This directory bundles the shape catalogs, photo-z inputs, spectroscopic calibration data, end-to-end simulations, and the example notebooks used to reproduce the DP1 weak-lensing / cluster-lensing analyses. The sub-directories below are organised by data type rather than by analysis step.
| Directory | Contents |
|---|---|
| catalogs/ | AnaCal shape catalogs on real DP1 data (FPFS moments + derivatives, multi-band fluxes, selection weights), one FITS per field, plus the spec-matched golden sample. |
| catalogs_sim/ |
Simulated AnaCal catalogs (same schema as
catalogs/) used to validate the shape-measurement
pipeline and to cross-check 2-D distributions against the real
data (cf. notebook 1.3).
|
| rail_data/ |
Photo-z inputs / outputs in tables_io /
RAIL format: per-source feature tables, cached point estimates
and per-source PDFs for ECDFS, EDFS, and Abell 360, plus the
training / test split and the helper scripts that built them.
|
| specz/ | Spectroscopic redshifts used to calibrate / validate photo-z: the ECDFS spec-z compilation plus a small helper that sky-matches it to the AnaCal catalog and applies the golden-sample selection (mag + trace + confidence cuts). |
| notebooks/ | Example notebooks demonstrating end-to-end analyses built on the directories above. See the dedicated notebook index for the full list and rendered HTML output. |
The notebooks/ directory holds the rendered analyses, grouped by topic:
| Notebook | Topic |
|---|---|
1_0_ecdfs_specz_sample.ipynb |
Build the ECDFS spec-z × AnaCal golden sample. |
1_1_ecdfs_photoz_6bands.ipynb |
6-band (ugrizy) photo-z evaluation on ECDFS. |
1_2_ecdfs_photoz_4bands.ipynb |
4-band (griz) photo-z evaluation on ECDFS. |
1_3_ecdfs_sim_histogram.ipynb |
2-D distribution comparison: real vs. simulated catalogs. |
2_1_edfs_photoz_estimate.ipynb |
EDFS photo-z estimation (cached source PDFs for clusters). |
2_2_edfs_erass_cluster1.ipynb |
EDFS-eRASS cluster 1: mass fit + aperture-mass S/N map. |
2_3_edfs_erass_cluster2.ipynb |
EDFS-eRASS cluster 2: shear profile + aperture-mass S/N map. |
3_1_a360_photoz_estimate.ipynb |
Abell 360 photo-z estimation. |
3_2_abell360_cluster.ipynb |
Abell 360 shear profile vs. NFW. |
The notebooks are run under a single conda environment
(dp1). To set up an equivalent environment from scratch:
conda create -n dp1 -c conda-forge --override-channels python=3.13
conda activate dp1
# Core scientific stack
conda install -c conda-forge \
numpy scipy matplotlib astropy emcee \
pandas pyarrow
# I/O helpers
conda install -c conda-forge \
fitsio tables_io "qp-prob>=1.0.1" healsparse
# Cluster lensing
conda install -c conda-forge clmm pyccl
# Photo-z (RAIL stack — note the pz-rail-* prefix on conda-forge)
conda install -c conda-forge \
pz-rail-base pz-rail-bpz pz-rail-flexzboost
# Jupyter / rendering
conda install -c conda-forge \
jupyter notebook nbconvert ipykernel
python -m ipykernel install --user --name dp1 --display-name "Python-dp1"
See rail-hub.readthedocs.io for the full list of optional RAIL extensions and pip-only extras.
After conda is done, install xlens from source:
git clone https://github.com/mr-superonion/xlens.git
cd xlens
pip install -e .
The notebooks ship with a small importable
notebooks/utils/ package that wraps the AnaCal shape
combination, tangential-shear binning, aperture-mass map, and the
spec-z sky-match helpers (the last is a re-export of
specz/match.py). It is pure Python — no extra packages
beyond the list above are needed.
Maintained by Xiangchong Li.