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import os
import numpy as np
import numpy.ma as ma
import matplotlib as mpl
mpl.use('Agg')
from matplotlib import pyplot as plt
from matplotlib.offsetbox import AnchoredText
from matplotlib import rc
rc('text', usetex=False)
rc('font', **{'family':'serif', 'serif':['Computer Modern'], 'size':18})
fix_sfr_mfr = [
(1, 1, 1, 1, 2, 0),
# (1, 1, 1, 1, 0, 0),
(1, 1, 1, 0, 1, 0),
]
# FR
# dimension = [3, 6]
dimension = [3, 4, 5, 6, 7, 8]
sigma_ratio = ['0.01']
energy_dependance = 'spectral'
spectral_index = -2
binning = [1e4, 1e7, 5]
fix_mixing = 'False'
fix_mixing_almost = 'False'
scale_region = "1E10"
# Likelihood
likelihood = 'golemfit'
confidence = 2.71 # 90% for 1DOF
outformat = ['png']
def gen_identifier(measured_ratio, source_ratio, dimension, sigma_ratio=0.01):
mr = np.array(measured_ratio) / float(np.sum(measured_ratio))
sr = np.array(source_ratio) / float(np.sum(source_ratio))
si = sigma_ratio
out = '_{0:03d}_{1:03d}_{2:03d}_{3:04d}_sfr_{4:03d}_{5:03d}_{6:03d}_DIM{7}_single_scale'.format(
int(mr[0]*100), int(mr[1]*100), int(mr[2]*100), int(si*1000),
int(sr[0]*100), int(sr[1]*100), int(sr[2]*100), dimension
)
return out
def get_units(dimension):
if dimension == 3: return r' / GeV'
if dimension == 4: return r''
if dimension == 5: return r' / GeV^{-1}'
if dimension == 6: return r' / GeV^{-2}'
if dimension == 7: return r' / GeV^{-3}'
if dimension == 8: return r' / GeV^{-4}'
def myround(x, base=5, up=False, down=False):
if up == down and up is True: assert 0
if up: return int(base * np.round(float(x)/base-0.5))
elif down: return int(base * np.round(float(x)/base+0.5))
else: int(base * np.round(float(x)/base))
colour = {0:'red', 1:'blue', 2:'green', 3:'purple', 4:'orange', 5:'black'}
fig = plt.figure(figsize=(7, 5))
ax = fig.add_subplot(111)
colour = {0:'red', 1:'blue', 2:'green', 3:'purple', 4:'orange', 5:'black'}
yranges = [np.inf, -np.inf]
legend_handles = []
ax.set_xlim(dimension[0]-1, dimension[-1]+1)
xticks = [''] + range(dimension[0], dimension[-1]+1) + ['']
ax.set_xticklabels(xticks)
ax.set_xlabel(r'BSM operator dimension ' + r'$d$')
ax.set_ylabel(r'${\rm log}_{10} \Lambda / GeV^{-d+4}$')
for i_dim, dim in enumerate(dimension):
for i_frs, frs in enumerate(fix_sfr_mfr):
outchain_head = '/data/user/smandalia/flavour_ratio/data/{0}/DIM{1}/SI_{2}/fix_ifr/0_01/'.format(likelihood, dim, spectral_index)
infile = outchain_head + '/bayes_factor/fr_fr_evidence' + gen_identifier(frs[:3], frs[-3:], dim) + '.npy'
try:
array = np.load(infile)
except IOError:
print 'failed to open {0}'.format(infile)
continue
print 'array', array
print 'array', array.shape
scale, llhs = array.T
print 'scale min', scale[np.argmin(llhs)]
null = llhs[np.argmin(llhs)]
# null = llhs[0]
# TODO(shivesh): negative or not?
reduced_ev = 2*(llhs - null)
print 'reduced_ev', reduced_ev
al = scale[reduced_ev < confidence]
if len(al) > 0:
label = '[{0}, {1}, {2}]'.format(frs[3], frs[4], frs[5])
lim = al[0]
print 'frs, dim, lim = ', frs, dim, lim
if lim < yranges[0]: yranges[0] = lim
if lim > yranges[1]: yranges[1] = lim+4
line = plt.Line2D(
(dim-0.1, dim+0.1), (lim, lim), lw=3, color=colour[i_frs], label=label
)
ax.add_line(line)
if i_dim == 0: legend_handles.append(line)
x_offset = i_frs*0.05 - 0.05
ax.annotate(
s='', xy=(dim+x_offset, lim), xytext=(dim+x_offset, lim+3),
arrowprops={'arrowstyle': '<-', 'lw': 1.2, 'color':colour[i_frs]}
)
else:
print 'No points for DIM {0} FRS {1} NULL {2}!'.format(dim, frs, null)
# print 'scales, reduced_ev', np.dstack([scale.data, reduced_ev.data])
yranges = (myround(yranges[0], up=True), myround(yranges[1], down=True))
ax.set_ylim(yranges)
ax.legend(handles=legend_handles, prop=dict(size=8), loc='upper right')
for ymaj in ax.yaxis.get_majorticklocs():
ax.axhline(y=ymaj, ls=':', color='gray', alpha=0.4, linewidth=1)
for xmaj in ax.xaxis.get_majorticklocs():
ax.axvline(x=xmaj, ls=':', color='gray', alpha=0.4, linewidth=1)
for of in outformat:
fig.savefig('../images/freq/full_corr.'+of, bbox_inches='tight', dpi=150)
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