Quickstart

Get the KPF-DRP installed and run a first reduction.

Installation

The pipeline runs in a dedicated conda environment (kpfpipe, Python 3.14). Clone the repository, create the environment, and install the package in editable mode:

git clone https://github.com/Keck-DataReductionPipelines/KPF-Pipeline.git
cd KPF-Pipeline
conda env create -f environment.yml
conda activate kpfpipe
pip install -e .

Running the DRP

The kpfpipe command has two main entry points: masters builds nightly calibration products (bias, dark, flat, wavelength solution), and science reduces science exposures into RVs. Both take an input data directory, an output directory, and the units to process.

Build the master calibrations for a night (identified by its YYYYMMDD datecode):

kpfpipe masters \
    --input_dir /path/to/data \
    --output_dir /path/to/output \
    --dates 20240405

Reduce one or more science frames (identified by their obs_ids):

kpfpipe science \
    --input_dir /path/to/data \
    --output_dir /path/to/output \
    --obs_ids KP.20240405.40113.57