import sys
import numpy
import parse_file
from scipy.special import gammaln as loggamma
from math import log, exp
populations = parse_file.all_lines
coverages = []
nonmutator_coverages = []
for population in populations:
sys.stderr.write("Processing %s...\t" %
parse_file.get_pretty_name(population))
# calculate mutation trajectories
# load mutations
mutations, depth_tuple = parse_file.parse_annotated_timecourse(population)
population_avg_depth_times, population_avg_depths, clone_avg_depth_times, clone_avg_depths = depth_tuple
coverages.extend(population_avg_depths[population_avg_depths >= 5])
if population in parse_file.complete_nonmutator_lines:
nonmutator_coverages.extend(
population_avg_depths[population_avg_depths >= 5])
sys.stderr.write("Done!\n")
hspace=0.2) #, hspace=0.08)
inner_grids = [inner_grid_1, inner_grid_2]
axes = []
for metapopulation_idx in xrange(0, 2):
axes.append([])
for population_idx in xrange(0, 6):
population = metapopulations[metapopulation_idx][population_idx]
axis = plt.Subplot(fig,
inner_grids[metapopulation_idx][population_idx])
fig.add_subplot(axis)
axes[metapopulation_idx].append(axis)
axis.set_title(parse_file.get_pretty_name(population),
fontsize=5,
y=0.96,
loc='left')
axis.spines['top'].set_visible(False)
axis.spines['right'].set_visible(False)
axis.get_yaxis().tick_left()
axis.get_yaxis().set_tick_params(direction='out', length=2)
axis.set_xticks([])
axis.set_ylabel('Fixed mutations')
axis.set_xlim([-1, 22])
if population_idx == 5:
axis.set_xlabel('Clones')
std_dx40 = 0
std_dx50 = individual_50k_std_dxs[i]
std_dx60 = individual_60k_std_dxs[i]
late_fitness_ts = numpy.array([t40, t50, t60])
late_fitness_xs = numpy.array([dx40, dx50, dx60])
late_fitness_std_xs = numpy.array([std_dx40, std_dx50, std_dx60])
late_fitness_trajectories[population] = (late_fitness_ts, late_fitness_xs, late_fitness_std_xs)
sys.stderr.write("Done!\n")
sys.stderr.write("Loading mutation data...\n")
for population in populations:
sys.stderr.write("Processing %s...\t" % parse_file.get_pretty_name(population))
# calculate mutation trajectories
# load mutations
mutations, depth_tuple = parse_file.parse_annotated_timecourse(population)
population_avg_depth_times, population_avg_depths, clone_avg_depth_times, clone_avg_depths = depth_tuple
dummy_times,fmajors,fminors,haplotype_trajectories = parse_file.parse_haplotype_timecourse(population)
state_times, state_trajectories = parse_file.parse_well_mixed_state_timecourse(population)
times = mutations[0][10]
Ms = numpy.zeros_like(times)*1.0
fixed_Ms = numpy.zeros_like(times)*1.0
transit_times[population] = []
pylab.figure(1,figsize=(5.45,3))
fig = pylab.gcf()
# make three panels panels
outer_grid = gridspec.GridSpec(1, 2, width_ratios=[4,1], wspace=0.125)
inner_grid_1 = gridspec.GridSpecFromSubplotSpec(4, 1, height_ratios=[1,1,1,1],
subplot_spec=outer_grid[0], hspace=0.30) #, hspace=0.08)
inner_grid_2 = gridspec.GridSpecFromSubplotSpec(len(remaining_populations), 1, height_ratios=([1]*len(remaining_populations)),
subplot_spec=outer_grid[1], hspace=0.25) #, hspace=0.08)
fixed_axis = plt.Subplot(fig, inner_grid_1[0])
fig.add_subplot(fixed_axis)
fixed_axis.set_title('%s basal clade' % parse_file.get_pretty_name(focal_population),loc='right',fontsize=5,y=0.89)
fixed_axis.spines['top'].set_visible(False)
fixed_axis.spines['right'].set_visible(False)
fixed_axis.get_xaxis().tick_bottom()
fixed_axis.get_yaxis().tick_left()
fixed_axis.get_yaxis().set_tick_params(direction='out',length=2,pad=0)
fixed_axis.get_xaxis().set_tick_params(direction='out',length=2,pad=0)
fixed_axis.set_xticks(generation_ticks)
fixed_axis.set_xticklabels([])
fixed_axis.set_yticks(frequency_ticks)
#fixed_axis.set_ylabel('Allele frequency, $f(t)$')
height_ratios=[1, 1, 1, 1],
subplot_spec=outer_grid[0],
hspace=0.30) #, hspace=0.08)
inner_grid_2 = gridspec.GridSpecFromSubplotSpec(
len(remaining_populations),
1,
height_ratios=([1] * len(remaining_populations)),
subplot_spec=outer_grid[1],
hspace=0.25) #, hspace=0.08)
fixed_axis = plt.Subplot(fig, inner_grid_1[0])
fig.add_subplot(fixed_axis)
fixed_axis.set_title('%s basal clade' %
parse_file.get_pretty_name(focal_population),
loc='right',
fontsize=5,
y=0.89)
fixed_axis.spines['top'].set_visible(False)
fixed_axis.spines['right'].set_visible(False)
fixed_axis.get_xaxis().tick_bottom()
fixed_axis.get_yaxis().tick_left()
fixed_axis.get_yaxis().set_tick_params(direction='out', length=2, pad=0)
fixed_axis.get_xaxis().set_tick_params(direction='out', length=2, pad=0)
fixed_axis.set_xticks(generation_ticks)
fixed_axis.set_xticklabels([])
fixed_axis.set_yticks(frequency_ticks)
replicate_str = "biological replicate 1"
if sample_name_str in seen_sample_names:
new_sample_name_str = sample_name_str
idx = 1
while new_sample_name_str in seen_sample_names:
idx += 1
new_sample_name_str = sample_name_str + (".%d" % idx)
sample_name_str = new_sample_name_str
replicate_str = "biological replicate %d" % idx
population_str = ",".join([parse_file.get_pretty_name(population) for population in populations])
if "BG" in strain_str:
description_str = "%s clonal isolate" % (population_str)
else:
description_str = "%s %s mixed population sample" % (population_str, timepoint_str)
column_strs = [" " for column in column_headers]
column_strs[sample_name_idx] = sample_name_str
column_strs[bioproject_accession_idx] = bioproject_accession_str
column_strs[organism_idx] = organsim_str
column_strs[strain_idx] = strain_str
hspace=0.25) #, hspace=0.08)
inner_grid_2 = gridspec.GridSpecFromSubplotSpec(
len(remaining_populations),
1,
height_ratios=([1] * len(remaining_populations)),
subplot_spec=outer_grid[1],
hspace=0.2) #, hspace=0.08)
focal_axes = []
for i in xrange(0, len(focal_populations)):
axis = plt.Subplot(fig, inner_grid_1[i])
focal_axes.append(axis)
fig.add_subplot(axis)
axis.set_title('%s' % parse_file.get_pretty_name(focal_populations[i]),
loc='right',
fontsize=5,
y=0.93)
axis.spines['top'].set_visible(False)
axis.spines['right'].set_visible(False)
axis.get_xaxis().tick_bottom()
axis.get_yaxis().tick_left()
axis.get_yaxis().set_tick_params(direction='out', length=3, pad=1)
axis.get_xaxis().set_tick_params(direction='out', length=3, pad=1)
axis.set_xticks(generation_ticks)
axis.set_yticks(frequency_ticks)
if i == (len(focal_populations) - 1):
axes = [axes]
#fig = plt.figure(figsize=(fig_width,fig_height))
#outer_grid = gridspec.GridSpec(len(populations), 1)
xticks = [5000 * i for i in xrange(0, 13)]
xticklabels = ['%dk' % (5 * i) for i in xrange(0, 13)]
xticklabels[0] = '0'
freq_axis = None
for population_idx in xrange(0, len(populations)):
population = populations[population_idx]
sys.stdout.write("Processing %s...\n" %
parse_file.get_pretty_name(population))
# calculate mutation trajectories
# load mutations
mutations, depth_tuple = parse_file.parse_annotated_timecourse(
population)
population_avg_depth_times, population_avg_depths, clone_avg_depth_times, clone_avg_depths = depth_tuple
dummy_times, fmajors, fminors, haplotype_trajectories = parse_file.parse_haplotype_timecourse(
population)
state_times, state_trajectories = parse_file.parse_well_mixed_state_timecourse(
population)
# set up figure axis
freq_axis = axes[population_idx]
xticklabels[0] = '0'
fixation_time_axis.set_xticks(xticks)
fixation_time_axis.set_xticklabels(xticklabels)
######
#
# Now do the calculations
#
######
#populations = ['m5','p2','p4','p1','p5','p6','p3','m4','m2','m1','m3','m6']
nonmutator_populations = ['m5', 'p2', 'p4', 'p1', 'm6', 'p5']
mutator_populations = ['m1', 'm2', 'm3', 'm4', 'p3', 'p6']
population_labels = {
population: parse_file.get_pretty_name(population)
for population in nonmutator_populations
}
population_colors = {
nonmutator_populations[i]: parse_file.nonmutator_line_colors[i]
for i in xrange(0, len(nonmutator_populations))
}
for i in xrange(0, len(mutator_populations)):
population_labels[mutator_populations[i]] = parse_file.get_pretty_name(
mutator_populations[i])
population_colors[
mutator_populations[i]] = parse_file.mutator_line_colors[i]
populations = mutator_populations + nonmutator_populations
axes = [axes]
#fig = plt.figure(figsize=(fig_width,fig_height))
#outer_grid = gridspec.GridSpec(len(populations), 1)
xticks = [5000*i for i in xrange(0,13)]
xticklabels = ['%dk' % (5*i) for i in xrange(0,13)]
xticklabels[0] = '0'
freq_axis = None
for population_idx in xrange(0,len(populations)):
population = populations[population_idx]
sys.stderr.write("Processing %s...\n" % parse_file.get_pretty_name(population))
# calculate mutation trajectories
# load mutations
mutations, depth_tuple = parse_file.parse_annotated_timecourse(population)
population_avg_depth_times, population_avg_depths, clone_avg_depth_times, clone_avg_depths = depth_tuple
dummy_times,fmajors,fminors,haplotype_trajectories = parse_file.parse_haplotype_timecourse(population)
state_times, state_trajectories = parse_file.parse_well_mixed_state_timecourse(population)
# set up figure axis
freq_axis = axes[population_idx]
if additional_titles[population_idx]=="":
title_text = parse_file.get_pretty_name(population)
