A package for analyzing the data from DeepLabCut

how to use this script

Install python 3.9 using miniconda only for install python and pip

conda create -n -y your_env_name python=3.9 
conda activate your_env_name

create a venv in the directory your want
It will create a .venv folder in your enviornment
https://code.visualstudio.com/docs/python/python-tutorial
```
conda activate your_env_name
python3 -m venv .venv
```
Deactivate the conda environment
Then activate the python venv enviroment

For windows user

Activate .venv in command prompt

(.venv) C:Users\yourname\....\directory_you_want>  ./.venv/Scripts/activate

Clone this repository using git bash

git clone https://github.com/lycantrope/deeplabcut-analysis.git && cd deeplabcut-analysis

Install all necessary packages

python -m pip install .

For Linux/MacOs user

source ./.venv/bin/activate
git clone https://github.com/lycantrope/deeplabcut-analysis.git && cd deeplabcut-analysis
python -m pip install .

Add a kernel to jupyter lab

pip install ipykernel
python -m ipykernel install --user --name=your_env_name

Add other kernel to current kernel (Both environment need ipykernel installed)

/path/to/kernel/env/bin/python -m ipykernel install --prefix=/path/to/jupyter/env --name 'python-my-env'

pyDLCbehavior package provides NovelObjectRecognitionAnalysis and YMazeAnalysis class objects, which can analyze the NOR test and Y-maze test.
These class objects have the result dataframe obtained from every experimental file, so the results can be summarized in Jupyter lab or Jupyter notebook using the following ipynb files.

novel_object_analysis.ipynb

Run this code in Jupyter.

[x] dataset requirements, preferably stored in the same directory
1. csv files exported from DLC.
2. pickle files exported from DLC.
3. avi files recorded in your NOR experiment.
[x] Set the directory path HOMEDIR that stores above datasets.
[x] Set the ROI to determine the coordinate of the center of each object
e.g.)
zone1: right upper object
zone2: left lower object

YMaze_analysis.ipynb