Notebooks
=========

Within the `notebooks/` directory, you will find as series of [Jupyter Notebooks](https://www.jupyter.org)
that demonstrate how to use **playNano** programmatically in an interactive environment. These notebooks cover
the entire workflow from loading and processing data to analysis and export.

.. image:: images/notebook_capture.png
   :alt: playNano demonstration notebook
   :align: center
   :width: 400px

These are useful as it allows the user to explore interactively and with rapid feedback the parameters that may
need adjusting in order to process a high-speed dataset. The notebooks can be found in the notebook/ directory after
cloning the GitHub repository.

To access these notebooks you will need to clone the **playNano** repository from [GitHub](https://github.com/derollins/playNano)
and install the optional notebook dependancies using ``pip install .[notebooks]``.

.. code-block:: bash

    git clone https://github.com/derollins/playNano.git   # Clone the repository
    cd playNano
    pip install -e . [notebooks]  # Install the package and notebook dependencies

**Current notebooks:**

- `playnano_demo_notebook.ipynb`: An overview of loading, processing, analysing, and exporting
  time-series AFM data using the *playNano* library API.
- `processing_demo.ipynb`: A step-by-step guide to applying processing filters and exploring and
  exporting results.

Running Notebooks
-----------------

Once installed Jupyter can be launched from the command line.

.. code-block:: bash

    jupyter notebook

This will open a browser window where you can navigate to the `notebooks/` directory and open the
notebooks.

Run each cell in the notebooks sequentially to see the workflow in action. Intially example data from
the test folder is used however you can change the paths to examine your own data and modify the processing
and analysis steps to begin to analyse your data.

The full API reference is available in the :doc:`api/modules` section of the documentation.