from __future__ import division, print_function, absolute_import
import functools
import operator
import sys
import warnings
import numbers
from collections import namedtuple
import inspect
import numpy as np
def _valarray(shape, value=np.nan, typecode=None):
"""Return an array of all value.
"""
out = np.ones(shape, dtype=bool) * value
if typecode is not None:
out = out.astype(typecode)
if not isinstance(out, np.ndarray):
out = np.asarray(out)
return out
def _lazywhere(cond, arrays, f, fillvalue=None, f2=None):
"""
np.where(cond, x, fillvalue) always evaluates x even where cond is False.
This one only evaluates f(arr1[cond], arr2[cond], ...).
For example,
>>> a, b = np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8])
>>> def f(a, b):
return a*b
>>> _lazywhere(a > 2, (a, b), f, np.nan)
array([ nan, nan, 21., 32.])
Notice it assumes that all `arrays` are of the same shape, or can be
broadcasted together.
"""
if fillvalue is None:
if f2 is None:
raise ValueError("One of (fillvalue, f2) must be given.")
else:
fillvalue = np.nan
else:
if f2 is not None:
raise ValueError("Only one of (fillvalue, f2) can be given.")
arrays = np.broadcast_arrays(*arrays)
temp = tuple(np.extract(cond, arr) for arr in arrays)
out = _valarray(np.shape(arrays[0]), value=fillvalue)
np.place(out, cond, f(*temp))
if f2 is not None:
temp = tuple(np.extract(~cond, arr) for arr in arrays)
np.place(out, ~cond, f2(*temp))
return out
def _aligned_zeros(shape, dtype=float, order="C", align=None):
"""Allocate a new ndarray with aligned memory.
Primary use case for this currently is working around a f2py issue
in Numpy 1.9.1, where dtype.alignment is such that np.zeros() does
not necessarily create arrays aligned up to it.
"""
dtype = np.dtype(dtype)
if align is None:
align = dtype.alignment
if not hasattr(shape, '__len__'):
shape = (shape,)
size = functools.reduce(operator.mul, shape) * dtype.itemsize
buf = np.empty(size + align + 1, np.uint8)
offset = buf.__array_interface__['data'][0] % align
if offset != 0:
offset = align - offset
# Note: slices producing 0-size arrays do not necessarily change
# data pointer --- so we use and allocate size+1
buf = buf[offset:offset+size+1][:-1]
data = np.ndarray(shape, dtype, buf, order=order)
data.fill(0)
return data
class DeprecatedImport(object):
"""
Deprecated import, with redirection + warning.
Examples
--------
Suppose you previously had in some module::
from foo import spam
If this has to be deprecated, do::
spam = DeprecatedImport("foo.spam", "baz")
to redirect users to use "baz" module instead.
"""
def __init__(self, old_module_name, new_module_name):
self._old_name = old_module_name
self._new_name = new_module_name
__import__(self._new_name)
self._mod = sys.modules[self._new_name]
def __dir__(self):
return dir(self._mod)
def __getattr__(self, name):
warnings.warn("Module %s is deprecated, use %s instead"
% (self._old_name, self._new_name),
DeprecationWarning)
return getattr(self._mod, name)
# copy-pasted from scikit-learn utils/validation.py
def check_random_state(seed):
"""Turn seed into a np.random.RandomState instance
If seed is None (or np.random), return the RandomState singleton used
by np.random.
If seed is an int, return a new RandomState instance seeded with seed.
If seed is already a RandomState instance, return it.
Otherwise raise ValueError.
"""
if seed is None or seed is np.random:
return np.random.mtrand._rand
if isinstance(seed, (numbers.Integral, np.integer)):
return np.random.RandomState(seed)
if isinstance(seed, np.random.RandomState):
return seed
raise ValueError('%r cannot be used to seed a numpy.random.RandomState'
' instance' % seed)
def _asarray_validated(a, check_finite=True,
sparse_ok=False, objects_ok=False, mask_ok=False,
as_inexact=False):
"""
Helper function for scipy argument validation.
Many scipy linear algebra functions do support arbitrary array-like
input arguments. Examples of commonly unsupported inputs include
matrices containing inf/nan, sparse matrix representations, and
matrices with complicated elements.
Parameters
----------
a : array_like
The array-like input.
check_finite : bool, optional
Whether to check that the input matrices contain only finite numbers.
Disabling may give a performance gain, but may result in problems
(crashes, non-termination) if the inputs do contain infinities or NaNs.
Default: True
sparse_ok : bool, optional
True if scipy sparse matrices are allowed.
objects_ok : bool, optional
True if arrays with dype('O') are allowed.
mask_ok : bool, optional
True if masked arrays are allowed.
as_inexact : bool, optional
True to convert the input array to a np.inexact dtype.
Returns
-------
ret : ndarray
The converted validated array.
"""
if not sparse_ok:
import scipy.sparse
if scipy.sparse.issparse(a):
msg = ('Sparse matrices are not supported by this function. '
'Perhaps one of the scipy.sparse.linalg functions '
'would work instead.')
raise ValueError(msg)
if not mask_ok:
if np.ma.isMaskedArray(a):
raise ValueError('masked arrays are not supported')
toarray = np.asarray_chkfinite if check_finite else np.asarray
a = toarray(a)
if not objects_ok:
if a.dtype is np.dtype('O'):
raise ValueError('object arrays are not supported')
if as_inexact:
if not np.issubdtype(a.dtype, np.inexact):
a = toarray(a, dtype=np.float_)
return a
# Add a replacement for inspect.getargspec() which is deprecated in python 3.5
# The version below is borrowed from Django,
# https://github.com/django/django/pull/4846
# Note an inconsistency between inspect.getargspec(func) and
# inspect.signature(func). If `func` is a bound method, the latter does *not*
# list `self` as a first argument, while the former *does*.
# Hence cook up a common ground replacement: `getargspec_no_self` which
# mimics `inspect.getargspec` but does not list `self`.
#
# This way, the caller code does not need to know whether it uses a legacy
# .getargspec or bright and shiny .signature.
try:
# is it python 3.3 or higher?
inspect.signature # @UndefinedVariable
# Apparently, yes. Wrap inspect.signature
ArgSpec = namedtuple('ArgSpec', ['args', 'varargs', 'keywords', 'defaults'])
def getargspec_no_self(func):
"""inspect.getargspec replacement using inspect.signature.
inspect.getargspec is deprecated in python 3. This is a replacement
based on the (new in python 3.3) `inspect.signature`.
Parameters
----------
func : callable
A callable to inspect
Returns
-------
argspec : ArgSpec(args, varargs, varkw, defaults)
This is similar to the result of inspect.getargspec(func) under
python 2.x.
NOTE: if the first argument of `func` is self, it is *not*, I repeat
*not* included in argspec.args.
This is done for consistency between inspect.getargspec() under
python 2.x, and inspect.signature() under python 3.x.
"""
sig = inspect.signature(func)
args = [
p.name for p in sig.parameters.values()
if p.kind == inspect.Parameter.POSITIONAL_OR_KEYWORD
]
varargs = [
p.name for p in sig.parameters.values()
if p.kind == inspect.Parameter.VAR_POSITIONAL
]
varargs = varargs[0] if varargs else None
varkw = [
p.name for p in sig.parameters.values()
if p.kind == inspect.Parameter.VAR_KEYWORD
]
varkw = varkw[0] if varkw else None
defaults = [
p.default for p in sig.parameters.values()
if (p.kind == inspect.Parameter.POSITIONAL_OR_KEYWORD and
p.default is not p.empty)
] or None
return ArgSpec(args, varargs, varkw, defaults)
except AttributeError:
# python 2.x
def getargspec_no_self(func):
"""inspect.getargspec replacement for compatibility with python 3.x.
inspect.getargspec is deprecated in python 3. This wraps it, and
*removes* `self` from the argument list of `func`, if present.
This is done for forward compatibility with python 3.
Parameters
----------
func : callable
A callable to inspect
Returns
-------
argspec : ArgSpec(args, varargs, varkw, defaults)
This is similar to the result of inspect.getargspec(func) under
python 2.x.
NOTE: if the first argument of `func` is self, it is *not*, I repeat
*not* included in argspec.args.
This is done for consistency between inspect.getargspec() under
python 2.x, and inspect.signature() under python 3.x.
"""
argspec = inspect.getargspec(func)
if argspec.args[0] == 'self':
argspec.args.pop(0)
return argspec