Wed May 23 16:23:12 CDT 2018

3 files changed, 0 insertions, 449 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)
diff --git a/bout/plot_corr.py b/bout/plot_corr.py
deleted file mode 100644
index a75fe8a..0000000
--- a/bout/plot_corr.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import os
-
-import numpy as np
-import numpy.ma as ma
-
-import scipy.interpolate as interpolate
-
-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 = 4.61 # 90% for 2DOF
-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'}
-
-labels = [r'$sin^2(2\mathcal{O}_{12})$', r'$sin^2(2\mathcal{O}_{13})$', r'$sin^2(2\mathcal{O}_{23})$']
-for i_dim, dim in enumerate(dimension):
-    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_corr/fr_co_evidence'+ gen_identifier(frs[:3], frs[-3:], dim) + '.npy'
-        # infile = '../mnrun/fr_co_evidence_033_033_033_0010_sfr_033_066_000_DIM6_single_scale.npy'
-        try:
-            array = ma.masked_invalid(np.load(infile))
-        except IOError:
-            print 'failed to open {0}'.format(infile)
-            continue
-        print 'array', array
-        print 'array', array.shape
-        for i_scen in xrange(len(labels)):
-            d = array[i_scen].reshape(len(array[i_scen])**2, 3)
-            fig = plt.figure(figsize=(7, 5))
-            ax = fig.add_subplot(111)
-            xranges = [np.inf, -np.inf]
-            legend_handles = []
-            ax.set_ylim(0, 1)
-            ax.set_ylabel(labels[i_scen])
-            xlabel = r'${\rm log}_{10} \Lambda' + get_units(dim) + r'$'
-            ax.set_xlabel(xlabel)
-
-            x = d[:,0]
-            y = d[:,1]
-            z = d[:,2]
-
-            print 'x', x
-            print 'y', y
-            print 'z', z
-            null_idx = np.argmin(z)
-            null = z[null_idx]
-            print 'null = {0}, for scale = {1}'.format(null, x[null_idx])
-            z = 2*(z - null)
-            print 'scale', x
-            print 'delta_llh', z
-
-            # x_ = np.linspace(np.min(x), np.max(x), 30)
-            # y_ = np.linspace(np.min(y), np.max(y), 30)
-            # z_ = interpolate.gridddata((x, y), z, (x_[None,:], y_[:,None]), method='nearest')
-
-            data = np.array([x, y, z, np.ones(x.shape)]).T
-            print 'data', data
-            data_clean = []
-            for d in data:
-                if not np.any(np.isnan(d)): data_clean.append(d)
-            data = np.vstack(data_clean)
-            sort_column = 3
-            data_sorted = data[data[:,sort_column].argsort()]
-            uni, c = np.unique(data[:,sort_column], return_counts=True)
-            print uni, c
-            print len(uni)
-            print np.unique(c)
-            
-            n = len(uni)
-            assert len(np.unique(c)) == 1
-            c = c[0]
-            col_array = []
-            for col in data_sorted.T:
-                col_array.append(col.reshape(n, c))
-            col_array = np.stack(col_array)
-            sep_arrays = []
-            for x_i in xrange(n):
-                sep_arrays.append(col_array[:,x_i])
-            
-            print len(sep_arrays)
-            sep_arrays = sep_arrays[0][:3]
-            print sep_arrays
-
-            llh_90_percent = (sep_arrays[2] < confidence)
-            data_90_percent = sep_arrays.T[llh_90_percent].T
-            print 'data_90_percent', data_90_percent
-
-            ax.tick_params(axis='x', labelsize=11)
-            ax.tick_params(axis='y', labelsize=11)
-
-            mini, maxi = np.min(x), np.max(x)
-            ax.set_xlim((mini, maxi))
-            ax.set_ylim(0, 1)
-            ax.grid(b=False)
-            
-            x_v = data_90_percent[0].round(decimals=4)
-            y_v = data_90_percent[1].round(decimals=4)
-            uniques = np.unique(x_v)
-            print 'uniques', uniques
-            if len(uniques) == 1: continue
-            bw = np.min(np.diff(uniques))
-            print 'bw', bw
-            print np.diff(uniques)
-            uni_x_split = np.split(uniques, np.where(np.diff(uniques) > bw*(1e20))[0] + 1)
-            print 'len(uni_x_split)', len(uni_x_split)
-            for uni_x in uni_x_split:
-                p_x_l, p_y_l = [], []
-                p_x_u, p_y_u = [], []
-                for uni in uni_x:
-                    idxes = np.where(x_v == uni)[0]
-                    ymin, ymax = 1, 0
-                    for idx in idxes:
-                        if y_v[idx] < ymin: ymin = y_v[idx]
-                        if y_v[idx] > ymax: ymax = y_v[idx]
-                    p_x_l.append(uni)
-                    p_y_l.append(ymin)
-                    p_x_u.append(uni)
-                    p_y_u.append(ymax)
-                p_x_l, p_y_l = np.array(p_x_l, dtype=np.float64), np.array(p_y_l, dtype=np.float64)
-                p_x_u, p_y_u = np.array(list(reversed(p_x_u)), dtype=np.float64), np.array(list(reversed(p_y_u)), dtype=np.float64)
-                p_x = np.hstack([p_x_l, p_x_u])
-                p_y = np.hstack([p_y_l, p_y_u])
-                p_x = np.r_[p_x, p_x[0]]
-                p_y = np.r_[p_y, p_y[0]]
-                print 'p_x', p_x
-                print 'p_y', p_y
-                try:
-                    tck, u = interpolate.splprep([p_x, p_y], s=1e-5, per=True)
-                    xi, yi = interpolate.splev(np.linspace(0, 1, 1000), tck)
-                except:
-                    xi, yi = p_x, p_y
-                ax.fill(xi, yi, 'r', edgecolor='r', linewidth=1)
-            
-            for of in outformat:
-                plt.savefig('../images/freq/lim_corr_DIM{0}_AN{1}_FRS{2}'.format(dim, i_scen, i_frs)+of, bbox_inches='tight', dpi=150)
-
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)