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############################################################################
# Copyright (c) Wolf Vollprecht, Johan Mabille and Sylvain Corlay #
# Copyright (c) QuantStack #
# #
# Distributed under the terms of the BSD 3-Clause License. #
# #
# The full license is in the file LICENSE, distributed with this software. #
############################################################################
import os
import sys
import subprocess
# Build the test extension
here = os.path.abspath(os.path.dirname(__file__))
subprocess.check_call([sys.executable, os.path.join(here, 'setup.py'), 'build_ext', '--inplace'], cwd=here)
# Test it!
from unittest import TestCase
import xtensor_python_test as xt
import numpy as np
class XtensorTest(TestCase):
def test_rm(self):
xt.test_rm(np.array([10], dtype=int))
def test_example1(self):
self.assertEqual(4, xt.example1([4, 5, 6]))
def test_example2(self):
x = np.array([[0., 1.], [2., 3.]])
res = np.array([[2., 3.], [4., 5.]])
y = xt.example2(x)
np.testing.assert_allclose(y, res, 1e-12)
def test_example3(self):
x = np.arange(2 * 3).reshape(2, 3)
xc = np.asfortranarray(x)
y = np.arange(2 * 3 * 4).reshape(2, 3, 4)
v = y[1:, 1:, 0]
z = np.arange(2 * 3 * 4 * 5).reshape(2, 3, 4, 5)
np.testing.assert_array_equal(xt.example3_xarray(x), x.T + 2)
np.testing.assert_array_equal(xt.example3_xarray_colmajor(xc), xc.T + 2)
np.testing.assert_array_equal(xt.example3_xtensor3(y), y.T + 2)
np.testing.assert_array_equal(xt.example3_xtensor2(x), x.T + 2)
np.testing.assert_array_equal(xt.example3_xtensor2(y[1:, 1:, 0]), v.T + 2)
np.testing.assert_array_equal(xt.example3_xtensor2_colmajor(xc), xc.T + 2)
np.testing.assert_array_equal(xt.example3_xfixed3(y), y.T + 2)
np.testing.assert_array_equal(xt.example3_xfixed2(x), x.T + 2)
np.testing.assert_array_equal(xt.example3_xfixed2_colmajor(xc), xc.T + 2)
with self.assertRaises(TypeError):
xt.example3_xtensor3(x)
with self.assertRaises(TypeError):
xt.example3_xfixed3(x)
with self.assertRaises(TypeError):
x = np.arange(3*2).reshape(3, 2)
xt.example3_xfixed2(x)
def test_broadcast_addition(self):
x = np.array([[2., 3., 4., 5.]])
y = np.array([[1., 2., 3., 4.],
[1., 2., 3., 4.],
[1., 2., 3., 4.]])
res = np.array([[3., 5., 7., 9.],
[3., 5., 7., 9.],
[3., 5., 7., 9.]])
z = xt.array_addition(x, y)
np.testing.assert_allclose(z, res, 1e-12)
def test_broadcast_subtraction(self):
x = np.array([[4., 5., 6., 7.]])
y = np.array([[4., 3., 2., 1.],
[4., 3., 2., 1.],
[4., 3., 2., 1.]])
res = np.array([[0., 2., 4., 6.],
[0., 2., 4., 6.],
[0., 2., 4., 6.]])
z = xt.array_subtraction(x, y)
np.testing.assert_allclose(z, res, 1e-12)
def test_broadcast_multiplication(self):
x = np.array([[1., 2., 3., 4.]])
y = np.array([[3., 2., 3., 2.],
[3., 2., 3., 2.],
[3., 2., 3., 2.]])
res = np.array([[3., 4., 9., 8.],
[3., 4., 9., 8.],
[3., 4., 9., 8.]])
z = xt.array_multiplication(x, y)
np.testing.assert_allclose(z, res, 1e-12)
def test_broadcast_division(self):
x = np.array([[8., 6., 4., 2.]])
y = np.array([[2., 2., 2., 2.],
[2., 2., 2., 2.],
[2., 2., 2., 2.]])
res = np.array([[4., 3., 2., 1.],
[4., 3., 2., 1.],
[4., 3., 2., 1.]])
z = xt.array_division(x, y)
np.testing.assert_allclose(z, res, 1e-12)
def test_vectorize(self):
x1 = np.array([[0, 1], [2, 3]])
x2 = np.array([0, 1])
res = np.array([[0, 2], [2, 4]])
y = xt.vectorize_example1(x1, x2)
np.testing.assert_array_equal(y, res)
def test_readme_example1(self):
v = np.arange(15).reshape(3, 5)
y = xt.readme_example1(v)
np.testing.assert_allclose(y, 1.2853996391883833, 1e-12)
def test_complex_overload_reg(self):
a = 23.23
c = 2.0 + 3.1j
self.assertEqual(xt.complex_overload_reg(a), a)
self.assertEqual(xt.complex_overload_reg(c), c)
def test_complex_overload(self):
a = np.random.rand(3, 3)
b = np.random.rand(3, 3)
c = a + b * 1j
y = xt.complex_overload(c)
np.testing.assert_allclose(np.imag(y), np.imag(c))
np.testing.assert_allclose(np.real(y), np.real(c))
x = xt.complex_overload(b)
self.assertEqual(x.dtype, b.dtype)
np.testing.assert_allclose(x, b)
def test_readme_example2(self):
x = np.arange(15).reshape(3, 5)
y = [1, 2, 3, 4, 5]
z = xt.readme_example2(x, y)
np.testing.assert_allclose(z,
[[-0.540302, 1.257618, 1.89929 , 0.794764, -1.040465],
[-1.499227, 0.136731, 1.646979, 1.643002, 0.128456],
[-1.084323, -0.583843, 0.45342 , 1.073811, 0.706945]], 1e-5)
def test_rect_to_polar(self):
x = np.ones(10, dtype=complex)
z = xt.rect_to_polar(x[::2]);
np.testing.assert_allclose(z, np.ones(5, dtype=float), 1e-5)
def test_shape_comparison(self):
x = np.ones([4, 4])
y = np.ones([5, 5])
z = np.zeros([4, 4])
self.assertFalse(xt.compare_shapes(x, y))
self.assertTrue(xt.compare_shapes(x, z))
def test_int_overload(self):
for dtype in [np.uint8, np.int8, np.uint16, np.int16, np.uint32, np.int32, np.uint64, np.int64]:
b = xt.int_overload(np.ones((10), dtype))
self.assertEqual(str(dtype.__name__), b)
def test_dtype(self):
var = xt.dtype_to_python()
self.assertEqual(var.dtype.names, ('a', 'b', 'c', 'x'))
exp_dtype = {
'a': (np.dtype('float64'), 0),
'b': (np.dtype('int32'), 8),
'c': (np.dtype('int8'), 12),
'x': (np.dtype(('<f8', (3,))), 16)
}
self.assertEqual(var.dtype.fields, exp_dtype)
self.assertEqual(var[0]['a'], 123)
self.assertEqual(var[0]['b'], 321)
self.assertEqual(var[0]['c'], ord('a'))
self.assertTrue(np.all(var[0]['x'] == [1, 2, 3]))
self.assertEqual(var[1]['a'], 111)
self.assertEqual(var[1]['b'], 222)
self.assertEqual(var[1]['c'], ord('x'))
self.assertTrue(np.all(var[1]['x'] == [5, 5, 5]))
d_dtype = np.dtype({'names':['a','b'], 'formats':['<f8','<i4'], 'offsets':[0,8], 'itemsize':16})
darr = np.array([(1, ord('p')), (123, ord('c'))], dtype=d_dtype)
self.assertEqual(darr[0]['a'], 1)
res = xt.dtype_from_python(darr)
self.assertEqual(res[0]['a'], 123.)
self.assertEqual(darr[0]['a'], 123.)
def test_char_array(self):
var = np.array(['hello', 'from', 'python'], dtype=np.dtype('|S20'));
self.assertEqual(var[0], b'hello')
xt.char_array(var)
self.assertEqual(var[0], b'hello')
self.assertEqual(var[1], b'from')
self.assertEqual(var[2], b'c++')
def test_col_row_major(self):
var = np.arange(50, dtype=float).reshape(2, 5, 5)
with self.assertRaises(RuntimeError):
xt.col_major_array(var)
with self.assertRaises(TypeError):
xt.row_major_tensor(var.T)
with self.assertRaises(TypeError):
xt.row_major_tensor(var[:, ::2, ::2])
with self.assertRaises(TypeError):
# raise for wrong dimension
xt.row_major_tensor(var[0, 0, :])
xt.row_major_tensor(var)
varF = np.arange(50, dtype=float).reshape(2, 5, 5, order='F')
xt.col_major_array(varF)
xt.col_major_array(varF[:, :, 0]) # still col major!
def test_xscalar(self):
var = np.arange(50, dtype=int)
self.assertTrue(np.sum(var) == xt.xscalar(var))
def test_bad_argument_call(self):
with self.assertRaises(TypeError):
xt.simple_array("foo")
with self.assertRaises(TypeError):
xt.simple_tensor("foo")
def test_diff_shape_overload(self):
self.assertEqual(1, xt.diff_shape_overload(np.ones(2)))
self.assertEqual(2, xt.diff_shape_overload(np.ones((2, 2))))
with self.assertRaises(TypeError):
# FIXME: the TypeError information is not informative
xt.diff_shape_overload(np.ones((2, 2, 2)))
def test_native_casters(self):
import gc
# check keep alive policy for get_strided_view()
gc.collect()
obj = xt.test_native_casters()
a = obj.get_strided_view()
obj = None
gc.collect()
_ = np.zeros((100, 100))
self.assertEqual(a.sum(), a.size)
# check keep alive policy for get_array_adapter()
gc.collect()
obj = xt.test_native_casters()
a = obj.get_array_adapter()
obj = None
gc.collect()
_ = np.zeros((100, 100))
self.assertEqual(a.sum(), a.size)
# check keep alive policy for get_array_adapter()
gc.collect()
obj = xt.test_native_casters()
a = obj.get_tensor_adapter()
obj = None
gc.collect()
_ = np.zeros((100, 100))
self.assertEqual(a.sum(), a.size)
# check keep alive policy for get_owning_array_adapter()
gc.collect()
obj = xt.test_native_casters()
a = obj.get_owning_array_adapter()
gc.collect()
_ = np.zeros((100, 100))
self.assertEqual(a.sum(), a.size)
# check keep alive policy for view_keep_alive_member_function()
gc.collect()
a = np.ones((100, 100))
b = obj.view_keep_alive_member_function(a)
obj = None
a = None
gc.collect()
_ = np.zeros((100, 100))
self.assertEqual(b.sum(), b.size)
# check shared buffer (insure that no copy is done)
obj = xt.test_native_casters()
arr = obj.get_array()
strided_view = obj.get_strided_view()
strided_view[0, 1] = -1
self.assertEqual(strided_view.shape, (1, 2))
self.assertEqual(arr[0, 2], -1)
adapter = obj.get_array_adapter()
self.assertEqual(adapter.shape, (2, 2))
adapter[1, 1] = -2
self.assertEqual(arr[0, 5], -2)
adapter = obj.get_tensor_adapter()
self.assertEqual(adapter.shape, (2, 2))
adapter[1, 1] = -3
self.assertEqual(arr[0, 5], -3)
class AttributeTest(TestCase):
def setUp(self):
self.c = xt.C()
def test_copy(self):
arr = self.c.copy
arr[0] = 1
self.assertEqual([0.]*4, self.c.copy.tolist())
def test_reference(self):
arr = self.c.ref
arr[0] = 1
self.assertEqual([1.] + [0.]*3, self.c.ref.tolist())