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875 lines
33 KiB
875 lines
33 KiB
from functools import reduce
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import pytest
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import numpy as np
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import numpy.core.umath as umath
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import numpy.core.fromnumeric as fromnumeric
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from numpy.testing import (
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assert_, assert_raises, assert_equal,
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)
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from numpy.ma import (
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MaskType, MaskedArray, absolute, add, all, allclose, allequal, alltrue,
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arange, arccos, arcsin, arctan, arctan2, array, average, choose,
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concatenate, conjugate, cos, cosh, count, divide, equal, exp, filled,
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getmask, greater, greater_equal, inner, isMaskedArray, less,
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less_equal, log, log10, make_mask, masked, masked_array, masked_equal,
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masked_greater, masked_greater_equal, masked_inside, masked_less,
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masked_less_equal, masked_not_equal, masked_outside,
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masked_print_option, masked_values, masked_where, maximum, minimum,
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multiply, nomask, nonzero, not_equal, ones, outer, product, put, ravel,
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repeat, resize, shape, sin, sinh, sometrue, sort, sqrt, subtract, sum,
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take, tan, tanh, transpose, where, zeros,
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)
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from numpy.compat import pickle
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pi = np.pi
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def eq(v, w, msg=''):
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result = allclose(v, w)
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if not result:
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print(f'Not eq:{msg}\n{v}\n----{w}')
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return result
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class TestMa:
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def setup_method(self):
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x = np.array([1., 1., 1., -2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
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y = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.])
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a10 = 10.
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m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
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m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1]
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xm = array(x, mask=m1)
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ym = array(y, mask=m2)
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z = np.array([-.5, 0., .5, .8])
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zm = array(z, mask=[0, 1, 0, 0])
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xf = np.where(m1, 1e+20, x)
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s = x.shape
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xm.set_fill_value(1e+20)
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self.d = (x, y, a10, m1, m2, xm, ym, z, zm, xf, s)
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def test_testBasic1d(self):
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# Test of basic array creation and properties in 1 dimension.
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(x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d
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assert_(not isMaskedArray(x))
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assert_(isMaskedArray(xm))
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assert_equal(shape(xm), s)
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assert_equal(xm.shape, s)
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assert_equal(xm.dtype, x.dtype)
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assert_equal(xm.size, reduce(lambda x, y:x * y, s))
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assert_equal(count(xm), len(m1) - reduce(lambda x, y:x + y, m1))
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assert_(eq(xm, xf))
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assert_(eq(filled(xm, 1.e20), xf))
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assert_(eq(x, xm))
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@pytest.mark.parametrize("s", [(4, 3), (6, 2)])
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def test_testBasic2d(self, s):
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# Test of basic array creation and properties in 2 dimensions.
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(x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d
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x.shape = s
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y.shape = s
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xm.shape = s
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ym.shape = s
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xf.shape = s
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assert_(not isMaskedArray(x))
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assert_(isMaskedArray(xm))
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assert_equal(shape(xm), s)
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assert_equal(xm.shape, s)
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assert_equal(xm.size, reduce(lambda x, y: x * y, s))
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assert_equal(count(xm), len(m1) - reduce(lambda x, y: x + y, m1))
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assert_(eq(xm, xf))
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assert_(eq(filled(xm, 1.e20), xf))
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assert_(eq(x, xm))
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def test_testArithmetic(self):
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# Test of basic arithmetic.
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(x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d
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a2d = array([[1, 2], [0, 4]])
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a2dm = masked_array(a2d, [[0, 0], [1, 0]])
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assert_(eq(a2d * a2d, a2d * a2dm))
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assert_(eq(a2d + a2d, a2d + a2dm))
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assert_(eq(a2d - a2d, a2d - a2dm))
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for s in [(12,), (4, 3), (2, 6)]:
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x = x.reshape(s)
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y = y.reshape(s)
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xm = xm.reshape(s)
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ym = ym.reshape(s)
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xf = xf.reshape(s)
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assert_(eq(-x, -xm))
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assert_(eq(x + y, xm + ym))
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assert_(eq(x - y, xm - ym))
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assert_(eq(x * y, xm * ym))
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with np.errstate(divide='ignore', invalid='ignore'):
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assert_(eq(x / y, xm / ym))
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assert_(eq(a10 + y, a10 + ym))
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assert_(eq(a10 - y, a10 - ym))
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assert_(eq(a10 * y, a10 * ym))
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with np.errstate(divide='ignore', invalid='ignore'):
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assert_(eq(a10 / y, a10 / ym))
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assert_(eq(x + a10, xm + a10))
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assert_(eq(x - a10, xm - a10))
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assert_(eq(x * a10, xm * a10))
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assert_(eq(x / a10, xm / a10))
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assert_(eq(x ** 2, xm ** 2))
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assert_(eq(abs(x) ** 2.5, abs(xm) ** 2.5))
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assert_(eq(x ** y, xm ** ym))
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assert_(eq(np.add(x, y), add(xm, ym)))
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assert_(eq(np.subtract(x, y), subtract(xm, ym)))
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assert_(eq(np.multiply(x, y), multiply(xm, ym)))
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with np.errstate(divide='ignore', invalid='ignore'):
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assert_(eq(np.divide(x, y), divide(xm, ym)))
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def test_testMixedArithmetic(self):
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na = np.array([1])
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ma = array([1])
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assert_(isinstance(na + ma, MaskedArray))
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assert_(isinstance(ma + na, MaskedArray))
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def test_testUfuncs1(self):
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# Test various functions such as sin, cos.
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(x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d
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assert_(eq(np.cos(x), cos(xm)))
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assert_(eq(np.cosh(x), cosh(xm)))
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assert_(eq(np.sin(x), sin(xm)))
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assert_(eq(np.sinh(x), sinh(xm)))
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assert_(eq(np.tan(x), tan(xm)))
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assert_(eq(np.tanh(x), tanh(xm)))
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with np.errstate(divide='ignore', invalid='ignore'):
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assert_(eq(np.sqrt(abs(x)), sqrt(xm)))
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assert_(eq(np.log(abs(x)), log(xm)))
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assert_(eq(np.log10(abs(x)), log10(xm)))
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assert_(eq(np.exp(x), exp(xm)))
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assert_(eq(np.arcsin(z), arcsin(zm)))
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assert_(eq(np.arccos(z), arccos(zm)))
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assert_(eq(np.arctan(z), arctan(zm)))
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assert_(eq(np.arctan2(x, y), arctan2(xm, ym)))
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assert_(eq(np.absolute(x), absolute(xm)))
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assert_(eq(np.equal(x, y), equal(xm, ym)))
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assert_(eq(np.not_equal(x, y), not_equal(xm, ym)))
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assert_(eq(np.less(x, y), less(xm, ym)))
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assert_(eq(np.greater(x, y), greater(xm, ym)))
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assert_(eq(np.less_equal(x, y), less_equal(xm, ym)))
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assert_(eq(np.greater_equal(x, y), greater_equal(xm, ym)))
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assert_(eq(np.conjugate(x), conjugate(xm)))
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assert_(eq(np.concatenate((x, y)), concatenate((xm, ym))))
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assert_(eq(np.concatenate((x, y)), concatenate((x, y))))
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assert_(eq(np.concatenate((x, y)), concatenate((xm, y))))
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assert_(eq(np.concatenate((x, y, x)), concatenate((x, ym, x))))
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def test_xtestCount(self):
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# Test count
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ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
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assert_(count(ott).dtype.type is np.intp)
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assert_equal(3, count(ott))
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assert_equal(1, count(1))
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assert_(eq(0, array(1, mask=[1])))
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ott = ott.reshape((2, 2))
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assert_(count(ott).dtype.type is np.intp)
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assert_(isinstance(count(ott, 0), np.ndarray))
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assert_(count(ott).dtype.type is np.intp)
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assert_(eq(3, count(ott)))
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assert_(getmask(count(ott, 0)) is nomask)
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assert_(eq([1, 2], count(ott, 0)))
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def test_testMinMax(self):
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# Test minimum and maximum.
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(x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d
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xr = np.ravel(x) # max doesn't work if shaped
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xmr = ravel(xm)
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# true because of careful selection of data
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assert_(eq(max(xr), maximum.reduce(xmr)))
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assert_(eq(min(xr), minimum.reduce(xmr)))
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def test_testAddSumProd(self):
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# Test add, sum, product.
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(x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d
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assert_(eq(np.add.reduce(x), add.reduce(x)))
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assert_(eq(np.add.accumulate(x), add.accumulate(x)))
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assert_(eq(4, sum(array(4), axis=0)))
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assert_(eq(4, sum(array(4), axis=0)))
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assert_(eq(np.sum(x, axis=0), sum(x, axis=0)))
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assert_(eq(np.sum(filled(xm, 0), axis=0), sum(xm, axis=0)))
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assert_(eq(np.sum(x, 0), sum(x, 0)))
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assert_(eq(np.prod(x, axis=0), product(x, axis=0)))
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assert_(eq(np.prod(x, 0), product(x, 0)))
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assert_(eq(np.prod(filled(xm, 1), axis=0),
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product(xm, axis=0)))
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if len(s) > 1:
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assert_(eq(np.concatenate((x, y), 1),
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concatenate((xm, ym), 1)))
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assert_(eq(np.add.reduce(x, 1), add.reduce(x, 1)))
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assert_(eq(np.sum(x, 1), sum(x, 1)))
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assert_(eq(np.prod(x, 1), product(x, 1)))
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def test_testCI(self):
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# Test of conversions and indexing
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x1 = np.array([1, 2, 4, 3])
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x2 = array(x1, mask=[1, 0, 0, 0])
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x3 = array(x1, mask=[0, 1, 0, 1])
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x4 = array(x1)
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# test conversion to strings
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str(x2) # raises?
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repr(x2) # raises?
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assert_(eq(np.sort(x1), sort(x2, fill_value=0)))
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# tests of indexing
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assert_(type(x2[1]) is type(x1[1]))
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assert_(x1[1] == x2[1])
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assert_(x2[0] is masked)
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assert_(eq(x1[2], x2[2]))
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assert_(eq(x1[2:5], x2[2:5]))
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assert_(eq(x1[:], x2[:]))
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assert_(eq(x1[1:], x3[1:]))
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x1[2] = 9
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x2[2] = 9
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assert_(eq(x1, x2))
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x1[1:3] = 99
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x2[1:3] = 99
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assert_(eq(x1, x2))
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x2[1] = masked
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assert_(eq(x1, x2))
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x2[1:3] = masked
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assert_(eq(x1, x2))
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x2[:] = x1
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x2[1] = masked
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assert_(allequal(getmask(x2), array([0, 1, 0, 0])))
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x3[:] = masked_array([1, 2, 3, 4], [0, 1, 1, 0])
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assert_(allequal(getmask(x3), array([0, 1, 1, 0])))
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x4[:] = masked_array([1, 2, 3, 4], [0, 1, 1, 0])
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assert_(allequal(getmask(x4), array([0, 1, 1, 0])))
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assert_(allequal(x4, array([1, 2, 3, 4])))
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x1 = np.arange(5) * 1.0
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x2 = masked_values(x1, 3.0)
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assert_(eq(x1, x2))
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assert_(allequal(array([0, 0, 0, 1, 0], MaskType), x2.mask))
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assert_(eq(3.0, x2.fill_value))
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x1 = array([1, 'hello', 2, 3], object)
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x2 = np.array([1, 'hello', 2, 3], object)
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s1 = x1[1]
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s2 = x2[1]
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assert_equal(type(s2), str)
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assert_equal(type(s1), str)
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assert_equal(s1, s2)
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assert_(x1[1:1].shape == (0,))
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def test_testCopySize(self):
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# Tests of some subtle points of copying and sizing.
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n = [0, 0, 1, 0, 0]
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m = make_mask(n)
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m2 = make_mask(m)
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assert_(m is m2)
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m3 = make_mask(m, copy=True)
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assert_(m is not m3)
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x1 = np.arange(5)
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y1 = array(x1, mask=m)
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assert_(y1._data is not x1)
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assert_(allequal(x1, y1._data))
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assert_(y1._mask is m)
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y1a = array(y1, copy=0)
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# For copy=False, one might expect that the array would just
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# passed on, i.e., that it would be "is" instead of "==".
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# See gh-4043 for discussion.
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assert_(y1a._mask.__array_interface__ ==
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y1._mask.__array_interface__)
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y2 = array(x1, mask=m3, copy=0)
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assert_(y2._mask is m3)
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assert_(y2[2] is masked)
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y2[2] = 9
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assert_(y2[2] is not masked)
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assert_(y2._mask is m3)
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assert_(allequal(y2.mask, 0))
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y2a = array(x1, mask=m, copy=1)
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assert_(y2a._mask is not m)
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assert_(y2a[2] is masked)
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y2a[2] = 9
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assert_(y2a[2] is not masked)
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assert_(y2a._mask is not m)
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assert_(allequal(y2a.mask, 0))
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y3 = array(x1 * 1.0, mask=m)
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assert_(filled(y3).dtype is (x1 * 1.0).dtype)
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x4 = arange(4)
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x4[2] = masked
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y4 = resize(x4, (8,))
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assert_(eq(concatenate([x4, x4]), y4))
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assert_(eq(getmask(y4), [0, 0, 1, 0, 0, 0, 1, 0]))
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y5 = repeat(x4, (2, 2, 2, 2), axis=0)
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assert_(eq(y5, [0, 0, 1, 1, 2, 2, 3, 3]))
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y6 = repeat(x4, 2, axis=0)
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assert_(eq(y5, y6))
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def test_testPut(self):
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# Test of put
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d = arange(5)
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n = [0, 0, 0, 1, 1]
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m = make_mask(n)
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m2 = m.copy()
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x = array(d, mask=m)
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assert_(x[3] is masked)
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assert_(x[4] is masked)
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x[[1, 4]] = [10, 40]
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assert_(x._mask is m)
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assert_(x[3] is masked)
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assert_(x[4] is not masked)
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assert_(eq(x, [0, 10, 2, -1, 40]))
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x = array(d, mask=m2, copy=True)
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x.put([0, 1, 2], [-1, 100, 200])
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assert_(x._mask is not m2)
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assert_(x[3] is masked)
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assert_(x[4] is masked)
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assert_(eq(x, [-1, 100, 200, 0, 0]))
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def test_testPut2(self):
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# Test of put
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d = arange(5)
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x = array(d, mask=[0, 0, 0, 0, 0])
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z = array([10, 40], mask=[1, 0])
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assert_(x[2] is not masked)
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assert_(x[3] is not masked)
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x[2:4] = z
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assert_(x[2] is masked)
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assert_(x[3] is not masked)
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assert_(eq(x, [0, 1, 10, 40, 4]))
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d = arange(5)
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x = array(d, mask=[0, 0, 0, 0, 0])
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y = x[2:4]
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z = array([10, 40], mask=[1, 0])
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assert_(x[2] is not masked)
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assert_(x[3] is not masked)
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y[:] = z
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assert_(y[0] is masked)
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assert_(y[1] is not masked)
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assert_(eq(y, [10, 40]))
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assert_(x[2] is masked)
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assert_(x[3] is not masked)
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assert_(eq(x, [0, 1, 10, 40, 4]))
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def test_testMaPut(self):
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(x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d
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m = [1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1]
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i = np.nonzero(m)[0]
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put(ym, i, zm)
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assert_(all(take(ym, i, axis=0) == zm))
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def test_testOddFeatures(self):
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# Test of other odd features
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x = arange(20)
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x = x.reshape(4, 5)
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x.flat[5] = 12
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assert_(x[1, 0] == 12)
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z = x + 10j * x
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assert_(eq(z.real, x))
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assert_(eq(z.imag, 10 * x))
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assert_(eq((z * conjugate(z)).real, 101 * x * x))
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z.imag[...] = 0.0
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x = arange(10)
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x[3] = masked
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assert_(str(x[3]) == str(masked))
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c = x >= 8
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assert_(count(where(c, masked, masked)) == 0)
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assert_(shape(where(c, masked, masked)) == c.shape)
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z = where(c, x, masked)
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assert_(z.dtype is x.dtype)
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assert_(z[3] is masked)
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assert_(z[4] is masked)
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assert_(z[7] is masked)
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assert_(z[8] is not masked)
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assert_(z[9] is not masked)
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assert_(eq(x, z))
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z = where(c, masked, x)
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assert_(z.dtype is x.dtype)
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assert_(z[3] is masked)
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assert_(z[4] is not masked)
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assert_(z[7] is not masked)
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assert_(z[8] is masked)
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assert_(z[9] is masked)
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z = masked_where(c, x)
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assert_(z.dtype is x.dtype)
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assert_(z[3] is masked)
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assert_(z[4] is not masked)
|
|
assert_(z[7] is not masked)
|
|
assert_(z[8] is masked)
|
|
assert_(z[9] is masked)
|
|
assert_(eq(x, z))
|
|
x = array([1., 2., 3., 4., 5.])
|
|
c = array([1, 1, 1, 0, 0])
|
|
x[2] = masked
|
|
z = where(c, x, -x)
|
|
assert_(eq(z, [1., 2., 0., -4., -5]))
|
|
c[0] = masked
|
|
z = where(c, x, -x)
|
|
assert_(eq(z, [1., 2., 0., -4., -5]))
|
|
assert_(z[0] is masked)
|
|
assert_(z[1] is not masked)
|
|
assert_(z[2] is masked)
|
|
assert_(eq(masked_where(greater(x, 2), x), masked_greater(x, 2)))
|
|
assert_(eq(masked_where(greater_equal(x, 2), x),
|
|
masked_greater_equal(x, 2)))
|
|
assert_(eq(masked_where(less(x, 2), x), masked_less(x, 2)))
|
|
assert_(eq(masked_where(less_equal(x, 2), x), masked_less_equal(x, 2)))
|
|
assert_(eq(masked_where(not_equal(x, 2), x), masked_not_equal(x, 2)))
|
|
assert_(eq(masked_where(equal(x, 2), x), masked_equal(x, 2)))
|
|
assert_(eq(masked_where(not_equal(x, 2), x), masked_not_equal(x, 2)))
|
|
assert_(eq(masked_inside(list(range(5)), 1, 3), [0, 199, 199, 199, 4]))
|
|
assert_(eq(masked_outside(list(range(5)), 1, 3), [199, 1, 2, 3, 199]))
|
|
assert_(eq(masked_inside(array(list(range(5)),
|
|
mask=[1, 0, 0, 0, 0]), 1, 3).mask,
|
|
[1, 1, 1, 1, 0]))
|
|
assert_(eq(masked_outside(array(list(range(5)),
|
|
mask=[0, 1, 0, 0, 0]), 1, 3).mask,
|
|
[1, 1, 0, 0, 1]))
|
|
assert_(eq(masked_equal(array(list(range(5)),
|
|
mask=[1, 0, 0, 0, 0]), 2).mask,
|
|
[1, 0, 1, 0, 0]))
|
|
assert_(eq(masked_not_equal(array([2, 2, 1, 2, 1],
|
|
mask=[1, 0, 0, 0, 0]), 2).mask,
|
|
[1, 0, 1, 0, 1]))
|
|
assert_(eq(masked_where([1, 1, 0, 0, 0], [1, 2, 3, 4, 5]),
|
|
[99, 99, 3, 4, 5]))
|
|
atest = ones((10, 10, 10), dtype=np.float32)
|
|
btest = zeros(atest.shape, MaskType)
|
|
ctest = masked_where(btest, atest)
|
|
assert_(eq(atest, ctest))
|
|
z = choose(c, (-x, x))
|
|
assert_(eq(z, [1., 2., 0., -4., -5]))
|
|
assert_(z[0] is masked)
|
|
assert_(z[1] is not masked)
|
|
assert_(z[2] is masked)
|
|
x = arange(6)
|
|
x[5] = masked
|
|
y = arange(6) * 10
|
|
y[2] = masked
|
|
c = array([1, 1, 1, 0, 0, 0], mask=[1, 0, 0, 0, 0, 0])
|
|
cm = c.filled(1)
|
|
z = where(c, x, y)
|
|
zm = where(cm, x, y)
|
|
assert_(eq(z, zm))
|
|
assert_(getmask(zm) is nomask)
|
|
assert_(eq(zm, [0, 1, 2, 30, 40, 50]))
|
|
z = where(c, masked, 1)
|
|
assert_(eq(z, [99, 99, 99, 1, 1, 1]))
|
|
z = where(c, 1, masked)
|
|
assert_(eq(z, [99, 1, 1, 99, 99, 99]))
|
|
|
|
def test_testMinMax2(self):
|
|
# Test of minimum, maximum.
|
|
assert_(eq(minimum([1, 2, 3], [4, 0, 9]), [1, 0, 3]))
|
|
assert_(eq(maximum([1, 2, 3], [4, 0, 9]), [4, 2, 9]))
|
|
x = arange(5)
|
|
y = arange(5) - 2
|
|
x[3] = masked
|
|
y[0] = masked
|
|
assert_(eq(minimum(x, y), where(less(x, y), x, y)))
|
|
assert_(eq(maximum(x, y), where(greater(x, y), x, y)))
|
|
assert_(minimum.reduce(x) == 0)
|
|
assert_(maximum.reduce(x) == 4)
|
|
|
|
def test_testTakeTransposeInnerOuter(self):
|
|
# Test of take, transpose, inner, outer products
|
|
x = arange(24)
|
|
y = np.arange(24)
|
|
x[5:6] = masked
|
|
x = x.reshape(2, 3, 4)
|
|
y = y.reshape(2, 3, 4)
|
|
assert_(eq(np.transpose(y, (2, 0, 1)), transpose(x, (2, 0, 1))))
|
|
assert_(eq(np.take(y, (2, 0, 1), 1), take(x, (2, 0, 1), 1)))
|
|
assert_(eq(np.inner(filled(x, 0), filled(y, 0)),
|
|
inner(x, y)))
|
|
assert_(eq(np.outer(filled(x, 0), filled(y, 0)),
|
|
outer(x, y)))
|
|
y = array(['abc', 1, 'def', 2, 3], object)
|
|
y[2] = masked
|
|
t = take(y, [0, 3, 4])
|
|
assert_(t[0] == 'abc')
|
|
assert_(t[1] == 2)
|
|
assert_(t[2] == 3)
|
|
|
|
def test_testInplace(self):
|
|
# Test of inplace operations and rich comparisons
|
|
y = arange(10)
|
|
|
|
x = arange(10)
|
|
xm = arange(10)
|
|
xm[2] = masked
|
|
x += 1
|
|
assert_(eq(x, y + 1))
|
|
xm += 1
|
|
assert_(eq(x, y + 1))
|
|
|
|
x = arange(10)
|
|
xm = arange(10)
|
|
xm[2] = masked
|
|
x -= 1
|
|
assert_(eq(x, y - 1))
|
|
xm -= 1
|
|
assert_(eq(xm, y - 1))
|
|
|
|
x = arange(10) * 1.0
|
|
xm = arange(10) * 1.0
|
|
xm[2] = masked
|
|
x *= 2.0
|
|
assert_(eq(x, y * 2))
|
|
xm *= 2.0
|
|
assert_(eq(xm, y * 2))
|
|
|
|
x = arange(10) * 2
|
|
xm = arange(10)
|
|
xm[2] = masked
|
|
x //= 2
|
|
assert_(eq(x, y))
|
|
xm //= 2
|
|
assert_(eq(x, y))
|
|
|
|
x = arange(10) * 1.0
|
|
xm = arange(10) * 1.0
|
|
xm[2] = masked
|
|
x /= 2.0
|
|
assert_(eq(x, y / 2.0))
|
|
xm /= arange(10)
|
|
assert_(eq(xm, ones((10,))))
|
|
|
|
x = arange(10).astype(np.float32)
|
|
xm = arange(10)
|
|
xm[2] = masked
|
|
x += 1.
|
|
assert_(eq(x, y + 1.))
|
|
|
|
def test_testPickle(self):
|
|
# Test of pickling
|
|
x = arange(12)
|
|
x[4:10:2] = masked
|
|
x = x.reshape(4, 3)
|
|
for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
|
|
s = pickle.dumps(x, protocol=proto)
|
|
y = pickle.loads(s)
|
|
assert_(eq(x, y))
|
|
|
|
def test_testMasked(self):
|
|
# Test of masked element
|
|
xx = arange(6)
|
|
xx[1] = masked
|
|
assert_(str(masked) == '--')
|
|
assert_(xx[1] is masked)
|
|
assert_equal(filled(xx[1], 0), 0)
|
|
|
|
def test_testAverage1(self):
|
|
# Test of average.
|
|
ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
|
|
assert_(eq(2.0, average(ott, axis=0)))
|
|
assert_(eq(2.0, average(ott, weights=[1., 1., 2., 1.])))
|
|
result, wts = average(ott, weights=[1., 1., 2., 1.], returned=True)
|
|
assert_(eq(2.0, result))
|
|
assert_(wts == 4.0)
|
|
ott[:] = masked
|
|
assert_(average(ott, axis=0) is masked)
|
|
ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
|
|
ott = ott.reshape(2, 2)
|
|
ott[:, 1] = masked
|
|
assert_(eq(average(ott, axis=0), [2.0, 0.0]))
|
|
assert_(average(ott, axis=1)[0] is masked)
|
|
assert_(eq([2., 0.], average(ott, axis=0)))
|
|
result, wts = average(ott, axis=0, returned=True)
|
|
assert_(eq(wts, [1., 0.]))
|
|
|
|
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)
|
|
assert_(allclose(average(x, axis=0), 2.5))
|
|
assert_(allclose(average(x, axis=0, weights=w1), 2.5))
|
|
y = array([arange(6), 2.0 * arange(6)])
|
|
assert_(allclose(average(y, None),
|
|
np.add.reduce(np.arange(6)) * 3. / 12.))
|
|
assert_(allclose(average(y, axis=0), np.arange(6) * 3. / 2.))
|
|
assert_(allclose(average(y, axis=1),
|
|
[average(x, axis=0), average(x, axis=0)*2.0]))
|
|
assert_(allclose(average(y, None, weights=w2), 20. / 6.))
|
|
assert_(allclose(average(y, axis=0, weights=w2),
|
|
[0., 1., 2., 3., 4., 10.]))
|
|
assert_(allclose(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_(allclose(average(masked_array(x, m1), axis=0), 2.5))
|
|
assert_(allclose(average(masked_array(x, m2), axis=0), 2.5))
|
|
assert_(average(masked_array(x, m4), axis=0) is masked)
|
|
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_(allclose(average(z, None), 20. / 6.))
|
|
assert_(allclose(average(z, axis=0),
|
|
[0., 1., 99., 99., 4.0, 7.5]))
|
|
assert_(allclose(average(z, axis=1), [2.5, 5.0]))
|
|
assert_(allclose(average(z, axis=0, weights=w2),
|
|
[0., 1., 99., 99., 4.0, 10.0]))
|
|
|
|
a = arange(6)
|
|
b = arange(6) * 3
|
|
r1, w1 = average([[a, b], [b, a]], axis=1, returned=True)
|
|
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=True)
|
|
assert_equal(shape(w2), shape(r2))
|
|
r2, w2 = average(ones((2, 2, 3)), returned=True)
|
|
assert_equal(shape(w2), shape(r2))
|
|
r2, w2 = average(ones((2, 2, 3)), weights=ones((2, 2, 3)), returned=True)
|
|
assert_(shape(w2) == shape(r2))
|
|
a2d = array([[1, 2], [0, 4]], float)
|
|
a2dm = masked_array(a2d, [[0, 0], [1, 0]])
|
|
a2da = average(a2d, axis=0)
|
|
assert_(eq(a2da, [0.5, 3.0]))
|
|
a2dma = average(a2dm, axis=0)
|
|
assert_(eq(a2dma, [1.0, 3.0]))
|
|
a2dma = average(a2dm, axis=None)
|
|
assert_(eq(a2dma, 7. / 3.))
|
|
a2dma = average(a2dm, axis=1)
|
|
assert_(eq(a2dma, [1.5, 4.0]))
|
|
|
|
def test_testToPython(self):
|
|
assert_equal(1, int(array(1)))
|
|
assert_equal(1.0, float(array(1)))
|
|
assert_equal(1, int(array([[[1]]])))
|
|
assert_equal(1.0, float(array([[1]])))
|
|
assert_raises(TypeError, float, array([1, 1]))
|
|
assert_raises(ValueError, bool, array([0, 1]))
|
|
assert_raises(ValueError, bool, array([0, 0], mask=[0, 1]))
|
|
|
|
def test_testScalarArithmetic(self):
|
|
xm = array(0, mask=1)
|
|
#TODO FIXME: Find out what the following raises a warning in r8247
|
|
with np.errstate(divide='ignore'):
|
|
assert_((1 / array(0)).mask)
|
|
assert_((1 + xm).mask)
|
|
assert_((-xm).mask)
|
|
assert_((-xm).mask)
|
|
assert_(maximum(xm, xm).mask)
|
|
assert_(minimum(xm, xm).mask)
|
|
assert_(xm.filled().dtype is xm._data.dtype)
|
|
x = array(0, mask=0)
|
|
assert_(x.filled() == x._data)
|
|
assert_equal(str(xm), str(masked_print_option))
|
|
|
|
def test_testArrayMethods(self):
|
|
a = array([1, 3, 2])
|
|
assert_(eq(a.any(), a._data.any()))
|
|
assert_(eq(a.all(), a._data.all()))
|
|
assert_(eq(a.argmax(), a._data.argmax()))
|
|
assert_(eq(a.argmin(), a._data.argmin()))
|
|
assert_(eq(a.choose(0, 1, 2, 3, 4),
|
|
a._data.choose(0, 1, 2, 3, 4)))
|
|
assert_(eq(a.compress([1, 0, 1]), a._data.compress([1, 0, 1])))
|
|
assert_(eq(a.conj(), a._data.conj()))
|
|
assert_(eq(a.conjugate(), a._data.conjugate()))
|
|
m = array([[1, 2], [3, 4]])
|
|
assert_(eq(m.diagonal(), m._data.diagonal()))
|
|
assert_(eq(a.sum(), a._data.sum()))
|
|
assert_(eq(a.take([1, 2]), a._data.take([1, 2])))
|
|
assert_(eq(m.transpose(), m._data.transpose()))
|
|
|
|
def test_testArrayAttributes(self):
|
|
a = array([1, 3, 2])
|
|
assert_equal(a.ndim, 1)
|
|
|
|
def test_testAPI(self):
|
|
assert_(not [m for m in dir(np.ndarray)
|
|
if m not in dir(MaskedArray) and
|
|
not m.startswith('_')])
|
|
|
|
def test_testSingleElementSubscript(self):
|
|
a = array([1, 3, 2])
|
|
b = array([1, 3, 2], mask=[1, 0, 1])
|
|
assert_equal(a[0].shape, ())
|
|
assert_equal(b[0].shape, ())
|
|
assert_equal(b[1].shape, ())
|
|
|
|
def test_assignment_by_condition(self):
|
|
# Test for gh-18951
|
|
a = array([1, 2, 3, 4], mask=[1, 0, 1, 0])
|
|
c = a >= 3
|
|
a[c] = 5
|
|
assert_(a[2] is masked)
|
|
|
|
def test_assignment_by_condition_2(self):
|
|
# gh-19721
|
|
a = masked_array([0, 1], mask=[False, False])
|
|
b = masked_array([0, 1], mask=[True, True])
|
|
mask = a < 1
|
|
b[mask] = a[mask]
|
|
expected_mask = [False, True]
|
|
assert_equal(b.mask, expected_mask)
|
|
|
|
|
|
class TestUfuncs:
|
|
def setup_method(self):
|
|
self.d = (array([1.0, 0, -1, pi / 2] * 2, mask=[0, 1] + [0] * 6),
|
|
array([1.0, 0, -1, pi / 2] * 2, mask=[1, 0] + [0] * 6),)
|
|
|
|
def test_testUfuncRegression(self):
|
|
f_invalid_ignore = [
|
|
'sqrt', 'arctanh', 'arcsin', 'arccos',
|
|
'arccosh', 'arctanh', 'log', 'log10', 'divide',
|
|
'true_divide', 'floor_divide', 'remainder', 'fmod']
|
|
for f in ['sqrt', 'log', 'log10', 'exp', 'conjugate',
|
|
'sin', 'cos', 'tan',
|
|
'arcsin', 'arccos', 'arctan',
|
|
'sinh', 'cosh', 'tanh',
|
|
'arcsinh',
|
|
'arccosh',
|
|
'arctanh',
|
|
'absolute', 'fabs', 'negative',
|
|
'floor', 'ceil',
|
|
'logical_not',
|
|
'add', 'subtract', 'multiply',
|
|
'divide', 'true_divide', 'floor_divide',
|
|
'remainder', 'fmod', 'hypot', 'arctan2',
|
|
'equal', 'not_equal', 'less_equal', 'greater_equal',
|
|
'less', 'greater',
|
|
'logical_and', 'logical_or', 'logical_xor']:
|
|
try:
|
|
uf = getattr(umath, f)
|
|
except AttributeError:
|
|
uf = getattr(fromnumeric, f)
|
|
mf = getattr(np.ma, f)
|
|
args = self.d[:uf.nin]
|
|
with np.errstate():
|
|
if f in f_invalid_ignore:
|
|
np.seterr(invalid='ignore')
|
|
if f in ['arctanh', 'log', 'log10']:
|
|
np.seterr(divide='ignore')
|
|
ur = uf(*args)
|
|
mr = mf(*args)
|
|
assert_(eq(ur.filled(0), mr.filled(0), f))
|
|
assert_(eqmask(ur.mask, mr.mask))
|
|
|
|
def test_reduce(self):
|
|
a = self.d[0]
|
|
assert_(not alltrue(a, axis=0))
|
|
assert_(sometrue(a, axis=0))
|
|
assert_equal(sum(a[:3], axis=0), 0)
|
|
assert_equal(product(a, axis=0), 0)
|
|
|
|
def test_minmax(self):
|
|
a = arange(1, 13).reshape(3, 4)
|
|
amask = masked_where(a < 5, a)
|
|
assert_equal(amask.max(), a.max())
|
|
assert_equal(amask.min(), 5)
|
|
assert_((amask.max(0) == a.max(0)).all())
|
|
assert_((amask.min(0) == [5, 6, 7, 8]).all())
|
|
assert_(amask.max(1)[0].mask)
|
|
assert_(amask.min(1)[0].mask)
|
|
|
|
def test_nonzero(self):
|
|
for t in "?bhilqpBHILQPfdgFDGO":
|
|
x = array([1, 0, 2, 0], mask=[0, 0, 1, 1])
|
|
assert_(eq(nonzero(x), [0]))
|
|
|
|
|
|
class TestArrayMethods:
|
|
|
|
def setup_method(self):
|
|
x = np.array([8.375, 7.545, 8.828, 8.5, 1.757, 5.928,
|
|
8.43, 7.78, 9.865, 5.878, 8.979, 4.732,
|
|
3.012, 6.022, 5.095, 3.116, 5.238, 3.957,
|
|
6.04, 9.63, 7.712, 3.382, 4.489, 6.479,
|
|
7.189, 9.645, 5.395, 4.961, 9.894, 2.893,
|
|
7.357, 9.828, 6.272, 3.758, 6.693, 0.993])
|
|
X = x.reshape(6, 6)
|
|
XX = x.reshape(3, 2, 2, 3)
|
|
|
|
m = np.array([0, 1, 0, 1, 0, 0,
|
|
1, 0, 1, 1, 0, 1,
|
|
0, 0, 0, 1, 0, 1,
|
|
0, 0, 0, 1, 1, 1,
|
|
1, 0, 0, 1, 0, 0,
|
|
0, 0, 1, 0, 1, 0])
|
|
mx = array(data=x, mask=m)
|
|
mX = array(data=X, mask=m.reshape(X.shape))
|
|
mXX = array(data=XX, mask=m.reshape(XX.shape))
|
|
|
|
self.d = (x, X, XX, m, mx, mX, mXX)
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def test_trace(self):
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(x, X, XX, m, mx, mX, mXX,) = self.d
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mXdiag = mX.diagonal()
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assert_equal(mX.trace(), mX.diagonal().compressed().sum())
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assert_(eq(mX.trace(),
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X.trace() - sum(mXdiag.mask * X.diagonal(),
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axis=0)))
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|
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def test_clip(self):
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(x, X, XX, m, mx, mX, mXX,) = self.d
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clipped = mx.clip(2, 8)
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assert_(eq(clipped.mask, mx.mask))
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assert_(eq(clipped._data, x.clip(2, 8)))
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assert_(eq(clipped._data, mx._data.clip(2, 8)))
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|
|
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def test_ptp(self):
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(x, X, XX, m, mx, mX, mXX,) = self.d
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(n, m) = X.shape
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assert_equal(mx.ptp(), mx.compressed().ptp())
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rows = np.zeros(n, np.float_)
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cols = np.zeros(m, np.float_)
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for k in range(m):
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cols[k] = mX[:, k].compressed().ptp()
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for k in range(n):
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rows[k] = mX[k].compressed().ptp()
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assert_(eq(mX.ptp(0), cols))
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assert_(eq(mX.ptp(1), rows))
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|
|
|
def test_swapaxes(self):
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|
(x, X, XX, m, mx, mX, mXX,) = self.d
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|
mXswapped = mX.swapaxes(0, 1)
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|
assert_(eq(mXswapped[-1], mX[:, -1]))
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|
mXXswapped = mXX.swapaxes(0, 2)
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assert_equal(mXXswapped.shape, (2, 2, 3, 3))
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|
|
|
def test_cumprod(self):
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|
(x, X, XX, m, mx, mX, mXX,) = self.d
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|
mXcp = mX.cumprod(0)
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|
assert_(eq(mXcp._data, mX.filled(1).cumprod(0)))
|
|
mXcp = mX.cumprod(1)
|
|
assert_(eq(mXcp._data, mX.filled(1).cumprod(1)))
|
|
|
|
def test_cumsum(self):
|
|
(x, X, XX, m, mx, mX, mXX,) = self.d
|
|
mXcp = mX.cumsum(0)
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|
assert_(eq(mXcp._data, mX.filled(0).cumsum(0)))
|
|
mXcp = mX.cumsum(1)
|
|
assert_(eq(mXcp._data, mX.filled(0).cumsum(1)))
|
|
|
|
def test_varstd(self):
|
|
(x, X, XX, m, mx, mX, mXX,) = self.d
|
|
assert_(eq(mX.var(axis=None), mX.compressed().var()))
|
|
assert_(eq(mX.std(axis=None), mX.compressed().std()))
|
|
assert_(eq(mXX.var(axis=3).shape, XX.var(axis=3).shape))
|
|
assert_(eq(mX.var().shape, X.var().shape))
|
|
(mXvar0, mXvar1) = (mX.var(axis=0), mX.var(axis=1))
|
|
for k in range(6):
|
|
assert_(eq(mXvar1[k], mX[k].compressed().var()))
|
|
assert_(eq(mXvar0[k], mX[:, k].compressed().var()))
|
|
assert_(eq(np.sqrt(mXvar0[k]),
|
|
mX[:, k].compressed().std()))
|
|
|
|
|
|
def eqmask(m1, m2):
|
|
if m1 is nomask:
|
|
return m2 is nomask
|
|
if m2 is nomask:
|
|
return m1 is nomask
|
|
return (m1 == m2).all()
|