1257 lines
42 KiB
Python
1257 lines
42 KiB
Python
from functools import partial
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from itertools import permutations
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import numpy as np
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import unittest
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from numba.core.compiler import compile_isolated, Flags
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from numba import jit, njit, from_dtype, typeof
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from numba.np.numpy_support import numpy_version
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from numba.core.errors import TypingError
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from numba.core import types, errors
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from numba.tests.support import (TestCase, MemoryLeakMixin, CompilationCache,
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tag)
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enable_pyobj_flags = Flags()
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enable_pyobj_flags.enable_pyobject = True
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no_pyobj_flags = Flags()
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no_pyobj_flags.nrt = True
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def from_generic(pyfuncs_to_use):
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"""Decorator for generic check functions.
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Iterates over 'pyfuncs_to_use', calling 'func' with the iterated
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item as first argument. Example:
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@from_generic(numpy_array_reshape, array_reshape)
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def check_only_shape(pyfunc, arr, shape, expected_shape):
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# Only check Numba result to avoid Numpy bugs
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self.memory_leak_setup()
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got = generic_run(pyfunc, arr, shape)
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self.assertEqual(got.shape, expected_shape)
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self.assertEqual(got.size, arr.size)
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del got
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self.memory_leak_teardown()
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"""
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def decorator(func):
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def result(*args, **kwargs):
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return [func(pyfunc, *args, **kwargs) for pyfunc in pyfuncs_to_use]
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return result
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return decorator
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def array_reshape(arr, newshape):
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return arr.reshape(newshape)
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def numpy_array_reshape(arr, newshape):
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return np.reshape(arr, newshape)
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def numpy_broadcast_to(arr, shape):
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return np.broadcast_to(arr, shape)
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def numpy_broadcast_shapes(*args):
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return np.broadcast_shapes(*args)
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def numpy_broadcast_arrays(*args):
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return np.broadcast_arrays(*args)
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def numpy_broadcast_to_indexing(arr, shape, idx):
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return np.broadcast_to(arr, shape)[idx]
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def flatten_array(a):
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return a.flatten()
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def ravel_array(a):
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return a.ravel()
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def ravel_array_size(a):
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return a.ravel().size
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def numpy_ravel_array(a):
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return np.ravel(a)
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def transpose_array(a):
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return a.transpose()
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def numpy_transpose_array(a):
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return np.transpose(a)
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def numpy_transpose_array_axes_kwarg(arr, axes):
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return np.transpose(arr, axes=axes)
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def numpy_transpose_array_axes_kwarg_copy(arr, axes):
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return np.transpose(arr, axes=axes).copy()
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def array_transpose_axes(arr, axes):
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return arr.transpose(axes)
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def array_transpose_axes_copy(arr, axes):
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return arr.transpose(axes).copy()
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def transpose_issue_4708(m, n):
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r1 = np.reshape(np.arange(m * n * 3), (m, 3, n))
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r2 = np.reshape(np.arange(n * 3), (n, 3))
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r_dif = (r1 - r2.T).T
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r_dif = np.transpose(r_dif, (2, 0, 1))
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z = r_dif + 1
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return z
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def squeeze_array(a):
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return a.squeeze()
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def expand_dims(a, axis):
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return np.expand_dims(a, axis)
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def atleast_1d(*args):
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return np.atleast_1d(*args)
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def atleast_2d(*args):
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return np.atleast_2d(*args)
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def atleast_3d(*args):
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return np.atleast_3d(*args)
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def as_strided1(a):
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# as_strided() with implicit shape
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strides = (a.strides[0] // 2,) + a.strides[1:]
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return np.lib.stride_tricks.as_strided(a, strides=strides)
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def as_strided2(a):
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# Rolling window example as in https://github.com/numba/numba/issues/1884
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window = 3
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shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
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strides = a.strides + (a.strides[-1],)
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return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)
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def add_axis2(a):
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return a[np.newaxis, :]
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def bad_index(arr, arr2d):
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x = arr.x,
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y = arr.y
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# note that `x` is a tuple, which causes a new axis to be created.
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arr2d[x, y] = 1.0
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def bad_float_index(arr):
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# 2D index required for this function because 1D index
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# fails typing
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return arr[1, 2.0]
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def numpy_fill_diagonal(arr, val, wrap=False):
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return np.fill_diagonal(arr, val, wrap)
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def numpy_shape(arr):
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return np.shape(arr)
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def numpy_flatnonzero(a):
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return np.flatnonzero(a)
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def numpy_argwhere(a):
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return np.argwhere(a)
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class TestArrayManipulation(MemoryLeakMixin, TestCase):
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"""
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Check shape-changing operations on arrays.
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"""
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def setUp(self):
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super(TestArrayManipulation, self).setUp()
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self.ccache = CompilationCache()
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def test_array_reshape(self):
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pyfuncs_to_use = [array_reshape, numpy_array_reshape]
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def generic_run(pyfunc, arr, shape):
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cres = compile_isolated(pyfunc, (typeof(arr), typeof(shape)))
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return cres.entry_point(arr, shape)
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@from_generic(pyfuncs_to_use)
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def check(pyfunc, arr, shape):
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expected = pyfunc(arr, shape)
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self.memory_leak_setup()
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got = generic_run(pyfunc, arr, shape)
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self.assertPreciseEqual(got, expected)
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del got
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self.memory_leak_teardown()
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@from_generic(pyfuncs_to_use)
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def check_only_shape(pyfunc, arr, shape, expected_shape):
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# Only check Numba result to avoid Numpy bugs
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self.memory_leak_setup()
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got = generic_run(pyfunc, arr, shape)
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self.assertEqual(got.shape, expected_shape)
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self.assertEqual(got.size, arr.size)
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del got
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self.memory_leak_teardown()
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@from_generic(pyfuncs_to_use)
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def check_err_shape(pyfunc, arr, shape):
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with self.assertRaises(NotImplementedError) as raises:
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generic_run(pyfunc, arr, shape)
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self.assertEqual(str(raises.exception),
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"incompatible shape for array")
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@from_generic(pyfuncs_to_use)
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def check_err_size(pyfunc, arr, shape):
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with self.assertRaises(ValueError) as raises:
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generic_run(pyfunc, arr, shape)
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self.assertEqual(str(raises.exception),
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"total size of new array must be unchanged")
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@from_generic(pyfuncs_to_use)
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def check_err_multiple_negative(pyfunc, arr, shape):
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with self.assertRaises(ValueError) as raises:
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generic_run(pyfunc, arr, shape)
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self.assertEqual(str(raises.exception),
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"multiple negative shape values")
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# C-contiguous
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arr = np.arange(24)
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check(arr, (24,))
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check(arr, (4, 6))
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check(arr, (8, 3))
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check(arr, (8, 1, 3))
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check(arr, (1, 8, 1, 1, 3, 1))
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arr = np.arange(24).reshape((2, 3, 4))
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check(arr, (24,))
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check(arr, (4, 6))
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check(arr, (8, 3))
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check(arr, (8, 1, 3))
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check(arr, (1, 8, 1, 1, 3, 1))
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check_err_size(arr, ())
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check_err_size(arr, (25,))
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check_err_size(arr, (8, 4))
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arr = np.arange(24).reshape((1, 8, 1, 1, 3, 1))
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check(arr, (24,))
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check(arr, (4, 6))
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check(arr, (8, 3))
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check(arr, (8, 1, 3))
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# F-contiguous
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arr = np.arange(24).reshape((2, 3, 4)).T
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check(arr, (4, 3, 2))
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check(arr, (1, 4, 1, 3, 1, 2, 1))
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check_err_shape(arr, (2, 3, 4))
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check_err_shape(arr, (6, 4))
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check_err_shape(arr, (2, 12))
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# Test negative shape value
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arr = np.arange(25).reshape(5,5)
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check(arr, -1)
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check(arr, (-1,))
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check(arr, (-1, 5))
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check(arr, (5, -1, 5))
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check(arr, (5, 5, -1))
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check_err_size(arr, (-1, 4))
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check_err_multiple_negative(arr, (-1, -2, 5, 5))
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check_err_multiple_negative(arr, (5, 5, -1, -1))
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# 0-sized arrays
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def check_empty(arr):
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check(arr, 0)
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check(arr, (0,))
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check(arr, (1, 0, 2))
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check(arr, (0, 55, 1, 0, 2))
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# -1 is buggy in Numpy with 0-sized arrays
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check_only_shape(arr, -1, (0,))
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check_only_shape(arr, (-1,), (0,))
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check_only_shape(arr, (0, -1), (0, 0))
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check_only_shape(arr, (4, -1), (4, 0))
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check_only_shape(arr, (-1, 0, 4), (0, 0, 4))
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check_err_size(arr, ())
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check_err_size(arr, 1)
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check_err_size(arr, (1, 2))
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arr = np.array([])
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check_empty(arr)
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check_empty(arr.reshape((3, 2, 0)))
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# Exceptions leak references
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self.disable_leak_check()
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def test_array_transpose_axes(self):
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pyfuncs_to_use = [numpy_transpose_array_axes_kwarg,
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numpy_transpose_array_axes_kwarg_copy,
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array_transpose_axes,
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array_transpose_axes_copy]
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def run(pyfunc, arr, axes):
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cres = self.ccache.compile(pyfunc, (typeof(arr), typeof(axes)))
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return cres.entry_point(arr, axes)
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@from_generic(pyfuncs_to_use)
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def check(pyfunc, arr, axes):
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expected = pyfunc(arr, axes)
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got = run(pyfunc, arr, axes)
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self.assertPreciseEqual(got, expected)
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self.assertEqual(got.flags.f_contiguous,
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expected.flags.f_contiguous)
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self.assertEqual(got.flags.c_contiguous,
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expected.flags.c_contiguous)
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@from_generic(pyfuncs_to_use)
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def check_err_axis_repeated(pyfunc, arr, axes):
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with self.assertRaises(ValueError) as raises:
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run(pyfunc, arr, axes)
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self.assertEqual(str(raises.exception),
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"repeated axis in transpose")
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@from_generic(pyfuncs_to_use)
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def check_err_axis_oob(pyfunc, arr, axes):
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with self.assertRaises(ValueError) as raises:
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run(pyfunc, arr, axes)
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self.assertEqual(str(raises.exception),
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"axis is out of bounds for array of given dimension")
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@from_generic(pyfuncs_to_use)
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def check_err_invalid_args(pyfunc, arr, axes):
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with self.assertRaises((TypeError, TypingError)):
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run(pyfunc, arr, axes)
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arrs = [np.arange(24),
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np.arange(24).reshape(4, 6),
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np.arange(24).reshape(2, 3, 4),
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np.arange(24).reshape(1, 2, 3, 4),
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np.arange(64).reshape(8, 4, 2)[::3,::2,:]]
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for i in range(len(arrs)):
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# First check `None`, the default, which is to reverse dims
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check(arrs[i], None)
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# Check supplied axis permutations
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for axes in permutations(tuple(range(arrs[i].ndim))):
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ndim = len(axes)
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neg_axes = tuple([x - ndim for x in axes])
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check(arrs[i], axes)
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check(arrs[i], neg_axes)
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@from_generic([transpose_issue_4708])
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def check_issue_4708(pyfunc, m, n):
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expected = pyfunc(m, n)
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got = njit(pyfunc)(m, n)
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# values in arrays are equals,
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# but stronger assertions not hold (layout and strides equality)
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np.testing.assert_equal(got, expected)
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check_issue_4708(3, 2)
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check_issue_4708(2, 3)
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check_issue_4708(5, 4)
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# Exceptions leak references
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self.disable_leak_check()
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check_err_invalid_args(arrs[1], "foo")
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check_err_invalid_args(arrs[1], ("foo",))
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check_err_invalid_args(arrs[1], 5.3)
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check_err_invalid_args(arrs[2], (1.2, 5))
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check_err_axis_repeated(arrs[1], (0, 0))
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check_err_axis_repeated(arrs[2], (2, 0, 0))
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check_err_axis_repeated(arrs[3], (3, 2, 1, 1))
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check_err_axis_oob(arrs[0], (1,))
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check_err_axis_oob(arrs[0], (-2,))
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check_err_axis_oob(arrs[1], (0, 2))
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check_err_axis_oob(arrs[1], (-3, 2))
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check_err_axis_oob(arrs[1], (0, -3))
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check_err_axis_oob(arrs[2], (3, 1, 2))
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check_err_axis_oob(arrs[2], (-4, 1, 2))
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check_err_axis_oob(arrs[3], (3, 1, 2, 5))
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check_err_axis_oob(arrs[3], (3, 1, 2, -5))
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with self.assertRaises(TypingError) as e:
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jit(nopython=True)(numpy_transpose_array)((np.array([0, 1]),))
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self.assertIn("np.transpose does not accept tuples",
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str(e.exception))
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def test_expand_dims(self):
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pyfunc = expand_dims
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def run(arr, axis):
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cres = self.ccache.compile(pyfunc, (typeof(arr), typeof(axis)))
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return cres.entry_point(arr, axis)
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def check(arr, axis):
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expected = pyfunc(arr, axis)
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self.memory_leak_setup()
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got = run(arr, axis)
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self.assertPreciseEqual(got, expected)
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del got
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self.memory_leak_teardown()
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def check_all_axes(arr):
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for axis in range(-arr.ndim - 1, arr.ndim + 1):
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check(arr, axis)
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# 1d
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arr = np.arange(5)
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check_all_axes(arr)
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# 3d (C, F, A)
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arr = np.arange(24).reshape((2, 3, 4))
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check_all_axes(arr)
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check_all_axes(arr.T)
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check_all_axes(arr[::-1])
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# 0d
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arr = np.array(42)
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check_all_axes(arr)
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def check_atleast_nd(self, pyfunc, cfunc):
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def check_result(got, expected):
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# We would like to check the result has the same contiguity,
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# but we can't rely on the "flags" attribute when there are
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# 1-sized dimensions.
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self.assertStridesEqual(got, expected)
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self.assertPreciseEqual(got.flatten(), expected.flatten())
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def check_single(arg):
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check_result(cfunc(arg), pyfunc(arg))
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def check_tuple(*args):
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expected_tuple = pyfunc(*args)
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got_tuple = cfunc(*args)
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self.assertEqual(len(got_tuple), len(expected_tuple))
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for got, expected in zip(got_tuple, expected_tuple):
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check_result(got, expected)
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# 0d
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a1 = np.array(42)
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a2 = np.array(5j)
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check_single(a1)
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check_tuple(a1, a2)
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# 1d
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b1 = np.arange(5)
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b2 = np.arange(6) + 1j
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b3 = b1[::-1]
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check_single(b1)
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check_tuple(b1, b2, b3)
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# 2d
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c1 = np.arange(6).reshape((2, 3))
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c2 = c1.T
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c3 = c1[::-1]
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check_single(c1)
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check_tuple(c1, c2, c3)
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# 3d
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d1 = np.arange(24).reshape((2, 3, 4))
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d2 = d1.T
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d3 = d1[::-1]
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check_single(d1)
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check_tuple(d1, d2, d3)
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# 4d
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e = np.arange(16).reshape((2, 2, 2, 2))
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check_single(e)
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# mixed dimensions
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check_tuple(a1, b2, c3, d2)
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def test_atleast_1d(self):
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pyfunc = atleast_1d
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cfunc = jit(nopython=True)(pyfunc)
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self.check_atleast_nd(pyfunc, cfunc)
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def test_atleast_2d(self):
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pyfunc = atleast_2d
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cfunc = jit(nopython=True)(pyfunc)
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self.check_atleast_nd(pyfunc, cfunc)
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def test_atleast_3d(self):
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pyfunc = atleast_3d
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cfunc = jit(nopython=True)(pyfunc)
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self.check_atleast_nd(pyfunc, cfunc)
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def check_as_strided(self, pyfunc):
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def run(arr):
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cres = self.ccache.compile(pyfunc, (typeof(arr),))
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return cres.entry_point(arr)
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def check(arr):
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|
expected = pyfunc(arr)
|
|
got = run(arr)
|
|
self.assertPreciseEqual(got, expected)
|
|
|
|
arr = np.arange(24)
|
|
check(arr)
|
|
check(arr.reshape((6, 4)))
|
|
check(arr.reshape((4, 1, 6)))
|
|
|
|
def test_as_strided(self):
|
|
self.check_as_strided(as_strided1)
|
|
self.check_as_strided(as_strided2)
|
|
|
|
def test_flatten_array(self, flags=enable_pyobj_flags, layout='C'):
|
|
a = np.arange(9).reshape(3, 3)
|
|
if layout == 'F':
|
|
a = a.T
|
|
|
|
pyfunc = flatten_array
|
|
arraytype1 = typeof(a)
|
|
if layout == 'A':
|
|
# Force A layout
|
|
arraytype1 = arraytype1.copy(layout='A')
|
|
|
|
self.assertEqual(arraytype1.layout, layout)
|
|
cr = compile_isolated(pyfunc, (arraytype1,), flags=flags)
|
|
cfunc = cr.entry_point
|
|
|
|
expected = pyfunc(a)
|
|
got = cfunc(a)
|
|
np.testing.assert_equal(expected, got)
|
|
|
|
def test_flatten_array_npm(self):
|
|
self.test_flatten_array(flags=no_pyobj_flags)
|
|
self.test_flatten_array(flags=no_pyobj_flags, layout='F')
|
|
self.test_flatten_array(flags=no_pyobj_flags, layout='A')
|
|
|
|
def test_ravel_array(self, flags=enable_pyobj_flags):
|
|
def generic_check(pyfunc, a, assume_layout):
|
|
# compile
|
|
arraytype1 = typeof(a)
|
|
self.assertEqual(arraytype1.layout, assume_layout)
|
|
cr = compile_isolated(pyfunc, (arraytype1,), flags=flags)
|
|
cfunc = cr.entry_point
|
|
|
|
expected = pyfunc(a)
|
|
got = cfunc(a)
|
|
# Check result matches
|
|
np.testing.assert_equal(expected, got)
|
|
# Check copying behavior
|
|
py_copied = (a.ctypes.data != expected.ctypes.data)
|
|
nb_copied = (a.ctypes.data != got.ctypes.data)
|
|
self.assertEqual(py_copied, assume_layout != 'C')
|
|
self.assertEqual(py_copied, nb_copied)
|
|
|
|
check_method = partial(generic_check, ravel_array)
|
|
check_function = partial(generic_check, numpy_ravel_array)
|
|
|
|
def check(*args, **kwargs):
|
|
check_method(*args, **kwargs)
|
|
check_function(*args, **kwargs)
|
|
|
|
# Check 2D
|
|
check(np.arange(9).reshape(3, 3), assume_layout='C')
|
|
check(np.arange(9).reshape(3, 3, order='F'), assume_layout='F')
|
|
check(np.arange(18).reshape(3, 3, 2)[:, :, 0], assume_layout='A')
|
|
|
|
# Check 3D
|
|
check(np.arange(18).reshape(2, 3, 3), assume_layout='C')
|
|
check(np.arange(18).reshape(2, 3, 3, order='F'), assume_layout='F')
|
|
check(np.arange(36).reshape(2, 3, 3, 2)[:, :, :, 0], assume_layout='A')
|
|
|
|
def test_ravel_array_size(self, flags=enable_pyobj_flags):
|
|
a = np.arange(9).reshape(3, 3)
|
|
|
|
pyfunc = ravel_array_size
|
|
arraytype1 = typeof(a)
|
|
cr = compile_isolated(pyfunc, (arraytype1,), flags=flags)
|
|
cfunc = cr.entry_point
|
|
|
|
expected = pyfunc(a)
|
|
got = cfunc(a)
|
|
np.testing.assert_equal(expected, got)
|
|
|
|
def test_ravel_array_npm(self):
|
|
self.test_ravel_array(flags=no_pyobj_flags)
|
|
|
|
def test_ravel_array_size_npm(self):
|
|
self.test_ravel_array_size(flags=no_pyobj_flags)
|
|
|
|
def test_transpose_array(self, flags=enable_pyobj_flags):
|
|
@from_generic([transpose_array, numpy_transpose_array])
|
|
def check(pyfunc):
|
|
a = np.arange(9).reshape(3, 3)
|
|
|
|
arraytype1 = typeof(a)
|
|
cr = compile_isolated(pyfunc, (arraytype1,), flags=flags)
|
|
cfunc = cr.entry_point
|
|
|
|
expected = pyfunc(a)
|
|
got = cfunc(a)
|
|
np.testing.assert_equal(expected, got)
|
|
|
|
check()
|
|
|
|
def test_transpose_array_npm(self):
|
|
self.test_transpose_array(flags=no_pyobj_flags)
|
|
|
|
def test_squeeze_array(self, flags=enable_pyobj_flags):
|
|
a = np.arange(2 * 1 * 3 * 1 * 4).reshape(2, 1, 3, 1, 4)
|
|
|
|
pyfunc = squeeze_array
|
|
arraytype1 = typeof(a)
|
|
cr = compile_isolated(pyfunc, (arraytype1,), flags=flags)
|
|
cfunc = cr.entry_point
|
|
|
|
expected = pyfunc(a)
|
|
got = cfunc(a)
|
|
np.testing.assert_equal(expected, got)
|
|
|
|
def test_squeeze_array_npm(self):
|
|
with self.assertRaises(errors.TypingError) as raises:
|
|
self.test_squeeze_array(flags=no_pyobj_flags)
|
|
|
|
self.assertIn("squeeze", str(raises.exception))
|
|
|
|
def test_add_axis2(self, flags=enable_pyobj_flags):
|
|
a = np.arange(9).reshape(3, 3)
|
|
|
|
pyfunc = add_axis2
|
|
arraytype1 = typeof(a)
|
|
cr = compile_isolated(pyfunc, (arraytype1,), flags=flags)
|
|
cfunc = cr.entry_point
|
|
|
|
expected = pyfunc(a)
|
|
got = cfunc(a)
|
|
np.testing.assert_equal(expected, got)
|
|
|
|
def test_add_axis2_npm(self):
|
|
with self.assertTypingError() as raises:
|
|
self.test_add_axis2(flags=no_pyobj_flags)
|
|
self.assertIn("unsupported array index type none in",
|
|
str(raises.exception))
|
|
|
|
def test_bad_index_npm(self):
|
|
with self.assertTypingError() as raises:
|
|
arraytype1 = from_dtype(np.dtype([('x', np.int32),
|
|
('y', np.int32)]))
|
|
arraytype2 = types.Array(types.int32, 2, 'C')
|
|
compile_isolated(bad_index, (arraytype1, arraytype2),
|
|
flags=no_pyobj_flags)
|
|
self.assertIn('unsupported array index type', str(raises.exception))
|
|
|
|
def test_bad_float_index_npm(self):
|
|
with self.assertTypingError() as raises:
|
|
compile_isolated(bad_float_index,
|
|
(types.Array(types.float64, 2, 'C'),))
|
|
self.assertIn('unsupported array index type float64',
|
|
str(raises.exception))
|
|
|
|
def test_fill_diagonal_basic(self):
|
|
pyfunc = numpy_fill_diagonal
|
|
cfunc = jit(nopython=True)(pyfunc)
|
|
|
|
def _shape_variations(n):
|
|
# square
|
|
yield (n, n)
|
|
# tall and thin
|
|
yield (2 * n, n)
|
|
# short and fat
|
|
yield (n, 2 * n)
|
|
# a bit taller than wide; odd numbers of rows and cols
|
|
yield ((2 * n + 1), (2 * n - 1))
|
|
# 4d, all dimensions same
|
|
yield (n, n, n, n)
|
|
# weird edge case
|
|
yield (1, 1, 1)
|
|
|
|
def _val_variations():
|
|
yield 1
|
|
yield 3.142
|
|
yield np.nan
|
|
yield -np.inf
|
|
yield True
|
|
yield np.arange(4)
|
|
yield (4,)
|
|
yield [8, 9]
|
|
yield np.arange(54).reshape(9, 3, 2, 1) # contiguous C
|
|
yield np.asfortranarray(np.arange(9).reshape(3, 3)) # contiguous F
|
|
yield np.arange(9).reshape(3, 3)[::-1] # non-contiguous
|
|
|
|
# contiguous arrays
|
|
def _multi_dimensional_array_variations(n):
|
|
for shape in _shape_variations(n):
|
|
yield np.zeros(shape, dtype=np.float64)
|
|
yield np.asfortranarray(np.ones(shape, dtype=np.float64))
|
|
|
|
# non-contiguous arrays
|
|
def _multi_dimensional_array_variations_strided(n):
|
|
for shape in _shape_variations(n):
|
|
tmp = np.zeros(tuple([x * 2 for x in shape]), dtype=np.float64)
|
|
slicer = tuple(slice(0, x * 2, 2) for x in shape)
|
|
yield tmp[slicer]
|
|
|
|
def _check_fill_diagonal(arr, val):
|
|
for wrap in None, True, False:
|
|
a = arr.copy()
|
|
b = arr.copy()
|
|
|
|
if wrap is None:
|
|
params = {}
|
|
else:
|
|
params = {'wrap': wrap}
|
|
|
|
pyfunc(a, val, **params)
|
|
cfunc(b, val, **params)
|
|
self.assertPreciseEqual(a, b)
|
|
|
|
for arr in _multi_dimensional_array_variations(3):
|
|
for val in _val_variations():
|
|
_check_fill_diagonal(arr, val)
|
|
|
|
for arr in _multi_dimensional_array_variations_strided(3):
|
|
for val in _val_variations():
|
|
_check_fill_diagonal(arr, val)
|
|
|
|
# non-numeric input arrays
|
|
arr = np.array([True] * 9).reshape(3, 3)
|
|
_check_fill_diagonal(arr, False)
|
|
_check_fill_diagonal(arr, [False, True, False])
|
|
_check_fill_diagonal(arr, np.array([True, False, True]))
|
|
|
|
def test_fill_diagonal_exception_cases(self):
|
|
pyfunc = numpy_fill_diagonal
|
|
cfunc = jit(nopython=True)(pyfunc)
|
|
val = 1
|
|
|
|
# Exceptions leak references
|
|
self.disable_leak_check()
|
|
|
|
# first argument unsupported number of dimensions
|
|
for a in np.array([]), np.ones(5):
|
|
with self.assertRaises(TypingError) as raises:
|
|
cfunc(a, val)
|
|
assert "The first argument must be at least 2-D" in str(raises.exception)
|
|
|
|
# multi-dimensional input where dimensions are not all equal
|
|
with self.assertRaises(ValueError) as raises:
|
|
a = np.zeros((3, 3, 4))
|
|
cfunc(a, val)
|
|
self.assertEqual("All dimensions of input must be of equal length", str(raises.exception))
|
|
|
|
# cases where val has incompatible type / value
|
|
def _assert_raises(arr, val):
|
|
with self.assertRaises(ValueError) as raises:
|
|
cfunc(arr, val)
|
|
self.assertEqual("Unable to safely conform val to a.dtype", str(raises.exception))
|
|
|
|
arr = np.zeros((3, 3), dtype=np.int32)
|
|
val = np.nan
|
|
_assert_raises(arr, val)
|
|
|
|
val = [3.3, np.inf]
|
|
_assert_raises(arr, val)
|
|
|
|
val = np.array([1, 2, 1e10], dtype=np.int64)
|
|
_assert_raises(arr, val)
|
|
|
|
arr = np.zeros((3, 3), dtype=np.float32)
|
|
val = [1.4, 2.6, -1e100]
|
|
_assert_raises(arr, val)
|
|
|
|
val = 1.1e100
|
|
_assert_raises(arr, val)
|
|
|
|
val = np.array([-1e100])
|
|
_assert_raises(arr, val)
|
|
|
|
def test_broadcast_to(self):
|
|
pyfunc = numpy_broadcast_to
|
|
cfunc = jit(nopython=True)(pyfunc)
|
|
|
|
# Tests taken from
|
|
# https://github.com/numpy/numpy/blob/75f852edf94a7293e7982ad516bee314d7187c2d/numpy/lib/tests/test_stride_tricks.py#L234-L257 # noqa: E501
|
|
data = [
|
|
[np.array(0), (0,)],
|
|
[np.array(0), (1,)],
|
|
[np.array(0), (3,)],
|
|
[np.ones(1), (1,)],
|
|
[np.ones(1), (2,)],
|
|
[np.ones(1), (1, 2, 3)],
|
|
[np.arange(3), (3,)],
|
|
[np.arange(3), (1, 3)],
|
|
[np.arange(3), (2, 3)],
|
|
# test if shape is not a tuple
|
|
[np.ones(0), 0],
|
|
[np.ones(1), 1],
|
|
[np.ones(1), 2],
|
|
# these cases with size 0 are strange, but they reproduce the behavior
|
|
# of broadcasting with ufuncs
|
|
[np.ones(1), (0,)],
|
|
[np.ones((1, 2)), (0, 2)],
|
|
[np.ones((2, 1)), (2, 0)],
|
|
# numpy accepts scalar values as first argument to np.broadcast_to
|
|
[2, (2, 2)],
|
|
# tuple input
|
|
[(1, 2), (2, 2)],
|
|
]
|
|
for input_array, shape in data:
|
|
expected = pyfunc(input_array, shape)
|
|
got = cfunc(input_array, shape)
|
|
self.assertPreciseEqual(got, expected)
|
|
|
|
def test_broadcast_to_raises(self):
|
|
pyfunc = numpy_broadcast_to
|
|
cfunc = jit(nopython=True)(pyfunc)
|
|
|
|
# Tests taken from
|
|
# https://github.com/numpy/numpy/blob/75f852edf94a7293e7982ad516bee314d7187c2d/numpy/lib/tests/test_stride_tricks.py#L260-L276 # noqa: E501
|
|
data = [
|
|
[np.zeros((0,)), (), TypingError,
|
|
'The argument "shape" must be a tuple or an integer.'],
|
|
[np.zeros((1,)), (), TypingError,
|
|
'The argument "shape" must be a tuple or an integer.'],
|
|
[np.zeros((3,)), (), TypingError,
|
|
'The argument "shape" must be a tuple or an integer.'],
|
|
[np.zeros((3,)), (1,), ValueError,
|
|
'operands could not be broadcast together with remapped shapes'],
|
|
[np.zeros((3,)), (2,), ValueError,
|
|
'operands could not be broadcast together with remapped shapes'],
|
|
[np.zeros((3,)), (4,), ValueError,
|
|
'operands could not be broadcast together with remapped shapes'],
|
|
[np.zeros((1, 2)), (2, 1), ValueError,
|
|
'operands could not be broadcast together with remapped shapes'],
|
|
[np.zeros((1, 1)), (1,), ValueError,
|
|
'input operand has more dimensions than allowed by the axis remapping'],
|
|
[np.zeros((2, 2)), (3,), ValueError,
|
|
'input operand has more dimensions than allowed by the axis remapping'],
|
|
[np.zeros((1,)), -1, ValueError,
|
|
'all elements of broadcast shape must be non-negative'],
|
|
[np.zeros((1,)), (-1,), ValueError,
|
|
'all elements of broadcast shape must be non-negative'],
|
|
[np.zeros((1, 2)), (-1, 2), ValueError,
|
|
'all elements of broadcast shape must be non-negative'],
|
|
[np.zeros((1, 2)), (1.1, 2.2), TypingError,
|
|
'The second argument "shape" must be a tuple of integers'],
|
|
['hello', (3,), TypingError,
|
|
'The first argument "array" must be array-like'],
|
|
]
|
|
self.disable_leak_check()
|
|
for arr, target_shape, err, msg in data:
|
|
with self.assertRaises(err) as raises:
|
|
cfunc(arr, target_shape)
|
|
self.assertIn(msg, str(raises.exception))
|
|
|
|
def test_broadcast_to_change_view(self):
|
|
pyfunc = numpy_broadcast_to
|
|
cfunc = jit(nopython=True)(pyfunc)
|
|
input_array = np.zeros(2, dtype=np.int32)
|
|
shape = (2, 2)
|
|
view = cfunc(input_array, shape)
|
|
input_array[0] = 10
|
|
|
|
self.assertEqual(input_array.sum(), 10)
|
|
self.assertEqual(view.sum(), 20)
|
|
|
|
def test_broadcast_to_indexing(self):
|
|
pyfunc = numpy_broadcast_to_indexing
|
|
cfunc = jit(nopython=True)(pyfunc)
|
|
data = [
|
|
[np.ones(2), (2, 2), (1,)],
|
|
]
|
|
for input_array, shape, idx in data:
|
|
expected = pyfunc(input_array, shape, idx)
|
|
got = cfunc(input_array, shape, idx)
|
|
self.assertPreciseEqual(got, expected)
|
|
|
|
@unittest.skipIf(numpy_version < (1, 20), "requires NumPy 1.20 or newer")
|
|
def test_broadcast_shapes(self):
|
|
pyfunc = numpy_broadcast_shapes
|
|
cfunc = jit(nopython=True)(pyfunc)
|
|
|
|
# Tests taken from
|
|
# https://github.com/numpy/numpy/blob/623bc1fae1d47df24e7f1e29321d0c0ba2771ce0/numpy/lib/tests/test_stride_tricks.py#L296-L334
|
|
data = [
|
|
# [[], ()], # cannot compute fingerprint of empty list
|
|
[()],
|
|
[(), ()],
|
|
[(7,)],
|
|
[(1, 2),],
|
|
[(1, 1)],
|
|
[(1, 1), (3, 4)],
|
|
[(6, 7), (5, 6, 1), (7,), (5, 1, 7)],
|
|
[(5, 6, 1)],
|
|
[(1, 3), (3, 1)],
|
|
[(1, 0), (0, 0)],
|
|
[(0, 1), (0, 0)],
|
|
[(1, 0), (0, 1)],
|
|
[(1, 1), (0, 0)],
|
|
[(1, 1), (1, 0)],
|
|
[(1, 1), (0, 1)],
|
|
[(), (0,)],
|
|
[(0,), (0, 0)],
|
|
[(0,), (0, 1)],
|
|
[(1,), (0, 0)],
|
|
[(), (0, 0)],
|
|
[(1, 1), (0,)],
|
|
[(1,), (0, 1)],
|
|
[(1,), (1, 0)],
|
|
[(), (1, 0)],
|
|
[(), (0, 1)],
|
|
[(1,), (3,)],
|
|
[2, (3, 2)],
|
|
]
|
|
for input_shape in data:
|
|
expected = pyfunc(*input_shape)
|
|
got = cfunc(*input_shape)
|
|
self.assertIsInstance(got, tuple)
|
|
self.assertPreciseEqual(expected, got)
|
|
|
|
@unittest.skipIf(numpy_version < (1, 20), "requires NumPy 1.20 or newer")
|
|
def test_broadcast_shapes_raises(self):
|
|
pyfunc = numpy_broadcast_shapes
|
|
cfunc = jit(nopython=True)(pyfunc)
|
|
|
|
self.disable_leak_check()
|
|
|
|
# Tests taken from
|
|
# https://github.com/numpy/numpy/blob/623bc1fae1d47df24e7f1e29321d0c0ba2771ce0/numpy/lib/tests/test_stride_tricks.py#L337-L351
|
|
data = [
|
|
[(3,), (4,)],
|
|
[(2, 3), (2,)],
|
|
[(3,), (3,), (4,)],
|
|
[(1, 3, 4), (2, 3, 3)],
|
|
[(1, 2), (3, 1), (3, 2), (10, 5)],
|
|
[2, (2, 3)],
|
|
]
|
|
for input_shape in data:
|
|
with self.assertRaises(ValueError) as raises:
|
|
cfunc(*input_shape)
|
|
|
|
self.assertIn("shape mismatch: objects cannot be broadcast to a single shape",
|
|
str(raises.exception))
|
|
|
|
@unittest.skipIf(numpy_version < (1, 20), "requires NumPy 1.20 or newer")
|
|
def test_broadcast_shapes_negative_dimension(self):
|
|
pyfunc = numpy_broadcast_shapes
|
|
cfunc = jit(nopython=True)(pyfunc)
|
|
|
|
self.disable_leak_check()
|
|
with self.assertRaises(ValueError) as raises:
|
|
cfunc((1, 2), (2), (-2))
|
|
|
|
self.assertIn("negative dimensions are not allowed", str(raises.exception))
|
|
|
|
@unittest.skipIf(numpy_version < (1, 20), "requires NumPy 1.20 or newer")
|
|
def test_broadcast_shapes_invalid_type(self):
|
|
pyfunc = numpy_broadcast_shapes
|
|
cfunc = jit(nopython=True)(pyfunc)
|
|
|
|
self.disable_leak_check()
|
|
|
|
inps = [
|
|
((1, 2), ('hello',)),
|
|
(3.4,),
|
|
('string',),
|
|
((1.2, 'a')),
|
|
(1, ((1.2, 'a'))),
|
|
]
|
|
|
|
for inp in inps:
|
|
with self.assertRaises(TypingError) as raises:
|
|
cfunc(*inp)
|
|
|
|
self.assertIn("must be either an int or tuple[int]", str(raises.exception))
|
|
|
|
def test_shape(self):
|
|
pyfunc = numpy_shape
|
|
cfunc = jit(nopython=True)(pyfunc)
|
|
|
|
def check(x):
|
|
expected = pyfunc(x)
|
|
got = cfunc(x)
|
|
self.assertPreciseEqual(got, expected)
|
|
|
|
# check arrays
|
|
for t in [(), (1,), (2, 3,), (4, 5, 6)]:
|
|
arr = np.empty(t)
|
|
check(arr)
|
|
|
|
# check some types that go via asarray
|
|
for t in [1, False, [1,], [[1, 2,],[3, 4]], (1,), (1, 2, 3)]:
|
|
check(arr)
|
|
|
|
with self.assertRaises(TypingError) as raises:
|
|
cfunc('a')
|
|
|
|
self.assertIn("The argument to np.shape must be array-like",
|
|
str(raises.exception))
|
|
|
|
def test_flatnonzero_basic(self):
|
|
pyfunc = numpy_flatnonzero
|
|
cfunc = jit(nopython=True)(pyfunc)
|
|
|
|
def a_variations():
|
|
yield np.arange(-5, 5)
|
|
yield np.full(5, fill_value=0)
|
|
yield np.array([])
|
|
a = self.random.randn(100)
|
|
a[np.abs(a) > 0.2] = 0.0
|
|
yield a
|
|
yield a.reshape(5, 5, 4)
|
|
yield a.reshape(50, 2, order='F')
|
|
yield a.reshape(25, 4)[1::2]
|
|
yield a * 1j
|
|
|
|
for a in a_variations():
|
|
expected = pyfunc(a)
|
|
got = cfunc(a)
|
|
self.assertPreciseEqual(expected, got)
|
|
|
|
def test_argwhere_basic(self):
|
|
pyfunc = numpy_argwhere
|
|
cfunc = jit(nopython=True)(pyfunc)
|
|
|
|
def a_variations():
|
|
yield np.arange(-5, 5) > 2
|
|
yield np.full(5, fill_value=0)
|
|
yield np.full(5, fill_value=1)
|
|
yield np.array([])
|
|
yield np.array([-1.0, 0.0, 1.0])
|
|
a = self.random.randn(100)
|
|
yield a > 0.2
|
|
yield a.reshape(5, 5, 4) > 0.5
|
|
yield a.reshape(50, 2, order='F') > 0.5
|
|
yield a.reshape(25, 4)[1::2] > 0.5
|
|
yield a == a - 1
|
|
yield a > -a
|
|
|
|
for a in a_variations():
|
|
expected = pyfunc(a)
|
|
got = cfunc(a)
|
|
self.assertPreciseEqual(expected, got)
|
|
|
|
@staticmethod
|
|
def array_like_variations():
|
|
yield ((1.1, 2.2), (3.3, 4.4), (5.5, 6.6))
|
|
yield (0.0, 1.0, 0.0, -6.0)
|
|
yield ([0, 1], [2, 3])
|
|
yield ()
|
|
yield np.nan
|
|
yield 0
|
|
yield 1
|
|
yield False
|
|
yield True
|
|
yield (True, False, True)
|
|
yield 2 + 1j
|
|
# the following are not array-like, but NumPy does not raise
|
|
yield None
|
|
yield 'a_string'
|
|
yield ''
|
|
|
|
|
|
def test_flatnonzero_array_like(self):
|
|
pyfunc = numpy_flatnonzero
|
|
cfunc = jit(nopython=True)(pyfunc)
|
|
|
|
for a in self.array_like_variations():
|
|
expected = pyfunc(a)
|
|
got = cfunc(a)
|
|
self.assertPreciseEqual(expected, got)
|
|
|
|
def test_argwhere_array_like(self):
|
|
pyfunc = numpy_argwhere
|
|
cfunc = jit(nopython=True)(pyfunc)
|
|
for a in self.array_like_variations():
|
|
expected = pyfunc(a)
|
|
got = cfunc(a)
|
|
self.assertPreciseEqual(expected, got)
|
|
|
|
def broadcast_arrays_assert_correct_shape(self, input_shapes, expected_shape):
|
|
# Broadcast a list of arrays with the given input shapes and check the
|
|
# common output shape.
|
|
pyfunc = numpy_broadcast_arrays
|
|
cfunc = jit(nopython=True)(pyfunc)
|
|
|
|
inarrays = [np.zeros(s) for s in input_shapes]
|
|
outarrays = cfunc(*inarrays)
|
|
expected = [expected_shape] * len(inarrays)
|
|
got = [a.shape for a in outarrays]
|
|
self.assertPreciseEqual(expected, got)
|
|
|
|
def test_broadcast_arrays_same_input_shapes(self):
|
|
# Tests taken from
|
|
# https://github.com/numpy/numpy/blob/623bc1fae1d47df24e7f1e29321d0c0ba2771ce0/numpy/lib/tests/test_stride_tricks.py#L83-L107 # noqa: E501
|
|
# Check that the final shape is just the input shape.
|
|
pyfunc = numpy_broadcast_arrays
|
|
cfunc = jit(nopython=True)(pyfunc)
|
|
|
|
data = [
|
|
# (),
|
|
(1,),
|
|
(3,),
|
|
(0, 1),
|
|
(0, 3),
|
|
(1, 0),
|
|
(3, 0),
|
|
(1, 3),
|
|
(3, 1),
|
|
(3, 3),
|
|
]
|
|
for shape in data:
|
|
input_shapes = [shape]
|
|
# Single input.
|
|
self.broadcast_arrays_assert_correct_shape(input_shapes, shape)
|
|
# Double input.
|
|
input_shapes2 = [shape, shape]
|
|
self.broadcast_arrays_assert_correct_shape(input_shapes2, shape)
|
|
# Triple input.
|
|
input_shapes3 = [shape, shape, shape]
|
|
self.broadcast_arrays_assert_correct_shape(input_shapes3, shape)
|
|
|
|
def test_broadcast_arrays_two_compatible_by_ones_input_shapes(self):
|
|
# Tests taken from
|
|
# https://github.com/numpy/numpy/blob/623bc1fae1d47df24e7f1e29321d0c0ba2771ce0/numpy/lib/tests/test_stride_tricks.py#L110-L132
|
|
# Check that two different input shapes of the same length, but some have
|
|
# ones, broadcast to the correct shape.
|
|
|
|
data = [
|
|
[[(1,), (3,)], (3,)],
|
|
[[(1, 3), (3, 3)], (3, 3)],
|
|
[[(3, 1), (3, 3)], (3, 3)],
|
|
[[(1, 3), (3, 1)], (3, 3)],
|
|
[[(1, 1), (3, 3)], (3, 3)],
|
|
[[(1, 1), (1, 3)], (1, 3)],
|
|
[[(1, 1), (3, 1)], (3, 1)],
|
|
[[(1, 0), (0, 0)], (0, 0)],
|
|
[[(0, 1), (0, 0)], (0, 0)],
|
|
[[(1, 0), (0, 1)], (0, 0)],
|
|
[[(1, 1), (0, 0)], (0, 0)],
|
|
[[(1, 1), (1, 0)], (1, 0)],
|
|
[[(1, 1), (0, 1)], (0, 1)],
|
|
]
|
|
for input_shapes, expected_shape in data:
|
|
self.broadcast_arrays_assert_correct_shape(input_shapes, expected_shape)
|
|
# Reverse the input shapes since broadcasting should be symmetric.
|
|
self.broadcast_arrays_assert_correct_shape(input_shapes[::-1], expected_shape)
|
|
|
|
def test_broadcast_arrays_two_compatible_by_prepending_ones_input_shapes(self):
|
|
# Tests taken from
|
|
# https://github.com/numpy/numpy/blob/623bc1fae1d47df24e7f1e29321d0c0ba2771ce0/numpy/lib/tests/test_stride_tricks.py#L135-L164
|
|
# Check that two different input shapes (of different lengths) broadcast
|
|
# to the correct shape.
|
|
|
|
data = [
|
|
[[(), (3,)], (3,)],
|
|
[[(3,), (3, 3)], (3, 3)],
|
|
[[(3,), (3, 1)], (3, 3)],
|
|
[[(1,), (3, 3)], (3, 3)],
|
|
[[(), (3, 3)], (3, 3)],
|
|
[[(1, 1), (3,)], (1, 3)],
|
|
[[(1,), (3, 1)], (3, 1)],
|
|
[[(1,), (1, 3)], (1, 3)],
|
|
[[(), (1, 3)], (1, 3)],
|
|
[[(), (3, 1)], (3, 1)],
|
|
[[(), (0,)], (0,)],
|
|
[[(0,), (0, 0)], (0, 0)],
|
|
[[(0,), (0, 1)], (0, 0)],
|
|
[[(1,), (0, 0)], (0, 0)],
|
|
[[(), (0, 0)], (0, 0)],
|
|
[[(1, 1), (0,)], (1, 0)],
|
|
[[(1,), (0, 1)], (0, 1)],
|
|
[[(1,), (1, 0)], (1, 0)],
|
|
[[(), (1, 0)], (1, 0)],
|
|
[[(), (0, 1)], (0, 1)],
|
|
]
|
|
for input_shapes, expected_shape in data:
|
|
self.broadcast_arrays_assert_correct_shape(input_shapes, expected_shape)
|
|
# Reverse the input shapes since broadcasting should be symmetric.
|
|
self.broadcast_arrays_assert_correct_shape(input_shapes[::-1], expected_shape)
|
|
|
|
def test_broadcast_arrays_scalar_input(self):
|
|
pyfunc = numpy_broadcast_arrays
|
|
cfunc = jit(nopython=True)(pyfunc)
|
|
data = [
|
|
[[True, False], (1,)],
|
|
[[1, 2], (1,)],
|
|
[[(1, 2), 2], (2,)],
|
|
]
|
|
for inarrays, expected_shape in data:
|
|
outarrays = cfunc(*inarrays)
|
|
got = [a.shape for a in outarrays]
|
|
expected = [expected_shape] * len(inarrays)
|
|
self.assertPreciseEqual(expected, got)
|
|
|
|
def test_broadcast_arrays_tuple_input(self):
|
|
pyfunc = numpy_broadcast_arrays
|
|
cfunc = jit(nopython=True)(pyfunc)
|
|
outarrays = cfunc((123, 456), (789,))
|
|
expected = [(2,), (2,)]
|
|
got = [a.shape for a in outarrays]
|
|
self.assertPreciseEqual(expected, got)
|
|
|
|
def test_broadcast_arrays_non_array_input(self):
|
|
pyfunc = numpy_broadcast_arrays
|
|
cfunc = jit(nopython=True)(pyfunc)
|
|
outarrays = cfunc(np.intp(2), np.zeros((1, 3), dtype=np.intp))
|
|
expected = [(1, 3), (1, 3)]
|
|
got = [a.shape for a in outarrays]
|
|
self.assertPreciseEqual(expected, got)
|
|
|
|
def test_broadcast_arrays_invalid_mixed_input_types(self):
|
|
pyfunc = numpy_broadcast_arrays
|
|
cfunc = jit(nopython=True)(pyfunc)
|
|
|
|
self.disable_leak_check()
|
|
|
|
with self.assertRaises(TypingError) as raises:
|
|
arr = np.arange(6).reshape((2, 3))
|
|
b = True
|
|
cfunc(arr, b)
|
|
self.assertIn('Mismatch of argument types', str(raises.exception))
|
|
|
|
def test_broadcast_arrays_invalid_input(self):
|
|
pyfunc = numpy_broadcast_arrays
|
|
cfunc = jit(nopython=True)(pyfunc)
|
|
|
|
self.disable_leak_check()
|
|
|
|
with self.assertRaises(TypingError) as raises:
|
|
arr = np.zeros(3, dtype=np.int64)
|
|
s = 'hello world'
|
|
cfunc(arr, s)
|
|
self.assertIn('Argument "1" must be array-like', str(raises.exception))
|
|
|
|
def test_broadcast_arrays_incompatible_shapes_raise_valueerror(self):
|
|
# Check that a ValueError is raised for incompatible shapes.
|
|
pyfunc = numpy_broadcast_arrays
|
|
cfunc = jit(nopython=True)(pyfunc)
|
|
|
|
self.disable_leak_check()
|
|
|
|
data = [
|
|
[(3,), (4,)],
|
|
[(2, 3), (2,)],
|
|
[(3,), (3,), (4,)],
|
|
[(1, 3, 4), (2, 3, 3)],
|
|
]
|
|
for input_shapes in data:
|
|
for shape in [input_shapes, input_shapes[::-1]]:
|
|
# Reverse the input shapes since broadcasting should be symmetric.
|
|
with self.assertRaises(ValueError) as raises:
|
|
inarrays = [np.zeros(s) for s in shape]
|
|
cfunc(*inarrays)
|
|
self.assertIn("shape mismatch: objects cannot be broadcast to a single shape",
|
|
str(raises.exception))
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|