Merge pull request #169 from danielskatz/patch-1

small changes for JOSS publication

Author: lizliz

paper/paper.bib

@@ -144,7 +144,7 @@ @Article{Myers2023c
 
 @Article{Jones2023,
   author        = {Jones, Benjamin and Wei, Guowei},
-  title         = {Persistent Directed Flag Laplacian},
+  title         = {Persistent Directed Flag {L}aplacian},
   year          = {2023},
   month         = dec,
   abstract      = {Topological data analysis (TDA) has had enormous success in science and engineering in the past decade. Persistent topological Laplacians (PTLs) overcome some limitations of persistent homology, a key technique in TDA, and provide substantial insight to the behavior of various geometric and topological objects. This work extends PTLs to directed flag complexes, which are an exciting generalization to flag complexes, also known as clique complexes, that arise naturally in many situations. We introduce the directed flag Laplacian and show that the proposed persistent directed flag Laplacian (PDFL) is a distinct way of analyzing these flag complexes. Example calculations are provided to demonstrate the potential of the proposed PDFL in real world applications.},
@@ -202,7 +202,7 @@ @PhdThesis{Tymochko2022
 
 @Article{Elchesen2022,
   author        = {Elchesen, Alex and Hartsock, Iryna and Perea, Jose A. and Rask, Tatum},
-  title         = {Learning on Persistence Diagrams as Radon Measures},
+  title         = {Learning on Persistence Diagrams as {R}adon Measures},
   year          = {2022},
   month         = dec,
   abstract      = {Persistence diagrams are common descriptors of the topological structure of data appearing in various classification and regression tasks. They can be generalized to Radon measures supported on the birth-death plane and endowed with an optimal transport distance. Examples of such measures are expectations of probability distributions on the space of persistence diagrams. In this paper, we develop methods for approximating continuous functions on the space of Radon measures supported on the birth-death plane, as well as their utilization in supervised learning tasks. Indeed, we show that any continuous function defined on a compact subset of the space of such measures (e.g., a classifier or regressor) can be approximated arbitrarily well by polynomial combinations of features computed using a continuous compactly supported function on the birth-death plane (a template). We provide insights into the structure of relatively compact subsets of the space of Radon measures, and test our approximation methodology on various data sets and supervised learning tasks.},
@@ -284,7 +284,7 @@ @Article{Bauer2021
 
 @Misc{Saul2019,
   author = {Nathaniel Saul and Chris Tralie},
-  title  = {Scikit-TDA: Topological Data Analysis for Python},
+  title  = {Scikit-TDA: Topological Data Analysis for {P}ython},
   year   = {2019},
   doi    = {10.5281/zenodo.2533369},
   url    = {https://doi.org/10.5281/zenodo.2533369},
@@ -349,7 +349,7 @@ @Article{Fasy2014
 @InProceedings{BinMasood2019,
   author    = {Bin Masood, Talha and Budin, Joseph and Falk, Martin and Favelier, Guillaume and Garth, Christoph and Gueunet, Charles and Guillou, Pierre and Hofmann, Lutz and Hristov, Petar and Kamakshidasan, Adhitya and Kappe, Christopher and Klacansky, Pavol and Laurin, Patrick and Levine, Joshua and Lukasczyk, Jonas and Sakurai, Daisuke and Soler, Maxime and Steneteg, Peter and Tierny, Julien and Usher, Will and Vidal, Jules and Wozniak, Michal},
   booktitle = {{TopoInVis}},
-  title     = {{An Overview of the Topology ToolKit}},
+  title     = {An Overview of the {Topology ToolKit}},
   year      = {2019},
 }
 
@@ -474,7 +474,7 @@ @Article{Tanweer2024_PREM
 @Conference{Myers2020,
   author    = {Myers, Audun D. and Melih Yesilli and Sarah Tymochko and Firas Khasawneh and Elizabeth Munch},
   booktitle = {Topological Data Analysis and Beyond Workshop at NeurIPS 2020},
-  title     = {Teaspoon: A comprehensive python package for topological signal processing},
+  title     = {Teaspoon: A comprehensive {P}ython package for topological signal processing},
   year      = {2020},
   image     = {teaspoon.png},
   keywords  = {conference},

paper/paper.md

@@ -62,11 +62,11 @@ bibliography: paper.bib
 The field of topological data analysis (TDA) has risen in the last two decades to become a mature field of research providing a unique perspective on data analysis.
 TDA consists of a suite of tools inspired by the field of Algebraic Topology, which encodes shape and structure in data in a quantitative manner.
 One particular subfield of work has focused on using TDA tools for the analysis of time series, colloquially known as Topological Signal Processing, or TSP.
-The python package \texttt{teaspoon} has been built specifically to cater to the needs of researchers working in the field of TSP, with the added benefit that the code can be used for other forms of input data beyond signal.
+The Python package \texttt{teaspoon} has been built specifically to cater to the needs of researchers working in the field of TSP, with the added benefit that the code can be used for other forms of input data beyond signal.
 
 Recent work has largely focused on the use of persistent homology and its variants for this context, thus this has been the main tool utilized in \texttt{teaspoon}.
 While a full discussion of the specifics of persistence is outside the scope of this brief paper, we give a brief introduction here and direct the interested reader to @Dey2021 and @Munch2017 for more information.
-Standard homology (see, e.g., @Hatcher) is a construction which builds a vector space for any input topological space $X$ and a given dimension $p$ of structure to be studied.
+Standard homology (see, e.g., @Hatcher) is a construction that builds a vector space for any input topological space $X$ and a given dimension $p$ of structure to be studied.
 Denoted $H_p(X)$, the $p=0$ dimensional homology encodes the structure of connected components; $p=1$ encodes loops; $p=2$ encodes voids; and higher dimensional versions exist without the interpretability of these lower dimensional versions.
 While homology is defined for a fixed topological space, persistent homology studies the changing homology for a changing topological space.
 Indeed, through a fundamental theorem of persistence [@CrawleyBoevey2015], we can use this sequence of vector spaces to determine when $p$-dimensional structures appear (or are born) and disappear (or die).
@@ -112,7 +112,7 @@ The transforms converting a time series into a mathematical structure available
 This includes both standard tools as well as newly developed techniques that incorporate topological information.
 For instance, the Takens embedding is included, which converts an input time series into a point cloud.
 However, there are also more recent techniques which convert a time series into a network, such as the Ordinal Partition Network (OPN) [@McCullough2015; @Myers2023c] or the Coarse Grained State Space (CGSS) network [@Wang2016; @Myers2023a].
-Similarly, standard entropy computations are included, as well as persistent (as in persistent homology) entropy. For more conventional time series analysis, a noise robust zero-crossing detection tool [@Tanweer2024_FODS] is included which detects all crossings of a discrete signal at once.
+Similarly, standard entropy computations are included, as well as persistent (as in persistent homology) entropy. For more conventional time series analysis, a noise robust zero-crossing detection tool [@Tanweer2024_FODS] is included, which detects all crossings of a discrete signal at once.
 This module also includes the *Texture Analysis* submodule, which provides techniques for comparing experimental and nominal surface textures in manufacturing/machining applications [@Chumley2023].
 The *Stochastic P-Bifurcation Detection* provides homological techniques for automatic and unbiased detection of Phenomenological Bifurcations in stochastic dynamical systems [@Tanweer2024_NODY; @Tanweer2024_PREM].
 
@@ -141,24 +141,24 @@ and kernel methods [@Reininghaus2015].
 
 The \texttt{teaspoon} package is focused on applications of TDA to time series with an emphasis on ease of usability in a Python environment.
 Optimization of the computation of persistence itself has been well studied by others and excellent code already exists for this aspect of the pipeline [@Otter2017].
-Where applicable, \texttt{teaspoon} uses these codebanks, particularly for persistent homology computations.
-Existing code banks include
+Where applicable, \texttt{teaspoon} uses these packages, particularly for persistent homology computations.
+Existing packages include
 Ripser [@Bauer2021],
 GUDHI [@Boissonnat2016],
 giotto-tda [@Tauzin2020],
 dionysus2 [@Morozov2019],
 scikit-tda [@Saul2019],
 R-TDA [@Fasy2014],
 and the Topology Toolkit (TTK) [@BinMasood2019].
-However, persistence in these codebanks is often provided in a very general context.
+However, persistence in these packages is often provided in a very general context.
 So, \texttt{teaspoon} fills the gap by providing tailored, well-documented tools for time series that can be used with a lower barrier to entry.
-This is not covered in other packages which are meant for broad applicability without specialization.
+This is not covered in other packages, which are meant for broad applicability without specialization.
 
 # Representative Publications Using Teaspoon
 
 The \texttt{teaspoon} package was started in 2017 as a GitLab repository, and was ported to GitHub in 2018.
 A previous but now outdated paper outlined the basic functionality of \texttt{teaspoon} at the time [@Myers2020].
-Because of its longevity, a non-exhaustive but extensive list of papers
+Because of its longevity, we provide a non-exhaustive but extensive list of papers
 [
 @Chumley2023;
 @Elchesen2022;
@@ -180,7 +180,7 @@ as well as theses
 @Yi2022;
  @Collins2022
 ]
-have utilized \texttt{teaspoon}.
+that have utilized \texttt{teaspoon}.
 
 # Acknowledgements