MeasureIA - The tool for measuring intrinsic alignment correlation functions in hydrodynamic simulations
Try it without installing anything: the example notebooks run on a seeded mock catalogue that ships with the
package, so they need no simulation data and no clone of this repository.
MeasureIA is a tool that can be used to easily measure intrinsic alignment correlation functions and clustering.
It includes measurement of wg+, wgg and the multipole moment estimator introduced in Singh et al (2024).
Two kinds of data are supported: simulation boxes in cartesian coordinates with periodic boundary conditions
(MeasureIABox), and lightcone or survey-like data in sky coordinates with a random catalogue (MeasureIALightcone).
Furthermore, the jackknife method is used to estimate the covariance matrix.
Outputs are saved in hdf5 files.
The estimators are cross-validated against halotools, treecorr and corr_pc; see the validation page for the comparisons and how to run them yourself.
You can find the documentation site here.
MeasureIA supports Python 3.10 - 3.14 and is tested on all of them. Full details, including how to install an unreleased version, are on the installation page.
pip install measureiaThat is the whole installation: every dependency is on PyPI.
If you manage your project with uv:
uv add measureia
uv run my_script.pyUse uv pip install measureia to install into an existing environment instead of a uv project.
Released versions are on PyPI; work in progress lives on the dev branch. To install a branch
directly, without cloning:
pip install "git+https://github.com/MarloesvL/measure_IA.git@dev"Clone the repository if you want to edit the code, run the test suite, or contribute:
git clone https://github.com/MarloesvL/measure_IA.git # or git@github.com:MarloesvL/measure_IA.git
cd measure_IA
uv sync # creates .venv with the locked dependencies, and a compatible Python
uv run pytest # optional: check the suite passes
uv run my_script.pySee CONTRIBUTING.md for the full development workflow.
If you use neither uv nor pip, the dependencies are listed in requirements.txt; in that case,
make sure your Python version is compatible (uv handles this for you).
See the example script 'example_measure_IA_box.py' or the jupyter notebook 'example_measureIA_box.ipynb' in the examples directory for short examples on how this package can be used. These run as-is on a seeded mock catalogue with a known intrinsic alignment signal (from 'measureia.mocks'), so no simulation or survey data is needed to try them out. Explanations on various input parameters are explained in the comments (and more fully in the docstrings of the methods and classes). Given the data dictionary in the correct format, the methods (with all optional parameters as their default) can be called as follows:
MeasureIA_test = MeasureIABox(data=data_dict, output_file_name="./outfile_name.hdf5", boxsize=205.)
# measure wgg, wg+
MeasureIA_test.measure_xi_w(dataset_name=dataset_name, corr_type="both", num_jk=27)
# measure multipoles
MeasureIA_test.measure_xi_multipoles(dataset_name=dataset_name, corr_type="both", num_jk=27)It is advisable to check out all the optional inputs in the examples.
The documentation site is at marloesvl.github.io/measure_IA. It covers the input dictionaries, the output file structure, the shape and sign conventions, the estimator definitions and the validation, alongside an API reference generated from the docstrings. All classes and the methods meant for use also have docstrings that provide the information needed. Please feel free to contact me for any additional questions.
Your output file with your own input of [output_file_name, snapshot, dataset_name, num_jk] will have the following structure:
[output_file_name]
└── Snapshot_[snapshot] Optional. If input [snapshot] is None, this group is omitted.
├── w_gg
│ ├── [dataset_name] w_gg values for each r_p bin
│ ├── [dataset_name]_rp r_p mean bin values
│ ├── [dataset_name]_mean_[num_jk] mean w_gg value of all jackknife realisations
│ ├── [dataset_name]_jackknife_cov_[num_jk] jackknife estimate of covariance matrix
│ ├── [dataset_name]_jackknife_[num_jk] sqrt of diagonal of covariance matrix (size of errorbars)
│ └── [dataset_name]_jk[num_jk] group containing all jackknife realisations for this dataset
│ ├── [dataset_name]_[i] jackknife realisations with i running from 0 to num_jk - 1
│ └── [dataset_name]_[i]_rp r_p bin values of each jackknife realisation
├── w_g_plus
│ ├── [dataset_name] w_g+ values for each r_p bin
│ ├── [dataset_name]_rp r_p mean bin values
│ ├── [dataset_name]_mean_[num_jk] mean w_g+ value of all jackknife realisations
│ ├── [dataset_name]_jackknife_cov_[num_jk] jackknife estimate of covariance matrix
│ ├── [dataset_name]_jackknife_[num_jk] sqrt of diagonal of covariance matrix (size of errorbars)
│ └── [dataset_name]_jk[num_jk] group containing all jackknife realisations for this dataset
│ ├── [dataset_name]_[i] jackknife realisations with i running from 0 to num_jk - 1
│ └── [dataset_name]_[i]_rp r_p bin values of each jackknife realisation
└── w
├── xi_gg
│ ├── [dataset_name] xi_gg grid in (r_p,pi)
│ ├── [dataset_name]_rp r_p mean bin values
│ ├── [dataset_name]_pi pi mean bin values
│ ├── [dataset_name]_RR_gg RR grid in (r_p,pi)
│ ├── [dataset_name]_DD DD grid in (r_p,pi) (pair counts)
│ └── [dataset_name]_jk[num_jk] group containing all jackknife realisations for this dataset
│ ├── [dataset_name]_[i] jackknife realisations with i running from 0 to num_jk - 1
│ └── [dataset_name]_[i]_[x] with x in [rp, pi, RR_gg, DD] as above
├── xi_g_plus
│ ├── [dataset_name] xi_g+ grid in (rp_,pi)
│ ├── [dataset_name]_rp r_p mean bin values
│ ├── [dataset_name]_pi pi mean bin values
│ ├── [dataset_name]_RR_g_plus RR grid in (r_p,pi)
│ ├── [dataset_name]_SplusD S+D grid in (r_p,pi)
│ └── [dataset_name]_jk[num_jk] group containing all jackknife realisations for this dataset
│ ├── [dataset_name]_[i] jackknife realisations with i running from 0 to num_jk - 1
│ └── [dataset_name]_[i]_[x] with x in [rp, pi, RR_g_plus, SplusD] as above
└── xi_g_cross
├── [dataset_name] xi_gx grid in (r_p,pi)
├── [dataset_name]_rp r_p mean bin values
├── [dataset_name]_pi pi mean bin values
├── [dataset_name]_RR_g_cross RR grid in (r_p,pi)
├── [dataset_name]_ScrossD SxD grid in (r_p,pi) (pair counts)
└── [dataset_name]_jk[num_jk] group containing all jackknife realisations for this dataset
├── [dataset_name]_[i] jackknife realisations with i running from 0 to num_jk - 1
└── [dataset_name]_[i]_[x] with x in [rp, pi, RR_g_cross, ScrossD] as above
If you choose to measure multipoles instead of wg+, all 'w' will be replaced by 'multipoles' - or both will appear, if you have measured both. For the multipoles, all xi_g+, DD (etc) grids are in (r, mu_r), not in (r_p, pi) and the suffixes of the bin values are also replaced by '_r' and '_mu_r' accordingly. In one file, multiple redshift (snapshot) measurements can be saved without being overwritten, as well as the jackknife information for different numbers of jackknife realisations (num_jk) for the same dataset.
Recently completed: the lightcone methods and their cross-code validation (against halotools, treecorr and corr_pc); multiprocessing support for the lightcone version; an optional responsivity factor for the shape calibration; NumPy 2 support and testing across Python 3.10 - 3.14; a documentation website; more exhaustive docstrings; and a 1.4x - 1.7x speed-up of the pair-counting kernel alongside a benchmark suite comparing MeasureIA against halotools and treecorr.
Planned developments include non-periodic versions of the box methods, e1/e2 input for the box methods, and further speed-up options. See the roadmap in the documentation for the current list.
Bug reports, feature requests and questions are all welcome as GitHub issues. See CONTRIBUTING.md for what makes a bug report easy to act on, how features are prioritised, and the workflow for contributing code. Note that pull requests which have not been discussed in an issue beforehand will not be accepted.
Please use the CITATION.cff file to cite this package properly. MeasureIA is archived on Zenodo under the DOI 10.5281/zenodo.17252215, which always resolves to the latest released version. If you need to cite the exact version you used, take that version's own DOI from the Zenodo record instead.