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dict2tex - Converting parameter dictionaries to LaTeX code

Purpose

Computational science often deals with mathematical models specified by large sets of parameters. In simulation research, it is common to store information on these parameters in dictionaries mapping parameter names to parameter values, occasionally supplemented by information on physical units and other properties. When documenting models in scientific manuscripts, information on model parameters is typically copied and pasted into the manuscript source code -- a process which is highly error prone and inefficient, in particular, when adjusting parameter values during manuscript production or in the review process. To improve the efficiency, correctness and reproducibility of computational science, it is therefore desirable to base both

  • model documentation, and
  • model implementation

on identical sources. This is an easy task, provided information on model parameters is stored in dictionaries such as

{
    "N": {
        "name": "$N$",
        "value": 10000,
        "unit": "",
        "docstring": "network size",
        "section": "network"
    },
    "dt": {
        "name": "$\\Delta{}t$",
        "value": 0.125,
        "unit": "ms",
        "docstring": "simulation time resolution",
        "section": "simulation"
    },
}

Here, each parameter (such as N or dt) is not only connected to a value and a physical unit, but also to a descriptor explaining the meaning of the parameter (docstring), LaTeX code for typesetting parameters in LaTeX (name), as well as a specification of the parameter type (section).

dict2tex.py contains a set of python functions that help to automate the conversion of such parameter dictionaries into LaTeX code used for preparing manuscripts. Two typical use cases are automatically generated parameter tables (example), and automatically generated files containing LaTeX macros, such as

macros.tex:
...
\def\PN{\ensuremath{N} }           %% network size
...
\def\Pdt{\ensuremath{\Delta{}t} }  %% simulation time resolution
...

Installation

  1. Clone this repository:

    git clone https://github.com/tomtetzlaff/dict2tex.git

    or

    git clone [email protected]:tomtetzlaff/dict2tex.git

  2. Enter the source directory:

    cd dict2tex

  3. Install the python module:

    pip install .

    or

    python setup.py install

Usage

After the installation, dict2tex can be imported in python using

import dict2tex

The two main functions are

  • dict2tex.tex_macros(): Creates LaTeX code for parameter macro definitions.
  • dict2tex.tex_table(): Creates LaTeX code for a parameter table.

See the python documentation of these functions (help()) for a detailed description.

The contents and style of the parameter tables and LaTeX macros are configurable. See example/example.py and example/config.yml.

Example

The example in the example folder demonstrates how to generate customized LaTeX macros and parameter tables from a toy parameter set. The full example can be run by executing

cd example
make

which produces the pdf file example.pdf.

File description

Sources

  • create_params_file.py: Creates a mock-up parameter set and stores it in params.json.
  • example.py: Loads params.json and converts it to LaTeX code for parameter tables and macro definitions.
  • example.tex: LaTeX main manuscript importing macros.tex, macros_table.tex, and parameter_table.tex.
  • config.yml: Configuration file used to specify table contents and style, macro names, and file names.

Targets

  • example.pdf: Final pdf document containing examples of parameter and macro tables, as well as text based on automatically generated macro definitions.
  • parameter_table.tex: LaTeX file containing a parameter table. Included in example.tex.
  • macros.tex: LaTeX file containing macro definitions. Included in example.tex.
  • macros_table.tex: LaTeX file containing a table showing macro definitions. Included in example.tex.

Requirements

The code and the example have been tested with python 3.9, and depend only on basic python packages such as json, numpy, and yaml.