Making a time series analysis package in python - advice or assistance sought

[Cameron Laird]

In article <1152274914.324079.22880 at s53g2000cws.googlegroups.com>,
 <beliavsky at aol.com> wrote:
>Ray Tomes wrote:
>> Hi Folks
>>
>> I am an old codger who has much experience with computers
>> in the distant past before all this object oriented stuff.
>> Also I have loads of software in such languages as FORTRAN
>> and BASIC, QBASIC etc that is very useful except that it
>> really doesn't like to run on modern operating systems and
>> has hopeless graphics resolution and lack of ease of use in
>> some ways.
>
>The Fortran code, which I assume is Fortran 77 or earlier, should run
>fine on "modern operating systems" using free (g77, g95, or gfortran)
>or commercial compilers.
>
>> My desire is to get all the facilities available in my
>> old programs working in a modern platform with flexible
>> and high-res graphics and easy to use. Ideally I might
>> find some good coders that are interested in the subject
>> who would assist me, alternatively some help in getting
>> started because there is so much info and so many resources
>> and libraries etc that I don't know where to start.
>>
>> My package will have the following capabilities:
>> 1. Able to read time series data in a variety of formats.
>> 2. Able to create, manipulate and save time series files.
>> 3. Able to do vector arithmetic on time series, including
>> dozens of functions.
>
>Fortran 90 and later versions have array operations, as does NumPy. You
>could convert parts of the FORTRAN code to F90
>
>> 4. Loop and macro facilities to simplify repetitive stuff.
>> 5. Flexible high-resolution graphic presentation.
>> 6. Built in functions to include:
>> FFT / fourier analysis, MESA / maximum entropy spectral analysis,
>> multiple regression, canonical correlation etc etc etc.
>> I have code for all these mostly in FORTRAN, some QBASIC.
>>
>> The applications of the package include:
>> 1. Analysis of time series data from many branches of science.
>> 2. Economic / business models including forecasting.
>> 3. Markets, stocks, commodities forecasting.
>> 4. Interdisciplinary causal analysis.
>> 5. Many more
>
>There exist public domain codes for many of the topics you mention, and
>I think several are part of NumPy. Many statistical algorithms are in
>R, for which the underlying C and Fortran code is available. I suggest
>that you identify which of your algorithms are not publicly available
>and focus on those, making an R package of them. I am interested in
>MESA. Then you can exploit the R graphics and language (called S) and
>have your work easily accessible to many users.
>

The original poster has received much good advice.  I'll reinforce
a couple of points:
1.  Flexibility, high usability, and appealing
    graphics indeed are worth the effort.  They
    can be achieved withOUT object orientation,
    though, and you absolutely should consider
    modernization of your existing *BASIC, 
    Fortran, and so on.  Don't let lack of a
    compiler block your progress; I'm sure we
    can help locate appropriate ones for you.
2.  Python is indeed a great vehicle for this
    sort of work, as I've argued in the past
    <URL: http://phaseit.net/claird/comp.programming/open_source_science.html >.
    For your particular circumstances, though,
    I applaud Mr. Beliavsky's suggestion that
    you look into R <URL:
    http://www-106.ibm.com/developerworks/linux/library/l-sc16.html >.
    You might get even quicker satisfaction,
    with a somewhat lower long-term ceiling,
    through Yorick <URL: http://wiki.tcl.tk/yorick >.

I understand that you were thinking in terms of enlistment of fellow
developers.  You might well be best off, though, with another round
of research and experimentation on your own.