[Numpy-discussion] Comparing NumPy/IDL Performance

Hi all, Myself and several colleagues have recently started work on a Python library for solar physics , in order to provide an alternative to the current mainstay for solar physics, which is written in IDL. One of the first steps we have ta

Re: [Numpy-discussion] Comparing NumPy/IDL Performance

hi Keith, I do not think that your primary concern should be with this kind of speed test at this stage : 1/ rest assured that this sort of tests have been performed in other contexts, and you can always do some hard work on high level computing languages like IDL and python to improve perform

Re: [Numpy-discussion] Comparing NumPy/IDL Performance

On Mon, Sep 26, 2011 at 3:19 PM, Keith Hughitt wrote: > Hi all, > Myself and several colleagues have recently started work on a Python library > for solar physics, in order to provide an alternative to the current > mainstay for solar physics, which is written in IDL. > One of the first steps we h

Re: [Numpy-discussion] Comparing NumPy/IDL Performance

On Mon, Sep 26, 2011 at 8:19 AM, Keith Hughitt wrote: > Hi all, > > Myself and several colleagues have recently started work on a Python > library for solar physics , in order to provide an > alternative to the current mainstay for solar > physics

Re: [Numpy-discussion] Comparing NumPy/IDL Performance

Hello Keith, While I also echo Johann's points about the arbitrariness and non-utility of benchmarking I'll briefly comment just on just a few tests to help out with getting things into idiomatic python/numpy: Tests 1 and 2 are fairly pointless (empty for loop and empty procedure) that won't a

Re: [Numpy-discussion] Comparing NumPy/IDL Performance

On Mon, Sep 26, 2011 at 8:24 AM, Zachary Pincus wrote: > Test 3: >    #Test 3 - Add 20 scalar ints >    nrep = 200 * scale_factor >    for i in range(nrep): >        a = i + 1 > > well, python looping is slow... one doesn't do such loops in idiomatic code > if the underlying intent can be

[Numpy-discussion] Trouble installing numpy

Using Source Forge download of NumPy installer package:   numpy-1.6.1-win32-superpack-python 2.7.exe.   Installation Wizard starts, but then installation fails with error message:   Python version 2.7 required, which was not found in the registry   Idle says it's using:   Python 2.7.2 64 bit AMD64

Re: [Numpy-discussion] Comparing NumPy/IDL Performance

One minor thing is you should use xrange rather than range. Although it will probably only make a difference for the empty loop ;) Otherwise, from what I can see, tests where numpy is really much worse are: - 1, 2, 3, 15, 18: Not numpy but Python related: for loops are not efficient - 6, 10: Maybe

Re: [Numpy-discussion] Trouble installing numpy

On Mon, Sep 26, 2011 at 9:43 AM, The Helmbolds wrote: > Using Source Forge download of NumPy installer package: > numpy-1.6.1-win32-superpack-python 2.7.exe. > > Installation Wizard starts, but then installation fails with error message: > Python version 2.7 required, which was not found in t

Re: [Numpy-discussion] Trouble installing numpy

You are probably trying to install the 32 bit version of numpy on your 64 bit Python. Either switch to 64 bit numpy or 32 bit Python. -=- Olivier 2011/9/26 The Helmbolds > Using Source Forge download of NumPy installer package: > numpy-1.6.1-win32-superpack-python 2.7.exe. > > Installation Wi

Re: [Numpy-discussion] Trouble installing numpy

t's holding up the installation, and what do I need to do to install >> numpy? >> >> > >Your Python is 64 bits, the numpy package is 32 bits and needs 32 bit >Python. If you need free 64 bit numpy on windows your best bet is probably >here <http://www.lfd.uci.edu/%7Eg

[Numpy-discussion] [ANN] ODE dy/dt = f(t) solver with guaranteed speficiable accuracy

hi all, now free solver interalg from OpenOpt framework (based on interval analysis) can solve ODE dy/dt = f(t) with guaranteed specifiable accuracy. See the ODE webpage for more details, there is an example of comparison with scipy.integrate.odeint, where latter fails to solve a