from yt.mods import *

from matplotlib import rc
rc('text', usetex=True)
rc('font', family='serif')

import pylab as pl
#from scipy import *
#import scipy.signal
from h5py import File
#import pyfits
import os
import string
from matplotlib import pyplot 
from matplotlib import pyplot as plt
from matplotlib import pylab
from matplotlib import colors
#from pyreadcol import *
import numpy as np
from matplotlib.ticker import NullFormatter

n=41

f=open('RD%04i_escape_fraction.txt'%n,'r')
lines=f.readlines()
f.close()

Mass = []
M1600 = []
for i in xrange(1,len(lines)):
 if glob.glob('../../Redshift15/SED/halo%05i.out'%(i-1)) ==  []:continue
 f=open('../../Redshift15/SED/halo%05i.out'%(i-1),'r')
 lines1=f.readlines()
 f.close()
 WL=[]
 MAB=[]
 for line in lines1:
    WL.append(float(line.split()[0]))
    MAB.append(float(line.split()[4]))
 f.close()
 WL=np.array(WL);MAB=np.array(MAB)
 M,=(MAB[WL==1595.0]+MAB[WL==1605.0])/2
 M1600.append(M)
 Mass.append(float(lines[i].split()[2]))

Mass=np.log10(Mass)
M1600=np.array(M1600)
x_max = 10 # int(Mass.max()+1)
x_min = 6.5

y_max = -8
y_min = -20

x_bins = 70    #60
y_bins = 24  #200

filename = 'virialmass_M1600'
fields = ["HaloNumber"]
fields_label = ['Number']
fields_min = [0]

#prof2d = BinnedProfile2D(sphere, d_bins, "Density", d_min, d_max, True,beta_bins, "beta", beta_min, beta_max, True)
data,xvals,yvals = pylab.histogram2d(Mass,M1600,bins=[x_bins,y_bins],range=[[x_min,x_max],[y_min,y_max]])


for field, cbarlabel,field_min in zip(fields,fields_label,fields_min):
    ylabel = r'$M_{1600}$'
    xlabel = r'$log_{10}(M_{vir}/M_{\odot})$'

    dy = yvals[1]-yvals[0]
    dx = xvals[1]-xvals[0]
    xvals1 = xvals[0:-1]+dx/2.0
    yvals1 = yvals[0:-1]+dy/2.0

    #data /= dlogx*dlogy

    intmin = 0
    intmax = data[0:,0:].max()

    fig = pl.figure(figsize=[8,6])
    ax = fig.add_subplot(111)

    mynorm = colors.Normalize([intmin, intmax])
    im = ax.contourf(xvals1, yvals1, data.T, np.linspace(intmin,intmax,3), cmap=pl.cm.binary, norm=mynorm)

    #if xvals.size == data.T.shape[0]:
    #    xvals = np.append(xvals, 2.*10.**np.log10(xvals[-1]) - 10.**np.log10(xvals[-2]))
    #if yvals.size == data.T.shape[0]:
    #    yvals = np.append(yvals, 2.*10.**np.log10(yvals[-1]) - 10.**np.log10(yvals[-2]))

    fontsize=24

    X, Y = np.meshgrid(xvals,yvals)
    im = ax.pcolor(X,Y,data.T,cmap=pl.cm.binary, norm=mynorm)
    #ax.set_xscale('log')
    #ax.set_yscale('log')
    cbar = fig.colorbar(im, fraction=0.05, format = '%i', ticks=np.linspace(intmin,intmax, (intmax-intmin)+1), pad=0.01, shrink=0.9)
    cbar.set_label('%s' % cbarlabel)

    pl.xlim(xvals.min(), xvals.max())
    #pl.ylim(yvals.min(), yvals.max())
    pl.ylim(yvals.max(), yvals.min())

    pl.xlabel('%s' % xlabel)
    pl.ylabel('%s'% ylabel)
    pl.savefig('RD%04i_%s_%s.png'% (n,filename,field),format='png',dpi=160)
    fig.clf()