Release of v1.2.0

Added by alexandre mary over 4 years ago

Commit commit:e0dcee6

Several improvements w.r.t. convention CF-1.6 and geometry.
(Almost full-) Compatibility with the forthcoming SURFEX v8.1 netCDF outputs.

Integration of EPYWEB: a Vortex+Epygram HTML interface for plotting fields from MF operational suites and OLIVE experiments.

Other new features
Pre-staging mode in usevortex
Function fields.psikhi2uv()
Function util.datetimerange()
Computation of wind direction

Porting fixes for Mageia6
More flexible colormaps scalings (as radar)

And a quantity of fixes !...

Release of v1.1.3

Added by alexandre mary over 4 years ago

Commit commit:b53faebc

Advanced netCDF support:
Read/write from/to netCDF files well better handled (examples to come).
Support for any kind of fields, including temporal dimension (from a single Point to a 4D field).
Facility: any field can be dumped to a netCDF file, thanks to the dump_to_nc() method.

Fields' internal data is 4D
Although rather transparent to the user, the internal data storage of fields is now always 4D. This modification brings great simplification in many places.
The getdata()/setdata() methods are hereby essential to use, with the optional d4=True.

Better handling of time dimension in fields
Corrections, modifications linked to previous item, and possibility to extract a time index or extend time dimension with another field (cf. methods).

Animation of fields
Fields with a temporal dimension now propose a plotanimation() method, that create a matplotlib animation, that can be saved to a MP4 file.

"Special resources": or how to get 3D/4D fields from 2D resource(s)
A new proxy function to create easily "special resources" (epygram.resources.special_resource()), that enable to:
- make 3D fields from a resource containing a series of horizontal fields distributed on the vertical;
- make 2D fields + temporal dimension from a series of 1-time resources;
- combination of both.

Better handling of matplotlib's figures and axes in plotfield()
Enables to have multiple plots on the same figure, and a more trustful superposition of plots.

Derivatives for Gauss grid fields (arpifs4py)
Similarly to the cartesian grids, through the compute_xy_spderivatives() method.


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