Downloading PyARTS ------------------ PyARTS is available by anonymous cvs from the ARTS website http://www.sat.uni-bremen.de/arts/. An up-to-date PyARTS source distribution can also be found at http://www.met.ed.ac.uk/~cdavis/PyARTS/latest Prerequisites ------------- * Python 2.3, (you will probably have this already) * A fortran compiler * NumPy and SciPy * matplotlib The versions of the above that I am currently using are numpy-0.9.8 and scipy-0.4.9. Some parts of PyARTS also require matplotlib, where I currently have matplotlib-0.86.2. Installation ------------ Once you have all of the above prerequisites installed, and checked out PyARTS from the ARTS cvs repository, run the following from the base directory. python setup.py install --home=~ This will install the package in ~/lib/python and also it will but some scripts in ~/bin. If you omit the --home argument python will try and install the modules in the standard 3rd party location (something like /usr/lib/python2.2/site-packages), which obviously wont happen unless you have superuser privileges In most cases the install command above will work, however, if like me, your Numeric package is not installed in the standard place (something like /usr/lib/python2.x/site-packages/Numeric), you need to use a slightly different command to build and install PyARTS: python setup.py build_src build_ext --include-dirs=/include/python install --home=~ Once installed you should modify your PYTHONPATH environment variable to include the installation directory (eg ~/lib/python). Testing your Installation ------------------------- There are several unit tests in the test/ folder of the distribution. These test both both the functionality and accuracy of the software. To run them all, and check that your installation is OK, type python testall.py -v If you would like to contribute to PyARTS, which is definitely encouraged, it is strongly recommended that the above command is run, and that all tests are successful, before committing your changes to CVS. Examples -------- Some example scripts are provided in the examples/ folder. These all should work as they only depend on data provided in the data/ folder. The testall.py script described above actually verifies that the examples run without error. At the time of writing the examples are: get_atm_fields.py A demonstration of the artsGetAtmFields function MCwith3Dboxcloud.py Creates a simple cloudy-sky scenario and performs a single radiative transfer calculation using the ARTS-MC module. mc_incoming_gen.py demonstrates the use of the create_incoming_lookup function which creates a sequential linear interpolation lookup table of incoming radiances that can be used by the ARTS Monte Carlo radiative transfer algorithm. geometry.py uses the plotting module to show ARTS RT simulation geometry. plot_refr_ind.py Uses the arts_scat module, and the plotting module to show the refractive index of ice at several EOS-MLS frequencies IWP_opt_pathCalc.py uses the arts.IWP_cloud_opt_pathCalc function to calculate the FOV averaged cloud optical path and ice water path for a simple 3D cloud scene Documentation ------------- Most modules in the package have reasonably complete docstring documentation. This means that in an interactive python session, online help on a given PyARTS class or function can be obtained by typing ``help(PyARTS_function_or_class)``. The docstring documentation can also be viewed in easily navigatable html documents by doing the following: ``/pydoc.py -p 1234`` and open ``http://localhost:1234`` in your web browser. There is a user guide in the doc/ folder of the distribution. A recent version of this document can be found at http://www.met.ed.ac.uk/~cdavis/PyARTS/userguide.pdf