Update application source code (v2.62)
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parent
18a8c7aa13
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184
src/Mmax.py
184
src/Mmax.py
@ -1,12 +1,12 @@
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import sys
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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# import os
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import scipy.io
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import logging
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from geopy.distance import geodesic
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from geopy.point import Point
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from util.base_logger import getDefaultLogger
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def main(Input_catalog, Input_injection_rate, time_win_in_hours, time_step_in_hour, time_win_type,
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@ -68,7 +68,8 @@ def main(Input_catalog, Input_injection_rate, time_win_in_hours, time_step_in_ho
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# Importing data
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logger.info("Import data")
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mat = scipy.io.loadmat(Input_catalog)
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Cat_structure = mat['Catalog']
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Cat_structure_name = scipy.io.whosmat(Input_catalog)[0][0]
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Cat_structure = mat[Cat_structure_name]
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Cat_id, Cat_t, Cat_m = [], [], []
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Cat_x, Cat_y, Cat_z = [], [], []
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Cat_lat, Cat_lon, Cat_elv, Cat_depth = [], [], [], []
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@ -475,181 +476,6 @@ def main(Input_catalog, Input_injection_rate, time_win_in_hours, time_step_in_ho
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return In_arr[:i_row,:]
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# Plotting models and parameters
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def Plot_feature(Model_Param_array,Output_dict):
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myVars = locals()
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# Function for findin min-max of all similar parameters
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def Extermom4All(Model_Param_array, itr_loc):
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Mat1D = np.reshape(Model_Param_array[:,itr_loc], -1)
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NotNone = np.isfinite(Mat1D)
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if np.min(Mat1D[NotNone])>0:
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return [np.min(Mat1D[NotNone])*0.95, np.max(Mat1D[NotNone])*1.05]
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elif np.min(Mat1D[NotNone])<0 and np.max(Mat1D[NotNone])>0:
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return [np.min(Mat1D[NotNone])*1.05, np.max(Mat1D[NotNone])*1.05]
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elif np.max(Mat1D[NotNone])<0:
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return [np.min(Mat1D[NotNone])*1.05, np.max(Mat1D[NotNone])*0.95]
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# Function for setting relevant lagends in the plot
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def Legend_label(loc):
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l = Output_dict_c['label'][loc]
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if Output_dict_c['b_method'][loc]:
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if Output_dict_c['cl'][loc]:
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l+='('+Output_dict_c['b_method'][loc]+', cl='+str(Output_dict_c['cl'][loc])+')'
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else:
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l+='('+Output_dict_c['b_method'][loc]+')'
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return l
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c_NotNone = [] # Removing all parameters with None or constant value
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for i in range(Model_Param_array.shape[1]):
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NotNone = np.isfinite(Model_Param_array[:,i])
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Eq_value = np.mean(Model_Param_array[:,i])
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if any(NotNone) and Eq_value != Model_Param_array[0,i]:
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c_NotNone.append(i)
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else:
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logger.info(f"No-PLOT: All values of {Output_dict['Type'][i]} are {Model_Param_array[0,i]}!")
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if len(c_NotNone) > 1:
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Model_Param_array = Model_Param_array[:,c_NotNone]
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# New output dictionary based on valid parameters for plotting
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Output_dict_c = {'Type':[], 'label':[], 'b_method':[], 'cl':[]}
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for i in range(len(c_NotNone)):
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Output_dict_c['Type'].append(Output_dict['Type'][c_NotNone[i]])
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Output_dict_c['label'].append(Output_dict['label'][c_NotNone[i]])
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Output_dict_c['b_method'].append(Output_dict['b_method'][c_NotNone[i]])
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Output_dict_c['cl'].append(Output_dict['cl'][c_NotNone[i]])
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coloring=['blue','g','r','c','m','y',
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'brown', 'darkolivegreen', 'teal', 'steelblue', 'slateblue',
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'purple', 'darksalmon', '#c5b0d5', '#c49c94',
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'#e377c2', '#f7b6d2', '#7f7f7f', '#c7c7c7', '#bcbd22', '#dbdb8d',
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'#17becf', '#9edae5',
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'brown', 'darkolivegreen', 'teal', 'steelblue', 'slateblue',]
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# All parameters to be plotted
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All_vars = Output_dict_c['Type'][2:]
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Uniqe_var = list(dict.fromkeys([s for s in All_vars if 'Standard Error' not in s])) #list(set(All_vars))
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# defining handels and labels to make final legend
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All_handels = ['p0']
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for i in range(1,len(All_vars)):
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All_handels.append('p'+str(i))
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handels = []
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labels = []
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# itr_loc: location of paramteres with similar type
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itr_loc = np.where(np.array(All_vars) == Uniqe_var[0])[0]+2
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fig, myVars[Output_dict_c['Type'][0]] = plt.subplots(1,1,figsize=(8+int(len(All_vars)/3),6))
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fig.subplots_adjust(right=1-len(Uniqe_var)*0.09)
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if Output_dict_c['label'][itr_loc[0]] == 'True Max-Mag': # plot with dash-line
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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)
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else:
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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]])
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handels.append(All_handels[itr_loc[0]-2])
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labels.append(Legend_label(itr_loc[0]))
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myVars[Output_dict_c['Type'][0]].set_ylabel(Output_dict_c['Type'][itr_loc[0]])
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myVars[Output_dict_c['Type'][0]].set_ylim(Extermom4All(Model_Param_array, itr_loc)[0], Extermom4All(Model_Param_array, itr_loc)[1])
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myVars[Output_dict_c['Type'][0]].set_xlabel('Day (From start of the recording)')
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if End_time:
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myVars[Output_dict_c['Type'][0]].set_xlim(0,End_time)
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# Plotting statndard error (if exists)
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if itr_loc[0]+1 < len(Output_dict_c['Type']) and Output_dict_c['Type'][itr_loc[0]+1] == 'Standard Error':
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myVars[Output_dict_c['Type'][0]].fill_between(Model_Param_array[:,1]/24/3600,
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Model_Param_array[:,itr_loc[0]] - Model_Param_array[:,itr_loc[0]+1],
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Model_Param_array[:,itr_loc[0]] + Model_Param_array[:,itr_loc[0]+1], color= coloring[itr_loc[0]], alpha=0.1)
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# Plotting similar parameters on one axis
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for j in range(1,len(itr_loc)):
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if Output_dict_c['label'][itr_loc[j]] == 'True Max-Mag': # plot with dash-line
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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)
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else:
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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]])
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handels.append(All_handels[itr_loc[j]-2])
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labels.append(Legend_label(itr_loc[j]))
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# Plotting statndard error (if exists)
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if itr_loc[0]+1 < len(Output_dict_c['Type']) and Output_dict_c['Type'][itr_loc[j]+1] == 'Standard Error':
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myVars[Output_dict_c['Type'][0]].fill_between(Model_Param_array[:,1]/24/3600,
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Model_Param_array[:,itr_loc[j]] - Model_Param_array[:,itr_loc[j]+1],
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Model_Param_array[:,itr_loc[j]] + Model_Param_array[:,itr_loc[j]+1], color= coloring[itr_loc[j]], alpha=0.1)
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first_itr = 0
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# Check if there is any more parameter to be plotted in second axes
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# The procedure is similar to last plots.
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if len(Uniqe_var) > 1:
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for i in range(1,len(Uniqe_var)):
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itr_loc = np.where(np.array(All_vars) == Uniqe_var[i])[0]+2
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myVars[Uniqe_var[i]] = myVars[Output_dict_c['Type'][0]].twinx()
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# if it is third or more axis, make a distance between them
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if first_itr == 0:
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first_itr += 1
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set_right = 1
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else:
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set_right = 1 + first_itr*0.2
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first_itr += 1
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myVars[Uniqe_var[i]].spines.right.set_position(("axes", set_right))
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if Output_dict_c['label'][itr_loc[0]] == 'True Max-Mag': # plot with dash-line
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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)
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else:
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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]])
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handels.append(All_handels[itr_loc[0]-2])
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labels.append(Legend_label(itr_loc[0]))
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myVars[Uniqe_var[i]].set_ylabel(Output_dict_c['Type'][itr_loc[0]])
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myVars[Uniqe_var[i]].yaxis.label.set_color(coloring[itr_loc[0]])
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myVars[Uniqe_var[i]].spines["right"].set_edgecolor(coloring[itr_loc[0]])
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myVars[Uniqe_var[i]].tick_params(axis='y', colors= coloring[itr_loc[0]])
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myVars[Uniqe_var[i]].set_ylim(Extermom4All(Model_Param_array, itr_loc)[0], Extermom4All(Model_Param_array, itr_loc)[1])
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if itr_loc[0]+1 < len(Output_dict_c['Type']) and Output_dict_c['Type'][itr_loc[0]+1] == 'Standard Error':
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myVars[Uniqe_var[i]].fill_between(Model_Param_array[:,1]/24/3600,
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Model_Param_array[:,itr_loc[0]] - Model_Param_array[:,itr_loc[0]+1],
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Model_Param_array[:,itr_loc[0]] + Model_Param_array[:,itr_loc[0]+1], color= coloring[itr_loc[0]], alpha=0.1)
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for j in range(1,len(itr_loc)):
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if Output_dict_c['label'][itr_loc[j]] == 'True Max-Mag': # plot with dash-line
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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)
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else:
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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]])
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handels.append(All_handels[itr_loc[j]-2])
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labels.append(Legend_label(itr_loc[j]))
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if itr_loc[j]+1 < len(Output_dict_c['Type']) and Output_dict_c['Type'][itr_loc[j]+1] == 'Standard Error':
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myVars[Uniqe_var[i]].fill_between(Model_Param_array[:,1]/24/3600,
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Model_Param_array[:,itr_loc[j]] - Model_Param_array[:,itr_loc[j]+1],
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Model_Param_array[:,itr_loc[j]] + Model_Param_array[:,itr_loc[j]+1], color= coloring[itr_loc[j]], alpha=0.1)
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# If there are timing, plot them as vertical lines
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if time_inj:
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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')
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handels.append('l1')
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labels.append('Start-inj')
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if time_shut_in:
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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')
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handels.append('l2')
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labels.append('Shut-in')
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if time_big_ev:
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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')
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handels.append('l3')
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labels.append('Large-Ev')
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box = myVars[Output_dict_c['Type'][0]].get_position()
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if len(handels) < 6:
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myVars[Output_dict_c['Type'][0]].set_position([box.x0, box.y0 + box.height * 0.1,
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box.width, box.height * 0.9])
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plt.legend([myVars[ii] for ii in handels], labels, loc='upper center',
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bbox_to_anchor=(0.5+0.06*first_itr, -0.15), fancybox=True, shadow=True, ncol=len(handels))
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elif len(handels) < 13:
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myVars[Output_dict_c['Type'][0]].set_position([box.x0, box.y0 + box.height * 0.04*int(len(handels)/2),
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box.width, box.height * (1 - 0.04*int(len(handels)/2))])
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plt.legend([myVars[ii] for ii in handels], labels, loc='upper center',
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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)
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else:
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myVars[Output_dict_c['Type'][0]].set_position([box.x0, box.y0 + box.height * 0.04*int(len(handels)/2),
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box.width, box.height * (1 - 0.04*int(len(handels)/2))])
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plt.legend([myVars[ii] for ii in handels], labels, loc='upper center',
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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)
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plt.title(Model_name)
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# plt.savefig(cwd+'/Results/'+Model_name+'.png', dpi=300)
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plt.savefig('PLOT_Mmax_param.png', dpi=300)
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# plt.show()
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# Run functions based on the configurations -------------------
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# Computing model
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if time_step_in_hour > time_win_in_hours:
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@ -681,7 +507,9 @@ def main(Input_catalog, Input_injection_rate, time_win_in_hours, time_step_in_ho
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if Plot_flag > 0:
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if Model_Param_array.any():
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logger.info("Plotting results")
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Plot_feature(Model_Param_array, Output_dict)
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import Mmax_plot # Import locally to ensure Mmax_plot is required only when Plot_flag > 0
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Mmax_plot.Plot_feature(Model_Param_array, Output_dict)
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else:
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logger.info("Model_Param_array is empty or not enough values to plot. Check 'csv' file.")
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206
src/Mmax_plot.py
Normal file
206
src/Mmax_plot.py
Normal file
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import matplotlib.pyplot as plt
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import numpy as np
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def Plot_feature(Model_Param_array,
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Output_dict,
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End_time=None,
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time_inj=None,
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time_shut_in=None,
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time_big_ev=None,
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Model_name="",
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logger=None):
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"""
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Plotting function extracted from Mmax.py for plotting models and parameters.
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Parameters
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----------
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Model_Param_array : np.ndarray
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Computed matrix of model parameters as rows in time.
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Output_dict : dict
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Dictionary describing each column in Model_Param_array.
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End_time : float, optional
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The last time to show in the X-axis (days), if desired.
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time_inj : float, optional
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Time of injection start (days), if you want a vertical line.
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time_shut_in : float, optional
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Time of shut-in (days), if you want a vertical line.
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time_big_ev : float, optional
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Time of large event (days), if you want a vertical line.
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Model_name : str, optional
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Model name used for the plot title.
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logger : logging.Logger, optional
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Logger for printing info messages. If None, no logging happens.
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"""
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myVars = locals()
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# Function for findin min-max of all similar parameters
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def Extermom4All(Model_Param_array, itr_loc):
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Mat1D = np.reshape(Model_Param_array[:,itr_loc], -1)
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NotNone = np.isfinite(Mat1D)
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if np.min(Mat1D[NotNone])>0:
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return [np.min(Mat1D[NotNone])*0.95, np.max(Mat1D[NotNone])*1.05]
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elif np.min(Mat1D[NotNone])<0 and np.max(Mat1D[NotNone])>0:
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return [np.min(Mat1D[NotNone])*1.05, np.max(Mat1D[NotNone])*1.05]
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elif np.max(Mat1D[NotNone])<0:
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return [np.min(Mat1D[NotNone])*1.05, np.max(Mat1D[NotNone])*0.95]
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# Function for setting relevant lagends in the plot
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def Legend_label(loc):
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l = Output_dict_c['label'][loc]
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if Output_dict_c['b_method'][loc]:
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if Output_dict_c['cl'][loc]:
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l+='('+Output_dict_c['b_method'][loc]+', cl='+str(Output_dict_c['cl'][loc])+')'
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else:
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l+='('+Output_dict_c['b_method'][loc]+')'
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return l
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c_NotNone = [] # Removing all parameters with None or constant value
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for i in range(Model_Param_array.shape[1]):
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NotNone = np.isfinite(Model_Param_array[:,i])
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Eq_value = np.mean(Model_Param_array[:,i])
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if any(NotNone) and Eq_value != Model_Param_array[0,i]:
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c_NotNone.append(i)
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else:
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logger.info(f"No-PLOT: All values of {Output_dict['Type'][i]} are {Model_Param_array[0,i]}!")
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if len(c_NotNone) > 1:
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Model_Param_array = Model_Param_array[:,c_NotNone]
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# New output dictionary based on valid parameters for plotting
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Output_dict_c = {'Type':[], 'label':[], 'b_method':[], 'cl':[]}
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for i in range(len(c_NotNone)):
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Output_dict_c['Type'].append(Output_dict['Type'][c_NotNone[i]])
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Output_dict_c['label'].append(Output_dict['label'][c_NotNone[i]])
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Output_dict_c['b_method'].append(Output_dict['b_method'][c_NotNone[i]])
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Output_dict_c['cl'].append(Output_dict['cl'][c_NotNone[i]])
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coloring=['blue','g','r','c','m','y',
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'brown', 'darkolivegreen', 'teal', 'steelblue', 'slateblue',
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'purple', 'darksalmon', '#c5b0d5', '#c49c94',
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'#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()
|
||||
Loading…
Reference in New Issue
Block a user