> Proof of concept or not, it's already used by the new assumptions system
> to detect inconsistencies. If you have any suggestions on how to improve
> the algorithm in pure Python, we'd love to hear them.
>
There's always more that can be done ;). I'd say the next obvious step
would be to imple
On Wed, Sep 4, 2013 at 7:53 PM, Aaron Meurer wrote:
> I think it would be better to implement the new IPython transformers.
> Then, people could add a very short IPython profile and start with
> something like ipython --profile sympy. This already has to use IPython, it
> might as well use the
On Sun, Oct 6, 2013 at 5:05 AM, Christian Muise
wrote:
>
>> Proof of concept or not, it's already used by the new assumptions system
>> to detect inconsistencies. If you have any suggestions on how to improve the
>> algorithm in pure Python, we'd love to hear them.
>
>
> There's always more that
I don't think so, but there was some discussion on this at
https://github.com/ipython/ipython/pull/2301. That pull request
implemented the basic API that we will want to use in IPython. There
is also a guide to AST at
http://greentreesnakes.readthedocs.org/en/latest/.
All the relevant code is in s
I've been wondering how much of the speed of CSymPy compared to SymPy is
due to the language (C++ vs Python) and how much is due to more
efficient algorithms. So I've created a simple proof of concept
[https://github.com/rlamy/pycsympy] implementing an algorithm similar to
what CSymPy uses in p
You can get similar speeds in regular SymPy. For me
In [59]: R, x, y, z, w = ring("x,y,z,w", ZZ, lex)
In [60]: %timeit (x + y + z + w)**60
1 loops, best of 3: 414 ms per loop
in pycsympy the fastest time was 405 ms.
I wasn't able to compile CSymPy because of
/Users/aaronmeurer/Documents/python
I have the following code, using the statistics module. It answers
questions like "If there's a 20% chance that my random variable is within
10 of the mean, what is the standard deviation?" with the call
getStdDev(10,20).
def getStdDev(distance,confidence):
x = Normal(0,1)
confidenceDis
This fails in solve
In [1]: from sympy.stats import *
In [2]: mu = Symbol('mu', real=True, bounded=True)
In [3]: sigma = Symbol('sigma', positive=True)
In [4]: X = Normal('X', mu, sigma)
In [5]: simplify(2 * P(X - mu < 10))
Out[5]:
⎛___⎞
⎜5⋅╲╱ 2 ⎟
erf⎜───⎟ + 1
⎝ σ ⎠
In [6
Hi Ronan,
On Sun, Oct 6, 2013 at 8:56 PM, Ronan Lamy wrote:
> I've been wondering how much of the speed of CSymPy compared to SymPy is due
> to the language (C++ vs Python) and how much is due to more efficient
> algorithms. So I've created a simple proof of concept
> [https://github.com/rlamy/py
> On Oct 6, 2013, at 11:18 PM, "Ondřej Čertík" wrote:
>
> Hi Ronan,
>
>> On Sun, Oct 6, 2013 at 8:56 PM, Ronan Lamy wrote:
>> I've been wondering how much of the speed of CSymPy compared to SymPy is due
>> to the language (C++ vs Python) and how much is due to more efficient
>> algorithms. So I'v
On Oct 6, 2013, at 10:13 PM, Matthew Rocklin wrote:
This fails in solve
In [1]: from sympy.stats import *
In [2]: mu = Symbol('mu', real=True, bounded=True)
In [3]: sigma = Symbol('sigma', positive=True)
In [4]: X = Normal('X', mu, sigma)
In [5]: simplify(2 * P(X - mu < 10))
Out[5]:
⎛
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