

.. _sphx_glr_gallery_pylab_examples_demo_agg_filter.py:


===============
Demo Agg Filter
===============





.. image:: /gallery/pylab_examples/images/sphx_glr_demo_agg_filter_001.png
    :align: center





.. code-block:: python

    import matplotlib.pyplot as plt

    import numpy as np
    import matplotlib.cm as cm
    import matplotlib.mlab as mlab


    def smooth1d(x, window_len):
        # copied from http://www.scipy.org/Cookbook/SignalSmooth

        s = np.r_[2*x[0] - x[window_len:1:-1], x, 2*x[-1] - x[-1:-window_len:-1]]
        w = np.hanning(window_len)
        y = np.convolve(w/w.sum(), s, mode='same')
        return y[window_len-1:-window_len+1]


    def smooth2d(A, sigma=3):

        window_len = max(int(sigma), 3)*2 + 1
        A1 = np.array([smooth1d(x, window_len) for x in np.asarray(A)])
        A2 = np.transpose(A1)
        A3 = np.array([smooth1d(x, window_len) for x in A2])
        A4 = np.transpose(A3)

        return A4


    class BaseFilter(object):
        def prepare_image(self, src_image, dpi, pad):
            ny, nx, depth = src_image.shape
            #tgt_image = np.zeros([pad*2+ny, pad*2+nx, depth], dtype="d")
            padded_src = np.zeros([pad*2 + ny, pad*2 + nx, depth], dtype="d")
            padded_src[pad:-pad, pad:-pad, :] = src_image[:, :, :]

            return padded_src  # , tgt_image

        def get_pad(self, dpi):
            return 0

        def __call__(self, im, dpi):
            pad = self.get_pad(dpi)
            padded_src = self.prepare_image(im, dpi, pad)
            tgt_image = self.process_image(padded_src, dpi)
            return tgt_image, -pad, -pad


    class OffsetFilter(BaseFilter):
        def __init__(self, offsets=None):
            if offsets is None:
                self.offsets = (0, 0)
            else:
                self.offsets = offsets

        def get_pad(self, dpi):
            return int(max(*self.offsets)/72.*dpi)

        def process_image(self, padded_src, dpi):
            ox, oy = self.offsets
            a1 = np.roll(padded_src, int(ox/72.*dpi), axis=1)
            a2 = np.roll(a1, -int(oy/72.*dpi), axis=0)
            return a2


    class GaussianFilter(BaseFilter):
        "simple gauss filter"

        def __init__(self, sigma, alpha=0.5, color=None):
            self.sigma = sigma
            self.alpha = alpha
            if color is None:
                self.color = (0, 0, 0)
            else:
                self.color = color

        def get_pad(self, dpi):
            return int(self.sigma*3/72.*dpi)

        def process_image(self, padded_src, dpi):
            #offsetx, offsety = int(self.offsets[0]), int(self.offsets[1])
            tgt_image = np.zeros_like(padded_src)
            aa = smooth2d(padded_src[:, :, -1]*self.alpha,
                          self.sigma/72.*dpi)
            tgt_image[:, :, -1] = aa
            tgt_image[:, :, :-1] = self.color
            return tgt_image


    class DropShadowFilter(BaseFilter):
        def __init__(self, sigma, alpha=0.3, color=None, offsets=None):
            self.gauss_filter = GaussianFilter(sigma, alpha, color)
            self.offset_filter = OffsetFilter(offsets)

        def get_pad(self, dpi):
            return max(self.gauss_filter.get_pad(dpi),
                       self.offset_filter.get_pad(dpi))

        def process_image(self, padded_src, dpi):
            t1 = self.gauss_filter.process_image(padded_src, dpi)
            t2 = self.offset_filter.process_image(t1, dpi)
            return t2


    from matplotlib.colors import LightSource


    class LightFilter(BaseFilter):
        "simple gauss filter"

        def __init__(self, sigma, fraction=0.5):
            self.gauss_filter = GaussianFilter(sigma, alpha=1)
            self.light_source = LightSource()
            self.fraction = fraction

        def get_pad(self, dpi):
            return self.gauss_filter.get_pad(dpi)

        def process_image(self, padded_src, dpi):
            t1 = self.gauss_filter.process_image(padded_src, dpi)
            elevation = t1[:, :, 3]
            rgb = padded_src[:, :, :3]

            rgb2 = self.light_source.shade_rgb(rgb, elevation,
                                               fraction=self.fraction)

            tgt = np.empty_like(padded_src)
            tgt[:, :, :3] = rgb2
            tgt[:, :, 3] = padded_src[:, :, 3]

            return tgt


    class GrowFilter(BaseFilter):
        "enlarge the area"

        def __init__(self, pixels, color=None):
            self.pixels = pixels
            if color is None:
                self.color = (1, 1, 1)
            else:
                self.color = color

        def __call__(self, im, dpi):
            pad = self.pixels
            ny, nx, depth = im.shape
            new_im = np.empty([pad*2 + ny, pad*2 + nx, depth], dtype="d")
            alpha = new_im[:, :, 3]
            alpha.fill(0)
            alpha[pad:-pad, pad:-pad] = im[:, :, -1]
            alpha2 = np.clip(smooth2d(alpha, self.pixels/72.*dpi) * 5, 0, 1)
            new_im[:, :, -1] = alpha2
            new_im[:, :, :-1] = self.color
            offsetx, offsety = -pad, -pad

            return new_im, offsetx, offsety


    from matplotlib.artist import Artist


    class FilteredArtistList(Artist):
        """
        A simple container to draw filtered artist.
        """

        def __init__(self, artist_list, filter):
            self._artist_list = artist_list
            self._filter = filter
            Artist.__init__(self)

        def draw(self, renderer):
            renderer.start_rasterizing()
            renderer.start_filter()
            for a in self._artist_list:
                a.draw(renderer)
            renderer.stop_filter(self._filter)
            renderer.stop_rasterizing()


    import matplotlib.transforms as mtransforms


    def filtered_text(ax):
        # mostly copied from contour_demo.py

        # prepare image
        delta = 0.025
        x = np.arange(-3.0, 3.0, delta)
        y = np.arange(-2.0, 2.0, delta)
        X, Y = np.meshgrid(x, y)
        Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
        Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
        # difference of Gaussians
        Z = 10.0 * (Z2 - Z1)

        # draw
        im = ax.imshow(Z, interpolation='bilinear', origin='lower',
                       cmap=cm.gray, extent=(-3, 3, -2, 2))
        levels = np.arange(-1.2, 1.6, 0.2)
        CS = ax.contour(Z, levels,
                        origin='lower',
                        linewidths=2,
                        extent=(-3, 3, -2, 2))

        ax.set_aspect("auto")

        # contour label
        cl = ax.clabel(CS, levels[1::2],  # label every second level
                       inline=1,
                       fmt='%1.1f',
                       fontsize=11)

        # change clable color to black
        from matplotlib.patheffects import Normal
        for t in cl:
            t.set_color("k")
            # to force TextPath (i.e., same font in all backends)
            t.set_path_effects([Normal()])

        # Add white glows to improve visibility of labels.
        white_glows = FilteredArtistList(cl, GrowFilter(3))
        ax.add_artist(white_glows)
        white_glows.set_zorder(cl[0].get_zorder() - 0.1)

        ax.xaxis.set_visible(False)
        ax.yaxis.set_visible(False)


    def drop_shadow_line(ax):
        # copied from examples/misc/svg_filter_line.py

        # draw lines
        l1, = ax.plot([0.1, 0.5, 0.9], [0.1, 0.9, 0.5], "bo-",
                      mec="b", mfc="w", lw=5, mew=3, ms=10, label="Line 1")
        l2, = ax.plot([0.1, 0.5, 0.9], [0.5, 0.2, 0.7], "ro-",
                      mec="r", mfc="w", lw=5, mew=3, ms=10, label="Line 1")

        gauss = DropShadowFilter(4)

        for l in [l1, l2]:

            # draw shadows with same lines with slight offset.

            xx = l.get_xdata()
            yy = l.get_ydata()
            shadow, = ax.plot(xx, yy)
            shadow.update_from(l)

            # offset transform
            ot = mtransforms.offset_copy(l.get_transform(), ax.figure,
                                         x=4.0, y=-6.0, units='points')

            shadow.set_transform(ot)

            # adjust zorder of the shadow lines so that it is drawn below the
            # original lines
            shadow.set_zorder(l.get_zorder() - 0.5)
            shadow.set_agg_filter(gauss)
            shadow.set_rasterized(True)  # to support mixed-mode renderers

        ax.set_xlim(0., 1.)
        ax.set_ylim(0., 1.)

        ax.xaxis.set_visible(False)
        ax.yaxis.set_visible(False)


    def drop_shadow_patches(ax):
        # Copied from barchart_demo.py
        N = 5
        menMeans = (20, 35, 30, 35, 27)

        ind = np.arange(N)  # the x locations for the groups
        width = 0.35       # the width of the bars

        rects1 = ax.bar(ind, menMeans, width, color='r', ec="w", lw=2)

        womenMeans = (25, 32, 34, 20, 25)
        rects2 = ax.bar(ind + width + 0.1, womenMeans, width, color='y', ec="w", lw=2)

        #gauss = GaussianFilter(1.5, offsets=(1,1), )
        gauss = DropShadowFilter(5, offsets=(1, 1), )
        shadow = FilteredArtistList(rects1 + rects2, gauss)
        ax.add_artist(shadow)
        shadow.set_zorder(rects1[0].get_zorder() - 0.1)

        ax.set_ylim(0, 40)

        ax.xaxis.set_visible(False)
        ax.yaxis.set_visible(False)


    def light_filter_pie(ax):
        fracs = [15, 30, 45, 10]
        explode = (0, 0.05, 0, 0)
        pies = ax.pie(fracs, explode=explode)
        ax.patch.set_visible(True)

        light_filter = LightFilter(9)
        for p in pies[0]:
            p.set_agg_filter(light_filter)
            p.set_rasterized(True)  # to support mixed-mode renderers
            p.set(ec="none",
                  lw=2)

        gauss = DropShadowFilter(9, offsets=(3, 4), alpha=0.7)
        shadow = FilteredArtistList(pies[0], gauss)
        ax.add_artist(shadow)
        shadow.set_zorder(pies[0][0].get_zorder() - 0.1)


    if 1:

        plt.figure(1, figsize=(6, 6))
        plt.subplots_adjust(left=0.05, right=0.95)

        ax = plt.subplot(221)
        filtered_text(ax)

        ax = plt.subplot(222)
        drop_shadow_line(ax)

        ax = plt.subplot(223)
        drop_shadow_patches(ax)

        ax = plt.subplot(224)
        ax.set_aspect(1)
        light_filter_pie(ax)
        ax.set_frame_on(True)

        plt.show()

**Total running time of the script:** ( 0 minutes  0.133 seconds)



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     :download:`Download Python source code: demo_agg_filter.py <demo_agg_filter.py>`



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