1195 lines
41 KiB
Python
1195 lines
41 KiB
Python
import warnings
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from numpy.testing import *
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import numpy.lib
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from numpy.lib import *
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from numpy.core import *
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from numpy import matrix, asmatrix
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import numpy as np
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class TestAny(TestCase):
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def test_basic(self):
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y1 = [0, 0, 1, 0]
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y2 = [0, 0, 0, 0]
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y3 = [1, 0, 1, 0]
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assert_(any(y1))
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assert_(any(y3))
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assert_(not any(y2))
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def test_nd(self):
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y1 = [[0, 0, 0], [0, 1, 0], [1, 1, 0]]
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assert_(any(y1))
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assert_array_equal(sometrue(y1, axis=0), [1, 1, 0])
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assert_array_equal(sometrue(y1, axis=1), [0, 1, 1])
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class TestAll(TestCase):
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def test_basic(self):
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y1 = [0, 1, 1, 0]
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y2 = [0, 0, 0, 0]
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y3 = [1, 1, 1, 1]
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assert_(not all(y1))
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assert_(all(y3))
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assert_(not all(y2))
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assert_(all(~array(y2)))
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def test_nd(self):
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y1 = [[0, 0, 1], [0, 1, 1], [1, 1, 1]]
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assert_(not all(y1))
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assert_array_equal(alltrue(y1, axis=0), [0, 0, 1])
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assert_array_equal(alltrue(y1, axis=1), [0, 0, 1])
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class TestAverage(TestCase):
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def test_basic(self):
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y1 = array([1, 2, 3])
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assert_(average(y1, axis=0) == 2.)
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y2 = array([1., 2., 3.])
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assert_(average(y2, axis=0) == 2.)
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y3 = [0., 0., 0.]
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assert_(average(y3, axis=0) == 0.)
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y4 = ones((4, 4))
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y4[0, 1] = 0
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y4[1, 0] = 2
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assert_almost_equal(y4.mean(0), average(y4, 0))
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assert_almost_equal(y4.mean(1), average(y4, 1))
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y5 = rand(5, 5)
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assert_almost_equal(y5.mean(0), average(y5, 0))
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assert_almost_equal(y5.mean(1), average(y5, 1))
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y6 = matrix(rand(5, 5))
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assert_array_equal(y6.mean(0), average(y6, 0))
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def test_weights(self):
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y = arange(10)
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w = arange(10)
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actual = average(y, weights=w)
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desired = (arange(10) ** 2).sum()*1. / arange(10).sum()
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assert_almost_equal(actual, desired)
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y1 = array([[1, 2, 3], [4, 5, 6]])
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w0 = [1, 2]
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actual = average(y1, weights=w0, axis=0)
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desired = array([3., 4., 5.])
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assert_almost_equal(actual, desired)
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w1 = [0, 0, 1]
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actual = average(y1, weights=w1, axis=1)
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desired = array([3., 6.])
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assert_almost_equal(actual, desired)
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# This should raise an error. Can we test for that ?
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# assert_equal(average(y1, weights=w1), 9./2.)
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# 2D Case
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w2 = [[0, 0, 1], [0, 0, 2]]
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desired = array([3., 6.])
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assert_array_equal(average(y1, weights=w2, axis=1), desired)
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assert_equal(average(y1, weights=w2), 5.)
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def test_returned(self):
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y = array([[1, 2, 3], [4, 5, 6]])
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# No weights
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avg, scl = average(y, returned=True)
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assert_equal(scl, 6.)
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avg, scl = average(y, 0, returned=True)
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assert_array_equal(scl, array([2., 2., 2.]))
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avg, scl = average(y, 1, returned=True)
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assert_array_equal(scl, array([3., 3.]))
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# With weights
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w0 = [1, 2]
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avg, scl = average(y, weights=w0, axis=0, returned=True)
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assert_array_equal(scl, array([3., 3., 3.]))
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w1 = [1, 2, 3]
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avg, scl = average(y, weights=w1, axis=1, returned=True)
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assert_array_equal(scl, array([6., 6.]))
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w2 = [[0, 0, 1], [1, 2, 3]]
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avg, scl = average(y, weights=w2, axis=1, returned=True)
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assert_array_equal(scl, array([1., 6.]))
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class TestSelect(TestCase):
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def _select(self, cond, values, default=0):
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output = []
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for m in range(len(cond)):
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output += [V[m] for V, C in zip(values, cond) if C[m]] or [default]
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return output
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def test_basic(self):
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choices = [array([1, 2, 3]),
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array([4, 5, 6]),
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array([7, 8, 9])]
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conditions = [array([0, 0, 0]),
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array([0, 1, 0]),
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array([0, 0, 1])]
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assert_array_equal(select(conditions, choices, default=15),
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self._select(conditions, choices, default=15))
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assert_equal(len(choices), 3)
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assert_equal(len(conditions), 3)
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class TestInsert(TestCase):
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def test_basic(self):
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a = [1, 2, 3]
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assert_equal(insert(a, 0, 1), [1, 1, 2, 3])
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assert_equal(insert(a, 3, 1), [1, 2, 3, 1])
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assert_equal(insert(a, [1, 1, 1], [1, 2, 3]), [1, 1, 2, 3, 2, 3])
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class TestAmax(TestCase):
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def test_basic(self):
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a = [3, 4, 5, 10, -3, -5, 6.0]
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assert_equal(amax(a), 10.0)
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b = [[3, 6.0, 9.0],
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[4, 10.0, 5.0],
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[8, 3.0, 2.0]]
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assert_equal(amax(b, axis=0), [8.0, 10.0, 9.0])
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assert_equal(amax(b, axis=1), [9.0, 10.0, 8.0])
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class TestAmin(TestCase):
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def test_basic(self):
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a = [3, 4, 5, 10, -3, -5, 6.0]
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assert_equal(amin(a), -5.0)
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b = [[3, 6.0, 9.0],
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[4, 10.0, 5.0],
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[8, 3.0, 2.0]]
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assert_equal(amin(b, axis=0), [3.0, 3.0, 2.0])
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assert_equal(amin(b, axis=1), [3.0, 4.0, 2.0])
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class TestPtp(TestCase):
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def test_basic(self):
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a = [3, 4, 5, 10, -3, -5, 6.0]
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assert_equal(ptp(a, axis=0), 15.0)
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b = [[3, 6.0, 9.0],
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[4, 10.0, 5.0],
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[8, 3.0, 2.0]]
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assert_equal(ptp(b, axis=0), [5.0, 7.0, 7.0])
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assert_equal(ptp(b, axis= -1), [6.0, 6.0, 6.0])
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class TestCumsum(TestCase):
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def test_basic(self):
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ba = [1, 2, 10, 11, 6, 5, 4]
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ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
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for ctype in [int8, uint8, int16, uint16, int32, uint32,
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float32, float64, complex64, complex128]:
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a = array(ba, ctype)
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a2 = array(ba2, ctype)
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assert_array_equal(cumsum(a, axis=0), array([1, 3, 13, 24, 30, 35, 39], ctype))
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assert_array_equal(cumsum(a2, axis=0), array([[1, 2, 3, 4], [6, 8, 10, 13],
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[16, 11, 14, 18]], ctype))
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assert_array_equal(cumsum(a2, axis=1),
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array([[1, 3, 6, 10],
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[5, 11, 18, 27],
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[10, 13, 17, 22]], ctype))
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class TestProd(TestCase):
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def test_basic(self):
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ba = [1, 2, 10, 11, 6, 5, 4]
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ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
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for ctype in [int16, uint16, int32, uint32,
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float32, float64, complex64, complex128]:
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a = array(ba, ctype)
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a2 = array(ba2, ctype)
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if ctype in ['1', 'b']:
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self.assertRaises(ArithmeticError, prod, a)
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self.assertRaises(ArithmeticError, prod, a2, 1)
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self.assertRaises(ArithmeticError, prod, a)
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else:
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assert_equal(prod(a, axis=0), 26400)
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assert_array_equal(prod(a2, axis=0),
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array([50, 36, 84, 180], ctype))
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assert_array_equal(prod(a2, axis= -1), array([24, 1890, 600], ctype))
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class TestCumprod(TestCase):
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def test_basic(self):
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ba = [1, 2, 10, 11, 6, 5, 4]
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ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
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for ctype in [int16, uint16, int32, uint32,
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float32, float64, complex64, complex128]:
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a = array(ba, ctype)
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a2 = array(ba2, ctype)
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if ctype in ['1', 'b']:
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self.assertRaises(ArithmeticError, cumprod, a)
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self.assertRaises(ArithmeticError, cumprod, a2, 1)
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self.assertRaises(ArithmeticError, cumprod, a)
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else:
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assert_array_equal(cumprod(a, axis= -1),
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array([1, 2, 20, 220,
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1320, 6600, 26400], ctype))
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assert_array_equal(cumprod(a2, axis=0),
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array([[ 1, 2, 3, 4],
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[ 5, 12, 21, 36],
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[50, 36, 84, 180]], ctype))
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assert_array_equal(cumprod(a2, axis= -1),
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array([[ 1, 2, 6, 24],
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[ 5, 30, 210, 1890],
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[10, 30, 120, 600]], ctype))
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class TestDiff(TestCase):
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def test_basic(self):
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x = [1, 4, 6, 7, 12]
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out = array([3, 2, 1, 5])
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out2 = array([-1, -1, 4])
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out3 = array([0, 5])
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assert_array_equal(diff(x), out)
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assert_array_equal(diff(x, n=2), out2)
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assert_array_equal(diff(x, n=3), out3)
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def test_nd(self):
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x = 20 * rand(10, 20, 30)
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out1 = x[:, :, 1:] - x[:, :, :-1]
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out2 = out1[:, :, 1:] - out1[:, :, :-1]
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out3 = x[1:, :, :] - x[:-1, :, :]
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out4 = out3[1:, :, :] - out3[:-1, :, :]
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assert_array_equal(diff(x), out1)
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assert_array_equal(diff(x, n=2), out2)
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assert_array_equal(diff(x, axis=0), out3)
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assert_array_equal(diff(x, n=2, axis=0), out4)
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class TestGradient(TestCase):
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def test_basic(self):
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x = array([[1, 1], [3, 4]])
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dx = [array([[2., 3.], [2., 3.]]),
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array([[0., 0.], [1., 1.]])]
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assert_array_equal(gradient(x), dx)
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def test_badargs(self):
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# for 2D array, gradient can take 0,1, or 2 extra args
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x = array([[1, 1], [3, 4]])
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assert_raises(SyntaxError, gradient, x, array([1., 1.]),
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array([1., 1.]), array([1., 1.]))
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def test_masked(self):
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# Make sure that gradient supports subclasses like masked arrays
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x = np.ma.array([[1, 1], [3, 4]])
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assert_equal(type(gradient(x)[0]), type(x))
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class TestAngle(TestCase):
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def test_basic(self):
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x = [1 + 3j, sqrt(2) / 2.0 + 1j * sqrt(2) / 2, 1, 1j, -1, -1j, 1 - 3j, -1 + 3j]
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y = angle(x)
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yo = [arctan(3.0 / 1.0), arctan(1.0), 0, pi / 2, pi, -pi / 2.0,
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- arctan(3.0 / 1.0), pi - arctan(3.0 / 1.0)]
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z = angle(x, deg=1)
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zo = array(yo) * 180 / pi
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assert_array_almost_equal(y, yo, 11)
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assert_array_almost_equal(z, zo, 11)
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class TestTrimZeros(TestCase):
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""" only testing for integer splits.
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"""
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def test_basic(self):
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a = array([0, 0, 1, 2, 3, 4, 0])
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res = trim_zeros(a)
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assert_array_equal(res, array([1, 2, 3, 4]))
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def test_leading_skip(self):
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a = array([0, 0, 1, 0, 2, 3, 4, 0])
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res = trim_zeros(a)
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assert_array_equal(res, array([1, 0, 2, 3, 4]))
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def test_trailing_skip(self):
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a = array([0, 0, 1, 0, 2, 3, 0, 4, 0])
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res = trim_zeros(a)
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assert_array_equal(res, array([1, 0, 2, 3, 0, 4]))
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class TestExtins(TestCase):
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def test_basic(self):
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a = array([1, 3, 2, 1, 2, 3, 3])
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b = extract(a > 1, a)
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assert_array_equal(b, [3, 2, 2, 3, 3])
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def test_place(self):
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a = array([1, 4, 3, 2, 5, 8, 7])
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place(a, [0, 1, 0, 1, 0, 1, 0], [2, 4, 6])
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assert_array_equal(a, [1, 2, 3, 4, 5, 6, 7])
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def test_both(self):
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a = rand(10)
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mask = a > 0.5
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ac = a.copy()
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c = extract(mask, a)
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place(a, mask, 0)
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place(a, mask, c)
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assert_array_equal(a, ac)
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class TestVectorize(TestCase):
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def test_simple(self):
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def addsubtract(a, b):
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if a > b:
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return a - b
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else:
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return a + b
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f = vectorize(addsubtract)
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r = f([0, 3, 6, 9], [1, 3, 5, 7])
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assert_array_equal(r, [1, 6, 1, 2])
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def test_scalar(self):
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def addsubtract(a, b):
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if a > b:
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return a - b
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else:
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return a + b
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f = vectorize(addsubtract)
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r = f([0, 3, 6, 9], 5)
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assert_array_equal(r, [5, 8, 1, 4])
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def test_large(self):
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x = linspace(-3, 2, 10000)
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f = vectorize(lambda x: x)
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y = f(x)
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assert_array_equal(y, x)
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def test_ufunc(self):
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import math
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f = vectorize(math.cos)
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args = array([0, 0.5*pi, pi, 1.5*pi, 2*pi])
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r1 = f(args)
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r2 = cos(args)
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assert_array_equal(r1, r2)
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def test_keywords(self):
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import math
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def foo(a, b=1):
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return a + b
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f = vectorize(foo)
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args = array([1,2,3])
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r1 = f(args)
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r2 = array([2,3,4])
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assert_array_equal(r1, r2)
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r1 = f(args, 2)
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r2 = array([3,4,5])
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assert_array_equal(r1, r2)
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def test_keywords_no_func_code(self):
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# This needs to test a function that has keywords but
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# no func_code attribute, since otherwise vectorize will
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# inspect the func_code.
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import random
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try:
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f = vectorize(random.randrange)
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except:
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raise AssertionError()
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class TestDigitize(TestCase):
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def test_forward(self):
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x = arange(-6, 5)
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bins = arange(-5, 5)
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assert_array_equal(digitize(x, bins), arange(11))
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def test_reverse(self):
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x = arange(5, -6, -1)
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bins = arange(5, -5, -1)
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assert_array_equal(digitize(x, bins), arange(11))
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def test_random(self):
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x = rand(10)
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bin = linspace(x.min(), x.max(), 10)
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assert_(all(digitize(x, bin) != 0))
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class TestUnwrap(TestCase):
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def test_simple(self):
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#check that unwrap removes jumps greather that 2*pi
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assert_array_equal(unwrap([1, 1 + 2 * pi]), [1, 1])
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#check that unwrap maintans continuity
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assert_(all(diff(unwrap(rand(10) * 100)) < pi))
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class TestFilterwindows(TestCase):
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def test_hanning(self):
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#check symmetry
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w = hanning(10)
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assert_array_almost_equal(w, flipud(w), 7)
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#check known value
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assert_almost_equal(sum(w, axis=0), 4.500, 4)
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def test_hamming(self):
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#check symmetry
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w = hamming(10)
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assert_array_almost_equal(w, flipud(w), 7)
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#check known value
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assert_almost_equal(sum(w, axis=0), 4.9400, 4)
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def test_bartlett(self):
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#check symmetry
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w = bartlett(10)
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assert_array_almost_equal(w, flipud(w), 7)
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#check known value
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assert_almost_equal(sum(w, axis=0), 4.4444, 4)
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def test_blackman(self):
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#check symmetry
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w = blackman(10)
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assert_array_almost_equal(w, flipud(w), 7)
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#check known value
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assert_almost_equal(sum(w, axis=0), 3.7800, 4)
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class TestTrapz(TestCase):
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def test_simple(self):
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r = trapz(exp(-1.0 / 2 * (arange(-10, 10, .1)) ** 2) / sqrt(2 * pi), dx=0.1)
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#check integral of normal equals 1
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assert_almost_equal(sum(r, axis=0), 1, 7)
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def test_ndim(self):
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|
x = linspace(0, 1, 3)
|
|
y = linspace(0, 2, 8)
|
|
z = linspace(0, 3, 13)
|
|
|
|
wx = ones_like(x) * (x[1] - x[0])
|
|
wx[0] /= 2
|
|
wx[-1] /= 2
|
|
wy = ones_like(y) * (y[1] - y[0])
|
|
wy[0] /= 2
|
|
wy[-1] /= 2
|
|
wz = ones_like(z) * (z[1] - z[0])
|
|
wz[0] /= 2
|
|
wz[-1] /= 2
|
|
|
|
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)
|
|
|
|
# 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)
|
|
assert_almost_equal(r, qy)
|
|
r = trapz(q, x=z[None, None, :], axis=2)
|
|
assert_almost_equal(r, qz)
|
|
|
|
# 1-d `x`
|
|
r = trapz(q, x=x, axis=0)
|
|
assert_almost_equal(r, qx)
|
|
r = trapz(q, x=y, axis=1)
|
|
assert_almost_equal(r, qy)
|
|
r = trapz(q, x=z, axis=2)
|
|
assert_almost_equal(r, qz)
|
|
|
|
def test_masked(self):
|
|
#Testing that masked arrays behave as if the function is 0 where
|
|
#masked
|
|
x = arange(5)
|
|
y = x * x
|
|
mask = x == 2
|
|
ym = np.ma.array(y, mask=mask)
|
|
r = 13.0 # sum(0.5 * (0 + 1) * 1.0 + 0.5 * (9 + 16))
|
|
assert_almost_equal(trapz(ym, x), r)
|
|
|
|
xm = np.ma.array(x, mask=mask)
|
|
assert_almost_equal(trapz(ym, xm), r)
|
|
|
|
xm = np.ma.array(x, mask=mask)
|
|
assert_almost_equal(trapz(y, xm), r)
|
|
|
|
def test_matrix(self):
|
|
#Test to make sure matrices give the same answer as ndarrays
|
|
x = linspace(0, 5)
|
|
y = x * x
|
|
r = trapz(y, x)
|
|
mx = matrix(x)
|
|
my = matrix(y)
|
|
mr = trapz(my, mx)
|
|
assert_almost_equal(mr, r)
|
|
|
|
|
|
class TestSinc(TestCase):
|
|
def test_simple(self):
|
|
assert_(sinc(0) == 1)
|
|
w = sinc(linspace(-1, 1, 100))
|
|
#check symmetry
|
|
assert_array_almost_equal(w, flipud(w), 7)
|
|
|
|
def test_array_like(self):
|
|
x = [0, 0.5]
|
|
y1 = sinc(array(x))
|
|
y2 = sinc(list(x))
|
|
y3 = sinc(tuple(x))
|
|
assert_array_equal(y1, y2)
|
|
assert_array_equal(y1, y3)
|
|
|
|
class TestHistogram(TestCase):
|
|
def setUp(self):
|
|
pass
|
|
|
|
def tearDown(self):
|
|
pass
|
|
|
|
def test_simple(self):
|
|
n = 100
|
|
v = rand(n)
|
|
(a, b) = histogram(v)
|
|
#check if the sum of the bins equals the number of samples
|
|
assert_equal(sum(a, axis=0), n)
|
|
#check that the bin counts are evenly spaced when the data is from a
|
|
# linear function
|
|
(a, b) = histogram(linspace(0, 10, 100))
|
|
assert_array_equal(a, 10)
|
|
|
|
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, array([2]))
|
|
assert_allclose(e, array([1., 2.]))
|
|
|
|
def test_normed(self):
|
|
# Check that the integral of the density equals 1.
|
|
n = 100
|
|
v = rand(n)
|
|
a, b = histogram(v, normed=True)
|
|
area = sum(a * diff(b))
|
|
assert_almost_equal(area, 1)
|
|
|
|
# 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)
|
|
area = sum(a * diff(b))
|
|
assert_almost_equal(area, 1)
|
|
|
|
def test_density(self):
|
|
# Check that the integral of the density equals 1.
|
|
n = 100
|
|
v = rand(n)
|
|
a, b = histogram(v, density=True)
|
|
area = sum(a * diff(b))
|
|
assert_almost_equal(area, 1)
|
|
|
|
# Check with non-constant bin widths
|
|
v = np.arange(10)
|
|
bins = [0,1,3,6,10]
|
|
a, b = histogram(v, bins, density=True)
|
|
assert_array_equal(a, .1)
|
|
assert_equal(sum(a*diff(b)), 1)
|
|
|
|
# Variale bin widths are especially useful to deal with
|
|
# infinities.
|
|
v = np.arange(10)
|
|
bins = [0,1,3,6,np.inf]
|
|
a, b = histogram(v, bins, density=True)
|
|
assert_array_equal(a, [.1,.1,.1,0.])
|
|
|
|
# Taken from a bug report from N. Becker on the numpy-discussion
|
|
# mailing list Aug. 6, 2010.
|
|
counts, dmy = np.histogram([1,2,3,4], [0.5,1.5,np.inf], density=True)
|
|
assert_equal(counts, [.25, 0])
|
|
|
|
def test_outliers(self):
|
|
# Check that outliers are not tallied
|
|
a = arange(10) + .5
|
|
|
|
# Lower outliers
|
|
h, b = histogram(a, range=[0, 9])
|
|
assert_equal(h.sum(), 9)
|
|
|
|
# Upper outliers
|
|
h, b = histogram(a, range=[1, 10])
|
|
assert_equal(h.sum(), 9)
|
|
|
|
# Normalization
|
|
h, b = histogram(a, range=[1, 9], normed=True)
|
|
assert_equal((h * diff(b)).sum(), 1)
|
|
|
|
# Weights
|
|
w = arange(10) + .5
|
|
h, b = histogram(a, range=[1, 9], weights=w, normed=True)
|
|
assert_equal((h * diff(b)).sum(), 1)
|
|
|
|
h, b = histogram(a, bins=8, range=[1, 9], weights=w)
|
|
assert_equal(h, w[1:-1])
|
|
|
|
def test_type(self):
|
|
# Check the type of the returned histogram
|
|
a = arange(10) + .5
|
|
h, b = histogram(a)
|
|
assert_(issubdtype(h.dtype, int))
|
|
|
|
h, b = histogram(a, normed=True)
|
|
assert_(issubdtype(h.dtype, float))
|
|
|
|
h, b = histogram(a, weights=ones(10, int))
|
|
assert_(issubdtype(h.dtype, int))
|
|
|
|
h, b = histogram(a, weights=ones(10, float))
|
|
assert_(issubdtype(h.dtype, float))
|
|
|
|
def test_weights(self):
|
|
v = rand(100)
|
|
w = ones(100) * 5
|
|
a, b = histogram(v)
|
|
na, nb = histogram(v, normed=True)
|
|
wa, wb = histogram(v, weights=w)
|
|
nwa, nwb = histogram(v, weights=w, normed=True)
|
|
assert_array_almost_equal(a * 5, wa)
|
|
assert_array_almost_equal(na, nwa)
|
|
|
|
# Check weights are properly applied.
|
|
v = linspace(0, 10, 10)
|
|
w = concatenate((zeros(5), ones(5)))
|
|
wa, wb = histogram(v, bins=arange(11), weights=w)
|
|
assert_array_almost_equal(wa, w)
|
|
|
|
# Check with integer weights
|
|
wa, wb = histogram([1, 2, 2, 4], bins=4, weights=[4, 3, 2, 1])
|
|
assert_array_equal(wa, [4, 5, 0, 1])
|
|
wa, wb = histogram([1, 2, 2, 4], bins=4, weights=[4, 3, 2, 1], normed=True)
|
|
assert_array_almost_equal(wa, array([4, 5, 0, 1]) / 10. / 3. * 4)
|
|
|
|
# Check weights with non-uniform bin widths
|
|
a,b = histogram(np.arange(9), [0,1,3,6,10], \
|
|
weights=[2,1,1,1,1,1,1,1,1], density=True)
|
|
assert_almost_equal(a, [.2, .1, .1, .075])
|
|
|
|
def test_empty(self):
|
|
a, b = histogram([], bins=([0,1]))
|
|
assert_array_equal(a, array([0]))
|
|
assert_array_equal(b, array([0, 1]))
|
|
|
|
|
|
class TestHistogramdd(TestCase):
|
|
def test_simple(self):
|
|
x = 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]])
|
|
H, edges = histogramdd(x, (2, 3, 3), range=[[-1, 1], [0, 3], [0, 3]])
|
|
answer = asarray([[[0, 1, 0], [0, 0, 1], [1, 0, 0]], [[0, 1, 0], [0, 0, 1],
|
|
[0, 0, 1]]])
|
|
assert_array_equal(H, answer)
|
|
# Check normalization
|
|
ed = [[-2, 0, 2], [0, 1, 2, 3], [0, 1, 2, 3]]
|
|
H, edges = histogramdd(x, bins=ed, normed=True)
|
|
assert_(all(H == answer / 12.))
|
|
# Check that H has the correct shape.
|
|
H, edges = histogramdd(x, (2, 3, 4), range=[[-1, 1], [0, 3], [0, 4]],
|
|
normed=True)
|
|
answer = asarray([[[0, 1, 0, 0], [0, 0, 1, 0], [1, 0, 0, 0]], [[0, 1, 0, 0],
|
|
[0, 0, 1, 0], [0, 0, 1, 0]]])
|
|
assert_array_almost_equal(H, answer / 6., 4)
|
|
# Check that a sequence of arrays is accepted and H has the correct
|
|
# shape.
|
|
z = [squeeze(y) for y in split(x, 3, axis=1)]
|
|
H, edges = histogramdd(z, bins=(4, 3, 2), range=[[-2, 2], [0, 3], [0, 2]])
|
|
answer = asarray([[[0, 0], [0, 0], [0, 0]],
|
|
[[0, 1], [0, 0], [1, 0]],
|
|
[[0, 1], [0, 0], [0, 0]],
|
|
[[0, 0], [0, 0], [0, 0]]])
|
|
assert_array_equal(H, answer)
|
|
|
|
Z = zeros((5, 5, 5))
|
|
Z[range(5), range(5), range(5)] = 1.
|
|
H, edges = histogramdd([arange(5), arange(5), arange(5)], 5)
|
|
assert_array_equal(H, Z)
|
|
|
|
def test_shape_3d(self):
|
|
# All possible permutations for bins of different lengths in 3D.
|
|
bins = ((5, 4, 6), (6, 4, 5), (5, 6, 4), (4, 6, 5), (6, 5, 4),
|
|
(4, 5, 6))
|
|
r = rand(10, 3)
|
|
for b in bins:
|
|
H, edges = histogramdd(r, b)
|
|
assert_(H.shape == b)
|
|
|
|
def test_shape_4d(self):
|
|
# All possible permutations for bins of different lengths in 4D.
|
|
bins = ((7, 4, 5, 6), (4, 5, 7, 6), (5, 6, 4, 7), (7, 6, 5, 4),
|
|
(5, 7, 6, 4), (4, 6, 7, 5), (6, 5, 7, 4), (7, 5, 4, 6),
|
|
(7, 4, 6, 5), (6, 4, 7, 5), (6, 7, 5, 4), (4, 6, 5, 7),
|
|
(4, 7, 5, 6), (5, 4, 6, 7), (5, 7, 4, 6), (6, 7, 4, 5),
|
|
(6, 5, 4, 7), (4, 7, 6, 5), (4, 5, 6, 7), (7, 6, 4, 5),
|
|
(5, 4, 7, 6), (5, 6, 7, 4), (6, 4, 5, 7), (7, 5, 6, 4))
|
|
|
|
r = rand(10, 4)
|
|
for b in bins:
|
|
H, edges = histogramdd(r, b)
|
|
assert_(H.shape == b)
|
|
|
|
def test_weights(self):
|
|
v = rand(100, 2)
|
|
hist, edges = histogramdd(v)
|
|
n_hist, edges = histogramdd(v, normed=True)
|
|
w_hist, edges = histogramdd(v, weights=ones(100))
|
|
assert_array_equal(w_hist, hist)
|
|
w_hist, edges = histogramdd(v, weights=ones(100) * 2, normed=True)
|
|
assert_array_equal(w_hist, n_hist)
|
|
w_hist, edges = histogramdd(v, weights=ones(100, int) * 2)
|
|
assert_array_equal(w_hist, 2 * hist)
|
|
|
|
def test_identical_samples(self):
|
|
x = zeros((10, 2), int)
|
|
hist, edges = histogramdd(x, bins=2)
|
|
assert_array_equal(edges[0], array([-0.5, 0. , 0.5]))
|
|
|
|
def test_empty(self):
|
|
a, b = histogramdd([[], []], bins=([0,1], [0,1]))
|
|
assert_array_max_ulp(a, array([[ 0.]]))
|
|
a, b = np.histogramdd([[], [], []], bins=2)
|
|
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."""
|
|
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])
|
|
assert_raises(ValueError, np.histogramdd, x, bins=[1, 1, 1, [1, 2, 2, 3]])
|
|
assert_raises(ValueError, np.histogramdd, x, bins=[1, 1, 1, [1, 2, 3, -3]])
|
|
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."""
|
|
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)
|
|
|
|
|
|
class TestUnique(TestCase):
|
|
def test_simple(self):
|
|
x = array([4, 3, 2, 1, 1, 2, 3, 4, 0])
|
|
assert_(all(unique(x) == [0, 1, 2, 3, 4]))
|
|
assert_(unique(array([1, 1, 1, 1, 1])) == array([1]))
|
|
x = ['widget', 'ham', 'foo', 'bar', 'foo', 'ham']
|
|
assert_(all(unique(x) == ['bar', 'foo', 'ham', 'widget']))
|
|
x = array([5 + 6j, 1 + 1j, 1 + 10j, 10, 5 + 6j])
|
|
assert_(all(unique(x) == [1 + 1j, 1 + 10j, 5 + 6j, 10]))
|
|
|
|
|
|
class TestCheckFinite(TestCase):
|
|
def test_simple(self):
|
|
a = [1, 2, 3]
|
|
b = [1, 2, inf]
|
|
c = [1, 2, nan]
|
|
numpy.lib.asarray_chkfinite(a)
|
|
assert_raises(ValueError, numpy.lib.asarray_chkfinite, b)
|
|
assert_raises(ValueError, numpy.lib.asarray_chkfinite, c)
|
|
|
|
|
|
class TestNaNFuncts(TestCase):
|
|
def setUp(self):
|
|
self.A = array([[[ nan, 0.01319214, 0.01620964],
|
|
[ 0.11704017, nan, 0.75157887],
|
|
[ 0.28333658, 0.1630199 , nan ]],
|
|
[[ 0.59541557, nan, 0.37910852],
|
|
[ nan, 0.87964135, nan ],
|
|
[ 0.70543747, nan, 0.34306596]],
|
|
[[ 0.72687499, 0.91084584, nan ],
|
|
[ 0.84386844, 0.38944762, 0.23913896],
|
|
[ nan, 0.37068164, 0.33850425]]])
|
|
|
|
def test_nansum(self):
|
|
assert_almost_equal(nansum(self.A), 8.0664079100000006)
|
|
assert_almost_equal(nansum(self.A, 0),
|
|
array([[ 1.32229056, 0.92403798, 0.39531816],
|
|
[ 0.96090861, 1.26908897, 0.99071783],
|
|
[ 0.98877405, 0.53370154, 0.68157021]]))
|
|
assert_almost_equal(nansum(self.A, 1),
|
|
array([[ 0.40037675, 0.17621204, 0.76778851],
|
|
[ 1.30085304, 0.87964135, 0.72217448],
|
|
[ 1.57074343, 1.6709751 , 0.57764321]]))
|
|
assert_almost_equal(nansum(self.A, 2),
|
|
array([[ 0.02940178, 0.86861904, 0.44635648],
|
|
[ 0.97452409, 0.87964135, 1.04850343],
|
|
[ 1.63772083, 1.47245502, 0.70918589]]))
|
|
|
|
def test_nanmin(self):
|
|
assert_almost_equal(nanmin(self.A), 0.01319214)
|
|
assert_almost_equal(nanmin(self.A, 0),
|
|
array([[ 0.59541557, 0.01319214, 0.01620964],
|
|
[ 0.11704017, 0.38944762, 0.23913896],
|
|
[ 0.28333658, 0.1630199 , 0.33850425]]))
|
|
assert_almost_equal(nanmin(self.A, 1),
|
|
array([[ 0.11704017, 0.01319214, 0.01620964],
|
|
[ 0.59541557, 0.87964135, 0.34306596],
|
|
[ 0.72687499, 0.37068164, 0.23913896]]))
|
|
assert_almost_equal(nanmin(self.A, 2),
|
|
array([[ 0.01319214, 0.11704017, 0.1630199 ],
|
|
[ 0.37910852, 0.87964135, 0.34306596],
|
|
[ 0.72687499, 0.23913896, 0.33850425]]))
|
|
assert_(np.isnan(nanmin([nan, nan])))
|
|
|
|
def test_nanargmin(self):
|
|
assert_almost_equal(nanargmin(self.A), 1)
|
|
assert_almost_equal(nanargmin(self.A, 0),
|
|
array([[1, 0, 0],
|
|
[0, 2, 2],
|
|
[0, 0, 2]]))
|
|
assert_almost_equal(nanargmin(self.A, 1),
|
|
array([[1, 0, 0],
|
|
[0, 1, 2],
|
|
[0, 2, 1]]))
|
|
assert_almost_equal(nanargmin(self.A, 2),
|
|
array([[1, 0, 1],
|
|
[2, 1, 2],
|
|
[0, 2, 2]]))
|
|
|
|
def test_nanmax(self):
|
|
assert_almost_equal(nanmax(self.A), 0.91084584000000002)
|
|
assert_almost_equal(nanmax(self.A, 0),
|
|
array([[ 0.72687499, 0.91084584, 0.37910852],
|
|
[ 0.84386844, 0.87964135, 0.75157887],
|
|
[ 0.70543747, 0.37068164, 0.34306596]]))
|
|
assert_almost_equal(nanmax(self.A, 1),
|
|
array([[ 0.28333658, 0.1630199 , 0.75157887],
|
|
[ 0.70543747, 0.87964135, 0.37910852],
|
|
[ 0.84386844, 0.91084584, 0.33850425]]))
|
|
assert_almost_equal(nanmax(self.A, 2),
|
|
array([[ 0.01620964, 0.75157887, 0.28333658],
|
|
[ 0.59541557, 0.87964135, 0.70543747],
|
|
[ 0.91084584, 0.84386844, 0.37068164]]))
|
|
assert_(np.isnan(nanmax([nan, nan])))
|
|
|
|
def test_nanmin_allnan_on_axis(self):
|
|
assert_array_equal(isnan(nanmin([[nan] * 2] * 3, axis=1)),
|
|
[True, True, True])
|
|
|
|
def test_nanmin_masked(self):
|
|
a = np.ma.fix_invalid([[2, 1, 3, nan], [5, 2, 3, nan]])
|
|
ctrl_mask = a._mask.copy()
|
|
test = np.nanmin(a, axis=1)
|
|
assert_equal(test, [1, 2])
|
|
assert_equal(a._mask, ctrl_mask)
|
|
assert_equal(np.isinf(a), np.zeros((2, 4), dtype=bool))
|
|
|
|
|
|
class TestNanFunctsIntTypes(TestCase):
|
|
|
|
int_types = (int8, int16, int32, int64, uint8, uint16, uint32, uint64)
|
|
|
|
def setUp(self, *args, **kwargs):
|
|
self.A = array([127, 39, 93, 87, 46])
|
|
|
|
def integer_arrays(self):
|
|
for dtype in self.int_types:
|
|
yield self.A.astype(dtype)
|
|
|
|
def test_nanmin(self):
|
|
min_value = min(self.A)
|
|
for A in self.integer_arrays():
|
|
assert_equal(nanmin(A), min_value)
|
|
|
|
def test_nanmax(self):
|
|
max_value = max(self.A)
|
|
for A in self.integer_arrays():
|
|
assert_equal(nanmax(A), max_value)
|
|
|
|
def test_nanargmin(self):
|
|
min_arg = argmin(self.A)
|
|
for A in self.integer_arrays():
|
|
assert_equal(nanargmin(A), min_arg)
|
|
|
|
def test_nanargmax(self):
|
|
max_arg = argmax(self.A)
|
|
for A in self.integer_arrays():
|
|
assert_equal(nanargmax(A), max_arg)
|
|
|
|
|
|
class TestCorrCoef(TestCase):
|
|
A = array([[ 0.15391142, 0.18045767, 0.14197213],
|
|
[ 0.70461506, 0.96474128, 0.27906989],
|
|
[ 0.9297531 , 0.32296769, 0.19267156]])
|
|
B = array([[ 0.10377691, 0.5417086 , 0.49807457],
|
|
[ 0.82872117, 0.77801674, 0.39226705],
|
|
[ 0.9314666 , 0.66800209, 0.03538394]])
|
|
res1 = array([[ 1. , 0.9379533 , -0.04931983],
|
|
[ 0.9379533 , 1. , 0.30007991],
|
|
[-0.04931983, 0.30007991, 1. ]])
|
|
res2 = array([[ 1. , 0.9379533 , -0.04931983,
|
|
0.30151751, 0.66318558, 0.51532523],
|
|
[ 0.9379533 , 1. , 0.30007991,
|
|
- 0.04781421, 0.88157256, 0.78052386],
|
|
[-0.04931983, 0.30007991, 1. ,
|
|
- 0.96717111, 0.71483595, 0.83053601],
|
|
[ 0.30151751, -0.04781421, -0.96717111,
|
|
1. , -0.51366032, -0.66173113],
|
|
[ 0.66318558, 0.88157256, 0.71483595,
|
|
- 0.51366032, 1. , 0.98317823],
|
|
[ 0.51532523, 0.78052386, 0.83053601,
|
|
- 0.66173113, 0.98317823, 1. ]])
|
|
|
|
def test_simple(self):
|
|
assert_almost_equal(corrcoef(self.A), self.res1)
|
|
assert_almost_equal(corrcoef(self.A, self.B), self.res2)
|
|
|
|
def test_ddof(self):
|
|
assert_almost_equal(corrcoef(self.A, ddof=-1), self.res1)
|
|
assert_almost_equal(corrcoef(self.A, self.B, ddof=-1), self.res2)
|
|
|
|
def test_empty(self):
|
|
assert_equal(corrcoef(np.array([])).size, 0)
|
|
assert_equal(corrcoef(np.array([]).reshape(0, 2)).shape, (0, 2))
|
|
|
|
|
|
class TestCov(TestCase):
|
|
def test_basic(self):
|
|
x = np.array([[0, 2], [1, 1], [2, 0]]).T
|
|
assert_allclose(np.cov(x), np.array([[ 1.,-1.], [-1.,1.]]))
|
|
|
|
def test_empty(self):
|
|
assert_equal(cov(np.array([])).size, 0)
|
|
assert_equal(cov(np.array([]).reshape(0, 2)).shape, (0, 2))
|
|
|
|
|
|
class Test_i0(TestCase):
|
|
def test_simple(self):
|
|
assert_almost_equal(i0(0.5), array(1.0634833707413234))
|
|
A = array([ 0.49842636, 0.6969809 , 0.22011976, 0.0155549])
|
|
assert_almost_equal(i0(A),
|
|
array([ 1.06307822, 1.12518299, 1.01214991, 1.00006049]))
|
|
B = array([[ 0.827002 , 0.99959078],
|
|
[ 0.89694769, 0.39298162],
|
|
[ 0.37954418, 0.05206293],
|
|
[ 0.36465447, 0.72446427],
|
|
[ 0.48164949, 0.50324519]])
|
|
assert_almost_equal(i0(B),
|
|
array([[ 1.17843223, 1.26583466],
|
|
[ 1.21147086, 1.0389829 ],
|
|
[ 1.03633899, 1.00067775],
|
|
[ 1.03352052, 1.13557954],
|
|
[ 1.0588429 , 1.06432317]]))
|
|
|
|
|
|
class TestKaiser(TestCase):
|
|
def test_simple(self):
|
|
assert_almost_equal(kaiser(0, 1.0), array([]))
|
|
assert_(isfinite(kaiser(1, 1.0)))
|
|
assert_almost_equal(kaiser(2, 1.0), array([ 0.78984831, 0.78984831]))
|
|
assert_almost_equal(kaiser(5, 1.0),
|
|
array([ 0.78984831, 0.94503323, 1. ,
|
|
0.94503323, 0.78984831]))
|
|
assert_almost_equal(kaiser(5, 1.56789),
|
|
array([ 0.58285404, 0.88409679, 1. ,
|
|
0.88409679, 0.58285404]))
|
|
|
|
def test_int_beta(self):
|
|
kaiser(3, 4)
|
|
|
|
|
|
class TestMsort(TestCase):
|
|
def test_simple(self):
|
|
A = array([[ 0.44567325, 0.79115165, 0.5490053 ],
|
|
[ 0.36844147, 0.37325583, 0.96098397],
|
|
[ 0.64864341, 0.52929049, 0.39172155]])
|
|
assert_almost_equal(msort(A),
|
|
array([[ 0.36844147, 0.37325583, 0.39172155],
|
|
[ 0.44567325, 0.52929049, 0.5490053 ],
|
|
[ 0.64864341, 0.79115165, 0.96098397]]))
|
|
|
|
|
|
class TestMeshgrid(TestCase):
|
|
def test_simple(self):
|
|
[X, Y] = meshgrid([1, 2, 3], [4, 5, 6, 7])
|
|
assert_(all(X == array([[1, 2, 3],
|
|
[1, 2, 3],
|
|
[1, 2, 3],
|
|
[1, 2, 3]])))
|
|
assert_(all(Y == array([[4, 4, 4],
|
|
[5, 5, 5],
|
|
[6, 6, 6],
|
|
[7, 7, 7]])))
|
|
|
|
|
|
class TestPiecewise(TestCase):
|
|
def test_simple(self):
|
|
# Condition is single bool list
|
|
x = piecewise([0, 0], [True, False], [1])
|
|
assert_array_equal(x, [1, 0])
|
|
|
|
# List of conditions: single bool list
|
|
x = piecewise([0, 0], [[True, False]], [1])
|
|
assert_array_equal(x, [1, 0])
|
|
|
|
# Conditions is single bool array
|
|
x = piecewise([0, 0], array([True, False]), [1])
|
|
assert_array_equal(x, [1, 0])
|
|
|
|
# Condition is single int array
|
|
x = piecewise([0, 0], array([1, 0]), [1])
|
|
assert_array_equal(x, [1, 0])
|
|
|
|
# List of conditions: int array
|
|
x = piecewise([0, 0], [array([1, 0])], [1])
|
|
assert_array_equal(x, [1, 0])
|
|
|
|
|
|
x = piecewise([0, 0], [[False, True]], [lambda x:-1])
|
|
assert_array_equal(x, [0, -1])
|
|
|
|
x = piecewise([1, 2], [[True, False], [False, True]], [3, 4])
|
|
assert_array_equal(x, [3, 4])
|
|
|
|
def test_default(self):
|
|
# No value specified for x[1], should be 0
|
|
x = piecewise([1, 2], [True, False], [2])
|
|
assert_array_equal(x, [2, 0])
|
|
|
|
# Should set x[1] to 3
|
|
x = piecewise([1, 2], [True, False], [2, 3])
|
|
assert_array_equal(x, [2, 3])
|
|
|
|
def test_0d(self):
|
|
x = array(3)
|
|
y = piecewise(x, x > 3, [4, 0])
|
|
assert_(y.ndim == 0)
|
|
assert_(y == 0)
|
|
|
|
|
|
class TestBincount(TestCase):
|
|
def test_simple(self):
|
|
y = np.bincount(np.arange(4))
|
|
assert_array_equal(y, np.ones(4))
|
|
|
|
def test_simple2(self):
|
|
y = np.bincount(np.array([1, 5, 2, 4, 1]))
|
|
assert_array_equal(y, np.array([0, 2, 1, 0, 1, 1]))
|
|
|
|
def test_simple_weight(self):
|
|
x = np.arange(4)
|
|
w = np.array([0.2, 0.3, 0.5, 0.1])
|
|
y = np.bincount(x, w)
|
|
assert_array_equal(y, w)
|
|
|
|
def test_simple_weight2(self):
|
|
x = np.array([1, 2, 4, 5, 2])
|
|
w = np.array([0.2, 0.3, 0.5, 0.1, 0.2])
|
|
y = np.bincount(x, w)
|
|
assert_array_equal(y, np.array([0, 0.2, 0.5, 0, 0.5, 0.1]))
|
|
|
|
def test_with_minlength(self):
|
|
x = np.array([0, 1, 0, 1, 1])
|
|
y = np.bincount(x, minlength=3)
|
|
assert_array_equal(y, np.array([2, 3, 0]))
|
|
|
|
def test_with_minlength_smaller_than_maxvalue(self):
|
|
x = np.array([0, 1, 1, 2, 2, 3, 3])
|
|
y = np.bincount(x, minlength=2)
|
|
assert_array_equal(y, np.array([1, 2, 2, 2]))
|
|
|
|
def test_with_minlength_and_weights(self):
|
|
x = np.array([1, 2, 4, 5, 2])
|
|
w = np.array([0.2, 0.3, 0.5, 0.1, 0.2])
|
|
y = np.bincount(x, w, 8)
|
|
assert_array_equal(y, np.array([0, 0.2, 0.5, 0, 0.5, 0.1, 0, 0]))
|
|
|
|
def test_empty(self):
|
|
x = np.array([], dtype=int)
|
|
y = np.bincount(x)
|
|
assert_array_equal(x,y)
|
|
|
|
def test_empty_with_minlength(self):
|
|
x = np.array([], dtype=int)
|
|
y = np.bincount(x, minlength=5)
|
|
assert_array_equal(y, np.zeros(5, dtype=int))
|
|
|
|
|
|
class TestInterp(TestCase):
|
|
def test_exceptions(self):
|
|
assert_raises(ValueError, interp, 0, [], [])
|
|
assert_raises(ValueError, interp, 0, [0], [1, 2])
|
|
|
|
def test_basic(self):
|
|
x = np.linspace(0, 1, 5)
|
|
y = np.linspace(0, 1, 5)
|
|
x0 = np.linspace(0, 1, 50)
|
|
assert_almost_equal(np.interp(x0, x, y), x0)
|
|
|
|
def test_right_left_behavior(self):
|
|
assert_equal(interp([-1, 0, 1], [0], [1]), [1,1,1])
|
|
assert_equal(interp([-1, 0, 1], [0], [1], left=0), [0,1,1])
|
|
assert_equal(interp([-1, 0, 1], [0], [1], right=0), [1,1,0])
|
|
assert_equal(interp([-1, 0, 1], [0], [1], left=0, right=0), [0,1,0])
|
|
|
|
def test_scalar_interpolation_point(self):
|
|
x = np.linspace(0, 1, 5)
|
|
y = np.linspace(0, 1, 5)
|
|
x0 = 0
|
|
assert_almost_equal(np.interp(x0, x, y), x0)
|
|
x0 = .3
|
|
assert_almost_equal(np.interp(x0, x, y), x0)
|
|
x0 = np.float32(.3)
|
|
assert_almost_equal(np.interp(x0, x, y), x0)
|
|
x0 = np.float64(.3)
|
|
assert_almost_equal(np.interp(x0, x, y), x0)
|
|
|
|
def test_zero_dimensional_interpolation_point(self):
|
|
x = np.linspace(0, 1, 5)
|
|
y = np.linspace(0, 1, 5)
|
|
x0 = np.array(.3)
|
|
assert_almost_equal(np.interp(x0, x, y), x0)
|
|
x0 = np.array(.3, dtype=object)
|
|
assert_almost_equal(np.interp(x0, x, y), .3)
|
|
|
|
|
|
def compare_results(res, desired):
|
|
for i in range(len(desired)):
|
|
assert_array_equal(res[i], desired[i])
|
|
|
|
|
|
def test_percentile_list():
|
|
assert_equal(np.percentile([1, 2, 3], 0), 1)
|
|
|
|
def test_percentile_out():
|
|
x = np.array([1, 2, 3])
|
|
y = np.zeros((3,))
|
|
p = (1, 2, 3)
|
|
np.percentile(x, p, out=y)
|
|
assert_equal(y, np.percentile(x, p))
|
|
|
|
x = np.array([[1, 2, 3],
|
|
[4, 5, 6]])
|
|
|
|
y = np.zeros((3, 3))
|
|
np.percentile(x, p, axis=0, out=y)
|
|
assert_equal(y, np.percentile(x, p, axis=0))
|
|
|
|
y = np.zeros((3, 2))
|
|
np.percentile(x, p, axis=1, out=y)
|
|
assert_equal(y, np.percentile(x, p, axis=1))
|
|
|
|
|
|
def test_median():
|
|
a0 = np.array(1)
|
|
a1 = np.arange(2)
|
|
a2 = np.arange(6).reshape(2, 3)
|
|
assert_allclose(np.median(a0), 1)
|
|
assert_allclose(np.median(a1), 0.5)
|
|
assert_allclose(np.median(a2), 2.5)
|
|
assert_allclose(np.median(a2, axis=0), [1.5, 2.5, 3.5])
|
|
assert_allclose(np.median(a2, axis=1), [1, 4])
|
|
|
|
|
|
if __name__ == "__main__":
|
|
run_module_suite()
|