Merge branch 'main' into quickstart_test

README.md

@@ -23,7 +23,7 @@ Phylotrackpy is essentially a wrapper around [Phylotracklib](https://empirical--
 
 ### Julia
 
-A phylogeny tracker [writting in Julia](https://github.com/jarbus/PhylogeneticTrees.jl/tree/master) is also available.
+A phylogeny tracker [written in Julia](https://github.com/jarbus/PhylogeneticTrees.jl/tree/master) is also available.
 
 ## Features
 
@@ -103,6 +103,28 @@ The above image represents an actual phylogeny measured from digital evolution.
 
 Contributions are welcome! See [CONTRIBUTING.md](CONTRIBUTING.md).
 
+## Citing
+
+If Phylotrack contributes to a scientific publication, please cite it as
+
+> Dolson, E., Rodriguez-Papa, S., & Moreno, M. A. (2024). Phylotrack: C++ and Python libraries for in silico phylogenetic tracking. arXiv preprint arXiv:2405.09389. https://doi.org/10.48550/arXiv.2405.09389
+
+```bibtex
+@misc{dolson2024phylotrack,
+      doi={10.48550/arXiv.2405.09389},
+      url={https://arxiv.org/abs/2405.09389},
+      title={Phylotrack: C++ and Python libraries for in silico phylogenetic tracking},
+      author={Emily Dolson and Santiago Rodriguez-Papa and Matthew Andres Moreno},
+      year={2024},
+      eprint={2405.09389},
+      archivePrefix={arXiv},
+      primaryClass={q-bio.PE}
+}
+```
+
+Consider also citing [pybind11](https://pybind11.readthedocs.io/en/stable/faq.html#how-to-cite-this-project) if you are using PhylotrackPy.
+And don't forget to leave a [star on GitHub](https://github.com/emilydolson/phylotrackpy/stargazers)!
+
 ## Developers
 
 - [Emily Dolson](https://emilyldolson.com) (lead developer)

joss/paper.bib

@@ -327,6 +327,7 @@ @misc{moreno2024analysis
   year = 2024,
   publisher = {arXiv},
   url = {https://arxiv.org/abs/2403.00246},
+  doi = {10.48550/arXiv.2403.00246},
   copyright = {arXiv.org perpetual, non-exclusive license},
   eprint = {2403.00246},
   archiveprefix = {arXiv},
@@ -506,3 +507,25 @@ @article{webbPhylogeniesCommunityEcology2002
   date = {2002-11},
   langid = {english},
 }
+@inproceedings{moreno2023toward,
+  author = {Moreno, Matthew Andres and Dolson, Emily and Rodriguez-Papa, Santiago},
+  title = "{Toward Phylogenetic Inference of Evolutionary Dynamics at Scale}",
+  volume = {ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference},
+  series = {ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference},
+  pages = {79},
+  year = {2023},
+  month = {07},
+  doi = {10.1162/isal_a_00694},
+  url = {https://doi.org/10.1162/isal_a_00694},
+  eprint = {https://direct.mit.edu/isal/proceedings-pdf/isal/35/79/2149068/isal_a_00694.pdf},
+}
+@misc{moreno2024ecology,
+  title={Ecology, Spatial Structure, and Selection Pressure Induce Strong Signatures in Phylogenetic Structure}, 
+  author={Matthew Andres Moreno and Santiago Rodriguez-Papa and Emily Dolson},
+  year={2024},
+  eprint={2405.07245},
+  url={https://arxiv.org/abs/2405.07245},
+  doi={10.48550/arXiv.2405.07245},
+  archivePrefix={arXiv},
+  primaryClass={q-bio.PE}
+}

joss/paper.md

@@ -39,7 +39,7 @@ Consequently, these populations undergo evolution [@pennock2007models], and can
 This experimental paradigm --- used across biological modeling, artificial life, and evolutionary computation --- complements research done using *in vitro* and *in vivo* systems by enabling experiments that would be impossible in the lab or field [@dolsonDigitalEvolutionEcology2021].
 One key benefit is complete, exact observability.
 For example, it is possible to perfectly record all parent-child relationships across simulation history, yielding complete phylogenies (ancestry trees).
-This information reveals when traits were gained or lost, and also facilitates inference of underlying evolutionary dynamics [@mooers1997inferring;@dolsonInterpretingTapeLife2020;@moreno2023toward].
+This information reveals when traits were gained or lost, and also facilitates inference of underlying evolutionary dynamics [@mooers1997inferring;@dolsonInterpretingTapeLife2020;@moreno2024ecology].
 
 The Phylotrack project provides libraries for tracking and analyzing phylogenies in *in silico* evolution.
 The project is composed of 1) Phylotracklib: a header-only C++ library, developed under the umbrella of the Empirical project [@ofria2020empirical], and 2) Phylotrackpy: a Python wrapper around Phylotracklib, created with Pybind11 [@pybind11].
@@ -119,7 +119,7 @@ Profiling data and hardware specifications are at <https://osf.io/52hzs/> [@fost
 ![Execution speed across population sizes. Error bars are SE.\label{fig:time}](assets/viz=plot-timeprof+x=population-size+y=generations-per-second+ext=.png){ width=50% }
 
 Figure \ref{fig:memory} shows generations evaluated per second at each population size.
-At population size 10, 1,000, and 100,000, we observed 3,923 (s.d. 257), 28,386 (s.d. 741), and 67,000 (s.d. 1825) agent reproduction events per second.
+At population size 10, 1,000, and 100,000, we observed 3,923 (s.d. 257), 28,386 (s.d. 741), and 67,000 (s.d. 1,825) agent reproduction events per second.
 Efficiency gains with population size likely arose from NumPy vectorized operations used to perform mutation and selection.
 
 ## Memory Usage

profile/requirements.txt

@@ -46,7 +46,7 @@ pandas==2.1.3
     #   teeplot
 phylotrackpy==0.1.17
     # via -r requirements.in
-pillow==10.2.0
+pillow==10.3.0
     # via matplotlib
 psutil==5.9.8
     # via memory-profiler