def _hap_comparator_ibdld(a, b):
'''Return a confidence measure in [0,1] of the equality of the haplotypes a and b.
Defines the confidence of equality based on a model of IBDLD posterior probabilities.'''
# TODO: replace this temporary implementation by ibdld_confidence()
d = diff.hap_diff(a, b)
return (1.0 * np.size(np.where(d == 0))) / max(
1, np.size(np.where(d != INDETERMINATE)))
def plot_comparison(template,
haps,
snps=None,
title=None,
xlabel=None,
x=None,
y=None,
template_y='TEMP',
colors=DEFAULT_COLORS):
'''Generate a recombination coloring plot of the difference between template
and each element in the list haps..'''
# Read or re-generate differences data
num_snps = len(template)
if np.size(haps.shape) == 1:
haps = np.transpose(np.array([haps]))
num_haps = haps.shape[1]
# d = (haps != np.transpose(np.tile(template, (num_haps,1)))).astype('int8')
d = np.transpose(
np.array([
diff.hap_diff(haps[:, j], template).astype('int8')
for j in xrange(0, num_haps)
]))
# Restrict view to snps, if specified
x = x if x is not None else np.arange(0, num_snps)
if snps is not None:
(x, d) = (x[snps], d[snps, :])
# Generate haplotype plots
P.clf()
P.hold(True)
hap_ticks = range(0, num_haps)
for j in hap_ticks:
difference = d[:, j]
for (value, color) in colors.iteritems():
hap = np.where(difference == value)
if hap:
P.plot(x[hap[0]],
np.zeros((np.size(hap), )) + j,
color,
markeredgecolor='gray'
if value == INDETERMINATE else color[0])
if title is not None:
P.title(title)
P.xlim([min(x) - 1, max(x) + 1])
P.ylim([-0.5, num_haps - 0.5])
P.xlabel(xlabel if xlabel else 'SNP #')
P.ylabel('Sample')
if y is not None:
P.yticks(hap_ticks, [template_y] + list(y))
return d
def plot_comparison(template, haps, snps=None,
title=None, xlabel=None, x=None, y=None, template_y='TEMP',
colors=DEFAULT_COLORS):
'''Generate a recombination coloring plot of the difference between template
and each element in the list haps..'''
# Read or re-generate differences data
num_snps = len(template)
if np.size(haps.shape) == 1:
haps = np.transpose(np.array([haps]))
num_haps = haps.shape[1]
# d = (haps != np.transpose(np.tile(template, (num_haps,1)))).astype('int8')
d = np.transpose(np.array([diff.hap_diff(haps[:, j], template).astype('int8')
for j in xrange(0, num_haps)]))
# Restrict view to snps, if specified
x = x if x is not None else np.arange(0, num_snps)
if snps is not None:
(x, d) = (x[snps], d[snps, :])
# Generate haplotype plots
P.clf()
P.hold(True)
hap_ticks = range(0, num_haps)
for j in hap_ticks:
difference = d[:, j]
for (value, color) in colors.iteritems():
hap = np.where(difference == value)
if hap:
P.plot(x[hap[0]], np.zeros((np.size(hap),)) + j, color,
markeredgecolor='gray' if value == INDETERMINATE else color[0])
if title is not None:
P.title(title)
P.xlim([min(x) - 1, max(x) + 1])
P.ylim([-0.5, num_haps - 0.5])
P.xlabel(xlabel if xlabel else 'SNP #')
P.ylabel('Sample')
if y is not None:
P.yticks(hap_ticks, [template_y] + list(y))
return d
def test_hap_diff(self):
'''Check haplotype difference functionality.'''
a = np.array([2, 2, 2, 1, 2, 1, 1, 0], dtype=np.byte)
b = np.array([0, 2, 2, 2, 1, 1, 1, 2], dtype=np.byte)
d = np.array([-1, 0, 0, 1, 1, 0, 0, -1], dtype=np.byte)
assert_equal(diff.hap_diff(a, b), d, 'Wrong haplotype difference')
def _hap_comparator_ibdld(a, b):
'''Return a confidence measure in [0,1] of the equality of the haplotypes a and b.
Defines the confidence of equality based on a model of IBDLD posterior probabilities.'''
# TODO: replace this temporary implementation by ibdld_confidence()
d = diff.hap_diff(a, b)
return (1.0 * np.size(np.where(d == 0))) / max(1, np.size(np.where(d != INDETERMINATE)))
def test_hap_diff(self):
'''Check haplotype difference functionality.'''
a = np.array([2, 2, 2, 1, 2, 1, 1, 0], dtype=np.byte)
b = np.array([0, 2, 2, 2, 1, 1, 1, 2], dtype=np.byte)
d = np.array([-1, 0, 0, 1, 1, 0, 0, -1], dtype=np.byte)
assert_equal(diff.hap_diff(a, b), d, 'Wrong haplotype difference')
