555 lines
22 KiB
Python
555 lines
22 KiB
Python
|
"""Functions copypasted from newer versions of numpy.
|
||
|
|
||
|
"""
|
||
|
from __future__ import division, print_function, absolute_import
|
||
|
|
||
|
import warnings
|
||
|
import sys
|
||
|
from warnings import WarningMessage
|
||
|
import re
|
||
|
from functools import wraps
|
||
|
import numpy as np
|
||
|
|
||
|
from scipy._lib._version import NumpyVersion
|
||
|
|
||
|
|
||
|
if NumpyVersion(np.__version__) > '1.7.0.dev':
|
||
|
_assert_warns = np.testing.assert_warns
|
||
|
else:
|
||
|
def _assert_warns(warning_class, func, *args, **kw):
|
||
|
r"""
|
||
|
Fail unless the given callable throws the specified warning.
|
||
|
|
||
|
This definition is copypasted from numpy 1.9.0.dev.
|
||
|
The version in earlier numpy returns None.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
warning_class : class
|
||
|
The class defining the warning that `func` is expected to throw.
|
||
|
func : callable
|
||
|
The callable to test.
|
||
|
*args : Arguments
|
||
|
Arguments passed to `func`.
|
||
|
**kwargs : Kwargs
|
||
|
Keyword arguments passed to `func`.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
The value returned by `func`.
|
||
|
|
||
|
"""
|
||
|
with warnings.catch_warnings(record=True) as l:
|
||
|
warnings.simplefilter('always')
|
||
|
result = func(*args, **kw)
|
||
|
if not len(l) > 0:
|
||
|
raise AssertionError("No warning raised when calling %s"
|
||
|
% func.__name__)
|
||
|
if not l[0].category is warning_class:
|
||
|
raise AssertionError("First warning for %s is not a "
|
||
|
"%s( is %s)" % (func.__name__, warning_class, l[0]))
|
||
|
return result
|
||
|
|
||
|
|
||
|
if NumpyVersion(np.__version__) >= '1.10.0':
|
||
|
from numpy import broadcast_to
|
||
|
else:
|
||
|
# Definition of `broadcast_to` from numpy 1.10.0.
|
||
|
|
||
|
def _maybe_view_as_subclass(original_array, new_array):
|
||
|
if type(original_array) is not type(new_array):
|
||
|
# if input was an ndarray subclass and subclasses were OK,
|
||
|
# then view the result as that subclass.
|
||
|
new_array = new_array.view(type=type(original_array))
|
||
|
# Since we have done something akin to a view from original_array, we
|
||
|
# should let the subclass finalize (if it has it implemented, i.e., is
|
||
|
# not None).
|
||
|
if new_array.__array_finalize__:
|
||
|
new_array.__array_finalize__(original_array)
|
||
|
return new_array
|
||
|
|
||
|
def _broadcast_to(array, shape, subok, readonly):
|
||
|
shape = tuple(shape) if np.iterable(shape) else (shape,)
|
||
|
array = np.array(array, copy=False, subok=subok)
|
||
|
if not shape and array.shape:
|
||
|
raise ValueError('cannot broadcast a non-scalar to a scalar array')
|
||
|
if any(size < 0 for size in shape):
|
||
|
raise ValueError('all elements of broadcast shape must be non-'
|
||
|
'negative')
|
||
|
broadcast = np.nditer(
|
||
|
(array,), flags=['multi_index', 'refs_ok', 'zerosize_ok'],
|
||
|
op_flags=['readonly'], itershape=shape, order='C').itviews[0]
|
||
|
result = _maybe_view_as_subclass(array, broadcast)
|
||
|
if not readonly and array.flags.writeable:
|
||
|
result.flags.writeable = True
|
||
|
return result
|
||
|
|
||
|
def broadcast_to(array, shape, subok=False):
|
||
|
return _broadcast_to(array, shape, subok=subok, readonly=True)
|
||
|
|
||
|
|
||
|
if NumpyVersion(np.__version__) >= '1.9.0':
|
||
|
from numpy import unique
|
||
|
else:
|
||
|
# the return_counts keyword was added in 1.9.0
|
||
|
def unique(ar, return_index=False, return_inverse=False, return_counts=False):
|
||
|
"""
|
||
|
Find the unique elements of an array.
|
||
|
|
||
|
Returns the sorted unique elements of an array. There are three optional
|
||
|
outputs in addition to the unique elements: the indices of the input array
|
||
|
that give the unique values, the indices of the unique array that
|
||
|
reconstruct the input array, and the number of times each unique value
|
||
|
comes up in the input array.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
ar : array_like
|
||
|
Input array. This will be flattened if it is not already 1-D.
|
||
|
return_index : bool, optional
|
||
|
If True, also return the indices of `ar` that result in the unique
|
||
|
array.
|
||
|
return_inverse : bool, optional
|
||
|
If True, also return the indices of the unique array that can be used
|
||
|
to reconstruct `ar`.
|
||
|
return_counts : bool, optional
|
||
|
If True, also return the number of times each unique value comes up
|
||
|
in `ar`.
|
||
|
|
||
|
.. versionadded:: 1.9.0
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
unique : ndarray
|
||
|
The sorted unique values.
|
||
|
unique_indices : ndarray, optional
|
||
|
The indices of the first occurrences of the unique values in the
|
||
|
(flattened) original array. Only provided if `return_index` is True.
|
||
|
unique_inverse : ndarray, optional
|
||
|
The indices to reconstruct the (flattened) original array from the
|
||
|
unique array. Only provided if `return_inverse` is True.
|
||
|
unique_counts : ndarray, optional
|
||
|
The number of times each of the unique values comes up in the
|
||
|
original array. Only provided if `return_counts` is True.
|
||
|
|
||
|
.. versionadded:: 1.9.0
|
||
|
|
||
|
Notes
|
||
|
-----
|
||
|
Taken over from numpy 1.12.0-dev (c8408bf9c). Omitted examples,
|
||
|
see numpy documentation for those.
|
||
|
|
||
|
"""
|
||
|
ar = np.asanyarray(ar).flatten()
|
||
|
|
||
|
optional_indices = return_index or return_inverse
|
||
|
optional_returns = optional_indices or return_counts
|
||
|
|
||
|
if ar.size == 0:
|
||
|
if not optional_returns:
|
||
|
ret = ar
|
||
|
else:
|
||
|
ret = (ar,)
|
||
|
if return_index:
|
||
|
ret += (np.empty(0, np.bool),)
|
||
|
if return_inverse:
|
||
|
ret += (np.empty(0, np.bool),)
|
||
|
if return_counts:
|
||
|
ret += (np.empty(0, np.intp),)
|
||
|
return ret
|
||
|
|
||
|
if optional_indices:
|
||
|
perm = ar.argsort(kind='mergesort' if return_index else 'quicksort')
|
||
|
aux = ar[perm]
|
||
|
else:
|
||
|
ar.sort()
|
||
|
aux = ar
|
||
|
flag = np.concatenate(([True], aux[1:] != aux[:-1]))
|
||
|
|
||
|
if not optional_returns:
|
||
|
ret = aux[flag]
|
||
|
else:
|
||
|
ret = (aux[flag],)
|
||
|
if return_index:
|
||
|
ret += (perm[flag],)
|
||
|
if return_inverse:
|
||
|
iflag = np.cumsum(flag) - 1
|
||
|
inv_idx = np.empty(ar.shape, dtype=np.intp)
|
||
|
inv_idx[perm] = iflag
|
||
|
ret += (inv_idx,)
|
||
|
if return_counts:
|
||
|
idx = np.concatenate(np.nonzero(flag) + ([ar.size],))
|
||
|
ret += (np.diff(idx),)
|
||
|
return ret
|
||
|
|
||
|
|
||
|
if NumpyVersion(np.__version__) > '1.12.0.dev':
|
||
|
polyvalfromroots = np.polynomial.polynomial.polyvalfromroots
|
||
|
else:
|
||
|
def polyvalfromroots(x, r, tensor=True):
|
||
|
r"""
|
||
|
Evaluate a polynomial specified by its roots at points x.
|
||
|
|
||
|
This function is copypasted from numpy 1.12.0.dev.
|
||
|
|
||
|
If `r` is of length `N`, this function returns the value
|
||
|
|
||
|
.. math:: p(x) = \prod_{n=1}^{N} (x - r_n)
|
||
|
|
||
|
The parameter `x` is converted to an array only if it is a tuple or a
|
||
|
list, otherwise it is treated as a scalar. In either case, either `x`
|
||
|
or its elements must support multiplication and addition both with
|
||
|
themselves and with the elements of `r`.
|
||
|
|
||
|
If `r` is a 1-D array, then `p(x)` will have the same shape as `x`. If
|
||
|
`r` is multidimensional, then the shape of the result depends on the
|
||
|
value of `tensor`. If `tensor is ``True`` the shape will be r.shape[1:]
|
||
|
+ x.shape; that is, each polynomial is evaluated at every value of `x`.
|
||
|
If `tensor` is ``False``, the shape will be r.shape[1:]; that is, each
|
||
|
polynomial is evaluated only for the corresponding broadcast value of
|
||
|
`x`. Note that scalars have shape (,).
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
x : array_like, compatible object
|
||
|
If `x` is a list or tuple, it is converted to an ndarray, otherwise
|
||
|
it is left unchanged and treated as a scalar. In either case, `x`
|
||
|
or its elements must support addition and multiplication with with
|
||
|
themselves and with the elements of `r`.
|
||
|
r : array_like
|
||
|
Array of roots. If `r` is multidimensional the first index is the
|
||
|
root index, while the remaining indices enumerate multiple
|
||
|
polynomials. For instance, in the two dimensional case the roots of
|
||
|
each polynomial may be thought of as stored in the columns of `r`.
|
||
|
tensor : boolean, optional
|
||
|
If True, the shape of the roots array is extended with ones on the
|
||
|
right, one for each dimension of `x`. Scalars have dimension 0 for
|
||
|
this action. The result is that every column of coefficients in `r`
|
||
|
is evaluated for every element of `x`. If False, `x` is broadcast
|
||
|
over the columns of `r` for the evaluation. This keyword is useful
|
||
|
when `r` is multidimensional. The default value is True.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
values : ndarray, compatible object
|
||
|
The shape of the returned array is described above.
|
||
|
|
||
|
See Also
|
||
|
--------
|
||
|
polyroots, polyfromroots, polyval
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> from numpy.polynomial.polynomial import polyvalfromroots
|
||
|
>>> polyvalfromroots(1, [1,2,3])
|
||
|
0.0
|
||
|
>>> a = np.arange(4).reshape(2,2)
|
||
|
>>> a
|
||
|
array([[0, 1],
|
||
|
[2, 3]])
|
||
|
>>> polyvalfromroots(a, [-1, 0, 1])
|
||
|
array([[ -0., 0.],
|
||
|
[ 6., 24.]])
|
||
|
>>> r = np.arange(-2, 2).reshape(2,2) # multidimensional coefficients
|
||
|
>>> r # each column of r defines one polynomial
|
||
|
array([[-2, -1],
|
||
|
[ 0, 1]])
|
||
|
>>> b = [-2, 1]
|
||
|
>>> polyvalfromroots(b, r, tensor=True)
|
||
|
array([[-0., 3.],
|
||
|
[ 3., 0.]])
|
||
|
>>> polyvalfromroots(b, r, tensor=False)
|
||
|
array([-0., 0.])
|
||
|
"""
|
||
|
r = np.array(r, ndmin=1, copy=0)
|
||
|
if r.dtype.char in '?bBhHiIlLqQpP':
|
||
|
r = r.astype(np.double)
|
||
|
if isinstance(x, (tuple, list)):
|
||
|
x = np.asarray(x)
|
||
|
if isinstance(x, np.ndarray):
|
||
|
if tensor:
|
||
|
r = r.reshape(r.shape + (1,)*x.ndim)
|
||
|
elif x.ndim >= r.ndim:
|
||
|
raise ValueError("x.ndim must be < r.ndim when tensor == "
|
||
|
"False")
|
||
|
return np.prod(x - r, axis=0)
|
||
|
|
||
|
|
||
|
try:
|
||
|
from numpy.testing import suppress_warnings
|
||
|
except ImportError:
|
||
|
class suppress_warnings(object):
|
||
|
"""
|
||
|
Context manager and decorator doing much the same as
|
||
|
``warnings.catch_warnings``.
|
||
|
|
||
|
However, it also provides a filter mechanism to work around
|
||
|
http://bugs.python.org/issue4180.
|
||
|
|
||
|
This bug causes Python before 3.4 to not reliably show warnings again
|
||
|
after they have been ignored once (even within catch_warnings). It
|
||
|
means that no "ignore" filter can be used easily, since following
|
||
|
tests might need to see the warning. Additionally it allows easier
|
||
|
specificity for testing warnings and can be nested.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
forwarding_rule : str, optional
|
||
|
One of "always", "once", "module", or "location". Analogous to
|
||
|
the usual warnings module filter mode, it is useful to reduce
|
||
|
noise mostly on the outmost level. Unsuppressed and unrecorded
|
||
|
warnings will be forwarded based on this rule. Defaults to "always".
|
||
|
"location" is equivalent to the warnings "default", match by exact
|
||
|
location the warning warning originated from.
|
||
|
|
||
|
Notes
|
||
|
-----
|
||
|
Filters added inside the context manager will be discarded again
|
||
|
when leaving it. Upon entering all filters defined outside a
|
||
|
context will be applied automatically.
|
||
|
|
||
|
When a recording filter is added, matching warnings are stored in the
|
||
|
``log`` attribute as well as in the list returned by ``record``.
|
||
|
|
||
|
If filters are added and the ``module`` keyword is given, the
|
||
|
warning registry of this module will additionally be cleared when
|
||
|
applying it, entering the context, or exiting it. This could cause
|
||
|
warnings to appear a second time after leaving the context if they
|
||
|
were configured to be printed once (default) and were already
|
||
|
printed before the context was entered.
|
||
|
|
||
|
Nesting this context manager will work as expected when the
|
||
|
forwarding rule is "always" (default). Unfiltered and unrecorded
|
||
|
warnings will be passed out and be matched by the outer level.
|
||
|
On the outmost level they will be printed (or caught by another
|
||
|
warnings context). The forwarding rule argument can modify this
|
||
|
behaviour.
|
||
|
|
||
|
Like ``catch_warnings`` this context manager is not threadsafe.
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> with suppress_warnings() as sup:
|
||
|
... sup.filter(DeprecationWarning, "Some text")
|
||
|
... sup.filter(module=np.ma.core)
|
||
|
... log = sup.record(FutureWarning, "Does this occur?")
|
||
|
... command_giving_warnings()
|
||
|
... # The FutureWarning was given once, the filtered warnings were
|
||
|
... # ignored. All other warnings abide outside settings (may be
|
||
|
... # printed/error)
|
||
|
... assert_(len(log) == 1)
|
||
|
... assert_(len(sup.log) == 1) # also stored in log attribute
|
||
|
|
||
|
Or as a decorator:
|
||
|
|
||
|
>>> sup = suppress_warnings()
|
||
|
>>> sup.filter(module=np.ma.core) # module must match exact
|
||
|
>>> @sup
|
||
|
>>> def some_function():
|
||
|
... # do something which causes a warning in np.ma.core
|
||
|
... pass
|
||
|
"""
|
||
|
def __init__(self, forwarding_rule="always"):
|
||
|
self._entered = False
|
||
|
|
||
|
# Suppressions are either instance or defined inside one with block:
|
||
|
self._suppressions = []
|
||
|
|
||
|
if forwarding_rule not in {"always", "module", "once", "location"}:
|
||
|
raise ValueError("unsupported forwarding rule.")
|
||
|
self._forwarding_rule = forwarding_rule
|
||
|
|
||
|
def _clear_registries(self):
|
||
|
if hasattr(warnings, "_filters_mutated"):
|
||
|
# clearing the registry should not be necessary on new pythons,
|
||
|
# instead the filters should be mutated.
|
||
|
warnings._filters_mutated()
|
||
|
return
|
||
|
# Simply clear the registry, this should normally be harmless,
|
||
|
# note that on new pythons it would be invalidated anyway.
|
||
|
for module in self._tmp_modules:
|
||
|
if hasattr(module, "__warningregistry__"):
|
||
|
module.__warningregistry__.clear()
|
||
|
|
||
|
def _filter(self, category=Warning, message="", module=None, record=False):
|
||
|
if record:
|
||
|
record = [] # The log where to store warnings
|
||
|
else:
|
||
|
record = None
|
||
|
if self._entered:
|
||
|
if module is None:
|
||
|
warnings.filterwarnings(
|
||
|
"always", category=category, message=message)
|
||
|
else:
|
||
|
module_regex = module.__name__.replace('.', r'\.') + '$'
|
||
|
warnings.filterwarnings(
|
||
|
"always", category=category, message=message,
|
||
|
module=module_regex)
|
||
|
self._tmp_modules.add(module)
|
||
|
self._clear_registries()
|
||
|
|
||
|
self._tmp_suppressions.append(
|
||
|
(category, message, re.compile(message, re.I), module, record))
|
||
|
else:
|
||
|
self._suppressions.append(
|
||
|
(category, message, re.compile(message, re.I), module, record))
|
||
|
|
||
|
return record
|
||
|
|
||
|
def filter(self, category=Warning, message="", module=None):
|
||
|
"""
|
||
|
Add a new suppressing filter or apply it if the state is entered.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
category : class, optional
|
||
|
Warning class to filter
|
||
|
message : string, optional
|
||
|
Regular expression matching the warning message.
|
||
|
module : module, optional
|
||
|
Module to filter for. Note that the module (and its file)
|
||
|
must match exactly and cannot be a submodule. This may make
|
||
|
it unreliable for external modules.
|
||
|
|
||
|
Notes
|
||
|
-----
|
||
|
When added within a context, filters are only added inside
|
||
|
the context and will be forgotten when the context is exited.
|
||
|
"""
|
||
|
self._filter(category=category, message=message, module=module,
|
||
|
record=False)
|
||
|
|
||
|
def record(self, category=Warning, message="", module=None):
|
||
|
"""
|
||
|
Append a new recording filter or apply it if the state is entered.
|
||
|
|
||
|
All warnings matching will be appended to the ``log`` attribute.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
category : class, optional
|
||
|
Warning class to filter
|
||
|
message : string, optional
|
||
|
Regular expression matching the warning message.
|
||
|
module : module, optional
|
||
|
Module to filter for. Note that the module (and its file)
|
||
|
must match exactly and cannot be a submodule. This may make
|
||
|
it unreliable for external modules.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
log : list
|
||
|
A list which will be filled with all matched warnings.
|
||
|
|
||
|
Notes
|
||
|
-----
|
||
|
When added within a context, filters are only added inside
|
||
|
the context and will be forgotten when the context is exited.
|
||
|
"""
|
||
|
return self._filter(category=category, message=message, module=module,
|
||
|
record=True)
|
||
|
|
||
|
def __enter__(self):
|
||
|
if self._entered:
|
||
|
raise RuntimeError("cannot enter suppress_warnings twice.")
|
||
|
|
||
|
self._orig_show = warnings.showwarning
|
||
|
self._filters = warnings.filters
|
||
|
warnings.filters = self._filters[:]
|
||
|
|
||
|
self._entered = True
|
||
|
self._tmp_suppressions = []
|
||
|
self._tmp_modules = set()
|
||
|
self._forwarded = set()
|
||
|
|
||
|
self.log = [] # reset global log (no need to keep same list)
|
||
|
|
||
|
for cat, mess, _, mod, log in self._suppressions:
|
||
|
if log is not None:
|
||
|
del log[:] # clear the log
|
||
|
if mod is None:
|
||
|
warnings.filterwarnings(
|
||
|
"always", category=cat, message=mess)
|
||
|
else:
|
||
|
module_regex = mod.__name__.replace('.', r'\.') + '$'
|
||
|
warnings.filterwarnings(
|
||
|
"always", category=cat, message=mess,
|
||
|
module=module_regex)
|
||
|
self._tmp_modules.add(mod)
|
||
|
warnings.showwarning = self._showwarning
|
||
|
self._clear_registries()
|
||
|
|
||
|
return self
|
||
|
|
||
|
def __exit__(self, *exc_info):
|
||
|
warnings.showwarning = self._orig_show
|
||
|
warnings.filters = self._filters
|
||
|
self._clear_registries()
|
||
|
self._entered = False
|
||
|
del self._orig_show
|
||
|
del self._filters
|
||
|
|
||
|
def _showwarning(self, message, category, filename, lineno,
|
||
|
*args, **kwargs):
|
||
|
use_warnmsg = kwargs.pop("use_warnmsg", None)
|
||
|
for cat, _, pattern, mod, rec in (
|
||
|
self._suppressions + self._tmp_suppressions)[::-1]:
|
||
|
if (issubclass(category, cat) and
|
||
|
pattern.match(message.args[0]) is not None):
|
||
|
if mod is None:
|
||
|
# Message and category match, either recorded or ignored
|
||
|
if rec is not None:
|
||
|
msg = WarningMessage(message, category, filename,
|
||
|
lineno, **kwargs)
|
||
|
self.log.append(msg)
|
||
|
rec.append(msg)
|
||
|
return
|
||
|
# Use startswith, because warnings strips the c or o from
|
||
|
# .pyc/.pyo files.
|
||
|
elif mod.__file__.startswith(filename):
|
||
|
# The message and module (filename) match
|
||
|
if rec is not None:
|
||
|
msg = WarningMessage(message, category, filename,
|
||
|
lineno, **kwargs)
|
||
|
self.log.append(msg)
|
||
|
rec.append(msg)
|
||
|
return
|
||
|
|
||
|
# There is no filter in place, so pass to the outside handler
|
||
|
# unless we should only pass it once
|
||
|
if self._forwarding_rule == "always":
|
||
|
if use_warnmsg is None:
|
||
|
self._orig_show(message, category, filename, lineno,
|
||
|
*args, **kwargs)
|
||
|
else:
|
||
|
self._orig_showmsg(use_warnmsg)
|
||
|
return
|
||
|
|
||
|
if self._forwarding_rule == "once":
|
||
|
signature = (message.args, category)
|
||
|
elif self._forwarding_rule == "module":
|
||
|
signature = (message.args, category, filename)
|
||
|
elif self._forwarding_rule == "location":
|
||
|
signature = (message.args, category, filename, lineno)
|
||
|
|
||
|
if signature in self._forwarded:
|
||
|
return
|
||
|
self._forwarded.add(signature)
|
||
|
if use_warnmsg is None:
|
||
|
self._orig_show(message, category, filename, lineno, *args,
|
||
|
**kwargs)
|
||
|
else:
|
||
|
self._orig_showmsg(use_warnmsg)
|
||
|
|
||
|
def __call__(self, func):
|
||
|
"""
|
||
|
Function decorator to apply certain suppressions to a whole
|
||
|
function.
|
||
|
"""
|
||
|
@wraps(func)
|
||
|
def new_func(*args, **kwargs):
|
||
|
with self:
|
||
|
return func(*args, **kwargs)
|
||
|
|
||
|
return new_func
|