Re: [matplotlib-devel] bbox of eps backend : test needed
On Sun, Nov 14, 2010 at 7:25 PM, Jae-Joon Lee wrote:
> Hi,
>
> I just committed a simple change that disables pstoeps in ps backend
> when "xpdf" distiller is used. Without this, output bbox was incorrect
> with "xpdf" distiller and usetex=False.
>
> Getting the correct bbox with the ps backend is very tricky, and it'll
> be appreciated if others help me test this.
>
> Here is a simple test script (this needs to be run with most recent
> svn version of matplotlib, and you need to able to use ustex=True!).
> It will produce 6 eps files. Please check if any of the eps files have
> incorrect bbox. And report which file has wrong bbox and your setup :
> your OS, gs version number (output from "gs -v"), pdftops version
> number (output from "pdftops -v"). It will be also good if you report
> your setup even when everything is okay.
>
> Regards,
>
> -JJ
>
> import matplotlib.pyplot as plt
>
> plt.plot([1,2])
>
> for distiller in ["False", "xpdf", "ghostscript"]:
>for usetex in ["True", "False"]:
>plt.rcParams["ps.usedistiller"]=distiller
>plt.rcParams["text.usetex"]=usetex
>
>plt.savefig("test_bbox_%s_%s.eps" % (distiller, usetex))
>
>
Evince shows a few of the eps files weirdly:
test_bbox_ghostscript_False.eps comes out as 8.5x11 inch while the other
come out as 8x6 inches.
test_bbox_xpdf_False.eps is the correct size, but the graph was placed as if
it was centered on a 8.5x11 page, thereby cutting off the bottom half of the
graph.
The system:
Fedora 13
pdftops 0.12.4
ghostscript 8.71
evince 2.30.3 (using libspectre 0.2.4)
I will try out my Ubuntu system tonight.
Ben Root
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[matplotlib-devel] plot_surface patch
Hi all, here's my first patch for matplotlib. Someone noticed at Stack Overflow that the plot_surface function in mplot3d wasn't especially fast for a lot of points (and small rstrides/cstrides) and using shading and a single color. I found some parts of the code that weren't vectorized. These are my changes so far. Summary of changes: 1. Changed from double looping over aranges to using xrange 2. Made the normalization of the normals and their dot product with the vector [-1,-1,0.5] to find the shading a vectorized operation. 3. Changed a list comprehension which calculated the colors using an iterative approach to using the already built-in vectorization of the Normalization class and using the np.outer function. The result is a numpy array rather than a list which actually speeds up things down the line. 4. removed the corners array from plot_surface which wasn't ever used or returned. It didn't really slow things down, but I'm thinking that it is cruft. For change number two, I made a separate function that generates the shades, but feel free to move that around if you prefer.. or maybe it should be a function that begins with a _ because it shouldn't be used externally. These changes give varying levels of speed improvement depending on the number of points and the rstrides/cstrides arguments. With larger numbers of points and small rstrides/cstrides, these changes can more than halve the running time. I have found no difference in output after my changes. I know there is more work to be done within the plot_surface function and I'll submit more changes soon. Justin Index: lib/mpl_toolkits/mplot3d/axes3d.py === --- lib/mpl_toolkits/mplot3d/axes3d.py (revision 8802) +++ lib/mpl_toolkits/mplot3d/axes3d.py (working copy) @@ -722,10 +722,9 @@ normals = [] #colset contains the data for coloring: either average z or the facecolor colset = [] -for rs in np.arange(0, rows-1, rstride): -for cs in np.arange(0, cols-1, cstride): +for rs in xrange(0, rows-1, rstride): +for cs in xrange(0, cols-1, cstride): ps = [] -corners = [] for a, ta in [(X, tX), (Y, tY), (Z, tZ)]: ztop = a[rs][cs:min(cols, cs+cstride+1)] zleft = ta[min(cols-1, cs+cstride)][rs:min(rows, rs+rstride+1)] @@ -733,7 +732,6 @@ zbase = zbase[::-1] zright = ta[cs][rs:min(rows, rs+rstride+1):] zright = zright[::-1] -corners.append([ztop[0], ztop[-1], zbase[0], zbase[-1]]) z = np.concatenate((ztop, zleft, zbase, zright)) ps.append(z) @@ -802,18 +800,21 @@ normals.append(np.cross(v1, v2)) return normals +def getshades(self, normals): +''' +Find the normalized vectors of the normals and dot them with +the vector [-1,-1,0.5] to get the proper shadings. +''' +return np.dot(normals/np.sqrt((normals**2).sum(axis=1))[:,np.newaxis], + np.array([[-1],[-1],[0.5]])).squeeze() + def _shade_colors(self, color, normals): ''' Shade *color* using normal vectors given by *normals*. *color* can also be an array of the same length as *normals*. ''' -shade = [] -for n in normals: -n = n / proj3d.mod(n) -shade.append(np.dot(n, [-1, -1, 0.5])) - -shade = np.array(shade) +shade = self.getshades(np.array(normals)) mask = ~np.isnan(shade) if len(shade[mask]) > 0: @@ -821,7 +822,7 @@ if art3d.iscolor(color): color = color.copy() color[3] = 1 -colors = [color * (0.5 + norm(v) * 0.5) for v in shade] +colors = np.outer(0.5 + norm(shade) * 0.5, color) else: colors = [np.array(colorConverter.to_rgba(c)) * \ (0.5 + norm(v) * 0.5) \ -- Beautiful is writing same markup. Internet Explorer 9 supports standards for HTML5, CSS3, SVG 1.1, ECMAScript5, and DOM L2 & L3. Spend less time writing and rewriting code and more time creating great experiences on the web. Be a part of the beta today http://p.sf.net/sfu/msIE9-sfdev2dev___ Matplotlib-devel mailing list Matplotlib-devel@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
[matplotlib-devel] plot_surface final patch (for now)
Hi again, I'll try to keep my messages in plain text from now on. I kept working on some more speed improvements for plot_surface functions and have included the diff that has those extra changes. Here are the main extra changes: 1. vectorized the code in _shade_colors when inputting a grid of colors by moving to an already vectorized to_rgba_array() function, using the vectorized Normalize class, and using numpy to multiply the two. Also, making the result be a numpy array stops set_face_color() function from redundantly calling to_rgba on each individual item again. This improvement speeds up the mplot3d example surface3d_demo3 by about 3x on my machine. 2. I preallocated the space for the two vectors that will be crossed for each area of points to make the normals and do a vectorized np.cross() afterwards instead. Shaves off a bit more time. 3. Made the functions that select points not include multiples of the corners of the rectangle of points (if that makes any sense). Not a huge savings, but still worthwhile I feel. I think that I'm done with working on these functions now. I hope that these changes are amenable. Let me know if there are concerns. Justin Peel -- Forwarded message -- From: J P Date: Tue, Nov 16, 2010 at 4:20 PM Subject: plot_surface patch To: Matplotlib-devel@lists.sourceforge.net Hi all, here's my first patch for matplotlib. Someone noticed at Stack Overflow that the plot_surface function in mplot3d wasn't especially fast for a lot of points (and small rstrides/cstrides) and using shading and a single color. I found some parts of the code that weren't vectorized. These are my changes so far. Summary of changes: 1. Changed from double looping over aranges to using xrange 2. Made the normalization of the normals and their dot product with the vector [-1,-1,0.5] to find the shading a vectorized operation. 3. Changed a list comprehension which calculated the colors using an iterative approach to using the already built-in vectorization of the Normalization class and using the np.outer function. The result is a numpy array rather than a list which actually speeds up things down the line. 4. removed the corners array from plot_surface which wasn't ever used or returned. It didn't really slow things down, but I'm thinking that it is cruft. For change number two, I made a separate function that generates the shades, but feel free to move that around if you prefer.. or maybe it should be a function that begins with a _ because it shouldn't be used externally. These changes give varying levels of speed improvement depending on the number of points and the rstrides/cstrides arguments. With larger numbers of points and small rstrides/cstrides, these changes can more than halve the running time. I have found no difference in output after my changes. I know there is more work to be done within the plot_surface function and I'll submit more changes soon. Justin Index: lib/mpl_toolkits/mplot3d/axes3d.py === --- lib/mpl_toolkits/mplot3d/axes3d.py (revision 8802) +++ lib/mpl_toolkits/mplot3d/axes3d.py (working copy) @@ -719,21 +719,24 @@ fcolors = self._shade_colors_lightsource(Z, cmap, lightsource) polys = [] -normals = [] +if cmap is None and shade: +totpts = int(np.ceil(float(rows - 1) / rstride) * \ + np.ceil(float(cols - 1) / cstride)) +v1 = np.empty((totpts,3)) +v2 = np.empty((totpts,3)) +which = 0 #colset contains the data for coloring: either average z or the facecolor colset = [] -for rs in np.arange(0, rows-1, rstride): -for cs in np.arange(0, cols-1, cstride): +for rs in xrange(0, rows-1, rstride): +for cs in xrange(0, cols-1, cstride): ps = [] -corners = [] for a, ta in [(X, tX), (Y, tY), (Z, tZ)]: -ztop = a[rs][cs:min(cols, cs+cstride+1)] -zleft = ta[min(cols-1, cs+cstride)][rs:min(rows, rs+rstride+1)] -zbase = a[min(rows-1, rs+rstride)][cs:min(cols, cs+cstride+1):] -zbase = zbase[::-1] -zright = ta[cs][rs:min(rows, rs+rstride+1):] -zright = zright[::-1] -corners.append([ztop[0], ztop[-1], zbase[0], zbase[-1]]) +ztop = a[rs,cs:min(cols, cs+cstride+1)] +zleft = a[rs+1:min(rows, rs+rstride+1), + min(cols-1, cs+cstride)] +zbase = a[min(rows-1, rs+rstride), + cs:min(cols-1, cs+cstride):][::-1] +zright = a[rs:min(rows-1, rs+rstride):,cs][::-1] z = np.concatenate((ztop, zleft, zbase, zright)) ps.append(z) @@ -758,10 +761,12 @@ # Only need vectors to shade if no c
