> My GSoC Application can be found at > https://github.com/sympy/sympy/wiki/GSoC-2012-Application--Bharath-M-R-:-Plotting-Module. > Can you please review the application and suggest any changes?
Before giving my feedback I should repeat yet one more time that I am not among those that decide which applications get accepted. # In synopsis: I think that you mean a backend and not a whole new module when you speak about svgfig. # In week 1: What do you mean by parse implicit functions? Anyway, you should not overcharge lambdify with stuff it was not meant to be. This is the reason that the old lambdify is such a mess. If you need some functionality from lambdify just use it in whatever abstraction you need to build. There are a few problems with the syntax that you are proposing. You are using strings instead of expressions, you are using lists instead of tuples and the order of the arguments is not the one used in other parts of sympy. # In week 3: The mpmath stuff should be discussed with them *much* in advance. # In week 4: I do not get the example with min and max (and they are not necessarily binary in python). Moreover, could you elaborate on what subpixel computation is and why do we need it. Finally, what is the use for `IntervalSet`. # Week 6 Branch cut plotting? # Week 7 Why is this not possible already by week 1. What is so special about x**(1/3) # Week 8 About "ploting every 2D function". Be aware that there are some nontrivial expression (the old lambdify does not work on them, the new one usually works). Will you be able to plot for example `Integral(awful_expression_of_x, (x,y,z)) < real(complex_valued_expression_of_y_and_z`)? # Week 9 Could you elaborate? I do not get a clear idea what you mean here. # Week 12 Elaborate on what do you need this adaptive method for? Is it part of all that is implemented already? Is it well abstracted, so there is no code duplication? One last important thing. I was left with the impression that you will rely on experimental_lambdify to return mpmath functions. This is not what experimental_lambdify does. The only cases when mpmath is used in this function is when there is no numpy function to translate to (which happens often), however even then mpmath is hidden in the evalf() methods of the expressions. The old lambdify can return mpmath functions but it works on a *very* limited subset of sympy expressions. In one sentence: if you rely only on mpmath or numpy for evaluation you will be able to plot only a very limited subset of expressions. Maybe a useful idea will be to implement your interval arithmetics directly in sympy independently of the library that actually does the computations. -- You received this message because you are subscribed to the Google Groups "sympy" group. To post to this group, send email to [email protected]. To unsubscribe from this group, send email to [email protected]. For more options, visit this group at http://groups.google.com/group/sympy?hl=en.
