#!/usr/bin/env python
# coding: utf-8


# nazvy proměnných jsou v angličtině
# Ale komenty teda ne)


import matplotlib
import matplotlib.pyplot as plt
matplotlib.rcParams['mathtext.fontset'] = 'stix'
matplotlib.rcParams['font.family'] = 'STIXGeneral'


#from scipy.special import gamma, factorial

#import numpy as np
##import numpy.ma as ma
#import scipy.stats as stats
#
##import scipy.integrate as integrate
##from scipy.spatial import ConvexHull
##from scipy.spatial import cKDTree
#
#from scipy import spatial
#
##import lukiskon as lk
#import g_models
#import f_models
#
#import whitebox
#
##import itertools
#
## patří to asi do user kódu
## pro zobrazení v interaktivním okně (IPython)
## get_ipython().run_line_magic('matplotlib', 'nbagg')
#
#
#
#
## Estimation of pf given a list of red and green points (Voronoi)
##
#
#def show_shape(patch):
#    ax=plt.gca()
#    ax.add_patch(patch)
#    #plt.axis('scaled')
#    plt.show()
#    
#def show_shape(patch, ax):
#    ax.add_patch(patch)
#    #plt.axis('scaled')
#    plt.show()
#
#def draw_voronoi(ax, ns):
#
#    plt=ax
#
#    #%% Draw R-space 
#    r = 4 # tato r-ko v local namespace, vid?
#
#    x = bx.sampled_plan.R[:ns,0]
#    y = bx.sampled_plan.R[:ns,1]
#
#    mask = bx.failsi[:ns]
#    
#
#    plt.plot(x[ mask], y[ mask], 'o', c='r' , markersize=1.0)
#    plt.plot(x[~mask], y[~mask], 'o', c='g' , markersize=1.0)
#
#    #for j, p in enumerate(sampled_plan_R):
#    #    plt.text(p[0]-0.0, p[1]+0.0, j, ha='right') # label the points
#
#    if gm.__class__.__name__ == 'S_ball':
#        xbound = np.linspace(-r, +r, 100)
#        plt.plot(xbound, +np.sqrt(r**2 - xbound**2), '--', c = 'blue', linewidth = 0.5)
#        plt.plot(xbound, -np.sqrt(r**2 - xbound**2), '--', c = 'blue', linewidth = 0.5)
#
#
#    if gm.__class__.__name__ == 'Sin2D':
#        xbound = np.linspace(-r, +r, 100)
#        plt.plot(xbound,- xbound/4 + np.sin(5*xbound) + 6, '--', c = 'blue', linewidth = 0.5)
#
#
#
#    tree = cKDTree(bx.sampled_plan.R[:ns])
#
#    nis = 1500000
#    pf = 0 # inicializace
#    ps = 0
#
#    points_weigths = np.zeros(len(bx))
#    near_neighbors = np.zeros(len(bx))
#    # loop over points (need to integrate red regions)
#
#
#    # set the minimum distance as the standard deviation of IS densisty
#    h_i = [stats.norm(0, 2) for j in range(nvar)] #! dosadit  standard deviation pddle chutí
#
#
#    # use IS sampling density with center equal to the current "red" point
#
#    # select nis = 100 points from IS density and 
#    # if the point has its nearest neighbor any red point from the sampled_plan, 
#
#    h_plan = np.zeros((nis, nvar))
#    for j in range(nvar):
#        h_plan[:, j] = h_i[j].rvs(nis) # realizace váhové funkce náhodné veličiny
#
#    # Rozptyl corrected IS
#    weights_sim = bx.f.new_sample(h_plan).pdf_R / np.prod([h_i[j].pdf(h_plan[:, j]) 
#                    for j in range(nvar)], axis=0) # [f1/h1, ..., fn/hn]
#
#
#
#    dd, ii = tree.query(h_plan)
#
#    Vor_mask = np.empty(len(ii), dtype=bool)# np.where(ii==i, True, False)
#
#    for k in range(len(ii)):
#        #points_weigths[ii[k]] = points_weigths[ii[k]] + weights_sim[k] / nis
#        #near_neighbors[ii[k]] = near_neighbors[ii[k]] + 1
#        Vor_mask[k] = bx.failsi[ii[k]]
#
#    pf = np.sum(weights_sim[Vor_mask])/nis
#    #print(pf)
#
#
#    # rohy. jasně
#    #plt.plot(сэрегъёс_R[:,0], сэрегъёс_R[:,1], 'o', c='b' )
#
#
#    x_grid = h_plan[:,0]
#    y_grid = h_plan[:,1]
#
#    marker_vole = matplotlib.markers.MarkerStyle(marker='.', fillstyle='full')
#    plt.scatter(x_grid[ Vor_mask], y_grid[ Vor_mask], c='xkcd:pale rose' , s=0.125**2, edgecolors='face', marker=marker_vole, alpha=1 ) #'YlOrRd'  cmap='Wistia',
#    plt.scatter(x_grid[~Vor_mask], y_grid[~Vor_mask], c='xkcd:light seafoam green', s=0.125**2, edgecolors='face', marker=marker_vole, alpha=1 ) #'YlOrRd'  cmap='Wistia',
#
#
#    #ax0.plot(x_w[fail_wgP], y_w[fail_wgP], 's', color='xkcd:mango', markersize=1)
#    #    ax0.plot(x_w[fail_wsP], y_w[fail_wsP], 's', color='xkcd:light seafoam green', markersize=1)
#    #    ax0.plot(x_w[fail_w1P], y_w[fail_w1P], 's', color='xkcd:pale rose', markersize=1)
#    #    ax0.plot(x_w[fail_w2P], y_w[fail_w2P], 's', color='xkcd:khaki', markersize=1)
#
#
#    #plt.xlim(-8, 8); plt.ylim(-8, 8)
#    lim = 6.0
#    ax.set_xlim(-lim, lim)
#    ax.set_ylim(-lim, lim)
#    ax.set_aspect('equal')
#    #ax.set_xticks([-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6])
#    #ax.set_yticks([-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6])
#    ax.set_xticks([-6,-4,-2,0,2,4,6])
#    ax.set_yticks([-6,-4,-2,0,2,4,6])
#    #plt.axis('equal')
#
#
#
#
#  
#
#    #pf = np.sum(points_weigths[failsi]*1/near_neighbors[failsi]) 
#    #pf = np.sum(points_weigths[failsi]) #/ nis / len(failure_points_indexes)
#
#    print(pf)
#
#
## In[66]:
#
#
#
#
#from scipy.spatial import cKDTree
#
## workaround :D
#from matplotlib.axes._axes import _log as matplotlib_axes_logger
#matplotlib_axes_logger.setLevel("DEBUG")
#
#fig, ([ax1, ax2], [ax3, ax4]) = plt.subplots(nrows=2, ncols=2, figsize=(7, 6), tight_layout=True)
#
#nsim = bx.nsim
#
#draw_voronoi(ax1, int(nsim/8))
#ax1.set_title('n={0}'.format(int(nsim/8)))
#
#draw_voronoi(ax2, int(nsim/4))
#ax2.set_title('n={0}'.format(int(nsim/4)))
#
#draw_voronoi(ax3, int(nsim/2))
#ax3.set_title('n={0}'.format(int(nsim/2)))
#
#draw_voronoi(ax4, nsim)
#ax4.set_title('n={0}'.format(nsim))
##ax1.set_ylabel('U prostor')
##ax3.set_ylabel('R prostor')
##ax1.set_title('Teselace v U prostoru')
##ax2.set_title('Teselace v R prostoru')
#
#
#ax1.set_xlabel('$x_{1}$')
#ax1.set_ylabel('$x_{2}$')
#ax2.set_xlabel('$x_{1}$')
#ax2.set_ylabel('$x_{2}$')
#ax3.set_xlabel('$x_{1}$')
#ax3.set_ylabel('$x_{2}$')
#ax4.set_xlabel('$x_{1}$')
#ax4.set_ylabel('$x_{2}$')
#
##ax1.set_xlim(-8, 8)
##ax1.set_ylim(-8, 8)
##ax1.set_aspect('equal')
#
## plot unit circles for better orientation
#
#for ax in [ax1, ax2, ax3, ax4]:
#    circle1 = plt.Circle((0, 0), 1, color='k', linewidth = 0.7, fill=False)
#    circle2 = plt.Circle((0, 0), 2, color='k', linewidth = 0.5, fill=False)
#    circle3 = plt.Circle((0, 0), 3, color='k', linewidth = 0.4, fill=False)
#    circle4 = plt.Circle((0, 0), 4, color='k', linewidth = 0.3, fill=False)
#    circle5 = plt.Circle((0, 0), 5, color='k', linewidth = 0.2, fill=False)
#    circle6 = plt.Circle((0, 0), 6, color='k', linewidth = 0.1, fill=False)
#    show_shape(circle1, ax)
#    show_shape(circle2, ax)
#    show_shape(circle3, ax)
##show_shape(circle4)
##show_shape(circle5)
##show_shape(circle6)
#
##plt.axis('square')
#
##fig.savefig("2x2_Deloneho_Zprod.png", dpi=300)
##fig.savefig("2x2.eps")
##fig.savefig("2x2.svg")
##fig.savefig("2x2.pdf")



def subplot3D(sample_box, ax3d, space='R'):
    Fails = getattr(sample_box.failure_samples, space)
    Succeses = getattr(sample_box.success_samples, space)
    xyz = [0,1,2]
    ax3d.scatter(Fails[:,xyz[0]], Fails[:,xyz[1]], Fails[:,xyz[2]], c='r', marker='.')
    ax3d.scatter(Succeses[:,xyz[0]], Succeses[:,xyz[1]], Succeses[:,xyz[2]], c='g', marker='.')
    
    ax3d.set_xlabel('X')
    ax3d.set_ylabel('Y')
    ax3d.set_zlabel('Z')
    


def plot3D(sample_box, space='R', filename=''):
    if not filename:
        filename = 'store/%s_%s_%s'%(sample_box.gm_signature, space, sample_box.nsim)
    fig = plt.figure()
    ax3d = fig.add_subplot(111, projection='3d')
    subplot3D(sample_box, ax3d, space)
    
    try: # jen pro formu zkusíme, vždyť musí funkce formálně něco dělat, žejo?
        fig.savefig(filename + ".png")#, dpi=300)
    except:
        pass # nic se neděje
        
        # vracím plt místo figury, protože nechcem sa trapiť ivent lupem
        # return plt insted of just fig to bypass event loop issues
    return plt


#
## nikdo mi neuvěří, že by tohle postačílo a nebylo by nutné tohlensto furt úpravovat
#def gp_plot(sample_box, space='R', terminal='png', filename=''):
#    if not filename:
#        filename = 'store/%s_%s_%s'%(sample_box.gm_signature, space, sample_box.nsim)
#    if space in ['Rn', 'GK', 'G']:
#        gp.c('set autoscale xy')
#        gp.c('set size square')
#        gp.c('set zeroaxis')
#    elif space in ['P', 'U']:
#        gp.c('set xrange [0:1]')
#        gp.c('set yrange [0:1]')
#        gp.c('set size square')
#        #gp.c('set autoscale')
#        gp.c('unset zeroaxis')
#    else: # R teda?
#        gp.c('set size noratio')
#        gp.c('set autoscale')
#        gp.c('unset zeroaxis')
#        
#    gp.c('set terminal ' + terminal)
#    gp.c('set output "%s.%s"'%(filename, terminal))
#    if os.name == 'posix':
#        gp.c('set decimalsign locale "POSIX"')
#    
#    # legenda
#    gp.c('unset key')
#    
#    # se mi zda, že gp bere data v řadcích
#    f_name = "%s_failure.dat" % (filename)
#    s_name = "%s_success.dat" % (filename)
#    gp.s(getattr(sample_box.failure_samples, space).T, f_name)
#    gp.s(getattr(sample_box.success_samples, space).T, s_name)
#    
#    
#    # rozkaz, který předaváme gnuplotovi
#    gp_plot = 'plot "%s" title "Success points" w p lc rgb "green", "%s" title "Failure points" w p lc rgb "red"' % (s_name, f_name)
#    
#    # Kružničky chcete?
#    # Кружочки ннада?
#    if space in ['Rn', 'G']:
#        gp.c('set parametric')
#        for i in range(5):
#            lw = 2 - i*0.3
#            gp_plot += ', cos(t)*%s,sin(t)*%s notitle w l lc rgb "black" lw %s'%(i+1, i+1, lw)
#            
#    # ne všichni majó definované hranice
#    try:
#        bounds = sample_box.get_2D_boundary()
#    
#        for i in range(len(bounds)):
#            bound = getattr(bounds[i], space).T 
#            gp.s(bound, "%s_boundary_%s.dat"%(filename, i+1))
#            gp_plot += ', "%s_boundary_%s.dat" notitle w l lc rgb "blue"'%(filename, i+1)
#    except AttributeError:
#        pass
#    
#    # Plot!
#    gp.c(gp_plot)
#
#
#
## nikdo mi neuvěří, že by tohle postačílo a nebylo by nutné tohlensto furt úpravovat
#def plot(data2D, terminal='png', filename=''):
#    if not filename:
#        filename = 'store/plot_%s'%(len(data2D[0]))
#        
#    gp.c('set terminal ' + terminal)
#    gp.c('set output "%s.%s"'%(filename, terminal))
#    if os.name == 'posix':
#        gp.c('set decimalsign locale "POSIX"')
#    
#    
#    # se mi zda, že gp bere data v řadcích
#    gp.s(data2D, filename+'.dat')
#    
#    # Plot!
#    # rozkaz, který předaváme gnuplotovi
#    gp.c('plot "%s.dat" ' % (filename))