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ipypublish 0.3.1

A workflow for creating and editing publication ready scientific reports, from one or more Jupyter Notebooks

Latest Version: 0.5.0

Project: https://github.com/chrisjsewell/ipypublish

A workflow for creating and editing publication ready scientific reports, from one or more Jupyter Notebooks, without leaving the browser!

For an example of the potential input/output, see Example.ipynb and Example.pdf .

Design Philosophy

In essence, the dream is to have the ultimate hybrid of Jupyter Notebook, WYSIWYG editor (e.g. MS Word) and document preparation system (e.g. TexMaker), being able to:

  • Dynamically (and reproducibly) explore data, run code and output the results
  • Dynamically edit and visualise the basic components of the document (text, math, figures, tables, references, citations, etc).
  • Have precise control over what elements are output to the final document and how they are layed out and typeset.
    • Also be able to output the same source document to different layouts and formats (pdf, html, presentation slides, etc).


  1. Create a notebook with some content!
  2. optionally create a .bib file and logo image
  3. Adjust the notebook and cell metadata.
  4. Clone the ipypublish GitHub repository and run the nbpublish.py script for either the specific notebook, or a folder containing multiple notebooks.
  5. A converted folder will be created, into which final .tex .pdf and _viewpdf.html files will be output, named by the notebook or folder input

The default latex template outputs all markdown cells (unless tagged latex_ignore), and then only code and output cells with latex metadata tags. See Example.ipynb and Example.pdf for an example of the potential input and output.

Setting up the environment

Using Conda is recommended for package management, in order to create self contained environments with specific versions of packages. The main external packages required are the Jupyter notebook, Jupyter nbconvert and Pandoc (for conversion to latex):

conda create --name ipyreport -c conda-forge jupyter pandoc

ipypublish can then be installed into this environment:

source activate ipyreport
pip install ipypublish

For converting to PDF, the TeX document preparation ecosystem is required (an in particular latexmk), which can be installed from:

ipypublish is automatically tested on update against python 2.7 and 3.6, for both Linux and OSX, using Travis CI. Therefore, to troubleshoot any installation/run issues, it is best to look at the travis config and travis test runs for working configurations.

For helpful extensions to the notebooks core capabilities (like a toc sidebar):

conda install --name ipyreport jupyter_contrib_nbextensions

A more extensive setup of useful packages (used to create the example) are listed in conda_packages.txt and an environment can be created directly from this using conda:

conda create --name ipyreport -c conda-forge -c matsci --file conda_packages.txt

Setting up a Notebook

For improved latex/pdf output, ipynb_latex_setup.py contains import and setup code for the notebook and a number of common packages and functions, including:

  • numpy, matplotlib, pandas, sympy, …
  • images_hconcat, images_vconcat and images_gridconcat functions, which use the PIL/Pillow package to create a single image from multiple images (with specified arrangement)

To use this script, in the first cell of a notebook, insert:

from ipypublish.scripts.ipynb_latex_setup import *

It is recommended that you also set this cell as an initialisation cell (i.e. have "init_cell": true in the metadata)

Converting Notebooks

The nbpublish script handles parsing the notebooks to nbconvert, with the appropriate converter. To see all options for this script:

nbpublish -h

For example, to convert the Example.ipynb notebook:

nbpublish -pdf example/notebooks/Example.ipynb

If a folder is input, then the .ipynb files it contains are processed and combined in ‘natural’ sorted order, i.e. 2_name.ipynb before 10_name.ipynb. By default, notebooks beginning ‘_’ are ignored.

All available converters are also listed by nbpublish -h. Two of note are:

  • latex_ipypublish_main.py is the default and converts cells to latex according to metadata tags on an ‘opt in’ basis.
  • latex_standard_article.py replicates the standard latex article template, which comes with nbconvert.
  • html_toc_toggle_input.py converts the entire notebook(s) to html and adds a table of contents sidebar and a button to toggle input code and output cells visible/hidden.
  • slides_standard converts the notebook to reveal.js slides, according to the standard nbconvert slide metadata. See the Live Slideshows section for using nbpresent to serve these slides to a webbrowser.

The current nbconvert --to pdf does not correctly resolve references and citations (since it copies the files to a temporary directory). Therefore nbconvert is only used for the initial nbconvert --to latex phase, followed by using latexmk to create the pdf and correctly resolve everything.

Creating a bespoke converter

nbconvert uses Jinja templates to specify the rules for how each element of the notebook should be converted, and also what each section of the latex file should contain. To create a custom template they employ an inheritance method to build up this template. However, in my experience this makes it;

  1. non-trivial to understand the full conversion process (having to go through the inheritance tree to find where particular methods have been implemented/overriden)
  2. difficult to swap in/out multiple rules

To improve this, ipypublish implements a pluginesque system to systematically append to blank template placeholders. For example, to create a document (with standard formatting) with a natbib bibliography where only input markdown is output, we could create the following dictionary:

my_tplx_dict = {
'meta_docstring':'with a natbib bibliography',

    ((( cell.source | citation2latex | strip_files_prefix | convert_pandoc('markdown', 'json',extra_args=[]) | resolve_references | convert_pandoc('json','latex') )))

    \usepackage[numbers, square, super, sort&compress]{natbib}
    \usepackage{doi} % hyperlink doi's

\bibliographystyle{unsrtnat} % sort citations by order of first appearance


The converter would then look like this:

from ipypublish.latex.create_tplx import create_tplx
from ipypublish.latex.standard import standard_article as doc
from ipypublish.latex.standard import standard_definitions as defs
from ipypublish.latex.standard import standard_packages as package

oformat = 'Latex'
template = create_tplx([package.tplx_dict,defs.tplx_dict,

config = {'TemplateExporter.filters':{},

Latex Metadata Tags

All information additional information, used to specify how a particular notebook/cell in latex is represented, is stored in the metadata under:

        "latex_doc": {}

Document Tags

To specify where the bibliography is:

"latex_doc": {
    "bibliography" : "path/to/bibliograph.bib"

The path can be absolute or relative.

For titlepage, enter in notebook metadata:

"latex_doc": {
  "titlepage": {
    "author": "Authors Name",
    "email": "authors@email.com",
    "supervisors": [
      "First Supervisor",
      "Second Supervisor"
    "title": "Main-Title",
    "subtitle": "Sub-Title",
    "tagline": "A tagline for the report.",
    "institution": [
    "logo": "path/to/logo_example.png"
  • all keys are optional
  • if there is no title, then the notebook filename will be used
  • if nbpublish.py is called on a folder, then the meta data from the first notebook will be used
  • logo should be the path (absolute or relative) to a logo image file

To control the output of contents tables:

"latex_doc": {
  "toc": true,
  "listfigures": true,
  "listtables": true,
  "listcode": true,

To override the default placement of figures and tables:

"latex_doc": {
    "figure": {
      "placement": "!bp"
    "table": {
      "placement": "!bp"

See Positioning_images_and_tables for placement options.

Cell Tags

To ignore any cell:

"latex_doc": {
    "ignore" : true

To output a code block:

"latex_doc": {
  "code": {
    "asfloat": true,
    "caption": "",
    "label": "code:example_sym",
    "widefigure": false,
    "placement": "H"

all extra tags are optional:

  • asfloat contitutes whether the code is wrapped in a codecell (float) environment or is inline.
  • all other tags work the same as figure (below).

For figures, enter in cell metadata:

"latex_doc": {
  "figure": {
    "caption": "Figure caption.",
    "label": "fig:flabel",
    "placement": "H",
    "widefigure": false
  • placement is optional and constitutes using a placement arguments for the figure (e.g. \begin{figure}[H]). See Positioning_images_and_tables.
  • widefigure is optional and constitutes expanding the figure to the page width (i.e. \begin{figure*}) (placement arguments will then be ignored)

For tables, enter in cell metadata:

"latex_doc": {
     "table": {
        "caption": "Table caption.",
        "label": "tbl:tlabel",
        "placement": "H",
            "alternate": "gray!20"

For equations, enter in cell metadata:

  "latex_doc": {
      "equation": {
        "label": "eqn:elabel"

label is optional

Captions in a Markdown cell

Especially for long captions, it would be prefered that they can be viewed and edited in a notebook Markdown cell, rather than hidden in the metadata. This can be achieved using the default latex template:

If a markdown input or latex output cell has the metadata tag:

 "latex_doc": {
    "caption": "fig:example_mpl"

Then, instead of it being input directly into the .tex file, it will be stored as a variable;

  • the variable’s name is created from the latex_caption value
  • the variable’s value is the first paragraph of the markdown text (i.e. nothing after a n)

If a subsequent figure, table or code cell has a label matching any stored variable name, for example:

"latex_doc": {
    "figure": {
    "caption": "",
    "label": "fig:example_mpl"

Then its caption will be overriden with that variable.

The manner in which this works can be found in Example.tex:

\newcommand{\kyfigcexampleumpl}{A matplotlib figure, with the caption set in the markdowncell above the figure.}

    \begin{center}\adjustimage{max size={0.9\linewidth}{0.4\paperheight}}{Example_files/Example_14_0.pdf}\end{center}

Note, this approach has the implicit contraint that caption cells must be above the corresponding figure/table to be output in the latex/pdf.

Citations and Bibliography

Using Zotero’s Firefox plugin and Zotero Better Bibtex for;

  • automated .bib file updating
  • drag and drop cite keys \cite{kirkeminde_thermodynamic_2012}
  • latexmk -bibtex -pdf (in nbpublish.py) handles creation of the bibliography
  • \usepackage{doi} turns the DOI numbers into url links
    • in Zotero-Better-Bibtex I have the option set to only export DOI, if both DOI and URL are present.

Please note, at the time of writing, Better BibTeX does not support Zotero 5.0 (issue#555). For now I have turned off auto-updates of Zotero, though this is probably not wise for long (Zotero 5 Discussion).

Can use:

<cite data-cite="kirkeminde_thermodynamic_2012">(Kirkeminde, 2012)</cite>

to make it look better in html, but not specifically available for drag and drop in Zotero

Live Slideshows

The nbpresent script handles serving reveal.js slides to a webbrowser. To see all options for this script:

nbpresent -h

The Reveal.js - Jupyter/IPython Slideshow Extension (RISE) notebook extension offers rendering as a Reveal.js-based slideshow, where you can execute code or show to the audience whatever you can show/do inside the notebook itself!

Dealing with external data

A goal for scientific publishing is automated reproducibility of analyses, which the Jupyter notebook excels at. But, more than that, it should be possible to efficiently reproduce the analysis with different data sets. This entails having one point of access to a data set within the notebook, rather than having copy-pasted data into variables, i.e. this:

data = read_in_data('data_key')
variable1 = data.key1
variable2 = data.key2

rather than this:

variable1 = 12345
variable2 = 'something'

The best-practice for accessing heirarchical data (in my opinion) is to use the JSON format (as long as the data isn’t relational), because it is:

  • applicable for any data structure
  • lightweight and easy to read and edit
  • has a simple read/write mapping to python objects (using json)
  • widely used (especially in web technologies)

A good way to store multiple bits of JSON data is in a mongoDB and accessing it via pymongo. This will also make it easy to move all the data to a cloud server at a later time, if required.

conda install pymongo

But, if the data is coming from files output from different simulation or experimental code, where the user has no control of the output format. Then writing JSON parsers may be the way to go, and this is where jsonextended comes in, which implements:

  • a lightweight plugin system to define bespoke classes for parsing different file extensions and data types.
  • a ‘lazy loader’ for treating an entire directory structure as a nested dictionary.

For example:

from jsonextended import plugins, edict
data = edict.LazyLoad('path/to/data')
variable1 = data.folder1.file1_json.key1
variable2 = data[['folder1','file1.json','key2']]
variable3 = data[['folder1','file2.csv','key1']]
variable4 = data[['folder2','subfolder1','file3.other','key1']]

If you are dealing with numerical data arrays which are to large to be loaded directly in to memory, then the h5py interface to the HDF5 binary data format, allows for the manipultion of even multi-terabyte datasets stored on disk, as if they were real NumPy arrays. These files are also supported by jsonextended lazy loading.


I also use the Firefox Split Pannel extension to view the {name}_viewpdf.html page and monitor changes to the pdf.

bookbook is another package with some conversion capabilities.


I took strong influence from:

File Type Py Version Uploaded on Size
ipypublish-0.3.1-py3-none-any.whl (md5) Python Wheel py3 2017-07-17 64KB