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Configuration

Per-notebook configuration

The pairing information for one or multiple notebooks can be set on the command line:

jupytext --set-formats ipynb,py [--sync] notebook.ipynb

You can pair a notebook to as many text representations as you want (see our World population notebook in the demo folder). Format specifications are of the form

[[root_folder//][path/][prefix]/][suffix.]ext[:format_name]

where

  • ext is one of ipynb, md, Rmd, jl, py, R, sh, cpp, q. Use the auto extension to have the script extension chosen according to the Jupyter kernel.
  • format_name (optional) is either light (default for scripts), nomarker, percent, hydrogen, sphinx (Python only), spin (R only) — see the format specifications.
  • root_folder, path, prefix and suffix allow to save the text representation to files with different names, or in different folders (see the configuration files examples).

Jupytext accepts a few additional options. These options should be added to the "jupytext" section in the metadata — use either the metadata editor or the --opt/--format-options argument on the command line.

  • comment_magics: By default, Jupyter magics are commented when notebooks are exported to any other format than markdown. If you prefer otherwise, use this boolean option, or its global counterpart (see below).
  • notebook_metadata_filter: By default, Jupytext only exports the kernelspec and jupytext metadata to the text files. Set "jupytext": {"notebook_metadata_filter": "-all"} if you want that the script has no notebook metadata at all. The value for notebook_metadata_filter is a comma separated list of additional/excluded (negated) entries, with all a keyword that allows to exclude all entries. Use dots to filter recursively the metadata. For instance, use notebook_metadata_filter="-jupytext.text_representation.jupytext_version" to remove the jupytext_version field in the jupytext.text_representation metadata.
  • cell_metadata_filter: By default, cell metadata autoscroll, collapsed, scrolled, trusted and ExecuteTime are not included in the text representation. Add or exclude more cell metadata with this option.

Jupytext configuration file

Possible locations and formats

Jupytext's contents manager, and the command line interface, can load some configuration options from a configuration file.

The configuration file should be either in the local or a parent directory, or in any directory listed in

from jupytext.config import global_jupytext_configuration_directories
list(global_jupytext_configuration_directories())

which include XDG_CONFIG_HOME (defaults to $HOME/.config) and XDG_CONFIG_DIR.

The name for the configuration file can be any of jupytext.config.JUPYTEXT_CONFIG_FILES, i.e. .jupytext (in TOML), jupytext.toml, jupytext.yml, jupytext.yaml, jupytext.json or jupytext.py, and their dot-file versions. Alternatively, if you are using it, you can also use your Python project's pyproject.toml file by adding configuration to a [tool.jupytext] table within it.

If you want to know, for a given directory, which configuration file Jupytext is using, please execute:

from jupytext.config import find_jupytext_configuration_file
find_jupytext_configuration_file('.')

If you want to limit the search for a configuration file to a given parent directory, you can create an empty .jupytext configuration file in that directory. Alternatively, you can set the search boundaries with an environment variable JUPYTEXT_CEILING_DIRECTORIES - a colon-separated list of absolute paths.

If JUPYTEXT_CEILING_DIRECTORIES is defined, Jupytext will stop searching for configuration files when it meets one of these path. This can be helpful to avoid searching for configuration files on slow filesystems. It can also be useful if you don't want to use a global configuration - for instance, when running pytest on Jupytext, we use JUPYTEXT_CEILING_DIRECTORIES="/tmp".

Configuring paired notebooks globally

The examples below assume that you use a .jupytext, jupytext.toml or .jupytext.toml Jupyter configuration file in TOML format. If you use another extension, please adapt the examples. For instance, if you want to use jupytext.yml in YAML format, replace the = sign with : and remove the double quotes. See also test_config.py for short examples in all the supported formats.

Also, the examples are for Jupytext 1.11.0 or later. If you are using an older version, you should consult the previous version of this documentation.

Say you want to always associate every .ipynb notebook with a .md file (and reciprocally). This is done by adding the following to your jupytext.toml or .jupytext.toml Jupyter configuration file:

# Always pair ipynb notebooks to md files
formats = "ipynb,md"

If you prefer to use a default ipynb - py:percent pairing, then that would be:

# Always pair ipynb notebooks to py:percent files
formats = "ipynb,py:percent"

or alternatively, using an explicit format list:

# Always pair ipynb notebooks to py:percent files
formats = ["ipynb", "py:percent"]

If you wish to use the pyproject.toml config file rather than jupytext.toml, you just need to create a [tool.jupytext] section in the pyproject.toml file, like here:

[tool.jupytext]
formats = "ipynb,py:percent"

You can pair notebooks in trees with a root_prefix separated with three slashes, e.g.

# Pair notebooks in subfolders of 'notebooks' to scripts in subfolders of 'scripts'
formats = "notebooks///ipynb,scripts///py:percent"

or alternatively, using a dict to map the prefix path to the format name:

# Pair notebooks in subfolders of 'notebooks' to scripts in subfolders of 'scripts'
[formats]
"notebooks/" = "ipynb"
"scripts/" = "py:percent"

Note that if you are using a pyproject.toml file with this dict format, you should make sure the table header is instead [tool.jupytext.formats].

The root_prefix is matched with the top-most parent folder of the matching name, not above the Jupytext configuration file.

For instance, with the pairing above, a notebook with path /home/user/jupyter/notebooks/project1/example.ipynb will be paired with the Python file /home/user/jupyter/scripts/project1/example.py.

In addition to the root_prefix, you can use symbolic links if you wish to distribute your notebook folders at different places. Be sure to use symbolic links on folders, not files (#696).

To disable the default pairing for an individual notebook, set formats to a single format, with e.g.:

jupytext --set-formats ipynb notebook.ipynb

Please note that, while Jupytext is Jupyter acts accordingly to both local or global Jupytext configuration files, the Jupytext menu in Jupyter, and the Jupyter commands in JupyterLab, only display the pairing information set in the notebooks itself and are not aware of the global configuration (#177).

Metadata filtering

You can specify which metadata to include or exclude in the text files created by Jupytext by setting notebook_metadata_filter (notebook metadata) and cell_metadata_filter (cell metadata) in the configuration file. They accept a string of comma separated keywords. A minus sign - in front of a keyword means exclusion.

Suppose you want to keep all the notebook metadata but widgets and varInspector in the YAML header. For cell metadata, you want to allow ExecuteTime and autoscroll, but not hide_output. You can set

notebook_metadata_filter = "all,-widgets,-varInspector"
cell_metadata_filter = "ExecuteTime,autoscroll,-hide_output"

If you want that the text files created by Jupytext have no metadata, you may use the global metadata filters below. Please note that with this setting, the metadata is only preserved in the .ipynb file.

notebook_metadata_filter = "-all"
cell_metadata_filter = "-all"

It is possible to filter nested metadata. For example, if you want to preserve the Jupytext metadata, but not the Jupytext version number, you can use:

notebook_metadata_filter = "-jupytext.text_representation.jupytext_version"

Finally, to hide the notebook metadata in an HTML comment in Markdown files, use the option hide_notebook_metadata.

More options

There are a couple more options available - please have a look at the JupytextConfiguration class in config.py.