# paper-behavior
This repository contains code to reproduce all figures of the behavior paper by the International Brain Laboratory. If reusing any part of this code please cite the [bioRxiv paper](https://www.biorxiv.org/content/10.1101/2020.01.17.909838v2) in which these figures appear. 

### Installation
These instructions require anaconda (https://www.anaconda.com/distribution/#download-section) for Python 3 and git (https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)

In an Anaconda prompt window:
1. Clone or download the paper-bahavior repository
2. Install the other dependencies by running `pip install -r requirements.txt`

To call the functions in this repo, either run python from within the `paper-bahavior` folder or
add the folder to your python path: 
```python
import sys
sys.path.extend([r'path/to/paper-behavior'])
```

### Obtain a DataJoint account through IBL JupyterHub
[IBL Jupyterhub](https://jupyterhub.internationalbrainlab.org) provides an online environment to explore the IBL behavior data pipeline.

1. Use your GitHub account to log in and go to the resource folder. 
2. Navigate to public_notebooks/Explore IBL pipeline.  The Notebook `04-Access the database locally
` provides the instruction to obtain the
 credentials to access the database. Copy the value of `dj.config`
3. In your local python IDE, do the following:
  a. `import datajoint as dj`
  b. set your local config variable `dj.config` with the values copied from JupyterHub
  c. `dj.config.save_local()`

You'll be able to run the code after the settings above.

### How to run the code
All the scripts start with the name of the figure they produce. The figure panels will appear in the `exported_figs` subfolder.

### Load figures without DataJoint
To load the figures from data saved in local CSV files, edit line 21 of
 `paper_behavior_functions.py` so that `QUERY = False`.

### Questions?
If you have any problems running this code, please open an issue or get in touch with the code's authors (written at the top of each script).

### Known issues
The data used in this paper have a number of issues.  The authors are confident that these issues
do not affect the results of the paper but nevertheless users should be aware of these
shortcomings.
  
1. NaN values may be found throughout the data.  These resulted from failures to either produce
 an event (for example the go cue tone wasn't played) or to record an event (e.g. the stimulus was
 produced but the photodiode failed to detect it).
2. Some events violated the task structure outlined in the paper.  For example during some sessions
 the go cue tone happened much later than the stimulus onset.  Although this conceivably
 affected the reaction time on some trials, it did not occur frequently enough to
 significantly affect the median reaction times.