Changes after 1D simulation has been added

inversion_plot_comp_paper2.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Mar 16 15:35:24 2021
+
+@author: xavier
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib import rc
+rc('text.latex', preamble=r'\usepackage{amsmath} \usepackage{amssymb}')
+rc('font',**{'family':'serif','sans-serif':['Times New Roman'],'size':15})
+rc('text', usetex=True)
+#Value for 1 width figure
+H = 3.;
+L = 3.;
+
+
+n_comp = 5
+path = ['YuanFanChoiceA/','YuanFanChoiceB/','YuanFanChoiceC/','EisenstatChoiceA/','Classic/']
+filename = ['paper2_inversion_nscgA','paper2_inversion_nscgB','paper2_inversion_nscgC','paper2_inversion_ncg','paper2_inversion_gd']
+lab = ['tr (a)', 'tr (b)', 'tr (c)', 'ls', 'gd']
+glob = ['trust-region','trust-region','trust-region','linesearch','linesearch']
+order = [2, 2, 2, 2, 1]
+col = ['C2','C2', 'C2', 'C1', 'C0']
+lin = ['-','--',':','-','-']
+max_forward = 180
+
+freqf = np.array([4.,6.,9.])
+n_freq = np.size(freqf)
+
+
+"""
+LOAD
+"""
+jcfs = []
+n_itcf = []
+n_scalecf = []
+n_itcfs = []
+
+for i in range(0,n_comp):
+    n_scalef = [[]]*n_freq; 
+    jfs = [[]]*n_freq;
+    n_itfs = [[]]*n_freq;
+    n_itf = [0]*n_freq
+    scalefs = [[]]*n_freq;
+    for f in range(0,n_freq):
+        n_scalef[f] = np.loadtxt(path[i]+filename[i]+'_globalminimum_historyg'+str(f)+'.csv',delimiter=';',max_rows=1,skiprows=3,dtype=np.integer)[1];
+        scalefs[f] = np.loadtxt(path[i]+filename[i]+'_globalminimum_historyg'+str(f)+'.csv',delimiter=';',usecols=2,max_rows=np.int(n_scalef[f]),skiprows=6,dtype=np.float);
+        jfs[f] = [[]]*n_scalef[f];
+        n_itfs[f] = np.zeros(n_scalef[f],np.integer);
+        line = 3 
+        for s in range(0,n_scalef[f]):        
+            n_itfs[f][s] = np.loadtxt(path[i]+filename[i]+'_localminimum_historyg'+str(f)+'.csv',delimiter=';',usecols = 1, max_rows=1,skiprows=line,dtype=np.integer);
+            n_itf[f] = n_itf[f]+n_itfs[f][s]
+            line=line+2
+            jfs[f][s] = np.loadtxt(path[i]+filename[i]+'_localminimum_historyg'+str(f)+'.csv',delimiter=';',max_rows=np.int(n_itfs[f][s]+1),skiprows=line,dtype=np.float)[:,0];
+            line=line+n_itfs[f][s]+1+1
+    jcfs.append(jfs)
+    n_itcf.append(n_itf)
+    n_itcfs.append(n_itfs)
+    n_scalecf.append(n_scalef)
+
+it_linecf = [[]*n_freq]*n_comp;
+it_descentcf = [[]*n_freq]*n_comp;
+bnd_descentcf = [[]*n_freq]*n_comp; 
+n_forwardcf = [[]*n_freq]*n_comp;
+successcf = [[]*n_freq]*n_comp;
+for i in range(0,n_comp):
+    it_linef = [[]]*n_freq
+    it_descentf = [[]]*n_freq
+    bnd_descentf = [[]]*n_freq
+    n_forwardf = [[]]*n_freq
+    successf = [[]]*n_freq
+    for f in range(0,n_freq):
+        n_forwardf[f] = np.zeros(n_itcf[i][f],np.integer)
+
+        if glob[i] == 'linesearch': 
+            it_linef[f] = np.loadtxt(path[i]+filename[i]+'_linesearch_historyg'+str(f)+'.csv',delimiter=';',usecols = 5, skiprows=1,dtype=np.integer)
+            n_forwardf[f] += it_linef[f] 
+        
+        if order[i] == 2:
+            it_descentf[f] = np.zeros(n_itcf[i][f],np.integer)
+            successf[f] = np.zeros(n_itcf[i][f],np.integer)
+            bnd_descentf[f] = np.zeros(n_itcf[i][f],np.integer)
+            line = 3 
+            for n in range(0,n_itcf[i][f]):        
+                it_descentf[f][n] = np.loadtxt(path[i]+filename[i]+'_descent_historyg'+str(f)+'.csv',delimiter=';',usecols=3,max_rows=1,skiprows=line,dtype=np.integer);
+                if glob[i] == 'trust-region':
+                    bnd_descentf[f][n] = np.loadtxt(path[i]+filename[i]+'_descent_historyg'+str(f)+'.csv',delimiter=';',usecols=6,max_rows=1,skiprows=line,dtype=np.integer);
+                successf[f][n] = np.loadtxt(path[i]+filename[i]+'_descent_historyg'+str(f)+'.csv',delimiter=';',usecols=0,max_rows=1,skiprows=line,dtype=np.integer);
+                line=line+it_descentf[f][n]+3
+            n_forwardf[f] += 2*it_descentf[f]
+        n_forwardf[f] += 2
+        n_forwardf[f] = np.cumsum(n_forwardf[f])
+        n_forwardf[f] = np.insert(n_forwardf[f],0,0)
+    n_forwardcf[i] = n_forwardf    
+    it_descentcf[i] = it_descentf
+    bnd_descentcf[i] = bnd_descentf
+    it_linecf[i] = it_linef
+    successcf[i] = successf
+
+"""
+CONVERGENCE PLOT
+"""
+fig = [[]]*n_freq
+for f in range(0,n_freq):
+    fig[f]=plt.figure(figsize=(L,H),tight_layout=False);
+    plt.subplots_adjust(top=0.95,right=0.98,bottom=0.09,left=0.16)
+    plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
+    plt.grid(zorder=0)
+    plt.xlim([0,max_forward])
+    plt.xticks(np.arange(0,161,40))
+    plt.ylim([1e-3,9])
+    plt.yscale('log')
+    for i in range(0,n_comp):
+        n_it=0
+        lines = [[]]*n_scalecf[i][f]
+        plt.plot(n_forwardcf[i][f][n_it],jcfs[i][f][0][0],color=col[i],marker='o',zorder=3,markersize=3.)
+        for s in range(0,n_scalecf[i][f]):
+            lines[s] = plt.plot(n_forwardcf[i][f][range(n_it,n_it+n_itcfs[i][f][s]+1)],jcfs[i][f][s][0:n_itcfs[i][f][s]+1],color=col[i],linestyle=lin[i],zorder=2,label=lab[i])
+            plt.plot(n_forwardcf[i][f][n_it+n_itcfs[i][f][s]],jcfs[i][f][s][n_itcfs[i][f][s]],color=col[i],marker='x',zorder=3,markersize=3.)
+            n_it=n_it+n_itcfs[i][f][s]
+        n_it = 0
+    #plt.legend()
+    plt.savefig('convergence'+str(f)+'.eps')
+        
+"""
+STATS
+"""
+print(''.rjust(30), 'Wave (tot.)'.rjust(30), 'Outer it. (tot.)'.rjust(30), 'Inner it. (mean)'.rjust(30), 'Failure (tot.)'.rjust(30), 'Bnd reached (tot.)'.rjust(30))
+for i in range(0,n_comp):
+        outter = np.sum(n_itcf[i])
+        inner = 0
+        failure = 0
+        boundary = 0
+        call = 0
+        for f in range(0,n_freq):
+            call += n_forwardcf[i][f][-1]
+            inner += np.sum(it_descentcf[i][f])
+            if glob[i] == 'linesearch':
+                failure += np.count_nonzero(it_linecf[i][f])
+            elif glob[i] == 'trust-region':
+                for s in range(0,n_scalecf[i][f]):
+                    failure += n_itcfs[i][f][s] - np.count_nonzero(np.diff(jcfs[i][f][s]))
+                    boundary += np.sum(bnd_descentcf[i][f])
+        print(path[i].rjust(30), str(call).rjust(30), str(outter).rjust(30), str(inner/outter).rjust(30), str(failure/outter).rjust(30), str(boundary/outter).rjust(30))
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