83 lines
2.3 KiB
Python
83 lines
2.3 KiB
Python
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"""Test of min-max 1D features of sparse array classes"""
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import pytest
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import numpy as np
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from numpy.testing import assert_equal, assert_array_equal
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from scipy.sparse import coo_array
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from scipy.sparse._sputils import isscalarlike
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def toarray(a):
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if isinstance(a, np.ndarray) or isscalarlike(a):
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return a
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return a.toarray()
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formats_for_minmax = [coo_array]
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@pytest.mark.parametrize("spcreator", formats_for_minmax)
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class Test_MinMaxMixin1D:
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def test_minmax(self, spcreator):
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D = np.arange(5)
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X = spcreator(D)
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assert_equal(X.min(), 0)
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assert_equal(X.max(), 4)
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assert_equal((-X).min(), -4)
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assert_equal((-X).max(), 0)
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def test_minmax_axis(self, spcreator):
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D = np.arange(50)
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X = spcreator(D)
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for axis in [0, -1]:
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assert_array_equal(
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toarray(X.max(axis=axis)), D.max(axis=axis, keepdims=True)
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)
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assert_array_equal(
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toarray(X.min(axis=axis)), D.min(axis=axis, keepdims=True)
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)
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for axis in [-2, 1]:
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with pytest.raises(ValueError, match="axis out of range"):
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X.min(axis=axis)
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with pytest.raises(ValueError, match="axis out of range"):
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X.max(axis=axis)
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def test_numpy_minmax(self, spcreator):
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dat = np.array([0, 1, 2])
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datsp = spcreator(dat)
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assert_array_equal(np.min(datsp), np.min(dat))
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assert_array_equal(np.max(datsp), np.max(dat))
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def test_argmax(self, spcreator):
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D1 = np.array([-1, 5, 2, 3])
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D2 = np.array([0, 0, -1, -2])
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D3 = np.array([-1, -2, -3, -4])
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D4 = np.array([1, 2, 3, 4])
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D5 = np.array([1, 2, 0, 0])
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for D in [D1, D2, D3, D4, D5]:
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mat = spcreator(D)
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assert_equal(mat.argmax(), np.argmax(D))
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assert_equal(mat.argmin(), np.argmin(D))
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assert_equal(mat.argmax(axis=0), np.argmax(D, axis=0))
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assert_equal(mat.argmin(axis=0), np.argmin(D, axis=0))
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D6 = np.empty((0,))
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for axis in [None, 0]:
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mat = spcreator(D6)
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with pytest.raises(ValueError, match="to an empty matrix"):
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mat.argmin(axis=axis)
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with pytest.raises(ValueError, match="to an empty matrix"):
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mat.argmax(axis=axis)
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