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. -- http://mail.python.org/mailman/listinfo/python-list