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Copy pathcbook.py
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2549 lines (2125 loc) · 83.4 KB
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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.
"""
import collections
import collections.abc
import contextlib
import functools
import gzip
import itertools
import math
import operator
import os
from pathlib import Path
import shlex
import subprocess
import sys
import time
import traceback
import types
import weakref
import numpy as np
try:
from numpy.exceptions import VisibleDeprecationWarning # numpy >= 1.25
except ImportError:
from numpy import VisibleDeprecationWarning
import matplotlib
from matplotlib import _api, _c_internal_utils, mlab
class _ExceptionInfo:
"""
A class to carry exception information around.
This is used to store and later raise exceptions. It's an alternative to
directly storing Exception instances that circumvents traceback-related
issues: caching tracebacks can keep user's objects in local namespaces
alive indefinitely, which can lead to very surprising memory issues for
users and result in incorrect tracebacks.
"""
def __init__(self, cls, *args, notes=None):
self._cls = cls
self._args = args
self._notes = notes if notes is not None else []
@classmethod
def from_exception(cls, exc):
return cls(type(exc), *exc.args, notes=getattr(exc, "__notes__", []))
def to_exception(self):
exc = self._cls(*self._args)
for note in self._notes:
exc.add_note(note)
return exc
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: "qt", "gtk3", "gtk4", "wx", "tk",
"macosx", "headless", ``None``.
"""
# Use ``sys.modules.get(name)`` rather than ``name in sys.modules`` as
# entries can also have been explicitly set to None.
QtWidgets = (
sys.modules.get("PyQt6.QtWidgets")
or sys.modules.get("PySide6.QtWidgets")
or sys.modules.get("PyQt5.QtWidgets")
or sys.modules.get("PySide2.QtWidgets")
)
if QtWidgets and QtWidgets.QApplication.instance():
return "qt"
Gtk = sys.modules.get("gi.repository.Gtk")
if Gtk:
if Gtk.MAJOR_VERSION == 4:
from gi.repository import GLib
if GLib.main_depth():
return "gtk4"
if Gtk.MAJOR_VERSION == 3 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
# Preemptively break reference cycle between locals and the frame.
del frame
macosx = sys.modules.get("matplotlib.backends._macosx")
if macosx and 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)
def _weak_or_strong_ref(func, callback):
"""
Return a `WeakMethod` wrapping *func* if possible, else a `_StrongRef`.
"""
try:
return weakref.WeakMethod(func, callback)
except TypeError:
return _StrongRef(func)
class _UnhashDict:
"""
A minimal dict-like class that also supports unhashable keys, storing them
in a list of key-value pairs.
This class only implements the interface needed for `CallbackRegistry`, and
tries to minimize the overhead for the hashable case.
"""
def __init__(self, pairs):
self._dict = {}
self._pairs = []
for k, v in pairs:
self[k] = v
def __setitem__(self, key, value):
try:
self._dict[key] = value
except TypeError:
for i, (k, v) in enumerate(self._pairs):
if k == key:
self._pairs[i] = (key, value)
break
else:
self._pairs.append((key, value))
def __getitem__(self, key):
try:
return self._dict[key]
except TypeError:
pass
for k, v in self._pairs:
if k == key:
return v
raise KeyError(key)
def pop(self, key, *args):
try:
if key in self._dict:
return self._dict.pop(key)
except TypeError:
for i, (k, v) in enumerate(self._pairs):
if k == key:
del self._pairs[i]
return v
if args:
return args[0]
raise KeyError(key)
def __iter__(self):
yield from self._dict
for k, v in self._pairs:
yield k
class CallbackRegistry:
"""
Handle registering, processing, blocking, 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
>>> with callbacks.blocked(signal='drink'):
... callbacks.process('drink', 123) # nothing will be called
>>> callbacks.process('drink', 123)
drink 123
>>> callbacks.disconnect(ondrink, signal='drink') # disconnect by func
>>> callbacks.process('drink', 123) # 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 not None, *exception_handler* must be a function that takes an
`Exception` as single parameter. It gets called with any `Exception`
raised by the callbacks during `CallbackRegistry.process`, and may
either re-raise the exception or handle it in another manner.
The default handler prints the exception (with `traceback.print_exc`) if
an interactive event loop is running; it re-raises the exception if no
interactive event loop is running.
signals : list, optional
If not None, *signals* is a list of signals that this registry handles:
attempting to `process` or to `connect` to a signal not in the list
throws a `ValueError`. The default, None, does not restrict the
handled signals.
"""
# 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, *, signals=None):
self._signals = None if signals is None else list(signals) # Copy it.
self.exception_handler = exception_handler
self.callbacks = {}
self._cid_gen = itertools.count()
self._func_cid_map = _UnhashDict([])
# A hidden variable that marks cids that need to be pickled.
self._pickled_cids = set()
def __getstate__(self):
return {
**vars(self),
# In general, callbacks may not be pickled, so we just drop them,
# unless directed otherwise by self._pickled_cids.
"callbacks": {s: {cid: proxy() for cid, proxy in d.items()
if cid in self._pickled_cids}
for s, d in self.callbacks.items()},
# It is simpler to reconstruct this from callbacks in __setstate__.
"_func_cid_map": None,
"_cid_gen": next(self._cid_gen)
}
def __setstate__(self, state):
cid_count = state.pop('_cid_gen')
vars(self).update(state)
self.callbacks = {
s: {cid: _weak_or_strong_ref(func, functools.partial(self._remove_proxy, s))
for cid, func in d.items()}
for s, d in self.callbacks.items()}
self._func_cid_map = _UnhashDict(
((s, proxy), cid)
for s, d in self.callbacks.items() for cid, proxy in d.items())
self._cid_gen = itertools.count(cid_count)
def connect(self, signal, func):
"""Register *func* to be called when signal *signal* is generated."""
if self._signals is not None:
_api.check_in_list(self._signals, signal=signal)
proxy = _weak_or_strong_ref(func, functools.partial(self._remove_proxy, signal))
try:
return self._func_cid_map[signal, proxy]
except KeyError:
cid = self._func_cid_map[signal, proxy] = next(self._cid_gen)
self.callbacks.setdefault(signal, {})[cid] = proxy
return cid
def _connect_picklable(self, signal, func):
"""
Like `.connect`, but the callback is kept when pickling/unpickling.
Currently internal-use only.
"""
cid = self.connect(signal, func)
self._pickled_cids.add(cid)
return cid
# Keep a reference to sys.is_finalizing, as sys may have been cleared out
# at that point.
def _remove_proxy(self, signal, proxy, *, _is_finalizing=sys.is_finalizing):
if _is_finalizing():
# Weakrefs can't be properly torn down at that point anymore.
return
cid = self._func_cid_map.pop((signal, proxy), None)
if cid is not None:
del self.callbacks[signal][cid]
self._pickled_cids.discard(cid)
else: # Not found
return
if len(self.callbacks[signal]) == 0: # Clean up empty dicts
del self.callbacks[signal]
@_api.rename_parameter("3.11", "cid", "cid_or_func")
def disconnect(self, cid_or_func, *, signal=None):
"""
Disconnect a callback.
Parameters
----------
cid_or_func : int or callable
If an int, disconnect the callback with that connection id.
If a callable, disconnect that function from signals.
signal : optional
Only used when *cid_or_func* is a callable. If given, disconnect
the function only from that specific signal. If not given,
disconnect from all signals the function is connected to.
Notes
-----
No error is raised if such a callback does not exist.
"""
if isinstance(cid_or_func, int):
if signal is not None:
raise ValueError(
"signal cannot be specified when disconnecting by cid")
for sig, proxy in self._func_cid_map:
if self._func_cid_map[sig, proxy] == cid_or_func:
break
else: # Not found
return
self._remove_proxy(sig, proxy)
elif signal is not None:
# Disconnect from a specific signal
proxy = _weak_or_strong_ref(cid_or_func, None)
self._remove_proxy(signal, proxy)
else:
# Disconnect from all signals
proxy = _weak_or_strong_ref(cid_or_func, None)
for sig, prx in list(self._func_cid_map):
if prx == proxy:
self._remove_proxy(sig, proxy)
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``.
"""
if self._signals is not None:
_api.check_in_list(self._signals, signal=s)
for ref in list(self.callbacks.get(s, {}).values()):
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
@contextlib.contextmanager
def blocked(self, *, signal=None):
"""
Block callback signals from being processed.
A context manager to temporarily block/disable callback signals
from being processed by the registered listeners.
Parameters
----------
signal : str, optional
The callback signal to block. The default is to block all signals.
"""
orig = self.callbacks
try:
if signal is None:
# Empty out the callbacks
self.callbacks = {}
else:
# Only remove the specific signal
self.callbacks = {k: orig[k] for k in orig if k != signal}
yield
finally:
self.callbacks = orig
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>"
def _local_over_kwdict(
local_var, kwargs, *keys,
warning_cls=_api.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:
_api.warn_external(f'"{key}" keyword argument will be ignored',
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 _strip_comment(s):
"""Strip everything from the first unquoted #."""
pos = 0
while True:
quote_pos = s.find('"', pos)
hash_pos = s.find('#', pos)
if quote_pos < 0:
without_comment = s if hash_pos < 0 else s[:hash_pos]
return without_comment.strip()
elif 0 <= hash_pos < quote_pos:
return s[:hash_pos].strip()
else:
closing_quote_pos = s.find('"', quote_pos + 1)
if closing_quote_pos < 0:
raise ValueError(
f"Missing closing quote in: {s!r}. If you need a double-"
'quote inside a string, use escaping: e.g. "the \" char"')
pos = closing_quote_pos + 1 # behind closing quote
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 isinstance(fname, str):
if fname.endswith('.gz'):
fh = gzip.open(fname, flag)
elif fname.endswith('.bz2'):
# python may not be compiled 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
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)
return fh if opened else contextlib.nullcontext(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):
"""
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, and *asfileobj* is `True`, the file is
loaded with `numpy.load`.
"""
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']:
return np.load(path)
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-simple-generator-for-flattening-nested-containers/
and Recipe 1.12 in cookbook
""" # noqa: E501
for item in seq:
if scalarp(item) or item is None:
yield item
else:
yield from flatten(item, scalarp)
class _Stack:
"""
Stack of elements with a movable cursor.
Mimics home/back/forward in a web browser.
"""
def __init__(self):
self._pos = -1
self._elements = []
def clear(self):
"""Empty the stack."""
self._pos = -1
self._elements = []
def __call__(self):
"""Return the current element, or None."""
return self._elements[self._pos] if self._elements else None
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."""
self._pos = max(self._pos - 1, 0)
return self()
def push(self, o):
"""
Push *o* to the stack after the current position, and return *o*.
Discard all later elements.
"""
self._elements[self._pos + 1:] = [o]
self._pos = len(self._elements) - 1
return o
def home(self):
"""
Push the first element onto the top of the stack.
The first element is returned.
"""
return self.push(self._elements[0]) if self._elements else None
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.
# Swap to native order.
x = x.byteswap(inplace=copy).view(x.dtype.newbyteorder('N'))
try:
xm = np.ma.masked_where(~(np.isfinite(x)), x, copy=False)
except TypeError:
if len(x.dtype.descr) == 1:
# Arrays with dtype 'object' get returned here.
# For example the 'c' kwarg of scatter, which supports multiple types.
# `plt.scatter([3, 4], [2, 5], c=[(1, 0, 0), 'y'])`
return x
else:
# In case of a dtype with multiple fields
# for example image data using a MultiNorm
try:
mask = np.empty(x.shape, dtype=np.dtype('bool, '*len(x.dtype.descr)))
for dd, dm in zip(x.dtype.descr, mask.dtype.descr):
mask[dm[0]] = ~np.isfinite(x[dd[0]])
xm = np.ma.array(x, mask=mask, copy=False)
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(f"[{key!r}]")
break
if key is next:
outstream.write(f"[key] = {val!r}")
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)
>>> list(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.WeakKeyDictionary(
{x: weakref.WeakSet([x]) for x in init})
self._ordering = weakref.WeakKeyDictionary()
for x in init:
if x not in self._ordering:
self._ordering[x] = len(self._ordering)
self._next_order = len(self._ordering) # Plain int to simplify pickling.
def __getstate__(self):
return {
**vars(self),
# Convert weak refs to strong ones.
"_mapping": {k: set(v) for k, v in self._mapping.items()},
"_ordering": {**self._ordering},
}
def __setstate__(self, state):
vars(self).update(state)
# Convert strong refs to weak ones.
self._mapping = weakref.WeakKeyDictionary(
{k: weakref.WeakSet(v) for k, v in self._mapping.items()})
self._ordering = weakref.WeakKeyDictionary(self._ordering)
def __contains__(self, item):
return item in self._mapping
def join(self, a, *args):
"""
Join given arguments into the same set. Accepts one or more arguments.
"""
mapping = self._mapping
try:
set_a = mapping[a]
except KeyError:
set_a = mapping[a] = weakref.WeakSet([a])
self._ordering[a] = self._next_order
self._next_order += 1
for arg in args:
try:
set_b = mapping[arg]
except KeyError:
set_b = mapping[arg] = weakref.WeakSet([arg])
self._ordering[arg] = self._next_order
self._next_order += 1
if set_b is not set_a:
if len(set_b) > len(set_a):
set_a, set_b = set_b, set_a
set_a.update(set_b)
for elem in set_b:
mapping[elem] = set_a
def joined(self, a, b):
"""Return whether *a* and *b* are members of the same set."""
return (self._mapping.get(a, object()) is self._mapping.get(b))
def remove(self, a):
"""Remove *a* from the grouper, doing nothing if it is not there."""
self._mapping.pop(a, {a}).remove(a)
self._ordering.pop(a, None)
def __iter__(self):
"""
Iterate over each of the disjoint sets as a list.
The iterator is invalid if interleaved with calls to join().
"""
unique_groups = {id(group): group for group in self._mapping.values()}
for group in unique_groups.values():
yield sorted(group, key=self._ordering.__getitem__)
def get_siblings(self, a, *, include_self=True):
"""
Return all the items joined with *a*.
*a* is included in the list if *include_self* is True.
"""
siblings = self._mapping.get(a, [a])
result = sorted(siblings, key=self._ordering.get)
if not include_self:
result.remove(a)
return result
class GrouperView:
"""Immutable view over a `.Grouper`."""
def __init__(self, grouper): self._grouper = grouper
def __contains__(self, item): return item in self._grouper
def __iter__(self): return iter(self._grouper)
def joined(self, a, b):
"""
Return whether *a* and *b* are members of the same set.
"""
return self._grouper.joined(a, b)
def get_siblings(self, a, *, include_self=True):
"""
Return all the items joined with *a*.
*a* is included in the list if *include_self* is True.
"""
return self._grouper.get_siblings(a, include_self=include_self)
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.
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