[Numpy-discussion] Release of Pyclimate0.0

Jon Saenz jsaenz at wm.lc.ehu.es
Tue Mar 28 04:46:17 CST 2000


<P><A HREF="http://lcdx00.wm.lc.ehu.es/~jsaenz/pyclimate">Pyclimate 0.0</A> - Climate variability analysis using Numeric Python (28-Mar-00)

					Tuesday, 03/28/2000
Hello, all.

We are making the first announce of a pre-alpha release (version 0.0) of 
our package pyclimate, which presents some tools used for climate 
variability analysis and which make extensive use of Numerical Python.
It is released under the GNU Public License.

We call them a pre-alpha release. Even though the routines are
quite debugged, they are yet growing and we are thinking in making
a stable release shortly after receiving some feedback from users.

The package contains:
IO functions
------------
-ASCII files (simple, but useful)
-ncstruct.py: netCDF structure copier. From a COARDS compliant netCDF
              file, this module creates a COARDS compliant file, 
	      copying the needed attributes, dimensions, 
	      auxiliary variables, comments, and so on in 
	      one call.

Time handling routines
----------------------
* JDTime.py -> Some C/Python functions to convert from date to Scaliger's 
	       Julian Day and from Julian Day to date. We are not trying to 
	       replace mxDate, but addressing a different problem. 
	       In particular, this module contains a routine
	       especially suited to handling monthly time steps
	       for climatological use.
* JDTimeHandler.py -> Python module which parses the units
                      attribute of the time variable in a COARDS
		      file and which offsets and scales adequately
		      the time values to read/save date fields.

Interface to DCDFLIB.C
----------------------
A C/Python interface to the free DCDFLIB.C library is provided. This library 
allows direct and inverse computations of parameters for several 
probability distribution functions like Chi^2, normal, binomial, F,
noncentral F, and many many more.

EOF analysis
------------
Empirical Orthogonal Function analysis based on the SVD decomposition of 
the data matrix and related functions to test the reliability/degeneracy
of eigenvalues (truncation rules). Monte Carlo test of the stability 
of eigenvectors to temporal subsampling.

SVD decomposition
-----------------
SVD decomposition of the correlation matrix of two datasets, functions
to compute the expansion coefficients, the squared cumulative covariance 
fraction and the homogeneous and heterogeneous correlation maps.
Monte Carlo test of the stability of singular vectors to temporal
subsampling.

Multivariate digital filter
---------------------------
Multivariate digital filter (high and low pass) based on the 
Kolmogorov-Zurbenko filter

Differential operators on the sphere
------------------------------------
Some classes to compute differential operators (gradient and divergence)
on a regular latitude/longitude grid.

PREREQUISITES
=============
To be able to use it, you will need:
   1. Python ;-)
   2. netCDF library 3.4 or later
   3. Scientific Python, by Konrad Hinsen
   4. DCDFLIB.C version 1.1

IF AND ONLY IF you really want to change the C code (JDTime.[hc] and 
pycdf.[hc]), then, you will also need SWIG.

COMPILATION
===========
There is no a automatic compilation/installation procedure, but the 
Makefile is quite straightforward.
After manually editing the Makefile for different platforms, the commands
  make
  make test -> Runs a (not infalible) regression test
  make install
will do it.
SORRY, we don't use it under Windows, only UNIX. Volunteers
that generate a Windows installation file would be appreciated, but we
will not do it.

DOCUMENTATION
=============
LaTeX, Postscript and PDF versions of the manual are included in the
distribution. However, we are preparing a new set of documentation
according to PSA rules.

AVAILABILITY
============
http://lcdx00.wm.lc.ehu.es/~jsaenz/pyclimate (Europe)
http://pyclimate.zubi.net/   (USA)
http://starship.python.net/crew/~jsaenz  (USA)

Any feedback from the users of the package will be really appreciated
by the authors. We will try to incorporate new developments, in case
we are able to do so. Our time availability is scarce.


Enjoy.


Jon Saenz, jsaenz at wm.lc.ehu.es
Juan Zubillaga, wmpzuesj at lg.ehu.es





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