def test_report_fail_percentage(self): a = np.array([1, 1, 1, 1]) b = np.array([1, 1, 1, 2]) try: assert_allclose(a, b) msg = '' except AssertionError as exc: msg = exc.args[0] assert_("mismatch 25.0%" in msg)
def test_equal_nan_default(self): # Make sure equal_nan default behavior remains unchanged. (All # of these functions use assert_array_compare under the hood.) # None of these should raise. a = np.array([np.nan]) b = np.array([np.nan]) assert_array_equal(a, b) assert_array_almost_equal(a, b) assert_array_less(a, b) assert_allclose(a, b)
def test_one_bin(self): # Ticket 632 hist, edges = histogram([1, 2, 3, 4], [1, 2]) assert_array_equal(hist, [ 2, ]) assert_array_equal(edges, [1, 2]) assert_raises(ValueError, histogram, [1, 2], bins=0) h, e = histogram([1, 2], bins=1) assert_equal(h, np.array([2])) assert_allclose(e, np.array([1., 2.]))
def test_ddof_corrcoef(self): # See gh-3336 x = np.ma.masked_equal([1, 2, 3, 4, 5], 4) y = np.array([2, 2.5, 3.1, 3, 5]) # this test can be removed after deprecation. with suppress_warnings() as sup: sup.filter(DeprecationWarning, "bias and ddof have no effect") r0 = np.ma.corrcoef(x, y, ddof=0) r1 = np.ma.corrcoef(x, y, ddof=1) # ddof should not have an effect (it gets cancelled out) assert_allclose(r0.data, r1.data)
def test_check_constant_pad_2d(self): arr = np.arange(4).reshape(2, 2) test = np.lib.pad(arr, ((1, 2), (1, 3)), mode='constant', constant_values=((1, 2), (3, 4))) expected = np.array( [[3, 1, 1, 4, 4, 4], [3, 0, 1, 4, 4, 4], [3, 2, 3, 4, 4, 4], [3, 2, 2, 4, 4, 4], [3, 2, 2, 4, 4, 4]] ) assert_allclose(test, expected)
def test_check_2d(self): arr = np.arange(20).reshape(4, 5).astype(np.float64) test = pad(arr, (2, 2), mode='linear_ramp', end_values=(0, 0)) expected = np.array( [[0., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0.5, 1., 1.5, 2., 1., 0.], [0., 0., 0., 1., 2., 3., 4., 2., 0.], [0., 2.5, 5., 6., 7., 8., 9., 4.5, 0.], [0., 5., 10., 11., 12., 13., 14., 7., 0.], [0., 7.5, 15., 16., 17., 18., 19., 9.5, 0.], [0., 3.75, 7.5, 8., 8.5, 9., 9.5, 4.75, 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0.]]) assert_allclose(test, expected)
def test_check_constant_odd_pad_amount(self): arr = np.arange(30).reshape(5, 6) test = pad(arr, ((1,), (2,)), mode='constant', constant_values=3) expected = np.array( [[ 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [ 3, 3, 0, 1, 2, 3, 4, 5, 3, 3], [ 3, 3, 6, 7, 8, 9, 10, 11, 3, 3], [ 3, 3, 12, 13, 14, 15, 16, 17, 3, 3], [ 3, 3, 18, 19, 20, 21, 22, 23, 3, 3], [ 3, 3, 24, 25, 26, 27, 28, 29, 3, 3], [ 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]] ) assert_allclose(test, expected)
def test_check_constant_float(self): # If input array is int, but constant_values are float, the dtype of # the array to be padded is kept arr = np.arange(30).reshape(5, 6) test = pad(arr, (1, 2), mode='constant', constant_values=1.1) expected = np.array( [[ 1, 1, 1, 1, 1, 1, 1, 1, 1], [ 1, 0, 1, 2, 3, 4, 5, 1, 1], [ 1, 6, 7, 8, 9, 10, 11, 1, 1], [ 1, 12, 13, 14, 15, 16, 17, 1, 1], [ 1, 18, 19, 20, 21, 22, 23, 1, 1], [ 1, 24, 25, 26, 27, 28, 29, 1, 1], [ 1, 1, 1, 1, 1, 1, 1, 1, 1], [ 1, 1, 1, 1, 1, 1, 1, 1, 1]] ) assert_allclose(test, expected)
def test_check_constant_float2(self): # If input array is float, and constant_values are float, the dtype of # the array to be padded is kept - here retaining the float constants arr = np.arange(30).reshape(5, 6) arr_float = arr.astype(np.float64) test = pad(arr_float, ((1, 2), (1, 2)), mode='constant', constant_values=1.1) expected = np.array( [[ 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1], [ 1.1, 0. , 1. , 2. , 3. , 4. , 5. , 1.1, 1.1], [ 1.1, 6. , 7. , 8. , 9. , 10. , 11. , 1.1, 1.1], [ 1.1, 12. , 13. , 14. , 15. , 16. , 17. , 1.1, 1.1], [ 1.1, 18. , 19. , 20. , 21. , 22. , 23. , 1.1, 1.1], [ 1.1, 24. , 25. , 26. , 27. , 28. , 29. , 1.1, 1.1], [ 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1], [ 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1]] ) assert_allclose(test, expected)
def test_basic(self): y = geomspace(1, 1e6) assert_(len(y) == 50) y = geomspace(1, 1e6, num=100) assert_(y[-1] == 10**6) y = geomspace(1, 1e6, endpoint=False) assert_(y[-1] < 10**6) y = geomspace(1, 1e6, num=7) assert_array_equal(y, [1, 10, 100, 1e3, 1e4, 1e5, 1e6]) y = geomspace(8, 2, num=3) assert_allclose(y, [8, 4, 2]) assert_array_equal(y.imag, 0) y = geomspace(-1, -100, num=3) assert_array_equal(y, [-1, -10, -100]) assert_array_equal(y.imag, 0) y = geomspace(-100, -1, num=3) assert_array_equal(y, [-100, -10, -1]) assert_array_equal(y.imag, 0)
def test_check_constant_float3(self): a = np.arange(100, dtype=float) a = pad(a, (25, 20), 'constant', constant_values=(-1.1, -1.2)) b = np.array( [-1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2] ) assert_allclose(a, b)
def test_check_simple(self): a = np.arange(100).astype('f') a = pad(a, (25, 20), 'linear_ramp', end_values=(4, 5)) b = np.array( [4.00, 3.84, 3.68, 3.52, 3.36, 3.20, 3.04, 2.88, 2.72, 2.56, 2.40, 2.24, 2.08, 1.92, 1.76, 1.60, 1.44, 1.28, 1.12, 0.96, 0.80, 0.64, 0.48, 0.32, 0.16, 0.00, 1.00, 2.00, 3.00, 4.00, 5.00, 6.00, 7.00, 8.00, 9.00, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0, 71.0, 72.0, 73.0, 74.0, 75.0, 76.0, 77.0, 78.0, 79.0, 80.0, 81.0, 82.0, 83.0, 84.0, 85.0, 86.0, 87.0, 88.0, 89.0, 90.0, 91.0, 92.0, 93.0, 94.0, 95.0, 96.0, 97.0, 98.0, 99.0, 94.3, 89.6, 84.9, 80.2, 75.5, 70.8, 66.1, 61.4, 56.7, 52.0, 47.3, 42.6, 37.9, 33.2, 28.5, 23.8, 19.1, 14.4, 9.7, 5.] ) assert_allclose(a, b, rtol=1e-5, atol=1e-5)
def test_upgrade(self): "Tests the upgrade method." converter = StringConverter() assert_equal(converter._status, 0) # test int assert_equal(converter.upgrade('0'), 0) assert_equal(converter._status, 1) # On systems where long defaults to 32-bit, the statuses will be # offset by one, so we check for this here. import numpy1.core.numeric as nx status_offset = int( nx.dtype(nx.int_).itemsize < nx.dtype(nx.int64).itemsize) # test int > 2**32 assert_equal(converter.upgrade('17179869184'), 17179869184) assert_equal(converter._status, 1 + status_offset) # test float assert_allclose(converter.upgrade('0.'), 0.0) assert_equal(converter._status, 2 + status_offset) # test complex assert_equal(converter.upgrade('0j'), complex('0j')) assert_equal(converter._status, 3 + status_offset) # test str # note that the longdouble type has been skipped, so the # _status increases by 2. Everything should succeed with # unicode conversion (5). for s in ['a', u'a', b'a']: res = converter.upgrade(s) assert_(type(res) is unicode) assert_equal(res, u'a') assert_equal(converter._status, 5 + status_offset)
def test_inf_edges(self): # Test using +/-inf bin edges works. See #1788. with np.errstate(invalid='ignore'): x = np.arange(6).reshape(3, 2) expected = np.array([[1, 0], [0, 1], [0, 1]]) h, e = np.histogramdd(x, bins=[3, [-np.inf, 2, 10]]) assert_allclose(h, expected) h, e = np.histogramdd(x, bins=[3, np.array([-1, 2, np.inf])]) assert_allclose(h, expected) h, e = np.histogramdd(x, bins=[3, [-np.inf, 3, np.inf]]) assert_allclose(h, expected)
def test_simple(self): x = 1e-3 y = 1e-9 assert_allclose(x, y, atol=1) assert_raises(AssertionError, assert_allclose, x, y) a = np.array([x, y, x, y]) b = np.array([x, y, x, x]) assert_allclose(a, b, atol=1) assert_raises(AssertionError, assert_allclose, a, b) b[-1] = y * (1 + 1e-8) assert_allclose(a, b) assert_raises(AssertionError, assert_allclose, a, b, rtol=1e-9) assert_allclose(6, 10, rtol=0.5) assert_raises(AssertionError, assert_allclose, 10, 6, rtol=0.5)
def test_pmt(self): res = np.pmt(0.08 / 12, 5 * 12, 15000) tgt = -304.145914 assert_allclose(res, tgt) # Test the edge case where rate == 0.0 res = np.pmt(0.0, 5 * 12, 15000) tgt = -250.0 assert_allclose(res, tgt) # Test the case where we use broadcast and # the arguments passed in are arrays. res = np.pmt([[0.0, 0.8], [0.3, 0.8]], [12, 3], [2000, 20000]) tgt = np.array([[-166.66667, -19311.258], [-626.90814, -19311.258]]) assert_allclose(res, tgt)
def test_array_scalar(self): lim1 = array([120, 100], dtype="int8") lim2 = array([-120, -100], dtype="int8") lim3 = array([1200, 1000], dtype="uint16") t1 = geomspace(lim1[0], lim1[1], 5) t2 = geomspace(lim2[0], lim2[1], 5) t3 = geomspace(lim3[0], lim3[1], 5) t4 = geomspace(120.0, 100.0, 5) t5 = geomspace(-120.0, -100.0, 5) t6 = geomspace(1200.0, 1000.0, 5) # t3 uses float32, t6 uses float64 assert_allclose(t1, t4, rtol=1e-2) assert_allclose(t2, t5, rtol=1e-2) assert_allclose(t3, t6, rtol=1e-5)
def test_equal_nan(self): a = np.array([np.nan]) b = np.array([np.nan]) # Should not raise: assert_allclose(a, b, equal_nan=True)
def test_complex(self): # Purely imaginary y = geomspace(1j, 16j, num=5) assert_allclose(y, [1j, 2j, 4j, 8j, 16j]) assert_array_equal(y.real, 0) y = geomspace(-4j, -324j, num=5) assert_allclose(y, [-4j, -12j, -36j, -108j, -324j]) assert_array_equal(y.real, 0) y = geomspace(1 + 1j, 1000 + 1000j, num=4) assert_allclose(y, [1 + 1j, 10 + 10j, 100 + 100j, 1000 + 1000j]) y = geomspace(-1 + 1j, -1000 + 1000j, num=4) assert_allclose(y, [-1 + 1j, -10 + 10j, -100 + 100j, -1000 + 1000j]) # Logarithmic spirals y = geomspace(-1, 1, num=3, dtype=complex) assert_allclose(y, [-1, 1j, +1]) y = geomspace(0 + 3j, -3 + 0j, 3) assert_allclose(y, [0 + 3j, -3 / sqrt(2) + 3j / sqrt(2), -3 + 0j]) y = geomspace(0 + 3j, 3 + 0j, 3) assert_allclose(y, [0 + 3j, 3 / sqrt(2) + 3j / sqrt(2), 3 + 0j]) y = geomspace(-3 + 0j, 0 - 3j, 3) assert_allclose(y, [-3 + 0j, -3 / sqrt(2) - 3j / sqrt(2), 0 - 3j]) y = geomspace(0 + 3j, -3 + 0j, 3) assert_allclose(y, [0 + 3j, -3 / sqrt(2) + 3j / sqrt(2), -3 + 0j]) y = geomspace(-2 - 3j, 5 + 7j, 7) assert_allclose(y, [ -2 - 3j, -0.29058977 - 4.15771027j, 2.08885354 - 4.34146838j, 4.58345529 - 3.16355218j, 6.41401745 - 0.55233457j, 6.75707386 + 3.11795092j, 5 + 7j ]) # Type promotion should prevent the -5 from becoming a NaN y = geomspace(3j, -5, 2) assert_allclose(y, [3j, -5]) y = geomspace(-5, 3j, 2) assert_allclose(y, [-5, 3j])
def test_min_int(self): a = np.array([np.iinfo(np.int_).min], dtype=np.int_) # Should not raise: assert_allclose(a, a)