",
-      "5109\n",
-      "model_vars.get('Gini', 0)[-1]=0.669\n",
-      "Condition not met at step 5110. No data to save.\n",
-      "5110\n",
-      "model_vars.get('Gini', 0)[-1]=0.6722\n",
-      "Condition not met at step 5111. No data to save.\n",
-      "5111\n",
-      "model_vars.get('Gini', 0)[-1]=0.6386000000000001\n",
-      "Condition not met at step 5112. No data to save.\n",
-      "5112\n",
-      "model_vars.get('Gini', 0)[-1]=0.643\n",
-      "Condition not met at step 5113. No data to save.\n",
-      "5113\n",
-      "model_vars.get('Gini', 0)[-1]=0.651\n",
-      "Condition not met at step 5114. No data to save.\n",
-      "5114\n",
-      "model_vars.get('Gini', 0)[-1]=0.6366\n",
-      "Condition not met at step 5115. No data to save.\n",
-      "5115\n",
-      "model_vars.get('Gini', 0)[-1]=0.634\n",
-      "Condition not met at step 5116. No data to save.\n",
-      "5116\n",
-      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
-      "Condition not met at step 5117. No data to save.\n",
-      "5117\n",
-      "model_vars.get('Gini', 0)[-1]=0.6398\n",
-      "Condition not met at step 5118. No data to save.\n",
-      "5118\n",
-      "model_vars.get('Gini', 0)[-1]=0.6504000000000001\n",
-      "Condition not met at step 5119. No data to save.\n",
-      "5119\n",
-      "model_vars.get('Gini', 0)[-1]=0.66\n",
-      "Condition not met at step 5120. No data to save.\n",
-      "5120\n",
-      "model_vars.get('Gini', 0)[-1]=0.6612\n",
-      "Condition not met at step 5121. No data to save.\n",
-      "5121\n",
-      "model_vars.get('Gini', 0)[-1]=0.6454\n",
-      "Condition not met at step 5122. No data to save.\n",
-      "5122\n",
-      "model_vars.get('Gini', 0)[-1]=0.6448\n",
-      "Condition not met at step 5123. No data to save.\n",
-      "5123\n",
-      "model_vars.get('Gini', 0)[-1]=0.6408\n",
-      "Condition not met at step 5124. No data to save.\n",
-      "5124\n",
-      "model_vars.get('Gini', 0)[-1]=0.6174\n",
-      "Condition not met at step 5125. No data to save.\n",
-      "5125\n",
-      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
-      "Condition not met at step 5126. No data to save.\n",
-      "5126\n",
-      "model_vars.get('Gini', 0)[-1]=0.6378\n",
-      "Condition not met at step 5127. No data to save.\n",
-      "5127\n",
-      "model_vars.get('Gini', 0)[-1]=0.6482\n",
-      "Condition not met at step 5128. No data to save.\n",
-      "5128\n",
-      "model_vars.get('Gini', 0)[-1]=0.6542\n",
-      "Condition not met at step 5129. No data to save.\n",
-      "5129\n",
-      "model_vars.get('Gini', 0)[-1]=0.6654\n",
-      "Condition not met at step 5130. No data to save.\n",
-      "5130\n",
-      "model_vars.get('Gini', 0)[-1]=0.6762\n",
-      "Condition not met at step 5131. No data to save.\n",
-      "5131\n",
-      "model_vars.get('Gini', 0)[-1]=0.688\n",
-      "Condition not met at step 5132. No data to save.\n",
-      "5132\n",
-      "model_vars.get('Gini', 0)[-1]=0.6896\n",
-      "Condition not met at step 5133. No data to save.\n",
-      "5133\n",
-      "model_vars.get('Gini', 0)[-1]=0.6744\n",
-      "Condition not met at step 5134. No data to save.\n",
-      "5134\n",
-      "model_vars.get('Gini', 0)[-1]=0.6838\n",
-      "Condition not met at step 5135. No data to save.\n",
-      "5135\n",
-      "model_vars.get('Gini', 0)[-1]=0.6588\n",
-      "Condition not met at step 5136. No data to save.\n",
-      "5136\n",
-      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
-      "Condition not met at step 5137. No data to save.\n",
-      "5137\n",
-      "model_vars.get('Gini', 0)[-1]=0.6514\n",
-      "Condition not met at step 5138. No data to save.\n",
-      "5138\n",
-      "model_vars.get('Gini', 0)[-1]=0.6462\n",
-      "Condition not met at step 5139. No data to save.\n",
-      "5139\n",
-      "model_vars.get('Gini', 0)[-1]=0.653\n",
-      "Condition not met at step 5140. No data to save.\n",
-      "5140\n",
-      "model_vars.get('Gini', 0)[-1]=0.6574\n",
-      "Condition not met at step 5141. No data to save.\n",
-      "5141\n",
-      "model_vars.get('Gini', 0)[-1]=0.6462\n",
-      "Condition not met at step 5142. No data to save.\n",
-      "5142\n",
-      "model_vars.get('Gini', 0)[-1]=0.6712\n",
-      "Condition not met at step 5143. No data to save.\n",
-      "5143\n",
-      "model_vars.get('Gini', 0)[-1]=0.6902\n",
-      "Condition not met at step 5144. No data to save.\n",
-      "5144\n",
-      "model_vars.get('Gini', 0)[-1]=0.6736\n",
-      "Condition not met at step 5145. No data to save.\n",
-      "5145\n",
-      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
-      "Condition not met at step 5146. No data to save.\n",
-      "5146\n",
-      "model_vars.get('Gini', 0)[-1]=0.6154\n",
-      "Condition not met at step 5147. No data to save.\n",
-      "5147\n",
-      "model_vars.get('Gini', 0)[-1]=0.6215999999999999\n",
-      "Condition not met at step 5148. No data to save.\n",
-      "5148\n",
-      "model_vars.get('Gini', 0)[-1]=0.6146\n",
-      "Condition not met at step 5149. No data to save.\n",
-      "5149\n",
-      "model_vars.get('Gini', 0)[-1]=0.6006\n",
-      "Condition not met at step 5150. No data to save.\n",
-      "5150\n",
-      "model_vars.get('Gini', 0)[-1]=0.6246\n",
-      "Condition not met at step 5151. No data to save.\n",
-      "5151\n",
-      "model_vars.get('Gini', 0)[-1]=0.6432\n",
-      "Condition not met at step 5152. No data to save.\n",
-      "5152\n",
-      "model_vars.get('Gini', 0)[-1]=0.6617999999999999\n",
-      "Condition not met at step 5153. No data to save.\n",
-      "5153\n",
-      "model_vars.get('Gini', 0)[-1]=0.6602\n",
-      "Condition not met at step 5154. No data to save.\n",
-      "5154\n",
-      "model_vars.get('Gini', 0)[-1]=0.681\n",
-      "Condition not met at step 5155. No data to save.\n",
-      "5155\n",
-      "model_vars.get('Gini', 0)[-1]=0.6732\n",
-      "Condition not met at step 5156. No data to save.\n",
-      "5156\n",
-      "model_vars.get('Gini', 0)[-1]=0.7098\n",
-      "Condition met. Appended special results for step 5157 to test_path/special_results.parquet\n",
-      "5157\n",
-      "model_vars.get('Gini', 0)[-1]=0.7012\n",
-      "Condition met. Appended special results for step 5158 to test_path/special_results.parquet\n",
-      "5158\n",
-      "model_vars.get('Gini', 0)[-1]=0.6858\n",
-      "Condition not met at step 5159. No data to save.\n",
-      "5159\n",
-      "model_vars.get('Gini', 0)[-1]=0.7116\n",
-      "Condition met. Appended special results for step 5160 to test_path/special_results.parquet\n",
-      "5160\n",
-      "model_vars.get('Gini', 0)[-1]=0.7008000000000001\n",
-      "Condition met. Appended special results for step 5161 to test_path/special_results.parquet\n",
-      "5161\n",
-      "model_vars.get('Gini', 0)[-1]=0.7214\n",
-      "Condition met. Appended special results for step 5162 to test_path/special_results.parquet\n",
-      "5162\n",
-      "model_vars.get('Gini', 0)[-1]=0.7242\n",
-      "Condition met. Appended special results for step 5163 to test_path/special_results.parquet\n",
-      "5163\n",
-      "model_vars.get('Gini', 0)[-1]=0.7378\n",
-      "Condition met. Appended special results for step 5164 to test_path/special_results.parquet\n",
-      "5164\n",
-      "model_vars.get('Gini', 0)[-1]=0.7388\n",
-      "Condition met. Appended special results for step 5165 to test_path/special_results.parquet\n",
-      "5165\n",
-      "model_vars.get('Gini', 0)[-1]=0.7148\n",
-      "Condition met. Appended special results for step 5166 to test_path/special_results.parquet\n",
-      "5166\n",
-      "model_vars.get('Gini', 0)[-1]=0.7242\n",
-      "Condition met. Appended special results for step 5167 to test_path/special_results.parquet\n",
-      "5167\n",
-      "model_vars.get('Gini', 0)[-1]=0.7406\n",
-      "Condition met. Appended special results for step 5168 to test_path/special_results.parquet\n",
-      "5168\n",
-      "model_vars.get('Gini', 0)[-1]=0.7436\n",
-      "Condition met. Appended special results for step 5169 to test_path/special_results.parquet\n",
-      "5169\n",
-      "model_vars.get('Gini', 0)[-1]=0.7323999999999999\n",
-      "Condition met. Appended special results for step 5170 to test_path/special_results.parquet\n",
-      "5170\n",
-      "model_vars.get('Gini', 0)[-1]=0.7170000000000001\n",
-      "Condition met. Appended special results for step 5171 to test_path/special_results.parquet\n",
-      "5171\n",
-      "model_vars.get('Gini', 0)[-1]=0.6859999999999999\n",
-      "Condition not met at step 5172. No data to save.\n",
-      "5172\n",
-      "model_vars.get('Gini', 0)[-1]=0.6744\n",
-      "Condition not met at step 5173. No data to save.\n",
-      "5173\n",
-      "model_vars.get('Gini', 0)[-1]=0.6756\n",
-      "Condition not met at step 5174. No data to save.\n",
-      "5174\n",
-      "model_vars.get('Gini', 0)[-1]=0.6866\n",
-      "Condition not met at step 5175. No data to save.\n",
-      "5175\n",
-      "model_vars.get('Gini', 0)[-1]=0.7056\n",
-      "Condition met. Appended special results for step 5176 to test_path/special_results.parquet\n",
-      "5176\n",
-      "model_vars.get('Gini', 0)[-1]=0.6950000000000001\n",
-      "Condition not met at step 5177. No data to save.\n",
-      "5177\n",
-      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
-      "Condition not met at step 5178. No data to save.\n",
-      "5178\n",
-      "model_vars.get('Gini', 0)[-1]=0.6522\n",
-      "Condition not met at step 5179. No data to save.\n",
-      "5179\n",
-      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
-      "Condition not met at step 5180. No data to save.\n",
-      "5180\n",
-      "model_vars.get('Gini', 0)[-1]=0.661\n",
-      "Condition not met at step 5181. No data to save.\n",
-      "5181\n",
-      "model_vars.get('Gini', 0)[-1]=0.653\n",
-      "Condition not met at step 5182. No data to save.\n",
-      "5182\n",
-      "model_vars.get('Gini', 0)[-1]=0.6474\n",
-      "Condition not met at step 5183. No data to save.\n",
-      "5183\n",
-      "model_vars.get('Gini', 0)[-1]=0.6298\n",
-      "Condition not met at step 5184. No data to save.\n",
-      "5184\n",
-      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
-      "Condition not met at step 5185. No data to save.\n",
-      "5185\n",
-      "model_vars.get('Gini', 0)[-1]=0.644\n",
-      "Condition not met at step 5186. No data to save.\n",
-      "5186\n",
-      "model_vars.get('Gini', 0)[-1]=0.6406000000000001\n",
-      "Condition not met at step 5187. No data to save.\n",
-      "5187\n",
-      "model_vars.get('Gini', 0)[-1]=0.6274\n",
-      "Condition not met at step 5188. No data to save.\n",
-      "5188\n",
-      "model_vars.get('Gini', 0)[-1]=0.6192\n",
-      "Condition not met at step 5189. No data to save.\n",
-      "5189\n",
-      "model_vars.get('Gini', 0)[-1]=0.631\n",
-      "Condition not met at step 5190. No data to save.\n",
-      "5190\n",
-      "model_vars.get('Gini', 0)[-1]=0.644\n",
-      "Condition not met at step 5191. No data to save.\n",
-      "5191\n",
-      "model_vars.get('Gini', 0)[-1]=0.639\n",
-      "Condition not met at step 5192. No data to save.\n",
-      "5192\n",
-      "model_vars.get('Gini', 0)[-1]=0.6362\n",
-      "Condition not met at step 5193. No data to save.\n",
-      "5193\n",
-      "model_vars.get('Gini', 0)[-1]=0.6408\n",
-      "Condition not met at step 5194. No data to save.\n",
-      "5194\n",
-      "model_vars.get('Gini', 0)[-1]=0.6275999999999999\n",
-      "Condition not met at step 5195. No data to save.\n",
-      "5195\n",
-      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
-      "Condition not met at step 5196. No data to save.\n",
-      "5196\n",
-      "model_vars.get('Gini', 0)[-1]=0.6548\n",
-      "Condition not met at step 5197. No data to save.\n",
-      "5197\n",
-      "model_vars.get('Gini', 0)[-1]=0.6696\n",
-      "Condition not met at step 5198. No data to save.\n",
-      "5198\n",
-      "model_vars.get('Gini', 0)[-1]=0.6844\n",
-      "Condition not met at step 5199. No data to save.\n",
-      "5199\n",
-      "CALLED\n",
-      "self.model._steps=5200\n",
-      " TEST self.model._steps=5200\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "5100  0.6580\n",
-      "5101  0.6764\n",
-      "5102  0.6952\n",
-      "5103  0.6768\n",
-      "5104  0.6866\n",
-      "...      ...\n",
-      "5195  0.6478\n",
-      "5196  0.6548\n",
-      "5197  0.6696\n",
-      "5198  0.6844\n",
-      "5199  0.6708\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_052.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_052.parquet'\n",
-      "Saving model to test_path/model_data_052.parquet\n",
-      "Saving agent to test_path/agent_data_052.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6708000000000001\n",
-      "Condition not met at step 5200. No data to save.\n",
-      "5200\n",
-      "model_vars.get('Gini', 0)[-1]=0.679\n",
-      "Condition not met at step 5201. No data to save.\n",
-      "5201\n",
-      "model_vars.get('Gini', 0)[-1]=0.685\n",
-      "Condition not met at step 5202. No data to save.\n",
-      "5202\n",
-      "model_vars.get('Gini', 0)[-1]=0.6684\n",
-      "Condition not met at step 5203. No data to save.\n",
-      "5203\n",
-      "model_vars.get('Gini', 0)[-1]=0.6638\n",
-      "Condition not met at step 5204. No data to save.\n",
-      "5204\n",
-      "model_vars.get('Gini', 0)[-1]=0.653\n",
-      "Condition not met at step 5205. No data to save.\n",
-      "5205\n",
-      "model_vars.get('Gini', 0)[-1]=0.6172\n",
-      "Condition not met at step 5206. No data to save.\n",
-      "5206\n",
-      "model_vars.get('Gini', 0)[-1]=0.5984\n",
-      "Condition not met at step 5207. No data to save.\n",
-      "5207\n",
-      "model_vars.get('Gini', 0)[-1]=0.636\n",
-      "Condition not met at step 5208. No data to save.\n",
-      "5208\n",
-      "model_vars.get('Gini', 0)[-1]=0.6298\n",
-      "Condition not met at step 5209. No data to save.\n",
-      "5209\n",
-      "model_vars.get('Gini', 0)[-1]=0.6352\n",
-      "Condition not met at step 5210. No data to save.\n",
-      "5210\n",
-      "model_vars.get('Gini', 0)[-1]=0.6522\n",
-      "Condition not met at step 5211. No data to save.\n",
-      "5211\n",
-      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
-      "Condition not met at step 5212. No data to save.\n",
-      "5212\n",
-      "model_vars.get('Gini', 0)[-1]=0.6554\n",
-      "Condition not met at step 5213. No data to save.\n",
-      "5213\n",
-      "model_vars.get('Gini', 0)[-1]=0.6596\n",
-      "Condition not met at step 5214. No data to save.\n",
-      "5214\n",
-      "model_vars.get('Gini', 0)[-1]=0.6396\n",
-      "Condition not met at step 5215. No data to save.\n",
-      "5215\n",
-      "model_vars.get('Gini', 0)[-1]=0.6488\n",
-      "Condition not met at step 5216. No data to save.\n",
-      "5216\n",
-      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
-      "Condition not met at step 5217. No data to save.\n",
-      "5217\n",
-      "model_vars.get('Gini', 0)[-1]=0.6716\n",
-      "Condition not met at step 5218. No data to save.\n",
-      "5218\n",
-      "model_vars.get('Gini', 0)[-1]=0.6508\n",
-      "Condition not met at step 5219. No data to save.\n",
-      "5219\n",
-      "model_vars.get('Gini', 0)[-1]=0.6768000000000001\n",
-      "Condition not met at step 5220. No data to save.\n",
-      "5220\n",
-      "model_vars.get('Gini', 0)[-1]=0.6699999999999999\n",
-      "Condition not met at step 5221. No data to save.\n",
-      "5221\n",
-      "model_vars.get('Gini', 0)[-1]=0.7072\n",
-      "Condition met. Appended special results for step 5222 to test_path/special_results.parquet\n",
-      "5222\n",
-      "model_vars.get('Gini', 0)[-1]=0.7183999999999999\n",
-      "Condition met. Appended special results for step 5223 to test_path/special_results.parquet\n",
-      "5223\n",
-      "model_vars.get('Gini', 0)[-1]=0.728\n",
-      "Condition met. Appended special results for step 5224 to test_path/special_results.parquet\n",
-      "5224\n",
-      "model_vars.get('Gini', 0)[-1]=0.7334\n",
-      "Condition met. Appended special results for step 5225 to test_path/special_results.parquet\n",
-      "5225\n",
-      "model_vars.get('Gini', 0)[-1]=0.7302\n",
-      "Condition met. Appended special results for step 5226 to test_path/special_results.parquet\n",
-      "5226\n",
-      "model_vars.get('Gini', 0)[-1]=0.6906\n",
-      "Condition not met at step 5227. No data to save.\n",
-      "5227\n",
-      "model_vars.get('Gini', 0)[-1]=0.6934\n",
-      "Condition not met at step 5228. No data to save.\n",
-      "5228\n",
-      "model_vars.get('Gini', 0)[-1]=0.6856\n",
-      "Condition not met at step 5229. No data to save.\n",
-      "5229\n",
-      "model_vars.get('Gini', 0)[-1]=0.6968000000000001\n",
-      "Condition not met at step 5230. No data to save.\n",
-      "5230\n",
-      "model_vars.get('Gini', 0)[-1]=0.6832\n",
-      "Condition not met at step 5231. No data to save.\n",
-      "5231\n",
-      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
-      "Condition not met at step 5232. No data to save.\n",
-      "5232\n",
-      "model_vars.get('Gini', 0)[-1]=0.6699999999999999\n",
-      "Condition not met at step 5233. No data to save.\n",
-      "5233\n",
-      "model_vars.get('Gini', 0)[-1]=0.6614\n",
-      "Condition not met at step 5234. No data to save.\n",
-      "5234\n",
-      "model_vars.get('Gini', 0)[-1]=0.6636\n",
-      "Condition not met at step 5235. No data to save.\n",
-      "5235\n",
-      "model_vars.get('Gini', 0)[-1]=0.6592\n",
-      "Condition not met at step 5236. No data to save.\n",
-      "5236\n",
-      "model_vars.get('Gini', 0)[-1]=0.6442\n",
-      "Condition not met at step 5237. No data to save.\n",
-      "5237\n",
-      "model_vars.get('Gini', 0)[-1]=0.6648000000000001\n",
-      "Condition not met at step 5238. No data to save.\n",
-      "5238\n",
-      "model_vars.get('Gini', 0)[-1]=0.6524000000000001\n",
-      "Condition not met at step 5239. No data to save.\n",
-      "5239\n",
-      "model_vars.get('Gini', 0)[-1]=0.646\n",
-      "Condition not met at step 5240. No data to save.\n",
-      "5240\n",
-      "model_vars.get('Gini', 0)[-1]=0.6662\n",
-      "Condition not met at step 5241. No data to save.\n",
-      "5241\n",
-      "model_vars.get('Gini', 0)[-1]=0.673\n",
-      "Condition not met at step 5242. No data to save.\n",
-      "5242\n",
-      "model_vars.get('Gini', 0)[-1]=0.6784\n",
-      "Condition not met at step 5243. No data to save.\n",
-      "5243\n",
-      "model_vars.get('Gini', 0)[-1]=0.6856\n",
-      "Condition not met at step 5244. No data to save.\n",
-      "5244\n",
-      "model_vars.get('Gini', 0)[-1]=0.6842\n",
-      "Condition not met at step 5245. No data to save.\n",
-      "5245\n",
-      "model_vars.get('Gini', 0)[-1]=0.6799999999999999\n",
-      "Condition not met at step 5246. No data to save.\n",
-      "5246\n",
-      "model_vars.get('Gini', 0)[-1]=0.6779999999999999\n",
-      "Condition not met at step 5247. No data to save.\n",
-      "5247\n",
-      "model_vars.get('Gini', 0)[-1]=0.652\n",
-      "Condition not met at step 5248. No data to save.\n",
-      "5248\n",
-      "model_vars.get('Gini', 0)[-1]=0.6568\n",
-      "Condition not met at step 5249. No data to save.\n",
-      "5249\n",
-      "model_vars.get('Gini', 0)[-1]=0.665\n",
-      "Condition not met at step 5250. No data to save.\n",
-      "5250\n",
-      "model_vars.get('Gini', 0)[-1]=0.6758\n",
-      "Condition not met at step 5251. No data to save.\n",
-      "5251\n",
-      "model_vars.get('Gini', 0)[-1]=0.6698\n",
-      "Condition not met at step 5252. No data to save.\n",
-      "5252\n",
-      "model_vars.get('Gini', 0)[-1]=0.6335999999999999\n",
-      "Condition not met at step 5253. No data to save.\n",
-      "5253\n",
-      "model_vars.get('Gini', 0)[-1]=0.6242000000000001\n",
-      "Condition not met at step 5254. No data to save.\n",
-      "5254\n",
-      "model_vars.get('Gini', 0)[-1]=0.606\n",
-      "Condition not met at step 5255. No data to save.\n",
-      "5255\n",
-      "model_vars.get('Gini', 0)[-1]=0.6304000000000001\n",
-      "Condition not met at step 5256. No data to save.\n",
-      "5256\n",
-      "model_vars.get('Gini', 0)[-1]=0.6315999999999999\n",
-      "Condition not met at step 5257. No data to save.\n",
-      "5257\n",
-      "model_vars.get('Gini', 0)[-1]=0.6496\n",
-      "Condition not met at step 5258. No data to save.\n",
-      "5258\n",
-      "model_vars.get('Gini', 0)[-1]=0.6214\n",
-      "Condition not met at step 5259. No data to save.\n",
-      "5259\n",
-      "model_vars.get('Gini', 0)[-1]=0.6332\n",
-      "Condition not met at step 5260. No data to save.\n",
-      "5260\n",
-      "model_vars.get('Gini', 0)[-1]=0.6342\n",
-      "Condition not met at step 5261. No data to save.\n",
-      "5261\n",
-      "model_vars.get('Gini', 0)[-1]=0.6182000000000001\n",
-      "Condition not met at step 5262. No data to save.\n",
-      "5262\n",
-      "model_vars.get('Gini', 0)[-1]=0.6082000000000001\n",
-      "Condition not met at step 5263. No data to save.\n",
-      "5263\n",
-      "model_vars.get('Gini', 0)[-1]=0.607\n",
-      "Condition not met at step 5264. No data to save.\n",
-      "5264\n",
-      "model_vars.get('Gini', 0)[-1]=0.5856\n",
-      "Condition not met at step 5265. No data to save.\n",
-      "5265\n",
-      "model_vars.get('Gini', 0)[-1]=0.5896\n",
-      "Condition not met at step 5266. No data to save.\n",
-      "5266\n",
-      "model_vars.get('Gini', 0)[-1]=0.6066\n",
-      "Condition not met at step 5267. No data to save.\n",
-      "5267\n",
-      "model_vars.get('Gini', 0)[-1]=0.6246\n",
-      "Condition not met at step 5268. No data to save.\n",
-      "5268\n",
-      "model_vars.get('Gini', 0)[-1]=0.621\n",
-      "Condition not met at step 5269. No data to save.\n",
-      "5269\n",
-      "model_vars.get('Gini', 0)[-1]=0.6096\n",
-      "Condition not met at step 5270. No data to save.\n",
-      "5270\n",
-      "model_vars.get('Gini', 0)[-1]=0.6038\n",
-      "Condition not met at step 5271. No data to save.\n",
-      "5271\n",
-      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
-      "Condition not met at step 5272. No data to save.\n",
-      "5272\n",
-      "model_vars.get('Gini', 0)[-1]=0.6532\n",
-      "Condition not met at step 5273. No data to save.\n",
-      "5273\n",
-      "model_vars.get('Gini', 0)[-1]=0.621\n",
-      "Condition not met at step 5274. No data to save.\n",
-      "5274\n",
-      "model_vars.get('Gini', 0)[-1]=0.6158\n",
-      "Condition not met at step 5275. No data to save.\n",
-      "5275\n",
-      "model_vars.get('Gini', 0)[-1]=0.6295999999999999\n",
-      "Condition not met at step 5276. No data to save.\n",
-      "5276\n",
-      "model_vars.get('Gini', 0)[-1]=0.5942000000000001\n",
-      "Condition not met at step 5277. No data to save.\n",
-      "5277\n",
-      "model_vars.get('Gini', 0)[-1]=0.639\n",
-      "Condition not met at step 5278. No data to save.\n",
-      "5278\n",
-      "model_vars.get('Gini', 0)[-1]=0.6058\n",
-      "Condition not met at step 5279. No data to save.\n",
-      "5279\n",
-      "model_vars.get('Gini', 0)[-1]=0.6158\n",
-      "Condition not met at step 5280. No data to save.\n",
-      "5280\n",
-      "model_vars.get('Gini', 0)[-1]=0.637\n",
-      "Condition not met at step 5281. No data to save.\n",
-      "5281\n",
-      "model_vars.get('Gini', 0)[-1]=0.6602\n",
-      "Condition not met at step 5282. No data to save.\n",
-      "5282\n",
-      "model_vars.get('Gini', 0)[-1]=0.6514\n",
-      "Condition not met at step 5283. No data to save.\n",
-      "5283\n",
-      "model_vars.get('Gini', 0)[-1]=0.644\n",
-      "Condition not met at step 5284. No data to save.\n",
-      "5284\n",
-      "model_vars.get('Gini', 0)[-1]=0.6355999999999999\n",
-      "Condition not met at step 5285. No data to save.\n",
-      "5285\n",
-      "model_vars.get('Gini', 0)[-1]=0.639\n",
-      "Condition not met at step 5286. No data to save.\n",
-      "5286\n",
-      "model_vars.get('Gini', 0)[-1]=0.6566000000000001\n",
-      "Condition not met at step 5287. No data to save.\n",
-      "5287\n",
-      "model_vars.get('Gini', 0)[-1]=0.6496\n",
-      "Condition not met at step 5288. No data to save.\n",
-      "5288\n",
-      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
-      "Condition not met at step 5289. No data to save.\n",
-      "5289\n",
-      "model_vars.get('Gini', 0)[-1]=0.6456\n",
-      "Condition not met at step 5290. No data to save.\n",
-      "5290\n",
-      "model_vars.get('Gini', 0)[-1]=0.6624\n",
-      "Condition not met at step 5291. No data to save.\n",
-      "5291\n",
-      "model_vars.get('Gini', 0)[-1]=0.6597999999999999\n",
-      "Condition not met at step 5292. No data to save.\n",
-      "5292\n",
-      "model_vars.get('Gini', 0)[-1]=0.651\n",
-      "Condition not met at step 5293. No data to save.\n",
-      "5293\n",
-      "model_vars.get('Gini', 0)[-1]=0.6686000000000001\n",
-      "Condition not met at step 5294. No data to save.\n",
-      "5294\n",
-      "model_vars.get('Gini', 0)[-1]=0.6748000000000001\n",
-      "Condition not met at step 5295. No data to save.\n",
-      "5295\n",
-      "model_vars.get('Gini', 0)[-1]=0.647\n",
-      "Condition not met at step 5296. No data to save.\n",
-      "5296\n",
-      "model_vars.get('Gini', 0)[-1]=0.6278\n",
-      "Condition not met at step 5297. No data to save.\n",
-      "5297\n",
-      "model_vars.get('Gini', 0)[-1]=0.6398\n",
-      "Condition not met at step 5298. No data to save.\n",
-      "5298\n",
-      "model_vars.get('Gini', 0)[-1]=0.6326\n",
-      "Condition not met at step 5299. No data to save.\n",
-      "5299\n",
-      "CALLED\n",
-      "self.model._steps=5300\n",
-      " TEST self.model._steps=5300\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "5200  0.6790\n",
-      "5201  0.6850\n",
-      "5202  0.6684\n",
-      "5203  0.6638\n",
-      "5204  0.6530\n",
-      "...      ...\n",
-      "5295  0.6470\n",
-      "5296  0.6278\n",
-      "5297  0.6398\n",
-      "5298  0.6326\n",
-      "5299  0.6286\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_053.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_053.parquet'\n",
-      "Saving model to test_path/model_data_053.parquet\n",
-      "Saving agent to test_path/agent_data_053.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6286\n",
-      "Condition not met at step 5300. No data to save.\n",
-      "5300\n",
-      "model_vars.get('Gini', 0)[-1]=0.6302\n",
-      "Condition not met at step 5301. No data to save.\n",
-      "5301\n",
-      "model_vars.get('Gini', 0)[-1]=0.6332\n",
-      "Condition not met at step 5302. No data to save.\n",
-      "5302\n",
-      "model_vars.get('Gini', 0)[-1]=0.646\n",
-      "Condition not met at step 5303. No data to save.\n",
-      "5303\n",
-      "model_vars.get('Gini', 0)[-1]=0.6546000000000001\n",
-      "Condition not met at step 5304. No data to save.\n",
-      "5304\n",
-      "model_vars.get('Gini', 0)[-1]=0.688\n",
-      "Condition not met at step 5305. No data to save.\n",
-      "5305\n",
-      "model_vars.get('Gini', 0)[-1]=0.7038\n",
-      "Condition met. Appended special results for step 5306 to test_path/special_results.parquet\n",
-      "5306\n",
-      "model_vars.get('Gini', 0)[-1]=0.7064\n",
-      "Condition met. Appended special results for step 5307 to test_path/special_results.parquet\n",
-      "5307\n",
-      "model_vars.get('Gini', 0)[-1]=0.6786000000000001\n",
-      "Condition not met at step 5308. No data to save.\n",
-      "5308\n",
-      "model_vars.get('Gini', 0)[-1]=0.6588\n",
-      "Condition not met at step 5309. No data to save.\n",
-      "5309\n",
-      "model_vars.get('Gini', 0)[-1]=0.6454\n",
-      "Condition not met at step 5310. No data to save.\n",
-      "5310\n",
-      "model_vars.get('Gini', 0)[-1]=0.6544\n",
-      "Condition not met at step 5311. No data to save.\n",
-      "5311\n",
-      "model_vars.get('Gini', 0)[-1]=0.664\n",
-      "Condition not met at step 5312. No data to save.\n",
-      "5312\n",
-      "model_vars.get('Gini', 0)[-1]=0.6724\n",
-      "Condition not met at step 5313. No data to save.\n",
-      "5313\n",
-      "model_vars.get('Gini', 0)[-1]=0.6462\n",
-      "Condition not met at step 5314. No data to save.\n",
-      "5314\n",
-      "model_vars.get('Gini', 0)[-1]=0.662\n",
-      "Condition not met at step 5315. No data to save.\n",
-      "5315\n",
-      "model_vars.get('Gini', 0)[-1]=0.6335999999999999\n",
-      "Condition not met at step 5316. No data to save.\n",
-      "5316\n",
-      "model_vars.get('Gini', 0)[-1]=0.6528\n",
-      "Condition not met at step 5317. No data to save.\n",
-      "5317\n",
-      "model_vars.get('Gini', 0)[-1]=0.6738\n",
-      "Condition not met at step 5318. No data to save.\n",
-      "5318\n",
-      "model_vars.get('Gini', 0)[-1]=0.6642\n",
-      "Condition not met at step 5319. No data to save.\n",
-      "5319\n",
-      "model_vars.get('Gini', 0)[-1]=0.6636\n",
-      "Condition not met at step 5320. No data to save.\n",
-      "5320\n",
-      "model_vars.get('Gini', 0)[-1]=0.6557999999999999\n",
-      "Condition not met at step 5321. No data to save.\n",
-      "5321\n",
-      "model_vars.get('Gini', 0)[-1]=0.664\n",
-      "Condition not met at step 5322. No data to save.\n",
-      "5322\n",
-      "model_vars.get('Gini', 0)[-1]=0.6708000000000001\n",
-      "Condition not met at step 5323. No data to save.\n",
-      "5323\n",
-      "model_vars.get('Gini', 0)[-1]=0.6768000000000001\n",
-      "Condition not met at step 5324. No data to save.\n",
-      "5324\n",
-      "model_vars.get('Gini', 0)[-1]=0.6968000000000001\n",
-      "Condition not met at step 5325. No data to save.\n",
-      "5325\n",
-      "model_vars.get('Gini', 0)[-1]=0.694\n",
-      "Condition not met at step 5326. No data to save.\n",
-      "5326\n",
-      "model_vars.get('Gini', 0)[-1]=0.6758\n",
-      "Condition not met at step 5327. No data to save.\n",
-      "5327\n",
-      "model_vars.get('Gini', 0)[-1]=0.6712\n",
-      "Condition not met at step 5328. No data to save.\n",
-      "5328\n",
-      "model_vars.get('Gini', 0)[-1]=0.6728000000000001\n",
-      "Condition not met at step 5329. No data to save.\n",
-      "5329\n",
-      "model_vars.get('Gini', 0)[-1]=0.6948000000000001\n",
-      "Condition not met at step 5330. No data to save.\n",
-      "5330\n",
-      "model_vars.get('Gini', 0)[-1]=0.7158\n",
-      "Condition met. Appended special results for step 5331 to test_path/special_results.parquet\n",
-      "5331\n",
-      "model_vars.get('Gini', 0)[-1]=0.7174\n",
-      "Condition met. Appended special results for step 5332 to test_path/special_results.parquet\n",
-      "5332\n",
-      "model_vars.get('Gini', 0)[-1]=0.7368\n",
-      "Condition met. Appended special results for step 5333 to test_path/special_results.parquet\n",
-      "5333\n",
-      "model_vars.get('Gini', 0)[-1]=0.7388\n",
-      "Condition met. Appended special results for step 5334 to test_path/special_results.parquet\n",
-      "5334\n",
-      "model_vars.get('Gini', 0)[-1]=0.7276\n",
-      "Condition met. Appended special results for step 5335 to test_path/special_results.parquet\n",
-      "5335\n",
-      "model_vars.get('Gini', 0)[-1]=0.722\n",
-      "Condition met. Appended special results for step 5336 to test_path/special_results.parquet\n",
-      "5336\n",
-      "model_vars.get('Gini', 0)[-1]=0.7176\n",
-      "Condition met. Appended special results for step 5337 to test_path/special_results.parquet\n",
-      "5337\n",
-      "model_vars.get('Gini', 0)[-1]=0.7058\n",
-      "Condition met. Appended special results for step 5338 to test_path/special_results.parquet\n",
-      "5338\n",
-      "model_vars.get('Gini', 0)[-1]=0.6838\n",
-      "Condition not met at step 5339. No data to save.\n",
-      "5339\n",
-      "model_vars.get('Gini', 0)[-1]=0.6762\n",
-      "Condition not met at step 5340. No data to save.\n",
-      "5340\n",
-      "model_vars.get('Gini', 0)[-1]=0.675\n",
-      "Condition not met at step 5341. No data to save.\n",
-      "5341\n",
-      "model_vars.get('Gini', 0)[-1]=0.6784\n",
-      "Condition not met at step 5342. No data to save.\n",
-      "5342\n",
-      "model_vars.get('Gini', 0)[-1]=0.6644\n",
-      "Condition not met at step 5343. No data to save.\n",
-      "5343\n",
-      "model_vars.get('Gini', 0)[-1]=0.6738\n",
-      "Condition not met at step 5344. No data to save.\n",
-      "5344\n",
-      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
-      "Condition not met at step 5345. No data to save.\n",
-      "5345\n",
-      "model_vars.get('Gini', 0)[-1]=0.6192\n",
-      "Condition not met at step 5346. No data to save.\n",
-      "5346\n",
-      "model_vars.get('Gini', 0)[-1]=0.6516\n",
-      "Condition not met at step 5347. No data to save.\n",
-      "5347\n",
-      "model_vars.get('Gini', 0)[-1]=0.6468\n",
-      "Condition not met at step 5348. No data to save.\n",
-      "5348\n",
-      "model_vars.get('Gini', 0)[-1]=0.6719999999999999\n",
-      "Condition not met at step 5349. No data to save.\n",
-      "5349\n",
-      "model_vars.get('Gini', 0)[-1]=0.687\n",
-      "Condition not met at step 5350. No data to save.\n",
-      "5350\n",
-      "model_vars.get('Gini', 0)[-1]=0.6826\n",
-      "Condition not met at step 5351. No data to save.\n",
-      "5351\n",
-      "model_vars.get('Gini', 0)[-1]=0.6734\n",
-      "Condition not met at step 5352. No data to save.\n",
-      "5352\n",
-      "model_vars.get('Gini', 0)[-1]=0.6772\n",
-      "Condition not met at step 5353. No data to save.\n",
-      "5353\n",
-      "model_vars.get('Gini', 0)[-1]=0.6658\n",
-      "Condition not met at step 5354. No data to save.\n",
-      "5354\n",
-      "model_vars.get('Gini', 0)[-1]=0.6788000000000001\n",
-      "Condition not met at step 5355. No data to save.\n",
-      "5355\n",
-      "model_vars.get('Gini', 0)[-1]=0.6818\n",
-      "Condition not met at step 5356. No data to save.\n",
-      "5356\n",
-      "model_vars.get('Gini', 0)[-1]=0.6624\n",
-      "Condition not met at step 5357. No data to save.\n",
-      "5357\n",
-      "model_vars.get('Gini', 0)[-1]=0.6936\n",
-      "Condition not met at step 5358. No data to save.\n",
-      "5358\n",
-      "model_vars.get('Gini', 0)[-1]=0.7016\n",
-      "Condition met. Appended special results for step 5359 to test_path/special_results.parquet\n",
-      "5359\n",
-      "model_vars.get('Gini', 0)[-1]=0.6719999999999999\n",
-      "Condition not met at step 5360. No data to save.\n",
-      "5360\n",
-      "model_vars.get('Gini', 0)[-1]=0.6694\n",
-      "Condition not met at step 5361. No data to save.\n",
-      "5361\n",
-      "model_vars.get('Gini', 0)[-1]=0.665\n",
-      "Condition not met at step 5362. No data to save.\n",
-      "5362\n",
-      "model_vars.get('Gini', 0)[-1]=0.687\n",
-      "Condition not met at step 5363. No data to save.\n",
-      "5363\n",
-      "model_vars.get('Gini', 0)[-1]=0.6624\n",
-      "Condition not met at step 5364. No data to save.\n",
-      "5364\n",
-      "model_vars.get('Gini', 0)[-1]=0.708\n",
-      "Condition met. Appended special results for step 5365 to test_path/special_results.parquet\n",
-      "5365\n",
-      "model_vars.get('Gini', 0)[-1]=0.7112\n",
-      "Condition met. Appended special results for step 5366 to test_path/special_results.parquet\n",
-      "5366\n",
-      "model_vars.get('Gini', 0)[-1]=0.6892\n",
-      "Condition not met at step 5367. No data to save.\n",
-      "5367\n",
-      "model_vars.get('Gini', 0)[-1]=0.649\n",
-      "Condition not met at step 5368. No data to save.\n",
-      "5368\n",
-      "model_vars.get('Gini', 0)[-1]=0.6366\n",
-      "Condition not met at step 5369. No data to save.\n",
-      "5369\n",
-      "model_vars.get('Gini', 0)[-1]=0.6759999999999999\n",
-      "Condition not met at step 5370. No data to save.\n",
-      "5370\n",
-      "model_vars.get('Gini', 0)[-1]=0.6786000000000001\n",
-      "Condition not met at step 5371. No data to save.\n",
-      "5371\n",
-      "model_vars.get('Gini', 0)[-1]=0.6552\n",
-      "Condition not met at step 5372. No data to save.\n",
-      "5372\n",
-      "model_vars.get('Gini', 0)[-1]=0.611\n",
-      "Condition not met at step 5373. No data to save.\n",
-      "5373\n",
-      "model_vars.get('Gini', 0)[-1]=0.6044\n",
-      "Condition not met at step 5374. No data to save.\n",
-      "5374\n",
-      "model_vars.get('Gini', 0)[-1]=0.6275999999999999\n",
-      "Condition not met at step 5375. No data to save.\n",
-      "5375\n",
-      "model_vars.get('Gini', 0)[-1]=0.6335999999999999\n",
-      "Condition not met at step 5376. No data to save.\n",
-      "5376\n",
-      "model_vars.get('Gini', 0)[-1]=0.6562\n",
-      "Condition not met at step 5377. No data to save.\n",
-      "5377\n",
-      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
-      "Condition not met at step 5378. No data to save.\n",
-      "5378\n",
-      "model_vars.get('Gini', 0)[-1]=0.6961999999999999\n",
-      "Condition not met at step 5379. No data to save.\n",
-      "5379\n",
-      "model_vars.get('Gini', 0)[-1]=0.6894\n",
-      "Condition not met at step 5380. No data to save.\n",
-      "5380\n",
-      "model_vars.get('Gini', 0)[-1]=0.6784\n",
-      "Condition not met at step 5381. No data to save.\n",
-      "5381\n",
-      "model_vars.get('Gini', 0)[-1]=0.6778\n",
-      "Condition not met at step 5382. No data to save.\n",
-      "5382\n",
-      "model_vars.get('Gini', 0)[-1]=0.6734\n",
-      "Condition not met at step 5383. No data to save.\n",
-      "5383\n",
-      "model_vars.get('Gini', 0)[-1]=0.6981999999999999\n",
-      "Condition not met at step 5384. No data to save.\n",
-      "5384\n",
-      "model_vars.get('Gini', 0)[-1]=0.6918\n",
-      "Condition not met at step 5385. No data to save.\n",
-      "5385\n",
-      "model_vars.get('Gini', 0)[-1]=0.6804\n",
-      "Condition not met at step 5386. No data to save.\n",
-      "5386\n",
-      "model_vars.get('Gini', 0)[-1]=0.6966\n",
-      "Condition not met at step 5387. No data to save.\n",
-      "5387\n",
-      "model_vars.get('Gini', 0)[-1]=0.698\n",
-      "Condition not met at step 5388. No data to save.\n",
-      "5388\n",
-      "model_vars.get('Gini', 0)[-1]=0.7\n",
-      "Condition not met at step 5389. No data to save.\n",
-      "5389\n",
-      "model_vars.get('Gini', 0)[-1]=0.6739999999999999\n",
-      "Condition not met at step 5390. No data to save.\n",
-      "5390\n",
-      "model_vars.get('Gini', 0)[-1]=0.6622\n",
-      "Condition not met at step 5391. No data to save.\n",
-      "5391\n",
-      "model_vars.get('Gini', 0)[-1]=0.6556\n",
-      "Condition not met at step 5392. No data to save.\n",
-      "5392\n",
-      "model_vars.get('Gini', 0)[-1]=0.6328\n",
-      "Condition not met at step 5393. No data to save.\n",
-      "5393\n",
-      "model_vars.get('Gini', 0)[-1]=0.6586000000000001\n",
-      "Condition not met at step 5394. No data to save.\n",
-      "5394\n",
-      "model_vars.get('Gini', 0)[-1]=0.6548\n",
-      "Condition not met at step 5395. No data to save.\n",
-      "5395\n",
-      "model_vars.get('Gini', 0)[-1]=0.6404000000000001\n",
-      "Condition not met at step 5396. No data to save.\n",
-      "5396\n",
-      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
-      "Condition not met at step 5397. No data to save.\n",
-      "5397\n",
-      "model_vars.get('Gini', 0)[-1]=0.6318\n",
-      "Condition not met at step 5398. No data to save.\n",
-      "5398\n",
-      "model_vars.get('Gini', 0)[-1]=0.6246\n",
-      "Condition not met at step 5399. No data to save.\n",
-      "5399\n",
-      "CALLED\n",
-      "self.model._steps=5400\n",
-      " TEST self.model._steps=5400\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "5300  0.6302\n",
-      "5301  0.6332\n",
-      "5302  0.6460\n",
-      "5303  0.6546\n",
-      "5304  0.6880\n",
-      "...      ...\n",
-      "5395  0.6404\n",
-      "5396  0.6364\n",
-      "5397  0.6318\n",
-      "5398  0.6246\n",
-      "5399  0.6476\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_054.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_054.parquet'\n",
-      "Saving model to test_path/model_data_054.parquet\n",
-      "Saving agent to test_path/agent_data_054.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6476\n",
-      "Condition not met at step 5400. No data to save.\n",
-      "5400\n",
-      "model_vars.get('Gini', 0)[-1]=0.6206\n",
-      "Condition not met at step 5401. No data to save.\n",
-      "5401\n",
-      "model_vars.get('Gini', 0)[-1]=0.6526000000000001\n",
-      "Condition not met at step 5402. No data to save.\n",
-      "5402\n",
-      "model_vars.get('Gini', 0)[-1]=0.6368\n",
-      "Condition not met at step 5403. No data to save.\n",
-      "5403\n",
-      "model_vars.get('Gini', 0)[-1]=0.6778\n",
-      "Condition not met at step 5404. No data to save.\n",
-      "5404\n",
-      "model_vars.get('Gini', 0)[-1]=0.667\n",
-      "Condition not met at step 5405. No data to save.\n",
-      "5405\n",
-      "model_vars.get('Gini', 0)[-1]=0.6542\n",
-      "Condition not met at step 5406. No data to save.\n",
-      "5406\n",
-      "model_vars.get('Gini', 0)[-1]=0.6288\n",
-      "Condition not met at step 5407. No data to save.\n",
-      "5407\n",
-      "model_vars.get('Gini', 0)[-1]=0.6532\n",
-      "Condition not met at step 5408. No data to save.\n",
-      "5408\n",
-      "model_vars.get('Gini', 0)[-1]=0.6642\n",
-      "Condition not met at step 5409. No data to save.\n",
-      "5409\n",
-      "model_vars.get('Gini', 0)[-1]=0.6732\n",
-      "Condition not met at step 5410. No data to save.\n",
-      "5410\n",
-      "model_vars.get('Gini', 0)[-1]=0.6934\n",
-      "Condition not met at step 5411. No data to save.\n",
-      "5411\n",
-      "model_vars.get('Gini', 0)[-1]=0.6798\n",
-      "Condition not met at step 5412. No data to save.\n",
-      "5412\n",
-      "model_vars.get('Gini', 0)[-1]=0.6906\n",
-      "Condition not met at step 5413. No data to save.\n",
-      "5413\n",
-      "model_vars.get('Gini', 0)[-1]=0.6808000000000001\n",
-      "Condition not met at step 5414. No data to save.\n",
-      "5414\n",
-      "model_vars.get('Gini', 0)[-1]=0.6848000000000001\n",
-      "Condition not met at step 5415. No data to save.\n",
-      "5415\n",
-      "model_vars.get('Gini', 0)[-1]=0.6648000000000001\n",
-      "Condition not met at step 5416. No data to save.\n",
-      "5416\n",
-      "model_vars.get('Gini', 0)[-1]=0.6636\n",
-      "Condition not met at step 5417. No data to save.\n",
-      "5417\n",
-      "model_vars.get('Gini', 0)[-1]=0.6862\n",
-      "Condition not met at step 5418. No data to save.\n",
-      "5418\n",
-      "model_vars.get('Gini', 0)[-1]=0.6834\n",
-      "Condition not met at step 5419. No data to save.\n",
-      "5419\n",
-      "model_vars.get('Gini', 0)[-1]=0.6758\n",
-      "Condition not met at step 5420. No data to save.\n",
-      "5420\n",
-      "model_vars.get('Gini', 0)[-1]=0.6714\n",
-      "Condition not met at step 5421. No data to save.\n",
-      "5421\n",
-      "model_vars.get('Gini', 0)[-1]=0.6614\n",
-      "Condition not met at step 5422. No data to save.\n",
-      "5422\n",
-      "model_vars.get('Gini', 0)[-1]=0.654\n",
-      "Condition not met at step 5423. No data to save.\n",
-      "5423\n",
-      "model_vars.get('Gini', 0)[-1]=0.6642\n",
-      "Condition not met at step 5424. No data to save.\n",
-      "5424\n",
-      "model_vars.get('Gini', 0)[-1]=0.6722\n",
-      "Condition not met at step 5425. No data to save.\n",
-      "5425\n",
-      "model_vars.get('Gini', 0)[-1]=0.6732\n",
-      "Condition not met at step 5426. No data to save.\n",
-      "5426\n",
-      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
-      "Condition not met at step 5427. No data to save.\n",
-      "5427\n",
-      "model_vars.get('Gini', 0)[-1]=0.6494\n",
-      "Condition not met at step 5428. No data to save.\n",
-      "5428\n",
-      "model_vars.get('Gini', 0)[-1]=0.6678\n",
-      "Condition not met at step 5429. No data to save.\n",
-      "5429\n",
-      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
-      "Condition not met at step 5430. No data to save.\n",
-      "5430\n",
-      "model_vars.get('Gini', 0)[-1]=0.6274\n",
-      "Condition not met at step 5431. No data to save.\n",
-      "5431\n",
-      "model_vars.get('Gini', 0)[-1]=0.629\n",
-      "Condition not met at step 5432. No data to save.\n",
-      "5432\n",
-      "model_vars.get('Gini', 0)[-1]=0.6306\n",
-      "Condition not met at step 5433. No data to save.\n",
-      "5433\n",
-      "model_vars.get('Gini', 0)[-1]=0.6314\n",
-      "Condition not met at step 5434. No data to save.\n",
-      "5434\n",
-      "model_vars.get('Gini', 0)[-1]=0.651\n",
-      "Condition not met at step 5435. No data to save.\n",
-      "5435\n",
-      "model_vars.get('Gini', 0)[-1]=0.6614\n",
-      "Condition not met at step 5436. No data to save.\n",
-      "5436\n",
-      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
-      "Condition not met at step 5437. No data to save.\n",
-      "5437\n",
-      "model_vars.get('Gini', 0)[-1]=0.6348\n",
-      "Condition not met at step 5438. No data to save.\n",
-      "5438\n",
-      "model_vars.get('Gini', 0)[-1]=0.6364000000000001\n",
-      "Condition not met at step 5439. No data to save.\n",
-      "5439\n",
-      "model_vars.get('Gini', 0)[-1]=0.6576\n",
-      "Condition not met at step 5440. No data to save.\n",
-      "5440\n",
-      "model_vars.get('Gini', 0)[-1]=0.662\n",
-      "Condition not met at step 5441. No data to save.\n",
-      "5441\n",
-      "model_vars.get('Gini', 0)[-1]=0.6138\n",
-      "Condition not met at step 5442. No data to save.\n",
-      "5442\n",
-      "model_vars.get('Gini', 0)[-1]=0.623\n",
-      "Condition not met at step 5443. No data to save.\n",
-      "5443\n",
-      "model_vars.get('Gini', 0)[-1]=0.6028\n",
-      "Condition not met at step 5444. No data to save.\n",
-      "5444\n",
-      "model_vars.get('Gini', 0)[-1]=0.6124\n",
-      "Condition not met at step 5445. No data to save.\n",
-      "5445\n",
-      "model_vars.get('Gini', 0)[-1]=0.6477999999999999\n",
-      "Condition not met at step 5446. No data to save.\n",
-      "5446\n",
-      "model_vars.get('Gini', 0)[-1]=0.6562\n",
-      "Condition not met at step 5447. No data to save.\n",
-      "5447\n",
-      "model_vars.get('Gini', 0)[-1]=0.6428\n",
-      "Condition not met at step 5448. No data to save.\n",
-      "5448\n",
-      "model_vars.get('Gini', 0)[-1]=0.6516\n",
-      "Condition not met at step 5449. No data to save.\n",
-      "5449\n",
-      "model_vars.get('Gini', 0)[-1]=0.6398\n",
-      "Condition not met at step 5450. No data to save.\n",
-      "5450\n",
-      "model_vars.get('Gini', 0)[-1]=0.643\n",
-      "Condition not met at step 5451. No data to save.\n",
-      "5451\n",
-      "model_vars.get('Gini', 0)[-1]=0.6342\n",
-      "Condition not met at step 5452. No data to save.\n",
-      "5452\n",
-      "model_vars.get('Gini', 0)[-1]=0.6592\n",
-      "Condition not met at step 5453. No data to save.\n",
-      "5453\n",
-      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
-      "Condition not met at step 5454. No data to save.\n",
-      "5454\n",
-      "model_vars.get('Gini', 0)[-1]=0.675\n",
-      "Condition not met at step 5455. No data to save.\n",
-      "5455\n",
-      "model_vars.get('Gini', 0)[-1]=0.6921999999999999\n",
-      "Condition not met at step 5456. No data to save.\n",
-      "5456\n",
-      "model_vars.get('Gini', 0)[-1]=0.7032\n",
-      "Condition met. Appended special results for step 5457 to test_path/special_results.parquet\n",
-      "5457\n",
-      "model_vars.get('Gini', 0)[-1]=0.7094\n",
-      "Condition met. Appended special results for step 5458 to test_path/special_results.parquet\n",
-      "5458\n",
-      "model_vars.get('Gini', 0)[-1]=0.6970000000000001\n",
-      "Condition not met at step 5459. No data to save.\n",
-      "5459\n",
-      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
-      "Condition not met at step 5460. No data to save.\n",
-      "5460\n",
-      "model_vars.get('Gini', 0)[-1]=0.6424000000000001\n",
-      "Condition not met at step 5461. No data to save.\n",
-      "5461\n",
-      "model_vars.get('Gini', 0)[-1]=0.6759999999999999\n",
-      "Condition not met at step 5462. No data to save.\n",
-      "5462\n",
-      "model_vars.get('Gini', 0)[-1]=0.6699999999999999\n",
-      "Condition not met at step 5463. No data to save.\n",
-      "5463\n",
-      "model_vars.get('Gini', 0)[-1]=0.6664\n",
-      "Condition not met at step 5464. No data to save.\n",
-      "5464\n",
-      "model_vars.get('Gini', 0)[-1]=0.6577999999999999\n",
-      "Condition not met at step 5465. No data to save.\n",
-      "5465\n",
-      "model_vars.get('Gini', 0)[-1]=0.6602\n",
-      "Condition not met at step 5466. No data to save.\n",
-      "5466\n",
-      "model_vars.get('Gini', 0)[-1]=0.6396\n",
-      "Condition not met at step 5467. No data to save.\n",
-      "5467\n",
-      "model_vars.get('Gini', 0)[-1]=0.6266\n",
-      "Condition not met at step 5468. No data to save.\n",
-      "5468\n",
-      "model_vars.get('Gini', 0)[-1]=0.6534\n",
-      "Condition not met at step 5469. No data to save.\n",
-      "5469\n",
-      "model_vars.get('Gini', 0)[-1]=0.6417999999999999\n",
-      "Condition not met at step 5470. No data to save.\n",
-      "5470\n",
-      "model_vars.get('Gini', 0)[-1]=0.6392\n",
-      "Condition not met at step 5471. No data to save.\n",
-      "5471\n",
-      "model_vars.get('Gini', 0)[-1]=0.615\n",
-      "Condition not met at step 5472. No data to save.\n",
-      "5472\n",
-      "model_vars.get('Gini', 0)[-1]=0.5933999999999999\n",
-      "Condition not met at step 5473. No data to save.\n",
-      "5473\n",
-      "model_vars.get('Gini', 0)[-1]=0.5920000000000001\n",
-      "Condition not met at step 5474. No data to save.\n",
-      "5474\n",
-      "model_vars.get('Gini', 0)[-1]=0.5774\n",
-      "Condition not met at step 5475. No data to save.\n",
-      "5475\n",
-      "model_vars.get('Gini', 0)[-1]=0.5860000000000001\n",
-      "Condition not met at step 5476. No data to save.\n",
-      "5476\n",
-      "model_vars.get('Gini', 0)[-1]=0.5938\n",
-      "Condition not met at step 5477. No data to save.\n",
-      "5477\n",
-      "model_vars.get('Gini', 0)[-1]=0.6148\n",
-      "Condition not met at step 5478. No data to save.\n",
-      "5478\n",
-      "model_vars.get('Gini', 0)[-1]=0.6326\n",
-      "Condition not met at step 5479. No data to save.\n",
-      "5479\n",
-      "model_vars.get('Gini', 0)[-1]=0.6408\n",
-      "Condition not met at step 5480. No data to save.\n",
-      "5480\n",
-      "model_vars.get('Gini', 0)[-1]=0.6368\n",
-      "Condition not met at step 5481. No data to save.\n",
-      "5481\n",
-      "model_vars.get('Gini', 0)[-1]=0.6396\n",
-      "Condition not met at step 5482. No data to save.\n",
-      "5482\n",
-      "model_vars.get('Gini', 0)[-1]=0.6768000000000001\n",
-      "Condition not met at step 5483. No data to save.\n",
-      "5483\n",
-      "model_vars.get('Gini', 0)[-1]=0.6839999999999999\n",
-      "Condition not met at step 5484. No data to save.\n",
-      "5484\n",
-      "model_vars.get('Gini', 0)[-1]=0.6756\n",
-      "Condition not met at step 5485. No data to save.\n",
-      "5485\n",
-      "model_vars.get('Gini', 0)[-1]=0.6702\n",
-      "Condition not met at step 5486. No data to save.\n",
-      "5486\n",
-      "model_vars.get('Gini', 0)[-1]=0.6738\n",
-      "Condition not met at step 5487. No data to save.\n",
-      "5487\n",
-      "model_vars.get('Gini', 0)[-1]=0.6844\n",
-      "Condition not met at step 5488. No data to save.\n",
-      "5488\n",
-      "model_vars.get('Gini', 0)[-1]=0.6946\n",
-      "Condition not met at step 5489. No data to save.\n",
-      "5489\n",
-      "model_vars.get('Gini', 0)[-1]=0.6634\n",
-      "Condition not met at step 5490. No data to save.\n",
-      "5490\n",
-      "model_vars.get('Gini', 0)[-1]=0.6756\n",
-      "Condition not met at step 5491. No data to save.\n",
-      "5491\n",
-      "model_vars.get('Gini', 0)[-1]=0.6848000000000001\n",
-      "Condition not met at step 5492. No data to save.\n",
-      "5492\n",
-      "model_vars.get('Gini', 0)[-1]=0.6966\n",
-      "Condition not met at step 5493. No data to save.\n",
-      "5493\n",
-      "model_vars.get('Gini', 0)[-1]=0.6941999999999999\n",
-      "Condition not met at step 5494. No data to save.\n",
-      "5494\n",
-      "model_vars.get('Gini', 0)[-1]=0.6984\n",
-      "Condition not met at step 5495. No data to save.\n",
-      "5495\n",
-      "model_vars.get('Gini', 0)[-1]=0.6759999999999999\n",
-      "Condition not met at step 5496. No data to save.\n",
-      "5496\n",
-      "model_vars.get('Gini', 0)[-1]=0.675\n",
-      "Condition not met at step 5497. No data to save.\n",
-      "5497\n",
-      "model_vars.get('Gini', 0)[-1]=0.6826\n",
-      "Condition not met at step 5498. No data to save.\n",
-      "5498\n",
-      "model_vars.get('Gini', 0)[-1]=0.6856\n",
-      "Condition not met at step 5499. No data to save.\n",
-      "5499\n",
-      "CALLED\n",
-      "self.model._steps=5500\n",
-      " TEST self.model._steps=5500\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "5400  0.6206\n",
-      "5401  0.6526\n",
-      "5402  0.6368\n",
-      "5403  0.6778\n",
-      "5404  0.6670\n",
-      "...      ...\n",
-      "5495  0.6760\n",
-      "5496  0.6750\n",
-      "5497  0.6826\n",
-      "5498  0.6856\n",
-      "5499  0.6806\n",
-      "\n",
-      "[100 rows x 1 columns]\n",
-      "3\n",
-      "model_file='test_path/model_data_055.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_055.parquet'\n",
-      "Saving model to test_path/model_data_055.parquet\n",
-      "Saving agent to test_path/agent_data_055.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.6806\n",
-      "Condition not met at step 5500. No data to save.\n",
-      "5500\n",
-      "model_vars.get('Gini', 0)[-1]=0.6888000000000001\n",
-      "Condition not met at step 5501. No data to save.\n",
-      "5501\n",
-      "model_vars.get('Gini', 0)[-1]=0.6708000000000001\n",
-      "Condition not met at step 5502. No data to save.\n",
-      "5502\n",
-      "model_vars.get('Gini', 0)[-1]=0.6882\n",
-      "Condition not met at step 5503. No data to save.\n",
-      "5503\n",
-      "model_vars.get('Gini', 0)[-1]=0.6648000000000001\n",
-      "Condition not met at step 5504. No data to save.\n",
-      "5504\n",
-      "model_vars.get('Gini', 0)[-1]=0.6682\n",
-      "Condition not met at step 5505. No data to save.\n",
-      "5505\n",
-      "model_vars.get('Gini', 0)[-1]=0.657\n",
-      "Condition not met at step 5506. No data to save.\n",
-      "5506\n",
-      "model_vars.get('Gini', 0)[-1]=0.6442\n",
-      "Condition not met at step 5507. No data to save.\n",
-      "5507\n",
-      "model_vars.get('Gini', 0)[-1]=0.65\n",
-      "Condition not met at step 5508. No data to save.\n",
-      "5508\n",
-      "model_vars.get('Gini', 0)[-1]=0.6497999999999999\n",
-      "Condition not met at step 5509. No data to save.\n",
-      "5509\n",
-      "model_vars.get('Gini', 0)[-1]=0.6366\n",
-      "Condition not met at step 5510. No data to save.\n",
-      "5510\n",
-      "model_vars.get('Gini', 0)[-1]=0.6335999999999999\n",
-      "Condition not met at step 5511. No data to save.\n",
-      "5511\n",
-      "model_vars.get('Gini', 0)[-1]=0.6252\n",
-      "Condition not met at step 5512. No data to save.\n",
-      "5512\n",
-      "model_vars.get('Gini', 0)[-1]=0.615\n",
-      "Condition not met at step 5513. No data to save.\n",
-      "5513\n",
-      "model_vars.get('Gini', 0)[-1]=0.615\n",
-      "Condition not met at step 5514. No data to save.\n",
-      "5514\n",
-      "model_vars.get('Gini', 0)[-1]=0.6412\n",
-      "Condition not met at step 5515. No data to save.\n",
-      "5515\n",
-      "model_vars.get('Gini', 0)[-1]=0.6214\n",
-      "Condition not met at step 5516. No data to save.\n",
-      "5516\n",
-      "model_vars.get('Gini', 0)[-1]=0.6255999999999999\n",
-      "Condition not met at step 5517. No data to save.\n",
-      "5517\n",
-      "model_vars.get('Gini', 0)[-1]=0.6322\n",
-      "Condition not met at step 5518. No data to save.\n",
-      "5518\n",
-      "model_vars.get('Gini', 0)[-1]=0.656\n",
-      "Condition not met at step 5519. No data to save.\n",
-      "5519\n",
-      "model_vars.get('Gini', 0)[-1]=0.6762\n",
-      "Condition not met at step 5520. No data to save.\n",
-      "5520\n",
-      "model_vars.get('Gini', 0)[-1]=0.6844\n",
-      "Condition not met at step 5521. No data to save.\n",
-      "5521\n",
-      "model_vars.get('Gini', 0)[-1]=0.6694\n",
-      "Condition not met at step 5522. No data to save.\n",
-      "5522\n",
-      "model_vars.get('Gini', 0)[-1]=0.6668000000000001\n",
-      "Condition not met at step 5523. No data to save.\n",
-      "5523\n",
-      "model_vars.get('Gini', 0)[-1]=0.6752\n",
-      "Condition not met at step 5524. No data to save.\n",
-      "5524\n",
-      "model_vars.get('Gini', 0)[-1]=0.6574\n",
-      "Condition not met at step 5525. No data to save.\n",
-      "5525\n",
-      "model_vars.get('Gini', 0)[-1]=0.6632\n",
-      "Condition not met at step 5526. No data to save.\n",
-      "5526\n",
-      "model_vars.get('Gini', 0)[-1]=0.6688000000000001\n",
-      "Condition not met at step 5527. No data to save.\n",
-      "5527\n",
-      "model_vars.get('Gini', 0)[-1]=0.6732\n",
-      "Condition not met at step 5528. No data to save.\n",
-      "5528\n",
-      "model_vars.get('Gini', 0)[-1]=0.6484000000000001\n",
-      "Condition not met at step 5529. No data to save.\n",
-      "5529\n",
-      "model_vars.get('Gini', 0)[-1]=0.6332\n",
-      "Condition not met at step 5530. No data to save.\n",
-      "5530\n",
-      "model_vars.get('Gini', 0)[-1]=0.6516\n",
-      "Condition not met at step 5531. No data to save.\n",
-      "5531\n",
-      "model_vars.get('Gini', 0)[-1]=0.6388\n",
-      "Condition not met at step 5532. No data to save.\n",
-      "5532\n",
-      "model_vars.get('Gini', 0)[-1]=0.6496\n",
-      "Condition not met at step 5533. No data to save.\n",
-      "5533\n",
-      "model_vars.get('Gini', 0)[-1]=0.6562\n",
-      "Condition not met at step 5534. No data to save.\n",
-      "5534\n",
-      "model_vars.get('Gini', 0)[-1]=0.6442\n",
-      "Condition not met at step 5535. No data to save.\n",
-      "5535\n",
-      "model_vars.get('Gini', 0)[-1]=0.6202000000000001\n",
-      "Condition not met at step 5536. No data to save.\n",
-      "5536\n",
-      "model_vars.get('Gini', 0)[-1]=0.621\n",
-      "Condition not met at step 5537. No data to save.\n",
-      "5537\n",
-      "model_vars.get('Gini', 0)[-1]=0.6486000000000001\n",
-      "Condition not met at step 5538. No data to save.\n",
-      "5538\n",
-      "model_vars.get('Gini', 0)[-1]=0.6644\n",
-      "Condition not met at step 5539. No data to save.\n",
-      "5539\n",
-      "model_vars.get('Gini', 0)[-1]=0.6322\n",
-      "Condition not met at step 5540. No data to save.\n",
-      "5540\n",
-      "model_vars.get('Gini', 0)[-1]=0.6384000000000001\n",
-      "Condition not met at step 5541. No data to save.\n",
-      "5541\n",
-      "model_vars.get('Gini', 0)[-1]=0.6368\n",
-      "Condition not met at step 5542. No data to save.\n",
-      "5542\n",
-      "model_vars.get('Gini', 0)[-1]=0.6404000000000001\n",
-      "Condition not met at step 5543. No data to save.\n",
-      "5543\n",
-      "model_vars.get('Gini', 0)[-1]=0.6352\n",
-      "Condition not met at step 5544. No data to save.\n",
-      "5544\n",
-      "model_vars.get('Gini', 0)[-1]=0.6295999999999999\n",
-      "Condition not met at step 5545. No data to save.\n",
-      "5545\n",
-      "model_vars.get('Gini', 0)[-1]=0.6679999999999999\n",
-      "Condition not met at step 5546. No data to save.\n",
-      "5546\n",
-      "model_vars.get('Gini', 0)[-1]=0.6882\n",
-      "Condition not met at step 5547. No data to save.\n",
-      "5547\n",
-      "model_vars.get('Gini', 0)[-1]=0.6686000000000001\n",
-      "Condition not met at step 5548. No data to save.\n",
-      "5548\n",
-      "model_vars.get('Gini', 0)[-1]=0.6722\n",
-      "Condition not met at step 5549. No data to save.\n",
-      "5549\n",
-      "model_vars.get('Gini', 0)[-1]=0.6758\n",
-      "Condition not met at step 5550. No data to save.\n",
-      "5550\n",
-      "model_vars.get('Gini', 0)[-1]=0.6930000000000001\n",
-      "Condition not met at step 5551. No data to save.\n",
-      "5551\n",
-      "model_vars.get('Gini', 0)[-1]=0.6732\n",
-      "Condition not met at step 5552. No data to save.\n",
-      "5552\n",
-      "model_vars.get('Gini', 0)[-1]=0.6606000000000001\n",
-      "Condition not met at step 5553. No data to save.\n",
-      "5553\n",
-      "model_vars.get('Gini', 0)[-1]=0.6368\n",
-      "Condition not met at step 5554. No data to save.\n",
-      "5554\n",
-      "FINAL CALLED\n",
-      "self.model._steps=5555\n",
-      " TEST self.model._steps=5555\n",
-      " TEST self._cache_interval=100\n",
-      "        Gini\n",
-      "5500  0.6888\n",
-      "5501  0.6708\n",
-      "5502  0.6882\n",
-      "5503  0.6648\n",
-      "5504  0.6682\n",
-      "5505  0.6570\n",
-      "5506  0.6442\n",
-      "5507  0.6500\n",
-      "5508  0.6498\n",
-      "5509  0.6366\n",
-      "5510  0.6336\n",
-      "5511  0.6252\n",
-      "5512  0.6150\n",
-      "5513  0.6150\n",
-      "5514  0.6412\n",
-      "5515  0.6214\n",
-      "5516  0.6256\n",
-      "5517  0.6322\n",
-      "5518  0.6560\n",
-      "5519  0.6762\n",
-      "5520  0.6844\n",
-      "5521  0.6694\n",
-      "5522  0.6668\n",
-      "5523  0.6752\n",
-      "5524  0.6574\n",
-      "5525  0.6632\n",
-      "5526  0.6688\n",
-      "5527  0.6732\n",
-      "5528  0.6484\n",
-      "5529  0.6332\n",
-      "5530  0.6516\n",
-      "5531  0.6388\n",
-      "5532  0.6496\n",
-      "5533  0.6562\n",
-      "5534  0.6442\n",
-      "5535  0.6202\n",
-      "5536  0.6210\n",
-      "5537  0.6486\n",
-      "5538  0.6644\n",
-      "5539  0.6322\n",
-      "5540  0.6384\n",
-      "5541  0.6368\n",
-      "5542  0.6404\n",
-      "5543  0.6352\n",
-      "5544  0.6296\n",
-      "5545  0.6680\n",
-      "5546  0.6882\n",
-      "5547  0.6686\n",
-      "5548  0.6722\n",
-      "5549  0.6758\n",
-      "5550  0.6930\n",
-      "5551  0.6732\n",
-      "5552  0.6606\n",
-      "5553  0.6368\n",
-      "5554  0.6510\n",
-      "3\n",
-      "model_file='test_path/model_data_056.parquet'\n",
-      "absolute_path='/mnt/c/wsl/github/mesa-forked/mesa/test_path/model_data_056.parquet'\n",
-      "Saving model to test_path/model_data_056.parquet\n",
-      "Saving agent to test_path/agent_data_056.parquet\n",
-      "model_vars.get('Gini', 0)[-1]=0.651\n",
-      "Condition not met at step 5555. No data to save.\n"
+      "all_records=<itertools.chain object at 0x7f77dfe55240>\n",
+      "rep_names=['Wealth']\n",
+      "Condition not met at step 1000. No data to save.\n"
      ]
     }
    ],
    "source": [
-    "for i in range(5555):\n",
+    "for i in range(number_of_steps):\n",
     "    print(i)\n",
     "    cacheable_model.model.step()\n",
-    "    cacheable_model.cache()\n"
+    "    cacheable_model.cache()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "248ea838-5c71-4838-b6ad-fdcfdc3a1c1e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "gini = model.datacollector.get_model_vars_dataframe()\n",
-    "# Plot the Gini coefficient over time\n",
-    "g = sns.lineplot(data=gini)\n",
-    "g.set(title=\"Gini Coefficient over Time\", ylabel=\"Gini Coefficient\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "9cf0a6c4-a976-4835-82e2-706f49b175d3",
-   "metadata": {},
+   "id": "f85769a6-c1a7-4049-aaaa-ed23c876b1eb",
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
@@ -18257,163 +2536,186 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.3272</td>\n",
+       "      <td>0.2602</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.4626</td>\n",
+       "      <td>0.4262</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.4626</td>\n",
+       "      <td>0.4538</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.5080</td>\n",
+       "      <td>0.4824</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5550</th>\n",
-       "      <td>0.6930</td>\n",
+       "      <th>995</th>\n",
+       "      <td>0.6944</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5551</th>\n",
-       "      <td>0.6732</td>\n",
+       "      <th>996</th>\n",
+       "      <td>0.6878</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5552</th>\n",
-       "      <td>0.6606</td>\n",
+       "      <th>997</th>\n",
+       "      <td>0.6712</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5553</th>\n",
-       "      <td>0.6368</td>\n",
+       "      <th>998</th>\n",
+       "      <td>0.6660</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5554</th>\n",
-       "      <td>0.6510</td>\n",
+       "      <th>999</th>\n",
+       "      <td>0.6166</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5555 rows × 1 columns</p>\n",
+       "<p>1000 rows × 1 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "        Gini\n",
-       "0     0.0000\n",
-       "1     0.3272\n",
-       "2     0.4626\n",
-       "3     0.4626\n",
-       "4     0.5080\n",
-       "...      ...\n",
-       "5550  0.6930\n",
-       "5551  0.6732\n",
-       "5552  0.6606\n",
-       "5553  0.6368\n",
-       "5554  0.6510\n",
+       "       Gini\n",
+       "0    0.0000\n",
+       "1    0.2602\n",
+       "2    0.4262\n",
+       "3    0.4538\n",
+       "4    0.4824\n",
+       "..      ...\n",
+       "995  0.6944\n",
+       "996  0.6878\n",
+       "997  0.6712\n",
+       "998  0.6660\n",
+       "999  0.6166\n",
        "\n",
-       "[5555 rows x 1 columns]"
+       "[1000 rows x 1 columns]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gini"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "c9515417-7c5f-45f8-8ffa-2f466288ea1f",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "        Gini\n",
-      "0     0.0000\n",
-      "1     0.3272\n",
-      "2     0.4626\n",
-      "3     0.4626\n",
-      "4     0.5080\n",
-      "...      ...\n",
-      "5550  0.6930\n",
-      "5551  0.6732\n",
-      "5552  0.6606\n",
-      "5553  0.6368\n",
-      "5554  0.6510\n",
-      "\n",
-      "[5555 rows x 1 columns]\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pyarrow.parquet as pq\n",
-    "import pandas as pd\n",
-    "import glob\n",
-    "\n",
-    "# Get a list of all Parquet files\n",
-    "model_files = glob.glob('test_path/model_data_*.parquet')\n",
-    "agent_files = glob.glob('test_path/agent_data_*.parquet')\n",
-    "\n",
-    "# Initialize lists to hold dataframes\n",
-    "model_dfs = []\n",
-    "agent_dfs = []\n",
-    "\n",
-    "# Read and append each file to the list\n",
-    "for model_file in model_files:\n",
-    "    table = pq.read_table(model_file)\n",
-    "    df = table.to_pandas()\n",
-    "    model_dfs.append(df)\n",
-    "\n",
-    "for agent_file in agent_files:\n",
-    "    table = pq.read_table(agent_file)\n",
-    "    df = table.to_pandas()\n",
-    "    agent_dfs.append(df)\n",
-    "\n",
-    "# Concatenate all DataFrames\n",
-    "model_df = pd.concat(model_dfs, ignore_index=True)\n",
-    "agent_df = pd.concat(agent_dfs, ignore_index=True)\n",
-    "\n",
-    "# Display the combined DataFrames\n",
-    "print(model_df)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "c6c73b47-b23c-4347-9951-b721ea5ed5da",
-   "metadata": {},
-   "outputs": [
+    },
     {
      "data": {
-      "image/png": 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",
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Wealth</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Step</th>\n",
+       "      <th>AgentID</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">999</th>\n",
+       "      <th>95</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "              Wealth\n",
+       "Step AgentID        \n",
+       "0    0             1\n",
+       "     1             1\n",
+       "     2             1\n",
+       "     3             1\n",
+       "     4             1\n",
+       "...              ...\n",
+       "999  95            6\n",
+       "     96            1\n",
+       "     97            1\n",
+       "     98            6\n",
+       "     99            0\n",
+       "\n",
+       "[100000 rows x 1 columns]"
       ]
      },
+     "execution_count": 10,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
+    "model_df, agent_df = cacheable_model.combine_dataframes()\n",
     "model_df\n",
-    "g = sns.lineplot(data=model_df)\n",
-    "g.set(title=\"Gini Coefficient over Time\", ylabel=\"Gini Coefficient\");"
+    "agent_df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "21cfeacd-dcfa-485f-848e-88cb619addfc",
+   "execution_count": 11,
+   "id": "33fdefa6-da87-40bd-8218-3ef74bbfa49f",
    "metadata": {
     "scrolled": true
    },
@@ -18449,161 +2751,70 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.3272</td>\n",
+       "      <td>0.2602</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.4626</td>\n",
+       "      <td>0.4262</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.4626</td>\n",
+       "      <td>0.4538</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.5080</td>\n",
+       "      <td>0.4824</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5550</th>\n",
-       "      <td>0.6930</td>\n",
+       "      <th>995</th>\n",
+       "      <td>0.6944</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5551</th>\n",
-       "      <td>0.6732</td>\n",
+       "      <th>996</th>\n",
+       "      <td>0.6878</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5552</th>\n",
-       "      <td>0.6606</td>\n",
+       "      <th>997</th>\n",
+       "      <td>0.6712</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5553</th>\n",
-       "      <td>0.6368</td>\n",
+       "      <th>998</th>\n",
+       "      <td>0.6660</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5554</th>\n",
-       "      <td>0.6510</td>\n",
+       "      <th>999</th>\n",
+       "      <td>0.6166</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5555 rows × 1 columns</p>\n",
+       "<p>1000 rows × 1 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "        Gini\n",
-       "0     0.0000\n",
-       "1     0.3272\n",
-       "2     0.4626\n",
-       "3     0.4626\n",
-       "4     0.5080\n",
-       "...      ...\n",
-       "5550  0.6930\n",
-       "5551  0.6732\n",
-       "5552  0.6606\n",
-       "5553  0.6368\n",
-       "5554  0.6510\n",
+       "       Gini\n",
+       "0    0.0000\n",
+       "1    0.2602\n",
+       "2    0.4262\n",
+       "3    0.4538\n",
+       "4    0.4824\n",
+       "..      ...\n",
+       "995  0.6944\n",
+       "996  0.6878\n",
+       "997  0.6712\n",
+       "998  0.6660\n",
+       "999  0.6166\n",
        "\n",
-       "[5555 rows x 1 columns]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "8c967f5e-4b91-44b8-b3f5-ab6874d0e28f",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['test_path/model_data_001.parquet',\n",
-       " 'test_path/model_data_002.parquet',\n",
-       " 'test_path/model_data_003.parquet',\n",
-       " 'test_path/model_data_004.parquet',\n",
-       " 'test_path/model_data_005.parquet',\n",
-       " 'test_path/model_data_006.parquet',\n",
-       " 'test_path/model_data_007.parquet',\n",
-       " 'test_path/model_data_008.parquet',\n",
-       " 'test_path/model_data_009.parquet',\n",
-       " 'test_path/model_data_010.parquet',\n",
-       " 'test_path/model_data_011.parquet',\n",
-       " 'test_path/model_data_012.parquet',\n",
-       " 'test_path/model_data_013.parquet',\n",
-       " 'test_path/model_data_014.parquet',\n",
-       " 'test_path/model_data_015.parquet',\n",
-       " 'test_path/model_data_016.parquet',\n",
-       " 'test_path/model_data_017.parquet',\n",
-       " 'test_path/model_data_018.parquet',\n",
-       " 'test_path/model_data_019.parquet',\n",
-       " 'test_path/model_data_020.parquet',\n",
-       " 'test_path/model_data_021.parquet',\n",
-       " 'test_path/model_data_022.parquet',\n",
-       " 'test_path/model_data_023.parquet',\n",
-       " 'test_path/model_data_024.parquet',\n",
-       " 'test_path/model_data_025.parquet',\n",
-       " 'test_path/model_data_026.parquet',\n",
-       " 'test_path/model_data_027.parquet',\n",
-       " 'test_path/model_data_028.parquet',\n",
-       " 'test_path/model_data_029.parquet',\n",
-       " 'test_path/model_data_030.parquet',\n",
-       " 'test_path/model_data_031.parquet',\n",
-       " 'test_path/model_data_032.parquet',\n",
-       " 'test_path/model_data_033.parquet',\n",
-       " 'test_path/model_data_034.parquet',\n",
-       " 'test_path/model_data_035.parquet',\n",
-       " 'test_path/model_data_036.parquet',\n",
-       " 'test_path/model_data_037.parquet',\n",
-       " 'test_path/model_data_038.parquet',\n",
-       " 'test_path/model_data_039.parquet',\n",
-       " 'test_path/model_data_040.parquet',\n",
-       " 'test_path/model_data_041.parquet',\n",
-       " 'test_path/model_data_042.parquet',\n",
-       " 'test_path/model_data_043.parquet',\n",
-       " 'test_path/model_data_044.parquet',\n",
-       " 'test_path/model_data_045.parquet',\n",
-       " 'test_path/model_data_046.parquet',\n",
-       " 'test_path/model_data_047.parquet',\n",
-       " 'test_path/model_data_048.parquet',\n",
-       " 'test_path/model_data_049.parquet',\n",
-       " 'test_path/model_data_050.parquet',\n",
-       " 'test_path/model_data_051.parquet',\n",
-       " 'test_path/model_data_052.parquet',\n",
-       " 'test_path/model_data_053.parquet',\n",
-       " 'test_path/model_data_054.parquet',\n",
-       " 'test_path/model_data_055.parquet',\n",
-       " 'test_path/model_data_056.parquet']"
+       "[1000 rows x 1 columns]"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "ea25fe67-a4fb-432c-8592-2a6b73dc28f0",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
+    },
     {
      "data": {
       "text/html": [
@@ -18625,114 +2836,139 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Gini</th>\n",
+       "      <th></th>\n",
+       "      <th>Wealth</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Step</th>\n",
+       "      <th>AgentID</th>\n",
+       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
        "      <th>0</th>\n",
-       "      <td>0.0000</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.3272</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.4626</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.4626</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.5080</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
+       "      <th>...</th>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5550</th>\n",
-       "      <td>0.6930</td>\n",
+       "      <th rowspan=\"5\" valign=\"top\">999</th>\n",
+       "      <th>25</th>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5551</th>\n",
-       "      <td>0.6732</td>\n",
+       "      <th>70</th>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5552</th>\n",
-       "      <td>0.6606</td>\n",
+       "      <th>30</th>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5553</th>\n",
-       "      <td>0.6368</td>\n",
+       "      <th>63</th>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5554</th>\n",
-       "      <td>0.6510</td>\n",
+       "      <th>56</th>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5555 rows × 1 columns</p>\n",
+       "<p>100000 rows × 1 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "        Gini\n",
-       "0     0.0000\n",
-       "1     0.3272\n",
-       "2     0.4626\n",
-       "3     0.4626\n",
-       "4     0.5080\n",
-       "...      ...\n",
-       "5550  0.6930\n",
-       "5551  0.6732\n",
-       "5552  0.6606\n",
-       "5553  0.6368\n",
-       "5554  0.6510\n",
+       "              Wealth\n",
+       "Step AgentID        \n",
+       "0    0             1\n",
+       "     1             1\n",
+       "     2             1\n",
+       "     3             1\n",
+       "     4             1\n",
+       "...              ...\n",
+       "999  25            1\n",
+       "     70            0\n",
+       "     30            1\n",
+       "     63            0\n",
+       "     56            1\n",
        "\n",
-       "[5555 rows x 1 columns]"
+       "[100000 rows x 1 columns]"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "model_df"
+    "import pyarrow.parquet as pq\n",
+    "import pandas as pd\n",
+    "import glob\n",
+    "\n",
+    "# Get a list of all Parquet files\n",
+    "model_files = glob.glob(f'{directory}/model_data_*.parquet')\n",
+    "agent_files = glob.glob(f'{directory}/agent_data_*.parquet')\n",
+    "special_files = glob.glob(f'{directory}/special_results.parquet')\n",
+    "\n",
+    "# Initialize lists to hold dataframes\n",
+    "model_dfs = []\n",
+    "agent_dfs = []\n",
+    "special_dfs = []\n",
+    "\n",
+    "# Read and append each file to the list\n",
+    "for model_file in model_files:\n",
+    "    table = pq.read_table(model_file)\n",
+    "    df = table.to_pandas()\n",
+    "    model_dfs.append(df)\n",
+    "\n",
+    "for agent_file in agent_files:\n",
+    "    table = pq.read_table(agent_file)\n",
+    "    df = table.to_pandas()\n",
+    "    agent_dfs.append(df)\n",
+    "\n",
+    "for special_file in special_files:\n",
+    "    table = pq.read_table(special_file)\n",
+    "    df = table.to_pandas()\n",
+    "    special_dfs.append(df)\n",
+    "\n",
+    "# Concatenate all DataFrames\n",
+    "model_df = pd.concat(model_dfs, ignore_index=True)\n",
+    "agent_df = pd.concat(agent_dfs)\n",
+    "special_df = pd.concat(special_dfs).set_index(\"Step\")\n",
+    "\n",
+    "# Display the combined DataFrames\n",
+    "model_df\n",
+    "agent_df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "50f26db2-06e0-49a1-996e-53e71a62ecbd",
+   "execution_count": 12,
+   "id": "3b983ae2-2903-4d6e-ba9a-6fe8a44e9239",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Gini    5555\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(gini == model_df).sum()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "1ac6d4b7-b74f-41e8-82eb-368d77355d91",
-   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -18756,942 +2992,2237 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gini</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>Step</th>\n",
+       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.7046</td>\n",
-       "      <td>42</td>\n",
+       "      <th>95</th>\n",
+       "      <td>0.7018</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.7010</td>\n",
-       "      <td>43</td>\n",
+       "      <th>105</th>\n",
+       "      <td>0.7040</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.7056</td>\n",
-       "      <td>44</td>\n",
+       "      <th>106</th>\n",
+       "      <td>0.7268</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.7204</td>\n",
-       "      <td>45</td>\n",
+       "      <th>107</th>\n",
+       "      <td>0.7086</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.7092</td>\n",
-       "      <td>46</td>\n",
+       "      <th>108</th>\n",
+       "      <td>0.7004</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
-       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>463</th>\n",
-       "      <td>0.7016</td>\n",
-       "      <td>5359</td>\n",
+       "      <th>948</th>\n",
+       "      <td>0.7176</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>464</th>\n",
-       "      <td>0.7080</td>\n",
-       "      <td>5365</td>\n",
+       "      <th>949</th>\n",
+       "      <td>0.7094</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>465</th>\n",
-       "      <td>0.7112</td>\n",
-       "      <td>5366</td>\n",
+       "      <th>950</th>\n",
+       "      <td>0.7156</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>466</th>\n",
-       "      <td>0.7032</td>\n",
-       "      <td>5457</td>\n",
+       "      <th>951</th>\n",
+       "      <td>0.7128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>467</th>\n",
-       "      <td>0.7094</td>\n",
-       "      <td>5458</td>\n",
+       "      <th>993</th>\n",
+       "      <td>0.7028</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>468 rows × 2 columns</p>\n",
+       "<p>91 rows × 1 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "       Gini  Step\n",
-       "0    0.7046    42\n",
-       "1    0.7010    43\n",
-       "2    0.7056    44\n",
-       "3    0.7204    45\n",
-       "4    0.7092    46\n",
-       "..      ...   ...\n",
-       "463  0.7016  5359\n",
-       "464  0.7080  5365\n",
-       "465  0.7112  5366\n",
-       "466  0.7032  5457\n",
-       "467  0.7094  5458\n",
+       "        Gini\n",
+       "Step        \n",
+       "95    0.7018\n",
+       "105   0.7040\n",
+       "106   0.7268\n",
+       "107   0.7086\n",
+       "108   0.7004\n",
+       "...      ...\n",
+       "948   0.7176\n",
+       "949   0.7094\n",
+       "950   0.7156\n",
+       "951   0.7128\n",
+       "993   0.7028\n",
        "\n",
-       "[468 rows x 2 columns]"
+       "[91 rows x 1 columns]"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "table = pq.read_table(\"test_path/special_results.parquet\")\n",
-    "df = table.to_pandas()\n",
-    "df"
+    "special_df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "e6bc2d5c-d1a5-4316-9aeb-1fdf75b0b8f1",
+   "execution_count": null,
+   "id": "67de6c92-efc5-44fd-b8b0-af9b0aa30635",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[      Gini\n",
-       " 0   0.0000\n",
-       " 1   0.3272\n",
-       " 2   0.4626\n",
-       " 3   0.4626\n",
-       " 4   0.5080\n",
-       " ..     ...\n",
-       " 95  0.6754\n",
-       " 96  0.6764\n",
-       " 97  0.6834\n",
-       " 98  0.6582\n",
-       " 99  0.6464\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "        Gini\n",
-       " 100  0.6650\n",
-       " 101  0.6506\n",
-       " 102  0.6734\n",
-       " 103  0.6656\n",
-       " 104  0.6450\n",
-       " ..      ...\n",
-       " 195  0.6086\n",
-       " 196  0.6014\n",
-       " 197  0.6130\n",
-       " 198  0.6184\n",
-       " 199  0.6284\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "        Gini\n",
-       " 200  0.6256\n",
-       " 201  0.6086\n",
-       " 202  0.6250\n",
-       " 203  0.6060\n",
-       " 204  0.6240\n",
-       " ..      ...\n",
-       " 295  0.6338\n",
-       " 296  0.6420\n",
-       " 297  0.6716\n",
-       " 298  0.6904\n",
-       " 299  0.6808\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "        Gini\n",
-       " 300  0.6764\n",
-       " 301  0.6888\n",
-       " 302  0.6702\n",
-       " 303  0.6726\n",
-       " 304  0.6638\n",
-       " ..      ...\n",
-       " 395  0.6800\n",
-       " 396  0.6448\n",
-       " 397  0.6418\n",
-       " 398  0.6556\n",
-       " 399  0.6790\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "        Gini\n",
-       " 400  0.6890\n",
-       " 401  0.6862\n",
-       " 402  0.6704\n",
-       " 403  0.6486\n",
-       " 404  0.6194\n",
-       " ..      ...\n",
-       " 495  0.7084\n",
-       " 496  0.6936\n",
-       " 497  0.6788\n",
-       " 498  0.6916\n",
-       " 499  0.6602\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "        Gini\n",
-       " 500  0.6610\n",
-       " 501  0.6544\n",
-       " 502  0.6406\n",
-       " 503  0.6584\n",
-       " 504  0.6760\n",
-       " ..      ...\n",
-       " 595  0.6540\n",
-       " 596  0.6926\n",
-       " 597  0.6836\n",
-       " 598  0.7032\n",
-       " 599  0.6522\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "        Gini\n",
-       " 600  0.6476\n",
-       " 601  0.6418\n",
-       " 602  0.6836\n",
-       " 603  0.6526\n",
-       " 604  0.6448\n",
-       " ..      ...\n",
-       " 695  0.6056\n",
-       " 696  0.6456\n",
-       " 697  0.6458\n",
-       " 698  0.6538\n",
-       " 699  0.6572\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "        Gini\n",
-       " 700  0.6500\n",
-       " 701  0.6500\n",
-       " 702  0.6402\n",
-       " 703  0.6274\n",
-       " 704  0.6596\n",
-       " ..      ...\n",
-       " 795  0.6366\n",
-       " 796  0.6130\n",
-       " 797  0.6186\n",
-       " 798  0.6078\n",
-       " 799  0.6270\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "        Gini\n",
-       " 800  0.6176\n",
-       " 801  0.6202\n",
-       " 802  0.6210\n",
-       " 803  0.6514\n",
-       " 804  0.6578\n",
-       " ..      ...\n",
-       " 895  0.6924\n",
-       " 896  0.6670\n",
-       " 897  0.7064\n",
-       " 898  0.7068\n",
-       " 899  0.6792\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "        Gini\n",
-       " 900  0.7038\n",
-       " 901  0.6784\n",
-       " 902  0.6872\n",
-       " 903  0.6596\n",
-       " 904  0.6486\n",
-       " ..      ...\n",
-       " 995  0.6922\n",
-       " 996  0.6850\n",
-       " 997  0.6806\n",
-       " 998  0.6510\n",
-       " 999  0.6530\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 1000  0.6574\n",
-       " 1001  0.6482\n",
-       " 1002  0.6254\n",
-       " 1003  0.6316\n",
-       " 1004  0.6486\n",
-       " ...      ...\n",
-       " 1095  0.6820\n",
-       " 1096  0.6684\n",
-       " 1097  0.6692\n",
-       " 1098  0.6672\n",
-       " 1099  0.6878\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 1100  0.6744\n",
-       " 1101  0.6722\n",
-       " 1102  0.6566\n",
-       " 1103  0.6402\n",
-       " 1104  0.6434\n",
-       " ...      ...\n",
-       " 1195  0.6562\n",
-       " 1196  0.6602\n",
-       " 1197  0.6420\n",
-       " 1198  0.6554\n",
-       " 1199  0.6302\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 1200  0.6602\n",
-       " 1201  0.6498\n",
-       " 1202  0.6450\n",
-       " 1203  0.6416\n",
-       " 1204  0.6194\n",
-       " ...      ...\n",
-       " 1295  0.6176\n",
-       " 1296  0.6446\n",
-       " 1297  0.6462\n",
-       " 1298  0.6282\n",
-       " 1299  0.6144\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 1300  0.5794\n",
-       " 1301  0.5938\n",
-       " 1302  0.6288\n",
-       " 1303  0.6124\n",
-       " 1304  0.6046\n",
-       " ...      ...\n",
-       " 1395  0.6232\n",
-       " 1396  0.6070\n",
-       " 1397  0.6194\n",
-       " 1398  0.6392\n",
-       " 1399  0.6406\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 1400  0.6554\n",
-       " 1401  0.6462\n",
-       " 1402  0.6474\n",
-       " 1403  0.6526\n",
-       " 1404  0.6426\n",
-       " ...      ...\n",
-       " 1495  0.5990\n",
-       " 1496  0.5984\n",
-       " 1497  0.5858\n",
-       " 1498  0.5858\n",
-       " 1499  0.5856\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 1500  0.6012\n",
-       " 1501  0.6192\n",
-       " 1502  0.6602\n",
-       " 1503  0.6812\n",
-       " 1504  0.6632\n",
-       " ...      ...\n",
-       " 1595  0.6454\n",
-       " 1596  0.6582\n",
-       " 1597  0.6726\n",
-       " 1598  0.6318\n",
-       " 1599  0.6750\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 1600  0.6614\n",
-       " 1601  0.6834\n",
-       " 1602  0.6880\n",
-       " 1603  0.6752\n",
-       " 1604  0.6766\n",
-       " ...      ...\n",
-       " 1695  0.6726\n",
-       " 1696  0.6748\n",
-       " 1697  0.6706\n",
-       " 1698  0.6786\n",
-       " 1699  0.6730\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 1700  0.6658\n",
-       " 1701  0.6540\n",
-       " 1702  0.6530\n",
-       " 1703  0.6748\n",
-       " 1704  0.6738\n",
-       " ...      ...\n",
-       " 1795  0.6408\n",
-       " 1796  0.6602\n",
-       " 1797  0.6676\n",
-       " 1798  0.6610\n",
-       " 1799  0.6514\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 1800  0.6714\n",
-       " 1801  0.6408\n",
-       " 1802  0.6296\n",
-       " 1803  0.6528\n",
-       " 1804  0.6374\n",
-       " ...      ...\n",
-       " 1895  0.6882\n",
-       " 1896  0.6914\n",
-       " 1897  0.6844\n",
-       " 1898  0.6692\n",
-       " 1899  0.6628\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 1900  0.6570\n",
-       " 1901  0.6568\n",
-       " 1902  0.6496\n",
-       " 1903  0.6518\n",
-       " 1904  0.6396\n",
-       " ...      ...\n",
-       " 1995  0.6664\n",
-       " 1996  0.6378\n",
-       " 1997  0.6748\n",
-       " 1998  0.6704\n",
-       " 1999  0.7110\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 2000  0.6892\n",
-       " 2001  0.7076\n",
-       " 2002  0.7010\n",
-       " 2003  0.6986\n",
-       " 2004  0.7022\n",
-       " ...      ...\n",
-       " 2095  0.6136\n",
-       " 2096  0.6152\n",
-       " 2097  0.6226\n",
-       " 2098  0.6160\n",
-       " 2099  0.6212\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 2100  0.6290\n",
-       " 2101  0.6408\n",
-       " 2102  0.6448\n",
-       " 2103  0.6540\n",
-       " 2104  0.6150\n",
-       " ...      ...\n",
-       " 2195  0.6788\n",
-       " 2196  0.6870\n",
-       " 2197  0.7068\n",
-       " 2198  0.6942\n",
-       " 2199  0.6918\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 2200  0.6854\n",
-       " 2201  0.6870\n",
-       " 2202  0.6954\n",
-       " 2203  0.6740\n",
-       " 2204  0.6710\n",
-       " ...      ...\n",
-       " 2295  0.6338\n",
-       " 2296  0.6366\n",
-       " 2297  0.6246\n",
-       " 2298  0.6198\n",
-       " 2299  0.6274\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 2300  0.6312\n",
-       " 2301  0.6636\n",
-       " 2302  0.6456\n",
-       " 2303  0.6352\n",
-       " 2304  0.6234\n",
-       " ...      ...\n",
-       " 2395  0.6728\n",
-       " 2396  0.6616\n",
-       " 2397  0.6836\n",
-       " 2398  0.6586\n",
-       " 2399  0.6722\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 2400  0.6744\n",
-       " 2401  0.6704\n",
-       " 2402  0.6748\n",
-       " 2403  0.6946\n",
-       " 2404  0.6826\n",
-       " ...      ...\n",
-       " 2495  0.6406\n",
-       " 2496  0.6262\n",
-       " 2497  0.6274\n",
-       " 2498  0.6262\n",
-       " 2499  0.6760\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 2500  0.6766\n",
-       " 2501  0.6570\n",
-       " 2502  0.6582\n",
-       " 2503  0.6530\n",
-       " 2504  0.6582\n",
-       " ...      ...\n",
-       " 2595  0.6860\n",
-       " 2596  0.7074\n",
-       " 2597  0.6884\n",
-       " 2598  0.6714\n",
-       " 2599  0.6552\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 2600  0.6246\n",
-       " 2601  0.6338\n",
-       " 2602  0.6050\n",
-       " 2603  0.6116\n",
-       " 2604  0.6238\n",
-       " ...      ...\n",
-       " 2695  0.5864\n",
-       " 2696  0.6312\n",
-       " 2697  0.6082\n",
-       " 2698  0.5956\n",
-       " 2699  0.6114\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 2700  0.6156\n",
-       " 2701  0.6080\n",
-       " 2702  0.6272\n",
-       " 2703  0.6298\n",
-       " 2704  0.6332\n",
-       " ...      ...\n",
-       " 2795  0.6548\n",
-       " 2796  0.6616\n",
-       " 2797  0.6782\n",
-       " 2798  0.6828\n",
-       " 2799  0.6620\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 2800  0.6664\n",
-       " 2801  0.6636\n",
-       " 2802  0.6406\n",
-       " 2803  0.6434\n",
-       " 2804  0.6484\n",
-       " ...      ...\n",
-       " 2895  0.7020\n",
-       " 2896  0.7060\n",
-       " 2897  0.7158\n",
-       " 2898  0.7270\n",
-       " 2899  0.7020\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 2900  0.6750\n",
-       " 2901  0.6644\n",
-       " 2902  0.6452\n",
-       " 2903  0.6470\n",
-       " 2904  0.6428\n",
-       " ...      ...\n",
-       " 2995  0.6776\n",
-       " 2996  0.6758\n",
-       " 2997  0.6648\n",
-       " 2998  0.6634\n",
-       " 2999  0.6394\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 3000  0.6186\n",
-       " 3001  0.6182\n",
-       " 3002  0.6096\n",
-       " 3003  0.6280\n",
-       " 3004  0.6492\n",
-       " ...      ...\n",
-       " 3095  0.6224\n",
-       " 3096  0.5896\n",
-       " 3097  0.6138\n",
-       " 3098  0.6548\n",
-       " 3099  0.6348\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 3100  0.6176\n",
-       " 3101  0.6246\n",
-       " 3102  0.6066\n",
-       " 3103  0.6264\n",
-       " 3104  0.6182\n",
-       " ...      ...\n",
-       " 3195  0.6356\n",
-       " 3196  0.6256\n",
-       " 3197  0.6416\n",
-       " 3198  0.6512\n",
-       " 3199  0.5912\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 3200  0.6280\n",
-       " 3201  0.6370\n",
-       " 3202  0.6604\n",
-       " 3203  0.6340\n",
-       " 3204  0.6162\n",
-       " ...      ...\n",
-       " 3295  0.6514\n",
-       " 3296  0.6618\n",
-       " 3297  0.6632\n",
-       " 3298  0.6654\n",
-       " 3299  0.6692\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 3300  0.6642\n",
-       " 3301  0.7062\n",
-       " 3302  0.7280\n",
-       " 3303  0.7180\n",
-       " 3304  0.7168\n",
-       " ...      ...\n",
-       " 3395  0.5936\n",
-       " 3396  0.6238\n",
-       " 3397  0.6232\n",
-       " 3398  0.6546\n",
-       " 3399  0.6640\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 3400  0.6626\n",
-       " 3401  0.6538\n",
-       " 3402  0.6572\n",
-       " 3403  0.6492\n",
-       " 3404  0.6386\n",
-       " ...      ...\n",
-       " 3495  0.6898\n",
-       " 3496  0.6606\n",
-       " 3497  0.6576\n",
-       " 3498  0.6604\n",
-       " 3499  0.6790\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 3500  0.7090\n",
-       " 3501  0.7062\n",
-       " 3502  0.7120\n",
-       " 3503  0.6938\n",
-       " 3504  0.7054\n",
-       " ...      ...\n",
-       " 3595  0.6466\n",
-       " 3596  0.6730\n",
-       " 3597  0.6938\n",
-       " 3598  0.6770\n",
-       " 3599  0.6718\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 3600  0.6856\n",
-       " 3601  0.6770\n",
-       " 3602  0.6754\n",
-       " 3603  0.6688\n",
-       " 3604  0.6642\n",
-       " ...      ...\n",
-       " 3695  0.6432\n",
-       " 3696  0.6644\n",
-       " 3697  0.6624\n",
-       " 3698  0.6758\n",
-       " 3699  0.6884\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 3700  0.6858\n",
-       " 3701  0.6748\n",
-       " 3702  0.6760\n",
-       " 3703  0.6604\n",
-       " 3704  0.6464\n",
-       " ...      ...\n",
-       " 3795  0.6304\n",
-       " 3796  0.6240\n",
-       " 3797  0.6180\n",
-       " 3798  0.5934\n",
-       " 3799  0.5876\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 3800  0.6190\n",
-       " 3801  0.6318\n",
-       " 3802  0.6034\n",
-       " 3803  0.6322\n",
-       " 3804  0.6234\n",
-       " ...      ...\n",
-       " 3895  0.6600\n",
-       " 3896  0.6490\n",
-       " 3897  0.6236\n",
-       " 3898  0.6242\n",
-       " 3899  0.6214\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 3900  0.5982\n",
-       " 3901  0.5790\n",
-       " 3902  0.5786\n",
-       " 3903  0.5740\n",
-       " 3904  0.5940\n",
-       " ...      ...\n",
-       " 3995  0.6608\n",
-       " 3996  0.6700\n",
-       " 3997  0.6604\n",
-       " 3998  0.6602\n",
-       " 3999  0.6558\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 4000  0.6534\n",
-       " 4001  0.6180\n",
-       " 4002  0.6200\n",
-       " 4003  0.6150\n",
-       " 4004  0.6076\n",
-       " ...      ...\n",
-       " 4095  0.6546\n",
-       " 4096  0.6422\n",
-       " 4097  0.6564\n",
-       " 4098  0.6382\n",
-       " 4099  0.6618\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 4100  0.6626\n",
-       " 4101  0.6690\n",
-       " 4102  0.6628\n",
-       " 4103  0.6602\n",
-       " 4104  0.6462\n",
-       " ...      ...\n",
-       " 4195  0.7122\n",
-       " 4196  0.7070\n",
-       " 4197  0.7290\n",
-       " 4198  0.7230\n",
-       " 4199  0.7214\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 4200  0.7042\n",
-       " 4201  0.7142\n",
-       " 4202  0.7242\n",
-       " 4203  0.6922\n",
-       " 4204  0.6664\n",
-       " ...      ...\n",
-       " 4295  0.6962\n",
-       " 4296  0.6776\n",
-       " 4297  0.6608\n",
-       " 4298  0.6378\n",
-       " 4299  0.6044\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 4300  0.6328\n",
-       " 4301  0.6124\n",
-       " 4302  0.6110\n",
-       " 4303  0.6012\n",
-       " 4304  0.5772\n",
-       " ...      ...\n",
-       " 4395  0.6734\n",
-       " 4396  0.6730\n",
-       " 4397  0.6712\n",
-       " 4398  0.6700\n",
-       " 4399  0.6712\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 4400  0.6628\n",
-       " 4401  0.6640\n",
-       " 4402  0.6932\n",
-       " 4403  0.7020\n",
-       " 4404  0.6788\n",
-       " ...      ...\n",
-       " 4495  0.6592\n",
-       " 4496  0.6544\n",
-       " 4497  0.6496\n",
-       " 4498  0.6482\n",
-       " 4499  0.6526\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 4500  0.6476\n",
-       " 4501  0.6484\n",
-       " 4502  0.6642\n",
-       " 4503  0.6734\n",
-       " 4504  0.6740\n",
-       " ...      ...\n",
-       " 4595  0.6558\n",
-       " 4596  0.6378\n",
-       " 4597  0.6602\n",
-       " 4598  0.6638\n",
-       " 4599  0.6902\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 4600  0.7034\n",
-       " 4601  0.7354\n",
-       " 4602  0.7234\n",
-       " 4603  0.6974\n",
-       " 4604  0.6846\n",
-       " ...      ...\n",
-       " 4695  0.5686\n",
-       " 4696  0.5356\n",
-       " 4697  0.5692\n",
-       " 4698  0.5406\n",
-       " 4699  0.5570\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 4700  0.5552\n",
-       " 4701  0.5780\n",
-       " 4702  0.5768\n",
-       " 4703  0.5990\n",
-       " 4704  0.6172\n",
-       " ...      ...\n",
-       " 4795  0.6620\n",
-       " 4796  0.6856\n",
-       " 4797  0.6602\n",
-       " 4798  0.6508\n",
-       " 4799  0.6674\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 4800  0.6742\n",
-       " 4801  0.6914\n",
-       " 4802  0.6712\n",
-       " 4803  0.6592\n",
-       " 4804  0.6118\n",
-       " ...      ...\n",
-       " 4895  0.6104\n",
-       " 4896  0.6074\n",
-       " 4897  0.6334\n",
-       " 4898  0.6054\n",
-       " 4899  0.6306\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 4900  0.6250\n",
-       " 4901  0.6354\n",
-       " 4902  0.6380\n",
-       " 4903  0.6444\n",
-       " 4904  0.6386\n",
-       " ...      ...\n",
-       " 4995  0.6632\n",
-       " 4996  0.6708\n",
-       " 4997  0.6530\n",
-       " 4998  0.5910\n",
-       " 4999  0.6036\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 5000  0.6046\n",
-       " 5001  0.6226\n",
-       " 5002  0.6248\n",
-       " 5003  0.6382\n",
-       " 5004  0.6644\n",
-       " ...      ...\n",
-       " 5095  0.6348\n",
-       " 5096  0.6528\n",
-       " 5097  0.6500\n",
-       " 5098  0.6350\n",
-       " 5099  0.6402\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 5100  0.6580\n",
-       " 5101  0.6764\n",
-       " 5102  0.6952\n",
-       " 5103  0.6768\n",
-       " 5104  0.6866\n",
-       " ...      ...\n",
-       " 5195  0.6478\n",
-       " 5196  0.6548\n",
-       " 5197  0.6696\n",
-       " 5198  0.6844\n",
-       " 5199  0.6708\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 5200  0.6790\n",
-       " 5201  0.6850\n",
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-       " 5204  0.6530\n",
-       " ...      ...\n",
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-       " 5298  0.6326\n",
-       " 5299  0.6286\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
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-       " 5301  0.6332\n",
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-       " 5303  0.6546\n",
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-       " ...      ...\n",
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-       " 5398  0.6246\n",
-       " 5399  0.6476\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 5400  0.6206\n",
-       " 5401  0.6526\n",
-       " 5402  0.6368\n",
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-       " ...      ...\n",
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-       " 5498  0.6856\n",
-       " 5499  0.6806\n",
-       " \n",
-       " [100 rows x 1 columns],\n",
-       "         Gini\n",
-       " 5500  0.6888\n",
-       " 5501  0.6708\n",
-       " 5502  0.6882\n",
-       " 5503  0.6648\n",
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-       " 5554  0.6510]"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_dfs\n"
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8740fdf0-50eb-40ee-a5a9-e6426ebebd3c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e416b4b7-29a1-4960-8ecb-991c9623ed07",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4e0ced9-2f26-4048-866d-04d823051ac6",
+   "metadata": {},
+   "source": [
+    "# caching for solar & grid visualisation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b9d8b823-36d3-42b2-bbf0-be3caf8a4335",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import networkx as nx\n",
+    "import solara\n",
+    "from matplotlib.figure import Figure\n",
+    "from matplotlib.ticker import MaxNLocator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e72f6761-7f53-45bc-be56-e9d7fd0246ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def _draw_grid(space, space_ax, agent_portrayal):\n",
+    "    def portray(g):\n",
+    "        x = []\n",
+    "        y = []\n",
+    "        s = []  # size\n",
+    "        c = []  # color\n",
+    "        for i in range(g.width):\n",
+    "            for j in range(g.height):\n",
+    "                content = g._grid[i][j]\n",
+    "                if not content:\n",
+    "                    continue\n",
+    "                if not hasattr(content, \"__iter__\"):\n",
+    "                    # Is a single grid\n",
+    "                    content = [content]\n",
+    "                for agent in content:\n",
+    "                    data = agent_portrayal(agent)\n",
+    "                    x.append(i)\n",
+    "                    y.append(j)\n",
+    "                    if \"size\" in data:\n",
+    "                        s.append(data[\"size\"])\n",
+    "                    if \"color\" in data:\n",
+    "                        c.append(data[\"color\"])\n",
+    "        out = {\"x\": x, \"y\": y}\n",
+    "        # This is the default value for the marker size, which auto-scales\n",
+    "        # according to the grid area.\n",
+    "        out[\"s\"] = (180 / max(g.width, g.height)) ** 2\n",
+    "        if len(s) > 0:\n",
+    "            out[\"s\"] = s\n",
+    "        if len(c) > 0:\n",
+    "            out[\"c\"] = c\n",
+    "        return out\n",
+    "\n",
+    "    space_ax.set_xlim(-1, space.width)\n",
+    "    space_ax.set_ylim(-1, space.height)\n",
+    "    space_ax.scatter(**portray(space))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "95bac82a-126f-4b57-a23e-34148a878866",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def agent_portrayal(agent):\n",
+    "    size = 10\n",
+    "    color = \"tab:red\"\n",
+    "    if agent.wealth > 0:\n",
+    "        size = 50\n",
+    "        color = \"tab:blue\"\n",
+    "    return {\"size\": size, \"color\": color}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "a2802713-ec93-43bd-81b6-4a2fd4e35114",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "space_fig = Figure()\n",
+    "space_ax = space_fig.subplots()\n",
+    "space = getattr(model, \"grid\", None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "2407f19d-b56a-49e9-94b5-7129ea068511",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d06979deaae46308c3f13ecb3fd07a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "Cannot show widget. You probably want to rerun the code cell above (<i>Click in the code cell, and press Shift+Enter <kbd>⇧</kbd>+<kbd>↩</kbd></i>)."
+      ],
+      "text/plain": [
+       "Cannot show ipywidgets in text"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_draw_grid(space, space_ax, agent_portrayal)\n",
+    "solara.FigureMatplotlib(space_fig, format=\"png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44fb646a-bdfe-453f-baa4-2542086223f9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8409abbd-d725-43ed-9479-e4f9c9670622",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "435ecab8-73f8-412d-bbd6-fd19ef097f97",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "9d237ce1-d933-44fc-8818-2f373c4e710c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mesa.agent import Agent\n",
+    "from cacheable_model import AgentSerializer\n",
+    "model = model\n",
+    "agents = [\n",
+    "    Agent(unique_id=1, model=model),\n",
+    "    Agent(unique_id=2, model=model)\n",
+    "]\n",
+    "\n",
+    "# Serialize and save agents to a Parquet file\n",
+    "AgentSerializer.save_agents_to_parquet(agents, 'agents.parquet')\n",
+    "\n",
+    "# Load agents from a Parquet file\n",
+    "loaded_agents = AgentSerializer.load_agents_from_parquet('agents.parquet', model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "4434b5e7-a772-4503-8342-7c97fafebb14",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<mesa.agent.Agent at 0x7f77b5ee28f0>, <mesa.agent.Agent at 0x7f77b5ee33a0>]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loaded_agents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dde8f54d-34ed-429d-99f7-b17c1b2cd5a5",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34da292b-a9ab-4a62-ab40-83bae6bd450a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "4979e557-b44a-49a4-8d24-87cdd5b35ef2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import networkx as nx\n",
+    "import solara\n",
+    "from matplotlib.figure import Figure\n",
+    "from matplotlib.ticker import MaxNLocator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6a68f845-c68f-4801-83d3-90e6437805f2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3af52f07-0156-485b-97d3-45c11964eb1f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d0526427-32c7-40c5-836c-96d0a23ebed1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "9a434237-2316-44ac-92b6-d836fb363987",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<mesa.space.MultiGrid at 0x7f77e00c47f0>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "space\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1fd18a2-7c7c-4198-a986-5e80e8219eb3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "6e8eece8-fcf5-49f7-99b2-c344f914e061",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'height': 10,\n",
+       " 'width': 10,\n",
+       " 'torus': True,\n",
+       " 'num_cells': 100,\n",
+       " '_grid': [[[<__main__.MoneyAgent at 0x7f77e00c5480>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c6440>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c6d40>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c6500>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c4dc0>],\n",
+       "   [],\n",
+       "   [],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c6b60>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c7df0>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c5b70>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c6050>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c4b50>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c7ee0>],\n",
+       "   [],\n",
+       "   [],\n",
+       "   []],\n",
+       "  [[],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c61a0>],\n",
+       "   [],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c5a20>],\n",
+       "   [],\n",
+       "   [],\n",
+       "   [],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c4d30>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c63b0>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c56c0>]],\n",
+       "  [[],\n",
+       "   [],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c6a10>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c5270>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c5cc0>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c5180>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c48e0>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c4b20>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c5720>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c6d70>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c6410>],\n",
+       "   [],\n",
+       "   [],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c6800>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c6350>]],\n",
+       "  [[<__main__.MoneyAgent at 0x7f77e00c6020>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c5120>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c57b0>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c59c0>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c5540>],\n",
+       "   [],\n",
+       "   [],\n",
+       "   [],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c64d0>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c5db0>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c4e20>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c57e0>],\n",
+       "   []],\n",
+       "  [[<__main__.MoneyAgent at 0x7f77e00c52a0>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c6ce0>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c6740>],\n",
+       "   [],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c66e0>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c4a90>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c6d10>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c5c60>],\n",
+       "   [],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c4e50>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c6bf0>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c6110>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c58a0>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c4be0>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c6320>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c5ae0>]],\n",
+       "  [[<__main__.MoneyAgent at 0x7f77e00c4a60>],\n",
+       "   [],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c5030>],\n",
+       "   [],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c5ea0>],\n",
+       "   [],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c6260>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c61d0>],\n",
+       "   [],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c45b0>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c69e0>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c5600>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c5780>]],\n",
+       "  [[],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c4fd0>],\n",
+       "   [],\n",
+       "   [],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c5e10>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c4f10>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c5f60>],\n",
+       "   [],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c6a40>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c50f0>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c5960>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c65f0>]],\n",
+       "  [[],\n",
+       "   [],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c5ba0>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c53f0>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c4a30>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c5c30>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c5c00>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c6950>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c7f40>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c5510>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c6c50>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c5a50>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c5d80>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c6890>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c5930>]],\n",
+       "  [[],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c6620>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c4ca0>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c6920>],\n",
+       "   [],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c6aa0>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c55d0>],\n",
+       "   [],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c4ee0>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c4cd0>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c62c0>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c66b0>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c6b00>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c7f10>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c4910>]],\n",
+       "  [[],\n",
+       "   [],\n",
+       "   [],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c7f70>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c5090>],\n",
+       "   [],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c6170>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c49a0>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c4c40>],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c68f0>,\n",
+       "    <__main__.MoneyAgent at 0x7f77e00c6530>],\n",
+       "   [],\n",
+       "   [<__main__.MoneyAgent at 0x7f77e00c5420>]]],\n",
+       " '_empties_built': False,\n",
+       " '_neighborhood_cache': {((3, 4), True, False, 1): ((2, 3),\n",
+       "   (2, 4),\n",
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+       "   (3, 8),\n",
+       "   (3, 9),\n",
+       "   (3, 0))},\n",
+       " 'cutoff_empties': 46.61556822380071,\n",
+       " 'properties': {},\n",
+       " '_empty_mask': array([[ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "          True],\n",
+       "        [ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
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+       "        [ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "          True],\n",
+       "        [ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "          True],\n",
+       "        [ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "          True],\n",
+       "        [ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "          True],\n",
+       "        [ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "          True],\n",
+       "        [ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "          True],\n",
+       "        [ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "          True],\n",
+       "        [ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "          True]])}"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "space.__dict__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "89fcf4a8-e5ef-4839-9c69-32a0f07c0412",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'unique_id': 73, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (0, 0), 'wealth': 2}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 43, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (0, 1), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 19, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (0, 1), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 58, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (0, 1), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 10, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (0, 2), 'wealth': 3}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 29, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (0, 5), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 4, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (0, 5), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 88, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (0, 6), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 52, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (0, 6), 'wealth': 2}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 7, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (0, 6), 'wealth': 2}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 65, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (0, 6), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 53, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (1, 1), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 98, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (1, 3), 'wealth': 6}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 11, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (1, 7), 'wealth': 4}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 45, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (1, 8), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 79, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (1, 9), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 32, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (2, 2), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 69, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (2, 2), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 92, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (2, 3), 'wealth': 3}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 62, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (2, 4), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 2, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (2, 4), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 6, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (2, 4), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 77, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (2, 4), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 18, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (2, 4), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 44, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (2, 5), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 34, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (2, 8), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 46, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (2, 9), 'wealth': 3}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 51, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (3, 0), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 63, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (3, 0), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 81, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (3, 1), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 99, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (3, 2), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 75, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (3, 2), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 55, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (3, 6), 'wealth': 2}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 93, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (3, 7), 'wealth': 7}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 12, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (3, 7), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 84, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (3, 8), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 70, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (4, 0), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 26, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (4, 0), 'wealth': 5}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 41, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (4, 1), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 39, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (4, 3), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 67, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (4, 4), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 23, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (4, 5), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 91, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (4, 5), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 13, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (4, 7), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 24, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (4, 8), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 66, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (4, 9), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 83, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (4, 9), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 14, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (4, 9), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 47, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (4, 9), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 86, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (4, 9), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 68, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (5, 0), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 21, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (5, 2), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 94, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (5, 4), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 49, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (5, 6), 'wealth': 2}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 50, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (5, 6), 'wealth': 2}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 0, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (5, 8), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 57, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (5, 9), 'wealth': 2}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 80, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (5, 9), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 78, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (5, 9), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 17, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (6, 1), 'wealth': 2}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 95, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (6, 4), 'wealth': 7}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 31, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (6, 4), 'wealth': 2}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 60, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (6, 4), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 27, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (6, 6), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 20, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (6, 7), 'wealth': 2}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 85, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (6, 8), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 42, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (6, 9), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 87, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (7, 2), 'wealth': 2}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 71, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (7, 3), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 5, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (7, 3), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 90, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (7, 4), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 89, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (7, 5), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 37, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (7, 6), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 61, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (7, 6), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 74, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (7, 6), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 25, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (7, 6), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 97, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (7, 7), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 96, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (7, 7), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 36, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (7, 8), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 82, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (7, 9), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 38, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (8, 1), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 8, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (8, 1), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 33, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (8, 1), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 28, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (8, 3), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 76, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (8, 4), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 16, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (8, 6), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 9, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (8, 6), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 48, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (8, 7), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 40, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (8, 8), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 30, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (8, 9), 'wealth': 2}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 64, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (8, 9), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 3, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (8, 9), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 59, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (9, 3), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 22, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (9, 4), 'wealth': 2}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 54, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (9, 6), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 1, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (9, 6), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 15, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (9, 6), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 35, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (9, 7), 'wealth': 4}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 56, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (9, 7), 'wealth': 1}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "{'unique_id': 72, 'model': <__main__.MoneyModel object at 0x7f77e00c45e0>, 'pos': (9, 9), 'wealth': 0}\n",
+      "type(agent)=<class '__main__.MoneyAgent'>\n",
+      "    pos_x  pos_y  unique_id  wealth\n",
+      "0       0      0         73       2\n",
+      "1       0      1         43       0\n",
+      "2       0      1         19       0\n",
+      "3       0      1         58       0\n",
+      "4       0      2         10       3\n",
+      "..    ...    ...        ...     ...\n",
+      "95      9      6          1       0\n",
+      "96      9      6         15       0\n",
+      "97      9      7         35       4\n",
+      "98      9      7         56       1\n",
+      "99      9      9         72       0\n",
+      "\n",
+      "[100 rows x 4 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "def cache_grid_state(grid, filename='grid_cache.parquet'):\n",
+    "    grid_state = {\n",
+    "        'width': grid.width,\n",
+    "        'height': grid.height,\n",
+    "        'agents': []\n",
+    "    }\n",
+    "    for x in range(grid.width):\n",
+    "        for y in range(grid.height):\n",
+    "            cell_contents = grid._grid[x][y]\n",
+    "            if cell_contents:\n",
+    "                if not hasattr(cell_contents, \"__iter__\"):\n",
+    "                    cell_contents = [cell_contents]\n",
+    "                for agent in cell_contents:\n",
+    "                    print(agent.__dict__)\n",
+    "                    print(f'{type(agent)=}')\n",
+    "                    agent_state = {\n",
+    "                        'pos_x': agent.pos[0],\n",
+    "                        'pos_y': agent.pos[1],\n",
+    "                        'unique_id': agent.unique_id,\n",
+    "                        'wealth': agent.wealth,\n",
+    "                        # **agent.__dict__\n",
+    "                    }\n",
+    "                    grid_state['agents'].append(agent_state)\n",
+    "    \n",
+    "    # Convert to DataFrame\n",
+    "    df = pd.DataFrame(grid_state['agents'])\n",
+    "    print(df)\n",
+    "    # Save DataFrame to Parquet\n",
+    "    df.to_parquet(filename)\n",
+    "\n",
+    "cache_grid_state(model.grid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "ad2f2e9b-5044-4ac3-b5ed-6a12862941c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pos_x</th>\n",
+       "      <th>pos_y</th>\n",
+       "      <th>unique_id</th>\n",
+       "      <th>wealth</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>73</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>43</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>58</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>9</td>\n",
+       "      <td>7</td>\n",
+       "      <td>35</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>9</td>\n",
+       "      <td>7</td>\n",
+       "      <td>56</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>72</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    pos_x  pos_y  unique_id  wealth\n",
+       "0       0      0         73       2\n",
+       "1       0      1         43       0\n",
+       "2       0      1         19       0\n",
+       "3       0      1         58       0\n",
+       "4       0      2         10       3\n",
+       "..    ...    ...        ...     ...\n",
+       "95      9      6          1       0\n",
+       "96      9      6         15       0\n",
+       "97      9      7         35       4\n",
+       "98      9      7         56       1\n",
+       "99      9      9         72       0\n",
+       "\n",
+       "[100 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "table = pd.read_parquet(\"grid_cache.parquet\")\n",
+    "table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "31f59c6d-39b0-42bd-a948-b8a7e9791b2e",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "Agent.__init__() got an unexpected keyword argument 'pos'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[25], line 21\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m grid\n\u001b[1;32m     20\u001b[0m \u001b[38;5;66;03m# Usage\u001b[39;00m\n\u001b[0;32m---> 21\u001b[0m model_grid \u001b[38;5;241m=\u001b[39m \u001b[43mreconstruct_grid\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mgrid_cache.parquet\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28mprint\u001b[39m(model_grid)\n",
+      "Cell \u001b[0;32mIn[25], line 15\u001b[0m, in \u001b[0;36mreconstruct_grid\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;66;03m# Add agents to the grid\u001b[39;00m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _, row \u001b[38;5;129;01min\u001b[39;00m df\u001b[38;5;241m.\u001b[39miterrows():\n\u001b[0;32m---> 15\u001b[0m     agent \u001b[38;5;241m=\u001b[39m \u001b[43mAgent\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpos\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mpos_x\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mpos_y\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwealth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrow\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mwealth\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     16\u001b[0m     grid\u001b[38;5;241m.\u001b[39madd_agent(agent, row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpos_x\u001b[39m\u001b[38;5;124m'\u001b[39m], row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpos_y\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m grid\n",
+      "\u001b[0;31mTypeError\u001b[0m: Agent.__init__() got an unexpected keyword argument 'pos'"
+     ]
+    }
+   ],
+   "source": [
+    "# not implemented fully\n",
+    "\n",
+    "import pandas as pd\n",
+    "from mesa.space import MultiGrid as Grid\n",
+    "# import mesa.space.MultiGrid as Grid \n",
+    "def reconstruct_grid(filename='grid_cache.parquet'):\n",
+    "    # Load the DataFrame from Parquet\n",
+    "    df = pd.read_parquet(filename)\n",
+    "    \n",
+    "    # Create a new Grid instance\n",
+    "    width = df['pos_x'].max() + 1  # Assuming positions start from 0\n",
+    "    height = df['pos_y'].max() + 1  # Assuming positions start from 0\n",
+    "    grid = Grid(width, height, False)\n",
+    "    \n",
+    "    # Add agents to the grid\n",
+    "    for _, row in df.iterrows():\n",
+    "        agent = Agent(pos=(row['pos_x'], row['pos_y']), wealth=row['wealth'])\n",
+    "        grid.add_agent(agent, row['pos_x'], row['pos_y'])\n",
+    "    \n",
+    "    return grid\n",
+    "\n",
+    "# Usage\n",
+    "model_grid = reconstruct_grid('grid_cache.parquet')\n",
+    "print(model_grid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7c983d9-a3fe-4427-96cc-7bd8c13e9a22",
+   "metadata": {},
+   "source": [
+    "# IGNORE BELOW KIV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c670be7b-fda7-414f-b0c7-e7ec58d70805",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def agent_portrayal(agent):\n",
+    "    size = 10\n",
+    "    color = \"tab:red\"\n",
+    "    if agent.wealth > 0:\n",
+    "        size = 50\n",
+    "        color = \"tab:blue\"\n",
+    "    return {\"size\": size, \"color\": color}\n",
+    "\n",
+    "class dummy:\n",
+    "\n",
+    "    def __init__(self):\n",
+    "\n",
+    "\n",
+    "def _draw_grid(space, space_ax, agent_portrayal):\n",
+    "    def portray(g):\n",
+    "        x = []\n",
+    "        y = []\n",
+    "        s = []  # size\n",
+    "        c = []  # color\n",
+    "        for i in range(g.width):\n",
+    "            for j in range(g.height):\n",
+    "                content = g._grid[i][j]\n",
+    "                if not content:\n",
+    "                    continue\n",
+    "                if not hasattr(content, \"__iter__\"):\n",
+    "                    # Is a single grid\n",
+    "                    content = [content]\n",
+    "                for agent in content:\n",
+    "                    data = agent_portrayal(agent)\n",
+    "                    x.append(i)\n",
+    "                    y.append(j)\n",
+    "                    if \"size\" in data:\n",
+    "                        s.append(data[\"size\"])\n",
+    "                    if \"color\" in data:\n",
+    "                        c.append(data[\"color\"])\n",
+    "        out = {\"x\": x, \"y\": y}\n",
+    "        # This is the default value for the marker size, which auto-scales\n",
+    "        # according to the grid area.\n",
+    "        out[\"s\"] = (180 / max(g.width, g.height)) ** 2\n",
+    "        if len(s) > 0:\n",
+    "            out[\"s\"] = s\n",
+    "        if len(c) > 0:\n",
+    "            out[\"c\"] = c\n",
+    "        return out\n",
+    "\n",
+    "    space_ax.set_xlim(-1, space.width)\n",
+    "    space_ax.set_ylim(-1, space.height)\n",
+    "    space_ax.scatter(**portray(space))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "264cd17e-5245-4f03-8af5-e54cd6799771",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "grid_state = {}\n",
+    "grid_state[\"height\"] = space.height\n",
+    "grid_state[\"width\"] = space.width\n",
+    "grid_state[\"torus\"] = space.torus\n",
+    "grid_state[\"num_cells\"] = space.num_cells\n",
+    "# grid_state[\"_grid\"] = [\n",
+    "#     [self.default_val() for _ in range(self.height)] for _ in range(self.width)\n",
+    "# ]\n",
+    "for i in range(space.height):\n",
+    "    for j in range(space.width):\n",
+    "        for agent_index in range(len(space._grid[i][j])):\n",
+    "            agent = space._grid[i][j][agent_index]\n",
+    "            agent_dict = {}\n",
+    "            agent_dict[\"unique_id\"] = agent.unique_id\n",
+    "            agent_dict[\"pos\"] = agent.pos\n",
+    "            agent_dict[\"wealth\"] = agent.wealth\n",
+    "            space._grid[i][j][agent_index] = agent_dict\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25d211cf-82c3-429d-80c0-41ac7294ff32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "space.__dict__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a21a324-7b7e-4365-8a2c-86491e8551a8",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(space.__dict__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a42a7b17-2c64-4c85-9bdb-c298f70c8fba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "space.__dict__.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb8870a6-a9e5-4114-a129-31cd150fb9fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_draw_grid(space, space_ax, agent_portrayal)\n",
+    "solara.FigureMatplotlib(space_fig, format=\"png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9abf2b97-ac6b-4be8-99ed-8f516bb83abb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model._steps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f90a3938-0f06-4e66-9bdc-16c31692fe18",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf55b523-d558-4eb3-9a74-6032dc8e341a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_draw_grid(space, space_ax, agent_portrayal)\n",
+    "solara.FigureMatplotlib(space_fig, format=\"png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37290b42-6398-4ee7-9844-ed4181f45e5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from typing import List, Any\n",
+    "\n",
+    "def read_grid_state(filename='grid_cache.parquet', model: Any = None) -> Any:\n",
+    "    \"\"\"Read the grid state from a Parquet file and reconstruct the grid with agents.\"\"\"\n",
+    "    # Load DataFrame from Parquet\n",
+    "    df = pd.read_parquet(filename)\n",
+    "    \n",
+    "    # Extract grid dimensions from the file\n",
+    "    width = int(df['width'].iloc[0])\n",
+    "    height = int(df['height'].iloc[0])\n",
+    "    \n",
+    "    # Initialize the grid\n",
+    "    grid = Grid(width=width, height=height)  # Adjust as needed for your grid class\n",
+    "\n",
+    "    # Reconstruct agents and place them in the grid\n",
+    "    agents = df[['pos_x', 'pos_y', 'wealth', 'unique_id']]\n",
+    "    for _, row in agents.iterrows():\n",
+    "        pos_x = int(row['pos_x'])\n",
+    "        pos_y = int(row['pos_y'])\n",
+    "        unique_id = int(row['unique_id'])\n",
+    "        wealth = float(row['wealth'])\n",
+    "        \n",
+    "        # Create agent instance (adjust based on how you create agents)\n",
+    "        agent = Agent(unique_id=unique_id, model=model)\n",
+    "        agent.pos = (pos_x, pos_y)\n",
+    "        agent.wealth = wealth\n",
+    "\n",
+    "        # Place agent in the grid\n",
+    "        grid._grid[pos_x][pos_y] = grid._grid[pos_x][pos_y] or []\n",
+    "        grid._grid[pos_x][pos_y].append(agent)\n",
+    "\n",
+    "    return grid\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2645afc6-a875-4a88-8a87-332b22700866",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Example of loading a grid from a Parquet file\n",
+    "  # Your model instance, or a placeholder if required\n",
+    "grid = read_grid_state('grid_cache.parquet', model=model)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "af6dd7cf-fddd-4ea6-ba1b-70fc75155f5c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "248ea838-5c71-4838-b6ad-fdcfdc3a1c1e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "gini = model.datacollector.get_model_vars_dataframe()\n",
+    "# Plot the Gini coefficient over time\n",
+    "g = sns.lineplot(data=gini)\n",
+    "g.set(title=\"Gini Coefficient over Time\", ylabel=\"Gini Coefficient\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9cf0a6c4-a976-4835-82e2-706f49b175d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gini"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "293d5002-5670-49e7-9d50-8bca632d743a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_wealth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5ecc197a-f2ca-4f7a-b66b-9e3767eb4c37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_wealth = model.datacollector.get_agent_vars_dataframe().sort_index()\n",
+    "agent_wealth\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e0cea053-db1e-4fc7-a5aa-52ada390f2ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_wealth.iloc[:,0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c9515417-7c5f-45f8-8ffa-2f466288ea1f",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import pyarrow.parquet as pq\n",
+    "import pandas as pd\n",
+    "import glob\n",
+    "\n",
+    "# Get a list of all Parquet files\n",
+    "model_files = glob.glob('test_path/model_data_*.parquet')\n",
+    "agent_files = glob.glob('test_path/agent_data_*.parquet')\n",
+    "\n",
+    "# Initialize lists to hold dataframes\n",
+    "model_dfs = []\n",
+    "agent_dfs = []\n",
+    "\n",
+    "# Read and append each file to the list\n",
+    "for model_file in model_files:\n",
+    "    table = pq.read_table(model_file)\n",
+    "    df = table.to_pandas()\n",
+    "    model_dfs.append(df)\n",
+    "\n",
+    "for agent_file in agent_files:\n",
+    "    table = pq.read_table(agent_file)\n",
+    "    df = table.to_pandas()\n",
+    "    agent_dfs.append(df)\n",
+    "    print(agent_dfs)\n",
+    "    print(\"----\")\n",
+    "\n",
+    "# Concatenate all DataFrames\n",
+    "model_df = pd.concat(model_dfs, ignore_index=True)\n",
+    "agent_df = pd.concat(agent_dfs)\n",
+    "\n",
+    "# Display the combined DataFrames\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6c73b47-b23c-4347-9951-b721ea5ed5da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_df\n",
+    "g = sns.lineplot(data=model_df)\n",
+    "g.set(title=\"Gini Coefficient over Time\", ylabel=\"Gini Coefficient\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21cfeacd-dcfa-485f-848e-88cb619addfc",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "model_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8c967f5e-4b91-44b8-b3f5-ab6874d0e28f",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "agent_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea25fe67-a4fb-432c-8592-2a6b73dc28f0",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "model_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "50f26db2-06e0-49a1-996e-53e71a62ecbd",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "(gini == model_df).sum()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ac6d4b7-b74f-41e8-82eb-368d77355d91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table = pq.read_table(\"test_path/special_results.parquet\")\n",
+    "df = table.to_pandas()\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e6bc2d5c-d1a5-4316-9aeb-1fdf75b0b8f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "# Read and combine the data\n",
+    "model_df, agent_df = cacheable_model.combine_dataframes()\n",
+    "\n",
+    "# Display the combined DataFrames\n",
+    "model_df\n",
+    "agent_df\n"
    ]
   }
  ],

From e4dac0e2edcd7837b77a89412cb399443cbbf7fd Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Sun, 4 Aug 2024 10:30:33 +0000
Subject: [PATCH 9/9] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 mesa/cacheable_model.py              | 99 ++++++++++++++++------------
 mesa/cacheable_wrapper_example.ipynb | 16 ++---
 mesa/datacollection_with_caching.py  | 28 +++++---
 mesa/example.ipynb                   | 36 +++++-----
 4 files changed, 101 insertions(+), 78 deletions(-)

diff --git a/mesa/cacheable_model.py b/mesa/cacheable_model.py
index 4f3fbc621e9..0d1bd15c594 100644
--- a/mesa/cacheable_model.py
+++ b/mesa/cacheable_model.py
@@ -1,18 +1,15 @@
-import os
 import contextlib
+import glob
 import itertools
-import types
-from copy import deepcopy
-from functools import partial
-from typing import Any, Callable
+import os
+from collections.abc import Callable
+from pathlib import Path
+from typing import Any
 
 import pyarrow as pa
 import pyarrow.parquet as pq
-from pathlib import Path
-from enum import Enum
-from mesa import Model
 
-import glob
+from mesa import Model
 
 with contextlib.suppress(ImportError):
     import pandas as pd
@@ -51,8 +48,9 @@ def __init__(
 
         self._cache_interval = 100
 
-        self._last_cached_step = 0 # inclusive since it is the bottom bound of slicing
+        self._last_cached_step = 0  # inclusive since it is the bottom bound of slicing
         self.condition_function = condition_function
+
     def get_agent_vars_dataframe(self):
         """Create a pandas DataFrame from the agent variables.
 
@@ -65,7 +63,9 @@ def get_agent_vars_dataframe(self):
                 "No agent reporters have been defined in the DataCollector, returning empty DataFrame."
             )
 
-        all_records = itertools.chain.from_iterable(self.model.datacollector._agent_records.values())
+        all_records = itertools.chain.from_iterable(
+            self.model.datacollector._agent_records.values()
+        )
         rep_names = list(self.model.datacollector.agent_reporters)
         print(f"{all_records=}")
         print(f"{rep_names=}")
@@ -76,9 +76,7 @@ def get_agent_vars_dataframe(self):
             index=["Step", "AgentID"],
         )
 
-
-
-        sliced_df = df.loc[self._last_cached_step:self.model._steps]
+        sliced_df = df.loc[self._last_cached_step : self.model._steps]
 
         return sliced_df
 
@@ -94,7 +92,9 @@ def get_model_vars_dataframe(self):
                 "No model reporters have been defined in the DataCollector, returning empty DataFrame."
             )
 
-        return pd.DataFrame(self.model.datacollector.model_vars)[self._last_cached_step:self.model._steps]
+        return pd.DataFrame(self.model.datacollector.model_vars)[
+            self._last_cached_step : self.model._steps
+        ]
 
     def _save_to_parquet(self, model):
         """Save the current cache of data to a Parquet file and clear the cache."""
@@ -110,11 +110,17 @@ def _save_to_parquet(self, model):
 
         absolute_path = os.path.abspath(model_file)
         if os.path.exists(absolute_path):
-            raise FileExistsError(f"A directory with the name {model_file} already exists.")
+            raise FileExistsError(
+                f"A directory with the name {model_file} already exists."
+            )
         if os.path.exists(model_file):
-            raise FileExistsError(f"A directory with the name {model_file} already exists.")
+            raise FileExistsError(
+                f"A directory with the name {model_file} already exists."
+            )
         if os.path.exists(agent_file):
-            raise FileExistsError(f"A directory with the name {agent_file} already exists.")
+            raise FileExistsError(
+                f"A directory with the name {agent_file} already exists."
+            )
 
         if not model_df.empty:
             model_table = pa.Table.from_pandas(model_df)
@@ -128,18 +134,19 @@ def cache(self):
         """Custom collect method to extend the original collect behavior."""
         # Implement your custom logic here
         # For example, let's say we want to write collected data to a cache file every `cache_step_rate` steps
-        if self.model._steps % self._cache_interval == 0:
-            self._save_to_parquet(self.model)
-            self._last_cached_step = self.model._steps
-        elif self.model._steps == self._total_steps:
+        if (
+            self.model._steps % self._cache_interval == 0
+            or self.model._steps == self._total_steps
+        ):
             self._save_to_parquet(self.model)
             self._last_cached_step = self.model._steps
 
-        if self.condition_function and self.save_special_results(self.condition_function):
+        if self.condition_function and self.save_special_results(
+            self.condition_function
+        ):
             pass
 
     def save_special_results(self, condition_function: Callable[[dict], bool]):
-
         model_vars = self.model.datacollector.model_vars
         self.cache_file_path.mkdir(parents=True, exist_ok=True)
 
@@ -147,26 +154,30 @@ def save_special_results(self, condition_function: Callable[[dict], bool]):
         special_results_file = f"{self.cache_file_path}/special_results.parquet"
         if condition_function(model_vars):
             step_data = {key: [value[-1]] for key, value in model_vars.items()}
-            step_data['Step'] = current_step
+            step_data["Step"] = current_step
             special_results_df = pd.DataFrame(step_data)
 
             # Append the current step data to the Parquet file
             if os.path.exists(special_results_file):
                 existing_data = pq.read_table(special_results_file).to_pandas()
-                combined_data = pd.concat([existing_data, special_results_df], ignore_index=True)
+                combined_data = pd.concat(
+                    [existing_data, special_results_df], ignore_index=True
+                )
                 special_results_table = pa.Table.from_pandas(combined_data)
             else:
                 special_results_table = pa.Table.from_pandas(special_results_df)
 
             pq.write_table(special_results_table, special_results_file)
 
-            print(f"Condition met. Appended special results for step {current_step} to {special_results_file}")
+            print(
+                f"Condition met. Appended special results for step {current_step} to {special_results_file}"
+            )
         else:
             print(f"Condition not met at step {current_step}. No data to save.")
 
     def read_model_data(self):
         """Read and combine all model data Parquet files into a single DataFrame."""
-        model_files = glob.glob(f'{self.cache_file_path}/model_data_*.parquet')
+        model_files = glob.glob(f"{self.cache_file_path}/model_data_*.parquet")
         model_dfs = []
 
         for model_file in model_files:
@@ -182,7 +193,7 @@ def read_model_data(self):
 
     def read_agent_data(self):
         """Read and combine all agent data Parquet files into a single DataFrame."""
-        agent_files = glob.glob(f'{self.cache_file_path}/agent_data_*.parquet')
+        agent_files = glob.glob(f"{self.cache_file_path}/agent_data_*.parquet")
         agent_dfs = []
 
         for agent_file in agent_files:
@@ -212,39 +223,43 @@ def combine_dataframes(self):
 
 
 # FOR GRID KIV IGNORE NOW
-import pandas as pd
-from typing import Dict, List, Any
-from random import Random
 from mesa.agent import Agent
+
+
 class AgentSerializer:
     @staticmethod
-    def agent_to_dict(agent: Agent) -> Dict[str, Any]:
+    def agent_to_dict(agent: Agent) -> dict[str, Any]:
         """Convert an Agent instance to a dictionary."""
         return {
-            'unique_id': agent.unique_id,
-            'model': str(agent.model),  # Convert model to a string or identifier
-            'pos': str(agent.pos) if agent.pos else None,  # Convert position to a string or identifier
+            "unique_id": agent.unique_id,
+            "model": str(agent.model),  # Convert model to a string or identifier
+            "pos": str(agent.pos)
+            if agent.pos
+            else None,  # Convert position to a string or identifier
         }
 
     @staticmethod
-    def dict_to_agent(agent_dict: Dict[str, Any], model: Any) -> Agent:
+    def dict_to_agent(agent_dict: dict[str, Any], model: Any) -> Agent:
         """Convert a dictionary to an Agent instance."""
-        unique_id = agent_dict['unique_id']
-        pos = agent_dict['pos'] if agent_dict['pos'] != 'None' else None
+        unique_id = agent_dict["unique_id"]
+        pos = agent_dict["pos"] if agent_dict["pos"] != "None" else None
         agent = Agent(unique_id=unique_id, model=model)
         agent.pos = pos
         return agent
 
     @staticmethod
-    def save_agents_to_parquet(agents: List[Agent], filename: str) -> None:
+    def save_agents_to_parquet(agents: list[Agent], filename: str) -> None:
         """Save a list of agents to a Parquet file."""
         agent_dicts = [AgentSerializer.agent_to_dict(agent) for agent in agents]
         df = pd.DataFrame(agent_dicts)
         df.to_parquet(filename)
 
     @staticmethod
-    def load_agents_from_parquet(filename: str, model: Any) -> List[Agent]:
+    def load_agents_from_parquet(filename: str, model: Any) -> list[Agent]:
         """Load agents from a Parquet file."""
         df = pd.read_parquet(filename)
-        agents = [AgentSerializer.dict_to_agent(row.to_dict(), model) for _, row in df.iterrows()]
+        agents = [
+            AgentSerializer.dict_to_agent(row.to_dict(), model)
+            for _, row in df.iterrows()
+        ]
         return agents
diff --git a/mesa/cacheable_wrapper_example.ipynb b/mesa/cacheable_wrapper_example.ipynb
index b83baae0372..9f9d39d7df3 100644
--- a/mesa/cacheable_wrapper_example.ipynb
+++ b/mesa/cacheable_wrapper_example.ipynb
@@ -4612,7 +4612,7 @@
     "                        # **agent.__dict__\n",
     "                    }\n",
     "                    grid_state['agents'].append(agent_state)\n",
-    "    \n",
+    "\n",
     "    # Convert to DataFrame\n",
     "    df = pd.DataFrame(grid_state['agents'])\n",
     "    print(df)\n",
@@ -4791,21 +4791,21 @@
     "\n",
     "import pandas as pd\n",
     "from mesa.space import MultiGrid as Grid\n",
-    "# import mesa.space.MultiGrid as Grid \n",
+    "# import mesa.space.MultiGrid as Grid\n",
     "def reconstruct_grid(filename='grid_cache.parquet'):\n",
     "    # Load the DataFrame from Parquet\n",
     "    df = pd.read_parquet(filename)\n",
-    "    \n",
+    "\n",
     "    # Create a new Grid instance\n",
     "    width = df['pos_x'].max() + 1  # Assuming positions start from 0\n",
     "    height = df['pos_y'].max() + 1  # Assuming positions start from 0\n",
     "    grid = Grid(width, height, False)\n",
-    "    \n",
+    "\n",
     "    # Add agents to the grid\n",
     "    for _, row in df.iterrows():\n",
     "        agent = Agent(pos=(row['pos_x'], row['pos_y']), wealth=row['wealth'])\n",
     "        grid.add_agent(agent, row['pos_x'], row['pos_y'])\n",
-    "    \n",
+    "\n",
     "    return grid\n",
     "\n",
     "# Usage\n",
@@ -4992,11 +4992,11 @@
     "    \"\"\"Read the grid state from a Parquet file and reconstruct the grid with agents.\"\"\"\n",
     "    # Load DataFrame from Parquet\n",
     "    df = pd.read_parquet(filename)\n",
-    "    \n",
+    "\n",
     "    # Extract grid dimensions from the file\n",
     "    width = int(df['width'].iloc[0])\n",
     "    height = int(df['height'].iloc[0])\n",
-    "    \n",
+    "\n",
     "    # Initialize the grid\n",
     "    grid = Grid(width=width, height=height)  # Adjust as needed for your grid class\n",
     "\n",
@@ -5007,7 +5007,7 @@
     "        pos_y = int(row['pos_y'])\n",
     "        unique_id = int(row['unique_id'])\n",
     "        wealth = float(row['wealth'])\n",
-    "        \n",
+    "\n",
     "        # Create agent instance (adjust based on how you create agents)\n",
     "        agent = Agent(unique_id=unique_id, model=model)\n",
     "        agent.pos = (pos_x, pos_y)\n",
diff --git a/mesa/datacollection_with_caching.py b/mesa/datacollection_with_caching.py
index 3e093f18da4..3c19ce436eb 100644
--- a/mesa/datacollection_with_caching.py
+++ b/mesa/datacollection_with_caching.py
@@ -33,16 +33,16 @@
     * For collecting agent-level variables, agents must have a unique_id
 """
 
-import os
 import contextlib
 import itertools
+import os
 import types
 from copy import deepcopy
 from functools import partial
+
 import pyarrow as pa
 import pyarrow.parquet as pq
 
-
 with contextlib.suppress(ImportError):
     import pandas as pd
 
@@ -62,7 +62,7 @@ def __init__(
         model_reporters=None,
         agent_reporters=None,
         tables=None,
-        output_dir='output_dir',
+        output_dir="output_dir",
     ):
         """
         Instantiate a DataCollector with lists of model and agent reporters.
@@ -120,9 +120,9 @@ def __init__(
         self.output_dir = output_dir
 
         if not output_dir:
-            raise ValueError('output_dir cannot be None')
+            raise ValueError("output_dir cannot be None")
         if not os.path.exists(output_dir):
-            print("Creating output directory {}".format(output_dir))
+            print(f"Creating output directory {output_dir}")
             os.makedirs(output_dir)
 
         if model_reporters is not None:
@@ -229,7 +229,9 @@ def collect(self, model):
                 # Check if function with arguments
                 elif isinstance(reporter, list):
                     self.model_vars[var].append(deepcopy(reporter[0](*reporter[1])))
-                    self.model_vars_cache[var].append(deepcopy(reporter[0](*reporter[1])))
+                    self.model_vars_cache[var].append(
+                        deepcopy(reporter[0](*reporter[1]))
+                    )
                 # Assume it's a callable otherwise (e.g., method)
                 # TODO: Check if method of a class explicitly
                 else:
@@ -336,11 +338,17 @@ def _save_to_parquet(self, model):
         absolute_path = os.path.abspath(model_file)
         print(f"{absolute_path=}")
         if os.path.exists(absolute_path):
-            raise FileExistsError(f"A directory with the name {model_file} already exists.")
+            raise FileExistsError(
+                f"A directory with the name {model_file} already exists."
+            )
         if os.path.exists(model_file):
-            raise FileExistsError(f"A directory with the name {model_file} already exists.")
+            raise FileExistsError(
+                f"A directory with the name {model_file} already exists."
+            )
         if os.path.exists(agent_file):
-            raise FileExistsError(f"A directory with the name {agent_file} already exists.")
+            raise FileExistsError(
+                f"A directory with the name {agent_file} already exists."
+            )
 
         if not model_df.empty:
             model_table = pa.Table.from_pandas(model_df)
@@ -358,4 +366,4 @@ def cache_remaining_data(self, model):
         """Cache the remaining data in model_vars_cache and _agent_records."""
         if not self.model_vars_cache:
             print("No model vars cached yet.")
-        self._save_to_parquet(model)
\ No newline at end of file
+        self._save_to_parquet(model)
diff --git a/mesa/example.ipynb b/mesa/example.ipynb
index 84f02283e0b..88de19ff3df 100644
--- a/mesa/example.ipynb
+++ b/mesa/example.ipynb
@@ -46,17 +46,16 @@
    },
    "outputs": [],
    "source": [
-    "import mesa\n",
-    "\n",
-    "# Data visualization tools.\n",
-    "import seaborn as sns\n",
-    "\n",
     "# Has multi-dimensional arrays and matrices. Has a large collection of\n",
     "# mathematical functions to operate on these arrays.\n",
-    "import numpy as np\n",
     "\n",
     "# Data manipulation and analysis.\n",
-    "import pandas as pd"
+    "import pandas as pd\n",
+    "\n",
+    "# Data visualization tools.\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import mesa"
    ]
   },
   {
@@ -1389,10 +1388,9 @@
    ],
    "source": [
     "import pyarrow.parquet as pq\n",
-    "import pandas as pd\n",
     "\n",
     "# Read a single Parquet file\n",
-    "table = pq.read_table('output_dir/model_data_1.parquet')\n",
+    "table = pq.read_table(\"output_dir/model_data_1.parquet\")\n",
     "\n",
     "# Convert to a pandas DataFrame\n",
     "df = table.to_pandas()\n",
@@ -1445,13 +1443,13 @@
     }
    ],
    "source": [
-    "import pyarrow.parquet as pq\n",
-    "import pandas as pd\n",
     "import glob\n",
     "\n",
+    "import pyarrow.parquet as pq\n",
+    "\n",
     "# Get a list of all Parquet files\n",
-    "model_files = glob.glob('output_dir/model_data_*.parquet')\n",
-    "agent_files = glob.glob('output_dir/agent_data_*.parquet')\n",
+    "model_files = glob.glob(\"output_dir/model_data_*.parquet\")\n",
+    "agent_files = glob.glob(\"output_dir/agent_data_*.parquet\")\n",
     "\n",
     "# Initialize lists to hold dataframes\n",
     "model_dfs = []\n",
@@ -1647,23 +1645,25 @@
    "source": [
     "import pyarrow.parquet as pq\n",
     "\n",
+    "\n",
     "def process_parquet_file(file_path):\n",
     "    # Read a single Parquet file\n",
     "    table = pq.read_table(file_path)\n",
-    "    \n",
+    "\n",
     "    # Convert to a pandas DataFrame\n",
     "    df = table.to_pandas()\n",
-    "    \n",
+    "\n",
     "    # Process the DataFrame (e.g., analyze, transform, etc.)\n",
     "    # For example, just print the first few rows\n",
     "    print(df.head())\n",
-    "    \n",
+    "\n",
     "    # Optionally, return or save the processed DataFrame\n",
     "    return df\n",
     "\n",
+    "\n",
     "# Get a list of all Parquet files\n",
-    "model_files = glob.glob('output_dir/model_data_*.parquet')\n",
-    "agent_files = glob.glob('output_dir/agent_data_*.parquet')\n",
+    "model_files = glob.glob(\"output_dir/model_data_*.parquet\")\n",
+    "agent_files = glob.glob(\"output_dir/agent_data_*.parquet\")\n",
     "\n",
     "# Process each file incrementally\n",
     "for model_file in model_files:\n",