axes

matplotlib.axes

class matplotlib.axes.Axes(fig, rect, axisbg=None, frameon=True, sharex=None, sharey=None, label='', xscale=None, yscale=None, **kwargs)

The Axes contains most of the figure elements: Axis, Tick, Line2D, Text, Polygon, etc., and sets the coordinate system.

The Axes instance supports callbacks through a callbacks attribute which is a CallbackRegistry instance. The events you can connect to are ‘xlim_changed’ and ‘ylim_changed’ and the callback will be called with func(ax) where ax is the Axes instance.

acorr(x, **kwargs)

Plot the autocorrelation of x.

Parameters:	x : sequence of scalar hold : boolean, optional, default: True detrend : callable, optional, default: mlab.detrend_none x is detrended by the detrend callable. Default is no normalization. normed : boolean, optional, default: True if True, normalize the data by the autocorrelation at the 0-th lag. usevlines : boolean, optional, default: True if True, Axes.vlines is used to plot the vertical lines from the origin to the acorr. Otherwise, Axes.plot is used. maxlags : integer, optional, default: 10 number of lags to show. If None, will return all 2 * len(x) - 1 lags.
Returns:	(lags, c, line, b) : where: lags are a length 2`maxlags+1 lag vector. c is the 2`maxlags+1 auto correlation vectorI line is a Line2D instance returned by plot. b is the x-axis.
Other Parameters:
	linestyle : Line2D prop, optional, default: None Only used if usevlines is False. marker : string, optional, default: ‘o’

Notes

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

• All arguments with the following names: ‘x’.

Examples

xcorr is top graph, and acorr is bottom graph.

(Source code, png, hires.png, pdf)

add_artist(a)

Add any Artist to the axes.

Use add_artist only for artists for which there is no dedicated “add” method; and if necessary, use a method such as update_datalim or update_datalim_numerix to manually update the dataLim if the artist is to be included in autoscaling.

Returns the artist.

add_callback(func)

Adds a callback function that will be called whenever one of the Artist‘s properties changes.

Returns an id that is useful for removing the callback with remove_callback() later.

add_collection(collection, autolim=True)

Add a Collection instance to the axes.

Returns the collection.

add_container(container)

Add a Container instance to the axes.

Returns the collection.

add_image(image)

Add a AxesImage to the axes.

Returns the image.

add_line(line)

Add a Line2D to the list of plot lines

Returns the line.

add_patch(p)

Add a Patch p to the list of axes patches; the clipbox will be set to the Axes clipping box. If the transform is not set, it will be set to transData.

Returns the patch.

add_table(tab)

Add a Table instance to the list of axes tables

Returns the table.

aname = 'Artist'

angle_spectrum(x, Fs=None, Fc=None, window=None, pad_to=None, sides=None, **kwargs)

Plot the angle spectrum.

Call signature:

angle_spectrum(x, Fs=2, Fc=0,  window=mlab.window_hanning,
               pad_to=None, sides='default', **kwargs)

Compute the angle spectrum (wrapped phase spectrum) of x. Data is padded to a length of pad_to and the windowing function window is applied to the signal.

x: 1-D array or sequence

Array or sequence containing the data

Keyword arguments:

Fs: scalar

The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. The default value is 2.

window: callable or ndarray

A function or a vector of length NFFT. To create window vectors see window_hanning(), window_none(), numpy.blackman(), numpy.hamming(), numpy.bartlett(), scipy.signal(), scipy.signal.get_window(), etc. The default is window_hanning(). If a function is passed as the argument, it must take a data segment as an argument and return the windowed version of the segment.

sides: [ ‘default’ | ‘onesided’ | ‘twosided’ ]

Specifies which sides of the spectrum to return. Default gives the default behavior, which returns one-sided for real data and both for complex data. ‘onesided’ forces the return of a one-sided spectrum, while ‘twosided’ forces two-sided.

pad_to: integer

The number of points to which the data segment is padded when performing the FFT. While not increasing the actual resolution of the spectrum (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. This corresponds to the n parameter in the call to fft(). The default is None, which sets pad_to equal to the length of the input signal (i.e. no padding).

Fc: integer

The center frequency of x (defaults to 0), which offsets the x extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband.

Returns the tuple (spectrum, freqs, line):

spectrum: 1-D array

The values for the angle spectrum in radians (real valued)

freqs: 1-D array

The frequencies corresponding to the elements in spectrum

line: a Line2D instance

The line created by this function

kwargs control the Line2D properties:

Property	Description
agg_filter	unknown
alpha	float (0.0 transparent through 1.0 opaque)
animated	[True | False]
antialiased or aa	[True | False]
axes	an Axes instance
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
color or c	any matplotlib color
contains	a callable function
dash_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
dash_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
dashes	sequence of on/off ink in points
drawstyle	[‘default’ | ‘steps’ | ‘steps-pre’ | ‘steps-mid’ | ‘steps-post’]
figure	a matplotlib.figure.Figure instance
fillstyle	[‘full’ | ‘left’ | ‘right’ | ‘bottom’ | ‘top’ | ‘none’]
gid	an id string
label	string or anything printable with ‘%s’ conversion.
linestyle or ls	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or lw	float value in points
marker	A valid marker style
markeredgecolor or mec	any matplotlib color
markeredgewidth or mew	float value in points
markerfacecolor or mfc	any matplotlib color
markerfacecoloralt or mfcalt	any matplotlib color
markersize or ms	float
markevery	[None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects	unknown
picker	float distance in points or callable pick function fn(artist, event)
pickradius	float distance in points
rasterized	[True | False | None]
sketch_params	unknown
snap	unknown
solid_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
solid_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
transform	a matplotlib.transforms.Transform instance
url	a url string
visible	[True | False]
xdata	1D array
ydata	1D array
zorder	any number

Example:

(Source code, png, hires.png, pdf)

See also

magnitude_spectrum()

angle_spectrum() plots the magnitudes of the corresponding frequencies.

phase_spectrum()

phase_spectrum() plots the unwrapped version of this function.

specgram()

specgram() can plot the angle spectrum of segments within the signal in a colormap.

Notes

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

• All arguments with the following names: ‘x’.

annotate(*args, **kwargs)

Annotate the point xy with text s.

Additional kwargs are passed to Text.

Parameters:

s : str The text of the annotation xy : iterable Length 2 sequence specifying the (x,y) point to annotate xytext : iterable, optional Length 2 sequence specifying the (x,y) to place the text at. If None, defaults to xy. xycoords : str, Artist, Transform, callable or tuple, optional The coordinate system that xy is given in. For a str the allowed values are: Property Description ‘figure points’ points from the lower left of the figure ‘figure pixels’ pixels from the lower left of the figure ‘figure fraction’ fraction of figure from lower left ‘axes points’ points from lower left corner of axes ‘axes pixels’ pixels from lower left corner of axes ‘axes fraction’ fraction of axes from lower left ‘data’ use the coordinate system of the object being annotated (default) ‘polar’ (theta,r) if not native ‘data’ coordinates If a Artist object is passed in the units are fraction if it’s bounding box. If a Transform object is passed in use that to transform xy to screen coordinates If a callable it must take a RendererBase object as input and return a Transform or Bbox object If a tuple must be length 2 tuple of str, Artist, Transform or callable objects. The first transform is used for the x coordinate and the second for y. See Annotating Axes for more details. Defaults to 'data' textcoords : str, Artist, Transform, callable or tuple, optional The coordinate system that xytext is given, which may be different than the coordinate system used for xy. All xycoords values are valid as well as the following strings: Property Description ‘offset points’ offset (in points) from the xy value ‘offset pixels’ offset (in pixels) from the xy value defaults to the input of xycoords arrowprops : dict, optional If not None, properties used to draw a FancyArrowPatch arrow between xy and xytext. If arrowprops does not contain the key 'arrowstyle' the allowed keys are: Key Description width the width of the arrow in points headwidth the width of the base of the arrow head in points headlength the length of the arrow head in points shrink fraction of total length to ‘shrink’ from both ends ? any key to matplotlib.patches.FancyArrowPatch If the arrowprops contains the key 'arrowstyle' the above keys are forbidden. The allowed values of 'arrowstyle' are: Name Attrs '-' None '->' head_length=0.4,head_width=0.2 '-[' widthB=1.0,lengthB=0.2,angleB=None '|-|' widthA=1.0,widthB=1.0 '-|>' head_length=0.4,head_width=0.2 '<-' head_length=0.4,head_width=0.2 '<->' head_length=0.4,head_width=0.2 '<|-' head_length=0.4,head_width=0.2 '<|-|>' head_length=0.4,head_width=0.2 'fancy' head_length=0.4,head_width=0.4,tail_width=0.4 'simple' head_length=0.5,head_width=0.5,tail_width=0.2 'wedge' tail_width=0.3,shrink_factor=0.5 Valid keys for FancyArrowPatch are: Key Description arrowstyle the arrow style connectionstyle the connection style relpos default is (0.5, 0.5) patchA default is bounding box of the text patchB default is None shrinkA default is 2 points shrinkB default is 2 points mutation_scale default is text size (in points) mutation_aspect default is 1. ? any key for matplotlib.patches.PathPatch Defaults to None annotation_clip : bool, optional Controls the visibility of the annotation when it goes outside the axes area. If True, the annotation will only be drawn when the xy is inside the axes. If False, the annotation will always be drawn regardless of its position. The default is None, which behave as True only if xycoords is “data”.

Returns:

Annotation

apply_aspect(position=None)

Use _aspect() and _adjustable() to modify the axes box or the view limits.

arrow(x, y, dx, dy, **kwargs)

Add an arrow to the axes.

Call signature:

arrow(x, y, dx, dy, **kwargs)

Draws arrow on specified axis from (x, y) to (x + dx, y + dy). Uses FancyArrow patch to construct the arrow.

The resulting arrow is affected by the axes aspect ratio and limits. This may produce an arrow whose head is not square with its stem. To create an arrow whose head is square with its stem, use annotate() for example:

ax.annotate("", xy=(0.5, 0.5), xytext=(0, 0),
    arrowprops=dict(arrowstyle="->"))

Optional kwargs control the arrow construction and properties:

Constructor arguments

width: float (default: 0.001)

width of full arrow tail

length_includes_head: [True | False] (default: False)

True if head is to be counted in calculating the length.

head_width: float or None (default: 3*width)

total width of the full arrow head

head_length: float or None (default: 1.5 * head_width)

length of arrow head

shape: [‘full’, ‘left’, ‘right’] (default: ‘full’)

draw the left-half, right-half, or full arrow

overhang: float (default: 0)

fraction that the arrow is swept back (0 overhang means triangular shape). Can be negative or greater than one.

head_starts_at_zero: [True | False] (default: False)

if True, the head starts being drawn at coordinate 0 instead of ending at coordinate 0.

Other valid kwargs (inherited from Patch) are:

Property	Description
agg_filter	unknown
alpha	float or None
animated	[True | False]
antialiased or aa	[True | False] or None for default
axes	an Axes instance
capstyle	[‘butt’ | ‘round’ | ‘projecting’]
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
color	matplotlib color spec
contains	a callable function
edgecolor or ec	mpl color spec, or None for default, or ‘none’ for no color
facecolor or fc	mpl color spec, or None for default, or ‘none’ for no color
figure	a matplotlib.figure.Figure instance
fill	[True | False]
gid	an id string
hatch	[‘/’ | ‘\’ | ‘|’ | ‘-‘ | ‘+’ | ‘x’ | ‘o’ | ‘O’ | ‘.’ | ‘*’]
joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
label	string or anything printable with ‘%s’ conversion.
linestyle or ls	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or lw	float or None for default
path_effects	unknown
picker	[None|float|boolean|callable]
rasterized	[True | False | None]
sketch_params	unknown
snap	unknown
transform	Transform instance
url	a url string
visible	[True | False]
zorder	any number

Example:

(Source code, png, hires.png, pdf)

autoscale(enable=True, axis='both', tight=None)

Autoscale the axis view to the data (toggle).

Convenience method for simple axis view autoscaling. It turns autoscaling on or off, and then, if autoscaling for either axis is on, it performs the autoscaling on the specified axis or axes.

enable: [True | False | None]

True (default) turns autoscaling on, False turns it off. None leaves the autoscaling state unchanged.

axis: [‘x’ | ‘y’ | ‘both’]

which axis to operate on; default is ‘both’

tight: [True | False | None]

If True, set view limits to data limits; if False, let the locator and margins expand the view limits; if None, use tight scaling if the only artist is an image, otherwise treat tight as False. The tight setting is retained for future autoscaling until it is explicitly changed.

Returns None.

autoscale_view(tight=None, scalex=True, scaley=True)

Autoscale the view limits using the data limits. You can selectively autoscale only a single axis, e.g., the xaxis by setting scaley to False. The autoscaling preserves any axis direction reversal that has already been done.

The data limits are not updated automatically when artist data are changed after the artist has been added to an Axes instance. In that case, use matplotlib.axes.Axes.relim() prior to calling autoscale_view.

axes

The Axes instance the artist resides in, or None.

axhline(y=0, xmin=0, xmax=1, **kwargs)

Add a horizontal line across the axis.

Parameters:	y : scalar, optional, default: 0 y position in data coordinates of the horizontal line. xmin : scalar, optional, default: 0 Should be between 0 and 1, 0 being the far left of the plot, 1 the far right of the plot. xmax : scalar, optional, default: 1 Should be between 0 and 1, 0 being the far left of the plot, 1 the far right of the plot.
Returns:	Line2D

See also

axhspan

for example plot and source code

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 instance
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
color or c	any matplotlib color
contains	a callable function
dash_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
dash_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
dashes	sequence of on/off ink in points
drawstyle	[‘default’ | ‘steps’ | ‘steps-pre’ | ‘steps-mid’ | ‘steps-post’]
figure	a matplotlib.figure.Figure instance
fillstyle	[‘full’ | ‘left’ | ‘right’ | ‘bottom’ | ‘top’ | ‘none’]
gid	an id string
label	string or anything printable with ‘%s’ conversion.
linestyle or ls	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or lw	float value in points
marker	A valid marker style
markeredgecolor or mec	any matplotlib color
markeredgewidth or mew	float value in points
markerfacecolor or mfc	any matplotlib color
markerfacecoloralt or mfcalt	any matplotlib color
markersize or ms	float
markevery	[None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects	unknown
picker	float distance in points or callable pick function fn(artist, event)
pickradius	float distance in points
rasterized	[True | False | None]
sketch_params	unknown
snap	unknown
solid_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
solid_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
transform	a matplotlib.transforms.Transform instance
url	a url string
visible	[True | False]
xdata	1D array
ydata	1D array
zorder	any number

axhspan(ymin, ymax, xmin=0, xmax=1, **kwargs)

Add a horizontal span (rectangle) across the axis.

Call signature:

axhspan(ymin, ymax, xmin=0, xmax=1, **kwargs)

y coords are in data units and x coords are in axes (relative 0-1) units.

Draw a horizontal span (rectangle) from ymin to ymax. With the default values of xmin = 0 and xmax = 1, this always spans the xrange, regardless of the xlim settings, even if you change them, e.g., with the set_xlim() command. That is, the horizontal extent is in axes coords: 0=left, 0.5=middle, 1.0=right but the y location is in data coordinates.

Return value is a matplotlib.patches.Polygon instance.

Examples:

• draw a gray rectangle from y = 0.25-0.75 that spans the horizontal extent of the axes:

  >>> axhspan(0.25, 0.75, facecolor='0.5', alpha=0.5)
  

Valid kwargs are Polygon properties:

Property	Description
agg_filter	unknown
alpha	float or None
animated	[True | False]
antialiased or aa	[True | False] or None for default
axes	an Axes instance
capstyle	[‘butt’ | ‘round’ | ‘projecting’]
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
color	matplotlib color spec
contains	a callable function
edgecolor or ec	mpl color spec, or None for default, or ‘none’ for no color
facecolor or fc	mpl color spec, or None for default, or ‘none’ for no color
figure	a matplotlib.figure.Figure instance
fill	[True | False]
gid	an id string
hatch	[‘/’ | ‘\’ | ‘|’ | ‘-‘ | ‘+’ | ‘x’ | ‘o’ | ‘O’ | ‘.’ | ‘*’]
joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
label	string or anything printable with ‘%s’ conversion.
linestyle or ls	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or lw	float or None for default
path_effects	unknown
picker	[None|float|boolean|callable]
rasterized	[True | False | None]
sketch_params	unknown
snap	unknown
transform	Transform instance
url	a url string
visible	[True | False]
zorder	any number

Example:

(Source code, png, hires.png, pdf)

axis(*v, **kwargs)

Set axis properties.

Valid signatures:

xmin, xmax, ymin, ymax = axis()
xmin, xmax, ymin, ymax = axis(list_arg)
xmin, xmax, ymin, ymax = axis(string_arg)
xmin, xmax, ymin, ymax = axis(**kwargs)

Parameters:

v : list of float or {‘on’, ‘off’, ‘equal’, ‘tight’, ‘scaled’, ‘normal’, ‘auto’, ‘image’, ‘square’} Optional positional argument Axis data limits set from a list; or a command relating to axes: Value Description ‘on’ Toggle axis lines and labels on ‘off’ Toggle axis lines and labels off ‘equal’ Equal scaling by changing limits ‘scaled’ Equal scaling by changing box dimensions ‘tight’ Limits set such that all data is shown ‘auto’ Automatic scaling, fill rectangle with data ‘normal’ Same as ‘auto’; deprecated ‘image’ ‘scaled’ with axis limits equal to data limits ‘square’ Square plot; similar to ‘scaled’, but initially forcing xmax-xmin = ymax-ymin emit : bool, optional Passed to set_{x,y}lim functions, if observers are notified of axis limit change xmin, ymin, xmax, ymax : float, optional The axis limits to be set

Returns:

xmin, xmax, ymin, ymax : float The axis limits

axvline(x=0, ymin=0, ymax=1, **kwargs)

Add a vertical line across the axes.

Parameters:	x : scalar, optional, default: 0 x position in data coordinates of the vertical line. ymin : scalar, optional, default: 0 Should be between 0 and 1, 0 being the bottom of the plot, 1 the top of the plot. ymax : scalar, optional, default: 1 Should be between 0 and 1, 0 being the bottom of the plot, 1 the top of the plot.
Returns:	Line2D

See also

axhspan

for example plot and source code

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 instance
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
color or c	any matplotlib color
contains	a callable function
dash_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
dash_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
dashes	sequence of on/off ink in points
drawstyle	[‘default’ | ‘steps’ | ‘steps-pre’ | ‘steps-mid’ | ‘steps-post’]
figure	a matplotlib.figure.Figure instance
fillstyle	[‘full’ | ‘left’ | ‘right’ | ‘bottom’ | ‘top’ | ‘none’]
gid	an id string
label	string or anything printable with ‘%s’ conversion.
linestyle or ls	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or lw	float value in points
marker	A valid marker style
markeredgecolor or mec	any matplotlib color
markeredgewidth or mew	float value in points
markerfacecolor or mfc	any matplotlib color
markerfacecoloralt or mfcalt	any matplotlib color
markersize or ms	float
markevery	[None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects	unknown
picker	float distance in points or callable pick function fn(artist, event)
pickradius	float distance in points
rasterized	[True | False | None]
sketch_params	unknown
snap	unknown
solid_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
solid_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
transform	a matplotlib.transforms.Transform instance
url	a url string
visible	[True | False]
xdata	1D array
ydata	1D array
zorder	any number

axvspan(xmin, xmax, ymin=0, ymax=1, **kwargs)

Add a vertical span (rectangle) across the axes.

Call signature:

axvspan(xmin, xmax, ymin=0, ymax=1, **kwargs)

x coords are in data units and y coords are in axes (relative 0-1) units.

Draw a vertical span (rectangle) from xmin to xmax. With the default values of ymin = 0 and ymax = 1, this always spans the yrange, regardless of the ylim settings, even if you change them, e.g., with the set_ylim() command. That is, the vertical extent is in axes coords: 0=bottom, 0.5=middle, 1.0=top but the y location is in data coordinates.

Return value is the matplotlib.patches.Polygon instance.

Examples:

• draw a vertical green translucent rectangle from x=1.25 to 1.55 that spans the yrange of the axes:

  >>> axvspan(1.25, 1.55, facecolor='g', alpha=0.5)
  

Valid kwargs are Polygon properties:

Property	Description
agg_filter	unknown
alpha	float or None
animated	[True | False]
antialiased or aa	[True | False] or None for default
axes	an Axes instance
capstyle	[‘butt’ | ‘round’ | ‘projecting’]
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
color	matplotlib color spec
contains	a callable function
edgecolor or ec	mpl color spec, or None for default, or ‘none’ for no color
facecolor or fc	mpl color spec, or None for default, or ‘none’ for no color
figure	a matplotlib.figure.Figure instance
fill	[True | False]
gid	an id string
hatch	[‘/’ | ‘\’ | ‘|’ | ‘-‘ | ‘+’ | ‘x’ | ‘o’ | ‘O’ | ‘.’ | ‘*’]
joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
label	string or anything printable with ‘%s’ conversion.
linestyle or ls	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or lw	float or None for default
path_effects	unknown
picker	[None|float|boolean|callable]
rasterized	[True | False | None]
sketch_params	unknown
snap	unknown
transform	Transform instance
url	a url string
visible	[True | False]
zorder	any number

See also

axhspan()

for example plot and source code

bar(left, height, width=0.8, bottom=None, **kwargs)

Make a bar plot.

Make a bar plot with rectangles bounded by:

left, left + width, bottom, bottom + height

(left, right, bottom and top edges)

Parameters:

left : sequence of scalars the x coordinates of the left sides of the bars height : sequence of scalars the heights of the bars width : scalar or array-like, optional the width(s) of the bars default: 0.8 bottom : scalar or array-like, optional the y coordinate(s) of the bars default: None color : scalar or array-like, optional the colors of the bar faces edgecolor : scalar or array-like, optional the colors of the bar edges linewidth : scalar or array-like, optional width of bar edge(s). If None, use default linewidth; If 0, don’t draw edges. default: None tick_label : string or array-like, optional the tick labels of the bars default: None xerr : scalar or array-like, optional if not None, will be used to generate errorbar(s) on the bar chart default: None yerr : scalar or array-like, optional if not None, will be used to generate errorbar(s) on the bar chart default: None ecolor : scalar or array-like, optional specifies the color of errorbar(s) default: None capsize : scalar, optional determines the length in points of the error bar caps default: None, which will take the value from the errorbar.capsize rcParam. error_kw : dict, optional dictionary of kwargs to be passed to errorbar method. ecolor and capsize may be specified here rather than as independent kwargs. align : {‘edge’, ‘center’}, optional If ‘edge’, aligns bars by their left edges (for vertical bars) and by their bottom edges (for horizontal bars). If ‘center’, interpret the left argument as the coordinates of the centers of the bars. To align on the align bars on the right edge pass a negative width. orientation : {‘vertical’, ‘horizontal’}, optional The orientation of the bars. log : boolean, optional If true, sets the axis to be log scale. default: False

Returns:

bars : matplotlib.container.BarContainer Container with all of the bars + errorbars

See also

barh

Plot a horizontal bar plot.

Notes

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

• All arguments with the following names: ‘linewidth’, ‘bottom’, ‘yerr’, ‘height’, ‘xerr’, ‘edgecolor’, ‘tick_label’, ‘color’, ‘left’, ‘width’, ‘ecolor’.

Examples

Example: A stacked bar chart.

(Source code, png, hires.png, pdf)

barbs(*args, **kw)

Plot a 2-D field of barbs.

Call signatures:

barb(U, V, **kw)
barb(U, V, C, **kw)
barb(X, Y, U, V, **kw)
barb(X, Y, U, V, C, **kw)

Arguments:

X, Y:

The x and y coordinates of the barb locations (default is head of barb; see pivot kwarg)

U, V:

Give the x and y components of the barb shaft

C:

An optional array used to map colors to the barbs

All arguments may be 1-D or 2-D arrays or sequences. If X and Y are absent, they will be generated as a uniform grid. If U and V are 2-D arrays but X and Y are 1-D, and if len(X) and len(Y) match the column and row dimensions of U, then X and Y will be expanded with numpy.meshgrid().

U, V, C may be masked arrays, but masked X, Y are not supported at present.

Keyword arguments:

length:

Length of the barb in points; the other parts of the barb are scaled against this. Default is 9

pivot: [ ‘tip’ | ‘middle’ ]

The part of the arrow that is at the grid point; the arrow rotates about this point, hence the name pivot. Default is ‘tip’

barbcolor: [ color | color sequence ]

Specifies the color all parts of the barb except any flags. This parameter is analagous to the edgecolor parameter for polygons, which can be used instead. However this parameter will override facecolor.

flagcolor: [ color | color sequence ]

Specifies the color of any flags on the barb. This parameter is analagous to the facecolor parameter for polygons, which can be used instead. However this parameter will override facecolor. If this is not set (and C has not either) then flagcolor will be set to match barbcolor so that the barb has a uniform color. If C has been set, flagcolor has no effect.

sizes:

A dictionary of coefficients specifying the ratio of a given feature to the length of the barb. Only those values one wishes to override need to be included. These features include:

• ‘spacing’ - space between features (flags, full/half barbs)
• ‘height’ - height (distance from shaft to top) of a flag or full barb
• ‘width’ - width of a flag, twice the width of a full barb
• ‘emptybarb’ - radius of the circle used for low magnitudes

fill_empty:

A flag on whether the empty barbs (circles) that are drawn should be filled with the flag color. If they are not filled, they will be drawn such that no color is applied to the center. Default is False

rounding:

A flag to indicate whether the vector magnitude should be rounded when allocating barb components. If True, the magnitude is rounded to the nearest multiple of the half-barb increment. If False, the magnitude is simply truncated to the next lowest multiple. Default is True

barb_increments:

A dictionary of increments specifying values to associate with different parts of the barb. Only those values one wishes to override need to be included.

• ‘half’ - half barbs (Default is 5)
• ‘full’ - full barbs (Default is 10)
• ‘flag’ - flags (default is 50)

flip_barb:

Either a single boolean flag or an array of booleans. Single boolean indicates whether the lines and flags should point opposite to normal for all barbs. An array (which should be the same size as the other data arrays) indicates whether to flip for each individual barb. Normal behavior is for the barbs and lines to point right (comes from wind barbs having these features point towards low pressure in the Northern Hemisphere.) Default is False

Barbs are traditionally used in meteorology as a way to plot the speed and direction of wind observations, but can technically be used to plot any two dimensional vector quantity. As opposed to arrows, which give vector magnitude by the length of the arrow, the barbs give more quantitative information about the vector magnitude by putting slanted lines or a triangle for various increments in magnitude, as show schematically below:

:     /\    \
:    /  \    \
:   /    \    \    \
:  /      \    \    \
: ------------------------------

The largest increment is given by a triangle (or “flag”). After those come full lines (barbs). The smallest increment is a half line. There is only, of course, ever at most 1 half line. If the magnitude is small and only needs a single half-line and no full lines or triangles, the half-line is offset from the end of the barb so that it can be easily distinguished from barbs with a single full line. The magnitude for the barb shown above would nominally be 65, using the standard increments of 50, 10, and 5.

linewidths and edgecolors can be used to customize the barb. Additional PolyCollection keyword arguments:

Property	Description
agg_filter	unknown
alpha	float or None
animated	[True | False]
antialiased or antialiaseds	Boolean or sequence of booleans
array	unknown
axes	an Axes instance
clim	a length 2 sequence of floats
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
cmap	a colormap or registered colormap name
color	matplotlib color arg or sequence of rgba tuples
contains	a callable function
edgecolor or edgecolors	matplotlib color spec or sequence of specs
facecolor or facecolors	matplotlib color spec or sequence of specs
figure	a matplotlib.figure.Figure instance
gid	an id string
hatch	[ ‘/’ | ‘\’ | ‘|’ | ‘-‘ | ‘+’ | ‘x’ | ‘o’ | ‘O’ | ‘.’ | ‘*’ ]
label	string or anything printable with ‘%s’ conversion.
linestyle or linestyles or dashes	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or linewidths or lw	float or sequence of floats
norm	unknown
offset_position	unknown
offsets	float or sequence of floats
path_effects	unknown
picker	[None|float|boolean|callable]
pickradius	unknown
rasterized	[True | False | None]
sketch_params	unknown
snap	unknown
transform	Transform instance
url	a url string
urls	unknown
visible	[True | False]
zorder	any number

Example:

(Source code)

(png, hires.png, pdf)

(png, hires.png, pdf)

Notes

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

• All positional and all keyword arguments.

barh(bottom, width, height=0.8, left=None, **kwargs)

Make a horizontal bar plot.

Make a horizontal bar plot with rectangles bounded by:

left, left + width, bottom, bottom + height

(left, right, bottom and top edges)

bottom, width, height, and left can be either scalars or sequences

Parameters:	bottom : scalar or array-like the y coordinate(s) of the bars width : scalar or array-like the width(s) of the bars height : sequence of scalars, optional, default: 0.8 the heights of the bars left : sequence of scalars the x coordinates of the left sides of the bars
Returns:	matplotlib.patches.Rectangle instances.
Other Parameters:
	color : scalar or array-like, optional the colors of the bars edgecolor : scalar or array-like, optional the colors of the bar edges linewidth : scalar or array-like, optional, default: None width of bar edge(s). If None, use default linewidth; If 0, don’t draw edges. tick_label : string or array-like, optional, default: None the tick labels of the bars xerr : scalar or array-like, optional, default: None if not None, will be used to generate errorbar(s) on the bar chart yerr : scalar or array-like, optional, default: None if not None, will be used to generate errorbar(s) on the bar chart ecolor : scalar or array-like, optional, default: None specifies the color of errorbar(s) capsize : scalar, optional determines the length in points of the error bar caps default: None, which will take the value from the errorbar.capsize rcParam. error_kw : dictionary of kwargs to be passed to errorbar method. ecolor and capsize may be specified here rather than as independent kwargs. align : [‘edge’ | ‘center’], optional, default: ‘edge’ If edge, aligns bars by their left edges (for vertical bars) and by their bottom edges (for horizontal bars). If center, interpret the left argument as the coordinates of the centers of the bars. log : boolean, optional, default: False If true, sets the axis to be log scale

See also

bar

Plot a vertical bar plot.

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 instance
capstyle	[‘butt’ | ‘round’ | ‘projecting’]
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
color	matplotlib color spec
contains	a callable function
edgecolor or ec	mpl color spec, or None for default, or ‘none’ for no color
facecolor or fc	mpl color spec, or None for default, or ‘none’ for no color
figure	a matplotlib.figure.Figure instance
fill	[True | False]
gid	an id string
hatch	[‘/’ | ‘\’ | ‘|’ | ‘-‘ | ‘+’ | ‘x’ | ‘o’ | ‘O’ | ‘.’ | ‘*’]
joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
label	string or anything printable with ‘%s’ conversion.
linestyle or ls	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or lw	float or None for default
path_effects	unknown
picker	[None|float|boolean|callable]
rasterized	[True | False | None]
sketch_params	unknown
snap	unknown
transform	Transform instance
url	a url string
visible	[True | False]
zorder	any number

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)

Make a box and whisker plot.

Call signature:

boxplot(self, x, notch=None, sym=None, vert=None, whis=None,
        positions=None, widths=None, patch_artist=False,
        bootstrap=None, usermedians=None, conf_intervals=None,
        meanline=False, showmeans=False, showcaps=True,
        showbox=True, showfliers=True, boxprops=None,
        labels=None, flierprops=None, medianprops=None,
        meanprops=None, capprops=None, whiskerprops=None,
        manage_xticks=True, autorange=False):

Make a box and whisker plot for each column of x or each vector in sequence x. The box extends from the lower to upper quartile values of the data, with a line at the median. The whiskers extend from the box to show the range of the data. Flier points are those past the end of the whiskers.

Parameters:	x : Array or a sequence of vectors. The input data. notch : bool, optional (False) If True, will produce a notched box plot. Otherwise, a rectangular boxplot is produced. sym : str, optional The default symbol for flier points. Enter an empty string (‘’) if you don’t want to show fliers. If None, then the fliers default to ‘b+’ If you want more control use the flierprops kwarg. vert : bool, optional (True) If True (default), makes the boxes vertical. If False, everything is drawn horizontally. whis : float, sequence, or string (default = 1.5) As a float, determines the reach of the whiskers past the first and third quartiles (e.g., Q3 + whis*IQR, IQR = interquartile range, Q3-Q1). Beyond the whiskers, data are considered outliers and are plotted as individual points. Set this to an unreasonably high value to force the whiskers to show the min and max values. Alternatively, set this to an ascending sequence of percentile (e.g., [5, 95]) to set the whiskers at specific percentiles of the data. Finally, whis can be the string 'range' to force the whiskers to the min and max of the data. bootstrap : int, optional Specifies whether to bootstrap the confidence intervals around the median for notched boxplots. If bootstrap is None, no bootstrapping is performed, and notches are calculated using a Gaussian-based asymptotic approximation (see McGill, R., Tukey, J.W., and Larsen, W.A., 1978, and Kendall and Stuart, 1967). Otherwise, bootstrap specifies the number of times to bootstrap the median to determine its 95% confidence intervals. Values between 1000 and 10000 are recommended. usermedians : array-like, optional An array or sequence whose first dimension (or length) is compatible with x. This overrides the medians computed by matplotlib for each element of usermedians that is not None. When an element of usermedians is None, the median will be computed by matplotlib as normal. conf_intervals : array-like, optional Array or sequence whose first dimension (or length) is compatible with x and whose second dimension is 2. When the an element of conf_intervals is not None, the notch locations computed by matplotlib are overridden (provided notch is True). When an element of conf_intervals is None, the notches are computed by the method specified by the other kwargs (e.g., bootstrap). positions : array-like, optional Sets the positions of the boxes. The ticks and limits are automatically set to match the positions. Defaults to range(1, N+1) where N is the number of boxes to be drawn. widths : scalar or array-like Sets the width of each box either with a scalar or a sequence. The default is 0.5, or 0.15*(distance between extreme positions), if that is smaller. patch_artist : bool, optional (False) If False produces boxes with the Line2D artist. Otherwise, boxes and drawn with Patch artists. labels : sequence, optional Labels for each dataset. Length must be compatible with dimensions of x. manage_xticks : bool, optional (True) If the function should adjust the xlim and xtick locations. autorange : bool, optional (False) When True and the data are distributed such that the 25th and 75th percentiles are equal, whis is set to 'range' such that the whisker ends are at the minimum and maximum of the data. meanline : bool, optional (False) If True (and showmeans is True), will try to render the mean as a line spanning the full width of the box according to meanprops (see below). Not recommended if shownotches is also True. Otherwise, means will be shown as points.
Returns:	result : dict A dictionary mapping each component of the boxplot to a list of the matplotlib.lines.Line2D instances created. That dictionary has the following keys (assuming vertical boxplots): boxes: the main body of the boxplot showing the quartiles and the median’s confidence intervals if enabled. medians: horizontal lines at the median of each box. whiskers: the vertical lines extending to the most extreme, non-outlier data points. caps: the horizontal lines at the ends of the whiskers. fliers: points representing data that extend beyond the whiskers (fliers). means: points or lines representing the means.
Other Parameters:
	The following boolean options toggle the drawing of individual components of the boxplots: showcaps: the caps on the ends of whiskers (default is True) showbox: the central box (default is True) showfliers: the outliers beyond the caps (default is True) showmeans: the arithmetic means (default is False) The remaining options can accept dictionaries that specify the style of the individual artists: capprops boxprops whiskerprops flierprops medianprops meanprops

Notes

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

• All positional and all keyword arguments.

Examples

(Source code, png, hires.png, pdf)

(png, hires.png, pdf)

broken_barh(xranges, yrange, **kwargs)

Plot horizontal bars.

Call signature:

broken_barh(self, xranges, yrange, **kwargs)

A collection of horizontal bars spanning yrange with a sequence of xranges.

Required arguments:

Argument	Description
xranges	sequence of (xmin, xwidth)
yrange	sequence of (ymin, ywidth)

kwargs are matplotlib.collections.BrokenBarHCollection properties:

Property	Description
agg_filter	unknown
alpha	float or None
animated	[True | False]
antialiased or antialiaseds	Boolean or sequence of booleans
array	unknown
axes	an Axes instance
clim	a length 2 sequence of floats
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
cmap	a colormap or registered colormap name
color	matplotlib color arg or sequence of rgba tuples
contains	a callable function
edgecolor or edgecolors	matplotlib color spec or sequence of specs
facecolor or facecolors	matplotlib color spec or sequence of specs
figure	a matplotlib.figure.Figure instance
gid	an id string
hatch	[ ‘/’ | ‘\’ | ‘|’ | ‘-‘ | ‘+’ | ‘x’ | ‘o’ | ‘O’ | ‘.’ | ‘*’ ]
label	string or anything printable with ‘%s’ conversion.
linestyle or linestyles or dashes	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or linewidths or lw	float or sequence of floats
norm	unknown
offset_position	unknown
offsets	float or sequence of floats
path_effects	unknown
picker	[None|float|boolean|callable]
pickradius	unknown
rasterized	[True | False | None]
sketch_params	unknown
snap	unknown
transform	Transform instance
url	a url string
urls	unknown
visible	[True | False]
zorder	any number

these can either be a single argument, i.e.,:

facecolors = 'black'

or a sequence of arguments for the various bars, i.e.,:

facecolors = ('black', 'red', 'green')

Example:

(Source code, png, hires.png, pdf)

Notes

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

• All positional and all keyword arguments.

bxp(bxpstats, positions=None, widths=None, vert=True, patch_artist=False, shownotches=False, showmeans=False, showcaps=True, showbox=True, showfliers=True, boxprops=None, whiskerprops=None, flierprops=None, medianprops=None, capprops=None, meanprops=None, meanline=False, manage_xticks=True)

Drawing function for box and whisker plots.

Call signature:

bxp(self, bxpstats, positions=None, widths=None, vert=True,
    patch_artist=False, shownotches=False, showmeans=False,
    showcaps=True, showbox=True, showfliers=True,
    boxprops=None, whiskerprops=None, flierprops=None,
    medianprops=None, capprops=None, meanprops=None,
    meanline=False, manage_xticks=True):

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:

bxpstats : list of dicts A list of dictionaries containing stats for each boxplot. Required keys are: med: The median (scalar float). q1: The first quartile (25th percentile) (scalar float). q3: The third quartile (75th percentile) (scalar float). whislo: Lower bound of the lower whisker (scalar float). whishi: Upper bound of the upper whisker (scalar float). Optional keys are: mean: The mean (scalar float). Needed if showmeans=True. fliers: Data beyond the whiskers (sequence of floats). Needed if showfliers=True. cilo & cihi: Lower and upper confidence intervals about the median. Needed if shownotches=True. label: Name of the dataset (string). If available, this will be used a tick label for the boxplot positions : array-like, default = [1, 2, ..., n] Sets the positions of the boxes. The ticks and limits are automatically set to match the positions. widths : array-like, default = 0.5 Either a scalar or a vector and sets the width of each box. The default is 0.5, or 0.15*(distance between extreme positions) if that is smaller. vert : bool, default = False If True (default), makes the boxes vertical. If False, makes horizontal boxes. patch_artist : bool, default = False If False produces boxes with the Line2D artist. If True produces boxes with the Patch artist. shownotches : bool, default = False If False (default), produces a rectangular box plot. If True, will produce a notched box plot showmeans : bool, default = False If True, will toggle on the rendering of the means showcaps : bool, default = True If True, will toggle on the rendering of the caps showbox : bool, default = True If True, will toggle on the rendering of the box showfliers : bool, default = True If True, will toggle on the rendering of the fliers boxprops : dict or None (default) If provided, will set the plotting style of the boxes whiskerprops : dict or None (default) If provided, will set the plotting style of the whiskers capprops : dict or None (default) If provided, will set the plotting style of the caps flierprops : dict or None (default) If provided will set the plotting style of the fliers medianprops : dict or None (default) If provided, will set the plotting style of the medians meanprops : dict or None (default) If provided, will set the plotting style of the means meanline : bool, default = False If True (and showmeans is True), will try to render the mean as a line spanning the full width of the box according to meanprops. Not recommended if shownotches is also True. Otherwise, means will be shown as points. manage_xticks : bool, default = True If the function should adjust the xlim and xtick locations.

Returns:

result : dict A dictionary mapping each component of the boxplot to a list of the matplotlib.lines.Line2D instances created. That dictionary has the following keys (assuming vertical boxplots): boxes: the main body of the boxplot showing the quartiles and the median’s confidence intervals if enabled. medians: horizontal lines at the median of each box. whiskers: the vertical lines extending to the most extreme, non-outlier data points. caps: the horizontal lines at the ends of the whiskers. fliers: points representing data that extend beyond the whiskers (fliers). means: points or lines representing the means.

Examples

(Source code, png, hires.png, pdf)

(png, hires.png, pdf)

can_pan()

Return True if this axes supports any pan/zoom button functionality.

can_zoom()

Return True if this axes supports the zoom box button functionality.

cla()

Clear the current axes.

clabel(CS, *args, **kwargs)

Label a contour plot.

Call signature:

clabel(cs, **kwargs)

Adds labels to line contours in cs, where cs is a ContourSet object returned by contour.

clabel(cs, v, **kwargs)

only labels contours listed in v.

Optional keyword arguments:

fontsize:

size in points or relative size e.g., ‘smaller’, ‘x-large’

colors:

• if None, the color of each label matches the color of the corresponding contour
• if one string color, e.g., colors = ‘r’ or colors = ‘red’, all labels will be plotted in this color
• if a tuple of matplotlib color args (string, float, rgb, etc), different labels will be plotted in different colors in the order specified

inline:

controls whether the underlying contour is removed or not. Default is True.

inline_spacing:

space in pixels to leave on each side of label when placing inline. Defaults to 5. This spacing will be exact for labels at locations where the contour is straight, less so for labels on curved contours.

fmt:

a format string for the label. Default is ‘%1.3f’ Alternatively, this can be a dictionary matching contour levels with arbitrary strings to use for each contour level (i.e., fmt[level]=string), or it can be any callable, such as a 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.

(Source code)

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(png, hires.png, pdf)

(png, hires.png, pdf)

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clear()

clear the axes

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

Plot the coherence between x and y.

Call signature:

cohere(x, y, NFFT=256, Fs=2, Fc=0, detrend = mlab.detrend_none,
       window = mlab.window_hanning, noverlap=0, pad_to=None,
       sides='default', scale_by_freq=None, **kwargs)

Plot the coherence between x and y. Coherence is the normalized cross spectral density:

Keyword arguments:

Fs: scalar

The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. The default value is 2.

window: callable or ndarray

A function or a vector of length NFFT. To create window vectors see window_hanning(), window_none(), numpy.blackman(), numpy.hamming(), numpy.bartlett(), scipy.signal(), scipy.signal.get_window(), etc. The default is window_hanning(). If a function is passed as the argument, it must take a data segment as an argument and return the windowed version of the segment.

sides: [ ‘default’ | ‘onesided’ | ‘twosided’ ]

Specifies which sides of the spectrum to return. Default gives the default behavior, which returns one-sided for real data and both for complex data. ‘onesided’ forces the return of a one-sided spectrum, while ‘twosided’ forces two-sided.

pad_to: integer

The number of points to which the data segment is padded when performing the FFT. This can be different from NFFT, which specifies the number of data points used. While not increasing the actual resolution of the spectrum (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. This corresponds to the n parameter in the call to fft(). The default is None, which sets pad_to equal to NFFT

NFFT: integer

The number of data points used in each block for the FFT. A power 2 is most efficient. The default value is 256. This should NOT be used to get zero padding, or the scaling of the result will be incorrect. Use pad_to for this instead.

detrend: [ ‘default’ | ‘constant’ | ‘mean’ | ‘linear’ | ‘none’] or

callable

The function applied to each segment before fft-ing, designed to remove the mean or linear trend. Unlike in MATLAB, where the detrend parameter is a vector, in matplotlib is it a function. The pylab module defines detrend_none(), detrend_mean(), and detrend_linear(), but you can use a custom function as well. You can also use a string to choose one of the functions. ‘default’, ‘constant’, and ‘mean’ call detrend_mean(). ‘linear’ calls detrend_linear(). ‘none’ calls detrend_none().

scale_by_freq: boolean

Specifies whether the resulting density values should be scaled by the scaling frequency, which gives density in units of Hz^-1. This allows for integration over the returned frequency values. The default is True for MATLAB compatibility.

noverlap: integer

The number of points of overlap between blocks. The default value is 0 (no overlap).

Fc: integer

The center frequency of x (defaults to 0), which offsets the x extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband.

The return value is a tuple (Cxy, f), where f are the frequencies of the coherence vector.

kwargs are applied to the lines.

References:

• Bendat & Piersol – Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986)

kwargs control the Line2D properties of the coherence plot:

Property	Description
agg_filter	unknown
alpha	float (0.0 transparent through 1.0 opaque)
animated	[True | False]
antialiased or aa	[True | False]
axes	an Axes instance
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
color or c	any matplotlib color
contains	a callable function
dash_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
dash_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
dashes	sequence of on/off ink in points
drawstyle	[‘default’ | ‘steps’ | ‘steps-pre’ | ‘steps-mid’ | ‘steps-post’]
figure	a matplotlib.figure.Figure instance
fillstyle	[‘full’ | ‘left’ | ‘right’ | ‘bottom’ | ‘top’ | ‘none’]
gid	an id string
label	string or anything printable with ‘%s’ conversion.
linestyle or ls	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or lw	float value in points
marker	A valid marker style
markeredgecolor or mec	any matplotlib color
markeredgewidth or mew	float value in points
markerfacecolor or mfc	any matplotlib color
markerfacecoloralt or mfcalt	any matplotlib color
markersize or ms	float
markevery	[None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects	unknown
picker	float distance in points or callable pick function fn(artist, event)
pickradius	float distance in points
rasterized	[True | False | None]
sketch_params	unknown
snap	unknown
solid_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
solid_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
transform	a matplotlib.transforms.Transform instance
url	a url string
visible	[True | False]
xdata	1D array
ydata	1D array
zorder	any number

Example:

(Source code, png, hires.png, pdf)

Notes

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

• All arguments with the following names: ‘y’, ‘x’.

contains(mouseevent)

Test whether the mouse event occured in the axes.

Returns True / False, {}

contains_point(point)

Returns True if the point (tuple of x,y) is inside the axes (the area defined by the its patch). A pixel coordinate is required.

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 automatically-chosen levels.

contour(Z,V)
contour(X,Y,Z,V)

draw contour lines at the values specified in sequence V, which must be in increasing order.

contourf(..., V)

fill the len(V)-1 regions between the values in V, which must be in increasing order.

contour(Z, **kwargs)

Use keyword args to control colors, linewidth, origin, cmap ... see below for more details.

X and Y must both be 2-D with the same shape as Z, or they must both be 1-D such that len(X) is the number of columns in Z and len(Y) is the number of rows in Z.

C = contour(...) returns a 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() and matplotlib.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.

contour-only keyword arguments:

linewidths: [ None | number | tuple of numbers ]

If linewidths is None, the default width in lines.linewidth in matplotlibrc is used.

If a number, all levels will be plotted with this linewidth.

If a tuple, different levels will be plotted with different linewidths in the order specified.

linestyles: [ None | ‘solid’ | ‘dashed’ | ‘dashdot’ | ‘dotted’ ]

If linestyles is None, the default is ‘solid’ unless the lines are monochrome. In that case, negative contours will take their linestyle from the matplotlibrc contour.negative_linestyle setting.

linestyles can also be an iterable of the above strings specifying a set of linestyles to be used. If this iterable is shorter than the number of contour levels it will be repeated as necessary.

contourf-only keyword arguments:

hatches:

A list of cross hatch patterns to use on the filled areas. If None, no hatching will be added to the contour. Hatching is supported in the PostScript, PDF, SVG and Agg backends only.

Note: contourf fills intervals that are closed at the top; that is, for boundaries z1 and z2, the filled region is:

z1 < z <= z2

There is one exception: if the lowest boundary coincides with the minimum value of the z array, then that minimum value will be included in the lowest interval.

Examples:

(Source code)

(png, hires.png, pdf)

(png, hires.png, pdf)

(png, hires.png, pdf)

(png, hires.png, pdf)

(png, hires.png, pdf)

(png, hires.png, pdf)

(Source code)

(png, hires.png, pdf)

(png, hires.png, pdf)

(png, hires.png, pdf)

(Source code, png, hires.png, pdf)

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 automatically-chosen levels.

contour(Z,V)
contour(X,Y,Z,V)

draw contour lines at the values specified in sequence V, which must be in increasing order.

contourf(..., V)

fill the len(V)-1 regions between the values in V, which must be in increasing order.

contour(Z, **kwargs)

Use keyword args to control colors, linewidth, origin, cmap ... see below for more details.

X and Y must both be 2-D with the same shape as Z, or they must both be 1-D such that len(X) is the number of columns in Z and len(Y) is the number of rows in Z.

C = contour(...) returns a 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() and matplotlib.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.

contour-only keyword arguments:

linewidths: [ None | number | tuple of numbers ]

If linewidths is None, the default width in lines.linewidth in matplotlibrc is used.

If a number, all levels will be plotted with this linewidth.

If a tuple, different levels will be plotted with different linewidths in the order specified.

linestyles: [ None | ‘solid’ | ‘dashed’ | ‘dashdot’ | ‘dotted’ ]

If linestyles is None, the default is ‘solid’ unless the lines are monochrome. In that case, negative contours will take their linestyle from the matplotlibrc contour.negative_linestyle setting.

linestyles can also be an iterable of the above strings specifying a set of linestyles to be used. If this iterable is shorter than the number of contour levels it will be repeated as necessary.

contourf-only keyword arguments:

hatches:

A list of cross hatch patterns to use on the filled areas. If None, no hatching will be added to the contour. Hatching is supported in the PostScript, PDF, SVG and Agg backends only.

Note: contourf fills intervals that are closed at the top; that is, for boundaries z1 and z2, the filled region is:

z1 < z <= z2

There is one exception: if the lowest boundary coincides with the minimum value of the z array, then that minimum value will be included in the lowest interval.

Examples:

(Source code)

(png, hires.png, pdf)

(png, hires.png, pdf)

(png, hires.png, pdf)

(png, hires.png, pdf)

(png, hires.png, pdf)

(png, hires.png, pdf)

(Source code)

(png, hires.png, pdf)

(png, hires.png, pdf)

(png, hires.png, pdf)

(Source code, png, hires.png, pdf)

convert_xunits(x)

For artists in an axes, if the xaxis has units support, convert x using xaxis unit type

convert_yunits(y)

For artists in an axes, if the yaxis has units support, convert y using yaxis unit type

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, **kwargs)

Plot the cross-spectral density.

Call signature:

csd(x, y, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
    window=mlab.window_hanning, noverlap=0, pad_to=None,
    sides='default', scale_by_freq=None, return_line=None, **kwargs)

The cross spectral density by Welch’s average periodogram method. The vectors x and y are divided into NFFT length segments. Each segment is detrended by function detrend and windowed by function window. noverlap gives the length of the overlap between segments. The product of the direct FFTs of x and y are averaged over each segment to compute , with a scaling to correct for power loss due to windowing.

If len(x) < NFFT or len(y) < NFFT, they will be zero padded to NFFT.

x, y: 1-D arrays or sequences

Arrays or sequences containing the data

Keyword arguments:

Fs: scalar

The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. The default value is 2.

window: callable or ndarray

A function or a vector of length NFFT. To create window vectors see window_hanning(), window_none(), numpy.blackman(), numpy.hamming(), numpy.bartlett(), scipy.signal(), scipy.signal.get_window(), etc. The default is window_hanning(). If a function is passed as the argument, it must take a data segment as an argument and return the windowed version of the segment.

sides: [ ‘default’ | ‘onesided’ | ‘twosided’ ]

Specifies which sides of the spectrum to return. Default gives the default behavior, which returns one-sided for real data and both for complex data. ‘onesided’ forces the return of a one-sided spectrum, while ‘twosided’ forces two-sided.

pad_to: integer

The number of points to which the data segment is padded when performing the FFT. This can be different from NFFT, which specifies the number of data points used. While not increasing the actual resolution of the spectrum (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. This corresponds to the n parameter in the call to fft(). The default is None, which sets pad_to equal to NFFT

NFFT: integer

The number of data points used in each block for the FFT. A power 2 is most efficient. The default value is 256. This should NOT be used to get zero padding, or the scaling of the result will be incorrect. Use pad_to for this instead.

detrend: [ ‘default’ | ‘constant’ | ‘mean’ | ‘linear’ | ‘none’] or

callable

The function applied to each segment before fft-ing, designed to remove the mean or linear trend. Unlike in MATLAB, where the detrend parameter is a vector, in matplotlib is it a function. The pylab module defines detrend_none(), detrend_mean(), and detrend_linear(), but you can use a custom function as well. You can also use a string to choose one of the functions. ‘default’, ‘constant’, and ‘mean’ call detrend_mean(). ‘linear’ calls detrend_linear(). ‘none’ calls detrend_none().

scale_by_freq: boolean

Specifies whether the resulting density values should be scaled by the scaling frequency, which gives density in units of Hz^-1. This allows for integration over the returned frequency values. The default is True for MATLAB compatibility.

noverlap: integer

The number of points of overlap between segments. The default value is 0 (no overlap).

Fc: integer

The center frequency of x (defaults to 0), which offsets the x extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband.

return_line: bool

Whether to include the line object plotted in the returned values. Default is False.

If return_line is False, returns the tuple (Pxy, freqs). If return_line is True, returns the tuple (Pxy, freqs. line):

Pxy: 1-D array

The values for the cross spectrum P_{xy} before scaling (complex valued)

freqs: 1-D array

The frequencies corresponding to the elements in Pxy

line: a Line2D instance

The line created by this function. Only returend if return_line is True.

For plotting, the power is plotted as for decibels, though P_{xy} itself is returned.

References:

Bendat & Piersol – Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986)

kwargs control the Line2D properties:

Property	Description
agg_filter	unknown
alpha	float (0.0 transparent through 1.0 opaque)
animated	[True | False]
antialiased or aa	[True | False]
axes	an Axes instance
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
color or c	any matplotlib color
contains	a callable function
dash_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
dash_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
dashes	sequence of on/off ink in points
drawstyle	[‘default’ | ‘steps’ | ‘steps-pre’ | ‘steps-mid’ | ‘steps-post’]
figure	a matplotlib.figure.Figure instance
fillstyle	[‘full’ | ‘left’ | ‘right’ | ‘bottom’ | ‘top’ | ‘none’]
gid	an id string
label	string or anything printable with ‘%s’ conversion.
linestyle or ls	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or lw	float value in points
marker	A valid marker style
markeredgecolor or mec	any matplotlib color
markeredgewidth or mew	float value in points
markerfacecolor or mfc	any matplotlib color
markerfacecoloralt or mfcalt	any matplotlib color
markersize or ms	float
markevery	[None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects	unknown
picker	float distance in points or callable pick function fn(artist, event)
pickradius	float distance in points
rasterized	[True | False | None]
sketch_params	unknown
snap	unknown
solid_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
solid_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
transform	a matplotlib.transforms.Transform instance
url	a url string
visible	[True | False]
xdata	1D array
ydata	1D array
zorder	any number

Example:

(Source code, png, hires.png, pdf)

See also

psd()

psd() is the equivalent to setting y=x.

Notes

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

• All arguments with the following names: ‘y’, ‘x’.

drag_pan(button, key, x, y)

Called when the mouse moves during a pan operation.

button is the mouse button number:

• 1: LEFT
• 2: MIDDLE
• 3: RIGHT

key is a “shift” key

x, y are the mouse coordinates in display coords.

Note

Intended to be overridden by new projection types.

draw(artist, renderer, *args, **kwargs)

Draw everything (plot lines, axes, labels)

draw_artist(a)

This method can only be used after an initial draw which caches the renderer. It is used to efficiently update Axes data (axis ticks, labels, etc are not updated)

end_pan()

Called when a pan operation completes (when the mouse button is up.)

Note

Intended to be overridden by new projection types.

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, **kwargs)

Plot an errorbar graph.

Call signature:

errorbar(x, y, yerr=None, xerr=None,
         fmt='', ecolor=None, elinewidth=None, capsize=None,
         barsabove=False, lolims=False, uplims=False,
         xlolims=False, xuplims=False, errorevery=1,
         capthick=None)

Plot x versus y with error deltas in yerr and xerr. Vertical errorbars are plotted if yerr is not None. Horizontal errorbars are plotted if xerr is not None.

x, y, xerr, and yerr can all be scalars, which plots a single error bar at x, y.

Optional keyword arguments:

xerr/yerr: [ scalar | N, Nx1, or 2xN array-like ]

If a scalar number, len(N) array-like object, or an Nx1 array-like object, errorbars are drawn at +/-value relative to the data.

If a sequence of shape 2xN, errorbars are drawn at -row1 and +row2 relative to the data.

fmt: [ ‘’ | ‘none’ | plot format string ]

The plot format symbol. If fmt is ‘none’ (case-insensitive), only the errorbars are plotted. This is used for adding errorbars to a bar plot, for example. Default is ‘’, an empty plot format string; properties are then identical to the defaults for plot().

ecolor: [ None | mpl color ]

A matplotlib color arg which gives the color the errorbar lines; if None, use the color of the line connecting the markers.

elinewidth: scalar

The linewidth of the errorbar lines. If None, use the linewidth.

capsize: scalar

The length of the error bar caps in points; if None, it will take the value from errorbar.capsize rcParam.

capthick: scalar

An alias kwarg to markeredgewidth (a.k.a. - mew). This setting is a more sensible name for the property that controls the thickness of the error bar cap in points. For backwards compatibility, if mew or markeredgewidth are given, then they will over-ride capthick. This may change in future releases.

barsabove: [ True | False ]

if True, will plot the errorbars above the plot symbols. Default is below.

lolims / uplims / xlolims / xuplims: [ False | True ]

These arguments can be used to indicate that a value gives only upper/lower limits. In that case a caret symbol is used to indicate this. lims-arguments may be of the same type as xerr and yerr. To use limits with inverted axes, set_xlim() or set_ylim() must be called before errorbar().

errorevery: positive integer

subsamples the errorbars. e.g., if errorevery=5, errorbars for every 5-th datapoint will be plotted. The data plot itself still shows all data points.

All other keyword arguments are passed on to the plot command for the markers. For example, this code makes big red squares with thick green edges:

x,y,yerr = rand(3,10)
errorbar(x, y, yerr, marker='s',
         mfc='red', mec='green', ms=20, mew=4)

where mfc, mec, ms and mew are aliases for the longer property names, markerfacecolor, markeredgecolor, markersize and markeredgewith.

valid kwargs for the marker properties are

Property	Description
agg_filter	unknown
alpha	float (0.0 transparent through 1.0 opaque)
animated	[True | False]
antialiased or aa	[True | False]
axes	an Axes instance
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
color or c	any matplotlib color
contains	a callable function
dash_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
dash_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
dashes	sequence of on/off ink in points
drawstyle	[‘default’ | ‘steps’ | ‘steps-pre’ | ‘steps-mid’ | ‘steps-post’]
figure	a matplotlib.figure.Figure instance
fillstyle	[‘full’ | ‘left’ | ‘right’ | ‘bottom’ | ‘top’ | ‘none’]
gid	an id string
label	string or anything printable with ‘%s’ conversion.
linestyle or ls	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or lw	float value in points
marker	A valid marker style
markeredgecolor or mec	any matplotlib color
markeredgewidth or mew	float value in points
markerfacecolor or mfc	any matplotlib color
markerfacecoloralt or mfcalt	any matplotlib color
markersize or ms	float
markevery	[None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects	unknown
picker	float distance in points or callable pick function fn(artist, event)
pickradius	float distance in points
rasterized	[True | False | None]
sketch_params	unknown
snap	unknown
solid_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
solid_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
transform	a matplotlib.transforms.Transform instance
url	a url string
visible	[True | False]
xdata	1D array
ydata	1D array
zorder	any number

Returns (plotline, caplines, barlinecols):

plotline: Line2D instance

x, y plot markers and/or line

caplines: list of error bar cap

Line2D instances

barlinecols: list of

LineCollection instances for the horizontal and vertical error ranges.

Example:

(Source code, png, hires.png, pdf)

Notes

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

• All arguments with the following names: ‘xerr’, ‘y’, ‘yerr’, ‘x’.

eventplot(positions, orientation='horizontal', lineoffsets=1, linelengths=1, linewidths=None, colors=None, linestyles='solid', **kwargs)

Plot identical parallel lines at specific positions.

Call signature:

eventplot(positions, orientation='horizontal', lineoffsets=0,
          linelengths=1, linewidths=None, color =None,
          linestyles='solid'

Plot parallel lines at the given positions. positions should be a 1D or 2D array-like object, with each row corresponding to a row or column of lines.

This type of plot is commonly used in neuroscience for representing neural events, where it is commonly called a spike raster, dot raster, or raster plot.

However, it is useful in any situation where you wish to show the timing or position of multiple sets of discrete events, such as the arrival times of people to a business on each day of the month or the date of hurricanes each year of the last century.

orientation

: [ ‘horizonal’ | ‘vertical’ ]

‘horizonal’ : the lines will be vertical and arranged in rows “vertical’ : lines will be horizontal and arranged in columns

lineoffsets :

A float or array-like containing floats.

linelengths :

A float or array-like containing floats.

linewidths :

A float or array-like containing floats.

colors

must be a sequence of RGBA tuples (e.g., arbitrary color strings, etc, not allowed) or a list of such sequences

linestyles :

[ ‘solid’ | ‘dashed’ | ‘dashdot’ | ‘dotted’ ] or an array of these values

For linelengths, linewidths, colors, and linestyles, if only a single value is given, that value is applied to all lines. If an array-like is given, it must have the same length as positions, and each value will be applied to the corresponding row or column in positions.

Returns a list of matplotlib.collections.EventCollection objects that were added.

kwargs are LineCollection properties:

Property	Description
agg_filter	unknown
alpha	float or None
animated	[True | False]
antialiased or antialiaseds	Boolean or sequence of booleans
array	unknown
axes	an Axes instance
clim	a length 2 sequence of floats
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
cmap	a colormap or registered colormap name
color	matplotlib color arg or sequence of rgba tuples
contains	a callable function
edgecolor or edgecolors	matplotlib color spec or sequence of specs
facecolor or facecolors	matplotlib color spec or sequence of specs
figure	a matplotlib.figure.Figure instance
gid	an id string
hatch	[ ‘/’ | ‘\’ | ‘|’ | ‘-‘ | ‘+’ | ‘x’ | ‘o’ | ‘O’ | ‘.’ | ‘*’ ]
label	string or anything printable with ‘%s’ conversion.
linestyle or linestyles or dashes	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or linewidths or lw	float or sequence of floats
norm	unknown
offset_position	unknown
offsets	float or sequence of floats
path_effects	unknown
paths	unknown
picker	[None|float|boolean|callable]
pickradius	unknown
rasterized	[True | False | None]
segments	unknown
sketch_params	unknown
snap	unknown
transform	Transform instance
url	a url string
urls	unknown
verts	unknown
visible	[True | False]
zorder	any number

Example:

(Source code, png, hires.png, pdf)

Notes

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

• All arguments with the following names: ‘linewidths’, ‘linelengths’, ‘lineoffsets’, ‘colors’, ‘positions’, ‘linestyles’.

fill(*args, **kwargs)

Plot filled polygons.

Call signature:

fill(*args, **kwargs)

args is a variable length argument, allowing for multiple x, y pairs with an optional color format string; see plot() for details on the argument parsing. For example, to plot a polygon with vertices at x, y in blue.:

ax.fill(x,y, 'b' )

An arbitrary number of x, y, color groups can be specified:

ax.fill(x1, y1, 'g', x2, y2, 'r')

Return value is a list of Patch instances that were added.

The same color strings that plot() supports are supported by the fill format string.

If you would like to fill below a curve, e.g., shade a region between 0 and y along x, use fill_between()

The closed kwarg will close the polygon when True (default).

kwargs control the Polygon properties:

Property	Description
agg_filter	unknown
alpha	float or None
animated	[True | False]
antialiased or aa	[True | False] or None for default
axes	an Axes instance
capstyle	[‘butt’ | ‘round’ | ‘projecting’]
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
color	matplotlib color spec
contains	a callable function
edgecolor or ec	mpl color spec, or None for default, or ‘none’ for no color
facecolor or fc	mpl color spec, or None for default, or ‘none’ for no color
figure	a matplotlib.figure.Figure instance
fill	[True | False]
gid	an id string
hatch	[‘/’ | ‘\’ | ‘|’ | ‘-‘ | ‘+’ | ‘x’ | ‘o’ | ‘O’ | ‘.’ | ‘*’]
joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
label	string or anything printable with ‘%s’ conversion.
linestyle or ls	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or lw	float or None for default
path_effects	unknown
picker	[None|float|boolean|callable]
rasterized	[True | False | None]
sketch_params	unknown
snap	unknown
transform	Transform instance
url	a url string
visible	[True | False]
zorder	any number

Example:

(Source code, png, hires.png, pdf)

Notes

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

• All arguments with the following names: ‘y’, ‘x’.

fill_between(x, y1, y2=0, where=None, interpolate=False, step=None, **kwargs)

Make filled polygons between two curves.

Create a PolyCollection filling the regions between y1 and y2 where where==True

Parameters:

x : array An N-length array of the x data y1 : array An N-length array (or scalar) of the y data y2 : array An N-length array (or scalar) of the y data where : array, optional If None, default to fill between everywhere. If not None, it is an N-length numpy boolean array and the fill will only happen over the regions where where==True. interpolate : bool, optional If True, interpolate between the two lines to find the precise point of intersection. Otherwise, the start and end points of the filled region will only occur on explicit values in the x array. step : {‘pre’, ‘post’, ‘mid’}, optional If not None, fill with step logic.

Notes

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

• All arguments with the following names: ‘y1’, ‘x’, ‘where’, ‘y2’.

Examples

(Source code)

(png, hires.png, pdf)

(png, hires.png, pdf)

(png, hires.png, pdf)

fill_betweenx(y, x1, x2=0, where=None, step=None, **kwargs)

Make filled polygons between two horizontal curves.

Call signature:

fill_betweenx(y, x1, x2=0, where=None, **kwargs)

Create a PolyCollection filling the regions between x1 and x2 where where==True

Parameters:

y : array An N-length array of the y data x1 : array An N-length array (or scalar) of the x data x2 : array, optional An N-length array (or scalar) of the x data where : array, optional If None, default to fill between everywhere. If not None, it is a N length numpy boolean array and the fill will only happen over the regions where where==True step : {‘pre’, ‘post’, ‘mid’}, optional If not None, fill with step logic.

Notes

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

• All arguments with the following names: ‘x2’, ‘y’, ‘where’, ‘x1’.

Examples

(Source code)

(png, hires.png, pdf)

(png, hires.png, pdf)

findobj(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.

format_coord(x, y)

Return a format string formatting the x, y coord

format_cursor_data(data)

Return cursor data string formatted.

format_xdata(x)

Return x string formatted. This function will use the attribute self.fmt_xdata if it is callable, else will fall back on the xaxis major formatter

format_ydata(y)

Return y string formatted. This function will use the fmt_ydata attribute if it is callable, else will fall back on the yaxis major formatter

get_adjustable()

get_agg_filter()

return filter function to be used for agg filter

get_alpha()

Return the alpha value used for blending - not supported on all backends

get_anchor()

get_animated()

Return the artist’s animated state

get_aspect()

get_autoscale_on()

Get whether autoscaling is applied for both axes on plot commands

get_autoscalex_on()

Get whether autoscaling for the x-axis is applied on plot commands

get_autoscaley_on()

Get whether autoscaling for the y-axis is applied on plot commands

get_axes()

Return the Axes instance the artist resides in, or None.

This has been deprecated in mpl 1.5, please use the axes property. Will be removed in 1.7 or 2.0.

get_axes_locator()

return axes_locator

get_axis_bgcolor()

Return the axis background color

get_axisbelow()

Get whether axis below is true or not

get_children()

return a list of child artists

get_clip_box()

Return artist clipbox

get_clip_on()

Return whether artist uses clipping

get_clip_path()

Return artist clip path

get_contains()

Return the _contains test used by the artist, or None for default.

get_cursor_data(event)

Get the cursor data for a given event.

get_cursor_props()

Return the cursor propertiess as a (linewidth, color) tuple, where linewidth is a float and color is an RGBA tuple

get_data_ratio()

Returns the aspect ratio of the raw data.

This method is intended to be overridden by new projection types.

get_data_ratio_log()

Returns the aspect ratio of the raw data in log scale. Will be used when both axis scales are in log.

get_default_bbox_extra_artists()

get_figure()

Return the Figure instance the artist belongs to.

get_frame_on()

Get whether the axes rectangle patch is drawn

get_gid()

Returns the group id

get_images()

return a list of Axes images contained by the Axes

get_label()

Get the label used for this artist in the legend.

get_legend()

Return the legend.Legend instance, or None if no legend is defined

get_legend_handles_labels(legend_handler_map=None)

Return handles and labels for legend

ax.legend() is equivalent to

h, l = ax.get_legend_handles_labels()
ax.legend(h, l)

get_lines()

Return a list of lines contained by the Axes

get_navigate()

Get whether the axes responds to navigation commands

get_navigate_mode()

Get the navigation toolbar button status: ‘PAN’, ‘ZOOM’, or None

get_path_effects()

get_picker()

Return the picker object used by this artist

get_position(original=False)

Return the a copy of the axes rectangle as a Bbox

get_rasterization_zorder()

Get zorder value below which artists will be rasterized

get_rasterized()

return True if the artist is to be rasterized

get_renderer_cache()

get_shared_x_axes()

Return a copy of the shared axes Grouper object for x axes

get_shared_y_axes()

Return a copy of the shared axes Grouper object for y axes

get_sketch_params()

Returns the sketch parameters for the artist.

Returns:	sketch_params : tuple or None A 3-tuple with the following elements: scale: The amplitude of the wiggle perpendicular to the source line. length: The length of the wiggle along the line. randomness: The scale factor by which the length is shrunken or expanded. May return None if no sketch parameters were set.

get_snap()

Returns the snap setting which may be:

• True: snap vertices to the nearest pixel center
• False: leave vertices as-is
• None: (auto) If the path contains only rectilinear line segments, round to the nearest pixel center

Only supported by the Agg and MacOSX backends.

get_tightbbox(renderer, call_axes_locator=True)

Return the tight bounding box of the axes. The dimension of the Bbox in canvas coordinate.

If call_axes_locator is False, it does not call the _axes_locator attribute, which is necessary to get the correct bounding box. call_axes_locator==False can be used if the caller is only intereted in the relative size of the tightbbox compared to the axes bbox.

get_title(loc='center')

Get an axes title.

Get 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.

Parameters:	loc : {‘center’, ‘left’, ‘right’}, str, optional Which title to get, defaults to ‘center’
Returns:	title: str The title text string.

get_transform()

Return the Transform instance used by this artist.

get_transformed_clip_path_and_affine()

Return the clip path with the non-affine part of its transformation applied, and the remaining affine part of its transformation.

get_url()

Returns the url

get_visible()

Return the artist’s visiblity

get_window_extent(*args, **kwargs)

get the axes bounding box in display space; args and kwargs are empty

get_xaxis()

Return the XAxis instance

get_xaxis_text1_transform(pad_points)

Get the transformation used for drawing x-axis labels, which will add the given amount of padding (in points) between the axes and the label. The x-direction is in data coordinates and the y-direction is in axis coordinates. Returns a 3-tuple of the form:

(transform, valign, halign)

where valign and halign are requested alignments for the text.

Note

This transformation is primarily used by the Axis class, and is meant to be overridden by new kinds of projections that may need to place axis elements in different locations.

get_xaxis_text2_transform(pad_points)

Get the transformation used for drawing the secondary x-axis labels, which will add the given amount of padding (in points) between the axes and the label. The x-direction is in data coordinates and the y-direction is in axis coordinates. Returns a 3-tuple of the form:

(transform, valign, halign)

where valign and halign are requested alignments for the text.

Note

This transformation is primarily used by the Axis class, and is meant to be overridden by new kinds of projections that may need to place axis elements in different locations.

get_xaxis_transform(which='grid')

Get the transformation used for drawing x-axis labels, ticks and gridlines. The x-direction is in data coordinates and the y-direction is in axis coordinates.

Note

This transformation is primarily used by the Axis class, and is meant to be overridden by new kinds of projections that may need to place axis elements in different locations.

get_xbound()

Returns the x-axis numerical bounds where:

lowerBound < upperBound

get_xgridlines()

Get the x grid lines as a list of Line2D instances

get_xlabel()

Get the xlabel text string.

get_xlim()

Get the x-axis range [left, right]

get_xmajorticklabels()

Get the xtick labels as a list of Text instances.

get_xminorticklabels()

Get the x minor tick labels as a list of matplotlib.text.Text instances.

get_xscale()

Return the xaxis scale string: linear, log, logit, symlog

get_xticklabels(minor=False, which=None)

Get the x tick labels as a list of Text instances.

Parameters:	minor : bool If True return the minor ticklabels, else return the major ticklabels which : None, (‘minor’, ‘major’, ‘both’) Overrides minor. Selects which ticklabels to return
Returns:	ret : list List of Text instances.

get_xticklines()

Get the xtick lines as a list of Line2D instances

get_xticks(minor=False)

Return the x ticks as a list of locations

get_yaxis()

Return the YAxis instance

get_yaxis_text1_transform(pad_points)

Get the transformation used for drawing y-axis labels, which will add the given amount of padding (in points) between the axes and the label. The x-direction is in axis coordinates and the y-direction is in data coordinates. Returns a 3-tuple of the form:

(transform, valign, halign)

where valign and halign are requested alignments for the text.

Note

This transformation is primarily used by the Axis class, and is meant to be overridden by new kinds of projections that may need to place axis elements in different locations.

get_yaxis_text2_transform(pad_points)

Get the transformation used for drawing the secondary y-axis labels, which will add the given amount of padding (in points) between the axes and the label. The x-direction is in axis coordinates and the y-direction is in data coordinates. Returns a 3-tuple of the form:

(transform, valign, halign)

where valign and halign are requested alignments for the text.

Note

This transformation is primarily used by the Axis class, and is meant to be overridden by new kinds of projections that may need to place axis elements in different locations.

get_yaxis_transform(which='grid')

Get the transformation used for drawing y-axis labels, ticks and gridlines. The x-direction is in axis coordinates and the y-direction is in data coordinates.

Note

This transformation is primarily used by the Axis class, and is meant to be overridden by new kinds of projections that may need to place axis elements in different locations.

get_ybound()

Return y-axis numerical bounds in the form of lowerBound < upperBound

get_ygridlines()

Get the y grid lines as a list of Line2D instances

get_ylabel()

Get the ylabel text string.

get_ylim()

Get the y-axis range [bottom, top]

get_ymajorticklabels()

Get the major y tick labels as a list of Text instances.

get_yminorticklabels()

Get the minor y tick labels as a list of Text instances.

get_yscale()

Return the yaxis scale string: linear, log, logit, symlog

get_yticklabels(minor=False, which=None)

Get the x tick labels as a list of Text instances.

Parameters:	minor : bool If True return the minor ticklabels, else return the major ticklabels which : None, (‘minor’, ‘major’, ‘both’) Overrides minor. Selects which ticklabels to return
Returns:	ret : list List of Text instances.

get_yticklines()

Get the ytick lines as a list of Line2D instances

get_yticks(minor=False)

Return the y ticks as a list of locations

get_zorder()

Return the Artist‘s zorder.

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

Turn the axes grids on or off.

Call signature:

grid(self, b=None, which='major', axis='both', **kwargs)

Set the axes grids on or off; b is a boolean. (For MATLAB compatibility, b may also be a string, ‘on’ or ‘off’.)

If b is None and len(kwargs)==0, toggle the grid state. If kwargs are supplied, it is assumed that you want a grid and b is thus set to True.

which can be ‘major’ (default), ‘minor’, or ‘both’ to control whether major tick grids, minor tick grids, or both are affected.

axis can be ‘both’ (default), ‘x’, or ‘y’ to control which set of gridlines are drawn.

kwargs are used to set the grid line properties, e.g.,:

ax.grid(color='r', linestyle='-', linewidth=2)

Valid Line2D kwargs are

Property	Description
agg_filter	unknown
alpha	float (0.0 transparent through 1.0 opaque)
animated	[True | False]
antialiased or aa	[True | False]
axes	an Axes instance
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
color or c	any matplotlib color
contains	a callable function
dash_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
dash_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
dashes	sequence of on/off ink in points
drawstyle	[‘default’ | ‘steps’ | ‘steps-pre’ | ‘steps-mid’ | ‘steps-post’]
figure	a matplotlib.figure.Figure instance
fillstyle	[‘full’ | ‘left’ | ‘right’ | ‘bottom’ | ‘top’ | ‘none’]
gid	an id string
label	string or anything printable with ‘%s’ conversion.
linestyle or ls	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or lw	float value in points
marker	A valid marker style
markeredgecolor or mec	any matplotlib color
markeredgewidth or mew	float value in points
markerfacecolor or mfc	any matplotlib color
markerfacecoloralt or mfcalt	any matplotlib color
markersize or ms	float
markevery	[None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects	unknown
picker	float distance in points or callable pick function fn(artist, event)
pickradius	float distance in points
rasterized	[True | False | None]
sketch_params	unknown
snap	unknown
solid_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
solid_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
transform	a matplotlib.transforms.Transform instance
url	a url string
visible	[True | False]
xdata	1D array
ydata	1D array
zorder	any number

has_data()

Return True if any artists have been added to axes.

This should not be used to determine whether the dataLim need to be updated, and may not actually be useful for anything.

have_units()

Return True if units are set on the x or y axes

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

Make a hexagonal binning plot.

Call signature:

hexbin(x, y, C = None, gridsize = 100, bins = None,
       xscale = 'linear', yscale = 'linear',
       cmap=None, norm=None, vmin=None, vmax=None,
       alpha=None, linewidths=None, edgecolors='none'
       reduce_C_function = np.mean, mincnt=None, marginals=True
       **kwargs)

Make a hexagonal binning plot of x versus y, where x, y are 1-D sequences of the same length, N. If C is None (the default), this is a histogram of the number of occurences of the observations at (x[i],y[i]).

If C is specified, it specifies values at the coordinate (x[i],y[i]). These values are accumulated for each hexagonal bin and then reduced according to reduce_C_function, which defaults to numpy’s mean function (np.mean). (If C is specified, it must also be a 1-D sequence of the same length as x and y.)

x, y and/or C may be masked arrays, in which case only unmasked points will be plotted.

Optional keyword arguments:

gridsize: [ 100 | integer ]

The number of hexagons in the x-direction, default is 100. The corresponding number of hexagons in the y-direction is chosen such that the hexagons are approximately regular. Alternatively, gridsize can be a tuple with two elements specifying the number of hexagons in the x-direction and the y-direction.

bins: [ None | ‘log’ | integer | sequence ]

If None, no binning is applied; the color of each hexagon directly corresponds to its count value.

If ‘log’, use a logarithmic scale for the color map. Internally, is used to determine the hexagon color.

If an integer, divide the counts in the specified number of bins, and color the hexagons accordingly.

If a sequence of values, the values of the lower bound of the bins to be used.

xscale: [ ‘linear’ | ‘log’ ]

Use a linear or log10 scale on the horizontal axis.

scale: [ ‘linear’ | ‘log’ ]

Use a linear or log10 scale on the vertical axis.

mincnt: [ None | a positive integer ]

If not None, only display cells with more than mincnt number of points in the cell

marginals: [ True | False ]

if marginals is True, plot the marginal density as colormapped rectagles along the bottom of the x-axis and left of the y-axis

extent: [ None | scalars (left, right, bottom, top) ]

The limits of the bins. The default assigns the limits based on gridsize, x, y, xscale and yscale.

Other keyword arguments controlling color mapping and normalization arguments:

cmap: [ None | Colormap ]

a matplotlib.colors.Colormap instance. If None, defaults to rc image.cmap.

norm: [ None | Normalize ]

matplotlib.colors.Normalize instance is used to scale luminance data to 0,1.

vmin / vmax: scalar

vmin and vmax are used in conjunction with norm to normalize luminance data. If either are None, the min and max of the color array C is used. Note if you pass a norm instance, your settings for vmin and vmax will be ignored.

alpha: scalar between 0 and 1, or None

the alpha value for the patches

linewidths: [ None | scalar ]

If None, defaults to rc lines.linewidth. Note that this is a tuple, and if you set the linewidths argument you must set it as a sequence of floats, as required by RegularPolyCollection.

Other keyword arguments controlling the Collection properties:

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

If 'none', draws the edges in the same color as the fill color. This is the default, as it avoids unsightly unpainted pixels between the hexagons.

If None, draws the outlines in the default color.

If a matplotlib color arg or sequence of rgba tuples, draws the outlines in the specified color.

Here are the standard descriptions of all the Collection kwargs:

Property	Description
agg_filter	unknown
alpha	float or None
animated	[True | False]
antialiased or antialiaseds	Boolean or sequence of booleans
array	unknown
axes	an Axes instance
clim	a length 2 sequence of floats
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
cmap	a colormap or registered colormap name
color	matplotlib color arg or sequence of rgba tuples
contains	a callable function
edgecolor or edgecolors	matplotlib color spec or sequence of specs
facecolor or facecolors	matplotlib color spec or sequence of specs
figure	a matplotlib.figure.Figure instance
gid	an id string
hatch	[ ‘/’ | ‘\’ | ‘|’ | ‘-‘ | ‘+’ | ‘x’ | ‘o’ | ‘O’ | ‘.’ | ‘*’ ]
label	string or anything printable with ‘%s’ conversion.
linestyle or linestyles or dashes	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or linewidths or lw	float or sequence of floats
norm	unknown
offset_position	unknown
offsets	float or sequence of floats
path_effects	unknown
picker	[None|float|boolean|callable]
pickradius	unknown
rasterized	[True | False | None]
sketch_params	unknown
snap	unknown
transform	Transform instance
url	a url string
urls	unknown
visible	[True | False]
zorder	any number

The return value is a PolyCollection instance; use get_array() on this PolyCollection to get the counts in each hexagon. If marginals is True, horizontal bar and vertical bar (both PolyCollections) will be attached to the return collection as attributes hbar and vbar.

Example:

(Source code, png, hires.png, pdf)

Notes

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

• All arguments with the following names: ‘y’, ‘x’.

hist(x, bins=10, range=None, normed=False, weights=None, cumulative=False, bottom=None, histtype='bar', align='mid', orientation='vertical', rwidth=None, log=False, color=None, label=None, stacked=False, **kwargs)

Plot a histogram.

Compute and draw the histogram of x. The return value is a tuple (n, bins, patches) or ([n0, n1, ...], bins, [patches0, patches1,...]) if the input contains multiple data.

Multiple data can be provided via x as a list of datasets of potentially different length ([x0, x1, ...]), or as a 2-D ndarray in which each column is a dataset. Note that the ndarray form is transposed relative to the list form.

Masked arrays are not supported at present.

Parameters:	x : (n,) array or sequence of (n,) arrays Input values, this takes either a single array or a sequency of arrays which are not required to be of the same length bins : integer or array_like, optional If an integer is given, bins + 1 bin edges are returned, consistently with numpy.histogram() for numpy version >= 1.3. Unequally spaced bins are supported if bins is a sequence. default is 10 range : tuple or None, optional The lower and upper range of the bins. Lower and upper outliers are ignored. If not provided, range is (x.min(), x.max()). Range has no effect if bins is a sequence. If bins is a sequence or range is specified, autoscaling is based on the specified bin range instead of the range of x. Default is None normed : boolean, optional If True, the first element of the return tuple will be the counts normalized to form a probability density, i.e., n/(len(x)`dbin), i.e., the integral of the histogram will sum to 1. If stacked is also True, the sum of the histograms is normalized to 1. Default is False weights : (n, ) array_like or None, optional An array of weights, of the same shape as x. Each value in x only contributes its associated weight towards the bin count (instead of 1). If normed is True, the weights are normalized, so that the integral of the density over the range remains 1. Default is None cumulative : boolean, optional If True, then a histogram is computed where each bin gives the counts in that bin plus all bins for smaller values. The last bin gives the total number of datapoints. If normed is also True then the histogram is normalized such that the last bin equals 1. If cumulative evaluates to less than 0 (e.g., -1), the direction of accumulation is reversed. In this case, if normed is also True, then the histogram is normalized such that the first bin equals 1. Default is False bottom : array_like, scalar, or None Location of the bottom baseline of each bin. If a scalar, the base line for each bin is shifted by the same amount. If an array, each bin is shifted independently and the length of bottom must match the number of bins. If None, defaults to 0. Default is None histtype : {‘bar’, ‘barstacked’, ‘step’, ‘stepfilled’}, optional The type of histogram to draw. ‘bar’ is a traditional bar-type histogram. If multiple data are given the bars are aranged side by side. ‘barstacked’ is a bar-type histogram where multiple data are stacked on top of each other. ‘step’ generates a lineplot that is by default unfilled. ‘stepfilled’ generates a lineplot that is by default filled. Default is ‘bar’ align : {‘left’, ‘mid’, ‘right’}, optional Controls how the histogram is plotted. ‘left’: bars are centered on the left bin edges. ‘mid’: bars are centered between the bin edges. ‘right’: bars are centered on the right bin edges. Default is ‘mid’ orientation : {‘horizontal’, ‘vertical’}, optional If ‘horizontal’, barh will be used for bar-type histograms and the bottom kwarg will be the left edges. rwidth : scalar or None, optional The relative width of the bars as a fraction of the bin width. If None, automatically compute the width. Ignored if histtype is ‘step’ or ‘stepfilled’. Default is None log : boolean, optional If True, the histogram axis will be set to a log scale. If log is True and x is a 1D array, empty bins will be filtered out and only the non-empty (n, bins, patches) will be returned. Default is False color : color or array_like of colors or None, optional Color spec or sequence of color specs, one per dataset. Default (None) uses the standard line color sequence. Default is None label : string or None, optional String, or sequence of strings to match multiple datasets. Bar charts yield multiple patches per dataset, but only the first gets the label, so that the legend command will work as expected. default is None stacked : boolean, optional If True, multiple data are stacked on top of each other If False multiple data are aranged side by side if histtype is ‘bar’ or on top of each other if histtype is ‘step’ Default is False
Returns:	n : array or list of arrays The values of the histogram bins. See normed and weights for a description of the possible semantics. If input x is an array, then this is an array of length nbins. If input is a sequence arrays [data1, data2,..], then this is a list of arrays with the values of the histograms for each of the arrays in the same order. bins : array The edges of the bins. Length nbins + 1 (nbins left edges and right edge of last bin). Always a single array even when multiple data sets are passed in. patches : list or list of lists Silent list of individual patches used to create the histogram or list of such list if multiple input datasets.
Other Parameters:
	kwargs : Patch properties

See also

hist2d

2D histograms

Notes

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

• All arguments with the following names: ‘weights’, ‘x’.

Examples

(Source code, png, hires.png, pdf)

hist2d(x, y, bins=10, range=None, normed=False, weights=None, cmin=None, cmax=None, **kwargs)

Make a 2D histogram plot.

Parameters:	x, y: array_like, shape (n, ) Input values bins: [None | int | [int, int] | array_like | [array, array]] The bin specification: If int, the number of bins for the two dimensions (nx=ny=bins). If [int, int], the number of bins in each dimension (nx, ny = bins). If array_like, the bin edges for the two dimensions (x_edges=y_edges=bins). If [array, array], the bin edges in each dimension (x_edges, y_edges = bins). The default value is 10. range : array_like shape(2, 2), optional, default: None The leftmost and rightmost edges of the bins along each dimension (if not specified explicitly in the bins parameters): [[xmin, xmax], [ymin, ymax]]. All values outside of this range will be considered outliers and not tallied in the histogram. normed : boolean, optional, default: False Normalize histogram. weights : array_like, shape (n, ), optional, default: None An array of values w_i weighing each sample (x_i, y_i). cmin : scalar, optional, default: None All bins that has count less than cmin will not be displayed and these count values in the return value count histogram will also be set to nan upon return cmax : scalar, optional, default: None All bins that has count more than cmax will not be displayed (set to none before passing to imshow) and these count values in the return value count histogram will also be set to nan upon return
Returns:	The return value is (counts, xedges, yedges, Image).
Other Parameters:
	kwargs : pcolorfast() properties.

See also

hist

1D histogram

Notes

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

• All arguments with the following names: ‘y’, ‘weights’, ‘x’.

Examples

(Source code, png, hires.png, pdf)

hitlist(event)

List the children of the artist which contain the mouse event event.

hlines(y, xmin, xmax, colors='k', linestyles='solid', label='', **kwargs)

Plot horizontal lines at each y from xmin to xmax.

Parameters:	y : scalar or sequence of scalar y-indexes where to plot the lines. xmin, xmax : scalar or 1D array_like Respective beginning and end of each line. If scalars are provided, all lines will have same length. colors : array_like of colors, optional, default: ‘k’ linestyles : [‘solid’ | ‘dashed’ | ‘dashdot’ | ‘dotted’], optional label : string, optional, default: ‘’
Returns:	lines : LineCollection
Other Parameters:
	kwargs : LineCollection properties.

See also

vlines

vertical lines

Notes

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

• All arguments with the following names: ‘xmax’, ‘y’, ‘xmin’.

Examples

(Source code, png, hires.png, pdf)

hold(b=None)

Call signature:

hold(b=None)

Set the hold state. If hold is None (default), toggle the hold state. Else set the hold state to boolean value b.

Examples:

# toggle hold
hold()

# turn hold on
hold(True)

# turn hold off
hold(False)

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

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, **kwargs)

Display an image on the axes.

Parameters:	X : array_like, shape (n, m) or (n, m, 3) or (n, m, 4) Display the image in X to current axes. X may be a float array, a uint8 array or a PIL image. If X is an array, it can have the following shapes: MxN – luminance (grayscale, float array only) MxNx3 – RGB (float or uint8 array) MxNx4 – RGBA (float or uint8 array) The value for each component of MxNx3 and MxNx4 float arrays should be in the range 0.0 to 1.0; MxN float arrays may be normalised. cmap : Colormap, optional, default: None If None, default to rc image.cmap value. cmap is ignored when X has RGB(A) information aspect : [‘auto’ | ‘equal’ | scalar], optional, default: None If ‘auto’, changes the image aspect ratio to match that of the axes. If ‘equal’, and extent is None, changes the axes aspect ratio to match that of the image. If extent is not None, the axes aspect ratio is changed to match that of the extent. If None, default to rc image.aspect value. interpolation : string, optional, default: None Acceptable values are ‘none’, ‘nearest’, ‘bilinear’, ‘bicubic’, ‘spline16’, ‘spline36’, ‘hanning’, ‘hamming’, ‘hermite’, ‘kaiser’, ‘quadric’, ‘catrom’, ‘gaussian’, ‘bessel’, ‘mitchell’, ‘sinc’, ‘lanczos’ If interpolation is None, default to rc image.interpolation. See also the filternorm and filterrad parameters. If interpolation is ‘none’, then no interpolation is performed on the Agg, ps and pdf backends. Other backends will fall back to ‘nearest’. norm : Normalize, optional, default: None A Normalize instance is used to scale luminance data to 0, 1. If None, use the default func:normalize. norm is only used if X is an array of floats. vmin, vmax : scalar, optional, default: None vmin and vmax are used in conjunction with norm to normalize luminance data. Note if you pass a norm instance, your settings for vmin and vmax will be ignored. alpha : scalar, optional, default: None The alpha blending value, between 0 (transparent) and 1 (opaque) origin : [‘upper’ | ‘lower’], optional, default: None Place the [0,0] index of the array in the upper left or lower left corner of the axes. If None, default to rc image.origin. extent : scalars (left, right, bottom, top), optional, default: None The location, in data-coordinates, of the lower-left and upper-right corners. If None, the image is positioned such that the pixel centers fall on zero-based (row, column) indices. shape : scalars (columns, rows), optional, default: None For raw buffer images filternorm : scalar, optional, default: 1 A parameter for the antigrain image resize filter. From the antigrain documentation, if filternorm = 1, the filter normalizes integer values and corrects the rounding errors. It doesn’t do anything with the source floating point values, it corrects only integers according to the rule of 1.0 which means that any sum of pixel weights must be equal to 1.0. So, the filter function must produce a graph of the proper shape. filterrad : scalar, optional, default: 4.0 The filter radius for filters that have a radius parameter, i.e. when interpolation is one of: ‘sinc’, ‘lanczos’ or ‘blackman’
Returns:	image : AxesImage
Other Parameters:
	kwargs : Artist properties.

See also

matshow

Plot a matrix or an array as an image.

Notes

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

• All positional and all keyword arguments.

Examples

(Source code, png, hires.png, pdf)

in_axes(mouseevent)

Return True if the given mouseevent (in display coords) is in the Axes

invert_xaxis()

Invert the x-axis.

invert_yaxis()

Invert the y-axis.

is_figure_set()

Returns True if the artist is assigned to a Figure.

is_transform_set()

Returns True if Artist has a transform explicitly set.

ishold()

return the HOLD status of the axes

legend(*args, **kwargs)

Places a legend on the axes.

To make a legend for lines which already exist on the axes (via plot for instance), simply call this function with an iterable of strings, one for each legend item. For example:

ax.plot([1, 2, 3])
ax.legend(['A simple line'])

However, in order to keep the “label” and the legend element instance together, it is preferable to specify the label either at artist creation, or by calling the set_label() method on the artist:

line, = ax.plot([1, 2, 3], label='Inline label')
# Overwrite the label by calling the method.
line.set_label('Label via method')
ax.legend()

Specific lines can be excluded from the automatic legend element selection by defining a label starting with an underscore. This is default for all artists, so calling legend() without any arguments and without setting the labels manually will result in no legend being drawn.

For full control of which artists have a legend entry, it is possible to pass an iterable of legend artists followed by an iterable of legend labels respectively:

legend((line1, line2, line3), ('label1', 'label2', 'label3'))

Parameters:

loc : int or string or pair of floats, default: ‘upper right’ The location of the legend. Possible codes are: Location String Location Code ‘best’ 0 ‘upper right’ 1 ‘upper left’ 2 ‘lower left’ 3 ‘lower right’ 4 ‘right’ 5 ‘center left’ 6 ‘center right’ 7 ‘lower center’ 8 ‘upper center’ 9 ‘center’ 10 Alternatively can be a 2-tuple giving x, y of the lower-left corner of the legend in axes coordinates (in which case bbox_to_anchor will be ignored). bbox_to_anchor : matplotlib.transforms.BboxBase instance or tuple of floats Specify any arbitrary location for the legend in bbox_transform coordinates (default Axes coordinates). For example, to put the legend’s upper right hand corner in the center of the axes the following keywords can be used: loc='upper right', bbox_to_anchor=(0.5, 0.5) ncol : integer The number of columns that the legend has. Default is 1. prop : None or matplotlib.font_manager.FontProperties or dict The font properties of the legend. If None (default), the current matplotlib.rcParams will be used. fontsize : int or float or {‘xx-small’, ‘x-small’, ‘small’, ‘medium’, ‘large’, ‘x-large’, ‘xx-large’} Controls the font size of the legend. If the value is numeric the size will be the absolute font size in points. String values are relative to the current default font size. This argument is only used if prop is not specified. numpoints : None or int The number of marker points in the legend when creating a legend entry for a line/matplotlib.lines.Line2D. Default is None which will take the value from the legend.numpoints rcParam. scatterpoints : None or int The number of marker points in the legend when creating a legend entry for a scatter plot/ matplotlib.collections.PathCollection. Default is None which will take the value from the legend.scatterpoints rcParam. scatteryoffsets : iterable of floats The vertical offset (relative to the font size) for the markers created for a scatter plot legend entry. 0.0 is at the base the legend text, and 1.0 is at the top. To draw all markers at the same height, set to [0.5]. Default [0.375, 0.5, 0.3125]. markerscale : None or int or float The relative size of legend markers compared with the originally drawn ones. Default is None which will take the value from the legend.markerscale rcParam. *markerfirst*: [ *True* | *False* ] if True, legend marker is placed to the left of the legend label if False, legend marker is placed to the right of the legend label frameon : None or bool Control whether a frame should be drawn around the legend. Default is None which will take the value from the legend.frameon rcParam. fancybox : None or bool Control whether round edges should be enabled around the FancyBboxPatch which makes up the legend’s background. Default is None which will take the value from the legend.fancybox rcParam. shadow : None or bool Control whether to draw a shadow behind the legend. Default is None which will take the value from the legend.shadow rcParam. framealpha : None or float Control the alpha transparency of the legend’s frame. Default is None which will take the value from the legend.framealpha rcParam. mode : {“expand”, None} If mode is set to "expand" the legend will be horizontally expanded to fill the axes area (or bbox_to_anchor if defines the legend’s size). bbox_transform : None or matplotlib.transforms.Transform The transform for the bounding box (bbox_to_anchor). For a value of None (default) the Axes’ transAxes transform will be used. title : str or None The legend’s title. Default is no title (None). borderpad : float or None The fractional whitespace inside the legend border. Measured in font-size units. Default is None which will take the value from the legend.borderpad rcParam. labelspacing : float or None The vertical space between the legend entries. Measured in font-size units. Default is None which will take the value from the legend.labelspacing rcParam. handlelength : float or None The length of the legend handles. Measured in font-size units. Default is None which will take the value from the legend.handlelength rcParam. handletextpad : float or None The pad between the legend handle and text. Measured in font-size units. Default is None which will take the value from the legend.handletextpad rcParam. borderaxespad : float or None The pad between the axes and legend border. Measured in font-size units. Default is None which will take the value from the legend.borderaxespad rcParam. columnspacing : float or None The spacing between columns. Measured in font-size units. Default is None which will take the value from the legend.columnspacing rcParam. handler_map : dict or None The custom dictionary mapping instances or types to a legend handler. This handler_map updates the default handler map found at matplotlib.legend.Legend.get_legend_handler_map().

Notes

Not all kinds of artist are supported by the legend command. See Legend guide for details.

Examples

(Source code, png, hires.png, pdf)

locator_params(axis='both', tight=None, **kwargs)

Control behavior of tick locators.

Keyword arguments:

axis

[‘x’ | ‘y’ | ‘both’] Axis on which to operate; default is ‘both’.

tight

[True | False | None] Parameter passed to autoscale_view(). Default is None, for no change.

Remaining keyword arguments are passed to directly to the set_params() method.

Typically one might want to reduce the maximum number of ticks and use tight bounds when plotting small subplots, for example:

ax.locator_params(tight=True, nbins=4)

Because the locator is involved in autoscaling, autoscale_view() is called automatically after the parameters are changed.

This presently works only for the MaxNLocator used by default on linear axes, but it may be generalized.

loglog(*args, **kwargs)

Make a plot with log scaling on both the x and y axis.

Call signature:

loglog(*args, **kwargs)

loglog() supports all the keyword arguments 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’ ]

Non-positive values in x or y can be masked as invalid, or clipped to a very small positive number

The remaining valid kwargs are Line2D properties:

Property	Description
agg_filter	unknown
alpha	float (0.0 transparent through 1.0 opaque)
animated	[True | False]
antialiased or aa	[True | False]
axes	an Axes instance
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
color or c	any matplotlib color
contains	a callable function
dash_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
dash_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
dashes	sequence of on/off ink in points
drawstyle	[‘default’ | ‘steps’ | ‘steps-pre’ | ‘steps-mid’ | ‘steps-post’]
figure	a matplotlib.figure.Figure instance
fillstyle	[‘full’ | ‘left’ | ‘right’ | ‘bottom’ | ‘top’ | ‘none’]
gid	an id string
label	string or anything printable with ‘%s’ conversion.
linestyle or ls	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or lw	float value in points
marker	A valid marker style
markeredgecolor or mec	any matplotlib color
markeredgewidth or mew	float value in points
markerfacecolor or mfc	any matplotlib color
markerfacecoloralt or mfcalt	any matplotlib color
markersize or ms	float
markevery	[None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects	unknown
picker	float distance in points or callable pick function fn(artist, event)
pickradius	float distance in points
rasterized	[True | False | None]
sketch_params	unknown
snap	unknown
solid_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
solid_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
transform	a matplotlib.transforms.Transform instance
url	a url string
visible	[True | False]
xdata	1D array
ydata	1D array
zorder	any number

Example:

(Source code, png, hires.png, pdf)

magnitude_spectrum(x, Fs=None, Fc=None, window=None, pad_to=None, sides=None, scale=None, **kwargs)

Plot the magnitude spectrum.

Call signature:

magnitude_spectrum(x, Fs=2, Fc=0,  window=mlab.window_hanning,
                   pad_to=None, sides='default', **kwargs)

Compute the magnitude spectrum of x. Data is padded to a length of pad_to and the windowing function window is applied to the signal.

x: 1-D array or sequence

Array or sequence containing the data

Keyword arguments:

Fs: scalar

The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. The default value is 2.

window: callable or ndarray

A function or a vector of length NFFT. To create window vectors see window_hanning(), window_none(), numpy.blackman(), numpy.hamming(), numpy.bartlett(), scipy.signal(), scipy.signal.get_window(), etc. The default is window_hanning(). If a function is passed as the argument, it must take a data segment as an argument and return the windowed version of the segment.

sides: [ ‘default’ | ‘onesided’ | ‘twosided’ ]

Specifies which sides of the spectrum to return. Default gives the default behavior, which returns one-sided for real data and both for complex data. ‘onesided’ forces the return of a one-sided spectrum, while ‘twosided’ forces two-sided.

pad_to: integer

The number of points to which the data segment is padded when performing the FFT. While not increasing the actual resolution of the spectrum (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. This corresponds to the n parameter in the call to fft(). The default is None, which sets pad_to equal to the length of the input signal (i.e. no padding).

scale: [ ‘default’ | ‘linear’ | ‘dB’ ]

The scaling of the values in the spec. ‘linear’ is no scaling. ‘dB’ returns the values in dB scale. When mode is ‘density’, this is dB power (10 * log10). Otherwise this is dB amplitude (20 * log10). ‘default’ is ‘linear’.

Fc: integer

The center frequency of x (defaults to 0), which offsets the x extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband.

Returns the tuple (spectrum, freqs, line):

spectrum: 1-D array

The values for the magnitude spectrum before scaling (real valued)

freqs: 1-D array

The frequencies corresponding to the elements in spectrum

line: a Line2D instance

The line created by this function

kwargs control the Line2D properties:

Property	Description
agg_filter	unknown
alpha	float (0.0 transparent through 1.0 opaque)
animated	[True | False]
antialiased or aa	[True | False]
axes	an Axes instance
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
color or c	any matplotlib color
contains	a callable function
dash_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
dash_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
dashes	sequence of on/off ink in points
drawstyle	[‘default’ | ‘steps’ | ‘steps-pre’ | ‘steps-mid’ | ‘steps-post’]
figure	a matplotlib.figure.Figure instance
fillstyle	[‘full’ | ‘left’ | ‘right’ | ‘bottom’ | ‘top’ | ‘none’]
gid	an id string
label	string or anything printable with ‘%s’ conversion.
linestyle or ls	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or lw	float value in points
marker	A valid marker style
markeredgecolor or mec	any matplotlib color
markeredgewidth or mew	float value in points
markerfacecolor or mfc	any matplotlib color
markerfacecoloralt or mfcalt	any matplotlib color
markersize or ms	float
markevery	[None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects	unknown
picker	float distance in points or callable pick function fn(artist, event)
pickradius	float distance in points
rasterized	[True | False | None]
sketch_params	unknown
snap	unknown
solid_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
solid_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
transform	a matplotlib.transforms.Transform instance
url	a url string
visible	[True | False]
xdata	1D array
ydata	1D array
zorder	any number

Example:

(Source code, png, hires.png, pdf)

See also

psd()

psd() plots the power spectral density.`.

angle_spectrum()

angle_spectrum() plots the angles of the corresponding frequencies.

phase_spectrum()

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

specgram()

specgram() can plot the magnitude spectrum of segments within the signal in a colormap.

Notes

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

• All arguments with the following names: ‘x’.

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.

matshow(Z, **kwargs)

Plot a matrix or array as an image.

The matrix will be shown the way it would be printed, with the first row at the top. Row and column numbering is zero-based.

Parameters:	Z : array_like shape (n, m) The matrix to be displayed.
Returns:	image : AxesImage
Other Parameters:
	kwargs : imshow arguments Sets origin to ‘upper’, ‘interpolation’ to ‘nearest’ and ‘aspect’ to equal.

See also

imshow

plot an image

Examples

(Source code)

(png, hires.png, pdf)

(png, hires.png, pdf)

(png, hires.png, pdf)

minorticks_off()

Remove minor ticks from the axes.

minorticks_on()

Add autoscaling minor ticks to the axes.

mouseover

name = 'rectilinear'

pchanged()

Fire an event when property changed, calling all of the registered callbacks.

pcolor(*args, **kwargs)

Create a pseudocolor plot of a 2-D array.

Note

pcolor can be very slow for large arrays; consider using the similar but much faster pcolormesh() instead.

Call signatures:

pcolor(C, **kwargs)
pcolor(X, Y, C, **kwargs)

C is the array of color values.

X and Y, if given, specify the (x, y) coordinates of the colored quadrilaterals; the quadrilateral for C[i,j] has corners at:

(X[i,   j],   Y[i,   j]),
(X[i,   j+1], Y[i,   j+1]),
(X[i+1, j],   Y[i+1, j]),
(X[i+1, j+1], Y[i+1, j+1]).

Ideally the dimensions of X and Y should be one greater than those of C; if the dimensions are the same, then the last row and column of C will be ignored.

Note that the column index corresponds to the x-coordinate, and the row index corresponds to y; for details, see the Grid Orientation section below.

If either or both of X and Y are 1-D arrays or column vectors, they will be expanded as needed into the appropriate 2-D arrays, making a rectangular grid.

X, Y and C may be masked arrays. If either C[i, j], or one of the vertices surrounding C[i,j] (X or Y at [i, j], [i+1, j], [i, j+1],[i+1, j+1]) is masked, nothing is plotted.

Keyword arguments:

cmap: [ None | Colormap ]

A 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 to normalize().

vmin/vmax: [ None | scalar ]

vmin and vmax are used in conjunction with norm to normalize luminance data. If either is None, it is autoscaled to the respective min or max of the color array C. If not None, vmin or vmax passed in here override any pre-existing values supplied in the norm instance.

shading: [ ‘flat’ | ‘faceted’ ]

If ‘faceted’, a black grid is drawn around each rectangle; if ‘flat’, edges are not drawn. Default is ‘flat’, contrary to MATLAB.

This kwarg is deprecated; please use ‘edgecolors’ instead:

• shading=’flat’ – edgecolors=’none’
• shading=’faceted – edgecolors=’k’

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

If None, the rc setting is used by default.

If 'none', edges will not be visible.

An mpl color or sequence of colors will set the edge color

alpha: 0 <= scalar <= 1 or None

the alpha blending value

snap: bool

Whether to snap the mesh to pixel boundaries.

Return value is a matplotlib.collections.Collection instance.

The grid orientation follows the MATLAB convention: an array C with shape (nrows, ncolumns) is plotted with the column number as X and the row number as Y, increasing up; hence it is plotted the way the array would be printed, except that the Y axis is reversed. That is, C is taken as C*(*y, x).

Similarly for meshgrid():

x = np.arange(5)
y = np.arange(3)
X, Y = np.meshgrid(x, y)

is equivalent to:

X = array([[0, 1, 2, 3, 4],
           [0, 1, 2, 3, 4],
           [0, 1, 2, 3, 4]])

Y = array([[0, 0, 0, 0, 0],
           [1, 1, 1, 1, 1],
           [2, 2, 2, 2, 2]])

so if you have:

C = rand(len(x), len(y))

then you need to transpose C:

pcolor(X, Y, C.T)

or:

pcolor(C.T)

MATLAB pcolor() always discards the last row and column of C, but matplotlib displays the last row and column if X and Y are not specified, or if X and Y have one more row and column than C.

kwargs can be used to control the PolyCollection properties:

Property	Description
agg_filter	unknown
alpha	float or None
animated	[True | False]
antialiased or antialiaseds	Boolean or sequence of booleans
array	unknown
axes	an Axes instance
clim	a length 2 sequence of floats
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
cmap	a colormap or registered colormap name
color	matplotlib color arg or sequence of rgba tuples
contains	a callable function
edgecolor or edgecolors	matplotlib color spec or sequence of specs
facecolor or facecolors	matplotlib color spec or sequence of specs
figure	a matplotlib.figure.Figure instance
gid	an id string
hatch	[ ‘/’ | ‘\’ | ‘|’ | ‘-‘ | ‘+’ | ‘x’ | ‘o’ | ‘O’ | ‘.’ | ‘*’ ]
label	string or anything printable with ‘%s’ conversion.
linestyle or linestyles or dashes	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or linewidths or lw	float or sequence of floats
norm	unknown
offset_position	unknown
offsets	float or sequence of floats
path_effects	unknown
picker	[None|float|boolean|callable]
pickradius	unknown
rasterized	[True | False | None]
sketch_params	unknown
snap	unknown
transform	Transform instance
url	a url string
urls	unknown
visible	[True | False]
zorder	any number

Note

The default antialiaseds is False if the default edgecolors*=”none” is used. This eliminates artificial lines at patch boundaries, and works regardless of the value of alpha. If *edgecolors is not “none”, then the default antialiaseds is taken from rcParams[‘patch.antialiased’], which defaults to True. Stroking the edges may be preferred if alpha is 1, but will cause artifacts otherwise.

See also

pcolormesh()

For an explanation of the differences between pcolor and pcolormesh.

Notes

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

• All positional and all keyword arguments.

pcolorfast(*args, **kwargs)

pseudocolor plot of a 2-D array

Experimental; this is a pcolor-type method that provides the fastest possible rendering with the Agg backend, and that can handle any quadrilateral grid. It supports only flat shading (no outlines), it lacks support for log scaling of the axes, and it does not have a pyplot wrapper.

Call signatures:

ax.pcolorfast(C, **kwargs)
ax.pcolorfast(xr, yr, C, **kwargs)
ax.pcolorfast(x, y, C, **kwargs)
ax.pcolorfast(X, Y, C, **kwargs)

C is the 2D array of color values corresponding to quadrilateral cells. Let (nr, nc) be its shape. C may be a masked array.

ax.pcolorfast(C, **kwargs) is equivalent to ax.pcolorfast([0,nc], [0,nr], C, **kwargs)

xr, yr specify the ranges of x and y corresponding to the rectangular region bounding C. If:

xr = [x0, x1]

and:

yr = [y0,y1]

then x goes from x0 to x1 as the second index of C goes from 0 to nc, etc. (x0, y0) is the outermost corner of cell (0,0), and (x1, y1) is the outermost corner of cell (nr-1, nc-1). All cells are rectangles of the same size. This is the fastest version.

x, y are 1D arrays of length nc +1 and nr +1, respectively, giving the x and y boundaries of the cells. Hence the cells are rectangular but the grid may be nonuniform. The speed is intermediate. (The grid is checked, and if found to be uniform the fast version is used.)

X and Y are 2D arrays with shape (nr +1, nc +1) that specify the (x,y) coordinates of the corners 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]). The cells need not be rectangular. This is the most general, but the slowest to render. It may produce faster and more compact output using ps, pdf, and svg backends, however.

Note that the column index corresponds to the x-coordinate, and the row index corresponds to y; for details, see the “Grid Orientation” section below.

Optional keyword arguments:

cmap: [ None | Colormap ]

A matplotlib.colors.Colormap instance from cm. 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 to normalize()

vmin/vmax: [ None | scalar ]

vmin and vmax are used in conjunction with norm to normalize luminance data. If either are None, the min and max of the color array C is used. If you pass a norm instance, vmin and vmax will be None.

alpha: 0 <= scalar <= 1 or None

the alpha blending value

Return value is an image if a regular or rectangular grid is specified, and a QuadMesh collection in the general quadrilateral case.

Notes

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

• All positional and all keyword arguments.

pcolormesh(*args, **kwargs)

Plot a quadrilateral mesh.

Call signatures:

pcolormesh(C)
pcolormesh(X, Y, C)
pcolormesh(C, **kwargs)

Create a pseudocolor plot of a 2-D array.

pcolormesh is similar to 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 to normalize().

vmin/vmax: [ None | scalar ]

vmin and vmax are used in conjunction with norm to normalize luminance data. If either is None, it is autoscaled to the respective min or max of the color array C. If not None, vmin or vmax passed in here override any pre-existing values supplied in the norm instance.

shading: [ ‘flat’ | ‘gouraud’ ]

‘flat’ indicates a solid color for each quad. When ‘gouraud’, each quad will be Gouraud shaded. When gouraud shading, edgecolors is ignored.

edgecolors: [None | 'None' | 'face' | color |

color sequence]

If None, the rc setting is used by default.

If 'None', edges will not be visible.

If 'face', edges will have the same color as the faces.

An mpl color or sequence of colors will set the edge color

alpha: 0 <= scalar <= 1 or None

the alpha blending value

Return value is a matplotlib.collections.QuadMesh object.

kwargs can be used to control the matplotlib.collections.QuadMesh properties:

Property	Description
agg_filter	unknown
alpha	float or None
animated	[True | False]
antialiased or antialiaseds	Boolean or sequence of booleans
array	unknown
axes	an Axes instance
clim	a length 2 sequence of floats
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
cmap	a colormap or registered colormap name
color	matplotlib color arg or sequence of rgba tuples
contains	a callable function
edgecolor or edgecolors	matplotlib color spec or sequence of specs
facecolor or facecolors	matplotlib color spec or sequence of specs
figure	a matplotlib.figure.Figure instance
gid	an id string
hatch	[ ‘/’ | ‘\’ | ‘|’ | ‘-‘ | ‘+’ | ‘x’ | ‘o’ | ‘O’ | ‘.’ | ‘*’ ]
label	string or anything printable with ‘%s’ conversion.
linestyle or linestyles or dashes	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or linewidths or lw	float or sequence of floats
norm	unknown
offset_position	unknown
offsets	float or sequence of floats
path_effects	unknown
picker	[None|float|boolean|callable]
pickradius	unknown
rasterized	[True | False | None]
sketch_params	unknown
snap	unknown
transform	Transform instance
url	a url string
urls	unknown
visible	[True | False]
zorder	any number

See also

pcolor()

For an explanation of the grid orientation and the expansion of 1-D X and/or Y to 2-D arrays.

Notes

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

• All positional and all keyword arguments.

phase_spectrum(x, Fs=None, Fc=None, window=None, pad_to=None, sides=None, **kwargs)

Plot the phase spectrum.

Call signature:

phase_spectrum(x, Fs=2, Fc=0,  window=mlab.window_hanning,
               pad_to=None, sides='default', **kwargs)

Compute the phase spectrum (unwrapped angle spectrum) of x. Data is padded to a length of pad_to and the windowing function window is applied to the signal.

x: 1-D array or sequence

Array or sequence containing the data

Keyword arguments:

Fs: scalar

The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. The default value is 2.

window: callable or ndarray

A function or a vector of length NFFT. To create window vectors see window_hanning(), window_none(), numpy.blackman(), numpy.hamming(), numpy.bartlett(), scipy.signal(), scipy.signal.get_window(), etc. The default is window_hanning(). If a function is passed as the argument, it must take a data segment as an argument and return the windowed version of the segment.

sides: [ ‘default’ | ‘onesided’ | ‘twosided’ ]

Specifies which sides of the spectrum to return. Default gives the default behavior, which returns one-sided for real data and both for complex data. ‘onesided’ forces the return of a one-sided spectrum, while ‘twosided’ forces two-sided.

pad_to: integer

The number of points to which the data segment is padded when performing the FFT. While not increasing the actual resolution of the spectrum (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. This corresponds to the n parameter in the call to fft(). The default is None, which sets pad_to equal to the length of the input signal (i.e. no padding).

Fc: integer

The center frequency of x (defaults to 0), which offsets the x extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband.

Returns the tuple (spectrum, freqs, line):

spectrum: 1-D array

The values for the phase spectrum in radians (real valued)

freqs: 1-D array

The frequencies corresponding to the elements in spectrum

line: a Line2D instance

The line created by this function

kwargs control the Line2D properties:

Property	Description
agg_filter	unknown
alpha	float (0.0 transparent through 1.0 opaque)
animated	[True | False]
antialiased or aa	[True | False]
axes	an Axes instance
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
color or c	any matplotlib color
contains	a callable function
dash_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
dash_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
dashes	sequence of on/off ink in points
drawstyle	[‘default’ | ‘steps’ | ‘steps-pre’ | ‘steps-mid’ | ‘steps-post’]
figure	a matplotlib.figure.Figure instance
fillstyle	[‘full’ | ‘left’ | ‘right’ | ‘bottom’ | ‘top’ | ‘none’]
gid	an id string
label	string or anything printable with ‘%s’ conversion.
linestyle or ls	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or lw	float value in points
marker	A valid marker style
markeredgecolor or mec	any matplotlib color
markeredgewidth or mew	float value in points
markerfacecolor or mfc	any matplotlib color
markerfacecoloralt or mfcalt	any matplotlib color
markersize or ms	float
markevery	[None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects	unknown
picker	float distance in points or callable pick function fn(artist, event)
pickradius	float distance in points
rasterized	[True | False | None]
sketch_params	unknown
snap	unknown
solid_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
solid_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
transform	a matplotlib.transforms.Transform instance
url	a url string
visible	[True | False]
xdata	1D array
ydata	1D array
zorder	any number

Example:

(Source code, png, hires.png, pdf)

See also

magnitude_spectrum()

magnitude_spectrum() plots the magnitudes of the corresponding frequencies.

angle_spectrum()

angle_spectrum() plots the wrapped version of this function.

specgram()

specgram() can plot the phase spectrum of segments within the signal in a colormap.

Notes

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

• All arguments with the following names: ‘x’.

pick(*args)

Call signature:

pick(mouseevent)

each child artist will fire a pick event if mouseevent is over the artist and the artist has picker set

pickable()

Return True if Artist is pickable.

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)

Plot a pie chart.

Call signature:

pie(x, explode=None, labels=None,
    colors=('b', 'g', 'r', 'c', 'm', 'y', 'k', 'w'),
    autopct=None, pctdistance=0.6, shadow=False,
    labeldistance=1.1, startangle=None, radius=None,
    counterclock=True, wedgeprops=None, textprops=None,
    center = (0, 0), frame = False )

Make a pie chart of array x. The fractional area of each wedge is given by x/sum(x). If sum(x) <= 1, then the values of x give the fractional area directly and the array will not be normalized. The wedges are plotted counterclockwise, by default starting from the x-axis.

Keyword arguments:

explode: [ None | len(x) sequence ]

If not None, is a len(x) array which specifies the fraction of the radius with which to offset each wedge.

colors: [ None | color sequence ]

A sequence of matplotlib color args through which the pie chart will cycle.

labels: [ None | len(x) sequence of strings ]

A sequence of strings providing the labels for each wedge

autopct: [ None | format string | format function ]

If not None, is a string or function used to label the wedges with their numeric value. The label will be placed inside the wedge. If it is a format string, the label will be fmt%pct. If it is a function, it will be called.

pctdistance: scalar

The ratio between the center of each pie slice and the start of the text generated by autopct. Ignored if autopct is None; default is 0.6.

labeldistance: scalar

The radial distance at which the pie labels are drawn

shadow: [ False | True ]

Draw a shadow beneath the pie.

startangle: [ None | Offset angle ]

If not None, rotates the start of the pie chart by angle degrees counterclockwise from the x-axis.

radius: [ None | scalar ] The radius of the pie, if radius is None it will be set to 1.

counterclock: [ False | True ]

Specify fractions direction, clockwise or counterclockwise.

wedgeprops: [ None | dict of key value pairs ]

Dict of arguments passed to the wedge objects making the pie. For example, you can pass in wedgeprops = { ‘linewidth’ : 3 } to set the width of the wedge border lines equal to 3. For more details, look at the doc/arguments of the wedge object. By default clip_on=False.

textprops: [ None | dict of key value pairs ]

Dict of arguments to pass to the text objects.

center: [ (0,0) | sequence of 2 scalars ] Center position of the chart.

frame: [ False | True ]

Plot axes frame with the chart.

The pie chart will probably look best if the figure and axes are square, or the Axes aspect is equal. e.g.:

figure(figsize=(8,8))
ax = axes([0.1, 0.1, 0.8, 0.8])

or:

axes(aspect=1)

Return value:

If autopct is None, return the tuple (patches, texts):

• patches is a sequence of 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.

Notes

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

• All arguments with the following names: ‘explode’, ‘colors’, ‘x’, ‘labels’.

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..N-1
plot(y, 'r+')     # ditto, but with red plusses

If x and/or y is 2-dimensional, then the corresponding columns will be plotted.

If used with labeled data, make sure that the color spec is not included as an element in data, as otherwise the last case plot("v","r", data={"v":..., "r":...) can be interpreted as the first case which would do plot(v, r) using the default line style and color.

If not used with labeled data (i.e., without a data argument), an arbitrary number of x, y, fmt groups can be specified, as in:

a.plot(x1, y1, 'g^', x2, y2, 'g-')

Return value is a list of lines that were added.

By default, each line is assigned a different style specified by a ‘style cycle’. To change this behavior, you can edit the axes.prop_cycle rcParam.

The following format string characters are accepted to control the line style or marker:

character	description
'-'	solid line style
'--'	dashed line style
'-.'	dash-dot line style
':'	dotted line style
'.'	point marker
','	pixel marker
'o'	circle marker
'v'	triangle_down marker
'^'	triangle_up marker
'<'	triangle_left marker
'>'	triangle_right marker
'1'	tri_down marker
'2'	tri_up marker
'3'	tri_left marker
'4'	tri_right marker
's'	square marker
'p'	pentagon marker
'*'	star marker
'h'	hexagon1 marker
'H'	hexagon2 marker
'+'	plus marker
'x'	x marker
'D'	diamond marker
'd'	thin_diamond marker
'|'	vline marker
'_'	hline marker

The following color abbreviations are supported:

character	color
‘b’	blue
‘g’	green
‘r’	red
‘c’	cyan
‘m’	magenta
‘y’	yellow
‘k’	black
‘w’	white

In addition, you can specify colors in many weird and wonderful ways, including full names ('green'), hex strings ('#008000'), RGB or RGBA tuples ((0,1,0,1)) or grayscale intensities as a string ('0.8'). Of these, the string specifications can be used in place of a fmt group, but the tuple forms can be used only as kwargs.

Line styles and colors are combined in a single format string, as in 'bo' for blue circles.

The kwargs can be used to set line properties (any property that has a set_* method). You can use this to set a line label (for auto legends), linewidth, anitialising, marker face color, etc. Here is an example:

plot([1,2,3], [1,2,3], 'go-', label='line 1', linewidth=2)
plot([1,2,3], [1,4,9], 'rs',  label='line 2')
axis([0, 4, 0, 10])
legend()

If you make multiple lines with one plot command, the kwargs apply to all those lines, e.g.:

plot(x1, y1, x2, y2, antialiased=False)

Neither line will be antialiased.

You do not need to use format strings, which are just abbreviations. All of the line properties can be controlled by keyword arguments. For example, you can set the color, marker, linestyle, and markercolor with:

plot(x, y, color='green', linestyle='dashed', marker='o',
     markerfacecolor='blue', markersize=12).

See Line2D for details.

The kwargs are Line2D properties:

Property	Description
agg_filter	unknown
alpha	float (0.0 transparent through 1.0 opaque)
animated	[True | False]
antialiased or aa	[True | False]
axes	an Axes instance
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
color or c	any matplotlib color
contains	a callable function
dash_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
dash_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
dashes	sequence of on/off ink in points
drawstyle	[‘default’ | ‘steps’ | ‘steps-pre’ | ‘steps-mid’ | ‘steps-post’]
figure	a matplotlib.figure.Figure instance
fillstyle	[‘full’ | ‘left’ | ‘right’ | ‘bottom’ | ‘top’ | ‘none’]
gid	an id string
label	string or anything printable with ‘%s’ conversion.
linestyle or ls	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or lw	float value in points
marker	A valid marker style
markeredgecolor or mec	any matplotlib color
markeredgewidth or mew	float value in points
markerfacecolor or mfc	any matplotlib color
markerfacecoloralt or mfcalt	any matplotlib color
markersize or ms	float
markevery	[None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects	unknown
picker	float distance in points or callable pick function fn(artist, event)
pickradius	float distance in points
rasterized	[True | False | None]
sketch_params	unknown
snap	unknown
solid_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
solid_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
transform	a matplotlib.transforms.Transform instance
url	a url string
visible	[True | False]
xdata	1D array
ydata	1D array
zorder	any number

kwargs scalex and scaley, if defined, are passed on to autoscale_view() to determine whether the x and y axes are autoscaled; the default is True.

Notes

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

• All arguments with the following names: ‘y’, ‘x’.

plot_date(x, y, fmt='o', tz=None, xdate=True, ydate=False, **kwargs)

Plot with data with dates.

Call signature:

plot_date(x, y, fmt='bo', tz=None, xdate=True,
          ydate=False, **kwargs)

Similar to the plot() command, except the x or y (or both) data is considered to be dates, and the axis is labeled accordingly.

x and/or y can be a sequence of dates represented as float days since 0001-01-01 UTC.

Keyword arguments:

fmt: string

The plot format string.

tz: [ None | timezone string | tzinfo instance]

The time zone to use in labeling dates. If None, defaults to rc value.

xdate: [ True | False ]

If True, the x-axis will be labeled with dates.

ydate: [ False | True ]

If True, the y-axis will be labeled with dates.

Note if you are using custom date tickers and formatters, it may be necessary to set the formatters/locators after the call to plot_date() since plot_date() will set the default tick locator to matplotlib.dates.AutoDateLocator (if the tick locator is not already set to a matplotlib.dates.DateLocator instance) and the default tick formatter to matplotlib.dates.AutoDateFormatter (if the tick formatter is not already set to a matplotlib.dates.DateFormatter instance).

Valid kwargs are Line2D properties:

Property	Description
agg_filter	unknown
alpha	float (0.0 transparent through 1.0 opaque)
animated	[True | False]
antialiased or aa	[True | False]
axes	an Axes instance
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
color or c	any matplotlib color
contains	a callable function
dash_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
dash_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
dashes	sequence of on/off ink in points
drawstyle	[‘default’ | ‘steps’ | ‘steps-pre’ | ‘steps-mid’ | ‘steps-post’]
figure	a matplotlib.figure.Figure instance
fillstyle	[‘full’ | ‘left’ | ‘right’ | ‘bottom’ | ‘top’ | ‘none’]
gid	an id string
label	string or anything printable with ‘%s’ conversion.
linestyle or ls	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or lw	float value in points
marker	A valid marker style
markeredgecolor or mec	any matplotlib color
markeredgewidth or mew	float value in points
markerfacecolor or mfc	any matplotlib color
markerfacecoloralt or mfcalt	any matplotlib color
markersize or ms	float
markevery	[None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects	unknown
picker	float distance in points or callable pick function fn(artist, event)
pickradius	float distance in points
rasterized	[True | False | None]
sketch_params	unknown
snap	unknown
solid_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
solid_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
transform	a matplotlib.transforms.Transform instance
url	a url string
visible	[True | False]
xdata	1D array
ydata	1D array
zorder	any number

See also

dates for helper functions

date2num(), num2date() and drange() for help on creating the required floating point dates.

Notes

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

• All arguments with the following names: ‘y’, ‘x’.

properties()

return a dictionary mapping property name -> value for all Artist props

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, **kwargs)

Plot the power spectral density.

Call signature:

psd(x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
    window=mlab.window_hanning, noverlap=0, pad_to=None,
    sides='default', scale_by_freq=None, return_line=None, **kwargs)

The power spectral density by Welch’s average periodogram method. The vector x is divided into NFFT length segments. Each segment is detrended by function detrend and windowed by function window. noverlap gives the length of the overlap between segments. The of each segment are averaged to compute , with a scaling to correct for power loss due to windowing.

If len(x) < NFFT, it will be zero padded to NFFT.

x: 1-D array or sequence

Array or sequence containing the data

Keyword arguments:

Fs: scalar

The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. The default value is 2.

window: callable or ndarray

A function or a vector of length NFFT. To create window vectors see window_hanning(), window_none(), numpy.blackman(), numpy.hamming(), numpy.bartlett(), scipy.signal(), scipy.signal.get_window(), etc. The default is window_hanning(). If a function is passed as the argument, it must take a data segment as an argument and return the windowed version of the segment.

sides: [ ‘default’ | ‘onesided’ | ‘twosided’ ]

Specifies which sides of the spectrum to return. Default gives the default behavior, which returns one-sided for real data and both for complex data. ‘onesided’ forces the return of a one-sided spectrum, while ‘twosided’ forces two-sided.

pad_to: integer

The number of points to which the data segment is padded when performing the FFT. This can be different from NFFT, which specifies the number of data points used. While not increasing the actual resolution of the spectrum (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. This corresponds to the n parameter in the call to fft(). The default is None, which sets pad_to equal to NFFT

NFFT: integer

The number of data points used in each block for the FFT. A power 2 is most efficient. The default value is 256. This should NOT be used to get zero padding, or the scaling of the result will be incorrect. Use pad_to for this instead.

detrend: [ ‘default’ | ‘constant’ | ‘mean’ | ‘linear’ | ‘none’] or

callable

The function applied to each segment before fft-ing, designed to remove the mean or linear trend. Unlike in MATLAB, where the detrend parameter is a vector, in matplotlib is it a function. The pylab module defines detrend_none(), detrend_mean(), and detrend_linear(), but you can use a custom function as well. You can also use a string to choose one of the functions. ‘default’, ‘constant’, and ‘mean’ call detrend_mean(). ‘linear’ calls detrend_linear(). ‘none’ calls detrend_none().

scale_by_freq: boolean

Specifies whether the resulting density values should be scaled by the scaling frequency, which gives density in units of Hz^-1. This allows for integration over the returned frequency values. The default is True for MATLAB compatibility.

noverlap: integer

The number of points of overlap between segments. The default value is 0 (no overlap).

Fc: integer

The center frequency of x (defaults to 0), which offsets the x extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband.

return_line: bool

Whether to include the line object plotted in the returned values. Default is False.

If return_line is False, returns the tuple (Pxx, freqs). If return_line is True, returns the tuple (Pxx, freqs. line):

Pxx: 1-D array

The values for the power spectrum P_{xx} before scaling (real valued)

freqs: 1-D array

The frequencies corresponding to the elements in Pxx

line: a Line2D instance

The line created by this function. Only returend if return_line is True.

For plotting, the power is plotted as for decibels, though Pxx itself is returned.

References:

Bendat & Piersol – Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986)

kwargs control the Line2D properties:

Property	Description
agg_filter	unknown
alpha	float (0.0 transparent through 1.0 opaque)
animated	[True | False]
antialiased or aa	[True | False]
axes	an Axes instance
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
color or c	any matplotlib color
contains	a callable function
dash_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
dash_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
dashes	sequence of on/off ink in points
drawstyle	[‘default’ | ‘steps’ | ‘steps-pre’ | ‘steps-mid’ | ‘steps-post’]
figure	a matplotlib.figure.Figure instance
fillstyle	[‘full’ | ‘left’ | ‘right’ | ‘bottom’ | ‘top’ | ‘none’]
gid	an id string
label	string or anything printable with ‘%s’ conversion.
linestyle or ls	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or lw	float value in points
marker	A valid marker style
markeredgecolor or mec	any matplotlib color
markeredgewidth or mew	float value in points
markerfacecolor or mfc	any matplotlib color
markerfacecoloralt or mfcalt	any matplotlib color
markersize or ms	float
markevery	[None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects	unknown
picker	float distance in points or callable pick function fn(artist, event)
pickradius	float distance in points
rasterized	[True | False | None]
sketch_params	unknown
snap	unknown
solid_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
solid_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
transform	a matplotlib.transforms.Transform instance
url	a url string
visible	[True | False]
xdata	1D array
ydata	1D array
zorder	any number

Example:

(Source code, png, hires.png, pdf)

See also

specgram()

specgram() differs in the default overlap; in not returning the mean of the segment periodograms; in returning the times of the segments; and in plotting a colormap instead of a line.

magnitude_spectrum()

magnitude_spectrum() plots the magnitude spectrum.

csd()

csd() plots the spectral density between two signals.

Notes

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

• All arguments with the following names: ‘x’.

quiver(*args, **kw)

Plot a 2-D field of arrows.

call signatures:

quiver(U, V, **kw)
quiver(U, V, C, **kw)
quiver(X, Y, U, V, **kw)
quiver(X, Y, U, V, C, **kw)

Arguments:

X, Y:

The x and y coordinates of the arrow locations (default is tail of arrow; see pivot kwarg)

U, V:

Give the x and y components of the arrow vectors

C:

An optional array used to map colors to the arrows

All arguments may be 1-D or 2-D arrays or sequences. If X and Y are absent, they will be generated as a uniform grid. If U and V are 2-D arrays but X and Y are 1-D, and if len(X) and len(Y) match the column and row dimensions of U, then X and Y will be expanded with numpy.meshgrid().

U, V, C may be masked arrays, but masked X, Y are not supported at present.

Keyword arguments:

units: [ ‘width’ | ‘height’ | ‘dots’ | ‘inches’ | ‘x’ | ‘y’ | ‘xy’ ]

Arrow units; the arrow dimensions except for length are in multiples of this unit.

• ‘width’ or ‘height’: the width or height of the axes
• ‘dots’ or ‘inches’: pixels or inches, based on the figure dpi
• ‘x’, ‘y’, or ‘xy’: X, Y, or sqrt(X^2+Y^2) data units

The arrows scale differently depending on the units. For ‘x’ or ‘y’, the arrows get larger as one zooms in; for other units, the arrow size is independent of the zoom state. For ‘width or ‘height’, the arrow size increases with the width and height of the axes, respectively, when the window is resized; for ‘dots’ or ‘inches’, resizing does not change the arrows.

angles: [ ‘uv’ | ‘xy’ | array ]

With the default ‘uv’, the arrow axis aspect ratio is 1, so that if U*==*V the orientation of the arrow on the plot is 45 degrees CCW from the horizontal axis (positive to the right). With ‘xy’, the arrow points from (x,y) to (x+u, y+v). Use this for plotting a gradient field, for example. Alternatively, arbitrary angles may be specified as an array of values in degrees, CCW from the horizontal axis. Note: inverting a data axis will correspondingly invert the arrows only with angles='xy'.

scale: [ None | float ]

Data units per arrow length unit, e.g., m/s per plot width; a smaller scale parameter makes the arrow longer. If None, a simple autoscaling algorithm is used, based on the average vector length and the number of vectors. The arrow length unit is given by the scale_units parameter

scale_units: None, or any of the units options.

For example, if scale_units is ‘inches’, scale is 2.0, and (u,v) = (1,0), then the vector will be 0.5 inches long. If scale_units is ‘width’, then the vector will be half the width of the axes.

If scale_units is ‘x’ then the vector will be 0.5 x-axis units. To plot vectors in the x-y plane, with u and v having the same units as x and y, use “angles=’xy’, scale_units=’xy’, scale=1”.

width:

Shaft width in arrow units; default depends on choice of units, above, and number of vectors; a typical starting value is about 0.005 times the width of the plot.

headwidth: scalar

Head width as multiple of shaft width, default is 3

headlength: scalar

Head length as multiple of shaft width, default is 5

headaxislength: scalar

Head length at shaft intersection, default is 4.5

minshaft: scalar

Length below which arrow scales, in units of head length. Do not set this to less than 1, or small arrows will look terrible! Default is 1

minlength: scalar

Minimum length as a multiple of shaft width; if an arrow length is less than this, plot a dot (hexagon) of this diameter instead. Default is 1.

pivot: [ ‘tail’ | ‘mid’ | ‘middle’ | ‘tip’ ]

The part of the arrow that is at the grid point; the arrow rotates about this point, hence the name pivot.

color: [ color | color sequence ]

This is a synonym for the PolyCollection facecolor kwarg. If C has been set, color has no effect.

The defaults give a slightly swept-back arrow; to make the head a triangle, make headaxislength the same as headlength. To make the arrow more pointed, reduce headwidth or increase headlength and headaxislength. To make the head smaller relative to the shaft, scale down all the head parameters. You will probably do best to leave minshaft alone.

linewidths and edgecolors can be used to customize the arrow outlines. Additional PolyCollection keyword arguments:

Property	Description
agg_filter	unknown
alpha	float or None
animated	[True | False]
antialiased or antialiaseds	Boolean or sequence of booleans
array	unknown
axes	an Axes instance
clim	a length 2 sequence of floats
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
cmap	a colormap or registered colormap name
color	matplotlib color arg or sequence of rgba tuples
contains	a callable function
edgecolor or edgecolors	matplotlib color spec or sequence of specs
facecolor or facecolors	matplotlib color spec or sequence of specs
figure	a matplotlib.figure.Figure instance
gid	an id string
hatch	[ ‘/’ | ‘\’ | ‘|’ | ‘-‘ | ‘+’ | ‘x’ | ‘o’ | ‘O’ | ‘.’ | ‘*’ ]
label	string or anything printable with ‘%s’ conversion.
linestyle or linestyles or dashes	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or linewidths or lw	float or sequence of floats
norm	unknown
offset_position	unknown
offsets	float or sequence of floats
path_effects	unknown
picker	[None|float|boolean|callable]
pickradius	unknown
rasterized	[True | False | None]
sketch_params	unknown
snap	unknown
transform	Transform instance
url	a url string
urls	unknown
visible	[True | False]
zorder	any number

quiverkey(*args, **kw)

Add a key to a quiver plot.

Call signature:

quiverkey(Q, X, Y, U, label, **kw)

Arguments:

Q:

The Quiver instance returned by a call to quiver.

X, Y:

The location of the key; additional explanation follows.

U:

The length of the key

label:

A string with the length and units of the key

Keyword arguments:

coordinates = [ ‘axes’ | ‘figure’ | ‘data’ | ‘inches’ ]

Coordinate system and units for X, Y: ‘axes’ and ‘figure’ are normalized coordinate systems with 0,0 in the lower left and 1,1 in the upper right; ‘data’ are the axes data coordinates (used for the locations of the vectors in the quiver plot itself); ‘inches’ is position in the figure in inches, with 0,0 at the lower left corner.

color:

overrides face and edge colors from Q.

labelpos = [ ‘N’ | ‘S’ | ‘E’ | ‘W’ ]

Position the label above, below, to the right, to the left of the arrow, respectively.

labelsep:

Distance in inches between the arrow and the label. Default is 0.1

labelcolor:

defaults to default 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.

redraw_in_frame()

This method can only be used after an initial draw which caches the renderer. It is used to efficiently update Axes data (axis ticks, labels, etc are not updated)

relim(visible_only=False)

Recompute the data limits based on current artists. If you want to exclude invisible artists from the calculation, set visible_only=True

At present, Collection instances are not supported.

remove()

Remove the artist from the figure if possible. The effect will not be visible until the figure is redrawn, e.g., with matplotlib.axes.Axes.draw_idle(). Call matplotlib.axes.Axes.relim() to update the axes limits if desired.

Note: relim() will not see collections even if the collection was added to axes with autolim = True.

Note: there is no support for removing the artist’s legend entry.

remove_callback(oid)

Remove a callback based on its id.

See also

add_callback()

For adding callbacks

reset_position()

Make the original position the active position

scatter(x, y, s=20, c=None, marker='o', cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=None, edgecolors=None, **kwargs)

Make a scatter plot of x vs y, where x and y are sequence like objects of the same length.

Parameters:	x, y : array_like, shape (n, ) Input data s : scalar or array_like, shape (n, ), optional, default: 20 size in points^2. c : color, sequence, or sequence of color, optional, default: ‘b’ c can be a single color format string, or a sequence of color specifications of length N, or a sequence of N numbers to be mapped to colors using the cmap and norm specified via kwargs (see below). Note that c should not be a single numeric RGB or RGBA sequence because that is indistinguishable from an array of values to be colormapped. c can be a 2-D array in which the rows are RGB or RGBA, however, including the case of a single row to specify the same color for all points. marker : MarkerStyle, optional, default: ‘o’ See markers for more information on the different styles of markers scatter supports. marker can be either an instance of the class or the text shorthand for a particular marker. cmap : Colormap, optional, default: None A Colormap instance or registered name. cmap is only used if c is an array of floats. If None, defaults to rc image.cmap. norm : Normalize, optional, default: None A Normalize instance is used to scale luminance data to 0, 1. norm is only used if c is an array of floats. If None, use the default normalize(). vmin, vmax : scalar, optional, default: None vmin and vmax are used in conjunction with norm to normalize luminance data. If either are None, the min and max of the color array is used. Note if you pass a norm instance, your settings for vmin and vmax will be ignored. alpha : scalar, optional, default: None The alpha blending value, between 0 (transparent) and 1 (opaque) linewidths : scalar or array_like, optional, default: None If None, defaults to (lines.linewidth,). edgecolors : color or sequence of color, optional, default: None If None, defaults to (patch.edgecolor). If ‘face’, the edge color will always be the same as the face color. If it is ‘none’, the patch boundary will not be drawn. For non-filled markers, the edgecolors kwarg is ignored; color is determined by c.
Returns:	paths : PathCollection
Other Parameters:
	kwargs : Collection properties

Notes

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

• All arguments with the following names: ‘y’, ‘linewidths’, ‘color’, ‘facecolor’, ‘facecolors’, ‘edgecolors’, ‘c’, ‘s’, ‘x’.

Examples

(Source code, png, hires.png, pdf)

semilogx(*args, **kwargs)

Make a plot with log scaling on the x axis.

Call signature:

semilogx(*args, **kwargs)

semilogx() supports all the keyword arguments of plot() and matplotlib.axes.Axes.set_xscale().

Notable keyword arguments:

basex: scalar > 1

Base of the x logarithm

subsx: [ None | sequence ]

The location of the minor xticks; None defaults to autosubs, which depend on the number of decades in the plot; see set_xscale() for details.

nonposx: [ ‘mask’ | ‘clip’ ]

Non-positive values in x can be masked as invalid, or clipped to a very small positive number

The remaining valid kwargs are Line2D properties:

Property	Description
agg_filter	unknown
alpha	float (0.0 transparent through 1.0 opaque)
animated	[True | False]
antialiased or aa	[True | False]
axes	an Axes instance
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
color or c	any matplotlib color
contains	a callable function
dash_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
dash_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
dashes	sequence of on/off ink in points
drawstyle	[‘default’ | ‘steps’ | ‘steps-pre’ | ‘steps-mid’ | ‘steps-post’]
figure	a matplotlib.figure.Figure instance
fillstyle	[‘full’ | ‘left’ | ‘right’ | ‘bottom’ | ‘top’ | ‘none’]
gid	an id string
label	string or anything printable with ‘%s’ conversion.
linestyle or ls	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or lw	float value in points
marker	A valid marker style
markeredgecolor or mec	any matplotlib color
markeredgewidth or mew	float value in points
markerfacecolor or mfc	any matplotlib color
markerfacecoloralt or mfcalt	any matplotlib color
markersize or ms	float
markevery	[None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects	unknown
picker	float distance in points or callable pick function fn(artist, event)
pickradius	float distance in points
rasterized	[True | False | None]
sketch_params	unknown
snap	unknown
solid_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
solid_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
transform	a matplotlib.transforms.Transform instance
url	a url string
visible	[True | False]
xdata	1D array
ydata	1D array
zorder	any number

See also

loglog()

For example code and figure

semilogy(*args, **kwargs)

Make a plot with log scaling on the y axis.

call signature:

semilogy(*args, **kwargs)

semilogy() supports all the keyword arguments of plot() and matplotlib.axes.Axes.set_yscale().

Notable keyword arguments:

basey: scalar > 1

Base of the y logarithm

subsy: [ None | sequence ]

The location of the minor yticks; None defaults to autosubs, which depend on the number of decades in the plot; see set_yscale() for details.

nonposy: [ ‘mask’ | ‘clip’ ]

Non-positive values in y can be masked as invalid, or clipped to a very small positive number

The remaining valid kwargs are Line2D properties:

Property	Description
agg_filter	unknown
alpha	float (0.0 transparent through 1.0 opaque)
animated	[True | False]
antialiased or aa	[True | False]
axes	an Axes instance
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
color or c	any matplotlib color
contains	a callable function
dash_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
dash_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
dashes	sequence of on/off ink in points
drawstyle	[‘default’ | ‘steps’ | ‘steps-pre’ | ‘steps-mid’ | ‘steps-post’]
figure	a matplotlib.figure.Figure instance
fillstyle	[‘full’ | ‘left’ | ‘right’ | ‘bottom’ | ‘top’ | ‘none’]
gid	an id string
label	string or anything printable with ‘%s’ conversion.
linestyle or ls	[‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
linewidth or lw	float value in points
marker	A valid marker style
markeredgecolor or mec	any matplotlib color
markeredgewidth or mew	float value in points
markerfacecolor or mfc	any matplotlib color
markerfacecoloralt or mfcalt	any matplotlib color
markersize or ms	float
markevery	[None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
path_effects	unknown
picker	float distance in points or callable pick function fn(artist, event)
pickradius	float distance in points
rasterized	[True | False | None]
sketch_params	unknown
snap	unknown
solid_capstyle	[‘butt’ | ‘round’ | ‘projecting’]
solid_joinstyle	[‘miter’ | ‘round’ | ‘bevel’]
transform	a matplotlib.transforms.Transform instance
url	a url string
visible	[True | False]
xdata	1D array
ydata	1D array
zorder	any number

See also

loglog()

For example code and figure

set(**kwargs)

A property batch setter. Pass kwargs to set properties. Will handle property name collisions (e.g., if both ‘color’ and ‘facecolor’ are specified, the property with higher priority gets set last).

set_adjustable(adjustable)

ACCEPTS: [ ‘box’ | ‘datalim’ | ‘box-forced’]

set_agg_filter(filter_func)

set agg_filter fuction.

set_alpha(alpha)

Set the alpha value used for blending - not supported on all backends.

ACCEPTS: float (0.0 transparent through 1.0 opaque)

set_anchor(anchor)

anchor

value	description
‘C’	Center
‘SW’	bottom left
‘S’	bottom
‘SE’	bottom right
‘E’	right
‘NE’	top right
‘N’	top
‘NW’	top left
‘W’	left

set_animated(b)

Set the artist’s animation state.

ACCEPTS: [True | False]

set_aspect(aspect, adjustable=None, anchor=None)

aspect

value	description
‘auto’	automatic; fill position rectangle with data
‘normal’	same as ‘auto’; deprecated
‘equal’	same scaling from data to plot units for x and y
num	a circle will be stretched such that the height is num times the width. aspect=1 is the same as aspect=’equal’.

adjustable

value	description
‘box’	change physical size of axes
‘datalim’	change xlim or ylim
‘box-forced’	same as ‘box’, but axes can be shared

‘box’ does not allow axes sharing, as this can cause unintended side effect. For cases when sharing axes is fine, use ‘box-forced’.

anchor

value	description
‘C’	centered
‘SW’	lower left corner
‘S’	middle of bottom edge
‘SE’	lower right corner
etc.

Deprecated since version 1.2: the option ‘normal’ for aspect is deprecated. Use ‘auto’ instead.

set_autoscale_on(b)

Set whether autoscaling is applied on plot commands

accepts: [ True | False ]

set_autoscalex_on(b)

Set whether autoscaling for the x-axis is applied on plot commands

accepts: [ True | False ]

set_autoscaley_on(b)

Set whether autoscaling for the y-axis is applied on plot commands

accepts: [ True | False ]

set_axes(axes)

Set the Axes instance in which the artist resides, if any.

This has been deprecated in mpl 1.5, please use the axes property. Will be removed in 1.7 or 2.0.

ACCEPTS: an Axes instance

set_axes_locator(locator)

set axes_locator

ACCEPT: a callable object which takes an axes instance and renderer and

returns a bbox.

set_axis_bgcolor(color)

set the axes background color

ACCEPTS: any matplotlib color - see colors()

set_axis_off()

turn off the axis

set_axis_on()

turn on the axis

set_axisbelow(b)

Set whether the axis ticks and gridlines are above or below most artists

ACCEPTS: [ True | False ]

set_clip_box(clipbox)

Set the artist’s clip Bbox.

ACCEPTS: a matplotlib.transforms.Bbox instance

set_clip_on(b)

Set whether artist uses clipping.

When False artists will be visible out side of the axes which can lead to unexpected results.

ACCEPTS: [True | False]

set_clip_path(path, transform=None)

Set the artist’s clip path, which may be:

• a Patch (or subclass) instance

• a Path instance, in which case

  an optional Transform instance may be provided, which will be applied to the path before using it for clipping.

• None, to remove the clipping path

For efficiency, if the path happens to be an axis-aligned rectangle, this method will set the clipping box to the corresponding rectangle and set the clipping path to None.

ACCEPTS: [ (Path, Transform) | Patch | None ]

set_color_cycle(clist)

Set the color cycle for any future plot commands on this Axes.

clist is a list of mpl color specifiers.

Deprecated since version 1.5.

set_contains(picker)

Replace the contains test used by this artist. The new picker should be a callable function which determines whether the artist is hit by the mouse event:

hit, props = picker(artist, mouseevent)

If the mouse event is over the artist, return hit = True and props is a dictionary of properties you want returned with the contains test.

ACCEPTS: a callable function

set_cursor_props(*args)

Set the cursor property as:

ax.set_cursor_props(linewidth, color)

or:

ax.set_cursor_props((linewidth, color))

ACCEPTS: a (float, color) tuple

set_figure(fig)

Set the class:Axes figure

accepts a class:Figure instance

set_frame_on(b)

Set whether the axes rectangle patch is drawn

ACCEPTS: [ True | False ]

set_gid(gid)

Sets the (group) id for the artist

ACCEPTS: an id string

set_label(s)

Set the label to s for auto legend.

ACCEPTS: string or anything printable with ‘%s’ conversion.

set_navigate(b)

Set whether the axes responds to navigation toolbar commands

ACCEPTS: [ True | False ]

set_navigate_mode(b)

Set the navigation toolbar button status;

Warning

this is not a user-API function.

set_path_effects(path_effects)

set path_effects, which should be a list of instances of matplotlib.patheffect._Base class or its derivatives.

set_picker(picker)

Set the epsilon for picking used by this artist

picker can be one of the following:

• None: picking is disabled for this artist (default)
• A boolean: if True then picking will be enabled and the artist will fire a pick event if the mouse event is over the artist
• A float: if picker is a number it is interpreted as an epsilon tolerance in points and the artist will fire off an event if it’s data is within epsilon of the mouse event. For some artists like lines and patch collections, the artist may provide additional data to the pick event that is generated, e.g., the indices of the data within epsilon of the pick event

• A function: if picker is callable, it is a user supplied function which determines whether the artist is hit by the mouse event:

  hit, props = picker(artist, mouseevent)
  

  to determine the hit test. if the mouse event is over the artist, return hit=True and props is a dictionary of properties you want added to the PickEvent attributes.

ACCEPTS: [None|float|boolean|callable]

set_position(pos, which='both')

Set the axes position with:

pos = [left, bottom, width, height]

in relative 0,1 coords, or pos can be a Bbox

There are two position variables: one which is ultimately used, but which may be modified by apply_aspect(), and a second which is the starting point for apply_aspect().

Optional keyword arguments:

which

value	description
‘active’	to change the first
‘original’	to change the second
‘both’	to change both

set_prop_cycle(*args, **kwargs)

Set the property cycle for any future plot commands on this Axes.

set_prop_cycle(arg) set_prop_cycle(label, itr) set_prop_cycle(label1=itr1[, label2=itr2[, ...]])

Form 1 simply sets given Cycler object.

Form 2 creates and sets a Cycler from a label and an iterable.

Form 3 composes and sets a Cycler as an inner product of the pairs of keyword arguments. In other words, all of the iterables are cycled simultaneously, as if through zip().

Parameters:

arg : Cycler Set the given Cycler. Can also be None to reset to the cycle defined by the current style. label : str The property key. Must be a valid Artist property. For example, ‘color’ or ‘linestyle’. Aliases are allowed, such as ‘c’ for ‘color’ and ‘lw’ for ‘linewidth’. itr : iterable Finite-length iterable of the property values. These values are validated and will raise a ValueError if invalid.

set_rasterization_zorder(z)

Set zorder value below which artists will be rasterized. Set to None to disable rasterizing of artists below a particular zorder.

set_rasterized(rasterized)

Force rasterized (bitmap) drawing in vector backend output.

Defaults to None, which implies the backend’s default behavior

ACCEPTS: [True | False | None]

set_sketch_params(scale=None, length=None, randomness=None)

Sets the sketch parameters.

Parameters:	scale : float, optional The amplitude of the wiggle perpendicular to the source line, in pixels. If scale is None, or not provided, no sketch filter will be provided. length : float, optional The length of the wiggle along the line, in pixels (default 128.0) randomness : float, optional The scale factor by which the length is shrunken or expanded (default 16.0)

set_snap(snap)

Sets the snap setting which may be:

• True: snap vertices to the nearest pixel center
• False: leave vertices as-is
• None: (auto) If the path contains only rectilinear line segments, round to the nearest pixel center

Only supported by the Agg and MacOSX backends.

set_title(label, fontdict=None, loc='center', **kwargs)

Set a title for the 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.

Parameters:	label : str Text to use for the title fontdict : dict A dictionary controlling the appearance of the title text, the default fontdict is: {'fontsize': rcParams['axes.titlesize'], 'fontweight' : rcParams['axes.titleweight'], 'verticalalignment': 'baseline', 'horizontalalignment': loc} loc : {‘center’, ‘left’, ‘right’}, str, optional Which title to set, defaults to ‘center’
Returns:	text : Text The matplotlib text instance representing the title
Other Parameters:
	kwargs : text properties Other keyword arguments are text properties, see Text for a list of valid text properties.

set_transform(t)

Set the Transform instance used by this artist.

ACCEPTS: Transform instance

set_url(url)

Sets the url for the artist

ACCEPTS: a url string

set_visible(b)

Set the artist’s visiblity.

ACCEPTS: [True | False]

set_xbound(lower=None, upper=None)

Set the lower and upper numerical bounds of the x-axis. This method will honor axes inversion regardless of parameter order. It will not change the _autoscaleXon attribute.

set_xlabel(xlabel, fontdict=None, labelpad=None, **kwargs)

Set the label for the xaxis.

Parameters:	xlabel : string x label labelpad : scalar, optional, default: None spacing in points between the label and the x-axis
Other Parameters:
	kwargs : Text properties

See also

text

for information on how override and the optional args work

set_xlim(left=None, right=None, emit=True, auto=False, **kw)

Call signature:

set_xlim(self, *args, **kwargs):

Set the data limits for the xaxis

Examples:

set_xlim((left, right))
set_xlim(left, right)
set_xlim(left=1) # right unchanged
set_xlim(right=1) # left unchanged

Keyword arguments:

left: scalar

The left xlim; xmin, the previous name, may still be used

right: scalar

The right xlim; xmax, the previous name, may still be used

emit: [ True | False ]

Notify observers of limit change

auto: [ True | False | None ]

Turn x autoscaling on (True), off (False; default), or leave unchanged (None)

Note, the left (formerly xmin) value may be greater than the right (formerly xmax). For example, suppose x is years before present. Then one might use:

set_ylim(5000, 0)

so 5000 years ago is on the left of the plot and the present is on the right.

Returns the current xlimits as a length 2 tuple

ACCEPTS: length 2 sequence of floats

set_xmargin(m)

Set padding of X data limits prior to autoscaling.

m times the data interval will be added to each end of that interval before it is used in autoscaling.

accepts: float in range 0 to 1

set_xscale(value, **kwargs)

Call signature:

set_xscale(value)

Set the scaling of the x-axis: ‘linear’ | ‘log’ | ‘logit’ | ‘symlog’

ACCEPTS: [‘linear’ | ‘log’ | ‘logit’ | ‘symlog’]

Different kwargs are accepted, depending on the scale:

‘linear’

‘log’

basex/basey:

The base of the logarithm

nonposx/nonposy: [‘mask’ | ‘clip’ ]

non-positive values in x or y can be masked as invalid, or clipped to a very small positive number

subsx/subsy:

Where to place the subticks between each major tick. Should be a sequence of integers. For example, in a log10 scale: [2, 3, 4, 5, 6, 7, 8, 9]

will place 8 logarithmically spaced minor ticks between each major tick.

‘logit’

nonpos: [‘mask’ | ‘clip’ ]

values beyond ]0, 1[ can be masked as invalid, or clipped to a number very close to 0 or 1

‘symlog’

basex/basey:

The base of the logarithm

linthreshx/linthreshy:

The range (-x, x) within which the plot is linear (to avoid having the plot go to infinity around zero).

subsx/subsy:

Where to place the subticks between each major tick. Should be a sequence of integers. For example, in a log10 scale: [2, 3, 4, 5, 6, 7, 8, 9]

will place 8 logarithmically spaced minor ticks between each major tick.

linscalex/linscaley:

This allows the linear range (-linthresh to linthresh) to be stretched relative to the logarithmic range. Its value is the number of decades to use for each half of the linear range. For example, when linscale == 1.0 (the default), the space used for the positive and negative halves of the linear range will be equal to one decade in the logarithmic range.

set_xticklabels(labels, fontdict=None, minor=False, **kwargs)

Call signature:

set_xticklabels(labels, fontdict=None, minor=False, **kwargs)

Set the xtick labels with list of strings labels. Return a list of axis text instances.

kwargs set the Text properties. Valid properties are

Property	Description
agg_filter	unknown
alpha	float (0.0 transparent through 1.0 opaque)
animated	[True | False]
axes	an Axes instance
backgroundcolor	any matplotlib color
bbox	FancyBboxPatch prop dict
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
color	any matplotlib color
contains	a callable function
family or fontname or fontfamily or name	[FONTNAME | ‘serif’ | ‘sans-serif’ | ‘cursive’ | ‘fantasy’ | ‘monospace’ ]
figure	a matplotlib.figure.Figure instance
fontproperties or font_properties	a matplotlib.font_manager.FontProperties instance
gid	an id string
horizontalalignment or ha	[ ‘center’ | ‘right’ | ‘left’ ]
label	string or anything printable with ‘%s’ conversion.
linespacing	float (multiple of font size)
multialignment	[‘left’ | ‘right’ | ‘center’ ]
path_effects	unknown
picker	[None|float|boolean|callable]
position	(x,y)
rasterized	[True | False | None]
rotation	[ angle in degrees | ‘vertical’ | ‘horizontal’ ]
rotation_mode	unknown
size or fontsize	[size in points | ‘xx-small’ | ‘x-small’ | ‘small’ | ‘medium’ | ‘large’ | ‘x-large’ | ‘xx-large’ ]
sketch_params	unknown
snap	unknown
stretch or fontstretch	[a numeric value in range 0-1000 | ‘ultra-condensed’ | ‘extra-condensed’ | ‘condensed’ | ‘semi-condensed’ | ‘normal’ | ‘semi-expanded’ | ‘expanded’ | ‘extra-expanded’ | ‘ultra-expanded’ ]
style or fontstyle	[ ‘normal’ | ‘italic’ | ‘oblique’]
text	string or anything printable with ‘%s’ conversion.
transform	Transform instance
url	a url string
usetex	unknown
variant or fontvariant	[ ‘normal’ | ‘small-caps’ ]
verticalalignment or ma or va	[ ‘center’ | ‘top’ | ‘bottom’ | ‘baseline’ ]
visible	[True | False]
weight or fontweight	[a numeric value in range 0-1000 | ‘ultralight’ | ‘light’ | ‘normal’ | ‘regular’ | ‘book’ | ‘medium’ | ‘roman’ | ‘semibold’ | ‘demibold’ | ‘demi’ | ‘bold’ | ‘heavy’ | ‘extra bold’ | ‘black’ ]
wrap	unknown
x	float
y	float
zorder	any number

ACCEPTS: sequence of strings

set_xticks(ticks, minor=False)

Set the x ticks with list of ticks

ACCEPTS: sequence of floats

set_ybound(lower=None, upper=None)

Set the lower and upper numerical bounds of the y-axis. This method will honor axes inversion regardless of parameter order. It will not change the _autoscaleYon attribute.

set_ylabel(ylabel, fontdict=None, labelpad=None, **kwargs)

Set the label for the yaxis

Parameters:	ylabel : string y label labelpad : scalar, optional, default: None spacing in points between the label and the x-axis
Other Parameters:
	kwargs : Text properties

See also

text

for information on how override and the optional args work

set_ylim(bottom=None, top=None, emit=True, auto=False, **kw)

Call signature:

set_ylim(self, *args, **kwargs):

Set the data limits for the yaxis

Examples:

set_ylim((bottom, top))
set_ylim(bottom, top)
set_ylim(bottom=1) # top unchanged
set_ylim(top=1) # bottom unchanged

Keyword arguments:

bottom: scalar

The bottom ylim; the previous name, ymin, may still be used

top: scalar

The top ylim; the previous name, ymax, may still be used

emit: [ True | False ]

Notify observers of limit change

auto: [ True | False | None ]

Turn y autoscaling on (True), off (False; default), or leave unchanged (None)

Note, the bottom (formerly ymin) value may be greater than the top (formerly ymax). For example, suppose y is depth in the ocean. Then one might use:

set_ylim(5000, 0)

so 5000 m depth is at the bottom of the plot and the surface, 0 m, is at the top.

Returns the current ylimits as a length 2 tuple

ACCEPTS: length 2 sequence of floats

set_ymargin(m)

Set padding of Y data limits prior to autoscaling.

m times the data interval will be added to each end of that interval before it is used in autoscaling.

accepts: float in range 0 to 1

set_yscale(value, **kwargs)

Call signature:

set_yscale(value)

Set the scaling of the y-axis: ‘linear’ | ‘log’ | ‘logit’ | ‘symlog’

ACCEPTS: [‘linear’ | ‘log’ | ‘logit’ | ‘symlog’]

Different kwargs are accepted, depending on the scale:

‘linear’

‘log’

basex/basey:

The base of the logarithm

nonposx/nonposy: [‘mask’ | ‘clip’ ]

non-positive values in x or y can be masked as invalid, or clipped to a very small positive number

subsx/subsy:

Where to place the subticks between each major tick. Should be a sequence of integers. For example, in a log10 scale: [2, 3, 4, 5, 6, 7, 8, 9]

will place 8 logarithmically spaced minor ticks between each major tick.

‘logit’

nonpos: [‘mask’ | ‘clip’ ]

values beyond ]0, 1[ can be masked as invalid, or clipped to a number very close to 0 or 1

‘symlog’

basex/basey:

The base of the logarithm

linthreshx/linthreshy:

The range (-x, x) within which the plot is linear (to avoid having the plot go to infinity around zero).

subsx/subsy:

Where to place the subticks between each major tick. Should be a sequence of integers. For example, in a log10 scale: [2, 3, 4, 5, 6, 7, 8, 9]

will place 8 logarithmically spaced minor ticks between each major tick.

linscalex/linscaley:

This allows the linear range (-linthresh to linthresh) to be stretched relative to the logarithmic range. Its value is the number of decades to use for each half of the linear range. For example, when linscale == 1.0 (the default), the space used for the positive and negative halves of the linear range will be equal to one decade in the logarithmic range.

set_yticklabels(labels, fontdict=None, minor=False, **kwargs)

Call signature:

set_yticklabels(labels, fontdict=None, minor=False, **kwargs)

Set the y tick labels with list of strings labels. Return a list of Text instances.

kwargs set Text properties for the labels. Valid properties are

Property	Description
agg_filter	unknown
alpha	float (0.0 transparent through 1.0 opaque)
animated	[True | False]
axes	an Axes instance
backgroundcolor	any matplotlib color
bbox	FancyBboxPatch prop dict
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
color	any matplotlib color
contains	a callable function
family or fontname or fontfamily or name	[FONTNAME | ‘serif’ | ‘sans-serif’ | ‘cursive’ | ‘fantasy’ | ‘monospace’ ]
figure	a matplotlib.figure.Figure instance
fontproperties or font_properties	a matplotlib.font_manager.FontProperties instance
gid	an id string
horizontalalignment or ha	[ ‘center’ | ‘right’ | ‘left’ ]
label	string or anything printable with ‘%s’ conversion.
linespacing	float (multiple of font size)
multialignment	[‘left’ | ‘right’ | ‘center’ ]
path_effects	unknown
picker	[None|float|boolean|callable]
position	(x,y)
rasterized	[True | False | None]
rotation	[ angle in degrees | ‘vertical’ | ‘horizontal’ ]
rotation_mode	unknown
size or fontsize	[size in points | ‘xx-small’ | ‘x-small’ | ‘small’ | ‘medium’ | ‘large’ | ‘x-large’ | ‘xx-large’ ]
sketch_params	unknown
snap	unknown
stretch or fontstretch	[a numeric value in range 0-1000 | ‘ultra-condensed’ | ‘extra-condensed’ | ‘condensed’ | ‘semi-condensed’ | ‘normal’ | ‘semi-expanded’ | ‘expanded’ | ‘extra-expanded’ | ‘ultra-expanded’ ]
style or fontstyle	[ ‘normal’ | ‘italic’ | ‘oblique’]
text	string or anything printable with ‘%s’ conversion.
transform	Transform instance
url	a url string
usetex	unknown
variant or fontvariant	[ ‘normal’ | ‘small-caps’ ]
verticalalignment or ma or va	[ ‘center’ | ‘top’ | ‘bottom’ | ‘baseline’ ]
visible	[True | False]
weight or fontweight	[a numeric value in range 0-1000 | ‘ultralight’ | ‘light’ | ‘normal’ | ‘regular’ | ‘book’ | ‘medium’ | ‘roman’ | ‘semibold’ | ‘demibold’ | ‘demi’ | ‘bold’ | ‘heavy’ | ‘extra bold’ | ‘black’ ]
wrap	unknown
x	float
y	float
zorder	any number

ACCEPTS: sequence of strings

set_yticks(ticks, minor=False)

Set the y ticks with list of ticks

ACCEPTS: sequence of floats

Keyword arguments:

minor: [ False | True ]

Sets the minor ticks if True

set_zorder(level)

Set the zorder for the artist. Artists with lower zorder values are drawn first.

ACCEPTS: any number

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, **kwargs)

Plot a spectrogram.

Call signature:

specgram(x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
         window=mlab.window_hanning, noverlap=128,
         cmap=None, xextent=None, pad_to=None, sides='default',
         scale_by_freq=None, mode='default', scale='default',
         **kwargs)

Compute and plot a spectrogram of data in x. Data are split into NFFT length segments and the spectrum of each section is computed. The windowing function window is applied to each segment, and the amount of overlap of each segment is specified with noverlap. The spectrogram is plotted as a colormap (using imshow).

x: 1-D array or sequence

Array or sequence containing the data

Keyword arguments:

Fs: scalar

The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. The default value is 2.

window: callable or ndarray

A function or a vector of length NFFT. To create window vectors see window_hanning(), window_none(), numpy.blackman(), numpy.hamming(), numpy.bartlett(), scipy.signal(), scipy.signal.get_window(), etc. The default is window_hanning(). If a function is passed as the argument, it must take a data segment as an argument and return the windowed version of the segment.

sides: [ ‘default’ | ‘onesided’ | ‘twosided’ ]

Specifies which sides of the spectrum to return. Default gives the default behavior, which returns one-sided for real data and both for complex data. ‘onesided’ forces the return of a one-sided spectrum, while ‘twosided’ forces two-sided.

pad_to: integer

The number of points to which the data segment is padded when performing the FFT. This can be different from NFFT, which specifies the number of data points used. While not increasing the actual resolution of the spectrum (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. This corresponds to the n parameter in the call to fft(). The default is None, which sets pad_to equal to NFFT

NFFT: integer

The number of data points used in each block for the FFT. A power 2 is most efficient. The default value is 256. This should NOT be used to get zero padding, or the scaling of the result will be incorrect. Use pad_to for this instead.

detrend: [ ‘default’ | ‘constant’ | ‘mean’ | ‘linear’ | ‘none’] or

callable

The function applied to each segment before fft-ing, designed to remove the mean or linear trend. Unlike in MATLAB, where the detrend parameter is a vector, in matplotlib is it a function. The pylab module defines detrend_none(), detrend_mean(), and detrend_linear(), but you can use a custom function as well. You can also use a string to choose one of the functions. ‘default’, ‘constant’, and ‘mean’ call detrend_mean(). ‘linear’ calls detrend_linear(). ‘none’ calls detrend_none().

scale_by_freq: boolean

Specifies whether the resulting density values should be scaled by the scaling frequency, which gives density in units of Hz^-1. This allows for integration over the returned frequency values. The default is True for MATLAB compatibility.

mode: [ ‘default’ | ‘psd’ | ‘magnitude’ | ‘angle’ | ‘phase’ ]

What sort of spectrum to use. Default is ‘psd’. which takes the power spectral density. ‘complex’ returns the complex-valued frequency spectrum. ‘magnitude’ returns the magnitude spectrum. ‘angle’ returns the phase spectrum without unwrapping. ‘phase’ returns the phase spectrum with unwrapping.

noverlap: integer

The number of points of overlap between blocks. The default value is 128.

scale: [ ‘default’ | ‘linear’ | ‘dB’ ]

The scaling of the values in the spec. ‘linear’ is no scaling. ‘dB’ returns the values in dB scale. When mode is ‘psd’, this is dB power (10 * log10). Otherwise this is dB amplitude (20 * log10). ‘default’ is ‘dB’ if mode is ‘psd’ or ‘magnitude’ and ‘linear’ otherwise. This must be ‘linear’ if mode is ‘angle’ or ‘phase’.

Fc: integer

The center frequency of x (defaults to 0), which offsets the x extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband.

cmap:

A matplotlib.colors.Colormap instance; if None, use default determined by rc

xextent:

The image extent along the x-axis. xextent = (xmin,xmax) The default is (0,max(bins)), where bins is the return value from specgram()

kwargs:

Additional kwargs are passed on to imshow which makes the specgram image

Note

detrend and scale_by_freq only apply when mode is set to ‘psd’

Returns the tuple (spectrum, freqs, t, im):

spectrum: 2-D array

columns are the periodograms of successive segments

freqs: 1-D array

The frequencies corresponding to the rows in spectrum

t: 1-D array

The times corresponding to midpoints of segments (i.e the columns in spectrum)

im: instance of class AxesImage

The image created by imshow containing the spectrogram

Example:

(Source code, png, hires.png, pdf)

See also

psd()

psd() differs in the default overlap; in returning the mean of the segment periodograms; in not returning times; and in generating a line plot instead of colormap.

magnitude_spectrum()

A single spectrum, similar to having a single segment when mode is ‘magnitude’. Plots a line instead of a colormap.

angle_spectrum()

A single spectrum, similar to having a single segment when mode is ‘angle’. Plots a line instead of a colormap.

phase_spectrum()

A single spectrum, similar to having a single segment when mode is ‘phase’. Plots a line instead of a colormap.

Notes

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

• All arguments with the following names: ‘x’.

spy(Z, precision=0, marker=None, markersize=None, aspect='equal', origin='upper', **kwargs)

Plot the sparsity pattern on a 2-D array.

spy(Z) plots the sparsity pattern of the 2-D array Z.

Parameters:

Z : sparse array (n, m) The array to be plotted. precision : float, optional, default: 0 If precision is 0, any non-zero value will be plotted; else, values of will be plotted. For scipy.sparse.spmatrix instances, there is a special case: if precision is ‘present’, any value present in the array will be plotted, even if it is identically zero. origin : [“upper”, “lower”], optional, default: “upper” Place the [0,0] index of the array in the upper left or lower left corner of the axes. aspect : [‘auto’ | ‘equal’ | scalar], optional, default: “equal” If ‘equal’, and extent is None, changes the axes aspect ratio to match that of the image. If extent is not None, the axes aspect ratio is changed to match that of the extent. If ‘auto’, changes the image aspect ratio to match that of the axes. If None, default to rc image.aspect value. Two plotting styles are available: image or marker. Both are available for full arrays, but only the marker style works for :class:`scipy.sparse.spmatrix` instances. If *marker* and *markersize* are *None*, an image will be returned and any remaining kwargs are passed to :func:`~matplotlib.pyplot.imshow`; else, a :class:`~matplotlib.lines.Line2D` object will be returned with the value of marker determining the marker type, and any remaining kwargs passed to the :meth:`~matplotlib.axes.Axes.plot` method. If *marker* and *markersize* are *None*, useful kwargs include: * *cmap* * *alpha*

See also

imshow

for image options.

plot

for plotting options

stackplot(x, *args, **kwargs)

Draws a stacked area plot.

x : 1d array of dimension N

y

: 2d array of dimension MxN, OR any number 1d arrays each of dimension

1xN. The data is assumed to be unstacked. Each of the following calls is legal:

stackplot(x, y)               # where y is MxN
stackplot(x, y1, y2, y3, y4)  # where y1, y2, y3, y4, are all 1xNm

Keyword arguments:

baseline

: [‘zero’, ‘sym’, ‘wiggle’, ‘weighted_wiggle’]

Method used to calculate the baseline. ‘zero’ is just a simple stacked plot. ‘sym’ is symmetric around zero and is sometimes called ThemeRiver. ‘wiggle’ minimizes the sum of the squared slopes. ‘weighted_wiggle’ does the same but weights to account for size of each layer. It is also called Streamgraph-layout. More details can be found at http://www.leebyron.com/else/streamgraph/.

labels : A list or tuple of labels to assign to each data series.

colors

: A list or tuple of colors. These will be cycled through and

used to colour the stacked areas. All other keyword arguments are passed to fill_between()

Returns r : A list of PolyCollection, one for each element in the stacked area plot.

stale

If the artist is ‘stale’ and needs to be re-drawn for the output to match the internal state of the artist.

start_pan(x, y, button)

Called when a pan operation has started.

x, y are the mouse coordinates in display coords. button is the mouse button number:

• 1: LEFT
• 2: MIDDLE
• 3: RIGHT

Note

Intended to be overridden by new projection types.

stem(*args, **kwargs)

Create a stem plot.

Call signatures:

stem(y, linefmt='b-', markerfmt='bo', basefmt='r-')
stem(x, y, linefmt='b-', markerfmt='bo', basefmt='r-')

A stem plot plots vertical lines (using linefmt) at each x location from the baseline to y, and places a marker there using markerfmt. A horizontal line at 0 is is plotted using basefmt.

If no x values are provided, the default is (0, 1, ..., len(y) - 1)

Return value is a tuple (markerline, stemlines, baseline).

See also

This document for details.

Example:

(Source code, png, hires.png, pdf)

Notes

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

• All positional and all keyword arguments.

step(x, y, *args, **kwargs)

Make a step plot.

Call signature:

step(x, y, *args, **kwargs)

Additional keyword args to step() are the same as those for plot().

x and y must be 1-D sequences, and it is assumed, but not checked, that x is uniformly increasing.

Keyword arguments:

where: [ ‘pre’ | ‘post’ | ‘mid’ ]

If ‘pre’ (the default), the interval from x[i] to x[i+1] has level y[i+1].

If ‘post’, that interval has level y[i].

If ‘mid’, the jumps in y occur half-way between the x-values.

Return value is a list of lines that were added.

Notes

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

• All arguments with the following names: ‘y’, ‘x’.

streamplot(x, y, u, v, density=1, linewidth=None, color=None, cmap=None, norm=None, arrowsize=1, arrowstyle='-|>', minlength=0.1, transform=None, zorder=1, start_points=None)

Draws streamlines of a vector flow.

x, y

: 1d arrays

an evenly spaced grid.

u, v

: 2d arrays

x and y-velocities. Number of rows should match length of y, and the number of columns should match x.

density

: float or 2-tuple

Controls the closeness of streamlines. When density = 1, the domain is divided into a 30x30 grid—density linearly scales this grid. Each cell in the grid can have, at most, one traversing streamline. For different densities in each direction, use [density_x, density_y].

linewidth

: numeric or 2d array

vary linewidth when given a 2d array with the same shape as velocities.

color

: matplotlib color code, or 2d array

Streamline color. When given an array with the same shape as velocities, color values are converted to colors using cmap.

cmap

: Colormap

Colormap used to plot streamlines and arrows. Only necessary when using an array input for color.

norm

: Normalize

Normalize object used to scale luminance data to 0, 1. If None, stretch (min, max) to (0, 1). Only necessary when color is an array.

arrowsize

: float

Factor scale arrow size.

arrowstyle

: str

Arrow style specification. See FancyArrowPatch.

minlength

: float

Minimum length of streamline in axes coordinates.

start_points: Nx2 array

Coordinates of starting points for the streamlines. In data coordinates, the same as the x and y arrays.

zorder

: int

any number

Returns:

stream_container

: StreamplotSet

Container object with attributes

• lines: matplotlib.collections.LineCollection of streamlines
• arrows: collection of matplotlib.patches.FancyArrowPatch objects representing arrows half-way along stream lines.

This container will probably change in the future to allow changes to the colormap, alpha, etc. for both lines and arrows, but these changes should be backward compatible.

table(**kwargs)

Add a table to the current axes.

Call signature:

table(cellText=None, cellColours=None,
      cellLoc='right', colWidths=None,
      rowLabels=None, rowColours=None, rowLoc='left',
      colLabels=None, colColours=None, colLoc='center',
      loc='bottom', bbox=None):

Returns a matplotlib.table.Table instance. For finer grained control over tables, use the Table class and add it to the axes with add_table().

Thanks to John Gill for providing the class and table.

kwargs control the Table properties:

Property	Description
agg_filter	unknown
alpha	float (0.0 transparent through 1.0 opaque)
animated	[True | False]
axes	an Axes instance
clip_box	a matplotlib.transforms.Bbox instance
clip_on	[True | False]
clip_path	[ (Path, Transform) | Patch | None ]
contains	a callable function
figure	a matplotlib.figure.Figure instance
fontsize	a float in points
gid	an id string
label	string or anything printable with ‘%s’ conversion.
path_effects	unknown
picker	[None|float|boolean|callable]
rasterized	[True | False | None]
sketch_params	unknown
snap	unknown
transform	Transform instance
url	a url string
visible	[True | False]
zorder	any number

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

Add text to the axes.

Add text in string s to axis at location x, y, data coordinates.

Parameters:	x, y : scalars data coordinates s : string text fontdict : dictionary, optional, default: None A dictionary to override the default text properties. If fontdict is None, the defaults are determined by your rc parameters. withdash : boolean, optional, default: False Creates a TextWithDash instance instead of a Text instance.
Other Parameters:
	kwargs : Text properties. Other miscellaneous text parameters.

Examples

Individual keyword arguments can be used to override any given parameter:

>>> text(x, y, s, fontsize=12)

The default transform specifies that text is in data coords, alternatively, you can specify text in axis coords (0,0 is lower-left and 1,1 is upper-right). The example below places text in the center of the axes:

>>> text(0.5, 0.5,'matplotlib', horizontalalignment='center',
...      verticalalignment='center',
...      transform=ax.transAxes)

You can put a rectangular box around the text instance (e.g., to set a background color) by using the keyword bbox. bbox is a dictionary of Rectangle properties. For example:

>>> text(x, y, s, bbox=dict(facecolor='red', alpha=0.5))

tick_params(axis='both', **kwargs)

Change the appearance of ticks and tick labels.

Keyword arguments:

axis

: [‘x’ | ‘y’ | ‘both’]

Axis on which to operate; default is ‘both’.

reset

: [True | False]

If True, set all parameters to defaults before processing other keyword arguments. Default is False.

which

: [‘major’ | ‘minor’ | ‘both’]

Default is ‘major’; apply arguments to which ticks.

direction

: [‘in’ | ‘out’ | ‘inout’]

Puts ticks inside the axes, outside the axes, or both.

length

Tick length in points.

width

Tick width in points.

color

Tick color; accepts any mpl color spec.

pad

Distance in points between tick and label.

labelsize

Tick label font size in points or as a string (e.g., ‘large’).

labelcolor

Tick label color; mpl color spec.

colors

Changes the tick color and the label color to the same value: mpl color spec.

zorder

Tick and label zorder.

bottom, top, left, right

: [bool | ‘on’ | ‘off’]

controls whether to draw the respective ticks.

labelbottom, labeltop, labelleft, labelright

Boolean or [‘on’ | ‘off’], controls whether to draw the respective tick labels.

Example:

ax.tick_params(direction='out', length=6, width=2, colors='r')

This will make all major ticks be red, pointing out of the box, and with dimensions 6 points by 2 points. Tick labels will also be red.

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 C-style (English) formatting. The default setting is controlled by the axes.formatter.use_locale rcparam.

Only the major ticks are affected. If the method is called when the ScalarFormatter is not the Formatter being used, an AttributeError will be raised.

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 automatically-chosen levels.

tricontour(..., Z, V)

draw contour lines at the values specified in sequence V, which must be in increasing order.

tricontourf(..., Z, V)

fill the (len(V)-1) regions between the values in V, which must be in increasing order.

tricontour(Z, **kwargs)

Use keyword args to control colors, linewidth, origin, cmap ... see below for more details.

C = tricontour(...) returns a 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() and matplotlib.colors.Colormap.set_over() methods.

xunits, yunits: [ None | registered units ]

Override axis units by specifying an instance of a matplotlib.units.ConversionInterface.

tricontour-only keyword arguments:

linewidths: [ None | number | tuple of numbers ]

If linewidths is None, the default width in lines.linewidth in matplotlibrc is used.

If a number, all levels will be plotted with this linewidth.

If a tuple, different levels will be plotted with different linewidths in the order specified

linestyles: [ None | ‘solid’ | ‘dashed’ | ‘dashdot’ | ‘dotted’ ]

If linestyles is None, the ‘solid’ is used.

linestyles can also be an iterable of the above strings specifying a set of linestyles to be used. If this iterable is shorter than the number of contour levels it will be repeated as necessary.

If contour is using a monochrome colormap and the contour level is less than 0, then the linestyle specified in contour.negative_linestyle in matplotlibrc will be used.

tricontourf-only keyword arguments:

antialiased: [ True | False ]

enable antialiasing

Note: tricontourf fills intervals that are closed at the top; that is, for boundaries z1 and z2, the filled region is:

z1 < z <= z2

There is one exception: if the lowest boundary coincides with the minimum value of the z array, then that minimum value will be included in the lowest interval.

Examples:

(Source code)

(png, hires.png, pdf)

(png, hires.png, pdf)

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 automatically-chosen levels.

tricontour(..., Z, V)

draw contour lines at the values specified in sequence V, which must be in increasing order.

tricontourf(..., Z, V)

fill the (len(V)-1) regions between the values in V, which must be in increasing order.

tricontour(Z, **kwargs)

Use keyword args to control colors, linewidth, origin, cmap ... see below for more details.

C = tricontour(...) returns a 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() and matplotlib.colors.Colormap.set_over() methods.

xunits, yunits: [ None | registered units ]

Override axis units by specifying an instance of a matplotlib.units.ConversionInterface.

tricontour-only keyword arguments:

linewidths: [ None | number | tuple of numbers ]

If linewidths is None, the default width in lines.linewidth in matplotlibrc is used.

If a number, all levels will be plotted with this linewidth.

If a tuple, different levels will be plotted with different linewidths in the order specified

linestyles: [ None | ‘solid’ | ‘dashed’ | ‘dashdot’ | ‘dotted’ ]

If linestyles is None, the ‘solid’ is used.

linestyles can also be an iterable of the above strings specifying a set of linestyles to be used. If this iterable is shorter than the number of contour levels it will be repeated as necessary.

If contour is using a monochrome colormap and the contour level is less than 0, then the linestyle specified in contour.negative_linestyle in matplotlibrc will be used.

tricontourf-only keyword arguments:

antialiased: [ True | False ]

enable antialiasing

Note: tricontourf fills intervals that are closed at the top; that is, for boundaries z1 and z2, the filled region is:

z1 < z <= z2

There is one exception: if the lowest boundary coincides with the minimum value of the z array, then that minimum value will be included in the lowest interval.

Examples:

(Source code)

(png, hires.png, pdf)

(png, hires.png, pdf)

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:

(Source code)

(png, hires.png, pdf)

(png, hires.png, pdf)

(png, hires.png, pdf)

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:

(Source code)

(png, hires.png, pdf)

(png, hires.png, pdf)

twinx()

Call signature:

ax = twinx()

create a twin of Axes for generating a plot with a sharex x-axis but independent y axis. The y-axis of self will have ticks on left and the returned axes will have ticks on the right.

Note

For those who are ‘picking’ artists while using twinx, pick events are only called for the artists in the top-most axes.

twiny()

Call signature:

ax = twiny()

create a twin of Axes for generating a plot with a shared y-axis but independent x axis. The x-axis of self will have ticks on bottom and the returned axes will have ticks on the top.

Note

For those who are ‘picking’ artists while using twiny, pick events are only called for the artists in the top-most axes.

update(props)

Update the properties of this Artist from the dictionary prop.

update_datalim(xys, updatex=True, updatey=True)

Update the data lim bbox with seq of xy tups or equiv. 2-D array

update_datalim_bounds(bounds)

Update the datalim to include the given Bbox bounds

update_datalim_numerix(x, y)

Update the data lim bbox with seq of xy tups

update_from(other)

Copy properties from other to self.

violin(vpstats, positions=None, vert=True, widths=0.5, showmeans=False, showextrema=True, showmedians=False)

Drawing function for violin plots.

Call signature:

violin(vpstats, positions=None, vert=True, widths=0.5,
       showmeans=False, showextrema=True, showmedians=False):

Draw a violin plot for each column of vpstats. Each filled area extends to represent the entire data range, with optional lines at the mean, the median, the minimum, and the maximum.

Parameters:

vpstats : list of dicts A list of dictionaries containing stats for each violin plot. Required keys are: coords: A list of scalars containing the coordinates that the violin’s kernel density estimate were evaluated at. vals: A list of scalars containing the values of the kernel density estimate at each of the coordinates given in coords. mean: The mean value for this violin’s dataset. median: The median value for this violin’s dataset. min: The minimum value for this violin’s dataset. max: The maximum value for this violin’s dataset. positions : array-like, default = [1, 2, ..., n] Sets the positions of the violins. The ticks and limits are automatically set to match the positions. vert : bool, default = True. If true, plots the violins veritcally. Otherwise, plots the violins horizontally. widths : array-like, default = 0.5 Either a scalar or a vector that sets the maximal width of each violin. The default is 0.5, which uses about half of the available horizontal space. showmeans : bool, default = False If true, will toggle rendering of the means. showextrema : bool, default = True If true, will toggle rendering of the extrema. showmedians : bool, default = False If true, will toggle rendering of the medians.

Returns:

result : dict A dictionary mapping each component of the violinplot to a list of the corresponding collection instances created. The dictionary has the following keys: bodies: A list of the matplotlib.collections.PolyCollection instances containing the filled area of each violin. cmeans: A matplotlib.collections.LineCollection instance created to identify the mean values of each of the violin’s distribution. cmins: A matplotlib.collections.LineCollection instance created to identify the bottom of each violin’s distribution. cmaxes: A matplotlib.collections.LineCollection instance created to identify the top of each violin’s distribution. cbars: A matplotlib.collections.LineCollection instance created to identify the centers of each violin’s distribution. cmedians: A matplotlib.collections.LineCollection instance created to identify the median values of each of the violin’s distribution.

violinplot(dataset, positions=None, vert=True, widths=0.5, showmeans=False, showextrema=True, showmedians=False, points=100, bw_method=None)

Make a violin plot.

Call signature:

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

Make a violin plot for each column of dataset or each vector in sequence dataset. Each filled area extends to represent the entire data range, with optional lines at the mean, the median, the minimum, and the maximum.

Parameters:

dataset : Array or a sequence of vectors. The input data. positions : array-like, default = [1, 2, ..., n] Sets the positions of the violins. The ticks and limits are automatically set to match the positions. vert : bool, default = True. If true, creates a vertical violin plot. Otherwise, creates a horizontal violin plot. widths : array-like, default = 0.5 Either a scalar or a vector that sets the maximal width of each violin. The default is 0.5, which uses about half of the available horizontal space. showmeans : bool, default = False If True, will toggle rendering of the means. showextrema : bool, default = True If True, will toggle rendering of the extrema. showmedians : bool, default = False If True, will toggle rendering of the medians. points : scalar, default = 100 Defines the number of points to evaluate each of the gaussian kernel density estimations at. bw_method : str, scalar or callable, optional The method used to calculate the estimator bandwidth. This can be ‘scott’, ‘silverman’, a scalar constant or a callable. If a scalar, this will be used directly as kde.factor. If a callable, it should take a GaussianKDE instance as its only parameter and return a scalar. If None (default), ‘scott’ is used.

Returns:

result : dict A dictionary mapping each component of the violinplot to a list of the corresponding collection instances created. The dictionary has the following keys: bodies: A list of the matplotlib.collections.PolyCollection instances containing the filled area of each violin. cmeans: A matplotlib.collections.LineCollection instance created to identify the mean values of each of the violin’s distribution. cmins: A matplotlib.collections.LineCollection instance created to identify the bottom of each violin’s distribution. cmaxes: A matplotlib.collections.LineCollection instance created to identify the top of each violin’s distribution. cbars: A matplotlib.collections.LineCollection instance created to identify the centers of each violin’s distribution. cmedians: A matplotlib.collections.LineCollection instance created to identify the median values of each of the violin’s distribution.

Notes

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

• All arguments with the following names: ‘dataset’.

vlines(x, ymin, ymax, colors='k', linestyles='solid', label='', **kwargs)

Plot vertical lines.

Plot vertical lines at each x from ymin to ymax.

Parameters:	x : scalar or 1D array_like x-indexes where to plot the lines. ymin, ymax : scalar or 1D array_like Respective beginning and end of each line. If scalars are provided, all lines will have same length. colors : array_like of colors, optional, default: ‘k’ linestyles : [‘solid’ | ‘dashed’ | ‘dashdot’ | ‘dotted’], optional label : string, optional, default: ‘’
Returns:	lines : LineCollection
Other Parameters:
	kwargs : LineCollection properties.

See also

hlines

horizontal lines

Notes

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

• All arguments with the following names: ‘ymin’, ‘x’, ‘colors’, ‘ymax’.

Examples

(Source code, png, hires.png, pdf)

xaxis_date(tz=None)

Sets up x-axis ticks and labels that treat the x data as dates.

tz is a timezone string or tzinfo instance. Defaults to rc value.

xaxis_inverted()

Returns True if the x-axis is inverted.

xcorr(x, y, normed=True, detrend=, usevlines=True, maxlags=10, **kwargs)

Plot the cross correlation between x and y.

Parameters:	x : sequence of scalars of length n y : sequence of scalars of length n hold : boolean, optional, default: True detrend : callable, optional, default: mlab.detrend_none x is detrended by the detrend callable. Default is no normalization. normed : boolean, optional, default: True if True, normalize the data by the autocorrelation at the 0-th lag. usevlines : boolean, optional, default: True if True, Axes.vlines is used to plot the vertical lines from the origin to the acorr. Otherwise, Axes.plot is used. maxlags : integer, optional, default: 10 number of lags to show. If None, will return all 2 * len(x) - 1 lags.
Returns:	(lags, c, line, b) : where: lags are a length 2`maxlags+1 lag vector. c is the 2`maxlags+1 auto correlation vectorI line is a Line2D instance returned by plot. b is the x-axis (none, if plot is used).
Other Parameters:
	linestyle : Line2D prop, optional, default: None Only used if usevlines is False. marker : string, optional, default: ‘o’

Notes

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

• All arguments with the following names: ‘y’, ‘x’.

yaxis_date(tz=None)

Sets up y-axis ticks and labels that treat the y data as dates.

tz is a timezone string or tzinfo instance. Defaults to rc value.

yaxis_inverted()

Returns True if the y-axis is inverted.

zorder = 0