Merge branch 'main' into orbitize_for_morgan

.github/workflows/joss-compile.yml

@@ -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: 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: paper/paper.pdf

docs/index.rst

@@ -55,6 +55,14 @@ User Guide:
 Changelog:
 ++++++++++
 
+**3.1.0 (2024-9-09)**
+
+- JOSS paper published! (@sblunt et al)
+
+**3.0.1 (2024-8-19)**
+
+- bugfix for issue #370 involving saving and loading hipparcos.py objects (@sblunt)
+
 **3.0.0 (2024-4-15)**
 
 - implementation of Hipparcos-Gaia catalog of accelerations fitting! (@semaphoreP)

docs/installation.rst

@@ -87,7 +87,8 @@ of the repo:
 
 .. code-block:: bash
 	
-	$ pip install -r requirements.txt -e .
+	$ pip install -r requirements.txt
+	$ pip install -e . --upgrade
 
 Issues?
 +++++++

orbitize/__init__.py

@@ -1,6 +1,6 @@
 import os
 
-__version__ = "3.0.0"
+__version__ = "3.1.0"
 
 # set Python env variable to keep track of example data dir
 orbitize_dir = os.path.dirname(__file__)

orbitize/results.py

@@ -197,7 +197,7 @@ def load_results(self, filename, append=False):
         iad_data = hf.get("IAD_datafile")
         if iad_data is not None:
 
-            tmpfile = 'thisisprettyhackysorrylmao'
+            tmpfile = 'tmpfile_OkToDeleteAfterFitFinishes'
             with open(tmpfile, 'w+') as f:
                 try:
                     for l in np.array(iad_data):
@@ -218,8 +218,6 @@ def load_results(self, filename, append=False):
                 renormalize_errors,
             )
 
-            os.system('rm {}'.format(tmpfile))
-
             # load Gaia data
             try:
                 gaia_num = int(hf.attrs['gaia_num'])
@@ -232,14 +230,14 @@ def load_results(self, filename, append=False):
             gaiagost_data = hf.get("Gaia_GOST")
             if gaiagost_data is not None:
 
-                tmpfile = 'thisisprettyhackysorrylmao'
+                tmpfile = 'tmpfile_OkToDeleteAfterFitFinishes'
                 tmptbl = table.Table(np.array(gaiagost_data))
                 tmptbl.write(tmpfile, format="ascii", overwrite=True)
 
                 gaia = orbitize.gaia.HGCALogProb(int(hip_num), hipparcos_IAD, tmpfile)
                 hipparcos_IAD = None # HGCA handles hipparocs, so don't want to pass Hipparcos also into the system
 
-                os.system('rm {}'.format(tmpfile))
+
         else:
             hipparcos_IAD = None
             gaia = None

paper/paper.md

@@ -0,0 +1,199 @@
+---
+title: 'orbitize! v3: Orbit fitting for the High-contrast Imaging Community'
+tags:
+  - Python
+  - astronomy
+  - Orbit fitting
+  - exoplanets
+  - high-contrast imaging
+authors:
+  - name: Sarah Blunt
+    orcid: 0000-0002-3199-2888
+    corresponding: true
+    affiliation: "1,2"
+  - name: Jason Jinfei Wang
+    orcid: 0000-0003-0774-6502
+    affiliation: "1"
+  - name: Lea Hirsch
+    affiliation: "11"
+  - name: Roberto Tejada
+    affiliation: "12"
+  - name: Vighnesh Nagpal
+    orcid: 0000-0001-5909-4433
+    affiliation: "13"
+  - name: Tirth Dharmesh Surti
+    affiliation: "5"
+  - name: Sofia Covarrubias
+    orcid: 0000-0003-1858-561X
+    affiliation: "2"
+  - name: Thea McKenna
+    orcid: 0009-0008-0290-2143
+    affiliation: "1,6,7,8"
+  - name: Rodrigo Ferrer Chávez
+    affiliation: "1"
+  - name: Jorge Llop-Sayson
+    orcid: 0000-0002-3414-784X
+    affiliation: "4"
+  - name: Mireya Arora
+    affiliation: "14"
+  - name: Amanda Chavez
+    affiliation: "1"
+  - name: Devin Cody
+    orcid: 0000-0002-7713-5937
+    affiliation: "9"
+  - name: Saanika Choudhary
+    affiliation: "1"
+  - name: Adam Smith
+    affiliation: "15"
+  - name: William Balmer
+    affiliation: "16"
+  - name: Tomas Stolker
+    orcid: 0000-0002-5823-3072
+    affiliation: "3"
+  - name: Hannah Gallamore
+    affiliation: "15"
+  - name: Clarissa R. Do Ó
+    orcid: 0000-0001-5173-2947
+    affiliation: "10"
+  - name: Eric L. Nielsen
+    affiliation: "15"
+  - name: Robert J. De Rosa
+    orcid: 0000-0002-4918-0247
+    affiliation: "17"
+
+affiliations:
+ - name: Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA), Northwestern University
+   index: 1
+ - name: California Institute of Technology
+   index: 2
+ - name: Leiden Observatory, Leiden University
+   index: 3
+ - name: Jet Propulsion Laboratory, California Institute of Technology
+   index: 4
+ - name: Department of Physics/Kavli Institute for Particle Astrophysics and Cosmology, Stanford University
+   index: 5
+ - name: Southeastern Universities Research Association
+   index: 6
+ - name: Astronomy Department, Cornell University
+   index: 7
+ - name: NASA Goddard Space Flight Center, Code 698
+   index: 8
+ - name: Google LLC
+   index: 9
+ - name: Department of Astronomy & Astrophysics, University of California San Diego
+   index: 10
+ - name: Department of Chemical & Physical Sciences, University of Toronto Mississauga
+   index: 11
+ - name: Department of Astrophysical Sciences, Princeton University
+   index: 12
+ - name: University of California, Berkeley
+   index: 13
+ - name: University of Michigan
+   index: 14
+ - name: New Mexico State University
+   index: 15
+ - name: Johns Hopkins University
+   index: 16
+ - name: European Southern Observatory
+   index: 17
+
+
+date: 14 March 2024
+bibliography: paper.bib
+
+---
+
+# Summary
+
+`orbitize!` is a package for Bayesian modeling of the orbital parameters of resolved binary 
+objects from time series measurements. It was developed with the needs of the high-contrast
+imaging community in mind, and has since also become widely used in the binary star community.
+A generic `orbitize!` use case involves translating relative astrometric time series, optionally 
+combined with radial velocity or astrometric time series, into a set of derived orbital posteriors.
+
+This paper is published alongside the release of `orbitize!` version 3.0, which 
+has seen significant enhancements in functionality and accessibility since the 
+release of version 1.0 [@Blunt:2020].
+
+# Statement of need
+
+The orbital parameters of directly-imaged planets and binary stars can tell us about
+their present-day dynamics and formation histories [@Bowler:2016], as well as about 
+their inherent physical characteristics (particularly mass, generally called "dynamical 
+mass" when derived from orbital constraints, e.g. @Brandt:2021, @Lacour:2021). 
+
+`orbitize!` is widely used in the exoplanet imaging and binary star communities to translate astrometric data to information about eccentricities [@Bowler:2020], obliquities [@Bryan:2020], 
+dynamical masses [@Lacour:2021], and more. 
+
+Each new released version of the `orbitize!` source code is automatically archived on Zenodo [@orbitize].
+
+# Major features added since v1
+
+For a detailed overview of the `orbitize!` API, core functionality (including information 
+about our Kepler solver), and initial verification, we refer the reader to @Blunt:2020. 
+This section lists major new features that have been added to the 
+code since the release of version 1.0 and directs the reader to more information about each of them.
+A full descriptive list of modifications to the code is maintained in our 
+[changelog](https://orbitize.readthedocs.io/en/latest/#changelog).
+
+Major new features since v1 include:
+
+1. The functionality to jointly fit the radial velocity (RV) time series for the primary star together with the secondary companion (see Section 3 of @Blunt:2023a). For the primary star, the RV data can either be directly input into orbitize! (as explained in the [Radial Velocity Tutorial](https://orbitize.readthedocs.io/en/latest/tutorials/RV_MCMC_Tutorial.html)) or be fitted separately and then used as priors (as detailed in the [Non-orbitize! Posteriors as Priors Tutorial](https://orbitize.readthedocs.io/en/latest/tutorials/Using_nonOrbitize_Posteriors_as_Priors.html)).
+
+2. The ability to jointly fit absolute astrometry of the primary star. `orbitize!` can fit
+    the Hipparcos-Gaia catalog of accelerations (@Brandt:2021; see the [HGCA Tutorial](https://orbitize.readthedocs.io/en/latest/tutorials/HGCA_tutorial.html)), as well as Hipparcos intermediate astrometric data and Gaia 
+    astrometry, following @Nielsen:2020 (see the [Hipparcos IAD Tutorial](https://orbitize.readthedocs.io/en/latest/tutorials/Hipparcos_IAD.html)). It can also handle arbitrary absolute astrometry (see the [Fitting Arbitrary Astrometry Tutorial](https://orbitize.readthedocs.io/en/latest/tutorials/abs_astrometry.html)).
+
+3. In addition to the MCMC and OFTI posterior computation algorithms documented in @Blunt:2020, 
+    `orbitize!` version 3 also implements a nested sampling backend, via `dynesty` (@Speagle:2020; see the [`dynesty` Tutorial](https://github.com/sblunt/orbitize/blob/main/docs/tutorials/dynesty_tutorial.ipynb).)
+
+4. `orbitize!` version 3 implements two prescriptions for handling multi-planet effects. 
+    Keplerian epicyclic motion of the primary star due to multiple orbiting bodies, 
+    following @Lacour:2021, is discussed in the [Multi-planet Tutorial](https://orbitize.readthedocs.io/en/latest/tutorials/Multiplanet_Tutorial.html), and N-body interactions are discussed in @Covarrubias:2022. The Keplerian epicyclic motion
+    prescription only accounts for star-planet interactions, treating the motion of the star as a sum of Keplerian orbit signals, 
+    while the N-body prescription models this effect as well as planet-planet interactions.
+
+5. The ability to fit in different orbital bases (@Ferrer-Chavez:2021, @Surti:2023; see the 
+    [Changing Bases](https://orbitize.readthedocs.io/en/latest/tutorials/Changing_bases_tutorial.html) tutorial), as well
+    as the ability to apply the observation-based priors derived in @ONeil:2019 (see the [Observation-based Priors Tutorial](https://github.com/sblunt/orbitize/blob/main/docs/tutorials/ONeil-ObsPriors.ipynb)).
+
+# Verification and Documentation
+
+`orbitize!` implements a full stack of automated testing and documentation building 
+practices. We use GitHub Actions to automatically run a suite of unit tests, maintained in [orbitize/tests](https://github.com/sblunt/orbitize/tree/main/tests),
+each time code is committed to the public repository or a pull request is opened. The Jupyter notebook
+tutorials, maintained in [orbitize/docs/tutorials](https://github.com/sblunt/orbitize/tree/main/docs/tutorials), are also run automatically when a 
+pull request to the `main` branch is opened. Documentation is built using `sphinx`, and hosted
+on readthedocs.org at [orbitize.info](https://orbitize.readthedocs.io/en/latest/). We also
+maintain a set of longer-running tests in [orbitize/tests/end-to-end-tests](https://github.com/sblunt/orbitize/tree/main/tests/end-to-end-tests) that show real
+scientific use cases of the code. These tests are not automatically run.
+
+`orbitize!` is documented through API docstrings describing individual functions, which are accessible on [our readthedocs page](https://orbitize.readthedocs.io/en/latest/api.html), a set of [Jupyter notebook tutorials](https://orbitize.readthedocs.io/en/latest/tutorials.html) walking the user through a particular application, a set of [frequently asked questions](https://orbitize.readthedocs.io/en/latest/faq.html),
+and an in-progress ["manual"](https://orbitize.readthedocs.io/en/orbitize-manual/manual.html) describing orbit fitting with `orbitize!` from first principles.
+
+# Comparison to Similar Packages
+
+Since the release of `orbitize!` version 1, other open source packages have been released that have 
+similar goals to `orbitize!`, notably `orvara` [@Brandt:2021] and `octofitter` [@Thompson:2023]. This is a wonderful development, as all packages benefit from open sharing of knowledge! `orbitize!`, `orvara`, and `octofitter` can 
+do many similar things, but each has unique features and strengths; as an example, `octofitter` is 
+extraordinarily fast, and enables joint astrometry extraction and orbit modeling, while `orbitize!` has unique 
+abilities to fit arbitrary absolute astrometry (i.e. not from Hipparcos or Gaia) and model data using an N-body backend. 
+`orvara` analytically marginalizes over parallax assuming a prior informed by Gaia, a significant speed advantage, while 
+`orbitize!` allows different parallax priors to be used. We recommend that users of each package compare the implementations 
+of the particular features they wish to use. 
+
+Best practices for orbit fitting, particularly using radial velocities, for which the treatment of stellar 
+activity is an active area of research, and absolute astrometry with Gaia and Hipparcos, for which the treatment of 
+correlated errors is an active area of research, evolve quickly. The philosophy of `orbitize!`
+is to, as much as possible, implement multiple approaches to a problem, evidenced by our multiple
+implementations of radial velocity joint fitting and absolute astrometry joint fitting (described above). 
+For detailed information about our particular implementations, we refer the reader to our [documentation](https://orbitize.readthedocs.io/en/latest/). 
+
+# Acknowledgements
+
+Our team gratefully acknowledges support from the Heising-Simons Foundation.  S.B. and J.J.W. are supported by NASA Grant 80NSSC23K0280. E.L.N. is supported by NASA Grants 21-ADAP21-0130 and 80NSSC21K0958.
+
+
+This paper describes additions made to `orbitize!` between versions 1.0.0 and 3.0.0. We are extremely grateful to Isabel Angelo, Henry Ngo, James Graham, Logan Pearce, and Malena Rice, who contribtued code to version 1.0.0, and are included as authors in @Blunt:2020.
+
+# References

tests/test_hipparcos.py

@@ -258,7 +258,10 @@ def test_save_load_2021():
     myResults = results.Results()
     myResults.load_results(filename)
 
-    os.system("rm tmp*.hdf5")
+    filename = "tmp3.hdf5"
+    myResults.save_results(filename)
+
+    os.system("rm tmp*")
 
 
 if __name__ == "__main__":