/pythia-datasets

Data repository for Project Pythia examples/notebooks

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pythia-datasets

Data repository for with sample data for the Pythia Foundations book.

Sample data sets

These files are used as sample data in Pythia Foundations and are downloaded by pythia_datasets package:

  • NARR_19930313_0000.nc
  • enso_data.csv
  • jan-17-co-asos.txt.xz
  • CESM2_sst_data.nc
  • CESM2_grid_variables.nc
  • daymet_v4_precip_sept_2013.nc

Adding new datasets

The scope of this data collection is to serve examples for Pythia Foundations. If you are adding new content to Foundations that requires a new dataset file, please follow these steps:

  1. Add the dataset file to the data/ directory
  2. From the command line, run python make_registry.py script to update the registry file residing in pythia_datasets/registry.txt
  3. Commit and push your changes to GitHub

Using datasets in notebooks and/or scripts

  • Ensure the pythia_datasets package is installed in your environment

    python -m pip install pythia-datasets

# or

python -m pip install git+https://github.com/ProjectPythia/pythia-datasets

  • Import DATASETS and inspect the registry to find out which datasets are available

    In [1]: from pythia_datasets import DATASETS

In [2]: DATASETS.registry_files
Out[2]: ['jan-17-co-asos.txt.xz', 'NARR_19930313_0000.nc']

  • To fetch a data file of interest, use the .fetch method and provide the filename of the data file. This will

    • download and cache the file if it doesn't exist already.
    • retrieve and return the local path
    In [4]: filepath = DATASETS.fetch('jan-17-co-asos.txt.xz')

In [5]: filepath
Out[5]: '/Users/abanihi/Library/Caches/pythia-datasets/jan-17-co-asos.txt.xz'

  • Once you have access to the local filepath, you can then use it to load your dataset into pandas or xarray or your package of choice:

    In [6]: df = pd.read_csv(filepath)

Changing the default data cache location

The default cache location (where the data are saved on your local system) is dependent on the operating system. You can use the locate() method to identify it:

from pythia_datasets import locate
locate()

The location can be overwritten by the PYTHIA_DATASETS_DIR environment variable to the desired destination.