Automated notion clustering for the knowledge LaTeX package
Project description
knowledge-clustering
Command-line tool to help with the use of the knowledge LaTeX package.
A tutorial on how to use both knowledge
and knowledge-clustering
can be found here.
Principle
The goal of knowledge-clustering
is to help the user write a LaTeX document with
the knowledge package.
It has three features:
- Clustering: provide suggestions to the user of what notions should be grouped together.
- Add quotes: find where you might have missed some quotes in your document.
- Anchor points: find where you might have missed anchor points in your document.
The clustering algorithm is meant to be used while writing your document, while the last two tools should be used when your document is (nearly) ready to be published, to check if everything is right.
Installation
To install (or upgrade) knowledge-clustering
, you need to have Python 3.9 (or a more recent version), and then run
python3 -m pip install --upgrade knowledge-clustering
and then
knowledge init
To check if you have the latest version of knowledge-clustering
, you can run
knowledge --version
Clustering notions
Syntax
Usage: knowledge cluster [OPTIONS]
Defines, as a comment and in the knowledge files, all the knowledges
occuring in the file.
Options:
-k, --knowledge FILE File containing the knowledges that are already
defined. Multiple files are allowed; new
knowledges will be written in the last one. If
the option is not specified, all .kl file in the
current directory (and subdirectory,
recursively) will be taken. If there are
multiple files, exactly one of them must end
with `default.kl`.
-d, --diagnose FILE Diagnose file produced by LaTeX. If the option
is not specified, the unique .diagnose file in
the current directory (and subdirectory,
recursively) is taken instead.
-l, --lang [en|fr] Language of your TeX document.
-S, --scope / --no-scope Print the scopes defined in the knowledge file
and print the possible meaning of those scope
inferred by knowledge-clustering.
-P, --print / --no-print Print all new knowledges.
-N, --no-update / --update Don't look on PyPI if a newer version of
knowledge-clustering is available.
-c, --config-file TEXT Specify the configuration file. By default the
configuration file in the folder
/Users/rmorvan/knowledge-
clustering/knowledge_clustering/data
corresponding to your language is used.
--help Show this message and exit.
Example
Example files can be found in the examples/
folder.
While writing some document, you have defined some knowledges in a file called preservation.kl
(distinct
from your main LaTeX
).
You continued writing your LaTeX
document (not provided in the examples/
folder)
for some time, and used some knowledges that were undefined.
When compiling, LaTeX
and the knowledge package
gives you a warning
and writes in a .diagnose
file some information explaining what went wrong. This .diagnose
file contains
a section called "Undefined knowledges" containing all knowledges used in your main LaTeX
file but not
defined in preservation.kl
. We reproduced this section
in the preservation.diagnose
file.
Normally, you would add every undefined knowledge, one after the other, in your
preservation.kl
. This is quite burdensome and can
largely be automated. This is precisely what knowledge-clustering
does: after running
knowledge cluster -k preservation.kl -d preservation.diagnose
your file preservation.diagnose
is left unchanged
but preservation.kl
is updated with comments.
The cluster
command is optional: you can also write knowledge -k preservation.kl -d preservation.diagnose
.
Now you simply have to check that the recommendations of knowledge-clustering
are
correct, and uncomment those lines.
Autofinder
If the current directory (and its recursive subdirectories) contains
a unique .diagnose
file and a unique .kl
file,
you can simply write knowledge cluster
(or knowledge
): the files will be automatically found.
Multiple knowledge files
If you have multiple knowledge files, you can use the -k
option multiple times.
For instance, you could write:
knowledge cluster -k 1.kl -k 2.kl -d ordinal.diagnose
Synonyms of knowledges defined in 1.kl
(resp. 2.kl
) will be defined, as comments,
in 1.kl
(resp. 2.kl
). New knowledges will always be added, as comments, to the last
file, which is 2.kl
in the example.
You can also use the autofinder in this case, using knowledge cluster
or knowledge
: if multiple .kl
files are present in the current directory (and
its recursive subdirectories), exactly one of them must end with default.kl
—this is
where new knowledges will be put.
Adding quotes
/!\ This feature is somewhat experimental.
Usage: knowledge addquotes [OPTIONS]
Finds knowledges defined in the knowledge files that appear in tex file
without quote symbols. Proposes to add quotes around them.
Options:
-t, --tex FILE Your TeX file. [required]
-k, --knowledge FILE File containing the knowledges that are already
defined. Multiple files are allowed; new
knowledges will be written in the last one. If
the option is not specified, all .kl file in the
current directory (and subdirectory,
recursively) will be taken. If there are
multiple files, exactly one of them must end
with `default.kl`.
-p, --print INTEGER When finding a match, number of lines (preceding
the match) that are printed in the prompt to the
user.
-N, --no-update / --update
--help Show this message and exit.
After running
knowledge addquotes -t mydocument.tex -k knowledges1.kl -k knowledges2.kl
your prompt will propose to add quotes around defined knowledges, and to define synonyms of knowledges that occur in your TeX file. For instance, if "algorithm" is a defined knowledge and "algorithms" occurs in your TeX file, then it will propose to you to define "algorithms" as a synonym of the knowledge "algorithm", and to add a pair of quotes around the string "algorithms" that occurs in your TeX file.
Whenever the algorithm finds a match for a knowledge, it will print the line of
the document where it found the match, and emphasize the string corresponding to the knowledge.
If you want to print more than one line, you can use the -p
(or --print
) option
to print more than one line.
Finding missing anchor points
Usage: knowledge anchor [OPTIONS]
Prints warning when a knowledge is introduced but is not preceded by an
anchor point.
Options:
-t, --tex FILE Your TeX file. [required]
-s, --space INTEGER Number of characters tolerated between an anchor
point and the introduction of a knowledge.
(Default value: 200)
-N, --no-update / --update
--help Show this message and exit.
When one runs
knowledge anchor -t mydocument.tex
the tool will print the lines of the document containing the
introduction of a knowledge that is not preceded by an anchor point.
The tolerance on how far away the anchor point can be from the
introduction of a knowledge can be changed with the -s
(or --space
)
option. The default value is 150 characters (corresponding to 2-3 lines in a
TeX document).
Devel using virtualenv
Using venv
and the --editable
option from pip
allows for an easy
setup of a development environment that will match a future user install without
the hassle.
For bash and Zsh users
python3 -m venv kl.venv
source ./kl.venv/bin/activate
python3 -m pip install --editable .
For fish users
python3 -m venv kl.venv
source ./kl.venv/bin/activate.fish
python3 -m pip install --editable .
FAQ
-
knowledge: command not found
after installingknowledge-clustering
Make sure you have Python>=3.9.
-
When running
knowledge
, I obtain a long message error indicating "Resource punkt not found."Run
knowledge init
. -
My shell doesn't autocomplete the command
knowledge
.Depending on whether you use
zsh
orbash
writeeval "`pip completion --<shellname>`"
(where
<shellname>
is eitherzsh
orbash
) in your.zshrc
(or.bashrc
) file and then, either launch a new terminal or runsource ~/.zshrc
(orsource ~/.bashrc
). -
Error: Got unexpected extra argument
when using multiple knowledge files.You should use the option
-k
before every knowledge file, like inknowledge cluster -k 1.kl -k 2.kl -d blabla.diagnose
-
I've updated
knowledge-clustering
but I still don't have the last version (which can be checked usingknowledge --version
): This can happen if you have multiple versions ofpython
(and multiple versions ofknowledge-clustering
).Type
where python3
, and uninstallknowledge-clustering
from everywhere (using<path>/python3 -m pip uninstall knowledge-clustering
). Try to then reinstallknowledge-clustering
by runningpython3 -m pip install --upgrade knowledge-clustering
.
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