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- matplotlib.artist.Artist
-
- Axes
-
- Subplot
class Axes(matplotlib.artist.Artist) |
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Emulate matlab's axes command, creating axes with
Axes(position=[left, bottom, width, height])
where all the arguments are fractions in [0,1] which specify the
fraction of the total figure window.
axisbg is the color of the axis background
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Methods defined here:
- __init__(self, fig, rect, axisbg=None, frameon=True)
- add_artist(self, a)
- Add any artist to the axes
- add_collection(self, collection)
- add_line(self, l)
- Add a line to the list of plot lines
- add_patch(self, p)
- Add a line to the list of plot lines
- add_table(self, tab)
- Add a table instance to the list of axes tables
- autoscale_view(self)
- bar(self, left, height, width=0.80000000000000004, bottom=0, color='b', yerr=None, xerr=None, ecolor='k', capsize=3)
- BAR(left, height)
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
- cla(self)
- Clear the current axes
- clear(self)
- cohere(self, x, y, NFFT=256, Fs=2, detrend=<function detrend_none>, window=<function 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 docs for psd and csd for information
about the function arguments NFFT, detrend, windowm noverlap, as
well as the methods used to compute Pxy, Pxx and Pyy.
Returns the tuple Cxy, freqs
Refs:
Bendat & Piersol -- Random Data: Analysis and Measurement
Procedures, John Wiley & Sons (1986)
- connect(self, s, func)
- csd(self, x, y, NFFT=256, Fs=2, detrend=<function detrend_none>, window=<function 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. noverlap gives the length of the
overlap between segments. The product of the direct FFTs of x and
y are averaged over each segment to compute Pxy, with a scaling to
correct for power loss due to windowing. Fs is the sampling
frequency.
NFFT must be a power of 2
detrend and window are functions, unlike in matlab where they are
vectors. For detrending you can use detrend_none, detrend_mean,
detrend_linear or a custom function. For windowing, you can use
window_none, window_hanning, or a custom function
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)
- disconnect(self, cid)
- disconnect from the Axes event.
- draw(self, renderer, *args, **kwargs)
- Draw everything (plot lines, axes, labels)
- errorbar(self, x, y, yerr=None, xerr=None, fmt='b-', ecolor=None, capsize=3)
- 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
errobar lines; if None, use the marker color.
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.
capsize is the size of the error bar caps in points
- fill(self, *args, **kwargs)
- Emulate matlab's fill command. *args is a variable length
argument, allowing for multiple x,y pairs with an optional
color format string. For example, all of the following are
legal, assuming a is the Axis instance:
a.fill(x,y) # plot polygon with vertices at x,y
a.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
a.fill(x1, y1, 'g', x2, y2, 'r')
Returns a list of patches that were added.
- get_axis_bgcolor(self)
- Return the axis background color
- get_child_artists(self)
- get_frame(self)
- Return the axes Rectangle frame
- get_images(self)
- get_legend(self)
- Return the Legend instance, or None if no legend is defined
- get_lines(self)
- get_position(self)
- Return the axes position
- get_xaxis(self)
- Return the XAxis instance
- get_xgridlines(self)
- Get the x grid lines as a list of Line2D instances
- get_xlim(self)
- Get the x axis range [xmin, xmax]
- get_xscale(self)
- return the xaxis scale string: log or linear
- get_xticklabels(self)
- Get the xtick labels as a list of Text instances
- get_xticklines(self)
- Get the xtick lines as a list of Line2D instances
- get_xticks(self)
- Return the x ticks as a list of locations
- get_yaxis(self)
- Return the YAxis instance
- get_ygridlines(self)
- Get the y grid lines as a list of Line2D instances
- get_ylim(self)
- Get the y axis range [ymin, ymax]
- get_yscale(self)
- return the yaxis scale string: log or linear
- get_yticklabels(self)
- Get the ytick labels as a list of Text instances
- get_yticklines(self)
- Get the ytick lines as a list of Line2D instances
- get_yticks(self)
- Return the y ticks as a list of locations
- grid(self, b)
- Set the axes grids on or off; b is a boolean
- has_data(self)
- hist(self, 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.
if noplot is True, just compute the histogram and return the
number of observations and the bins as an (n, bins) tuple.
If noplot is False, compute the histogram and plot it, returning
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)
- hlines(self, 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
- hold(self, 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
- imshow(self, X, cmap=None, norm=None, aspect=None, interpolation=None, alpha=1.0, vmin=None, vmax=None, origin=None, extent=None)
- IMSHOW(X) - plot image in array X to current axes, resampling to scale
to axes size
IMSHOW(X, **kwargs) - Use keyword args to control image scaling,
colormapping etc. See below for details
Display the image in array X to current axes. 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
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
* 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.
* 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
- in_axes(self, xwin, ywin)
- legend(self, *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. You can set the label of a line by
doing plot(x, y, label='my data') or
line.set_label('my data')
legend( LABELS )
>>> legend( ('label1', 'label2', 'label3') )
Make a legend for Line2D instances lines1, line2, line3
legend( LINES, LABELS )
>>> legend( (line1, line2, line3), ('label1', 'label2', 'label3') )
Make a legend at LOC
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 LOC location codes are
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
- loglog(self, *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)
- panx(self, numsteps)
- Pan the x axis numsteps (plus pan right, minus pan left)
- pany(self, numsteps)
- Pan the x axis numsteps (plus pan up, minus pan down)
- pcolor(self, *args, **kwargs)
- 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
Note, the behavior of meshgrid in matlab 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
- pcolor_classic(self, *args, **kwargs)
- 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')
returns a list of patch objects
Note, the behavior of meshgrid in matlab 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
- plot(self, *args, **kwargs)
- Emulate matlab's plot command. *args is a variable length
argument, allowing for multiple x,y pairs with an optional
format string. For example, all of the following are legal,
assuming a is the Axis instance:
a.plot(x,y) # plot Numeric arrays y vs x
a.plot(x,y, 'bo') # plot Numeric arrays y vs x with blue circles
a.plot(y) # plot y using x as index array 0..N-1
a.plot(y, 'r+') # ditto 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
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
The kwargs that are 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.
- plot_date(self, d, y, converter=None, fmt='bo', **kwargs)
- plot_date(d, y, converter, fmt='bo', **kwargs)
d is a sequence of dates; converter is a dates.DateConverter
instance that converts your dates to seconds since the epoch for
plotting. 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.
pass converter = None if your dates are already in epoch format
- psd(self, x, NFFT=256, Fs=2, detrend=<function detrend_none>, window=<function 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 must be a power of 2
-- detrend and window are functions, unlike in matlab where they
are vectors. For detrending you can use detrend_none,
detrend_mean, detrend_linear or a custom function. For
windowing, you can use window_none, window_hanning, or a custom
function
-- if length x < NFFT, it will be zero padded to NFFT
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)
- scatter(self, x, y, s=20, c='b', marker='o', cmap=None, norm=None, vmin=None, vmax=None, alpha=1.0)
- 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' : octogon
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
- scatter_classic(self, 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
- semilogx(self, *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)
- semilogy(self, *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)
- set_axis_bgcolor(self, color)
- set_axis_off(self)
- set_axis_on(self)
- set_frame_on(self, b)
- Set whether the axes rectangle patch is drawn with boolean b
- set_image_extent(self, xmin, xmax, ymin, ymax)
- Set the data units of the image. This is useful if you want to
plot other things over the image, eg, lines or scatter
- set_position(self, pos)
- Set the axes position with pos = left, bottom, width, height
in relative 0,1 coords
- set_title(self, label, fontdict=None, **kwargs)
- Set the title for the xaxis
See the text docstring for information of how override and the
optional args work
- set_xlabel(self, xlabel, fontdict=None, **kwargs)
- Set the label for the xaxis
See the text docstring for information of how override and the
optional args work
- set_xlim(self, v, emit=True)
- Set the limits for the xaxis; v = [xmin, xmax]
if emit is false, do not trigger an event
- set_xscale(self, value, basex=10, subsx=None)
- Set the xscaling: 'log' or 'linear'
if value is 'log', the additional kwargs have the following meaning
basex: base of the logarithm
subsx: the location of the minor ticks; None defaults to range(2,basex)
- set_xticklabels(self, labels, fontdict=None, **kwargs)
- Set the xtick labels with list of strings labels
Return a list of axis text instances
- set_xticks(self, ticks)
- Set the x ticks with list of ticks
- set_ylabel(self, ylabel, fontdict=None, **kwargs)
- Set the label for the yaxis
Defaults override is
override = {
'verticalalignment' : 'center',
'horizontalalignment' : 'right',
'rotation'='vertical' : }
See the text doctstring for information of how override and
the optional args work
- set_ylim(self, v, emit=True)
- Set the limits for the xaxis; v = [ymin, ymax]
if emit is false, do not trigger an event
- set_yscale(self, value, basey=10, subsy=None)
- Set the yscaling: 'log' or 'linear'
if value is 'log', the additional kwargs have the following meaning
basey: base of the logarithm
subsy: the location of the minor ticks; None are the default range(2,basex)
- set_yticklabels(self, labels, fontdict=None, **kwargs)
- Set the ytick labels with list of strings labels.
Return a list of Text instances
- set_yticks(self, ticks)
- Set the y ticks with list of ticks
- specgram(self, x, NFFT=256, Fs=2, detrend=<function detrend_none>, window=<function 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
See help(psd) for information on the other arguments
cmap is a colormap; if None use default determined by rc
return value is Pxx, freqs, bins, im
bins are the time points the spectrogram is calculated over
freqs is an array of frequencies
Pxx is a len(times) x len(freqs) array of power
im is a matplotlib image
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
- stem(self, 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.
- table(self, cellText=None, cellColours=None, cellLoc='right', colWidths=None, rowLabels=None, rowColours=None, rowLoc='left', colLabels=None, colColours=None, colLoc='center', loc='bottom', bbox=None)
- Create a table and add it to the 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.
- text(self, x, y, text, fontdict=None, **kwargs)
- Add text to axis at location x,y (data coords)
fontdict is a dictionary to override the default text properties.
If fontdict is None, the default is
If len(args) the override dictionary will be:
'verticalalignment' : 'bottom',
'horizontalalignment' : 'left'
'transform' : self.transData
**kwargs can in turn be used to override the override, as in
a.text(x,y,label, fontsize=12)
will have verticalalignment=bottom and
horizontalalignment=left but will have a fontsize of 12
The Text defaults are
'color' : 'k',
'fontproperties' : see FontProperties
'horizontalalignment' : 'left'
'rotation' : 'horizontal',
'verticalalignment' : 'bottom',
'transform' : self.transData,
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
ax = subplot(111)
text(0.5, 0.5,'matplotlib',
horizontalalignment='center',
verticalalignment='center',
transform = ax.transAxes,
)
- update_datalim(self, xys)
- Update the data lim bbox with seq of xy tups
- vlines(self, 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
- zoomx(self, numsteps)
- Zoom in on the x xaxis numsteps (plus for zoom in, minus for zoom out)
- zoomy(self, numsteps)
- Zoom in on the x xaxis numsteps (plus for zoom in, minus for zoom out)
Methods inherited from matplotlib.artist.Artist:
- get_alpha(self)
- Return the alpha value used for blending - not supported on
all backends
- get_clip_on(self)
- Return whether artist uses clipping
- get_transform(self)
- return the Transformation instance used by this artist
- get_visible(self)
- return the artist's visiblity
- is_figure_set(self)
- is_transform_set(self)
- Artist has transform explicity let
- set_alpha(self, alpha)
- Set the alpha value used for blending - not supported on
all backends
- set_clip_box(self, clipbox)
- Set the artist's clip Bbox
- set_clip_on(self, b)
- Set whether artist uses clipping
- set_figure(self, fig)
- Set the figure instance the artist belong to
- set_lod(self, on)
- Set Level of Detail on or off. If on, the artists may examine
things like the pixel width of the axes and draw a subset of
their contents accordingly
- set_transform(self, t)
- set the Transformation instance used by this artist
- set_visible(self, b)
- set the artist's visiblity
Data and other attributes inherited from matplotlib.artist.Artist:
- aname = 'Artist'
|
class Subplot(Axes) |
| |
Emulate matlab's 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
|
| |
- Method resolution order:
- Subplot
- Axes
- matplotlib.artist.Artist
Methods defined here:
- __init__(self, fig, *args, **kwargs)
- is_first_col(self)
- is_first_row(self)
- is_last_col(self)
- is_last_row(self)
Methods inherited from Axes:
- add_artist(self, a)
- Add any artist to the axes
- add_collection(self, collection)
- add_line(self, l)
- Add a line to the list of plot lines
- add_patch(self, p)
- Add a line to the list of plot lines
- add_table(self, tab)
- Add a table instance to the list of axes tables
- autoscale_view(self)
- bar(self, left, height, width=0.80000000000000004, bottom=0, color='b', yerr=None, xerr=None, ecolor='k', capsize=3)
- BAR(left, height)
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
- cla(self)
- Clear the current axes
- clear(self)
- cohere(self, x, y, NFFT=256, Fs=2, detrend=<function detrend_none>, window=<function 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 docs for psd and csd for information
about the function arguments NFFT, detrend, windowm noverlap, as
well as the methods used to compute Pxy, Pxx and Pyy.
Returns the tuple Cxy, freqs
Refs:
Bendat & Piersol -- Random Data: Analysis and Measurement
Procedures, John Wiley & Sons (1986)
- connect(self, s, func)
- csd(self, x, y, NFFT=256, Fs=2, detrend=<function detrend_none>, window=<function 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. noverlap gives the length of the
overlap between segments. The product of the direct FFTs of x and
y are averaged over each segment to compute Pxy, with a scaling to
correct for power loss due to windowing. Fs is the sampling
frequency.
NFFT must be a power of 2
detrend and window are functions, unlike in matlab where they are
vectors. For detrending you can use detrend_none, detrend_mean,
detrend_linear or a custom function. For windowing, you can use
window_none, window_hanning, or a custom function
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)
- disconnect(self, cid)
- disconnect from the Axes event.
- draw(self, renderer, *args, **kwargs)
- Draw everything (plot lines, axes, labels)
- errorbar(self, x, y, yerr=None, xerr=None, fmt='b-', ecolor=None, capsize=3)
- 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
errobar lines; if None, use the marker color.
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.
capsize is the size of the error bar caps in points
- fill(self, *args, **kwargs)
- Emulate matlab's fill command. *args is a variable length
argument, allowing for multiple x,y pairs with an optional
color format string. For example, all of the following are
legal, assuming a is the Axis instance:
a.fill(x,y) # plot polygon with vertices at x,y
a.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
a.fill(x1, y1, 'g', x2, y2, 'r')
Returns a list of patches that were added.
- get_axis_bgcolor(self)
- Return the axis background color
- get_child_artists(self)
- get_frame(self)
- Return the axes Rectangle frame
- get_images(self)
- get_legend(self)
- Return the Legend instance, or None if no legend is defined
- get_lines(self)
- get_position(self)
- Return the axes position
- get_xaxis(self)
- Return the XAxis instance
- get_xgridlines(self)
- Get the x grid lines as a list of Line2D instances
- get_xlim(self)
- Get the x axis range [xmin, xmax]
- get_xscale(self)
- return the xaxis scale string: log or linear
- get_xticklabels(self)
- Get the xtick labels as a list of Text instances
- get_xticklines(self)
- Get the xtick lines as a list of Line2D instances
- get_xticks(self)
- Return the x ticks as a list of locations
- get_yaxis(self)
- Return the YAxis instance
- get_ygridlines(self)
- Get the y grid lines as a list of Line2D instances
- get_ylim(self)
- Get the y axis range [ymin, ymax]
- get_yscale(self)
- return the yaxis scale string: log or linear
- get_yticklabels(self)
- Get the ytick labels as a list of Text instances
- get_yticklines(self)
- Get the ytick lines as a list of Line2D instances
- get_yticks(self)
- Return the y ticks as a list of locations
- grid(self, b)
- Set the axes grids on or off; b is a boolean
- has_data(self)
- hist(self, 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.
if noplot is True, just compute the histogram and return the
number of observations and the bins as an (n, bins) tuple.
If noplot is False, compute the histogram and plot it, returning
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)
- hlines(self, 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
- hold(self, 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
- imshow(self, X, cmap=None, norm=None, aspect=None, interpolation=None, alpha=1.0, vmin=None, vmax=None, origin=None, extent=None)
- IMSHOW(X) - plot image in array X to current axes, resampling to scale
to axes size
IMSHOW(X, **kwargs) - Use keyword args to control image scaling,
colormapping etc. See below for details
Display the image in array X to current axes. 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
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
* 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.
* 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
- in_axes(self, xwin, ywin)
- legend(self, *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. You can set the label of a line by
doing plot(x, y, label='my data') or
line.set_label('my data')
legend( LABELS )
>>> legend( ('label1', 'label2', 'label3') )
Make a legend for Line2D instances lines1, line2, line3
legend( LINES, LABELS )
>>> legend( (line1, line2, line3), ('label1', 'label2', 'label3') )
Make a legend at LOC
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 LOC location codes are
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
- loglog(self, *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)
- panx(self, numsteps)
- Pan the x axis numsteps (plus pan right, minus pan left)
- pany(self, numsteps)
- Pan the x axis numsteps (plus pan up, minus pan down)
- pcolor(self, *args, **kwargs)
- 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
Note, the behavior of meshgrid in matlab 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
- pcolor_classic(self, *args, **kwargs)
- 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')
returns a list of patch objects
Note, the behavior of meshgrid in matlab 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
- plot(self, *args, **kwargs)
- Emulate matlab's plot command. *args is a variable length
argument, allowing for multiple x,y pairs with an optional
format string. For example, all of the following are legal,
assuming a is the Axis instance:
a.plot(x,y) # plot Numeric arrays y vs x
a.plot(x,y, 'bo') # plot Numeric arrays y vs x with blue circles
a.plot(y) # plot y using x as index array 0..N-1
a.plot(y, 'r+') # ditto 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
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
The kwargs that are 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.
- plot_date(self, d, y, converter=None, fmt='bo', **kwargs)
- plot_date(d, y, converter, fmt='bo', **kwargs)
d is a sequence of dates; converter is a dates.DateConverter
instance that converts your dates to seconds since the epoch for
plotting. 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.
pass converter = None if your dates are already in epoch format
- psd(self, x, NFFT=256, Fs=2, detrend=<function detrend_none>, window=<function 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 must be a power of 2
-- detrend and window are functions, unlike in matlab where they
are vectors. For detrending you can use detrend_none,
detrend_mean, detrend_linear or a custom function. For
windowing, you can use window_none, window_hanning, or a custom
function
-- if length x < NFFT, it will be zero padded to NFFT
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)
- scatter(self, x, y, s=20, c='b', marker='o', cmap=None, norm=None, vmin=None, vmax=None, alpha=1.0)
- 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' : octogon
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
- scatter_classic(self, 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
- semilogx(self, *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)
- semilogy(self, *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)
- set_axis_bgcolor(self, color)
- set_axis_off(self)
- set_axis_on(self)
- set_frame_on(self, b)
- Set whether the axes rectangle patch is drawn with boolean b
- set_image_extent(self, xmin, xmax, ymin, ymax)
- Set the data units of the image. This is useful if you want to
plot other things over the image, eg, lines or scatter
- set_position(self, pos)
- Set the axes position with pos = left, bottom, width, height
in relative 0,1 coords
- set_title(self, label, fontdict=None, **kwargs)
- Set the title for the xaxis
See the text docstring for information of how override and the
optional args work
- set_xlabel(self, xlabel, fontdict=None, **kwargs)
- Set the label for the xaxis
See the text docstring for information of how override and the
optional args work
- set_xlim(self, v, emit=True)
- Set the limits for the xaxis; v = [xmin, xmax]
if emit is false, do not trigger an event
- set_xscale(self, value, basex=10, subsx=None)
- Set the xscaling: 'log' or 'linear'
if value is 'log', the additional kwargs have the following meaning
basex: base of the logarithm
subsx: the location of the minor ticks; None defaults to range(2,basex)
- set_xticklabels(self, labels, fontdict=None, **kwargs)
- Set the xtick labels with list of strings labels
Return a list of axis text instances
- set_xticks(self, ticks)
- Set the x ticks with list of ticks
- set_ylabel(self, ylabel, fontdict=None, **kwargs)
- Set the label for the yaxis
Defaults override is
override = {
'verticalalignment' : 'center',
'horizontalalignment' : 'right',
'rotation'='vertical' : }
See the text doctstring for information of how override and
the optional args work
- set_ylim(self, v, emit=True)
- Set the limits for the xaxis; v = [ymin, ymax]
if emit is false, do not trigger an event
- set_yscale(self, value, basey=10, subsy=None)
- Set the yscaling: 'log' or 'linear'
if value is 'log', the additional kwargs have the following meaning
basey: base of the logarithm
subsy: the location of the minor ticks; None are the default range(2,basex)
- set_yticklabels(self, labels, fontdict=None, **kwargs)
- Set the ytick labels with list of strings labels.
Return a list of Text instances
- set_yticks(self, ticks)
- Set the y ticks with list of ticks
- specgram(self, x, NFFT=256, Fs=2, detrend=<function detrend_none>, window=<function 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
See help(psd) for information on the other arguments
cmap is a colormap; if None use default determined by rc
return value is Pxx, freqs, bins, im
bins are the time points the spectrogram is calculated over
freqs is an array of frequencies
Pxx is a len(times) x len(freqs) array of power
im is a matplotlib image
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
- stem(self, 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.
- table(self, cellText=None, cellColours=None, cellLoc='right', colWidths=None, rowLabels=None, rowColours=None, rowLoc='left', colLabels=None, colColours=None, colLoc='center', loc='bottom', bbox=None)
- Create a table and add it to the 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.
- text(self, x, y, text, fontdict=None, **kwargs)
- Add text to axis at location x,y (data coords)
fontdict is a dictionary to override the default text properties.
If fontdict is None, the default is
If len(args) the override dictionary will be:
'verticalalignment' : 'bottom',
'horizontalalignment' : 'left'
'transform' : self.transData
**kwargs can in turn be used to override the override, as in
a.text(x,y,label, fontsize=12)
will have verticalalignment=bottom and
horizontalalignment=left but will have a fontsize of 12
The Text defaults are
'color' : 'k',
'fontproperties' : see FontProperties
'horizontalalignment' : 'left'
'rotation' : 'horizontal',
'verticalalignment' : 'bottom',
'transform' : self.transData,
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
ax = subplot(111)
text(0.5, 0.5,'matplotlib',
horizontalalignment='center',
verticalalignment='center',
transform = ax.transAxes,
)
- update_datalim(self, xys)
- Update the data lim bbox with seq of xy tups
- vlines(self, 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
- zoomx(self, numsteps)
- Zoom in on the x xaxis numsteps (plus for zoom in, minus for zoom out)
- zoomy(self, numsteps)
- Zoom in on the x xaxis numsteps (plus for zoom in, minus for zoom out)
Methods inherited from matplotlib.artist.Artist:
- get_alpha(self)
- Return the alpha value used for blending - not supported on
all backends
- get_clip_on(self)
- Return whether artist uses clipping
- get_transform(self)
- return the Transformation instance used by this artist
- get_visible(self)
- return the artist's visiblity
- is_figure_set(self)
- is_transform_set(self)
- Artist has transform explicity let
- set_alpha(self, alpha)
- Set the alpha value used for blending - not supported on
all backends
- set_clip_box(self, clipbox)
- Set the artist's clip Bbox
- set_clip_on(self, b)
- Set whether artist uses clipping
- set_figure(self, fig)
- Set the figure instance the artist belong to
- set_lod(self, on)
- Set Level of Detail on or off. If on, the artists may examine
things like the pixel width of the axes and draw a subset of
their contents accordingly
- set_transform(self, t)
- set the Transformation instance used by this artist
- set_visible(self, b)
- set the artist's visiblity
Data and other attributes inherited from matplotlib.artist.Artist:
- aname = 'Artist'
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