You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
479 lines
16 KiB
Python
479 lines
16 KiB
Python
import functools
|
|
import operator
|
|
import sys
|
|
import warnings
|
|
import numbers
|
|
from collections import namedtuple
|
|
import inspect
|
|
import math
|
|
|
|
import numpy as np
|
|
|
|
try:
|
|
from numpy.random import Generator as Generator
|
|
except ImportError:
|
|
class Generator(): # type: ignore[no-redef]
|
|
pass
|
|
|
|
|
|
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], ...).
|
|
|
|
Examples
|
|
--------
|
|
|
|
>>> 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)
|
|
tcode = np.mintypecode([a.dtype.char for a in arrays])
|
|
out = np.full(np.shape(arrays[0]), fill_value=fillvalue, dtype=tcode)
|
|
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 _lazyselect(condlist, choicelist, arrays, default=0):
|
|
"""
|
|
Mimic `np.select(condlist, choicelist)`.
|
|
|
|
Notice, it assumes that all `arrays` are of the same shape or can be
|
|
broadcasted together.
|
|
|
|
All functions in `choicelist` must accept array arguments in the order
|
|
given in `arrays` and must return an array of the same shape as broadcasted
|
|
`arrays`.
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.arange(6)
|
|
>>> np.select([x <3, x > 3], [x**2, x**3], default=0)
|
|
array([ 0, 1, 4, 0, 64, 125])
|
|
|
|
>>> _lazyselect([x < 3, x > 3], [lambda x: x**2, lambda x: x**3], (x,))
|
|
array([ 0., 1., 4., 0., 64., 125.])
|
|
|
|
>>> a = -np.ones_like(x)
|
|
>>> _lazyselect([x < 3, x > 3],
|
|
... [lambda x, a: x**2, lambda x, a: a * x**3],
|
|
... (x, a), default=np.nan)
|
|
array([ 0., 1., 4., nan, -64., -125.])
|
|
|
|
"""
|
|
arrays = np.broadcast_arrays(*arrays)
|
|
tcode = np.mintypecode([a.dtype.char for a in arrays])
|
|
out = np.full(np.shape(arrays[0]), fill_value=default, dtype=tcode)
|
|
for index in range(len(condlist)):
|
|
func, cond = choicelist[index], condlist[index]
|
|
if np.all(cond is False):
|
|
continue
|
|
cond, _ = np.broadcast_arrays(cond, arrays[0])
|
|
temp = tuple(np.extract(cond, arr) for arr in arrays)
|
|
np.place(out, cond, func(*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
|
|
|
|
|
|
def _prune_array(array):
|
|
"""Return an array equivalent to the input array. If the input
|
|
array is a view of a much larger array, copy its contents to a
|
|
newly allocated array. Otherwise, return the input unchanged.
|
|
"""
|
|
if array.base is not None and array.size < array.base.size // 2:
|
|
return array.copy()
|
|
return array
|
|
|
|
|
|
def prod(iterable):
|
|
"""
|
|
Product of a sequence of numbers.
|
|
|
|
Faster than np.prod for short lists like array shapes, and does
|
|
not overflow if using Python integers.
|
|
"""
|
|
product = 1
|
|
for x in iterable:
|
|
product *= x
|
|
return product
|
|
|
|
|
|
def float_factorial(n: int) -> float:
|
|
"""Compute the factorial and return as a float
|
|
|
|
Returns infinity when result is too large for a double
|
|
"""
|
|
return float(math.factorial(n)) if n < 171 else np.inf
|
|
|
|
|
|
class DeprecatedImport(object):
|
|
"""
|
|
Deprecated import with redirection and 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.
|
|
If seed is a new-style np.random.Generator, 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
|
|
try:
|
|
# Generator is only available in numpy >= 1.17
|
|
if isinstance(seed, np.random.Generator):
|
|
return seed
|
|
except AttributeError:
|
|
pass
|
|
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.getfullargspec()/
|
|
# The version below is borrowed from Django,
|
|
# https://github.com/django/django/pull/4846.
|
|
|
|
# Note an inconsistency between inspect.getfullargspec(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: `getfullargspec_no_self` which
|
|
# mimics `inspect.getfullargspec` but does not list `self`.
|
|
#
|
|
# This way, the caller code does not need to know whether it uses a legacy
|
|
# .getfullargspec or a bright and shiny .signature.
|
|
|
|
FullArgSpec = namedtuple('FullArgSpec',
|
|
['args', 'varargs', 'varkw', 'defaults',
|
|
'kwonlyargs', 'kwonlydefaults', 'annotations'])
|
|
|
|
def getfullargspec_no_self(func):
|
|
"""inspect.getfullargspec replacement using inspect.signature.
|
|
|
|
If func is a bound method, do not list the 'self' parameter.
|
|
|
|
Parameters
|
|
----------
|
|
func : callable
|
|
A callable to inspect
|
|
|
|
Returns
|
|
-------
|
|
fullargspec : FullArgSpec(args, varargs, varkw, defaults, kwonlyargs,
|
|
kwonlydefaults, annotations)
|
|
|
|
NOTE: if the first argument of `func` is self, it is *not*, I repeat
|
|
*not*, included in fullargspec.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 in [inspect.Parameter.POSITIONAL_OR_KEYWORD,
|
|
inspect.Parameter.POSITIONAL_ONLY]
|
|
]
|
|
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 = tuple(
|
|
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
|
|
kwonlyargs = [
|
|
p.name for p in sig.parameters.values()
|
|
if p.kind == inspect.Parameter.KEYWORD_ONLY
|
|
]
|
|
kwdefaults = {p.name: p.default for p in sig.parameters.values()
|
|
if p.kind == inspect.Parameter.KEYWORD_ONLY and
|
|
p.default is not p.empty}
|
|
annotations = {p.name: p.annotation for p in sig.parameters.values()
|
|
if p.annotation is not p.empty}
|
|
return FullArgSpec(args, varargs, varkw, defaults, kwonlyargs,
|
|
kwdefaults or None, annotations)
|
|
|
|
|
|
class MapWrapper(object):
|
|
"""
|
|
Parallelisation wrapper for working with map-like callables, such as
|
|
`multiprocessing.Pool.map`.
|
|
|
|
Parameters
|
|
----------
|
|
pool : int or map-like callable
|
|
If `pool` is an integer, then it specifies the number of threads to
|
|
use for parallelization. If ``int(pool) == 1``, then no parallel
|
|
processing is used and the map builtin is used.
|
|
If ``pool == -1``, then the pool will utilize all available CPUs.
|
|
If `pool` is a map-like callable that follows the same
|
|
calling sequence as the built-in map function, then this callable is
|
|
used for parallelization.
|
|
"""
|
|
def __init__(self, pool=1):
|
|
self.pool = None
|
|
self._mapfunc = map
|
|
self._own_pool = False
|
|
|
|
if callable(pool):
|
|
self.pool = pool
|
|
self._mapfunc = self.pool
|
|
else:
|
|
from multiprocessing import Pool
|
|
# user supplies a number
|
|
if int(pool) == -1:
|
|
# use as many processors as possible
|
|
self.pool = Pool()
|
|
self._mapfunc = self.pool.map
|
|
self._own_pool = True
|
|
elif int(pool) == 1:
|
|
pass
|
|
elif int(pool) > 1:
|
|
# use the number of processors requested
|
|
self.pool = Pool(processes=int(pool))
|
|
self._mapfunc = self.pool.map
|
|
self._own_pool = True
|
|
else:
|
|
raise RuntimeError("Number of workers specified must be -1,"
|
|
" an int >= 1, or an object with a 'map' method")
|
|
|
|
def __enter__(self):
|
|
return self
|
|
|
|
def terminate(self):
|
|
if self._own_pool:
|
|
self.pool.terminate()
|
|
|
|
def join(self):
|
|
if self._own_pool:
|
|
self.pool.join()
|
|
|
|
def close(self):
|
|
if self._own_pool:
|
|
self.pool.close()
|
|
|
|
def __exit__(self, exc_type, exc_value, traceback):
|
|
if self._own_pool:
|
|
self.pool.close()
|
|
self.pool.terminate()
|
|
|
|
def __call__(self, func, iterable):
|
|
# only accept one iterable because that's all Pool.map accepts
|
|
try:
|
|
return self._mapfunc(func, iterable)
|
|
except TypeError as e:
|
|
# wrong number of arguments
|
|
raise TypeError("The map-like callable must be of the"
|
|
" form f(func, iterable)") from e
|
|
|
|
|
|
def rng_integers(gen, low, high=None, size=None, dtype='int64',
|
|
endpoint=False):
|
|
"""
|
|
Return random integers from low (inclusive) to high (exclusive), or if
|
|
endpoint=True, low (inclusive) to high (inclusive). Replaces
|
|
`RandomState.randint` (with endpoint=False) and
|
|
`RandomState.random_integers` (with endpoint=True).
|
|
|
|
Return random integers from the "discrete uniform" distribution of the
|
|
specified dtype. If high is None (the default), then results are from
|
|
0 to low.
|
|
|
|
Parameters
|
|
----------
|
|
gen: {None, np.random.RandomState, np.random.Generator}
|
|
Random number generator. If None, then the np.random.RandomState
|
|
singleton is used.
|
|
low: int or array-like of ints
|
|
Lowest (signed) integers to be drawn from the distribution (unless
|
|
high=None, in which case this parameter is 0 and this value is used
|
|
for high).
|
|
high: int or array-like of ints
|
|
If provided, one above the largest (signed) integer to be drawn from
|
|
the distribution (see above for behavior if high=None). If array-like,
|
|
must contain integer values.
|
|
size: None
|
|
Output shape. If the given shape is, e.g., (m, n, k), then m * n * k
|
|
samples are drawn. Default is None, in which case a single value is
|
|
returned.
|
|
dtype: {str, dtype}, optional
|
|
Desired dtype of the result. All dtypes are determined by their name,
|
|
i.e., 'int64', 'int', etc, so byteorder is not available and a specific
|
|
precision may have different C types depending on the platform.
|
|
The default value is np.int_.
|
|
endpoint: bool, optional
|
|
If True, sample from the interval [low, high] instead of the default
|
|
[low, high) Defaults to False.
|
|
|
|
Returns
|
|
-------
|
|
out: int or ndarray of ints
|
|
size-shaped array of random integers from the appropriate distribution,
|
|
or a single such random int if size not provided.
|
|
"""
|
|
if isinstance(gen, Generator):
|
|
return gen.integers(low, high=high, size=size, dtype=dtype,
|
|
endpoint=endpoint)
|
|
else:
|
|
if gen is None:
|
|
# default is RandomState singleton used by np.random.
|
|
gen = np.random.mtrand._rand
|
|
if endpoint:
|
|
# inclusive of endpoint
|
|
# remember that low and high can be arrays, so don't modify in
|
|
# place
|
|
if high is None:
|
|
return gen.randint(low + 1, size=size, dtype=dtype)
|
|
if high is not None:
|
|
return gen.randint(low, high=high + 1, size=size, dtype=dtype)
|
|
|
|
# exclusive
|
|
return gen.randint(low, high=high, size=size, dtype=dtype)
|