CODEWITHSUNDEEP
pythonnumpymatplotlibphysicsdifferential-equations
Coshin
Coshin

Reputation: 53

How can I work around overflow error in matplotlib?

I'm solving a set of coupled differential equations with odeint package from scipy.integrate.

For the integration time I have:

 t=numpy.linspace(0,8e+9,5e+06)

where 5e+06 is the timestep.

I then plot the equations I have as such: 

plt.xscale('symlog') #x axis logarithmic scale
plt.yscale('log',basey=2) #Y axis logarithmic scale
plt.gca().set_ylim(8, 100000) #Changing y axis ticks
ax = plt.gca()
ax.yaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter())
ax.xaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter())
plt.title("Example graph")
plt.xlabel("time (yr)")
plt.ylabel("quantity a")
plt.plot(t,a,"r-", label = 'Example graph')
plt.legend(loc='best')

where a is time dependent variable. (This is just one graph from many.)

However, the graphs look a bit jagged, rather than oscillatory and I obtain this error:

OverflowError: Exceeded cell block limit (set 'agg.path.chunksize' rcparam)

I'm not overly sure what this error means, I've looked at other answers but don't know how to implement the 'agg.path.chunksize'.

Also, the integration + plotting takes around 7 hours and that is with some CPU processing hacks, so I really do not want to implement anything that would increase the time.

How can I overcome this error?

I have attempted to reduce the timestep, however I obtain this error instead: 

Excess work done on this call (perhaps wrong Dfun type).
Run with full_output = 1 to get quantitative information.

Upvotes: 3

Views: 4680

Answers (1)

ImportanceOfBeingErnest
ImportanceOfBeingErnest

Reputation: 339470

As the error message suggests, you may set the chunksize to a larger value.

plt.rcParams['agg.path.chunksize'] = 1000

However you may also critically reflect why this error occurs in the first place. It would only occur if you are trying to plot an unreasonably large amount of data on the graph. Meaning, if you try to plot 200000000 points, the renderer might have problems to keep them all in memory. But one should probably ask oneself, why is it necessary to plot so many points? A screen may display some 2000 points in lateral direction, a printed paper maybe 6000. Using more points does not make sense, generally speaking.

Now if the solution of your differential equations requires a large point density, it does not automatically mean that you need to plot them all.

E.g. one could just plot every 100th point, 

plt.plot(x[::100], y[::100])

most probably without even affecting the visual plot appearance.

Upvotes: 8

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