Codes/Notebooks to analyze Fiber Photometry data
See also related package for analysing event data from TDT and MedAssociate systems: med-associates-utils
This package allows you to load data produced by TDT Synapse software, and eases working with the resulting data. Also includes a growing library of analysis routines.
We recommend you use anaconda virtual environments. Two main ways to install. The first, create the virtual environment, and install this package directly (more useful on "production" systems). The second, clone the repository, and then pass to anaconda the environment.yaml file during environment creation (more useful for development).
conda create -n fptools python=3.12
pip install git+https://github.com/tischfieldlab/fptools.git
OR
git clone https://github.com/tischfieldlab/fptools.git
cd fptools
conda env create -f environment.yml
Please checkout the jupyter notebooks available in the notebooks directory.
Please see the notebook 01_Data_Loading.ipynb for a full example of data loading and manipulation basics.
To load data from the tanks produced by the TDT software, use the function load_data() in the fptools.io module. load_data() takes a parameter tank_path which is the directory to search recursively for blocks.
> from fptools.io import load_data
> sessions = load_data(r'C:\path\to\data\tank')
100%|██████████| 30/30 [00:00<00:00, 2068.67it/s]
This will return to you a SessionCollection object, which in many ways behaves like a python list, but with some extra functionality.
We can ask the SessionCollection to describe itself, which will tell us the number of sessions, as well their contents.
> sessions.describe()
Number of sessions: 36
Signals present in data with counts:
(36) "Dopamine"
(36) "Isosbestic"
Epocs present in data with counts:
(36) "Cam1"
(36) "P1SC"
(36) "UnNP"
(36) "URM_"
(36) "Nose"
(36) "Tick"
(36) "RNP"
(36) "RMG"
Assuming we added a genotype to each session metadata, we could filter to only WT sessions via the following code:
wt_sessions = sessions.filter(lambda s: s.metadata['genotype'] == 'WT')