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.CallbackRegistry(exception_handler=<function _exception_printer>)[source]¶
Bases:
object
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_handlercallable, optional
If not None, exception_handler must be a function that takes an
Exception
as single parameter. It gets called with anyException
raised by the callbacks duringCallbackRegistry.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.
- blocked(*, 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
- signalstr, optional
The callback signal to block. The default is to block all signals.
- class matplotlib.cbook.Grouper(init=())[source]¶
Bases:
object
A disjoint-set data structure.
Objects can be joined using
join()
, tested for connectedness usingjoined()
, 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
- class matplotlib.cbook.Stack(default=None)[source]¶
Bases:
object
Stack of elements with a movable cursor.
Mimics home/back/forward in a web browser.
- bubble(o)[source]¶
Raise all references of o to the top of the stack, and return it.
- Raises
- ValueError
If o is not in the stack.
- matplotlib.cbook.boxplot_stats(X, whis=1.5, bootstrap=None, labels=None, autorange=False)[source]¶
Return a list of dictionaries of statistics used to draw a series of box and whisker plots using
bxp
.- Parameters
- Xarray-like
Data that will be represented in the boxplots. Should have 2 or fewer dimensions.
- whisfloat 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 belowQ3 + whis*(Q3-Q1)
, where Q1 and Q3 are the first and third quartiles. The default value ofwhis = 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.
- bootstrapint, optional
Number of times the confidence intervals around the median should be bootstrapped (percentile method).
- labelsarray-like, optional
Labels for each dataset. Length must be compatible with dimensions of X.
- autorangebool, 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)
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.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-D masked arrays
1-D ndarrays
ndarrays with more than one dimension
other non-string iterables
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().
- matplotlib.cbook.deprecated(*args, **kwargs)[source]¶
[Deprecated]
Notes
Deprecated since version 3.4:
- matplotlib.cbook.file_requires_unicode(x)[source]¶
Return whether the given writable file-like object requires Unicode to be written to it.
- matplotlib.cbook.flatten(seq, scalarp=<function is_scalar_or_string>)[source]¶
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
- matplotlib.cbook.get_sample_data(fname, asfileobj=True, *, np_load=False)[source]¶
Return a sample data file. fname is a path relative to the
mpl-data/sample_data
directory. If asfileobj isTrue
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, asfileobj is True, and np_load is True, the file is loaded with
numpy.load
. np_load currently defaults to False but will default to True in a future release.
- matplotlib.cbook.index_of(y)[source]¶
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 apandas.Series
), if that fails, userange(len(y))
.This will be extended in the future to deal with more types of labeled data.
- Parameters
- yfloat or array-like
- Returns
- x, yndarray
The x and y values to plot.
- matplotlib.cbook.is_math_text(s)[source]¶
Return whether the string s contains math expressions.
This is done by checking whether s contains an even number of non-escaped dollar signs.
- 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 whether obj looks like a file object with a write method.
- matplotlib.cbook.ls_mapper = {'-': 'solid', '--': 'dashed', '-.': 'dashdot', ':': 'dotted'}¶
Maps short codes for line style to their full name used by backends.
- matplotlib.cbook.ls_mapper_r = {'dashdot': '-.', 'dashed': '--', 'dotted': ':', 'solid': '-'}¶
Maps full names for line styles used by backends to their short codes.
- class matplotlib.cbook.maxdict(maxsize)[source]¶
Bases:
dict
A dictionary with a maximum size.
Notes
This doesn't override all the relevant methods to constrain the size, just
__setitem__
, so use with caution.
- matplotlib.cbook.normalize_kwargs(kw, alias_mapping=None)[source]¶
Helper function to normalize kwarg inputs.
- Parameters
- kwdict 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_mappingdict 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.
- matplotlib.cbook.open_file_cm(path_or_file, mode='r', encoding=None)[source]¶
Pass through file objects and context-manage path-likes.
- matplotlib.cbook.print_cycles(objects, outstream=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>, show_progress=False)[source]¶
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_progressbool
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 to2N
points which when connected linearly give a step function which changes values at the middle of the intervals.- Parameters
- xarray
The x location of the steps. May be empty.
- y1, ..., yparray
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 lengthN
, each of these arrays will be length2N
.
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 to2N + 1
points, which when connected linearly give a step function which changes values at the end of the intervals.- Parameters
- xarray
The x location of the steps. May be empty.
- y1, ..., yparray
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 lengthN
, each of these arrays will be length2N + 1
. ForN=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 to2N - 1
points, which when connected linearly give a step function which changes values at the beginning of the intervals.- Parameters
- xarray
The x location of the steps. May be empty.
- y1, ..., yparray
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 lengthN
, each of these arrays will be length2N + 1
. ForN=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]¶
[Deprecated] Return the memory consumed by the process.
Notes
Deprecated since version 3.5.
- matplotlib.cbook.safe_first_element(obj)[source]¶
Return the first element in obj.
This is an type-independent way of obtaining the first element, supporting both index access and the iterator protocol.
- matplotlib.cbook.sanitize_sequence(data)[source]¶
Convert dictview objects to list. Other inputs are returned unchanged.
- class matplotlib.cbook.silent_list(type, seq=None)[source]¶
Bases:
list
A list with a short
repr()
.This is meant to be used for a homogeneous list of artists, so that they don't cause long, meaningless output.
Instead of
[<matplotlib.lines.Line2D object at 0x7f5749fed3c8>, <matplotlib.lines.Line2D object at 0x7f5749fed4e0>, <matplotlib.lines.Line2D object at 0x7f5758016550>]
one will get
<a list of 3 Line2D objects>
If
self.type
is None, the type name is obtained from the first item in the list (if any).
- matplotlib.cbook.simple_linear_interpolation(a, steps)[source]¶
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
- aarray, shape (n, ...)
- stepsint
- Returns
- array
shape
((n - 1) * steps + 1, ...)
- matplotlib.cbook.strip_math(s)[source]¶
Remove latex formatting from mathtext.
Only handles fully math and fully non-math strings.
- matplotlib.cbook.to_filehandle(fname, flag='r', return_opened=False, encoding=None)[source]¶
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
- fnamestr or path-like or file-like
If
str
oros.PathLike
, the file is opened using the flags specified by flag and encoding. If a file-like object, it is passed through.- flagstr, default: 'r'
Passed as the mode argument to
open
when fname isstr
oros.PathLike
; ignored if fname is file-like.- return_openedbool, 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.
- encodingstr or None, default: None
Passed as the mode argument to
open
when fname isstr
oros.PathLike
; ignored if fname is file-like.
- Returns
- fhfile-like
- openedbool
opened is only returned if return_opened is True.
- matplotlib.cbook.violin_stats(X, method, points=100, quantiles=None)[source]¶
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
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
- Xarray-like
Sample data that will be used to produce the gaussian kernel density estimates. Must have 2 or fewer dimensions.
- methodcallable
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.- pointsint, default: 100
Defines the number of points to evaluate each of the gaussian kernel density estimates at.
- quantilesarray-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.