""" 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 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 # premetively 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 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 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 = {} # 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, self._remove_proxy) for cid, func in d.items()} for s, d in self.callbacks.items()} self._func_cid_map = { s: {proxy: cid for cid, proxy in d.items()} for s, d in self.callbacks.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) self._func_cid_map.setdefault(signal, {}) proxy = _weak_or_strong_ref(func, self._remove_proxy) if proxy in self._func_cid_map[signal]: return self._func_cid_map[signal][proxy] cid = next(self._cid_gen) self._func_cid_map[signal][proxy] = cid self.callbacks.setdefault(signal, {}) self.callbacks[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, proxy, *, _is_finalizing=sys.is_finalizing): if _is_finalizing(): # Weakrefs can't be properly torn down at that point anymore. return for signal, proxy_to_cid in list(self._func_cid_map.items()): cid = proxy_to_cid.pop(proxy, None) if cid is not None: del self.callbacks[signal][cid] self._pickled_cids.discard(cid) break else: # Not found return # Clean up empty dicts 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. """ self._pickled_cids.discard(cid) # Clean up callbacks for signal, cid_to_proxy in list(self.callbacks.items()): proxy = cid_to_proxy.pop(cid, None) if proxy is not None: break else: # Not found return proxy_to_cid = self._func_cid_map[signal] for current_proxy, current_cid in list(proxy_to_cid.items()): if current_cid == cid: assert proxy is current_proxy del proxy_to_cid[current_proxy] # Clean up empty dicts if len(self.callbacks[signal]) == 0: del self.callbacks[signal] del self._func_cid_map[signal] 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 :: [, , ] one will get :: 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"" else: return "" 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) @_api.delete_parameter( "3.8", "np_load", alternative="open(get_sample_data(..., asfileobj=False))") def get_sample_data(fname, asfileobj=True, *, np_load=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']: if np_load: return np.load(path) else: 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) @_api.deprecated("3.8") 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) 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: 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 @_api.deprecated("3.8", alternative="none, you no longer need to clean a Grouper") def clean(self): """Clean dead weak references from the dictionary.""" 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): """Return all of the items joined with *a*, including itself.""" siblings = self._mapping.get(a, [a]) return sorted(siblings, key=self._ordering.get) 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 self._grouper.joined(a, b) def get_siblings(self, a): return self._grouper.get_siblings(a) 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. 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") try: x = np.asanyarray(x) except (VisibleDeprecationWarning, ValueError): # NumPy 1.19 raises a warning about ragged arrays, but we want # to accept basically anything here. x = np.asanyarray(x, dtype=object) 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 def _broadcast_with_masks(*args, compress=False): """ Broadcast inputs, combining all masked arrays. Parameters ---------- *args : array-like The inputs to broadcast. compress : bool, default: False Whether to compress the masked arrays. If False, the masked values are replaced by NaNs. Returns ------- list of array-like The broadcasted and masked inputs. """ # extract the masks, if any masks = [k.mask for k in args if isinstance(k, np.ma.MaskedArray)] # broadcast to match the shape bcast = np.broadcast_arrays(*args, *masks) inputs = bcast[:len(args)] masks = bcast[len(args):] if masks: # combine the masks into one mask = np.logical_or.reduce(masks) # put mask on and compress if compress: inputs = [np.ma.array(k, mask=mask).compressed() for k in inputs] else: inputs = [np.ma.array(k, mask=mask, dtype=float).filled(np.nan).ravel() for k in inputs] else: inputs = [np.ravel(k) for k in inputs] return inputs def boxplot_stats(X, whis=1.5, bootstrap=None, labels=None, autorange=False): r""" Return a list of dictionaries of statistics used to draw a series of box and whisker plots using `~.Axes.bxp`. Parameters ---------- X : array-like Data that will be represented in the boxplots. Should have 2 or fewer dimensions. whis : float or (float, float), default: 1.5 The position of the whiskers. If a float, the lower whisker is at the lowest datum above ``Q1 - whis*(Q3-Q1)``, and the upper whisker at the highest datum below ``Q3 + whis*(Q3-Q1)``, where Q1 and Q3 are the first and third quartiles. The default value of ``whis = 1.5`` corresponds to Tukey's original definition of boxplots. If a pair of floats, they indicate the percentiles at which to draw the whiskers (e.g., (5, 95)). In particular, setting this to (0, 100) results in whiskers covering the whole range of the data. In the edge case where ``Q1 == Q3``, *whis* is automatically set to (0, 100) (cover the whole range of the data) if *autorange* is True. Beyond the whiskers, data are considered outliers and are plotted as individual points. bootstrap : int, optional Number of times the confidence intervals around the median should be bootstrapped (percentile method). labels : list of str, 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 (0, 100) such that the whisker ends are at the minimum and maximum of the data. Returns ------- 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) iqr interquartile range 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['iqr'] = np.nan stats['cilo'] = np.nan stats['cihi'] = np.nan stats['whislo'] = np.nan stats['whishi'] = np.nan continue # up-convert to an array, just to be safe x = np.ma.asarray(x) x = x.data[~x.mask].ravel() # 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 = (0, 100) # conf. interval around median stats['cilo'], stats['cihi'] = _compute_conf_interval( x, med, stats['iqr'], bootstrap ) # lowest/highest non-outliers if np.iterable(whis) and not isinstance(whis, str): loval, hival = np.percentile(x, whis) elif np.isreal(whis): loval = q1 - whis * stats['iqr'] hival = q3 + whis * stats['iqr'] else: raise ValueError('whis must be a float or list of percentiles') # 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.concatenate([ x[x < stats['whislo']], x[x > stats['whishi']], ]) # add in the remaining stats stats['q1'], stats['med'], stats['q3'] = q1, med, q3 return bxpstats #: Maps short codes for line style to their full name used by backends. ls_mapper = {'-': 'solid', '--': 'dashed', '-.': 'dashdot', ':': 'dotted'} #: Maps full names for line styles used by backends to their short codes. ls_mapper_r = {v: k for k, v in ls_mapper.items()} 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])) def is_math_text(s): """ Return whether the string *s* contains math expressions. This is done by checking whether *s* contains an even number of non-escaped dollar signs. """ 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): """Convert scalars to 1D arrays; pass-through arrays as is.""" # Unpack in case of e.g. Pandas or xarray object x = _unpack_to_numpy(x) # plot requires `shape` and `ndim`. If passed an # object that doesn't provide them, then force to numpy array. # Note this will strip unit information. if (not hasattr(x, 'shape') or not hasattr(x, 'ndim') or len(x.shape) < 1): return np.atleast_1d(x) else: return 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 `numpy.asanyarray` 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. """ # Unpack in case of e.g. Pandas or xarray object X = _unpack_to_numpy(X) # Iterate over columns for ndarrays. if isinstance(X, np.ndarray): X = X.T 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(f'{name} must have 2 or fewer dimensions') # Iterate over list of iterables. if len(X) == 0: return [[]] result = [] is_1d = True for xi in X: # check if this is iterable, except for strings which we # treat as singletons. if not isinstance(xi, str): try: iter(xi) except TypeError: pass else: is_1d = False xi = np.asanyarray(xi) nd = np.ndim(xi) if nd > 1: raise ValueError(f'{name} must have 2 or fewer dimensions') result.append(xi.reshape(-1)) if is_1d: # 1D array of scalars: directly return it. return [np.reshape(result, -1)] else: # 2D array, or 1D array of iterables: use flattened version. return result def violin_stats(X, method, points=100, quantiles=None): """ Return 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 with the same keys to `~.axes.Axes.violinplot` instead of using Matplotlib to do the calculations. See the *Returns* section below for the keys that must be present in the dictionaries. 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 : int, default: 100 Defines the number of points to evaluate each of the gaussian kernel density estimates at. quantiles : array-like, default: None Defines (if not None) a list of floats in interval [0, 1] for each column of data, which represents the quantiles that will be rendered for that column of data. Must have 2 or fewer dimensions. 1D array will be treated as a singleton list containing them. Returns ------- list of dict 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. - quantiles: The quantile values 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") # Want quantiles to be as the same shape as data sequences if quantiles is not None and len(quantiles) != 0: quantiles = _reshape_2D(quantiles, "quantiles") # Else, mock quantiles if it's none or empty else: quantiles = [[]] * len(X) # quantiles should have the same size as dataset if len(X) != len(quantiles): raise ValueError("List of violinplot statistics and quantiles values" " must have the same length") # Zip x and quantiles for (x, q) in zip(X, quantiles): # Dictionary of results for this distribution stats = {} # Calculate basic stats for the distribution min_val = np.min(x) max_val = np.max(x) quantile_val = np.percentile(x, 100 * q) # 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 stats['quantiles'] = np.atleast_1d(quantile_val) # Append to output vpstats.append(stats) return vpstats 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 ------- 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 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 ------- 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 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 ------- 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} def index_of(y): """ A helper function to create reasonable x values for the given *y*. This is used for plotting (x, y) if x values are not explicitly given. First try ``y.index`` (assuming *y* is a `pandas.Series`), if that fails, use ``range(len(y))``. This will be extended in the future to deal with more types of labeled data. Parameters ---------- y : float or array-like Returns ------- x, y : ndarray The x and y values to plot. """ try: return y.index.to_numpy(), y.to_numpy() except AttributeError: pass try: y = _check_1d(y) except (VisibleDeprecationWarning, ValueError): # NumPy 1.19 will warn on ragged input, and we can't actually use it. pass else: return np.arange(y.shape[0], dtype=float), y raise ValueError('Input could not be cast to an at-least-1D NumPy array') def safe_first_element(obj): """ Return the first element in *obj*. This is a type-independent way of obtaining the first element, supporting both index access and the iterator protocol. """ 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)) def _safe_first_finite(obj): """ Return the first finite element in *obj* if one is available and skip_nonfinite is True. Otherwise, return the first element. This is a method for internal use. This is a type-independent way of obtaining the first finite element, supporting both index access and the iterator protocol. """ def safe_isfinite(val): if val is None: return False try: return math.isfinite(val) except (TypeError, ValueError): # if the outer object is 2d, then val is a 1d array, and # - math.isfinite(numpy.zeros(3)) raises TypeError # - math.isfinite(torch.zeros(3)) raises ValueError pass try: return np.isfinite(val) if np.isscalar(val) else True except TypeError: # This is something that NumPy cannot make heads or tails of, # assume "finite" return True if isinstance(obj, np.flatiter): # TODO do the finite filtering on this return obj[0] elif isinstance(obj, collections.abc.Iterator): raise RuntimeError("matplotlib does not support generators as input") else: for val in obj: if safe_isfinite(val): return val return safe_first_element(obj) def sanitize_sequence(data): """ Convert dictview objects to list. Other inputs are returned unchanged. """ return (list(data) if isinstance(data, collections.abc.MappingView) else data) def normalize_kwargs(kw, alias_mapping=None): """ Helper function to normalize kwarg inputs. Parameters ---------- kw : dict or None A dict of keyword arguments. None is explicitly supported and treated as an empty dict, to support functions with an optional parameter of the form ``props=None``. alias_mapping : dict or Artist subclass or Artist instance, 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. If an Artist subclass or instance is passed, use its properties alias mapping. Raises ------ TypeError To match what Python raises if invalid arguments/keyword arguments are passed to a callable. """ from matplotlib.artist import Artist if kw is None: return {} # deal with default value of alias_mapping if alias_mapping is None: alias_mapping = {} elif (isinstance(alias_mapping, type) and issubclass(alias_mapping, Artist) or isinstance(alias_mapping, Artist)): alias_mapping = getattr(alias_mapping, "_alias_map", {}) to_canonical = {alias: canonical for canonical, alias_list in alias_mapping.items() for alias in alias_list} canonical_to_seen = {} ret = {} # output dictionary for k, v in kw.items(): canonical = to_canonical.get(k, k) if canonical in canonical_to_seen: raise TypeError(f"Got both {canonical_to_seen[canonical]!r} and " f"{k!r}, which are aliases of one another") canonical_to_seen[canonical] = k ret[canonical] = v return ret @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 _array_perimeter(arr): """ Get the elements on the perimeter of *arr*. Parameters ---------- arr : ndarray, shape (M, N) The input array. Returns ------- 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], )) def _unfold(arr, axis, size, step): """ Append an extra dimension containing sliding windows along *axis*. All windows are of size *size* and begin with every *step* elements. Parameters ---------- arr : ndarray, shape (N_1, ..., N_k) The input array axis : int Axis along which the windows are extracted size : int Size of the windows step : int Stride between first elements of subsequent windows. Returns ------- ndarray, shape (N_1, ..., 1 + (N_axis-size)/step, ..., N_k, size) Examples -------- >>> i, j = np.ogrid[:3, :7] >>> a = i*10 + j >>> a array([[ 0, 1, 2, 3, 4, 5, 6], [10, 11, 12, 13, 14, 15, 16], [20, 21, 22, 23, 24, 25, 26]]) >>> _unfold(a, axis=1, size=3, step=2) array([[[ 0, 1, 2], [ 2, 3, 4], [ 4, 5, 6]], [[10, 11, 12], [12, 13, 14], [14, 15, 16]], [[20, 21, 22], [22, 23, 24], [24, 25, 26]]]) """ new_shape = [*arr.shape, size] new_strides = [*arr.strides, arr.strides[axis]] new_shape[axis] = (new_shape[axis] - size) // step + 1 new_strides[axis] = new_strides[axis] * step return np.lib.stride_tricks.as_strided(arr, shape=new_shape, strides=new_strides, writeable=False) def _array_patch_perimeters(x, rstride, cstride): """ Extract perimeters of patches from *arr*. Extracted patches are of size (*rstride* + 1) x (*cstride* + 1) and share perimeters with their neighbors. The ordering of the vertices matches that returned by ``_array_perimeter``. Parameters ---------- x : ndarray, shape (N, M) Input array rstride : int Vertical (row) stride between corresponding elements of each patch cstride : int Horizontal (column) stride between corresponding elements of each patch Returns ------- ndarray, shape (N/rstride * M/cstride, 2 * (rstride + cstride)) """ assert rstride > 0 and cstride > 0 assert (x.shape[0] - 1) % rstride == 0 assert (x.shape[1] - 1) % cstride == 0 # We build up each perimeter from four half-open intervals. Here is an # illustrated explanation for rstride == cstride == 3 # # T T T R # L R # L R # L B B B # # where T means that this element will be in the top array, R for right, # B for bottom and L for left. Each of the arrays below has a shape of: # # (number of perimeters that can be extracted vertically, # number of perimeters that can be extracted horizontally, # cstride for top and bottom and rstride for left and right) # # Note that _unfold doesn't incur any memory copies, so the only costly # operation here is the np.concatenate. top = _unfold(x[:-1:rstride, :-1], 1, cstride, cstride) bottom = _unfold(x[rstride::rstride, 1:], 1, cstride, cstride)[..., ::-1] right = _unfold(x[:-1, cstride::cstride], 0, rstride, rstride) left = _unfold(x[1:, :-1:cstride], 0, rstride, rstride)[..., ::-1] return (np.concatenate((top, right, bottom, left), axis=2) .reshape(-1, 2 * (rstride + cstride))) @contextlib.contextmanager def _setattr_cm(obj, **kwargs): """ Temporarily set some attributes; restore original state at context exit. """ sentinel = object() origs = {} for attr in kwargs: orig = getattr(obj, attr, sentinel) if attr in obj.__dict__ or orig is sentinel: # if we are pulling from the instance dict or the object # does not have this attribute we can trust the above origs[attr] = orig else: # if the attribute is not in the instance dict it must be # from the class level cls_orig = getattr(type(obj), attr) # if we are dealing with a property (but not a general descriptor) # we want to set the original value back. if isinstance(cls_orig, property): origs[attr] = orig # otherwise this is _something_ we are going to shadow at # the instance dict level from higher up in the MRO. We # are going to assume we can delattr(obj, attr) to clean # up after ourselves. It is possible that this code will # fail if used with a non-property custom descriptor which # implements __set__ (and __delete__ does not act like a # stack). However, this is an internal tool and we do not # currently have any custom descriptors. else: origs[attr] = sentinel try: for attr, val in kwargs.items(): setattr(obj, attr, val) yield finally: for attr, orig in origs.items(): if orig is sentinel: delattr(obj, attr) else: setattr(obj, attr, orig) 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 PyQt<=5.1 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 _get_nonzero_slices(buf): """ Return the bounds of the nonzero region of a 2D array as a pair of slices. ``buf[_get_nonzero_slices(buf)]`` is the smallest sub-rectangle in *buf* that encloses all non-zero entries in *buf*. If *buf* is fully zero, then ``(slice(0, 0), slice(0, 0))`` is returned. """ x_nz, = buf.any(axis=0).nonzero() y_nz, = buf.any(axis=1).nonzero() if len(x_nz) and len(y_nz): l, r = x_nz[[0, -1]] b, t = y_nz[[0, -1]] return slice(b, t + 1), slice(l, r + 1) else: return slice(0, 0), slice(0, 0) def _pformat_subprocess(command): """Pretty-format a subprocess command for printing/logging purposes.""" return (command if isinstance(command, str) else " ".join(shlex.quote(os.fspath(arg)) for arg in command)) 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', _pformat_subprocess(command)) proc = subprocess.run(command, capture_output=True, **kwargs) if proc.returncode: stdout = proc.stdout if isinstance(stdout, bytes): stdout = stdout.decode() stderr = proc.stderr if isinstance(stderr, bytes): stderr = stderr.decode() raise RuntimeError( f"The command\n" f" {_pformat_subprocess(command)}\n" f"failed and generated the following output:\n" f"{stdout}\n" f"and the following error:\n" f"{stderr}") if proc.stdout: logger.debug("stdout:\n%s", proc.stdout) if proc.stderr: logger.debug("stderr:\n%s", proc.stderr) return proc.stdout def _setup_new_guiapp(): """ Perform OS-dependent setup when Matplotlib creates a new GUI application. """ # Windows: If not explicit app user model id has been set yet (so we're not # already embedded), then set it to "matplotlib", so that taskbar icons are # correct. try: _c_internal_utils.Win32_GetCurrentProcessExplicitAppUserModelID() except OSError: _c_internal_utils.Win32_SetCurrentProcessExplicitAppUserModelID( "matplotlib") def _format_approx(number, precision): """ Format the number with at most the number of decimals given as precision. Remove trailing zeros and possibly the decimal point. """ return f'{number:.{precision}f}'.rstrip('0').rstrip('.') or '0' def _g_sig_digits(value, delta): """ Return the number of significant digits to %g-format *value*, assuming that it is known with an error of *delta*. """ if delta == 0: if value == 0: # if both value and delta are 0, np.spacing below returns 5e-324 # which results in rather silly results return 3 # delta = 0 may occur when trying to format values over a tiny range; # in that case, replace it by the distance to the closest float. delta = abs(np.spacing(value)) # If e.g. value = 45.67 and delta = 0.02, then we want to round to 2 digits # after the decimal point (floor(log10(0.02)) = -2); 45.67 contributes 2 # digits before the decimal point (floor(log10(45.67)) + 1 = 2): the total # is 4 significant digits. A value of 0 contributes 1 "digit" before the # decimal point. # For inf or nan, the precision doesn't matter. return max( 0, (math.floor(math.log10(abs(value))) + 1 if value else 1) - math.floor(math.log10(delta))) if math.isfinite(value) else 0 def _unikey_or_keysym_to_mplkey(unikey, keysym): """ Convert a Unicode key or X keysym to a Matplotlib key name. The Unicode key is checked first; this avoids having to list most printable keysyms such as ``EuroSign``. """ # For non-printable characters, gtk3 passes "\0" whereas tk passes an "". if unikey and unikey.isprintable(): return unikey key = keysym.lower() if key.startswith("kp_"): # keypad_x (including kp_enter). key = key[3:] if key.startswith("page_"): # page_{up,down} key = key.replace("page_", "page") if key.endswith(("_l", "_r")): # alt_l, ctrl_l, shift_l. key = key[:-2] if sys.platform == "darwin" and key == "meta": # meta should be reported as command on mac key = "cmd" key = { "return": "enter", "prior": "pageup", # Used by tk. "next": "pagedown", # Used by tk. }.get(key, key) return key @functools.cache def _make_class_factory(mixin_class, fmt, attr_name=None): """ Return a function that creates picklable classes inheriting from a mixin. After :: factory = _make_class_factory(FooMixin, fmt, attr_name) FooAxes = factory(Axes) ``Foo`` is a class that inherits from ``FooMixin`` and ``Axes`` and **is picklable** (picklability is what differentiates this from a plain call to `type`). Its ``__name__`` is set to ``fmt.format(Axes.__name__)`` and the base class is stored in the ``attr_name`` attribute, if not None. Moreover, the return value of ``factory`` is memoized: calls with the same ``Axes`` class always return the same subclass. """ @functools.cache def class_factory(axes_class): # if we have already wrapped this class, declare victory! if issubclass(axes_class, mixin_class): return axes_class # The parameter is named "axes_class" for backcompat but is really just # a base class; no axes semantics are used. base_class = axes_class class subcls(mixin_class, base_class): # Better approximation than __module__ = "matplotlib.cbook". __module__ = mixin_class.__module__ def __reduce__(self): return (_picklable_class_constructor, (mixin_class, fmt, attr_name, base_class), self.__getstate__()) subcls.__name__ = subcls.__qualname__ = fmt.format(base_class.__name__) if attr_name is not None: setattr(subcls, attr_name, base_class) return subcls class_factory.__module__ = mixin_class.__module__ return class_factory def _picklable_class_constructor(mixin_class, fmt, attr_name, base_class): """Internal helper for _make_class_factory.""" factory = _make_class_factory(mixin_class, fmt, attr_name) cls = factory(base_class) return cls.__new__(cls) def _is_torch_array(x): """Check if 'x' is a PyTorch Tensor.""" try: # we're intentionally not attempting to import torch. If somebody # has created a torch array, torch should already be in sys.modules return isinstance(x, sys.modules['torch'].Tensor) except Exception: # TypeError, KeyError, AttributeError, maybe others? # we're attempting to access attributes on imported modules which # may have arbitrary user code, so we deliberately catch all exceptions return False def _is_jax_array(x): """Check if 'x' is a JAX Array.""" try: # we're intentionally not attempting to import jax. If somebody # has created a jax array, jax should already be in sys.modules return isinstance(x, sys.modules['jax'].Array) except Exception: # TypeError, KeyError, AttributeError, maybe others? # we're attempting to access attributes on imported modules which # may have arbitrary user code, so we deliberately catch all exceptions return False def _unpack_to_numpy(x): """Internal helper to extract data from e.g. pandas and xarray objects.""" if isinstance(x, np.ndarray): # If numpy, return directly return x if hasattr(x, 'to_numpy'): # Assume that any to_numpy() method actually returns a numpy array return x.to_numpy() if hasattr(x, 'values'): xtmp = x.values # For example a dict has a 'values' attribute, but it is not a property # so in this case we do not want to return a function if isinstance(xtmp, np.ndarray): return xtmp if _is_torch_array(x) or _is_jax_array(x): xtmp = x.__array__() # In case __array__() method does not return a numpy array in future if isinstance(xtmp, np.ndarray): return xtmp return x def _auto_format_str(fmt, value): """ Apply *value* to the format string *fmt*. This works both with unnamed %-style formatting and unnamed {}-style formatting. %-style formatting has priority. If *fmt* is %-style formattable that will be used. Otherwise, {}-formatting is applied. Strings without formatting placeholders are passed through as is. Examples -------- >>> _auto_format_str('%.2f m', 0.2) '0.20 m' >>> _auto_format_str('{} m', 0.2) '0.2 m' >>> _auto_format_str('const', 0.2) 'const' >>> _auto_format_str('%d or {}', 0.2) '0 or {}' """ try: return fmt % (value,) except (TypeError, ValueError): return fmt.format(value)