# Load gene coverage information for species_name
sys.stderr.write("Loading pangenome data for %s...\n" % species_name)
dummy_samples, gene_names, gene_presence_matrix, gene_depth_matrix, marker_coverages, gene_reads_matrix = parse_midas_data.parse_pangenome_data(species_name,allowed_samples=snp_samples, disallowed_genes=shared_pangenome_genes)
sys.stderr.write("Done!\n")
desired_triplet_idxs = []
for dummy1, initial_sample, middle_sample, final_sample in triplet_map[species_name]:
panel_idx+=1
if (initial_sample in dummy_samples) and (middle_sample in dummy_samples) and (final_sample in dummy_samples):
initial_idx = numpy.nonzero(dummy_samples==initial_sample)[0][0]
middle_idx = numpy.nonzero(dummy_samples==middle_sample)[0][0]
final_idx = numpy.nonzero(dummy_samples==final_sample)[0][0]
copynum_trajectories = gene_diversity_utils.calculate_triplet_gene_copynums(gene_depth_matrix, marker_coverages, initial_idx, middle_idx, final_idx)
visnos = numpy.array([1,2,3])
for i in xrange(0,len(copynum_trajectories)):
cs = numpy.clip(copynum_trajectories[i],5e-02,2)
sfs_axes[panel_idx].plot(visnos, cs,'.-',alpha=0.5,markersize=2,markeredgewidth=0)
sfs_axes[panel_idx].set_ylabel('%s\n%s' % (figure_utils.get_abbreviated_species_name(species_name), initial_sample))
fig.savefig('%s/haploid_triplet_gene_sweeps.png' % (parse_midas_data.analysis_directory),bbox_inches='tight',dpi=300)
sys.stderr.write("Done!\n")
# Plot joint SFS!
for pair_idx in xrange(0, len(triplet_map[species_name])):
dummy, sample_i, sample_j, sample_k = triplet_map[species_name][
pair_idx]
panel_idx += 1
freqs = triplet_freqs[pair_idx]
visnos = numpy.array([1, 2, 3])
for i in xrange(0, len(freqs)):
sfs_axes[panel_idx].plot(visnos,
freqs[i],
'.-',
alpha=0.5,
markersize=2,
markeredgewidth=0)
sfs_axes[panel_idx].set_ylabel(
'%s\n%s' %
(figure_utils.get_abbreviated_species_name(species_name),
sample_i))
fig.savefig('%s/haploid_triplet_sweeps.png' %
(parse_midas_data.analysis_directory),
bbox_inches='tight',
dpi=300)
sys.stderr.write("Done!\n")
snp_axes = []
zoomed_snp_axes = []
###################
#
# SNP change panel
#
###################
for example_idx in xrange(0, len(examples)):
#
snp_axis = plt.Subplot(divergence_fig, divergence_grid[example_idx, 0])
divergence_fig.add_subplot(snp_axis)
#
snp_axis.set_ylabel('Divergence, $d$')
snp_axis.set_xlabel('%s QP host pairs (random, $n=%d$)' %
(figure_utils.get_abbreviated_species_name(
examples[example_idx]), num_random_pairs))
snp_axis.set_ylim([1e-06, 1e-01])
snp_axis.set_xlim([-512, 5])
snp_axis.set_xticks([])
#
snp_axis.spines['top'].set_visible(False)
snp_axis.spines['right'].set_visible(False)
snp_axis.get_xaxis().tick_bottom()
snp_axis.get_yaxis().tick_left()
#
snp_axes.append(snp_axis)
#
zoomed_snp_axis = plt.Subplot(divergence_fig, divergence_grid[example_idx,
1])
divergence_fig.add_subplot(zoomed_snp_axis)
#