Ammer_et_al_2023

Ammer_et_al_2023

Data and code for 'Multi-level visual motion opponency in Drosophila, Nature Neuroscience, 2023'

This repository contains all data and code needed to reproduce the Main and Extended Data Figures of the publication and is grouped in folders that correspond to the Figures. Numerical data are generally provided in numpy-format and accompanied by analysis code provided in ipynb (Jupyter Notebook) files that allow reproduction of the manuscript's Figures.

Whereas most analysis code is written in Python 2.7.15, some scripts are written in Python 3.8.8. Note that all analysis codes necessitate the importation of open source Python libraries:

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os
import scipy.io
from scipy import stats

In addition, we provide two custom-written libraries in the dataset that are needed for data analysis and modelling that need to be imported in the respective notebooks:

import octopus as oct
import blindschleiche as bs

Python versions and libraries needed for executing the script are listed in the first cell of every Jupyter notebook.

Additionally, some Figures contain Excel files or Image files in png format. Supplementary Videos are provided as mp4 files.