def test_testAverage1(self):
# Test of average.
ott = array([0., 1., 2., 3.], mask=[True, False, False, False])
assert_equal(2.0, average(ott, axis=0))
assert_equal(2.0, average(ott, weights=[1., 1., 2., 1.]))
result, wts = average(ott, weights=[1., 1., 2., 1.], returned=1)
assert_equal(2.0, result)
self.assertTrue(wts == 4.0)
ott[:] = masked
assert_equal(average(ott, axis=0).mask, [True])
ott = array([0., 1., 2., 3.], mask=[True, False, False, False])
ott = ott.reshape(2, 2)
ott[:, 1] = masked
assert_equal(average(ott, axis=0), [2.0, 0.0])
assert_equal(average(ott, axis=1).mask[0], [True])
assert_equal([2., 0.], average(ott, axis=0))
result, wts = average(ott, axis=0, returned=1)
assert_equal(wts, [1., 0.])
def test_testAverage1(self):
# Test of average.
ott = array([0., 1., 2., 3.], mask=[True, False, False, False])
assert_equal(2.0, average(ott, axis=0))
assert_equal(2.0, average(ott, weights=[1., 1., 2., 1.]))
result, wts = average(ott, weights=[1., 1., 2., 1.], returned=1)
assert_equal(2.0, result)
self.assertTrue(wts == 4.0)
ott[:] = masked
assert_equal(average(ott, axis=0).mask, [True])
ott = array([0., 1., 2., 3.], mask=[True, False, False, False])
ott = ott.reshape(2, 2)
ott[:, 1] = masked
assert_equal(average(ott, axis=0), [2.0, 0.0])
assert_equal(average(ott, axis=1).mask[0], [True])
assert_equal([2., 0.], average(ott, axis=0))
result, wts = average(ott, axis=0, returned=1)
assert_equal(wts, [1., 0.])
def test_testAverage3(self):
# Yet more tests of average!
a = arange(6)
b = arange(6) * 3
r1, w1 = average([[a, b], [b, a]], axis=1, returned=1)
assert_equal(shape(r1), shape(w1))
assert_equal(r1.shape, w1.shape)
r2, w2 = average(ones((2, 2, 3)), axis=0, weights=[3, 1], returned=1)
assert_equal(shape(w2), shape(r2))
r2, w2 = average(ones((2, 2, 3)), returned=1)
assert_equal(shape(w2), shape(r2))
r2, w2 = average(ones((2, 2, 3)), weights=ones((2, 2, 3)), returned=1)
assert_equal(shape(w2), shape(r2))
a2d = array([[1, 2], [0, 4]], float)
a2dm = masked_array(a2d, [[False, False], [True, False]])
a2da = average(a2d, axis=0)
assert_equal(a2da, [0.5, 3.0])
a2dma = average(a2dm, axis=0)
assert_equal(a2dma, [1.0, 3.0])
a2dma = average(a2dm, axis=None)
assert_equal(a2dma, 7. / 3.)
a2dma = average(a2dm, axis=1)
assert_equal(a2dma, [1.5, 4.0])
def test_testAverage3(self):
# Yet more tests of average!
a = arange(6)
b = arange(6) * 3
r1, w1 = average([[a, b], [b, a]], axis=1, returned=1)
assert_equal(shape(r1), shape(w1))
assert_equal(r1.shape, w1.shape)
r2, w2 = average(ones((2, 2, 3)), axis=0, weights=[3, 1], returned=1)
assert_equal(shape(w2), shape(r2))
r2, w2 = average(ones((2, 2, 3)), returned=1)
assert_equal(shape(w2), shape(r2))
r2, w2 = average(ones((2, 2, 3)), weights=ones((2, 2, 3)), returned=1)
assert_equal(shape(w2), shape(r2))
a2d = array([[1, 2], [0, 4]], float)
a2dm = masked_array(a2d, [[False, False], [True, False]])
a2da = average(a2d, axis=0)
assert_equal(a2da, [0.5, 3.0])
a2dma = average(a2dm, axis=0)
assert_equal(a2dma, [1.0, 3.0])
a2dma = average(a2dm, axis=None)
assert_equal(a2dma, 7. / 3.)
a2dma = average(a2dm, axis=1)
assert_equal(a2dma, [1.5, 4.0])
def test_complex(self):
# Test with complex data.
# (Regression test for https://github.com/numpy/numpy/issues/2684)
mask = np.array([[0, 0, 0, 1, 0], [0, 1, 0, 0, 0]], dtype=bool)
a = masked_array([[0, 1 + 2j, 3 + 4j, 5 + 6j, 7 + 8j],
[9j, 0 + 1j, 2 + 3j, 4 + 5j, 7 + 7j]],
mask=mask)
av = average(a)
expected = np.average(a.compressed())
assert_almost_equal(av.real, expected.real)
assert_almost_equal(av.imag, expected.imag)
av0 = average(a, axis=0)
expected0 = average(a.real, axis=0) + average(a.imag, axis=0) * 1j
assert_almost_equal(av0.real, expected0.real)
assert_almost_equal(av0.imag, expected0.imag)
av1 = average(a, axis=1)
expected1 = average(a.real, axis=1) + average(a.imag, axis=1) * 1j
assert_almost_equal(av1.real, expected1.real)
assert_almost_equal(av1.imag, expected1.imag)
# Test with the 'weights' argument.
wts = np.array([[0.5, 1.0, 2.0, 1.0, 0.5], [1.0, 1.0, 1.0, 1.0, 1.0]])
wav = average(a, weights=wts)
expected = np.average(a.compressed(), weights=wts[~mask])
assert_almost_equal(wav.real, expected.real)
assert_almost_equal(wav.imag, expected.imag)
wav0 = average(a, weights=wts, axis=0)
expected0 = (average(a.real, weights=wts, axis=0) +
average(a.imag, weights=wts, axis=0) * 1j)
assert_almost_equal(wav0.real, expected0.real)
assert_almost_equal(wav0.imag, expected0.imag)
wav1 = average(a, weights=wts, axis=1)
expected1 = (average(a.real, weights=wts, axis=1) +
average(a.imag, weights=wts, axis=1) * 1j)
assert_almost_equal(wav1.real, expected1.real)
assert_almost_equal(wav1.imag, expected1.imag)
def test_onintegers_with_mask(self):
# Test average on integers with mask
a = average(array([1, 2]))
assert_equal(a, 1.5)
a = average(array([1, 2, 3, 4], mask=[False, False, True, True]))
assert_equal(a, 1.5)
def test_testAverage2(self):
# More tests of average.
w1 = [0, 1, 1, 1, 1, 0]
w2 = [[0, 1, 1, 1, 1, 0], [1, 0, 0, 0, 0, 1]]
x = arange(6, dtype=np.float_)
assert_equal(average(x, axis=0), 2.5)
assert_equal(average(x, axis=0, weights=w1), 2.5)
y = array([arange(6, dtype=np.float_), 2.0 * arange(6)])
assert_equal(average(y, None), np.add.reduce(np.arange(6)) * 3. / 12.)
assert_equal(average(y, axis=0), np.arange(6) * 3. / 2.)
assert_equal(
average(y, axis=1),
[average(x, axis=0), average(x, axis=0) * 2.0])
assert_equal(average(y, None, weights=w2), 20. / 6.)
assert_equal(average(y, axis=0, weights=w2), [0., 1., 2., 3., 4., 10.])
assert_equal(
average(y, axis=1),
[average(x, axis=0), average(x, axis=0) * 2.0])
m1 = zeros(6)
m2 = [0, 0, 1, 1, 0, 0]
m3 = [[0, 0, 1, 1, 0, 0], [0, 1, 1, 1, 1, 0]]
m4 = ones(6)
m5 = [0, 1, 1, 1, 1, 1]
assert_equal(average(masked_array(x, m1), axis=0), 2.5)
assert_equal(average(masked_array(x, m2), axis=0), 2.5)
assert_equal(average(masked_array(x, m4), axis=0).mask, [True])
assert_equal(average(masked_array(x, m5), axis=0), 0.0)
assert_equal(count(average(masked_array(x, m4), axis=0)), 0)
z = masked_array(y, m3)
assert_equal(average(z, None), 20. / 6.)
assert_equal(average(z, axis=0), [0., 1., 99., 99., 4.0, 7.5])
assert_equal(average(z, axis=1), [2.5, 5.0])
assert_equal(average(z, axis=0, weights=w2),
[0., 1., 99., 99., 4.0, 10.0])
def test_complex(self):
# Test with complex data.
# (Regression test for https://github.com/numpy/numpy/issues/2684)
mask = np.array([[0, 0, 0, 1, 0],
[0, 1, 0, 0, 0]], dtype=bool)
a = masked_array([[0, 1+2j, 3+4j, 5+6j, 7+8j],
[9j, 0+1j, 2+3j, 4+5j, 7+7j]],
mask=mask)
av = average(a)
expected = np.average(a.compressed())
assert_almost_equal(av.real, expected.real)
assert_almost_equal(av.imag, expected.imag)
av0 = average(a, axis=0)
expected0 = average(a.real, axis=0) + average(a.imag, axis=0)*1j
assert_almost_equal(av0.real, expected0.real)
assert_almost_equal(av0.imag, expected0.imag)
av1 = average(a, axis=1)
expected1 = average(a.real, axis=1) + average(a.imag, axis=1)*1j
assert_almost_equal(av1.real, expected1.real)
assert_almost_equal(av1.imag, expected1.imag)
# Test with the 'weights' argument.
wts = np.array([[0.5, 1.0, 2.0, 1.0, 0.5],
[1.0, 1.0, 1.0, 1.0, 1.0]])
wav = average(a, weights=wts)
expected = np.average(a.compressed(), weights=wts[~mask])
assert_almost_equal(wav.real, expected.real)
assert_almost_equal(wav.imag, expected.imag)
wav0 = average(a, weights=wts, axis=0)
expected0 = (average(a.real, weights=wts, axis=0) +
average(a.imag, weights=wts, axis=0)*1j)
assert_almost_equal(wav0.real, expected0.real)
assert_almost_equal(wav0.imag, expected0.imag)
wav1 = average(a, weights=wts, axis=1)
expected1 = (average(a.real, weights=wts, axis=1) +
average(a.imag, weights=wts, axis=1)*1j)
assert_almost_equal(wav1.real, expected1.real)
assert_almost_equal(wav1.imag, expected1.imag)
def test_onintegers_with_mask(self):
# Test average on integers with mask
a = average(array([1, 2]))
assert_equal(a, 1.5)
a = average(array([1, 2, 3, 4], mask=[False, False, True, True]))
assert_equal(a, 1.5)
def test_testAverage2(self):
# More tests of average.
w1 = [0, 1, 1, 1, 1, 0]
w2 = [[0, 1, 1, 1, 1, 0], [1, 0, 0, 0, 0, 1]]
x = arange(6, dtype=np.float_)
assert_equal(average(x, axis=0), 2.5)
assert_equal(average(x, axis=0, weights=w1), 2.5)
y = array([arange(6, dtype=np.float_), 2.0 * arange(6)])
assert_equal(average(y, None), np.add.reduce(np.arange(6)) * 3. / 12.)
assert_equal(average(y, axis=0), np.arange(6) * 3. / 2.)
assert_equal(average(y, axis=1),
[average(x, axis=0), average(x, axis=0) * 2.0])
assert_equal(average(y, None, weights=w2), 20. / 6.)
assert_equal(average(y, axis=0, weights=w2),
[0., 1., 2., 3., 4., 10.])
assert_equal(average(y, axis=1),
[average(x, axis=0), average(x, axis=0) * 2.0])
m1 = zeros(6)
m2 = [0, 0, 1, 1, 0, 0]
m3 = [[0, 0, 1, 1, 0, 0], [0, 1, 1, 1, 1, 0]]
m4 = ones(6)
m5 = [0, 1, 1, 1, 1, 1]
assert_equal(average(masked_array(x, m1), axis=0), 2.5)
assert_equal(average(masked_array(x, m2), axis=0), 2.5)
assert_equal(average(masked_array(x, m4), axis=0).mask, [True])
assert_equal(average(masked_array(x, m5), axis=0), 0.0)
assert_equal(count(average(masked_array(x, m4), axis=0)), 0)
z = masked_array(y, m3)
assert_equal(average(z, None), 20. / 6.)
assert_equal(average(z, axis=0), [0., 1., 99., 99., 4.0, 7.5])
assert_equal(average(z, axis=1), [2.5, 5.0])
assert_equal(average(z, axis=0, weights=w2),
[0., 1., 99., 99., 4.0, 10.0])
def generate_n_poisson_weights_for_average(n, n_events_in_bins = [100, 120]):
mean = average(n_events_in_bins)
return generate_n_poisson_weights(n, mean)
import pandas as pd
import gc
import numpy as np
import timeit as t
import LoanAggregateCalculator
from numpy.ma.extras import average
a = np.arange(100)
aa = np.arange(100, 200)
test_object = LoanAggregateCalculator.LoanAggregateCalculator()
s = pd.Series(a)
ss = pd.Series(aa)
i = np.random.choice(a, size=10)
#timer1 = t.repeat("ss[i]", "from __main__ import ss,i \ gc.enable()", number=1000)
# convert to micro seconds
#print(str(average(timer1)*10)+' us')
'text = "sample string"; char = "g"'
gc.enable()
timer2 = t.repeat("test_object.calculate_aggregate()",
setup='gc.enable();' + 'from __main__ import test_object',
number=1)
# convert to micro seconds
print(str(average(timer2) * 10) + ' us')
def generate_n_poisson_weights_for_average(n, n_events_in_bins = [100, 120]):
mean = average(n_events_in_bins)
return generate_n_poisson_weights(n, mean)
