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from setuptools import setup, find_packages
from codecs import open
import os
import sys

from netlib import version

# Based on https://github.com/pypa/sampleproject/blob/master/setup.py
# and https://python-packaging-user-guide.readthedocs.org/
# and https://caremad.io/2014/11/distributing-a-cffi-project/

here = os.path.abspath(os.path.dirname(__file__))

with open(os.path.join(here, 'README.rst'), encoding='utf-8') as f:
    long_description = f.read()

setup(
    name="netlib",
    version=version.VERSION,
    description="A collection of network utilities used by pathod and mitmproxy.",
    long_description=long_description,
    url="http://github.com/mitmproxy/netlib",
    author="Aldo Cortesi",
    author_email="aldo@corte.si",
    license="MIT",
    classifiers=[
        "License :: OSI Approved :: MIT License",
        "Development Status :: 3 - Alpha",
        "Operating System :: POSIX",
        "Programming Language :: Python",
        "Programming Language :: Python :: 2",
        "Programming Language :: Python :: 2.7",
        "Programming Language :: Python :: 3",
        "Programming Language :: Python :: 3.5",
        "Programming Language :: Python :: Implementation :: CPython",
        "Programming Language :: Python :: Implementation :: PyPy",
        "Topic :: Internet",
        "Topic :: Internet :: WWW/HTTP",
        "Topic :: Internet :: WWW/HTTP :: HTTP Servers",
        "Topic :: Software Development :: Testing",
        "Topic :: Software Development :: Testing :: Traffic Generation",
    ],
    packages=find_packages(),
    install_requires=[
        "pyasn1>=0.1.9, <0.2",
        "pyOpenSSL>=0.15.1, <0.16",
        "cryptography>=1.2.2, <1.3",
        "passlib>=1.6.5, <1.7",
        "hpack>=2.1.0, <3.0",
        "hyperframe>=3.2.0, <4.0",
        "six>=1.10.0, <1.11",
        "certifi>=2015.11.20.1",  # no semver here - this should always be on the last release!
        "backports.ssl_match_hostname>=3.5.0.1, <3.6",
    ],
    extras_require={
        # Do not use a range operator here: https://bitbucket.org/pypa/setuptools/issues/380
        # Ubuntu Trusty and other still ship with setuptools < 17.1
        ':python_version == "2.7"': [
            "ipaddress>=1.0.15, <1.1",
        ],
        'dev': [
            "mock>=1.3.0, <1.4",
            "pytest>=2.8.7, <2.9",
            "pytest-xdist>=1.14, <1.15",
            "pytest-cov>=2.2.1, <2.3",
            "pytest-timeout>=1.0.0, <1.1",
            "coveralls>=1.1, <1.2"
        ]
    },
)
n>): assert np.isclose(m.vectorized_func3(np.array(3 + 7j)), [6 + 14j]) for f in [m.vectorized_func, m.vectorized_func2]: 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) """ ) 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) """ ) 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) """ ) 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) """ ) def test_type_selection(): 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." ) def test_docs(doc): assert ( doc(m.vectorized_func) == """ vectorized_func(arg0: numpy.ndarray[numpy.int32], arg1: numpy.ndarray[numpy.float32], arg2: numpy.ndarray[numpy.float64]) -> object """ # noqa: E501 line too long ) def test_trivial_broadcasting(): trivial, vectorized_is_trivial = m.trivial, m.vectorized_is_trivial 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( np.array([[1, 3, 5], [7, 9, 11]]), np.array([[2, 4, 6], [8, 10, 12]]), 3 ) 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 ) 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") 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 y1 = np.array(z1, order="F") y2 = np.array(y1) y3 = np.array(y1) 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 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 def test_passthrough_arguments(doc): assert doc(m.vec_passthrough) == ( "vec_passthrough(" + ", ".join( [ "arg0: float", "arg1: numpy.ndarray[numpy.float64]", "arg2: numpy.ndarray[numpy.float64]", "arg3: numpy.ndarray[numpy.int32]", "arg4: int", "arg5: m.numpy_vectorize.NonPODClass", "arg6: numpy.ndarray[numpy.float64]", ] ) + ") -> object" ) 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( m.vec_passthrough(1, b, c, d, 10000, m.NonPODClass(100000), g) == np.array( [ [1111111, 2111121, 3111131], [1112111, 2112121, 3112131], [1113111, 2113121, 3113131], ] ) ) def test_method_vectorization(): o = m.VectorizeTestClass(3) 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(): 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) assert isinstance(z, np.ndarray) assert z.shape == (1,) z = m.vectorized_func(1, [[[2]]], 3) assert isinstance(z, np.ndarray) assert z.shape == (1, 1, 1) def test_vectorized_noreturn(): x = m.NonPODClass(0) assert x.value == 0 m.add_to(x, [1, 2, 3, 4]) assert x.value == 10 m.add_to(x, 1) assert x.value == 11 m.add_to(x, [[1, 1], [2, 3]]) assert x.value == 18