Added licence file

Reviewed-on: #4
Reviewed-by: asia <asia@noreply.example.org>

LICENCE.txt

@@ -0,0 +1,408 @@
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README.md

@@ -1,3 +1,6 @@
-MaxMagnitudeDPModels app official repository
+# MaxMagnitudeDPModels — Official Application Repository
 
-Link to remote: https://epos-apps.grid.cyfronet.pl/official-apps/MaxMagnitudeDPModels
+This repository contains the source code and configuration files for the `MaxMagnitudeDPModels` application used within the [EPISODES Platform](https://EpisodesPlatform.eu/).
+
+📦 To test or modify this application in the EPISODES Platform environment, follow the guide:  
+https://docs.cyfronet.pl/display/ISDOC/Editing+application+codes+guide

src/Mmax.py

@@ -1,12 +1,12 @@
 import sys
 import numpy as np
-import matplotlib.pyplot as plt
 import pandas as pd
 # import os
 import scipy.io
 import logging
 from geopy.distance import geodesic
 from geopy.point import Point
+
 from util.base_logger import getDefaultLogger
 
 def main(Input_catalog, Input_injection_rate, time_win_in_hours, time_step_in_hour, time_win_type,
@@ -68,7 +68,8 @@ def main(Input_catalog, Input_injection_rate, time_win_in_hours, time_step_in_ho
     # Importing data
     logger.info("Import data")
     mat = scipy.io.loadmat(Input_catalog)
-    Cat_structure = mat['Catalog']
+    Cat_structure_name = scipy.io.whosmat(Input_catalog)[0][0]
+    Cat_structure = mat[Cat_structure_name]
     Cat_id, Cat_t, Cat_m = [], [], []
     Cat_x, Cat_y, Cat_z = [], [], []
     Cat_lat, Cat_lon, Cat_elv, Cat_depth = [], [], [], []
@@ -205,7 +206,10 @@ def main(Input_catalog, Input_injection_rate, time_win_in_hours, time_step_in_ho
         Feat_dic['Param'][0]['C'] = C
 
     if f_indx in [0,1,2,4]:
-        Feat_dic['Param'][0]['b_method'] = b_method
+        if not b_method:
+            raise ValueError("Please choose an option for b-value")
+        else:
+            Feat_dic['Param'][0]['b_method'] = b_method
 
     if f_indx in [0,4]:
         for cl_i in cl:
@@ -475,181 +479,6 @@ def main(Input_catalog, Input_injection_rate, time_win_in_hours, time_step_in_ho
 
         return In_arr[:i_row,:]
 
-    # Plotting models and parameters
-    def Plot_feature(Model_Param_array,Output_dict):
-        myVars = locals()
-
-        # Function for findin min-max of all similar parameters
-        def Extermom4All(Model_Param_array, itr_loc):
-            Mat1D = np.reshape(Model_Param_array[:,itr_loc], -1)
-            NotNone = np.isfinite(Mat1D)
-            if np.min(Mat1D[NotNone])>0:
-                return [np.min(Mat1D[NotNone])*0.95, np.max(Mat1D[NotNone])*1.05]
-            elif np.min(Mat1D[NotNone])<0 and np.max(Mat1D[NotNone])>0:
-                return [np.min(Mat1D[NotNone])*1.05, np.max(Mat1D[NotNone])*1.05]
-            elif np.max(Mat1D[NotNone])<0:
-                return [np.min(Mat1D[NotNone])*1.05, np.max(Mat1D[NotNone])*0.95]
-
-        # Function for setting relevant lagends in the plot
-        def Legend_label(loc):
-            l = Output_dict_c['label'][loc]
-            if Output_dict_c['b_method'][loc]:
-                if Output_dict_c['cl'][loc]:
-                    l+='('+Output_dict_c['b_method'][loc]+', cl='+str(Output_dict_c['cl'][loc])+')'
-                else:
-                    l+='('+Output_dict_c['b_method'][loc]+')'
-
-            return l
-
-        c_NotNone = [] # Removing all parameters with None or constant value
-        for i in range(Model_Param_array.shape[1]):
-            NotNone = np.isfinite(Model_Param_array[:,i])
-            Eq_value = np.mean(Model_Param_array[:,i])
-            if any(NotNone) and Eq_value != Model_Param_array[0,i]:
-                c_NotNone.append(i)
-            else:
-                logger.info(f"No-PLOT: All values of {Output_dict['Type'][i]} are {Model_Param_array[0,i]}!")
-
-        if len(c_NotNone) > 1:
-            Model_Param_array = Model_Param_array[:,c_NotNone]
-            # New output dictionary based on valid parameters for plotting
-            Output_dict_c = {'Type':[], 'label':[], 'b_method':[], 'cl':[]}
-            for i in range(len(c_NotNone)):
-                Output_dict_c['Type'].append(Output_dict['Type'][c_NotNone[i]])
-                Output_dict_c['label'].append(Output_dict['label'][c_NotNone[i]])
-                Output_dict_c['b_method'].append(Output_dict['b_method'][c_NotNone[i]])
-                Output_dict_c['cl'].append(Output_dict['cl'][c_NotNone[i]])
-
-            coloring=['blue','g','r','c','m','y',
-                        'brown', 'darkolivegreen', 'teal', 'steelblue', 'slateblue',
-                        'purple', 'darksalmon', '#c5b0d5', '#c49c94',
-                        '#e377c2', '#f7b6d2', '#7f7f7f', '#c7c7c7', '#bcbd22', '#dbdb8d',
-                        '#17becf', '#9edae5',
-                        'brown', 'darkolivegreen', 'teal', 'steelblue', 'slateblue',]
-            # All parameters to be plotted
-            All_vars = Output_dict_c['Type'][2:]
-            Uniqe_var = list(dict.fromkeys([s for s in All_vars if 'Standard Error' not in s])) #list(set(All_vars))
-
-            # defining handels and labels to make final legend
-            All_handels = ['p0']
-            for i in range(1,len(All_vars)):
-                All_handels.append('p'+str(i))
-            handels = []
-            labels = []
-
-            # itr_loc: location of paramteres with similar type
-            itr_loc = np.where(np.array(All_vars) == Uniqe_var[0])[0]+2
-            fig, myVars[Output_dict_c['Type'][0]] = plt.subplots(1,1,figsize=(8+int(len(All_vars)/3),6))
-            fig.subplots_adjust(right=1-len(Uniqe_var)*0.09)
-            if Output_dict_c['label'][itr_loc[0]] == 'True Max-Mag': # plot with dash-line
-                myVars[All_handels[itr_loc[0]-2]], = myVars[Output_dict_c['Type'][0]].plot(Model_Param_array[:,1]/24/3600, Model_Param_array[:,itr_loc[0]], c= 'k', ls='--', lw = 2)
-            else:
-                myVars[All_handels[itr_loc[0]-2]], = myVars[Output_dict_c['Type'][0]].plot(Model_Param_array[:,1]/24/3600, Model_Param_array[:,itr_loc[0]], c= coloring[itr_loc[0]])
-            handels.append(All_handels[itr_loc[0]-2])
-            labels.append(Legend_label(itr_loc[0]))
-            myVars[Output_dict_c['Type'][0]].set_ylabel(Output_dict_c['Type'][itr_loc[0]])
-            myVars[Output_dict_c['Type'][0]].set_ylim(Extermom4All(Model_Param_array, itr_loc)[0], Extermom4All(Model_Param_array, itr_loc)[1])
-            myVars[Output_dict_c['Type'][0]].set_xlabel('Day (From start of the recording)')
-            if End_time:
-                myVars[Output_dict_c['Type'][0]].set_xlim(0,End_time)
-
-            # Plotting statndard error (if exists)
-            if  itr_loc[0]+1 < len(Output_dict_c['Type']) and Output_dict_c['Type'][itr_loc[0]+1] == 'Standard Error':
-                myVars[Output_dict_c['Type'][0]].fill_between(Model_Param_array[:,1]/24/3600,
-                                                            Model_Param_array[:,itr_loc[0]] -  Model_Param_array[:,itr_loc[0]+1],
-                                                            Model_Param_array[:,itr_loc[0]] +  Model_Param_array[:,itr_loc[0]+1], color= coloring[itr_loc[0]], alpha=0.1)
-            # Plotting similar parameters on one axis
-            for j in range(1,len(itr_loc)):
-                    if Output_dict_c['label'][itr_loc[j]] == 'True Max-Mag': # plot with dash-line
-                        myVars[All_handels[itr_loc[j]-2]], = myVars[Output_dict_c['Type'][0]].plot(Model_Param_array[:,1]/24/3600, Model_Param_array[:,itr_loc[j]], c= 'k', ls='--', lw = 2)
-                    else:
-                        myVars[All_handels[itr_loc[j]-2]], = myVars[Output_dict_c['Type'][0]].plot(Model_Param_array[:,1]/24/3600, Model_Param_array[:,itr_loc[j]], c= coloring[itr_loc[j]])
-                    handels.append(All_handels[itr_loc[j]-2])
-                    labels.append(Legend_label(itr_loc[j]))
-
-                    # Plotting statndard error (if exists)
-                    if  itr_loc[0]+1 < len(Output_dict_c['Type']) and Output_dict_c['Type'][itr_loc[j]+1] == 'Standard Error':
-                        myVars[Output_dict_c['Type'][0]].fill_between(Model_Param_array[:,1]/24/3600,
-                                                            Model_Param_array[:,itr_loc[j]] -  Model_Param_array[:,itr_loc[j]+1],
-                                                            Model_Param_array[:,itr_loc[j]] +  Model_Param_array[:,itr_loc[j]+1], color= coloring[itr_loc[j]], alpha=0.1)
-            first_itr = 0
-            # Check if there is any more parameter to be plotted in second axes
-            # The procedure is similar to last plots.
-            if len(Uniqe_var) > 1:
-                for i in range(1,len(Uniqe_var)):
-                    itr_loc = np.where(np.array(All_vars) == Uniqe_var[i])[0]+2
-                    myVars[Uniqe_var[i]] = myVars[Output_dict_c['Type'][0]].twinx()
-                    # if it is third or more axis, make a distance between them
-                    if first_itr == 0:
-                        first_itr += 1
-                        set_right = 1
-                    else:
-                        set_right = 1 + first_itr*0.2
-                        first_itr += 1
-                        myVars[Uniqe_var[i]].spines.right.set_position(("axes", set_right))
-                    if Output_dict_c['label'][itr_loc[0]] == 'True Max-Mag': # plot with dash-line
-                        myVars[All_handels[itr_loc[0]-2]], = myVars[Uniqe_var[i]].plot(Model_Param_array[:,1]/24/3600, Model_Param_array[:,itr_loc[0]], c= 'k', ls='--', lw = 2)
-                    else:
-                        myVars[All_handels[itr_loc[0]-2]], = myVars[Uniqe_var[i]].plot(Model_Param_array[:,1]/24/3600, Model_Param_array[:,itr_loc[0]], c= coloring[itr_loc[0]])
-                    handels.append(All_handels[itr_loc[0]-2])
-                    labels.append(Legend_label(itr_loc[0]))
-                    myVars[Uniqe_var[i]].set_ylabel(Output_dict_c['Type'][itr_loc[0]])
-                    myVars[Uniqe_var[i]].yaxis.label.set_color(coloring[itr_loc[0]])
-                    myVars[Uniqe_var[i]].spines["right"].set_edgecolor(coloring[itr_loc[0]])
-                    myVars[Uniqe_var[i]].tick_params(axis='y', colors= coloring[itr_loc[0]])
-                    myVars[Uniqe_var[i]].set_ylim(Extermom4All(Model_Param_array, itr_loc)[0], Extermom4All(Model_Param_array, itr_loc)[1])
-                    if itr_loc[0]+1 < len(Output_dict_c['Type']) and Output_dict_c['Type'][itr_loc[0]+1] == 'Standard Error':
-                        myVars[Uniqe_var[i]].fill_between(Model_Param_array[:,1]/24/3600,
-                                                            Model_Param_array[:,itr_loc[0]] -  Model_Param_array[:,itr_loc[0]+1],
-                                                            Model_Param_array[:,itr_loc[0]] +  Model_Param_array[:,itr_loc[0]+1], color= coloring[itr_loc[0]], alpha=0.1)
-
-                    for j in range(1,len(itr_loc)):
-                        if Output_dict_c['label'][itr_loc[j]] == 'True Max-Mag': # plot with dash-line
-                            myVars[All_handels[itr_loc[j]-2]], = myVars[Uniqe_var[i]].plot(Model_Param_array[:,1]/24/3600, Model_Param_array[:,itr_loc[j]], c= 'k', ls = '--', lw = 2)
-                        else:
-                            myVars[All_handels[itr_loc[j]-2]], = myVars[Uniqe_var[i]].plot(Model_Param_array[:,1]/24/3600, Model_Param_array[:,itr_loc[j]], c= coloring[itr_loc[j]])
-                        handels.append(All_handels[itr_loc[j]-2])
-                        labels.append(Legend_label(itr_loc[j]))
-                        if itr_loc[j]+1 < len(Output_dict_c['Type']) and Output_dict_c['Type'][itr_loc[j]+1] == 'Standard Error':
-                            myVars[Uniqe_var[i]].fill_between(Model_Param_array[:,1]/24/3600,
-                                                            Model_Param_array[:,itr_loc[j]] -  Model_Param_array[:,itr_loc[j]+1],
-                                                            Model_Param_array[:,itr_loc[j]] +  Model_Param_array[:,itr_loc[j]+1], color= coloring[itr_loc[j]], alpha=0.1)
-
-            # If there are timing, plot them as vertical lines
-            if time_inj:
-                myVars['l1'], = plt.plot([time_inj,time_inj], [Extermom4All(Model_Param_array, itr_loc)[0],Extermom4All(Model_Param_array, itr_loc)[1]], ls='--', c='k')
-                handels.append('l1')
-                labels.append('Start-inj')
-            if time_shut_in:
-                myVars['l2'], = plt.plot([time_shut_in,time_shut_in], [Extermom4All(Model_Param_array, itr_loc)[0],Extermom4All(Model_Param_array, itr_loc)[1]], ls='-.', c='k')
-                handels.append('l2')
-                labels.append('Shut-in')
-            if time_big_ev:
-                myVars['l3'], = plt.plot([time_big_ev,time_big_ev], [Extermom4All(Model_Param_array, itr_loc)[0],Extermom4All(Model_Param_array, itr_loc)[1]], ls='dotted', c='k')
-                handels.append('l3')
-                labels.append('Large-Ev')
-
-            box = myVars[Output_dict_c['Type'][0]].get_position()
-            if len(handels) < 6:
-                myVars[Output_dict_c['Type'][0]].set_position([box.x0, box.y0 + box.height * 0.1,
-                        box.width, box.height * 0.9])
-                plt.legend([myVars[ii] for ii in handels], labels, loc='upper center',
-                        bbox_to_anchor=(0.5+0.06*first_itr, -0.15), fancybox=True, shadow=True, ncol=len(handels))
-            elif len(handels) < 13:
-                myVars[Output_dict_c['Type'][0]].set_position([box.x0, box.y0 + box.height * 0.04*int(len(handels)/2),
-                        box.width, box.height * (1 - 0.04*int(len(handels)/2))])
-                plt.legend([myVars[ii] for ii in handels], labels, loc='upper center',
-                        bbox_to_anchor=(0.5+0.1*first_itr, -0.04*int(len(handels)/2)), fancybox=True, shadow=True, ncol=int(len(handels)/2)+1, handleheight=2)
-            else:
-                myVars[Output_dict_c['Type'][0]].set_position([box.x0, box.y0 + box.height * 0.04*int(len(handels)/2),
-                        box.width, box.height * (1 - 0.04*int(len(handels)/2))])
-                plt.legend([myVars[ii] for ii in handels], labels, loc='upper center',
-                        bbox_to_anchor=(0.6+0.1*first_itr, -0.04*int(len(handels)/2)), fancybox=True, shadow=True, ncol=int(len(handels)/2)+1, handleheight=2)
-            plt.title(Model_name)
-            # plt.savefig(cwd+'/Results/'+Model_name+'.png', dpi=300)
-            plt.savefig('PLOT_Mmax_param.png', dpi=300)
-            # plt.show()
-
     # Run functions based on the configurations -------------------
     # Computing model
     if time_step_in_hour > time_win_in_hours:
@@ -681,7 +510,9 @@ def main(Input_catalog, Input_injection_rate, time_win_in_hours, time_step_in_ho
     if Plot_flag > 0:
         if Model_Param_array.any():
             logger.info("Plotting results")
-            Plot_feature(Model_Param_array, Output_dict)
+
+            import Mmax_plot # Import locally to ensure Mmax_plot is required only when Plot_flag > 0
+            Mmax_plot.Plot_feature(Model_Param_array, Output_dict)
         else:
             logger.info("Model_Param_array is empty or not enough values to plot. Check 'csv' file.")
 

src/Mmax_plot.py

@@ -0,0 +1,206 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+def Plot_feature(Model_Param_array,
+                 Output_dict,
+                 End_time=None,
+                 time_inj=None,
+                 time_shut_in=None,
+                 time_big_ev=None,
+                 Model_name="",
+                 logger=None):
+    """
+    Plotting function extracted from Mmax.py for plotting models and parameters.
+
+    Parameters
+    ----------
+    Model_Param_array : np.ndarray
+        Computed matrix of model parameters as rows in time.
+    Output_dict : dict
+        Dictionary describing each column in Model_Param_array.
+    End_time : float, optional
+        The last time to show in the X-axis (days), if desired.
+    time_inj : float, optional
+        Time of injection start (days), if you want a vertical line.
+    time_shut_in : float, optional
+        Time of shut-in (days), if you want a vertical line.
+    time_big_ev : float, optional
+        Time of large event (days), if you want a vertical line.
+    Model_name : str, optional
+        Model name used for the plot title.
+    logger : logging.Logger, optional
+        Logger for printing info messages. If None, no logging happens.
+    """
+    myVars = locals()
+
+    # Function for findin min-max of all similar parameters
+    def Extermom4All(Model_Param_array, itr_loc):
+        Mat1D = np.reshape(Model_Param_array[:,itr_loc], -1)
+        NotNone = np.isfinite(Mat1D)
+        if np.min(Mat1D[NotNone])>0:
+            return [np.min(Mat1D[NotNone])*0.95, np.max(Mat1D[NotNone])*1.05]
+        elif np.min(Mat1D[NotNone])<0 and np.max(Mat1D[NotNone])>0:
+            return [np.min(Mat1D[NotNone])*1.05, np.max(Mat1D[NotNone])*1.05]
+        elif np.max(Mat1D[NotNone])<0:
+            return [np.min(Mat1D[NotNone])*1.05, np.max(Mat1D[NotNone])*0.95]
+
+    # Function for setting relevant lagends in the plot
+    def Legend_label(loc):
+        l = Output_dict_c['label'][loc]
+        if Output_dict_c['b_method'][loc]:
+            if Output_dict_c['cl'][loc]:
+                l+='('+Output_dict_c['b_method'][loc]+', cl='+str(Output_dict_c['cl'][loc])+')'
+            else:
+                l+='('+Output_dict_c['b_method'][loc]+')'
+
+        return l
+
+    c_NotNone = [] # Removing all parameters with None or constant value
+    for i in range(Model_Param_array.shape[1]):
+        NotNone = np.isfinite(Model_Param_array[:,i])
+        Eq_value = np.mean(Model_Param_array[:,i])
+        if any(NotNone) and Eq_value != Model_Param_array[0,i]:
+            c_NotNone.append(i)
+        else:
+            logger.info(f"No-PLOT: All values of {Output_dict['Type'][i]} are {Model_Param_array[0,i]}!")
+
+    if len(c_NotNone) > 1:
+        Model_Param_array = Model_Param_array[:,c_NotNone]
+        # New output dictionary based on valid parameters for plotting
+        Output_dict_c = {'Type':[], 'label':[], 'b_method':[], 'cl':[]}
+        for i in range(len(c_NotNone)):
+            Output_dict_c['Type'].append(Output_dict['Type'][c_NotNone[i]])
+            Output_dict_c['label'].append(Output_dict['label'][c_NotNone[i]])
+            Output_dict_c['b_method'].append(Output_dict['b_method'][c_NotNone[i]])
+            Output_dict_c['cl'].append(Output_dict['cl'][c_NotNone[i]])
+
+        coloring=['blue','g','r','c','m','y',
+                  'brown', 'darkolivegreen', 'teal', 'steelblue', 'slateblue',
+                  'purple', 'darksalmon', '#c5b0d5', '#c49c94',
+                  '#e377c2', '#f7b6d2', '#7f7f7f', '#c7c7c7', '#bcbd22', '#dbdb8d',
+                  '#17becf', '#9edae5',
+                  'brown', 'darkolivegreen', 'teal', 'steelblue', 'slateblue',]
+        # All parameters to be plotted
+        All_vars = Output_dict_c['Type'][2:]
+        Uniqe_var = list(dict.fromkeys([s for s in All_vars if 'Standard Error' not in s])) #list(set(All_vars))
+
+        # defining handels and labels to make final legend
+        All_handels = ['p0']
+        for i in range(1,len(All_vars)):
+            All_handels.append('p'+str(i))
+        handels = []
+        labels = []
+
+        # itr_loc: location of paramteres with similar type
+        itr_loc = np.where(np.array(All_vars) == Uniqe_var[0])[0]+2
+        fig, myVars[Output_dict_c['Type'][0]] = plt.subplots(1,1,figsize=(8+int(len(All_vars)/3),6))
+        fig.subplots_adjust(right=1-len(Uniqe_var)*0.09)
+        if Output_dict_c['label'][itr_loc[0]] == 'True Max-Mag': # plot with dash-line
+            myVars[All_handels[itr_loc[0]-2]], = myVars[Output_dict_c['Type'][0]].plot(Model_Param_array[:,1]/24/3600, Model_Param_array[:,itr_loc[0]], c= 'k', ls='--', lw = 2)
+        else:
+            myVars[All_handels[itr_loc[0]-2]], = myVars[Output_dict_c['Type'][0]].plot(Model_Param_array[:,1]/24/3600, Model_Param_array[:,itr_loc[0]], c= coloring[itr_loc[0]])
+        handels.append(All_handels[itr_loc[0]-2])
+        labels.append(Legend_label(itr_loc[0]))
+        myVars[Output_dict_c['Type'][0]].set_ylabel(Output_dict_c['Type'][itr_loc[0]])
+        myVars[Output_dict_c['Type'][0]].set_ylim(Extermom4All(Model_Param_array, itr_loc)[0], Extermom4All(Model_Param_array, itr_loc)[1])
+        myVars[Output_dict_c['Type'][0]].set_xlabel('Day (From start of the recording)')
+        if End_time:
+            myVars[Output_dict_c['Type'][0]].set_xlim(0,End_time)
+
+        # Plotting statndard error (if exists)
+        if  itr_loc[0]+1 < len(Output_dict_c['Type']) and Output_dict_c['Type'][itr_loc[0]+1] == 'Standard Error':
+            myVars[Output_dict_c['Type'][0]].fill_between(Model_Param_array[:,1]/24/3600,
+                                                          Model_Param_array[:,itr_loc[0]] -  Model_Param_array[:,itr_loc[0]+1],
+                                                          Model_Param_array[:,itr_loc[0]] +  Model_Param_array[:,itr_loc[0]+1], color= coloring[itr_loc[0]], alpha=0.1)
+        # Plotting similar parameters on one axis
+        for j in range(1,len(itr_loc)):
+            if Output_dict_c['label'][itr_loc[j]] == 'True Max-Mag': # plot with dash-line
+                myVars[All_handels[itr_loc[j]-2]], = myVars[Output_dict_c['Type'][0]].plot(Model_Param_array[:,1]/24/3600, Model_Param_array[:,itr_loc[j]], c= 'k', ls='--', lw = 2)
+            else:
+                myVars[All_handels[itr_loc[j]-2]], = myVars[Output_dict_c['Type'][0]].plot(Model_Param_array[:,1]/24/3600, Model_Param_array[:,itr_loc[j]], c= coloring[itr_loc[j]])
+            handels.append(All_handels[itr_loc[j]-2])
+            labels.append(Legend_label(itr_loc[j]))
+
+            # Plotting statndard error (if exists)
+            if  itr_loc[0]+1 < len(Output_dict_c['Type']) and Output_dict_c['Type'][itr_loc[j]+1] == 'Standard Error':
+                myVars[Output_dict_c['Type'][0]].fill_between(Model_Param_array[:,1]/24/3600,
+                                                              Model_Param_array[:,itr_loc[j]] -  Model_Param_array[:,itr_loc[j]+1],
+                                                              Model_Param_array[:,itr_loc[j]] +  Model_Param_array[:,itr_loc[j]+1], color= coloring[itr_loc[j]], alpha=0.1)
+        first_itr = 0
+        # Check if there is any more parameter to be plotted in second axes
+        # The procedure is similar to last plots.
+        if len(Uniqe_var) > 1:
+            for i in range(1,len(Uniqe_var)):
+                itr_loc = np.where(np.array(All_vars) == Uniqe_var[i])[0]+2
+                myVars[Uniqe_var[i]] = myVars[Output_dict_c['Type'][0]].twinx()
+                # if it is third or more axis, make a distance between them
+                if first_itr == 0:
+                    first_itr += 1
+                    set_right = 1
+                else:
+                    set_right = 1 + first_itr*0.2
+                    first_itr += 1
+                    myVars[Uniqe_var[i]].spines.right.set_position(("axes", set_right))
+                if Output_dict_c['label'][itr_loc[0]] == 'True Max-Mag': # plot with dash-line
+                    myVars[All_handels[itr_loc[0]-2]], = myVars[Uniqe_var[i]].plot(Model_Param_array[:,1]/24/3600, Model_Param_array[:,itr_loc[0]], c= 'k', ls='--', lw = 2)
+                else:
+                    myVars[All_handels[itr_loc[0]-2]], = myVars[Uniqe_var[i]].plot(Model_Param_array[:,1]/24/3600, Model_Param_array[:,itr_loc[0]], c= coloring[itr_loc[0]])
+                handels.append(All_handels[itr_loc[0]-2])
+                labels.append(Legend_label(itr_loc[0]))
+                myVars[Uniqe_var[i]].set_ylabel(Output_dict_c['Type'][itr_loc[0]])
+                myVars[Uniqe_var[i]].yaxis.label.set_color(coloring[itr_loc[0]])
+                myVars[Uniqe_var[i]].spines["right"].set_edgecolor(coloring[itr_loc[0]])
+                myVars[Uniqe_var[i]].tick_params(axis='y', colors= coloring[itr_loc[0]])
+                myVars[Uniqe_var[i]].set_ylim(Extermom4All(Model_Param_array, itr_loc)[0], Extermom4All(Model_Param_array, itr_loc)[1])
+                if itr_loc[0]+1 < len(Output_dict_c['Type']) and Output_dict_c['Type'][itr_loc[0]+1] == 'Standard Error':
+                    myVars[Uniqe_var[i]].fill_between(Model_Param_array[:,1]/24/3600,
+                                                      Model_Param_array[:,itr_loc[0]] -  Model_Param_array[:,itr_loc[0]+1],
+                                                      Model_Param_array[:,itr_loc[0]] +  Model_Param_array[:,itr_loc[0]+1], color= coloring[itr_loc[0]], alpha=0.1)
+
+                for j in range(1,len(itr_loc)):
+                    if Output_dict_c['label'][itr_loc[j]] == 'True Max-Mag': # plot with dash-line
+                        myVars[All_handels[itr_loc[j]-2]], = myVars[Uniqe_var[i]].plot(Model_Param_array[:,1]/24/3600, Model_Param_array[:,itr_loc[j]], c= 'k', ls = '--', lw = 2)
+                    else:
+                        myVars[All_handels[itr_loc[j]-2]], = myVars[Uniqe_var[i]].plot(Model_Param_array[:,1]/24/3600, Model_Param_array[:,itr_loc[j]], c= coloring[itr_loc[j]])
+                    handels.append(All_handels[itr_loc[j]-2])
+                    labels.append(Legend_label(itr_loc[j]))
+                    if itr_loc[j]+1 < len(Output_dict_c['Type']) and Output_dict_c['Type'][itr_loc[j]+1] == 'Standard Error':
+                        myVars[Uniqe_var[i]].fill_between(Model_Param_array[:,1]/24/3600,
+                                                          Model_Param_array[:,itr_loc[j]] -  Model_Param_array[:,itr_loc[j]+1],
+                                                          Model_Param_array[:,itr_loc[j]] +  Model_Param_array[:,itr_loc[j]+1], color= coloring[itr_loc[j]], alpha=0.1)
+
+        # If there are timing, plot them as vertical lines
+        if time_inj:
+            myVars['l1'], = plt.plot([time_inj,time_inj], [Extermom4All(Model_Param_array, itr_loc)[0],Extermom4All(Model_Param_array, itr_loc)[1]], ls='--', c='k')
+            handels.append('l1')
+            labels.append('Start-inj')
+        if time_shut_in:
+            myVars['l2'], = plt.plot([time_shut_in,time_shut_in], [Extermom4All(Model_Param_array, itr_loc)[0],Extermom4All(Model_Param_array, itr_loc)[1]], ls='-.', c='k')
+            handels.append('l2')
+            labels.append('Shut-in')
+        if time_big_ev:
+            myVars['l3'], = plt.plot([time_big_ev,time_big_ev], [Extermom4All(Model_Param_array, itr_loc)[0],Extermom4All(Model_Param_array, itr_loc)[1]], ls='dotted', c='k')
+            handels.append('l3')
+            labels.append('Large-Ev')
+
+        box = myVars[Output_dict_c['Type'][0]].get_position()
+        if len(handels) < 6:
+            myVars[Output_dict_c['Type'][0]].set_position([box.x0, box.y0 + box.height * 0.1,
+                                                           box.width, box.height * 0.9])
+            plt.legend([myVars[ii] for ii in handels], labels, loc='upper center',
+                       bbox_to_anchor=(0.5+0.06*first_itr, -0.15), fancybox=True, shadow=True, ncol=len(handels))
+        elif len(handels) < 13:
+            myVars[Output_dict_c['Type'][0]].set_position([box.x0, box.y0 + box.height * 0.04*int(len(handels)/2),
+                                                           box.width, box.height * (1 - 0.04*int(len(handels)/2))])
+            plt.legend([myVars[ii] for ii in handels], labels, loc='upper center',
+                       bbox_to_anchor=(0.5+0.1*first_itr, -0.04*int(len(handels)/2)), fancybox=True, shadow=True, ncol=int(len(handels)/2)+1, handleheight=2)
+        else:
+            myVars[Output_dict_c['Type'][0]].set_position([box.x0, box.y0 + box.height * 0.04*int(len(handels)/2),
+                                                           box.width, box.height * (1 - 0.04*int(len(handels)/2))])
+            plt.legend([myVars[ii] for ii in handels], labels, loc='upper center',
+                       bbox_to_anchor=(0.6+0.1*first_itr, -0.04*int(len(handels)/2)), fancybox=True, shadow=True, ncol=int(len(handels)/2)+1, handleheight=2)
+        plt.title(Model_name)
+        # plt.savefig(cwd+'/Results/'+Model_name+'.png', dpi=300)
+        plt.savefig('PLOT_Mmax_param.png', dpi=300)
+        # plt.show()

src/maxmagnitude_wrapper.py

@@ -23,8 +23,6 @@ import argparse
 from Mmax import main as Mmax
 
 def main(argv):
-    raise ValueError("Test error from repo official-apps/MaxMagnitudeDPModels branch experimental-test-live-code-editing")
-
     parser = argparse.ArgumentParser()
     parser.add_argument("Input_catalog", help="Input catalog: path to input file of type 'catalog'")
     parser.add_argument("Input_injection_rate", help="Input injection rate: path to input file of type 'injection_rate'")