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""" A collection of utility functions and classes. Originally, many (but not all) were from the Python Cookbook -- hence the name cbook.
This module is safe to import from anywhere within matplotlib; it imports matplotlib only at runtime. """
mplDeprecation, deprecated, warn_deprecated, MatplotlibDeprecationWarning)
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
traceback.print_exc()
""" Wrapper similar to a weakref, but keeping a strong reference to the object. """
return self._obj
return isinstance(other, _StrongRef) and self._obj == other._obj
"""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).
# 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).
return {'exception_handler': self.exception_handler}
self.__init__(**state)
"""Register *func* to be called when signal *s* is generated. """ return self._func_cid_map[s][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]
"""Disconnect the callback registered with callback id *cid*. """ else:
""" Process signal *s*.
All of the functions registered to receive callbacks on *s* will be called with ``*args`` and ``**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
""" 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 """ self.extend(seq)
def __repr__(self): return '<a list of %d %s objects>' % (len(self), self.type)
# store a dictionary of this SilentList's state return {'type': self.type, 'seq': self[:]}
self.type = state['type'] self.extend(state['seq'])
""" A class for issuing warnings about keyword arguments that will be ignored by matplotlib """
""" 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: warnings.warn('"%s" keyword argument will be ignored' % key, IgnoredKeywordWarning) return out
"""remove latex formatting from mathtext""" remove = (r'\mathdefault', r'\rm', r'\cal', r'\tt', r'\it', '\\', '{', '}') s = s[1:-1] for r in remove: s = s.replace(r, '') return s
""" 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 """
"""return true if *obj* is iterable"""
"""Returns true if *obj* can be hashed""" except TypeError: return False
"""return true if *obj* looks like a file object with a *write* method""" return callable(getattr(obj, 'write', None))
""" Returns `True` if 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 is_numlike(obj): """return true if *obj* looks like a number""" return isinstance(obj, (numbers.Number, np.number))
""" *fname* can be an `os.PathLike` or a file handle. Support for gzipped files is automatic, if the filename ends in .gz. *flag* is a read/write flag for :func:`file` """ fname = os.fspath(fname) # get rid of 'U' in flag for gzipped files. flag = flag.replace('U', '') fh = gzip.open(fname, flag) # 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: elif hasattr(fname, 'seek'): fh = fname opened = False else: raise ValueError('fname must be a PathLike or file handle') return fh
r"""Pass through file objects and context-manage `.PathLike`\s.""" else: yield fh
"""Return whether the given object is a scalar or string like."""
"""Parses the string argument as a boolean""" warn_deprecated("2.2", "Passing one of 'on', 'true', 'off', 'false' as a " "boolean is deprecated; use an actual boolean " "(True/False) instead.") if s.lower() in ['on', 'true']: return True if s.lower() in ['off', 'false']: return False raise ValueError('String "%s" must be one of: ' '"on", "off", "true", or "false"' % s)
""" 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
""" Returns 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)
""" 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)
self._cache = {}
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
def get_realpath_and_stat(path):
# 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. # A cache to hold the regexs that actually remove the indent.
""" 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.
return s
# This is the number of spaces to remove from the left-hand side.
# 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.
""" 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
""" A dictionary with a maximum size; this doesn't override all the relevant methods to constrain the size, just setitem, so use with caution """
""" Stack of elements with a movable cursor.
Mimics home/back/forward in a web browser. """
"""Return the current element, or None.""" if not len(self._elements): return self._default else: return self._elements[self._pos]
return len(self._elements)
return self._elements[ind]
"""Move the position forward and return the current element.""" self._pos = min(self._pos + 1, len(self._elements) - 1) return self()
"""Move the position back and return the current element.""" if self._pos > 0: self._pos -= 1 return self()
""" Push *o* to the stack at current position. Discard all later elements.
*o* is returned. """
""" 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()
"""Return whether the stack is empty.""" return len(self._elements) == 0
"""Empty the stack."""
""" Raise *o* to the top of the stack. *o* must be present in the stack.
*o* is returned. """ raise ValueError('Unknown element o') else:
"""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)
"""return the memory consumed by process""" from subprocess import Popen, PIPE pid = os.getpid() if sys.platform == 'sunos5': try: a2 = Popen(['ps', '-p', '%d' % pid, '-o', 'osz'], stdout=PIPE).stdout.readlines() except OSError: raise NotImplementedError( "report_memory works on Sun OS only if " "the 'ps' program is found") mem = int(a2[-1].strip()) elif sys.platform == 'linux': try: a2 = Popen(['ps', '-p', '%d' % pid, '-o', 'rss,sz'], stdout=PIPE).stdout.readlines() except OSError: raise NotImplementedError( "report_memory works on Linux only if " "the 'ps' program is found") mem = int(a2[1].split()[1]) elif sys.platform == 'darwin': try: a2 = Popen(['ps', '-p', '%d' % pid, '-o', 'rss,vsz'], stdout=PIPE).stdout.readlines() except OSError: raise NotImplementedError( "report_memory works on Mac OS only if " "the 'ps' program is found") mem = int(a2[1].split()[0]) elif sys.platform == 'win32': try: a2 = Popen(["tasklist", "/nh", "/fi", "pid eq %d" % pid], stdout=PIPE).stdout.read() except OSError: raise NotImplementedError( "report_memory works on Windows only if " "the 'tasklist' program is found") mem = int(a2.strip().split()[-2].replace(',', '')) else: raise NotImplementedError( "We don't have a memory monitor for %s" % sys.platform) return mem
"""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))
# 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')
except TypeError: return x
""" *objects* A list of objects to find cycles in. It is often useful to pass in gc.garbage to find the cycles that are preventing some objects from being garbage collected.
*outstream* The stream for output.
*show_progress* If True, print the number of objects reached as they are found. """ import gc from types import FrameType
def print_path(path): for i, step in enumerate(path): # next "wraps around" next = path[(i + 1) % len(path)]
outstream.write(" %s -- " % type(step)) if isinstance(step, dict): for key, val in step.items(): if val is next: outstream.write("[{!r}]".format(key)) break if key is next: outstream.write("[key] = {!r}".format(val)) break elif isinstance(step, list): outstream.write("[%d]" % step.index(next)) elif isinstance(step, tuple): outstream.write("( tuple )") else: outstream.write(repr(step)) outstream.write(" ->\n") outstream.write("\n")
def recurse(obj, start, all, current_path): if show_progress: outstream.write("%d\r" % len(all))
all[id(obj)] = None
referents = gc.get_referents(obj) for referent in referents: # If we've found our way back to the start, this is # a cycle, so print it out if referent is start: print_path(current_path)
# Don't go back through the original list of objects, or # through temporary references to the object, since those # are just an artifact of the cycle detector itself. elif referent is objects or isinstance(referent, 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("Examining: %r\n" % (obj,)) recurse(obj, obj, {}, [])
""" 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
"""
"""Clean dead weak references from the dictionary."""
""" Join given arguments into the same set. Accepts one or more arguments. """
set_a, set_b = set_b, set_a
"""Returns True if *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)))
self.clean() set_a = self._mapping.pop(weakref.ref(a), None) if set_a: set_a.remove(weakref.ref(a))
""" 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]
"""Returns all of the items joined with *a*, including itself."""
""" 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. """ .reshape((len(x),) + a.shape[1:]))
""" 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().
""" return () raise ValueError("First argument must be a sequence") raise ValueError("Masked arrays must be 1-D") else: else: xd = x except: # Fixme: put in tuple of possible exceptions? pass for i, x in enumerate(margs): if seqlist[i]: margs[i] = x.take(igood, axis=0)
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 to the 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 this to an ascending sequence of percentile (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 arithemetic 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 tricksyness, 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 = np.compress(x <= hival, x) if len(wiskhi) == 0 or np.max(wiskhi) < q3: stats['whishi'] = q3 else: stats['whishi'] = np.max(wiskhi)
# get low extreme wisklo = np.compress(x >= loval, x) 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([ np.compress(x < stats['whislo'], x), np.compress(x > stats['whishi'], x) ])
# 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.
def align_iterators(func, *iterables): """ This generator takes a bunch of iterables that are ordered by func It sends out ordered tuples::
(func(row), [rows from all iterators matching func(row)])
It is used by :func:`matplotlib.mlab.recs_join` to join record arrays """ class myiter: def __init__(self, it): self.it = it self.key = self.value = None self.iternext()
def iternext(self): try: self.value = next(self.it) self.key = func(self.value) except StopIteration: self.value = self.key = None
def __call__(self, key): retval = None if key == self.key: retval = self.value self.iternext() elif self.key and key > self.key: raise ValueError("Iterator has been left behind") return retval
# This can be made more efficient by not computing the minimum key for each # iteration iters = [myiter(it) for it in iterables] minvals = minkey = True while True: minvals = ([_f for _f in [it.key for it in iters] if _f]) if minvals: minkey = min(minvals) yield (minkey, [it(minkey) for it in iters]) else: break
""" 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]))
# Did we find an even number of non-escaped dollar signs? # If so, treat is as math text.
""" Convert a sequence to a float array; if input was a masked array, masked values are converted to nans. """ return np.ma.asarray(x, float).filled(np.nan) else:
''' Converts a sequence of less than 1 dimension, to an array of 1 dimension; leaves everything else untouched. ''' else: except (IndexError, TypeError): return np.atleast_1d(x)
""" 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 X.ndim == 1 and X.dtype.type != np.object_: # 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))
""" 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
""" 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
""" 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
""" 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
'steps': pts_to_prestep, 'steps-pre': pts_to_prestep, 'steps-post': pts_to_poststep, 'steps-mid': pts_to_midstep}
""" 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. """
# 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")
"""Converts dictview object to list""" else data)
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 alias_mapping = dict()
# make a local so we can pop # output dictionary
# hit all alias mappings
# the alias lists are ordered from lowest to highest priority # so we know to use the last value in this list # if canonical is not in the alias_list assume highest priority # if we found anything in this set of aliases put it in the return # dict warnings.warn("Saw kwargs {seen!r} which are all aliases for " "{canon!r}. Kept value from {used!r}".format( seen=seen, canon=canonical, used=seen[-1]))
# 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
raise TypeError("The required keys {keys!r} " "are not in kwargs".format(keys=fail_keys))
raise TypeError("The forbidden keys {keys!r} " "are in kwargs".format(keys=fail_keys))
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))
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. """
""" 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. """
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
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
if self.remove: for path in self.lock_path, self.path: try: os.rmdir(path) except OSError: pass
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. """ 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)) finally:
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))
"""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 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. """
"""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). """
raise ValueError( "Neither getter nor setter exists for {!r}".format(prop))
# Need to decide on conflict resolution policy. raise NotImplementedError("Parent class already defines aliases")
""" 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], ))
def _setattr_cm(obj, **kwargs): """Temporarily set some attributes; restore original state at context exit. """ finally: delattr(obj, attr) else:
""" `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|\.)", frame.f_globals["__name__"]): break frame = frame.f_back warnings.warn(message, category, stacklevel)
return key in self._od
return iter(self._od)
return len(self._od)
self._od.pop(key, None)
# Agg's buffers are unmultiplied RGBA8888, which neither PyQt4 nor cairo # support; however, both do support premultiplied ARGB32.
""" 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
""" 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 |