Here's how to format Markdown cells in Jupyter notebooks: Headings. Use the number sign (#) followed by a blank space for notebook titles and section headings: #for titles. ##for major headings. ###for subheadings. ####for 4th level subheadings. Use the following code to emphasize text. An example Markdown cell in Jupyter Notebook. Data Tip: Learn more about Markdown for Jupyter Notebook. If you render your Jupyter Notebook file to HTML or PDF, this Markdown will appear as formatted text in the output document. Data Tip: In fact, this web page that you are reading right now is generated from a Markdown document! Now, press Run from the Jupyter Notebook toolbar. You can also create a Markdown cell where you can write text as a Markdown. Just select ‘Markdown’ from the Jupyter Notebook toolbar. Notice how the In symbol disappears from the side of the cell. Markdown texts are useful in writing essential guidelines and comments for the code. MyST Markdown overview Special content blocks References and cross-references Control the page layout Citations and bibliographies Math and equations Images and figures Add metadata to your book pages Hide or remove content Types of content source files Markdown files Jupyter Notebook files.

One of the typographical error types that isn’t picked up in the recipe I used in Spellchecking Jupyter Notebooks with pyspelling is the repeated word error type (for example, the the).

A quick way to spot repeated words is to use egrep on the command line over a set of notebooks-as-markdown (via Jupytext) files: egrep -o 'b(w+)s+1b' */.md/*.md

I do seem to get some false positives with this, generating an output file of the report and then doing a quick filter on that wouls tidy that up.

An alternative route might be to extend pyspelling and look at tokenised word pairs for duplicates. Packages such as spacy also support things like Rule-Based Phrase Text Extraction and Matching at a token-based, as well as regex, level. Spacy also has extensions for hunspell [spacy_hunspell]. A wide range of contextual spell checkers are also available (for example, neuspell seems to offer a meta-tool over several of them), although care would need to be taken when it comes to (not) detecting US vs UK English spellings as typos. For nltk based spell-checking, see eg sussex_nltk/spell.

Note that adding an autofix would be easy enough but may make for false positives if there is a legitimate repeated word pair in a text. Falsely autocorrecting that, then detecting the created error / tracking down the incorrect deletion so it can be repaired, would be non-trivial.

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Increasingly, I think it might be useful to generate a form with suggested autocorrections and checkboxes pre-checked by default that could be used to script corrections might be useful. It could also generate a change history.

For checking grammar, the Java based LanguageTool seems to be one of the most popular tools out there, being as it is the engine behind the OpenOffice spellchecker. Python wrappers are available for it (for example, jxmorris12/language_tool_python).

in case of running a notebook server on bioclust, we need to use a virtual environment for the installation

Config file

to generate the config file for the first time, run:

Markdown For Jupyter Notebooks Cheat Sheet

the file will be generated on:

Markdown For Jupiter Notebooks 2017

~/.jupyter/jupyter_notebook_config.py

Markdown Cell Python

With the same notebook server, you can run several kernels