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self.grid else f'{self.sim_type}_{self.magnitude}_{self.rup_type}{self.iteration}_s{self.station}_{self.direction}' - self.drift_title = f'Drift per story plot | {self.id}' - self.spectrums_title = f'Story PSa plot | {self.id}' + self.drift_title = f'Drift per story Plot | {self.id}' + self.spectrums_title = f'Story PSa Plot | {self.id}' self.base_shear_ss_title = f'Base Shear Plot | {self.id}' def plotConfig(self, title:str, x = 19.2, y = 10.8): @@ -314,15 +314,19 @@ def plotModelDrift(self, max_corner_x: list, max_center_x: list, max_corner_y: l # Plot center drift y = [i for i in range(1, self.stories+1)] if self.x_direction: - candidate = np.array(max_center_x).max() + 0.0005 if self.station == 1 else 0 + candidate = np.array(max_center_x).max() + 0.0005 if self.station == 1 else 0 ax.set_xlim(xlim_inf, candidate) if candidate > axes[0,0].get_xlim()[1] else ax.set_xlim(xlim_inf, axes[0,0].get_xlim()[1]) color = color_1 if self.grid and not line_color else 'red' - ax.plot(max_center_x, y, label='max_center_x', color=color, linewidth=0.5, markersize=5) + linewidth = 0.5 if self.grid and not line_color else 0.6 + marker = 'v' if line_color else None + ax.plot(max_center_x, y, label='max_center_x', marker=marker, color=color, linewidth=0.5, markersize=2) else: - candidate = np.array(max_center_y).max() + 0.0005 if self.station == 1 else 0 + candidate = np.array(max_center_y).max() + 0.0005 if self.station == 1 else 0 ax.set_xlim(xlim_inf, candidate) if candidate > axes[0,0].get_xlim()[1] else ax.set_xlim(xlim_inf, axes[0,0].get_xlim()[1]) color = color_1 if self.grid and not line_color else 'blue' - ax.plot(max_center_y, y, label='max_center_y', color=color, linewidth=0.5, markersize=5) + linewidth = 0.5 if self.grid and not line_color else 0.6 + marker = 'd' if line_color else None + ax.plot(max_center_y, y, label='max_center_y', marker=marker, color=color, linewidth=linewidth, markersize=2) # Plot NCH433 limits ax.axvline(x=0.002, color='black', linestyle='--', linewidth=0.55, alpha = 0.9, label='NCh433 Limit - 5.9.2 = 0.002') @@ -361,7 +365,9 @@ def plotLocalStoriesSpectrums(self, row = (self.station - 1) // 3 col = (self.station - 1) % 3 ax = axes[row, col] - + # Set local title + ax.set_title(f'Station {self.station}') + # Setup axis formatter = FuncFormatter(self.to_empty) ax.set_xlabel('T (s)') if self.station in [7,8,9] else ax.xaxis.set_major_formatter(formatter) @@ -421,12 +427,15 @@ def plotMeanStoriesSpectrums(self, row = (self.station - 1) // 3 col = (self.station - 1) % 3 ax = axes[row, col] - + + # Set local title + ax.set_title(f'Station {self.station}') + # Setup axis formatter = FuncFormatter(self.to_empty) ax.set_xlabel('T (s)') if self.station in [7,8,9] else ax.xaxis.set_major_formatter(formatter) ax.set_ylabel(f'Acceleration in {direction.upper()} (m/s/s)') if self.station in [4] else ax.set_ylabel('') - + # Make plot spectrum T = np.linspace(0.003, 2, 1000) spa_lst = [] @@ -445,14 +454,14 @@ def plotMeanStoriesSpectrums(self, ax.plot(T, Spe, label=f'Story {story}', linestyle=line_style, color=color, linewidth=0.5) handles, labels = axes[0, 0].get_legend_handles_labels() - fig.legend(handles[-5:], labels[-5:], loc='upper right', bbox_to_anchor=(1, 1), bbox_transform=fig.transFigure) + fig.legend(handles[-5:], labels[-5:], loc='upper right', bbox_to_anchor=(1, 1), bbox_transform=fig.transFigure, fontsize='small') if save_fig: self.plotSave(fig) - def plotShearBaseOverTime(self, time:np.ndarray, time_shear_fma:list[float], Qmax:float, dir_:str, axes: plt.Axes=None, - save_fig:bool=True, fig_size:tuple[float, float]=(19.2, 10.8), mean=False): + save_fig:bool=True, fig_size:tuple[float, float]=(19.2, 10.8), mean=False, leyend=False, + ylim_inf=-100, ylim_sup=100)->plt.Axes|NDArray[plt.Axes]: # Input params if dir_ not in ['x','X','y','Y']: raise ValueError(f'dir must be x, y! Current: {dir}') @@ -468,18 +477,39 @@ def plotShearBaseOverTime(self, time:np.ndarray, time_shear_fma:list[float], Qma row = (self.station - 1) // 3 col = (self.station - 1) % 3 ax = axes[row, col] - - ax.axhline(y=Qmax, color='red', linestyle='--', linewidth=0.5, alpha = 0.9, label='NCh433 Qmax - 6.3.7.1') if not mean and self.iteration == 1 else None - ax.axhline(y=-Qmax, color='red', linestyle='--', linewidth=0.5, alpha = 0.9, label=None) if not mean and self.iteration == 1 else None - - formatter = FuncFormatter(self.to_empty) - ax.set_xlabel('Time (s)') if self.station in [7,8,9] else ax.xaxis.set_major_formatter(formatter) - ax.set_ylabel(f'Shear in {dir_.upper()} direction (kN)') if self.station in [4] else ax.set_ylabel('') + # Set local title + ax.set_title(f'Station {self.station}') + + # Plot alpha = 0.1 if not mean else 1 ax.plot(time, time_shear_fma, label='Shear Base Series', color='blue', linewidth=0.5, alpha=alpha) + ax.axhline(y=Qmax, color='red', linestyle='--', linewidth=0.5, alpha = 0.9, label='NCh433 Qmax - 6.3.7.1') + ax.axhline(y=-Qmax, color='red', linestyle='--', linewidth=0.5, alpha = 0.9, label=None) + + # Setup axis + formatter1 = FuncFormatter(self.to_empty) + formatter2 = FuncFormatter(self.to_equal) + ax.set_ylim(ylim_inf, ylim_sup) if self.station ==1 and self.iteration == 1 else ax.set_ylim(axes[0, 0].get_ylim()) + ax.set_xlabel('Time (s)') if self.station in [8] else ax.xaxis.set_major_formatter(formatter1) + ax.set_ylabel(f'Shear in {dir_.upper()} direction (kN)') if self.station in [4] else ax.set_ylabel('') + ax.xaxis.set_major_formatter(formatter2) if self.station in [7,8,9] else ax.xaxis.set_major_formatter(formatter1) + ax.yaxis.set_major_formatter(formatter2) if self.station in [1,4,7] else ax.yaxis.set_major_formatter(formatter1) + + # Update lim negative if candidate is less than the current limit + #candidate_neg = np.array(time_shear_fma).min() - 1000 if self.station == 1 else 0 + #ax.set_ylim(candidate_neg, axes[0,0].get_ylim()[1]) if candidate_neg < axes[0,0].get_ylim()[0] else ax.set_ylim(axes[0,0].get_ylim()[0], axes[0,0].get_ylim()[1]) + #candidate_pos = np.array(time_shear_fma).max() + 1000 if self.station == 1 else 0 + #ax.set_ylim(axes[0,0].get_ylim()[0], candidate_pos) if candidate_pos > axes[0,0].get_ylim()[1] else ax.set_ylim(axes[0,0].get_ylim()[0], axes[0,0].get_ylim()[1]) + + candidate_neg = np.array(time_shear_fma).min() - 1000 if self.station == 1 and mean else 0 + ax.set_ylim(candidate_neg, axes[0,0].get_ylim()[1]) if candidate_neg < axes[0,0].get_ylim()[0] else ax.set_ylim(axes[0,0].get_ylim()[0], axes[0,0].get_ylim()[1]) + candidate_pos = np.array(time_shear_fma).max() + 1000 if self.station == 1 and mean else 0 + ax.set_ylim(axes[0,0].get_ylim()[0], candidate_pos) if candidate_pos > axes[0,0].get_ylim()[1] else ax.set_ylim(axes[0,0].get_ylim()[0], axes[0,0].get_ylim()[1]) + handles, labels = axes[0, 0].get_legend_handles_labels() - fig.legend(handles[-5:], labels[-5:], loc='upper right', bbox_to_anchor=(1, 1), bbox_transform=fig.transFigure) + if leyend: + fig.legend(handles[-5:], labels[-5:], loc='upper right', bbox_to_anchor=(1, 1), bbox_transform=fig.transFigure) if save_fig: self.plotSave(fig) return axes diff --git a/Python Scripts/PySeesTKO/pyseestko/queries.py b/Python Scripts/PySeesTKO/pyseestko/queries.py index 0cf61885..5e3f2055 100644 --- a/Python Scripts/PySeesTKO/pyseestko/queries.py +++ b/Python Scripts/PySeesTKO/pyseestko/queries.py @@ -7,7 +7,6 @@ from pyseestko.utilities import initialize_ssh_tunnel #type: ignore from pyseestko.utilities import checkMainQueryInput #type: ignore from pyseestko.utilities import save_df_to_csv_paths #type: ignore -from pyseestko.utilities import getDriftResultsDF #type: ignore from pathlib import Path from typing import List, Dict, Tuple from tqdm import tqdm @@ -56,8 +55,7 @@ def executeMainQuery( windows : bool = True, project_path: Path = git_path/ 'DataBase-Outputs', verbose : bool = True, - ) -> Tuple[Tuple[dict, pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame], - Dict[str, List[pd.DataFrame]], Dict[str, pd.DataFrame]]: + ) -> Tuple[Dict[str, pd.DataFrame], Dict[str, pd.DataFrame], Dict[str, pd.DataFrame]]: """ This function will execute the main query to get the results from the database The logic is the following: @@ -132,26 +130,11 @@ def executeMainQuery( # -------------------------------------- RETURN THE RESULTS -------------------------------------- drift_tple = (None, None, None, None, None) spectra_tple = (None, None) - base_shear_tple = None + base_shear_tple = (None, None) - # Compute Drift Results - if save_drift: - 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_max_x = getDriftResultsDF(sim_type_lst, nsubs_lst, iteration_lst, station_lst, - [df['Max x'].max() for df in drifts_df_dict.values()]) - drift_df_max_y = getDriftResultsDF(sim_type_lst, nsubs_lst, iteration_lst, station_lst, - [df['Max y'].max() for df in drifts_df_dict.values()]) - drift_df_mean_x = getDriftResultsDF(sim_type_lst, nsubs_lst, iteration_lst, station_lst, - [df['Max x'].mean() for df in drifts_df_dict.values()]) - drift_df_mean_y = getDriftResultsDF(sim_type_lst, nsubs_lst, iteration_lst, station_lst, - [df['Max y'].mean() for df in drifts_df_dict.values()]) - drift_tple = (drifts_df_dict, drift_df_max_x, drift_df_max_y, drift_df_mean_x, drift_df_mean_y) + - return drift_tple, spectra_df_dict, base_shear_df_dict + return drift_tple, spectra_tple, base_shear_tple def queryMetricsInSinglePlots( # Params @@ -267,7 +250,7 @@ def queryMetricsInGridPlots( # Optional params windows : bool = True, verbose : bool = True, - )-> Tuple[Dict[str, pd.DataFrame], Dict[str, List[pd.DataFrame]], Dict[str, pd.DataFrame]]: + )-> Tuple[Dict[str, pd.DataFrame], Dict[str, pd.DataFrame], Dict[str, pd.DataFrame]]: """ This function will execute the main query to get the results from the database in grid plots The grid is going to be a 3x3 grid with the drifts, spectra and base shear. @@ -282,11 +265,12 @@ def queryMetricsInGridPlots( save_drift = project_path / 'Drift Output' if save_drift else None save_spectra = project_path / 'Story Spectra Output' if save_spectra else None save_b_shear = project_path / 'Base Shear Output' if save_b_shear else None - + nch = NCh433_2012('Las Condes', 'B', 2) # Iterate over the subs, then over the sim_type and then over the stations so we can get all the results for sim_type in sim_types: for nsubs in nsubs_lst: structure_weight = 22241.3 if nsubs == 4 else 18032.3 + Qmax = nch.computeMaxBaseShear_c6_3_7_2(structure_weight) # Drift params drift_axes = [np.full((3, 3), None), np.full((3, 3), None)] @@ -333,7 +317,8 @@ def queryMetricsInGridPlots( # Plot mean spectra _plotMeanSpectraColor(spectra_df_dict, plotter, spectra_axes, save_fig, fig_size) if save_spectra else None - _plotMeanBaseShearColor(base_shear_df_dict, plotter, base_shear_axes, save_fig, fig_size) if save_b_shear else None + # Plot mean base shear + _plotMeanBaseShearColor(base_shear_df_dict, plotter, base_shear_axes, save_fig, fig_size, Qmax) if save_b_shear else None # Update tqdm pbar.update(1) @@ -370,13 +355,31 @@ def _plotMeanSpectraColor(spectra_df_dict: Dict[str, pd.DataFrame], plotter: Plo plotter.setup_direction(x_direction=False) plotter.plotMeanStoriesSpectrums(mean_spectras_y, 'y', [1,5,10,15,20], save_fig, spectra_axes[1], fig_size) -def _plotMeanBaseShearColor(base_shear_df_dict: Dict[str, pd.DataFrame], plotter: Plotting, spectra_axes:plt.Axes, save_fig:bool, fig_size:bool): +def _plotMeanBaseShearColor(base_shear_df_dict: Dict[str, pd.DataFrame], plotter: Plotting, base_shear_axes:plt.Axes, save_fig:bool, fig_size:bool, Qmax:float): mean_base_shear_x = pd.concat([df['Shear X'] for df in list(base_shear_df_dict.values())[-5:]], axis=1).mean(axis=1) mean_base_shear_y = pd.concat([df['Shear Y'] for df in list(base_shear_df_dict.values())[-5:]], axis=1).mean(axis=1) plotter.setup_direction(x_direction=True) - plotter.plotShearBaseOverTime(mean_base_shear_x.index, mean_base_shear_x.values, 0, 'x', spectra_axes[0], save_fig, fig_size, mean=True) + plotter.plotShearBaseOverTime( + time = mean_base_shear_x.index, + time_shear_fma = mean_base_shear_x.values, + Qmax = Qmax, + dir_ = 'x', + axes = base_shear_axes[0], + save_fig = save_fig, + fig_size = fig_size, + leyend = False, + mean = True) plotter.setup_direction(x_direction=False) - plotter.plotShearBaseOverTime(mean_base_shear_y.index, mean_base_shear_y.values, 0, 'y', spectra_axes[1], save_fig, fig_size, mean=True) + plotter.plotShearBaseOverTime( + time = mean_base_shear_y.index, + time_shear_fma = mean_base_shear_y.values, + Qmax = Qmax, + dir_ = 'y', + axes = base_shear_axes[1], + save_fig = save_fig, + fig_size = fig_size, + leyend = False, + mean = True) def getDriftDFs(drifts_df_lst:List[pd.DataFrame]): """ @@ -474,7 +477,7 @@ def getReplicaCummStatisticDriftDFs(drifts_df_lst:List[pd.DataFrame], statistic: return df1, df2, drift_df -def getSpectraDfs(spectra_df_lst:List[pd.DataFrame]): +def DEPgetSpectraDfs(spectra_df_lst:List[pd.DataFrame]): """ This function will get the mean spectra dataframes of the x and y directions} and return the spectra dataframe of the x direction, the spectra dataframe of the y direction @@ -810,6 +813,7 @@ def getAllResults(self, # Plot the data self.plotter.save_path = Path(save_b_shear) + self.plotter.setup_direction(x_direction=True) shear_axes_x = self.plotter.plotShearBaseOverTime( time = time_series, time_shear_fma = shear_x, @@ -820,6 +824,7 @@ def getAllResults(self, fig_size = fig_size, mean = False ) + self.plotter.setup_direction(x_direction=False) shear_axes_y = self.plotter.plotShearBaseOverTime( time = time_series, time_shear_fma = shear_y, diff --git a/Python Scripts/PySeesTKO/pyseestko/utilities.py b/Python Scripts/PySeesTKO/pyseestko/utilities.py index f57903ba..ef6a9741 100644 --- a/Python Scripts/PySeesTKO/pyseestko/utilities.py +++ b/Python Scripts/PySeesTKO/pyseestko/utilities.py @@ -118,6 +118,46 @@ def getDriftResultsDF( drift_df['Zone'] = drift_df['Station'].apply(assignZonesToStationsInDF) return drift_df +def getSpectraResultsDF( + sim_type_lst : List[str], + nsubs_lst : List[str], + iteration_lst : List[str], + station_lst : List[str], + spectra_df_dict: Dict[str, pd.DataFrame], + ): + max_spectra_lst_x = [(df.iloc[:,:5]).mean().max() for df in spectra_df_dict.values()] + max_spectra_lst_y = [(df.iloc[:,5:]).mean().max() for df in spectra_df_dict.values()] + spectra_df = pd.DataFrame({ + 'Sim_Type' : sim_type_lst, + 'Nsubs' : nsubs_lst, + 'Iteration' : iteration_lst, + 'Station' : station_lst, + 'Max_Spectra_X': max_spectra_lst_x, + 'Max_Spectra_Y': max_spectra_lst_y + }) + spectra_df['Zone'] = spectra_df['Station'].apply(assignZonesToStationsInDF) + return spectra_df + +def getSBaseResultsDF( + sim_type_lst : List[str], + nsubs_lst : List[str], + iteration_lst : List[str], + station_lst : List[str], + base_shear_df_dict: Dict[str, pd.DataFrame] + ): + mean_base_shear_lst_x = [abs(df['Shear X'].mean()) for df in base_shear_df_dict.values()] + mean_base_shear_lst_y = [abs(df['Shear Y'].mean()) for df in base_shear_df_dict.values()] + base_shear_df = pd.DataFrame({ + 'Sim_Type' : sim_type_lst, + 'Nsubs' : nsubs_lst, + 'Iteration' : iteration_lst, + 'Station' : station_lst, + 'BShear_X' : mean_base_shear_lst_x, + 'BShear_Y' : mean_base_shear_lst_y + }) + base_shear_df['Zone'] = base_shear_df['Station'].apply(assignZonesToStationsInDF) + return base_shear_df + # ================================================================================== # =============================== UTILITY FUNCTIONS ================================ diff --git a/Python Scripts/query_data.py b/Python Scripts/query_data.py index affa0e67..9463a265 100644 --- a/Python Scripts/query_data.py +++ b/Python Scripts/query_data.py @@ -1,12 +1,16 @@ #%% ================================== INIT AND CONNECT TO DATABASE ================================== # Import modules -from pyseestko.utilities import getMappings #type: ignore -from pyseestko import queries as query #type: ignore -from matplotlib import pyplot as plt -from pathlib import Path - +from pyseestko.utilities import getMappings, load_module #type: ignore +from pyseestko import queries as query #type: ignore +from matplotlib import pyplot as plt +from pathlib import Path +from pyseestko.utilities import getDriftResultsDF #type: ignore +from pyseestko.utilities import getSpectraResultsDF #type: ignore +from pyseestko.utilities import getSBaseResultsDF #type: ignore # Temp imports import pandas as pd +import winsound + # DataBase user params user = 'omarson' @@ -14,6 +18,9 @@ host = 'localhost' database = 'stkodatabase' +# Debugging +path_to_queries = Path("C:/Users/oioya/OneDrive - miuandes.cl/Escritorio/Git-Updated/Thesis-Project-Simulation-Data-Analysis/Python Scripts/PySeesTKO/pyseestko/queries.py") +#query = load_module('query', path_to_queries) #%% ========================================== INPUT PARAMS ========================================== # Define paths to save the plots @@ -21,7 +28,7 @@ # Define the parameters sim_types = [i for i in range(1,4)] # 1 = FB, 2 = AB, 3 = DRM -nsubs_lst = [2,4] # Can be: 2,4 +nsubs_lst = [2,4] # Can be: 2,4 iterations = [i for i in range(1,6)] # Can be: {1,2,3,4,5,6,7,8,9,10} stations = [i for i in range(1,10)] # Can be: {0,1,2,3,4,5,6,7,8,9} @@ -36,7 +43,7 @@ # Get the drifts, spectra and base shear dataframes for the selected simulation types, nsubs, # iterations and stations # -------------------------------------- Execyte the main query -------------------------------------- -drift_tple, spectra_df_dict, base_shear_df_dict = query.executeMainQuery( +drifts_df_dict, spectra_df_dict, base_shear_df_dict = query.executeMainQuery( # Main params sim_types = sim_types, nsubs_lst = nsubs_lst, @@ -51,52 +58,62 @@ host = host, database = database, # Save params - save_drift = False, - save_spectra = False, + save_drift = True, + save_spectra = True, save_b_shear = True, - xlim_sup = 0.025, + save_results = save_csvs, + # Plot params + show_plots = show_plots, + xlim_sup = 0.003, #0.025 NOTE: see how to put it as a tuple for x,y + grid = True, + fig_size = (8.25, 11), + dpi = 150, + file_type = 'pdf', # Extra params project_path = project_path, - show_plots = show_plots, - save_results = save_csvs, verbose = False, - grid = True, ) -drift_df_max_x, drift_df_max_y, drift_df_mean_x, drift_df_mean_y = drift_tple - +winsound.Beep(1000, 500) # --------------------------------------- LOAD DATA FOR ANOVA ---------------------------------------- # Spectra results: -#max_spectra_lst_x = [df[0].max().max() for df in spectra_df_dict.values()] -#max_spectra_lst_y = [df[1].max().max() for df in spectra_df_dict.values()] +max_spectra_lst_x = [(df.iloc[:,:5]).mean().max() for df in spectra_df_dict.values()] +max_spectra_lst_y = [(df.iloc[:,5:]).mean().max() for df in spectra_df_dict.values()] ## Base shear results: -#mean_base_shear_lst = [abs(df['Shear X'].mean()) for df in base_shear_df_dict.values()] -#mean_base_shear_lst = [abs(df['Shear Y'].mean()) for df in base_shear_df_dict.values()] +#mean_base_shear_lst_x = [abs(df['Shear X'].mean()) for df in base_shear_df_dict.values()] +#mean_base_shear_lst_y = [abs(df['Shear Y'].mean()) for df in base_shear_df_dict.values()] # %% -for sim_type in sim_types: - for nsubs in nsubs_lst: - for station in stations: - for iteration in iterations: - print(f"Sim_type: {sim_type}, nsubs: {nsubs}, station: {station}, iteration: {iteration}") - - +# Compute Drift Results +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_max_x = getDriftResultsDF(sim_type_lst, nsubs_lst, iteration_lst, station_lst, + [df['Max x'].max() for df in drifts_df_dict.values()]) +drift_df_max_y = getDriftResultsDF(sim_type_lst, nsubs_lst, iteration_lst, station_lst, + [df['Max y'].max() for df in drifts_df_dict.values()]) +drift_df_mean_x = getDriftResultsDF(sim_type_lst, nsubs_lst, iteration_lst, station_lst, + [df['Max x'].mean() for df in drifts_df_dict.values()]) +drift_df_mean_y = getDriftResultsDF(sim_type_lst, nsubs_lst, iteration_lst, station_lst, + [df['Max y'].mean() for df in drifts_df_dict.values()]) +drift_tple = (drifts_df_dict, drift_df_max_x, drift_df_max_y, drift_df_mean_x, drift_df_mean_y) + +# Compute Spectra Results +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 = getSpectraResultsDF(sim_type_lst, nsubs_lst, iteration_lst, station_lst, + spectra_df_dict) + +# Compute Base Shear Results +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 = getSBaseResultsDF(sim_type_lst, nsubs_lst, iteration_lst, station_lst, + base_shear_df_dict) - for sim_type in sim_types: - for nsubs in nsubs_lst: - structure_weight = 22241.3 if nsubs == 4 else 18032.3 - for station in stations: - for iteration in iterations: - # Init the classes - plotter = Plotting(sim_type, stories, # The class that plots the data - nsubs, magnitude, - iteration, rupture_type, - station, show_plots=show_plots) - query = ProjectQueries(user, password, host, database, # The class that queries the database - sim_type, linearity, - mag_map.get(magnitude, 'None'), - rup_map.get(rupture_type, 'None'), iteration, - loc_map.get(station, 'None'), - stories, nsubs, plotter, windows=windows, verbose=verbose) \ No newline at end of file