You may also try saving as SVG and then loading in Inkscape and using its various "artistic" vector plugins to achieve similar results.
Mike John Hunter wrote: > On Fri, May 7, 2010 at 1:09 PM, Eric Firing <efir...@hawaii.edu> wrote: > >> On 05/06/2010 08:02 PM, oyster wrote: >> >>> I know matplotlib mimics matlab, which offers scientific look. >>> but can we use matplotlib to get a skecth(in other words, >>> hand-drawing) style for bar/pie/etc in none formalist paper? for >>> example, http://teethgrinder.co.uk/open-flash-chart/gallery-bar-7.php >>> thanks >>> >> With a considerable effort you could use mpl's capabilities to program >> this sort of thing, but it will not be added as an option. >> > > JJ's demo_ribbon_box is pretty close -- you just need to create a base > image you want stretched to the bar height. > > import matplotlib.pyplot as plt > import numpy as np > from matplotlib.image import BboxImage > > from matplotlib._png import read_png > import matplotlib.colors > from matplotlib.cbook import get_sample_data > > class RibbonBox(object): > > original_image = read_png(get_sample_data("Minduka_Present_Blue_Pack.png", > asfileobj=False)) > cut_location = 70 > b_and_h = original_image[:,:,2] > color = original_image[:,:,2] - original_image[:,:,0] > alpha = original_image[:,:,3] > nx = original_image.shape[1] > > def __init__(self, color): > rgb = matplotlib.colors.colorConverter.to_rgb(color) > > im = np.empty(self.original_image.shape, > self.original_image.dtype) > > > im[:,:,:3] = self.b_and_h[:,:,np.newaxis] > im[:,:,:3] -= self.color[:,:,np.newaxis]*(1.-np.array(rgb)) > im[:,:,3] = self.alpha > > self.im = im > > > def get_stretched_image(self, stretch_factor): > stretch_factor = max(stretch_factor, 1) > ny, nx, nch = self.im.shape > ny2 = int(ny*stretch_factor) > > stretched_image = np.empty((ny2, nx, nch), > self.im.dtype) > cut = self.im[self.cut_location,:,:] > stretched_image[:,:,:] = cut > stretched_image[:self.cut_location,:,:] = \ > self.im[:self.cut_location,:,:] > stretched_image[-(ny-self.cut_location):,:,:] = \ > self.im[-(ny-self.cut_location):,:,:] > > self._cached_im = stretched_image > return stretched_image > > > > class RibbonBoxImage(BboxImage): > zorder = 1 > > def __init__(self, bbox, color, > cmap = None, > norm = None, > interpolation=None, > origin=None, > filternorm=1, > filterrad=4.0, > resample = False, > **kwargs > ): > > BboxImage.__init__(self, bbox, > cmap = cmap, > norm = norm, > interpolation=interpolation, > origin=origin, > filternorm=filternorm, > filterrad=filterrad, > resample = resample, > **kwargs > ) > > self._ribbonbox = RibbonBox(color) > self._cached_ny = None > > > def draw(self, renderer, *args, **kwargs): > > bbox = self.get_window_extent(renderer) > stretch_factor = bbox.height / bbox.width > > ny = int(stretch_factor*self._ribbonbox.nx) > if self._cached_ny != ny: > arr = self._ribbonbox.get_stretched_image(stretch_factor) > self.set_array(arr) > self._cached_ny = ny > > BboxImage.draw(self, renderer, *args, **kwargs) > > > if 1: > from matplotlib.transforms import Bbox, TransformedBbox > from matplotlib.ticker import ScalarFormatter > > fig = plt.gcf() > fig.clf() > ax = plt.subplot(111) > > years = np.arange(2004, 2009) > box_colors = [(0.8, 0.2, 0.2), > (0.2, 0.8, 0.2), > (0.2, 0.2, 0.8), > (0.7, 0.5, 0.8), > (0.3, 0.8, 0.7), > ] > heights = np.random.random(years.shape) * 7000 + 3000 > > fmt = ScalarFormatter(useOffset=False) > ax.xaxis.set_major_formatter(fmt) > > for year, h, bc in zip(years, heights, box_colors): > bbox0 = Bbox.from_extents(year-0.4, 0., year+0.4, h) > bbox = TransformedBbox(bbox0, ax.transData) > rb_patch = RibbonBoxImage(bbox, bc, interpolation="bicubic") > > ax.add_artist(rb_patch) > > ax.annotate(r"%d" % (int(h/100.)*100), > (year, h), va="bottom", ha="center") > > patch_gradient = BboxImage(ax.bbox, > interpolation="bicubic", > zorder=0.1, > ) > gradient = np.zeros((2, 2, 4), dtype=np.float) > gradient[:,:,:3] = [1, 1, 0.] > gradient[:,:,3] = [[0.1, 0.3],[0.3, 0.5]] # alpha channel > patch_gradient.set_array(gradient) > ax.add_artist(patch_gradient) > > > ax.set_xlim(years[0]-0.5, years[-1]+0.5) > ax.set_ylim(0, 10000) > > fig.savefig('ribbon_box.png') > plt.show() > > ------------------------------------------------------------------------ > > ------------------------------------------------------------------------ > > ------------------------------------------------------------------------------ > > ------------------------------------------------------------------------ > > _______________________________________________ > Matplotlib-users mailing list > Matplotlib-users@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/matplotlib-users -- Michael Droettboom Science Software Branch Operations and Engineering Division Space Telescope Science Institute Operated by AURA for NASA ------------------------------------------------------------------------------ _______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
