You may be interested to know that the current 'state of the art' for
handling tensor symmetries (i.e. simplifying expressions which are
identical because of tensor symmetries) is based on the
'double coset algorithm', which you can find in
R Portugal, "Algorithmic simplification of tensor expre
Hi Ondrej,
> I'll play with it and reply later here. Have you considered creating a
> cadabra package for Sage? They may even include it by default.
I'm not sure that that is easy to do; in any case, no, I haven't done
that yet.
> In SymPy we will also be optionally using a Cythonized core, but
>
> Personally I would like to see more cohesion among tensor-like projects.
> There are a lot of tensor-like or indexed things in SymPy that all have
> valuable logic, none of which stem from some common framework. The
> original idea behind tensor core was to have some base layer from which
>
> Thanks for the email. Would you be willing to license your code under
> BSD style license?
>
Let's leave the licensing issues out of the discussion for the time being
if you don't mind. We'll find a way to make this work.
Btw, since your code is in C++, maybe you can help us with CSymPy
>
> So how do you propose Cadabra would work with SymPy or CSymPy? Would
> there be some core in Cadabra, that works well with SymPy, that people can
> use to
> build useful things upon it, or just use it for calculations?
The way this will work is that there are 'cadabra.Ex' objects which
I know you didn't ask for this, but I can strongly recommend using pybind11
(https://github.com/pybind/pybind11) if you want nice C++ bindings for
Python. It shields you from a lot of low-level details.
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If anyone, including you, is going to spend serious time on including some
of that functionality, I would suggest you have a look first at what is
already present in Cadabra (https://cadabra.science). It is possible to
re-use its algorithms elsewhere by writing a bit of Python glue code, and
po
Hi David,
I tend to agree with most of your criticism of Jupyter. What do you intend
to use in order to render mathematics? In the notebook system for Cadabra
(https://cadabra.science) I went the route of feeding things through LaTeX
& dvipng and then render the resulting png, which can be made
Just for comparison: this is part of David's example in Cadabra:
[image: cadabra_screenshot.png]
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> I should say at this point, that I use my own format for the workbook
> files - because that gives me far more freedom to add features - but if I
> can get a spec of Jupyter's output format
>
Those specs are all on the Jupyter site. Cadabra uses a very similar format
(and in fact also has a
> Is there a way to compute the gradient and hessian matrices of a vector
> field like u = (x) i + (2yz) j + (3xy) k.
>
I know you didn't ask for this, but just in case it is of any help, here's
how you do it in Cadabra (https://cadabra.science).
Cadabra uses Sympy under the hood, so it's at
If I have an expression with a bunch of unevaluated 'Integral', 'Sum' and
so on, what is the recommended way to apply 'doit()' on all of these in one
shot?
Cheers,
Kasper
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Hi all,
I would like to make a preliminary announcement of an update to
my Cadabra computer algebra system for tensor field theory problems.
The reason being that you can now use the Cadabra notebook frontend to
do mixed Cadabra/Sympy or even pure Sympy computations with it.
You can see it as a
Hi Aman,
> I would like to make a *gui* interface for sympy for making it more
> user friendly. I would like to use *Tkinter* module for it since it
> is cross-platform and I already have experience with it.
If you, or anyone, is seriously considering this, may I suggest to
have a peek at my Cada
The standard way to make LaTeX automatically break displayed equations is
to use the 'breqn' package. This requires changing your 'equation'
environment to a 'dmath' environment as well, together with a number of
smaller things (see the manual for details), which Jupyter may be able to
do for y
Hi all,
The Cadabra computer algebra project (http://cadabra.science), which
makes use of parts of SymPy under the hood, has secured funding for a
3.5 year research student position leading to a PhD in Mathematics. The
official announcement can be found at
http://cadabra.science/cadabra_phd_po
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