pyplot
matplotlib.pyplot
Provides a MATLABlike plotting framework.
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)

matplotlib.pyplot.acorr(x, hold=None, data=None, **kwargs)

Plot the autocorrelation of
x
.Parameters: x : sequence of scalar
hold : boolean, optional, 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, normalize the data by the autocorrelation at the 0th lag.
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:
Other Parameters: linestyle :
Line2D
prop, optional, default: NoneOnly used if usevlines is False.
marker : string, optional, default: ?o?
Notes
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?.
Additional kwargs: hold = [TrueFalse] overrides default hold state
Examples
xcorr
is top graph, andacorr
is bottom graph.(Source code, png, hires.png, pdf)

matplotlib.pyplot.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.
 x: 1D array or sequence
 Array or sequence containing the data
Keyword arguments:
 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
window_hanning()
,window_none()
,numpy.blackman()
,numpy.hamming()
,numpy.bartlett()
,scipy.signal()
,scipy.signal.get_window()
, etc. The default iswindow_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 onesided for real data and both for complex data. ?onesided? forces the return of a onesided spectrum, while ?twosided? forces twosided.
 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.
Returns the tuple (spectrum, freqs, line):
 spectrum: 1D array
 The values for the angle spectrum in radians (real valued)
 freqs: 1D array
 The frequencies corresponding to the elements in spectrum

line: a Line2D instance
 The line created by this function
kwargs control the
Line2D
properties:Property Description agg_filter
unknown alpha
float (0.0 transparent through 1.0 opaque) animated
[True  False] antialiased
or aa[True  False] axes
an Axes
instanceclip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
or cany 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?  ?stepspre?  ?stepsmid?  ?stepspost?] figure
a matplotlib.figure.Figure
instancefillstyle
[?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, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or lwfloat value in points marker
A valid marker style
markeredgecolor
or mecany matplotlib color markeredgewidth
or mewfloat value in points markerfacecolor
or mfcany matplotlib color markerfacecoloralt
or mfcaltany matplotlib color markersize
or msfloat markevery
[None  int  length2 tuple of int  slice  list/array of int  float  length2 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 matplotlib.transforms.Transform
instanceurl
a url string visible
[True  False] xdata
1D array ydata
1D array zorder
any number Example:
(Source code, png, hires.png, pdf)
See also

magnitude_spectrum()

angle_spectrum()
plots the magnitudes of the corresponding frequencies. 
phase_spectrum()

phase_spectrum()
plots the unwrapped version of this function. 
specgram()

specgram()
can plot the angle spectrum of segments within the signal in a colormap.
Notes
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?.
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.annotate(*args, **kwargs)

Annotate the point
xy
with texts
.Additional kwargs are passed to
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
Artist
object is passed in the units are fraction if it?s bounding box.If a
Transform
object is passed in use that to transformxy
to screen coordinatesIf a callable it must take a
RendererBase
object as input and return aTransform
orBbox
objectIf 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 Annotating Axes for more details.
Defaults to
'data'
textcoords : str,
Artist
,Transform
, callable or tuple, optionalThe coordinate system that
xytext
is given, which may be different than the coordinate system used forxy
.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
FancyArrowPatch
arrow betweenxy
andxytext
.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 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
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 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 thexy
is inside the axes. IfFalse
, the annotation will always be drawn regardless of its position.The default is
None
, which behave asTrue
only if xycoords is ?data?.Returns: Annotation

matplotlib.pyplot.arrow(x, y, dx, dy, hold=None, **kwargs)

Add an arrow to the axes.
Call signature:
arrow(x, y, dx, dy, **kwargs)
Draws arrow on specified axis from (x, y) to (x + dx, y + dy). Uses FancyArrow patch to construct the arrow.
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
annotate()
for example:ax.annotate("", xy=(0.5, 0.5), xytext=(0, 0), arrowprops=dict(arrowstyle=">"))
Optional kwargs 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 lefthalf, righthalf, 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
Patch
) are:Property Description agg_filter
unknown alpha
float or None animated
[True  False] antialiased
or aa[True  False] or None for default axes
an Axes
instancecapstyle
[?butt?  ?round?  ?projecting?] clip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
matplotlib color spec contains
a callable function edgecolor
or ecmpl color spec, or None for default, or ?none? for no color facecolor
or fcmpl color spec, or None for default, or ?none? for no color figure
a matplotlib.figure.Figure
instancefill
[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, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or lwfloat or None for default path_effects
unknown picker
[Nonefloatbooleancallable] rasterized
[True  False  None] sketch_params
unknown snap
unknown transform
Transform
instanceurl
a url string visible
[True  False] zorder
any number Example:
(Source code, png, hires.png, pdf)
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.autoscale(enable=True, axis='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.

matplotlib.pyplot.autumn()

set the default colormap to autumn and apply to current image if any. See help(colormaps) for more information

matplotlib.pyplot.axes(*args, **kwargs)

Add an axes to the figure.
The axes is added at position rect specified by:

axes()
by itself creates a default fullsubplot(111)
window axis. 
axes(rect, axisbg='w')
where rect = [left, bottom, width, height] in normalized (0, 1) units. axisbg is the background color for the axis, default white. 
axes(h)
where h is an axes instance makes h the current axis. AnAxes
instance is returned.
kwarg Accepts Description axisbg color the axes background color frameon [TrueFalse] display the frame? sharex otherax current axes shares xaxis attribute with otherax sharey otherax current axes shares yaxis attribute with otherax polar [TrueFalse] use a polar axes? aspect [str  num] [?equal?, ?auto?] or a number. If a number the ratio of xunit/yunit in screenspace. Also see set_aspect()
.Examples:

examples/pylab_examples/axes_demo.py
places custom axes. 
examples/pylab_examples/shared_axis_demo.py
uses sharex and sharey.


matplotlib.pyplot.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: Notes
kwargs are passed to
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
Line2D
properties, with the exception of ?transform?:Property Description agg_filter
unknown alpha
float (0.0 transparent through 1.0 opaque) animated
[True  False] antialiased
or aa[True  False] axes
an Axes
instanceclip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
or cany 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?  ?stepspre?  ?stepsmid?  ?stepspost?] figure
a matplotlib.figure.Figure
instancefillstyle
[?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, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or lwfloat value in points marker
A valid marker style
markeredgecolor
or mecany matplotlib color markeredgewidth
or mewfloat value in points markerfacecolor
or mfcany matplotlib color markerfacecoloralt
or mfcaltany matplotlib color markersize
or msfloat markevery
[None  int  length2 tuple of int  slice  list/array of int  float  length2 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 matplotlib.transforms.Transform
instanceurl
a url string visible
[True  False] xdata
1D array ydata
1D array zorder
any number 

matplotlib.pyplot.axhspan(ymin, ymax, xmin=0, xmax=1, hold=None, **kwargs)

Add a horizontal span (rectangle) across the axis.
Call signature:
axhspan(ymin, ymax, xmin=0, xmax=1, **kwargs)
y coords are in data units and x coords are in axes (relative 01) units.
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
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.Return value is a
matplotlib.patches.Polygon
instance.Examples:

draw a gray rectangle from y = 0.250.75 that spans the horizontal extent of the axes:
>>> axhspan(0.25, 0.75, facecolor='0.5', alpha=0.5)
Valid kwargs are
Polygon
properties:Property Description agg_filter
unknown alpha
float or None animated
[True  False] antialiased
or aa[True  False] or None for default axes
an Axes
instancecapstyle
[?butt?  ?round?  ?projecting?] clip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
matplotlib color spec contains
a callable function edgecolor
or ecmpl color spec, or None for default, or ?none? for no color facecolor
or fcmpl color spec, or None for default, or ?none? for no color figure
a matplotlib.figure.Figure
instancefill
[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, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or lwfloat or None for default path_effects
unknown picker
[Nonefloatbooleancallable] rasterized
[True  False  None] sketch_params
unknown snap
unknown transform
Transform
instanceurl
a url string visible
[True  False] zorder
any number Example:
(Source code, png, hires.png, pdf)
Additional kwargs: hold = [TrueFalse] overrides default hold state


matplotlib.pyplot.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 (xmaxxmin) and (ymaxymin) 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
See also

xlim(),
ylim()
 For setting the x and ylimits individually.


matplotlib.pyplot.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: 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
Line2D
properties, with the exception of ?transform?:Property Description agg_filter
unknown alpha
float (0.0 transparent through 1.0 opaque) animated
[True  False] antialiased
or aa[True  False] axes
an Axes
instanceclip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
or cany 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?  ?stepspre?  ?stepsmid?  ?stepspost?] figure
a matplotlib.figure.Figure
instancefillstyle
[?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, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or lwfloat value in points marker
A valid marker style
markeredgecolor
or mecany matplotlib color markeredgewidth
or mewfloat value in points markerfacecolor
or mfcany matplotlib color markerfacecoloralt
or mfcaltany matplotlib color markersize
or msfloat markevery
[None  int  length2 tuple of int  slice  list/array of int  float  length2 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 matplotlib.transforms.Transform
instanceurl
a url string visible
[True  False] xdata
1D array ydata
1D array zorder
any number 

matplotlib.pyplot.axvspan(xmin, xmax, ymin=0, ymax=1, hold=None, **kwargs)

Add a vertical span (rectangle) across the axes.
Call signature:
axvspan(xmin, xmax, ymin=0, ymax=1, **kwargs)
x coords are in data units and y coords are in axes (relative 01) units.
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
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.Return value is the
matplotlib.patches.Polygon
instance.Examples:

draw a vertical green translucent rectangle from x=1.25 to 1.55 that spans the yrange of the axes:
>>> axvspan(1.25, 1.55, facecolor='g', alpha=0.5)
Valid kwargs are
Polygon
properties:Property Description agg_filter
unknown alpha
float or None animated
[True  False] antialiased
or aa[True  False] or None for default axes
an Axes
instancecapstyle
[?butt?  ?round?  ?projecting?] clip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
matplotlib color spec contains
a callable function edgecolor
or ecmpl color spec, or None for default, or ?none? for no color facecolor
or fcmpl color spec, or None for default, or ?none? for no color figure
a matplotlib.figure.Figure
instancefill
[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, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or lwfloat or None for default path_effects
unknown picker
[Nonefloatbooleancallable] rasterized
[True  False  None] sketch_params
unknown snap
unknown transform
Transform
instanceurl
a url string visible
[True  False] zorder
any number See also

axhspan()
 for example plot and source code
Additional kwargs: hold = [TrueFalse] overrides default hold state


matplotlib.pyplot.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 arraylike, optional
the width(s) of the bars default: 0.8
bottom : scalar or arraylike, optional
the y coordinate(s) of the bars default: None
color : scalar or arraylike, optional
the colors of the bar faces
edgecolor : scalar or arraylike, optional
the colors of the bar edges
linewidth : scalar or arraylike, optional
width of bar edge(s). If None, use default linewidth; If 0, don?t draw edges. default: None
tick_label : string or arraylike, optional
the tick labels of the bars default: None
xerr : scalar or arraylike, optional
if not None, will be used to generate errorbar(s) on the bar chart default: None
yerr : scalar or arraylike, optional
if not None, will be used to generate errorbar(s) on the bar chart default: None
ecolor : scalar or arraylike, 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
rcParam
.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 : {?edge?, ?center?}, 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 negativewidth
.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
See also

barh
 Plot a horizontal bar plot.
Notes
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: ?linewidth?, ?bottom?, ?yerr?, ?height?, ?xerr?, ?edgecolor?, ?tick_label?, ?color?, ?left?, ?width?, ?ecolor?.
Additional kwargs: hold = [TrueFalse] overrides default hold state
Examples
Example: A stacked bar chart.
(Source code, png, hires.png, pdf)


matplotlib.pyplot.barbs(*args, **kw)

Plot a 2D 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 1D or 2D arrays or sequences. If X and Y are absent, they will be generated as a uniform grid. If U and V are 2D arrays but X and Y are 1D, and if
len(X)
andlen(Y)
match the column and row dimensions of U, then X and Y will be expanded withnumpy.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 halfbarb 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:
: /\ \ : / \ \ : / \ \ \ : / \ \ \ : 
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 halfline and no full lines or triangles, the halfline 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
PolyCollection
keyword arguments:Property Description agg_filter
unknown alpha
float or None animated
[True  False] antialiased
or antialiasedsBoolean or sequence of booleans array
unknown axes
an Axes
instanceclim
a length 2 sequence of floats clip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]cmap
a colormap or registered colormap name color
matplotlib color arg or sequence of rgba tuples contains
a callable function edgecolor
or edgecolorsmatplotlib color spec or sequence of specs facecolor
or facecolorsmatplotlib color spec or sequence of specs figure
a matplotlib.figure.Figure
instancegid
an id string hatch
[ ?/?  ?\?  ??  ??  ?+?  ?x?  ?o?  ?O?  ?.?  ?*? ] label
string or anything printable with ?%s? conversion. linestyle
or linestyles or dashes[?solid?  ?dashed?, ?dashdot?, ?dotted?  (offset, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or linewidths or lwfloat or sequence of floats norm
unknown offset_position
unknown offsets
float or sequence of floats path_effects
unknown picker
[Nonefloatbooleancallable] pickradius
unknown rasterized
[True  False  None] sketch_params
unknown snap
unknown transform
Transform
instanceurl
a url string urls
unknown visible
[True  False] zorder
any number Example:
Notes
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.
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.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
, andleft
can be either scalars or sequencesParameters: bottom : scalar or arraylike
the y coordinate(s) of the bars
width : scalar or arraylike
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 arraylike, optional
the colors of the bars
edgecolor : scalar or arraylike, optional
the colors of the bar edges
linewidth : scalar or arraylike, optional, default: None
width of bar edge(s). If None, use default linewidth; If 0, don?t draw edges.
tick_label : string or arraylike, optional, default: None
the tick labels of the bars
xerr : scalar or arraylike, optional, default: None
if not None, will be used to generate errorbar(s) on the bar chart
yerr : scalar or arraylike, optional, default: None
if not None, will be used to generate errorbar(s) on the bar chart
ecolor : scalar or arraylike, 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
rcParam
.error_kw :
dictionary of kwargs to be passed to errorbar method.
ecolor
andcapsize
may be specified here rather than as independent kwargs.align : [?edge?  ?center?], optional, default: ?edge?
If
edge
, aligns bars by their left edges (for vertical bars) and by their bottom edges (for horizontal bars). Ifcenter
, interpret theleft
argument as the coordinates of the centers of the bars.log : boolean, optional, default: False
If true, sets the axis to be log scale
Notes
The optional arguments
color
,edgecolor
,linewidth
,xerr
, andyerr
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
andyerr
are passed directly toerrorbar()
, so they can also have shape 2xN for independent specification of lower and upper errors.Other optional kwargs:
Property Description agg_filter
unknown alpha
float or None animated
[True  False] antialiased
or aa[True  False] or None for default axes
an Axes
instancecapstyle
[?butt?  ?round?  ?projecting?] clip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
matplotlib color spec contains
a callable function edgecolor
or ecmpl color spec, or None for default, or ?none? for no color facecolor
or fcmpl color spec, or None for default, or ?none? for no color figure
a matplotlib.figure.Figure
instancefill
[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, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or lwfloat or None for default path_effects
unknown picker
[Nonefloatbooleancallable] rasterized
[True  False  None] sketch_params
unknown snap
unknown transform
Transform
instanceurl
a url string visible
[True  False] zorder
any number 

matplotlib.pyplot.bone()

set the default colormap to bone and apply to current image if any. See help(colormaps) for more information

matplotlib.pyplot.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.

matplotlib.pyplot.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, hold=None, data=None)

Make a box and whisker plot.
Call signature:
boxplot(self, x, notch=None, sym=None, vert=None, whis=None, positions=None, widths=None, patch_artist=False, bootstrap=None, usermedians=None, conf_intervals=None, meanline=False, showmeans=False, showcaps=True, showbox=True, showfliers=True, boxprops=None, labels=None, flierprops=None, medianprops=None, meanprops=None, capprops=None, whiskerprops=None, manage_xticks=True, autorange=False):
Make a box and whisker plot for each column of
x
or each vector in sequencex
. 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.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. IfFalse
, 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, Q3Q1). 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 Gaussianbased 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 : arraylike, optional
An array or sequence whose first dimension (or length) is compatible with
x
. This overrides the medians computed by matplotlib for each element ofusermedians
that is notNone
. When an element ofusermedians
is None, the median will be computed by matplotlib as normal.conf_intervals : arraylike, optional
Array or sequence whose first dimension (or length) is compatible with
x
and whose second dimension is 2. When the an element ofconf_intervals
is not None, the notch locations computed by matplotlib are overridden (providednotch
isTrue
). When an element ofconf_intervals
isNone
, the notches are computed by the method specified by the other kwargs (e.g.,bootstrap
).positions : arraylike, 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 arraylike
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
(andshowmeans
isTrue
), will try to render the mean as a line spanning the full width of the box according tomeanprops
(see below). Not recommended ifshownotches
is also True. Otherwise, means will be shown as points.Returns: result : dict
A dictionary mapping each component of the boxplot to a list of the
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, nonoutlier 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.
Other Parameters: The following boolean options toggle the drawing of individual
components of the boxplots:
 showcaps: the caps on the ends of whiskers (default is True)
 showbox: the central box (default is True)
 showfliers: the outliers beyond the caps (default is True)
 showmeans: the arithmetic means (default is False)
The remaining options can accept dictionaries that specify the
style of the individual artists:
 capprops
 boxprops
 whiskerprops
 flierprops
 medianprops
 meanprops
Notes
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.
Additional kwargs: hold = [TrueFalse] overrides default hold state
Examples
(Source code, png, hires.png, pdf)


matplotlib.pyplot.broken_barh(xranges, yrange, hold=None, data=None, **kwargs)

Plot horizontal bars.
Call signature:
broken_barh(self, xranges, yrange, **kwargs)
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
matplotlib.collections.BrokenBarHCollection
properties:Property Description agg_filter
unknown alpha
float or None animated
[True  False] antialiased
or antialiasedsBoolean or sequence of booleans array
unknown axes
an Axes
instanceclim
a length 2 sequence of floats clip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]cmap
a colormap or registered colormap name color
matplotlib color arg or sequence of rgba tuples contains
a callable function edgecolor
or edgecolorsmatplotlib color spec or sequence of specs facecolor
or facecolorsmatplotlib color spec or sequence of specs figure
a matplotlib.figure.Figure
instancegid
an id string hatch
[ ?/?  ?\?  ??  ??  ?+?  ?x?  ?o?  ?O?  ?.?  ?*? ] label
string or anything printable with ?%s? conversion. linestyle
or linestyles or dashes[?solid?  ?dashed?, ?dashdot?, ?dotted?  (offset, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or linewidths or lwfloat or sequence of floats norm
unknown offset_position
unknown offsets
float or sequence of floats path_effects
unknown picker
[Nonefloatbooleancallable] pickradius
unknown rasterized
[True  False  None] sketch_params
unknown snap
unknown transform
Transform
instanceurl
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:
(Source code, png, hires.png, pdf)
Notes
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.
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.cla()

Clear the current axes.

matplotlib.pyplot.clabel(CS, *args, **kwargs)

Label a contour plot.
Call signature:
clabel(cs, **kwargs)
Adds labels to line contours in cs, where cs is a
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?, ?xlarge?
 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
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.
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.clf()

Clear the current figure.

matplotlib.pyplot.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)

matplotlib.pyplot.close(*args)

Close a figure window.
close()
by itself closes the current figureclose(h)
where h is aFigure
instance, closes that figureclose(num)
closes figure number numclose(name)
where name is a string, closes figure with that labelclose('all')
closes all the figure windows

matplotlib.pyplot.cohere(x, y, NFFT=256, Fs=2, Fc=0, detrend=, window=, noverlap=0, pad_to=None, sides='default', scale_by_freq=None, hold=None, data=None, **kwargs)

Plot the coherence between x and y.
Call signature:
cohere(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, **kwargs)
Plot the coherence between x and y. Coherence is the normalized cross spectral density:
Keyword arguments:
 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
window_hanning()
,window_none()
,numpy.blackman()
,numpy.hamming()
,numpy.bartlett()
,scipy.signal()
,scipy.signal.get_window()
, etc. The default iswindow_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 onesided for real data and both for complex data. ?onesided? forces the return of a onesided spectrum, while ?twosided? forces twosided.
 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 ffting, 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
pylab
module definesdetrend_none()
,detrend_mean()
, anddetrend_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? calldetrend_mean()
. ?linear? callsdetrend_linear()
. ?none? callsdetrend_none()
.  scale_by_freq: boolean
 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.
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)
kwargs control the
Line2D
properties of the coherence plot:Property Description agg_filter
unknown alpha
float (0.0 transparent through 1.0 opaque) animated
[True  False] antialiased
or aa[True  False] axes
an Axes
instanceclip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
or cany 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?  ?stepspre?  ?stepsmid?  ?stepspost?] figure
a matplotlib.figure.Figure
instancefillstyle
[?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, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or lwfloat value in points marker
A valid marker style
markeredgecolor
or mecany matplotlib color markeredgewidth
or mewfloat value in points markerfacecolor
or mfcany matplotlib color markerfacecoloralt
or mfcaltany matplotlib color markersize
or msfloat markevery
[None  int  length2 tuple of int  slice  list/array of int  float  length2 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 matplotlib.transforms.Transform
instanceurl
a url string visible
[True  False] xdata
1D array ydata
1D array zorder
any number Example:
(Source code, png, hires.png, pdf)
Notes
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: ?y?, ?x?.
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.colorbar(mappable=None, cax=None, ax=None, **kw)

Add a colorbar to a plot.
Function signatures for the
pyplot
interface; all but the first are also method signatures for thecolorbar()
method:colorbar(**kwargs) colorbar(mappable, **kwargs) colorbar(mappable, cax=cax, **kwargs) colorbar(mappable, ax=ax, **kwargs)
arguments:
 mappable
 the
Image
,ContourSet
, etc. to which the colorbar applies; this argument is mandatory for thecolorbar()
method but optional for thecolorbar()
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 outof 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 twoelement 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 ScalarFormatter
is used. If a format string is given, e.g., ?%.3f?, that is used. An alternativeFormatter
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
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:

Colorbar
instance; see also its base class,ColorbarBase
. Call theset_label()
method to label the colorbar.

matplotlib.pyplot.colors()

This is a donothing 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 builtin 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, axisbg=(0.1843, 0.3098, 0.3098))
Here is an example that creates a pale turquoise title:
title('Is this the best color?', color='#afeeee')

matplotlib.pyplot.connect(s, func)

Connect event with string s to func. The signature of func is:
def func(event)
where event is a
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 theAxes
the event occurs is over, and additionally, the variablesevent.xdata
andevent.ydata
will be defined. This is the mouse location in data coords. SeeKeyEvent
andMouseEvent
for more info.Return value is a connection id that can be used with
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)

matplotlib.pyplot.contour(*args, **kwargs)

Plot contours.
contour()
andcontourf()
draw contour lines and filled contours, respectively. Except as noted, function signatures and return values are the same for both versions.contourf()
differs from the MATLAB version in that it does not draw the polygon edges. To draw edges, add line contours with calls tocontour()
.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 automaticallychosen 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 2D with the same shape as Z, or they must both be 1D such that
len(X)
is the number of columns in Z andlen(Y)
is the number of rows in Z.C = contour(...)
returns aQuadContourSet
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
Colormap
instance or None. If cmap is None and colors is None, a default Colormap is used.  norm: [ None  Normalize ]
 A
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
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
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
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
matplotlib.colors.Colormap.set_under()
andmatplotlib.colors.Colormap.set_over()
methods.  xunits, yunits: [ None  registered units ]
 Override axis units by specifying an instance of a
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.
contouronly keyword arguments:
 linewidths: [ None  number  tuple of numbers ]

If linewidths is None, the default width in
lines.linewidth
inmatplotlibrc
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.
contourfonly 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:
(Source code, png, hires.png, pdf)
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.contourf(*args, **kwargs)

Plot contours.
contour()
andcontourf()
draw contour lines and filled contours, respectively. Except as noted, function signatures and return values are the same for both versions.contourf()
differs from the MATLAB version in that it does not draw the polygon edges. To draw edges, add line contours with calls tocontour()
.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 automaticallychosen 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 2D with the same shape as Z, or they must both be 1D such that
len(X)
is the number of columns in Z andlen(Y)
is the number of rows in Z.C = contour(...)
returns aQuadContourSet
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
Colormap
instance or None. If cmap is None and colors is None, a default Colormap is used.  norm: [ None  Normalize ]
 A
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
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
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
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
matplotlib.colors.Colormap.set_under()
andmatplotlib.colors.Colormap.set_over()
methods.  xunits, yunits: [ None  registered units ]
 Override axis units by specifying an instance of a
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.
contouronly keyword arguments:
 linewidths: [ None  number  tuple of numbers ]

If linewidths is None, the default width in
lines.linewidth
inmatplotlibrc
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.
contourfonly 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:
(Source code, png, hires.png, pdf)
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.cool()

set the default colormap to cool and apply to current image if any. See help(colormaps) for more information

matplotlib.pyplot.copper()

set the default colormap to copper and apply to current image if any. See help(colormaps) for more information

matplotlib.pyplot.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 crossspectral 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 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 , 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.
 x, y: 1D arrays or sequences
 Arrays or sequences containing the data
Keyword arguments:
 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
window_hanning()
,window_none()
,numpy.blackman()
,numpy.hamming()
,numpy.bartlett()
,scipy.signal()
,scipy.signal.get_window()
, etc. The default iswindow_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 onesided for real data and both for complex data. ?onesided? forces the return of a onesided spectrum, while ?twosided? forces twosided.
 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 ffting, 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
pylab
module definesdetrend_none()
,detrend_mean()
, anddetrend_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? calldetrend_mean()
. ?linear? callsdetrend_linear()
. ?none? callsdetrend_none()
.  scale_by_freq: boolean
 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.
If return_line is False, returns the tuple (Pxy, freqs). If return_line is True, returns the tuple (Pxy, freqs. line):
 Pxy: 1D array
 The values for the cross spectrum
P_{xy}
before scaling (complex valued)  freqs: 1D array
 The frequencies corresponding to the elements in Pxy

line: a Line2D instance
 The line created by this function. Only returend if return_line is True.
For plotting, the power is plotted as for decibels, though
P_{xy}
itself is returned. References:
 Bendat & Piersol ? Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986)
kwargs control the Line2D properties:
Property Description agg_filter
unknown alpha
float (0.0 transparent through 1.0 opaque) animated
[True  False] antialiased
or aa[True  False] axes
an Axes
instanceclip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
or cany 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?  ?stepspre?  ?stepsmid?  ?stepspost?] figure
a matplotlib.figure.Figure
instancefillstyle
[?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, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or lwfloat value in points marker
A valid marker style
markeredgecolor
or mecany matplotlib color markeredgewidth
or mewfloat value in points markerfacecolor
or mfcany matplotlib color markerfacecoloralt
or mfcaltany matplotlib color markersize
or msfloat markevery
[None  int  length2 tuple of int  slice  list/array of int  float  length2 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 matplotlib.transforms.Transform
instanceurl
a url string visible
[True  False] xdata
1D array ydata
1D array zorder
any number Example:
(Source code, png, hires.png, pdf)
Notes
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: ?y?, ?x?.
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.delaxes(*args)

Remove an axes from the current figure. If ax doesn?t exist, an error will be raised.
delaxes()
: delete the current axes

matplotlib.pyplot.disconnect(cid)

Disconnect callback id cid
Example usage:
cid = canvas.mpl_connect('button_press_event', on_press) #...later canvas.mpl_disconnect(cid)

matplotlib.pyplot.draw()

Redraw the current figure.
This is used in interactive mode to update a figure that has been altered, but not automatically redrawn. This should be only rarely needed, but there may be ways to modify the state of a figure with out marking it as
stale
. Please report these cases as bugs.A more objectoriented alternative, given any
Figure
instance,fig
, that was created using apyplot
function, is:fig.canvas.draw_idle()

matplotlib.pyplot.errorbar(x, y, yerr=None, xerr=None, fmt='', 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.
Call signature:
errorbar(x, y, yerr=None, xerr=None, fmt='', ecolor=None, elinewidth=None, capsize=None, barsabove=False, lolims=False, uplims=False, xlolims=False, xuplims=False, errorevery=1, capthick=None)
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.
Optional keyword arguments:
 xerr/yerr: [ scalar  N, Nx1, or 2xN arraylike ]

If a scalar number, len(N) arraylike object, or an Nx1 arraylike object, errorbars are drawn at +/value relative to the data.
If a sequence of shape 2xN, errorbars are drawn at row1 and +row2 relative to the data.
 fmt: [ ??  ?none?  plot format string ]
 The plot format symbol. If fmt is ?none? (caseinsensitive), 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
plot()
.  ecolor: [ None  mpl color ]
 A matplotlib color arg which gives the color the errorbar lines; if None, use the color of the line connecting the markers.
 elinewidth: scalar
 The linewidth of the errorbar lines. If None, use the linewidth.
 capsize: scalar
 The length of the error bar caps in points; if None, it will take the value from
errorbar.capsize
rcParam
.  capthick: scalar
 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 override capthick. This may change in future releases.
 barsabove: [ True  False ]
 if True, will plot the errorbars above the plot symbols. Default is below.
 lolims / uplims / xlolims / xuplims: [ False  True ]
 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. limsarguments may be of the same type as xerr and yerr. To use limits with inverted axes,
set_xlim()
orset_ylim()
must be called beforeerrorbar()
.  errorevery: positive integer
 subsamples the errorbars. e.g., if errorevery=5, errorbars for every 5th datapoint will be plotted. The data plot itself still shows all data points.
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 markeredgewith.
valid kwargs for the marker properties are
Property Description agg_filter
unknown alpha
float (0.0 transparent through 1.0 opaque) animated
[True  False] antialiased
or aa[True  False] axes
an Axes
instanceclip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
or cany 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?  ?stepspre?  ?stepsmid?  ?stepspost?] figure
a matplotlib.figure.Figure
instancefillstyle
[?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, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or lwfloat value in points marker
A valid marker style
markeredgecolor
or mecany matplotlib color markeredgewidth
or mewfloat value in points markerfacecolor
or mfcany matplotlib color markerfacecoloralt
or mfcaltany matplotlib color markersize
or msfloat markevery
[None  int  length2 tuple of int  slice  list/array of int  float  length2 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 matplotlib.transforms.Transform
instanceurl
a url string visible
[True  False] xdata
1D array ydata
1D array zorder
any number Returns (plotline, caplines, barlinecols):

plotline: Line2D instance
 x, y plot markers and/or line
 caplines: list of error bar cap

Line2D
instances  barlinecols: list of

LineCollection
instances for the horizontal and vertical error ranges.
Example:
(Source code, png, hires.png, pdf)
Notes
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: ?xerr?, ?y?, ?yerr?, ?x?.
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.eventplot(positions, orientation='horizontal', lineoffsets=1, linelengths=1, linewidths=None, colors=None, linestyles='solid', hold=None, data=None, **kwargs)

Plot identical parallel lines at specific positions.
Call signature:
eventplot(positions, orientation='horizontal', lineoffsets=0, linelengths=1, linewidths=None, color =None, linestyles='solid'
Plot parallel lines at the given positions. positions should be a 1D or 2D arraylike 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? : the lines will be vertical and arranged in rows ?vertical? : lines will be horizontal and arranged in columns
 lineoffsets :
 A float or arraylike containing floats.
 linelengths :
 A float or arraylike containing floats.
 linewidths :
 A float or arraylike 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 arraylike 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 matplotlib.collections.EventCollection
objects that were added.
kwargs are LineCollection
properties:
Property  Description 

agg_filter  unknown 
alpha  float or None 
animated  [True  False] 
antialiased or antialiaseds  Boolean or sequence of booleans 
array  unknown 
axes  an Axes instance 
clim  a length 2 sequence of floats 
clip_box  a matplotlib.transforms.Bbox instance 
clip_on  [True  False] 
clip_path  [ (Path , Transform )  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 matplotlib.figure.Figure instance 
gid  an id string 
hatch  [ ?/?  ?\?  ??  ??  ?+?  ?x?  ?o?  ?O?  ?.?  ?*? ] 
label  string or anything printable with ?%s? conversion. 
linestyle or linestyles or dashes  [?solid?  ?dashed?, ?dashdot?, ?dotted?  (offset, onoffdashseq)  ''  ''  '.'  ':'  '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  [Nonefloatbooleancallable] 
pickradius  unknown 
rasterized  [True  False  None] 
segments  unknown 
sketch_params  unknown 
snap  unknown 
transform 
Transform instance 
url  a url string 
urls  unknown 
verts  unknown 
visible  [True  False] 
zorder  any number 
Example:
(Source code, png, hires.png, pdf)
Notes
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: ?linewidths?, ?linelengths?, ?lineoffsets?, ?colors?, ?positions?, ?linestyles?.
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.figimage(*args, **kwargs)

Adds a nonresampled image to the figure.
call signatures:
figimage(X, **kwargs)
adds a nonresampled 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 resize the Figure to match the given image size. xo or yo An integer, the x and y image offset in pixels cmap a matplotlib.colors.Colormap
instance, e.g., cm.jet. If None, default to the rcimage.cmap
valuenorm a matplotlib.colors.Normalize
instance. The default is normalization(). This scales luminance > 01vminvmax are used to scale a luminance image to 01. 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 (
imshow()
) which will be resampled to fit the current axes. If you want a resampled image to fill the entire figure, you can define anAxes
with size [0,1,0,1].An
matplotlib.image.FigureImage
instance is returned.(Source code, png, hires.png, pdf)
Additional kwargs are Artist kwargs passed on to
FigureImage
Addition kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.figlegend(handles, labels, loc, **kwargs)

Place a legend in the figure.
 labels
 a sequence of strings
 handles
 a sequence of
Line2D
orPatch
instances  loc
 can be a string or an integer specifying the legend location
A
matplotlib.legend.Legend
instance is returned.Example:
figlegend( (line1, line2, line3), ('label1', 'label2', 'label3'), 'upper right' )
See also

matplotlib.pyplot.fignum_exists(num)

matplotlib.pyplot.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 01 coords). See
text()
for the meaning of the other arguments.kwargs control the
Text
properties:Property Description agg_filter
unknown alpha
float (0.0 transparent through 1.0 opaque) animated
[True  False] axes
an Axes
instancebackgroundcolor
any matplotlib color bbox
FancyBboxPatch prop dict clip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
any matplotlib color contains
a callable function family
or fontname or fontfamily or name[FONTNAME  ?serif?  ?sansserif?  ?cursive?  ?fantasy?  ?monospace? ] figure
a matplotlib.figure.Figure
instancefontproperties
or font_propertiesa matplotlib.font_manager.FontProperties
instancegid
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
[Nonefloatbooleancallable] position
(x,y) rasterized
[True  False  None] rotation
[ angle in degrees  ?vertical?  ?horizontal? ] rotation_mode
unknown size
or fontsize[size in points  ?xxsmall?  ?xsmall?  ?small?  ?medium?  ?large?  ?xlarge?  ?xxlarge? ] sketch_params
unknown snap
unknown stretch
or fontstretch[a numeric value in range 01000  ?ultracondensed?  ?extracondensed?  ?condensed?  ?semicondensed?  ?normal?  ?semiexpanded?  ?expanded?  ?extraexpanded?  ?ultraexpanded? ] style
or fontstyle[ ?normal?  ?italic?  ?oblique?] text
string or anything printable with ?%s? conversion. transform
Transform
instanceurl
a url string usetex
unknown variant
or fontvariant[ ?normal?  ?smallcaps? ] verticalalignment
or ma or va[ ?center?  ?top?  ?bottom?  ?baseline? ] visible
[True  False] weight
or fontweight[a numeric value in range 01000  ?ultralight?  ?light?  ?normal?  ?regular?  ?book?  ?medium?  ?roman?  ?semibold?  ?demibold?  ?demi?  ?bold?  ?heavy?  ?extra bold?  ?black? ] wrap
unknown x
float y
float zorder
any number

matplotlib.pyplot.figure(num=None, figsize=None, dpi=None, facecolor=None, edgecolor=None, frameon=True, FigureClass=, **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?snum
.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

matplotlib.pyplot.fill(*args, **kwargs)

Plot filled polygons.
Call signature:
fill(*args, **kwargs)
args is a variable length argument, allowing for multiple x, y pairs with an optional color format string; see
plot()
for details on the argument parsing. For example, to plot a polygon with vertices at x, y in blue.:ax.fill(x,y, 'b' )
An arbitrary number of x, y, color groups can be specified:
ax.fill(x1, y1, 'g', x2, y2, 'r')
Return value is a list of
Patch
instances that were added.The same color strings that
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
fill_between()
The closed kwarg will close the polygon when True (default).
kwargs control the
Polygon
properties:Property Description agg_filter
unknown alpha
float or None animated
[True  False] antialiased
or aa[True  False] or None for default axes
an Axes
instancecapstyle
[?butt?  ?round?  ?projecting?] clip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
matplotlib color spec contains
a callable function edgecolor
or ecmpl color spec, or None for default, or ?none? for no color facecolor
or fcmpl color spec, or None for default, or ?none? for no color figure
a matplotlib.figure.Figure
instancefill
[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, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or lwfloat or None for default path_effects
unknown picker
[Nonefloatbooleancallable] rasterized
[True  False  None] sketch_params
unknown snap
unknown transform
Transform
instanceurl
a url string visible
[True  False] zorder
any number Example:
(Source code, png, hires.png, pdf)
Notes
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: ?y?, ?x?.
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.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
PolyCollection
filling the regions between y1 and y2 wherewhere==True
Parameters: x : array
An Nlength array of the x data
y1 : array
An Nlength array (or scalar) of the y data
y2 : array
An Nlength array (or scalar) of the y data
where : array, optional
If
None
, default to fill between everywhere. If notNone
, it is an Nlength numpy boolean array and the fill will only happen over the regions wherewhere==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
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: ?y1?, ?x?, ?where?, ?y2?.
Additional kwargs: hold = [TrueFalse] overrides default hold state
Examples

matplotlib.pyplot.fill_betweenx(y, x1, x2=0, where=None, step=None, hold=None, data=None, **kwargs)

Make filled polygons between two horizontal curves.
Call signature:
fill_betweenx(y, x1, x2=0, where=None, **kwargs)
Create a
PolyCollection
filling the regions between x1 and x2 wherewhere==True
Parameters: y : array
An Nlength array of the y data
x1 : array
An Nlength array (or scalar) of the x data
x2 : array, optional
An Nlength 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
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: ?x2?, ?y?, ?where?, ?x1?.
Additional kwargs: hold = [TrueFalse] overrides default hold state
Examples

matplotlib.pyplot.findobj(o=None, match=None, include_self=True)

Find artist objects.
Recursively find all
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.

matplotlib.pyplot.flag()

set the default colormap to flag and apply to current image if any. See help(colormaps) for more information

matplotlib.pyplot.gca(**kwargs)

Get the current
Axes
instance on the current figure matching the given keyword args, or create one.See also

matplotlib.figure.Figure.gca
 The figure?s gca method.
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.


matplotlib.pyplot.gcf()

Get a reference to the current figure.

matplotlib.pyplot.gci()

Get the current colorable artist. Specifically, returns the current
ScalarMappable
instance (image or patch collection), or None if no images or patch collections have been defined. The commandsimshow()
andfigimage()
createImage
instances, and the commandspcolor()
andscatter()
createCollection
instances. The current image is an attribute of the current axes, or the nearest earlier axes in the current figure that contains an image.

matplotlib.pyplot.get_current_fig_manager()

matplotlib.pyplot.get_figlabels()

Return a list of existing figure labels.

matplotlib.pyplot.get_fignums()

Return a list of existing figure numbers.

matplotlib.pyplot.get_plot_commands()

Get a sorted list of all of the plotting commands.

matplotlib.pyplot.ginput(*args, **kwargs)

Call signature:
ginput(self, n=1, timeout=30, show_clicks=True, mouse_add=1, mouse_pop=3, mouse_stop=2)
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.

matplotlib.pyplot.gray()

set the default colormap to gray and apply to current image if any. See help(colormaps) for more information

matplotlib.pyplot.grid(b=None, which='major', axis='both', **kwargs)

Turn the axes grids on or off.
Call signature:
grid(self, b=None, which='major', axis='both', **kwargs)
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
Line2D
kwargs areProperty Description agg_filter
unknown alpha
float (0.0 transparent through 1.0 opaque) animated
[True  False] antialiased
or aa[True  False] axes
an Axes
instanceclip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
or cany 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?  ?stepspre?  ?stepsmid?  ?stepspost?] figure
a matplotlib.figure.Figure
instancefillstyle
[?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, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or lwfloat value in points marker
A valid marker style
markeredgecolor
or mecany matplotlib color markeredgewidth
or mewfloat value in points markerfacecolor
or mfcany matplotlib color markerfacecoloralt
or mfcaltany matplotlib color markersize
or msfloat markevery
[None  int  length2 tuple of int  slice  list/array of int  float  length2 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 matplotlib.transforms.Transform
instanceurl
a url string visible
[True  False] xdata
1D array ydata
1D array zorder
any number

matplotlib.pyplot.hexbin(x, y, C=None, gridsize=100, bins=None, xscale='linear', yscale='linear', extent=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, edgecolors='none', reduce_C_function=, mincnt=None, marginals=False, hold=None, data=None, **kwargs)

Make a hexagonal binning plot.
Call signature:
hexbin(x, y, C = None, gridsize = 100, bins = None, xscale = 'linear', yscale = 'linear', cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, edgecolors='none' reduce_C_function = np.mean, mincnt=None, marginals=True **kwargs)
Make a hexagonal binning plot of x versus y, where x, y are 1D 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 1D sequence of the same length as x and y.)
x, y and/or C may be masked arrays, in which case only unmasked points will be plotted.
Optional keyword arguments:
 gridsize: [ 100  integer ]
 The number of hexagons in the xdirection, default is 100. The corresponding number of hexagons in the ydirection 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 xdirection and the ydirection.
 bins: [ None  ?log?  integer  sequence ]

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, 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? ]
 Use a linear or log10 scale on the horizontal axis.
 scale: [ ?linear?  ?log? ]
 Use a linear or log10 scale on the vertical axis.
 mincnt: [ None  a positive integer ]
 If not None, only display cells with more than mincnt number of points in the cell
 marginals: [ True  False ]
 if marginals is True, plot the marginal density as colormapped rectagles along the bottom of the xaxis and left of the yaxis
 extent: [ None  scalars (left, right, bottom, top) ]
 The limits of the bins. The default assigns the limits based on gridsize, x, y, xscale and yscale.
Other keyword arguments controlling color mapping and normalization arguments:
 cmap: [ None  Colormap ]
 a
matplotlib.colors.Colormap
instance. If None, defaults to rcimage.cmap
.  norm: [ None  Normalize ]

matplotlib.colors.Normalize
instance is used to scale luminance data to 0,1.  vmin / vmax: scalar
 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 C is used. Note if you pass a norm instance, your settings for vmin and vmax will be ignored.
 alpha: scalar between 0 and 1, or None
 the alpha value for the patches
 linewidths: [ None  scalar ]
 If None, defaults to rc lines.linewidth. Note that this is a tuple, and if you set the linewidths argument you must set it as a sequence of floats, as required by
RegularPolyCollection
.
Other keyword arguments controlling the Collection properties:

edgecolors: [ None  'none'  mpl color  color sequence ]

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 the outlines in the default color.
If a matplotlib color arg or sequence of rgba tuples, draws the outlines in the specified color.
Here are the standard descriptions of all the
Collection
kwargs:Property Description agg_filter
unknown alpha
float or None animated
[True  False] antialiased
or antialiasedsBoolean or sequence of booleans array
unknown axes
an Axes
instanceclim
a length 2 sequence of floats clip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]cmap
a colormap or registered colormap name color
matplotlib color arg or sequence of rgba tuples contains
a callable function edgecolor
or edgecolorsmatplotlib color spec or sequence of specs facecolor
or facecolorsmatplotlib color spec or sequence of specs figure
a matplotlib.figure.Figure
instancegid
an id string hatch
[ ?/?  ?\?  ??  ??  ?+?  ?x?  ?o?  ?O?  ?.?  ?*? ] label
string or anything printable with ?%s? conversion. linestyle
or linestyles or dashes[?solid?  ?dashed?, ?dashdot?, ?dotted?  (offset, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or linewidths or lwfloat or sequence of floats norm
unknown offset_position
unknown offsets
float or sequence of floats path_effects
unknown picker
[Nonefloatbooleancallable] pickradius
unknown rasterized
[True  False  None] sketch_params
unknown snap
unknown transform
Transform
instanceurl
a url string urls
unknown visible
[True  False] zorder
any number The return value is a
PolyCollection
instance; useget_array()
on thisPolyCollection
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.Example:
(Source code, png, hires.png, pdf)
Notes
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: ?y?, ?x?.
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.hist(x, bins=10, range=None, normed=False, weights=None, cumulative=False, bottom=None, histtype='bar', align='mid', orientation='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 2D 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, optional
If an integer is given,
bins + 1
bin edges are returned, consistently withnumpy.histogram()
for numpy version >= 1.3.Unequally spaced bins are supported if
bins
is a sequence.default is 10
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 ifbins
is a sequence.If
bins
is a sequence orrange
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 inx
only contributes its associated weight towards the bin count (instead of 1). Ifnormed
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. Ifnormed
is alsoTrue
then the histogram is normalized such that the last bin equals 1. Ifcumulative
evaluates to less than 0 (e.g., 1), the direction of accumulation is reversed. In this case, ifnormed
is alsoTrue
, 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 bartype histogram. If multiple data are given the bars are aranged side by side.
 ?barstacked? is a bartype 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?,
barh
will be used for bartype 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. Iflog
isTrue
andx
is a 1D array, empty bins will be filtered out and only the nonempty (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 IfFalse
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 :
Patch
propertiesSee also

hist2d
 2D histograms
Notes
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?.
Additional kwargs: hold = [TrueFalse] overrides default hold state
Examples
(Source code, png, hires.png, pdf)

matplotlib.pyplot.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 :
pcolorfast()
properties.See also

hist
 1D histogram
Notes
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: ?y?, ?weights?, ?x?.
Additional kwargs: hold = [TrueFalse] overrides default hold state
Examples
(Source code, png, hires.png, pdf)

matplotlib.pyplot.hlines(y, xmin, xmax, colors='k', linestyles='solid', label='', hold=None, data=None, **kwargs)

Plot horizontal lines at each
y
fromxmin
toxmax
.Parameters: y : scalar or sequence of scalar
yindexes 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 :
LineCollection
Other Parameters: kwargs :
LineCollection
properties.See also

vlines
 vertical lines
Notes
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?, ?y?, ?xmin?.
Additional kwargs: hold = [TrueFalse] overrides default hold state
Examples
(Source code, png, hires.png, pdf)


matplotlib.pyplot.hold(b=None)

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 be added to the current axes. When hold is False, the current axes and figure will be cleared on the next plot command.

matplotlib.pyplot.hot()

set the default colormap to hot and apply to current image if any. See help(colormaps) for more information

matplotlib.pyplot.hsv()

set the default colormap to hsv and apply to current image if any. See help(colormaps) for more information

matplotlib.pyplot.imread(*args, **kwargs)

Read an image from a file into an array.
fname may be a string path, a valid URL, or a Python filelike 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
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 is installed, it will use it to load the image and return an array (if possible) which can be used with
imshow()
. Note, URL strings may not be compatible with PIL. Check the PIL documentation for more information.

matplotlib.pyplot.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 filelike 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.

matplotlib.pyplot.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 a float array, a uint8 array or a PIL image. IfX
is an array, it can have the following shapes: MxN ? luminance (grayscale, float array only)
 MxNx3 ? RGB (float or uint8 array)
 MxNx4 ? RGBA (float or uint8 array)
The value for each component of MxNx3 and MxNx4 float arrays should be in the range 0.0 to 1.0; MxN float arrays may be normalised.
cmap :
Colormap
, optional, default: NoneIf None, default to rc
image.cmap
value.cmap
is ignored whenX
has RGB(A) informationaspect : [?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. Ifextent
is notNone
, 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 rcimage.interpolation
. See also thefilternorm
andfilterrad
parameters. Ifinterpolation
is ?none?, then no interpolation is performed on the Agg, ps and pdf backends. Other backends will fall back to ?nearest?.norm :
Normalize
, optional, default: NoneA
Normalize
instance is used to scale luminance data to 0, 1. IfNone
, use the default func:normalize
.norm
is only used ifX
is an array of floats.vmin, vmax : scalar, optional, default: None
vmin
andvmax
are used in conjunction with norm to normalize luminance data. Note if you pass anorm
instance, your settings forvmin
andvmax
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 datacoordinates, of the lowerleft and upperright corners. If
None
, the image is positioned such that the pixel centers fall on zerobased (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 :
AxesImage
Other Parameters: kwargs :
Artist
properties.See also

matshow
 Plot a matrix or an array as an image.
Notes
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.
Additional kwargs: hold = [TrueFalse] overrides default hold state
Examples
(Source code, png, hires.png, pdf)

matplotlib.pyplot.inferno()

set the default colormap to inferno and apply to current image if any. See help(colormaps) for more information

matplotlib.pyplot.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.

matplotlib.pyplot.ioff()

Turn interactive mode off.

matplotlib.pyplot.ion()

Turn interactive mode on.

matplotlib.pyplot.ishold()

Return the hold status of the current axes.

matplotlib.pyplot.isinteractive()

Return status of interactive mode.

matplotlib.pyplot.jet()

set the default colormap to jet and apply to current image if any. See help(colormaps) for more information

matplotlib.pyplot.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
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
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 2tuple giving
x, y
of the lowerleft corner of the legend in axes coordinates (in which casebbox_to_anchor
will be ignored).bbox_to_anchor :
matplotlib.transforms.BboxBase
instance or tuple of floatsSpecify 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
matplotlib.font_manager.FontProperties
or dictThe font properties of the legend. If None (default), the current
matplotlib.rcParams
will be used.fontsize : int or float or {?xxsmall?, ?xsmall?, ?small?, ?medium?, ?large?, ?xlarge?, ?xxlarge?}
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/
matplotlib.lines.Line2D
. Default isNone
which will take the value from thelegend.numpoints
rcParam
.scatterpoints : None or int
The number of marker points in the legend when creating a legend entry for a scatter plot/
matplotlib.collections.PathCollection
. Default isNone
which will take the value from thelegend.scatterpoints
rcParam
.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 thelegend.markerscale
rcParam
.*markerfirst*: [ *True*  *False* ]
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 a frame should be drawn around the legend. Default is
None
which will take the value from thelegend.frameon
rcParam
.fancybox : None or bool
Control whether round edges should be enabled around the
FancyBboxPatch
which makes up the legend?s background. Default isNone
which will take the value from thelegend.fancybox
rcParam
.shadow : None or bool
Control whether to draw a shadow behind the legend. Default is
None
which will take the value from thelegend.shadow
rcParam
.framealpha : None or float
Control the alpha transparency of the legend?s frame. Default is
None
which will take the value from thelegend.framealpha
rcParam
.mode : {?expand?, None}
If
mode
is set to"expand"
the legend will be horizontally expanded to fill the axes area (orbbox_to_anchor
if defines the legend?s size).bbox_transform : None or
matplotlib.transforms.Transform
The transform for the bounding box (
bbox_to_anchor
). For a value ofNone
(default) the 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 fontsize units. Default is
None
which will take the value from thelegend.borderpad
rcParam
.labelspacing : float or None
The vertical space between the legend entries. Measured in fontsize units. Default is
None
which will take the value from thelegend.labelspacing
rcParam
.handlelength : float or None
The length of the legend handles. Measured in fontsize units. Default is
None
which will take the value from thelegend.handlelength
rcParam
.handletextpad : float or None
The pad between the legend handle and text. Measured in fontsize units. Default is
None
which will take the value from thelegend.handletextpad
rcParam
.borderaxespad : float or None
The pad between the axes and legend border. Measured in fontsize units. Default is
None
which will take the value from thelegend.borderaxespad
rcParam
.columnspacing : float or None
The spacing between columns. Measured in fontsize units. Default is
None
which will take the value from thelegend.columnspacing
rcParam
.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 atmatplotlib.legend.Legend.get_legend_handler_map()
.Notes
Not all kinds of artist are supported by the legend command. See Legend guide for details.
Examples
(Source code, png, hires.png, pdf)

matplotlib.pyplot.locator_params(axis='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
autoscale_view()
. Default is None, for no change.
Remaining keyword arguments are passed to directly to the
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,
autoscale_view()
is called automatically after the parameters are changed.This presently works only for the
MaxNLocator
used by default on linear axes, but it may be generalized.

matplotlib.pyplot.loglog(*args, **kwargs)

Make a plot with log scaling on both the x and y axis.
Call signature:
loglog(*args, **kwargs)
loglog()
supports all the keyword arguments ofplot()
andmatplotlib.axes.Axes.set_xscale()
/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
matplotlib.axes.Axes.set_xscale()
/matplotlib.axes.Axes.set_yscale()
for details  nonposx/nonposy: [?mask?  ?clip? ]
 Nonpositive values in x or y can be masked as invalid, or clipped to a very small positive number
The remaining valid kwargs are
Line2D
properties:Property Description agg_filter
unknown alpha
float (0.0 transparent through 1.0 opaque) animated
[True  False] antialiased
or aa[True  False] axes
an Axes
instanceclip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
or cany 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?  ?stepspre?  ?stepsmid?  ?stepspost?] figure
a matplotlib.figure.Figure
instancefillstyle
[?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, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or lwfloat value in points marker
A valid marker style
markeredgecolor
or mecany matplotlib color markeredgewidth
or mewfloat value in points markerfacecolor
or mfcany matplotlib color markerfacecoloralt
or mfcaltany matplotlib color markersize
or msfloat markevery
[None  int  length2 tuple of int  slice  list/array of int  float  length2 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 matplotlib.transforms.Transform
instanceurl
a url string visible
[True  False] xdata
1D array ydata
1D array zorder
any number Example:
(Source code, png, hires.png, pdf)
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.magma()

set the default colormap to magma and apply to current image if any. See help(colormaps) for more information

matplotlib.pyplot.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.
 x: 1D array or sequence
 Array or sequence containing the data
Keyword arguments:
 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
window_hanning()
,window_none()
,numpy.blackman()
,numpy.hamming()
,numpy.bartlett()
,scipy.signal()
,scipy.signal.get_window()
, etc. The default iswindow_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 onesided for real data and both for complex data. ?onesided? forces the return of a onesided spectrum, while ?twosided? forces twosided.
 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.
Returns the tuple (spectrum, freqs, line):
 spectrum: 1D array
 The values for the magnitude spectrum before scaling (real valued)
 freqs: 1D array
 The frequencies corresponding to the elements in spectrum

line: a Line2D instance
 The line created by this function
kwargs control the
Line2D
properties:Property Description agg_filter
unknown alpha
float (0.0 transparent through 1.0 opaque) animated
[True  False] antialiased
or aa[True  False] axes
an Axes
instanceclip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
or cany 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?  ?stepspre?  ?stepsmid?  ?stepspost?] figure
a matplotlib.figure.Figure
instancefillstyle
[?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, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or lwfloat value in points marker
A valid marker style
markeredgecolor
or mecany matplotlib color markeredgewidth
or mewfloat value in points markerfacecolor
or mfcany matplotlib color markerfacecoloralt
or mfcaltany matplotlib color markersize
or msfloat markevery
[None  int  length2 tuple of int  slice  list/array of int  float  length2 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 matplotlib.transforms.Transform
instanceurl
a url string visible
[True  False] xdata
1D array ydata
1D array zorder
any number Example:
(Source code, png, hires.png, pdf)
See also

psd()

psd()
plots the power spectral density.`. 
angle_spectrum()

angle_spectrum()
plots the angles of the corresponding frequencies. 
phase_spectrum()

phase_spectrum()
plots the phase (unwrapped angle) of the corresponding frequencies. 
specgram()

specgram()
can plot the magnitude spectrum of segments within the signal in a colormap.
Notes
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?.
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.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
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.

matplotlib.pyplot.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
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,
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 howmatshow()
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.

matplotlib.pyplot.minorticks_off()

Remove minor ticks from the current plot.

matplotlib.pyplot.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.

matplotlib.pyplot.over(func, *args, **kwargs)

Call a function with hold(True).
Calls:
func(*args, **kwargs)
with
hold(True)
and then restores the hold state.

matplotlib.pyplot.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 noninteractive backend is in use, this executes time.sleep(interval).
This can be used for crude animation. For more complex animation, see
matplotlib.animation
.This function is experimental; its behavior may be changed or extended in a future release.

matplotlib.pyplot.pcolor(*args, **kwargs)

Create a pseudocolor plot of a 2D array.
Note
pcolor can be very slow for large arrays; consider using the similar but much faster
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 xcoordinate, and the row index corresponds to y; for details, see the Grid Orientation section below.
If either or both of X and Y are 1D arrays or column vectors, they will be expanded as needed into the appropriate 2D 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
matplotlib.colors.Colormap
instance. If None, use rc settings.  norm: [ None  Normalize ]
 An
matplotlib.colors.Normalize
instance is used to scale luminance data to 0,1. If None, defaults tonormalize()
.  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 preexisting 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
matplotlib.collections.Collection
instance.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
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
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
PolyCollection
properties:Property Description agg_filter
unknown alpha
float or None animated
[True  False] antialiased
or antialiasedsBoolean or sequence of booleans array
unknown axes
an Axes
instanceclim
a length 2 sequence of floats clip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]cmap
a colormap or registered colormap name color
matplotlib color arg or sequence of rgba tuples contains
a callable function edgecolor
or edgecolorsmatplotlib color spec or sequence of specs facecolor
or facecolorsmatplotlib color spec or sequence of specs figure
a matplotlib.figure.Figure
instancegid
an id string hatch
[ ?/?  ?\?  ??  ??  ?+?  ?x?  ?o?  ?O?  ?.?  ?*? ] label
string or anything printable with ?%s? conversion. linestyle
or linestyles or dashes[?solid?  ?dashed?, ?dashdot?, ?dotted?  (offset, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or linewidths or lwfloat or sequence of floats norm
unknown offset_position
unknown offsets
float or sequence of floats path_effects
unknown picker
[Nonefloatbooleancallable] pickradius
unknown rasterized
[True  False  None] sketch_params
unknown snap
unknown transform
Transform
instanceurl
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.
See also

pcolormesh()
 For an explanation of the differences between pcolor and pcolormesh.
Notes
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.
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.pcolormesh(*args, **kwargs)

Plot a quadrilateral mesh.
Call signatures:
pcolormesh(C) pcolormesh(X, Y, C) pcolormesh(C, **kwargs)
Create a pseudocolor plot of a 2D array.
pcolormesh is similar to
pcolor()
, but uses a different mechanism and returns a different object; pcolor returns aPolyCollection
but pcolormesh returns aQuadMesh
. 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,
pcolor()
simply does not draw quadrilaterals with masked colors or vertices.Keyword arguments:
 cmap: [ None  Colormap ]
 A
matplotlib.colors.Colormap
instance. If None, use rc settings.  norm: [ None  Normalize ]
 A
matplotlib.colors.Normalize
instance is used to scale luminance data to 0,1. If None, defaults tonormalize()
.  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 preexisting 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
matplotlib.collections.QuadMesh
object.kwargs can be used to control the
matplotlib.collections.QuadMesh
properties:Property Description agg_filter
unknown alpha
float or None animated
[True  False] antialiased
or antialiasedsBoolean or sequence of booleans array
unknown axes
an Axes
instanceclim
a length 2 sequence of floats clip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]cmap
a colormap or registered colormap name color
matplotlib color arg or sequence of rgba tuples contains
a callable function edgecolor
or edgecolorsmatplotlib color spec or sequence of specs facecolor
or facecolorsmatplotlib color spec or sequence of specs figure
a matplotlib.figure.Figure
instancegid
an id string hatch
[ ?/?  ?\?  ??  ??  ?+?  ?x?  ?o?  ?O?  ?.?  ?*? ] label
string or anything printable with ?%s? conversion. linestyle
or linestyles or dashes[?solid?  ?dashed?, ?dashdot?, ?dotted?  (offset, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or linewidths or lwfloat or sequence of floats norm
unknown offset_position
unknown offsets
float or sequence of floats path_effects
unknown picker
[Nonefloatbooleancallable] pickradius
unknown rasterized
[True  False  None] sketch_params
unknown snap
unknown transform
Transform
instanceurl
a url string urls
unknown visible
[True  False] zorder
any number See also

pcolor()
 For an explanation of the grid orientation and the expansion of 1D X and/or Y to 2D arrays.
Notes
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.
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.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.
 x: 1D array or sequence
 Array or sequence containing the data
Keyword arguments:
 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
window_hanning()
,window_none()
,numpy.blackman()
,numpy.hamming()
,numpy.bartlett()
,scipy.signal()
,scipy.signal.get_window()
, etc. The default iswindow_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 onesided for real data and both for complex data. ?onesided? forces the return of a onesided spectrum, while ?twosided? forces twosided.
 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.
Returns the tuple (spectrum, freqs, line):
 spectrum: 1D array
 The values for the phase spectrum in radians (real valued)
 freqs: 1D array
 The frequencies corresponding to the elements in spectrum

line: a Line2D instance
 The line created by this function
kwargs control the
Line2D
properties:Property Description agg_filter
unknown alpha
float (0.0 transparent through 1.0 opaque) animated
[True  False] antialiased
or aa[True  False] axes
an Axes
instanceclip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
or cany 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?  ?stepspre?  ?stepsmid?  ?stepspost?] figure
a matplotlib.figure.Figure
instancefillstyle
[?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, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or lwfloat value in points marker
A valid marker style
markeredgecolor
or mecany matplotlib color markeredgewidth
or mewfloat value in points markerfacecolor
or mfcany matplotlib color markerfacecoloralt
or mfcaltany matplotlib color markersize
or msfloat markevery
[None  int  length2 tuple of int  slice  list/array of int  float  length2 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 matplotlib.transforms.Transform
instanceurl
a url string visible
[True  False] xdata
1D array ydata
1D array zorder
any number Example:
(Source code, png, hires.png, pdf)
See also

magnitude_spectrum()

magnitude_spectrum()
plots the magnitudes of the corresponding frequencies. 
angle_spectrum()

angle_spectrum()
plots the wrapped version of this function. 
specgram()

specgram()
can plot the phase spectrum of segments within the signal in a colormap.
Notes
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?.
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.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.
Call signature:
pie(x, explode=None, labels=None, colors=('b', 'g', 'r', 'c', 'm', 'y', 'k', 'w'), 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 )
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 xaxis.
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.
 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 xaxis.
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
matplotlib.patches.Wedge
instances 
texts is a list of the label
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
Text
instances for the numeric labels. 
patches is a sequence of
Notes
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: ?explode?, ?colors?, ?x?, ?labels?.
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.pink()

set the default colormap to pink and apply to current image if any. See help(colormaps) for more information

matplotlib.pyplot.plasma()

set the default colormap to plasma and apply to current image if any. See help(colormaps) for more information

matplotlib.pyplot.plot(*args, **kwargs)

Plot lines and/or markers to the
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..N1 plot(y, 'r+') # ditto, but with red plusses
If x and/or y is 2dimensional, 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 doplot(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 '.'
dashdot 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 afmt
group, but the tuple forms can be used only askwargs
.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
Line2D
for details.The kwargs are
Line2D
properties:Property Description agg_filter
unknown alpha
float (0.0 transparent through 1.0 opaque) animated
[True  False] antialiased
or aa[True  False] axes
an Axes
instanceclip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
or cany 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?  ?stepspre?  ?stepsmid?  ?stepspost?] figure
a matplotlib.figure.Figure
instancefillstyle
[?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, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or lwfloat value in points marker
A valid marker style
markeredgecolor
or mecany matplotlib color markeredgewidth
or mewfloat value in points markerfacecolor
or mfcany matplotlib color markerfacecoloralt
or mfcaltany matplotlib color markersize
or msfloat markevery
[None  int  length2 tuple of int  slice  list/array of int  float  length2 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 matplotlib.transforms.Transform
instanceurl
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
autoscale_view()
to determine whether the x and y axes are autoscaled; the default is True.Notes
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: ?y?, ?x?.
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.plot_date(x, y, fmt='o', tz=None, xdate=True, ydate=False, hold=None, data=None, **kwargs)

Plot with data with dates.
Call signature:
plot_date(x, y, fmt='bo', tz=None, xdate=True, ydate=False, **kwargs)
Similar to the
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 00010101 UTC.
Keyword arguments:
 fmt: string
 The plot format string.

tz: [ None  timezone string  tzinfo instance]
 The time zone to use in labeling dates. If None, defaults to rc value.
 xdate: [ True  False ]
 If True, the xaxis will be labeled with dates.
 ydate: [ False  True ]
 If True, the yaxis will be labeled with dates.
Note if you are using custom date tickers and formatters, it may be necessary to set the formatters/locators after the call to
plot_date()
sinceplot_date()
will set the default tick locator tomatplotlib.dates.AutoDateLocator
(if the tick locator is not already set to amatplotlib.dates.DateLocator
instance) and the default tick formatter tomatplotlib.dates.AutoDateFormatter
(if the tick formatter is not already set to amatplotlib.dates.DateFormatter
instance).Valid kwargs are
Line2D
properties:Property Description agg_filter
unknown alpha
float (0.0 transparent through 1.0 opaque) animated
[True  False] antialiased
or aa[True  False] axes
an Axes
instanceclip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
or cany 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?  ?stepspre?  ?stepsmid?  ?stepspost?] figure
a matplotlib.figure.Figure
instancefillstyle
[?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, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or lwfloat value in points marker
A valid marker style
markeredgecolor
or mecany matplotlib color markeredgewidth
or mewfloat value in points markerfacecolor
or mfcany matplotlib color markerfacecoloralt
or mfcaltany matplotlib color markersize
or msfloat markevery
[None  int  length2 tuple of int  slice  list/array of int  float  length2 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 matplotlib.transforms.Transform
instanceurl
a url string visible
[True  False] xdata
1D array ydata
1D array zorder
any number See also
dates
for helper functionsdate2num()
,num2date()
anddrange()
for help on creating the required floating point dates.Notes
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: ?y?, ?x?.
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.plotfile(fname, cols=(0, ), plotfuncs=None, comments='#', skiprows=0, checkrows=5, delimiter=', ', 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
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
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.

matplotlib.pyplot.polar(*args, **kwargs)

Make a polar plot.
call signature:
polar(theta, r, **kwargs)
Multiple theta, r arguments are supported, with format strings, as in
plot()
.

matplotlib.pyplot.prism()

set the default colormap to prism and apply to current image if any. See help(colormaps) for more information

matplotlib.pyplot.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 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 of each segment are averaged to compute , with a scaling to correct for power loss due to windowing.
If len(x) < NFFT, it will be zero padded to NFFT.
 x: 1D array or sequence
 Array or sequence containing the data
Keyword arguments:
 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
window_hanning()
,window_none()
,numpy.blackman()
,numpy.hamming()
,numpy.bartlett()
,scipy.signal()
,scipy.signal.get_window()
, etc. The default iswindow_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 onesided for real data and both for complex data. ?onesided? forces the return of a onesided spectrum, while ?twosided? forces twosided.
 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 ffting, 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
pylab
module definesdetrend_none()
,detrend_mean()
, anddetrend_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? calldetrend_mean()
. ?linear? callsdetrend_linear()
. ?none? callsdetrend_none()
.  scale_by_freq: boolean
 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.
If return_line is False, returns the tuple (Pxx, freqs). If return_line is True, returns the tuple (Pxx, freqs. line):
 Pxx: 1D array
 The values for the power spectrum
P_{xx}
before scaling (real valued)  freqs: 1D array
 The frequencies corresponding to the elements in Pxx

line: a Line2D instance
 The line created by this function. Only returend if return_line is True.
For plotting, the power is plotted as for decibels, though Pxx itself is returned.
 References:
 Bendat & Piersol ? Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986)
kwargs control the
Line2D
properties:Property Description agg_filter
unknown alpha
float (0.0 transparent through 1.0 opaque) animated
[True  False] antialiased
or aa[True  False] axes
an Axes
instanceclip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
or cany 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?  ?stepspre?  ?stepsmid?  ?stepspost?] figure
a matplotlib.figure.Figure
instancefillstyle
[?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, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or lwfloat value in points marker
A valid marker style
markeredgecolor
or mecany matplotlib color markeredgewidth
or mewfloat value in points markerfacecolor
or mfcany matplotlib color markerfacecoloralt
or mfcaltany matplotlib color markersize
or msfloat markevery
[None  int  length2 tuple of int  slice  list/array of int  float  length2 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 matplotlib.transforms.Transform
instanceurl
a url string visible
[True  False] xdata
1D array ydata
1D array zorder
any number Example:
(Source code, png, hires.png, pdf)
See also

specgram()

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. 
magnitude_spectrum()

magnitude_spectrum()
plots the magnitude spectrum. 
csd()

csd()
plots the spectral density between two signals.
Notes
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?.
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.quiver(*args, **kw)

Plot a 2D 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 1D or 2D arrays or sequences. If X and Y are absent, they will be generated as a uniform grid. If U and V are 2D arrays but X and Y are 1D, and if
len(X)
andlen(Y)
match the column and row dimensions of U, then X and Y will be expanded withnumpy.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 xaxis units. To plot vectors in the xy 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
PolyCollection
facecolor kwarg. If C has been set, color has no effect.
The defaults give a slightly sweptback 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
PolyCollection
keyword arguments:Property Description agg_filter
unknown alpha
float or None animated
[True  False] antialiased
or antialiasedsBoolean or sequence of booleans array
unknown axes
an Axes
instanceclim
a length 2 sequence of floats clip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]cmap
a colormap or registered colormap name color
matplotlib color arg or sequence of rgba tuples contains
a callable function edgecolor
or edgecolorsmatplotlib color spec or sequence of specs facecolor
or facecolorsmatplotlib color spec or sequence of specs figure
a matplotlib.figure.Figure
instancegid
an id string hatch
[ ?/?  ?\?  ??  ??  ?+?  ?x?  ?o?  ?O?  ?.?  ?*? ] label
string or anything printable with ?%s? conversion. linestyle
or linestyles or dashes[?solid?  ?dashed?, ?dashdot?, ?dotted?  (offset, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or linewidths or lwfloat or sequence of floats norm
unknown offset_position
unknown offsets
float or sequence of floats path_effects
unknown picker
[Nonefloatbooleancallable] pickradius
unknown rasterized
[True  False  None] sketch_params
unknown snap
unknown transform
Transform
instanceurl
a url string urls
unknown visible
[True  False] zorder
any number Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.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
Text
color.  fontproperties:
 A dictionary with keyword arguments accepted by the
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.
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.rc(*args, **kwargs)

Set the current rc params. Group is the grouping for the rc, e.g., for
lines.linewidth
the group islines
, foraxes.facecolor
, the group isaxes
, 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
rcdefaults()
to restore the default rc params after changes.

matplotlib.pyplot.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.

matplotlib.pyplot.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

matplotlib.pyplot.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,
rgrid()
simply returns the tuple (lines, labels), where lines is an array of radial gridlines (Line2D
instances) and labels is an array of tick labels (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' )

matplotlib.pyplot.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 filelike object, or possibly some backenddependent object such as
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.

matplotlib.pyplot.sca(ax)

Set the current Axes instance to ax.
The current Figure is updated to the parent of ax.

matplotlib.pyplot.scatter(x, y, s=20, c=None, marker='o', 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, where x and y are sequence like objects of the same length.
Parameters: x, y : array_like, shape (n, )
Input data
s : scalar or array_like, shape (n, ), optional, default: 20
size in points^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 lengthN
, or a sequence ofN
numbers to be mapped to colors using thecmap
andnorm
specified via kwargs (see below). Note thatc
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 2D 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 :
MarkerStyle
, optional, default: ?o?See
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 :
Colormap
, optional, default: NoneA
Colormap
instance or registered name.cmap
is only used ifc
is an array of floats. If None, defaults to rcimage.cmap
.norm :
Normalize
, optional, default: NoneA
Normalize
instance is used to scale luminance data to 0, 1.norm
is only used ifc
is an array of floats. IfNone
, use the defaultnormalize()
.vmin, vmax : scalar, optional, default: None
vmin
andvmax
are used in conjunction withnorm
to normalize luminance data. If either areNone
, the min and max of the color array is used. Note if you pass anorm
instance, your settings forvmin
andvmax
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,).
edgecolors : color or sequence of color, optional, default: None
If None, defaults to (patch.edgecolor). 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 nonfilled markers, the
edgecolors
kwarg is ignored; color is determined byc
.Returns: paths :
PathCollection
Other Parameters: kwargs :
Collection
propertiesNotes
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: ?y?, ?linewidths?, ?color?, ?facecolor?, ?facecolors?, ?edgecolors?, ?c?, ?s?, ?x?.
Additional kwargs: hold = [TrueFalse] overrides default hold state
Examples
(Source code, png, hires.png, pdf)

matplotlib.pyplot.sci(im)

Set the current image. This image will be the target of colormap commands like
jet()
,hot()
orclim()
). The current image is an attribute of the current axes.

matplotlib.pyplot.semilogx(*args, **kwargs)

Make a plot with log scaling on the x axis.
Call signature:
semilogx(*args, **kwargs)
semilogx()
supports all the keyword arguments ofplot()
andmatplotlib.axes.Axes.set_xscale()
.Notable keyword arguments:
 basex: scalar > 1
 Base of the x logarithm
 subsx: [ None  sequence ]
 The location of the minor xticks; None defaults to autosubs, which depend on the number of decades in the plot; see
set_xscale()
for details.  nonposx: [ ?mask?  ?clip? ]
 Nonpositive values in x can be masked as invalid, or clipped to a very small positive number
The remaining valid kwargs are
Line2D
properties:Property Description agg_filter
unknown alpha
float (0.0 transparent through 1.0 opaque) animated
[True  False] antialiased
or aa[True  False] axes
an Axes
instanceclip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
or cany 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?  ?stepspre?  ?stepsmid?  ?stepspost?] figure
a matplotlib.figure.Figure
instancefillstyle
[?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, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or lwfloat value in points marker
A valid marker style
markeredgecolor
or mecany matplotlib color markeredgewidth
or mewfloat value in points markerfacecolor
or mfcany matplotlib color markerfacecoloralt
or mfcaltany matplotlib color markersize
or msfloat markevery
[None  int  length2 tuple of int  slice  list/array of int  float  length2 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 matplotlib.transforms.Transform
instanceurl
a url string visible
[True  False] xdata
1D array ydata
1D array zorder
any number See also

loglog()
 For example code and figure
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.semilogy(*args, **kwargs)

Make a plot with log scaling on the y axis.
call signature:
semilogy(*args, **kwargs)
semilogy()
supports all the keyword arguments ofplot()
andmatplotlib.axes.Axes.set_yscale()
.Notable keyword arguments:
 basey: scalar > 1
 Base of the y logarithm
 subsy: [ None  sequence ]
 The location of the minor yticks; None defaults to autosubs, which depend on the number of decades in the plot; see
set_yscale()
for details.  nonposy: [ ?mask?  ?clip? ]
 Nonpositive values in y can be masked as invalid, or clipped to a very small positive number
The remaining valid kwargs are
Line2D
properties:Property Description agg_filter
unknown alpha
float (0.0 transparent through 1.0 opaque) animated
[True  False] antialiased
or aa[True  False] axes
an Axes
instanceclip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
or cany 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?  ?stepspre?  ?stepsmid?  ?stepspost?] figure
a matplotlib.figure.Figure
instancefillstyle
[?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, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or lwfloat value in points marker
A valid marker style
markeredgecolor
or mecany matplotlib color markeredgewidth
or mewfloat value in points markerfacecolor
or mfcany matplotlib color markerfacecoloralt
or mfcaltany matplotlib color markersize
or msfloat markevery
[None  int  length2 tuple of int  slice  list/array of int  float  length2 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 matplotlib.transforms.Transform
instanceurl
a url string visible
[True  False] xdata
1D array ydata
1D array zorder
any number See also

loglog()
 For example code and figure
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.set_cmap(cmap)

Set the default colormap. Applies to the current image if any. See help(colormaps) for more information.
cmap must be a
Colormap
instance, or the name of a registered colormap.See
matplotlib.cm.register_cmap()
andmatplotlib.cm.get_cmap()
.

matplotlib.pyplot.setp(*args, **kwargs)

Set a property on an artist object.
matplotlib supports the use of
setp()
(?set property?) andgetp()
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
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')
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

matplotlib.pyplot.show(*args, **kw)

Display a figure. When running in ipython with its pylab mode, display all figures and return to the ipython prompt.
In noninteractive 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 noninteractive 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.

matplotlib.pyplot.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).
 x: 1D array or sequence
 Array or sequence containing the data
Keyword arguments:
 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
window_hanning()
,window_none()
,numpy.blackman()
,numpy.hamming()
,numpy.bartlett()
,scipy.signal()
,scipy.signal.get_window()
, etc. The default iswindow_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 onesided for real data and both for complex data. ?onesided? forces the return of a onesided spectrum, while ?twosided? forces twosided.
 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 ffting, 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
pylab
module definesdetrend_none()
,detrend_mean()
, anddetrend_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? calldetrend_mean()
. ?linear? callsdetrend_linear()
. ?none? callsdetrend_none()
.  scale_by_freq: boolean
 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 complexvalued 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
matplotlib.colors.Colormap
instance; if None, use default determined by rc  xextent:
 The image extent along the xaxis. xextent = (xmin,xmax) The default is (0,max(bins)), where bins is the return value from
specgram()
 kwargs:
 Additional kwargs are passed on to imshow which makes the specgram image
Note
detrend and scale_by_freq only apply when mode is set to ?psd?
Returns the tuple (spectrum, freqs, t, im):
 spectrum: 2D array
 columns are the periodograms of successive segments
 freqs: 1D array
 The frequencies corresponding to the rows in spectrum
 t: 1D array
 The times corresponding to midpoints of segments (i.e the columns in spectrum)

im: instance of class
AxesImage
 The image created by imshow containing the spectrogram
Example:
(Source code, png, hires.png, pdf)
See also

psd()

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. 
magnitude_spectrum()
 A single spectrum, similar to having a single segment when mode is ?magnitude?. Plots a line instead of a colormap.

angle_spectrum()
 A single spectrum, similar to having a single segment when mode is ?angle?. Plots a line instead of a colormap.

phase_spectrum()
 A single spectrum, similar to having a single segment when mode is ?phase?. Plots a line instead of a colormap.
Notes
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?.
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.spectral()

set the default colormap to spectral and apply to current image if any. See help(colormaps) for more information

matplotlib.pyplot.spring()

set the default colormap to spring and apply to current image if any. See help(colormaps) for more information

matplotlib.pyplot.spy(Z, precision=0, marker=None, markersize=None, aspect='equal', hold=None, **kwargs)

Plot the sparsity pattern on a 2D array.
spy(Z)
plots the sparsity pattern of the 2D array Z.Parameters: Z : sparse array (n, m)
The array to be plotted.
precision : float, optional, default: 0
If precision is 0, any nonzero value will be plotted; else, values of will be plotted.
For
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. Ifextent
is notNone
, 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*

matplotlib.pyplot.stackplot(x, *args, **kwargs)

Draws a stacked area plot.
x : 1d array of dimension N
 y

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://www.leebyron.com/else/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 andused to colour the stacked areas. All other keyword arguments are passed to fill_between()
Returns r : A list of PolyCollection
, one for each element in the stacked area plot.
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.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).
See also
This document for details.
Example:
(Source code, png, hires.png, pdf)
Notes
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.
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.step(x, y, *args, **kwargs)

Make a step plot.
Call signature:
step(x, y, *args, **kwargs)
Additional keyword args to
step()
are the same as those forplot()
.x and y must be 1D sequences, and it is assumed, but not checked, that x is uniformly increasing.
Keyword arguments:
 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 halfway between the xvalues.
Return value is a list of lines that were added.
Notes
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: ?y?, ?x?.
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.streamplot(x, y, u, v, density=1, linewidth=None, color=None, cmap=None, norm=None, arrowsize=1, arrowstyle='>', minlength=0.1, transform=None, zorder=1, start_points=None, hold=None, data=None)

Draws streamlines of a vector flow.
 x, y
 an evenly spaced grid.
 u, v
: 2d arraysx and yvelocities. Number of rows should match length of y, and the number of columns should match x. density : float or 2tupleControls 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 arrayvary linewidth when given a 2d array with the same shape as velocities. color : matplotlib color code, or 2d arrayStreamline color. When given an array with the same shape as velocities, color values are converted to colors using cmap. cmap : Colormap
Colormap used to plot streamlines and arrows. Only necessary when using an array input for color. norm : 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 : floatFactor scale arrow size. arrowstyle : strArrow style specification. See FancyArrowPatch
. minlength : floatMinimum 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 : intany number
Returns:
 stream_container
: StreamplotSet
Container object with attributes
 lines:
matplotlib.collections.LineCollection
of streamlines  arrows: collection of
matplotlib.patches.FancyArrowPatch
objects representing arrows halfway 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.
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.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
subaxes, 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 ofnrows * 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 preexisting 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, axisbg='y') # creates 2nd subplot with yellow background
If you do not want this behavior, use the
add_subplot()
method or theaxes()
function instead.Keyword arguments:
 axisbg:
 The background color of the subplot, which can be any valid color specifier. See
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
matplotlib.projections
.
See also
Example:
(Source code, png, hires.png, pdf)

matplotlib.pyplot.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 0based.
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)

matplotlib.pyplot.subplot_tool(targetfig=None)

Launch a subplot tool window for a figure.
A
matplotlib.widgets.SubplotTool
instance is returned.

matplotlib.pyplot.subplots(nrows=1, ncols=1, sharex=False, sharey=False, squeeze=True, subplot_kw=None, gridspec_kw=None, **fig_kw)

Create a figure with a set of subplots already made.
This utility wrapper makes it convenient to create common layouts of subplots, including the enclosing figure object, in a single call.
Keyword arguments:
 nrows
 Number of rows of the subplot grid. Defaults to 1.
 ncols
: intNumber of columns of the subplot grid. Defaults to 1. sharex : string or boolIf True, the X axis will be shared amongst all subplots. If True and you have multiple rows, the x tick labels on all but the last row of plots will have visible set to False If a string must be one of ?row?, ?col?, ?all?, or ?none?. ?all? has the same effect as True, ?none? has the same effect as False. If ?row?, each subplot row will share a X axis. If ?col?, each subplot column will share a X axis and the x tick labels on all but the last row will have visible set to False. sharey : string or boolIf True, the Y axis will be shared amongst all subplots. If True and you have multiple columns, the y tick labels on all but the first column of plots will have visible set to False If a string must be one of ?row?, ?col?, ?all?, or ?none?. ?all? has the same effect as True, ?none? has the same effect as False. If ?row?, each subplot row will share a Y axis and the y tick labels on all but the first column will have visible set to False. If ?col?, each subplot column will share a Y axis. squeeze : bool
If True, extra dimensions are squeezed out from the returned axis object:
 if only one subplot is constructed (nrows=ncols=1), the resulting single Axis object is returned as a scalar.
 for Nx1 or 1xN subplots, the returned object is a 1d numpy object array of Axis objects are returned as numpy 1d arrays.
 for NxM subplots with N>1 and M>1 are returned as a 2d array.
If False, no squeezing at all is done: the returned axis object is always a 2d array containing Axis instances, even if it ends up being 1x1.
subplot_kw
: dictDict with keywords passed to the add_subplot()
call used to create each subplots. gridspec_kw : dictDict with keywords passed to the GridSpec
constructor used to create the grid the subplots are placed on. fig_kw : dictDict with keywords passed to the figure()
call. Note that all keywords not recognized above will be automatically included here.
Returns:
fig, ax : tuple

fig is the
matplotlib.figure.Figure
object  ax can be either a single axis object or an array of axis objects if more than one subplot was created. The dimensions of the resulting array can be controlled with the squeeze keyword, see above.
Examples:
x = np.linspace(0, 2*np.pi, 400) y = np.sin(x**2) # Just a figure and one subplot f, ax = plt.subplots() ax.plot(x, y) ax.set_title('Simple plot') # Two subplots, unpack 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) # Four polar axes plt.subplots(2, 2, subplot_kw=dict(polar=True)) # 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 a X and Y axis with all subplots plt.subplots(2, 2, sharex='all', sharey='all') # same as plt.subplots(2, 2, sharex=True, sharey=True)

matplotlib.pyplot.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 hspace = 0.2 # the amount of height reserved for white space between subplots
The actual defaults are controlled by the rc file

matplotlib.pyplot.summer()

set the default colormap to summer and apply to current image if any. See help(colormaps) for more information

matplotlib.pyplot.suptitle(*args, **kwargs)

Add a centered title to the figure.
kwargs are
matplotlib.text.Text
properties. Using figure coordinates, the defaults are: x
 The x location of the text in figure coords
 y
: 0.98The y location of the text in figure coords horizontalalignment : ?center?The horizontal alignment of the text verticalalignment : ?top?The vertical alignment of the text
A matplotlib.text.Text
instance is returned.
Example:
fig.suptitle('this is the figure title', fontsize=12)

matplotlib.pyplot.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.

matplotlib.pyplot.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
matplotlib.table.Table
instance. For finer grained control over tables, use theTable
class and add it to the axes withadd_table()
.Thanks to John Gill for providing the class and table.
kwargs control the
Table
properties:Property Description agg_filter
unknown alpha
float (0.0 transparent through 1.0 opaque) animated
[True  False] axes
an Axes
instanceclip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]contains
a callable function figure
a matplotlib.figure.Figure
instancefontsize
a float in points gid
an id string label
string or anything printable with ?%s? conversion. path_effects
unknown picker
[Nonefloatbooleancallable] rasterized
[True  False  None] sketch_params
unknown snap
unknown transform
Transform
instanceurl
a url string visible
[True  False] zorder
any number

matplotlib.pyplot.text(x, y, s, fontdict=None, withdash=False, **kwargs)

Add text to the axes.
Add text in string
s
to axis at locationx
,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
TextWithDash
instance instead of aText
instance.Other Parameters: kwargs :
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 lowerleft and 1,1 is upperright). 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 ofRectangle
properties. For example:>>> text(x, y, s, bbox=dict(facecolor='red', alpha=0.5))

matplotlib.pyplot.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 (
Line2D
instances) and labels is an array of tick labels (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):
Note that on input, the labels argument is a list of strings, and on output it is a list of
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') )

matplotlib.pyplot.tick_params(axis='both', **kwargs)

Change the appearance of ticks and tick labels.
Keyword arguments:
 axis
 Axis on which to operate; default is ?both?.
 reset
: [True  False]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 Tick length in points. width Tick width in points. color Tick color; accepts any mpl color spec. pad Distance in points between tick and label. labelsize Tick label font size in points or as a string (e.g., ?large?). labelcolor Tick label color; mpl color spec. colors Changes the tick color and the label color to the same value: mpl color spec. zorder Tick and label zorder. bottom, top, left, right : [bool  ?on?  ?off?]controls whether to draw the respective ticks. labelbottom, labeltop, labelleft, labelright Boolean or [?on?  ?off?], controls whether to draw the respective tick labels.
Example:
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.

matplotlib.pyplot.ticklabel_format(**kwargs)

Change the
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 Cstyle (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
ScalarFormatter
is not theFormatter
being used, anAttributeError
will be raised.

matplotlib.pyplot.tight_layout(pad=1.08, h_pad=None, w_pad=None, rect=None)

Automatically adjust subplot parameters to give specified padding.
Parameters:
 pad
 padding between the figure edge and the edges of subplots, as a fraction of the fontsize.
 h_pad, w_pad
: floatpadding (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).

matplotlib.pyplot.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.
See also
See
text()
for adding text to the current axesParameters: 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 :
Text
The matplotlib text instance representing the title
Other Parameters: kwargs : text properties
Other keyword arguments are text properties, see
Text
for a list of valid text properties.

matplotlib.pyplot.tricontour(*args, **kwargs)

Draw contours on an unstructured triangular grid.
tricontour()
andtricontourf()
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
matplotlib.tri.Triangulation
object, ortricontour(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
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 automaticallychosen 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 aTriContourSet
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
Colormap
instance or None. If cmap is None and colors is None, a default Colormap is used.  norm: [ None  Normalize ]
 A
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
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
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
matplotlib.colors.Colormap.set_under()
andmatplotlib.colors.Colormap.set_over()
methods.  xunits, yunits: [ None  registered units ]
 Override axis units by specifying an instance of a
matplotlib.units.ConversionInterface
.
tricontouronly keyword arguments:
 linewidths: [ None  number  tuple of numbers ]

If linewidths is None, the default width in
lines.linewidth
inmatplotlibrc
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
inmatplotlibrc
will be used.
tricontourfonly 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:
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.tricontourf(*args, **kwargs)

Draw contours on an unstructured triangular grid.
tricontour()
andtricontourf()
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
matplotlib.tri.Triangulation
object, ortricontour(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
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 automaticallychosen 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 aTriContourSet
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
Colormap
instance or None. If cmap is None and colors is None, a default Colormap is used.  norm: [ None  Normalize ]
 A
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
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
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
matplotlib.colors.Colormap.set_under()
andmatplotlib.colors.Colormap.set_over()
methods.  xunits, yunits: [ None  registered units ]
 Override axis units by specifying an instance of a
matplotlib.units.ConversionInterface
.
tricontouronly keyword arguments:
 linewidths: [ None  number  tuple of numbers ]

If linewidths is None, the default width in
lines.linewidth
inmatplotlibrc
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
inmatplotlibrc
will be used.
tricontourfonly 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:
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.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
matplotlib.tri.Triangulation
object, ortripcolor(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
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
pcolor()
.Example:
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.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
matplotlib.tri.Triangulation
object, ortriplot(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
Triangulation
for a explanation of these possibilities.The remaining args and kwargs are the same as for
plot()
.Return a list of 2
Line2D
containing respectively: the lines plotted for triangles edges
 the markers plotted for triangles nodes
Example:
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.twinx(ax=None)

Make a second axes that shares the xaxis. 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.
See also

examples/api_examples/two_scales.py
 For an example


matplotlib.pyplot.twiny(ax=None)

Make a second axes that shares the yaxis. 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.

matplotlib.pyplot.uninstall_repl_displayhook()

Uninstalls the matplotlib display hook.

matplotlib.pyplot.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.
Call signature:
violinplot(dataset, positions=None, vert=True, widths=0.5, showmeans=False, showextrema=True, showmedians=False, points=100, bw_method=None):
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

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
: arraylike, 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 thematplotlib.collections.PolyCollection
instances containing the filled area of each violin. 
cmeans
: Amatplotlib.collections.LineCollection
instance created to identify the mean values of each of the violin?s distribution. 
cmins
: Amatplotlib.collections.LineCollection
instance created to identify the bottom of each violin?s distribution. 
cmaxes
: Amatplotlib.collections.LineCollection
instance created to identify the top of each violin?s distribution. 
cbars
: Amatplotlib.collections.LineCollection
instance created to identify the centers of each violin?s distribution. 
cmedians
: Amatplotlib.collections.LineCollection
instance created to identify the median values of each of the violin?s distribution.
Notes
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?.
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.viridis()

set the default colormap to viridis and apply to current image if any. See help(colormaps) for more information

matplotlib.pyplot.vlines(x, ymin, ymax, colors='k', linestyles='solid', label='', hold=None, data=None, **kwargs)

Plot vertical lines.
Plot vertical lines at each
x
fromymin
toymax
.Parameters: x : scalar or 1D array_like
xindexes 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 :
LineCollection
Other Parameters: kwargs :
LineCollection
properties.See also

hlines
 horizontal lines
Notes
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: ?ymin?, ?x?, ?colors?, ?ymax?.
Additional kwargs: hold = [TrueFalse] overrides default hold state
Examples
(Source code, png, hires.png, pdf)


Call signature:
waitforbuttonpress(self, timeout=1)
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.

matplotlib.pyplot.winter()

set the default colormap to winter and apply to current image if any. See help(colormaps) for more information

matplotlib.pyplot.xcorr(x, y, normed=True, detrend=, usevlines=True, maxlags=10, hold=None, data=None, **kwargs)

Plot the cross correlation between x and y.
Parameters: x : sequence of scalars of length n
y : sequence of scalars of length n
hold : boolean, optional, 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, normalize the data by the autocorrelation at the 0th lag.
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:
Other Parameters: linestyle :
Line2D
prop, optional, default: NoneOnly used if usevlines is False.
marker : string, optional, default: ?o?
Notes
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: ?y?, ?x?.
Additional kwargs: hold = [TrueFalse] overrides default hold state

matplotlib.pyplot.xkcd(scale=1, length=100, randomness=2)

Turns on xkcd sketchstyle 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 XKCDstyle fig1 = plt.figure() # ... # This figure will be in regular style fig2 = plt.figure()

matplotlib.pyplot.xlabel(s, *args, **kwargs)

Set the x axis label of the current axis.
Default override is:
override = { 'fontsize' : 'small', 'verticalalignment' : 'top', 'horizontalalignment' : 'center' }
See also

text()
 For information on how override and the optional args work


matplotlib.pyplot.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 xaxis.
The new axis limits are returned as a length 2 tuple.

matplotlib.pyplot.xscale(*args, **kwargs)

Set the scaling of the xaxis.
call signature:
xscale(scale, **kwargs)
The available scales are: ?linear?  ?log?  ?logit?  ?symlog?
Different keywords may be accepted, depending on the scale:
?linear?
?log?
 basex/basey:
 The base of the logarithm
 nonposx/nonposy: [?mask?  ?clip? ]
 nonpositive 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.

matplotlib.pyplot.xticks(*args, **kwargs)

Get or set the xlimits 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
Text
properties. For example, to rotate long labels:xticks( arange(12), calendar.month_name[1:13], rotation=17 )

matplotlib.pyplot.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' : }
See also

text()
 For information on how override and the optional args work.


matplotlib.pyplot.ylim(*args, **kwargs)

Get or set the ylimits 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 yaxis.
The new axis limits are returned as a length 2 tuple.

matplotlib.pyplot.yscale(*args, **kwargs)

Set the scaling of the yaxis.
call signature:
yscale(scale, **kwargs)
The available scales are: ?linear?  ?log?  ?logit?  ?symlog?
Different keywords may be accepted, depending on the scale:
?linear?
?log?
 basex/basey:
 The base of the logarithm
 nonposx/nonposy: [?mask?  ?clip? ]
 nonpositive 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.

matplotlib.pyplot.yticks(*args, **kwargs)

Get or set the ylimits 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
Text
properties. For example, to rotate long labels:yticks( arange(12), calendar.month_name[1:13], rotation=45 )
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