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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
import sys
from ClassifyMin import *
from HelperFunctions import *
# from CellScript import *
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.cm as cm
from vtk.util import numpy_support
from pyevtk.hl import gridToVTK
import time
import matplotlib.ticker as ticker
import matplotlib as mpl
from matplotlib.ticker import MultipleLocator,FormatStrFormatter,MaxNLocator
import pandas as pd
# from matplotlib import rc
# rc('text', usetex=True) # Use LaTeX font
#
# import seaborn as sns
# sns.set(color_codes=True)
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
InputFile = "/inputs/computeMuGamma.parset"
OutputFile = "/outputs/outputMuGamma.txt"
# --------- 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
# rho1 = 1.0
# alpha = 2.56140350877193 #2.56140350877193, 4.0852130325814535
# beta = 2.0 #5.0
# theta = 1.0/4.0
# theta = 1.0/8.0 # 0.5
# theta = 0.075 # 0.5
# mu1 = 10.0
# rho1 = 1.0
alpha = 10.0
# beta = 40.0
# theta = 0.125
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rho1 = 1.0
# alpha = 2.0
beta = 2.0 #5.0
# theta = 1.0/4.0
theta = 1.0/8.0
#
# mu1 = 10.0
# rho1 = 1.0
# alpha = 10.0
# beta = 2.0 #5.0
# theta = 1.0/8.0
# #
# mu1 = 10.0
# rho1 = 1.0
# # alpha = 10.02021333
# alpha = 10.0
# beta = 2.0
# theta = 0.125
# mu1 = 10.0
# rho1 = 1.0
# # alpha = 10.02021333
# alpha = 9.0
# beta = 2.0
# theta = 0.075
#
# mu1 = 10.0
# rho1 = 1.0
# alpha = 4.0
# beta = 10.0
# theta = 1.0/4.0
# alpha = 10 #1.05263158
# beta = 40.0 #5.0
# # theta = 1.0/4.0
# theta = 1.0/8.0 # 0.5
#
#
# alpha = 2.0
# beta = 2.0 #5.0
# theta = 1/4.0
# rho1 = 1.0
# mu1 = 10.0
# InterestingParameterSet :
# mu1 = 10.0
# rho1 = 1.0
# alpha = 10
# beta = 40.0
# theta = 0.124242
# gamma = 0.75
#set gamma either to 1. '0' 2. 'infinity' or 3. a numerical positive value
# gamma = '0'
# gamma = 'infinity'
# gamma = 0.5
# gamma = 0.25
print('mu1: ', mu1)
print('rho1: ', rho1)
print('alpha: ', alpha)
print('beta: ', beta)
print('theta: ', theta)
# print('gamma:', gamma)
print('----------------------------')
# ----------------------------------------------------------------
gamma_min = 0.01
gamma_max = 3.0
Gamma_Values = np.linspace(gamma_min, gamma_max, num=400) # TODO variable Input Parameters...alpha,beta...
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print('(Input) Gamma_Values:', Gamma_Values)
# mu_gamma = []
# Gamma_Values = '0'
# Get values for mu_Gamma
GetMuGammaVec = np.vectorize(GetMuGamma)
muGammas = GetMuGammaVec(beta,theta,Gamma_Values,mu1,rho1, InputFilePath ,OutputFilePath )
print('muGammas:', muGammas)
q12 = 0.0
q1 = (1.0/6.0)*harmonicMean(mu1, beta, theta)
q2 = (1.0/6.0)*arithmeticMean(mu1, beta, theta)
print('q1: ', q1)
print('q2: ', q2)
b1 = prestrain_b1(rho1, beta, alpha,theta)
b2 = prestrain_b2(rho1, beta, alpha,theta)
q3_star = math.sqrt(q1*q2)
print('q3_star:', q3_star)
# TODO these have to be compatible with input parameters!!!
# compute certain ParameterValues that this makes sense
# b1 = q3_star
# b2 = q1
print('b1: ', b1)
print('b2: ', b2)
print('ratio(left) =', b2/b1)
print('ratio(right) = ', q1/q3_star)
# return classifyMin(q1, q2, q3, q12, b1, b2, print_Cases, print_Output)
# classifyMin_anaVec = np.vectorize(classifyMin_ana)
# G, angles, Types, curvature = classifyMin_anaVec(alpha, beta, theta, muGammas, mu1, rho1)
classifyMin_anaVec = np.vectorize(classifyMin_ana)
G, angles, Types, curvature = classifyMin_anaVec(alpha, beta, theta, muGammas, mu1, rho1)
# _,angles,_,_ = classifyMin_anaVec(alpha, beta, theta, muGammas, mu1, rho1)
print('angles:', angles)
idx = find_nearestIdx(muGammas, q3_star)
print('GammaValue Idx closest to q_3^*', idx)
gammaClose = Gamma_Values[idx]
print('GammaValue(Idx) with mu_gamma closest to q_3^*', gammaClose)
determinantVec = np.vectorize(determinant)
detValues = determinantVec(q1,q2,muGammas,q12)
print('detValues:', detValues)
detZeroidx = find_nearestIdx(detValues, 0)
print('idx where det nearest to zero', idx)
gammaClose = Gamma_Values[detZeroidx]
print('gammaClose:', gammaClose)
# --- Convert to numpy array
Gamma_Values = np.array(Gamma_Values)
angles = np.array(angles)
# ---------------- Create Plot -------------------
# plt.figure()
mpl.rcParams['text.usetex'] = True
mpl.rcParams["font.family"] = "serif"
mpl.rcParams["font.size"] = "9"
width = 6.28 *0.5
height = width / 1.618
fig = plt.figure()
# ax = plt.axes((0.15,0.21 ,0.75,0.75))
ax = plt.axes((0.15,0.21 ,0.8,0.75))
ax.tick_params(axis='x',which='major', direction='out',pad=5)
ax.tick_params(axis='y',which='major', length=3, width=1, direction='out',pad=3)
# ax.xaxis.set_major_locator(MultipleLocator(0.1))
# ax.xaxis.set_minor_locator(MultipleLocator(0.1))
# ax.yaxis.set_major_locator(plt.MultipleLocator(np.pi / 8))
ax.yaxis.set_minor_locator(plt.MultipleLocator(np.pi / 16))
ax.yaxis.set_major_formatter(plt.FuncFormatter(format_func))
ax.grid(True,which='major',axis='both',alpha=0.3)
# ax.set(xlim=(1.760, 1.880), ylim=(-0.1, np.pi/4.0))
ax.set(ylim=(-0.1, np.pi/4.0))
# # plt.title(r'angle$-\mu_\gamma(\gamma)$-Plot')
# # plt.title(r'angle$-\gamma$-Plot')
# plt.plot(Gamma_Values, angles)
# plt.scatter(Gamma_Values, angles)
# plt.plot(muGammas, angles)
# plt.scatter(muGammas, angles)
# # plt.axis([0, 6, 0, 20])
# # plt.axhline(y = 1.90476, color = 'b', linestyle = ':', label='$q_1$')
# # plt.axhline(y = 2.08333, color = 'r', linestyle = 'dashed', label='$q_2$')
# plt.axvline(x = q1, color = 'b', linestyle = ':', label='$q_1$')
# plt.axvline(x = q2, color = 'r', linestyle = 'dashed', label='$q_2$')
# # plt.axvline(x = q3_star, color = 'r', linestyle = 'dashed', label='$\gamma^*$')
# f,ax=plt.subplots(1)
# ax.plot(muGammas, angles)
# ax.scatter(muGammas, angles)
# print('muGammas:', muGammas)
# print('angles:', angles)
ax.plot(muGammas, angles, 'royalblue', zorder=3 )
# ax.scatter(Gamma_Values, angles)
ax.set_xlabel(r"$q_3(\gamma)$")
# ax.set_xlabel(r"$\gamma$")
# ax.set_ylabel(r"angle $\angle$")
ax.set_ylabel(r"angle $\alpha$")
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# plt.xlabel("$q_3$")
# plt.xlabel("$\gamma$")
# plt.ylabel("angle")
# ax.grid(True)
# ax.yaxis.set_major_locator(plt.MultipleLocator(np.pi / 2))
# ax.yaxis.set_minor_locator(plt.MultipleLocator(np.pi / 12))
# 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))
# ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%g $\pi$'))
# ax.yaxis.set_major_locator(ticker.MultipleLocator(base=0.25))
# ax.yaxis.set_major_formatter(ticker.FuncFormatter(
# lambda val,pos: '{:.0g}$\pi$'.format(2*val/np.pi) if val !=0 else '0'))
# ax.yaxis.set_major_locator(ticker.MultipleLocator(base=0.5*np.pi))
# ---------------------------- show pi values ------------------------------------
# ax.axvline(x = q1, color = 'b', linestyle = ':', label='$q_1$')
# ax.axvline(x = q2, color = 'r', linestyle = 'dashed', label='$q_2$')
# ax.legend()
# # ax.set(xlim=(1.750, 1.880), ylim=(0, math.pi/2.0))
# ax.set(xlim=(1.760, 1.880), ylim=(-0.1, np.pi/4.0))
# # ax.set_yticks([0, np.pi/4 ,np.pi/2])
# # labels = ['$0$', r'$\pi/4$', r'$\pi/2$']
# ax.set_yticks([0, np.pi/8, np.pi/4 ])
# labels = ['$0$',r'$\pi/8$', r'$\pi/4$']
# ax.set_yticklabels(labels)
# ---------------------------------------------------------------
# ax.set(xlim=(1.750, 1.880), ylim=(0, math.pi/2.0))
# ax.set(xlim=(1.760, 1.880), ylim=(-0.1, np.pi/4.0))
# ax.set(xlim=(1.760, 1.880), ylim=(-0.1, np.pi/4.0))
# ax.set_yticks([0, np.pi/4 ,np.pi/2])
# labels = ['$0$', r'$\pi/4$', r'$\pi/2$']
# OLD :
ax.set_yticks([0, np.pi/8, np.pi/4 ])
labels = ['$0$',r'$\pi/8$', r'$\pi/4$']
ax.set_yticklabels(labels)
# Plot Gamma Value that is closest to q3_star
# ax.axvline(x = gammaClose, color = 'midnightblue', linestyle = 'dashed', label='$\gamma^*$')
# ax.axvspan(gamma_min, gammaClose, color='red', alpha=0.2)
# ax.axvspan(gammaClose, gamma_max, color='green', alpha=0.2)
ax.axvline(x = q3_star, color = 'midnightblue', linestyle = 'dashed', label='$q_3^*$')
ax.axvline(x = q1, color = 'forestgreen', linestyle = 'dashed', label='$q_1$')
ax.axvline(x = q2, color = 'darkorange', linestyle = 'dashed', label='$q_2$')
# ax.legend(loc='upper center')
ax.legend(loc='upper left', bbox_to_anchor=(0.55, 0.85))
# ax.legend(loc='upper left')
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# plt.xlabel("$q_3(\gamma)$")
# plt.xlabel("$\gamma$")
# plt.ylabel("angle")
# plt.legend(loc='upper center')
fig.set_size_inches(width, height)
fig.savefig('Plot-Angle-q3.pdf')
plt.show()
# plt.figure()
# plt.title(r'angle$-\mu_\gamma(\gamma)$-Plot')
# plt.plot(muGammas, angles)
# plt.scatter(muGammas, angles)
# # plt.axis([0, 6, 0, 20])
# # plt.axhline(y = 1.90476, color = 'b', linestyle = ':', label='$q_1$')
# # plt.axhline(y = 2.08333, color = 'r', linestyle = 'dashed', label='$q_2$')
# plt.axvline(x = 1.90476, color = 'b', linestyle = ':', label='$q_1$')
# plt.axvline(x = 2.08333, color = 'r', linestyle = 'dashed', label='$q_2$')
# plt.xlabel("$\mu_\gamma$")
# plt.ylabel("angle")
# plt.legend()
# plt.show()
#