solve codacy issues

Author: BenMGeo

_episodes/first_example_recipe.md

@@ -1,5 +1,4 @@
 ---
-
 title: "Running a recipe (First example)"
 teaching: 20
 exercises: 25
@@ -16,8 +15,7 @@ objectives:
 keypoints:
 - "A recipe does not break by fiddling with it"
 - "Log information is useful when interpreting the first warnings/errors"
-- "The dataset section resembles the filename or directory structure (e.g. CMIP subdirectories)."
-
+- "The dataset section resembles the filename or directory structure (e.g. CMIP subdirectories)"
 ---
 
 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, modify a recipe, explore and run some basic recipe debugging.
@@ -26,32 +24,32 @@ This episode describes how ESMValTool recipes work, how to run a recipe and how
 
 Recipes are the instructions that you give to ESMValTool that tell it what you want to do. This includes four main sections: datasets, preprocessors, diagnostics and description.
 
-- datasets: what datasets you want to use, including
-  - the time range and time resolution,
-  - the MIP, ensemble member,
-  - the experiment (i.e. historical, ssp125, etc.),
-  - and the grid type (CMIP6 only).
+  - datasets: what datasets you want to use, including
+    - the time range and time resolution,
+    - the MIP, ensemble member,
+    - the experiment (i.e. historical, ssp125, etc.),
+    - and the grid type (CMIP6 only).
 
-- preprocessors: general operations applied to a dataset before handling it in a diagnostic, listing
-  - which preprocessor modules to apply,
-  - the order to apply them,
-  - and the preprocessor arguments.
+  - preprocessors: general operations applied to a dataset before handling it in a diagnostic, listing
+    - which preprocessor modules to apply,
+    - the order to apply them,
+    - and the preprocessor arguments.
 
-  This section can also be optional, if no preprocessing is needed.
+    This section can also be optional, if no preprocessing is needed.
 
-- diagnostics: all the information about the diagnostic, including
-  - list of variables to evaluate (with their respective configurations),
-  - the desired diagnostic script to use,
-  - and additional diagnostic script options or arguments, if needed.
+  - diagnostics: all the information about the diagnostic, including
+    - list of variables to evaluate (with their respective configurations),
+    - the desired diagnostic script to use,
+    - and additional diagnostic script options or arguments, if needed.
 
-  Also include additional datasets beyond those included in the datasets section mentioned above, for instance variable specific observational data.
+    Also include additional datasets beyond those included in the datasets section mentioned above, for instance variable specific observational data.
 
-- description: a brief description of the recipe, including
-  - who wrote the recipe and who maintains it,
-  - which project is responsible for it,
-  - and which publications, references are linked with the recipe.
+  - description: a brief description of the recipe, including
+    - who wrote the recipe and who maintains it,
+    - which project is responsible for it,
+    - and which publications, references are linked with the recipe.
 
-  Note that the authors, publications and references are to be named in the config-references.yml
+    Note that the authors, publications and references are to be named in the config-references.yml
 
 The information you provide in the recipe is not only affecting the processes you are starting, but also the directory names your output will be structured in.
 For additional reeds, please have a look at the recipe format description in the [ESMValTool manual](https://docs.esmvaltool.org/projects/esmvalcore/en/latest/recipe/overview.html#recipe-section-diagnostics).
@@ -64,10 +62,8 @@ Once you’ve set up your conda environment and installed ESMValTool (see episod
 esmvaltool -c configuration recipe
 ~~~
 
-
 To try your hand with a basic recipe, please work through this episode.
 
-
 ## Introduction to the example recipe
 The recipe presented here is a simple, basic recipe that takes a single dataset and produces a time series plot.
 
@@ -120,7 +116,6 @@ Please download the following recipe into your ESMValTool working area with the
 > ~~~
 {: .solution}
 
-
 > ## Explore the recipe
 > Use the command and investigate the sample recipe.
 > ~~~bash
@@ -129,50 +124,50 @@ Please download the following recipe into your ESMValTool working area with the
 {: .challenge}
 
 Please note the following sections:
-- documentation: lines 4-20
+  - documentation: lines 4-20
 
-  The documentation consists of the following information:
-  - description: a short description of the recipe
-  - authors: a list of authors (linked to esmvaltool/config-references.yml)
-  - maintainer: a list of maintainers (linked to esmvaltool/config-references.yml)
-  - references: a list of references (linked to a bibtexfile in esmvaltool/references with the same name)
-  - projects: a list of projects (linked to esmvaltool/config-references.yml)
+    The documentation consists of the following information:
+    - description: a short description of the recipe
+    - authors: a list of authors (linked to esmvaltool/config-references.yml)
+    - maintainer: a list of maintainers (linked to esmvaltool/config-references.yml)
+    - references: a list of references (linked to a bibtexfile in esmvaltool/references with the same name)
+    - projects: a list of projects (linked to esmvaltool/config-references.yml)
 
 
-- datasets: lines 22-23
+  - datasets: lines 22-23
 
-  The dataset definition consists of a list of python dictionaries with the information on the datasets.
-  - dataset name (key: dataset)
-  - project (key: project)
-  - experiment (key: exp)
-  - mip (for CMIP data, key: mip)
-  - ensemble member (key: ensemble)
-  - time range (e.g. key-value-pair: start_year: 1982, end_year: 1990)
-  - model grid (for CMIP6 data only, key: grid)
-  - alias (key: alias; use the alias for e.g. a more human readable name)
+    The dataset definition consists of a list of python dictionaries with the information on the datasets.
+    - dataset name (key: dataset)
+    - project (key: project)
+    - experiment (key: exp)
+    - mip (for CMIP data, key: mip)
+    - ensemble member (key: ensemble)
+    - time range (e.g. key-value-pair: start_year: 1982, end_year: 1990)
+    - model grid (for CMIP6 data only, key: grid)
+    - alias (key: alias; use the alias for e.g. a more human readable name)
 
 
-- preprocessors: lines 25-28
+  - preprocessors: lines 25-28
 
-  The definition for different preprocessors or combinations.
-  If no preprocessing is needed, the preprocessor can be set to an empty python dictionary (`{}`).
-  Here, we produce annual means. The preprocessor is called with its name (here: prep_timeseries), later in the diagnostic (line 39).
-  (See episode #5 LINK for more details.)
+    The definition for different preprocessors or combinations.
+    If no preprocessing is needed, the preprocessor can be set to an empty python dictionary (`{}`).
+    Here, we produce annual means. The preprocessor is called with its name (here: prep_timeseries), later in the diagnostic (line 39).
+    (See episode #5 LINK for more details.)
 
 
-- diagnostic section: lines 30-42
+  - diagnostic section: lines 30-42
 
-  The information of which diagnostic script to run with which variables.
-  The diagnostics section has some indents that are free to call.
-  - the first indent (here: diag_timeseries_temperature) is the diagnostic’s name (a string without whitespace), used for setting up the respective directories
-  - description: a short description of the diagnostic
-  - variables: a definition of all variables that are used in this diagnostic
-  - the next indent (here: timeseries_variable) is the variables’ names (a string without whitespace) for the diagnostic to use
-  - short_name: the variable name as listed in the dataset
-  - preprocessor: the preprocessor(s) applied to the variable before running the diagnostic
-  - scripts: a definition of all scripts that are used in this diagnostic
-  - the next indent (here: timeseries_diag) is the scripts’ names (a string without whitespace) for the script to use
-  - script: a executable script with a directory relative to the `esmvaltool/diag_scripts/` directory
+    The information of which diagnostic script to run with which variables.
+    The diagnostics section has some indents that are free to call.
+    - the first indent (here: diag_timeseries_temperature) is the diagnostic’s name (a string without whitespace), used for setting up the respective directories
+    - description: a short description of the diagnostic
+    - variables: a definition of all variables that are used in this diagnostic
+    - the next indent (here: timeseries_variable) is the variables’ names (a string without whitespace) for the diagnostic to use
+    - short_name: the variable name as listed in the dataset
+    - preprocessor: the preprocessor(s) applied to the variable before running the diagnostic
+    - scripts: a definition of all scripts that are used in this diagnostic
+    - the next indent (here: timeseries_diag) is the scripts’ names (a string without whitespace) for the script to use
+    - script: a executable script with a directory relative to the `esmvaltool/diag_scripts/` directory
 
 > ## Please answer the following questions:
 > What is the short_name of the variable being analyzed?
@@ -184,7 +179,6 @@ Please note the following sections:
 > What do you think running this recipe will produce?
 {: .challenge}
 
-
 > ## What is the short_name of the variable being analyzed?
 > thetaoga -  Global Average Sea Water Potential Temperature
 {: .solution}
@@ -220,10 +214,10 @@ Please note the following sections:
 {: .solution}
 
 Each time you run the ESMValTool, it will produce a new output directory within your specified work directory with the name of the recipe and the tagged runtime. This folder should contain four folders:
-- run
-- work
-- preproc
-- plots
+  - run
+  - work
+  - preproc
+  - plots
 
 > ## Inspect the output:
 > Now that you have run the esmvaltool command for the first time, please locate your output directory.
@@ -311,7 +305,6 @@ The snippets for the edits can be found below:
 > http://clipc-services.ceda.ac.uk/dreq/index/CMORvar.html
 {: .callout}
 
-
 ## Common issues & tips
 
 > ## esmvaltool not found
@@ -322,22 +315,18 @@ The snippets for the edits can be found below:
 > Which computing machine are you using? Does your user-config.yml file reflect your machine's settings? Is the dataset’s name in the correct order?
 {: .solution}
 
-
 > ## Diagnostic path problems
 > The directory path to your diagnostics code is set relative to the esmvaltool/diag_scripts subdirectory. Is the code placed in this subdirectory? Is it spelled correctly?
 {: .solution}
 
-
 > ## FX files not found
 > There is no FX file for your corresponding dataset. Are your datasets’ names spelled correctly? Is there a FX file for this dataset?
 {: .solution}
 
-
 > ## The preprocessor works but the diagnostic fails
 > If your preprocessor works fine but your diagnostic script fails, congratulations! A failed diagnostic means that you won’t need to re-run the preprocessor. In your “run/main_log.txt” run output, you should see a line that reads: “To re-run this diagnostic script, run:”, followed by a line with a command that will allow you to re-run your diagnostic script only. Append this line with the “-i” option after the python script you call to re-run your diagnostic.
 {: .solution}
 
-
 > ## Your recipe’s name/project/reference isn’t recognised by ESMValTool. Error message is `ValueError: Tag 'NAME' does not exist in section 'authors' of path/esmvaltool/config-references.yml`
 > Most likely, you added your own name to the recipe in the description section, but didn’t add it to the esmvaltool/config-references.yml file, where the names are linked to an email address, institute, and ORCID Identity.
 {: .solution}