Initial commit

Author: Bergam0t

examples/1_simplest_case/model_classes.py

@@ -0,0 +1,109 @@
+# ## Executing a model
+import pandas as pd
+import numpy as np
+from simulation_summary_functions import SimulationSummary
+
+def single_run(scenario, 
+               model,
+               rc_period=600,
+               random_no_set=1,
+               return_detailed_logs=False,
+               ):
+    '''
+    Perform a single run of the model and return the results
+
+    Parameters:
+    -----------
+
+    scenario: Scenario object
+        The scenario/paramaters to run
+
+    rc_period: int
+        The length of the simulation run that collects results
+
+    random_no_set: int or None, optional (default=DEFAULT_RNG_SET)
+        Controls the set of random seeds used by the stochastic parts of the 
+        model.  Set to different ints to get different results.  Set to None
+        for a random set of seeds.
+
+    Returns:
+    --------
+        pandas.DataFrame:
+        results from single run.
+    '''
+    # set random number set - this controls sampling for the run.
+    scenario.set_random_no_set(random_no_set)
+
+    # create an instance of the model
+    model = model(scenario)
+
+    # run the model
+    model.run(results_collection_period=rc_period)
+
+    # run results
+    summary = SimulationSummary(model)
+
+    if return_detailed_logs:
+        return {
+            'full_event_log': pd.DataFrame(model.full_event_log),
+            'patient_log':  pd.DataFrame(summary.patient_log),
+            'utilisation_audit': pd.DataFrame(model.utilisation_audit),
+            'summary_df': summary.summary_frame()
+        }
+
+    summary_df = summary.summary_frame()
+
+    return summary_df
+
+
+def multiple_replications(scenario,
+                          model,
+                          rc_period=600,
+                          n_reps=10,
+                          return_detailed_logs=False):
+    '''
+    Perform multiple replications of the model.
+
+    Params:
+    ------
+    scenario: Scenario
+        Parameters/arguments to configurethe model
+
+    rc_period: float, optional (default=DEFAULT_RESULTS_COLLECTION_PERIOD)
+        results collection period.  
+        the number of minutes to run the model to collect results
+
+    n_reps: int, optional (default=DEFAULT_N_REPS)
+        Number of independent replications to run.
+
+    Returns:
+    --------
+    pandas.DataFrame
+    '''
+
+    if return_detailed_logs:
+        results = [{'rep': rep+1,
+                    'results': single_run(scenario,
+                                          model,
+                                          rc_period,
+                                          random_no_set=(scenario.random_number_set)+rep,
+                                          return_detailed_logs=True)}
+                   for rep in range(n_reps)]
+
+        # format and return results in a dataframe
+
+        return results
+
+
+    # If not returning detailed logs, do some additional steps before returning the summary df
+    results = [single_run(scenario,
+                          model,
+                          rc_period,
+                          random_no_set=(scenario.random_number_set)+rep)
+               for rep in range(n_reps)]
+
+    # format and return results in a dataframe
+    df_results = pd.concat(results)
+    df_results.index = np.arange(1, len(df_results)+1)
+    df_results.index.name = 'rep'
+    return df_results