pymotif 0.0.1

jupyter widget bindings for the motif library

Pymotif

A Python package that lets you plot Motif graphs within Jupyter Notebook / Jupyter Lab:

It's that easy to get started!

Features

• Seamless integration into existing Jupyter workflows
• Multiple data import options
• Programmatic graph manipulation
• Easy code sharing and reuse

Installation

You can install using pip (we recommend using virtual environments):

pip install pymotif

And it should work. In some cases, you may also need to install and enable Jupyter extensions:

# Jupyter Lab
jupyter labextension install @jupyter-widgets/jupyterlab-manager

# For Jupyter Lab <= 2, you may need to install the extension manually
jupyter labextension install @cylynx/pymotif

# For Jupyter Notebook <= 5.2, you may need to enable nbextensions
jupyter nbextension enable --py [--sys-prefix|--user|--system] pymotif

Demo

Demo notebooks can be found in the examples folder. For a start, check out examples/introduction.ipynb, which gives a quick overview of the available functionality!

Motif Class

As shown above, using Motif in Jupyter involves importing and instantiating the Motif class from pymotif.

Instantiation

from pymotif import Motif

motif = Motif()
motif.plot()    # or just 'motif'

Motif() accepts various instantiation parameters (refer to Motif's __init__ method for updated information):

All parameters are optional.
Only one graph import (json_path, nx_graph, neo4j_graph, or csv_path) can be passed each time.

json_path: str
    Path to a local JSON file containing the graph data.
    If this is used, all other params will be ignored.

nx_graph: nx.Graph
    A networkx graph to be rendered

neo4j_graph: neo4j.graph.Graph
    A neo4j graph to be rendered, obtained from the neo4j.Result.graph() method.
    Ref: https://neo4j.com/docs/api/python-driver/current/api.html#graph

csv_path: str
    Path to a local CSV edgelist file

style: dict
    The rendered graph's style. Its format depends on Motif's StyleOptions interface:
    https://github.com/cylynx/motif.gl/blob/c79ba6549407979a4ec0214cc6c7c7d0f2a3be41/packages/motif/src/redux/graph/types.ts#L206

title: str
    The rendered graph's title

Other params are ignored when using JSON files because the file itself may also contain pre-defined styles, titles, or other settings.

Example Usage

# import a csv file and set a title
motif = Motif(csv_path=<YOUR CSV PATH>, title='my first csv import')

# import a json file. as mentioned above, using json ignores all other params
motif = Motif(json_path=<YOUR JSON PATH>, title='ignored parameter')

# import a networkx graph and arrange it in a grid layout
style = {'layout': {'type': 'grid'}}
motif = Motif(nx_graph=<YOUR NETWORKX GRAPH>, style=style)

Attributes

There is only one class attribute for now:

state: dict
    There are 2 possible keys: data, style.
    Data is a list of graph data describing what will be rendered in the widget.
    Style is a dict describing how the graphs will be rendered.

    Follows the TLoadFormat interface defined in Motif's types.ts:
    https://github.com/cylynx/motif.gl/blob/master/packages/motif/src/redux/graph/types.ts#L283

Example Usage

m = Motif(<YOUR PARAMS>)

# check graph's initial state
m.state

# stuff happens
...

# sanity check
m.state

This may be useful for debugging your graph objects at various points in time throughout your analysis.

Methods

def add_graph(self, **kwargs):
    """
    Adds another graph to an existing Motif widget.
    Takes the same parameters as __init__.
    If provided, graph settings here will overwrite those set previously (e.g. style).
    """


def set_style(self, style: dict, overwrite=False):
    """
    Allows updating the style of an existing widget.

    ------------
     Parameters
    ------------
    style: dict
        The rendered graph's style
    overwrite=False:
        If True, overwrites all existing styles with the passed 'style' param.
        If False, merges 'style' param with existing styles
    """


def plot(self):
    """ Plots the graphs' current state as a Jupyter widget """

Example Usage

# create a new graph
m = Motif(<YOUR PARAMS>)

# add another previously-saved graph from a JSON file
m.add_graph(json_path=<YOUR JSON PATH>)

# adjust and overwrite the combined graphs' style
m.set_style(style=<YOUR STYLE>, overwrite=True)

# plot the combined graph
m.plot()

Development

This section contains instructions for developing Pymotif locally.

For a more thorough walkthrough check out the official Jupyter widgets guide:
https://ipywidgets.readthedocs.io/en/latest/examples/Widget%20Custom.html

Create a new conda environment with the dependencies

To create the environment, execute the following command:

conda create -n motif -c conda-forge jupyterlab nodejs python

Then activate the environment with:

conda activate motif

Build and install the widget for development

Since the widget contains a Python part, you need to install the package in editable mode:

npm run pymotif:build // In root directory to link it with monorepo setup
python -m pip install -e .

Install required Python packages:

python -m pip install -r requirements.txt

If you are using JupyterLab:

jupyter labextension develop --overwrite .

If you are using the Classic Notebook:

jupyter nbextension install --sys-prefix --symlink --overwrite --py pymotif
jupyter nbextension enable --sys-prefix --py pymotif

To continuously monitor the project for changes and automatically trigger a rebuild, start Jupyter in watch mode:

jupyter lab --watch

And in a separate session, begin watching the source directory for changes:

npm run pymotif  // In root directory to link it with monorepo setup

After a change wait for the build to finish and then refresh your browser and the changes should take effect.

If you make a change to the python code then you will need to restart the notebook kernel to have it take effect.

Publishing

1. Update the version in package.json
2. Relase the @cylynx/pymotif packages:

npm login
npm run pymotif:publish

3. Bundle the python package: python setup.py sdist bdist_wheel
4. Update the version in pymotif/_version.py
5. If frontend version dependency has changed, update pymotif/_frontend.py
6. Publish the package to PyPI:

pip install twine
twine upload dist/pymotif*