matplotlib.pyplot
¶Provides a MATLABlike plotting framework.
pylab
combines pyplot with numpy into a single namespace.
This is convenient for interactive work, but for programming it
is recommended that the namespaces be kept separate, e.g.:
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0, 5, 0.1);
y = np.sin(x)
plt.plot(x, y)
matplotlib.pyplot.
acorr
(x, hold=None, data=None, **kwargs)¶Plot the autocorrelation of x
.
Parameters:  x : sequence of scalar hold : boolean, optional, deprecated, default: True detrend : callable, optional, default:
normed : boolean, optional, default: True
usevlines : boolean, optional, default: True
maxlags : integer, optional, default: 10


Returns:  (lags, c, line, b) : where: 
Other Parameters:  
linestyle :
marker : string, optional, default: ‘o’ 
Notes
The cross correlation is performed with numpy.correlate()
with
mode
= 2.
Examples
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
Fs : scalar
window : callable or ndarray
sides : [ ‘default’  ‘onesided’  ‘twosided’ ]
pad_to : integer
Fc : integer
**kwargs :



Returns:  spectrum : 1D array
freqs : 1D array
line : a

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.
Examples
(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
xy : iterable
xytext : iterable, optional
xycoords : str, Artist, Transform, callable or tuple, optional
textcoords : str,
arrowprops : dict, optional
annotation_clip : bool, optional



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
y : float
dx : float
dy : float



Returns:  a : FancyArrow


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

Notes
The resulting arrow is affected by the axes aspect ratio and limits.
This may produce an arrow whose head is not square with its stem. To
create an arrow whose head is square with its stem, use
annotate()
for example:
ax.annotate("", xy=(0.5, 0.5), xytext=(0, 0),
arrowprops=dict(arrowstyle=">"))
Examples
(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
xmin : scalar, optional, default: 0
xmax : scalar, optional, default: 1


Returns: 
See also
axhspan
Notes
kwargs are passed to Line2D
and can be used
to control the line properties.
Examples
draw a thick red hline at ‘y’ = 0 that spans the xrange:
>>> axhline(linewidth=4, color='r')
draw a default hline at ‘y’ = 1 that spans the xrange:
>>> axhline(y=1)
draw a default hline at ‘y’ = .5 that spans the middle half of the xrange:
>>> axhline(y=.5, xmin=0.25, xmax=0.75)
Valid kwargs are Line2D
properties,
with the exception of ‘transform’:
Property Description agg_filter
unknown alpha
float (0.0 transparent through 1.0 opaque) animated
[True  False] antialiased
or aa[True  False] axes
an Axes
instanceclip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
or cany matplotlib color contains
a callable function dash_capstyle
[‘butt’  ‘round’  ‘projecting’] dash_joinstyle
[‘miter’  ‘round’  ‘bevel’] dashes
sequence of on/off ink in points drawstyle
[‘default’  ‘steps’  ‘stepspre’  ‘stepsmid’  ‘stepspost’] figure
a matplotlib.figure.Figure
instancefillstyle
[‘full’  ‘left’  ‘right’  ‘bottom’  ‘top’  ‘none’] gid
an id string label
string or anything printable with ‘%s’ conversion. linestyle
or ls[‘solid’  ‘dashed’, ‘dashdot’, ‘dotted’  (offset, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or lwfloat value in points marker
A valid marker style
markeredgecolor
or mecany matplotlib color markeredgewidth
or mewfloat value in points markerfacecolor
or mfcany matplotlib color markerfacecoloralt
or mfcaltany matplotlib color markersize
or msfloat markevery
[None  int  length2 tuple of int  slice  list/array of int  float  length2 tuple of float] path_effects
unknown picker
float distance in points or callable pick function fn(artist, event)
pickradius
float distance in points rasterized
[True  False  None] sketch_params
unknown snap
unknown solid_capstyle
[‘butt’  ‘round’  ‘projecting’] solid_joinstyle
[‘miter’  ‘round’  ‘bevel’] transform
a matplotlib.transforms.Transform
instanceurl
a url string visible
[True  False] xdata
1D array ydata
1D array zorder
any number
matplotlib.pyplot.
axhspan
(ymin, ymax, xmin=0, xmax=1, hold=None, **kwargs)¶Add a horizontal span (rectangle) across the axis.
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
ymax : float
xmin : float, optional, default: 0
xmax : float, optional, default: 1



Returns:  Polygon : 

Other Parameters:  
kwargs :

See also
axvspan
Examples
(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
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
ymin : scalar, optional, default: 0
ymax : scalar, optional, default: 1


Returns: 
See also
axhspan
Examples
draw a thick red vline at x = 0 that spans the yrange:
>>> axvline(linewidth=4, color='r')
draw a default vline at x = 1 that spans the yrange:
>>> axvline(x=1)
draw a default vline at x = .5 that spans the middle half of the yrange:
>>> axvline(x=.5, ymin=0.25, ymax=0.75)
Valid kwargs are Line2D
properties,
with the exception of ‘transform’:
Property Description agg_filter
unknown alpha
float (0.0 transparent through 1.0 opaque) animated
[True  False] antialiased
or aa[True  False] axes
an Axes
instanceclip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
or cany matplotlib color contains
a callable function dash_capstyle
[‘butt’  ‘round’  ‘projecting’] dash_joinstyle
[‘miter’  ‘round’  ‘bevel’] dashes
sequence of on/off ink in points drawstyle
[‘default’  ‘steps’  ‘stepspre’  ‘stepsmid’  ‘stepspost’] figure
a matplotlib.figure.Figure
instancefillstyle
[‘full’  ‘left’  ‘right’  ‘bottom’  ‘top’  ‘none’] gid
an id string label
string or anything printable with ‘%s’ conversion. linestyle
or ls[‘solid’  ‘dashed’, ‘dashdot’, ‘dotted’  (offset, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or lwfloat value in points marker
A valid marker style
markeredgecolor
or mecany matplotlib color markeredgewidth
or mewfloat value in points markerfacecolor
or mfcany matplotlib color markerfacecoloralt
or mfcaltany matplotlib color markersize
or msfloat markevery
[None  int  length2 tuple of int  slice  list/array of int  float  length2 tuple of float] path_effects
unknown picker
float distance in points or callable pick function fn(artist, event)
pickradius
float distance in points rasterized
[True  False  None] sketch_params
unknown snap
unknown solid_capstyle
[‘butt’  ‘round’  ‘projecting’] solid_joinstyle
[‘miter’  ‘round’  ‘bevel’] transform
a matplotlib.transforms.Transform
instanceurl
a url string visible
[True  False] xdata
1D array ydata
1D array zorder
any number
matplotlib.pyplot.
axvspan
(xmin, xmax, ymin=0, ymax=1, hold=None, **kwargs)¶Add a vertical span (rectangle) across the axes.
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
xmax : scalar
ymin : scalar, optional
ymax : scalar, optional


Returns:  rectangle : matplotlib.patches.Polygon

Other Parameters:  
**kwargs

See also
Examples
Draw a vertical, green, translucent rectangle from x = 1.25 to x = 1.55 that spans the yrange of the axes.
>>> axvspan(1.25, 1.55, facecolor='g', alpha=0.5)
matplotlib.pyplot.
bar
(left, height, width=0.8, bottom=None, hold=None, data=None, **kwargs)¶Make a bar plot.
Make a bar plot with rectangles bounded by:
left
,left
+width
,bottom
,bottom
+height
 (left, right, bottom and top edges)
Parameters:  left : sequence of scalars
height : sequence of scalars
width : scalar or arraylike, optional
bottom : scalar or arraylike, optional
color : scalar or arraylike, optional
edgecolor : scalar or arraylike, optional
linewidth : scalar or arraylike, optional
tick_label : string or arraylike, optional
xerr : scalar or arraylike, optional
yerr : scalar or arraylike, optional
ecolor : scalar or arraylike, optional
capsize : scalar, optional
error_kw : dict, optional
align : {‘center’, ‘edge’}, optional
orientation : {‘vertical’, ‘horizontal’}, optional
log : boolean, optional


Returns:  bars : matplotlib.container.BarContainer

See also
barh
Notes
The optional arguments color
, edgecolor
, linewidth
,
xerr
, and yerr
can be either scalars or sequences of
length equal to the number of bars. This enables you to use
bar as the basis for stacked bar charts, or candlestick plots.
Detail: xerr
and yerr
are passed directly to
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
instancecapstyle
[‘butt’  ‘round’  ‘projecting’] clip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
matplotlib color spec contains
a callable function edgecolor
or ecmpl color spec, None, ‘none’, or ‘auto’ facecolor
or fcmpl color spec, or None for default, or ‘none’ for no color figure
a matplotlib.figure.Figure
instancefill
[True  False] gid
an id string hatch
[‘/’  ‘\’  ‘’  ‘‘  ‘+’  ‘x’  ‘o’  ‘O’  ‘.’  ‘*’] joinstyle
[‘miter’  ‘round’  ‘bevel’] label
string or anything printable with ‘%s’ conversion. linestyle
or ls[‘solid’  ‘dashed’, ‘dashdot’, ‘dotted’  (offset, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or lwfloat or None for default path_effects
unknown picker
[Nonefloatbooleancallable] rasterized
[True  False  None] sketch_params
unknown snap
unknown transform
Transform
instanceurl
a url string visible
[True  False] zorder
any number
Examples
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:
 X, Y:
 The x and y coordinates of the barb locations (default is head of barb; see pivot kwarg)
 U, V:
 Give the x and y components of the barb shaft
 C:
 An optional array used to map colors to the barbs
All arguments may be 1D or 2D arrays or sequences. If X and Y
are absent, they will be generated as a uniform grid. If U and V
are 2D arrays but X and Y are 1D, and if len(X)
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:
 length:
 Length of the barb in points; the other parts of the barb are scaled against this. Default is 9
 pivot: [ ‘tip’  ‘middle’ ]
 The part of the arrow that is at the grid point; the arrow rotates about this point, hence the name pivot. Default is ‘tip’
 barbcolor: [ color  color sequence ]
 Specifies the color all parts of the barb except any flags. This parameter is analagous to the edgecolor parameter for polygons, which can be used instead. However this parameter will override facecolor.
 flagcolor: [ color  color sequence ]
 Specifies the color of any flags on the barb. This parameter is analagous to the facecolor parameter for polygons, which can be used instead. However this parameter will override facecolor. If this is not set (and C has not either) then flagcolor will be set to match barbcolor so that the barb has a uniform color. If C has been set, flagcolor has no effect.
 sizes:
A dictionary of coefficients specifying the ratio of a given feature to the length of the barb. Only those values one wishes to override need to be included. These features include:
 ‘spacing’  space between features (flags, full/half barbs)
 ‘height’  height (distance from shaft to top) of a flag or full barb
 ‘width’  width of a flag, twice the width of a full barb
 ‘emptybarb’  radius of the circle used for low magnitudes
 fill_empty:
 A flag on whether the empty barbs (circles) that are drawn should be filled with the flag color. If they are not filled, they will be drawn such that no color is applied to the center. Default is False
 rounding:
 A flag to indicate whether the vector magnitude should be rounded when allocating barb components. If True, the magnitude is rounded to the nearest multiple of the halfbarb increment. If False, the magnitude is simply truncated to the next lowest multiple. Default is True
 barb_increments:
A dictionary of increments specifying values to associate with different parts of the barb. Only those values one wishes to override need to be included.
 ‘half’  half barbs (Default is 5)
 ‘full’  full barbs (Default is 10)
 ‘flag’  flags (default is 50)
 flip_barb:
 Either a single boolean flag or an array of booleans. Single boolean indicates whether the lines and flags should point opposite to normal for all barbs. An array (which should be the same size as the other data arrays) indicates whether to flip for each individual barb. Normal behavior is for the barbs and lines to point right (comes from wind barbs having these features point towards low pressure in the Northern Hemisphere.) Default is False
Barbs are traditionally used in meteorology as a way to plot the speed and direction of wind observations, but can technically be used to plot any two dimensional vector quantity. As opposed to arrows, which give vector magnitude by the length of the arrow, the barbs give more quantitative information about the vector magnitude by putting slanted lines or a triangle for various increments in magnitude, as show schematically below:
: /\ \
: / \ \
: / \ \ \
: / \ \ \
: 
The largest increment is given by a triangle (or “flag”). After those come full lines (barbs). The smallest increment is a half line. There is only, of course, ever at most 1 half line. If the magnitude is small and only needs a single halfline and no full lines or triangles, the halfline is offset from the end of the barb so that it can be easily distinguished from barbs with a single full line. The magnitude for the barb shown above would nominally be 65, using the standard increments of 50, 10, and 5.
linewidths and edgecolors can be used to customize the barb.
Additional PolyCollection
keyword
arguments:
Property Description agg_filter
unknown alpha
float or None animated
[True  False] antialiased
or antialiasedsBoolean or sequence of booleans array
unknown axes
an Axes
instanceclim
a length 2 sequence of floats clip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]cmap
a colormap or registered colormap name color
matplotlib color arg or sequence of rgba tuples contains
a callable function edgecolor
or edgecolorsmatplotlib color spec or sequence of specs facecolor
or facecolorsmatplotlib color spec or sequence of specs figure
a matplotlib.figure.Figure
instancegid
an id string hatch
[ ‘/’  ‘\’  ‘’  ‘‘  ‘+’  ‘x’  ‘o’  ‘O’  ‘.’  ‘*’ ] label
string or anything printable with ‘%s’ conversion. linestyle
or dashes or linestyles[‘solid’  ‘dashed’, ‘dashdot’, ‘dotted’  (offset, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or linewidths or lwfloat or sequence of floats norm
unknown offset_position
unknown offsets
float or sequence of floats path_effects
unknown picker
[Nonefloatbooleancallable] pickradius
unknown rasterized
[True  False  None] sketch_params
unknown snap
unknown transform
Transform
instanceurl
a url string urls
unknown visible
[True  False] zorder
any number
Example:
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
 (left, right, bottom and top edges)
bottom
, width
, height
, and left
can be either scalars
or sequences
Parameters:  bottom : scalar or arraylike
width : scalar or arraylike
height : sequence of scalars, optional, default: 0.8
left : sequence of scalars


Returns: 

Other Parameters:  
color : scalar or arraylike, optional
edgecolor : scalar or arraylike, optional
linewidth : scalar or arraylike, optional, default: None
tick_label : string or arraylike, optional, default: None
xerr : scalar or arraylike, optional, default: None
yerr : scalar or arraylike, optional, default: None
ecolor : scalar or arraylike, optional, default: None
capsize : scalar, optional
error_kw :
align : {‘center’, ‘edge’}, optional
log : boolean, optional, default: False

See also
bar
Notes
The optional arguments color
, edgecolor
, linewidth
,
xerr
, and yerr
can be either scalars or sequences of
length equal to the number of bars. This enables you to use
bar as the basis for stacked bar charts, or candlestick plots.
Detail: xerr
and yerr
are passed directly to
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
instancecapstyle
[‘butt’  ‘round’  ‘projecting’] clip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
matplotlib color spec contains
a callable function edgecolor
or ecmpl color spec, None, ‘none’, or ‘auto’ facecolor
or fcmpl color spec, or None for default, or ‘none’ for no color figure
a matplotlib.figure.Figure
instancefill
[True  False] gid
an id string hatch
[‘/’  ‘\’  ‘’  ‘‘  ‘+’  ‘x’  ‘o’  ‘O’  ‘.’  ‘*’] joinstyle
[‘miter’  ‘round’  ‘bevel’] label
string or anything printable with ‘%s’ conversion. linestyle
or ls[‘solid’  ‘dashed’, ‘dashdot’, ‘dotted’  (offset, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or lwfloat or None for default path_effects
unknown picker
[Nonefloatbooleancallable] rasterized
[True  False  None] sketch_params
unknown snap
unknown transform
Transform
instanceurl
a url string visible
[True  False] zorder
any number
matplotlib.pyplot.
bone
()¶set the default colormap to bone and apply to current image if any. See help(colormaps) for more information
matplotlib.pyplot.
box
(on=None)¶Turn the axes box on or off. on may be a boolean or a string, ‘on’ or ‘off’.
If on is None, toggle state.
matplotlib.pyplot.
boxplot
(x, notch=None, sym=None, vert=None, whis=None, positions=None, widths=None, patch_artist=None, bootstrap=None, usermedians=None, conf_intervals=None, meanline=None, showmeans=None, showcaps=None, showbox=None, showfliers=None, boxprops=None, labels=None, flierprops=None, medianprops=None, meanprops=None, capprops=None, whiskerprops=None, manage_xticks=True, 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.
notch : bool, optional (False)
sym : str, optional
vert : bool, optional (True)
whis : float, sequence, or string (default = 1.5)
bootstrap : int, optional
usermedians : arraylike, optional
conf_intervals : arraylike, optional
positions : arraylike, optional
widths : scalar or arraylike
patch_artist : bool, optional (False)
labels : sequence, optional
manage_xticks : bool, optional (True)
autorange : bool, optional (False)
meanline : bool, optional (False)
zorder : scalar, optional (None)


Returns:  result : dict

Other Parameters:  
showcaps : bool, optional (True)
showbox : bool, optional (True)
showfliers : bool, optional (True)
showmeans : bool, optional (False)
capprops : dict, optional (None)
boxprops : dict, optional (None)
whiskerprops : dict, optional (None)
flierprops : dict, optional (None)
medianprops : dict, optional (None)
meanprops : dict, optional (None)

Examples
(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 antialiasedsBoolean or sequence of booleans array
unknown axes
an Axes
instanceclim
a length 2 sequence of floats clip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]cmap
a colormap or registered colormap name color
matplotlib color arg or sequence of rgba tuples contains
a callable function edgecolor
or edgecolorsmatplotlib color spec or sequence of specs facecolor
or facecolorsmatplotlib color spec or sequence of specs figure
a matplotlib.figure.Figure
instancegid
an id string hatch
[ ‘/’  ‘\’  ‘’  ‘‘  ‘+’  ‘x’  ‘o’  ‘O’  ‘.’  ‘*’ ] label
string or anything printable with ‘%s’ conversion. linestyle
or dashes or linestyles[‘solid’  ‘dashed’, ‘dashdot’, ‘dotted’  (offset, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or linewidths or lwfloat or sequence of floats norm
unknown offset_position
unknown offsets
float or sequence of floats path_effects
unknown picker
[Nonefloatbooleancallable] pickradius
unknown rasterized
[True  False  None] sketch_params
unknown snap
unknown transform
Transform
instanceurl
a url string urls
unknown visible
[True  False] zorder
any number
these can either be a single argument, i.e.,:
facecolors = 'black'
or a sequence of arguments for the various bars, i.e.,:
facecolors = ('black', 'red', 'green')
Example:
(Source code, png, 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:
 fontsize:
 size in points or relative size e.g., ‘smaller’, ‘xlarge’
 colors:
 if None, the color of each label matches the color of the corresponding contour
 if one string color, e.g., colors = ‘r’ or colors = ‘red’, all labels will be plotted in this color
 if a tuple of matplotlib color args (string, float, rgb, etc), different labels will be plotted in different colors in the order specified
 inline:
 controls whether the underlying contour is removed or not. Default is True.
 inline_spacing:
 space in pixels to leave on each side of label when placing inline. Defaults to 5. This spacing will be exact for labels at locations where the contour is straight, less so for labels on curved contours.
 fmt:
 a format string for the label. Default is ‘%1.3f’ Alternatively, this can be a dictionary matching contour levels with arbitrary strings to use for each contour level (i.e., fmt[level]=string), or it can be any callable, such as a
Formatter
instance, that returns a string when called with a numeric contour level. manual:
if True, contour labels will be placed manually using mouse clicks. Click the first button near a contour to add a label, click the second button (or potentially both mouse buttons at once) to finish adding labels. The third button can be used to remove the last label added, but only if labels are not inline. Alternatively, the keyboard can be used to select label locations (enter to end label placement, delete or backspace act like the third mouse button, and any other key will select a label location).
manual can be an iterable object of x,y tuples. Contour labels will be created as if mouse is clicked at each x,y positions.
 rightside_up:
 if True (default), label rotations will always be plus or minus 90 degrees from level.
 use_clabeltext:
 if True (default is False), ClabelText class (instead of matplotlib.Text) is used to create labels. ClabelText recalculates rotation angles of texts during the drawing time, therefore this can be used if aspect of the axes changes.
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
window : callable or ndarray
sides : [ ‘default’  ‘onesided’  ‘twosided’ ]
pad_to : integer
NFFT : integer
detrend : {‘default’, ‘constant’, ‘mean’, ‘linear’, ‘none’} or callable
scale_by_freq : boolean, optional
noverlap : integer
Fc : integer
**kwargs :



Returns:  The return value is a tuple (Cxy, f), where f are the frequencies of the coherence vector. kwargs are applied to the lines. 
References
Bendat & Piersol – Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986)
Examples
(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:
 mappable
 the
Image
,ContourSet
, etc. to which the colorbar applies; this argument is mandatory for thecolorbar()
method but optional for thecolorbar()
function, which sets the default to the current image.
keyword arguments:
 cax
 None  axes object into which the colorbar will be drawn
 ax
 None  parent axes object(s) from which space for a new colorbar axes will be stolen. If a list of axes is given they will all be resized to make room for the colorbar axes.
 use_gridspec
 False  If cax is None, a new cax is created as an instance of Axes. If ax is an instance of Subplot and use_gridspec is True, cax is created as an instance of Subplot using the grid_spec module.
Additional keyword arguments are of two kinds:
axes properties:
Property Description orientation vertical or horizontal fraction 0.15; fraction of original axes to use for colorbar pad 0.05 if vertical, 0.15 if horizontal; fraction of original axes between colorbar and new image axes shrink 1.0; fraction by which to shrink the colorbar aspect 20; ratio of long to short dimensions anchor (0.0, 0.5) if vertical; (0.5, 1.0) if horizontal; the anchor point of the colorbar axes panchor (1.0, 0.5) if vertical; (0.5, 0.0) if horizontal; the anchor point of the colorbar parent axes. If False, the parent axes’ anchor will be unchanged colorbar properties:
Property Description extend [ ‘neither’  ‘both’  ‘min’  ‘max’ ] If not ‘neither’, make pointed end(s) for outof range values. These are set for a given colormap using the colormap set_under and set_over methods. extendfrac [ None  ‘auto’  length  lengths ] If set to None, both the minimum and maximum triangular colorbar extensions with have a length of 5% of the interior colorbar length (this is the default setting). If set to ‘auto’, makes the triangular colorbar extensions the same lengths as the interior boxes (when spacing is set to ‘uniform’) or the same lengths as the respective adjacent interior boxes (when spacing is set to ‘proportional’). If a scalar, indicates the length of both the minimum and maximum triangular colorbar extensions as a fraction of the interior colorbar length. A twoelement sequence of fractions may also be given, indicating the lengths of the minimum and maximum colorbar extensions respectively as a fraction of the interior colorbar length. extendrect [ False  True ] If False the minimum and maximum colorbar extensions will be triangular (the default). If True the extensions will be rectangular. spacing [ ‘uniform’  ‘proportional’ ] Uniform spacing gives each discrete color the same space; proportional makes the space proportional to the data interval. ticks [ None  list of ticks  Locator object ] If None, ticks are determined automatically from the input. format [ None  format string  Formatter object ] If None, the ScalarFormatter
is used. If a format string is given, e.g., ‘%.3f’, that is used. An alternativeFormatter
object may be given instead.drawedges [ False  True ] If true, draw lines at color boundaries. The following will probably be useful only in the context of indexed colors (that is, when the mappable has norm=NoNorm()), or other unusual circumstances.
Property Description boundaries None or a sequence values None or a sequence which must be of length 1 less than the sequence of boundaries. For each region delimited by adjacent entries in boundaries, the color mapped to the corresponding value in values will be used.
If mappable is a ContourSet
, its extend
kwarg is included automatically.
Note that the shrink kwarg provides a simple way to keep a vertical colorbar, for example, from being taller than the axes of the mappable to which the colorbar is attached; but it is a manual method requiring some trial and error. If the colorbar is too tall (or a horizontal colorbar is too wide) use a smaller value of shrink.
For more precise control, you can manually specify the positions of the axes objects in which the mappable and the colorbar are drawn. In this case, do not use any of the axes properties kwargs.
It is known that some vector graphics viewer (svg and pdf) renders white gaps between segments of the colorbar. This is due to bugs in the viewers not matplotlib. As a workaround the colorbar can be rendered with overlapping segments:
cbar = colorbar()
cbar.solids.set_edgecolor("face")
draw()
However this has negative consequences in other circumstances. Particularly with semi transparent images (alpha < 1) and colorbar extensions and is not enabled by default see (issue #1188).
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:
 corner_mask: [ True  False  ‘legacy’ ]
Enable/disable corner masking, which only has an effect if Z is a masked array. If False, any quad touching a masked point is masked out. If True, only the triangular corners of quads nearest those points are always masked out, other triangular corners comprising three unmasked points are contoured as usual. If ‘legacy’, the old contouring algorithm is used, which is equivalent to False and is deprecated, only remaining whilst the new algorithm is tested fully.
If not specified, the default is taken from rcParams[‘contour.corner_mask’], which is True unless it has been modified.
 colors: [ None  string  (mpl_colors) ]
If None, the colormap specified by cmap will be used.
If a string, like ‘r’ or ‘red’, all levels will be plotted in this color.
If a tuple of matplotlib color args (string, float, rgb, etc), different levels will be plotted in different colors in the order specified.
 alpha: float
 The alpha blending value
 cmap: [ None  Colormap ]
 A cm
Colormap
instance or None. If cmap is None and colors is None, a default Colormap is used. norm: [ None  Normalize ]
 A
matplotlib.colors.Normalize
instance for scaling data values to colors. If norm is None and colors is None, the default linear scaling is used. vmin, vmax: [ None  scalar ]
 If not None, either or both of these values will be supplied to the
matplotlib.colors.Normalize
instance, overriding the default color scaling based on levels. levels: [level0, level1, ..., leveln]
 A list of floating point numbers indicating the level curves to draw, in increasing order; e.g., to draw just the zero contour pass
levels=[0]
 origin: [ None  ‘upper’  ‘lower’  ‘image’ ]
If None, the first value of Z will correspond to the lower left corner, location (0,0). If ‘image’, the rc value for
image.origin
will be used.This keyword is not active if X and Y are specified in the call to contour.
extent: [ None  (x0,x1,y0,y1) ]
If origin is not None, then extent is interpreted as in
matplotlib.pyplot.imshow()
: it gives the outer pixel boundaries. In this case, the position of Z[0,0] is the center of the pixel, not a corner. If origin is None, then (x0, y0) is the position of Z[0,0], and (x1, y1) is the position of Z[1,1].This keyword is not active if X and Y are specified in the call to contour.
 locator: [ None  ticker.Locator subclass ]
 If locator is None, the default
MaxNLocator
is used. The locator is used to determine the contour levels if they are not given explicitly via the V argument. extend: [ ‘neither’  ‘both’  ‘min’  ‘max’ ]
 Unless this is ‘neither’, contour levels are automatically added to one or both ends of the range so that all data are included. These added ranges are then mapped to the special colormap values which default to the ends of the colormap range, but can be set via
matplotlib.colors.Colormap.set_under()
andmatplotlib.colors.Colormap.set_over()
methods. xunits, yunits: [ None  registered units ]
 Override axis units by specifying an instance of a
matplotlib.units.ConversionInterface
. antialiased: [ True  False ]
 enable antialiasing, overriding the defaults. For filled contours, the default is True. For line contours, it is taken from rcParams[‘lines.antialiased’].
 nchunk: [ 0  integer ]
 If 0, no subdivision of the domain. Specify a positive integer to divide the domain into subdomains of nchunk by nchunk quads. Chunking reduces the maximum length of polygons generated by the contouring algorithm which reduces the rendering workload passed on to the backend and also requires slightly less RAM. It can however introduce rendering artifacts at chunk boundaries depending on the backend, the antialiased flag and value of alpha.
contouronly keyword arguments:
 linewidths: [ None  number  tuple of numbers ]
If linewidths is None, the default width in
lines.linewidth
inmatplotlibrc
is used.If a number, all levels will be plotted with this linewidth.
If a tuple, different levels will be plotted with different linewidths in the order specified.
 linestyles: [ None  ‘solid’  ‘dashed’  ‘dashdot’  ‘dotted’ ]
If linestyles is None, the default is ‘solid’ unless the lines are monochrome. In that case, negative contours will take their linestyle from the
matplotlibrc
contour.negative_linestyle
setting.linestyles can also be an iterable of the above strings specifying a set of linestyles to be used. If this iterable is shorter than the number of contour levels it will be repeated as necessary.
contourfonly keyword arguments:
 hatches:
 A list of cross hatch patterns to use on the filled areas. If None, no hatching will be added to the contour. Hatching is supported in the PostScript, PDF, SVG and Agg backends only.
Note: contourf fills intervals that are closed at the top; that is, for boundaries z1 and z2, the filled region is:
z1 < z <= z2
There is one exception: if the lowest boundary coincides with the minimum value of the z array, then that minimum value will be included in the lowest interval.
Examples:
(Source code, png, 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:
 corner_mask: [ True  False  ‘legacy’ ]
Enable/disable corner masking, which only has an effect if Z is a masked array. If False, any quad touching a masked point is masked out. If True, only the triangular corners of quads nearest those points are always masked out, other triangular corners comprising three unmasked points are contoured as usual. If ‘legacy’, the old contouring algorithm is used, which is equivalent to False and is deprecated, only remaining whilst the new algorithm is tested fully.
If not specified, the default is taken from rcParams[‘contour.corner_mask’], which is True unless it has been modified.
 colors: [ None  string  (mpl_colors) ]
If None, the colormap specified by cmap will be used.
If a string, like ‘r’ or ‘red’, all levels will be plotted in this color.
If a tuple of matplotlib color args (string, float, rgb, etc), different levels will be plotted in different colors in the order specified.
 alpha: float
 The alpha blending value
 cmap: [ None  Colormap ]
 A cm
Colormap
instance or None. If cmap is None and colors is None, a default Colormap is used. norm: [ None  Normalize ]
 A
matplotlib.colors.Normalize
instance for scaling data values to colors. If norm is None and colors is None, the default linear scaling is used. vmin, vmax: [ None  scalar ]
 If not None, either or both of these values will be supplied to the
matplotlib.colors.Normalize
instance, overriding the default color scaling based on levels. levels: [level0, level1, ..., leveln]
 A list of floating point numbers indicating the level curves to draw, in increasing order; e.g., to draw just the zero contour pass
levels=[0]
 origin: [ None  ‘upper’  ‘lower’  ‘image’ ]
If None, the first value of Z will correspond to the lower left corner, location (0,0). If ‘image’, the rc value for
image.origin
will be used.This keyword is not active if X and Y are specified in the call to contour.
extent: [ None  (x0,x1,y0,y1) ]
If origin is not None, then extent is interpreted as in
matplotlib.pyplot.imshow()
: it gives the outer pixel boundaries. In this case, the position of Z[0,0] is the center of the pixel, not a corner. If origin is None, then (x0, y0) is the position of Z[0,0], and (x1, y1) is the position of Z[1,1].This keyword is not active if X and Y are specified in the call to contour.
 locator: [ None  ticker.Locator subclass ]
 If locator is None, the default
MaxNLocator
is used. The locator is used to determine the contour levels if they are not given explicitly via the V argument. extend: [ ‘neither’  ‘both’  ‘min’  ‘max’ ]
 Unless this is ‘neither’, contour levels are automatically added to one or both ends of the range so that all data are included. These added ranges are then mapped to the special colormap values which default to the ends of the colormap range, but can be set via
matplotlib.colors.Colormap.set_under()
andmatplotlib.colors.Colormap.set_over()
methods. xunits, yunits: [ None  registered units ]
 Override axis units by specifying an instance of a
matplotlib.units.ConversionInterface
. antialiased: [ True  False ]
 enable antialiasing, overriding the defaults. For filled contours, the default is True. For line contours, it is taken from rcParams[‘lines.antialiased’].
 nchunk: [ 0  integer ]
 If 0, no subdivision of the domain. Specify a positive integer to divide the domain into subdomains of nchunk by nchunk quads. Chunking reduces the maximum length of polygons generated by the contouring algorithm which reduces the rendering workload passed on to the backend and also requires slightly less RAM. It can however introduce rendering artifacts at chunk boundaries depending on the backend, the antialiased flag and value of alpha.
contouronly keyword arguments:
 linewidths: [ None  number  tuple of numbers ]
If linewidths is None, the default width in
lines.linewidth
inmatplotlibrc
is used.If a number, all levels will be plotted with this linewidth.
If a tuple, different levels will be plotted with different linewidths in the order specified.
 linestyles: [ None  ‘solid’  ‘dashed’  ‘dashdot’  ‘dotted’ ]
If linestyles is None, the default is ‘solid’ unless the lines are monochrome. In that case, negative contours will take their linestyle from the
matplotlibrc
contour.negative_linestyle
setting.linestyles can also be an iterable of the above strings specifying a set of linestyles to be used. If this iterable is shorter than the number of contour levels it will be repeated as necessary.
contourfonly keyword arguments:
 hatches:
 A list of cross hatch patterns to use on the filled areas. If None, no hatching will be added to the contour. Hatching is supported in the PostScript, PDF, SVG and Agg backends only.
Note: contourf fills intervals that are closed at the top; that is, for boundaries z1 and z2, the filled region is:
z1 < z <= z2
There is one exception: if the lowest boundary coincides with the minimum value of the z array, then that minimum value will be included in the lowest interval.
Examples:
(Source code, png, 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
Fs : scalar
window : callable or ndarray
sides : [ ‘default’  ‘onesided’  ‘twosided’ ]
pad_to : integer
NFFT : integer
detrend : {‘default’, ‘constant’, ‘mean’, ‘linear’, ‘none’} or callable
scale_by_freq : boolean, optional
noverlap : integer
Fc : integer
return_line : bool
**kwargs :



Returns:  Pxy : 1D array
freqs : 1D array
line : a

See also
Notes
For plotting, the power is plotted as
for decibels, though P_{xy}
itself
is returned.
References
Bendat & Piersol – Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986)
Examples
(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 y : scalar xerr/yerr : scalar or arraylike, shape(n,1) or shape(2,n), optional
fmt : plot format string, optional, default: None
ecolor : mpl color, optional, default: None
elinewidth : scalar, optional, default: None
capsize : scalar, optional, default: None
capthick : scalar, optional, default: None
barsabove : bool, optional, default: False
lolims / uplims / xlolims / xuplims : bool, optional, default:None
errorevery : positive integer, optional, default:1



Returns:  plotline :
caplines : list of
barlinecols : list of


Other Parameters:  
kwargs : All other keyword arguments are passed on to the plot

Examples
(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.
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 antialiasedsBoolean or sequence of booleans array
unknown axes
an Axes
instanceclim
a length 2 sequence of floats clip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]cmap
a colormap or registered colormap name color
matplotlib color arg or sequence of rgba tuples contains
a callable function edgecolor
or edgecolorsmatplotlib color spec or sequence of specs facecolor
or facecolorsmatplotlib color spec or sequence of specs figure
a matplotlib.figure.Figure
instancegid
an id string hatch
[ ‘/’  ‘\’  ‘’  ‘‘  ‘+’  ‘x’  ‘o’  ‘O’  ‘.’  ‘*’ ] label
string or anything printable with ‘%s’ conversion. linestyle
or dashes or linestyles[‘solid’  ‘dashed’, ‘dashdot’, ‘dotted’  (offset, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or linewidths or lwfloat or sequence of floats norm
unknown offset_position
unknown offsets
float or sequence of floats path_effects
unknown paths
unknown picker
[Nonefloatbooleancallable] pickradius
unknown rasterized
[True  False  None] segments
unknown sketch_params
unknown snap
unknown transform
Transform
instanceurl
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 rcimage.cmap
valuenorm a matplotlib.colors.Normalize
instance. The default is normalization(). This scales luminance > 01vminvmax are used to scale a luminance image to 01. If either is None, the min and max of the luminance values will be used. Note if you pass a norm instance, the settings for vmin and vmax will be ignored. alpha the alpha blending value, default is None origin [ ‘upper’  ‘lower’ ] Indicates where the [0,0] index of the array is in the upper left or lower left corner of the axes. Defaults to the rc image.origin value
figimage complements the axes image
(imshow()
) which will be resampled
to fit the current axes. If you want a resampled image to
fill the entire figure, you can define 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
instancebackgroundcolor
any matplotlib color bbox
FancyBboxPatch prop dict clip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
any matplotlib color contains
a callable function family
or fontfamily or fontname or name[FONTNAME  ‘serif’  ‘sansserif’  ‘cursive’  ‘fantasy’  ‘monospace’ ] figure
a matplotlib.figure.Figure
instancefontproperties
or font_propertiesa matplotlib.font_manager.FontProperties
instancegid
an id string horizontalalignment
or ha[ ‘center’  ‘right’  ‘left’ ] label
string or anything printable with ‘%s’ conversion. linespacing
float (multiple of font size) multialignment
[‘left’  ‘right’  ‘center’ ] path_effects
unknown picker
[Nonefloatbooleancallable] position
(x,y) rasterized
[True  False  None] rotation
[ angle in degrees  ‘vertical’  ‘horizontal’ ] rotation_mode
unknown size
or fontsize[size in points  ‘xxsmall’  ‘xsmall’  ‘small’  ‘medium’  ‘large’  ‘xlarge’  ‘xxlarge’ ] sketch_params
unknown snap
unknown stretch
or fontstretch[a numeric value in range 01000  ‘ultracondensed’  ‘extracondensed’  ‘condensed’  ‘semicondensed’  ‘normal’  ‘semiexpanded’  ‘expanded’  ‘extraexpanded’  ‘ultraexpanded’ ] style
or fontstyle[ ‘normal’  ‘italic’  ‘oblique’] text
string or anything printable with ‘%s’ conversion. transform
Transform
instanceurl
a url string usetex
unknown variant
or fontvariant[ ‘normal’  ‘smallcaps’ ] verticalalignment
or ma or va[ ‘center’  ‘top’  ‘bottom’  ‘baseline’ ] visible
[True  False] weight
or fontweight[a numeric value in range 01000  ‘ultralight’  ‘light’  ‘normal’  ‘regular’  ‘book’  ‘medium’  ‘roman’  ‘semibold’  ‘demibold’  ‘demi’  ‘bold’  ‘heavy’  ‘extra bold’  ‘black’ ] wrap
unknown x
float y
float zorder
any number
matplotlib.pyplot.
figure
(num=None, figsize=None, dpi=None, facecolor=None, edgecolor=None, frameon=True, FigureClass=<class 'matplotlib.figure.Figure'>, **kwargs)¶Creates a new figure.
Parameters:  num : integer or string, optional, default: none
figsize : tuple of integers, optional, default: None
dpi : integer, optional, default: None
facecolor :
edgecolor :


Returns:  figure : Figure

Notes
If you are creating many figures, make sure you explicitly call “close” on the figures you are not using, because this will enable pylab to properly clean up the memory.
rcParams defines the default values, which can be modified in the matplotlibrc file
matplotlib.pyplot.
fill
(*args, **kwargs)¶Plot filled polygons.
Parameters:  args : a variable length argument


Returns:  a list of 
Other Parameters:  
kwargs : 
Notes
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()
Examples
(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
y1 : array
y2 : array
where : array, optional
interpolate : bool, optional
step : {‘pre’, ‘post’, ‘mid’}, optional


See also
Notes
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 antialiasedsBoolean or sequence of booleans array
unknown axes
an Axes
instanceclim
a length 2 sequence of floats clip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]cmap
a colormap or registered colormap name color
matplotlib color arg or sequence of rgba tuples contains
a callable function edgecolor
or edgecolorsmatplotlib color spec or sequence of specs facecolor
or facecolorsmatplotlib color spec or sequence of specs figure
a matplotlib.figure.Figure
instancegid
an id string hatch
[ ‘/’  ‘\’  ‘’  ‘‘  ‘+’  ‘x’  ‘o’  ‘O’  ‘.’  ‘*’ ] label
string or anything printable with ‘%s’ conversion. linestyle
or dashes or linestyles[‘solid’  ‘dashed’, ‘dashdot’, ‘dotted’  (offset, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or linewidths or lwfloat or sequence of floats norm
unknown offset_position
unknown offsets
float or sequence of floats path_effects
unknown picker
[Nonefloatbooleancallable] pickradius
unknown rasterized
[True  False  None] sketch_params
unknown snap
unknown transform
Transform
instanceurl
a url string urls
unknown visible
[True  False] zorder
any number
Examples
matplotlib.pyplot.
fill_betweenx
(y, x1, x2=0, where=None, step=None, hold=None, data=None, **kwargs)¶Make filled polygons between two horizontal curves.
Create a PolyCollection
filling the regions between x1 and x2 where
where==True
Parameters:  y : array
x1 : array
x2 : array, optional
where : array, optional
step : {‘pre’, ‘post’, ‘mid’}, optional


See also
Notes
PolyCollection
kwargs control the Polygon
properties:
Property Description agg_filter
unknown alpha
float or None animated
[True  False] antialiased
or antialiasedsBoolean or sequence of booleans array
unknown axes
an Axes
instanceclim
a length 2 sequence of floats clip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]cmap
a colormap or registered colormap name color
matplotlib color arg or sequence of rgba tuples contains
a callable function edgecolor
or edgecolorsmatplotlib color spec or sequence of specs facecolor
or facecolorsmatplotlib color spec or sequence of specs figure
a matplotlib.figure.Figure
instancegid
an id string hatch
[ ‘/’  ‘\’  ‘’  ‘‘  ‘+’  ‘x’  ‘o’  ‘O’  ‘.’  ‘*’ ] label
string or anything printable with ‘%s’ conversion. linestyle
or dashes or linestyles[‘solid’  ‘dashed’, ‘dashdot’, ‘dotted’  (offset, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or linewidths or lwfloat or sequence of floats norm
unknown offset_position
unknown offsets
float or sequence of floats path_effects
unknown picker
[Nonefloatbooleancallable] pickradius
unknown rasterized
[True  False  None] sketch_params
unknown snap
unknown transform
Transform
instanceurl
a url string urls
unknown visible
[True  False] zorder
any number
Examples
matplotlib.pyplot.
findobj
(o=None, match=None, include_self=True)¶Find artist objects.
Recursively find all Artist
instances
contained in self.
match can be
 None: return all objects contained in artist.
 function with signature
boolean = match(artist)
used to filter matches class instance: e.g., Line2D. Only return artists of class type.
If include_self is True (default), include self in the list to be checked for a match.
matplotlib.pyplot.
flag
()¶set the default colormap to flag and apply to current image if any. See help(colormaps) for more information
matplotlib.pyplot.
gca
(**kwargs)¶Get the current Axes
instance on the
current figure matching the given keyword args, or create one.
See also
matplotlib.figure.Figure.gca
Examples
To get the current polar axes on the current figure:
plt.gca(projection='polar')
If the current axes doesn’t exist, or isn’t a polar one, the appropriate axes will be created and then returned.
matplotlib.pyplot.
gcf
()¶Get a reference to the current figure.
matplotlib.pyplot.
gci
()¶Get the current colorable artist. Specifically, returns the
current ScalarMappable
instance (image or
patch collection), or None if no images or patch collections
have been defined. The 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
instanceclip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
or cany matplotlib color contains
a callable function dash_capstyle
[‘butt’  ‘round’  ‘projecting’] dash_joinstyle
[‘miter’  ‘round’  ‘bevel’] dashes
sequence of on/off ink in points drawstyle
[‘default’  ‘steps’  ‘stepspre’  ‘stepsmid’  ‘stepspost’] figure
a matplotlib.figure.Figure
instancefillstyle
[‘full’  ‘left’  ‘right’  ‘bottom’  ‘top’  ‘none’] gid
an id string label
string or anything printable with ‘%s’ conversion. linestyle
or ls[‘solid’  ‘dashed’, ‘dashdot’, ‘dotted’  (offset, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or lwfloat value in points marker
A valid marker style
markeredgecolor
or mecany matplotlib color markeredgewidth
or mewfloat value in points markerfacecolor
or mfcany matplotlib color markerfacecoloralt
or mfcaltany matplotlib color markersize
or msfloat markevery
[None  int  length2 tuple of int  slice  list/array of int  float  length2 tuple of float] path_effects
unknown picker
float distance in points or callable pick function fn(artist, event)
pickradius
float distance in points rasterized
[True  False  None] sketch_params
unknown snap
unknown solid_capstyle
[‘butt’  ‘round’  ‘projecting’] solid_joinstyle
[‘miter’  ‘round’  ‘bevel’] transform
a matplotlib.transforms.Transform
instanceurl
a url string visible
[True  False] xdata
1D array ydata
1D array zorder
any number
matplotlib.pyplot.
hexbin
(x, y, C=None, gridsize=100, bins=None, xscale='linear', yscale='linear', extent=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, edgecolors='none', reduce_C_function=<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
bins : {‘log’} or int or sequence, optional, default is None
xscale : {‘linear’, ‘log’}, optional, default is ‘linear’
yscale : {‘linear’, ‘log’}, optional, default is ‘linear’
mincnt : int > 0, optional, default is None
marginals : bool, optional, default is False
extent : scalar, optional, default is None


Returns:  object

Other Parameters:  
cmap : object, optional, default is None
norm : object, optional, default is None
vmin, vmax : scalar, optional, default is None
alpha : scalar between 0 and 1, optional, default is None
linewidths : scalar, optional, default is None
edgecolors : {‘none’} or mpl color, optional, default is ‘none’

Notes
The standard descriptions of all the
Collection
parameters:
Property Description agg_filter
unknown alpha
float or None animated
[True  False] antialiased
or antialiasedsBoolean or sequence of booleans array
unknown axes
an Axes
instanceclim
a length 2 sequence of floats clip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]cmap
a colormap or registered colormap name color
matplotlib color arg or sequence of rgba tuples contains
a callable function edgecolor
or edgecolorsmatplotlib color spec or sequence of specs facecolor
or facecolorsmatplotlib color spec or sequence of specs figure
a matplotlib.figure.Figure
instancegid
an id string hatch
[ ‘/’  ‘\’  ‘’  ‘‘  ‘+’  ‘x’  ‘o’  ‘O’  ‘.’  ‘*’ ] label
string or anything printable with ‘%s’ conversion. linestyle
or dashes or linestyles[‘solid’  ‘dashed’, ‘dashdot’, ‘dotted’  (offset, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or linewidths or lwfloat or sequence of floats norm
unknown offset_position
unknown offsets
float or sequence of floats path_effects
unknown picker
[Nonefloatbooleancallable] pickradius
unknown rasterized
[True  False  None] sketch_params
unknown snap
unknown transform
Transform
instanceurl
a url string urls
unknown visible
[True  False] zorder
any number
Note
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>]:
Examples
(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
bins : integer or array_like or ‘auto’, optional
range : tuple or None, optional
normed : boolean, optional
weights : (n, ) array_like or None, optional
cumulative : boolean, optional
bottom : array_like, scalar, or None
histtype : {‘bar’, ‘barstacked’, ‘step’, ‘stepfilled’}, optional
align : {‘left’, ‘mid’, ‘right’}, optional
orientation : {‘horizontal’, ‘vertical’}, optional
rwidth : scalar or None, optional
log : boolean, optional
color : color or array_like of colors or None, optional
label : string or None, optional
stacked : boolean, optional


Returns:  n : array or list of arrays
bins : array
patches : list or list of lists

Other Parameters:  
kwargs : 
See also
hist2d
Notes
Until numpy release 1.5, the underlying numpy histogram function was
incorrect with normed`=`True
if bin sizes were unequal. MPL
inherited that error. It is now corrected within MPL when using
earlier numpy versions.
Examples
(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, )
bins: [None  int  [int, int]  array_like  [array, array]]
range : array_like shape(2, 2), optional, default: None
normed : boolean, optional, default: False
weights : array_like, shape (n, ), optional, default: None
cmin : scalar, optional, default: None
cmax : scalar, optional, default: None


Returns:  The return value is 
Other Parameters:  
kwargs : 
See also
hist
Notes
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
.
Examples
(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
xmin, xmax : scalar or 1D array_like
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
Examples
(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)
cmap :
aspect : [‘auto’  ‘equal’  scalar], optional, default: None
interpolation : string, optional, default: None
norm : vmin, vmax : scalar, optional, default: None
alpha : scalar, optional, default: None
origin : [‘upper’  ‘lower’], optional, default: None
extent : scalars (left, right, bottom, top), optional, default: None
shape : scalars (columns, rows), optional, default: None
filternorm : scalar, optional, default: 1
filterrad : scalar, optional, default: 4.0


Returns:  image : 
Other Parameters:  
kwargs : 
See also
matshow
Notes
Unless extent is used, pixel centers will be located at integer coordinates. In other words: the origin will coincide with the center of pixel (0, 0).
Examples
(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’
bbox_to_anchor :
ncol : integer
prop : None or
fontsize : int or float or {‘xxsmall’, ‘xsmall’, ‘small’, ‘medium’, ‘large’, ‘xlarge’, ‘xxlarge’}
numpoints : None or int
scatterpoints : None or int
scatteryoffsets : iterable of floats
markerscale : None or int or float
markerfirst : bool
frameon : None or bool
fancybox : None or bool
shadow : None or bool
framealpha : None or float
facecolor : None or “inherit” or a color spec edgecolor : None or “inherit” or a color spec mode : {“expand”, None}
bbox_transform : None or
title : str or None
borderpad : float or None
labelspacing : float or None
handlelength : float or None
handletextpad : float or None
borderaxespad : float or None
columnspacing : float or None
handler_map : dict or None


Notes
Not all kinds of artist are supported by the legend command. See Legend guide for details.
Examples
(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:
 basex/basey: scalar > 1
 Base of the x/y logarithm
 subsx/subsy: [ None  sequence ]
 The location of the minor x/y ticks; None defaults to autosubs, which depend on the number of decades in the plot; see
matplotlib.axes.Axes.set_xscale()
/matplotlib.axes.Axes.set_yscale()
for details nonposx/nonposy: [‘mask’  ‘clip’ ]
 Nonpositive values in x or y can be masked as invalid, or clipped to a very small positive number
The remaining valid kwargs are
Line2D
properties:
Property Description agg_filter
unknown alpha
float (0.0 transparent through 1.0 opaque) animated
[True  False] antialiased
or aa[True  False] axes
an Axes
instanceclip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
or cany matplotlib color contains
a callable function dash_capstyle
[‘butt’  ‘round’  ‘projecting’] dash_joinstyle
[‘miter’  ‘round’  ‘bevel’] dashes
sequence of on/off ink in points drawstyle
[‘default’  ‘steps’  ‘stepspre’  ‘stepsmid’  ‘stepspost’] figure
a matplotlib.figure.Figure
instancefillstyle
[‘full’  ‘left’  ‘right’  ‘bottom’  ‘top’  ‘none’] gid
an id string label
string or anything printable with ‘%s’ conversion. linestyle
or ls[‘solid’  ‘dashed’, ‘dashdot’, ‘dotted’  (offset, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or lwfloat value in points marker
A valid marker style
markeredgecolor
or mecany matplotlib color markeredgewidth
or mewfloat value in points markerfacecolor
or mfcany matplotlib color markerfacecoloralt
or mfcaltany matplotlib color markersize
or msfloat markevery
[None  int  length2 tuple of int  slice  list/array of int  float  length2 tuple of float] path_effects
unknown picker
float distance in points or callable pick function fn(artist, event)
pickradius
float distance in points rasterized
[True  False  None] sketch_params
unknown snap
unknown solid_capstyle
[‘butt’  ‘round’  ‘projecting’] solid_joinstyle
[‘miter’  ‘round’  ‘bevel’] transform
a matplotlib.transforms.Transform
instanceurl
a url string visible
[True  False] xdata
1D array ydata
1D array zorder
any number
Example:
(Source code, png, 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
Fs : scalar
window : callable or ndarray
sides : [ ‘default’  ‘onesided’  ‘twosided’ ]
pad_to : integer
scale : [ ‘default’  ‘linear’  ‘dB’ ]
Fc : integer
**kwargs :



Returns:  spectrum : 1D array
freqs : 1D array
line : a

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.
Examples
(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:
 cmap: [ None  Colormap ]
 A
matplotlib.colors.Colormap
instance. If None, use rc settings. norm: [ None  Normalize ]
 An
matplotlib.colors.Normalize
instance is used to scale luminance data to 0,1. If None, defaults tonormalize()
. vmin/vmax: [ None  scalar ]
 vmin and vmax are used in conjunction with norm to normalize luminance data. If either is None, it is autoscaled to the respective min or max of the color array C. If not None, vmin or vmax passed in here override any preexisting values supplied in the norm instance.
 shading: [ ‘flat’  ‘faceted’ ]
If ‘faceted’, a black grid is drawn around each rectangle; if ‘flat’, edges are not drawn. Default is ‘flat’, contrary to MATLAB.
 This kwarg is deprecated; please use ‘edgecolors’ instead:
 shading=’flat’ – edgecolors=’none’
 shading=’faceted – edgecolors=’k’
 edgecolors: [ None 
'none'
 color  color sequence]If None, the rc setting is used by default.
If
'none'
, edges will not be visible.An mpl color or sequence of colors will set the edge color
 alpha:
0 <= scalar <= 1
or None the alpha blending value
 snap: bool
 Whether to snap the mesh to pixel boundaries.
Return value is a matplotlib.collections.Collection
instance.
The grid orientation follows the MATLAB convention: an array C with shape (nrows, ncolumns) is plotted with the column number as X and the row number as Y, increasing up; hence it is plotted the way the array would be printed, except that the Y axis is reversed. That is, C is taken as C*(*y, x).
Similarly for meshgrid()
:
x = np.arange(5)
y = np.arange(3)
X, Y = np.meshgrid(x, y)
is equivalent to:
X = array([[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4]])
Y = array([[0, 0, 0, 0, 0],
[1, 1, 1, 1, 1],
[2, 2, 2, 2, 2]])
so if you have:
C = rand(len(x), len(y))
then you need to transpose C:
pcolor(X, Y, C.T)
or:
pcolor(C.T)
MATLAB pcolor()
always discards the last row and column
of C, but matplotlib displays the last row and column if X and
Y are not specified, or if X and Y have one more row and
column than C.
kwargs can be used to control the
PolyCollection
properties:
Property Description agg_filter
unknown alpha
float or None animated
[True  False] antialiased
or antialiasedsBoolean or sequence of booleans array
unknown axes
an Axes
instanceclim
a length 2 sequence of floats clip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]cmap
a colormap or registered colormap name color
matplotlib color arg or sequence of rgba tuples contains
a callable function edgecolor
or edgecolorsmatplotlib color spec or sequence of specs facecolor
or facecolorsmatplotlib color spec or sequence of specs figure
a matplotlib.figure.Figure
instancegid
an id string hatch
[ ‘/’  ‘\’  ‘’  ‘‘  ‘+’  ‘x’  ‘o’  ‘O’  ‘.’  ‘*’ ] label
string or anything printable with ‘%s’ conversion. linestyle
or dashes or linestyles[‘solid’  ‘dashed’, ‘dashdot’, ‘dotted’  (offset, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or linewidths or lwfloat or sequence of floats norm
unknown offset_position
unknown offsets
float or sequence of floats path_effects
unknown picker
[Nonefloatbooleancallable] pickradius
unknown rasterized
[True  False  None] sketch_params
unknown snap
unknown transform
Transform
instanceurl
a url string urls
unknown visible
[True  False] zorder
any number
Note
The default antialiaseds is False if the default edgecolors*=”none” is used. This eliminates artificial lines at patch boundaries, and works regardless of the value of alpha. If *edgecolors is not “none”, then the default antialiaseds is taken from rcParams[‘patch.antialiased’], which defaults to True. Stroking the edges may be preferred if alpha is 1, but will cause artifacts otherwise.
See also
pcolormesh()
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:
 cmap: [ None  Colormap ]
 A
matplotlib.colors.Colormap
instance. If None, use rc settings. norm: [ None  Normalize ]
 A
matplotlib.colors.Normalize
instance is used to scale luminance data to 0,1. If None, defaults tonormalize()
. vmin/vmax: [ None  scalar ]
 vmin and vmax are used in conjunction with norm to normalize luminance data. If either is None, it is autoscaled to the respective min or max of the color array C. If not None, vmin or vmax passed in here override any preexisting values supplied in the norm instance.
 shading: [ ‘flat’  ‘gouraud’ ]
 ‘flat’ indicates a solid color for each quad. When ‘gouraud’, each quad will be Gouraud shaded. When gouraud shading, edgecolors is ignored.
 edgecolors: [None 
'None'
'face'
 color  color sequence]If None, the rc setting is used by default.
If
'None'
, edges will not be visible.If
'face'
, edges will have the same color as the faces.An mpl color or sequence of colors will set the edge color
 alpha:
0 <= scalar <= 1
or None the alpha blending value
Return value is a matplotlib.collections.QuadMesh
object.
kwargs can be used to control the
matplotlib.collections.QuadMesh
properties:
Property Description agg_filter
unknown alpha
float or None animated
[True  False] antialiased
or antialiasedsBoolean or sequence of booleans array
unknown axes
an Axes
instanceclim
a length 2 sequence of floats clip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]cmap
a colormap or registered colormap name color
matplotlib color arg or sequence of rgba tuples contains
a callable function edgecolor
or edgecolorsmatplotlib color spec or sequence of specs facecolor
or facecolorsmatplotlib color spec or sequence of specs figure
a matplotlib.figure.Figure
instancegid
an id string hatch
[ ‘/’  ‘\’  ‘’  ‘‘  ‘+’  ‘x’  ‘o’  ‘O’  ‘.’  ‘*’ ] label
string or anything printable with ‘%s’ conversion. linestyle
or dashes or linestyles[‘solid’  ‘dashed’, ‘dashdot’, ‘dotted’  (offset, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or linewidths or lwfloat or sequence of floats norm
unknown offset_position
unknown offsets
float or sequence of floats path_effects
unknown picker
[Nonefloatbooleancallable] pickradius
unknown rasterized
[True  False  None] sketch_params
unknown snap
unknown transform
Transform
instanceurl
a url string urls
unknown visible
[True  False] zorder
any number
See also
pcolor()
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
Fs : scalar
window : callable or ndarray
sides : [ ‘default’  ‘onesided’  ‘twosided’ ]
pad_to : integer
Fc : integer
**kwargs :



Returns:  spectrum : 1D array
freqs : 1D array
line : a

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.
Examples
(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.
Keyword arguments:
 explode: [ None  len(x) sequence ]
 If not None, is a
len(x)
array which specifies the fraction of the radius with which to offset each wedge. colors: [ None  color sequence ]
 A sequence of matplotlib color args through which the pie chart will cycle. If
None
, will use the colors in the currently active cycle. labels: [ None  len(x) sequence of strings ]
 A sequence of strings providing the labels for each wedge
 autopct: [ None  format string  format function ]
 If not None, is a string or function used to label the wedges with their numeric value. The label will be placed inside the wedge. If it is a format string, the label will be
fmt%pct
. If it is a function, it will be called. pctdistance: scalar
 The ratio between the center of each pie slice and the start of the text generated by autopct. Ignored if autopct is None; default is 0.6.
 labeldistance: scalar
 The radial distance at which the pie labels are drawn
 shadow: [ False  True ]
 Draw a shadow beneath the pie.
 startangle: [ None  Offset angle ]
 If not None, rotates the start of the pie chart by angle degrees counterclockwise from the xaxis.
radius: [ None  scalar ] The radius of the pie, if radius is None it will be set to 1.
 counterclock: [ False  True ]
 Specify fractions direction, clockwise or counterclockwise.
 wedgeprops: [ None  dict of key value pairs ]
 Dict of arguments passed to the wedge objects making the pie. For example, you can pass in wedgeprops = { ‘linewidth’ : 3 } to set the width of the wedge border lines equal to 3. For more details, look at the doc/arguments of the wedge object. By default
clip_on=False
. textprops: [ None  dict of key value pairs ]
 Dict of arguments to pass to the text objects.
center: [ (0,0)  sequence of 2 scalars ] Center position of the chart.
 frame: [ False  True ]
 Plot axes frame with the chart.
The pie chart will probably look best if the figure and axes are square, or the Axes aspect is equal. e.g.:
figure(figsize=(8,8))
ax = axes([0.1, 0.1, 0.8, 0.8])
or:
axes(aspect=1)
If autopct is None, return the tuple (patches, texts):
 patches is a sequence of
matplotlib.patches.Wedge
instances texts is a list of the label
matplotlib.text.Text
instances.
If autopct is not None, return the tuple (patches,
texts, autotexts), where patches and texts are as
above, and autotexts is a list of
Text
instances for the numeric
labels.
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
instanceclip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]color
or cany matplotlib color contains
a callable function dash_capstyle
[‘butt’  ‘round’  ‘projecting’] dash_joinstyle
[‘miter’  ‘round’  ‘bevel’] dashes
sequence of on/off ink in points drawstyle
[‘default’  ‘steps’  ‘stepspre’  ‘stepsmid’  ‘stepspost’] figure
a matplotlib.figure.Figure
instancefillstyle
[‘full’  ‘left’  ‘right’  ‘bottom’  ‘top’  ‘none’] gid
an id string label
string or anything printable with ‘%s’ conversion. linestyle
or ls[‘solid’  ‘dashed’, ‘dashdot’, ‘dotted’  (offset, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or lwfloat value in points marker
A valid marker style
markeredgecolor
or mecany matplotlib color markeredgewidth
or mewfloat value in points markerfacecolor
or mfcany matplotlib color markerfacecoloralt
or mfcaltany matplotlib color markersize
or msfloat markevery
[None  int  length2 tuple of int  slice  list/array of int  float  length2 tuple of float] path_effects
unknown picker
float distance in points or callable pick function fn(artist, event)
pickradius
float distance in points rasterized
[True  False  None] sketch_params
unknown snap
unknown solid_capstyle
[‘butt’  ‘round’  ‘projecting’] solid_joinstyle
[‘miter’  ‘round’  ‘bevel’] transform
a matplotlib.transforms.Transform
instanceurl
a url string visible
[True  False] xdata
1D array ydata
1D array zorder
any number
kwargs scalex and scaley, if defined, are passed on to
autoscale_view()
to determine
whether the x and y axes are autoscaled; the default is
True.
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
tz : [ None  timezone string 
xdate : boolean
ydate : boolean



Returns:  lines 

Other Parameters:  
kwargs : properties :
.. note::

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
Fs : scalar
window : callable or ndarray
sides : [ ‘default’  ‘onesided’  ‘twosided’ ]
pad_to : integer
NFFT : integer
detrend : {‘default’, ‘constant’, ‘mean’, ‘linear’, ‘none’} or callable
scale_by_freq : boolean, optional
noverlap : integer
Fc : integer
return_line : bool
**kwargs :



Returns:  Pxx : 1D array
freqs : 1D array
line : a

See also
specgram()
specgram()
differs in the default overlap; in not returning the mean of the segment periodograms; in returning the times of the segments; and in plotting a colormap instead of a line.magnitude_spectrum()
magnitude_spectrum()
plots the magnitude spectrum.csd()
csd()
plots the spectral density between two signals.
Notes
For plotting, the power is plotted as for decibels, though Pxx itself is returned.
References
Bendat & Piersol – Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986)
Examples
(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)
Arguments:
 X, Y:
 The x and y coordinates of the arrow locations (default is tail of arrow; see pivot kwarg)
 U, V:
 Give the x and y components of the arrow vectors
 C:
 An optional array used to map colors to the arrows
All arguments may be 1D or 2D arrays or sequences. If X and Y
are absent, they will be generated as a uniform grid. If U and V
are 2D arrays but X and Y are 1D, and if len(X)
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:
 units: [ ‘width’  ‘height’  ‘dots’  ‘inches’  ‘x’  ‘y’  ‘xy’ ]
Arrow units; the arrow dimensions except for length are in multiples of this unit.
 ‘width’ or ‘height’: the width or height of the axes
 ‘dots’ or ‘inches’: pixels or inches, based on the figure dpi
 ‘x’, ‘y’, or ‘xy’: X, Y, or sqrt(X^2+Y^2) data units
The arrows scale differently depending on the units. For ‘x’ or ‘y’, the arrows get larger as one zooms in; for other units, the arrow size is independent of the zoom state. For ‘width or ‘height’, the arrow size increases with the width and height of the axes, respectively, when the window is resized; for ‘dots’ or ‘inches’, resizing does not change the arrows.
 angles: [ ‘uv’  ‘xy’  array ]
 With the default ‘uv’, the arrow axis aspect ratio is 1, so that if U*==*V the orientation of the arrow on the plot is 45 degrees CCW from the horizontal axis (positive to the right). With ‘xy’, the arrow points from (x,y) to (x+u, y+v). Use this for plotting a gradient field, for example. Alternatively, arbitrary angles may be specified as an array of values in degrees, CCW from the horizontal axis. Note: inverting a data axis will correspondingly invert the arrows only with
angles='xy'
. scale: [ None  float ]
 Data units per arrow length unit, e.g., m/s per plot width; a smaller scale parameter makes the arrow longer. If None, a simple autoscaling algorithm is used, based on the average vector length and the number of vectors. The arrow length unit is given by the scale_units parameter
 scale_units: None, or any of the units options.
For example, if scale_units is ‘inches’, scale is 2.0, and
(u,v) = (1,0)
, then the vector will be 0.5 inches long. If scale_units is ‘width’, then the vector will be half the width of the axes.If scale_units is ‘x’ then the vector will be 0.5 xaxis units. To plot vectors in the xy plane, with u and v having the same units as x and y, use “angles=’xy’, scale_units=’xy’, scale=1”.
 width:
 Shaft width in arrow units; default depends on choice of units, above, and number of vectors; a typical starting value is about 0.005 times the width of the plot.
 headwidth: scalar
 Head width as multiple of shaft width, default is 3
 headlength: scalar
 Head length as multiple of shaft width, default is 5
 headaxislength: scalar
 Head length at shaft intersection, default is 4.5
 minshaft: scalar
 Length below which arrow scales, in units of head length. Do not set this to less than 1, or small arrows will look terrible! Default is 1
 minlength: scalar
 Minimum length as a multiple of shaft width; if an arrow length is less than this, plot a dot (hexagon) of this diameter instead. Default is 1.
 pivot: [ ‘tail’  ‘mid’  ‘middle’  ‘tip’ ]
 The part of the arrow that is at the grid point; the arrow rotates about this point, hence the name pivot.
 color: [ color  color sequence ]
 This is a synonym for the
PolyCollection
facecolor kwarg. If C has been set, color has no effect.
The defaults give a slightly sweptback arrow; to make the head a triangle, make headaxislength the same as headlength. To make the arrow more pointed, reduce headwidth or increase headlength and headaxislength. To make the head smaller relative to the shaft, scale down all the head parameters. You will probably do best to leave minshaft alone.
linewidths and edgecolors can be used to customize the arrow
outlines. Additional PolyCollection
keyword arguments:
Property Description agg_filter
unknown alpha
float or None animated
[True  False] antialiased
or antialiasedsBoolean or sequence of booleans array
unknown axes
an Axes
instanceclim
a length 2 sequence of floats clip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]cmap
a colormap or registered colormap name color
matplotlib color arg or sequence of rgba tuples contains
a callable function edgecolor
or edgecolorsmatplotlib color spec or sequence of specs facecolor
or facecolorsmatplotlib color spec or sequence of specs figure
a matplotlib.figure.Figure
instancegid
an id string hatch
[ ‘/’  ‘\’  ‘’  ‘‘  ‘+’  ‘x’  ‘o’  ‘O’  ‘.’  ‘*’ ] label
string or anything printable with ‘%s’ conversion. linestyle
or dashes or linestyles[‘solid’  ‘dashed’, ‘dashdot’, ‘dotted’  (offset, onoffdashseq)  ''
''
'.'
':'
'None'
' '
''
]linewidth
or linewidths or lwfloat or sequence of floats norm
unknown offset_position
unknown offsets
float or sequence of floats path_effects
unknown picker
[Nonefloatbooleancallable] pickradius
unknown rasterized
[True  False  None] sketch_params
unknown snap
unknown transform
Transform
instanceurl
a url string urls
unknown visible
[True  False] zorder
any number
matplotlib.pyplot.
quiverkey
(*args, **kw)¶Add a key to a quiver plot.
Call signature:
quiverkey(Q, X, Y, U, label, **kw)
Arguments:
 Q:
 The Quiver instance returned by a call to quiver.
 X, Y:
 The location of the key; additional explanation follows.
 U:
 The length of the key
 label:
 A string with the length and units of the key
Keyword arguments:
 coordinates = [ ‘axes’  ‘figure’  ‘data’  ‘inches’ ]
 Coordinate system and units for X, Y: ‘axes’ and ‘figure’ are normalized coordinate systems with 0,0 in the lower left and 1,1 in the upper right; ‘data’ are the axes data coordinates (used for the locations of the vectors in the quiver plot itself); ‘inches’ is position in the figure in inches, with 0,0 at the lower left corner.
 color:
 overrides face and edge colors from Q.
 labelpos = [ ‘N’  ‘S’  ‘E’  ‘W’ ]
 Position the label above, below, to the right, to the left of the arrow, respectively.
 labelsep:
 Distance in inches between the arrow and the label. Default is 0.1
 labelcolor:
 defaults to default
Text
color. fontproperties:
 A dictionary with keyword arguments accepted by the
FontProperties
initializer: family, style, variant, size, weight
Any additional keyword arguments are used to override vector properties taken from Q.
The positioning of the key depends on X, Y, coordinates, and labelpos. If labelpos is ‘N’ or ‘S’, X, Y give the position of the middle of the key arrow. If labelpos is ‘E’, X, Y positions the head, and if labelpos is ‘W’, X, Y positions the tail; in either of these two cases, X, Y is somewhere in the middle of the arrow+label key object.
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 rcdefaults()
to restore the default
rc params after changes.
matplotlib.pyplot.
rc_context
(rc=None, fname=None)¶Return a context manager for managing rc settings.
This allows one to do:
with mpl.rc_context(fname='screen.rc'):
plt.plot(x, a)
with mpl.rc_context(fname='print.rc'):
plt.plot(x, b)
plt.plot(x, c)
The ‘a’ vs ‘x’ and ‘c’ vs ‘x’ plots would have settings from ‘screen.rc’, while the ‘b’ vs ‘x’ plot would have settings from ‘print.rc’.
A dictionary can also be passed to the context manager:
with mpl.rc_context(rc={'text.usetex': True}, fname='screen.rc'):
plt.plot(x, a)
The ‘rc’ dictionary takes precedence over the settings loaded from ‘fname’. Passing a dictionary only is also valid.
matplotlib.pyplot.
rcdefaults
()¶Restore the default rc params. These are not the params loaded by the rc file, but mpl’s internal params. See rc_file_defaults for reloading the default params from the rc file
matplotlib.pyplot.
rgrids
(*args, **kwargs)¶Get or set the radial gridlines on a polar plot.
call signatures:
lines, labels = rgrids()
lines, labels = rgrids(radii, labels=None, angle=22.5, **kwargs)
When called with no arguments, rgrid()
simply returns the
tuple (lines, labels), where lines is an array of radial
gridlines (Line2D
instances) and
labels is an array of tick labels
(Text
instances). When called with
arguments, the labels will appear at the specified radial
distances and angles.
labels, if not None, is a len(radii) list of strings of the labels to use at each angle.
If labels is None, the rformatter will be used
Examples:
# set the locations of the radial gridlines and labels
lines, labels = rgrids( (0.25, 0.5, 1.0) )
# set the locations and labels of the radial gridlines and labels
lines, labels = rgrids( (0.25, 0.5, 1.0), ('Tom', 'Dick', 'Harry' )
matplotlib.pyplot.
savefig
(*args, **kwargs)¶Save the current figure.
Call signature:
savefig(fname, dpi=None, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches=None, pad_inches=0.1,
frameon=None)
The output formats available depend on the backend being used.
Arguments:
 fname:
A string containing a path to a filename, or a Python filelike object, or possibly some backenddependent object such as
PdfPages
.If format is None and fname is a string, the output format is deduced from the extension of the filename. If the filename has no extension, the value of the rc parameter
savefig.format
is used.If fname is not a string, remember to specify format to ensure that the correct backend is used.
Keyword arguments:
 dpi: [ None 
scalar > 0
 ‘figure’] The resolution in dots per inch. If None it will default to the value
savefig.dpi
in the matplotlibrc file. If ‘figure’ it will set the dpi to be the value of the figure. facecolor, edgecolor:
 the colors of the figure rectangle
 orientation: [ ‘landscape’  ‘portrait’ ]
 not supported on all backends; currently only on postscript output
 papertype:
 One of ‘letter’, ‘legal’, ‘executive’, ‘ledger’, ‘a0’ through ‘a10’, ‘b0’ through ‘b10’. Only supported for postscript output.
 format:
 One of the file extensions supported by the active backend. Most backends support png, pdf, ps, eps and svg.
 transparent:
 If True, the axes patches will all be transparent; the figure patch will also be transparent unless facecolor and/or edgecolor are specified via kwargs. This is useful, for example, for displaying a plot on top of a colored background on a web page. The transparency of these patches will be restored to their original values upon exit of this function.
 frameon:
 If True, the figure patch will be colored, if False, the figure background will be transparent. If not provided, the rcParam ‘savefig.frameon’ will be used.
 bbox_inches:
 Bbox in inches. Only the given portion of the figure is saved. If ‘tight’, try to figure out the tight bbox of the figure.
 pad_inches:
 Amount of padding around the figure when bbox_inches is ‘tight’.
 bbox_extra_artists:
 A list of extra artists that will be considered when the tight bbox is calculated.
matplotlib.pyplot.
sca
(ax)¶Set the current Axes instance to ax.
The current Figure is updated to the parent of ax.
matplotlib.pyplot.
scatter
(x, y, s=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, )
s : scalar or array_like, shape (n, ), optional
c : color, sequence, or sequence of color, optional, default: ‘b’
marker :
cmap :
norm :
vmin, vmax : scalar, optional, default: None
alpha : scalar, optional, default: None
linewidths : scalar or array_like, optional, default: None
verts : sequence of (x, y), optional
edgecolors : color or sequence of color, optional, default: None


Returns:  paths : 
Other Parameters:  
kwargs : 
See also
plot
Notes
The plot
function will be faster for scatterplots where markers
don’t vary in size or color.
Any or all of x
, y
, s
, and c
may be masked arrays, in which
case all masks will be combined and only unmasked points will be
plotted.
Fundamentally, scatter works with 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
.
Examples
(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
subsx : array_like, optional
nonposx : string, optional, {‘mask’, ‘clip’}



Returns: 


Other Parameters:  
:class:`~matplotlib.lines.Line2D` properties:

See also
loglog
Notes
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


Returns: 

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
Fs : scalar
window : callable or ndarray
sides : [ ‘default’  ‘onesided’  ‘twosided’ ]
pad_to : integer
NFFT : integer
detrend : {‘default’, ‘constant’, ‘mean’, ‘linear’, ‘none’} or callable
scale_by_freq : boolean, optional
mode : [ ‘default’  ‘psd’  ‘magnitude’  ‘angle’  ‘phase’ ]
noverlap : integer
scale : [ ‘default’  ‘linear’  ‘dB’ ]
Fc : integer
cmap :
xextent : [None  (xmin, xmax)]
**kwargs :


Returns:  spectrum : 2D array
freqs : 1D array
t : 1D array
im : instance of class

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()
Notes
detrend and scale_by_freq only apply when mode is set to ‘psd’
Examples
(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)
precision : float, optional, default: 0
origin : [“upper”, “lower”], optional, default: “upper”
aspect : [‘auto’  ‘equal’  scalar], optional, default: “equal”
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
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:
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.
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
y : array_like


Returns:  list

Other Parameters:  
where : [ ‘pre’  ‘post’  ‘mid’ ]

Notes
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.
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].Colormap
Normalize
FancyArrowPatch
.x
and y
arrays.Returns:
 stream_container : StreamplotSet
Container object with attributes
 lines:
matplotlib.collections.LineCollection
of streamlines arrows: collection of
matplotlib.patches.FancyArrowPatch
objects representing arrows halfway along stream lines.This container will probably change in the future to allow changes to the colormap, alpha, etc. for both lines and arrows, but these changes should be backward compatible.
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 new subplot with a position which is entirely inside a preexisting axes will trigger the larger axes to be deleted:
import matplotlib.pyplot as plt
# plot a line, implicitly creating a subplot(111)
plt.plot([1,2,3])
# now create a subplot which represents the top plot of a grid
# with 2 rows and 1 column. Since this subplot will overlap the
# first, the plot (and its axes) previously created, will be removed
plt.subplot(211)
plt.plot(range(12))
plt.subplot(212, 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:
 facecolor:
 The background color of the subplot, which can be any valid color specifier. See
matplotlib.colors
for more information. polar:
 A boolean flag indicating whether the subplot plot should be a polar projection. Defaults to False.
 projection:
 A string giving the name of a custom projection to be used for the subplot. This projection must have been previously registered. See
matplotlib.projections
.
See also
Example:
(Source code, png, 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
sharex, sharey : bool or {‘none’, ‘all’, ‘row’, ‘col’}, default: False
squeeze : bool, optional, default: True
subplot_kw : dict, optional
gridspec_kw : dict, optional
fig_kw : dict, optional


Returns:  fig : ax : Axes object or array of Axes objects.

Examples
First create some toy data:
>>> x = np.linspace(0, 2*np.pi, 400)
>>> y = np.sin(x**2)
Creates just a figure and only one subplot
>>> fig, ax = plt.subplots()
>>> ax.plot(x, y)
>>> ax.set_title('Simple plot')
Creates two subplots and unpacks the output array immediately
>>> f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
>>> ax1.plot(x, y)
>>> ax1.set_title('Sharing Y axis')
>>> ax2.scatter(x, y)
Creates four polar axes, and accesses them through the returned array
>>> fig, axes = plt.subplots(2, 2, subplot_kw=dict(polar=True))
>>> axes[0, 0].plot(x, y)
>>> axes[1, 1].scatter(x, y)
Share a X axis with each column of subplots
>>> plt.subplots(2, 2, sharex='col')
Share a Y axis with each row of subplots
>>> plt.subplots(2, 2, sharey='row')
Share both X and Y axes with all subplots
>>> plt.subplots(2, 2, sharex='all', sharey='all')
Note that this is the same as
>>> plt.subplots(2, 2, sharex=True, sharey=True)
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
 The x location of the text in figure coords
 y : 0.98
 The y location of the text in figure coords
 horizontalalignment : ‘center’
 The horizontal alignment of the text
 verticalalignment : ‘top’
 The vertical alignment of the text
If the fontproperties
keyword argument is given then the
rcParams defaults for fontsize
(figure.titlesize
) and
fontweight
(figure.titleweight
) will be ignored in favour
of the FontProperties
defaults.
A 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
instanceclip_box
a matplotlib.transforms.Bbox
instanceclip_on
[True  False] clip_path
[ ( Path
,Transform
) Patch
 None ]contains
a callable function figure
a matplotlib.figure.Figure
instancefontsize
a float in points gid
an id string label
string or anything printable with ‘%s’ conversion. path_effects
unknown picker
[Nonefloatbooleancallable] rasterized
[True  False  None] sketch_params
unknown snap
unknown transform
Transform
instanceurl
a url string visible
[True  False] zorder
any number
matplotlib.pyplot.
text
(x, y, s, fontdict=None, withdash=False, **kwargs)¶Add text to the axes.
Add text in string s
to axis at location x
, y
, data
coordinates.
Parameters:  x, y : scalars
s : string
fontdict : dictionary, optional, default: None
withdash : boolean, optional, default: False


Other Parameters:  
kwargs :

Examples
Individual keyword arguments can be used to override any given parameter:
>>> text(x, y, s, fontsize=12)
The default transform specifies that text is in data coords, alternatively, you can specify text in axis coords (0,0 is lowerleft and 1,1 is upperright). The example below places text in the center of the axes:
>>> text(0.5, 0.5,'matplotlib', horizontalalignment='center',
... verticalalignment='center',
... transform=ax.transAxes)
You can put a rectangular box around the text instance (e.g., to
set a background color) by using the keyword bbox
. bbox
is
a dictionary 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


Other Parameters:  
axis : {‘x’, ‘y’, ‘both’}
reset : bool
which : {‘major’, ‘minor’, ‘both’}
direction : {‘in’, ‘out’, ‘inout’}
length : float
width : float
color : color
pad : float
labelsize : float or str
labelcolor : color
colors : color
zorder : float
bottom, top, left, right : bool or {‘on’, ‘off’}
labelbottom, labeltop, labelleft, labelright : bool or {‘on’, ‘off’}

Examples
Usage
ax.tick_params(direction='out', length=6, width=2, colors='r')
This will make all major ticks be red, pointing out of the box, and with dimensions 6 points by 2 points. Tick labels will also be red.
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_inches
.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
fontdict : dict
loc : {‘center’, ‘left’, ‘right’}, str, optional


Returns:  text :

Other Parameters:  
kwargs : text properties

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:
 colors: [ None  string  (mpl_colors) ]
If None, the colormap specified by cmap will be used.
If a string, like ‘r’ or ‘red’, all levels will be plotted in this color.
If a tuple of matplotlib color args (string, float, rgb, etc), different levels will be plotted in different colors in the order specified.
 alpha: float
 The alpha blending value
 cmap: [ None  Colormap ]
 A cm
Colormap
instance or None. If cmap is None and colors is None, a default Colormap is used. norm: [ None  Normalize ]
 A
matplotlib.colors.Normalize
instance for scaling data values to colors. If norm is None and colors is None, the default linear scaling is used. levels [level0, level1, ..., leveln]
 A list of floating point numbers indicating the level curves to draw, in increasing order; e.g., to draw just the zero contour pass
levels=[0]
 origin: [ None  ‘upper’  ‘lower’  ‘image’ ]
If None, the first value of Z will correspond to the lower left corner, location (0,0). If ‘image’, the rc value for
image.origin
will be used.This keyword is not active if X and Y are specified in the call to contour.
extent: [ None  (x0,x1,y0,y1) ]
If origin is not None, then extent is interpreted as in
matplotlib.pyplot.imshow()
: it gives the outer pixel boundaries. In this case, the position of Z[0,0] is the center of the pixel, not a corner. If origin is None, then (x0, y0) is the position of Z[0,0], and (x1, y1) is the position of Z[1,1].This keyword is not active if X and Y are specified in the call to contour.
 locator: [ None  ticker.Locator subclass ]
 If locator is None, the default
MaxNLocator
is used. The locator is used to determine the contour levels if they are not given explicitly via the V argument. extend: [ ‘neither’  ‘both’  ‘min’  ‘max’ ]
 Unless this is ‘neither’, contour levels are automatically added to one or both ends of the range so that all data are included. These added ranges are then mapped to the special colormap values which default to the ends of the colormap range, but can be set via
matplotlib.colors.Colormap.set_under()
andmatplotlib.colors.Colormap.set_over()
methods. xunits, yunits: [ None  registered units ]
 Override axis units by specifying an instance of a
matplotlib.units.ConversionInterface
.
tricontouronly keyword arguments:
 linewidths: [ None  number  tuple of numbers ]
If linewidths is None, the default width in
lines.linewidth
inmatplotlibrc
is used.If a number, all levels will be plotted with this linewidth.
If a tuple, different levels will be plotted with different linewidths in the order specified
 linestyles: [ None  ‘solid’  ‘dashed’  ‘dashdot’  ‘dotted’ ]
If linestyles is None, the ‘solid’ is used.
linestyles can also be an iterable of the above strings specifying a set of linestyles to be used. If this iterable is shorter than the number of contour levels it will be repeated as necessary.
If contour is using a monochrome colormap and the contour level is less than 0, then the linestyle specified in
contour.negative_linestyle
inmatplotlibrc
will be used.
tricontourfonly keyword arguments:
 antialiased: [ True  False ]
 enable antialiasing
Note: tricontourf fills intervals that are closed at the top; that is, for boundaries z1 and z2, the filled region is:
z1 < z <= z2
There is one exception: if the lowest boundary coincides with the minimum value of the z array, then that minimum value will be included in the lowest interval.
Examples:
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:
 colors: [ None  string  (mpl_colors) ]
If None, the colormap specified by cmap will be used.
If a string, like ‘r’ or ‘red’, all levels will be plotted in this color.
If a tuple of matplotlib color args (string, float, rgb, etc), different levels will be plotted in different colors in the order specified.
 alpha: float
 The alpha blending value
 cmap: [ None  Colormap ]
 A cm
Colormap
instance or None. If cmap is None and colors is None, a default Colormap is used. norm: [ None  Normalize ]
 A
matplotlib.colors.Normalize
instance for scaling data values to colors. If norm is None and colors is None, the default linear scaling is used. levels [level0, level1, ..., leveln]
 A list of floating point numbers indicating the level curves to draw, in increasing order; e.g., to draw just the zero contour pass
levels=[0]
 origin: [ None  ‘upper’  ‘lower’  ‘image’ ]
If None, the first value of Z will correspond to the lower left corner, location (0,0). If ‘image’, the rc value for
image.origin
will be used.This keyword is not active if X and Y are specified in the call to contour.
extent: [ None  (x0,x1,y0,y1) ]
If origin is not None, then extent is interpreted as in
matplotlib.pyplot.imshow()
: it gives the outer pixel boundaries. In this case, the position of Z[0,0] is the center of the pixel, not a corner. If origin is None, then (x0, y0) is the position of Z[0,0], and (x1, y1) is the position of Z[1,1].This keyword is not active if X and Y are specified in the call to contour.
 locator: [ None  ticker.Locator subclass ]
 If locator is None, the default
MaxNLocator
is used. The locator is used to determine the contour levels if they are not given explicitly via the V argument. extend: [ ‘neither’  ‘both’  ‘min’  ‘max’ ]
 Unless this is ‘neither’, contour levels are automatically added to one or both ends of the range so that all data are included. These added ranges are then mapped to the special colormap values which default to the ends of the colormap range, but can be set via
matplotlib.colors.Colormap.set_under()
andmatplotlib.colors.Colormap.set_over()
methods. xunits, yunits: [ None  registered units ]
 Override axis units by specifying an instance of a
matplotlib.units.ConversionInterface
.
tricontouronly keyword arguments:
 linewidths: [ None  number  tuple of numbers ]
If linewidths is None, the default width in
lines.linewidth
inmatplotlibrc
is used.If a number, all levels will be plotted with this linewidth.
If a tuple, different levels will be plotted with different linewidths in the order specified
 linestyles: [ None  ‘solid’  ‘dashed’  ‘dashdot’  ‘dotted’ ]
If linestyles is None, the ‘solid’ is used.
linestyles can also be an iterable of the above strings specifying a set of linestyles to be used. If this iterable is shorter than the number of contour levels it will be repeated as necessary.
If contour is using a monochrome colormap and the contour level is less than 0, then the linestyle specified in
contour.negative_linestyle
inmatplotlibrc
will be used.
tricontourfonly keyword arguments:
 antialiased: [ True  False ]
 enable antialiasing
Note: tricontourf fills intervals that are closed at the top; that is, for boundaries z1 and z2, the filled region is:
z1 < z <= z2
There is one exception: if the lowest boundary coincides with the minimum value of the z array, then that minimum value will be included in the lowest interval.
Examples:
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:
 the lines plotted for triangles edges
 the markers plotted for triangles nodes
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.
positions : arraylike, default = [1, 2, ..., n]
vert : bool, default = True.
widths : arraylike, default = 0.5
showmeans : bool, default = False
showextrema : bool, default = True
showmedians : bool, default = False
points : scalar, default = 100
bw_method : str, scalar or callable, optional


Returns:  result : dict
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
ymin, ymax : scalar or 1D array_like
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
Examples
(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:
normed : boolean, optional, default: True
usevlines : boolean, optional, default: True
maxlags : integer, optional, default: 10


Returns:  (lags, c, line, b) : where: 
Other Parameters:  
linestyle :
marker : string, optional, default: ‘o’ 
Notes
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
length : float, optional
randomness : float, optional


Notes
This function works by a number of rcParams, so it will probably override others you have set before.
If you want the effects of this function to be temporary, it can be used as a context manager, for example:
with plt.xkcd():
# This figure will be in XKCDstyle
fig1 = plt.figure()
# ...
# This figure will be in regular style
fig2 = plt.figure()
matplotlib.pyplot.
xlabel
(s, *args, **kwargs)¶Set the x axis label of the current axis.
Default override is:
override = {
'fontsize' : 'small',
'verticalalignment' : 'top',
'horizontalalignment' : 'center'
}
See also
text()
matplotlib.pyplot.
xlim
(*args, **kwargs)¶Get or set the x limits of the current axes.
xmin, xmax = xlim() # return the current xlim
xlim( (xmin, xmax) ) # set the xlim to xmin, xmax
xlim( xmin, xmax ) # set the xlim to xmin, xmax
If you do not specify args, you can pass the xmin and xmax as kwargs, e.g.:
xlim(xmax=3) # adjust the max leaving min unchanged
xlim(xmin=1) # adjust the min leaving max unchanged
Setting limits turns autoscaling off for the xaxis.
The new axis limits are returned as a length 2 tuple.
matplotlib.pyplot.
xscale
(*args, **kwargs)¶Set the scaling of the xaxis.
call signature:
xscale(scale, **kwargs)
The available scales are: ‘linear’  ‘log’  ‘logit’  ‘symlog’
Different keywords may be accepted, depending on the scale:
‘linear’
‘log’
 basex/basey:
 The base of the logarithm
 nonposx/nonposy: [‘mask’  ‘clip’ ]
 nonpositive values in x or y can be masked as invalid, or clipped to a very small positive number
 subsx/subsy:
Where to place the subticks between each major tick. Should be a sequence of integers. For example, in a log10 scale:
[2, 3, 4, 5, 6, 7, 8, 9]
will place 8 logarithmically spaced minor ticks between each major tick.
‘logit’
 nonpos: [‘mask’  ‘clip’ ]
 values beyond ]0, 1[ can be masked as invalid, or clipped to a number very close to 0 or 1
‘symlog’
 basex/basey:
 The base of the logarithm
 linthreshx/linthreshy:
 The range (x, x) within which the plot is linear (to avoid having the plot go to infinity around zero).
 subsx/subsy:
Where to place the subticks between each major tick. Should be a sequence of integers. For example, in a log10 scale:
[2, 3, 4, 5, 6, 7, 8, 9]
will place 8 logarithmically spaced minor ticks between each major tick.
 linscalex/linscaley:
 This allows the linear range (linthresh to linthresh) to be stretched relative to the logarithmic range. Its value is the number of decades to use for each half of the linear range. For example, when linscale == 1.0 (the default), the space used for the positive and negative halves of the linear range will be equal to one decade in the logarithmic range.
matplotlib.pyplot.
xticks
(*args, **kwargs)¶Get or set the xlimits of the current tick locations and labels.
# return locs, labels where locs is an array of tick locations and
# labels is an array of tick labels.
locs, labels = xticks()
# set the locations of the xticks
xticks( arange(6) )
# set the locations and labels of the xticks
xticks( arange(5), ('Tom', 'Dick', 'Harry', 'Sally', 'Sue') )
The keyword args, if any, are Text
properties. For example, to rotate long labels:
xticks( arange(12), calendar.month_name[1:13], rotation=17 )
matplotlib.pyplot.
ylabel
(s, *args, **kwargs)¶Set the y axis label of the current axis.
Defaults override is:
override = {
'fontsize' : 'small',
'verticalalignment' : 'center',
'horizontalalignment' : 'right',
'rotation'='vertical' : }
See also
text()
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 s