collection of calibration routines to run on the HERA instrument.
Project description
hera_cal
The hera_cal package includes modules and scripts for the calibration and LST-binning of Hydrogen Epoch of Reionization Array (HERA) data, along with various helpful methods for filtering and smoothing of data and calibration solutions. These are meant for use interatively, as part of offline analysis (e.g. IDR 2.2), or as part of HERA's realtime analysis pipeline using hera_opm
.
This package only officially supports python 3, though most functionality will still work in python 2.
Package Details
Modules
I/O and Data Handling
-
hera_cal.io
: containsHERACal
andHERAData
that wrappyuvdata
equivalents and enable easy I/O of data and calibration files. -
hera_cal.datacontainer
: contains theDataContainer
object, a dictionary-like container for visibility data with various useful abstractions
Calibration
-
hera_cal.redcal
: redundant calibration module, withfirstcal
,logcal
,lincal
, andomnical
and helper functions for finding and manipulating sets of redundant baselines. -
hera_cal.abscal
: absolute calibration module, largely used to calibrate out redcal degeneraices post-redundant calibration using an externally calibrated data set. -
hera_cal.apply_cal
: functions to apply calibration solutions (and flags) to data in memory or on disk -
hera_cal.reflections
: functions for fitting per-antenna cable reflections and other per-baseline high-delay systematics (e.g. cross-talk) -
hera_cal.tempcal
: functions for calibrating using external temperature data
LST-Binning
hera_cal.lstbin
: module for LST-binning, including aligning, rephasing, and MAD clipping, and associated I/O
Filtering and Smoothing
-
hera_cal.smooth_cal
: utilities for smoothing calibration solutions in frequency, time, or both -
hera_cal.vis_clean
: base module interface to aipy CLEAN for low- and high-pass filtering visibility data along the time or frequency axis -
hera_cal.delay_filter
: specialization ofvis_clean
for performing delay filtering (e.g. wedge filtering) of visibility data -
hera_cal.frf
: specialization ofvis_clean
for performing fringe-rate (e.g. time) filtering of visibility data
Other Utilities
-
hera_cal.noise
: utilities for calculating visibility noise from interleaved differences and for predicting visibility noise from autocorrelations -
hera_cal.autos
: module for extracting and saving autocorrelation data -
hera_cal.utils
: grabbag of useful functions, including polarization string handling, FFT-based delay fitting, time and LST math, solar position calculation, chi^2 calculations, etc. -
hera_cal.flag_utils
: utilities for applying, synthesizing, and factoring flags
Scripts
apply_cal.py
: apply calibration solutions (as associated antenna-based flags) to dataauto_reflection_run.py
: estimate cable reflection gains from autocorrelationsdelay_filter_run.py
: perform delay filtering outside the wedgeextract_autos.py
: extract autocorrelation visibilities and save themextract_hh.py
: extract data only from the core HERA Hexlstbin_run.py
: run the LST-binnernoise_from_autos.py
: infer noise on visibilities and save as per-antenna noise standard deviationpost_redcal_abscal_run.py
: run abscal post-redundant calibration and save updated calibration solutionsredcal_run.py
: run redundant calibration and save firstcal and omnical visibility abd calibration solutionssmooth_cal_run.py
: smooth calibration solutions in time, frequency, or both
Documentation
The only guaranteed up-to-date documentation of individual functions and classes are their docstrings.
The IDR2.2 Release Memo is a jupyter notebook that can run at NRAO and contains useful examples of data access and visualization.
Many modules have instructional notebooks avaible here, though some of those are out of date.
While hera_cal
has a Read the Docs, it is wildly out of date.
Installation
Preferred installation method is pip install .
in top-level directory. Alternatively,
one can use python setup.py install
. This will attempt to install all dependencies.
If you prefer to explicitly manage dependencies, see below.
Dependencies
Those who use conda
(preferred) may wish to install the following manually before
installing hera_cal
:
conda install -c conda-forge "numpy>=1.10" scipy scikit-learn h5py astropy pyuvdata
(note that h5py
is a dependency of hera_qm
, not hera_cal
).
Other dependencies that will be installed from PyPI on-the-fly are:
hera_cal
also has the optional dependency of aipy
, and some
functions will not work without this dependency. To install all optional dependencies, use
pip install .[all]
or pip install git+git://github.com/HERA-Team/hera_cal.git[all]
.
Development Environment
To install a full development environment for hera_cal
, it can be useful to work with
a fresh conda
environment. These steps will get you up and running::
$ conda create -n hera_cal python=3
$ conda activate hera_cal
$ conda env update -n hera_cal -f environment.yml
$ pip install -e .
This installs extra packages than those required to use hera_cal
, including hera_sim
and pytest
.
If you are developing hera_cal
please install pre-commit: pip install pre-commit
and
then pre-commit install
in the top-level directory. This will check your style before
you make commits.
Running tests
Tests use the pytest
framework. To run all tests, call pytest
or
python -m pytest
from the base directory of the repo.
Issues and Contribution
Issues are tracked here. Please submit bugs, feature requests, etc. Contributions to this repo via pull request are welcome, though they require thorough peer review before merging into the master branch. To the best of our ability, all code should be covered with tests. The primary maintainer of hera_cal
is @jsdillon. Other maintiners who can update the master branch include @AaronParsons, @nkern, @adampbeardsley, and @plaplant.
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