CatOrNot/venv/lib/python3.7/site-packages/numpy/ma/tests/test_deprecations.py

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2020-01-06 16:11:15 +01:00
"""Test deprecation and future warnings.
"""
from __future__ import division, absolute_import, print_function
import numpy as np
from numpy.testing import assert_warns
from numpy.ma.testutils import assert_equal
from numpy.ma.core import MaskedArrayFutureWarning
class TestArgsort(object):
""" gh-8701 """
def _test_base(self, argsort, cls):
arr_0d = np.array(1).view(cls)
argsort(arr_0d)
arr_1d = np.array([1, 2, 3]).view(cls)
argsort(arr_1d)
# argsort has a bad default for >1d arrays
arr_2d = np.array([[1, 2], [3, 4]]).view(cls)
result = assert_warns(
np.ma.core.MaskedArrayFutureWarning, argsort, arr_2d)
assert_equal(result, argsort(arr_2d, axis=None))
# should be no warnings for explicitly specifying it
argsort(arr_2d, axis=None)
argsort(arr_2d, axis=-1)
def test_function_ndarray(self):
return self._test_base(np.ma.argsort, np.ndarray)
def test_function_maskedarray(self):
return self._test_base(np.ma.argsort, np.ma.MaskedArray)
def test_method(self):
return self._test_base(np.ma.MaskedArray.argsort, np.ma.MaskedArray)
class TestMinimumMaximum(object):
def test_minimum(self):
assert_warns(DeprecationWarning, np.ma.minimum, np.ma.array([1, 2]))
def test_maximum(self):
assert_warns(DeprecationWarning, np.ma.maximum, np.ma.array([1, 2]))
def test_axis_default(self):
# NumPy 1.13, 2017-05-06
data1d = np.ma.arange(6)
data2d = data1d.reshape(2, 3)
ma_min = np.ma.minimum.reduce
ma_max = np.ma.maximum.reduce
# check that the default axis is still None, but warns on 2d arrays
result = assert_warns(MaskedArrayFutureWarning, ma_max, data2d)
assert_equal(result, ma_max(data2d, axis=None))
result = assert_warns(MaskedArrayFutureWarning, ma_min, data2d)
assert_equal(result, ma_min(data2d, axis=None))
# no warnings on 1d, as both new and old defaults are equivalent
result = ma_min(data1d)
assert_equal(result, ma_min(data1d, axis=None))
assert_equal(result, ma_min(data1d, axis=0))
result = ma_max(data1d)
assert_equal(result, ma_max(data1d, axis=None))
assert_equal(result, ma_max(data1d, axis=0))