#!/usr/bin/env python
#file test_parse.py
from numpy import array
from cogent.util.unit_test import TestCase, main
from cogent.maths.stats.rarefaction import (subsample,
naive_histogram,
wrap_numpy_histogram,
rarefaction,
subsample_freq_dist_nonzero,
subsample_random,
subsample_multinomial)
__author__ = "Rob Knight"
__copyright__ = "Copyright 2007-2012, The Cogent Project"
__credits__ = ["Rob Knight"]
__license__ = "GPL"
__version__ = "1.5.3-dev"
__maintainer__ = "Rob Knight"
__email__ = "rob@spot.colorado.edu"
__status__ = "Production"
class TopLevelTests(TestCase):
"""Tests of top-level functions"""
def test_subsample(self):
"""subsample should return a random subsample of a vector"""
a = array([0,5,0])
self.assertEqual(subsample(a,5), array([0,5,0]))
self.assertEqual(subsample(a,2), array([0,2,0]))
b = array([2,0,1])
# selecting 2 counts from the vector 1000 times yields each of the
# two possible results at least once each
b = array([2,0,1])
actual = {}
for i in range(1000):
e = subsample(b,2)
actual[tuple(e)] = None
self.assertEqual(actual, {(1,0,1):None,(2,0,0):None})
obs = subsample(b,2)
assert (obs == array([1,0,1])).all() or (obs == array([2,0,0])).all()
def test_subsample_freq_dist_nonzero(self):
"""subsample_freq_dist_nonzero should return a random subsample of a vector
"""
a = array([0,5,0])
self.assertEqual(subsample_freq_dist_nonzero(a,5), array([0,5,0]))
self.assertEqual(subsample_freq_dist_nonzero(a,2), array([0,2,0]))
# selecting 35 counts from the vector 1000 times yields each at least
# two different results
b = array([2,0,1,2,1,8,6,0,3,3,5,0,0,0,5])
actual = {}
for i in range(100):
e = subsample_freq_dist_nonzero(b,35)
self.assertTrue(e.sum(),35)
actual[tuple(e)] = None
self.assertTrue(len(actual) > 1)
# selecting 2 counts from the vector 1000 times yields each of the
# two possible results at least once each (note that an issue with an
# inital buggy version of subsample_freq_dist_nonzero was detected with
# this test, so don't remove - )
b = array([2,0,1])
actual = {}
for i in range(1000):
e = subsample_freq_dist_nonzero(b,2)
actual[tuple(e)] = None
self.assertTrue(e.sum() == 2)
self.assertEqual(actual, {(1,0,1):None,(2,0,0):None})
def test_subsample_random(self):
"""subsample_random should return a random subsample of a vector
"""
a = array([0,5,0])
self.assertEqual(subsample_random(a,5), array([0,5,0]))
self.assertEqual(subsample_random(a,2), array([0,2,0]))
# selecting 35 counts from the vector 1000 times yields each at least
# two different results
b = array([2,0,1,2,1,8,6,0,3,3,5,0,0,0,5])
actual = {}
for i in range(100):
e = subsample_random(b,35)
self.assertTrue(e.sum(),35)
actual[tuple(e)] = None
self.assertTrue(len(actual) > 1)
# selecting 2 counts from the vector 1000 times yields each of the
# two possible results at least once each
b = array([2,0,1])
actual = {}
for i in range(1000):
e = subsample_random(b,2)
actual[tuple(e)] = None
self.assertTrue(e.sum() == 2)
self.assertEqual(actual, {(1,0,1):None,(2,0,0):None})
def test_subsample_multinomial(self):
"""subsample_multinomial should return a random subsample of a vector
"""
# selecting 35 counts from the vector 1000 times yields each at least
# two different results
actual = {}
for i in range(100):
b = array([2,0,1,2,1,8,6,0,3,3,5,0,0,0,5])
e = subsample_multinomial(b,35)
self.assertTrue(e.sum(),35)
actual[tuple(e)] = None
self.assertTrue(len(actual) > 1)
def test_naive_histogram(self):
"""naive_histogram should produce expected result"""
vals = array([1,0,0,3])
self.assertEqual(naive_histogram(vals), array([2,1,0,1]))
self.assertEqual(naive_histogram(vals, 4), array([2,1,0,1,0]))
def test_wrap_numpy_histogram(self):
"""wrap_numpy_histogram should provide expected result"""
vals = array([1,0,0,3])
h_f = wrap_numpy_histogram(3)
self.assertEqual(h_f(vals), array([2,1,0,1]))
h_f = wrap_numpy_histogram(4)
self.assertEqual(h_f(vals, 4), array([2,1,0,1,0]))
def test_rarefaction(self):
"""rarefaction should produce expected curve"""
vals = array([5,0,0,3,0,10], dtype=int)
res = [r.copy() for r in rarefaction(vals, stride=1)]
self.assertEqual(len(res), 18)
for i, r in enumerate(res):
self.assertEqual(r.sum(), i+1)
#make sure we didn't add any bad counts
for pos in [1,2,4]:
self.assertEqual(r[pos], 0)
#when we get to end should recapture orig vals
self.assertEqual(r, vals)
res = [r.copy() for r in rarefaction(vals, stride=3)]
self.assertEqual(len(res), 6)
for i, r in enumerate(res):
self.assertEqual(r.sum(), 3*(i+1))
#make sure we didn't add any bad counts
for pos in [1,2,4]:
self.assertEqual(r[pos], 0)
#when we get to end should recapture orig vals
self.assertEqual(r, vals)
#repeat everything above using alt. input format
orig_vals = vals.copy()
vals = array([0,0,0,0,0,3,3,3,5,5,5,5,5,5,5,5,5,5], dtype=int)
res = [r.copy() for r in rarefaction(vals, stride=1, is_counts=False)]
self.assertEqual(len(res), 18)
for i, r in enumerate(res):
self.assertEqual(r.sum(), i+1)
#make sure we didn't add any bad counts
for pos in [1,2,4]:
self.assertEqual(r[pos], 0)
#when we get to end should recapture orig vals
self.assertEqual(r, orig_vals)
res = [r.copy() for r in rarefaction(vals, stride=3, is_counts=False)]
self.assertEqual(len(res), 6)
for i, r in enumerate(res):
self.assertEqual(r.sum(), 3*(i+1))
#make sure we didn't add any bad counts
for pos in [1,2,4]:
self.assertEqual(r[pos], 0)
#when we get to end should recapture orig vals
self.assertEqual(r, orig_vals)
if __name__ =='__main__':
main()
