%pylab inline

import requests

import numpy as np
from matplotlib import pyplot as plt

r=requests.get('http://bicepkeck.org/B2_3yr_rlikelihood_20140314.txt')

header = r.text.split('\n')[:12]

rawLikelihoodData = r.text.split('\n')[13:]

likelihoodScan = []
temp = [i.split('\t') for i in rawLikelihoodData]
temp
for x,y in temp[:-1]:
    likelihoodScan.append([float(x), float(y)])
likelihoodScan = np.array(likelihoodScan)

np.sum(likelihoodScan[:,1])

lmax = likelihoodScan[:,1].max()
rpeak = np.where(likelihoodScan[:,1]==lmax)[0][0]

rminBicep, rmaxBicep = rpeak,rpeak
integral,cutoff=0.,lmax-lmax/1000
while integral<0.68:
    rRange = np.where(likelihoodScan[:,1]>cutoff)
    rminBicep=np.min(rRange)
    rmaxBicep=np.max(rRange)
    integral = np.sum(likelihoodScan[rminBicep:rmaxBicep,1])
    cutoff-=lmax/1000
print '[%f, %f]'%( rminBicep, rmaxBicep)

plt.plot(likelihoodScan[:,0],likelihoodScan[:,1])
plt.plot(likelihoodScan[rminBicep:rmaxBicep,0],likelihoodScan[rminBicep:rmaxBicep,1],'g')
plt.plot([rminBicep/1000.,rminBicep/1000.],[0.,likelihoodScan[rminBicep,1]],'g')
plt.plot([rmaxBicep/1000.,rmaxBicep/1000.],[0.,likelihoodScan[rmaxBicep,1]],'g')
plt.xlabel('r')
plt.ylabel('Likelihood')

minLogL = -2 * np.log(np.max(likelihoodScan[:,1]))
rmin, rmax = 0,0
for r, l in likelihoodScan:
    if -2*np.log(l)-minLogL < 1:
        rmin= r
        break
        
for r, l in likelihoodScan[200:]:
    if -2*np.log(l)-minLogL > 1:
        rmax=r
        break
print '[%f, %f]'%( rmin, rmax)

plt.plot(likelihoodScan[:,0],-2* np.log(likelihoodScan[:,1])-minLogL)
plt.plot([0,0.8],[1.,1.],'r')
plt.plot([rmaxBicep/1000.,rmaxBicep/1000.],[0.,1.],'g')
plt.plot([rminBicep/1000.,rminBicep/1000.],[0.,1.],'g')
plt.plot([rmax,rmax],[0.,1.],'r')
plt.plot([rmin,rmin],[0.,1.],'r')
plt.xlabel('r')
plt.ylabel('-2 ln Likleihood')
plt.ylim([0,2])
plt.xlim([0.1,0.3])

np.sum(likelihoodScan[147:260,1])

significance = np.sqrt(-2*np.log(likelihoodScan[0,1]))

significance