I have around 100 python files, that each create one figure using matplotlib.
Since I want to use all CPU cores, I basically did "for filename in files:
execfile(filename)" using a python script. However, this does not produce
the same output as running each file separately (for instance axes, figure
size are sometime wrong).
I _think_ I narrowed it down to this: In all files I need to do
"matplotlib.rcParams(update)". I guess that this influences the matplotlib
rc parameters and thus somehow values from some figures are used for others.
So, my question is, how can I do something like
"matplotlib.rcParams(update)" so that it does not influence other scripts
that are run in parallel using 'execfile'? Or, how do I set rc parameters
for one specific script?
BTW, I tried 'pp' and 'multiprocessing', same problem with both.
Any help is greatly appreciated!
Cheers, Martin
···
--
View this message in context: http://old.nabble.com/matplotlib.rcParams(update)-and-parallel-python-tp32669484p32669484.html
Sent from the matplotlib - users mailing list archive at Nabble.com.
The problem is that the rc params are module level in matplotlib so in
a persistent process like ipython if one script modifies the rc
params, subsequent files executed in the same process will be
affected. You can restore the rc params to their default state by
doing before each call to execfile
import matplotlib
matplotlib.rc_file_defaults()
We face the same issue in the "plot_directive" which we use when
building the matplotlib documentation. We define a function
"clear_state" in
https://github.com/matplotlib/matplotlib/blob/master/lib/matplotlib/sphinxext/plot_directive.py#L484
which closes all open figures, restore the rc defaults, and the
updates the defaults to an rc dictionary of the parameters we want for
each run.
···
On Mon, Oct 17, 2011 at 2:57 PM, tinux <hoffmann.martin@...287...> wrote:
I have around 100 python files, that each create one figure using matplotlib.
Since I want to use all CPU cores, I basically did "for filename in files:
execfile(filename)" using a python script. However, this does not produce
the same output as running each file separately (for instance axes, figure
size are sometime wrong).
I _think_ I narrowed it down to this: In all files I need to do
"matplotlib.rcParams(update)". I guess that this influences the matplotlib
rc parameters and thus somehow values from some figures are used for others.
So, my question is, how can I do something like
"matplotlib.rcParams(update)" so that it does not influence other scripts
that are run in parallel using 'execfile'? Or, how do I set rc parameters
for one specific script?
BTW, I tried 'pp' and 'multiprocessing', same problem with both.
Thank you for the tip. I actually had played around with
matplotlib.rcdefaults() before, but that didn't work. I tried now the
using exactly the clear_state function you suggested. Since it still
didn't work, I finally found out that I had a very similar problem with
another module from which I determine axes, figure size and other
things. So, thank you very much, that helped me a lot and now things
work the way I want!
···
On Die 18 Okt 2011 04:45:27 CEST, John Hunter wrote:
On Mon, Oct 17, 2011 at 2:57 PM, tinux <hoffmann.martin@...287...> wrote:
I have around 100 python files, that each create one figure using matplotlib.
Since I want to use all CPU cores, I basically did "for filename in files:
execfile(filename)" using a python script. However, this does not produce
the same output as running each file separately (for instance axes, figure
size are sometime wrong).
I _think_ I narrowed it down to this: In all files I need to do
"matplotlib.rcParams(update)". I guess that this influences the matplotlib
rc parameters and thus somehow values from some figures are used for others.
So, my question is, how can I do something like
"matplotlib.rcParams(update)" so that it does not influence other scripts
that are run in parallel using 'execfile'? Or, how do I set rc parameters
for one specific script?
BTW, I tried 'pp' and 'multiprocessing', same problem with both.
The problem is that the rc params are module level in matplotlib so in
a persistent process like ipython if one script modifies the rc
params, subsequent files executed in the same process will be
affected. You can restore the rc params to their default state by
doing before each call to execfile
import matplotlib
matplotlib.rc_file_defaults()
We face the same issue in the "plot_directive" which we use when
building the matplotlib documentation. We define a function
"clear_state" in
https://github.com/matplotlib/matplotlib/blob/master/lib/matplotlib/sphinxext/plot_directive.py#L484
which closes all open figures, restore the rc defaults, and the
updates the defaults to an rc dictionary of the parameters we want for
each run.