This repo contains scripts to:

• Download spreadsheets containing specifications for AMF data products from a folder in Google Drive

• Generate check suites for the IOOS compliance checker based on the contents of the spreadsheets

• Generate controlled vocabulary files from the spreadsheets

The checks are generated in YAML format for use with the cc-yaml plugin for compliance-checker. The code for the checks themselves is implemented in compliance-check-lib.

Depencendies for Compliance Checker and compliance-check-lib include some packages that must be compiled from source, which can be tricky to set up. The recommended way to get set up is to use a CentOS 6 machine and do the following:

• Install the JASMIN Analysis Platform

• Install the following packages: yum install python27-netCDF4 python27-iris python27-cf python27-virtualenv python27-cf_units

(alternatively use a JASMIN VM which will already have the JAP and those packages installed)

Then create a Python 2.7 virtual environment and install the required python packages:

virtalenv -p python2.7 --system-site-packages venv
source venv/bin/activate

pip install git+https://github.com/ncasuk/amf-check-writer \
            git+https://github.com/cedadev/compliance-checker \
            git+https://github.com/cedadev/compliance-check-lib \
            git+https://github.com/cedadev/cc-yaml

# Download spreadsheets. See 'authentication' section below for first time usage
download-from-drive /tmp/spreadsheets

# Create controlled vocabulary files from spreadsheets. For first time usage
# you may need to create the pyessv archive directory:
# mkdir -p ~/.esdoc/pyessv-archive
create-cvs /tmp/spreadsheets /tmp/cvs

# Create YAML checks from spreadsheets
create-yaml-checks /tmp/spreadsheets /tmp/yaml

# Run a check; e.g:
compliance-checker --yaml /tmp/yaml/AMF_product_radiation_land.yml \
                   --test product_radiation_land_checks \
                   <dataset>

# or using amf-checker wrapper script:
amf-checker <dataset>

Usage: download-from-drive [--secrets <secrets JSON>] <output dir>.

This script recursively finds all spreadsheets under a folder in Google Drive and saves each worksheet as a .tsv file (the root folder ID is hardcoded in amf_check_writer/download-from-drive.py).

The directory structure of the Drive folder is preserved, and a directory for each spreadsheet is created. The individual sheets are saved as <sheet name>.tsv inside the spreadsheet directory.

For example, after running download-from-drive /tmp/mysheets with a test folder:

$ tree /tmp/mysheets
/tmp/mysheets
├── first-spreadsheet.xlsx
│   ├── Sheet1.tsv
│   └── Sheet2.tsv
└── sub-folder
    ├── second-spreadsheet.xlsx
    │   └── Sheet1.tsv
    └── sub-sub-dir
        └── other-spreadsheet.xlsx
            └── my-sheet.tsv

5 directories, 4 files

Downloding spreadsheets from Google Drive requires the script to authenticate as your Google account. This is done using a JSON file obtained from the Google API dashboard.

• Visit https://console.developers.google.com/apis/dashboard

• Select a project from the dropdown in the header bar, or create a new project (blue button named 'Create project')

• Click the 'Enable APIs and Services' button in the header bar

• Search for 'Google Drive API'. Click the result and press 'Enable'. Return to the dashboard and do the same for 'Google Sheets API'

• Return to the dashboard and click 'Credentials' in the sidebar on the left (key icon)

• Click 'Create credentials' and select 'OAuth client ID'. Select 'Other' for application type and follow the prompts. Dismiss the popup that appears.

• You should see the newly created credentials in the table. On the right hand side of the table there is a download icon ('Download JSON'). Click it and save the JSON file.

• Run download-from-drive and use the --secrets option to point to the JSON file just downloaded. Credentials are cached in ~/.credentials after initial authentication, so --secrets is only required the first time.

• You will be given a URL to visit in a web browser and prompted for a verification code. This lets you sign into a Google account and give permission for the app to access your data on Google drive/sheets.

Alternatively follow the quickstart guide on the Google sheets site to enable the sheets API and create credentials (this also allows you to create a new project):

https://developers.google.com/sheets/api/quickstart/python

After this visit the API dashboard to enable the Drive API, as detailed above. You do not need to create another credentials JSON file.

Usage: create-cvs [--pyessv-dir <pyessv root>] <spreadsheets dir> <output dir>.

This script reads .tsv files downloaded with download-from-drive, and generates controlled vocabularies in JSON format from various worksheets. Each file is saved in <output dir> as AMF_<name>.json.

CVs are created for:

• List of instruments and their names and descriptions
• List of platforms
• List of data products
• List of creators (AMF_scientist.json)
• Variable names and expected attributes (and values) for each data product
• Dimension names and expected attributes (and values) for each data product
• Variable/dimension names and attributes common to all data products (AMF_product_common_{variable,dimension}_{air,land,sea}.json)

The format of the CVs is specific to each type.

Each CV is also saved with pyessv and written to pyessv's archive directory. The directory can be overridden with the --pyessv-dir option. Beware that if you use a non-standard pyessv archive directory, you must set PYESSV_ARCHIVE_HOME environment variable accordingly when running compliance-checker or amf-checker.

Usage: create-yaml-checks <spreadsheets dir> <output dir>.

This script reads .tsv files and produces YAML checks to be used with cc-yaml and compliance-check-lib.

Similar to create-cvs, checks are saved in <output dir> as AMF_name.yml. Checks are created for:

• Variable/dimension specifications (common and per-product)
• Global attribute checks
• File info (name, size etc...) and file structure

For each data product/deployment mode combination, a check AMF_product_<name>_<mode>.yml is created that includes global checks and the relevant variable/dimensions checks for the product and mode. e.g.:

AMF_product_soil_land.yml:

suite_name: product_soil_land_checks
checks:
# Global checks
- {__INCLUDE__: AMF_file_info.yml}
- {__INCLUDE__: AMF_file_structure.yml}
- {__INCLUDE__: AMF_global_attrs.yml}
# Common checks for 'land' deployment mode
- {__INCLUDE__: AMF_product_common_dimension_land.yml}
- {__INCLUDE__: AMF_product_common_variable_land.yml}
# Product specific
- {__INCLUDE__: AMF_product_soil_dimension.yml}
- {__INCLUDE__: AMF_product_soil_variable.yml}

Usage: amf-checker [--yaml-dir <yaml dir>] [-o <output dir>] [-f <output format>] <dataset>...

Wrapper script around compliance-checker to automatically find and run the relevant YAML checks for AMF datasets. See --help output for detailed help on the meaning of the available options.

<dataset> can be either the path to a NetCDF file or a directory, in which case all files in the directory are checked. Multiple files/directories can be given, so shell globs can be used: e.g.

amf-checker /path/to/data/*.nc

There are tests - run using:

pytest amf_check_writer/tests.py