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, deprecated, default: True detrend : callable, optional, default: x is detrended by the normed : boolean, optional, default: True if True, input vectors are normalised to unit length. usevlines : boolean, optional, default: True if True, Axes.vlines is used to plot the vertical lines from the origin to the acorr. Otherwise, Axes.plot is used. maxlags : integer, optional, default: 10 number of lags to show. If None, will return all 2 * len(x)  1 lags. 

Returns: 
(lags, c, line, b) : where: 
Other Parameters:  
linestyle : Only used if usevlines is False. marker : string, optional, default: ‘o’ 
The cross correlation is performed with numpy.correlate()
with mode
= 2.
xcorr
is top graph, and acorr
is bottom graph.
(Source code, png, pdf)
Note
In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:
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.
Parameters: 
x : 1D array or sequence Array or sequence containing the data Fs : scalar The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. The default value is 2. window : callable or ndarray A function or a vector of length NFFT. To create window vectors see 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. **kwargs : Keyword arguments control the
 

Returns: 
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 The line created by this function 
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.(Source code, png, pdf)
matplotlib.pyplot.annotate(*args, **kwargs)
Annotate the point xy
with text s
.
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 xycoords : str, Artist, Transform, callable or tuple, optional The coordinate system that For a
If a If a If a callable it must take a If a See Advanced Annotation for more details. Defaults to textcoords : str, The coordinate system that All
defaults to the input of arrowprops : dict, optional If not None, properties used to draw a If
If the
Valid keys for
Defaults to None annotation_clip : bool, optional Controls the visibility of the annotation when it goes outside the axes area. If The default is  

Returns: 
Annotation 
matplotlib.pyplot.arrow(x, y, dx, dy, hold=None, **kwargs)
Add an arrow to the axes.
Draws arrow on specified axis from (x
, y
) to (x
+ dx
, y
+ dy
). Uses FancyArrow patch to construct the arrow.
Parameters: 
x : float Xcoordinate of the arrow base y : float Ycoordinate of the arrow base dx : float Length of arrow along xcoordinate dy : float Length of arrow along ycoordinate  

Returns: 
a : FancyArrow patches.FancyArrow object  
Other Parameters:  
Optional kwargs (inherited from FancyArrow patch) control the arrow construction and properties: Constructor arguments
Other valid kwargs (inherited from :class:`Patch`) are:

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=">"))
(Source code, png, pdf)
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.
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 full subplot(111)
window axis.axes(rect, facecolor='w')
where rect = [left, bottom, width, height] in normalized (0, 1) units. facecolor is the background color for the axis, default white.axes(h)
where h is an axes instance makes h the current axis. An Axes
instance is returned.kwarg  Accepts  Description 

facecolor  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: 
See also
axhspan
kwargs are passed to Line2D
and can be used to control the line properties.
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 instance 
clip_box  a matplotlib.transforms.Bbox instance 
clip_on  [True  False] 
clip_path  [ (Path , Transform )  Patch  None ] 
color or c  any matplotlib color 
contains  a callable function 
dash_capstyle  [‘butt’  ‘round’  ‘projecting’] 
dash_joinstyle  [‘miter’  ‘round’  ‘bevel’] 
dashes  sequence of on/off ink in points 
drawstyle  [‘default’  ‘steps’  ‘stepspre’  ‘stepsmid’  ‘stepspost’] 
figure  a matplotlib.figure.Figure instance 
fillstyle  [‘full’  ‘left’  ‘right’  ‘bottom’  ‘top’  ‘none’] 
gid  an id string 
label  string or anything printable with ‘%s’ conversion. 
linestyle or ls  [‘solid’  ‘dashed’, ‘dashdot’, ‘dotted’  (offset, onoffdashseq)  ''  ''  '.'  ':'  'None'  ' '  '' ] 
linewidth or lw  float value in points 
marker  A valid marker style 
markeredgecolor or mec  any matplotlib color 
markeredgewidth or mew  float value in points 
markerfacecolor or mfc  any matplotlib color 
markerfacecoloralt or mfcalt  any matplotlib color 
markersize or ms  float 
markevery  [None  int  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 instance 
url  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.
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.
Parameters: 
ymin : float Lower limit of the horizontal span in data units. ymax : float Upper limit of the horizontal span in data units. xmin : float, optional, default: 0 Lower limit of the vertical span in axes (relative 01) units. xmax : float, optional, default: 1 Upper limit of the vertical span in axes (relative 01) units.  

Returns: 
Polygon :  
Other Parameters:  
kwargs :

See also
axvspan
(Source code, png, pdf)
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()
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: 
See also
axhspan
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 instance 
clip_box  a matplotlib.transforms.Bbox instance 
clip_on  [True  False] 
clip_path  [ (Path , Transform )  Patch  None ] 
color or c  any matplotlib color 
contains  a callable function 
dash_capstyle  [‘butt’  ‘round’  ‘projecting’] 
dash_joinstyle  [‘miter’  ‘round’  ‘bevel’] 
dashes  sequence of on/off ink in points 
drawstyle  [‘default’  ‘steps’  ‘stepspre’  ‘stepsmid’  ‘stepspost’] 
figure  a matplotlib.figure.Figure instance 
fillstyle  [‘full’  ‘left’  ‘right’  ‘bottom’  ‘top’  ‘none’] 
gid  an id string 
label  string or anything printable with ‘%s’ conversion. 
linestyle or ls  [‘solid’  ‘dashed’, ‘dashdot’, ‘dotted’  (offset, onoffdashseq)  ''  ''  '.'  ':'  'None'  ' '  '' ] 
linewidth or lw  float value in points 
marker  A valid marker style 
markeredgecolor or mec  any matplotlib color 
markeredgewidth or mew  float value in points 
markerfacecolor or mfc  any matplotlib color 
markerfacecoloralt or mfcalt  any matplotlib color 
markersize or ms  float 
markevery  [None  int  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 instance 
url  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.
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.
Parameters: 
xmin : scalar Number indicating the first Xaxis coordinate of the vertical span rectangle in data units. xmax : scalar Number indicating the second Xaxis coordinate of the vertical span rectangle in data units. ymin : scalar, optional Number indicating the first Yaxis coordinate of the vertical span rectangle in relative Yaxis units (01). Default to 0. ymax : scalar, optional Number indicating the second Yaxis coordinate of the vertical span rectangle in relative Yaxis units (01). Default to 1. 

Returns: 
rectangle : matplotlib.patches.Polygon Vertical span (rectangle) from (xmin, ymin) to (xmax, ymax). 
Other Parameters:  
**kwargs Optional parameters are properties of the class matplotlib.patches.Polygon. 
See also
Draw a vertical, green, translucent rectangle from x = 1.25 to x = 1.55 that spans the yrange of the axes.
>>> axvspan(1.25, 1.55, facecolor='g', alpha=0.5)
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
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 error_kw : dict, optional dictionary of kwargs to be passed to errorbar method. ecolor and capsize may be specified here rather than as independent kwargs. align : {‘center’, ‘edge’}, optional If ‘edge’, aligns bars by their left edges (for vertical bars) and by their bottom edges (for horizontal bars). If ‘center’, interpret the 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
The optional arguments color
, edgecolor
, linewidth
, xerr
, and yerr
can be either scalars or sequences of length equal to the number of bars. This enables you to use bar as the basis for stacked bar charts, or candlestick plots. Detail: xerr
and yerr
are passed directly to errorbar()
, 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 instance 
capstyle  [‘butt’  ‘round’  ‘projecting’] 
clip_box  a matplotlib.transforms.Bbox instance 
clip_on  [True  False] 
clip_path  [ (Path , Transform )  Patch  None ] 
color  matplotlib color spec 
contains  a callable function 
edgecolor or ec  mpl color spec, None, ‘none’, or ‘auto’ 
facecolor or fc  mpl color spec, or None for default, or ‘none’ for no color 
figure  a matplotlib.figure.Figure instance 
fill  [True  False] 
gid  an id string 
hatch  [‘/’  ‘\’  ‘’  ‘‘  ‘+’  ‘x’  ‘o’  ‘O’  ‘.’  ‘*’] 
joinstyle  [‘miter’  ‘round’  ‘bevel’] 
label  string or anything printable with ‘%s’ conversion. 
linestyle or ls  [‘solid’  ‘dashed’, ‘dashdot’, ‘dotted’  (offset, onoffdashseq)  ''  ''  '.'  ':'  'None'  ' '  '' ] 
linewidth or lw  float or None for default 
path_effects  unknown 
picker  [Nonefloatbooleancallable] 
rasterized  [True  False  None] 
sketch_params  unknown 
snap  unknown 
transform 
Transform instance 
url  a url string 
visible  [True  False] 
zorder  any number 
Example: A stacked bar chart.
(Source code, png, pdf)
Note
In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:
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:
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)
and len(Y)
match the column and row dimensions of U, then X and Y will be expanded with numpy.meshgrid()
.
U, V, C may be masked arrays, but masked X, Y are not supported at present.
Keyword arguments:
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:
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.
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 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 dashes or linestyles  [‘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 
picker  [Nonefloatbooleancallable] 
pickradius  unknown 
rasterized  [True  False  None] 
sketch_params  unknown 
snap  unknown 
transform 
Transform instance 
url  a url string 
urls  unknown 
visible  [True  False] 
zorder  any number 
Example:
Note
In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:
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
bottom
, width
, height
, and left
can be either scalars or sequences
Parameters: 
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: 

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 error_kw : dictionary of kwargs to be passed to errorbar method. align : {‘center’, ‘edge’}, optional If ‘edge’, aligns bars by their left edges (for vertical bars) and by their bottom edges (for horizontal bars). If ‘center’, interpret the log : boolean, optional, default: False If true, sets the axis to be log scale 
See also
bar
The optional arguments color
, edgecolor
, linewidth
, xerr
, and yerr
can be either scalars or sequences of length equal to the number of bars. This enables you to use bar as the basis for stacked bar charts, or candlestick plots. Detail: xerr
and yerr
are passed directly to errorbar()
, 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 instance 
capstyle  [‘butt’  ‘round’  ‘projecting’] 
clip_box  a matplotlib.transforms.Bbox instance 
clip_on  [True  False] 
clip_path  [ (Path , Transform )  Patch  None ] 
color  matplotlib color spec 
contains  a callable function 
edgecolor or ec  mpl color spec, None, ‘none’, or ‘auto’ 
facecolor or fc  mpl color spec, or None for default, or ‘none’ for no color 
figure  a matplotlib.figure.Figure instance 
fill  [True  False] 
gid  an id string 
hatch  [‘/’  ‘\’  ‘’  ‘‘  ‘+’  ‘x’  ‘o’  ‘O’  ‘.’  ‘*’] 
joinstyle  [‘miter’  ‘round’  ‘bevel’] 
label  string or anything printable with ‘%s’ conversion. 
linestyle or ls  [‘solid’  ‘dashed’, ‘dashdot’, ‘dotted’  (offset, onoffdashseq)  ''  ''  '.'  ':'  'None'  ' '  '' ] 
linewidth or lw  float or None for default 
path_effects  unknown 
picker  [Nonefloatbooleancallable] 
rasterized  [True  False  None] 
sketch_params  unknown 
snap  unknown 
transform 
Transform instance 
url  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, autorange=False, zorder=None, hold=None, data=None)
Make a box and whisker plot.
Make a box and whisker plot for each column of x
or each vector in sequence x
. The box extends from the lower to upper quartile values of the data, with a line at the median. The whiskers extend from the box to show the range of the data. Flier points are those past the end of the whiskers.
Parameters: 
x : Array or a sequence of vectors. The input data. notch : bool, optional (False) If Note In cases where the values of the CI are less than the lower quartile or greater than the upper quartile, the notches will extend beyond the box, giving it a distinctive “flipped” appearance. This is expected behavior and consistent with other statistical visualization packages. sym : str, optional The default symbol for flier points. Enter an empty string (‘’) if you don’t want to show fliers. If vert : bool, optional (True) If whis : float, sequence, or string (default = 1.5) As a float, determines the reach of the whiskers to the beyond the first and third quartiles. In other words, where IQR is the interquartile range ( bootstrap : int, optional Specifies whether to bootstrap the confidence intervals around the median for notched boxplots. If usermedians : arraylike, optional An array or sequence whose first dimension (or length) is compatible with conf_intervals : arraylike, optional Array or sequence whose first dimension (or length) is compatible with positions : arraylike, optional Sets the positions of the boxes. The ticks and limits are automatically set to match the positions. Defaults to widths : scalar or arraylike Sets the width of each box either with a scalar or a sequence. The default is 0.5, or patch_artist : bool, optional (False) If labels : sequence, optional Labels for each dataset. Length must be compatible with dimensions of manage_xticks : bool, optional (True) If the function should adjust the xlim and xtick locations. autorange : bool, optional (False) When meanline : bool, optional (False) If zorder : scalar, optional (None) Sets the zorder of the boxplot. 

Returns: 
result : dict A dictionary mapping each component of the boxplot to a list of the

Other Parameters:  
showcaps : bool, optional (True) Show the caps on the ends of whiskers. showbox : bool, optional (True) Show the central box. showfliers : bool, optional (True) Show the outliers beyond the caps. showmeans : bool, optional (False) Show the arithmetic means. capprops : dict, optional (None) Specifies the style of the caps. boxprops : dict, optional (None) Specifies the style of the box. whiskerprops : dict, optional (None) Specifies the style of the whiskers. flierprops : dict, optional (None) Specifies the style of the fliers. medianprops : dict, optional (None) Specifies the style of the median. meanprops : dict, optional (None) Specifies the style of the mean. 
(Source code, png, pdf)
Note
In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:
matplotlib.pyplot.broken_barh(xranges, yrange, hold=None, data=None, **kwargs)
Plot horizontal bars.
A collection of horizontal bars spanning yrange with a sequence of xranges.
Required arguments:
Argument  Description 

xranges  sequence of (xmin, xwidth) 
yrange  sequence of (ymin, ywidth) 
kwargs are matplotlib.collections.BrokenBarHCollection
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 dashes or linestyles  [‘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 
picker  [Nonefloatbooleancallable] 
pickradius  unknown 
rasterized  [True  False  None] 
sketch_params  unknown 
snap  unknown 
transform 
Transform instance 
url  a url string 
urls  unknown 
visible  [True  False] 
zorder  any number 
these can either be a single argument, i.e.,:
facecolors = 'black'
or a sequence of arguments for the various bars, i.e.,:
facecolors = ('black', 'red', 'green')
Example:
(Source code, png, pdf)
Note
In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:
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:
Formatter
instance, that returns a string when called with a numeric contour level.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.
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 figure
close(h)
where h is a Figure
instance, closes that figure
close(num)
closes figure number num
close(name)
where name is a string, closes figure with that label
close('all')
closes all the figure windows
matplotlib.pyplot.cohere(x, y, NFFT=256, Fs=2, Fc=0, detrend=<function detrend_none>, window=<function window_hanning>, noverlap=0, pad_to=None, sides='default', scale_by_freq=None, hold=None, data=None, **kwargs)
Plot the coherence between x and y.
Plot the coherence between x and y. Coherence is the normalized cross spectral density:
Parameters: 
Fs : scalar The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. The default value is 2. window : callable or ndarray A function or a vector of length NFFT. To create window vectors see 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 scale_by_freq : boolean, optional Specifies whether the resulting density values should be scaled by the scaling frequency, which gives density in units of Hz^1. This allows for integration over the returned frequency values. The default is True for MATLAB compatibility. noverlap : integer The number of points of overlap between blocks. The default value is 0 (no overlap). Fc : integer The center frequency of x (defaults to 0), which offsets the x extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband. **kwargs : Keyword arguments control the
 

Returns: 
The return value is a tuple (Cxy, f), where f are the frequencies of the coherence vector. kwargs are applied to the lines. 
Bendat & Piersol – Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986)
(Source code, png, pdf)
Note
In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:
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 the colorbar()
method:
colorbar(**kwargs) colorbar(mappable, **kwargs) colorbar(mappable, cax=cax, **kwargs) colorbar(mappable, ax=ax, **kwargs)
arguments:
Image
, ContourSet
, etc. to which the colorbar applies; this argument is mandatory for the colorbar()
method but optional for the colorbar()
function, which sets the default to the current image.keyword arguments:
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 alternative Formatter object may be given instead. 
drawedges  [ False  True ] If true, draw lines at color boundaries. 
The following will probably be useful only in the context of indexed colors (that is, when the mappable has norm=NoNorm()), or other unusual circumstances.
Property  Description 

boundaries  None or a sequence 
values  None or a sequence which must be of length 1 less than the sequence of boundaries. For each region delimited by adjacent entries in boundaries, the color mapped to the corresponding value in values will be used. 
If mappable is a 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).
Colorbar
instance; see also its base class, ColorbarBase
. Call the set_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, facecolor=(0.1843, 0.3098, 0.3098))
Here is an example that creates a pale turquoise title:
title('Is this the best color?', color='#afeeee')
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
For the location events (button and key press/release), if the mouse is over the axes, the variable event.inaxes
will be set to the Axes
the event occurs is over, and additionally, the variables event.xdata
and event.ydata
will be defined. This is the mouse location in data coords. See KeyEvent
and MouseEvent
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()
and contourf()
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 to contour()
.
Call signatures:
contour(Z)
make a contour plot of an array Z. The level values are chosen automatically.
contour(X,Y,Z)
X, Y specify the (x, y) coordinates of the surface
contour(Z,N) contour(X,Y,Z,N)
contour up to N 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 and len(Y)
is the number of rows in Z.
C = contour(...)
returns a QuadContourSet
object.
Optional keyword arguments:
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.
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.
Colormap
instance or None. If cmap is None and colors is None, a default Colormap is used.matplotlib.colors.Normalize
instance for scaling data values to colors. If norm is None and colors is None, the default linear scaling is used.matplotlib.colors.Normalize
instance, overriding the default color scaling based on levels.levels=[0]
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.
MaxNLocator
is used. The locator is used to determine the contour levels if they are not given explicitly via the V argument.matplotlib.colors.Colormap.set_under()
and matplotlib.colors.Colormap.set_over()
methods.matplotlib.units.ConversionInterface
.contouronly keyword arguments:
If linewidths is None, the default width in lines.linewidth
in matplotlibrc
is used.
If a number, all levels will be plotted with this linewidth.
If a tuple, different levels will be plotted with different linewidths in the order specified.
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:
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, pdf)
matplotlib.pyplot.contourf(*args, **kwargs)
Plot contours.
contour()
and contourf()
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 to contour()
.
Call signatures:
contour(Z)
make a contour plot of an array Z. The level values are chosen automatically.
contour(X,Y,Z)
X, Y specify the (x, y) coordinates of the surface
contour(Z,N) contour(X,Y,Z,N)
contour up to N 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 and len(Y)
is the number of rows in Z.
C = contour(...)
returns a QuadContourSet
object.
Optional keyword arguments:
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.
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.
Colormap
instance or None. If cmap is None and colors is None, a default Colormap is used.matplotlib.colors.Normalize
instance for scaling data values to colors. If norm is None and colors is None, the default linear scaling is used.matplotlib.colors.Normalize
instance, overriding the default color scaling based on levels.levels=[0]
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.
MaxNLocator
is used. The locator is used to determine the contour levels if they are not given explicitly via the V argument.matplotlib.colors.Colormap.set_under()
and matplotlib.colors.Colormap.set_over()
methods.matplotlib.units.ConversionInterface
.contouronly keyword arguments:
If linewidths is None, the default width in lines.linewidth
in matplotlibrc
is used.
If a number, all levels will be plotted with this linewidth.
If a tuple, different levels will be plotted with different linewidths in the order specified.
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:
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, pdf)
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.
Parameters: 
x, y : 1D arrays or sequences Arrays or sequences containing the data Fs : scalar The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. The default value is 2. window : callable or ndarray A function or a vector of length NFFT. To create window vectors see 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 scale_by_freq : boolean, optional Specifies whether the resulting density values should be scaled by the scaling frequency, which gives density in units of Hz^1. This allows for integration over the returned frequency values. The default is True for MATLAB compatibility. noverlap : integer The number of points of overlap between segments. The default value is 0 (no overlap). Fc : integer The center frequency of x (defaults to 0), which offsets the x extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband. return_line : bool Whether to include the line object plotted in the returned values. Default is False. **kwargs : Keyword arguments control the
 

Returns: 
Pxy : 1D array The values for the cross spectrum freqs : 1D array The frequencies corresponding to the elements in Pxy line : a The line created by this function. Only returned if return_line is True. 
See also
For plotting, the power is plotted as for decibels, though P_{xy}
itself is returned.
Bendat & Piersol – Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986)
(Source code, png, pdf)
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 to update a figure that has been altered, but not automatically redrawn. If interactive mode is on (ion()
), this should be only rarely needed, but there may be ways to modify the state of a figure without marking it as stale
. Please report these cases as bugs.
A more objectoriented alternative, given any Figure
instance, fig
, that was created using a pyplot
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.
Plot x versus y with error deltas in yerr and xerr. Vertical errorbars are plotted if yerr is not None. Horizontal errorbars are plotted if xerr is not None.
x, y, xerr, and yerr can all be scalars, which plots a single error bar at x, y.
Parameters: 
x : scalar or arraylike y : scalar or arraylike xerr/yerr : scalar or arraylike, shape(N,) or shape(2,N), optional If a scalar number, len(N) arraylike object, or a Nelement arraylike object, errorbars are drawn at +/value relative to the data. Default is None. If a sequence of shape 2xN, errorbars are drawn at row1 and +row2 relative to the data. fmt : plot format string, optional, default: None The plot format symbol. If fmt is ‘none’ (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 ecolor : mpl color, optional, default: None A matplotlib color arg which gives the color the errorbar lines; if None, use the color of the line connecting the markers. elinewidth : scalar, optional, default: None The linewidth of the errorbar lines. If None, use the linewidth. capsize : scalar, optional, default: None The length of the error bar caps in points; if None, it will take the value from capthick : scalar, optional, default: None An alias kwarg to markeredgewidth (a.k.a.  mew). This setting is a more sensible name for the property that controls the thickness of the error bar cap in points. For backwards compatibility, if mew or markeredgewidth are given, then they will override capthick. This may change in future releases. barsabove : bool, optional, default: False if True , will plot the errorbars above the plot symbols. Default is below. lolims / uplims / xlolims / xuplims : bool, optional, default:None These arguments can be used to indicate that a value gives only upper/lower limits. In that case a caret symbol is used to indicate this. limsarguments may be of the same type as xerr and yerr. To use limits with inverted axes, errorevery : positive integer, optional, default:1 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.  

Returns: 
plotline : x, y plot markers and/or line caplines : list of error bar cap barlinecols : list of horizontal and vertical error ranges.  
Other Parameters:  
kwargs : All other keyword arguments are passed on to the plot command for the markers. For example, this code makes big red squares with thick green edges: x,y,yerr = rand(3,10) errorbar(x, y, yerr, marker='s', mfc='red', mec='green', ms=20, mew=4) where mfc, mec, ms and mew are aliases for the longer property names, markerfacecolor, markeredgecolor, markersize and markeredgewidth. valid kwargs for the marker properties are

(Source code, png, pdf)
Note
In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:
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.
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 : [ ‘horizontal’  ‘vertical’ ]
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 dashes or linestyles  [‘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, pdf)
Note
In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:
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:
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 rc image.cmap value 
norm  a matplotlib.colors.Normalize instance. The default is normalization(). This scales luminance > 01 
vminvmax  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 an Axes
with extent [0,0,1,1].
An matplotlib.image.FigureImage
instance is returned.
(Source code, png, pdf)
Additional kwargs are Artist kwargs passed on to FigureImage
matplotlib.pyplot.figlegend(handles, labels, loc, **kwargs)
Place a legend in the figure.
Line2D
or Patch
instancesA 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 instance 
backgroundcolor  any matplotlib color 
bbox  FancyBboxPatch prop dict 
clip_box  a matplotlib.transforms.Bbox instance 
clip_on  [True  False] 
clip_path  [ (Path , Transform )  Patch  None ] 
color  any matplotlib color 
contains  a callable function 
family or fontfamily or fontname or name  [FONTNAME  ‘serif’  ‘sansserif’  ‘cursive’  ‘fantasy’  ‘monospace’ ] 
figure  a matplotlib.figure.Figure instance 
fontproperties or font_properties  a matplotlib.font_manager.FontProperties instance 
gid  an id string 
horizontalalignment or ha  [ ‘center’  ‘right’  ‘left’ ] 
label  string or anything printable with ‘%s’ conversion. 
linespacing  float (multiple of font size) 
multialignment  [‘left’  ‘right’  ‘center’ ] 
path_effects  unknown 
picker  [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 instance 
url  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=<class 'matplotlib.figure.Figure'>, **kwargs)
Creates a new figure.
Parameters: 
num : integer or string, optional, default: none If not provided, a new figure will be created, and the figure number will be incremented. The figure objects holds this number in a 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. 
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.
Parameters: 
args : a variable length argument It allowing for multiple x, y pairs with an optional color format string; see ax.fill(x, y) ax.fill(x, y, "b") ax.fill(x, y, "b", x, y, "r") An arbitrary number of x, y, color groups can be specified:: ax.fill(x1, y1, ‘g’, x2, y2, ‘r’) 

Returns: 
a list of 
Other Parameters:  
kwargs : 
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()
(Source code, png, pdf)
Note
In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:
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 where where==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 interpolate : bool, optional If step : {‘pre’, ‘post’, ‘mid’}, optional If not None, fill with step logic. 

See also
Additional Keyword args passed on to the PolyCollection
.
kwargs control the Polygon
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 dashes or linestyles  [‘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 
picker  [Nonefloatbooleancallable] 
pickradius  unknown 
rasterized  [True  False  None] 
sketch_params  unknown 
snap  unknown 
transform 
Transform instance 
url  a url string 
urls  unknown 
visible  [True  False] 
zorder  any number 
matplotlib.pyplot.fill_betweenx(y, x1, x2=0, where=None, step=None, hold=None, data=None, **kwargs)
Make filled polygons between two horizontal curves.
Create a PolyCollection
filling the regions between x1 and x2 where where==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 step : {‘pre’, ‘post’, ‘mid’}, optional If not None, fill with step logic. 

See also
PolyCollection
kwargs control the Polygon
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 dashes or linestyles  [‘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 
picker  [Nonefloatbooleancallable] 
pickradius  unknown 
rasterized  [True  False  None] 
sketch_params  unknown 
snap  unknown 
transform 
Transform instance 
url  a url string 
urls  unknown 
visible  [True  False] 
zorder  any number 
matplotlib.pyplot.findobj(o=None, match=None, include_self=True)
Find artist objects.
Recursively find all Artist
instances contained in self.
match can be
boolean = match(artist)
used to filter matchesIf 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
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 commands imshow()
and figimage()
create Image
instances, and the commands pcolor()
and scatter()
create Collection
instances. The current image is an attribute of the current axes, or the nearest earlier axes in the current figure that contains an image.
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)
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.
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 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 instance 
clip_box  a matplotlib.transforms.Bbox instance 
clip_on  [True  False] 
clip_path  [ (Path , Transform )  Patch  None ] 
color or c  any matplotlib color 
contains  a callable function 
dash_capstyle  [‘butt’  ‘round’  ‘projecting’] 
dash_joinstyle  [‘miter’  ‘round’  ‘bevel’] 
dashes  sequence of on/off ink in points 
drawstyle  [‘default’  ‘steps’  ‘stepspre’  ‘stepsmid’  ‘stepspost’] 
figure  a matplotlib.figure.Figure instance 
fillstyle  [‘full’  ‘left’  ‘right’  ‘bottom’  ‘top’  ‘none’] 
gid  an id string 
label  string or anything printable with ‘%s’ conversion. 
linestyle or ls  [‘solid’  ‘dashed’, ‘dashdot’, ‘dotted’  (offset, onoffdashseq)  ''  ''  '.'  ':'  'None'  ' '  '' ] 
linewidth or lw  float value in points 
marker  A valid marker style 
markeredgecolor or mec  any matplotlib color 
markeredgewidth or mew  float value in points 
markerfacecolor or mfc  any matplotlib color 
markerfacecoloralt or mfcalt  any matplotlib color 
markersize or ms  float 
markevery  [None  int  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 instance 
url  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=<function mean>, mincnt=None, marginals=False, hold=None, data=None, **kwargs)
Make a hexagonal binning plot.
Make a hexagonal binning plot of x versus y, where x, y are 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.)
Parameters: 
x, y : array or masked array C : array or masked array, optional, default is None gridsize : int or (int, int), optional, default is 100 The number of hexagons in the 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 : {‘log’} or int or sequence, optional, default is None If None, no binning is applied; the color of each hexagon directly corresponds to its count value. If ‘log’, use a logarithmic scale for the color map. Internally, is used to determine the hexagon color. If an integer, divide the counts in the specified number of bins, and color the hexagons accordingly. If a sequence of values, the values of the lower bound of the bins to be used. xscale : {‘linear’, ‘log’}, optional, default is ‘linear’ Use a linear or log10 scale on the horizontal axis. yscale : {‘linear’, ‘log’}, optional, default is ‘linear’ Use a linear or log10 scale on the vertical axis. mincnt : int > 0, optional, default is None If not None, only display cells with more than mincnt number of points in the cell marginals : bool, optional, default is False if marginals is True, plot the marginal density as colormapped rectagles along the bottom of the xaxis and left of the yaxis extent : scalar, optional, default is None The limits of the bins. The default assigns the limits based on gridsize, x, y, xscale and yscale. If xscale or yscale is set to ‘log’, the limits are expected to be the exponent for a power of 10. E.g. for xlimits of 1 and 50 in ‘linear’ scale and ylimits of 10 and 1000 in ‘log’ scale, enter (1, 50, 1, 3). Order of scalars is (left, right, bottom, top). 

Returns: 
object a If marginals is True, horizontal bar and vertical bar (both PolyCollections) will be attached to the return collection as attributes hbar and vbar. 
Other Parameters:  
cmap : object, optional, default is None a norm : object, optional, default is None
vmin, vmax : scalar, optional, default is None vmin and vmax are used in conjunction with norm to normalize luminance data. If None, the min and max of the color array C are used. Note if you pass a norm instance your settings for vmin and vmax will be ignored. alpha : scalar between 0 and 1, optional, default is None the alpha value for the patches linewidths : scalar, optional, default is None If None, defaults to 1.0. edgecolors : {‘none’} or mpl color, optional, default is ‘none’ If ‘none’, draws the edges in the same color as the fill color. This is the default, as it avoids unsightly unpainted pixels between the hexagons. If None, draws outlines in the default color. If a matplotlib color arg, draws outlines in the specified color. 
The standard descriptions of all the Collection
parameters:
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 dashes or linestyles  [‘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 
picker  [Nonefloatbooleancallable] 
pickradius  unknown 
rasterized  [True  False  None] 
sketch_params  unknown 
snap  unknown 
transform 
Transform instance 
url  a url string 
urls  unknown 
visible  [True  False] 
zorder  any number 
Note
In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:
(Source code, png, pdf)
matplotlib.pyplot.hist(x, bins=None, 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 or ‘auto’, optional If an integer is given, Unequally spaced bins are supported if If Numpy 1.11 is installed, may also be Default is taken from the rcParam range : tuple or None, optional The lower and upper range of the bins. Lower and upper outliers are ignored. If not provided, If Default is normed : boolean, optional If Default is weights : (n, ) array_like or None, optional An array of weights, of the same shape as Default is cumulative : boolean, optional If Default is 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 histtype : {‘bar’, ‘barstacked’, ‘step’, ‘stepfilled’}, optional The type of histogram to draw.
Default is ‘bar’ align : {‘left’, ‘mid’, ‘right’}, optional Controls how the histogram is plotted.
Default is ‘mid’ orientation : {‘horizontal’, ‘vertical’}, optional If ‘horizontal’, rwidth : scalar or None, optional The relative width of the bars as a fraction of the bin width. If Ignored if Default is log : boolean, optional If Default is color : color or array_like of colors or None, optional Color spec or sequence of color specs, one per dataset. Default ( Default is 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 stacked : boolean, optional If Default is 

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 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 : 
See also
hist2d
Until numpy release 1.5, the underlying numpy histogram function was incorrect with normed`=`True
if bin sizes were unequal. MPL inherited that error. It is now corrected within MPL when using earlier numpy versions.
(Source code, png, pdf)
Note
In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:
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:
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 
Other Parameters:  
cmap : {Colormap, string}, optional A norm : Normalize, optional A vmin/vmax : {None, scalar}, optional Arguments passed to the alpha : The alpha blending value. 
See also
hist
Rendering the histogram with a logarithmic color scale is accomplished by passing a colors.LogNorm
instance to the norm keyword argument. Likewise, powerlaw normalization (similar in effect to gamma correction) can be accomplished with colors.PowerNorm
.
(Source code, png, pdf)
Note
In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:
matplotlib.pyplot.hlines(y, xmin, xmax, colors='k', linestyles='solid', label='', hold=None, data=None, **kwargs)
Plot horizontal lines at each y
from xmin
to xmax
.
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 : 
Other Parameters:  
kwargs : 
See also
vlines
(Source code, png, pdf)
Note
In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:
matplotlib.pyplot.hold(b=None)
Deprecated since version 2.0: pyplot.hold is deprecated. Future behavior will be consistent with the longtime default: plot commands add elements without first clearing the Axes and/or Figure.
Set the hold state. If b is None (default), toggle the hold state, else set the hold state to boolean value b:
hold() # toggle hold hold(True) # hold is on hold(False) # hold is off
When hold is True, subsequent plot commands will add elements to the current axes. When hold is False, the current axes and figure will be cleared on the next plot command.
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.
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
The value for each component of MxNx3 and MxNx4 float arrays should be in the range 0.0 to 1.0. MxN arrays are mapped to colors based on the cmap : If None, default to rc aspect : [‘auto’  ‘equal’  scalar], optional, default: None If ‘auto’, changes the image aspect ratio to match that of the axes. If ‘equal’, and If None, default to rc 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 norm : A vmin, vmax : scalar, optional, default: None
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 extent : scalars (left, right, bottom, top), optional, default: None The location, in datacoordinates, of the lowerleft and upperright corners. If 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 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 : 
Other Parameters:  
kwargs : 
See also
matshow
Unless extent is used, pixel centers will be located at integer coordinates. In other words: the origin will coincide with the center of pixel (0, 0).
(Source code, png, pdf)
Note
In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:
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()
Deprecated since version 2.0: pyplot.hold is deprecated. Future behavior will be consistent with the longtime default: plot commands add elements without first clearing the Axes and/or Figure.
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:
Alternatively can be a 2tuple giving bbox_to_anchor : Specify any arbitrary location for the legend in 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 The font properties of the legend. If None (default), the current 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 numpoints : None or int The number of marker points in the legend when creating a legend entry for a line/ scatterpoints : None or int The number of marker points in the legend when creating a legend entry for a scatter plot/ 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 markerscale : None or int or float The relative size of legend markers compared with the originally drawn ones. Default is markerfirst : bool if True, legend marker is placed to the left of the legend label if False, legend marker is placed to the right of the legend label frameon : None or bool Control whether the legend should be drawn on a patch (frame). Default is fancybox : None or bool Control whether round edges should be enabled around the shadow : None or bool Control whether to draw a shadow behind the legend. Default is framealpha : None or float Control the alpha transparency of the legend’s background. Default is facecolor : None or “inherit” or a color spec Control the legend’s background color. Default is edgecolor : None or “inherit” or a color spec Control the legend’s background patch edge color. Default is mode : {“expand”, None} If bbox_transform : None or The transform for the bounding box ( title : str or None The legend’s title. Default is no title ( borderpad : float or None The fractional whitespace inside the legend border. Measured in fontsize units. Default is labelspacing : float or None The vertical space between the legend entries. Measured in fontsize units. Default is handlelength : float or None The length of the legend handles. Measured in fontsize units. Default is handletextpad : float or None The pad between the legend handle and text. Measured in fontsize units. Default is borderaxespad : float or None The pad between the axes and legend border. Measured in fontsize units. Default is columnspacing : float or None The spacing between columns. Measured in fontsize units. Default is handler_map : dict or None The custom dictionary mapping instances or types to a legend handler. This 

Not all kinds of artist are supported by the legend command. See Legend guide for details.
(Source code, png, pdf)
matplotlib.pyplot.locator_params(axis='both', tight=None, **kwargs)
Control behavior of tick locators.
Keyword arguments:
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.
loglog()
supports all the keyword arguments of plot()
and matplotlib.axes.Axes.set_xscale()
/ matplotlib.axes.Axes.set_yscale()
.
Notable keyword arguments:
matplotlib.axes.Axes.set_xscale()
/ matplotlib.axes.Axes.set_yscale()
for detailsThe 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 instance 
clip_box  a matplotlib.transforms.Bbox instance 
clip_on  [True  False] 
clip_path  [ (Path , Transform )  Patch  None ] 
color or c  any matplotlib color 
contains  a callable function 
dash_capstyle  [‘butt’  ‘round’  ‘projecting’] 
dash_joinstyle  [‘miter’  ‘round’  ‘bevel’] 
dashes  sequence of on/off ink in points 
drawstyle  [‘default’  ‘steps’  ‘stepspre’  ‘stepsmid’  ‘stepspost’] 
figure  a matplotlib.figure.Figure instance 
fillstyle  [‘full’  ‘left’  ‘right’  ‘bottom’  ‘top’  ‘none’] 
gid  an id string 
label  string or anything printable with ‘%s’ conversion. 
linestyle or ls  [‘solid’  ‘dashed’, ‘dashdot’, ‘dotted’  (offset, onoffdashseq)  ''  ''  '.'  ':'  'None'  ' '  '' ] 
linewidth or lw  float value in points 
marker  A valid marker style 
markeredgecolor or mec  any matplotlib color 
markeredgewidth or mew  float value in points 
markerfacecolor or mfc  any matplotlib color 
markerfacecoloralt or mfcalt  any matplotlib color 
markersize or ms  float 
markevery  [None  int  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 instance 
url  a url string 
visible  [True  False] 
xdata  1D array 
ydata  1D array 
zorder  any number 
Example:
(Source code, png, pdf)
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.
Parameters: 
x : 1D array or sequence Array or sequence containing the data Fs : scalar The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. The default value is 2. window : callable or ndarray A function or a vector of length NFFT. To create window vectors see 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. **kwargs : Keyword arguments control the
 

Returns: 
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 The line created by this function 
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.(Source code, png, pdf)
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.
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 how matshow()
tries to set the figure aspect ratio to be the one of the array, if you provide the number of an already existing figure, strange things may happen.
If fignum is False or 0, a new figure window will NOT be created.
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.nipy_spectral()
set the default colormap to nipy_spectral and apply to current image if any. See help(colormaps) for more information
matplotlib.pyplot.over(func, *args, **kwargs)
Deprecated since version 2.0: pyplot.hold is deprecated. Future behavior will be consistent with the longtime default: plot commands add elements without first clearing the Axes and/or Figure.
Call a function with hold(True).
Calls:
func(*args, **kwargs)
with hold(True)
and then restores the hold state.
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:
matplotlib.colors.Colormap
instance. If None, use rc settings.matplotlib.colors.Normalize
instance is used to scale luminance data to 0,1. If None, defaults to normalize()
.If ‘faceted’, a black grid is drawn around each rectangle; if ‘flat’, edges are not drawn. Default is ‘flat’, contrary to MATLAB.
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
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 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 dashes or linestyles  [‘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 
picker  [Nonefloatbooleancallable] 
pickradius  unknown 
rasterized  [True  False  None] 
sketch_params  unknown 
snap  unknown 
transform 
Transform instance 
url  a url string 
urls  unknown 
visible  [True  False] 
zorder  any number 
Note
The default antialiaseds is False if the default edgecolors*=”none” is used. This eliminates artificial lines at patch boundaries, and works regardless of the value of alpha. If *edgecolors is not “none”, then the default antialiaseds is taken from rcParams[‘patch.antialiased’], which defaults to True. Stroking the edges may be preferred if alpha is 1, but will cause artifacts otherwise.
See also
pcolormesh()
Note
In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:
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 a PolyCollection
but pcolormesh returns a QuadMesh
. It is much faster, so it is almost always preferred for large arrays.
C may be a masked array, but X and Y may not. Masked array support is implemented via cmap and norm; in contrast, pcolor()
simply does not draw quadrilaterals with masked colors or vertices.
Keyword arguments:
matplotlib.colors.Colormap
instance. If None, use rc settings.matplotlib.colors.Normalize
instance is used to scale luminance data to 0,1. If None, defaults to normalize()
.edgecolors: [None  'None'  'face'  color 
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
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 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 dashes or linestyles  [‘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 
picker  [Nonefloatbooleancallable] 
pickradius  unknown 
rasterized  [True  False  None] 
sketch_params  unknown 
snap  unknown 
transform 
Transform instance 
url  a url string 
urls  unknown 
visible  [True  False] 
zorder  any number 
See also
pcolor()
Note
In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:
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.
Parameters: 
x : 1D array or sequence Array or sequence containing the data Fs : scalar The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. The default value is 2. window : callable or ndarray A function or a vector of length NFFT. To create window vectors see 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. **kwargs : Keyword arguments control the
 

Returns: 
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 The line created by this function 
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.(Source code, png, pdf)
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.
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.
Parameters: 
x : arraylike The input array used to make the pie chart. explode : arraylike, optional, default: None If not None, is a labels : list, optional, default: None A sequence of strings providing the labels for each wedge colors : arraylike, optional, default: None A sequence of matplotlib color args through which the pie chart will cycle. If autopct : None (default), string, or function, optional 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 pctdistance : float, optional, default: 0.6 The ratio between the center of each pie slice and the start of the text generated by autopct. Ignored if autopct is None. shadow : bool, optional, default: False Draw a shadow beneath the pie. labeldistance : float, optional, default: 1.1 The radial distance at which the pie labels are drawn startangle : float, optional, default: None If not None, rotates the start of the pie chart by angle degrees counterclockwise from the xaxis. radius : float, optional, default: None The radius of the pie, if radius is None it will be set to 1. counterclock : bool, optional, default: True Specify fractions direction, clockwise or counterclockwise. wedgeprops : dict, optional, default: None 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 textprops : dict, optional, default: None Dict of arguments to pass to the text objects. center : list of float, optional, default: (0, 0) Center position of the chart. Takes value (0, 0) or is a sequence of 2 scalars. frame : bool, optional, default: False Plot axes frame with the chart if true. 

Returns: 
patches : list A sequence of texts : list A is a list of the label autotexts : list A is a list of 
The pie chart will probably look best if the figure and axes are square, or the Axes aspect is equal.
(Source code, png, pdf)
Note
In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:
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 do plot(v, r)
using the default line style and color.
If not used with labeled data (i.e., without a data argument), an arbitrary number of x, y, fmt groups can be specified, as in:
a.plot(x1, y1, 'g^', x2, y2, 'g')
Return value is a list of lines that were added.
By default, each line is assigned a different style specified by a ‘style cycle’. To change this behavior, you can edit the axes.prop_cycle rcParam.
The following format string characters are accepted to control the line style or marker:
character  description 

''  solid line style 
''  dashed line style 
'.'  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 a fmt
group, but the tuple forms can be used only as kwargs
.
Line styles and colors are combined in a single format string, as in 'bo'
for blue circles.
The kwargs can be used to set line properties (any property that has a set_*
method). You can use this to set a line label (for auto legends), linewidth, anitialising, marker face color, etc. Here is an example:
plot([1,2,3], [1,2,3], 'go', label='line 1', linewidth=2) plot([1,2,3], [1,4,9], 'rs', label='line 2') axis([0, 4, 0, 10]) legend()
If you make multiple lines with one plot command, the kwargs apply to all those lines, e.g.:
plot(x1, y1, x2, y2, antialiased=False)
Neither line will be antialiased.
You do not need to use format strings, which are just abbreviations. All of the line properties can be controlled by keyword arguments. For example, you can set the color, marker, linestyle, and markercolor with:
plot(x, y, color='green', linestyle='dashed', marker='o', markerfacecolor='blue', markersize=12).
See 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 instance 
clip_box  a matplotlib.transforms.Bbox instance 
clip_on  [True  False] 
clip_path  [ (Path , Transform )  Patch  None ] 
color or c  any matplotlib color 
contains  a callable function 
dash_capstyle  [‘butt’  ‘round’  ‘projecting’] 
dash_joinstyle  [‘miter’  ‘round’  ‘bevel’] 
dashes  sequence of on/off ink in points 
drawstyle  [‘default’  ‘steps’  ‘stepspre’  ‘stepsmid’  ‘stepspost’] 
figure  a matplotlib.figure.Figure instance 
fillstyle  [‘full’  ‘left’  ‘right’  ‘bottom’  ‘top’  ‘none’] 
gid  an id string 
label  string or anything printable with ‘%s’ conversion. 
linestyle or ls  [‘solid’  ‘dashed’, ‘dashdot’, ‘dotted’  (offset, onoffdashseq)  ''  ''  '.'  ':'  'None'  ' '  '' ] 
linewidth or lw  float value in points 
marker  A valid marker style 
markeredgecolor or mec  any matplotlib color 
markeredgewidth or mew  float value in points 
markerfacecolor or mfc  any matplotlib color 
markerfacecoloralt or mfcalt  any matplotlib color 
markersize or ms  float 
markevery  [None  int  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 instance 
url  a url string 
visible  [True  False] 
xdata  1D array 
ydata  1D array 
zorder  any number 
kwargs scalex and scaley, if defined, are passed on to autoscale_view()
to determine whether the x and y axes are autoscaled; the default is True.
Note
In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:
matplotlib.pyplot.plot_date(x, y, fmt='o', tz=None, xdate=True, ydate=False, hold=None, data=None, **kwargs)
A plot with data that contains dates.
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.
Note if you are using custom date tickers and formatters, it may be necessary to set the formatters/locators after the call to meth:plot_date
since meth:plot_date
will set the default tick locator to class:matplotlib.dates.AutoDateLocator
(if the tick locator is not already set to a class:matplotlib.dates.DateLocator
instance) and the default tick formatter to class:matplotlib.dates.AutoDateFormatter
(if the tick formatter is not already set to a class:matplotlib.dates.DateFormatter
instance).
Parameters: 
fmt : string The plot format string. tz : [ None  timezone string  The time zone to use in labeling dates. If None, defaults to rc value. xdate : boolean If True, the xaxis will be labeled with dates. ydate : boolean If True, the yaxis will be labeled with dates.  

Returns: 
lines  
Other Parameters:  
kwargs : properties :
.. note:: In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:

See also
matplotlib.dates
matplotlib.dates.date2num
matplotlib.dates.num2date
matplotlib.dates.drange
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'
.
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.
Parameters: 
x : 1D array or sequence Array or sequence containing the data Fs : scalar The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. The default value is 2. window : callable or ndarray A function or a vector of length NFFT. To create window vectors see 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 scale_by_freq : boolean, optional Specifies whether the resulting density values should be scaled by the scaling frequency, which gives density in units of Hz^1. This allows for integration over the returned frequency values. The default is True for MATLAB compatibility. noverlap : integer The number of points of overlap between segments. The default value is 0 (no overlap). Fc : integer The center frequency of x (defaults to 0), which offsets the x extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband. return_line : bool Whether to include the line object plotted in the returned values. Default is False. **kwargs : Keyword arguments control the
 

Returns: 
Pxx : 1D array The values for the power spectrum freqs : 1D array The frequencies corresponding to the elements in Pxx line : a The line created by this function. Only returned if return_line is True. 
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.For plotting, the power is plotted as for decibels, though Pxx itself is returned.
Bendat & Piersol – Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986)
(Source code, png, pdf)
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)
U and V are the arrow data, X and Y set the locaiton of the arrows, and C sets the color of the arrows. These 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 and X and Y are 1D, and if len(X)
and len(Y)
match the column and row dimensions of U, then X and Y will be expanded with numpy.meshgrid()
.
The default settings autoscales the length of the arrows to a reasonable size. To change this behavior see the scale and scale_units kwargs.
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.
Parameters: 
X : 1D or 2D array, sequence, optional The x coordinates of the arrow locations Y : 1D or 2D array, sequence, optional The y coordinates of the arrow locations U : 1D or 2D array or masked array, sequence The x components of the arrow vectors V : 1D or 2D array or masked array, sequence The y components of the arrow vectors C : 1D or 2D array, sequence, optional The arrow colors units : [ ‘width’  ‘height’  ‘dots’  ‘inches’  ‘x’  ‘y’  ‘xy’ ] The arrow dimensions (except for length) are measured in multiples of this unit. ‘width’ or ‘height’: the width or height of the axis ‘dots’ or ‘inches’: pixels or inches, based on the figure dpi ‘x’, ‘y’, or ‘xy’: respectively X, Y, or in 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, optional Method for determining the angle of the arrows. Default is ‘uv’. ‘uv’: the arrow axis aspect ratio is 1 so that if U*==*V the orientation of the arrow on the plot is 45 degrees counterclockwise from the horizontal axis (positive to the right). ‘xy’: arrows point 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, counterclockwise from the horizontal axis. Note: inverting a data axis will correspondingly invert the arrows only with scale : None, float, optional Number of data units per arrow length unit, e.g., m/s per plot width; a smaller scale parameter makes the arrow longer. Default is None. 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 : [ ‘width’  ‘height’  ‘dots’  ‘inches’  ‘x’  ‘y’  ‘xy’ ], None, optional If the scale kwarg is None, the arrow length unit. Default is None. e.g. scale_units is ‘inches’, scale is 2.0, and If scale_units is ‘width’/’height’, then the vector will be half the width/height 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 width : scalar, optional 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, optional Head width as multiple of shaft width, default is 3 headlength : scalar, optional Head length as multiple of shaft width, default is 5 headaxislength : scalar, optional Head length at shaft intersection, default is 4.5 minshaft : scalar, optional 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, optional 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’ ], optional 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 ], optional This is a synonym for the 

See also
quiverkey
Additional PolyCollection
keyword arguments:
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 dashes or linestyles  [‘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 
picker  [Nonefloatbooleancallable] 
pickradius  unknown 
rasterized  [True  False  None] 
sketch_params  unknown 
snap  unknown 
transform 
Transform instance 
url  a url string 
urls  unknown 
visible  [True  False] 
zorder  any number 
(Source code, png, pdf)
matplotlib.pyplot.quiverkey(*args, **kw)
Add a key to a quiver plot.
Call signature:
quiverkey(Q, X, Y, U, label, **kw)
Arguments:
Keyword arguments:
Text
color.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.
matplotlib.pyplot.rc(*args, **kwargs)
Set the current rc params. Group is the grouping for the rc, e.g., for lines.linewidth
the group is lines
, for axes.facecolor
, the group is axes
, and so on. Group may also be a list or tuple of group names, e.g., (xtick, ytick). kwargs is a dictionary attribute name/value pairs, e.g.,:
rc('lines', linewidth=2, color='r')
sets the current rc params and is equivalent to:
rcParams['lines.linewidth'] = 2 rcParams['lines.color'] = 'r'
The following aliases are available to save typing for interactive users:
Alias  Property 

‘lw’  ‘linewidth’ 
‘ls’  ‘linestyle’ 
‘c’  ‘color’ 
‘fc’  ‘facecolor’ 
‘ec’  ‘edgecolor’ 
‘mew’  ‘markeredgewidth’ 
‘aa’  ‘antialiased’ 
Thus you could abbreviate the above rc command as:
rc('lines', lw=2, c='r')
Note you can use python’s kwargs dictionary facility to store dictionaries of default parameters. e.g., you can customize the font rc as follows:
font = {'family' : 'monospace', 'weight' : 'bold', 'size' : 'larger'} rc('font', **font) # pass in the font dict as kwargs
This enables you to easily switch between several configurations. Use matplotlib.style.use('default')
or 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 rc params from Matplotlib’s internal defaults.
See also
rc_file_defaults
matplotlib.style.use
style.use('default')
to restore the default style.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:
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’]
savefig.dpi
in the matplotlibrc file. If ‘figure’ it will set the dpi to be the value of the figure.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=None, c=None, marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=None, edgecolors=None, hold=None, data=None, **kwargs)
Make a scatter plot of x
vs y
Marker size is scaled by s
and marker color is mapped to c
Parameters: 
x, y : array_like, shape (n, ) Input data s : scalar or array_like, shape (n, ), optional size in points^2. Default is c : color, sequence, or sequence of color, optional, default: ‘b’
marker : See cmap : A norm : A vmin, vmax : scalar, optional, default: None
alpha : scalar, optional, default: None The alpha blending value, between 0 (transparent) and 1 (opaque) linewidths : scalar or array_like, optional, default: None If None, defaults to (lines.linewidth,). verts : sequence of (x, y), optional If edgecolors : color or sequence of color, optional, default: None If None, defaults to ‘face’ If ‘face’, the edge color will always be the same as the face color. If it is ‘none’, the patch boundary will not be drawn. For nonfilled markers, the 

Returns: 
paths : 
Other Parameters:  
kwargs : 
See also
plot
plot
function will be faster for scatterplots where markers don’t vary in size or color. Any or all of x
, y
, s
, and c
may be masked arrays, in which case all masks will be combined and only unmasked points will be plotted.
Fundamentally, scatter works with 1D arrays; x
, y
, s
, and c
may be input as 2D arrays, but within scatter they will be flattened. The exception is c
, which will be flattened only if its size matches the size of x
and y
.
(Source code, png, pdf)
Note
In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:
matplotlib.pyplot.sci(im)
Set the current image. This image will be the target of colormap commands like jet()
, hot()
or clim()
). 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.
Parameters: 
basex : float, optional Base of the x logarithm. The scalar should be larger than 1. subsx : array_like, optional The location of the minor xticks; None defaults to autosubs, which depend on the number of decades in the plot; see nonposx : string, optional, {‘mask’, ‘clip’} Nonpositive values in x can be masked as invalid, or clipped to a very small positive number.  

Returns: 
Logscaled plot on the x axis.  
Other Parameters:  
:class:`~matplotlib.lines.Line2D` properties:

See also
loglog
This function supports all the keyword arguments of plot()
and matplotlib.axes.Axes.set_xscale()
.
matplotlib.pyplot.semilogy(*args, **kwargs)
Make a plot with log scaling on the y
axis.
Parameters: 
basey : scalar > 1 Base of the


Returns: 
Line instance of the plot. 
Other Parameters:  
kwargs :
===================================================================================== =============================================================================================================================================== Property Description ===================================================================================== =============================================================================================================================================== :meth:`agg_filter <matplotlib.artist.Artist.set_agg_filter>` unknown :meth:`alpha <matplotlib.artist.Artist.set_alpha>` float (0.0 transparent through 1.0 opaque) :meth:`animated <matplotlib.artist.Artist.set_animated>` [True  False] :meth:`antialiased <matplotlib.lines.Line2D.set_antialiased>` or aa [True  False] :meth:`axes <matplotlib.artist.Artist.set_axes>` an :class:`~matplotlib.axes.Axes` instance :meth:`clip_box <matplotlib.artist.Artist.set_clip_box>` a :class:`matplotlib.transforms.Bbox` instance :meth:`clip_on <matplotlib.artist.Artist.set_clip_on>` [True  False] :meth:`clip_path <matplotlib.artist.Artist.set_clip_path>` [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`)  :class:`~matplotlib.patches.Patch`  None ] :meth:`color <matplotlib.lines.Line2D.set_color>` or c any matplotlib color :meth:`contains <matplotlib.artist.Artist.set_contains>` a callable function :meth:`dash_capstyle <matplotlib.lines.Line2D.set_dash_capstyle>` [‘butt’  ‘round’  ‘projecting’] :meth:`dash_joinstyle <matplotlib.lines.Line2D.set_dash_joinstyle>` [‘miter’  ‘round’  ‘bevel’] :meth:`dashes <matplotlib.lines.Line2D.set_dashes>` sequence of on/off ink in points :meth:`drawstyle <matplotlib.lines.Line2D.set_drawstyle>` [‘default’  ‘steps’  ‘stepspre’  ‘stepsmid’  ‘stepspost’] :meth:`figure <matplotlib.artist.Artist.set_figure>` a :class:`matplotlib.figure.Figure` instance :meth:`fillstyle <matplotlib.lines.Line2D.set_fillstyle>` [‘full’  ‘left’  ‘right’  ‘bottom’  ‘top’  ‘none’] :meth:`gid <matplotlib.artist.Artist.set_gid>` an id string :meth:`label <matplotlib.artist.Artist.set_label>` string or anything printable with ‘%s’ conversion. :meth:`linestyle <matplotlib.lines.Line2D.set_linestyle>` or ls [‘solid’  ‘dashed’, ‘dashdot’, ‘dotted’  (offset, onoffdashseq)  ``’‘``  ``’–’``  ``’.’``  ``’:’``  ``’None’``  ``’ ‘``  ``’‘``] :meth:`linewidth <matplotlib.lines.Line2D.set_linewidth>` or lw float value in points :meth:`marker <matplotlib.lines.Line2D.set_marker>` :mod:`A valid marker style <matplotlib.markers>` :meth:`markeredgecolor <matplotlib.lines.Line2D.set_markeredgecolor>` or mec any matplotlib color :meth:`markeredgewidth <matplotlib.lines.Line2D.set_markeredgewidth>` or mew float value in points :meth:`markerfacecolor <matplotlib.lines.Line2D.set_markerfacecolor>` or mfc any matplotlib color :meth:`markerfacecoloralt <matplotlib.lines.Line2D.set_markerfacecoloralt>` or mfcalt any matplotlib color :meth:`markersize <matplotlib.lines.Line2D.set_markersize>` or ms float :meth:`markevery <matplotlib.lines.Line2D.set_markevery>` [None  int  length2 tuple of int  slice  list/array of int  float  length2 tuple of float] :meth:`path_effects <matplotlib.artist.Artist.set_path_effects>` unknown :meth:`picker <matplotlib.lines.Line2D.set_picker>` float distance in points or callable pick function ``fn(artist, event)`` :meth:`pickradius <matplotlib.lines.Line2D.set_pickradius>` float distance in points :meth:`rasterized <matplotlib.artist.Artist.set_rasterized>` [True  False  None] :meth:`sketch_params <matplotlib.artist.Artist.set_sketch_params>` unknown :meth:`snap <matplotlib.artist.Artist.set_snap>` unknown :meth:`solid_capstyle <matplotlib.lines.Line2D.set_solid_capstyle>` [‘butt’  ‘round’  ‘projecting’] :meth:`solid_joinstyle <matplotlib.lines.Line2D.set_solid_joinstyle>` [‘miter’  ‘round’  ‘bevel’] :meth:`transform <matplotlib.lines.Line2D.set_transform>` a :class:`matplotlib.transforms.Transform` instance :meth:`url <matplotlib.artist.Artist.set_url>` a url string :meth:`visible <matplotlib.artist.Artist.set_visible>` [True  False] :meth:`xdata <matplotlib.lines.Line2D.set_xdata>` 1D array :meth:`ydata <matplotlib.lines.Line2D.set_ydata>` 1D array :meth:`zorder <matplotlib.artist.Artist.set_zorder>` any number ===================================================================================== =============================================================================================================================================== 
See also
loglog()
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()
and matplotlib.cm.get_cmap()
.
matplotlib.pyplot.setp(*args, **kwargs)
Set a property on an artist object.
matplotlib supports the use of setp()
(“set property”) and getp()
to set and get object properties, as well as to do introspection on the object. For example, to set the linestyle of a line to be dashed, you can do:
>>> line, = plot([1,2,3]) >>> setp(line, linestyle='')
If you want to know the valid types of arguments, you can provide the name of the property you want to set without a value:
>>> setp(line, 'linestyle') linestyle: [ ''  ''  '.'  ':'  'steps'  'None' ]
If you want to see all the properties that can be set, and their possible values, you can do:
>>> setp(line) ... long output listing omitted
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).
Parameters: 
x : 1D array or sequence Array or sequence containing the data. Fs : scalar The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. The default value is 2. window : callable or ndarray A function or a vector of length NFFT. To create window vectors see 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 scale_by_freq : boolean, optional Specifies whether the resulting density values should be scaled by the scaling frequency, which gives density in units of Hz^1. This allows for integration over the returned frequency values. The default is True for MATLAB compatibility. mode : [ ‘default’  ‘psd’  ‘magnitude’  ‘angle’  ‘phase’ ] What sort of spectrum to use. Default is ‘psd’, which takes the power spectral density. ‘complex’ returns the 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 xextent : [None  (xmin, xmax)] The image extent along the xaxis. The default sets xmin to the left border of the first bin (spectrum column) and xmax to the right border of the last bin. Note that for noverlap>0 the width of the bins is smaller than those of the segments. **kwargs : Additional kwargs are passed on to imshow which makes the specgram image 

Returns: 
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 The image created by imshow containing the spectrogram 
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()
angle_spectrum()
phase_spectrum()
detrend and scale_by_freq only apply when mode is set to ‘psd’
(Source code, png, pdf)
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', **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 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 If ‘auto’, changes the image aspect ratio to match that of the axes. If None, default to rc 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 : 2d array of dimension MxN, OR any number 1d arrays each of dimension
1xN. The data is assumed to be unstacked. Each of the following calls is legal:
stackplot(x, y) # where y is MxN stackplot(x, y1, y2, y3, y4) # where y1, y2, y3, y4, are all 1xNm
Keyword arguments:
baseline : [‘zero’, ‘sym’, ‘wiggle’, ‘weighted_wiggle’]
ThemeRiver
. ‘wiggle’ minimizes the sum of the squared slopes. ‘weighted_wiggle’ does the same but weights to account for size of each layer. It is also called Streamgraph
layout. More details can be found at http://leebyron.com/streamgraph/.labels : A list or tuple of labels to assign to each data series.
colors : A list or tuple of colors. These will be cycled through and
fill_between()
Returns r : A list of PolyCollection
, one for each element in the stacked area plot.
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, pdf)
Note
In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:
matplotlib.pyplot.step(x, y, *args, **kwargs)
Make a step plot.
Parameters: 
x : array_like 1D sequence, and it is assumed, but not checked, that it is uniformly increasing. y : array_like 1D sequence, and it is assumed, but not checked, that it is uniformly increasing. 

Returns: 
list List of lines that were added. 
Other Parameters:  
where : [ ‘pre’  ‘post’  ‘mid’ ] If ‘pre’ (the default), the interval from x[i] to x[i+1] has level y[i+1]. If ‘post’, that interval has level y[i]. If ‘mid’, the jumps in y occur halfway between the xvalues. 
Additional parameters are the same as those for plot()
.
Note
In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:
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=None, start_points=None, hold=None, data=None)
Draws streamlines of a vector flow.
x, y : 1d arrays
u, v : 2d arrays
density : float or 2tuple
density = 1
, the domain is divided into a 30x30 grid—density linearly scales this grid. Each cell in the grid can have, at most, one traversing streamline. For different densities in each direction, use [density_x, density_y].linewidth : numeric or 2d array
color : matplotlib color code, or 2d array
cmap : Colormap
norm : Normalize
arrowsize : float
arrowstyle : str
FancyArrowPatch
.minlength : float
x
and y
arrays.zorder : int
Returns:
stream_container : StreamplotSet
Container object with attributes
matplotlib.collections.LineCollection
of streamlinesmatplotlib.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.
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 of nrows * ncols
.
In the case when nrows, ncols and plot_number are all less than 10, a convenience exists, such that the a 3 digit number can be given instead, where the hundreds represent nrows, the tens represent ncols and the units represent plot_number. For instance:
subplot(211)
produces a subaxes in a figure which represents the top plot (i.e. the first) in a 2 row by 1 column notional grid (no grid actually exists, but conceptually this is how the returned subplot has been positioned).
Note
Creating a subplot will delete any preexisting subplot that overlaps with it beyond sharing a boundary:
import matplotlib.pyplot as plt # plot a line, implicitly creating a subplot(111) plt.plot([1,2,3]) # now create a subplot which represents the top plot of a grid # with 2 rows and 1 column. Since this subplot will overlap the # first, the plot (and its axes) previously created, will be removed plt.subplot(211) plt.plot(range(12)) plt.subplot(212, facecolor='y') # creates 2nd subplot with yellow background
If you do not want this behavior, use the add_subplot()
method or the axes()
function instead.
Keyword arguments:
matplotlib.colors
for more information.matplotlib.projections
.See also
Example:
(Source code, 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 and a set of subplots
This utility wrapper makes it convenient to create common layouts of subplots, including the enclosing figure object, in a single call.
Parameters: 
nrows, ncols : int, optional, default: 1 Number of rows/columns of the subplot grid. sharex, sharey : bool or {‘none’, ‘all’, ‘row’, ‘col’}, default: False Controls sharing of properties among x (
When subplots have a shared xaxis along a column, only the x tick labels of the bottom subplot are visible. Similarly, when subplots have a shared yaxis along a row, only the y tick labels of the first column subplot are visible. squeeze : bool, optional, default: True
subplot_kw : dict, optional Dict with keywords passed to the gridspec_kw : dict, optional Dict with keywords passed to the **fig_kw : All additional keyword arguments are passed to the 

Returns: 
fig : ax : Axes object or array of Axes objects. ax can be either a single 
First create some toy data:
>>> x = np.linspace(0, 2*np.pi, 400) >>> y = np.sin(x**2)
Creates just a figure and only one subplot
>>> fig, ax = plt.subplots() >>> ax.plot(x, y) >>> ax.set_title('Simple plot')
Creates two subplots and unpacks the output array immediately
>>> f, (ax1, ax2) = plt.subplots(1, 2, sharey=True) >>> ax1.plot(x, y) >>> ax1.set_title('Sharing Y axis') >>> ax2.scatter(x, y)
Creates four polar axes, and accesses them through the returned array
>>> fig, axes = plt.subplots(2, 2, subplot_kw=dict(polar=True)) >>> axes[0, 0].plot(x, y) >>> axes[1, 1].scatter(x, y)
Share a X axis with each column of subplots
>>> plt.subplots(2, 2, sharex='col')
Share a Y axis with each row of subplots
>>> plt.subplots(2, 2, sharey='row')
Share both X and Y axes with all subplots
>>> plt.subplots(2, 2, sharex='all', sharey='all')
Note that this is the same as
>>> plt.subplots(2, 2, sharex=True, sharey=True)
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, # expressed as a fraction of the average axis width hspace = 0.2 # the amount of height reserved for white space between subplots, # expressed as a fraction of the average axis height
The actual defaults are controlled by the rc file
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 : 0.5
y : 0.98
horizontalalignment : ‘center’
verticalalignment : ‘top’
If the fontproperties
keyword argument is given then the rcParams defaults for fontsize
(figure.titlesize
) and fontweight
(figure.titleweight
) will be ignored in favour of the FontProperties
defaults.
A 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. Either cellText
or cellColours
must be provided. For finer grained control over tables, use the Table
class and add it to the axes with add_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 instance 
clip_box  a matplotlib.transforms.Bbox instance 
clip_on  [True  False] 
clip_path  [ (Path , Transform )  Patch  None ] 
contains  a callable function 
figure  a matplotlib.figure.Figure instance 
fontsize  a float in points 
gid  an id string 
label  string or anything printable with ‘%s’ conversion. 
path_effects  unknown 
picker  [Nonefloatbooleancallable] 
rasterized  [True  False  None] 
sketch_params  unknown 
snap  unknown 
transform 
Transform instance 
url  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 location x
, y
, data coordinates.
Parameters: 
x, y : scalars data coordinates s : string text fontdict : dictionary, optional, default: None A dictionary to override the default text properties. If fontdict is None, the defaults are determined by your rc parameters. withdash : boolean, optional, default: False Creates a 

Other Parameters:  
kwargs : Other miscellaneous text parameters. 
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 of Rectangle
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.
Parameters: 
axis : {‘x’, ‘y’, ‘both’}, optional Which axis to apply the parameters to. 

Other Parameters:  
axis : {‘x’, ‘y’, ‘both’} Axis on which to operate; default is ‘both’. reset : bool If True, set all parameters to defaults before processing other keyword arguments. Default is False. which : {‘major’, ‘minor’, ‘both’} Default is ‘major’; apply arguments to which ticks. direction : {‘in’, ‘out’, ‘inout’} Puts ticks inside the axes, outside the axes, or both. length : float Tick length in points. width : float Tick width in points. color : color Tick color; accepts any mpl color spec. pad : float Distance in points between tick and label. labelsize : float or str Tick label font size in points or as a string (e.g., ‘large’). labelcolor : color Tick label color; mpl color spec. colors : color Changes the tick color and the label color to the same value: mpl color spec. zorder : float Tick and label zorder. bottom, top, left, right : bool or {‘on’, ‘off’} controls whether to draw the respective ticks. labelbottom, labeltop, labelleft, labelright : bool or {‘on’, ‘off’} controls whether to draw the respective tick labels. 
Usage
ax.tick_params(direction='out', length=6, width=2, colors='r')
This will make all major ticks be red, pointing out of the box, and with dimensions 6 points by 2 points. Tick labels will also be red.
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 the Formatter
being used, an AttributeError
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 : float
h_pad, w_pad : float
pad_inches
.rect : if rect is given, it is interpreted as a rectangle
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 axes
Parameters: 
label : str Text to use for the title fontdict : dict A dictionary controlling the appearance of the title text, the default {‘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 : The matplotlib text instance representing the title 
Other Parameters:  
kwargs : text properties Other keyword arguments are text properties, see 
matplotlib.pyplot.tricontour(*args, **kwargs)
Draw contours on an unstructured triangular grid. tricontour()
and tricontourf()
draw contour lines and filled contours, respectively. Except as noted, function signatures and return values are the same for both versions.
The triangulation can be specified in one of two ways; either:
tricontour(triangulation, ...)
where triangulation is a matplotlib.tri.Triangulation
object, or
tricontour(x, y, ...) tricontour(x, y, triangles, ...) tricontour(x, y, triangles=triangles, ...) tricontour(x, y, mask=mask, ...) tricontour(x, y, triangles, mask=mask, ...)
in which case a Triangulation object will be created. See 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 a TriContourSet
object.
Optional keyword arguments:
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.
Colormap
instance or None. If cmap is None and colors is None, a default Colormap is used.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=[0]
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.
MaxNLocator
is used. The locator is used to determine the contour levels if they are not given explicitly via the V argument.matplotlib.colors.Colormap.set_under()
and matplotlib.colors.Colormap.set_over()
methods.matplotlib.units.ConversionInterface
.tricontouronly keyword arguments:
If linewidths is None, the default width in lines.linewidth
in matplotlibrc
is used.
If a number, all levels will be plotted with this linewidth.
If a tuple, different levels will be plotted with different linewidths in the order specified
If linestyles is None, the ‘solid’ is used.
linestyles can also be an iterable of the above strings specifying a set of linestyles to be used. If this iterable is shorter than the number of contour levels it will be repeated as necessary.
If contour is using a monochrome colormap and the contour level is less than 0, then the linestyle specified in contour.negative_linestyle
in matplotlibrc
will be used.
tricontourfonly keyword arguments:
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:
matplotlib.pyplot.tricontourf(*args, **kwargs)
Draw contours on an unstructured triangular grid. tricontour()
and tricontourf()
draw contour lines and filled contours, respectively. Except as noted, function signatures and return values are the same for both versions.
The triangulation can be specified in one of two ways; either:
tricontour(triangulation, ...)
where triangulation is a matplotlib.tri.Triangulation
object, or
tricontour(x, y, ...) tricontour(x, y, triangles, ...) tricontour(x, y, triangles=triangles, ...) tricontour(x, y, mask=mask, ...) tricontour(x, y, triangles, mask=mask, ...)
in which case a Triangulation object will be created. See 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 a TriContourSet
object.
Optional keyword arguments:
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.
Colormap
instance or None. If cmap is None and colors is None, a default Colormap is used.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=[0]
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.
MaxNLocator
is used. The locator is used to determine the contour levels if they are not given explicitly via the V argument.matplotlib.colors.Colormap.set_under()
and matplotlib.colors.Colormap.set_over()
methods.matplotlib.units.ConversionInterface
.tricontouronly keyword arguments:
If linewidths is None, the default width in lines.linewidth
in matplotlibrc
is used.
If a number, all levels will be plotted with this linewidth.
If a tuple, different levels will be plotted with different linewidths in the order specified
If linestyles is None, the ‘solid’ is used.
linestyles can also be an iterable of the above strings specifying a set of linestyles to be used. If this iterable is shorter than the number of contour levels it will be repeated as necessary.
If contour is using a monochrome colormap and the contour level is less than 0, then the linestyle specified in contour.negative_linestyle
in matplotlibrc
will be used.
tricontourfonly keyword arguments:
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:
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, or
tripcolor(x, y, ...) tripcolor(x, y, triangles, ...) tripcolor(x, y, triangles=triangles, ...) tripcolor(x, y, mask=mask, ...) tripcolor(x, y, triangles, mask=mask, ...)
in which case a Triangulation object will be created. See 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:
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, or
triplot(x, y, ...) triplot(x, y, triangles, ...) triplot(x, y, triangles=triangles, ...) triplot(x, y, mask=mask, ...) triplot(x, y, triangles, mask=mask, ...)
in which case a Triangulation object will be created. See 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:
Example:
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
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.
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 : arraylike, default = [1, 2, ..., n] Sets the positions of the violins. The ticks and limits are automatically set to match the positions. vert : bool, default = True. If true, creates a vertical violin plot. Otherwise, creates a horizontal violin plot. widths : 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 showextrema : bool, default = True If showmedians : bool, default = False If 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 

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:
Note In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:

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
from ymin
to ymax
.
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 : 
Other Parameters:  
kwargs : 
See also
hlines
(Source code, png, pdf)
Note
In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:
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=<function detrend_none>, usevlines=True, maxlags=10, hold=None, data=None, **kwargs)
Plot the cross correlation between x and y.
The correlation with lag k is defined as sum_n x[n+k] * conj(y[n]).
Parameters: 
x : sequence of scalars of length n y : sequence of scalars of length n hold : boolean, optional, deprecated, default: True detrend : callable, optional, default: x is detrended by the normed : boolean, optional, default: True if True, input vectors are normalised to unit length. usevlines : boolean, optional, default: True if True, Axes.vlines is used to plot the vertical lines from the origin to the acorr. Otherwise, Axes.plot is used. maxlags : integer, optional, default: 10 number of lags to show. If None, will return all 2 * len(x)  1 lags. 

Returns: 
(lags, c, line, b) : where: 
Other Parameters:  
linestyle : Only used if usevlines is False. marker : string, optional, default: ‘o’ 
The cross correlation is performed with numpy.correlate()
with mode
= 2.
Note
In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:
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. 

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()
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’
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’
‘symlog’
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.
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()
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’
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’
‘symlog’
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.
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 )
© 2012–2017 Matplotlib Development Team. All rights reserved.
Licensed under the Matplotlib License Agreement.
http://matplotlib.org/2.0.2/api/pyplot_api.html