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Jun 16, 2024
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results 10 #11

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105 changes: 6 additions & 99 deletions Python Scripts/query_data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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,
Expand All @@ -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)
Expand All @@ -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)
Expand All @@ -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 ==========================================