# HG changeset patch
# User luca_milaz
# Date 1721587673 0
# Node ID d9b29c64efc98cf576d58cbd910c51b78613ee13
# Parent  df1eee095863f1946dce7b30eb1acd8308882b02
Uploaded

diff -r df1eee095863 -r d9b29c64efc9 marea_2/ras_to_bounds.py
--- a/marea_2/ras_to_bounds.py	Sun Jul 21 18:47:41 2024 +0000
+++ b/marea_2/ras_to_bounds.py	Sun Jul 21 18:47:53 2024 +0000
@@ -39,8 +39,6 @@
     
     parser.add_argument("-meo", "--medium", type = str,
         help = "path to input file with custom medium, if provided")
-    
-    parser.add_argument("-men", "--medium_name", type = str, help = "custom medium name")
 
     parser.add_argument('-ol', '--out_log', 
                         help = "Output log")
@@ -56,7 +54,7 @@
     
     parser.add_argument('-rs', '--ras_selector',
                         required = True,
-                        type=str,
+                        type=bool,
                         help = 'ras selector')
     
     ARGS = parser.parse_args()
@@ -77,10 +75,42 @@
         log.write(s + "\n\n")
     print(s)
 
+def generate_bounds(model:cobra.Model, medium:dict, ras=None) -> pd.DataFrame:
+    model_new = model.copy()
+    rxns_ids = []
+    for rxn in model.reactions:
+        rxns_ids.append(rxn.id)
+    for reaction in medium.keys():
+        if(medium[reaction] != None):
+            model_new.reactions.get_by_id(reaction).lower_bound=-float(medium[reaction])
+    df_FVA = cobra.flux_analysis.flux_variability_analysis(model_new,fraction_of_optimum=0,processes=1).round(8)
+    for reaction in rxns_ids:
+        model_new.reactions.get_by_id(reaction).lower_bound=float(df_FVA.loc[reaction,"minimum"])
+        model_new.reactions.get_by_id(reaction).upper_bound=float(df_FVA.loc[reaction,"maximum"])
+    
+    if(ras is not None):
+        for reaction in rxns_ids:
+            if reaction in ras.keys():
+                lower_bound=model_new.reactions.get_by_id(reaction).lower_bound
+                upper_bound=model_new.reactions.get_by_id(reaction).upper_bound
+                valMax=float((upper_bound)*ras[reaction])
+                valMin=float((lower_bound)*ras[reaction])
+                if upper_bound!=0 and lower_bound==0:
+                    model_new.reactions.get_by_id(reaction).upper_bound=valMax
+                if upper_bound==0 and lower_bound!=0:
+                    model_new.reactions.get_by_id(reaction).lower_bound=valMin
+                if upper_bound!=0 and lower_bound!=0:
+                    model_new.reactions.get_by_id(reaction).lower_bound=valMin
+                    model_new.reactions.get_by_id(reaction).upper_bound=valMax
+    rxns = []
+    for reaction in model_new.reactions:
+        rxns.append(reaction.id)
 
-def write_to_file(dataset: pd.DataFrame, name: str, keep_index:bool=False)->None:
-    dataset.to_csv(ARGS.output_folder + name + ".csv", sep = '\t', index = keep_index)
+    bounds = pd.DataFrame(columns = ["lower_bound", "upper_bound"], index=rxns)
 
+    for reaction in model.reactions:
+        bounds.loc[reaction.id] = [reaction.lower_bound, reaction.upper_bound]
+    return bounds
 
 
 ############################# main ###########################################
@@ -91,35 +121,46 @@
     Returns:
         None
     """
-    if not os.path.exists('model_generator'):
-        os.makedirs('model_generator')
+    if not os.path.exists('ras_to_bounds'):
+        os.makedirs('ras_to_bounds')
 
-    num_processors = cpu_count()
+    ARGS.output_folder = 'ras_to_bounds/'
 
     global ARGS
     ARGS = process_args(sys.argv)
-
-    ARGS.output_folder = 'model_generator/'
     
     ARGS.output_types = ARGS.output_type.split(",")
 
-    ras = pd.read_table(ARGS.input_ras, header=0, sep=r'\s+', index_col = 0).T
-    ras.replace("None", None, inplace=True)
-    ras = ras.astype(float)
+    boundsPath = utils.FilePath("bounds", ".csv", prefix = ARGS.output_folder)
+    mediumPath = utils.FilePath("medium", ".csv", prefix = ARGS.output_folder)
 
-    #medium has rows cells and columns medium reactions, not common reactions set to None
-    medium = pd.read_csv(ARGS.input_medium, sep = '\t', header = 0, engine='python', index_col = 0)
-    medium = ras.astype(float)
-
+    if(ARGS.ras_selector == True):
+        ras = pd.read_table(ARGS.input_ras, header=0, sep=r'\s+', index_col = 0).T
+        ras.replace("None", None, inplace=True)
+        ras = ras.astype(float)
+    
     model_type :utils.Model = ARGS.model_selector
     if model_type is utils.Model.Custom:
         model = model_type.getCOBRAmodel(customPath = utils.FilePath.fromStrPath(ARGS.model), customExtension = utils.FilePath.fromStrPath(ARGS.model_name).ext)
     else:
         model = model_type.getCOBRAmodel(toolDir=ARGS.tool_dir)
 
-    '''for index, row in ras.iterrows(): #iterate over cells RAS
-        generate_model(model, index, row, medium.loc[index], ARGS.output_model_format)'''
+    if(ARGS.medium_selector == "Custom"):
+        medium = pd.read_csv(ARGS.input_medium, index_col = 0)
+        medium = medium.astype(float)
+        medium = medium['medium'].to_dict()
+    else:
+        df_mediums = pd.read_csv(ARGS.tool_dir + "/local/medium/medium.csv", index_col = 0)
+        medium = df_mediums[[ARGS.medium_selector]]
+        medium = medium[ARGS.medium_selector].to_dict()
 
+    if(ARGS.ras_selector == True):
+        bounds = generate_bounds(model, medium, ras = ras)
+    else:
+        bounds = generate_bounds(model, medium)
+
+    bounds.to_csv(boundsPath.show())
+    medium.to_csv(mediumPath.show())
     pass
         
 ##############################################################################