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axes

matplotlib.axes

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

Bases: matplotlib.artist.Artist

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.

Call signature:

acorr(x, normed=True, detrend=mlab.detrend_none, usevlines=True,
      maxlags=10, **kwargs)

If normed = True, normalize the data by the autocorrelation at 0-th lag. x is detrended by the detrend callable (default no normalization).

Data are plotted as plot(lags, c, **kwargs)

Return value is a tuple (lags, c, line) where:

  • lags are a length 2*maxlags+1 lag vector
  • c is the 2*maxlags+1 auto correlation vector
  • line is a Line2D instance returned by plot()

The default linestyle is None and the default marker is 'o', though these can be overridden with keyword args. The cross correlation is performed with numpy.correlate() with mode = 2.

If usevlines is True, vlines() rather than plot() is used to draw vertical lines from the origin to the acorr. Otherwise, the plot style is determined by the kwargs, which are Line2D properties.

maxlags is a positive integer detailing the number of lags to show. The default value of None will return all (2*len(x)-1) lags.

The return value is a tuple (lags, c, linecol, b) where

See also

plot() or vlines() For documentation on valid kwargs.

Example:

xcorr() is top graph, and acorr() is bottom graph.

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

../_images/xcorr_demo.png
add_artist(a)

Add any Artist to the axes.

Returns the artist.

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_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.

annotate(*args, **kwargs)

Create an annotation: a piece of text referring to a data point.

Call signature:

annotate(s, xy, xytext=None, xycoords='data',
         textcoords='data', arrowprops=None, **kwargs)

Keyword arguments:

Annotate the x, y point xy with text s at x, y location xytext. (If xytext = None, defaults to xy, and if textcoords = None, defaults to xycoords).

arrowprops, if not None, is a dictionary of line properties (see matplotlib.lines.Line2D) for the arrow that connects annotation to the point.

If the dictionary has a key arrowstyle, a FancyArrowPatch instance is created with the given dictionary and is drawn. Otherwise, a YAArow patch instance is created and drawn. Valid keys for YAArow are

Key Description
width the width of the arrow in points
frac the fraction of the arrow length occupied by the head
headwidth the width of the base of the arrow head in points
shrink oftentimes it is convenient to have the arrowtip and base a bit away from the text and point being annotated. If d is the distance between the text and annotated point, shrink will shorten the arrow so the tip and base are shink percent of the distance d away from the endpoints. ie, shrink=0.05 is 5%
? any key for matplotlib.patches.polygon

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

xycoords and textcoords are strings that indicate the coordinates of xy and xytext.

Property Description
‘figure points’ points from the lower left corner of the figure
‘figure pixels’ pixels from the lower left corner of the figure
‘figure fraction’ 0,0 is lower left of figure and 1,1 is upper, right
‘axes points’ points from lower left corner of axes
‘axes pixels’ pixels from lower left corner of axes
‘axes fraction’ 0,1 is lower left of axes and 1,1 is upper right
‘data’ use the coordinate system of the object being annotated (default)
‘offset points’ Specify an offset (in points) from the xy value
‘polar’ you can specify theta, r for the annotation, even in cartesian plots. Note that if you are using a polar axes, you do not need to specify polar for the coordinate system since that is the native “data” coordinate system.

If a ‘points’ or ‘pixels’ option is specified, values will be added to the bottom-left and if negative, values will be subtracted from the top-right. Eg:

# 10 points to the right of the left border of the axes and
# 5 points below the top border
xy=(10,-5), xycoords='axes points'

You may use an instance of Transform or Artist. See Annotating Axes for more details.

The annotation_clip attribute contols 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”.

Additional kwargs are Text properties:

Property Description
agg_filter unknown
alpha float (0.0 transparent through 1.0 opaque)
animated [True | False]
axes an Axes instance
backgroundcolor any matplotlib color
bbox rectangle prop dict
clip_box a matplotlib.transforms.Bbox instance
clip_on [True | False]
clip_path [ (Path, Transform) | Patch | None ]
color any matplotlib color
contains a callable function
family or fontfamily or fontname or name [ FONTNAME | ‘serif’ | ‘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)
lod [True | False]
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’ ]
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
variant or fontvariant [ ‘normal’ | ‘small-caps’ ]
verticalalignment or va or ma [ ‘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’ ]
x float
y float
zorder any number

(Source code)

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.

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
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’ | ‘.’ | ‘*’ ]
label string or anything printable with ‘%s’ conversion.
linestyle or ls [‘solid’ | ‘dashed’ | ‘dashdot’ | ‘dotted’]
linewidth or lw float or None for default
lod [True | False]
path_effects unknown
picker [None|float|boolean|callable]
rasterized [True | False | None]
snap unknown
transform Transform instance
url a url string
visible [True | False]
zorder any number

Example:

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

../_images/arrow_demo.png
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, eg, 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.

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

Add a horizontal line across the axis.

Call signature:

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

Draw a horizontal line at y from xmin to xmax. With the default values of xmin = 0 and xmax = 1, this line will always span the horizontal extent of the axes, regardless of the xlim settings, even if you change them, eg. 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 the Line2D instance. kwargs are the same as kwargs to plot, and can be used to control the line properties. Eg.,

  • 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 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
data 2D array (rows are x, y) or two 1D arrays
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 [ '-' | '--' | '-.' | ':' | 'None' | ' ' | '' ] and any drawstyle in combination with a linestyle, e.g. 'steps--'.
linewidth or lw float value in points
lod [True | False]
marker [ 7 | 4 | 5 | 6 | 'o' | 'D' | 'h' | 'H' | '_' | '' | 'None' | None | ' ' | '8' | 'p' | ',' | '+' | '.' | 's' | '*' | 'd' | 3 | 0 | 1 | 2 | '1' | '3' | '4' | '2' | 'v' | '<' | '>' | '^' | '|' | 'x' | '$...$' | tuple | Nx2 array ]
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 | integer | (startind, stride)
picker float distance in points or callable pick function fn(artist, event)
pickradius float distance in points
rasterized [True | False | None]
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

axhspan()
for example plot and source code
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, eg. 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
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’ | ‘.’ | ‘*’ ]
label string or anything printable with ‘%s’ conversion.
linestyle or ls [‘solid’ | ‘dashed’ | ‘dashdot’ | ‘dotted’]
linewidth or lw float or None for default
lod [True | False]
path_effects unknown
picker [None|float|boolean|callable]
rasterized [True | False | None]
snap unknown
transform Transform instance
url a url string
visible [True | False]
zorder any number

Example:

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

../_images/axhspan_demo.png
axis(*v, **kwargs)

Convenience method for manipulating the x and y view limits and the aspect ratio of the plot. For details, see axis().

kwargs are passed on to set_xlim() and set_ylim()

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

Add a vertical line across the axes.

Call signature:

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

Draw a vertical line at x from ymin to ymax. With the default values of ymin = 0 and ymax = 1, this line will always span the vertical extent of the axes, regardless of the ylim settings, even if you change them, eg. with the set_ylim() command. That is, the vertical extent is in axes coords: 0=bottom, 0.5=middle, 1.0=top but the x location is in data coordinates.

Return value is the Line2D instance. kwargs are the same as kwargs to plot, and can be used to control the line properties. Eg.,

  • 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 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
data 2D array (rows are x, y) or two 1D arrays
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 [ '-' | '--' | '-.' | ':' | 'None' | ' ' | '' ] and any drawstyle in combination with a linestyle, e.g. 'steps--'.
linewidth or lw float value in points
lod [True | False]
marker [ 7 | 4 | 5 | 6 | 'o' | 'D' | 'h' | 'H' | '_' | '' | 'None' | None | ' ' | '8' | 'p' | ',' | '+' | '.' | 's' | '*' | 'd' | 3 | 0 | 1 | 2 | '1' | '3' | '4' | '2' | 'v' | '<' | '>' | '^' | '|' | 'x' | '$...$' | tuple | Nx2 array ]
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 | integer | (startind, stride)
picker float distance in points or callable pick function fn(artist, event)
pickradius float distance in points
rasterized [True | False | None]
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

axhspan()
for example plot and source code
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, eg. 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
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’ | ‘.’ | ‘*’ ]
label string or anything printable with ‘%s’ conversion.
linestyle or ls [‘solid’ | ‘dashed’ | ‘dashdot’ | ‘dotted’]
linewidth or lw float or None for default
lod [True | False]
path_effects unknown
picker [None|float|boolean|callable]
rasterized [True | False | None]
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.

Call signature:

bar(left, height, width=0.8, bottom=0, **kwargs)

Make a bar plot with rectangles bounded by:

left, left + width, bottom, bottom + height
(left, right, bottom and top edges)

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

Return value is a list of matplotlib.patches.Rectangle instances.

Required arguments:

Argument Description
left the x coordinates of the left sides of the bars
height the heights of the bars

Optional keyword arguments:

Keyword Description
width the widths of the bars
bottom the y coordinates of the bottom edges of the bars
color the colors of the bars
edgecolor the colors of the bar edges
linewidth width of bar edges; None means use default linewidth; 0 means don’t draw edges.
xerr if not None, will be used to generate errorbars on the bar chart
yerr if not None, will be used to generate errorbars on the bar chart
ecolor specifies the color of any errorbar
capsize (default 3) determines the length in points of the error bar caps
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’ (default) | ‘center’
orientation ‘vertical’ | ‘horizontal’
log [False|True] False (default) leaves the orientation axis as-is; True sets it to log scale

For vertical bars, align = ‘edge’ aligns bars by their left edges in left, while align = ‘center’ interprets these values as the x coordinates of the bar centers. For horizontal bars, align = ‘edge’ aligns bars by their bottom edges in bottom, while align = ‘center’ interprets these values as the y coordinates of the bar centers.

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
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’ | ‘.’ | ‘*’ ]
label string or anything printable with ‘%s’ conversion.
linestyle or ls [‘solid’ | ‘dashed’ | ‘dashdot’ | ‘dotted’]
linewidth or lw float or None for default
lod [True | False]
path_effects unknown
picker [None|float|boolean|callable]
rasterized [True | False | None]
snap unknown
transform Transform instance
url a url string
visible [True | False]
zorder any number

Example: A stacked bar chart.

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

../_images/bar_stacked.png
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
colorbar unknown
contains a callable function
edgecolor or edgecolors matplotlib color arg or sequence of rgba tuples
facecolor or facecolors matplotlib color arg or sequence of rgba tuples
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) ]
linewidth or lw or linewidths float or sequence of floats
lod [True | False]
norm unknown
offset_position unknown
offsets float or sequence of floats
paths unknown
picker [None|float|boolean|callable]
pickradius unknown
rasterized [True | False | None]
snap unknown
transform Transform instance
url a url string
urls unknown
visible [True | False]
zorder any number

Example:

(Source code)

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

Make a horizontal bar plot.

Call signature:

barh(bottom, width, height=0.8, left=0, **kwargs)

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

Return value is a list of matplotlib.patches.Rectangle instances.

Required arguments:

Argument Description
bottom the vertical positions of the bottom edges of the bars
width the lengths of the bars

Optional keyword arguments:

Keyword Description
height the heights (thicknesses) of the bars
left the x coordinates of the left edges of the bars
color the colors of the bars
edgecolor the colors of the bar edges
linewidth width of bar edges; None means use default linewidth; 0 means don’t draw edges.
xerr if not None, will be used to generate errorbars on the bar chart
yerr if not None, will be used to generate errorbars on the bar chart
ecolor specifies the color of any errorbar
capsize (default 3) determines the length in points of the error bar caps
align ‘edge’ (default) | ‘center’
log [False|True] False (default) leaves the horizontal axis as-is; True sets it to log scale

Setting align = ‘edge’ aligns bars by their bottom edges in bottom, while align = ‘center’ interprets these values as the y coordinates of the bar centers.

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 barh as the basis for stacked bar charts, or candlestick plots.

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
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’ | ‘.’ | ‘*’ ]
label string or anything printable with ‘%s’ conversion.
linestyle or ls [‘solid’ | ‘dashed’ | ‘dashdot’ | ‘dotted’]
linewidth or lw float or None for default
lod [True | False]
path_effects unknown
picker [None|float|boolean|callable]
rasterized [True | False | None]
snap unknown
transform Transform instance
url a url string
visible [True | False]
zorder any number
boxplot(x, notch=False, sym='b+', vert=True, whis=1.5, positions=None, widths=None, patch_artist=False, bootstrap=None, usermedians=None, conf_intervals=None)

Make a box and whisker plot.

Call signature:

boxplot(x, notch=False, sym='+', vert=True, whis=1.5,
        positions=None, widths=None, patch_artist=False,
        bootstrap=None, usermedians=None, conf_intervals=None)

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.

Function Arguments:

x :
Array or a sequence of vectors.
notch : [ False (default) | True ]
If False (default), produces a rectangular box plot. If True, will produce a notched box plot
sym : [ default ‘b+’ ]
The default symbol for flier points. Enter an empty string (‘’) if you don’t want to show fliers.
vert : [ False | True (default) ]
If True (default), makes the boxes vertical. If False, makes horizontal boxes.
whis : [ default 1.5 ]
Defines the length of the whiskers as a function of the inner quartile range. They extend to the most extreme data point within ( whis*(75%-25%) ) data range.
bootstrap : [ None (default) | integer ]
Specifies whether to bootstrap the confidence intervals around the median for notched boxplots. If bootstrap==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 it’s 95% confidence intervals. Values between 1000 and 10000 are recommended.
usermedians : [ default None ]
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 == None, the median will be computed directly as normal.
conf_intervals : [ default None ]
Array or sequence whose first dimension (or length) is compatible with x and whose second dimension is 2. When the current element of conf_intervals is not None, the notch locations computed by matplotlib are overridden (assuming notch is True). When an element of conf_intervals is None, boxplot compute notches the method specified by the other kwargs (e.g. bootstrap).
positions : [ default 1,2,...,n ]
Sets the horizontal positions of the boxes. The ticks and limits are automatically set to match the positions.
widths : [ 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.
patch_artist : [ False (default) | True ]
If False produces boxes with the Line2D artist If True produces boxes with the Patch artist

Returns 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: horizonal lines at the median of each box.
  • whiskers: the vertical lines extending to the most extreme, n-outlier data points.
  • caps: the horizontal lines at the ends of the whiskers.
  • fliers: points representing data that extend beyone the whiskers (outliers).

Example:

(Source code)

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
colorbar unknown
contains a callable function
edgecolor or edgecolors matplotlib color arg or sequence of rgba tuples
facecolor or facecolors matplotlib color arg or sequence of rgba tuples
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) ]
linewidth or lw or linewidths float or sequence of floats
lod [True | False]
norm unknown
offset_position unknown
offsets float or sequence of floats
paths unknown
picker [None|float|boolean|callable]
pickradius unknown
rasterized [True | False | None]
snap unknown
transform Transform instance
url a url string
urls unknown
visible [True | False]
zorder any number

these can either be a single argument, ie:

facecolors = 'black'

or a sequence of arguments for the various bars, ie:

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

Example:

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

../_images/broken_barh.png
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 eg ‘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)

clear()

clear the axes

cohere(x, y, NFFT=256, Fs=2, Fc=0, detrend=<function detrend_none at 0x2523ed8>, window=<function window_hanning at 0x252f1b8>, 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:

NFFT: integer
The number of data points used in each block for the FFT. Must be even; 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.
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.
detrend: 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.
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.
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 psd (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
sides: [ ‘default’ | ‘onesided’ | ‘twosided’ ]
Specifies which sides of the PSD 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 PSD, while ‘twosided’ forces two-sided.
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
data 2D array (rows are x, y) or two 1D arrays
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 [ '-' | '--' | '-.' | ':' | 'None' | ' ' | '' ] and any drawstyle in combination with a linestyle, e.g. 'steps--'.
linewidth or lw float value in points
lod [True | False]
marker [ 7 | 4 | 5 | 6 | 'o' | 'D' | 'h' | 'H' | '_' | '' | 'None' | None | ' ' | '8' | 'p' | ',' | '+' | '.' | 's' | '*' | 'd' | 3 | 0 | 1 | 2 | '1' | '3' | '4' | '2' | 'v' | '<' | '>' | '^' | '|' | 'x' | '$...$' | tuple | Nx2 array ]
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 | integer | (startind, stride)
picker float distance in points or callable pick function fn(artist, event)
pickradius float distance in points
rasterized [True | False | None]
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)

../_images/cohere_demo.png
connect(s, func)

Register observers to be notified when certain events occur. Register with callback functions with the following signatures. The function has the following signature:

func(ax)  # where ax is the instance making the callback.

The following events can be connected to:

‘xlim_changed’,’ylim_changed’

The connection id is is returned - you can use this with disconnect to disconnect from the axes event

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 N automatically-chosen levels.

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

draw contour lines at the values specified in sequence V

contourf(..., V)

fill the len(V)-1 regions between the values in V

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:

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; eg 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’].

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:

nchunk: [ 0 | integer ]
If 0, no subdivision of the domain. Specify a positive integer to divide the domain into subdomains of roughly nchunk by nchunk points. This may never actually be advantageous, so this option may be removed. Chunking introduces artifacts at the chunk boundaries unless antialiased is False.
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)

(Source code)

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 N automatically-chosen levels.

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

draw contour lines at the values specified in sequence V

contourf(..., V)

fill the len(V)-1 regions between the values in V

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:

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; eg 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’].

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:

nchunk: [ 0 | integer ]
If 0, no subdivision of the domain. Specify a positive integer to divide the domain into subdomains of roughly nchunk by nchunk points. This may never actually be advantageous, so this option may be removed. Chunking introduces artifacts at the chunk boundaries unless antialiased is False.
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)

(Source code)

csd(x, y, NFFT=256, Fs=2, Fc=0, detrend=<function detrend_none at 0x2523ed8>, window=<function window_hanning at 0x252f1b8>, noverlap=0, pad_to=None, sides='default', scale_by_freq=None, **kwargs)

Plot 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, **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. 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.

Returns the tuple (Pxy, freqs). P is the cross spectrum (complex valued), and is plotted.

Keyword arguments:

NFFT: integer
The number of data points used in each block for the FFT. Must be even; 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.
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.
detrend: 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.
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.
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 psd (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
sides: [ ‘default’ | ‘onesided’ | ‘twosided’ ]
Specifies which sides of the PSD 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 PSD, while ‘twosided’ forces two-sided.
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.
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
data 2D array (rows are x, y) or two 1D arrays
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 [ '-' | '--' | '-.' | ':' | 'None' | ' ' | '' ] and any drawstyle in combination with a linestyle, e.g. 'steps--'.
linewidth or lw float value in points
lod [True | False]
marker [ 7 | 4 | 5 | 6 | 'o' | 'D' | 'h' | 'H' | '_' | '' | 'None' | None | ' ' | '8' | 'p' | ',' | '+' | '.' | 's' | '*' | 'd' | 3 | 0 | 1 | 2 | '1' | '3' | '4' | '2' | 'v' | '<' | '>' | '^' | '|' | 'x' | '$...$' | tuple | Nx2 array ]
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 | integer | (startind, stride)
picker float distance in points or callable pick function fn(artist, event)
pickradius float distance in points
rasterized [True | False | None]
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)

../_images/csd_demo.png
disconnect(cid)

disconnect from the Axes event.

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=3, 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=3,
         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: ‘-‘
The plot format symbol. If fmt is None, only the errorbars are plotted. This is used for adding errorbars to a bar plot, for example.
ecolor: [ None | mpl color ]
A matplotlib color arg which gives the color the errorbar lines; if None, use the marker color.
elinewidth: scalar
The linewidth of the errorbar lines. If None, use the linewidth.
capsize: scalar
The length of the error bar caps in points
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.
errorevery: positive integer
subsamples the errorbars. Eg if everyerror=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
data 2D array (rows are x, y) or two 1D arrays
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 [ '-' | '--' | '-.' | ':' | 'None' | ' ' | '' ] and any drawstyle in combination with a linestyle, e.g. 'steps--'.
linewidth or lw float value in points
lod [True | False]
marker [ 7 | 4 | 5 | 6 | 'o' | 'D' | 'h' | 'H' | '_' | '' | 'None' | None | ' ' | '8' | 'p' | ',' | '+' | '.' | 's' | '*' | 'd' | 3 | 0 | 1 | 2 | '1' | '3' | '4' | '2' | 'v' | '<' | '>' | '^' | '|' | 'x' | '$...$' | tuple | Nx2 array ]
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 | integer | (startind, stride)
picker float distance in points or callable pick function fn(artist, event)
pickradius float distance in points
rasterized [True | False | None]
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)

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, eg. 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
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’ | ‘.’ | ‘*’ ]
label string or anything printable with ‘%s’ conversion.
linestyle or ls [‘solid’ | ‘dashed’ | ‘dashdot’ | ‘dotted’]
linewidth or lw float or None for default
lod [True | False]
path_effects unknown
picker [None|float|boolean|callable]
rasterized [True | False | None]
snap unknown
transform Transform instance
url a url string
visible [True | False]
zorder any number

Example:

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

../_images/fill_demo.png
fill_between(x, y1, y2=0, where=None, interpolate=False, **kwargs)

Make filled polygons between two curves.

Call signature:

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

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

x :
An N-length array of the x data
y1 :
An N-length array (or scalar) of the y data
y2 :
An N-length array (or scalar) of the y data
where :
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 :
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.
kwargs :
Keyword args passed on to the PolyCollection.

kwargs control the Polygon properties:

Property Description
agg_filter unknown
alpha float or None
animated [True | False]
antialiased or antialiaseds Boolean or sequence of booleans
array unknown
axes an Axes instance
clim a length 2 sequence of floats
clip_box a matplotlib.transforms.Bbox instance
clip_on [True | False]
clip_path [ (Path, Transform) | Patch | None ]
cmap a colormap or registered colormap name
color matplotlib color arg or sequence of rgba tuples
colorbar unknown
contains a callable function
edgecolor or edgecolors matplotlib color arg or sequence of rgba tuples
facecolor or facecolors matplotlib color arg or sequence of rgba tuples
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) ]
linewidth or lw or linewidths float or sequence of floats
lod [True | False]
norm unknown
offset_position unknown
offsets float or sequence of floats
paths unknown
picker [None|float|boolean|callable]
pickradius unknown
rasterized [True | False | None]
snap unknown
transform Transform instance
url a url string
urls unknown
visible [True | False]
zorder any number

(Source code)

See also

fill_betweenx()
for filling between two sets of x-values
fill_betweenx(y, x1, x2=0, where=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

y :
An N-length array of the y data
x1 :
An N-length array (or scalar) of the x data
x2 :
An N-length array (or scalar) of the x data
where :
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
kwargs :
keyword args passed on to the PolyCollection

kwargs control the Polygon properties:

Property Description
agg_filter unknown
alpha float or None
animated [True | False]
antialiased or antialiaseds Boolean or sequence of booleans
array unknown
axes an Axes instance
clim a length 2 sequence of floats
clip_box a matplotlib.transforms.Bbox instance
clip_on [True | False]
clip_path [ (Path, Transform) | Patch | None ]
cmap a colormap or registered colormap name
color matplotlib color arg or sequence of rgba tuples
colorbar unknown
contains a callable function
edgecolor or edgecolors matplotlib color arg or sequence of rgba tuples
facecolor or facecolors matplotlib color arg or sequence of rgba tuples
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) ]
linewidth or lw or linewidths float or sequence of floats
lod [True | False]
norm unknown
offset_position unknown
offsets float or sequence of floats
paths unknown
picker [None|float|boolean|callable]
pickradius unknown
rasterized [True | False | None]
snap unknown
transform Transform instance
url a url string
urls unknown
visible [True | False]
zorder any number

(Source code)

See also

fill_between()
for filling between two sets of y-values
format_coord(x, y)

Return a format string formatting the x, y coord

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

frame
get_adjustable()
get_anchor()
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_locator()

return axes_locator

get_axis_bgcolor()

Return the axis background color

get_axisbelow()

Get whether axis below is true or not

get_child_artists()

Return a list of artists the axes contains.

Deprecated since version 0.98.

get_children()

return a list of child artists

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_frame()

Return the axes Rectangle frame

get_frame_on()

Get whether the axes rectangle patch is drawn

get_images()

return a list of Axes images contained by the Axes

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_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_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_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()

Get the title text string.

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, symlog

get_xticklabels(minor=False)

Get the x tick labels as a 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, symlog

get_yticklabels(minor=False)

Get the y tick labels as a 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

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, eg:

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
data 2D array (rows are x, y) or two 1D arrays
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 [ '-' | '--' | '-.' | ':' | 'None' | ' ' | '' ] and any drawstyle in combination with a linestyle, e.g. 'steps--'.
linewidth or lw float value in points
lod [True | False]
marker [ 7 | 4 | 5 | 6 | 'o' | 'D' | 'h' | 'H' | '_' | '' | 'None' | None | ' ' | '8' | 'p' | ',' | '+' | '.' | 's' | '*' | 'd' | 3 | 0 | 1 | 2 | '1' | '3' | '4' | '2' | 'v' | '<' | '>' | '^' | '|' | 'x' | '$...$' | tuple | Nx2 array ]
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 | integer | (startind, stride)
picker float distance in points or callable pick function fn(artist, event)
pickradius float distance in points
rasterized [True | False | None]
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.

hexbin(x, y, C=None, gridsize=100, bins=None, xscale='linear', yscale='linear', extent=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, edgecolors='none', reduce_C_function=<function mean at 0x14f7938>, 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
colorbar unknown
contains a callable function
edgecolor or edgecolors matplotlib color arg or sequence of rgba tuples
facecolor or facecolors matplotlib color arg or sequence of rgba tuples
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) ]
linewidth or lw or linewidths float or sequence of floats
lod [True | False]
norm unknown
offset_position unknown
offsets float or sequence of floats
paths unknown
picker [None|float|boolean|callable]
pickradius unknown
rasterized [True | False | None]
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)

../_images/hexbin_demo.png
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.

Call signature:

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)

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.

Keyword arguments:

bins:
Either an integer number of bins or a sequence giving the bins. If bins is an integer, bins + 1 bin edges will be returned, consistent with numpy.histogram() for numpy version >= 1.3, and with the new = True argument in earlier versions. Unequally spaced bins are supported if bins is a sequence.
range:

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.

normed:

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). In a probability density, the integral of the histogram should be 1; you can verify that with a trapezoidal integration of the probability density function:

pdf, bins, patches = ax.hist(...)
print np.sum(pdf * np.diff(bins))

Note

Until numpy release 1.5, the underlying numpy histogram function was incorrect with normed*=*True if bin sizes were unequal. MPL inherited that error. It is now corrected within MPL when using earlier numpy versions

weights:
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.
cumulative:
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.
histtype: [ ‘bar’ | ‘barstacked’ | ‘step’ | ‘stepfilled’ ]

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.
align: [‘left’ | ‘mid’ | ‘right’ ]

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.
orientation: [ ‘horizontal’ | ‘vertical’ ]
If ‘horizontal’, barh() will be used for bar-type histograms and the bottom kwarg will be the left edges.
rwidth:
The relative width of the bars as a fraction of the bin width. If None, automatically compute the width. Ignored if histtype = ‘step’ or ‘stepfilled’.
log:
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.
color:
Color spec or sequence of color specs, one per dataset. Default (None) uses the standard line color sequence.
label:

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:

ax.hist(10+2*np.random.randn(1000), label='men')
ax.hist(12+3*np.random.randn(1000), label='women', alpha=0.5)
ax.legend()
stacked:

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’

.

kwargs are used to update the properties of the Patch instances returned by hist:

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
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’ | ‘.’ | ‘*’ ]
label string or anything printable with ‘%s’ conversion.
linestyle or ls [‘solid’ | ‘dashed’ | ‘dashdot’ | ‘dotted’]
linewidth or lw float or None for default
lod [True | False]
path_effects unknown
picker [None|float|boolean|callable]
rasterized [True | False | None]
snap unknown
transform Transform instance
url a url string
visible [True | False]
zorder any number

Example:

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

../_images/histogram_demo.png
hist2d(x, y, bins=10, range=None, normed=False, weights=None, cmin=None, cmax=None, **kwargs)

Make a 2D histogram plot.

Call signature:

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

Make a 2d histogram plot of x versus y, where x, y are 1-D sequences of the same length.

The return value is (counts, xedges, yedges, Image).

Optional keyword arguments: 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: [None | array_like shape(2,2)]
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:[True|False]
Normalize histogram. The default value is False
weights: [None | array]
An array of values w_i weighing each sample (x_i, y_i).
cmin : [None| scalar]
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 : [None| scalar]
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

Remaining keyword arguments are passed directly to pcolorfast().

Rendering the histogram with a logarithmic color scale is accomplished by passing a colors.LogNorm instance to the norm keyword argument.

Example:

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

../_images/hist2d_demo.png
hlines(y, xmin, xmax, colors='k', linestyles='solid', label='', **kwargs)

Plot horizontal lines.

call signature:

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

Plot horizontal lines at each y from xmin to xmax.

Returns the LineCollection that was added.

Required arguments:

y:
a 1-D numpy array or iterable.
xmin and xmax:
can be scalars or len(x) numpy arrays. If they are scalars, then the respective values are constant, else the widths of the lines are determined by xmin and xmax.

Optional keyword arguments:

colors:
a line collections color argument, either a single color or a len(y) list of colors
linestyles:
[ ‘solid’ | ‘dashed’ | ‘dashdot’ | ‘dotted’ ]

Example:

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

../_images/hline_demo.png
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.

Call signature:

imshow(X, cmap=None, norm=None, aspect=None, interpolation=None,
       alpha=None, vmin=None, vmax=None, origin=None, extent=None,
       **kwargs)

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, X 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.

An matplotlib.image.AxesImage instance is returned.

Keyword arguments:

cmap: [ None | Colormap ]

A matplotlib.colors.Colormap instance, eg. cm.jet. If None, default to rc image.cmap value.

cmap is ignored when X has RGB(A) information

aspect: [ None | ‘auto’ | ‘equal’ | scalar ]

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:

Acceptable values are None, ‘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: [ None | Normalize ]

An matplotlib.colors.Normalize instance; if None, default is normalization(). This scales luminance -> 0-1

norm is only used for an MxN float array.

vmin/vmax: [ None | scalar ]
Used to scale a luminance image to 0-1. If either is None, the min and max of the luminance values will be used. Note if norm is not None, the settings for vmin and vmax will be ignored.
alpha: scalar
The alpha blending value, between 0 (transparent) and 1 (opaque) or None
origin: [ None | ‘upper’ | ‘lower’ ]
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: [ None | scalars (left, right, bottom, top) ]
Data limits for the axes. The default assigns zero-based row, column indices to the x, y centers of the pixels.
shape: [ None | scalars (columns, rows) ]
For raw buffer images
filternorm:
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:
The filter radius for filters that have a radius parameter, i.e. when interpolation is one of: ‘sinc’, ‘lanczos’ or ‘blackman’

Additional kwargs are Artist 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
gid an id string
label string or anything printable with ‘%s’ conversion.
lod [True | False]
picker [None|float|boolean|callable]
rasterized [True | False | None]
snap unknown
transform Transform instance
url a url string
visible [True | False]
zorder any number

Example:

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

../_images/image_demo.png
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.

ishold()

return the HOLD status of the axes

legend(*args, **kwargs)

Place a legend on the current axes.

Call signature:

legend(*args, **kwargs)

Places legend at location loc. Labels are a sequence of strings and loc can be a string or an integer specifying the legend location.

To make a legend with existing lines:

legend()

legend() by itself will try and build a legend using the label property of the lines/patches/collections. You can set the label of a line by doing:

plot(x, y, label='my data')

or:

line.set_label('my data').

If label is set to ‘_nolegend_’, the item will not be shown in legend.

To automatically generate the legend from labels:

legend( ('label1', 'label2', 'label3') )

To make a legend for a list of lines and labels:

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

To make a legend at a given location, using a location argument:

legend( ('label1', 'label2', 'label3'), loc='upper left')

or:

legend( (line1, line2, line3),  ('label1', 'label2', 'label3'), loc=2)

The location 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

Users can specify any arbitrary location for the legend using the bbox_to_anchor keyword argument. bbox_to_anchor can be an instance of BboxBase(or its derivatives) or a tuple of 2 or 4 floats. For example,

loc = ‘upper right’, bbox_to_anchor = (0.5, 0.5)

will place the legend so that the upper right corner of the legend at the center of the axes.

The legend location can be specified in other coordinate, by using the bbox_transform keyword.

The loc itslef can be a 2-tuple giving x,y of the lower-left corner of the legend in axes coords (bbox_to_anchor is ignored).

Keyword arguments:

prop: [ None | FontProperties | dict ]
A matplotlib.font_manager.FontProperties instance. If prop is a dictionary, a new instance will be created with prop. If None, use rc settings.
fontsize: [ size in points | ‘xx-small’ | ‘x-small’ | ‘small’ | ‘medium’ | ‘large’ | ‘x-large’ | ‘xx-large’ ]
Set the font size. May be either a size string, relative to the default font size, or an absolute font size in points. This argument is only used if prop is not specified.
numpoints: integer
The number of points in the legend for line
scatterpoints: integer
The number of points in the legend for scatter plot
scatteroffsets: list of floats
a list of yoffsets for scatter symbols in legend
markerscale: [ None | scalar ]
The relative size of legend markers vs. original. If None, use rc settings.
frameon: [ True | False ]
if True, draw a frame around the legend. The default is set by the rcParam ‘legend.frameon’
fancybox: [ None | False | True ]
if True, draw a frame with a round fancybox. If None, use rc settings
shadow: [ None | False | True ]
If True, draw a shadow behind legend. If None, use rc settings.
ncol : integer
number of columns. default is 1
mode : [ “expand” | None ]
if mode is “expand”, the legend will be horizontally expanded to fill the axes area (or bbox_to_anchor)
bbox_to_anchor : an instance of BboxBase or a tuple of 2 or 4 floats
the bbox that the legend will be anchored.
bbox_transform : [ an instance of Transform | None ]
the transform for the bbox. transAxes if None.
title : string
the legend title

Padding and spacing between various elements use following keywords parameters. These values are measure in font-size units. E.g., a fontsize of 10 points and a handlelength=5 implies a handlelength of 50 points. Values from rcParams will be used if None.

Keyword Description
borderpad the fractional whitespace inside the legend border
labelspacing the vertical space between the legend entries
handlelength the length of the legend handles
handletextpad the pad between the legend handle and text
borderaxespad the pad between the axes and legend border
columnspacing the spacing between columns

Note

Not all kinds of artist are supported by the legend command. See LINK (FIXME) for details.

Example:

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

../_images/legend_demo.png

See also

Legend guide.

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
data 2D array (rows are x, y) or two 1D arrays
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 [ '-' | '--' | '-.' | ':' | 'None' | ' ' | '' ] and any drawstyle in combination with a linestyle, e.g. 'steps--'.
linewidth or lw float value in points
lod [True | False]
marker [ 7 | 4 | 5 | 6 | 'o' | 'D' | 'h' | 'H' | '_' | '' | 'None' | None | ' ' | '8' | 'p' | ',' | '+' | '.' | 's' | '*' | 'd' | 3 | 0 | 1 | 2 | '1' | '3' | '4' | '2' | 'v' | '<' | '>' | '^' | '|' | 'x' | '$...$' | tuple | Nx2 array ]
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 | integer | (startind, stride)
picker float distance in points or callable pick function fn(artist, event)
pickradius float distance in points
rasterized [True | False | None]
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)

../_images/log_demo.png
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.

Argument:
Z anything that can be interpreted as a 2-D array

kwargs all are passed to imshow(). matshow() sets defaults for origin, interpolation, and aspect; if you want row zero to be at the bottom instead of the top, you can set the origin kwarg to “lower”.

Returns: an matplotlib.image.AxesImage instance.

minorticks_off()

Remove minor ticks from the axes.

minorticks_on()

Add autoscaling minor ticks to the axes.

name = 'rectilinear'
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 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

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 = 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:

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
colorbar unknown
contains a callable function
edgecolor or edgecolors matplotlib color arg or sequence of rgba tuples
facecolor or facecolors matplotlib color arg or sequence of rgba tuples
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) ]
linewidth or lw or linewidths float or sequence of floats
lod [True | False]
norm unknown
offset_position unknown
offsets float or sequence of floats
paths unknown
picker [None|float|boolean|callable]
pickradius unknown
rasterized [True | False | None]
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.
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 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.

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
colorbar unknown
contains a callable function
edgecolor or edgecolors matplotlib color arg or sequence of rgba tuples
facecolor or facecolors matplotlib color arg or sequence of rgba tuples
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) ]
linewidth or lw or linewidths float or sequence of floats
lod [True | False]
norm unknown
offset_position unknown
offsets float or sequence of floats
paths unknown
picker [None|float|boolean|callable]
pickradius unknown
rasterized [True | False | None]
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.
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

pie(x, explode=None, labels=None, colors=None, autopct=None, pctdistance=0.6, shadow=False, labeldistance=1.1, startangle=None, radius=None)

Plot a pie chart.

Call signature:

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

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.

The pie chart will probably look best if the figure and axes are square. Eg.:

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

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

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.

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.

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 color specified by a ‘color cycle’. To change this behavior, you can edit the axes.color_cycle rcParam. Alternatively, you can use set_default_color_cycle().

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, antialised=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
data 2D array (rows are x, y) or two 1D arrays
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 [ '-' | '--' | '-.' | ':' | 'None' | ' ' | '' ] and any drawstyle in combination with a linestyle, e.g. 'steps--'.
linewidth or lw float value in points
lod [True | False]
marker [ 7 | 4 | 5 | 6 | 'o' | 'D' | 'h' | 'H' | '_' | '' | 'None' | None | ' ' | '8' | 'p' | ',' | '+' | '.' | 's' | '*' | 'd' | 3 | 0 | 1 | 2 | '1' | '3' | '4' | '2' | 'v' | '<' | '>' | '^' | '|' | 'x' | '$...$' | tuple | Nx2 array ]
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 | integer | (startind, stride)
picker float distance in points or callable pick function fn(artist, event)
pickradius float distance in points
rasterized [True | False | None]
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.

plot_date(x, y, fmt='bo', 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
data 2D array (rows are x, y) or two 1D arrays
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 [ '-' | '--' | '-.' | ':' | 'None' | ' ' | '' ] and any drawstyle in combination with a linestyle, e.g. 'steps--'.
linewidth or lw float value in points
lod [True | False]
marker [ 7 | 4 | 5 | 6 | 'o' | 'D' | 'h' | 'H' | '_' | '' | 'None' | None | ' ' | '8' | 'p' | ',' | '+' | '.' | 's' | '*' | 'd' | 3 | 0 | 1 | 2 | '1' | '3' | '4' | '2' | 'v' | '<' | '>' | '^' | '|' | 'x' | '$...$' | tuple | Nx2 array ]
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 | integer | (startind, stride)
picker float distance in points or callable pick function fn(artist, event)
pickradius float distance in points
rasterized [True | False | None]
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.

psd(x, NFFT=256, Fs=2, Fc=0, detrend=<function detrend_none at 0x2523ed8>, window=<function window_hanning at 0x252f1b8>, noverlap=0, pad_to=None, sides='default', scale_by_freq=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, **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 Pxx, with a scaling to correct for power loss due to windowing. Fs is the sampling frequency.

Keyword arguments:

NFFT: integer
The number of data points used in each block for the FFT. Must be even; 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.
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.
detrend: 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.
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.
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 psd (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
sides: [ ‘default’ | ‘onesided’ | ‘twosided’ ]
Specifies which sides of the PSD 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 PSD, while ‘twosided’ forces two-sided.
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.

Returns the tuple (Pxx, freqs).

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
data 2D array (rows are x, y) or two 1D arrays
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 [ '-' | '--' | '-.' | ':' | 'None' | ' ' | '' ] and any drawstyle in combination with a linestyle, e.g. 'steps--'.
linewidth or lw float value in points
lod [True | False]
marker [ 7 | 4 | 5 | 6 | 'o' | 'D' | 'h' | 'H' | '_' | '' | 'None' | None | ' ' | '8' | 'p' | ',' | '+' | '.' | 's' | '*' | 'd' | 3 | 0 | 1 | 2 | '1' | '3' | '4' | '2' | 'v' | '<' | '>' | '^' | '|' | 'x' | '$...$' | tuple | Nx2 array ]
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 | integer | (startind, stride)
picker float distance in points or callable pick function fn(artist, event)
pickradius float distance in points
rasterized [True | False | None]
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)

../_images/psd_demo.png
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 the window is resized; for ‘dots’ or ‘inches’, resizing does not change the arrows.

angles: [ ‘uv’ | ‘xy’ | array ]
With the default ‘uv’, the arrow aspect ratio is 1, so that if U*==*V the angle of the arrow on the plot is 45 degrees CCW from the x-axis. With ‘xy’, the arrow points from (x,y) to (x+u, y+v). Alternatively, arbitrary angles may be specified as an array of values in degrees, CCW from the x-axis.
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’ | ‘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
colorbar unknown
contains a callable function
edgecolor or edgecolors matplotlib color arg or sequence of rgba tuples
facecolor or facecolors matplotlib color arg or sequence of rgba tuples
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) ]
linewidth or lw or linewidths float or sequence of floats
lod [True | False]
norm unknown
offset_position unknown
offsets float or sequence of floats
paths unknown
picker [None|float|boolean|callable]
pickradius unknown
rasterized [True | False | None]
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()

Recompute the data limits based on current artists.

At present, Collection instances are not supported.

reset_position()

Make the original position the active position

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

Make a scatter plot.

Call signatures:

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

Make a scatter plot of x versus y, where x, y are converted to 1-D sequences which must be of the same length, N.

Keyword arguments:

s:
size in points^2. It is a scalar or an array of the same length as x and y.
c:
a color. 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.
marker:
can be one of:
marker description
7 caretdown
4 caretleft
5 caretright
6 caretup
'o' circle
'D' diamond
'h' hexagon1
'H' hexagon2
'_' hline
'' nothing
'None' nothing
None nothing
' ' nothing
'8' octagon
'p' pentagon
',' pixel
'+' plus
'.' point
's' square
'*' star
'd' thin_diamond
3 tickdown
0 tickleft
1 tickright
2 tickup
'1' tri_down
'3' tri_left
'4' tri_right
'2' tri_up
'v' triangle_down
'<' triangle_left
'>' triangle_right
'^' triangle_up
'|' vline
'x' x
'$...$' render the string using mathtext.
verts a list of (x, y) pairs used for Path vertices.
path a Path instance.
(numsides, style, angle) see below

The marker can also be a tuple (numsides, style, angle), which will create a custom, regular symbol.

numsides:
the number of sides
style:

the style of the regular symbol:

Value Description
0 a regular polygon
1 a star-like symbol
2 an asterisk
3 a circle (numsides and angle is ignored)
angle:
the angle of rotation of the symbol, in degrees

For backward compatibility, the form (verts, 0) is also accepted, but it is equivalent to just verts for giving a raw set of vertices that define the shape.

Any or all of x, y, s, and c may be masked arrays, in which case all masks will be combined and only unmasked points will be plotted.

Other keyword arguments: the color mapping and normalization arguments will be used only if c is an array of floats.

cmap: [ None | Colormap ]
A matplotlib.colors.Colormap instance or registered name. If None, defaults to rc image.cmap. cmap is only used if c is an array of floats.
norm: [ None | Normalize ]
A matplotlib.colors.Normalize instance is used to scale luminance data to 0, 1. If None, use the default normalize(). norm is only used if c is an array of floats.
vmin/vmax:
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: 0 <= scalar <= 1 or None
The alpha value for the patches
linewidths: [ None | scalar | sequence ]
If None, defaults to (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.

Optional kwargs control the Collection properties; in particular:

edgecolors:
The string ‘none’ to plot faces with no outlines
facecolors:
The string ‘none’ to plot unfilled outlines

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
colorbar unknown
contains a callable function
edgecolor or edgecolors matplotlib color arg or sequence of rgba tuples
facecolor or facecolors matplotlib color arg or sequence of rgba tuples
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) ]
linewidth or lw or linewidths float or sequence of floats
lod [True | False]
norm unknown
offset_position unknown
offsets float or sequence of floats
paths unknown
picker [None|float|boolean|callable]
pickradius unknown
rasterized [True | False | None]
snap unknown
transform Transform instance
url a url string
urls unknown
visible [True | False]
zorder any number

A Collection instance is returned.

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
data 2D array (rows are x, y) or two 1D arrays
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 [ '-' | '--' | '-.' | ':' | 'None' | ' ' | '' ] and any drawstyle in combination with a linestyle, e.g. 'steps--'.
linewidth or lw float value in points
lod [True | False]
marker [ 7 | 4 | 5 | 6 | 'o' | 'D' | 'h' | 'H' | '_' | '' | 'None' | None | ' ' | '8' | 'p' | ',' | '+' | '.' | 's' | '*' | 'd' | 3 | 0 | 1 | 2 | '1' | '3' | '4' | '2' | 'v' | '<' | '>' | '^' | '|' | 'x' | '$...$' | tuple | Nx2 array ]
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 | integer | (startind, stride)
picker float distance in points or callable pick function fn(artist, event)
pickradius float distance in points
rasterized [True | False | None]
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
data 2D array (rows are x, y) or two 1D arrays
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 [ '-' | '--' | '-.' | ':' | 'None' | ' ' | '' ] and any drawstyle in combination with a linestyle, e.g. 'steps--'.
linewidth or lw float value in points
lod [True | False]
marker [ 7 | 4 | 5 | 6 | 'o' | 'D' | 'h' | 'H' | '_' | '' | 'None' | None | ' ' | '8' | 'p' | ',' | '+' | '.' | 's' | '*' | 'd' | 3 | 0 | 1 | 2 | '1' | '3' | '4' | '2' | 'v' | '<' | '>' | '^' | '|' | 'x' | '$...$' | tuple | Nx2 array ]
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 | integer | (startind, stride)
picker float distance in points or callable pick function fn(artist, event)
pickradius float distance in points
rasterized [True | False | None]
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_adjustable(adjustable)

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

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_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.  
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_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_color_cycle(clist)

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

clist is a list of mpl color specifiers.

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_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_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_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_title(label, fontdict=None, **kwargs)

Call signature:

set_title(label, fontdict=None, **kwargs):

Set the title for the axes.

kwargs are Text properties:

Property Description
agg_filter unknown
alpha float (0.0 transparent through 1.0 opaque)
animated [True | False]
axes an Axes instance
backgroundcolor any matplotlib color
bbox rectangle prop dict
clip_box a matplotlib.transforms.Bbox instance
clip_on [True | False]
clip_path [ (Path, Transform) | Patch | None ]
color any matplotlib color
contains a callable function
family or fontfamily or fontname or name [ FONTNAME | ‘serif’ | ‘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)
lod [True | False]
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’ ]
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
variant or fontvariant [ ‘normal’ | ‘small-caps’ ]
verticalalignment or va or ma [ ‘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’ ]
x float
y float
zorder any number

ACCEPTS: str

See also

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

Call signature:

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

Set the label for the xaxis.

labelpad is the spacing in points between the label and the x-axis

Valid kwargs are Text properties:

Property Description
agg_filter unknown
alpha float (0.0 transparent through 1.0 opaque)
animated [True | False]
axes an Axes instance
backgroundcolor any matplotlib color
bbox rectangle prop dict
clip_box a matplotlib.transforms.Bbox instance
clip_on [True | False]
clip_path [ (Path, Transform) | Patch | None ]
color any matplotlib color
contains a callable function
family or fontfamily or fontname or name [ FONTNAME | ‘serif’ | ‘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)
lod [True | False]
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’ ]
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
variant or fontvariant [ ‘normal’ | ‘small-caps’ ]
verticalalignment or va or ma [ ‘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’ ]
x float
y float
zorder any number

ACCEPTS: str

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’ | ‘symlog’

ACCEPTS: [‘linear’ | ‘log’ | ‘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.

‘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 rectangle prop dict
clip_box a matplotlib.transforms.Bbox instance
clip_on [True | False]
clip_path [ (Path, Transform) | Patch | None ]
color any matplotlib color
contains a callable function
family or fontfamily or fontname or name [ FONTNAME | ‘serif’ | ‘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)
lod [True | False]
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’ ]
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
variant or fontvariant [ ‘normal’ | ‘small-caps’ ]
verticalalignment or va or ma [ ‘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’ ]
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)

Call signature:

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

Set the label for the yaxis

labelpad is the spacing in points between the label and the y-axis

Valid kwargs are Text properties:

Property Description
agg_filter unknown
alpha float (0.0 transparent through 1.0 opaque)
animated [True | False]
axes an Axes instance
backgroundcolor any matplotlib color
bbox rectangle prop dict
clip_box a matplotlib.transforms.Bbox instance
clip_on [True | False]
clip_path [ (Path, Transform) | Patch | None ]
color any matplotlib color
contains a callable function
family or fontfamily or fontname or name [ FONTNAME | ‘serif’ | ‘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)
lod [True | False]
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’ ]
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
variant or fontvariant [ ‘normal’ | ‘small-caps’ ]
verticalalignment or va or ma [ ‘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’ ]
x float
y float
zorder any number

ACCEPTS: str

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’ | ‘symlog’

ACCEPTS: [‘linear’ | ‘log’ | ‘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.

‘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 rectangle prop dict
clip_box a matplotlib.transforms.Bbox instance
clip_on [True | False]
clip_path [ (Path, Transform) | Patch | None ]
color any matplotlib color
contains a callable function
family or fontfamily or fontname or name [ FONTNAME | ‘serif’ | ‘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)
lod [True | False]
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’ ]
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
variant or fontvariant [ ‘normal’ | ‘small-caps’ ]
verticalalignment or va or ma [ ‘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’ ]
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
specgram(x, NFFT=256, Fs=2, Fc=0, detrend=<function detrend_none at 0x2523ed8>, window=<function window_hanning at 0x252f1b8>, noverlap=128, cmap=None, xextent=None, pad_to=None, sides='default', scale_by_freq=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, **kwargs)

Compute a spectrogram of data in x. Data are split into NFFT length segments and the PSD 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.

Keyword arguments:

NFFT: integer
The number of data points used in each block for the FFT. Must be even; 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.
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.
detrend: 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.
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.
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 psd (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
sides: [ ‘default’ | ‘onesided’ | ‘twosided’ ]
Specifies which sides of the PSD 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 PSD, while ‘twosided’ forces two-sided.
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 128.
Fc: integer
The center frequency of x (defaults to 0), which offsets the y 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

Return value is (Pxx, freqs, bins, im):

  • bins are the time points the spectrogram is calculated over
  • freqs is an array of frequencies
  • Pxx is an array of shape (len(times), len(freqs)) of power
  • im is a AxesImage instance

Note: If x is real (i.e. non-complex), only the positive spectrum is shown. If x is complex, both positive and negative parts of the spectrum are shown. This can be overridden using the sides keyword argument.

Example:

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

../_images/specgram_demo.png
spy(Z, precision=0, marker=None, markersize=None, aspect='equal', **kwargs)

Plot the sparsity pattern on a 2-D array.

Call signature:

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

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

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.

The array will be plotted as it would be printed, with the first index (row) increasing down and the second index (column) increasing to the right.

By default aspect is ‘equal’, so that each array element occupies a square space; set the aspect kwarg to ‘auto’ to allow the plot to fill the plot box, or to any scalar number to specify the aspect ratio of an array element directly.

Two plotting styles are available: image or marker. Both are available for full arrays, but only the marker style works for scipy.sparse.spmatrix instances.

If marker and markersize are None, an image will be returned and any remaining kwargs are passed to imshow(); else, a Line2D object will be returned with the value of marker determining the marker type, and any remaining kwargs passed to the plot() method.

If marker and markersize are None, useful kwargs include:

  • cmap
  • alpha

See also

imshow()
For image options.

For controlling colors, e.g. cyan background and red marks, use:

cmap = mcolors.ListedColormap(['c','r'])

If marker or markersize is not None, useful kwargs include:

  • marker
  • markersize
  • color

Useful values for marker include:

  • ‘s’ square (default)
  • ‘o’ circle
  • ‘.’ point
  • ‘,’ pixel

See also

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:

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.

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(x, y, linefmt='b-', markerfmt='bo', basefmt='r-', bottom=None, label=None)

Create a stem plot.

Call signature:

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.

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

See also

This document for details.

Example:

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

../_images/stem_plot.png
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 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.

streamplot(x, y, u, v, density=1, linewidth=None, color=None, cmap=None, norm=None, arrowsize=1, arrowstyle='-|>', minlength=0.1, transform=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 25x25 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.

Returns:

stream_container : StreamplotSet

Container object with attributes

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.
lod [True | False]
picker [None|float|boolean|callable]
rasterized [True | False | None]
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.

Call signature:

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

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

Keyword arguments:

fontdict:
A dictionary to override the default text properties. If fontdict is None, the defaults are determined by your rc parameters.
withdash: [ False | True ]
Creates a TextWithDash instance instead of a Text instance.

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 (eg. to set a background color) by using the keyword bbox. bbox is a dictionary of matplotlib.patches.Rectangle properties. For example:

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

Valid kwargs are Text properties:

Property Description
agg_filter unknown
alpha float (0.0 transparent through 1.0 opaque)
animated [True | False]
axes an Axes instance
backgroundcolor any matplotlib color
bbox rectangle prop dict
clip_box a matplotlib.transforms.Bbox instance
clip_on [True | False]
clip_path [ (Path, Transform) | Patch | None ]
color any matplotlib color
contains a callable function
family or fontfamily or fontname or name [ FONTNAME | ‘serif’ | ‘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)
lod [True | False]
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’ ]
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
variant or fontvariant [ ‘normal’ | ‘small-caps’ ]
verticalalignment or va or ma [ ‘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’ ]
x float
y float
zorder any number
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 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

tricontourf(..., Z, V)

fill the (len(V)-1) regions between the values in V

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; eg 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
nchunk: [ 0 | integer ]
If 0, no subdivision of the domain. Specify a positive integer to divide the domain into subdomains of roughly nchunk by nchunk points. This may never actually be advantageous, so this option may be removed. Chunking introduces artifacts at the chunk boundaries unless antialiased is False.

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)

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 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

tricontourf(..., Z, V)

fill the (len(V)-1) regions between the values in V

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; eg 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
nchunk: [ 0 | integer ]
If 0, no subdivision of the domain. Specify a positive integer to divide the domain into subdomains of roughly nchunk by nchunk points. This may never actually be advantageous, so this option may be removed. Chunking introduces artifacts at the chunk boundaries unless antialiased is False.

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)

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 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. shading of ‘faceted’ is deprecated; please use edgecolors instead.

The remaining kwargs are the same as for pcolor().

Example:

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 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().

Example:

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_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

vlines(x, ymin, ymax, colors='k', linestyles='solid', label='', **kwargs)

Plot vertical lines.

Call signature:

vlines(x, ymin, ymax, color='k', linestyles='solid')

Plot vertical lines at each x from ymin to ymax. ymin or ymax can be scalars or len(x) numpy arrays. If they are scalars, then the respective values are constant, else the heights of the lines are determined by ymin and ymax.

colors :
A line collection’s color args, either a single color or a len(x) list of colors

linestyles : [ ‘solid’ | ‘dashed’ | ‘dashdot’ | ‘dotted’ ]

Returns the matplotlib.collections.LineCollection that was 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
colorbar unknown
contains a callable function
edgecolor or edgecolors matplotlib color arg or sequence of rgba tuples
facecolor or facecolors matplotlib color arg or sequence of rgba tuples
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) ]
linewidth or lw or linewidths float or sequence of floats
lod [True | False]
norm unknown
offset_position unknown
offsets float or sequence of floats
paths unknown
picker [None|float|boolean|callable]
pickradius unknown
rasterized [True | False | None]
segments unknown
snap unknown
transform Transform instance
url a url string
urls unknown
verts unknown
visible [True | False]
zorder any number
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=<function detrend_none at 0x2523ed8>, usevlines=True, maxlags=10, **kwargs)

Plot the cross correlation between x and y.

Call signature:

xcorr(self, x, y, normed=True, detrend=mlab.detrend_none,
  usevlines=True, maxlags=10, **kwargs)

If normed = True, normalize the data by the cross correlation at 0-th lag. x and y are detrended by the detrend callable (default no normalization). x and y must be equal length.

Data are plotted as plot(lags, c, **kwargs)

Return value is a tuple (lags, c, line) where:

  • lags are a length 2*maxlags+1 lag vector

  • c is the 2*maxlags+1 auto correlation vector

  • line is a Line2D instance

    returned by plot().

The default linestyle is None and the default marker is ‘o’, though these can be overridden with keyword args. The cross correlation is performed with numpy.correlate() with mode = 2.

If usevlines is True:

vlines() rather than plot() is used to draw vertical lines from the origin to the xcorr. Otherwise the plotstyle is determined by the kwargs, which are Line2D properties.

The return value is a tuple (lags, c, linecol, b) where linecol is the matplotlib.collections.LineCollection instance and b is the x-axis.

maxlags is a positive integer detailing the number of lags to show. The default value of None will return all (2*len(x)-1) lags.

Example:

xcorr() is top graph, and acorr() is bottom graph.

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

../_images/xcorr_demo.png
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.

matplotlib.axes.Subplot

alias of AxesSubplot

class matplotlib.axes.SubplotBase(fig, *args, **kwargs)

Base class for subplots, which are Axes instances with additional methods to facilitate generating and manipulating a set of Axes within a figure.

fig is a matplotlib.figure.Figure instance.

args is the tuple (numRows, numCols, plotNum), where the array of subplots in the figure has dimensions numRows, numCols, and where plotNum is the number of the subplot being created. plotNum starts at 1 in the upper left corner and increases to the right.

If numRows <= numCols <= plotNum < 10, args can be the decimal integer numRows * 100 + numCols * 10 + plotNum.

change_geometry(numrows, numcols, num)

change subplot geometry, eg. from 1,1,1 to 2,2,3

get_geometry()

get the subplot geometry, eg 2,2,3

get_subplotspec()

get the SubplotSpec instance associated with the subplot

is_first_col()
is_first_row()
is_last_col()
is_last_row()
label_outer()

set the visible property on ticklabels so xticklabels are visible only if the subplot is in the last row and yticklabels are visible only if the subplot is in the first column

set_subplotspec(subplotspec)

set the SubplotSpec instance associated with the subplot

update_params()

update the subplot position from fig.subplotpars

matplotlib.axes.set_default_color_cycle(clist)

Change the default cycle of colors that will be used by the plot command. This must be called before creating the Axes to which it will apply; it will apply to all future axes.

clist is a sequence of mpl color specifiers.

See also: set_color_cycle().

Note

Deprecated 2010/01/03. Set rcParams[‘axes.color_cycle’] directly.

matplotlib.axes.subplot_class_factory(axes_class=None)