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pybind11/tests/test_numpy_vectorize.py

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Port tests to pytest Use simple asserts and pytest's powerful introspection to make testing simpler. This merges the old .py/.ref file pairs into simple .py files where the expected values are right next to the code being tested. This commit does not touch the C++ part of the code and replicates the Python tests exactly like the old .ref-file-based approach.
2016-08-12 11:50:00 +00:00
import pytest
Update all remaining tests to new test styles This udpates all the remaining tests to the new test suite code and comment styles started in #898. For the most part, the test coverage here is unchanged, with a few minor exceptions as noted below. - test_constants_and_functions: this adds more overload tests with overloads with different number of arguments for more comprehensive overload_cast testing. The test style conversion broke the overload tests under MSVC 2015, prompting the additional tests while looking for a workaround. - test_eigen: this dropped the unused functions `get_cm_corners` and `get_cm_corners_const`--these same tests were duplicates of the same things provided (and used) via ReturnTester methods. - test_opaque_types: this test had a hidden dependence on ExampleMandA which is now fixed by using the global UserType which suffices for the relevant test. - test_methods_and_attributes: this required some additions to UserType to make it usable as a replacement for the test's previous SimpleType: UserType gained a value mutator, and the `value` property is not mutable (it was previously readonly). Some overload tests were also added to better test overload_cast (as described above). - test_numpy_array: removed the untemplated mutate_data/mutate_data_t: the templated versions with an empty parameter pack expand to the same thing. - test_stl: this was already mostly in the new style; this just tweaks things a bit, localizing a class, and adding some missing `// test_whatever` comments. - test_virtual_functions: like `test_stl`, this was mostly in the new test style already, but needed some `// test_whatever` comments. This commit also moves the inherited virtual example code to the end of the file, after the main set of tests (since it is less important than the other tests, and rather length); it also got renamed to `test_inherited_virtuals` (from `test_inheriting_repeat`) because it tests both inherited virtual approaches, not just the repeat approach.
2017-07-25 20:47:36 +00:00
from pybind11_tests import numpy_vectorize as m
Port tests to pytest Use simple asserts and pytest's powerful introspection to make testing simpler. This merges the old .py/.ref file pairs into simple .py files where the expected values are right next to the code being tested. This commit does not touch the C++ part of the code and replicates the Python tests exactly like the old .ref-file-based approach.
2016-08-12 11:50:00 +00:00
Move requires_numpy, etc. decorators to globals test_eigen.py and test_numpy_*.py have the same @pytest.requires_eigen_and_numpy or @pytest.requires_numpy on every single test; this changes them to use pytest's global `pytestmark = ...` instead to disable the entire module when numpy and/or eigen aren't available.
2017-01-24 16:26:51 +00:00
pytestmark = pytest.requires_numpy
Port tests to pytest Use simple asserts and pytest's powerful introspection to make testing simpler. This merges the old .py/.ref file pairs into simple .py files where the expected values are right next to the code being tested. This commit does not touch the C++ part of the code and replicates the Python tests exactly like the old .ref-file-based approach.
2016-08-12 11:50:00 +00:00
with pytest.suppress(ImportError):
import numpy as np
def test_vectorize(capture):
Update all remaining tests to new test styles This udpates all the remaining tests to the new test suite code and comment styles started in #898. For the most part, the test coverage here is unchanged, with a few minor exceptions as noted below. - test_constants_and_functions: this adds more overload tests with overloads with different number of arguments for more comprehensive overload_cast testing. The test style conversion broke the overload tests under MSVC 2015, prompting the additional tests while looking for a workaround. - test_eigen: this dropped the unused functions `get_cm_corners` and `get_cm_corners_const`--these same tests were duplicates of the same things provided (and used) via ReturnTester methods. - test_opaque_types: this test had a hidden dependence on ExampleMandA which is now fixed by using the global UserType which suffices for the relevant test. - test_methods_and_attributes: this required some additions to UserType to make it usable as a replacement for the test's previous SimpleType: UserType gained a value mutator, and the `value` property is not mutable (it was previously readonly). Some overload tests were also added to better test overload_cast (as described above). - test_numpy_array: removed the untemplated mutate_data/mutate_data_t: the templated versions with an empty parameter pack expand to the same thing. - test_stl: this was already mostly in the new style; this just tweaks things a bit, localizing a class, and adding some missing `// test_whatever` comments. - test_virtual_functions: like `test_stl`, this was mostly in the new test style already, but needed some `// test_whatever` comments. This commit also moves the inherited virtual example code to the end of the file, after the main set of tests (since it is less important than the other tests, and rather length); it also got renamed to `test_inherited_virtuals` (from `test_inheriting_repeat`) because it tests both inherited virtual approaches, not just the repeat approach.
2017-07-25 20:47:36 +00:00
assert np.isclose(m.vectorized_func3(np.array(3 + 7j)), [6 + 14j])
Port tests to pytest Use simple asserts and pytest's powerful introspection to make testing simpler. This merges the old .py/.ref file pairs into simple .py files where the expected values are right next to the code being tested. This commit does not touch the C++ part of the code and replicates the Python tests exactly like the old .ref-file-based approach.
2016-08-12 11:50:00 +00:00
Update all remaining tests to new test styles This udpates all the remaining tests to the new test suite code and comment styles started in #898. For the most part, the test coverage here is unchanged, with a few minor exceptions as noted below. - test_constants_and_functions: this adds more overload tests with overloads with different number of arguments for more comprehensive overload_cast testing. The test style conversion broke the overload tests under MSVC 2015, prompting the additional tests while looking for a workaround. - test_eigen: this dropped the unused functions `get_cm_corners` and `get_cm_corners_const`--these same tests were duplicates of the same things provided (and used) via ReturnTester methods. - test_opaque_types: this test had a hidden dependence on ExampleMandA which is now fixed by using the global UserType which suffices for the relevant test. - test_methods_and_attributes: this required some additions to UserType to make it usable as a replacement for the test's previous SimpleType: UserType gained a value mutator, and the `value` property is not mutable (it was previously readonly). Some overload tests were also added to better test overload_cast (as described above). - test_numpy_array: removed the untemplated mutate_data/mutate_data_t: the templated versions with an empty parameter pack expand to the same thing. - test_stl: this was already mostly in the new style; this just tweaks things a bit, localizing a class, and adding some missing `// test_whatever` comments. - test_virtual_functions: like `test_stl`, this was mostly in the new test style already, but needed some `// test_whatever` comments. This commit also moves the inherited virtual example code to the end of the file, after the main set of tests (since it is less important than the other tests, and rather length); it also got renamed to `test_inherited_virtuals` (from `test_inheriting_repeat`) because it tests both inherited virtual approaches, not just the repeat approach.
2017-07-25 20:47:36 +00:00
for f in [m.vectorized_func, m.vectorized_func2]:
Port tests to pytest Use simple asserts and pytest's powerful introspection to make testing simpler. This merges the old .py/.ref file pairs into simple .py files where the expected values are right next to the code being tested. This commit does not touch the C++ part of the code and replicates the Python tests exactly like the old .ref-file-based approach.
2016-08-12 11:50:00 +00:00
with capture:
assert np.isclose(f(1, 2, 3), 6)
assert capture == "my_func(x:int=1, y:float=2, z:float=3)"
with capture:
assert np.isclose(f(np.array(1), np.array(2), 3), 6)
assert capture == "my_func(x:int=1, y:float=2, z:float=3)"
with capture:
assert np.allclose(f(np.array([1, 3]), np.array([2, 4]), 3), [6, 36])
assert capture == """
my_func(x:int=1, y:float=2, z:float=3)
my_func(x:int=3, y:float=4, z:float=3)
"""
vectorize: trivial handling for F-order arrays This extends the trivial handling to support trivial handling for Fortran-order arrays (i.e. column major): if inputs aren't all C-contiguous, but *are* all F-contiguous, the resulting array will be F-contiguous and we can do trivial processing. For anything else (e.g. C-contiguous, or inputs requiring non-trivial processing), the result is in (numpy-default) C-contiguous layout.
2017-03-19 00:11:59 +00:00
with capture:
a = np.array([[1, 2], [3, 4]], order='F')
b = np.array([[10, 20], [30, 40]], order='F')
c = 3
result = f(a, b, c)
assert np.allclose(result, a * b * c)
assert result.flags.f_contiguous
# All inputs are F order and full or singletons, so we the result is in col-major order:
assert capture == """
my_func(x:int=1, y:float=10, z:float=3)
my_func(x:int=3, y:float=30, z:float=3)
my_func(x:int=2, y:float=20, z:float=3)
my_func(x:int=4, y:float=40, z:float=3)
"""
Port tests to pytest Use simple asserts and pytest's powerful introspection to make testing simpler. This merges the old .py/.ref file pairs into simple .py files where the expected values are right next to the code being tested. This commit does not touch the C++ part of the code and replicates the Python tests exactly like the old .ref-file-based approach.
2016-08-12 11:50:00 +00:00
with capture:
a, b, c = np.array([[1, 3, 5], [7, 9, 11]]), np.array([[2, 4, 6], [8, 10, 12]]), 3
assert np.allclose(f(a, b, c), a * b * c)
assert capture == """
my_func(x:int=1, y:float=2, z:float=3)
my_func(x:int=3, y:float=4, z:float=3)
my_func(x:int=5, y:float=6, z:float=3)
my_func(x:int=7, y:float=8, z:float=3)
my_func(x:int=9, y:float=10, z:float=3)
my_func(x:int=11, y:float=12, z:float=3)
"""
with capture:
a, b, c = np.array([[1, 2, 3], [4, 5, 6]]), np.array([2, 3, 4]), 2
assert np.allclose(f(a, b, c), a * b * c)
assert capture == """
my_func(x:int=1, y:float=2, z:float=2)
my_func(x:int=2, y:float=3, z:float=2)
my_func(x:int=3, y:float=4, z:float=2)
my_func(x:int=4, y:float=2, z:float=2)
my_func(x:int=5, y:float=3, z:float=2)
my_func(x:int=6, y:float=4, z:float=2)
"""
with capture:
a, b, c = np.array([[1, 2, 3], [4, 5, 6]]), np.array([[2], [3]]), 2
assert np.allclose(f(a, b, c), a * b * c)
assert capture == """
my_func(x:int=1, y:float=2, z:float=2)
my_func(x:int=2, y:float=2, z:float=2)
my_func(x:int=3, y:float=2, z:float=2)
my_func(x:int=4, y:float=3, z:float=2)
my_func(x:int=5, y:float=3, z:float=2)
my_func(x:int=6, y:float=3, z:float=2)
"""
Stop forcing c-contiguous in py::vectorize The only part of the vectorize code that actually needs c-contiguous is the "trivial" broadcast; for non-trivial arguments, the code already uses strides properly (and so handles C-style, F-style, neither, slices, etc.) This commit rewrites `broadcast` to additionally check for C-contiguous storage, then takes off the `c_style` flag for the arguments, which will keep the functionality more or less the same, except for no longer requiring an array copy for non-c-contiguous input arrays. Additionally, if we're given a singleton slice (e.g. a[0::4, 0::4] for a 4x4 or smaller array), we no longer fail triviality because the trivial code path never actually uses the strides on a singleton.
2017-03-15 03:57:56 +00:00
with capture:
a, b, c = np.array([[1, 2, 3], [4, 5, 6]], order='F'), np.array([[2], [3]]), 2
assert np.allclose(f(a, b, c), a * b * c)
assert capture == """
my_func(x:int=1, y:float=2, z:float=2)
my_func(x:int=2, y:float=2, z:float=2)
my_func(x:int=3, y:float=2, z:float=2)
my_func(x:int=4, y:float=3, z:float=2)
my_func(x:int=5, y:float=3, z:float=2)
my_func(x:int=6, y:float=3, z:float=2)
"""
with capture:
a, b, c = np.array([[1, 2, 3], [4, 5, 6]])[::, ::2], np.array([[2], [3]]), 2
assert np.allclose(f(a, b, c), a * b * c)
assert capture == """
my_func(x:int=1, y:float=2, z:float=2)
my_func(x:int=3, y:float=2, z:float=2)
my_func(x:int=4, y:float=3, z:float=2)
my_func(x:int=6, y:float=3, z:float=2)
"""
with capture:
a, b, c = np.array([[1, 2, 3], [4, 5, 6]], order='F')[::, ::2], np.array([[2], [3]]), 2
assert np.allclose(f(a, b, c), a * b * c)
assert capture == """
my_func(x:int=1, y:float=2, z:float=2)
my_func(x:int=3, y:float=2, z:float=2)
my_func(x:int=4, y:float=3, z:float=2)
my_func(x:int=6, y:float=3, z:float=2)
"""
Port tests to pytest Use simple asserts and pytest's powerful introspection to make testing simpler. This merges the old .py/.ref file pairs into simple .py files where the expected values are right next to the code being tested. This commit does not touch the C++ part of the code and replicates the Python tests exactly like the old .ref-file-based approach.
2016-08-12 11:50:00 +00:00
Simplify tests by replacing output capture with asserts where possible The C++ part of the test code is modified to achieve this. As a result, this kind of test: ```python with capture: kw_func1(5, y=10) assert capture == "kw_func(x=5, y=10)" ``` can be replaced with a simple: `assert kw_func1(5, y=10) == "x=5, y=10"`
2016-08-12 20:28:31 +00:00
def test_type_selection():
Update all remaining tests to new test styles This udpates all the remaining tests to the new test suite code and comment styles started in #898. For the most part, the test coverage here is unchanged, with a few minor exceptions as noted below. - test_constants_and_functions: this adds more overload tests with overloads with different number of arguments for more comprehensive overload_cast testing. The test style conversion broke the overload tests under MSVC 2015, prompting the additional tests while looking for a workaround. - test_eigen: this dropped the unused functions `get_cm_corners` and `get_cm_corners_const`--these same tests were duplicates of the same things provided (and used) via ReturnTester methods. - test_opaque_types: this test had a hidden dependence on ExampleMandA which is now fixed by using the global UserType which suffices for the relevant test. - test_methods_and_attributes: this required some additions to UserType to make it usable as a replacement for the test's previous SimpleType: UserType gained a value mutator, and the `value` property is not mutable (it was previously readonly). Some overload tests were also added to better test overload_cast (as described above). - test_numpy_array: removed the untemplated mutate_data/mutate_data_t: the templated versions with an empty parameter pack expand to the same thing. - test_stl: this was already mostly in the new style; this just tweaks things a bit, localizing a class, and adding some missing `// test_whatever` comments. - test_virtual_functions: like `test_stl`, this was mostly in the new test style already, but needed some `// test_whatever` comments. This commit also moves the inherited virtual example code to the end of the file, after the main set of tests (since it is less important than the other tests, and rather length); it also got renamed to `test_inherited_virtuals` (from `test_inheriting_repeat`) because it tests both inherited virtual approaches, not just the repeat approach.
2017-07-25 20:47:36 +00:00
assert m.selective_func(np.array([1], dtype=np.int32)) == "Int branch taken."
assert m.selective_func(np.array([1.0], dtype=np.float32)) == "Float branch taken."
assert m.selective_func(np.array([1.0j], dtype=np.complex64)) == "Complex float branch taken."
Port tests to pytest Use simple asserts and pytest's powerful introspection to make testing simpler. This merges the old .py/.ref file pairs into simple .py files where the expected values are right next to the code being tested. This commit does not touch the C++ part of the code and replicates the Python tests exactly like the old .ref-file-based approach.
2016-08-12 11:50:00 +00:00
def test_docs(doc):
Update all remaining tests to new test styles This udpates all the remaining tests to the new test suite code and comment styles started in #898. For the most part, the test coverage here is unchanged, with a few minor exceptions as noted below. - test_constants_and_functions: this adds more overload tests with overloads with different number of arguments for more comprehensive overload_cast testing. The test style conversion broke the overload tests under MSVC 2015, prompting the additional tests while looking for a workaround. - test_eigen: this dropped the unused functions `get_cm_corners` and `get_cm_corners_const`--these same tests were duplicates of the same things provided (and used) via ReturnTester methods. - test_opaque_types: this test had a hidden dependence on ExampleMandA which is now fixed by using the global UserType which suffices for the relevant test. - test_methods_and_attributes: this required some additions to UserType to make it usable as a replacement for the test's previous SimpleType: UserType gained a value mutator, and the `value` property is not mutable (it was previously readonly). Some overload tests were also added to better test overload_cast (as described above). - test_numpy_array: removed the untemplated mutate_data/mutate_data_t: the templated versions with an empty parameter pack expand to the same thing. - test_stl: this was already mostly in the new style; this just tweaks things a bit, localizing a class, and adding some missing `// test_whatever` comments. - test_virtual_functions: like `test_stl`, this was mostly in the new test style already, but needed some `// test_whatever` comments. This commit also moves the inherited virtual example code to the end of the file, after the main set of tests (since it is less important than the other tests, and rather length); it also got renamed to `test_inherited_virtuals` (from `test_inheriting_repeat`) because it tests both inherited virtual approaches, not just the repeat approach.
2017-07-25 20:47:36 +00:00
assert doc(m.vectorized_func) == """
Improve py::array_t scalar type information (#724) * Add value_type member alias to py::array_t (resolve #632) * Use numpy scalar name in py::array_t function signatures (e.g. float32/64 instead of just float)
2017-03-13 18:17:18 +00:00
vectorized_func(arg0: numpy.ndarray[int32], arg1: numpy.ndarray[float32], arg2: numpy.ndarray[float64]) -> object
Set maximum line length for Python style checker (#552)
2016-12-12 23:59:28 +00:00
""" # noqa: E501 line too long
Stop forcing c-contiguous in py::vectorize The only part of the vectorize code that actually needs c-contiguous is the "trivial" broadcast; for non-trivial arguments, the code already uses strides properly (and so handles C-style, F-style, neither, slices, etc.) This commit rewrites `broadcast` to additionally check for C-contiguous storage, then takes off the `c_style` flag for the arguments, which will keep the functionality more or less the same, except for no longer requiring an array copy for non-c-contiguous input arrays. Additionally, if we're given a singleton slice (e.g. a[0::4, 0::4] for a 4x4 or smaller array), we no longer fail triviality because the trivial code path never actually uses the strides on a singleton.
2017-03-15 03:57:56 +00:00
def test_trivial_broadcasting():
Update all remaining tests to new test styles This udpates all the remaining tests to the new test suite code and comment styles started in #898. For the most part, the test coverage here is unchanged, with a few minor exceptions as noted below. - test_constants_and_functions: this adds more overload tests with overloads with different number of arguments for more comprehensive overload_cast testing. The test style conversion broke the overload tests under MSVC 2015, prompting the additional tests while looking for a workaround. - test_eigen: this dropped the unused functions `get_cm_corners` and `get_cm_corners_const`--these same tests were duplicates of the same things provided (and used) via ReturnTester methods. - test_opaque_types: this test had a hidden dependence on ExampleMandA which is now fixed by using the global UserType which suffices for the relevant test. - test_methods_and_attributes: this required some additions to UserType to make it usable as a replacement for the test's previous SimpleType: UserType gained a value mutator, and the `value` property is not mutable (it was previously readonly). Some overload tests were also added to better test overload_cast (as described above). - test_numpy_array: removed the untemplated mutate_data/mutate_data_t: the templated versions with an empty parameter pack expand to the same thing. - test_stl: this was already mostly in the new style; this just tweaks things a bit, localizing a class, and adding some missing `// test_whatever` comments. - test_virtual_functions: like `test_stl`, this was mostly in the new test style already, but needed some `// test_whatever` comments. This commit also moves the inherited virtual example code to the end of the file, after the main set of tests (since it is less important than the other tests, and rather length); it also got renamed to `test_inherited_virtuals` (from `test_inheriting_repeat`) because it tests both inherited virtual approaches, not just the repeat approach.
2017-07-25 20:47:36 +00:00
trivial, vectorized_is_trivial = m.trivial, m.vectorized_is_trivial
Stop forcing c-contiguous in py::vectorize The only part of the vectorize code that actually needs c-contiguous is the "trivial" broadcast; for non-trivial arguments, the code already uses strides properly (and so handles C-style, F-style, neither, slices, etc.) This commit rewrites `broadcast` to additionally check for C-contiguous storage, then takes off the `c_style` flag for the arguments, which will keep the functionality more or less the same, except for no longer requiring an array copy for non-c-contiguous input arrays. Additionally, if we're given a singleton slice (e.g. a[0::4, 0::4] for a 4x4 or smaller array), we no longer fail triviality because the trivial code path never actually uses the strides on a singleton.
2017-03-15 03:57:56 +00:00
vectorize: trivial handling for F-order arrays This extends the trivial handling to support trivial handling for Fortran-order arrays (i.e. column major): if inputs aren't all C-contiguous, but *are* all F-contiguous, the resulting array will be F-contiguous and we can do trivial processing. For anything else (e.g. C-contiguous, or inputs requiring non-trivial processing), the result is in (numpy-default) C-contiguous layout.
2017-03-19 00:11:59 +00:00
assert vectorized_is_trivial(1, 2, 3) == trivial.c_trivial
assert vectorized_is_trivial(np.array(1), np.array(2), 3) == trivial.c_trivial
assert vectorized_is_trivial(np.array([1, 3]), np.array([2, 4]), 3) == trivial.c_trivial
assert trivial.c_trivial == vectorized_is_trivial(
Stop forcing c-contiguous in py::vectorize The only part of the vectorize code that actually needs c-contiguous is the "trivial" broadcast; for non-trivial arguments, the code already uses strides properly (and so handles C-style, F-style, neither, slices, etc.) This commit rewrites `broadcast` to additionally check for C-contiguous storage, then takes off the `c_style` flag for the arguments, which will keep the functionality more or less the same, except for no longer requiring an array copy for non-c-contiguous input arrays. Additionally, if we're given a singleton slice (e.g. a[0::4, 0::4] for a 4x4 or smaller array), we no longer fail triviality because the trivial code path never actually uses the strides on a singleton.
2017-03-15 03:57:56 +00:00
np.array([[1, 3, 5], [7, 9, 11]]), np.array([[2, 4, 6], [8, 10, 12]]), 3)
vectorize: trivial handling for F-order arrays This extends the trivial handling to support trivial handling for Fortran-order arrays (i.e. column major): if inputs aren't all C-contiguous, but *are* all F-contiguous, the resulting array will be F-contiguous and we can do trivial processing. For anything else (e.g. C-contiguous, or inputs requiring non-trivial processing), the result is in (numpy-default) C-contiguous layout.
2017-03-19 00:11:59 +00:00
assert vectorized_is_trivial(
np.array([[1, 2, 3], [4, 5, 6]]), np.array([2, 3, 4]), 2) == trivial.non_trivial
assert vectorized_is_trivial(
np.array([[1, 2, 3], [4, 5, 6]]), np.array([[2], [3]]), 2) == trivial.non_trivial
Stop forcing c-contiguous in py::vectorize The only part of the vectorize code that actually needs c-contiguous is the "trivial" broadcast; for non-trivial arguments, the code already uses strides properly (and so handles C-style, F-style, neither, slices, etc.) This commit rewrites `broadcast` to additionally check for C-contiguous storage, then takes off the `c_style` flag for the arguments, which will keep the functionality more or less the same, except for no longer requiring an array copy for non-c-contiguous input arrays. Additionally, if we're given a singleton slice (e.g. a[0::4, 0::4] for a 4x4 or smaller array), we no longer fail triviality because the trivial code path never actually uses the strides on a singleton.
2017-03-15 03:57:56 +00:00
z1 = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype='int32')
z2 = np.array(z1, dtype='float32')
z3 = np.array(z1, dtype='float64')
vectorize: trivial handling for F-order arrays This extends the trivial handling to support trivial handling for Fortran-order arrays (i.e. column major): if inputs aren't all C-contiguous, but *are* all F-contiguous, the resulting array will be F-contiguous and we can do trivial processing. For anything else (e.g. C-contiguous, or inputs requiring non-trivial processing), the result is in (numpy-default) C-contiguous layout.
2017-03-19 00:11:59 +00:00
assert vectorized_is_trivial(z1, z2, z3) == trivial.c_trivial
assert vectorized_is_trivial(1, z2, z3) == trivial.c_trivial
assert vectorized_is_trivial(z1, 1, z3) == trivial.c_trivial
assert vectorized_is_trivial(z1, z2, 1) == trivial.c_trivial
assert vectorized_is_trivial(z1[::2, ::2], 1, 1) == trivial.non_trivial
assert vectorized_is_trivial(1, 1, z1[::2, ::2]) == trivial.c_trivial
assert vectorized_is_trivial(1, 1, z3[::2, ::2]) == trivial.non_trivial
assert vectorized_is_trivial(z1, 1, z3[1::4, 1::4]) == trivial.c_trivial
Stop forcing c-contiguous in py::vectorize The only part of the vectorize code that actually needs c-contiguous is the "trivial" broadcast; for non-trivial arguments, the code already uses strides properly (and so handles C-style, F-style, neither, slices, etc.) This commit rewrites `broadcast` to additionally check for C-contiguous storage, then takes off the `c_style` flag for the arguments, which will keep the functionality more or less the same, except for no longer requiring an array copy for non-c-contiguous input arrays. Additionally, if we're given a singleton slice (e.g. a[0::4, 0::4] for a 4x4 or smaller array), we no longer fail triviality because the trivial code path never actually uses the strides on a singleton.
2017-03-15 03:57:56 +00:00
y1 = np.array(z1, order='F')
y2 = np.array(y1)
y3 = np.array(y1)
vectorize: trivial handling for F-order arrays This extends the trivial handling to support trivial handling for Fortran-order arrays (i.e. column major): if inputs aren't all C-contiguous, but *are* all F-contiguous, the resulting array will be F-contiguous and we can do trivial processing. For anything else (e.g. C-contiguous, or inputs requiring non-trivial processing), the result is in (numpy-default) C-contiguous layout.
2017-03-19 00:11:59 +00:00
assert vectorized_is_trivial(y1, y2, y3) == trivial.f_trivial
assert vectorized_is_trivial(y1, 1, 1) == trivial.f_trivial
assert vectorized_is_trivial(1, y2, 1) == trivial.f_trivial
assert vectorized_is_trivial(1, 1, y3) == trivial.f_trivial
assert vectorized_is_trivial(y1, z2, 1) == trivial.non_trivial
assert vectorized_is_trivial(z1[1::4, 1::4], y2, 1) == trivial.f_trivial
assert vectorized_is_trivial(y1[1::4, 1::4], z2, 1) == trivial.c_trivial
Update all remaining tests to new test styles This udpates all the remaining tests to the new test suite code and comment styles started in #898. For the most part, the test coverage here is unchanged, with a few minor exceptions as noted below. - test_constants_and_functions: this adds more overload tests with overloads with different number of arguments for more comprehensive overload_cast testing. The test style conversion broke the overload tests under MSVC 2015, prompting the additional tests while looking for a workaround. - test_eigen: this dropped the unused functions `get_cm_corners` and `get_cm_corners_const`--these same tests were duplicates of the same things provided (and used) via ReturnTester methods. - test_opaque_types: this test had a hidden dependence on ExampleMandA which is now fixed by using the global UserType which suffices for the relevant test. - test_methods_and_attributes: this required some additions to UserType to make it usable as a replacement for the test's previous SimpleType: UserType gained a value mutator, and the `value` property is not mutable (it was previously readonly). Some overload tests were also added to better test overload_cast (as described above). - test_numpy_array: removed the untemplated mutate_data/mutate_data_t: the templated versions with an empty parameter pack expand to the same thing. - test_stl: this was already mostly in the new style; this just tweaks things a bit, localizing a class, and adding some missing `// test_whatever` comments. - test_virtual_functions: like `test_stl`, this was mostly in the new test style already, but needed some `// test_whatever` comments. This commit also moves the inherited virtual example code to the end of the file, after the main set of tests (since it is less important than the other tests, and rather length); it also got renamed to `test_inherited_virtuals` (from `test_inheriting_repeat`) because it tests both inherited virtual approaches, not just the repeat approach.
2017-07-25 20:47:36 +00:00
assert m.vectorized_func(z1, z2, z3).flags.c_contiguous
assert m.vectorized_func(y1, y2, y3).flags.f_contiguous
assert m.vectorized_func(z1, 1, 1).flags.c_contiguous
assert m.vectorized_func(1, y2, 1).flags.f_contiguous
assert m.vectorized_func(z1[1::4, 1::4], y2, 1).flags.f_contiguous
assert m.vectorized_func(y1[1::4, 1::4], z2, 1).flags.c_contiguous
vectorize: pass-through of non-vectorizable args This extends py::vectorize to automatically pass through non-vectorizable arguments. This removes the need for the documented "explicitly exclude an argument" workaround. Vectorization now applies to arithmetic, std::complex, and POD types, passed as plain value or by const lvalue reference (previously only pass-by-value types were supported). Non-const lvalue references and any other types are passed through as-is. Functions with rvalue reference arguments (whether vectorizable or not) are explicitly prohibited: an rvalue reference is inherently not something that can be passed multiple times and is thus unsuitable to being in a vectorized function. The vectorize returned value is also now more sensitive to inputs: previously it would return by value when all inputs are of size 1; this is now amended to having all inputs of size 1 *and* 0 dimensions. Thus if you pass in, for example, [[1]], you get back a 1x1, 2D array, while previously you got back just the resulting single value. Vectorization of member function specializations is now also supported via `py::vectorize(&Class::method)`; this required passthrough support for the initial object pointer on the wrapping function pointer.
2017-03-26 03:51:40 +00:00
def test_passthrough_arguments(doc):
Update all remaining tests to new test styles This udpates all the remaining tests to the new test suite code and comment styles started in #898. For the most part, the test coverage here is unchanged, with a few minor exceptions as noted below. - test_constants_and_functions: this adds more overload tests with overloads with different number of arguments for more comprehensive overload_cast testing. The test style conversion broke the overload tests under MSVC 2015, prompting the additional tests while looking for a workaround. - test_eigen: this dropped the unused functions `get_cm_corners` and `get_cm_corners_const`--these same tests were duplicates of the same things provided (and used) via ReturnTester methods. - test_opaque_types: this test had a hidden dependence on ExampleMandA which is now fixed by using the global UserType which suffices for the relevant test. - test_methods_and_attributes: this required some additions to UserType to make it usable as a replacement for the test's previous SimpleType: UserType gained a value mutator, and the `value` property is not mutable (it was previously readonly). Some overload tests were also added to better test overload_cast (as described above). - test_numpy_array: removed the untemplated mutate_data/mutate_data_t: the templated versions with an empty parameter pack expand to the same thing. - test_stl: this was already mostly in the new style; this just tweaks things a bit, localizing a class, and adding some missing `// test_whatever` comments. - test_virtual_functions: like `test_stl`, this was mostly in the new test style already, but needed some `// test_whatever` comments. This commit also moves the inherited virtual example code to the end of the file, after the main set of tests (since it is less important than the other tests, and rather length); it also got renamed to `test_inherited_virtuals` (from `test_inheriting_repeat`) because it tests both inherited virtual approaches, not just the repeat approach.
2017-07-25 20:47:36 +00:00
assert doc(m.vec_passthrough) == (
"vec_passthrough(" + ", ".join([
"arg0: float",
"arg1: numpy.ndarray[float64]",
"arg2: numpy.ndarray[float64]",
"arg3: numpy.ndarray[int32]",
"arg4: int",
"arg5: m.numpy_vectorize.NonPODClass",
"arg6: numpy.ndarray[float64]"]) + ") -> object")
vectorize: pass-through of non-vectorizable args This extends py::vectorize to automatically pass through non-vectorizable arguments. This removes the need for the documented "explicitly exclude an argument" workaround. Vectorization now applies to arithmetic, std::complex, and POD types, passed as plain value or by const lvalue reference (previously only pass-by-value types were supported). Non-const lvalue references and any other types are passed through as-is. Functions with rvalue reference arguments (whether vectorizable or not) are explicitly prohibited: an rvalue reference is inherently not something that can be passed multiple times and is thus unsuitable to being in a vectorized function. The vectorize returned value is also now more sensitive to inputs: previously it would return by value when all inputs are of size 1; this is now amended to having all inputs of size 1 *and* 0 dimensions. Thus if you pass in, for example, [[1]], you get back a 1x1, 2D array, while previously you got back just the resulting single value. Vectorization of member function specializations is now also supported via `py::vectorize(&Class::method)`; this required passthrough support for the initial object pointer on the wrapping function pointer.
2017-03-26 03:51:40 +00:00
b = np.array([[10, 20, 30]], dtype='float64')
c = np.array([100, 200]) # NOT a vectorized argument
d = np.array([[1000], [2000], [3000]], dtype='int')
g = np.array([[1000000, 2000000, 3000000]], dtype='int') # requires casting
assert np.all(
Update all remaining tests to new test styles This udpates all the remaining tests to the new test suite code and comment styles started in #898. For the most part, the test coverage here is unchanged, with a few minor exceptions as noted below. - test_constants_and_functions: this adds more overload tests with overloads with different number of arguments for more comprehensive overload_cast testing. The test style conversion broke the overload tests under MSVC 2015, prompting the additional tests while looking for a workaround. - test_eigen: this dropped the unused functions `get_cm_corners` and `get_cm_corners_const`--these same tests were duplicates of the same things provided (and used) via ReturnTester methods. - test_opaque_types: this test had a hidden dependence on ExampleMandA which is now fixed by using the global UserType which suffices for the relevant test. - test_methods_and_attributes: this required some additions to UserType to make it usable as a replacement for the test's previous SimpleType: UserType gained a value mutator, and the `value` property is not mutable (it was previously readonly). Some overload tests were also added to better test overload_cast (as described above). - test_numpy_array: removed the untemplated mutate_data/mutate_data_t: the templated versions with an empty parameter pack expand to the same thing. - test_stl: this was already mostly in the new style; this just tweaks things a bit, localizing a class, and adding some missing `// test_whatever` comments. - test_virtual_functions: like `test_stl`, this was mostly in the new test style already, but needed some `// test_whatever` comments. This commit also moves the inherited virtual example code to the end of the file, after the main set of tests (since it is less important than the other tests, and rather length); it also got renamed to `test_inherited_virtuals` (from `test_inheriting_repeat`) because it tests both inherited virtual approaches, not just the repeat approach.
2017-07-25 20:47:36 +00:00
m.vec_passthrough(1, b, c, d, 10000, m.NonPODClass(100000), g) ==
vectorize: pass-through of non-vectorizable args This extends py::vectorize to automatically pass through non-vectorizable arguments. This removes the need for the documented "explicitly exclude an argument" workaround. Vectorization now applies to arithmetic, std::complex, and POD types, passed as plain value or by const lvalue reference (previously only pass-by-value types were supported). Non-const lvalue references and any other types are passed through as-is. Functions with rvalue reference arguments (whether vectorizable or not) are explicitly prohibited: an rvalue reference is inherently not something that can be passed multiple times and is thus unsuitable to being in a vectorized function. The vectorize returned value is also now more sensitive to inputs: previously it would return by value when all inputs are of size 1; this is now amended to having all inputs of size 1 *and* 0 dimensions. Thus if you pass in, for example, [[1]], you get back a 1x1, 2D array, while previously you got back just the resulting single value. Vectorization of member function specializations is now also supported via `py::vectorize(&Class::method)`; this required passthrough support for the initial object pointer on the wrapping function pointer.
2017-03-26 03:51:40 +00:00
np.array([[1111111, 2111121, 3111131],
[1112111, 2112121, 3112131],
[1113111, 2113121, 3113131]]))
def test_method_vectorization():
Update all remaining tests to new test styles This udpates all the remaining tests to the new test suite code and comment styles started in #898. For the most part, the test coverage here is unchanged, with a few minor exceptions as noted below. - test_constants_and_functions: this adds more overload tests with overloads with different number of arguments for more comprehensive overload_cast testing. The test style conversion broke the overload tests under MSVC 2015, prompting the additional tests while looking for a workaround. - test_eigen: this dropped the unused functions `get_cm_corners` and `get_cm_corners_const`--these same tests were duplicates of the same things provided (and used) via ReturnTester methods. - test_opaque_types: this test had a hidden dependence on ExampleMandA which is now fixed by using the global UserType which suffices for the relevant test. - test_methods_and_attributes: this required some additions to UserType to make it usable as a replacement for the test's previous SimpleType: UserType gained a value mutator, and the `value` property is not mutable (it was previously readonly). Some overload tests were also added to better test overload_cast (as described above). - test_numpy_array: removed the untemplated mutate_data/mutate_data_t: the templated versions with an empty parameter pack expand to the same thing. - test_stl: this was already mostly in the new style; this just tweaks things a bit, localizing a class, and adding some missing `// test_whatever` comments. - test_virtual_functions: like `test_stl`, this was mostly in the new test style already, but needed some `// test_whatever` comments. This commit also moves the inherited virtual example code to the end of the file, after the main set of tests (since it is less important than the other tests, and rather length); it also got renamed to `test_inherited_virtuals` (from `test_inheriting_repeat`) because it tests both inherited virtual approaches, not just the repeat approach.
2017-07-25 20:47:36 +00:00
o = m.VectorizeTestClass(3)
vectorize: pass-through of non-vectorizable args This extends py::vectorize to automatically pass through non-vectorizable arguments. This removes the need for the documented "explicitly exclude an argument" workaround. Vectorization now applies to arithmetic, std::complex, and POD types, passed as plain value or by const lvalue reference (previously only pass-by-value types were supported). Non-const lvalue references and any other types are passed through as-is. Functions with rvalue reference arguments (whether vectorizable or not) are explicitly prohibited: an rvalue reference is inherently not something that can be passed multiple times and is thus unsuitable to being in a vectorized function. The vectorize returned value is also now more sensitive to inputs: previously it would return by value when all inputs are of size 1; this is now amended to having all inputs of size 1 *and* 0 dimensions. Thus if you pass in, for example, [[1]], you get back a 1x1, 2D array, while previously you got back just the resulting single value. Vectorization of member function specializations is now also supported via `py::vectorize(&Class::method)`; this required passthrough support for the initial object pointer on the wrapping function pointer.
2017-03-26 03:51:40 +00:00
x = np.array([1, 2], dtype='int')
y = np.array([[10], [20]], dtype='float32')
assert np.all(o.method(x, y) == [[14, 15], [24, 25]])
def test_array_collapse():
Update all remaining tests to new test styles This udpates all the remaining tests to the new test suite code and comment styles started in #898. For the most part, the test coverage here is unchanged, with a few minor exceptions as noted below. - test_constants_and_functions: this adds more overload tests with overloads with different number of arguments for more comprehensive overload_cast testing. The test style conversion broke the overload tests under MSVC 2015, prompting the additional tests while looking for a workaround. - test_eigen: this dropped the unused functions `get_cm_corners` and `get_cm_corners_const`--these same tests were duplicates of the same things provided (and used) via ReturnTester methods. - test_opaque_types: this test had a hidden dependence on ExampleMandA which is now fixed by using the global UserType which suffices for the relevant test. - test_methods_and_attributes: this required some additions to UserType to make it usable as a replacement for the test's previous SimpleType: UserType gained a value mutator, and the `value` property is not mutable (it was previously readonly). Some overload tests were also added to better test overload_cast (as described above). - test_numpy_array: removed the untemplated mutate_data/mutate_data_t: the templated versions with an empty parameter pack expand to the same thing. - test_stl: this was already mostly in the new style; this just tweaks things a bit, localizing a class, and adding some missing `// test_whatever` comments. - test_virtual_functions: like `test_stl`, this was mostly in the new test style already, but needed some `// test_whatever` comments. This commit also moves the inherited virtual example code to the end of the file, after the main set of tests (since it is less important than the other tests, and rather length); it also got renamed to `test_inherited_virtuals` (from `test_inheriting_repeat`) because it tests both inherited virtual approaches, not just the repeat approach.
2017-07-25 20:47:36 +00:00
assert not isinstance(m.vectorized_func(1, 2, 3), np.ndarray)
assert not isinstance(m.vectorized_func(np.array(1), 2, 3), np.ndarray)
z = m.vectorized_func([1], 2, 3)
vectorize: pass-through of non-vectorizable args This extends py::vectorize to automatically pass through non-vectorizable arguments. This removes the need for the documented "explicitly exclude an argument" workaround. Vectorization now applies to arithmetic, std::complex, and POD types, passed as plain value or by const lvalue reference (previously only pass-by-value types were supported). Non-const lvalue references and any other types are passed through as-is. Functions with rvalue reference arguments (whether vectorizable or not) are explicitly prohibited: an rvalue reference is inherently not something that can be passed multiple times and is thus unsuitable to being in a vectorized function. The vectorize returned value is also now more sensitive to inputs: previously it would return by value when all inputs are of size 1; this is now amended to having all inputs of size 1 *and* 0 dimensions. Thus if you pass in, for example, [[1]], you get back a 1x1, 2D array, while previously you got back just the resulting single value. Vectorization of member function specializations is now also supported via `py::vectorize(&Class::method)`; this required passthrough support for the initial object pointer on the wrapping function pointer.
2017-03-26 03:51:40 +00:00
assert isinstance(z, np.ndarray)
assert z.shape == (1, )
Update all remaining tests to new test styles This udpates all the remaining tests to the new test suite code and comment styles started in #898. For the most part, the test coverage here is unchanged, with a few minor exceptions as noted below. - test_constants_and_functions: this adds more overload tests with overloads with different number of arguments for more comprehensive overload_cast testing. The test style conversion broke the overload tests under MSVC 2015, prompting the additional tests while looking for a workaround. - test_eigen: this dropped the unused functions `get_cm_corners` and `get_cm_corners_const`--these same tests were duplicates of the same things provided (and used) via ReturnTester methods. - test_opaque_types: this test had a hidden dependence on ExampleMandA which is now fixed by using the global UserType which suffices for the relevant test. - test_methods_and_attributes: this required some additions to UserType to make it usable as a replacement for the test's previous SimpleType: UserType gained a value mutator, and the `value` property is not mutable (it was previously readonly). Some overload tests were also added to better test overload_cast (as described above). - test_numpy_array: removed the untemplated mutate_data/mutate_data_t: the templated versions with an empty parameter pack expand to the same thing. - test_stl: this was already mostly in the new style; this just tweaks things a bit, localizing a class, and adding some missing `// test_whatever` comments. - test_virtual_functions: like `test_stl`, this was mostly in the new test style already, but needed some `// test_whatever` comments. This commit also moves the inherited virtual example code to the end of the file, after the main set of tests (since it is less important than the other tests, and rather length); it also got renamed to `test_inherited_virtuals` (from `test_inheriting_repeat`) because it tests both inherited virtual approaches, not just the repeat approach.
2017-07-25 20:47:36 +00:00
z = m.vectorized_func(1, [[[2]]], 3)
vectorize: pass-through of non-vectorizable args This extends py::vectorize to automatically pass through non-vectorizable arguments. This removes the need for the documented "explicitly exclude an argument" workaround. Vectorization now applies to arithmetic, std::complex, and POD types, passed as plain value or by const lvalue reference (previously only pass-by-value types were supported). Non-const lvalue references and any other types are passed through as-is. Functions with rvalue reference arguments (whether vectorizable or not) are explicitly prohibited: an rvalue reference is inherently not something that can be passed multiple times and is thus unsuitable to being in a vectorized function. The vectorize returned value is also now more sensitive to inputs: previously it would return by value when all inputs are of size 1; this is now amended to having all inputs of size 1 *and* 0 dimensions. Thus if you pass in, for example, [[1]], you get back a 1x1, 2D array, while previously you got back just the resulting single value. Vectorization of member function specializations is now also supported via `py::vectorize(&Class::method)`; this required passthrough support for the initial object pointer on the wrapping function pointer.
2017-03-26 03:51:40 +00:00
assert isinstance(z, np.ndarray)
assert z.shape == (1, 1, 1)
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