1999 lines
77 KiB
Python
1999 lines
77 KiB
Python
"""Tests suite for MaskedArray.
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Adapted from the original test_ma by Pierre Gerard-Marchant
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:author: Pierre Gerard-Marchant
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:contact: pierregm_at_uga_dot_edu
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"""
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import itertools
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import warnings
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import pytest
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import numpy as np
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from numpy._core.numeric import normalize_axis_tuple
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from numpy.ma.core import (
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MaskedArray,
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arange,
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array,
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count,
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getmaskarray,
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masked,
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masked_array,
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nomask,
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ones,
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shape,
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zeros,
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)
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from numpy.ma.extras import (
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_covhelper,
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apply_along_axis,
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apply_over_axes,
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atleast_1d,
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atleast_2d,
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atleast_3d,
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average,
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clump_masked,
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clump_unmasked,
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compress_nd,
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compress_rowcols,
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corrcoef,
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cov,
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diagflat,
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dot,
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ediff1d,
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flatnotmasked_contiguous,
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in1d,
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intersect1d,
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isin,
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mask_rowcols,
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masked_all,
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masked_all_like,
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median,
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mr_,
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ndenumerate,
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notmasked_contiguous,
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notmasked_edges,
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polyfit,
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setdiff1d,
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setxor1d,
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stack,
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union1d,
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unique,
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vstack,
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)
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from numpy.ma.testutils import (
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assert_,
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assert_almost_equal,
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assert_array_equal,
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assert_equal,
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)
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from numpy.testing import assert_warns, suppress_warnings
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class TestGeneric:
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#
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def test_masked_all(self):
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# Tests masked_all
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# Standard dtype
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test = masked_all((2,), dtype=float)
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control = array([1, 1], mask=[1, 1], dtype=float)
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assert_equal(test, control)
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# Flexible dtype
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dt = np.dtype({'names': ['a', 'b'], 'formats': ['f', 'f']})
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test = masked_all((2,), dtype=dt)
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control = array([(0, 0), (0, 0)], mask=[(1, 1), (1, 1)], dtype=dt)
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assert_equal(test, control)
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test = masked_all((2, 2), dtype=dt)
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control = array([[(0, 0), (0, 0)], [(0, 0), (0, 0)]],
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mask=[[(1, 1), (1, 1)], [(1, 1), (1, 1)]],
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dtype=dt)
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assert_equal(test, control)
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# Nested dtype
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dt = np.dtype([('a', 'f'), ('b', [('ba', 'f'), ('bb', 'f')])])
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test = masked_all((2,), dtype=dt)
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control = array([(1, (1, 1)), (1, (1, 1))],
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mask=[(1, (1, 1)), (1, (1, 1))], dtype=dt)
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assert_equal(test, control)
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test = masked_all((2,), dtype=dt)
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control = array([(1, (1, 1)), (1, (1, 1))],
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mask=[(1, (1, 1)), (1, (1, 1))], dtype=dt)
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assert_equal(test, control)
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test = masked_all((1, 1), dtype=dt)
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control = array([[(1, (1, 1))]], mask=[[(1, (1, 1))]], dtype=dt)
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assert_equal(test, control)
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def test_masked_all_with_object_nested(self):
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# Test masked_all works with nested array with dtype of an 'object'
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# refers to issue #15895
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my_dtype = np.dtype([('b', ([('c', object)], (1,)))])
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masked_arr = np.ma.masked_all((1,), my_dtype)
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assert_equal(type(masked_arr['b']), np.ma.core.MaskedArray)
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assert_equal(type(masked_arr['b']['c']), np.ma.core.MaskedArray)
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assert_equal(len(masked_arr['b']['c']), 1)
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assert_equal(masked_arr['b']['c'].shape, (1, 1))
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assert_equal(masked_arr['b']['c']._fill_value.shape, ())
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def test_masked_all_with_object(self):
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# same as above except that the array is not nested
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my_dtype = np.dtype([('b', (object, (1,)))])
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masked_arr = np.ma.masked_all((1,), my_dtype)
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assert_equal(type(masked_arr['b']), np.ma.core.MaskedArray)
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assert_equal(len(masked_arr['b']), 1)
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assert_equal(masked_arr['b'].shape, (1, 1))
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assert_equal(masked_arr['b']._fill_value.shape, ())
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def test_masked_all_like(self):
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# Tests masked_all
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# Standard dtype
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base = array([1, 2], dtype=float)
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test = masked_all_like(base)
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control = array([1, 1], mask=[1, 1], dtype=float)
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assert_equal(test, control)
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# Flexible dtype
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dt = np.dtype({'names': ['a', 'b'], 'formats': ['f', 'f']})
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base = array([(0, 0), (0, 0)], mask=[(1, 1), (1, 1)], dtype=dt)
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test = masked_all_like(base)
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control = array([(10, 10), (10, 10)], mask=[(1, 1), (1, 1)], dtype=dt)
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assert_equal(test, control)
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# Nested dtype
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dt = np.dtype([('a', 'f'), ('b', [('ba', 'f'), ('bb', 'f')])])
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control = array([(1, (1, 1)), (1, (1, 1))],
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mask=[(1, (1, 1)), (1, (1, 1))], dtype=dt)
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test = masked_all_like(control)
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assert_equal(test, control)
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def check_clump(self, f):
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for i in range(1, 7):
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for j in range(2**i):
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k = np.arange(i, dtype=int)
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ja = np.full(i, j, dtype=int)
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a = masked_array(2**k)
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a.mask = (ja & (2**k)) != 0
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s = 0
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for sl in f(a):
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s += a.data[sl].sum()
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if f == clump_unmasked:
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assert_equal(a.compressed().sum(), s)
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else:
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a.mask = ~a.mask
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assert_equal(a.compressed().sum(), s)
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def test_clump_masked(self):
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# Test clump_masked
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a = masked_array(np.arange(10))
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a[[0, 1, 2, 6, 8, 9]] = masked
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#
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test = clump_masked(a)
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control = [slice(0, 3), slice(6, 7), slice(8, 10)]
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assert_equal(test, control)
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self.check_clump(clump_masked)
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def test_clump_unmasked(self):
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# Test clump_unmasked
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a = masked_array(np.arange(10))
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a[[0, 1, 2, 6, 8, 9]] = masked
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test = clump_unmasked(a)
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control = [slice(3, 6), slice(7, 8), ]
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assert_equal(test, control)
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self.check_clump(clump_unmasked)
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def test_flatnotmasked_contiguous(self):
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# Test flatnotmasked_contiguous
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a = arange(10)
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# No mask
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test = flatnotmasked_contiguous(a)
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assert_equal(test, [slice(0, a.size)])
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# mask of all false
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a.mask = np.zeros(10, dtype=bool)
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assert_equal(test, [slice(0, a.size)])
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# Some mask
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a[(a < 3) | (a > 8) | (a == 5)] = masked
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test = flatnotmasked_contiguous(a)
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assert_equal(test, [slice(3, 5), slice(6, 9)])
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#
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a[:] = masked
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test = flatnotmasked_contiguous(a)
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assert_equal(test, [])
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class TestAverage:
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# Several tests of average. Why so many ? Good point...
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def test_testAverage1(self):
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# Test of average.
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ott = array([0., 1., 2., 3.], mask=[True, False, False, False])
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assert_equal(2.0, average(ott, axis=0))
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assert_equal(2.0, average(ott, weights=[1., 1., 2., 1.]))
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result, wts = average(ott, weights=[1., 1., 2., 1.], returned=True)
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assert_equal(2.0, result)
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assert_(wts == 4.0)
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ott[:] = masked
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assert_equal(average(ott, axis=0).mask, [True])
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ott = array([0., 1., 2., 3.], mask=[True, False, False, False])
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ott = ott.reshape(2, 2)
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ott[:, 1] = masked
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assert_equal(average(ott, axis=0), [2.0, 0.0])
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assert_equal(average(ott, axis=1).mask[0], [True])
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assert_equal([2., 0.], average(ott, axis=0))
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result, wts = average(ott, axis=0, returned=True)
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assert_equal(wts, [1., 0.])
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def test_testAverage2(self):
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# More tests of average.
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w1 = [0, 1, 1, 1, 1, 0]
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w2 = [[0, 1, 1, 1, 1, 0], [1, 0, 0, 0, 0, 1]]
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x = arange(6, dtype=np.float64)
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assert_equal(average(x, axis=0), 2.5)
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assert_equal(average(x, axis=0, weights=w1), 2.5)
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y = array([arange(6, dtype=np.float64), 2.0 * arange(6)])
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assert_equal(average(y, None), np.add.reduce(np.arange(6)) * 3. / 12.)
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assert_equal(average(y, axis=0), np.arange(6) * 3. / 2.)
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assert_equal(average(y, axis=1),
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[average(x, axis=0), average(x, axis=0) * 2.0])
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assert_equal(average(y, None, weights=w2), 20. / 6.)
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assert_equal(average(y, axis=0, weights=w2),
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[0., 1., 2., 3., 4., 10.])
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assert_equal(average(y, axis=1),
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[average(x, axis=0), average(x, axis=0) * 2.0])
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m1 = zeros(6)
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m2 = [0, 0, 1, 1, 0, 0]
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m3 = [[0, 0, 1, 1, 0, 0], [0, 1, 1, 1, 1, 0]]
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m4 = ones(6)
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m5 = [0, 1, 1, 1, 1, 1]
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assert_equal(average(masked_array(x, m1), axis=0), 2.5)
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assert_equal(average(masked_array(x, m2), axis=0), 2.5)
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assert_equal(average(masked_array(x, m4), axis=0).mask, [True])
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assert_equal(average(masked_array(x, m5), axis=0), 0.0)
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assert_equal(count(average(masked_array(x, m4), axis=0)), 0)
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z = masked_array(y, m3)
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assert_equal(average(z, None), 20. / 6.)
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assert_equal(average(z, axis=0), [0., 1., 99., 99., 4.0, 7.5])
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assert_equal(average(z, axis=1), [2.5, 5.0])
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assert_equal(average(z, axis=0, weights=w2),
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[0., 1., 99., 99., 4.0, 10.0])
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def test_testAverage3(self):
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# Yet more tests of average!
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a = arange(6)
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b = arange(6) * 3
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r1, w1 = average([[a, b], [b, a]], axis=1, returned=True)
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assert_equal(shape(r1), shape(w1))
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assert_equal(r1.shape, w1.shape)
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r2, w2 = average(ones((2, 2, 3)), axis=0, weights=[3, 1], returned=True)
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assert_equal(shape(w2), shape(r2))
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r2, w2 = average(ones((2, 2, 3)), returned=True)
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assert_equal(shape(w2), shape(r2))
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r2, w2 = average(ones((2, 2, 3)), weights=ones((2, 2, 3)), returned=True)
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assert_equal(shape(w2), shape(r2))
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a2d = array([[1, 2], [0, 4]], float)
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a2dm = masked_array(a2d, [[False, False], [True, False]])
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a2da = average(a2d, axis=0)
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assert_equal(a2da, [0.5, 3.0])
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a2dma = average(a2dm, axis=0)
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assert_equal(a2dma, [1.0, 3.0])
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a2dma = average(a2dm, axis=None)
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assert_equal(a2dma, 7. / 3.)
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a2dma = average(a2dm, axis=1)
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assert_equal(a2dma, [1.5, 4.0])
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def test_testAverage4(self):
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# Test that `keepdims` works with average
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x = np.array([2, 3, 4]).reshape(3, 1)
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b = np.ma.array(x, mask=[[False], [False], [True]])
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w = np.array([4, 5, 6]).reshape(3, 1)
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actual = average(b, weights=w, axis=1, keepdims=True)
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desired = masked_array([[2.], [3.], [4.]], [[False], [False], [True]])
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assert_equal(actual, desired)
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def test_weight_and_input_dims_different(self):
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# this test mirrors a test for np.average()
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# in lib/test/test_function_base.py
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y = np.arange(12).reshape(2, 2, 3)
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w = np.array([0., 0., 1., .5, .5, 0., 0., .5, .5, 1., 0., 0.])\
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.reshape(2, 2, 3)
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m = np.full((2, 2, 3), False)
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yma = np.ma.array(y, mask=m)
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subw0 = w[:, :, 0]
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actual = average(yma, axis=(0, 1), weights=subw0)
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desired = masked_array([7., 8., 9.], mask=[False, False, False])
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assert_almost_equal(actual, desired)
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m = np.full((2, 2, 3), False)
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m[:, :, 0] = True
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m[0, 0, 1] = True
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yma = np.ma.array(y, mask=m)
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actual = average(yma, axis=(0, 1), weights=subw0)
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desired = masked_array(
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[np.nan, 8., 9.],
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mask=[True, False, False])
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assert_almost_equal(actual, desired)
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m = np.full((2, 2, 3), False)
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yma = np.ma.array(y, mask=m)
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subw1 = w[1, :, :]
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actual = average(yma, axis=(1, 2), weights=subw1)
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desired = masked_array([2.25, 8.25], mask=[False, False])
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assert_almost_equal(actual, desired)
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# here the weights have the wrong shape for the specified axes
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with pytest.raises(
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ValueError,
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match="Shape of weights must be consistent with "
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"shape of a along specified axis"):
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average(yma, axis=(0, 1, 2), weights=subw0)
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with pytest.raises(
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ValueError,
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match="Shape of weights must be consistent with "
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"shape of a along specified axis"):
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average(yma, axis=(0, 1), weights=subw1)
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# swapping the axes should be same as transposing weights
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actual = average(yma, axis=(1, 0), weights=subw0)
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desired = average(yma, axis=(0, 1), weights=subw0.T)
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assert_almost_equal(actual, desired)
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def test_onintegers_with_mask(self):
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# Test average on integers with mask
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a = average(array([1, 2]))
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assert_equal(a, 1.5)
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a = average(array([1, 2, 3, 4], mask=[False, False, True, True]))
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assert_equal(a, 1.5)
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def test_complex(self):
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# Test with complex data.
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# (Regression test for https://github.com/numpy/numpy/issues/2684)
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mask = np.array([[0, 0, 0, 1, 0],
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[0, 1, 0, 0, 0]], dtype=bool)
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a = masked_array([[0, 1 + 2j, 3 + 4j, 5 + 6j, 7 + 8j],
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[9j, 0 + 1j, 2 + 3j, 4 + 5j, 7 + 7j]],
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mask=mask)
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av = average(a)
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expected = np.average(a.compressed())
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assert_almost_equal(av.real, expected.real)
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assert_almost_equal(av.imag, expected.imag)
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av0 = average(a, axis=0)
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expected0 = average(a.real, axis=0) + average(a.imag, axis=0) * 1j
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assert_almost_equal(av0.real, expected0.real)
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assert_almost_equal(av0.imag, expected0.imag)
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av1 = average(a, axis=1)
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expected1 = average(a.real, axis=1) + average(a.imag, axis=1) * 1j
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assert_almost_equal(av1.real, expected1.real)
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assert_almost_equal(av1.imag, expected1.imag)
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# Test with the 'weights' argument.
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wts = np.array([[0.5, 1.0, 2.0, 1.0, 0.5],
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[1.0, 1.0, 1.0, 1.0, 1.0]])
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wav = average(a, weights=wts)
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expected = np.average(a.compressed(), weights=wts[~mask])
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assert_almost_equal(wav.real, expected.real)
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assert_almost_equal(wav.imag, expected.imag)
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wav0 = average(a, weights=wts, axis=0)
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expected0 = (average(a.real, weights=wts, axis=0) +
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average(a.imag, weights=wts, axis=0) * 1j)
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assert_almost_equal(wav0.real, expected0.real)
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assert_almost_equal(wav0.imag, expected0.imag)
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wav1 = average(a, weights=wts, axis=1)
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expected1 = (average(a.real, weights=wts, axis=1) +
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average(a.imag, weights=wts, axis=1) * 1j)
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assert_almost_equal(wav1.real, expected1.real)
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assert_almost_equal(wav1.imag, expected1.imag)
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@pytest.mark.parametrize(
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'x, axis, expected_avg, weights, expected_wavg, expected_wsum',
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[([1, 2, 3], None, [2.0], [3, 4, 1], [1.75], [8.0]),
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([[1, 2, 5], [1, 6, 11]], 0, [[1.0, 4.0, 8.0]],
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[1, 3], [[1.0, 5.0, 9.5]], [[4, 4, 4]])],
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)
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def test_basic_keepdims(self, x, axis, expected_avg,
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weights, expected_wavg, expected_wsum):
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avg = np.ma.average(x, axis=axis, keepdims=True)
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assert avg.shape == np.shape(expected_avg)
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assert_array_equal(avg, expected_avg)
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wavg = np.ma.average(x, axis=axis, weights=weights, keepdims=True)
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assert wavg.shape == np.shape(expected_wavg)
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assert_array_equal(wavg, expected_wavg)
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wavg, wsum = np.ma.average(x, axis=axis, weights=weights,
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returned=True, keepdims=True)
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assert wavg.shape == np.shape(expected_wavg)
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assert_array_equal(wavg, expected_wavg)
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assert wsum.shape == np.shape(expected_wsum)
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assert_array_equal(wsum, expected_wsum)
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def test_masked_weights(self):
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# Test with masked weights.
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# (Regression test for https://github.com/numpy/numpy/issues/10438)
|
|
a = np.ma.array(np.arange(9).reshape(3, 3),
|
|
mask=[[1, 0, 0], [1, 0, 0], [0, 0, 0]])
|
|
weights_unmasked = masked_array([5, 28, 31], mask=False)
|
|
weights_masked = masked_array([5, 28, 31], mask=[1, 0, 0])
|
|
|
|
avg_unmasked = average(a, axis=0,
|
|
weights=weights_unmasked, returned=False)
|
|
expected_unmasked = np.array([6.0, 5.21875, 6.21875])
|
|
assert_almost_equal(avg_unmasked, expected_unmasked)
|
|
|
|
avg_masked = average(a, axis=0, weights=weights_masked, returned=False)
|
|
expected_masked = np.array([6.0, 5.576271186440678, 6.576271186440678])
|
|
assert_almost_equal(avg_masked, expected_masked)
|
|
|
|
# weights should be masked if needed
|
|
# depending on the array mask. This is to avoid summing
|
|
# masked nan or other values that are not cancelled by a zero
|
|
a = np.ma.array([1.0, 2.0, 3.0, 4.0],
|
|
mask=[False, False, True, True])
|
|
avg_unmasked = average(a, weights=[1, 1, 1, np.nan])
|
|
|
|
assert_almost_equal(avg_unmasked, 1.5)
|
|
|
|
a = np.ma.array([
|
|
[1.0, 2.0, 3.0, 4.0],
|
|
[5.0, 6.0, 7.0, 8.0],
|
|
[9.0, 1.0, 2.0, 3.0],
|
|
], mask=[
|
|
[False, True, True, False],
|
|
[True, False, True, True],
|
|
[True, False, True, False],
|
|
])
|
|
|
|
avg_masked = np.ma.average(a, weights=[1, np.nan, 1], axis=0)
|
|
avg_expected = np.ma.array([1.0, np.nan, np.nan, 3.5],
|
|
mask=[False, True, True, False])
|
|
|
|
assert_almost_equal(avg_masked, avg_expected)
|
|
assert_equal(avg_masked.mask, avg_expected.mask)
|
|
|
|
|
|
class TestConcatenator:
|
|
# Tests for mr_, the equivalent of r_ for masked arrays.
|
|
|
|
def test_1d(self):
|
|
# Tests mr_ on 1D arrays.
|
|
assert_array_equal(mr_[1, 2, 3, 4, 5, 6], array([1, 2, 3, 4, 5, 6]))
|
|
b = ones(5)
|
|
m = [1, 0, 0, 0, 0]
|
|
d = masked_array(b, mask=m)
|
|
c = mr_[d, 0, 0, d]
|
|
assert_(isinstance(c, MaskedArray))
|
|
assert_array_equal(c, [1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1])
|
|
assert_array_equal(c.mask, mr_[m, 0, 0, m])
|
|
|
|
def test_2d(self):
|
|
# Tests mr_ on 2D arrays.
|
|
a_1 = np.random.rand(5, 5)
|
|
a_2 = np.random.rand(5, 5)
|
|
m_1 = np.round(np.random.rand(5, 5), 0)
|
|
m_2 = np.round(np.random.rand(5, 5), 0)
|
|
b_1 = masked_array(a_1, mask=m_1)
|
|
b_2 = masked_array(a_2, mask=m_2)
|
|
# append columns
|
|
d = mr_['1', b_1, b_2]
|
|
assert_(d.shape == (5, 10))
|
|
assert_array_equal(d[:, :5], b_1)
|
|
assert_array_equal(d[:, 5:], b_2)
|
|
assert_array_equal(d.mask, np.r_['1', m_1, m_2])
|
|
d = mr_[b_1, b_2]
|
|
assert_(d.shape == (10, 5))
|
|
assert_array_equal(d[:5, :], b_1)
|
|
assert_array_equal(d[5:, :], b_2)
|
|
assert_array_equal(d.mask, np.r_[m_1, m_2])
|
|
|
|
def test_masked_constant(self):
|
|
actual = mr_[np.ma.masked, 1]
|
|
assert_equal(actual.mask, [True, False])
|
|
assert_equal(actual.data[1], 1)
|
|
|
|
actual = mr_[[1, 2], np.ma.masked]
|
|
assert_equal(actual.mask, [False, False, True])
|
|
assert_equal(actual.data[:2], [1, 2])
|
|
|
|
|
|
class TestNotMasked:
|
|
# Tests notmasked_edges and notmasked_contiguous.
|
|
|
|
def test_edges(self):
|
|
# Tests unmasked_edges
|
|
data = masked_array(np.arange(25).reshape(5, 5),
|
|
mask=[[0, 0, 1, 0, 0],
|
|
[0, 0, 0, 1, 1],
|
|
[1, 1, 0, 0, 0],
|
|
[0, 0, 0, 0, 0],
|
|
[1, 1, 1, 0, 0]],)
|
|
test = notmasked_edges(data, None)
|
|
assert_equal(test, [0, 24])
|
|
test = notmasked_edges(data, 0)
|
|
assert_equal(test[0], [(0, 0, 1, 0, 0), (0, 1, 2, 3, 4)])
|
|
assert_equal(test[1], [(3, 3, 3, 4, 4), (0, 1, 2, 3, 4)])
|
|
test = notmasked_edges(data, 1)
|
|
assert_equal(test[0], [(0, 1, 2, 3, 4), (0, 0, 2, 0, 3)])
|
|
assert_equal(test[1], [(0, 1, 2, 3, 4), (4, 2, 4, 4, 4)])
|
|
#
|
|
test = notmasked_edges(data.data, None)
|
|
assert_equal(test, [0, 24])
|
|
test = notmasked_edges(data.data, 0)
|
|
assert_equal(test[0], [(0, 0, 0, 0, 0), (0, 1, 2, 3, 4)])
|
|
assert_equal(test[1], [(4, 4, 4, 4, 4), (0, 1, 2, 3, 4)])
|
|
test = notmasked_edges(data.data, -1)
|
|
assert_equal(test[0], [(0, 1, 2, 3, 4), (0, 0, 0, 0, 0)])
|
|
assert_equal(test[1], [(0, 1, 2, 3, 4), (4, 4, 4, 4, 4)])
|
|
#
|
|
data[-2] = masked
|
|
test = notmasked_edges(data, 0)
|
|
assert_equal(test[0], [(0, 0, 1, 0, 0), (0, 1, 2, 3, 4)])
|
|
assert_equal(test[1], [(1, 1, 2, 4, 4), (0, 1, 2, 3, 4)])
|
|
test = notmasked_edges(data, -1)
|
|
assert_equal(test[0], [(0, 1, 2, 4), (0, 0, 2, 3)])
|
|
assert_equal(test[1], [(0, 1, 2, 4), (4, 2, 4, 4)])
|
|
|
|
def test_contiguous(self):
|
|
# Tests notmasked_contiguous
|
|
a = masked_array(np.arange(24).reshape(3, 8),
|
|
mask=[[0, 0, 0, 0, 1, 1, 1, 1],
|
|
[1, 1, 1, 1, 1, 1, 1, 1],
|
|
[0, 0, 0, 0, 0, 0, 1, 0]])
|
|
tmp = notmasked_contiguous(a, None)
|
|
assert_equal(tmp, [
|
|
slice(0, 4, None),
|
|
slice(16, 22, None),
|
|
slice(23, 24, None)
|
|
])
|
|
|
|
tmp = notmasked_contiguous(a, 0)
|
|
assert_equal(tmp, [
|
|
[slice(0, 1, None), slice(2, 3, None)],
|
|
[slice(0, 1, None), slice(2, 3, None)],
|
|
[slice(0, 1, None), slice(2, 3, None)],
|
|
[slice(0, 1, None), slice(2, 3, None)],
|
|
[slice(2, 3, None)],
|
|
[slice(2, 3, None)],
|
|
[],
|
|
[slice(2, 3, None)]
|
|
])
|
|
#
|
|
tmp = notmasked_contiguous(a, 1)
|
|
assert_equal(tmp, [
|
|
[slice(0, 4, None)],
|
|
[],
|
|
[slice(0, 6, None), slice(7, 8, None)]
|
|
])
|
|
|
|
|
|
class TestCompressFunctions:
|
|
|
|
def test_compress_nd(self):
|
|
# Tests compress_nd
|
|
x = np.array(list(range(3 * 4 * 5))).reshape(3, 4, 5)
|
|
m = np.zeros((3, 4, 5)).astype(bool)
|
|
m[1, 1, 1] = True
|
|
x = array(x, mask=m)
|
|
|
|
# axis=None
|
|
a = compress_nd(x)
|
|
assert_equal(a, [[[ 0, 2, 3, 4],
|
|
[10, 12, 13, 14],
|
|
[15, 17, 18, 19]],
|
|
[[40, 42, 43, 44],
|
|
[50, 52, 53, 54],
|
|
[55, 57, 58, 59]]])
|
|
|
|
# axis=0
|
|
a = compress_nd(x, 0)
|
|
assert_equal(a, [[[ 0, 1, 2, 3, 4],
|
|
[ 5, 6, 7, 8, 9],
|
|
[10, 11, 12, 13, 14],
|
|
[15, 16, 17, 18, 19]],
|
|
[[40, 41, 42, 43, 44],
|
|
[45, 46, 47, 48, 49],
|
|
[50, 51, 52, 53, 54],
|
|
[55, 56, 57, 58, 59]]])
|
|
|
|
# axis=1
|
|
a = compress_nd(x, 1)
|
|
assert_equal(a, [[[ 0, 1, 2, 3, 4],
|
|
[10, 11, 12, 13, 14],
|
|
[15, 16, 17, 18, 19]],
|
|
[[20, 21, 22, 23, 24],
|
|
[30, 31, 32, 33, 34],
|
|
[35, 36, 37, 38, 39]],
|
|
[[40, 41, 42, 43, 44],
|
|
[50, 51, 52, 53, 54],
|
|
[55, 56, 57, 58, 59]]])
|
|
|
|
a2 = compress_nd(x, (1,))
|
|
a3 = compress_nd(x, -2)
|
|
a4 = compress_nd(x, (-2,))
|
|
assert_equal(a, a2)
|
|
assert_equal(a, a3)
|
|
assert_equal(a, a4)
|
|
|
|
# axis=2
|
|
a = compress_nd(x, 2)
|
|
assert_equal(a, [[[ 0, 2, 3, 4],
|
|
[ 5, 7, 8, 9],
|
|
[10, 12, 13, 14],
|
|
[15, 17, 18, 19]],
|
|
[[20, 22, 23, 24],
|
|
[25, 27, 28, 29],
|
|
[30, 32, 33, 34],
|
|
[35, 37, 38, 39]],
|
|
[[40, 42, 43, 44],
|
|
[45, 47, 48, 49],
|
|
[50, 52, 53, 54],
|
|
[55, 57, 58, 59]]])
|
|
|
|
a2 = compress_nd(x, (2,))
|
|
a3 = compress_nd(x, -1)
|
|
a4 = compress_nd(x, (-1,))
|
|
assert_equal(a, a2)
|
|
assert_equal(a, a3)
|
|
assert_equal(a, a4)
|
|
|
|
# axis=(0, 1)
|
|
a = compress_nd(x, (0, 1))
|
|
assert_equal(a, [[[ 0, 1, 2, 3, 4],
|
|
[10, 11, 12, 13, 14],
|
|
[15, 16, 17, 18, 19]],
|
|
[[40, 41, 42, 43, 44],
|
|
[50, 51, 52, 53, 54],
|
|
[55, 56, 57, 58, 59]]])
|
|
a2 = compress_nd(x, (0, -2))
|
|
assert_equal(a, a2)
|
|
|
|
# axis=(1, 2)
|
|
a = compress_nd(x, (1, 2))
|
|
assert_equal(a, [[[ 0, 2, 3, 4],
|
|
[10, 12, 13, 14],
|
|
[15, 17, 18, 19]],
|
|
[[20, 22, 23, 24],
|
|
[30, 32, 33, 34],
|
|
[35, 37, 38, 39]],
|
|
[[40, 42, 43, 44],
|
|
[50, 52, 53, 54],
|
|
[55, 57, 58, 59]]])
|
|
|
|
a2 = compress_nd(x, (-2, 2))
|
|
a3 = compress_nd(x, (1, -1))
|
|
a4 = compress_nd(x, (-2, -1))
|
|
assert_equal(a, a2)
|
|
assert_equal(a, a3)
|
|
assert_equal(a, a4)
|
|
|
|
# axis=(0, 2)
|
|
a = compress_nd(x, (0, 2))
|
|
assert_equal(a, [[[ 0, 2, 3, 4],
|
|
[ 5, 7, 8, 9],
|
|
[10, 12, 13, 14],
|
|
[15, 17, 18, 19]],
|
|
[[40, 42, 43, 44],
|
|
[45, 47, 48, 49],
|
|
[50, 52, 53, 54],
|
|
[55, 57, 58, 59]]])
|
|
|
|
a2 = compress_nd(x, (0, -1))
|
|
assert_equal(a, a2)
|
|
|
|
def test_compress_rowcols(self):
|
|
# Tests compress_rowcols
|
|
x = array(np.arange(9).reshape(3, 3),
|
|
mask=[[1, 0, 0], [0, 0, 0], [0, 0, 0]])
|
|
assert_equal(compress_rowcols(x), [[4, 5], [7, 8]])
|
|
assert_equal(compress_rowcols(x, 0), [[3, 4, 5], [6, 7, 8]])
|
|
assert_equal(compress_rowcols(x, 1), [[1, 2], [4, 5], [7, 8]])
|
|
x = array(x._data, mask=[[0, 0, 0], [0, 1, 0], [0, 0, 0]])
|
|
assert_equal(compress_rowcols(x), [[0, 2], [6, 8]])
|
|
assert_equal(compress_rowcols(x, 0), [[0, 1, 2], [6, 7, 8]])
|
|
assert_equal(compress_rowcols(x, 1), [[0, 2], [3, 5], [6, 8]])
|
|
x = array(x._data, mask=[[1, 0, 0], [0, 1, 0], [0, 0, 0]])
|
|
assert_equal(compress_rowcols(x), [[8]])
|
|
assert_equal(compress_rowcols(x, 0), [[6, 7, 8]])
|
|
assert_equal(compress_rowcols(x, 1,), [[2], [5], [8]])
|
|
x = array(x._data, mask=[[1, 0, 0], [0, 1, 0], [0, 0, 1]])
|
|
assert_equal(compress_rowcols(x).size, 0)
|
|
assert_equal(compress_rowcols(x, 0).size, 0)
|
|
assert_equal(compress_rowcols(x, 1).size, 0)
|
|
|
|
def test_mask_rowcols(self):
|
|
# Tests mask_rowcols.
|
|
x = array(np.arange(9).reshape(3, 3),
|
|
mask=[[1, 0, 0], [0, 0, 0], [0, 0, 0]])
|
|
assert_equal(mask_rowcols(x).mask,
|
|
[[1, 1, 1], [1, 0, 0], [1, 0, 0]])
|
|
assert_equal(mask_rowcols(x, 0).mask,
|
|
[[1, 1, 1], [0, 0, 0], [0, 0, 0]])
|
|
assert_equal(mask_rowcols(x, 1).mask,
|
|
[[1, 0, 0], [1, 0, 0], [1, 0, 0]])
|
|
x = array(x._data, mask=[[0, 0, 0], [0, 1, 0], [0, 0, 0]])
|
|
assert_equal(mask_rowcols(x).mask,
|
|
[[0, 1, 0], [1, 1, 1], [0, 1, 0]])
|
|
assert_equal(mask_rowcols(x, 0).mask,
|
|
[[0, 0, 0], [1, 1, 1], [0, 0, 0]])
|
|
assert_equal(mask_rowcols(x, 1).mask,
|
|
[[0, 1, 0], [0, 1, 0], [0, 1, 0]])
|
|
x = array(x._data, mask=[[1, 0, 0], [0, 1, 0], [0, 0, 0]])
|
|
assert_equal(mask_rowcols(x).mask,
|
|
[[1, 1, 1], [1, 1, 1], [1, 1, 0]])
|
|
assert_equal(mask_rowcols(x, 0).mask,
|
|
[[1, 1, 1], [1, 1, 1], [0, 0, 0]])
|
|
assert_equal(mask_rowcols(x, 1,).mask,
|
|
[[1, 1, 0], [1, 1, 0], [1, 1, 0]])
|
|
x = array(x._data, mask=[[1, 0, 0], [0, 1, 0], [0, 0, 1]])
|
|
assert_(mask_rowcols(x).all() is masked)
|
|
assert_(mask_rowcols(x, 0).all() is masked)
|
|
assert_(mask_rowcols(x, 1).all() is masked)
|
|
assert_(mask_rowcols(x).mask.all())
|
|
assert_(mask_rowcols(x, 0).mask.all())
|
|
assert_(mask_rowcols(x, 1).mask.all())
|
|
|
|
@pytest.mark.parametrize("axis", [None, 0, 1])
|
|
@pytest.mark.parametrize(["func", "rowcols_axis"],
|
|
[(np.ma.mask_rows, 0), (np.ma.mask_cols, 1)])
|
|
def test_mask_row_cols_axis_deprecation(self, axis, func, rowcols_axis):
|
|
# Test deprecation of the axis argument to `mask_rows` and `mask_cols`
|
|
x = array(np.arange(9).reshape(3, 3),
|
|
mask=[[1, 0, 0], [0, 0, 0], [0, 0, 0]])
|
|
|
|
with assert_warns(DeprecationWarning):
|
|
res = func(x, axis=axis)
|
|
assert_equal(res, mask_rowcols(x, rowcols_axis))
|
|
|
|
def test_dot(self):
|
|
# Tests dot product
|
|
n = np.arange(1, 7)
|
|
#
|
|
m = [1, 0, 0, 0, 0, 0]
|
|
a = masked_array(n, mask=m).reshape(2, 3)
|
|
b = masked_array(n, mask=m).reshape(3, 2)
|
|
c = dot(a, b, strict=True)
|
|
assert_equal(c.mask, [[1, 1], [1, 0]])
|
|
c = dot(b, a, strict=True)
|
|
assert_equal(c.mask, [[1, 1, 1], [1, 0, 0], [1, 0, 0]])
|
|
c = dot(a, b, strict=False)
|
|
assert_equal(c, np.dot(a.filled(0), b.filled(0)))
|
|
c = dot(b, a, strict=False)
|
|
assert_equal(c, np.dot(b.filled(0), a.filled(0)))
|
|
#
|
|
m = [0, 0, 0, 0, 0, 1]
|
|
a = masked_array(n, mask=m).reshape(2, 3)
|
|
b = masked_array(n, mask=m).reshape(3, 2)
|
|
c = dot(a, b, strict=True)
|
|
assert_equal(c.mask, [[0, 1], [1, 1]])
|
|
c = dot(b, a, strict=True)
|
|
assert_equal(c.mask, [[0, 0, 1], [0, 0, 1], [1, 1, 1]])
|
|
c = dot(a, b, strict=False)
|
|
assert_equal(c, np.dot(a.filled(0), b.filled(0)))
|
|
assert_equal(c, dot(a, b))
|
|
c = dot(b, a, strict=False)
|
|
assert_equal(c, np.dot(b.filled(0), a.filled(0)))
|
|
#
|
|
m = [0, 0, 0, 0, 0, 0]
|
|
a = masked_array(n, mask=m).reshape(2, 3)
|
|
b = masked_array(n, mask=m).reshape(3, 2)
|
|
c = dot(a, b)
|
|
assert_equal(c.mask, nomask)
|
|
c = dot(b, a)
|
|
assert_equal(c.mask, nomask)
|
|
#
|
|
a = masked_array(n, mask=[1, 0, 0, 0, 0, 0]).reshape(2, 3)
|
|
b = masked_array(n, mask=[0, 0, 0, 0, 0, 0]).reshape(3, 2)
|
|
c = dot(a, b, strict=True)
|
|
assert_equal(c.mask, [[1, 1], [0, 0]])
|
|
c = dot(a, b, strict=False)
|
|
assert_equal(c, np.dot(a.filled(0), b.filled(0)))
|
|
c = dot(b, a, strict=True)
|
|
assert_equal(c.mask, [[1, 0, 0], [1, 0, 0], [1, 0, 0]])
|
|
c = dot(b, a, strict=False)
|
|
assert_equal(c, np.dot(b.filled(0), a.filled(0)))
|
|
#
|
|
a = masked_array(n, mask=[0, 0, 0, 0, 0, 1]).reshape(2, 3)
|
|
b = masked_array(n, mask=[0, 0, 0, 0, 0, 0]).reshape(3, 2)
|
|
c = dot(a, b, strict=True)
|
|
assert_equal(c.mask, [[0, 0], [1, 1]])
|
|
c = dot(a, b)
|
|
assert_equal(c, np.dot(a.filled(0), b.filled(0)))
|
|
c = dot(b, a, strict=True)
|
|
assert_equal(c.mask, [[0, 0, 1], [0, 0, 1], [0, 0, 1]])
|
|
c = dot(b, a, strict=False)
|
|
assert_equal(c, np.dot(b.filled(0), a.filled(0)))
|
|
#
|
|
a = masked_array(n, mask=[0, 0, 0, 0, 0, 1]).reshape(2, 3)
|
|
b = masked_array(n, mask=[0, 0, 1, 0, 0, 0]).reshape(3, 2)
|
|
c = dot(a, b, strict=True)
|
|
assert_equal(c.mask, [[1, 0], [1, 1]])
|
|
c = dot(a, b, strict=False)
|
|
assert_equal(c, np.dot(a.filled(0), b.filled(0)))
|
|
c = dot(b, a, strict=True)
|
|
assert_equal(c.mask, [[0, 0, 1], [1, 1, 1], [0, 0, 1]])
|
|
c = dot(b, a, strict=False)
|
|
assert_equal(c, np.dot(b.filled(0), a.filled(0)))
|
|
#
|
|
a = masked_array(np.arange(8).reshape(2, 2, 2),
|
|
mask=[[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
|
|
b = masked_array(np.arange(8).reshape(2, 2, 2),
|
|
mask=[[[0, 0], [0, 0]], [[0, 0], [0, 1]]])
|
|
c = dot(a, b, strict=True)
|
|
assert_equal(c.mask,
|
|
[[[[1, 1], [1, 1]], [[0, 0], [0, 1]]],
|
|
[[[0, 0], [0, 1]], [[0, 0], [0, 1]]]])
|
|
c = dot(a, b, strict=False)
|
|
assert_equal(c.mask,
|
|
[[[[0, 0], [0, 1]], [[0, 0], [0, 0]]],
|
|
[[[0, 0], [0, 0]], [[0, 0], [0, 0]]]])
|
|
c = dot(b, a, strict=True)
|
|
assert_equal(c.mask,
|
|
[[[[1, 0], [0, 0]], [[1, 0], [0, 0]]],
|
|
[[[1, 0], [0, 0]], [[1, 1], [1, 1]]]])
|
|
c = dot(b, a, strict=False)
|
|
assert_equal(c.mask,
|
|
[[[[0, 0], [0, 0]], [[0, 0], [0, 0]]],
|
|
[[[0, 0], [0, 0]], [[1, 0], [0, 0]]]])
|
|
#
|
|
a = masked_array(np.arange(8).reshape(2, 2, 2),
|
|
mask=[[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
|
|
b = 5.
|
|
c = dot(a, b, strict=True)
|
|
assert_equal(c.mask, [[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
|
|
c = dot(a, b, strict=False)
|
|
assert_equal(c.mask, [[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
|
|
c = dot(b, a, strict=True)
|
|
assert_equal(c.mask, [[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
|
|
c = dot(b, a, strict=False)
|
|
assert_equal(c.mask, [[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
|
|
#
|
|
a = masked_array(np.arange(8).reshape(2, 2, 2),
|
|
mask=[[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
|
|
b = masked_array(np.arange(2), mask=[0, 1])
|
|
c = dot(a, b, strict=True)
|
|
assert_equal(c.mask, [[1, 1], [1, 1]])
|
|
c = dot(a, b, strict=False)
|
|
assert_equal(c.mask, [[1, 0], [0, 0]])
|
|
|
|
def test_dot_returns_maskedarray(self):
|
|
# See gh-6611
|
|
a = np.eye(3)
|
|
b = array(a)
|
|
assert_(type(dot(a, a)) is MaskedArray)
|
|
assert_(type(dot(a, b)) is MaskedArray)
|
|
assert_(type(dot(b, a)) is MaskedArray)
|
|
assert_(type(dot(b, b)) is MaskedArray)
|
|
|
|
def test_dot_out(self):
|
|
a = array(np.eye(3))
|
|
out = array(np.zeros((3, 3)))
|
|
res = dot(a, a, out=out)
|
|
assert_(res is out)
|
|
assert_equal(a, res)
|
|
|
|
|
|
class TestApplyAlongAxis:
|
|
# Tests 2D functions
|
|
def test_3d(self):
|
|
a = arange(12.).reshape(2, 2, 3)
|
|
|
|
def myfunc(b):
|
|
return b[1]
|
|
|
|
xa = apply_along_axis(myfunc, 2, a)
|
|
assert_equal(xa, [[1, 4], [7, 10]])
|
|
|
|
# Tests kwargs functions
|
|
def test_3d_kwargs(self):
|
|
a = arange(12).reshape(2, 2, 3)
|
|
|
|
def myfunc(b, offset=0):
|
|
return b[1 + offset]
|
|
|
|
xa = apply_along_axis(myfunc, 2, a, offset=1)
|
|
assert_equal(xa, [[2, 5], [8, 11]])
|
|
|
|
|
|
class TestApplyOverAxes:
|
|
# Tests apply_over_axes
|
|
def test_basic(self):
|
|
a = arange(24).reshape(2, 3, 4)
|
|
test = apply_over_axes(np.sum, a, [0, 2])
|
|
ctrl = np.array([[[60], [92], [124]]])
|
|
assert_equal(test, ctrl)
|
|
a[(a % 2).astype(bool)] = masked
|
|
test = apply_over_axes(np.sum, a, [0, 2])
|
|
ctrl = np.array([[[28], [44], [60]]])
|
|
assert_equal(test, ctrl)
|
|
|
|
|
|
class TestMedian:
|
|
def test_pytype(self):
|
|
r = np.ma.median([[np.inf, np.inf], [np.inf, np.inf]], axis=-1)
|
|
assert_equal(r, np.inf)
|
|
|
|
def test_inf(self):
|
|
# test that even which computes handles inf / x = masked
|
|
r = np.ma.median(np.ma.masked_array([[np.inf, np.inf],
|
|
[np.inf, np.inf]]), axis=-1)
|
|
assert_equal(r, np.inf)
|
|
r = np.ma.median(np.ma.masked_array([[np.inf, np.inf],
|
|
[np.inf, np.inf]]), axis=None)
|
|
assert_equal(r, np.inf)
|
|
# all masked
|
|
r = np.ma.median(np.ma.masked_array([[np.inf, np.inf],
|
|
[np.inf, np.inf]], mask=True),
|
|
axis=-1)
|
|
assert_equal(r.mask, True)
|
|
r = np.ma.median(np.ma.masked_array([[np.inf, np.inf],
|
|
[np.inf, np.inf]], mask=True),
|
|
axis=None)
|
|
assert_equal(r.mask, True)
|
|
|
|
def test_non_masked(self):
|
|
x = np.arange(9)
|
|
assert_equal(np.ma.median(x), 4.)
|
|
assert_(type(np.ma.median(x)) is not MaskedArray)
|
|
x = range(8)
|
|
assert_equal(np.ma.median(x), 3.5)
|
|
assert_(type(np.ma.median(x)) is not MaskedArray)
|
|
x = 5
|
|
assert_equal(np.ma.median(x), 5.)
|
|
assert_(type(np.ma.median(x)) is not MaskedArray)
|
|
# integer
|
|
x = np.arange(9 * 8).reshape(9, 8)
|
|
assert_equal(np.ma.median(x, axis=0), np.median(x, axis=0))
|
|
assert_equal(np.ma.median(x, axis=1), np.median(x, axis=1))
|
|
assert_(np.ma.median(x, axis=1) is not MaskedArray)
|
|
# float
|
|
x = np.arange(9 * 8.).reshape(9, 8)
|
|
assert_equal(np.ma.median(x, axis=0), np.median(x, axis=0))
|
|
assert_equal(np.ma.median(x, axis=1), np.median(x, axis=1))
|
|
assert_(np.ma.median(x, axis=1) is not MaskedArray)
|
|
|
|
def test_docstring_examples(self):
|
|
"test the examples given in the docstring of ma.median"
|
|
x = array(np.arange(8), mask=[0] * 4 + [1] * 4)
|
|
assert_equal(np.ma.median(x), 1.5)
|
|
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
|
|
assert_(type(np.ma.median(x)) is not MaskedArray)
|
|
x = array(np.arange(10).reshape(2, 5), mask=[0] * 6 + [1] * 4)
|
|
assert_equal(np.ma.median(x), 2.5)
|
|
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
|
|
assert_(type(np.ma.median(x)) is not MaskedArray)
|
|
ma_x = np.ma.median(x, axis=-1, overwrite_input=True)
|
|
assert_equal(ma_x, [2., 5.])
|
|
assert_equal(ma_x.shape, (2,), "shape mismatch")
|
|
assert_(type(ma_x) is MaskedArray)
|
|
|
|
def test_axis_argument_errors(self):
|
|
msg = "mask = %s, ndim = %s, axis = %s, overwrite_input = %s"
|
|
for ndmin in range(5):
|
|
for mask in [False, True]:
|
|
x = array(1, ndmin=ndmin, mask=mask)
|
|
|
|
# Valid axis values should not raise exception
|
|
args = itertools.product(range(-ndmin, ndmin), [False, True])
|
|
for axis, over in args:
|
|
try:
|
|
np.ma.median(x, axis=axis, overwrite_input=over)
|
|
except Exception:
|
|
raise AssertionError(msg % (mask, ndmin, axis, over))
|
|
|
|
# Invalid axis values should raise exception
|
|
args = itertools.product([-(ndmin + 1), ndmin], [False, True])
|
|
for axis, over in args:
|
|
try:
|
|
np.ma.median(x, axis=axis, overwrite_input=over)
|
|
except np.exceptions.AxisError:
|
|
pass
|
|
else:
|
|
raise AssertionError(msg % (mask, ndmin, axis, over))
|
|
|
|
def test_masked_0d(self):
|
|
# Check values
|
|
x = array(1, mask=False)
|
|
assert_equal(np.ma.median(x), 1)
|
|
x = array(1, mask=True)
|
|
assert_equal(np.ma.median(x), np.ma.masked)
|
|
|
|
def test_masked_1d(self):
|
|
x = array(np.arange(5), mask=True)
|
|
assert_equal(np.ma.median(x), np.ma.masked)
|
|
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
|
|
assert_(type(np.ma.median(x)) is np.ma.core.MaskedConstant)
|
|
x = array(np.arange(5), mask=False)
|
|
assert_equal(np.ma.median(x), 2.)
|
|
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
|
|
assert_(type(np.ma.median(x)) is not MaskedArray)
|
|
x = array(np.arange(5), mask=[0, 1, 0, 0, 0])
|
|
assert_equal(np.ma.median(x), 2.5)
|
|
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
|
|
assert_(type(np.ma.median(x)) is not MaskedArray)
|
|
x = array(np.arange(5), mask=[0, 1, 1, 1, 1])
|
|
assert_equal(np.ma.median(x), 0.)
|
|
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
|
|
assert_(type(np.ma.median(x)) is not MaskedArray)
|
|
# integer
|
|
x = array(np.arange(5), mask=[0, 1, 1, 0, 0])
|
|
assert_equal(np.ma.median(x), 3.)
|
|
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
|
|
assert_(type(np.ma.median(x)) is not MaskedArray)
|
|
# float
|
|
x = array(np.arange(5.), mask=[0, 1, 1, 0, 0])
|
|
assert_equal(np.ma.median(x), 3.)
|
|
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
|
|
assert_(type(np.ma.median(x)) is not MaskedArray)
|
|
# integer
|
|
x = array(np.arange(6), mask=[0, 1, 1, 1, 1, 0])
|
|
assert_equal(np.ma.median(x), 2.5)
|
|
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
|
|
assert_(type(np.ma.median(x)) is not MaskedArray)
|
|
# float
|
|
x = array(np.arange(6.), mask=[0, 1, 1, 1, 1, 0])
|
|
assert_equal(np.ma.median(x), 2.5)
|
|
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
|
|
assert_(type(np.ma.median(x)) is not MaskedArray)
|
|
|
|
def test_1d_shape_consistency(self):
|
|
assert_equal(np.ma.median(array([1, 2, 3], mask=[0, 0, 0])).shape,
|
|
np.ma.median(array([1, 2, 3], mask=[0, 1, 0])).shape)
|
|
|
|
def test_2d(self):
|
|
# Tests median w/ 2D
|
|
(n, p) = (101, 30)
|
|
x = masked_array(np.linspace(-1., 1., n),)
|
|
x[:10] = x[-10:] = masked
|
|
z = masked_array(np.empty((n, p), dtype=float))
|
|
z[:, 0] = x[:]
|
|
idx = np.arange(len(x))
|
|
for i in range(1, p):
|
|
np.random.shuffle(idx)
|
|
z[:, i] = x[idx]
|
|
assert_equal(median(z[:, 0]), 0)
|
|
assert_equal(median(z), 0)
|
|
assert_equal(median(z, axis=0), np.zeros(p))
|
|
assert_equal(median(z.T, axis=1), np.zeros(p))
|
|
|
|
def test_2d_waxis(self):
|
|
# Tests median w/ 2D arrays and different axis.
|
|
x = masked_array(np.arange(30).reshape(10, 3))
|
|
x[:3] = x[-3:] = masked
|
|
assert_equal(median(x), 14.5)
|
|
assert_(type(np.ma.median(x)) is not MaskedArray)
|
|
assert_equal(median(x, axis=0), [13.5, 14.5, 15.5])
|
|
assert_(type(np.ma.median(x, axis=0)) is MaskedArray)
|
|
assert_equal(median(x, axis=1), [0, 0, 0, 10, 13, 16, 19, 0, 0, 0])
|
|
assert_(type(np.ma.median(x, axis=1)) is MaskedArray)
|
|
assert_equal(median(x, axis=1).mask, [1, 1, 1, 0, 0, 0, 0, 1, 1, 1])
|
|
|
|
def test_3d(self):
|
|
# Tests median w/ 3D
|
|
x = np.ma.arange(24).reshape(3, 4, 2)
|
|
x[x % 3 == 0] = masked
|
|
assert_equal(median(x, 0), [[12, 9], [6, 15], [12, 9], [18, 15]])
|
|
x.shape = (4, 3, 2)
|
|
assert_equal(median(x, 0), [[99, 10], [11, 99], [13, 14]])
|
|
x = np.ma.arange(24).reshape(4, 3, 2)
|
|
x[x % 5 == 0] = masked
|
|
assert_equal(median(x, 0), [[12, 10], [8, 9], [16, 17]])
|
|
|
|
def test_neg_axis(self):
|
|
x = masked_array(np.arange(30).reshape(10, 3))
|
|
x[:3] = x[-3:] = masked
|
|
assert_equal(median(x, axis=-1), median(x, axis=1))
|
|
|
|
def test_out_1d(self):
|
|
# integer float even odd
|
|
for v in (30, 30., 31, 31.):
|
|
x = masked_array(np.arange(v))
|
|
x[:3] = x[-3:] = masked
|
|
out = masked_array(np.ones(()))
|
|
r = median(x, out=out)
|
|
if v == 30:
|
|
assert_equal(out, 14.5)
|
|
else:
|
|
assert_equal(out, 15.)
|
|
assert_(r is out)
|
|
assert_(type(r) is MaskedArray)
|
|
|
|
def test_out(self):
|
|
# integer float even odd
|
|
for v in (40, 40., 30, 30.):
|
|
x = masked_array(np.arange(v).reshape(10, -1))
|
|
x[:3] = x[-3:] = masked
|
|
out = masked_array(np.ones(10))
|
|
r = median(x, axis=1, out=out)
|
|
if v == 30:
|
|
e = masked_array([0.] * 3 + [10, 13, 16, 19] + [0.] * 3,
|
|
mask=[True] * 3 + [False] * 4 + [True] * 3)
|
|
else:
|
|
e = masked_array([0.] * 3 + [13.5, 17.5, 21.5, 25.5] + [0.] * 3,
|
|
mask=[True] * 3 + [False] * 4 + [True] * 3)
|
|
assert_equal(r, e)
|
|
assert_(r is out)
|
|
assert_(type(r) is MaskedArray)
|
|
|
|
@pytest.mark.parametrize(
|
|
argnames='axis',
|
|
argvalues=[
|
|
None,
|
|
1,
|
|
(1, ),
|
|
(0, 1),
|
|
(-3, -1),
|
|
]
|
|
)
|
|
def test_keepdims_out(self, axis):
|
|
mask = np.zeros((3, 5, 7, 11), dtype=bool)
|
|
# Randomly set some elements to True:
|
|
w = np.random.random((4, 200)) * np.array(mask.shape)[:, None]
|
|
w = w.astype(np.intp)
|
|
mask[tuple(w)] = np.nan
|
|
d = masked_array(np.ones(mask.shape), mask=mask)
|
|
if axis is None:
|
|
shape_out = (1,) * d.ndim
|
|
else:
|
|
axis_norm = normalize_axis_tuple(axis, d.ndim)
|
|
shape_out = tuple(
|
|
1 if i in axis_norm else d.shape[i] for i in range(d.ndim))
|
|
out = masked_array(np.empty(shape_out))
|
|
result = median(d, axis=axis, keepdims=True, out=out)
|
|
assert result is out
|
|
assert_equal(result.shape, shape_out)
|
|
|
|
def test_single_non_masked_value_on_axis(self):
|
|
data = [[1., 0.],
|
|
[0., 3.],
|
|
[0., 0.]]
|
|
masked_arr = np.ma.masked_equal(data, 0)
|
|
expected = [1., 3.]
|
|
assert_array_equal(np.ma.median(masked_arr, axis=0),
|
|
expected)
|
|
|
|
def test_nan(self):
|
|
for mask in (False, np.zeros(6, dtype=bool)):
|
|
dm = np.ma.array([[1, np.nan, 3], [1, 2, 3]])
|
|
dm.mask = mask
|
|
|
|
# scalar result
|
|
r = np.ma.median(dm, axis=None)
|
|
assert_(np.isscalar(r))
|
|
assert_array_equal(r, np.nan)
|
|
r = np.ma.median(dm.ravel(), axis=0)
|
|
assert_(np.isscalar(r))
|
|
assert_array_equal(r, np.nan)
|
|
|
|
r = np.ma.median(dm, axis=0)
|
|
assert_equal(type(r), MaskedArray)
|
|
assert_array_equal(r, [1, np.nan, 3])
|
|
r = np.ma.median(dm, axis=1)
|
|
assert_equal(type(r), MaskedArray)
|
|
assert_array_equal(r, [np.nan, 2])
|
|
r = np.ma.median(dm, axis=-1)
|
|
assert_equal(type(r), MaskedArray)
|
|
assert_array_equal(r, [np.nan, 2])
|
|
|
|
dm = np.ma.array([[1, np.nan, 3], [1, 2, 3]])
|
|
dm[:, 2] = np.ma.masked
|
|
assert_array_equal(np.ma.median(dm, axis=None), np.nan)
|
|
assert_array_equal(np.ma.median(dm, axis=0), [1, np.nan, 3])
|
|
assert_array_equal(np.ma.median(dm, axis=1), [np.nan, 1.5])
|
|
|
|
def test_out_nan(self):
|
|
o = np.ma.masked_array(np.zeros((4,)))
|
|
d = np.ma.masked_array(np.ones((3, 4)))
|
|
d[2, 1] = np.nan
|
|
d[2, 2] = np.ma.masked
|
|
assert_equal(np.ma.median(d, 0, out=o), o)
|
|
o = np.ma.masked_array(np.zeros((3,)))
|
|
assert_equal(np.ma.median(d, 1, out=o), o)
|
|
o = np.ma.masked_array(np.zeros(()))
|
|
assert_equal(np.ma.median(d, out=o), o)
|
|
|
|
def test_nan_behavior(self):
|
|
a = np.ma.masked_array(np.arange(24, dtype=float))
|
|
a[::3] = np.ma.masked
|
|
a[2] = np.nan
|
|
assert_array_equal(np.ma.median(a), np.nan)
|
|
assert_array_equal(np.ma.median(a, axis=0), np.nan)
|
|
|
|
a = np.ma.masked_array(np.arange(24, dtype=float).reshape(2, 3, 4))
|
|
a.mask = np.arange(a.size) % 2 == 1
|
|
aorig = a.copy()
|
|
a[1, 2, 3] = np.nan
|
|
a[1, 1, 2] = np.nan
|
|
|
|
# no axis
|
|
assert_array_equal(np.ma.median(a), np.nan)
|
|
assert_(np.isscalar(np.ma.median(a)))
|
|
|
|
# axis0
|
|
b = np.ma.median(aorig, axis=0)
|
|
b[2, 3] = np.nan
|
|
b[1, 2] = np.nan
|
|
assert_equal(np.ma.median(a, 0), b)
|
|
|
|
# axis1
|
|
b = np.ma.median(aorig, axis=1)
|
|
b[1, 3] = np.nan
|
|
b[1, 2] = np.nan
|
|
assert_equal(np.ma.median(a, 1), b)
|
|
|
|
# axis02
|
|
b = np.ma.median(aorig, axis=(0, 2))
|
|
b[1] = np.nan
|
|
b[2] = np.nan
|
|
assert_equal(np.ma.median(a, (0, 2)), b)
|
|
|
|
def test_ambigous_fill(self):
|
|
# 255 is max value, used as filler for sort
|
|
a = np.array([[3, 3, 255], [3, 3, 255]], dtype=np.uint8)
|
|
a = np.ma.masked_array(a, mask=a == 3)
|
|
assert_array_equal(np.ma.median(a, axis=1), 255)
|
|
assert_array_equal(np.ma.median(a, axis=1).mask, False)
|
|
assert_array_equal(np.ma.median(a, axis=0), a[0])
|
|
assert_array_equal(np.ma.median(a), 255)
|
|
|
|
def test_special(self):
|
|
for inf in [np.inf, -np.inf]:
|
|
a = np.array([[inf, np.nan], [np.nan, np.nan]])
|
|
a = np.ma.masked_array(a, mask=np.isnan(a))
|
|
assert_equal(np.ma.median(a, axis=0), [inf, np.nan])
|
|
assert_equal(np.ma.median(a, axis=1), [inf, np.nan])
|
|
assert_equal(np.ma.median(a), inf)
|
|
|
|
a = np.array([[np.nan, np.nan, inf], [np.nan, np.nan, inf]])
|
|
a = np.ma.masked_array(a, mask=np.isnan(a))
|
|
assert_array_equal(np.ma.median(a, axis=1), inf)
|
|
assert_array_equal(np.ma.median(a, axis=1).mask, False)
|
|
assert_array_equal(np.ma.median(a, axis=0), a[0])
|
|
assert_array_equal(np.ma.median(a), inf)
|
|
|
|
# no mask
|
|
a = np.array([[inf, inf], [inf, inf]])
|
|
assert_equal(np.ma.median(a), inf)
|
|
assert_equal(np.ma.median(a, axis=0), inf)
|
|
assert_equal(np.ma.median(a, axis=1), inf)
|
|
|
|
a = np.array([[inf, 7, -inf, -9],
|
|
[-10, np.nan, np.nan, 5],
|
|
[4, np.nan, np.nan, inf]],
|
|
dtype=np.float32)
|
|
a = np.ma.masked_array(a, mask=np.isnan(a))
|
|
if inf > 0:
|
|
assert_equal(np.ma.median(a, axis=0), [4., 7., -inf, 5.])
|
|
assert_equal(np.ma.median(a), 4.5)
|
|
else:
|
|
assert_equal(np.ma.median(a, axis=0), [-10., 7., -inf, -9.])
|
|
assert_equal(np.ma.median(a), -2.5)
|
|
assert_equal(np.ma.median(a, axis=1), [-1., -2.5, inf])
|
|
|
|
for i in range(10):
|
|
for j in range(1, 10):
|
|
a = np.array([([np.nan] * i) + ([inf] * j)] * 2)
|
|
a = np.ma.masked_array(a, mask=np.isnan(a))
|
|
assert_equal(np.ma.median(a), inf)
|
|
assert_equal(np.ma.median(a, axis=1), inf)
|
|
assert_equal(np.ma.median(a, axis=0),
|
|
([np.nan] * i) + [inf] * j)
|
|
|
|
def test_empty(self):
|
|
# empty arrays
|
|
a = np.ma.masked_array(np.array([], dtype=float))
|
|
with suppress_warnings() as w:
|
|
w.record(RuntimeWarning)
|
|
assert_array_equal(np.ma.median(a), np.nan)
|
|
assert_(w.log[0].category is RuntimeWarning)
|
|
|
|
# multiple dimensions
|
|
a = np.ma.masked_array(np.array([], dtype=float, ndmin=3))
|
|
# no axis
|
|
with suppress_warnings() as w:
|
|
w.record(RuntimeWarning)
|
|
warnings.filterwarnings('always', '', RuntimeWarning)
|
|
assert_array_equal(np.ma.median(a), np.nan)
|
|
assert_(w.log[0].category is RuntimeWarning)
|
|
|
|
# axis 0 and 1
|
|
b = np.ma.masked_array(np.array([], dtype=float, ndmin=2))
|
|
assert_equal(np.ma.median(a, axis=0), b)
|
|
assert_equal(np.ma.median(a, axis=1), b)
|
|
|
|
# axis 2
|
|
b = np.ma.masked_array(np.array(np.nan, dtype=float, ndmin=2))
|
|
with warnings.catch_warnings(record=True) as w:
|
|
warnings.filterwarnings('always', '', RuntimeWarning)
|
|
assert_equal(np.ma.median(a, axis=2), b)
|
|
assert_(w[0].category is RuntimeWarning)
|
|
|
|
def test_object(self):
|
|
o = np.ma.masked_array(np.arange(7.))
|
|
assert_(type(np.ma.median(o.astype(object))), float)
|
|
o[2] = np.nan
|
|
assert_(type(np.ma.median(o.astype(object))), float)
|
|
|
|
|
|
class TestCov:
|
|
|
|
def setup_method(self):
|
|
self.data = array(np.random.rand(12))
|
|
|
|
def test_covhelper(self):
|
|
x = self.data
|
|
# Test not mask output type is a float.
|
|
assert_(_covhelper(x, rowvar=True)[1].dtype, np.float32)
|
|
assert_(_covhelper(x, y=x, rowvar=False)[1].dtype, np.float32)
|
|
# Test not mask output is equal after casting to float.
|
|
mask = x > 0.5
|
|
assert_array_equal(
|
|
_covhelper(
|
|
np.ma.masked_array(x, mask), rowvar=True
|
|
)[1].astype(bool),
|
|
~mask.reshape(1, -1),
|
|
)
|
|
assert_array_equal(
|
|
_covhelper(
|
|
np.ma.masked_array(x, mask), y=x, rowvar=False
|
|
)[1].astype(bool),
|
|
np.vstack((~mask, ~mask)),
|
|
)
|
|
|
|
def test_1d_without_missing(self):
|
|
# Test cov on 1D variable w/o missing values
|
|
x = self.data
|
|
assert_almost_equal(np.cov(x), cov(x))
|
|
assert_almost_equal(np.cov(x, rowvar=False), cov(x, rowvar=False))
|
|
assert_almost_equal(np.cov(x, rowvar=False, bias=True),
|
|
cov(x, rowvar=False, bias=True))
|
|
|
|
def test_2d_without_missing(self):
|
|
# Test cov on 1 2D variable w/o missing values
|
|
x = self.data.reshape(3, 4)
|
|
assert_almost_equal(np.cov(x), cov(x))
|
|
assert_almost_equal(np.cov(x, rowvar=False), cov(x, rowvar=False))
|
|
assert_almost_equal(np.cov(x, rowvar=False, bias=True),
|
|
cov(x, rowvar=False, bias=True))
|
|
|
|
def test_1d_with_missing(self):
|
|
# Test cov 1 1D variable w/missing values
|
|
x = self.data
|
|
x[-1] = masked
|
|
x -= x.mean()
|
|
nx = x.compressed()
|
|
assert_almost_equal(np.cov(nx), cov(x))
|
|
assert_almost_equal(np.cov(nx, rowvar=False), cov(x, rowvar=False))
|
|
assert_almost_equal(np.cov(nx, rowvar=False, bias=True),
|
|
cov(x, rowvar=False, bias=True))
|
|
#
|
|
try:
|
|
cov(x, allow_masked=False)
|
|
except ValueError:
|
|
pass
|
|
#
|
|
# 2 1D variables w/ missing values
|
|
nx = x[1:-1]
|
|
assert_almost_equal(np.cov(nx, nx[::-1]), cov(x, x[::-1]))
|
|
assert_almost_equal(np.cov(nx, nx[::-1], rowvar=False),
|
|
cov(x, x[::-1], rowvar=False))
|
|
assert_almost_equal(np.cov(nx, nx[::-1], rowvar=False, bias=True),
|
|
cov(x, x[::-1], rowvar=False, bias=True))
|
|
|
|
def test_2d_with_missing(self):
|
|
# Test cov on 2D variable w/ missing value
|
|
x = self.data
|
|
x[-1] = masked
|
|
x = x.reshape(3, 4)
|
|
valid = np.logical_not(getmaskarray(x)).astype(int)
|
|
frac = np.dot(valid, valid.T)
|
|
xf = (x - x.mean(1)[:, None]).filled(0)
|
|
assert_almost_equal(cov(x),
|
|
np.cov(xf) * (x.shape[1] - 1) / (frac - 1.))
|
|
assert_almost_equal(cov(x, bias=True),
|
|
np.cov(xf, bias=True) * x.shape[1] / frac)
|
|
frac = np.dot(valid.T, valid)
|
|
xf = (x - x.mean(0)).filled(0)
|
|
assert_almost_equal(cov(x, rowvar=False),
|
|
(np.cov(xf, rowvar=False) *
|
|
(x.shape[0] - 1) / (frac - 1.)))
|
|
assert_almost_equal(cov(x, rowvar=False, bias=True),
|
|
(np.cov(xf, rowvar=False, bias=True) *
|
|
x.shape[0] / frac))
|
|
|
|
|
|
class TestCorrcoef:
|
|
|
|
def setup_method(self):
|
|
self.data = array(np.random.rand(12))
|
|
self.data2 = array(np.random.rand(12))
|
|
|
|
def test_ddof(self):
|
|
# ddof raises DeprecationWarning
|
|
x, y = self.data, self.data2
|
|
expected = np.corrcoef(x)
|
|
expected2 = np.corrcoef(x, y)
|
|
with suppress_warnings() as sup:
|
|
warnings.simplefilter("always")
|
|
assert_warns(DeprecationWarning, corrcoef, x, ddof=-1)
|
|
sup.filter(DeprecationWarning, "bias and ddof have no effect")
|
|
# ddof has no or negligible effect on the function
|
|
assert_almost_equal(np.corrcoef(x, ddof=0), corrcoef(x, ddof=0))
|
|
assert_almost_equal(corrcoef(x, ddof=-1), expected)
|
|
assert_almost_equal(corrcoef(x, y, ddof=-1), expected2)
|
|
assert_almost_equal(corrcoef(x, ddof=3), expected)
|
|
assert_almost_equal(corrcoef(x, y, ddof=3), expected2)
|
|
|
|
def test_bias(self):
|
|
x, y = self.data, self.data2
|
|
expected = np.corrcoef(x)
|
|
# bias raises DeprecationWarning
|
|
with suppress_warnings() as sup:
|
|
warnings.simplefilter("always")
|
|
assert_warns(DeprecationWarning, corrcoef, x, y, True, False)
|
|
assert_warns(DeprecationWarning, corrcoef, x, y, True, True)
|
|
assert_warns(DeprecationWarning, corrcoef, x, bias=False)
|
|
sup.filter(DeprecationWarning, "bias and ddof have no effect")
|
|
# bias has no or negligible effect on the function
|
|
assert_almost_equal(corrcoef(x, bias=1), expected)
|
|
|
|
def test_1d_without_missing(self):
|
|
# Test cov on 1D variable w/o missing values
|
|
x = self.data
|
|
assert_almost_equal(np.corrcoef(x), corrcoef(x))
|
|
assert_almost_equal(np.corrcoef(x, rowvar=False),
|
|
corrcoef(x, rowvar=False))
|
|
with suppress_warnings() as sup:
|
|
sup.filter(DeprecationWarning, "bias and ddof have no effect")
|
|
assert_almost_equal(np.corrcoef(x, rowvar=False, bias=True),
|
|
corrcoef(x, rowvar=False, bias=True))
|
|
|
|
def test_2d_without_missing(self):
|
|
# Test corrcoef on 1 2D variable w/o missing values
|
|
x = self.data.reshape(3, 4)
|
|
assert_almost_equal(np.corrcoef(x), corrcoef(x))
|
|
assert_almost_equal(np.corrcoef(x, rowvar=False),
|
|
corrcoef(x, rowvar=False))
|
|
with suppress_warnings() as sup:
|
|
sup.filter(DeprecationWarning, "bias and ddof have no effect")
|
|
assert_almost_equal(np.corrcoef(x, rowvar=False, bias=True),
|
|
corrcoef(x, rowvar=False, bias=True))
|
|
|
|
def test_1d_with_missing(self):
|
|
# Test corrcoef 1 1D variable w/missing values
|
|
x = self.data
|
|
x[-1] = masked
|
|
x -= x.mean()
|
|
nx = x.compressed()
|
|
assert_almost_equal(np.corrcoef(nx), corrcoef(x))
|
|
assert_almost_equal(np.corrcoef(nx, rowvar=False),
|
|
corrcoef(x, rowvar=False))
|
|
with suppress_warnings() as sup:
|
|
sup.filter(DeprecationWarning, "bias and ddof have no effect")
|
|
assert_almost_equal(np.corrcoef(nx, rowvar=False, bias=True),
|
|
corrcoef(x, rowvar=False, bias=True))
|
|
try:
|
|
corrcoef(x, allow_masked=False)
|
|
except ValueError:
|
|
pass
|
|
# 2 1D variables w/ missing values
|
|
nx = x[1:-1]
|
|
assert_almost_equal(np.corrcoef(nx, nx[::-1]), corrcoef(x, x[::-1]))
|
|
assert_almost_equal(np.corrcoef(nx, nx[::-1], rowvar=False),
|
|
corrcoef(x, x[::-1], rowvar=False))
|
|
with suppress_warnings() as sup:
|
|
sup.filter(DeprecationWarning, "bias and ddof have no effect")
|
|
# ddof and bias have no or negligible effect on the function
|
|
assert_almost_equal(np.corrcoef(nx, nx[::-1]),
|
|
corrcoef(x, x[::-1], bias=1))
|
|
assert_almost_equal(np.corrcoef(nx, nx[::-1]),
|
|
corrcoef(x, x[::-1], ddof=2))
|
|
|
|
def test_2d_with_missing(self):
|
|
# Test corrcoef on 2D variable w/ missing value
|
|
x = self.data
|
|
x[-1] = masked
|
|
x = x.reshape(3, 4)
|
|
|
|
test = corrcoef(x)
|
|
control = np.corrcoef(x)
|
|
assert_almost_equal(test[:-1, :-1], control[:-1, :-1])
|
|
with suppress_warnings() as sup:
|
|
sup.filter(DeprecationWarning, "bias and ddof have no effect")
|
|
# ddof and bias have no or negligible effect on the function
|
|
assert_almost_equal(corrcoef(x, ddof=-2)[:-1, :-1],
|
|
control[:-1, :-1])
|
|
assert_almost_equal(corrcoef(x, ddof=3)[:-1, :-1],
|
|
control[:-1, :-1])
|
|
assert_almost_equal(corrcoef(x, bias=1)[:-1, :-1],
|
|
control[:-1, :-1])
|
|
|
|
|
|
class TestPolynomial:
|
|
#
|
|
def test_polyfit(self):
|
|
# Tests polyfit
|
|
# On ndarrays
|
|
x = np.random.rand(10)
|
|
y = np.random.rand(20).reshape(-1, 2)
|
|
assert_almost_equal(polyfit(x, y, 3), np.polyfit(x, y, 3))
|
|
# ON 1D maskedarrays
|
|
x = x.view(MaskedArray)
|
|
x[0] = masked
|
|
y = y.view(MaskedArray)
|
|
y[0, 0] = y[-1, -1] = masked
|
|
#
|
|
(C, R, K, S, D) = polyfit(x, y[:, 0], 3, full=True)
|
|
(c, r, k, s, d) = np.polyfit(x[1:], y[1:, 0].compressed(), 3,
|
|
full=True)
|
|
for (a, a_) in zip((C, R, K, S, D), (c, r, k, s, d)):
|
|
assert_almost_equal(a, a_)
|
|
#
|
|
(C, R, K, S, D) = polyfit(x, y[:, -1], 3, full=True)
|
|
(c, r, k, s, d) = np.polyfit(x[1:-1], y[1:-1, -1], 3, full=True)
|
|
for (a, a_) in zip((C, R, K, S, D), (c, r, k, s, d)):
|
|
assert_almost_equal(a, a_)
|
|
#
|
|
(C, R, K, S, D) = polyfit(x, y, 3, full=True)
|
|
(c, r, k, s, d) = np.polyfit(x[1:-1], y[1:-1, :], 3, full=True)
|
|
for (a, a_) in zip((C, R, K, S, D), (c, r, k, s, d)):
|
|
assert_almost_equal(a, a_)
|
|
#
|
|
w = np.random.rand(10) + 1
|
|
wo = w.copy()
|
|
xs = x[1:-1]
|
|
ys = y[1:-1]
|
|
ws = w[1:-1]
|
|
(C, R, K, S, D) = polyfit(x, y, 3, full=True, w=w)
|
|
(c, r, k, s, d) = np.polyfit(xs, ys, 3, full=True, w=ws)
|
|
assert_equal(w, wo)
|
|
for (a, a_) in zip((C, R, K, S, D), (c, r, k, s, d)):
|
|
assert_almost_equal(a, a_)
|
|
|
|
def test_polyfit_with_masked_NaNs(self):
|
|
x = np.random.rand(10)
|
|
y = np.random.rand(20).reshape(-1, 2)
|
|
|
|
x[0] = np.nan
|
|
y[-1, -1] = np.nan
|
|
x = x.view(MaskedArray)
|
|
y = y.view(MaskedArray)
|
|
x[0] = masked
|
|
y[-1, -1] = masked
|
|
|
|
(C, R, K, S, D) = polyfit(x, y, 3, full=True)
|
|
(c, r, k, s, d) = np.polyfit(x[1:-1], y[1:-1, :], 3, full=True)
|
|
for (a, a_) in zip((C, R, K, S, D), (c, r, k, s, d)):
|
|
assert_almost_equal(a, a_)
|
|
|
|
|
|
class TestArraySetOps:
|
|
|
|
def test_unique_onlist(self):
|
|
# Test unique on list
|
|
data = [1, 1, 1, 2, 2, 3]
|
|
test = unique(data, return_index=True, return_inverse=True)
|
|
assert_(isinstance(test[0], MaskedArray))
|
|
assert_equal(test[0], masked_array([1, 2, 3], mask=[0, 0, 0]))
|
|
assert_equal(test[1], [0, 3, 5])
|
|
assert_equal(test[2], [0, 0, 0, 1, 1, 2])
|
|
|
|
def test_unique_onmaskedarray(self):
|
|
# Test unique on masked data w/use_mask=True
|
|
data = masked_array([1, 1, 1, 2, 2, 3], mask=[0, 0, 1, 0, 1, 0])
|
|
test = unique(data, return_index=True, return_inverse=True)
|
|
assert_equal(test[0], masked_array([1, 2, 3, -1], mask=[0, 0, 0, 1]))
|
|
assert_equal(test[1], [0, 3, 5, 2])
|
|
assert_equal(test[2], [0, 0, 3, 1, 3, 2])
|
|
#
|
|
data.fill_value = 3
|
|
data = masked_array(data=[1, 1, 1, 2, 2, 3],
|
|
mask=[0, 0, 1, 0, 1, 0], fill_value=3)
|
|
test = unique(data, return_index=True, return_inverse=True)
|
|
assert_equal(test[0], masked_array([1, 2, 3, -1], mask=[0, 0, 0, 1]))
|
|
assert_equal(test[1], [0, 3, 5, 2])
|
|
assert_equal(test[2], [0, 0, 3, 1, 3, 2])
|
|
|
|
def test_unique_allmasked(self):
|
|
# Test all masked
|
|
data = masked_array([1, 1, 1], mask=True)
|
|
test = unique(data, return_index=True, return_inverse=True)
|
|
assert_equal(test[0], masked_array([1, ], mask=[True]))
|
|
assert_equal(test[1], [0])
|
|
assert_equal(test[2], [0, 0, 0])
|
|
#
|
|
# Test masked
|
|
data = masked
|
|
test = unique(data, return_index=True, return_inverse=True)
|
|
assert_equal(test[0], masked_array(masked))
|
|
assert_equal(test[1], [0])
|
|
assert_equal(test[2], [0])
|
|
|
|
def test_ediff1d(self):
|
|
# Tests mediff1d
|
|
x = masked_array(np.arange(5), mask=[1, 0, 0, 0, 1])
|
|
control = array([1, 1, 1, 4], mask=[1, 0, 0, 1])
|
|
test = ediff1d(x)
|
|
assert_equal(test, control)
|
|
assert_equal(test.filled(0), control.filled(0))
|
|
assert_equal(test.mask, control.mask)
|
|
|
|
def test_ediff1d_tobegin(self):
|
|
# Test ediff1d w/ to_begin
|
|
x = masked_array(np.arange(5), mask=[1, 0, 0, 0, 1])
|
|
test = ediff1d(x, to_begin=masked)
|
|
control = array([0, 1, 1, 1, 4], mask=[1, 1, 0, 0, 1])
|
|
assert_equal(test, control)
|
|
assert_equal(test.filled(0), control.filled(0))
|
|
assert_equal(test.mask, control.mask)
|
|
#
|
|
test = ediff1d(x, to_begin=[1, 2, 3])
|
|
control = array([1, 2, 3, 1, 1, 1, 4], mask=[0, 0, 0, 1, 0, 0, 1])
|
|
assert_equal(test, control)
|
|
assert_equal(test.filled(0), control.filled(0))
|
|
assert_equal(test.mask, control.mask)
|
|
|
|
def test_ediff1d_toend(self):
|
|
# Test ediff1d w/ to_end
|
|
x = masked_array(np.arange(5), mask=[1, 0, 0, 0, 1])
|
|
test = ediff1d(x, to_end=masked)
|
|
control = array([1, 1, 1, 4, 0], mask=[1, 0, 0, 1, 1])
|
|
assert_equal(test, control)
|
|
assert_equal(test.filled(0), control.filled(0))
|
|
assert_equal(test.mask, control.mask)
|
|
#
|
|
test = ediff1d(x, to_end=[1, 2, 3])
|
|
control = array([1, 1, 1, 4, 1, 2, 3], mask=[1, 0, 0, 1, 0, 0, 0])
|
|
assert_equal(test, control)
|
|
assert_equal(test.filled(0), control.filled(0))
|
|
assert_equal(test.mask, control.mask)
|
|
|
|
def test_ediff1d_tobegin_toend(self):
|
|
# Test ediff1d w/ to_begin and to_end
|
|
x = masked_array(np.arange(5), mask=[1, 0, 0, 0, 1])
|
|
test = ediff1d(x, to_end=masked, to_begin=masked)
|
|
control = array([0, 1, 1, 1, 4, 0], mask=[1, 1, 0, 0, 1, 1])
|
|
assert_equal(test, control)
|
|
assert_equal(test.filled(0), control.filled(0))
|
|
assert_equal(test.mask, control.mask)
|
|
#
|
|
test = ediff1d(x, to_end=[1, 2, 3], to_begin=masked)
|
|
control = array([0, 1, 1, 1, 4, 1, 2, 3],
|
|
mask=[1, 1, 0, 0, 1, 0, 0, 0])
|
|
assert_equal(test, control)
|
|
assert_equal(test.filled(0), control.filled(0))
|
|
assert_equal(test.mask, control.mask)
|
|
|
|
def test_ediff1d_ndarray(self):
|
|
# Test ediff1d w/ a ndarray
|
|
x = np.arange(5)
|
|
test = ediff1d(x)
|
|
control = array([1, 1, 1, 1], mask=[0, 0, 0, 0])
|
|
assert_equal(test, control)
|
|
assert_(isinstance(test, MaskedArray))
|
|
assert_equal(test.filled(0), control.filled(0))
|
|
assert_equal(test.mask, control.mask)
|
|
#
|
|
test = ediff1d(x, to_end=masked, to_begin=masked)
|
|
control = array([0, 1, 1, 1, 1, 0], mask=[1, 0, 0, 0, 0, 1])
|
|
assert_(isinstance(test, MaskedArray))
|
|
assert_equal(test.filled(0), control.filled(0))
|
|
assert_equal(test.mask, control.mask)
|
|
|
|
def test_intersect1d(self):
|
|
# Test intersect1d
|
|
x = array([1, 3, 3, 3], mask=[0, 0, 0, 1])
|
|
y = array([3, 1, 1, 1], mask=[0, 0, 0, 1])
|
|
test = intersect1d(x, y)
|
|
control = array([1, 3, -1], mask=[0, 0, 1])
|
|
assert_equal(test, control)
|
|
|
|
def test_setxor1d(self):
|
|
# Test setxor1d
|
|
a = array([1, 2, 5, 7, -1], mask=[0, 0, 0, 0, 1])
|
|
b = array([1, 2, 3, 4, 5, -1], mask=[0, 0, 0, 0, 0, 1])
|
|
test = setxor1d(a, b)
|
|
assert_equal(test, array([3, 4, 7]))
|
|
#
|
|
a = array([1, 2, 5, 7, -1], mask=[0, 0, 0, 0, 1])
|
|
b = [1, 2, 3, 4, 5]
|
|
test = setxor1d(a, b)
|
|
assert_equal(test, array([3, 4, 7, -1], mask=[0, 0, 0, 1]))
|
|
#
|
|
a = array([1, 2, 3])
|
|
b = array([6, 5, 4])
|
|
test = setxor1d(a, b)
|
|
assert_(isinstance(test, MaskedArray))
|
|
assert_equal(test, [1, 2, 3, 4, 5, 6])
|
|
#
|
|
a = array([1, 8, 2, 3], mask=[0, 1, 0, 0])
|
|
b = array([6, 5, 4, 8], mask=[0, 0, 0, 1])
|
|
test = setxor1d(a, b)
|
|
assert_(isinstance(test, MaskedArray))
|
|
assert_equal(test, [1, 2, 3, 4, 5, 6])
|
|
#
|
|
assert_array_equal([], setxor1d([], []))
|
|
|
|
def test_setxor1d_unique(self):
|
|
# Test setxor1d with assume_unique=True
|
|
a = array([1, 2, 5, 7, -1], mask=[0, 0, 0, 0, 1])
|
|
b = [1, 2, 3, 4, 5]
|
|
test = setxor1d(a, b, assume_unique=True)
|
|
assert_equal(test, array([3, 4, 7, -1], mask=[0, 0, 0, 1]))
|
|
#
|
|
a = array([1, 8, 2, 3], mask=[0, 1, 0, 0])
|
|
b = array([6, 5, 4, 8], mask=[0, 0, 0, 1])
|
|
test = setxor1d(a, b, assume_unique=True)
|
|
assert_(isinstance(test, MaskedArray))
|
|
assert_equal(test, [1, 2, 3, 4, 5, 6])
|
|
#
|
|
a = array([[1], [8], [2], [3]])
|
|
b = array([[6, 5], [4, 8]])
|
|
test = setxor1d(a, b, assume_unique=True)
|
|
assert_(isinstance(test, MaskedArray))
|
|
assert_equal(test, [1, 2, 3, 4, 5, 6])
|
|
|
|
def test_isin(self):
|
|
# the tests for in1d cover most of isin's behavior
|
|
# if in1d is removed, would need to change those tests to test
|
|
# isin instead.
|
|
a = np.arange(24).reshape([2, 3, 4])
|
|
mask = np.zeros([2, 3, 4])
|
|
mask[1, 2, 0] = 1
|
|
a = array(a, mask=mask)
|
|
b = array(data=[0, 10, 20, 30, 1, 3, 11, 22, 33],
|
|
mask=[0, 1, 0, 1, 0, 1, 0, 1, 0])
|
|
ec = zeros((2, 3, 4), dtype=bool)
|
|
ec[0, 0, 0] = True
|
|
ec[0, 0, 1] = True
|
|
ec[0, 2, 3] = True
|
|
c = isin(a, b)
|
|
assert_(isinstance(c, MaskedArray))
|
|
assert_array_equal(c, ec)
|
|
# compare results of np.isin to ma.isin
|
|
d = np.isin(a, b[~b.mask]) & ~a.mask
|
|
assert_array_equal(c, d)
|
|
|
|
def test_in1d(self):
|
|
# Test in1d
|
|
a = array([1, 2, 5, 7, -1], mask=[0, 0, 0, 0, 1])
|
|
b = array([1, 2, 3, 4, 5, -1], mask=[0, 0, 0, 0, 0, 1])
|
|
test = in1d(a, b)
|
|
assert_equal(test, [True, True, True, False, True])
|
|
#
|
|
a = array([5, 5, 2, 1, -1], mask=[0, 0, 0, 0, 1])
|
|
b = array([1, 5, -1], mask=[0, 0, 1])
|
|
test = in1d(a, b)
|
|
assert_equal(test, [True, True, False, True, True])
|
|
#
|
|
assert_array_equal([], in1d([], []))
|
|
|
|
def test_in1d_invert(self):
|
|
# Test in1d's invert parameter
|
|
a = array([1, 2, 5, 7, -1], mask=[0, 0, 0, 0, 1])
|
|
b = array([1, 2, 3, 4, 5, -1], mask=[0, 0, 0, 0, 0, 1])
|
|
assert_equal(np.invert(in1d(a, b)), in1d(a, b, invert=True))
|
|
|
|
a = array([5, 5, 2, 1, -1], mask=[0, 0, 0, 0, 1])
|
|
b = array([1, 5, -1], mask=[0, 0, 1])
|
|
assert_equal(np.invert(in1d(a, b)), in1d(a, b, invert=True))
|
|
|
|
assert_array_equal([], in1d([], [], invert=True))
|
|
|
|
def test_union1d(self):
|
|
# Test union1d
|
|
a = array([1, 2, 5, 7, 5, -1], mask=[0, 0, 0, 0, 0, 1])
|
|
b = array([1, 2, 3, 4, 5, -1], mask=[0, 0, 0, 0, 0, 1])
|
|
test = union1d(a, b)
|
|
control = array([1, 2, 3, 4, 5, 7, -1], mask=[0, 0, 0, 0, 0, 0, 1])
|
|
assert_equal(test, control)
|
|
|
|
# Tests gh-10340, arguments to union1d should be
|
|
# flattened if they are not already 1D
|
|
x = array([[0, 1, 2], [3, 4, 5]], mask=[[0, 0, 0], [0, 0, 1]])
|
|
y = array([0, 1, 2, 3, 4], mask=[0, 0, 0, 0, 1])
|
|
ez = array([0, 1, 2, 3, 4, 5], mask=[0, 0, 0, 0, 0, 1])
|
|
z = union1d(x, y)
|
|
assert_equal(z, ez)
|
|
#
|
|
assert_array_equal([], union1d([], []))
|
|
|
|
def test_setdiff1d(self):
|
|
# Test setdiff1d
|
|
a = array([6, 5, 4, 7, 7, 1, 2, 1], mask=[0, 0, 0, 0, 0, 0, 0, 1])
|
|
b = array([2, 4, 3, 3, 2, 1, 5])
|
|
test = setdiff1d(a, b)
|
|
assert_equal(test, array([6, 7, -1], mask=[0, 0, 1]))
|
|
#
|
|
a = arange(10)
|
|
b = arange(8)
|
|
assert_equal(setdiff1d(a, b), array([8, 9]))
|
|
a = array([], np.uint32, mask=[])
|
|
assert_equal(setdiff1d(a, []).dtype, np.uint32)
|
|
|
|
def test_setdiff1d_char_array(self):
|
|
# Test setdiff1d_charray
|
|
a = np.array(['a', 'b', 'c'])
|
|
b = np.array(['a', 'b', 's'])
|
|
assert_array_equal(setdiff1d(a, b), np.array(['c']))
|
|
|
|
|
|
class TestShapeBase:
|
|
|
|
def test_atleast_2d(self):
|
|
# Test atleast_2d
|
|
a = masked_array([0, 1, 2], mask=[0, 1, 0])
|
|
b = atleast_2d(a)
|
|
assert_equal(b.shape, (1, 3))
|
|
assert_equal(b.mask.shape, b.data.shape)
|
|
assert_equal(a.shape, (3,))
|
|
assert_equal(a.mask.shape, a.data.shape)
|
|
assert_equal(b.mask.shape, b.data.shape)
|
|
|
|
def test_shape_scalar(self):
|
|
# the atleast and diagflat function should work with scalars
|
|
# GitHub issue #3367
|
|
# Additionally, the atleast functions should accept multiple scalars
|
|
# correctly
|
|
b = atleast_1d(1.0)
|
|
assert_equal(b.shape, (1,))
|
|
assert_equal(b.mask.shape, b.shape)
|
|
assert_equal(b.data.shape, b.shape)
|
|
|
|
b = atleast_1d(1.0, 2.0)
|
|
for a in b:
|
|
assert_equal(a.shape, (1,))
|
|
assert_equal(a.mask.shape, a.shape)
|
|
assert_equal(a.data.shape, a.shape)
|
|
|
|
b = atleast_2d(1.0)
|
|
assert_equal(b.shape, (1, 1))
|
|
assert_equal(b.mask.shape, b.shape)
|
|
assert_equal(b.data.shape, b.shape)
|
|
|
|
b = atleast_2d(1.0, 2.0)
|
|
for a in b:
|
|
assert_equal(a.shape, (1, 1))
|
|
assert_equal(a.mask.shape, a.shape)
|
|
assert_equal(a.data.shape, a.shape)
|
|
|
|
b = atleast_3d(1.0)
|
|
assert_equal(b.shape, (1, 1, 1))
|
|
assert_equal(b.mask.shape, b.shape)
|
|
assert_equal(b.data.shape, b.shape)
|
|
|
|
b = atleast_3d(1.0, 2.0)
|
|
for a in b:
|
|
assert_equal(a.shape, (1, 1, 1))
|
|
assert_equal(a.mask.shape, a.shape)
|
|
assert_equal(a.data.shape, a.shape)
|
|
|
|
b = diagflat(1.0)
|
|
assert_equal(b.shape, (1, 1))
|
|
assert_equal(b.mask.shape, b.data.shape)
|
|
|
|
|
|
class TestNDEnumerate:
|
|
|
|
def test_ndenumerate_nomasked(self):
|
|
ordinary = np.arange(6.).reshape((1, 3, 2))
|
|
empty_mask = np.zeros_like(ordinary, dtype=bool)
|
|
with_mask = masked_array(ordinary, mask=empty_mask)
|
|
assert_equal(list(np.ndenumerate(ordinary)),
|
|
list(ndenumerate(ordinary)))
|
|
assert_equal(list(ndenumerate(ordinary)),
|
|
list(ndenumerate(with_mask)))
|
|
assert_equal(list(ndenumerate(with_mask)),
|
|
list(ndenumerate(with_mask, compressed=False)))
|
|
|
|
def test_ndenumerate_allmasked(self):
|
|
a = masked_all(())
|
|
b = masked_all((100,))
|
|
c = masked_all((2, 3, 4))
|
|
assert_equal(list(ndenumerate(a)), [])
|
|
assert_equal(list(ndenumerate(b)), [])
|
|
assert_equal(list(ndenumerate(b, compressed=False)),
|
|
list(zip(np.ndindex((100,)), 100 * [masked])))
|
|
assert_equal(list(ndenumerate(c)), [])
|
|
assert_equal(list(ndenumerate(c, compressed=False)),
|
|
list(zip(np.ndindex((2, 3, 4)), 2 * 3 * 4 * [masked])))
|
|
|
|
def test_ndenumerate_mixedmasked(self):
|
|
a = masked_array(np.arange(12).reshape((3, 4)),
|
|
mask=[[1, 1, 1, 1],
|
|
[1, 1, 0, 1],
|
|
[0, 0, 0, 0]])
|
|
items = [((1, 2), 6),
|
|
((2, 0), 8), ((2, 1), 9), ((2, 2), 10), ((2, 3), 11)]
|
|
assert_equal(list(ndenumerate(a)), items)
|
|
assert_equal(len(list(ndenumerate(a, compressed=False))), a.size)
|
|
for coordinate, value in ndenumerate(a, compressed=False):
|
|
assert_equal(a[coordinate], value)
|
|
|
|
|
|
class TestStack:
|
|
|
|
def test_stack_1d(self):
|
|
a = masked_array([0, 1, 2], mask=[0, 1, 0])
|
|
b = masked_array([9, 8, 7], mask=[1, 0, 0])
|
|
|
|
c = stack([a, b], axis=0)
|
|
assert_equal(c.shape, (2, 3))
|
|
assert_array_equal(a.mask, c[0].mask)
|
|
assert_array_equal(b.mask, c[1].mask)
|
|
|
|
d = vstack([a, b])
|
|
assert_array_equal(c.data, d.data)
|
|
assert_array_equal(c.mask, d.mask)
|
|
|
|
c = stack([a, b], axis=1)
|
|
assert_equal(c.shape, (3, 2))
|
|
assert_array_equal(a.mask, c[:, 0].mask)
|
|
assert_array_equal(b.mask, c[:, 1].mask)
|
|
|
|
def test_stack_masks(self):
|
|
a = masked_array([0, 1, 2], mask=True)
|
|
b = masked_array([9, 8, 7], mask=False)
|
|
|
|
c = stack([a, b], axis=0)
|
|
assert_equal(c.shape, (2, 3))
|
|
assert_array_equal(a.mask, c[0].mask)
|
|
assert_array_equal(b.mask, c[1].mask)
|
|
|
|
d = vstack([a, b])
|
|
assert_array_equal(c.data, d.data)
|
|
assert_array_equal(c.mask, d.mask)
|
|
|
|
c = stack([a, b], axis=1)
|
|
assert_equal(c.shape, (3, 2))
|
|
assert_array_equal(a.mask, c[:, 0].mask)
|
|
assert_array_equal(b.mask, c[:, 1].mask)
|
|
|
|
def test_stack_nd(self):
|
|
# 2D
|
|
shp = (3, 2)
|
|
d1 = np.random.randint(0, 10, shp)
|
|
d2 = np.random.randint(0, 10, shp)
|
|
m1 = np.random.randint(0, 2, shp).astype(bool)
|
|
m2 = np.random.randint(0, 2, shp).astype(bool)
|
|
a1 = masked_array(d1, mask=m1)
|
|
a2 = masked_array(d2, mask=m2)
|
|
|
|
c = stack([a1, a2], axis=0)
|
|
c_shp = (2,) + shp
|
|
assert_equal(c.shape, c_shp)
|
|
assert_array_equal(a1.mask, c[0].mask)
|
|
assert_array_equal(a2.mask, c[1].mask)
|
|
|
|
c = stack([a1, a2], axis=-1)
|
|
c_shp = shp + (2,)
|
|
assert_equal(c.shape, c_shp)
|
|
assert_array_equal(a1.mask, c[..., 0].mask)
|
|
assert_array_equal(a2.mask, c[..., 1].mask)
|
|
|
|
# 4D
|
|
shp = (3, 2, 4, 5,)
|
|
d1 = np.random.randint(0, 10, shp)
|
|
d2 = np.random.randint(0, 10, shp)
|
|
m1 = np.random.randint(0, 2, shp).astype(bool)
|
|
m2 = np.random.randint(0, 2, shp).astype(bool)
|
|
a1 = masked_array(d1, mask=m1)
|
|
a2 = masked_array(d2, mask=m2)
|
|
|
|
c = stack([a1, a2], axis=0)
|
|
c_shp = (2,) + shp
|
|
assert_equal(c.shape, c_shp)
|
|
assert_array_equal(a1.mask, c[0].mask)
|
|
assert_array_equal(a2.mask, c[1].mask)
|
|
|
|
c = stack([a1, a2], axis=-1)
|
|
c_shp = shp + (2,)
|
|
assert_equal(c.shape, c_shp)
|
|
assert_array_equal(a1.mask, c[..., 0].mask)
|
|
assert_array_equal(a2.mask, c[..., 1].mask)
|