"Python scripting for scientific visualization software
ParaView. Historically, pv_atmos
has been developed to work with geophysical, and in particular,
atmospheric model data (hence the name). However, pv_atmos has evolved
into a very general package, and contains
routines for visualizing netCDF data, and the capability to show
arbitrary axes and labels in a large variety of geometries (linear and
logarithmic axes, spherical geometry).
This package is described in an open access peer-reviewed article:
Jucker, M 2014. Scientific Visualisation of Atmospheric Data with ParaView.
Journal of Open Research Software 2(1):e4, DOI:
http://dx.doi.org/10.5334/jors.al.
Please cite this work if you use this software for your publications.
The modules and functions within them include
pv_atmos.basic
Provides functionality to read data on a 2D or 3D linear or
logarithmic coordinates grid, including time evolution (if present) from
a netCDF file. The netCDF should loosely correspond to the
Climate and Forecast (FC) conventions.
The important attribute is the time coordinate: ParaView will be
looking for the "units: xxxx since xxxx" attribute to decide which
dimension corresponds to time.
LoadData()
Read a netCDF file, convert linear to logarithmic (e.g. pressure to
log-pressure) coordinate if desired, and transform according to prefered
aspect ratio.
Cart2Spherical()
Transform rectangular geometry into a sphere with given radius.
Make3D()
Take a dataset with (a) 2D variable(s), and expand the chosen
variable as third dimension. Classic example: Ocean bathymetry on a
lon-lat grid.
TransformCoords()
If not already done when loading the data, apply coordinate
transformation in Cartesian coordinates, according to specified aspect
ratio and logarithmic coordinates.
MakeSelectable()
In order to be able to switch any filter's visibility on/off in the GUI's pipeline, call this helper function.
DeleteAll(), HideAll(), ShowAll()
Delete, hide, or show all filters present in the pipeline.
CartWind2Sphere()
Converts zonal and meridional winds (or any velocity) from m/s into
degrees longitude per time step and degrees latitude per time step. It
can also convert pressure velocity
from hPa/s into the new vertical coordinate measure per time step.
Sphere2xyz(), xyz2Sphere()
Convert a given point in spherical (Cartesian) coordinates into
Cartesian (spherical) coordinates, given the transformations applied to
the data. Helpful to position labels, camera, etc.
pv_atmos.grids
Provides the possibility to add axes, grid lines, planes (cuts), and labels. In
case of spherical geometry, one can also add shells, which are spheres
of a radius corresponding to a given vertical level. Planes
and shells contain data information, and can therefore be used for data
analysis as well as grid information.
These routines are not limited to any kind of data, and can be used with
any data, or even without data, to add a custom grid to a visualization.
AddGrid()
Add a full grid, including grid lines at custom levels of all
dimensions. This includes the appropriate lables of the grid lines, and
labeling the axes.
SphericalShells()
Similar to AddGrid() in Cartesian coordinates, this adds shells
around a sphere to serve as grid. These shells are labeled with the
appropriate level value, and a water mark can be added to the outermost
shell.
AddGridPlane()
Add one grid plane along one dimension.
AddGridLabel()
Add one label along one dimension.
AddAxisLabel()
Label one given axis
SphericalLabels()
Label any number of vertical levels in spherical geometry.
WaterMark()
Add a water mark to one of the spherical shells. Nice to brand your viz.
LonLat2Polar()
Project 2D longitude-latitude or 3D longitude-latitude-vertical data
onto polar coordinates around the North or South pole. This has a little
3D twist, in that the projection can be domed in the vertical.
https://github.com/mjucker/pv_atmos
Automatic parsing of files for dependency-hierarchy creation in case of use and include statements;