def _distribution_summary_plot(self, phylum_rel_dists, taxa_for_dist_inference, plot_file):
"""Summary plot showing the distribution of taxa at each taxonomic rank under different rootings.
Parameters
----------
phylum_rel_dists: phylum_rel_dists[phylum][rank_index][taxon] -> relative divergences
Relative divergence of taxon at each rank for different phylum-level rootings.
taxa_for_dist_inference : iterable
Taxa to considered when inferring distributions.
plot_file : str
Desired name of output plot.
"""
self.fig.clear()
self.fig.set_size_inches(12, 6)
ax = self.fig.add_subplot(111)
# determine median relative distance for each taxa
medians_for_taxa = self.taxa_median_rd(phylum_rel_dists)
# create percentile and classification boundary lines
percentiles = {}
for i, rank in enumerate(sorted(medians_for_taxa.keys())):
v = [np_median(dists) for taxon, dists in medians_for_taxa[rank].items() if taxon in taxa_for_dist_inference]
if not v:
# not taxa at rank suitable for creating classification boundaries
continue
p10, p50, p90 = np_percentile(v, [10, 50, 90])
ax.plot((p10, p10), (i, i + 0.25), c=(0.3, 0.3, 0.3), lw=2, zorder=2)
ax.plot((p50, p50), (i, i + 0.5), c=(0.3, 0.3, 0.3), lw=2, zorder=2)
ax.plot((p90, p90), (i, i + 0.25), c=(0.3, 0.3, 0.3), lw=2, zorder=2)
for b in [-0.2, -0.1, 0.1, 0.2]:
boundary = p50 + b
if boundary < 1.0 and boundary > 0.0:
if abs(b) == 0.1:
c = (1.0, 0.65, 0.0) # orange
else:
c = (1.0, 0.0, 0.0)
ax.plot((boundary, boundary), (i, i + 0.5), c=c, lw=2, zorder=2)
percentiles[i] = [p10, p50, p90]
# create scatter plot and results table
x = []
y = []
c = []
labels = []
rank_labels = []
for i, rank in enumerate(sorted(medians_for_taxa.keys())):
rank_label = Taxonomy.rank_labels[rank]
rank_labels.append(rank_label + ' (%d)' % len(medians_for_taxa[rank]))
mono = []
poly = []
no_inference = []
for clade_label, dists in medians_for_taxa[rank].items():
md = np_median(dists)
x.append(md)
y.append(i)
labels.append(clade_label)
if is_integer(clade_label.split('^')[-1]):
# taxa with a numerical suffix after a caret indicate
# polyphyletic groups when decorated with tax2tree
c.append((1.0, 0.0, 0.0))
poly.append(md)
elif clade_label not in taxa_for_dist_inference:
c.append((0.3, 0.3, 0.3))
no_inference.append(md)
else:
c.append((0.0, 0.0, 1.0))
mono.append(md)
# histogram for each rank
n = 0
if len(mono) > 0:
mono = np_array(mono)
no_inference = np_array(no_inference)
poly = np_array(poly)
binwidth = 0.025
bins = np_arange(0, 1.0 + binwidth, binwidth)
mono_max_count = max(np_histogram(mono, bins=bins)[0])
mono_weights = np_ones_like(mono) * (1.0 / mono_max_count)
w = float(len(mono)) / (len(mono) + len(poly) + len(no_inference))
n, b, p = ax.hist(mono, bins=bins,
color=(0.0, 0.0, 1.0),
alpha=0.25,
weights=0.9 * w * mono_weights,
bottom=i,
lw=0,
zorder=0)
if len(no_inference) > 0:
no_inference_max_count = max(np_histogram(no_inference, bins=bins)[0])
no_inference_weights = np_ones_like(no_inference) * (1.0 / no_inference_max_count)
ax.hist(no_inference, bins=bins,
color=(0.3, 0.3, 0.3),
alpha=0.25,
weights=0.9 * (1.0 - w) * no_inference_weights,
bottom=i + n,
lw=0,
zorder=0)
if len(poly) > 0:
poly_max_count = max(np_histogram(poly, bins=bins)[0])
poly_weights = np_ones_like(poly) * (1.0 / poly_max_count)
ax.hist(poly, bins=bins,
color=(1.0, 0.0, 0.0),
alpha=0.25,
weights=0.9 * (1.0 - w) * poly_weights,
bottom=i + n,
lw=0,
zorder=0)
scatter = ax.scatter(x, y, alpha=0.5, s=48, c=c, zorder=1)
# set plot elements
ax.grid(color=(0.8, 0.8, 0.8), linestyle='dashed')
ax.set_xlabel('relative distance')
ax.set_xticks(np_arange(0, 1.05, 0.1))
ax.set_xlim([-0.01, 1.01])
ax.set_ylabel('rank (no. taxa)')
ax.set_yticks(range(0, len(medians_for_taxa)))
ax.set_ylim([-0.2, len(medians_for_taxa) - 0.01])
ax.set_yticklabels(rank_labels)
self.prettify(ax)
# make plot interactive
mpld3.plugins.clear(self.fig)
mpld3.plugins.connect(self.fig, mpld3.plugins.PointLabelTooltip(scatter, labels=labels))
mpld3.plugins.connect(self.fig, mpld3.plugins.MousePosition(fontsize=10))
mpld3.save_html(self.fig, plot_file[0:plot_file.rfind('.')] + '.html')
self.fig.tight_layout(pad=1)
self.fig.savefig(plot_file, dpi=self.dpi)
def _distribution_summary_plot(self, phylum_rel_dists, taxa_for_dist_inference, plot_file):
"""Summary plot showing the distribution of taxa at each taxonomic rank under different rootings.
Parameters
----------
phylum_rel_dists: phylum_rel_dists[phylum][rank_index][taxon] -> relative divergences
Relative divergence of taxon at each rank for different phylum-level rootings.
taxa_for_dist_inference : iterable
Taxa to considered when inferring distributions.
plot_file : str
Desired name of output plot.
"""
self.fig.clear()
self.fig.set_size_inches(12, 6)
ax = self.fig.add_subplot(111)
# determine median relative distance for each taxa
medians_for_taxa = self.taxa_median_rd(phylum_rel_dists)
# create percentile and classification boundary lines
percentiles = {}
for i, rank in enumerate(sorted(medians_for_taxa.keys())):
v = [np_median(dists) for taxon, dists in medians_for_taxa[rank].iteritems() if taxon in taxa_for_dist_inference]
p10, p50, p90 = np_percentile(v, [10, 50, 90])
ax.plot((p10, p10), (i, i + 0.25), c=(0.3, 0.3, 0.3), lw=2, zorder=2)
ax.plot((p50, p50), (i, i + 0.5), c=(0.3, 0.3, 0.3), lw=2, zorder=2)
ax.plot((p90, p90), (i, i + 0.25), c=(0.3, 0.3, 0.3), lw=2, zorder=2)
for b in [-0.2, -0.1, 0.1, 0.2]:
boundary = p50 + b
if boundary < 1.0 and boundary > 0.0:
if abs(b) == 0.1:
c = (1.0, 0.65, 0.0) # orange
else:
c = (1.0, 0.0, 0.0)
ax.plot((boundary, boundary), (i, i + 0.5), c=c, lw=2, zorder=2)
percentiles[i] = [p10, p50, p90]
# create scatter plot and results table
x = []
y = []
c = []
labels = []
rank_labels = []
for i, rank in enumerate(sorted(medians_for_taxa.keys())):
rank_label = Taxonomy.rank_labels[rank]
rank_labels.append(rank_label + ' (%d)' % len(medians_for_taxa[rank]))
mono = []
poly = []
no_inference = []
for clade_label, dists in medians_for_taxa[rank].iteritems():
md = np_median(dists)
x.append(md)
y.append(i)
labels.append(clade_label)
if is_integer(clade_label.split('^')[-1]):
# taxa with a numerical suffix after a caret indicate
# polyphyletic groups when decorated with tax2tree
c.append((1.0, 0.0, 0.0))
poly.append(md)
elif clade_label not in taxa_for_dist_inference:
c.append((0.3, 0.3, 0.3))
no_inference.append(md)
else:
c.append((0.0, 0.0, 1.0))
mono.append(md)
# histogram for each rank
mono = np_array(mono)
no_inference = np_array(no_inference)
poly = np_array(poly)
binwidth = 0.025
bins = np_arange(0, 1.0 + binwidth, binwidth)
mono_max_count = max(np_histogram(mono, bins=bins)[0])
mono_weights = np_ones_like(mono) * (1.0 / mono_max_count)
w = float(len(mono)) / (len(mono) + len(poly) + len(no_inference))
n, b, p = ax.hist(mono, bins=bins,
color=(0.0, 0.0, 1.0),
alpha=0.25,
weights=0.9 * w * mono_weights,
bottom=i,
lw=0,
zorder=0)
if len(no_inference) > 0:
no_inference_max_count = max(np_histogram(no_inference, bins=bins)[0])
no_inference_weights = np_ones_like(no_inference) * (1.0 / no_inference_max_count)
ax.hist(no_inference, bins=bins,
color=(0.3, 0.3, 0.3),
alpha=0.25,
weights=0.9 * (1.0 - w) * no_inference_weights,
bottom=i + n,
lw=0,
zorder=0)
if len(poly) > 0:
poly_max_count = max(np_histogram(poly, bins=bins)[0])
poly_weights = np_ones_like(poly) * (1.0 / poly_max_count)
ax.hist(poly, bins=bins,
color=(1.0, 0.0, 0.0),
alpha=0.25,
weights=0.9 * (1.0 - w) * poly_weights,
bottom=i + n,
lw=0,
zorder=0)
scatter = ax.scatter(x, y, alpha=0.5, s=48, c=c, zorder=1)
# set plot elements
ax.grid(color=(0.8, 0.8, 0.8), linestyle='dashed')
ax.set_xlabel('relative distance')
ax.set_xticks(np_arange(0, 1.05, 0.1))
ax.set_xlim([-0.01, 1.01])
ax.set_ylabel('rank (no. taxa)')
ax.set_yticks(xrange(0, len(medians_for_taxa)))
ax.set_ylim([-0.2, len(medians_for_taxa) - 0.01])
ax.set_yticklabels(rank_labels)
self.prettify(ax)
# make plot interactive
mpld3.plugins.clear(self.fig)
mpld3.plugins.connect(self.fig, mpld3.plugins.PointLabelTooltip(scatter, labels=labels))
mpld3.plugins.connect(self.fig, mpld3.plugins.MousePosition(fontsize=10))
mpld3.save_html(self.fig, plot_file[0:plot_file.rfind('.')] + '.html')
self.fig.tight_layout(pad=1)
self.fig.savefig(plot_file, dpi=self.dpi)
def _distribution_plot(self, rel_dists, taxa_for_dist_inference, distribution_table, plot_file):
"""Create plot showing the distribution of taxa at each taxonomic rank.
Parameters
----------
rel_dists: d[rank_index][taxon] -> relative divergence
Relative divergence of taxa at each rank.
taxa_for_dist_inference : iterable
Taxa to considered when inferring distributions.
distribution_table : str
Desired name of output table with distribution information.
plot_file : str
Desired name of output plot.
"""
self.fig.clear()
self.fig.set_size_inches(12, 6)
ax = self.fig.add_subplot(111)
# create normal distributions
for i, rank in enumerate(sorted(rel_dists.keys())):
v = [dist for taxa, dist in rel_dists[rank].items() if taxa in taxa_for_dist_inference]
if len(v) < 2:
continue
u = np_mean(v)
rv = norm(loc=u, scale=np_std(v))
x = np_linspace(rv.ppf(0.001), rv.ppf(0.999), 1000)
nd = rv.pdf(x)
# ax.plot(x, 0.75 * (nd / max(nd)) + i, 'b-', alpha=0.6, zorder=2)
# ax.plot((u, u), (i, i + 0.5), 'b-', zorder=2)
# create percentile and classifciation boundary lines
percentiles = {}
for i, rank in enumerate(sorted(rel_dists.keys())):
v = [dist for taxa, dist in rel_dists[rank].items() if taxa in taxa_for_dist_inference]
if len(v) == 0:
continue
p10, p50, p90 = np_percentile(v, [10, 50, 90])
ax.plot((p10, p10), (i, i + 0.25), c=(0.3, 0.3, 0.3), lw=2, zorder=2)
ax.plot((p50, p50), (i, i + 0.5), c=(0.3, 0.3, 0.3), lw=2, zorder=2)
ax.plot((p90, p90), (i, i + 0.25), c=(0.3, 0.3, 0.3), lw=2, zorder=2)
for b in [-0.2, -0.1, 0.1, 0.2]:
boundary = p50 + b
if boundary < 1.0 and boundary > 0.0:
if abs(b) == 0.1:
c = (1.0, 0.65, 0.0) # orange
else:
c = (1.0, 0.0, 0.0)
ax.plot((boundary, boundary), (i, i + 0.5), c=c, lw=2, zorder=2)
percentiles[i] = [p10, p50, p90]
# create scatter plot and results table
fout = open(distribution_table, 'w')
fout.write('Taxa\tRelative Distance\tP10\tMedian\tP90\tPercentile outlier\n')
x = []
y = []
c = []
labels = []
rank_labels = []
for i, rank in enumerate(sorted(rel_dists.keys())):
rank_label = Taxonomy.rank_labels[rank]
rank_labels.append(rank_label + ' (%d)' % len(rel_dists[rank]))
mono = []
poly = []
no_inference = []
for clade_label, dist in rel_dists[rank].items():
x.append(dist)
y.append(i)
labels.append(clade_label)
if is_integer(clade_label.split('^')[-1]):
# taxa with a numerical suffix after a caret indicate
# polyphyletic groups when decorated with tax2tree
c.append((1.0, 0.0, 0.0))
poly.append(dist)
elif clade_label not in taxa_for_dist_inference:
c.append((0.3, 0.3, 0.3))
no_inference.append(dist)
else:
c.append((0.0, 0.0, 1.0))
mono.append(dist)
# report results
v = [clade_label, dist]
if i in percentiles:
p10, p50, p90 = percentiles[i]
percentile_outlier = not (dist >= p10 and dist <= p90)
v += percentiles[i] + [str(percentile_outlier)]
else:
percentile_outlier = 'Insufficent data to calculate percentiles'
v += [-1,-1,-1] + [str(percentile_outlier)]
fout.write('%s\t%.2f\t%.2f\t%.2f\t%.2f\t%s\n' % tuple(v))
# histogram for each rank
mono = np_array(mono)
no_inference = np_array(no_inference)
poly = np_array(poly)
binwidth = 0.025
bins = np_arange(0, 1.0 + binwidth, binwidth)
d = len(mono) + len(poly) + len(no_inference)
if d == 0:
break
w = float(len(mono)) / d
n = 0
if len(mono) > 0:
mono_max_count = max(np_histogram(mono, bins=bins)[0])
mono_weights = np_ones_like(mono) * (1.0 / mono_max_count)
n, b, p = ax.hist(mono, bins=bins,
color=(0.0, 0.0, 1.0),
alpha=0.25,
weights=0.9 * w * mono_weights,
bottom=i,
lw=0,
zorder=0)
if len(no_inference) > 0:
no_inference_max_count = max(np_histogram(no_inference, bins=bins)[0])
no_inference_weights = np_ones_like(no_inference) * (1.0 / no_inference_max_count)
ax.hist(no_inference, bins=bins,
color=(0.3, 0.3, 0.3),
alpha=0.25,
weights=0.9 * (1.0 - w) * no_inference_weights,
bottom=i + n,
lw=0,
zorder=0)
if len(poly) > 0:
poly_max_count = max(np_histogram(poly, bins=bins)[0])
poly_weights = np_ones_like(poly) * (1.0 / poly_max_count)
ax.hist(poly, bins=bins,
color=(1.0, 0.0, 0.0),
alpha=0.25,
weights=0.9 * (1.0 - w) * poly_weights,
bottom=i + n,
lw=0,
zorder=0)
fout.close()
# overlay scatter plot elements
scatter = ax.scatter(x, y, alpha=0.5, s=48, c=c, zorder=1)
# set plot elements
ax.grid(color=(0.8, 0.8, 0.8), linestyle='dashed')
ax.set_xlabel('relative distance')
ax.set_xticks(np_arange(0, 1.05, 0.1))
ax.set_xlim([-0.05, 1.05])
ax.set_ylabel('rank (no. taxa)')
ax.set_yticks(range(0, len(rel_dists)))
ax.set_ylim([-0.2, len(rel_dists) - 0.01])
ax.set_yticklabels(rank_labels)
self.prettify(ax)
# make plot interactive
mpld3.plugins.clear(self.fig)
mpld3.plugins.connect(self.fig, mpld3.plugins.PointLabelTooltip(scatter, labels=labels))
mpld3.plugins.connect(self.fig, mpld3.plugins.MousePosition(fontsize=10))
mpld3.save_html(self.fig, plot_file[0:plot_file.rfind('.')] + '.html')
self.fig.tight_layout(pad=1)
self.fig.savefig(plot_file, dpi=self.dpi)
def _distribution_plot(self, rel_dists, taxa_for_dist_inference, distribution_table, plot_file):
"""Create plot showing the distribution of taxa at each taxonomic rank.
Parameters
----------
rel_dists: d[rank_index][taxon] -> relative divergence
Relative divergence of taxa at each rank.
taxa_for_dist_inference : iterable
Taxa to considered when inferring distributions.
distribution_table : str
Desired name of output table with distribution information.
plot_file : str
Desired name of output plot.
"""
self.fig.clear()
self.fig.set_size_inches(12, 6)
ax = self.fig.add_subplot(111)
# create normal distributions
for i, rank in enumerate(sorted(rel_dists.keys())):
v = [dist for taxa, dist in rel_dists[rank].iteritems() if taxa in taxa_for_dist_inference]
if len(v) < 2:
continue
u = np_mean(v)
rv = norm(loc=u, scale=np_std(v))
x = np_linspace(rv.ppf(0.001), rv.ppf(0.999), 1000)
nd = rv.pdf(x)
# ax.plot(x, 0.75 * (nd / max(nd)) + i, 'b-', alpha=0.6, zorder=2)
# ax.plot((u, u), (i, i + 0.5), 'b-', zorder=2)
# create percentile and classifciation boundary lines
percentiles = {}
for i, rank in enumerate(sorted(rel_dists.keys())):
v = [dist for taxa, dist in rel_dists[rank].iteritems() if taxa in taxa_for_dist_inference]
if len(v) == 0:
continue
p10, p50, p90 = np_percentile(v, [10, 50, 90])
ax.plot((p10, p10), (i, i + 0.25), c=(0.3, 0.3, 0.3), lw=2, zorder=2)
ax.plot((p50, p50), (i, i + 0.5), c=(0.3, 0.3, 0.3), lw=2, zorder=2)
ax.plot((p90, p90), (i, i + 0.25), c=(0.3, 0.3, 0.3), lw=2, zorder=2)
for b in [-0.2, -0.1, 0.1, 0.2]:
boundary = p50 + b
if boundary < 1.0 and boundary > 0.0:
if abs(b) == 0.1:
c = (1.0, 0.65, 0.0) # orange
else:
c = (1.0, 0.0, 0.0)
ax.plot((boundary, boundary), (i, i + 0.5), c=c, lw=2, zorder=2)
percentiles[i] = [p10, p50, p90]
# create scatter plot and results table
fout = open(distribution_table, 'w')
fout.write('Taxa\tRelative Distance\tP10\tMedian\tP90\tPercentile outlier\n')
x = []
y = []
c = []
labels = []
rank_labels = []
for i, rank in enumerate(sorted(rel_dists.keys())):
rank_label = Taxonomy.rank_labels[rank]
rank_labels.append(rank_label + ' (%d)' % len(rel_dists[rank]))
mono = []
poly = []
no_inference = []
for clade_label, dist in rel_dists[rank].iteritems():
x.append(dist)
y.append(i)
labels.append(clade_label)
if is_integer(clade_label.split('^')[-1]):
# taxa with a numerical suffix after a caret indicate
# polyphyletic groups when decorated with tax2tree
c.append((1.0, 0.0, 0.0))
poly.append(dist)
elif clade_label not in taxa_for_dist_inference:
c.append((0.3, 0.3, 0.3))
no_inference.append(dist)
else:
c.append((0.0, 0.0, 1.0))
mono.append(dist)
# report results
v = [clade_label, dist]
if i in percentiles:
p10, p50, p90 = percentiles[i]
percentile_outlier = not (dist >= p10 and dist <= p90)
v += percentiles[i] + [str(percentile_outlier)]
else:
percentile_outlier = 'Insufficent data to calculate percentiles'
v += [-1,-1,-1] + [str(percentile_outlier)]
fout.write('%s\t%.2f\t%.2f\t%.2f\t%.2f\t%s\n' % tuple(v))
# histogram for each rank
mono = np_array(mono)
no_inference = np_array(no_inference)
poly = np_array(poly)
binwidth = 0.025
bins = np_arange(0, 1.0 + binwidth, binwidth)
w = float(len(mono)) / (len(mono) + len(poly) + len(no_inference))
n = 0
if len(mono) > 0:
mono_max_count = max(np_histogram(mono, bins=bins)[0])
mono_weights = np_ones_like(mono) * (1.0 / mono_max_count)
n, b, p = ax.hist(mono, bins=bins,
color=(0.0, 0.0, 1.0),
alpha=0.25,
weights=0.9 * w * mono_weights,
bottom=i,
lw=0,
zorder=0)
if len(no_inference) > 0:
no_inference_max_count = max(np_histogram(no_inference, bins=bins)[0])
no_inference_weights = np_ones_like(no_inference) * (1.0 / no_inference_max_count)
ax.hist(no_inference, bins=bins,
color=(0.3, 0.3, 0.3),
alpha=0.25,
weights=0.9 * (1.0 - w) * no_inference_weights,
bottom=i + n,
lw=0,
zorder=0)
if len(poly) > 0:
poly_max_count = max(np_histogram(poly, bins=bins)[0])
poly_weights = np_ones_like(poly) * (1.0 / poly_max_count)
ax.hist(poly, bins=bins,
color=(1.0, 0.0, 0.0),
alpha=0.25,
weights=0.9 * (1.0 - w) * poly_weights,
bottom=i + n,
lw=0,
zorder=0)
fout.close()
# overlay scatter plot elements
scatter = ax.scatter(x, y, alpha=0.5, s=48, c=c, zorder=1)
# set plot elements
ax.grid(color=(0.8, 0.8, 0.8), linestyle='dashed')
ax.set_xlabel('relative distance')
ax.set_xticks(np_arange(0, 1.05, 0.1))
ax.set_xlim([-0.05, 1.05])
ax.set_ylabel('rank (no. taxa)')
ax.set_yticks(xrange(0, len(rel_dists)))
ax.set_ylim([-0.2, len(rel_dists) - 0.01])
ax.set_yticklabels(rank_labels)
self.prettify(ax)
# make plot interactive
mpld3.plugins.clear(self.fig)
mpld3.plugins.connect(self.fig, mpld3.plugins.PointLabelTooltip(scatter, labels=labels))
mpld3.plugins.connect(self.fig, mpld3.plugins.MousePosition(fontsize=10))
mpld3.save_html(self.fig, plot_file[0:plot_file.rfind('.')] + '.html')
self.fig.tight_layout(pad=1)
self.fig.savefig(plot_file, dpi=self.dpi)
