Fixed indexing bug.
Fixed incorrect computation of optical depth when cloud ice is not available.
Users can use fill values for cloud free areas.
Users can now specify the optical depth threshold that triggers the calculation.
Fixes#45.
Release 1.1.1
- Added script for building on Cheyenne with maxed out Intel settings, which
also required a patch for numpy.distutils.
- Fixed a few unicode characters hiding in a docstring that were causing
problems on Cheyenne, and also building the docs with Sphinx on Python 2.x.
- Fix issue with np.amax not working with xarray on Cheyenne, causing an error
with the mdbz product.
- Fix cape_2d private variable bug when running with multiple CPUs.
- Computational routines now support multiple cores using OpenMP. See
:ref:`using_omp` for details on how to use this new feature.
- The CAPE routines should be noticeably faster, even in the single threaded
case (thank you supreethms1809!).
- :meth:`wrf.getvar` now works correctly with non-gridded NetCDF variables
- The cloud fraction diagnostic has changed:
- Users can now select their own cloud threshold levels, and can choose
between a vertical coordinate defined as height (AGL), height (MSL), or
pressure.
- The default vertical coordinate type has been changed to be height (AGL).
This ensures that clouds appear over mountainous regions. If you need
the old behavior, set the *vert_type* argument to 'pressure'.
- Fixed a bug involving the cloud threshold search algorithm, where if the
surface was higher than the threshold for a cloud level, the algorithm
would use whatever was there before (uninitialized variable bug). This
caused some interesting visualization issues when plotted. Now, whenever
the surface is above a cloud level threshold, a fill value is used to
indicate that data is unavailable for that location.
- The cartopy object for LambertConformal should now work correctly in the
southern hemisphere.
- Fixed a bug with the PolarStereographic projection missing a geobounds
argument (thank you hanschen!).
- Renamed the modules containing the 'get_product' routines used
by :meth:`wrf.getvar` to avoid naming conflicts with the raw computational
routine names. Users should be using :meth:`wrf.getvar` instead of these
routines, but for those that imported the 'get_product' routines
directly, you will need to modify your code.
- Fixed a uniqueness issue with the internal coordinate cache that was causing
crashes when input data is changed to a different file in a jupyter notebook
cell.
- Added code to better support building wheels on Windows (thank you letmaik!)
- Improved support for scipy.io.netcdf objects.
- Added a new 'zstag' diagnostic that returns the height values for the
vertically staggered grid.
- A DOI is now available for wrf-python. Please cite wrf-python if you are
using it for your research. (See :ref:`citation`)
- Fixed issue with vertcross and interpline not working correctly when a
projection object is used. Users will now have to supply the lower left
latitude and longitude corner point.
- Beginning with numpy 1.14, wrf-python can be built using the MSVC
compiler with gfortran. WRF-Python can now be built for Python 3.5+ on
services like AppVeyor.
Since the size of OpenMP constants differs from system to system, there is now a fortran program that will print the KIND sizes, and the ompgen.F90 file is generated by python using a string template. This only needs to be performed if using OpenMP.
Version 1.1.0
- Release 1.1.0
- Computational routines now support multiple cores using OpenMP. See
:ref:`using_omp` for details on how to use this new feature.
- The CAPE routines should be noticeably faster, even in the single threaded
case (thank you supreethms1809!).
- :meth:`wrf.getvar` now works correctly with non-gridded NetCDF variables
- The cloud fraction diagnostic has changed:
- Users can now select their own cloud threshold levels, and can choose
between a vertical coordinate defined as height (AGL), height (MSL), or
pressure.
- The default vertical coordinate type has been changed to be height (AGL).
This ensures that clouds appear over mountainous regions. If you need
the old behavior, set the *vert_type* argument to 'pressure'.
- Fixed a bug involving the cloud threshold search algorithm, where if the
surface was higher than the threshold for a cloud level, the algorithm
would use whatever was there before (uninitialized variable bug). This
caused some interesting visualization issues when plotted. Now, whenever
the surface is above a cloud level threshold, a fill value is used to
indicate that data is unavailable for that location.
- The cartopy object for LambertConformal should now work correctly in the
southern hemisphere.
- Fixed a bug with the PolarStereographic projection missing a geobounds
argument (thank you hanschen!).
- Renamed the modules containing the 'get_product' routines used
by :meth:`wrf.getvar` to avoid naming conflicts with the raw computational
routine names. Users should be using :meth:`wrf.getvar` instead of these
routines, but for those that imported the 'get_product' routines
directly, you will need to modify your code.
- Fixed a uniqueness issue with the internal coordinate cache that was causing
crashes when input data is changed to a different file in a jupyter notebook
cell.
- Added code to better support building wheels on Windows (thank you letmaik!)
- Improved support for scipy.io.netcdf objects.
- Added a new 'zstag' diagnostic that returns the height values for the
vertically staggered grid.
- A DOI is now available for wrf-python. Please cite wrf-python if you are
using it for your research. (See :ref:`citation`)
- Fixed issue with vertcross and interpline not working correctly when a
projection object is used. Users will now have to supply the lower left
latitude and longitude corner point.
- Beginning with numpy 1.14, wrf-python can be built using the MSVC
compiler with gfortran. WRF-Python can now be built for Python 3.5+ on
services like AppVeyor.
Bill Ladwig