Tsdat Tools

This repository contains helpful scripts and notes for several tsdat-related tools.

Some tools are available as jupyter notebooks, and others are available as a command-line utility.

To get access to the command-line utilities, just run:

pip install tsdat-tools

To use all the other tools, we recommend cloning this repository.

Data to Yaml

The goal of this tool is to reduce the tediousness of writing tsdat configuration files for data that you can already read and convert into an xr.Dataset object in tsdat. It generates two output files: dataset.yaml and retriever.yaml, which are used by tsdat to define metadata and how the input variables should be mapped to output variables.

If your file is in one of the following formats, this tool can already do this for you. Formats supported out-of-box:

• netCDF: Files ending with .nc or .cdf will use the tsdat.NetCDFReader class
• csv: Files ending with .csv will use the tsdat.CSVReader class
• parquet: Files ending with .parquet or .pq or .pqt will use the tsdat.ParquetReader class
• zarr: Files/folders ending with .zarr will use the tsdat.ZarrReader class

Usage

Then you can run the tool with:

tsdat-tools data2yaml path/to/data/file --input-config path/to/current/dataset.yaml

Full usage instructions can be obtained using the --help flag:

>>> tsdat-tools data2yaml --help

Usage: tsdat-tools data2yaml [OPTIONS] DATAPATH

╭─ Arguments ─────────────────────────────────────────────────────────────────────────────────────────────╮
│ *    datapath   PATH  Path to the input data file that should be used to generate tsdat configurations. │
│                       [default: None]                                                                   │
│                       [required]                                                                        │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Options ───────────────────────────────────────────────────────────────────────────────────────────────╮
│ --outdir                               DIRECTORY                      The path to the directory where   │
│                                                                       the 'dataset.yaml' and            │
│                                                                       'retriever.yaml' files should be  │
│                                                                       written.                          │
│                                                                       [default: .]                      │
│ --input-config                         PATH                           Path to a dataset.yaml file to be │
│                                                                       used in addition to               │
│                                                                       configurations derived from the   │
│                                                                       input data file. Configurations   │
│                                                                       defined here take priority over   │
│                                                                       auto-detected properties in the   │
│                                                                       input file.                       │
│                                                                       [default: None]                   │
│ --help                                                                Show this message and exit.       │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────╯

This tool is designed to be run in the following workflow:

1. Generate new ingest/pipeline from cookiecutter template (e.g., make cookies command)
2. Put an example data file for your pipeline in the test/data/input folder
3. Clean up the autogenerated dataset.yaml file.
   • Add metadata and remove any unused variables
   • Don't add additional variables yet; just make sure that the info in the current file is accurate
4. Commit your changes in git or back up your changes so you can compare before & after the script runs.
5. Run this script, passing it the path to your input data file and using the --input-config option to tell it where your cleaned dataset.yaml file is. By default this will generate a new dataset.yaml file in the current working directory (location of pwd on the command line), but you can also use the --outdir option to specify the path where it should write to.
6. Review the changes the script made to each file. Note that it is not capable of standardizing units or other metadata, so you will still need to clean those up manually.
7. Continue with the rest of the ingest/pipeline development steps

Excel to Yaml

Please consult the documentation in the excel2yaml/README.md file for more information about this tool.

NetCDF to CSV

Please consult the documentation in the netcdf2csv/README.md file for more information about this tool.