Comment #4 on issue 3694 by smi...@gmail.com: Better Simplification of
Gammas
http://code.google.com/p/sympy/issues/detail?id=3694
Not as nice as I'd hoped...this is similar to the TR* routines of
Fu...making sure the expression is in the form that will lead to
simplification is tricky.
Updates:
Status: Fixed
Comment #3 on issue 3628 by smi...@gmail.com: trigsimp introduces extra
factor
http://code.google.com/p/sympy/issues/detail?id=3628
(No comment was entered for this change.)
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Comment #2 on issue 3689 by smi...@gmail.com: Remove DeferredVector
http://code.google.com/p/sympy/issues/detail?id=3689
The docstring shows an example.
A vector whose components are deferred (e.g. for use with lambdify)
Examples
from sympy import DeferredVector,
Comment #2 on issue 3692 by skirpic...@gmail.com:
(Symbol(n,integer=True,even=True)/2).is_integer = False !
http://code.google.com/p/sympy/issues/detail?id=3692
https://github.com/sympy/sympy/pull/1896
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Comment #5 on issue 3694 by smi...@gmail.com: Better Simplification of
Gammas
http://code.google.com/p/sympy/issues/detail?id=3694
OK, nice again:
e = (-gamma(k)*gamma(k + 2) + gamma(k + 1)**2)/gamma(k)**2
assert combsimp(e) == k**2
assert combsimp(1/e) == 1/k**2
e =
Comment #3 on issue 3693 by skirpic...@gmail.com: solve() returns only
subset of solutions for polynomial eqs
http://code.google.com/p/sympy/issues/detail?id=3693
This seems to be fixed.. Close?
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Comment #3 on issue 3689 by asmeu...@gmail.com: Remove DeferredVector
http://code.google.com/p/sympy/issues/detail?id=3689
Another example usage:
https://github.com/sympy/sympy/blob/master/sympy/external/tests/test_numpy.py#L244.
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Issue 3287: Merge DeferredVector and MatrixSymbol
http://code.google.com/p/sympy/issues/detail?id=3287
This issue is now blocking issue sympy:3689.
See http://code.google.com/p/sympy/issues/detail?id=3689
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Updates:
Blockedon: sympy:3287
Comment #4 on issue 3689 by asmeu...@gmail.com: Remove DeferredVector
http://code.google.com/p/sympy/issues/detail?id=3689
Issue 3287 and issue 1268 are related. In fact, this might just be
considered a duplicate of issue 3287.
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On Wed, Mar 13, 2013 at 8:58 PM, Duane Nykamp dqnyk...@comcast.net wrote:
I see. Explicitly doing substitutions of the form (Symbol('oldvar'),
Symbol('newvar')) makes sense.
However, there unicode becomes a problem. Trying to use Symbol(varname)
raises a
AssertionError: type 'unicode'
On Thu, Mar 14, 2013 at 12:34 AM, Aaron Meurer asmeu...@gmail.com wrote:
This validates what I've often said, which is that if you use the
right algorithms and the right data structures, then Python can be
just as fast as a compiled alternative (especially since the right
data structures
Why not a middle ground? For some things it would be silly not to
support n dimensions, like dot product, but for others, like cross
product, the n-dimensional generalization is more complicated, and
(if I understand correctly), not even technically a vector. For those
cases, you could give
Hello all,
I have implemented a module giving steps for most derivatives and some
integrals for SymPy Gamma. However, it was suggested that at least some of
this functionality should be added to SymPy itself. If so, what
functionality should be added and how should it be integrated into SymPy?
Why not a middle ground?
Indeed. I like this idea a lot. +1
I think that Raoul's post takes care of the requirement of the list. Also,
I will still take some time to write the proposal on the wiki. First, I
want to be really clear on exactly what to implement and, more importantly,
how to
This seems very nice. Have you looked at the rewrite rules and
strategies modules that are currently developed and talked about on
the mailing list? Given that you follow the same paradigm I guess this
would be the correct path to merge your work. I am a bit worried that
you might have
Le 14/03/2013 15:22, David Li a écrit :
The code makes use of context managers, which would need to be replaced
in order to maintain Python 2.5 compatibility.
That's not a problem, you can use 'from __future__ import with_block'.
OTOH, .format() is 2.7+ only, so it is a compatibility
Le 14/03/2013 09:42, Ondřej Čertík a écrit :
Mateusz and I discussed this last few days (I visited him in Wroclaw) and
I think we both agreed that a good idea is for me (or others) to
finish the experimental
C++ core (https://github.com/certik/csympy) so that we can run some
benchmarks and play
Alright, thank you all for the feedback!
I just started looking at the strategies module, I think I could map my
rules to rules in the strategies module and have intsteps/diffsteps be a
strategy; I'll look into this some more. I can get rid of the format
function easily, I'm not really using
The driving idea behind strategies is that you should make lots of little
functions that each do one thing. These should separately be composed
together to make larger functions. This way the little mathematical
transformations can be reused in the future or can be wrapped with the kind
of
Slightly off topic: I think that many people would be interested to
see a blog post on planet.sympy.org about this development. On one
hand you have done a lot of work on gamma itself, on the other your
work with this integrator shows a nice way to extend sympy without
worrying too much about
Le 14/03/2013 20:16, Ondřej Čertík a écrit :
On Thu, Mar 14, 2013 at 7:50 PM, Ronan Lamy ronan.l...@gmail.com wrote:
Le 14/03/2013 09:42, Ondřej Čertík a écrit :
In general, I think it's quite amazing how fast Python actually is,
that sympy can compete very well
with for example Maxima, if
On Thu, Mar 14, 2013 at 3:42 AM, Ondřej Čertík ondrej.cer...@gmail.com wrote:
On Thu, Mar 14, 2013 at 12:34 AM, Aaron Meurer asmeu...@gmail.com wrote:
This validates what I've often said, which is that if you use the
right algorithms and the right data structures, then Python can be
just as
I support the idea of being smarter about running fewer Python functions.
Your post brought up a couple experiences I had trying to get unification
based rewriterules to run efficiently.
- expand currently works by recursively calling _eval_expand_hint on
an expression. So if you have
Normal unification is decently fast. A simple implementation in another
project is herehttps://github.com/logpy/logpy/blob/master/logpy/core.py#L58.
However our implementation of unification in an associative/commutative
context is naive and painfully slow. AC matching is a hard problem but in
@aaron I have sorted out the issues into Sympy-Bot and Reviews.sympy.org
Bot:
144 142 139 121 120 108 95 94 89 80 73 63 62 56 21 2
Review.sympy.org:
138 132 120 119 117 103 102 90 59 57 55 51 50 49 48 47 46 45 42 35 16
Are the issues 57, 48 still valid?
On Wed, Mar 13, 2013 at 8:26 PM,
On Fri, Mar 15, 2013 at 6:56 AM, Aaron Meurer asmeu...@gmail.com wrote:
How fast is this? Does it scale well for large expressions with many
variables? Can it be modified to return the mapping?
My symatch branch does what your asking...it's called `symbol_match`
as I recall. I'm stalled
I'd love to see a SymPy presence at the conference, even if it's just
attendees.
This year, sponsorships are open to everyone, not just students:
http://conference.scipy.org/scipy2013/reg_fin_aid.php.
Aaron Meurer
On Wed, Mar 13, 2013 at 1:43 PM, Matthew Rocklin mrock...@gmail.com wrote:
The
I'd just like to clarify if my post was a bit ambiguous. I would like to
implement both of these in a single project
1. Adding lie groups support for first order ODE's
2. Series solutions to first and second order ODE's
I would like to know if it would be good.
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Manoj Kumar,
Mech
On Fri, Mar 15, 2013 at 6:24 AM, Aaron Meurer asmeu...@gmail.com wrote:
On Thu, Mar 14, 2013 at 3:42 AM, Ondřej Čertík ondrej.cer...@gmail.com
wrote:
On Thu, Mar 14, 2013 at 12:34 AM, Aaron Meurer asmeu...@gmail.com wrote:
This validates what I've often said, which is that if you use the
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