[scripts] update with additional functions

run_analyses.py

@@ -1,7 +1,6 @@
 import os
 import logging
 import argparse
-import multiprocessing
 
 from code.analyses.baseline_response_properties import run_baseline_analysis
 from code.analyses.receptive_field_estimation import run_receptive_field_analysis
@@ -9,6 +8,7 @@ from code.analyses.phase_analysis import phase_analysis
 from code.analyses.driven_response_properties import run_driven_response_analysis
 from code.analyses.homogeneous_populations import run_homogeneous_population_analysis
 from code.analyses.heterogeneous_populations import run_heterogeneous_population_analysis
+from code.util import DelayType, default_job_count
 
 logging.basicConfig(level=logging._nameToLevel["WARN"], force=True)
 
@@ -18,36 +18,45 @@ def run_analyses(args):
     data_folder = "raw_data"
     results_folder = "derived_data"
     stimulus_folder = "stimuli"
-    num_cores = args.jobs
 
     base_results = run_baseline_analysis(list_folder, data_folder)
-    rf_results = run_receptive_field_analysis(list_folder, data_folder, num_cores=num_cores)
+    rf_results = run_receptive_field_analysis(list_folder, data_folder, num_cores=args.jobs)
     base_results.to_csv(os.path.join(results_folder, "baseline_properties.csv"), sep=";")
     rf_results.to_csv(os.path.join(results_folder, "receptivefield_positions.csv"), sep=";")
 
     full_df = phase_analysis(results_folder)
     full_df.to_csv(os.path.join(results_folder, "Figure2_baseline_properties.csv"), sep=";")
 
-    driven_results = run_driven_response_analysis(list_folder, data_folder, results_folder, num_cores=num_cores)
+    driven_results = run_driven_response_analysis(list_folder, data_folder, results_folder, num_cores=args.jobs)
     driven_results.to_csv(os.path.join(results_folder, "whitenoise_trials.csv"), sep=";")
 
     # NOTE: The following analyses will take a while... depending on your hardware this will be a few hours
     whitenoise_trial_info = os.path.join(results_folder, "whitenoise_trials.csv")
-    homogeneous_results = run_homogeneous_population_analysis(whitenoise_trial_info, data_folder, stimulus_folder, num_cores=num_cores)
+    homogeneous_results = run_homogeneous_population_analysis(whitenoise_trial_info, data_folder, stimulus_folder, num_cores=args.jobs)
     homogeneous_results.to_csv(os.path.join(results_folder, "homogeneous_populationcoding.csv"), sep=";")
-    population_sizes=[2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30]
-    delays=[0.0, 0.0005, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.0125, 0.015]
-    kernels=[0.000125, 0.00025, 0.0005, 0.001, 0.002, 0.003, 0.005, 0.01, 0.015, 0.02, 0.025]
-    heterogeneous_results = run_heterogeneous_population_analysis(whitenoise_trial_info, data_folder, stimulus_folder,
-                                                                  population_sizes, delays, kernels, num_cores=num_cores)
+
+    population_sizes      = [2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30]
+    delays                = [-1.0, 0.0, 0.000125, 0.00025, 0.0005, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.0125, 0.015]
+    kernels               = [0.000125, 0.00025, 0.0005, 0.001, 0.002, 0.003, 0.005, 0.01, 0.015, 0.02, 0.025]
+    delay_types           = [DelayType.Gaussian]
+    heterogeneous_results = None
+
+    for dt in delay_types:
+        results = run_heterogeneous_population_analysis(whitenoise_trial_info, data_folder, 
+                                                        stimulus_folder, population_sizes, 
+                                                        delays, kernels, num_cores=args.jobs,delay_type=dt)
+        if heterogeneous_results is None:
+            heterogeneous_results = results
+        else:
+            heterogeneous_results = heterogeneous_results.append(results, ignore_index=True)
     heterogeneous_results.to_csv(os.path.join(results_folder, "heterogeneous_populationcoding.csv"), sep=";")
 
 
 def create_parser():
     parser = argparse.ArgumentParser(description="Command line tool for the data analysis of the Hladnik & Grewe population coding project. \nThis may take a while... On a single core, this may easily exceed 10 hours...")
 
-    dflt_num_cores = int(multiprocessing.cpu_count() / 2)
-    parser.add_argument("-j", "--jobs", type=int, default=dflt_num_cores, help=f"The number of parallel processes spawned for the analyses (defaults to {dflt_num_cores})")
+    default_num_cores = default_job_count()
+    parser.add_argument("-j", "--jobs", type=int, default=default_num_cores, help=f"The number of parallel processes spawned for the analyses (defaults to {default_num_cores})")
     parser.set_defaults(func=run_analyses)
     return parser
 

run_simulation.py

@@ -1,21 +1,28 @@
 import argparse
-import multiprocessing
 
 from code.simulations.lif_simulation import main as run_simulations
+from code.simulations.lif_simulation import command_line_parser as lif_parser
+from code.simulations.lif_surface import command_line_parser as surface_parser
+
 
 def create_parser():
     parser = argparse.ArgumentParser(description="Command line tool for running the LIF simulations of the Hladnik & Grewe population coding project. This may take a while! On a single core, this may easily exceed 6 hours ...")
-    dflt_num_cores = int(multiprocessing.cpu_count() / 2)
+    subparsers = parser.add_subparsers(title="commands",
+                                       help="Sub commands for plotting different tasks",
+                                       description="")
 
-    parser.add_argument("-j", "--jobs", type=int, default=dflt_num_cores, help=f"The number of parallel processes spawned for the simulation (defaults to {dflt_num_cores})")
-    parser.set_defaults(func=run_simulations)
+    lif_parser(subparsers)
+    surface_parser(subparsers)
     return parser
 
 
 def main():
     parser = create_parser()
     args = parser.parse_args()
-    args.func(args)
+    if hasattr(args, "func"):
+        args.func(args)
+    else:
+        parser.print_help()
 
 
 if __name__ == "__main__":