|Aug 22, 2011|
|9 years 4 months ||More size_t related issues have been found (thanks to the new
snowpack server) and fixed. Basically, please remember that any
variable used with/for the return value of a call to size() MUST be
of type size_t (this becomes specially relevant in 64 bi
|Aug 16, 2011|
|9 years 5 months ||Cleaning up the CMakeLists, adding a version string as well as a new
method: getLibVersion() to get the said version string (with
compilation date and time). A BuildVersion cmake macro has been
written and will be shared with snowpack (and later with
|9 years 5 months ||Several HACKS have been removed (either the HACK had been fixed but
the comment was still there, or it has now been fixed, or after more
(careful) consideration, it has been decided that the HACK was not a
|9 years 5 months ||Better error messages when compiling in debug as well as for
dimensions issues (adding arrays of incompatible dimensions, invalid
|9 years 5 months ||The VW_MAX field was not properly handled in SMETIO. The failure
count has been slightly improved in SNIO (to decrease the number of
failures when a model could recover: if ILWR is not provided but TSS
is, we don't count it as a failure anymore).
|Aug 8, 2011|
|9 years 5 months ||A Fit1D object is now created by specifying which regression model
to use through an enum. Assignment operators as well as copy
constructors have also been implemented (and tested).
The config.dox file has been updated by doxygen to match more recen
|Jul 30, 2011|
|9 years 5 months ||An update of libsmet. Added SMETReader::get_filename() and
SMETReader::get_header_intvalue(string key) with which a header
value can be directly converted into an integer.
Added libsmet.h to MeteoIO.h
|Jul 28, 2011|
|9 years 5 months ||libsmet: Speedup when reading binary files implemented analogously
to ASCII files.
|9 years 5 months ||1) Fix for parsing easting, norhting from SMET files: setEPSG(...)
must be called before setXY(...)
2) Speedup when reading large SMET ASCII files
|9 years 5 months ||Added more comments to libsmet.h
|9 years 5 months ||A new library for dealing with SMET files has been developed. The
plugin SMETIO builds on top of this library now. It provides:
- A SMETReader class, to read SMET files and parse the header info
- A SMETWriter class to write SMET files and set the he
|Jul 27, 2011|
|9 years 5 months ||A (more) intelligent handling of buffered grids has been
implemented: the grids are stored in the equivalent of a circular
buffer of a given size (user defined, or 10 by default). This
prevents running out of memory when processing large numbers of g
|Jul 26, 2011|
|9 years 5 months ||Fixing installation of meteostats header files.
|Jul 20, 2011|
|9 years 5 months ||Some methods have been moved into another class (like the simple
linear interpolation between two points that is now in
ResamplingAlgorithms), some renammed (like the new weightedMean that
replaces the ill-nammed "linearInterpolate"). The
|Jul 19, 2011|
|9 years 6 months ||The paths have been fixed, it compiles again...
|9 years 6 months ||Reorganizing all the statistical elements... this commit won't
compile, this is just to move files around. Wait for next commit
|9 years 6 months ||Bugfix: The nr_stations variable was not initialized in all use
cases but then used to resize vec_streampos. This lead to a
std::bad_alloc and a seg fault when trying to load SMETIO for
writing only (in_meteo != "SMET").
|9 years 6 months ||Speed optimizations when reading data with the GEOtop plugin are now
more elaborate. This leads to significant performance gains when
reading meteo data in GEOtop format.
|Jul 18, 2011|
|9 years 6 months ||The standard linear regression as well as our tweaked "noisy
linear" regression have been integrated into the Fit1D class.
This means that we should now only use this class for statistical
models. The implementation that is in libinterpol1D will be m
|Jul 15, 2011|
|9 years 6 months ||The 1D regression component is now coming into shape: the Fit1D
class receives two vectors (of X and Y), compute a specified
regression model, then transparently provides modelled values
(calling fit1D.f()). Introducing a new regression model consist