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pyLTR: Python Tools for the LTR

pyLTR is a repository of Python modules & packages useful to the CISM
community. This includes:

  • Solar Wind file processing
  • Standard visualizations used for testing & validation
  • Simple data processing

pyLTR ships with LTR version 2.2.0 and newer

After downloading LTR, look in misc/pyLTR

Setup

Requirements

  • Python >= 2.4
  • MatPlotLib >= 0.99.1.1
  • NumPy >= 1.4.0
  • SciPy >= 0.7.2
  • pyHDF >= 0.8.2
  • Nose >= 0.11.3 (optional)
  • Fortran compiler (optional)

These can be easily installed in several ways:

pyLTR has has been successfully tested on Intel Macs running OSX 10.5 (Leopard) and 10.6 (Snow Leopard), 10.7 (Lion), 32 & 64 bit Linux environments, and IBM AIX systems.

Install

Once the prerequisites are installed:

  1. Execute 'install.sh'
  2. Set up your environment in one of two ways:
    1. Append /path/to/pyLTR (not /path/to/pyLTR/pyLTR) to $PYTHONPATH:
      export PYTHONPATH=/path/to/LTR-para/misc/pyLTR:$PYTHONPATH
      
    2. Setup a soft link
      ln -s /path/to/LTR-para/misc/pyLTR/pyLTR /path/to/lib/python2.x/site-packages/pyLTR
      

Notes

  • EPD includes its own Python-2.6 Make sure you modify install.sh to point to the correct python. Strange things may happen if you attempt to install with Python-2.5 when the requirements were built using Python-2.6.
  • the academic version of EPD is available for 32-bit and 64-bit. When using the 32-bit version, install.sh may complain about missing libraries unless you set the CFLAGS environment variable to use 32-bit libraries (i.e. for GCC, export CFLAGS="-m32").

Although not required, it's a good idea to install Nose & run test.sh to make sure everything has been installed properly (note: Nose should be included with the Enthought Python Distribution).

Usage

pyLTR ships with a variety of convenience scripts in the "scripts" subdirectory.  Learn what a script does by passing the "--about" command-line argument, like so:

Execute with the {{--help }}flag for usage information. For example:

LTR-para/misc/pyLTR/scripts/solarWind.py --about

Execute with the --help flag for usage information. For example:

.LTR-para/misc/pyLTR/scripts/solarWind.py --help

solarWind.py

Generates TIEGCM (NetCDF) or LFM (SW-SM-DAT) solar wind file. Can convert from a variety of data sources (eg. CCMC, Enlil, LFM, MAS, OMNI).

Sample usage:

solarWind.py --filename=OMNI_HRO_1MIN_27372.txt -o TIEGCM

When output is LFM (ie. flag -o LFM), this will generate coefficients for a linear Bx fit; save the SW-SM-DAT file; and generate a Python Pickle file storing the data for further analysis.

Visualization

See the "Usage" section above to learn how to use some of the following visualization tools:

MIX output

MIX output can be visualized with a variety of tools:

  • 2d Potential plots of polar caps
    • mixEfieldSummary.py: 1-panel plot of electric field
    • mixIonSummary.py: 6-panel plot of MIX potential, energy, flux, conductances and field-aligned current
  • Time series
    • mixTimeSeries.py plots hemispheric power, cross polar cap potential and current density

LFM output

Magnetosphere:

  • dst.py: Disturbance Storm Time (DST) index

Ionosphere:

  • lfmionIonSummary.py:

Advanced

Want to write your own custom scripts using the pyLTR developers API? Take a look at source code of files from both the "examples" and "scripts" directories.

BXZERO, BY_COEF, BZ_COEF

The solar wind processor computes a linear regression fit to Bx and writes the coefficients BXZERO, BYCOEF, BZCOEF:

Write these numbers down and use them as input to the corresponding MakeItSo prompt.

Note on BXZERO

BXZERO is currently unsupported due to a bug within the LFM.  If prompted, set BXZERO=0.0 This will be fixed in a future release of the code (refs #143).

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