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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
Last synced: 3 months ago
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Provides analysis for the MPAS components of E3SM
- Host: GitHub
- URL: https://github.com/MPAS-Dev/MPAS-Analysis
- Owner: MPAS-Dev
- License: other
- Created: 2016-06-22T20:29:57.000Z (over 8 years ago)
- Default Branch: develop
- Last Pushed: 2024-05-23T16:43:43.000Z (6 months ago)
- Last Synced: 2024-06-11T17:03:43.545Z (5 months ago)
- Topics: climate-analysis, climate-model, e3sm, mpas, mpas-analysis
- Language: Python
- Homepage:
- Size: 320 MB
- Stars: 50
- Watchers: 19
- Forks: 49
- Open Issues: 37
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- open-sustainable-technology - 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. (Climate Change / Climate Data Processing and Analysis)
README
# MPAS-Analysis
[](https://dev.azure.com/MPAS-Dev/MPAS-Analysis%20testing/_build/latest?definitionId=2&branchName=develop)
Analysis for simulations produced with Model for Prediction Across Scales
(MPAS) components and the Energy Exascale Earth System Model (E3SM), which
used those components.
## conda-forge
### Current build status
All platforms:
### Current release info
| Name | Downloads | Version | Platforms |
| --- | --- | --- | --- |
| [](https://anaconda.org/conda-forge/mpas-analysis) | [](https://anaconda.org/conda-forge/mpas-analysis) | [](https://anaconda.org/conda-forge/mpas-analysis) | [](https://anaconda.org/conda-forge/mpas-analysis) |
## Documentation
[https://mpas-dev.github.io/MPAS-Analysis/latest/](https://mpas-dev.github.io/MPAS-Analysis/latest/)
## 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 -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 -e .
cd ../../../analysis/MPAS-Analysis
python -m pip install -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
```