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.CondaPkg/env/Lib/site-packages/scipy/sparse/tests/test_minmax1d.py

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+"""Test of min-max 1D features of sparse array classes"""
+
+import pytest
+
+import numpy as np
+
+from numpy.testing import assert_equal, assert_array_equal
+
+from scipy.sparse import coo_array, csr_array, csc_array, bsr_array
+from scipy.sparse import coo_matrix, csr_matrix, csc_matrix, bsr_matrix
+from scipy.sparse._sputils import isscalarlike
+
+
+def toarray(a):
+    if isinstance(a, np.ndarray) or isscalarlike(a):
+        return a
+    return a.toarray()
+
+
+formats_for_minmax = [bsr_array, coo_array, csc_array, csr_array]
+formats_for_minmax_supporting_1d = [coo_array, csr_array]
+
+
+@pytest.mark.parametrize("spcreator", formats_for_minmax_supporting_1d)
+class Test_MinMaxMixin1D:
+    def test_minmax(self, spcreator):
+        D = np.arange(5)
+        X = spcreator(D)
+
+        assert_equal(X.min(), 0)
+        assert_equal(X.max(), 4)
+        assert_equal((-X).min(), -4)
+        assert_equal((-X).max(), 0)
+
+    def test_minmax_axis(self, spcreator):
+        D = np.arange(50)
+        X = spcreator(D)
+
+        for axis in [0, -1]:
+            assert_array_equal(
+                toarray(X.max(axis=axis)), D.max(axis=axis, keepdims=True)
+            )
+            assert_array_equal(
+                toarray(X.min(axis=axis)), D.min(axis=axis, keepdims=True)
+            )
+        for axis in [-2, 1]:
+            with pytest.raises(ValueError, match="axis out of range"):
+                X.min(axis=axis)
+            with pytest.raises(ValueError, match="axis out of range"):
+                X.max(axis=axis)
+
+    def test_numpy_minmax(self, spcreator):
+        dat = np.array([0, 1, 2])
+        datsp = spcreator(dat)
+        assert_array_equal(np.min(datsp), np.min(dat))
+        assert_array_equal(np.max(datsp), np.max(dat))
+
+
+    def test_argmax(self, spcreator):
+        D1 = np.array([-1, 5, 2, 3])
+        D2 = np.array([0, 0, -1, -2])
+        D3 = np.array([-1, -2, -3, -4])
+        D4 = np.array([1, 2, 3, 4])
+        D5 = np.array([1, 2, 0, 0])
+
+        for D in [D1, D2, D3, D4, D5]:
+            mat = spcreator(D)
+
+            assert_equal(mat.argmax(), np.argmax(D))
+            assert_equal(mat.argmin(), np.argmin(D))
+
+            assert_equal(mat.argmax(axis=0), np.argmax(D, axis=0))
+            assert_equal(mat.argmin(axis=0), np.argmin(D, axis=0))
+
+        D6 = np.empty((0,))
+
+        for axis in [None, 0]:
+            mat = spcreator(D6)
+            with pytest.raises(ValueError, match="to an empty matrix"):
+                mat.argmin(axis=axis)
+            with pytest.raises(ValueError, match="to an empty matrix"):
+                mat.argmax(axis=axis)
+
+
+@pytest.mark.parametrize("spcreator", formats_for_minmax)
+class Test_ShapeMinMax2DWithAxis:
+    def test_minmax(self, spcreator):
+        dat = np.array([[-1, 5, 0, 3], [0, 0, -1, -2], [0, 0, 1, 2]])
+        datsp = spcreator(dat)
+
+        for (spminmax, npminmax) in [
+            (datsp.min, np.min),
+            (datsp.max, np.max),
+            (datsp.nanmin, np.nanmin),
+            (datsp.nanmax, np.nanmax),
+        ]:
+            for ax, result_shape in [(0, (4,)), (1, (3,))]:
+                assert_equal(toarray(spminmax(axis=ax)), npminmax(dat, axis=ax))
+                assert_equal(spminmax(axis=ax).shape, result_shape)
+                assert spminmax(axis=ax).format == "coo"
+
+        for spminmax in [datsp.argmin, datsp.argmax]:
+            for ax in [0, 1]:
+                assert isinstance(spminmax(axis=ax), np.ndarray)
+
+        # verify spmatrix behavior
+        spmat_form = {
+            'coo': coo_matrix,
+            'csr': csr_matrix,
+            'csc': csc_matrix,
+            'bsr': bsr_matrix,
+        }
+        datspm = spmat_form[datsp.format](dat)
+
+        for spm, npm in [
+            (datspm.min, np.min),
+            (datspm.max, np.max),
+            (datspm.nanmin, np.nanmin),
+            (datspm.nanmax, np.nanmax),
+        ]:
+            for ax, result_shape in [(0, (1, 4)), (1, (3, 1))]:
+                assert_equal(toarray(spm(axis=ax)), npm(dat, axis=ax, keepdims=True))
+                assert_equal(spm(axis=ax).shape, result_shape)
+                assert spm(axis=ax).format == "coo"
+
+        for spminmax in [datspm.argmin, datspm.argmax]:
+            for ax in [0, 1]:
+                assert isinstance(spminmax(axis=ax), np.ndarray)