Reflect dimension in approx repr for numpy arrays.
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@ -82,14 +82,18 @@ class ApproxNumpy(ApproxBase):
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"""
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def __repr__(self):
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# It might be nice to rewrite this function to account for the
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# shape of the array...
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import numpy as np
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list_scalars = []
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for x in np.ndindex(self.expected.shape):
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list_scalars.append(self._approx_scalar(np.asscalar(self.expected[x])))
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def recursive_map(f, x):
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if isinstance(x, list):
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return list(recursive_map(f, xi) for xi in x)
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else:
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return f(x)
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list_scalars = recursive_map(
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self._approx_scalar,
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self.expected.tolist())
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return "approx({!r})".format(list_scalars)
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if sys.version_info[0] == 2:
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@ -59,17 +59,19 @@ class TestApprox(object):
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),
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)
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def test_repr_0d_array(self, plus_minus):
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def test_repr_nd_array(self, plus_minus):
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# Make sure that arrays of all different dimensions are repr'd
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# correctly.
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np = pytest.importorskip("numpy")
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np_array = np.array(5.)
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assert approx(np_array) == 5.0
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string_expected = "approx([5.0 {} 5.0e-06])".format(plus_minus)
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assert repr(approx(np_array)) == string_expected
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np_array = np.array([5.])
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assert approx(np_array) == 5.0
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assert repr(approx(np_array)) == string_expected
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examples = [
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(np.array(5.), 'approx(5.0 {pm} 5.0e-06)'),
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(np.array([5.]), 'approx([5.0 {pm} 5.0e-06])'),
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(np.array([[5.]]), 'approx([[5.0 {pm} 5.0e-06]])'),
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(np.array([[5., 6.]]), 'approx([[5.0 {pm} 5.0e-06, 6.0 {pm} 6.0e-06]])'),
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(np.array([[5.], [6.]]), 'approx([[5.0 {pm} 5.0e-06], [6.0 {pm} 6.0e-06]])'),
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]
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for np_array, repr_string in examples:
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assert repr(approx(np_array)) == repr_string.format(pm=plus_minus)
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def test_operator_overloading(self):
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assert 1 == approx(1, rel=1e-6, abs=1e-12)
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