Training your Pooch¶

The problem¶

You develop a Python library called plumbus for analysing data emitted by interdimensional portals. You want to distribute sample data so that your users can easily try out the library by copying and pasting from the docs. You want to have a plumbus.datasets module that defines functions like fetch_c137() that will return the data loaded as a pandas.DataFrame for convenient access.

Assumptions¶

We’ll setup a Pooch to solve your data distribution needs. In this example, we’ll work with the follow assumptions:

Your sample data are in a folder of your Github repository.
You use git tags to mark releases of your project in the history.
Your project has a variable that defines the version string.
The version string contains an indicator that the current commit is not a release (like 'v1.2.3+12.d908jdl' or 'v0.1+dev').

Let’s say that this is the layout of your repository on Github:

doc/
    ...
data/
    README.md
    c137.csv
    cronen.csv
plumbus/
    __init__.py
    ...
    datasets.py
setup.py
...

The sample data are stored in the data folder of your repository.

Setup¶

Pooch can download and cache your data files to the users computer automatically. This is what the plumbus/datasets.py file would look like:

"""
Load sample data.
"""
import pandas
import pooch

from . import version  # The version string of your project


GOODBOY = pooch.create(
    # Use the default cache folder for the OS
    path=pooch.os_cache("plumbus"),
    # The remote data is on Github
    base_url="https://github.com/rick/plumbus/raw/{version}/data/",
    version=version,
    # If this is a development version, get the data from the master branch
    version_dev="master",
    # The registry specifies the files that can be fetched from the local storage
    registry={
        "c137.csv": "19uheidhlkjdwhoiwuhc0uhcwljchw9ochwochw89dcgw9dcgwc",
        "cronen.csv": "1upodh2ioduhw9celdjhlfvhksgdwikdgcowjhcwoduchowjg8w",
    },
)


def fetch_c137():
    """
    Load the C-137 sample data as a pandas.DataFrame.
    """
    # The file will be downloaded automatically the first time this is run.
    fname = GOODBOY.fetch("c137.csv")
    data = pandas.read_csv(fname)
    return data


def fetch_cronen():
    """
    Load the Cronenberg sample data as a pandas.DataFrame.
    """
    fname = GOODBOY.fetch("cronen.csv")
    data = pandas.read_csv(fname)
    return data

When the user calls plumbus.datasets.fetch_c137() for the first time, the data file will be downloaded and stored in the local storage. In this case, we’re using pooch.os_cache to set the local folder to the default cache location for your OS. You could also provide any other path if you prefer. See the documentation for pooch.create for more options.

Hashes¶

Pooch uses SHA256 hashes to check if files are up-to-date or possibly corrupted:

If a file exists in the local folder, Pooch will check that its hash matches the one in the registry. If it doesn’t, we’ll assume that it needs to be updated.
If a file needs to be updated or doesn’t exist, Pooch will download it from the remote source and check the hash. If the hash doesn’t match, an exception is raised to warn of possible file corruption.

You can generate hashes for your data files using the terminal:

$ openssl sha256 data/c137.csv
SHA256(data/c137.csv)= baee0894dba14b12085eacb204284b97e362f4f3e5a5807693cc90ef415c1b2d

Or using the pooch.file_hash function (which is a convenient way of calling Python’s hashlib):

import pooch
print(pooch.file_hash("data/c137.csv"))

Versioning¶

The files from different version of your project will be kept in separate folders to make sure they don’t conflict with each other. This way, you can safely update data files while maintaining backward compatibility. For example, if path=".plumbus" and version="v0.1", the data folder will be .plumbus/v0.1.

When your project updates, Pooch will automatically setup a separate folder for the new data files based on the given version string. The remote URL will also be updated. Notice that there is a format specifier {version} in the URL that Pooch substitutes for you.

User-defined paths¶

In the above example, the location of the local storage in the users computer is hard-coded. There is no way for them to change it to something else. To avoid being a tyrant, you can allow the user to define the path argument using an environment variable:

GOODBOY = pooch.create(
    # This is still the default in case the environment variable isn't defined
    path=pooch.os_cache("plumbus"),
    base_url="https://github.com/rick/plumbus/raw/{version}/data/",
    version=version,
    version_dev="master",
    registry={
        "c137.csv": "19uheidhlkjdwhoiwuhc0uhcwljchw9ochwochw89dcgw9dcgwc",
        "cronen.csv": "1upodh2ioduhw9celdjhlfvhksgdwikdgcowjhcwoduchowjg8w",
    },
    # The name of the environment variable that can overwrite the path argument
    env="PLUMBUS_DATA_DIR",
)

In this case, if the user defines the PLUMBUS_DATA_DIR environment variable, we’ll use its value instead of path. Pooch will still append the value of version to the path, so the value of PLUMBUS_DATA_DIR should not include a version number.

Subdirectories¶

You can have data files in subdirectories of the remote data store. These files will be saved to the same subdirectories in the local storage folder. Note, however, that the names of these files in the registry must use Unix-style separators ('/') even on Windows. We will handle the appropriate conversions.

So you have 1000 data files¶

If your project has a large number of data files, it can be tedious to list them in a dictionary. In these cases, it’s better to store the file names and hashes in a file and use pooch.Pooch.load_registry to read them:

import os

GOODBOY = pooch.create(
    # Use the default cache folder for the OS
    path=pooch.os_cache("plumbus"),
    # The remote data is on Github
    base_url="https://github.com/rick/plumbus/raw/{version}/data/",
    version=version,
    # If this is a development version, get the data from the master branch
    version_dev="master",
    # We'll load it from a file later
    registry=None,
)
GOODBOY.load_registry(os.path.join(os.path.dirname(__file__), "registry.txt"))

The registry.txt file in this case is in the same directory as the datasets.py module and should be shipped with the package. It’s contents are:

c137.csv 19uheidhlkjdwhoiwuhc0uhcwljchw9ochwochw89dcgw9dcgwc
cronen.csv 1upodh2ioduhw9celdjhlfvhksgdwikdgcowjhcwoduchowjg8w

To make sure the registry file is shipped with your package, include the following in your MANIFEST.in file:

include plumbus/registry.txt

And the following entry in the setup function of your setup.py:

setup(
    ...
    package_data={"plumbus": ["registry.txt"]},
    ...
)

Creating a registry file¶

If you have many data files, creating the registry and keeping it updated can be a challenge. Function pooch.make_registry will create a registry file with all contents of a directory. For example, we can generate the registry file for our fictitious project from the command-line:

$ python -c "import pooch; pooch.make_registry('data', 'plumbus/registry.txt')"