Merge branch 'main' into use-pyproject

.github/workflows/compile-paper.yaml

@@ -0,0 +1,23 @@
+on: [push]
+
+jobs:
+ paper:
+ runs-on: ubuntu-latest
+ name: Paper Draft
+ steps:
+ - name: Checkout
+ uses: actions/checkout@v4
+ - name: Build draft PDF
+ uses: openjournals/openjournals-draft-action@master
+ with:
+ journal: joss
+ # This should be the path to the paper within your repo.
+ paper-path: joss-paper/paper.md
+ - name: Upload
+ uses: actions/upload-artifact@v1
+ with:
+ name: paper
+ # This is the output path where Pandoc will write the compiled
+ # PDF. Note, this should be the same directory as the input
+ # paper.md
+ path: joss-paper/paper.pdf

.github/workflows/run-docs-code.yaml

@@ -21,7 +21,7 @@ jobs:
  fetch-depth: 1
 
  - name: Setup Miniconda
- uses: conda-incubator/[email protected].1
+ uses: conda-incubator/[email protected].3
  with:
  auto-update-conda: true
  mamba-version: "*"

.github/workflows/test-with-warnings.yaml

@@ -22,7 +22,7 @@ jobs:
  fetch-depth: 1
 
  - name: Setup Miniconda
- uses: conda-incubator/[email protected].1
+ uses: conda-incubator/[email protected].3
  with:
  auto-update-conda: true
  mamba-version: "*"

.github/workflows/testsuite.yaml

@@ -22,7 +22,7 @@ jobs:
  fetch-depth: 1
 
  - name: Setup Miniconda
- uses: conda-incubator/[email protected].1
+ uses: conda-incubator/[email protected].3
  with:
  auto-update-conda: true
  mamba-version: "*"
@@ -46,7 +46,7 @@ jobs:
  run: |
  python -m pytest --cov=py21cmsense --cov-config=.coveragerc --cov-report xml:./coverage.xml
 
- - uses: codecov/codecov-action@v3
+ - uses: codecov/codecov-action@v4
  if: success()
  with:
  file: ./coverage.xml #optional

.pre-commit-config.yaml

@@ -2,7 +2,7 @@ exclude: '^docs/conf.py'
 
 repos:
 - repo: https://github.com/pre-commit/pre-commit-hooks
- rev: v4.5.0
+ rev: v4.6.0
  hooks:
  - id: trailing-whitespace
  - id: check-added-large-files

joss-paper/2dps.png:Zone.Identifier

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+[ZoneTransfer]
+ZoneId=3
+ReferrerUrl=https://21cmsense.readthedocs.io/en/latest/tutorials/understanding_21cmsense.html
+HostUrl=https://21cmsense.readthedocs.io/en/latest/_images/tutorials_understanding_21cmsense_75_0.png

joss-paper/paper.md

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+---
+title: '21cmSense v2: A modular, open-source 21 cm sensitivity calculator'
+tags:
+ - Python
+ - astronomy
+ - 21 cm Cosmology
+authors:
+ - name: Steven G. Murray
+ orcid: 0000-0003-3059-3823
+ equal-contrib: true
+ affiliation: 1 # (Multiple affiliations must be quoted)
+ corresponding: true
+ - name: Jonathan Pober
+ equal-contrib: true # (This is how you can denote equal contributions between multiple authors)
+ affiliation: 2
+ orcid: 0000-0002-3492-0433
+ - name: Matthew Kolopanis
+ affiliation: 3
+ orcid: 0000-0002-2950-2974
+affiliations:
+ - name: Scuola Normale Superiore, Italy
+ index: 1
+ - name: Department of Physics, Brown University, Providence, RI, USA
+ index: 2
+ - name: School of Earth and Space Exploration, Arizona State University, Tempe, AZ, USA
+ index: 3
+date: 18 January 2024
+bibliography: paper.bib
+---
+
+# Summary
+
+The 21cm line of neutral hydrogen is a powerful probe of the high-redshift
+universe (Cosmic Dawn and the Epoch of Reionization), with an unprecedented potential to
+inform us about key processes of early galaxy formation, the first stars and even
+cosmology and structure formation [@Liu2020], via intensity mapping.
+It is the subject of a number of current and upcoming
+low-frequency radio experiments, including the MWA [@mwa], LOFAR [@lofar], HERA [@hera]
+and the SKA [@Pritchard2015], which complement the detailed information concerning the
+brightest sources in these early epochs from powerful optical and near-infrared telescopes
+such as the JWST [@jwst].
+
+
+21cmSense is a Python package that provides a modular framework for calculating the
+sensitivity of these experiments, in order to enhance the process of their design.
+This paper presents version v2.0.0 of 21cmSense, which has been re-written from the ground up
+to be more modular and extensible, and to provide a more user-friendly interface -- as
+well as converting the well-used legacy package, presented in [@Pober2013,@Pober2014] from Python 2 to 3.
+
+21cmSense can compute sensitivity estimates for both map-making [@fhd] and
+delay-spectrum [@Parsons2012] approaches to power-spectrum estimation.
+The full sensitivity calculation is rather involved and
+computationally expensive in its most general form, however 21cmSense uses a few
+key assumptions to accelerate the calculation:
+
+1. Each baseline (pair of antennas) in the interferometer intrinsically measures a dense
+ blob of 2D spatial Fourier modes of the sky intensity distribution, centred at a
+ particular Fourier coordinate $(u,v)$ given by the displacement vector between the
+ antennas forming the baseline, and covering an area in this $(u,v)$-space that is given
+ by the Fourier-transform of the primary beam of the instrument.
+ The Fourier-space representation of the sky is thus
+ built up by collecting many baselines that cover the so-called "$(u,v)$-plane".
+ ``21cmSense`` approximates this process of synthesising many baselines by
+ nearest-grid-point interpolation onto a regular grid in the $(u,v)$-plane.
+ Furthermore, importantly the $(u,v)$-grid is chosen to have cells that are comparable
+ to the instrument's Fourier-space beam size, so that a particular baseline essentially
+ measures a single cell in the grid, and no more.
+ This maximizes resolution while keeping the covariance between cells small.
+ This removes the need for tracking the full covariance between cells, and also removes
+ the need to perform a beam convolution, which can be expensive.
+2. We do not consider flagging of visibilities due to RFI and other systematics, which
+ can complicate the propagation of uncertainties.
+
+Some of the key new features introduced in this version of 21cmSense include:
+
+1. Simplified, modular library API: the calculation has been split into modules that can
+ be used independently (for example, a class defining the `Observatory`, the
+ `Observation` and the `Sensitivity`). These can be used interactively via Jupyter
+ [@jupyter] or other interactive interfaces for Python, or called as library functions
+ in other code.
+2. Command-line interface: the library can be called from the command-line, allowing
+ for easy scripting and automation of sensitivity calculations.
+3. More accurate cosmological calculations using `astropy` [@Robitaille2013; @astropy]
+4. Improved documentation and examples, including a Jupyter notebook that walks through
+ the calculation step-by-step.
+5. Generalization of the sensitivity calculation. The `Sensitivity` class is an abstract
+ class from which the sensitivity of differing summary statistics can be defined.
+ Currently, its only implementation is the `PowerSpectrum` class, which computes the
+ classic sensitivity of the power spectrum. However, the framework
+ can be extended to other summaries, for example wavelets [@Trott2016a].
+6. Improved speed: the new version of 21cmSense is significantly faster than the legacy
+ version, due to a number of vectorization improvements in the code.
+7. Built-in profiles for several major experiments: MWA, HERA and SKA-1. These can be
+ used as-is, or as a starting point for defining a custom instrument.
+
+An example of the predicted sensitivity of the HERA experiment after a year's observation
+at $z=8.5$ is shown in Figure \ref{sense}, corresponding to the sampling of the $(u,v)$-grid
+shown in Figure \ref{uvsampling}. The sensivity here is a signal-to-noise,
+assuming a signal magnitude computed using a semi-numerical model from the 21cmFAST
+code [@Mesinger2011; @Murray2020], using parameters from [@Munoz2022].
+This figure also demonstrates that the new
+21cmSense is capable of producing sensitivity predictions in the cylindrically-averaged
+2D power spectrum space, which is helpful for upcoming experiments.
+
+![Sampling of the $(u,v)$-plane for the HERA experiment during a full year of observations.\label{uvsampling}](uv-sampling.pdf)
+
+![Predicted sensitivity of 1000 hours (one year) of HERA observations, as a function of perpendicular and line-of-sight fourier scale. The sensitivity is represented as the signal-to-noise on each $k$-mode, assuming a particular astrophysical model.\label{sense}](2dps.pdf)
+
+# Statement of need
+
+`21cmSense` provides a simple interface for computing the expected sensitivity of
+radio interferometers that aim to measure the 21cm line of neutral hydrogen.
+This field is growing rapidly, with a number of experiments currently underway or in
+the planning stages. Historically, `21cmSense` has been a trusted tool for the design of
+these experiments [@Pober2013; @Pober2014; @Greig2020] and for forecasting parameter
+constraints [@Greig2015; @Greig2017; @Greig2018].
+This overhauled, modularized version of `21cmSense` provides a more user-friendly
+interface, improved performance, and the extensibility required for the next generation,
+as evidenced by its usage in the literature [@Brietman2024,@Schosser2024].
+
+# Acknowledgements
+
+We acknowledge helpful conversations with Danny Jacobs.
+
+# References

joss-paper/uv-sampling.png:Zone.Identifier

@@ -0,0 +1,4 @@
+[ZoneTransfer]
+ZoneId=3
+ReferrerUrl=https://21cmsense.readthedocs.io/en/latest/tutorials/getting_started.html
+HostUrl=https://21cmsense.readthedocs.io/en/latest/_images/tutorials_getting_started_51_1.png