I've been using python extensively to extract data from various external pieces of equipment (ranging from arduinos to oscilloscopes), and I'm looking for a simplistic way to plot stuff.
There's already some answers to similar questions on stack overflow:
What is the best real time plotting widget for wxPython?
With most pointing to this fine piece of code by Eli Bendersky
http://eli.thegreenplace.net/2008/08/01/matplotlib-with-wxpython-guis/
But the scope of the code is far more complicated that what I'm looking for. I'm looking for something rather minimalistic that just plots the data in real-time as it streams from a source -- it doesn't need a GUI, radio buttons, knobs and sliders, or anything like that.
It seems that solutions such as calling pylab.plot() or pylab.show() in a loop doesn't seem to give the correct behavior.
Does anyone have suggestions?
Well, this isn't a wxPython answer but I've used Chaco for this sort of thing and it's pretty straight forward. There is a nice example of a realtime spectrum analyzer that may be similar to your use case and a nice tutorial. So, if you aren't tied to wxPython for other reasons, that might be worth a look.
Besides the matplotlib examples you've found, there's also wx.lib.plot and several answers here: http://wxpython-users.1045709.n5.nabble.com/real-time-data-plots-td2344816.html
To use real time plotting you need to send signals to the GUI loop. If you use interactive mode (Ipython) then you might also like to use threads.
I have written some decorators to handle the GUI and threading in a really easy and clean way. They work for the QT backend.
https://gist.github.com/Vrekrer/106c49a3ae6d420937aa
A sample code for Ipython will look like this
#%pylab qt
#https://gist.github.com/Vrekrer/106c49a3ae6d420937aa
import QThreadDecorators
import time
#QThreadDecorators.GUI_Safe
def myplot(x,y):
#This will plot a new line for each call (ok for an example)
plot(x, y, 'bo-')
grid(True)
#QThreadDecorators.AsQThread
def myLoop(x):
y = x * nan
for i, xi in enumerate(x):
#get some data
time.sleep(1)
y[i] = xi**2
#plot in real time
myplot(x,y)
#just call the function and it will run on a thread
myLoop( arange(20) )
Related
I used to work in Matlab and it is really convenient (when working with big arrays/matrices and nested functions) to visualize intermediate results during debugging using plot function.
In Python I cannot plot anything in debug mode: a window with figure plot is never loaded (I am using Spyder IDE for coding and matplotlib.pyplot for plotting).
This is really annoying when debugging nested function and classes.
Does anyone know a good solution? Of course, I can always output intermediate results, however it is not convenient.
Thanks,
Mikhail
Ok, I found a way to show the plot without breaking the debugging process.
All you need to do is to issue plt.pause(1) command, which will display the plots, and then one can continue the debugging process.
I'm just wondering if it exists an equivalent to the Matlab figure window in Python where we can modify plots directly from the figure window, or add some features (text, box , arrow, and so on), or make curve fitting, etc.
Matplotlib is good, but it is not as high-level as the Matlab figure. We need to code everything and if we want to modify plots, we need to modify the code directly (except for some basic stuffs like modifing the line color)
With matplotlib, you will indeed remain in the "code it all" workflow. This is not directly the answer you expect but the matplotlib documentation recently gained a very instructive figure that will probably help you if you stay with matplotlib: http://matplotlib.org/examples/showcase/anatomy.html shows the "anatomy" of the figure with all the proper designations for the parts of the figure.
Overall, I could always find examples of what I needed in their excellent gallery http://matplotlib.org/gallery.html
In my opinion, you'll save time by coding these customizations instead of doing them by hand. You may indeed feel otherwise but if not there is a ton of examples of matplotlib code on SO, in their docs and a large community of people around it :-)
I have been looking for a way to be able to select which series are visible on a plot, after a plot is created.
I need this as i often have plots with many series. they are too many to plot at the same time, and i need to quickly and interactively select which series are visible. Ideally there will be a window with a list of series in the plot and checkboxes, where the series with the checked checkbox is visible.
Does anyone know if this has been already implemented somewhere?, if not then can someone guide me of how can i do it myself?
Thanks!
Omar
It all depends on how much effort you are willing to do and what the exact requirements are, but you can bet it has already been implemented somewhere :-)
If the aim is mainly to not clutter the image, it may be sufficient to use the built-in capabilities; you can find relevant code in the matplotlib examples library:
http://matplotlib.org/examples/event_handling/legend_picking.html
http://matplotlib.org/examples/widgets/check_buttons.html
If you really want to have a UI, so you can guard the performance by limiting the amount of plots / data, you would typically use a GUI toolbox such as GTK, QT or WX. Look here for some articles and example code:
http://eli.thegreenplace.net/2009/05/23/more-pyqt-plotting-demos/
A list with checkboxes will be fine if you have a few plots or less, but for more plots a popup menu would probably be better. I am not sure whether either of these is possible with matplotlib though.
The way I implemented this once was to use a slider to select the plot from a list - basically you use the slider to set the index of the series that should be shown. I had a few hundred series per dataset, so it was a good way to quickly glance through them.
My code for setting this up was roughly like this:
fig = pyplot.figure()
slax = self.fig.add_axes((0.1,0.05,0.35,0.05))
sl = matplotlib.widgets.Slider(slax, "Trace #", 0, len(plotlist), valinit=0.0)
def update_trace():
ax.clear()
tracenum = int(np.floor(sl.val))
ax.plot(plotlist[tracenum])
fig.canvas.draw()
sl.on_changed(update_trace)
ax = self.fig.add_axes((0.6, 0.2, 0.35, 0.7))
fig.add_subplot(axes=self.traceax)
update_trace()
Here's an example:
Now that plot.ly has opened sourced their libraries, it is a really good choice for interactive plots in python. See for example: https://plot.ly/python/legend/#legend-names. You can click on the legend traces and select/deselect traces.
If you want to embed in an Ipython/Jupyter Notebook, that is also straightforward: https://plot.ly/ipython-notebooks/gallery/
In my day job as a PhD student, I do geological modeling. In my spare time (mainly for fun), I am learning Python and trying to write a simple program to view 3D geocellular models.
geological model http://img710.imageshack.us/img710/6503/sgems.png
The geocellular model is just a 3D grid where every grid cell has some value (as shown in the right figure). So, I would want my viewer to be able to display a 3D grid model like the picture on the right side. As well, I would like it to be able to display cross sections through the model in the x, y and z directions (this is shown in the left figure).
I would also want the models to be able to rotate around all three axes and zoom in and out.
I've done some preliminary investigation (mainly here) and it seems like VisVis and VTK are two potential options. I am trying to use wxPython for the main GUI and it looks like both options will work with wxPython as far as I can tell.
Questions:
Am I right when I say that I think VisVis and VTK would work for what I want? Is one preferable to the other?
Which of these two options would be the easiest to implement?
Is there another option that I also should consider?
Keep in mind that I'm newish to Python and very new to wxPython.
What you are looking for is called voxel visualization, voxel grid or such. I would seriously consider MayaVi (never used it, but I keep eye on it), it seems to have something very close here.
Paraview, built atop VTK just like MayaVi, might be a good option, too.
I think going straight to VTK for visualization is difficult, it is too low-level and will probably make you just frustrated. That said, you will want to save your data in as VTK datasets for opening in MayaVi/Paraview; it is not difficult, you just have to pick the right structure (vtkGrid, vtkUnstructedGrid, ...).
In my case, I chose to use directly the VTK bindings for Python. To be honest I found it simpler to get going with VTK than Mayavi, partly because the documentation is better (many many examples!). It felt like Mayavi was adding another layer of complexity on my way to get the job done. But tom10 is right. After you've started, using Mayavi may be easier.
Apart from that, Mayavi offers a library called TVTK which is a more pythonic version of the VTK bindings but in the end I chose plain VTK in order to minimize the number of dependencies. But you should check it out. Perhaps it will be just what you are searching for.
At the beginning I found very helpful this tutorial. It is not about Python, it is about tcl, but translating the examples is trivial and it helps you understand the way vtk works.
Also, in order to get you started, you can check the examples at the VTK Wiki. If they are not enough, you can always check the C++ examples and translate them to Python. The translation is not difficult as the names of methods and properties are the same. If you do, you are encouraged to add the examples at the wiki. There are even more examples in the source.
While you are learning VTK, you will (re)discover that Ipython is awesome! Having the whole VTK namespace at your fingertips helps enormously.
In case you need more specific help, the vtk-users mailing list is quite active. Lastly there are books about VTK, and some of them are free!They are not about Python though.
I haven't tried wxPython and VTK together, but that is because I prefer PyQt4 over wxPython. AFAIK there are no problems with the integration of VTK with either library. In any case, before spending time writing a GUI, check out thoroughly ParaView. It probably already does what you want, and if it doesn't, it is python scriptable too! (I've never checked it though).
Just as a simple example of using Mayavi's mlab interface to do this (With some geologic data, even!):
from mayavi import mlab
import geoprobe
vol = geoprobe.volume('Volumes/example.vol')
data = vol.load() #"data" here is just a 3D numpy array of uint8's
fig = mlab.figure(bgcolor=(1., 1., 1.), fgcolor=(0., 0., 0.), size=(800,800))
grid = mlab.pipeline.scalar_field(data)
# Have things display in kilometers with no vertical exxageration
# Each voxel actually represents a 12.5 by 18.5 by 5 meter volume.
grid.spacing = [vol.dxW / 1000, vol.dyW / 1000, vol.dz / 1000]
# Now, let's display a few cut planes. These are interactive, and are set up to
# be dragged around through the volume. If you'd prefer non-interactive cut
# planes, have a look at mlab.pipeline.scalar_cut_plane instead.
orientations = ['x', 'x', 'y', 'z']
starting_positions = [vol.nx//4, 3*vol.nx//4, vol.ny//2, vol.nz]
for orientation, start_pos in zip(orientations, starting_positions):
plane = mlab.pipeline.image_plane_widget(grid, colormap='gray',
plane_orientation='%s_axes' % orientation, slice_index=start_pos)
# High values should be black, low values should be white...
plane.module_manager.scalar_lut_manager.reverse_lut = True
mlab.show()
(The data and data-format-handling code (the geoprobe module) are available here: http://code.google.com/p/python-geoprobe/ )
While I'd agree that learning VTK is better in the long run, you can get up-and-running quite quickly with Mayavi. The big advantage is not having to jump through hoops to get your data into VTK format. TVTK and Mayavi allow you to directly use numpy arrays.
If you want an easier way of getting into the VTK/MayaVi world (see eudoxos' fine answer), look at the mlab API to it. This brings matplotlib-like convenience to basic volume visualizations and I've yet to find the need to dig deeper into the underlying platform.
vpython is simpler to use than mayavi, but it has fewer features.
http://vpython.org/contents/bounce_example.html
Matlab and Mathematica both have features that allow the user to manipulate the contents of a plot using, say, a slider. For example, if you have a set of 3D data, it is easy to make a small program that lets you view 2D slices of a set of 3D data where the user can slide a slider to move through which slice is displayed. Is there anything in python that allows for this sort of capability without tons of effort? For example, is it possible to do such a thing in matplotlib, or something similar? I
Thanks.
My first thought would be Mayavi, which is great for data visualization, especially in 3D. It relies on VTK. It is included in the Enthought flavoured version of Python, together with Chaco for 2D plotting. To get an idea, look at Travis Vaught's nice screencast in Multidimensional Data Visualization in Python - Mixing Chaco and Mayavi.
It also possible to embed basic interactive functionalities (like slider) to Matplotlib, see matplotlib.widgets and the widget examples.
Finally, you can use rpy (or better, rpy2) and benefit from the R interface.
Have you looked at Vtk? http://www.vtk.org/ One of their demos does exactly what you're asking.
In principle, you can do it by help of MathGL. This is cross-platform GPL library for plotting.
For each mouse clicks you can find the x,y,z position in plot or clicked object and adjust (replot) some other information correspondingly. However you should handle mouse clicks by yourself (for example, using PyQt).
Another option is to use python within the SAGE computation interface/environment, which has the #interact command (see here for specific help on this command)