Running your first recipe

Last updated on 2024-11-26 | Edit this page

Estimated time: 30 minutes

Overview

Questions

  • How to run a recipe?
  • What happens when I run a recipe?

Objectives

  • Run an existing ESMValTool recipe
  • Examine the log information
  • Navigate the output created by ESMValTool
  • Make small adjustments to an existing recipe

This episode describes how ESMValTool recipes work, how to run a recipe and how to explore the recipe output. By the end of this episode, you should be able to run your first recipe, look at the recipe output, and make small modifications.

Running an existing recipe

The recipe format has briefly been introduced in the [Introduction][lesson-introduction] episode. To see all the recipes that are shipped with ESMValTool, type

BASH 

esmvaltool recipes list

We will start by running [examples/recipe_python.yml](https://docs.esmvaltool. org/en/latest/recipes/recipe_examples.html)

esmvaltool run examples/recipe_python.yml

or if you have the user configuration file in your current directory then

esmvaltool run --config_file ./config-user.yml examples/recipe_python.yml

If everything is okay, you should see that ESMValTool is printing a lot of output to the command line. The final message should be “Run was successful”. The exact output varies depending on your machine, but it should look something like the example log output on terminal below.

{% include example_output.txt %}

Pro tip: ESMValTool search paths

You might wonder how ESMValTool was able find the recipe file, even though it’s not in your working directory. All the recipe paths printed from esmvaltool recipes list are relative to ESMValTool’s installation location. This is where ESMValTool will look if it cannot find the file by following the path from your working directory.

Investigating the log messages

Let’s dissect what’s happening here.

Output files and directories

After the banner and general information, the output starts with some important locations.

  1. Did ESMValTool use the right config file?
  2. What is the path to the example recipe?
  3. What is the main output folder generated by ESMValTool?
  4. Can you guess what the different output directories are for?
  5. ESMValTool creates two log files. What is the difference?
  1. The config file should be the one we edited in the previous episode, something like /home/<username>/.esmvaltool/config-user.yml or ~/esmvaltool_tutorial/config-user.yml.
  2. ESMValTool found the recipe in its installation directory, something like /home/users/username/mambaforge/envs/esmvaltool/bin/esmvaltool/recipes/examples/ or if you are using a pre-installed module on a server, something like /apps/jasmin/community/esmvaltool/ESMValTool_<version> /esmvaltool/recipes/examples/recipe_python.yml, where <version> is the latest release.
  3. ESMValTool creates a time-stamped output directory for every run. In this case, it should be something like recipe_python_YYYYMMDD_HHMMSS. This folder is made inside the output directory specified in the previous episode: ~/esmvaltool_tutorial/esmvaltool_output.
  4. There should be four output folders:
  • plots/: this is where output figures are stored.
  • preproc/: this is where pre-processed data are stored.
  • run/: this is where esmvaltool stores general information about the run, such as log messages and a copy of the recipe file.
  • work/: this is where output files (not figures) are stored.
  1. The log files are:
  • main_log.txt is a copy of the command-line output
  • main_log_debug.txt contains more detailed information that may be useful for debugging.

Debugging: No ‘preproc’ directory?

If you’re missing the preproc directory, then your config-user.yml file has the value remove_preproc_dir set to true (this is used to save disk space). Please set this value to false and run the recipe again.

After the output locations, there are two main sections that can be distinguished in the log messages:

  • Creating tasks
  • Executing tasks

Analyse the tasks

List all the tasks that ESMValTool is executing for this recipe. Can you guess what this recipe does?

Just after all the ‘creating tasks’ and before ‘executing tasks’, we find the following line in the output:

[134535] INFO    These tasks will be executed: map/tas, timeseries/tas_global,
timeseries/script1, map/script1, timeseries/tas_amsterdam

So there are three tasks related to timeseries: global temperature, Amsterdam temperature, and a script (tas: near-surface air temperature). And then there are two tasks related to a map: something with temperature, and again a script.

Examining the recipe file

To get more insight into what is happening, we will have a look at the recipe file itself. Use the following command to copy the recipe to your working directory

BASH 

esmvaltool recipes get examples/recipe_python.yml

Now you should see the recipe file in your working directory (type ls to verify). Use the nano editor to open this file:

BASH 

nano recipe_python.yml

For reference, you can also view the recipe by unfolding the box below.

YAML 

# ESMValTool
# recipe_python.yml
#
# See https://docs.esmvaltool.org/en/latest/recipes/recipe_examples.html
# for a description of this recipe.
#
# See https://docs.esmvaltool.org/projects/esmvalcore/en/latest/recipe/overview.html
# for a description of the recipe format.
---
documentation:
 description: |
   Example recipe that plots a map and timeseries of temperature.

 title: Recipe that runs an example diagnostic written in Python.

 authors:
   - andela_bouwe
   - righi_mattia

 maintainer:
   - schlund_manuel

 references:
   - acknow_project

 projects:
   - esmval
   - c3s-magic

datasets:
 - {dataset: BCC-ESM1, project: CMIP6, exp: historical, ensemble: r1i1p1f1, grid: gn}
 - {dataset: bcc-csm1-1, project: CMIP5, exp: historical, ensemble: r1i1p1}

preprocessors:
 # See https://docs.esmvaltool.org/projects/esmvalcore/en/latest/recipe/preprocessor.html
 # for a description of the preprocessor functions.

 to_degrees_c:
   convert_units:
     units: degrees_C

 annual_mean_amsterdam:
   extract_location:
     location: Amsterdam
     scheme: linear
   annual_statistics:
     operator: mean
   multi_model_statistics:
     statistics:
       - mean
     span: overlap
   convert_units:
     units: degrees_C

 annual_mean_global:
   area_statistics:
     operator: mean
   annual_statistics:
     operator: mean
   convert_units:
     units: degrees_C

diagnostics:

 map:
   description: Global map of temperature in January 2000.
   themes:
     - phys
   realms:
     - atmos
   variables:
     tas:
       mip: Amon
       preprocessor: to_degrees_c
       timerange: 2000/P1M
       caption: |
         Global map of {long_name} in January 2000 according to {dataset}.
   scripts:
     script1:
       script: examples/diagnostic.py
       quickplot:
         plot_type: pcolormesh
         cmap: Reds

 timeseries:
   description: Annual mean temperature in Amsterdam and global mean since 1850.
   themes:
     - phys
   realms:
     - atmos
   variables:
     tas_amsterdam:
       short_name: tas
       mip: Amon
       preprocessor: annual_mean_amsterdam
       timerange: 1850/2000
       caption: Annual mean {long_name} in Amsterdam according to {dataset}.
     tas_global:
       short_name: tas
       mip: Amon
       preprocessor: annual_mean_global
       timerange: 1850/2000
       caption: Annual global mean {long_name} according to {dataset}.
   scripts:
     script1:
       script: examples/diagnostic.py
       quickplot:
         plot_type: plot

Do you recognize the basic recipe structure that was introduced in episode 1?

  • Documentation with relevant (citation) information
  • Datasets that should be analysed
  • Preprocessors groups of common preprocessing steps
  • Diagnostics scripts performing more specific evaluation steps

Analyse the recipe

Try to answer the following questions:

  1. Who wrote this recipe?
  2. Who should be approached if there is a problem with this recipe?
  3. How many datasets are analyzed?
  4. What does the preprocessor called annual_mean_global do?
  5. Which script is applied for the diagnostic called map?
  6. Can you link specific lines in the recipe to the tasks that we saw before?
  7. How is the location of the city specified?
  8. How is the temporal range of the data specified?

  1. The example recipe is written by Bouwe Andela and Mattia Righi.

  2. Manuel Schlund is listed as the maintainer of this recipe.

  3. Two datasets are analysed:

  • CMIP6 data from the model BCC-ESM1
  • CMIP5 data from the model bcc-csm1-1

  1. The preprocessor annual_mean_global computes an area mean as well as annual means

  2. The diagnostic called map executes a script referred to as script1. This is a python script named examples/diagnostic.py

  3. There are two diagnostics: map and timeseries. Under the diagnostic map we find two tasks:

  • a preprocessor task called tas, applying the preprocessor called to_degrees_c to the variable tas.
  • a diagnostic task called script1, applying the script examples/diagnostic.py to the preprocessed data (map/tas).

Under the diagnostic timeseries we find three tasks:

  • a preprocessor task called tas_amsterdam, applying the preprocessor called annual_mean_amsterdam to the variable tas.
  • a preprocessor task called tas_global, applying the preprocessor called annual_mean_global to the variable tas.
  • a diagnostic task called script1, applying the script examples/diagnostic.py to the preprocessed data (timeseries/tas_global and timeseries/tas_amsterdam).

  1. The extract_location preprocessor is used to get data for a specific location here. ESMValTool interpolates to the location based on the chosen scheme. Can you tell the scheme used here? For more ways to extract areas, see the [Area operations][preproc-area-manipulation] page.

  2. The timerange tag is used to extract data from a specific time period here. The start time is 01/01/2000 and the span of time to calculate means is 1 Month given by P1M. For more options on how to specify time ranges, see the [timerange documentation][timeranges].

Pro tip: short names and variable groups

The preprocessor tasks in ESMValTool are called ‘variable groups’. For the diagnostic timeseries, we have two variable groups: tas_amsterdam and tas_global. Both of them operate on the variable tas (as indicated by the short_name), but they apply different preprocessors. For the diagnostic map the variable group itself is named tas, and you’ll notice that we do not explicitly provide the short_name. This is a shorthand built into ESMValTool.

Output files

Have another look at the output directory created by the ESMValTool run.

Which files/folders are created by each task?

  • map/tas: creates /preproc/map/tas, which contains preprocessed data for each of the input datasets, a file called metadata.yml describing the contents of these datasets and provenance information in the form of .xml files.
  • timeseries/tas_global: creates /preproc/timeseries/tas_global, which contains preprocessed data for each of the input datasets, a metadata.yml file and provenance information in the form of .xml files.
  • timeseries/tas_amsterdam: creates /preproc/timeseries/tas_amsterdam, which contains preprocessed data for each of the input datasets, plus a combined MultiModelMean, a metadata.yml file and provenance files.
  • map/script1: creates /run/map/script1 with general information and a log of the diagnostic script run. It also creates /plots/map/script1/ and /work/map/script1, which contain output figures and output datasets, respectively. For each output file, there is also corresponding provenance information in the form of .xml, .bibtex and .txt files.
  • timeseries/script1: creates /run/timeseries/script1 with general information and a log of the diagnostic script run. It also creates /plots/timeseries/script1 and /work/timeseries/script1, which contain output figures and output datasets, respectively. For each output file, there is also corresponding provenance information in the form of .xml, .bibtex and .txt files.

Pro tip: diagnostic logs

When you run ESMValTool, any log messages from the diagnostic script are not printed on the terminal. But they are written to the log.txt files in the folder /run/<diag_name>/log.txt.

ESMValTool does print a command that can be used to re-run a diagnostic script. When you use this the output will be printed to the command line.

Modifying the example recipe

Let’s make a small modification to the example recipe. Notice that now that you have copied and edited the recipe, you can use

esmvaltool run recipe_python.yml

to refer to your local file rather than the default version shipped with ESMValTool.

Change your location

Modify and run the recipe to analyse the temperature for your own location.

In principle, you only have to modify the location in the preprocessor called annual_mean_amsterdam. However, it is good practice to also replace all instances of amsterdam with the correct name of your location. Otherwise the log messages and output will be confusing. You are free to modify the names of preprocessors or diagnostics.

In the diff file below you will see the changes we have made to the file. The top 2 lines are the filenames and the lines like @@ -39,9 +39,9 @@ represent the line numbers in the original and modified file, respectively. For more info on this format, see here.

DIFF 

--- recipe_python.yml	
+++ recipe_python_london.yml	
@@ -39,9 +39,9 @@
    convert_units:
      units: degrees_C

-  annual_mean_amsterdam:
+  annual_mean_london:
    extract_location:
-      location: Amsterdam
+      location: London
      scheme: linear
    annual_statistics:
      operator: mean
@@ -83,7 +83,7 @@
          cmap: Reds

  timeseries:
-    description: Annual mean temperature in Amsterdam and global mean since 1850.
+    description: Annual mean temperature in London and global mean since 1850.
    themes:
      - phys
    realms:
@@ -92,9 +92,9 @@
      tas_amsterdam:
        short_name: tas
        mip: Amon
-        preprocessor: annual_mean_amsterdam
+        preprocessor: annual_mean_london
        timerange: 1850/2000
-        caption: Annual mean {long_name} in Amsterdam according to {dataset}.
+        caption: Annual mean {long_name} in London according to {dataset}.
      tas_global:
        short_name: tas
        mip: Amon

Key Points

  • ESMValTool recipes work ‘out of the box’ (if input data is available)
  • There are strong links between the recipe, log file, and output folders
  • Recipes can easily be modified to re-use existing code for your own use case