importnb 2023.11.1
import jupyter notebooks as python modules and scripts.
Requires Python: >=3.8

# `importnb` imports notebooks as python modules.

if you're here, then there is a chance you have a notebook (`.ipynb`) in a directory saved as `Untitled.ipynb`. it is just sitting there, but what if it could be used as a python module? `importnb` is here to answer that question.


## basic example 
use `importnb`'s  `Notebook` finder and loader to import notebooks as modules

    # with the new api
    from importnb import imports
    with imports("ipynb"):
        import Untitled

    # with the explicit api
    from importnb import imports
    with Notebook():
        import Untitled




### What does this snippet do?

> the snippet begins `with` a context manager that modifies the files python can discover.
it will find  the `Untitled.ipynb` notebook and import it as a module with `__name__` `Untitled`. 
the `__file__` description will have `.ipynb` as an extension. 

maybe when we give notebooks new life they eventually earn a better name than `Untitled`?

## run a notebook as a script

the `importnb` command line interface mimics python's. it permits running notebooks files, modules, and raw json data. 

the commands below execute a notebook module and file respectively.

    importnb -m Untitled         # call the Untitled module as __main__
    importnb Untitled.ipynb      # call the Untitled file as __main__

## installing `importnb`

use either `pip` or `conda/mamba` 

    pip install importnb
    conda install -cconda-forge importnb
    mamba install -cconda-forge importnb



## `importnb` features

* `importnb.Notebook` offers parameters to customize how modules are imported
* imports Jupyter notebooks as python modules
  * fuzzy finding conventions for finding files that are not valid python names
* works with top-level await statements
* integration with `pytest`
* extensible machinery and entry points
* translates Jupyter notebook files (ie `.ipynb` files) line-for-line to python source providing natural error messages
* command line interface for running notebooks as python scripts
* has no required dependencies

### customizing parameters

the `Notebook` object has a few features that can be toggled:

* `lazy:bool=False` lazy load the module, the namespace is populated when the module is access the first time.
* `position:int=0` the relative position of the import loader in the `sys.path_hooks`
* `fuzzy:bool=True` use fuzzy searching syntax when underscores are encountered.
* `include_markdown_docstring:bool=True` markdown blocks preceding function/class defs become docstrings.
* `include_magic:bool=True` ignore any ipython magic syntaxes
* `only_defs:bool=False` import only function and class definitions. ignore intermediate * expressions.
* `no_magic:bool=False` execute `IPython` magic statements from the loader.

these features are defined in the `importnb.loader.Interface` class and they can be controlled throught the command line interface.

### importing notebooks

the primary goal of this library is to make it easy to reuse python code in notebooks. below are a few ways to invoke python's import system within the context manager.

    with importnb.imports("ipynb"):
        import Untitled
        import Untitled as nb
        __import__("Untitled")
        from importlib import import_module
        import_module("Untitled")

#### import data files

there is support for discovering data files. when discovered, data from disk on loaded and stored on the module with rich reprs.

    with importnb.imports("toml", "json", "yaml"):
        pass

all the available entry points are found with

    from importnb.entry_points import list_aliases
    list_aliases()

#### loading directly from file 

    Untitled = Notebook.load("Untitled.ipynb")


### fuzzy finding

often notebooks have names that are not valid python files names that are restricted alphanumeric characters and an `_`.  the `importnb` fuzzy finder converts python's import convention into globs that will find modules matching specific patters. consider the statement:

    with importnb.Notebook():
        import U_titl__d                        # U*titl**d.ipynb

`importnb` translates `U_titl__d` to a glob format that matches the pattern `U*titl**d.ipynb` when searching for the source. that means that `importnb` should fine `Untitled.ipynb` as the source for the import[^unless].

    with importnb.Notebook():
        import _ntitled                        # *ntitled.ipynb
        import __d                     # **d.ipynb
        import U__                        # U**.ipynb

a primary motivation for this feature is name notebooks as if they were blog posts using the `YYYY-MM-DD-title-here.ipynb` convention. there are a few ways we could this file explicitly. the fuzzy finder syntax could like any of the following:

    with importnb.Notebook():
        import __title_here
        import YYYY_MM_DD_title_here
        import __MM_DD_title_here

#### fuzzy name ambiguity

it is possible that a fuzzy import may be ambiguous are return multiple files.
the `importnb` fuzzy finder will prefer the most recently changed file.

ambiguity can be avoided by using more explicit fuzzy imports that will reduce collisions.
another option is use python's explicit import functions.


    with importnb.Notebook():
        __import__("YYYY-MM-DD-title-here")
        import_module("YYYY-MM-DD-title-here")


#### importing your most recently changed notebook

an outcome of resolving the most recently changed is that you can import your most recent notebook with:

        import __                        # **.ipynb

### integrations

#### `pytest`

since `importnb` transforms notebooks to python documents we can use these as source for tests.
`importnb`s `pytest` extension is not fancy, it only allows for conventional pytest test discovery. 

`nbval` is alternative testing tools that validates notebook outputs. this style is near to using notebooks as `doctest` while `importnb` primarily adds the ability to write `unittest`s in notebooks. adding tests to notebooks help preserve them over time.

#### extensible

the `importnb.Notebook` machinery is extensible. it allows other file formats to be used. for example, `pidgy` uses `importnb` to import `markdown` files as compiled python code.

    class MyLoader(importnb.Notebook): pass

    
---

## developer

```bash
pip install -e.      # install in development mode
hatch run test:cov   # test 
```

* `importnb` uses `hatch` for testing in python and `IPython`

---

## appendix
### line-for-line translation and natural error messages

a challenge with Jupyter notebooks is that they are `json` data. this poses problems:

1. every valid line of code in a Jupyter notebook is a quoted `json` string
2. `json` parsers don't have a reason to return line numbers.

#### the problem with quoted code

#### line-for-line `json` parser

python's `json` module is not pluggable in the way we need to find line numbers. since `importnb` is meant to be dependency free on installation we couldn't look to any other packages like `ujson` or `json5`. 

the need for line numbers is enough that we ship a standalone `json` grammar parser. to do this without extra dependencies we use the `lark` grammar package at build time:
* we've defined a `json.g`ramar
* we use `hatch` hooks to invoke `lark-standalone` that generates a standalone parser for the grammar. the generated file is shipped with the package.

the result of `importnb` is `json` data translated into vertically sparse, valid python code.

#### reproducibility caution with the fuzzy finder 

⚠️ fuzzy finding is not reproducible as your system will change over time. in python, "explicit is better than implicit" so defining strong fuzzy strings is best practice if you MUST use esotric names. an alternative option is to use the `importlib.import_module` machinery


[pip]: #
[conda]: #
[mamba]: #
[pidgy]: #