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Plotter_HN
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#!/usr/bin/env python
import os, sys
import numpy as np
import matplotlib.pyplot as plt
#import matplotlib as mpl
#import colormaps as cmaps
import pickle
import scipy.stats as sp
from os.path import join
import errno
#import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from contur.Plotting import *
from contur import Legacy as ctr
#from TesterFunctions import *
def mkdir_p(path):
try:
os.makedirs(path)
except OSError as exc: # Python >2.5
if exc.errno == errno.EEXIST and os.path.isdir(path):
pass
else:
raise
def writeOutput(output, h):
f = open(h, 'w')
for item in output:
f.write(str(item) + "\n")
f.close()
# quick function to return the bound from neutrinoloess double beta decay
#
def existingLimit1(VeN):
ndb=5.0e-08
return (VeN/10000.)**2/ndb
# def existingLimit1(mNH):
# ndb=5.0e-02
# return 100.*np.sqrt(mNH*ndb)
mkdir_p('plots')
for root, dirs, files in os.walk('.',topdown=False):
maps = {}
#for dirs
for filename in files:
#walk and look for .map files
if '.map' in filename and 'svn' not in filename and '.hg' not in filename:
filename = join(root,filename)
with open(filename,'r+b') as f:
maps[filename.strip(".map")] = pickle.load(f)
if not maps:
continue
##use longest member of map to make base map, just in case
data = maps[max(maps, key=lambda x: len(maps[x]))][:]
for i in range(0,len(data)):
data[i]=list(data[i])
data[i][2] = 1.0
data[i][3] = []
data[i][4] = []
data[i][5] = []
data[i][6] = []
i=0
for key in maps:
i+=1
for i in range(0,len(maps[key])):
for j in range(0,len(data)):
if maps[key][i][0] == data[j][0] and maps[key][i][1] == data[j][1]:
data[j][3].extend(maps[key][i][3])
data[j][4].extend(maps[key][i][4])
data[j][5].extend(maps[key][i][5])
data[j][6].extend(maps[key][i][6])
for listelement in data:
tmp = ctr.confLevel(listelement[3],listelement[4],listelement[5],listelement[6])
if (tmp > 0.0):
data[data.index(listelement)][2]= tmp
else:
data[data.index(listelement)][2]= 1.0
temp = []
temp = zip(zip(*data)[0],zip(*data)[1],zip(*data)[2])
temp.sort(key=lambda x: x[0])
writeOutput(temp,"combinedCL_" + root.strip('./') + ".dat")
#find the grid spacings:
dx= ( min(filter(lambda x: x> min(zip(*data)[1]), (zip(*data)[1]))) - min(zip(*data)[1]))/2.0
dy= ( min(filter(lambda x: x> min(zip(*data)[0]), (zip(*data)[0]))) - min(zip(*data)[0]))/2.0
##build the contour grid, NOTE: different to the map grid
contourXaxis=[]
contourYaxis=[]
x=min(zip(*data)[1])
y=min(zip(*data)[0])
#lets make a second grid for contouring
#for x in range(0, min(zip(*data)[1]), dx*2):
while x <= max(zip(*data)[1]):
contourXaxis.append(x)
x= x+ dx*2
while y <= max(zip(*data)[0]):
contourYaxis.append(y)
y= y+ dy*2
#Build the map grid
#this could be redone intelligently I think...
x_grid_min= min(zip(*data)[1])-dx
y_grid_min= (min(zip(*data)[0])-dy)
x_grid_max= max(zip(*data)[1])+dx
y_grid_max= (max(zip(*data)[0])+dy)
yy,xx =np.mgrid[y_grid_min:y_grid_max+dy:2*dy,x_grid_min:x_grid_max+dx:2*dx]
c=np.zeros([len(xx[0,:])-1,len(yy[:,0])-1])
for i in range(0,len(zip(*data)[1])):
xcounter=0
ycounter=0
for xarg2 in xx[1]:
if zip(*data)[1][i] > xarg2:
xcounter+=1
for yarg2 in yy[:,xcounter]:
if zip(*data)[0][i] > yarg2:
ycounter+=1
c[xcounter-1][ycounter-1]=zip(*data)[2][i]
###Plotting functions
fig=plt.figure(figsize=fig_dims)
ax = fig.add_subplot(1,1,1)
##try and pick ticks intelligently
my_locator_x = MaxNLocator((int(max(contourXaxis)) / 500) + 1)
my_locator_y = MaxNLocator((int(max(contourYaxis)) / 500) + 1)
minorLocator_x = MaxNLocator(int(len(contourXaxis)) + 1)
minorLocator_y = MaxNLocator(int(len(contourYaxis)) + 1)
ax.yaxis.set_major_locator(my_locator_x)
ax.xaxis.set_major_locator(my_locator_y)
ax.xaxis.set_minor_locator(minorLocator_y)
ax.yaxis.set_minor_locator(minorLocator_x)
del my_locator_x
del my_locator_y
del minorLocator_x
del minorLocator_y
plt.pcolormesh(yy,xx,c.T,cmap=plt.cm.magma, vmin=0, vmax=1)
plt.axis([y_grid_min, y_grid_max,x_grid_min, x_grid_max])
##axis labels
plt.xlabel(r"$M_{\nu H}$ [GeV]")
plt.ylabel(r"$VeN \times 10^{4}$")
# plt.xlabel(r"$M_{\phi}$ [GeV]")
# plt.ylabel(r"$\Lambda$ [GeV]")
##suppress title
#plt.title("ATLAS + CMS Combined all channels",y=1.01)
##supress colorbar, make in a separate plot
#plt.colorbar().set_label("CL of exclusion")
##save the fig and pad it for better layout
fig.tight_layout(pad=0.1)
plt.savefig("./plots/combinedCL_" + root.strip('./') + ".pdf")
plt.savefig("./plots/combinedCL_" + root.strip('./') + ".png")
############################################################################################################
############################################################################################################
############################################################################################################
############################################################################################################
###brand new plot for contours, dont need to fully reset?
fig=plt.figure(figsize=fig_dims)
ax = fig.add_subplot(1,1,1)
##try and pick ticks intelligently
my_locator_x = MaxNLocator((int(max(contourXaxis)) / 500)+1)
my_locator_y = MaxNLocator((int(max(contourYaxis)) / 500)+1)
minorLocator_x = MaxNLocator(int(len(contourXaxis)) +1)
minorLocator_y = MaxNLocator(int(len(contourYaxis)) +1)
ax.yaxis.set_major_locator(my_locator_x)
ax.xaxis.set_major_locator(my_locator_y)
ax.xaxis.set_minor_locator(minorLocator_y)
ax.yaxis.set_minor_locator(minorLocator_x)
##set axis size, again contour axis different to heatmap axis
#plt.axis([0.0, max(zip(*data)[0]),0.0, max(zip(*data)[1])])
plt.axis([min(zip(*data)[0]), max(zip(*data)[0]),min(zip(*data)[1]), max(zip(*data)[1])])
contourXaxis,contourYaxis=contourYaxis,contourXaxis
##draw a filled contour region for the CL excl
CS=plt.contourf(contourXaxis,contourYaxis,c,levels=[0.90,1.0],label="CL",cmap=plt.cm.magma, alpha =0.8)
##and a black outline
CS2=plt.contour(CS, colors = 'black')
#CS2=plab.contour(contourXaxis,contourYaxis,c.T,levels=[0.95,1.0], colors = 'black')
###plot the existing limit unitarity bound
y=np.array(contourYaxis)
x=existingLimit1(y)
plt.plot(x,y,color='navy')
#x[0] = 10.0
ax.fill_between(x,y,y_grid_max,facecolor='navy',alpha=0.4)
###contour labeler
fmt={}
strs=['95\% CL','CL']
for l , s in zip(CS2.levels, strs):
fmt[l]=s
###Messing with manually placed labels for contours
#manual_loc=[(800,1300),(400,300)]
#plab.clabel(CS2, CS2.levels[::2], inline=1, fmt=fmt, fontsize=10, manual=manual_loc)
#txt = plab.text(700,1100,'95\% CL',backgroundcolor='white')
#plab.clabel(CS2, inline=1, fontsize=10, manual=manual_loc)
# plt.xlabel(r"$M_{\phi}$ [GeV]")
# plt.ylabel(r"$\Lambda$ [GeV]")
plt.xlabel(r"$M_{\nu H}$ [GeV]")
plt.ylabel(r"$VeN \times 10^{4}}$")
artists, labels = CS.legend_elements()
##potential legend info, edit here:
#plt.legend(artists, labels, handleheight=2,loc=2)
#plt.legend(handles=[CS])
#plt.rc('text', usetex=False)
#plt.rc('font', family='sans')
#, fontsize=12
#plab.title("CL Contour",y=1.01)
#legend = ax.legend(loc='upper center', shadow=True)
#plab.colorbar(ticks=[0,0.5,1.0]).set_label("CL of exclusion")
##save the fig and pad it for better layout
fig.tight_layout(pad=0.1)
#fig.savefig('figure.pdf')
plt.savefig("./plots/contur_" + root.strip('./') + ".pdf")
plt.savefig("./plots/contur_" + root.strip('./') + ".png")
print "Plotting maps: " + root.strip('./')
############################################################################################################
############################################################################################################
############################################################################################################
############################################################################################################
##print the colorbar on a separate plot
fig=plt.figure(figsize=[fig_dims[0]*2,0.5])
ax = fig.add_subplot(1,1,1)
import matplotlib as mpl
norm = mpl.colors.Normalize(vmin=0, vmax=1)
cb = mpl.colorbar.ColorbarBase(ax, cmap=plt.cm.magma, orientation='horizontal', norm=norm)
cb.set_label("CL of exclusion")
fig.tight_layout(pad=0.1)
plt.savefig('./plots/colorbarkey.pdf')

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