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 ClassifyMin_New 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
from matplotlib.ticker import MultipleLocator,FormatStrFormatter,MaxNLocator

from mpl_toolkits.axes_grid1.inset_locator import inset_axes

import matplotlib as mpl
import seaborn as sns
import matplotlib.colors as mcolors
import time

from scipy.ndimage.filters import gaussian_filter
# from scipy import ndimage
# print(sys.executable)

# --------------------------------------------------------------------
# START :
# INPUT (Parameters):   alpha, beta, theta, gamma, mu1, rho1
#
# -Option 1 : (Case lambda = 0 => q12 = 0)
#   compute q1,q2,b1,b2 from Formula
#       Option 1.1 :
#           set mu_gamma = 'q1' or 'q2' (extreme regimes: gamma \in {0,\infty})
#       Option 1.2 :
#           compute mu_gamma with 'Compute_MuGamma' (2D problem much faster then Cell-Problem)
# -Option 2 :
#   compute Q_hom & B_eff by running 'Cell-Problem'
#
# -> CLASSIFY ...
#
# OUTPUT: Minimizer G, angle , type, curvature
# -----------------------------------------------------------------------
#
#
# def GetMuGamma(beta,theta,gamma,mu1,rho1, InputFilePath = os.path.dirname(os.getcwd()) +"/inputs/computeMuGamma.parset",
#                 OutputFilePath = os.path.dirname(os.getcwd()) + "/outputs/outputMuGamma.txt" ):
#     # ------------------------------------ get mu_gamma ------------------------------
#     # ---Scenario 1.1: extreme regimes
#     if gamma == '0':
#         print('extreme regime: gamma = 0')
#         mu_gamma = (1.0/6.0)*arithmeticMean(mu1, beta, theta) # = q2
#         print("mu_gamma:", mu_gamma)
#     elif gamma == 'infinity':
#         print('extreme regime: gamma = infinity')
#         mu_gamma = (1.0/6.0)*harmonicMean(mu1, beta, theta)   # = q1
#         print("mu_gamma:", mu_gamma)
#     else:
#         # --- Scenario 1.2:  compute mu_gamma with 'Compute_MuGamma' (much faster than running full Cell-Problem)
#         # print("Run computeMuGamma for Gamma = ", gamma)
#         with open(InputFilePath, 'r') as file:
#             filedata = file.read()
#         filedata = re.sub('(?m)^gamma=.*','gamma='+str(gamma),filedata)
#         # filedata = re.sub('(?m)^alpha=.*','alpha='+str(alpha),filedata)
#         filedata = re.sub('(?m)^beta=.*','beta='+str(beta),filedata)
#         filedata = re.sub('(?m)^theta=.*','theta='+str(theta),filedata)
#         filedata = re.sub('(?m)^mu1=.*','mu1='+str(mu1),filedata)
#         filedata = re.sub('(?m)^rho1=.*','rho1='+str(rho1),filedata)
#         f = open(InputFilePath,'w')
#         f.write(filedata)
#         f.close()
#         # --- Run Cell-Problem
#
#         # Check Time
#         # t = time.time()
#         # subprocess.run(['./build-cmake/src/Cell-Problem', './inputs/cellsolver.parset'],
#         #                                      capture_output=True, text=True)
#         # --- Run Cell-Problem_muGama   -> faster
#         # subprocess.run(['./build-cmake/src/Cell-Problem_muGamma', './inputs/cellsolver.parset'],
#         #                                              capture_output=True, text=True)
#         # --- Run Compute_muGamma (2D Problem much much faster)
#
#         subprocess.run(['./build-cmake/src/Compute_MuGamma', './inputs/computeMuGamma.parset'],
#                                                              capture_output=True, text=True)
#         # print('elapsed time:', time.time() - t)
#
#         #Extract mu_gamma from Output-File                                           TODO: GENERALIZED THIS FOR QUANTITIES OF INTEREST
#         with open(OutputFilePath, 'r') as file:
#             output = file.read()
#         tmp = re.search(r'(?m)^mu_gamma=.*',output).group()                           # Not necessary for Intention of Program t output Minimizer etc.....
#         s = re.findall(r"[-+]?\d*\.\d+|\d+", tmp)
#         mu_gamma = float(s[0])
#         # print("mu_gamma:", mu_gammaValue)
#     # --------------------------------------------------------------------------------------
#     return mu_gamma
#



# ----------- SETUP PATHS
# InputFile  = "/inputs/cellsolver.parset"
# OutputFile = "/outputs/output.txt"
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)


# -------------------------- Input Parameters --------------------
# mu1 = 10.0               # TODO : here must be the same values as in the Parset for computeMuGamma
mu1 = 1.0
rho1 = 1.0
alpha = 2.0
beta = 2.0
# beta = 5.0
theta = 1.0/4.0
#set gamma either to 1. '0' 2. 'infinity' or 3. a numerical positive value
gamma = '0'
# gamma = 'infinity'
# gamma = 0.5
# gamma = 0.25
# gamma = 1.0

# gamma = 5.0

#added
# lambda1 = 10.0
lambda1 = 0.0

#Test:
# rho1 = -1.0



print('---- Input parameters: -----')
print('mu1: ', mu1)
print('rho1: ', rho1)
print('alpha: ', alpha)
print('beta: ', beta)
print('theta: ', theta)
print('gamma:', gamma)

print('lambda1: ', lambda1)
print('----------------------------')
# ----------------------------------------------------------------

#
# gamma_min = 0.5
# gamma_max = 1.0
#
# # gamma_min = 1
# # gamma_max = 1
# Gamma_Values = np.linspace(gamma_min, gamma_max, num=3)
# # #
# # # Gamma_Values = np.linspace(gamma_min, gamma_max, num=13)    # TODO variable Input Parameters...alpha,beta...
# print('(Input) Gamma_Values:', Gamma_Values)

print('type of gamma:', type(gamma))
# # #
Gamma_Values = ['0', 'infinity']
# Gamma_Values = ['infinity']
# Gamma_Values = ['0']
print('(Input) Gamma_Values:', Gamma_Values)

for gamma in Gamma_Values:

    print('Run for gamma = ', gamma)
    print('type of gamma:', type(gamma))
        # muGamma = GetMuGamma(beta,theta,gamma,mu1,rho1,InputFilePath)
        # # muGamma = GetMuGamma(beta,theta,gamma,mu1,rho1)
        # print('Test MuGamma:', muGamma)

        # ------- Options --------
        # print_Cases = True
        # print_Output = True

                            #TODO
    # generalCase = True #Read Output from Cell-Problem instead of using Lemma1.4 (special case)
    generalCase = False

    # make_3D_plot = True
    # make_3D_PhaseDiagram = True
    make_2D_plot = False
    make_2D_PhaseDiagram = False
    make_3D_plot = False
    make_3D_PhaseDiagram = False
    make_2D_plot = True
    make_2D_PhaseDiagram = True
    #

    # --- Define effective quantities: q1, q2 , q3 = mu_gamma, q12 ---
    # q1 = harmonicMean(mu1, beta, theta)
    # q2 = arithmeticMean(mu1, beta, theta)
    # --- Set q12
    # q12 = 0.0  # (analytical example)              # TEST / TODO read from Cell-Output





    # b1 = prestrain_b1(rho1, beta, alpha, theta)
    # b2 = prestrain_b2(rho1, beta, alpha, theta)
    #
    # print('---- Input parameters: -----')
    # print('mu1: ', mu1)
    # print('rho1: ', rho1)
    # print('alpha: ', alpha)
    # print('beta: ', beta)
    # print('theta: ', theta)
    # print("q1: ", q1)
    # print("q2: ", q2)
    # print("mu_gamma: ", mu_gamma)
    # print("q12: ", q12)
    # print("b1: ", b1)
    # print("b2: ", b2)
    # print('----------------------------')
    # print("machine epsilon", sys.float_info.epsilon)

    # G, angle, type, kappa = classifyMin(q1, q2, mu_gamma, q12,  b1, b2, print_Cases, print_Output)
    # Test = f(1,2 ,q1,q2,mu_gamma,q12,b1,b2)
    # print("Test", Test)

    # ---------------------- MAKE PLOT / Write to VTK------------------------------------------------------------------------------

    # SamplePoints_3D = 10 # Number of sample points in each direction
    # SamplePoints_2D = 10 # Number of sample points in each direction
    SamplePoints_3D = 300 # Number of sample points in each direction
    # SamplePoints_3D = 150 # Number of sample points in each direction
    # SamplePoints_3D = 100 # Number of sample points in each direction
    # SamplePoints_3D = 200 # Number of sample points in each direction
    # SamplePoints_3D = 400 # Number of sample points in each direction
    # SamplePoints_2D = 7500 # Number of sample points in each direction
    # SamplePoints_2D = 4000 # 4000 # Number of sample points in each direction
    SamplePoints_2D = 400 # 4000  # Number of sample points in each direction
    # SamplePoints_2D = 500 # 4000    # Number of sample points in each direction
    # SamplePoints_2D = 100 # 4000  # Number of sample points in each direction
    # SamplePoints_2D = 2000 # 4000 # Number of sample points in each direction
    # SamplePoints_2D = 1000   # 4000 # Number of sample points in each direction
    # SamplePoints_2D = 1500 # 4000 # Number of sa

    if make_3D_PhaseDiagram:
        alphas_ = np.linspace(-20, 20, SamplePoints_3D)
        # alphas_ = np.linspace(-10, 10, SamplePoints_3D)

        # betas_  = np.linspace(0.01,40.01,SamplePoints_3D) # Full Range
        # betas_  = np.linspace(0.01,20.01,SamplePoints_3D) # FULL Range



        # betas_  = np.linspace(0.01,0.99,SamplePoints_3D)  # weird part
        betas_  = np.linspace(1.01,40.01,SamplePoints_3D)     #TEST !!!!!  For Beta <1 weird tings happen...
        thetas_ = np.linspace(0.01,0.99,SamplePoints_3D)


        # TEST
        # alphas_ = np.linspace(-2, 2, SamplePoints_3D)
        # betas_  = np.linspace(1.01,10.01,SamplePoints_3D)
        # print('betas:', betas_)

        # TEST :
        # alphas_ = np.linspace(-40, 40, SamplePoints_3D)
        # betas_  = np.linspace(0.01,80.01,SamplePoints_3D) # Full Range

        # print('type of alphas', type(alphas_))
        # print('Test:', type(np.array([mu_gamma])) )
        alphas, betas, thetas = np.meshgrid(alphas_, betas_, thetas_, indexing='ij')
        classifyMin_anaVec = np.vectorize(classifyMin_ana)

        # Get MuGamma values ...
        GetMuGammaVec = np.vectorize(GetMuGamma)
        muGammas = GetMuGammaVec(betas, thetas, gamma, mu1, rho1)
        # Classify Minimizers....
        G, angles, Types, curvature = classifyMin_anaVec(alphas, betas, thetas, muGammas,  mu1, rho1)   # Sets q12 to zero!!!

        # G, angles, Types, curvature = classifyMin_anaVec(alphas, betas, thetas, muGammas,  mu1, rho1, True, True)
        # print('size of G:', G.shape)
        # print('G:', G)

        # Option to print angles
        # print('angles:', angles)


        # Out = classifyMin_anaVec(alphas,betas,thetas)
        # T = Out[2]
        # --- Write to VTK

        GammaString = str(gamma)
        VTKOutputName = "outputs/PhaseDiagram3D" + "Gamma" + GammaString
        gridToVTK(VTKOutputName , alphas, betas, thetas, pointData = {'Type': Types, 'angles': angles, 'curvature': curvature} )
        print('Written to VTK-File:', VTKOutputName )

    if make_2D_PhaseDiagram:
        # alphas_ = np.linspace(-20, 20, SamplePoints_2D)
        # alphas_ = np.linspace(0, 1, SamplePoints_2D)
        thetas_ = np.linspace(0.01,0.99,SamplePoints_2D)
        alphas_ = np.linspace(-5, 5, SamplePoints_2D)
        # alphas_ = np.linspace(-5, 15, SamplePoints_2D)
        # thetas_ = np.linspace(0.05,0.25,SamplePoints_2D)


        # good range:
        # alphas_ = np.linspace(9, 10, SamplePoints_2D)
        # thetas_ = np.linspace(0.075,0.14,SamplePoints_2D)

        # range used:
        # alphas_ = np.linspace(8, 10, SamplePoints_2D)
        # thetas_ = np.linspace(0.05,0.16,SamplePoints_2D)

            # alphas_ = np.linspace(8, 12, SamplePoints_2D)
            # thetas_ = np.linspace(0.05,0.2,SamplePoints_2D)
        # betas_  = np.linspace(0.01,40.01,1)
        #fix to one value:
        betas_ = 2.0;
        # betas_ = 10.0;
        # betas_ = 5.0;
        # betas_ = 0.5;


        #intermediate Values
        alphas_ = np.linspace(-2, 1, SamplePoints_2D)
        # alphas_ = np.linspace(-1.5, 1, SamplePoints_2D)
        # thetas_ = np.linspace(0.4,0.6,SamplePoints_2D)
        # betas_ = 10.0;

        # TEST
        # alphas_ = np.linspace(-8, 8, SamplePoints_2D)
        # thetas_ = np.linspace(0.01,0.99,SamplePoints_2D)
        # betas_ = 1.0; #TEST Problem: disvison by zero if alpha = 9, theta = 0.1 !
        # betas_ = 0.9;
        # betas_ = 0.5;  #TEST!!!
        # alphas, betas, thetas = np.meshgrid(alphas_, betas_, thetas_, indexing='ij')
        betas = betas_
        alphas, thetas = np.meshgrid(alphas_, thetas_, indexing='ij')

        if generalCase:
            classifyMin_matVec = np.vectorize(classifyMin_mat)
            GetCellOutputVec = np.vectorize(GetCellOutput, otypes=[np.ndarray, np.ndarray])
            Q, B = GetCellOutputVec(alphas,betas,thetas,gamma,mu1,rho1,lambda1, InputFilePath ,OutputFilePath )


            # print('type of Q:', type(Q))
            # print('Q:', Q)
            G, angles, Types, curvature = classifyMin_matVec(Q,B)

        else:
            classifyMin_anaVec = np.vectorize(classifyMin_ana)
            GetMuGammaVec = np.vectorize(GetMuGamma)
            # muGammas = GetMuGammaVec(betas,thetas,gamma,mu1,rho1,InputFilePath ,OutputFilePath )
            # G, angles, Types, curvature = classifyMin_anaVec(alphas,betas,thetas, muGammas,  mu1, rho1)    # Sets q12 to zero!!!
            muGammas = GetMuGammaVec(betas,thetas,gamma,mu1,rho1,InputFilePath ,OutputFilePath )

            if gamma == '0':
                G, angles_0, Types, curvature = classifyMin_anaVec(alphas,betas,thetas, muGammas,  mu1, rho1)    # Sets q12 to zero!!!
            if gamma == 'infinity':
                G, angles_inf, Types, curvature = classifyMin_anaVec(alphas,betas,thetas, muGammas,  mu1, rho1)    # Sets q12 to zero!!!

            # print('size of G:', G.shape)
            # print('G:', G)
            # print('Types:', Types)
            # Out = classifyMin_anaVec(alphas,betas,thetas)
            # T = Out[2]
            # --- Write to VTK
            # VTKOutputName = + path + "./PhaseDiagram2DNEW"

        # print('angles:',angles)
        # GammaString = str(gamma)
        # VTKOutputName = "outputs/PhaseDiagram2D" + "Gamma_" + GammaString
        # gridToVTK(VTKOutputName , alphas, betas, thetas, pointData = {'Type': Types, 'angles': angles, 'curvature': curvature} )
        # print('Written to VTK-File:', VTKOutputName )


# --- Make 3D Scatter plot
if(make_3D_plot or make_2D_plot):


    # Styling
    plt.style.use("seaborn-darkgrid")
    plt.style.use("seaborn-whitegrid")
    plt.style.use("seaborn")
    # plt.style.use("seaborn-paper")
    # 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})

    ### ADJUST GRID:
    mpl.rcParams['axes.labelpad'] = 5
    mpl.rcParams['grid.linewidth'] = 0.25
    mpl.rcParams['grid.alpha'] = 0.9 # 0.75
    mpl.rcParams['grid.linestyle'] = '-'
    mpl.rcParams['grid.color']   = 'gray'#'black'

    # mpl.rcParams['axes.axisbelow'] = True
    # mpl.rcParams.update({"axes.grid" : True, "grid.color": "gray"})
    # mpl.rcParams["axes.grid"] = False
    # fig = plt.figure()
    # ax = fig.add_subplot(111, projection='3d')
    # colors = cm.plasma(Types)
    colors = cm.coolwarm(angles_inf)


    width = 6.28
    # height = width / 1.618
    height = width / 2.5
    # height = width
    # fig, ax = plt.subplots()
    fig,ax = plt.subplots(nrows=1,ncols=2,figsize=(width,height), sharey=True)
    # ax = plt.axes((0.15,0.21 ,0.8,0.75))




    # if make_2D_plot: pnt3d=ax.scatter(alphas,thetas,c=Types.flat)
    # if make_3D_plot: pnt3d=ax.scatter(alphas,betas,thetas,c=Types.flat)
    #
    # if make_2D_plot: pnt3d=ax.scatter(alphas,thetas,c=angles.flat)
    # if make_3D_plot: pnt3d=ax.scatter(alphas,betas,thetas,c=angles.flat)


    # pnt=ax.scatter(alphas,thetas,c=angles,cmap='coolwarm')
    # # ax.colorbar()
    # CS = ax.contourf(alphas, thetas, angles,6, cmap=plt.cm.coolwarm, linestyle=dashed)
    # # CS = ax.contour(alphas, thetas, angles,6, colors='k')
    # ax.clabel(CS, inline=True, fontsize=7.5)
    # # ax.set_title('Simplest default with labels')

    # matplotlib.rcParams['contour.linestyles'] = 'dashed'
    cmap=mpl.cm.coolwarm
    # cmap = sns.color_palette("flare", as_cmap=True)
    # cmap = sns.color_palette('species')
    ### GET COLORS :
    deep_colors = sns.color_palette("pastel")
    print('deep_colors.as_hex():',deep_colors.as_hex())


    diverging_colors = sns.color_palette("RdBu", 10)
    print('diverging_colors.as_hex():',diverging_colors.as_hex())

    pal = sns.color_palette("Blues")
    pal = sns.color_palette()
    print(pal.as_hex())

    # flatui = ["#9b59b6", "#3498db", "#95a5a6", "#e74c3c", "#34495e", "#2ecc71"]
    flatui = ["coral","white", "cornflowerblue"]
    flatui = ["cornflowerblue", "coral"]
    flatui = ['#4c72b0','white', '#c44e52']
    flatui = ['#4c72b0','white', '#8de5a1']
    flatui = ['#a1c9f4', '#ffb482','#ff9f9b'] #Test colors
    flatui = ['#4c72b0','white', '#ffb482']
    flatui = ['#4c72b0','white', '#ff9f9b']
    flatui = ['#4c72b0','white', '#ab162a']

    # flatui = ['#4c72b0','white', '#eb9172']
    # flatui = ['#4c72b0','white', '#64b5cd']
    cmap = mpl.colors.ListedColormap(sns.color_palette(flatui).as_hex())
    cmap = mpl.colors.ListedColormap(sns.color_palette(flatui).as_hex())
    cmap = mpl.colors.ListedColormap(sns.color_palette("RdBu_r", 10).as_hex())
    cmap = mpl.colors.ListedColormap(sns.color_palette("coolwarm", 10).as_hex())  #Discrete CMAP
    cmap = sns.color_palette("coolwarm", as_cmap=True)
    # cmap = sns.color_palette("vlag", as_cmap=True)
    # cmap = sns.color_palette("icefire", as_cmap=True)
    # cmap = sns.color_palette("Spectral_r", as_cmap=True)
    # cmap = sns.color_palette("flare_r", as_cmap=True)
    # cmap = sns.diverging_palette(220, 20, as_cmap=True)
    # cmap = sns.diverging_palette(250, 30, l=65, center="dark", as_cmap=True)
    # cmap = mpl.colors.ListedColormap(sns.color_palette().as_hex())
    # cmap = mpl.colors.LinearSegmentedColormap.from_list("", sns.color_palette(flatui).as_hex())

    ### REVERSE COLORMAP :
    # cmap = mpl.colors.ListedColormap(cmap.colors[::-1])
    # cmap = cmap.reverse()

    # cmap = mpl.colors.LinearSegmentedColormap.from_list("", ["blue","violet","red"])
    # cmap = mpl.colors.LinearSegmentedColormap.from_list("", ["cornflowerblue","coral"])

    # plt.imshow(angles_0, extent=[-2, 1, 0, 1],origin='lower',
                      # cmap=cmap, alpha=1.0)
    divnorm=mcolors.TwoSlopeNorm(vmin=angles_0.min(), vcenter=(angles_0.max()+angles_0.min())/2, vmax=angles_0.max())
    ax[0].imshow(angles_0.T, extent=[-2, 1, 0, 1], origin='lower', norm = divnorm,
                      cmap=cmap, alpha=0.9, aspect=2.5)
    # CS =  ax[0].contour(alphas, thetas, angles_0, colors='k',linewidths=(0.75), extent=(-2, 1, 0, 1), zorder=5)
    levels = np.arange(0.0, 1.58, 1)
    # angles_0 = gaussian_filter(angles_0, sigma=0.1)  # Try to smooth contour
    # CS =  ax[0].contour(alphas, thetas, angles_0, levels, colors='k',linewidths=(0.75), extent=(-2, 1, 0, 1), zorder=5)
    # CS =  ax[0].contour(alphas, thetas, angles_0, levels=[0.0], colors='black',linewidths=(0.75), extent=(-2, 1, 0, 1), zorder=5)
    CS =  ax[0].contour(alphas, thetas, angles_0, levels=1, colors='black',linewidths=(0.5), extent=(-2, 1, 0, 1), zorder=5)
    # ax[0].clabel(CS, inline=True, fontsize=8, colors='black')
    # manual_locations = [(-0.75, 0.2),(0.5,0.6)]
    # ax[0].clabel(CS, inline=True, fontsize=8, colors='black', manual=manual_locations)
    # CS_0 = ax[0].contourf(alphas, thetas, angles_0, 10, cmap=plt.cm.coolwarm)
    # CS_02 = ax[0].contour(CS_0, levels=CS_0.levels[::2], colors='black',inline=True, linewidths=(0.5,))



    # ax.clabel(CS2, inline=True, fontsize=9, colors='black')
    # ax.clabel(CS2, inline=True, inline_spacing=3, rightside_up=True, colors='k', fontsize=8)
    # manual_locations = [
    #     (-0.5, 0.3), (-0.7, 0.4), (-0.8, 0.5), (-0.9, 0.6), (-1,0.7)]
    manual_locations = [
        (-0.4, 0.2),(-0.6, 0.3), (-0.7, 0.4), (-0.8, 0.5), (-0.9, 0.6), (-1,0.7)]
    # ax.clabel(CS2, inline=True, fontsize=6, colors='black', manual=manual_locations)
    # ax.clabel(CS2, inline=True, fontsize=6, colors='black')
    # ax.clabel(CS2, CS2.levels, inline=True, fontsize=10)
    # ax.clabel(CS,  fontsize=5, colors='black')
    # cbar = fig.colorbar(CS,label=r'angle $\alpha$', ticks=[0, np.pi/8, np.pi/4, 3*np.pi/8 , np.pi/2 ])
    # cbar = fig.colorbar(CS_0, ticks=[0, np.pi/2 ])
    # cbar.ax.set_yticklabels(['$0$', r'$\pi/2$'])
    # cbar.ax.set_title(r'angle $\alpha$')


    divnorm=mcolors.TwoSlopeNorm(vmin=angles_inf.min(), vcenter=(angles_inf.max()+angles_inf.min())/2, vmax=angles_inf.max())

    Im = ax[1].imshow(angles_inf.T, extent=[-2, 1, 0, 1], origin='lower', norm = divnorm,
                      cmap=cmap, alpha=0.9, aspect=2.5)
    # CS_1 =  ax[1].contour(alphas, thetas, angles_inf, colors='k',linewidths=(0.75), extent=(-2, 1, 0, 1), zorder=5)

    levels = np.arange(0.25, 1.6, 0.25)
    # levels = np.arange(0.1, 1.58, 0.25)

    # angles_inf = gaussian_filter(angles_inf, sigma=2)  # Try to smooth contour

    CS_1 =  ax[1].contour(alphas, thetas, angles_inf,levels, colors='black',linewidths=(0.5), extent=(-2, 1, 0, 1), zorder=5)
    # ax[1].clabel(CS_1, inline=True, fontsize=8, colors='black')
    # manual_locations = [
    #     (-0.5, 0.15), (-0.7, 0.3), (-0.8, 0.45), (-0.9, 0.6), (-1,0.75)]
    manual_locations = [
        (-0.4, 0.1), (-0.6,0.30), (-0.7, 0.45), (-0.8, 0.6), (-0.9, 0.75), (-1,0.90)]
    ax[1].clabel(CS_1, inline=True, fontsize=10, colors='black', manual=manual_locations)


    # CS_1 = ax[1].contourf(alphas, thetas, angles_inf, 10, cmap=plt.cm.coolwarm)
    # # CS = ax.contourf(alphas, thetas, angles, 10, cmap='RdBu')
    # CS_12 = ax[1].contour(CS_1, levels=CS_1.levels[::2], colors='black',inline=True, linewidths=(0.5,))
    # # ax.clabel(CS2, inline=True, fontsize=9, colors='black')
    # # ax.clabel(CS2, inline=True, inline_spacing=3, rightside_up=True, colors='k', fontsize=8)
    # # manual_locations = [
    # #     (-0.5, 0.3), (-0.7, 0.4), (-0.8, 0.5), (-0.9, 0.6), (-1,0.7)]
    # manual_locations = [
    #     (-0.5, 0.15), (-0.7, 0.3), (-0.8, 0.45), (-0.9, 0.6), (-1,0.75)]
    # # ax[1].clabel(CS_12, inline=True, fontsize=10, colors='black', manual=manual_locations)
    # ax[1].clabel(CS_12, inline=True, fontsize=8, colors='black', manual=manual_locations)
    # # ax[1].clabel(CS_12, inline=True, fontsize=8, colors='black')


    axins1 = inset_axes(ax[1],
                       width="5%",  # width = 5% of parent_bbox width
                       height="100%",  # height : 50%
                       loc='lower left',
                       bbox_to_anchor=(1.05, 0., 1, 1),
                       bbox_transform=ax[1].transAxes,
                       borderpad=0,
                       )

    # ax.clabel(CS2, CS2.levels, inline=True, fontsize=10)
    # ax.clabel(CS,  fontsize=5, colors='black')
    # cbar = fig.colorbar(CS,label=r'angle $\alpha$', ticks=[0, np.pi/8, np.pi/4, 3*np.pi/8 , np.pi/2 ])
    # cbar = fig.colorbar(CS_1, ticks=[0, np.pi/8, np.pi/4, 3*np.pi/8 , np.pi/2 ])

    # cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
    cbar = fig.colorbar(Im, cax=axins1, ticks=[0, np.pi/8, np.pi/4, 3*np.pi/8 , np.pi/2 ])
    # cbar = fig.colorbar(CS_1, cax=cbar_ax, shrink=0.2, location='right', ticks=[0, np.pi/8, np.pi/4, 3*np.pi/8 , np.pi/2 ])
    # cbar = fig.colorbar(CS_1,  ax=ax[:], shrink=0.8, location='right', ticks=[0, np.pi/8, np.pi/4, 3*np.pi/8 , np.pi/2 ])

    cbar.ax.set_yticklabels(['$0$',r'$\pi/8$', r'$\pi/4$' ,r'$3\pi/8$' , r'$\pi/2$'])
    # cbar.ax.set_title(r'angle $\alpha$')
    # cbar.ax.set_title(r'angle $\alpha$', fontsize=10)
    cbar.ax.set_title(r'$\alpha$', fontsize=10)
    # cbar=plt.colorbar(pnt3d)
    # cbar.set_label("Values (units)")
    # plt.axvline(x = 8, color = 'b', linestyle = ':', label='$q_1$')
    # plt.axhline(y = 0.083333333, color = 'b', linestyle = ':', label='$q_1$')

    ax[0].set_xlabel(r'$\theta_\rho$',fontsize=10)
    # ax[0].yaxis.set_major_locator(MultipleLocator(0.1))
    # ax[0].xaxis.set_major_locator(MultipleLocator(1))
    ax[0].yaxis.set_major_locator(MultipleLocator(0.1))
    ax[0].xaxis.set_major_locator(MultipleLocator(0.5))
    ax[0].set_ylabel(r'$\theta$   ',fontsize=10, rotation=0, labelpad=10)
    ax[0].tick_params(axis='x', labelsize=10 )
    ax[0].tick_params(axis='y', labelsize=10)

    ax[0].set_title(r"$0<\gamma\ll1$")

    ax[1].set_xlabel(r'$\theta_\rho$',fontsize=10)
    # ax.xaxis.set_minor_locator(MultipleLocator(0.5))
    # ax[1].yaxis.set_major_locator(MultipleLocator(0.1))
    # ax[1].xaxis.set_major_locator(MultipleLocator(1))
    ax[1].yaxis.set_major_locator(MultipleLocator(0.1))
    ax[1].xaxis.set_major_locator(MultipleLocator(0.5))
    ax[1].tick_params(axis='x', labelsize=10 )
    ax[1].tick_params(axis='y', labelsize=10 )

    ax[1].set_title(r"$\gamma \gg 1$")
    # ax.set_ylabel('beta')
    # ax[1].set_ylabel(r'$\theta$   ',fontsize=10, rotation=0)
    # if make_3D_plot: ax.set_zlabel('theta')
    # plt.subplots_adjust(bottom=0.2)
    # plt.subplots_adjust(wspace=0.22, hspace=0.1)
    plt.subplots_adjust(hspace=0.15, wspace=0.1)
    plt.subplots_adjust(bottom=0.2)
    # fig.subplots_adjust(right=0.75)


    # ax[0].grid( linestyle = '-', linewidth = 0.25, alpha=0.5, zorder=1)
    # ax[1].grid( linestyle = '--', linewidth = 0.25, zorder=1)
    # ax[1].set_axisbelow(True)
    # ax[1].yaxis.grid(color='gray', linestyle='dashed')

    # # Hide grid lines
    # ax.grid(False)



    fig.set_size_inches(width, height)
    outputName = '2D-PhaseDiagram-Angle.pdf'
    fig.savefig(outputName, dpi=300, format='pdf')
    # fig.savefig('Plot-Contour.pdf')
    plt.show()
    # plt.savefig('common_labels.png', dpi=300)
    # print('T:', T)
    # print('Type 1 occured here:', np.where(T == 1))
    # print('Type 2 occured here:', np.where(T == 2))


    # print(alphas_)
    # print(betas_)





# ALTERNATIVE
# colors = ("red", "green", "blue")
# groups = ("Type 1", "Type2", "Type3")
#
# # Create plot
# fig = plt.figure()
# ax = fig.add_subplot(1, 1, 1)
#
# for data, color, group in zip(Types, colors, groups):
#     # x, y = data
#     ax.scatter(alphas, thetas, alpha=0.8, c=color, edgecolors='none', label=group)
#
# plt.title('Matplot scatter plot')
# plt.legend(loc=2)
# plt.show()