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"""
A collection of utility functions and classes. Originally, many
(but not all) were from the Python Cookbook -- hence the name cbook.
This module is safe to import from anywhere within Matplotlib;
it imports Matplotlib only at runtime.
"""
import collections
import collections.abc
import contextlib
import functools
import gzip
import itertools
import operator
import os
from pathlib import Path
import re
import shlex
import subprocess
import sys
import time
import traceback
import types
import warnings
import weakref
import numpy as np
import matplotlib
from matplotlib import _c_internal_utils
from matplotlib._api import warn_external as _warn_external
from matplotlib._api.deprecation import (
deprecated, warn_deprecated,
_rename_parameter, _delete_parameter, _make_keyword_only,
_deprecate_method_override, _deprecate_privatize_attribute,
_suppress_matplotlib_deprecation_warning,
MatplotlibDeprecationWarning, mplDeprecation)
def _get_running_interactive_framework():
"""
Return the interactive framework whose event loop is currently running, if
any, or "headless" if no event loop can be started, or None.
Returns
-------
Optional[str]
One of the following values: "qt5", "qt4", "gtk3", "wx", "tk",
"macosx", "headless", ``None``.
"""
QtWidgets = (sys.modules.get("PyQt5.QtWidgets")
or sys.modules.get("PySide2.QtWidgets"))
if QtWidgets and QtWidgets.QApplication.instance():
return "qt5"
QtGui = (sys.modules.get("PyQt4.QtGui")
or sys.modules.get("PySide.QtGui"))
if QtGui and QtGui.QApplication.instance():
return "qt4"
Gtk = sys.modules.get("gi.repository.Gtk")
if Gtk and Gtk.main_level():
return "gtk3"
wx = sys.modules.get("wx")
if wx and wx.GetApp():
return "wx"
tkinter = sys.modules.get("tkinter")
if tkinter:
codes = {tkinter.mainloop.__code__, tkinter.Misc.mainloop.__code__}
for frame in sys._current_frames().values():
while frame:
if frame.f_code in codes:
return "tk"
frame = frame.f_back
if 'matplotlib.backends._macosx' in sys.modules:
if sys.modules["matplotlib.backends._macosx"].event_loop_is_running():
return "macosx"
if not _c_internal_utils.display_is_valid():
return "headless"
return None
def _exception_printer(exc):
if _get_running_interactive_framework() in ["headless", None]:
raise exc
else:
traceback.print_exc()
class _StrongRef:
"""
Wrapper similar to a weakref, but keeping a strong reference to the object.
"""
def __init__(self, obj):
self._obj = obj
def __call__(self):
return self._obj
def __eq__(self, other):
return isinstance(other, _StrongRef) and self._obj == other._obj
def __hash__(self):
return hash(self._obj)
class CallbackRegistry:
"""
Handle registering and disconnecting for a set of signals and callbacks:
>>> def oneat(x):
... print('eat', x)
>>> def ondrink(x):
... print('drink', x)
>>> from matplotlib.cbook import CallbackRegistry
>>> callbacks = CallbackRegistry()
>>> id_eat = callbacks.connect('eat', oneat)
>>> id_drink = callbacks.connect('drink', ondrink)
>>> callbacks.process('drink', 123)
drink 123
>>> callbacks.process('eat', 456)
eat 456
>>> callbacks.process('be merry', 456) # nothing will be called
>>> callbacks.disconnect(id_eat)
>>> callbacks.process('eat', 456) # nothing will be called
In practice, one should always disconnect all callbacks when they are
no longer needed to avoid dangling references (and thus memory leaks).
However, real code in Matplotlib rarely does so, and due to its design,
it is rather difficult to place this kind of code. To get around this,
and prevent this class of memory leaks, we instead store weak references
to bound methods only, so when the destination object needs to die, the
CallbackRegistry won't keep it alive.
Parameters
----------
exception_handler : callable, optional
If provided must have signature ::
def handler(exc: Exception) -> None:
If not None this function will be called with any `Exception`
subclass raised by the callbacks in `CallbackRegistry.process`.
The handler may either consume the exception or re-raise.
The callable must be pickle-able.
The default handler is ::
def h(exc):
traceback.print_exc()
"""
# We maintain two mappings:
# callbacks: signal -> {cid -> weakref-to-callback}
# _func_cid_map: signal -> {weakref-to-callback -> cid}
def __init__(self, exception_handler=_exception_printer):
self.exception_handler = exception_handler
self.callbacks = {}
self._cid_gen = itertools.count()
self._func_cid_map = {}
def __getstate__(self):
# In general, callbacks may not be pickled, so we just drop them.
return {**vars(self), "callbacks": {}, "_func_cid_map": {}}
def connect(self, s, func):
"""Register *func* to be called when signal *s* is generated."""
self._func_cid_map.setdefault(s, {})
try:
proxy = weakref.WeakMethod(func, self._remove_proxy)
except TypeError:
proxy = _StrongRef(func)
if proxy in self._func_cid_map[s]:
return self._func_cid_map[s][proxy]
cid = next(self._cid_gen)
self._func_cid_map[s][proxy] = cid
self.callbacks.setdefault(s, {})
self.callbacks[s][cid] = proxy
return cid
# Keep a reference to sys.is_finalizing, as sys may have been cleared out
# at that point.
def _remove_proxy(self, proxy, *, _is_finalizing=sys.is_finalizing):
if _is_finalizing():
# Weakrefs can't be properly torn down at that point anymore.
return
for signal, proxies in list(self._func_cid_map.items()):
try:
del self.callbacks[signal][proxies[proxy]]
except KeyError:
pass
if len(self.callbacks[signal]) == 0:
del self.callbacks[signal]
del self._func_cid_map[signal]
def disconnect(self, cid):
"""
Disconnect the callback registered with callback id *cid*.
No error is raised if such a callback does not exist.
"""
for eventname, callbackd in list(self.callbacks.items()):
try:
del callbackd[cid]
except KeyError:
continue
else:
for signal, functions in list(self._func_cid_map.items()):
for function, value in list(functions.items()):
if value == cid:
del functions[function]
return
def process(self, s, *args, **kwargs):
"""
Process signal *s*.
All of the functions registered to receive callbacks on *s* will be
called with ``*args`` and ``**kwargs``.
"""
for cid, ref in list(self.callbacks.get(s, {}).items()):
func = ref()
if func is not None:
try:
func(*args, **kwargs)
# this does not capture KeyboardInterrupt, SystemExit,
# and GeneratorExit
except Exception as exc:
if self.exception_handler is not None:
self.exception_handler(exc)
else:
raise
class silent_list(list):
"""
A list with a short ``repr()``.
This is meant to be used for a homogeneous list of artists, so that they
don't cause long, meaningless output.
Instead of ::
[<matplotlib.lines.Line2D object at 0x7f5749fed3c8>,
<matplotlib.lines.Line2D object at 0x7f5749fed4e0>,
<matplotlib.lines.Line2D object at 0x7f5758016550>]
one will get ::
<a list of 3 Line2D objects>
If ``self.type`` is None, the type name is obtained from the first item in
the list (if any).
"""
def __init__(self, type, seq=None):
self.type = type
if seq is not None:
self.extend(seq)
def __repr__(self):
if self.type is not None or len(self) != 0:
tp = self.type if self.type is not None else type(self[0]).__name__
return f"<a list of {len(self)} {tp} objects>"
else:
return "<an empty list>"
@deprecated("3.3")
class IgnoredKeywordWarning(UserWarning):
"""
A class for issuing warnings about keyword arguments that will be ignored
by Matplotlib.
"""
pass
@deprecated("3.3", alternative="normalize_kwargs")
def local_over_kwdict(local_var, kwargs, *keys):
"""
Enforces the priority of a local variable over potentially conflicting
argument(s) from a kwargs dict. The following possible output values are
considered in order of priority::
local_var > kwargs[keys[0]] > ... > kwargs[keys[-1]]
The first of these whose value is not None will be returned. If all are
None then None will be returned. Each key in keys will be removed from the
kwargs dict in place.
Parameters
----------
local_var : any object
The local variable (highest priority).
kwargs : dict
Dictionary of keyword arguments; modified in place.
keys : str(s)
Name(s) of keyword arguments to process, in descending order of
priority.
Returns
-------
any object
Either local_var or one of kwargs[key] for key in keys.
Raises
------
IgnoredKeywordWarning
For each key in keys that is removed from kwargs but not used as
the output value.
"""
return _local_over_kwdict(local_var, kwargs, *keys, IgnoredKeywordWarning)
def _local_over_kwdict(
local_var, kwargs, *keys, warning_cls=MatplotlibDeprecationWarning):
out = local_var
for key in keys:
kwarg_val = kwargs.pop(key, None)
if kwarg_val is not None:
if out is None:
out = kwarg_val
else:
_warn_external('"%s" keyword argument will be ignored' % key,
warning_cls)
return out
def strip_math(s):
"""
Remove latex formatting from mathtext.
Only handles fully math and fully non-math strings.
"""
if len(s) >= 2 and s[0] == s[-1] == "$":
s = s[1:-1]
for tex, plain in [
(r"\times", "x"), # Specifically for Formatter support.
(r"\mathdefault", ""),
(r"\rm", ""),
(r"\cal", ""),
(r"\tt", ""),
(r"\it", ""),
("\\", ""),
("{", ""),
("}", ""),
]:
s = s.replace(tex, plain)
return s
def is_writable_file_like(obj):
"""Return whether *obj* looks like a file object with a *write* method."""
return callable(getattr(obj, 'write', None))
def file_requires_unicode(x):
"""
Return whether the given writable file-like object requires Unicode to be
written to it.
"""
try:
x.write(b'')
except TypeError:
return True
else:
return False
def to_filehandle(fname, flag='r', return_opened=False, encoding=None):
"""
Convert a path to an open file handle or pass-through a file-like object.
Consider using `open_file_cm` instead, as it allows one to properly close
newly created file objects more easily.
Parameters
----------
fname : str or path-like or file-like
If `str` or `os.PathLike`, the file is opened using the flags specified
by *flag* and *encoding*. If a file-like object, it is passed through.
flag : str, default: 'r'
Passed as the *mode* argument to `open` when *fname* is `str` or
`os.PathLike`; ignored if *fname* is file-like.
return_opened : bool, default: False
If True, return both the file object and a boolean indicating whether
this was a new file (that the caller needs to close). If False, return
only the new file.
encoding : str or None, default: None
Passed as the *mode* argument to `open` when *fname* is `str` or
`os.PathLike`; ignored if *fname* is file-like.
Returns
-------
fh : file-like
opened : bool
*opened* is only returned if *return_opened* is True.
"""
if isinstance(fname, os.PathLike):
fname = os.fspath(fname)
if "U" in flag:
warn_deprecated("3.3", message="Passing a flag containing 'U' to "
"to_filehandle() is deprecated since %(since)s and "
"will be removed %(removal)s.")
flag = flag.replace("U", "")
if isinstance(fname, str):
if fname.endswith('.gz'):
fh = gzip.open(fname, flag)
elif fname.endswith('.bz2'):
# python may not be complied with bz2 support,
# bury import until we need it
import bz2
fh = bz2.BZ2File(fname, flag)
else:
fh = open(fname, flag, encoding=encoding)
opened = True
elif hasattr(fname, 'seek'):
fh = fname
opened = False
else:
raise ValueError('fname must be a PathLike or file handle')
if return_opened:
return fh, opened
return fh
@contextlib.contextmanager
def open_file_cm(path_or_file, mode="r", encoding=None):
r"""Pass through file objects and context-manage path-likes."""
fh, opened = to_filehandle(path_or_file, mode, True, encoding)
if opened:
with fh:
yield fh
else:
yield fh
def is_scalar_or_string(val):
"""Return whether the given object is a scalar or string like."""
return isinstance(val, str) or not np.iterable(val)
def get_sample_data(fname, asfileobj=True, *, np_load=False):
"""
Return a sample data file. *fname* is a path relative to the
:file:`mpl-data/sample_data` directory. If *asfileobj* is `True`
return a file object, otherwise just a file path.
Sample data files are stored in the 'mpl-data/sample_data' directory within
the Matplotlib package.
If the filename ends in .gz, the file is implicitly ungzipped. If the
filename ends with .npy or .npz, *asfileobj* is True, and *np_load* is
True, the file is loaded with `numpy.load`. *np_load* currently defaults
to False but will default to True in a future release.
"""
path = _get_data_path('sample_data', fname)
if asfileobj:
suffix = path.suffix.lower()
if suffix == '.gz':
return gzip.open(path)
elif suffix in ['.npy', '.npz']:
if np_load:
return np.load(path)
else:
warn_deprecated(
"3.3", message="In a future release, get_sample_data "
"will automatically load numpy arrays. Set np_load to "
"True to get the array and suppress this warning. Set "
"asfileobj to False to get the path to the data file and "
"suppress this warning.")
return path.open('rb')
elif suffix in ['.csv', '.xrc', '.txt']:
return path.open('r')
else:
return path.open('rb')
else:
return str(path)
def _get_data_path(*args):
"""
Return the `pathlib.Path` to a resource file provided by Matplotlib.
``*args`` specify a path relative to the base data path.
"""
return Path(matplotlib.get_data_path(), *args)
def flatten(seq, scalarp=is_scalar_or_string):
"""
Return a generator of flattened nested containers.
For example:
>>> from matplotlib.cbook import flatten
>>> l = (('John', ['Hunter']), (1, 23), [[([42, (5, 23)], )]])
>>> print(list(flatten(l)))
['John', 'Hunter', 1, 23, 42, 5, 23]
By: Composite of Holger Krekel and Luther Blissett
From: https://code.activestate.com/recipes/121294/
and Recipe 1.12 in cookbook
"""
for item in seq:
if scalarp(item) or item is None:
yield item
else:
yield from flatten(item, scalarp)
@deprecated("3.3", alternative="os.path.realpath and os.stat")
@functools.lru_cache()
def get_realpath_and_stat(path):
realpath = os.path.realpath(path)
stat = os.stat(realpath)
stat_key = (stat.st_ino, stat.st_dev)
return realpath, stat_key
# A regular expression used to determine the amount of space to
# remove. It looks for the first sequence of spaces immediately
# following the first newline, or at the beginning of the string.
_find_dedent_regex = re.compile(r"(?:(?:\n\r?)|^)( *)\S")
# A cache to hold the regexs that actually remove the indent.
_dedent_regex = {}
class maxdict(dict):
"""
A dictionary with a maximum size.
Notes
-----
This doesn't override all the relevant methods to constrain the size,
just ``__setitem__``, so use with caution.
"""
def __init__(self, maxsize):
dict.__init__(self)
self.maxsize = maxsize
self._killkeys = []
def __setitem__(self, k, v):
if k not in self:
if len(self) >= self.maxsize:
del self[self._killkeys[0]]
del self._killkeys[0]
self._killkeys.append(k)
dict.__setitem__(self, k, v)
class Stack:
"""
Stack of elements with a movable cursor.
Mimics home/back/forward in a web browser.
"""
def __init__(self, default=None):
self.clear()
self._default = default
def __call__(self):
"""Return the current element, or None."""
if not self._elements:
return self._default
else:
return self._elements[self._pos]
def __len__(self):
return len(self._elements)
def __getitem__(self, ind):
return self._elements[ind]
def forward(self):
"""Move the position forward and return the current element."""
self._pos = min(self._pos + 1, len(self._elements) - 1)
return self()
def back(self):
"""Move the position back and return the current element."""
if self._pos > 0:
self._pos -= 1
return self()
def push(self, o):
"""
Push *o* to the stack at current position. Discard all later elements.
*o* is returned.
"""
self._elements = self._elements[:self._pos + 1] + [o]
self._pos = len(self._elements) - 1
return self()
def home(self):
"""
Push the first element onto the top of the stack.
The first element is returned.
"""
if not self._elements:
return
self.push(self._elements[0])
return self()
def empty(self):
"""Return whether the stack is empty."""
return len(self._elements) == 0
def clear(self):
"""Empty the stack."""
self._pos = -1
self._elements = []
def bubble(self, o):
"""
Raise all references of *o* to the top of the stack, and return it.
Raises
------
ValueError
If *o* is not in the stack.
"""
if o not in self._elements:
raise ValueError('Given element not contained in the stack')
old_elements = self._elements.copy()
self.clear()
top_elements = []
for elem in old_elements:
if elem == o:
top_elements.append(elem)
else:
self.push(elem)
for _ in top_elements:
self.push(o)
return o
def remove(self, o):
"""
Remove *o* from the stack.
Raises
------
ValueError
If *o* is not in the stack.
"""
if o not in self._elements:
raise ValueError('Given element not contained in the stack')
old_elements = self._elements.copy()
self.clear()
for elem in old_elements:
if elem != o:
self.push(elem)
def report_memory(i=0): # argument may go away
"""Return the memory consumed by the process."""
def call(command, os_name):
try:
return subprocess.check_output(command)
except subprocess.CalledProcessError as err:
raise NotImplementedError(
"report_memory works on %s only if "
"the '%s' program is found" % (os_name, command[0])
) from err
pid = os.getpid()
if sys.platform == 'sunos5':
lines = call(['ps', '-p', '%d' % pid, '-o', 'osz'], 'Sun OS')
mem = int(lines[-1].strip())
elif sys.platform == 'linux':
lines = call(['ps', '-p', '%d' % pid, '-o', 'rss,sz'], 'Linux')
mem = int(lines[1].split()[1])
elif sys.platform == 'darwin':
lines = call(['ps', '-p', '%d' % pid, '-o', 'rss,vsz'], 'Mac OS')
mem = int(lines[1].split()[0])
elif sys.platform == 'win32':
lines = call(["tasklist", "/nh", "/fi", "pid eq %d" % pid], 'Windows')
mem = int(lines.strip().split()[-2].replace(',', ''))
else:
raise NotImplementedError(
"We don't have a memory monitor for %s" % sys.platform)
return mem
def safe_masked_invalid(x, copy=False):
x = np.array(x, subok=True, copy=copy)
if not x.dtype.isnative:
# If we have already made a copy, do the byteswap in place, else make a
# copy with the byte order swapped.
x = x.byteswap(inplace=copy).newbyteorder('N') # Swap to native order.
try:
xm = np.ma.masked_invalid(x, copy=False)
xm.shrink_mask()
except TypeError:
return x
return xm
def print_cycles(objects, outstream=sys.stdout, show_progress=False):
"""
Print loops of cyclic references in the given *objects*.
It is often useful to pass in ``gc.garbage`` to find the cycles that are
preventing some objects from being garbage collected.
Parameters
----------
objects
A list of objects to find cycles in.
outstream
The stream for output.
show_progress : bool
If True, print the number of objects reached as they are found.
"""
import gc
def print_path(path):
for i, step in enumerate(path):
# next "wraps around"
next = path[(i + 1) % len(path)]
outstream.write(" %s -- " % type(step))
if isinstance(step, dict):
for key, val in step.items():
if val is next:
outstream.write("[{!r}]".format(key))
break
if key is next:
outstream.write("[key] = {!r}".format(val))
break
elif isinstance(step, list):
outstream.write("[%d]" % step.index(next))
elif isinstance(step, tuple):
outstream.write("( tuple )")
else:
outstream.write(repr(step))
outstream.write(" ->\n")
outstream.write("\n")
def recurse(obj, start, all, current_path):
if show_progress:
outstream.write("%d\r" % len(all))
all[id(obj)] = None
referents = gc.get_referents(obj)
for referent in referents:
# If we've found our way back to the start, this is
# a cycle, so print it out
if referent is start:
print_path(current_path)
# Don't go back through the original list of objects, or
# through temporary references to the object, since those
# are just an artifact of the cycle detector itself.
elif referent is objects or isinstance(referent, types.FrameType):
continue
# We haven't seen this object before, so recurse
elif id(referent) not in all:
recurse(referent, start, all, current_path + [obj])
for obj in objects:
outstream.write(f"Examining: {obj!r}\n")
recurse(obj, obj, {}, [])
class Grouper:
"""
A disjoint-set data structure.
Objects can be joined using :meth:`join`, tested for connectedness
using :meth:`joined`, and all disjoint sets can be retrieved by
using the object as an iterator.
The objects being joined must be hashable and weak-referenceable.
Examples
--------
>>> from matplotlib.cbook import Grouper
>>> class Foo:
... def __init__(self, s):
... self.s = s
... def __repr__(self):
... return self.s
...
>>> a, b, c, d, e, f = [Foo(x) for x in 'abcdef']
>>> grp = Grouper()
>>> grp.join(a, b)
>>> grp.join(b, c)
>>> grp.join(d, e)
>>> sorted(map(tuple, grp))
[(a, b, c), (d, e)]
>>> grp.joined(a, b)
True
>>> grp.joined(a, c)
True
>>> grp.joined(a, d)
False
"""
def __init__(self, init=()):
self._mapping = {weakref.ref(x): [weakref.ref(x)] for x in init}
def __contains__(self, item):
return weakref.ref(item) in self._mapping
def clean(self):
"""Clean dead weak references from the dictionary."""
mapping = self._mapping
to_drop = [key for key in mapping if key() is None]
for key in to_drop:
val = mapping.pop(key)
val.remove(key)
def join(self, a, *args):
"""
Join given arguments into the same set. Accepts one or more arguments.
"""
mapping = self._mapping
set_a = mapping.setdefault(weakref.ref(a), [weakref.ref(a)])
for arg in args:
set_b = mapping.get(weakref.ref(arg), [weakref.ref(arg)])
if set_b is not set_a:
if len(set_b) > len(set_a):
set_a, set_b = set_b, set_a
set_a.extend(set_b)
for elem in set_b:
mapping[elem] = set_a
self.clean()
def joined(self, a, b):
"""Return whether *a* and *b* are members of the same set."""
self.clean()
return (self._mapping.get(weakref.ref(a), object())
is self._mapping.get(weakref.ref(b)))
def remove(self, a):
self.clean()
set_a = self._mapping.pop(weakref.ref(a), None)
if set_a:
set_a.remove(weakref.ref(a))
def __iter__(self):
"""
Iterate over each of the disjoint sets as a list.
The iterator is invalid if interleaved with calls to join().
"""
self.clean()
unique_groups = {id(group): group for group in self._mapping.values()}
for group in unique_groups.values():
yield [x() for x in group]
def get_siblings(self, a):
"""Return all of the items joined with *a*, including itself."""
self.clean()
siblings = self._mapping.get(weakref.ref(a), [weakref.ref(a)])
return [x() for x in siblings]
def simple_linear_interpolation(a, steps):
"""
Resample an array with ``steps - 1`` points between original point pairs.
Along each column of *a*, ``(steps - 1)`` points are introduced between
each original values; the values are linearly interpolated.
Parameters
----------
a : array, shape (n, ...)
steps : int
Returns
-------
array
shape ``((n - 1) * steps + 1, ...)``
"""
fps = a.reshape((len(a), -1))
xp = np.arange(len(a)) * steps
x = np.arange((len(a) - 1) * steps + 1)
return (np.column_stack([np.interp(x, xp, fp) for fp in fps.T])
.reshape((len(x),) + a.shape[1:]))
def delete_masked_points(*args):
"""
Find all masked and/or non-finite points in a set of arguments,
and return the arguments with only the unmasked points remaining.
Arguments can be in any of 5 categories:
1) 1-D masked arrays
2) 1-D ndarrays
3) ndarrays with more than one dimension
4) other non-string iterables
5) anything else
The first argument must be in one of the first four categories;
any argument with a length differing from that of the first
argument (and hence anything in category 5) then will be
passed through unchanged.
Masks are obtained from all arguments of the correct length
in categories 1, 2, and 4; a point is bad if masked in a masked
array or if it is a nan or inf. No attempt is made to
extract a mask from categories 2, 3, and 4 if `numpy.isfinite`
does not yield a Boolean array.
All input arguments that are not passed unchanged are returned
as ndarrays after removing the points or rows corresponding to
masks in any of the arguments.
A vastly simpler version of this function was originally
written as a helper for Axes.scatter().
"""
if not len(args):
return ()
if is_scalar_or_string(args[0]):
raise ValueError("First argument must be a sequence")
nrecs = len(args[0])
margs = []
seqlist = [False] * len(args)
for i, x in enumerate(args):
if not isinstance(x, str) and np.iterable(x) and len(x) == nrecs:
seqlist[i] = True
if isinstance(x, np.ma.MaskedArray):
if x.ndim > 1:
raise ValueError("Masked arrays must be 1-D")
else:
x = np.asarray(x)
margs.append(x)
masks = [] # list of masks that are True where good
for i, x in enumerate(margs):
if seqlist[i]:
if x.ndim > 1:
continue # Don't try to get nan locations unless 1-D.
if isinstance(x, np.ma.MaskedArray):
masks.append(~np.ma.getmaskarray(x)) # invert the mask
xd = x.data
else:
xd = x
try:
mask = np.isfinite(xd)
if isinstance(mask, np.ndarray):
masks.append(mask)
except Exception: # Fixme: put in tuple of possible exceptions?
pass
if len(masks):
mask = np.logical_and.reduce(masks)
igood = mask.nonzero()[0]
if len(igood) < nrecs:
for i, x in enumerate(margs):
if seqlist[i]:
margs[i] = x[igood]
for i, x in enumerate(margs):
if seqlist[i] and isinstance(x, np.ma.MaskedArray):
margs[i] = x.filled()
return margs
def _combine_masks(*args):
"""
Find all masked and/or non-finite points in a set of arguments,
and return the arguments as masked arrays with a common mask.
Arguments can be in any of 5 categories:
1) 1-D masked arrays
2) 1-D ndarrays
3) ndarrays with more than one dimension
4) other non-string iterables
5) anything else
The first argument must be in one of the first four categories;
any argument with a length differing from that of the first
argument (and hence anything in category 5) then will be
passed through unchanged.
Masks are obtained from all arguments of the correct length
in categories 1, 2, and 4; a point is bad if masked in a masked
array or if it is a nan or inf. No attempt is made to
extract a mask from categories 2 and 4 if `numpy.isfinite`
does not yield a Boolean array. Category 3 is included to
support RGB or RGBA ndarrays, which are assumed to have only
valid values and which are passed through unchanged.