pspipe
pspipe is the data-analysis pipeline used by the LOFAR Epoch of Reionization
and NenuFAR Cosmic Dawn projects. It turns calibrated low-frequency radio
MeasurementSets into images, weighted Kelvin visibility cubes, power spectra,
and foreground-separated products. It tracks observations and processing
revisions so that large, multi-night analyses remain reproducible.
It provides two commands:
psdbcreates configurations and manages observations and processing revisions;pspiperuns processing tasks on observations registered in that database.
The pipeline uses WSClean for imaging and
ps_eor for cubes, flagging, power spectra,
and ML-GPR. Optional MeasurementSet-processing tasks also call
DP3 and
NenuCal.
Documentation
The pspipe documentation follows the complete workflow from installation and calibrated MeasurementSets to images, visibility cubes, power spectra, multi-night combinations, and foreground removal. New users should begin with the getting-started page and then follow the user guide.
The documentation also includes the full configuration, command-line, and Python API references.
Installation
pspipe requires Python 3.10 or newer:
pip install pspipe
For ML-GPR:
pip install "pspipe[ml-gpr]"
Minimal workflow
After installation, create a workspace, register an observation, make its images, and convert them to visibility cubes:
mkdir pspipe-analysis && cd pspipe-analysis
psdb init nenufar /path/to/analysis-data first_run
psdb add_obs first_run.toml NIGHT_001 -m /path/to/ms_lists/NIGHT_001
psdb list_all_obs first_run.toml
pspipe image first_run.toml NIGHT_001
psdb list_rev_img first_run.toml -o NIGHT_001
pspipe gen_vis_cube first_run.toml NIGHT_001
Review the generated configuration before running: in particular the MeasurementSet data column, image and cube UV ranges, field of view, frequency bins, window, and primary beam. Inspect the FITS images and visibility cubes between stages; the separate commands above make those validation gates explicit. Once a setup is established, tasks can be chained with commas. The user guide continues from this point by loading the visibility data and producing power spectra from Python.
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