Mercurial > repos > bimib > marea_2_0
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(-) [+] |
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--- 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)