| | |
- matplotlib.axes.Axes(matplotlib.artist.Artist)
-
- Axes3DI
- Axes3D
- Scaler
class Axes3D |
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Wrapper for Axes3DI
Provides set_xlim, set_ylim etc.
2D functions can be caught here and mapped
to their 3D approximations.
This should probably be the case for plot etc...
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Methods defined here:
- __getattr__(self, k)
- __init__(self, fig, *args, **kwargs)
- __setattr__(self, k, v)
- add_3DCollection(self, patches)
- add_collection(self, polys, zs=None, dir='z')
- bar(self, left, height, z=0, dir='z', *args, **kwargs)
- scatter(self, xs, ys, zs=None, dir='z', *args, **kwargs)
- set_xlim(self, *args, **kwargs)
- set_ylim(self, *args, **kwargs)
- set_zlim(self, *args, **kwargs)
- text(self, x, y, text, *args, **kwargs)
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class Axes3DI(matplotlib.axes.Axes) |
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Wrap an Axes object
The x,y data coordinates, which are manipulated by set_xlim and
set_ylim are used as the target view coordinates by the 3D
transformations. These coordinates are mostly invisible to the
outside world.
set_w_xlim, set_w_ylim and set_w_zlim manipulate the 3D world
coordinates which are scaled to represent the data and are stored
in the xy_dataLim, zz_datalim bboxes.
The axes representing the x,y,z world dimensions are self.w_xaxis,
self.w_yaxis and self.w_zaxis. They can probably be controlled in
more or less the normal ways.
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- Method resolution order:
- Axes3DI
- matplotlib.axes.Axes
- matplotlib.artist.Artist
Methods defined here:
- __init__(self, fig, rect=[0.0, 0.0, 1.0, 1.0], *args, **kwargs)
- add_lines(self, lines, *args, **kwargs)
- ahvline(self, x, y)
- ahvxplane(self, x)
- ahvyplane(self, y)
- auto_scale_xyz(self, X, Y, Z=None, had_data=None)
- autoscale_view(self, scalex=True, scaley=True, scalez=True)
- button_press(self, event)
- button_release(self, event)
- clabel(self, *args, **kwargs)
- contour3D(self, X, Y, Z, *args, **kwargs)
- contourf3D(self, X, Y, Z, *args, **kwargs)
- create_axes(self)
- draw(self, renderer)
- format_coord(self, xd, yd)
- Given the 2D view coordinates attempt to guess a 3D coordinate
Looks for the nearest edge to the point and then assumes that the point is
at the same z location as the nearest point on the edge.
- format_xdata(self, x)
- Return x string formatted. This function will use the attribute
self.fmt_xdata if it is callable, else will fall back on the xaxis
major formatter
- format_ydata(self, y)
- Return y string formatted. This function will use the attribute
self.fmt_ydata if it is callable, else will fall back on the yaxis
major formatter
- format_zdata(self, z)
- Return y string formatted. This function will use the attribute
self.fmt_ydata if it is callable, else will fall back on the yaxis
major formatter
- get_axis_position(self)
- get_proj(self)
- Create the projection matrix from the current viewing
position.
elev stores the elevation angle in the z plane
azim stores the azimuth angle in the x,y plane
dist is the distance of the eye viewing point from the object
point.
- get_w_lims(self)
- get_w_xlim(self)
- get_w_ylim(self)
- get_w_zlim(self)
- mouse_init(self)
- nset_xlim(self, *args)
- nset_ylim(self, *args)
- on_move(self, event)
- Mouse moving
button-1 rotates
button-3 zooms
- panx(self, numsteps)
- pany(self, numsteps)
- plot(self, *args, **kwargs)
- plot3D(self, xs, ys, zs, *args, **kwargs)
- plot3d = plot3D(self, xs, ys, zs, *args, **kwargs)
- plot_surface(self, X, Y, Z, *args, **kwargs)
- plot_wireframe(self, X, Y, Z, *args, **kwargs)
- really_set_xlim(self, vmin, vmax)
- really_set_ylim(self, vmin, vmax)
- scatter3D(self, xs, ys, zs, *args, **kwargs)
- scatter3d = scatter3D(self, xs, ys, zs, *args, **kwargs)
- set_top_view(self)
- set_w_xlim(self, *args, **kwargs)
- set_w_ylim(self, *args, **kwargs)
- set_w_zlim(self, *args, **kwargs)
- set_xlabel(self, xlabel, fontdict=None, **kwargs)
- set_ylabel(self, ylabel, fontdict=None, **kwargs)
- set_zlabel(self, zlabel, fontdict=None, **kwargs)
- text3D(self, x, y, z, s, *args, **kwargs)
- tunit_cube(self, vals=None, M=None)
- tunit_edges(self, vals=None, M=None)
- unit_cube(self, vals=None)
- update_datalim(self, xys)
- update_datalim_numerix(self, x, y)
- view_init(self, elev, azim)
- vlim_argument(self, get_lim, *args)
Methods inherited from matplotlib.axes.Axes:
- acorr(self, x, **kwargs)
- ACORR(x, normed=False, detrend=detrend_none, usevlines=False,
maxlags=None, **kwargs)
Plot the autocorrelation of x. If normed=True, normalize the
data but the autocorrelation at 0-th lag. x is detrended by
the detrend callable (default no normalization.
data are plotted as plot(lags, c, **kwargs)
return value is lags, c, line where lags are a length
2*maxlags+1 lag vector, c is the 2*maxlags+1 auto correlation
vector, and line is a Line2D instance returned by plot. The
default linestyle is None and the default marker is 'o',
though these can be overridden with keyword args. The cross
correlation is performed with numerix cross_correlate with
mode=2.
If usevlines is True, Axes.vlines rather than Axes.plot is used
to draw vertical lines from the origin to the acorr.
Otherwise the plotstyle is determined by the kwargs, which are
Line2D properties. If usevlines, the return value is lags, c,
linecol, b where linecol is the LineCollection and b is the x-axis
if usevlines=True, kwargs are passed onto Axes.vlines
if usevlines=False, kwargs are passed onto Axes.plot
maxlags is a positive integer detailing the number of lags to show.
The default value of None will return all (2*len(x)-1) lags.
See the respective function for documentation on valid kwargs
- add_artist(self, a)
- Add any artist to the axes
- add_collection(self, collection, autolim=False)
- add a Collection instance to Axes
- add_line(self, line)
- Add a line to the list of plot lines
- add_patch(self, p)
- Add a patch to the list of Axes patches; the clipbox will be
set to the Axes clipping box. If the transform is not set, it
wil be set to self.transData.
- add_table(self, tab)
- Add a table instance to the list of axes tables
- annotate(self, *args, **kwargs)
- annotate(self, s, xy, textloc,
xycoords='data', textcoords='data',
lineprops=None,
markerprops=None
**props)
alpha: float
animated: [True | False]
axes: an axes instance
backgroundcolor: any matplotlib color
bbox: rectangle prop dict plus key 'pad' which is a pad in points
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
color: any matplotlib color
family: [ 'serif' | 'sans-serif' | 'cursive' | 'fantasy' | 'monospace' ]
figure: a matplotlib.figure.Figure instance
fontproperties: a matplotlib.font_manager.FontProperties instance
horizontalalignment or ha: [ 'center' | 'right' | 'left' ]
label: any string
lod: [True | False]
multialignment: ['left' | 'right' | 'center' ]
name or fontname: string eg, ['Sans' | 'Courier' | 'Helvetica' ...]
picker: [None|float|boolean|callable]
position: (x,y)
rotation: [ angle in degrees 'vertical' | 'horizontal'
size or fontsize: [ size in points | relative size eg 'smaller', 'x-large' ]
style or fontstyle: [ 'normal' | 'italic' | 'oblique']
text: string or anything printable with '%s' conversion
transform: a matplotlib.transform transformation instance
variant: [ 'normal' | 'small-caps' ]
verticalalignment or va: [ 'center' | 'top' | 'bottom' ]
visible: [True | False]
weight or fontweight: [ 'normal' | 'bold' | 'heavy' | 'light' | 'ultrabold' | 'ultralight']
x: float
y: float
zorder: any number
- apply_aspect(self, data_ratio=None)
- Use self._aspect and self._adjustable to modify the
axes box or the view limits.
The data_ratio kwarg is set to 1 for polar axes. It is
used only when _adjustable is 'box'.
- arrow(self, x, y, dx, dy, **kwargs)
- Draws arrow on specified axis from (x,y) to (x+dx,y+dy).
Optional kwargs control the arrow properties:
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: an axes instance
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
edgecolor or ec: any matplotlib color
facecolor or fc: any matplotlib color
figure: a matplotlib.figure.Figure instance
fill: [True | False]
hatch: unknown
label: any string
linewidth or lw: float
lod: [True | False]
picker: [None|float|boolean|callable]
transform: a matplotlib.transform transformation instance
visible: [True | False]
zorder: any number
- axhline(self, y=0, xmin=0, xmax=1, **kwargs)
- AXHLINE(y=0, xmin=0, xmax=1, **kwargs)
Axis Horizontal Line
Draw a horizontal line at y from xmin to xmax. With the default
values of xmin=0 and xmax=1, this line will always span the horizontal
extent of the axes, regardless of the xlim settings, even if you
change them, eg with the xlim command. That is, the horizontal extent
is in axes coords: 0=left, 0.5=middle, 1.0=right but the y location is
in data coordinates.
Return value is the Line2D instance. kwargs are the same as kwargs to
plot, and can be used to control the line properties. Eg
# draw a thick red hline at y=0 that spans the xrange
axhline(linewidth=4, color='r')
# draw a default hline at y=1 that spans the xrange
axhline(y=1)
# draw a default hline at y=.5 that spans the the middle half of
# the xrange
axhline(y=.5, xmin=0.25, xmax=0.75)
Valid kwargs are Line2D properties
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: unknown
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
color or c: any matplotlib color
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
data: (array xdata, array ydata)
figure: a matplotlib.figure.Figure instance
label: any string
linestyle or ls: [ '-' | '--' | '-.' | ':' | 'steps' | 'None' | ' ' | '' ]
linewidth or lw: float value in points
lod: [True | False]
marker: [ '+' | ',' | '.' | '1' | '2' | '3' | '4'
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markersize or ms: float
picker: [None|float|boolean|callable]
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a matplotlib.transform transformation instance
visible: [True | False]
xdata: array
ydata: array
zorder: any number
- axhspan(self, ymin, ymax, xmin=0, xmax=1, **kwargs)
- AXHSPAN(ymin, ymax, xmin=0, xmax=1, **kwargs)
Axis Horizontal Span. ycoords are in data units and x
coords are in axes (relative 0-1) units
Draw a horizontal span (regtangle) from ymin to ymax. With the
default values of xmin=0 and xmax=1, this always span the xrange,
regardless of the xlim settings, even if you change them, eg with the
xlim command. That is, the horizontal extent is in axes coords:
0=left, 0.5=middle, 1.0=right but the y location is in data
coordinates.
kwargs are the kwargs to Patch, eg
antialiased, aa
linewidth, lw
edgecolor, ec
facecolor, fc
the terms on the right are aliases
Return value is the patches.Polygon instance.
#draws a gray rectangle from y=0.25-0.75 that spans the horizontal
#extent of the axes
axhspan(0.25, 0.75, facecolor='0.5', alpha=0.5)
Valid kwargs are Polygon properties
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: an axes instance
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
edgecolor or ec: any matplotlib color
facecolor or fc: any matplotlib color
figure: a matplotlib.figure.Figure instance
fill: [True | False]
hatch: unknown
label: any string
linewidth or lw: float
lod: [True | False]
picker: [None|float|boolean|callable]
transform: a matplotlib.transform transformation instance
visible: [True | False]
zorder: any number
- axis(self, *v, **kwargs)
- Convenience method for manipulating the x and y view limits
and the aspect ratio of the plot.
kwargs are passed on to set_xlim and set_ylim -- see their docstrings for details
- axvline(self, x=0, ymin=0, ymax=1, **kwargs)
- AXVLINE(x=0, ymin=0, ymax=1, **kwargs)
Axis Vertical Line
Draw a vertical line at x from ymin to ymax. With the default values
of ymin=0 and ymax=1, this line will always span the vertical extent
of the axes, regardless of the xlim settings, even if you change them,
eg with the xlim command. That is, the vertical extent is in axes
coords: 0=bottom, 0.5=middle, 1.0=top but the x location is in data
coordinates.
Return value is the Line2D instance. kwargs are the same as
kwargs to plot, and can be used to control the line properties. Eg
# draw a thick red vline at x=0 that spans the yrange
l = axvline(linewidth=4, color='r')
# draw a default vline at x=1 that spans the yrange
l = axvline(x=1)
# draw a default vline at x=.5 that spans the the middle half of
# the yrange
axvline(x=.5, ymin=0.25, ymax=0.75)
Valid kwargs are Line2D properties
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: unknown
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
color or c: any matplotlib color
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
data: (array xdata, array ydata)
figure: a matplotlib.figure.Figure instance
label: any string
linestyle or ls: [ '-' | '--' | '-.' | ':' | 'steps' | 'None' | ' ' | '' ]
linewidth or lw: float value in points
lod: [True | False]
marker: [ '+' | ',' | '.' | '1' | '2' | '3' | '4'
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markersize or ms: float
picker: [None|float|boolean|callable]
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a matplotlib.transform transformation instance
visible: [True | False]
xdata: array
ydata: array
zorder: any number
- axvspan(self, xmin, xmax, ymin=0, ymax=1, **kwargs)
- AXVSPAN(xmin, xmax, ymin=0, ymax=1, **kwargs)
axvspan : Axis Vertical Span. xcoords are in data units and y coords
are in axes (relative 0-1) units
Draw a vertical span (regtangle) from xmin to xmax. With the default
values of ymin=0 and ymax=1, this always span the yrange, regardless
of the ylim settings, even if you change them, eg with the ylim
command. That is, the vertical extent is in axes coords: 0=bottom,
0.5=middle, 1.0=top but the y location is in data coordinates.
kwargs are the kwargs to Patch, eg
antialiased, aa
linewidth, lw
edgecolor, ec
facecolor, fc
the terms on the right are aliases
return value is the patches.Polygon instance.
# draw a vertical green translucent rectangle from x=1.25 to 1.55 that
# spans the yrange of the axes
axvspan(1.25, 1.55, facecolor='g', alpha=0.5)
Valid kwargs are Polygon properties
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: an axes instance
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
edgecolor or ec: any matplotlib color
facecolor or fc: any matplotlib color
figure: a matplotlib.figure.Figure instance
fill: [True | False]
hatch: unknown
label: any string
linewidth or lw: float
lod: [True | False]
picker: [None|float|boolean|callable]
transform: a matplotlib.transform transformation instance
visible: [True | False]
zorder: any number
- bar(self, left, height, width=0.80000000000000004, bottom=None, color=None, edgecolor=None, linewidth=None, yerr=None, xerr=None, ecolor=None, capsize=3, align='edge', orientation='vertical', log=False, **kwargs)
- BAR(left, height, width=0.8, bottom=0,
color=None, edgecolor=None, linewidth=None,
yerr=None, xerr=None, ecolor=None, capsize=3,
align='edge', orientation='vertical', log=False)
Make a bar plot with rectangles bounded by
left, left+width, bottom, bottom+height
(left, right, bottom and top edges)
left, height, width, and bottom can be either scalars or sequences
Return value is a list of Rectangle patch instances
left - the x coordinates of the left sides of the bars
height - the heights of the bars
Optional arguments:
width - the widths of the bars
bottom - the y coordinates of the bottom edges of the bars
color - the colors of the bars
edgecolor - the colors of the bar edges
linewidth - width of bar edges; None means use default
linewidth; 0 means don't draw edges.
xerr and yerr, if not None, will be used to generate errorbars
on the bar chart
ecolor specifies the color of any errorbar
capsize (default 3) determines the length in points of the error
bar caps
align = 'edge' (default) | 'center'
orientation = 'vertical' | 'horizontal'
log = False | True - False (default) leaves the orientation
axis as-is; True sets it to log scale
For vertical bars, align='edge' aligns bars by their left edges in
left, while 'center' interprets these values as the x coordinates of
the bar centers. For horizontal bars, 'edge' aligns bars by their
bottom edges in bottom, while 'center' interprets these values as the
y coordinates of the bar centers.
The optional arguments color, edgecolor, linewidth, xerr, and yerr can
be either scalars or sequences of length equal to the number of bars.
This enables you to use bar as the basis for stacked bar charts, or
candlestick plots.
Optional kwargs:
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: an axes instance
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
edgecolor or ec: any matplotlib color
facecolor or fc: any matplotlib color
figure: a matplotlib.figure.Figure instance
fill: [True | False]
hatch: unknown
label: any string
linewidth or lw: float
lod: [True | False]
picker: [None|float|boolean|callable]
transform: a matplotlib.transform transformation instance
visible: [True | False]
zorder: any number
- barh(self, bottom, width, height=0.80000000000000004, left=None, **kwargs)
- BARH(bottom, width, height=0.8, left=0, **kwargs)
Make a horizontal bar plot with rectangles bounded by
left, left+width, bottom, bottom+height
(left, right, bottom and top edges)
bottom, width, height, and left can be either scalars or sequences
Return value is a list of Rectangle patch instances
bottom - the vertical positions of the bottom edges of the bars
width - the lengths of the bars
Optional arguments:
height - the heights (thicknesses) of the bars
left - the x coordinates of the left edges of the bars
color - the colors of the bars
edgecolor - the colors of the bar edges
linewidth - width of bar edges; None means use default
linewidth; 0 means don't draw edges.
xerr and yerr, if not None, will be used to generate errorbars
on the bar chart
ecolor specifies the color of any errorbar
capsize (default 3) determines the length in points of the error
bar caps
align = 'edge' (default) | 'center'
log = False | True - False (default) leaves the horizontal
axis as-is; True sets it to log scale
Setting align='edge' aligns bars by their bottom edges in bottom,
while 'center' interprets these values as the y coordinates of the bar
centers.
The optional arguments color, edgecolor, linewidth, xerr, and yerr can
be either scalars or sequences of length equal to the number of bars.
This enables you to use barh as the basis for stacked bar charts, or
candlestick plots.
Optional kwargs:
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: an axes instance
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
edgecolor or ec: any matplotlib color
facecolor or fc: any matplotlib color
figure: a matplotlib.figure.Figure instance
fill: [True | False]
hatch: unknown
label: any string
linewidth or lw: float
lod: [True | False]
picker: [None|float|boolean|callable]
transform: a matplotlib.transform transformation instance
visible: [True | False]
zorder: any number
- boxplot(self, x, notch=0, sym='b+', vert=1, whis=1.5, positions=None, widths=None)
- boxplot(x, notch=0, sym='+', vert=1, whis=1.5,
positions=None, widths=None)
Make a box and whisker plot for each column of x or
each vector in sequence x.
The box extends from the lower to upper quartile values
of the data, with a line at the median. The whiskers
extend from the box to show the range of the data. Flier
points are those past the end of the whiskers.
notch = 0 (default) produces a rectangular box plot.
notch = 1 will produce a notched box plot
sym (default 'b+') is the default symbol for flier points.
Enter an empty string ('') if you don't want to show fliers.
vert = 1 (default) makes the boxes vertical.
vert = 0 makes horizontal boxes. This seems goofy, but
that's how Matlab did it.
whis (default 1.5) defines the length of the whiskers as
a function of the inner quartile range. They extend to the
most extreme data point within ( whis*(75%-25%) ) data range.
positions (default 1,2,...,n) sets the horizontal positions of
the boxes. The ticks and limits are automatically set to match
the positions.
widths is either a scalar or a vector and sets the width of
each box. The default is 0.5, or 0.15*(distance between extreme
positions) if that is smaller.
x is an array or a sequence of vectors.
Returns a list of the lines added.
- broken_barh(self, xranges, yrange, **kwargs)
- A collection of horizontal bars spanning yrange with a sequence of
xranges
xranges : sequence of (xmin, xwidth)
yrange : (ymin, ywidth)
kwargs are collections.BrokenBarHCollection properties
alpha: float
animated: [True | False]
array: unknown
axes: an axes instance
clim: a length 2 sequence of floats
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
cmap: a colormap
color: matplotlib color arg or sequence of rgba tuples
colorbar: unknown
edgecolor: matplotlib color arg or sequence of rgba tuples
facecolor: matplotlib color arg or sequence of rgba tuples
figure: a matplotlib.figure.Figure instance
label: any string
linewidth: float or sequence of floats
lod: [True | False]
norm: unknown
picker: [None|float|boolean|callable]
transform: a matplotlib.transform transformation instance
visible: [True | False]
zorder: any number
these can either be a single argument, ie facecolors='black'
or a sequence of arguments for the various bars, ie
facecolors='black', 'red', 'green'
- cla(self)
- Clear the current axes
- clear(self)
- clear the axes
- cohere(self, x, y, NFFT=256, Fs=2, detrend=<function detrend_none at 0x108ac08>, window=<function window_hanning at 0x108a0c8>, noverlap=0, **kwargs)
- COHERE(x, y, NFFT=256, Fs=2, detrend=detrend_none,
window=window_hanning, noverlap=0, **kwargs)
cohere the coherence between x and y. Coherence is the normalized
cross spectral density
Cxy = |Pxy|^2/(Pxx*Pyy)
The return value is (Cxy, f), where f are the frequencies of the
coherence vector.
See the PSD help for a description of the optional parameters.
kwargs are applied to the lines
Returns the tuple Cxy, freqs
Refs: Bendat & Piersol -- Random Data: Analysis and Measurement
Procedures, John Wiley & Sons (1986)
kwargs control the Line2D properties of the coherence plot:
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: unknown
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
color or c: any matplotlib color
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
data: (array xdata, array ydata)
figure: a matplotlib.figure.Figure instance
label: any string
linestyle or ls: [ '-' | '--' | '-.' | ':' | 'steps' | 'None' | ' ' | '' ]
linewidth or lw: float value in points
lod: [True | False]
marker: [ '+' | ',' | '.' | '1' | '2' | '3' | '4'
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markersize or ms: float
picker: [None|float|boolean|callable]
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a matplotlib.transform transformation instance
visible: [True | False]
xdata: array
ydata: array
zorder: any number
- connect(self, s, func)
- Register observers to be notified when certain events occur. Register
with callback functions with the following signatures. The function
has the following signature
func(ax) # where ax is the instance making the callback.
The following events can be connected to:
'xlim_changed','ylim_changed'
The connection id is is returned - you can use this with
disconnect to disconnect from the axes event
- contour(self, *args, **kwargs)
- contour and contourf draw contour lines and filled contours,
respectively. Except as noted, function signatures and return
values are the same for both versions.
contourf differs from the Matlab (TM) version in that it does not
draw the polygon edges, because the contouring engine yields
simply connected regions with branch cuts. To draw the edges,
add line contours with calls to contour.
Function signatures
contour(Z) - make a contour plot of an array Z. The level
values are chosen automatically.
contour(X,Y,Z) - X,Y specify the (x,y) coordinates of the surface
contour(Z,N) and contour(X,Y,Z,N) - contour N automatically-chosen
levels.
contour(Z,V) and contour(X,Y,Z,V) - draw len(V) contour lines,
at the values specified in sequence V
contourf(..., V) - fill the (len(V)-1) regions between the
values in V
contour(Z, **kwargs) - Use keyword args to control colors, linewidth,
origin, cmap ... see below
X, Y, and Z must be arrays with the same dimensions.
Z may be a masked array, but filled contouring may not handle
internal masked regions correctly.
C = contour(...) returns a ContourSet object.
Optional keyword args are shown with their defaults below (you must
use kwargs for these):
* colors = None; or one of the following:
- a tuple of matplotlib color args (string, float, rgb, etc),
different levels will be plotted in different colors in the order
specified
- one string color, e.g. colors = 'r' or colors = 'red', all levels
will be plotted in this color
- if colors == None, the colormap specified by cmap will be used
* alpha=1.0 : the alpha blending value
* cmap = None: a cm Colormap instance from matplotlib.cm.
- if cmap == None and colors == None, a default Colormap is used.
* norm = None: a matplotlib.colors.Normalize instance for
scaling data values to colors.
- if norm == None, and colors == None, the default
linear scaling is used.
* origin = None: 'upper'|'lower'|'image'|None.
If 'image', the rc value for image.origin will be used.
If None (default), the first value of Z will correspond
to the lower left corner, location (0,0).
This keyword is active only if contourf is called with
one or two arguments, that is, without explicitly
specifying X and Y.
* extent = None: (x0,x1,y0,y1); also active only if X and Y
are not specified. If origin is not None, then extent is
interpreted as in imshow: it gives the outer pixel boundaries.
In this case, the position of Z[0,0] is the center of the
pixel, not a corner.
If origin is None, then (x0,y0) is the position of Z[0,0],
and (x1,y1) is the position of Z[-1,-1].
* locator = None: an instance of a ticker.Locator subclass;
default is MaxNLocator. It is used to determine the
contour levels if they are not given explicitly via the
V argument.
***** New: *****
* extend = 'neither', 'both', 'min', 'max'
Unless this is 'neither' (default), contour levels are
automatically added to one or both ends of the range so that
all data are included. These added ranges are then
mapped to the special colormap values which default to
the ends of the colormap range, but can be set via
Colormap.set_under() and Colormap.set_over() methods.
To replace clip_ends=True and V = [-100, 2, 1, 0, 1, 2, 100],
use extend='both' and V = [2, 1, 0, 1, 2].
****************
contour only:
* linewidths = None: or one of these:
- a number - all levels will be plotted with this linewidth,
e.g. linewidths = 0.6
- a tuple of numbers, e.g. linewidths = (0.4, 0.8, 1.2) different
levels will be plotted with different linewidths in the order
specified
- if linewidths == None, the default width in lines.linewidth in
matplotlibrc is used
contourf only:
***** Obsolete: ****
* clip_ends = True
If False, the limits for color scaling are set to the
minimum and maximum contour levels.
True (default) clips the scaling limits. Example:
if the contour boundaries are V = [-100, 2, 1, 0, 1, 2, 100],
then the scaling limits will be [-100, 100] if clip_ends
is False, and [-3, 3] if clip_ends is True.
* linewidths = None or a number; default of 0.05 works for
Postscript; a value of about 0.5 seems better for Agg.
* antialiased = True (default) or False; if False, there is
no need to increase the linewidths for Agg, but True gives
nicer color boundaries. If antialiased is True and linewidths
is too small, then there may be light-colored lines at the
color boundaries caused by the antialiasing.
* nchunk = 0 (default) for no subdivision of the domain;
specify a positive integer to divide the domain into
subdomains of roughly nchunk by nchunk points. This may
never actually be advantageous, so this option may be
removed. Chunking introduces artifacts at the chunk
boundaries unless antialiased = False, or linewidths is
set to a large enough value for the particular renderer and
resolution.
- contourf(self, *args, **kwargs)
- contour and contourf draw contour lines and filled contours,
respectively. Except as noted, function signatures and return
values are the same for both versions.
contourf differs from the Matlab (TM) version in that it does not
draw the polygon edges, because the contouring engine yields
simply connected regions with branch cuts. To draw the edges,
add line contours with calls to contour.
Function signatures
contour(Z) - make a contour plot of an array Z. The level
values are chosen automatically.
contour(X,Y,Z) - X,Y specify the (x,y) coordinates of the surface
contour(Z,N) and contour(X,Y,Z,N) - contour N automatically-chosen
levels.
contour(Z,V) and contour(X,Y,Z,V) - draw len(V) contour lines,
at the values specified in sequence V
contourf(..., V) - fill the (len(V)-1) regions between the
values in V
contour(Z, **kwargs) - Use keyword args to control colors, linewidth,
origin, cmap ... see below
X, Y, and Z must be arrays with the same dimensions.
Z may be a masked array, but filled contouring may not handle
internal masked regions correctly.
C = contour(...) returns a ContourSet object.
Optional keyword args are shown with their defaults below (you must
use kwargs for these):
* colors = None; or one of the following:
- a tuple of matplotlib color args (string, float, rgb, etc),
different levels will be plotted in different colors in the order
specified
- one string color, e.g. colors = 'r' or colors = 'red', all levels
will be plotted in this color
- if colors == None, the colormap specified by cmap will be used
* alpha=1.0 : the alpha blending value
* cmap = None: a cm Colormap instance from matplotlib.cm.
- if cmap == None and colors == None, a default Colormap is used.
* norm = None: a matplotlib.colors.Normalize instance for
scaling data values to colors.
- if norm == None, and colors == None, the default
linear scaling is used.
* origin = None: 'upper'|'lower'|'image'|None.
If 'image', the rc value for image.origin will be used.
If None (default), the first value of Z will correspond
to the lower left corner, location (0,0).
This keyword is active only if contourf is called with
one or two arguments, that is, without explicitly
specifying X and Y.
* extent = None: (x0,x1,y0,y1); also active only if X and Y
are not specified. If origin is not None, then extent is
interpreted as in imshow: it gives the outer pixel boundaries.
In this case, the position of Z[0,0] is the center of the
pixel, not a corner.
If origin is None, then (x0,y0) is the position of Z[0,0],
and (x1,y1) is the position of Z[-1,-1].
* locator = None: an instance of a ticker.Locator subclass;
default is MaxNLocator. It is used to determine the
contour levels if they are not given explicitly via the
V argument.
***** New: *****
* extend = 'neither', 'both', 'min', 'max'
Unless this is 'neither' (default), contour levels are
automatically added to one or both ends of the range so that
all data are included. These added ranges are then
mapped to the special colormap values which default to
the ends of the colormap range, but can be set via
Colormap.set_under() and Colormap.set_over() methods.
To replace clip_ends=True and V = [-100, 2, 1, 0, 1, 2, 100],
use extend='both' and V = [2, 1, 0, 1, 2].
****************
contour only:
* linewidths = None: or one of these:
- a number - all levels will be plotted with this linewidth,
e.g. linewidths = 0.6
- a tuple of numbers, e.g. linewidths = (0.4, 0.8, 1.2) different
levels will be plotted with different linewidths in the order
specified
- if linewidths == None, the default width in lines.linewidth in
matplotlibrc is used
contourf only:
***** Obsolete: ****
* clip_ends = True
If False, the limits for color scaling are set to the
minimum and maximum contour levels.
True (default) clips the scaling limits. Example:
if the contour boundaries are V = [-100, 2, 1, 0, 1, 2, 100],
then the scaling limits will be [-100, 100] if clip_ends
is False, and [-3, 3] if clip_ends is True.
* linewidths = None or a number; default of 0.05 works for
Postscript; a value of about 0.5 seems better for Agg.
* antialiased = True (default) or False; if False, there is
no need to increase the linewidths for Agg, but True gives
nicer color boundaries. If antialiased is True and linewidths
is too small, then there may be light-colored lines at the
color boundaries caused by the antialiasing.
* nchunk = 0 (default) for no subdivision of the domain;
specify a positive integer to divide the domain into
subdomains of roughly nchunk by nchunk points. This may
never actually be advantageous, so this option may be
removed. Chunking introduces artifacts at the chunk
boundaries unless antialiased = False, or linewidths is
set to a large enough value for the particular renderer and
resolution.
- csd(self, x, y, NFFT=256, Fs=2, detrend=<function detrend_none at 0x108ac08>, window=<function window_hanning at 0x108a0c8>, noverlap=0, **kwargs)
- CSD(x, y, NFFT=256, Fs=2, detrend=detrend_none,
window=window_hanning, noverlap=0, **kwargs)
The cross spectral density Pxy by Welches average periodogram method.
The vectors x and y are divided into NFFT length segments. Each
segment is detrended by function detrend and windowed by function
window. The product of the direct FFTs of x and y are averaged over
each segment to compute Pxy, with a scaling to correct for power loss
due to windowing.
See the PSD help for a description of the optional parameters.
Returns the tuple Pxy, freqs. Pxy is the cross spectrum (complex
valued), and 10*log10(|Pxy|) is plotted
Refs:
Bendat & Piersol -- Random Data: Analysis and Measurement
Procedures, John Wiley & Sons (1986)
kwargs control the Line2D properties:
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: unknown
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
color or c: any matplotlib color
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
data: (array xdata, array ydata)
figure: a matplotlib.figure.Figure instance
label: any string
linestyle or ls: [ '-' | '--' | '-.' | ':' | 'steps' | 'None' | ' ' | '' ]
linewidth or lw: float value in points
lod: [True | False]
marker: [ '+' | ',' | '.' | '1' | '2' | '3' | '4'
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markersize or ms: float
picker: [None|float|boolean|callable]
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a matplotlib.transform transformation instance
visible: [True | False]
xdata: array
ydata: array
zorder: any number
- disconnect(self, cid)
- disconnect from the Axes event.
- draw_artist(self, a)
- This method can only be used after an initial draw which
caches the renderer. It is used to efficiently update Axes
data (axis ticks, labels, etc are not updated)
- errorbar(self, x, y, yerr=None, xerr=None, fmt='-', ecolor=None, capsize=3, barsabove=False, **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.
So you can add additional key=value pairs to control the
errorbar markers. For example, this code makes big red
squares with thick green edges
>>> x,y,yerr = rand(3,10)
>>> errorbar(x, y, yerr, marker='s',
mfc='red', mec='green', ms=20, mew=4)
mfc, mec, ms and mew are aliases for the longer property
names, markerfacecolor, markeredgecolor, markersize and
markeredgewith.
valid kwargs for the marker properties are
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: unknown
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
color or c: any matplotlib color
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
data: (array xdata, array ydata)
figure: a matplotlib.figure.Figure instance
label: any string
linestyle or ls: [ '-' | '--' | '-.' | ':' | 'steps' | 'None' | ' ' | '' ]
linewidth or lw: float value in points
lod: [True | False]
marker: [ '+' | ',' | '.' | '1' | '2' | '3' | '4'
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markersize or ms: float
picker: [None|float|boolean|callable]
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a matplotlib.transform transformation instance
visible: [True | False]
xdata: array
ydata: array
zorder: any number
Return value is a length 3 tuple. The first element is the
Line2D instance for the y symbol lines. The second element is
a list of error bar cap lines, the third element is a list of
line collections for the horizontal and vertical error ranges
- fill(self, *args, **kwargs)
- FILL(*args, **kwargs)
plot filled polygons. *args is a variable length argument, allowing
for multiple x,y pairs with an optional color format string; see plot
for details on the argument parsing. For example, all of the
following are legal, assuming ax is an Axes instance:
ax.fill(x,y) # plot polygon with vertices at x,y
ax.fill(x,y, 'b' ) # plot polygon with vertices at x,y in blue
An arbitrary number of x, y, color groups can be specified, as in
ax.fill(x1, y1, 'g', x2, y2, 'r')
Return value is a list of patches that were added
The same color strings that plot supports are supported by the fill
format string.
kwargs control the Polygon properties:
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: an axes instance
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
edgecolor or ec: any matplotlib color
facecolor or fc: any matplotlib color
figure: a matplotlib.figure.Figure instance
fill: [True | False]
hatch: unknown
label: any string
linewidth or lw: float
lod: [True | False]
picker: [None|float|boolean|callable]
transform: a matplotlib.transform transformation instance
visible: [True | False]
zorder: any number
- get_adjustable(self)
- get_anchor(self)
- get_aspect(self)
- get_autoscale_on(self)
- Get whether autoscaling is applied on plot commands
- get_axis_bgcolor(self)
- Return the axis background color
- get_axisbelow(self)
- Get whether axist below is true or not
- get_child_artists(self)
- Return a list of artists the axes contains. Deprecated
- get_children(self)
- return a list of child artists
- get_cursor_props(self)
- return the cursor props as a linewidth, color tuple where
linewidth is a float and color is an RGBA tuple
- get_frame(self)
- Return the axes Rectangle frame
- get_frame_on(self)
- Get whether the axes rectangle patch is drawn
- get_images(self)
- return a list of Axes images contained by the Axes
- get_legend(self)
- Return the Legend instance, or None if no legend is defined
- get_lines(self)
- Return a list of lines contained by the Axes
- get_navigate(self)
- Get whether the axes responds to navigation commands
- get_navigate_mode(self)
- Get the navigation toolbar button status: 'PAN', 'ZOOM', or None
- get_position(self, original=False)
- Return the axes rectangle left, bottom, width, height
- get_renderer_cache(self)
- get_window_extent(self, *args, **kwargs)
- get the axes bounding box in display space; args and kwargs are empty
- 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=None, **kwargs)
- GRID(self, b=None, **kwargs)
Set the axes grids on or off; b is a boolean
if b is None and len(kwargs)==0, toggle the grid state. if
kwargs are supplied, it is assumed that you want a grid and b
is thus set to True
kawrgs are used to set the grid line properties, eg
ax.grid(color='r', linestyle='-', linewidth=2)
Valid Line2D kwargs are
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: unknown
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
color or c: any matplotlib color
dash_capstyle: ['butt' | 'round' | 'projecting']
dash_joinstyle: ['miter' | 'round' | 'bevel']
dashes: sequence of on/off ink in points
data: (array xdata, array ydata)
figure: a matplotlib.figure.Figure instance
label: any string
linestyle or ls: [ '-' | '--' | '-.' | ':' | 'steps' | 'None' | ' ' | '' ]
linewidth or lw: float value in points
lod: [True | False]
marker: [ '+' | ',' | '.' | '1' | '2' | '3' | '4'
markeredgecolor or mec: any matplotlib color
markeredgewidth or mew: float value in points
markerfacecolor or mfc: any matplotlib color
markersize or ms: float
picker: [None|float|boolean|callable]
solid_capstyle: ['butt' | 'round' | 'projecting']
solid_joinstyle: ['miter' | 'round' | 'bevel']
transform: a matplotlib.transform transformation instance
visible: [True | False]
xdata: array
ydata: array
zorder: any number
- has_data(self)
- Return true if any artists have been added to axes.
This should not be used to determine whether the dataLim
need to be updated, and may not actually be useful for
anything.
- hist(self, x, bins=10, normed=0, bottom=None, align='edge', orientation='vertical', width=None, log=False, **kwargs)
- HIST(x, bins=10, normed=0, bottom=None,
align='edge', orientation='vertical', width=None,
log=False, **kwargs)
Compute the histogram of x. bins is either an integer number of
bins or a sequence giving the bins. x are the data to be binned.
The return values is (n, bins, patches)
If normed is true, the first element of the return tuple will
be the counts normalized to form a probability density, ie,
n/(len(x)*dbin). In a probability density, the integral of
the histogram should be one (we assume equally spaced bins);
you can verify that with
# trapezoidal integration of the probability density function
from matplotlib.mlab import trapz
pdf, bins, patches = ax.hist(...)
print trapz(bins, pdf)
align = 'edge' | 'center'. Interprets bins either as edge
or center values
orientation = 'horizontal' | 'vertical'. If horizontal, barh
will be used and the "bottom" kwarg will be the left edges.
width: the width of the bars. If None, automatically compute
the width.
log: if True, the histogram axis will be set to a log scale
kwargs are used to update the properties of the
hist Rectangles:
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
axes: an axes instance
clip_box: a matplotlib.transform.Bbox instance
clip_on: [True | False]
clip_path: an agg.path_storage instance
edgecolor or ec: any matplotlib color
facecolor or fc: any matplotlib color
figure: a matplotlib.figure.Figure instance
fill: [True | False]
hatch: unknown
label: any string
linewidth or lw: float
lod: [True | False]
picker: [None|float|boolean|callable]
transform: a matplotlib.transform transformation instance
visible: [True | False]
zorder: any number
- hlines(self, y, xmin, xmax, colors='k', linestyle='solid', label='', **kwargs)
- HLINES(y, xmin, xmax, colors='k', linestyle='solid', **kwargs)
plot horizontal lines at each y from xmin to xmax. xmin or xmax can
be scalars or len(x) numpy arrays. If they are scalars, then the
respective values are constant, else the widths of the lines are
determined by xmin and xmax
colors is a line collections color args, either a single color or a len(x) list of colors
linestyle is one of solid|dashed|dashdot|dotted
Returns the LineCollection that was added
- hold(self, b=None)
- HOLD(b=None)
Set the hold state. If hold is None (default), toggle the
hold state. Else set the hold state to boolean value b.
Eg
hold() # toggle hold
hold(True) # hold is on
hold(False) # hold is off
When hold is True, subsequent plot commands will be added to
the current axes. When hold is False, the current axes and
figure will be cleared on the next plot command
- imshow(self, X, cmap=None, norm=None, aspect=None, interpolation=None, alpha=1.0, vmin=None, vmax=None, origin=None, extent=None, shape=None, filternorm=1, filterrad=4.0, imlim=None, **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, a
UInt8 array or a PIL image. If X is an array, X can have the following
shapes:
MxN : luminance (grayscale, float array only)
MxNx3 : RGB (float or UInt8 array)
MxNx4 : RGBA (float or UInt8 array)
The value for each component of MxNx3 and MxNx4 float arrays should be
in the range 0.0 to 1.0; MxN float arrays may be normalised.
A 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: auto, equal, or a number. If None, default to rc
image.aspect value
* interpolation is one of:
'nearest', 'bilinear', 'bicubic', 'spline16', 'spline36',
'hanning', 'hamming', 'hermite', 'kaiser', 'quadric',
'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc',
'lanczos', 'blackman'
if interpolation is None, default to rc
image.interpolation. See also th the filternorm and
filterrad parameters
* norm is a matplotlib.colors.Normalize instance; default is
normalization(). This scales luminance -> 0-1 (only used for an
MxN float array).
* vmin and vmax are used to scale a luminance image to 0-1. If
either is None, the min and max of the luminance values will be
used. Note if you pass a norm instance, the settings for vmin and
vmax will be ignored.
* alpha = 1.0 : the alpha blending value
* origin is 'upper' or 'lower', to place the [0,0]
index of the array in the upper left or lower left corner of
the axes. If None, default to rc image.origin
* extent is (left, right, bottom, top) data values of the
axes. The default assigns zero-based row, column indices
to the x, y centers of the pixels.
* shape is for raw buffer images
* filternorm is a parameter for the antigrain image resize
filter. From the antigrain documentation, if normalize=1,
the filter normalizes integer values and corrects the
rounding errors. It doesn't do anything with the source
floating point values, it corrects only integers according
to the rule of 1.0 which means that any sum of pixel
weights must be equal to 1.0. So, the filter function
must produce a graph of the proper shape.
* filterrad: the filter radius for filters that have a radius
parameter, ie when interpolation is one of: 'sinc',
'lanczos' or 'blackman'
Additional kwargs are matplotlib.artist
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