from __future__ import division, generators

import math, sys

from numerix import absolute, arange, array, asarray, ones, divide,\
     transpose, log, log10, Float, Float32, ravel, zeros,\
     Int16, Int32, Int, Float64, ceil, indices, \
     shape, which, where, sqrt, asum, compress, maximum, minimum

import numerix.ma as ma

import matplotlib.mlab
from artist import Artist, setp
from axis import XAxis, YAxis
from cbook import iterable, is_string_like, flatten, enumerate, \
     allequal, dict_delall, popd, popall, silent_list
from collections import RegularPolyCollection, PolyCollection, LineCollection
from colors import colorConverter, normalize, Colormap, LinearSegmentedColormap, looks_like_color
import cm
#from cm import ColormapJet, Grayscale, ScalarMappable
from cm import ScalarMappable
from contour import ContourSupport, ContourLabeler
import _image
from ticker import AutoLocator, LogLocator, NullLocator
from ticker import ScalarFormatter, LogFormatter, LogFormatterExponent, LogFormatterMathtext, NullFormatter

from image import AxesImage
from legend import Legend
from lines import Line2D, lineStyles, lineMarkers

from matplotlib.mlab import meshgrid, detrend_none, detrend_linear, \
     window_none, window_hanning, linspace, prctile
from matplotlib.numerix.mlab import flipud, amin, amax

from matplotlib import rcParams
from patches import Patch, Rectangle, Circle, Polygon, Arrow, Wedge, Shadow, bbox_artist
from table import Table
from text import Text, TextWithDash, _process_text_args
from transforms import Bbox, Point, Value, Affine, NonseparableTransformation
from transforms import  FuncXY, Func, LOG10, IDENTITY, POLAR
from transforms import get_bbox_transform, unit_bbox, one, origin, zero
from transforms import blend_xy_sep_transform, Interval
from font_manager import FontProperties

import matplotlib

if matplotlib._havedate:
    from dates import YearLocator, MonthLocator, WeekdayLocator, \
             DayLocator, HourLocator, MinuteLocator, DateFormatter,\
             SEC_PER_MIN, SEC_PER_HOUR, SEC_PER_DAY, SEC_PER_WEEK


def _process_plot_format(fmt):
    """
    Process a matlab(TM) style color/line style format string.  Return a
    linestyle, color tuple as a result of the processing.  Default
    values are ('-', 'b').  Example format strings include

    'ko'    : black circles
    '.b'    : blue dots
    'r--'   : red dashed lines

    See Line2D.lineStyles and GraphicsContext.colors for all possible
    styles and color format string.

    """

    colors = {
        'b' : 1,
        'g' : 1,
        'r' : 1,
        'c' : 1,
        'm' : 1,
        'y' : 1,
        'k' : 1,
        'w' : 1,
        }


    linestyle = 'None'
    marker = 'None'
    color = rcParams['lines.color']
    
    # handle the multi char special cases and strip them from the
    # string
    if fmt.find('--')>=0:
        linestyle = '--'
        fmt = fmt.replace('--', '')
    if fmt.find('-.')>=0:
        linestyle = '-.'
        fmt = fmt.replace('-.', '')

    chars = [c for c in fmt]

    for c in chars:
        if lineStyles.has_key(c):
            if linestyle != 'None':
                raise ValueError, 'Illegal format string "%s"; two linestyle symbols' % fmt

            linestyle = c
        elif lineMarkers.has_key(c):
            if marker != 'None':
                raise ValueError, 'Illegal format string "%s"; two marker symbols' % fmt
            marker = c
        elif colors.has_key(c):
            color = c
        else:
            err = 'Unrecognized character %c in format string' % c
            raise ValueError, err

    if linestyle == 'None' and marker == 'None':
        linestyle = rcParams['lines.linestyle']

    return linestyle, marker, color

class _process_plot_var_args:
    """

    Process variable length arguments to the plot command, so that
    plot commands like the following are supported

      plot(t, s)
      plot(t1, s1, t2, s2)
      plot(t1, s1, 'ko', t2, s2)
      plot(t1, s1, 'ko', t2, s2, 'r--', t3, e3)

    an arbitrary number of x, y, fmt are allowed
    """

    def __init__(self, command='plot'):
        self.command = command
        self._clear_color_cycle()

    def _clear_color_cycle(self):
        self.colors = ['b','g','r','c','m','y','k']
        # if the default line color is a color format string, move it up
        # in the que
        try: ind = self.colors.index(rcParams['lines.color'])
        except ValueError:
            self.firstColor = rcParams['lines.color']
        else:
            self.colors[0], self.colors[ind] = self.colors[ind], self.colors[0]
            self.firstColor = self.colors[0]

        self.Ncolors = len(self.colors)

        self.count = 0

    def __call__(self, *args, **kwargs):
        ret =  self._grab_next_args(*args, **kwargs)
        return ret

    def set_lineprops(self, line, **kwargs):
        assert self.command == 'plot', 'set_lineprops only works with "plot"'
        for key, val in kwargs.items():
            funcName = "set_%s"%key
            if not hasattr(line,funcName):
                raise TypeError, 'There is no line property "%s"'%key
            func = getattr(line,funcName)
            func(val)

    def set_patchprops(self, fill_poly, **kwargs):
        assert self.command == 'fill', 'set_patchprops only works with "fill"'
        for key, val in kwargs.items():
            funcName = "set_%s"%key
            if not hasattr(fill_poly,funcName):
                raise TypeError, 'There is no patch property "%s"'%key
            func = getattr(fill_poly,funcName)
            func(val)


    def is_filled(self, marker):
        filled = ('o', '^', 'v', '<', '>', 's',
                  'd', 'D', 'h', 'H',
                  'p')
        return marker in filled


    def _plot_1_arg(self, y, **kwargs):
        assert self.command == 'plot', 'fill needs at least 2 arguments'
        if self.count==0:
            color = self.firstColor
        else:
            color = self.colors[int(self.count % self.Ncolors)]

        assert(iterable(y))
        try: N=max(y.shape)
        except AttributeError: N = len(y)
        ret =  Line2D(arange(N), y,
                      color = color,
                      markerfacecolor=color,
                      )
        self.set_lineprops(ret, **kwargs)
        self.count += 1
        return ret

    def _plot_2_args(self, tup2, **kwargs):
        if is_string_like(tup2[1]):

            assert self.command == 'plot', 'fill needs at least 2 non-string arguments'
            y, fmt = tup2
            assert(iterable(y))
            linestyle, marker, color = _process_plot_format(fmt)

            if self.is_filled(marker): mec = None # use default
            else: mec = color                     # use current color
            try: N=max(y.shape)
            except AttributeError: N = len(y)

            ret =  Line2D(xdata=arange(N), ydata=y,
                          color=color, linestyle=linestyle, marker=marker,
                          markerfacecolor=color,
                          markeredgecolor=mec,
                          )
            self.set_lineprops(ret, **kwargs)
            return ret
        else:

            x,y = tup2
            #print self.count, self.Ncolors, self.count % self.Ncolors
            assert(iterable(x))
            assert(iterable(y))
            if self.command == 'plot':
                c = self.colors[self.count % self.Ncolors]
                ret =  Line2D(x, y,
                              color = c,
                              markerfacecolor = c,
                              )
                self.set_lineprops(ret, **kwargs)
                self.count += 1
            elif self.command == 'fill':
                ret = Polygon( zip(x,y), fill=True, )
                self.set_patchprops(ret, **kwargs)

            return ret

    def _plot_3_args(self, tup3, **kwargs):
        if self.command == 'plot':
            x, y, fmt = tup3
            assert(iterable(x))
            assert(iterable(y))

            linestyle, marker, color = _process_plot_format(fmt)
            if self.is_filled(marker): mec = None # use default
            else: mec = color                     # use current color

            ret = Line2D(x, y, color=color,
                         linestyle=linestyle, marker=marker,
                         markerfacecolor=color,
                         markeredgecolor=mec,
                         )
            self.set_lineprops(ret, **kwargs)
        if self.command == 'fill':
            x, y, facecolor = tup3
            ret = Polygon(zip(x,y),
                          facecolor = facecolor,
                          fill=True,
                          )
            self.set_patchprops(ret, **kwargs)
        return ret

    def _grab_next_args(self, *args, **kwargs):

        remaining = args
        while 1:
            if len(remaining)==0: return
            if len(remaining)==1:
                yield self._plot_1_arg(remaining[0], **kwargs)
                remaining = []
                continue
            if len(remaining)==2:
                yield self._plot_2_args(remaining, **kwargs)
                remaining = []
                continue
            if len(remaining)==3:
                if not is_string_like(remaining[2]):
                    raise ValueError, 'third arg must be a format string'
                yield self._plot_3_args(remaining, **kwargs)
                remaining=[]
                continue
            if is_string_like(remaining[2]):
                yield self._plot_3_args(remaining[:3], **kwargs)
                remaining=remaining[3:]
            else:
                yield self._plot_2_args(remaining[:2], **kwargs)
                remaining=remaining[2:]
            #yield self._plot_2_args(remaining[:2])
            #remaining=args[2:]

BinOpType=type(zero())
def makeValue(v):
    if type(v) == BinOpType:
        return v
    else:
        return Value(v)


class Axes(Artist):
    """
    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

    """

    scaled = {IDENTITY : 'linear',
              LOG10 : 'log',
              }
    def __init__(self, fig, rect,
                 axisbg = None, # defaults to rc axes.facecolor
                 frameon = True,
                 sharex=None, # use Axes instance's xaxis info
                 sharey=None, # use Axes instance's yaxis info
                 label='',
                 **kwargs
                 ):
        Artist.__init__(self)
        self._position = map(makeValue, rect)
        # must be set before set_figure
        self._sharex = sharex
        self._sharey = sharey
        self.set_label(label)
        self.set_figure(fig)

        # this call may differ for non-sep axes, eg polar
        self._init_axis()


        if axisbg is None: axisbg = rcParams['axes.facecolor']
        self._axisbg = axisbg
        self._frameon = frameon


        self._hold = rcParams['axes.hold']
        self._connected = {} # a dict from events to (id, func)
        self.cla()

        # funcs used to format x and y - fall back on major formatters
        self.fmt_xdata = None
        self.fmt_ydata = None


        self._contourHelper = ContourSupport(self)
        self._contourLabeler = ContourLabeler(self)

        self.set_cursor_props((1,'k')) # set the cursor properties for axes

        # a mapping from artists to 1 -- would use a set but we are
        # python2.2 compliant
        self.animated = {}  
        self._cachedRenderer = None
        self.set_navigate(True)
        if len(kwargs): setp(self, **kwargs)
        
    def _init_axis(self):
        "move this out of __init__ because non-separable axes don't use it"
        self.xaxis = XAxis(self)
        self.yaxis = YAxis(self)

    def 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
        """
        if len(args)==1:
            lw, c = args[0]
        elif len(args)==2:
            lw, c = args
        else:
            raise ValueError('args must be a (linewidth, color) tuple')
        c =colorConverter.to_rgba(c)
        self._cursorProps = lw, c

    def get_cursor_props(self):
        """return the cursor props as a linewidth, color tuple where
        linewidth is a float and color is an RGBA tuple"""
        return self._cursorProps
        
    def set_figure(self, fig):
        """
        Set the Axes figure

        ACCEPTS: a Figure instance
        """
        Artist.set_figure(self, fig)

        l, b, w, h = self._position
        xmin = fig.bbox.ll().x()
        xmax = fig.bbox.ur().x()
        ymin = fig.bbox.ll().y()
        ymax = fig.bbox.ur().y()
        figw = xmax-xmin
        figh = ymax-ymin
        self.left   =  l*figw
        self.bottom =  b*figh
        self.right  =  (l+w)*figw
        self.top    =  (b+h)*figh


        self.bbox = Bbox( Point(self.left, self.bottom),
                          Point(self.right, self.top ),
                          )
        #these will be updated later as data is added
        self._set_lim_and_transforms()

    def _set_lim_and_transforms(self):
        """
        set the dataLim and viewLim BBox attributes and the
        transData and transAxes Transformation attributes
        """


        if self._sharex is not None:
            left=self._sharex.viewLim.ll().x()
            right=self._sharex.viewLim.ur().x()
        else:
            left=zero()
            right=one()
        if self._sharey is not None:
            bottom=self._sharey.viewLim.ll().y()
            top=self._sharey.viewLim.ur().y()
        else:
            bottom=zero()
            top=one()

        self.viewLim = Bbox(Point(left, bottom), Point(right, top))
        self.dataLim = unit_bbox()


        self.transData = get_bbox_transform(self.viewLim, self.bbox)
        self.transAxes = get_bbox_transform(unit_bbox(), self.bbox)

        if self._sharex:
            self.transData.set_funcx(self._sharex.transData.get_funcx())

        if self._sharey:
            self.transData.set_funcy(self._sharey.transData.get_funcy())

    def 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)
        """
        trans = blend_xy_sep_transform( self.transAxes, self.transData)
        l, = self.plot([xmin,xmax], [y,y], transform=trans, **kwargs)
        return l


    def 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)

        """

        trans = blend_xy_sep_transform( self.transData, self.transAxes )
        l, = self.plot([x,x], [ymin,ymax] , transform=trans, **kwargs)
        return l


    def 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)
        """
        trans = blend_xy_sep_transform( self.transAxes, self.transData  )
        verts = (xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin)
        p = Polygon(verts, **kwargs)
        p.set_transform(trans)
        self.add_patch(p)
        return p


    def 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)

        """


        trans = blend_xy_sep_transform( self.transData, self.transAxes   )
        verts = [(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin)]
        p = Polygon(verts, **kwargs)
        p.set_transform(trans)
        self.add_patch(p)

        return p

    def 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
        """
        try: return self.fmt_xdata(x)
        except TypeError:
            func = self.xaxis.get_major_formatter().format_data
            val = func(x)
            return val

    def 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
        """
        try: return self.fmt_ydata(y)
        except TypeError:
            func = self.yaxis.get_major_formatter().format_data
            val =  func(y)
            return val

    def format_coord(self, x, y):
        'return a format string formatting the x, y coord'
        xs = self.format_xdata(x)
        ys = self.format_ydata(y)
        return  'x=%s, y=%s'%(xs,ys)

    def has_data(self):
        'return true if any artists have been added to axes'
        return (
            len(self.collections) +
            len(self.images) +
            len(self.lines) +
            len(self.patches))>0


    def _set_artist_props(self, a):
        'set the boilerplate props for artists added to axes'
        a.set_figure(self.figure)
        if not a.is_transform_set():
            a.set_transform(self.transData)
        a.axes = self

    def cla(self):
        'Clear the current axes'

        self.xaxis.cla()
        self.yaxis.cla()


        if self._sharex is not None:
            self.xaxis.major = self._sharex.xaxis.major
            self.xaxis.minor = self._sharex.xaxis.minor
        if self._sharey is not None:
            self.yaxis.major = self._sharey.yaxis.major
            self.yaxis.minor = self._sharey.yaxis.minor

        self._get_lines = _process_plot_var_args()
        self._get_patches_for_fill = _process_plot_var_args('fill')

        self._gridOn = rcParams['axes.grid']
        self.lines = []
        self.patches = []
        self.texts = []     # text in axis coords
        self.tables = []
        self.artists = []
        self.images = []
        self.legend_ = None
        self.collections = []  # collection.Collection instances

        self.images = []

        self._autoscaleon = True
        
        self.grid(self._gridOn)
        self.title =  Text(
            x=0.5, y=1.02, text='',
            fontproperties=FontProperties(size=rcParams['axes.titlesize']),
            verticalalignment='bottom',
            horizontalalignment='center',
            )
        self.title.set_transform(self.transAxes)

        self._set_artist_props(self.title)

        self.axesPatch = Rectangle(
            xy=(0,0), width=1, height=1,
            facecolor=self._axisbg,
            edgecolor=rcParams['axes.edgecolor'],
            )
        self.axesPatch.set_figure(self.figure)
        self.axesPatch.set_transform(self.transAxes)
        self.axesPatch.set_linewidth(rcParams['axes.linewidth'])
        self.axison = True

    def add_artist(self, a):
        'Add any artist to the axes'
        self.artists.append(a)
        self._set_artist_props(a)

    def add_collection(self, collection):
        'add a Collection instance to Axes'
        self.collections.append(collection)
        self._set_artist_props(collection)
        collection.set_clip_box(self.bbox)

    def get_images(self):
        'return a list of Axes images contained by the Axes'
        return silent_list('AxesImage', self.images)

    def get_xscale(self):
        'return the xaxis scale string: log or linear'
        return self.scaled[self.transData.get_funcx().get_type()]

    def get_yscale(self):
        'return the yaxis scale string: log or linear'
        return self.scaled[self.transData.get_funcy().get_type()]

    def update_datalim(self, xys):
        'Update the data lim bbox with seq of xy tups'
        # if no data is set currently, the bbox will ignore it's
        # limits and set the bound to be the bounds of the xydata.
        # Otherwise, it will compute the bounds of it's current data
        # and the data in xydata
        self.dataLim.update(xys, not self.has_data())
        

    def update_datalim_numerix(self, x, y):
        'Update the data lim bbox with seq of xy tups'
        # if no data is set currently, the bbox will ignore it's
        # limits and set the bound to be the bounds of the xydata.
        # Otherwise, it will compute the bounds of it's current data
        # and the data in xydata
        #print type(x), type(y)
        self.dataLim.update_numerix(x, y, not self.has_data())

    def add_line(self, l):
        'Add a line to the list of plot lines'
        self._set_artist_props(l)
        l.set_clip_box(self.bbox)
        xdata = l.get_xdata(valid_only=True)
        ydata = l.get_ydata(valid_only=True)

        if l.get_transform() != self.transData:
            xys = self._get_verts_in_data_coords(
                l.get_transform(), zip(xdata, ydata))
            xdata = array([x for x,y in xys])
            ydata = array([y for x,y in xys])

        self.update_datalim_numerix( xdata, ydata )
        #self.update_datalim(zip(xdata, ydata))
        self.lines.append(l)

    def _get_verts_in_data_coords(self, trans, xys):
        if trans == self.transData:
            return xys
        # data is not in axis data units.  We must transform it to
        # display and then back to data to get it in data units
        xys = trans.seq_xy_tups(xys)
        return [ self.transData.inverse_xy_tup(xy) for xy in xys]

    def 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.
        """

        self._set_artist_props(p)
        p.set_clip_box(self.bbox)
        xys = self._get_verts_in_data_coords(
            p.get_transform(), p.get_verts())
        #for x,y in xys: print x,y
        self.update_datalim(xys)
        self.patches.append(p)
        
    def add_table(self, tab):
        'Add a table instance to the list of axes tables'
        self._set_artist_props(tab)
        self.tables.append(tab)


    def autoscale_view(self):
        'autoscale the view limits using the data limits'
        # if image data only just use the datalim

        if not self._autoscaleon: return
        if (len(self.images)>0 and
            len(self.lines)==0 and
            len(self.patches)==0):

            self.set_xlim(self.dataLim.intervalx().get_bounds())

            self.set_ylim(self.dataLim.intervaly().get_bounds())
            return


        locator = self.xaxis.get_major_locator()
        self.set_xlim(locator.autoscale())
        locator = self.yaxis.get_major_locator()
        self.set_ylim(locator.autoscale())


    def 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.
        

        """
        if not self._hold: self.cla()
        do_scale = True
        S = 1.0
        if len(args)==0:
            # ( U, V )
            U = asarray(U)
            V = asarray(V)
            X,Y = meshgrid( arange(U.shape[0]), arange(U.shape[1]) )
        elif len(args)==1:
            # ( U, V, S )
            U = asarray(U)
            V = asarray(V)
            X,Y = meshgrid( arange(U.shape[0]), arange(U.shape[1]) )
            S = float(args[0])
            do_scale = ( S != 0.0 )
        elif len(args)==2:
            # ( X, Y, U, V )
            X = asarray(U)
            Y = asarray(V)
            U = asarray(args[0])
            V = asarray(args[1])
        elif len(args)==3:
            # ( X, Y, U, V )
            X = asarray(U)
            Y = asarray(V)
            U = asarray(args[0])
            V = asarray(args[1])
            S = float(args[2])
            do_scale = ( S != 0.0 )

        assert U.shape == V.shape
        assert X.shape == Y.shape
        assert U.shape == X.shape
        
        arrows = []
        N = sqrt( U**2+V**2 )
        if do_scale:
            Nmax = maximum.reduce(maximum.reduce(N))
            U *= (S/Nmax)
            V *= (S/Nmax)
            N /= Nmax
            
        alpha = kwargs.get('alpha', 1.0)
        width = kwargs.get('width', 0.25)
        norm = kwargs.get('norm', None)
        cmap = kwargs.get('cmap', None)
        vmin = kwargs.get('vmin', None)
        vmax = kwargs.get('vmax', None)
        color = kwargs.get('color', None)
        shading = kwargs.get('shading', 'faceted')

        C = None
        I,J = U.shape
        if color is not None and not looks_like_color(color):
            clr = asarray(color)
            if clr.shape==U.shape:
                C = array([ clr[i,j] for i in xrange(I)  for j in xrange(J)])
            elif clr.shape == () and color:
                # a scalar (1, True,...)
                C = array([ N[i,j] for i in xrange(I)  for j in xrange(J)])
            else:
                color = (0.,0.,0.,1.)
        elif color is None:
            color = (0.,0.,0.,1.)
        else:
            color = colorConverter.to_rgba( color, alpha )


        arrows = [ Arrow(X[i,j],Y[i,j],U[i,j],V[i,j],0.1*S ).get_verts()
                   for i in xrange(I) for j in xrange(J) ]
        collection = PolyCollection(
            arrows,
            edgecolors = 'None',
            facecolors = (color,),
            antialiaseds = (0,),
            linewidths = (width,),
            )
        if C is not None:
            collection.set_array( C )
        else:
            collection.set_facecolor( (color,) )
        collection.set_cmap(cmap)
        collection.set_norm(norm)
        if norm is not None:
            collection.set_clim( vmin, vmax )
        self.add_collection( collection )
        lims = asarray(arrows)
        _max = maximum.reduce( maximum.reduce( lims ))
        _min = minimum.reduce( minimum.reduce( lims ))
        self.update_datalim( [ tuple(_min), tuple(_max) ] )
        self.autoscale_view()
        return arrows
                


    def 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
        """
        if not self._hold: self.cla()

        # left = asarray(left) - width/2
        left = asarray(left)
        height = asarray(height)

        patches = []


        # if color looks like a color string, an RGB tuple or a
        # scalar, then repeat it by len(x)
        if (is_string_like(color) or
            (iterable(color) and len(color)==3 and len(left)!=3) or
            not iterable(color)):
            color = [color]*len(left)


        if not iterable(bottom):
            bottom = array([bottom]*len(left), Float)
        else:
            bottom = asarray(bottom)
        if not iterable(width):
            width = array([width]*len(left), Float)
        else:
            width = asarray(width)

        N = len(left)
        assert len(bottom)==N, 'bar arg bottom must be len(left)'
        assert len(width)==N, 'bar arg width must be len(left) or scalar'
        assert len(height)==N, 'bar arg height must be len(left) or scalar'
        assert len(color)==N, 'bar arg color must be len(left) or scalar'

        args = zip(left, bottom, width, height, color)
        for l, b, w, h, c in args:
            if h<0:
                b += h
                h = abs(h)
            r = Rectangle(
                xy=(l, b), width=w, height=h,
                facecolor=c,
                )
            self.add_patch(r)
            patches.append(r)


        if xerr is not None or yerr is not None:
            self.errorbar(
                left+0.5*width, bottom+height,
                yerr=yerr, xerr=xerr,
                fmt=None, ecolor=ecolor, capsize=capsize)
        self.autoscale_view()
        return patches

    def 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
        
        """
        if not self._hold: self.cla()
        holdStatus = self._hold
        lines = []
        x = asarray(x)
        
        # if we've got a vector, reshape it
        rank = len(x.shape)
        if 1 == rank:
            x.shape = -1, 1
        
        row, col = x.shape
        
        # get some plot info
        num_plots = range(1, col + 1)
        box_width = col * min(0.15, 0.5/col)

        # loop through columns, adding each to plot
        self.hold(True)
        for i in num_plots:
            d = x[:,i-1]
            # get median and quartiles
            q1, med, q3 = prctile(d,[25,50,75])
            # get high extreme
            iq = q3 - q1
            hi_val = q3 + whis*iq
            wisk_hi = compress( d <= hi_val , d )
            if len(wisk_hi) == 0:
                wisk_hi = q3
            else:
                wisk_hi = max(wisk_hi)
            # get low extreme
            lo_val = q1 - whis*iq
            wisk_lo = compress( d >= lo_val, d )
            if len(wisk_lo) == 0:
                wisk_lo = q1
            else:
                wisk_lo = min(wisk_lo)
            # get fliers - if we are showing them
            flier_hi = []
            flier_lo = []
            flier_hi_x = []
            flier_lo_x = []
            if len(sym) != 0:
                flier_hi = compress( d > wisk_hi, d )
                flier_lo = compress( d < wisk_lo, d )            
                flier_hi_x = ones(flier_hi.shape[0]) * i
                flier_lo_x = ones(flier_lo.shape[0]) * i            

            # get x locations for fliers, whisker, whisker cap and box sides
            box_x_min = i - box_width * 0.5
            box_x_max = i + box_width * 0.5

            wisk_x = ones(2) * i
            
            cap_x_min = i - box_width * 0.25
            cap_x_max = i + box_width * 0.25
            cap_x = [cap_x_min, cap_x_max]
            
            # get y location for median
            med_y = [med, med]
            
            # calculate 'regular' plot
            if notch == 0:
                # make our box vectors
                box_x = [box_x_min, box_x_max, box_x_max, box_x_min, box_x_min ]
                box_y = [q1, q1, q3, q3, q1 ]                
                # make our median line vectors
                med_x = [box_x_min, box_x_max]
            # calculate 'notch' plot
            else:
                notch_max = med + 1.57*iq/sqrt(row)
                notch_min = med - 1.57*iq/sqrt(row)
                if notch_max > q3:
                    notch_max = q3
                if notch_min < q1:
                    notch_min = q1
                # make our notched box vectors
                box_x = [box_x_min, box_x_max, box_x_max, cap_x_max, box_x_max, box_x_max, box_x_min, box_x_min, cap_x_min, box_x_min, box_x_min ]
                box_y = [q1, q1, notch_min, med, notch_max, q3, q3, notch_max, med, notch_min, q1]
                # make our median line vectors
                med_x = [cap_x_min, cap_x_max]
                med_y = [med, med]
            
            # make a vertical plot . . .
            if 1 == vert:                        
                l = self.plot(wisk_x, [q1, wisk_lo], 'b--',
                              wisk_x, [q3, wisk_hi], 'b--',
                              cap_x, [wisk_hi, wisk_hi], 'k-',
                              cap_x, [wisk_lo, wisk_lo], 'k-',
                              box_x, box_y, 'b-',
                              med_x, med_y, 'r-',
                              flier_hi_x, flier_hi, sym,
                              flier_lo_x, flier_lo, sym )
                lines.extend(l)
            # or perhaps a horizontal plot
            else:
                l = self.plot([q1, wisk_lo], wisk_x, 'b--',
                              [q3, wisk_hi], wisk_x, 'b--',
                              [wisk_hi, wisk_hi], cap_x, 'k-',
                              [wisk_lo, wisk_lo], cap_x, 'k-',
                              box_y, box_x, 'b-',
                              med_y, med_x, 'r-',
                              flier_hi, flier_hi_x, sym,
                              flier_lo, flier_lo_x, sym )
                lines.extend(l)
            

        # fix our axes/ticks up a little
        if 1 == vert:        
            self.set_xlim([0.5, 0.5+col])
            self.set_xticks(num_plots)
        else:
            self.set_ylim([0.5, 0.5+col])
            self.set_yticks(num_plots)
        
        # reset hold status
        self.hold(holdStatus)

        return lines

    def 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
        """
        if not self._hold: self.cla()

        # left = asarray(left) - width/2
        x = asarray(x)
        y = asarray(y)

        patches = []


        # if color looks like a color string, and RGB tuple or a
        # scalar, then repeat it by len(x)
        if (is_string_like(color) or
            (iterable(color) and len(color)==3 and len(left)!=3) or
            not iterable(color)):
            color = [color]*len(x)


        if not iterable(left):
            left = array([left]*len(x), Float)
        else:
            left = asarray(left)
        if not iterable(height):
            height = array([height]*len(x), Float)
        else:
            height = asarray(height)

        N = len(x)
        assert len(left)==N, 'bar arg left must be len(x)'
        assert len(height)==N, 'bar arg height must be len(x) or scalar'
        assert len(y)==N, 'bar arg y must be len(x) or scalar'
        assert len(color)==N, 'bar arg color must be len(x) or scalar'

        width = x
        right = left+x
        bottom = y - height/2.

        args = zip(left, bottom, width, height, color)
        for l, b, w, h, c in args:
            if h<0:
                b += h
                h = abs(h)
            r = Rectangle(
                xy=(l, b), width=w, height=h,
                facecolor=c,
                )
            self.add_patch(r)
            patches.append(r)

        if xerr is not None or yerr is not None:
            self.errorbar(
                right, y,
                yerr=yerr, xerr=xerr,
                fmt=None, ecolor=ecolor, capsize=capsize)
        self.autoscale_view()
        return patches

    def clear(self):
        'clear the axes'
        self.cla()

    def clabel(self, *args, **kwargs):
        return self._contourLabeler.clabel(*args, **kwargs)
    clabel.__doc__ = ContourLabeler.clabel.__doc__

    def contour(self, *args, **kwargs):
        return self._contourHelper.contour(*args, **kwargs)
    contour.__doc__ = ContourSupport.contour.__doc__

    def contourf(self, *args, **kwargs):
        return self._contourHelper.contourf(*args, **kwargs)
    contourf.__doc__ = ContourSupport.contourf.__doc__
    
    
    def 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)
        """
        if not self._hold: self.cla()
        cxy, freqs = matplotlib.mlab.cohere(x, y, NFFT, Fs, detrend, window, noverlap)

        self.plot(freqs, cxy)
        self.set_xlabel('Frequency')
        self.set_ylabel('Coherence')
        self.grid(True)

        return cxy, freqs


    def 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)

        """
        if not self._hold: self.cla()
        pxy, freqs = matplotlib.mlab.csd(x, y, NFFT, Fs, detrend, window, noverlap)
        pxy.shape = len(freqs),
        # pxy is complex

        self.plot(freqs, 10*log10(absolute(pxy)))
        self.set_xlabel('Frequency')
        self.set_ylabel('Cross Spectrum Magnitude (dB)')
        self.grid(True)
        vmin, vmax = self.viewLim.intervaly().get_bounds()

        intv = vmax-vmin
        step = 10*int(log10(intv))

        ticks = arange(math.floor(vmin), math.ceil(vmax)+1, step)
        self.set_yticks(ticks)

        return pxy, freqs

    def 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)        
        """
        assert self._cachedRenderer is not None
        a.draw(self._cachedRenderer)

    def redraw_in_frame(self):
        """
        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)        
        """
        assert self._cachedRenderer is not None
        self.draw(self._cachedRenderer, inframe=True)

    def get_renderer_cache(self):
        return self._cachedRenderer
    
    def draw(self, renderer, inframe=False):
        "Draw everything (plot lines, axes, labels)"
        if not self.get_visible(): return
        renderer.open_group('axes')

        self.transData.freeze()  # eval the lazy objects
        self.transAxes.freeze()  # eval the lazy objects
        if self.axison:
            if self._frameon: self.axesPatch.draw(renderer)

        if len(self.images)==1:
            im = self.images[0]
            im.draw(renderer)
        elif len(self.images)>1:

            # make a composite image blending alpha
            # list of (_image.Image, ox, oy)

            if not allequal([im.origin for im in self.images]):
                raise ValueError('Composite images with different origins not supported')
            else:
                    origin = self.images[0].origin

            ims = [(im.make_image(renderer),0,0) for im in self.images if im.get_visible()]


            im = _image.from_images(self.bbox.height(), self.bbox.width(), ims)
            im.is_grayscale = False
            l, b, w, h = self.bbox.get_bounds()
            ox = l
            oy = self.figure.bbox.height()-(b+h)
            renderer.draw_image(ox, oy, im, origin, self.bbox)

        if self.axison and not inframe:
            self.xaxis.draw(renderer)
            self.yaxis.draw(renderer)



        artists = []
        artists.extend(self.collections)
        artists.extend(self.patches)
        artists.extend(self.lines)
        artists.extend(self.texts)

        # keep track of i to guarantee stable sort for python 2.2
        dsu = [ (a.zorder, i, a) for i, a in enumerate(artists)
                if a not in self.animated]
        dsu.sort()

        for zorder, i, a in dsu:
            a.draw(renderer)

        self.title.draw(renderer)
        if 0: bbox_artist(self.title, renderer)
        # optional artists
        for a in self.artists:
            a.draw(renderer)


        if self.legend_ is not None:
            self.legend_.draw(renderer)


        for table in self.tables:
            table.draw(renderer)

        self.transData.thaw()  # release the lazy objects
        self.transAxes.thaw()  # release the lazy objects
        renderer.close_group('axes')

        self._cachedRenderer = renderer


    def __draw_animate(self):
        # ignore for now; broken
        if self._lastRenderer is None:
            raise RuntimeError('You must first call ax.draw()')
        dsu = [(a.zorder, a) for a in self.animated.keys()]
        dsu.sort()
        renderer = self._lastRenderer
        renderer.blit()
        for tmp, a in dsu:
            a.draw(renderer)

    def 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.
              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.  


        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.
        """
        if not self._hold: self.cla()
        # make sure all the args are iterable arrays
        if not iterable(x): x = asarray([x])
        else: x = asarray(x)

        if not iterable(y): y = asarray([y])
        else: y = asarray(y)

        if xerr is not None:
            if not iterable(xerr): xerr = asarray([xerr])
            else: xerr = asarray(xerr)

        if yerr is not None:
            if not iterable(yerr): yerr = asarray([yerr])
            else: yerr = asarray(yerr)

        l0 = None

        if barsabove and fmt is not None:
            l0, = self.plot(x,y,fmt,**kwargs)

        caplines = []
        barlines = []

        if xerr is not None:
            if len(xerr.shape) == 1:
                left  = x-xerr
                right = x+xerr
            else:
                left  = x-xerr[0]
                right = x+xerr[1]

            barlines.extend( self.hlines(y, x, left) )
            barlines.extend( self.hlines(y, x, right) )
            caplines.extend( self.plot(left, y, '|', ms=2*capsize) )
            caplines.extend( self.plot(right, y, '|', ms=2*capsize) )

        if yerr is not None:
            if len(yerr.shape) == 1:
                lower = y-yerr
                upper = y+yerr
            else:
                lower = y-yerr[0]
                upper = y+yerr[1]

            barlines.extend( self.vlines(x, y, upper ) )
            barlines.extend( self.vlines(x, y, lower ) )
            caplines.extend( self.plot(x, lower, '_', ms=2*capsize) )
            caplines.extend( self.plot(x, upper, '_', ms=2*capsize) )

        if not barsabove and fmt is not None:
            l0, = self.plot(x,y,fmt,**kwargs)

        if ecolor is None and l0 is None:
            ecolor = rcParams['lines.color']
        elif ecolor is None:
            ecolor = l0.get_color()

        for l in barlines:
            l.set_color(ecolor)
        for l in caplines:
            l.set_color(ecolor)
            l.set_markerfacecolor(ecolor)
            l.set_markeredgecolor(ecolor)

        self.autoscale_view()

        ret = silent_list('Line2D errorbar', caplines+barlines)
        return (l0, ret)

    def 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.
        """
        if not self._hold: self.cla()
        patches = []
        for poly in self._get_patches_for_fill(*args, **kwargs):
            self.add_patch( poly )
            patches.append( poly )
        self.autoscale_view()
        return patches

    def get_axis_bgcolor(self):
        'Return the axis background color'
        return self._axisbg

    def get_child_artists(self):
        """
        Return a list of artists the axes contains.  Deprecated
        """
        artists = [self.title, self.axesPatch, self.xaxis, self.yaxis]
        artists.extend(self.lines)
        artists.extend(self.patches)
        artists.extend(self.texts)
        artists.extend(self.collections)
        if self.legend_ is not None:
            artists.append(self.legend_)
        return silent_list('Artist', artists)

    def get_frame(self):
        'Return the axes Rectangle frame'
        return self.axesPatch

    def get_legend(self):
        'Return the Legend instance, or None if no legend is defined'
        return self.legend_


    def get_lines(self):
        'Return a list of lines contained by the Axes'
        return silent_list('Line2D', self.lines)

    def get_xaxis(self):
        'Return the XAxis instance'
        return self.xaxis

    def get_xgridlines(self):
        'Get the x grid lines as a list of Line2D instances'
        return silent_list('Line2D xgridline', self.xaxis.get_gridlines())

    def get_xlim(self):
        'Get the x axis range [xmin, xmax]'
        return self.viewLim.intervalx().get_bounds()


    def get_xticklabels(self):
        'Get the xtick labels as a list of Text instances'
        return silent_list('Text xticklabel', self.xaxis.get_ticklabels())

    def get_xticklines(self):
        'Get the xtick lines as a list of Line2D instances'
        return silent_list('Text xtickline', self.xaxis.get_ticklines())


    def get_xticks(self):
        'Return the x ticks as a list of locations'
        return self.xaxis.get_ticklocs()

    def get_yaxis(self):
        'Return the YAxis instance'
        return self.yaxis

    def get_ylim(self):
        'Get the y axis range [ymin, ymax]'
        return self.viewLim.intervaly().get_bounds()

    def get_ygridlines(self):
        'Get the y grid lines as a list of Line2D instances'
        return silent_list('Line2D ygridline', self.yaxis.get_gridlines())

    def get_yticklabels(self):
        'Get the ytick labels as a list of Text instances'
        return silent_list('Text yticklabel', self.yaxis.get_ticklabels())

    def get_yticklines(self):
        'Get the ytick lines as a list of Line2D instances'
        return silent_list('Line2D ytickline', self.yaxis.get_ticklines())

    def get_yticks(self):
        'Return the y ticks as a list of locations'
        return self.yaxis.get_ticklocs()

    def get_frame_on(self):
        """
        Get whether the axes rectangle patch is drawn
        """
        return self._frameon

    def get_navigate(self):
        """
        Get whether the axes responds to navigation commands
        """
        return self._navigate


    def get_autoscale_on(self):
        """
        Get whether autoscaling is applied on plot commands
        """
        return self._autoscaleon

    def grid(self, b=None):
        """
        Set the axes grids on or off; b is a boolean
        if b is None, toggle the grid state
        """
        self.xaxis.grid(b)
        self.yaxis.grid(b)

    def hist(self, x, bins=10, normed=0, bottom=0,
             orientation='vertical', **kwargs):
        """
        HIST(x, bins=10, normed=0, bottom=0, orientiation='vertical', **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 distribtion, ie,
        n/(len(x)*dbin)


        orientation = 'horizontal' | 'vertical'.  If horizontal, barh
        will be used and the "bottom" kwarg will be the left.

        kwargs are used to update the properties of the
        hist bars
        """
        if not self._hold: self.cla()
        n,bins = matplotlib.mlab.hist(x, bins, normed)
        width = 0.9*(bins[1]-bins[0])
        if orientation=='horizontal':
            patches = self.barh(n, bins, height=width, left=bottom)
        else:
            patches = self.bar(bins, n, width=width, bottom=bottom)
        for p in patches:
            p.update(kwargs)
        return n, bins, silent_list('Patch', patches)

    def 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
            
        """
        if b is None: self._hold = not self._hold
        else: self._hold = b

    def set_frame_on(self, b):
        """
        Set whether the axes rectangle patch is drawn

        ACCEPTS: True|False
        """
        self._frameon = b

    def set_navigate(self, b):
        """
        Set whether the axes responds to navigation toolbar commands

        ACCEPTS: True|False
        """
        self._navigate = b

    def set_autoscale_on(self, b):
        """
        Set whether the axes rectangle patch is drawn

        ACCEPTS: True|False
        """
        self._autoscaleon = b

    def 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):
        """

        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: free or preserve.  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 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

          * 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'

    """

        if not self._hold: self.cla()

        if norm is not None: assert(isinstance(norm, normalize))
        if cmap is not None: assert(isinstance(cmap, Colormap))

        im = AxesImage(self, cmap, norm, aspect, interpolation, origin, extent,
                       filternorm=filternorm,
                       filterrad=filterrad)
        if norm is None and shape is None:
            im.set_clim(vmin, vmax)

        im.set_data(X)
        im.set_alpha(alpha)

        xmin, xmax, ymin, ymax = im.get_extent()

        corners = (xmin, ymin), (xmax, ymax)
        self.update_datalim(corners)
        self.set_xlim((xmin, xmax))
        self.set_ylim((ymin, ymax))
        self.images.append(im)

        return im

    def in_axes(self, xwin, ywin):
        'return True is the point xwin, ywin (display coords) are in the Axes'
        return self.bbox.contains(xwin, ywin)

    def 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
        """
        linestyle, marker, color = _process_plot_format(fmt)

        # todo: fix me for y is scalar and xmin and xmax are iterable
        y = asarray(y)
        xmin = asarray(xmin)
        xmax = asarray(xmax)

        if len(xmin)==1:
            xmin = xmin*ones(y.shape, y.typecode())
        if len(xmax)==1:
            xmax = xmax*ones(y.shape, y.typecode())

        if len(xmin)!=len(y):
            raise ValueError, 'xmin and y are unequal sized sequences'
        if len(xmax)!=len(y):
            raise ValueError, 'xmax and y are unequal sized sequences'

        lines = []
        for (thisY, thisMin, thisMax) in zip(y,xmin,xmax):
            line = Line2D(
                [thisMin, thisMax], [thisY, thisY],
                color=color, linestyle=linestyle, marker=marker,
                )
            self.add_line( line )
            lines.append(line)
        return lines


    def 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'). If label is set to '_nolegend_', the item will not be shown
          in legend.

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

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

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

        The location codes are

          'best' : 0,          
          'upper right'  : 1, (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 get_handles():
            handles = self.lines
            handles.extend(self.patches)
            handles.extend([c for c in self.collections if isinstance(c, LineCollection)])
            return handles


        if len(args)==0:
            handles = []
            labels = []
            for line in get_handles():
                label = line.get_label()
                if label != '_nolegend_':
                    handles.append(line)
                    labels.append(label)
            loc = popd(kwargs, 'loc', 1)

        elif len(args)==1:
            # LABELS
            labels = args[0]
            handles = [h for h, label in zip(get_handles(), labels)]
            loc = popd(kwargs, 'loc', 1)

        elif len(args)==2:
            if is_string_like(args[1]) or isinstance(args[1], int):
                # LABELS, LOC
                labels, loc = args
                handles = [h for h, label in zip(get_handles(), labels)]
            else:
                # LINES, LABELS
                handles, labels = args
                loc = popd(kwargs, 'loc', 1)

        elif len(args)==3:
            # LINES, LABELS, LOC
            handles, labels, loc = args
        else:
            raise RuntimeError('Invalid arguments to legend')


        handles = flatten(handles)
        self.legend_ = Legend(self, handles, labels, loc, **kwargs)
        return self.legend_

    def 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
        """
        if not self._hold: self.cla()

        dx = {'basex': kwargs.get('basex', 10),
              'subsx': kwargs.get('subsx', None),
              }
        dy = {'basey': kwargs.get('basey', 10),
              'subsy': kwargs.get('subsy', None),
              }

        self.set_xscale('log', **dx)
        self.set_yscale('log', **dy)
        dict_delall( kwargs, ('basex', 'basey', 'subsx', 'subsy'))

        b =  self._hold
        self._hold = True # we've already processed the hold
        l = self.plot(*args, **kwargs)
        self._hold = b    # restore the hold

        return l


    def panx(self, numsteps):
        'Pan the x axis numsteps (plus pan right, minus pan left)'
        self.xaxis.pan(numsteps)
        xmin, xmax = self.viewLim.intervalx().get_bounds()
        for line in self.lines:
            line.set_xclip(xmin, xmax)
        self._send_xlim_event()

    def pany(self, numsteps):
        'Pan the x axis numsteps (plus pan up, minus pan down)'
        self.yaxis.pan(numsteps)
        self._send_ylim_event()



    def 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

        X,Y and C may be masked arrays.  If either C[i,j], or one
        of the vertices surrounding C[i,j] (X or Y at [i,j],[i+1,j],
        [i,j+1],[i=1,j+1]) is masked, nothing is plotted.

        Optional 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
        """
        if not self._hold: self.cla()

        alpha = kwargs.get('alpha', 1.0)
        norm = kwargs.get('norm', None)
        cmap = kwargs.get('cmap', None)
        vmin = kwargs.get('vmin', None)
        vmax = kwargs.get('vmax', None)
        shading = kwargs.get('shading', 'faceted')

        if len(args)==1:
            C = args[0]
            numRows, numCols = C.shape
            X, Y = meshgrid(arange(numCols+1), arange(numRows+1) )
        elif len(args)==3:
            X, Y, C = args
        else:
            raise TypeError, 'Illegal arguments to pcolor; see help(pcolor)'

        Nx, Ny = X.shape

        # convert to MA, if necessary.
        C = ma.asarray(C)
        X = ma.asarray(X)
        Y = ma.asarray(Y)
        mask = ma.getmaskarray(X)+ma.getmaskarray(Y)
        xymask = mask[0:-1,0:-1]+mask[1:,1:]+mask[0:-1,1:]+mask[1:,0:-1]
        # don't plot if C or any of the surrounding vertices are masked.
        mask = ma.getmaskarray(C)[0:Nx-1,0:Ny-1]+xymask

        X1 = compress(ravel(mask==0),ravel(ma.filled(X[0:-1,0:-1])))
        Y1 = compress(ravel(mask==0),ravel(ma.filled(Y[0:-1,0:-1])))
        X2 = compress(ravel(mask==0),ravel(ma.filled(X[1:,0:-1])))
        Y2 = compress(ravel(mask==0),ravel(ma.filled(Y[1:,0:-1])))
        X3 = compress(ravel(mask==0),ravel(ma.filled(X[1:,1:])))
        Y3 = compress(ravel(mask==0),ravel(ma.filled(Y[1:,1:])))
        X4 = compress(ravel(mask==0),ravel(ma.filled(X[0:-1,1:])))
        Y4 = compress(ravel(mask==0),ravel(ma.filled(Y[0:-1,1:])))
        verts = zip(zip(X1,Y1),zip(X2,Y2),zip(X3,Y3),zip(X4,Y4))

        C = compress(ravel(mask==0),ravel(ma.filled(C[0:Nx-1,0:Ny-1])))

        if shading == 'faceted':
            edgecolors =  (0,0,0,1),
        else:
            edgecolors = 'None'

        collection = PolyCollection(
            verts,
            edgecolors   = edgecolors,
            antialiaseds = (0,),
            linewidths   = (0.25,),
            )

        collection.set_alpha(alpha)
        collection.set_array(C)
        if norm is not None: assert(isinstance(norm, normalize))
        if cmap is not None: assert(isinstance(cmap, Colormap))
        collection.set_cmap(cmap)
        collection.set_norm(norm)

        if norm is not None:
            collection.set_clim(vmin, vmax)

        self.grid(False)

        x = X.compressed()
        y = Y.compressed()
        minx = amin(x)
        maxx = amax(x)
        miny = amin(y)
        maxy = amax(y)

        corners = (minx, miny), (maxx, maxy)
        self.update_datalim( corners)
        self.autoscale_view()

        # add the collection last
        self.add_collection(collection)
        return collection

    def 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
        """

        if not self._hold: self.cla()
        shading = kwargs.get('shading', 'faceted')
        cmap = kwargs.get('cmap', cm.get_cmap())
        norm = kwargs.get('norm', normalize())
        alpha = kwargs.get('alpha', 1.0)

        if len(args)==1:
            C = args[0]
            numRows, numCols = C.shape
            X, Y = meshgrid(arange(numCols+1), arange(numRows+1) )
        elif len(args)==3:
            X, Y, C = args
        else:
            raise TypeError, 'Illegal arguments to pcolor; see help(pcolor)'

        Nx, Ny = X.shape

        if isinstance(norm, normalize) and not norm.scaled():
            norm.autoscale(C)

        nc = norm(C)
        RGBA = cmap(nc, alpha)

        patches = []

        for i in range(Nx-1):
            for j in range(Ny-1):
                color = tuple(RGBA[i,j,:3])
                left = X[i,j]
                bottom = Y[i,j]
                width = X[i,j+1]-left
                height = Y[i+1,j]-bottom
                rect = Rectangle(
                    (left, bottom), width, height,
                    )
                rect.set_facecolor(color)
                rect.set_alpha(RGBA[i,j,3])
                if shading == 'faceted':
                    rect.set_linewidth(0.25)
                    rect.set_edgecolor('k')
                else:
                    rect.set_edgecolor(color)
                self.patches.append(rect)
                rect.set_figure(self.figure)
                rect.set_transform(self.transData)
                patches.append(rect)
        self.grid(0)

        x = ravel(X)
        y = ravel(Y)
        minx = amin(x)
        maxx = amax(x)
        miny = amin(y)
        maxy = amax(y)

        corners = (minx, miny), (maxx, maxy)


        self.update_datalim( corners)
        self.autoscale_view()
        return patches


    def 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
  """
        self.set_frame_on(False)

        x = asarray(x).astype(Float32)

        sx = float(asum(x))
        if sx>1: x = divide(x,sx)

        if labels is None: labels = ['']*len(x)
        if explode is None: explode = [0]*len(x)
        assert(len(x)==len(labels))
        assert(len(x)==len(explode))


        center = 0,0
        radius = 1
        theta1 = 0
        i = 0
        texts = []
        slices = []
        autotexts = []
        for frac, label, expl in zip(x,labels, explode):
            x, y = center
            theta2 = theta1 + frac
            thetam = 2*math.pi*0.5*(theta1+theta2)
            x += expl*math.cos(thetam)
            y += expl*math.sin(thetam)

            w = Wedge((x,y), radius, 360.*theta1, 360.*theta2,
                      facecolor=colors[i%len(colors)])
            slices.append(w)
            self.add_patch(w)
            self.set_label(label)

            if shadow:
                # make sure to add a shadow after the call to
                # add_patch so the figure and transform props will be
                # set
                shad = Shadow(w, -0.02, -0.02,
                              #props={'facecolor':w.get_facecolor()}
                              )
                shad.set_zorder(0.9*w.get_zorder())
                self.add_patch(shad)


            xt = x + 1.1*radius*math.cos(thetam)
            yt = y + 1.1*radius*math.sin(thetam)

            t = self.text(xt, yt, label,
                          size=rcParams['tick.labelsize'],
                          horizontalalignment='center',
                          verticalalignment='center')

            texts.append(t)

            if autopct is not None:
                xt = x + 0.6*radius*math.cos(thetam)
                yt = y + 0.6*radius*math.sin(thetam)
                if is_string_like(autopct):
                    s = autopct%(100.*frac)
                elif callable(autopct):
                    s = autopct(100.*frac)
                else:
                    raise TypeError('autopct must be callable or a format string')

                t = self.text(xt, yt, s,
                              horizontalalignment='center',
                              verticalalignment='center')
                autotexts.append(t)


            theta1 = theta2
            i += 1

        self.set_xlim((-1.25, 1.25))
        self.set_ylim((-1.25, 1.25))
        self.set_xticks([])
        self.set_yticks([])

        if autopct is None: return slices, texts
        else: return slices, texts, autotexts

    def 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, antialised=False)

        Neither line will be antialiased.
        """

        if not self._hold: self.cla()
        lines = []
        for line in self._get_lines(*args, **kwargs):
            self.add_line(line)
            lines.append(line)
        lines = [line for line in lines] # consume the generator
        self.autoscale_view()
        return lines

    def plot_date(self, d, y, fmt='bo', tz=None, **kwargs):
        """
        PLOT_DATE(d, y, 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
        """

        if not matplotlib._havedate:
            raise SystemExit('plot_date: no dates support - dates require python2.3')

        if not self._hold: self.cla()

        ret = self.plot(d, y, fmt, **kwargs)

        span  = self.dataLim.intervalx().span()

        if span==0: span = 1/24.
        
        minutes = span*24*60
        hours  = span*24
        days   = span
        weeks  = span/7.
        months = span/31. # approx
        years  = span/365.

        numticks = 5
        if years>numticks:
            locator = YearLocator(int(years/numticks), tz=tz)  # define
            fmt = '%Y'
        elif months>numticks:
            locator = MonthLocator(tz=tz)
            fmt = '%b %Y'
        elif weeks>numticks:
            locator = WeekdayLocator(tz=tz)
            fmt = '%a, %b %d'
        elif days>numticks:
            locator = DayLocator(interval=int(math.ceil(days/numticks)), tz=tz)
            fmt = '%b %d'
        elif hours>numticks:
            locator = HourLocator(interval=int(math.ceil(hours/numticks)), tz=tz)
            fmt = '%H:%M\n%b %d'
        elif minutes>numticks:
            locator = MinuteLocator(interval=int(math.ceil(minutes/numticks)), tz=tz)
            fmt = '%H:%M:%S'
        else:
            locator = MinuteLocator(tz=tz)
            fmt = '%H:%M:%S'


        formatter = DateFormatter(fmt, tz=tz)
        self.xaxis.set_major_locator(locator)
        self.xaxis.set_major_formatter(formatter)
        self.autoscale_view()

        return ret

    def 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)

        """
        if not self._hold: self.cla()
        pxx, freqs = matplotlib.mlab.psd(x, NFFT, Fs, detrend, window, noverlap)
        pxx.shape = len(freqs),

        self.plot(freqs, 10*log10(pxx))
        self.set_xlabel('Frequency')
        self.set_ylabel('Power Spectrum (dB)')
        self.grid(True)
        vmin, vmax = self.viewLim.intervaly().get_bounds()
        intv = vmax-vmin
        logi = int(log10(intv))
        if logi==0: logi=.1
        step = 10*logi
        #print vmin, vmax, step, intv, math.floor(vmin), math.ceil(vmax)+1
        ticks = arange(math.floor(vmin), math.ceil(vmax)+1, step)
        self.set_yticks(ticks)

        return pxx, freqs

    def get_position(self):
        'Return the axes rectangle left, bottom, width, height'
        return [val.get() for val in self._position]

    def 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
        """
        for num,val in zip(pos, self._position):
            val.set(num)

    def 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.
        """
        if not self._hold: self.cla()
        markerline, = self.plot(x, y, markerfmt)

        stemlines = []
        for thisx, thisy in zip(x, y):
            l, = self.plot([thisx,thisx], [0, thisy], linefmt)
            stemlines.append(l)

        baseline, = self.plot([amin(x), amax(x)], [0,0], basefmt)
        return markerline, stemlines, baseline


    def set_axis_off(self):
        """
        turn off the axis

        ACCEPTS: void
        """

        self.axison = False

    def set_axis_on(self):
        """
        turn on the axis

        ACCEPTS: void
        """

        self.axison = True

    def scatter(self, x, y, s=20, c='b', marker='o', cmap=None, norm=None,
                    vmin=None, vmax=None, alpha=1.0, linewidths=None,
                    faceted=True,
                    **kwargs):
        """
    SCATTER(x, y, s=20, c='b', marker='o', cmap=None, norm=None,
            vmin=None, vmax=None, alpha=1.0, linewidths=None,
            faceted=True, **kwargs)        
        Supported function signatures:

            SCATTER(x, y) - 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

          * linewidths, if None, defaults to (lines.linewidth,).  Note
            that this is a tuple, and if you set the linewidths
            argument you must set it as a sequence of floats, as
            required by PolyCollection -- see
            matplotlib.collections.PolyCollection for details

         * faceted: if True, will use the default edgecolor for the
           markers.  If False, will set the edgecolors to be the same
           as the facecolors
           """

        if not self._hold: self.cla()

        syms =  { # a dict from symbol to (numsides, angle)
            's' : (4, math.pi/4.0),  # square
            'o' : (20, 0),           # circle
            '^' : (3,0),             # triangle up
            '>' : (3,math.pi/2.0),   # triangle right
            'v' : (3,math.pi),       # triangle down
            '<' : (3,3*math.pi/2.0), # triangle left
            'd' : (4,0),             # diamond
            'p' : (5,0),             # pentagram
            'h' : (6,0),             # hexagon
            '8' : (8,0),             # octagon
            }



        if not syms.has_key(marker):
            raise ValueError('Unknown marker symbol to scatter')


        numsides, rotation = syms[marker]


        if not is_string_like(c) and iterable(c) and len(c)==len(x):
            colors = None
        else:
            colors = ( colorConverter.to_rgba(c, alpha), )


        if not iterable(s):
            scales = (s,)
        else:
            scales = s

        if faceted: edgecolors = None
        else: edgecolors = 'None'
        
        collection = RegularPolyCollection(
            self.figure.dpi,
            numsides, rotation, scales,
            facecolors = colors,
            edgecolors = edgecolors,
            linewidths = linewidths,
            offsets = zip(x,y),
            transOffset = self.transData,
            )
        collection.set_alpha(alpha)
        collection.update(kwargs)

        if colors is None:
            if norm is not None: assert(isinstance(norm, normalize))
            if cmap is not None: assert(isinstance(cmap, Colormap))
            collection.set_array(c)
            collection.set_cmap(cmap)
            collection.set_norm(norm)

            if norm is None:
                collection.set_clim(vmin, vmax)

        minx = amin(x)
        maxx = amax(x)
        miny = amin(y)
        maxy = amax(y)

        w = maxx-minx
        h = maxy-miny

        # the pad is a little hack to deal with the fact that we don't
        # want to transform all the symbols whose scales are in points
        # to data coords to get the exact bounding box for efficiency
        # reasons.  It can be done right if this is deemed important
        padx, pady = 0.05*w, 0.05*h
        corners = (minx-padx, miny-pady), (maxx+padx, maxy+pady)
        self.update_datalim( corners)
        self.autoscale_view()

        # add the collection last
        self.add_collection(collection)
        return collection


    def 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
        """
        if not self._hold: self.cla()
        if is_string_like(c):
            c = [c]*len(x)
        elif not iterable(c):
            c = [c]*len(x)
        else:
            norm = normalize()
            norm(c)
            c = cm.jet(c)

        if s is None:
            s = [abs(0.015*(amax(y)-amin(y)))]*len(x)
        elif not iterable(s):
            s = [s]*len(x)

        if len(c)!=len(x):
            raise ValueError, 'c and x are not equal lengths'
        if len(s)!=len(x):
            raise ValueError, 's and x are not equal lengths'

        patches = []
        for thisX, thisY, thisS, thisC in zip(x,y,s,c):
            #print thisX, thisY, thisS, thisC
            circ = Circle( (thisX, thisY),
                           radius=thisS,
                           )
            circ.set_facecolor(thisC)
            self.add_patch(circ)
            patches.append(circ)
        self.autoscale_view()
        return patches

    def 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

        """
        # hi fernando
        if not self._hold: self.cla()
        d = {'basex': kwargs.get('basex', 10),
             'subsx': kwargs.get('subsx', None),
             }

        self.set_xscale('log', **d)
        dict_delall( kwargs, ('basex', 'subsx'))
        b =  self._hold
        self._hold = True # we've already processed the hold
        l = self.plot(*args, **kwargs)
        self._hold = b    # restore the hold
        return l


    def 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

        """
        # hi fernando
        if not self._hold: self.cla()

        d = {'basey': kwargs.get('basey', 10),
             'subsy': kwargs.get('subsy', None),
             }
        self.set_yscale('log', **d)
        dict_delall( kwargs, ('basey', 'subsy'))
        b =  self._hold
        self._hold = True # we've already processed the hold
        l = self.plot(*args, **kwargs)
        self._hold = b    # restore the hold

        return l


    def set_axis_bgcolor(self, color):
        """
        set the axes background color

        ACCEPTS: any matplotlib color - see help(colors)
        """

        self._axisbg = color
        self.axesPatch.set_facecolor(color)

    def 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
        """
        default = {
            'fontsize':rcParams['axes.titlesize'],
            'verticalalignment' : 'bottom',
            'horizontalalignment' : 'center'
            }

        self.title.set_text(label)
        self.title.update(default)
        if fontdict is not None: self.title.update(fontdict)
        self.title.update(kwargs)
        return self.title

    def 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
        """

        label = self.xaxis.get_label()
        label.set_text(xlabel)
        if fontdict is not None: label.update(fontdict)
        label.update(kwargs)
        return label

    def _send_xlim_event(self):
        for cid, func in self._connected.get('xlim_changed', []):
            func(self)

    def _send_ylim_event(self):
        for cid, func in self._connected.get('ylim_changed', []):
            func(self)


    def set_xlim(self, *args, **kwargs):    
        """
        set_xlim(self, *args, **kwargs):

        Set the limits for the xaxis; v = [xmin, xmax]
        
        set_xlim((valmin, valmax))
        set_xlim(valmin, valmax)
        set_xlim(xmin=1) # xmax unchanged
        set_xlim(xmax=1) # xmin unchanged

        Valid kwargs:

        xmin : the min of the xlim
        xmax : the max of the xlim        
        emit : notify observers of lim change
        

        Returns the current xlimits as a length 2 tuple

        ACCEPTS: len(2) sequence of floats
        """
        
        vmin, vmax = self.get_xlim()

        xmin = popd(kwargs, 'xmin', None)
        xmax = popd(kwargs, 'xmax', None)
        emit = popd(kwargs, 'emit', False)                
        if len(args)!=0 and (xmin is not None or xmax is not None):
            raise TypeError('You cannot pass args and xmin/xmax kwargs')

        if len(args)==0:
            if xmin is not None: vmin = xmin
            if xmax is not None: vmax = xmax
        elif len(args)==1:
            vmin, vmax = args[0]
        elif len(args)==2:
            vmin, vmax = args
        else:
            raise ValueError('args must be length 0, 1 or 2')
        
            
        
        if self.transData.get_funcx().get_type()==LOG10 and min(vmin, vmax)<=0:
            raise ValueError('Cannot set nonpositive limits with log transform')
        
        self.viewLim.intervalx().set_bounds(vmin, vmax)
        if emit: self._send_xlim_event()
        return vmin, vmax

    def 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
        """

        #if subsx is None: subsx = range(2, basex)
        assert(value.lower() in ('log', 'linear', ))
        if value == 'log':
            self.xaxis.set_major_locator(LogLocator(basex))
            self.xaxis.set_major_formatter(LogFormatterMathtext(basex))
            self.xaxis.set_minor_locator(LogLocator(basex,subsx))
            self.transData.get_funcx().set_type(LOG10)
            minx, maxx = self.get_xlim()
            if min(minx, maxx)<=0:
                self.autoscale_view()
        elif value == 'linear':
            self.xaxis.set_major_locator(AutoLocator())
            self.xaxis.set_major_formatter(ScalarFormatter())
            self.xaxis.set_minor_locator(NullLocator())
            self.xaxis.set_minor_formatter(NullFormatter())
            self.transData.get_funcx().set_type( IDENTITY )

    def 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
        """
        return self.xaxis.set_ticklabels(labels, fontdict, **kwargs)

    def set_xticks(self, ticks):
        """
        Set the x ticks with list of ticks

        ACCEPTS: sequence of floats
        """
        return self.xaxis.set_ticks(ticks)


    def 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
        """
        label = self.yaxis.get_label()
        label.set_text(ylabel)

        if fontdict is not None: label.update(fontdict)
        label.update(kwargs)
        return label


    def set_ylim(self, *args, **kwargs):    
        """
        set_ylim(self, *args, **kwargs):

        Set the limits for the yaxis; v = [ymin, ymax]
        
        set_ylim((valmin, valmax))
        set_ylim(valmin, valmax)
        set_ylim(ymin=1) # ymax unchanged
        set_ylim(ymax=1) # ymin unchanged

        Valid kwargs:

        ymin : the min of the ylim
        ymax : the max of the ylim        
        emit : notify observers of lim change
        

        Returns the current ylimits as a length 2 tuple

        ACCEPTS: len(2) sequence of floats
        """
        
        vmin, vmax = self.get_ylim()

        ymin = popd(kwargs, 'ymin', None)
        ymax = popd(kwargs, 'ymax', None)
        emit = popd(kwargs, 'emit', False)                
        if len(args)!=0 and (ymin is not None or ymax is not None):
            raise TypeError('You cannot pass args and ymin/ymax kwargs')

        if len(args)==0:
            if ymin is not None: vmin = ymin
            if ymax is not None: vmax = ymax
        elif len(args)==1:
            vmin, vmax = args[0]
        elif len(args)==2:
            vmin, vmax = args
        else:
            raise ValueError('args must be length 0, 1 or 2')
        
            
        
        if self.transData.get_funcy().get_type()==LOG10 and min(vmin, vmax)<=0:
            raise ValueError('Cannot set nonpositive limits with log transform')
        
        self.viewLim.intervaly().set_bounds(vmin, vmax)
        if emit: self._send_ylim_event()
        return vmin, vmax

    def 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
        """

        #if subsy is None: subsy = range(2, basey)
        assert(value.lower() in ('log', 'linear', ))

        if value == 'log':
            self.yaxis.set_major_locator(LogLocator(basey))
            self.yaxis.set_major_formatter(LogFormatterMathtext(basey))
            self.yaxis.set_minor_locator(LogLocator(basey,subsy))
            self.transData.get_funcy().set_type(LOG10)
            miny, maxy = self.get_ylim()
            if min(miny, maxy)<=0:
                self.autoscale_view()

        elif value == 'linear':
            self.yaxis.set_major_locator(AutoLocator())
            self.yaxis.set_major_formatter(ScalarFormatter())
            self.yaxis.set_minor_locator(NullLocator())
            self.yaxis.set_minor_formatter(NullFormatter())
            self.transData.get_funcy().set_type( IDENTITY )

    def 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
        """
        return self.yaxis.set_ticklabels(labels, fontdict, **kwargs)

    def set_yticks(self, ticks):
        """
        Set the y ticks with list of ticks

        ACCEPTS: sequence of floats
        """
        return self.yaxis.set_ticks(ticks)


    def 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.
    """
        if not self._hold: self.cla()

        Pxx, freqs, bins = matplotlib.mlab.specgram(x, NFFT, Fs, detrend,
             window, noverlap)


        Z = 10*log10(Pxx)
        Z =  flipud(Z)

        if xextent is None: xextent = 0, amax(bins)
        xmin, xmax = xextent
        extent = xmin, xmax, 0, amax(freqs)
        im = self.imshow(Z, cmap, extent=extent)

        return Pxx, freqs, bins, im

    def ishold(self):
        'return the HOLD status of the axes'
        return self._hold

    def 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
        """
        x,y,z = matplotlib.mlab.get_xyz_where(Z, Z>0)
        return self.plot(x+0.5,y+0.5, linestyle='None',
                         marker=marker,markersize=markersize, **kwargs)

    def spy2(self, Z):
        """
        SPY2(Z) plots the sparsity pattern of the matrix S as an image

        The image instance is returned
        """

        #binary colormap min white, max black
        cmapdata = {
             'red'  :  ((0., 1., 1.), (1., 0., 0.)),
             'green':  ((0., 1., 1.), (1., 0., 0.)),
             'blue' :  ((0., 1., 1.), (1., 0., 0.))
             }
        binary =  LinearSegmentedColormap('binary',  cmapdata, 2)
        Z = where(Z>0,1.,0.)
        return self.imshow(transpose(Z), interpolation='nearest', cmap=binary)

    def 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.
        """
        # Check we have some cellText
        if cellText is None:
            # assume just colours are needed
            rows = len(cellColours)
            cols = len(cellColours[0])
            cellText = [[''] * rows] * cols

        rows = len(cellText)
        cols = len(cellText[0])
        for row in cellText:
            assert len(row) == cols

        if cellColours is not None:
            assert len(cellColours) == rows
            for row in cellColours:
                assert len(row) == cols
        else:
            cellColours = ['w' * cols] * rows

        # Set colwidths if not given
        if colWidths is None:
            colWidths = [1.0/cols] * cols

        # Check row and column labels
        rowLabelWidth = 0
        if rowLabels is None:
            if rowColours is not None:
                rowLabels = [''] * cols
                rowLabelWidth = colWidths[0]
        elif rowColours is None:
            rowColours = 'w' * rows

        if rowLabels is not None:
            assert len(rowLabels) == rows

        offset = 0
        if colLabels is None:
            if colColours is not None:
                colLabels = [''] * rows
                offset = 1
        elif colColours is None:
            colColours = 'w' * cols
            offset = 1

        if rowLabels is not None:
            assert len(rowLabels) == rows

        # Set up cell colours if not given
        if cellColours is None:
            cellColours = ['w' * cols] * rows

        # Now create the table
        table = Table(self, loc, bbox)
        height = table._approx_text_height()

        # Add the cells
        for row in xrange(rows):
            for col in xrange(cols):
                table.add_cell(row+offset, col,
                               width=colWidths[col], height=height,
                               text=cellText[row][col],
                               facecolor=cellColours[row][col],
                               loc=cellLoc)
        # Do column labels
        if colLabels is not None:
            for col in xrange(cols):
                table.add_cell(0, col,
                               width=colWidths[col], height=height,
                               text=colLabels[col], facecolor=colColours[col],
                               loc=colLoc)

        # Do row labels
        if rowLabels is not None:
            for row in xrange(rows):
                table.add_cell(row+offset, -1,
                               width=rowLabelWidth, height=height,
                               text=rowLabels[row], facecolor=rowColours[row],
                               loc=rowLoc)
            if rowLabelWidth == 0:
                table.auto_set_column_width(-1)

        self.add_table(table)
        return table


    def text(self, x, y, s, fontdict=None,
             withdash=False, **kwargs):
        """
        TEXT(x, y, s, fontdict=None, **kwargs)

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

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

          withdash=True will create a TextWithDash instance instead
          of a Text instance.

        Individual keyword 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,
            )

        """
        default = {
            'verticalalignment' : 'bottom',
            'horizontalalignment' : 'left',
            #'verticalalignment' : 'top',
            'transform' : self.transData,
            }

        # At some point if we feel confident that TextWithDash
        # is robust as a drop-in replacement for Text and that
        # the performance impact of the heavier-weight class
        # isn't too significant, it may make sense to eliminate
        # the withdash kwarg and simply delegate whether there's
        # a dash to TextWithDash and dashlength.
        if withdash:
            t = TextWithDash(
                x=x, y=y, text=s,
                )
        else:
            t = Text(
                x=x, y=y, text=s,
                )
        self._set_artist_props(t)

        t.update(default)
        if fontdict is not None: t.update(fontdict)
        t.update(kwargs)
        self.texts.append(t)

        if t.get_clip_on():  t.set_clip_box(self.bbox)
        return t


    def toggle_log_lineary(self):
        'toggle between log and linear on the y axis'
        funcy = self.transData.get_funcy().get_type()
        if funcy==LOG10: self.set_yscale('linear')
        elif funcy==IDENTITY: self.set_yscale('log')

    def 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
        """
        x = asarray(x)
        ymin = asarray(ymin)
        ymax = asarray(ymax)

        if len(ymin)==1:
            ymin = ymin*ones(x.shape, x.typecode())
        if len(ymax)==1:
            ymax = ymax*ones(x.shape, x.typecode())


        if len(ymin)!=len(x):
            raise ValueError, 'ymin and x are unequal sized sequences'
        if len(ymax)!=len(x):
            raise ValueError, 'ymax and x are unequal sized sequences'

        Y = transpose(array([ymin, ymax]))
        lines = []
        for thisX, thisY in zip(x,Y):
            line = Line2D(
                [thisX, thisX], thisY, color=color, linestyle='-',
                )
            self.add_line(line)
            lines.append(line)
        return lines


    def zoomx(self, numsteps):
        'Zoom in on the x xaxis numsteps (plus for zoom in, minus for zoom out)'
        self.xaxis.zoom(numsteps)
        xmin, xmax = self.viewLim.intervalx().get_bounds()
        for line in self.lines:
            line.set_xclip(xmin, xmax)
        self._send_xlim_event()

    def zoomy(self, numsteps):
        'Zoom in on the x xaxis numsteps (plus for zoom in, minus for zoom out)'
        self.yaxis.zoom(numsteps)
        self._send_ylim_event()

    _cid = 0
    _events = ('xlim_changed', 'ylim_changed')

    def 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

        """ 

        if s not in Axes._events:
            raise ValueError('You can only connect to the following axes events: %s' % ', '.join(Axes._events))

        cid = Axes._cid
        self._connected.setdefault(s, []).append((cid, func))
        Axes._cid += 1
        return cid

    def disconnect(self, cid):
        'disconnect from the Axes event.'
        for key, val in self._connected.items():
            for item in val:
                if item[0] == cid:
                    self._connected[key].remove(item)
                    return

    def pick(self, x, y, trans=None, among=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.
        
        The selection of artists from amongst which the pick function
        finds an artist can be narrowed using the optional keyword
        argument among. If provided, this should be either a sequence
        of permitted artists or a function taking an artist as its
        argument and returning a true value if and only if that artist
        can be selected.
        
        Note this algorithm calculates distance to the vertices of the
        polygon, so if you want to pick a patch, click on the edge!
        """
        if trans is not None:
            xywin = trans.xy_tup((x,y))
        else:
            xywin = x,y

        def dist_points(p1, p2):
            'return the distance between two points'
            x1, y1 = p1
            x2, y2 = p2
            return math.sqrt((x1-x2)**2+(y1-y2)**2)

        def dist_x_y(p1, x, y):
            'x and y are arrays; return the distance to the closest point'
            x1, y1 = p1
            return min(sqrt((x-x1)**2+(y-y1)**2))

        def dist(a):
            if isinstance(a, Text):
                bbox = a.get_window_extent()
                l,b,w,h = bbox.get_bounds()
                verts = (l,b), (l,b+h), (l+w,b+h), (l+w, b)
                xt, yt = zip(*verts)
            elif isinstance(a, Patch):
                verts = a.get_verts()
                tverts = a.get_transform().seq_xy_tups(verts)
                xt, yt = zip(*tverts)
            elif isinstance(a, Line2D):
                xdata = a.get_xdata(valid_only = True)
                ydata = a.get_ydata(valid_only = True)
                xt, yt = a.get_transform().numerix_x_y(xdata, ydata)

            return dist_x_y(xywin, asarray(xt), asarray(yt))

        artists = self.lines + self.patches + self.texts
        if callable(among):
            artists = filter(test, artists)
        elif iterable(among):
            amongd = dict([(k,1) for k in among])
            artists = [a for a in artists if a in amongd]
        elif among is None:
            pass
        else:
            raise ValueError('among mut be callable or iterable')
        if not len(artists): return None
        ds = [ (dist(a),a) for a in artists]
        ds.sort()
        return ds[0][1]


class SubplotBase:
    """
    Emulate matlab's(TM) 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
    """

    def __init__(self, fig, *args, **kwargs):
        """
        fig is a figure instance

        args is a varargs to specify the subplot

        """

        self.figure = fig

        if len(args)==1:
            s = str(args[0])
            if len(s) != 3:
                raise ValueError('Argument to subplot must be a 3 digits long')
            rows, cols, num = map(int, s)
        elif len(args)==3:
            rows, cols, num = args
        else:
            raise ValueError(  'Illegal argument to subplot')


        total = rows*cols
        num -= 1    # convert from matlab to python indexing ie num in range(0,total)
        if num >= total:
            raise ValueError( 'Subplot number exceeds total subplots')
        self._rows = rows
        self._cols = cols
        self._num = num

        self.update_params()

    def update_params(self):
        'update the subplot position from fig.subplotpars'

        rows = self._rows
        cols = self._cols
        num = self._num

        pars = self.figure.subplotpars
        left = pars.left
        right = pars.right
        bottom = pars.bottom
        top = pars.top
        wspace = pars.wspace
        hspace = pars.hspace        
        totWidth = right-left
        totHeight = top-bottom

        figH = totHeight/(rows + hspace*(rows-1))
        sepH = hspace*figH

        figW = totWidth/(cols + wspace*(cols-1))
        sepW = wspace*figW

        rowNum, colNum =  divmod(num, cols)

        figBottom = top - (rowNum+1)*figH - rowNum*sepH
        figLeft = left + colNum*(figW + sepW)

        self.figBottom = figBottom
        self.figLeft = figLeft
        self.figW = figW
        self.figH = figH
        self.rowNum = rowNum
        self.colNum = colNum
        self.numRows = rows
        self.numCols = cols

        if 0:
            print 'rcn', rows, cols, num
            print 'lbrt', left, bottom, right, top
            print 'self.figBottom', self.figBottom
            print 'self.figLeft', self.figLeft
            print 'self.figW', self.figW
            print 'self.figH', self.figH
            print 'self.rowNum', self.rowNum
            print 'self.colNum', self.colNum
            print 'self.numRows', self.numRows
            print 'self.numCols', self.numCols

        
    def is_first_col(self):
        return self.colNum==0

    def is_first_row(self):
        return self.rowNum==0

    def is_last_row(self):
        return self.rowNum==self.numRows-1


    def is_last_col(self):
        return self.colNum==self.numCols-1

class Subplot(SubplotBase, Axes):
    """
    Emulate matlab's(TM) 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
    """
    def __init__(self, fig, *args, **kwargs):
        SubplotBase.__init__(self, fig, *args)
        Axes.__init__(self, fig, [self.figLeft, self.figBottom,
                                  self.figW, self.figH], **kwargs)



class PolarAxes(Axes):
    """

    Make a PolarAxes.  The rectangular bounding box of the axes is given by


       PolarAxes(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

    Attributes:
      thetagridlines  : a list of Line2D for the theta grids
      rgridlines      : a list of Line2D for the radial grids
      thetagridlabels : a list of Text for the theta grid labels
      rgridlabels     : a list of Text for the theta grid labels

    """

    RESOLUTION = 200

    def __init__(self, *args, **kwarg):
        Axes.__init__(self, *args, **kwarg)
        self.cla()
        
    def _init_axis(self):
        "nuthin to do"
        pass
    def _set_lim_and_transforms(self):
        """
        set the dataLim and viewLim BBox attributes and the
        transData and transAxes Transformation attributes
        """

        # the lim are r, theta

        self.dataLim = Bbox( Point( Value(5/4.*math.pi), Value(math.sqrt(2))),
                             Point( Value(1/4.*math.pi), Value(math.sqrt(2))))
        self.viewLim = Bbox( Point( Value(5/4.*math.pi), Value(math.sqrt(2))),
                             Point( Value(1/4.*math.pi), Value(math.sqrt(2))))

        self.transData = NonseparableTransformation(self.viewLim, self.bbox, FuncXY(POLAR))
        self.transAxes = get_bbox_transform(unit_bbox(), self.bbox)


    def cla(self):
        'Clear the current axes'

        # init these w/ some arbitrary numbers - they'll be updated as
        # data is added to the axes

        self._get_lines = _process_plot_var_args()
        self._get_patches_for_fill = _process_plot_var_args('fill')

        self._gridOn = rcParams['polaraxes.grid']
        self.thetagridlabels = []
        self.thetagridlines = []
        self.rgridlabels = []
        self.rgridlines = []

        self.lines = []
        self.images = []
        self.patches = []                
        self.collections = []
        self.texts = []     # text in axis coords

        self.grid(self._gridOn)
        self.title =  Text(
            x=0.5, y=1.05, text='',
            fontproperties=FontProperties(size=rcParams['axes.titlesize']),
            verticalalignment='bottom',
            horizontalalignment='center',
            )
        self.title.set_transform(self.transAxes)

        self._set_artist_props(self.title)


        self.thetas = linspace(0,2*math.pi, self.RESOLUTION)
        verts = zip(self.thetas, ones(self.RESOLUTION))
        self.axesPatch = Polygon(
            verts,
            facecolor=self._axisbg,
            edgecolor=rcParams['axes.edgecolor'],
            )


        self.axesPatch.set_figure(self.figure)
        self.axesPatch.set_transform(self.transData)
        self.axesPatch.set_linewidth(rcParams['axes.linewidth'])
        self.axison = True

        # we need to set a view and data interval from 0->rmax to make
        # the formatter and locator work correctly
        self.rintv = Interval(Value(0), Value(1))
        self.rintd = Interval(Value(0), Value(1))

        self.rformatter  = ScalarFormatter()
        self.rformatter.set_view_interval(self.rintv)
        self.rformatter.set_data_interval(self.rintd)
        self.rlocator = AutoLocator()
        self.rlocator.set_view_interval(self.rintv)
        self.rlocator.set_data_interval(self.rintd)

        angles = arange(0, 360, 45)
        radii = arange(0.2, 1.1, 0.2)
        self.set_thetagrids(angles)
        self.set_rgrids(radii)

    def grid(self, b):
        'Set the axes grids on or off; b is a boolean'
        self._gridOn = b



    def legend(self, *args, **kwargs):
        """
        LEGEND(*args, **kwargs)
        Not implemented for polar yet -- use figlegend    
        """
        raise NotImplementedError('legend not implemented for polar yet -- use figlegend')

    def table(self, *args, **kwargs):
        """
        TABLE(*args, **kwargs)
        Not implemented for polar axes
        """
        raise NotImplementedError('legend not implemented for polar axes')

    def autoscale_view(self):
        'set the view limits to include all the data in the axes'
        self.rintd.set_bounds(0, self.get_rmax())
        rmin, rmax = self.rlocator.autoscale()
        self.rintv.set_bounds(rmin, rmax)

        self.axesPatch.xy = zip(self.thetas, rmax*ones(self.RESOLUTION))
        val = rmax*math.sqrt(2)
        self.viewLim.intervaly().set_bounds(val, val)

        ticks = self.rlocator()
        self.set_rgrids(ticks)

        for t in self.thetagridlabels:
            t.set_y(1.05*rmax)

        r = linspace(0, rmax, self.RESOLUTION)
        for l in self.thetagridlines:
            l.set_ydata(r)

    def set_rgrids(self, radii, labels=None, angle=22.5, **kwargs):
        """
        set the radial locations and labels of the r grids

        The labels will appear at radial distances radii at angle

        labels, if not None, is a len(radii) list of strings of the
        labels to use at each angle.

        if labels is None, the self.rformatter will be used

        Return value is a list of lines, labels where the lines are
        matplotlib.Line2D instances and the labels are matplotlib.Text
        instances

        ACCEPTS: sequence of floats
        """

        popall(self.rgridlines)
        theta = linspace(0,2*math.pi, self.RESOLUTION)
        ls = rcParams['grid.linestyle']
        color = rcParams['grid.color']
        lw = rcParams['grid.linewidth']

        for r in radii:
            r = ones(self.RESOLUTION)*r
            line = Line2D(theta, r, linestyle=ls, color=color, linewidth=lw)
            line.set_transform(self.transData)
            self.rgridlines.append(line)


        popall(self.rgridlabels)


        color = rcParams['tick.color']


        props=FontProperties(size=rcParams['tick.labelsize'])
        if labels is None:
            labels = [self.rformatter(r,0) for r in radii]
        for r,l in zip(radii, labels):
            t = Text(angle/180.*math.pi, r, l,
                     fontproperties=props, color=color,
                     horizontalalignment='center', verticalalignment='center')
            t.set_transform(self.transData)
            t.update(kwargs)
            self._set_artist_props(t)
            t.set_clip_on(False)
            self.rgridlabels.append(t)

        return self.rgridlines, self.rgridlabels


    def set_thetagrids(self, angles, labels=None, fmt='%d', frac = 1.1,
                       **kwargs):
        """
        set the angles at which to place the theta grids (these
        gridlines are equal along the theta dimension).  angles is in
        degrees

        labels, if not None, is a len(angles) list of strings of the
        labels to use at each angle.

        if labels is None, the labels with be fmt%angle

        frac is the fraction of the polar axes radius at which to
        place the label (1 is the edge).Eg 1.05 isd outside the axes
        and 0.95 is inside the axes

        kwargs are optional text properties for the labels

        Return value is a list of lines, labels where the lines are
        matplotlib.Line2D instances and the labels are matplotlib.Text
        instances

        ACCEPTS: sequence of floats
        """
        popall(self.thetagridlines)
        ox, oy = 0,0
        ls = rcParams['grid.linestyle']
        color = rcParams['grid.color']
        lw = rcParams['grid.linewidth']

        r = linspace(0, self.get_rmax(), self.RESOLUTION)
        for a in angles:
            theta = ones(self.RESOLUTION)*a/180.*math.pi
            line = Line2D(theta, r, linestyle=ls, color=color, linewidth=lw)
            line.set_transform(self.transData)
            self.thetagridlines.append(line)

        popall(self.thetagridlabels)

        color = rcParams['tick.color']

        props=FontProperties(size=rcParams['tick.labelsize'])
        r = frac*self.get_rmax()
        if labels is None:
            labels = [fmt%a for a in angles]
        for a,l in zip(angles, labels):
            t = Text(a/180.*math.pi, r, l, fontproperties=props, color=color,
                     horizontalalignment='center', verticalalignment='center')
            t.set_transform(self.transData)
            t.update(kwargs)
            self._set_artist_props(t)
            t.set_clip_on(False)
            self.thetagridlabels.append(t)
        return self.thetagridlines, self.thetagridlabels




    def get_rmax(self):
        'get the maximum radius in the view limits dimension'
        vmin, vmax = self.dataLim.intervaly().get_bounds()
        return max(vmin, vmax)

    def draw(self, renderer):
        if not self.get_visible(): return
        renderer.open_group('polar_axes')
        self.transData.freeze()  # eval the lazy objects
        self.transAxes.freeze()  # eval the lazy objects
        #self._update_axes()
        if self.axison:
            if self._frameon: self.axesPatch.draw(renderer)


        if self._gridOn:
            for l in self.rgridlines+self.thetagridlines:
                l.draw(renderer)

        for t in self.thetagridlabels+self.rgridlabels:
            t.draw(renderer)


        artists = []
        artists.extend(self.lines)
        artists.extend(self.texts)
        artists.extend(self.collections)

        dsu = [ (a.zorder, a) for a in artists]
        dsu.sort()

        for zorder, a in dsu:
            a.draw(renderer)

        self.title.draw(renderer)
        self.transData.thaw()  # release the lazy objects
        self.transAxes.thaw()  # release the lazy objects
        renderer.close_group('polar_axes')


    def format_coord(self, theta, r):
        'return a format string formatting the coordinate'
        theta /= math.pi
        return 'theta=%1.2fpi, r=%1.3f'%(theta, r)


    def has_data(self):
        'return true if any artists have been added to axes'
        return len(self.lines)+len(self.collections)

    def set_xlabel(self, xlabel, fontdict=None, **kwargs):
        'xlabel not implemented'
        raise NotImplementedError('xlabel not defined for polar axes (yet)')

    def set_ylabel(self, ylabel, fontdict=None, **kwargs):
        'ylabel not implemented'
        raise NotImplementedError('ylabel not defined for polar axes (yet)')


    def set_xlim(self, v, emit=True):
        """
        SET_XLIM(v, emit=True)

        A do nothing impl until we can figure out how to handle interaction
        ACCEPTS: len(2) sequence of floats
        """
        #warnings.warn('Navigation set_ylim not implemented for polar')
        self.viewLim.intervalx().set_bounds(*v)
        if emit: self._send_xlim_event()


    def set_ylim(self, v, emit=True):
        """
        SET_YLIM(v, emit=True)

        ACCEPTS: len(2) sequence of floats
        """
        #warnings.warn('Navigation set_xlim not implemented for polar')
        self.viewLim.intervaly().set_bounds(*v)
        if emit: self._send_ylim_event()

    def get_xscale(self):
        'return the xaxis scale string'
        return 'polar'

    def get_yscale(self):
        'return the yaxis scale string'
        return 'polar'

    def toggle_log_lineary(self):
        'toggle between log and linear axes ignored for polar'
        pass

class PolarSubplot(SubplotBase, PolarAxes):
    """
    Create a polar subplot with

      PolarSubplot(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
    """
    def __init__(self, fig, *args, **kwargs):
        SubplotBase.__init__(self, fig, *args)
        PolarAxes.__init__(self, fig, [self.figLeft, self.figBottom, self.figW, self.figH], **kwargs)


"""
# this is some discarded code I was using to find the minimum positive
# data point for some log scaling fixes.  I realized there was a
# cleaner way to do it, but am keeping this around as an example for
# how to get the data out of the axes.  Might want to make something
# like this a method one day, or better yet make get_verts and Artist
# method

            minx, maxx = self.get_xlim()
            if minx<=0 or maxx<=0:
                # find the min pos value in the data
                xs = []
                for line in self.lines:
                    xs.extend(line.get_xdata(valid_only = True))
                for patch in self.patches:
                    xs.extend([x for x,y in patch.get_verts()])
                for collection in self.collections:
                    xs.extend([x for x,y in collection.get_verts()])
                posx = [x for x in xs if x>0]
                if len(posx):

                    minx = min(posx)
                    maxx = max(posx)
                    # warning, probably breaks inverted axis
                    self.set_xlim((0.1*minx, maxx))
"""
