# Maze Analysis Repository

MM 5/2/2021

This repo accompanies our preprint of 1/15/2021

**Matthew Rosenberg, Tony Zhang, Pietro Perona, Markus Meister (2021) Mice in a labyrinth: Rapid learning, sudden insight, and efficient exploration (https://doi.org/10.1101/2021.01.14.426746)**

It contains all the data and code needed to reproduce the published analysis, as well as subsequent changes and additions made in response to peer review. 

## Contents of the repo
`Maze_Analysis_3A`,...,`Maze_Analysis_3D`. These four jupyter notebooks cover the published preprint. They gradually develop the various topics of analysis, starting from raw data, producing figure panels and numerical results for the article along the way. They contain a good number of comments and mathematical sections to guide the user.

`Maze_Analysis_3E` covers changes and additions made in response to peer review. Some of this material appears in the revised article, see `Revision.pdf`.

`code/`: Contains python files with routines accessed from multiple notebooks.

`outdata/`: A place for data files, both input and output. 

`outdata - tf files only/`: Just the raw data, the starting point for all analysis.

`figs/`: A place for PDF files that make up the figure panels in the article.

`apparatus/`: Instructions and files for building the maze used in the article.

## How to reproduce all the analysis starting from raw data

0. Read our paper.The version of Jan 2021 is included in the repo. Then read at least the start of `Maze_Analysis_3A`.  
1. Empty the `outdata/` directory. Fill it with the contents of `outdata - tf files only/`. Now you're starting with the raw trajectories of animals in the maze.
2. Empty the `figs/` directory.
3. Run the notebooks `Maze_Analysis_3A`,...,`Maze_Analysis_3D` in alphabetical sequence.
4. Now the `figs/` directory should contain all the figure panels plus a few extras. 

## How to find code for a specific figure panel
- The names of all the figure panels (as numbered in the preprint of Jan 2021) appear as level-3 headings in the notebooks. Look through these to find your figure of interest. Or...
- In the `figs/` directory find the name of the PDF file of interest, and search for that name in the notebooks.

## How to view the raw videos
You can find these on Youtube:

- [Rewarded animals](https://www.youtube.com/playlist?list=PLm5UsX091_2X0ph_ldO3_lC9KFxqYpqo5)
- [Unrewarded animals](https://www.youtube.com/playlist?list=PLm5UsX091_2VTPPMrEEkTsFT8xbFdNi9I)