[Numpy-discussion] element wise help
Chris Colbert
sccolbert@gmail....
Thu May 7 14:10:23 CDT 2009
the user of the program inputs the transform in a text field. So I have no
way of know the function apriori.
that doesn't mean I still couldn't throw the exec and eval commands into
another function just to clean things up.
Chris
On Thu, May 7, 2009 at 2:45 PM, <josef.pktd@gmail.com> wrote:
> On Thu, May 7, 2009 at 2:11 PM, Chris Colbert <sccolbert@gmail.com> wrote:
> > alright I got it working. Thanks!
> >
> > This version is an astonishingly 1900x faster than my original
> > implementation which had two for loops. Both versions are below:
> >
> > thanks again!
> >
> > ### new fast code ####
> >
> > b = 4.7
> > n = arange(1, N+1, 1.0).reshape(N, -1)
> > n1 = (-1)**n
> > prefix = exp(b) / timearray
> >
> > arg1 = {'S': b / timearray}
> > exec('from numpy import *', arg1)
> > term1 = (0.5) * eval(transform, arg1)
> >
> > temp1 = b + (1J * pi * n)
> > temp2 = temp1 / timearray
> > arg2 = {'S': temp2}
> > exec('from numpy import *', arg2)
> > term2 = (eval(transform, arg2) * n1).sum(axis=0).real
> >
> > f = prefix * (term1 + term2)
> >
> > return f
>
> If you don't do code generation and have control over transform, then,
> I think, it would be more readable to replace the exec and eval by a
> function call to transform.
>
> I haven't found a case yet where eval is necessary, except for code
> generation as in sympy.
>
> Josef
>
>
>
> >
> > ##### old slow code ######
> > b = 4.7
> > f = []
> >
> > for t in timearray:
> > rsum = 0.0
> > for n in range(1, N+1):
> > arg1 = {'S': ((b/t) + (1J*n*pi/t))}
> > exec('from numpy import *', arg1)
> > tempval = eval(transform, arg1)*((-1)**n)
> > rsum = rsum + tempval.real
> > arg2 = {'S': b/t}
> > exec('from numpy import *', arg2)
> > tempval2 = eval(transform, arg2)*0.5
> > fval = (exp(b) / t) * (tempval2 + rsum)
> > f.append(fval)
> >
> > return f
> >
> >
> >
> >
> > On Thu, May 7, 2009 at 1:41 PM, Chris Colbert <sccolbert@gmail.com>
> wrote:
> >>
> >> its part of a larger program for designing PID controllers. This
> >> particular function numerical calculates the inverse laplace transform
> using
> >> riemann sums.
> >>
> >> The exec statements, from what i gather, allow the follow eval statement
> >> to be executed in the scope of numpy and its functions. I don't get how
> it
> >> works either, but it doesnt work without it.
> >>
> >> I've just about got something working using broadcasting and will post
> it
> >> soon.
> >>
> >> chris
> >>
> >> On Thu, May 7, 2009 at 1:37 PM, <josef.pktd@gmail.com> wrote:
> >>>
> >>> On Thu, May 7, 2009 at 1:08 PM, Chris Colbert <sccolbert@gmail.com>
> >>> wrote:
> >>> > let me just post my code:
> >>> >
> >>> > t is the time array and n is also an array.
> >>> >
> >>> > For every value of time t, these operations are performed on the
> entire
> >>> > array n. Then, n is summed to a scalar which represents the system
> >>> > response
> >>> > at time t.
> >>> >
> >>> > I would like to eliminate this for loop if possible.
> >>> >
> >>> > Chris
> >>> >
> >>> > #### code ####
> >>> >
> >>> > b = 4.7
> >>> > f = []
> >>> > n = arange(1, N+1, 1)
> >>> >
> >>> > for t in timearray:
> >>> > arg1 = {'S': ((b/t) + (1J*n*pi/t))}
> >>> > exec('from numpy import *', arg1)
> >>> > tempval = eval(transform, arg1)*((-1)**n)
> >>> > rsum = tempval.real.sum()
> >>> > arg2 = {'S': b/t}
> >>> > exec('from numpy import *', arg2)
> >>> > tempval2 = eval(transform, arg2)*0.5
> >>> > fval = (exp(b) / t) * (tempval2 + rsum)
> >>> > f.append(fval)
> >>> >
> >>> >
> >>> > #### /code #####
> >>> >
> >>>
> >>> I don't understand what the exec statements are doing, I never use it.
> >>> what is transform?
> >>> Can you use regular functions instead or is there a special reason for
> >>> the exec and eval?
> >>>
> >>> In these expressions ((b/t) + (1J*n*pi/t)), (exp(b) / t)
> >>> broadcasting can be used.
> >>>
> >>> Whats the size of t and n?
> >>>
> >>> Josef
> >>> _______________________________________________
> >>> Numpy-discussion mailing list
> >>> Numpy-discussion@scipy.org
> >>> http://mail.scipy.org/mailman/listinfo/numpy-discussion
> >>
> >
> >
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> >
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