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Python-based Analysis of Fruit fly decision-making data for associative learning under different reward stimulations (Turner Lab, HHMI, 2023)

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neurorishika/FlyOptoStimAnalysis

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FlyOptoStimAnalysis

Author: Rishika Mohanta

Latest Build Date: 2023-07-21 22:37:40

About the Project

Project description is being updated. Please check back later.

Instructions

This is a Poetry-enabled python project. Poetry installs a virtual environment in the project directory and all packages are installed in this virtual environment. This means that you do not need to install any packages in your system. The virtual environment is automatically activated when you run the project through Poetry.

If you use VS Code, you can set the Python interpreter to the Poetry virtual environment .venv in the project directory for script execution and debugging and use the Poetry virtual environment .venv for the Jupyter kernel.

First, you need to setup a git alias for tree generation by running the following command on the terminal:

git config --global alias.tree '! git ls-tree --full-name --name-only -t -r HEAD | sed -e "s/[^-][^\/]*\//   |/g" -e "s/|\([^ ]\)/|-- \1/"'

To run the project, make sure you have Poetry installed and run the following commands in the project directory:

poetry run python utils/update.py
poetry run python utils/build.py

To run the Jupyter notebook, run the following command in the project directory:

poetry run jupyter notebook

Project Organization

The project is organized as follows:

.gitignore
LICENSE
README.md
analysis
   |-- .gitkeep
   |-- pulse-learning-analysis.ipynb
flyoptostim
   |-- __init__.py
   |-- rdp_client.py
poetry.lock
poetry.toml
processed_data
   |-- .gitkeep
   |-- pulStr_20-07-2023
   |   |-- bootstrap_optimization_results_100.pkl
   |   |-- bootstrap_optimization_results_20.pkl
   |   |-- bootstrap_optimization_results_200.pkl
   |   |-- bootstrap_optimization_results_50.pkl
   |   |-- bootstrap_optimization_results_500.pkl
   |   |-- global_optimization_results.pkl
   |-- ql_fit_boot.pkl
   |-- ql_fit_go.pkl
pyproject.toml
scripts
   |-- .gitkeep
tests
   |-- __init__.py
utils
   |-- build.py
   |-- quickstart.py
   |-- update.py

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Python-based Analysis of Fruit fly decision-making data for associative learning under different reward stimulations (Turner Lab, HHMI, 2023)

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