def test_calc_distrib_for_sample(self):
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
snps = VariationsArrays()
snps.put_chunks(hdf5.iterate_chunks())
distrib, _ = calc_field_distrib_for_a_sample(hdf5, field='/calls/DP',
sample='1_17_1_gbs',
n_bins=15)
assert distrib.shape == (15,)
distrib2, _ = calc_field_distrib_for_a_sample(snps, field='/calls/DP',
n_bins=15,
sample='1_17_1_gbs',
chunk_size=None)
assert numpy.all(distrib == distrib2)
distrib3, _ = calc_field_distrib_for_a_sample(snps, field='/calls/DP',
n_bins=15,
sample='1_17_1_gbs',
chunk_size=50)
assert numpy.all(distrib3 == distrib2)
vars_ = VariationsArrays()
vars_['/calls/DP'] = numpy.array([[10, 5, 15],
[0, 15, 10]])
vars_['/calls/GT'] = numpy.array([[[0, 0], [0, 1], [1, 1]],
[[0, 0], [0, 1], [1, 1]]])
vars_.samples = list(range(3))
distrib, _ = calc_field_distribs_per_sample(vars_, field='/calls/DP',
n_bins=16)
expec = [[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1]]
assert numpy.all(expec == distrib)
assert numpy.all(calc_depth(vars_) == [10, 5, 15, 0, 15, 10])
distrib, _ = calc_field_distribs_per_sample(vars_, field='/calls/DP',
n_bins=16,
mask_field='/calls/GT',
mask_func=call_is_het)
expec = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
assert numpy.all(expec == distrib)
assert numpy.all(calc_depth(vars_) == [10, 5, 15, 0, 15, 10])
distrib, _ = calc_field_distribs_per_sample(vars_, field='/calls/DP',
n_bins=16,
mask_field='/calls/GT',
mask_func=call_is_hom)
expec = [[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1]]
assert numpy.all(expec == distrib)
assert numpy.all(calc_depth(vars_) == [10, 5, 15, 0, 15, 10])
def test_calc_distrib_for_sample(self):
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
snps = VariationsArrays()
snps.put_chunks(hdf5.iterate_chunks())
distrib, _ = calc_field_distrib_for_a_sample(hdf5, field='/calls/DP',
sample='1_17_1_gbs',
n_bins=15)
assert distrib.shape == (15,)
distrib2, _ = calc_field_distrib_for_a_sample(snps, field='/calls/DP',
n_bins=15,
sample='1_17_1_gbs',
chunk_size=None)
assert numpy.all(distrib == distrib2)
distrib3, _ = calc_field_distrib_for_a_sample(snps, field='/calls/DP',
n_bins=15,
sample='1_17_1_gbs',
chunk_size=50)
assert numpy.all(distrib3 == distrib2)
vars_ = VariationsArrays()
vars_['/calls/DP'] = numpy.array([[10, 5, 15],
[0, 15, 10]])
vars_['/calls/GT'] = numpy.array([[[0, 0], [0, 1], [1, 1]],
[[0, 0], [0, 1], [1, 1]]])
vars_.samples = list(range(3))
distrib, _ = calc_field_distribs_per_sample(vars_, field='/calls/DP',
n_bins=16)
expec = [[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1]]
assert numpy.all(expec == distrib)
assert numpy.all(calc_depth(vars_) == [10, 5, 15, 0, 15, 10])
distrib, _ = calc_field_distribs_per_sample(vars_, field='/calls/DP',
n_bins=16,
mask_field='/calls/GT',
mask_func=call_is_het)
expec = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
assert numpy.all(expec == distrib)
assert numpy.all(calc_depth(vars_) == [10, 5, 15, 0, 15, 10])
distrib, _ = calc_field_distribs_per_sample(vars_, field='/calls/DP',
n_bins=16,
mask_field='/calls/GT',
mask_func=call_is_hom)
expec = [[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1]]
assert numpy.all(expec == distrib)
assert numpy.all(calc_depth(vars_) == [10, 5, 15, 0, 15, 10])
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 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)
