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https://github.com/jupyterphysscilab/jupyter-instructortools

Tools for making JupyterPhysSciLab notebook templates for student handouts
https://github.com/jupyterphysscilab/jupyter-instructortools

educational-software educational-technology jupyter jupyter-notebook pandas plotting python

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Tools for making JupyterPhysSciLab notebook templates for student handouts

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README 

        
# jupyter-instructortools

[Introduction](#introduction) |

[Usage](#usage) | 

[Current Menu Items](#available-menu-items) | 

[Typical workflow](#typical-workflow) | [Install](#install) | 

[Issues or Comments](#issues-or-comments) | 

[License](#this-software-is-distributed-under-the-gnu-v3-licensehttpsgnuorglicenses)

## Introduction

This adds a menu to Jupyter that automates some useful tasks an

instructor might want to do while building a notebook template for an 

assignment. This is part of the

[Jupyter Physical Science Lab project](https://jupyterphysscilab.github.io/Documentation/),

but can be used independently of the rest of the project.



## Usage:

The menu that provides access to most of the tools is activated using the 

"Activate menu" item in the "JPSL Instructor Tools" section of the Jupyter 

Lab command palette (figure 1). By default it is inactive at the beginning 

of a session.



![Instructor Tool commands in Jupyter Lab command palette](https://raw.githubusercontent.com/JupyterPhysSciLab/jupyter-instructortools/master/JPSL_Instructor_Tools_palette_cmds.png)



__Figure 1:__ Instructor Tool commands available in the Jupyter lab 

command 

palette.



The "Disallow menu in Notebook" option prevents the use of the Instructor 

Tools menu with the _currently focused notebook_. This should only be done to 

the final form of the notebook that will be distributed to students, because 

it is very difficult to reverse.



### Available menu items

* Create a data input table.

    * Table column and row labels can be locked once set.

    * Number of rows and columns must be chosen on initial creation.

    * Table will survive deletion of all cell output data.

    * The code cell that creates the table  is protected.

    * Table creation code will work in a plain vanilla Jupyter install, but the 

      contents of the table cannot be edited.

    * See the [jupyter-datainputtable](https://github.com/JupyterPhysSciLab/jupyter-datainputtable)

      extension for more details.

* Add some highlight bars to a selected markdown cell. These can be removed by 

  deleting the associated`
`:

  * A vertical cyan bracket at the left of the cell.

  * A vertical red bracket at the left of the cell.

  * A horizontal green start bar (fading down to beige). Useful for indicating

    the beginning of an instruction block.

  * A horizontal brown stop bar (fading down from beige to brown). Useful 

    for indicating the end of an instruction block.

* Protect/unprotect selected cells. Protected cells cannot be 

  edited or deleted by the user. This is a good way to prevent instructions

  and example code from being damaged by students.

* Set/un-set selected cells as allowed to be hidden. This can be used to mark

  cells to hide before producing a pdf to turn in.

* Set/un-set selected cells to collapse code before printing.

* Set/un-set selected cells to collapse code in JPSL.

* Temporarily highlight each kind of cell marked for hiding or code collapse.

* Temporarily completely hide (not collapse) code (all or just selected cells).

* Insert boilerplate instructions on initializing a

  notebook into a selected markdown cell.

* Insert boilerplate instructions on preparing a pdf version of the notebook 

  to turn in with instructor selected parts hidden.



  

### Typical workflow

Work in a virtual environment that includes this tool plus all the tools

the students will have access to (see [Install](#install)).



1. Start the jupyter notebook server (from the command line use `jupyter 

   lab` or `jupyter notebook`).

1. Open a new notebook and activate the menu using the "Activate menu" item 

   in the "JPSL Instructor Tools" section of the Jupyter Lab command palette 

   (figure 1). By default it is inactive at the beginning of a session.

1. Build the exercise including instructions, examples, tables (use the menu) 

   and imports.

1. Collect all the necessary imports into a code cell that will be the 

   first code cell in the worksheet. You may want introductory material 

   before this cell.

1. Use the menu to protect any cells you do not want students to 

   accidentally alter.

1. Use the menu to tag cells so they can be hidden. This allows students to 

   print a compressed version of the notebook for grading. Consider hiding 

   most of the instructions.

1. Restart the kernel and clear all cell outputs. Delete or emtpy any cells 

   that have things you want the students to be filling in.

1. Save the notebook and make a duplicate of it. Continue working with the 

   duplicate.

1. Work through the notebook as if you were a student, make adjustments as 

    you go. Iterate restarting the kernel, clearing the cell outputs, saving,

    duplicating and working though until satisfied.

1. Save the final version of the worksheet. Duplicate it.

1. Open the duplicate worksheet. Make sure all the appropriate cell data is 

    cleared. Then select "Disallow menu in Notebook" item in the "JPSL 

   Instructor Tools" section of the Jupyter Lab command palette (figure 1). 

   This will deactivate the menu and block students from easily 

    reinstalling it. Save the notebook and distribute this copy to students.



## Requirements

* JupyterLab >= 4.0.0

* notebook >= 7.0.0

* jupyter-datainputtable >=0.8.0



## Install



Installation using pip into a virtual environment is recommended. My 

favorite way to manage virtual environments is using

[pipenv](https://pipenv.pypa.io/en/latest/). You should also consider

[venv](https://docs.python.org/3/library/venv.html) which is part of the 

standard Python library and [hatch](https://hatch.pypa.io/latest/) for 

development.



__Option 1__: Recommended, as this will install all of the Jupyter Physical 

Science Lab packages an Instructor might need. Use the

[JPSLInstructor pseudo package](https://github.com/JupyterPhysSciLab/JPSLInstructor).



```bash

pip install JPSLInstructor

```



__Option 2__: Install just this package and its minimal requirements. You 

may want to do this if you are just making worksheets, do not need live 

data acquisition or want to use a very specific set of packages.



```bash

pip install jupyter_instructortools

```



## Contributing 



### Development install

Note: You will need NodeJS to build the extension package.



The `jlpm` command is JupyterLab's pinned version of

[yarn](https://yarnpkg.com/) that is installed with JupyterLab. You may use

`yarn` or `npm` in lieu of `jlpm` below.



```bash

# Clone the repo to your local environment

# Change directory to the jupyter_datainputtable directory

# Install package in development mode

pip install -e "."

# Link your development version of the extension with JupyterLab

jupyter labextension develop . --overwrite

# Rebuild extension Typescript source after making changes

jlpm build

```



You can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.



```bash

# Watch the source directory in one terminal, automatically rebuilding when needed

jlpm watch

# Run JupyterLab in another terminal

jupyter lab

```



With the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).



By default, the `jlpm build` command generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:



```bash

jupyter lab build --minimize=False

```



### Development uninstall



```bash

pip uninstall jupyter_instructortools

```



In development mode, you will also need to remove the symlink created by `jupyter labextension develop`

command. To find its location, you can run `jupyter labextension list` to figure out where the `labextensions`

folder is located. Then you can remove the symlink named 

`jupyter-instructortools` within that folder.



### Testing the extension (currently incomplete)



#### Frontend tests



This extension is using [Jest](https://jestjs.io/) for JavaScript code testing.



To execute them, execute:



```sh

jlpm

jlpm test

```



#### Integration tests



This extension uses [Playwright](https://playwright.dev/docs/intro) for the integration tests (aka user level tests).

More precisely, the JupyterLab helper [Galata](https://github.com/jupyterlab/jupyterlab/tree/master/galata) is used to handle testing the extension in JupyterLab.



More information are provided within the [ui-tests](./ui-tests/README.md) README.



### Packaging the extension



See [RELEASE](RELEASE.md)

## Issues or comments



Issues or bugs should be reported via the project's [issues pages](https://github.com/JupyterPhysSciLab/jupyter-instructortools/issues).



Copyright - Jonathan Gutow, 2020 - 2024.