This code will be for reproducing work of Lopez Corredoira in arXiv:1312.0003.
In [1]:
import healpix_util as hu
import astropy as ap
import numpy as np
from astropy.io import fits
from astropy.table import Table
import astropy.io.ascii as ascii
from astropy.constants import c
import matplotlib.pyplot as plt
import math
import scipy.special as sp
from scipy import integrate
Co-moving distance between any two sources at redshift $z$ separated by $\Delta z$ and $\Delta\theta$ in the redshift space is
$s(\Delta z, z\Delta\theta)\approxeq \frac{c}{H_0}x(z)\sqrt{(\Delta z)^2+y^2(z)(z\Delta\theta)^2}$
Here $x(z)$ and $y(z)$ are model independent parameters. $y(z)$ characterizes Alcock-Paczynski ratio.
$x(z)$ and $y(z)$ functions for $\Lambda CDM$ and Linear coasting models are
For $\Lambda CDM$
$x(z)=\frac{1}{\sqrt{\Omega_m(1+z)^3+\Omega_m}}$
$y(z)=\frac{1}{z}\int_0^z dx\sqrt{\frac{\Omega_m(1+z)^3+\Omega_\Lambda}{\Omega_m(1+x)^3+\Omega_\Lambda}}$
For Linear coasting in a flat universe
$x(z)=\frac{1}{E(z)}=\frac{1}{1+z}$
$y(z)=\frac{1+z}{z}\ln(1+z)$
For Milne Universe (open empty Universe that is also linearly coasting)
$x(z)=\frac{1}{E(z)}=\frac{1}{1+z}$
$y(z)=1+\frac{z}{2}$
In [7]:
z=np.arange(0.01,5,0.01)
om=0.3
ol=0.7
yLCDM0=np.sqrt(om*(1+z)**3+ol)/z
LCDMf = lambda x: 1.0/math.sqrt(om*(1+x)**3+ol)
np.vectorize(LCDMf)
def LCDMfint(zt):
return integrate.quad(LCDMf, 0, zt)
LCDMfint=np.vectorize(LCDMfint)
yLCDM=yLCDM0*LCDMfint(z)[0]
#Ez = lambda z: 1/np.sqrt(0.3*(1+z)**3+0.7)
#np.vectorize(Ez)
#def ylcdm(z):
# for i in range(len(z)):
# return 1/z[i]*np.sqrt(0.3*(1+z[i])**3+0.7)/integrate.quad(Ez,0,z[i])
#print ylcdm(z)
#ylcdm=1/z*np.
ylc=(1+z)/z*np.log(1+z)
def ymilne(z):
return 1+z/2
plt.figure()
plt.xlabel('redshift(z)')
plt.ylabel('y(z)')
plt.plot(z,yLCDM,'red',label='yLCDM')
plt.plot(z,ylc,'green',label='yLC')
#plt.plot(z,ylcdm)
plt.plot(z,ymilne(z),'blue',label='yMilne')
plt.legend()
plt.savefig("yz_plots.pdf")
#plt.axes()
#plt.plot(z,ylcdm(z))
plt.show()
In [18]:
#z=np.arange(0,5,0.01)
z[4]
print z[6]
#for val in z:
#print z
0.06
In [ ]:
In [4]:
help(plt)
Help on module matplotlib.pyplot in matplotlib:
NAME
matplotlib.pyplot - Provides a MATLAB-like plotting framework.
FILE
/Users/rohin/anaconda/lib/python2.7/site-packages/matplotlib/pyplot.py
DESCRIPTION
:mod:`~matplotlib.pylab` combines pyplot with numpy into a single namespace.
This is convenient for interactive work, but for programming it
is recommended that the namespaces be kept separate, e.g.::
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0, 5, 0.1);
y = np.sin(x)
plt.plot(x, y)
FUNCTIONS
acorr(x, hold=None, data=None, **kwargs)
Plot the autocorrelation of `x`.
Parameters
----------
x : sequence of scalar
hold : boolean, optional, *deprecated*, default: True
detrend : callable, optional, default: `mlab.detrend_none`
x is detrended by the `detrend` callable. Default is no
normalization.
normed : boolean, optional, default: True
if True, input vectors are normalised to unit length.
usevlines : boolean, optional, default: True
if True, Axes.vlines is used to plot the vertical lines from the
origin to the acorr. Otherwise, Axes.plot is used.
maxlags : integer, optional, default: 10
number of lags to show. If None, will return all 2 * len(x) - 1
lags.
Returns
-------
(lags, c, line, b) : where:
- `lags` are a length 2`maxlags+1 lag vector.
- `c` is the 2`maxlags+1 auto correlation vectorI
- `line` is a `~matplotlib.lines.Line2D` instance returned by
`plot`.
- `b` is the x-axis.
Other parameters
----------------
linestyle : `~matplotlib.lines.Line2D` prop, optional, default: None
Only used if usevlines is False.
marker : string, optional, default: 'o'
Notes
-----
The cross correlation is performed with :func:`numpy.correlate` with
`mode` = 2.
Examples
--------
`~matplotlib.pyplot.xcorr` is top graph, and
`~matplotlib.pyplot.acorr` is bottom graph.
.. plot:: mpl_examples/pylab_examples/xcorr_demo.py
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All arguments with the following names: 'x'.
angle_spectrum(x, Fs=None, Fc=None, window=None, pad_to=None, sides=None, hold=None, data=None, **kwargs)
Plot the angle spectrum.
Call signature::
angle_spectrum(x, Fs=2, Fc=0, window=mlab.window_hanning,
pad_to=None, sides='default', **kwargs)
Compute the angle spectrum (wrapped phase spectrum) of *x*.
Data is padded to a length of *pad_to* and the windowing function
*window* is applied to the signal.
Parameters
----------
x : 1-D array or sequence
Array or sequence containing the data
Fs : scalar
The sampling frequency (samples per time unit). It is used
to calculate the Fourier frequencies, freqs, in cycles per time
unit. The default value is 2.
window : callable or ndarray
A function or a vector of length *NFFT*. To create window
vectors see :func:`window_hanning`, :func:`window_none`,
:func:`numpy.blackman`, :func:`numpy.hamming`,
:func:`numpy.bartlett`, :func:`scipy.signal`,
:func:`scipy.signal.get_window`, etc. The default is
:func:`window_hanning`. If a function is passed as the
argument, it must take a data segment as an argument and
return the windowed version of the segment.
sides : [ 'default' | 'onesided' | 'twosided' ]
Specifies which sides of the spectrum to return. Default gives the
default behavior, which returns one-sided for real data and both
for complex data. 'onesided' forces the return of a one-sided
spectrum, while 'twosided' forces two-sided.
pad_to : integer
The number of points to which the data segment is padded when
performing the FFT. While not increasing the actual resolution of
the spectrum (the minimum distance between resolvable peaks),
this can give more points in the plot, allowing for more
detail. This corresponds to the *n* parameter in the call to fft().
The default is None, which sets *pad_to* equal to the length of the
input signal (i.e. no padding).
Fc : integer
The center frequency of *x* (defaults to 0), which offsets
the x extents of the plot to reflect the frequency range used
when a signal is acquired and then filtered and downsampled to
baseband.
**kwargs :
Keyword arguments control the :class:`~matplotlib.lines.Line2D`
properties:
agg_filter: unknown
alpha: float (0.0 transparent through 1.0 opaque)
animated: [True | False]
antialiased or aa: [True | False]
axes: an :class:`~matplotlib.axes.Axes` instance
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
color or c: any matplotlib color
contains: a callable function
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post']
figure: a :class:`matplotlib.figure.Figure` instance
fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none']
gid: an id string
label: string or anything printable with '%s' conversion.
linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or lw: float value in points
marker: :mod:`A valid marker style <matplotlib.markers>`
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markerfacecoloralt or mfcalt: any matplotlib color
markersize or ms: float
markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects: unknown
picker: float distance in points or callable pick function ``fn(artist, event)``
pickradius: float distance in points
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a :class:`matplotlib.transforms.Transform` instance
url: a url string
visible: [True | False]
xdata: 1D array
ydata: 1D array
zorder: any number
Returns
-------
spectrum : 1-D array
The values for the angle spectrum in radians (real valued)
freqs : 1-D array
The frequencies corresponding to the elements in *spectrum*
line : a :class:`~matplotlib.lines.Line2D` instance
The line created by this function
Examples
--------
.. plot:: mpl_examples/pylab_examples/spectrum_demo.py
See Also
--------
:func:`magnitude_spectrum`
:func:`angle_spectrum` plots the magnitudes of the corresponding
frequencies.
:func:`phase_spectrum`
:func:`phase_spectrum` plots the unwrapped version of this
function.
:func:`specgram`
:func:`specgram` can plot the angle spectrum of segments within the
signal in a colormap.
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All arguments with the following names: 'x'.
annotate(*args, **kwargs)
Annotate the point ``xy`` with text ``s``.
Additional kwargs are passed to `~matplotlib.text.Text`.
Parameters
----------
s : str
The text of the annotation
xy : iterable
Length 2 sequence specifying the *(x,y)* point to annotate
xytext : iterable, optional
Length 2 sequence specifying the *(x,y)* to place the text
at. If None, defaults to ``xy``.
xycoords : str, Artist, Transform, callable or tuple, optional
The coordinate system that ``xy`` is given in.
For a `str` the allowed values are:
================= ===============================================
Property Description
================= ===============================================
'figure points' points from the lower left of the figure
'figure pixels' pixels from the lower left of the figure
'figure fraction' fraction of figure from lower left
'axes points' points from lower left corner of axes
'axes pixels' pixels from lower left corner of axes
'axes fraction' fraction of axes from lower left
'data' use the coordinate system of the object being
annotated (default)
'polar' *(theta,r)* if not native 'data' coordinates
================= ===============================================
If a `~matplotlib.artist.Artist` object is passed in the units are
fraction if it's bounding box.
If a `~matplotlib.transforms.Transform` object is passed
in use that to transform ``xy`` to screen coordinates
If a callable it must take a
`~matplotlib.backend_bases.RendererBase` object as input
and return a `~matplotlib.transforms.Transform` or
`~matplotlib.transforms.Bbox` object
If a `tuple` must be length 2 tuple of str, `Artist`,
`Transform` or callable objects. The first transform is
used for the *x* coordinate and the second for *y*.
See :ref:`plotting-guide-annotation` for more details.
Defaults to ``'data'``
textcoords : str, `Artist`, `Transform`, callable or tuple, optional
The coordinate system that ``xytext`` is given, which
may be different than the coordinate system used for
``xy``.
All ``xycoords`` values are valid as well as the following
strings:
================= =========================================
Property Description
================= =========================================
'offset points' offset (in points) from the *xy* value
'offset pixels' offset (in pixels) from the *xy* value
================= =========================================
defaults to the input of ``xycoords``
arrowprops : dict, optional
If not None, properties used to draw a
`~matplotlib.patches.FancyArrowPatch` arrow between ``xy`` and
``xytext``.
If `arrowprops` does not contain the key ``'arrowstyle'`` the
allowed keys are:
========== ======================================================
Key Description
========== ======================================================
width the width of the arrow in points
headwidth the width of the base of the arrow head in points
headlength the length of the arrow head in points
shrink fraction of total length to 'shrink' from both ends
? any key to :class:`matplotlib.patches.FancyArrowPatch`
========== ======================================================
If the `arrowprops` contains the key ``'arrowstyle'`` the
above keys are forbidden. The allowed values of
``'arrowstyle'`` are:
============ =============================================
Name Attrs
============ =============================================
``'-'`` None
``'->'`` head_length=0.4,head_width=0.2
``'-['`` widthB=1.0,lengthB=0.2,angleB=None
``'|-|'`` widthA=1.0,widthB=1.0
``'-|>'`` head_length=0.4,head_width=0.2
``'<-'`` head_length=0.4,head_width=0.2
``'<->'`` head_length=0.4,head_width=0.2
``'<|-'`` head_length=0.4,head_width=0.2
``'<|-|>'`` head_length=0.4,head_width=0.2
``'fancy'`` head_length=0.4,head_width=0.4,tail_width=0.4
``'simple'`` head_length=0.5,head_width=0.5,tail_width=0.2
``'wedge'`` tail_width=0.3,shrink_factor=0.5
============ =============================================
Valid keys for `~matplotlib.patches.FancyArrowPatch` are:
=============== ==================================================
Key Description
=============== ==================================================
arrowstyle the arrow style
connectionstyle the connection style
relpos default is (0.5, 0.5)
patchA default is bounding box of the text
patchB default is None
shrinkA default is 2 points
shrinkB default is 2 points
mutation_scale default is text size (in points)
mutation_aspect default is 1.
? any key for :class:`matplotlib.patches.PathPatch`
=============== ==================================================
Defaults to None
annotation_clip : bool, optional
Controls the visibility of the annotation when it goes
outside the axes area.
If `True`, the annotation will only be drawn when the
``xy`` is inside the axes. If `False`, the annotation will
always be drawn regardless of its position.
The default is `None`, which behave as `True` only if
*xycoords* is "data".
Returns
-------
Annotation
arrow(x, y, dx, dy, hold=None, **kwargs)
Add an arrow to the axes.
Draws arrow on specified axis from (`x`, `y`) to (`x` + `dx`,
`y` + `dy`). Uses FancyArrow patch to construct the arrow.
Parameters
----------
x : float
X-coordinate of the arrow base
y : float
Y-coordinate of the arrow base
dx : float
Length of arrow along x-coordinate
dy : float
Length of arrow along y-coordinate
Returns
-------
a : FancyArrow
patches.FancyArrow object
Other Parameters
-----------------
Optional kwargs (inherited from FancyArrow patch) control the arrow
construction and properties:
Constructor arguments
*width*: float (default: 0.001)
width of full arrow tail
*length_includes_head*: [True | False] (default: False)
True if head is to be counted in calculating the length.
*head_width*: float or None (default: 3*width)
total width of the full arrow head
*head_length*: float or None (default: 1.5 * head_width)
length of arrow head
*shape*: ['full', 'left', 'right'] (default: 'full')
draw the left-half, right-half, or full arrow
*overhang*: float (default: 0)
fraction that the arrow is swept back (0 overhang means
triangular shape). Can be negative or greater than one.
*head_starts_at_zero*: [True | False] (default: False)
if True, the head starts being drawn at coordinate 0
instead of ending at coordinate 0.
Other valid kwargs (inherited from :class:`Patch`) are:
agg_filter: unknown
alpha: float or None
animated: [True | False]
antialiased or aa: [True | False] or None for default
axes: an :class:`~matplotlib.axes.Axes` instance
capstyle: ['butt' | 'round' | 'projecting']
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
color: matplotlib color spec
contains: a callable function
edgecolor or ec: mpl color spec, None, 'none', or 'auto'
facecolor or fc: mpl color spec, or None for default, or 'none' for no color
figure: a :class:`matplotlib.figure.Figure` instance
fill: [True | False]
gid: an id string
hatch: ['/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*']
joinstyle: ['miter' | 'round' | 'bevel']
label: string or anything printable with '%s' conversion.
linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or lw: float or None for default
path_effects: unknown
picker: [None|float|boolean|callable]
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
transform: :class:`~matplotlib.transforms.Transform` instance
url: a url string
visible: [True | False]
zorder: any number
Notes
-----
The resulting arrow is affected by the axes aspect ratio and limits.
This may produce an arrow whose head is not square with its stem. To
create an arrow whose head is square with its stem, use
:meth:`annotate` for example::
ax.annotate("", xy=(0.5, 0.5), xytext=(0, 0),
arrowprops=dict(arrowstyle="->"))
Examples
--------
.. plot:: mpl_examples/pylab_examples/arrow_demo.py
autoscale(enable=True, axis=u'both', tight=None)
Autoscale the axis view to the data (toggle).
Convenience method for simple axis view autoscaling.
It turns autoscaling on or off, and then,
if autoscaling for either axis is on, it performs
the autoscaling on the specified axis or axes.
*enable*: [True | False | None]
True (default) turns autoscaling on, False turns it off.
None leaves the autoscaling state unchanged.
*axis*: ['x' | 'y' | 'both']
which axis to operate on; default is 'both'
*tight*: [True | False | None]
If True, set view limits to data limits;
if False, let the locator and margins expand the view limits;
if None, use tight scaling if the only artist is an image,
otherwise treat *tight* as False.
The *tight* setting is retained for future autoscaling
until it is explicitly changed.
Returns None.
autumn()
set the default colormap to autumn and apply to current image if any.
See help(colormaps) for more information
axes(*args, **kwargs)
Add an axes to the figure.
The axes is added at position *rect* specified by:
- ``axes()`` by itself creates a default full ``subplot(111)`` window axis.
- ``axes(rect, facecolor='w')`` where *rect* = [left, bottom, width,
height] in normalized (0, 1) units. *facecolor* is the background
color for the axis, default white.
- ``axes(h)`` where *h* is an axes instance makes *h* the current
axis. An :class:`~matplotlib.axes.Axes` instance is returned.
========= ============== ==============================================
kwarg Accepts Description
========= ============== ==============================================
facecolor color the axes background color
frameon [True|False] display the frame?
sharex otherax current axes shares xaxis attribute
with otherax
sharey otherax current axes shares yaxis attribute
with otherax
polar [True|False] use a polar axes?
aspect [str | num] ['equal', 'auto'] or a number. If a number
the ratio of x-unit/y-unit in screen-space.
Also see
:meth:`~matplotlib.axes.Axes.set_aspect`.
========= ============== ==============================================
Examples:
* :file:`examples/pylab_examples/axes_demo.py` places custom axes.
* :file:`examples/pylab_examples/shared_axis_demo.py` uses
*sharex* and *sharey*.
axhline(y=0, xmin=0, xmax=1, hold=None, **kwargs)
Add a horizontal line across the axis.
Parameters
----------
y : scalar, optional, default: 0
y position in data coordinates of the horizontal line.
xmin : scalar, optional, default: 0
Should be between 0 and 1, 0 being the far left of the plot, 1 the
far right of the plot.
xmax : scalar, optional, default: 1
Should be between 0 and 1, 0 being the far left of the plot, 1 the
far right of the plot.
Returns
-------
:class:`~matplotlib.lines.Line2D`
Notes
-----
kwargs are passed to :class:`~matplotlib.lines.Line2D` and can be used
to control the line properties.
Examples
--------
* draw a thick red hline at 'y' = 0 that spans the xrange::
>>> axhline(linewidth=4, color='r')
* draw a default hline at 'y' = 1 that spans the xrange::
>>> axhline(y=1)
* draw a default hline at 'y' = .5 that spans the middle half of
the xrange::
>>> axhline(y=.5, xmin=0.25, xmax=0.75)
Valid kwargs are :class:`~matplotlib.lines.Line2D` properties,
with the exception of 'transform':
agg_filter: unknown
alpha: float (0.0 transparent through 1.0 opaque)
animated: [True | False]
antialiased or aa: [True | False]
axes: an :class:`~matplotlib.axes.Axes` instance
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
color or c: any matplotlib color
contains: a callable function
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post']
figure: a :class:`matplotlib.figure.Figure` instance
fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none']
gid: an id string
label: string or anything printable with '%s' conversion.
linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or lw: float value in points
marker: :mod:`A valid marker style <matplotlib.markers>`
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markerfacecoloralt or mfcalt: any matplotlib color
markersize or ms: float
markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects: unknown
picker: float distance in points or callable pick function ``fn(artist, event)``
pickradius: float distance in points
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a :class:`matplotlib.transforms.Transform` instance
url: a url string
visible: [True | False]
xdata: 1D array
ydata: 1D array
zorder: any number
See also
--------
axhspan : for example plot and source code
axhspan(ymin, ymax, xmin=0, xmax=1, hold=None, **kwargs)
Add a horizontal span (rectangle) across the axis.
Draw a horizontal span (rectangle) from *ymin* to *ymax*.
With the default values of *xmin* = 0 and *xmax* = 1, this
always spans the xrange, regardless of the xlim settings, even
if you change them, e.g., with the :meth:`set_xlim` command.
That is, the horizontal extent is in axes coords: 0=left,
0.5=middle, 1.0=right but the *y* location is in data
coordinates.
Parameters
----------
ymin : float
Lower limit of the horizontal span in data units.
ymax : float
Upper limit of the horizontal span in data units.
xmin : float, optional, default: 0
Lower limit of the vertical span in axes (relative
0-1) units.
xmax : float, optional, default: 1
Upper limit of the vertical span in axes (relative
0-1) units.
Returns
-------
Polygon : `~matplotlib.patches.Polygon`
Other Parameters
----------------
kwargs : `~matplotlib.patches.Polygon` properties.
agg_filter: unknown
alpha: float or None
animated: [True | False]
antialiased or aa: [True | False] or None for default
axes: an :class:`~matplotlib.axes.Axes` instance
capstyle: ['butt' | 'round' | 'projecting']
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
color: matplotlib color spec
contains: a callable function
edgecolor or ec: mpl color spec, None, 'none', or 'auto'
facecolor or fc: mpl color spec, or None for default, or 'none' for no color
figure: a :class:`matplotlib.figure.Figure` instance
fill: [True | False]
gid: an id string
hatch: ['/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*']
joinstyle: ['miter' | 'round' | 'bevel']
label: string or anything printable with '%s' conversion.
linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or lw: float or None for default
path_effects: unknown
picker: [None|float|boolean|callable]
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
transform: :class:`~matplotlib.transforms.Transform` instance
url: a url string
visible: [True | False]
zorder: any number
See Also
--------
axvspan : Add a vertical span (rectangle) across the axes.
Examples
--------
.. plot:: mpl_examples/pylab_examples/axhspan_demo.py
axis(*v, **kwargs)
Convenience method to get or set axis properties.
Calling with no arguments::
>>> axis()
returns the current axes limits ``[xmin, xmax, ymin, ymax]``.::
>>> axis(v)
sets the min and max of the x and y axes, with
``v = [xmin, xmax, ymin, ymax]``.::
>>> axis('off')
turns off the axis lines and labels.::
>>> axis('equal')
changes limits of *x* or *y* axis so that equal increments of *x*
and *y* have the same length; a circle is circular.::
>>> axis('scaled')
achieves the same result by changing the dimensions of the plot box instead
of the axis data limits.::
>>> axis('tight')
changes *x* and *y* axis limits such that all data is shown. If
all data is already shown, it will move it to the center of the
figure without modifying (*xmax* - *xmin*) or (*ymax* -
*ymin*). Note this is slightly different than in MATLAB.::
>>> axis('image')
is 'scaled' with the axis limits equal to the data limits.::
>>> axis('auto')
and::
>>> axis('normal')
are deprecated. They restore default behavior; axis limits are automatically
scaled to make the data fit comfortably within the plot box.
if ``len(*v)==0``, you can pass in *xmin*, *xmax*, *ymin*, *ymax*
as kwargs selectively to alter just those limits without changing
the others.
>>> axis('square')
changes the limit ranges (*xmax*-*xmin*) and (*ymax*-*ymin*) of
the *x* and *y* axes to be the same, and have the same scaling,
resulting in a square plot.
The xmin, xmax, ymin, ymax tuple is returned
.. seealso::
:func:`xlim`, :func:`ylim`
For setting the x- and y-limits individually.
axvline(x=0, ymin=0, ymax=1, hold=None, **kwargs)
Add a vertical line across the axes.
Parameters
----------
x : scalar, optional, default: 0
x position in data coordinates of the vertical line.
ymin : scalar, optional, default: 0
Should be between 0 and 1, 0 being the bottom of the plot, 1 the
top of the plot.
ymax : scalar, optional, default: 1
Should be between 0 and 1, 0 being the bottom of the plot, 1 the
top of the plot.
Returns
-------
:class:`~matplotlib.lines.Line2D`
Examples
--------
* draw a thick red vline at *x* = 0 that spans the yrange::
>>> axvline(linewidth=4, color='r')
* draw a default vline at *x* = 1 that spans the yrange::
>>> axvline(x=1)
* draw a default vline at *x* = .5 that spans the middle half of
the yrange::
>>> axvline(x=.5, ymin=0.25, ymax=0.75)
Valid kwargs are :class:`~matplotlib.lines.Line2D` properties,
with the exception of 'transform':
agg_filter: unknown
alpha: float (0.0 transparent through 1.0 opaque)
animated: [True | False]
antialiased or aa: [True | False]
axes: an :class:`~matplotlib.axes.Axes` instance
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
color or c: any matplotlib color
contains: a callable function
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post']
figure: a :class:`matplotlib.figure.Figure` instance
fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none']
gid: an id string
label: string or anything printable with '%s' conversion.
linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or lw: float value in points
marker: :mod:`A valid marker style <matplotlib.markers>`
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markerfacecoloralt or mfcalt: any matplotlib color
markersize or ms: float
markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects: unknown
picker: float distance in points or callable pick function ``fn(artist, event)``
pickradius: float distance in points
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a :class:`matplotlib.transforms.Transform` instance
url: a url string
visible: [True | False]
xdata: 1D array
ydata: 1D array
zorder: any number
See also
--------
axhspan : for example plot and source code
axvspan(xmin, xmax, ymin=0, ymax=1, hold=None, **kwargs)
Add a vertical span (rectangle) across the axes.
Draw a vertical span (rectangle) from `xmin` to `xmax`. With
the default values of `ymin` = 0 and `ymax` = 1. This always
spans the yrange, regardless of the ylim settings, even if you
change them, e.g., with the :meth:`set_ylim` command. That is,
the vertical extent is in axes coords: 0=bottom, 0.5=middle,
1.0=top but the y location is in data coordinates.
Parameters
----------
xmin : scalar
Number indicating the first X-axis coordinate of the vertical
span rectangle in data units.
xmax : scalar
Number indicating the second X-axis coordinate of the vertical
span rectangle in data units.
ymin : scalar, optional
Number indicating the first Y-axis coordinate of the vertical
span rectangle in relative Y-axis units (0-1). Default to 0.
ymax : scalar, optional
Number indicating the second Y-axis coordinate of the vertical
span rectangle in relative Y-axis units (0-1). Default to 1.
Returns
-------
rectangle : matplotlib.patches.Polygon
Vertical span (rectangle) from (xmin, ymin) to (xmax, ymax).
Other Parameters
----------------
**kwargs
Optional parameters are properties of the class
matplotlib.patches.Polygon.
See Also
--------
axhspan
Examples
--------
Draw a vertical, green, translucent rectangle from x = 1.25 to
x = 1.55 that spans the yrange of the axes.
>>> axvspan(1.25, 1.55, facecolor='g', alpha=0.5)
bar(left, height, width=0.8, bottom=None, hold=None, data=None, **kwargs)
Make a bar plot.
Make a bar plot with rectangles bounded by:
`left`, `left` + `width`, `bottom`, `bottom` + `height`
(left, right, bottom and top edges)
Parameters
----------
left : sequence of scalars
the x coordinates of the left sides of the bars
height : sequence of scalars
the heights of the bars
width : scalar or array-like, optional
the width(s) of the bars
default: 0.8
bottom : scalar or array-like, optional
the y coordinate(s) of the bars
default: None
color : scalar or array-like, optional
the colors of the bar faces
edgecolor : scalar or array-like, optional
the colors of the bar edges
linewidth : scalar or array-like, optional
width of bar edge(s). If None, use default
linewidth; If 0, don't draw edges.
default: None
tick_label : string or array-like, optional
the tick labels of the bars
default: None
xerr : scalar or array-like, optional
if not None, will be used to generate errorbar(s) on the bar chart
default: None
yerr : scalar or array-like, optional
if not None, will be used to generate errorbar(s) on the bar chart
default: None
ecolor : scalar or array-like, optional
specifies the color of errorbar(s)
default: None
capsize : scalar, optional
determines the length in points of the error bar caps
default: None, which will take the value from the
``errorbar.capsize`` :data:`rcParam<matplotlib.rcParams>`.
error_kw : dict, optional
dictionary of kwargs to be passed to errorbar method. *ecolor* and
*capsize* may be specified here rather than as independent kwargs.
align : {'center', 'edge'}, optional
If 'edge', aligns bars by their left edges (for vertical bars) and
by their bottom edges (for horizontal bars). If 'center', interpret
the `left` argument as the coordinates of the centers of the bars.
To align on the align bars on the right edge pass a negative
`width`.
orientation : {'vertical', 'horizontal'}, optional
The orientation of the bars.
log : boolean, optional
If true, sets the axis to be log scale.
default: False
Returns
-------
bars : matplotlib.container.BarContainer
Container with all of the bars + errorbars
Notes
-----
The optional arguments `color`, `edgecolor`, `linewidth`,
`xerr`, and `yerr` can be either scalars or sequences of
length equal to the number of bars. This enables you to use
bar as the basis for stacked bar charts, or candlestick plots.
Detail: `xerr` and `yerr` are passed directly to
:meth:`errorbar`, so they can also have shape 2xN for
independent specification of lower and upper errors.
Other optional kwargs:
agg_filter: unknown
alpha: float or None
animated: [True | False]
antialiased or aa: [True | False] or None for default
axes: an :class:`~matplotlib.axes.Axes` instance
capstyle: ['butt' | 'round' | 'projecting']
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
color: matplotlib color spec
contains: a callable function
edgecolor or ec: mpl color spec, None, 'none', or 'auto'
facecolor or fc: mpl color spec, or None for default, or 'none' for no color
figure: a :class:`matplotlib.figure.Figure` instance
fill: [True | False]
gid: an id string
hatch: ['/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*']
joinstyle: ['miter' | 'round' | 'bevel']
label: string or anything printable with '%s' conversion.
linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or lw: float or None for default
path_effects: unknown
picker: [None|float|boolean|callable]
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
transform: :class:`~matplotlib.transforms.Transform` instance
url: a url string
visible: [True | False]
zorder: any number
See also
--------
barh: Plot a horizontal bar plot.
Examples
--------
**Example:** A stacked bar chart.
.. plot:: mpl_examples/pylab_examples/bar_stacked.py
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All arguments with the following names: 'bottom', 'color', 'ecolor', 'edgecolor', 'height', 'left', 'linewidth', 'tick_label', 'width', 'xerr', 'yerr'.
barbs(*args, **kw)
Plot a 2-D field of barbs.
Call signatures::
barb(U, V, **kw)
barb(U, V, C, **kw)
barb(X, Y, U, V, **kw)
barb(X, Y, U, V, C, **kw)
Arguments:
*X*, *Y*:
The x and y coordinates of the barb locations
(default is head of barb; see *pivot* kwarg)
*U*, *V*:
Give the x and y components of the barb shaft
*C*:
An optional array used to map colors to the barbs
All arguments may be 1-D or 2-D arrays or sequences. If *X* and *Y*
are absent, they will be generated as a uniform grid. If *U* and *V*
are 2-D arrays but *X* and *Y* are 1-D, and if ``len(X)`` and ``len(Y)``
match the column and row dimensions of *U*, then *X* and *Y* will be
expanded with :func:`numpy.meshgrid`.
*U*, *V*, *C* may be masked arrays, but masked *X*, *Y* are not
supported at present.
Keyword arguments:
*length*:
Length of the barb in points; the other parts of the barb
are scaled against this.
Default is 9
*pivot*: [ 'tip' | 'middle' ]
The part of the arrow that is at the grid point; the arrow rotates
about this point, hence the name *pivot*. Default is 'tip'
*barbcolor*: [ color | color sequence ]
Specifies the color all parts of the barb except any flags. This
parameter is analagous to the *edgecolor* parameter for polygons,
which can be used instead. However this parameter will override
facecolor.
*flagcolor*: [ color | color sequence ]
Specifies the color of any flags on the barb. This parameter is
analagous to the *facecolor* parameter for polygons, which can be
used instead. However this parameter will override facecolor. If
this is not set (and *C* has not either) then *flagcolor* will be
set to match *barbcolor* so that the barb has a uniform color. If
*C* has been set, *flagcolor* has no effect.
*sizes*:
A dictionary of coefficients specifying the ratio of a given
feature to the length of the barb. Only those values one wishes to
override need to be included. These features include:
- 'spacing' - space between features (flags, full/half barbs)
- 'height' - height (distance from shaft to top) of a flag or
full barb
- 'width' - width of a flag, twice the width of a full barb
- 'emptybarb' - radius of the circle used for low magnitudes
*fill_empty*:
A flag on whether the empty barbs (circles) that are drawn should
be filled with the flag color. If they are not filled, they will
be drawn such that no color is applied to the center. Default is
False
*rounding*:
A flag to indicate whether the vector magnitude should be rounded
when allocating barb components. If True, the magnitude is
rounded to the nearest multiple of the half-barb increment. If
False, the magnitude is simply truncated to the next lowest
multiple. Default is True
*barb_increments*:
A dictionary of increments specifying values to associate with
different parts of the barb. Only those values one wishes to
override need to be included.
- 'half' - half barbs (Default is 5)
- 'full' - full barbs (Default is 10)
- 'flag' - flags (default is 50)
*flip_barb*:
Either a single boolean flag or an array of booleans. Single
boolean indicates whether the lines and flags should point
opposite to normal for all barbs. An array (which should be the
same size as the other data arrays) indicates whether to flip for
each individual barb. Normal behavior is for the barbs and lines
to point right (comes from wind barbs having these features point
towards low pressure in the Northern Hemisphere.) Default is
False
Barbs are traditionally used in meteorology as a way to plot the speed
and direction of wind observations, but can technically be used to
plot any two dimensional vector quantity. As opposed to arrows, which
give vector magnitude by the length of the arrow, the barbs give more
quantitative information about the vector magnitude by putting slanted
lines or a triangle for various increments in magnitude, as show
schematically below::
: /\ \
: / \ \
: / \ \ \
: / \ \ \
: ------------------------------
.. note the double \ at the end of each line to make the figure
.. render correctly
The largest increment is given by a triangle (or "flag"). After those
come full lines (barbs). The smallest increment is a half line. There
is only, of course, ever at most 1 half line. If the magnitude is
small and only needs a single half-line and no full lines or
triangles, the half-line is offset from the end of the barb so that it
can be easily distinguished from barbs with a single full line. The
magnitude for the barb shown above would nominally be 65, using the
standard increments of 50, 10, and 5.
linewidths and edgecolors can be used to customize the barb.
Additional :class:`~matplotlib.collections.PolyCollection` keyword
arguments:
agg_filter: unknown
alpha: float or None
animated: [True | False]
antialiased or antialiaseds: Boolean or sequence of booleans
array: unknown
axes: an :class:`~matplotlib.axes.Axes` instance
clim: a length 2 sequence of floats
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
cmap: a colormap or registered colormap name
color: matplotlib color arg or sequence of rgba tuples
contains: a callable function
edgecolor or edgecolors: matplotlib color spec or sequence of specs
facecolor or facecolors: matplotlib color spec or sequence of specs
figure: a :class:`matplotlib.figure.Figure` instance
gid: an id string
hatch: [ '/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ]
label: string or anything printable with '%s' conversion.
linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or linewidths or lw: float or sequence of floats
norm: unknown
offset_position: unknown
offsets: float or sequence of floats
path_effects: unknown
picker: [None|float|boolean|callable]
pickradius: unknown
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
transform: :class:`~matplotlib.transforms.Transform` instance
url: a url string
urls: unknown
visible: [True | False]
zorder: any number
**Example:**
.. plot:: mpl_examples/pylab_examples/barb_demo.py
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All positional and all keyword arguments.
barh(bottom, width, height=0.8, left=None, hold=None, **kwargs)
Make a horizontal bar plot.
Make a horizontal bar plot with rectangles bounded by:
`left`, `left` + `width`, `bottom`, `bottom` + `height`
(left, right, bottom and top edges)
`bottom`, `width`, `height`, and `left` can be either scalars
or sequences
Parameters
----------
bottom : scalar or array-like
the y coordinate(s) of the bars
width : scalar or array-like
the width(s) of the bars
height : sequence of scalars, optional, default: 0.8
the heights of the bars
left : sequence of scalars
the x coordinates of the left sides of the bars
Returns
-------
`matplotlib.patches.Rectangle` instances.
Other parameters
----------------
color : scalar or array-like, optional
the colors of the bars
edgecolor : scalar or array-like, optional
the colors of the bar edges
linewidth : scalar or array-like, optional, default: None
width of bar edge(s). If None, use default
linewidth; If 0, don't draw edges.
tick_label : string or array-like, optional, default: None
the tick labels of the bars
xerr : scalar or array-like, optional, default: None
if not None, will be used to generate errorbar(s) on the bar chart
yerr : scalar or array-like, optional, default: None
if not None, will be used to generate errorbar(s) on the bar chart
ecolor : scalar or array-like, optional, default: None
specifies the color of errorbar(s)
capsize : scalar, optional
determines the length in points of the error bar caps
default: None, which will take the value from the
``errorbar.capsize`` :data:`rcParam<matplotlib.rcParams>`.
error_kw :
dictionary of kwargs to be passed to errorbar method. `ecolor` and
`capsize` may be specified here rather than as independent kwargs.
align : {'center', 'edge'}, optional
If 'edge', aligns bars by their left edges (for vertical
bars) and by their bottom edges (for horizontal bars). If
'center', interpret the `bottom` argument as the
coordinates of the centers of the bars. To align on the
align bars on the top edge pass a negative 'height'.
log : boolean, optional, default: False
If true, sets the axis to be log scale
Notes
-----
The optional arguments `color`, `edgecolor`, `linewidth`,
`xerr`, and `yerr` can be either scalars or sequences of
length equal to the number of bars. This enables you to use
bar as the basis for stacked bar charts, or candlestick plots.
Detail: `xerr` and `yerr` are passed directly to
:meth:`errorbar`, so they can also have shape 2xN for
independent specification of lower and upper errors.
Other optional kwargs:
agg_filter: unknown
alpha: float or None
animated: [True | False]
antialiased or aa: [True | False] or None for default
axes: an :class:`~matplotlib.axes.Axes` instance
capstyle: ['butt' | 'round' | 'projecting']
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
color: matplotlib color spec
contains: a callable function
edgecolor or ec: mpl color spec, None, 'none', or 'auto'
facecolor or fc: mpl color spec, or None for default, or 'none' for no color
figure: a :class:`matplotlib.figure.Figure` instance
fill: [True | False]
gid: an id string
hatch: ['/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*']
joinstyle: ['miter' | 'round' | 'bevel']
label: string or anything printable with '%s' conversion.
linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or lw: float or None for default
path_effects: unknown
picker: [None|float|boolean|callable]
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
transform: :class:`~matplotlib.transforms.Transform` instance
url: a url string
visible: [True | False]
zorder: any number
See also
--------
bar: Plot a vertical bar plot.
bone()
set the default colormap to bone and apply to current image if any.
See help(colormaps) for more information
box(on=None)
Turn the axes box on or off. *on* may be a boolean or a string,
'on' or 'off'.
If *on* is *None*, toggle state.
boxplot(x, notch=None, sym=None, vert=None, whis=None, positions=None, widths=None, patch_artist=None, bootstrap=None, usermedians=None, conf_intervals=None, meanline=None, showmeans=None, showcaps=None, showbox=None, showfliers=None, boxprops=None, labels=None, flierprops=None, medianprops=None, meanprops=None, capprops=None, whiskerprops=None, manage_xticks=True, autorange=False, zorder=None, hold=None, data=None)
Make a box and whisker plot.
Make a box and whisker plot for each column of ``x`` or each
vector in sequence ``x``. The box extends from the lower to
upper quartile values of the data, with a line at the median.
The whiskers extend from the box to show the range of the
data. Flier points are those past the end of the whiskers.
Parameters
----------
x : Array or a sequence of vectors.
The input data.
notch : bool, optional (False)
If `True`, will produce a notched box plot. Otherwise, a
rectangular boxplot is produced. The notches represent the
confidence interval (CI) around the median. See the entry
for the ``bootstrap`` parameter for information regarding
how the locations of the notches are computed.
.. note::
In cases where the values of the CI are less than the
lower quartile or greater than the upper quartile, the
notches will extend beyond the box, giving it a
distinctive "flipped" appearance. This is expected
behavior and consistent with other statistical
visualization packages.
sym : str, optional
The default symbol for flier points. Enter an empty string
('') if you don't want to show fliers. If `None`, then the
fliers default to 'b+' If you want more control use the
flierprops kwarg.
vert : bool, optional (True)
If `True` (default), makes the boxes vertical. If `False`,
everything is drawn horizontally.
whis : float, sequence, or string (default = 1.5)
As a float, determines the reach of the whiskers past the
first and third quartiles (e.g., Q3 + whis*IQR,
IQR = interquartile range, Q3-Q1). Beyond the whiskers, data
are considered outliers and are plotted as individual
points. Set this to an unreasonably high value to force the
whiskers to show the min and max values. Alternatively, 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 min and max of the data.
bootstrap : int, optional
Specifies whether to bootstrap the confidence intervals
around the median for notched boxplots. If ``bootstrap`` is
None, no bootstrapping is performed, and notches are
calculated using a Gaussian-based asymptotic approximation
(see McGill, R., Tukey, J.W., and Larsen, W.A., 1978, and
Kendall and Stuart, 1967). Otherwise, bootstrap specifies
the number of times to bootstrap the median to determine its
95% confidence intervals. Values between 1000 and 10000 are
recommended.
usermedians : array-like, optional
An array or sequence whose first dimension (or length) is
compatible with ``x``. This overrides the medians computed
by matplotlib for each element of ``usermedians`` that is not
`None`. When an element of ``usermedians`` is None, the median
will be computed by matplotlib as normal.
conf_intervals : array-like, optional
Array or sequence whose first dimension (or length) is
compatible with ``x`` and whose second dimension is 2. When
the an element of ``conf_intervals`` is not None, the
notch locations computed by matplotlib are overridden
(provided ``notch`` is `True`). When an element of
``conf_intervals`` is `None`, the notches are computed by the
method specified by the other kwargs (e.g., ``bootstrap``).
positions : array-like, optional
Sets the positions of the boxes. The ticks and limits are
automatically set to match the positions. Defaults to
`range(1, N+1)` where N is the number of boxes to be drawn.
widths : scalar or array-like
Sets the width of each box either with a scalar or a
sequence. The default is 0.5, or ``0.15*(distance between
extreme positions)``, if that is smaller.
patch_artist : bool, optional (False)
If `False` produces boxes with the Line2D artist. Otherwise,
boxes and drawn with Patch artists.
labels : sequence, optional
Labels for each dataset. Length must be compatible with
dimensions of ``x``.
manage_xticks : bool, optional (True)
If the function should adjust the xlim and xtick locations.
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.
meanline : bool, optional (False)
If `True` (and ``showmeans`` is `True`), will try to render
the mean as a line spanning the full width of the box
according to ``meanprops`` (see below). Not recommended if
``shownotches`` is also True. Otherwise, means will be shown
as points.
zorder : scalar, optional (None)
Sets the zorder of the boxplot.
Other Parameters
----------------
showcaps : bool, optional (True)
Show the caps on the ends of whiskers.
showbox : bool, optional (True)
Show the central box.
showfliers : bool, optional (True)
Show the outliers beyond the caps.
showmeans : bool, optional (False)
Show the arithmetic means.
capprops : dict, optional (None)
Specifies the style of the caps.
boxprops : dict, optional (None)
Specifies the style of the box.
whiskerprops : dict, optional (None)
Specifies the style of the whiskers.
flierprops : dict, optional (None)
Specifies the style of the fliers.
medianprops : dict, optional (None)
Specifies the style of the median.
meanprops : dict, optional (None)
Specifies the style of the mean.
Returns
-------
result : dict
A dictionary mapping each component of the boxplot to a list
of the :class:`matplotlib.lines.Line2D` instances
created. That dictionary has the following keys (assuming
vertical boxplots):
- ``boxes``: the main body of the boxplot showing the
quartiles and the median's confidence intervals if
enabled.
- ``medians``: horizontal lines at the median of each box.
- ``whiskers``: the vertical lines extending to the most
extreme, non-outlier data points.
- ``caps``: the horizontal lines at the ends of the
whiskers.
- ``fliers``: points representing data that extend beyond
the whiskers (fliers).
- ``means``: points or lines representing the means.
Examples
--------
.. plot:: mpl_examples/statistics/boxplot_demo.py
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All positional and all keyword arguments.
broken_barh(xranges, yrange, hold=None, data=None, **kwargs)
Plot horizontal bars.
A collection of horizontal bars spanning *yrange* with a sequence of
*xranges*.
Required arguments:
========= ==============================
Argument Description
========= ==============================
*xranges* sequence of (*xmin*, *xwidth*)
*yrange* sequence of (*ymin*, *ywidth*)
========= ==============================
kwargs are
:class:`matplotlib.collections.BrokenBarHCollection`
properties:
agg_filter: unknown
alpha: float or None
animated: [True | False]
antialiased or antialiaseds: Boolean or sequence of booleans
array: unknown
axes: an :class:`~matplotlib.axes.Axes` instance
clim: a length 2 sequence of floats
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
cmap: a colormap or registered colormap name
color: matplotlib color arg or sequence of rgba tuples
contains: a callable function
edgecolor or edgecolors: matplotlib color spec or sequence of specs
facecolor or facecolors: matplotlib color spec or sequence of specs
figure: a :class:`matplotlib.figure.Figure` instance
gid: an id string
hatch: [ '/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ]
label: string or anything printable with '%s' conversion.
linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or linewidths or lw: float or sequence of floats
norm: unknown
offset_position: unknown
offsets: float or sequence of floats
path_effects: unknown
picker: [None|float|boolean|callable]
pickradius: unknown
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
transform: :class:`~matplotlib.transforms.Transform` instance
url: a url string
urls: unknown
visible: [True | False]
zorder: any number
these can either be a single argument, i.e.,::
facecolors = 'black'
or a sequence of arguments for the various bars, i.e.,::
facecolors = ('black', 'red', 'green')
**Example:**
.. plot:: mpl_examples/pylab_examples/broken_barh.py
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All positional and all keyword arguments.
cla()
Clear the current axes.
clabel(CS, *args, **kwargs)
Label a contour plot.
Call signature::
clabel(cs, **kwargs)
Adds labels to line contours in *cs*, where *cs* is a
:class:`~matplotlib.contour.ContourSet` object returned by
contour.
::
clabel(cs, v, **kwargs)
only labels contours listed in *v*.
Optional keyword arguments:
*fontsize*:
size in points or relative size e.g., 'smaller', 'x-large'
*colors*:
- if *None*, the color of each label matches the color of
the corresponding contour
- if one string color, e.g., *colors* = 'r' or *colors* =
'red', all labels will be plotted in this color
- if a tuple of matplotlib color args (string, float, rgb, etc),
different labels will be plotted in different colors in the order
specified
*inline*:
controls whether the underlying contour is removed or
not. Default is *True*.
*inline_spacing*:
space in pixels to leave on each side of label when
placing inline. Defaults to 5. This spacing will be
exact for labels at locations where the contour is
straight, less so for labels on curved contours.
*fmt*:
a format string for the label. Default is '%1.3f'
Alternatively, this can be a dictionary matching contour
levels with arbitrary strings to use for each contour level
(i.e., fmt[level]=string), or it can be any callable, such
as a :class:`~matplotlib.ticker.Formatter` instance, that
returns a string when called with a numeric contour level.
*manual*:
if *True*, contour labels will be placed manually using
mouse clicks. Click the first button near a contour to
add a label, click the second button (or potentially both
mouse buttons at once) to finish adding labels. The third
button can be used to remove the last label added, but
only if labels are not inline. Alternatively, the keyboard
can be used to select label locations (enter to end label
placement, delete or backspace act like the third mouse button,
and any other key will select a label location).
*manual* can be an iterable object of x,y tuples. Contour labels
will be created as if mouse is clicked at each x,y positions.
*rightside_up*:
if *True* (default), label rotations will always be plus
or minus 90 degrees from level.
*use_clabeltext*:
if *True* (default is False), ClabelText class (instead of
matplotlib.Text) is used to create labels. ClabelText
recalculates rotation angles of texts during the drawing time,
therefore this can be used if aspect of the axes changes.
.. plot:: mpl_examples/pylab_examples/contour_demo.py
clf()
Clear the current figure.
clim(vmin=None, vmax=None)
Set the color limits of the current image.
To apply clim to all axes images do::
clim(0, 0.5)
If either *vmin* or *vmax* is None, the image min/max respectively
will be used for color scaling.
If you want to set the clim of multiple images,
use, for example::
for im in gca().get_images():
im.set_clim(0, 0.05)
close(*args)
Close a figure window.
``close()`` by itself closes the current figure
``close(h)`` where *h* is a :class:`Figure` instance, closes that figure
``close(num)`` closes figure number *num*
``close(name)`` where *name* is a string, closes figure with that label
``close('all')`` closes all the figure windows
cohere(x, y, NFFT=256, Fs=2, Fc=0, detrend=<function detrend_none>, window=<function window_hanning>, noverlap=0, pad_to=None, sides=u'default', scale_by_freq=None, hold=None, data=None, **kwargs)
Plot the coherence between *x* and *y*.
Plot the coherence between *x* and *y*. Coherence is the
normalized cross spectral density:
.. math::
C_{xy} = \frac{|P_{xy}|^2}{P_{xx}P_{yy}}
Parameters
----------
Fs : scalar
The sampling frequency (samples per time unit). It is used
to calculate the Fourier frequencies, freqs, in cycles per time
unit. The default value is 2.
window : callable or ndarray
A function or a vector of length *NFFT*. To create window
vectors see :func:`window_hanning`, :func:`window_none`,
:func:`numpy.blackman`, :func:`numpy.hamming`,
:func:`numpy.bartlett`, :func:`scipy.signal`,
:func:`scipy.signal.get_window`, etc. The default is
:func:`window_hanning`. If a function is passed as the
argument, it must take a data segment as an argument and
return the windowed version of the segment.
sides : [ 'default' | 'onesided' | 'twosided' ]
Specifies which sides of the spectrum to return. Default gives the
default behavior, which returns one-sided for real data and both
for complex data. 'onesided' forces the return of a one-sided
spectrum, while 'twosided' forces two-sided.
pad_to : integer
The number of points to which the data segment is padded when
performing the FFT. This can be different from *NFFT*, which
specifies the number of data points used. While not increasing
the actual resolution of the spectrum (the minimum distance between
resolvable peaks), this can give more points in the plot,
allowing for more detail. This corresponds to the *n* parameter
in the call to fft(). The default is None, which sets *pad_to*
equal to *NFFT*
NFFT : integer
The number of data points used in each block for the FFT.
A power 2 is most efficient. The default value is 256.
This should *NOT* be used to get zero padding, or the scaling of the
result will be incorrect. Use *pad_to* for this instead.
detrend : {'default', 'constant', 'mean', 'linear', 'none'} or callable
The function applied to each segment before fft-ing,
designed to remove the mean or linear trend. Unlike in
MATLAB, where the *detrend* parameter is a vector, in
matplotlib is it a function. The :mod:`~matplotlib.pylab`
module defines :func:`~matplotlib.pylab.detrend_none`,
:func:`~matplotlib.pylab.detrend_mean`, and
:func:`~matplotlib.pylab.detrend_linear`, but you can use
a custom function as well. You can also use a string to choose
one of the functions. 'default', 'constant', and 'mean' call
:func:`~matplotlib.pylab.detrend_mean`. 'linear' calls
:func:`~matplotlib.pylab.detrend_linear`. 'none' calls
:func:`~matplotlib.pylab.detrend_none`.
scale_by_freq : boolean, optional
Specifies whether the resulting density values should be scaled
by the scaling frequency, which gives density in units of Hz^-1.
This allows for integration over the returned frequency values.
The default is True for MATLAB compatibility.
noverlap : integer
The number of points of overlap between blocks. The
default value is 0 (no overlap).
Fc : integer
The center frequency of *x* (defaults to 0), which offsets
the x extents of the plot to reflect the frequency range used
when a signal is acquired and then filtered and downsampled to
baseband.
**kwargs :
Keyword arguments control the :class:`~matplotlib.lines.Line2D`
properties of the coherence plot:
agg_filter: unknown
alpha: float (0.0 transparent through 1.0 opaque)
animated: [True | False]
antialiased or aa: [True | False]
axes: an :class:`~matplotlib.axes.Axes` instance
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
color or c: any matplotlib color
contains: a callable function
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post']
figure: a :class:`matplotlib.figure.Figure` instance
fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none']
gid: an id string
label: string or anything printable with '%s' conversion.
linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or lw: float value in points
marker: :mod:`A valid marker style <matplotlib.markers>`
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markerfacecoloralt or mfcalt: any matplotlib color
markersize or ms: float
markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects: unknown
picker: float distance in points or callable pick function ``fn(artist, event)``
pickradius: float distance in points
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a :class:`matplotlib.transforms.Transform` instance
url: a url string
visible: [True | False]
xdata: 1D array
ydata: 1D array
zorder: any number
Returns
-------
The return value is a tuple (*Cxy*, *f*), where *f* are the
frequencies of the coherence vector.
kwargs are applied to the lines.
References
----------
Bendat & Piersol -- Random Data: Analysis and Measurement Procedures,
John Wiley & Sons (1986)
Examples
--------
.. plot:: mpl_examples/pylab_examples/cohere_demo.py
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All arguments with the following names: 'x', 'y'.
colorbar(mappable=None, cax=None, ax=None, **kw)
Add a colorbar to a plot.
Function signatures for the :mod:`~matplotlib.pyplot` interface; all
but the first are also method signatures for the
:meth:`~matplotlib.figure.Figure.colorbar` method::
colorbar(**kwargs)
colorbar(mappable, **kwargs)
colorbar(mappable, cax=cax, **kwargs)
colorbar(mappable, ax=ax, **kwargs)
arguments:
*mappable*
the :class:`~matplotlib.image.Image`,
:class:`~matplotlib.contour.ContourSet`, etc. to
which the colorbar applies; this argument is mandatory for the
:meth:`~matplotlib.figure.Figure.colorbar` method but optional for the
:func:`~matplotlib.pyplot.colorbar` function, which sets the
default to the current image.
keyword arguments:
*cax*
None | axes object into which the colorbar will be drawn
*ax*
None | parent axes object(s) from which space for a new
colorbar axes will be stolen. If a list of axes is given
they will all be resized to make room for the colorbar axes.
*use_gridspec*
False | If *cax* is None, a new *cax* is created as an instance of
Axes. If *ax* is an instance of Subplot and *use_gridspec* is True,
*cax* is created as an instance of Subplot using the
grid_spec module.
Additional keyword arguments are of two kinds:
axes properties:
============= ====================================================
Property Description
============= ====================================================
*orientation* vertical or horizontal
*fraction* 0.15; fraction of original axes to use for colorbar
*pad* 0.05 if vertical, 0.15 if horizontal; fraction
of original axes between colorbar and new image axes
*shrink* 1.0; fraction by which to shrink the colorbar
*aspect* 20; ratio of long to short dimensions
*anchor* (0.0, 0.5) if vertical; (0.5, 1.0) if horizontal;
the anchor point of the colorbar axes
*panchor* (1.0, 0.5) if vertical; (0.5, 0.0) if horizontal;
the anchor point of the colorbar parent axes. If
False, the parent axes' anchor will be unchanged
============= ====================================================
colorbar properties:
============ ====================================================
Property Description
============ ====================================================
*extend* [ 'neither' | 'both' | 'min' | 'max' ]
If not 'neither', make pointed end(s) for out-of-
range values. These are set for a given colormap
using the colormap set_under and set_over methods.
*extendfrac* [ *None* | 'auto' | length | lengths ]
If set to *None*, both the minimum and maximum
triangular colorbar extensions with have a length of
5% of the interior colorbar length (this is the
default setting). If set to 'auto', makes the
triangular colorbar extensions the same lengths as
the interior boxes (when *spacing* is set to
'uniform') or the same lengths as the respective
adjacent interior boxes (when *spacing* is set to
'proportional'). If a scalar, indicates the length
of both the minimum and maximum triangular colorbar
extensions as a fraction of the interior colorbar
length. A two-element sequence of fractions may also
be given, indicating the lengths of the minimum and
maximum colorbar extensions respectively as a
fraction of the interior colorbar length.
*extendrect* [ *False* | *True* ]
If *False* the minimum and maximum colorbar extensions
will be triangular (the default). If *True* the
extensions will be rectangular.
*spacing* [ 'uniform' | 'proportional' ]
Uniform spacing gives each discrete color the same
space; proportional makes the space proportional to
the data interval.
*ticks* [ None | list of ticks | Locator object ]
If None, ticks are determined automatically from the
input.
*format* [ None | format string | Formatter object ]
If None, the
:class:`~matplotlib.ticker.ScalarFormatter` is used.
If a format string is given, e.g., '%.3f', that is
used. An alternative
:class:`~matplotlib.ticker.Formatter` object may be
given instead.
*drawedges* [ False | True ] If true, draw lines at color
boundaries.
============ ====================================================
The following will probably be useful only in the context of
indexed colors (that is, when the mappable has norm=NoNorm()),
or other unusual circumstances.
============ ===================================================
Property Description
============ ===================================================
*boundaries* None or a sequence
*values* None or a sequence which must be of length 1 less
than the sequence of *boundaries*. For each region
delimited by adjacent entries in *boundaries*, the
color mapped to the corresponding value in values
will be used.
============ ===================================================
If *mappable* is a :class:`~matplotlib.contours.ContourSet`, its *extend*
kwarg is included automatically.
Note that the *shrink* kwarg provides a simple way to keep a vertical
colorbar, for example, from being taller than the axes of the mappable
to which the colorbar is attached; but it is a manual method requiring
some trial and error. If the colorbar is too tall (or a horizontal
colorbar is too wide) use a smaller value of *shrink*.
For more precise control, you can manually specify the positions of
the axes objects in which the mappable and the colorbar are drawn. In
this case, do not use any of the axes properties kwargs.
It is known that some vector graphics viewer (svg and pdf) renders white gaps
between segments of the colorbar. This is due to bugs in the viewers not
matplotlib. As a workaround the colorbar can be rendered with overlapping
segments::
cbar = colorbar()
cbar.solids.set_edgecolor("face")
draw()
However this has negative consequences in other circumstances. Particularly
with semi transparent images (alpha < 1) and colorbar extensions and is not
enabled by default see (issue #1188).
returns:
:class:`~matplotlib.colorbar.Colorbar` instance; see also its base class,
:class:`~matplotlib.colorbar.ColorbarBase`. Call the
:meth:`~matplotlib.colorbar.ColorbarBase.set_label` method
to label the colorbar.
colormaps()
Matplotlib provides a number of colormaps, and others can be added using
:func:`~matplotlib.cm.register_cmap`. This function documents the built-in
colormaps, and will also return a list of all registered colormaps if called.
You can set the colormap for an image, pcolor, scatter, etc,
using a keyword argument::
imshow(X, cmap=cm.hot)
or using the :func:`set_cmap` function::
imshow(X)
pyplot.set_cmap('hot')
pyplot.set_cmap('jet')
In interactive mode, :func:`set_cmap` will update the colormap post-hoc,
allowing you to see which one works best for your data.
All built-in colormaps can be reversed by appending ``_r``: For instance,
``gray_r`` is the reverse of ``gray``.
There are several common color schemes used in visualization:
Sequential schemes
for unipolar data that progresses from low to high
Diverging schemes
for bipolar data that emphasizes positive or negative deviations from a
central value
Cyclic schemes
meant for plotting values that wrap around at the
endpoints, such as phase angle, wind direction, or time of day
Qualitative schemes
for nominal data that has no inherent ordering, where color is used
only to distinguish categories
The base colormaps are derived from those of the same name provided
with Matlab:
========= =======================================================
Colormap Description
========= =======================================================
autumn sequential linearly-increasing shades of red-orange-yellow
bone sequential increasing black-white color map with
a tinge of blue, to emulate X-ray film
cool linearly-decreasing shades of cyan-magenta
copper sequential increasing shades of black-copper
flag repetitive red-white-blue-black pattern (not cyclic at
endpoints)
gray sequential linearly-increasing black-to-white
grayscale
hot sequential black-red-yellow-white, to emulate blackbody
radiation from an object at increasing temperatures
hsv cyclic red-yellow-green-cyan-blue-magenta-red, formed
by changing the hue component in the HSV color space
inferno perceptually uniform shades of black-red-yellow
jet a spectral map with dark endpoints, blue-cyan-yellow-red;
based on a fluid-jet simulation by NCSA [#]_
magma perceptually uniform shades of black-red-white
pink sequential increasing pastel black-pink-white, meant
for sepia tone colorization of photographs
plasma perceptually uniform shades of blue-red-yellow
prism repetitive red-yellow-green-blue-purple-...-green pattern
(not cyclic at endpoints)
spring linearly-increasing shades of magenta-yellow
summer sequential linearly-increasing shades of green-yellow
viridis perceptually uniform shades of blue-green-yellow
winter linearly-increasing shades of blue-green
========= =======================================================
For the above list only, you can also set the colormap using the
corresponding pylab shortcut interface function, similar to Matlab::
imshow(X)
hot()
jet()
The next set of palettes are from the `Yorick scientific visualisation
package <http://dhmunro.github.io/yorick-doc/>`_, an evolution of
the GIST package, both by David H. Munro:
============ =======================================================
Colormap Description
============ =======================================================
gist_earth mapmaker's colors from dark blue deep ocean to green
lowlands to brown highlands to white mountains
gist_heat sequential increasing black-red-orange-white, to emulate
blackbody radiation from an iron bar as it grows hotter
gist_ncar pseudo-spectral black-blue-green-yellow-red-purple-white
colormap from National Center for Atmospheric
Research [#]_
gist_rainbow runs through the colors in spectral order from red to
violet at full saturation (like *hsv* but not cyclic)
gist_stern "Stern special" color table from Interactive Data
Language software
============ =======================================================
The following colormaps are based on the `ColorBrewer
<http://colorbrewer2.org>`_ color specifications and designs developed by
Cynthia Brewer:
ColorBrewer Diverging (luminance is highest at the midpoint, and
decreases towards differently-colored endpoints):
======== ===================================
Colormap Description
======== ===================================
BrBG brown, white, blue-green
PiYG pink, white, yellow-green
PRGn purple, white, green
PuOr orange, white, purple
RdBu red, white, blue
RdGy red, white, gray
RdYlBu red, yellow, blue
RdYlGn red, yellow, green
Spectral red, orange, yellow, green, blue
======== ===================================
ColorBrewer Sequential (luminance decreases monotonically):
======== ====================================
Colormap Description
======== ====================================
Blues white to dark blue
BuGn white, light blue, dark green
BuPu white, light blue, dark purple
GnBu white, light green, dark blue
Greens white to dark green
Greys white to black (not linear)
Oranges white, orange, dark brown
OrRd white, orange, dark red
PuBu white, light purple, dark blue
PuBuGn white, light purple, dark green
PuRd white, light purple, dark red
Purples white to dark purple
RdPu white, pink, dark purple
Reds white to dark red
YlGn light yellow, dark green
YlGnBu light yellow, light green, dark blue
YlOrBr light yellow, orange, dark brown
YlOrRd light yellow, orange, dark red
======== ====================================
ColorBrewer Qualitative:
(For plotting nominal data, :class:`ListedColormap` is used,
not :class:`LinearSegmentedColormap`. Different sets of colors are
recommended for different numbers of categories.)
* Accent
* Dark2
* Paired
* Pastel1
* Pastel2
* Set1
* Set2
* Set3
Other miscellaneous schemes:
============= =======================================================
Colormap Description
============= =======================================================
afmhot sequential black-orange-yellow-white blackbody
spectrum, commonly used in atomic force microscopy
brg blue-red-green
bwr diverging blue-white-red
coolwarm diverging blue-gray-red, meant to avoid issues with 3D
shading, color blindness, and ordering of colors [#]_
CMRmap "Default colormaps on color images often reproduce to
confusing grayscale images. The proposed colormap
maintains an aesthetically pleasing color image that
automatically reproduces to a monotonic grayscale with
discrete, quantifiable saturation levels." [#]_
cubehelix Unlike most other color schemes cubehelix was designed
by D.A. Green to be monotonically increasing in terms
of perceived brightness. Also, when printed on a black
and white postscript printer, the scheme results in a
greyscale with monotonically increasing brightness.
This color scheme is named cubehelix because the r,g,b
values produced can be visualised as a squashed helix
around the diagonal in the r,g,b color cube.
gnuplot gnuplot's traditional pm3d scheme
(black-blue-red-yellow)
gnuplot2 sequential color printable as gray
(black-blue-violet-yellow-white)
ocean green-blue-white
rainbow spectral purple-blue-green-yellow-orange-red colormap
with diverging luminance
seismic diverging blue-white-red
nipy_spectral black-purple-blue-green-yellow-red-white spectrum,
originally from the Neuroimaging in Python project
terrain mapmaker's colors, blue-green-yellow-brown-white,
originally from IGOR Pro
============= =======================================================
The following colormaps are redundant and may be removed in future
versions. It's recommended to use the names in the descriptions
instead, which produce identical output:
========= =======================================================
Colormap Description
========= =======================================================
gist_gray identical to *gray*
gist_yarg identical to *gray_r*
binary identical to *gray_r*
spectral identical to *nipy_spectral* [#]_
========= =======================================================
.. rubric:: Footnotes
.. [#] Rainbow colormaps, ``jet`` in particular, are considered a poor
choice for scientific visualization by many researchers: `Rainbow Color
Map (Still) Considered Harmful
<http://ieeexplore.ieee.org/document/4118486/?arnumber=4118486>`_
.. [#] Resembles "BkBlAqGrYeOrReViWh200" from NCAR Command
Language. See `Color Table Gallery
<http://www.ncl.ucar.edu/Document/Graphics/color_table_gallery.shtml>`_
.. [#] See `Diverging Color Maps for Scientific Visualization
<http://www.kennethmoreland.com/color-maps/>`_ by Kenneth Moreland.
.. [#] See `A Color Map for Effective Black-and-White Rendering of
Color-Scale Images
<http://www.mathworks.com/matlabcentral/fileexchange/2662-cmrmap-m>`_
by Carey Rappaport
.. [#] Changed to distinguish from ColorBrewer's *Spectral* map.
:func:`spectral` still works, but
``set_cmap('nipy_spectral')`` is recommended for clarity.
colors()
This is a do-nothing function to provide you with help on how
matplotlib handles colors.
Commands which take color arguments can use several formats to
specify the colors. For the basic built-in colors, you can use a
single letter
===== =======
Alias Color
===== =======
'b' blue
'g' green
'r' red
'c' cyan
'm' magenta
'y' yellow
'k' black
'w' white
===== =======
For a greater range of colors, you have two options. You can
specify the color using an html hex string, as in::
color = '#eeefff'
or you can pass an R,G,B tuple, where each of R,G,B are in the
range [0,1].
You can also use any legal html name for a color, for example::
color = 'red'
color = 'burlywood'
color = 'chartreuse'
The example below creates a subplot with a dark
slate gray background::
subplot(111, facecolor=(0.1843, 0.3098, 0.3098))
Here is an example that creates a pale turquoise title::
title('Is this the best color?', color='#afeeee')
connect(s, func)
Connect event with string *s* to *func*. The signature of *func* is::
def func(event)
where event is a :class:`matplotlib.backend_bases.Event`. The
following events are recognized
- 'button_press_event'
- 'button_release_event'
- 'draw_event'
- 'key_press_event'
- 'key_release_event'
- 'motion_notify_event'
- 'pick_event'
- 'resize_event'
- 'scroll_event'
- 'figure_enter_event',
- 'figure_leave_event',
- 'axes_enter_event',
- 'axes_leave_event'
- 'close_event'
For the location events (button and key press/release), if the
mouse is over the axes, the variable ``event.inaxes`` will be
set to the :class:`~matplotlib.axes.Axes` the event occurs is
over, and additionally, the variables ``event.xdata`` and
``event.ydata`` will be defined. This is the mouse location
in data coords. See
:class:`~matplotlib.backend_bases.KeyEvent` and
:class:`~matplotlib.backend_bases.MouseEvent` for more info.
Return value is a connection id that can be used with
:meth:`~matplotlib.backend_bases.Event.mpl_disconnect`.
Example usage::
def on_press(event):
print('you pressed', event.button, event.xdata, event.ydata)
cid = canvas.mpl_connect('button_press_event', on_press)
contour(*args, **kwargs)
Plot contours.
:func:`~matplotlib.pyplot.contour` and
:func:`~matplotlib.pyplot.contourf` draw contour lines and
filled contours, respectively. Except as noted, function
signatures and return values are the same for both versions.
:func:`~matplotlib.pyplot.contourf` differs from the MATLAB
version in that it does not draw the polygon edges.
To draw edges, add line contours with
calls to :func:`~matplotlib.pyplot.contour`.
Call signatures::
contour(Z)
make a contour plot of an array *Z*. The level values are chosen
automatically.
::
contour(X,Y,Z)
*X*, *Y* specify the (x, y) coordinates of the surface
::
contour(Z,N)
contour(X,Y,Z,N)
contour up to *N* automatically-chosen levels.
::
contour(Z,V)
contour(X,Y,Z,V)
draw contour lines at the values specified in sequence *V*,
which must be in increasing order.
::
contourf(..., V)
fill the ``len(V)-1`` regions between the values in *V*,
which must be in increasing order.
::
contour(Z, **kwargs)
Use keyword args to control colors, linewidth, origin, cmap ... see
below for more details.
*X* and *Y* must both be 2-D with the same shape as *Z*, or they
must both be 1-D such that ``len(X)`` is the number of columns in
*Z* and ``len(Y)`` is the number of rows in *Z*.
``C = contour(...)`` returns a
:class:`~matplotlib.contour.QuadContourSet` object.
Optional keyword arguments:
*corner_mask*: [ *True* | *False* | 'legacy' ]
Enable/disable corner masking, which only has an effect if *Z* is
a masked array. If *False*, any quad touching a masked point is
masked out. If *True*, only the triangular corners of quads
nearest those points are always masked out, other triangular
corners comprising three unmasked points are contoured as usual.
If 'legacy', the old contouring algorithm is used, which is
equivalent to *False* and is deprecated, only remaining whilst the
new algorithm is tested fully.
If not specified, the default is taken from
rcParams['contour.corner_mask'], which is True unless it has
been modified.
*colors*: [ *None* | string | (mpl_colors) ]
If *None*, the colormap specified by cmap will be used.
If a string, like 'r' or 'red', all levels will be plotted in this
color.
If a tuple of matplotlib color args (string, float, rgb, etc),
different levels will be plotted in different colors in the order
specified.
*alpha*: float
The alpha blending value
*cmap*: [ *None* | Colormap ]
A cm :class:`~matplotlib.colors.Colormap` instance or
*None*. If *cmap* is *None* and *colors* is *None*, a
default Colormap is used.
*norm*: [ *None* | Normalize ]
A :class:`matplotlib.colors.Normalize` instance for
scaling data values to colors. If *norm* is *None* and
*colors* is *None*, the default linear scaling is used.
*vmin*, *vmax*: [ *None* | scalar ]
If not *None*, either or both of these values will be
supplied to the :class:`matplotlib.colors.Normalize`
instance, overriding the default color scaling based on
*levels*.
*levels*: [level0, level1, ..., leveln]
A list of floating point numbers indicating the level
curves to draw, in increasing order; e.g., to draw just
the zero contour pass ``levels=[0]``
*origin*: [ *None* | 'upper' | 'lower' | 'image' ]
If *None*, the first value of *Z* will correspond to the
lower left corner, location (0,0). If 'image', the rc
value for ``image.origin`` will be used.
This keyword is not active if *X* and *Y* are specified in
the call to contour.
*extent*: [ *None* | (x0,x1,y0,y1) ]
If *origin* is not *None*, then *extent* is interpreted as
in :func:`matplotlib.pyplot.imshow`: it gives the outer
pixel boundaries. In this case, the position of Z[0,0]
is the center of the pixel, not a corner. If *origin* is
*None*, then (*x0*, *y0*) is the position of Z[0,0], and
(*x1*, *y1*) is the position of Z[-1,-1].
This keyword is not active if *X* and *Y* are specified in
the call to contour.
*locator*: [ *None* | ticker.Locator subclass ]
If *locator* is *None*, the default
:class:`~matplotlib.ticker.MaxNLocator` is used. The
locator is used to determine the contour levels if they
are not given explicitly via the *V* argument.
*extend*: [ 'neither' | 'both' | 'min' | 'max' ]
Unless this is 'neither', contour levels are automatically
added to one or both ends of the range so that all data
are included. These added ranges are then mapped to the
special colormap values which default to the ends of the
colormap range, but can be set via
:meth:`matplotlib.colors.Colormap.set_under` and
:meth:`matplotlib.colors.Colormap.set_over` methods.
*xunits*, *yunits*: [ *None* | registered units ]
Override axis units by specifying an instance of a
:class:`matplotlib.units.ConversionInterface`.
*antialiased*: [ *True* | *False* ]
enable antialiasing, overriding the defaults. For
filled contours, the default is *True*. For line contours,
it is taken from rcParams['lines.antialiased'].
*nchunk*: [ 0 | integer ]
If 0, no subdivision of the domain. Specify a positive integer to
divide the domain into subdomains of *nchunk* by *nchunk* quads.
Chunking reduces the maximum length of polygons generated by the
contouring algorithm which reduces the rendering workload passed
on to the backend and also requires slightly less RAM. It can
however introduce rendering artifacts at chunk boundaries depending
on the backend, the *antialiased* flag and value of *alpha*.
contour-only keyword arguments:
*linewidths*: [ *None* | number | tuple of numbers ]
If *linewidths* is *None*, the default width in
``lines.linewidth`` in ``matplotlibrc`` is used.
If a number, all levels will be plotted with this linewidth.
If a tuple, different levels will be plotted with different
linewidths in the order specified.
*linestyles*: [ *None* | 'solid' | 'dashed' | 'dashdot' | 'dotted' ]
If *linestyles* is *None*, the default is 'solid' unless
the lines are monochrome. In that case, negative
contours will take their linestyle from the ``matplotlibrc``
``contour.negative_linestyle`` setting.
*linestyles* can also be an iterable of the above strings
specifying a set of linestyles to be used. If this
iterable is shorter than the number of contour levels
it will be repeated as necessary.
contourf-only keyword arguments:
*hatches*:
A list of cross hatch patterns to use on the filled areas.
If None, no hatching will be added to the contour.
Hatching is supported in the PostScript, PDF, SVG and Agg
backends only.
Note: contourf fills intervals that are closed at the top; that
is, for boundaries *z1* and *z2*, the filled region is::
z1 < z <= z2
There is one exception: if the lowest boundary coincides with
the minimum value of the *z* array, then that minimum value
will be included in the lowest interval.
**Examples:**
.. plot:: mpl_examples/pylab_examples/contour_demo.py
.. plot:: mpl_examples/pylab_examples/contourf_demo.py
.. plot:: mpl_examples/pylab_examples/contour_corner_mask.py
contourf(*args, **kwargs)
Plot contours.
:func:`~matplotlib.pyplot.contour` and
:func:`~matplotlib.pyplot.contourf` draw contour lines and
filled contours, respectively. Except as noted, function
signatures and return values are the same for both versions.
:func:`~matplotlib.pyplot.contourf` differs from the MATLAB
version in that it does not draw the polygon edges.
To draw edges, add line contours with
calls to :func:`~matplotlib.pyplot.contour`.
Call signatures::
contour(Z)
make a contour plot of an array *Z*. The level values are chosen
automatically.
::
contour(X,Y,Z)
*X*, *Y* specify the (x, y) coordinates of the surface
::
contour(Z,N)
contour(X,Y,Z,N)
contour up to *N* automatically-chosen levels.
::
contour(Z,V)
contour(X,Y,Z,V)
draw contour lines at the values specified in sequence *V*,
which must be in increasing order.
::
contourf(..., V)
fill the ``len(V)-1`` regions between the values in *V*,
which must be in increasing order.
::
contour(Z, **kwargs)
Use keyword args to control colors, linewidth, origin, cmap ... see
below for more details.
*X* and *Y* must both be 2-D with the same shape as *Z*, or they
must both be 1-D such that ``len(X)`` is the number of columns in
*Z* and ``len(Y)`` is the number of rows in *Z*.
``C = contour(...)`` returns a
:class:`~matplotlib.contour.QuadContourSet` object.
Optional keyword arguments:
*corner_mask*: [ *True* | *False* | 'legacy' ]
Enable/disable corner masking, which only has an effect if *Z* is
a masked array. If *False*, any quad touching a masked point is
masked out. If *True*, only the triangular corners of quads
nearest those points are always masked out, other triangular
corners comprising three unmasked points are contoured as usual.
If 'legacy', the old contouring algorithm is used, which is
equivalent to *False* and is deprecated, only remaining whilst the
new algorithm is tested fully.
If not specified, the default is taken from
rcParams['contour.corner_mask'], which is True unless it has
been modified.
*colors*: [ *None* | string | (mpl_colors) ]
If *None*, the colormap specified by cmap will be used.
If a string, like 'r' or 'red', all levels will be plotted in this
color.
If a tuple of matplotlib color args (string, float, rgb, etc),
different levels will be plotted in different colors in the order
specified.
*alpha*: float
The alpha blending value
*cmap*: [ *None* | Colormap ]
A cm :class:`~matplotlib.colors.Colormap` instance or
*None*. If *cmap* is *None* and *colors* is *None*, a
default Colormap is used.
*norm*: [ *None* | Normalize ]
A :class:`matplotlib.colors.Normalize` instance for
scaling data values to colors. If *norm* is *None* and
*colors* is *None*, the default linear scaling is used.
*vmin*, *vmax*: [ *None* | scalar ]
If not *None*, either or both of these values will be
supplied to the :class:`matplotlib.colors.Normalize`
instance, overriding the default color scaling based on
*levels*.
*levels*: [level0, level1, ..., leveln]
A list of floating point numbers indicating the level
curves to draw, in increasing order; e.g., to draw just
the zero contour pass ``levels=[0]``
*origin*: [ *None* | 'upper' | 'lower' | 'image' ]
If *None*, the first value of *Z* will correspond to the
lower left corner, location (0,0). If 'image', the rc
value for ``image.origin`` will be used.
This keyword is not active if *X* and *Y* are specified in
the call to contour.
*extent*: [ *None* | (x0,x1,y0,y1) ]
If *origin* is not *None*, then *extent* is interpreted as
in :func:`matplotlib.pyplot.imshow`: it gives the outer
pixel boundaries. In this case, the position of Z[0,0]
is the center of the pixel, not a corner. If *origin* is
*None*, then (*x0*, *y0*) is the position of Z[0,0], and
(*x1*, *y1*) is the position of Z[-1,-1].
This keyword is not active if *X* and *Y* are specified in
the call to contour.
*locator*: [ *None* | ticker.Locator subclass ]
If *locator* is *None*, the default
:class:`~matplotlib.ticker.MaxNLocator` is used. The
locator is used to determine the contour levels if they
are not given explicitly via the *V* argument.
*extend*: [ 'neither' | 'both' | 'min' | 'max' ]
Unless this is 'neither', contour levels are automatically
added to one or both ends of the range so that all data
are included. These added ranges are then mapped to the
special colormap values which default to the ends of the
colormap range, but can be set via
:meth:`matplotlib.colors.Colormap.set_under` and
:meth:`matplotlib.colors.Colormap.set_over` methods.
*xunits*, *yunits*: [ *None* | registered units ]
Override axis units by specifying an instance of a
:class:`matplotlib.units.ConversionInterface`.
*antialiased*: [ *True* | *False* ]
enable antialiasing, overriding the defaults. For
filled contours, the default is *True*. For line contours,
it is taken from rcParams['lines.antialiased'].
*nchunk*: [ 0 | integer ]
If 0, no subdivision of the domain. Specify a positive integer to
divide the domain into subdomains of *nchunk* by *nchunk* quads.
Chunking reduces the maximum length of polygons generated by the
contouring algorithm which reduces the rendering workload passed
on to the backend and also requires slightly less RAM. It can
however introduce rendering artifacts at chunk boundaries depending
on the backend, the *antialiased* flag and value of *alpha*.
contour-only keyword arguments:
*linewidths*: [ *None* | number | tuple of numbers ]
If *linewidths* is *None*, the default width in
``lines.linewidth`` in ``matplotlibrc`` is used.
If a number, all levels will be plotted with this linewidth.
If a tuple, different levels will be plotted with different
linewidths in the order specified.
*linestyles*: [ *None* | 'solid' | 'dashed' | 'dashdot' | 'dotted' ]
If *linestyles* is *None*, the default is 'solid' unless
the lines are monochrome. In that case, negative
contours will take their linestyle from the ``matplotlibrc``
``contour.negative_linestyle`` setting.
*linestyles* can also be an iterable of the above strings
specifying a set of linestyles to be used. If this
iterable is shorter than the number of contour levels
it will be repeated as necessary.
contourf-only keyword arguments:
*hatches*:
A list of cross hatch patterns to use on the filled areas.
If None, no hatching will be added to the contour.
Hatching is supported in the PostScript, PDF, SVG and Agg
backends only.
Note: contourf fills intervals that are closed at the top; that
is, for boundaries *z1* and *z2*, the filled region is::
z1 < z <= z2
There is one exception: if the lowest boundary coincides with
the minimum value of the *z* array, then that minimum value
will be included in the lowest interval.
**Examples:**
.. plot:: mpl_examples/pylab_examples/contour_demo.py
.. plot:: mpl_examples/pylab_examples/contourf_demo.py
.. plot:: mpl_examples/pylab_examples/contour_corner_mask.py
cool()
set the default colormap to cool and apply to current image if any.
See help(colormaps) for more information
copper()
set the default colormap to copper and apply to current image if any.
See help(colormaps) for more information
csd(x, y, NFFT=None, Fs=None, Fc=None, detrend=None, window=None, noverlap=None, pad_to=None, sides=None, scale_by_freq=None, return_line=None, hold=None, data=None, **kwargs)
Plot the cross-spectral density.
Call signature::
csd(x, y, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=0, pad_to=None,
sides='default', scale_by_freq=None, return_line=None, **kwargs)
The cross spectral density :math:`P_{xy}` by Welch's average
periodogram method. The vectors *x* and *y* are divided into
*NFFT* length segments. Each segment is detrended by function
*detrend* and windowed by function *window*. *noverlap* gives
the length of the overlap between segments. The product of
the direct FFTs of *x* and *y* are averaged over each segment
to compute :math:`P_{xy}`, with a scaling to correct for power
loss due to windowing.
If len(*x*) < *NFFT* or len(*y*) < *NFFT*, they will be zero
padded to *NFFT*.
Parameters
----------
x, y : 1-D arrays or sequences
Arrays or sequences containing the data
Fs : scalar
The sampling frequency (samples per time unit). It is used
to calculate the Fourier frequencies, freqs, in cycles per time
unit. The default value is 2.
window : callable or ndarray
A function or a vector of length *NFFT*. To create window
vectors see :func:`window_hanning`, :func:`window_none`,
:func:`numpy.blackman`, :func:`numpy.hamming`,
:func:`numpy.bartlett`, :func:`scipy.signal`,
:func:`scipy.signal.get_window`, etc. The default is
:func:`window_hanning`. If a function is passed as the
argument, it must take a data segment as an argument and
return the windowed version of the segment.
sides : [ 'default' | 'onesided' | 'twosided' ]
Specifies which sides of the spectrum to return. Default gives the
default behavior, which returns one-sided for real data and both
for complex data. 'onesided' forces the return of a one-sided
spectrum, while 'twosided' forces two-sided.
pad_to : integer
The number of points to which the data segment is padded when
performing the FFT. This can be different from *NFFT*, which
specifies the number of data points used. While not increasing
the actual resolution of the spectrum (the minimum distance between
resolvable peaks), this can give more points in the plot,
allowing for more detail. This corresponds to the *n* parameter
in the call to fft(). The default is None, which sets *pad_to*
equal to *NFFT*
NFFT : integer
The number of data points used in each block for the FFT.
A power 2 is most efficient. The default value is 256.
This should *NOT* be used to get zero padding, or the scaling of the
result will be incorrect. Use *pad_to* for this instead.
detrend : {'default', 'constant', 'mean', 'linear', 'none'} or callable
The function applied to each segment before fft-ing,
designed to remove the mean or linear trend. Unlike in
MATLAB, where the *detrend* parameter is a vector, in
matplotlib is it a function. The :mod:`~matplotlib.pylab`
module defines :func:`~matplotlib.pylab.detrend_none`,
:func:`~matplotlib.pylab.detrend_mean`, and
:func:`~matplotlib.pylab.detrend_linear`, but you can use
a custom function as well. You can also use a string to choose
one of the functions. 'default', 'constant', and 'mean' call
:func:`~matplotlib.pylab.detrend_mean`. 'linear' calls
:func:`~matplotlib.pylab.detrend_linear`. 'none' calls
:func:`~matplotlib.pylab.detrend_none`.
scale_by_freq : boolean, optional
Specifies whether the resulting density values should be scaled
by the scaling frequency, which gives density in units of Hz^-1.
This allows for integration over the returned frequency values.
The default is True for MATLAB compatibility.
noverlap : integer
The number of points of overlap between segments.
The default value is 0 (no overlap).
Fc : integer
The center frequency of *x* (defaults to 0), which offsets
the x extents of the plot to reflect the frequency range used
when a signal is acquired and then filtered and downsampled to
baseband.
return_line : bool
Whether to include the line object plotted in the returned values.
Default is False.
**kwargs :
Keyword arguments control the :class:`~matplotlib.lines.Line2D`
properties:
agg_filter: unknown
alpha: float (0.0 transparent through 1.0 opaque)
animated: [True | False]
antialiased or aa: [True | False]
axes: an :class:`~matplotlib.axes.Axes` instance
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
color or c: any matplotlib color
contains: a callable function
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post']
figure: a :class:`matplotlib.figure.Figure` instance
fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none']
gid: an id string
label: string or anything printable with '%s' conversion.
linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or lw: float value in points
marker: :mod:`A valid marker style <matplotlib.markers>`
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markerfacecoloralt or mfcalt: any matplotlib color
markersize or ms: float
markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects: unknown
picker: float distance in points or callable pick function ``fn(artist, event)``
pickradius: float distance in points
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a :class:`matplotlib.transforms.Transform` instance
url: a url string
visible: [True | False]
xdata: 1D array
ydata: 1D array
zorder: any number
Returns
-------
Pxy : 1-D array
The values for the cross spectrum `P_{xy}` before scaling
(complex valued)
freqs : 1-D array
The frequencies corresponding to the elements in *Pxy*
line : a :class:`~matplotlib.lines.Line2D` instance
The line created by this function.
Only returned if *return_line* is True.
Notes
-----
For plotting, the power is plotted as
:math:`10\log_{10}(P_{xy})` for decibels, though `P_{xy}` itself
is returned.
References
----------
Bendat & Piersol -- Random Data: Analysis and Measurement Procedures,
John Wiley & Sons (1986)
Examples
--------
.. plot:: mpl_examples/pylab_examples/csd_demo.py
See Also
--------
:func:`psd`
:func:`psd` is the equivalent to setting y=x.
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All arguments with the following names: 'x', 'y'.
delaxes(*args)
Remove an axes from the current figure. If *ax*
doesn't exist, an error will be raised.
``delaxes()``: delete the current axes
disconnect(cid)
Disconnect callback id cid
Example usage::
cid = canvas.mpl_connect('button_press_event', on_press)
#...later
canvas.mpl_disconnect(cid)
draw()
Redraw the current figure.
This is used to update a figure that has been altered, but not
automatically re-drawn. If interactive mode is on (:func:`.ion()`), this
should be only rarely needed, but there may be ways to modify the state of
a figure without marking it as `stale`. Please report these cases as
bugs.
A more object-oriented alternative, given any
:class:`~matplotlib.figure.Figure` instance, :attr:`fig`, that
was created using a :mod:`~matplotlib.pyplot` function, is::
fig.canvas.draw_idle()
errorbar(x, y, yerr=None, xerr=None, fmt=u'', ecolor=None, elinewidth=None, capsize=None, barsabove=False, lolims=False, uplims=False, xlolims=False, xuplims=False, errorevery=1, capthick=None, hold=None, data=None, **kwargs)
Plot an errorbar graph.
Plot x versus y with error deltas in yerr and xerr.
Vertical errorbars are plotted if yerr is not None.
Horizontal errorbars are plotted if xerr is not None.
x, y, xerr, and yerr can all be scalars, which plots a
single error bar at x, y.
Parameters
----------
x : scalar
y : scalar
xerr/yerr : scalar or array-like, shape(n,1) or shape(2,n), optional
If a scalar number, len(N) array-like object, or an Nx1
array-like object, errorbars are drawn at +/-value relative
to the data. Default is None.
If a sequence of shape 2xN, errorbars are drawn at -row1
and +row2 relative to the data.
fmt : plot format string, optional, default: None
The plot format symbol. If fmt is 'none' (case-insensitive),
only the errorbars are plotted. This is used for adding
errorbars to a bar plot, for example. Default is '',
an empty plot format string; properties are
then identical to the defaults for :meth:`plot`.
ecolor : mpl color, optional, default: None
A matplotlib color arg which gives the color the errorbar lines;
if None, use the color of the line connecting the markers.
elinewidth : scalar, optional, default: None
The linewidth of the errorbar lines. If None, use the linewidth.
capsize : scalar, optional, default: None
The length of the error bar caps in points; if None, it will
take the value from ``errorbar.capsize``
:data:`rcParam<matplotlib.rcParams>`.
capthick : scalar, optional, default: None
An alias kwarg to markeredgewidth (a.k.a. - mew). This
setting is a more sensible name for the property that
controls the thickness of the error bar cap in points. For
backwards compatibility, if mew or markeredgewidth are given,
then they will over-ride capthick. This may change in future
releases.
barsabove : bool, optional, default: False
if True , will plot the errorbars above the plot
symbols. Default is below.
lolims / uplims / xlolims / xuplims : bool, optional, default:None
These arguments can be used to indicate that a value gives
only upper/lower limits. In that case a caret symbol is
used to indicate this. lims-arguments may be of the same
type as *xerr* and *yerr*. To use limits with inverted
axes, :meth:`set_xlim` or :meth:`set_ylim` must be called
before :meth:`errorbar`.
errorevery : positive integer, optional, default:1
subsamples the errorbars. e.g., if errorevery=5, errorbars for
every 5-th datapoint will be plotted. The data plot itself still
shows all data points.
Returns
-------
plotline : :class:`~matplotlib.lines.Line2D` instance
x, y plot markers and/or line
caplines : list of :class:`~matplotlib.lines.Line2D` instances
error bar cap
barlinecols : list of :class:`~matplotlib.collections.LineCollection`
horizontal and vertical error ranges.
Other Parameters
----------------
kwargs : All other keyword arguments are passed on to the plot
command for the markers. For example, this code makes big red
squares with thick green edges::
x,y,yerr = rand(3,10)
errorbar(x, y, yerr, marker='s', mfc='red',
mec='green', ms=20, mew=4)
where mfc, mec, ms and mew are aliases for the longer
property names, markerfacecolor, markeredgecolor, markersize
and markeredgewidth.
valid kwargs for the marker properties are
agg_filter: unknown
alpha: float (0.0 transparent through 1.0 opaque)
animated: [True | False]
antialiased or aa: [True | False]
axes: an :class:`~matplotlib.axes.Axes` instance
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
color or c: any matplotlib color
contains: a callable function
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post']
figure: a :class:`matplotlib.figure.Figure` instance
fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none']
gid: an id string
label: string or anything printable with '%s' conversion.
linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or lw: float value in points
marker: :mod:`A valid marker style <matplotlib.markers>`
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markerfacecoloralt or mfcalt: any matplotlib color
markersize or ms: float
markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects: unknown
picker: float distance in points or callable pick function ``fn(artist, event)``
pickradius: float distance in points
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a :class:`matplotlib.transforms.Transform` instance
url: a url string
visible: [True | False]
xdata: 1D array
ydata: 1D array
zorder: any number
Examples
--------
.. plot:: mpl_examples/statistics/errorbar_demo.py
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All arguments with the following names: 'x', 'xerr', 'y', 'yerr'.
eventplot(positions, orientation=u'horizontal', lineoffsets=1, linelengths=1, linewidths=None, colors=None, linestyles=u'solid', hold=None, data=None, **kwargs)
Plot identical parallel lines at specific positions.
Plot parallel lines at the given positions. positions should be a 1D
or 2D array-like object, with each row corresponding to a row or column
of lines.
This type of plot is commonly used in neuroscience for representing
neural events, where it is commonly called a spike raster, dot raster,
or raster plot.
However, it is useful in any situation where you wish to show the
timing or position of multiple sets of discrete events, such as the
arrival times of people to a business on each day of the month or the
date of hurricanes each year of the last century.
*orientation* : [ 'horizonal' | 'vertical' ]
'horizonal' : the lines will be vertical and arranged in rows
"vertical' : lines will be horizontal and arranged in columns
*lineoffsets* :
A float or array-like containing floats.
*linelengths* :
A float or array-like containing floats.
*linewidths* :
A float or array-like containing floats.
*colors*
must be a sequence of RGBA tuples (e.g., arbitrary color
strings, etc, not allowed) or a list of such sequences
*linestyles* :
[ 'solid' | 'dashed' | 'dashdot' | 'dotted' ] or an array of these
values
For linelengths, linewidths, colors, and linestyles, if only a single
value is given, that value is applied to all lines. If an array-like
is given, it must have the same length as positions, and each value
will be applied to the corresponding row or column in positions.
Returns a list of :class:`matplotlib.collections.EventCollection`
objects that were added.
kwargs are :class:`~matplotlib.collections.LineCollection` properties:
agg_filter: unknown
alpha: float or None
animated: [True | False]
antialiased or antialiaseds: Boolean or sequence of booleans
array: unknown
axes: an :class:`~matplotlib.axes.Axes` instance
clim: a length 2 sequence of floats
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
cmap: a colormap or registered colormap name
color: matplotlib color arg or sequence of rgba tuples
contains: a callable function
edgecolor or edgecolors: matplotlib color spec or sequence of specs
facecolor or facecolors: matplotlib color spec or sequence of specs
figure: a :class:`matplotlib.figure.Figure` instance
gid: an id string
hatch: [ '/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ]
label: string or anything printable with '%s' conversion.
linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or linewidths or lw: float or sequence of floats
norm: unknown
offset_position: unknown
offsets: float or sequence of floats
path_effects: unknown
paths: unknown
picker: [None|float|boolean|callable]
pickradius: unknown
rasterized: [True | False | None]
segments: unknown
sketch_params: unknown
snap: unknown
transform: :class:`~matplotlib.transforms.Transform` instance
url: a url string
urls: unknown
verts: unknown
visible: [True | False]
zorder: any number
**Example:**
.. plot:: mpl_examples/pylab_examples/eventplot_demo.py
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All arguments with the following names: 'colors', 'linelengths', 'lineoffsets', 'linestyles', 'linewidths', 'positions'.
figimage(*args, **kwargs)
Adds a non-resampled image to the figure.
call signatures::
figimage(X, **kwargs)
adds a non-resampled array *X* to the figure.
::
figimage(X, xo, yo)
with pixel offsets *xo*, *yo*,
*X* must be a float array:
* If *X* is MxN, assume luminance (grayscale)
* If *X* is MxNx3, assume RGB
* If *X* is MxNx4, assume RGBA
Optional keyword arguments:
========= =========================================================
Keyword Description
========= =========================================================
resize a boolean, True or False. If "True", then re-size the
Figure to match the given image size.
xo or yo An integer, the *x* and *y* image offset in pixels
cmap a :class:`matplotlib.colors.Colormap` instance, e.g.,
cm.jet. If *None*, default to the rc ``image.cmap``
value
norm a :class:`matplotlib.colors.Normalize` instance. The
default is normalization(). This scales luminance -> 0-1
vmin|vmax are used to scale a luminance image to 0-1. If either
is *None*, the min and max of the luminance values will
be used. Note if you pass a norm instance, the settings
for *vmin* and *vmax* will be ignored.
alpha the alpha blending value, default is *None*
origin [ 'upper' | 'lower' ] Indicates where the [0,0] index of
the array is in the upper left or lower left corner of
the axes. Defaults to the rc image.origin value
========= =========================================================
figimage complements the axes image
(:meth:`~matplotlib.axes.Axes.imshow`) which will be resampled
to fit the current axes. If you want a resampled image to
fill the entire figure, you can define an
:class:`~matplotlib.axes.Axes` with extent [0,0,1,1].
An :class:`matplotlib.image.FigureImage` instance is returned.
.. plot:: mpl_examples/pylab_examples/figimage_demo.py
Additional kwargs are Artist kwargs passed on to
:class:`~matplotlib.image.FigureImage`
figlegend(handles, labels, loc, **kwargs)
Place a legend in the figure.
*labels*
a sequence of strings
*handles*
a sequence of :class:`~matplotlib.lines.Line2D` or
:class:`~matplotlib.patches.Patch` instances
*loc*
can be a string or an integer specifying the legend
location
A :class:`matplotlib.legend.Legend` instance is returned.
Example::
figlegend( (line1, line2, line3),
('label1', 'label2', 'label3'),
'upper right' )
.. seealso::
:func:`~matplotlib.pyplot.legend`
fignum_exists(num)
figtext(*args, **kwargs)
Add text to figure.
Call signature::
text(x, y, s, fontdict=None, **kwargs)
Add text to figure at location *x*, *y* (relative 0-1
coords). See :func:`~matplotlib.pyplot.text` for the meaning
of the other arguments.
kwargs control the :class:`~matplotlib.text.Text` properties:
agg_filter: unknown
alpha: float (0.0 transparent through 1.0 opaque)
animated: [True | False]
axes: an :class:`~matplotlib.axes.Axes` instance
backgroundcolor: any matplotlib color
bbox: FancyBboxPatch prop dict
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
color: any matplotlib color
contains: a callable function
family or fontfamily or fontname or name: [FONTNAME | 'serif' | 'sans-serif' | 'cursive' | 'fantasy' | 'monospace' ]
figure: a :class:`matplotlib.figure.Figure` instance
fontproperties or font_properties: a :class:`matplotlib.font_manager.FontProperties` instance
gid: an id string
horizontalalignment or ha: [ 'center' | 'right' | 'left' ]
label: string or anything printable with '%s' conversion.
linespacing: float (multiple of font size)
multialignment: ['left' | 'right' | 'center' ]
path_effects: unknown
picker: [None|float|boolean|callable]
position: (x,y)
rasterized: [True | False | None]
rotation: [ angle in degrees | 'vertical' | 'horizontal' ]
rotation_mode: unknown
size or fontsize: [size in points | 'xx-small' | 'x-small' | 'small' | 'medium' | 'large' | 'x-large' | 'xx-large' ]
sketch_params: unknown
snap: unknown
stretch or fontstretch: [a numeric value in range 0-1000 | 'ultra-condensed' | 'extra-condensed' | 'condensed' | 'semi-condensed' | 'normal' | 'semi-expanded' | 'expanded' | 'extra-expanded' | 'ultra-expanded' ]
style or fontstyle: [ 'normal' | 'italic' | 'oblique']
text: string or anything printable with '%s' conversion.
transform: :class:`~matplotlib.transforms.Transform` instance
url: a url string
usetex: unknown
variant or fontvariant: [ 'normal' | 'small-caps' ]
verticalalignment or ma or va: [ 'center' | 'top' | 'bottom' | 'baseline' ]
visible: [True | False]
weight or fontweight: [a numeric value in range 0-1000 | 'ultralight' | 'light' | 'normal' | 'regular' | 'book' | 'medium' | 'roman' | 'semibold' | 'demibold' | 'demi' | 'bold' | 'heavy' | 'extra bold' | 'black' ]
wrap: unknown
x: float
y: float
zorder: any number
figure(num=None, figsize=None, dpi=None, facecolor=None, edgecolor=None, frameon=True, FigureClass=<class 'matplotlib.figure.Figure'>, **kwargs)
Creates a new figure.
Parameters
----------
num : integer or string, optional, default: none
If not provided, a new figure will be created, and the figure number
will be incremented. The figure objects holds this number in a `number`
attribute.
If num is provided, and a figure with this id already exists, make
it active, and returns a reference to it. If this figure does not
exists, create it and returns it.
If num is a string, the window title will be set to this figure's
`num`.
figsize : tuple of integers, optional, default: None
width, height in inches. If not provided, defaults to rc
figure.figsize.
dpi : integer, optional, default: None
resolution of the figure. If not provided, defaults to rc figure.dpi.
facecolor :
the background color. If not provided, defaults to rc figure.facecolor
edgecolor :
the border color. If not provided, defaults to rc figure.edgecolor
Returns
-------
figure : Figure
The Figure instance returned will also be passed to new_figure_manager
in the backends, which allows to hook custom Figure classes into the
pylab interface. Additional kwargs will be passed to the figure init
function.
Notes
-----
If you are creating many figures, make sure you explicitly call "close"
on the figures you are not using, because this will enable pylab
to properly clean up the memory.
rcParams defines the default values, which can be modified in the
matplotlibrc file
fill(*args, **kwargs)
Plot filled polygons.
Parameters
----------
args : a variable length argument
It allowing for multiple
*x*, *y* pairs with an optional color format string; see
:func:`~matplotlib.pyplot.plot` for details on the argument
parsing. For example, each of the following is legal::
ax.fill(x, y)
ax.fill(x, y, "b")
ax.fill(x, y, "b", x, y, "r")
An arbitrary number of *x*, *y*, *color* groups can be specified::
ax.fill(x1, y1, 'g', x2, y2, 'r')
Returns
-------
a list of :class:`~matplotlib.patches.Patch`
Other Parameters
----------------
kwargs : :class:`~matplotlib.patches.Polygon` properties
Notes
-----
The same color strings that :func:`~matplotlib.pyplot.plot`
supports are supported by the fill format string.
If you would like to fill below a curve, e.g., shade a region
between 0 and *y* along *x*, use :meth:`fill_between`
Examples
--------
.. plot:: mpl_examples/lines_bars_and_markers/fill_demo.py
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All arguments with the following names: 'x', 'y'.
fill_between(x, y1, y2=0, where=None, interpolate=False, step=None, hold=None, data=None, **kwargs)
Make filled polygons between two curves.
Create a :class:`~matplotlib.collections.PolyCollection`
filling the regions between *y1* and *y2* where
``where==True``
Parameters
----------
x : array
An N-length array of the x data
y1 : array
An N-length array (or scalar) of the y data
y2 : array
An N-length array (or scalar) of the y data
where : array, optional
If `None`, default to fill between everywhere. If not `None`,
it is an N-length numpy boolean array and the fill will
only happen over the regions where ``where==True``.
interpolate : bool, optional
If `True`, interpolate between the two lines to find the
precise point of intersection. Otherwise, the start and
end points of the filled region will only occur on explicit
values in the *x* array.
step : {'pre', 'post', 'mid'}, optional
If not None, fill with step logic.
Notes
-----
Additional Keyword args passed on to the
:class:`~matplotlib.collections.PolyCollection`.
kwargs control the :class:`~matplotlib.patches.Polygon` properties:
agg_filter: unknown
alpha: float or None
animated: [True | False]
antialiased or antialiaseds: Boolean or sequence of booleans
array: unknown
axes: an :class:`~matplotlib.axes.Axes` instance
clim: a length 2 sequence of floats
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
cmap: a colormap or registered colormap name
color: matplotlib color arg or sequence of rgba tuples
contains: a callable function
edgecolor or edgecolors: matplotlib color spec or sequence of specs
facecolor or facecolors: matplotlib color spec or sequence of specs
figure: a :class:`matplotlib.figure.Figure` instance
gid: an id string
hatch: [ '/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ]
label: string or anything printable with '%s' conversion.
linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or linewidths or lw: float or sequence of floats
norm: unknown
offset_position: unknown
offsets: float or sequence of floats
path_effects: unknown
picker: [None|float|boolean|callable]
pickradius: unknown
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
transform: :class:`~matplotlib.transforms.Transform` instance
url: a url string
urls: unknown
visible: [True | False]
zorder: any number
Examples
--------
.. plot:: mpl_examples/pylab_examples/fill_between_demo.py
See Also
--------
:meth:`fill_betweenx`
for filling between two sets of x-values
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All arguments with the following names: 'where', 'x', 'y1', 'y2'.
fill_betweenx(y, x1, x2=0, where=None, step=None, hold=None, data=None, **kwargs)
Make filled polygons between two horizontal curves.
Create a :class:`~matplotlib.collections.PolyCollection`
filling the regions between *x1* and *x2* where
``where==True``
Parameters
----------
y : array
An N-length array of the y data
x1 : array
An N-length array (or scalar) of the x data
x2 : array, optional
An N-length array (or scalar) of the x data
where : array, optional
If *None*, default to fill between everywhere. If not *None*,
it is a N length numpy boolean array and the fill will
only happen over the regions where ``where==True``
step : {'pre', 'post', 'mid'}, optional
If not None, fill with step logic.
Notes
-----
keyword args passed on to the
:class:`~matplotlib.collections.PolyCollection`
kwargs control the :class:`~matplotlib.patches.Polygon` properties:
agg_filter: unknown
alpha: float or None
animated: [True | False]
antialiased or antialiaseds: Boolean or sequence of booleans
array: unknown
axes: an :class:`~matplotlib.axes.Axes` instance
clim: a length 2 sequence of floats
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
cmap: a colormap or registered colormap name
color: matplotlib color arg or sequence of rgba tuples
contains: a callable function
edgecolor or edgecolors: matplotlib color spec or sequence of specs
facecolor or facecolors: matplotlib color spec or sequence of specs
figure: a :class:`matplotlib.figure.Figure` instance
gid: an id string
hatch: [ '/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ]
label: string or anything printable with '%s' conversion.
linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or linewidths or lw: float or sequence of floats
norm: unknown
offset_position: unknown
offsets: float or sequence of floats
path_effects: unknown
picker: [None|float|boolean|callable]
pickradius: unknown
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
transform: :class:`~matplotlib.transforms.Transform` instance
url: a url string
urls: unknown
visible: [True | False]
zorder: any number
Examples
--------
.. plot:: mpl_examples/pylab_examples/fill_betweenx_demo.py
See Also
--------
:meth:`fill_between`
for filling between two sets of y-values
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All arguments with the following names: 'where', 'x1', 'x2', 'y'.
findobj(o=None, match=None, include_self=True)
Find artist objects.
Recursively find all :class:`~matplotlib.artist.Artist` instances
contained in self.
*match* can be
- None: return all objects contained in artist.
- function with signature ``boolean = match(artist)``
used to filter matches
- class instance: e.g., Line2D. Only return artists of class type.
If *include_self* is True (default), include self in the list to be
checked for a match.
flag()
set the default colormap to flag and apply to current image if any.
See help(colormaps) for more information
gca(**kwargs)
Get the current :class:`~matplotlib.axes.Axes` instance on the
current figure matching the given keyword args, or create one.
Examples
--------
To get the current polar axes on the current figure::
plt.gca(projection='polar')
If the current axes doesn't exist, or isn't a polar one, the appropriate
axes will be created and then returned.
See Also
--------
matplotlib.figure.Figure.gca : The figure's gca method.
gcf()
Get a reference to the current figure.
gci()
Get the current colorable artist. Specifically, returns the
current :class:`~matplotlib.cm.ScalarMappable` instance (image or
patch collection), or *None* if no images or patch collections
have been defined. The commands :func:`~matplotlib.pyplot.imshow`
and :func:`~matplotlib.pyplot.figimage` create
:class:`~matplotlib.image.Image` instances, and the commands
:func:`~matplotlib.pyplot.pcolor` and
:func:`~matplotlib.pyplot.scatter` create
:class:`~matplotlib.collections.Collection` instances. The
current image is an attribute of the current axes, or the nearest
earlier axes in the current figure that contains an image.
get_current_fig_manager()
get_figlabels()
Return a list of existing figure labels.
get_fignums()
Return a list of existing figure numbers.
get_plot_commands()
Get a sorted list of all of the plotting commands.
ginput(*args, **kwargs)
Blocking call to interact with the figure.
This will wait for *n* clicks from the user and return a list of the
coordinates of each click.
If *timeout* is zero or negative, does not timeout.
If *n* is zero or negative, accumulate clicks until a middle click
(or potentially both mouse buttons at once) terminates the input.
Right clicking cancels last input.
The buttons used for the various actions (adding points, removing
points, terminating the inputs) can be overriden via the
arguments *mouse_add*, *mouse_pop* and *mouse_stop*, that give
the associated mouse button: 1 for left, 2 for middle, 3 for
right.
The keyboard can also be used to select points in case your mouse
does not have one or more of the buttons. The delete and backspace
keys act like right clicking (i.e., remove last point), the enter key
terminates input and any other key (not already used by the window
manager) selects a point.
gray()
set the default colormap to gray and apply to current image if any.
See help(colormaps) for more information
grid(b=None, which=u'major', axis=u'both', **kwargs)
Turn the axes grids on or off.
Set the axes grids on or off; *b* is a boolean. (For MATLAB
compatibility, *b* may also be a string, 'on' or 'off'.)
If *b* is *None* and ``len(kwargs)==0``, toggle the grid state. If
*kwargs* are supplied, it is assumed that you want a grid and *b*
is thus set to *True*.
*which* can be 'major' (default), 'minor', or 'both' to control
whether major tick grids, minor tick grids, or both are affected.
*axis* can be 'both' (default), 'x', or 'y' to control which
set of gridlines are drawn.
*kwargs* are used to set the grid line properties, e.g.,::
ax.grid(color='r', linestyle='-', linewidth=2)
Valid :class:`~matplotlib.lines.Line2D` kwargs are
agg_filter: unknown
alpha: float (0.0 transparent through 1.0 opaque)
animated: [True | False]
antialiased or aa: [True | False]
axes: an :class:`~matplotlib.axes.Axes` instance
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
color or c: any matplotlib color
contains: a callable function
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post']
figure: a :class:`matplotlib.figure.Figure` instance
fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none']
gid: an id string
label: string or anything printable with '%s' conversion.
linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or lw: float value in points
marker: :mod:`A valid marker style <matplotlib.markers>`
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markerfacecoloralt or mfcalt: any matplotlib color
markersize or ms: float
markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects: unknown
picker: float distance in points or callable pick function ``fn(artist, event)``
pickradius: float distance in points
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a :class:`matplotlib.transforms.Transform` instance
url: a url string
visible: [True | False]
xdata: 1D array
ydata: 1D array
zorder: any number
hexbin(x, y, C=None, gridsize=100, bins=None, xscale=u'linear', yscale=u'linear', extent=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, edgecolors=u'none', reduce_C_function=<function mean>, mincnt=None, marginals=False, hold=None, data=None, **kwargs)
Make a hexagonal binning plot.
Make a hexagonal binning plot of *x* versus *y*, where *x*,
*y* are 1-D sequences of the same length, *N*. If *C* is *None*
(the default), this is a histogram of the number of occurences
of the observations at (x[i],y[i]).
If *C* is specified, it specifies values at the coordinate
(x[i],y[i]). These values are accumulated for each hexagonal
bin and then reduced according to *reduce_C_function*, which
defaults to numpy's mean function (np.mean). (If *C* is
specified, it must also be a 1-D sequence of the same length
as *x* and *y*.)
Parameters
----------
x, y : array or masked array
C : array or masked array, optional, default is *None*
gridsize : int or (int, int), optional, default is 100
The number of hexagons in the *x*-direction, default is
100. The corresponding number of hexagons in the
*y*-direction is chosen such that the hexagons are
approximately regular. Alternatively, gridsize can be a
tuple with two elements specifying the number of hexagons
in the *x*-direction and the *y*-direction.
bins : {'log'} or int or sequence, optional, default is *None*
If *None*, no binning is applied; the color of each hexagon
directly corresponds to its count value.
If 'log', use a logarithmic scale for the color
map. Internally, :math:`log_{10}(i+1)` is used to
determine the hexagon color.
If an integer, divide the counts in the specified number
of bins, and color the hexagons accordingly.
If a sequence of values, the values of the lower bound of
the bins to be used.
xscale : {'linear', 'log'}, optional, default is 'linear'
Use a linear or log10 scale on the horizontal axis.
yscale : {'linear', 'log'}, optional, default is 'linear'
Use a linear or log10 scale on the vertical axis.
mincnt : int > 0, optional, default is *None*
If not *None*, only display cells with more than *mincnt*
number of points in the cell
marginals : bool, optional, default is *False*
if marginals is *True*, plot the marginal density as
colormapped rectagles along the bottom of the x-axis and
left of the y-axis
extent : scalar, optional, default is *None*
The limits of the bins. The default assigns the limits
based on *gridsize*, *x*, *y*, *xscale* and *yscale*.
If *xscale* or *yscale* is set to 'log', the limits are
expected to be the exponent for a power of 10. E.g. for
x-limits of 1 and 50 in 'linear' scale and y-limits
of 10 and 1000 in 'log' scale, enter (1, 50, 1, 3).
Order of scalars is (left, right, bottom, top).
Other parameters
----------------
cmap : object, optional, default is *None*
a :class:`matplotlib.colors.Colormap` instance. If *None*,
defaults to rc ``image.cmap``.
norm : object, optional, default is *None*
:class:`matplotlib.colors.Normalize` instance is used to
scale luminance data to 0,1.
vmin, vmax : scalar, optional, default is *None*
*vmin* and *vmax* are used in conjunction with *norm* to
normalize luminance data. If *None*, the min and max of the
color array *C* are used. Note if you pass a norm instance
your settings for *vmin* and *vmax* will be ignored.
alpha : scalar between 0 and 1, optional, default is *None*
the alpha value for the patches
linewidths : scalar, optional, default is *None*
If *None*, defaults to 1.0.
edgecolors : {'none'} or mpl color, optional, default is 'none'
If 'none', draws the edges in the same color as the fill color.
This is the default, as it avoids unsightly unpainted pixels
between the hexagons.
If *None*, draws outlines in the default color.
If a matplotlib color arg, draws outlines in the specified color.
Returns
-------
object
a :class:`~matplotlib.collections.PolyCollection` instance; use
:meth:`~matplotlib.collections.PolyCollection.get_array` on
this :class:`~matplotlib.collections.PolyCollection` to get
the counts in each hexagon.
If *marginals* is *True*, horizontal
bar and vertical bar (both PolyCollections) will be attached
to the return collection as attributes *hbar* and *vbar*.
Examples
--------
.. plot:: mpl_examples/pylab_examples/hexbin_demo.py
Notes
--------
The standard descriptions of all the
:class:`~matplotlib.collections.Collection` parameters:
agg_filter: unknown
alpha: float or None
animated: [True | False]
antialiased or antialiaseds: Boolean or sequence of booleans
array: unknown
axes: an :class:`~matplotlib.axes.Axes` instance
clim: a length 2 sequence of floats
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
cmap: a colormap or registered colormap name
color: matplotlib color arg or sequence of rgba tuples
contains: a callable function
edgecolor or edgecolors: matplotlib color spec or sequence of specs
facecolor or facecolors: matplotlib color spec or sequence of specs
figure: a :class:`matplotlib.figure.Figure` instance
gid: an id string
hatch: [ '/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ]
label: string or anything printable with '%s' conversion.
linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or linewidths or lw: float or sequence of floats
norm: unknown
offset_position: unknown
offsets: float or sequence of floats
path_effects: unknown
picker: [None|float|boolean|callable]
pickradius: unknown
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
transform: :class:`~matplotlib.transforms.Transform` instance
url: a url string
urls: unknown
visible: [True | False]
zorder: any number
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All arguments with the following names: 'x', 'y'.
hist(x, bins=None, range=None, normed=False, weights=None, cumulative=False, bottom=None, histtype=u'bar', align=u'mid', orientation=u'vertical', rwidth=None, log=False, color=None, label=None, stacked=False, hold=None, data=None, **kwargs)
Plot a histogram.
Compute and draw the histogram of *x*. The return value is a
tuple (*n*, *bins*, *patches*) or ([*n0*, *n1*, ...], *bins*,
[*patches0*, *patches1*,...]) if the input contains multiple
data.
Multiple data can be provided via *x* as a list of datasets
of potentially different length ([*x0*, *x1*, ...]), or as
a 2-D ndarray in which each column is a dataset. Note that
the ndarray form is transposed relative to the list form.
Masked arrays are not supported at present.
Parameters
----------
x : (n,) array or sequence of (n,) arrays
Input values, this takes either a single array or a sequency of
arrays which are not required to be of the same length
bins : integer or array_like or 'auto', optional
If an integer is given, `bins + 1` bin edges are returned,
consistently with :func:`numpy.histogram` for numpy version >=
1.3.
Unequally spaced bins are supported if `bins` is a sequence.
If Numpy 1.11 is installed, may also be ``'auto'``.
Default is taken from the rcParam ``hist.bins``.
range : tuple or None, optional
The lower and upper range of the bins. Lower and upper outliers
are ignored. If not provided, `range` is (x.min(), x.max()). Range
has no effect if `bins` is a sequence.
If `bins` is a sequence or `range` is specified, autoscaling
is based on the specified bin range instead of the
range of x.
Default is ``None``
normed : boolean, optional
If `True`, the first element of the return tuple will
be the counts normalized to form a probability density, i.e.,
``n/(len(x)`dbin)``, i.e., the integral of the histogram will sum
to 1. If *stacked* is also *True*, the sum of the histograms is
normalized to 1.
Default is ``False``
weights : (n, ) array_like or None, optional
An array of weights, of the same shape as `x`. Each value in `x`
only contributes its associated weight towards the bin count
(instead of 1). If `normed` is True, the weights are normalized,
so that the integral of the density over the range remains 1.
Default is ``None``
cumulative : boolean, optional
If `True`, then a histogram is computed where each bin gives the
counts in that bin plus all bins for smaller values. The last bin
gives the total number of datapoints. If `normed` is also `True`
then the histogram is normalized such that the last bin equals 1.
If `cumulative` evaluates to less than 0 (e.g., -1), the direction
of accumulation is reversed. In this case, if `normed` is also
`True`, then the histogram is normalized such that the first bin
equals 1.
Default is ``False``
bottom : array_like, scalar, or None
Location of the bottom baseline of each bin. If a scalar,
the base line for each bin is shifted by the same amount.
If an array, each bin is shifted independently and the length
of bottom must match the number of bins. If None, defaults to 0.
Default is ``None``
histtype : {'bar', 'barstacked', 'step', 'stepfilled'}, optional
The type of histogram to draw.
- 'bar' is a traditional bar-type histogram. If multiple data
are given the bars are aranged side by side.
- 'barstacked' is a bar-type histogram where multiple
data are stacked on top of each other.
- 'step' generates a lineplot that is by default
unfilled.
- 'stepfilled' generates a lineplot that is by default
filled.
Default is 'bar'
align : {'left', 'mid', 'right'}, optional
Controls how the histogram is plotted.
- 'left': bars are centered on the left bin edges.
- 'mid': bars are centered between the bin edges.
- 'right': bars are centered on the right bin edges.
Default is 'mid'
orientation : {'horizontal', 'vertical'}, optional
If 'horizontal', `~matplotlib.pyplot.barh` will be used for
bar-type histograms and the *bottom* kwarg will be the left edges.
rwidth : scalar or None, optional
The relative width of the bars as a fraction of the bin width. If
`None`, automatically compute the width.
Ignored if `histtype` is 'step' or 'stepfilled'.
Default is ``None``
log : boolean, optional
If `True`, the histogram axis will be set to a log scale. If `log`
is `True` and `x` is a 1D array, empty bins will be filtered out
and only the non-empty (`n`, `bins`, `patches`) will be returned.
Default is ``False``
color : color or array_like of colors or None, optional
Color spec or sequence of color specs, one per dataset. Default
(`None`) uses the standard line color sequence.
Default is ``None``
label : string or None, optional
String, or sequence of strings to match multiple datasets. Bar
charts yield multiple patches per dataset, but only the first gets
the label, so that the legend command will work as expected.
default is ``None``
stacked : boolean, optional
If `True`, multiple data are stacked on top of each other If
`False` multiple data are aranged side by side if histtype is
'bar' or on top of each other if histtype is 'step'
Default is ``False``
Returns
-------
n : array or list of arrays
The values of the histogram bins. See **normed** and **weights**
for a description of the possible semantics. If input **x** is an
array, then this is an array of length **nbins**. If input is a
sequence arrays ``[data1, data2,..]``, then this is a list of
arrays with the values of the histograms for each of the arrays
in the same order.
bins : array
The edges of the bins. Length nbins + 1 (nbins left edges and right
edge of last bin). Always a single array even when multiple data
sets are passed in.
patches : list or list of lists
Silent list of individual patches used to create the histogram
or list of such list if multiple input datasets.
Other Parameters
----------------
kwargs : `~matplotlib.patches.Patch` properties
See also
--------
hist2d : 2D histograms
Notes
-----
Until numpy release 1.5, the underlying numpy histogram function was
incorrect with `normed`=`True` if bin sizes were unequal. MPL
inherited that error. It is now corrected within MPL when using
earlier numpy versions.
Examples
--------
.. plot:: mpl_examples/statistics/histogram_demo_features.py
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All arguments with the following names: 'weights', 'x'.
hist2d(x, y, bins=10, range=None, normed=False, weights=None, cmin=None, cmax=None, hold=None, data=None, **kwargs)
Make a 2D histogram plot.
Parameters
----------
x, y: array_like, shape (n, )
Input values
bins: [None | int | [int, int] | array_like | [array, array]]
The bin specification:
- If int, the number of bins for the two dimensions
(nx=ny=bins).
- If [int, int], the number of bins in each dimension
(nx, ny = bins).
- If array_like, the bin edges for the two dimensions
(x_edges=y_edges=bins).
- If [array, array], the bin edges in each dimension
(x_edges, y_edges = bins).
The default value is 10.
range : array_like shape(2, 2), optional, default: None
The leftmost and rightmost edges of the bins along each dimension
(if not specified explicitly in the bins parameters): [[xmin,
xmax], [ymin, ymax]]. All values outside of this range will be
considered outliers and not tallied in the histogram.
normed : boolean, optional, default: False
Normalize histogram.
weights : array_like, shape (n, ), optional, default: None
An array of values w_i weighing each sample (x_i, y_i).
cmin : scalar, optional, default: None
All bins that has count less than cmin will not be displayed and
these count values in the return value count histogram will also
be set to nan upon return
cmax : scalar, optional, default: None
All bins that has count more than cmax will not be displayed (set
to none before passing to imshow) and these count values in the
return value count histogram will also be set to nan upon return
Returns
-------
The return value is ``(counts, xedges, yedges, Image)``.
Other parameters
----------------
kwargs : :meth:`pcolorfast` properties.
See also
--------
hist : 1D histogram
Notes
-----
Rendering the histogram with a logarithmic color scale is
accomplished by passing a :class:`colors.LogNorm` instance to
the *norm* keyword argument. Likewise, power-law normalization
(similar in effect to gamma correction) can be accomplished with
:class:`colors.PowerNorm`.
Examples
--------
.. plot:: mpl_examples/pylab_examples/hist2d_demo.py
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All arguments with the following names: 'weights', 'x', 'y'.
hlines(y, xmin, xmax, colors=u'k', linestyles=u'solid', label=u'', hold=None, data=None, **kwargs)
Plot horizontal lines at each `y` from `xmin` to `xmax`.
Parameters
----------
y : scalar or sequence of scalar
y-indexes where to plot the lines.
xmin, xmax : scalar or 1D array_like
Respective beginning and end of each line. If scalars are
provided, all lines will have same length.
colors : array_like of colors, optional, default: 'k'
linestyles : ['solid' | 'dashed' | 'dashdot' | 'dotted'], optional
label : string, optional, default: ''
Returns
-------
lines : `~matplotlib.collections.LineCollection`
Other parameters
----------------
kwargs : `~matplotlib.collections.LineCollection` properties.
See also
--------
vlines : vertical lines
Examples
--------
.. plot:: mpl_examples/pylab_examples/vline_hline_demo.py
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All arguments with the following names: 'xmax', 'xmin', 'y'.
hold(*args, **kwargs)
.. deprecated:: 2.0
pyplot.hold is deprecated.
Future behavior will be consistent with the long-time default:
plot commands add elements without first clearing the
Axes and/or Figure.
Set the hold state. If *b* is None (default), toggle the
hold state, else set the hold state to boolean value *b*::
hold() # toggle hold
hold(True) # hold is on
hold(False) # hold is off
When *hold* is *True*, subsequent plot commands will add elements to
the current axes. When *hold* is *False*, the current axes and
figure will be cleared on the next plot command.
hot()
set the default colormap to hot and apply to current image if any.
See help(colormaps) for more information
hsv()
set the default colormap to hsv and apply to current image if any.
See help(colormaps) for more information
imread(*args, **kwargs)
Read an image from a file into an array.
*fname* may be a string path, a valid URL, or a Python
file-like object. If using a file object, it must be opened in binary
mode.
If *format* is provided, will try to read file of that type,
otherwise the format is deduced from the filename. If nothing can
be deduced, PNG is tried.
Return value is a :class:`numpy.array`. For grayscale images, the
return array is MxN. For RGB images, the return value is MxNx3.
For RGBA images the return value is MxNx4.
matplotlib can only read PNGs natively, but if `PIL
<http://www.pythonware.com/products/pil/>`_ is installed, it will
use it to load the image and return an array (if possible) which
can be used with :func:`~matplotlib.pyplot.imshow`. Note, URL strings
may not be compatible with PIL. Check the PIL documentation for more
information.
imsave(*args, **kwargs)
Save an array as in image file.
The output formats available depend on the backend being used.
Arguments:
*fname*:
A string containing a path to a filename, or a Python file-like object.
If *format* is *None* and *fname* is a string, the output
format is deduced from the extension of the filename.
*arr*:
An MxN (luminance), MxNx3 (RGB) or MxNx4 (RGBA) array.
Keyword arguments:
*vmin*/*vmax*: [ None | scalar ]
*vmin* and *vmax* set the color scaling for the image by fixing the
values that map to the colormap color limits. If either *vmin*
or *vmax* is None, that limit is determined from the *arr*
min/max value.
*cmap*:
cmap is a colors.Colormap instance, e.g., cm.jet.
If None, default to the rc image.cmap value.
*format*:
One of the file extensions supported by the active
backend. Most backends support png, pdf, ps, eps and svg.
*origin*
[ 'upper' | 'lower' ] Indicates where the [0,0] index of
the array is in the upper left or lower left corner of
the axes. Defaults to the rc image.origin value.
*dpi*
The DPI to store in the metadata of the file. This does not affect the
resolution of the output image.
imshow(X, cmap=None, norm=None, aspect=None, interpolation=None, alpha=None, vmin=None, vmax=None, origin=None, extent=None, shape=None, filternorm=1, filterrad=4.0, imlim=None, resample=None, url=None, hold=None, data=None, **kwargs)
Display an image on the axes.
Parameters
----------
X : array_like, shape (n, m) or (n, m, 3) or (n, m, 4)
Display the image in `X` to current axes. `X` may be an
array or a PIL image. If `X` is an array, it
can have the following shapes and types:
- MxN -- values to be mapped (float or int)
- MxNx3 -- RGB (float or uint8)
- MxNx4 -- RGBA (float or uint8)
The value for each component of MxNx3 and MxNx4 float arrays
should be in the range 0.0 to 1.0. MxN arrays are mapped
to colors based on the `norm` (mapping scalar to scalar)
and the `cmap` (mapping the normed scalar to a color).
cmap : `~matplotlib.colors.Colormap`, optional, default: None
If None, default to rc `image.cmap` value. `cmap` is ignored
if `X` is 3-D, directly specifying RGB(A) values.
aspect : ['auto' | 'equal' | scalar], optional, default: None
If 'auto', changes the image aspect ratio to match that of the
axes.
If 'equal', and `extent` is None, changes the axes aspect ratio to
match that of the image. If `extent` is not `None`, the axes
aspect ratio is changed to match that of the extent.
If None, default to rc ``image.aspect`` value.
interpolation : string, optional, default: None
Acceptable values are 'none', 'nearest', 'bilinear', 'bicubic',
'spline16', 'spline36', 'hanning', 'hamming', 'hermite', 'kaiser',
'quadric', 'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc',
'lanczos'
If `interpolation` is None, default to rc `image.interpolation`.
See also the `filternorm` and `filterrad` parameters.
If `interpolation` is 'none', then no interpolation is performed
on the Agg, ps and pdf backends. Other backends will fall back to
'nearest'.
norm : `~matplotlib.colors.Normalize`, optional, default: None
A `~matplotlib.colors.Normalize` instance is used to scale
a 2-D float `X` input to the (0, 1) range for input to the
`cmap`. If `norm` is None, use the default func:`normalize`.
If `norm` is an instance of `~matplotlib.colors.NoNorm`,
`X` must be an array of integers that index directly into
the lookup table of the `cmap`.
vmin, vmax : scalar, optional, default: None
`vmin` and `vmax` are used in conjunction with norm to normalize
luminance data. Note if you pass a `norm` instance, your
settings for `vmin` and `vmax` will be ignored.
alpha : scalar, optional, default: None
The alpha blending value, between 0 (transparent) and 1 (opaque)
origin : ['upper' | 'lower'], optional, default: None
Place the [0,0] index of the array in the upper left or lower left
corner of the axes. If None, default to rc `image.origin`.
extent : scalars (left, right, bottom, top), optional, default: None
The location, in data-coordinates, of the lower-left and
upper-right corners. If `None`, the image is positioned such that
the pixel centers fall on zero-based (row, column) indices.
shape : scalars (columns, rows), optional, default: None
For raw buffer images
filternorm : scalar, optional, default: 1
A parameter for the antigrain image resize filter. From the
antigrain documentation, if `filternorm` = 1, the filter
normalizes integer values and corrects the rounding errors. It
doesn't do anything with the source floating point values, it
corrects only integers according to the rule of 1.0 which means
that any sum of pixel weights must be equal to 1.0. So, the
filter function must produce a graph of the proper shape.
filterrad : scalar, optional, default: 4.0
The filter radius for filters that have a radius parameter, i.e.
when interpolation is one of: 'sinc', 'lanczos' or 'blackman'
Returns
-------
image : `~matplotlib.image.AxesImage`
Other parameters
----------------
kwargs : `~matplotlib.artist.Artist` properties.
See also
--------
matshow : Plot a matrix or an array as an image.
Notes
-----
Unless *extent* is used, pixel centers will be located at integer
coordinates. In other words: the origin will coincide with the center
of pixel (0, 0).
Examples
--------
.. plot:: mpl_examples/pylab_examples/image_demo.py
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All positional and all keyword arguments.
inferno()
set the default colormap to inferno and apply to current image if any.
See help(colormaps) for more information
install_repl_displayhook()
Install a repl display hook so that any stale figure are automatically
redrawn when control is returned to the repl.
This works with IPython terminals and kernels,
as well as vanilla python shells.
ioff()
Turn interactive mode off.
ion()
Turn interactive mode on.
ishold(*args, **kwargs)
.. deprecated:: 2.0
pyplot.hold is deprecated.
Future behavior will be consistent with the long-time default:
plot commands add elements without first clearing the
Axes and/or Figure.
Return the hold status of the current axes.
isinteractive()
Return status of interactive mode.
jet()
set the default colormap to jet and apply to current image if any.
See help(colormaps) for more information
legend(*args, **kwargs)
Places a legend on the axes.
To make a legend for lines which already exist on the axes
(via plot for instance), simply call this function with an iterable
of strings, one for each legend item. For example::
ax.plot([1, 2, 3])
ax.legend(['A simple line'])
However, in order to keep the "label" and the legend element
instance together, it is preferable to specify the label either at
artist creation, or by calling the
:meth:`~matplotlib.artist.Artist.set_label` method on the artist::
line, = ax.plot([1, 2, 3], label='Inline label')
# Overwrite the label by calling the method.
line.set_label('Label via method')
ax.legend()
Specific lines can be excluded from the automatic legend element
selection by defining a label starting with an underscore.
This is default for all artists, so calling :meth:`legend` without
any arguments and without setting the labels manually will result in
no legend being drawn.
For full control of which artists have a legend entry, it is possible
to pass an iterable of legend artists followed by an iterable of
legend labels respectively::
legend((line1, line2, line3), ('label1', 'label2', 'label3'))
Parameters
----------
loc : int or string or pair of floats, default: 'upper right'
The location of the legend. Possible codes are:
=============== =============
Location String Location Code
=============== =============
'best' 0
'upper right' 1
'upper left' 2
'lower left' 3
'lower right' 4
'right' 5
'center left' 6
'center right' 7
'lower center' 8
'upper center' 9
'center' 10
=============== =============
Alternatively can be a 2-tuple giving ``x, y`` of the lower-left
corner of the legend in axes coordinates (in which case
``bbox_to_anchor`` will be ignored).
bbox_to_anchor : :class:`matplotlib.transforms.BboxBase` instance or tuple of floats
Specify any arbitrary location for the legend in `bbox_transform`
coordinates (default Axes coordinates).
For example, to put the legend's upper right hand corner in the
center of the axes the following keywords can be used::
loc='upper right', bbox_to_anchor=(0.5, 0.5)
ncol : integer
The number of columns that the legend has. Default is 1.
prop : None or :class:`matplotlib.font_manager.FontProperties` or dict
The font properties of the legend. If None (default), the current
:data:`matplotlib.rcParams` will be used.
fontsize : int or float or {'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large'}
Controls the font size of the legend. If the value is numeric the
size will be the absolute font size in points. String values are
relative to the current default font size. This argument is only
used if `prop` is not specified.
numpoints : None or int
The number of marker points in the legend when creating a legend
entry for a line/:class:`matplotlib.lines.Line2D`.
Default is ``None`` which will take the value from the
``legend.numpoints`` :data:`rcParam<matplotlib.rcParams>`.
scatterpoints : None or int
The number of marker points in the legend when creating a legend
entry for a scatter plot/
:class:`matplotlib.collections.PathCollection`.
Default is ``None`` which will take the value from the
``legend.scatterpoints`` :data:`rcParam<matplotlib.rcParams>`.
scatteryoffsets : iterable of floats
The vertical offset (relative to the font size) for the markers
created for a scatter plot legend entry. 0.0 is at the base the
legend text, and 1.0 is at the top. To draw all markers at the
same height, set to ``[0.5]``. Default ``[0.375, 0.5, 0.3125]``.
markerscale : None or int or float
The relative size of legend markers compared with the originally
drawn ones. Default is ``None`` which will take the value from
the ``legend.markerscale`` :data:`rcParam <matplotlib.rcParams>`.
markerfirst : bool
if *True*, legend marker is placed to the left of the legend label
if *False*, legend marker is placed to the right of the legend
label
frameon : None or bool
Control whether the legend should be drawn on a patch (frame).
Default is ``None`` which will take the value from the
``legend.frameon`` :data:`rcParam<matplotlib.rcParams>`.
fancybox : None or bool
Control whether round edges should be enabled around
the :class:`~matplotlib.patches.FancyBboxPatch` which
makes up the legend's background.
Default is ``None`` which will take the value from the
``legend.fancybox`` :data:`rcParam<matplotlib.rcParams>`.
shadow : None or bool
Control whether to draw a shadow behind the legend.
Default is ``None`` which will take the value from the
``legend.shadow`` :data:`rcParam<matplotlib.rcParams>`.
framealpha : None or float
Control the alpha transparency of the legend's background.
Default is ``None`` which will take the value from the
``legend.framealpha`` :data:`rcParam<matplotlib.rcParams>`.
facecolor : None or "inherit" or a color spec
Control the legend's background color.
Default is ``None`` which will take the value from the
``legend.facecolor`` :data:`rcParam<matplotlib.rcParams>`.
If ``"inherit"``, it will take the ``axes.facecolor``
:data:`rcParam<matplotlib.rcParams>`.
edgecolor : None or "inherit" or a color spec
Control the legend's background patch edge color.
Default is ``None`` which will take the value from the
``legend.edgecolor`` :data:`rcParam<matplotlib.rcParams>`.
If ``"inherit"``, it will take the ``axes.edgecolor``
:data:`rcParam<matplotlib.rcParams>`.
mode : {"expand", None}
If `mode` is set to ``"expand"`` the legend will be horizontally
expanded to fill the axes area (or `bbox_to_anchor` if defines
the legend's size).
bbox_transform : None or :class:`matplotlib.transforms.Transform`
The transform for the bounding box (`bbox_to_anchor`). For a value
of ``None`` (default) the Axes'
:data:`~matplotlib.axes.Axes.transAxes` transform will be used.
title : str or None
The legend's title. Default is no title (``None``).
borderpad : float or None
The fractional whitespace inside the legend border.
Measured in font-size units.
Default is ``None`` which will take the value from the
``legend.borderpad`` :data:`rcParam<matplotlib.rcParams>`.
labelspacing : float or None
The vertical space between the legend entries.
Measured in font-size units.
Default is ``None`` which will take the value from the
``legend.labelspacing`` :data:`rcParam<matplotlib.rcParams>`.
handlelength : float or None
The length of the legend handles.
Measured in font-size units.
Default is ``None`` which will take the value from the
``legend.handlelength`` :data:`rcParam<matplotlib.rcParams>`.
handletextpad : float or None
The pad between the legend handle and text.
Measured in font-size units.
Default is ``None`` which will take the value from the
``legend.handletextpad`` :data:`rcParam<matplotlib.rcParams>`.
borderaxespad : float or None
The pad between the axes and legend border.
Measured in font-size units.
Default is ``None`` which will take the value from the
``legend.borderaxespad`` :data:`rcParam<matplotlib.rcParams>`.
columnspacing : float or None
The spacing between columns.
Measured in font-size units.
Default is ``None`` which will take the value from the
``legend.columnspacing`` :data:`rcParam<matplotlib.rcParams>`.
handler_map : dict or None
The custom dictionary mapping instances or types to a legend
handler. This `handler_map` updates the default handler map
found at :func:`matplotlib.legend.Legend.get_legend_handler_map`.
Notes
-----
Not all kinds of artist are supported by the legend command.
See :ref:`plotting-guide-legend` for details.
Examples
--------
.. plot:: mpl_examples/api/legend_demo.py
locator_params(axis=u'both', tight=None, **kwargs)
Control behavior of tick locators.
Keyword arguments:
*axis*
['x' | 'y' | 'both'] Axis on which to operate;
default is 'both'.
*tight*
[True | False | None] Parameter passed to :meth:`autoscale_view`.
Default is None, for no change.
Remaining keyword arguments are passed to directly to the
:meth:`~matplotlib.ticker.MaxNLocator.set_params` method.
Typically one might want to reduce the maximum number
of ticks and use tight bounds when plotting small
subplots, for example::
ax.locator_params(tight=True, nbins=4)
Because the locator is involved in autoscaling,
:meth:`autoscale_view` is called automatically after
the parameters are changed.
This presently works only for the
:class:`~matplotlib.ticker.MaxNLocator` used
by default on linear axes, but it may be generalized.
loglog(*args, **kwargs)
Make a plot with log scaling on both the *x* and *y* axis.
:func:`~matplotlib.pyplot.loglog` supports all the keyword
arguments of :func:`~matplotlib.pyplot.plot` and
:meth:`matplotlib.axes.Axes.set_xscale` /
:meth:`matplotlib.axes.Axes.set_yscale`.
Notable keyword arguments:
*basex*/*basey*: scalar > 1
Base of the *x*/*y* logarithm
*subsx*/*subsy*: [ *None* | sequence ]
The location of the minor *x*/*y* ticks; *None* defaults
to autosubs, which depend on the number of decades in the
plot; see :meth:`matplotlib.axes.Axes.set_xscale` /
:meth:`matplotlib.axes.Axes.set_yscale` for details
*nonposx*/*nonposy*: ['mask' | 'clip' ]
Non-positive values in *x* or *y* can be masked as
invalid, or clipped to a very small positive number
The remaining valid kwargs are
:class:`~matplotlib.lines.Line2D` properties:
agg_filter: unknown
alpha: float (0.0 transparent through 1.0 opaque)
animated: [True | False]
antialiased or aa: [True | False]
axes: an :class:`~matplotlib.axes.Axes` instance
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
color or c: any matplotlib color
contains: a callable function
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post']
figure: a :class:`matplotlib.figure.Figure` instance
fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none']
gid: an id string
label: string or anything printable with '%s' conversion.
linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or lw: float value in points
marker: :mod:`A valid marker style <matplotlib.markers>`
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markerfacecoloralt or mfcalt: any matplotlib color
markersize or ms: float
markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects: unknown
picker: float distance in points or callable pick function ``fn(artist, event)``
pickradius: float distance in points
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a :class:`matplotlib.transforms.Transform` instance
url: a url string
visible: [True | False]
xdata: 1D array
ydata: 1D array
zorder: any number
**Example:**
.. plot:: mpl_examples/pylab_examples/log_demo.py
magma()
set the default colormap to magma and apply to current image if any.
See help(colormaps) for more information
magnitude_spectrum(x, Fs=None, Fc=None, window=None, pad_to=None, sides=None, scale=None, hold=None, data=None, **kwargs)
Plot the magnitude spectrum.
Call signature::
magnitude_spectrum(x, Fs=2, Fc=0, window=mlab.window_hanning,
pad_to=None, sides='default', **kwargs)
Compute the magnitude spectrum of *x*. Data is padded to a
length of *pad_to* and the windowing function *window* is applied to
the signal.
Parameters
----------
x : 1-D array or sequence
Array or sequence containing the data
Fs : scalar
The sampling frequency (samples per time unit). It is used
to calculate the Fourier frequencies, freqs, in cycles per time
unit. The default value is 2.
window : callable or ndarray
A function or a vector of length *NFFT*. To create window
vectors see :func:`window_hanning`, :func:`window_none`,
:func:`numpy.blackman`, :func:`numpy.hamming`,
:func:`numpy.bartlett`, :func:`scipy.signal`,
:func:`scipy.signal.get_window`, etc. The default is
:func:`window_hanning`. If a function is passed as the
argument, it must take a data segment as an argument and
return the windowed version of the segment.
sides : [ 'default' | 'onesided' | 'twosided' ]
Specifies which sides of the spectrum to return. Default gives the
default behavior, which returns one-sided for real data and both
for complex data. 'onesided' forces the return of a one-sided
spectrum, while 'twosided' forces two-sided.
pad_to : integer
The number of points to which the data segment is padded when
performing the FFT. While not increasing the actual resolution of
the spectrum (the minimum distance between resolvable peaks),
this can give more points in the plot, allowing for more
detail. This corresponds to the *n* parameter in the call to fft().
The default is None, which sets *pad_to* equal to the length of the
input signal (i.e. no padding).
scale : [ 'default' | 'linear' | 'dB' ]
The scaling of the values in the *spec*. 'linear' is no scaling.
'dB' returns the values in dB scale. When *mode* is 'density',
this is dB power (10 * log10). Otherwise this is dB amplitude
(20 * log10). 'default' is 'linear'.
Fc : integer
The center frequency of *x* (defaults to 0), which offsets
the x extents of the plot to reflect the frequency range used
when a signal is acquired and then filtered and downsampled to
baseband.
**kwargs :
Keyword arguments control the :class:`~matplotlib.lines.Line2D`
properties:
agg_filter: unknown
alpha: float (0.0 transparent through 1.0 opaque)
animated: [True | False]
antialiased or aa: [True | False]
axes: an :class:`~matplotlib.axes.Axes` instance
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
color or c: any matplotlib color
contains: a callable function
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post']
figure: a :class:`matplotlib.figure.Figure` instance
fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none']
gid: an id string
label: string or anything printable with '%s' conversion.
linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or lw: float value in points
marker: :mod:`A valid marker style <matplotlib.markers>`
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markerfacecoloralt or mfcalt: any matplotlib color
markersize or ms: float
markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects: unknown
picker: float distance in points or callable pick function ``fn(artist, event)``
pickradius: float distance in points
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a :class:`matplotlib.transforms.Transform` instance
url: a url string
visible: [True | False]
xdata: 1D array
ydata: 1D array
zorder: any number
Returns
-------
spectrum : 1-D array
The values for the magnitude spectrum before scaling (real valued)
freqs : 1-D array
The frequencies corresponding to the elements in *spectrum*
line : a :class:`~matplotlib.lines.Line2D` instance
The line created by this function
Examples
--------
.. plot:: mpl_examples/pylab_examples/spectrum_demo.py
See Also
--------
:func:`psd`
:func:`psd` plots the power spectral density.`.
:func:`angle_spectrum`
:func:`angle_spectrum` plots the angles of the corresponding
frequencies.
:func:`phase_spectrum`
:func:`phase_spectrum` plots the phase (unwrapped angle) of the
corresponding frequencies.
:func:`specgram`
:func:`specgram` can plot the magnitude spectrum of segments within
the signal in a colormap.
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All arguments with the following names: 'x'.
margins(*args, **kw)
Set or retrieve autoscaling margins.
signatures::
margins()
returns xmargin, ymargin
::
margins(margin)
margins(xmargin, ymargin)
margins(x=xmargin, y=ymargin)
margins(..., tight=False)
All three forms above set the xmargin and ymargin parameters.
All keyword parameters are optional. A single argument
specifies both xmargin and ymargin. The *tight* parameter
is passed to :meth:`autoscale_view`, which is executed after
a margin is changed; the default here is *True*, on the
assumption that when margins are specified, no additional
padding to match tick marks is usually desired. Setting
*tight* to *None* will preserve the previous setting.
Specifying any margin changes only the autoscaling; for example,
if *xmargin* is not None, then *xmargin* times the X data
interval will be added to each end of that interval before
it is used in autoscaling.
matshow(A, fignum=None, **kw)
Display an array as a matrix in a new figure window.
The origin is set at the upper left hand corner and rows (first
dimension of the array) are displayed horizontally. The aspect
ratio of the figure window is that of the array, unless this would
make an excessively short or narrow figure.
Tick labels for the xaxis are placed on top.
With the exception of *fignum*, keyword arguments are passed to
:func:`~matplotlib.pyplot.imshow`. You may set the *origin*
kwarg to "lower" if you want the first row in the array to be
at the bottom instead of the top.
*fignum*: [ None | integer | False ]
By default, :func:`matshow` creates a new figure window with
automatic numbering. If *fignum* is given as an integer, the
created figure will use this figure number. Because of how
:func:`matshow` tries to set the figure aspect ratio to be the
one of the array, if you provide the number of an already
existing figure, strange things may happen.
If *fignum* is *False* or 0, a new figure window will **NOT** be created.
minorticks_off()
Remove minor ticks from the current plot.
minorticks_on()
Display minor ticks on the current plot.
Displaying minor ticks reduces performance; turn them off using
minorticks_off() if drawing speed is a problem.
nipy_spectral()
set the default colormap to nipy_spectral and apply to current image if any.
See help(colormaps) for more information
over(*args, **kwargs)
.. deprecated:: 2.0
pyplot.hold is deprecated.
Future behavior will be consistent with the long-time default:
plot commands add elements without first clearing the
Axes and/or Figure.
Call a function with hold(True).
Calls::
func(*args, **kwargs)
with ``hold(True)`` and then restores the hold state.
pause(interval)
Pause for *interval* seconds.
If there is an active figure it will be updated and displayed,
and the GUI event loop will run during the pause.
If there is no active figure, or if a non-interactive backend
is in use, this executes time.sleep(interval).
This can be used for crude animation. For more complex
animation, see :mod:`matplotlib.animation`.
This function is experimental; its behavior may be changed
or extended in a future release.
pcolor(*args, **kwargs)
Create a pseudocolor plot of a 2-D array.
.. note::
pcolor can be very slow for large arrays; consider
using the similar but much faster
:func:`~matplotlib.pyplot.pcolormesh` instead.
Call signatures::
pcolor(C, **kwargs)
pcolor(X, Y, C, **kwargs)
*C* is the array of color values.
*X* and *Y*, if given, specify the (*x*, *y*) coordinates of
the colored quadrilaterals; the quadrilateral for C[i,j] has
corners at::
(X[i, j], Y[i, j]),
(X[i, j+1], Y[i, j+1]),
(X[i+1, j], Y[i+1, j]),
(X[i+1, j+1], Y[i+1, j+1]).
Ideally the dimensions of *X* and *Y* should be one greater
than those of *C*; if the dimensions are the same, then the
last row and column of *C* will be ignored.
Note that the column index corresponds to the
*x*-coordinate, and the row index corresponds to *y*; for
details, see the :ref:`Grid Orientation
<axes-pcolor-grid-orientation>` section below.
If either or both of *X* and *Y* are 1-D arrays or column vectors,
they will be expanded as needed into the appropriate 2-D arrays,
making a rectangular grid.
*X*, *Y* and *C* may be masked arrays. If either C[i, j], or one
of the vertices surrounding C[i,j] (*X* or *Y* at [i, j], [i+1, j],
[i, j+1],[i+1, j+1]) is masked, nothing is plotted.
Keyword arguments:
*cmap*: [ *None* | Colormap ]
A :class:`matplotlib.colors.Colormap` instance. If *None*, use
rc settings.
*norm*: [ *None* | Normalize ]
An :class:`matplotlib.colors.Normalize` instance is used
to scale luminance data to 0,1. If *None*, defaults to
:func:`normalize`.
*vmin*/*vmax*: [ *None* | scalar ]
*vmin* and *vmax* are used in conjunction with *norm* to
normalize luminance data. If either is *None*, it
is autoscaled to the respective min or max
of the color array *C*. If not *None*, *vmin* or
*vmax* passed in here override any pre-existing values
supplied in the *norm* instance.
*shading*: [ 'flat' | 'faceted' ]
If 'faceted', a black grid is drawn around each rectangle; if
'flat', edges are not drawn. Default is 'flat', contrary to
MATLAB.
This kwarg is deprecated; please use 'edgecolors' instead:
* shading='flat' -- edgecolors='none'
* shading='faceted -- edgecolors='k'
*edgecolors*: [ *None* | ``'none'`` | color | color sequence]
If *None*, the rc setting is used by default.
If ``'none'``, edges will not be visible.
An mpl color or sequence of colors will set the edge color
*alpha*: ``0 <= scalar <= 1`` or *None*
the alpha blending value
*snap*: bool
Whether to snap the mesh to pixel boundaries.
Return value is a :class:`matplotlib.collections.Collection`
instance.
.. _axes-pcolor-grid-orientation:
The grid orientation follows the MATLAB convention: an
array *C* with shape (*nrows*, *ncolumns*) is plotted with
the column number as *X* and the row number as *Y*, increasing
up; hence it is plotted the way the array would be printed,
except that the *Y* axis is reversed. That is, *C* is taken
as *C*(*y*, *x*).
Similarly for :func:`meshgrid`::
x = np.arange(5)
y = np.arange(3)
X, Y = np.meshgrid(x, y)
is equivalent to::
X = array([[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4]])
Y = array([[0, 0, 0, 0, 0],
[1, 1, 1, 1, 1],
[2, 2, 2, 2, 2]])
so if you have::
C = rand(len(x), len(y))
then you need to transpose C::
pcolor(X, Y, C.T)
or::
pcolor(C.T)
MATLAB :func:`pcolor` always discards the last row and column
of *C*, but matplotlib displays the last row and column if *X* and
*Y* are not specified, or if *X* and *Y* have one more row and
column than *C*.
kwargs can be used to control the
:class:`~matplotlib.collections.PolyCollection` properties:
agg_filter: unknown
alpha: float or None
animated: [True | False]
antialiased or antialiaseds: Boolean or sequence of booleans
array: unknown
axes: an :class:`~matplotlib.axes.Axes` instance
clim: a length 2 sequence of floats
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
cmap: a colormap or registered colormap name
color: matplotlib color arg or sequence of rgba tuples
contains: a callable function
edgecolor or edgecolors: matplotlib color spec or sequence of specs
facecolor or facecolors: matplotlib color spec or sequence of specs
figure: a :class:`matplotlib.figure.Figure` instance
gid: an id string
hatch: [ '/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ]
label: string or anything printable with '%s' conversion.
linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or linewidths or lw: float or sequence of floats
norm: unknown
offset_position: unknown
offsets: float or sequence of floats
path_effects: unknown
picker: [None|float|boolean|callable]
pickradius: unknown
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
transform: :class:`~matplotlib.transforms.Transform` instance
url: a url string
urls: unknown
visible: [True | False]
zorder: any number
.. note::
The default *antialiaseds* is False if the default
*edgecolors*="none" is used. This eliminates artificial lines
at patch boundaries, and works regardless of the value of
alpha. If *edgecolors* is not "none", then the default
*antialiaseds* is taken from
rcParams['patch.antialiased'], which defaults to *True*.
Stroking the edges may be preferred if *alpha* is 1, but
will cause artifacts otherwise.
.. seealso::
:func:`~matplotlib.pyplot.pcolormesh`
For an explanation of the differences between
pcolor and pcolormesh.
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All positional and all keyword arguments.
pcolormesh(*args, **kwargs)
Plot a quadrilateral mesh.
Call signatures::
pcolormesh(C)
pcolormesh(X, Y, C)
pcolormesh(C, **kwargs)
Create a pseudocolor plot of a 2-D array.
pcolormesh is similar to :func:`~matplotlib.pyplot.pcolor`,
but uses a different mechanism and returns a different
object; pcolor returns a
:class:`~matplotlib.collections.PolyCollection` but pcolormesh
returns a
:class:`~matplotlib.collections.QuadMesh`. It is much faster,
so it is almost always preferred for large arrays.
*C* may be a masked array, but *X* and *Y* may not. Masked
array support is implemented via *cmap* and *norm*; in
contrast, :func:`~matplotlib.pyplot.pcolor` simply does not
draw quadrilaterals with masked colors or vertices.
Keyword arguments:
*cmap*: [ *None* | Colormap ]
A :class:`matplotlib.colors.Colormap` instance. If *None*, use
rc settings.
*norm*: [ *None* | Normalize ]
A :class:`matplotlib.colors.Normalize` instance is used to
scale luminance data to 0,1. If *None*, defaults to
:func:`normalize`.
*vmin*/*vmax*: [ *None* | scalar ]
*vmin* and *vmax* are used in conjunction with *norm* to
normalize luminance data. If either is *None*, it
is autoscaled to the respective min or max
of the color array *C*. If not *None*, *vmin* or
*vmax* passed in here override any pre-existing values
supplied in the *norm* instance.
*shading*: [ 'flat' | 'gouraud' ]
'flat' indicates a solid color for each quad. When
'gouraud', each quad will be Gouraud shaded. When gouraud
shading, edgecolors is ignored.
*edgecolors*: [*None* | ``'None'`` | ``'face'`` | color |
color sequence]
If *None*, the rc setting is used by default.
If ``'None'``, edges will not be visible.
If ``'face'``, edges will have the same color as the faces.
An mpl color or sequence of colors will set the edge color
*alpha*: ``0 <= scalar <= 1`` or *None*
the alpha blending value
Return value is a :class:`matplotlib.collections.QuadMesh`
object.
kwargs can be used to control the
:class:`matplotlib.collections.QuadMesh` properties:
agg_filter: unknown
alpha: float or None
animated: [True | False]
antialiased or antialiaseds: Boolean or sequence of booleans
array: unknown
axes: an :class:`~matplotlib.axes.Axes` instance
clim: a length 2 sequence of floats
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
cmap: a colormap or registered colormap name
color: matplotlib color arg or sequence of rgba tuples
contains: a callable function
edgecolor or edgecolors: matplotlib color spec or sequence of specs
facecolor or facecolors: matplotlib color spec or sequence of specs
figure: a :class:`matplotlib.figure.Figure` instance
gid: an id string
hatch: [ '/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ]
label: string or anything printable with '%s' conversion.
linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or linewidths or lw: float or sequence of floats
norm: unknown
offset_position: unknown
offsets: float or sequence of floats
path_effects: unknown
picker: [None|float|boolean|callable]
pickradius: unknown
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
transform: :class:`~matplotlib.transforms.Transform` instance
url: a url string
urls: unknown
visible: [True | False]
zorder: any number
.. seealso::
:func:`~matplotlib.pyplot.pcolor`
For an explanation of the grid orientation
(:ref:`Grid Orientation <axes-pcolor-grid-orientation>`)
and the expansion of 1-D *X* and/or *Y* to 2-D arrays.
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All positional and all keyword arguments.
phase_spectrum(x, Fs=None, Fc=None, window=None, pad_to=None, sides=None, hold=None, data=None, **kwargs)
Plot the phase spectrum.
Call signature::
phase_spectrum(x, Fs=2, Fc=0, window=mlab.window_hanning,
pad_to=None, sides='default', **kwargs)
Compute the phase spectrum (unwrapped angle spectrum) of *x*.
Data is padded to a length of *pad_to* and the windowing function
*window* is applied to the signal.
Parameters
----------
x : 1-D array or sequence
Array or sequence containing the data
Fs : scalar
The sampling frequency (samples per time unit). It is used
to calculate the Fourier frequencies, freqs, in cycles per time
unit. The default value is 2.
window : callable or ndarray
A function or a vector of length *NFFT*. To create window
vectors see :func:`window_hanning`, :func:`window_none`,
:func:`numpy.blackman`, :func:`numpy.hamming`,
:func:`numpy.bartlett`, :func:`scipy.signal`,
:func:`scipy.signal.get_window`, etc. The default is
:func:`window_hanning`. If a function is passed as the
argument, it must take a data segment as an argument and
return the windowed version of the segment.
sides : [ 'default' | 'onesided' | 'twosided' ]
Specifies which sides of the spectrum to return. Default gives the
default behavior, which returns one-sided for real data and both
for complex data. 'onesided' forces the return of a one-sided
spectrum, while 'twosided' forces two-sided.
pad_to : integer
The number of points to which the data segment is padded when
performing the FFT. While not increasing the actual resolution of
the spectrum (the minimum distance between resolvable peaks),
this can give more points in the plot, allowing for more
detail. This corresponds to the *n* parameter in the call to fft().
The default is None, which sets *pad_to* equal to the length of the
input signal (i.e. no padding).
Fc : integer
The center frequency of *x* (defaults to 0), which offsets
the x extents of the plot to reflect the frequency range used
when a signal is acquired and then filtered and downsampled to
baseband.
**kwargs :
Keyword arguments control the :class:`~matplotlib.lines.Line2D`
properties:
agg_filter: unknown
alpha: float (0.0 transparent through 1.0 opaque)
animated: [True | False]
antialiased or aa: [True | False]
axes: an :class:`~matplotlib.axes.Axes` instance
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
color or c: any matplotlib color
contains: a callable function
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post']
figure: a :class:`matplotlib.figure.Figure` instance
fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none']
gid: an id string
label: string or anything printable with '%s' conversion.
linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or lw: float value in points
marker: :mod:`A valid marker style <matplotlib.markers>`
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markerfacecoloralt or mfcalt: any matplotlib color
markersize or ms: float
markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects: unknown
picker: float distance in points or callable pick function ``fn(artist, event)``
pickradius: float distance in points
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a :class:`matplotlib.transforms.Transform` instance
url: a url string
visible: [True | False]
xdata: 1D array
ydata: 1D array
zorder: any number
Returns
-------
spectrum : 1-D array
The values for the phase spectrum in radians (real valued)
freqs : 1-D array
The frequencies corresponding to the elements in *spectrum*
line : a :class:`~matplotlib.lines.Line2D` instance
The line created by this function
Examples
--------
.. plot:: mpl_examples/pylab_examples/spectrum_demo.py
See Also
--------
:func:`magnitude_spectrum`
:func:`magnitude_spectrum` plots the magnitudes of the
corresponding frequencies.
:func:`angle_spectrum`
:func:`angle_spectrum` plots the wrapped version of this function.
:func:`specgram`
:func:`specgram` can plot the phase spectrum of segments within the
signal in a colormap.
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All arguments with the following names: 'x'.
pie(x, explode=None, labels=None, colors=None, autopct=None, pctdistance=0.6, shadow=False, labeldistance=1.1, startangle=None, radius=None, counterclock=True, wedgeprops=None, textprops=None, center=(0, 0), frame=False, hold=None, data=None)
Plot a pie chart.
Make a pie chart of array *x*. The fractional area of each
wedge is given by x/sum(x). If sum(x) <= 1, then the values
of x give the fractional area directly and the array will not
be normalized. The wedges are plotted counterclockwise,
by default starting from the x-axis.
Keyword arguments:
*explode*: [ *None* | len(x) sequence ]
If not *None*, is a ``len(x)`` array which specifies the
fraction of the radius with which to offset each wedge.
*colors*: [ *None* | color sequence ]
A sequence of matplotlib color args through which the pie chart
will cycle. If `None`, will use the colors in the currently
active cycle.
*labels*: [ *None* | len(x) sequence of strings ]
A sequence of strings providing the labels for each wedge
*autopct*: [ *None* | format string | format function ]
If not *None*, is a string or function used to label the wedges
with their numeric value. The label will be placed inside the
wedge. If it is a format string, the label will be ``fmt%pct``.
If it is a function, it will be called.
*pctdistance*: scalar
The ratio between the center of each pie slice and the
start of the text generated by *autopct*. Ignored if
*autopct* is *None*; default is 0.6.
*labeldistance*: scalar
The radial distance at which the pie labels are drawn
*shadow*: [ *False* | *True* ]
Draw a shadow beneath the pie.
*startangle*: [ *None* | Offset angle ]
If not *None*, rotates the start of the pie chart by *angle*
degrees counterclockwise from the x-axis.
*radius*: [ *None* | scalar ]
The radius of the pie, if *radius* is *None* it will be set to 1.
*counterclock*: [ *False* | *True* ]
Specify fractions direction, clockwise or counterclockwise.
*wedgeprops*: [ *None* | dict of key value pairs ]
Dict of arguments passed to the wedge objects making the pie.
For example, you can pass in wedgeprops = { 'linewidth' : 3 }
to set the width of the wedge border lines equal to 3.
For more details, look at the doc/arguments of the wedge object.
By default `clip_on=False`.
*textprops*: [ *None* | dict of key value pairs ]
Dict of arguments to pass to the text objects.
*center*: [ (0,0) | sequence of 2 scalars ]
Center position of the chart.
*frame*: [ *False* | *True* ]
Plot axes frame with the chart.
The pie chart will probably look best if the figure and axes are
square, or the Axes aspect is equal. e.g.::
figure(figsize=(8,8))
ax = axes([0.1, 0.1, 0.8, 0.8])
or::
axes(aspect=1)
Return value:
If *autopct* is *None*, return the tuple (*patches*, *texts*):
- *patches* is a sequence of
:class:`matplotlib.patches.Wedge` instances
- *texts* is a list of the label
:class:`matplotlib.text.Text` instances.
If *autopct* is not *None*, return the tuple (*patches*,
*texts*, *autotexts*), where *patches* and *texts* are as
above, and *autotexts* is a list of
:class:`~matplotlib.text.Text` instances for the numeric
labels.
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All arguments with the following names: 'colors', 'explode', 'labels', 'x'.
pink()
set the default colormap to pink and apply to current image if any.
See help(colormaps) for more information
plasma()
set the default colormap to plasma and apply to current image if any.
See help(colormaps) for more information
plot(*args, **kwargs)
Plot lines and/or markers to the
:class:`~matplotlib.axes.Axes`. *args* is a variable length
argument, allowing for multiple *x*, *y* pairs with an
optional format string. For example, each of the following is
legal::
plot(x, y) # plot x and y using default line style and color
plot(x, y, 'bo') # plot x and y using blue circle markers
plot(y) # plot y using x as index array 0..N-1
plot(y, 'r+') # ditto, but with red plusses
If *x* and/or *y* is 2-dimensional, then the corresponding columns
will be plotted.
If used with labeled data, make sure that the color spec is not
included as an element in data, as otherwise the last case
``plot("v","r", data={"v":..., "r":...)``
can be interpreted as the first case which would do ``plot(v, r)``
using the default line style and color.
If not used with labeled data (i.e., without a data argument),
an arbitrary number of *x*, *y*, *fmt* groups can be specified, as in::
a.plot(x1, y1, 'g^', x2, y2, 'g-')
Return value is a list of lines that were added.
By default, each line is assigned a different style specified by a
'style cycle'. To change this behavior, you can edit the
axes.prop_cycle rcParam.
The following format string characters are accepted to control
the line style or marker:
================ ===============================
character description
================ ===============================
``'-'`` solid line style
``'--'`` dashed line style
``'-.'`` dash-dot line style
``':'`` dotted line style
``'.'`` point marker
``','`` pixel marker
``'o'`` circle marker
``'v'`` triangle_down marker
``'^'`` triangle_up marker
``'<'`` triangle_left marker
``'>'`` triangle_right marker
``'1'`` tri_down marker
``'2'`` tri_up marker
``'3'`` tri_left marker
``'4'`` tri_right marker
``'s'`` square marker
``'p'`` pentagon marker
``'*'`` star marker
``'h'`` hexagon1 marker
``'H'`` hexagon2 marker
``'+'`` plus marker
``'x'`` x marker
``'D'`` diamond marker
``'d'`` thin_diamond marker
``'|'`` vline marker
``'_'`` hline marker
================ ===============================
The following color abbreviations are supported:
========== ========
character color
========== ========
'b' blue
'g' green
'r' red
'c' cyan
'm' magenta
'y' yellow
'k' black
'w' white
========== ========
In addition, you can specify colors in many weird and
wonderful ways, including full names (``'green'``), hex
strings (``'#008000'``), RGB or RGBA tuples (``(0,1,0,1)``) or
grayscale intensities as a string (``'0.8'``). Of these, the
string specifications can be used in place of a ``fmt`` group,
but the tuple forms can be used only as ``kwargs``.
Line styles and colors are combined in a single format string, as in
``'bo'`` for blue circles.
The *kwargs* can be used to set line properties (any property that has
a ``set_*`` method). You can use this to set a line label (for auto
legends), linewidth, anitialising, marker face color, etc. Here is an
example::
plot([1,2,3], [1,2,3], 'go-', label='line 1', linewidth=2)
plot([1,2,3], [1,4,9], 'rs', label='line 2')
axis([0, 4, 0, 10])
legend()
If you make multiple lines with one plot command, the kwargs
apply to all those lines, e.g.::
plot(x1, y1, x2, y2, antialiased=False)
Neither line will be antialiased.
You do not need to use format strings, which are just
abbreviations. All of the line properties can be controlled
by keyword arguments. For example, you can set the color,
marker, linestyle, and markercolor with::
plot(x, y, color='green', linestyle='dashed', marker='o',
markerfacecolor='blue', markersize=12).
See :class:`~matplotlib.lines.Line2D` for details.
The kwargs are :class:`~matplotlib.lines.Line2D` properties:
agg_filter: unknown
alpha: float (0.0 transparent through 1.0 opaque)
animated: [True | False]
antialiased or aa: [True | False]
axes: an :class:`~matplotlib.axes.Axes` instance
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
color or c: any matplotlib color
contains: a callable function
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post']
figure: a :class:`matplotlib.figure.Figure` instance
fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none']
gid: an id string
label: string or anything printable with '%s' conversion.
linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or lw: float value in points
marker: :mod:`A valid marker style <matplotlib.markers>`
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markerfacecoloralt or mfcalt: any matplotlib color
markersize or ms: float
markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects: unknown
picker: float distance in points or callable pick function ``fn(artist, event)``
pickradius: float distance in points
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a :class:`matplotlib.transforms.Transform` instance
url: a url string
visible: [True | False]
xdata: 1D array
ydata: 1D array
zorder: any number
kwargs *scalex* and *scaley*, if defined, are passed on to
:meth:`~matplotlib.axes.Axes.autoscale_view` to determine
whether the *x* and *y* axes are autoscaled; the default is
*True*.
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All arguments with the following names: 'x', 'y'.
plot_date(x, y, fmt=u'o', tz=None, xdate=True, ydate=False, hold=None, data=None, **kwargs)
A plot with data that contains dates.
Similar to the :func:`~matplotlib.pyplot.plot` command, except
the *x* or *y* (or both) data is considered to be dates, and the
axis is labeled accordingly.
*x* and/or *y* can be a sequence of dates represented as float
days since 0001-01-01 UTC.
Note if you are using custom date tickers and formatters, it
may be necessary to set the formatters/locators after the call
to meth:`plot_date` since meth:`plot_date` will set the
default tick locator to
class:`matplotlib.dates.AutoDateLocator` (if the tick
locator is not already set to a
class:`matplotlib.dates.DateLocator` instance) and the
default tick formatter to
class:`matplotlib.dates.AutoDateFormatter` (if the tick
formatter is not already set to a
class:`matplotlib.dates.DateFormatter` instance).
Parameters
----------
fmt : string
The plot format string.
tz : [ *None* | timezone string | :class:`tzinfo` instance]
The time zone to use in labeling dates. If *None*, defaults to rc
value.
xdate : boolean
If *True*, the *x*-axis will be labeled with dates.
ydate : boolean
If *True*, the *y*-axis will be labeled with dates.
Returns
-------
lines
See Also
--------
matplotlib.dates : helper functions on dates
matplotlib.dates.date2num : how to convert dates to num
matplotlib.dates.num2date : how to convert num to dates
matplotlib.dates.drange : how floating point dates
Other Parameters
----------------
kwargs : :class:`matplotlib.lines.Line2D`
properties : agg_filter: unknown
alpha: float (0.0 transparent through 1.0 opaque)
animated: [True | False]
antialiased or aa: [True | False]
axes: an :class:`~matplotlib.axes.Axes` instance
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
color or c: any matplotlib color
contains: a callable function
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post']
figure: a :class:`matplotlib.figure.Figure` instance
fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none']
gid: an id string
label: string or anything printable with '%s' conversion.
linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or lw: float value in points
marker: :mod:`A valid marker style <matplotlib.markers>`
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markerfacecoloralt or mfcalt: any matplotlib color
markersize or ms: float
markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects: unknown
picker: float distance in points or callable pick function ``fn(artist, event)``
pickradius: float distance in points
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a :class:`matplotlib.transforms.Transform` instance
url: a url string
visible: [True | False]
xdata: 1D array
ydata: 1D array
zorder: any number
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All arguments with the following names: 'x', 'y'.
plotfile(fname, cols=(0,), plotfuncs=None, comments=u'#', skiprows=0, checkrows=5, delimiter=u',', names=None, subplots=True, newfig=True, **kwargs)
Plot the data in in a file.
*cols* is a sequence of column identifiers to plot. An identifier
is either an int or a string. If it is an int, it indicates the
column number. If it is a string, it indicates the column header.
matplotlib will make column headers lower case, replace spaces with
underscores, and remove all illegal characters; so ``'Adj Close*'``
will have name ``'adj_close'``.
- If len(*cols*) == 1, only that column will be plotted on the *y* axis.
- If len(*cols*) > 1, the first element will be an identifier for
data for the *x* axis and the remaining elements will be the
column indexes for multiple subplots if *subplots* is *True*
(the default), or for lines in a single subplot if *subplots*
is *False*.
*plotfuncs*, if not *None*, is a dictionary mapping identifier to
an :class:`~matplotlib.axes.Axes` plotting function as a string.
Default is 'plot', other choices are 'semilogy', 'fill', 'bar',
etc. You must use the same type of identifier in the *cols*
vector as you use in the *plotfuncs* dictionary, e.g., integer
column numbers in both or column names in both. If *subplots*
is *False*, then including any function such as 'semilogy'
that changes the axis scaling will set the scaling for all
columns.
*comments*, *skiprows*, *checkrows*, *delimiter*, and *names*
are all passed on to :func:`matplotlib.pylab.csv2rec` to
load the data into a record array.
If *newfig* is *True*, the plot always will be made in a new figure;
if *False*, it will be made in the current figure if one exists,
else in a new figure.
kwargs are passed on to plotting functions.
Example usage::
# plot the 2nd and 4th column against the 1st in two subplots
plotfile(fname, (0,1,3))
# plot using column names; specify an alternate plot type for volume
plotfile(fname, ('date', 'volume', 'adj_close'),
plotfuncs={'volume': 'semilogy'})
Note: plotfile is intended as a convenience for quickly plotting
data from flat files; it is not intended as an alternative
interface to general plotting with pyplot or matplotlib.
plotting()
============================ =============================================================================================================================================================================================================================================
Function Description
============================ =============================================================================================================================================================================================================================================
`acorr` Plot the autocorrelation of `x`.
`angle_spectrum` Plot the angle spectrum.
`annotate` Annotate the point ``xy`` with text ``s``.
`arrow` Add an arrow to the axes.
`autoscale` Autoscale the axis view to the data (toggle).
`axes` Add an axes to the figure.
`axhline` Add a horizontal line across the axis.
`axhspan` Add a horizontal span (rectangle) across the axis.
`axis` Convenience method to get or set axis properties.
`axvline` Add a vertical line across the axes.
`axvspan` Add a vertical span (rectangle) across the axes.
`bar` Make a bar plot.
`barbs` Plot a 2-D field of barbs.
`barh` Make a horizontal bar plot.
`box` Turn the axes box on or off.
`boxplot` Make a box and whisker plot.
`broken_barh` Plot horizontal bars.
`cla` Clear the current axes.
`clabel` Label a contour plot.
`clf` Clear the current figure.
`clim` Set the color limits of the current image.
`close` Close a figure window.
`cohere` Plot the coherence between *x* and *y*.
`colorbar` Add a colorbar to a plot.
`contour` Plot contours.
`contourf` Plot contours.
`csd` Plot the cross-spectral density.
`delaxes` Remove an axes from the current figure.
`draw` Redraw the current figure.
`errorbar` Plot an errorbar graph.
`eventplot` Plot identical parallel lines at specific positions.
`figimage` Adds a non-resampled image to the figure.
`figlegend` Place a legend in the figure.
`fignum_exists`
`figtext` Add text to figure.
`figure` Creates a new figure.
`fill` Plot filled polygons.
`fill_between` Make filled polygons between two curves.
`fill_betweenx` Make filled polygons between two horizontal curves.
`findobj` Find artist objects.
`gca` Get the current :class:`~matplotlib.axes.Axes` instance on the current figure matching the given keyword args, or create one.
`gcf` Get a reference to the current figure.
`gci` Get the current colorable artist.
`get_figlabels` Return a list of existing figure labels.
`get_fignums` Return a list of existing figure numbers.
`grid` Turn the axes grids on or off.
`hexbin` Make a hexagonal binning plot.
`hist` Plot a histogram.
`hist2d` Make a 2D histogram plot.
`hlines` Plot horizontal lines at each `y` from `xmin` to `xmax`.
`hold` ..
`imread` Read an image from a file into an array.
`imsave` Save an array as in image file.
`imshow` Display an image on the axes.
`install_repl_displayhook` Install a repl display hook so that any stale figure are automatically redrawn when control is returned to the repl.
`ioff` Turn interactive mode off.
`ion` Turn interactive mode on.
`ishold` ..
`isinteractive` Return status of interactive mode.
`legend` Places a legend on the axes.
`locator_params` Control behavior of tick locators.
`loglog` Make a plot with log scaling on both the *x* and *y* axis.
`magnitude_spectrum` Plot the magnitude spectrum.
`margins` Set or retrieve autoscaling margins.
`matshow` Display an array as a matrix in a new figure window.
`minorticks_off` Remove minor ticks from the current plot.
`minorticks_on` Display minor ticks on the current plot.
`over` ..
`pause` Pause for *interval* seconds.
`pcolor` Create a pseudocolor plot of a 2-D array.
`pcolormesh` Plot a quadrilateral mesh.
`phase_spectrum` Plot the phase spectrum.
`pie` Plot a pie chart.
`plot` Plot lines and/or markers to the :class:`~matplotlib.axes.Axes`.
`plot_date` A plot with data that contains dates.
`plotfile` Plot the data in in a file.
`polar` Make a polar plot.
`psd` Plot the power spectral density.
`quiver` Plot a 2-D field of arrows.
`quiverkey` Add a key to a quiver plot.
`rc` Set the current rc params.
`rc_context` Return a context manager for managing rc settings.
`rcdefaults` Restore the default rc params.
`rgrids` Get or set the radial gridlines on a polar plot.
`savefig` Save the current figure.
`sca` Set the current Axes instance to *ax*.
`scatter` Make a scatter plot of `x` vs `y` Marker size is scaled by `s` and marker color is mapped to `c` Parameters ---------- x, y : array_like, shape (n, ) Input data s : scalar or array_like, shape (n, ), optional size in points^2.
`sci` Set the current image.
`semilogx` Make a plot with log scaling on the *x* axis.
`semilogy` Make a plot with log scaling on the `y` axis.
`set_cmap` Set the default colormap.
`setp` Set a property on an artist object.
`show` Display a figure.
`specgram` Plot a spectrogram.
`spy` Plot the sparsity pattern on a 2-D array.
`stackplot` Draws a stacked area plot.
`stem` Create a stem plot.
`step` Make a step plot.
`streamplot` Draws streamlines of a vector flow.
`subplot` Return a subplot axes positioned by the given grid definition.
`subplot2grid` Create a subplot in a grid.
`subplot_tool` Launch a subplot tool window for a figure.
`subplots` Create a figure and a set of subplots This utility wrapper makes it convenient to create common layouts of subplots, including the enclosing figure object, in a single call.
`subplots_adjust` Tune the subplot layout.
`suptitle` Add a centered title to the figure.
`switch_backend` Switch the default backend.
`table` Add a table to the current axes.
`text` Add text to the axes.
`thetagrids` Get or set the theta locations of the gridlines in a polar plot.
`tick_params` Change the appearance of ticks and tick labels.
`ticklabel_format` Change the `~matplotlib.ticker.ScalarFormatter` used by default for linear axes.
`tight_layout` Automatically adjust subplot parameters to give specified padding.
`title` Set a title of the current axes.
`tricontour` Draw contours on an unstructured triangular grid.
`tricontourf` Draw contours on an unstructured triangular grid.
`tripcolor` Create a pseudocolor plot of an unstructured triangular grid.
`triplot` Draw a unstructured triangular grid as lines and/or markers.
`twinx` Make a second axes that shares the *x*-axis.
`twiny` Make a second axes that shares the *y*-axis.
`uninstall_repl_displayhook` Uninstalls the matplotlib display hook.
`violinplot` Make a violin plot.
`vlines` Plot vertical lines.
`xcorr` Plot the cross correlation between *x* and *y*.
`xkcd` Turns on `xkcd <http://xkcd.com/>`_ sketch-style drawing mode.
`xlabel` Set the *x* axis label of the current axis.
`xlim` Get or set the *x* limits of the current axes.
`xscale` Set the scaling of the *x*-axis.
`xticks` Get or set the *x*-limits of the current tick locations and labels.
`ylabel` Set the *y* axis label of the current axis.
`ylim` Get or set the *y*-limits of the current axes.
`yscale` Set the scaling of the *y*-axis.
`yticks` Get or set the *y*-limits of the current tick locations and labels.
============================ =============================================================================================================================================================================================================================================
polar(*args, **kwargs)
Make a polar plot.
call signature::
polar(theta, r, **kwargs)
Multiple *theta*, *r* arguments are supported, with format
strings, as in :func:`~matplotlib.pyplot.plot`.
prism()
set the default colormap to prism and apply to current image if any.
See help(colormaps) for more information
psd(x, NFFT=None, Fs=None, Fc=None, detrend=None, window=None, noverlap=None, pad_to=None, sides=None, scale_by_freq=None, return_line=None, hold=None, data=None, **kwargs)
Plot the power spectral density.
Call signature::
psd(x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=0, pad_to=None,
sides='default', scale_by_freq=None, return_line=None, **kwargs)
The power spectral density :math:`P_{xx}` by Welch's average
periodogram method. The vector *x* is divided into *NFFT* length
segments. Each segment is detrended by function *detrend* and
windowed by function *window*. *noverlap* gives the length of
the overlap between segments. The :math:`|\mathrm{fft}(i)|^2`
of each segment :math:`i` are averaged to compute :math:`P_{xx}`,
with a scaling to correct for power loss due to windowing.
If len(*x*) < *NFFT*, it will be zero padded to *NFFT*.
Parameters
----------
x : 1-D array or sequence
Array or sequence containing the data
Fs : scalar
The sampling frequency (samples per time unit). It is used
to calculate the Fourier frequencies, freqs, in cycles per time
unit. The default value is 2.
window : callable or ndarray
A function or a vector of length *NFFT*. To create window
vectors see :func:`window_hanning`, :func:`window_none`,
:func:`numpy.blackman`, :func:`numpy.hamming`,
:func:`numpy.bartlett`, :func:`scipy.signal`,
:func:`scipy.signal.get_window`, etc. The default is
:func:`window_hanning`. If a function is passed as the
argument, it must take a data segment as an argument and
return the windowed version of the segment.
sides : [ 'default' | 'onesided' | 'twosided' ]
Specifies which sides of the spectrum to return. Default gives the
default behavior, which returns one-sided for real data and both
for complex data. 'onesided' forces the return of a one-sided
spectrum, while 'twosided' forces two-sided.
pad_to : integer
The number of points to which the data segment is padded when
performing the FFT. This can be different from *NFFT*, which
specifies the number of data points used. While not increasing
the actual resolution of the spectrum (the minimum distance between
resolvable peaks), this can give more points in the plot,
allowing for more detail. This corresponds to the *n* parameter
in the call to fft(). The default is None, which sets *pad_to*
equal to *NFFT*
NFFT : integer
The number of data points used in each block for the FFT.
A power 2 is most efficient. The default value is 256.
This should *NOT* be used to get zero padding, or the scaling of the
result will be incorrect. Use *pad_to* for this instead.
detrend : {'default', 'constant', 'mean', 'linear', 'none'} or callable
The function applied to each segment before fft-ing,
designed to remove the mean or linear trend. Unlike in
MATLAB, where the *detrend* parameter is a vector, in
matplotlib is it a function. The :mod:`~matplotlib.pylab`
module defines :func:`~matplotlib.pylab.detrend_none`,
:func:`~matplotlib.pylab.detrend_mean`, and
:func:`~matplotlib.pylab.detrend_linear`, but you can use
a custom function as well. You can also use a string to choose
one of the functions. 'default', 'constant', and 'mean' call
:func:`~matplotlib.pylab.detrend_mean`. 'linear' calls
:func:`~matplotlib.pylab.detrend_linear`. 'none' calls
:func:`~matplotlib.pylab.detrend_none`.
scale_by_freq : boolean, optional
Specifies whether the resulting density values should be scaled
by the scaling frequency, which gives density in units of Hz^-1.
This allows for integration over the returned frequency values.
The default is True for MATLAB compatibility.
noverlap : integer
The number of points of overlap between segments.
The default value is 0 (no overlap).
Fc : integer
The center frequency of *x* (defaults to 0), which offsets
the x extents of the plot to reflect the frequency range used
when a signal is acquired and then filtered and downsampled to
baseband.
return_line : bool
Whether to include the line object plotted in the returned values.
Default is False.
**kwargs :
Keyword arguments control the :class:`~matplotlib.lines.Line2D`
properties:
agg_filter: unknown
alpha: float (0.0 transparent through 1.0 opaque)
animated: [True | False]
antialiased or aa: [True | False]
axes: an :class:`~matplotlib.axes.Axes` instance
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
color or c: any matplotlib color
contains: a callable function
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post']
figure: a :class:`matplotlib.figure.Figure` instance
fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none']
gid: an id string
label: string or anything printable with '%s' conversion.
linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or lw: float value in points
marker: :mod:`A valid marker style <matplotlib.markers>`
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markerfacecoloralt or mfcalt: any matplotlib color
markersize or ms: float
markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects: unknown
picker: float distance in points or callable pick function ``fn(artist, event)``
pickradius: float distance in points
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a :class:`matplotlib.transforms.Transform` instance
url: a url string
visible: [True | False]
xdata: 1D array
ydata: 1D array
zorder: any number
Returns
-------
Pxx : 1-D array
The values for the power spectrum `P_{xx}` before scaling
(real valued)
freqs : 1-D array
The frequencies corresponding to the elements in *Pxx*
line : a :class:`~matplotlib.lines.Line2D` instance
The line created by this function.
Only returned if *return_line* is True.
Notes
-----
For plotting, the power is plotted as
:math:`10\log_{10}(P_{xx})` for decibels, though *Pxx* itself
is returned.
References
----------
Bendat & Piersol -- Random Data: Analysis and Measurement Procedures,
John Wiley & Sons (1986)
Examples
--------
.. plot:: mpl_examples/pylab_examples/psd_demo.py
See Also
--------
:func:`specgram`
:func:`specgram` differs in the default overlap; in not returning
the mean of the segment periodograms; in returning the times of the
segments; and in plotting a colormap instead of a line.
:func:`magnitude_spectrum`
:func:`magnitude_spectrum` plots the magnitude spectrum.
:func:`csd`
:func:`csd` plots the spectral density between two signals.
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All arguments with the following names: 'x'.
quiver(*args, **kw)
Plot a 2-D field of arrows.
call signatures::
quiver(U, V, **kw)
quiver(U, V, C, **kw)
quiver(X, Y, U, V, **kw)
quiver(X, Y, U, V, C, **kw)
Arguments:
*X*, *Y*:
The x and y coordinates of the arrow locations (default is tail of
arrow; see *pivot* kwarg)
*U*, *V*:
Give the x and y components of the arrow vectors
*C*:
An optional array used to map colors to the arrows
All arguments may be 1-D or 2-D arrays or sequences. If *X* and *Y*
are absent, they will be generated as a uniform grid. If *U* and *V*
are 2-D arrays but *X* and *Y* are 1-D, and if ``len(X)`` and ``len(Y)``
match the column and row dimensions of *U*, then *X* and *Y* will be
expanded with :func:`numpy.meshgrid`.
*U*, *V*, *C* may be masked arrays, but masked *X*, *Y* are not
supported at present.
Keyword arguments:
*units*: [ 'width' | 'height' | 'dots' | 'inches' | 'x' | 'y' | 'xy' ]
Arrow units; the arrow dimensions *except for length* are in
multiples of this unit.
* 'width' or 'height': the width or height of the axes
* 'dots' or 'inches': pixels or inches, based on the figure dpi
* 'x', 'y', or 'xy': *X*, *Y*, or sqrt(X^2+Y^2) data units
The arrows scale differently depending on the units. For
'x' or 'y', the arrows get larger as one zooms in; for other
units, the arrow size is independent of the zoom state. For
'width or 'height', the arrow size increases with the width and
height of the axes, respectively, when the window is resized;
for 'dots' or 'inches', resizing does not change the arrows.
*angles*: [ 'uv' | 'xy' | array ]
With the default 'uv', the arrow axis aspect ratio is 1, so that
if *U*==*V* the orientation of the arrow on the plot is 45 degrees
CCW from the horizontal axis (positive to the right).
With 'xy', the arrow points from (x,y) to (x+u, y+v).
Use this for plotting a gradient field, for example.
Alternatively, arbitrary angles may be specified as an array
of values in degrees, CCW from the horizontal axis.
Note: inverting a data axis will correspondingly invert the
arrows *only* with `angles='xy'`.
*scale*: [ *None* | float ]
Data units per arrow length unit, e.g., m/s per plot width; a smaller
scale parameter makes the arrow longer. If *None*, a simple
autoscaling algorithm is used, based on the average vector length
and the number of vectors. The arrow length unit is given by
the *scale_units* parameter
*scale_units*: *None*, or any of the *units* options.
For example, if *scale_units* is 'inches', *scale* is 2.0, and
``(u,v) = (1,0)``, then the vector will be 0.5 inches long.
If *scale_units* is 'width', then the vector will be half the width
of the axes.
If *scale_units* is 'x' then the vector will be 0.5 x-axis
units. To plot vectors in the x-y plane, with u and v having
the same units as x and y, use
"angles='xy', scale_units='xy', scale=1".
*width*:
Shaft width in arrow units; default depends on choice of units,
above, and number of vectors; a typical starting value is about
0.005 times the width of the plot.
*headwidth*: scalar
Head width as multiple of shaft width, default is 3
*headlength*: scalar
Head length as multiple of shaft width, default is 5
*headaxislength*: scalar
Head length at shaft intersection, default is 4.5
*minshaft*: scalar
Length below which arrow scales, in units of head length. Do not
set this to less than 1, or small arrows will look terrible!
Default is 1
*minlength*: scalar
Minimum length as a multiple of shaft width; if an arrow length
is less than this, plot a dot (hexagon) of this diameter instead.
Default is 1.
*pivot*: [ 'tail' | 'mid' | 'middle' | 'tip' ]
The part of the arrow that is at the grid point; the arrow rotates
about this point, hence the name *pivot*.
*color*: [ color | color sequence ]
This is a synonym for the
:class:`~matplotlib.collections.PolyCollection` facecolor kwarg.
If *C* has been set, *color* has no effect.
The defaults give a slightly swept-back arrow; to make the head a
triangle, make *headaxislength* the same as *headlength*. To make the
arrow more pointed, reduce *headwidth* or increase *headlength* and
*headaxislength*. To make the head smaller relative to the shaft,
scale down all the head parameters. You will probably do best to leave
minshaft alone.
linewidths and edgecolors can be used to customize the arrow
outlines. Additional :class:`~matplotlib.collections.PolyCollection`
keyword arguments:
agg_filter: unknown
alpha: float or None
animated: [True | False]
antialiased or antialiaseds: Boolean or sequence of booleans
array: unknown
axes: an :class:`~matplotlib.axes.Axes` instance
clim: a length 2 sequence of floats
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
cmap: a colormap or registered colormap name
color: matplotlib color arg or sequence of rgba tuples
contains: a callable function
edgecolor or edgecolors: matplotlib color spec or sequence of specs
facecolor or facecolors: matplotlib color spec or sequence of specs
figure: a :class:`matplotlib.figure.Figure` instance
gid: an id string
hatch: [ '/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ]
label: string or anything printable with '%s' conversion.
linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or linewidths or lw: float or sequence of floats
norm: unknown
offset_position: unknown
offsets: float or sequence of floats
path_effects: unknown
picker: [None|float|boolean|callable]
pickradius: unknown
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
transform: :class:`~matplotlib.transforms.Transform` instance
url: a url string
urls: unknown
visible: [True | False]
zorder: any number
quiverkey(*args, **kw)
Add a key to a quiver plot.
Call signature::
quiverkey(Q, X, Y, U, label, **kw)
Arguments:
*Q*:
The Quiver instance returned by a call to quiver.
*X*, *Y*:
The location of the key; additional explanation follows.
*U*:
The length of the key
*label*:
A string with the length and units of the key
Keyword arguments:
*coordinates* = [ 'axes' | 'figure' | 'data' | 'inches' ]
Coordinate system and units for *X*, *Y*: 'axes' and 'figure' are
normalized coordinate systems with 0,0 in the lower left and 1,1
in the upper right; 'data' are the axes data coordinates (used for
the locations of the vectors in the quiver plot itself); 'inches'
is position in the figure in inches, with 0,0 at the lower left
corner.
*color*:
overrides face and edge colors from *Q*.
*labelpos* = [ 'N' | 'S' | 'E' | 'W' ]
Position the label above, below, to the right, to the left of the
arrow, respectively.
*labelsep*:
Distance in inches between the arrow and the label. Default is
0.1
*labelcolor*:
defaults to default :class:`~matplotlib.text.Text` color.
*fontproperties*:
A dictionary with keyword arguments accepted by the
:class:`~matplotlib.font_manager.FontProperties` initializer:
*family*, *style*, *variant*, *size*, *weight*
Any additional keyword arguments are used to override vector
properties taken from *Q*.
The positioning of the key depends on *X*, *Y*, *coordinates*, and
*labelpos*. If *labelpos* is 'N' or 'S', *X*, *Y* give the position
of the middle of the key arrow. If *labelpos* is 'E', *X*, *Y*
positions the head, and if *labelpos* is 'W', *X*, *Y* positions the
tail; in either of these two cases, *X*, *Y* is somewhere in the
middle of the arrow+label key object.
rc(*args, **kwargs)
Set the current rc params. Group is the grouping for the rc, e.g.,
for ``lines.linewidth`` the group is ``lines``, for
``axes.facecolor``, the group is ``axes``, and so on. Group may
also be a list or tuple of group names, e.g., (*xtick*, *ytick*).
*kwargs* is a dictionary attribute name/value pairs, e.g.,::
rc('lines', linewidth=2, color='r')
sets the current rc params and is equivalent to::
rcParams['lines.linewidth'] = 2
rcParams['lines.color'] = 'r'
The following aliases are available to save typing for interactive
users:
===== =================
Alias Property
===== =================
'lw' 'linewidth'
'ls' 'linestyle'
'c' 'color'
'fc' 'facecolor'
'ec' 'edgecolor'
'mew' 'markeredgewidth'
'aa' 'antialiased'
===== =================
Thus you could abbreviate the above rc command as::
rc('lines', lw=2, c='r')
Note you can use python's kwargs dictionary facility to store
dictionaries of default parameters. e.g., you can customize the
font rc as follows::
font = {'family' : 'monospace',
'weight' : 'bold',
'size' : 'larger'}
rc('font', **font) # pass in the font dict as kwargs
This enables you to easily switch between several configurations.
Use :func:`~matplotlib.pyplot.rcdefaults` to restore the default
rc params after changes.
rc_context(rc=None, fname=None)
Return a context manager for managing rc settings.
This allows one to do::
with mpl.rc_context(fname='screen.rc'):
plt.plot(x, a)
with mpl.rc_context(fname='print.rc'):
plt.plot(x, b)
plt.plot(x, c)
The 'a' vs 'x' and 'c' vs 'x' plots would have settings from
'screen.rc', while the 'b' vs 'x' plot would have settings from
'print.rc'.
A dictionary can also be passed to the context manager::
with mpl.rc_context(rc={'text.usetex': True}, fname='screen.rc'):
plt.plot(x, a)
The 'rc' dictionary takes precedence over the settings loaded from
'fname'. Passing a dictionary only is also valid.
rcdefaults()
Restore the default rc params. These are not the params loaded by
the rc file, but mpl's internal params. See rc_file_defaults for
reloading the default params from the rc file
rgrids(*args, **kwargs)
Get or set the radial gridlines on a polar plot.
call signatures::
lines, labels = rgrids()
lines, labels = rgrids(radii, labels=None, angle=22.5, **kwargs)
When called with no arguments, :func:`rgrid` simply returns the
tuple (*lines*, *labels*), where *lines* is an array of radial
gridlines (:class:`~matplotlib.lines.Line2D` instances) and
*labels* is an array of tick labels
(:class:`~matplotlib.text.Text` instances). When called with
arguments, the labels will appear at the specified radial
distances and angles.
*labels*, if not *None*, is a len(*radii*) list of strings of the
labels to use at each angle.
If *labels* is None, the rformatter will be used
Examples::
# set the locations of the radial gridlines and labels
lines, labels = rgrids( (0.25, 0.5, 1.0) )
# set the locations and labels of the radial gridlines and labels
lines, labels = rgrids( (0.25, 0.5, 1.0), ('Tom', 'Dick', 'Harry' )
savefig(*args, **kwargs)
Save the current figure.
Call signature::
savefig(fname, dpi=None, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches=None, pad_inches=0.1,
frameon=None)
The output formats available depend on the backend being used.
Arguments:
*fname*:
A string containing a path to a filename, or a Python
file-like object, or possibly some backend-dependent object
such as :class:`~matplotlib.backends.backend_pdf.PdfPages`.
If *format* is *None* and *fname* is a string, the output
format is deduced from the extension of the filename. If
the filename has no extension, the value of the rc parameter
``savefig.format`` is used.
If *fname* is not a string, remember to specify *format* to
ensure that the correct backend is used.
Keyword arguments:
*dpi*: [ *None* | ``scalar > 0`` | 'figure']
The resolution in dots per inch. If *None* it will default to
the value ``savefig.dpi`` in the matplotlibrc file. If 'figure'
it will set the dpi to be the value of the figure.
*facecolor*, *edgecolor*:
the colors of the figure rectangle
*orientation*: [ 'landscape' | 'portrait' ]
not supported on all backends; currently only on postscript output
*papertype*:
One of 'letter', 'legal', 'executive', 'ledger', 'a0' through
'a10', 'b0' through 'b10'. Only supported for postscript
output.
*format*:
One of the file extensions supported by the active
backend. Most backends support png, pdf, ps, eps and svg.
*transparent*:
If *True*, the axes patches will all be transparent; the
figure patch will also be transparent unless facecolor
and/or edgecolor are specified via kwargs.
This is useful, for example, for displaying
a plot on top of a colored background on a web page. The
transparency of these patches will be restored to their
original values upon exit of this function.
*frameon*:
If *True*, the figure patch will be colored, if *False*, the
figure background will be transparent. If not provided, the
rcParam 'savefig.frameon' will be used.
*bbox_inches*:
Bbox in inches. Only the given portion of the figure is
saved. If 'tight', try to figure out the tight bbox of
the figure.
*pad_inches*:
Amount of padding around the figure when bbox_inches is
'tight'.
*bbox_extra_artists*:
A list of extra artists that will be considered when the
tight bbox is calculated.
sca(ax)
Set the current Axes instance to *ax*.
The current Figure is updated to the parent of *ax*.
scatter(x, y, s=None, c=None, marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=None, edgecolors=None, hold=None, data=None, **kwargs)
Make a scatter plot of `x` vs `y`
Marker size is scaled by `s` and marker color is mapped to `c`
Parameters
----------
x, y : array_like, shape (n, )
Input data
s : scalar or array_like, shape (n, ), optional
size in points^2. Default is `rcParams['lines.markersize'] ** 2`.
c : color, sequence, or sequence of color, optional, default: 'b'
`c` can be a single color format string, or a sequence of color
specifications of length `N`, or a sequence of `N` numbers to be
mapped to colors using the `cmap` and `norm` specified via kwargs
(see below). Note that `c` should not be a single numeric RGB or
RGBA sequence because that is indistinguishable from an array of
values to be colormapped. `c` can be a 2-D array in which the
rows are RGB or RGBA, however, including the case of a single
row to specify the same color for all points.
marker : `~matplotlib.markers.MarkerStyle`, optional, default: 'o'
See `~matplotlib.markers` for more information on the different
styles of markers scatter supports. `marker` can be either
an instance of the class or the text shorthand for a particular
marker.
cmap : `~matplotlib.colors.Colormap`, optional, default: None
A `~matplotlib.colors.Colormap` instance or registered name.
`cmap` is only used if `c` is an array of floats. If None,
defaults to rc `image.cmap`.
norm : `~matplotlib.colors.Normalize`, optional, default: None
A `~matplotlib.colors.Normalize` instance is used to scale
luminance data to 0, 1. `norm` is only used if `c` is an array of
floats. If `None`, use the default :func:`normalize`.
vmin, vmax : scalar, optional, default: None
`vmin` and `vmax` are used in conjunction with `norm` to normalize
luminance data. If either are `None`, the min and max of the
color array is used. Note if you pass a `norm` instance, your
settings for `vmin` and `vmax` will be ignored.
alpha : scalar, optional, default: None
The alpha blending value, between 0 (transparent) and 1 (opaque)
linewidths : scalar or array_like, optional, default: None
If None, defaults to (lines.linewidth,).
verts : sequence of (x, y), optional
If `marker` is None, these vertices will be used to
construct the marker. The center of the marker is located
at (0,0) in normalized units. The overall marker is rescaled
by ``s``.
edgecolors : color or sequence of color, optional, default: None
If None, defaults to 'face'
If 'face', the edge color will always be the same as
the face color.
If it is 'none', the patch boundary will not
be drawn.
For non-filled markers, the `edgecolors` kwarg
is ignored and forced to 'face' internally.
Returns
-------
paths : `~matplotlib.collections.PathCollection`
Other parameters
----------------
kwargs : `~matplotlib.collections.Collection` properties
See Also
--------
plot : to plot scatter plots when markers are identical in size and
color
Notes
-----
* The `plot` function will be faster for scatterplots where markers
don't vary in size or color.
* Any or all of `x`, `y`, `s`, and `c` may be masked arrays, in which
case all masks will be combined and only unmasked points will be
plotted.
Fundamentally, scatter works with 1-D arrays; `x`, `y`, `s`, and `c`
may be input as 2-D arrays, but within scatter they will be
flattened. The exception is `c`, which will be flattened only if its
size matches the size of `x` and `y`.
Examples
--------
.. plot:: mpl_examples/shapes_and_collections/scatter_demo.py
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All arguments with the following names: 'c', 'color', 'edgecolors', 'facecolor', 'facecolors', 'linewidths', 's', 'x', 'y'.
sci(im)
Set the current image. This image will be the target of colormap
commands like :func:`~matplotlib.pyplot.jet`,
:func:`~matplotlib.pyplot.hot` or
:func:`~matplotlib.pyplot.clim`). The current image is an
attribute of the current axes.
semilogx(*args, **kwargs)
Make a plot with log scaling on the *x* axis.
Parameters
----------
basex : float, optional
Base of the *x* logarithm. The scalar should be larger
than 1.
subsx : array_like, optional
The location of the minor xticks; *None* defaults to
autosubs, which depend on the number of decades in the
plot; see :meth:`~matplotlib.axes.Axes.set_xscale` for
details.
nonposx : string, optional, {'mask', 'clip'}
Non-positive values in *x* can be masked as
invalid, or clipped to a very small positive number.
Returns
-------
`~matplotlib.pyplot.plot`
Log-scaled plot on the *x* axis.
Other Parameters
----------------
:class:`~matplotlib.lines.Line2D` properties:
agg_filter: unknown
alpha: float (0.0 transparent through 1.0 opaque)
animated: [True | False]
antialiased or aa: [True | False]
axes: an :class:`~matplotlib.axes.Axes` instance
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
color or c: any matplotlib color
contains: a callable function
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post']
figure: a :class:`matplotlib.figure.Figure` instance
fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none']
gid: an id string
label: string or anything printable with '%s' conversion.
linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or lw: float value in points
marker: :mod:`A valid marker style <matplotlib.markers>`
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markerfacecoloralt or mfcalt: any matplotlib color
markersize or ms: float
markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects: unknown
picker: float distance in points or callable pick function ``fn(artist, event)``
pickradius: float distance in points
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a :class:`matplotlib.transforms.Transform` instance
url: a url string
visible: [True | False]
xdata: 1D array
ydata: 1D array
zorder: any number
See Also
--------
loglog : For example code and figure.
Notes
-----
This function supports all the keyword arguments of
:func:`~matplotlib.pyplot.plot` and
:meth:`matplotlib.axes.Axes.set_xscale`.
semilogy(*args, **kwargs)
Make a plot with log scaling on the `y` axis.
Parameters
----------
basey : scalar > 1
Base of the `y` logarithm.
subsy : None or iterable
The location of the minor yticks. None defaults to
autosubs, which depend on the number of decades in the
plot. See :meth:`~matplotlib.axes.Axes.set_yscale` for
details.
nonposy : {'mask' | 'clip'} str
Non-positive values in `y` can be masked as
invalid, or clipped to a very small positive number.
Returns
-------
`~matplotlib.lines.Line2D`
Line instance of the plot.
Other Parameters
----------------
kwargs : `~matplotlib.lines.Line2D` properties,
`~matplotlib.pylab.plot` and
`matplotlib.axes.Axes.set_yscale` arguments.
agg_filter: unknown
alpha: float (0.0 transparent through 1.0 opaque)
animated: [True | False]
antialiased or aa: [True | False]
axes: an :class:`~matplotlib.axes.Axes` instance
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
color or c: any matplotlib color
contains: a callable function
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post']
figure: a :class:`matplotlib.figure.Figure` instance
fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none']
gid: an id string
label: string or anything printable with '%s' conversion.
linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
linewidth or lw: float value in points
marker: :mod:`A valid marker style <matplotlib.markers>`
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markerfacecoloralt or mfcalt: any matplotlib color
markersize or ms: float
markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects: unknown
picker: float distance in points or callable pick function ``fn(artist, event)``
pickradius: float distance in points
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a :class:`matplotlib.transforms.Transform` instance
url: a url string
visible: [True | False]
xdata: 1D array
ydata: 1D array
zorder: any number
See also
--------
:meth:`loglog`: For example code and figure.
set_cmap(cmap)
Set the default colormap. Applies to the current image if any.
See help(colormaps) for more information.
*cmap* must be a :class:`~matplotlib.colors.Colormap` instance, or
the name of a registered colormap.
See :func:`matplotlib.cm.register_cmap` and
:func:`matplotlib.cm.get_cmap`.
setp(*args, **kwargs)
Set a property on an artist object.
matplotlib supports the use of :func:`setp` ("set property") and
:func:`getp` to set and get object properties, as well as to do
introspection on the object. For example, to set the linestyle of a
line to be dashed, you can do::
>>> line, = plot([1,2,3])
>>> setp(line, linestyle='--')
If you want to know the valid types of arguments, you can provide the
name of the property you want to set without a value::
>>> setp(line, 'linestyle')
linestyle: [ '-' | '--' | '-.' | ':' | 'steps' | 'None' ]
If you want to see all the properties that can be set, and their
possible values, you can do::
>>> setp(line)
... long output listing omitted
:func:`setp` operates on a single instance or a list of instances.
If you are in query mode introspecting the possible values, only
the first instance in the sequence is used. When actually setting
values, all the instances will be set. e.g., suppose you have a
list of two lines, the following will make both lines thicker and
red::
>>> x = arange(0,1.0,0.01)
>>> y1 = sin(2*pi*x)
>>> y2 = sin(4*pi*x)
>>> lines = plot(x, y1, x, y2)
>>> setp(lines, linewidth=2, color='r')
:func:`setp` works with the MATLAB style string/value pairs or
with python kwargs. For example, the following are equivalent::
>>> setp(lines, 'linewidth', 2, 'color', 'r') # MATLAB style
>>> setp(lines, linewidth=2, color='r') # python style
show(*args, **kw)
Display a figure.
When running in ipython with its pylab mode, display all
figures and return to the ipython prompt.
In non-interactive mode, display all figures and block until
the figures have been closed; in interactive mode it has no
effect unless figures were created prior to a change from
non-interactive to interactive mode (not recommended). In
that case it displays the figures but does not block.
A single experimental keyword argument, *block*, may be
set to True or False to override the blocking behavior
described above.
specgram(x, NFFT=None, Fs=None, Fc=None, detrend=None, window=None, noverlap=None, cmap=None, xextent=None, pad_to=None, sides=None, scale_by_freq=None, mode=None, scale=None, vmin=None, vmax=None, hold=None, data=None, **kwargs)
Plot a spectrogram.
Call signature::
specgram(x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=128,
cmap=None, xextent=None, pad_to=None, sides='default',
scale_by_freq=None, mode='default', scale='default',
**kwargs)
Compute and plot a spectrogram of data in *x*. Data are split into
*NFFT* length segments and the spectrum of each section is
computed. The windowing function *window* is applied to each
segment, and the amount of overlap of each segment is
specified with *noverlap*. The spectrogram is plotted as a colormap
(using imshow).
Parameters
----------
x : 1-D array or sequence
Array or sequence containing the data.
Fs : scalar
The sampling frequency (samples per time unit). It is used
to calculate the Fourier frequencies, freqs, in cycles per time
unit. The default value is 2.
window : callable or ndarray
A function or a vector of length *NFFT*. To create window
vectors see :func:`window_hanning`, :func:`window_none`,
:func:`numpy.blackman`, :func:`numpy.hamming`,
:func:`numpy.bartlett`, :func:`scipy.signal`,
:func:`scipy.signal.get_window`, etc. The default is
:func:`window_hanning`. If a function is passed as the
argument, it must take a data segment as an argument and
return the windowed version of the segment.
sides : [ 'default' | 'onesided' | 'twosided' ]
Specifies which sides of the spectrum to return. Default gives the
default behavior, which returns one-sided for real data and both
for complex data. 'onesided' forces the return of a one-sided
spectrum, while 'twosided' forces two-sided.
pad_to : integer
The number of points to which the data segment is padded when
performing the FFT. This can be different from *NFFT*, which
specifies the number of data points used. While not increasing
the actual resolution of the spectrum (the minimum distance between
resolvable peaks), this can give more points in the plot,
allowing for more detail. This corresponds to the *n* parameter
in the call to fft(). The default is None, which sets *pad_to*
equal to *NFFT*
NFFT : integer
The number of data points used in each block for the FFT.
A power 2 is most efficient. The default value is 256.
This should *NOT* be used to get zero padding, or the scaling of the
result will be incorrect. Use *pad_to* for this instead.
detrend : {'default', 'constant', 'mean', 'linear', 'none'} or callable
The function applied to each segment before fft-ing,
designed to remove the mean or linear trend. Unlike in
MATLAB, where the *detrend* parameter is a vector, in
matplotlib is it a function. The :mod:`~matplotlib.pylab`
module defines :func:`~matplotlib.pylab.detrend_none`,
:func:`~matplotlib.pylab.detrend_mean`, and
:func:`~matplotlib.pylab.detrend_linear`, but you can use
a custom function as well. You can also use a string to choose
one of the functions. 'default', 'constant', and 'mean' call
:func:`~matplotlib.pylab.detrend_mean`. 'linear' calls
:func:`~matplotlib.pylab.detrend_linear`. 'none' calls
:func:`~matplotlib.pylab.detrend_none`.
scale_by_freq : boolean, optional
Specifies whether the resulting density values should be scaled
by the scaling frequency, which gives density in units of Hz^-1.
This allows for integration over the returned frequency values.
The default is True for MATLAB compatibility.
mode : [ 'default' | 'psd' | 'magnitude' | 'angle' | 'phase' ]
What sort of spectrum to use. Default is 'psd', which takes
the power spectral density. 'complex' returns the complex-valued
frequency spectrum. 'magnitude' returns the magnitude spectrum.
'angle' returns the phase spectrum without unwrapping. 'phase'
returns the phase spectrum with unwrapping.
noverlap : integer
The number of points of overlap between blocks. The
default value is 128.
scale : [ 'default' | 'linear' | 'dB' ]
The scaling of the values in the *spec*. 'linear' is no scaling.
'dB' returns the values in dB scale. When *mode* is 'psd',
this is dB power (10 * log10). Otherwise this is dB amplitude
(20 * log10). 'default' is 'dB' if *mode* is 'psd' or
'magnitude' and 'linear' otherwise. This must be 'linear'
if *mode* is 'angle' or 'phase'.
Fc : integer
The center frequency of *x* (defaults to 0), which offsets
the x extents of the plot to reflect the frequency range used
when a signal is acquired and then filtered and downsampled to
baseband.
cmap :
A :class:`matplotlib.colors.Colormap` instance; if *None*, use
default determined by rc
xextent : [None | (xmin, xmax)]
The image extent along the x-axis. The default sets *xmin* to the
left border of the first bin (*spectrum* column) and *xmax* to the
right border of the last bin. Note that for *noverlap>0* the width
of the bins is smaller than those of the segments.
**kwargs :
Additional kwargs are passed on to imshow which makes the
specgram image
Notes
-----
*detrend* and *scale_by_freq* only apply when *mode* is set to
'psd'
Returns
-------
spectrum : 2-D array
Columns are the periodograms of successive segments.
freqs : 1-D array
The frequencies corresponding to the rows in *spectrum*.
t : 1-D array
The times corresponding to midpoints of segments (i.e., the columns
in *spectrum*).
im : instance of class :class:`~matplotlib.image.AxesImage`
The image created by imshow containing the spectrogram
Examples
--------
.. plot:: mpl_examples/pylab_examples/specgram_demo.py
See Also
--------
:func:`psd`
:func:`psd` differs in the default overlap; in returning the mean
of the segment periodograms; in not returning times; and in
generating a line plot instead of colormap.
:func:`magnitude_spectrum`
A single spectrum, similar to having a single segment when *mode*
is 'magnitude'. Plots a line instead of a colormap.
:func:`angle_spectrum`
A single spectrum, similar to having a single segment when *mode*
is 'angle'. Plots a line instead of a colormap.
:func:`phase_spectrum`
A single spectrum, similar to having a single segment when *mode*
is 'phase'. Plots a line instead of a colormap.
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All arguments with the following names: 'x'.
spectral()
set the default colormap to spectral and apply to current image if any.
See help(colormaps) for more information
spring()
set the default colormap to spring and apply to current image if any.
See help(colormaps) for more information
spy(Z, precision=0, marker=None, markersize=None, aspect=u'equal', **kwargs)
Plot the sparsity pattern on a 2-D array.
``spy(Z)`` plots the sparsity pattern of the 2-D array *Z*.
Parameters
----------
Z : sparse array (n, m)
The array to be plotted.
precision : float, optional, default: 0
If *precision* is 0, any non-zero value will be plotted; else,
values of :math:`|Z| > precision` will be plotted.
For :class:`scipy.sparse.spmatrix` instances, there is a special
case: if *precision* is 'present', any value present in the array
will be plotted, even if it is identically zero.
origin : ["upper", "lower"], optional, default: "upper"
Place the [0,0] index of the array in the upper left or lower left
corner of the axes.
aspect : ['auto' | 'equal' | scalar], optional, default: "equal"
If 'equal', and `extent` is None, changes the axes aspect ratio to
match that of the image. If `extent` is not `None`, the axes
aspect ratio is changed to match that of the extent.
If 'auto', changes the image aspect ratio to match that of the
axes.
If None, default to rc ``image.aspect`` value.
Two plotting styles are available: image or marker. Both
are available for full arrays, but only the marker style
works for :class:`scipy.sparse.spmatrix` instances.
If *marker* and *markersize* are *None*, an image will be
returned and any remaining kwargs are passed to
:func:`~matplotlib.pyplot.imshow`; else, a
:class:`~matplotlib.lines.Line2D` object will be returned with
the value of marker determining the marker type, and any
remaining kwargs passed to the
:meth:`~matplotlib.axes.Axes.plot` method.
If *marker* and *markersize* are *None*, useful kwargs include:
* *cmap*
* *alpha*
See also
--------
imshow : for image options.
plot : for plotting options
stackplot(x, *args, **kwargs)
Draws a stacked area plot.
*x* : 1d array of dimension N
*y* : 2d array of dimension MxN, OR any number 1d arrays each of dimension
1xN. The data is assumed to be unstacked. Each of the following
calls is legal::
stackplot(x, y) # where y is MxN
stackplot(x, y1, y2, y3, y4) # where y1, y2, y3, y4, are all 1xNm
Keyword arguments:
*baseline* : ['zero', 'sym', 'wiggle', 'weighted_wiggle']
Method used to calculate the baseline. 'zero' is just a
simple stacked plot. 'sym' is symmetric around zero and
is sometimes called `ThemeRiver`. 'wiggle' minimizes the
sum of the squared slopes. 'weighted_wiggle' does the
same but weights to account for size of each layer.
It is also called `Streamgraph`-layout. More details
can be found at http://leebyron.com/streamgraph/.
*labels* : A list or tuple of labels to assign to each data series.
*colors* : A list or tuple of colors. These will be cycled through and
used to colour the stacked areas.
All other keyword arguments are passed to
:func:`~matplotlib.Axes.fill_between`
Returns *r* : A list of
:class:`~matplotlib.collections.PolyCollection`, one for each
element in the stacked area plot.
stem(*args, **kwargs)
Create a stem plot.
Call signatures::
stem(y, linefmt='b-', markerfmt='bo', basefmt='r-')
stem(x, y, linefmt='b-', markerfmt='bo', basefmt='r-')
A stem plot plots vertical lines (using *linefmt*) at each *x*
location from the baseline to *y*, and places a marker there
using *markerfmt*. A horizontal line at 0 is is plotted using
*basefmt*.
If no *x* values are provided, the default is (0, 1, ..., len(y) - 1)
Return value is a tuple (*markerline*, *stemlines*,
*baseline*).
.. seealso::
This
`document <http://www.mathworks.com/help/techdoc/ref/stem.html>`_
for details.
**Example:**
.. plot:: mpl_examples/pylab_examples/stem_plot.py
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All positional and all keyword arguments.
step(x, y, *args, **kwargs)
Make a step plot.
Parameters
----------
x : array_like
1-D sequence, and it is assumed, but not checked,
that it is uniformly increasing.
y : array_like
1-D sequence, and it is assumed, but not checked,
that it is uniformly increasing.
Returns
-------
list
List of lines that were added.
Other parameters
----------------
where : [ 'pre' | 'post' | 'mid' ]
If 'pre' (the default), the interval from
x[i] to x[i+1] has level y[i+1].
If 'post', that interval has level y[i].
If 'mid', the jumps in *y* occur half-way between the
*x*-values.
Notes
-----
Additional parameters are the same as those for
:func:`~matplotlib.pyplot.plot`.
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All arguments with the following names: 'x', 'y'.
streamplot(x, y, u, v, density=1, linewidth=None, color=None, cmap=None, norm=None, arrowsize=1, arrowstyle=u'-|>', minlength=0.1, transform=None, zorder=None, start_points=None, hold=None, data=None)
Draws streamlines of a vector flow.
*x*, *y* : 1d arrays
an *evenly spaced* grid.
*u*, *v* : 2d arrays
x and y-velocities. Number of rows should match length of y, and
the number of columns should match x.
*density* : float or 2-tuple
Controls the closeness of streamlines. When `density = 1`, the domain
is divided into a 30x30 grid---*density* linearly scales this grid.
Each cell in the grid can have, at most, one traversing streamline.
For different densities in each direction, use [density_x, density_y].
*linewidth* : numeric or 2d array
vary linewidth when given a 2d array with the same shape as velocities.
*color* : matplotlib color code, or 2d array
Streamline color. When given an array with the same shape as
velocities, *color* values are converted to colors using *cmap*.
*cmap* : :class:`~matplotlib.colors.Colormap`
Colormap used to plot streamlines and arrows. Only necessary when using
an array input for *color*.
*norm* : :class:`~matplotlib.colors.Normalize`
Normalize object used to scale luminance data to 0, 1. If None, stretch
(min, max) to (0, 1). Only necessary when *color* is an array.
*arrowsize* : float
Factor scale arrow size.
*arrowstyle* : str
Arrow style specification.
See :class:`~matplotlib.patches.FancyArrowPatch`.
*minlength* : float
Minimum length of streamline in axes coordinates.
*start_points*: Nx2 array
Coordinates of starting points for the streamlines.
In data coordinates, the same as the ``x`` and ``y`` arrays.
*zorder* : int
any number
Returns:
*stream_container* : StreamplotSet
Container object with attributes
- lines: `matplotlib.collections.LineCollection` of streamlines
- arrows: collection of `matplotlib.patches.FancyArrowPatch`
objects representing arrows half-way along stream
lines.
This container will probably change in the future to allow changes
to the colormap, alpha, etc. for both lines and arrows, but these
changes should be backward compatible.
subplot(*args, **kwargs)
Return a subplot axes positioned by the given grid definition.
Typical call signature::
subplot(nrows, ncols, plot_number)
Where *nrows* and *ncols* are used to notionally split the figure
into ``nrows * ncols`` sub-axes, and *plot_number* is used to identify
the particular subplot that this function is to create within the notional
grid. *plot_number* starts at 1, increments across rows first and has a
maximum of ``nrows * ncols``.
In the case when *nrows*, *ncols* and *plot_number* are all less than 10,
a convenience exists, such that the a 3 digit number can be given instead,
where the hundreds represent *nrows*, the tens represent *ncols* and the
units represent *plot_number*. For instance::
subplot(211)
produces a subaxes in a figure which represents the top plot (i.e. the
first) in a 2 row by 1 column notional grid (no grid actually exists,
but conceptually this is how the returned subplot has been positioned).
.. note::
Creating a new subplot with a position which is entirely inside a
pre-existing axes will trigger the larger axes to be deleted::
import matplotlib.pyplot as plt
# plot a line, implicitly creating a subplot(111)
plt.plot([1,2,3])
# now create a subplot which represents the top plot of a grid
# with 2 rows and 1 column. Since this subplot will overlap the
# first, the plot (and its axes) previously created, will be removed
plt.subplot(211)
plt.plot(range(12))
plt.subplot(212, facecolor='y') # creates 2nd subplot with yellow background
If you do not want this behavior, use the
:meth:`~matplotlib.figure.Figure.add_subplot` method or the
:func:`~matplotlib.pyplot.axes` function instead.
Keyword arguments:
*facecolor*:
The background color of the subplot, which can be any valid
color specifier. See :mod:`matplotlib.colors` for more
information.
*polar*:
A boolean flag indicating whether the subplot plot should be
a polar projection. Defaults to *False*.
*projection*:
A string giving the name of a custom projection to be used
for the subplot. This projection must have been previously
registered. See :mod:`matplotlib.projections`.
.. seealso::
:func:`~matplotlib.pyplot.axes`
For additional information on :func:`axes` and
:func:`subplot` keyword arguments.
:file:`examples/pie_and_polar_charts/polar_scatter_demo.py`
For an example
**Example:**
.. plot:: mpl_examples/subplots_axes_and_figures/subplot_demo.py
subplot2grid(shape, loc, rowspan=1, colspan=1, **kwargs)
Create a subplot in a grid. The grid is specified by *shape*, at
location of *loc*, spanning *rowspan*, *colspan* cells in each
direction. The index for loc is 0-based. ::
subplot2grid(shape, loc, rowspan=1, colspan=1)
is identical to ::
gridspec=GridSpec(shape[0], shape[1])
subplotspec=gridspec.new_subplotspec(loc, rowspan, colspan)
subplot(subplotspec)
subplot_tool(targetfig=None)
Launch a subplot tool window for a figure.
A :class:`matplotlib.widgets.SubplotTool` instance is returned.
subplots(nrows=1, ncols=1, sharex=False, sharey=False, squeeze=True, subplot_kw=None, gridspec_kw=None, **fig_kw)
Create a figure and a set of subplots
This utility wrapper makes it convenient to create common layouts of
subplots, including the enclosing figure object, in a single call.
Parameters
----------
nrows, ncols : int, optional, default: 1
Number of rows/columns of the subplot grid.
sharex, sharey : bool or {'none', 'all', 'row', 'col'}, default: False
Controls sharing of properties among x (`sharex`) or y (`sharey`)
axes:
- True or 'all': x- or y-axis will be shared among all
subplots.
- False or 'none': each subplot x- or y-axis will be
independent.
- 'row': each subplot row will share an x- or y-axis.
- 'col': each subplot column will share an x- or y-axis.
When subplots have a shared x-axis along a column, only the x tick
labels of the bottom subplot are visible. Similarly, when subplots
have a shared y-axis along a row, only the y tick labels of the first
column subplot are visible.
squeeze : bool, optional, default: True
- If True, extra dimensions are squeezed out from the returned Axes
object:
- if only one subplot is constructed (nrows=ncols=1), the
resulting single Axes object is returned as a scalar.
- for Nx1 or 1xN subplots, the returned object is a 1D numpy
object array of Axes objects are returned as numpy 1D arrays.
- for NxM, subplots with N>1 and M>1 are returned as a 2D arrays.
- If False, no squeezing at all is done: the returned Axes object is
always a 2D array containing Axes instances, even if it ends up
being 1x1.
subplot_kw : dict, optional
Dict with keywords passed to the
:meth:`~matplotlib.figure.Figure.add_subplot` call used to create each
subplot.
gridspec_kw : dict, optional
Dict with keywords passed to the
:class:`~matplotlib.gridspec.GridSpec` constructor used to create the
grid the subplots are placed on.
fig_kw : dict, optional
Dict with keywords passed to the :func:`figure` call. Note that all
keywords not recognized above will be automatically included here.
Returns
-------
fig : :class:`matplotlib.figure.Figure` object
ax : Axes object or array of Axes objects.
ax can be either a single :class:`matplotlib.axes.Axes` object or an
array of Axes objects if more than one subplot was created. The
dimensions of the resulting array can be controlled with the squeeze
keyword, see above.
Examples
--------
First create some toy data:
>>> x = np.linspace(0, 2*np.pi, 400)
>>> y = np.sin(x**2)
Creates just a figure and only one subplot
>>> fig, ax = plt.subplots()
>>> ax.plot(x, y)
>>> ax.set_title('Simple plot')
Creates two subplots and unpacks the output array immediately
>>> f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
>>> ax1.plot(x, y)
>>> ax1.set_title('Sharing Y axis')
>>> ax2.scatter(x, y)
Creates four polar axes, and accesses them through the returned array
>>> fig, axes = plt.subplots(2, 2, subplot_kw=dict(polar=True))
>>> axes[0, 0].plot(x, y)
>>> axes[1, 1].scatter(x, y)
Share a X axis with each column of subplots
>>> plt.subplots(2, 2, sharex='col')
Share a Y axis with each row of subplots
>>> plt.subplots(2, 2, sharey='row')
Share both X and Y axes with all subplots
>>> plt.subplots(2, 2, sharex='all', sharey='all')
Note that this is the same as
>>> plt.subplots(2, 2, sharex=True, sharey=True)
See Also
--------
figure
subplot
subplots_adjust(*args, **kwargs)
Tune the subplot layout.
call signature::
subplots_adjust(left=None, bottom=None, right=None, top=None,
wspace=None, hspace=None)
The parameter meanings (and suggested defaults) are::
left = 0.125 # the left side of the subplots of the figure
right = 0.9 # the right side of the subplots of the figure
bottom = 0.1 # the bottom of the subplots of the figure
top = 0.9 # the top of the subplots of the figure
wspace = 0.2 # the amount of width reserved for blank space between subplots,
# expressed as a fraction of the average axis width
hspace = 0.2 # the amount of height reserved for white space between subplots,
# expressed as a fraction of the average axis height
The actual defaults are controlled by the rc file
summer()
set the default colormap to summer and apply to current image if any.
See help(colormaps) for more information
suptitle(*args, **kwargs)
Add a centered title to the figure.
kwargs are :class:`matplotlib.text.Text` properties. Using figure
coordinates, the defaults are:
x : 0.5
The x location of the text in figure coords
y : 0.98
The y location of the text in figure coords
horizontalalignment : 'center'
The horizontal alignment of the text
verticalalignment : 'top'
The vertical alignment of the text
If the `fontproperties` keyword argument is given then the
rcParams defaults for `fontsize` (`figure.titlesize`) and
`fontweight` (`figure.titleweight`) will be ignored in favour
of the `FontProperties` defaults.
A :class:`matplotlib.text.Text` instance is returned.
Example::
fig.suptitle('this is the figure title', fontsize=12)
switch_backend(newbackend)
Switch the default backend. This feature is **experimental**, and
is only expected to work switching to an image backend. e.g., if
you have a bunch of PostScript scripts that you want to run from
an interactive ipython session, you may want to switch to the PS
backend before running them to avoid having a bunch of GUI windows
popup. If you try to interactively switch from one GUI backend to
another, you will explode.
Calling this command will close all open windows.
table(**kwargs)
Add a table to the current axes.
Call signature::
table(cellText=None, cellColours=None,
cellLoc='right', colWidths=None,
rowLabels=None, rowColours=None, rowLoc='left',
colLabels=None, colColours=None, colLoc='center',
loc='bottom', bbox=None):
Returns a :class:`matplotlib.table.Table` instance. Either `cellText`
or `cellColours` must be provided. For finer grained control over
tables, use the :class:`~matplotlib.table.Table` class and add it to
the axes with :meth:`~matplotlib.axes.Axes.add_table`.
Thanks to John Gill for providing the class and table.
kwargs control the :class:`~matplotlib.table.Table`
properties:
agg_filter: unknown
alpha: float (0.0 transparent through 1.0 opaque)
animated: [True | False]
axes: an :class:`~matplotlib.axes.Axes` instance
clip_box: a :class:`matplotlib.transforms.Bbox` instance
clip_on: [True | False]
clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]
contains: a callable function
figure: a :class:`matplotlib.figure.Figure` instance
fontsize: a float in points
gid: an id string
label: string or anything printable with '%s' conversion.
path_effects: unknown
picker: [None|float|boolean|callable]
rasterized: [True | False | None]
sketch_params: unknown
snap: unknown
transform: :class:`~matplotlib.transforms.Transform` instance
url: a url string
visible: [True | False]
zorder: any number
text(x, y, s, fontdict=None, withdash=False, **kwargs)
Add text to the axes.
Add text in string `s` to axis at location `x`, `y`, data
coordinates.
Parameters
----------
x, y : scalars
data coordinates
s : string
text
fontdict : dictionary, optional, default: None
A dictionary to override the default text properties. If fontdict
is None, the defaults are determined by your rc parameters.
withdash : boolean, optional, default: False
Creates a `~matplotlib.text.TextWithDash` instance instead of a
`~matplotlib.text.Text` instance.
Other parameters
----------------
kwargs : `~matplotlib.text.Text` properties.
Other miscellaneous text parameters.
Examples
--------
Individual keyword arguments can be used to override any given
parameter::
>>> text(x, y, s, fontsize=12)
The default transform specifies that text is in data coords,
alternatively, you can specify text in axis coords (0,0 is
lower-left and 1,1 is upper-right). The example below places
text in the center of the axes::
>>> text(0.5, 0.5,'matplotlib', horizontalalignment='center',
... verticalalignment='center',
... transform=ax.transAxes)
You can put a rectangular box around the text instance (e.g., to
set a background color) by using the keyword `bbox`. `bbox` is
a dictionary of `~matplotlib.patches.Rectangle`
properties. For example::
>>> text(x, y, s, bbox=dict(facecolor='red', alpha=0.5))
thetagrids(*args, **kwargs)
Get or set the theta locations of the gridlines in a polar plot.
If no arguments are passed, return a tuple (*lines*, *labels*)
where *lines* is an array of radial gridlines
(:class:`~matplotlib.lines.Line2D` instances) and *labels* is an
array of tick labels (:class:`~matplotlib.text.Text` instances)::
lines, labels = thetagrids()
Otherwise the syntax is::
lines, labels = thetagrids(angles, labels=None, fmt='%d', frac = 1.1)
set the angles at which to place the theta grids (these gridlines
are equal along the theta dimension).
*angles* is in degrees.
*labels*, if not *None*, is a len(angles) list of strings of the
labels to use at each angle.
If *labels* is *None*, the labels will be ``fmt%angle``.
*frac* is the fraction of the polar axes radius at which to place
the label (1 is the edge). e.g., 1.05 is outside the axes and 0.95
is inside the axes.
Return value is a list of tuples (*lines*, *labels*):
- *lines* are :class:`~matplotlib.lines.Line2D` instances
- *labels* are :class:`~matplotlib.text.Text` instances.
Note that on input, the *labels* argument is a list of strings,
and on output it is a list of :class:`~matplotlib.text.Text`
instances.
Examples::
# set the locations of the radial gridlines and labels
lines, labels = thetagrids( range(45,360,90) )
# set the locations and labels of the radial gridlines and labels
lines, labels = thetagrids( range(45,360,90), ('NE', 'NW', 'SW','SE') )
tick_params(axis=u'both', **kwargs)
Change the appearance of ticks and tick labels.
Parameters
----------
axis : {'x', 'y', 'both'}, optional
Which axis to apply the parameters to.
Other Parameters
----------------
axis : {'x', 'y', 'both'}
Axis on which to operate; default is 'both'.
reset : bool
If *True*, set all parameters to defaults
before processing other keyword arguments. Default is
*False*.
which : {'major', 'minor', 'both'}
Default is 'major'; apply arguments to *which* ticks.
direction : {'in', 'out', 'inout'}
Puts ticks inside the axes, outside the axes, or both.
length : float
Tick length in points.
width : float
Tick width in points.
color : color
Tick color; accepts any mpl color spec.
pad : float
Distance in points between tick and label.
labelsize : float or str
Tick label font size in points or as a string (e.g., 'large').
labelcolor : color
Tick label color; mpl color spec.
colors : color
Changes the tick color and the label color to the same value:
mpl color spec.
zorder : float
Tick and label zorder.
bottom, top, left, right : bool or {'on', 'off'}
controls whether to draw the respective ticks.
labelbottom, labeltop, labelleft, labelright : bool or {'on', 'off'}
controls whether to draw the
respective tick labels.
Examples
--------
Usage ::
ax.tick_params(direction='out', length=6, width=2, colors='r')
This will make all major ticks be red, pointing out of the box,
and with dimensions 6 points by 2 points. Tick labels will
also be red.
ticklabel_format(**kwargs)
Change the `~matplotlib.ticker.ScalarFormatter` used by
default for linear axes.
Optional keyword arguments:
============ =========================================
Keyword Description
============ =========================================
*style* [ 'sci' (or 'scientific') | 'plain' ]
plain turns off scientific notation
*scilimits* (m, n), pair of integers; if *style*
is 'sci', scientific notation will
be used for numbers outside the range
10`m`:sup: to 10`n`:sup:.
Use (0,0) to include all numbers.
*useOffset* [True | False | offset]; if True,
the offset will be calculated as needed;
if False, no offset will be used; if a
numeric offset is specified, it will be
used.
*axis* [ 'x' | 'y' | 'both' ]
*useLocale* If True, format the number according to
the current locale. This affects things
such as the character used for the
decimal separator. If False, use
C-style (English) formatting. The
default setting is controlled by the
axes.formatter.use_locale rcparam.
============ =========================================
Only the major ticks are affected.
If the method is called when the
:class:`~matplotlib.ticker.ScalarFormatter` is not the
:class:`~matplotlib.ticker.Formatter` being used, an
:exc:`AttributeError` will be raised.
tight_layout(pad=1.08, h_pad=None, w_pad=None, rect=None)
Automatically adjust subplot parameters to give specified padding.
Parameters:
pad : float
padding between the figure edge and the edges of subplots, as a fraction of the font-size.
h_pad, w_pad : float
padding (height/width) between edges of adjacent subplots.
Defaults to `pad_inches`.
rect : if rect is given, it is interpreted as a rectangle
(left, bottom, right, top) in the normalized figure
coordinate that the whole subplots area (including
labels) will fit into. Default is (0, 0, 1, 1).
title(s, *args, **kwargs)
Set a title of the current axes.
Set one of the three available axes titles. The available titles are
positioned above the axes in the center, flush with the left edge,
and flush with the right edge.
.. seealso::
See :func:`~matplotlib.pyplot.text` for adding text
to the current axes
Parameters
----------
label : str
Text to use for the title
fontdict : dict
A dictionary controlling the appearance of the title text,
the default `fontdict` is:
{'fontsize': rcParams['axes.titlesize'],
'fontweight' : rcParams['axes.titleweight'],
'verticalalignment': 'baseline',
'horizontalalignment': loc}
loc : {'center', 'left', 'right'}, str, optional
Which title to set, defaults to 'center'
Returns
-------
text : :class:`~matplotlib.text.Text`
The matplotlib text instance representing the title
Other parameters
----------------
kwargs : text properties
Other keyword arguments are text properties, see
:class:`~matplotlib.text.Text` for a list of valid text
properties.
tricontour(*args, **kwargs)
Draw contours on an unstructured triangular grid.
:func:`~matplotlib.pyplot.tricontour` and
:func:`~matplotlib.pyplot.tricontourf` draw contour lines and
filled contours, respectively. Except as noted, function
signatures and return values are the same for both versions.
The triangulation can be specified in one of two ways; either::
tricontour(triangulation, ...)
where triangulation is a :class:`matplotlib.tri.Triangulation`
object, or
::
tricontour(x, y, ...)
tricontour(x, y, triangles, ...)
tricontour(x, y, triangles=triangles, ...)
tricontour(x, y, mask=mask, ...)
tricontour(x, y, triangles, mask=mask, ...)
in which case a Triangulation object will be created. See
:class:`~matplotlib.tri.Triangulation` for a explanation of
these possibilities.
The remaining arguments may be::
tricontour(..., Z)
where *Z* is the array of values to contour, one per point
in the triangulation. The level values are chosen
automatically.
::
tricontour(..., Z, N)
contour *N* automatically-chosen levels.
::
tricontour(..., Z, V)
draw contour lines at the values specified in sequence *V*,
which must be in increasing order.
::
tricontourf(..., Z, V)
fill the (len(*V*)-1) regions between the values in *V*,
which must be in increasing order.
::
tricontour(Z, **kwargs)
Use keyword args to control colors, linewidth, origin, cmap ... see
below for more details.
``C = tricontour(...)`` returns a
:class:`~matplotlib.contour.TriContourSet` object.
Optional keyword arguments:
*colors*: [ *None* | string | (mpl_colors) ]
If *None*, the colormap specified by cmap will be used.
If a string, like 'r' or 'red', all levels will be plotted in this
color.
If a tuple of matplotlib color args (string, float, rgb, etc),
different levels will be plotted in different colors in the order
specified.
*alpha*: float
The alpha blending value
*cmap*: [ *None* | Colormap ]
A cm :class:`~matplotlib.colors.Colormap` instance or
*None*. If *cmap* is *None* and *colors* is *None*, a
default Colormap is used.
*norm*: [ *None* | Normalize ]
A :class:`matplotlib.colors.Normalize` instance for
scaling data values to colors. If *norm* is *None* and
*colors* is *None*, the default linear scaling is used.
*levels* [level0, level1, ..., leveln]
A list of floating point numbers indicating the level
curves to draw, in increasing order; e.g., to draw just
the zero contour pass ``levels=[0]``
*origin*: [ *None* | 'upper' | 'lower' | 'image' ]
If *None*, the first value of *Z* will correspond to the
lower left corner, location (0,0). If 'image', the rc
value for ``image.origin`` will be used.
This keyword is not active if *X* and *Y* are specified in
the call to contour.
*extent*: [ *None* | (x0,x1,y0,y1) ]
If *origin* is not *None*, then *extent* is interpreted as
in :func:`matplotlib.pyplot.imshow`: it gives the outer
pixel boundaries. In this case, the position of Z[0,0]
is the center of the pixel, not a corner. If *origin* is
*None*, then (*x0*, *y0*) is the position of Z[0,0], and
(*x1*, *y1*) is the position of Z[-1,-1].
This keyword is not active if *X* and *Y* are specified in
the call to contour.
*locator*: [ *None* | ticker.Locator subclass ]
If *locator* is None, the default
:class:`~matplotlib.ticker.MaxNLocator` is used. The
locator is used to determine the contour levels if they
are not given explicitly via the *V* argument.
*extend*: [ 'neither' | 'both' | 'min' | 'max' ]
Unless this is 'neither', contour levels are automatically
added to one or both ends of the range so that all data
are included. These added ranges are then mapped to the
special colormap values which default to the ends of the
colormap range, but can be set via
:meth:`matplotlib.colors.Colormap.set_under` and
:meth:`matplotlib.colors.Colormap.set_over` methods.
*xunits*, *yunits*: [ *None* | registered units ]
Override axis units by specifying an instance of a
:class:`matplotlib.units.ConversionInterface`.
tricontour-only keyword arguments:
*linewidths*: [ *None* | number | tuple of numbers ]
If *linewidths* is *None*, the default width in
``lines.linewidth`` in ``matplotlibrc`` is used.
If a number, all levels will be plotted with this linewidth.
If a tuple, different levels will be plotted with different
linewidths in the order specified
*linestyles*: [ *None* | 'solid' | 'dashed' | 'dashdot' | 'dotted' ]
If *linestyles* is *None*, the 'solid' is used.
*linestyles* can also be an iterable of the above strings
specifying a set of linestyles to be used. If this
iterable is shorter than the number of contour levels
it will be repeated as necessary.
If contour is using a monochrome colormap and the contour
level is less than 0, then the linestyle specified
in ``contour.negative_linestyle`` in ``matplotlibrc``
will be used.
tricontourf-only keyword arguments:
*antialiased*: [ *True* | *False* ]
enable antialiasing
Note: tricontourf fills intervals that are closed at the top; that
is, for boundaries *z1* and *z2*, the filled region is::
z1 < z <= z2
There is one exception: if the lowest boundary coincides with
the minimum value of the *z* array, then that minimum value
will be included in the lowest interval.
**Examples:**
.. plot:: mpl_examples/pylab_examples/tricontour_demo.py
tricontourf(*args, **kwargs)
Draw contours on an unstructured triangular grid.
:func:`~matplotlib.pyplot.tricontour` and
:func:`~matplotlib.pyplot.tricontourf` draw contour lines and
filled contours, respectively. Except as noted, function
signatures and return values are the same for both versions.
The triangulation can be specified in one of two ways; either::
tricontour(triangulation, ...)
where triangulation is a :class:`matplotlib.tri.Triangulation`
object, or
::
tricontour(x, y, ...)
tricontour(x, y, triangles, ...)
tricontour(x, y, triangles=triangles, ...)
tricontour(x, y, mask=mask, ...)
tricontour(x, y, triangles, mask=mask, ...)
in which case a Triangulation object will be created. See
:class:`~matplotlib.tri.Triangulation` for a explanation of
these possibilities.
The remaining arguments may be::
tricontour(..., Z)
where *Z* is the array of values to contour, one per point
in the triangulation. The level values are chosen
automatically.
::
tricontour(..., Z, N)
contour *N* automatically-chosen levels.
::
tricontour(..., Z, V)
draw contour lines at the values specified in sequence *V*,
which must be in increasing order.
::
tricontourf(..., Z, V)
fill the (len(*V*)-1) regions between the values in *V*,
which must be in increasing order.
::
tricontour(Z, **kwargs)
Use keyword args to control colors, linewidth, origin, cmap ... see
below for more details.
``C = tricontour(...)`` returns a
:class:`~matplotlib.contour.TriContourSet` object.
Optional keyword arguments:
*colors*: [ *None* | string | (mpl_colors) ]
If *None*, the colormap specified by cmap will be used.
If a string, like 'r' or 'red', all levels will be plotted in this
color.
If a tuple of matplotlib color args (string, float, rgb, etc),
different levels will be plotted in different colors in the order
specified.
*alpha*: float
The alpha blending value
*cmap*: [ *None* | Colormap ]
A cm :class:`~matplotlib.colors.Colormap` instance or
*None*. If *cmap* is *None* and *colors* is *None*, a
default Colormap is used.
*norm*: [ *None* | Normalize ]
A :class:`matplotlib.colors.Normalize` instance for
scaling data values to colors. If *norm* is *None* and
*colors* is *None*, the default linear scaling is used.
*levels* [level0, level1, ..., leveln]
A list of floating point numbers indicating the level
curves to draw, in increasing order; e.g., to draw just
the zero contour pass ``levels=[0]``
*origin*: [ *None* | 'upper' | 'lower' | 'image' ]
If *None*, the first value of *Z* will correspond to the
lower left corner, location (0,0). If 'image', the rc
value for ``image.origin`` will be used.
This keyword is not active if *X* and *Y* are specified in
the call to contour.
*extent*: [ *None* | (x0,x1,y0,y1) ]
If *origin* is not *None*, then *extent* is interpreted as
in :func:`matplotlib.pyplot.imshow`: it gives the outer
pixel boundaries. In this case, the position of Z[0,0]
is the center of the pixel, not a corner. If *origin* is
*None*, then (*x0*, *y0*) is the position of Z[0,0], and
(*x1*, *y1*) is the position of Z[-1,-1].
This keyword is not active if *X* and *Y* are specified in
the call to contour.
*locator*: [ *None* | ticker.Locator subclass ]
If *locator* is None, the default
:class:`~matplotlib.ticker.MaxNLocator` is used. The
locator is used to determine the contour levels if they
are not given explicitly via the *V* argument.
*extend*: [ 'neither' | 'both' | 'min' | 'max' ]
Unless this is 'neither', contour levels are automatically
added to one or both ends of the range so that all data
are included. These added ranges are then mapped to the
special colormap values which default to the ends of the
colormap range, but can be set via
:meth:`matplotlib.colors.Colormap.set_under` and
:meth:`matplotlib.colors.Colormap.set_over` methods.
*xunits*, *yunits*: [ *None* | registered units ]
Override axis units by specifying an instance of a
:class:`matplotlib.units.ConversionInterface`.
tricontour-only keyword arguments:
*linewidths*: [ *None* | number | tuple of numbers ]
If *linewidths* is *None*, the default width in
``lines.linewidth`` in ``matplotlibrc`` is used.
If a number, all levels will be plotted with this linewidth.
If a tuple, different levels will be plotted with different
linewidths in the order specified
*linestyles*: [ *None* | 'solid' | 'dashed' | 'dashdot' | 'dotted' ]
If *linestyles* is *None*, the 'solid' is used.
*linestyles* can also be an iterable of the above strings
specifying a set of linestyles to be used. If this
iterable is shorter than the number of contour levels
it will be repeated as necessary.
If contour is using a monochrome colormap and the contour
level is less than 0, then the linestyle specified
in ``contour.negative_linestyle`` in ``matplotlibrc``
will be used.
tricontourf-only keyword arguments:
*antialiased*: [ *True* | *False* ]
enable antialiasing
Note: tricontourf fills intervals that are closed at the top; that
is, for boundaries *z1* and *z2*, the filled region is::
z1 < z <= z2
There is one exception: if the lowest boundary coincides with
the minimum value of the *z* array, then that minimum value
will be included in the lowest interval.
**Examples:**
.. plot:: mpl_examples/pylab_examples/tricontour_demo.py
tripcolor(*args, **kwargs)
Create a pseudocolor plot of an unstructured triangular grid.
The triangulation can be specified in one of two ways; either::
tripcolor(triangulation, ...)
where triangulation is a :class:`matplotlib.tri.Triangulation`
object, or
::
tripcolor(x, y, ...)
tripcolor(x, y, triangles, ...)
tripcolor(x, y, triangles=triangles, ...)
tripcolor(x, y, mask=mask, ...)
tripcolor(x, y, triangles, mask=mask, ...)
in which case a Triangulation object will be created. See
:class:`~matplotlib.tri.Triangulation` for a explanation of these
possibilities.
The next argument must be *C*, the array of color values, either
one per point in the triangulation if color values are defined at
points, or one per triangle in the triangulation if color values
are defined at triangles. If there are the same number of points
and triangles in the triangulation it is assumed that color
values are defined at points; to force the use of color values at
triangles use the kwarg *facecolors*=C instead of just *C*.
*shading* may be 'flat' (the default) or 'gouraud'. If *shading*
is 'flat' and C values are defined at points, the color values
used for each triangle are from the mean C of the triangle's
three points. If *shading* is 'gouraud' then color values must be
defined at points.
The remaining kwargs are the same as for
:meth:`~matplotlib.axes.Axes.pcolor`.
**Example:**
.. plot:: mpl_examples/pylab_examples/tripcolor_demo.py
triplot(*args, **kwargs)
Draw a unstructured triangular grid as lines and/or markers.
The triangulation to plot can be specified in one of two ways;
either::
triplot(triangulation, ...)
where triangulation is a :class:`matplotlib.tri.Triangulation`
object, or
::
triplot(x, y, ...)
triplot(x, y, triangles, ...)
triplot(x, y, triangles=triangles, ...)
triplot(x, y, mask=mask, ...)
triplot(x, y, triangles, mask=mask, ...)
in which case a Triangulation object will be created. See
:class:`~matplotlib.tri.Triangulation` for a explanation of these
possibilities.
The remaining args and kwargs are the same as for
:meth:`~matplotlib.axes.Axes.plot`.
Return a list of 2 :class:`~matplotlib.lines.Line2D` containing
respectively:
- the lines plotted for triangles edges
- the markers plotted for triangles nodes
**Example:**
.. plot:: mpl_examples/pylab_examples/triplot_demo.py
twinx(ax=None)
Make a second axes that shares the *x*-axis. The new axes will
overlay *ax* (or the current axes if *ax* is *None*). The ticks
for *ax2* will be placed on the right, and the *ax2* instance is
returned.
.. seealso::
:file:`examples/api_examples/two_scales.py`
For an example
twiny(ax=None)
Make a second axes that shares the *y*-axis. The new axis will
overlay *ax* (or the current axes if *ax* is *None*). The ticks
for *ax2* will be placed on the top, and the *ax2* instance is
returned.
uninstall_repl_displayhook()
Uninstalls the matplotlib display hook.
.. warning
Need IPython >= 2 for this to work. For IPython < 2 will raise a
``NotImplementedError``
.. warning
If you are using vanilla python and have installed another
display hook this will reset ``sys.displayhook`` to what ever
function was there when matplotlib installed it's displayhook,
possibly discarding your changes.
violinplot(dataset, positions=None, vert=True, widths=0.5, showmeans=False, showextrema=True, showmedians=False, points=100, bw_method=None, hold=None, data=None)
Make a violin plot.
Make a violin plot for each column of *dataset* or each vector in
sequence *dataset*. Each filled area extends to represent the
entire data range, with optional lines at the mean, the median,
the minimum, and the maximum.
Parameters
----------
dataset : Array or a sequence of vectors.
The input data.
positions : array-like, default = [1, 2, ..., n]
Sets the positions of the violins. The ticks and limits are
automatically set to match the positions.
vert : bool, default = True.
If true, creates a vertical violin plot.
Otherwise, creates a horizontal violin plot.
widths : array-like, default = 0.5
Either a scalar or a vector that sets the maximal width of
each violin. The default is 0.5, which uses about half of the
available horizontal space.
showmeans : bool, default = False
If `True`, will toggle rendering of the means.
showextrema : bool, default = True
If `True`, will toggle rendering of the extrema.
showmedians : bool, default = False
If `True`, will toggle rendering of the medians.
points : scalar, default = 100
Defines the number of points to evaluate each of the
gaussian kernel density estimations at.
bw_method : str, scalar or callable, optional
The method used to calculate the estimator bandwidth. This can be
'scott', 'silverman', a scalar constant or a callable. If a
scalar, this will be used directly as `kde.factor`. If a
callable, it should take a `GaussianKDE` instance as its only
parameter and return a scalar. If None (default), 'scott' is used.
Returns
-------
result : dict
A dictionary mapping each component of the violinplot to a
list of the corresponding collection instances created. The
dictionary has the following keys:
- ``bodies``: A list of the
:class:`matplotlib.collections.PolyCollection` instances
containing the filled area of each violin.
- ``cmeans``: A
:class:`matplotlib.collections.LineCollection` instance
created to identify the mean values of each of the
violin's distribution.
- ``cmins``: A
:class:`matplotlib.collections.LineCollection` instance
created to identify the bottom of each violin's
distribution.
- ``cmaxes``: A
:class:`matplotlib.collections.LineCollection` instance
created to identify the top of each violin's
distribution.
- ``cbars``: A
:class:`matplotlib.collections.LineCollection` instance
created to identify the centers of each violin's
distribution.
- ``cmedians``: A
:class:`matplotlib.collections.LineCollection` instance
created to identify the median values of each of the
violin's distribution.
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All arguments with the following names: 'dataset'.
viridis()
set the default colormap to viridis and apply to current image if any.
See help(colormaps) for more information
vlines(x, ymin, ymax, colors=u'k', linestyles=u'solid', label=u'', hold=None, data=None, **kwargs)
Plot vertical lines.
Plot vertical lines at each `x` from `ymin` to `ymax`.
Parameters
----------
x : scalar or 1D array_like
x-indexes where to plot the lines.
ymin, ymax : scalar or 1D array_like
Respective beginning and end of each line. If scalars are
provided, all lines will have same length.
colors : array_like of colors, optional, default: 'k'
linestyles : ['solid' | 'dashed' | 'dashdot' | 'dotted'], optional
label : string, optional, default: ''
Returns
-------
lines : `~matplotlib.collections.LineCollection`
Other parameters
----------------
kwargs : `~matplotlib.collections.LineCollection` properties.
See also
--------
hlines : horizontal lines
Examples
--------
.. plot:: mpl_examples/pylab_examples/vline_hline_demo.py
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All arguments with the following names: 'colors', 'x', 'ymax', 'ymin'.
waitforbuttonpress(*args, **kwargs)
Blocking call to interact with the figure.
This will return True is a key was pressed, False if a mouse
button was pressed and None if *timeout* was reached without
either being pressed.
If *timeout* is negative, does not timeout.
winter()
set the default colormap to winter and apply to current image if any.
See help(colormaps) for more information
xcorr(x, y, normed=True, detrend=<function detrend_none>, usevlines=True, maxlags=10, hold=None, data=None, **kwargs)
Plot the cross correlation between *x* and *y*.
The correlation with lag k is defined as sum_n x[n+k] * conj(y[n]).
Parameters
----------
x : sequence of scalars of length n
y : sequence of scalars of length n
hold : boolean, optional, *deprecated*, default: True
detrend : callable, optional, default: `mlab.detrend_none`
x is detrended by the `detrend` callable. Default is no
normalization.
normed : boolean, optional, default: True
if True, input vectors are normalised to unit length.
usevlines : boolean, optional, default: True
if True, Axes.vlines is used to plot the vertical lines from the
origin to the acorr. Otherwise, Axes.plot is used.
maxlags : integer, optional, default: 10
number of lags to show. If None, will return all 2 * len(x) - 1
lags.
Returns
-------
(lags, c, line, b) : where:
- `lags` are a length 2`maxlags+1 lag vector.
- `c` is the 2`maxlags+1 auto correlation vectorI
- `line` is a `~matplotlib.lines.Line2D` instance returned by
`plot`.
- `b` is the x-axis (none, if plot is used).
Other parameters
----------------
linestyle : `~matplotlib.lines.Line2D` prop, optional, default: None
Only used if usevlines is False.
marker : string, optional, default: 'o'
Notes
-----
The cross correlation is performed with :func:`numpy.correlate` with
`mode` = 2.
.. note::
In addition to the above described arguments, this function can take a
**data** keyword argument. If such a **data** argument is given, the
following arguments are replaced by **data[<arg>]**:
* All arguments with the following names: 'x', 'y'.
xkcd(scale=1, length=100, randomness=2)
Turns on `xkcd <http://xkcd.com/>`_ sketch-style drawing mode.
This will only have effect on things drawn after this function is
called.
For best results, the "Humor Sans" font should be installed: it is
not included with matplotlib.
Parameters
----------
scale : float, optional
The amplitude of the wiggle perpendicular to the source line.
length : float, optional
The length of the wiggle along the line.
randomness : float, optional
The scale factor by which the length is shrunken or expanded.
Notes
-----
This function works by a number of rcParams, so it will probably
override others you have set before.
If you want the effects of this function to be temporary, it can
be used as a context manager, for example::
with plt.xkcd():
# This figure will be in XKCD-style
fig1 = plt.figure()
# ...
# This figure will be in regular style
fig2 = plt.figure()
xlabel(s, *args, **kwargs)
Set the *x* axis label of the current axis.
Default override is::
override = {
'fontsize' : 'small',
'verticalalignment' : 'top',
'horizontalalignment' : 'center'
}
.. seealso::
:func:`~matplotlib.pyplot.text`
For information on how override and the optional args work
xlim(*args, **kwargs)
Get or set the *x* limits of the current axes.
::
xmin, xmax = xlim() # return the current xlim
xlim( (xmin, xmax) ) # set the xlim to xmin, xmax
xlim( xmin, xmax ) # set the xlim to xmin, xmax
If you do not specify args, you can pass the xmin and xmax as
kwargs, e.g.::
xlim(xmax=3) # adjust the max leaving min unchanged
xlim(xmin=1) # adjust the min leaving max unchanged
Setting limits turns autoscaling off for the x-axis.
The new axis limits are returned as a length 2 tuple.
xscale(*args, **kwargs)
Set the scaling of the *x*-axis.
call signature::
xscale(scale, **kwargs)
The available scales are: u'linear' | u'log' | u'logit' | u'symlog'
Different keywords may be accepted, depending on the scale:
'linear'
'log'
*basex*/*basey*:
The base of the logarithm
*nonposx*/*nonposy*: ['mask' | 'clip' ]
non-positive values in *x* or *y* can be masked as
invalid, or clipped to a very small positive number
*subsx*/*subsy*:
Where to place the subticks between each major tick.
Should be a sequence of integers. For example, in a log10
scale: ``[2, 3, 4, 5, 6, 7, 8, 9]``
will place 8 logarithmically spaced minor ticks between
each major tick.
'logit'
*nonpos*: ['mask' | 'clip' ]
values beyond ]0, 1[ can be masked as invalid, or clipped to a number
very close to 0 or 1
'symlog'
*basex*/*basey*:
The base of the logarithm
*linthreshx*/*linthreshy*:
The range (-*x*, *x*) within which the plot is linear (to
avoid having the plot go to infinity around zero).
*subsx*/*subsy*:
Where to place the subticks between each major tick.
Should be a sequence of integers. For example, in a log10
scale: ``[2, 3, 4, 5, 6, 7, 8, 9]``
will place 8 logarithmically spaced minor ticks between
each major tick.
*linscalex*/*linscaley*:
This allows the linear range (-*linthresh* to *linthresh*)
to be stretched relative to the logarithmic range. Its
value is the number of decades to use for each half of the
linear range. For example, when *linscale* == 1.0 (the
default), the space used for the positive and negative
halves of the linear range will be equal to one decade in
the logarithmic range.
xticks(*args, **kwargs)
Get or set the *x*-limits of the current tick locations and labels.
::
# return locs, labels where locs is an array of tick locations and
# labels is an array of tick labels.
locs, labels = xticks()
# set the locations of the xticks
xticks( arange(6) )
# set the locations and labels of the xticks
xticks( arange(5), ('Tom', 'Dick', 'Harry', 'Sally', 'Sue') )
The keyword args, if any, are :class:`~matplotlib.text.Text`
properties. For example, to rotate long labels::
xticks( arange(12), calendar.month_name[1:13], rotation=17 )
ylabel(s, *args, **kwargs)
Set the *y* axis label of the current axis.
Defaults override is::
override = {
'fontsize' : 'small',
'verticalalignment' : 'center',
'horizontalalignment' : 'right',
'rotation'='vertical' : }
.. seealso::
:func:`~matplotlib.pyplot.text`
For information on how override and the optional args
work.
ylim(*args, **kwargs)
Get or set the *y*-limits of the current axes.
::
ymin, ymax = ylim() # return the current ylim
ylim( (ymin, ymax) ) # set the ylim to ymin, ymax
ylim( ymin, ymax ) # set the ylim to ymin, ymax
If you do not specify args, you can pass the *ymin* and *ymax* as
kwargs, e.g.::
ylim(ymax=3) # adjust the max leaving min unchanged
ylim(ymin=1) # adjust the min leaving max unchanged
Setting limits turns autoscaling off for the y-axis.
The new axis limits are returned as a length 2 tuple.
yscale(*args, **kwargs)
Set the scaling of the *y*-axis.
call signature::
yscale(scale, **kwargs)
The available scales are: u'linear' | u'log' | u'logit' | u'symlog'
Different keywords may be accepted, depending on the scale:
'linear'
'log'
*basex*/*basey*:
The base of the logarithm
*nonposx*/*nonposy*: ['mask' | 'clip' ]
non-positive values in *x* or *y* can be masked as
invalid, or clipped to a very small positive number
*subsx*/*subsy*:
Where to place the subticks between each major tick.
Should be a sequence of integers. For example, in a log10
scale: ``[2, 3, 4, 5, 6, 7, 8, 9]``
will place 8 logarithmically spaced minor ticks between
each major tick.
'logit'
*nonpos*: ['mask' | 'clip' ]
values beyond ]0, 1[ can be masked as invalid, or clipped to a number
very close to 0 or 1
'symlog'
*basex*/*basey*:
The base of the logarithm
*linthreshx*/*linthreshy*:
The range (-*x*, *x*) within which the plot is linear (to
avoid having the plot go to infinity around zero).
*subsx*/*subsy*:
Where to place the subticks between each major tick.
Should be a sequence of integers. For example, in a log10
scale: ``[2, 3, 4, 5, 6, 7, 8, 9]``
will place 8 logarithmically spaced minor ticks between
each major tick.
*linscalex*/*linscaley*:
This allows the linear range (-*linthresh* to *linthresh*)
to be stretched relative to the logarithmic range. Its
value is the number of decades to use for each half of the
linear range. For example, when *linscale* == 1.0 (the
default), the space used for the positive and negative
halves of the linear range will be equal to one decade in
the logarithmic range.
yticks(*args, **kwargs)
Get or set the *y*-limits of the current tick locations and labels.
::
# return locs, labels where locs is an array of tick locations and
# labels is an array of tick labels.
locs, labels = yticks()
# set the locations of the yticks
yticks( arange(6) )
# set the locations and labels of the yticks
yticks( arange(5), ('Tom', 'Dick', 'Harry', 'Sally', 'Sue') )
The keyword args, if any, are :class:`~matplotlib.text.Text`
properties. For example, to rotate long labels::
yticks( arange(12), calendar.month_name[1:13], rotation=45 )
DATA
absolute_import = _Feature((2, 5, 0, 'alpha', 1), (3, 0, 0, 'alpha', 0...
division = _Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192...
print_function = _Feature((2, 6, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0)...
rcParams = RcParams({u'_internal.classic_mode': False,
...or.width...
rcParamsDefault = RcParams({u'_internal.classic_mode': False,
...o...
unicode_literals = _Feature((2, 6, 0, 'alpha', 2), (3, 0, 0, 'alpha', ...
In [ ]:
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