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test_old_ma.py
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from __future__ import division, absolute_import, print_function from functools import reduce import numpy as np import numpy.core.umath as umath import numpy.core.fromnumeric as fromnumeric from numpy.testing import TestCase, run_module_suite, assert_ from numpy.ma.testutils import assert_array_equal from numpy.ma import ( MaskType, MaskedArray, absolute, add, all, allclose, allequal, alltrue, arange, arccos, arcsin, arctan, arctan2, array, average, choose, concatenate, conjugate, cos, cosh, count, divide, equal, exp, filled, getmask, greater, greater_equal, inner, isMaskedArray, less, less_equal, log, log10, make_mask, masked, masked_array, masked_equal, masked_greater, masked_greater_equal, masked_inside, masked_less, masked_less_equal, masked_not_equal, masked_outside, masked_print_option, masked_values, masked_where, maximum, minimum, multiply, nomask, nonzero, not_equal, ones, outer, product, put, ravel, repeat, resize, shape, sin, sinh, sometrue, sort, sqrt, subtract, sum, take, tan, tanh, transpose, where, zeros, ) pi = np.pi def eq(v, w, msg=''): result = allclose(v, w) if not result: print("Not eq:%s\n%s\n----%s" % (msg, str(v), str(w))) return result class TestMa(TestCase): def setUp(self): x = np.array([1., 1., 1., -2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.]) y = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.]) a10 = 10. m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0] m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1] xm = array(x, mask=m1) ym = array(y, mask=m2) z = np.array([-.5, 0., .5, .8]) zm = array(z, mask=[0, 1, 0, 0]) xf = np.where(m1, 1e+20, x) s = x.shape xm.set_fill_value(1e+20) self.d = (x, y, a10, m1, m2, xm, ym, z, zm, xf, s) def test_testBasic1d(self): # Test of basic array creation and properties in 1 dimension. (x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d self.assertFalse(isMaskedArray(x)) self.assertTrue(isMaskedArray(xm)) self.assertEqual(shape(xm), s) self.assertEqual(xm.shape, s) self.assertEqual(xm.dtype, x.dtype) self.assertEqual(xm.size, reduce(lambda x, y:x * y, s)) self.assertEqual(count(xm), len(m1) - reduce(lambda x, y:x + y, m1)) self.assertTrue(eq(xm, xf)) self.assertTrue(eq(filled(xm, 1.e20), xf)) self.assertTrue(eq(x, xm)) def test_testBasic2d(self): # Test of basic array creation and properties in 2 dimensions. for s in [(4, 3), (6, 2)]: (x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d x.shape = s y.shape = s xm.shape = s ym.shape = s xf.shape = s self.assertFalse(isMaskedArray(x)) self.assertTrue(isMaskedArray(xm)) self.assertEqual(shape(xm), s) self.assertEqual(xm.shape, s) self.assertEqual(xm.size, reduce(lambda x, y:x * y, s)) self.assertEqual(count(xm), len(m1) - reduce(lambda x, y:x + y, m1)) self.assertTrue(eq(xm, xf)) self.assertTrue(eq(filled(xm, 1.e20), xf)) self.assertTrue(eq(x, xm)) self.setUp() def test_testArithmetic(self): # Test of basic arithmetic. (x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d a2d = array([[1, 2], [0, 4]]) a2dm = masked_array(a2d, [[0, 0], [1, 0]]) self.assertTrue(eq(a2d * a2d, a2d * a2dm)) self.assertTrue(eq(a2d + a2d, a2d + a2dm)) self.assertTrue(eq(a2d - a2d, a2d - a2dm)) for s in [(12,), (4, 3), (2, 6)]: x = x.reshape(s) y = y.reshape(s) xm = xm.reshape(s) ym = ym.reshape(s) xf = xf.reshape(s) self.assertTrue(eq(-x, -xm)) self.assertTrue(eq(x + y, xm + ym)) self.assertTrue(eq(x - y, xm - ym)) self.assertTrue(eq(x * y, xm * ym)) with np.errstate(divide='ignore', invalid='ignore'): self.assertTrue(eq(x / y, xm / ym)) self.assertTrue(eq(a10 + y, a10 + ym)) self.assertTrue(eq(a10 - y, a10 - ym)) self.assertTrue(eq(a10 * y, a10 * ym)) with np.errstate(divide='ignore', invalid='ignore'): self.assertTrue(eq(a10 / y, a10 / ym)) self.assertTrue(eq(x + a10, xm + a10)) self.assertTrue(eq(x - a10, xm - a10)) self.assertTrue(eq(x * a10, xm * a10)) self.assertTrue(eq(x / a10, xm / a10)) self.assertTrue(eq(x ** 2, xm ** 2)) self.assertTrue(eq(abs(x) ** 2.5, abs(xm) ** 2.5)) self.assertTrue(eq(x ** y, xm ** ym)) self.assertTrue(eq(np.add(x, y), add(xm, ym))) self.assertTrue(eq(np.subtract(x, y), subtract(xm, ym))) self.assertTrue(eq(np.multiply(x, y), multiply(xm, ym))) with np.errstate(divide='ignore', invalid='ignore'): self.assertTrue(eq(np.divide(x, y), divide(xm, ym))) def test_testMixedArithmetic(self): na = np.array([1]) ma = array([1]) self.assertTrue(isinstance(na + ma, MaskedArray)) self.assertTrue(isinstance(ma + na, MaskedArray)) def test_testUfuncs1(self): # Test various functions such as sin, cos. (x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d self.assertTrue(eq(np.cos(x), cos(xm))) self.assertTrue(eq(np.cosh(x), cosh(xm))) self.assertTrue(eq(np.sin(x), sin(xm))) self.assertTrue(eq(np.sinh(x), sinh(xm))) self.assertTrue(eq(np.tan(x), tan(xm))) self.assertTrue(eq(np.tanh(x), tanh(xm))) with np.errstate(divide='ignore', invalid='ignore'): self.assertTrue(eq(np.sqrt(abs(x)), sqrt(xm))) self.assertTrue(eq(np.log(abs(x)), log(xm))) self.assertTrue(eq(np.log10(abs(x)), log10(xm))) self.assertTrue(eq(np.exp(x), exp(xm))) self.assertTrue(eq(np.arcsin(z), arcsin(zm))) self.assertTrue(eq(np.arccos(z), arccos(zm))) self.assertTrue(eq(np.arctan(z), arctan(zm))) self.assertTrue(eq(np.arctan2(x, y), arctan2(xm, ym))) self.assertTrue(eq(np.absolute(x), absolute(xm))) self.assertTrue(eq(np.equal(x, y), equal(xm, ym))) self.assertTrue(eq(np.not_equal(x, y), not_equal(xm, ym))) self.assertTrue(eq(np.less(x, y), less(xm, ym))) self.assertTrue(eq(np.greater(x, y), greater(xm, ym))) self.assertTrue(eq(np.less_equal(x, y), less_equal(xm, ym))) self.assertTrue(eq(np.greater_equal(x, y), greater_equal(xm, ym))) self.assertTrue(eq(np.conjugate(x), conjugate(xm))) self.assertTrue(eq(np.concatenate((x, y)), concatenate((xm, ym)))) self.assertTrue(eq(np.concatenate((x, y)), concatenate((x, y)))) self.assertTrue(eq(np.concatenate((x, y)), concatenate((xm, y)))) self.assertTrue(eq(np.concatenate((x, y, x)), concatenate((x, ym, x)))) def test_xtestCount(self): # Test count ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0]) self.assertTrue(count(ott).dtype.type is np.intp) self.assertEqual(3, count(ott)) self.assertEqual(1, count(1)) self.assertTrue(eq(0, array(1, mask=[1]))) ott = ott.reshape((2, 2)) self.assertTrue(count(ott).dtype.type is np.intp) assert_(isinstance(count(ott, 0), np.ndarray)) self.assertTrue(count(ott).dtype.type is np.intp) self.assertTrue(eq(3, count(ott))) assert_(getmask(count(ott, 0)) is nomask) self.assertTrue(eq([1, 2], count(ott, 0))) def test_testMinMax(self): # Test minimum and maximum. (x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d xr = np.ravel(x) # max doesn't work if shaped xmr = ravel(xm) # true because of careful selection of data self.assertTrue(eq(max(xr), maximum(xmr))) self.assertTrue(eq(min(xr), minimum(xmr))) def test_testAddSumProd(self): # Test add, sum, product. (x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d self.assertTrue(eq(np.add.reduce(x), add.reduce(x))) self.assertTrue(eq(np.add.accumulate(x), add.accumulate(x))) self.assertTrue(eq(4, sum(array(4), axis=0))) self.assertTrue(eq(4, sum(array(4), axis=0))) self.assertTrue(eq(np.sum(x, axis=0), sum(x, axis=0))) self.assertTrue(eq(np.sum(filled(xm, 0), axis=0), sum(xm, axis=0))) self.assertTrue(eq(np.sum(x, 0), sum(x, 0))) self.assertTrue(eq(np.product(x, axis=0), product(x, axis=0))) self.assertTrue(eq(np.product(x, 0), product(x, 0))) self.assertTrue(eq(np.product(filled(xm, 1), axis=0), product(xm, axis=0))) if len(s) > 1: self.assertTrue(eq(np.concatenate((x, y), 1), concatenate((xm, ym), 1))) self.assertTrue(eq(np.add.reduce(x, 1), add.reduce(x, 1))) self.assertTrue(eq(np.sum(x, 1), sum(x, 1))) self.assertTrue(eq(np.product(x, 1), product(x, 1))) def test_testCI(self): # Test of conversions and indexing x1 = np.array([1, 2, 4, 3]) x2 = array(x1, mask=[1, 0, 0, 0]) x3 = array(x1, mask=[0, 1, 0, 1]) x4 = array(x1) # test conversion to strings str(x2) # raises? repr(x2) # raises? assert_(eq(np.sort(x1), sort(x2, fill_value=0))) # tests of indexing assert_(type(x2[1]) is type(x1[1])) assert_(x1[1] == x2[1]) assert_(x2[0] is masked) assert_(eq(x1[2], x2[2])) assert_(eq(x1[2:5], x2[2:5])) assert_(eq(x1[:], x2[:])) assert_(eq(x1[1:], x3[1:])) x1[2] = 9 x2[2] = 9 assert_(eq(x1, x2)) x1[1:3] = 99 x2[1:3] = 99 assert_(eq(x1, x2)) x2[1] = masked assert_(eq(x1, x2)) x2[1:3] = masked assert_(eq(x1, x2)) x2[:] = x1 x2[1] = masked assert_(allequal(getmask(x2), array([0, 1, 0, 0]))) x3[:] = masked_array([1, 2, 3, 4], [0, 1, 1, 0]) assert_(allequal(getmask(x3), array([0, 1, 1, 0]))) x4[:] = masked_array([1, 2, 3, 4], [0, 1, 1, 0]) assert_(allequal(getmask(x4), array([0, 1, 1, 0]))) assert_(allequal(x4, array([1, 2, 3, 4]))) x1 = np.arange(5) * 1.0 x2 = masked_values(x1, 3.0) assert_(eq(x1, x2)) assert_(allequal(array([0, 0, 0, 1, 0], MaskType), x2.mask)) assert_(eq(3.0, x2.fill_value)) x1 = array([1, 'hello', 2, 3], object) x2 = np.array([1, 'hello', 2, 3], object) s1 = x1[1] s2 = x2[1] self.assertEqual(type(s2), str) self.assertEqual(type(s1), str) self.assertEqual(s1, s2) assert_(x1[1:1].shape == (0,)) def test_testCopySize(self): # Tests of some subtle points of copying and sizing. n = [0, 0, 1, 0, 0] m = make_mask(n) m2 = make_mask(m) self.assertTrue(m is m2) m3 = make_mask(m, copy=1) self.assertTrue(m is not m3) x1 = np.arange(5) y1 = array(x1, mask=m) self.assertTrue(y1._data is not x1) self.assertTrue(allequal(x1, y1._data)) self.assertTrue(y1.mask is m) y1a = array(y1, copy=0) self.assertTrue(y1a.mask is y1.mask) y2 = array(x1, mask=m, copy=0) self.assertTrue(y2.mask is m) self.assertTrue(y2[2] is masked) y2[2] = 9 self.assertTrue(y2[2] is not masked) self.assertTrue(y2.mask is not m) self.assertTrue(allequal(y2.mask, 0)) y3 = array(x1 * 1.0, mask=m) self.assertTrue(filled(y3).dtype is (x1 * 1.0).dtype) x4 = arange(4) x4[2] = masked y4 = resize(x4, (8,)) self.assertTrue(eq(concatenate([x4, x4]), y4)) self.assertTrue(eq(getmask(y4), [0, 0, 1, 0, 0, 0, 1, 0])) y5 = repeat(x4, (2, 2, 2, 2), axis=0) self.assertTrue(eq(y5, [0, 0, 1, 1, 2, 2, 3, 3])) y6 = repeat(x4, 2, axis=0) self.assertTrue(eq(y5, y6)) def test_testPut(self): # Test of put d = arange(5) n = [0, 0, 0, 1, 1] m = make_mask(n) x = array(d, mask=m) self.assertTrue(x[3] is masked) self.assertTrue(x[4] is masked) x[[1, 4]] = [10, 40] self.assertTrue(x.mask is not m) self.assertTrue(x[3] is masked) self.assertTrue(x[4] is not masked) self.assertTrue(eq(x, [0, 10, 2, -1, 40])) x = array(d, mask=m) x.put([0, 1, 2], [-1, 100, 200]) self.assertTrue(eq(x, [-1, 100, 200, 0, 0])) self.assertTrue(x[3] is masked) self.assertTrue(x[4] is masked) def test_testMaPut(self): (x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d m = [1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1] i = np.nonzero(m)[0] put(ym, i, zm) assert_(all(take(ym, i, axis=0) == zm)) def test_testOddFeatures(self): # Test of other odd features x = arange(20) x = x.reshape(4, 5) x.flat[5] = 12 assert_(x[1, 0] == 12) z = x + 10j * x assert_(eq(z.real, x)) assert_(eq(z.imag, 10 * x)) assert_(eq((z * conjugate(z)).real, 101 * x * x)) z.imag[...] = 0.0 x = arange(10) x[3] = masked assert_(str(x[3]) == str(masked)) c = x >= 8 assert_(count(where(c, masked, masked)) == 0) assert_(shape(where(c, masked, masked)) == c.shape) z = where(c, x, masked) assert_(z.dtype is x.dtype) assert_(z[3] is masked) assert_(z[4] is masked) assert_(z[7] is masked) assert_(z[8] is not masked) assert_(z[9] is not masked) assert_(eq(x, z)) z = where(c, masked, x) assert_(z.dtype is x.dtype) assert_(z[3] is masked) assert_(z[4] is not masked) assert_(z[7] is not masked) assert_(z[8] is masked) assert_(z[9] is masked) z = masked_where(c, x) assert_(z.dtype is x.dtype) assert_(z[3] is masked) assert_(z[4] is not masked) assert_(z[7] is not masked) assert_(z[8] is masked) assert_(z[9] is masked) assert_(eq(x, z)) x = array([1., 2., 3., 4., 5.]) c = array([1, 1, 1, 0, 0]) x[2] = masked z = where(c, x, -x) assert_(eq(z, [1., 2., 0., -4., -5])) c[0] = masked z = where(c, x, -x) assert_(eq(z, [1., 2., 0., -4., -5])) assert_(z[0] is masked) assert_(z[1] is not masked) assert_(z[2] is masked) assert_(eq(masked_where(greater(x, 2), x), masked_greater(x, 2))) assert_(eq(masked_where(greater_equal(x, 2), x), masked_greater_equal(x, 2))) assert_(eq(masked_where(less(x, 2), x), masked_less(x, 2))) assert_(eq(masked_where(less_equal(x, 2), x), masked_less_equal(x, 2))) assert_(eq(masked_where(not_equal(x, 2), x), masked_not_equal(x, 2))) assert_(eq(masked_where(equal(x, 2), x), masked_equal(x, 2))) assert_(eq(masked_where(not_equal(x, 2), x), masked_not_equal(x, 2))) assert_(eq(masked_inside(list(range(5)), 1, 3), [0, 199, 199, 199, 4])) assert_(eq(masked_outside(list(range(5)), 1, 3), [199, 1, 2, 3, 199])) assert_(eq(masked_inside(array(list(range(5)), mask=[1, 0, 0, 0, 0]), 1, 3).mask, [1, 1, 1, 1, 0])) assert_(eq(masked_outside(array(list(range(5)), mask=[0, 1, 0, 0, 0]), 1, 3).mask, [1, 1, 0, 0, 1])) assert_(eq(masked_equal(array(list(range(5)), mask=[1, 0, 0, 0, 0]), 2).mask, [1, 0, 1, 0, 0])) assert_(eq(masked_not_equal(array([2, 2, 1, 2, 1], mask=[1, 0, 0, 0, 0]), 2).mask, [1, 0, 1, 0, 1])) assert_(eq(masked_where([1, 1, 0, 0, 0], [1, 2, 3, 4, 5]), [99, 99, 3, 4, 5])) atest = ones((10, 10, 10), dtype=np.float32) btest = zeros(atest.shape, MaskType) ctest = masked_where(btest, atest) assert_(eq(atest, ctest)) z = choose(c, (-x, x)) assert_(eq(z, [1., 2., 0., -4., -5])) assert_(z[0] is masked) assert_(z[1] is not masked) assert_(z[2] is masked) x = arange(6) x[5] = masked y = arange(6) * 10 y[2] = masked c = array([1, 1, 1, 0, 0, 0], mask=[1, 0, 0, 0, 0, 0]) cm = c.filled(1) z = where(c, x, y) zm = where(cm, x, y) assert_(eq(z, zm)) assert_(getmask(zm) is nomask) assert_(eq(zm, [0, 1, 2, 30, 40, 50])) z = where(c, masked, 1) assert_(eq(z, [99, 99, 99, 1, 1, 1])) z = where(c, 1, masked) assert_(eq(z, [99, 1, 1, 99, 99, 99])) def test_testMinMax2(self): # Test of minumum, maximum. assert_(eq(minimum([1, 2, 3], [4, 0, 9]), [1, 0, 3])) assert_(eq(maximum([1, 2, 3], [4, 0, 9]), [4, 2, 9])) x = arange(5) y = arange(5) - 2 x[3] = masked y[0] = masked assert_(eq(minimum(x, y), where(less(x, y), x, y))) assert_(eq(maximum(x, y), where(greater(x, y), x, y))) assert_(minimum(x) == 0) assert_(maximum(x) == 4) def test_testTakeTransposeInnerOuter(self): # Test of take, transpose, inner, outer products x = arange(24) y = np.arange(24) x[5:6] = masked x = x.reshape(2, 3, 4) y = y.reshape(2, 3, 4) assert_(eq(np.transpose(y, (2, 0, 1)), transpose(x, (2, 0, 1)))) assert_(eq(np.take(y, (2, 0, 1), 1), take(x, (2, 0, 1), 1))) assert_(eq(np.inner(filled(x, 0), filled(y, 0)), inner(x, y))) assert_(eq(np.outer(filled(x, 0), filled(y, 0)), outer(x, y))) y = array(['abc', 1, 'def', 2, 3], object) y[2] = masked t = take(y, [0, 3, 4]) assert_(t[0] == 'abc') assert_(t[1] == 2) assert_(t[2] == 3) def test_testInplace(self): # Test of inplace operations and rich comparisons y = arange(10) x = arange(10) xm = arange(10) xm[2] = masked x += 1 assert_(eq(x, y + 1)) xm += 1 assert_(eq(x, y + 1)) x = arange(10) xm = arange(10) xm[2] = masked x -= 1 assert_(eq(x, y - 1)) xm -= 1 assert_(eq(xm, y - 1)) x = arange(10) * 1.0 xm = arange(10) * 1.0 xm[2] = masked x *= 2.0 assert_(eq(x, y * 2)) xm *= 2.0 assert_(eq(xm, y * 2)) x = arange(10) * 2 xm = arange(10) xm[2] = masked x //= 2 assert_(eq(x, y)) xm //= 2 assert_(eq(x, y)) x = arange(10) * 1.0 xm = arange(10) * 1.0 xm[2] = masked x /= 2.0 assert_(eq(x, y / 2.0)) xm /= arange(10) assert_(eq(xm, ones((10,)))) x = arange(10).astype(np.float32) xm = arange(10) xm[2] = masked x += 1. assert_(eq(x, y + 1.)) def test_testPickle(self): # Test of pickling import pickle x = arange(12) x[4:10:2] = masked x = x.reshape(4, 3) s = pickle.dumps(x) y = pickle.loads(s) assert_(eq(x, y)) def test_testMasked(self): # Test of masked element xx = arange(6) xx[1] = masked self.assertTrue(str(masked) == '--') self.assertTrue(xx[1] is masked) self.assertEqual(filled(xx[1], 0), 0) def test_testAverage1(self): # Test of average. ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0]) self.assertTrue(eq(2.0, average(ott, axis=0))) self.assertTrue(eq(2.0, average(ott, weights=[1., 1., 2., 1.]))) result, wts = average(ott, weights=[1., 1., 2., 1.], returned=1) self.assertTrue(eq(2.0, result)) self.assertTrue(wts == 4.0) ott[:] = masked self.assertTrue(average(ott, axis=0) is masked) ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0]) ott = ott.reshape(2, 2) ott[:, 1] = masked self.assertTrue(eq(average(ott, axis=0), [2.0, 0.0])) self.assertTrue(average(ott, axis=1)[0] is masked) self.assertTrue(eq([2., 0.], average(ott, axis=0))) result, wts = average(ott, axis=0, returned=1) self.assertTrue(eq(wts, [1., 0.])) def test_testAverage2(self): # More tests of average. w1 = [0, 1, 1, 1, 1, 0] w2 = [[0, 1, 1, 1, 1, 0], [1, 0, 0, 0, 0, 1]] x = arange(6) self.assertTrue(allclose(average(x, axis=0), 2.5)) self.assertTrue(allclose(average(x, axis=0, weights=w1), 2.5)) y = array([arange(6), 2.0 * arange(6)]) self.assertTrue(allclose(average(y, None), np.add.reduce(np.arange(6)) * 3. / 12.)) self.assertTrue(allclose(average(y, axis=0), np.arange(6) * 3. / 2.)) self.assertTrue(allclose(average(y, axis=1), [average(x, axis=0), average(x, axis=0)*2.0])) self.assertTrue(allclose(average(y, None, weights=w2), 20. / 6.)) self.assertTrue(allclose(average(y, axis=0, weights=w2), [0., 1., 2., 3., 4., 10.])) self.assertTrue(allclose(average(y, axis=1), [average(x, axis=0), average(x, axis=0)*2.0])) m1 = zeros(6) m2 = [0, 0, 1, 1, 0, 0] m3 = [[0, 0, 1, 1, 0, 0], [0, 1, 1, 1, 1, 0]] m4 = ones(6) m5 = [0, 1, 1, 1, 1, 1] self.assertTrue(allclose(average(masked_array(x, m1), axis=0), 2.5)) self.assertTrue(allclose(average(masked_array(x, m2), axis=0), 2.5)) self.assertTrue(average(masked_array(x, m4), axis=0) is masked) self.assertEqual(average(masked_array(x, m5), axis=0), 0.0) self.assertEqual(count(average(masked_array(x, m4), axis=0)), 0) z = masked_array(y, m3) self.assertTrue(allclose(average(z, None), 20. / 6.)) self.assertTrue(allclose(average(z, axis=0), [0., 1., 99., 99., 4.0, 7.5])) self.assertTrue(allclose(average(z, axis=1), [2.5, 5.0])) self.assertTrue(allclose(average(z, axis=0, weights=w2), [0., 1., 99., 99., 4.0, 10.0])) a = arange(6) b = arange(6) * 3 r1, w1 = average([[a, b], [b, a]], axis=1, returned=1) self.assertEqual(shape(r1), shape(w1)) self.assertEqual(r1.shape, w1.shape) r2, w2 = average(ones((2, 2, 3)), axis=0, weights=[3, 1], returned=1) self.assertEqual(shape(w2), shape(r2)) r2, w2 = average(ones((2, 2, 3)), returned=1) self.assertEqual(shape(w2), shape(r2)) r2, w2 = average(ones((2, 2, 3)), weights=ones((2, 2, 3)), returned=1) self.assertTrue(shape(w2) == shape(r2)) a2d = array([[1, 2], [0, 4]], float) a2dm = masked_array(a2d, [[0, 0], [1, 0]]) a2da = average(a2d, axis=0) self.assertTrue(eq(a2da, [0.5, 3.0])) a2dma = average(a2dm, axis=0) self.assertTrue(eq(a2dma, [1.0, 3.0])) a2dma = average(a2dm, axis=None) self.assertTrue(eq(a2dma, 7. / 3.)) a2dma = average(a2dm, axis=1) self.assertTrue(eq(a2dma, [1.5, 4.0])) def test_testToPython(self): self.assertEqual(1, int(array(1))) self.assertEqual(1.0, float(array(1))) self.assertEqual(1, int(array([[[1]]]))) self.assertEqual(1.0, float(array([[1]]))) self.assertRaises(TypeError, float, array([1, 1])) self.assertRaises(ValueError, bool, array([0, 1])) self.assertRaises(ValueError, bool, array([0, 0], mask=[0, 1])) def test_testScalarArithmetic(self): xm = array(0, mask=1) #TODO FIXME: Find out what the following raises a warning in r8247 with np.errstate(divide='ignore'): self.assertTrue((1 / array(0)).mask) self.assertTrue((1 + xm).mask) self.assertTrue((-xm).mask) self.assertTrue((-xm).mask) self.assertTrue(maximum(xm, xm).mask) self.assertTrue(minimum(xm, xm).mask) self.assertTrue(xm.filled().dtype is xm._data.dtype) x = array(0, mask=0) self.assertTrue(x.filled() == x._data) self.assertEqual(str(xm), str(masked_print_option)) def test_testArrayMethods(self): a = array([1, 3, 2]) self.assertTrue(eq(a.any(), a._data.any())) self.assertTrue(eq(a.all(), a._data.all())) self.assertTrue(eq(a.argmax(), a._data.argmax())) self.assertTrue(eq(a.argmin(), a._data.argmin())) self.assertTrue(eq(a.choose(0, 1, 2, 3, 4), a._data.choose(0, 1, 2, 3, 4))) self.assertTrue(eq(a.compress([1, 0, 1]), a._data.compress([1, 0, 1]))) self.assertTrue(eq(a.conj(), a._data.conj())) self.assertTrue(eq(a.conjugate(), a._data.conjugate())) m = array([[1, 2], [3, 4]]) self.assertTrue(eq(m.diagonal(), m._data.diagonal())) self.assertTrue(eq(a.sum(), a._data.sum())) self.assertTrue(eq(a.take([1, 2]), a._data.take([1, 2]))) self.assertTrue(eq(m.transpose(), m._data.transpose())) def test_testArrayAttributes(self): a = array([1, 3, 2]) self.assertEqual(a.ndim, 1) def test_testAPI(self): self.assertFalse([m for m in dir(np.ndarray) if m not in dir(MaskedArray) and not m.startswith('_')]) def test_testSingleElementSubscript(self): a = array([1, 3, 2]) b = array([1, 3, 2], mask=[1, 0, 1]) self.assertEqual(a[0].shape, ()) self.assertEqual(b[0].shape, ()) self.assertEqual(b[1].shape, ()) class TestUfuncs(TestCase): def setUp(self): self.d = (array([1.0, 0, -1, pi / 2] * 2, mask=[0, 1] + [0] * 6), array([1.0, 0, -1, pi / 2] * 2, mask=[1, 0] + [0] * 6),) def test_testUfuncRegression(self): f_invalid_ignore = [ 'sqrt', 'arctanh', 'arcsin', 'arccos', 'arccosh', 'arctanh', 'log', 'log10', 'divide', 'true_divide', 'floor_divide', 'remainder', 'fmod'] for f in ['sqrt', 'log', 'log10', 'exp', 'conjugate', 'sin', 'cos', 'tan', 'arcsin', 'arccos', 'arctan', 'sinh', 'cosh', 'tanh', 'arcsinh', 'arccosh', 'arctanh', 'absolute', 'fabs', 'negative', 'floor', 'ceil', 'logical_not', 'add', 'subtract', 'multiply', 'divide', 'true_divide', 'floor_divide', 'remainder', 'fmod', 'hypot', 'arctan2', 'equal', 'not_equal', 'less_equal', 'greater_equal', 'less', 'greater', 'logical_and', 'logical_or', 'logical_xor']: try: uf = getattr(umath, f) except AttributeError: uf = getattr(fromnumeric, f) mf = getattr(np.ma, f) args = self.d[:uf.nin] with np.errstate(): if f in f_invalid_ignore: np.seterr(invalid='ignore') if f in ['arctanh', 'log', 'log10']: np.seterr(divide='ignore') ur = uf(*args) mr = mf(*args) self.assertTrue(eq(ur.filled(0), mr.filled(0), f)) self.assertTrue(eqmask(ur.mask, mr.mask)) def test_reduce(self): a = self.d[0] self.assertFalse(alltrue(a, axis=0)) self.assertTrue(sometrue(a, axis=0)) self.assertEqual(sum(a[:3], axis=0), 0) self.assertEqual(product(a, axis=0), 0) def test_minmax(self): a = arange(1, 13).reshape(3, 4) amask = masked_where(a < 5, a) self.assertEqual(amask.max(), a.max()) self.assertEqual(amask.min(), 5) self.assertTrue((amask.max(0) == a.max(0)).all()) self.assertTrue((amask.min(0) == [5, 6, 7, 8]).all()) self.assertTrue(amask.max(1)[0].mask) self.assertTrue(amask.min(1)[0].mask) def test_nonzero(self): for t in "?bhilqpBHILQPfdgFDGO": x = array([1, 0, 2, 0], mask=[0, 0, 1, 1]) self.assertTrue(eq(nonzero(x), [0])) class TestArrayMethods(TestCase): def setUp(self): x = np.array([8.375, 7.545, 8.828, 8.5, 1.757, 5.928, 8.43, 7.78, 9.865, 5.878, 8.979, 4.732, 3.012, 6.022, 5.095, 3.116, 5.238, 3.957, 6.04, 9.63, 7.712, 3.382, 4.489, 6.479, 7.189, 9.645, 5.395, 4.961, 9.894, 2.893, 7.357, 9.828, 6.272, 3.758, 6.693, 0.993]) X = x.reshape(6, 6) XX = x.reshape(3, 2, 2, 3) m = np.array([0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0]) mx = array(data=x, mask=m) mX = array(data=X, mask=m.reshape(X.shape)) mXX = array(data=XX, mask=m.reshape(XX.shape)) self.d = (x, X, XX, m, mx, mX, mXX) def test_trace(self): (x, X, XX, m, mx, mX, mXX,) = self.d mXdiag = mX.diagonal() self.assertEqual(mX.trace(), mX.diagonal().compressed().sum()) self.assertTrue(eq(mX.trace(), X.trace() - sum(mXdiag.mask * X.diagonal(), axis=0))) def test_clip(self): (x, X, XX, m, mx, mX, mXX,) = self.d clipped = mx.clip(2, 8) self.assertTrue(eq(clipped.mask, mx.mask)) self.assertTrue(eq(clipped._data, x.clip(2, 8))) self.assertTrue(eq(clipped._data, mx._data.clip(2, 8))) def test_ptp(self): (x, X, XX, m, mx, mX, mXX,) = self.d (n, m) = X.shape self.assertEqual(mx.ptp(), mx.compressed().ptp()) rows = np.zeros(n, np.float_) cols = np.zeros(m, np.float_) for k in range(m): cols[k] = mX[:, k].compressed().ptp() for k in range(n): rows[k] = mX[k].compressed().ptp() self.assertTrue(eq(mX.ptp(0), cols)) self.assertTrue(eq(mX.ptp(1), rows)) def test_swapaxes(self): (x, X, XX, m, mx, mX, mXX,) = self.d mXswapped = mX.swapaxes(0, 1) self.assertTrue(eq(mXswapped[-1], mX[:, -1])) mXXswapped = mXX.swapaxes(0, 2) self.assertEqual(mXXswapped.shape, (2, 2, 3, 3)) def test_cumprod(self): (x, X, XX, m, mx, mX, mXX,) = self.d mXcp = mX.cumprod(0) self.assertTrue(eq(mXcp._data, mX.filled(1).cumprod(0))) mXcp = mX.cumprod(1) self.assertTrue(eq(mXcp._data, mX.filled(1).cumprod(1))) def test_cumsum(self): (x, X, XX, m, mx, mX, mXX,) = self.d mXcp = mX.cumsum(0) self.assertTrue(eq(mXcp._data, mX.filled(0).cumsum(0))) mXcp = mX.cumsum(1) self.assertTrue(eq(mXcp._data, mX.filled(0).cumsum(1))) def test_varstd(self): (x, X, XX, m, mx, mX, mXX,) = self.d self.assertTrue(eq(mX.var(axis=None), mX.compressed().var())) self.assertTrue(eq(mX.std(axis=None), mX.compressed().std())) self.assertTrue(eq(mXX.var(axis=3).shape, XX.var(axis=3).shape)) self.assertTrue(eq(mX.var().shape, X.var().shape)) (mXvar0, mXvar1) = (mX.var(axis=0), mX.var(axis=1)) for k in range(6): self.assertTrue(eq(mXvar1[k], mX[k].compressed().var())) self.assertTrue(eq(mXvar0[k], mX[:, k].compressed().var())) self.assertTrue(eq(np.sqrt(mXvar0[k]), mX[:, k].compressed().std())) def eqmask(m1, m2): if m1 is nomask: return m2 is nomask if m2 is nomask: return m1 is nomask return (m1 == m2).all() if __name__ == "__main__": run_module_suite()