You can subscribe to this list here.
2003 
_{Jan}

_{Feb}

_{Mar}

_{Apr}

_{May}
(3) 
_{Jun}

_{Jul}

_{Aug}
(12) 
_{Sep}
(12) 
_{Oct}
(56) 
_{Nov}
(65) 
_{Dec}
(37) 

2005 
_{Jan}
(184) 
_{Feb}
(267) 
_{Mar}
(214) 
_{Apr}
(286) 
_{May}
(320) 
_{Jun}
(299) 
_{Jul}
(348) 
_{Aug}
(283) 
_{Sep}
(355) 
_{Oct}
(293) 
_{Nov}
(232) 
_{Dec}
(203) 
2008 
_{Jan}
(312) 
_{Feb}
(359) 
_{Mar}
(454) 
_{Apr}
(287) 
_{May}
(340) 
_{Jun}
(450) 
_{Jul}
(403) 
_{Aug}
(324) 
_{Sep}
(349) 
_{Oct}
(385) 
_{Nov}
(363) 
_{Dec}
(437) 
2009 
_{Jan}
(500) 
_{Feb}
(301) 
_{Mar}
(409) 
_{Apr}
(486) 
_{May}
(545) 
_{Jun}
(391) 
_{Jul}
(518) 
_{Aug}
(497) 
_{Sep}
(492) 
_{Oct}
(429) 
_{Nov}
(357) 
_{Dec}
(310) 
2011 
_{Jan}
(321) 
_{Feb}
(413) 
_{Mar}
(299) 
_{Apr}
(215) 
_{May}
(284) 
_{Jun}
(203) 
_{Jul}
(207) 
_{Aug}
(314) 
_{Sep}
(321) 
_{Oct}
(259) 
_{Nov}
(347) 
_{Dec}
(209) 
2006 
_{Jan}
(352) 
_{Feb}
(358) 
_{Mar}
(403) 
_{Apr}
(313) 
_{May}
(165) 
_{Jun}
(281) 
_{Jul}
(316) 
_{Aug}
(228) 
_{Sep}
(279) 
_{Oct}
(243) 
_{Nov}
(315) 
_{Dec}
(345) 
2010 
_{Jan}
(371) 
_{Feb}
(657) 
_{Mar}
(519) 
_{Apr}
(432) 
_{May}
(312) 
_{Jun}
(416) 
_{Jul}
(477) 
_{Aug}
(386) 
_{Sep}
(419) 
_{Oct}
(435) 
_{Nov}
(320) 
_{Dec}
(202) 
2004 
_{Jan}
(59) 
_{Feb}
(78) 
_{Mar}
(153) 
_{Apr}
(205) 
_{May}
(184) 
_{Jun}
(123) 
_{Jul}
(171) 
_{Aug}
(156) 
_{Sep}
(190) 
_{Oct}
(120) 
_{Nov}
(154) 
_{Dec}
(223) 
2012 
_{Jan}
(322) 
_{Feb}
(414) 
_{Mar}
(377) 
_{Apr}
(179) 
_{May}
(173) 
_{Jun}
(234) 
_{Jul}
(151) 
_{Aug}

_{Sep}

_{Oct}

_{Nov}

_{Dec}

2007 
_{Jan}
(260) 
_{Feb}
(323) 
_{Mar}
(340) 
_{Apr}
(319) 
_{May}
(290) 
_{Jun}
(296) 
_{Jul}
(221) 
_{Aug}
(292) 
_{Sep}
(242) 
_{Oct}
(248) 
_{Nov}
(242) 
_{Dec}
(332) 
S  M  T  W  T  F  S 


1
(7) 
2

3

4
(1) 
5

6
(3) 
7
(1) 
8
(5) 
9
(15) 
10
(15) 
11
(6) 
12
(7) 
13

14
(3) 
15
(10) 
16
(11) 
17
(17) 
18
(4) 
19
(6) 
20

21
(1) 
22
(5) 
23
(4) 
24

25

26
(7) 
27
(2) 
28
(4) 
29
(9) 
30
(11) 




From: John Hunter <jdhunter@ac...>  20041111 15:49:55

>>>>> "Shin" == Shin <sdhyok@...> writes: Shin> My default mode of matplotlib is interactive mode, but in Shin> some programs I like to turn off the interactive model Shin> temporarily so postpone drawing until I call show(), because Shin> of performance concern. Any way for swith the mode in a Shin> script? Thanks in advance. from matplotlib import interactive from matplotlib.matlab import * plot([1,2,3]) interactive(False) # turn off interactive mode xlabel('hi mom') ylabel('bye') title('all done') interactive(False) # turn it back on draw() # draw the canvas JDH Shin>  This Shin> SF.Net email is sponsored by: Sybase ASE Linux Express Shin> Edition  download now for FREE LinuxWorld Reader's Choice Shin> Award Winner for best database on Linux. Shin> http://ads.osdn.com/?ad_id=5588&alloc_id=12065&op=click Shin> _______________________________________________ Shin> Matplotlibusers mailing list Shin> Matplotlibusers@... Shin> https://lists.sourceforge.net/lists/listinfo/matplotlibusers 
From: John Hunter <jdhunter@ac...>  20041111 15:25:53

>>>>> "Nils" == Nils Wagner <nwagner@...> writes: Nils> Hi all, Structure plots provide a quick visual check on the Nils> sparsity pattern of the matrix. A structure plot is a Nils> rectangular array of dots; a dot is black if the Nils> corresponding matrix element is nonzero otherwise it is Nils> white. Nils> Is it possible to generate such plots with scipy or should Nils> we switch over to matplotlib ? Here's another implementation that uses images  likely to be much faster for very large matrices. import random from matplotlib.colors import LinearSegmentedColormap from matplotlib.matlab import * def spy2(Z): """ SPY(Z) plots the sparsity pattern of the matrix S as an image """ #binary colormap min white, max black cmapdata = { 'red' : ((0., 1., 1.), (1., 0., 0.)), 'green': ((0., 1., 1.), (1., 0., 0.)), 'blue' : ((0., 1., 1.), (1., 0., 0.)) } binary = LinearSegmentedColormap('binary', cmapdata, 2) Z = where(Z>0,1.,0.) imshow(transpose(Z), interpolation='nearest', cmap=binary) def get_sparse_matrix(M,N,frac=0.1): 'return a MxN sparse matrix with frac elements randomly filled' data = zeros((M,N))*0. for i in range(int(M*N*frac)): x = random.randint(0,M1) y = random.randint(0,N1) data[x,y] = rand() return data data = get_sparse_matrix(50,60) spy2(data) show() 
From: John Hunter <jdhunter@ac...>  20041111 15:14:56

>>>>> "Nils" == Nils Wagner <nwagner@...> writes: Nils> Hi all, Structure plots provide a quick visual check on the Nils> sparsity pattern of the matrix. A structure plot is a Nils> rectangular array of dots; a dot is black if the Nils> corresponding matrix element is nonzero otherwise it is Nils> white. Nils> Is it possible to generate such plots with scipy or should Nils> we switch over to matplotlib ? A quick matplotlib implementation is below. In matlab this function is called "spy" and Alexander Schmolck requested this in an earlier post. The spy implementation uses plot markers which are fixed sizes (in points). For large matrices, you'll likely want to use a smaller markersize. Perhaps better would be to use a polygon collection setup so that the marker sizes filled the boundaries of the matrix cell. This would take a little more work, and would also have a different call signature that matlab's, since matlab also uses plots markers . If you have any thoughts on how you would like the implementation to work, please share them... JDH from matplotlib.matlab import * def get_xyz_where(Z, Cond): """ Z and Cond are MxN matrices. Z are data and Cond is a boolean matrix where some condition is satisfied. Return value is x,y,z where x and y are the indices into Z and z are the values of Z at those indices. x,y,z are 1D arrays This is a lot easier in numarray  is there a more elegant way to do this that works on both numeric and numarray? """ M,N = Z.shape z = ravel(Z) ind = nonzero( ravel(Cond) ) x = arange(M); x.shape = M,1 X = repeat(x, N, 1) x = ravel(X) y = arange(N); y.shape = 1,N Y = repeat(y, M) y = ravel(Y) x = take(x, ind) y = take(y, ind) z = take(z, ind) return x,y,z def spy(Z, marker='s', markersize=10, **kwargs): """ SPY(Z, **kwrags) plots the sparsity pattern of the matrix S. kwargs give the marker properties  see help(plot) for more information on marker properties """ x,y,z = get_xyz_where(Z, Z>0) plot(x+0.5,y+0.5, linestyle='None', marker=marker,markersize=markersize, **kwargs) M,N = 25,20 data = zeros((M,N))*0. data[:,12] = rand(M) data[5,:] = rand(N) spy(data) axis([0,M,0,N]) show() 
From: Nils Wagner <nwagner@me...>  20041111 14:12:44

Hi all, Structure plots provide a quick visual check on the sparsity pattern of the matrix. A structure plot is a rectangular array of dots; a dot is black if the corresponding matrix element is nonzero otherwise it is white. Is it possible to generate such plots with scipy or should we switch over to matplotlib ? Nils Reference: http://math.nist.gov/MatrixMarket/structureplots.html 
From: Jochen Voss <voss@se...>  20041111 09:52:14

Hello, On Wed, 10 Nov 2004 16:26:47 +0000 (GMT) Andy Baerdmore wrote > Anyway, the upshot is matplotlib runs but it is not finding the > sans font and makes a poor substitute in its place. For exampe the output > from simple_demo.py is : ... I think that this might be a bug in Vittorio's Debian packages. There was a problem with the matplotlib font loading code which made it only find fonts installed under /usr/share/matplotlib. Since Debian hast the fonts under /usr/share/fonts they were not found. Maybe this was not fixed in his packages? You can try my alternative packages at http://seehuhn.de/debian/, which hopefully work. Download pythonmatplotlib_0.63.42.1_i386.deb =66rom there and install it manually with e.g. dpkg i pythonmatplotlib_0.63.42.1_i386.deb I hope this helps, Jochen =20 http://seehuhn.de/ 
From: Shin <sdhyok@em...>  20041111 06:21:38

My default mode of matplotlib is interactive mode, but in some programs I like to turn off the interactive model temporarily so postpone drawing until I call show(), because of performance concern. Any way for swith the mode in a script? Thanks in advance. Daehyok Shin UNCCH 