test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/miniconda3/envs/py38/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os \n",
    " \n",
    "import cv2 \n",
    "import numpy as np \n",
    "import requests \n",
    "import torch \n",
    "import torch.onnx \n",
    "from torch import nn \n",
    " \n",
    "class SuperResolutionNet(nn.Module): \n",
    "    def __init__(self, upscale_factor): \n",
    "        super().__init__() \n",
    "        self.upscale_factor = upscale_factor \n",
    "        self.img_upsampler = nn.Upsample( \n",
    "            scale_factor=self.upscale_factor, \n",
    "            mode='bicubic', \n",
    "            align_corners=False) \n",
    " \n",
    "        self.conv1 = nn.Conv2d(3,64,kernel_size=9,padding=4) \n",
    "        self.conv2 = nn.Conv2d(64,32,kernel_size=1,padding=0) \n",
    "        self.conv3 = nn.Conv2d(32,3,kernel_size=5,padding=2) \n",
    " \n",
    "        self.relu = nn.ReLU() \n",
    " \n",
    "    def forward(self, x): \n",
    "        x = self.img_upsampler(x) \n",
    "        out = self.relu(self.conv1(x)) \n",
    "        out = self.relu(self.conv2(out)) \n",
    "        out = self.conv3(out) \n",
    "        return out \n",
    " \n",
    "# Download checkpoint and test image \n",
    "urls = ['https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth', \n",
    "    'https://raw.githubusercontent.com/open-mmlab/mmediting/master/tests/data/face/000001.png'] \n",
    "names = ['srcnn.pth', 'face.png'] \n",
    "for url, name in zip(urls, names): \n",
    "    if not os.path.exists(name): \n",
    "        open(name, 'wb').write(requests.get(url).content) \n",
    " \n",
    "def init_torch_model(): \n",
    "    torch_model = SuperResolutionNet(upscale_factor=3) \n",
    " \n",
    "    state_dict = torch.load('srcnn.pth')['state_dict'] \n",
    " \n",
    "    # Adapt the checkpoint \n",
    "    for old_key in list(state_dict.keys()): \n",
    "        new_key = '.'.join(old_key.split('.')[1:]) \n",
    "        state_dict[new_key] = state_dict.pop(old_key) \n",
    " \n",
    "    torch_model.load_state_dict(state_dict) \n",
    "    torch_model.eval() \n",
    "    return torch_model \n",
    " \n",
    "model = init_torch_model() \n",
    "input_img = cv2.imread('face.png').astype(np.float32) \n",
    " \n",
    "# HWC to NCHW \n",
    "input_img = np.transpose(input_img, [2, 0, 1]) \n",
    "input_img = np.expand_dims(input_img, 0) \n",
    " \n",
    "# Inference \n",
    "torch_output = model(torch.from_numpy(input_img)).detach().numpy() \n",
    " \n",
    "# NCHW to HWC \n",
    "torch_output = np.squeeze(torch_output, 0) \n",
    "torch_output = np.clip(torch_output, 0, 255) \n",
    "torch_output = np.transpose(torch_output, [1, 2, 0]).astype(np.uint8) \n",
    " \n",
    "# Show image \n",
    "cv2.imwrite(\"face_torch.png\", torch_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0)\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "probs = torch.tensor([0.1, 0.2, 0.3, 0.4, 0.5])\n",
    "probs = torch.distributions.Categorical(probs=probs)\n",
    "action = probs.sample()\n",
    "print(action)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal(loc: 0.0, scale: 1.0)\n",
      "Sample: 0.14062602818012238\n",
      "Log Probability Density: -0.9288263320922852\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.distributions as dist\n",
    "\n",
    "# 创建一个均值为0，标准差为1的正态分布\n",
    "mean = 0.0\n",
    "std = 1.0\n",
    "normal_dist = dist.Normal(mean, std)\n",
    "print(normal_dist)\n",
    "\n",
    "# 从分布中采样\n",
    "sample = normal_dist.sample()\n",
    "\n",
    "# 计算样本的概率密度\n",
    "prob_density = normal_dist.log_prob(sample)\n",
    "\n",
    "# 打印结果\n",
    "print(f\"Sample: {sample}\")\n",
    "print(f\"Log Probability Density: {prob_density}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "create_net\n",
      "Net init\n",
      "the x is:  520\n"
     ]
    }
   ],
   "source": [
    "class Net:\n",
    "    def __init__(self, x=100):\n",
    "        print(\"Net init\")\n",
    "        print('the x is: ', x)\n",
    "    \n",
    "    @classmethod\n",
    "    def create_net(cls, m):\n",
    "        print(\"create_net\")\n",
    "        cls(m)\n",
    "\n",
    "\n",
    "y = 520\n",
    "Net.create_net(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values([[9303, 5001, 690, 9624, 4210, 3292, 4955, 6776, 9684, 7200, 2110, 7076, 9872, 5898, 2265, 3973, 3992, 1451, 9857, 6583, 7729, 7630, 869, 1073, 110, 7181, 6160, 2075, 3709, 5726, 5590, 8224]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "dummy_prompt_token_ids = np.random.randint(10000,\n",
    "                                            size=(8, 32))\n",
    "dummy_inputs = [{\n",
    "    \"prompt_token_ids\": batch\n",
    "} for batch in dummy_prompt_token_ids.tolist()]\n",
    "dummy_inputs[0].values()\n",
    "# dummy_inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "finfo(resolution=1, min=-57344, max=57344, eps=0.25, smallest_normal=6.10352e-05, tiny=6.10352e-05, dtype=float8_e5m2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch.nn as nn\n",
    "import torch\n",
    "\n",
    "finfo = torch.finfo(torch.float8_e5m2)\n",
    "finfo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processed prompts:   0%|          | 0/10 [00:00<?, ?it/s, est. speed input: 0.00 toks/s, output: 0.00 toks/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00010514259338378906\n",
      "0.00012612342834472656\n",
      "0.00013399124145507812\n",
      "0.00014209747314453125\n",
      "0.00014901161193847656\n",
      "0.00015592575073242188\n",
      "0.00016307830810546875\n",
      "0.00016999244689941406\n",
      "0.0001761913299560547\n",
      "0.00018310546875\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "pbar = tqdm(\n",
    "                total=10,\n",
    "                desc=\"Processed prompts\",\n",
    "                dynamic_ncols=True,\n",
    "                postfix=(f\"est. speed input: {0:.2f} toks/s, \"\n",
    "                         f\"output: {0:.2f} toks/s\"),\n",
    "            )\n",
    "\n",
    "for i in range(10):\n",
    "    print(pbar.format_dict['elapsed'])\n",
    "    pbar.update(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_classmethod\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "class Test:\n",
    "    def __init__(self, c=0):\n",
    "        print(c)\n",
    "        self.rank = 0\n",
    "    \n",
    "    @classmethod\n",
    "    def test_classmethod(cls, rank=9):\n",
    "        print(\"test_classmethod\")\n",
    "        cls(rank)\n",
    "Test.test_classmethod()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[0.6197, 0.8165],\n",
      "         [0.5405, 0.2133],\n",
      "         [0.9509, 0.8153],\n",
      "         [0.6293, 0.1230]],\n",
      "\n",
      "        [[0.6598, 0.7861],\n",
      "         [0.3918, 0.6148],\n",
      "         [0.4171, 0.8185],\n",
      "         [0.9584, 0.9309]],\n",
      "\n",
      "        [[0.0933, 0.2751],\n",
      "         [0.1459, 0.4152],\n",
      "         [0.6703, 0.9592],\n",
      "         [0.6810, 0.3643]]])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "t = torch.rand(3, 4, 2)\n",
    "print(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.return_types.topk(\n",
       "values=tensor([0.9592, 0.9584, 0.9509, 0.9309, 0.8185, 0.8165, 0.8153, 0.7861, 0.6810,\n",
       "        0.6703]),\n",
       "indices=tensor([21, 14,  4, 15, 13,  1,  5,  9, 22, 20]))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = t.reshape(-1).topk(k=10)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[False,  True],\n",
       "         [False, False],\n",
       "         [ True,  True],\n",
       "         [False, False]],\n",
       "\n",
       "        [[False,  True],\n",
       "         [False, False],\n",
       "         [False,  True],\n",
       "         [ True,  True]],\n",
       "\n",
       "        [[False, False],\n",
       "         [False, False],\n",
       "         [False,  True],\n",
       "         [ True, False]]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q = t > a.values[-1]\n",
    "q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[0.0000, 0.8165],\n",
       "         [0.0000, 0.0000],\n",
       "         [0.9509, 0.8153],\n",
       "         [0.0000, 0.0000]],\n",
       "\n",
       "        [[0.0000, 0.7861],\n",
       "         [0.0000, 0.0000],\n",
       "         [0.0000, 0.8185],\n",
       "         [0.9584, 0.9309]],\n",
       "\n",
       "        [[0.0000, 0.0000],\n",
       "         [0.0000, 0.0000],\n",
       "         [0.0000, 0.9592],\n",
       "         [0.6810, 0.0000]]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ou = t * q\n",
    "ou"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[0.6197, 0.0000],\n",
       "         [0.5405, 0.2133],\n",
       "         [0.0000, 0.0000],\n",
       "         [0.6293, 0.1230]],\n",
       "\n",
       "        [[0.6598, 0.0000],\n",
       "         [0.3918, 0.6148],\n",
       "         [0.4171, 0.0000],\n",
       "         [0.0000, 0.0000]],\n",
       "\n",
       "        [[0.0933, 0.2751],\n",
       "         [0.1459, 0.4152],\n",
       "         [0.6703, 0.0000],\n",
       "         [0.0000, 0.3643]]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ou1 = t*~q\n",
    "ou1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = torch.tensor([1, 2, 3, 0])\n",
    "s.numel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'纬度'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m/opt/miniconda3/envs/py38/lib/python3.8/site-packages/pandas/core/indexes/base.py:3653\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3652\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3653\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3654\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32m/opt/miniconda3/envs/py38/lib/python3.8/site-packages/pandas/_libs/index.pyx:147\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m/opt/miniconda3/envs/py38/lib/python3.8/site-packages/pandas/_libs/index.pyx:176\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7080\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7088\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: '纬度'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 16\u001b[0m\n\u001b[1;32m     11\u001b[0m china_map \u001b[38;5;241m=\u001b[39m folium\u001b[38;5;241m.\u001b[39mMap(location\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m35.3\u001b[39m, \u001b[38;5;241m100.8\u001b[39m], zoom_start\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m)\n\u001b[1;32m     13\u001b[0m \u001b[38;5;66;03m# 添加热力图层\u001b[39;00m\n\u001b[1;32m     14\u001b[0m heat_data \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m     15\u001b[0m     [lat, lon, value]\n\u001b[0;32m---> 16\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m lat, lon, value \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m纬度\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m, df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m经度\u001b[39m\u001b[38;5;124m'\u001b[39m], df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m值\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m     17\u001b[0m ]\n\u001b[1;32m     19\u001b[0m folium\u001b[38;5;241m.\u001b[39mplugins\u001b[38;5;241m.\u001b[39mHeatMap(heat_data)\u001b[38;5;241m.\u001b[39madd_to(china_map)\n\u001b[1;32m     21\u001b[0m \u001b[38;5;66;03m# 保存地图\u001b[39;00m\n",
      "File \u001b[0;32m/opt/miniconda3/envs/py38/lib/python3.8/site-packages/pandas/core/frame.py:3761\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   3760\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3761\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3762\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   3763\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m/opt/miniconda3/envs/py38/lib/python3.8/site-packages/pandas/core/indexes/base.py:3655\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3653\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3654\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3655\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3656\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3657\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3658\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3659\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3660\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: '纬度'"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "provinces = ['安徽', '北京', '福建', '甘肃', '广西', '贵州', '河南', '黑龙江', '吉林', '江苏', '江西', '南京', '内蒙古', '宁夏', '青海', '山东', '陕西', '上海', '四川', '武汉', '新疆', '云南', '浙江']\n",
    "values = [2, 2, 3, 2, 1, 1, 3, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 2]\n",
    "\n",
    "\n",
    "# 创建热力图\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.pcolor(np.array([[value1, value2], [value3, value4]]), cmap='viridis')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.yticks()\n",
    "plt.title('中国省份热力图')\n",
    "plt.ylabel('省份')\n",
    "plt.xlabel('热力值')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/v5/echarts.min', 'china':'https://assets.pyecharts.org/assets/v5/maps/china'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"d956413a7ecb41d99b9ab2b17674971d\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts', 'china'], function(echarts) {\n",
       "                var chart_d956413a7ecb41d99b9ab2b17674971d = echarts.init(\n",
       "                    document.getElementById('d956413a7ecb41d99b9ab2b17674971d'), 'white', {renderer: 'canvas'});\n",
       "                var option_d956413a7ecb41d99b9ab2b17674971d = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"aria\": {\n",
       "        \"enabled\": false\n",
       "    },\n",
       "    \"color\": [\n",
       "        \"#5470c6\",\n",
       "        \"#91cc75\",\n",
       "        \"#fac858\",\n",
       "        \"#ee6666\",\n",
       "        \"#73c0de\",\n",
       "        \"#3ba272\",\n",
       "        \"#fc8452\",\n",
       "        \"#9a60b4\",\n",
       "        \"#ea7ccc\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"map\",\n",
       "            \"name\": \"\\u5355\\u4f4d\\u6570\\u91cf\",\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"map\": \"china\",\n",
       "            \"data\": [\n",
       "                {\n",
       "                    \"name\": \"\\u5b89\\u5fbd\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5317\\u4eac\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u798f\\u5efa\",\n",
       "                    \"value\": 3\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u7518\\u8083\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5e7f\\u897f\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u8d35\\u5dde\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6cb3\\u5357\",\n",
       "                    \"value\": 3\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9ed1\\u9f99\\u6c5f\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5409\\u6797\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6c5f\\u82cf\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6c5f\\u897f\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5357\\u4eac\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5185\\u8499\\u53e4\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5b81\\u590f\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9752\\u6d77\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5c71\\u4e1c\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9655\\u897f\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e0a\\u6d77\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u56db\\u5ddd\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6b66\\u6c49\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u65b0\\u7586\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e91\\u5357\",\n",
       "                    \"value\": 2\n",
       "                }\n",
       "            ],\n",
       "            \"roam\": true,\n",
       "            \"aspectScale\": 0.75,\n",
       "            \"nameProperty\": \"name\",\n",
       "            \"selectedMode\": false,\n",
       "            \"zoom\": 1,\n",
       "            \"zlevel\": 0,\n",
       "            \"z\": 2,\n",
       "            \"seriesLayoutBy\": \"column\",\n",
       "            \"datasetIndex\": 0,\n",
       "            \"mapValueCalculation\": \"sum\",\n",
       "            \"showLegendSymbol\": true,\n",
       "            \"emphasis\": {}\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u5355\\u4f4d\\u6570\\u91cf\"\n",
       "            ],\n",
       "            \"selected\": {},\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14,\n",
       "            \"backgroundColor\": \"transparent\",\n",
       "            \"borderColor\": \"#ccc\",\n",
       "            \"borderRadius\": 0,\n",
       "            \"pageButtonItemGap\": 5,\n",
       "            \"pageButtonPosition\": \"end\",\n",
       "            \"pageFormatter\": \"{current}/{total}\",\n",
       "            \"pageIconColor\": \"#2f4554\",\n",
       "            \"pageIconInactiveColor\": \"#aaa\",\n",
       "            \"pageIconSize\": 15,\n",
       "            \"animationDurationUpdate\": 800,\n",
       "            \"selector\": false,\n",
       "            \"selectorPosition\": \"auto\",\n",
       "            \"selectorItemGap\": 7,\n",
       "            \"selectorButtonGap\": 10\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"enterable\": false,\n",
       "        \"confine\": false,\n",
       "        \"appendToBody\": false,\n",
       "        \"transitionDuration\": 0.4,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5,\n",
       "        \"order\": \"seriesAsc\"\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"text\": \"\\u4e2d\\u56fd\\u7701\\u4efd\\u70ed\\u529b\\u56fe\",\n",
       "            \"target\": \"blank\",\n",
       "            \"subtarget\": \"blank\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"textAlign\": \"auto\",\n",
       "            \"textVerticalAlign\": \"auto\",\n",
       "            \"triggerEvent\": false\n",
       "        }\n",
       "    ],\n",
       "    \"visualMap\": {\n",
       "        \"show\": true,\n",
       "        \"type\": \"continuous\",\n",
       "        \"min\": 0,\n",
       "        \"max\": 3,\n",
       "        \"inRange\": {\n",
       "            \"color\": [\n",
       "                \"#50a3ba\",\n",
       "                \"#eac763\",\n",
       "                \"#d94e5d\"\n",
       "            ]\n",
       "        },\n",
       "        \"calculable\": true,\n",
       "        \"inverse\": false,\n",
       "        \"splitNumber\": 5,\n",
       "        \"hoverLink\": true,\n",
       "        \"orient\": \"vertical\",\n",
       "        \"padding\": 5,\n",
       "        \"showLabel\": true,\n",
       "        \"itemWidth\": 20,\n",
       "        \"itemHeight\": 140,\n",
       "        \"borderWidth\": 0\n",
       "    }\n",
       "};\n",
       "                chart_d956413a7ecb41d99b9ab2b17674971d.setOption(option_d956413a7ecb41d99b9ab2b17674971d);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x12ddab2b0>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pyecharts.charts import Map\n",
    "from pyecharts import options as opts\n",
    "\n",
    "# 假设你的数据已经存储在一个CSV文件中\n",
    "provinces = ['安徽', '北京', '福建', '甘肃', '广西', '贵州', '河南', '黑龙江', '吉林', '江苏', '江西', '南京', '内蒙古', '宁夏', '青海', '山东', '陕西', '上海', '四川', '武汉', '新疆', '云南', '浙江']\n",
    "values = [2, 2, 3, 2, 1, 1, 3, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 2]\n",
    "\n",
    "\n",
    "# 创建热力图\n",
    "map_chart = (\n",
    "    Map()\n",
    "    .add(\n",
    "        \"单位数量\",\n",
    "        [list(z) for z in zip(provinces, values)],\n",
    "        \"china\"\n",
    "    )\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"中国省份热力图\"),\n",
    "        visualmap_opts=opts.VisualMapOpts(max_=max(values)),\n",
    "    )\n",
    ")\n",
    "\n",
    "# 渲染图表到本地HTML文件\n",
    "map_chart.render('china_heatmap.html')\n",
    "\n",
    "# 如果你使用Jupyter Notebook, 可以直接显示图表\n",
    "map_chart.render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/v5/echarts.min', 'china':'https://assets.pyecharts.org/assets/v5/maps/china'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"241b30b2e21240ee936bd744ac643ad9\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts', 'china'], function(echarts) {\n",
       "                var chart_241b30b2e21240ee936bd744ac643ad9 = echarts.init(\n",
       "                    document.getElementById('241b30b2e21240ee936bd744ac643ad9'), 'white', {renderer: 'canvas'});\n",
       "                var option_241b30b2e21240ee936bd744ac643ad9 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"aria\": {\n",
       "        \"enabled\": false\n",
       "    },\n",
       "    \"color\": [\n",
       "        \"#5470c6\",\n",
       "        \"#91cc75\",\n",
       "        \"#fac858\",\n",
       "        \"#ee6666\",\n",
       "        \"#73c0de\",\n",
       "        \"#3ba272\",\n",
       "        \"#fc8452\",\n",
       "        \"#9a60b4\",\n",
       "        \"#ea7ccc\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"map\",\n",
       "            \"name\": \"\\u5355\\u4f4d\\u6570\\u91cf\",\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"map\": \"china\",\n",
       "            \"data\": [\n",
       "                {\n",
       "                    \"name\": \"\\u5b89\\u5fbd\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5317\\u4eac\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u798f\\u5efa\",\n",
       "                    \"value\": 3\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u7518\\u8083\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5e7f\\u897f\",\n",
       "                    \"value\": 1\n",
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       "                {\n",
       "                    \"name\": \"\\u8d35\\u5dde\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6cb3\\u5357\",\n",
       "                    \"value\": 3\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9ed1\\u9f99\\u6c5f\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5409\\u6797\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6c5f\\u82cf\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6c5f\\u897f\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5357\\u4eac\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5185\\u8499\\u53e4\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5b81\\u590f\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9752\\u6d77\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5c71\\u4e1c\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9655\\u897f\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e0a\\u6d77\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u56db\\u5ddd\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6b66\\u6c49\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u65b0\\u7586\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e91\\u5357\",\n",
       "                    \"value\": 2\n",
       "                }\n",
       "            ],\n",
       "            \"roam\": true,\n",
       "            \"aspectScale\": 0.75,\n",
       "            \"nameProperty\": \"name\",\n",
       "            \"selectedMode\": false,\n",
       "            \"zoom\": 1,\n",
       "            \"zlevel\": 0,\n",
       "            \"z\": 2,\n",
       "            \"seriesLayoutBy\": \"column\",\n",
       "            \"datasetIndex\": 0,\n",
       "            \"mapValueCalculation\": \"sum\",\n",
       "            \"showLegendSymbol\": true,\n",
       "            \"emphasis\": {}\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u5355\\u4f4d\\u6570\\u91cf\"\n",
       "            ],\n",
       "            \"selected\": {},\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14,\n",
       "            \"backgroundColor\": \"transparent\",\n",
       "            \"borderColor\": \"#ccc\",\n",
       "            \"borderRadius\": 0,\n",
       "            \"pageButtonItemGap\": 5,\n",
       "            \"pageButtonPosition\": \"end\",\n",
       "            \"pageFormatter\": \"{current}/{total}\",\n",
       "            \"pageIconColor\": \"#2f4554\",\n",
       "            \"pageIconInactiveColor\": \"#aaa\",\n",
       "            \"pageIconSize\": 15,\n",
       "            \"animationDurationUpdate\": 800,\n",
       "            \"selector\": false,\n",
       "            \"selectorPosition\": \"auto\",\n",
       "            \"selectorItemGap\": 7,\n",
       "            \"selectorButtonGap\": 10\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"enterable\": false,\n",
       "        \"confine\": false,\n",
       "        \"appendToBody\": false,\n",
       "        \"transitionDuration\": 0.4,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5,\n",
       "        \"order\": \"seriesAsc\"\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"text\": \"\\u4e2d\\u56fd\\u7701\\u4efd\\u70ed\\u529b\\u56fe\",\n",
       "            \"target\": \"blank\",\n",
       "            \"subtarget\": \"blank\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"textAlign\": \"auto\",\n",
       "            \"textVerticalAlign\": \"auto\",\n",
       "            \"triggerEvent\": false\n",
       "        }\n",
       "    ],\n",
       "    \"visualMap\": {\n",
       "        \"show\": true,\n",
       "        \"type\": \"piecewise\",\n",
       "        \"min\": 0,\n",
       "        \"max\": 3,\n",
       "        \"inRange\": {\n",
       "            \"color\": [\n",
       "                \"#FFFF00\",\n",
       "                \"#FFA500\",\n",
       "                \"#FF0000\"\n",
       "            ]\n",
       "        },\n",
       "        \"calculable\": true,\n",
       "        \"inverse\": false,\n",
       "        \"splitNumber\": 5,\n",
       "        \"hoverLink\": true,\n",
       "        \"orient\": \"vertical\",\n",
       "        \"padding\": 5,\n",
       "        \"showLabel\": true,\n",
       "        \"itemWidth\": 20,\n",
       "        \"itemHeight\": 14,\n",
       "        \"borderWidth\": 0,\n",
       "        \"pieces\": [\n",
       "            {\n",
       "                \"min\": 3,\n",
       "                \"max\": 3,\n",
       "                \"label\": \"3\",\n",
       "                \"color\": \"#FFB6C1\"\n",
       "            },\n",
       "            {\n",
       "                \"min\": 2,\n",
       "                \"max\": 2,\n",
       "                \"label\": \"2\",\n",
       "                \"color\": \"#FFA500\"\n",
       "            },\n",
       "            {\n",
       "                \"min\": 1,\n",
       "                \"max\": 1,\n",
       "                \"label\": \"1\",\n",
       "                \"color\": \"#FFFF00\"\n",
       "            }\n",
       "        ]\n",
       "    }\n",
       "};\n",
       "                chart_241b30b2e21240ee936bd744ac643ad9.setOption(option_241b30b2e21240ee936bd744ac643ad9);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x12eed2af0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pyecharts.charts import Map\n",
    "from pyecharts import options as opts\n",
    "\n",
    "# 假设你的数据已经存储在一个CSV文件中\n",
    "provinces = ['安徽', '北京', '福建', '甘肃', '广西', '贵州', '河南', '黑龙江', '吉林', '江苏', '江西', '南京', '内蒙古', '宁夏', '青海', '山东', '陕西', '上海', '四川', '武汉', '新疆', '云南', '浙江']\n",
    "values = [2, 2, 3, 2, 1, 1, 3, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 2]\n",
    "\n",
    "# 创建热力图\n",
    "map_chart = (\n",
    "    Map()\n",
    "    .add(\n",
    "        \"单位数量\",\n",
    "        [list(z) for z in zip(provinces, values)],\n",
    "        \"china\"\n",
    "    )\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"中国省份热力图\"),\n",
    "        visualmap_opts=opts.VisualMapOpts(\n",
    "            is_piecewise=True,\n",
    "            pieces=[\n",
    "                {\"min\": 3, \"max\": 3, \"label\": \"3\", \"color\": \"#FFB6C1\"},\n",
    "                {\"min\": 2, \"max\": 2, \"label\": \"2\", \"color\": \"#FFA500\"},\n",
    "                {\"min\": 1, \"max\": 1, \"label\": \"1\", \"color\": \"#FFFF00\"}\n",
    "            ],\n",
    "            max_=3,\n",
    "            is_calculable=True,\n",
    "            range_color=[\"#FFFF00\", \"#FFA500\", \"#FF0000\"]\n",
    "        )\n",
    "    )\n",
    ")\n",
    "\n",
    "# 渲染图表到本地HTML文件\n",
    "map_chart.render('china_heatmap.html')\n",
    "\n",
    "# 如果你使用Jupyter Notebook, 可以直接显示图表\n",
    "map_chart.render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/v5/echarts.min', 'china':'https://assets.pyecharts.org/assets/v5/maps/china'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"7dbe61ec6c68449cb48c78411744cfa1\" style=\"width:1000px; height:800px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts', 'china'], function(echarts) {\n",
       "                var chart_7dbe61ec6c68449cb48c78411744cfa1 = echarts.init(\n",
       "                    document.getElementById('7dbe61ec6c68449cb48c78411744cfa1'), 'light', {renderer: 'canvas'});\n",
       "                var option_7dbe61ec6c68449cb48c78411744cfa1 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"aria\": {\n",
       "        \"enabled\": false\n",
       "    },\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"map\",\n",
       "            \"name\": \"\\u5355\\u4f4d\\u6570\\u91cf\",\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"map\": \"china\",\n",
       "            \"data\": [\n",
       "                {\n",
       "                    \"name\": \"\\u5b89\\u5fbd\\u7701\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5317\\u4eac\\u5e02\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u798f\\u5efa\\u7701\",\n",
       "                    \"value\": 3\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u7518\\u8083\\u7701\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5e7f\\u897f\\u58ee\\u65cf\\u81ea\\u6cbb\\u533a\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u8d35\\u5dde\\u7701\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6cb3\\u5357\\u7701\",\n",
       "                    \"value\": 3\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9ed1\\u9f99\\u6c5f\\u7701\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6e56\\u5317\\u7701\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5409\\u6797\\u7701\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6c5f\\u82cf\\u7701\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6c5f\\u897f\\u7701\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5185\\u8499\\u53e4\\u81ea\\u6cbb\\u533a\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5b81\\u590f\\u56de\\u65cf\\u81ea\\u6cbb\\u533a\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9752\\u6d77\\u7701\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5c71\\u4e1c\\u7701\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9655\\u897f\\u7701\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e0a\\u6d77\\u5e02\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u56db\\u5ddd\\u7701\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u65b0\\u7586\\u7ef4\\u543e\\u5c14\\u81ea\\u6cbb\\u533a\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e91\\u5357\\u7701\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6d59\\u6c5f\\u7701\",\n",
       "                    \"value\": 1\n",
       "                }\n",
       "            ],\n",
       "            \"roam\": true,\n",
       "            \"aspectScale\": 0.75,\n",
       "            \"nameProperty\": \"name\",\n",
       "            \"selectedMode\": false,\n",
       "            \"zoom\": 1,\n",
       "            \"zlevel\": 0,\n",
       "            \"z\": 2,\n",
       "            \"seriesLayoutBy\": \"column\",\n",
       "            \"datasetIndex\": 0,\n",
       "            \"mapValueCalculation\": \"sum\",\n",
       "            \"showLegendSymbol\": true,\n",
       "            \"emphasis\": {}\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u5355\\u4f4d\\u6570\\u91cf\"\n",
       "            ],\n",
       "            \"selected\": {},\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14,\n",
       "            \"backgroundColor\": \"transparent\",\n",
       "            \"borderColor\": \"#ccc\",\n",
       "            \"borderRadius\": 0,\n",
       "            \"pageButtonItemGap\": 5,\n",
       "            \"pageButtonPosition\": \"end\",\n",
       "            \"pageFormatter\": \"{current}/{total}\",\n",
       "            \"pageIconColor\": \"#2f4554\",\n",
       "            \"pageIconInactiveColor\": \"#aaa\",\n",
       "            \"pageIconSize\": 15,\n",
       "            \"animationDurationUpdate\": 800,\n",
       "            \"selector\": false,\n",
       "            \"selectorPosition\": \"auto\",\n",
       "            \"selectorItemGap\": 7,\n",
       "            \"selectorButtonGap\": 10\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"enterable\": false,\n",
       "        \"confine\": false,\n",
       "        \"appendToBody\": false,\n",
       "        \"transitionDuration\": 0.4,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5,\n",
       "        \"order\": \"seriesAsc\"\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"target\": \"blank\",\n",
       "            \"subtarget\": \"blank\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"textAlign\": \"auto\",\n",
       "            \"textVerticalAlign\": \"auto\",\n",
       "            \"triggerEvent\": false\n",
       "        }\n",
       "    ],\n",
       "    \"visualMap\": {\n",
       "        \"show\": true,\n",
       "        \"type\": \"piecewise\",\n",
       "        \"min\": 0,\n",
       "        \"max\": 3,\n",
       "        \"inRange\": {\n",
       "            \"color\": [\n",
       "                \"#50a3ba\",\n",
       "                \"#eac763\",\n",
       "                \"#d94e5d\"\n",
       "            ]\n",
       "        },\n",
       "        \"calculable\": true,\n",
       "        \"inverse\": false,\n",
       "        \"splitNumber\": 5,\n",
       "        \"hoverLink\": true,\n",
       "        \"orient\": \"vertical\",\n",
       "        \"padding\": 5,\n",
       "        \"showLabel\": true,\n",
       "        \"itemWidth\": 20,\n",
       "        \"itemHeight\": 14,\n",
       "        \"borderWidth\": 0,\n",
       "        \"pieces\": [\n",
       "            {\n",
       "                \"min\": 3,\n",
       "                \"max\": 3,\n",
       "                \"label\": \"3\",\n",
       "                \"color\": \"#FF0000\"\n",
       "            },\n",
       "            {\n",
       "                \"min\": 2,\n",
       "                \"max\": 2,\n",
       "                \"label\": \"2\",\n",
       "                \"color\": \"#FFA500\"\n",
       "            },\n",
       "            {\n",
       "                \"min\": 1,\n",
       "                \"max\": 1,\n",
       "                \"label\": \"1\",\n",
       "                \"color\": \"#FFFF00\"\n",
       "            }\n",
       "        ]\n",
       "    }\n",
       "};\n",
       "                chart_7dbe61ec6c68449cb48c78411744cfa1.setOption(option_7dbe61ec6c68449cb48c78411744cfa1);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x1035ec550>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pyecharts.charts import Map\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.globals import ThemeType\n",
    "\n",
    "# 手动输入数据\n",
    "data = pd.DataFrame({\n",
    "    '省份': ['安徽省', '北京市', '福建省', '甘肃省', '广西壮族自治区', \n",
    "           '贵州省', '河南省', '黑龙江省', '湖北省', '吉林省', \n",
    "           '江苏省', '江西省', '内蒙古自治区', '宁夏回族自治区', '青海省', \n",
    "           '山东省', '陕西省', '上海市', '四川省', '新疆维吾尔自治区', \n",
    "           '云南省', '浙江省'],\n",
    "    '数量': [2, 2, 3, 2, 1, \n",
    "           1, 3, 1, 1, 2, \n",
    "           2, 1, 1, 1, 1, \n",
    "           1, 2, 2, 1, 2, \n",
    "           2, 1]\n",
    "})\n",
    "\n",
    "# 创建省份和数量的列表\n",
    "provinces = data['省份'].tolist()\n",
    "values = data['数量'].tolist()\n",
    "\n",
    "# 创建热力图\n",
    "map_chart = (\n",
    "    Map(init_opts=opts.InitOpts(width=\"1000px\", height=\"800px\", theme=ThemeType.LIGHT))\n",
    "    .add(\n",
    "        \"单位数量\",\n",
    "        [list(z) for z in zip(provinces, values)],\n",
    "        \"china\"\n",
    "    )\n",
    "    .set_global_opts(\n",
    "        # title_opts=opts.TitleOpts(title=\"中国省份热力图\"),\n",
    "        visualmap_opts=opts.VisualMapOpts(\n",
    "            is_piecewise=True,\n",
    "            pieces=[\n",
    "                {\"min\": 3, \"max\": 3, \"label\": \"3\", \"color\": \"#FF0000\"},\n",
    "                {\"min\": 2, \"max\": 2, \"label\": \"2\", \"color\": \"#FFA500\"},\n",
    "                {\"min\": 1, \"max\": 1, \"label\": \"1\", \"color\": \"#FFFF00\"}\n",
    "            ],\n",
    "            max_=3,\n",
    "            is_calculable=True\n",
    "        )\n",
    "    )\n",
    ")\n",
    "\n",
    "# 渲染图表到本地HTML文件\n",
    "map_chart.render('china_heatmap1.html')\n",
    "\n",
    "# 如果你使用Jupyter Notebook, 可以直接显示图表\n",
    "map_chart.render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/v5/echarts.min', 'china':'https://assets.pyecharts.org/assets/v5/maps/china'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"1554fc6698d44739857f8ac4f93233a7\" style=\"width:1000px; height:800px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts', 'china'], function(echarts) {\n",
       "                var chart_1554fc6698d44739857f8ac4f93233a7 = echarts.init(\n",
       "                    document.getElementById('1554fc6698d44739857f8ac4f93233a7'), 'light', {renderer: 'canvas'});\n",
       "                var option_1554fc6698d44739857f8ac4f93233a7 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"aria\": {\n",
       "        \"enabled\": false\n",
       "    },\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"map\",\n",
       "            \"name\": \"\\u5355\\u4f4d\\u6570\\u91cf\",\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"margin\": 8,\n",
       "                \"fontSize\": 12\n",
       "            },\n",
       "            \"map\": \"china\",\n",
       "            \"data\": [\n",
       "                {\n",
       "                    \"name\": \"\\u5b89\\u5fbd\\u7701\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5317\\u4eac\\u5e02\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u798f\\u5efa\\u7701\",\n",
       "                    \"value\": 3\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u7518\\u8083\\u7701\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5e7f\\u897f\\u58ee\\u65cf\\u81ea\\u6cbb\\u533a\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u8d35\\u5dde\\u7701\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6cb3\\u5357\\u7701\",\n",
       "                    \"value\": 3\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9ed1\\u9f99\\u6c5f\\u7701\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6e56\\u5317\\u7701\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5409\\u6797\\u7701\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6c5f\\u82cf\\u7701\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6c5f\\u897f\\u7701\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5185\\u8499\\u53e4\\u81ea\\u6cbb\\u533a\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5b81\\u590f\\u56de\\u65cf\\u81ea\\u6cbb\\u533a\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9752\\u6d77\\u7701\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5c71\\u4e1c\\u7701\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9655\\u897f\\u7701\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e0a\\u6d77\\u5e02\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u56db\\u5ddd\\u7701\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u65b0\\u7586\\u7ef4\\u543e\\u5c14\\u81ea\\u6cbb\\u533a\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e91\\u5357\\u7701\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6d59\\u6c5f\\u7701\",\n",
       "                    \"value\": 1\n",
       "                }\n",
       "            ],\n",
       "            \"roam\": true,\n",
       "            \"aspectScale\": 0.75,\n",
       "            \"nameProperty\": \"name\",\n",
       "            \"selectedMode\": false,\n",
       "            \"zoom\": 1,\n",
       "            \"zlevel\": 0,\n",
       "            \"z\": 2,\n",
       "            \"seriesLayoutBy\": \"column\",\n",
       "            \"datasetIndex\": 0,\n",
       "            \"mapValueCalculation\": \"sum\",\n",
       "            \"showLegendSymbol\": true,\n",
       "            \"emphasis\": {}\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u5355\\u4f4d\\u6570\\u91cf\"\n",
       "            ],\n",
       "            \"selected\": {},\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14,\n",
       "            \"backgroundColor\": \"transparent\",\n",
       "            \"borderColor\": \"#ccc\",\n",
       "            \"borderRadius\": 0,\n",
       "            \"pageButtonItemGap\": 5,\n",
       "            \"pageButtonPosition\": \"end\",\n",
       "            \"pageFormatter\": \"{current}/{total}\",\n",
       "            \"pageIconColor\": \"#2f4554\",\n",
       "            \"pageIconInactiveColor\": \"#aaa\",\n",
       "            \"pageIconSize\": 15,\n",
       "            \"animationDurationUpdate\": 800,\n",
       "            \"selector\": false,\n",
       "            \"selectorPosition\": \"auto\",\n",
       "            \"selectorItemGap\": 7,\n",
       "            \"selectorButtonGap\": 10\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"enterable\": false,\n",
       "        \"confine\": false,\n",
       "        \"appendToBody\": false,\n",
       "        \"transitionDuration\": 0.4,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5,\n",
       "        \"order\": \"seriesAsc\"\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"text\": \"\\u4e2d\\u56fd\\u7701\\u4efd\\u70ed\\u529b\\u56fe\",\n",
       "            \"target\": \"blank\",\n",
       "            \"subtarget\": \"blank\",\n",
       "            \"left\": \"center\",\n",
       "            \"top\": \"10px\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"textAlign\": \"auto\",\n",
       "            \"textVerticalAlign\": \"auto\",\n",
       "            \"triggerEvent\": false\n",
       "        }\n",
       "    ],\n",
       "    \"visualMap\": {\n",
       "        \"show\": true,\n",
       "        \"type\": \"piecewise\",\n",
       "        \"min\": 0,\n",
       "        \"max\": 3,\n",
       "        \"inRange\": {\n",
       "            \"color\": [\n",
       "                \"#50a3ba\",\n",
       "                \"#eac763\",\n",
       "                \"#d94e5d\"\n",
       "            ]\n",
       "        },\n",
       "        \"calculable\": true,\n",
       "        \"inverse\": false,\n",
       "        \"splitNumber\": 5,\n",
       "        \"hoverLink\": true,\n",
       "        \"orient\": \"vertical\",\n",
       "        \"left\": \"left\",\n",
       "        \"bottom\": \"10px\",\n",
       "        \"padding\": 5,\n",
       "        \"showLabel\": true,\n",
       "        \"itemWidth\": 20,\n",
       "        \"itemHeight\": 14,\n",
       "        \"borderWidth\": 0,\n",
       "        \"pieces\": [\n",
       "            {\n",
       "                \"min\": 3,\n",
       "                \"max\": 3,\n",
       "                \"label\": \"3\",\n",
       "                \"color\": \"#FF0000\"\n",
       "            },\n",
       "            {\n",
       "                \"min\": 2,\n",
       "                \"max\": 2,\n",
       "                \"label\": \"2\",\n",
       "                \"color\": \"#FFA500\"\n",
       "            },\n",
       "            {\n",
       "                \"min\": 1,\n",
       "                \"max\": 1,\n",
       "                \"label\": \"1\",\n",
       "                \"color\": \"#FFFF00\"\n",
       "            }\n",
       "        ]\n",
       "    }\n",
       "};\n",
       "                chart_1554fc6698d44739857f8ac4f93233a7.setOption(option_1554fc6698d44739857f8ac4f93233a7);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x135da9e20>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pyecharts.charts import Map\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.globals import ThemeType\n",
    "\n",
    "# 手动输入数据\n",
    "data = pd.DataFrame({\n",
    "    '省份': ['安徽省', '北京市', '福建省', '甘肃省', '广西壮族自治区', \n",
    "           '贵州省', '河南省', '黑龙江省', '湖北省', '吉林省', \n",
    "           '江苏省', '江西省', '内蒙古自治区', '宁夏回族自治区', '青海省', \n",
    "           '山东省', '陕西省', '上海市', '四川省', '新疆维吾尔自治区', \n",
    "           '云南省', '浙江省'],\n",
    "    '数量': [2, 2, 3, 2, 1, \n",
    "           1, 3, 1, 1, 1, \n",
    "           2, 1, 1, 1, 1, \n",
    "           1, 2, 2, 1, 2, \n",
    "           2, 1]\n",
    "})\n",
    "# 创建省份和数量的列表\n",
    "provinces = data['省份'].tolist()\n",
    "values = data['数量'].tolist()\n",
    "\n",
    "# 创建热力图\n",
    "map_chart = (\n",
    "    Map(init_opts=opts.InitOpts(width=\"1000px\", height=\"800px\", theme=ThemeType.LIGHT))\n",
    "    .add(\n",
    "        \"单位数量\",\n",
    "        [list(z) for z in zip(provinces, values)],\n",
    "        \"china\",\n",
    "        label_opts=opts.LabelOpts(is_show=True, font_size=12)\n",
    "    )\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"中国省份热力图\", pos_left='center', pos_top='10px'),\n",
    "        visualmap_opts=opts.VisualMapOpts(\n",
    "            is_piecewise=True,\n",
    "            pieces=[\n",
    "                {\"min\": 3, \"max\": 3, \"label\": \"3\", \"color\": \"#FF0000\"},\n",
    "                {\"min\": 2, \"max\": 2, \"label\": \"2\", \"color\": \"#FFA500\"},\n",
    "                {\"min\": 1, \"max\": 1, \"label\": \"1\", \"color\": \"#FFFF00\"}\n",
    "            ],\n",
    "            max_=3,\n",
    "            is_calculable=True,\n",
    "            pos_left='left',\n",
    "            pos_bottom='10px'\n",
    "        )\n",
    "    )\n",
    ")\n",
    "\n",
    "# 渲染图表到本地HTML文件\n",
    "map_chart.render('china_heatmap1.html')\n",
    "\n",
    "# 如果你使用Jupyter Notebook, 可以直接显示图表\n",
    "map_chart.render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/v5/echarts.min', 'china':'https://assets.pyecharts.org/assets/v5/maps/china'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"d567c72be8614243913fd0a4ac4848c4\" style=\"width:1000px; height:800px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts', 'china'], function(echarts) {\n",
       "                var chart_d567c72be8614243913fd0a4ac4848c4 = echarts.init(\n",
       "                    document.getElementById('d567c72be8614243913fd0a4ac4848c4'), 'light', {renderer: 'canvas'});\n",
       "                var option_d567c72be8614243913fd0a4ac4848c4 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"aria\": {\n",
       "        \"enabled\": false\n",
       "    },\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"map\",\n",
       "            \"name\": \"\\u5355\\u4f4d\\u6570\\u91cf\",\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"inside\",\n",
       "                \"rotate\": 0,\n",
       "                \"margin\": 8,\n",
       "                \"fontSize\": 8,\n",
       "                \"align\": \"center\",\n",
       "                \"verticalAlign\": \"middle\",\n",
       "                \"formatter\": \"{b}\"\n",
       "            },\n",
       "            \"map\": \"china\",\n",
       "            \"data\": [\n",
       "                {\n",
       "                    \"name\": \"\\u5b89\\u5fbd\\u7701\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5317\\u4eac\\u5e02\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u798f\\u5efa\\u7701\",\n",
       "                    \"value\": 3\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u7518\\u8083\\u7701\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5e7f\\u897f\\u58ee\\u65cf\\u81ea\\u6cbb\\u533a\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u8d35\\u5dde\\u7701\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6cb3\\u5357\\u7701\",\n",
       "                    \"value\": 3\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9ed1\\u9f99\\u6c5f\\u7701\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6e56\\u5317\\u7701\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5409\\u6797\\u7701\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6c5f\\u82cf\\u7701\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6c5f\\u897f\\u7701\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5185\\u8499\\u53e4\\u81ea\\u6cbb\\u533a\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5b81\\u590f\\u56de\\u65cf\\u81ea\\u6cbb\\u533a\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9752\\u6d77\\u7701\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5c71\\u4e1c\\u7701\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9655\\u897f\\u7701\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e0a\\u6d77\\u5e02\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u56db\\u5ddd\\u7701\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u65b0\\u7586\\u7ef4\\u543e\\u5c14\\u81ea\\u6cbb\\u533a\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e91\\u5357\\u7701\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6d59\\u6c5f\\u7701\",\n",
       "                    \"value\": 1\n",
       "                }\n",
       "            ],\n",
       "            \"roam\": true,\n",
       "            \"aspectScale\": 0.75,\n",
       "            \"nameProperty\": \"name\",\n",
       "            \"selectedMode\": false,\n",
       "            \"zoom\": 1,\n",
       "            \"zlevel\": 0,\n",
       "            \"z\": 2,\n",
       "            \"seriesLayoutBy\": \"column\",\n",
       "            \"datasetIndex\": 0,\n",
       "            \"mapValueCalculation\": \"sum\",\n",
       "            \"showLegendSymbol\": true,\n",
       "            \"emphasis\": {}\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u5355\\u4f4d\\u6570\\u91cf\"\n",
       "            ],\n",
       "            \"selected\": {},\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14,\n",
       "            \"backgroundColor\": \"transparent\",\n",
       "            \"borderColor\": \"#ccc\",\n",
       "            \"borderRadius\": 0,\n",
       "            \"pageButtonItemGap\": 5,\n",
       "            \"pageButtonPosition\": \"end\",\n",
       "            \"pageFormatter\": \"{current}/{total}\",\n",
       "            \"pageIconColor\": \"#2f4554\",\n",
       "            \"pageIconInactiveColor\": \"#aaa\",\n",
       "            \"pageIconSize\": 15,\n",
       "            \"animationDurationUpdate\": 800,\n",
       "            \"selector\": false,\n",
       "            \"selectorPosition\": \"auto\",\n",
       "            \"selectorItemGap\": 7,\n",
       "            \"selectorButtonGap\": 10\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"enterable\": false,\n",
       "        \"confine\": false,\n",
       "        \"appendToBody\": false,\n",
       "        \"transitionDuration\": 0.4,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5,\n",
       "        \"order\": \"seriesAsc\"\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"target\": \"blank\",\n",
       "            \"subtarget\": \"blank\",\n",
       "            \"left\": \"center\",\n",
       "            \"top\": \"10px\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"textAlign\": \"auto\",\n",
       "            \"textVerticalAlign\": \"auto\",\n",
       "            \"triggerEvent\": false\n",
       "        }\n",
       "    ],\n",
       "    \"visualMap\": {\n",
       "        \"show\": true,\n",
       "        \"type\": \"piecewise\",\n",
       "        \"min\": 0,\n",
       "        \"max\": 3,\n",
       "        \"inRange\": {\n",
       "            \"color\": [\n",
       "                \"#50a3ba\",\n",
       "                \"#eac763\",\n",
       "                \"#d94e5d\"\n",
       "            ]\n",
       "        },\n",
       "        \"calculable\": true,\n",
       "        \"inverse\": false,\n",
       "        \"splitNumber\": 5,\n",
       "        \"hoverLink\": true,\n",
       "        \"orient\": \"vertical\",\n",
       "        \"left\": \"left\",\n",
       "        \"bottom\": \"10px\",\n",
       "        \"padding\": 5,\n",
       "        \"showLabel\": true,\n",
       "        \"itemWidth\": 20,\n",
       "        \"itemHeight\": 14,\n",
       "        \"borderWidth\": 0,\n",
       "        \"pieces\": [\n",
       "            {\n",
       "                \"min\": 3,\n",
       "                \"max\": 3,\n",
       "                \"label\": \"3\",\n",
       "                \"color\": \"#FF0000\"\n",
       "            },\n",
       "            {\n",
       "                \"min\": 2,\n",
       "                \"max\": 2,\n",
       "                \"label\": \"2\",\n",
       "                \"color\": \"#FFA500\"\n",
       "            },\n",
       "            {\n",
       "                \"min\": 1,\n",
       "                \"max\": 1,\n",
       "                \"label\": \"1\",\n",
       "                \"color\": \"#FFFF00\"\n",
       "            }\n",
       "        ]\n",
       "    }\n",
       "};\n",
       "                chart_d567c72be8614243913fd0a4ac4848c4.setOption(option_d567c72be8614243913fd0a4ac4848c4);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x147884100>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pyecharts.charts import Map\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.globals import ThemeType\n",
    "\n",
    "# 手动输入数据\n",
    "data = pd.DataFrame({\n",
    "    '省份': ['安徽省', '北京市', '福建省', '甘肃省', '广西壮族自治区', \n",
    "           '贵州省', '河南省', '黑龙江省', '湖北省', '吉林省', \n",
    "           '江苏省', '江西省', '内蒙古自治区', '宁夏回族自治区', '青海省', \n",
    "           '山东省', '陕西省', '上海市', '四川省', '新疆维吾尔自治区', \n",
    "           '云南省', '浙江省'],\n",
    "    '数量': [2, 2, 3, 2, 1, \n",
    "           1, 3, 1, 1, 2, \n",
    "           2, 1, 1, 1, 1, \n",
    "           1, 2, 2, 1, 2, \n",
    "           2, 1]\n",
    "})\n",
    "\n",
    "# 创建省份和数量的列表\n",
    "provinces = data['省份'].tolist()\n",
    "values = data['数量'].tolist()\n",
    "\n",
    "# 创建热力图\n",
    "map_chart = (\n",
    "    Map(init_opts=opts.InitOpts(width=\"1000px\", height=\"800px\", theme=ThemeType.LIGHT))\n",
    "    .add(\n",
    "        \"单位数量\",\n",
    "        [list(z) for z in zip(provinces, values)],\n",
    "        \"china\",\n",
    "        label_opts=opts.LabelOpts(\n",
    "            is_show=True,\n",
    "            font_size=8,\n",
    "            position=\"inside\",\n",
    "            formatter=\"{b}\",\n",
    "            # font_style=\"italic\",\n",
    "            rotate=0,\n",
    "            horizontal_align='center',\n",
    "            vertical_align='middle'\n",
    "        )\n",
    "    )\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(pos_left='center', pos_top='10px'),\n",
    "        visualmap_opts=opts.VisualMapOpts(\n",
    "            is_piecewise=True,\n",
    "            pieces=[\n",
    "                {\"min\": 3, \"max\": 3, \"label\": \"3\",\"color\": \"#FF0000\"},\n",
    "                {\"min\": 2, \"max\": 2, \"label\": \"2\", \"color\": \"#FFA500\"},\n",
    "                {\"min\": 1, \"max\": 1, \"label\": \"1\", \"color\": \"#FFFF00\"}\n",
    "            ],\n",
    "            max_=3,\n",
    "            is_calculable=True,\n",
    "            pos_left='left',\n",
    "            pos_bottom='10px'\n",
    "        )\n",
    "    )\n",
    ")\n",
    "\n",
    "# 渲染图表到本地HTML文件\n",
    "map_chart.render('china_heatmap.html')\n",
    "\n",
    "# 如果你使用Jupyter Notebook, 可以直接显示图表\n",
    "map_chart.render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = 'sooth_quant'\n",
    "b = t.find('smooth')\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.2667,  0.8704, -0.3118, -0.4162],\n",
       "        [ 0.1082,  0.4435, -1.8497,  1.0578],\n",
       "        [ 0.0849, -0.2407,  0.0647,  1.5056],\n",
       "        [-1.3744, -0.2795,  1.2123, -0.6132]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "t = torch.randn(4, 4)\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.2667, 0.8704, 1.2123, 1.5056])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = t.max(0)\n",
    "b[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(3)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = torch.argmax(b[0])\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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em41/Tc6EWinh28IKXPOftSjtXOYh8mZdKhbDhw/He++9hyVLlmDu3LkoLS3F6NGj0djYeNrPycvLg9FodDwSExN7HNpXSJKE56/PQWp0MKoaTbjjwy0wWTgtTnQ6jsFND9pxs7skScLN5/fCwj+PQJReiz2VTbjqtTVYtqtSdDSiHpHkHizu1dXVoVevXnjxxRdx2223nfI5JpMJJtMvZ2Q0NDQgMTER9fX1MBgM3X1pn3KgphlXvbYGDW0WTB2WhLwpWaIjEXmcY00mDPnXjwCAgscvgUGnFpzIeaoa2nDXR1uxufPwsnsnpOLucf2gUIg7YI3o1xoaGmA0Gs/673ePbjcNCQlBamoqSkpKTvscrVYLg8Fw0oNO1jsiCK9O7RjmXLDxEA8vIjoF+/kgyRFBPlUqACDKoMP828/HLSN6AQBe+nEP/vzBFjS0tQtORtR1PSoWTU1N2LdvH2JjY52Vx2+NSYvCA5ekAQAe+6oIWw7WCk5E5Fm8bf+KrtKoFPjn1Zl47nfZ0KgU+HFXJSa/thZ7K0+/1EzkibpULB544AGsXLkSBw4cwLp163DNNddAqVRi6tSprsrnV+4a0xeXZ3WcjHjHh1tRyWFOIgd7scjy8sHNs7luaCI+u2ME4ow67K9pxuTX1+K7wgrRsYjOWZeKxeHDhzF16lSkpaXh+uuvR3h4ONavX4/IyEhX5fMrkiThud/lID1Gj2oOcxKdxH5HSI6PXrE4UXZCCL6+exRGJIej2WzFnR9txb+X7IbVxv0uyPP1aHizO851+MOfHTzWjKteW4v61nbceF4i8qZkQZI4xEX+q7KhDcOfXgaFBBQ9cSkCNSrRkdzCYrXh2SW78dbqUgAdB5u9euNAhAR63+Zg5P3cMrxJrtErvGOYUyEBCzeV4SMOc5Kfyy+rAwCkRuv9plQAgEqpwCNXDMArNw6ETq3Aqj3VmPTaGuw8wv2AyHOxWHioi1Ij8dfLOs5hefyrHdh0gMOc5L/sd4Rk+cD+Fd1x9cB4LLprJJLCAlFW24opc9fiy+3lomMRnRKLhQf7y4XJuDI7FhabjDs/3IqK+lbRkYiEyLffEZIYIjaIQP1jDfhqxkhcmBqJtnYbZi7cjie/2QmLlSckk2dhsfBgkiTh37/LRnqMHjVNJtzx4Va0tXOYk/yLLMsotG/l7adXLOxCAjV499bzkDu2LwDgnTWluPmdDahpMp3lM4nch8XCwwVqVJj3+6EwBqiRX1aHR78s4kmI5FcOH2/F8ZZ2qJUS0mP1ouMIp1RIePDSdLxx8xAEaZRYv78Wk+asccyhEInGYuEFksID8dpNHcOcn2w+jA/XHxQdicht7PtXpMcYoFUpBafxHJdlxuDLGSORHBmEivo2XPfmz/hkc5noWEQsFt5idEokHprYMcz5xNc7sWH/McGJiNzjlxNN/XsZ5FT6RemxOHckJvSPhtliw18/K8A/FhdxvwsSisXCi9w+OhlX5cTBYpORO38rjtRxmJN8n69v5d1TBp0a834/BPddnApJAj5YfxDvrTsgOhb5MRYLLyJJEp69Nhv9Yw2oaTLjzg+3cJiTfJrNJqOo3F4sQsSG8WAKhYR7xqfgn1dlAABe+KEY5fzBgwRhsfAyARol5v1+CEID1cg/XI9HFnGYk3xX6bFmNJos0KkVSIkKFh3H400b3gtDe4WixWzFo4v53kBisFh4ocSwQLx202AoJODzrYfxPi97ko+yz1dkxBmhUvLt6mwUCgl5U7KgVkpYtrsK3xUdFR2J/BD/pnqpkf0i8PDl/QEAT367C+s5zEk+yHGiqZ/vX9EVKdF63HlRxz4Xj3+1Aw1t7YITkb9hsfBit43qg8kD42C1ycj9aCvXVMnn2ItFTiKLRVfcNbYfkiOCUNVowr+X7BYdh/wMi4UXkyQJeVOyMSDWgGPNZtzxAYc5yXdYrDbsOGK/YhEiNoyX0amVeOqaLADAh+sPYctBnjVE7sNi4eUCNErMu2UIwoI0KCyvx1WvrcGqPdWiYxH12N6qJrS12xCsVSE5Ikh0HK8zom84rhuSAACY/UUhzBaeKULuwWLhAxJCAzF32mCEBKqxp7IJt/x3I/7w7kaUVDWJjkbUbYWdyyCZ8QYoFJLgNN7p4cv7IyxIgz2VTXhr9X7RcchPsFj4iOHJ4Vj5wFj8cWQfqBQSlhdX49KXV+Hxr3bgeLNZdDyiLsvvvCMkh/tXdFtokAb/uLJjyPuVZXtRWtMsOBH5AxYLH2IMVOPRSQPww70XYkL/aFhtMt5bdwBjnl+Bd9aU8lIoeZXCzo2xsrjjZo9MHhiP0SkRMFtseGRRIfe2IJdjsfBByZHBeHv6UHz0p+FIj9GjvrUdT36zE5e+vAo/7qzkGwt5PJPFil0VDQB4xaKnJEnCvyZnQqtSYN2+Y/hia7noSOTjWCx82Mh+Efj2ntF4ZkoWIoI1KK1pxp/+bzNufmeD402byBMVH21Eu1VGaKAaCaEBouN4vV7hQZg5IQUA8K9vd6KWy6PkQiwWPk6pkHDjsCQsf2AM7hzTFxqVAmtLjuGKV1dj9hcFqG40iY5I9Bv59o2xEkIgSRzcdIbbRycjPUaP4y3teOrbXaLjkA9jsfATep0af7ssHcvuuwhXZMfCJgMLNpZh7PMr8J8VJdz/gjxKof2odO646TRqpQJPT8mC1HkUwNqSGtGRyEexWPiZxLBAvH7TYHx2xwjkJBjRZLLg30uKMeHFlfim4AjnL8gj8Kh01xicFIrfn98LAPDIokL+QEEuwWLhp4b2DsOiu0bipRtyEGPQ4fDxVsyYvw3XvfEz8svqRMcjP9ZqtmJPZSMAHpXuCg9emoZogxYHjrXgtZ9KRMchH8Ri4ccUCgnXDErATw9chFkTUhCgVmLzweO4+vW1uO/j7aio59kj5H47jtTDJgNRei1ijDrRcXyOXqfGE1dlAADeWLkPxUcbBSciX8NiQQjUqDBrQiqWPzAGUwbHAwC+2FaOsc+vwEtL96DFbBGckPwJl0Fc79KMGFw8IBoWm4yHFxXCZuMSKDkPiwU5xBh1ePH6gfhqxkic1zsUbe02vLJsL8Y9vxKfbznMNx9yiwL74CaXQVxGkiQ8cVUGgjRKbDl4HPM3HhIdiXwIiwX9RnZCCD75ywj8Z9pgJIQG4GhDG+7/NB+T/7MWmw7wlERyrQLuuOkWcSEBeODSNADAs0t2o6qhTXAi8hUsFnRKkiTh8qxY/HjfRfjbZekI1qpQcLge173xM3I/2oqy2hbREckHNbS1Y391x3kWvNXU9W4Z0Rs5CUY0tlnwxNc7RcchH8FiQWekUytx55i+WP7AGEwdlgSFBHxbWIHxL6zEM9/tRmNbu+iI5EOKOq9WxIcEIDxYKziN71MqJDw9JQtKhYRvCyuwbFel6EjkA1gs6JxE6rXIm5KFb+8ZjVH9ImC22vDGyn0Y+/wKzN9wCFbOX5AT2Ac3cxJ5tcJdMuKM+NOoPgCAR7/cgWYTh7WpZ1gsqEv6xxrwwW3D8M70oUiOCEJNkxkPLyrEFa+u5k5+1GOF9q2840PEBvEzMyekICE0AOV1rXhx6R7RccjLsVhQl0mShPH9o7Fk1oV49MoBMAaosftoI6a9vQF/en8T9lc3iY5IXiq/846QHA5uulWgRoV/Tc4EALy7ttRR8Ii6g8WCuk2jUuCPo/pg5YNjcOsFvaFSSPhxVxUueWkVnvh6B+paeIIinbvaZjMOH+/YlC2Dg5tuNyYtClflxMEmAw99UQCL1SY6EnkpFgvqsZBADR6/KgNLZl2IcelRsNhkvLv2AC56bgXeXVuKdr5B0Tmw71+RHBEEY4BabBg/9Y8rB8CgU2HHkQa8t+6A6DjkpVgsyGn6RQXjv7eehw9uG4a0aD3qW9vxxNc7cenLq7BsVyUPOKMzcsxXcBlEmEi9Fg9f3h8A8MIPe3hbOXULiwU53eiUSHx7zyg8dU0mwoM02F/djNve34xb/ruR5xLQaeU7tvIOERvEz10/NBHDeoehtd2KR78s4g8E1GUsFuQSKqUC04b3wvIHx+AvFyVDo1Rg9d4aTHxlFR5eVIiaJpPoiORhCsvrAPCMENEUCglPT8mERqnA8uJqfFtYIToSeRkWC3Ipg06N2RP748f7LsLEzBjYZGD+hkMY+9wKvLFyH0wWq+iI5AEqG9pQ2WCCQgIy4gyi4/i9flF63DmmLwDgia93or6VG+HRuWOxILdICg/E3JuH4OM/n4/MeAMaTRY8891uTHhxJf5XWMHLrX7OvjFWSpQegRqV4DQEAHeN7YvkyCBUN5rw7JLdouOQF2GxILcanhyOr3JH4fnrchBt0KKsthV3fbQVN7y5nvfO+7FfTjTlMoin0KqUePqaLAAdVxl5ACGdKxYLcjuFQsLvhiRg+QNjcM/4FOjUCmw8UIurXl+D+z/JRyVPWfQ7BY7BTRYLT3J+cjhuGJoIAHj4i0KYLbx1nM6OxYKECdSocN/Fqfjp/jGYPDAOsgx8vvUwxjy3Aq/8uBetZs5f+ANZlk+4YhEiNAv91uzL0xERrMHeqia8uXKf6DjkBVgsSLi4kAC8fOMgLM4dicFJIWhtt+KlH/dg3AsrsGjbYdh4wJlPO3y8Fcdb2qFWSkiP1YuOQ78SEqjBP64cAACYs7yEW/bTWbFYkMcYmBiCz++8AHOmDkJ8SAAq6ttw78f5uGbuOmw5yPVdX2VfBkmPMUCrUgpOQ6dyVU4cLkyNhNliwyOLuLcFnRmLBXkUSZIwKScOy+6/CA9emoYgjRL5ZXW4du7PmDF/Kw4f506Avqagc/8K7rjpuSRJwlOTM6FTK/Dz/mP4bMth0ZHIg7FYkEfSqZXIHdsPyx8cgxuGJkKSgG8KKjDuhZX495LdaDJZREckJyko67hiwRNNPVtiWCBmTUgFADz1v104xk3u6DRYLMijRel1ePZ32fjm7lEYkRwOs8WG/6zYhzHPrcDCjYdg5fyFV7PZZBSVd54REh8iNgyd1W2j+iA9Ro+6lnY89e0u0XHIQ7FYkFfIiDNi/u3DMe/3Q9A7PBA1TSY89EUhrpyzButKakTHo24qPdaMRpMFWpUCqdHBouPQWaiVCjxzbTYkCfhiWzlW760WHYk8EIsFeQ1JknBJRgx+uPci/P2K/tDrVNhV0YCb3t6A2/9vM0prmkVHpC6yb4qWEWeASsm3I28wMDEE00f0BgD8fXER2tp5WzidjH+TyetoVAr8aXQyVj44FreM6AWlQsLSnZW45KWVmLuC99l7k3zuX+GV7r8kFTEGHQ4ea8Gry/aKjkMehsWCvFZYkAb/vDoTS2aOxpi0SLRbZTy7ZDcq6ltFR6NzVMgdN72SXqfGE1dnAADmrdqP3UcbBCciT8JiQV4vJVqP9/4wDIOSQgAAy3dz3dcbWKw2FB2xF4sQsWGoyy7NiMGlGdGw2GTM/qKQG9mRA4sF+YxxaVEAgJ92VwlOQueipLoJbe02BGtVSI4IEh2HuuGJqzIRrFVh26E6fLThoOg45CFYLMhnjOvfUSzWltRwoMwL2PevyIw3QKGQBKeh7ogx6vDgpWkAgH8vKeYBggSAxYJ8yIBYA2IMOrS2W7GhlFuAezr7jptcBvFuN5/fCwMTQ9BosuDxr3aIjkMegMWCfIYkSRibHgkA+GlXpeA0dDY8Kt03KBUS8qZkQaWQ8F3RUSzdyb97/o7FgnzKWPucRXEVD0ryYCaLFbsqOu4kyOaOm16vf6wBfxqdDAB49Msibrnv51gsyKeM7BcBjUqBstpW7OPxzh6r+Ggj2q0yQgLVSAwLEB2HnGDm+BQkhnWcSvzCD8Wi45BALBbkU4K0KpyfHA6Ad4d4MvsySFa8EZLEwU1fEKBR4qnJWQCA99cdQH5ZndhAJAyLBfmccWkdcxbLdrFYeKqCzh03czi46VMuTI3E5IFxsMnA7C8KYbHaREciAVgsyOeMS48GAGw+eBz1re2C09CpOK5YcHDT5/z9ygEICVRjZ0UD/ru2VHQcEqBHxeKZZ56BJEmYNWuWk+IQ9VxSeCD6RQXDapN5+qIHajVbsbeqY/6FVyx8T0SwFg9P7A8AeGnpXpTVtghORO7W7WKxadMmvPnmm8jOznZmHiKnGJfOXTg91c6KelhtMiL1WkQbtKLjkAtcNzQBw/uEobXdir8vLuIdWn6mW8WiqakJ06ZNw1tvvYXQ0FBnZyLqMfttpyuKq2HlGQYeJb9zx82cBA5u+ipJkvD0lCxolAqs3FONrwsqREciN+pWscjNzcUVV1yBCRMmnPW5JpMJDQ0NJz2IXG1o71DodSrUNpsdR3OTZygst98REiI2CLlU38hg5I7tBwD459c7UN/CeSd/0eVisXDhQmzduhV5eXnn9Py8vDwYjUbHIzExscshibpKrVTgwtSOu0OWcznEo9iLXnYiBzd93R1jktE3Mgg1TWY8s2SX6DjkJl0qFmVlZZg5cyY++ugj6HS6c/qc2bNno76+3vEoKyvrVlCiruJpp56nsa0d+6ubAQDZ8SwWvk6rUiJvSscc3oKNZdjIM3z8QpeKxZYtW1BVVYXBgwdDpVJBpVJh5cqVePXVV6FSqWC1/vZESa1WC4PBcNKDyB3GpEVCkoAdRxpwtJ6nLnoC+zJIfEgAwoM5uOkPhvUJw9RhHVeqZ39RAJOFJw/7ui4Vi/Hjx6OwsBDbt293PIYOHYpp06Zh+/btUCqVrspJ1GXhwVoMTAwBACwv5lULT1DIg8f80kOX9UdEsBb7qpvxxor9ouOQi3WpWOj1emRmZp70CAoKQnh4ODIzM12VkajbuBziWX450TREbBByK2OgGo9OGgAAeH15Cc/x8XHceZN82tjO/SzW7K1BWzsvwYpWUF4HgFcs/NGk7FhclBoJs9WGh78o5N4WPqzHxWLFihV4+eWXnRCFyPky4gyINmjR2m7FBg6OCVXbbEZZbSsAIJODm35HkiT8a3ImAtRKbCitxaebD4uORC7CKxbk0yRJcuzCydtOxbIPbvaJCIIxQC04DYmQGBaIey9OAQA89b9dqGkyCU5ErsBiQT7Pvgvnst2VvPwqUEHnMdpcBvFvfxzZBwNiDahvbce/vtkpOg65AIsF+byR/SKgUSpQVtvKoTGBCso5uEmASqlA3pQsKCRg8fYjWLWHBwX6GhYL8nlBWhWGJ4cB4N0hIhXYd9zkFQu/l5MYgukX9AYAPLK4EK1mDlb7EhYL8gvjedqpUJUNbahsMEEhdQzUEt1/SRpijTqU1bbilWV7RcchJ2KxIL8wLj0aALD5wHHUt/IwJHez71+REqVHoEYlOA15gmCtCv+8umP/o7dW78euCh5Q6StYLMgvJIUHom9kECw2Gav3ck3X3Qq5DEKncPGAaEzMjIHVJmP2F4Ww2jhc7QtYLMhvjONyiDD53MqbTuPxqzKg16qwvawOH64/KDoOOQGLBfkN+3LIyuJq2PiTkdvIsuzYw4J3hNCvRRt0+OtlaQCA574v5oGBPoDFgvzG0N6h0OtUONZsRn7npXlyvcPHW1HbbIZaKSE9Vi86DnmgacN7YVBSCJpMFjz2VZHoONRDLBbkN9RKBS5MiQTA5RB3sl+tSI8xQKviCcj0WwqFhLwpWVApJHy/oxLf7zgqOhL1AIsF+ZWxnLNwO/vVoSzOV9AZpMcY8OcLkwEAj325A41tvHvLW7FYkF8ZkxYJSQJ2HGlAZQPXct2hsHNwM4fFgs7invEp6BUeiKMNbXjhhz2i41A3sViQX4kI1iKnc4CQh5K5ns0mO4pFVnyI2DDk8XRqJZ6anAUAeP/nA9jeeb4MeRcWC/I79ttOl7FYuNyBY81oNFmgVSmQGh0sOg55gVEpEZgyKB6yDMz+ohDtVpvoSNRFLBbkd+zFYm1JDUwWnlHgSvYdNzPiDFAp+XZD5+aRK/ojNFCNXRUNeGdNqeg41EX8m05+JyPOgGiDFi1mKzbsrxUdx6cVHOb+FdR14cFaPHx5fwDAyz/uQVlti+BE1BUsFuR3JEnC2DTeHeIOPNGUuut3QxIwIjkcbe02PLK4CLLMTe28BYsF+aUTbzvlG5ZrWKw27DjScbAUr1hQV0mShKeuyYRGpcCqPdX4Kv+I6Eh0jlgsyC+N6hcBjVKBQ7Ut2FfdLDqOTyqpbkJruxXBWhWSI4JExyEvlBwZjLvH9gMA/PPrnahrMQtOROeCxYL8UpBWheHJYQB426mr2OcrMuMNUCgkwWnIW/3lor5IiQrGsWYz8v63W3QcOgcsFuS3eNqpa/0yXxEiNAd5N41KgaendOxt8fHmMmzYf0xwIjobFgvyW/ZiselALRq4fbDTFfKodHKS83qH4abhSQCA2YsKeZu4h2OxIL/VKzwIyZFBsNhkrN5TIzqOTzFbbNhV0QgAyOaOm+QEf7ssHZF6LfZXN+M/y/eJjkNnwGJBfm08l0NcovhoI8xWG0IC1UgMCxAdh3yAMUCNxyYNAADMXbEPJVVNghPR6bBYkF+z33a6orgKNhtvO3UWx4mm8UZIEgc3yTmuyIrF2LRImK02PLyokH9nPRSLBfm183qHQa9V4Viz2fGPIfXcLyeahogNQj5FkiQ8OTkTAWolNpbW4tMtZaIj0SmwWJBfUysVGJ0aAYC3nTqT44oFBzfJyRJCA3H/JakAgKe+3YXqRpPgRPRrLBbk98alRwMAfipmsXCGVrMVezvXv3nFglzh1gt6IzPegIY2C578ZqfoOPQrLBbk98akRUKSgKLyBlQ2tImO4/V2VtTDapMRqdci2qAVHYd8kEqpQN412VBIwFf5R7CCPxR4FBYL8nsRwVrHJk5cDum5Asd8BQc3yXWyEoz4w8g+AIC/Ly5Ci9kiOBHZsVgQARjH006dxl4ssrh/BbnYfRenIj4kAIePt+KVH/eKjkOdWCyIAIzv31Es1pTUcFe/HnJs5Z3IwU1yrSCtCv+8OgMA8PaaUuw4Ui84EQEsFkQAgIw4A6L0WrSYrdiwv1Z0HK/V2NaO/TUdp8Vmx7NYkOuN7x+NK7JiYbXJePiLQli5t4VwLBZE6Lg/fiyXQ3qsqLwBsgzEhwQgPJiDm+Qej00aAL1OhfzD9fjg5wOi4/g9FguiTvZdOJcXV0GW+VNPd/xyoimvVpD7RBl0+Ntl6QCA574vxpG6VsGJ/BuLBVGnUSkR0CgVOHisxXE5n7qmoNx+ommI2CDkd24aloQhvULRbLbioS+43bdILBZEnYK1KgxPDgMA/LSLyyHdwSsWJIpCIeGZKVnQqRVYtacab6/ZLzqS32KxIDoB5yy673izGWW1HZegMzm4SQKkROvx2KSOu0T+vaQY28vqxAbyUywWRCcY1zlnselALRra2gWn8S72ZZA+EUEwBqgFpyF/deN5ibgiOxYWm4y7F2zl32MBWCyITtA7IgjJkUGw2GSs2VsjOo5XKeQyCHkASZKQNyULCaEBKKttxewvCjmM7WYsFkS/Yt+FcxnnLLok37HjJosFiWXQqTFn6iCoFBK+LajAwk08Xt2dWCyIfsW+HLJyTxUny7ug0H5GSGKI2CBEAAYlheLBS9MAAI9/tQN7KhsFJ/IfLBZEvzK0dxiCtSrUNJkdcwN0ZlUNbTja0AaF1LGLKZEnuH10Mi5MjYTJYsOM+VvRauZ2/e7AYkH0KxqVAhemRgDg3SHnyn7wWEqUHoEaleA0RB0UCgkvXp+DSL0Weyqb8M9vdoqO5BdYLIhO4ZfbTisFJ/EO9v0rsji4SR4mIliLl28YCEkCFmw8hG8KjoiO5PNYLIhOYUxnsSgqb0BVQ5vgNJ7PvmSUw2JBHmhkvwjkjukHAJj9eSHKalsEJ/JtLBZEpxCp1zr+kVxezOWQM5Fl2bEUksWtvMlDzZqQgqG9QtFosmDGgm1ot9pER/JZLBZEpzEuPRoA5yzOpryuFbXNZqiVEvrH6kXHITollVKBV6YOgkGnQn5ZHZ7/vlh0JJ/FYkF0GvbbTlfvrYHJwmny07FfrUiL0UOrUgpOQ3R68SEB+PfvcgAAb67ajxW8GukSLBZEp5ERZ0CkXosWsxUbS2tFx/FY9mLBE03JG1yWGYNbRvQCANz/ST5nqFyAxYLoNBQKCWPTIgFwOeRMHCeacsdN8hIPX94f6TF6HGs2Y9bH22HlRnhOxWJBdAYnzlnwvIHfstlkFJbzigV5F51aidduGowAtRLr9h3DGyv3iY7kU1gsiM5gVEoE1EoJB4+1YH9Ns+g4HufAsWY0tlmgVSmQEh0sOg7ROesXFYwnJ2cCAF5cugebD3C501lYLIjOIFirwvA+4QCA5VwO+Q371YqMOAPUSr6dkHe5dnA8rhkUD6tNxsyF21HXYhYdySfwnYDoLOx3h3DO4rfyy7gMQt5LkiQ8OTkTvcMDUV7Xir9+VsAlTydgsSA6C3ux2Fhai8a2dsFpPEtheR0AIJs7bpKXCtaq8NpNg6FWSvhhZyU+WH9QdCSvx2JBdBa9I4KQHBEEi03G6r01ouN4DIvVhqLyBgAsFuTdMuONmD2xPwDgX9/swo4jPNW4J1gsiM7BWC6H/Ma+6ma0tlsRpFEiOYKDm+Td/jCyN8anR8FsteHuBdvQbLKIjuS1WCyIzsH4zmKxorgKNt7zDgDI79y/IjPeCIVCEhuGqIckScJz1+UgxqDD/upmPPbVDtGRvBaLBdE5GNo7DMFaFWqazI47IfxdYeeOmzmJIWKDEDlJWJAGr9w4EAoJ+GzLYSzadlh0JK/EYkF0DjQqBUanRAAAlnE5BMAvO25mccdN8iHDk8Mxc3wqAODvi4pQyv1ruqxLxWLu3LnIzs6GwWCAwWDAiBEj8N1337kqG5FHsc9ZcD8LwGyxYVdFIwAgh7eako+ZMa4fhvcJQ7PZirsXbOUhhF3UpWKRkJCAZ555Blu2bMHmzZsxbtw4XH311dixg2tR5PvGpnUUi8Lyer8/uKj4aCPMVhtCAtVIDAsQHYfIqZQKCa/cOAihgWoUlTfgme92i47kVbpULCZNmoTLL78cKSkpSE1NxVNPPYXg4GCsX7/eVfmIPEakXoucztsqVxRXC04jVkHn/hVZ8UZIEgc3yffEGHV44fqOI9bfXXsAS3dWCk7kPbo9Y2G1WrFw4UI0NzdjxIgRp32eyWRCQ0PDSQ8ib2VfDlm227/fZAocO25yvoJ817j0aNw2qg8A4MHP8lFR3yo4kXfocrEoLCxEcHAwtFot7rjjDixatAgDBgw47fPz8vJgNBodj8TExB4FJhLJvgvnmr01fr3uWsATTclP/PWyNGTFG1HX0o6ZC7bDYrWJjuTxulws0tLSsH37dmzYsAF33nknpk+fjp07d572+bNnz0Z9fb3jUVZW1qPARCJlxhkRqdei2WzFptLjouMI0Wq2Yk9lx+Amr1iQr9OqlJgzdRCCNEpsPFCLOT+ViI7k8bpcLDQaDfr164chQ4YgLy8POTk5eOWVV077fK1W67iLxP4g8lYKhYSxaZEA/HcXzp0VDbDaZETqtYgx6ETHIXK53hFBeHpKFgBgzk978fO+Y4ITebYe72Nhs9lgMpmckYXIK/xy2ql/zlnY96/I5uAm+ZGrB8bj+qEJsMnArI+3obaZR6yfTpeKxezZs7Fq1SocOHAAhYWFmD17NlasWIFp06a5Kh+RxxmVEgm1UsKBYy3YX90kOo7b2Xfc5HwF+ZvHr8pA38ggVDaY8MCn+Txi/TS6VCyqqqpwyy23IC0tDePHj8emTZvw/fff4+KLL3ZVPiKPE6xVYXifcAD+uRxiPyOE8xXkbwI1HUesa1QK/LS7Cu+sKRUdySN1qVi88847OHDgAEwmE6qqqvDjjz+yVJBf8tfTThvb2rG/c4vjLBYL8kP9Yw34x5Udd0I+u2S3Y2mQfsGzQoi6wT5nsbG0Fo1t7YLTuE9ReQNkGYgPCUBEsFZ0HCIhbh6ehMsyYtBulXH3gm1+9R5wLlgsiLqhT0QQ+kQEwWKTsWZvjeg4blPYueMml0HIn0mShGevzUZ8SAAOHmvBI4uKOG9xAhYLom4a54fLIfmdg5tcBiF/ZwxU49WpA6FUSPgq/wg+3cIj1u1YLIi6yV4slhdXwWbzj59W7HeE8ERTImBIrzDcf0nHEeuPfbkDJVWNghN5BhYLom46r3cYgrUq1DSZUdi5xbUvO95sxqHaFgBAZjyvWBABwB0X9sWofhFobbdixvxtaGv3363+7VgsiLpJo1JgVL8IAP6xHGIvT30igmAMUAtOQ+QZFAoJL96Qg4hgDXYfbcS/vj39ERf+gsWCqAfG9f9lOcTX2W+ry+LVCqKTROl1ePH6gQCAD9cfwneFFWIDCcZiQdQDYzrPDSk4XI+qhjbBaVyr4DCPSic6nQtTI3HHRX0BAH/9vABlncuG/ojFgqgHovQ6xz+0K4qrBadxrQJu5U10RvdfkoqBiSFobLNg5sJtaPfTI9ZZLIh6aGya7992WtXQhqMNbVBIQEYcTygmOhW1UoE5UwdBr1Nh66E6vLR0j+hIQrBYEPXQ+M45i9V7q2G2+OZPKParFf2ighGkVQlOQ+S5EsMC8ey12QCAuSv3YfVe376SeSosFkQ9lBlnRESwFs1mKzaW1oqO4xIF5VwGITpXl2fFYtrwJMgycO/H+ahuNImO5FYsFkQ9pFBIGNs5xOmryyEFPNGUqEv+ceUApEXrUdNkwn2fbPebTfQAFgsipzhxF05fI8uyY8dNXrEgOjc6tRKv3TQIOrUCq/fW4M1V+0VHchsWCyInGJUSAbVSQmlNM/ZXN4mO41Tlda041myGSiEhPUYvOg6R10iJ1uOJqzIAAM//UIwtB48LTuQeLBZETqDXqTGsTxgA31sOsV+tSI/VQ6dWCk5D5F2uH5qISTlxsNpk3LNgG+pbff+IdRYLIiex33bqa8shjhNN40PEBiHyQpIk4alrMpEUFojyulY89HmBzx+xzmJB5CT2OYuNpbVoMlkEp3GewvI6AEAOBzeJusWgU2PO1EFQKSR8V3QU8zceEh3JpVgsiJwkOTIYfSKC0G6VscZH7l232WTHHhZZLBZE3ZaTGIK/XZYOAPjn1zux+2iD4ESuw2JB5ET25ZBlu3xjOeRgbQsa2yzQqhRIjebgJlFP3DaqD8amRcJksWHG/G1oMfvOlc0TsVgQOdEvt51W+8R96/b9KwbEGaBW8u2CqCcUCgnPX5eDKL0WJVVNeOIr3zxine8URE40rE8YgjRK1DSZUHSkXnScHrMvg+Rw/woipwgP1uLlGwdCkoCPN5fhy+3loiM5HYsFkRNpVAqMTvGdXTjtVyyy4jlfQeQsF/SNwN1j+wEAHllUhIPHmgUnci4WCyInsy+HeHuxsNpkFJV3DJjlJLJYEDnTPeNTcF7vUDSZLLh7wTafOsCQxYLIycakd1yxKDhcj6rGNsFpuq+kqgmt7VYEaZToExEsOg6RT1EpFXjlxkEwBqhRcLgez32/W3Qkp2GxIHKyKL3OsXSwoth7bzu1L4NkxhuhVEhiwxD5oLiQADx/XQ4A4K3Vpfhpd6XgRM7BYkHkAo67Q7x4OaTAcfAYl0GIXOXiAdG49YLeAIAHPi3A0Xrvvcppx2JB5AL2YrF6b43Xrp0WlPNEUyJ3mH15OjLiDKhtNmPWx9tg9fJb1VksiFwgK96IiGAtmkwWbDpQKzpOl5ktNuw60jG4ySsWRK6lVSkxZ+ogBGqUWL+/Fq8vLxEdqUdYLIhcQKGQMCbNe2873VPZCLPVBmOAGklhgaLjEPm85Mhg/GtyJgDg5R/3YGOp9/1AYsdiQeQi4714ziK/c3AzO8EISeLgJpE7TBmcgCmD42GTgZkLt+F4s1l0pG5hsSBykVEpEVArJeyvaUZpjXdtgFPIwU0iIZ68OhN9IoJQUd+GBz/zziPWWSyIXESvU+O83mEAvG85JN9+oml8iNggRH4mSKvCnKmDoFEq8OOuSry37oDoSF3GYkHkQt5422lbuxV7KhsBcMdNIhEy4414+PKOI9bz/rcbReXede4QiwWRC9mLxYbSY2gyeccRyTuONMBqkxERrEWMQSc6DpFfmn5Bb1w8IBpmqw13L9jmNe8fAIsFkUslRwajd3gg2q0y1uz1jl04CzsHN3M4uEkkjCRJeO532Ygz6lBa04xHFxeJjnTOWCyIXGyslx1KZt9xM4uDm0RChQRq8MrUQVBIwBfbyvH5lsOiI50TFgsiF3PMWRRXw+YFO+rZd9zM4Y6bRMKd1zsM905IBQD848si7KtuEpzo7FgsiFxsWJ8wBGmUqG40oeiIZw9hNZksjjcuXrEg8gx3je2HEcnhaDFbcff8bWhrt4qOdEYsFkQuplUpMSolAoDnL4cUlddDloH4kABEBGtFxyEiAEqFhJdvHIiwIA12VjTgme88+4h1FgsiN/CW207tR6Xbj30nIs8QbdDhhes7jlh/b90BfL/jqOBEp8diQeQGY9M6ikX+4XpUN5oEpzk9x1Hp3L+CyOOMTYvCny9MBgD89bMClNe1Ck50aiwWRG4QZdA5rgIsL/bcqxaOYsEdN4k80gOXpCEnwYj61nbMXLANFqtNdKTfYLEgcpOxHr4cUtdixqHaFgBcCiHyVBqVAnOmDoZeq8Lmg8fxyrK9oiP9BosFkZvY5yxW762B2eJ5P2XYr1b0Dg+EMVAtOA0RnU5SeCCenpIFAHhteQnWldQITnQyFgsiN8mONyIiWIMmkwWbD9SKjvMbheX2E01DxAYhorOalBOHG89LhCwDMz/ejpomz5ndYrEgchOFQsKYziHOZR64HJJfVgeAR6UTeYvHJmWgX1QwqhtNeODTfI/ZgI/FgsiNPPm2U16xIPIuARolXrtpELQqBVYUV+PtNftFRwLAYkHkVqNSIqBSSNhf04wDNc2i4zhUNbahor4NCgnIiDOIjkNE5yg9xoBHJw0AAPx7STG2d155FInFgsiNDDo1hvUJA+BZu3AWdg5u9osKRpBWJTgNEXXFTcOScEVWLCw2GXcv2IqGtnaheVgsiNxsnAeedppvP9GU+1cQeR1JkvD0lCwkhAagrLYVs78ohCyLm7dgsSByM/t+FhtKj6HJZBGcpkNh51beOdxxk8grGQPUeHXqIKgUEr4tqMAagbeg8ponkZslRwShV3ggDh5rwZq9NbgsM0ZoHlmWHXtYcGMsIu81OCkUsy/vj0CNEqP6RQjLwSsWRG4mSZJH3R1ypL4Nx5rNUCkk9I/l4CaRN7ttVB9MHZYESZKEZWCxIBLAMWdRXCX83vOCzinytBg9dGql0CxE5P1YLIgEGNYnDIEaJaobTdhxpEFolgLuX0FETsRiQSSAVvXLGqjou0MKOgc3ueMmETkDiwWRIOP7/7IcIsqJg5ssFkTkDCwWRIKM7Tw3JL+sDtWNYg4QOnCsBY1tFmhVCqRG64VkICLfwmJBJEiUQYfM+I67MFYIumphXwYZEGeAWsm3AyLqOb6TEAk0rvOqxXJhxaJzGYT7VxCRk7BYEAk0rn80AGD1nhqYLTa3v37hYd4RQkTO1aVikZeXh/POOw96vR5RUVGYPHkyiouLXZWNyOdlxxsREaxBo8mCzQdq3fraVpuMoiMc3CQi5+pSsVi5ciVyc3Oxfv16LF26FO3t7bjkkkvQ3Ow5xz8TeROFQsJFqWIOJdtX3YQWsxVBGiWSI4Pd+tpE5Lu6VCyWLFmCW2+9FRkZGcjJycF7772HQ4cOYcuWLa7KR+TzTtyF053yO3fczIg3QqkQt/0vEfmWHh1CVl/fcRk1LCzstM8xmUwwmX65la6hQewug0SeZnRqBFQKCfurm3Ggphm9I4Lc8rqFnTtu5nAZhIicqNvDmzabDbNmzcLIkSORmZl52ufl5eXBaDQ6HomJid19SSKfZNCpcV7vjnLuzuWQfPuJphzcJCIn6naxyM3NRVFRERYuXHjG582ePRv19fWOR1lZWXdfkshnOU47ddNyiNliw66KjquHvGJBRM7UrWIxY8YMfPPNN1i+fDkSEhLO+FytVguDwXDSg4hONrazWGzYX4tmk8Xlr7enshFmiw3GADWSwgJd/npE5D+6VCxkWcaMGTOwaNEi/PTTT+jTp4+rchH5lb6RQegVHgiz1YY1JTUuf70TzweRJA5uEpHzdKlY5Obm4sMPP8T8+fOh1+tx9OhRHD16FK2tra7KR+QXJElynB3y0y7XL4fYt/LO4o6bRORkXSoWc+fORX19PcaMGYPY2FjH4+OPP3ZVPiK/ceKchSzLLn2tAu64SUQu0qXbTV39Zkfkz4YnhyFQo0RVowk7jjQg00VXE9rarSiubATAHTeJyPl4VgiRh9CqlBjVLwIAsMyFyyE7KxpgtcmICNYi1qhz2esQkX9isSDyIO7YhbOgc8dNDm4SkSuwWBB5EPttpwWH61DTZDrLs7unoJwHjxGR67BYEHmQaIMOGXEGyDKworjaJa9x4q2mRETOxmJB5GHG25dDdlc6/Ws3mSzYV90EAMiKD3H61yciYrEg8jD25ZDVe2rQbrU59WsXlddDloE4ow6Req1TvzYREcBiQeRxchJCEB6kQaPJgk0Hap36tQu5fwURuRiLBZGHUSgkXJQWCQBY7uTTTvPtO25yvoKIXITFgsgDjU+PBgAsc3KxKOy8IySHVyyIyEVYLIg80OjUCKgUEvZXN+PgsWanfM26FjMOHmsBwDNCiMh1WCyIPJBBp8bQ3qEAgJ+cdNXCfrWid3ggjIFqp3xNIqJfY7Eg8lCOXTidVCzs+1dkcRmEiFyIxYLIQ43rnLPYsL8WzSZLj7+e/aj0HA5uEpELsVgQeai+kUFICguE2WrDmpKaHn89xxULzlcQkQuxWBB5KEmSHMshPb3ttKqxDRX1bZAkuOw4diIigMWCyKONPWHOQpblbn8d+8ZY/SKDEaRVOSUbEdGpsFgQebDhfcIQqFGiqtGEHUcauv11CrjjJhG5CYsFkQfTqZUY2S8CQM/uDrEPbvJEUyJyNRYLIg/X09tOZVl27GHBYkFErsZiQeThxqZ1FIv8w3WoaTJ1+fOP1LehpskMlUJC/1iDs+MREZ2ExYLIw8UYdciIM0CWgRXF1V3+/MLOZZC0GD10aqWT0xERnYzFgsgL9OS20/zDXAYhIvdhsSDyAvbbTlftqUa71dalzy3kHSFE5EYsFkReICchBGFBGjSaLNh84Pg5f54sy447QrjjJhG5A4sFkRdQKiSMSYsEAPy0u/KcP+/gsRY0tFmgUSmQFqN3VTwiIgcWCyIv0Z3bTvM7r1YMiDVAreRfdyJyPb7TEHmJ0SmRUCok7KtuxqFjLef0Ofb5Cp5oSkTuwmJB5CWMAWoM7RUK4NyXQxwnmnJwk4jchMWCyIuM79+xHLLsHJZDrDYZRUd4xYKI3IvFgsiL2OcsNuyvRbPJcsbn7qtuQovZikCNEsmRwe6IR0TEYkHkTfpGBiMxLABmqw1rS2rO+Fz7MkhmvBFKheSOeERELBZE3kSSJIzrPDtkefGZl0McJ5py/woiciMWCyIvM65/NICO205lWT7t8+xXLLITQ9wRi4gIAIsFkdcZ3icMAWolKhtM2HGk4ZTPMVts2FnR8Xu8YkFE7sRiQeRldGolRvaLAHD6Q8n2VDbCbLHBoFOhV3igO+MRkZ9jsSDyQo5dOE8zZ1FwwsFjksTBTSJyHxYLIi9kLxbby+pwrMn0m98vLK8DwKPSicj9WCyIvFCMUYcBsQbIMrCiuPo3v59fZr9iwWJBRO7FYkHkpU63HNLWbsWeykYAHUshRETuxGJB5KXGdW7vvaq4Gu1Wm+PjOysaYLHJiAjWINaoExWPiPwUiwWRl8pJCEFYkAaNJgs2Hzju+HghBzeJSCAWCyIvpVRIGJMaCeDkXTjzO3fczOL+FUQkAIsFkRcba5+zOGE/C/sVi5xEFgsicj8WCyIvdmFqJJQKCSVVTTh0rAVNJgtKqpsAAFnxIWLDEZFfYrEg8mLGADWG9goFAPy0uxI7yushy0CcUYdIvVZwOiLyRywWRF7ul9tOqx07bmZx/woiEoTFgsjL2YvF+n3HsH7/MQDcv4KIxGGxIPJy/aKCkRgWALPVhmWdQ5zccZOIRGGxIPJykiRhXFrUSR/L5uAmEQnCYkHkA+y3nQJAr/BAGAPVAtMQkT9jsSDyAecnhyNArQTA+QoiEovFgsgH6NRKXJgaAQCO20+JiERQiQ5ARM7x5ORMjE6JxPVDE0VHISI/xmJB5COi9DrcfH4v0TGIyM9xKYSIiIichsWCiIiInIbFgoiIiJyGxYKIiIichsWCiIiInIbFgoiIiJyGxYKIiIichsWCiIiInKbLxWLVqlWYNGkS4uLiIEkSFi9e7IJYRERE5I26XCyam5uRk5OD119/3RV5iIiIyIt1eUvviRMnYuLEia7IQkRERF7O5WeFmEwmmEwmx68bGhpc/ZJEREQkiMuHN/Py8mA0Gh2PxESevEhEROSrXH7FYvbs2bjvvvscv66vr0dSUhKvXBAREXkR+7/bsiyf8XkuLxZarRZardbxa3swXrkgIiLyPo2NjTAajaf9fZcXi1+Li4tDWVkZ9Ho9JEly2tdtaGhAYmIiysrKYDAYnPZ1vYm//xnw+/fv7x/gn4G/f/8A/wxc+f3LsozGxkbExcWd8XldLhZNTU0oKSlx/Lq0tBTbt29HWFgYkpKSzvr5CoUCCQkJXX3Zc2YwGPzyP6YT+fufAb9///7+Af4Z+Pv3D/DPwFXf/5muVNh1uVhs3rwZY8eOdfzaPj8xffp0vPfee139ckRERORDulwsxowZc9bBDSIiIvJPPnNWiFarxWOPPXbSoKi/8fc/A37//v39A/wz8PfvH+CfgSd8/5LMyw9ERETkJD5zxYKIiIjEY7EgIiIip2GxICIiIqdhsSAiIiKnYbEgIiIip/GZYvH666+jd+/e0Ol0GD58ODZu3Cg6ktusWrUKkyZNQlxcHCRJwuLFi0VHcqu8vDycd9550Ov1iIqKwuTJk1FcXCw6ltvMnTsX2dnZjp32RowYge+++050LGGeeeYZSJKEWbNmiY7iNo8//jgkSTrpkZ6eLjqWW5WXl+Pmm29GeHg4AgICkJWVhc2bN4uO5Ta9e/f+zX8DkiQhNzfX7Vl8olh8/PHHuO+++/DYY49h69atyMnJwaWXXoqqqirR0dyiubkZOTk5eP3110VHEWLlypXIzc3F+vXrsXTpUrS3t+OSSy5Bc3Oz6GhukZCQgGeeeQZbtmzB5s2bMW7cOFx99dXYsWOH6Ghut2nTJrz55pvIzs4WHcXtMjIyUFFR4XisWbNGdCS3OX78OEaOHAm1Wo3vvvsOO3fuxAsvvIDQ0FDR0dxm06ZNJ/3/v3TpUgDAdddd5/4wsg8YNmyYnJub6/i11WqV4+Li5Ly8PIGpxAAgL1q0SHQMoaqqqmQA8sqVK0VHESY0NFR+++23Rcdwq8bGRjklJUVeunSpfNFFF8kzZ84UHcltHnvsMTknJ0d0DGH+9re/yaNGjRIdw6PMnDlT7tu3r2yz2dz+2l5/xcJsNmPLli2YMGGC42MKhQITJkzAzz//LDAZiVJfXw8ACAsLE5zE/axWKxYuXIjm5maMGDFCdBy3ys3NxRVXXHHSe4E/2bt3L+Li4pCcnIxp06bh0KFDoiO5zVdffYWhQ4fiuuuuQ1RUFAYNGoS33npLdCxhzGYzPvzwQ/zxj3906ini58rri0VNTQ2sViuio6NP+nh0dDSOHj0qKBWJYrPZMGvWLIwcORKZmZmi47hNYWEhgoODodVqcccdd2DRokUYMGCA6Fhus3DhQmzduhV5eXmiowgxfPhwvPfee1iyZAnmzp2L0tJSjB49Go2NjaKjucX+/fsxd+5cpKSk4Pvvv8edd96Je+65B++//77oaEIsXrwYdXV1uPXWW4W8fpcPISPyZLm5uSgqKvKr9WUASEtLw/bt21FfX4/PPvsM06dPx8qVK/2iXJSVlWHmzJlYunQpdDqd6DhCTJw40fG/s7OzMXz4cPTq1QuffPIJbrvtNoHJ3MNms2Ho0KF4+umnAQCDBg1CUVER3njjDUyfPl1wOvd75513MHHiRMTFxQl5fa+/YhEREQGlUonKysqTPl5ZWYmYmBhBqUiEGTNm4JtvvsHy5cuRkJAgOo5baTQa9OvXD0OGDEFeXh5ycnLwyiuviI7lFlu2bEFVVRUGDx4MlUoFlUqFlStX4tVXX4VKpYLVahUd0e1CQkKQmpqKkpIS0VHcIjY29jclun///n61HGR38OBB/Pjjj/jTn/4kLIPXFwuNRoMhQ4Zg2bJljo/ZbDYsW7bM79aY/ZUsy5gxYwYWLVqEn376CX369BEdSTibzQaTySQ6hluMHz8ehYWF2L59u+MxdOhQTJs2Ddu3b4dSqRQd0e2ampqwb98+xMbGio7iFiNHjvzNLeZ79uxBr169BCUS591330VUVBSuuOIKYRl8Yinkvvvuw/Tp0zF06FAMGzYML7/8Mpqbm/GHP/xBdDS3aGpqOuknk9LSUmzfvh1hYWFISkoSmMw9cnNzMX/+fHz55ZfQ6/WO2Rqj0YiAgADB6Vxv9uzZmDhxIpKSktDY2Ij58+djxYoV+P7770VHcwu9Xv+beZqgoCCEh4f7zZzNAw88gEmTJqFXr144cuQIHnvsMSiVSkydOlV0NLe49957ccEFF+Dpp5/G9ddfj40bN2LevHmYN2+e6GhuZbPZ8O6772L69OlQqQT+8+72+1BcZM6cOXJSUpKs0WjkYcOGyevXrxcdyW2WL18uA/jNY/r06aKjucWpvncA8rvvvis6mlv88Y9/lHv16iVrNBo5MjJSHj9+vPzDDz+IjiWUv91uesMNN8ixsbGyRqOR4+Pj5RtuuEEuKSkRHcutvv76azkzM1PWarVyenq6PG/ePNGR3O7777+XAcjFxcVCc0iyLMtiKg0RERH5Gq+fsSAiIiLPwWJBRERETsNiQURERE7DYkFEREROw2JBRERETsNiQURERE7DYkFEREROw2JBRERETsNiQURERE7DYkFEREROw2JBRERETvP//HPHQxVM+O0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "t = [5.8, 4.7, 4.2, 1.2, 5.5, 4.8, 3.4, 2.2]\n",
    "\n",
    "plt.plot(t)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0],\n",
       "        [1, 0, 0, 0, 0],\n",
       "        [0, 2, 0, 0, 0],\n",
       "        [0, 0, 3, 0, 0]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "t = torch.diag(torch.tensor([1, 2, 3]), -2)\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.5000, 0.0000],\n",
       "        [0.0000, 0.5000]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = torch.eye(2)*2\n",
    "t.inverse()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0., 0., 0.,  ..., 0., 0., 0.],\n",
      "        [0., 0., 0.,  ..., 0., 0., 0.],\n",
      "        [0., 0., 0.,  ..., 0., 0., 0.],\n",
      "        ...,\n",
      "        [0., 0., 0.,  ..., 0., 0., 0.],\n",
      "        [0., 0., 0.,  ..., 0., 0., 0.],\n",
      "        [0., 0., 0.,  ..., 0., 0., 0.]])\n",
      "tensor([[2.0000, 0.0100, 0.0100,  ..., 0.0000, 0.0000, 0.0000],\n",
      "        [0.0100, 2.0000, 0.0100,  ..., 0.0000, 0.0000, 0.0000],\n",
      "        [0.0100, 0.0100, 2.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "        ...,\n",
      "        [0.0000, 0.0000, 0.0000,  ..., 2.0000, 0.0100, 0.0100],\n",
      "        [0.0000, 0.0000, 0.0000,  ..., 0.0100, 2.0000, 0.0100],\n",
      "        [0.0000, 0.0000, 0.0000,  ..., 0.0100, 0.0100, 2.0000]])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[1.0000, 0.0100, 0.0100,  ..., 0.0000, 0.0000, 0.0000],\n",
       "        [0.0100, 1.0000, 0.0100,  ..., 0.0000, 0.0000, 0.0000],\n",
       "        [0.0100, 0.0100, 1.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
       "        ...,\n",
       "        [0.0000, 0.0000, 0.0000,  ..., 1.0000, 0.0100, 0.0100],\n",
       "        [0.0000, 0.0000, 0.0000,  ..., 0.0100, 1.0000, 0.0100],\n",
       "        [0.0000, 0.0000, 0.0000,  ..., 0.0100, 0.0100, 1.0000]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qkvmask_num = int(4096/90)+1\n",
    "fc1mask_num = int((4096/31)/90)+1\n",
    "\n",
    "values = torch.tensor([1 for i1 in range(512)])\n",
    "maskqkv = torch.zeros(512, 512)\n",
    "print(maskqkv)\n",
    "for i1 in range(qkvmask_num):\n",
    "    if i1 == 0:\n",
    "        mask1 = torch.diag(values[:len(values)-i1], i1)\n",
    "        mask2 = torch.diag(values[:len(values)-i1], -i1)\n",
    "    else:\n",
    "        mask1 = torch.diag(0.01*values[:len(values)-i1], i1)\n",
    "        mask2 = torch.diag(0.01*values[:len(values)-i1], -i1)\n",
    "    maskqkv = maskqkv + mask1 + mask2\n",
    "print(maskqkv)\n",
    "maskqkv = maskqkv - torch.eye(512)\n",
    "maskqkv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 定义矩阵A\n",
    "A = np.array([[1.5, 2.2], [2.1, 3.6]])\n",
    "\n",
    "# 变换（1）：A乘以一个对角矩阵diag(3,4)得到B\n",
    "diag_matrix = np.diag([3.7, 4.2])\n",
    "B = A @ diag_matrix\n",
    "\n",
    "# 变换（2）：B加上一个偏移量0.5得到C\n",
    "C = B + 0.5\n",
    "\n",
    "# 变换（3）：A乘以一个矩阵[[1,3],[2,4]]得到D\n",
    "transform_matrix = np.array([[1.5, 0.6], [1.6, 2.7]])\n",
    "D = A @ transform_matrix\n",
    "\n",
    "# 绘制向量图\n",
    "fig, axs = plt.subplots(1, 3, figsize=(15, 5))\n",
    "\n",
    "# 绘制矩阵B的向量图\n",
    "for i in range(B.shape[0]):\n",
    "    axs[0].quiver(0, 0, B[i, 0], B[i, 1], angles='xy', scale_units='xy', scale=1, color='b')\n",
    "axs[0].set_xlim(-1, 15)\n",
    "axs[0].set_ylim(-1, 15)\n",
    "axs[0].axhline(0, color='black',linewidth=0.5)\n",
    "axs[0].axvline(0, color='black',linewidth=0.5)\n",
    "axs[0].grid(True)\n",
    "axs[0].set_aspect('equal')\n",
    "axs[0].set_title('Matrix B: A * diag(3,4)')\n",
    "\n",
    "# 绘制矩阵C的向量图\n",
    "for i in range(C.shape[0]):\n",
    "    axs[1].quiver(0, 0, C[i, 0], C[i, 1], angles='xy', scale_units='xy', scale=1, color='r')\n",
    "axs[1].set_xlim(-1, 15)\n",
    "axs[1].set_ylim(-1, 15)\n",
    "axs[1].axhline(0, color='black',linewidth=0.5)\n",
    "axs[1].axvline(0, color='black',linewidth=0.5)\n",
    "axs[1].grid(True)\n",
    "axs[1].set_aspect('equal')\n",
    "axs[1].set_title('Matrix C: B + 0.5')\n",
    "\n",
    "# 绘制矩阵D的向量图\n",
    "for i in range(D.shape[0]):\n",
    "    axs[2].quiver(0, 0, D[i, 0], D[i, 1], angles='xy', scale_units='xy', scale=1, color='g')\n",
    "axs[2].set_xlim(-1, 15)\n",
    "axs[2].set_ylim(-1, 15)\n",
    "axs[2].axhline(0, color='black',linewidth=0.5)\n",
    "axs[2].axvline(0, color='black',linewidth=0.5)\n",
    "axs[2].grid(True)\n",
    "axs[2].set_aspect('equal')\n",
    "axs[2].set_title('Matrix D: A * [[1,3],[2,4]]')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 定义矩阵A\n",
    "A = np.array([[1.5, 2.2], [2.1, 3.6]])\n",
    "\n",
    "# 变换（1）：A乘以一个对角矩阵diag([3.7, 4.2])得到B\n",
    "diag_matrix = np.diag([3.7, 4.2])\n",
    "B = A @ diag_matrix\n",
    "\n",
    "# 变换（2）：B在xoy平面向右平移0.5得到C\n",
    "C = B + np.array([0.5, 0])\n",
    "\n",
    "# 变换（3）：A乘以一个矩阵[[1.1, 3.4], [2.2, 4.6]]得到D\n",
    "transform_matrix = np.array([[1.1, 3.4], [2.2, 4.6]])\n",
    "D = A @ transform_matrix\n",
    "\n",
    "# 创建绘图对象\n",
    "fig, axs = plt.subplots(1, 3, figsize=(18, 6))\n",
    "\n",
    "# 函数：绘制向量图\n",
    "def plot_vectors(ax, vectors, colors, title, linestyle='-'):\n",
    "    for vector, color in zip(vectors, colors):\n",
    "        ax.quiver(0, 0, vector[0], vector[1], angles='xy', scale_units='xy', scale=1, color=color, linestyle=linestyle)\n",
    "    ax.axhline(0, color='black',linewidth=0.5)\n",
    "    ax.axvline(0, color='black',linewidth=0.5)\n",
    "    ax.grid(True)\n",
    "    ax.set_aspect('equal')\n",
    "    ax.set_title(title)\n",
    "    ax.set_xlim(-1, 20)\n",
    "    ax.set_ylim(-1, 20)\n",
    "\n",
    "# 矩阵A的向量（虚线表示）\n",
    "vectors_A = [A[0], A[1]]\n",
    "colors_A = ['grey', 'grey']\n",
    "\n",
    "# 绘制矩阵B的向量图\n",
    "plot_vectors(axs[0], vectors_A, colors_A, 'Matrix B: A * diag([3.7, 4.2])', linestyle='dashed')\n",
    "plot_vectors(axs[0], B, ['b', 'b'], 'Matrix B: A * diag([3.7, 4.2])')\n",
    "\n",
    "# 绘制矩阵C的向量图\n",
    "plot_vectors(axs[1], vectors_A, colors_A, 'Matrix C: B + [0.5, 0]', linestyle='dashed')\n",
    "plot_vectors(axs[1], C, ['r', 'r'], 'Matrix C: B + [0.5, 0]')\n",
    "\n",
    "# 绘制矩阵D的向量图\n",
    "plot_vectors(axs[2], vectors_A, colors_A, 'Matrix D: A * [[1.1, 3.4], [2.2, 4.6]]', linestyle='dashed')\n",
    "plot_vectors(axs[2], D, ['g', 'g'], 'Matrix D: A * [[1.1, 3.4], [2.2, 4.6]]')\n",
    "\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 定义矩阵A\n",
    "A = np.array([[1.5, 2.2], [2.1, 3.6]])\n",
    "\n",
    "# 变换（1）：A乘以一个对角矩阵diag([3.7, 4.2])得到B\n",
    "diag_matrix = np.diag([3.7, 4.2])\n",
    "B = A @ diag_matrix\n",
    "\n",
    "# 变换（2）：B在xoy平面向右平移0.5得到C\n",
    "C = B + np.array([0.5, 0])\n",
    "\n",
    "# 变换（3）：A乘以一个矩阵[[1.1, 3.4], [2.2, 4.6]]得到D\n",
    "transform_matrix = np.array([[1.2, 1.2], [4.1, 2.4]])\n",
    "D = A @ transform_matrix\n",
    "\n",
    "# 创建绘图对象\n",
    "fig, axs = plt.subplots(1, 3, figsize=(18, 6))\n",
    "\n",
    "# 函数：绘制向量图\n",
    "def plot_vectors(ax, vectors, colors, title, linestyle='-'):\n",
    "    for vector, color in zip(vectors, colors):\n",
    "        ax.quiver(0, 0, vector[0], vector[1], angles='xy', scale_units='xy', scale=1, color=color, linestyle=linestyle)\n",
    "    ax.axhline(0, color='black', linewidth=0.5)\n",
    "    ax.axvline(0, color='black', linewidth=0.5)\n",
    "    ax.grid(True)\n",
    "    ax.set_aspect('equal')\n",
    "    ax.set_title(title)\n",
    "    ax.set_xlim(-1, 20)\n",
    "    ax.set_ylim(-1, 20)\n",
    "\n",
    "def plot_vectors_with_offset(ax, vectors, offset, colors, title, linestyle='-'):\n",
    "    for vector, color in zip(vectors, colors):\n",
    "        ax.quiver(offset[0], offset[1], vector[0], vector[1], angles='xy', scale_units='xy', scale=1, color=color, linestyle=linestyle)\n",
    "    ax.axhline(0, color='black', linewidth=0.5)\n",
    "    ax.axvline(0, color='black', linewidth=0.5)\n",
    "    ax.grid(True)\n",
    "    ax.set_aspect('equal')\n",
    "    ax.set_title(title)\n",
    "    ax.set_xlim(-1, 20)\n",
    "    ax.set_ylim(-1, 20)\n",
    "\n",
    "# 矩阵A的向量（虚线表示）\n",
    "vectors_A = [A[0], A[1]]\n",
    "colors_A = ['grey', 'grey']\n",
    "\n",
    "# 绘制矩阵B的向量图\n",
    "plot_vectors(axs[0], vectors_A, colors_A, 'Matrix B: A * diag([3.7, 4.2])', linestyle='dashed')\n",
    "plot_vectors(axs[0], B, ['b', 'b'], 'Matrix B: A * diag([3.7, 4.2])')\n",
    "\n",
    "# 绘制矩阵C的向量图\n",
    "plot_vectors(axs[1], vectors_A, colors_A, 'Matrix C: B + [0.5, 0]', linestyle='dashed')\n",
    "plot_vectors_with_offset(axs[1], C, [0.5, 0], ['r', 'r'], 'Matrix C: B + [0.5, 0]')\n",
    "\n",
    "# 绘制矩阵D的向量图\n",
    "plot_vectors(axs[2], vectors_A, colors_A, 'Matrix D: A * [[1.1, 3.4], [2.2, 4.6]]', linestyle='dashed')\n",
    "plot_vectors(axs[2], D, ['g', 'g'], 'Matrix D: A * [[1.1, 3.4], [2.2, 4.6]]')\n",
    "\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\x1b[32m(asctime)s (name)s]\\x1b[0m'"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from termcolor import colored\n",
    "\n",
    "colored('(asctime)s (name)s]', 'green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.7500, 0.6000, 0.5000],\n",
       "        [0.4286, 0.3750, 0.3333]])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "a1 = torch.tensor([3])\n",
    "a2 = torch.tensor([[4, 5, 6],[7,8,9]])\n",
    "a1 / a2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import requests\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 图片的URL\n",
    "image_url = \"https://pic3.zhimg.com/80/v2-4563e810d05ec25ce283c7f54a271e2e_1440w.webp\"\n",
    "\n",
    "# 发送HTTP请求获取图片内容\n",
    "response = requests.get(image_url)\n",
    "response.raise_for_status()  # 确保请求成功\n",
    "\n",
    "# 将图片内容转换为Image对象\n",
    "image = Image.open(BytesIO(response.content))\n",
    "\n",
    "# 显示图片\n",
    "plt.imshow(image)\n",
    "plt.axis('off')  # 隐藏坐标轴\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import torch\n",
    "except:\n",
    "    print(\"Please install PyTorch first.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 269556.81it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "for idx, i in enumerate(tqdm(range(100))):\n",
    "    print(idx)\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from matplotlib import cm\n",
    "\n",
    "plt.style.use('_mpl-gallery')\n",
    "\n",
    "# Make data\n",
    "X = np.arange(-5, 5, 0.25)\n",
    "Y = np.arange(-5, 5, 0.25)\n",
    "X, Y = np.meshgrid(X, Y)\n",
    "R = np.sqrt(X**2 + Y**2)\n",
    "Z = np.sin(R)\n",
    "\n",
    "# Plot the surface\n",
    "fig, ax = plt.subplots(subplot_kw={\"projection\": \"3d\"})\n",
    "ax.plot_surface(X, Y, Z, vmin=Z.min() * 2, cmap=cm.Blues)\n",
    "\n",
    "ax.set(xticklabels=[],\n",
    "       yticklabels=[],\n",
    "       zticklabels=[])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.style.use('_mpl-gallery')\n",
    "\n",
    "# Prepare some coordinates\n",
    "x, y, z = np.indices((8, 8, 8))\n",
    "\n",
    "# Draw cuboids in the top left and bottom right corners\n",
    "cube1 = (x < 3) & (y < 3) & (z < 3)\n",
    "cube2 = (x >= 5) & (y >= 5) & (z >= 5)\n",
    "\n",
    "# Combine the objects into a single boolean array\n",
    "voxelarray = cube1 | cube2\n",
    "\n",
    "# Plot\n",
    "fig, ax = plt.subplots(subplot_kw={\"projection\": \"3d\"})\n",
    "ax.voxels(voxelarray, edgecolor='k')\n",
    "\n",
    "ax.set(xticklabels=[],\n",
    "       yticklabels=[],\n",
    "       zticklabels=[])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.style.use('_mpl-gallery-nogrid')\n",
    "\n",
    "# make data\n",
    "x = np.linspace(-4, 4, 6)\n",
    "y = np.linspace(-4, 4, 6)\n",
    "X, Y = np.meshgrid(x, y)\n",
    "U = X + Y\n",
    "V = Y - X\n",
    "\n",
    "# plot\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.quiver(X, Y, U, V, color=\"C0\", angles='xy',\n",
    "          scale_units='xy', scale=5, width=.015)\n",
    "\n",
    "ax.set(xlim=(-5, 5), ylim=(-5, 5))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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