Newer
Older
import numpy as np
import matplotlib.pyplot as plt
import sympy as sym
import math
import os
import subprocess
import fileinput
import re
import matlab.engine
from HelperFunctions import *
# from ClassifyMin import *
from ClassifyMin_New import *
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
import matplotlib.ticker as tickers
import matplotlib as mpl
from matplotlib.ticker import MultipleLocator,FormatStrFormatter,MaxNLocator
import pandas as pd
# import tikzplotlib
# # from pylab import *
# from tikzplotlib import save as tikz_save
# Needed ?
mpl.use('pdf')
# from subprocess import Popen, PIPE
#import sys
###################### makePlot.py #########################
# Generalized Plot-Script giving the option to define
# quantity of interest and the parameter it depends on
# to create a plot
#
# Input: Define y & x for "x-y plot" as Strings
# - Run the 'Cell-Problem' for the different Parameter-Points
# (alternatively run 'Compute_MuGamma' if quantity of interest
# is q3=muGamma for a significant Speedup)
###########################################################
# figsize argument takes inputs in inches
# and we have the width of our document in pts.
# To set the figure size we construct a function
# to convert from pts to inches and to determine
# an aesthetic figure height using the golden ratio:
# def set_size(width, fraction=1):
# """Set figure dimensions to avoid scaling in LaTeX.
#
# Parameters
# ----------
# width: float
# Document textwidth or columnwidth in pts
# fraction: float, optional
# Fraction of the width which you wish the figure to occupy
#
# Returns
# -------
# fig_dim: tuple
# Dimensions of figure in inches
# """
# # Width of figure (in pts)
# fig_width_pt = width * fraction
#
# # Convert from pt to inches
# inches_per_pt = 1 / 72.27
#
# # Golden ratio to set aesthetic figure height
# # https://disq.us/p/2940ij3
# golden_ratio = (5**.5 - 1) / 2
#
# # Figure width in inches
# fig_width_in = fig_width_pt * inches_per_pt
# # Figure height in inches
# fig_height_in = fig_width_in * golden_ratio
#
# fig_dim = (fig_width_in, fig_height_in)
#
# return fig_dim
#
def format_func(value, tick_number):
# # find number of multiples of pi/2
# N = int(np.round(2 * value / np.pi))
# if N == 0:
# return "0"
# elif N == 1:
# return r"$\pi/2$"
# elif N == 2:
# return r"$\pi$"
# elif N % 2 > 0:
# return r"${0}\pi/2$".format(N)
# else:
# return r"${0}\pi$".format(N // 2)
# find number of multiples of pi/2
N = int(np.round(4 * value / np.pi))
if N == 0:
return "0"
elif N == 1:
return r"$\pi/4$"
elif N == 2:
return r"$\pi/2$"
elif N % 2 > 0:
return r"${0}\pi/2$".format(N)
else:
return r"${0}\pi$".format(N // 2)
def find_nearest(array, value):
array = np.asarray(array)
idx = (np.abs(array - value)).argmin()
return array[idx]
def find_nearestIdx(array, value):
array = np.asarray(array)
idx = (np.abs(array - value)).argmin()
return idx
# TODO
# - Fallunterscheidung (Speedup) falls gesuchter value mu_gamma = q3
# - Also Add option to plot Minimization Output
# ----- Setup Paths -----
# InputFile = "/inputs/cellsolver.parset"
# OutputFile = "/outputs/output.txt"
InputFile = "/inputs/computeMuGamma.parset"
OutputFile = "/outputs/outputMuGamma.txt"
# path = os.getcwd()
# InputFilePath = os.getcwd()+InputFile
# OutputFilePath = os.getcwd()+OutputFile
# --------- Run from src folder:
path_parent = os.path.dirname(os.getcwd())
os.chdir(path_parent)
path = os.getcwd()
print(path)
InputFilePath = os.getcwd()+InputFile
OutputFilePath = os.getcwd()+OutputFile
print("InputFilepath: ", InputFilePath)
print("OutputFilepath: ", OutputFilePath)
print("Path: ", path)
#---------------------------------------------------------------
print('---- Input parameters: -----')
mu1 = 1.0 #10.0
# lambda1 = 10.0
rho1 = 1.0
alpha = 5.0
beta = 10.0
# alpha = 2.0
# beta = 2.0
theta = 1.0/8.0 #1.0/4.0
lambda1 = 0.0
# gamma = 1.0/4.0
# TEST:
alpha=3.0;
# # INTERESTING!:
alpha = 3
beta = 10.0
theta= 1/8
theta = 0.5
# gamma = 0.5
print('mu1: ', mu1)
print('rho1: ', rho1)
print('alpha: ', alpha)
print('beta: ', beta)
print('theta: ', theta)
print('gamma:', gamma)
print('----------------------------')
# --- define Interval of x-va1ues:
# xmin = 0.01
# xmax = 0.41
# xmax = 0.99
xmin = -2.0
# compare with interpolant between endpoints
# xmin = -0.7014028056112225
# xmax = 0.70
compare_interpolant = False
# xmin = -5.0
# xmax = 5.0
Jumps = False
Jumps = True
X_Values = np.linspace(xmin, xmax, num=numPoints)
print(X_Values)
Y_Values = []
Curvature_alpha0 = []
Curvature_alphaNeg0125 = []
Curvature_alphaNeg025 = []
Curvature_alphaNeg05 = []
Curvature_alphaNeg075 = []
Curvature_alphaNeg1 = []
Curvature_alpha3 = []
Curvature_alphaNeg5 = []
for alpha in X_Values:
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
q12 = 0.0
q1 = (1.0/6.0)*harmonicMean(mu1, beta, theta)
q2 = (1.0/6.0)*arithmeticMean(mu1, beta, theta)
b1 = prestrain_b1(rho1, beta, alpha,theta)
b2 = prestrain_b2(rho1, beta, alpha,theta)
b3 = 0.0
q3 = GetMuGamma(beta,theta,gamma,mu1,rho1,InputFilePath ,OutputFilePath)
G, angle, Type, curvature = classifyMin_ana(alpha,beta,theta, q3, mu1, rho1)
Y_Values.append(curvature)
# G, angle, Type, curvature = classifyMin_ana(-1.0,beta,theta, q3, mu1, rho1)
# Curvature_alphaNeg1.append(curvature)
# G, angle, Type, curvature = classifyMin_ana(-0.5,beta,theta, q3, mu1, rho1)
# Curvature_alphaNeg05 .append(curvature)
# G, angle, Type, curvature = classifyMin_ana(-0.25,beta,theta, q3, mu1, rho1)
# Curvature_alphaNeg025.append(curvature)
# G, angle, Type, curvature = classifyMin_ana(3.0,beta,theta, q3, mu1, rho1)
# Curvature_alpha3.append(curvature)
# G, angle, Type, curvature = classifyMin_ana(-0.75,beta,theta, q3, mu1, rho1)
# Curvature_alphaNeg075.append(curvature)
# G, angle, Type, curvature = classifyMin_ana(0,beta,theta, q3, mu1, rho1)
# Curvature_alpha0.append(curvature)
# G, angle, Type, curvature = classifyMin_ana(-0.125,beta,theta, q3, mu1, rho1)
# Curvature_alphaNeg0125.append(curvature)
# G, angle, Type, curvature = classifyMin_ana(-5.0,beta,theta, q3, mu1, rho1)
# Curvature_alphaNeg5.append(curvature)
print("(Output) Values of Curvature: ", Y_Values)
idx = find_nearestIdx(Y_Values, 0)
print(' Idx of value closest to 0', idx)
ValueClose = Y_Values[idx]
print('GammaValue(Idx) with mu_gamma closest to q_3^*', ValueClose)
# jumpThreshold = 0.5
jumpThreshold = 0.05
# jumpThreshold = 0.01
# Find Indices where the difference between the next one is larger than epsilon...
jump_idx = []
jump_xValues = []
jump_yValues = []
tmp = X_Values[0]
for idx, x in enumerate(X_Values):
print(idx, x)
if idx > 0:
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
print('jump candidate')
jump_idx.append(idx)
jump_xValues.append(x)
jump_yValues.append(Y_Values[idx])
print("Jump Indices", jump_idx)
print("Jump X-values:", jump_xValues)
print("Jump Y-values:", jump_yValues)
y_plotValues = [Y_Values[0]]
x_plotValues = [X_Values[0]]
# y_plotValues.extend(jump_yValues)
for i in jump_idx:
y_plotValues.extend([Y_Values[i-1], Y_Values[i]])
x_plotValues.extend([X_Values[i-1], X_Values[i]])
y_plotValues.append(Y_Values[-1])
# x_plotValues = [X_Values[0]]
# x_plotValues.extend(jump_xValues)
x_plotValues.append(X_Values[-1])
print("y_plotValues:", y_plotValues)
print("x_plotValues:", x_plotValues)
# Y_Values[np.diff(y) >= 0.5] = np.nan
#get values bigger than jump position
# gamma = infty
# x_rest = X_Values[X_Values>x_plotValues[1]]
# Y_Values = np.array(Y_Values) #convert the np array
# y_rest = Y_Values[X_Values>x_plotValues[1]]
#
#
# # gamma = 0
# x_rest = X_Values[X_Values>x_plotValues[3]]
# Y_Values = np.array(Y_Values) #convert the np array
# y_rest = Y_Values[X_Values>x_plotValues[3]]
# gamma between
# Y_Values = np.array(Y_Values) #convert the np array
# X_Values = np.array(X_Values) #convert the np array
#
# x_one = X_Values[X_Values>x_plotValues[3]]
# # ax.scatter(X_Values, Y_Values)
# y_rest = Y_Values[X_Values>x_plotValues[3]]
# ax.plot(X_Values[X_Values>0.135], Y_Values[X_Values<0.135])
#
#
#
# y_rest = Y_Values[np.nonzero(X_Values>x_plotValues[1]]
# print('X_Values:', X_Values)
# print('Y_Values:', Y_Values)
# print('x_rest:', x_rest)
# print('y_rest:', y_rest)
# print('np.nonzero(X_Values>x_plotValues[1]', np.nonzero(X_Values>x_plotValues[1]) )
# --- Convert to numpy array
Y_Values = np.array(Y_Values)
X_Values = np.array(X_Values)
Curvature_alphaNeg1 = np.array(Curvature_alphaNeg1)
Curvature_alphaNeg05 = np.array(Curvature_alphaNeg05)
Curvature_alphaNeg025 = np.array(Curvature_alphaNeg025)
Curvature_alphaNeg075 = np.array(Curvature_alphaNeg075)
Curvature_alpha3 = np.array(Curvature_alpha3)
Curvature_alphaNeg0 = np.array(Curvature_alpha0)
Curvature_alphaNeg0125 = np.array(Curvature_alphaNeg0125)
Curvature_alphaNeg5 = np.array(Curvature_alphaNeg5)
# ---------------- Create Plot -------------------
# mpl.rcParams['text.usetex'] = True
# mpl.rcParams["font.family"] = "serif"
# mpl.rcParams["font.size"] = "9"
# Styling
plt.style.use("seaborn-darkgrid")
plt.style.use("seaborn-whitegrid")
plt.style.use("seaborn")
# plt.style.use('ggplot')
# plt.rcParams["font.family"] = "Avenir"
# plt.rcParams["font.size"] = 16
# plt.style.use("seaborn-darkgrid")
mpl.rcParams['text.usetex'] = True
mpl.rcParams["font.family"] = "serif"
mpl.rcParams["font.size"] = "10"
# mpl.rcParams['xtick.labelsize'] = 16mpl.rcParams['xtick.major.size'] = 2.5
# mpl.rcParams['xtick.bottom'] = True
# mpl.rcParams['ticks'] = True
mpl.rcParams['xtick.bottom'] = True
mpl.rcParams['xtick.major.size'] = 3
mpl.rcParams['xtick.minor.size'] = 1.5
mpl.rcParams['xtick.major.width'] = 0.75
mpl.rcParams['ytick.left'] = True
mpl.rcParams['ytick.major.size'] = 3
mpl.rcParams['ytick.minor.size'] = 1.5
mpl.rcParams['ytick.major.width'] = 0.75
mpl.rcParams.update({'font.size': 10})
mpl.rcParams['axes.labelpad'] = 2
### ADJUST GRID:
mpl.rcParams['grid.linewidth'] = 0.25
mpl.rcParams['grid.alpha'] = 0.9 # 0.75
mpl.rcParams['grid.linestyle'] = '-'
mpl.rcParams['grid.color'] = 'gray'#'black'
#---- Scale Figure apropriately to fit tex-File Width
# width = 452.9679
# width as measured in inkscape
width = 6.28 *0.5
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
height = width / 1.618
#setup canvas first
fig = plt.figure() #main
# fig, ax = plt.subplots()
# fig, (ax, ax2) = plt.subplots(ncols=2)
# fig,axes = plt.subplots(nrows=1,ncols=2,figsize=(width,height)) # more than one plot
# fig.subplots_adjust(left=.15, bottom=.16, right=.99, top=.97) #TEST
# TEST
# mpl.rcParams['figure.figsize'] = (width+0.1,height+0.1)
# fig = plt.figure(figsize=(width+0.1,height+0.1))
# mpl.rcParams['figure.figsize'] = (width,height)
# fig = plt.figure(figsize=(10,6)) # default is [6.4,4.8] 6.4 is the width, 4.8 is the height
# fig = plt.figure(figsize=(width,height)) # default is [6.4,4.8] 6.4 is the width, 4.8 is the height
# fig = plt.figure(figsize=set_size(width))
# fig = plt.subplots(1, 1, figsize=set_size(width))
# --- To create a figure half the width of your document:#
# fig = plt.figure(figsize=set_size(width, fraction=0.5))
#--- You must select the correct size of the plot in advance
# fig.set_size_inches(3.54,3.54)
# ax = plt.axes((0.15,0.18,0.8,0.8))
# ax = plt.axes((0.15,0.18,0.6,0.6))
ax = plt.axes((0.15,0.2,0.75,0.75))
# ax = plt.axes((0.1,0.1,0.5,0.8))
# ax = plt.axes((0.1,0.1,1,1))
# ax = plt.axes()
# ax.spines['right'].set_visible(False)
# ax.spines['left'].set_visible(False)
# ax.spines['bottom'].set_visible(False)
# ax.spines['top'].set_visible(False)
# ax.tick_params(axis='x',which='major',direction='out',length=10,width=5,color='red',pad=15,labelsize=15,labelcolor='green',
# labelrotation=15)
# ax.tick_params(axis='x',which='major', direction='out',pad=5,labelsize=10)
# ax.tick_params(axis='y',which='major', length=5, width=1, direction='out',pad=5,labelsize=10)
ax.tick_params(axis='x',which='major', direction='out',pad=3)
ax.tick_params(axis='y',which='major', length=3, width=1, direction='out',pad=3)
# ax.xaxis.set_major_locator(MultipleLocator(0.05))
# ax.xaxis.set_minor_locator(MultipleLocator(0.025))
ax.xaxis.set_major_locator(MultipleLocator(0.1))
ax.xaxis.set_minor_locator(MultipleLocator(0.05))
ax.xaxis.set_major_locator(MultipleLocator(0.5))
ax.xaxis.set_minor_locator(MultipleLocator(0.25))
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
#---- print data-types
print(ax.xaxis.get_major_locator())
print(ax.xaxis.get_minor_locator())
print(ax.xaxis.get_major_formatter())
print(ax.xaxis.get_minor_formatter())
#---- Hide Ticks or Labels
# ax.yaxis.set_major_locator(plt.NullLocator())
# ax.xaxis.set_major_formatter(plt.NullFormatter())
#---- Reducing or Increasing the Number of Ticks
# ax.xaxis.set_major_locator(plt.MaxNLocator(3))
# ax.yaxis.set_major_locator(plt.MaxNLocator(3))
#----- Fancy Tick Formats
# ax.yaxis.set_major_locator(plt.MultipleLocator(np.pi / 4))
# ax.yaxis.set_minor_locator(plt.MultipleLocator(np.pi / 12))
# ax.yaxis.set_major_formatter(plt.FuncFormatter(format_func))
# --- manually change ticks&labels:
# ax.set_xticks([0.2,1])
# ax.set_xticklabels(['pos1','pos2'])
# ax.set_yticks([0, np.pi/8, np.pi/4 ])
# labels = ['$0$',r'$\pi/8$', r'$\pi/4$']
# ax.set_yticklabels(labels)
a=ax.yaxis.get_major_locator()
b=ax.yaxis.get_major_formatter()
c = ax.get_xticks()
d = ax.get_xticklabels()
print('xticks:',c)
print('xticklabels:',d)
ax.grid(True,which='major',axis='both',alpha=0.3)
# plt.figure()
# f,ax=plt.subplots(1)
# plt.title(r''+ yName + '-Plot')
# plt.plot(X_Values, Y_Values,linewidth=2, '.k')
# plt.plot(X_Values, Y_Values,'.k',markersize=1)
# plt.plot(X_Values, Y_Values,'.',markersize=0.8)
# plt.plot(X_Values, Y_Values)
# ax.plot([[0],X_Values[-1]], [Y_Values[0],Y_Values[-1]])
# Gamma = '0'
# ax.plot([x_plotValues[0],x_plotValues[1]], [y_plotValues[0],y_plotValues[1]] , 'b')
#
# ax.plot([x_plotValues[1],x_plotValues[3]], [y_plotValues[2],y_plotValues[3]] , 'b')
#
# ax.plot(x_rest, y_rest, 'b')
# Gamma between
# x jump values (gamma 0): [0.13606060606060608, 0.21090909090909093]
# ax.plot([[0,jump_xValues[0]], [0, 0]] , 'b')
# ax.plot([jump_xValues[0],xmin], [y_plotValues[2],y_plotValues[2]] , 'b')
# ax.plot([[0,0.13606060606060608], [0, 0]] , 'b')
# ax.plot([[0.13606060606060608,xmin], [(math.pi/2),(math.pi/2)]], 'b')
# jump_xValues[0]
# --- leave out jumps:
# ax.scatter(X_Values, Y_Values)
# ax.set_xlabel(r"prestrain ratio $\theta_\rho$")
ax.set_xlabel(r"$\theta_\rho$")
if Jumps:
# # --- leave out jumps:
# if gamma == 'infinity':
# ax.plot(X_Values[X_Values>=jump_xValues[0]], Y_Values[X_Values>=jump_xValues[0]] , 'royalblue')
# ax.plot(X_Values[X_Values<jump_xValues[0]], Y_Values[X_Values<jump_xValues[0]], 'royalblue')
## Plot every other line.. not the jumps..
# if gamma == '0':
# tmp = 1
# for idx, x in enumerate(x_plotValues):
# if idx > 0 and tmp == 1:
# # plt.plot([x_plotValues[idx-1],x_plotValues[idx]] ,[y_plotValues[idx-1],y_plotValues[idx]] )
# ax.plot([x_plotValues[idx-1],x_plotValues[idx]] ,[y_plotValues[idx-1],y_plotValues[idx]], 'royalblue', zorder=2)
# tmp = 0
# else:
# tmp = 1
for x in jump_xValues:
plt.axvline(x,ymin=0, ymax= 1, color = 'orange',alpha=0.5, linestyle = 'dashed', linewidth=1, zorder=1)
# plt.axvline(x,ymin=0, ymax= 1, color = 'orange',alpha=0.5, linestyle = 'dashed', label=r'$\theta_*$')
# print('jump_idx[0]:',jump_idx[0])
# print('X_Values[jump_idx[0]]',X_Values[jump_idx[0]])
# print('X_Values[jump_idx[1]]',X_Values[jump_idx[1]])
# print('Y_Values[jump_idx[0]]',Y_Values[jump_idx[0]])
# print('Y_Values[jump_idx[1]]',Y_Values[jump_idx[1]])
# Better use for-loop!!
if gamma == '0':
ax.scatter([X_Values[jump_idx[0]], X_Values[jump_idx[1]]],[Y_Values[jump_idx[0]],Y_Values[jump_idx[1]]],s=6, marker='o', cmap=None, norm=None, facecolor = 'black',
edgecolor = 'black', vmin=None, vmax=None, alpha=None, linewidths=None, zorder=5)
# ax.text(X_Values[jump_idx[0]]+0.05, Y_Values[jump_idx[0]]+0.02, r"$2$", size=6, bbox=dict(boxstyle="circle",facecolor='white', alpha=1.0, pad=0.1, linewidth=0.5)
# )
#
# ax.text(X_Values[jump_idx[1]]+0.05, Y_Values[jump_idx[1]]+0.02, r"$1$", size=6, bbox=dict(boxstyle="circle",facecolor='white', alpha=1.0, pad=0.1, linewidth=0.5))
ax.text(X_Values[jump_idx[0]]+0.10, Y_Values[jump_idx[0]]+0.10, r"$1$", size=8, bbox=dict(boxstyle="circle",facecolor='white', alpha=1.0, pad=0.1, linewidth=0.5)
ax.text(X_Values[jump_idx[1]]+0.10, Y_Values[jump_idx[1]]+0.10, r"$2$", size=8, bbox=dict(boxstyle="circle",facecolor='white', alpha=1.0, pad=0.1, linewidth=0.5))
ax.scatter([X_Values[jump_idx[0]]],[Y_Values[jump_idx[0]]],s=8, marker='o', cmap=None, norm=None, facecolor = 'black',
edgecolor = 'black', vmin=None, vmax=None, alpha=None, linewidths=None, zorder=5)
# ax.text(X_Values[jump_idx[0]]+0.05, Y_Values[jump_idx[0]]+0.02, r"$1$", size=6, bbox=dict(boxstyle="circle",facecolor='white', alpha=1.0, pad=0.1, linewidth=0.5)
# )
ax.text(X_Values[jump_idx[0]]+0.10, Y_Values[jump_idx[0]]+0.10, r"$1$", size=8, bbox=dict(boxstyle="circle",facecolor='white', alpha=1.0, pad=0.1, linewidth=0.5)
)
# ax.text(X_Values[jump_idx[1]]+0.05, Y_Values[jump_idx[1]]+0.02, r"$1$", size=6, bbox=dict(boxstyle="circle",facecolor='white', alpha=1.0, pad=0.1, linewidth=0.5))
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
# plt.axvline(x_plotValues[1],ymin=0, ymax= 1, color = 'g',alpha=0.5, linestyle = 'dashed')
# plt.axhline(y = 1.90476, color = 'b', linestyle = ':', label='$q_1$')
# plt.axhline(y = 2.08333, color = 'r', linestyle = 'dashed', label='$q_2$')
# plt.legend()
# -- SETUP LEGEND
# ax.legend(prop={'size': 11})
# ax.legend()
# ------------------ SAVE FIGURE
# tikzplotlib.save("TesTout.tex")
# plt.close()
# mpl.rcParams.update(mpl.rcParamsDefault)
# plt.savefig("graph.pdf",
# #This is simple recomendation for publication plots
# dpi=1000,
# # Plot will be occupy a maximum of available space
# bbox_inches='tight',
# )
# plt.savefig("graph.pdf")
# ---- ADD additional scatter:
# ax.scatter(X_Values,Y_Values,s=1,c='black',zorder=4)
# Find transition point
# lastIdx = len(Y_Values)-1
#
# for idx, y in enumerate(Y_Values):
# if idx != lastIdx:
# if abs(y-0) < 0.01 and abs(Y_Values[idx+1] - 0) > 0.05:
# transition_point1 = X_Values[idx+1]
# print('transition point1:', transition_point1 )
# if abs(y-0.5*np.pi) < 0.01 and abs(Y_Values[idx+1] -0.5*np.pi)>0.01:
# transition_point2 = X_Values[idx]
# print('transition point2:', transition_point2 )
# if abs(y-0) > 0.01 and abs(Y_Values[idx+1] - 0) < 0.01:
# transition_point3 = X_Values[idx+1]
# print('transition point3:', transition_point3 )
# Add transition Points
if gamma == '0':
# transition_point1 = 0.13663316582914573
# transition_point2 = 0.20899497487437185
# plt.axvline(transition_point1,ymin=0, ymax= 1, color = 'orange',alpha=0.5, linestyle = 'dashed', linewidth=1)
# plt.axvline(transition_point2,ymin=0, ymax= 1, color = 'orange',alpha=0.5, linestyle = 'dashed', linewidth=1)
plt.axvline(jump_xValues[0],ymin=0, ymax= 1, color = 'orange',alpha=0.5, linestyle = 'dashed', linewidth=1)
# ax.plot(X_Values[X_Values<jump_xValues[0]], Y_Values[X_Values<jump_xValues[0]], 'royalblue')
# ax.plot(X_Values[X_Values>jump_xValues[0]], Y_Values[X_Values>jump_xValues[0]], 'royalblue')
# l1 = ax.scatter(X_Values,Y_Values,s=1, marker='o', edgecolor = 'forestgreen', cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, zorder=4)
l1 = ax.scatter(X_Values,Y_Values,s=0.75, marker='o', edgecolor = 'forestgreen', cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, zorder=4)
# l1 = ax.plot(X_Values,Y_Values,s=1, marker='o', edgecolor = 'forestgreen', cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, zorder=4)
# l1 = ax.plot(X_Values,Y_Values, color='forestgreen', linewidth=1.5, zorder=3, label = 'test')
# plt.axvline(jump_xValues[0],ymin=0, ymax= 1, color = 'orange',alpha=0.5, linestyle = 'dashed', linewidth=1)
#
# ax.plot(X_Values[X_Values<jump_xValues[0]], Y_Values[X_Values<jump_xValues[0]], 'royalblue')
# ax.plot(X_Values[np.where(np.logical_and(X_Values>jump_xValues[0], X_Values<jump_xValues[1])) ], Y_Values[np.where(np.logical_and(X_Values>jump_xValues[0] ,X_Values<jump_xValues[1] ))] ,'royalblue')
# ax.plot(X_Values[X_Values>jump_xValues[1]], Y_Values[X_Values>jump_xValues[1]], 'royalblue')
# # ax.plot(x_plotValues,y_plotValues, 'royalblue')
# ax.scatter([transition_point1, transition_point2],[jump_yValues[0], jump_yValues[1]],s=6, marker='o', cmap=None, norm=None, facecolor = 'black',
# edgecolor = 'black', vmin=None, vmax=None, alpha=None, linewidths=None, zorder=3)
#
# ax.text(transition_point1-0.02 , jump_yValues[0]-0.02, r"$4$", size=6, bbox=dict(boxstyle="circle",facecolor='white', alpha=1.0, pad=0.1, linewidth=0.5)
# )
#
# ax.text(transition_point2+0.012 , jump_yValues[1]+0.02, r"$5$", size=6, bbox=dict(boxstyle="circle",facecolor='white', alpha=1.0, pad=0.1, linewidth=0.5)
# )
else :
plt.axvline(jump_xValues[0],ymin=0, ymax= 1, color = 'orange',alpha=0.5, linestyle = 'dashed', linewidth=1)
# ax.plot(X_Values[X_Values<jump_xValues[0]], Y_Values[X_Values<jump_xValues[0]], 'royalblue')
# ax.plot(X_Values[X_Values>jump_xValues[0]], Y_Values[X_Values>jump_xValues[0]], 'royalblue')
# l1 = ax.scatter(X_Values,Y_Values,s=1, marker='o', edgecolor = 'forestgreen', cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, zorder=4)
l1 = ax.scatter(X_Values,Y_Values,s=0.75, marker='o', edgecolor = 'forestgreen', cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, zorder=4)
# idx1 = find_nearestIdx(X_Values, transition_point1)
# idx2 = find_nearestIdx(X_Values, transition_point2)
# print('idx1', idx1)
# print('idx2', idx2)
# Y_TP1 = Y_Values[idx1]
# Y_TP2 = Y_Values[idx2]
# print('Y_TP1', Y_TP1)
# print('Y_TP2', Y_TP2)
# ax.scatter([transition_point1, transition_point2],[Y_TP1, Y_TP2],s=6, marker='o', cmap=None, norm=None, facecolor = 'black',
# edgecolor = 'black', vmin=None, vmax=None, alpha=None, linewidths=None, zorder=3)
# ax.text(transition_point1-0.02 , Y_TP1-0.02, r"$6$", size=6, bbox=dict(boxstyle="circle",facecolor='white', alpha=1.0, pad=0.1, linewidth=0.5)
# ax.text(transition_point2+0.015 , Y_TP2+0.020, r"$7$", size=6, bbox=dict(boxstyle="circle",facecolor='white', alpha=1.0, pad=0.1, linewidth=0.5))
# ax.scatter(jump_xValues,jump_yValues,s=6, marker='o', cmap=None, norm=None, facecolor = 'black',
# edgecolor = 'black', vmin=None, vmax=None, alpha=None, linewidths=None, zorder=3)
# ax.text(jump_xValues[0]+0.05 , jump_yValues[0]+0.02, r"$6$", size=6, bbox=dict(boxstyle="circle",facecolor='white', alpha=1.0, pad=0.1, linewidth=0.5))
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
else:
# ax.scatter(X_Values,Y_Values,s=1, marker='o', cmap=None, norm=None, facecolor = 'blue',
# edgecolor = 'none', vmin=None, vmax=None, alpha=None, linewidths=None, zorder=3)
# ---------------------------------------------------------------
# l1 = ax.scatter(X_Values,Curvature_alphaNeg5,s=1, marker='o', cmap=None, norm=None, vmin=None, vmax=None, alpha=1.0, linewidths=None, zorder=3)
# l2 = ax.scatter(X_Values,Curvature_alphaNeg1,s=1, marker='o', cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, zorder=3, label=r"$\theta_\rho = -1.0$")
# l3 = ax.scatter(X_Values,Curvature_alphaNeg075,s=1, marker='o', cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, zorder=3)
# l4 = ax.scatter(X_Values,Curvature_alphaNeg05,s=1, marker='o', cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, zorder=3)
# l5 = ax.scatter(X_Values,Curvature_alphaNeg025,s=1, marker='o', cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, zorder=3)
# l6 = ax.scatter(X_Values,Curvature_alphaNeg0125,s=1, marker='o', cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, zorder=3)
# l7 = ax.scatter(X_Values,Curvature_alpha0,s=1, marker='o', edgecolor = 'black',cmap=None, norm=None, vmin=None, vmax=None, alpha=0.75, linewidths=None, zorder=4)
# l8 = ax.scatter(X_Values,Curvature_alpha3,s=1, marker='o', cmap=None, norm=None, vmin=None, vmax=None, alpha=1.0, linewidths=None, zorder=4)
# # l4 = ax.scatter(X_Values,Curvature_alpha3,s=1, marker='o', markerfacecolor='red',markeredgecolor='black',markeredgewidth=2, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.5, linewidths=None, zorder=3)
#
# ax.legend(handles=[l1,l2,l3,l4, l5, l6, l7, l8],
# labels= [r"$\theta_\rho = -5.0$", r"$\theta_\rho = -1.0$",r"$\theta_\rho = -0.75$", r"$\theta_\rho = -0.5$", r"$\theta_\rho = -0.25$", r"$\theta_\rho = -0.125$", r"$\theta_\rho = 0$", r"$\theta_\rho = 3.0$" ],
# loc='upper left',
# bbox_to_anchor=(1,1))
# ---------------------------------------------------------------
# line_labels = [r"$\theta_\rho = -1.0$", r"$\theta_\rho = -0.5$", r"$\theta_\rho = -0.25$", r"$\theta_\rho = 3.0$"]
# ax.set_yticks([0, np.pi/8, np.pi/4, 3*np.pi/8 , np.pi/2, 5*np.pi/8 ])
# labels = ['$0$',r'$\pi/8$', r'$\pi/4$' ,r'$3\pi/8$' , r'$\pi/2$',r'$5\pi/8$']
# ax.set_yticklabels(labels)
# ax.set_yticks([1.570786327, np.pi/2 ])
# labels = [r'$\pi/2-0.0005 $' , r'$\pi/2$']
# ax.set_yticklabels(labels)
# fig.legend([l1, l2, l3, l4], # The line objects
# labels=line_labels, # The labels for each line
# # loc="upper center", # Position of legend
# loc='upperleft', bbox_to_anchor=(1,1),
# borderaxespad=0.15 # Small spacing around legend box
# # title="Legend Title" # Title for the legend
# )
# l1 = ax.plot(X_Values,Y_Values, color='forestgreen', linewidth=1.5, zorder=3, label = 'test')
l1 = ax.scatter(X_Values,Y_Values,s=1, marker='o', edgecolor = 'forestgreen', cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, zorder=4)
# l1 = ax.scatter(X_Values,Y_Values,s=6, marker='o', edgecolor = 'forestgreen', cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, zorder=4)
#compare with interpolant between endpoints
if compare_interpolant:
ax.plot([X_Values[0],X_Values[-1]],[Y_Values[0],Y_Values[-1]])
Outputname = 'Plot-Curvature-Alpha_Gamma' + str(gamma) + '.pdf'
# tikz_save('someplot.tex', figureheight='5cm', figurewidth='9cm')
# tikz_save('fig.tikz',
# figureheight = '\\figureheight',
# figurewidth = '\\figurewidth')
# ----------------------------------------
plt.show()
# #---------------------------------------------------------------