Developer: Getting Started

This mini-tutorial is meant as the starting point for other tutorials for developers. It describes the process for creating a fork of the MPAS-Analysis repo, cloning the repository (and your fork) locally, making a git worktree for development, and creating a conda environment that includes the mpas_analysis package and all of its dependencies, installed in a mode appropriate for development.

1. Getting started on GitHub

1.1 Forking MPAS-Analysis

If you would like to contribute to MPAS-Analysis, you will need to create your own fork of the repository. Go to the link and click on Fork near the top right corner of the page. The Owner should be your GitHub username and the Repository name should be MPAS-Analysis. Check the box for “Copy the develop branch only”. Click “Create fork”.

1.2 Adding SSH keys

If you have not already done so, you should add SSH keys to GitHub that allow you to push to your fork from the machine(s) where you will do your development. Instructions can be found here.

1.3 Local git configuration

It will be convenient to have some basic configuration for git taken care of before we clone the repository. Here are some recommended config options to set. Edit your ~/.gitconfig (create it if it doesn’t exist).

[user]
        name = Xylar Asay-Davis
        email = xylarstorm@gmail.com
[core]
        editor = vim
[color]
        ui = true
[alias]
        logg = log --graph --oneline --decorate
[rebase]
        autosquash = true

Obviously, change [user] config options to appropriate values for you. You must use the email address associated with your GitHub account. Otherwise, your commits will not be associated with your GitHub user name.

2. Cloning the repository

You will want to clone both the main MPAS-Analysis repository and your own fork. The MPAS-Analysis development tutorials assume that you will be developing branches in different worktrees and recommend a directory structure appropriate for this approach.

Note

If you are on a machine with an old version of git, you may need to add:

module load git

to your .bashrc. You want a pretty recent version of git so you have the git worktree command.

Begin by creating a “base” directory for development in a convenient location for keeping code. This should not be on a “scratch” or other temporary drive on an HPC machine. The base directory should be named MPAS-Analysis, mpas-analysis or something similar.

$ mkdir mpas-analysis
$ cd mpas-analysis

Within the base directory, clone the main repository into a directory called develop (the default branch is the develop branch):

$ git clone git@github.com:MPAS-Dev/MPAS-Analysis.git develop
$ cd develop

Add your fork as a “remote”:

$ git remote add <username>/MPAS-Analysis git@github.com:<username>/MPAS-Analysis.git

Make sure to replace <username> with your GitHub username.

3. Making a worktree

To do your development, first make sure you are in the develop directory within your base directory (e.g. mpas-analysis/develop). Then, “fetch” and changes that might have happened on the develop branch so you are using the latest version as a starting point:

$ git fetch --all -p

This will fetch all branches from both the main repository and your fork. It will also prune (-p) any branches you might have deleted.

Then, make a worktree for developing your new feature:

$ git worktree add ../add_my_fancy_task

The last argument (add_my_fancy_task in this example) is both the name of a directory within the base directory (mpas-analysis) and the name of the branch you will be developing.

Go into that directory to do your development:

$ cd ../add_my_fancy_task

4. Making a conda environment

MPAS-Analysis relies on several packages that are only available as conda packages from the conda-forge channel. The first step for running MPAS-Analysis is to create a conda environment with all the needed packages.

4.1 Installing Mambaforge

If you have not yet installed Anaconda, Miniconda or Mambaforge, you will need to begin there. The concept behind Anaconda is that just about everything you would need for a typical python workflow is included. The concept behind Miniconda and Mambaforge is that you create different environments for different purposes. This allows for greater flexibility and tends to lead to fewer conflicts between incompatible packages, particularly when using a channel other than the defaults supplied by Anaconda. Since we will use the conda-forge channel and the mamba tools to speed up installation, the Mambaforge approach is strongly recommended. The main advantage of Mambaforge over Miniconda is that it automatically takes care of a few steps that we otherwise need to do manually.

First download the Mambaforge installer for your operating system, then run it:

$ /bin/bash Mambaforge-Linux-x86_64.sh

Note

MPAS-Analysis and many of the packages it depends on support OSX and Linux but not Windows.

If you are on an HPC system, you can still install Miniconda into your home directory. Typically, you will need the Linux version.

Note

At this time, we don’t have experience with installing or running MPAS-Analysis on ARM or Power8/9 architectures.

You will be asked to agree to the terms and conditions. Type yes to continue.

You will be prompted with a location to install. In this tutorial, we assume that Mambaforge is installed in the default location, ~/mambaforge. If you are using Miniconda or chose to install Mambaforge somewhere else, just make sure to make the appropriate substitution whenever you see a reference to this path below.

Note

On some HPC machines (particularly at LANL Institutional Computing and NERSC) the space in your home directory is quite limited. You may want to install Mambaforge in an alternative location to avoid running out of space.

You will see prompt like this:

Do you wish the installer to initialize Mambaforge
by running conda init? [yes|no]
[no] >>>

You may wish to skip the step (answer no) if you are working on a system where you will also be using other conda environments, most notably E3SM-Unified (which has its own Miniconda installation). If you do not run conda init, you have to manually activate conda whenever you need it. For bash and similar shells, this is:

$ source ~/mambaforge/etc/profile.d/conda.sh
$ conda activate

If you use csh, tcsh or related shells, this becomes:

> source ~/mambaforge/etc/profile.d/conda.csh
> conda activate

You may wish to create an alias in your .bashrc or .cshrc to make this easier. For example:

alias init_conda="source ~/mambaforge/etc/profile.d/conda.sh; conda activate"

4.2 One-time Miniconda setup

If you installed Miniconda, rather than Mambaforge, you will need to add the conda-forge channel and make sure it always takes precedence for packages available on that channel:

$ conda config --add channels conda-forge
$ conda config --set channel_priority strict

Then, you will need to install the mamba package:

$ conda install -y mamba

If you installed Mambaforge, these steps will happen automatically.

4.3 Create a development environment

You can create a new conda environment called mpas_dev and install the dependencies that MPAS-Analysis needs by running the following in the worktree where you are doing your development:

$ mamba create -y -n mpas_dev --file dev-spec.txt "esmf=*=nompi_*"

The last argument is only needed on HPC machines because the conda version of MPI doesn’t work properly on these machines. You can omit it if you’re setting up the conda environment on your laptop.

Then, you can activate the environment and install MPAS-Analysis in “edit” mode by running:

$ conda activate mpas_dev
$ python -m pip install --no-deps --no-build-isolation -e .

In this mode, any edits you make to the code in the worktree will be available in the conda environment. If you run mpas_analysis on the command line, it will know about the changes.

Note

If you add or remove files in the code, you will need to re-install MPAS-Analysis in the conda environment by rerunning

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

4.4 Activating the environment

Each time you open a new terminal window, to activate the mpas_dev environment, you will need to run either for bash:

$ source ~/mambaforge/etc/profile.d/conda.sh
$ conda activate mpas_dev

or for csh:

> source ~/mambaforge/etc/profile.d/conda.csh
> conda activate mpas_dev

You can skip the source command if you chose to initialize Mambaforge or Miniconda3 so it loads automatically. You can also use the init_conda alias for this step if you defined one.

4.5 Switching worktrees

If you switch to a different worktree, it is safest to rerun the whole process for creating the mpas_dev conda environment. If you know that the dependencies are the same as the worktree used to create mpas_dev, You can just reinstall mpas_analysis itself by rerunning

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

in the new worktree. If you forget this step, you will find that changes you make in the worktree don’t affect the mpas_dev conda environment you are using.

5. Editing code

You may, of course, edit the MPAS-Analysis code using whatever tool you like. I strongly recommend editing on your laptop and using PyCharm community edition to do the editing. PyCharm provides many features including flagging deviations from preferred coding style guidelines known as PEP8 and syntax error detection using the mpas_dev conda environment you created.

6. Running MPAS-Analysis on a laptop

If you wish to run MPAS-Analysis on your laptop (or desktop machine), you will need to follow steps 2-6 of the User: Getting Started tutorial.

7. Running MPAS-Analysis on an E3SM supported machine

7.1 Configuring MPAS-Analysis

We configure MPAS-Analysis is with Python cfg (also called ini) files:

[runs]
# mainRunName is a name that identifies the simulation being analyzed.
mainRunName = runName

[execute]
...

The default config file contains thousands of config options, which gives a lot of flexibility to MPAS-Analysis but can be more than bit overwhelming to new users and developers.

The file example_e3sm.cfg provides you with an example with some of the most common config options you might need to change on an E3SM supported machine. If you specify the name of the supported machine with the --machine (or -m) flag when you call mpas_analysis, there are several config options that will be set for you automatically.

First, you should copy this file to a new name for a specific run (say myrun.cfg). Then, you should modify any config options you want to change in your new config file. At a minimum, you need to specify:

mainRunName in [runs]: A name for the run to be included plot titles and legends (best if it’s not super long)
baseDirectory in [input]: The directory for the simulation results to analyze
mpasMeshName in [input]: The official name of the MPAS-Ocean and -Seaice mesh
baseDirectory in [output]: The directory for the analysis results

We will cover these and a few other common options in this tutorial. With the exception of a few paths that you will need to provide, the config options displayed below are the ones appropriate for the example E3SM simulation using the QU480 MPAS mesh.

7.1.1 [runs]

The [runs] section contains options related to which E3SM simulation(s) are being analyzed:

[runs]
## options related to the run to be analyzed and control runs to be
## compared against

# mainRunName is a name that identifies the simulation being analyzed.
mainRunName = A_WCYCL1850.ne4_oQU480.anvil

The mainRunName can be any useful name that will appear at the top of each web page of the analysis output and in the legends or titles of the figures. Often, this is the full name of the E3SM simulation but sometimes it is convenient to have a shorter name. In this case, we use part of the run name but leave off the date of the simulation to keep it a little shorter.

7.1.2 [execute]

The [execute] section contains options related to serial or parallel execution of the individual “tasks” that make up an MPAS-Analysis run. For the most part, you can let MPAS-Analysis take care of this on supported machines. The exception is that, in a development conda environment, you will be using a version of ESMF that cannot run in parallel so you will need the following:

[execute]
## options related to executing parallel tasks

# the number of MPI tasks to use in creating mapping files (1 means tasks run in
# serial, the default)
mapMpiTasks = 1

# "None" if ESMF should perform mapping file generation in serial without a
# command, or one of "srun" or "mpirun" if it should be run in parallel (or in
# serial but with a command)
mapParallelExec = None

If you are running into trouble with MPAS-Analysis, such as running out of memory, you may want to explore other config options from this section.

7.1.3 [input]

The [input] section provides paths to the E3SM simulation data and the name of the MPAS-Ocean and MPAS-Seaice mesh.

[input]
## options related to reading in the results to be analyzed

# directory containing model results
baseDirectory = /lcrc/group/e3sm/ac.xylar/acme_scratch/anvil/20200305.A_WCYCL1850.ne4_oQU480.anvil

# Note: an absolute path can be supplied for any of these subdirectories.
# A relative path is assumed to be relative to baseDirectory.
# In this example, results are assumed to be in <baseDirecory>/run

# subdirectory containing restart files
runSubdirectory = run
# subdirectory for ocean history files
oceanHistorySubdirectory = archive/ocn/hist
# subdirectory for sea ice history files
seaIceHistorySubdirectory = archive/ice/hist

# names of namelist and streams files, either a path relative to baseDirectory
# or an absolute path.
oceanNamelistFileName = run/mpaso_in
oceanStreamsFileName = run/streams.ocean
seaIceNamelistFileName = run/mpassi_in
seaIceStreamsFileName = run/streams.seaice

# name of the ocean and sea-ice mesh (e.g. EC30to60E2r2, WC14to60E2r3,
# ECwISC30to60E2r1, SOwISC12to60E2r4, oQU240, etc.)
mpasMeshName = oQU480

The baseDirectory is the path for the E3SM simulation. Here are paths to some very low resolution simulations you can use on various supported machines:

Anvil or Chrysalis:

/lcrc/group/e3sm/ac.xylar/acme_scratch/anvil/20200305.A_WCYCL1850.ne4_oQU480.anvil
/lcrc/group/e3sm/ac.xylar/acme_scratch/anvil/20201025.GMPAS-IAF.T62_oQU240wLI.anvil

Cori and Perlmutter:

/global/cfs/cdirs/e3sm/xylar/20200305.A_WCYCL1850.ne4_oQU480.anvil

Compy:

/compyfs/asay932/analysis_testing/test_output/20200305.A_WCYCL1850.ne4_oQU480.anvil

The mpasMeshName is the official name of the MPAS-Ocean and -Seaice mesh used in the simulation, which should be in the simulation name and must be a directory on the inputdata server In this example, this is oQU480, meaning the quasi-uniform 480-km mesh for the ocean and sea ice.

The runSubdirectory must contain valid MPAS-Ocean and MPAS-Seaice restart files, used to get information about the MPAS mesh and the ocean vertical grid.

The oceanHistorySubdirectory must contain MPAS-Ocean monthly mean output files, typically named:

mpaso.hist.am.timeSeriesStatsMonthly.YYYY-MM-DD.nc

Similarly, seaIceHistorySubdirectory contains the MPAS-Seaice monthly mean output:

mpassi.hist.am.timeSeriesStatsMonthly.YYYY-MM-DD.nc

In this example, we are using a run where short-term archiving has been used so the output is not in the run directory.

Finally, MPAS-Analysis needs a set of “namelists” and “streams” files that provide information on the E3SM configuration for MPAS-Ocean and MPAS-Seaice, and about the output files, respectively. These are typically also found in the run directory.

7.1.4 [output]

The [output] section provides a path where the output from the analysis run will be written, the option to output the results web pages to another location, and a list of analysis to be generated (or explicitly skipped).

[output]
## options related to writing out plots, intermediate cached data sets, logs,
## etc.

# The subdirectory for the analysis and output on the web portal
subdir = ${runs:mainRunName}/clim_${climatology:startYear}-${climatology:endYear}_ts_${timeSeries:startYear}-${timeSeries:endYear}

# directory where analysis should be written
# NOTE: This directory path must be specific to each test case.
baseDirectory = /lcrc/group/e3sm/${web_portal:username}/analysis/${output:subdir}

# provide an absolute path to put HTML in an alternative location (e.g. a web
# portal)
htmlSubdirectory = ${web_portal:base_path}/${web_portal:username}/analysis/${output:subdir}

# a list of analyses to generate.  Valid names can be seen by running:
#   mpas_analysis --list
# This command also lists tags for each analysis.
# Shortcuts exist to generate (or not generate) several types of analysis.
# These include:
#   'all' -- all analyses will be run
#   'all_publicObs' -- all analyses for which observations are available on the
#                      public server (the default)
#   'all_<tag>' -- all analysis with a particular tag will be run
#   'all_<component>' -- all analyses from a given component (either 'ocean'
#                        or 'seaIce') will be run
#   'only_<component>', 'only_<tag>' -- all analysis from this component or
#                                       with this tag will be run, and all
#                                       analysis for other components or
#                                       without the tag will be skipped
#   'no_<task_name>' -- skip the given task
#   'no_<component>', 'no_<tag>' -- in analogy to 'all_*', skip all analysis
#                                   tasks from the given component or with
#                                   the given tag.  Do
#                                      mpas_analysis --list
#                                   to list all task names and their tags
# an equivalent syntax can be used on the command line to override this
# option:
#    mpas_analysis analysis.cfg --generate \
#         only_ocean,no_timeSeries,timeSeriesSST
generate = ['all', 'no_BGC', 'no_icebergs', 'no_index', 'no_eke',
            'no_landIceCavities']

In this example, I have made liberal use of extended interpolation in the config file to make use of config options in other config options.

subdir is the subdirectory for both the analysis and the output on the web portal. It typically indicates the run being used and the years covered by the climatology (and sometimes the time series as in this example). See 7.1.5. [climatology], [timeSeries] and [index] for more info on these config options.

baseDirectory is any convenient location for the output. In this example, I have used a typical path on Anvil or Chrysalis, including the ${web_portal:username} that will be populated automatically on a supported machine and ${output:subdir}, the subdirectory from above.

htmlSubdirectory is set using the location of the web portal, which is automatically determined on an E3SM machine, the user name, and the same subdirectory used for analysis output. You can modify the path as needed to match your own preferred workflow.

Note

On some E3SM supported machines like Chicoma, there is no web portal so you will want to just manually replace the part of the basePath given by /lcrc/group/e3sm/${web_portal:username} in the example above.

You will need to just put the web output in an html subdirectory within the analysis output:

htmlSubdirectory = html

and copy this from the supercomputer to your laptop to view it in your browser.

Finally, the generate option provides a python list of flags that can be used to determine which analysis will be generated. In this case, we are turning off some analysis that will not work because some features (biogeochemistry, icebergs, eddy kinetic energy and land-ice cavities) are not available in this run and one (the El Niño climate index) is not useful.

7.1.5. [climatology], [timeSeries] and [index]

These options determine the start and end years of climatologies (time averages over a particular month, season or the full year), time series or the El Niño climate index.

[climatology]
## options related to producing climatologies, typically to compare against
## observations and previous runs

# the first year over which to average climatalogies
startYear = 3
# the last year over which to average climatalogies
endYear = 5

[timeSeries]
## options related to producing time series plots, often to compare against
## observations and previous runs

# start and end years for timeseries analysis.  Out-of-bounds values will lead
# to an error.
startYear = 1
endYear = 5

[index]
## options related to producing nino index.

# start and end years for El Nino 3.4 analysis.  Out-of-bounds values will lead
# to an error.
startYear = 1
endYear = 5

For each of these, options a full year of data must exist for that year to be included in the analysis.

For the example QU480 simulation, only 5 years of output are available, so we are doing a climatology over the last 3 years (3 to 5) and displaying time series over the full 5 years. (If the El Niño index weren’t disabled, it would also be displayed over the full 5 years.)

7.2 Running MPAS-Analysis

The hard work is done. Now that we have a config file, we are ready to run.

To run MPAS-Analysis, you should either create a job script or log into an interactive session on a compute node. Then, activate the mpas_dev conda environment as in 4.4 Activating the environment.

On many file systems, MPAS-Analysis and other python-based software that used NetCDF files based on the HDF5 file structure can experience file access errors unless the following environment variable is set as follows in bash:

$ export HDF5_USE_FILE_LOCKING=FALSE

or under csh:

> setenv HDF5_USE_FILE_LOCKING FALSE

Then, running MPAS-Analysis is as simple as:

$ mpas_analysis -m <machine> myrun.cfg

where <machine> is the name of the machine (all lowercase). On Cori, we only support the Haswell nodes (so the machine name is cori-haswell). For now, we only support CPU nodes on Perlmutter (pm-cpu) and Chicoma (chicoma-cpu).

Typical output is the analysis is running correctly looks something like:

$ mpas_analysis -m anvil myrun.cfg
Detected E3SM supported machine: anvil
Using the following config files:
   /gpfs/fs1/home/ac.xylar/code/mpas-analysis/add_my_fancy_task/mpas_analysis/default.cfg
   /gpfs/fs1/home/ac.xylar/anvil/mambaforge/envs/mpas_dev/lib/python3.10/site-packages/mache/machines/anvil.cfg
   /gpfs/fs1/home/ac.xylar/code/mpas-analysis/add_my_fancy_task/mpas_analysis/configuration/anvil.cfg
   /gpfs/fs1/home/ac.xylar/code/mpas-analysis/add_my_fancy_task/mpas_analysis/__main__.py
   /gpfs/fs1/home/ac.xylar/code/mpas-analysis/add_my_fancy_task/myrun.cfg
copying /gpfs/fs1/home/ac.xylar/code/mpas-analysis/add_my_fancy_task/myrun.cfg to HTML dir.

running: /gpfs/fs1/home/ac.xylar/anvil/mambaforge/envs/mpas_dev/bin/ESMF_RegridWeightGen --source /lcrc/group/e3sm/ac.xylar/analysis/A_WCYCL1850.ne4_oQU480.anvil/clim_3-5_ts_1-5/mapping/tmp76l7of28/src_mesh.nc --destination /lcrc/group/e3sm/ac.xylar/analysis/A_WCYCL1850.ne4_oQU480.anvil/clim_3-5_ts_1-5/mapping/tmp76l7of28/dst_mesh.nc --weight /lcrc/group/e3sm/ac.xylar/analysis/A_WCYCL1850.ne4_oQU480.anvil/clim_3-5_ts_1-5/mapping/map_oQU480_to_0.5x0.5degree_bilinear.nc --method bilinear --netcdf4 --no_log --src_loc center --src_regional --ignore_unmapped
running: /gpfs/fs1/home/ac.xylar/anvil/mambaforge/envs/mpas_dev/bin/ESMF_RegridWeightGen --source /lcrc/group/e3sm/ac.xylar/analysis/A_WCYCL1850.ne4_oQU480.anvil/clim_3-5_ts_1-5/mapping/tmpj94wpf9y/src_mesh.nc --destination /lcrc/group/e3sm/ac.xylar/analysis/A_WCYCL1850.ne4_oQU480.anvil/clim_3-5_ts_1-5/mapping/tmpj94wpf9y/dst_mesh.nc --weight /lcrc/group/e3sm/ac.xylar/analysis/A_WCYCL1850.ne4_oQU480.anvil/clim_3-5_ts_1-5/mapping/map_oQU480_to_6000.0x6000.0km_10.0km_Antarctic_stereo_bilinear.nc --method bilinear --netcdf4 --no_log --src_loc center --src_regional --dst_regional --ignore_unmapped
running: /gpfs/fs1/home/ac.xylar/anvil/mambaforge/envs/mpas_dev/bin/ESMF_RegridWeightGen --source /lcrc/group/e3sm/ac.xylar/analysis/A_WCYCL1850.ne4_oQU480.anvil/clim_3-5_ts_1-5/mapping/tmp6zm13a0s/src_mesh.nc --destination /lcrc/group/e3sm/ac.xylar/analysis/A_WCYCL1850.ne4_oQU480.anvil/clim_3-5_ts_1-5/mapping/tmp6zm13a0s/dst_mesh.nc --weight /lcrc/group/e3sm/ac.xylar/analysis/A_WCYCL1850.ne4_oQU480.anvil/clim_3-5_ts_1-5/mapping/map_oQU480_to_WOCE_transects_5km_bilinear.nc --method bilinear --netcdf4 --no_log --src_loc center --src_regional --dst_regional --ignore_unmapped
Preprocessing SOSE transect data...
  temperature
  salinity
  potentialDensity
  zonalVelocity
  meridionalVelocity
  velMag
  Done.
running: /gpfs/fs1/home/ac.xylar/anvil/mambaforge/envs/mpas_dev/bin/ESMF_RegridWeightGen --source /lcrc/group/e3sm/ac.xylar/analysis/A_WCYCL1850.ne4_oQU480.anvil/clim_3-5_ts_1-5/mapping/tmpe2a9yblb/src_mesh.nc --destination /lcrc/group/e3sm/ac.xylar/analysis/A_WCYCL1850.ne4_oQU480.anvil/clim_3-5_ts_1-5/mapping/tmpe2a9yblb/dst_mesh.nc --weight /lcrc/group/e3sm/ac.xylar/analysis/A_WCYCL1850.ne4_oQU480.anvil/clim_3-5_ts_1-5/mapping/map_oQU480_to_SOSE_transects_5km_bilinear.nc --method bilinear --netcdf4 --no_log --src_loc center --src_regional --dst_regional --ignore_unmapped

Running tasks: 100% |##########################################| Time:  0:06:42

Log files for executed tasks can be found in /lcrc/group/e3sm/ac.xylar/analysis/A_WCYCL1850.ne4_oQU480.anvil/clim_3-5_ts_1-5/logs
Total setup time: 0:02:13.78
Total run time: 0:08:55.86
Generating webpage for viewing results...
Web page: https://web.lcrc.anl.gov/public/e3sm/diagnostic_output/ac.xylar/analysis/A_WCYCL1850.ne4_oQU480.anvil/clim_3-5_ts_1-5

The first part of the output, before the progress bar, is the “setup” phase where MPAS-Analysis is checking if the requested analysis can be run on the simulation results. The specific output shown here is related to 1) listing the config files used to determine the final set of config options used in the analysis, and 2) creating mapping files that are used to interpolate between the oQU480 mesh and the various grids MPAS-Analysis uses to compare with observations. Since MPAS-Analysis didn’t know about that oQU480 mesh ahead of time, it is creating mapping files and regions masks for this mesh on the fly.

The mpas_analysis command-line tool has several more options you can explore with

$ mpas_analysis --help

These include listing the available analysis tasks and their tags, purging a previous analysis run before running the analysis again, plotting all available color maps, and outputting verbose python error messages when the analysis fails during the setup phase (before a progress bar appears).

7.3 Viewing the Output

The primary output from MPAS-Analysis is a set of web pages, each containing galleries of figures. The output can be found in the directory you provided in 7.1.4 [output] and given in the last line of the analysis output (if you are on a supported machine with a web portal), e.g.:

Web page: https://web.lcrc.anl.gov/public/e3sm/diagnostic_output/ac.xylar/analysis/A_WCYCL1850.ne4_oQU480.anvil/clim_3-5_ts_1-5

Note

On Cori and Perlmutter, you will need to change the permissions so you can see the webpage online:

$ chmod -R ugo+rX /global/cfs/cdirs/e3sm/www/<username>

where <username> is your NERSC username.

If the web page is incomplete, it presumably means there was an error during the analysis run, since the web page is generated as the final step. Check the analysis output and then the log files for individual analysis tasks to see what when wrong. See 7 Troubleshooting or ask for help if you run into trouble.

The main web page has links to the ocean and sea-ice web pages as well as some “provenance” information about which version of MPAS-Analysis you were using and how it was configured.

The web page generated by this tutorial should look something like this (somewhat outdated) example output.