Version 3.1.1
matplotlib
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Source code for matplotlib.cbook

"""
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 glob
import gzip
import itertools
import locale
import numbers
import operator
import os
from pathlib import Path
import re
import subprocess
import sys
import time
import traceback
import types
import warnings
import weakref
from weakref import WeakMethod

import numpy as np

import matplotlib
from .deprecation import (
    deprecated, warn_deprecated,
    _rename_parameter, _delete_parameter, _make_keyword_only,
    _suppress_matplotlib_deprecation_warning,
    MatplotlibDeprecationWarning, mplDeprecation)


[docs]@deprecated("3.0") def unicode_safe(s): if isinstance(s, bytes): try: # On some systems, locale.getpreferredencoding returns None, # which can break unicode; and the sage project reports that # some systems have incorrect locale specifications, e.g., # an encoding instead of a valid locale name. Another # pathological case that has been reported is an empty string. # On some systems, getpreferredencoding sets the locale, which has # side effects. Passing False eliminates those side effects. preferredencoding = locale.getpreferredencoding( matplotlib.rcParams['axes.formatter.use_locale']).strip() if not preferredencoding: preferredencoding = None except (ValueError, ImportError, AttributeError): preferredencoding = None if preferredencoding is None: return str(s) else: return str(s, preferredencoding) return s
def _exception_printer(exc): 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)
[docs]class CallbackRegistry(object): """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 -> callback} # _func_cid_map: signal -> {callback -> cid} # (actually, callbacks are weakrefs to the actual callbacks). def __init__(self, exception_handler=_exception_printer): self.exception_handler = exception_handler self.callbacks = {} self._cid_gen = itertools.count() self._func_cid_map = {} # In general, callbacks may not be pickled; thus, we simply recreate an # empty dictionary at unpickling. In order to ensure that `__setstate__` # (which just defers to `__init__`) is called, `__getstate__` must # return a truthy value (for pickle protocol>=3, i.e. Py3, the # *actual* behavior is that `__setstate__` will be called as long as # `__getstate__` does not return `None`, but this is undocumented -- see # http://bugs.python.org/issue12290). def __getstate__(self): return {'exception_handler': self.exception_handler} def __setstate__(self, state): self.__init__(**state)
[docs] def connect(self, s, func): """Register *func* to be called when signal *s* is generated. """ self._func_cid_map.setdefault(s, {}) try: proxy = 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
def _remove_proxy(self, proxy): 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]
[docs] def disconnect(self, cid): """Disconnect the callback registered with callback id *cid*. """ 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
[docs] 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
[docs]class silent_list(list): """ override repr when returning a list of matplotlib artists to prevent long, meaningless output. This is meant to be used for a homogeneous list of a given type """ def __init__(self, type, seq=None): self.type = type if seq is not None: self.extend(seq) def __repr__(self): return '<a list of %d %s objects>' % (len(self), self.type) __str__ = __repr__ def __getstate__(self): # store a dictionary of this SilentList's state return {'type': self.type, 'seq': self[:]} def __setstate__(self, state): self.type = state['type'] self.extend(state['seq'])
[docs]class IgnoredKeywordWarning(UserWarning): """ A class for issuing warnings about keyword arguments that will be ignored by matplotlib """ pass
[docs]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 ------- out : 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 """ 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, IgnoredKeywordWarning) return out
[docs]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
[docs]@deprecated('3.0', alternative='types.SimpleNamespace') class Bunch(types.SimpleNamespace): """ Often we want to just collect a bunch of stuff together, naming each item of the bunch; a dictionary's OK for that, but a small do- nothing class is even handier, and prettier to use. Whenever you want to group a few variables:: >>> point = Bunch(datum=2, squared=4, coord=12) >>> point.datum """ pass
[docs]@deprecated('3.1', alternative='np.iterable') def iterable(obj): """return true if *obj* is iterable""" try: iter(obj) except TypeError: return False return True
[docs]@deprecated("3.1", alternative="isinstance(..., collections.abc.Hashable)") def is_hashable(obj): """Returns true if *obj* can be hashed""" try: hash(obj) except TypeError: return False return True
[docs]def is_writable_file_like(obj): """Return whether *obj* looks like a file object with a *write* method.""" return callable(getattr(obj, 'write', None))
[docs]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
[docs]@deprecated('3.0', alternative='isinstance(..., numbers.Number)') def is_numlike(obj): """return true if *obj* looks like a number""" return isinstance(obj, (numbers.Number, np.number))
[docs]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 PathLike or file-like object 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'): # get rid of 'U' in flag for gzipped files. flag = flag.replace('U', '') 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 # get rid of 'U' in flag for bz2 files flag = flag.replace('U', '') 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
[docs]@contextlib.contextmanager def open_file_cm(path_or_file, mode="r", encoding=None): r"""Pass through file objects and context-manage `.PathLike`\s.""" fh, opened = to_filehandle(path_or_file, mode, True, encoding) if opened: with fh: yield fh else: yield fh
[docs]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)
[docs]def get_sample_data(fname, asfileobj=True): """ Return a sample data file. *fname* is a path relative to the `mpl-data/sample_data` directory. If *asfileobj* is `True` return a file object, otherwise just a file path. Set the rc parameter examples.directory to the directory where we should look, if sample_data files are stored in a location different than default (which is 'mpl-data/sample_data` at the same level of 'matplotlib` Python module files). If the filename ends in .gz, the file is implicitly ungzipped. """ # Don't trigger deprecation warning when just fetching. if dict.__getitem__(matplotlib.rcParams, 'examples.directory'): root = matplotlib.rcParams['examples.directory'] else: root = os.path.join(matplotlib._get_data_path(), 'sample_data') path = os.path.join(root, fname) if asfileobj: if os.path.splitext(fname)[-1].lower() in ['.csv', '.xrc', '.txt']: mode = 'r' else: mode = 'rb' base, ext = os.path.splitext(fname) if ext == '.gz': return gzip.open(path, mode) else: return open(path, mode) else: return path
[docs]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)
[docs]@deprecated("3.0") def mkdirs(newdir, mode=0o777): """ make directory *newdir* recursively, and set *mode*. Equivalent to :: > mkdir -p NEWDIR > chmod MODE NEWDIR """ # this functionality is now in core python as of 3.2 # LPY DROP os.makedirs(newdir, mode=mode, exist_ok=True)
[docs]@deprecated('3.0') class GetRealpathAndStat(object): def __init__(self): self._cache = {} def __call__(self, path): result = self._cache.get(path) if result is None: realpath = os.path.realpath(path) if sys.platform == 'win32': stat_key = realpath else: stat = os.stat(realpath) stat_key = (stat.st_ino, stat.st_dev) result = realpath, stat_key self._cache[path] = result return result
[docs]@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 = {}
[docs]@deprecated("3.1", alternative="inspect.cleandoc") def dedent(s): """ Remove excess indentation from docstring *s*. Discards any leading blank lines, then removes up to n whitespace characters from each line, where n is the number of leading whitespace characters in the first line. It differs from textwrap.dedent in its deletion of leading blank lines and its use of the first non-blank line to determine the indentation. It is also faster in most cases. """ # This implementation has a somewhat obtuse use of regular # expressions. However, this function accounted for almost 30% of # matplotlib startup time, so it is worthy of optimization at all # costs. if not s: # includes case of s is None return '' match = _find_dedent_regex.match(s) if match is None: return s # This is the number of spaces to remove from the left-hand side. nshift = match.end(1) - match.start(1) if nshift == 0: return s # Get a regex that will remove *up to* nshift spaces from the # beginning of each line. If it isn't in the cache, generate it. unindent = _dedent_regex.get(nshift, None) if unindent is None: unindent = re.compile("\n\r? {0,%d}" % nshift) _dedent_regex[nshift] = unindent result = unindent.sub("\n", s).strip() return result
[docs]@deprecated("3.0") def listFiles(root, patterns='*', recurse=1, return_folders=0): """ Recursively list files from Parmar and Martelli in the Python Cookbook """ import os.path import fnmatch # Expand patterns from semicolon-separated string to list pattern_list = patterns.split(';') results = [] for dirname, dirs, files in os.walk(root): # Append to results all relevant files (and perhaps folders) for name in files: fullname = os.path.normpath(os.path.join(dirname, name)) if return_folders or os.path.isfile(fullname): for pattern in pattern_list: if fnmatch.fnmatch(name, pattern): results.append(fullname) break # Block recursion if recursion was disallowed if not recurse: break return results
[docs]class maxdict(dict): """ A dictionary with a maximum size; 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)
[docs]class Stack(object): """ 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 len(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]
[docs] def forward(self): """Move the position forward and return the current element.""" self._pos = min(self._pos + 1, len(self._elements) - 1) return self()
[docs] def back(self): """Move the position back and return the current element.""" if self._pos > 0: self._pos -= 1 return self()
[docs] 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()
[docs] def home(self): """ Push the first element onto the top of the stack. The first element is returned. """ if not len(self._elements): return self.push(self._elements[0]) return self()
[docs] def empty(self): """Return whether the stack is empty.""" return len(self._elements) == 0
[docs] def clear(self): """Empty the stack.""" self._pos = -1 self._elements = []
[docs] def bubble(self, o): """ Raise *o* to the top of the stack. *o* must be present in the stack. *o* is returned. """ if o not in self._elements: raise ValueError('Unknown element o') old = self._elements[:] self.clear() bubbles = [] for thiso in old: if thiso == o: bubbles.append(thiso) else: self.push(thiso) for _ in bubbles: self.push(o) return o
[docs] def remove(self, o): """Remove *o* from the stack.""" if o not in self._elements: raise ValueError('Unknown element o') old = self._elements[:] self.clear() for thiso in old: if thiso != o: self.push(thiso)
[docs]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: raise NotImplementedError( "report_memory works on %s only if " "the '%s' program is found" % (os_name, command[0]) ) 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
_safezip_msg = 'In safezip, len(args[0])=%d but len(args[%d])=%d'
[docs]@deprecated("3.1") def safezip(*args): """make sure *args* are equal len before zipping""" Nx = len(args[0]) for i, arg in enumerate(args[1:]): if len(arg) != Nx: raise ValueError(_safezip_msg % (Nx, i + 1, len(arg))) return list(zip(*args))
[docs]def safe_masked_invalid(x, copy=False): x = np.array(x, subok=True, copy=copy) if not x.dtype.isnative: # Note that the argument to `byteswap` is 'inplace', # thus if we have already made a copy, do the byteswap in # place, else make a copy with the byte order swapped. # Be explicit that we are swapping the byte order of the dtype x = x.byteswap(copy).newbyteorder('S') try: xm = np.ma.masked_invalid(x, copy=False) xm.shrink_mask() except TypeError: return x return xm
[docs]class Grouper(object): """ This class provides a lightweight way to group arbitrary objects together into disjoint sets when a full-blown graph data structure would be overkill. 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. For example: >>> from matplotlib.cbook import Grouper >>> class Foo(object): ... 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
[docs] 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)
[docs] 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()
[docs] 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)))
[docs] 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]
[docs] 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]
[docs]def simple_linear_interpolation(a, steps): """ Resample an array with ``steps - 1`` points between original point pairs. Parameters ---------- a : array, shape (n, ...) steps : int Returns ------- array, shape ``((n - 1) * steps + 1, ...)`` Along each column of *a*, ``(steps - 1)`` points are introduced between each original values; the values are linearly interpolated. """ 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:]))
[docs]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 :meth:`np.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 :meth:`np.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. All input arguments that are not passed unchanged are returned as masked arrays if any masked points are found, otherwise as ndarrays. """ 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 = [] # Output args; some may be modified. seqlist = [False] * len(args) # Flags: True if output will be masked. masks = [] # List of masks. for i, x in enumerate(args): if is_scalar_or_string(x) or len(x) != nrecs: margs.append(x) # Leave it unmodified. else: if isinstance(x, np.ma.MaskedArray) and x.ndim > 1: raise ValueError("Masked arrays must be 1-D") x = np.asanyarray(x) if x.ndim == 1: x = safe_masked_invalid(x) seqlist[i] = True if np.ma.is_masked(x): masks.append(np.ma.getmaskarray(x)) margs.append(x) # Possibly modified. if len(masks): mask = np.logical_or.reduce(masks) for i, x in enumerate(margs): if seqlist[i]: margs[i] = np.ma.array(x, mask=mask) return margs
[docs]def boxplot_stats(X, whis=1.5, bootstrap=None, labels=None, autorange=False): """ Returns list of dictionaries of statistics used to draw a series of box and whisker plots. The `Returns` section enumerates the required keys of the dictionary. Users can skip this function and pass a user-defined set of dictionaries to the new `axes.bxp` method instead of relying on MPL to do the calculations. Parameters ---------- X : array-like Data that will be represented in the boxplots. Should have 2 or fewer dimensions. whis : float, string, or sequence (default = 1.5) As a float, determines the reach of the whiskers beyond the first and third quartiles. In other words, where IQR is the interquartile range (`Q3-Q1`), the upper whisker will extend to last datum less than `Q3 + whis*IQR`. Similarly, the lower whisker will extend to the first datum greater than `Q1 - whis*IQR`. Beyond the whiskers, data are considered outliers and are plotted as individual points. This can be set to an ascending sequence of percentiles (e.g., [5, 95]) to set the whiskers at specific percentiles of the data. Finally, `whis` can be the string ``'range'`` to force the whiskers to the minimum and maximum of the data. In the edge case that the 25th and 75th percentiles are equivalent, `whis` can be automatically set to ``'range'`` via the `autorange` option. bootstrap : int, optional Number of times the confidence intervals around the median should be bootstrapped (percentile method). labels : array-like, optional Labels for each dataset. Length must be compatible with dimensions of `X`. autorange : bool, optional (False) When `True` and the data are distributed such that the 25th and 75th percentiles are equal, ``whis`` is set to ``'range'`` such that the whisker ends are at the minimum and maximum of the data. Returns ------- bxpstats : list of dict A list of dictionaries containing the results for each column of data. Keys of each dictionary are the following: ======== =================================== Key Value Description ======== =================================== label tick label for the boxplot mean arithmetic mean value med 50th percentile q1 first quartile (25th percentile) q3 third quartile (75th percentile) cilo lower notch around the median cihi upper notch around the median whislo end of the lower whisker whishi end of the upper whisker fliers outliers ======== =================================== Notes ----- Non-bootstrapping approach to confidence interval uses Gaussian- based asymptotic approximation: .. math:: \\mathrm{med} \\pm 1.57 \\times \\frac{\\mathrm{iqr}}{\\sqrt{N}} General approach from: McGill, R., Tukey, J.W., and Larsen, W.A. (1978) "Variations of Boxplots", The American Statistician, 32:12-16. """ def _bootstrap_median(data, N=5000): # determine 95% confidence intervals of the median M = len(data) percentiles = [2.5, 97.5] bs_index = np.random.randint(M, size=(N, M)) bsData = data[bs_index] estimate = np.median(bsData, axis=1, overwrite_input=True) CI = np.percentile(estimate, percentiles) return CI def _compute_conf_interval(data, med, iqr, bootstrap): if bootstrap is not None: # Do a bootstrap estimate of notch locations. # get conf. intervals around median CI = _bootstrap_median(data, N=bootstrap) notch_min = CI[0] notch_max = CI[1] else: N = len(data) notch_min = med - 1.57 * iqr / np.sqrt(N) notch_max = med + 1.57 * iqr / np.sqrt(N) return notch_min, notch_max # output is a list of dicts bxpstats = [] # convert X to a list of lists X = _reshape_2D(X, "X") ncols = len(X) if labels is None: labels = itertools.repeat(None) elif len(labels) != ncols: raise ValueError("Dimensions of labels and X must be compatible") input_whis = whis for ii, (x, label) in enumerate(zip(X, labels)): # empty dict stats = {} if label is not None: stats['label'] = label # restore whis to the input values in case it got changed in the loop whis = input_whis # note tricksiness, append up here and then mutate below bxpstats.append(stats) # if empty, bail if len(x) == 0: stats['fliers'] = np.array([]) stats['mean'] = np.nan stats['med'] = np.nan stats['q1'] = np.nan stats['q3'] = np.nan stats['cilo'] = np.nan stats['cihi'] = np.nan stats['whislo'] = np.nan stats['whishi'] = np.nan stats['med'] = np.nan continue # up-convert to an array, just to be safe x = np.asarray(x) # arithmetic mean stats['mean'] = np.mean(x) # medians and quartiles q1, med, q3 = np.percentile(x, [25, 50, 75]) # interquartile range stats['iqr'] = q3 - q1 if stats['iqr'] == 0 and autorange: whis = 'range' # conf. interval around median stats['cilo'], stats['cihi'] = _compute_conf_interval( x, med, stats['iqr'], bootstrap ) # lowest/highest non-outliers if np.isscalar(whis): if np.isreal(whis): loval = q1 - whis * stats['iqr'] hival = q3 + whis * stats['iqr'] elif whis in ['range', 'limit', 'limits', 'min/max']: loval = np.min(x) hival = np.max(x) else: raise ValueError('whis must be a float, valid string, or list ' 'of percentiles') else: loval = np.percentile(x, whis[0]) hival = np.percentile(x, whis[1]) # get high extreme wiskhi = x[x <= hival] if len(wiskhi) == 0 or np.max(wiskhi) < q3: stats['whishi'] = q3 else: stats['whishi'] = np.max(wiskhi) # get low extreme wisklo = x[x >= loval] if len(wisklo) == 0 or np.min(wisklo) > q1: stats['whislo'] = q1 else: stats['whislo'] = np.min(wisklo) # compute a single array of outliers stats['fliers'] = np.hstack([ x[x < stats['whislo']], x[x > stats['whishi']], ]) # add in the remaining stats stats['q1'], stats['med'], stats['q3'] = q1, med, q3 return bxpstats
# The ls_mapper maps short codes for line style to their full name used by # backends; the reverse mapper is for mapping full names to short ones. ls_mapper = {'-': 'solid', '--': 'dashed', '-.': 'dashdot', ':': 'dotted'} ls_mapper_r = {v: k for k, v in ls_mapper.items()}
[docs]def contiguous_regions(mask): """ Return a list of (ind0, ind1) such that mask[ind0:ind1].all() is True and we cover all such regions """ mask = np.asarray(mask, dtype=bool) if not mask.size: return [] # Find the indices of region changes, and correct offset idx, = np.nonzero(mask[:-1] != mask[1:]) idx += 1 # List operations are faster for moderately sized arrays idx = idx.tolist() # Add first and/or last index if needed if mask[0]: idx = [0] + idx if mask[-1]: idx.append(len(mask)) return list(zip(idx[::2], idx[1::2]))
[docs]def is_math_text(s): # Did we find an even number of non-escaped dollar signs? # If so, treat is as math text. s = str(s) dollar_count = s.count(r'$') - s.count(r'\$') even_dollars = (dollar_count > 0 and dollar_count % 2 == 0) return even_dollars
def _to_unmasked_float_array(x): """ Convert a sequence to a float array; if input was a masked array, masked values are converted to nans. """ if hasattr(x, 'mask'): return np.ma.asarray(x, float).filled(np.nan) else: return np.asarray(x, float) def _check_1d(x): ''' Converts a sequence of less than 1 dimension, to an array of 1 dimension; leaves everything else untouched. ''' if not hasattr(x, 'shape') or len(x.shape) < 1: return np.atleast_1d(x) else: try: x[:, None] return x except (IndexError, TypeError): return np.atleast_1d(x) def _reshape_2D(X, name): """ Use Fortran ordering to convert ndarrays and lists of iterables to lists of 1D arrays. Lists of iterables are converted by applying `np.asarray` to each of their elements. 1D ndarrays are returned in a singleton list containing them. 2D ndarrays are converted to the list of their *columns*. *name* is used to generate the error message for invalid inputs. """ # Iterate over columns for ndarrays, over rows otherwise. X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X)) if len(X) == 0: return [[]] elif X.ndim == 1 and np.ndim(X[0]) == 0: # 1D array of scalars: directly return it. return [X] elif X.ndim in [1, 2]: # 2D array, or 1D array of iterables: flatten them first. return [np.reshape(x, -1) for x in X] else: raise ValueError("{} must have 2 or fewer dimensions".format(name))
[docs]def violin_stats(X, method, points=100): """ Returns a list of dictionaries of data which can be used to draw a series of violin plots. See the `Returns` section below to view the required keys of the dictionary. Users can skip this function and pass a user-defined set of dictionaries to the `axes.vplot` method instead of using MPL to do the calculations. Parameters ---------- X : array-like Sample data that will be used to produce the gaussian kernel density estimates. Must have 2 or fewer dimensions. method : callable The method used to calculate the kernel density estimate for each column of data. When called via `method(v, coords)`, it should return a vector of the values of the KDE evaluated at the values specified in coords. points : scalar, default = 100 Defines the number of points to evaluate each of the gaussian kernel density estimates at. Returns ------- A list of dictionaries containing the results for each column of data. The dictionaries contain at least the following: - coords: A list of scalars containing the coordinates this particular kernel density estimate was evaluated at. - vals: A list of scalars containing the values of the kernel density estimate at each of the coordinates given in `coords`. - mean: The mean value for this column of data. - median: The median value for this column of data. - min: The minimum value for this column of data. - max: The maximum value for this column of data. """ # List of dictionaries describing each of the violins. vpstats = [] # Want X to be a list of data sequences X = _reshape_2D(X, "X") for x in X: # Dictionary of results for this distribution stats = {} # Calculate basic stats for the distribution min_val = np.min(x) max_val = np.max(x) # Evaluate the kernel density estimate coords = np.linspace(min_val, max_val, points) stats['vals'] = method(x, coords) stats['coords'] = coords # Store additional statistics for this distribution stats['mean'] = np.mean(x) stats['median'] = np.median(x) stats['min'] = min_val stats['max'] = max_val # Append to output vpstats.append(stats) return vpstats
[docs]def pts_to_prestep(x, *args): """ Convert continuous line to pre-steps. Given a set of ``N`` points, convert to ``2N - 1`` points, which when connected linearly give a step function which changes values at the beginning of the intervals. Parameters ---------- x : array The x location of the steps. May be empty. y1, ..., yp : array y arrays to be turned into steps; all must be the same length as ``x``. Returns ------- out : array The x and y values converted to steps in the same order as the input; can be unpacked as ``x_out, y1_out, ..., yp_out``. If the input is length ``N``, each of these arrays will be length ``2N + 1``. For ``N=0``, the length will be 0. Examples -------- >> x_s, y1_s, y2_s = pts_to_prestep(x, y1, y2) """ steps = np.zeros((1 + len(args), max(2 * len(x) - 1, 0))) # In all `pts_to_*step` functions, only assign *once* using `x` and `args`, # as converting to an array may be expensive. steps[0, 0::2] = x steps[0, 1::2] = steps[0, 0:-2:2] steps[1:, 0::2] = args steps[1:, 1::2] = steps[1:, 2::2] return steps
[docs]def pts_to_poststep(x, *args): """ Convert continuous line to post-steps. Given a set of ``N`` points convert to ``2N + 1`` points, which when connected linearly give a step function which changes values at the end of the intervals. Parameters ---------- x : array The x location of the steps. May be empty. y1, ..., yp : array y arrays to be turned into steps; all must be the same length as ``x``. Returns ------- out : array The x and y values converted to steps in the same order as the input; can be unpacked as ``x_out, y1_out, ..., yp_out``. If the input is length ``N``, each of these arrays will be length ``2N + 1``. For ``N=0``, the length will be 0. Examples -------- >> x_s, y1_s, y2_s = pts_to_poststep(x, y1, y2) """ steps = np.zeros((1 + len(args), max(2 * len(x) - 1, 0))) steps[0, 0::2] = x steps[0, 1::2] = steps[0, 2::2] steps[1:, 0::2] = args steps[1:, 1::2] = steps[1:, 0:-2:2] return steps
[docs]def pts_to_midstep(x, *args): """ Convert continuous line to mid-steps. Given a set of ``N`` points convert to ``2N`` points which when connected linearly give a step function which changes values at the middle of the intervals. Parameters ---------- x : array The x location of the steps. May be empty. y1, ..., yp : array y arrays to be turned into steps; all must be the same length as ``x``. Returns ------- out : array The x and y values converted to steps in the same order as the input; can be unpacked as ``x_out, y1_out, ..., yp_out``. If the input is length ``N``, each of these arrays will be length ``2N``. Examples -------- >> x_s, y1_s, y2_s = pts_to_midstep(x, y1, y2) """ steps = np.zeros((1 + len(args), 2 * len(x))) x = np.asanyarray(x) steps[0, 1:-1:2] = steps[0, 2::2] = (x[:-1] + x[1:]) / 2 steps[0, :1] = x[:1] # Also works for zero-sized input. steps[0, -1:] = x[-1:] steps[1:, 0::2] = args steps[1:, 1::2] = steps[1:, 0::2] return steps
STEP_LOOKUP_MAP = {'default': lambda x, y: (x, y), 'steps': pts_to_prestep, 'steps-pre': pts_to_prestep, 'steps-post': pts_to_poststep, 'steps-mid': pts_to_midstep}
[docs]def index_of(y): """ A helper function to get the index of an input to plot against if x values are not explicitly given. Tries to get `y.index` (works if this is a pd.Series), if that fails, return np.arange(y.shape[0]). This will be extended in the future to deal with more types of labeled data. Parameters ---------- y : scalar or array-like The proposed y-value Returns ------- x, y : ndarray The x and y values to plot. """ try: return y.index.values, y.values except AttributeError: y = _check_1d(y) return np.arange(y.shape[0], dtype=float), y
[docs]def safe_first_element(obj): if isinstance(obj, collections.abc.Iterator): # needed to accept `array.flat` as input. # np.flatiter reports as an instance of collections.Iterator # but can still be indexed via []. # This has the side effect of re-setting the iterator, but # that is acceptable. try: return obj[0] except TypeError: pass raise RuntimeError("matplotlib does not support generators " "as input") return next(iter(obj))
[docs]def sanitize_sequence(data): """Converts dictview object to list""" return (list(data) if isinstance(data, collections.abc.MappingView) else data)
[docs]def normalize_kwargs(kw, alias_mapping=None, required=(), forbidden=(), allowed=None): """Helper function to normalize kwarg inputs The order they are resolved are: 1. aliasing 2. required 3. forbidden 4. allowed This order means that only the canonical names need appear in `allowed`, `forbidden`, `required` Parameters ---------- alias_mapping, dict, optional A mapping between a canonical name to a list of aliases, in order of precedence from lowest to highest. If the canonical value is not in the list it is assumed to have the highest priority. required : iterable, optional A tuple of fields that must be in kwargs. forbidden : iterable, optional A list of keys which may not be in kwargs allowed : tuple, optional A tuple of allowed fields. If this not None, then raise if `kw` contains any keys not in the union of `required` and `allowed`. To allow only the required fields pass in ``()`` for `allowed` Raises ------ TypeError To match what python raises if invalid args/kwargs are passed to a callable. """ # deal with default value of alias_mapping if alias_mapping is None: alias_mapping = dict() # make a local so we can pop kw = dict(kw) # output dictionary ret = dict() # hit all alias mappings for canonical, alias_list in alias_mapping.items(): # the alias lists are ordered from lowest to highest priority # so we know to use the last value in this list tmp = [] seen = [] for a in alias_list: try: tmp.append(kw.pop(a)) seen.append(a) except KeyError: pass # if canonical is not in the alias_list assume highest priority if canonical not in alias_list: try: tmp.append(kw.pop(canonical)) seen.append(canonical) except KeyError: pass # if we found anything in this set of aliases put it in the return # dict if tmp: ret[canonical] = tmp[-1] if len(tmp) > 1: warn_deprecated( "3.1", message=f"Saw kwargs {seen!r} which are all " f"aliases for {canonical!r}. Kept value from " f"{seen[-1]!r}. Passing multiple aliases for the same " f"property will raise a TypeError %(removal)s.") # at this point we know that all keys which are aliased are removed, update # the return dictionary from the cleaned local copy of the input ret.update(kw) fail_keys = [k for k in required if k not in ret] if fail_keys: raise TypeError("The required keys {keys!r} " "are not in kwargs".format(keys=fail_keys)) fail_keys = [k for k in forbidden if k in ret] if fail_keys: raise TypeError("The forbidden keys {keys!r} " "are in kwargs".format(keys=fail_keys)) if allowed is not None: allowed_set = {*required, *allowed} fail_keys = [k for k in ret if k not in allowed_set] if fail_keys: raise TypeError( "kwargs contains {keys!r} which are not in the required " "{req!r} or allowed {allow!r} keys".format( keys=fail_keys, req=required, allow=allowed)) return ret
[docs]@deprecated("3.1") def get_label(y, default_name): try: return y.name except AttributeError: return default_name
_lockstr = """\ LOCKERROR: matplotlib is trying to acquire the lock {!r} and has failed. This maybe due to any other process holding this lock. If you are sure no other matplotlib process is running try removing these folders and trying again. """
[docs]@deprecated("3.0") class Locked(object): """ Context manager to handle locks. Based on code from conda. (c) 2012-2013 Continuum Analytics, Inc. / https://www.continuum.io/ All Rights Reserved conda is distributed under the terms of the BSD 3-clause license. Consult LICENSE_CONDA or https://opensource.org/licenses/BSD-3-Clause. """ LOCKFN = '.matplotlib_lock'
[docs] class TimeoutError(RuntimeError): pass
def __init__(self, path): self.path = path self.end = "-" + str(os.getpid()) self.lock_path = os.path.join(self.path, self.LOCKFN + self.end) self.pattern = os.path.join(self.path, self.LOCKFN + '-*') self.remove = True def __enter__(self): retries = 50 sleeptime = 0.1 while retries: files = glob.glob(self.pattern) if files and not files[0].endswith(self.end): time.sleep(sleeptime) retries -= 1 else: break else: err_str = _lockstr.format(self.pattern) raise self.TimeoutError(err_str) if not files: try: os.makedirs(self.lock_path) except OSError: pass else: # PID lock already here --- someone else will remove it. self.remove = False def __exit__(self, exc_type, exc_value, traceback): if self.remove: for path in self.lock_path, self.path: try: os.rmdir(path) except OSError: pass
@contextlib.contextmanager def _lock_path(path): """ Context manager for locking a path. Usage:: with _lock_path(path): ... Another thread or process that attempts to lock the same path will wait until this context manager is exited. The lock is implemented by creating a temporary file in the parent directory, so that directory must exist and be writable. """ path = Path(path) lock_path = path.with_name(path.name + ".matplotlib-lock") retries = 50 sleeptime = 0.1 for _ in range(retries): try: with lock_path.open("xb"): break except FileExistsError: time.sleep(sleeptime) else: raise TimeoutError("""\ Lock error: Matplotlib failed to acquire the following lock file: {} This maybe due to another process holding this lock file. If you are sure no other Matplotlib process is running, remove this file and try again.""".format( lock_path)) try: yield finally: lock_path.unlink() def _topmost_artist( artists, _cached_max=functools.partial(max, key=operator.attrgetter("zorder"))): """Get the topmost artist of a list. In case of a tie, return the *last* of the tied artists, as it will be drawn on top of the others. `max` returns the first maximum in case of ties, so we need to iterate over the list in reverse order. """ return _cached_max(reversed(artists)) def _str_equal(obj, s): """Return whether *obj* is a string equal to string *s*. This helper solely exists to handle the case where *obj* is a numpy array, because in such cases, a naive ``obj == s`` would yield an array, which cannot be used in a boolean context. """ return isinstance(obj, str) and obj == s def _str_lower_equal(obj, s): """Return whether *obj* is a string equal, when lowercased, to string *s*. This helper solely exists to handle the case where *obj* is a numpy array, because in such cases, a naive ``obj == s`` would yield an array, which cannot be used in a boolean context. """ return isinstance(obj, str) and obj.lower() == s def _define_aliases(alias_d, cls=None): """Class decorator for defining property aliases. Use as :: @cbook._define_aliases({"property": ["alias", ...], ...}) class C: ... For each property, if the corresponding ``get_property`` is defined in the class so far, an alias named ``get_alias`` will be defined; the same will be done for setters. If neither the getter nor the setter exists, an exception will be raised. The alias map is stored as the ``_alias_map`` attribute on the class and can be used by `~.normalize_kwargs` (which assumes that higher priority aliases come last). """ if cls is None: # Return the actual class decorator. return functools.partial(_define_aliases, alias_d) def make_alias(name): # Enforce a closure over *name*. @functools.wraps(getattr(cls, name)) def method(self, *args, **kwargs): return getattr(self, name)(*args, **kwargs) return method for prop, aliases in alias_d.items(): exists = False for prefix in ["get_", "set_"]: if prefix + prop in vars(cls): exists = True for alias in aliases: method = make_alias(prefix + prop) method.__name__ = prefix + alias method.__doc__ = "Alias for `{}`.".format(prefix + prop) setattr(cls, prefix + alias, method) if not exists: raise ValueError( "Neither getter nor setter exists for {!r}".format(prop)) if hasattr(cls, "_alias_map"): # Need to decide on conflict resolution policy. raise NotImplementedError("Parent class already defines aliases") cls._alias_map = alias_d return cls def _array_perimeter(arr): """ Get the elements on the perimeter of ``arr``, Parameters ---------- arr : ndarray, shape (M, N) The input array Returns ------- perimeter : ndarray, shape (2*(M - 1) + 2*(N - 1),) The elements on the perimeter of the array:: [arr[0,0] ... arr[0,-1] ... arr[-1, -1] ... arr[-1,0] ...] Examples -------- >>> i, j = np.ogrid[:3,:4] >>> a = i*10 + j >>> a array([[ 0, 1, 2, 3], [10, 11, 12, 13], [20, 21, 22, 23]]) >>> _array_perimeter(a) array([ 0, 1, 2, 3, 13, 23, 22, 21, 20, 10]) """ # note we use Python's half-open ranges to avoid repeating # the corners forward = np.s_[0:-1] # [0 ... -1) backward = np.s_[-1:0:-1] # [-1 ... 0) return np.concatenate(( arr[0, forward], arr[forward, -1], arr[-1, backward], arr[backward, 0], )) @contextlib.contextmanager def _setattr_cm(obj, **kwargs): """Temporarily set some attributes; restore original state at context exit. """ sentinel = object() origs = [(attr, getattr(obj, attr, sentinel)) for attr in kwargs] try: for attr, val in kwargs.items(): setattr(obj, attr, val) yield finally: for attr, orig in origs: if orig is sentinel: delattr(obj, attr) else: setattr(obj, attr, orig) def _warn_external(message, category=None): """ `warnings.warn` wrapper that sets *stacklevel* to "outside Matplotlib". The original emitter of the warning can be obtained by patching this function back to `warnings.warn`, i.e. ``cbook._warn_external = warnings.warn`` (or ``functools.partial(warnings.warn, stacklevel=2)``, etc.). """ frame = sys._getframe() for stacklevel in itertools.count(1): # lgtm[py/unused-loop-variable] if frame is None: # when called in embedded context may hit frame is None break if not re.match(r"\A(matplotlib|mpl_toolkits)(\Z|\.)", # Work around sphinx-gallery not setting __name__. frame.f_globals.get("__name__", "")): break frame = frame.f_back warnings.warn(message, category, stacklevel) class _OrderedSet(collections.abc.MutableSet): def __init__(self): self._od = collections.OrderedDict() def __contains__(self, key): return key in self._od def __iter__(self): return iter(self._od) def __len__(self): return len(self._od) def add(self, key): self._od.pop(key, None) self._od[key] = None def discard(self, key): self._od.pop(key, None) # Agg's buffers are unmultiplied RGBA8888, which neither PyQt4 nor cairo # support; however, both do support premultiplied ARGB32. def _premultiplied_argb32_to_unmultiplied_rgba8888(buf): """ Convert a premultiplied ARGB32 buffer to an unmultiplied RGBA8888 buffer. """ rgba = np.take( # .take() ensures C-contiguity of the result. buf, [2, 1, 0, 3] if sys.byteorder == "little" else [1, 2, 3, 0], axis=2) rgb = rgba[..., :-1] alpha = rgba[..., -1] # Un-premultiply alpha. The formula is the same as in cairo-png.c. mask = alpha != 0 for channel in np.rollaxis(rgb, -1): channel[mask] = ( (channel[mask].astype(int) * 255 + alpha[mask] // 2) // alpha[mask]) return rgba def _unmultiplied_rgba8888_to_premultiplied_argb32(rgba8888): """ Convert an unmultiplied RGBA8888 buffer to a premultiplied ARGB32 buffer. """ if sys.byteorder == "little": argb32 = np.take(rgba8888, [2, 1, 0, 3], axis=2) rgb24 = argb32[..., :-1] alpha8 = argb32[..., -1:] else: argb32 = np.take(rgba8888, [3, 0, 1, 2], axis=2) alpha8 = argb32[..., :1] rgb24 = argb32[..., 1:] # Only bother premultiplying when the alpha channel is not fully opaque, # as the cost is not negligible. The unsafe cast is needed to do the # multiplication in-place in an integer buffer. if alpha8.min() != 0xff: np.multiply(rgb24, alpha8 / 0xff, out=rgb24, casting="unsafe") return argb32 def _check_and_log_subprocess(command, logger, **kwargs): """ Run *command*, returning its stdout output if it succeeds. If it fails (exits with nonzero return code), raise an exception whose text includes the failed command and captured stdout and stderr output. Regardless of the return code, the command is logged at DEBUG level on *logger*. In case of success, the output is likewise logged. """ logger.debug('%s', str(command)) proc = subprocess.run( command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, **kwargs) if proc.returncode: raise RuntimeError( f"The command\n" f" {str(command)}\n" f"failed and generated the following output:\n" f"{proc.stdout.decode('utf-8')}\n" f"and the following error:\n" f"{proc.stderr.decode('utf-8')}") logger.debug("stdout:\n%s", proc.stdout) logger.debug("stderr:\n%s", proc.stderr) return proc.stdout def _check_not_matrix(**kwargs): """ If any value in *kwargs* is a `np.matrix`, raise a TypeError with the key name in its message. """ for k, v in kwargs.items(): if isinstance(v, np.matrix): raise TypeError(f"Argument {k!r} cannot be a np.matrix") def _check_in_list(values, **kwargs): """ For each *key, value* pair in *kwargs*, check that *value* is in *values*; if not, raise an appropriate ValueError. Examples -------- >>> cbook._check_in_list(["foo", "bar"], arg=arg, other_arg=other_arg) """ for k, v in kwargs.items(): if v not in values: raise ValueError( "{!r} is not a valid value for {}; supported values are {}" .format(v, k, ', '.join(map(repr, values))))