584 lines
20 KiB
Python
584 lines
20 KiB
Python
import inspect
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import sys
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import os
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import tempfile
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from io import StringIO
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from unittest import mock
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import numpy as np
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from numpy.testing import (
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assert_, assert_equal, assert_raises, assert_raises_regex)
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from numpy.core.overrides import (
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_get_implementing_args, array_function_dispatch,
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verify_matching_signatures, ARRAY_FUNCTION_ENABLED)
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from numpy.compat import pickle
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import pytest
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requires_array_function = pytest.mark.skipif(
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not ARRAY_FUNCTION_ENABLED,
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reason="__array_function__ dispatch not enabled.")
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def _return_not_implemented(self, *args, **kwargs):
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return NotImplemented
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# need to define this at the top level to test pickling
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@array_function_dispatch(lambda array: (array,))
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def dispatched_one_arg(array):
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"""Docstring."""
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return 'original'
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@array_function_dispatch(lambda array1, array2: (array1, array2))
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def dispatched_two_arg(array1, array2):
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"""Docstring."""
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return 'original'
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class TestGetImplementingArgs:
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def test_ndarray(self):
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array = np.array(1)
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args = _get_implementing_args([array])
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assert_equal(list(args), [array])
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args = _get_implementing_args([array, array])
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assert_equal(list(args), [array])
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args = _get_implementing_args([array, 1])
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assert_equal(list(args), [array])
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args = _get_implementing_args([1, array])
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assert_equal(list(args), [array])
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def test_ndarray_subclasses(self):
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class OverrideSub(np.ndarray):
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__array_function__ = _return_not_implemented
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class NoOverrideSub(np.ndarray):
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pass
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array = np.array(1).view(np.ndarray)
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override_sub = np.array(1).view(OverrideSub)
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no_override_sub = np.array(1).view(NoOverrideSub)
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args = _get_implementing_args([array, override_sub])
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assert_equal(list(args), [override_sub, array])
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args = _get_implementing_args([array, no_override_sub])
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assert_equal(list(args), [no_override_sub, array])
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args = _get_implementing_args(
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[override_sub, no_override_sub])
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assert_equal(list(args), [override_sub, no_override_sub])
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def test_ndarray_and_duck_array(self):
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class Other:
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__array_function__ = _return_not_implemented
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array = np.array(1)
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other = Other()
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args = _get_implementing_args([other, array])
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assert_equal(list(args), [other, array])
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args = _get_implementing_args([array, other])
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assert_equal(list(args), [array, other])
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def test_ndarray_subclass_and_duck_array(self):
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class OverrideSub(np.ndarray):
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__array_function__ = _return_not_implemented
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class Other:
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__array_function__ = _return_not_implemented
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array = np.array(1)
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subarray = np.array(1).view(OverrideSub)
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other = Other()
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assert_equal(_get_implementing_args([array, subarray, other]),
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[subarray, array, other])
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assert_equal(_get_implementing_args([array, other, subarray]),
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[subarray, array, other])
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def test_many_duck_arrays(self):
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class A:
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__array_function__ = _return_not_implemented
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class B(A):
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__array_function__ = _return_not_implemented
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class C(A):
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__array_function__ = _return_not_implemented
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class D:
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__array_function__ = _return_not_implemented
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a = A()
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b = B()
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c = C()
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d = D()
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assert_equal(_get_implementing_args([1]), [])
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assert_equal(_get_implementing_args([a]), [a])
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assert_equal(_get_implementing_args([a, 1]), [a])
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assert_equal(_get_implementing_args([a, a, a]), [a])
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assert_equal(_get_implementing_args([a, d, a]), [a, d])
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assert_equal(_get_implementing_args([a, b]), [b, a])
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assert_equal(_get_implementing_args([b, a]), [b, a])
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assert_equal(_get_implementing_args([a, b, c]), [b, c, a])
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assert_equal(_get_implementing_args([a, c, b]), [c, b, a])
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def test_too_many_duck_arrays(self):
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namespace = dict(__array_function__=_return_not_implemented)
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types = [type('A' + str(i), (object,), namespace) for i in range(33)]
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relevant_args = [t() for t in types]
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actual = _get_implementing_args(relevant_args[:32])
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assert_equal(actual, relevant_args[:32])
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with assert_raises_regex(TypeError, 'distinct argument types'):
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_get_implementing_args(relevant_args)
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class TestNDArrayArrayFunction:
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@requires_array_function
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def test_method(self):
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class Other:
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__array_function__ = _return_not_implemented
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class NoOverrideSub(np.ndarray):
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pass
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class OverrideSub(np.ndarray):
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__array_function__ = _return_not_implemented
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array = np.array([1])
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other = Other()
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no_override_sub = array.view(NoOverrideSub)
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override_sub = array.view(OverrideSub)
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result = array.__array_function__(func=dispatched_two_arg,
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types=(np.ndarray,),
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args=(array, 1.), kwargs={})
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assert_equal(result, 'original')
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result = array.__array_function__(func=dispatched_two_arg,
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types=(np.ndarray, Other),
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args=(array, other), kwargs={})
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assert_(result is NotImplemented)
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result = array.__array_function__(func=dispatched_two_arg,
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types=(np.ndarray, NoOverrideSub),
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args=(array, no_override_sub),
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kwargs={})
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assert_equal(result, 'original')
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result = array.__array_function__(func=dispatched_two_arg,
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types=(np.ndarray, OverrideSub),
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args=(array, override_sub),
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kwargs={})
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assert_equal(result, 'original')
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with assert_raises_regex(TypeError, 'no implementation found'):
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np.concatenate((array, other))
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expected = np.concatenate((array, array))
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result = np.concatenate((array, no_override_sub))
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assert_equal(result, expected.view(NoOverrideSub))
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result = np.concatenate((array, override_sub))
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assert_equal(result, expected.view(OverrideSub))
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def test_no_wrapper(self):
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# This shouldn't happen unless a user intentionally calls
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# __array_function__ with invalid arguments, but check that we raise
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# an appropriate error all the same.
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array = np.array(1)
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func = lambda x: x
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with assert_raises_regex(AttributeError, '_implementation'):
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array.__array_function__(func=func, types=(np.ndarray,),
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args=(array,), kwargs={})
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@requires_array_function
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class TestArrayFunctionDispatch:
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def test_pickle(self):
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for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
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roundtripped = pickle.loads(
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pickle.dumps(dispatched_one_arg, protocol=proto))
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assert_(roundtripped is dispatched_one_arg)
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def test_name_and_docstring(self):
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assert_equal(dispatched_one_arg.__name__, 'dispatched_one_arg')
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if sys.flags.optimize < 2:
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assert_equal(dispatched_one_arg.__doc__, 'Docstring.')
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def test_interface(self):
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class MyArray:
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def __array_function__(self, func, types, args, kwargs):
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return (self, func, types, args, kwargs)
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original = MyArray()
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(obj, func, types, args, kwargs) = dispatched_one_arg(original)
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assert_(obj is original)
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assert_(func is dispatched_one_arg)
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assert_equal(set(types), {MyArray})
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# assert_equal uses the overloaded np.iscomplexobj() internally
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assert_(args == (original,))
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assert_equal(kwargs, {})
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def test_not_implemented(self):
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class MyArray:
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def __array_function__(self, func, types, args, kwargs):
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return NotImplemented
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array = MyArray()
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with assert_raises_regex(TypeError, 'no implementation found'):
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dispatched_one_arg(array)
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@requires_array_function
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class TestVerifyMatchingSignatures:
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def test_verify_matching_signatures(self):
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verify_matching_signatures(lambda x: 0, lambda x: 0)
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verify_matching_signatures(lambda x=None: 0, lambda x=None: 0)
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verify_matching_signatures(lambda x=1: 0, lambda x=None: 0)
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with assert_raises(RuntimeError):
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verify_matching_signatures(lambda a: 0, lambda b: 0)
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with assert_raises(RuntimeError):
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verify_matching_signatures(lambda x: 0, lambda x=None: 0)
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with assert_raises(RuntimeError):
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verify_matching_signatures(lambda x=None: 0, lambda y=None: 0)
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with assert_raises(RuntimeError):
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verify_matching_signatures(lambda x=1: 0, lambda y=1: 0)
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def test_array_function_dispatch(self):
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with assert_raises(RuntimeError):
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@array_function_dispatch(lambda x: (x,))
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def f(y):
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pass
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# should not raise
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@array_function_dispatch(lambda x: (x,), verify=False)
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def f(y):
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pass
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def _new_duck_type_and_implements():
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"""Create a duck array type and implements functions."""
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HANDLED_FUNCTIONS = {}
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class MyArray:
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def __array_function__(self, func, types, args, kwargs):
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if func not in HANDLED_FUNCTIONS:
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return NotImplemented
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if not all(issubclass(t, MyArray) for t in types):
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return NotImplemented
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return HANDLED_FUNCTIONS[func](*args, **kwargs)
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def implements(numpy_function):
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"""Register an __array_function__ implementations."""
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def decorator(func):
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HANDLED_FUNCTIONS[numpy_function] = func
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return func
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return decorator
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return (MyArray, implements)
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@requires_array_function
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class TestArrayFunctionImplementation:
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def test_one_arg(self):
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MyArray, implements = _new_duck_type_and_implements()
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@implements(dispatched_one_arg)
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def _(array):
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return 'myarray'
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assert_equal(dispatched_one_arg(1), 'original')
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assert_equal(dispatched_one_arg(MyArray()), 'myarray')
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def test_optional_args(self):
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MyArray, implements = _new_duck_type_and_implements()
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@array_function_dispatch(lambda array, option=None: (array,))
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def func_with_option(array, option='default'):
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return option
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@implements(func_with_option)
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def my_array_func_with_option(array, new_option='myarray'):
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return new_option
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# we don't need to implement every option on __array_function__
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# implementations
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assert_equal(func_with_option(1), 'default')
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assert_equal(func_with_option(1, option='extra'), 'extra')
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assert_equal(func_with_option(MyArray()), 'myarray')
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with assert_raises(TypeError):
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func_with_option(MyArray(), option='extra')
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# but new options on implementations can't be used
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result = my_array_func_with_option(MyArray(), new_option='yes')
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assert_equal(result, 'yes')
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with assert_raises(TypeError):
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func_with_option(MyArray(), new_option='no')
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def test_not_implemented(self):
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MyArray, implements = _new_duck_type_and_implements()
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@array_function_dispatch(lambda array: (array,), module='my')
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def func(array):
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return array
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array = np.array(1)
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assert_(func(array) is array)
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assert_equal(func.__module__, 'my')
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with assert_raises_regex(
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TypeError, "no implementation found for 'my.func'"):
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func(MyArray())
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class TestNDArrayMethods:
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def test_repr(self):
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# gh-12162: should still be defined even if __array_function__ doesn't
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# implement np.array_repr()
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class MyArray(np.ndarray):
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def __array_function__(*args, **kwargs):
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return NotImplemented
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array = np.array(1).view(MyArray)
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assert_equal(repr(array), 'MyArray(1)')
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assert_equal(str(array), '1')
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class TestNumPyFunctions:
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def test_set_module(self):
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assert_equal(np.sum.__module__, 'numpy')
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assert_equal(np.char.equal.__module__, 'numpy.char')
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assert_equal(np.fft.fft.__module__, 'numpy.fft')
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assert_equal(np.linalg.solve.__module__, 'numpy.linalg')
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def test_inspect_sum(self):
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signature = inspect.signature(np.sum)
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assert_('axis' in signature.parameters)
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@requires_array_function
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def test_override_sum(self):
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MyArray, implements = _new_duck_type_and_implements()
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@implements(np.sum)
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def _(array):
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return 'yes'
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assert_equal(np.sum(MyArray()), 'yes')
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@requires_array_function
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def test_sum_on_mock_array(self):
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# We need a proxy for mocks because __array_function__ is only looked
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# up in the class dict
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class ArrayProxy:
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def __init__(self, value):
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self.value = value
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def __array_function__(self, *args, **kwargs):
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return self.value.__array_function__(*args, **kwargs)
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def __array__(self, *args, **kwargs):
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return self.value.__array__(*args, **kwargs)
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proxy = ArrayProxy(mock.Mock(spec=ArrayProxy))
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proxy.value.__array_function__.return_value = 1
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result = np.sum(proxy)
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assert_equal(result, 1)
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proxy.value.__array_function__.assert_called_once_with(
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np.sum, (ArrayProxy,), (proxy,), {})
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proxy.value.__array__.assert_not_called()
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@requires_array_function
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def test_sum_forwarding_implementation(self):
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class MyArray(np.ndarray):
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def sum(self, axis, out):
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return 'summed'
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def __array_function__(self, func, types, args, kwargs):
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return super().__array_function__(func, types, args, kwargs)
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# note: the internal implementation of np.sum() calls the .sum() method
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array = np.array(1).view(MyArray)
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assert_equal(np.sum(array), 'summed')
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class TestArrayLike:
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def setup(self):
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class MyArray():
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def __init__(self, function=None):
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self.function = function
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def __array_function__(self, func, types, args, kwargs):
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try:
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my_func = getattr(self, func.__name__)
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except AttributeError:
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return NotImplemented
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return my_func(*args, **kwargs)
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self.MyArray = MyArray
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class MyNoArrayFunctionArray():
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def __init__(self, function=None):
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self.function = function
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self.MyNoArrayFunctionArray = MyNoArrayFunctionArray
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def add_method(self, name, arr_class, enable_value_error=False):
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def _definition(*args, **kwargs):
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# Check that `like=` isn't propagated downstream
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assert 'like' not in kwargs
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if enable_value_error and 'value_error' in kwargs:
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raise ValueError
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return arr_class(getattr(arr_class, name))
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setattr(arr_class, name, _definition)
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def func_args(*args, **kwargs):
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return args, kwargs
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@requires_array_function
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def test_array_like_not_implemented(self):
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self.add_method('array', self.MyArray)
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ref = self.MyArray.array()
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with assert_raises_regex(TypeError, 'no implementation found'):
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array_like = np.asarray(1, like=ref)
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_array_tests = [
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('array', *func_args((1,))),
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('asarray', *func_args((1,))),
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('asanyarray', *func_args((1,))),
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('ascontiguousarray', *func_args((2, 3))),
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('asfortranarray', *func_args((2, 3))),
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('require', *func_args((np.arange(6).reshape(2, 3),),
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requirements=['A', 'F'])),
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('empty', *func_args((1,))),
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('full', *func_args((1,), 2)),
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('ones', *func_args((1,))),
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('zeros', *func_args((1,))),
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('arange', *func_args(3)),
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('frombuffer', *func_args(b'\x00' * 8, dtype=int)),
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('fromiter', *func_args(range(3), dtype=int)),
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('fromstring', *func_args('1,2', dtype=int, sep=',')),
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('loadtxt', *func_args(lambda: StringIO('0 1\n2 3'))),
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('genfromtxt', *func_args(lambda: StringIO(u'1,2.1'),
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dtype=[('int', 'i8'), ('float', 'f8')],
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delimiter=',')),
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]
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@pytest.mark.parametrize('function, args, kwargs', _array_tests)
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@pytest.mark.parametrize('numpy_ref', [True, False])
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@requires_array_function
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def test_array_like(self, function, args, kwargs, numpy_ref):
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self.add_method('array', self.MyArray)
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self.add_method(function, self.MyArray)
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np_func = getattr(np, function)
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my_func = getattr(self.MyArray, function)
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if numpy_ref is True:
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ref = np.array(1)
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else:
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ref = self.MyArray.array()
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like_args = tuple(a() if callable(a) else a for a in args)
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|
array_like = np_func(*like_args, **kwargs, like=ref)
|
|
|
|
if numpy_ref is True:
|
|
assert type(array_like) is np.ndarray
|
|
|
|
np_args = tuple(a() if callable(a) else a for a in args)
|
|
np_arr = np_func(*np_args, **kwargs)
|
|
|
|
# Special-case np.empty to ensure values match
|
|
if function == "empty":
|
|
np_arr.fill(1)
|
|
array_like.fill(1)
|
|
|
|
assert_equal(array_like, np_arr)
|
|
else:
|
|
assert type(array_like) is self.MyArray
|
|
assert array_like.function is my_func
|
|
|
|
@pytest.mark.parametrize('function, args, kwargs', _array_tests)
|
|
@pytest.mark.parametrize('ref', [1, [1], "MyNoArrayFunctionArray"])
|
|
@requires_array_function
|
|
def test_no_array_function_like(self, function, args, kwargs, ref):
|
|
self.add_method('array', self.MyNoArrayFunctionArray)
|
|
self.add_method(function, self.MyNoArrayFunctionArray)
|
|
np_func = getattr(np, function)
|
|
|
|
# Instantiate ref if it's the MyNoArrayFunctionArray class
|
|
if ref == "MyNoArrayFunctionArray":
|
|
ref = self.MyNoArrayFunctionArray.array()
|
|
|
|
like_args = tuple(a() if callable(a) else a for a in args)
|
|
|
|
with assert_raises_regex(TypeError,
|
|
'The `like` argument must be an array-like that implements'):
|
|
np_func(*like_args, **kwargs, like=ref)
|
|
|
|
@pytest.mark.parametrize('numpy_ref', [True, False])
|
|
def test_array_like_fromfile(self, numpy_ref):
|
|
self.add_method('array', self.MyArray)
|
|
self.add_method("fromfile", self.MyArray)
|
|
|
|
if numpy_ref is True:
|
|
ref = np.array(1)
|
|
else:
|
|
ref = self.MyArray.array()
|
|
|
|
data = np.random.random(5)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
fname = os.path.join(tmpdir, "testfile")
|
|
data.tofile(fname)
|
|
|
|
array_like = np.fromfile(fname, like=ref)
|
|
if numpy_ref is True:
|
|
assert type(array_like) is np.ndarray
|
|
np_res = np.fromfile(fname, like=ref)
|
|
assert_equal(np_res, data)
|
|
assert_equal(array_like, np_res)
|
|
else:
|
|
assert type(array_like) is self.MyArray
|
|
assert array_like.function is self.MyArray.fromfile
|
|
|
|
@requires_array_function
|
|
def test_exception_handling(self):
|
|
self.add_method('array', self.MyArray, enable_value_error=True)
|
|
|
|
ref = self.MyArray.array()
|
|
|
|
with assert_raises(ValueError):
|
|
np.array(1, value_error=True, like=ref)
|