1549 lines
52 KiB
Python
1549 lines
52 KiB
Python
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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 import jit, njit, from_dtype, typeof
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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
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enable_pyobj_flags = {'forceobj': True}
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no_pyobj_flags = {'_nrt': True, 'nopython': 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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@njit
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def array_reshape(arr, newshape):
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return arr.reshape(newshape)
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@njit
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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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@njit
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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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@njit
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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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@njit
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def array_transpose_axes(arr, axes):
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return arr.transpose(axes)
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@njit
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def array_transpose_axes_copy(arr, axes):
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return arr.transpose(axes).copy()
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@njit
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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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@njit
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def sliding_window_view(x, window_shape, axis=None):
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return np.lib.stride_tricks.sliding_window_view(x, window_shape, axis=axis)
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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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def numpy_resize(a, new_shape):
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return np.resize(a, new_shape)
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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 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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return pyfunc(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.py_func(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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@from_generic(pyfuncs_to_use)
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def check(pyfunc, arr, axes):
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expected = pyfunc.py_func(arr, axes)
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got = 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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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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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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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.py_func(m, n)
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got = 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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||
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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)
|
||
|
|
check_err_invalid_args(arrs[2], (1.2, 5))
|
||
|
|
|
||
|
|
check_err_axis_repeated(arrs[1], (0, 0))
|
||
|
|
check_err_axis_repeated(arrs[2], (2, 0, 0))
|
||
|
|
check_err_axis_repeated(arrs[3], (3, 2, 1, 1))
|
||
|
|
|
||
|
|
check_err_axis_oob(arrs[0], (1,))
|
||
|
|
check_err_axis_oob(arrs[0], (-2,))
|
||
|
|
check_err_axis_oob(arrs[1], (0, 2))
|
||
|
|
check_err_axis_oob(arrs[1], (-3, 2))
|
||
|
|
check_err_axis_oob(arrs[1], (0, -3))
|
||
|
|
check_err_axis_oob(arrs[2], (3, 1, 2))
|
||
|
|
check_err_axis_oob(arrs[2], (-4, 1, 2))
|
||
|
|
check_err_axis_oob(arrs[3], (3, 1, 2, 5))
|
||
|
|
check_err_axis_oob(arrs[3], (3, 1, 2, -5))
|
||
|
|
|
||
|
|
with self.assertRaises(TypingError) as e:
|
||
|
|
jit(nopython=True)(numpy_transpose_array)((np.array([0, 1]),))
|
||
|
|
self.assertIn("np.transpose does not accept tuples",
|
||
|
|
str(e.exception))
|
||
|
|
|
||
|
|
def test_numpy_resize_basic(self):
|
||
|
|
pyfunc = numpy_resize
|
||
|
|
cfunc = njit(pyfunc)
|
||
|
|
def inputs():
|
||
|
|
# Taken from https://github.com/numpy/numpy/blob/f0b2fca91a1f5f50ff696895072f6fe9e69c1466/numpy/core/tests/test_numeric.py#L24-L64 noqa: E501
|
||
|
|
yield np.array([[1, 2], [3, 4]]), (2, 4)
|
||
|
|
yield np.array([[1, 2], [3, 4]]), (4, 2)
|
||
|
|
yield np.array([[1, 2], [3, 4]]), (4, 3)
|
||
|
|
yield np.array([[1, 2], [3, 4]]), (0,)
|
||
|
|
yield np.array([[1, 2], [3, 4]]), (0, 2)
|
||
|
|
yield np.array([[1, 2], [3, 4]]), (2, 0)
|
||
|
|
yield np.zeros(0, dtype = float), (2, 1)
|
||
|
|
# other
|
||
|
|
yield np.array([[1, 2], [3, 4]]), (4,)
|
||
|
|
yield np.array([[1, 2], [3, 4]]), 4
|
||
|
|
yield np.zeros((1, 3), dtype = int), (2, 1)
|
||
|
|
yield np.array([], dtype = float), (4, 2)
|
||
|
|
yield [0, 1, 2, 3], (2, 3)
|
||
|
|
yield 4, (2, 3)
|
||
|
|
|
||
|
|
for a, new_shape in inputs():
|
||
|
|
self.assertPreciseEqual(pyfunc(a, new_shape), cfunc(a, new_shape))
|
||
|
|
|
||
|
|
def test_numpy_resize_exception(self):
|
||
|
|
# Exceptions leak references
|
||
|
|
self.disable_leak_check()
|
||
|
|
|
||
|
|
cfunc = njit(numpy_resize)
|
||
|
|
|
||
|
|
with self.assertRaises(TypingError) as raises:
|
||
|
|
cfunc("abc", (2, 3))
|
||
|
|
self.assertIn(('The argument "a" must be array-like'),
|
||
|
|
str(raises.exception))
|
||
|
|
|
||
|
|
with self.assertRaises(TypingError) as raises:
|
||
|
|
cfunc(np.array([[0,1],[2,3]]), "abc")
|
||
|
|
self.assertIn(('The argument "new_shape" must be an integer or '
|
||
|
|
'a tuple of integers'),
|
||
|
|
str(raises.exception))
|
||
|
|
|
||
|
|
with self.assertRaises(ValueError) as raises:
|
||
|
|
cfunc(np.array([[0,1],[2,3]]), (-2, 3))
|
||
|
|
self.assertIn(('All elements of `new_shape` must be non-negative'),
|
||
|
|
str(raises.exception))
|
||
|
|
|
||
|
|
with self.assertRaises(ValueError) as raises:
|
||
|
|
cfunc(np.array([[0,1],[2,3]]), -4)
|
||
|
|
self.assertIn(('All elements of `new_shape` must be non-negative'),
|
||
|
|
str(raises.exception))
|
||
|
|
|
||
|
|
def test_expand_dims(self):
|
||
|
|
pyfunc = expand_dims
|
||
|
|
cfunc = njit(pyfunc)
|
||
|
|
|
||
|
|
def check(arr, axis):
|
||
|
|
expected = pyfunc(arr, axis)
|
||
|
|
self.memory_leak_setup()
|
||
|
|
got = cfunc(arr, axis)
|
||
|
|
self.assertPreciseEqual(got, expected)
|
||
|
|
del got
|
||
|
|
self.memory_leak_teardown()
|
||
|
|
|
||
|
|
def check_all_axes(arr):
|
||
|
|
for axis in range(-arr.ndim - 1, arr.ndim + 1):
|
||
|
|
check(arr, axis)
|
||
|
|
|
||
|
|
# 1d
|
||
|
|
arr = np.arange(5)
|
||
|
|
check_all_axes(arr)
|
||
|
|
# 3d (C, F, A)
|
||
|
|
arr = np.arange(24).reshape((2, 3, 4))
|
||
|
|
check_all_axes(arr)
|
||
|
|
check_all_axes(arr.T)
|
||
|
|
check_all_axes(arr[::-1])
|
||
|
|
# 0d
|
||
|
|
arr = np.array(42)
|
||
|
|
check_all_axes(arr)
|
||
|
|
|
||
|
|
def test_expand_dims_exceptions(self):
|
||
|
|
pyfunc = expand_dims
|
||
|
|
cfunc = jit(nopython=True)(pyfunc)
|
||
|
|
arr = np.arange(5)
|
||
|
|
|
||
|
|
with self.assertTypingError() as raises:
|
||
|
|
cfunc('hello', 3)
|
||
|
|
self.assertIn('First argument "a" must be an array', str(raises.exception))
|
||
|
|
|
||
|
|
with self.assertTypingError() as raises:
|
||
|
|
cfunc(arr, 'hello')
|
||
|
|
self.assertIn('Argument "axis" must be an integer', str(raises.exception))
|
||
|
|
|
||
|
|
|
||
|
|
def check_atleast_nd(self, pyfunc, cfunc):
|
||
|
|
def check_result(got, expected):
|
||
|
|
# We would like to check the result has the same contiguity,
|
||
|
|
# but we can't rely on the "flags" attribute when there are
|
||
|
|
# 1-sized dimensions.
|
||
|
|
self.assertStridesEqual(got, expected)
|
||
|
|
self.assertPreciseEqual(got.flatten(), expected.flatten())
|
||
|
|
|
||
|
|
def check_single(arg):
|
||
|
|
check_result(cfunc(arg), pyfunc(arg))
|
||
|
|
|
||
|
|
def check_tuple(*args):
|
||
|
|
expected_tuple = pyfunc(*args)
|
||
|
|
got_tuple = cfunc(*args)
|
||
|
|
self.assertEqual(len(got_tuple), len(expected_tuple))
|
||
|
|
for got, expected in zip(got_tuple, expected_tuple):
|
||
|
|
check_result(got, expected)
|
||
|
|
|
||
|
|
# 0d
|
||
|
|
a1 = np.array(42)
|
||
|
|
a2 = np.array(5j)
|
||
|
|
check_single(a1)
|
||
|
|
check_tuple(a1, a2)
|
||
|
|
# 1d
|
||
|
|
b1 = np.arange(5)
|
||
|
|
b2 = np.arange(6) + 1j
|
||
|
|
b3 = b1[::-1]
|
||
|
|
check_single(b1)
|
||
|
|
check_tuple(b1, b2, b3)
|
||
|
|
# 2d
|
||
|
|
c1 = np.arange(6).reshape((2, 3))
|
||
|
|
c2 = c1.T
|
||
|
|
c3 = c1[::-1]
|
||
|
|
check_single(c1)
|
||
|
|
check_tuple(c1, c2, c3)
|
||
|
|
# 3d
|
||
|
|
d1 = np.arange(24).reshape((2, 3, 4))
|
||
|
|
d2 = d1.T
|
||
|
|
d3 = d1[::-1]
|
||
|
|
check_single(d1)
|
||
|
|
check_tuple(d1, d2, d3)
|
||
|
|
# 4d
|
||
|
|
e = np.arange(16).reshape((2, 2, 2, 2))
|
||
|
|
check_single(e)
|
||
|
|
# mixed dimensions
|
||
|
|
check_tuple(a1, b2, c3, d2)
|
||
|
|
|
||
|
|
def test_atleast_1d(self):
|
||
|
|
pyfunc = atleast_1d
|
||
|
|
cfunc = jit(nopython=True)(pyfunc)
|
||
|
|
self.check_atleast_nd(pyfunc, cfunc)
|
||
|
|
|
||
|
|
def test_atleast_2d(self):
|
||
|
|
pyfunc = atleast_2d
|
||
|
|
cfunc = jit(nopython=True)(pyfunc)
|
||
|
|
self.check_atleast_nd(pyfunc, cfunc)
|
||
|
|
|
||
|
|
def test_atleast_3d(self):
|
||
|
|
pyfunc = atleast_3d
|
||
|
|
cfunc = jit(nopython=True)(pyfunc)
|
||
|
|
self.check_atleast_nd(pyfunc, cfunc)
|
||
|
|
|
||
|
|
def check_as_strided(self, pyfunc):
|
||
|
|
cfunc = njit(pyfunc)
|
||
|
|
|
||
|
|
def check(arr):
|
||
|
|
expected = pyfunc(arr)
|
||
|
|
got = cfunc(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_sliding_window_view(self):
|
||
|
|
def check(arr, window_shape, axis):
|
||
|
|
# Our version is always writeable (NumPy default is False).
|
||
|
|
expected = np.lib.stride_tricks.sliding_window_view(
|
||
|
|
arr, window_shape, axis, writeable=True
|
||
|
|
)
|
||
|
|
got = sliding_window_view(arr, window_shape, axis)
|
||
|
|
self.assertPreciseEqual(got, expected)
|
||
|
|
|
||
|
|
# 1d array, different ways of specifying the axis.
|
||
|
|
arr1 = np.arange(24)
|
||
|
|
for axis in [None, 0, -1, (0,)]:
|
||
|
|
with self.subTest(f"1d array, axis={axis}"):
|
||
|
|
check(arr1, 5, axis)
|
||
|
|
|
||
|
|
# 2d array, 1d window.
|
||
|
|
arr2 = np.arange(200).reshape(10, 20)
|
||
|
|
for axis in [0, -1]:
|
||
|
|
with self.subTest(f"2d array, axis={axis}"):
|
||
|
|
check(arr2, 5, axis)
|
||
|
|
|
||
|
|
# 2d array, 2d window.
|
||
|
|
for axis in [None, (0, 1), (1, 0), (1, -2)]:
|
||
|
|
with self.subTest(f"2d array, axis={axis}"):
|
||
|
|
check(arr2, (5, 8), axis)
|
||
|
|
|
||
|
|
# 4d array, 2d window.
|
||
|
|
arr4 = np.arange(200).reshape(4, 5, 5, 2)
|
||
|
|
for axis in [(1, 2), (-2, -3)]:
|
||
|
|
with self.subTest(f"4d array, axis={axis}"):
|
||
|
|
check(arr4, (3, 2), axis)
|
||
|
|
|
||
|
|
# Repeated axis.
|
||
|
|
with self.subTest("2d array, repeated axes"):
|
||
|
|
check(arr2, (5, 3, 3), (0, 1, 0))
|
||
|
|
|
||
|
|
def test_sliding_window_view_errors(self):
|
||
|
|
def _raises(msg, *args):
|
||
|
|
with self.assertRaises(ValueError) as raises:
|
||
|
|
sliding_window_view(*args)
|
||
|
|
self.assertIn(msg, str(raises.exception))
|
||
|
|
|
||
|
|
def _typing_error(msg, *args):
|
||
|
|
with self.assertRaises(errors.TypingError) as raises:
|
||
|
|
sliding_window_view(*args)
|
||
|
|
self.assertIn(msg, str(raises.exception))
|
||
|
|
|
||
|
|
# Exceptions leak references
|
||
|
|
self.disable_leak_check()
|
||
|
|
|
||
|
|
arr1 = np.arange(24)
|
||
|
|
arr2 = np.arange(200).reshape(10, 20)
|
||
|
|
|
||
|
|
# Window shape cannot be larger than dimension or negative.
|
||
|
|
with self.subTest("1d window shape too large"):
|
||
|
|
_raises("window_shape cannot be larger", arr1, 25, None)
|
||
|
|
with self.subTest("2d window shape too large"):
|
||
|
|
_raises("window_shape cannot be larger", arr2, (4, 21), None)
|
||
|
|
with self.subTest("1d window negative size"):
|
||
|
|
_raises("`window_shape` cannot contain negative", arr1, -1, None)
|
||
|
|
with self.subTest("2d window with a negative size"):
|
||
|
|
_raises("`window_shape` cannot contain negative", arr2, (4, -3), None)
|
||
|
|
|
||
|
|
# window_shape and axis parameters must be compatible.
|
||
|
|
with self.subTest("1d array, 2d window shape"):
|
||
|
|
_raises("matching length window_shape and axis", arr1, (10, 2), None)
|
||
|
|
with self.subTest("2d window shape, only one axis given"):
|
||
|
|
_raises("matching length window_shape and axis", arr2, (10, 2), 1)
|
||
|
|
with self.subTest("1d window shape, 2 axes given"):
|
||
|
|
_raises("matching length window_shape and axis", arr1, 5, (0, 0))
|
||
|
|
|
||
|
|
# Axis values out of bounds.
|
||
|
|
with self.subTest("1d array, second axis"):
|
||
|
|
_raises("Argument axis out of bounds", arr1, 4, 1)
|
||
|
|
with self.subTest("1d array, axis -2"):
|
||
|
|
_raises("Argument axis out of bounds", arr1, 4, -2)
|
||
|
|
with self.subTest("2d array, fourth axis"):
|
||
|
|
_raises("Argument axis out of bounds", arr2, (4, 4), (0, 3))
|
||
|
|
with self.subTest("2d array, axis -3"):
|
||
|
|
_raises("Argument axis out of bounds", arr2, (4, 4), (0, -3))
|
||
|
|
|
||
|
|
# Useful messages for unsupported types.
|
||
|
|
with self.subTest("window_shape=None"):
|
||
|
|
_typing_error(
|
||
|
|
"window_shape must be an integer or tuple of integer", arr1, None
|
||
|
|
)
|
||
|
|
with self.subTest("window_shape=float"):
|
||
|
|
_typing_error(
|
||
|
|
"window_shape must be an integer or tuple of integer", arr1, 3.1
|
||
|
|
)
|
||
|
|
with self.subTest("window_shape=tuple(float)"):
|
||
|
|
_typing_error(
|
||
|
|
"window_shape must be an integer or tuple of integer", arr1, (3.1,)
|
||
|
|
)
|
||
|
|
with self.subTest("axis=float"):
|
||
|
|
_typing_error(
|
||
|
|
"axis must be None, an integer or tuple of integer", arr1, 4, 3.1
|
||
|
|
)
|
||
|
|
with self.subTest("axis=tuple(float)"):
|
||
|
|
_typing_error(
|
||
|
|
"axis must be None, an integer or tuple of integer", arr1, 4, (3.1,)
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
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)
|
||
|
|
cfunc = jit((arraytype1,), **flags)(pyfunc)
|
||
|
|
|
||
|
|
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)
|
||
|
|
cfunc = jit((arraytype1,), **flags)(pyfunc)
|
||
|
|
|
||
|
|
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)
|
||
|
|
cfunc = jit((arraytype1,), **flags)(pyfunc)
|
||
|
|
|
||
|
|
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)
|
||
|
|
cfunc = jit((arraytype1,), **flags)(pyfunc)
|
||
|
|
|
||
|
|
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)
|
||
|
|
cfunc = jit((arraytype1,), **flags)(pyfunc)
|
||
|
|
|
||
|
|
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_axis(self):
|
||
|
|
@njit
|
||
|
|
def np_new_axis_getitem(a, idx):
|
||
|
|
return a[idx]
|
||
|
|
|
||
|
|
@njit
|
||
|
|
def np_new_axis_setitem(a, idx, item):
|
||
|
|
a[idx] = item
|
||
|
|
return a
|
||
|
|
|
||
|
|
a = np.arange(4 * 5 * 6 * 7).reshape((4, 5, 6, 7))
|
||
|
|
idx_cases = [
|
||
|
|
(slice(None), np.newaxis),
|
||
|
|
(np.newaxis, slice(None)),
|
||
|
|
(slice(1), np.newaxis, 1),
|
||
|
|
(np.newaxis, 2, slice(None)),
|
||
|
|
(slice(1), Ellipsis, np.newaxis, 1),
|
||
|
|
(1, np.newaxis, Ellipsis),
|
||
|
|
(np.newaxis, slice(1), np.newaxis, 1),
|
||
|
|
(1, Ellipsis, None, np.newaxis),
|
||
|
|
(np.newaxis, slice(1), Ellipsis, np.newaxis, 1),
|
||
|
|
(1, np.newaxis, np.newaxis, Ellipsis),
|
||
|
|
(np.newaxis, 1, np.newaxis, Ellipsis),
|
||
|
|
(slice(3), 1, np.newaxis, None),
|
||
|
|
(np.newaxis, 1, Ellipsis, None),
|
||
|
|
]
|
||
|
|
pyfunc_getitem = np_new_axis_getitem.py_func
|
||
|
|
cfunc_getitem = np_new_axis_getitem
|
||
|
|
|
||
|
|
pyfunc_setitem = np_new_axis_setitem.py_func
|
||
|
|
cfunc_setitem = np_new_axis_setitem
|
||
|
|
|
||
|
|
for idx in idx_cases:
|
||
|
|
expected = pyfunc_getitem(a, idx)
|
||
|
|
got = cfunc_getitem(a, idx)
|
||
|
|
np.testing.assert_equal(expected, got)
|
||
|
|
|
||
|
|
a_empty = np.zeros_like(a)
|
||
|
|
item = a[idx]
|
||
|
|
|
||
|
|
expected = pyfunc_setitem(a_empty.copy(), idx, item)
|
||
|
|
got = cfunc_setitem(a_empty.copy(), idx, item)
|
||
|
|
np.testing.assert_equal(expected, got)
|
||
|
|
|
||
|
|
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')
|
||
|
|
njit((arraytype1, arraytype2))(bad_index)
|
||
|
|
self.assertIn('Unsupported array index type', str(raises.exception))
|
||
|
|
|
||
|
|
def test_bad_float_index_npm(self):
|
||
|
|
with self.assertTypingError() as raises:
|
||
|
|
njit((types.Array(types.float64, 2, 'C'),))(bad_float_index)
|
||
|
|
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_0d_array(self):
|
||
|
|
pyfunc = numpy_broadcast_to
|
||
|
|
cfunc = jit(nopython=True)(pyfunc)
|
||
|
|
|
||
|
|
inputs = [
|
||
|
|
np.array(123),
|
||
|
|
123,
|
||
|
|
True,
|
||
|
|
# can't do np.asarray() on the types below
|
||
|
|
# 'hello',
|
||
|
|
# np.timedelta64(10, 'Y'),
|
||
|
|
# np.datetime64(10, 'Y'),
|
||
|
|
]
|
||
|
|
|
||
|
|
shape = ()
|
||
|
|
for arr in inputs:
|
||
|
|
expected = pyfunc(arr, shape)
|
||
|
|
got = cfunc(arr, shape)
|
||
|
|
self.assertPreciseEqual(expected, got)
|
||
|
|
# ensure that np.broadcast_to returned a read-only array
|
||
|
|
self.assertFalse(got.flags['WRITEABLE'])
|
||
|
|
|
||
|
|
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,
|
||
|
|
'Cannot broadcast a non-scalar to a scalar array'],
|
||
|
|
[np.zeros((1,)), (), TypingError,
|
||
|
|
'Cannot broadcast a non-scalar to a scalar array'],
|
||
|
|
[np.zeros((3,)), (), TypingError,
|
||
|
|
'Cannot broadcast a non-scalar to a scalar array'],
|
||
|
|
[(), (), TypingError,
|
||
|
|
'Cannot broadcast a non-scalar to a scalar array'],
|
||
|
|
[(123,), (), TypingError,
|
||
|
|
'Cannot broadcast a non-scalar to a scalar array'],
|
||
|
|
[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'],
|
||
|
|
[3, (2, 'a'), TypingError,
|
||
|
|
'object cannot be interpreted as an integer'],
|
||
|
|
]
|
||
|
|
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_corner_cases(self):
|
||
|
|
@njit
|
||
|
|
def _broadcast_to_1():
|
||
|
|
return np.broadcast_to('a', (2, 3))
|
||
|
|
|
||
|
|
expected = _broadcast_to_1.py_func()
|
||
|
|
got = _broadcast_to_1()
|
||
|
|
self.assertPreciseEqual(expected, got)
|
||
|
|
|
||
|
|
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)
|
||
|
|
|
||
|
|
def test_broadcast_to_array_attrs(self):
|
||
|
|
# See issue #8534. This tests that broadcast array attributes have the
|
||
|
|
# correct value when accessed.
|
||
|
|
@njit
|
||
|
|
def foo(arr):
|
||
|
|
ret = np.broadcast_to(arr, (2, 3))
|
||
|
|
return ret, ret.size, ret.shape, ret.strides
|
||
|
|
|
||
|
|
arr = np.arange(3)
|
||
|
|
|
||
|
|
expected = foo.py_func(arr)
|
||
|
|
got = foo(arr)
|
||
|
|
self.assertPreciseEqual(expected, got)
|
||
|
|
|
||
|
|
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)
|
||
|
|
|
||
|
|
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))
|
||
|
|
|
||
|
|
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))
|
||
|
|
|
||
|
|
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))
|
||
|
|
|
||
|
|
def test_readonly_after_flatten(self):
|
||
|
|
# Reproduces Issue #8370
|
||
|
|
def unfold_flatten(x, y):
|
||
|
|
r, c = x.shape
|
||
|
|
a = np.broadcast_to(x, (y, r, c))
|
||
|
|
b = np.swapaxes(a, 0, 1)
|
||
|
|
cc = b.flatten()
|
||
|
|
d = np.reshape(cc, (-1, c))
|
||
|
|
d[y - 1:, :] = d[: 1 - y]
|
||
|
|
return d
|
||
|
|
|
||
|
|
pyfunc = unfold_flatten
|
||
|
|
cfunc = jit(nopython=True)(pyfunc)
|
||
|
|
|
||
|
|
# If issue #8370 is not fixed: This will fail.
|
||
|
|
res_nb = cfunc(np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]]), 2)
|
||
|
|
res_py = pyfunc(np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]]), 2)
|
||
|
|
|
||
|
|
np.testing.assert_array_equal(res_py, res_nb)
|
||
|
|
|
||
|
|
def test_readonly_after_ravel(self):
|
||
|
|
# Reproduces another suggested problem in Issue #8370
|
||
|
|
def unfold_ravel(x, y):
|
||
|
|
r, c = x.shape
|
||
|
|
a = np.broadcast_to(x, (y, r, c))
|
||
|
|
b = np.swapaxes(a, 0, 1)
|
||
|
|
cc = b.ravel()
|
||
|
|
d = np.reshape(cc, (-1, c))
|
||
|
|
d[y - 1:, :] = d[: 1 - y]
|
||
|
|
return d
|
||
|
|
|
||
|
|
pyfunc = unfold_ravel
|
||
|
|
cfunc = jit(nopython=True)(pyfunc)
|
||
|
|
|
||
|
|
# If issue #8370 is not fixed: This will fail.
|
||
|
|
res_nb = cfunc(np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]]), 2)
|
||
|
|
res_py = pyfunc(np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]]), 2)
|
||
|
|
|
||
|
|
np.testing.assert_array_equal(res_py, res_nb)
|
||
|
|
|
||
|
|
def test_mutability_after_ravel(self):
|
||
|
|
# Reproduces another suggested problem in Issue #8370
|
||
|
|
# Namely that ravel should only return a writable array
|
||
|
|
# if a copy took place... otherwise leave it as it is.
|
||
|
|
self.disable_leak_check()
|
||
|
|
a_c = np.arange(9).reshape((3, 3)).copy()
|
||
|
|
a_f = a_c.copy(order='F')
|
||
|
|
a_c.flags.writeable = False
|
||
|
|
a_f.flags.writeable = False
|
||
|
|
|
||
|
|
def try_ravel_w_copy(a):
|
||
|
|
result = a.ravel()
|
||
|
|
return result
|
||
|
|
|
||
|
|
pyfunc = try_ravel_w_copy
|
||
|
|
cfunc = jit(nopython=True)(pyfunc)
|
||
|
|
|
||
|
|
ret_c = cfunc(a_c)
|
||
|
|
ret_f = cfunc(a_f)
|
||
|
|
|
||
|
|
msg = 'No copy was performed, so the ' \
|
||
|
|
'resulting array must not be writeable'
|
||
|
|
self.assertTrue(not ret_c.flags.writeable, msg)
|
||
|
|
|
||
|
|
msg = 'A copy was performed, yet the resulting array is not modifiable'
|
||
|
|
self.assertTrue(ret_f.flags.writeable, msg)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
unittest.main()
|