Re: [Matplotlib-users] Format date tick labels
Thanks, Ben.
Your fix works when the view interval is greater than 1 minute, but not so
much when the view interval is less than one minute.
BTW, what I am trying to accomplish is to use matplotlib to plot
time-series data that can be as long as several days and as short as a few
milliseconds. So, do you think I am better off to handle the format
manually by myself instead of deriving the format from auto locator? I am
skeptical about the auto locator now because it doesn't seem to be designed
for intervals less than 1 second.
Cheers,
Jianbao
On Mon, Oct 8, 2012 at 6:16 AM, Benjamin Root ben.r...@ou.edu wrote:
On Sun, Oct 7, 2012 at 6:47 PM, Jianbao Tao jianbao@gmail.com wrote:
fig = figure()
tsta = num2epoch(date2num(datetime.datetime.now()))
tarr = tsta + arange(0, 60*60*0.5, 0.1)# half hour, dt =
0.1 sec
x = np.array(num2date(epoch2num(tarr)))
nt = len(tarr)
y = randn(nt)
ax = fig.add_subplot(111)
ax.plot(x, y)
fig.canvas.draw()
# Make a formatter instance.
locator = mpl.dates.AutoDateLocator()
formatter = mpl.dates.AutoDateFormatter(locator)
# Customize the scaling.
formatter.scaled = {
365.0 : '%Y', # view interval 356 days
30. : '%b %Y', # view interval 30 days but less than
365 days
1.0 : '%b %d', # view interval 1 day but less than 30
days
1./24. : '%H:%M',# view interval 1 hour but less than 24
hours
1./24./60. : '%M:%S', # view interval 1 min but less than 1 hour
1./24./60./60. : '%S', # view interval 1 min
}
# Apply the formatter and redraw the plot.
ax.get_xaxis().set_major_formatter(formatter)
fig.canvas.draw()
Confirmed. It seems like that code fell out of maintenance a bit
somehow. Luckily, there is a quick fix to get your code working again.
Just add the following two lines after you set the major formatter, but
before your final draw:
locator.set_axis(ax.xaxis)
locator.refresh()
Note, if you zoom in and out after that call, I doubt it will re-adjust
your formatter. Could you file a bug report, please?
Cheers!
Ben Root
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[Matplotlib-users] But report: Date axis formatter problem
Problem: The autodatelocator and autodateformatter don't seem to work
properly. One, the formatter doesn't seem to work immediately after being
applied to an axis. A manual call to the locator seems necessary. Two, the
autodatelocator doesn't seem to be able to handle view intervals less than
1 second, i.e., the tick labels don't show digits beyond second. You can
see this by zooming the example figure from the code below to a level
shorter than one second.
1. Operating system: OS X 10.8.2
2. matplotlib version: 1.2.0rc2
3. I installed matplotlib via:
pip install git+https://github.com/matplotlib/matplotlib.git#egg=matplotlib-dev
4. Backend: TkAgg, but I don't think the problem depends on the backend.
5. Code:
#- code
-
# Running in ipython --pylab mode.
fig = figure()
tsta = num2epoch(date2num(datetime.datetime.now()))
tarr = tsta + arange(0, 60*30., 0.01) # half hour, dt = 0.01 sec
x = np.array(num2date(epoch2num(tarr)))
nt = len(tarr)
y = randn(nt)
ax = fig.add_subplot(111)
ax.plot(x, y)
fig.canvas.draw() # Show an overall view of the data
locator = mpl.dates.AutoDateLocator()
formatter = mpl.dates.AutoDateFormatter(locator)
formatter.scaled = {
365.0 : '%Y',
30. : '%b %Y',
1.0 : '%b %d',
1./24. : '%H:%M',
1./24./60. : '%M:%S',
1./24./60./60. : '%S',
}
ax.get_xaxis().set_major_formatter(formatter) # Won't work immediately.
locator.set_axis(ax.xaxis) # Have to manually make this call and the one
below.
locator.refresh() # Another manual call.
fig.canvas.draw()
# end of code
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Re: [Matplotlib-users] But report: Date axis formatter problem
Bug to the bug report: In the subject, it should be 'Bug report', rather
than 'But report'. Oops :-(
Jianbao
On Mon, Oct 8, 2012 at 9:37 AM, Jianbao Tao jianbao@gmail.com wrote:
Problem: The autodatelocator and autodateformatter don't seem to work
properly. One, the formatter doesn't seem to work immediately after being
applied to an axis. A manual call to the locator seems necessary. Two, the
autodatelocator doesn't seem to be able to handle view intervals less than
1 second, i.e., the tick labels don't show digits beyond second. You can
see this by zooming the example figure from the code below to a level
shorter than one second.
1. Operating system: OS X 10.8.2
2. matplotlib version: 1.2.0rc2
3. I installed matplotlib via:
pip install
git+https://github.com/matplotlib/matplotlib.git#egg=matplotlib-dev
4. Backend: TkAgg, but I don't think the problem depends on the backend.
5. Code:
#- code
-
# Running in ipython --pylab mode.
fig = figure()
tsta = num2epoch(date2num(datetime.datetime.now()))
tarr = tsta + arange(0, 60*30., 0.01) # half hour, dt = 0.01 sec
x = np.array(num2date(epoch2num(tarr)))
nt = len(tarr)
y = randn(nt)
ax = fig.add_subplot(111)
ax.plot(x, y)
fig.canvas.draw() # Show an overall view of the data
locator = mpl.dates.AutoDateLocator()
formatter = mpl.dates.AutoDateFormatter(locator)
formatter.scaled = {
365.0 : '%Y',
30. : '%b %Y',
1.0 : '%b %d',
1./24. : '%H:%M',
1./24./60. : '%M:%S',
1./24./60./60. : '%S',
}
ax.get_xaxis().set_major_formatter(formatter) # Won't work immediately.
locator.set_axis(ax.xaxis) # Have to manually make this call and the one
below.
locator.refresh() # Another manual call.
fig.canvas.draw()
# end of code
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[Matplotlib-users] Options for speeding up matplotlib, spectrogram with log scale axis, etc.
Hi all, A little background: I am from the space physics field where a lot of people watch/analyze satellite data for a living. This is a field currently dominated by IDL in terms of visualization/analysis software. I was a happy IDL user until I saw those very, very, I mean, seriously, very, very pretty matplotlib plots a couple of weeks ago. Although I was happy with IDL most of the time, I always hated the feel of IDL plots on screen. So, I decided to make my move from IDL to python + numpy + scipy + matplotlib. However, this is not a trivial move. One major thing that makes me stick to IDL in the first place is the Tplot package (bundled into THEMIS Data Analysis Software, a.k.a., TDAShttp://themis.ssl.berkeley.edu/software.shtml) developed at my own lab, the Space Sciences Lab at UC Berkeley. I must have something equivalent to Tplot to work efficiently on the python platform. In order to do that, there are two problems to solve. First, a utility module is required to load data that are in NASA CDF format. Second, a 2D plotting application is required with the following features: 1) Able to handle large amount vector data, 2) able to display spectrogram with log scale axis quickly, and 3) convenient toolbar to navigate the data. I have written a module that can quickly load data in CDF files in cython, with help from the cython and the numpy communities. I have also gotten the third plotting feature working with a customized navigation toolbar, thanks to the help I received in this mailing list. However, I haven't figured out how to get the first two plotting features. Matplotlib is known for its slow speed when it comes to large data sets. However, it seems some other packages can plot large data sets very fast, although not as pretty as matplotlib. So, I am wondering what makes matplotlib so slow. Is it because the anti-aliasing engine? If so, is it possible to turn it on or off flexibly to compromise between performance and quality? Also, is it possible to convert the bottle-neck bit of the code into cython to speed up matplotlib? As for spectrograms with log scale axis, I found a working solution from Stack Overflowhttp://stackoverflow.com/questions/10812189/creating-a-log-frequency-axis-spectrogram-using-specgram-in-matplotlib, but it is simply too slow. So, again, why is it so slow? So, for my purposes, my real problem now is the slow speed of matplotlib. I tried other packages, such as pyqtgraph, pyqwt, and Chaco/Traits. They seem to be faster, but they have serious problems too. Pyqtgraph seems very promising, but it seems to be in an infant stage for now with serious bugs. For example, I can't get it working together with matplotlib. PyQwt/guiqwt is reasonably robust, but it has too many dependencies in my opinion, and doesn't seem to have a wide user base. Chaco/Traits seems another viable possibility, especially considering the fact that it is actually supported by a company, but I didn't get a chance to see their performance and quality because I can't install Enable, a necessary bit for Chaco, on my mac. (But the fact that Chaco/Traits is supported by a real company is a real plus to me. If I can't eventually speed up matplotlib, I will probably give it another shot.) I have one idea to speed up line plots in matplotlib on screen, which is basically down-sampling the data before plotting. Basically, my idea is to down-sample the data into a level that one pixel only corresponds to one data point. Apparently, one must have enough information to determine the mapping between the data and the pixels on screen. However, such an overhead is just to maintain some house-keeping information, which I suppose is minimal. I have no idea how to speed up the log-scale spectrogram plot at the moment. :-( So, the bottom line: What are the options to speed up matplotlib? Your comments and insights are very much appreciated. :-) Thank you for reading. Cheers, Jianbao -- Don't let slow site performance ruin your business. Deploy New Relic APM Deploy New Relic app performance management and know exactly what is happening inside your Ruby, Python, PHP, Java, and .NET app Try New Relic at no cost today and get our sweet Data Nerd shirt too! http://p.sf.net/sfu/newrelic-dev2dev___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
Re: [Matplotlib-users] Options for speeding up matplotlib, spectrogram with log scale axis, etc.
Hi Andre, Thanks for your message. I like it. :-) I do have a .edu email. I didn't try to install Chaco with EPD because I tend to be skeptical when it comes to a bundled package with a lot of stuff. I like it to be as simple as possible. But it seems that I am probably better off to install EPD as a whole. Cheers, Jianbao On Mon, Oct 8, 2012 at 11:17 AM, Andre' Walker-Loud walksl...@gmail.comwrote: Hi Jianbao, One option for getting Chaco is to install the Enthought python disctribution http://www.enthought.com/ you can see from their package index, they install Chaco (and all needed libraries to make it work) http://www.enthought.com/products/epdlibraries.php If you have an email ending in .edu you can automatically get their academic version (fully functioning version - you just have to verify you are doing academic research). Since you mentioned you were at UC Berkeley, I assume you have .edu. Their python installation works nicely, and installs itself in /Library/Frameworks/Python.framework/ so it plays nicely with the Mac GUI environment. Also, it will not overwrite any other installation you have - it makes its own install dir. UNFORTUNATELY - at the moment, it appears they are writing their new academic software licenses, so you can not download it right now. But there message promises it will soon be available again. I have found the Enthought installation to be MUCH more reliable than FINK or MacPorts (Enthought is also a private company - hence the quality installers etc, and they like to support academic work). Cheers, Andre On Oct 8, 2012, at 10:55 AM, Jianbao Tao wrote: Hi all, A little background: I am from the space physics field where a lot of people watch/analyze satellite data for a living. This is a field currently dominated by IDL in terms of visualization/analysis software. I was a happy IDL user until I saw those very, very, I mean, seriously, very, very pretty matplotlib plots a couple of weeks ago. Although I was happy with IDL most of the time, I always hated the feel of IDL plots on screen. So, I decided to make my move from IDL to python + numpy + scipy + matplotlib. However, this is not a trivial move. One major thing that makes me stick to IDL in the first place is the Tplot package (bundled into THEMIS Data Analysis Software, a.k.a., TDAS) developed at my own lab, the Space Sciences Lab at UC Berkeley. I must have something equivalent to Tplot to work efficiently on the python platform. In order to do that, there are two problems to solve. First, a utility module is required to load data that are in NASA CDF format. Second, a 2D plotting application is required with the following features: 1) Able to handle large amount vector data, 2) able to display spectrogram with log scale axis quickly, and 3) convenient toolbar to navigate the data. I have written a module that can quickly load data in CDF files in cython, with help from the cython and the numpy communities. I have also gotten the third plotting feature working with a customized navigation toolbar, thanks to the help I received in this mailing list. However, I haven't figured out how to get the first two plotting features. Matplotlib is known for its slow speed when it comes to large data sets. However, it seems some other packages can plot large data sets very fast, although not as pretty as matplotlib. So, I am wondering what makes matplotlib so slow. Is it because the anti-aliasing engine? If so, is it possible to turn it on or off flexibly to compromise between performance and quality? Also, is it possible to convert the bottle-neck bit of the code into cython to speed up matplotlib? As for spectrograms with log scale axis, I found a working solution from Stack Overflow, but it is simply too slow. So, again, why is it so slow? So, for my purposes, my real problem now is the slow speed of matplotlib. I tried other packages, such as pyqtgraph, pyqwt, and Chaco/Traits. They seem to be faster, but they have serious problems too. Pyqtgraph seems very promising, but it seems to be in an infant stage for now with serious bugs. For example, I can't get it working together with matplotlib. PyQwt/guiqwt is reasonably robust, but it has too many dependencies in my opinion, and doesn't seem to have a wide user base. Chaco/Traits seems another viable possibility, especially considering the fact that it is actually supported by a company, but I didn't get a chance to see their performance and quality because I can't install Enable, a necessary bit for Chaco, on my mac. (But the fact that Chaco/Traits is supported by a real company is a real plus to me. If I can't eventually speed up matplotlib, I will probably give it another shot.) I have one idea to speed up line plots in matplotlib on screen, which is basically down-sampling the data before plotting. Basically, my idea is to down-sample the data into a level
[Matplotlib-users] Format date tick labels
Hi,
I am having trouble to customize the format of date axis tick labels. Below
is a snippet to demonstrate my problem:
#- code
# Make an example plot.
fig = figure()
tsta = num2epoch(date2num(datetime.datetime.now()))
tarr = tsta + arange(0, 60*60*0.5, 0.1)# half hour, dt =
0.1 sec
x = np.array(num2date(epoch2num(tarr)))
nt = len(tarr)
y = randn(nt)
ax = fig.add_subplot(111)
ax.plot(x, y)
fig.canvas.draw()
# Make a formatter instance.
locator = mpl.dates.AutoDateLocator()
formatter = mpl.dates.AutoDateFormatter(locator)
# Customize the scaling.
formatter.scaled = {
365.0 : '%Y', # view interval 356 days
30. : '%b %Y', # view interval 30 days but less than 365
days
1.0 : '%b %d', # view interval 1 day but less than 30 days
1./24. : '%H:%M',# view interval 1 hour but less than 24 hours
1./24./60. : '%M:%S', # view interval 1 min but less than 1 hour
1./24./60./60. : '%S', # view interval 1 min
}
# Apply the formatter and redraw the plot.
ax.get_xaxis().set_major_formatter(formatter)
fig.canvas.draw()
#-- end of code
-
I listed my expectation about the new scaling above. However, what I got
was simply '%Y' after I applied the formatter. So, how can I make the new
formatter work, please? Thank you very much.
Jianbao
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Re: [Matplotlib-users] Matplotlib produced plots in academic journal articles
I think that is a great idea. I think it is worthwhile to put a highlighted spot, or whatever, that shows matplotlib plots in academic publications. Additionally, it is good for enlarging the matplotlib user base to ask people to acknowledge matplotlib in their papers if they use matplotlib to make plots, and share links of their publications. Of course, matplotlib.orgshould provide some sort of platform for people to share that kind of information, such as a public email address. Such acknowledgement is not a hard thing to do, and I think most people, if not all, that benefit from matplotlib would be more than happy to do so. :-) Jianbao Message: 4 Date: Thu, 4 Oct 2012 22:31:34 -0600 From: G?khan Sever gokhanse...@gmail.com Subject: [Matplotlib-users] Matplotlib produced plots in academic journal articles To: matplotlib-users@lists.sourceforge.net Message-ID: CAE5kuyh17jsDcaejwx= xekryyys9kf5zw9q7yigziaketio...@mail.gmail.com Content-Type: text/plain; charset=utf-8 Hello, Is there any collection of articles that shows academic articles using matplotlib produced plots? I have come across a few recent articles in my field with plots produced by matplotlib. Though, the mpl page shows some nice examples of publication quality plots, it would be nice to have a discipline specific collection of academic paper citations/links (hopefully mostly open-access titles) to raise awareness of mpl usage in academia by attracting other language users. What do you think? -- G?khan -- Don't let slow site performance ruin your business. Deploy New Relic APM Deploy New Relic app performance management and know exactly what is happening inside your Ruby, Python, PHP, Java, and .NET app Try New Relic at no cost today and get our sweet Data Nerd shirt too! http://p.sf.net/sfu/newrelic-dev2dev___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
[Matplotlib-users] Problem with shared axis
Hi, I am working on a time-series data browser based on matplotlib. In general, it shows a N_row x 1_col stack of axes, which share the x axis, the time axis. It is nice that matplotlib offers the sharex option so that the data can be zoomed simultaneously in time. However, one problem with the sharex option is that it not only shares the axis range (or limits, if you will), but also the axis appearance, which is not always desirable. In my case, I want the tick labels to be shown only on the bottom subplot. However, that doesn't seem to be achievable with sharex. The follow snippet demonstrates my example: #- code import matplotlib.pyplot as plt fig = plt.figure() ax1 = fig.add_subplot(211) ax2 = fig.add_subplot(212, sharex=ax1) ax1.get_xaxis().set_ticklabels([]) # This also suppresses x tick labels of ax2. fig.canvas.draw() #-- end of code Is there a workaround, hopefully simple and straightforward, to share range (or limits) only among axes? Better yet, can this feature be added, like a keyword sharexrange, in the future, if it is not already there? Of course, the situation should be similar for y axis, too. Thank you very much. Jianbao -- Don't let slow site performance ruin your business. Deploy New Relic APM Deploy New Relic app performance management and know exactly what is happening inside your Ruby, Python, PHP, Java, and .NET app Try New Relic at no cost today and get our sweet Data Nerd shirt too! http://p.sf.net/sfu/newrelic-dev2dev___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
[Matplotlib-users] Development advice needed
Dear all, As some of you might have noticed, I am asking questions frequently recently, most of which are naive ones. The reason for this is that I recently decided to develop a satellite data viewer with matplotlib, and I am new to both python and matplotlib. Here is a little background of this decision. I am a postdoc in the space physics field, where a lot of people watch and analyze satellite data for a living. The data are time-series data by nature. As for now, a lot of people use packages based on IDL to navigate their data. I myself is one of them too. For now. :-) One big problem with IDL is that it is very expensive because it doesn't have a broad enough user base to drive their cost down. Another big problem is that the company that is developing IDL doesn't seem to work in the right direction. For example, more than 99% of the time of more than 99% of the IDL users use the so-called direct graphics system in IDL, but IDL hasn't upgraded this system since, I don't know, maybe early 90s. Compared to what matplotlib can offer, the on-screen graphics quality of the IDL direct graphics is simply ugly, which is a big reason why I want to switch to matplotlib. There are also some other frequently-used nice features in matplotlib that IDL doesn't have. After reading the matplotlib documents and trying out several little examples for a few days, I now have a feeling that matplotlib at least has most of the infrastructure ready for my purposes. One thing that bothers me a little bit is that the plotting speed seems to be a little slow. But IDL had the same problem in the first place too. As computers became faster and faster, that problem just became less and less important. I expect the same thing will happen to matplotlib too. Now let me turn to technical stuff. What I want is a time-series plotting system like the following. First, it manages all the figure windows it generates, including the positions and looks of the figure windows. A common use case for such a system would be that the user is also analyzing the data while watching the time-series data,and hence the user likely needs to plot some temporary results, such as a snapshot of particle distribution function, but doesn't want to screw up the time-series plot. Therefore, it will be nice that the plotting windows of the time-series data are managed particularly. Second, it should come with a navigation toolbar that facilitates the data navigation. The current navigation toolbar widget is nice and probably suit more than 50% percent of my needs, but it's not sufficient. Third, the system should have minimal dependencies for the sake of portability and installation easiness. As for now, I don't want any dependencies beyond numpy, scipy, and matplotlib. Ipython would be a highly recommended tool, but the system should be just fine without it. After weighing all the options, I sense that I will probably be better off to use the matplotlib library directly, rather than the convenient utilities provided by pyplot. However, I am having a hard time to find good instructions for using the matplotlib infrastructure. So, I would like to hear some references on that. I also would like to hear general advice about how to construct such a system so that its structure is consistent with matplotlib conventions. Other comments and advice are warmly welcome too. :-) Thank you very much for reading this far. Jianbao -- Don't let slow site performance ruin your business. Deploy New Relic APM Deploy New Relic app performance management and know exactly what is happening inside your Ruby, Python, PHP, Java, and .NET app Try New Relic at no cost today and get our sweet Data Nerd shirt too! http://p.sf.net/sfu/newrelic-dev2dev___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
Re: [Matplotlib-users] Development advice needed
Dear Anthony, Thank you so much for your advice. I embedded my response below. Jianbao On Wed, Oct 3, 2012 at 10:49 AM, Anthony Floyd anthonyfl...@gmail.comwrote: Hi Jianbao, First some context: at the company I work for, we've been using matplotlib to do much of what you want to do for the past 4 years. We have created our own application for plotting, interrogating, and manipulating time-series data coming from both simulations and measurements, although from a completely different domain (in our case it's virtual manufacturing of composite materials). In the past two years, we've also been using matplotlib to plot in more-or-less realtime data from a cloud industrial sensors (temperature, pressure, etc). Do you have any references, such as screen shots, gallery, examples, or whatever? I am very curious to see what people can do with matplotlib. After reading the matplotlib documents and trying out several little examples for a few days, I now have a feeling that matplotlib at least has most of the infrastructure ready for my purposes. One thing that bothers me a little bit is that the plotting speed seems to be a little slow. But IDL had the same problem in the first place too. As computers became faster and faster, that problem just became less and less important. I expect the same thing will happen to matplotlib too. This is true, matplotlib can be slow, particularly for large data sets and many data sets. The trick is to downsample (and use tiling if you're going to be panning around a lot) what you're actually plotting before handing it off to the plot. I think more recent versions of matplotlib handle some of this for you, but we've found that it's faster to do the downsampling ourselves. As a matter of fact, I considered writing intermediate routines to handle downsampling before feeding data in matplotlib. However, you will have to do anti-alias filtering for that. So, I wasn't sure downsampling would boost the speed anyway. But based on your experience, this is probably a good idea. :-) Now let me turn to technical stuff. What I want is a time-series plotting [...] sufficient. Third, the system should have minimal dependencies for the sake of portability and installation easiness. As for now, I don't want any dependencies beyond numpy, scipy, and matplotlib. Ipython would be a highly recommended tool, but the system should be just fine without it. You're going to need more than that. At the very least you're going to need a widget framework like wxPython, pyQT, pyGTK, or some such. These will provide you with all the window management, widget controls, and so on. Our preference is wxPython but YMMV. One of my concerns about third-party widget framework is that sometimes it is difficult to install them. In fact, I tried to install wxPython on my Mac (10.8 OS X) last night, but didn't succeed. Another concern of mine is that I don't know how efficient or how easy to interact with a thrid-party widget framework from a python interpreter. However, again, based on your reply, it doesn't seem to be a big issue after all. After weighing all the options, I sense that I will probably be better off to use the matplotlib library directly, rather than the convenient utilities provided by pyplot. However, I am having a hard time to find good instructions for using the matplotlib infrastructure. So, I would like to hear some references on that. I also would like to hear general advice about how to construct such a system so that its structure is consistent with matplotlib conventions. Other comments and advice are warmly welcome too. Absolutely, you'll want to use the API rather than the utility functions. The best reference for that is the online documentation at matplotlib.org. In the past we've found the source code documentation (or, say, that generated by doxygen) more helpful than the Sphinx documentation, but frankly our matplotlib bits are pretty stable now and we haven't had to use the documentation for a while (perhaps it's better now). Good luck! We've been very happy with our design choices, and get nothing but positive feedback on how our plots look and feel. matplotlib and the amazing active community around it have everything to do with that. I am very glad to hear that. :-) Anthony. -- Don't let slow site performance ruin your business. Deploy New Relic APM Deploy New Relic app performance management and know exactly what is happening inside your Ruby, Python, PHP, Java, and .NET app Try New Relic at no cost today and get our sweet Data Nerd shirt too! http://p.sf.net/sfu/newrelic-dev2dev___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
Re: [Matplotlib-users] Development advice needed
Thank you so much, Anthony. After weighing the options, I decided to go for Tkinter. The major reason for this is portability. BTW, I checked out your website. Those screenshots are quite impressive. :-) Jianbao On Wed, Oct 3, 2012 at 3:27 PM, Anthony Floyd anthonyfl...@gmail.comwrote: Hi Jianbao, Do you have any references, such as screen shots, gallery, examples, or whatever? I am very curious to see what people can do with matplotlib. If you can find a Windows machine (or a Windows VM) and stomach a 60 MB download, visit http://www.convergent.ca/products/raven/downloads.html and grab the RAVEN Viewer and Demo RAVEN Workspace. When starting the program for the first time, don't worry about selecting a license, select Viewer Only. Open the demo file. All plotting, annotating, legends, etc are handled by matplotlib. wxPython provides the rest of the GUI elements. The entirety of the program except for the engineering backend (which isn't exposed in the viewer anyway) is written in Python. If you can't get to a Windows box, then just visit http://www.convergent.ca/raven to get a sense of the application. Cheers, Anthony. -- Don't let slow site performance ruin your business. Deploy New Relic APM Deploy New Relic app performance management and know exactly what is happening inside your Ruby, Python, PHP, Java, and .NET app Try New Relic at no cost today and get our sweet Data Nerd shirt too! http://p.sf.net/sfu/newrelic-dev2dev___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
[Matplotlib-users] How to modify the navigation toolbar easily in a matplotlib figure window?
Is it possible to do something like the following to modify the navigation toolbar in matplotlib? 1. Generate a figure window, such as by *fig = figure()* 2. Get a reference of the navigation toolbar, such as by *tbar = fig.get_navigation_toolbar()* or better yet, just by *tbar = fig.navtbar* 3. Modify the toolbar through the reference *tbar*, such as delete/add/edit a button by something like this: *tbar.add_button()*, *tbar.remove_button(a reference to a button)*, *tbar.edit_button(a reference to a button)*. 4. Update the figure by *fig.canvas.draw()* Thank you very much. -- Don't let slow site performance ruin your business. Deploy New Relic APM Deploy New Relic app performance management and know exactly what is happening inside your Ruby, Python, PHP, Java, and .NET app Try New Relic at no cost today and get our sweet Data Nerd shirt too! http://p.sf.net/sfu/newrelic-dev2dev___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
[Matplotlib-users] Create a figure window without navigation toolbar
Hi, I know one can make a figure window without toolbar by doing mpl.rcParams['toolbar'] = 'None' However, this approach is kind of annoying if one just wants to remove the toolbar for one figure window and to keep the default behavior to be with a toolbar. So, I am wondering if it is possible to do something like: fig = figure(toolbar=False) # Create a new figure window with no toolbar. If it is not possible now, I am wondering if the matplotlib team has any intention to implement that feature. I would think it is not hard to do so, since it is so easy to achieve that goal by setting a parameter. But I could very much be wrong, as I know nothing about how things work under the hood for matplotlib. Anyway, I am mostly just curious, since I have already got a workaround for that. But it would be nice to know other's people's approaches/ideas. :-) Thank you very much. Jianbao -- Don't let slow site performance ruin your business. Deploy New Relic APM Deploy New Relic app performance management and know exactly what is happening inside your Ruby, Python, PHP, Java, and .NET app Try New Relic at no cost today and get our sweet Data Nerd shirt too! http://p.sf.net/sfu/newrelic-dev2dev___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
[Matplotlib-users] Control the position of a figure window
Hi, Is it possible to specify the position of a figure window when one is created? This will be a killing feature if one wants to put the figure window at the right place in the screen automatically. It is annoying if ones has to drag a new figure to a comfortable place in the screen every time a new figure is created. Jianbao -- Don't let slow site performance ruin your business. Deploy New Relic APM Deploy New Relic app performance management and know exactly what is happening inside your Ruby, Python, PHP, Java, and .NET app Try New Relic at no cost today and get our sweet Data Nerd shirt too! http://p.sf.net/sfu/newrelic-dev2dev___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
