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-rw-r--r--bout/plot.py129
1 files changed, 0 insertions, 129 deletions
diff --git a/bout/plot.py b/bout/plot.py
deleted file mode 100644
index 717cf81..0000000
--- a/bout/plot.py
+++ /dev/null
@@ -1,129 +0,0 @@
-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, 6]
-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'}
-
-for i_dim, dim in enumerate(dimension):
- fig = plt.figure(figsize=(7, 5))
- ax = fig.add_subplot(111)
- yranges = [np.inf, -np.inf]
- legend_handles = []
- xticks = [r'$\mathcal{O}_{12}$', r'$\mathcal{O}_{13}$', r'$\mathcal{O}_{23}$']
- ax.set_xlim(0, len(xticks)+1)
- ax.set_xticklabels([''] + xticks + [''])
- ax.set_xlabel(r'BSM operator angle')
- ylabel = r'${\rm log}_{10} \Lambda' + get_units(dim) + r'$'
- ax.set_ylabel(ylabel)
- for i_frs, frs in enumerate(fix_sfr_mfr):
- print '== DIM{0}'.format(dim)
- print '== FRS = {0}'.format(frs)
- 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 + '/angles_limit/fr_anfr_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
- for i_th in xrange(len(xticks)):
- scale, llhs = array[i_th].T
- min_llh = np.min(llhs)
- delta_llh = 2*(llhs - min_llh)
- print 'scale', scale
- print 'delta_llh', delta_llh
- al = scale[delta_llh < 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(
- (i_th+1-0.1, i_th+1+0.1), (lim, lim), lw=3, color=colour[i_frs], label=label
- )
- ax.add_line(line)
- if i_th == 0: legend_handles.append(line)
- x_offset = i_frs*0.05 - 0.05
- ax.annotate(
- s='', xy=(i_th+1+x_offset, lim), xytext=(i_th+1+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, min_llh)
- try:
- yranges = (myround(yranges[0], up=True), myround(yranges[1], down=True))
- # ax.set_ylim(yranges)
- ax.set_ylim([-30, -20])
- except: pass
-
- ax.legend(handles=legend_handles, prop=dict(size=8), loc='upper right',
- title='dimension {0}'.format(dim))
- 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/lim_DIM{0}.'.format(dim)+of, bbox_inches='tight', dpi=150)