Flojoy / Python

The Python repo for Flojoy infrastructure. Including the client for Flojoy's desktop app, the @flojoy decorator, and various utilities for node creation.

The @flojoy decorator

Usage:

from scipy import signal
import numpy as np
from flojoy import flojoy, DataContainer

@flojoy
def BUTTER(v, params):
    ''' Apply a butterworth filter to an input vector '''

    logger.debug('Butterworth inputs:', v)

    x = v[0].x
    sig = v[0].y

    sos = signal.butter(10, 15, 'hp', fs=1000, output='sos')
    filtered = signal.sosfilt(sig, sig)

    return DataContainer(x = x, y = filtered)

The @flojoy decorator automatically injects vector(s) passed from the previous node, as well as any control parameters set in the CTRL panel UI.

Please see https://github.com/flojoy-io/flojoy-desktop/tree/main/PYTHON/FUNCTIONS for more usage examples.

Supported data types

See https://github.com/flojoy-io/flojoy-python/issues/4

Reconcilers

Flojoy nodes should try to accomodate any reasonable combination of inputs that a first-time Flojoy Studio user might try.

For example, the ADD node should make a best effort to do something reasonable when a matrix is added to a DataFrame, or a 2 matrices of a different size are added.

For this reason, we've created the Reconciler class to handle the process of turning different data types into compatible, easily added objects.

An Example

Here's an example of the Reconciler in action from the ADD node. Just initialize the Reconciler and pass it pairs of primitives from within your DataContainers.

def ADD(dc_inputs: list[DataContainer], params: dict) -> DataContainer:
  ...

    dc_reconciler = Reconciler()
    cur_sum = dc_inputs[0]
    for operand_n in dc_inputs[1:]:
        # reconcile the types of the inputs before calling numpy
        reconciled_lhs, reconciled_rhs = dc_reconciler.reconcile(cur_sum.y, operand_n.y)
        cur_sum = np.add(reconciled_lhs, reconciled_rhs)
    return DataContainer(x=dc_inputs[0].x, y=cur_sum)

Customizing your Reconcilers for different Node Categories

Have a different idea about how you want your DataContainers to be interoperable? Subclass Reconciler and override the specific type pairs you're interested in.

For example, let's say we're writing image processing nodes, and we want special behavior when dealing with certain image-like DataContainers.

class ImageProcessingReconciler(Reconciler):
    def __init__():
        super().__init__()
    # override only for the pair of types you're interested in!
    def image__image(self, lhs, rhs):
      # special handling for reconciling two different images
      ...
      return reconciled_lhs, reconciled_rhs
    def grayscale__image(self, lhs, rhs):
      # special handling for reconciling a grayscale and an image
      ...
      return reconciled_lhs, reconciled_rhs

Publish Package on PYPI

Uploading file via Twine

You have to install twine package first if not installed already. To install twine run following command: pip install twine

• Update version in setup.py. For example: version = '0.0.1' for prod version and version = '0.0.1-dev' for dev version release,
• Run following command to make a distribution file: python3 setup.py sdist
• To upload files on PYPI, run following command: twine upload dist/*

Note: You'll need username and password to make a release. Please get in touch with the team for credentials.