def __call__(self):
        'Return the locations of the ticks'
        self.verify_intervals()
        b=self._base

        vmin, vmax = self.viewInterval.get_bounds()
        vmin = math.log(vmin)/math.log(b)
        vmax = math.log(vmax)/math.log(b)

        if vmax<vmin:
            vmin, vmax = vmax, vmin
        ticklocs = []

        numdec = math.floor(vmax)-math.ceil(vmin)
        if self._subs is None: # autosub
            if numdec>10: subs = array([1.0])
            elif numdec>6: subs = arange(2.0, b, 2.0)
            else: subs = arange(2.0, b)
        else:
            subs = self._subs
        for decadeStart in b**arange(math.floor(vmin),math.ceil(vmax)):
            ticklocs.extend( subs*decadeStart )

        if(len(subs) and subs[0]==1.0):
            ticklocs.append(b**math.ceil(vmax))


        ticklocs = array(ticklocs)
        ind = nonzero(logical_and(ticklocs>=b**vmin ,
                                  ticklocs<=b**vmax))


        ticklocs = take(ticklocs,ind)
        return ticklocs

def makeMappingArray(N, data):
    """Create an N-element 1-d lookup table

    data represented by a list of x,y0,y1 mapping correspondences.
    Each element in this list represents how a value between 0 and 1
    (inclusive) represented by x is mapped to a corresponding value
    between 0 and 1 (inclusive). The two values of y are to allow
    for discontinuous mapping functions (say as might be found in a
    sawtooth) where y0 represents the value of y for values of x
    <= to that given, and y1 is the value to be used for x > than
    that given). The list must start with x=0, end with x=1, and
    all values of x must be in increasing order. Values between
    the given mapping points are determined by simple linear interpolation.

    The function returns an array "result" where result[x*(N-1)]
    gives the closest value for values of x between 0 and 1.
    """
    try:
        adata = array(data)
    except:
        raise TypeError("data must be convertable to an array")
    shape = adata.shape
    if len(shape) != 2 and shape[1] != 3:
        raise ValueError("data must be nx3 format")

    x  = adata[:,0]
    y0 = adata[:,1]
    y1 = adata[:,2]

    if x[0] != 0. or x[-1] != 1.0:
        raise ValueError(
           "data mapping points must start with x=0. and end with x=1")
    if sometrue(sort(x)-x):
        raise ValueError(
           "data mapping points must have x in increasing order")
    # begin generation of lookup table
    x = x * (N-1)
    lut = zeros((N,), Float)
    xind = arange(float(N))
    ind = searchsorted(x, xind)[1:-1]

    lut[1:-1] = ( divide(xind[1:-1] - take(x,ind-1),
                         take(x,ind)-take(x,ind-1) )
                  *(take(y0,ind)-take(y1,ind-1)) + take(y1,ind-1))
    lut[0] = y1[0]
    lut[-1] = y0[-1]
    # ensure that the lut is confined to values between 0 and 1 by clipping it
    clip(lut, 0.0, 1.0)
    #lut = where(lut > 1., 1., lut)
    #lut = where(lut < 0., 0., lut)
    return lut

    def __call__(self, X, alpha=1.0):
        """
        X is either a scalar or an array (of any dimension).
        If scalar, a tuple of rgba values is returned, otherwise
        an array with the new shape = oldshape+(4,). If the X-values
        are integers, then they are used as indices into the array.
        If they are floating point, then they must be in the
        interval (0.0, 1.0).
        Alpha must be a scalar.
        """

        if not self._isinit: self._init()
        alpha = min(alpha, 1.0) # alpha must be between 0 and 1
        alpha = max(alpha, 0.0)
        self._lut[:-3, -1] = alpha
        mask_bad = None
        if not iterable(X):
            vtype = 'scalar'
            xa = array([X])
        else:
            vtype = 'array'
            xma = ma.asarray(X)
            xa = xma.filled(0)
            mask_bad = ma.getmask(xma)
        if typecode(xa) in typecodes['Float']:
            putmask(xa, xa==1.0, 0.9999999) #Treat 1.0 as slightly less than 1.
            xa = (xa * self.N).astype(Int)
        # Set the over-range indices before the under-range;
        # otherwise the under-range values get converted to over-range.
        putmask(xa, xa>self.N-1, self._i_over)
        putmask(xa, xa<0, self._i_under)
        if mask_bad is not None and mask_bad.shape == xa.shape:
            putmask(xa, mask_bad, self._i_bad)
        rgba = take(self._lut, xa)
        if vtype == 'scalar':
            rgba = tuple(rgba[0,:])
        return rgba