len(species_names) + 0.5],
color='#fee0d2')
divergence_axis.text(exp((log(1e-06) + log(low_divergence_threshold)) / 2),
len(species_names) + 0.5 +
(len(species_names) + 2) * 0.02,
"'closely\nrelated'",
fontsize=5,
fontstyle='italic',
ha='center',
color='#fc9272')
# get better haploid species names
pretty_species_names = []
for species_name in species_names:
base_name = figure_utils.get_pretty_species_name(species_name)
pretty_name = base_name
if pretty_name in pretty_species_names:
idx = 1
while pretty_name in pretty_species_names:
idx += 1
pretty_name = base_name + (" %d" % (idx))
pretty_species_names.append(pretty_name)
yticks = numpy.arange(0, len(species_names))
yticklabels = [
"%s, n=%d" % (pretty_species_names[i], sample_sizes[i])
for i in xrange(0, len(sample_sizes))
]
snp_axis.set_ylim([-0.02, 1.02])
snp_axis.set_xticklabels([])
snp_axes.append(snp_axis)
gene_axis = plt.Subplot(fig, outer_grid[1, example_idx])
fig.add_subplot(gene_axis)
line, = gene_axis.semilogy([0.5, 3.5], [0.5, 0.5], ':', color='0.7')
line.set_dashes((1, 1))
line, = gene_axis.semilogy([0.5, 3.5], [0.05, 0.05], ':', color='0.7')
line.set_dashes((1, 1))
gene_axis.set_xlim([0.6, 3.4])
gene_axis.set_xticks([1, 2, 3])
gene_axis.set_ylim([0.02, 2])
gene_axes.append(gene_axis)
snp_axis.set_title(figure_utils.get_pretty_species_name(
examples[example_idx][0]),
fontsize=6)
gene_axis.set_xlabel('Visit number')
if example_idx == 0:
snp_axis.set_ylabel('SNV frequency')
gene_axis.set_ylabel('Gene copynum')
else:
snp_axis.set_yticklabels([])
gene_axis.set_yticklabels([])
# Now actually plot
for example_idx in xrange(0, len(examples)):
species_name = examples[example_idx][0]
sys.stderr.write("Loading whitelisted genes...\n")
#depth_axis.set_ylim([1e01,3e03])
#depth_axis.set_xticks([])
#depth_axis.set_title(species_name,fontsize=fontsize)
polymorphism_axis = plt.Subplot(fig, polymorphism_grid[1])
fig.add_subplot(polymorphism_axis)
polymorphism_axis.set_xlabel("Ranked samples (n=%d)" % len(desired_samples))
polymorphism_axis.set_ylabel(
"Within-sample polymorphism ($0.2 \leq f \leq 0.8$)")
polymorphism_axis.set_ylim([1e-06, 2e-01])
polymorphism_axis.set_xticks([])
polymorphism_axis.set_title(figure_utils.get_pretty_species_name(species_name),
fontsize=fontsize)
#polymorphism_axis.set_title(species_name,fontsize=fontsize)
##############################################################################
#
# Panel (b). Three example (folded) SFSs for focal species
#
##############################################################################
sfs_grid = gridspec.GridSpecFromSubplotSpec(4,
1,
height_ratios=[1, 1, 1, 1],
subplot_spec=focal_grid[0],
hspace=0.25)
sample_sizes = numpy.array(sample_sizes)
passed_species = species_phylogeny_utils.sort_phylogenetically(
passed_species,
first_entry=focal_speciess[0],
second_sorting_attribute=(-1 * sample_sizes))
#passed_species = species_phylogeny_utils.sort_phylogenetically(passed_species)
num_passed_species = len(passed_species)
inconsistency_axis.plot([1], [-1],
'-',
color=focal_colors[0],
linewidth=1,
alpha=1,
label=figure_utils.get_pretty_species_name(
focal_speciess[0], include_number=False))
inconsistency_axis.plot([1], [-1],
'-',
color=focal_colors[1],
linewidth=1,
alpha=1,
label=figure_utils.get_pretty_species_name(
focal_speciess[1], include_number=False))
inconsistency_axis.plot([1], [-1],
'r-',
linewidth=0.5,
alpha=0.3,
label='Other species')
inconsistency_axis.legend(loc='lower left',
frameon=False,
fontsize=5,
species_axis.set_xlim([-1.5, num_passed_species - 0.5])
#species_axis.set_ylabel('LD, $\sigma^2_d$')
species_axis.spines['top'].set_visible(False)
species_axis.spines['right'].set_visible(False)
species_axis.set_ylabel('Linkage Disequilibrium, $\sigma^2_d$')
species_axis.get_xaxis().tick_bottom()
species_axis.get_yaxis().tick_left()
xticks = numpy.arange(0, num_passed_species)
#xticklabels = ["%s (%d)" % (species_names[i],sample_sizes[i]) for i in xrange(0,len(sample_sizes))]
#xticklabels = ["%s" % (passed_species[i]) for i in xrange(0, num_passed_species)]
xticklabels = [
"%s" % (figure_utils.get_pretty_species_name(passed_species[i]))
for i in xrange(0, num_passed_species)
]
species_axis.set_xticks(xticks)
species_axis.set_xticklabels(xticklabels, rotation='vertical', fontsize=4)
#species_axis.set_ylim([2e-02,2])
####################################################
#
# Set up Figure (2 panels, arranged in 2x1 grid)
#
####################################################
pylab.figure(4, figsize=(4, 1))
#
# Set up Figure (3 panels, arranged in 3x1 grid)
#
####################################################
pylab.figure(3, figsize=(7, 1.25))
ld_fig = pylab.gcf()
# make three panels panels
ld_grid = gridspec.GridSpec(1, 3, width_ratios=[1, 1, 1], wspace=0.25)
ld_axis_1 = plt.Subplot(ld_fig, ld_grid[0])
ld_fig.add_subplot(ld_axis_1)
ld_axis_1.set_title(
figure_utils.get_pretty_species_name('Bacteroides_fragilis_54507'),
fontsize=5)
ld_axis_1.set_ylabel('Linkage disequilibrium, $\sigma^2_d$')
ld_axis_1.set_xlabel('Distance between SNPs, $\ell$')
ld_axis_1.spines['top'].set_visible(False)
ld_axis_1.spines['right'].set_visible(False)
ld_axis_1.spines['bottom'].set_zorder(22)
ld_axis_1.get_xaxis().tick_bottom()
ld_axis_1.get_yaxis().tick_left()
ld_axis_1.set_xlim([2, 1e04])
ld_axis_1.set_ylim([1e-02, 1])
ld_axis_1.text(6e03,
3.5e-03,
'Genome-\nwide',
horizontalalignment='center',
fontsize='5')
clade_fst_axis = plt.Subplot(fig, outer_grid[0])
fig.add_subplot(clade_fst_axis)
clade_fst_axis.set_ylabel('Fst (clades)')
clade_fst_axis.set_ylim([-0.05,1.05])
clade_fst_axis.set_xlim([-1,len(species_names)])
clade_fst_axis.spines['top'].set_visible(False)
clade_fst_axis.spines['right'].set_visible(False)
clade_fst_axis.get_xaxis().tick_bottom()
clade_fst_axis.get_yaxis().tick_left()
xticks = numpy.arange(0,len(species_names))
#xticklabels = ["%s (%d)" % (species_names[i],sample_sizes[i]) for i in xrange(0,len(sample_sizes))]
xticklabels = ["%s" % figure_utils.get_pretty_species_name(species_names[i]) for i in xrange(0, len(species_names))]
clade_fst_axis.set_xticks(xticks)
clade_fst_axis.set_xticklabels(xticklabels, rotation='vertical',fontsize=4)
# Plot percentiles of divergence distribution
for species_idx in xrange(0,len(species_names)):
species_name = species_names[species_idx]
#clade_fst_axis.plot([-1,len(species_names)+1],[0.2,0.2],'k:',linewidth=0.5)
if species_name in clade_Fst:
observed_fst, bootstrapped_fsts = clade_Fst[species_name]