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cbook

matplotlib.cbook

A collection of utility functions and classes. Originally, many (but not all) were from the Python Cookbook -- hence the name cbook.

This module is safe to import from anywhere within matplotlib; it imports matplotlib only at runtime.

class matplotlib.cbook.Bunch(**kwargs)[source]

Bases: types.SimpleNamespace

Deprecated since version 3.0: The Bunch class was deprecated in Matplotlib 3.0 and will be removed in 3.2. Use types.SimpleNamespace instead.

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
class matplotlib.cbook.CallbackRegistry(exception_handler=<function _exception_printer>)[source]

Bases: object

Handle registering and disconnecting for a set of signals and callbacks:

>>> def oneat(x):
...    print('eat', x)
>>> def ondrink(x):
...    print('drink', x)
>>> from matplotlib.cbook import CallbackRegistry
>>> callbacks = CallbackRegistry()
>>> id_eat = callbacks.connect('eat', oneat)
>>> id_drink = callbacks.connect('drink', ondrink)
>>> callbacks.process('drink', 123)
drink 123
>>> callbacks.process('eat', 456)
eat 456
>>> callbacks.process('be merry', 456) # nothing will be called
>>> callbacks.disconnect(id_eat)
>>> callbacks.process('eat', 456)      # nothing will be called

In practice, one should always disconnect all callbacks when they are no longer needed to avoid dangling references (and thus memory leaks). However, real code in Matplotlib rarely does so, and due to its design, it is rather difficult to place this kind of code. To get around this, and prevent this class of memory leaks, we instead store weak references to bound methods only, so when the destination object needs to die, the CallbackRegistry won't keep it alive.

Parameters:
exception_handler : callable, optional

If provided must have signature

def handler(exc: Exception) -> None:

If not None this function will be called with any Exception subclass raised by the callbacks in CallbackRegistry.process. The handler may either consume the exception or re-raise.

The callable must be pickle-able.

The default handler is

def h(exc):
    traceback.print_exc()
connect(s, func)[source]

Register func to be called when signal s is generated.

disconnect(cid)[source]

Disconnect the callback registered with callback id cid.

process(s, *args, **kwargs)[source]

Process signal s.

All of the functions registered to receive callbacks on s will be called with *args and **kwargs.

class matplotlib.cbook.GetRealpathAndStat(**kwargs)[source]

Bases: object

Deprecated since version 3.0: The GetRealpathAndStat class was deprecated in Matplotlib 3.0 and will be removed in 3.2.

class matplotlib.cbook.Grouper(init=())[source]

Bases: object

This class provides a lightweight way to group arbitrary objects together into disjoint sets when a full-blown graph data structure would be overkill.

Objects can be joined using join(), tested for connectedness using 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()[source]

Clean dead weak references from the dictionary.

get_siblings(a)[source]

Returns all of the items joined with a, including itself.

join(a, *args)[source]

Join given arguments into the same set. Accepts one or more arguments.

joined(a, b)[source]

Returns True if a and b are members of the same set.

remove(a)[source]
exception matplotlib.cbook.IgnoredKeywordWarning[source]

Bases: UserWarning

A class for issuing warnings about keyword arguments that will be ignored by matplotlib

class matplotlib.cbook.Locked(**kwargs)[source]

Bases: object

Deprecated since version 3.0: The Locked class was deprecated in Matplotlib 3.0 and will be removed in 3.2.

Context manager to handle locks.

Based on code from conda.

(c) 2012-2013 Continuum Analytics, Inc. / https://www.continuum.io/ All Rights Reserved

conda is distributed under the terms of the BSD 3-clause license. Consult LICENSE_CONDA or https://opensource.org/licenses/BSD-3-Clause.

LOCKFN = '.matplotlib_lock'
exception TimeoutError[source]

Bases: RuntimeError

class matplotlib.cbook.Stack(default=None)[source]

Bases: object

Stack of elements with a movable cursor.

Mimics home/back/forward in a web browser.

back()[source]

Move the position back and return the current element.

bubble(o)[source]

Raise o to the top of the stack. o must be present in the stack.

o is returned.

clear()[source]

Empty the stack.

empty()[source]

Return whether the stack is empty.

forward()[source]

Move the position forward and return the current element.

home()[source]

Push the first element onto the top of the stack.

The first element is returned.

push(o)[source]

Push o to the stack at current position. Discard all later elements.

o is returned.

remove(o)[source]

Remove o from the stack.

matplotlib.cbook.align_iterators(func, *iterables)[source]

Deprecated since version 2.2: The align_iterators function was deprecated in Matplotlib 2.2 and will be removed in 3.1.

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 matplotlib.mlab.recs_join() to join record arrays

matplotlib.cbook.boxplot_stats(X, whis=1.5, bootstrap=None, labels=None, autorange=False)[source]

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:

\[\mathrm{med} \pm 1.57 \times \frac{\mathrm{iqr}}{\sqrt{N}}\]

General approach from: McGill, R., Tukey, J.W., and Larsen, W.A. (1978) "Variations of Boxplots", The American Statistician, 32:12-16.

matplotlib.cbook.contiguous_regions(mask)[source]

Return a list of (ind0, ind1) such that mask[ind0:ind1].all() is True and we cover all such regions

matplotlib.cbook.dedent(s)[source]

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.

matplotlib.cbook.delete_masked_points(*args)[source]

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 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().

matplotlib.cbook.file_requires_unicode(x)[source]

Returns True if the given writable file-like object requires Unicode to be written to it.

matplotlib.cbook.flatten(seq, scalarp=<function is_scalar_or_string>)[source]

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

matplotlib.cbook.get_label(y, default_name)[source]
matplotlib.cbook.get_realpath_and_stat[source]
matplotlib.cbook.get_sample_data(fname, asfileobj=True)[source]

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.

matplotlib.cbook.index_of(y)[source]

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.

matplotlib.cbook.is_hashable(obj)[source]

Returns true if obj can be hashed

matplotlib.cbook.is_math_text(s)[source]
matplotlib.cbook.is_numlike(obj)[source]

Deprecated since version 3.0: isinstance(..., numbers.Number)

return true if obj looks like a number

matplotlib.cbook.is_scalar_or_string(val)[source]

Return whether the given object is a scalar or string like.

matplotlib.cbook.is_writable_file_like(obj)[source]

return true if obj looks like a file object with a write method

matplotlib.cbook.iterable(obj)[source]

return true if obj is iterable

matplotlib.cbook.listFiles(root, patterns='*', recurse=1, return_folders=0)[source]

Deprecated since version 3.0: The listFiles function was deprecated in Matplotlib 3.0 and will be removed in 3.2.

Recursively list files

from Parmar and Martelli in the Python Cookbook

matplotlib.cbook.local_over_kwdict(local_var, kwargs, *keys)[source]

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

class matplotlib.cbook.maxdict(maxsize)[source]

Bases: dict

A dictionary with a maximum size; this doesn't override all the relevant methods to constrain the size, just setitem, so use with caution

matplotlib.cbook.mkdirs(newdir, mode=511)[source]

Deprecated since version 3.0: The mkdirs function was deprecated in Matplotlib 3.0 and will be removed in 3.2.

make directory newdir recursively, and set mode. Equivalent to

> mkdir -p NEWDIR
> chmod MODE NEWDIR
matplotlib.cbook.normalize_kwargs(kw, alias_mapping=None, required=(), forbidden=(), allowed=None)[source]

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.

matplotlib.cbook.open_file_cm(path_or_file, mode='r', encoding=None)[source]

Pass through file objects and context-manage PathLikes.

matplotlib.cbook.print_cycles(objects, outstream=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='UTF-8'>, show_progress=False)[source]
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.
matplotlib.cbook.pts_to_midstep(x, *args)[source]

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)

matplotlib.cbook.pts_to_poststep(x, *args)[source]

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)

matplotlib.cbook.pts_to_prestep(x, *args)[source]

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)

matplotlib.cbook.report_memory(i=0)[source]

return the memory consumed by process

matplotlib.cbook.safe_first_element(obj)[source]
matplotlib.cbook.safe_masked_invalid(x, copy=False)[source]
matplotlib.cbook.safezip(*args)[source]

make sure args are equal len before zipping

matplotlib.cbook.sanitize_sequence(data)[source]

Converts dictview object to list

class matplotlib.cbook.silent_list(type, seq=None)[source]

Bases: list

override repr when returning a list of matplotlib artists to prevent long, meaningless output. This is meant to be used for a homogeneous list of a given type

matplotlib.cbook.simple_linear_interpolation(a, steps)[source]

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.
matplotlib.cbook.strip_math(s)[source]

remove latex formatting from mathtext

matplotlib.cbook.to_filehandle(fname, flag='rU', return_opened=False, encoding=None)[source]

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 file()

matplotlib.cbook.unicode_safe(s)[source]

Deprecated since version 3.0: The unicode_safe function was deprecated in Matplotlib 3.0 and will be removed in 3.2.

matplotlib.cbook.violin_stats(X, method, points=100)[source]

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.