Installing Python Modules for Kalpana

This post was contributed by CCHT collaborator Jason Fleming. This is a LONG post, but as he notes, it is a stream of consciousness about how he compiled the modules needed by Kalpana. Hopefully his experience will be helpful to new users. After the modules have been compiled, then Kalpana is very easy to use. So please don’t be discouraged!

The actual script kalapana.py does not require installation, it is merely executed. However, it relies on several Python modules that most users (or their IT supporters) will need to install before working with Kalpana. These modules are:

  • matplotlib – main python module used for data visualization.
  • pylab – Imports plotting and numerics libraries in a single name space.
  • shapely – used to construct geometric objects like Points, Polygons and LineStrings.
  • fiona – for writing .shp files.
  • netCDF4 – reading and writing netCDF files.
  • datetime – dates and time calculation, manipulation, and formatting.
  • time – contains time related functions for measuring the performance of the code itself.
  • numpy – to facilitate scientific computing; used primarily in Kalpana for working with n-dimensional numpy arrays, which are ideal for storing large amounts of data.
  • collections – accessing OrderedDict, which is a dictionary subclass that remembers the order in which entries were added, whereas an ordinary dictionary does not do so).
  • simplekml – writing kml (Google Earth) files.

In the process of installing Python modules on a desktop platform, as well as an HPC platform, I’ve found that the process can be very challenging. The biggest issue is conflicting module version dependencies, which can be handled by the use of virtual execution environments for Python (explained below).

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News: Developing Storm Surge Visualization

2015/03/10 – CCEE
Developing Storm Surge Visualization

ncsu-engr

When tropical storms approach, local, state and federal emergency managers seek predictions of storm surge and coastal flooding. In a project supported by NC Sea Grant, Dr. Casey Dietrich and Ph.D. student Rosemary Cyriac are improving the dissemination of flooding predictions to end-users by producing predictions in popular file formats. The Coastal Emergency Risk Assessment (CERA, http://nc-cera.renci.org/) provides a Web-based interface for visualizing surge predictions from computer models. Dr. Dietrich’s team is working with emergency managers in North Carolina’s coastal counties and with other decision makers. Results from daily model simulations are sent to these individuals, and they are widely used to predict inundation and flooding levels. Such predictions are also needed for engineering design and evacuation decisions. Model outputs are converted into formats compatible with commonly used visualization software, such as ArcGIS and Google Earth. By providing predictions to local emergency managers in a useful format, the information can be more easily integrated with other data, thereby making the information more accessible to those who most need it.