Manual python package install: Download the. See below how to run pv_atmos once it's installed. However, be advised that the functions depend on paraview.simple, which is not available as independent python package. This is a regular python package, which can be installed with pip. Journal of Open Research Software 2(1):e4, DOI. Scientific Visualisation of Atmospheric Data with ParaView. Version 1.0 corresponds to the description of Jucker, M 2014. See releases with changelogs in the releases panel of the GitHubĭistribution. This has a little 3D twist, in that the projection can be domed in the vertical. Project 2D longitude-latitude or 3D longitude-latitude-vertical data onto polar coordinates around the North or South pole. WaterMark()Īdd a water mark to one of the spherical shells. Label any number of vertical levels in spherical geometry. AddGridLabel()Īdd one label along one dimension. AddGridPlane()Īdd one grid plane along one dimension. These shells are labeled with the appropriate level value, and a water mark can be added to the outermost shell. Similar to AddGrid() in Cartesian coordinates, this adds shells around a sphere to serve as grid. This includes the appropriate lables of the grid lines, and labeling the axes. FunctionsĪdd a full grid, including grid lines at custom levels of all dimensions. These routines are not limited to any kind of data, and can be used withĪny data, or even without data, to add a custom grid to a visualization. PlanesĪnd shells contain data information, and can therefore be used for data Of a radius corresponding to a given vertical level. InĬase of spherical geometry, one can also add shells, which are spheres Provides the possibility to add axes, grid lines, planes (cuts), and labels. Sphere2xyz(), xyz2Sphere()Ĭonvert a given point in spherical (Cartesian) coordinates into Cartesian (spherical) coordinates, given the transformations applied to the data. It can also convert pressure velocityįrom hPa/s into the new vertical coordinate measure per time step. CartWind2Sphere()Ĭonverts zonal and meridional winds (or any velocity) from m/s into degrees longitude per time step and degrees latitude per time step. DeleteAll(), HideAll(), ShowAll()ĭelete, hide, or show all filters present in the pipeline. In order to be able to switch any filter's visibility on/off in the GUI's pipeline, call this helper function. If not already done when loading the data, apply coordinate transformation in Cartesian coordinates, according to specified aspect ratio and logarithmic coordinates. Classic example: Ocean bathymetry on a lon-lat grid. Take a dataset with (a) 2D variable(s), and expand the chosen variable as third dimension. Transform rectangular geometry into a sphere with given radius. pressure to log-pressure) coordinate if desired, and transform according to prefered aspect ratio. Read a netCDF file, convert linear to logarithmic (e.g. Helper functions for the above to work are: Higher-order functions that you might want to use regularly are: 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.Īll functions are written in CamelCase, and variables in camelCase (sorry, it seems that both versions refer to the same animal). The netCDF should loosely correspond to the Climate and Forecast (FC) conventions. Provides functionality to read data on a 2D or 3D linear or logarithmic coordinates grid, including time evolution (if present) from a netCDF file. A list of all function within the modules is provided here. For more information on each function, please use ComponentsĬomponents are briefly described below. Please cite this work if you use this software for your publications. This package is described in an open access peer-reviewed article: 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). However, pv_atmos has evolved into a very general package, and contains Historically, pv_atmos has been developed to work with geophysical, and in particular, atmospheric model data (hence the name). Python scripting for scientific visualization software
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