diff --git a/Python Scripts/query_data.py b/Python Scripts/query_data.py index d92c6346..f2c50f71 100644 --- a/Python Scripts/query_data.py +++ b/Python Scripts/query_data.py @@ -61,7 +61,7 @@ # Save params save_drift = False, save_spectra = False, - save_b_shear = True, + save_b_shear = False, save_results = save_csvs, # Plot params show_plots = show_plots, @@ -85,49 +85,7 @@ # --------------------------------------- DRIFT ---------------------------------------- # Compute Drift Results if all(isinstance(value, pd.DataFrame) for value in drifts_df_dict.values()): - sim_type_lst = [key.split('_')[0] for key in drifts_df_dict.keys()] - nsubs_lst = [key.split('_')[1] for key in drifts_df_dict.keys()] - iteration_lst = [key.split('_')[4] for key in drifts_df_dict.keys()] - station_lst = [key.split('_')[5] for key in drifts_df_dict.keys()] - - drift_df = pd.DataFrame({ - 'Sim_Type' : sim_type_lst, - 'Nsubs' : nsubs_lst, - 'Iteration' : iteration_lst, - 'Station' : station_lst}) - - dfx = pd.DataFrame([df['CM x'] for df in drifts_df_dict.values()]) - dfy = pd.DataFrame([df['CM y'] for df in drifts_df_dict.values()]) - dfy = dfy.reset_index() - dfx = dfx.reset_index() - dfy = dfy.iloc[:,1:] - dfx = dfx.iloc[:,1:] - drift_df_x = pd.concat([drift_df, dfx], axis=1) - drift_df_y = pd.concat([drift_df, dfy], axis=1) - rename_dict = { - 1 : 's1', - 2 : 's2', - 3 : 's3', - 4 : 's4', - 5 : 's5', - 6 : 's6', - 7 : 's7', - 8 : 's8', - 9 : 's9', - 10 : 's10', - 11 : 's11', - 12 : 's12', - 13 : 's13', - 14 : 's14', - 15 : 's15', - 16 : 's16', - 17 : 's17', - 18 : 's18', - 19 : 's19', - 20 : 's20', - } - drift_df_x = drift_df_x.rename(columns=rename_dict).copy()[['Sim_Type', 'Nsubs', 'Iteration', 'Station', 's1','s5','s10','s15','s20']] - drift_df_y = drift_df_y.rename(columns=rename_dict).copy()[['Sim_Type', 'Nsubs', 'Iteration', 'Station', 's1','s5','s10','s15','s20']] + drift_df_x, drift_df_y = getDriftResultsDF(drifts_df_dict) else: drift_df_x = pd.read_csv(project_path / 'drift_per_story_X_df.csv', index_col=0) drift_df_y = pd.read_csv(project_path / 'drift_per_story_Y_df.csv', index_col=0) @@ -136,43 +94,7 @@ # --------------------------------------- SPECTRUM ---------------------------------------- # We will have the acceleration at the period equal to mode 3 = 0.83s if all(isinstance(value, pd.DataFrame) for value in spectra_df_dict.values()): - sim_type_lst = [key.split('_')[0] for key in spectra_df_dict.keys()] - nsubs_lst = [key.split('_')[1] for key in spectra_df_dict.keys()] - iteration_lst = [key.split('_')[4] for key in spectra_df_dict.keys()] - station_lst = [key.split('_')[5] for key in spectra_df_dict.keys()] - spectra_df = pd.DataFrame({ - 'Sim_Type' : sim_type_lst, - 'Nsubs' : nsubs_lst, - 'Iteration' : iteration_lst, - 'Station' : station_lst,}) - spectra_df['Zone'] = spectra_df['Station'].apply(assignZonesToStationsInDF) - columns_x = ['Story 1 x', 'Story 5 x', 'Story 10 x', 'Story 15 x', 'Story 20 x'] - columns_y = ['Story 1 y', 'Story 5 y', 'Story 10 y', 'Story 15 y', 'Story 20 y'] - - dfx = pd.DataFrame([df[columns_x].iloc[416] for df in spectra_df_dict.values()]) - dfy = pd.DataFrame([df[columns_y].iloc[416] for df in spectra_df_dict.values()]) - dfy = dfy.reset_index() - dfx = dfx.reset_index() - dfy = dfy.iloc[:,1:] - dfx = dfx.iloc[:,1:] - spectra_df_x = pd.concat([spectra_df, dfx], axis=1) - spectra_df_y = pd.concat([spectra_df, dfy], axis=1) - rename_dict = { - 'Story 1 x' : 's1', - 'Story 5 x' : 's5', - 'Story 10 x' : 's10', - 'Story 15 x' : 's15', - 'Story 20 x' : 's20', - } - spectra_df_x = spectra_df_x.rename(columns=rename_dict) - rename_dict = { - 'Story 1 y' : 's1', - 'Story 5 y' : 's5', - 'Story 10 y' : 's10', - 'Story 15 y' : 's15', - 'Story 20 y' : 's20', - } - spectra_df_y = spectra_df_y.rename(columns=rename_dict) + spectra_df_x, spectra_df_y = getSpectraResultsDF(spectra_df_dict) else: spectra_df_x = pd.read_csv(project_path / 'spectra_per_story_X_df.csv', index_col=0) @@ -181,25 +103,10 @@ #%% Compute Base Shear Results # --------------------------------------- BASE SHEAR ---------------------------------------- if all(isinstance(value, pd.DataFrame) for value in base_shear_df_dict.values()): - sim_type_lst = [key.split('_')[0] for key in base_shear_df_dict.keys()] - nsubs_lst = [key.split('_')[1] for key in base_shear_df_dict.keys()] - iteration_lst = [key.split('_')[4] for key in base_shear_df_dict.keys()] - station_lst = [key.split('_')[5] for key in base_shear_df_dict.keys()] - base_shear_df = pd.DataFrame({ - 'Sim_Type' : sim_type_lst, - 'Nsubs' : nsubs_lst, - 'Iteration' : iteration_lst, - 'Station' : station_lst,}) - dfx = pd.DataFrame([df['Shear X'].abs().max() for df in base_shear_df_dict.values()], columns=['MaxShearX']) - dfy = pd.DataFrame([df['Shear Y'].abs().max() for df in base_shear_df_dict.values()], columns=['MaxShearY']) - dfy = dfy.reset_index() - dfx = dfx.reset_index() - dfy = dfy.iloc[:,1:] - dfx = dfx.iloc[:,1:] - base_shear_df_x = pd.concat([base_shear_df, dfx], axis=1) - base_shear_df_y = pd.concat([base_shear_df, dfy], axis=1) + base_shear_df_x, base_shear_df_y = getSBaseResultsDF(base_shear_df_dict) else: base_shear_df_x = pd.read_csv(project_path / 'max_base_shear_X_df.csv', index_col=0) base_shear_df_y = pd.read_csv(project_path / 'max_base_shear_Y_df.csv', index_col=0) -# %% +# %% ========================================== ANOVA ========================================== +