Bindfit datakit

Bindfit is a binding constant fitting tool designed to work with classical supramolecular titration data obtained from NMR, UV, Fluorescence and other methods.

• 🗒️ Datakit documentation
• 💻 Bindfit Python library GitHub

Usage

Initialise virtualenv and install DataStudio CLI:

python -m venv .venv
pip install datastudio-cli

# Test it works
ds

Ensure you build the Docker container first:

# Build Bindfit container
cd bindfit/container
./build.sh

# Build python-run-base if not built
# Navigate to python-run-base repository
./build.sh

You can now run the datakit:

cd bindfit-datakit

ds reset                                   # Clear any previous outputs
ds init                                    # Initialise the default run
ds load data ./data/nmr11.csv              # Load input data
ds set model "nmr1to1"                     # Set fit model
ds set method "nelder-mead"                # Set fit method
ds set inputParams.k.init 314              # Set initial parameter guess
ds run                                     # Run algorithm
ds show outputParams                       # View optimised parameters
ds show summary                            # View fit summary
ds view fitGraphMatplotlib                 # View fit graph

Development

Requirements

• Docker
• Python (to run CLI)
• opendatastudio/cli
• opendatastudio/containers

Set up pre-commit hooks

Set up included Flake (flake.nix) with direnv (.envrc) to automatically load development environment.

Install/run pre-commit hooks:

pre-commit install
pre-commit run --all-files

Build execution container

Build base execution container:

# Navigate to python-run-base repository
./build.sh

Build bindfit-datakit container:

# Navigate back to bindfit-datakit repository
cd containers
./build.sh

Install CLI

python -m venv .venv
source .venv/bin/activate
pip install -e /PATH/TO/CLI

ds  # Check CLI is installed

See Usage for command reference.