diff --git a/act/analysis.py b/act/analysis.py
index ca3733c..8e7b2d9 100644
--- a/act/analysis.py
+++ b/act/analysis.py
@@ -144,14 +144,9 @@ def save_mse_corr(
 
 
 def print_run_stats(config: SimulationConfig):
-    output_folder = utils.get_output_folder_name(config)
+    model_data_dir = utils.get_last_model_data_folder_name(config)
+
     target_params = config["optimization_parameters"].get("target_params")
-    if(config["run_mode"] == "segregated"):
-        segregation_index = utils.get_segregation_index(config)
-        segregation_dir = f"seg_module_{segregation_index+1}/"
-        model_data_dir = os.path.join(output_folder, segregation_dir)
-    else:
-        model_data_dir = output_folder + "model_data/"
     pred_passive_json_path = model_data_dir + "pred_passive_properties.json"
     metrics = pd.read_csv(model_data_dir + "metrics.csv")
     preds_df = pd.read_csv(model_data_dir + "pred.csv", index_col=0)
@@ -169,7 +164,7 @@ def print_run_stats(config: SimulationConfig):
             ]
 
     preds = np.array(preds_df)
-    print(output_folder)
+    print(model_data_dir)
     print(f"Med MSE: {metrics['mse'].median():.4f} ({metrics['mse'].std():.4f})")
     print(f"Med Corr: {metrics['corr'].median():.4f} ({metrics['corr'].std():.4f})")
     print()
diff --git a/act/simulator.py b/act/simulator.py
index 5b9ffd4..cabf826 100644
--- a/act/simulator.py
+++ b/act/simulator.py
@@ -72,12 +72,12 @@ def _run_generate_target_traces(config: SimulationConfig, ignore_segregation=Fal
 
 
 def _run(config: SimulationConfig):
+    
     if config["optimization_parameters"]["num_epochs"] < 1:
         raise ValueError("Number of epochs is expected to be >= 1.")
-
-    output_folder = utils.get_output_folder_name(config) + "_temp_/"
-    if not os.path.exists(output_folder):
-        os.mkdir(output_folder)
+    
+    output_folder = utils.create_model_data_folder(config)
+    segregation_index = utils.get_segregation_index(config)
 
     # if there is a target_cell specified then use it too
     os.mkdir(temp_modfiles_dir)
@@ -88,7 +88,6 @@ def _run(config: SimulationConfig):
     os.system(f"nrnivmodl {temp_modfiles_dir}")
     ltohto = False
     logger = ACTLogger()
-    segregation_index = utils.get_segregation_index(config)  # if needed
     if config["run_mode"] == "segregated" and config["segregation"][
         segregation_index
     ].get("use_lto_amps", False):
@@ -374,20 +373,20 @@ def _run(config: SimulationConfig):
     learned_params = {param: predict for param, predict in zip(params, predictions)}
     if config["run_mode"] == "segregated":
         # save a copy of the outputs for future development
-        base_output_folder = utils.get_output_folder_name(config)
-        run_output_folder_name = f"{config['run_mode']}"
-        seg_folder = os.path.join(
-            base_output_folder, f"seg_module_{segregation_index+1}"
-        )
-        shutil.move(output_folder, seg_folder)
+        #base_output_folder = utils.get_output_folder_name(config)
+        #run_output_folder_name = f"{config['run_mode']}"
+        #seg_folder = os.path.join(
+            #base_output_folder, f"{random_seed}-seed_module_{segregation_index+1}"
+        #)
+        #shutil.move(output_folder, seg_folder)
         utils.update_segregation(config, learned_params)
     else:
-        base_output_folder = utils.get_output_folder_name(config)
-        orig_folder = os.path.join(
-            base_output_folder, "model_data"
-        )
-        shutil.move(output_folder, orig_folder)
-        utils.save_learned_params(learned_params)
+        #base_output_folder = utils.get_output_folder_name(config)
+        #orig_folder = os.path.join(
+            #base_output_folder, f"{random_seed}-seed_model_data"
+        #)
+        #shutil.move(output_folder, orig_folder)
+        utils.save_learned_params(learned_params, config)
 
 
 def run_generate_target_traces(
diff --git a/act/utils.py b/act/utils.py
index fe6629d..e04e23d 100644
--- a/act/utils.py
+++ b/act/utils.py
@@ -23,7 +23,6 @@
 pc = h.ParallelContext()  # object to access MPI methods
 MPI_RANK = int(pc.id())
 
-
 def create_output_folder(config: SimulationConfig, overwrite=True) -> str:
     if(config["output"]["auto_structure"] == True):
         print("AUTO STRUCTURED")
@@ -45,14 +44,81 @@ def create_output_folder(config: SimulationConfig, overwrite=True) -> str:
 def get_output_folder_name(config: SimulationConfig) -> str:
     cell_name = config["cell"]["name"]
     num_slices = f"{config['optimization_parameters']['parametric_distribution']['n_slices']}"
-    random_seed = f"{config['optimization_parameters']['random_seed']}"
     run_mode = f"{config['run_mode']}"  #"segregated" "origin
     if(run_mode == "segregated"):
         run_mode_name = "seg"
     else:
         run_mode_name = "orig"
 
-    return f"./output/{cell_name}_{run_mode_name}_{num_slices}-slice_{random_seed}-seed/"
+    return f"./output/{cell_name}_{run_mode_name}_{num_slices}-slice/"
+
+def get_sim_data_folder_name(config: SimulationConfig) -> str:
+    segregation_index = get_segregation_index(config)
+    if config["run_mode"] == "segregated":
+        sim_dir = get_output_folder_name(config) + "sim_data/" + f"module_{segregation_index+1}/"
+    else:
+        sim_dir = get_output_folder_name(config) + "sim_data/" 
+    return sim_dir
+
+def get_param_values_file(config: SimulationConfig) -> str:
+    random_seed = f"{config['optimization_parameters']['random_seed']}"
+    return get_output_folder_name(config) + "sim_data/"  + f"parameter_values_{random_seed}-seed.json"
+
+def get_sim_output_folder_name(config: SimulationConfig) -> str:
+    return get_sim_data_folder_name(config) + "output/"
+
+def create_model_data_folder(config: SimulationConfig) -> str:
+    segregation_index = get_segregation_index(config)
+    random_seed = f"{config['optimization_parameters']['random_seed']}"
+    if config["run_mode"] == "segregated":
+        
+        output_folder = get_output_folder_name(config) + "model_data/" 
+
+        if not os.path.exists(output_folder):
+            os.mkdir(output_folder)
+
+        output_folder = output_folder + f"{random_seed}-seed/"
+
+        if not os.path.exists(output_folder):
+            os.mkdir(output_folder)
+
+        output_folder = output_folder + f"module_{segregation_index+1}/"
+        
+        if not os.path.exists(output_folder):
+            os.mkdir(output_folder)
+    else:
+        output_folder = get_output_folder_name(config) + "model_data/" 
+
+        if not os.path.exists(output_folder):
+            os.mkdir(output_folder)
+
+        output_folder = output_folder + f"{random_seed}-seed/"
+
+        if not os.path.exists(output_folder):
+            os.mkdir(output_folder)
+    
+    return output_folder
+
+def get_model_data_folder_name(config: SimulationConfig) -> str:
+    output_dir = get_output_folder_name(config)
+    random_seed = f"{config['optimization_parameters']['random_seed']}"
+    segregation_index = get_segregation_index(config)
+    if(config["run_mode"] == "segregated"):
+        model_data_dir = output_dir + "model_data/"+ f"{random_seed}-seed/" + f"module_{segregation_index+1}/"
+    else:
+        model_data_dir = output_dir + "model_data/"+ f"{random_seed}-seed/"
+    return model_data_dir
+
+def get_last_model_data_folder_name(config: SimulationConfig) -> str:
+    # This is used for the plotting scripts because the module # was incremented, so we need the "last run"
+    output_dir = get_output_folder_name(config)
+    random_seed = f"{config['optimization_parameters']['random_seed']}"
+    segregation_index = get_segregation_index(config)
+    if(config["run_mode"] == "segregated"):
+        model_data_dir = output_dir + "model_data/"+ f"{random_seed}-seed/" + f"module_{segregation_index}/"
+    else:
+        model_data_dir = output_dir + "model_data/"+ f"{random_seed}-seed/"
+    return model_data_dir
 
 def set_cell_parameters(cell, parameter_list: list, parameter_values: list) -> None:
     for sec in cell.all:
@@ -198,8 +264,7 @@ def cleanup_simulation():
 
 
 def get_segregation_index(config: SimulationConfig):
-    output_dir = get_output_folder_name(config)
-    parameter_values_file = output_dir + "sim_data" + "parameter_values.json"
+    parameter_values_file = get_param_values_file(config)
 
     if config["run_mode"] != "segregated":
         return -1
@@ -215,7 +280,7 @@ def get_segregation_index(config: SimulationConfig):
 def load_preset_params(config: SimulationConfig):
     # Returns a dict of learned params from segregation
     # if segregation is not used then returns an empty dict
-    parameter_values_file = "parameter_values.json"
+    parameter_values_file = get_param_values_file(config)
 
     if config["run_mode"] != "segregated":
         return {}
@@ -239,7 +304,7 @@ def load_preset_params(config: SimulationConfig):
 def load_learned_params(config: SimulationConfig):
     # Returns a dict of learned params from segregation
     # if segregation is not used then returns an empty dict
-    parameter_values_file = "parameter_values.json"
+    parameter_values_file = get_param_values_file(config)
 
     if not os.path.exists(parameter_values_file):
         return {}
@@ -250,7 +315,7 @@ def load_learned_params(config: SimulationConfig):
 
 
 def get_learned_variability(config: SimulationConfig):
-    parameter_values_file = "parameter_values.json"
+    parameter_values_file = get_param_values_file(config)
     lv = 0
     if os.path.exists(parameter_values_file):
         with open(parameter_values_file, "r") as fp:
@@ -261,7 +326,7 @@ def get_learned_variability(config: SimulationConfig):
 
 
 def get_learned_variability_params(config: SimulationConfig):
-    parameter_values_file = "parameter_values.json"
+    parameter_values_file = get_param_values_file(config)
     lvp = []
     if os.path.exists(parameter_values_file):
         with open(parameter_values_file, "r") as fp:
@@ -278,8 +343,8 @@ def update_segregation(config: SimulationConfig, learned_params):
     # And updates the parameter_values.json if that parameter was
     # in the current segregation index
     # learned_params = {'channel'(str):value(float),}
-    output_dir = get_output_folder_name(config) + "sim_data/"
-    parameter_values_file = output_dir + "parameter_values.json"
+    output_dir = get_sim_data_folder_name(config)
+    parameter_values_file = get_param_values_file(config)
     if os.path.exists(parameter_values_file):
         print(f"Updating {parameter_values_file} for learned parameters")
         with open(parameter_values_file, "r") as fp:
@@ -346,8 +411,8 @@ def update_segregation(config: SimulationConfig, learned_params):
         )
 
 
-def save_learned_params(learned_params):
-    parameter_values_file = "parameter_values.json"
+def save_learned_params(learned_params, config: SimulationConfig):
+    parameter_values_file = get_param_values_file(config)
     if os.path.exists(parameter_values_file):
         print(f"Updating {parameter_values_file} for learned parameters")
         with open(parameter_values_file, "r") as fp:
@@ -360,9 +425,9 @@ def save_learned_params(learned_params):
 
 
 def build_parametric_network(config: SimulationConfig):
-    output_dir = get_output_folder_name(config) + "sim_data/"
+    output_dir = get_sim_data_folder_name(config)
     config_file = output_dir + "simulation_act_simulation_config.json"
-    parameter_values_file = output_dir + "parameter_values.json"
+    parameter_values_file = get_param_values_file(config)
 
     params = [p["channel"] for p in config["optimization_parameters"]["params"]]
 
@@ -615,10 +680,10 @@ def generate_parametric_traces(config: SimulationConfig):
     traces for a large collection of cells and generates an h5
     file for injestion later.
     """
-    output_dir = get_output_folder_name(config) + "sim_data/"
+    output_dir = get_sim_data_folder_name(config)
     passive_properties = config.get("cell", {}).get("passive_properties", None)
     config_file = output_dir + "simulation_act_simulation_config.json"
-    parameter_values_file = output_dir + "parameter_values.json"
+    parameter_values_file = get_param_values_file(config)
     with open(parameter_values_file) as f:
         param_dict = json.load(f)
         params = param_dict["parameters"]
@@ -673,8 +738,8 @@ def load_parametric_traces(config: SimulationConfig, drop_ramp=False):
     """
     Return a torch tensor of all traces in the specified h5 file
     """
-    output_dir = get_output_folder_name(config) + "sim_data/"
-    parameter_values_file = output_dir + "parameter_values.json"
+    output_dir = get_sim_data_folder_name(config)
+    parameter_values_file = get_param_values_file(config)
     traces_file = output_dir + "output/v_report.h5"
 
     if not os.path.exists(parameter_values_file) or not os.path.exists(traces_file):
diff --git a/simulation/analyze_res.py b/simulation/analyze_res.py
index d5d0886..b1f27e5 100644
--- a/simulation/analyze_res.py
+++ b/simulation/analyze_res.py
@@ -7,11 +7,13 @@
 from simulation_configs import selected_config
 
 from act import analysis
+import meta_sweep
 
-
-def main():
+def main(config):
     analysis.print_run_stats(selected_config)
 
 
 if __name__ == "__main__":
-    main()
+    if '--sweep' in sys.argv:
+        selected_config = meta_sweep.get_meta_params_for_sweep()
+    main(selected_config)
diff --git a/simulation/batch_generate_traces_sweep.sh b/simulation/batch_generate_traces_sweep.sh
new file mode 100644
index 0000000..677237f
--- /dev/null
+++ b/simulation/batch_generate_traces_sweep.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+
+#SBATCH -N 1
+#SBATCH -n 48
+#SBATCH -W
+#SBATCH --qos=normal
+#SBATCH --job-name=act
+#SBATCH --output=output/bmtk_sim.out
+#SBATCH --time 0-12:00
+
+START=$(date)
+mpiexec nrniv -mpi -python generate_traces.py --sweep
+#mpiexec ./components_homogenous/mechanisms/x86_64/special -mpi run_network.py simulation_configECP_base_homogenous.json
+END=$(date)
+
+printf "Start: $START \nEnd:   $END\n"
+
+echo "Done running model at $(date)"
\ No newline at end of file
diff --git a/simulation/generate_arma_stats.py b/simulation/generate_arma_stats.py
index fbd3ada..75a29c6 100644
--- a/simulation/generate_arma_stats.py
+++ b/simulation/generate_arma_stats.py
@@ -4,20 +4,42 @@
 from act import utils
 from simulation_configs import selected_config
 import warnings
+import os.path
+import meta_sweep
+
 
 warnings.filterwarnings("ignore")
 
 if __name__ == "__main__":
-    traces, params, amps = utils.load_parametric_traces(selected_config)
-    segregation_index = utils.get_segregation_index(selected_config)
-
-    arima_order = (10, 0, 10)
-    if selected_config.get("summary_features", {}).get("arima_order"):
-        arima_order = tuple(selected_config["summary_features"]["arima_order"])
-    if selected_config["run_mode"] == "segregated" and selected_config["segregation"][segregation_index].get("arima_order",None):
-        print(f"custom arima order for segregation set")
-        arima_order = tuple(selected_config["segregation"][segregation_index]["arima_order"])
-    print(f"ARIMA order set to {arima_order}")
-
-    output_dir = utils.get_output_folder_name(selected_config) + "sim_data/output/arima_stats.json"
-    utils.arima_coefs_proc_map(traces, output_file=output_dir, arima_order=arima_order)
+    if '--sweep' in sys.argv:
+        selected_config = meta_sweep.get_meta_params_for_sweep()
+
+    output_dir = utils.get_sim_output_folder_name(selected_config)
+    arma_stats_file = output_dir + "arima_stats.json"
+    arma_stats_exists = os.path.exists(arma_stats_file)
+    generate_arma = selected_config["optimization_parameters"]["generate_arma"]
+
+    if (arma_stats_exists):
+        print("--------------------------------------------------------------------")
+        print(f"ARMA STATS ALREADY GENERATED - Using stats from: {arma_stats_file}")
+        print("--------------------------------------------------------------------")
+    elif (not generate_arma):
+        print("-------------------------------------------------")
+        print("ARMA STATS TURNED OFF IN SIMULATION CONFIGURATION")
+        print("-------------------------------------------------")
+    else:
+        traces, params, amps = utils.load_parametric_traces(selected_config)
+        segregation_index = utils.get_segregation_index(selected_config)
+
+        arima_order = (10, 0, 10)
+        if selected_config.get("summary_features", {}).get("arima_order"):
+            arima_order = tuple(selected_config["summary_features"]["arima_order"])
+        if selected_config["run_mode"] == "segregated" and selected_config["segregation"][segregation_index].get("arima_order",None):
+            print(f"custom arima order for segregation set")
+            arima_order = tuple(selected_config["segregation"][segregation_index]["arima_order"])
+        print(f"ARIMA order set to {arima_order}")
+
+        print(output_dir)
+
+        utils.arima_coefs_proc_map(traces, output_file=arma_stats_file, arima_order=arima_order)
+        
diff --git a/simulation/generate_target_traces.py b/simulation/generate_target_traces.py
index ed34e93..5a8183d 100644
--- a/simulation/generate_target_traces.py
+++ b/simulation/generate_target_traces.py
@@ -1,16 +1,31 @@
 import sys
 sys.path.append("../")
+import os
 from act import simulator
+from act import utils
+import meta_sweep
 
 from simulation_configs import selected_config
 
+
+
 if __name__ == "__main__":
 
     ignore_segregation = False
     if '--ignore_segregation' in sys.argv:
         ignore_segregation = True
         print('ignoring segregation, typically used for generating final traces')
+    if '--sweep' in sys.argv:
+        selected_config = meta_sweep.get_meta_params_for_sweep()
 
-    print("generating traces...")
-    simulator.run_generate_target_traces(selected_config, subprocess=False, ignore_segregation=ignore_segregation)
-    print("done")
+    # Check if target traces are already saved
+    target_dir = utils.get_output_folder_name(selected_config) + "target/"
+    target_file_name = selected_config["optimization_parameters"]["target_V_file"]
+    if(os.path.exists(target_dir + target_file_name)):
+        print("-----------------------------------------------")
+        print(f"TARGET DATA ALREADY GENERATED AT: {target_dir}")
+        print("-----------------------------------------------")
+    else:
+        print("generating traces...")
+        simulator.run_generate_target_traces(selected_config, subprocess=False, ignore_segregation=ignore_segregation)
+        print("done")
diff --git a/simulation/generate_traces.py b/simulation/generate_traces.py
index ff3c67c..e06542b 100644
--- a/simulation/generate_traces.py
+++ b/simulation/generate_traces.py
@@ -1,14 +1,27 @@
 from simulation_configs import selected_config
 
+import os
+
 import sys
 sys.path.append("../")
+import meta_sweep
 
-from act.utils import build_parametric_network, generate_parametric_traces
+from act.utils import build_parametric_network, generate_parametric_traces, get_sim_output_folder_name
 
 if __name__ == "__main__":
+    if '--sweep' in sys.argv:
+        selected_config = meta_sweep.get_meta_params_for_sweep()
     if "build" in sys.argv:
         print("Building Network")
         build_parametric_network(selected_config)
     else:
-        print("Generating Traces")
-        generate_parametric_traces(selected_config)
+        # Check if data already exists: spikes.h5 and v_report.h5
+        sim_data_dir = get_sim_output_folder_name(selected_config)
+
+        if (os.path.exists(sim_data_dir + "spikes.h5")) and (os.path.exists(sim_data_dir + "v_report.h5")):
+            print("------------------------------------------------------------")
+            print(f"DATA ALREADY GENERATED FOR THIS MODULE AT: {sim_data_dir}")
+            print("------------------------------------------------------------")
+        else:
+            print("Generating Traces")
+            generate_parametric_traces(selected_config)
diff --git a/simulation/meta_sweep.py b/simulation/meta_sweep.py
index 59c62b1..54c6bdc 100644
--- a/simulation/meta_sweep.py
+++ b/simulation/meta_sweep.py
@@ -1,56 +1,46 @@
 import json
-import subprocess
+import numpy as np
 
-def set_meta_params():
-    x = 1
+from simulation_configs import selected_config
 
+def get_meta_params_for_sweep():
+    meta_data = json.load(open('meta_params.json'))
+    run_num = meta_data["run_num"]
+    
+    list_param_lens = []
+    parameter_slice_length = len(meta_data["parameter_slice_list"])
+    list_param_lens.append(parameter_slice_length)
+
+    random_seed_length = len(meta_data["random_seed_list"])
+    list_param_lens.append(random_seed_length)
+
+    index_list = get_index_tuple(run_num,list_param_lens)
+
+    config = selected_config
+
+    config["optimization_parameters"]["parametric_distribution"]["n_slices"] = meta_data["parameter_slice_list"][index_list[0]]
+    config["optimization_parameters"]["random_seed"] = meta_data["random_seed_list"][index_list[1]]
+
+    return config
 
-def run_meta_sweep():
+
+def get_index_tuple(run_num, list_param_lens):
+    return np.unravel_index(run_num, list_param_lens)
+
+def increment_config_sweep_number():
+    # Increment which configuration we are on
+    meta_data = json.load(open('meta_params.json'))
+    with open('meta_params.json', 'w') as json_file:
+        meta_data["run_num"] = meta_data["run_num"] + 1
+        json.dump(meta_data, json_file)
+
+def get_number_of_configs():
     meta_data = json.load(open('meta_params.json'))
-    print(meta_data)
-    run_mode_list = meta_data["run_mode_list"]
-    print(run_mode_list)
-    print(type(run_mode_list))
-    cell_list = meta_data["cell_list"]
-    model_type_list = meta_data["model_type_list"]
-    parameter_slice_list = meta_data["parameter_slice_list"]
-    random_seed_list = meta_data["random_seed_list"]
-    generate_arma_stats = meta_data["generate_arma_stats"]
-
-    if "orig" in run_mode_list:
-        for cell in cell_list :
-            for model_type in model_type_list :
-                for slices in parameter_slice_list :
-                    for seed in random_seed_list :
-                        # Set the parameters
-                        print("SETTING PARAMETERS")
-
-                        # Run the Pipeline
-                        print(f"PARAMS: orig_{cell}_{model_type}_{slices}_{seed}")
-                        #subprocess.run("python generate_target_traces.py") 
-                        print(f"Target")
-                        #subprocess.run(["python generate_traces.py", "build"]) 
-                        print(f"Build")
-                        #subprocess.run("sbatch batch_generate_traces.sh") 
-                        print(f"Simulate")
-                        if (generate_arma_stats == True) and (seed == random_seed_list[0]):
-                            print(f"Generating ARMA stats")
-                            #subprocess.run("python generate_arma_stats.py") 
-
-                        #subprocess.run("python run_simulation.py") 
-                        print(f"Run Model")
-                        #subprocess.run("python analyze_res.py") 
-                        print(f"Results")
-                        #subprocess.run("python plot_fi.py") 
-                        print(f"FI Plot")
-                        #subprocess.run("python plot_learned_parameters.py") 
-                        print(f"Final")
-
-
-
-    if "seg" in run_mode_list:
-        pass
-
-# python generate_target_traces.py && python generate_traces.py build && sbatch batch_generate_traces.sh -W && python generate_arma_stats.py && python run_simulation.py && python analyze_res.py && python plot_fi.py && python plot_learned_parameters.py
-if __name__ == "__main__":
-    run_meta_sweep()
+    list_param_lens = []
+    parameter_slice_length = len(meta_data["parameter_slice_list"])
+    list_param_lens.append(parameter_slice_length)
+
+    random_seed_length = len(meta_data["random_seed_list"])
+    list_param_lens.append(random_seed_length)
+
+    return np.prod(list_param_lens)
\ No newline at end of file
diff --git a/simulation/plot_arima.py b/simulation/plot_arima.py
index ad44fa1..dd74068 100644
--- a/simulation/plot_arima.py
+++ b/simulation/plot_arima.py
@@ -10,6 +10,7 @@
 import numpy as np
 from statsmodels.graphics.tsaplots import plot_predict
 import time
+import meta_sweep
 
 warnings.filterwarnings("ignore")
 
@@ -49,6 +50,8 @@ def plot_arima(cell_id, pq=10):
 
 
 if __name__ == "__main__":
+    if '--sweep' in sys.argv:
+        selected_config = meta_sweep.get_meta_params_for_sweep()
     traces, params, amps = utils.load_parametric_traces(selected_config)
 
     traces = traces.cpu().detach().tolist()
diff --git a/simulation/plot_fi.py b/simulation/plot_fi.py
index 0fdadf8..4433a6f 100644
--- a/simulation/plot_fi.py
+++ b/simulation/plot_fi.py
@@ -5,23 +5,17 @@
 import sys
 sys.path.append("../")
 from act import analysis, utils
+import meta_sweep
 
 
-def main(extra_trace, extra_trace_label, title=None):
-    config = selected_config
+def main(config, extra_trace, extra_trace_label, title=None):
 
     inj_dur = config["simulation_parameters"]["h_i_dur"]
-
+    random_seed = f"{config['optimization_parameters']['random_seed']}"
     output_folder = utils.get_output_folder_name(config)
     target_params = config["optimization_parameters"].get("target_params")
-    if(config["run_mode"] == "segregated"):
-        segregation_index = utils.get_segregation_index(config)
-        segregation_dir = f"seg_module_{segregation_index+1}/"
-        model_data_dir = os.path.join(output_folder, segregation_dir)
-        fi_file = model_data_dir + f"{output_folder[9:-1]}_FI.png"
-    else:
-        model_data_dir = output_folder + "model_data/"
-        fi_file = model_data_dir + f"{output_folder[9:-1]}_FI.png"
+    model_data_dir = utils.get_last_model_data_folder_name(config)
+    fi_file = model_data_dir + f"FI.png"
 
     traces_file = model_data_dir + "traces.h5"
     simulated_traces, target_traces, amps = utils.load_final_traces(traces_file)
@@ -72,4 +66,6 @@ def main(extra_trace, extra_trace_label, title=None):
     if args.title:
         title = args.title
 
-    main(extra_trace, extra_label, title=title)
+    if '--sweep' in sys.argv:
+        selected_config = meta_sweep.get_meta_params_for_sweep()
+    main(selected_config, extra_trace, extra_label, title=title)
diff --git a/simulation/plot_learned_parameters.py b/simulation/plot_learned_parameters.py
index f5d162b..bb119d2 100644
--- a/simulation/plot_learned_parameters.py
+++ b/simulation/plot_learned_parameters.py
@@ -9,30 +9,27 @@
 sys.path.append("../")
 from act.analysis import save_plot, plot_fi_curves
 from act.target_utils import load_target_traces
-from act.utils import load_learned_params, get_fi_curve, get_fi_curve_error, get_output_folder_name
+from act import utils
 from act.optim import ACTOptimizer
 from act.target_utils import DEFAULT_TARGET_V_LTO_FILE, DEFAULT_TARGET_V_HTO_FILE
 
 from simulation_configs import selected_config
+import meta_sweep
 
 # will have to generate target traces (python generate_target_traces.py --ignore_segregation)
 
 temp_modfiles_dir = "temp_modfiles"
 
-def save_fi(config, simulated_traces, target_traces, amps):
+def save_fi(config, simulated_traces, target_traces, amps, output_file):
     inj_dur = config["simulation_parameters"]["h_i_dur"]
-
-    output_file = os.path.join(
-        get_output_folder_name(selected_config), "final" , "final_fi_curve"
-    )
-
-    simulated_curve = get_fi_curve(simulated_traces, amps, inj_dur=inj_dur)
-    target_curve = get_fi_curve(target_traces, amps, inj_dur=inj_dur)
+    
+    simulated_curve = utils.get_fi_curve(simulated_traces, amps, inj_dur=inj_dur)
+    target_curve = utils.get_fi_curve(target_traces, amps, inj_dur=inj_dur)
 
     simulated_label = config["output"]["simulated_label"]
     target_label = config["output"]["target_label"]
 
-    err1 = get_fi_curve_error(simulated_traces, target_traces, amps, inj_dur=inj_dur)
+    err1 = utils.get_fi_curve_error(simulated_traces, target_traces, amps, inj_dur=inj_dur)
     simulated_label = simulated_label + f" (err: {err1})"
 
     curves_list = [
@@ -78,17 +75,40 @@ def run(simulation_config):
     )
     
     # create output folders
-    output_folder = os.path.join(
-        get_output_folder_name(selected_config), "final"
-    )
-    if not os.path.exists(output_folder):
-        os.makedirs(output_folder, exist_ok=True)
+    segregation_index = utils.get_segregation_index(simulation_config)
+    random_seed = f"{simulation_config['optimization_parameters']['random_seed']}"
+    if simulation_config["run_mode"] == "segregated":
+        
+        output_folder = utils.get_output_folder_name(simulation_config) + "final/" 
+
+        if not os.path.exists(output_folder):
+            os.mkdir(output_folder)
+
+        output_folder = output_folder + f"{random_seed}-seed/"
+
+        if not os.path.exists(output_folder):
+            os.mkdir(output_folder)
+
+        output_folder = output_folder + f"module_{segregation_index}/"
+        
+        if not os.path.exists(output_folder):
+            os.mkdir(output_folder)
+    else:
+        output_folder = utils.get_output_folder_name(simulation_config) + "final/" 
+
+        if not os.path.exists(output_folder):
+            os.mkdir(output_folder)
+
+        output_folder = output_folder + f"{random_seed}-seed/"
+
+        if not os.path.exists(output_folder):
+            os.mkdir(output_folder)
        
     dt = simulation_config["simulation_parameters"]["h_dt"]
     amps = simulation_config["optimization_parameters"]["amps"]
     simulated_label = simulation_config["output"]["simulated_label"]
     target_label = simulation_config["output"]["target_label"]
-    learned_params = load_learned_params(simulation_config)
+    learned_params = utils.load_learned_params(simulation_config)
     parameters = [k for k,v in learned_params.items()]
     target_params = [v for k,v in learned_params.items()]
     
@@ -112,7 +132,8 @@ def run(simulation_config):
         )
     print("saving fi")
     sv_tensor = torch.cat(sv_list)
-    save_fi(simulation_config, sv_tensor, target_V, torch.tensor(amps))
+    output_file = output_folder + "final_fi_curve.png"
+    save_fi(simulation_config, sv_tensor, target_V, torch.tensor(amps), output_file)
 
     if os.path.exists(DEFAULT_TARGET_V_LTO_FILE): # we have generated an LTO file before and should simulate at end
         # need to go case by case
@@ -273,6 +294,8 @@ def run(simulation_config):
 
 if __name__ == '__main__':
     # will have to generate target traces (python generate_target_traces.py --ignore_segregation)
+    if '--sweep' in sys.argv:
+        selected_config = meta_sweep.get_meta_params_for_sweep()
     try:
         run(selected_config)
     except:
diff --git a/simulation/plot_loss.py b/simulation/plot_loss.py
index 8b4d6be..6ecd2ef 100644
--- a/simulation/plot_loss.py
+++ b/simulation/plot_loss.py
@@ -5,7 +5,10 @@
 from act import utils
 from simulation_configs import selected_config
 import os
+import meta_sweep
 
+if '--sweep' in sys.argv:
+    selected_config = meta_sweep.get_meta_params_for_sweep()
 output_dir = utils.get_output_folder_name(selected_config)
 
 if(selected_config["run_mode"] == "segregated"):
diff --git a/simulation/plot_target_v.py b/simulation/plot_target_v.py
index 41cbd6e..3f87b59 100644
--- a/simulation/plot_target_v.py
+++ b/simulation/plot_target_v.py
@@ -4,9 +4,11 @@
 sys.path.append("../")
 from act import utils
 from simulation_configs import selected_config
+import meta_sweep
 
 
-
+if '--sweep' in sys.argv:
+    selected_config = meta_sweep.get_meta_params_for_sweep()
 output_dir = utils.get_output_folder_name(selected_config)
 
 target_v_file = output_dir + "target/target_v.json"
diff --git a/simulation/plot_traces.py b/simulation/plot_traces.py
index 4f10291..78628eb 100644
--- a/simulation/plot_traces.py
+++ b/simulation/plot_traces.py
@@ -9,6 +9,7 @@
 import numpy as np
 
 from simulation_configs import selected_config
+import meta_sweep
 
 config = selected_config
 
@@ -82,6 +83,8 @@ def stats(traces, params_dict):
 
 
 if __name__ == "__main__":
+    if '--sweep' in sys.argv:
+        selected_config = meta_sweep.get_meta_params_for_sweep()
     output_dir = utils.get_output_folder_name(selected_config)
     traces_path = output_dir + "sim_data/output/v_report.h5"
     params_path = output_dir + "sim_data/parameter_values.json"
diff --git a/simulation/run_full_pipeline.py b/simulation/run_full_pipeline.py
new file mode 100644
index 0000000..c30d88e
--- /dev/null
+++ b/simulation/run_full_pipeline.py
@@ -0,0 +1,59 @@
+import subprocess
+from simulation_configs import selected_config
+import sys
+
+import meta_sweep
+
+# See how many runs are needed for this configuration. 
+# Original: 1 run   -   Segregated: n runs depending on # of modules
+
+def run_single_config():
+    run_mode = selected_config["run_mode"]
+    if run_mode == "original":
+        number_of_runs = 1
+    elif run_mode == "segregated":
+        number_of_runs = len(selected_config["segregation"])
+
+    for i in range(0,number_of_runs):
+        # The whole pipeline is run through these commands
+        subprocess.run(["python", "generate_target_traces.py"]) 
+        subprocess.run(["python", "generate_traces.py", "build"]) 
+        subprocess.run(["sbatch", "--wait", "batch_generate_traces.sh"]) 
+        subprocess.run(["python", "generate_arma_stats.py"]) 
+        subprocess.run(["python", "run_simulation.py"]) 
+        subprocess.run(["python", "analyze_res.py"]) 
+        subprocess.run(["python", "plot_fi.py"]) 
+        subprocess.run(["python", "plot_learned_parameters.py"]) 
+
+def run_sweep_of_config():
+    run_mode = selected_config["run_mode"]
+    if run_mode == "original":
+        number_of_runs = 1
+    elif run_mode == "segregated":
+        number_of_runs = len(selected_config["segregation"])
+
+    number_of_configs = meta_sweep.get_number_of_configs()
+
+    print("------------------------------------------------------")
+    print(f"REQUESTING {number_of_configs} CONFIGURATIONS TO BE RUN")
+    print("------------------------------------------------------")
+
+    for i in range(0,number_of_configs):
+        for j in range(0,number_of_runs):
+            # The whole pipeline is run through these commands
+            subprocess.run(["python", "generate_target_traces.py", "--sweep"]) 
+            subprocess.run(["python", "generate_traces.py", "build", "--sweep"]) 
+            subprocess.run(["sbatch", "--wait", "batch_generate_traces_sweep.sh"]) 
+            subprocess.run(["python", "generate_arma_stats.py", "--sweep"]) 
+            subprocess.run(["python", "run_simulation.py", "--sweep"]) 
+            subprocess.run(["python", "analyze_res.py", "--sweep"]) 
+            subprocess.run(["python", "plot_fi.py", "--sweep"]) 
+            subprocess.run(["python", "plot_learned_parameters.py", "--sweep"]) 
+        
+        meta_sweep.increment_config_sweep_number()
+
+if __name__ == "__main__":
+    if '--sweep' in sys.argv:
+        run_sweep_of_config()
+    else:
+        run_single_config()
\ No newline at end of file
diff --git a/simulation/run_orig_pipeline.sh b/simulation/run_orig_pipeline.sh
deleted file mode 100644
index 1ced368..0000000
--- a/simulation/run_orig_pipeline.sh
+++ /dev/null
@@ -1,25 +0,0 @@
-#!/bin/bash
-
-#SBATCH -N 1
-#SBATCH -n 1
-#SBATCH --qos=normal
-#SBATCH --job-name=act-orig-pipeline
-#SBATCH --output=output/run_orig_pipeline.out
-#SBATCH --time 0-12:00
-
-
-
-START=$(date)
-python generate_target_traces.py
-python generate_traces.py build
-sbatch --wait batch_generate_traces.sh
-python generate_arma_stats.py
-python run_simulation.py
-python analyze_res.py
-python plot_fi.py
-python plot_learned_parameters.py
-END=$(date)
-
-printf "Start: $START \nEnd:   $END\n"
-
-echo "Done running model at $(date)"
\ No newline at end of file
diff --git a/simulation/run_pipeline.sh b/simulation/run_pipeline.sh
new file mode 100644
index 0000000..91c8049
--- /dev/null
+++ b/simulation/run_pipeline.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+
+#SBATCH -N 1
+#SBATCH -n 1
+#SBATCH --qos=normal
+#SBATCH --job-name=act-orig-pipeline
+#SBATCH --output=output/run_pipeline.out
+#SBATCH --time 0-12:00
+
+
+
+START=$(date)
+python run_full_pipeline.py
+END=$(date)
+
+printf "Start: $START \nEnd:   $END\n"
+
+echo "Done running model at $(date)"
\ No newline at end of file
diff --git a/simulation/run_seg_pipeline.sh b/simulation/run_seg_pipeline.sh
deleted file mode 100644
index 1ced368..0000000
--- a/simulation/run_seg_pipeline.sh
+++ /dev/null
@@ -1,25 +0,0 @@
-#!/bin/bash
-
-#SBATCH -N 1
-#SBATCH -n 1
-#SBATCH --qos=normal
-#SBATCH --job-name=act-orig-pipeline
-#SBATCH --output=output/run_orig_pipeline.out
-#SBATCH --time 0-12:00
-
-
-
-START=$(date)
-python generate_target_traces.py
-python generate_traces.py build
-sbatch --wait batch_generate_traces.sh
-python generate_arma_stats.py
-python run_simulation.py
-python analyze_res.py
-python plot_fi.py
-python plot_learned_parameters.py
-END=$(date)
-
-printf "Start: $START \nEnd:   $END\n"
-
-echo "Done running model at $(date)"
\ No newline at end of file
diff --git a/simulation/run_simulation.py b/simulation/run_simulation.py
index 63c6be6..5bccc68 100644
--- a/simulation/run_simulation.py
+++ b/simulation/run_simulation.py
@@ -2,6 +2,9 @@
 import sys
 sys.path.append("../")
 from act import simulator
+import meta_sweep
 
 if __name__ == "__main__":
+    if '--sweep' in sys.argv:
+        selected_config = meta_sweep.get_meta_params_for_sweep()
     p = simulator.run(selected_config, subprocess=False)
diff --git a/simulation/run_sweep.sh b/simulation/run_sweep.sh
new file mode 100644
index 0000000..d478e68
--- /dev/null
+++ b/simulation/run_sweep.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+
+#SBATCH -N 1
+#SBATCH -n 1
+#SBATCH --qos=normal
+#SBATCH --job-name=act-orig-pipeline
+#SBATCH --output=output/run_pipeline.out
+#SBATCH --time 0-12:00
+
+
+
+START=$(date)
+python run_full_pipeline.py --sweep
+END=$(date)
+
+printf "Start: $START \nEnd:   $END\n"
+
+echo "Done running model at $(date)"
\ No newline at end of file
diff --git a/simulation/simulation_configs.py b/simulation/simulation_configs.py
index 3089a2c..5f8b001 100644
--- a/simulation/simulation_configs.py
+++ b/simulation/simulation_configs.py
@@ -82,6 +82,7 @@
         "num_amps_to_match": 1,
         "num_epochs": 10,
         "random_seed": 42,
+        "generate_arma": True,
         "skip_match_voltage": True,
         "parametric_distribution": {  # sample the parameter space for training if n_slices is > 1
             "n_slices": 5,
@@ -205,6 +206,7 @@
         "num_amps_to_match": 12,
         "num_epochs": 10,
         "random_seed": 42,
+        "generate_arma": True,
         "skip_match_voltage": True,
         "use_random_forest": True,
         "parametric_distribution": {  # sample the parameter space for training if n_slices is > 1
@@ -330,6 +332,7 @@
         "num_amps_to_match": 1,
         "num_epochs": 10,
         "random_seed": 42,
+        "generate_arma": False,
         "skip_match_voltage": True,
         "parametric_distribution": {  # sample the parameter space for training if n_slices is > 1
             "n_slices": 5,
@@ -453,6 +456,7 @@
         "num_amps_to_match": 12,
         "num_epochs": 10,
         "random_seed": 42,
+        "generate_arma": False,
         "skip_match_voltage": True,
         "use_random_forest": True,
         "parametric_distribution": {  # sample the parameter space for training if n_slices is > 1
@@ -571,6 +575,7 @@
         "num_amps_to_match": 1,
         "num_epochs": 10,
         "random_seed": 42,
+        "generate_arma": False,
         "skip_match_voltage": True,
         "parametric_distribution": {  # sample the parameter space for training if n_slices is > 1
             "n_slices": 5,
@@ -664,6 +669,7 @@
         "num_amps_to_match": 12,
         "num_epochs": 10,
         "random_seed": 42,
+        "generate_arma": False,
         "skip_match_voltage": True,
         "use_random_forest": True,
         "parametric_distribution": {  # sample the parameter space for training if n_slices is > 1
@@ -766,7 +772,8 @@
         "num_repeats": 1,
         "num_amps_to_match": 1,
         "num_epochs": 10,
-        "random_seed": 42,
+        "random_seed": 45,
+        "generate_arma": False,
         "skip_match_voltage": True,
         "parametric_distribution": {  # sample the parameter space for training if n_slices is > 1
             "n_slices": 2,
@@ -861,11 +868,12 @@
         "num_repeats": 1,
         "num_amps_to_match": 12,
         "num_epochs": 10,
-        "random_seed": 42,
+        "random_seed": 53,
+        "generate_arma": False,
         "skip_match_voltage": True,
         "use_random_forest": True,
         "parametric_distribution": {  # sample the parameter space for training if n_slices is > 1
-            "n_slices": 3,
+            "n_slices": 2,
         },
         "decimate_factor": 10,
     },
@@ -898,4 +906,4 @@
 #                                                 SELECTED_CONFIG
 # ===================================================================================================================
 # ===================================================================================================================
-selected_config = Burster_S3_orig
+selected_config = LA_A_seg
diff --git a/simulation/slurm-6441.out b/simulation/slurm-6441.out
deleted file mode 100644
index 33de957..0000000
--- a/simulation/slurm-6441.out
+++ /dev/null
@@ -1,772 +0,0 @@
-/home/mwsrgf/act-venv/lib64/python3.9/site-packages/torch/__init__.py:614: UserWarning: torch.set_default_tensor_type() is deprecated as of PyTorch 2.1, please use torch.set_default_dtype() and torch.set_default_device() as alternatives. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:451.)
-  _C._set_default_tensor_type(t)
-Warning: no DISPLAY environment variable.
---No graphics will be displayed.
-/home/mwsrgf/proj/ACT/simulation
-Mod files: "temp_modfiles/temp_modfiles/capool.mod" "temp_modfiles/temp_modfiles/cas.mod" "temp_modfiles/temp_modfiles/cat.mod" "temp_modfiles/temp_modfiles/inhsyn.mod" "temp_modfiles/temp_modfiles/ka.mod" "temp_modfiles/temp_modfiles/kca.mod" "temp_modfiles/temp_modfiles/kdr.mod" "temp_modfiles/temp_modfiles/leak.mod" "temp_modfiles/temp_modfiles/na.mod"
-
-Creating 'x86_64' directory for .o files.
-
- -> Compiling mod_func.cpp
- -> NMODL ../temp_modfiles/capool.mod
- -> NMODL ../temp_modfiles/cas.mod
- -> NMODL ../temp_modfiles/cat.mod
-Translating capool.mod into /home/mwsrgf/proj/ACT/simulation/x86_64/capool.c
- -> NMODL ../temp_modfiles/inhsyn.mod
-Translating cas.mod into /home/mwsrgf/proj/ACT/simulation/x86_64/cas.c
-Translating cat.mod into /home/mwsrgf/proj/ACT/simulation/x86_64/cat.c
-Thread Safe
-Thread Safe
-Thread Safe
- -> NMODL ../temp_modfiles/ka.mod
-Translating inhsyn.mod into /home/mwsrgf/proj/ACT/simulation/x86_64/inhsyn.c
- -> NMODL ../temp_modfiles/kca.mod
- -> NMODL ../temp_modfiles/kdr.mod
-Thread Safe
-Translating ka.mod into /home/mwsrgf/proj/ACT/simulation/x86_64/ka.c
- -> NMODL ../temp_modfiles/leak.mod
-Thread Safe
-Translating kca.mod into /home/mwsrgf/proj/ACT/simulation/x86_64/kca.c
-Translating kdr.mod into /home/mwsrgf/proj/ACT/simulation/x86_64/kdr.c
-Thread Safe
-Thread Safe
- -> NMODL ../temp_modfiles/na.mod
-Translating leak.mod into /home/mwsrgf/proj/ACT/simulation/x86_64/leak.c
-Thread Safe
- -> Compiling capool.c
- -> Compiling cas.c
- -> Compiling cat.c
-Translating na.mod into /home/mwsrgf/proj/ACT/simulation/x86_64/na.c
-Thread Safe
- -> Compiling inhsyn.c
- -> Compiling ka.c
- -> Compiling kca.c
- -> Compiling kdr.c
- -> Compiling leak.c
- -> Compiling na.c
- => LINKING shared library ./libnrnmech.so
- => LINKING executable ./special LDFLAGS are:    -pthread
-Successfully created x86_64/special
-generating traces...
-
-(2024-02-18 22:45:48.151818)-[START]
-loading hoc file ../data/BursterS3/orig/template.hoc
-Setting eleak = -49
-Setting Burster[0].soma[0].gbar_leak = 0.00003538
-Setting Burster[0].soma[0].cm = 1.52762500
-Generating trace for 0.0 pA
-h.celsius set to 31.0
-Generating trace for 500.0 pA
-h.celsius set to 31.0
-Generating trace for 1000.0 pA
-h.celsius set to 31.0
-Generating trace for 1500.0 pA
-h.celsius set to 31.0
-Generating trace for 2000.0 pA
-h.celsius set to 31.0
-h.celsius set to 31.0
-Saving target voltage data to: ./output/Burster_orig_2-slice_42-seed/target/target_v.json
-done
-/home/mwsrgf/act-venv/lib64/python3.9/site-packages/torch/__init__.py:614: UserWarning: torch.set_default_tensor_type() is deprecated as of PyTorch 2.1, please use torch.set_default_dtype() and torch.set_default_device() as alternatives. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:451.)
-  _C._set_default_tensor_type(t)
-numprocs=1
-WARNING:root:No edges have been made for this network, skipping saving of edges file.
-/home/mwsrgf/proj/ACT/simulation/output/Burster_orig_2-slice_42-seed/sim_data/components/mechanisms
-Mod files: "./capool.mod" "./cas.mod" "./cat.mod" "./inhsyn.mod" "./ka.mod" "./kca.mod" "./kdr.mod" "./leak.mod" "./na.mod"
-
- -> Compiling mod_func.cpp
- -> NMODL ../capool.mod
- -> NMODL ../cas.mod
- -> NMODL ../cat.mod
-Translating capool.mod into /home/mwsrgf/proj/ACT/simulation/output/Burster_orig_2-slice_42-seed/sim_data/components/mechanisms/x86_64/capool.c
-Translating cas.mod into /home/mwsrgf/proj/ACT/simulation/output/Burster_orig_2-slice_42-seed/sim_data/components/mechanisms/x86_64/cas.c
- -> NMODL ../inhsyn.mod
-Translating cat.mod into /home/mwsrgf/proj/ACT/simulation/output/Burster_orig_2-slice_42-seed/sim_data/components/mechanisms/x86_64/cat.c
-Thread Safe
-Thread Safe
-Thread Safe
- -> NMODL ../ka.mod
-Translating inhsyn.mod into /home/mwsrgf/proj/ACT/simulation/output/Burster_orig_2-slice_42-seed/sim_data/components/mechanisms/x86_64/inhsyn.c
- -> NMODL ../kca.mod
- -> NMODL ../kdr.mod
-Thread Safe
- -> NMODL ../leak.mod
-Translating ka.mod into /home/mwsrgf/proj/ACT/simulation/output/Burster_orig_2-slice_42-seed/sim_data/components/mechanisms/x86_64/ka.c
-Translating kca.mod into /home/mwsrgf/proj/ACT/simulation/output/Burster_orig_2-slice_42-seed/sim_data/components/mechanisms/x86_64/kca.c
-Translating kdr.mod into /home/mwsrgf/proj/ACT/simulation/output/Burster_orig_2-slice_42-seed/sim_data/components/mechanisms/x86_64/kdr.c
-Thread Safe
-Thread Safe
-Thread Safe
- -> NMODL ../na.mod
-Translating leak.mod into /home/mwsrgf/proj/ACT/simulation/output/Burster_orig_2-slice_42-seed/sim_data/components/mechanisms/x86_64/leak.c
- -> Compiling capool.c
-Thread Safe
- -> Compiling cas.c
- -> Compiling cat.c
-Translating na.mod into /home/mwsrgf/proj/ACT/simulation/output/Burster_orig_2-slice_42-seed/sim_data/components/mechanisms/x86_64/na.c
-Thread Safe
- -> Compiling inhsyn.c
- -> Compiling ka.c
- -> Compiling kca.c
- -> Compiling kdr.c
- -> Compiling leak.c
- -> Compiling na.c
- => LINKING shared library ./libnrnmech.so
-Successfully created x86_64/special
-/home/mwsrgf/proj/ACT/simulation
-Building Network
-Setting celsius to default value of 31.0
-Number of cells to be generated: 320  (5 amps * 64 cells per amp (parameters ^ splits))
-Submitted batch job 6442
-/home/mwsrgf/act-venv/lib64/python3.9/site-packages/torch/__init__.py:614: UserWarning: torch.set_default_tensor_type() is deprecated as of PyTorch 2.1, please use torch.set_default_dtype() and torch.set_default_device() as alternatives. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:451.)
-  _C._set_default_tensor_type(t)
-numprocs=1
-loading large file (./output/Burster_orig_2-slice_42-seed/sim_data/output/v_report.h5)
-decimate_factor set - reducing dataset by 10x
-ARIMA order set to (4, 0, 4)
-processing cell 115/320
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-problem processing cell 66: LU decomposition error. | setting all values to 0.0
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-/home/mwsrgf/act-venv/lib64/python3.9/site-packages/torch/__init__.py:614: UserWarning: torch.set_default_tensor_type() is deprecated as of PyTorch 2.1, please use torch.set_default_dtype() and torch.set_default_device() as alternatives. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:451.)
-  _C._set_default_tensor_type(t)
-Warning: no DISPLAY environment variable.
---No graphics will be displayed.
-/home/mwsrgf/proj/ACT/simulation
-Mod files: "temp_modfiles/temp_modfiles/capool.mod" "temp_modfiles/temp_modfiles/cas.mod" "temp_modfiles/temp_modfiles/cat.mod" "temp_modfiles/temp_modfiles/inhsyn.mod" "temp_modfiles/temp_modfiles/ka.mod" "temp_modfiles/temp_modfiles/kca.mod" "temp_modfiles/temp_modfiles/kdr.mod" "temp_modfiles/temp_modfiles/leak.mod" "temp_modfiles/temp_modfiles/na.mod"
-
-Creating 'x86_64' directory for .o files.
-
- -> Compiling mod_func.cpp
- -> NMODL ../temp_modfiles/capool.mod
- -> NMODL ../temp_modfiles/cas.mod
- -> NMODL ../temp_modfiles/cat.mod
-Translating capool.mod into /home/mwsrgf/proj/ACT/simulation/x86_64/capool.c
- -> NMODL ../temp_modfiles/inhsyn.mod
-Translating cas.mod into /home/mwsrgf/proj/ACT/simulation/x86_64/cas.c
-Translating cat.mod into /home/mwsrgf/proj/ACT/simulation/x86_64/cat.c
-Thread Safe
-Thread Safe
-Thread Safe
- -> NMODL ../temp_modfiles/ka.mod
-Translating inhsyn.mod into /home/mwsrgf/proj/ACT/simulation/x86_64/inhsyn.c
- -> NMODL ../temp_modfiles/kca.mod
-Thread Safe
- -> NMODL ../temp_modfiles/kdr.mod
-Translating ka.mod into /home/mwsrgf/proj/ACT/simulation/x86_64/ka.c
- -> NMODL ../temp_modfiles/leak.mod
-Thread Safe
-Translating kca.mod into /home/mwsrgf/proj/ACT/simulation/x86_64/kca.c
-Translating kdr.mod into /home/mwsrgf/proj/ACT/simulation/x86_64/kdr.c
-Thread Safe
- -> NMODL ../temp_modfiles/na.mod
-Thread Safe
-Translating leak.mod into /home/mwsrgf/proj/ACT/simulation/x86_64/leak.c
-Thread Safe
- -> Compiling capool.c
- -> Compiling cas.c
- -> Compiling cat.c
-Translating na.mod into /home/mwsrgf/proj/ACT/simulation/x86_64/na.c
-Thread Safe
- -> Compiling inhsyn.c
- -> Compiling ka.c
- -> Compiling kca.c
- -> Compiling kdr.c
- -> Compiling leak.c
- -> Compiling na.c
- => LINKING shared library ./libnrnmech.so
- => LINKING executable ./special LDFLAGS are:    -pthread
-Successfully created x86_64/special
-/home/mwsrgf/act-venv/lib64/python3.9/site-packages/torch/utils/_device.py:77: UserWarning: The use of `x.T` on tensors of dimension other than 2 to reverse their shape is deprecated and it will throw an error in a future release. Consider `x.mT` to transpose batches of matrices or `x.permute(*torch.arange(x.ndim - 1, -1, -1))` to reverse the dimensions of a tensor. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3614.)
-  return func(*args, **kwargs)
-/home/mwsrgf/proj/ACT/simulation/../act/utils.py:936: RuntimeWarning: Mean of empty slice.
-  amplitude = x[peaks].mean()
-/home/mwsrgf/act-venv/lib64/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in divide
-  ret = ret.dtype.type(ret / rcount)
-
-(2024-02-18 22:47:26.890992)-[START]
-(2024-02-18 22:47:26.891026)-[INFO]: Number of amplitudes: 5
-Loading ./output/Burster_orig_2-slice_42-seed/target/target_v.json for target traces
-decimate_factor set - reducing generated target voltage by 10x
-(2024-02-18 22:47:27.192908)-[INFO]: Target voltage shape: torch.Size([5, 1000])
-loading hoc file ../data/BursterS3/orig/template.hoc
-Setting eleak = -49
-Setting Burster[0].soma[0].gbar_leak = 0.00003538
-Setting Burster[0].soma[0].cm = 1.52762500
-Extracting first 10 spikes for summary features
-loading large file (./output/Burster_orig_2-slice_42-seed/sim_data/output/v_report.h5)
-decimate_factor set - reducing dataset by 10x
-224/320 spiking traces extracted.
-Dropping 188 traces, mean value >-50 between 550:750ms
-Overriding model class to RandomForest
-resampling of traces not needed, same shape as target
-fitting random forest
-Done fitting random forest
-Feature importance
-Avg Max Spike Height : 27.66
-Avg Min Spike Height : 17.38
-Spike 2 time : 11.12
-Interspike Interval : 10.69
-Spike 1 time : 7.98
-Num Spikes : 7.59
-Spike 4 time : 5.93
-Spike 3 time : 4.52
-Spike 8 time : 1.29
-Spike 9 time : 1.24
-Spike 5 time : 1.22
-Spike 10 time : 1.19
-Spike 6 time : 1.11
-Spike 7 time : 1.1
-Amps supplied: [0.0, 0.5, 1.0, 1.5, 2.0]
-Amps predicted: [1.2613682746887207, 1.2623332738876343, 1.2595967054367065, 1.2493733167648315, 1.2554583549499512]
-Amps error: [-1.2613682746887207, -0.7623332738876343, -0.25959670543670654, 0.25062668323516846, 0.7445416450500488]
-Amps error sum: -1.2881299257278442
-writing training run stats for repeat 1
-done
-1 predictions: [[0.18810045719146729, 0.1242673322558403, 0.008672761730849743, 0.0033556190319359303, 0.10391533374786377, 0.02894076146185398], [0.17917466163635254, 0.1292133331298828, 0.008972762152552605, 0.003495618933811784, 0.0945313349366188, 0.030052760615944862], [0.17763373255729675, 0.1311986744403839, 0.009088762104511261, 0.0035256189294159412, 0.09398733079433441, 0.030204761773347855], [0.1702098548412323, 0.13502933084964752, 0.009320762008428574, 0.003619618946686387, 0.08555533736944199, 0.03111676126718521], [0.17104965448379517, 0.13550333678722382, 0.00929276179522276, 0.0035996190272271633, 0.08739133179187775, 0.030964761972427368]]
-Calculating error...
-h.celsius set to 31.0
-h.celsius set to 31.0
-h.celsius set to 31.0
-h.celsius set to 31.0
-h.celsius set to 31.0
-h.celsius set to 31.0
-h.celsius set to 31.0
-h.celsius set to 31.0
-h.celsius set to 31.0
-h.celsius set to 31.0
-h.celsius set to 31.0
-h.celsius set to 31.0
-h.celsius set to 31.0
-h.celsius set to 31.0
-h.celsius set to 31.0
-h.celsius set to 31.0
-h.celsius set to 31.0
-h.celsius set to 31.0
-h.celsius set to 31.0
-h.celsius set to 31.0
-h.celsius set to 31.0
-h.celsius set to 31.0
-h.celsius set to 31.0
-h.celsius set to 31.0
-h.celsius set to 31.0
-decimate_factor set - reducing sims voltage by 10x
-Repeat 1 mean: [0.17723367214202881, 0.13104240149259566, 0.009069561958312988, 0.0035192189738154412, 0.09307613372802734, 0.030255961418151855] | variance: [4.1918117947190576e-05, 1.7013404974699498e-05, 5.602178017975745e-08, 8.78143708878728e-09, 4.1863099728343126e-05, 6.034075001057348e-07]
-All predictions: [[0.18810045719146729, 0.1242673322558403, 0.008672761730849743, 0.0033556190319359303, 0.10391533374786377, 0.02894076146185398], [0.17917466163635254, 0.1292133331298828, 0.008972762152552605, 0.003495618933811784, 0.0945313349366188, 0.030052760615944862], [0.17763373255729675, 0.1311986744403839, 0.009088762104511261, 0.0035256189294159412, 0.09398733079433441, 0.030204761773347855], [0.1702098548412323, 0.13502933084964752, 0.009320762008428574, 0.003619618946686387, 0.08555533736944199, 0.03111676126718521], [0.17104965448379517, 0.13550333678722382, 0.00929276179522276, 0.0035996190272271633, 0.08739133179187775, 0.030964761972427368]]
-Err per prediction: [148.31556701660156, 153.6968231201172, 154.41632080078125, 160.08599853515625, 158.44515991210938]
-FI Err per prediction: [3.2, 2.4, 3.2, 2.8, 2.8]
-Amplitude/Frequency err per prediction [264.0, 234.0, 220.0, 194.0, 208.0]
-Best prediction: [0.17917466163635254, 0.1292133331298828, 0.008972762152552605, 0.003495618933811784, 0.0945313349366188, 0.030052760615944862]
-loading hoc file ../data/BursterS3/orig/template.hoc
-Setting eleak = -49
-Setting Burster[1].soma[0].gbar_leak = 0.00003538
-Setting Burster[1].soma[0].cm = 1.52762500
-h.celsius set to 31.0
-decimate_factor set - reducing sims voltage by 10x
-h.celsius set to 31.0
-decimate_factor set - reducing sims voltage by 10x
-h.celsius set to 31.0
-decimate_factor set - reducing sims voltage by 10x
-h.celsius set to 31.0
-decimate_factor set - reducing sims voltage by 10x
-h.celsius set to 31.0
-decimate_factor set - reducing sims voltage by 10x
-h.celsius set to 31.0
-loading hoc file ../data/BursterS3/orig/template.hoc
-Setting eleak = -49
-Setting Burster[2].soma[0].gbar_leak = 0.00003538
-Setting Burster[2].soma[0].cm = 1.52762500
-h.celsius set to 31.0
-decimate_factor set - reducing sims voltage by 10x
-loading hoc file ../data/BursterS3/orig/template.hoc
-Setting eleak = -49
-Setting Burster[3].soma[0].gbar_leak = 0.00003538
-Setting Burster[3].soma[0].cm = 1.52762500
-h.celsius set to 31.0
-decimate_factor set - reducing sims voltage by 10x
-loading hoc file ../data/BursterS3/orig/template.hoc
-Setting eleak = -49
-Setting Burster[4].soma[0].gbar_leak = 0.00003538
-Setting Burster[4].soma[0].cm = 1.52762500
-h.celsius set to 31.0
-decimate_factor set - reducing sims voltage by 10x
-loading hoc file ../data/BursterS3/orig/template.hoc
-Setting eleak = -49
-Setting Burster[5].soma[0].gbar_leak = 0.00003538
-Setting Burster[5].soma[0].cm = 1.52762500
-h.celsius set to 31.0
-decimate_factor set - reducing sims voltage by 10x
-loading hoc file ../data/BursterS3/orig/template.hoc
-Setting eleak = -49
-Setting Burster[6].soma[0].gbar_leak = 0.00003538
-Setting Burster[6].soma[0].cm = 1.52762500
-h.celsius set to 31.0
-decimate_factor set - reducing sims voltage by 10x
-/home/mwsrgf/proj/ACT/simulation/analyze_res.py:6: DeprecationWarning: 
-Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),
-(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)
-but was not found to be installed on your system.
-If this would cause problems for you,
-please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466
-        
-  import pandas as pd
-/home/mwsrgf/act-venv/lib64/python3.9/site-packages/torch/__init__.py:614: UserWarning: torch.set_default_tensor_type() is deprecated as of PyTorch 2.1, please use torch.set_default_dtype() and torch.set_default_device() as alternatives. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:451.)
-  _C._set_default_tensor_type(t)
-Warning: no DISPLAY environment variable.
---No graphics will be displayed.
-./output/Burster_orig_2-slice_42-seed/
-Med MSE: 125.1478 (32.1713)
-Med Corr: 0.4629 (0.2671)
-
-Predicted values:
-    gbar_na  gbar_kdr  gbar_cas  gbar_cat   gbar_ka  gbar_kca
-0  0.179175  0.129213  0.008973  0.003496  0.094531  0.030053
-Target values:
-   gbar_na  gbar_kdr  gbar_cas  gbar_cat  gbar_ka  gbar_kca
-0     0.13       0.1      0.01     0.005     0.17      0.02
-Error:
-    gbar_na  gbar_kdr  gbar_cas  gbar_cat   gbar_ka  gbar_kca
-0  0.049175  0.029213 -0.001027 -0.001504 -0.075469  0.010053
-
-Pred MAE: 0.0277
-
-Model ACT-Original Passive properties:
-{
-  "leak_conductance_variable": "gbar_leak",
-  "leak_reversal_variable": "eleak",
-  "r_in": 50.117835998535156,
-  "tau": 26.1,
-  "v_rest": -52.25846481323242
-}
-----------
-
-
-User Trace Passive properties:
-{
-  "v_rest": -49,
-  "r_in": 90,
-  "tau": 43.175,
-  "leak_conductance_variable": "gbar_leak",
-  "leak_reversal_variable": "eleak"
-}
-----------
-
-
-Passive properties errror
-v_rest: -3.26
-r_in: -39.88
-tau: -17.07
-----------
-
-target spikes: tensor([ 5.,  7.,  8., 10., 11.])
-simulated spikes: tensor([ 8.,  7.,  7.,  9., 10.])
-diff: tensor([ 3.,  0., -1., -1., -1.])
-Simulated and target FI curve error [SUM((simulated-target)/target)/n]: 1.2
-/home/mwsrgf/act-venv/lib64/python3.9/site-packages/torch/__init__.py:614: UserWarning: torch.set_default_tensor_type() is deprecated as of PyTorch 2.1, please use torch.set_default_dtype() and torch.set_default_device() as alternatives. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:451.)
-  _C._set_default_tensor_type(t)
-Warning: no DISPLAY environment variable.
---No graphics will be displayed.
-./output/Burster_orig_2-slice_42-seed/model_data/Burster_orig_2-slice_42-seed_FI.png
-Model ACT-Original (err: 2.4): [   0.  500. 1000. 1500. 2000.] pA : [16. 14. 14. 18. 20.] Hz
-User Trace: [   0.  500. 1000. 1500. 2000.] pA : [10. 14. 16. 20. 22.] Hz
-Warning: no DISPLAY environment variable.
---No graphics will be displayed.
-/home/mwsrgf/act-venv/lib64/python3.9/site-packages/torch/__init__.py:614: UserWarning: torch.set_default_tensor_type() is deprecated as of PyTorch 2.1, please use torch.set_default_dtype() and torch.set_default_device() as alternatives. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:451.)
-  _C._set_default_tensor_type(t)
-/home/mwsrgf/proj/ACT/simulation
-Mod files: "temp_modfiles/temp_modfiles/capool.mod" "temp_modfiles/temp_modfiles/cas.mod" "temp_modfiles/temp_modfiles/cat.mod" "temp_modfiles/temp_modfiles/inhsyn.mod" "temp_modfiles/temp_modfiles/ka.mod" "temp_modfiles/temp_modfiles/kca.mod" "temp_modfiles/temp_modfiles/kdr.mod" "temp_modfiles/temp_modfiles/leak.mod" "temp_modfiles/temp_modfiles/na.mod"
-
-Creating 'x86_64' directory for .o files.
-
- -> Compiling mod_func.cpp
- -> NMODL ../temp_modfiles/capool.mod
- -> NMODL ../temp_modfiles/cas.mod
- -> NMODL ../temp_modfiles/cat.mod
- -> NMODL ../temp_modfiles/inhsyn.mod
-Translating cas.mod into /home/mwsrgf/proj/ACT/simulation/x86_64/cas.c
-Translating capool.mod into /home/mwsrgf/proj/ACT/simulation/x86_64/capool.c
-Translating cat.mod into /home/mwsrgf/proj/ACT/simulation/x86_64/cat.c
-Thread Safe
-Thread Safe
-Thread Safe
- -> NMODL ../temp_modfiles/ka.mod
-Translating inhsyn.mod into /home/mwsrgf/proj/ACT/simulation/x86_64/inhsyn.c
- -> NMODL ../temp_modfiles/kdr.mod
- -> NMODL ../temp_modfiles/kca.mod
-Thread Safe
-Translating ka.mod into /home/mwsrgf/proj/ACT/simulation/x86_64/ka.c
- -> NMODL ../temp_modfiles/leak.mod
-Thread Safe
-Translating kdr.mod into /home/mwsrgf/proj/ACT/simulation/x86_64/kdr.c
-Translating kca.mod into /home/mwsrgf/proj/ACT/simulation/x86_64/kca.c
-Thread Safe
-Thread Safe
- -> NMODL ../temp_modfiles/na.mod
-Translating leak.mod into /home/mwsrgf/proj/ACT/simulation/x86_64/leak.c
-Thread Safe
- -> Compiling capool.c
- -> Compiling cas.c
-Translating na.mod into /home/mwsrgf/proj/ACT/simulation/x86_64/na.c
- -> Compiling cat.c
-Thread Safe
- -> Compiling inhsyn.c
- -> Compiling ka.c
- -> Compiling kca.c
- -> Compiling kdr.c
- -> Compiling leak.c
- -> Compiling na.c
- => LINKING shared library ./libnrnmech.so
- => LINKING executable ./special LDFLAGS are:    -pthread
-Successfully created x86_64/special
-Loading ./output/Burster_orig_2-slice_42-seed/target/target_v.json for target traces
-loading hoc file ../data/BursterS3/orig/template.hoc
-Setting eleak = -49
-Setting Burster[0].soma[0].gbar_leak = 0.00003538
-Setting Burster[0].soma[0].cm = 1.52762500
-Generating trace for 0.0 pA
-Setting params: {}
-h.celsius set to 31.0
-Generating trace for 500.0 pA
-Setting params: {}
-h.celsius set to 31.0
-Generating trace for 1000.0 pA
-Setting params: {}
-h.celsius set to 31.0
-Generating trace for 1500.0 pA
-Setting params: {}
-h.celsius set to 31.0
-Generating trace for 2000.0 pA
-Setting params: {}
-h.celsius set to 31.0
-saving fi
-Model ACT-Original (err: 0.0): [   0.  500. 1000. 1500. 2000.] pA : [10. 14. 16. 20. 22.] Hz
-User Trace: [   0.  500. 1000. 1500. 2000.] pA : [10. 14. 16. 20. 22.] Hz
-Learned Params
-{}
-
-Target Params
-{'gbar_na': 0.13, 'gbar_kdr': 0.1, 'gbar_cas': 0.01, 'gbar_cat': 0.005, 'gbar_ka': 0.17, 'gbar_kca': 0.02}
-
-Difference
-{}
-
-Difference (percent)
-{}
-
-done
-Start: Sun Feb 18 22:45:40 CST 2024 
-End:   Sun Feb 18 22:48:05 CST 2024
-Done running model at Sun Feb 18 22:48:05 CST 2024