Defining Parameters

Ways to define parameters

There are three ways to input parameters to the diagnostics:

Command line: For example: python run_e3sm_diags.py --variables T PRECT or e3sm_diags -p myparam.py --variables T PRECT will set the variables parameter to ['T', 'PRECT'].
Parameters file: In the command python run_e3sm_diags.py, the parameters file is run_e3sm_diags.py. In the command e3sm_diags -p myparams.py, the parameters file is myparams.py.
Diagnostics file: In the command python run_e3sm_diags.py -d mydiags.cfg or e3sm_diags -d mydiags.cfg, the diagnostics file is mydiags.cfg.

Each of these ways have a level of priority, with the command line input having the highest priority and the diagnostics file having the lowest priority.

Examples

Say that we have the following files:

run_e3sm_diags.py:

import os
from e3sm_diags.parameter.core_parameter import CoreParameter
from e3sm_diags.run import runner

param = CoreParameter()

param.reference_data_path = '/p/user_pub/e3sm/e3sm_diags_data/obs_for_e3sm_diags/climatology/'
param.test_data_path = '/p/user_pub/e3sm/e3sm_diags_data/test_model_data_for_acme_diags/climatology/'
param.test_name = '20161118.beta0.FC5COSP.ne30_ne30.edison'
prefix = '/var/www/acme/acme-diags/zhang40/tests/'
param.results_dir = os.path.join(prefix, 'lat_lon_demo')

param.seasons = ['DJF', 'MAM', 'JJA', 'SON']
param.variables = ['T']
runner.sets_to_run = ['lat_lon']
runner.run_diags([param])

myparams.py:

sets = ['lat_lon']
variables = ['T']
seasons = ['DJF', 'MAM', 'JJA', 'SON']

mydiags.cfg:

[#]
variables = ["PRECT"]
regions = ["global"]
seasons = ["ANN"]

[#]
variables = ["SST"]
regions = ["ocean"]
seasons = ["JJA"]

Running with just the parameters file

Running python run_e3sm_diags.py will just run the lat-lon contour diagnostics once with the parameters being:

variables = ['T']
seasons = ['DJF', 'MAM', 'JJA', 'SON']

Running with the diagnostics file

The cfg files support all of the non-programatic parameters covered below in the available parameters section. cfg files are also the way of defining multiple diagnostics runs. In mydiags.cfg defined above, we have two runs.

Running e3sm_diags -d mydiags.cfg will have two runs with the following parameters

Run 1:

variables = ['PRECT']
regions = ['global']
seasons = ['ANN']

Run 2:

variables = ['SST']
regions = ['ocean']
seasons = ['JJA']

Running e3sm_diags -p myparams.py -d mydiags.cfg (or python run_e3sm_diags.py -d mydiags.cfg) will also have two runs, but the parameters in myparams.py (or run_e3sm_diags.py) will take priority over the ones described in mydiags.cfg. So the runs will be:

Run 1:

variables = ['T']
regions = ['global']
seasons = ['DJF', 'MAM', 'JJA', 'SON']

Run 2:

variables = ['T']
regions = ['ocean']
seasons = ['DJF', 'MAM', 'JJA', 'SON']

Running with command line arguments

Command line arguments take precedence over both the parameters file and the diagnostics file.

So, running python run_e3sm_diags.py -d mydiags.cfg --variables PREH2O or e3sm_diags -p myparams.py -d mydiags.cfg --variables PREH2O will have the variables in both runs be PREH2O:

Run 1:

variables = ['PREH2O']
regions = ['global']
seasons = ['DJF', 'MAM', 'JJA', 'SON']

Run 2:

variables = ['PREH2O']
regions = ['ocean']
seasons = ['DJF', 'MAM', 'JJA', 'SON']

Selecting certain parameters

When you run e3sm_diags with a file passed in via -p, the parameters in that file are inserted into each diagnostics from default diagnostic files like this, overwriting any duplicates in the process. A single diagnostics starts with [#]. If you provide your own cfg file with -d, the same happens.

For example, say we have the following parameters in a Python file:

reference_data_path = '/global/cfs/cdirs/e3sm/e3sm_diags/obs_for_acme_diags/'
test_data_path = '/global/cfs/cdirs/e3sm/e3sm_diags/test_model_data_for_acme_diags/'

test_name = '20161118.beta0.FC5COSP.ne30_ne30.edison'

variables = ['PRECT']
sets = ['lat_lon']

Since we’re running the lat_lon plotset, and since it defaults to model_vs_obs, it will open this file. Each of the parameters in the Python file will be inserted into each of the diagnostics runs.

So each of the 100+ lat_lon diagnostics will be done with variables = ['PRECT']. However, this is nonsensical. What we want to do is to “select” the diagnostics from here that use PRECT.

Using the selectors parameter

In the above Python file, we can designate the variables parameter to be a “selector”.

First, find the default selectors used here and copy what current parameters are used as selectors. This is the value of self.selectors.

In your Python file, paste these along with any parameters you want as selectors. It should look something like this:

reference_data_path = '/global/cfs/cdirs/e3sm/e3sm_diags/obs_for_acme_diags/'
test_data_path = '/global/cfs/cdirs/e3sm/e3sm_diags/test_model_data_for_acme_diags/'

test_name = '20161118.beta0.FC5COSP.ne30_ne30.edison'

variables = ['PRECT']
sets = ['lat_lon']
# 'sets' and 'seasons' were our default values. We added 'variables'
selectors = ['sets', 'seasons', 'variables']

If you run e3sm_diags now like this, you’ll only run the diagnostics that had the variables originally as 'PRECT'.

Remember that you can use any of the parameters defined here as selectors.

Say we only wanted to select the diagnostics using 'PRECT' and using specific observational data. We can do the following:

reference_data_path = '/global/cfs/cdirs/e3sm/e3sm_diags/obs_for_acme_diags/'
test_data_path = '/global/cfs/cdirs/e3sm/e3sm_diags/test_model_data_for_acme_diags/'

test_name = '20161118.beta0.FC5COSP.ne30_ne30.edison'

variables = ['PRECT']
sets = ['lat_lon']
ref_name = ['GPCP_v2.2', 'ERA-Interim']
# 'sets' and 'seasons' were our default values.
selectors = ['sets', 'seasons', 'variables', 'ref_name']