Merge branch 'master' of github.com:cadCAD-org/cadCAD

.github/workflows/cadcad-ci.yml

@@ -7,6 +7,7 @@ on:
     branches: [ "master" ]
   pull_request:
     branches: [ "master" ]
+  workflow_dispatch:
 
 permissions:
   contents: read

cadCAD/configuration/utils/__init__.py

@@ -186,7 +186,7 @@ def config_sim(config_dict: ConfigurationDict):
                 raise Exception('When sweeping, `M` list lengths should either be 1 and/or equal. More than two distinct lengths are not allowed')
             elif (distinct_param_value_lengths == 1) and (0 in param_values_length_set):
                 return config_dict
-            elif (1 in param_values_length_set):
+            elif (distinct_param_value_lengths == 1) or (1 in param_values_length_set):
                 return [{**config_dict, "M": M} 
 
                         for M in flatten_tabulated_dict(tabulate_dict(params))]

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
@@ -41,14 +41,16 @@ def easy_run(
     assign_params: Union[bool, set] = True,
     drop_substeps=True,
     exec_mode='local',
+    deepcopy_off=False,
 ) -> pd.DataFrame:
     """
     Run cadCAD simulations without headaches.
     """
 
     # 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()
@@ -64,7 +66,7 @@ def easy_run(
         _exec_mode = ExecutionMode().local_mode
     elif exec_mode == 'single':
         _exec_mode = ExecutionMode().single_mode
-    exec_context = ExecutionContext(_exec_mode)
+    exec_context = ExecutionContext(_exec_mode, additional_objs={'deepcopy_off': deepcopy_off})
     executor = Executor(exec_context=exec_context, configs=configs)
 
     # Execute the cadCAD experiment
@@ -91,22 +93,27 @@ 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:

setup.py

@@ -33,6 +33,6 @@
     author_email="[email protected]",
     license="LICENSE.txt",
     packages=find_packages(),
-    install_requires=["pandas", "funcy", "dill", "pathos", "numpy", "pytz", "six"],
+    install_requires=["pandas", "funcy", "dill", "pathos", "numpy", "pytz", "six", "tqdm"],
     python_requires=">=3.9.0",
 )

testing/test_param_count.py

@@ -0,0 +1,77 @@
+from cadCAD.configuration import Experiment
+from cadCAD.configuration.utils import config_sim
+from cadCAD.engine import Executor, ExecutionContext, ExecutionMode
+import pytest
+
+P_no_lst = {'pA': 1, 'pB': 2, 'pC': 3}
+P_single_lst = {'pA': [1], 'pB': [1], 'pC': [3]}
+P_single_swp = {'pA': [4, 5, 6], 'pB': [1], 'pC': [3]}
+P_all_swp = {'pA': [7, 8, 9], 'pB': [1, 2, 3], 'pC': [1, 2, 3]}
+P_all_but_one_swp = {'pA': [7, 8, 9], 'pB': [1, 2, 3], 'pC': [1]}
+Ps = [P_no_lst, P_single_lst, P_single_swp, P_all_swp, P_all_but_one_swp]
+
+CONFIG_SIGNATURES_TO_TEST = [(3, 3, 3, 3, 3), (1, 3, 3, 3, 3),
+ (3, 1, 3, 3, 3), (1, 1, 3, 3, 3),
+ (3, 3, 1, 3, 3), (1, 3, 1, 3, 3), (1, 1, 1, 3, 3)]
+
+
+def run_experiment(exp: Experiment, mode: str):
+    exec_context = ExecutionContext(mode)
+    executor = Executor(exec_context=exec_context, configs=exp.configs)
+    (records, tensor_field, _) = executor.execute()
+    return records
+
+
+def param_count_test_suf_generator(provided_params):
+    def s_test_param_count(params, _2, _3, _4, _5):
+        assert params.keys() == provided_params.keys(), 'Params are not matching'
+        return ('varA', None)
+    return s_test_param_count
+
+
+def param_count_test_policy_generator(provided_params):
+    def p_test_param_count(params, _2, _3, _4):
+        assert params.keys() == provided_params.keys(), 'Params are not matching'
+        return {'sigA': None}
+    return p_test_param_count
+
+def create_experiments(N_simulations=3, N_sweeps=3, N_runs=3, N_timesteps=3, N_substeps=3, params={}) -> Experiment:
+
+    INITIAL_STATE = {'varA': None}
+    PSUBs = [{'policies': {'sigA': param_count_test_policy_generator(params)}, 'variables': {'varA': param_count_test_suf_generator(params)}}] * N_substeps
+
+    SIM_CONFIG = config_sim(
+        {
+            "N": N_runs,
+            "T": range(N_timesteps),
+            "M": params,  # Optional
+        }
+    )
+
+    exp = Experiment()
+    for i_sim in range(N_simulations):
+        exp.append_model(
+            sim_configs=SIM_CONFIG,
+            initial_state=INITIAL_STATE,
+            partial_state_update_blocks=PSUBs
+        )
+    return exp
+
+
+def expected_rows(N_simulations, N_sweeps, N_runs, N_timesteps, N_substeps,P) -> int:
+    return N_simulations * N_sweeps *  N_runs * (N_timesteps * N_substeps + 1)
+
+
+@pytest.mark.parametrize("N_sim,N_sw,N_r,N_t,N_s", CONFIG_SIGNATURES_TO_TEST)
+@pytest.mark.parametrize("P", Ps)
+def test_row_count_single(N_sim, N_sw, N_r, N_t, N_s, P):
+    args = (N_sim, N_sw, N_r, N_t, N_s, P)
+    len(run_experiment(create_experiments(*args), 'single_proc'))
+
+
+
+@pytest.mark.parametrize("N_sim,N_sw,N_r,N_t,N_s", CONFIG_SIGNATURES_TO_TEST)
+@pytest.mark.parametrize("P", Ps)
+def test_row_count_local(N_sim, N_sw, N_r, N_t, N_s, P):
+    args = (N_sim, N_sw, N_r, N_t, N_s, P)
+    len(run_experiment(create_experiments(*args), 'local_proc'))

testing/tests/cadCAD_memory_address.json

@@ -1 +1 @@
-{"memory_address": "0x111857380"}
+{"memory_address": "0x111444900"}

testing/tools/test_tools.py

@@ -0,0 +1,66 @@
+from cadCAD.tools.execution import easy_run
+
+# def test_empty_easy_run():
+#     state_variables = {
+#     }
+#
+#     params = {
+#     }
+#
+#     psubs = [
+#         {
+#             'policies': {},
+#             'variables': {},
+#         },
+#     ]
+#
+#     N_timesteps = 2
+#
+#     N_samples = 1
+#
+#     results = easy_run(
+#         state_variables,
+#         params,
+#         psubs,
+#         N_timesteps,
+#         N_samples,
+#     )
+#     print(results)
+
+
+
+def test_easy_run():
+    state_variables = {
+        'a':0.5,
+    }
+
+    params = {
+        'c':[1, 2],
+        'd':[1, 2],
+    }
+
+    def p(params, substep, state_history, previous_state):
+        a_delta = 1 - params['c'] * previous_state['a']
+        return {'a_delta': a_delta}
+
+    def s(params, substep, state_history, previous_state, policy_input):
+        return 'a', previous_state['a'] + policy_input['a_delta']
+
+    psubs = [
+        {
+            'policies': {'p': p},
+            'variables': {'a': s},
+        },
+    ]
+
+    N_timesteps = 2
+
+    N_samples = 1
+
+    results = easy_run(
+        state_variables,
+        params,
+        psubs,
+        N_timesteps,
+        N_samples,
+    )