treat2p 0.1.8

Treatment pipeline for suite2p extracted fluorescent traces over time.

Treat2p

Made to be used with suite2p, to extend it's capabilities with treatment ones, on top of the nice roi signal extraction features of suite2p.

Made after the matlab developments of Pierre Pierre Marie Gardères

Getting started

To install this package, you simply need to hop into your own virtual environment and run :

pip install treat2p

or

uv add treat2p

How to use :

In short, to treat some plane/channel data that suite2p extracted already:

import treat2p
suite2p_path = r"C:\Users\yourname\Desktop\suite2p"
outputs, stats, ops = treat2p.run_treat2p(suite2p_path, plane = 0, chan = 1)

run_treat2p takes all the arguments that goes into the various stages of the pipeline, registers, them, and stores them in the ops.npy file under the treat2p key.

The stages of treat2p pipeline are (for each roi):

• neuropil factor estimation (and correction)
• slow trend estimation (and correction)
• deltaF/F0 normalization
• "mean centered" normalization

For a bit more explanations, please see the jupyter notebook tutorial here.

Getting started

Made to be used with suite2p, to extend it's capabilities with treatment ones, on top of the nice roi signal extraction features of suite2p.

Made following the matlab developments of Pierre Marie Gardères

Installation

To install this package, you simply need to hop into your own virtual environment and run :

=== "pip" .sh pip install treat2p === "uv" .sh uv add treat2p

How to use

In short, to treat some plane/channel data that suite2p extracted already:

import treat2p
suite2p_path = r"C:\Users\yourname\Desktop\suite2p"
outputs, stats, ops = treat2p.run_treat2p(suite2p_path, plane = 0, chan = 1)

run_treat2p Takes all the arguments that goes into the various stages of the pipeline, registers, them, and stores them in the ops.npy file under the treat2p key.

The stages of treat2p pipeline are (for each roi):

• neuropil factor estimation (and correction)
• slow trend estimation (and correction)
  • deltaF/F0 normalization normalisation or
  • "mean centered" normalization

For a bit more explanations, please see the jupyter notebook tutorial here.