def __call__(self, value, clip=None):
        if clip is None:
            clip = self.clip

        result, is_scalar = self.process_value(value)

        result = ma.masked_less_equal(result, 0, copy=False)

        self.autoscale_None(result)
        vmin, vmax = self.vmin, self.vmax
        if vmin > vmax:
            raise ValueError("minvalue must be less than or equal to maxvalue")
        elif vmin <= 0:
            raise ValueError("values must all be positive")
        elif vmin == vmax:
            result.fill(0)
        else:
            if clip:
                mask = ma.getmask(result)
                result = ma.array(np.clip(result.filled(vmax), vmin, vmax), mask=mask)
            # in-place equivalent of above can be much faster
            resdat = result.data
            mask = result.mask
            if mask is np.ma.nomask:
                mask = resdat <= 0
            else:
                mask |= resdat <= 0
            cbook._putmask(resdat, mask, 1)
            np.log(resdat, resdat)
            resdat -= np.log(vmin)
            resdat /= np.log(vmax) - np.log(vmin)
            result = np.ma.array(resdat, mask=mask, copy=False)
        if is_scalar:
            result = result[0]
        return result

    def _h_arrows(self, length):
        """ length is in arrow width units """
        # It might be possible to streamline the code
        # and speed it up a bit by using complex (x,y)
        # instead of separate arrays; but any gain would be slight.
        minsh = self.minshaft * self.headlength
        N = len(length)
        length = length.reshape(N, 1)
        # This number is chosen based on when pixel values overflow in Agg
        # causing rendering errors
        # length = np.minimum(length, 2 ** 16)
        np.clip(length, 0, 2 ** 16, out=length)
        # x, y: normal horizontal arrow
        x = np.array([0, -self.headaxislength,
                      -self.headlength, 0],
                     np.float64)
        x = x + np.array([0, 1, 1, 1]) * length
        y = 0.5 * np.array([1, 1, self.headwidth, 0], np.float64)
        y = np.repeat(y[np.newaxis, :], N, axis=0)
        # x0, y0: arrow without shaft, for short vectors
        x0 = np.array([0, minsh - self.headaxislength,
                       minsh - self.headlength, minsh], np.float64)
        y0 = 0.5 * np.array([1, 1, self.headwidth, 0], np.float64)
        ii = [0, 1, 2, 3, 2, 1, 0, 0]
        X = x.take(ii, 1)
        Y = y.take(ii, 1)
        Y[:, 3:-1] *= -1
        X0 = x0.take(ii)
        Y0 = y0.take(ii)
        Y0[3:-1] *= -1
        shrink = length / minsh if minsh != 0. else 0.
        X0 = shrink * X0[np.newaxis, :]
        Y0 = shrink * Y0[np.newaxis, :]
        short = np.repeat(length < minsh, 8, axis=1)
        # Now select X0, Y0 if short, otherwise X, Y
        cbook._putmask(X, short, X0)
        cbook._putmask(Y, short, Y0)
        if self.pivot == 'middle':
            X -= 0.5 * X[:, 3, np.newaxis]
        elif self.pivot == 'tip':
            X = X - X[:, 3, np.newaxis]   # numpy bug? using -= does not
                                          # work here unless we multiply
                                          # by a float first, as with 'mid'.
        elif self.pivot != 'tail':
            raise ValueError(("Quiver.pivot must have value in {{'middle', "
                              "'tip', 'tail'}} not {0}").format(self.pivot))

        tooshort = length < self.minlength
        if tooshort.any():
            # Use a heptagonal dot:
            th = np.arange(0, 8, 1, np.float64) * (np.pi / 3.0)
            x1 = np.cos(th) * self.minlength * 0.5
            y1 = np.sin(th) * self.minlength * 0.5
            X1 = np.repeat(x1[np.newaxis, :], N, axis=0)
            Y1 = np.repeat(y1[np.newaxis, :], N, axis=0)
            tooshort = np.repeat(tooshort, 8, 1)
            cbook._putmask(X, tooshort, X1)
            cbook._putmask(Y, tooshort, Y1)
        # Mask handling is deferred to the caller, _make_verts.
        return X, Y

    def __call__(self, X, alpha=None, bytes=False):
        """
        Parameters
        ----------
        X : scalar, ndarray
            The data value(s) to convert to RGBA.
            For floats, X should be in the interval ``[0.0, 1.0]`` to
            return the RGBA values ``X*100`` percent along the Colormap line.
            For integers, X should be in the interval ``[0, Colormap.N)`` to
            return RGBA values *indexed* from the Colormap with index ``X``.
        alpha : float, None
            Alpha must be a scalar between 0 and 1, or None.
        bytes : bool
            If False (default), the returned RGBA values will be floats in the
            interval ``[0, 1]`` otherwise they will be uint8s in the interval
            ``[0, 255]``.

        Returns
        -------
        Tuple of RGBA values if X is scalar, othewise an array of
        RGBA values with a shape of ``X.shape + (4, )``.

        """
        # See class docstring for arg/kwarg documentation.
        if not self._isinit:
            self._init()
        mask_bad = None
        if not cbook.iterable(X):
            vtype = 'scalar'
            xa = np.array([X])
        else:
            vtype = 'array'
            xma = ma.array(X, copy=True)  # Copy here to avoid side effects.
            mask_bad = xma.mask           # Mask will be used below.
            xa = xma.filled()             # Fill to avoid infs, etc.
            del xma

        # Calculations with native byteorder are faster, and avoid a
        # bug that otherwise can occur with putmask when the last
        # argument is a numpy scalar.
        if not xa.dtype.isnative:
            xa = xa.byteswap().newbyteorder()

        if xa.dtype.kind == "f":
            # Treat 1.0 as slightly less than 1.
            vals = np.array([1, 0], dtype=xa.dtype)
            almost_one = np.nextafter(*vals)
            cbook._putmask(xa, xa == 1.0, almost_one)
            # The following clip is fast, and prevents possible
            # conversion of large positive values to negative integers.

            xa *= self.N
            if NP_CLIP_OUT:
                np.clip(xa, -1, self.N, out=xa)
            else:
                xa = np.clip(xa, -1, self.N)

            # ensure that all 'under' values will still have negative
            # value after casting to int
            cbook._putmask(xa, xa < 0.0, -1)
            xa = xa.astype(int)
        # Set the over-range indices before the under-range;
        # otherwise the under-range values get converted to over-range.
        cbook._putmask(xa, xa > self.N - 1, self._i_over)
        cbook._putmask(xa, xa < 0, self._i_under)
        if mask_bad is not None:
            if mask_bad.shape == xa.shape:
                cbook._putmask(xa, mask_bad, self._i_bad)
            elif mask_bad:
                xa.fill(self._i_bad)
        if bytes:
            lut = (self._lut * 255).astype(np.uint8)
        else:
            lut = self._lut.copy()  # Don't let alpha modify original _lut.

        if alpha is not None:
            alpha = min(alpha, 1.0)  # alpha must be between 0 and 1
            alpha = max(alpha, 0.0)
            if bytes:
                alpha = int(alpha * 255)
            if (lut[-1] == 0).all():
                lut[:-1, -1] = alpha
                # All zeros is taken as a flag for the default bad
                # color, which is no color--fully transparent.  We
                # don't want to override this.
            else:
                lut[:, -1] = alpha
                # If the bad value is set to have a color, then we
                # override its alpha just as for any other value.

        rgba = np.empty(shape=xa.shape + (4,), dtype=lut.dtype)
        lut.take(xa, axis=0, mode='clip', out=rgba)
                    #  twice as fast as lut[xa];
                    #  using the clip or wrap mode and providing an
                    #  output array speeds it up a little more.
        if vtype == 'scalar':
            rgba = tuple(rgba[0, :])
        return rgba

    def __call__(self, X, alpha=None, bytes=False):
        """
        *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 between 0 and 1, or None.
        If bytes is False, the rgba values will be floats on a
        0-1 scale; if True, they will be uint8, 0-255.
        """

        if not self._isinit:
            self._init()
        mask_bad = None
        if not cbook.iterable(X):
            vtype = "scalar"
            xa = np.array([X])
        else:
            vtype = "array"
            xma = ma.array(X, copy=False)
            mask_bad = xma.mask
            xa = xma.data.copy()  # Copy here to avoid side effects.
            del xma
            # masked values are substituted below; no need to fill them here

        if xa.dtype.char in np.typecodes["Float"]:
            # Treat 1.0 as slightly less than 1.
            cbook._putmask(xa, xa == 1.0, np.nextafter(xa.dtype.type(1), xa.dtype.type(0)))
            # The following clip is fast, and prevents possible
            # conversion of large positive values to negative integers.

            xa *= self.N
            if NP_CLIP_OUT:
                np.clip(xa, -1, self.N, out=xa)
            else:
                xa = np.clip(xa, -1, self.N)

            # ensure that all 'under' values will still have negative
            # value after casting to int
            cbook._putmask(xa, xa < 0.0, -1)
            xa = xa.astype(int)
        # Set the over-range indices before the under-range;
        # otherwise the under-range values get converted to over-range.
        cbook._putmask(xa, xa > self.N - 1, self._i_over)
        cbook._putmask(xa, xa < 0, self._i_under)
        if mask_bad is not None:
            if mask_bad.shape == xa.shape:
                cbook._putmask(xa, mask_bad, self._i_bad)
            elif mask_bad:
                xa.fill(self._i_bad)
        if bytes:
            lut = (self._lut * 255).astype(np.uint8)
        else:
            lut = self._lut.copy()  # Don't let alpha modify original _lut.

        if alpha is not None:
            alpha = min(alpha, 1.0)  # alpha must be between 0 and 1
            alpha = max(alpha, 0.0)
            if bytes:
                alpha = int(alpha * 255)
            if (lut[-1] == 0).all():
                lut[:-1, -1] = alpha
                # All zeros is taken as a flag for the default bad
                # color, which is no color--fully transparent.  We
                # don't want to override this.
            else:
                lut[:, -1] = alpha
                # If the bad value is set to have a color, then we
                # override its alpha just as for any other value.

        rgba = np.empty(shape=xa.shape + (4,), dtype=lut.dtype)
        lut.take(xa, axis=0, mode="clip", out=rgba)
        #  twice as fast as lut[xa];
        #  using the clip or wrap mode and providing an
        #  output array speeds it up a little more.
        if vtype == "scalar":
            rgba = tuple(rgba[0, :])
        return rgba

def _newcall_(self, X, alpha=None, bytes=False):
    if not self._isinit:
        self._init()
    mask_bad = None
    if not cbook.iterable(X):
        vtype = 'scalar'
        xa = np.array([X])
    else:
        vtype = 'array'
        xma = ma.array(X, copy=True)  # Copy here to avoid side effects.
        mask_bad = xma.mask           # Mask will be used below.
        xa = xma.filled()             # Fill to avoid infs, etc.
        del xma

    self._lut[0] = [0, 0, 0, 1]
    # Calculations with native byteorder are faster, and avoid a
    # bug that otherwise can occur with putmask when the last
    # argument is a numpy scalar.
    if not xa.dtype.isnative:
        xa = xa.byteswap().newbyteorder()

    if xa.dtype.kind == "f":
        # Treat 1.0 as slightly less than 1.
        vals = np.array([1, 0], dtype=xa.dtype)
        almost_one = np.nextafter(*vals)
        cbook._putmask(xa, xa == 1.0, almost_one)
        # The following clip is fast, and prevents possible
        # conversion of large positive values to negative integers.

        xa *= self.N
        np.clip(xa, -1, self.N, out=xa)

        # ensure that all 'under' values will still have negative
        # value after casting to int
        cbook._putmask(xa, xa < 0.0, -1)
        xa = xa.astype(int)
    # Set the over-range indices before the under-range;
    # otherwise the under-range values get converted to over-range.
    cbook._putmask(xa, xa > self.N - 1, self._i_over)
    cbook._putmask(xa, xa < 0, self._i_under)
    if mask_bad is not None:
        if mask_bad.shape == xa.shape:
            cbook._putmask(xa, mask_bad, self._i_bad)
        elif mask_bad:
            xa.fill(self._i_bad)
    if bytes:
        lut = (self._lut * 255).astype(np.uint8)
    else:
        lut = self._lut.copy()  # Don't let alpha modify original _lut.

    if alpha is not None:
        alpha = min(alpha, 1.0)  # alpha must be between 0 and 1
        alpha = max(alpha, 0.0)
        if bytes:
            alpha = int(alpha * 255)
        if (lut[-1] == 0).all():
            lut[:-1, -1] = alpha
            # All zeros is taken as a flag for the default bad
            # color, which is no color--fully transparent.  We
            # don't want to override this.
        else:
            lut[:, -1] = alpha
            # If the bad value is set to have a color, then we
            # override its alpha just as for any other value.

    rgba = np.empty(shape=xa.shape + (4,), dtype=lut.dtype)
    lut.take(xa, axis=0, mode='clip', out=rgba)
    if vtype == 'scalar':
        rgba = tuple(rgba[0, :])
    return rgba