results 3

Python Scripts/PySeesTKO/pyseestko/plotting.py

@@ -228,8 +228,8 @@ def setup_direction(self, x_direction:bool=True):
         self.direction = 'X' if x_direction else 'Y'
         self.id        = f'{self.sim_type} | {self.stories} stories - {self.nsubs} subs - {self.direction}dir' if self.grid else f'{self.sim_type} |  {self.magnitude}Mw | Station {self.station} | {self.stories} stories - {self.nsubs} subs - {self.direction}dir'
         self.file_name = f'{self.sim_type}_20f{self.nsubs}_{self.direction}' if 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

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, 

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 ================================