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This repository contains code to reproduce figures from the IBL reproducible ephys paper

Installation

Making a new python environment (optional)

Install Anaconda and git, and follow their installer instructions to add each to the system path

Create new python environment

conda create --name ibl_repro_ephys python=3.9

Activate environment

conda activate ibl_repro_ephys

Downloading and Installing repo

Clone the repo

git clone https://github.com/int-brain-lab/paper-reproducible-ephys.git

Navigate to repo

cd paper-reproducible-ephys

Install requirements and repo

pip install -e .

Configuration

Setting up ONE credentials

Open an ipython terminal

from one.api import ONE
pw = 'international'
one = ONE(silent=True, password=pw)

Setting up saving scripts

By default data and figures will be saved into a folder with the figure name e.g fig_hist. To find this location on you computer (for example for figure 1) you can type the

from reproducible_ephys_functions import save_data_path, save_figure_path
print(save_data_path(figure='fig_hist'))
print(save_figure_path(figure='fig_hist'))

If you want to override the location where the data and figures are saved you can create a script in the repo directory, that is called reproducible_ephys_paths.py and add the following:

FIG_PATH = '/path/where/to/save/your/figures/'

DATA_PATH = '/path/where/to/save/your/data/

Getting Started

Reproducing the figures

In each figure subfolder there is a README that contains instructions for how to replicate the analysis and generate the figures in the paper.

The subfolders correspond to the following figures

  • Figure 1 - fig_intro, fig_data_quality
  • Figure 2 - fig_hist
  • Figure 3 - fig_ephysfeatures
  • Figure 4 - fig_taskmodulation
  • Figure 5 - fig_PCA
  • Figure 6 - fig_spatial
  • Figure 7 - fig_encodingRRR
  • Figure 8 - fig_mtnn
  • Figure 9 - fig_decoding

Finding the insertions used for analysis

The list of insertions probe insertions considered for analysis in this version of the paper can be found in the following way

from one.api import ONE
from reproducible_ephys_functions import get_insertions

one = ONE()
insertions = get_insertions(level=0, one=one, freeze='freeze_2024_03')

More detail about insertions used for each figure

A detailed overview of the criteria and insertions that have been used for each figure can be found in this spreadsheet

Running RIGOR metrics on your data

To run the RIGOR metrics on your own data please refer to this notebook