diff options
Diffstat (limited to 'numpy/lib/tests/test_function_base.py')
| -rw-r--r-- | numpy/lib/tests/test_function_base.py | 377 |
1 files changed, 223 insertions, 154 deletions
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py index e11822e8e..a82fab6a7 100644 --- a/numpy/lib/tests/test_function_base.py +++ b/numpy/lib/tests/test_function_base.py @@ -9,14 +9,22 @@ from numpy.testing import ( assert_almost_equal, assert_array_almost_equal, assert_raises, assert_allclose, assert_array_max_ulp, assert_warns, assert_raises_regex, dec, clear_and_catch_warnings - ) +) import numpy.lib.function_base as nfb from numpy.random import rand -from numpy.lib import * +from numpy.lib import ( + add_newdoc_ufunc, angle, average, bartlett, blackman, corrcoef, cov, + delete, diff, digitize, extract, flipud, gradient, hamming, hanning, + histogram, histogramdd, i0, insert, interp, kaiser, meshgrid, msort, + piecewise, place, select, setxor1d, sinc, split, trapz, trim_zeros, + unwrap, unique, vectorize, +) + from numpy.compat import long class TestAny(TestCase): + def test_basic(self): y1 = [0, 0, 1, 0] y2 = [0, 0, 0, 0] @@ -33,6 +41,7 @@ class TestAny(TestCase): class TestAll(TestCase): + def test_basic(self): y1 = [0, 1, 1, 0] y2 = [0, 0, 0, 0] @@ -50,6 +59,7 @@ class TestAll(TestCase): class TestCopy(TestCase): + def test_basic(self): a = np.array([[1, 2], [3, 4]]) a_copy = np.copy(a) @@ -77,6 +87,7 @@ class TestCopy(TestCase): class TestAverage(TestCase): + def test_basic(self): y1 = np.array([1, 2, 3]) assert_(average(y1, axis=0) == 2.) @@ -102,7 +113,7 @@ class TestAverage(TestCase): y = np.arange(10) w = np.arange(10) actual = average(y, weights=w) - desired = (np.arange(10) ** 2).sum()*1. / np.arange(10).sum() + desired = (np.arange(10) ** 2).sum() * 1. / np.arange(10).sum() assert_almost_equal(actual, desired) y1 = np.array([[1, 2, 3], [4, 5, 6]]) @@ -230,6 +241,7 @@ class TestSelect(TestCase): class TestInsert(TestCase): + def test_basic(self): a = [1, 2, 3] assert_equal(insert(a, 0, 1), [1, 1, 2, 3]) @@ -247,7 +259,7 @@ class TestInsert(TestCase): with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', FutureWarning) assert_equal( - insert(a, np.array([True]*4), 9), [1, 9, 9, 9, 9, 2, 3]) + insert(a, np.array([True] * 4), 9), [1, 9, 9, 9, 9, 2, 3]) assert_(w[0].category is FutureWarning) def test_multidim(self): @@ -272,25 +284,25 @@ class TestInsert(TestCase): a = np.arange(4).reshape(2, 2) assert_equal(insert(a[:, :1], 1, a[:, 1], axis=1), a) - assert_equal(insert(a[:1, :], 1, a[1, :], axis=0), a) + assert_equal(insert(a[:1,:], 1, a[1,:], axis=0), a) # negative axis value a = np.arange(24).reshape((2, 3, 4)) - assert_equal(insert(a, 1, a[:, :, 3], axis=-1), - insert(a, 1, a[:, :, 3], axis=2)) - assert_equal(insert(a, 1, a[:, 2, :], axis=-2), - insert(a, 1, a[:, 2, :], axis=1)) + assert_equal(insert(a, 1, a[:,:, 3], axis=-1), + insert(a, 1, a[:,:, 3], axis=2)) + assert_equal(insert(a, 1, a[:, 2,:], axis=-2), + insert(a, 1, a[:, 2,:], axis=1)) # invalid axis value - assert_raises(IndexError, insert, a, 1, a[:, 2, :], axis=3) - assert_raises(IndexError, insert, a, 1, a[:, 2, :], axis=-4) + assert_raises(IndexError, insert, a, 1, a[:, 2,:], axis=3) + assert_raises(IndexError, insert, a, 1, a[:, 2,:], axis=-4) # negative axis value - a = np.arange(24).reshape((2,3,4)) - assert_equal(insert(a, 1, a[:,:,3], axis=-1), - insert(a, 1, a[:,:,3], axis=2)) - assert_equal(insert(a, 1, a[:,2,:], axis=-2), - insert(a, 1, a[:,2,:], axis=1)) + a = np.arange(24).reshape((2, 3, 4)) + assert_equal(insert(a, 1, a[:,:, 3], axis=-1), + insert(a, 1, a[:,:, 3], axis=2)) + assert_equal(insert(a, 1, a[:, 2,:], axis=-2), + insert(a, 1, a[:, 2,:], axis=1)) def test_0d(self): # This is an error in the future @@ -330,6 +342,7 @@ class TestInsert(TestCase): class TestAmax(TestCase): + def test_basic(self): a = [3, 4, 5, 10, -3, -5, 6.0] assert_equal(np.amax(a), 10.0) @@ -341,6 +354,7 @@ class TestAmax(TestCase): class TestAmin(TestCase): + def test_basic(self): a = [3, 4, 5, 10, -3, -5, 6.0] assert_equal(np.amin(a), -5.0) @@ -352,6 +366,7 @@ class TestAmin(TestCase): class TestPtp(TestCase): + def test_basic(self): a = [3, 4, 5, 10, -3, -5, 6.0] assert_equal(np.ptp(a, axis=0), 15.0) @@ -363,6 +378,7 @@ class TestPtp(TestCase): class TestCumsum(TestCase): + def test_basic(self): ba = [1, 2, 10, 11, 6, 5, 4] ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]] @@ -384,6 +400,7 @@ class TestCumsum(TestCase): class TestProd(TestCase): + def test_basic(self): ba = [1, 2, 10, 11, 6, 5, 4] ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]] @@ -392,9 +409,9 @@ class TestProd(TestCase): a = np.array(ba, ctype) a2 = np.array(ba2, ctype) if ctype in ['1', 'b']: - self.assertRaises(ArithmeticError, prod, a) - self.assertRaises(ArithmeticError, prod, a2, 1) - self.assertRaises(ArithmeticError, prod, a) + self.assertRaises(ArithmeticError, np.prod, a) + self.assertRaises(ArithmeticError, np.prod, a2, 1) + self.assertRaises(ArithmeticError, np.prod, a) else: assert_equal(np.prod(a, axis=0), 26400) assert_array_equal(np.prod(a2, axis=0), @@ -404,6 +421,7 @@ class TestProd(TestCase): class TestCumprod(TestCase): + def test_basic(self): ba = [1, 2, 10, 11, 6, 5, 4] ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]] @@ -412,9 +430,9 @@ class TestCumprod(TestCase): a = np.array(ba, ctype) a2 = np.array(ba2, ctype) if ctype in ['1', 'b']: - self.assertRaises(ArithmeticError, cumprod, a) - self.assertRaises(ArithmeticError, cumprod, a2, 1) - self.assertRaises(ArithmeticError, cumprod, a) + self.assertRaises(ArithmeticError, np.cumprod, a) + self.assertRaises(ArithmeticError, np.cumprod, a2, 1) + self.assertRaises(ArithmeticError, np.cumprod, a) else: assert_array_equal(np.cumprod(a, axis=-1), np.array([1, 2, 20, 220, @@ -430,6 +448,7 @@ class TestCumprod(TestCase): class TestDiff(TestCase): + def test_basic(self): x = [1, 4, 6, 7, 12] out = np.array([3, 2, 1, 5]) @@ -441,10 +460,10 @@ class TestDiff(TestCase): def test_nd(self): x = 20 * rand(10, 20, 30) - out1 = x[:, :, 1:] - x[:, :, :-1] - out2 = out1[:, :, 1:] - out1[:, :, :-1] - out3 = x[1:, :, :] - x[:-1, :, :] - out4 = out3[1:, :, :] - out3[:-1, :, :] + out1 = x[:,:, 1:] - x[:,:, :-1] + out2 = out1[:,:, 1:] - out1[:,:, :-1] + out3 = x[1:,:,:] - x[:-1,:,:] + out4 = out3[1:,:,:] - out3[:-1,:,:] assert_array_equal(diff(x), out1) assert_array_equal(diff(x, n=2), out2) assert_array_equal(diff(x, axis=0), out3) @@ -452,6 +471,7 @@ class TestDiff(TestCase): class TestDelete(TestCase): + def setUp(self): self.a = np.arange(5) self.nd_a = np.arange(5).repeat(2).reshape(1, 5, 2) @@ -466,8 +486,8 @@ class TestDelete(TestCase): indices = indices[(indices >= 0) & (indices < 5)] assert_array_equal(setxor1d(a_del, self.a[indices, ]), self.a, err_msg=msg) - xor = setxor1d(nd_a_del[0, :, 0], self.nd_a[0, indices, 0]) - assert_array_equal(xor, self.nd_a[0, :, 0], err_msg=msg) + xor = setxor1d(nd_a_del[0,:, 0], self.nd_a[0, indices, 0]) + assert_array_equal(xor, self.nd_a[0,:, 0], err_msg=msg) def test_slices(self): lims = [-6, -2, 0, 1, 2, 4, 5] @@ -516,6 +536,7 @@ class TestDelete(TestCase): class TestGradient(TestCase): + def test_basic(self): v = [[1, 1], [3, 4]] x = np.array(v) @@ -582,6 +603,7 @@ class TestGradient(TestCase): class TestAngle(TestCase): + def test_basic(self): x = [1 + 3j, np.sqrt(2) / 2.0 + 1j * np.sqrt(2) / 2, 1, 1j, -1, -1j, 1 - 3j, -1 + 3j] @@ -597,8 +619,12 @@ class TestAngle(TestCase): class TestTrimZeros(TestCase): - """ only testing for integer splits. + + """ + Only testing for integer splits. + """ + def test_basic(self): a = np.array([0, 0, 1, 2, 3, 4, 0]) res = trim_zeros(a) @@ -616,6 +642,7 @@ class TestTrimZeros(TestCase): class TestExtins(TestCase): + def test_basic(self): a = np.array([1, 3, 2, 1, 2, 3, 3]) b = extract(a > 1, a) @@ -632,7 +659,7 @@ class TestExtins(TestCase): place(a, [1, 0, 1, 0, 1, 0, 1], [8, 9]) assert_array_equal(a, [8, 2, 9, 4, 8, 6, 9]) assert_raises_regex(ValueError, "Cannot insert from an empty array", - lambda: place(a, [0, 0, 0, 0, 0, 1, 0], [])) + lambda: place(a, [0, 0, 0, 0, 0, 1, 0], [])) def test_both(self): a = rand(10) @@ -645,12 +672,14 @@ class TestExtins(TestCase): class TestVectorize(TestCase): + def test_simple(self): def addsubtract(a, b): if a > b: return a - b else: return a + b + f = vectorize(addsubtract) r = f([0, 3, 6, 9], [1, 3, 5, 7]) assert_array_equal(r, [1, 6, 1, 2]) @@ -661,6 +690,7 @@ class TestVectorize(TestCase): return a - b else: return a + b + f = vectorize(addsubtract) r = f([0, 3, 6, 9], 5) assert_array_equal(r, [5, 8, 1, 4]) @@ -674,16 +704,16 @@ class TestVectorize(TestCase): def test_ufunc(self): import math f = vectorize(math.cos) - args = np.array([0, 0.5*np.pi, np.pi, 1.5*np.pi, 2*np.pi]) + args = np.array([0, 0.5 * np.pi, np.pi, 1.5 * np.pi, 2 * np.pi]) r1 = f(args) r2 = np.cos(args) assert_array_almost_equal(r1, r2) def test_keywords(self): - import math def foo(a, b=1): return a + b + f = vectorize(foo) args = np.array([1, 2, 3]) r1 = f(args) @@ -699,16 +729,16 @@ class TestVectorize(TestCase): # inspect the func_code. import random try: - f = vectorize(random.randrange) + vectorize(random.randrange) # Should succeed except: raise AssertionError() def test_keywords2_ticket_2100(self): - r"""Test kwarg support: enhancement ticket 2100""" - import math + # Test kwarg support: enhancement ticket 2100 def foo(a, b=1): return a + b + f = vectorize(foo) args = np.array([1, 2, 3]) r1 = f(a=args) @@ -721,13 +751,14 @@ class TestVectorize(TestCase): assert_array_equal(r1, r2) def test_keywords3_ticket_2100(self): - """Test excluded with mixed positional and kwargs: ticket 2100""" + # Test excluded with mixed positional and kwargs: ticket 2100 def mypolyval(x, p): _p = list(p) res = _p.pop(0) while _p: - res = res*x + _p.pop(0) + res = res * x + _p.pop(0) return res + vpolyval = np.vectorize(mypolyval, excluded=['p', 1]) ans = [3, 6] assert_array_equal(ans, vpolyval(x=[0, 1], p=[1, 2, 3])) @@ -735,49 +766,54 @@ class TestVectorize(TestCase): assert_array_equal(ans, vpolyval([0, 1], [1, 2, 3])) def test_keywords4_ticket_2100(self): - """Test vectorizing function with no positional args.""" + # Test vectorizing function with no positional args. @vectorize def f(**kw): res = 1.0 for _k in kw: res *= kw[_k] return res + assert_array_equal(f(a=[1, 2], b=[3, 4]), [3, 8]) def test_keywords5_ticket_2100(self): - """Test vectorizing function with no kwargs args.""" + # Test vectorizing function with no kwargs args. @vectorize def f(*v): return np.prod(v) + assert_array_equal(f([1, 2], [3, 4]), [3, 8]) def test_coverage1_ticket_2100(self): def foo(): return 1 + f = vectorize(foo) assert_array_equal(f(), 1) def test_assigning_docstring(self): def foo(x): return x + doc = "Provided documentation" f = vectorize(foo, doc=doc) assert_equal(f.__doc__, doc) def test_UnboundMethod_ticket_1156(self): - """Regression test for issue 1156""" + # Regression test for issue 1156 class Foo: b = 2 def bar(self, a): - return a**self.b + return a ** self.b + assert_array_equal(vectorize(Foo().bar)(np.arange(9)), - np.arange(9)**2) + np.arange(9) ** 2) assert_array_equal(vectorize(Foo.bar)(Foo(), np.arange(9)), - np.arange(9)**2) + np.arange(9) ** 2) def test_execution_order_ticket_1487(self): - """Regression test for dependence on execution order: issue 1487""" + # Regression test for dependence on execution order: issue 1487 f1 = vectorize(lambda x: x) res1a = f1(np.arange(3)) res1b = f1(np.arange(0.1, 3)) @@ -788,24 +824,23 @@ class TestVectorize(TestCase): assert_equal(res1b, res2b) def test_string_ticket_1892(self): - """Test vectorization over strings: issue 1892.""" + # Test vectorization over strings: issue 1892. f = np.vectorize(lambda x: x) - s = '0123456789'*10 + s = '0123456789' * 10 assert_equal(s, f(s)) - #z = f(np.array([s,s])) - #assert_array_equal([s,s], f(s)) def test_cache(self): - """Ensure that vectorized func called exactly once per argument.""" + # Ensure that vectorized func called exactly once per argument. _calls = [0] @vectorize def f(x): _calls[0] += 1 - return x**2 + return x ** 2 + f.cache = True x = np.arange(5) - assert_array_equal(f(x), x*x) + assert_array_equal(f(x), x * x) assert_equal(_calls[0], len(x)) def test_otypes(self): @@ -816,6 +851,7 @@ class TestVectorize(TestCase): class TestDigitize(TestCase): + def test_forward(self): x = np.arange(-6, 5) bins = np.arange(-5, 5) @@ -871,7 +907,7 @@ class TestDigitize(TestCase): assert_raises(ValueError, digitize, x, bins) def test_casting_error(self): - x = [1, 2, 3+1.j] + x = [1, 2, 3 + 1.j] bins = [1, 2, 3] assert_raises(TypeError, digitize, x, bins) x, bins = bins, x @@ -879,48 +915,51 @@ class TestDigitize(TestCase): class TestUnwrap(TestCase): + def test_simple(self): - #check that unwrap removes jumps greather that 2*pi + # check that unwrap removes jumps greather that 2*pi assert_array_equal(unwrap([1, 1 + 2 * np.pi]), [1, 1]) - #check that unwrap maintans continuity + # check that unwrap maintans continuity assert_(np.all(diff(unwrap(rand(10) * 100)) < np.pi)) class TestFilterwindows(TestCase): + def test_hanning(self): - #check symmetry + # check symmetry w = hanning(10) assert_array_almost_equal(w, flipud(w), 7) - #check known value + # check known value assert_almost_equal(np.sum(w, axis=0), 4.500, 4) def test_hamming(self): - #check symmetry + # check symmetry w = hamming(10) assert_array_almost_equal(w, flipud(w), 7) - #check known value + # check known value assert_almost_equal(np.sum(w, axis=0), 4.9400, 4) def test_bartlett(self): - #check symmetry + # check symmetry w = bartlett(10) assert_array_almost_equal(w, flipud(w), 7) - #check known value + # check known value assert_almost_equal(np.sum(w, axis=0), 4.4444, 4) def test_blackman(self): - #check symmetry + # check symmetry w = blackman(10) assert_array_almost_equal(w, flipud(w), 7) - #check known value + # check known value assert_almost_equal(np.sum(w, axis=0), 3.7800, 4) class TestTrapz(TestCase): + def test_simple(self): x = np.arange(-10, 10, .1) - r = trapz(np.exp(-.5*x**2) / np.sqrt(2*np.pi), dx=0.1) - #check integral of normal equals 1 + r = trapz(np.exp(-.5 * x ** 2) / np.sqrt(2 * np.pi), dx=0.1) + # check integral of normal equals 1 assert_almost_equal(r, 1, 7) def test_ndim(self): @@ -938,18 +977,18 @@ class TestTrapz(TestCase): wz[0] /= 2 wz[-1] /= 2 - q = x[:, None, None] + y[None, :, None] + z[None, None, :] + q = x[:, None, None] + y[None,:, None] + z[None, None,:] qx = (q * wx[:, None, None]).sum(axis=0) - qy = (q * wy[None, :, None]).sum(axis=1) - qz = (q * wz[None, None, :]).sum(axis=2) + qy = (q * wy[None,:, None]).sum(axis=1) + qz = (q * wz[None, None,:]).sum(axis=2) # n-d `x` r = trapz(q, x=x[:, None, None], axis=0) assert_almost_equal(r, qx) - r = trapz(q, x=y[None, :, None], axis=1) + r = trapz(q, x=y[None,:, None], axis=1) assert_almost_equal(r, qy) - r = trapz(q, x=z[None, None, :], axis=2) + r = trapz(q, x=z[None, None,:], axis=2) assert_almost_equal(r, qz) # 1-d `x` @@ -961,8 +1000,8 @@ class TestTrapz(TestCase): assert_almost_equal(r, qz) def test_masked(self): - #Testing that masked arrays behave as if the function is 0 where - #masked + # Testing that masked arrays behave as if the function is 0 where + # masked x = np.arange(5) y = x * x mask = x == 2 @@ -977,7 +1016,7 @@ class TestTrapz(TestCase): assert_almost_equal(trapz(y, xm), r) def test_matrix(self): - #Test to make sure matrices give the same answer as ndarrays + # Test to make sure matrices give the same answer as ndarrays x = np.linspace(0, 5) y = x * x r = trapz(y, x) @@ -988,10 +1027,11 @@ class TestTrapz(TestCase): class TestSinc(TestCase): + def test_simple(self): assert_(sinc(0) == 1) w = sinc(np.linspace(-1, 1, 100)) - #check symmetry + # check symmetry assert_array_almost_equal(w, flipud(w), 7) def test_array_like(self): @@ -1004,6 +1044,7 @@ class TestSinc(TestCase): class TestHistogram(TestCase): + def setUp(self): pass @@ -1014,10 +1055,10 @@ class TestHistogram(TestCase): n = 100 v = rand(n) (a, b) = histogram(v) - #check if the sum of the bins equals the number of samples + # check if the sum of the bins equals the number of samples assert_equal(np.sum(a, axis=0), n) - #check that the bin counts are evenly spaced when the data is from a - # linear function + # check that the bin counts are evenly spaced when the data is from + # a linear function (a, b) = histogram(np.linspace(0, 10, 100)) assert_array_equal(a, 10) @@ -1039,7 +1080,8 @@ class TestHistogram(TestCase): area = np.sum(a * diff(b)) assert_almost_equal(area, 1) - # Check with non-constant bin widths (buggy but backwards compatible) + # Check with non-constant bin widths (buggy but backwards + # compatible) v = np.arange(10) bins = [0, 1, 5, 9, 10] a, b = histogram(v, bins, normed=True) @@ -1059,7 +1101,7 @@ class TestHistogram(TestCase): bins = [0, 1, 3, 6, 10] a, b = histogram(v, bins, density=True) assert_array_equal(a, .1) - assert_equal(np.sum(a*diff(b)), 1) + assert_equal(np.sum(a * diff(b)), 1) # Variale bin widths are especially useful to deal with # infinities. @@ -1102,20 +1144,20 @@ class TestHistogram(TestCase): # Check the type of the returned histogram a = np.arange(10) + .5 h, b = histogram(a) - assert_(issubdtype(h.dtype, int)) + assert_(np.issubdtype(h.dtype, int)) h, b = histogram(a, normed=True) - assert_(issubdtype(h.dtype, float)) + assert_(np.issubdtype(h.dtype, float)) h, b = histogram(a, weights=np.ones(10, int)) - assert_(issubdtype(h.dtype, int)) + assert_(np.issubdtype(h.dtype, int)) h, b = histogram(a, weights=np.ones(10, float)) - assert_(issubdtype(h.dtype, float)) + assert_(np.issubdtype(h.dtype, float)) def test_f32_rounding(self): # gh-4799, check that the rounding of the edges works with float32 - x = np.array([276.318359 , -69.593948 , 21.329449], dtype=np.float32) + x = np.array([276.318359, -69.593948, 21.329449], dtype=np.float32) y = np.array([5005.689453, 4481.327637, 6010.369629], dtype=np.float32) counts_hist, xedges, yedges = np.histogram2d(x, y, bins=100) assert_equal(counts_hist.sum(), 3.) @@ -1160,11 +1202,11 @@ class TestHistogram(TestCase): # Check with custom bins wa, wb = histogram(values, bins=[0, 2, 3], weights=weights) - assert_array_almost_equal(wa, np.array([1, 1]) + 1j * np.array([2,3])) + assert_array_almost_equal(wa, np.array([1, 1]) + 1j * np.array([2, 3])) # Check with even bins - wa, wb = histogram(values, bins=2, range=[1,3], weights=weights) - assert_array_almost_equal(wa, np.array([1, 1]) + 1j * np.array([2,3])) + wa, wb = histogram(values, bins=2, range=[1, 3], weights=weights) + assert_array_almost_equal(wa, np.array([1, 1]) + 1j * np.array([2, 3])) # Decimal weights from decimal import Decimal @@ -1176,10 +1218,9 @@ class TestHistogram(TestCase): assert_array_almost_equal(wa, [Decimal(1), Decimal(5)]) # Check with even bins - wa, wb = histogram(values, bins=2, range=[1,3], weights=weights) + wa, wb = histogram(values, bins=2, range=[1, 3], weights=weights) assert_array_almost_equal(wa, [Decimal(1), Decimal(5)]) - def test_empty(self): a, b = histogram([], bins=([0, 1])) assert_array_equal(a, np.array([0])) @@ -1187,6 +1228,7 @@ class TestHistogram(TestCase): class TestHistogramdd(TestCase): + def test_simple(self): x = np.array([[-.5, .5, 1.5], [-.5, 1.5, 2.5], [-.5, 2.5, .5], [.5, .5, 1.5], [.5, 1.5, 2.5], [.5, 2.5, 2.5]]) @@ -1270,8 +1312,8 @@ class TestHistogramdd(TestCase): assert_array_max_ulp(a, np.zeros((2, 2, 2))) def test_bins_errors(self): - """There are two ways to specify bins. Check for the right errors when - mixing those.""" + # There are two ways to specify bins. Check for the right errors + # when mixing those. x = np.arange(8).reshape(2, 4) assert_raises(ValueError, np.histogramdd, x, bins=[-1, 2, 4, 5]) assert_raises(ValueError, np.histogramdd, x, bins=[1, 0.99, 1, 1]) @@ -1282,7 +1324,7 @@ class TestHistogramdd(TestCase): assert_(np.histogramdd(x, bins=[1, 1, 1, [1, 2, 3, 4]])) def test_inf_edges(self): - """Test using +/-inf bin edges works. See #1788.""" + # 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]]) @@ -1294,31 +1336,31 @@ class TestHistogramdd(TestCase): assert_allclose(h, expected) def test_rightmost_binedge(self): - """Test event very close to rightmost binedge. - See Github issue #4266""" + # Test event very close to rightmost binedge. See Github issue #4266 x = [0.9999999995] - bins = [[0.,0.5,1.0]] + bins = [[0., 0.5, 1.0]] hist, _ = histogramdd(x, bins=bins) assert_(hist[0] == 0.0) assert_(hist[1] == 1.) x = [1.0] - bins = [[0.,0.5,1.0]] + bins = [[0., 0.5, 1.0]] hist, _ = histogramdd(x, bins=bins) assert_(hist[0] == 0.0) assert_(hist[1] == 1.) x = [1.0000000001] - bins = [[0.,0.5,1.0]] + bins = [[0., 0.5, 1.0]] hist, _ = histogramdd(x, bins=bins) assert_(hist[0] == 0.0) assert_(hist[1] == 1.) x = [1.0001] - bins = [[0.,0.5,1.0]] + bins = [[0., 0.5, 1.0]] hist, _ = histogramdd(x, bins=bins) assert_(hist[0] == 0.0) assert_(hist[1] == 0.0) class TestUnique(TestCase): + def test_simple(self): x = np.array([4, 3, 2, 1, 1, 2, 3, 4, 0]) assert_(np.all(unique(x) == [0, 1, 2, 3, 4])) @@ -1330,6 +1372,7 @@ class TestUnique(TestCase): class TestCheckFinite(TestCase): + def test_simple(self): a = [1, 2, 3] b = [1, 2, np.inf] @@ -1339,14 +1382,17 @@ class TestCheckFinite(TestCase): assert_raises(ValueError, np.lib.asarray_chkfinite, c) def test_dtype_order(self): - """Regression test for missing dtype and order arguments""" + # Regression test for missing dtype and order arguments a = [1, 2, 3] a = np.lib.asarray_chkfinite(a, order='F', dtype=np.float64) assert_(a.dtype == np.float64) class catch_warn_nfb(clear_and_catch_warnings): - """ Context manager to catch, reset warnings in function_base module + + """ + Context manager to catch, reset warnings in function_base module + """ class_modules = (nfb,) @@ -1430,7 +1476,7 @@ class TestCov(TestCase): res2 = np.array([[0.4, -0.4], [-0.4, 0.4]]) unit_frequencies = np.ones(3, dtype=np.integer) weights = np.array([1.0, 4.0, 1.0]) - res3 = np.array([[2./3., -2./3.], [-2./3., 2./3.]]) + res3 = np.array([[2. / 3., -2. / 3.], [-2. / 3., 2. / 3.]]) unit_weights = np.ones(3) x3 = np.array([0.3942, 0.5969, 0.7730, 0.9918, 0.7964]) @@ -1483,19 +1529,19 @@ class TestCov(TestCase): assert_raises(RuntimeError, cov, self.x1, fweights=f) f = np.ones(2, dtype=np.integer) assert_raises(RuntimeError, cov, self.x1, fweights=f) - f = -1*np.ones(3, dtype=np.integer) + f = -1 * np.ones(3, dtype=np.integer) assert_raises(ValueError, cov, self.x1, fweights=f) def test_aweights(self): assert_allclose(cov(self.x1, aweights=self.weights), self.res3) - assert_allclose(cov(self.x1, aweights=3.0*self.weights), + assert_allclose(cov(self.x1, aweights=3.0 * self.weights), cov(self.x1, aweights=self.weights)) assert_allclose(cov(self.x1, aweights=self.unit_weights), self.res1) w = np.ones((2, 3)) assert_raises(RuntimeError, cov, self.x1, aweights=w) w = np.ones(2) assert_raises(RuntimeError, cov, self.x1, aweights=w) - w = -1.0*np.ones(3) + w = -1.0 * np.ones(3) assert_raises(ValueError, cov, self.x1, aweights=w) def test_unit_fweights_and_aweights(self): @@ -1512,7 +1558,7 @@ class TestCov(TestCase): aweights=self.weights), self.res3) assert_allclose(cov(self.x1, fweights=self.unit_frequencies, - aweights=3.0*self.weights), + aweights=3.0 * self.weights), cov(self.x1, aweights=self.weights)) assert_allclose(cov(self.x1, fweights=self.unit_frequencies, aweights=self.unit_weights), @@ -1520,6 +1566,7 @@ class TestCov(TestCase): class Test_I0(TestCase): + def test_simple(self): assert_almost_equal( i0(0.5), @@ -1545,6 +1592,7 @@ class Test_I0(TestCase): class TestKaiser(TestCase): + def test_simple(self): assert_(np.isfinite(kaiser(1, 1.0))) assert_almost_equal(kaiser(0, 1.0), @@ -1563,6 +1611,7 @@ class TestKaiser(TestCase): class TestMsort(TestCase): + def test_simple(self): A = np.array([[0.44567325, 0.79115165, 0.54900530], [0.36844147, 0.37325583, 0.96098397], @@ -1575,6 +1624,7 @@ class TestMsort(TestCase): class TestMeshgrid(TestCase): + def test_simple(self): [X, Y] = meshgrid([1, 2, 3], [4, 5, 6, 7]) assert_array_equal(X, np.array([[1, 2, 3], @@ -1628,6 +1678,7 @@ class TestMeshgrid(TestCase): class TestPiecewise(TestCase): + def test_simple(self): # Condition is single bool list x = piecewise([0, 0], [True, False], [1]) @@ -1678,10 +1729,11 @@ class TestPiecewise(TestCase): def test_0d_comparison(self): x = 3 - y = piecewise(x, [x <= 3, x > 3], [4, 0]) + piecewise(x, [x <= 3, x > 3], [4, 0]) # Should succeed. class TestBincount(TestCase): + def test_simple(self): y = np.bincount(np.arange(4)) assert_array_equal(y, np.ones(4)) @@ -1747,6 +1799,7 @@ class TestBincount(TestCase): class TestInterp(TestCase): + def test_exceptions(self): assert_raises(ValueError, interp, 0, [], []) assert_raises(ValueError, interp, 0, [0], [1, 2]) @@ -1832,10 +1885,10 @@ class TestScoreatpercentile(TestCase): def test_2D(self): x = np.array([[1, 1, 1], - [1, 1, 1], - [4, 4, 3], - [1, 1, 1], - [1, 1, 1]]) + [1, 1, 1], + [4, 4, 3], + [1, 1, 1], + [1, 1, 1]]) assert_array_equal(np.percentile(x, 50, axis=0), [1, 1, 1]) def test_linear(self): @@ -1888,7 +1941,8 @@ class TestScoreatpercentile(TestCase): assert_equal(np.percentile(x, (25, 50), axis=1).shape, (2, 3, 5, 6)) assert_equal(np.percentile(x, (25, 50), axis=2).shape, (2, 3, 4, 6)) assert_equal(np.percentile(x, (25, 50), axis=3).shape, (2, 3, 4, 5)) - assert_equal(np.percentile(x, (25, 50, 75), axis=1).shape, (3, 3, 5, 6)) + assert_equal( + np.percentile(x, (25, 50, 75), axis=1).shape, (3, 3, 5, 6)) assert_equal(np.percentile(x, (25, 50), interpolation="higher").shape, (2,)) assert_equal(np.percentile(x, (25, 50, 75), @@ -1909,10 +1963,10 @@ class TestScoreatpercentile(TestCase): x = np.arange(12).reshape(3, 4) assert_equal(np.percentile(x, 50), 5.5) self.assertTrue(np.isscalar(np.percentile(x, 50))) - r0 = np.array([ 4., 5., 6., 7.]) + r0 = np.array([4., 5., 6., 7.]) assert_equal(np.percentile(x, 50, axis=0), r0) assert_equal(np.percentile(x, 50, axis=0).shape, r0.shape) - r1 = np.array([ 1.5, 5.5, 9.5]) + r1 = np.array([1.5, 5.5, 9.5]) assert_almost_equal(np.percentile(x, 50, axis=1), r1) assert_equal(np.percentile(x, 50, axis=1).shape, r1.shape) @@ -1930,11 +1984,11 @@ class TestScoreatpercentile(TestCase): x = np.arange(12).reshape(3, 4) assert_equal(np.percentile(x, 50, interpolation='lower'), 5.) self.assertTrue(np.isscalar(np.percentile(x, 50))) - r0 = np.array([ 4., 5., 6., 7.]) + r0 = np.array([4., 5., 6., 7.]) c0 = np.percentile(x, 50, interpolation='lower', axis=0) assert_equal(c0, r0) assert_equal(c0.shape, r0.shape) - r1 = np.array([ 1., 5., 9.]) + r1 = np.array([1., 5., 9.]) c1 = np.percentile(x, 50, interpolation='lower', axis=1) assert_almost_equal(c1, r1) assert_equal(c1.shape, r1.shape) @@ -2006,7 +2060,7 @@ class TestScoreatpercentile(TestCase): def test_percentile_empty_dim(self): # empty dims are preserved - d = np.arange(11*2).reshape(11, 1, 2, 1) + d = np.arange(11 * 2).reshape(11, 1, 2, 1) assert_array_equal(np.percentile(d, 50, axis=0).shape, (1, 2, 1)) assert_array_equal(np.percentile(d, 50, axis=1).shape, (11, 2, 1)) assert_array_equal(np.percentile(d, 50, axis=2).shape, (11, 1, 1)) @@ -2032,7 +2086,6 @@ class TestScoreatpercentile(TestCase): assert_array_equal(np.array(np.percentile(d, [10, 50], axis=3)).shape, (2, 11, 1, 2)) - def test_percentile_no_overwrite(self): a = np.array([2, 3, 4, 1]) np.percentile(a, [50], overwrite_input=False) @@ -2076,19 +2129,19 @@ class TestScoreatpercentile(TestCase): d = np.arange(3 * 5 * 7 * 11).reshape(3, 5, 7, 11) np.random.shuffle(d) assert_equal(np.percentile(d, 25, axis=(0, 1, 2))[0], - np.percentile(d[:, :, :, 0].flatten(), 25)) + np.percentile(d[:,:,:, 0].flatten(), 25)) assert_equal(np.percentile(d, [10, 90], axis=(0, 1, 3))[:, 1], - np.percentile(d[:, :, 1, :].flatten(), [10, 90])) + np.percentile(d[:,:, 1,:].flatten(), [10, 90])) assert_equal(np.percentile(d, 25, axis=(3, 1, -4))[2], - np.percentile(d[:, :, 2, :].flatten(), 25)) + np.percentile(d[:,:, 2,:].flatten(), 25)) assert_equal(np.percentile(d, 25, axis=(3, 1, 2))[2], - np.percentile(d[2, :, :, :].flatten(), 25)) + np.percentile(d[2,:,:,:].flatten(), 25)) assert_equal(np.percentile(d, 25, axis=(3, 2))[2, 1], - np.percentile(d[2, 1, :, :].flatten(), 25)) + np.percentile(d[2, 1,:,:].flatten(), 25)) assert_equal(np.percentile(d, 25, axis=(1, -2))[2, 1], - np.percentile(d[2, :, :, 1].flatten(), 25)) + np.percentile(d[2,:,:, 1].flatten(), 25)) assert_equal(np.percentile(d, 25, axis=(1, 3))[2, 2], - np.percentile(d[2, :, 2, :].flatten(), 25)) + np.percentile(d[2,:, 2,:].flatten(), 25)) def test_extended_axis_invalid(self): d = np.ones((3, 5, 7, 11)) @@ -2117,6 +2170,7 @@ class TestScoreatpercentile(TestCase): keepdims=True).shape, (2, 1, 1, 7, 1)) assert_equal(np.percentile(d, [1, 7], axis=(0, 3), keepdims=True).shape, (2, 1, 5, 7, 1)) + def test_out(self): o = np.zeros((4,)) d = np.ones((3, 4)) @@ -2130,7 +2184,6 @@ class TestScoreatpercentile(TestCase): assert_equal(np.percentile(d, 2, out=o), o) assert_equal(np.percentile(d, 2, interpolation='nearest', out=o), o) - def test_out_nan(self): with warnings.catch_warnings(record=True): warnings.filterwarnings('always', '', RuntimeWarning) @@ -2138,13 +2191,16 @@ class TestScoreatpercentile(TestCase): d = np.ones((3, 4)) d[2, 1] = np.nan assert_equal(np.percentile(d, 0, 0, out=o), o) - assert_equal(np.percentile(d, 0, 0, interpolation='nearest', out=o), o) + assert_equal( + np.percentile(d, 0, 0, interpolation='nearest', out=o), o) o = np.zeros((3,)) assert_equal(np.percentile(d, 1, 1, out=o), o) - assert_equal(np.percentile(d, 1, 1, interpolation='nearest', out=o), o) + assert_equal( + np.percentile(d, 1, 1, interpolation='nearest', out=o), o) o = np.zeros(()) assert_equal(np.percentile(d, 1, out=o), o) - assert_equal(np.percentile(d, 1, interpolation='nearest', out=o), o) + assert_equal( + np.percentile(d, 1, interpolation='nearest', out=o), o) def test_nan_behavior(self): a = np.arange(24, dtype=float) @@ -2171,7 +2227,8 @@ class TestScoreatpercentile(TestCase): # axis0 zerod b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), 0.3, 0) - b[2, 3] = np.nan; b[1, 2] = np.nan + b[2, 3] = np.nan + b[1, 2] = np.nan with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', RuntimeWarning) assert_equal(np.percentile(a, 0.3, 0), b) @@ -2179,47 +2236,57 @@ class TestScoreatpercentile(TestCase): # axis0 not zerod b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), [0.3, 0.6], 0) - b[:, 2, 3] = np.nan; b[:, 1, 2] = np.nan + b[:, 2, 3] = np.nan + b[:, 1, 2] = np.nan with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', RuntimeWarning) assert_equal(np.percentile(a, [0.3, 0.6], 0), b) # axis1 zerod b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), 0.3, 1) - b[1, 3] = np.nan; b[1, 2] = np.nan + b[1, 3] = np.nan + b[1, 2] = np.nan with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', RuntimeWarning) assert_equal(np.percentile(a, 0.3, 1), b) # axis1 not zerod - b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), [0.3, 0.6], 1) - b[:, 1, 3] = np.nan; b[:, 1, 2] = np.nan + b = np.percentile( + np.arange(24, dtype=float).reshape(2, 3, 4), [0.3, 0.6], 1) + b[:, 1, 3] = np.nan + b[:, 1, 2] = np.nan with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', RuntimeWarning) assert_equal(np.percentile(a, [0.3, 0.6], 1), b) # axis02 zerod - b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), 0.3, (0, 2)) - b[1] = np.nan; b[2] = np.nan + b = np.percentile( + np.arange(24, dtype=float).reshape(2, 3, 4), 0.3, (0, 2)) + b[1] = np.nan + b[2] = np.nan with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', RuntimeWarning) assert_equal(np.percentile(a, 0.3, (0, 2)), b) # axis02 not zerod b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), [0.3, 0.6], (0, 2)) - b[:, 1] = np.nan; b[:, 2] = np.nan + b[:, 1] = np.nan + b[:, 2] = np.nan with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', RuntimeWarning) assert_equal(np.percentile(a, [0.3, 0.6], (0, 2)), b) # axis02 not zerod with nearest interpolation b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), [0.3, 0.6], (0, 2), interpolation='nearest') - b[:, 1] = np.nan; b[:, 2] = np.nan + b[:, 1] = np.nan + b[:, 2] = np.nan with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', RuntimeWarning) assert_equal(np.percentile( a, [0.3, 0.6], (0, 2), interpolation='nearest'), b) + class TestMedian(TestCase): + def test_basic(self): a0 = np.array(1) a1 = np.arange(2) @@ -2245,7 +2312,6 @@ class TestMedian(TestCase): assert_equal(np.median(a).ndim, 0) assert_(w[0].category is RuntimeWarning) - def test_axis_keyword(self): a3 = np.array([[2, 3], [0, 1], @@ -2307,6 +2373,7 @@ class TestMedian(TestCase): def test_subclass(self): # gh-3846 class MySubClass(np.ndarray): + def __new__(cls, input_array, info=None): obj = np.asarray(input_array).view(cls) obj.info = info @@ -2315,7 +2382,7 @@ class TestMedian(TestCase): def mean(self, axis=None, dtype=None, out=None): return -7 - a = MySubClass([1,2,3]) + a = MySubClass([1, 2, 3]) assert_equal(np.median(a), -7) def test_out(self): @@ -2353,39 +2420,42 @@ class TestMedian(TestCase): a[1, 2, 3] = np.nan a[1, 1, 2] = np.nan - #no axis + # no axis with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', RuntimeWarning) assert_equal(np.median(a), np.nan) assert_equal(np.median(a).ndim, 0) assert_(w[0].category is RuntimeWarning) - #axis0 + # axis0 b = np.median(np.arange(24, dtype=float).reshape(2, 3, 4), 0) - b[2, 3] = np.nan; b[1, 2] = np.nan + b[2, 3] = np.nan + b[1, 2] = np.nan with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', RuntimeWarning) assert_equal(np.median(a, 0), b) assert_equal(len(w), 1) - #axis1 + # axis1 b = np.median(np.arange(24, dtype=float).reshape(2, 3, 4), 1) - b[1, 3] = np.nan; b[1, 2] = np.nan + b[1, 3] = np.nan + b[1, 2] = np.nan with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', RuntimeWarning) assert_equal(np.median(a, 1), b) assert_equal(len(w), 1) - #axis02 + # axis02 b = np.median(np.arange(24, dtype=float).reshape(2, 3, 4), (0, 2)) - b[1] = np.nan; b[2] = np.nan + b[1] = np.nan + b[2] = np.nan with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', RuntimeWarning) assert_equal(np.median(a, (0, 2)), b) assert_equal(len(w), 1) def test_object(self): - o = np.arange(7.); + o = np.arange(7.) assert_(type(np.median(o.astype(object))), float) o[2] = np.nan assert_(type(np.median(o.astype(object))), float) @@ -2406,19 +2476,19 @@ class TestMedian(TestCase): d = np.arange(3 * 5 * 7 * 11).reshape(3, 5, 7, 11) np.random.shuffle(d) assert_equal(np.median(d, axis=(0, 1, 2))[0], - np.median(d[:, :, :, 0].flatten())) + np.median(d[:,:,:, 0].flatten())) assert_equal(np.median(d, axis=(0, 1, 3))[1], - np.median(d[:, :, 1, :].flatten())) + np.median(d[:,:, 1,:].flatten())) assert_equal(np.median(d, axis=(3, 1, -4))[2], - np.median(d[:, :, 2, :].flatten())) + np.median(d[:,:, 2,:].flatten())) assert_equal(np.median(d, axis=(3, 1, 2))[2], - np.median(d[2, :, :, :].flatten())) + np.median(d[2,:,:,:].flatten())) assert_equal(np.median(d, axis=(3, 2))[2, 1], - np.median(d[2, 1, :, :].flatten())) + np.median(d[2, 1,:,:].flatten())) assert_equal(np.median(d, axis=(1, -2))[2, 1], - np.median(d[2, :, :, 1].flatten())) + np.median(d[2,:,:, 1].flatten())) assert_equal(np.median(d, axis=(1, 3))[2, 2], - np.median(d[2, :, 2, :].flatten())) + np.median(d[2,:, 2,:].flatten())) def test_extended_axis_invalid(self): d = np.ones((3, 5, 7, 11)) @@ -2444,7 +2514,6 @@ class TestMedian(TestCase): (1, 1, 7, 1)) - class TestAdd_newdoc_ufunc(TestCase): def test_ufunc_arg(self): |