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Commit 9f8437ba authored by Klaus Böhnlein's avatar Klaus Böhnlein
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Add plos for a_star

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src/Plot_aStar_elliptic.py 0 → 100644
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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 *
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 = 10.0
# lambda1 = 10.0
rho1 = 1.0
alpha = 5.0
beta = 10.0
theta = 1.0/4.0
lambda1 = 0.0
gamma = 1.0/4.0
gamma = 'infinity' #Elliptic Setting
# gamma = '0' #Hyperbolic Setting
# gamma = 0.5
print('mu1: ', mu1)
print('rho1: ', rho1)
print('alpha: ', alpha)
print('beta: ', beta)
print('theta: ', theta)
print('gamma:', gamma)
print('----------------------------')
# TODO? : Ask User for Input ...
# function = input("Enter value you want to plot (y-value):\n")
# print(f'You entered {function}')
# parameter = input("Enter Parameter this value depends on (x-value) :\n")
# print(f'You entered {parameter}')
# Add Option to change NumberOfElements used for computation of Cell-Problem
# --- Define Quantity of interest:
# Options: 'q1', 'q2', 'q3', 'q12' ,'q21', 'q31', 'q13' , 'q23', 'q32' , 'b1', 'b2' ,'b3'
# TODO: EXTRA (MInimization Output) 'Minimizer (norm?)' 'angle', 'type', 'curvature'
# yName = 'q12'
# # yName = 'b1'
# yName = 'q3'
# yName = 'angle'
# yName = 'curvature'
yName = 'MinVec'
# --- Define Parameter this function/quantity depends on:
# Options: mu1 ,lambda1, rho1 , alpha, beta, theta, gamma
# xName = 'theta'
# xName = 'gamma'
# xName = 'lambda1'
xName = 'theta'
# --- define Interval of x-va1ues:
# xmin = 0.15
xmin = 0.01
xmax = 0.41
# xmin = 0.18 #Achtung bei manchen werten von theta ist integration in ComputeMuGama/Cell_problem schlecht!
# xmax = 0.41 # Materialfunktion muss von Gitter aufgelöst werden
# müssen vielfache von (1/2^i) sein wobei i integer
# xmin = 0.18 #Achtung bei manchen werten von theta ist integration in ComputeMuGama/Cell_problem schlecht!
# xmax = 0.23
# xmin = 0.01
# xmax = 3.0
numPoints = 70
# numPoints = 50
X_Values = np.linspace(xmin, xmax, num=numPoints)
print(X_Values)
Y_Values = []
for theta in X_Values:
print('Situation of Lemma1.4')
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
# if gamma == '0':
# q3 = q2
# if gamma == 'infinity':
# q3 = q1
q3 = GetMuGamma(beta,theta,gamma,mu1,rho1,InputFilePath ,OutputFilePath)
if yName == 'q1': # TODO: Better use dictionary?...
print('q1 used')
Y_Values.append(q1)
elif yName =='q2':
print('q2 used')
Y_Values.append(q2)
elif yName =='q3':
print('q3 used')
Y_Values.append(q3)
elif yName =='q12':
print('q12 used')
Y_Values.append(q12)
elif yName =='b1':
print('b1 used')
Y_Values.append(b1)
elif yName =='b2':
print('b2 used')
Y_Values.append(b2)
elif yName =='b3':
print('b3 used')
Y_Values.append(b3)
elif yName == 'angle' or yName =='type' or yName =='curvature' or yName =='MinVec':
G, angle, Type, curvature = classifyMin_ana(alpha,beta,theta, q3, mu1, rho1)
if yName =='angle':
print('angle used')
Y_Values.append(angle)
if yName =='type':
print('angle used')
Y_Values.append(type)
if yName =='curvature':
print('angle used')
Y_Values.append(curvature)
if yName =='MinVec':
print('MinVec used')
Y_Values.append(G)
print("(Output) Values of " + yName + ": ", 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)
#
#
#
# # 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:
# if abs(Y_Values[idx]-Y_Values[idx-1]) > 1:
# 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)
Y_arr = np.asarray(Y_Values, dtype=float)
X_Values = np.asarray(X_Values, dtype=float)
print('X_Values:', X_Values)
print('Y_arr:', Y_arr)
# ---------------- Create Plot -------------------
#--- change plot style: SEABORN
# plt.style.use("seaborn-paper")
#--- Adjust gobal matplotlib variables
# mpl.rcParams['pdf.fonttype'] = 42
# mpl.rcParams['ps.fonttype'] = 42
mpl.rcParams['text.usetex'] = True
mpl.rcParams["font.family"] = "serif"
mpl.rcParams["font.size"] = "9"
# plt.rc('font', family='serif', serif='Times')
# plt.rc('font', family='serif')
# # plt.rc('text', usetex=True) #also works...
# plt.rc('xtick', labelsize=8)
# plt.rc('ytick', labelsize=8)
# plt.rc('axes', labelsize=8)
#---- Scale Figure apropriately to fit tex-File Width
# width = 452.9679
# width as measured in inkscape
width = 6.28 *0.5
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((0.1,0.1,0.8,0.8))
# 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))
#---- 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.set_yticks([0, np.pi/8, np.pi/4 ])
#
# 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)
# ax.plot(Y_arr[:,0], Y_arr[:,1] , 'royalblue')
print('Y_arr[:,0]:', Y_arr[:,0])
print('Y_arr[:,1]:', Y_arr[:,1])
ax.plot(Y_arr[:,0], Y_arr[:,1] , 'royalblue', # data
marker='o', # each marker will be rendered as a circle
markersize=2, # marker size
markerfacecolor='orange', # marker facecolor
markeredgecolor='black', # marker edgecolor
markeredgewidth=0.5, # marker edge width
# linestyle='--', # line style will be dash line
linewidth=1,
zorder = 3) # line width
# plt.figure()
#--- Coordinate Axes:
ax.spines.left.set_position('zero')
ax.spines.right.set_color('none')
ax.spines.bottom.set_position('zero')
ax.spines.top.set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.set(xlim=(-25, 15), ylim=(-3, 3))
#-- Decorate the spins
arrow_length = 8 # In points
# X-axis arrow
ax.annotate('x', xy=(1, 0), xycoords=('axes fraction', 'data'),
xytext=(arrow_length, 0), textcoords='offset points',
ha='left', va='center',
arrowprops=dict(arrowstyle='<|-', fc='black'))
# Y-axis arrow
ax.annotate('y', xy=(0, 1), xycoords=('data', 'axes fraction'),
xytext=(0, arrow_length), textcoords='offset points',
ha='center', va='bottom',
arrowprops=dict(arrowstyle='<|-', fc='black'))
# ax.scatter(Y_arr[21,0],Y_arr[21,1], s=6, marker='o', cmap=None, norm=None, facecolor = 'forestgreen',
# edgecolor = 'black', vmin=None, vmax=None, alpha=None, linewidths=None, zorder=5)
# ax.text(Y_arr[21,0]-0.25 , Y_arr[21,1]+0.15, r"$1$", size=4, bbox=dict(boxstyle="circle",facecolor='white', alpha=1.0, pad=0.1, linewidth=0.5))
# ax.text(Y_arr[21,0] , Y_arr[21,1], r"$1$", size=2, bbox=dict(boxstyle="circle",facecolor='white', alpha=1.0, pad=0.1, linewidth=0.5), zorder=5)
ax.scatter(Y_arr[21,0] , Y_arr[21,1], s=4, marker='o', cmap=None, norm=None, facecolor = 'forestgreen',
edgecolor = 'black', vmin=None, vmax=None, alpha=None, linewidths=0.5, zorder=5)
ax.scatter(Y_arr[31,0] , Y_arr[31,1], s=4, marker='o', cmap=None, norm=None, facecolor = 'forestgreen',
edgecolor = 'black', vmin=None, vmax=None, alpha=None, linewidths=0.5, zorder=5)
ax.scatter(Y_arr[40,0] , Y_arr[40,1], s=4, marker='o', cmap=None, norm=None, facecolor = 'forestgreen',
edgecolor = 'black', vmin=None, vmax=None, alpha=None, linewidths=0.5, zorder=5)
ax.annotate( 1 , (Y_arr[21,0] , Y_arr[21,1]), xytext=(Y_arr[21,0]-0.35 , Y_arr[21,1]+1),
bbox=dict(boxstyle="circle",facecolor='white', alpha=1.0, pad=0.1, linewidth=0.5),
arrowprops = dict(arrowstyle="simple",color='blue', linewidth=0.1), fontsize=6)
ax.annotate( 2 , (Y_arr[31,0] , Y_arr[31,1]), xytext=(Y_arr[31,0]+4 , Y_arr[31,1]-0.08),
bbox=dict(boxstyle="circle",facecolor='white', alpha=1.0, pad=0.1, linewidth=0.5),
arrowprops = dict(arrowstyle="simple",color='blue', linewidth=0.1), fontsize=6)
ax.annotate( 3 , (Y_arr[40,0] , Y_arr[40,1]), xytext=(Y_arr[40,0]-0.35 , Y_arr[40,1]+1),
bbox=dict(boxstyle="circle",facecolor='white', alpha=1.0, pad=0.1, linewidth=0.5),
arrowprops = dict(arrowstyle="simple",color='blue', linewidth=0.1), fontsize=6)
# arrowprops = dict(arrowstyle="simple",color='blue', linewidth=0.1, shrink=0.05), fontsize=4)
# 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)
# # --- 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')
# # ax.plot(X_Values[X_Values>=jump_xValues[0]], Y_Values[X_Values>=jump_xValues[0]])
# # ax.plot(X_Values[X_Values<jump_xValues[0]], Y_Values[X_Values<jump_xValues[0]])
# ax.plot(X_Values[X_Values>0.136], Y_Values[X_Values>0.136])
# ax.plot(X_Values[X_Values<0.135], Y_Values[X_Values<0.135])
# ax.scatter(X_Values, Y_Values)
# ax.plot(X_Values, Y_Values)
# plt.plot(x_plotValues, y_plotValues,'.')
# plt.scatter(X_Values, Y_Values, alpha=0.3)
# plt.scatter(X_Values, Y_Values)
# plt.plot(X_Values, Y_Values,'.')
# plt.plot([X_Values[0],X_Values[-1]], [Y_Values[0],Y_Values[-1]])
# plt.axis([0, 6, 0, 20])
# ax.set_xlabel(r"volume fraction $\theta$", size=11)
# ax.set_ylabel(r"angle $\angle$", size=11)
# ax.set_xlabel(r"volume fraction $\theta$")
# ax.set_ylabel(r"angle $\angle$")
# ax.set_ylabel(r"$a^*$")
# plt.ylabel('$\kappa$')
# ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%g $\pi$'))
# ax.yaxis.set_major_locator(ticker.MultipleLocator(base=0.1))
# 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' )
# 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)
# plt.axvline(x,ymin=0, ymax= 1, color = 'orange',alpha=0.5, linestyle = 'dashed', label=r'$\theta_*$')
# 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")
#
# # 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':
# ax.scatter([transition_point1, transition_point2],[np.pi/2,np.pi/2],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 , np.pi/2-0.02, r"$1$", size=6, bbox=dict(boxstyle="circle",facecolor='white', alpha=1.0, pad=0.1, linewidth=0.5)
# )
#
# ax.text(transition_point2+0.012 , np.pi/2-0.02, r"$2$", size=6, bbox=dict(boxstyle="circle",facecolor='white', alpha=1.0, pad=0.1, linewidth=0.5)
# )
# else:
# ax.scatter([transition_point1, transition_point2, transition_point3 ],[np.pi/2,np.pi/2,0 ],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 , np.pi/2-0.02, r"$1$", size=6, bbox=dict(boxstyle="circle",facecolor='white', alpha=1.0, pad=0.1, linewidth=0.5)
# )
#
# ax.text(transition_point2 +0.015 , np.pi/2-0.02, r"$2$", size=6, bbox=dict(boxstyle="circle",facecolor='white', alpha=1.0, pad=0.1, linewidth=0.5)
# )
#
# ax.text(transition_point3 +0.005 , 0+0.06, r"$3$", size=6, bbox=dict(boxstyle="circle",facecolor='white', alpha=1.0, pad=0.1, linewidth=0.5)
# )
fig.set_size_inches(width, height)
fig.savefig('Plot-aStar_elliptic.pdf')
# tikz_save('someplot.tex', figureheight='5cm', figurewidth='9cm')
# tikz_save('fig.tikz',
# figureheight = '\\figureheight',
# figurewidth = '\\figurewidth')
# ----------------------------------------
plt.show()
# #---------------------------------------------------------------
src/Plot_aStar_hyperbolic.py 0 → 100644
+ 770
− 0
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