| | |
- arange(...)
- arange(start, stop=None, step=1, typecode=None)
Just like range() except it returns an array whose type can be
specified by the keyword argument typecode.
- array(...)
- array(sequence, typecode=None, copy=1, savespace=0) will return a new array formed from the given (potentially nested) sequence with type given by typecode. If no typecode is given, then the type will be determined as the minimum type required to hold the objects in sequence. If copy is zero and sequence is already an array, a reference will be returned. If savespace is nonzero, the new array will maintain its precision in operations.
- arrayrange = arange(...)
- arange(start, stop=None, step=1, typecode=None)
Just like range() except it returns an array whose type can be
specified by the keyword argument typecode.
- 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
- 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)
Addition 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)
Addition kwargs: hold = [True|False] overrides default hold state
- axis(*v)
- Set/Get the axis properties::
axis() returns the current axis as a length a length 4 vector
axis(v) where v = [xmin, xmax, ymin, ymax] sets the min and max of the x
and y axis limits
axis('off') turns off the axis lines and labels
axis('equal') sets the xlim width and ylim height to be to be
identical. The longer of the two intervals is chosen
- 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)
Addition 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)
Addition kwargs: hold = [True|False] overrides default hold state
- bar(*args, **kwargs)
- BAR(left, height, width=0.8, bottom=0,
color='b', yerr=None, xerr=None, ecolor='k', capsize=3)
Make a bar plot with rectangles at
left, left+width, 0, height
left and height are Numeric arrays.
Return value is a list of Rectangle patch instances
BAR(left, height, width, bottom,
color, yerr, xerr, capsize, yoff)
xerr and yerr, if not None, will be used to generate errorbars
on the bar chart
color specifies the color of the bar
ecolor specifies the color of any errorbar
capsize determines the length in points of the error bar caps
The optional arguments color, width and bottom can be either
scalars or len(x) sequences
This enables you to use bar as the basis for stacked bar
charts, or candlestick plots
Addition kwargs: hold = [True|False] overrides default hold state
- barh(*args, **kwargs)
- BARH(x, y, height=0.8, left=0,
color='b', yerr=None, xerr=None, ecolor='k', capsize=3)
BARH(x, y)
The y values give the heights of the center of the bars. The
x values give the length of the bars.
Return value is a list of Rectangle patch instances
Optional arguments
height - the height (thickness) of the bar
left - the x coordinate of the left side of the bar
color specifies the color of the bar
xerr and yerr, if not None, will be used to generate errorbars
on the bar chart
ecolor specifies the color of any errorbar
capsize determines the length in points of the error bar caps
The optional arguments color, height and left can be either
scalars or len(x) sequences
Addition 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
- choose(...)
- choose(a, (b1,b2,...))
- cla(*args, **kwargs)
- Clear the current axes
- 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, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=0)
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.
Returns the tuple Cxy, freqs
Refs: Bendat & Piersol -- Random Data: Analysis and Measurement
Procedures, John Wiley & Sons (1986)
Addition kwargs: hold = [True|False] overrides default hold state
- colorbar(tickfmt='%1.1f')
- Create a colorbar for current mappable image (see gci)
tickfmt is a format string to format the colorbar ticks
return value is the colorbar axes instance
- colormaps()
- matplotlib provides the following colormaps.
autumn bone cool copper flag gray hot hsv jet pink prism
spring summer winter
You can set the colormap for an image, pcolor, scatter, etc,
either as a keyword argument
>>> 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
'key_press_event'
'button_press_event'
'button_release_event'
'motion_notify_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(z, x = None, y = None, levels = None, colors = None)
plots contour lines of an image z
z is a 2D array of image values
x and y are 2D arrays with coordinates of z values in the
two directions. x and y do not need to be evenly spaced but must
be of the same shape as z
levels can be a list of level values or the number of levels to be
plotted. If levels == None, a default number of 7 evenly spaced
levels is plotted.
colors is one of these:
- 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 default color for lines.color in
.matplotlibrc is used.
linewidths is one of:
- 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
reg is a 2D region number array with the same dimensions as x and
y. The values of reg should be positive region numbers, and zero fro
zones wich do not exist.
triangle - triangulation array - must be the same shape as reg
alpha : the default transparency of contour lines
fmt is a format string for adding a label to each collection.
Currently this is useful for auto-legending and may be useful down
the road for legend labeling
origin = 'upper'|'lower'|None. If None, the rc value for image.origin
will be used
More information on reg and triangle arrays is in _contour.c
Return value is levels, collections where levels is a list of contour
levels used and collections is a list of
matplotlib.collections.LineCollection instances
Addition 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
- cross_correlate(...)
- cross_correlate(a,v, mode=0)
- csd(*args, **kwargs)
- CSD(x, y, NFFT=256, Fs=2, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=0)
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*log10(|Pxy|) is plotted
Refs:
Bendat & Piersol -- Random Data: Analysis and Measurement
Procedures, John Wiley & Sons (1986)
Addition 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)
- Connect s to func. return an id that can be used with disconnect
Method should return None
- draw()
- redraw the current figure
- errorbar(*args, **kwargs)
- ERRORBAR(x, y, yerr=None, xerr=None,
fmt='b-', ecolor=None, capsize=3, barsabove=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
kwargs are passed on to the plot command for the markers
Return value is a length 2 tuple. The first element is a list of
y symbol lines. The second element is a list of error bar lines.
Addition 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 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
- figure(num=1, figsize=None, dpi=None, facecolor=None, edgecolor=None, frameon=True)
- figure(num = 1, figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k')
Create a new figure and return a handle to it
If figure(num) already exists, make it active and return the
handle to it.
figure(1)
figsize - width in height x 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
- 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 a is the Axis 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.
The kwargs that are can be used to set line properties (any
property that has a set_* method). You can use this to set edge
color, face color, etc.
Addition 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
- fromstring(...)
- fromstring(string, typecode='l', count=-1) returns a new 1d array initialized from the raw binary data in string. If count is positive, the new array will have count elements, otherwise it's size is determined by the size of string.
- 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 collecitons have been
defined. The commands imshow and figimage create images
instances, and the commands pcolor and scatter create patch
collection instances
- get(o, *args)
- Return the value of handle property s
h is an instance of a class, eg a Line2D or an Axes or Text.
if s is 'somename', this function returns
o.get_somename()
get can be used to query all the gettable properties with get(o)
Many properties have aliases for shorter typing, eg 'lw' is an
alias for 'linewidth'. In the output, aliases and full property
names will be listed as
property or alias = value
eg
linewidth or lw = 2
- 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)
- Set the axes grids on or off; b is a boolean
if b is None, toggle the grid state
- hist(*args, **kwargs)
- HIST(x, bins=10, normed=0, bottom=0)
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 distribtion, ie,
n/(len(x)*dbin)
Addition kwargs: hold = [True|False] overrides default hold state
- hlines(*args, **kwargs)
- HLINES(y, xmin, xmax, fmt='k-')
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
Returns a list of line instances that were added
Addition 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
- 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 numerix 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 numarray/Numeric 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 or a
PIL image. If X is a float array, X can have the following shapes
MxN : luminance (grayscale)
MxNx3 : RGB
MxNx4 : RGBA
A matplotlib.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: free or preserve. if None, default to rc
image.aspect value
* interpolation is one of: bicubic bilinear blackman100 blackman256
blackman64 nearest sinc144 sinc256 sinc64 spline16 or spline36.
If None, default to rc image.interpolation
* norm is a matplotlib.colors.normalize instance; default is
normalization(). This scales luminance -> 0-1 (Ignored when X is
PIL image).
* 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. If None, default to rc image.origin
* extent is a data xmin, xmax, ymin, ymax for making image plots
registered with data plots. Default is the image dimensions
in pixels
Addition 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')
# 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, (currently not supported, defaults to upper right)
'upper right' : 1, (default)
'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
numpoints = 4 # the number of points in the legend line
prop = FontProperties('smaller') # the font properties
pad = 0.2 # the fractional whitespace inside the legend border
# The kwarg dimensions are in axes coords
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
shadow = False # if True, draw a shadow behind legend
- load(fname)
- Load ASCII data from fname into an array and return the array.
The data must be regular, same number of values in every row
fname can be a filename or a file handle
matfile data is not currently supported, but see
Nigel Wade's matfile ftp://ion.le.ac.uk/matfile/matfile.tar.gz
Example usage:
x,y = load('test.dat') # data in two columns
X = load('test.dat') # a matrix of data
x = load('test.dat') # a single column of data
- 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 range(2,basex)
* basey: base of the y logarithm
* subsy: the location of the minor yticks; None defaults to range(2,basey)
Addition kwargs: hold = [True|False] overrides default hold state
- over(func, *args, **kwargs)
- Call func(*args, **kwargs) with hold(True) and then restore the hold state
- pcolor(*args, **kwargs)
- PCOLOR(*args, **kwargs)
Function signatures
PCOLOR(C) - make a pseudocolor plot of matrix C
PCOLOR(X, Y, C) - a pseudo color plot of C on the matrices X and Y
PCOLOR(C, **kwargs) - Use keywork args to control colormapping and
scaling; see below
Optional keywork args are shown with their defaults below (you must
use kwargs for these):
* cmap = cm.jet : a cm Colormap instance from matplotlib.cm.
defaults to cm.jet
* norm = normalize() : matplotlib.colors.normalize 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', edge colors are same as
face colors
* alpha=1.0 : the alpha blending value
Return value is a matplotlib.collections.PatchCollection
object
Grid Orientation
The behavior of meshgrid in matlab(TM) is a bit counterintuitive for
x and y arrays. For example,
x = arange(7)
y = arange(5)
X, Y = meshgrid(x,y)
Z = rand( len(x), len(y))
pcolor(X, Y, Z)
will fail in matlab and pylab. You will probably be
happy with
pcolor(X, Y, transpose(Z))
Likewise, for nonsquare Z,
pcolor(transpose(Z))
will make the x and y axes in the plot agree with the numrows and
numcols of Z
Addition kwargs: hold = [True|False] overrides default hold state
- pcolor_classic(*args, **kwargs)
- PCOLOR_CLASSIC(self, *args, **kwargs)
Function signatures
pcolor(C) - make a pseudocolor plot of matrix C
pcolor(X, Y, C) - a pseudo color plot of C on the matrices X and Y
pcolor(C, cmap=cm.jet) - make a pseudocolor plot of matrix C using
rectangle patches using a colormap jet. Colormaps are avalible
in matplotlib.cm. You must pass this as a kwarg.
pcolor(C, norm=normalize()) - the normalization function used
` to scale your color data to 0-1. must be passed as a kwarg.
pcolor(C, alpha=0.5) - set the alpha of the pseudocolor plot.
Must be used as a kwarg
Shading:
The optional keyword arg shading ('flat' or 'faceted') will
determine whether a black grid is drawn around each pcolor square.
Default 'faceteted' e.g., pcolor(C, shading='flat') pcolor(X, Y,
C, shading='faceted')
Return value is a list of patch objects.
Grid orientation
The behavior of meshgrid in matlab(TM) is a bit counterintuitive for x
and y arrays. For example,
x = arange(7)
y = arange(5)
X, Y = meshgrid(x,y)
Z = rand( len(x), len(y))
pcolor(X, Y, Z)
will fail in matlab and matplotlib. You will probably be
happy with
pcolor(X, Y, transpose(Z))
Likewise, for nonsquare Z,
pcolor(transpose(Z))
will make the x and y axes in the plot agree with the numrows
and numcols of Z
Addition kwargs: hold = [True|False] overrides default hold state
- pie(*args, **kwargs)
- 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
- 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 matplotlib.patches.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
Addition 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
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 strings are supported
b : blue
g : green
r : red
c : cyan
m : magenta
y : yellow
k : black
w : white
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, antialising=False)
Neither line will be antialiased.
Addition kwargs: hold = [True|False] overrides default hold state
- plot_date(*args, **kwargs)
- PLOT_DATE(d, y, converter, fmt='bo', tz=None, **kwargs)
d is a sequence of dates represented as float days since
0001-01-01 UTC and y are the y values at those dates. fmt is
a plot format string. kwargs are passed on to plot. See plot
for more information.
See matplotlib.dates for helper functions date2num, num2date
and drange for help on creating the required floating point dates
tz is the timezone - defaults to rc value
Addition kwargs: hold = [True|False] overrides default hold state
- plotting()
- Plotting commands
axes - Create a new axes
axis - Set or return the current axis limits
bar - make a bar chart
cla - clear current axes
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
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
pcolor - make a pseudocolor plot
plot - make a line plot
psd - make a plot of power spectral density
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
- 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, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=0)
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.
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*log10(pxx)) for decibels,
though pxx itself is returned
Refs:
Bendat & Piersol -- Random Data: Analysis and Measurement
Procedures, John Wiley & Sons (1986)
Addition kwargs: hold = [True|False] overrides default hold state
- raise_msg_to_str(msg)
- msg is a return arg from a raise. Join with new lines
- 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. 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'
'mfc' : 'markerfacecolor'
'mec' : 'markeredgecolor'
'mew' : 'markeredgewidth'
'aa' : 'antialiased'
'l' : 'lines'
'a' : 'axes'
'f' : 'figure'
'p' : 'patches'
'g' : 'grid'
Thus you could abbreviate the above rc command as
rc('l', 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
- reshape(...)
- reshape(a, (d1, d2, ..., dn)). Change the shape of a to be an n-dimensional array with dimensions given by d1...dn. Note: the size specified for the new array must be exactly equal to the size of the old one or an error will occur.
- 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' )
- save(fname, X, fmt='%1.4f')
- Save the data in X to file fname using fmt string to convert the
data to strings
fname can be a filename or a file handle
Example usage:
save('test.out', X) # X is an array
save('test1.out', (x,y,z)) # x,y,z equal sized 1D arrays
save('test2.out', x) # x is 1D
save('test3.out', x, fmt='%1.4e') # use exponential notation
- savefig(*args, **kwargs)
- SAVEFIG(fname, dpi=150, facecolor='w', edgecolor='w',
orientation='portrait'):
Save the current figure to filename fname. dpi is the resolution
in dots per inch.
Output file types currently supported are jpeg and png and will be
deduced by the extension to fname
facecolor and edgecolor are the colors os the figure rectangle
orientation is either 'landscape' or 'portrait' - not supported on
all backends; currently only on postscript output.
- scatter(*args, **kwargs)
- SCATTER(x, y, s=20, c='b', marker='o', cmap=None, norm=None,
vmin=None, vmax=None, alpha=1.0)
Supported function signatures:
SCATTER(x, y) - make a scatter plot of x vs y
SCATTER(x, y, s) - make a scatter plot of x vs y with size in area
given by s
SCATTER(x, y, s, c) - make a scatter plot of x vs y with size in area
given by s and colors given by c
SCATTER(x, y, s, c, **kwargs) - control colormapping and scaling
with keyword args; see below
Make a scatter plot of x versus y. s is a size in points^2 a scalar
or an array of the same length as x or y. c is a color and can be a
single color format string or an length(x) array of intensities which
will be mapped by the matplotlib.colors.colormap instance cmap
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
s is a size argument in points squared.
Other keyword args; the color mapping and normalization arguments will
on be used if c is an array of floats
* cmap = cm.jet : a cm Colormap instance from matplotlib.cm.
defaults to rc image.cmap
* norm = normalize() : matplotlib.colors.normalize 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
Addition kwargs: hold = [True|False] overrides default hold state
- scatter_classic(*args, **kwargs)
- SCATTER_CLASSIC(x, y, s=None, c='b')
Make a scatter plot of x versus y. s is a size (in data coords) and
can be either a scalar or an array of the same length as x or y. c is
a color and can be a single color format string or an length(x) array
of intensities which will be mapped by the colormap jet.
If size is None a default size will be used
Addition kwargs: hold = [True|False] overrides default hold state
- searchsorted = binarysearch(...)
- binarysearch(a,v)
- 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
range(2,basex)
Addition 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: the location of the minor ticks; None defaults to
range(2,basey)
Addition kwargs: hold = [True|False] overrides default hold state
- set(h, *args, **kwargs)
- matlab(TM) and pylab allow you to use set and get 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])
>>> set(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
>>> set(line, 'linestyle')
linestyle: [ '-' | '--' | '-.' | ':' | 'steps' | 'None' ]
If you want to see all the properties that can be set, and their
possible values, you can do
>>> set(line)
... long output listing omitted'
set operates on a single instance or a list of instances. If you are
in quey mode introspecting the possible values, only the first
instance in the sequnce 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)
>>> set(lines, linewidth=2, color='r')
Set works with the matlab(TM) style string/value pairs or with python
kwargs. For example, the following are equivalent
>>> set(lines, 'linewidth', 2, 'color', r') # matlab style
>>> set(lines, linewidth=2, color='r') # python style
- specgram(*args, **kwargs)
- SPECGRAM(x, NFFT=256, Fs=2, 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 matplotlib.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 matplotlib.image.AxesImage.
Addition kwargs: hold = [True|False] overrides default hold state
- 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, **kwrags) plots the sparsity pattern of the matrix S
using plot markers.
kwargs give the marker properties - see help(plot) for more
information on marker properties
The line handles are returned
Addition kwargs: hold = [True|False] overrides default hold state
- spy2(*args, **kwargs)
- SPY2(Z) plots the sparsity pattern of the matrix S as an image
The image instance is returned
Addition 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.
Addition 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')
- summer()
- set the default colormap to summer and apply to current image if any. See help(colormaps) for more information
- 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.
- take(...)
- take(a, indices, axis=0). Selects the elements in indices from array a along the given axis.
- 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.
Individual keyword arguemnts 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,
)
- 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
- 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
Returns a list of lines that were added
Addition 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
- 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
- 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
- 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.
- zeros(...)
- zeros((d1,...,dn),typecode='l',savespace=0) will return a new array of shape (d1,...,dn) and type typecode with all it's entries initialized to zero. If savespace is nonzero the array will be a spacesaver array.
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