I have a Python code that contains a function where a figure is created in order to be saved as a pdf (it never shows on the screen during execution). For some reason, the execution of this subroutine keeps the figure open and blocks the following operations in the code. I tried to use the cla(), clf() and clear() functions but I could not get it to work...
Here is a partial view of the subroutine:
def trace_pdf(a,b,c,d):
x = np.linspace(0,100,a)
fig2 = plt.figure()
ax2 = fig2.add_subplot(111)
ax2.plot(b,c,'b', label='BA',linewidth=3.5)
ax2.set_title('a pdf like no other')
fig2.savefig('file.pdf', format='pdf')
fig2.clf()
fig2.clear()
I do not see why my code is blocked... (I checked that if I comment the call to the trace_pdf function, everything works fine).
So here is what I did to fix my problem. I decided to try to run my function as an independent process so I added to my code:
from multiprocessing import Process, Queue
def trace_pdf(a,b,c,d):
x = np.linspace(0,100,a)
fig2 = plt.figure()
ax2 = fig2.add_subplot(111)
ax2.plot(b,c,'b', label='BA',linewidth=3.5)
ax2.set_title('a pdf like no other')
fig2.savefig('file.pdf', format='pdf')
plt.close()
trace_pdf = Process(target=trace_pdf, args=(a,b,c,d))
trace_pdf.start()
This way, the plt_close() call does not impact the main graphic interface as I believe its action is limited to the separate process... For details regarding running a function as an independent process I used this post.
Related
I have an App made using Qt4 Designer which inserts a matplotlib figure into a container widget.
The code to generate the figure comes from another module, obspy:
self.st.plot(fig = self.rawDataPlot)
https://docs.obspy.org/packages/autogen/obspy.core.stream.Stream.plot.html
Normally, this would create and show a matplotlib figure for the st object's data, which is time-series. When the fig parameter is specified this tells self.st.plot to plot to an existing matplotlib figure instance.
The code I have to generate the figure and then position it in my GUI widget is:
def addmpl(self, fig, layout, window): # code to add mpl figure to Qt4 widget
self.canvas = FigureCanvas(fig)
layout.addWidget(self.canvas)
self.canvas.draw()
self.toolbar = NavigationToolbar(self.canvas,
window, coordinates=True)
layout.addWidget(self.toolbar)
self.rawDataPlot = plt.figure() # code to create a mpl figure instance
self.st.plot(fig = self.rawDataPlot) # plot time-series data to existing matplotlib figure instance
self.addmpl(self.rawDataPlot, self.mplvl, self.mplwindow) # add mpl figure to Qt4 widget
What I want to do is instantiate a matplot figure (for use by self.st.plot) but in a way which avoids using plt.figure(), as I have read that this is bad practice when using object-oriented programming.
If I replace plt.figure() with Figure() (from matplotlib.figure.Figure()) I get an error:
AttributeError: 'NoneType' object has no attribute 'draw'
As it stands, the App runs fine if I use plt.figure(), but is there a clean way to avoid using is and is it even necessary for my case?
PS, the code snippets here are taken from a larger source, but I think it gets the point across..
In principle both methods should work.
Whether you set self.rawDataPlot = plt.figure() or self.rawDataPlot = Figure() does not make a huge difference, assuming the imports are correct.
So the error is most probably triggered within the self.st.plot() function. (In general, if you report errors, append the traceback.)
Looking at the source of obspy.core.stream.Stream.plot there is a keyword argument
:param draw: If True, the figure canvas is explicitly re-drawn, which
ensures that existing figures are fresh. It makes no difference
for figures that are not yet visible.
Defaults to True.
That means that apparently the plot function tries to draw the canvas, which in the case of providing a Figure() hasn't yet been set.
A good guess would therfore be to call
self.rawDataPlot = Figure()
self.st.plot(fig = self.rawDataPlot, draw=False)
and see if the problem persists.
This might be a general question. I'm modifying a Python code wrote by former colleague. The main purpose of the code is
Read some file from local
Pop out a GUI to do some modification
Save the file to local
The GUI is wrote with Python and Tkinter. I'm not very familiar with Tkinter actually. Right now, I want to implement an auto-save function, which runs alongside Tkinter's mainloop(), and save modified files automatically for every 5 minutes. I think I will need a second thread to do this. But I'm not sure how. Any ideas or examples will be much appreciated!! Thanks
Just like the comment says, use 'after' recursion.
import Tkinter
root = Tkinter.Tk()
def autosave():
# do something you want
root.after(60000 * 5, autosave) # time in milliseconds
autosave()
root.mainloop()
Threaded solution is possible too:
import threading
import time
import Tkinter
root = Tkinter.Tk()
def autosave():
while True:
# do something you want
time.sleep(60 * 5)
saver = threading.Thread(target=autosave)
saver.start()
root.mainloop()
before leaving I use sys.exit() to kill all running threads and gui. Not sure is it proper way to do it or not.
I would like to overwrite an existing plot I made in python with a new function call. I would like to produce a plot, look at it, then call the same function again with different arguments to produce another plot. I would like the second plot to replace the first plot. That is, I don't want two figure windows open; just the original window overwritten.
I have tried using interactive mode when plotting (ion()), placing plot() and show() calls in different places, and clearing figures. The problems I have are that: 1. I can never overwrite and existing window, I always open more 2. show() blocks the code from continuing and I am unable to perform the 2nd function call 3. I use interactive mode and the window appears for a second before going away
What I'm trying to do seems simple enough, but I'm having great difficulty. Any help is appreciated.
Easiest solution
There are many ways to do this, the easiest of which is to reset the plot's Line2D using its set_ydata(...) method and pyplot.pause. There are versions of matplotlib (<0.9, I believe) which don't have pyplot.pause, so you may need to update yours. Here's a simple minimal working example:
import numpy as np
from matplotlib import pyplot as plt
ph, = plt.plot(np.random.rand(100))
def change_plot():
ph.set_ydata(np.random.rand(100))
plt.pause(1)
while True:
change_plot()
Other approaches
Using pyplot.ion and pyplot.ioff, as detailed here. I tend to use these when I'm doing exploratory data analysis with a Python shell.
Using the matplotlib.animation package, as detailed in this very comprehensible example. This is a much more robust approach than the easy solution above, and permits all kinds of useful/fun options, such as outputting the animation to a video file, etc.
Instead of using the set_ydata method of the Lines object, you can always clear the axes (pyplot.cla()) and call the plotting command again. For example, if you are using pyplot.contour, the returned QuadContourSet has no set_zdata method, but this will work:
import numpy as np
from matplotlib import pyplot as plt
X,Y = np.meshgrid(np.arange(100),np.arange(100))
def change_plot():
Z = np.random.random(X.shape)
plt.cla()
ph = plt.contour(X,Y,Z)
plt.pause(1)
while True:
change_plot()
write your plotting function like
def my_plotter(ax, data, style):
ax.cla()
# ax.whatever for the rest of your plotting
return artists_added
and then call it like
data = some_function()
arts = my_plotter(plt.gca(), data, ...)
or do
fig, ax = plt.subplots()
and then call your plotting function like
arts = my_plotter(ax, data, ...)
I had almost the same issue and I solved it by assigning a name for each plot.
def acc(train_acc, test_acc, savename):
plt.figure(savename) # If you remove this line, the plots will be added to the same plot. But, when you assign a figure, each plot will appear in a separate plot.
ep = np.arange(len(train_acc)) + 1
plt.plot(ep, train_acc, color="blue", linewidth=1, linestyle="-", label="Train")
plt.plot(ep, test_acc, color="red", linewidth=1, linestyle="-", label="Test")
plt.title("NDCG")
plt.xlabel("Iteration")
plt.ylabel("NDCG#K")
plt.legend(loc='lower right')
plt.savefig(savename)
I'd like to either disable the VTK warning window or, better yet, catch them to handle with my application's logging system. My application is using an embedded mayavi view, and I don't want error windows popping up that I have no control over. The following code demonstrates the warning window.
import numpy as np
from mayavi import mlab
x1 = np.array([1, 1, 2, 3])
y1 = np.array([1, 1, 4, 2])
z1 = np.array([1, 1, 5, 1])
mlab.plot3d(x1, y1, z1)
mlab.show()
Ok, I've done some research and discovered that vtk.vtkObject.GlobalWarningDisplayOff() will disable the window completely, which is nice. Better yet the followingcode will log the warnings to a file (found it here):
def redirect_vtk_messages ():
""" Can be used to redirect VTK related error messages to a
file."""
import tempfile
tempfile.template = 'vtk-err'
f = tempfile.mktemp('.log')
log = vtkpython.vtkFileOutputWindow()
log.SetFlush(1)
log.SetFileName(f)
log.SetInstance(log)
So while this is nice, I'm still unable to pipe the warnings directly into a logging handler. I'd rather not have to have a vtk_log file next to my regular log files. Also I might want to handle the warnings in my GUI somehow, or give the user options on how to handle them and constantly watching a log file for changes seems like a poor way to do that.
Any suggestions on a robust pythonic way to handle vtk warnings in an application which embeds mayavi/vtk?
I don't know whether this will work in the Mayavi environment, but this works for Python wrappings to VTK
# pipe vtk output errors to file
errOut = vtk.vtkFileOutputWindow()
errOut.SetFileName("VTK Error Out.txt")
vtkStdErrOut = vtk.vtkOutputWindow()
vtkStdErrOut.SetInstance(errOut)
I guess this partially answer your question, but you could implement an error observer in python as explained here http://public.kitware.com/pipermail/vtkusers/2012-June/074703.html and add it to the vtk class you are interested.
In c++ I find much simpler to redirect the output to stderr (this example is for windows):
vtkSmartPointer<vtkWin32OutputWindow> myOutputWindow = vtkSmartPointer<vtkWin32OutputWindow>::New();
myOutputWindow->SetSendToStdErr(true);
vtkOutputWindow::SetInstance(myOutputWindow);
In python I tried
ow = vtk.vtkOutputWindow()
ow.SendToStdErrOn()
it sends the error to console, but I still see the vtk window and it doesn't really seem catching the errors.
Another option could be to recompile vtk with VTK_USE_DISPLAY turned off ( http://osdir.com/ml/python-enthought-devel/2009-11/msg00164.html). I am not going to try this because I am using the vtk distribution already compiled in paraview
You can create a subclass deriving from vtkOutputWindow and implement your message handling in the method void DisplayText(const char* someText). I did this is in a C++ project to redirect all output to cerr and even suppress specific warnings.
An approach I found similar to #SciCompLover's answer that suppresses the output window while also printing to the console:
import vtk
vtk_out = vtk.vtkOutputWindow()
vtk_out.SetInstance(vtk_out)
Tested on Mayavi 4.7.1 with VTK 8.2.0 on Windows and MacOS.
I was following the tutorial "Writing a graphical applications for scientific programming using TraitsUI
http://code.enthought.com/projects/traits/docs/html/tutorials/traits_ui_scientific_app.html
and tested the following code snippet:
from enthought.traits.api import *
from enthought.traits.ui.api import *
class Camera(HasTraits):
""" Camera object """
gain = Enum(1, 2, 3,
desc="the gain index of the camera",
label="gain", )
exposure = CInt(10,
desc="the exposure time, in ms",
label="Exposure", )
def capture(self):
""" Captures an image on the camera and returns it """
print "capturing an image at %i ms exposure, gain: %i" % (
self.exposure, self.gain )
if __name__ == "__main__":
camera = Camera()
camera.configure_traits()
camera.capture()
If I run this at the command line it works as advertised. A GUI pops up. You adjust the parameters, and when you click "OK" it returns the modified values. But when I run the same code from within the Canopy editor by clicking the run button the default parameters print immediately; then the window pops up. When you then adjust the parameters in the GUI and click "OK" the GUI exits but the new parameter values don't print.
It is as if somehow camera.capture() is running before camera.configure_traits.
First, I would suggest using this newer version of the tutorial: http://docs.enthought.com/traitsui/tutorials/traits_ui_scientific_app.html
The one you linked to references materials for TraitsUI version 3, whereas the one above is for the version you're likely using (version 4). The newer tutorial uses the newer module names, traitsui.api instead of enthought.traits.ui.api for example.
As to why Canopy displays the values immediately, this is the expected behavior when running the program:
if __name__ == "__main__":
camera = Camera()
camera.configure_traits()
camera.capture()
When run as __main__ (i.e., not imported as a module by another script), the script does these three things in order: creates an instance of Camera(), pops up the GUI (configure_traits), and then executes the capture method that prints the current values (which are "1" and "10" by default).
The OK/Cancel buttons are not hooked into setting these values. As a test, try changing the exposure or gain but instead of clicking the buttons, try inspecting these attributes from within Canopy's IPython prompt while the GUI is still open: camera.gain or camera.exposure should return the newly set values.