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def normalize_values(a_values, b_values):
"""
Trim two lists of values to contain only the values they both share
Given two lists of sample values, trim each list so that it contains
only the samples that contain a value in both lists. Also returns
the number of such samples.
>>> normalize_values([2.3, None, None, 3.2, 4.1, 5], [3.4, 7.2, 1.3, None, 6.2, 4.1])
([2.3, 4.1, 5], [3.4, 6.2, 4.1], 3)
"""
min_length = min(len(a_values), len(b_values))
a_new = []
b_new = []
for counter in range(min_length):
if (a_values[counter] or a_values[counter] == 0) and (b_values[counter] or b_values[counter] == 0):
a_new.append(a_values[counter])
b_new.append(b_values[counter])
num_overlap = len(a_new)
assert num_overlap == len(b_new), "Lengths should be the same"
return a_new, b_new, num_overlap
def common_keys(a_samples, b_samples):
"""
>>> a = dict(BXD1 = 9.113, BXD2 = 9.825, BXD14 = 8.985, BXD15 = 9.300)
>>> b = dict(BXD1 = 9.723, BXD3 = 9.825, BXD14 = 9.124, BXD16 = 9.300)
>>> sorted(common_keys(a, b))
['BXD1', 'BXD14']
"""
return set(a_samples.keys()).intersection(set(b_samples.keys()))
def normalize_values_with_samples(a_samples, b_samples):
common_samples = common_keys(a_samples, b_samples)
a_new = {}
b_new = {}
for sample in common_samples:
a_new[sample] = a_samples[sample]
b_new[sample] = b_samples[sample]
num_overlap = len(a_new)
assert num_overlap == len(b_new), "Lengths should be the same"
return a_new, b_new, num_overlap
if __name__ == '__main__':
import doctest
doctest.testmod()
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