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‎.nojekyll‎

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‎02_shell_files/figure-commonmark/cell-18-output-1.png‎

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‎02_shell_files/figure-html/cell-18-output-1.png‎

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‎index.html‎

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‎index.html.md‎

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# execnb
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<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
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[![CI](https://github.com/fastai/execnb/actions/workflows/test.yaml/badge.svg)](https://github.com/fastai/execnb/actions/workflows/test.yaml)
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[![Deploy to GitHub
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Pages](https://github.com/fastai/execnb/actions/workflows/deploy.yaml/badge.svg)](https://github.com/fastai/execnb/actions/workflows/deploy.yaml)
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## Install
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Either:
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pip install execnb
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or if you use conda:
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conda install -c fastai execnb
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(You can replace `conda` with `mamba` in the line above if you have
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mamba installed.)
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## How to use
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Use
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[`CaptureShell`](https://AnswerDotAI.github.io/execnb/shell.html#captureshell)
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to run Jupyter code and capture notebook outputs, without running a
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Jupyter server (or even having it installed):
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``` python
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from execnb.nbio import *
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from execnb.shell import *
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from fastcore.utils import *
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```
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``` python
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s = CaptureShell()
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s.run('1+1')
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```
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[{'data': {'text/plain': ['2']},
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'metadata': {},
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'output_type': 'execute_result',
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'execution_count': 1}]
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To execute a notebook and save it with outputs filled in, use
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[`CaptureShell.execute`](https://AnswerDotAI.github.io/execnb/shell.html#captureshell.execute):
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``` python
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try:
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s.execute('../tests/clean.ipynb', 'tmp.ipynb')
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print(read_nb('tmp.ipynb').cells[1].outputs)
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finally: Path('tmp.ipynb').unlink()
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```
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[{'name': 'stdout', 'output_type': 'stream', 'text': ['1\n']}, {'data': {'text/plain': ['2']}, 'execution_count': 3, 'metadata': {}, 'output_type': 'execute_result'}]
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You can also execute notebooks from the command line with
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[`exec_nb`](https://AnswerDotAI.github.io/execnb/shell.html#exec_nb):
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``` python
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!exec_nb --help
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```
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usage: exec_nb [-h] [--dest DEST] [--exc_stop] [--inject_code INJECT_CODE]
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[--inject_path INJECT_PATH] [--inject_idx INJECT_IDX]
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src
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Execute notebook from `src` and save with outputs to `dest`
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positional arguments:
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src Notebook path to read from
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optional arguments:
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-h, --help show this help message and exit
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--dest DEST Notebook path to write to (default: )
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--exc_stop Stop on exceptions? (default: False)
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--inject_code INJECT_CODE Code to inject into a cell
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--inject_path INJECT_PATH Path to file containing code to inject into a cell
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--inject_idx INJECT_IDX Cell to replace with `inject_code` (default: 0)

‎nbio.html‎

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‎nbio.html.md‎

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# nbio
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<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
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## Reading a notebook
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A notebook is just a json file.
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<details open class="code-fold">
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<summary>Exported source</summary>
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``` python
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def _read_json(self, encoding=None, errors=None):
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return loads(Path(self).read_text(encoding=encoding, errors=errors))
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```
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</details>
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``` python
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minimal_fn = Path('../tests/minimal.ipynb')
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minimal_txt = AttrDict(_read_json(minimal_fn))
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```
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It contains two sections, the `metadata`…:
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``` python
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minimal_txt.metadata
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```
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{'kernelspec': {'display_name': 'Python 3 (ipykernel)',
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'language': 'python',
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'name': 'python3'}}
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…and, more importantly, the `cells`:
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``` python
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minimal_txt.cells
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```
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[{'cell_type': 'markdown',
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'metadata': {},
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'source': ['## A minimal notebook']},
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{'cell_type': 'code',
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'execution_count': None,
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'metadata': {},
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'outputs': [{'data': {'text/plain': ['2']},
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'execution_count': None,
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'metadata': {},
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'output_type': 'execute_result'}],
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'source': ['# Do some arithmetic\n', '1+1']}]
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The second cell here is a `code` cell, however it contains no outputs,
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because it hasn’t been executed yet. To execute a notebook, we first
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need to convert it into a format suitable for `nbclient` (which expects
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some `dict` keys to be available as attrs, and some available as regular
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`dict` keys). Normally, `nbformat` is used for this step, but it’s
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rather slow and inflexible, so we’ll write our own function based on
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`fastcore`’s handy `dict2obj`, which makes all keys available as both
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attrs *and* keys.
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------------------------------------------------------------------------
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<a
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href="https://github.com/AnswerDotAI/execnb/blob/main/execnb/nbio.py#L21"
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target="_blank" style="float:right; font-size:smaller">source</a>
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### NbCell
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> NbCell (idx, cell)
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*`dict` subclass that also provides access to keys as attrs*
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We use an `AttrDict` subclass which has some basic functionality for
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accessing notebook cells.
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------------------------------------------------------------------------
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<a
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href="https://github.com/AnswerDotAI/execnb/blob/main/execnb/nbio.py#L50"
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target="_blank" style="float:right; font-size:smaller">source</a>
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### dict2nb
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> dict2nb (js=None, **kwargs)
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*Convert dict `js` to an `AttrDict`,*
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We can now convert our JSON into this `nbclient`-compatible format,
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which pretty prints the source code of cells in notebooks.
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``` python
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minimal = dict2nb(minimal_txt)
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cell = minimal.cells[1]
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cell
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```
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``` json
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{ 'cell_type': 'code',
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'execution_count': None,
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'idx_': 1,
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'metadata': {},
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'outputs': [ { 'data': {'text/plain': ['2']},
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'execution_count': None,
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'metadata': {},
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'output_type': 'execute_result'}],
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'source': '# Do some arithmetic\n1+1'}
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```
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The abstract syntax tree of source code cells is available in the
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`parsed_` property:
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``` python
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cell.parsed_(), cell.parsed_()[0].value.op
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```
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([<ast.Expr>], <ast.Add>)
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------------------------------------------------------------------------
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<a
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href="https://github.com/AnswerDotAI/execnb/blob/main/execnb/nbio.py#L57"
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target="_blank" style="float:right; font-size:smaller">source</a>
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### read_nb
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> read_nb (path)
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*Return notebook at `path`*
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This reads the JSON for the file at `path` and converts it with
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[`dict2nb`](https://AnswerDotAI.github.io/execnb/nbio.html#dict2nb). For
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instance:
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``` python
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minimal = read_nb(minimal_fn)
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str(minimal.cells[0])
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```
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"{'cell_type': 'markdown', 'metadata': {}, 'source': '## A minimal notebook', 'idx_': 0}"
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The file name read is stored in `path_`:
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``` python
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minimal.path_
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```
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'../tests/minimal.ipynb'
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## Creating a notebook
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------------------------------------------------------------------------
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<a
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href="https://github.com/AnswerDotAI/execnb/blob/main/execnb/nbio.py#L64"
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target="_blank" style="float:right; font-size:smaller">source</a>
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### new_nb
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> new_nb (cells=None, meta=None, nbformat=4, nbformat_minor=5)
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*Returns an empty new notebook*
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Use this function when creating a new notebook. Useful for when you
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don’t want to create a notebook on disk first and then read it.
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------------------------------------------------------------------------
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<a
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href="https://github.com/AnswerDotAI/execnb/blob/main/execnb/nbio.py#L69"
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target="_blank" style="float:right; font-size:smaller">source</a>
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### mk_cell
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> mk_cell (text, cell_type='code', **kwargs)
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*Create an
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[`NbCell`](https://AnswerDotAI.github.io/execnb/nbio.html#nbcell)
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containing `text`*
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<table>
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<thead>
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<tr>
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<th></th>
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<th><strong>Type</strong></th>
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<th><strong>Default</strong></th>
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<th><strong>Details</strong></th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>text</td>
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<td></td>
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<td></td>
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<td><code>source</code> attr in cell</td>
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</tr>
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<tr>
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<td>cell_type</td>
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<td>str</td>
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<td>code</td>
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<td><code>cell_type</code> attr in cell</td>
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</tr>
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<tr>
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<td>kwargs</td>
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<td>VAR_KEYWORD</td>
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<td></td>
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<td></td>
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</tr>
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</tbody>
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</table>
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``` python
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mk_cell('print(1)', execution_count=0)
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```
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``` json
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{ 'cell_type': 'code',
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'directives_': {},
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'execution_count': 0,
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'idx_': 0,
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'metadata': {},
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'source': 'print(1)'}
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```
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## Writing a notebook
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------------------------------------------------------------------------
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<a
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href="https://github.com/AnswerDotAI/execnb/blob/main/execnb/nbio.py#L81"
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target="_blank" style="float:right; font-size:smaller">source</a>
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### nb2dict
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> nb2dict (d, k=None)
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*Convert parsed notebook to `dict`*
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This returns the exact same dict as is read from the notebook JSON.
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``` python
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minimal_fn = Path('../tests/minimal.ipynb')
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minimal = read_nb(minimal_fn)
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minimal_dict = _read_json(minimal_fn)
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assert minimal_dict==nb2dict(minimal)
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```
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------------------------------------------------------------------------
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<a
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href="https://github.com/AnswerDotAI/execnb/blob/main/execnb/nbio.py#L89"
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target="_blank" style="float:right; font-size:smaller">source</a>
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### nb2str
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> nb2str (nb)
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*Convert `nb` to a `str`*
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To save a notebook we first need to convert it to a `str`:
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``` python
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print(nb2str(minimal)[:45])
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```
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{
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"cells": [
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{
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"cell_type": "markdown",
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------------------------------------------------------------------------
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<a
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href="https://github.com/AnswerDotAI/execnb/blob/main/execnb/nbio.py#L95"
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target="_blank" style="float:right; font-size:smaller">source</a>
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### write_nb
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> write_nb (nb, path)
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*Write `nb` to `path`*
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This returns the exact same string as saved by Jupyter.
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``` python
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tmp = Path('tmp.ipynb')
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try:
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minimal_txt = minimal_fn.read_text()
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write_nb(minimal, tmp)
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assert minimal_txt==tmp.read_text()
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finally: tmp.unlink()
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```
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Here’s how to put all the pieces of `execnb.nbio` together:
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``` python
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nb = new_nb([mk_cell('print(1)')])
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path = Path('test.ipynb')
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write_nb(nb, path)
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nb2 = read_nb(path)
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print(nb2.cells)
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path.unlink()
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```
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[{'cell_type': 'code', 'metadata': {}, 'source': 'print(1)', 'idx_': 0}]

‎robots.txt‎

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Sitemap: https://AnswerDotAI.github.io/execnb/sitemap.xml

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