Metadata-Version: 2.1
Name: nbparameterise
Version: 0.5
Summary: Re-run a notebook substituting input parameters in the first cell.
Author-email: Thomas Kluyver <thomas@kluyver.me.uk>
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
Classifier: License :: OSI Approved :: MIT License
Classifier: Framework :: Jupyter
Requires-Dist: nbconvert
Requires-Dist: astsearch
Project-URL: Source, https://github.com/takluyver/nbparameterise

This is a tool to run notebooks with input values. When you write the notebook,
these are defined in the first code cell, with regular assignments like this:

.. code-block:: python

    stock = 'YHOO'
    days_back = 600

Nbparameterise handles finding and extracting these parameters, and replacing
them with input values. You can then run the notebook with the new values.
This can be used for:

- Batch processing: run the same code on a list of different inputs. See
  ``examples/batch.py``.
- Simple user interfaces: build an input form based on the parameters, and run
  the notebook when the user submits the form. See ``examples/webapp.py`` for
  an implementation of this with an HTML form.

Nbparameterise can identify and replace numbers, strings, booleans (True/False),
lists and dicts - the types which can be represented in JSON (apart from None).
It's designed to change parameter values but keep their types, although this
isn't enforced.

Extra information about the parameters, such as names to display in a user
interface, can be stored in notebook metadata.

Nbparameterise is written in Python 3, but it can handle notebooks that use
Python 2.

Usage:

.. code-block:: python

    import nbclient
    import nbformat
    from nbparameterise import (
        extract_parameters, replace_definitions, parameter_values
    )

    with open("Stock display.ipynb") as f:
        nb = nbformat.read(f, as_version=4)

    # Get a list of Parameter objects
    orig_parameters = extract_parameters(nb)

    # Update one or more parameters
    params = parameter_values(orig_parameters, stock='GOOG')

    # Make a notebook object with these definitions
    new_nb = replace_definitions(nb, params)

    # Execute the notebook with the new parameters
    nbclient.execute(new_nb)

