Getting started

MeasureIA measures intrinsic-alignment correlation functions — the projected correlations \(w_{gg}\) and \(w_{g+}\) and their multipoles \(\tilde\xi_{gg,0}\), \(\tilde\xi_{g+,2}\) — together with their jackknife covariance. It works on two kinds of data:

	Class	Data	Randoms	Shapes	Jackknife
Box	MeasureIABox	Cartesian positions in a periodic box	analytic	axis direction + axis ratio q	sub-boxes (\(x^3\))
Lightcone	MeasureIALightcone	sky coordinates (RA, DEC, redshift)	explicit random catalogue	ellipticity/shear e1, e2	sky patches (k-means)

Use MeasureIABox for periodic hydrodynamic simulation snapshots, and MeasureIALightcone for lightcone / survey-like data where you have a random catalogue and shear-style shape measurements.

Install

See Installation. In short (Python 3.10–3.14):

pip install measureia

A first measurement

Box:

from measureia import MeasureIABox
import numpy as np

data = {
    "Position": np.array([]), "Position_shape_sample": np.array([]),
    "Axis_Direction": np.array([]), "q": np.array([]), "LOS": 2,
}
mi = MeasureIABox(data, output_file_name="./out.hdf5", boxsize=205.0)
mi.measure_xi_w(dataset_name="ds1", corr_type="both", num_jk=27, temp_file_path="./")

Lightcone:

from measureia import MeasureIALightcone
import numpy as np

data = {
    "RA": np.array([]), "DEC": np.array([]), "Redshift": np.array([]),
    "RA_shape_sample": np.array([]), "DEC_shape_sample": np.array([]),
    "Redshift_shape_sample": np.array([]), "e1": np.array([]), "e2": np.array([]),
}
randoms_data = {"RA": np.array([]), "DEC": np.array([]), "Redshift": np.array([])}
mi = MeasureIALightcone(data, randoms_data, output_file_name="./out.hdf5")
mi.measure_xi_w("galaxies", dataset_name="ds1", corr_type="both", num_jk=27, temp_file_path="./")

Where to go next

• Input — the full data dictionaries for the box and the lightcone.
• Usage — worked examples, including multipoles and multiprocessing.
• Conventions — the shape/sign conventions (especially the e1/e2 convention).
• Estimator definitions — the mathematics of the estimators.
• Output structure — how results are stored in the HDF5 output file.
• Per-galaxy contributions — resolving the box signal per shape galaxy, the input for regressing alignment on galaxy properties.