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Merged
merged 11 commits into from
Dec 9, 2019
Merged

Bug pylint 2885 #732

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be4075b
The shape attribute is an numpy.ndarray
hippo91 Dec 8, 2019
302052f
The ufunc functions are now instance s of FakeUfunc class that mimics…
hippo91 Dec 8, 2019
2de6eb3
geterrobj et seterrobj are not ufunc objects
hippo91 Dec 8, 2019
a26e111
Takes into account the fact that
hippo91 Dec 8, 2019
09859b2
Adds a special Ufunc class for ufunc objects that return pair of ndarray
hippo91 Dec 8, 2019
74faff8
Adds unittest for ufunc objects returning pair of ndarray
hippo91 Dec 8, 2019
91f4ea2
Ufunc objects are no more functions but instances a fake Ufunc class
hippo91 Dec 8, 2019
5d5d3a0
Reactivates tests for functions whose result is inferred as an ndarra…
hippo91 Dec 8, 2019
fc996da
Adds two entries dealing with numpy
hippo91 Dec 9, 2019
271bcc7
Corrects links toward issues
hippo91 Dec 9, 2019
d427e15
Reformatting according to black
hippo91 Dec 9, 2019
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10 changes: 10 additions & 0 deletions ChangeLog
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,16 @@ What's New in astroid 2.4.0?
============================
Release Date: TBA

* All the ``numpy ufunc`` functions derived now from a common class that
implements the specific ``reduce``, ``accumulate``, ``reduceat``,
``outer`` and ``at`` methods.

Close PyCQA/pylint#2885

* The ``shape`` attribute of a ``numpy ndarray`` is now a ``ndarray``

Close PyCQA/pylint#3139

* Don't ignore special methods when inspecting gi classes

Close #728
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178 changes: 107 additions & 71 deletions astroid/brain/brain_numpy_core_umath.py
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Expand Up @@ -16,84 +16,120 @@ def numpy_core_umath_transform():
)
return astroid.parse(
"""
class FakeUfunc:
def __init__(self):
self.__doc__ = str()
self.__name__ = str()
self.nin = 0
self.nout = 0
self.nargs = 0
self.ntypes = 0
self.types = None
self.identity = None
self.signature = None

@classmethod
def reduce(cls, a, axis=None, dtype=None, out=None):
return numpy.ndarray([0, 0])

@classmethod
def accumulate(cls, array, axis=None, dtype=None, out=None):
return numpy.ndarray([0, 0])

@classmethod
def reduceat(cls, a, indices, axis=None, dtype=None, out=None):
return numpy.ndarray([0, 0])

@classmethod
def outer(cls, A, B, **kwargs):
return numpy.ndarray([0, 0])

@classmethod
def at(cls, a, indices, b=None):
return numpy.ndarray([0, 0])

class FakeUfuncOneArg(FakeUfunc):
def __call__(self, x, {opt_args:s}):
return numpy.ndarray([0, 0])

class FakeUfuncOneArgBis(FakeUfunc):
def __call__(self, x, {opt_args:s}):
return numpy.ndarray([0, 0]), numpy.ndarray([0, 0])

class FakeUfuncTwoArgs(FakeUfunc):
def __call__(self, x1, x2, {opt_args:s}):
return numpy.ndarray([0, 0])

# Constants
e = 2.718281828459045
euler_gamma = 0.5772156649015329

# No arg functions
def geterrobj(): return []

# One arg functions
def seterrobj(errobj): return None

# One arg functions with optional kwargs
def arccos(x, {opt_args:s}): return numpy.ndarray((0, 0))
def arccosh(x, {opt_args:s}): return numpy.ndarray((0, 0))
def arcsin(x, {opt_args:s}): return numpy.ndarray((0, 0))
def arcsinh(x, {opt_args:s}): return numpy.ndarray((0, 0))
def arctan(x, {opt_args:s}): return numpy.ndarray((0, 0))
def arctanh(x, {opt_args:s}): return numpy.ndarray((0, 0))
def cbrt(x, {opt_args:s}): return numpy.ndarray((0, 0))
def conj(x, {opt_args:s}): return numpy.ndarray((0, 0))
def conjugate(x, {opt_args:s}): return numpy.ndarray((0, 0))
def cosh(x, {opt_args:s}): return numpy.ndarray((0, 0))
def deg2rad(x, {opt_args:s}): return numpy.ndarray((0, 0))
def degrees(x, {opt_args:s}): return numpy.ndarray((0, 0))
def exp2(x, {opt_args:s}): return numpy.ndarray((0, 0))
def expm1(x, {opt_args:s}): return numpy.ndarray((0, 0))
def fabs(x, {opt_args:s}): return numpy.ndarray((0, 0))
def frexp(x, {opt_args:s}): return (numpy.ndarray((0, 0)), numpy.ndarray((0, 0)))
def isfinite(x, {opt_args:s}): return numpy.ndarray((0, 0))
def isinf(x, {opt_args:s}): return numpy.ndarray((0, 0))
def log(x, {opt_args:s}): return numpy.ndarray((0, 0))
def log1p(x, {opt_args:s}): return numpy.ndarray((0, 0))
def log2(x, {opt_args:s}): return numpy.ndarray((0, 0))
def logical_not(x, {opt_args:s}): return numpy.ndarray((0, 0))
def modf(x, {opt_args:s}): return (numpy.ndarray((0, 0)), numpy.ndarray((0, 0)))
def negative(x, {opt_args:s}): return numpy.ndarray((0, 0))
def rad2deg(x, {opt_args:s}): return numpy.ndarray((0, 0))
def radians(x, {opt_args:s}): return numpy.ndarray((0, 0))
def reciprocal(x, {opt_args:s}): return numpy.ndarray((0, 0))
def rint(x, {opt_args:s}): return numpy.ndarray((0, 0))
def sign(x, {opt_args:s}): return numpy.ndarray((0, 0))
def signbit(x, {opt_args:s}): return numpy.ndarray((0, 0))
def sinh(x, {opt_args:s}): return numpy.ndarray((0, 0))
def spacing(x, {opt_args:s}): return numpy.ndarray((0, 0))
def square(x, {opt_args:s}): return numpy.ndarray((0, 0))
def tan(x, {opt_args:s}): return numpy.ndarray((0, 0))
def tanh(x, {opt_args:s}): return numpy.ndarray((0, 0))
def trunc(x, {opt_args:s}): return numpy.ndarray((0, 0))
arccos = FakeUfuncOneArg()
arccosh = FakeUfuncOneArg()
arcsin = FakeUfuncOneArg()
arcsinh = FakeUfuncOneArg()
arctan = FakeUfuncOneArg()
arctanh = FakeUfuncOneArg()
cbrt = FakeUfuncOneArg()
conj = FakeUfuncOneArg()
conjugate = FakeUfuncOneArg()
cosh = FakeUfuncOneArg()
deg2rad = FakeUfuncOneArg()
exp2 = FakeUfuncOneArg()
expm1 = FakeUfuncOneArg()
fabs = FakeUfuncOneArg()
frexp = FakeUfuncOneArgBis()
isfinite = FakeUfuncOneArg()
isinf = FakeUfuncOneArg()
log = FakeUfuncOneArg()
log1p = FakeUfuncOneArg()
log2 = FakeUfuncOneArg()
logical_not = FakeUfuncOneArg()
modf = FakeUfuncOneArgBis()
negative = FakeUfuncOneArg()
rad2deg = FakeUfuncOneArg()
reciprocal = FakeUfuncOneArg()
rint = FakeUfuncOneArg()
sign = FakeUfuncOneArg()
signbit = FakeUfuncOneArg()
sinh = FakeUfuncOneArg()
spacing = FakeUfuncOneArg()
square = FakeUfuncOneArg()
tan = FakeUfuncOneArg()
tanh = FakeUfuncOneArg()
trunc = FakeUfuncOneArg()

# Two args functions with optional kwargs
def bitwise_and(x1, x2, {opt_args:s}): return numpy.ndarray((0, 0))
def bitwise_or(x1, x2, {opt_args:s}): return numpy.ndarray((0, 0))
def bitwise_xor(x1, x2, {opt_args:s}): return numpy.ndarray((0, 0))
def copysign(x1, x2, {opt_args:s}): return numpy.ndarray((0, 0))
def divide(x1, x2, {opt_args:s}): return numpy.ndarray((0, 0))
def equal(x1, x2, {opt_args:s}): return numpy.ndarray((0, 0))
def floor_divide(x1, x2, {opt_args:s}): return numpy.ndarray((0, 0))
def fmax(x1, x2, {opt_args:s}): return numpy.ndarray((0, 0))
def fmin(x1, x2, {opt_args:s}): return numpy.ndarray((0, 0))
def fmod(x1, x2, {opt_args:s}): return numpy.ndarray((0, 0))
def greater(x1, x2, {opt_args:s}): return numpy.ndarray((0, 0))
def hypot(x1, x2, {opt_args:s}): return numpy.ndarray((0, 0))
def ldexp(x1, x2, {opt_args:s}): return numpy.ndarray((0, 0))
def left_shift(x1, x2, {opt_args:s}): return numpy.ndarray((0, 0))
def less(x1, x2, {opt_args:s}): return numpy.ndarray((0, 0))
def logaddexp(x1, x2, {opt_args:s}): return numpy.ndarray((0, 0))
def logaddexp2(x1, x2, {opt_args:s}): return numpy.ndarray((0, 0))
def logical_and(x1, x2, {opt_args:s}): return numpy.ndarray([0, 0])
def logical_or(x1, x2, {opt_args:s}): return numpy.ndarray([0, 0])
def logical_xor(x1, x2, {opt_args:s}): return numpy.ndarray([0, 0])
def maximum(x1, x2, {opt_args:s}): return numpy.ndarray((0, 0))
def minimum(x1, x2, {opt_args:s}): return numpy.ndarray((0, 0))
def nextafter(x1, x2, {opt_args:s}): return numpy.ndarray((0, 0))
def not_equal(x1, x2, {opt_args:s}): return numpy.ndarray((0, 0))
def power(x1, x2, {opt_args:s}): return numpy.ndarray((0, 0))
def remainder(x1, x2, {opt_args:s}): return numpy.ndarray((0, 0))
def right_shift(x1, x2, {opt_args:s}): return numpy.ndarray((0, 0))
def subtract(x1, x2, {opt_args:s}): return numpy.ndarray((0, 0))
def true_divide(x1, x2, {opt_args:s}): return numpy.ndarray((0, 0))
bitwise_and = FakeUfuncTwoArgs()
bitwise_or = FakeUfuncTwoArgs()
bitwise_xor = FakeUfuncTwoArgs()
copysign = FakeUfuncTwoArgs()
divide = FakeUfuncTwoArgs()
equal = FakeUfuncTwoArgs()
floor_divide = FakeUfuncTwoArgs()
fmax = FakeUfuncTwoArgs()
fmin = FakeUfuncTwoArgs()
fmod = FakeUfuncTwoArgs()
greater = FakeUfuncTwoArgs()
hypot = FakeUfuncTwoArgs()
ldexp = FakeUfuncTwoArgs()
left_shift = FakeUfuncTwoArgs()
less = FakeUfuncTwoArgs()
logaddexp = FakeUfuncTwoArgs()
logaddexp2 = FakeUfuncTwoArgs()
logical_and = FakeUfuncTwoArgs()
logical_or = FakeUfuncTwoArgs()
logical_xor = FakeUfuncTwoArgs()
maximum = FakeUfuncTwoArgs()
minimum = FakeUfuncTwoArgs()
nextafter = FakeUfuncTwoArgs()
not_equal = FakeUfuncTwoArgs()
power = FakeUfuncTwoArgs()
remainder = FakeUfuncTwoArgs()
right_shift = FakeUfuncTwoArgs()
subtract = FakeUfuncTwoArgs()
true_divide = FakeUfuncTwoArgs()
""".format(
opt_args=ufunc_optional_keyword_arguments
)
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2 changes: 1 addition & 1 deletion astroid/brain/brain_numpy_ndarray.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,7 +29,7 @@ def __init__(self, shape, dtype=float, buffer=None, offset=0,
self.nbytes = None
self.ndim = None
self.real = None
self.shape = None
self.shape = numpy.ndarray([0, 0])
self.size = None
self.strides = None

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