This is in response to a Question about the speed and memory use of an animated DEplot. The problems are that the example's animation was slow to create, and prohibitively expensive to save in a Document.

An alternative approach is to combine multiple calls to plots:-odeplot with a call to plots:-fieldplot to supply the background flow arrows. This is a lot faster. It takes less memory to create and run, but the GUI may still consume too much resources saving it. The good news is that it's so much faster that it's not inconvenient to re-run the entire thing from scratch. And so it's quite feasible to remove all the expensive output from the Document prior to saving and thus avoid the whole resources problem.

The original questioner also wanted to visualize with resect to two varying parameters. So I've also done an implementation of that using Embedded Components and two Sliders.

Here is the old DEplot animation. It takes about 40 sec to create it animation on an Intel i7.

Here is the new combined odeplot+fieldplot animation. It takes a second or two to create its animation on an Intel i7.

Here is the DEplot in Embedded Components. It's very slow, and the image doesn't change smoothly with the sliders.

Here is the new combined odeplot+fieldplot in Embedded Components. Its image changes pretty smoothly with the sliders.

I encourage completely quitting the GUI (not just restart, or close Document and re-Open) between comparison runs of these implementations, of you want to get a really good feel for the effects of both running them as well as saving them (with and without all output).

For the Embedded Component documents, the functioning code resides inside the "initialize" button. (right-click, go to Component Properties, Action When Clicked, only if you want to inspect it.) To run those two  Documents, execute all the commands (use the triple-exclam from the menubar if you like), and then press the "initialize" button, and then move the sliders.

The difference in performance is related partly to the use of hardware datatype Arrays in the PLOT structures generated by odeplot and fieldplot. (But an `arrow` primitive would help even more!)

And (I think) there is improvement by virtue of using dsolve/numeric/parameters in the use of `odeplot`. That saves overhead from repeated cold invocations of dsolve/numeric. And DEplot doesn't support that, since it expects as argument the system of DEs and ICs. The newer `odeplot` command accepts the procedure returned by dsolve/numeric, and thus allows for efficient repeated setting of parameter values. The `fieldplot` command doesn't need the solution of the DE system at all: it just needs the DEs.

I would have considered wrapping the whole combined approach up into a single command, but it might have to accept separate options for the view ranges, in order to always look its best. A smart version might be able to deduce the computed ranges from the odeplot output, and then create the background fieldplot based on that.

Inside a procedure, local variables are evaluated only one level. Of what good is this, one might ask?

Well, for one thing it allows you to do checks or manipulations of an unevaluated function call without having that function call be evaluated over again. I mean, for function calls to routines which don't happen to remember earlier results.

This is a revision of an Answer

There have been some recent posts about interpolating data.

Attached below is a worksheet that shows some possibilities, with the functionality centering on the CurveFitting:-ArrayInterpolation command.

This is quick summary of parts of a broader document which covers both 2-d and 3-d methods (for regular grids), where I've left out the higher-efficiency methods and instead roughed in some examples involving integration and differentiation.

I've elected not to follow the 3-d Example from the ArrayInterpolation command's help-page, although using a pre-formed grid is a very fast approach to obtain just an interpolated 3-d plot. I also prefer to use the plots:-surfdata command rather than the plots:-matrixplot command, since the former let's one get the axes' tickmarks correct for the x- and y-data ranges.

The scenario is that you have a grid of data points in two dimensions (x- and y, or P- and T-, or what have you).

For each point (ie, for each 2-d pair of values) you have an associated value (or height in z, say). Hence you actually have data points in 3-d space.

How you obtained the associated (z) values depends on your own particular data collection method, or your own program. How you got the data is irrelevant here. What matters is that you have the finite number of data values, and no other easy way to generate data values at more points (let alone data for arbitrary new points). Below, we'll just create the data (once, at the start) for this example using an entirely made-up formula.

The presumption is that you might wish to plot a smooth surface that connects the 3-d data.

But you might also wish to write some program which requires interpolated (z) values at some new (x,y) 2-d points.  And you do not yet know what these 2-d point pairs are. So a pre-formed Array of points  at which to interpolate may not suffice.

Instead of using a pre-formed Array of output points, we'll contruct a procedure named `B` which can be supplied with a new (x,y) 2-d output point and (if that point lies within the original range) return an interpolated (z) value.

This procedure `B` can also be plotted, using the usual `plot3d` comamnd. It won't plot quite as fast as would a pre-computed and pre-interpolated finer grid of (x,y) values, but it should plot nicely. And the surface can be made quite smooth, by merely increasing the number of plotted points using plot3d's usual numpoints option. (Maple does not currently do "adaptive" 3-d plotting, so there's also no advantage in that respect.) But `B` does solve the secondary task, of being able to compute for any subsequent (x,y) point.

We can even integrate and differentiate `B` numerically. Of course we should keep in mind that this is somewhat error prone, since on top of usual issues with numerical differentiation there is also fact that we make the choice of interpolation method! The entire interpolated surface will differ considerably according to whether a spline, cubic (or other) interpolating scheme is chosen!

We'll use P and T as the x- and y- grid points, below, since "a name is just a name" and our choice of variables is arbitarary.

plot_interp.mw

One useful feature of the `evalf` command is that it remembers previous results. But it also stores the current value of Digits as well as its input argument, to be associated with the remembered result.

There are two reasons for this. The fancier reason is so that, when Digits is reduced from that of an earlier successful computation, `evalf` can simply round off the earlier result to the desired number of decimal digits. The more basic reason is that `evalf` might...

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