def test_to_normalized(self):
"""correctly recalibrate CategoryFreqs"""
freqs = number.CategoryFreqs({"A": 4, "C": 2, "G": 4}, total=12)
self.assertEqual(freqs["A"], 4 / 12)
freqs = freqs.to_normalized()
self.assertEqual(freqs["A"], 4 / 10)
# from an empty dict
freqs = number.CategoryFreqs()
d = freqs.to_normalized()
self.assertEqual(d.to_dict(), {})
def test_categoryfreqs_entropy(self):
"""correctly returns frequencies"""
vals = numpy.array([4 / 12, 3 / 12, 4 / 12, 1 / 12])
expect = -(vals * numpy.log2(vals)).sum()
freqs = number.CategoryFreqs({
"A": 4,
"C": 3,
"G": 4,
"T": 1
},
total=12)
assert_allclose(freqs.entropy, expect)