guppy-neuro 2.0.0a8

Guided Photometry Analysis in Python, a free and open-source fiber photometry data analysis tool.

Installation

GuPPy can be run on Windows, Mac or Linux. It requires Python 3.10 or greater.

Step 1: Install Conda

We recommend installing GuPPy inside a conda virtual environment to avoid conflicts with other Python packages on your system.

1. Download the Miniconda installer for your operating system from the official Miniconda page.

   • Windows: Download the .exe installer and run it, following the on-screen prompts.
   • macOS: Download the .pkg installer (or the .sh script) and follow the on-screen prompts. Alternatively, run the shell script in a terminal:

     bash Miniconda3-latest-MacOSX-x86_64.sh
     

   • Linux: Download the .sh installer and run it in a terminal:

     bash Miniconda3-latest-Linux-x86_64.sh
     

2. After installation, open a new terminal (or Command Prompt / Anaconda Prompt on Windows) and verify conda is available:

   conda --version
   

Step 2: Create and Activate a Conda Environment

1. Create a new conda environment named guppy_env with Python 3.12:

   conda create -n guppy_env python=3.12
   

2. Activate the environment:

   conda activate guppy_env
   

   Your terminal prompt should now show (guppy_env) to indicate the environment is active. You will need to activate this environment each time you open a new terminal before using GuPPy.

Step 3: Install GuPPy

With the guppy_env environment active, install GuPPy using one of the two methods below.

Option A: Install via PyPI (Recommended)

Install the latest stable release directly from PyPI:

pip install guppy-neuro

Option B: Install from GitHub (Latest Development Version)

This option gives you access to the latest features and bug fixes that may not yet be in the stable release. You will need git installed (installation instructions).

1. Clone the repository:

   git clone https://github.com/LernerLab/GuPPy.git
   

2. Navigate into the cloned directory:

   cd GuPPy
   

3. Install the package in editable mode:

   pip install -e .
   

Usage

In a terminal or command prompt, you can start using GuPPy by running the following command:

guppy

This will launch the GuPPy user interface, where you can begin analyzing your fiber photometry data.

Documentation

Note: The Wiki and video tutorials below refer to GuPPy v1.3.0 and earlier. Updated documentation for v2.0 is coming soon.

Wiki

• The full instructions along with detailed descriptions of each step to run the GuPPy tool is on Github Wiki Page.

Tutorial Videos

• Installation steps
• Explaining Input Parameters GUI
• Individual Analysis steps
• Artifacts Removal
• Group Analysis steps
• Use of csv file as an input
• Use of Neurophotometrics data as an input

Sample Data

• Sample data for the user to go through the tool in the start. This folder of sample data has two types of sample data recorded with a TDT system : 1) Clean Data 2) Data with artifacts (to practice removing them) 3) Neurophotometrics data 4) Doric system data. Finally, it has a control channel, signal channel and event timestamps file in a 'csv' format to get an idea of how to structure other data in the 'csv' file format accepted by GuPPy.

Discussions

• GuPPy was initially developed keeping our data (FP data recorded using TDT systems) in mind. GuPPy now supports data collected using Neurophotometrics, Doric system and also other data types/formats using 'csv' files as input, but these are less extensively tested because of lack of sample data. If you have any issues, please get in touch on the chat room or by raising an issue, so that we can continue to improve this tool.

Citation

• If you use GuPPy for your research, please cite Venus N. Sherathiya, Michael D. Schaid, Jillian L. Seiler, Gabriela C. Lopez, and Talia N. Lerner GuPPy, a Python toolbox for the analysis of fiber photometry data

Venus N. Sherathiya, Michael D. Schaid, Jillian L. Seiler, Gabriela C. Lopez, and Talia N. Lerner GuPPy, a Python toolbox for the analysis of fiber photometry data. Sci Rep 11, 24212 (2021). https://doi.org/10.1038/s41598-021-03626-9

Contributors

• Venus Sherathiya
• Michael Schaid
• Jillian Seiler
• Gabriela Lopez
• Talia Lerner
• Paul Adkisson-Floro