changeset 211:bb4c61924e03 draft

Uploaded
author luca_milaz
date Fri, 05 Jul 2024 12:01:52 +0000
parents eca31a79e4b6
children 7f2e0ca1b8f1
files marea_2_0/flux_sampling.py
diffstat 1 files changed, 20 insertions(+), 23 deletions(-) [+]
line wrap: on
line diff
--- a/marea_2_0/flux_sampling.py	Fri Jul 05 11:28:51 2024 +0000
+++ b/marea_2_0/flux_sampling.py	Fri Jul 05 12:01:52 2024 +0000
@@ -150,10 +150,6 @@
 
 def model_sampler(model_input:str, model_name:str)-> List[pd.DataFrame]:
 
-    df_mean = pd.DataFrame()
-    df_median= pd.DataFrame()
-    df_quantiles= pd.DataFrame()
-
     model = load_custom_model(
         utils.FilePath.fromStrPath(model_input), utils.FilePath.fromStrPath(model_name).ext)
     
@@ -176,22 +172,24 @@
 
     return df_mean, df_median, df_quantiles
 
-def fluxes_statistics(model_name: str,  output_types:List, df_mean:pd.DataFrame, df_median:pd.DataFrame, df_quantiles:pd.DataFrame)-> List[pd.DataFrame]:
+def fluxes_statistics(model_name: str,  output_types:List)-> List[pd.DataFrame]:
+
+    df_mean = pd.DataFrame()
+    df_median= pd.DataFrame()
+    df_quantiles= pd.DataFrame()
 
     df_samples = pd.read_csv(ARGS.output_folder  +  model_name + '.csv', sep = '\t')
     for output_type in output_types:
         if(output_type == "mean"):
-            df_temp = df_samples.mean()
-            df_temp = df_temp.to_frame().T
-            df_temp = df_temp.reset_index(drop=True)
-            df_temp.index = [model_name]
-            df_mean = pd.concat([df_mean, df_temp])
+            df_mean = df_samples.mean()
+            df_mean = df_mean.to_frame().T
+            df_mean = df_mean.reset_index(drop=True)
+            df_mean.index = [model_name]
         elif(output_type == "median"):
-            df_temp = df_samples.median()
-            df_temp = df_temp.to_frame().T
-            df_temp = df_temp.reset_index(drop=True)
-            df_temp.index = [model_name]
-            df_median = pd.concat([df_median, df_temp])
+            df_median = df_samples.median()
+            df_median = df_median.to_frame().T
+            df_median = df_median.reset_index(drop=True)
+            df_median.index = [model_name]
         elif(output_type == "quantiles"):
             df_quantile = df_samples.quantile([0.25, 0.5, 0.75])
             newRow = []
@@ -203,11 +201,10 @@
                 cols.append(rxn + "_q2")
                 newRow.append(df_quantile[rxn].loc[0.75])
                 cols.append(rxn + "_q3")
-            df_temp = pd.DataFrame(columns=cols)
-            df_temp.loc[0] = newRow
-            df_temp = df_temp.reset_index(drop=True)
-            df_temp.index = [model_name]
-            df_quantiles = pd.concat([df_quantiles, df_temp])
+            df_quantiles = pd.DataFrame(columns=cols)
+            df_quantiles.loc[0] = newRow
+            df_quantiles = df_quantiles.reset_index(drop=True)
+            df_quantiles.index = [model_name]
     
     return df_mean, df_median, df_quantiles
     
@@ -268,9 +265,9 @@
  
     results = Parallel(n_jobs=num_processors)(delayed(model_sampler)(model_input, model_name) for model_input, model_name in zip(models_input, models_name))
 
-    all_mean = pd.concat([result[0] for result in results], ignore_index=True)
-    all_median = pd.concat([result[1] for result in results], ignore_index=True)
-    all_quantiles = pd.concat([result[2] for result in results], ignore_index=True)
+    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)
+    all_quantiles = pd.concat([result[2] for result in results], ignore_index=False)
 
     if("mean" in ARGS.output_types):
         all_mean = all_mean.fillna(0.0)