<P><A HREF="http://lcdx00.wm.lc.ehu.es/~jsaenz/pyclimate">Pyclimate 0.0</A>  Climate variability analysis using Numeric Python (28Mar00)
Tuesday, 03/28/2000 Hello, all.
We are making the first announce of a prealpha 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 prealpha 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 KolmogorovZurbenko 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@wm.lc.ehu.es Juan Zubillaga, wmpzuesj@lg.ehu.es
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Jon Saenz