Thanks. I think I got it. I just submitted the pull request.
I think there already was a test for the derivative with respect to first
argument, but it also had the wrong sign.
James
On Thursday, September 26, 2013 7:30:29 PM UTC-5, Aaron Meurer wrote:
>
> No problems. We have a guide written u
I guess the problem is that y*1.0 is translated into y*Float(1.0), and
Float(1.0) is the same as Float(1.0, 15), and it goes with the higher
precision. I don't know why it does this. It seems to me that it
should go with the smallest number of digits, not the largest, but
maybe there's a good reaso
My idea was to put assumptions on Wild expressions. I guess using the
new assumptions system, the assumptions do not need to be, nor should
they be, actually tied to the symbols. This will also make it easy to
add assumptions about general expressions, not just Wilds (and make it
easier to do the s
No problems. We have a guide written up at
https://github.com/sympy/sympy/wiki/development-workflow. Or just ask
here or on our IRC channel if you need help.
Aaron Meurer
On Thu, Sep 26, 2013 at 3:13 PM, James Fiedler wrote:
> Hi,
>
> Yeah, I'm willing to do that. I'm actually trying to learn Gi
The solution to this is to call apart on the arguments. For instance
In [9]: apart(2/(s**2*(s**2 + 1)))
Out[9]:
2 2
- ── + ──
22
s + 1 s
In [10]: inverse_laplace_transform(-2/(s**2 + 1), s, t)
Out[10]: -2⋅sin(t)⋅Heaviside(t)
In [11]: inverse_laplace_transform(2/s**2,
Hi,
Sympy is directly using inverse Millen formula
Integral(exp(s*t)*F, (s, c - I*oo, c + I*oo))
to evaluate the inverse_laplace_transform function. But it is not working
well with some functions which can be easily evaluated by hand. For example
1. A NotImplementedError is raised when I tr
Hi,
Yeah, I'm willing to do that. I'm actually trying to learn Git now, so this
would be good practice. Of course, that also means it might take me a
minute to figure out how to do this.
James
On Thursday, September 26, 2013 3:32:45 PM UTC-5, Tom Bachmann wrote:
>
> Hi,
>
> good catch. Thank
On Thursday, September 26, 2013 8:39:28 PM UTC+2, Ondřej Čertík wrote:
>
> On Thu, Sep 26, 2013 at 11:45 AM, F. B. >
> wrote:
> > What's your idea to include assumptions in pattern matching?
> >
> > I have some hypothetical uses:
> >
> > a = Wild('a', assumptions=[Q.is_positive])
> > a = W
Hi,
good catch. Thanks for reporting this. I wrote the code, made a small
misstake, and overlooked adding a test which would easily have uncovered
this.
Here is a one-line fix:
diff --git a/sympy/functions/special/gamma_functions.py
b/sympy/functions/special/gamma_functions.py
index 86e292
Is this intended behavior of evalf or N? It evaluates to the precision
specified, but future calculations involving the number return to the
previous precision.
In [56]: x=Float("1.234567")
In [57]: y=N(x,2)
In [58]: y
Out[58]: 1.2
In [59]: y*1.0
Out[59]: 1.234375
I was thinking it
Hello,
The example below demonstrates what looks to me like a bug in the
lowergamma derivative. The example shows that the derivative is very
different from approximations to it.
Is this a bug, or have I just messed up something?
In [1]: import sympy
>
> In [2]: sympy.__version__
> Out[2]: '
On Thu, Sep 26, 2013 at 11:45 AM, F. B. wrote:
> What's your idea to include assumptions in pattern matching?
>
> I have some hypothetical uses:
>
> a = Wild('a', assumptions=[Q.is_positive])
> a = Wild('a'); a & Q.is_positive(a)
> expr.match(expr2, assumptions= ... )
What difference would it mak
What's your idea to include assumptions in pattern matching?
I have some hypothetical uses:
1. a = Wild('a', assumptions=[Q.is_positive])
2. a = Wild('a'); a & Q.is_positive(a)
3. expr.match(expr2, assumptions= ... )
Any better ideas?
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You received this message because you are subs
Hi Harsh,
Please have a look at our Development Workflow page[1] to get started. If
you have already gone through this, submit a pull request so that we can
review it and check it in. Start small and just push your preliminary ideas
first so that we can discuss and refine it.
-Saurabh
[1] htt
Hello,
I was going through the sympy source-code to find out how to
computationally calculate the Inverse Laplace Transform of Functions. While
reading the code I found a TODO
```
# TODO handle derivatives etc
```
in symp.integrals.transforms line 152
I would like to solve this. So, can y
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