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hera_cal

codecov

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: contains HERACal and HERAData that wrap pyuvdata equivalents and enable easy I/O of data and calibration files.

  • hera_cal.datacontainer: contains the DataContainer object, a dictionary-like container for visibility data with various useful abstractions

Calibration

  • hera_cal.redcal: redundant calibration module, with firstcal, logcal, lincal, and omnical 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 of vis_clean for performing delay filtering (e.g. wedge filtering) of visibility data

  • hera_cal.frf: specialization of vis_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 data
  • auto_reflection_run.py: estimate cable reflection gains from autocorrelations
  • delay_filter_run.py: perform delay filtering outside the wedge
  • extract_autos.py: extract autocorrelation visibilities and save them
  • extract_hh.py: extract data only from the core HERA Hex
  • lstbin_run.py: run the LST-binner
  • noise_from_autos.py: infer noise on visibilities and save as per-antenna noise standard deviation
  • post_redcal_abscal_run.py: run abscal post-redundant calibration and save updated calibration solutions
  • redcal_run.py: run redundant calibration and save firstcal and omnical visibility abd calibration solutions
  • smooth_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.