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https://github.com/MPAS-Dev/MPAS-Analysis

Provides analysis for the MPAS components of E3SM
https://github.com/MPAS-Dev/MPAS-Analysis

climate-analysis climate-model e3sm mpas mpas-analysis

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Provides analysis for the MPAS components of E3SM

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# MPAS-Analysis



Analysis for simulations produced with Model for Prediction Across Scales

(MPAS) components and the Energy Exascale Earth System Model (E3SM), which

used those components.



![sea surface temperature](docs/users_guide/_static/sst_example.png)



## conda-forge



### Current build status



All platforms:

    

      

        

      

    

  



### Current release info



| Name | Downloads | Version | Platforms |

| --- | --- | --- | --- |

| [![Conda Recipe](https://img.shields.io/badge/recipe-mpas--analysis-green.svg)](https://anaconda.org/conda-forge/mpas-analysis) | [![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/mpas-analysis.svg)](https://anaconda.org/conda-forge/mpas-analysis) | [![Conda Version](https://img.shields.io/conda/vn/conda-forge/mpas-analysis.svg)](https://anaconda.org/conda-forge/mpas-analysis) | [![Conda Platforms](https://img.shields.io/conda/pn/conda-forge/mpas-analysis.svg)](https://anaconda.org/conda-forge/mpas-analysis) |



## Documentation



[https://mpas-dev.github.io/MPAS-Analysis/develop/](https://mpas-dev.github.io/MPAS-Analysis/develop/)



## Installation for users



MPAS-Analysis is available as an anaconda package via the `conda-forge` channel:



```

conda config --add channels conda-forge

conda create -n mpas-analysis mpas-analysis

conda activate mpas-analysis

```



## Installation for developers



To use the latest version for developers, get the code from:

 [https://github.com/MPAS-Dev/MPAS-Analysis](https://github.com/MPAS-Dev/MPAS-Analysis)



Then, you will need to set up a conda environment from the MPAS-Analysis repo.

This environment will include the required dependencies for the development

branch from `dev-spec.txt` and will install the `mpas_analysis` package into

the conda environment in a way that points directly to the local branch (so

changes you make to the code directly affect `mpas_analysis` in the conda

environment):



``` bash

conda config --add channels conda-forge

conda config --set channel_priority strict

conda create -y -n mpas_dev --file dev-spec.txt

conda activate mpas_dev

python -m pip install --no-deps --no-build-isolation -e .

```



If you are developing another conda package at the same time (this is common

for MPAS-Tools or geometric\_features), you should first comment out the other

package in `dev-spec.txt`.  Then, you can install both packages in the same

development environment, e.g.:

``` bash

conda create -y -n mpas_dev --file tools/MPAS-Tools/conda_package/dev-spec.txt \

    --file analysis/MPAS-Analysis/dev-spec.txt

conda activate mpas_dev

cd tools/MPAS-Tools/conda_package

python -m pip install --no-deps --no-build-isolation -e .

cd ../../../analysis/MPAS-Analysis

python -m pip install --no-deps --no-build-isolation -e .

```

Obviously, the paths to the repos may be different in your local clones.  With

the `mpas_dev` environment as defined above, you can make changes to both

`mpas_tools` and `mpas-analysis` packages in their respective branches, and

these changes will be reflected when refer to the packages or call their

respective entry points (command-line tools).



## Download analysis input data



If you installed the `mpas-analysis` package, download the data that is

necessary to MPAS-Analysis by running:



```

download_analysis_data -o /path/to/mpas_analysis/diagnostics

```



where `/path/to/mpas_analysis/diagnostics` is the main folder that will contain

two subdirectories:



* `mpas_analysis`, which includes mapping and region mask files for

  standard resolution MPAS meshes

* `observations`, which includes the pre-processed observations listed in the

  [Observations table](https://mpas-dev.github.io/MPAS-Analysis/latest/observations.html)

  and used to evaluate the model results



Once you have downloaded the analysis data, you will point to its location

(your equivalent of `path/to/mpas_analysis/diagnostics` above) in the config

option `baseDirectory` in the `[diagnostics]` section.



## List Analysis



If you installed the `mpas-analysis` package, list the available analysis tasks

by running:



```

mpas_analysis --list

```



This lists all tasks and their tags.  These can be used in the `generate`

command-line option or config option.  See `mpas_analysis/default.cfg`

for more details.



## Running the analysis



  1. Create and empty config file (say `myrun.cfg`), copy `example.cfg`,

     or copy one of the example files in the `configs` directory (if using a

     git repo) or download one from the

     [example configs directory](https://github.com/MPAS-Dev/MPAS-Analysis/tree/develop/configs).

  2. Either modify config options in your new file or copy and modify config

     options from `mpas_analysis/default.cfg` (in a git repo) or directly

     from GitHub:

     [default.cfg](https://github.com/MPAS-Dev/MPAS-Analysis/tree/develop/mpas_analysis/default.cfg).

  3. If you installed the `mpas-analysis` package, run:

     `mpas_analysis myrun.cfg`.  This will read the configuration

     first from `mpas_analysis/default.cfg` and then replace that

     configuration with any changes from from `myrun.cfg`

  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

     `mpas_analysis/default.cfg` for more details on this option.



  **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]')

  * You do not need to copy all options from `mpas_analysis/default.cfg`.

    This file will automatically be used for any options you do not include

    in your custom config file.

  * You should **not** modify `mpas_analysis/default.cfg` directly.



## List of MPAS output files that are needed by MPAS-Analysis:



  * mpas-o files:

      * `mpaso.hist.am.timeSeriesStatsMonthly.*.nc` (Note: since OHC

        anomalies are computed wrt the first year of the simulation,

        if OHC diagnostics is activated, the analysis will need the

        first full year of `mpaso.hist.am.timeSeriesStatsMonthly.*.nc`

        files, no matter what `[timeSeries]/startYear` and

        `[timeSeries]/endYear`  are. This is especially important to know if

        short term archiving is used in the run to analyze: in that case, set

        `[input]/runSubdirectory`, `[input]/oceanHistorySubdirectory` and

        `[input]/seaIceHistorySubdirectory` to the appropriate run and archive

        directories and choose `[timeSeries]/startYear` and

        `[timeSeries]/endYear` to include only data that have been short-term

        archived).

      * `mpaso.hist.am.meridionalHeatTransport.0001-03-01.nc` (or any

        `hist.am.meridionalHeatTransport` file)

      * `mpaso.rst.0002-01-01_00000.nc` (or any other mpas-o restart file)

      * `streams.ocean`

      * `mpaso_in`

  * mpas-seaice files:

      * `mpasseaice.hist.am.timeSeriesStatsMonthly.*.nc`

      * `mpasseaice.rst.0002-01-01_00000.nc` (or any other mpas-seaice restart

        file)

      * `streams.seaice`

      * `mpassi_in`



Note: for older runs, mpas-seaice files will be named:

  * `mpascice.hist.am.timeSeriesStatsMonthly.*.nc`

  * `mpascice.rst.0002-01-01_00000.nc`

  * `streams.cice`

  * `mpas-cice_in`

  Also, for older runs `mpaso_in` will be named:

  * `mpas-o_in`



## Purge Old Analysis



To purge old analysis (delete the whole output directory) before running run

the analysis, add the `--purge` flag.  If you installed `mpas-analysis` as

a package, run:



```

mpas_analysis --purge 

```



All of the subdirectories listed in `output` will be deleted along with the

climatology subdirectories in `oceanObservations` and `seaIceObservations`.



It is a good policy to use the purge flag for most changes to the config file,

for example, updating the start and/or end years of climatologies (and

sometimes time series), changing the resolution of a comparison grid, renaming

the run, changing the seasons over which climatologies are computed for a given

task, updating the code to the latest version.



Cases where it is reasonable not to purge would be, for example, changing

options that only affect plotting (color map, ticks, ranges, font sizes, etc.),

rerunning with a different set of tasks specified by the `generate` option

(though this will often cause climatologies to be re-computed with new

variables and may not save time compared with purging), generating only the

final website with `--html_only`, and re-running after the simulation has

progressed to extend time series (however, not recommended for changing the

bounds on climatologies, see above).



## Running in parallel via a queueing system



If you are running from a git repo:



  1. If you are running from a git repo, copy the appropriate job script file

     from `configs/` to the root directory (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. If using the `mpas-analysis` conda package, download the job script and/or

     sample config file from the

     [example configs directory](https://github.com/MPAS-Dev/MPAS-Analysis/tree/develop/configs).

  3. Modify the number of parallel tasks, the run name, the output directory

     and the path to the config file for the run.

  4. 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.

  5. 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.



## Customizing plots or creating new ones



There are three main ways to either customize the plots that MPAS-Analysis

already makes or creating new ones:



1. customize the config file. Some features, such as colormaps and colorbar

   limits for color shaded plot or depth ranges for ocean region time series,

   can be customized: look at `mpas_analysis/default.cfg` for available

   customization for each analysis task.

2. read in the analysis data computed by MPAS-Analysis into custom scripts. When

   running MPAS-Analysis with the purpose of generating both climatologies

   and time series, the following data sets are generated:

   * `[baseDirectory]/clim/mpas/avg/unmasked_[mpasMeshName]`: MPAS-Ocean

     and MPAS-seaice climatologies on the native grid.

   * `[baseDirectory]/clim/mpas/avg/remapped`: remapped climatologies

     for each chosen task (climatology files are stored in different

     subdirectories according to the task name).

   * `[baseDirectory]/clim/obs`: observational climatologies.

   * `[baseDirectory]/clim/mpas/avg/mocStreamfunction_years[startYear]-[endYear].nc`.

   * `[baseDirectory]/clim/mpas/avg/meridionalHeatTransport_years[startYear]-[endYear].nc`.

   * `[baseDirectory]/timeseries`: various time series data.

   Custom scripts can then utilize these datasets to generate custom plots.

3. add a new analysis task to MPAS-Analysis (see below).



## Instructions for creating a new analysis task



Analysis tasks can be found in a directory corresponding to each component,

e.g., `mpas_analysis/ocean` for MPAS-Ocean. Shared functionality is contained

within the `mpas_analysis/shared` directory.



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 `mpas_analysis/default.cfg` (and possibly any machine-specific

   config files in `configs/`)

4. import new analysis task in `mpas_analysis//__init__.py`

5. add new analysis task to `mpas_analysis/__main__.py` under

   `build_analysis_list`, see below.



A new analysis task can be added with:

```

   analyses.append(.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.



## Generating Documentation



Create a development environment as described above in "Installation for

developers".  Then run:

To generate the `sphinx` documentation, run:

```

cd docs

make clean

make html

```

The results can be viewed in your web browser by opening:

```

_build/html/index.html

```