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

GuPPy can be run on Windows, Mac or Linux.

Follow the instructions below to install GuPPy :

• Current Users : Download new code updates by following steps 1.a to 1.c, then visit the Github Wiki page to get started on your analysis
• New Users : Follow all the installation steps and then visit the Github Wiki page to get started on your analysis

1. Download the Guppy code
   a. Press the green button labeled “Code” on the top right corner and that will initiate a pull down menu.
   

   b. Click on Download ZIP. (Ensure that you save this ZIP locally, not in any external cloud storage such as iCloud, OneDrive, Box, etc. We suggest saving it in your User folder on the C drive)
   

   c. Once downloaded, open the ZIP file and you should have a folder named “GuPPy-main”. Place this GuPPy-main folder wherever is most convenient (avoiding cloud storage).
   

   d. Inside the GuPPy-main folder there is a subfolder named “GuPPy”. Take note of the GuPPy subfolder location or path. It will be important for future steps in the GuPPy workflow
   

   • Mac: Right click folder → Click Get Info → Text next to “Where:”
     ~ Ex: /Users/LernerLab/Desktop/GuPPy-main
     
   • Windows/Linux: Right click folder → Properties → Text next to “Location:”
     

2. Anaconda is a distribution of the Python and R programming languages for scientific computing. Install Anaconda. Install Anaconda based on your operating system (Mac, Windows or Linux) by following the prompts when you run the downloaded installation file.

3. Once installed, open an Anaconda Prompt window (for windows) or Terminal window (for Mac or Linux). You can search for "anaconda prompt" or "terminal" on your computer to open this window.

4. Find the location where GuPPy folder is located (from Step 1d) and execute the following command on the Anaconda Prompt or terminal window:

cd path_to_GuPPy_folder

• Ex: cd /Users/LernerLab/Desktop/GuPPy-main

5. Next, execute the following commands, in this specific order, on Anaconda Prompt or terminal window:
   
   • Note : filename in the first command should be replaced by spec_file_windows10.txt or spec_file_mac.txt or spec_file_linux.txt (based on your OS)
     
   • Some of these commands will initiate various transactions. Wait until they are all done before executing the next line
     
   • If the Anaconda Prompt or Terminal window asks: Proceed ([y]/n)? Respond with y
     

conda create --name guppy --file filename
conda activate guppy
conda install -c anaconda ipykernel
python -m ipykernel install --user --name=guppy

6. Open the GuPPy-main folder and click into the GuPPy subfolder. In this subfolder, there will be a file named savingInputParameters.ipynb Identify the path/location of this file as similarly described in Step 1d.

7. On the terminal window or Anaconda prompt window, use the savingInputParameters.ipynb path/location to execute the following command:

cd path_to_file

• Ex: cd /Users/LernerLab/Desktop/GuPPy-main/GuPPy

8. Lastly, execute the following command to open the GuPPy User Interface:

panel serve --show savingInputParameters.ipynb

GuPPy is now officially downloaded and ready to use!

• 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). It also has sample data recorded with a Neurophotometrics system. 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.

• GuPPy was initially developed keeping our data (FP data recorded using TDT systems) in mind. GuPPy now supports data collected using Neurophotometrics 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.

• Venus Sherathiya
• Michael Schaid
• Jillian Seiler
• Gabriela Lopez
• Talia Lerner