Convert IPYNB to TEX

Why Convert IPYNB to TEX?

Converting IPYNB to TEX is essential for researchers who develop their analyses in Jupyter Notebooks but need to publish results in academic journals that require LaTeX submissions. This conversion bridges the gap between exploratory data science workflows and formal academic publishing by transforming interactive notebooks into professionally typeset LaTeX documents.

Jupyter Notebooks already support LaTeX math expressions in markdown cells, and these are directly preserved during conversion. Code cells are wrapped in verbatim or listings environments for proper display, while markdown content is translated to LaTeX sections, paragraphs, and formatting commands. The result is a complete .tex file ready for compilation.

This workflow is particularly popular in machine learning research. Researchers prototype experiments in Jupyter, then convert their notebooks to LaTeX for conference submissions to venues like NeurIPS, ICML, or ACL. The conversion preserves the analytical narrative while producing publication-quality formatting.

Key Benefits of Converting IPYNB to TEX:

• Academic Publishing: Produce journal-ready LaTeX from research notebooks
• Math Preservation: LaTeX expressions in markdown cells transfer directly
• Code Listings: Source code appears in properly formatted listing environments
• Professional Typography: Benefit from LaTeX's superior typesetting engine
• Bibliography Support: Add BibTeX references to the converted document
• Cross-References: Use LaTeX labels and refs for figures, tables, and sections
• Overleaf Compatible: Upload directly to Overleaf for collaborative editing

Practical Examples

Example 1: Research Paper to TeX

Input IPYNB file (notebook.ipynb):

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": ["# Neural Network Classification\n", "## Abstract\n", "We present a deep learning approach to image classification\n", "achieving 97.3% accuracy on the benchmark dataset."]
    },
    {
      "cell_type": "code",
      "source": ["import torch\n", "import torch.nn as nn\n", "\n", "model = nn.Sequential(\n", "    nn.Linear(784, 256),\n", "    nn.ReLU(),\n", "    nn.Linear(256, 10)\n", ")"]
    }
  ]
}

Output TEX file (notebook.tex):

\documentclass{article}
\usepackage[utf8]{inputenc}
\usepackage{listings}
\usepackage{hyperref}

\title{notebook}
\date{\today}

\begin{document}
\maketitle

\section{Neural Network Classification}

\subsection{Abstract}
We present a deep learning approach to image classification
achieving 97.3\% accuracy on the benchmark dataset.

\begin{lstlisting}[language=Python]
import torch
import torch.nn as nn

model = nn.Sequential(
    nn.Linear(784, 256),
    nn.ReLU(),
    nn.Linear(256, 10)
)
\end{lstlisting}

\end{document}

Example 2: Math Formulas to TeX

Input IPYNB file (analysis.ipynb):

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": ["## Loss Function\n", "The cross-entropy loss is defined as:\n", "\n", "$$L = -\\sum_{i} y_i \\log(\\hat{y}_i)$$"]
    },
    {
      "cell_type": "code",
      "source": ["def cross_entropy(y_true, y_pred):\n", "    return -np.sum(y_true * np.log(y_pred))"]
    }
  ]
}

Output TEX file (analysis.tex):

\documentclass{article}
\usepackage[utf8]{inputenc}
\usepackage{amsmath}
\usepackage{listings}

\title{analysis}
\date{\today}

\begin{document}
\maketitle

\subsection{Loss Function}
The cross-entropy loss is defined as:

$$L = -\sum_{i} y_i \log(\hat{y}_i)$$

\begin{lstlisting}[language=Python]
def cross_entropy(y_true, y_pred):
    return -np.sum(y_true * np.log(y_pred))
\end{lstlisting}

\end{document}

Example 3: Conference Paper Preparation

Input IPYNB file (research.ipynb):

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": ["# Reinforcement Learning for Robotics\n", "## Introduction\n", "This paper explores Q-learning applications in robotic control."]
    },
    {
      "cell_type": "code",
      "source": ["# Q-learning update rule\n", "Q[state, action] = Q[state, action] + alpha * (\n", "    reward + gamma * np.max(Q[next_state]) - Q[state, action]\n", ")"]
    },
    {
      "cell_type": "markdown",
      "source": ["## Results\n", "The agent converged after 500 episodes with a\n", "mean reward of $R = 245.8 \\pm 12.3$."]
    }
  ]
}

Output TEX file (research.tex):

\documentclass{article}
\usepackage[utf8]{inputenc}
\usepackage{amsmath}
\usepackage{listings}

\title{research}
\date{\today}

\begin{document}
\maketitle

\section{Reinforcement Learning for Robotics}

\subsection{Introduction}
This paper explores Q-learning applications in robotic control.

\begin{lstlisting}[language=Python]
# Q-learning update rule
Q[state, action] = Q[state, action] + alpha * (
    reward + gamma * np.max(Q[next_state]) - Q[state, action]
)
\end{lstlisting}

\subsection{Results}
The agent converged after 500 episodes with a
mean reward of $R = 245.8 \pm 12.3$.

\end{document}

Frequently Asked Questions (FAQ)

Q: Are LaTeX math expressions from notebook markdown cells preserved?

A: Yes, LaTeX mathematical expressions written in notebook markdown cells (both inline $ and display $$ notation) are directly preserved in the TEX output. They will compile correctly with any standard LaTeX distribution.

Q: How are code cells represented in the LaTeX document?

A: Code cells are wrapped in verbatim or lstlisting environments that preserve indentation, syntax, and formatting. You can customize the code display by modifying the listings package settings in the document preamble.

Q: Can I compile the output TEX file directly?

A: Yes, the generated TEX file includes a complete document preamble with necessary packages and can be compiled with pdflatex, xelatex, or lualatex. You may need to install additional LaTeX packages if they are not already available in your TeX distribution.

Q: Can I upload the output to Overleaf?

A: Absolutely. The generated .tex file can be uploaded directly to Overleaf for collaborative editing and online compilation. Overleaf includes all standard LaTeX packages needed to compile the document.

Q: How are markdown headings mapped to LaTeX sections?

A: Markdown heading levels are mapped to corresponding LaTeX sectioning commands: # becomes \section, ## becomes \subsection, ### becomes \subsubsection, and so on. This preserves the document hierarchy from the notebook.

Q: Are notebook images included in the TEX output?

A: Embedded images in notebook outputs are not included in the text-based TEX file. However, you can manually add \includegraphics commands to reference image files exported separately from the notebook.

Q: Is this similar to Jupyter's built-in nbconvert to LaTeX?

A: The conversion produces similar output to nbconvert's LaTeX export. Our online converter provides a convenient way to perform this conversion without installing Jupyter or nbconvert locally, directly from your browser.

Q: Can I add a bibliography to the converted document?

A: Yes, after conversion you can add \bibliography and \cite commands to the TEX source. Create a .bib file with your references and compile with bibtex or biblatex to generate a complete academic document with citations.