def make_mean_hist(hists, debug=False):
""" return the hist with bin contents the mean over <hists> of each bin """
binvals = {}
for hist in hists:
if debug:
print ' sub',
for ib in range(0, hist.n_bins + 2):
low_edge = hist.low_edges[ib]
if low_edge not in binvals:
binvals[low_edge] = 0.
binvals[low_edge] += hist.bin_contents[ib]
if debug:
print ' ', low_edge, hist.bin_contents[ib],
if debug:
print ''
binlist = sorted(binvals.keys())
meanhist = Hist(len(binlist) - 2, binlist[1], binlist[-1], binlist[1:-1])
if debug:
print ' mean',
for ib in range(len(binlist)):
meanhist.set_ibin(ib, binvals[binlist[ib]])
if debug:
print ' ', meanhist.low_edges[ib], meanhist.bin_contents[ib],
if debug:
print ''
meanhist.normalize()
return meanhist
def make_mean_hist(hists, debug=False):
""" return the hist with bin contents the mean over <hists> of each bin """
binvals = {}
for hist in hists:
if debug:
print ' sub',
for ib in range(0, hist.n_bins + 2):
low_edge = hist.low_edges[ib]
if low_edge not in binvals:
binvals[low_edge] = 0.
binvals[low_edge] += hist.bin_contents[ib]
if debug:
print ' ', low_edge, hist.bin_contents[ib],
if debug:
print ''
binlist = sorted(binvals.keys())
meanhist = Hist(len(binlist) - 2, binlist[1], binlist[-1], binlist[1 : -1])
if debug:
print ' mean',
for ib in range(len(binlist)):
meanhist.set_ibin(ib, binvals[binlist[ib]])
if debug:
print ' ', meanhist.low_edges[ib], meanhist.bin_contents[ib],
if debug:
print ''
meanhist.normalize()
return meanhist
def make_hist_from_dict_of_counts(values, var_type, hist_label, log='', xmin_force=0.0, xmax_force=0.0, normalize=False, sort=False):
""" Fill a histogram with values from a dictionary (each key will correspond to one bin) """
assert var_type == 'int' or var_type == 'string' # floats should be handled by Hist class in hist.py
if len(values) == 0:
print 'WARNING no values for %s in make_hist' % hist_label
return TH1D(hist_label, '', 1, 0, 1)
bin_labels = sorted(values)
if not sort and var_type == 'string': # for strings, sort so most common value is to left side
bin_labels = sorted(values, key=values.get, reverse=True)
if var_type == 'string':
n_bins = len(values)
else:
n_bins = bin_labels[-1] - bin_labels[0] + 1
hist = None
xbins = [0. for _ in range(n_bins+1)] # NOTE the +1 is 'cause you need the lower edge of the overflow bin
if xmin_force == xmax_force: # if boundaries aren't set explicitly, work out what they should be
if var_type == 'string':
set_bins(bin_labels, n_bins, 'x' in log, xbins, var_type)
hist = Hist(n_bins, xbins[0], xbins[-1], xbins=xbins)
else:
hist = Hist(n_bins, bin_labels[0] - 0.5, bin_labels[-1] + 0.5) # for integers, just go from the first to the last bin label (they're sorted)
else:
hist = Hist(n_bins, xmin_force, xmax_force)
for ival in range(len(values)):
if var_type == 'string':
label = bin_labels[ival]
ibin = ival + 1
else:
label = ''
ibin = hist.find_bin(bin_labels[ival])
hist.set_ibin(ibin, values[bin_labels[ival]], error=math.sqrt(values[bin_labels[ival]]), label=label)
# make sure there's no overflows
if hist.bin_contents[0] != 0.0 or hist.bin_contents[-1] != 0.0:
for ibin in range(hist.n_bins + 2):
print '%d %f %f' % (ibin, hist.low_edges[ibin], hist.bin_contents[ibin])
raise Exception('overflows in ' + hist_label)
if normalize:
hist.normalize()
hist.ytitle = 'freq'
else:
hist.ytitle = 'counts'
roothist = make_hist_from_my_hist_class(hist, hist_label)
return roothist
def make_hist_from_dict_of_counts(values, var_type, hist_label, log='', xmin_force=0.0, xmax_force=0.0, normalize=False, sort=False):
""" Fill a histogram with values from a dictionary (each key will correspond to one bin) """
assert var_type == 'int' or var_type == 'string' # floats should be handled by Hist class in hist.py
if len(values) == 0:
print 'WARNING no values for %s in make_hist' % hist_label
return Hist(1, 0, 1)
bin_labels = sorted(values)
if not sort and var_type == 'string': # for strings, sort so most common value is to left side
bin_labels = sorted(values, key=values.get, reverse=True)
if var_type == 'string':
n_bins = len(values)
else:
n_bins = bin_labels[-1] - bin_labels[0] + 1
hist = None
xbins = [0. for _ in range(n_bins+1)] # NOTE the +1 is 'cause you need the lower edge of the overflow bin
if xmin_force == xmax_force: # if boundaries aren't set explicitly, work out what they should be
if var_type == 'string':
set_bins(bin_labels, n_bins, 'x' in log, xbins, var_type)
hist = Hist(n_bins, xbins[0], xbins[-1], xbins=xbins)
else:
hist = Hist(n_bins, bin_labels[0] - 0.5, bin_labels[-1] + 0.5) # for integers, just go from the first to the last bin label (they're sorted)
else:
hist = Hist(n_bins, xmin_force, xmax_force)
for ival in range(len(values)):
if var_type == 'string':
label = bin_labels[ival]
ibin = ival + 1
else:
label = ''
ibin = hist.find_bin(bin_labels[ival])
hist.set_ibin(ibin, values[bin_labels[ival]], error=math.sqrt(values[bin_labels[ival]]), label=label)
# make sure there's no overflows
if hist.bin_contents[0] != 0.0 or hist.bin_contents[-1] != 0.0:
for ibin in range(hist.n_bins + 2):
print '%d %f %f' % (ibin, hist.low_edges[ibin], hist.bin_contents[ibin])
raise Exception('overflows in ' + hist_label)
if normalize:
hist.normalize()
hist.ytitle = 'freq'
else:
hist.ytitle = 'counts'
return hist
def plot(self, plotdir, only_csv=False, only_overall=False):
if not self.finalized:
self.finalize()
overall_plotdir = plotdir + '/overall'
for gene in self.freqs:
if only_overall:
continue
freqs = self.freqs[gene]
if len(freqs) == 0:
if gene not in glutils.dummy_d_genes.values():
print ' %s no mutefreqer obs for %s' % (utils.color('red', 'warning'), utils.color_gene(gene))
continue
sorted_positions = sorted(freqs.keys())
genehist = Hist(sorted_positions[-1] - sorted_positions[0] + 1, sorted_positions[0] - 0.5, sorted_positions[-1] + 0.5, xtitle='position', ytitle='mut freq', title=gene)
for position in sorted_positions:
hi_diff = abs(freqs[position]['freq'] - freqs[position]['freq_hi_err'])
lo_diff = abs(freqs[position]['freq'] - freqs[position]['freq_lo_err'])
err = 0.5*(hi_diff + lo_diff)
genehist.set_ibin(genehist.find_bin(position), freqs[position]['freq'], error=err)
xline = None
figsize = [7, 4]
if utils.get_region(gene) in utils.conserved_codons[self.glfo['chain']]:
codon = utils.conserved_codons[self.glfo['chain']][utils.get_region(gene)]
xline = self.glfo[codon + '-positions'][gene]
if utils.get_region(gene) == 'v':
figsize[0] *= 3.5
elif utils.get_region(gene) == 'j':
figsize[0] *= 2
plotting.draw_no_root(self.per_gene_mean_rates[gene], plotdir=plotdir + '/per-gene/' + utils.get_region(gene), plotname=utils.sanitize_name(gene), errors=True, write_csv=True, only_csv=only_csv, shift_overflows=True)
# per-position plots:
plotting.draw_no_root(genehist, plotdir=plotdir + '/per-gene-per-position/' + utils.get_region(gene), plotname=utils.sanitize_name(gene), errors=True, write_csv=True, xline=xline, figsize=figsize, only_csv=only_csv, shift_overflows=True)
# # per-position, per-base plots:
# paramutils.make_mutefreq_plot(plotdir + '/' + utils.get_region(gene) + '-per-base', utils.sanitize_name(gene), plotting_info) # needs translation to mpl UPDATE fcn is fixed, but I can't be bothered uncommenting this at the moment
# make mean mute freq hists
for rstr in ['all', 'cdr3'] + utils.regions:
if rstr == 'all':
bounds = (0.0, 0.4)
else:
bounds = (0.0, 0.6 if rstr == 'd' else 0.4)
plotting.draw_no_root(self.mean_rates[rstr], plotname=rstr+'_mean-freq', plotdir=overall_plotdir, stats='mean', bounds=bounds, write_csv=True, only_csv=only_csv, shift_overflows=True)
plotting.draw_no_root(self.mean_n_muted[rstr], plotname=rstr+'_mean-n-muted', plotdir=overall_plotdir, stats='mean', write_csv=True, only_csv=only_csv, shift_overflows=True)
if not only_csv: # write html file and fix permissiions
for substr in self.subplotdirs:
plotting.make_html(plotdir + '/' + substr)
def make_mean_hist(hists):
""" return the hist with bin contents the mean over <hists> of each bin """
binvals = {}
for hist in hists: # I could probably do this with list comprehensions or something, but this way handles different bin bounds
for ib in range(0, hist.n_bins + 2):
low_edge = hist.low_edges[ib]
if low_edge not in binvals:
binvals[low_edge] = []
binvals[low_edge].append(hist.bin_contents[ib])
binlist = sorted(binvals.keys())
meanhist = Hist(len(binlist) - 2, binlist[1], binlist[-1], xbins=binlist[1 :])
for ib in range(len(binlist)):
vlist = binvals[binlist[ib]]
meanhist.set_ibin(ib, numpy.mean(vlist), error=(numpy.std(vlist, ddof=1) / math.sqrt(len(vlist))))
# meanhist.normalize()
return meanhist
def make_mean_hist(hists):
""" return the hist with bin contents the mean over <hists> of each bin """
binvals = {}
for hist in hists: # I could probably do this with list comprehensions or something, but this way handles different bin bounds
for ib in range(0, hist.n_bins + 2):
low_edge = hist.low_edges[ib]
if low_edge not in binvals:
binvals[low_edge] = []
binvals[low_edge].append(hist.bin_contents[ib])
binlist = sorted(binvals.keys())
meanhist = Hist(len(binlist) - 2, binlist[1], binlist[-1], xbins=binlist[1 :])
for ib in range(len(binlist)):
vlist = binvals[binlist[ib]]
meanhist.set_ibin(ib, numpy.mean(vlist), error=(numpy.std(vlist, ddof=1) / math.sqrt(len(vlist))))
# meanhist.normalize()
return meanhist
def plot(self, base_plotdir, cyst_positions=None, tryp_positions=None, only_csv=False):
if not self.finalized:
self.finalize()
plotdir = base_plotdir + '/mute-freqs'
overall_plotdir = plotdir + '/overall'
utils.prep_dir(overall_plotdir, multilings=('*.csv', '*.svg'))
for region in utils.regions:
utils.prep_dir(plotdir + '/' + region, multilings=('*.csv', '*.svg'))
# utils.prep_dir(plotdir + '/' + region + '-per-base/plots', multilings=('*.csv', '*.png'))
if self.tigger:
utils.prep_dir(plotdir + '/tigger', multilings=('*.csv', '*.svg'))
for gene in self.freqs:
freqs = self.freqs[gene]
sorted_positions = sorted(freqs.keys())
genehist = Hist(sorted_positions[-1] - sorted_positions[0] + 1, sorted_positions[0] - 0.5, sorted_positions[-1] + 0.5, xtitle='fixme', ytitle='fixme') #, title=utils.sanitize_name(gene))
for position in sorted_positions:
hi_diff = abs(freqs[position]['freq'] - freqs[position]['freq_hi_err'])
lo_diff = abs(freqs[position]['freq'] - freqs[position]['freq_lo_err'])
err = 0.5*(hi_diff + lo_diff)
genehist.set_ibin(genehist.find_bin(position), freqs[position]['freq'], error=err)
xline = None
figsize = [3, 3]
if utils.get_region(gene) == 'v' and cyst_positions is not None:
xline = cyst_positions[gene]
figsize[0] *= 3.5
elif utils.get_region(gene) == 'j' and tryp_positions is not None:
xline = tryp_positions[gene]
figsize[0] *= 2
plotting.draw_no_root(genehist, plotdir=plotdir + '/' + utils.get_region(gene), plotname=utils.sanitize_name(gene), errors=True, write_csv=True, xline=xline, figsize=figsize, only_csv=only_csv)
# paramutils.make_mutefreq_plot(plotdir + '/' + utils.get_region(gene) + '-per-base', utils.sanitize_name(gene), plotting_info) # needs translation to mpl
# make mean mute freq hists
plotting.draw_no_root(self.mean_rates['all'], plotname='all-mean-freq', plotdir=overall_plotdir, stats='mean', bounds=(0.0, 0.4), write_csv=True, only_csv=only_csv)
for region in utils.regions:
plotting.draw_no_root(self.mean_rates[region], plotname=region+'-mean-freq', plotdir=overall_plotdir, stats='mean', bounds=(0.0, 0.4), write_csv=True, only_csv=only_csv)
if self.tigger:
self.tigger_plot(only_csv)
if not only_csv: # write html file and fix permissiions
plotting.make_html(overall_plotdir)
for region in utils.regions:
plotting.make_html(plotdir + '/' + region, n_columns=1)
def make_fraction_plot(hright, hwrong, plotdir, plotname, xlabel, ylabel, xbounds, only_csv=False, write_csv=False):
if 'fraction_uncertainty' not in sys.modules:
import fraction_uncertainty
# NOTE should really merge this with draw_no_root()
xvals = hright.get_bin_centers() #ignore_overflows=True)
right = hright.bin_contents
wrong = hwrong.bin_contents
yvals = [float(r) / (r + w) if r + w > 0. else 0. for r, w in zip(right, wrong)]
# remove values corresponding to bins with no entries
while yvals.count(0.) > 0:
iv = yvals.index(0.)
xvals.pop(iv)
right.pop(iv)
wrong.pop(iv)
yvals.pop(iv)
tmphilos = [sys.modules['fraction_uncertainty'].err(r, r + w) for r, w in zip(right, wrong)]
yerrs = [err[1] - err[0] for err in tmphilos]
# print '%s' % region
# for iv in range(len(xvals)):
# print ' %5.2f %5.0f / %5.0f = %5.2f +/- %.3f' % (xvals[iv], right[iv], right[iv] + wrong[iv], yvals[iv], yerrs[iv])
if write_csv:
hist_for_csv = Hist(hright.n_bins, hright.xmin, hright.xmax)
bincenters = hright.get_bin_centers()
for ibin in range(hright.n_bins):
bcenter = bincenters[ibin]
if bcenter in xvals: # if we didn't remove it
iy = xvals.index(bcenter)
hist_for_csv.set_ibin(ibin, yvals[iy], error=yerrs[iy])
hist_for_csv.write(plotdir + '/' + plotname + '.csv')
if not only_csv:
fig, ax = mpl_init()
ax.errorbar(xvals, yvals, yerr=yerrs, markersize=10, linewidth=1, marker='.')
if xlabel == 'support':
ax.plot((0, 1), (0, 1), color='black', linestyle='--', linewidth=3) # line with slope 1 and intercept 0
mpl_finish(ax, plotdir, plotname, xlabel=xlabel, ylabel=ylabel, title=plotconfig.plot_titles.get(plotname, plotname), xbounds=xbounds, ybounds=(-0.1, 1.1))
plt.close()
def make_fraction_plot(hright, hwrong, plotdir, plotname, xlabel, ylabel, xbounds, only_csv=False, write_csv=False):
if 'fraction_uncertainty' not in sys.modules:
import fraction_uncertainty
# NOTE should really merge this with draw_no_root()
xvals = hright.get_bin_centers() #ignore_overflows=True)
right = hright.bin_contents
wrong = hwrong.bin_contents
yvals = [float(r) / (r + w) if r + w > 0. else 0. for r, w in zip(right, wrong)]
# remove values corresponding to bins with no entries
while yvals.count(0.) > 0:
iv = yvals.index(0.)
xvals.pop(iv)
right.pop(iv)
wrong.pop(iv)
yvals.pop(iv)
tmphilos = [sys.modules['fraction_uncertainty'].err(r, r + w) for r, w in zip(right, wrong)]
yerrs = [err[1] - err[0] for err in tmphilos]
# print '%s' % region
# for iv in range(len(xvals)):
# print ' %5.2f %5.0f / %5.0f = %5.2f +/- %.3f' % (xvals[iv], right[iv], right[iv] + wrong[iv], yvals[iv], yerrs[iv])
if write_csv:
hist_for_csv = Hist(hright.n_bins, hright.xmin, hright.xmax)
bincenters = hright.get_bin_centers()
for ibin in range(hright.n_bins):
bcenter = bincenters[ibin]
if bcenter in xvals: # if we didn't remove it
iy = xvals.index(bcenter)
hist_for_csv.set_ibin(ibin, yvals[iy], error=yerrs[iy])
hist_for_csv.write(plotdir + '/' + plotname + '.csv')
if not only_csv:
fig, ax = mpl_init()
ax.errorbar(xvals, yvals, yerr=yerrs, markersize=10, linewidth=1, marker='.')
if xlabel == 'support':
ax.plot((0, 1), (0, 1), color='black', linestyle='--', linewidth=3) # line with slope 1 and intercept 0
mpl_finish(ax, plotdir, plotname, xlabel=xlabel, ylabel=ylabel, title=plotconfig.plot_titles.get(plotname, plotname), xbounds=xbounds, ybounds=(-0.1, 1.1))
plt.close()
def plot(self, base_plotdir, cyst_positions=None, tryp_positions=None, only_csv=False):
if not self.finalized:
self.finalize()
plotdir = base_plotdir + '/mute-freqs'
utils.prep_dir(plotdir + '/plots', multilings=('*.csv', '*.svg'))
for region in utils.regions:
utils.prep_dir(plotdir + '/' + region + '/plots', multilings=('*.csv', '*.svg'))
# utils.prep_dir(plotdir + '/' + region + '-per-base/plots', multilings=('*.csv', '*.png'))
for gene in self.counts:
counts, plotting_info = self.counts[gene], self.plotting_info[gene]
sorted_positions = sorted(counts)
genehist = Hist(sorted_positions[-1] - sorted_positions[0] + 1, sorted_positions[0] - 0.5, sorted_positions[-1] + 0.5, xtitle='fixme', ytitle='fixme') #, title=utils.sanitize_name(gene))
for position in sorted_positions:
hi_diff = abs(counts[position]['freq'] - counts[position]['freq_hi_err'])
lo_diff = abs(counts[position]['freq'] - counts[position]['freq_lo_err'])
err = 0.5*(hi_diff + lo_diff)
genehist.set_ibin(genehist.find_bin(position), counts[position]['freq'], error=err)
xline = None
figsize = [3, 3]
if utils.get_region(gene) == 'v' and cyst_positions is not None:
xline = cyst_positions[gene]['cysteine-position']
figsize[0] *= 3.5
elif utils.get_region(gene) == 'j' and tryp_positions is not None:
xline = int(tryp_positions[gene])
figsize[0] *= 2
plotting.draw_no_root(genehist, plotdir=plotdir + '/' + utils.get_region(gene), plotname=utils.sanitize_name(gene), errors=True, write_csv=True, xline=xline, figsize=figsize, only_csv=only_csv)
# paramutils.make_mutefreq_plot(plotdir + '/' + utils.get_region(gene) + '-per-base', utils.sanitize_name(gene), plotting_info) # needs translation to mpl
# make mean mute freq hists
plotting.draw_no_root(self.mean_rates['all'], plotname='all-mean-freq', plotdir=plotdir, stats='mean', bounds=(0.0, 0.4), write_csv=True, only_csv=only_csv)
for region in utils.regions:
plotting.draw_no_root(self.mean_rates[region], plotname=region+'-mean-freq', plotdir=plotdir, stats='mean', bounds=(0.0, 0.4), write_csv=True, only_csv=only_csv)
if not only_csv: # write html file and fix permissiions
check_call(['./bin/makeHtml', plotdir, '3', 'null', 'svg'])
for region in utils.regions:
check_call(['./bin/makeHtml', plotdir + '/' + region, '1', 'null', 'svg'])
# check_call(['./bin/makeHtml', plotdir + '/' + region + '-per-base', '1', 'null', 'png'])
check_call(['./bin/permissify-www', plotdir]) # NOTE this should really permissify starting a few directories higher up
def make_mean_hist(hists, debug=False):
""" return the hist with bin contents the mean over <hists> of each bin """
binvals = {}
all_data = None
for hist in hists:
if debug:
print ' sub',
for ib in range(0, hist.n_bins + 2):
low_edge = hist.low_edges[ib]
if low_edge not in binvals:
binvals[low_edge] = 0.
binvals[low_edge] += hist.bin_contents[ib]
if debug:
print ' ', low_edge, hist.bin_contents[ib],
if all_data is not None and hist.all_data is None:
raise Exception('tried to average hists with and without all_data set')
if hist.all_data is not None:
if all_data is None:
all_data = []
all_data += hist.all_data
if debug:
print ''
binlist = sorted(binvals.keys())
meanhist = Hist(len(binlist) - 2, binlist[1], binlist[-1], binlist[1 : -1])
meanhist.all_data = all_data
if debug:
print ' mean',
for ib in range(len(binlist)):
meanhist.set_ibin(ib, binvals[binlist[ib]])
if debug:
print ' ', meanhist.low_edges[ib], meanhist.bin_contents[ib],
if debug:
print ''
meanhist.normalize()
return meanhist
def plot(self, plotdir, only_csv=False, only_overall=False):
import plotting
if not self.finalized:
self.finalize()
overall_plotdir = plotdir + '/overall'
for gene in self.freqs:
if only_overall:
continue
freqs = self.freqs[gene]
if len(freqs) == 0:
if gene not in glutils.dummy_d_genes.values():
print ' %s no mutefreqer obs for %s' % (utils.color(
'red', 'warning'), utils.color_gene(gene))
continue
sorted_positions = sorted(freqs.keys())
genehist = Hist(sorted_positions[-1] - sorted_positions[0] + 1,
sorted_positions[0] - 0.5,
sorted_positions[-1] + 0.5,
xtitle='position',
ytitle='mut freq',
title=gene)
for position in sorted_positions:
hi_diff = abs(freqs[position]['freq'] -
freqs[position]['freq_hi_err'])
lo_diff = abs(freqs[position]['freq'] -
freqs[position]['freq_lo_err'])
err = 0.5 * (hi_diff + lo_diff)
genehist.set_ibin(genehist.find_bin(position),
freqs[position]['freq'],
error=err)
xline = None
figsize = [7, 4]
if utils.get_region(gene) in utils.conserved_codons[
self.glfo['locus']]:
xline = utils.cdn_pos(self.glfo, utils.get_region(gene), gene)
if utils.get_region(gene) == 'v':
figsize[0] *= 3.5
elif utils.get_region(gene) == 'j':
figsize[0] *= 2
plotting.draw_no_root(self.per_gene_mean_rates[gene],
plotdir=plotdir + '/per-gene/' +
utils.get_region(gene),
plotname=utils.sanitize_name(gene),
errors=True,
write_csv=True,
only_csv=only_csv,
shift_overflows=True)
# per-position plots:
plotting.draw_no_root(genehist,
plotdir=plotdir + '/per-gene-per-position/' +
utils.get_region(gene),
plotname=utils.sanitize_name(gene),
errors=True,
write_csv=True,
xline=xline,
figsize=figsize,
only_csv=only_csv,
shift_overflows=True)
# # per-position, per-base plots:
# paramutils.make_mutefreq_plot(plotdir + '/' + utils.get_region(gene) + '-per-base', utils.sanitize_name(gene), plotting_info) # needs translation to mpl UPDATE fcn is fixed, but I can't be bothered uncommenting this at the moment
# make mean mute freq hists
for rstr in ['all', 'cdr3'] + utils.regions:
if rstr == 'all':
bounds = (0.0, 0.4)
else:
bounds = (0.0, 0.6 if rstr == 'd' else 0.4)
plotting.draw_no_root(self.mean_rates[rstr],
plotname=rstr + '_mean-freq',
plotdir=overall_plotdir,
stats='mean',
bounds=bounds,
write_csv=True,
only_csv=only_csv,
shift_overflows=True)
plotting.draw_no_root(self.mean_n_muted[rstr],
plotname=rstr + '_mean-n-muted',
plotdir=overall_plotdir,
stats='mean',
write_csv=True,
only_csv=only_csv,
shift_overflows=True)
if not only_csv: # write html file and fix permissiions
for substr in self.subplotdirs:
plotting.make_html(plotdir + '/' + substr)
def make_hist_from_dict_of_counts(
values,
var_type,
hist_label,
is_log_x=False,
xmin_force=0.0,
xmax_force=0.0,
sort_by_counts=False,
default_n_bins=30
): # default_n_bins is only used if is_log_x set we're doing auto log bins
""" Fill a histogram with values from a dictionary (each key will correspond to one bin) """
assert var_type == 'int' or var_type == 'string' # floats should be handled by Hist class in hist.py
if len(values) == 0:
print 'WARNING no values for %s in make_hist' % hist_label
return Hist(1, 0, 1)
bin_labels = sorted(
values
) # by default sort by keys in dict (i.e. these aren't usually actually string "labels")
if sort_by_counts: # instead sort by counts
bin_labels = sorted(values, key=values.get, reverse=True)
if var_type == 'string':
n_bins = len(values)
else:
n_bins = bin_labels[-1] - bin_labels[
0] + 1 if not is_log_x else default_n_bins
hist = None
xbins = [
0. for _ in range(n_bins + 1)
] # NOTE the +1 is 'cause you need the lower edge of the overflow bin
if xmin_force == xmax_force: # if boundaries aren't set explicitly, work out what they should be
if var_type == 'string':
set_bins(bin_labels, n_bins, is_log_x, xbins, var_type)
hist = Hist(n_bins, xbins[0], xbins[-1], xbins=xbins)
else:
if is_log_x: # get automatic log-spaced bins
set_bins(bin_labels, n_bins, is_log_x, xbins, var_type)
hist = Hist(n_bins, xbins[0], xbins[-1], xbins=xbins)
else:
hist = Hist(
n_bins, bin_labels[0] - 0.5, bin_labels[-1] + 0.5
) # for integers, just go from the first to the last bin label (they're sorted)
else:
hist = Hist(n_bins, xmin_force, xmax_force)
for ival in range(len(values)):
if var_type == 'string':
label = bin_labels[ival]
ibin = ival + 1
else:
label = ''
ibin = hist.find_bin(bin_labels[ival])
hist.set_ibin(ibin,
values[bin_labels[ival]],
error=math.sqrt(values[bin_labels[ival]]),
label=label)
# make sure there's no overflows
if hist.bin_contents[0] != 0.0 or hist.bin_contents[-1] != 0.0:
for ibin in range(hist.n_bins + 2):
print '%d %f %f' % (ibin, hist.low_edges[ibin],
hist.bin_contents[ibin])
raise Exception('overflows in ' + hist_label)
return hist
