Mercurial > repos > bimib > marea_2

changeset 189:0eca2d3b7509 draft

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Uploaded
author luca_milaz
date Tue, 30 Jul 2024 20:08:44 +0000
parents 3e711b9fa866
children c1bf1291390f
files marea_2/flux_simulation.py
diffstat 1 files changed, 13 insertions(+), 12 deletions(-) [+]
line wrap: on
line diff
--- a/marea_2/flux_simulation.py	Tue Jul 30 20:08:31 2024 +0000
+++ b/marea_2/flux_simulation.py	Tue Jul 30 20:08:44 2024 +0000
@@ -36,14 +36,18 @@
                         required = True,
                         type=str,
                         help = 'inputs bounds')
+    
+    parser.add_argument('-ni', '--names',
+                        required = True,
+                        type=str,
+                        help = 'cell names')
  
     parser.add_argument(
         '-ms', '--model_selector', 
         type = utils.Model, default = utils.Model.ENGRO2, choices = [utils.Model.ENGRO2, utils.Model.Custom],
         help = 'chose which type of model you want use')
     
-    parser.add_argument("-mo", "--model", type = str,
-        help = "path to input file with custom rules, if provided")
+    parser.add_argument("-mo", "--model", type = str)
     
     parser.add_argument("-mn", "--model_name", type = str, help = "custom mode name")
     
@@ -182,19 +186,15 @@
     pass
 
 
-def model_sampler(model_input:cobra.Model, model_name:str)-> List[pd.DataFrame]:
+def model_sampler(model_input_original:cobra.Model, bounds_path:str, cell_name:str)-> List[pd.DataFrame]:
 
-    
-    bounds_df = read_dataset(model_name, "bounds dataset")
+    model_input = model_input_original.copy()
+    bounds_df = read_dataset(bounds_path, "bounds dataset")
     for rxn_index, row in bounds_df.iterrows():
         model_input.reactions.get_by_id(rxn_index).lower_bound = row.lower_bound
         model_input.reactions.get_by_id(rxn_index).upper_bound = row.upper_bound
-
-    utils.logWarning(
-        "Sampling model: " + model_name,
-        ARGS.out_log)
     
-    name = model_name.split('.')[0]
+    name = cell_name.split('.')[0]
     
     if ARGS.algorithm == 'OPTGP':
         OPTGP_sampler(model_input, name, ARGS.n_samples, ARGS.thinning, ARGS.n_batches, ARGS.seed)
@@ -210,7 +210,7 @@
     returnList = []
     returnList.append(df_mean, df_median, df_quantiles)
 
-    df_pFBA, df_FVA, df_sensitivity = fluxes_analysis(name, model_name, ARGS.output_type_analysis)
+    df_pFBA, df_FVA, df_sensitivity = fluxes_analysis(model_input, name, ARGS.output_type_analysis)
 
     if("pFBA" in ARGS.output_type_analysis):
         returnList.append(df_pFBA)
@@ -325,11 +325,12 @@
         model = model_type.getCOBRAmodel(toolDir=ARGS.tool_dir)
     
     ARGS.bounds = ARGS.input.split(",")
+    ARGS.bounds_name = ARGS.names.split(",")
     ARGS.output_types = ARGS.output_type.split(",")
     ARGS.output_type_analysis = ARGS.output_type_analysis.split(",")
 
 
-    results = Parallel(n_jobs=num_processors)(delayed(model_sampler)(model, model_name) for model_name in ARGS.bounds)
+    results = Parallel(n_jobs=num_processors)(delayed(model_sampler)(model, bounds_path, cell_name) for bounds_path, cell_name in zip(ARGS.bounds, ARGS.bounds_name))
 
     all_mean = pd.concat([result[0] for result in results], ignore_index=False)
     all_median = pd.concat([result[1] for result in results], ignore_index=False)