def sources_at_point_pair(self, chrom1, pos1, chrom2, pos2, strain_names):
""" Prints the range of the 2D interval and the counts of subspecific combos at 2 loci in the genome
:param chrom1: chromosome of one locus
:param pos1: position of one locus
:param chrom2: chromosome of another locus
:param pos2: position of another locus
:param strain_names: list of strain names to analyze
"""
coords = [self.genome_index(chrom1, pos1), self.genome_index(chrom2, pos2)]
mins = [0] * 2
maxes = [np.sum(self.sizes)] * 2
coords.sort()
output = {}
samples = [[[] for _ in subspecies.iter_subspecies(True)] for _ in subspecies.iter_subspecies(True)]
key = [subspecies.to_string(s) for s in subspecies.iter_subspecies(True)]
for strain_name in strain_names:
intervals = self.sample_dict[strain_name][0]
sources = self.sample_dict[strain_name][1]
# find interval containing each location
i = 0
interval_indices = [None, None]
for loc_num in xrange(2):
while intervals[i] < coords[loc_num]:
i += 1
if i > 0:
mins[loc_num] = max(mins[loc_num], intervals[i - 1])
maxes[loc_num] = min(maxes[loc_num], intervals[i])
interval_indices[loc_num] = i
samples[subspecies.to_ordinal(sources[interval_indices[0]])][
subspecies.to_ordinal(sources[interval_indices[1]])].append(strain_name)
output['Key'] = key
output['Samples'] = samples
output['Intervals'] = [
self.chrom_and_pos(mins[0], maxes[0]),
self.chrom_and_pos(mins[1], maxes[1])
]
return output
def build_pairwise_matrix(self, strain_names, elem_intervals):
# 3d matrix. First index is combo, remaining 2d matrices are counts for pairwise intervals
source_counts = np.zeros([(subspecies.NUM_SUBSPECIES + 1) ** 2, len(elem_intervals), len(elem_intervals)],
dtype=np.int16)
for strain_name in strain_names:
intervals, sources = self.sample_dict[strain_name]
# map this strain's intervals onto the elementary intervals
breaks = np.insert(np.searchsorted(elem_intervals, intervals), 0, -1)
for row in xrange(len(intervals)):
for col in xrange(row, len(intervals)): # only upper triangle
source = subspecies.combine(sources[row], sources[col])
source_ordinate = subspecies.to_ordinal(source)
source_counts[source_ordinate, breaks[row] + 1:breaks[row + 1] + 1,
breaks[col] + 1:breaks[col + 1] + 1] += 1
return source_counts
def pairwise_frequencies(self, strain_names):
""" For every locus pair and every label pair, count the number of strains which have those
labels at those pairs of loci.
:param strain_names: list of strain names to analyze (must be a subset of the output from preprocess())
"""
output = [[[], [], [], []] for _ in xrange(subspecies.NUM_SUBSPECIES**2)]
for strain_name in strain_names:
intervals, sources = self.sample_dict[strain_name]
for i in xrange(len(intervals)):
# only upper triangle is meaningful
if subspecies.is_known(sources[i]):
for j in xrange(i, len(intervals)):
if subspecies.is_known(sources[j]):
combo_output = output[subspecies.to_ordinal(subspecies.combine(sources[i], sources[j]))]
combo_output[0].append(intervals[i-1])
combo_output[1].append(intervals[i])
combo_output[2].append(intervals[j-1])
combo_output[3].append(intervals[j])
return output, [subspecies.to_color(i, True) for i in xrange(subspecies.NUM_SUBSPECIES**2)]
