Boris Barbour wrote: > Hi, > > I have lots of data acquired via analogue to digital conversion. The data is > consequently represented as integers (often 16 bit resolution). To obtain the > correct signal and plot it, these data must of course be multiplied by a > floating point scale factor. This seems potentially wasteful of resources > (time and memory), especially as I would prefer to keep the original data > untouched.
I don't understand this last clause; scaling your original integer data prior to plotting does not in any way inhibit your storage and use of that original integer data. > > It occurs to me that a more efficient plotting method would be to plot the > original data but scale the axes by the appropriate factor. In that way a > simple numpy array view could be passed to plot. Does a method for doing this > exist? I think I can do it in a rather convoluted way by plotting the > original data and then superimposing empty axes at the adjusted scale. > However, I haven't yet tested this and I'm a bit skeptical about the overhead > of two plots. Another possibility might be the units mechanism, but according > to the documentation that is discouraged, and it might be awkward to > implement. > > If the possibility doesn't exist, I wonder whether it might be feasible - and > not too difficult - to add to the axis methods? One could add a scale > parameter with a default value of 1 that should not affect existing code. For ordinary plots in matplotlib the data will be converted to double precision anyway, and the time required for you to do your own scaling and conversion is utterly negligible compared to the total plotting time. I don't think it will make any difference in memory usage, either. Matplotlib uses asarray(), so there will not be a copy if the input is already a double precision array. It sounds like you may be thinking about optimizations in the wrong place. Are you actually running up against speed or memory problems? Eric ------------------------------------------------------------------------- This SF.Net email is sponsored by the Moblin Your Move Developer's challenge Build the coolest Linux based applications with Moblin SDK & win great prizes Grand prize is a trip for two to an Open Source event anywhere in the world http://moblin-contest.org/redirect.php?banner_id=100&url=/ _______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
