pyuvdata defines a pythonic interface to interferometric data sets. Currently pyuvdata supports reading and writing of miriad, uvfits, CASA measurement sets and uvh5 files and reading of FHD (Fast Holographic Deconvolution) visibility save files, SMA Mir files and MWA correlator FITS files.
API documentation and a tutorial is hosted on ReadTheDocs.
The main goals are:
To provide a high quality, well documented path to convert between data formats
Support the direct use of datasets from python with minimal software
Provide precise data definition via both human readable code and high quality online documentation
pyuvdata has four major user classes:
UVData: supports interferometric data (visibilities) and associated metadata
UVCal: supports interferometric calibration solutions (antenna-based) and associated metadata (Note that this is a fairly new object, consider it to be a beta version)
UVBeam: supports primary beams (E-field or power) and associated metadata (Note that this is a fairly new object, consider it to be a beta version)
UVFlag: A class to handle the manipulation and combination of flags for data sets. Also can convert raw data quality metrics into flags using thresholding. (This object is very new and experimental. Consider it to be a beta version)
UVData File standard notes
miriad has been thoroughly tested with aipy-style miriad files and minimally tested with ATCA files
uvfits conforms to AIPS memo 117 (as of March 2020). It is tested against FHD, CASA, and AIPS. However AIPS is limited to <80 antennas and CASA uvfits import does not seem to support >255 antennas. Users with data sets containing > 255 antennas should use the measurement set writer instead.
CASA measurement sets, primarily conforming to CASA Memo 229, with some elements taken from the proposed v3.0 format documented in CASA Memo 264. Measurement sets are tested against VLA and MWA data sets, (the latter filled via cotter), with some manual verification haven been performed against ALMA and SMA data sets, the latter filled using the
importuvfitstask of CASA. tested against ALMA-filled datasets and with SMA datasets
uvh5 is an HDF5-based file format defined by the HERA collaboration, details in the uvh5 memo. Note that this is a somewhat new format, so it may evolve a bit but we will strive to make future versions backwards compatible with the current format. It is probably not compatible with other interferometric HDF5 files defined by other groups.
FHD (read-only support, tested against MWA and PAPER data)
MIR (read-only support, though experimental write functions are available, tested against SMA data)
MWA correlator FITS files (read-only support, tested against Cotter outputs and FHD)
UVCal file formats
calfits: a new format defined in pyuvdata, details in the calfits_memo. Note that this format was recently defined and may change in coming versions, based on user needs. Consider it to be a beta version, but we will strive to make future versions backwards compatible with the current format.
FHD calibration files (read-only support)
UVBeam file formats
regularly gridded fits for both E-field and power beams
non-standard HEALPix fits for both E-field and power beams (in an ImageHDU rather than a binary table to support frequency, polarization and E-field vector axes)
read support for CST beam text files, with a defined yaml file format for metadata, details here: cst settings file
UVCal: object and calfits file format (beta version)
UVBeam: object and beamfits file format (beta version)
UVFlag: object and HDF5 file format. (beta version)
Mir: object (part of UVData class) (beta version)
MirParser: object and python interface for MIR file format (beta version)
Known Issues and Planned Improvements
UVBeam: support phased-array antenna beams (e.g. MWA beams).
UVFlag: Adding requires a high level knowledge of individual objects. (see issue #653)
For details see the issue log.
pyuvdata has been used with data from the following telescopes. If you use it on data from a telescope we don’t have listed here please let us know how it went via an issue! We would like to make pyuvdata generally useful to the community for as many telescopes as possible.
We use a
generation.major.minor version number format. We use the
generation number for very significant improvements or major
major number to indicate substantial package changes
(intended to be released every 3-4 months) and the
minor number to
release smaller incremental updates (intended to be released
approximately monthly and which usually do not include breaking API
changes). We do our best to provide a significant period (usually 2
major generations) of deprecation warnings for all breaking changes to
the API. We track all changes in our
pyuvdata was originally developed in the low frequency 21cm community to support the development of and interchange of data between calibration and foreground subtraction pipelines. Particular focus has been paid to supporting drift and phased array modes.
Please cite pyuvdata by citing our JOSS paper:
Hazelton et al, (2017), pyuvdata: an interface for astronomical interferometeric datasets in python, Journal of Open Source Software, 2(10), 140, doi:10.21105/joss.00140
Simple installation via conda is available for users, developers should follow the directions under Developer Installation below.
For simple installation, the latest stable version is available via
conda install -c conda-forge pyuvdata) or pip
pip install pyuvdata).
There are some optional dependencies that are required for specific functionality, which will not be installed automatically by conda or pip. See Dependencies for details on installing optional dependencies.
Note that as of v2.2,
pyuvdata is only supported on python 3.7+.
astropy >= 5.0.4
h5py >= 3.1
numpy >= 1.20
pyerfa >= 2.0
scipy >= 1.5
astropy-healpix >= 0.6 (for working with beams in HEALPix formats)
astroquery >= 0.4.4 (for enabling phasing to ephemeris objects using JPL-Horizons)
hdf5plugin >= 3.1.0 (for enabling bitshuffle and other hdf5 compression filters in uvh5 files)
lunarsky >=0.2.1 (for working with simulated datasets for lunar telescopes)
novas and novas_de405 (for using the NOVAS library for astrometry)
python-casacore >= 3.3.1 (for working with CASA measurement sets)
pyyaml >= 5.1 (for working with settings files for CST beam files)
The numpy and astropy versions are important, so make sure these are up to date.
We suggest using conda to install all the dependencies. If you want to
install python-casacore and astropy-healpix, you’ll need to add
conda-forge as a channel (
conda config --add channels conda-forge).
If you do not want to use conda, the packages are also available on PyPI
(python-casacore may require more effort, see details for that package
below). You can install the optional dependencies via pip by specifying
an option when you install pyuvdata, as in
pip install pyuvdata[healpix] which will install all the required
packages for using the HEALPix functionality in pyuvdata. The options
that can be passed in this way are: [
dev]. The first set (
various specific functionality while
all will install all optional
dependencies. The last three (
dev) may be useful
for developers of pyuvdata.
python-casacore requires the casacore c++ libraries. It can be installed
easily using conda (
python-casacore on conda-forge).
If you do not want to use conda, the casacore c++ libraries are available for ubuntu through the kern suite. On OSX, casacore is available through the ska-sa brew tap. The python-casacore library (with manual install instructions) is available at https://github.com/casacore/python-casacore
Clone the repository using
git clone https://github.com/RadioAstronomySoftwareGroup/pyuvdata.git
Navigate into the pyuvdata directory and run
pip install . (note
python setup.py install does not work). Note that this will
attempt to automatically install any missing dependencies. If you use
anaconda or another package manager you might prefer to first install
the dependencies as described in Dependencies.
To install without dependencies, run
pip install --no-deps .
To compile the binary extension modules such that you can successfully
import pyuvdata from the top-level directory of your Git
python setup.py build_ext --inplace
If you want to do development on pyuvdata, in addition to the other dependencies you will need the following packages:
pytest >= 6.2
pytest-cases >= 3.6.9
cython >=0.23 (This is necessary for coverage reporting of cython extensions)
One other package, pytest-xdist, is not required, but can be used to
speed up running the test suite by running tests in parallel. To use it
call pytest with the
-n auto option.
One way to ensure you have all the needed packages is to use the
environment.yaml file to create a new environment that will
contain all the optional dependencies along with dependencies required
for testing and development (
conda env create -f environment.yaml).
Alternatively, you can specify
installing pyuvdata (as in
pip install pyuvdata[dev]) to install the
packages needed for testing (including coverage and linting) and
dev includes everything in
To use pre-commit to prevent committing code that does not follow our
style, you’ll need to run
pre-commit install in the top level
pytest package to execute test suite. From the source
pyuvdata directory run
python -m pytest.
UVFlag module requires the
The primary interface to data from python is via the UVData object. It provides import functionality from all supported file formats (UVFITS, Miriad, UVH5, FHD, CASA measurement sets, SMA Mir, MWA correlator FITS) and export to UVFITS, Miriad, CASA measurement sets and UVH5 formats and can be interacted with directly. Similarly, the primary calibration, beam, and flag interfaces are via the UVCal, UVBeam, and UVflag objects. The attributes of the UVData, UVCal, UVBeam, and UVFlag objects are described in the UVData Parameters, UVCal Parameters, UVBeam Parameters and UVFlag Parameters pages on ReadTheDocs.
pyuvdata is maintained by the RASG Managers, which currently include:
Adam Beardsley (Winona State University)
Bryna Hazelton (University of Washington)
Daniel Jacobs (Arizona State University)
Matt Kolopanis (Arizona State University)
Paul La Plante (University of Nevada, Las Vegas)
Jonathan Pober (Brown University)
Support for pyuvdata was provided by NSF awards #1835421 and #1835120.