1 files changed, 0 insertions, 127 deletions
diff --git a/bout/plot_full.py b/bout/plot_full.py
deleted file mode 100644
index f2e1919..0000000
--- a/bout/plot_full.py
+++ /dev/null
@@ -1,127 +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, 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)