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) |
---|---|---|---|---|---|---|---|---|---|---|---|---|
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) |
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) |
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) |
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) |
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) |
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) |
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) |
2012 |
Jan
(322) |
Feb
(414) |
Mar
(377) |
Apr
(179) |
May
(173) |
Jun
(234) |
Jul
(295) |
Aug
(239) |
Sep
(276) |
Oct
(355) |
Nov
(144) |
Dec
(108) |
2013 |
Jan
(170) |
Feb
(89) |
Mar
(204) |
Apr
(133) |
May
(142) |
Jun
(89) |
Jul
(160) |
Aug
(180) |
Sep
(69) |
Oct
(136) |
Nov
(83) |
Dec
(32) |
2014 |
Jan
(71) |
Feb
(90) |
Mar
(161) |
Apr
(117) |
May
(78) |
Jun
(94) |
Jul
(60) |
Aug
(83) |
Sep
(102) |
Oct
(132) |
Nov
(154) |
Dec
(96) |
2015 |
Jan
(45) |
Feb
(138) |
Mar
(176) |
Apr
(132) |
May
(119) |
Jun
(124) |
Jul
(77) |
Aug
(31) |
Sep
(34) |
Oct
(22) |
Nov
(23) |
Dec
(9) |
2016 |
Jan
(26) |
Feb
(17) |
Mar
(10) |
Apr
(8) |
May
(4) |
Jun
(8) |
Jul
(6) |
Aug
(5) |
Sep
(9) |
Oct
(4) |
Nov
|
Dec
|
2017 |
Jan
(5) |
Feb
(7) |
Mar
(1) |
Apr
(5) |
May
|
Jun
(3) |
Jul
(6) |
Aug
(1) |
Sep
|
Oct
(2) |
Nov
(1) |
Dec
|
2018 |
Jan
|
Feb
|
Mar
|
Apr
(1) |
May
|
Jun
|
Jul
|
Aug
|
Sep
|
Oct
|
Nov
|
Dec
|
2020 |
Jan
|
Feb
|
Mar
|
Apr
|
May
(1) |
Jun
|
Jul
|
Aug
|
Sep
|
Oct
|
Nov
|
Dec
|
2025 |
Jan
(1) |
Feb
|
Mar
|
Apr
|
May
|
Jun
|
Jul
|
Aug
|
Sep
|
Oct
|
Nov
|
Dec
|
S | M | T | W | T | F | S |
---|---|---|---|---|---|---|
|
|
|
|
1
(11) |
2
(2) |
3
(2) |
4
|
5
(1) |
6
(6) |
7
(9) |
8
(5) |
9
(1) |
10
(1) |
11
(1) |
12
(7) |
13
(6) |
14
(3) |
15
(1) |
16
(10) |
17
(1) |
18
(2) |
19
(3) |
20
(14) |
21
(14) |
22
(18) |
23
(5) |
24
(5) |
25
(1) |
26
(22) |
27
(5) |
28
(21) |
29
(25) |
30
(3) |
|
Additional functionality for segplot could be; instead of providing maxdelta, be able to supply start and stop indices, or perhaps a list of start and stop indices. Another alternative would be to supply x and y ranges which specify to only join the dots when both points are within the range. It might also be worth thinking about factoring out the segment breaking code. Then you could do things like apply regression fit lines to individual segments etc. Gary ----- Original Message ----- > Any suggestions for a name, or additional functionality? > > JDH -- ___________________________________________________________ Sign-up for Ads Free at Mail.com http://promo.mail.com/adsfreejump.htm
>>>>> "Peter" == Peter Groszkowski <pgr...@ge...> writes: Peter> Hi everyone: I was wondering whether it is possible to tell Peter> matplotlib how/when to connect data points. Consider this Peter> simple script: Peter> from matplotlib.matlab import * figure(1) t = Peter> [0,1,2,3,4,5,105,106,107] s = [1,4,5,3,9,11,-5,-8,3] Peter> plot(t, s, antialiased=False) grid(True) show() Peter> There are no data points between t=5 and t=105. By default Peter> the points (5,11) and (105,-5) are connected, but I would Peter> like to tell matplotlib NOT to do so. In my case I would Peter> like to pass the plot function a variable telling it what Peter> to do. So for example would have: Peter> plot(t, s, max_delta=40) Peter> This would mean that the points are only to be connected if Peter> the difference between the adjacent t values is less than Peter> 40. In my case this is relevant because sometimes there Peter> are "holes" in my data, and connecting the points makes the Peter> plots look very messy. Peter> Would anyone find something like this useful? Would it be Peter> difficult to implement? Certainly not difficult, and probably useful enough to put in the standard distro. Eg, in a stock market trading example, you would have lots of quotes, minute by minute, punctuated by long intervals overnight where the market is closed. If you set maxdelta appropriately, you could draw connected lines only within trading days. Here is a sample implementation from matplotlib.matlab import * def segplot(x, y, fmt, maxdelta, **kwargs): """ Plot x versus y, breaking the plot at any point where x[i] - x[i-1] > maxdelta. kwargs are passed on to plot """ x = asarray(x) y = asarray(y) d = diff(x) lines = [] ind = nonzero(greater(d, maxdelta)) ind = ind+1 if not len(ind): lines.extend( plot(x,y,fmt,**kwargs) ) else: allind = [0] allind.extend(ind) allind.append(len(x)) for i1,i2 in zip(allind[:-1], allind[1:]): lines.extend( plot(x[i1:i2], y[i1:i2], fmt, **kwargs) ) return lines t = [0,1,2,3,4,5,105,106,107,187, 200, 212, 300, 320] s = [1,4,5,3,9,11,-5,-8,3,12, 15, 12, -1, 3] segplot(t, s, 'b-o', 40, antialiased=False) grid(True) show() I'm inclined not to make this part of plot, since plot processes a variable number of arguments it makes it a little difficult. Certainly doable, but I'm hesitant to put too much on plot because it might become unwieldy. But a new function, like segment plot, would be easy enough to include. Any suggestions for a name, or additional functionality? JDH
>>>>> "Dominique" == Dominique Orban <Dom...@po...> writes: Dominique> Many thanks for your reply and suggestions. I see what Dominique> is happening. Nearest neighbor interpolation has the Dominique> colors right, but i was trying to get "more Dominique> interpolation". I guess looking at the great pictures Dominique> that imshow() produces i was hoping for a result such Dominique> as that of Dominique> X = rand(10) pcolor( X ) shading 'interp' As far as I recall, matlab's pcolor also loses an edge due to interpolation. Perhaps the main difference is that in matlab, the axes limits are set by default so that you don't see it. I remember being surprised by this many moons ago the first time I used pcolor in matlab. Dominique> in Matlab. The Matplotlib picture is just as good Dominique> really, except for the border. Why are the other Dominique> borders not white as well? Is the interpolation Dominique> "directional"? Why aren't pixels on the border only Dominique> interpolated with their neighbors inside the image, and Dominique> not those outside (these have less neighbors than Dominique> pixels in the middle)? I'll have to think about this some more. There is also something funny about how the tick labeling currently works for images, because the X[0,0] coord is upper left but is labeled as 0,10. Perhaps ticks should be off be default, or labeled with the y axis descending. Those who have opinions please weigh in. BTW, the developer of agg, Maxim, is fairly responsive, so if you want to pursue this issue after reading some of the code I point to below on the agg mailing list http://lists.sourceforge.net/lists/listinfo/vector-agg-general Maxim can probably provide some additional guidance. Dominique> Perhaps you can point me to the part of the code (c++ i Dominique> assume) which does the interpolation? Should i grab the Dominique> CVS repository for that? Then maybe i can play around Dominique> and see if i can achieve the effect i am looking for. The code is available in the matplotlib src distribution. The module is src/_image.cpp, which uses agg for image manipulation; see the function Image_resize. All the agg code is also in the matplotlib src distro, eg, agg2/include. agg2 doesn't have a lot of documentation - hence I spend a lot of time reading src files, eg agg_conv_transform.h agg_span_image_filter_rgb24.h agg_span_image_filter_rgba32.h agg_span_interpolator_linear.h The latest agg snapshot it http://www.antigrain.com/agg2.tar.gz which has lots of examples in the examples dir. Agg can do a lot with images, some of which would be nice to add to the matplotlib interface.... JDH
John Hunter wrote: >>>>>>"Dominique" == Dominique Orban <Dom...@po...> writes: > > > Dominique> When using imshow(), why does there always seem to be a > Dominique> blank zone along the southern and eastern edges of the > Dominique> figure? For instance: > > Dominique> X = rand(10,10) imshow(X) > > Dominique> plots a luminance image of X, which seems fine, except > Dominique> for the lower and rightmost edges, which are blank. I > Dominique> may be misunderstanding the purpose of imshow, but > Dominique> skimming through the code didn't give me an answer. I > Dominique> am using matplotlib 0.52 on WinXP with either GTKAgg or > Dominique> TkAgg. > > Hi Dominique, > > Your example did point me to a small bug in the image module, but it > is mostly unrelated to what you are observing. In the axes.py > function imshow, replace > > self.set_image_extent(0, numcols-1, 0, numrows-1) > with > self.set_image_extent(0, numcols, 0, numrows) > > > This only affects the tick labeling (not the actual image display) > but it was wrong before and should be changed. > > Now run this script > > from matplotlib.matlab import * > X = rand(10,10) > > subplot(211) > im = imshow(X) > im.set_interpolation('nearest') > > subplot(212) > im = imshow(X) > show() > > The key thing is that the white border you are seeing arises from > interpolation. The points on the bottom and right have no neighbors > in those directions, and so they interpolate to the background color, > which is white. > > You can set the axis limits so that these regions don't appear, or use > nearest neighbor interpolation. > > Let me know if these suggestions don't work for you. > > JDH John, Many thanks for your reply and suggestions. I see what is happening. Nearest neighbor interpolation has the colors right, but i was trying to get "more interpolation". I guess looking at the great pictures that imshow() produces i was hoping for a result such as that of X = rand(10) pcolor( X ) shading 'interp' in Matlab. The Matplotlib picture is just as good really, except for the border. Why are the other borders not white as well? Is the interpolation "directional"? Why aren't pixels on the border only interpolated with their neighbors inside the image, and not those outside (these have less neighbors than pixels in the middle)? Perhaps you can point me to the part of the code (c++ i assume) which does the interpolation? Should i grab the CVS repository for that? Then maybe i can play around and see if i can achieve the effect i am looking for. Thanks again ! Dominique
>>>>> "Dominique" == Dominique Orban <Dom...@po...> writes: Dominique> When using imshow(), why does there always seem to be a Dominique> blank zone along the southern and eastern edges of the Dominique> figure? For instance: Dominique> X = rand(10,10) imshow(X) Dominique> plots a luminance image of X, which seems fine, except Dominique> for the lower and rightmost edges, which are blank. I Dominique> may be misunderstanding the purpose of imshow, but Dominique> skimming through the code didn't give me an answer. I Dominique> am using matplotlib 0.52 on WinXP with either GTKAgg or Dominique> TkAgg. Hi Dominique, Your example did point me to a small bug in the image module, but it is mostly unrelated to what you are observing. In the axes.py function imshow, replace self.set_image_extent(0, numcols-1, 0, numrows-1) with self.set_image_extent(0, numcols, 0, numrows) This only affects the tick labeling (not the actual image display) but it was wrong before and should be changed. Now run this script from matplotlib.matlab import * X = rand(10,10) subplot(211) im = imshow(X) im.set_interpolation('nearest') subplot(212) im = imshow(X) show() The key thing is that the white border you are seeing arises from interpolation. The points on the bottom and right have no neighbors in those directions, and so they interpolate to the background color, which is white. You can set the axis limits so that these regions don't appear, or use nearest neighbor interpolation. Let me know if these suggestions don't work for you. JDH
Hi john , I was doing a pure TeX plot (a bunch of equations inside a box) and I noticed that the \sqrt{}command does not work even though it listed in the help page for mathtext. \frac and \dfrac would be a nice addition too... feel free to used this little script as an example of another use of mathtext... Flavio