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

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merged 1 commit into from
Mar 14, 2024
Merged

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

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26 changes: 16 additions & 10 deletions cadCAD/tools/execution/easy_run.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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()
Expand Down Expand Up @@ -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:
Expand Down