- 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
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 .
pip install ipykernel
python -m ipykernel install --user --name=your_env_name
/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.
Run this code in Jupyter.
- dataset requirements, preferably stored in the same directory
- csv files exported from DLC.
- pickle files exported from DLC.
- avi files recorded in your NOR experiment.
-
Set the directory path
HOMEDIR
that stores above datasets. -
Set the ROI to determine the coordinate of the center of each object
e.g.)
zone1: right upper object
zone2: left lower object
Run this code in Jupyter.
- dataset requirements, preferably stored in the same directory
- csv files exported from DLC.
- pickle files exported from DLC.
- Set the directory path
HOMEDIR
that stores above datasets.