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 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 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 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_single_joyplot(self,
sorted_clusters,
annotations,
repertoire_size,
plotdir,
plotname,
plot_high_mutation=False,
cluster_indices=None,
title=None,
debug=False):
def gety(minval, maxval, xmax, x):
slope = (maxval - minval) / xmax
return slope * x + minval
def getnmutelist(cluster):
return annotations[':'.join(cluster)]['n_mutations']
colors = ['#006600', '#3399ff', '#ffa500']
# goldenrod '#daa520'
# red '#cc0000',
# dark red '#990012'
# purple '#a821c7'
# grey '#808080'
dpi = 80
xpixels = 450
ypixels = max(400, 10 * len(sorted_clusters))
fig, ax = self.plotting.mpl_init(figsize=(xpixels / dpi,
ypixels / dpi))
min_linewidth = 0.3
max_linewidth = 12
# min_alpha = 0.1
# max_alpha = 1.
# linewidth = 7
alpha = 0.55
ymin, ymax = 9999, 0
iclust_global = 0 # index within this plot
yticks, yticklabels = [], []
high_mutation_clusters = []
biggest_n_mutations = None
if debug:
print ' %s %d x %d %s' % (plotname, xpixels, ypixels,
utils.color('red', 'high mutation')
if plot_high_mutation else '')
print ' size frac yval median mean'
for csize, cluster_group in itertools.groupby(sorted_clusters,
key=lambda c: len(c)):
cluster_group = sorted(list(cluster_group),
key=lambda c: numpy.median(getnmutelist(c)))
n_clusters = len(cluster_group)
repfracstr = self.get_repfracstr(csize, repertoire_size)
for iclust in range(len(
cluster_group)): # index within the clusters of this size
cluster = cluster_group[iclust]
nmutelist = sorted(getnmutelist(cluster))
nmedian = numpy.median(nmutelist)
nmean = numpy.mean(
nmutelist) # maybe should use this instead of the median?
if biggest_n_mutations is None or nmutelist[
-1] > biggest_n_mutations:
biggest_n_mutations = nmutelist[-1]
if nmedian > self.n_max_mutations and not plot_high_mutation:
high_mutation_clusters.append(cluster)
continue
yval = len(sorted_clusters) - iclust_global
if yval < ymin:
ymin = yval
if yval > ymax:
ymax = yval
yticks.append(yval)
# yticklabels.append('%d' % csize)
yticklabels.append(repfracstr)
base_color = colors[iclust_global % len(colors)]
qti_n_muted = {}
if self.args.queries_to_include is not None:
queries_to_include_in_this_cluster = set(cluster) & set(
self.args.queries_to_include)
if len(queries_to_include_in_this_cluster) > 0:
unsorted_nmutelist = getnmutelist(cluster)
qti_n_muted = {
uid: unsorted_nmutelist[cluster.index(uid)]
for uid in queries_to_include_in_this_cluster
} # add a red line for each of 'em (i.e. color that hist bin red)
if plot_high_mutation:
xtext = 1.1
elif float(nmedian) / self.n_max_mutations < 0.5:
xtext = 0.75
else:
xtext = 0.1
ax.text(xtext * self.n_max_mutations,
yval,
' '.join(
sorted(queries_to_include_in_this_cluster,
key=lambda q: qti_n_muted[q])),
color='red',
fontsize=8)
if debug:
print ' %5s %-10s %4.1f %6.1f %6.1f' % (
'%d' % csize if iclust == 0 else '', repfracstr
if iclust == 0 else '', yval, nmedian, nmean)
nbins = nmutelist[-1] - nmutelist[0] + 1
hist = Hist(nbins, nmutelist[0] - 0.5, nmutelist[-1] + 0.5)
for nm in nmutelist:
hist.fill(nm)
assert hist.overflow_contents() == 0. # includes underflows
xmax = max(
hist.bin_contents) # NOTE no relation to <ymax> above
for ibin in range(1, hist.n_bins + 1):
linewidth = gety(min_linewidth, max_linewidth, xmax,
hist.bin_contents[ibin])
color = base_color
# alpha = gety(min_alpha, max_alpha, xmax, hist.bin_contents[ibin])
for nmuted in qti_n_muted.values():
if hist.find_bin(nmuted) == ibin:
color = 'red'
if hist.bin_contents[ibin] == 0.:
color = 'grey'
linewidth = min_linewidth
alpha = 0.4
ax.plot([hist.low_edges[ibin], hist.low_edges[ibin + 1]],
[yval, yval],
color=color,
linewidth=linewidth,
alpha=alpha,
solid_capstyle='butt')
if cluster_indices is not None:
xtext = nmutelist[
-1] if plot_high_mutation else self.n_max_mutations # NOTE reuse of <xtext> (arg)
xwidth = ax.get_xlim()[1] - ax.get_xlim(
)[0] if plot_high_mutation else self.n_max_mutations
ax.text(0.05 * xwidth + xtext,
yval,
str(cluster_indices[':'.join(cluster)]),
color=base_color,
fontsize=6,
alpha=alpha,
fontdict={'weight': 'bold'})
ax.text(0.12 * xwidth + xtext,
yval,
str(csize),
color=base_color,
fontsize=6,
alpha=alpha,
fontdict={'weight': 'bold'})
iclust_global += 1
xbounds = [-0.2, self.n_max_mutations] if not plot_high_mutation else [
self.n_max_mutations, biggest_n_mutations
]
ybounds = [0.95 * ymin, 1.05 * ymax]
n_ticks = 5
if len(yticks) > n_ticks:
yticks = [
yticks[i] for i in range(0, len(yticks),
int(len(yticks) / float(n_ticks - 1)))
]
yticklabels = [
yticklabels[i]
for i in range(0, len(yticklabels),
int(len(yticklabels) / float(n_ticks - 1)))
]
self.plotting.mpl_finish(
ax,
plotdir,
plotname,
xlabel='N mutations',
ylabel='fraction of repertoire',
title=title, # ylabel = 'clonal family size'
xbounds=xbounds,
ybounds=ybounds,
yticks=yticks,
yticklabels=yticklabels,
adjust={'left': 0.25})
return high_mutation_clusters
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