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
(1) |
2
(10) |
3
(29) |
4
(56) |
5
(44) |
6
(26) |
7
(12) |
8
(1) |
9
(2) |
10
(11) |
11
(28) |
12
(17) |
13
(6) |
14
(17) |
15
(7) |
16
(1) |
17
(8) |
18
(8) |
19
(7) |
20
(2) |
21
(8) |
22
(4) |
23
(6) |
24
(1) |
25
(2) |
26
(8) |
27
(3) |
28
(5) |
29
(1) |
30
|
31
(1) |
|
|
|
|
|
Hi Jack, In \matplotlib\axes.py, Axes.format_xdata() func = self.xaxis.get_major_formatter().format_data_short ->func = self.xaxis.get_major_formatter().format_data same for Axes.format_ydata() -Yongtao On Dec 22, 2007 1:46 PM, Jack Sankey <jac...@gm...> wrote: > Hello, > > When you make a figure and move the mouse around inside the axes, the > x- and y-values appear in the status bar. Is there a way to change the > precision of this data? It's only tracking 3 significant figures and I > need more (say you're zoomed in on some data with a large offset). > > Is there a way to change this in matplotlibrc or some global > preference? If not, is it a figure property? > > Thanks in advance, > Jack > > ------------------------------------------------------------------------- > This SF.net email is sponsored by: Microsoft > Defy all challenges. Microsoft(R) Visual Studio 2005. > http://clk.atdmt.com/MRT/go/vse0120000070mrt/direct/01/ > _______________________________________________ > Matplotlib-users mailing list > Mat...@li... > https://lists.sourceforge.net/lists/listinfo/matplotlib-users >
Hello, When you make a figure and move the mouse around inside the axes, the x- and y-values appear in the status bar. Is there a way to change the precision of this data? It's only tracking 3 significant figures and I need more (say you're zoomed in on some data with a large offset). Is there a way to change this in matplotlibrc or some global preference? If not, is it a figure property? Thanks in advance, Jack
Beautiful! Many thanks John. Gary R. John Hunter wrote: <snip> > You can manually turn off autoscaling on the axes instance with the > following, and both scatter and plot should then work as you want. > > ax1 = subplot(121) > axis('off') > ax1.imshow(rand(20,20)) > ax2 = subplot(122) > axis('off') > ax2.imshow(rand(20,20)) > > ax1.set_autoscale_on(False) > #ax1.scatter([5,10],[5,10]) # note 2 > ax1.plot([5,10],[5,10], 'o') # note 3 > show()
IyEvdXNyL2Jpbi9lbnYgcHl0aG9uCiMgZW1iZWRkaW5nX2luX3d4LnB5CiMKCiIiIgpDb3B5cmln aHQgKEMpIEplcmVteSBPJ0Rvbm9naHVlLCAyMDAzCgpMaWNlbnNlOiBUaGlzIHdvcmsgaXMgbGlj ZW5zZWQgdW5kZXIgdGhlIFBTRi4gQSBjb3B5IHNob3VsZCBiZSBpbmNsdWRlZAp3aXRoIHRoaXMg c291cmNlIGNvZGUsIGFuZCBpcyBhbHNvIGF2YWlsYWJsZSBhdApodHRwOi8vd3d3LnB5dGhvbi5v cmcvcHNmL2xpY2Vuc2UuaHRtbAoKVGhpcyBpcyBhIHNhbXBsZSBzaG93aW5nIGhvdyB0byBlbWJl ZCBhIG1hdHBsb3RsaWIgZmlndXJlIGluIGEgd3hQYW5lbC4KClRoZSBleGFtcGxlIGltcGxlbWVu dHMgdGhlIGZ1bGwgbmF2aWdhdGlvbiB0b29sYmFyLCBzbyB5b3UgY2FuIGF1dG9tYXRpY2FsbHkK aW5oZXJpdCBzdGFuZGFyZCBtYXRwbG90bGliIGZlYXR1cmVzIHN1Y2ggYXMgdGhlIGFiaWxpdHkg dG8gem9vbSwgcGFuIGFuZApzYXZlIGZpZ3VyZXMgaW4gdGhlIHN1cHBvcnRlZCBmb3JtYXRzLgoK VGhlcmUgYXJlIGEgZmV3IHNtYWxsIGNvbXBsZXhpdGllcyB3b3J0aCBub3RpbmcgaW4gdGhlIGV4 YW1wbGU6CgoxKSBCeSBkZWZhdWx0LCBhIHd4RnJhbWUgY2FuIGNvbnRhaW4gYSB0b29sYmFyIChh ZGRlZCB3aXRoIFNldFRvb2xCYXIoKSkKICAgYnV0IHRoaXMgaXMgYXQgdGhlIHRvcCBvZiB0aGUg ZnJhbWUuIE1hdHBsb3RsaWIgZGVmYXVsdCBpcyB0byBwdXQgdGhlCiAgIGNvbnRyb2xzIGF0IHRo ZSBib3R0b20gb2YgdGhlIGZyYW1lLCBzbyB5b3UgaGF2ZSB0byBtYW5hZ2UgdGhlIHRvb2xiYXIK ICAgeW91cnNlbGYuIEkgaGF2ZSBkb25lIHRoaXMgYnkgcHV0dGluZyB0aGUgZmlndXJlIGFuZCB0 b29sYmFyIGludG8gYQogICBzaXplciwgYnV0IHRoaXMgbWVhbnMgdGhhdCB5b3UgbmVlZCB0byBv dmVycmlkZSBHZXRUb29sQmFyIGZvciB5b3VyCiAgIHd4RnJhbWUgc28gdGhhdCB0aGUgZmlndXJl IG1hbmFnZXIgY2FuIGZpbmQgdGhlIHRvb2xiYXIuCgoyKSBJIGhhdmUgaW1wbGVtZW50ZWQgYSBm aWd1cmUgbWFuYWdlciB0byBsb29rIGFmdGVyIHRoZSBwbG90cyBhbmQgYXhlcy4KICAgSWYgeW91 IGRvbid0IHdhbnQgYSB0b29sYmFyLCBpdCBpcyBzaW1wbGVyIHRvIGFkZCB0aGUgZmlndXJlIGRp cmVjdGx5CiAgIGFuZCBub3Qgd29ycnkuIEhvd2V2ZXIsIHRoZSBmaWd1cmUgbWFuYWdlciBsb29r cyBhZnRlciBjbGlwcGluZyBvZiB0aGUKICAgZmlndXJlIGNvbnRlbnRzLCBzbyB5b3Ugd2lsbCBu ZWVkIGl0IGlmIHlvdSB3YW50IHRvIG5hdmlnYXRlCgozKSBUaGVyZSBpcyBhIGJ1ZyBpbiB0aGUg d2F5IGluIHdoaWNoIG15IGNvcHkgb2Ygd3hQeXRob24gY2FsY3VsYXRlcwogICB0b29sYmFyIHdp ZHRoIG9uIFdpbjMyLCBzbyB0aGVyZSBpcyBhIHRyaWNreSBsaW5lIHRvIGVuc3VyZSB0aGF0IHRo ZQogICB3aWR0aCBvZiB0aGUgdG9vbGJhdCBpcyB0aGUgc2FtZSBhcyB0aGUgd2lkdGggb2YgdGhl IGZpZ3VyZS4KCjQpIERlcGVuZGluZyBvbiB0aGUgcGFyYW1ldGVycyB5b3UgcGFzcyB0byB0aGUg c2l6ZXIsIHlvdSBjYW4gbWFrZSB0aGUKICAgZmlndXJlIHJlc2l6YWJsZSBvciBub3QuCiIiIgoK aW1wb3J0IG1hdHBsb3RsaWIKbWF0cGxvdGxpYi51c2UoJ1dYJykKZnJvbSBtYXRwbG90bGliLmJh Y2tlbmRzLmJhY2tlbmRfd3ggaW1wb3J0IFRvb2xiYXIsIEZpZ3VyZUNhbnZhc1d4LFwKICAgICBG aWd1cmVNYW5hZ2VyCgpmcm9tIG1hdHBsb3RsaWIuZmlndXJlIGltcG9ydCBGaWd1cmUKZnJvbSBt YXRwbG90bGliLmF4ZXMgaW1wb3J0IFN1YnBsb3QKaW1wb3J0ICBudW1weQojZnJvbSB3eCBpbXBv cnQgKgppbXBvcnQgd3gKCgpjbGFzcyBQbG90RmlndXJlKHd4LkZyYW1lKToKICAgIGRlZiBfX2lu aXRfXyhzZWxmKToKICAgICAgICB3eC5GcmFtZS5fX2luaXRfXyhzZWxmLCBOb25lLCAtMSwgIlRl c3QgZW1iZWRkZWQgd3hGaWd1cmUiKQoKICAgICAgICBzZWxmLmZpZyA9IEZpZ3VyZSgoOSw4KSwg NzUpCiAgICAgICAgc2VsZi5jYW52YXMgPSBGaWd1cmVDYW52YXNXeChzZWxmLCAtMSwgc2VsZi5m aWcpCiAgICAgICAgc2VsZi50b29sYmFyID0gVG9vbGJhcihzZWxmLmNhbnZhcykKICAgICAgICBz ZWxmLnRvb2xiYXIuUmVhbGl6ZSgpCgogICAgICAgICMgT24gV2luZG93cywgZGVmYXVsdCBmcmFt ZSBzaXplIGJlaGF2aW91ciBpcyBpbmNvcnJlY3QKICAgICAgICAjIHlvdSBkb24ndCBuZWVkIHRo aXMgdW5kZXIgTGludXgKICAgICAgICB0dywgdGggPSBzZWxmLnRvb2xiYXIuR2V0U2l6ZVR1cGxl KCkKICAgICAgICBmdywgZmggPSBzZWxmLmNhbnZhcy5HZXRTaXplVHVwbGUoKQogICAgICAgIHNl bGYudG9vbGJhci5TZXRTaXplKHd4LlNpemUoZncsIHRoKSkKCiAgICAgICAgIyBDcmVhdGUgYSBm aWd1cmUgbWFuYWdlciB0byBtYW5hZ2UgdGhpbmdzCiAgICAgICAgc2VsZi5maWdtZ3IgPSBGaWd1 cmVNYW5hZ2VyKHNlbGYuY2FudmFzLCAxLCBzZWxmKQogICAgICAgICMgTm93IHB1dCBhbGwgaW50 byBhIHNpemVyCiAgICAgICAgc2l6ZXIgPSB3eC5Cb3hTaXplcih3eC5WRVJUSUNBTCkKICAgICAg ICAjIFRoaXMgd2F5IG9mIGFkZGluZyB0byBzaXplciBhbGxvd3MgcmVzaXppbmcKICAgICAgICBz aXplci5BZGQoc2VsZi5jYW52YXMsIDEsIHd4LkxFRlR8d3guVE9QfHd4LkdST1cpCiAgICAgICAg IyBCZXN0IHRvIGFsbG93IHRoZSB0b29sYmFyIHRvIHJlc2l6ZSEKICAgICAgICBzaXplci5BZGQo c2VsZi50b29sYmFyLCAwLCB3eC5HUk9XKQogICAgICAgIHNlbGYuU2V0U2l6ZXIoc2l6ZXIpCiAg ICAgICAgc2VsZi5GaXQoKQoKICAgIGRlZiBwbG90X2RhdGEoc2VsZik6CiAgICAgICAgIyBVc2Ug dGhzIGxpbmUgaWYgdXNpbmcgYSB0b29sYmFyCiAgICAgICAgYSA9IHNlbGYuZmlnLmFkZF9zdWJw bG90KDExMSkKCiAgICAgICAgIyBPciB0aGlzIG9uZSBpZiB0aGVyZSBpcyBubyB0b29sYmFyCiAg ICAgICAgI2EgPSBTdWJwbG90KHNlbGYuZmlnLCAxMTEpCgogICAgICAgIHQgPSBudW1weS5hcmFu Z2UoMC4wLDMuMCwwLjAxKQogICAgICAgIHMgPSBudW1weS5zaW4oMipudW1weS5waSp0KQogICAg ICAgIGMgPSBudW1weS5jb3MoMipudW1weS5waSp0KQogICAgICAgIGEucGxvdCh0LHMpCiAgICAg ICAgYS5wbG90KHQsYykKICAgICAgICBzZWxmLnRvb2xiYXIudXBkYXRlKCkKCiAgICBkZWYgR2V0 VG9vbEJhcihzZWxmKToKICAgICAgICAjIFlvdSB3aWxsIG5lZWQgdG8gb3ZlcnJpZGUgR2V0VG9v bEJhciBpZiB5b3UgYXJlIHVzaW5nIGFuCiAgICAgICAgIyB1bm1hbmFnZWQgdG9vbGJhciBpbiB5 b3VyIGZyYW1lCiAgICAgICAgcmV0dXJuIHNlbGYudG9vbGJhcgoKaWYgX19uYW1lX18gPT0gJ19f bWFpbl9fJzoKICAgIGFwcCA9IHd4LlB5U2ltcGxlQXBwKDApCiAgICBmcmFtZSA9IFBsb3RGaWd1 cmUoKQogICAgZnJhbWUucGxvdF9kYXRhKCkKICAgIGZyYW1lLlNob3coKQogICAgYXBwLk1haW5M b29wKCkK