matplotlib.axes.Axes

matplotlib.axes.Axes Class Reference

Inheritance diagram for matplotlib.axes.Axes:

matplotlib.artist.Artist matplotlib.axes.PolarAxes matplotlib.axes.Subplot matplotlib.axes.PolarSubplot List of all members.

Public Member Functions

def set_cursor_props
def get_cursor_props
def set_figure
def axhline
def axvline
def axhspan
def axvspan
def format_xdata
def format_ydata
def quiver
def bar
def boxplot
def barh
def cohere
def csd
def errorbar
def fill
def get_child_artists
def grid
def hist
def hold
def set_frame_on
def imshow
def hlines
def legend
def loglog
def pcolor
def pcolor_classic
def pie
def plot
def plot_date
def psd
def set_position
def stem
def set_axis_off
def set_axis_on
def scatter
def scatter_classic
def semilogx
def semilogy
def set_axis_bgcolor
def set_title
def set_xlabel
def set_xscale
def set_xticklabels
def set_xticks
def set_ylabel
def set_yscale
def set_yticklabels
def set_yticks
def specgram
def spy
def spy2
def table
def text
def vlines
def connect
def pick

Detailed Description

Emulate matlab's (TM) 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

Member Function Documentation

def matplotlib.axes.Axes.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)

def matplotlib.axes.Axes.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)

def matplotlib.axes.Axes.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)

def matplotlib.axes.Axes.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)

def matplotlib.axes.Axes.bar (   self,
  left,
  height,
  width = 0.8,
  bottom = 0,
  color = 'b',
  yerr = None,
  xerr = None,
  ecolor = 'k',
  capsize = 3 
) 

BAR(left, height, width=0.8, bottom=0,
    color='b', yerr=None, xerr=None, ecolor='k', capsize=3)

Make a bar plot with rectangles at

  left, left+width, 0, height

left and height are Numeric arrays.

Return value is a list of Rectangle patch instances

BAR(left, height, width, bottom,
    color, yerr, xerr, capsize, yoff)

    xerr and yerr, if not None, will be used to generate errorbars
      on the bar chart

    color specifies the color of the bar

    ecolor specifies the color of any errorbar

    capsize determines the length in points of the error bar caps


The optional arguments color, width and bottom can be either
scalars or len(x) sequences

This enables you to use bar as the basis for stacked bar
charts, or candlestick plots

def matplotlib.axes.Axes.barh (   self,
  x,
  y,
  height = 0.8,
  left = 0,
  color = 'b',
  yerr = None,
  xerr = None,
  ecolor = 'k',
  capsize = 3 
) 

BARH(x, y, height=0.8, left=0,
     color='b', yerr=None, xerr=None, ecolor='k', capsize=3)

    BARH(x, y)

    The y values give the heights of the center of the bars.  The
    x values give the length of the bars.

    Return value is a list of Rectangle patch instances

Optional arguments

    height - the height (thickness)  of the bar

    left  - the x coordinate of the left side of the bar

    color specifies the color of the bar

    xerr and yerr, if not None, will be used to generate errorbars
    on the bar chart

    ecolor specifies the color of any errorbar

    capsize determines the length in points of the error bar caps

The optional arguments color, height and left can be either
scalars or len(x) sequences

def matplotlib.axes.Axes.boxplot (   self,
  x,
  notch = 0,
  sym = 'b+',
  vert = 1,
  whis = 1.5 
) 

boxplot(x, notch=0, sym='+', vert=1, whis=1.5)

Make a box and whisker plot for each column of 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.

x is a Numeric array

Returns a list of the lines added

def matplotlib.axes.Axes.cohere (   self,
  x,
  y,
  NFFT = 256,
  Fs = 2,
  detrend = detrend_none,
  window = window_hanning,
  noverlap = 0 
) 

COHERE(x, y, NFFT=256, Fs=2, detrend=detrend_none,
      window=window_hanning, noverlap=0)

cohere the coherence between x and y.  Coherence is the normalized
cross spectral density

  Cxy = |Pxy|^2/(Pxx*Pyy)

The return value is (Cxy, f), where f are the frequencies of the
coherence vector.

See the PSD help for a description of the optional parameters.

Returns the tuple Cxy, freqs

Refs: Bendat & Piersol -- Random Data: Analysis and Measurement
  Procedures, John Wiley & Sons (1986)

def matplotlib.axes.Axes.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

def matplotlib.axes.Axes.csd (   self,
  x,
  y,
  NFFT = 256,
  Fs = 2,
  detrend = detrend_none,
  window = window_hanning,
  noverlap = 0 
) 

CSD(x, y, NFFT=256, Fs=2, detrend=detrend_none,
    window=window_hanning, noverlap=0)

The cross spectral density Pxy by Welches average periodogram method.
The vectors x and y are divided into NFFT length segments.  Each
segment is detrended by function detrend and windowed by function
window.  The product of the direct FFTs of x and y are averaged over
each segment to compute Pxy, with a scaling to correct for power loss
due to windowing.

See the PSD help for a description of the optional parameters.

Returns the tuple Pxy, freqs.  Pxy is the cross spectrum (complex
valued), and 10*log10(|Pxy|) is plotted

Refs:
  Bendat & Piersol -- Random Data: Analysis and Measurement
    Procedures, John Wiley & Sons (1986)

def matplotlib.axes.Axes.errorbar (   self,
  x,
  y,
  yerr = None,
  xerr = None,
  fmt = 'b-',
  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

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.

def matplotlib.axes.Axes.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 a is the Axis instance:

  ax.fill(x,y)            # plot polygon with vertices at x,y
  ax.fill(x,y, 'b' )      # plot polygon with vertices at x,y in blue

An arbitrary number of x, y, color groups can be specified, as in
  ax.fill(x1, y1, 'g', x2, y2, 'r')

Return value is a list of patches that were added

The same color strings that plot supports are supported by the fill
format string.

The kwargs that are can be used to set line properties (any
property that has a set_* method).  You can use this to set edge
color, face color, etc.

def matplotlib.axes.Axes.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

def matplotlib.axes.Axes.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

def matplotlib.axes.Axes.get_child_artists (   self  )  

Return a list of artists the axes contains.  Deprecated

def matplotlib.axes.Axes.get_cursor_props (   self  )  

return the cursor props as a linewidth, color tuple where
linewidth is a float and color is an RGBA tuple

def matplotlib.axes.Axes.grid (   self,
  b = None 
) 

Set the axes grids on or off; b is a boolean
if b is None, toggle the grid state

Reimplemented in matplotlib.axes.PolarAxes.

def matplotlib.axes.Axes.hist (   self,
  x,
  bins = 10,
  normed = 0,
  bottom = 0,
  kwargs 
) 

HIST(x, bins=10, normed=0, bottom=0)

Compute the histogram of x.  bins is either an integer number of
bins or a sequence giving the bins.  x are the data to be binned.

The return values is (n, bins, patches)

If normed is true, the first element of the return tuple will be the
counts normalized to form a probability distribtion, ie,
n/(len(x)*dbin)

kwargs are used to update the properties of the hist bars

def matplotlib.axes.Axes.hlines (   self,
  y,
  xmin,
  xmax,
  fmt = 'k-' 
) 

HLINES(y, xmin, xmax, fmt='k-')

plot horizontal lines at each y from xmin to xmax.  xmin or xmax can
be scalars or len(x) numpy arrays.  If they are scalars, then the
respective values are constant, else the widths of the lines are
determined by xmin and xmax

Returns a list of line instances that were added

def matplotlib.axes.Axes.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

def matplotlib.axes.Axes.imshow (   self,
  X,
  cmap = None,
  norm = None,
  aspect = None,
  interpolation = None,
  alpha = 1.0,
  vmin = None,
  vmax = None,
  origin = None,
  extent = None 
) 

IMSHOW(X, cmap=None, norm=None, aspect=None, interpolation=None,
       alpha=1.0, vmin=None, vmax=None, origin=None, extent=None)

IMSHOW(X) - plot image X to current axes, resampling to scale to axes
    size (X may be numarray/Numeric array or PIL image)

IMSHOW(X, **kwargs) - Use keyword args to control image scaling,
colormapping etc. See below for details


Display the image in X to current axes.  X may be a float array or a
PIL image. If X is a float array, X can have the following shapes

    MxN    : luminance (grayscale)

    MxNx3  : RGB

    MxNx4  : RGBA

A matplotlib.image.AxesImage instance is returned

The following kwargs are allowed:

  * cmap is a cm colormap instance, eg cm.jet.  If None, default to rc
    image.cmap value (Ignored when X has RGB(A) information)

  * aspect is one of: free or preserve.  if None, default to rc
    image.aspect value

  * interpolation is one of: bicubic bilinear blackman100 blackman256
    blackman64 nearest sinc144 sinc256 sinc64 spline16 or spline36.
    If None, default to rc image.interpolation

  * norm is a matplotlib.colors.normalize instance; default is
    normalization().  This scales luminance -> 0-1 (Ignored when X is
    PIL image).

  * vmin and vmax are used to scale a luminance image to 0-1.  If
    either is None, the min and max of the luminance values will be
    used.  Note if you pass a norm instance, the settings for vmin and
    vmax will be ignored.

  * alpha = 1.0 : the alpha blending value

  * origin is either upper or lower, which indicates where the [0,0]
    index of the array is in the upper left or lower left corner of
    the axes.  If None, default to rc image.origin

  * extent is a data xmin, xmax, ymin, ymax for making image plots
    registered with data plots.  Default is the image dimensions
    in pixels

def matplotlib.axes.Axes.legend (   self,
  args,
  kwargs 
) 

LEGEND(*args, **kwargs)

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

USAGE:

  Make a legend with existing lines

  >>> legend()

  legend by itself will try and build a legend using the label
  property of the lines/patches/collections.  You can set the label of
  a line by doing plot(x, y, label='my data') or line.set_label('my
  data')

    # automatically generate the legend from labels
    legend( ('label1', 'label2', 'label3') )

    # Make a legend for a list of lines and labels
    legend( (line1, line2, line3), ('label1', 'label2', 'label3') )

    # Make a legend at a given location, using a location argument
    # legend( LABELS, LOC )  or
    # legend( LINES, LABELS, LOC )
    legend( ('label1', 'label2', 'label3'), loc='upper left')
    legend( (line1, line2, line3),  ('label1', 'label2', 'label3'), loc=2)

The location codes are

  'best' : 0,          
  'upper right'  : 1, (default)
  'upper left'   : 2,
  'lower left'   : 3,
  'lower right'  : 4,
  'right'        : 5,
  'center left'  : 6,
  'center right' : 7,
  'lower center' : 8,
  'upper center' : 9,
  'center'       : 10,

If none of these are suitable, loc can be a 2-tuple giving x,y
in axes coords, ie,

  loc = 0, 1 is left top
  loc = 0.5, 0.5 is center, center

and so on.  The following kwargs are supported

  numpoints = 4         # the number of points in the legend line
  prop = FontProperties('smaller')  # the font properties
  pad = 0.2             # the fractional whitespace inside the legend border

  # The kwarg dimensions are in axes coords
  labelsep = 0.005     # the vertical space between the legend entries
  handlelen = 0.05     # the length of the legend lines
  handletextsep = 0.02 # the space between the legend line and legend text
  axespad = 0.02       # the border between the axes and legend edge
  shadow = False       # if True, draw a shadow behind legend

def matplotlib.axes.Axes.loglog (   self,
  args,
  kwargs 
) 

LOGLOG(*args, **kwargs)

Make a loglog plot with log scaling on the a and y axis.  The args
to semilog x are the same as the args to plot.  See help plot for
more info.

Optional keyword args supported are any of the kwargs
supported by plot or set_xscale or set_yscale.  Notable, for
log scaling:

  * basex: base of the x logarithm

  * subsx: the location of the minor ticks; None defaults to autosubs,
    which depend on the number of decades in the plot

  * basey: base of the y logarithm

  * subsy: the location of the minor yticks; None defaults to autosubs,
    which depend on the number of decades in the plot

def matplotlib.axes.Axes.pcolor (   self,
  args,
  kwargs 
) 

PCOLOR(*args, **kwargs)

Function signatures

  PCOLOR(C) - make a pseudocolor plot of matrix C

  PCOLOR(X, Y, C) - a pseudo color plot of C on the matrices X and Y

  PCOLOR(C, **kwargs) - Use keywork args to control colormapping and
                scaling; see below

Optional keywork args are shown with their defaults below (you must
use kwargs for these):

  * cmap = cm.jet : a cm Colormap instance from matplotlib.cm.
    defaults to cm.jet

  * norm = normalize() : matplotlib.colors.normalize is used to scale
    luminance data to 0,1.

  * vmin=None and vmax=None : vmin and vmax are used in conjunction
    with norm to normalize luminance data.  If either are None, the
    min and max of the color array C is used.  If you pass a norm
    instance, vmin and vmax will be None

  * shading = 'flat' : or 'faceted'.  If 'faceted', a black grid is
    drawn around each rectangle; if 'flat', edge colors are same as
    face colors

  * alpha=1.0 : the alpha blending value

Return value is a matplotlib.collections.PatchCollection
object

Grid Orientation

    The behavior of meshgrid in matlab(TM) is a bit counterintuitive for
    x and y arrays.  For example,

x = arange(7)
y = arange(5)
X, Y = meshgrid(x,y)

Z = rand( len(x), len(y))
pcolor(X, Y, Z)

    will fail in matlab and pylab.  You will probably be
    happy with

pcolor(X, Y, transpose(Z))

    Likewise, for nonsquare Z,

pcolor(transpose(Z))

    will make the x and y axes in the plot agree with the numrows and
    numcols of Z

def matplotlib.axes.Axes.pcolor_classic (   self,
  args,
  kwargs 
) 

PCOLOR_CLASSIC(self, *args, **kwargs)

Function signatures

    pcolor(C) - make a pseudocolor plot of matrix C

    pcolor(X, Y, C) - a pseudo color plot of C on the matrices X and Y

    pcolor(C, cmap=cm.jet) - make a pseudocolor plot of matrix C using
      rectangle patches using a colormap jet.  Colormaps are avalible
      in matplotlib.cm.  You must pass this as a kwarg.

    pcolor(C, norm=normalize()) - the normalization function used
`     to scale your color data to 0-1.  must be passed as a kwarg.

    pcolor(C, alpha=0.5) - set the alpha of the pseudocolor plot.
      Must be used as a kwarg

Shading:

    The optional keyword arg shading ('flat' or 'faceted') will
    determine whether a black grid is drawn around each pcolor square.
    Default 'faceteted' e.g., pcolor(C, shading='flat') pcolor(X, Y,
    C, shading='faceted')

Return value is a list of patch objects.

Grid orientation

    The behavior of meshgrid in matlab(TM) is a bit counterintuitive for x
    and y arrays.  For example,

  x = arange(7)
  y = arange(5)
  X, Y = meshgrid(x,y)

  Z = rand( len(x), len(y))
  pcolor(X, Y, Z)

    will fail in matlab and matplotlib.  You will probably be
    happy with

pcolor(X, Y, transpose(Z))

    Likewise, for nonsquare Z,

pcolor(transpose(Z))

    will make the x and y axes in the plot agree with the numrows
    and numcols of Z

def matplotlib.axes.Axes.pick (   self,
  x,
  y,
  trans = None 
) 

Return the artist under point that is closest to the x, y.  if trans
is None, x, and y are in window coords, 0,0 = lower left.  Otherwise,
trans is a matplotlib transform that specifies the coordinate system
of x, y.

Note this algorithm calculates distance to the vertices of the
polygon, so if you want to pick a patch, click on the edge!

def matplotlib.axes.Axes.pie (   self,
  x,
  explode = None,
  labels = None,
  colors = ('b', 'g',
  r,
  c,
  m,
  y,
  k,
  w,
  autopct = None,
  shadow = False 
) 

Make a pie chart of array x.  The fractional area of each wedge is
given by x/sum(x).  If sum(x)<=1, then the values of x give the
fractional area directly and the array will not be normalized.

  - explode, if not None, is a len(x) array which specifies the
    fraction of the radius to offset that wedge.

  - colors is a sequence of matplotlib color args that the pie chart
    will cycle.

  - labels, if not None, is a len(x) list of labels.

  - autopct, if not None, is a string or function used to label the
    wedges with their numeric value.  The label will be placed inside
    the wedge.  If it is a format string, the label will be fmt%pct.
    If it is a function, it will be called

  - shadow, if True, will draw a shadow beneath the pie.

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

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

Return value:

  If autopct is None, return a list of (patches, texts), where patches
  is a sequence of matplotlib.patches.Wedge instances and texts is a
  list of the label Text instnaces

  If autopct is not None, return (patches, texts, autotexts), where
  patches and texts are as above, and autotexts is a list of text
  instances for the numeric labels

def matplotlib.axes.Axes.plot (   self,
  args,
  kwargs 
) 

PLOT(*args, **kwargs)

Plot lines and/or markers to the Axes.  *args is a variable length
argument, allowing for multiple x,y pairs with an optional format
string.  For example, each of the following is legal

    plot(x,y)            # plot x and y using the default line style and color
    plot(x,y, 'bo')      # plot x and y using blue circle markers
    plot(y)              # plot y using x as index array 0..N-1
    plot(y, 'r+')        # ditto, but with red plusses

An arbitrary number of x, y, fmt groups can be specified, as in

    a.plot(x1, y1, 'g^', x2, y2, 'g-')

Return value is a list of lines that were added.

The following line styles are supported:

    -     : solid line
    --    : dashed line
    -.    : dash-dot line
    :     : dotted line
    .     : points
    ,     : pixels
    o     : circle symbols
    ^     : triangle up symbols
    v     : triangle down symbols
    <     : triangle left symbols
    >     : triangle right symbols
    s     : square symbols
    +     : plus symbols
    x     : cross symbols
    D     : diamond symbols
    d     : thin diamond symbols
    1     : tripod down symbols
    2     : tripod up symbols
    3     : tripod left symbols
    4     : tripod right symbols
    h     : hexagon symbols
    H     : rotated hexagon symbols
    p     : pentagon symbols
    |     : vertical line symbols
    _     : horizontal line symbols
    steps : use gnuplot style 'steps' # kwarg only

The following color strings are supported

    b  : blue
    g  : green
    r  : red
    c  : cyan
    m  : magenta
    y  : yellow
    k  : black
    w  : white

Line styles and colors are combined in a single format string, as in
'bo' for blue circles.

The **kwargs can be used to set line properties (any property that has
a set_* method).  You can use this to set a line label (for auto
legends), linewidth, anitialising, marker face color, etc.  Here is an
example:

    plot([1,2,3], [1,2,3], 'go-', label='line 1', linewidth=2)
    plot([1,2,3], [1,4,9], 'rs',  label='line 2')
    axis([0, 4, 0, 10])
    legend()

If you make multiple lines with one plot command, the kwargs apply
to all those lines, eg

    plot(x1, y1, x2, y2, antialising=False)

Neither line will be antialiased.

def matplotlib.axes.Axes.plot_date (   self,
  d,
  y,
  fmt = 'bo',
  tz = None,
  kwargs 
) 

PLOT_DATE(d, y, converter, fmt='bo', tz=None, **kwargs)

d is a sequence of dates represented as float days since
0001-01-01 UTC and y are the y values at those dates.  fmt is
a plot format string.  kwargs are passed on to plot.  See plot
for more information.

See matplotlib.dates for helper functions date2num, num2date
and drange for help on creating the required floating point dates

tz is the timezone - defaults to rc value

def matplotlib.axes.Axes.psd (   self,
  x,
  NFFT = 256,
  Fs = 2,
  detrend = detrend_none,
  window = window_hanning,
  noverlap = 0 
) 

PSD(x, NFFT=256, Fs=2, detrend=detrend_none,
    window=window_hanning, noverlap=0)

The power spectral density by Welches average periodogram method.  The
vector x is divided into NFFT length segments.  Each segment is
detrended by function detrend and windowed by function window.
noperlap gives the length of the overlap between segments.  The
absolute(fft(segment))**2 of each segment are averaged to compute Pxx,
with a scaling to correct for power loss due to windowing.  Fs is the
sampling frequency.

    NFFT is the length of the fft segment; must be a power of 2

    Fs is the sampling frequency.

    detrend - the function applied to each segment before fft-ing,
      designed to remove the mean or linear trend.  Unlike in matlab,
      where the detrend parameter is a vector, in matplotlib is it a
      function.  The mlab module defines detrend_none, detrend_mean,
      detrend_linear, but you can use a custom function as well.

    window - the function used to window the segments.  window is a
      function, unlike in matlab(TM) where it is a vector.  mlab defines
      window_none, window_hanning, but you can use a custom function
      as well.

    noverlap gives the length of the overlap between segments.

Returns the tuple Pxx, freqs

For plotting, the power is plotted as 10*log10(pxx)) for decibels,
though pxx itself is returned

Refs:

  Bendat & Piersol -- Random Data: Analysis and Measurement
  Procedures, John Wiley & Sons (1986)

def matplotlib.axes.Axes.quiver (   self,
  U,
  V,
  args,
  kwargs 
) 

QUIVER( X, Y, U, V )
QUIVER( U, V )
QUIVER( X, Y, U, V, S)
QUIVER( U, V, S )
QUIVER( ..., color=None, width=1.0, cmap=None,norm=None )

Make a vector plot (U, V) with arrows on a grid (X, Y)

The optional arguments color and width are used to specify the color and width
of the arrow. color can be an array of colors in which case the arrows can be
colored according to another dataset.

If cm is specied and color is None, the colormap is used to give a color
according to the vector's length.

If color is a scalar field, the colormap is used to map the scalar to a color
If a colormap is specified and color is an array of color triplets, then the
colormap is ignored

width is a scalar that controls the width of the arrows

if S is specified it is used to scale the vectors. Use S=0 to disable automatic
scaling.
If S!=0, vectors are scaled to fit within the grid and then are multiplied by S.

def matplotlib.axes.Axes.scatter (   self,
  x,
  y,
  s = 20,
  c = 'b',
  marker = 'o',
  cmap = None,
  norm = None,
  vmin = None,
  vmax = None,
  alpha = 1.0,
  kwargs 
) 

SCATTER(x, y, s=20, c='b', marker='o', cmap=None, norm=None,
vmin=None, vmax=None, alpha=1.0)

Supported function signatures:

    SCATTER(x, y) - make a scatter plot of x vs y

    SCATTER(x, y, s) - make a scatter plot of x vs y with size in area
      given by s

    SCATTER(x, y, s, c) - make a scatter plot of x vs y with size in area
      given by s and colors given by c

    SCATTER(x, y, s, c, **kwargs) - control colormapping and scaling
      with keyword args; see below

Make a scatter plot of x versus y.  s is a size in points^2 a scalar
or an array of the same length as x or y.  c is a color and can be a
single color format string or an length(x) array of intensities which
will be mapped by the matplotlib.colors.colormap instance cmap

The marker can be one of

    's' : square
    'o' : circle
    '^' : triangle up
    '>' : triangle right
    'v' : triangle down
    '<' : triangle left
    'd' : diamond
    'p' : pentagram
    'h' : hexagon
    '8' : octagon

s is a size argument in points squared.

Other keyword args; the color mapping and normalization arguments will
on be used if c is an array of floats

  * cmap = cm.jet : a cm Colormap instance from matplotlib.cm.
    defaults to rc image.cmap

  * norm = normalize() : matplotlib.colors.normalize is used to
    scale luminance data to 0,1.

  * vmin=None and vmax=None : vmin and vmax are used in conjunction
    with norm to normalize luminance data.  If either are None, the
    min and max of the color array C is used.  Note if you pass a norm
    instance, your settings for vmin and vmax will be ignored

  * alpha =1.0 : the alpha value for the patches

def matplotlib.axes.Axes.scatter_classic (   self,
  x,
  y,
  s = None,
  c = 'b' 
) 

SCATTER_CLASSIC(x, y, s=None, c='b')

Make a scatter plot of x versus y.  s is a size (in data coords) and
can be either a scalar or an array of the same length as x or y.  c is
a color and can be a single color format string or an length(x) array
of intensities which will be mapped by the colormap jet.

If size is None a default size will be used

def matplotlib.axes.Axes.semilogx (   self,
  args,
  kwargs 
) 

SEMILOGX(*args, **kwargs)

Make a semilog plot with log scaling on the x axis.  The args to
semilog x are the same as the args to plot.  See help plot for more
info.

Optional keyword args supported are any of the kwargs supported by
plot or set_xscale.  Notable, for log scaling:

    * basex: base of the logarithm

    * subsx: the location of the minor ticks; None defaults to autosubs,
    which depend on the number of decades in the plot

def matplotlib.axes.Axes.semilogy (   self,
  args,
  kwargs 
) 

SEMILOGY(*args, **kwargs):

Make a semilog plot with log scaling on the y axis.  The args to
semilogy are the same as the args to plot.  See help plot for more
info.

Optional keyword args supported are any of the kwargs supported by
plot or set_yscale.  Notable, for log scaling:

    * basey: base of the logarithm

    * subsy: the location of the minor ticks; None defaults to autosubs,
    which depend on the number of decades in the plot

def matplotlib.axes.Axes.set_axis_bgcolor (   self,
  color 
) 

set the axes background color

ACCEPTS: any matplotlib color - see help(colors)

def matplotlib.axes.Axes.set_axis_off (   self  )  

turn off the axis

ACCEPTS: void

def matplotlib.axes.Axes.set_axis_on (   self  )  

turn on the axis

ACCEPTS: void

def matplotlib.axes.Axes.set_cursor_props (   self,
  args 
) 

Set the cursor property as
ax.set_cursor_props(linewidth, color)  OR
ax.set_cursor_props((linewidth, color))

ACCEPTS: a (float, color) tuple

def matplotlib.axes.Axes.set_figure (   self,
  fig 
) 

Set the Axes figure

ACCEPTS: a Figure instance

Reimplemented from matplotlib.artist.Artist.

def matplotlib.axes.Axes.set_frame_on (   self,
  b 
) 

Set whether the axes rectangle patch is drawn

ACCEPTS: True|False

def matplotlib.axes.Axes.set_position (   self,
  pos 
) 

Set the axes position with pos = [left, bottom, width, height]
in relative 0,1 coords

ACCEPTS: len(4) sequence of floats

def matplotlib.axes.Axes.set_title (   self,
  label,
  fontdict = None,
  kwargs 
) 

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

Set the title for the xaxis.  See the text docstring for information
of how override and the optional args work

ACCEPTS: str

def matplotlib.axes.Axes.set_xlabel (   self,
  xlabel,
  fontdict = None,
  kwargs 
) 

SET_XLABEL(xlabel, fontdict=None, **kwargs)

Set the label for the xaxis.  See the text docstring for information
of how override and the optional args work.

ACCEPTS: str

Reimplemented in matplotlib.axes.PolarAxes.

def matplotlib.axes.Axes.set_xscale (   self,
  value,
  basex = 10,
  subsx = None 
) 

SET_XSCALE(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 autosubs,
    which depend on the number of decades in the plot

ACCEPTS: str

def matplotlib.axes.Axes.set_xticklabels (   self,
  labels,
  fontdict = None,
  kwargs 
) 

SET_XTICKLABELS(labels, fontdict=None, **kwargs)

Set the xtick labels with list of strings labels Return a list of axis
text instances

ACCEPTS: sequence of strings

def matplotlib.axes.Axes.set_xticks (   self,
  ticks 
) 

Set the x ticks with list of ticks

ACCEPTS: sequence of floats

def matplotlib.axes.Axes.set_ylabel (   self,
  ylabel,
  fontdict = None,
  kwargs 
) 

SET_YLABEL(ylabel, fontdict=None, **kwargs)

Set the label for the yaxis

See the text doctstring for information of how override and
the optional args work

ACCEPTS: str

Reimplemented in matplotlib.axes.PolarAxes.

def matplotlib.axes.Axes.set_yscale (   self,
  value,
  basey = 10,
  subsy = None 
) 

SET_YSCALE(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
      is to autosub

ACCEPTS: str

def matplotlib.axes.Axes.set_yticklabels (   self,
  labels,
  fontdict = None,
  kwargs 
) 

SET_YTICKLABELS(labels, fontdict=None, **kwargs)

Set the ytick labels with list of strings labels.  Return a list of
Text instances

ACCEPTS: sequence of strings

def matplotlib.axes.Axes.set_yticks (   self,
  ticks 
) 

Set the y ticks with list of ticks

ACCEPTS: sequence of floats

def matplotlib.axes.Axes.specgram (   self,
  x,
  NFFT = 256,
  Fs = 2,
  detrend = detrend_none,
  window = window_hanning,
  noverlap = 128,
  cmap = None,
  xextent = None 
) 

SPECGRAM(x, NFFT=256, Fs=2, detrend=detrend_none,
 window=window_hanning, noverlap=128,
 cmap=None, xextent=None)

Compute a spectrogram of data in x.  Data are split into NFFT length
segements and the PSD of each section is computed.  The windowing
function window is applied to each segment, and the amount of overlap
of each segment is specified with noverlap.

    * cmap is a colormap; if None use default determined by rc

    * xextent is the image extent in the xaxes xextent=xmin, xmax -
      default 0, max(bins), 0, max(freqs) where bins is the return
      value from matplotlib.matplotlib.mlab.specgram

    * See help(psd) for information on the other keyword arguments.

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

    bins are the time points the spectrogram is calculated over

    freqs is an array of frequencies

    Pxx is a len(times) x len(freqs) array of power

    im is a matplotlib.image.AxesImage.

def matplotlib.axes.Axes.spy (   self,
  Z,
  marker = 's',
  markersize = 10,
  kwargs 
) 

SPY(Z, **kwrags) plots the sparsity pattern of the matrix S
using plot markers.

kwargs give the marker properties - see help(plot) for more
information on marker properties

The line handles are returned

def matplotlib.axes.Axes.spy2 (   self,
  Z 
) 

SPY2(Z) plots the sparsity pattern of the matrix S as an image

The image instance is returned

def matplotlib.axes.Axes.stem (   self,
  x,
  y,
  linefmt = 'b-',
  markerfmt = 'bo',
  basefmt = 'r-' 
) 

STEM(x, y, linefmt='b-', markerfmt='bo', basefmt='r-')

A stem plot plots vertical lines (using linefmt) at each x location
from the baseline to y, and places a marker there using markerfmt.  A
horizontal line at 0 is is plotted using basefmt

Return value is (markerline, stemlines, baseline) .

See
http://www.mathworks.com/access/helpdesk/help/techdoc/ref/stem.html
for details and examples/stem_plot.py for a demo.

def matplotlib.axes.Axes.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 
) 

TABLE(cellText=None, cellColours=None,
      cellLoc='right', colWidths=None,
      rowLabels=None, rowColours=None, rowLoc='left',
      colLabels=None, colColours=None, colLoc='center',
      loc='bottom', bbox=None):

Add a table to the current axes.  Returns a table instance.  For
finer grained control over tables, use the Table class and add it
to the axes with add_table.

Thanks to John Gill for providing the class and table.

def matplotlib.axes.Axes.text (   self,
  x,
  y,
  s,
  fontdict = None,
  kwargs 
) 

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

Add text in string s to axis at location x,y (data coords)

  fontdict is a dictionary to override the default text properties.
  If fontdict is None, the defaults are determined by your rc
  parameters.

Individual keyword arguemnts can be used to override any given
parameter

    text(x, y, s, fontsize=12)

The default transform specifies that text is in data coords,
alternatively, you can specify text in axis coords (0,0 lower left and
1,1 upper right).  The example below places text in the center of the
axes

    text(0.5, 0.5,'matplotlib',
 horizontalalignment='center',
 verticalalignment='center',
 transform = ax.transAxes,
    )

def matplotlib.axes.Axes.vlines (   self,
  x,
  ymin,
  ymax,
  color = 'k' 
) 

VLINES(x, ymin, ymax, color='k')

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

Returns a list of lines that were added

The documentation for this class was generated from the following file:
Generated on Tue Jul 4 15:42:43 2006 for matplotlib by  doxygen 1.4.7