fix: easy supports list/array/sequence type as parameter (#348)

This change avoids using df.assign for adding parameters to the config
dataframe, this allows for lists to be used as parameters.

Fixes #347

cadCAD/tools/execution/easy_run.py

@@ -2,7 +2,7 @@
 import types
 from typing import Dict, Union
 
-import pandas as pd # type: ignore
+import pandas as pd  # type: ignore
 from cadCAD.configuration import Experiment
 from cadCAD.configuration.utils import config_sim
 from cadCAD.engine import ExecutionContext, ExecutionMode, Executor
@@ -47,8 +47,9 @@ def easy_run(
     """
 
     # Set-up sim_config
-    simulation_parameters = {'N': N_samples, 'T': range(N_timesteps), 'M': params}
-    sim_config = config_sim(simulation_parameters) # type: ignore
+    simulation_parameters = {'N': N_samples,
+                             'T': range(N_timesteps), 'M': params}
+    sim_config = config_sim(simulation_parameters)  # type: ignore
 
     # Create a new experiment
     exp = Experiment()
@@ -91,22 +92,27 @@ def easy_run(
         if assign_params == True:
             pass
         else:
-            params_set &= assign_params # type: ignore
+            params_set &= assign_params  # type: ignore
 
         # Logic for getting the assign params criteria
         if type(assign_params) is list:
-            selected_params = set(assign_params) & params_set # type: ignore
+            selected_params = set(assign_params) & params_set  # type: ignore
         elif type(assign_params) is set:
             selected_params = assign_params & params_set
         else:
             selected_params = params_set
+        # Attribute parameters to each row*
+        params_dict = select_config_M_dict(configs, 0, selected_params)
+
+        # Handles all cases of parameter types including list
+        for key, value in params_dict.items():
+            df[key] = df.apply(lambda _: value, axis=1)
 
-        # Attribute parameters to each row
-        df = df.assign(**select_config_M_dict(configs, 0, selected_params))
         for i, (_, n_df) in enumerate(df.groupby(['simulation', 'subset', 'run'])):
-            df.loc[n_df.index] = n_df.assign(
-                **select_config_M_dict(configs, i, selected_params)
-            )
+            params_dict = select_config_M_dict(configs, i, selected_params)
+            for key, value in params_dict.items():
+                df.loc[n_df.index, key] = df.loc[n_df.index].apply(
+                    lambda _: value, axis=1)
 
     # Based on Vitor Marthendal (@marthendalnunes) snippet
     if use_label == True: