| | |
- acorr(*args, **kwargs)
- ACORR(x, normed=False, detrend=mlab.detrend_none, usevlines=False,
maxlags=None, **kwargs)
Plot the autocorrelation of x. If normed=True, normalize the
data but 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 lags, c, line where lags are a length
2*maxlags+1 lag vector, c is the 2*maxlags+1 auto correlation
vector, and 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, Axes.vlines rather than Axes.plot is used
to draw vertical lines from the origin to the acorr.
Otherwise the plotstyle is determined by the kwargs, which are
Line2D properties. If usevlines, the return value is lags, c,
linecol, b where linecol is the collections.LineCollection and b is the x-axis
if usevlines=True, kwargs are passed onto Axes.vlines
if usevlines=False, kwargs are passed onto Axes.plot
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.
See the respective function for documentation on valid kwargs
Additional kwargs: hold = [True|False] overrides default hold state
- annotate(*args, **kwargs)
- annotate(s, xy,
xytext=None,
xycoords='data',
textcoords='data',
arrowprops=None,
**props)
Annotate the x,y point xy with text s at x,y location xytext
(xytext if None defaults to xy and textcoords if 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. Valid keys are
- 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: often times 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
xycoords and textcoords are strings that indicate the
coordinates of xy and xytext.
'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)
'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 left, bottom 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'
Additional kwargs are Text properties:
alpha: float
animated: [True | False]
axes: an axes instance
backgroundcolor: any matplotlib color
bbox: rectangle prop dict plus key 'pad' which is a pad in points
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
color: any matplotlib color
contains: unknown
family: [ 'serif' | 'sans-serif' | 'cursive' | 'fantasy' | 'monospace' ]
figure: a matplotlib.figure.Figure instance
fontproperties: a matplotlib.font_manager.FontProperties instance
horizontalalignment or ha: [ 'center' | 'right' | 'left' ]
label: any string
linespacing: float
lod: [True | False]
multialignment: ['left' | 'right' | 'center' ]
name or fontname: string eg, ['Sans' | 'Courier' | 'Helvetica' ...]
picker: [None|float|boolean|callable]
position: (x,y)
rotation: [ angle in degrees 'vertical' | 'horizontal'
size or fontsize: [ size in points | relative size eg 'smaller', 'x-large' ]
style or fontstyle: [ 'normal' | 'italic' | 'oblique']
text: string or anything printable with '%s' conversion
transform: a matplotlib.transform transformation instance
variant: [ 'normal' | 'small-caps' ]
verticalalignment or va: [ 'center' | 'top' | 'bottom' | 'baseline' ]
visible: [True | False]
weight or fontweight: [ 'normal' | 'bold' | 'heavy' | 'light' | 'ultrabold' | 'ultralight']
x: float
y: float
zorder: any number
- arrow(*args, **kwargs)
- Draws arrow on specified axis from (x,y) to (x+dx,y+dy).
Optional kwargs control the arrow properties:
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: an axes instance
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
contains: unknown
edgecolor or ec: any matplotlib color
facecolor or fc: any matplotlib color
figure: a matplotlib.figure.Figure instance
fill: [True | False]
hatch: unknown
label: any string
linewidth or lw: float
lod: [True | False]
picker: [None|float|boolean|callable]
transform: a matplotlib.transform transformation instance
visible: [True | False]
zorder: any number
Additional kwargs: hold = [True|False] overrides default hold state
- autumn()
- set the default colormap to autumn and apply to current image if any.
See help(colormaps) for more information
- axes(*args, **kwargs)
- Add an axes at positon rect specified by::
axes() by itself creates a default full subplot(111) window axis
axes(rect, axisbg='w') where rect=[left, bottom, width, height] in
normalized (0,1) units. axisbg is the background color for the
axis, default white
axes(h) where h is an axes instance makes h the
current axis An Axes instance is returned
kwargs:
axisbg=color : the axes background color
frameon=False : don't display the frame
sharex=otherax : the current axes shares xaxis attribute with otherax
sharey=otherax : the current axes shares yaxis attribute with otherax
polar=True|False : use a polar axes or not
Examples
examples/axes_demo.py places custom axes.
examples/shared_axis_demo.py uses sharex and sharey
- axhline(*args, **kwargs)
- AXHLINE(y=0, xmin=0, xmax=1, **kwargs)
Axis Horizontal Line
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 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
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: unknown
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
color or c: any matplotlib color
contains: unknown
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
data: (npy.array xdata, npy.array ydata)
figure: a matplotlib.figure.Figure instance
label: any string
linestyle or ls: [ '-' | '--' | '-.' | ':' | 'steps' | 'None' | ' ' | '' ]
linewidth or lw: float value in points
lod: [True | False]
marker: [ '+' | ',' | '.' | '1' | '2' | '3' | '4'
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markersize or ms: float
picker: unknown
pickradius: unknown
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a matplotlib.transform transformation instance
visible: [True | False]
xdata: npy.array
ydata: npy.array
zorder: any number
Additional kwargs: hold = [True|False] overrides default hold state
- axhspan(*args, **kwargs)
- AXHSPAN(ymin, ymax, xmin=0, xmax=1, **kwargs)
Axis Horizontal Span. ycoords are in data units and x
coords are in axes (relative 0-1) units
Draw a horizontal span (regtangle) from ymin to ymax. With the
default values of xmin=0 and xmax=1, this always span the xrange,
regardless of the xlim settings, even if you change them, eg with the
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.
kwargs are the kwargs to Patch, eg
antialiased, aa
linewidth, lw
edgecolor, ec
facecolor, fc
the terms on the right are aliases
Return value is the patches.Polygon instance.
#draws 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
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: an axes instance
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
contains: unknown
edgecolor or ec: any matplotlib color
facecolor or fc: any matplotlib color
figure: a matplotlib.figure.Figure instance
fill: [True | False]
hatch: unknown
label: any string
linewidth or lw: float
lod: [True | False]
picker: [None|float|boolean|callable]
transform: a matplotlib.transform transformation instance
visible: [True | False]
zorder: any number
Additional kwargs: hold = [True|False] overrides default hold state
- axis(*v, **kwargs)
- Set/Get the axis properties::
v = axis() returns the current axes as v = [xmin, xmax, ymin, ymax]
axis(v) where v = [xmin, xmax, ymin, ymax] sets the min and max
of the x and y axes
axis('off') turns off the axis lines and labels
axis('equal') changes limits of x or y axis so that equal
increments of x and y have the same length; a circle
is circular.
axis('scaled') achieves the same result by changing the
dimensions of the plot box instead of the axis data
limits.
axis('tight') changes x and y axis limits such that all data is
shown. If all data is already shown, it will move it to the center
of the figure without modifying (xmax-xmin) or (ymax-ymin). Note
this is slightly different than in matlab.
axis('image') is 'scaled' with the axis limits equal to the
data limits.
axis('auto') or 'normal' (deprecated) restores default behavior;
axis limits are automatically scaled to make the data fit
comfortably within the plot box.
if len(*v)==0, you can pass in xmin, xmax, ymin, ymax as kwargs
selectively to alter just those limits w/o changing the others.
See help(xlim) and help(ylim) for more information
The xmin, xmax, ymin, ymax tuple is returned
- axvline(*args, **kwargs)
- AXVLINE(x=0, ymin=0, ymax=1, **kwargs)
Axis Vertical Line
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 xlim settings, even if you change them,
eg with the xlim 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
l = axvline(linewidth=4, color='r')
# draw a default vline at x=1 that spans the yrange
l = 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
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: unknown
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
color or c: any matplotlib color
contains: unknown
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
data: (npy.array xdata, npy.array ydata)
figure: a matplotlib.figure.Figure instance
label: any string
linestyle or ls: [ '-' | '--' | '-.' | ':' | 'steps' | 'None' | ' ' | '' ]
linewidth or lw: float value in points
lod: [True | False]
marker: [ '+' | ',' | '.' | '1' | '2' | '3' | '4'
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markersize or ms: float
picker: unknown
pickradius: unknown
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a matplotlib.transform transformation instance
visible: [True | False]
xdata: npy.array
ydata: npy.array
zorder: any number
Additional kwargs: hold = [True|False] overrides default hold state
- axvspan(*args, **kwargs)
- AXVSPAN(xmin, xmax, ymin=0, ymax=1, **kwargs)
axvspan : Axis Vertical Span. xcoords are in data units and y coords
are in axes (relative 0-1) units
Draw a vertical span (regtangle) from xmin to xmax. With the default
values of ymin=0 and ymax=1, this always span the yrange, regardless
of the ylim settings, even if you change them, eg with the 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.
kwargs are the kwargs to Patch, eg
antialiased, aa
linewidth, lw
edgecolor, ec
facecolor, fc
the terms on the right are aliases
return value is the patches.Polygon instance.
# 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
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: an axes instance
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
contains: unknown
edgecolor or ec: any matplotlib color
facecolor or fc: any matplotlib color
figure: a matplotlib.figure.Figure instance
fill: [True | False]
hatch: unknown
label: any string
linewidth or lw: float
lod: [True | False]
picker: [None|float|boolean|callable]
transform: a matplotlib.transform transformation instance
visible: [True | False]
zorder: any number
Additional kwargs: hold = [True|False] overrides default hold state
- bar(*args, **kwargs)
- BAR(left, height, width=0.8, bottom=0,
color=None, edgecolor=None, linewidth=None,
yerr=None, xerr=None, ecolor=None, capsize=3,
align='edge', orientation='vertical', log=False)
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 Rectangle patch instances
left - the x coordinates of the left sides of the bars
height - the heights of the bars
Optional arguments:
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 and 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'
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 'center' interprets these values as the x coordinates of
the bar centers. For horizontal bars, 'edge' aligns bars by their
bottom edges in bottom, while '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.
Optional kwargs:
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: an axes instance
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
contains: unknown
edgecolor or ec: any matplotlib color
facecolor or fc: any matplotlib color
figure: a matplotlib.figure.Figure instance
fill: [True | False]
hatch: unknown
label: any string
linewidth or lw: float
lod: [True | False]
picker: [None|float|boolean|callable]
transform: a matplotlib.transform transformation instance
visible: [True | False]
zorder: any number
Additional kwargs: hold = [True|False] overrides default hold state
- barh(*args, **kwargs)
- 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 Rectangle patch instances
bottom - the vertical positions of the bottom edges of the bars
width - the lengths of the bars
Optional arguments:
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 and 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 '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.
Optional kwargs:
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: an axes instance
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
contains: unknown
edgecolor or ec: any matplotlib color
facecolor or fc: any matplotlib color
figure: a matplotlib.figure.Figure instance
fill: [True | False]
hatch: unknown
label: any string
linewidth or lw: float
lod: [True | False]
picker: [None|float|boolean|callable]
transform: a matplotlib.transform transformation instance
visible: [True | False]
zorder: any number
Additional kwargs: hold = [True|False] overrides default hold state
- bone()
- set the default colormap to bone and apply to current image if any.
See help(colormaps) for more information
- box(on=None)
- Turn the axes box on or off according to 'on'
If on is None, toggle state
- boxplot(*args, **kwargs)
- boxplot(x, notch=0, sym='+', vert=1, whis=1.5,
positions=None, widths=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.
notch = 0 (default) produces a rectangular box plot.
notch = 1 will produce a notched box plot
sym (default 'b+') is the default symbol for flier points.
Enter an empty string ('') if you don't want to show fliers.
vert = 1 (default) makes the boxes vertical.
vert = 0 makes horizontal boxes. This seems goofy, but
that's how Matlab did it.
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.
positions (default 1,2,...,n) sets the horizontal positions of
the boxes. The ticks and limits are automatically set to match
the positions.
widths is 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.
x is an array or a sequence of vectors.
Returns a list of the lines added.
Additional kwargs: hold = [True|False] overrides default hold state
- broken_barh(*args, **kwargs)
- A collection of horizontal bars spanning yrange with a sequence of
xranges
xranges : sequence of (xmin, xwidth)
yrange : (ymin, ywidth)
kwargs are collections.BrokenBarHCollection properties
alpha: float
animated: [True | False]
array: unknown
axes: an axes instance
clim: a length 2 sequence of floats
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
cmap: a colormap
color: matplotlib color arg or sequence of rgba tuples
colorbar: unknown
contains: unknown
edgecolor: matplotlib color arg or sequence of rgba tuples
edgecolors: unknown
facecolor: matplotlib color arg or sequence of rgba tuples
facecolors: unknown
figure: a matplotlib.figure.Figure instance
label: any string
linewidth: float or sequence of floats
linewidths: float or sequence of floats
lod: [True | False]
lw: float or sequence of floats
norm: unknown
picker: [None|float|boolean|callable]
transform: a matplotlib.transform transformation instance
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'
Additional kwargs: hold = [True|False] overrides default hold state
- cla(*args, **kwargs)
- Clear the current axes
- clabel(*args, **kwargs)
- clabel(CS, **kwargs) - add labels to line contours in CS,
where CS is a ContourSet object returned by contour.
clabel(CS, V, **kwargs) - only label contours listed in V
keyword arguments:
* fontsize = None: as described in http://matplotlib.sf.net/fonts.html
* colors = None:
- a tuple of matplotlib color args (string, float, rgb, etc),
different labels will be plotted in different colors in the order
specified
- one string color, e.g. colors = 'r' or colors = 'red', all labels
will be plotted in this color
- if colors == None, the color of each label matches the color
of the corresponding contour
* inline = True: controls whether the underlying contour is removed
(inline = True) or not (False)
* fmt = '%1.3f': a format string for the label
Additional kwargs: hold = [True|False] overrides default hold state
- clf()
- Clear the current figure
- clim(vmin=None, vmax=None)
- Set the color limits of the current image
To apply clim to all axes images do
clim(0, 0.5)
If either vmin or vmax is None, the image min/max respectively
will be used for color scaling.
If you want to set the clim of multiple images,
use, for example for im in gca().get_images(): im.set_clim(0,
0.05)
- close(*args)
- Close a figure window
close() by itself closes the current figure
close(num) closes figure number num
close(h) where h is a figure handle(instance) closes that figure
close('all') closes all the figure windows
- cohere(*args, **kwargs)
- COHERE(x, y, NFFT=256, Fs=2, Fc=0, detrend = mlab.detrend_none,
window = mlab.window_hanning, noverlap=0, **kwargs)
cohere the coherence between x and y. Coherence is the normalized
cross spectral density
Cxy = |Pxy|^2/(Pxx*Pyy)
The return value is (Cxy, f), where f are the frequencies of the
coherence vector.
See the PSD help for a description of the optional parameters.
kwargs are applied to the lines
Returns the tuple Cxy, freqs
Refs: Bendat & Piersol -- Random Data: Analysis and Measurement
Procedures, John Wiley & Sons (1986)
kwargs control the Line2D properties of the coherence plot:
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: unknown
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
color or c: any matplotlib color
contains: unknown
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
data: (npy.array xdata, npy.array ydata)
figure: a matplotlib.figure.Figure instance
label: any string
linestyle or ls: [ '-' | '--' | '-.' | ':' | 'steps' | 'None' | ' ' | '' ]
linewidth or lw: float value in points
lod: [True | False]
marker: [ '+' | ',' | '.' | '1' | '2' | '3' | '4'
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markersize or ms: float
picker: unknown
pickradius: unknown
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a matplotlib.transform transformation instance
visible: [True | False]
xdata: npy.array
ydata: npy.array
zorder: any number
Additional kwargs: hold = [True|False] overrides default hold state
- colorbar(mappable=None, cax=None, ax=None, **kw)
- Add a colorbar to a plot.
Function signatures for the pyplot interface; all but the first are
also method signatures for the Figure.colorbar method:
colorbar(**kwargs)
colorbar(mappable, **kwargs)
colorbar(mappable, cax=cax, **kwargs)
colorbar(mappable, ax=ax, **kwargs)
arguments:
mappable: the image, ContourSet, etc. to which the colorbar applies;
this argument is mandatory for the Figure.colorbar
method but optional for the pyplot.colorbar function,
which sets the default to the current image.
keyword arguments:
cax: None | axes object into which the colorbar will be drawn
ax: None | parent axes object from which space for a new
colorbar axes will be stolen
**kwargs are in two groups:
axes properties:
fraction = 0.15; fraction of original axes to use for colorbar
pad = 0.05 if vertical, 0.15 if horizontal; fraction
of original axes between colorbar and
new image axes
shrink = 1.0; fraction by which to shrink the colorbar
aspect = 20; ratio of long to short dimensions
colorbar properties:
extend='neither', 'both', 'min', 'max'
If not 'neither', make pointed end(s) for out-of-range
values. These are set for a given colormap using the
colormap set_under and set_over methods.
spacing='uniform', 'proportional'
Uniform spacing gives each discrete color the same space;
proportional makes the space proportional to the data interval.
ticks=None, list of ticks, Locator object
If None, ticks are determined automatically from the input.
format=None, format string, Formatter object
If none, the ScalarFormatter is used.
If a format string is given, e.g. '%.3f', that is used.
An alternative Formatter object may be given instead.
drawedges=False, True
If true, draw lines at color boundaries.
The following will probably be useful only in the context of
indexed colors (that is, when the mappable has norm=NoNorm()),
or other unusual circumstances.
boundaries=None or a sequence
values=None or a sequence which must be of length 1 less than the
sequence of boundaries.
For each region delimited by adjacent entries in
boundaries, the color mapped to the corresponding
value in values will be used.
If mappable is a ContourSet, its extend kwarg is included automatically.
Note that the shrink kwarg provides a simple way to keep
a vertical colorbar, for example, from being taller than
the axes of the mappable to which the colorbar is attached;
but it is a manual method requiring some trial and error.
If the colorbar is too tall (or a horizontal colorbar is
too wide) use a smaller value of shrink.
For more precise control, you can manually specify the
positions of the axes objects in which the mappable and
the colorbar are drawn. In this case, do not use any of the
axes properties kwargs.
- colormaps()
- matplotlib provides the following colormaps.
autumn bone cool copper flag gray hot hsv jet pink prism
spring summer winter spectral
You can set the colormap for an image, pcolor, scatter, etc,
either as a keyword argumentdef con
>>> imshow(X, cmap=cm.hot)
or post-hoc using the corresponding pylab interface function
>>> imshow(X)
>>> hot()
>>> jet()
In interactive mode, this will update the colormap allowing you to
see which one works best for your data.
- colors()
- This is a do nothing function to provide you with help on how
matplotlib handles colors.
Commands which take color arguments can use several formats to
specify the colors. For the basic builtin colors, you can use a
single letter
b : blue
g : green
r : red
c : cyan
m : magenta
y : yellow
k : black
w : white
For a greater range of colors, you have two options. You can
specify the color using an html hex string, as in
color = '#eeefff'
or you can pass an R,G,B tuple, where each of R,G,B are in the
range [0,1].
You can also use any legal html name for a color, like 'red',
'burlywood' and 'chartreuse'
The example below creates a subplot with a dark
slate gray background
subplot(111, axisbg=(0.1843, 0.3098, 0.3098))
Here is an example that creates a pale turqoise title
title('Is this the best color?', color='#afeeee')
- connect(s, func)
- Connect event with string s to func. The signature of func is
def func(event)
where event is a MplEvent. The following events are recognized
'resize_event',
'draw_event',
'key_press_event',
'key_release_event',
'button_press_event',
'button_release_event',
'scroll_event',
'motion_notify_event',
'pick_event',
For the three events above, if the mouse is over the axes,
the variable event.inaxes will be set to the axes it is over,
and additionally, the variables event.xdata and event.ydata
will be defined. This is the mouse location in data coords.
See backend_bases.MplEvent.
return value is a connection id that can be used with
mpl_disconnect
- contour(*args, **kwargs)
- 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 (TM) version in that it does not
draw the polygon edges, because the contouring engine yields
simply connected regions with branch cuts. To draw the edges,
add line contours with calls to contour.
Function 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) and contour(X,Y,Z,N) - contour N automatically-chosen
levels.
contour(Z,V) and contour(X,Y,Z,V) - draw len(V) 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
X, Y, and Z must be arrays with the same dimensions.
Z may be a masked array, but filled contouring may not handle
internal masked regions correctly.
C = contour(...) returns a ContourSet object.
Optional keyword args are shown with their defaults below (you must
use kwargs for these):
* colors = None; or one of the following:
- a tuple of matplotlib color args (string, float, rgb, etc),
different levels will be plotted in different colors in the order
specified
- one string color, e.g. colors = 'r' or colors = 'red', all levels
will be plotted in this color
- if colors == None, the colormap specified by cmap will be used
* alpha=1.0 : the alpha blending value
* cmap = None: a cm Colormap instance from matplotlib.cm.
- if cmap == None and colors == None, a default Colormap is used.
* norm = None: a matplotlib.colors.Normalize instance for
scaling data values to colors.
- if norm == None, and colors == None, the default
linear scaling is used.
* origin = None: 'upper'|'lower'|'image'|None.
If 'image', the rc value for image.origin will be used.
If None (default), the first value of Z will correspond
to the lower left corner, location (0,0).
This keyword is active only if contourf is called with
one or two arguments, that is, without explicitly
specifying X and Y.
* extent = None: (x0,x1,y0,y1); also active only if X and Y
are not specified. If origin is not None, then extent is
interpreted as in 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].
* locator = None: an instance of a ticker.Locator subclass;
default is MaxNLocator. It 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' (default), 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
Colormap.set_under() and Colormap.set_over() methods.
****************
contour only:
* linewidths = None: or one of these:
- a number - all levels will be plotted with this linewidth,
e.g. linewidths = 0.6
- a tuple of numbers, e.g. linewidths = (0.4, 0.8, 1.2) different
levels will be plotted with different linewidths in the order
specified
- if linewidths == None, the default width in lines.linewidth in
matplotlibrc is used
contourf only:
* antialiased = True (default) or False
* nchunk = 0 (default) for 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 = False
Additional kwargs: hold = [True|False] overrides default hold state
- contourf(*args, **kwargs)
- 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 (TM) version in that it does not
draw the polygon edges, because the contouring engine yields
simply connected regions with branch cuts. To draw the edges,
add line contours with calls to contour.
Function 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) and contour(X,Y,Z,N) - contour N automatically-chosen
levels.
contour(Z,V) and contour(X,Y,Z,V) - draw len(V) 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
X, Y, and Z must be arrays with the same dimensions.
Z may be a masked array, but filled contouring may not handle
internal masked regions correctly.
C = contour(...) returns a ContourSet object.
Optional keyword args are shown with their defaults below (you must
use kwargs for these):
* colors = None; or one of the following:
- a tuple of matplotlib color args (string, float, rgb, etc),
different levels will be plotted in different colors in the order
specified
- one string color, e.g. colors = 'r' or colors = 'red', all levels
will be plotted in this color
- if colors == None, the colormap specified by cmap will be used
* alpha=1.0 : the alpha blending value
* cmap = None: a cm Colormap instance from matplotlib.cm.
- if cmap == None and colors == None, a default Colormap is used.
* norm = None: a matplotlib.colors.Normalize instance for
scaling data values to colors.
- if norm == None, and colors == None, the default
linear scaling is used.
* origin = None: 'upper'|'lower'|'image'|None.
If 'image', the rc value for image.origin will be used.
If None (default), the first value of Z will correspond
to the lower left corner, location (0,0).
This keyword is active only if contourf is called with
one or two arguments, that is, without explicitly
specifying X and Y.
* extent = None: (x0,x1,y0,y1); also active only if X and Y
are not specified. If origin is not None, then extent is
interpreted as in 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].
* locator = None: an instance of a ticker.Locator subclass;
default is MaxNLocator. It 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' (default), 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
Colormap.set_under() and Colormap.set_over() methods.
****************
contour only:
* linewidths = None: or one of these:
- a number - all levels will be plotted with this linewidth,
e.g. linewidths = 0.6
- a tuple of numbers, e.g. linewidths = (0.4, 0.8, 1.2) different
levels will be plotted with different linewidths in the order
specified
- if linewidths == None, the default width in lines.linewidth in
matplotlibrc is used
contourf only:
* antialiased = True (default) or False
* nchunk = 0 (default) for 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 = False
Additional kwargs: hold = [True|False] overrides default hold state
- cool()
- set the default colormap to cool and apply to current image if any.
See help(colormaps) for more information
- copper()
- set the default colormap to copper and apply to current image if any.
See help(colormaps) for more information
- csd(*args, **kwargs)
- CSD(x, y, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window=window_hanning, noverlap=0, **kwargs)
The cross spectral density Pxy by Welches 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 Pxy, with a scaling to correct for power loss
due to windowing.
See the PSD help for a description of the optional parameters.
Returns the tuple Pxy, freqs. Pxy is the cross spectrum (complex
valued), and 10*npy.log10(|Pxy|) is plotted
Refs:
Bendat & Piersol -- Random Data: Analysis and Measurement
Procedures, John Wiley & Sons (1986)
kwargs control the Line2D properties:
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: unknown
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
color or c: any matplotlib color
contains: unknown
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
data: (npy.array xdata, npy.array ydata)
figure: a matplotlib.figure.Figure instance
label: any string
linestyle or ls: [ '-' | '--' | '-.' | ':' | 'steps' | 'None' | ' ' | '' ]
linewidth or lw: float value in points
lod: [True | False]
marker: [ '+' | ',' | '.' | '1' | '2' | '3' | '4'
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markersize or ms: float
picker: unknown
pickradius: unknown
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a matplotlib.transform transformation instance
visible: [True | False]
xdata: npy.array
ydata: npy.array
zorder: any number
Additional kwargs: hold = [True|False] overrides default hold state
- delaxes(*args)
- delaxes(ax) - remove ax from the current figure. If ax doesn't
exist an error will be raised.
delaxes(): delete the current axes
- disconnect(cid)
- disconnect callback id cid
- draw()
- redraw the current figure
- errorbar(*args, **kwargs)
- ERRORBAR(x, y, yerr=None, xerr=None,
fmt='b-', ecolor=None, capsize=3, barsabove=False,
lolims=False, uplims=False,
xlolims=False, xuplims=False)
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
xerr and yerr may be any of:
a rank-0, Nx1 Numpy array - symmetric errorbars +/- value
an N-element list or tuple - symmetric errorbars +/- value
a rank-1, Nx2 Numpy array - asymmetric errorbars -column1/+column2
Alternatively, x, y, xerr, and yerr can all be scalars, which
plots a single error bar at x, y.
fmt is the plot format symbol for y. if fmt is None, just
plot the errorbars with no line symbols. This can be useful
for creating a bar plot with errorbars
ecolor is a matplotlib color arg which gives the color the
errorbar lines; if None, use the marker color.
capsize is the size of the error bar caps in points
barsabove, if True, will plot the errorbars above the plot symbols
- default is below
lolims, uplims, xlolims, xuplims: 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.
kwargs are passed on to the plot command for the markers.
So you can add additional key=value pairs to control the
errorbar 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)
mfc, mec, ms and mew are aliases for the longer property
names, markerfacecolor, markeredgecolor, markersize and
markeredgewith.
valid kwargs for the marker properties are
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: unknown
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
color or c: any matplotlib color
contains: unknown
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
data: (npy.array xdata, npy.array ydata)
figure: a matplotlib.figure.Figure instance
label: any string
linestyle or ls: [ '-' | '--' | '-.' | ':' | 'steps' | 'None' | ' ' | '' ]
linewidth or lw: float value in points
lod: [True | False]
marker: [ '+' | ',' | '.' | '1' | '2' | '3' | '4'
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markersize or ms: float
picker: unknown
pickradius: unknown
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a matplotlib.transform transformation instance
visible: [True | False]
xdata: npy.array
ydata: npy.array
zorder: any number
Return value is a length 3 tuple. The first element is the
Line2D instance for the y symbol lines. The second element is
a list of error bar cap lines, the third element is a list of
line collections for the horizontal and vertical error ranges
Additional kwargs: hold = [True|False] overrides default hold state
- figimage(*args, **kwargs)
- FIGIMAGE(X) # add non-resampled array to figure
FIGIMAGE(X, xo, yo) # with pixel offsets
FIGIMAGE(X, **kwargs) # control interpolation ,scaling, etc
Add a nonresampled figure to the figure from array X. xo and yo are
offsets in pixels
X must be a float array
If X is MxN, assume luminance (grayscale)
If X is MxNx3, assume RGB
If X is MxNx4, assume RGBA
The following kwargs are allowed:
* cmap is a cm colormap instance, eg cm.jet. If None, default to
the rc image.cmap valuex
* norm is a matplotlib.colors.Normalize instance; default is
normalization(). This scales luminance -> 0-1
* vmin and vmax are 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 you pass a norm instance, the settings for vmin and
vmax will be ignored.
* alpha = 1.0 : the alpha blending value
* origin is either 'upper' or 'lower', which indicates where the [0,0]
index of the array is in the upper left or lower left corner of
the axes. Defaults to the rc image.origin value
This complements the axes image (Axes.imshow) which will be resampled
to fit the current axes. If you want a resampled image to fill the
entire figure, you can define an Axes with size [0,1,0,1].
A image.FigureImage instance is returned.
Addition kwargs: hold = [True|False] overrides default hold state
- figlegend(handles, labels, loc, **kwargs)
- Place a legend in the figure. Labels are a sequence of
strings, handles is a sequence of line or patch instances, and
loc can be a string r an integer specifying the legend
location
USAGE:
legend( (line1, line2, line3),
('label1', 'label2', 'label3'),
'upper right')
See help(legend) for information about the location codes
A matplotlib.legend.Legend instance is returned
- figtext(*args, **kwargs)
- Add text to figure at location x,y (relative 0-1 coords) See
the help for Axis text for the meaning of the other arguments
kwargs control the Text properties:
alpha: float
animated: [True | False]
axes: an axes instance
backgroundcolor: any matplotlib color
bbox: rectangle prop dict plus key 'pad' which is a pad in points
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
color: any matplotlib color
contains: unknown
family: [ 'serif' | 'sans-serif' | 'cursive' | 'fantasy' | 'monospace' ]
figure: a matplotlib.figure.Figure instance
fontproperties: a matplotlib.font_manager.FontProperties instance
horizontalalignment or ha: [ 'center' | 'right' | 'left' ]
label: any string
linespacing: float
lod: [True | False]
multialignment: ['left' | 'right' | 'center' ]
name or fontname: string eg, ['Sans' | 'Courier' | 'Helvetica' ...]
picker: [None|float|boolean|callable]
position: (x,y)
rotation: [ angle in degrees 'vertical' | 'horizontal'
size or fontsize: [ size in points | relative size eg 'smaller', 'x-large' ]
style or fontstyle: [ 'normal' | 'italic' | 'oblique']
text: string or anything printable with '%s' conversion
transform: a matplotlib.transform transformation instance
variant: [ 'normal' | 'small-caps' ]
verticalalignment or va: [ 'center' | 'top' | 'bottom' | 'baseline' ]
visible: [True | False]
weight or fontweight: [ 'normal' | 'bold' | 'heavy' | 'light' | 'ultrabold' | 'ultralight']
x: float
y: float
zorder: any number
- figure(num=None, figsize=None, dpi=None, facecolor=None, edgecolor=None, frameon=True, FigureClass=<class matplotlib.figure.Figure at 0x8ad86bc>, **kwargs)
- figure(num = None, figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k')
Create a new figure and return a handle to it. If num=None, the figure
number will be incremented and a new figure will be created. The returned
figure objects have a .number attribute holding this number.
If num is an integer, and figure(num) already exists, make it
active and return the handle to it. If figure(num) does not exist
it will be created. Numbering starts at 1, matlab style
figure(1)
If you are creating many figures, make sure you explicitly call "close"
on the figures you are not using, because this will enable pylab
to properly clean up the memory.
kwargs:
figsize - width x height in inches; defaults to rc figure.figsize
dpi - resolution; defaults to rc figure.dpi
facecolor - the background color; defaults to rc figure.facecolor
edgecolor - the border color; defaults to rc figure.edgecolor
rcParams gives the default values from the matplotlibrc file
FigureClass is a Figure or derived class that will be passed on to
new_figure_manager in the backends which allows you to hook custom
Figureclasses into the pylab interface. Additional kwargs will be
passed on to your figure init function
- fill(*args, **kwargs)
- FILL(*args, **kwargs)
plot filled polygons. *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, all of the
following are legal, assuming ax is an Axes instance:
ax.fill(x,y) # plot polygon with vertices at x,y
ax.fill(x,y, 'b' ) # plot polygon with vertices at x,y in blue
An arbitrary number of x, y, color groups can be specified, as in
ax.fill(x1, y1, 'g', x2, y2, 'r')
Return value is a list of patches 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 mlab.poly_between, eg
xs, ys = poly_between(x, 0, y)
axes.fill(xs, ys, facecolor='red', alpha=0.5)
See examples/fill_between.py for more examples.
kwargs control the Polygon properties:
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: an axes instance
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
contains: unknown
edgecolor or ec: any matplotlib color
facecolor or fc: any matplotlib color
figure: a matplotlib.figure.Figure instance
fill: [True | False]
hatch: unknown
label: any string
linewidth or lw: float
lod: [True | False]
picker: [None|float|boolean|callable]
transform: a matplotlib.transform transformation instance
visible: [True | False]
zorder: any number
Additional kwargs: hold = [True|False] overrides default hold state
- flag()
- set the default colormap to flag and apply to current image if any.
See help(colormaps) for more information
- gca(**kwargs)
- Return the current axis instance. This can be used to control
axis properties either using set or the Axes methods.
Example:
plot(t,s)
set(gca(), 'xlim', [0,10]) # set the x axis limits
or
plot(t,s)
a = gca()
a.set_xlim([0,10]) # does the same
- gcf()
- Return a handle to the current figure
- gci()
- get the current ScalarMappable instance (image or patch
collection), or None if no images or patch collections have been
defined. The commands imshow and figimage create images
instances, and the commands pcolor and scatter create patch
collection instances
- get_current_fig_manager()
- get_plot_commands()
- gray()
- set the default colormap to gray and apply to current image if any.
See help(colormaps) for more information
- grid(*args, **kwargs)
- GRID(self, b=None, **kwargs)
Set the axes grids on or off; b is a boolean
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
kawrgs are used to set the grid line properties, eg
ax.grid(color='r', linestyle='-', linewidth=2)
Valid Line2D kwargs are
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: unknown
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
color or c: any matplotlib color
contains: unknown
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
data: (npy.array xdata, npy.array ydata)
figure: a matplotlib.figure.Figure instance
label: any string
linestyle or ls: [ '-' | '--' | '-.' | ':' | 'steps' | 'None' | ' ' | '' ]
linewidth or lw: float value in points
lod: [True | False]
marker: [ '+' | ',' | '.' | '1' | '2' | '3' | '4'
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markersize or ms: float
picker: unknown
pickradius: unknown
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a matplotlib.transform transformation instance
visible: [True | False]
xdata: npy.array
ydata: npy.array
zorder: any number
- hist(*args, **kwargs)
- HIST(x, bins=10, normed=0, bottom=None,
align='edge', orientation='vertical', width=None,
log=False, **kwargs)
Compute the histogram of x. bins is either an integer number of
bins or a sequence giving the bins. x are the data to be binned.
The return values is (n, bins, patches)
If normed is true, the first element of the return tuple will
be the counts normalized to form a probability density, ie,
n/(len(x)*dbin). In a probability density, the integral of
the histogram should be one (we assume equally spaced bins);
you can verify that with
# trapezoidal integration of the probability density function
pdf, bins, patches = ax.hist(...)
print npy.trapz(pdf, bins)
align = 'edge' | 'center'. Interprets bins either as edge
or center values
orientation = 'horizontal' | 'vertical'. If horizontal, barh
will be used and the "bottom" kwarg will be the left edges.
width: the width of the bars. If None, automatically compute
the width.
log: if True, the histogram axis will be set to a log scale
kwargs are used to update the properties of the
hist Rectangles:
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: an axes instance
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
contains: unknown
edgecolor or ec: any matplotlib color
facecolor or fc: any matplotlib color
figure: a matplotlib.figure.Figure instance
fill: [True | False]
hatch: unknown
label: any string
linewidth or lw: float
lod: [True | False]
picker: [None|float|boolean|callable]
transform: a matplotlib.transform transformation instance
visible: [True | False]
zorder: any number
Additional kwargs: hold = [True|False] overrides default hold state
- hlines(*args, **kwargs)
- HLINES(y, xmin, xmax, colors='k', linestyle='solid', **kwargs)
plot horizontal lines at each y from xmin to xmax. xmin or 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
colors is a line collections color args, either a single color
or a len(x) list of colors
linestyle is one of solid|dashed|dashdot|dotted
Returns the LineCollection that was added
Additional kwargs: hold = [True|False] overrides default hold state
- 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.
Eg
hold() # toggle hold
hold(True) # hold is on
hold(False) # hold is off
When hold is True, subsequent plot commands will be added to the
current axes. When hold is False, the current axes and figure
will be cleared on the next plot command
- hot()
- set the default colormap to hot and apply to current image if any.
See help(colormaps) for more information
- hsv()
- set the default colormap to hsv and apply to current image if any.
See help(colormaps) for more information
- imread(*args, **kwargs)
- return image file in fname as numpy array
Return value is a MxNx4 array of 0-1 normalized floats
- imshow(*args, **kwargs)
- IMSHOW(X, cmap=None, norm=None, aspect=None, interpolation=None,
alpha=1.0, vmin=None, vmax=None, origin=None, extent=None)
IMSHOW(X) - plot image X to current axes, resampling to scale to axes
size (X may be numpy array or PIL image)
IMSHOW(X, **kwargs) - Use keyword args to control image scaling,
colormapping etc. See below for details
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.
A image.AxesImage instance is returned
The following kwargs are allowed:
* cmap is a cm colormap instance, eg cm.jet. If None, default to rc
image.cmap value (Ignored when X has RGB(A) information)
* aspect is one of: auto, equal, or a number. If None, default to rc
image.aspect value
* interpolation is one of:
'nearest', 'bilinear', 'bicubic', 'spline16', 'spline36',
'hanning', 'hamming', 'hermite', 'kaiser', 'quadric',
'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc',
'lanczos', 'blackman'
if interpolation is None, default to rc
image.interpolation. See also th the filternorm and
filterrad parameters
* norm is a mcolors.Normalize instance; default is
normalization(). This scales luminance -> 0-1 (only used for an
MxN float array).
* vmin and vmax are 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 you pass a norm instance, the settings for vmin and
vmax will be ignored.
* alpha = 1.0 : the alpha blending value
* origin is 'upper' or 'lower', to 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 is (left, right, bottom, top) data values of the
axes. The default assigns zero-based row, column indices
to the x, y centers of the pixels.
* shape is for raw buffer images
* filternorm is a parameter for the antigrain image resize
filter. From the antigrain documentation, if normalize=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, ie when interpolation is one of: 'sinc',
'lanczos' or 'blackman'
Additional kwargs are martist properties
Additional kwargs: hold = [True|False] overrides default hold state
- ioff()
- turn interactive mode off
- ion()
- turn interactive mode on
- ishold()
- Return the hold status of the current axes
- isinteractive()
- Return the interactive status
- jet()
- set the default colormap to jet and apply to current image if any.
See help(colormaps) for more information
- legend(*args, **kwargs)
- LEGEND(*args, **kwargs)
Place a legend on the current axes at location loc. Labels are a
sequence of strings and loc can be a string or an integer specifying
the legend location
USAGE:
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.
# automatically generate the legend from labels
legend( ('label1', 'label2', 'label3') )
# Make a legend for a list of lines and labels
legend( (line1, line2, line3), ('label1', 'label2', 'label3') )
# Make a legend at a given location, using a location argument
# legend( LABELS, LOC ) or
# legend( LINES, LABELS, LOC )
legend( ('label1', 'label2', 'label3'), loc='upper left')
legend( (line1, line2, line3), ('label1', 'label2', 'label3'), loc=2)
The location codes are
'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,
If none of these are suitable, loc can be a 2-tuple giving x,y
in axes coords, ie,
loc = 0, 1 is left top
loc = 0.5, 0.5 is center, center
and so on. The following kwargs are supported:
isaxes=True # whether this is an axes legend
numpoints = 4 # the number of points in the legend line
prop = FontProperties(size='smaller') # the font property
pad = 0.2 # the fractional whitespace inside the legend border
markerscale = 0.6 # the relative size of legend markers vs. original
shadow # if True, draw a shadow behind legend
labelsep = 0.005 # the vertical space between the legend entries
handlelen = 0.05 # the length of the legend lines
handletextsep = 0.02 # the space between the legend line and legend text
axespad = 0.02 # the border between the axes and legend edge
- loglog(*args, **kwargs)
- LOGLOG(*args, **kwargs)
Make a loglog plot with log scaling on the a and y axis. The args
to semilog x are the same as the args to plot. See help plot for
more info.
Optional keyword args supported are any of the kwargs
supported by plot or set_xscale or set_yscale. Notable, for
log scaling:
* basex: base of the x logarithm
* subsx: the location of the minor ticks; None defaults to
autosubs, which depend on the number of decades in the
plot; see set_xscale for details
* basey: base of the y logarithm
* subsy: 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
The remaining valid kwargs are Line2D properties:
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: unknown
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
color or c: any matplotlib color
contains: unknown
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
data: (npy.array xdata, npy.array ydata)
figure: a matplotlib.figure.Figure instance
label: any string
linestyle or ls: [ '-' | '--' | '-.' | ':' | 'steps' | 'None' | ' ' | '' ]
linewidth or lw: float value in points
lod: [True | False]
marker: [ '+' | ',' | '.' | '1' | '2' | '3' | '4'
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markersize or ms: float
picker: unknown
pickradius: unknown
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a matplotlib.transform transformation instance
visible: [True | False]
xdata: npy.array
ydata: npy.array
zorder: any number
Additional kwargs: hold = [True|False] overrides default hold state
- matshow(A, fignum=None, **kw)
- Display an array as a matrix in a new figure window.
The origin is set at the upper left hand corner and rows (first dimension
of the array) are displayed horizontally. The aspect ratio of the figure
window is that of the array, unless this would make an excessively
short or narrow figure.
Tick labels for the xaxis are placed on top.
With one exception, keyword arguments are passed to
imshow().
Special keyword argument which is NOT passed to imshow():
- fignum(None): by default, matshow() creates a new figure window with
automatic numbering. If fignum is given as an integer, the created
figure will use this figure number. Because of how matshow() tries to
set the figure aspect ratio to be the one of the array, if you provide
the number of an already existing figure, strange things may happen.
if fignum is False or 0, a new figure window will NOT be created.
Example usage:
def samplemat(dims):
aa = zeros(dims)
for i in range(min(dims)):
aa[i,i] = i
return aa
dimlist = [(12,12),(128,64),(64,512),(2048,256)]
for d in dimlist:
im = matshow(samplemat(d))
show()
- over(func, *args, **kwargs)
- Call func(*args, **kwargs) with hold(True) and then restore the hold state
- pcolor(*args, **kwargs)
- pcolor(*args, **kwargs): pseudocolor plot of a 2-D array
Function 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.
Optional keyword args are shown with their defaults below (you must
use kwargs for these):
* cmap = cm.jet : a cm Colormap instance from cm
* norm = Normalize() : mcolors.Normalize instance
is used to scale luminance data to 0,1.
* vmin=None and vmax=None : vmin and vmax are used in conjunction
with norm to normalize luminance data. If either are None, the
min and max of the color array C is used. If you pass a norm
instance, vmin and vmax will be None
* shading = 'flat' : or 'faceted'. If 'faceted', a black grid is
drawn around each rectangle; if 'flat', edges are not drawn.
Default is 'flat', contrary to Matlab(TM).
This kwarg is deprecated;
please use the edgecolors kwarg instead:
shading='flat' --> edgecolors='None'
shading='faceted --> edgecolors='k'
edgecolors can also be None to specify the rcParams
default, or any mpl color or sequence of colors.
* alpha=1.0 : the alpha blending value
Return value is a mcoll.PatchCollection
object
Grid Orientation
The orientation follows the Matlab(TM) 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 = npy.arange(5)
y = npy.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)
Dimensions
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:
alpha: float
animated: [True | False]
array: unknown
axes: an axes instance
clim: a length 2 sequence of floats
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
cmap: a colormap
color: matplotlib color arg or sequence of rgba tuples
colorbar: unknown
contains: unknown
edgecolor: matplotlib color arg or sequence of rgba tuples
edgecolors: unknown
facecolor: matplotlib color arg or sequence of rgba tuples
facecolors: unknown
figure: a matplotlib.figure.Figure instance
label: any string
linewidth: float or sequence of floats
linewidths: float or sequence of floats
lod: [True | False]
lw: float or sequence of floats
norm: unknown
picker: [None|float|boolean|callable]
transform: a matplotlib.transform transformation instance
visible: [True | False]
zorder: any number
Additional kwargs: hold = [True|False] overrides default hold state
- pcolormesh(*args, **kwargs)
- PCOLORMESH(*args, **kwargs)
Function signatures
PCOLORMESH(C) - make a pseudocolor plot of matrix C
PCOLORMESH(X, Y, C) - a pseudo color plot of C on the matrices X and Y
PCOLORMESH(C, **kwargs) - Use keyword args to control colormapping and
scaling; see below
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.
Optional keyword args are shown with their defaults below (you must
use kwargs for these):
* cmap = cm.jet : a cm Colormap instance from cm.
* norm = Normalize() : colors.Normalize instance
is used to scale luminance data to 0,1. Instantiate it
with clip=False if C is a masked array.
* vmin=None and vmax=None : vmin and vmax are used in conjunction
with norm to normalize luminance data. If either are None, the
min and max of the color array C is used.
* shading = 'flat' : or 'faceted'. If 'faceted', a black grid is
drawn around each rectangle; if 'flat', edges are not drawn.
Default is 'flat', contrary to Matlab(TM).
This kwarg is deprecated;
please use the edgecolors kwarg instead:
shading='flat' --> edgecolors='None'
shading='faceted --> edgecolors='k'
More flexible specification of edgecolors, as in pcolor,
is not presently supported.
* alpha=1.0 : the alpha blending value
Return value is a collections.PatchCollection
object
See pcolor for an explantion of the grid orientation and the
expansion of 1-D X and/or Y to 2-D arrays.
kwargs can be used to control the collections.QuadMesh polygon
collection properties:
alpha: float
animated: [True | False]
array: unknown
axes: an axes instance
clim: a length 2 sequence of floats
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
cmap: a colormap
color: matplotlib color arg or sequence of rgba tuples
colorbar: unknown
contains: unknown
edgecolor: matplotlib color arg or sequence of rgba tuples
edgecolors: unknown
facecolor: matplotlib color arg or sequence of rgba tuples
facecolors: unknown
figure: a matplotlib.figure.Figure instance
label: any string
linewidth: float or sequence of floats
linewidths: float or sequence of floats
lod: [True | False]
lw: float or sequence of floats
norm: unknown
picker: [None|float|boolean|callable]
transform: a matplotlib.transform transformation instance
visible: [True | False]
zorder: any number
Additional kwargs: hold = [True|False] overrides default hold state
- pie(*args, **kwargs)
- PIE(x, explode=None, labels=None,
colors=('b', 'g', 'r', 'c', 'm', 'y', 'k', 'w'),
autopct=None, pctdistance=0.6, labeldistance=1.1, shadow=False)
Make a pie chart of array x. The fractional area of each wedge is
given by x/sum(x). If sum(x)<=1, then the values of x give the
fractional area directly and the array will not be normalized.
- explode, if not None, is a len(x) array which specifies the
fraction of the radius to offset that wedge.
- colors is a sequence of matplotlib color args that the pie chart
will cycle.
- labels, if not None, is a len(x) list of labels.
- autopct, 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 is 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 is the radial distance at which the pie labels are drawn
- shadow, if True, will draw a shadow beneath the pie.
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 a list of (patches, texts), where patches
is a sequence of mpatches.Wedge instances and texts is a
list of the label Text instnaces
If autopct is not None, return (patches, texts, autotexts), where
patches and texts are as above, and autotexts is a list of text
instances for the numeric labels
Additional kwargs: hold = [True|False] overrides default hold state
- pink()
- set the default colormap to pink and apply to current image if any.
See help(colormaps) for more information
- plot(*args, **kwargs)
- 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 the 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.
The following line styles are supported:
- : solid line
-- : dashed line
-. : dash-dot line
: : dotted line
. : points
, : pixels
o : circle symbols
^ : triangle up symbols
v : triangle down symbols
< : triangle left symbols
> : triangle right symbols
s : square symbols
+ : plus symbols
x : cross symbols
D : diamond symbols
d : thin diamond symbols
1 : tripod down symbols
2 : tripod up symbols
3 : tripod left symbols
4 : tripod right symbols
h : hexagon symbols
H : rotated hexagon symbols
p : pentagon symbols
| : vertical line symbols
_ : horizontal line symbols
steps : use gnuplot style 'steps' # kwarg only
The following color abbreviations are supported
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, eg
plot(x1, y1, x2, y2, antialised=False)
Neither line will be antialiased.
The kwargs are Line2D properties:
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: unknown
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
color or c: any matplotlib color
contains: unknown
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
data: (npy.array xdata, npy.array ydata)
figure: a matplotlib.figure.Figure instance
label: any string
linestyle or ls: [ '-' | '--' | '-.' | ':' | 'steps' | 'None' | ' ' | '' ]
linewidth or lw: float value in points
lod: [True | False]
marker: [ '+' | ',' | '.' | '1' | '2' | '3' | '4'
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markersize or ms: float
picker: unknown
pickradius: unknown
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a matplotlib.transform transformation instance
visible: [True | False]
xdata: npy.array
ydata: npy.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; default True. See Axes.autoscale_view for more
information
Additional kwargs: hold = [True|False] overrides default hold state
- plot_date(*args, **kwargs)
- 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 or y (or both) can be a sequence of dates represented as
float days since 0001-01-01 UTC.
fmt is a plot format string.
tz is the time zone to use in labelling dates. Defaults to rc value.
If xdate is True, the x-axis will be labeled with dates.
If ydate is 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 ticker.AutoDateLocator (if the tick locator is not already set to
a ticker.DateLocator instance) and the default tick formatter to
AutoDateFormatter (if the tick formatter is not already set to
a DateFormatter instance).
Valid kwargs are Line2D properties:
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: unknown
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
color or c: any matplotlib color
contains: unknown
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
data: (npy.array xdata, npy.array ydata)
figure: a matplotlib.figure.Figure instance
label: any string
linestyle or ls: [ '-' | '--' | '-.' | ':' | 'steps' | 'None' | ' ' | '' ]
linewidth or lw: float value in points
lod: [True | False]
marker: [ '+' | ',' | '.' | '1' | '2' | '3' | '4'
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markersize or ms: float
picker: unknown
pickradius: unknown
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a matplotlib.transform transformation instance
visible: [True | False]
xdata: npy.array
ydata: npy.array
zorder: any number
See dates for helper functions date2num, num2date
and drange for help on creating the required floating point dates
Additional kwargs: hold = [True|False] overrides default hold state
- plotfile(fname, cols=(0,), plotfuncs=None, comments='#', skiprows=0, checkrows=5, delimiter=',', **kwargs)
- plot the data in fname
cols is a sequence of column identifiers to plot. An identifier
is either an int or a string. if it is an int, it indicates the
column number. If it is a string, it indicates the column header.
mpl will make column headers lower case, replace spaces with
underscores, and remove all illegal characters; so 'Adj Close*'
will have name 'adj_close'
if len(cols)==1, only that column will be plotted on the y axis.
if len(cols)>1, the first element will be an identifier for data
for the x axis and the remaining elements will be the column
indexes for multiple subplots
plotfuncs, if not None, is a dictionary mapping identifier to an
Axes plotting function as a string. Default is 'plot', other
choices are 'semilogy', 'fill', 'bar', etc... You must use the
same type of identifier in the cols vector as you use in the
plotfuncs dictionary, eg integer column numbers in both or column
names in both.
comments, skiprows, checkrows, and delimiter are all passed on to
matplotlib.mlab.csv2rec to load the data into a record array. See
the help there fore more information.
kwargs are passed on to plotting functions
Example usage:
# plot the 2nd and 4th column against the 1st in two subplots
plotfile(fname, (0,1,3))
# plot using column names; specify an alternate plot type for volume
plotfile(fname, ('date', 'volume', 'adj_close'), plotfuncs={'volume': 'semilogy'})
- plotting()
- Plotting commands
axes - Create a new axes
axis - Set or return the current axis limits
bar - make a bar chart
boxplot - make a box and whiskers chart
cla - clear current axes
clabel - label a contour plot
clf - clear a figure window
close - close a figure window
colorbar - add a colorbar to the current figure
cohere - make a plot of coherence
contour - make a contour plot
contourf - make a filled contour plot
csd - make a plot of cross spectral density
draw - force a redraw of the current figure
errorbar - make an errorbar graph
figlegend - add a legend to the figure
figimage - add an image to the figure, w/o resampling
figtext - add text in figure coords
figure - create or change active figure
fill - make filled polygons
gca - return the current axes
gcf - return the current figure
gci - get the current image, or None
get - get a handle graphics property
hist - make a histogram
hold - set the hold state on current axes
legend - add a legend to the axes
loglog - a log log plot
imread - load image file into array
imshow - plot image data
matshow - display a matrix in a new figure preserving aspect
pcolor - make a pseudocolor plot
plot - make a line plot
plotfile - plot data from a flat file
psd - make a plot of power spectral density
quiver - make a direction field (arrows) plot
rc - control the default params
savefig - save the current figure
scatter - make a scatter plot
set - set a handle graphics property
semilogx - log x axis
semilogy - log y axis
show - show the figures
specgram - a spectrogram plot
stem - make a stem plot
subplot - make a subplot (numrows, numcols, axesnum)
table - add a table to the axes
text - add some text at location x,y to the current axes
title - add a title to the current axes
xlabel - add an xlabel to the current axes
ylabel - add a ylabel to the current axes
autumn - set the default colormap to autumn
bone - set the default colormap to bone
cool - set the default colormap to cool
copper - set the default colormap to copper
flag - set the default colormap to flag
gray - set the default colormap to gray
hot - set the default colormap to hot
hsv - set the default colormap to hsv
jet - set the default colormap to jet
pink - set the default colormap to pink
prism - set the default colormap to prism
spring - set the default colormap to spring
summer - set the default colormap to summer
winter - set the default colormap to winter
spectral - set the default colormap to spectral
- polar(*args, **kwargs)
- POLAR(theta, r)
Make a polar plot. Multiple theta, r arguments are supported,
with format strings, as in plot.
- prism()
- set the default colormap to prism and apply to current image if any.
See help(colormaps) for more information
- psd(*args, **kwargs)
- PSD(x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=0, **kwargs)
The power spectral density by Welches average periodogram method. The
vector x is divided into NFFT length segments. Each segment is
detrended by function detrend and windowed by function window.
noperlap gives the length of the overlap between segments. The
absolute(fft(segment))**2 of each segment are averaged to compute Pxx,
with a scaling to correct for power loss due to windowing. Fs is the
sampling frequency.
* NFFT is the length of the fft segment; must be a power of 2
* Fs is the sampling frequency.
* Fc is the center frequency of x (defaults to 0), which offsets
the yextents of the image to reflect the frequency range used
when a signal is acquired and then filtered and downsampled to
baseband.
* detrend - 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 mlab module defines detrend_none, detrend_mean,
detrend_linear, but you can use a custom function as well.
* window - the function used to window the segments. window is a
function, unlike in matlab(TM) where it is a vector. mlab defines
window_none, window_hanning, but you can use a custom function
as well.
* noverlap gives the length of the overlap between segments.
Returns the tuple Pxx, freqs
For plotting, the power is plotted as 10*npy.log10(pxx) for decibels,
though pxx itself is returned
Refs:
Bendat & Piersol -- Random Data: Analysis and Measurement
Procedures, John Wiley & Sons (1986)
kwargs control the Line2D properties:
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: unknown
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
color or c: any matplotlib color
contains: unknown
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
data: (npy.array xdata, npy.array ydata)
figure: a matplotlib.figure.Figure instance
label: any string
linestyle or ls: [ '-' | '--' | '-.' | ':' | 'steps' | 'None' | ' ' | '' ]
linewidth or lw: float value in points
lod: [True | False]
marker: [ '+' | ',' | '.' | '1' | '2' | '3' | '4'
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markersize or ms: float
picker: unknown
pickradius: unknown
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a matplotlib.transform transformation instance
visible: [True | False]
xdata: npy.array
ydata: npy.array
zorder: any number
Additional kwargs: hold = [True|False] overrides default hold state
- quiver(*args, **kwargs)
- Plot a 2-D field of arrows.
Function 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 give 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 is 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 meshgrid.
U, V, C may be masked arrays, but masked X, Y are not
supported at present.
Keyword arguments (default given first):
* units = 'width' | 'height' | 'dots' | 'inches' | 'x' | 'y'
arrow units; the arrow dimensions *except for length*
are in multiples of this unit.
* scale = None | float
data units per arrow 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.
Arrow dimensions and scales can be in any of several units:
'width' or 'height': the width or height of the axes
'dots' or 'inches': pixels or inches, based on the figure dpi
'x' or 'y': X or Y data units
In all cases 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.
The arrows scale differently depending on the units, however.
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.
* 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 = 3 head width as multiple of shaft width
* headlength = 5 head length as multiple of shaft width
* headaxislength = 4.5 head length at shaft intersection
* minshaft = 1 length below which arrow scales, in units
of head length. Do not set this to less
than 1, or small arrows will look terrible!
* minlength = 1 minimum length as a multiple of shaft width;
if an arrow length is less than this, plot a
dot (hexagon) of this diameter instead.
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.
* 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 = 'k' | any matplotlib color spec or sequence of color specs.
This is a synonym for the PolyCollection facecolor kwarg.
If C has been set, 'color' has no effect.
* All PolyCollection kwargs are valid, in the sense that they
will be passed on to the PolyCollection constructor.
In particular, one might want to use, for example:
linewidths = (1,), edgecolors = ('g',)
to make the arrows have green outlines of unit width.
Additional kwargs: hold = [True|False] overrides default hold state
- quiverkey(*args, **kwargs)
- Add a key to a quiver plot.
Function signature:
quiverkey(Q, X, Y, U, label, **kw)
Arguments:
Q is the Quiver instance returned by a call to quiver.
X, Y give the location of the key; additional explanation follows.
U is the length of the key
label is a string with the length and units of the key
Keyword arguments (default given first):
* 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 = 0.1 inches distance between the arrow and the label
* labelcolor (defaults to default Text color)
* fontproperties is a dictionary with keyword arguments accepted
by the FontProperties initializer: family, style, variant,
size, weight
Any additional keyword arguments are used to override vector
properties taken from Q.
The positioning of the key depends on X, Y, coordinates, and
labelpos. If labelpos is 'N' or 'S', X,Y give the position
of the middle of the key arrow. If labelpos is 'E', X,Y
positions the head, and if labelpos is 'W', X,Y positions the
tail; in either of these two cases, X,Y is somewhere in the middle
of the arrow+label key object.
Additional kwargs: hold = [True|False] overrides default hold state
- rc(*args, **kwargs)
- Set the current rc params. Group is the grouping for the rc, eg
for lines.linewidth the group is 'lines', for axes.facecolor, the
group is 'axes', and so on. Group may also be a list or tuple
of group names, eg ('xtick','ytick'). kwargs is a list of
attribute name/value pairs, eg
rc('lines', linewidth=2, color='r')
sets the current rc params and is equivalent to
rcParams['lines.linewidth'] = 2
rcParams['lines.color'] = 'r'
The following aliases are available to save typing for interactive
users
'lw' : 'linewidth'
'ls' : 'linestyle'
'c' : 'color'
'fc' : 'facecolor'
'ec' : 'edgecolor'
'mew' : 'markeredgewidth'
'aa' : 'antialiased'
Thus you could abbreviate the above rc command as
rc('lines', lw=2, c='r')
Note you can use python's kwargs dictionary facility to store
dictionaries of default parameters. Eg, you can customize the
font rc as follows
font = {'family' : 'monospace',
'weight' : 'bold',
'size' : 'larger',
}
rc('font', **font) # pass in the font dict as kwargs
This enables you to easily switch between several configurations.
Use rcdefaults to restore the default rc params after changes.
- rcdefaults()
- Restore the default rc params - the ones that were created at
matplotlib load time
- rgrids(*args, **kwargs)
- Set/Get the radial locations of the gridlines and ticklabels
With no args, simply return lines, labels where lines is an
array of radial gridlines (Line2D instances) and labels is an
array of tick labels (Text instances).
lines, labels = rgrids()
With arguments, the syntax is
lines, labels = RGRIDS(radii, labels=None, angle=22.5, **kwargs)
The labels will appear at radial distances radii at angle
labels, if not None, is a len(radii) list of strings of the
labels to use at each angle.
if labels is None, the self.rformatter will be used
Return value is a list of lines, labels where the lines are
matplotlib.Line2D instances and the labels are matplotlib.Text
instances. Note that on input the labels argument is a list of
strings, and on output it is a list of Text instances
Examples
# set the locations of the radial gridlines and labels
lines, labels = rgrids( (0.25, 0.5, 1.0) )
# set the locations and labels of the radial gridlines and labels
lines, labels = rgrids( (0.25, 0.5, 1.0), ('Tom', 'Dick', 'Harry' )
- savefig(*args, **kwargs)
- SAVEFIG(fname, dpi=None, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None):
Save the current figure.
fname - the filename to save the current figure to. The
output formats supported depend on the backend being
used. and are deduced by the extension to fname.
Possibilities are eps, jpeg, pdf, png, ps, svg. fname
can also be a file or file-like object - cairo backend
only.
dpi - is the resolution in dots per inch. If
None it will default to the value savefig.dpi in the
matplotlibrc file
facecolor and edgecolor are the colors of the figure rectangle
orientation is either 'landscape' or 'portrait' - not supported on
all backends; currently only on postscript output
papertype is is one of 'letter', 'legal', 'executive', 'ledger', 'a0'
through 'a10', or 'b0' through 'b10' - only supported for postscript
output
format - one of the file extensions supported by the active backend.
- scatter(*args, **kwargs)
- SCATTER(x, y, s=20, c='b', marker='o', cmap=None, norm=None,
vmin=None, vmax=None, alpha=1.0, linewidths=None,
faceted=True, **kwargs)
Supported function signatures:
SCATTER(x, y, **kwargs)
SCATTER(x, y, s, **kwargs)
SCATTER(x, y, s, c, **kwargs)
Make a scatter plot of x versus y, where x, y are 1-D sequences
of the same length, N.
Arguments s and c can also be given as kwargs; this is encouraged
for readability.
s is a size in points^2. It is a scalar
or an array of the same length as x and y.
c is a color and 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.
The marker can be one of
's' : square
'o' : circle
'^' : triangle up
'>' : triangle right
'v' : triangle down
'<' : triangle left
'd' : diamond
'p' : pentagram
'h' : hexagon
'8' : octagon
If marker is None and verts is not None, verts is a sequence
of (x,y) vertices for a custom scatter symbol.
s is a size argument in points squared.
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 args; the color mapping and normalization arguments will
be used only if c is an array of floats
* cmap = cm.jet : a colors.Colormap instance from cm.
defaults to rc image.cmap
* norm = colors.Normalize() : colors.Normalize instance
is used to scale luminance data to 0,1.
* vmin=None and vmax=None : vmin and vmax are used in conjunction
with norm to normalize luminance data. If either are None, the
min and max of the color array C is used. Note if you pass a norm
instance, your settings for vmin and vmax will be ignored
* alpha =1.0 : the alpha value for the patches
* linewidths, 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 -- see
collections.RegularPolyCollection for details
* faceted: if True, will use the default edgecolor for the
markers. If False, will set the edgecolors to be the same
as the facecolors.
This kwarg is deprecated;
please use the edgecolors kwarg instead:
shading='flat' --> edgecolors='None'
shading='faceted --> edgecolors=None
edgecolors also can be any mpl color or sequence of colors.
Optional kwargs control the PatchCollection properties:
alpha: float
animated: [True | False]
array: unknown
axes: an axes instance
clim: a length 2 sequence of floats
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
cmap: a colormap
color: matplotlib color arg or sequence of rgba tuples
colorbar: unknown
contains: unknown
edgecolor: matplotlib color arg or sequence of rgba tuples
edgecolors: unknown
facecolor: matplotlib color arg or sequence of rgba tuples
facecolors: unknown
figure: a matplotlib.figure.Figure instance
label: any string
linewidth: float or sequence of floats
linewidths: float or sequence of floats
lod: [True | False]
lw: float or sequence of floats
norm: unknown
picker: [None|float|boolean|callable]
transform: a matplotlib.transform transformation instance
visible: [True | False]
zorder: any number
A Collection instance is returned
Additional kwargs: hold = [True|False] overrides default hold state
- sci(im)
- Set the current image (the target of colormap commands like jet, hot or clim)
- semilogx(*args, **kwargs)
- SEMILOGX(*args, **kwargs)
Make a semilog plot with log scaling on the x axis. The args to
semilog x are the same as the args to plot. See help plot for more
info.
Optional keyword args supported are any of the kwargs supported by
plot or set_xscale. Notable, for log scaling:
* basex: base of the logarithm
* subsx: the location of the minor ticks; None defaults to
autosubs, which depend on the number of decades in the
plot; see set_xscale for details
The remaining valid kwargs are Line2D properties:
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: unknown
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
color or c: any matplotlib color
contains: unknown
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
data: (npy.array xdata, npy.array ydata)
figure: a matplotlib.figure.Figure instance
label: any string
linestyle or ls: [ '-' | '--' | '-.' | ':' | 'steps' | 'None' | ' ' | '' ]
linewidth or lw: float value in points
lod: [True | False]
marker: [ '+' | ',' | '.' | '1' | '2' | '3' | '4'
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markersize or ms: float
picker: unknown
pickradius: unknown
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a matplotlib.transform transformation instance
visible: [True | False]
xdata: npy.array
ydata: npy.array
zorder: any number
Additional kwargs: hold = [True|False] overrides default hold state
- semilogy(*args, **kwargs)
- SEMILOGY(*args, **kwargs):
Make a semilog plot with log scaling on the y axis. The args to
semilogy are the same as the args to plot. See help plot for more
info.
Optional keyword args supported are any of the kwargs supported by
plot or set_yscale. Notable, for log scaling:
* basey: base of the logarithm
* subsy: a sequence of the location of the minor ticks;
None defaults to autosubs, which depend on the number of
decades in the plot; see set_yscale for details
The remaining valid kwargs are Line2D properties:
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: unknown
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
color or c: any matplotlib color
contains: unknown
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
data: (npy.array xdata, npy.array ydata)
figure: a matplotlib.figure.Figure instance
label: any string
linestyle or ls: [ '-' | '--' | '-.' | ':' | 'steps' | 'None' | ' ' | '' ]
linewidth or lw: float value in points
lod: [True | False]
marker: [ '+' | ',' | '.' | '1' | '2' | '3' | '4'
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markersize or ms: float
picker: unknown
pickradius: unknown
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a matplotlib.transform transformation instance
visible: [True | False]
xdata: npy.array
ydata: npy.array
zorder: any number
Additional kwargs: hold = [True|False] overrides default hold state
- setp(*args, **kwargs)
- matplotlib supports the use of setp ("set property") and getp to set
and get object properties, as well as to do introspection on the
object For example, to set the linestyle of a line to be dashed, you
can do
>>> line, = plot([1,2,3])
>>> setp(line, linestyle='--')
If you want to know the valid types of arguments, you can provide the
name of the property you want to set without a value
>>> setp(line, 'linestyle')
linestyle: [ '-' | '--' | '-.' | ':' | 'steps' | 'None' ]
If you want to see all the properties that can be set, and their
possible values, you can do
>>> setp(line)
... long output listing omitted'
setp operates on a single instance or a list of instances. If you
are in query mode introspecting the possible values, only the first
instance in the sequence is used. When actually setting values,
all the instances will be set. Eg, suppose you have a list of two
lines, the following will make both lines thicker and red
>>> x = arange(0,1.0,0.01)
>>> y1 = sin(2*pi*x)
>>> y2 = sin(4*pi*x)
>>> lines = plot(x, y1, x, y2)
>>> setp(lines, linewidth=2, color='r')
setp works with the matlab(TM) style string/value pairs or with
python kwargs. For example, the following are equivalent
>>> setp(lines, 'linewidth', 2, 'color', r') # matlab style
>>> setp(lines, linewidth=2, color='r') # python style
- specgram(*args, **kwargs)
- SPECGRAM(x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window = mlab.window_hanning, noverlap=128,
cmap=None, xextent=None)
Compute a spectrogram of data in x. Data are split into NFFT length
segements 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.
* cmap is a colormap; if None use default determined by rc
* xextent is the image extent in the xaxes xextent=xmin, xmax -
default 0, max(bins), 0, max(freqs) where bins is the return
value from mlab.specgram
* See help(psd) for information on the other keyword arguments.
Return value is (Pxx, freqs, bins, im), where
bins are the time points the spectrogram is calculated over
freqs is an array of frequencies
Pxx is a len(times) x len(freqs) array of power
im is a image.AxesImage.
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.
Additional kwargs: hold = [True|False] overrides default hold state
- spectral()
- set the default colormap to spectral and apply to current image if any.
See help(colormaps) for more information
- spring()
- set the default colormap to spring and apply to current image if any.
See help(colormaps) for more information
- spy(*args, **kwargs)
- spy(Z) plots the sparsity pattern of the 2-D array Z
If precision is None, any non-zero value will be plotted;
else, values of absolute(Z)>precision will be plotted.
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 axes plot method.
If marker and markersize are None, useful kwargs include:
cmap
alpha
See documentation for imshow() for details.
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
See documentation for plot() for details.
Useful values for marker include:
's' square (default)
'o' circle
'.' point
',' pixel
Additional kwargs: hold = [True|False] overrides default hold state
- stem(*args, **kwargs)
- 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 (markerline, stemlines, baseline) .
See
http://www.mathworks.com/access/helpdesk/help/techdoc/ref/stem.html
for details and examples/stem_plot.py for a demo.
Additional kwargs: hold = [True|False] overrides default hold state
- step(*args, **kwargs)
- step(x, y, *args, **kwargs)
x and y must be 1-D sequences, and it is assumed, but not checked,
that x is uniformly increasing.
Make a step plot. The args and keyword args to step are the same
as the args to plot. See help plot for more info.
Additional keyword args for step:
* where: can be 'pre', 'post' or 'mid'; if 'pre', the
interval from x[i] to x[i+1] has level y[i];
if 'post', that interval has level y[i+1];
and if 'mid', the jumps in y occur half-way
between the x-values. Default is 'pre'.
Additional kwargs: hold = [True|False] overrides default hold state
- subplot(*args, **kwargs)
- Create a subplot command, creating axes with
subplot(numRows, numCols, plotNum)
where plotNum=1 is the first plot number and increasing plotNums
fill rows first. max(plotNum)==numRows*numCols
You can leave out the commas if numRows<=numCols<=plotNum<10, as
in
subplot(211) # 2 rows, 1 column, first (upper) plot
subplot(111) is the default axis
The background color of the subplot can be specified via keyword
argument 'axisbg', which takes a color string or gdk.Color as value, as in
subplot(211, axisbg='y')
See help(axes) for additional information on axes and subplot
keyword arguments.
New subplots that overlap old will delete the old axes. If you do
not want this behavior, use fig.add_subplot or the axes command. Eg
from pylab import *
plot([1,2,3]) # implicitly creates subplot(111)
subplot(211) # overlaps, subplot(111) is killed
plot(rand(12), rand(12))
- subplot_tool(targetfig=None)
- Launch a subplot tool window for targetfig (default gcf)
A matplotlib.widgets.SubplotTool instance is returned
- subplots_adjust(*args, **kwargs)
- subplots_adjust(left=None, bottom=None, right=None, top=None,
wspace=None, hspace=None)
Tune the subplot layout via the figure.SubplotParams mechanism.
The parameter meanings (and suggested defaults) are
left = 0.125 # the left side of the subplots of the figure
right = 0.9 # the right side of the subplots of the figure
bottom = 0.1 # the bottom of the subplots of the figure
top = 0.9 # the top of the subplots of the figure
wspace = 0.2 # the amount of width reserved for blank space between subplots
hspace = 0.2 # the amount of height reserved for white space between subplots
The actual defaults are controlled by the rc file
- summer()
- set the default colormap to summer and apply to current image if any.
See help(colormaps) for more information
- switch_backend(newbackend)
- Swtich the default backend to newbackend. This feature is
EXPERIMENTAL, and is only expected to work switching to an image
backend. Eg, if you have a bunch of PS scripts that you want to
run from an interactive ipython session, you may want to switch to
the PS backend before running them to avoid having a bunch of GUI
windows popup. If you try to interactively switch from one GUI
backend to another, you will explode.
Calling this command will close all open windows.
- table(*args, **kwargs)
- 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):
Add a table to the current axes. Returns a 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:
alpha: float
animated: [True | False]
axes: an axes instance
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
contains: unknown
figure: a matplotlib.figure.Figure instance
fontsize: a float in points
label: any string
lod: [True | False]
picker: [None|float|boolean|callable]
transform: a matplotlib.transform transformation instance
visible: [True | False]
zorder: any number
- text(*args, **kwargs)
- TEXT(x, y, s, fontdict=None, **kwargs)
Add text in string s to axis at location x,y (data coords)
fontdict is a dictionary to override the default text properties.
If fontdict is None, the defaults are determined by your rc
parameters.
withdash=True will create 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 lower left and
1,1 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 patches.Rectangle properties (see help
for Rectangle for a list of these). For example
text(x, y, s, bbox=dict(facecolor='red', alpha=0.5))
Valid kwargs are Text properties
alpha: float
animated: [True | False]
axes: an axes instance
backgroundcolor: any matplotlib color
bbox: rectangle prop dict plus key 'pad' which is a pad in points
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
color: any matplotlib color
contains: unknown
family: [ 'serif' | 'sans-serif' | 'cursive' | 'fantasy' | 'monospace' ]
figure: a matplotlib.figure.Figure instance
fontproperties: a matplotlib.font_manager.FontProperties instance
horizontalalignment or ha: [ 'center' | 'right' | 'left' ]
label: any string
linespacing: float
lod: [True | False]
multialignment: ['left' | 'right' | 'center' ]
name or fontname: string eg, ['Sans' | 'Courier' | 'Helvetica' ...]
picker: [None|float|boolean|callable]
position: (x,y)
rotation: [ angle in degrees 'vertical' | 'horizontal'
size or fontsize: [ size in points | relative size eg 'smaller', 'x-large' ]
style or fontstyle: [ 'normal' | 'italic' | 'oblique']
text: string or anything printable with '%s' conversion
transform: a matplotlib.transform transformation instance
variant: [ 'normal' | 'small-caps' ]
verticalalignment or va: [ 'center' | 'top' | 'bottom' | 'baseline' ]
visible: [True | False]
weight or fontweight: [ 'normal' | 'bold' | 'heavy' | 'light' | 'ultrabold' | 'ultralight']
x: float
y: float
zorder: any number
- thetagrids(*args, **kwargs)
- Set/Get the theta locations of the gridlines and ticklabels
If no arguments are passed, return lines, labels where lines is an
array of radial gridlines (Line2D instances) and labels is an
array of tick labels (Text instances).
lines, labels = thetagrids()
Otherwise the syntax is
lines, labels = THETAGRIDS(angles, labels=None, fmt='%d', frac = 1.1)
set the angles at which to place the theta grids (these gridlines
are equal along the theta dimension). angles is in degrees
labels, if not None, is a len(angles) list of strings of the
labels to use at each angle.
if labels is None, the labels with be fmt%angle
frac is the fraction of the polar axes radius at which to place
the label (1 is the edge).Eg 1.05 isd outside the axes and 0.95
is inside the axes
Return value is a list of lines, labels where the lines are
matplotlib.Line2D instances and the labels are matplotlib.Text
instances. Note that on input the labels argument is a list of
strings, and on output it is a list of Text instances
Examples:
# set the locations of the radial gridlines and labels
lines, labels = thetagrids( range(45,360,90) )
# set the locations and labels of the radial gridlines and labels
lines, labels = thetagrids( range(45,360,90), ('NE', 'NW', 'SW','SE') )
- title(s, *args, **kwargs)
- Set the title of the current axis to s
Default font override is:
override = {
'fontsize' : 'medium',
'verticalalignment' : 'bottom',
'horizontalalignment' : 'center'
}
See the text docstring for information of how override and the
optional args work
- twinx(ax=None)
- Make a second axes overlay ax (or the current axes if ax is None)
sharing the xaxis. The ticks for ax2 will be placed on the right,
and the ax2 instance is returned. See examples/two_scales.py
- twiny(ax=None)
- Make a second axes overlay ax (or the current axes if ax is None)
sharing the yaxis. The ticks for ax2 will be placed on the top,
and the ax2 instance is returned.
- vlines(*args, **kwargs)
- VLINES(x, ymin, ymax, color='k')
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 is a line collections color args, either a single color
or a len(x) list of colors
linestyle is one of solid|dashed|dashdot|dotted
Returns the collections.LineCollection that was added
kwargs are collections.LineCollection properties:
alpha: float or sequence of floats
animated: [True | False]
array: unknown
axes: an axes instance
clim: a length 2 sequence of floats
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
cmap: a colormap
color: matplotlib color arg or sequence of rgba tuples
colorbar: unknown
contains: unknown
figure: a matplotlib.figure.Figure instance
label: any string
linestyle: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) ]
linewidth: float or sequence of floats
linewidths: float or sequence of floats
lod: [True | False]
lw: float or sequence of floats
norm: unknown
picker: [None|float|boolean|callable]
pickradius: unknown
segments: unknown
transform: a matplotlib.transform transformation instance
verts: unknown
visible: [True | False]
zorder: any number
Additional kwargs: hold = [True|False] overrides default hold state
- winter()
- set the default colormap to winter and apply to current image if any.
See help(colormaps) for more information
- xcorr(*args, **kwargs)
- XCORR(x, y, normed=False, detrend=mlab.detrend_none, usevlines=False, **kwargs):
Plot the cross correlation between x and y. If normed=True,
normalize the data but 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 lags, c, line where lags are a length
2*maxlags+1 lag vector, c is the 2*maxlags+1 auto correlation
vector, and 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, Axes.vlines rather than Axes.plot is used
to draw vertical lines from the origin to the acorr.
Otherwise the plotstyle is determined by the kwargs, which are
Line2D properties. If usevlines, the return value is lags, c,
linecol, b where linecol is the collections.LineCollection and b is the x-axis
if usevlines=True, kwargs are passed onto Axes.vlines
if usevlines=False, kwargs are passed onto Axes.plot
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.
See the respective function for documentation on valid kwargs
Additional kwargs: hold = [True|False] overrides default hold state
- xlabel(s, *args, **kwargs)
- Set the x axis label of the current axis to s
Default override is
override = {
'fontsize' : 'small',
'verticalalignment' : 'top',
'horizontalalignment' : 'center'
}
See the text docstring for information of how override and
the optional args work
- xlim(*args, **kwargs)
- Set/Get the xlimits of the current axes
xmin, xmax = xlim() : return the current xlim
xlim( (xmin, xmax) ) : set the xlim to xmin, xmax
xlim( xmin, xmax ) : set the xlim to xmin, xmax
If you do not specify args, you can pass the xmin and xmax as
kwargs, eg
xlim(xmax=3) # adjust the max leaving min unchanged
xlim(xmin=1) # adjust the min leaving max unchanged
The new axis limits are returned as a length 2 tuple
- xticks(*args, **kwargs)
- Set/Get the xlimits of the current ticklocs, labels
# return locs, labels where locs is an array of tick locations and
# labels is an array of tick labels.
locs, labels = xticks()
# set the locations of the xticks
xticks( arange(6) )
# set the locations and labels of the xticks
xticks( arange(5), ('Tom', 'Dick', 'Harry', 'Sally', 'Sue') )
The keyword args, if any, are text properties; see text for more
information on text properties.
- ylabel(s, *args, **kwargs)
- Set the y axis label of the current axis to s
Defaults override is
override = {
'fontsize' : 'small',
'verticalalignment' : 'center',
'horizontalalignment' : 'right',
'rotation'='vertical' : }
See the text docstring for information of how override and the
optional args work
- ylim(*args, **kwargs)
- Set/Get the ylimits of the current axes
ymin, ymax = ylim() : return the current ylim
ylim( (ymin, ymax) ) : set the ylim to ymin, ymax
ylim( ymin, ymax ) : set the ylim to ymin, ymax
If you do not specify args, you can pass the ymin and ymax as
kwargs, eg
ylim(ymax=3) # adjust the max leaving min unchanged
ylim(ymin=1) # adjust the min leaving max unchanged
The new axis limits are returned as a length 2 tuple
- yticks(*args, **kwargs)
- Set/Get the ylimits of the current ticklocs, labels
# return locs, labels where locs is an array of tick locations and
# labels is an array of tick labels.
locs, labels = yticks()
# set the locations of the yticks
yticks( arange(6) )
# set the locations and labels of the yticks
yticks( arange(5), ('Tom', 'Dick', 'Harry', 'Sally', 'Sue') )
The keyword args, if any, are text properties; see text for more
information on text properties.
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