Setup for ci

notebooks/demo/tutorial_data-quality_atmodat-checker.ipynb

 %% Cell type:markdown id:77da5fe5-80a7-4952-86e4-f39b3f06ddef tags:
 
 ## ATMODAT Standard Compliance Checker
 
 This notebook introduces you to the [atmodat checker](https://github.com/AtMoDat/atmodat_data_checker) which contains checks to ensure compliance with the ATMODAT Standard.
 
 > Its core functionality is based on the [IOOS compliance checker](https://github.com/ioos/compliance-checker). The ATMODAT Standard Compliance Checker library makes use of [cc-yaml](https://github.com/cedadev/cc-yaml), which provides a plugin for the IOOS compliance checker that generates check suites from YAML descriptions. Furthermore, the Compliance Check Library is used as the basis to define generic, reusable compliance checks.
 
 In addition, the compliance to the **CF Conventions 1.4 or higher** is verified with the [CF checker](https://github.com/cedadev/cf-checker).
 
 %% Cell type:markdown id:edb35c53-dc33-4f1f-a4af-5a8ea69e5dfe tags:
 
 In this notebook, you will learn
 
 - [how to use an environment on DKRZ HPC mistral or levante](#Preparation)
 - [how to run checks with the atmodat data checker](#Application)
 - [to understand the results of the checker and further analyse it with pandas](#Results)
 - [how you could proceed to cure the data with  xarray if it does not pass the QC](#Curation)
 
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 ### Preparation
 
 On DKRZ's High-performance computer PC, we provide a `conda` environment which are useful for working with data in DKRZ’s CMIP Data Pool.
 
 **Option 1: Activate checker libraries for working with a comand-line shell**
 
 If you like to work with shell commands, you can simply activate the environment. Prior to this, you may have
 to load a module with a recent python interpreter
 
 ```bash
 module load python3/unstable
 #The following line activates the quality-assurance environment mit den checker libraries so that you can execute them with shell commands:
 source activate /work/bm0021/conda-envs/quality-assurance
 ```
 
 %% Cell type:markdown id:dff94c1c-8aa1-42aa-9486-f6d5a6df1884 tags:
 
 **Option 2: Create a kernel with checker libraries to work with jupyter notebooks**
 
 With `ipykernel` you can install a *kernel* which can be used within a jupyter server like [jupyterhub](https://jupyterhub.dkrz.de). `ipykernel` creates the kernel based on the activated environment.
 
 ```bash
 module load python3/unstable
 #The following line activates the quality-assurance environment mit den checker libraries so that you can execute them with shell commands:
 source activate /work/bm0021/conda-envs/quality-assurance
 python -m ipykernel install --user --name qualitychecker --display-name="qualitychecker"
 ```
 
 If you run this command from within a jupyter server, you have to restart the jupyterserver afterwards to be able to select the new *quality checker* kernel.
 
 %% Cell type:markdown id:95f9ba22-f84c-42e4-9952-ff6ef4f7b86d tags:
 
 **Expert mode**: Running the jupyter server from a different environment than the environment in which atmodat is installed
 
 Make sure that you:
 
 1. Install the `cfunits` package to the jupyter environment via `conda install cfunits -c conda-forge -p $jupyterenv` and restart the kernel.
 1. Add the atmodat environment to the `PATH` environment variable inside the notebook. Otherwise, the notebook's shell does not find the application `run_checks`. You can modify environment variables with the `os` package and its command `os.envrion`. The environment of the kernel can be found with `sys` and `sys.executable`. The following block sets the environment variable `PATH` correctly:
 
 %% Cell type:code id:955fcaff-3b3f-4e5e-8c56-59ed90a4bca2 tags:
 
 ``` python
 import sys
 import os
 os.environ["PATH"]=os.environ["PATH"]+":"+os.path.sep.join(sys.executable.split('/')[:-1])
 ```
 
 %% Cell type:code id:72c0158e-1fbb-420b-8976-329579e397b9 tags:
 
 ``` python
 #As long as there is the installation bug, we have to manually get the Atmodat CVs:
 if not "AtMoDat_CVs" in [dirpath.split(os.path.sep)[-1]
                          for (dirpath, dirs, files) in os.walk(os.path.sep.join(sys.executable.split('/')[:-2]))] :
     !git clone https://github.com/AtMoDat/AtMoDat_CVs.git {os.path.sep.join(sys.executable.split('/')[:-2])}/lib/python3.9/site-packages/atmodat_checklib/AtMoDat_CVs
 ```
 
 %% Cell type:markdown id:3d0c7dc2-4e14-4738-92c5-b8c107916656 tags:
 
 ### Data to be checked
 
 In this tutorial, we will check a small subset of CMIP6 data which we gain via `intake`:
 
 %% Cell type:code id:75e90932-4e2f-478c-b7b5-d82b9fd347c9 tags:
 
 ``` python
 import intake
 # Path to master catalog on the DKRZ server
 col_url = "https://dkrz.de/s/intake"
 col_url = "https://gitlab.dkrz.de/data-infrastructure-services/intake-esm/-/raw/master/esm-collections/cloud-access/dkrz_catalog.yaml"
 parent_col=intake.open_catalog([col_url])
 list(parent_col)
 
 # Open the catalog with the intake package and name it "col" as short for "collection"
 col=parent_col["dkrz_cmip6_disk"]
 ```
 
 %% Cell type:code id:d30edc41-2561-43b1-879f-5e5d58784e4e tags:
 
 ``` python
 # We just use the first file from the CMIP6 catalog and copy it to the local disk because we make some experiments from it
 exp_file=col.df["uri"].values[0]
 exp_file
 ```
 
 %% Cell type:markdown id:f1476f21-6f58-4430-9602-f18d8fa79460 tags:
 
 ### Application
 
 The command `run_checks` can be executed from any directory from within the atmodat conda environment.
 
 The atmodat checker contains two modules:
 - one that checks the global attributes for compliance with the ATMODAT standard
 - another that performs a standard CF check (building upon the cfchecks library).
 
 %% Cell type:markdown id:365507aa-33a6-42df-9b35-7ead7da006b6 tags:
 
 Show usage instructions of `run_checks`
 
 %% Cell type:code id:76dabfbf-839b-4dca-844c-514cf82f0b66 tags:
 
 ``` python
 !run_checks -h
 ```
 
 %% Cell type:markdown id:2c04701c-bc27-4460-b80e-d32daf4a7376 tags:
 
 The results of the performed checks are provided in the checker_output directory. By default, `run_checks` assumes writing permissions in the path where the atmodat checker is installed. If this is not the case, you must specify an output directory where you possess writing permissions with the `-op output_path`.
 
 In the following block, we set the *output path* to the current working directory which we get via the bash command `pwd`. We apply `run_checks` for the `exp_file` which we downloaded in the chapter before.
 
 %% Cell type:code id:c3ef1468-6ce9-4869-a173-2374eca5bc2c tags:
 
 ``` python
 cwd=!pwd
 cwd=cwd[0]
 !run_checks -f {exp_file} -op {cwd} -s
 ```
 
 %% Cell type:markdown id:13e20408-b6fa-4d39-be02-41db2109c980 tags:
 
 Now, we have a directory `atmodat_checker_output` in the `op`. For each run of `run_checks`, a new directory is created inside of `op` named by the timestamp. Additionally, a directory *latest* always shows the output of the most recent run.
 
 %% Cell type:code id:601f3486-91e2-4ff5-9f8e-324f10f799b5 tags:
 
 ``` python
 !ls {os.path.sep.join([cwd, "atmodat_checker_output"])}
 ```
 
 %% Cell type:markdown id:fa5ef2a4-a1da-4fa0-873f-902884ea4db6 tags:
 
 As we ran `run_checks` with the option `-s`, one output is the *short_summary.txt* file which we `cat` in the following:
 
 %% Cell type:code id:9f6c38fd-199b-413e-9821-6535235be83c tags:
 
 ``` python
 output_dir_string=os.path.sep.join(["atmodat_checker_output","latest"])
 output_path=os.path.sep.join([cwd, output_dir_string])
 !cat {os.path.sep.join([output_path, "short_summary.txt"])}
 ```
 
 %% Cell type:markdown id:99d2ba16-52c2-4cb6-b82b-226e75463aab tags:
 
 ### Results
 
 The short summary contains information about versions, the timestamp of execution, the ratio of passed checks on attributes and errors written by the CF checker.
 
 - cfchecks routine only issues a warning/information message if variable metadata are completely missing.
 - Zero errors in the cfchecks routine does not necessarily mean that a data file is CF compliant!
 
 We can also have a look into the detailled output including the exact error message in the *long_summary_* files which are subdivided into severe levels.
 
 %% Cell type:code id:9600c713-1203-430b-a4a6-bf70ec441221 tags:
 
 ``` python
 !cat {os.path.sep.join([output_path,"long_summary_recommended.csv"])}
 ```
 
 %% Cell type:code id:b9fa72d6-6e5f-433a-81f0-40e4cd5a94cd tags:
 
 ``` python
 !cat {os.path.sep.join([output_path,"long_summary_mandatory.csv"])}
 ```
 
 %% Cell type:markdown id:b94a7c75-abc6-4792-aa5f-65467c6522de tags:
 
 We can open the *.csv* files with `pandas` to further analyse the output.
 
 %% Cell type:code id:f02ea2c4-7238-4afd-aef0-565aa5a5787f tags:
 
 ``` python
 import pandas as pd
 recommend_df=pd.read_csv(os.path.sep.join([output_path,"long_summary_recommended.csv"]))
 recommend_df
 ```
 
 %% Cell type:markdown id:6453b4ca-288e-4c49-8c93-da4524ef5792 tags:
 
 There may be **missing** global attributes wich are recommended by the *atmodat standard*. We can find them with pandas:
 
 %% Cell type:code id:f0a7e6db-f79a-448f-8046-bb4bf3bcef9d tags:
 
 ``` python
 missing_recommend_atts=list(
     recommend_df.loc[recommend_df["Error Message"]=="global attribute is not present"]["Global Attribute"]
 )
 missing_recommend_atts
 ```
 
 %% Cell type:markdown id:06283c25-c5b6-450f-bfe9-d65e8fe26623 tags:
 
 ### Curation
 
 Let's try first steps to *cure* the file by adding a missing attribute with `xarray`. We can open the file into an *xarray dataset* with:
 
 %% Cell type:code id:b294cd89-d55c-421f-82e2-4cf42ece7d62 tags:
 
 ``` python
 import xarray as xr
 exp_file_ds=xr.open_dataset(exp_file)
 exp_file_ds
 ```
 
 %% Cell type:markdown id:f02bc09f-94dc-4e0f-b12f-9798549e90e8 tags:
 
 We can **handle and add attributes** via the `dict`-type attribute `.attrs`. Applied on the dataset, it shows all *global attributes* of the file:
 
 %% Cell type:code id:fc0ffe80-4288-4ac3-a599-3239f37f461d tags:
 
 ``` python
 exp_file_ds.attrs
 ```
 
 %% Cell type:markdown id:6f61190e-49bc-40da-8b33-30f3debd1895 tags:
 
 We add all missing attributes and set a dummy value for them:
 
 %% Cell type:code id:3fd18adf-fe43-4d47-b565-d082b80b970d tags:
 
 ``` python
 for att in missing_recommend_atts:
     exp_file_ds.attrs[att]="Dummy"
 ```
 
 %% Cell type:markdown id:56e26094-0ad6-42a9-afaf-5c482ee8ca87 tags:
 
 We save the modified dataset with the `to_netcdf` function:
 
 %% Cell type:code id:8050d724-da0d-417a-992e-24bb5aae0c82 tags:
 
 ``` python
-exp_file_ds.to_netcdf(exp_file+".modified.nc")
+exp_file_ds.to_netcdf("testfile-modified.nc")
 ```
 
 %% Cell type:markdown id:5794c6ce-fff2-4c6e-8c08-aaf5dd342f8d tags:
 
 Now, lets run `run_checks` again.
 
 We can also only provide a directory instead of a file as an argument with the option `-p`. The checker will find all `.nc` files inside that directory.
 
 %% Cell type:code id:6c3698f7-62a4-4297-bfbf-d6447a0f006a tags:
 
 ``` python
 !run_checks -p {cwd} -op {cwd} -s
 ```
 
 %% Cell type:markdown id:c72647ee-7497-42df-ae68-f6a2d4ea87ad tags:
 
 Using the *latest* directory, here is the new summary:
 
 %% Cell type:code id:51d2eff6-2a31-47b7-a706-f2555e03b9c3 tags:
 
 ``` python
 !cat {os.path.sep.join([output_path,"short_summary.txt"])}
 ```
 
 %% Cell type:markdown id:1c9205ec-4f5f-4173-bb0d-1896785a9d04 tags:
 
 You can see that the checks do not fail for the modified file when subtracting the earlier failes from the sum of new passed checks.