in the docs directory.
1. Add a new internal document -- testserver.dox -- to describe
how to set up and maintain the dap test server.
2. It moves the internal documentation (internal.dox, indexing.dox,
and testserver.dox) to later in the documentation table of contents.
3. Cleanup the formatting of the internal documents.
4. Cleanup some minor doxygen issues in other files.
re: github issue https://github.com/Unidata/netcdf-fortran/issues/82
This was originally discovered in the Fortran tests, but is
a problem in the C library.
The problem only occurred when using HDF5-1.10.x. The reason it
failed is that starting with 1.10, the hid_t type was changed
from 32 bits to 64 bits.
The function libsrc4/nc4memcb.c#NC4_image_init was using type int (doh!)
to return the hdf fileid instead of hid_t type. This, of course,
caused the id to be truncated and in turn later use of the id
caused hdf5 to fail.
Fix is trivial: replace int with hid_t. This also requires a related
change in nc4mem.c.
Also added the test case derived from the original Fortran code.
You would think I would learn...
corresponding HDF5 operations.
re: github issue https://github.com/Unidata/netcdf-c/issues/908
also in reference to https://github.com/pydata/xarray/issues/2004
The netcdf-c library has implemented the nc_get_vars and nc_put_vars
operations as element at a time. This has resulted in very slow
operation.
This pr attempts to improve the situation for netcdf-4/hdf5 files
by using the slab operations provided by the hdf5 library. The new
implementation passes the get/put vars stride information down to
the hdf5 slab operations.
The result appears to improve performance significantly. Some simple
tests on large 2-D arrays shows speedups in excess of 150.
Misc. other changes:
1. fix bug in ncgen/semantics.c; using a list's allocated length
instead of actual length.
2. Added a temporary hook in the netcdf library plus a performance
test case (tst_varsperf.c) to estimate the speedup. After users
have had some experience with this, I will remove it, probably
after the 4.7 release.
