Skip to main content

A collection of simulation routines describing the HERA instrument.

Project description

Build Status Coverage Status Documentation Status

Basic simulation package for HERA-like redundant interferometric arrays.

Features

  • Systematic Models: Many models of instrumental systematics in various forms, eg. thermal noise, RFI, bandpass gains, cross-talk, cable reflections and foregrounds.

  • HERA-tuned: All models have defaults tuned to HERA, with various default “sets” available (eg.H1C, H2C)

  • Interoperability: Interoperability with pyuvdata datasets and pyuvsim configurations.

  • Ease-of-use: High-level interface for adding multiple systematics to existing visibilities in a self-consistent way.

  • Visibility Simulation: A high-level interface for visbility simulation that is compatible with the configuration definition from pyuvsim but is able to call multiple simulator implementations.

  • Convenience: Methods for adjusting simulated data to match the times/baselines of a reference dataset.

Documentation

At ReadTheDocs. In particular, for a tutorial and overview of available features, check out the tour.

Installation

Conda users

If you are using conda, the following command will install all dependencies which it can handle natively:

$ conda install -c conda-forge numpy scipy pyuvdata attrs h5py healpy pyyaml

If you are creating a new development environment, consider using the included environment file:

$ conda env create -f ci/tests.yaml

This will create a fresh environment with all required dependencies, as well as those required for testing. Then follow the pip-only instructions below to install hera_sim itself.

Pip-only install

Simply use pip install -e . or run pip install git+git://github.com/HERA-Team/hera_sim.

Developer install

For a development install (tests and documentation), run pip install -e .[dev].

Other optional extras can be installed as well. To use baseline-dependent averaging functionality, install the extra [bda]. For the ability to simulate redundant gains, install [cal]. To enable GPU functionality on some of the methods (especially visibility simulators), install [gpu].

As the repository is becoming quite large, you may also wish to perform a shallow clone to retrieve only the recent commits and history. This makes the clone faster and avoid bottleneck in CI pipelines.

Provide an argument --depth 1 to the git clone command to copy only the latest revision of the repository.

git clone -–depth [depth] git@github.com:HERA-Team/hera_sim.git

Versioning

We use semantic versioning (major.minor.patch) for the hera_sim package (see SemVer documentation). To briefly summarize, new major versions include API-breaking changes, new minor versions add new features in a backwards-compatible way, and new patch versions implement backwards-compatible bug fixes.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

hera_sim-2.4.0.tar.gz (4.6 MB view details)

Uploaded Source

Built Distribution

hera_sim-2.4.0-py2.py3-none-any.whl (469.6 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file hera_sim-2.4.0.tar.gz.

File metadata

  • Download URL: hera_sim-2.4.0.tar.gz
  • Upload date:
  • Size: 4.6 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for hera_sim-2.4.0.tar.gz
Algorithm Hash digest
SHA256 f855d60eaa4a67fbe286c776bb5b61abf77ab4433c9dbaf50d4ee746c9c29525
MD5 1aa929f8ee14b579c35006f0ee8507c4
BLAKE2b-256 23bcd3b9dcdd7f9f5a94a4d212b3f16d862d27b818f28696937e7296e0bddd34

See more details on using hashes here.

File details

Details for the file hera_sim-2.4.0-py2.py3-none-any.whl.

File metadata

  • Download URL: hera_sim-2.4.0-py2.py3-none-any.whl
  • Upload date:
  • Size: 469.6 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for hera_sim-2.4.0-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 cd36673e52fb6503855a20a73c0ff6aef9d2aca2196dc684c921b252a990462d
MD5 aff305d06ed22da79246fad9ba0018e4
BLAKE2b-256 83ed8b932b2c9abf9c7bd1ae607b57de87d1c345c8c3e32015c0945dcdbef559

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page