def test_translate_colors(self):
"""Checks translate_colors is sane"""
# Sets up knowns values
known_def_8 = array([[0.83529412, 0.24313725, 0.30980392],
[0.95686275, 0.42745098, 0.26274510],
[0.99215686, 0.68235294, 0.38039216],
[0.99607843, 0.87843137, 0.54509804],
[0.90196078, 0.96078431, 0.59607843],
[0.67058824, 0.86666667, 0.64313725],
[0.40000000, 0.76078431, 0.64705882],
[0.19607843, 0.53333333, 0.74117647]])
known_PuRd_9 = array([[0.96862745, 0.95686275, 0.97647059],
[0.90588235, 0.88235294, 0.93725490],
[0.83137255, 0.72549020, 0.85490196],
[0.78823529, 0.58039216, 0.78039216],
[0.87450980, 0.39607843, 0.69019608],
[0.90588235, 0.16078431, 0.54117647],
[0.80784314, 0.07058824, 0.33725490],
[0.59607843, 0.00000000, 0.26274510],
[0.40392157, 0.00000000, 0.12156863]])
# Test the calls
with self.assertRaises(ValueError):
translate_colors(5, 'Winchester')
with self.assertRaises(ValueError):
translate_colors(13)
def_map = translate_colors(8)
PuRd_map = translate_colors(9, 'PuRd')
# Checks the outputs are sane
assert_almost_equal(known_def_8, def_map)
assert_almost_equal(known_PuRd_9, PuRd_map)
def test_translate_colors(self):
"""Checks translate_colors is sane"""
# Sets up knowns values
known_def_8 = array([[0.83529412, 0.24313725, 0.30980392],
[0.95686275, 0.42745098, 0.26274510],
[0.99215686, 0.68235294, 0.38039216],
[0.99607843, 0.87843137, 0.54509804],
[0.90196078, 0.96078431, 0.59607843],
[0.67058824, 0.86666667, 0.64313725],
[0.40000000, 0.76078431, 0.64705882],
[0.19607843, 0.53333333, 0.74117647]])
known_PuRd_9 = array([[0.96862745, 0.95686275, 0.97647059],
[0.90588235, 0.88235294, 0.93725490],
[0.83137255, 0.72549020, 0.85490196],
[0.78823529, 0.58039216, 0.78039216],
[0.87450980, 0.39607843, 0.69019608],
[0.90588235, 0.16078431, 0.54117647],
[0.80784314, 0.07058824, 0.33725490],
[0.59607843, 0.00000000, 0.26274510],
[0.40392157, 0.00000000, 0.12156863]])
# Test the calls
with self.assertRaises(ValueError):
translate_colors(5, 'Winchester')
with self.assertRaises(ValueError):
translate_colors(13)
def_map = translate_colors(8)
PuRd_map = translate_colors(9, 'PuRd')
# Checks the outputs are sane
assert_almost_equal(known_def_8, def_map)
assert_almost_equal(known_PuRd_9, PuRd_map)
def main(tax_table, output_dir, samples_to_analyze=None):
"""Generates pie chart of the most abundant twelve taxa in the sample
INPUTS:
otu_table -- a biom formatted taxonomy table at the desired level of
resolution
output_dir -- the location of the directory where output files should
be stored.
samples_to_analyze -- a list of sample ids to plot. If no value is
passed, then all samples in the biom table are analyzed.
OUTPUTS:
A pdf of the piechart summarizing the most abundant taxa will be
generated and saved to the output directory. These will follow the
naming convention PIECHART_<SAMPLEID>.pdf.
"""
# Creates the text around hte file name
FILENAME_BEFORE = 'piechart_'
FILENAME_AFTER = '.pdf'
# Handles string cleaning
RENDER = 'LATEX'
UNCLASSIFIED = False
# Sets up the rare threshhold for
RARE_THRESH = 0.0
SUM_MIN = 1
# Sets up axis parameters
AXIS_LENGTH = 7.25
AXIS_BORDER = 0.01
AXIS_TITLE = 0
AXIS_LEGEND = 7
# Modifies the axis limits
AX_LIMS = [-1.05, 1.05]
# Sets up constants for getting the colormap and plotting
MAP_NAME = 'BrBG'
NUM_SHOW = 12
OTHER_COLOR = array([[85/255, 85/255, 85/255]])
# Sets up plotting parameters
FIG_LEGEND = True
FIG_COLOR_EDGE = False
FIG_LEG_FRAME = False
FIG_LEG_OFFSET = [0.95, 0.025, 1.0, 0.95]
# Sets up the the legend font
LEG_FONT = FontProperties()
LEG_FONT.set_size(28)
LEG_FONT.set_family('sans-serif')
# Sets the general font properties
use_latex = True
rc_font_family = 'sans-serif'
rc_font = ['Helvetica', 'Arial']
# Sets up the colormap
colormap = translate_colors((NUM_SHOW-1), MAP_NAME)
colormap = vstack((colormap, OTHER_COLOR))
# Sets up plotting constants
(axis_dims, fig_dims) = calculate_dimensions_rectangle(
axis_width=AXIS_LENGTH, axis_height=AXIS_LENGTH, border=AXIS_BORDER,
title=AXIS_TITLE, legend=AXIS_LEGEND)
# Walks over a taxa tree and prioritizes based on taxonomy
(tree, all_taxa) = build_tree_from_taxontable(tax_table)
# Sets up samples for which tables are being generated
if samples_to_analyze is not None:
samples_to_test = samples_to_analyze
else:
samples_to_test = all_taxa.keys()
# Checks the samples exist
if samples_to_test:
samples_to_test = set(samples_to_test)
tmp = {k: v for k, v in all_taxa.items() if k in samples_to_test}
all_taxa = tmp
if not samples_to_test:
raise ValueError("No samples!")
# Walks over the table
filt_fun = lambda v, i, md: v.sum() > 0
for samp, filtered_table, rare, unique in sample_rare_unique(tree,
tax_table,
all_taxa,
RARE_THRESH):
# abund_fun = lambda v, i, md: i in all_taxa[samp]
filtered_table = tax_table.filterObservations(filt_fun)
sample_data = filtered_table.sampleData(samp)
taxa = filtered_table.ObservationIds
# Calculates abundance and limits to the top n samples.
abund_rank = calculate_abundance(sample=sample_data,
taxa=taxa,
sum_min=SUM_MIN)
abund_rank = abund_rank[:(NUM_SHOW-1)]
# Cleans the greengenes strings and adds an "Other" Category for
# missing taxa
[sample_tax, sample_freq] = [list(a) for a in zip(*abund_rank)]
clean_tax = [clean_greengenes_string(tax, RENDER,
unclassified=UNCLASSIFIED)
for tax in sample_tax]
clean_tax.append('Other')
sample_freq.append(1-sum(sample_freq))
# Sets up the sample filename
filename = pjoin(output_dir, '%s%s%s' % (FILENAME_BEFORE, samp,
FILENAME_AFTER))
# Creates the pie chart
render_single_pie(data_vec=sample_freq,
group_names=clean_tax,
axis_dims=axis_dims,
fig_dims=fig_dims,
file_out=filename,
legend=FIG_LEGEND,
colors=colormap,
show_edge=FIG_COLOR_EDGE,
legend_frame=FIG_LEG_FRAME,
rc_font=rc_font,
legend_offset=FIG_LEG_OFFSET,
rc_fam=rc_font_family,
legend_font=LEG_FONT,
use_latex=use_latex,
x_lims=AX_LIMS,
y_lims=AX_LIMS)
def main(tax_table, output_dir, samples_to_analyze=None):
"""Generates pie chart of the most abundant twelve taxa in the sample
INPUTS:
otu_table -- a biom formatted taxonomy table at the desired level of
resolution
output_dir -- the location of the directory where output files should
be stored.
samples_to_analyze -- a list of sample ids to plot. If no value is
passed, then all samples in the biom table are analyzed.
OUTPUTS:
A pdf of the piechart summarizing the most abundant taxa will be
generated and saved to the output directory. These will follow the
naming convention PIECHART_<SAMPLEID>.pdf.
"""
# Creates the text around hte file name
FILENAME_BEFORE = 'piechart_'
FILENAME_AFTER = '.pdf'
# Handles string cleaning
RENDER = 'LATEX'
UNCLASSIFIED = False
# Sets up the rare threshhold for
RARE_THRESH = 0.0
SUM_MIN = 1
# Sets up axis parameters
AXIS_LENGTH = 7.25
AXIS_BORDER = 0.01
AXIS_TITLE = 0
AXIS_LEGEND = 7
# Modifies the axis limits
AX_LIMS = [-1.05, 1.05]
# Sets up constants for getting the colormap and plotting
MAP_NAME = 'BrBG'
NUM_SHOW = 12
OTHER_COLOR = array([[85 / 255, 85 / 255, 85 / 255]])
# Sets up plotting parameters
FIG_LEGEND = True
FIG_COLOR_EDGE = False
FIG_LEG_FRAME = False
FIG_LEG_OFFSET = [0.95, 0.025, 1.0, 0.95]
# Sets up the the legend font
LEG_FONT = FontProperties()
LEG_FONT.set_size(28)
LEG_FONT.set_family('sans-serif')
# Sets the general font properties
use_latex = True
rc_font_family = 'sans-serif'
rc_font = ['Helvetica', 'Arial']
# Sets up the colormap
colormap = translate_colors((NUM_SHOW - 1), MAP_NAME)
colormap = vstack((colormap, OTHER_COLOR))
# Sets up plotting constants
(axis_dims,
fig_dims) = calculate_dimensions_rectangle(axis_width=AXIS_LENGTH,
axis_height=AXIS_LENGTH,
border=AXIS_BORDER,
title=AXIS_TITLE,
legend=AXIS_LEGEND)
# Walks over a taxa tree and prioritizes based on taxonomy
(tree, all_taxa) = build_tree_from_taxontable(tax_table)
# Sets up samples for which tables are being generated
if samples_to_analyze is not None:
samples_to_test = samples_to_analyze
else:
samples_to_test = all_taxa.keys()
# Checks the samples exist
if samples_to_test:
samples_to_test = set(samples_to_test)
tmp = {k: v for k, v in all_taxa.items() if k in samples_to_test}
all_taxa = tmp
if not samples_to_test:
raise ValueError("No samples!")
# Walks over the table
filt_fun = lambda v, i, md: v.sum() > 0
for samp, filtered_table, rare, unique in sample_rare_unique(
tree, tax_table, all_taxa, RARE_THRESH):
# abund_fun = lambda v, i, md: i in all_taxa[samp]
filtered_table = tax_table.filterObservations(filt_fun)
sample_data = filtered_table.sampleData(samp)
taxa = filtered_table.ObservationIds
# Calculates abundance and limits to the top n samples.
abund_rank = calculate_abundance(sample=sample_data,
taxa=taxa,
sum_min=SUM_MIN)
abund_rank = abund_rank[:(NUM_SHOW - 1)]
# Cleans the greengenes strings and adds an "Other" Category for
# missing taxa
[sample_tax, sample_freq] = [list(a) for a in zip(*abund_rank)]
clean_tax = [
clean_greengenes_string(tax, RENDER, unclassified=UNCLASSIFIED)
for tax in sample_tax
]
clean_tax.append('Other')
sample_freq.append(1 - sum(sample_freq))
# Sets up the sample filename
filename = pjoin(output_dir,
'%s%s%s' % (FILENAME_BEFORE, samp, FILENAME_AFTER))
# Creates the pie chart
render_single_pie(data_vec=sample_freq,
group_names=clean_tax,
axis_dims=axis_dims,
fig_dims=fig_dims,
file_out=filename,
legend=FIG_LEGEND,
colors=colormap,
show_edge=FIG_COLOR_EDGE,
legend_frame=FIG_LEG_FRAME,
rc_font=rc_font,
legend_offset=FIG_LEG_OFFSET,
rc_fam=rc_font_family,
legend_font=LEG_FONT,
use_latex=use_latex,
x_lims=AX_LIMS,
y_lims=AX_LIMS)
