def test_std(self):
"""Should produce a standard deviation of 1.0 for a std normal dist"""
expected = 1.58113883008
self.assertFloatEqual(std(array([1,2,3,4,5])), expected)
expected_a = array([expected, expected, expected, expected, expected])
a = array([[1,2,3,4,5],[5,1,2,3,4],[4,5,1,2,3],[3,4,5,1,2],[2,3,4,5,1]])
self.assertFloatEqual(std(a,axis=0), expected_a)
self.assertFloatEqual(std(a,axis=1), expected_a)
self.assertRaises(ValueError, std, a, 5)
def test_std(self):
"""Should produce a standard deviation of 1.0 for a std normal dist"""
expected = 1.58113883008
self.assertFloatEqual(std(array([1, 2, 3, 4, 5])), expected)
expected_a = array([expected, expected, expected, expected, expected])
a = array([[1, 2, 3, 4, 5], [5, 1, 2, 3, 4], [4, 5, 1, 2, 3],
[3, 4, 5, 1, 2], [2, 3, 4, 5, 1]])
self.assertFloatEqual(std(a, axis=0), expected_a)
self.assertFloatEqual(std(a, axis=1), expected_a)
self.assertRaises(ValueError, std, a, 5)
def test_std_2d(self):
"""Should produce from 2darray the same stdevs as scipy.stats.std"""
inp = array([[1, 2, 3], [4, 5, 6]])
exps = ( #tuple(scipy_std(inp, ax) for ax in [None, 0, 1])
1.8708286933869707, array([2.12132034, 2.12132034,
2.12132034]), array([1., 1.]))
results = tuple(std(inp, ax) for ax in [None, 0, 1])
for obs, exp in zip(results, exps):
testing.assert_almost_equal(obs, exp)
def test_std_2d(self):
"""Should produce from 2darray the same stdevs as scipy.stats.std"""
inp = array([[1,2,3],[4,5,6]])
exps = ( #tuple(scipy_std(inp, ax) for ax in [None, 0, 1])
1.8708286933869707,
array([ 2.12132034, 2.12132034, 2.12132034]),
array([ 1., 1.]))
results = tuple(std(inp, ax) for ax in [None, 0, 1])
for obs, exp in zip(results, exps):
testing.assert_almost_equal(obs, exp)
def test_std_3d(self):
"""Should produce from 3darray the same std devs as scipy.stats.std"""
inp3d = array( #2,2,3
[[[0, 2, 2], [3, 4, 5]], [[1, 9, 0], [9, 10, 1]]])
exp3d = ( #for axis None, 0, 1, 2: calc from scipy.stats.std
3.63901418552,
array([[0.70710678, 4.94974747, 1.41421356],
[4.24264069, 4.24264069, 2.82842712]]),
array([[2.12132034, 1.41421356, 2.12132034],
[5.65685425, 0.70710678, 0.70710678]]),
array([[1.15470054, 1.], [4.93288286, 4.93288286]]))
res = tuple(std(inp3d, ax) for ax in [None, 0, 1, 2])
for obs, exp in zip(res, exp3d):
testing.assert_almost_equal(obs, exp)
def test_std_3d(self):
"""Should produce from 3darray the same std devs as scipy.stats.std"""
inp3d = array(#2,2,3
[[[ 0, 2, 2],
[ 3, 4, 5]],
[[ 1, 9, 0],
[ 9, 10, 1]]])
exp3d = (#for axis None, 0, 1, 2: calc from scipy.stats.std
3.63901418552,
array([[ 0.70710678, 4.94974747, 1.41421356],
[ 4.24264069, 4.24264069, 2.82842712]]),
array([[ 2.12132034, 1.41421356, 2.12132034],
[ 5.65685425, 0.70710678, 0.70710678]]),
array([[ 1.15470054, 1. ],
[ 4.93288286, 4.93288286]]))
res = tuple(std(inp3d, ax) for ax in [None, 0, 1, 2])
for obs, exp in zip(res, exp3d):
testing.assert_almost_equal(obs, exp)
def change_length_analysis(n, branch_lengths=\
[0.05, 0.1, 0.15, 0.2, 0.25,0.3, 0.35, 0.4], leaves=16, slen=1000):
header = ['mean_three_m','mean_three_sd','var_three_m', 'var_three_sd', \
'ratio_three_m', 'ratio_three_sd']
conditions = {'one_q':balanced_one_q_tree, 'two_q':balanced_two_q_tree, \
'multi_q':balanced_multi_q_tree}
condition_names = ['one_q', 'two_q', 'multi_q']
result_f = mean_var_eigen
full_header = ['length'] + \
[name+i for name in condition_names for i in header]
print '\t'.join(full_header)
for b in lengths:
result = [b]
for c in condition_names:
tree_f = conditions[c]
make_tree = lambda:tree_f(n=leaves,seq_length=slen,length=b,\
perturb=False)
samples = list(tree_stats(n, make_tree, result_f))
means = average(samples)
stdevs = std(samples)
for i in zip(means, stdevs):
result.extend(i)
print '\t'.join(map(str, result))
def change_length_analysis(n, branch_lengths=\
[0.05, 0.1, 0.15, 0.2, 0.25,0.3, 0.35, 0.4], leaves=16, slen=1000):
header = ['mean_three_m','mean_three_sd','var_three_m', 'var_three_sd', \
'ratio_three_m', 'ratio_three_sd']
conditions = {'one_q':balanced_one_q_tree, 'two_q':balanced_two_q_tree, \
'multi_q':balanced_multi_q_tree}
condition_names = ['one_q', 'two_q', 'multi_q']
result_f = mean_var_eigen
full_header = ['length'] + \
[name+i for name in condition_names for i in header]
print '\t'.join(full_header)
for b in lengths:
result = [b]
for c in condition_names:
tree_f = conditions[c]
make_tree = lambda:tree_f(n=leaves,seq_length=slen,length=b,\
perturb=False)
samples = list(tree_stats(n, make_tree, result_f))
means = average(samples)
stdevs = std(samples)
for i in zip(means, stdevs):
result.extend(i)
print '\t'.join(map(str, result))
def var(x):
return std(x)**2
def var(x):
return std(x)**2
