MPAS-Analysis

A repository for the development and maintenance of MPAS analysis tools.

Analysis is stored in a directory corresponding to each core component, e.g., ocean for MPAS-Ocean. Shared functionality is contained within the shared directory.

Installation

This analysis repository presumes that the following python packages are available:

• numpy
• scipy
• matplotlib
• netCDF4
• xarray >= 0.9.1
• dask
• bottleneck
• basemap
• lxml
• nco >= 4.5.4
• pyproj

You can easily install them via the conda command:

conda config --add channels conda-forge
conda install numpy scipy matplotlib netCDF4 xarray dask bottleneck basemap \
    lxml nco pyproj

Running the analysis

1. Create and empty config file (say config.myrun) or copy one of the example files in the configs directory.

2. Copy and modify any config options you want to change from config.default into your new config file.

   Requirements for custom config files:

   • At minimum you should set baseDirectory under [output] to the folder where output is stored. NOTE this value should be a unique directory for each run being analyzed. If multiple runs are analyzed in the same directory, cached results from a previous analysis will not be updated correctly.
   • Any options you copy into the config file must include the appropriate section header (e.g. '[run]' or '[output]')
   • The entire config.default does not need to be used. This fill will automatically be used for any options you do not include in your custom config file.
   • Given the automatic sourcing of config.default you should not alter config.default directly.

3. run: ./run_analysis.py config.myrun. This will read the configuraiton first from config.default and then replace that configuraiton with any changes from from config.myrun

4. If you want to run a subset of the analysis, you can either set the generate option under [output] in your config file or use the --generate flag on the command line. See the comments in config.default for more details on this option.

Running in parallel

1. Copy the appropriate job script file from configs/<machine_name> to the same directory as run_analysis.py (or another directory if preferred). The default script, configs/job_script.default.bash, is appropriate for a laptop or desktop computer with multiple cores.
2. Modify the number of nodes (equal to the number of parallel tasks), the run name and optionally the output directory and the path to the config file for the run (default: ./configs/<machine_name>/config.<run_name>) Note: in job_script.default.bash, the number of parallel tasks is set manually, since there are no nodes.
3. Note: the number of parallel tasks can be anything between 1 and the number of analysis tasks to be performed. If there are more tasks than parallel tasks, later tasks will simply wait until earlier tasks have finished.
4. Submit the job using the modified job script

If a job script for your machine is not available, try modifying the default job script in configs/job_script.default.bash or one of the job scripts for another machine to fit your needs.

Instructions for creating a new analysis task

1. create a new task by copying mpas_analysis/analysis_task_template.py to the appropriate folder (ocean, sea_ice, etc.) and modifying it as described in the template. Take a look at mpas_analysis/shared/analysis_task.py for additional guidance.
2. note, no changes need to be made to mpas_analysis/shared/analysis_task.py
3. modify config.default (and possibly any machine-specific config files in configs/<machine>)
4. import new analysis task in mpas_analysis/<component>/__init__.py
5. if necessary, update variable maps in mpas_analysis/shared/variable_namelist_stream_maps/<component>_maps.py
6. add new analysis task to run_analysis.py under build_analysis_list:

      analyses.append(<component>.MyTask(config, myArg='argValue'))

   This will add a new object of the MyTask class to a list of analysis tasks created in build_analysis_list. Later on in run_analysis, it will first go through the list to make sure each task needs to be generated (by calling check_generate, which is defined in AnalysisTask), then, will call setup_and_check on each task (to make sure the appropriate AM is on and files are present), and will finally call run on each task that is to be generated and is set up properly.