def plot_call_field_distribs_per_gt_type(variations, field, max_value,
data_dir, chunk_size=SNPS_PER_CHUNK):
# Field distribution per sample
field_name = field.split('/')[-1]
fpath = join(data_dir, '{}_distribution_per_sample.png'.format(field_name))
mask_funcs = [call_is_het, call_is_hom]
names = ['Heterozygous', 'Homozygous']
distribs = []
for mask_func in mask_funcs:
dp_distribs, bins = calc_field_distribs_per_sample(variations,
field=field,
range_=(0, max_value),
n_bins=max_value,
chunk_size=chunk_size,
mask_func=mask_func,
mask_field=GT_FIELD)
distribs.append(dp_distribs)
title = '{} distribution per sample'.format(field_name)
mpl_params = {'set_xlabel': {'args': ['Samples'], 'kwargs': {}},
'set_ylabel': {'args': [field_name], 'kwargs': {}},
'set_title': {'args': [title], 'kwargs': {}}}
figsize = (variations[GT_FIELD].shape[1], 7)
plot_boxplot_from_distribs_series(distribs, fhand=open(fpath, 'w'),
mpl_params=mpl_params, figsize=figsize,
colors=['pink', 'tan'],
labels=names,
xticklabels=variations.samples)
# Overall field distributions
fpath = join(data_dir, '{}_distribution.png'.format(field_name))
fhand = open(fpath, 'w')
fig = Figure(figsize=(20, 15))
canvas = FigureCanvas(fig)
i = 1
for distrib, name in zip(distribs, names):
distrib = numpy.sum(dp_distribs, axis=0)
distrib_cum = calc_cum_distrib(distrib)
axes = fig.add_subplot(len(names) * 100 + 20 + i)
i += 1
title = '{} distribution all samples {}'.format(field_name, name)
plot_distrib(distrib, bins, axes=axes,
mpl_params={'set_xlabel': {'args': [field_name],
'kwargs': {}},
'set_ylabel': {'args': ['Number of GTs'],
'kwargs': {}},
'set_title': {'args': [title], 'kwargs': {}}})
distrib_cum = distrib_cum/distrib_cum[0] * 100
axes = fig.add_subplot(len(names) * 100 + 20 + i)
i += 1
title = '{} cumulative distribution all samples {}'.format(field_name,
name)
plot_distrib(distrib_cum, bins, axes=axes,
mpl_params={'set_xlabel': {'args': [field_name],
'kwargs': {}},
'set_ylabel': {'args': ['% calls > Depth '],
'kwargs': {}},
'set_title': {'args': [title], 'kwargs': {}}})
canvas.print_figure(fhand)
def test_plot_boxplot_series(self):
distribs = numpy.array(
[
[0, 0, 0, 0, 0, 1, 3, 5, 3, 1],
[0, 0, 0, 0, 1, 3, 5, 3, 1, 0],
[0, 1, 3, 5, 3, 1, 0, 0, 0, 0],
[1, 3, 5, 3, 1, 0, 0, 0, 0, 0],
]
)
mpl_params = {
"set_xlabel": {"args": ["Samples"], "kwargs": {}},
"set_ylabel": {"args": ["Depth"], "kwargs": {}},
}
distribs_series = [distribs[[0, 2], :], distribs[[1, 3], :]]
with NamedTemporaryFile(suffix=".png") as fhand:
plot_boxplot_from_distribs_series(distribs_series, fhand=fhand, mpl_params=mpl_params)
def test_plot_boxplot_series(self):
distribs = numpy.array([[0, 0, 0, 0, 0, 1, 3, 5, 3, 1],
[0, 0, 0, 0, 1, 3, 5, 3, 1, 0],
[0, 1, 3, 5, 3, 1, 0, 0, 0, 0],
[1, 3, 5, 3, 1, 0, 0, 0, 0, 0]])
mpl_params = {
'set_xlabel': {
'args': ['Samples'],
'kwargs': {}
},
'set_ylabel': {
'args': ['Depth'],
'kwargs': {}
}
}
distribs_series = [distribs[[0, 2], :], distribs[[1, 3], :]]
with NamedTemporaryFile(suffix='.png') as fhand:
plot_boxplot_from_distribs_series(distribs_series,
fhand=fhand,
mpl_params=mpl_params)
def plot_call_field_distribs_per_gt_type(variations,
field,
max_value,
data_dir,
chunk_size=SNPS_PER_CHUNK):
# Field distribution per sample
field_name = field.split('/')[-1]
fpath = join(data_dir, '{}_distribution_per_sample.png'.format(field_name))
mask_funcs = [call_is_het, call_is_hom]
names = ['Heterozygous', 'Homozygous']
distribs = []
for mask_func in mask_funcs:
dp_distribs, bins = calc_field_distribs_per_sample(
variations,
field=field,
range_=(0, max_value),
n_bins=max_value,
chunk_size=chunk_size,
mask_func=mask_func,
mask_field=GT_FIELD)
distribs.append(dp_distribs)
title = '{} distribution per sample'.format(field_name)
mpl_params = {
'set_xlabel': {
'args': ['Samples'],
'kwargs': {}
},
'set_ylabel': {
'args': [field_name],
'kwargs': {}
},
'set_title': {
'args': [title],
'kwargs': {}
}
}
figsize = (variations[GT_FIELD].shape[1], 7)
plot_boxplot_from_distribs_series(distribs,
fhand=open(fpath, 'w'),
mpl_params=mpl_params,
figsize=figsize,
colors=['pink', 'tan'],
labels=names,
xticklabels=variations.samples)
# Overall field distributions
fpath = join(data_dir, '{}_distribution.png'.format(field_name))
fhand = open(fpath, 'w')
fig = Figure(figsize=(20, 15))
canvas = FigureCanvas(fig)
i = 1
for distrib, name in zip(distribs, names):
distrib = numpy.sum(dp_distribs, axis=0)
distrib_cum = calc_cum_distrib(distrib)
axes = fig.add_subplot(len(names) * 100 + 20 + i)
i += 1
title = '{} distribution all samples {}'.format(field_name, name)
plot_distrib(distrib,
bins,
axes=axes,
mpl_params={
'set_xlabel': {
'args': [field_name],
'kwargs': {}
},
'set_ylabel': {
'args': ['Number of GTs'],
'kwargs': {}
},
'set_title': {
'args': [title],
'kwargs': {}
}
})
distrib_cum = distrib_cum / distrib_cum[0] * 100
axes = fig.add_subplot(len(names) * 100 + 20 + i)
i += 1
title = '{} cumulative distribution all samples {}'.format(
field_name, name)
plot_distrib(distrib_cum,
bins,
axes=axes,
mpl_params={
'set_xlabel': {
'args': [field_name],
'kwargs': {}
},
'set_ylabel': {
'args': ['% calls > Depth '],
'kwargs': {}
},
'set_title': {
'args': [title],
'kwargs': {}
}
})
canvas.print_figure(fhand)