From 19de4a28c9530180596219859546a900e5396d4e Mon Sep 17 00:00:00 2001 From: markur <98951648+markur4@users.noreply.github.com> Date: Fri, 8 Mar 2024 13:39:49 +0100 Subject: [PATCH] paper edits --- paper.md | 55 +++++++++++++++++++++++++++--------------------------- paper.pdf | Bin 448602 -> 449935 bytes 2 files changed, 27 insertions(+), 28 deletions(-) diff --git a/paper.md b/paper.md index 5627bbe..59b2a58 100644 --- a/paper.md +++ b/paper.md @@ -29,50 +29,50 @@ aas-doi: 10.3847/xxxxx <- update this with the DOI from AAS once you know it. # aas-journal: Astrophysical Journal <- The name of the AAS journal. --- + # Summary `plotastic` addresses the challenges of transitioning from exploratory data analysis to hypothesis testing in Python's data science ecosystem. Bridging the gap between `seaborn` and `pingouin`, this library offers a unified environment for plotting and statistical analysis. It simplifies -the workflow with a user-friendly syntax and seamless integration with +the workflow with user-friendly syntax and seamless integration with familiar `seaborn` parameters (y, x, hue, row, col). Inspired by `seaborn`'s consistency, `plotastic` utilizes a `DataAnalysis` object to -intelligently pass parameters to `pingouin` statistical functions. The -library systematically groups the data according to the needs of -statistical tests and plots, conducts visualisation, analyses and -supports extensive customization options. In essence, `plotastic` -establishes a protocol for configuring statical analyses through -plotting parameters. This approach streamlines the process, translating -`seaborn` parameters into statistical terms, allowing researchers to -focus on correct statistical testing and less about specific syntax and +intelligently pass parameters to `pingouin` statistical functions. +Hence, statistics and plotting are performed on the same set of +parameters, so that the strength of `seaborn` in visualizing +multidimensional data is extended onto statistical analysis. In essence, +`plotastic` translates `seaborn` parameters into statistical terms, +configures statistical protocols based on intuitive plotting syntax and +returns a `matplotlib` figure with known customization options and more. +This approach streamlines data analysis, allowing researchers to focus +on correct statistical testing and less about specific syntax and implementations. + # Statement of need Python's data science ecosystem provides powerful tools for both visualization and statistical testing. However, the transition from exploratory data analysis to hypothesis testing can be cumbersome, requiring users to switch between libraries and adapt to different -syntaxes. - -`seaborn` has become a popular choice for plotting in Python, offering -an intuitive interface. Its statistical functionality focuses on -descriptive plots and bootstrapped confidence intervals - +syntaxes. `seaborn` has become a popular choice for plotting in Python, +offering an intuitive interface. Its statistical functionality focuses +on descriptive plots and bootstrapped confidence intervals [@waskomSeabornStatisticalData2021]. The library `pingouin` offers an extensive set of statistical tests, but it lacks integration with common plotting capabilities [@vallatPingouinStatisticsPython2018]. -`statannotations` integrates statistical testing with plot annotations, but - uses a complex interface and is limited to pairwise comparisons. + `statannotations` integrates statistical testing with plot annotations, + but uses a complex interface and is limited to pairwise comparisons [@charlierTrevismdStatannotationsV02022]. `plotastic` addresses this gap by offering a unified environment for plotting and statistical analysis. With an emphasis on user-friendly -syntax and integration with familiar `seaborn` parameters, it simplifies -the process for users already comfortable `seaborn`. The library ensures -a smooth workflow, from data import to hypothesis testing and +syntax and integration of familiar `seaborn` parameters, it simplifies +the process for users already comfortable with `seaborn`. The library +ensures a smooth workflow, from data import to hypothesis testing and visualization. # Example @@ -169,11 +169,10 @@ of statistical analysis and plotting, leveraging the capabilities of @charlierTrevismdStatannotationsV02022]. It utilizes long-format `pandas` `DataFrames` as its primary input, aligning with the conventions of `seaborn` and ensuring compatibility with existing data -structures [@wickhamTidyData2014a; -@mckinneyPandasFoundationalPython2011]. +structures [@wickhamTidyData2014a; @reback2020pandas; @mckinneyDataStructuresStatistical2010]. -`plotastic` was inspired by `seaborn`'s intuitive and consistent usage -of the same set of parameters (y, x, hue, row, col) found in each of its +`plotastic` was inspired by `seaborn` using the same set of intuitive +and consistent parameters (y, x, hue, row, col) found in each of its plotting functions [@waskomSeabornStatisticalData2021]. These parameters intuitively delineate the data dimensions plotted, yielding 'facetted' subplots, each presenting y against x. This allows for rapid and @@ -208,24 +207,24 @@ on the x-axis, which is achieved by splitting the data by all grouping dimensions (hue, row, col) (\autoref{tab:sphericity}). For omnibus and posthoc analyses, data is grouped by the row and col dimensions in parallel to the `matplotlib` axes, before performing one two-factor -analysis per axes using x and hue as the within/between-factors. +analysis per axis using x and hue as the within/between-factors. (\autoref{tab:RMANOVA}). `DataAnalysis` visualizes data through predefined plotting functions designed for drawing multi-layered plots. A notable emphasis within `plotastic` is placed on showcasing individual datapoints alongside aggregated means or medians. In detail, each plotting function -initializes `matplotlib` figure and axes using `plt.subplots()` while +initializes a `matplotlib` figure and axes using `plt.subplots()` while returning a `DataAnalysis` object for method chaining. Axes are populated by `seaborn` plotting functions (e.g., `sns.boxplot()`), leveraging automated aggregation and error bar displays. Keyword arguments are passed to these `seaborn` functions, ensuring the same degree of customization as in seaborn. Users can further customize plots by chaining `DataAnalysis` methods or by applying common `matplotlib` code -to override plotastic settings. Figures are exported using +to override `plotastic` settings. Figures are exported using `plt.savefig()`. -`plotastic` also focuses on in annotating statistical information within +`plotastic` also focuses on annotating statistical information within plots, seamlessly incorporating p-values from pairwise comparisons using `statannotations` [@charlierTrevismdStatannotationsV02022]. This integration simplifies the interface and expands options for pair diff --git a/paper.pdf b/paper.pdf index 0148a1a79afb165065973b826e27f1e383c26938..1cae41d7ceb7f3eaf7da3491760cba81c22f3b6a 100644 GIT binary patch delta 29879 zcmV(_K-9n5up5uN8?YY>12!-@myv@76o2hK%Z?;DZukC*{DIO+)Eg)?&{ypH$A-xpH4zmRrV$^228p12_NaU%tcX^536+!T=}*^yH?7Lp>Q2A@W9%UWm4Z8ktW}EU^y~N2-(IzT3tE49 z)B2pWzF4gz{(igE>-?^Dg3o{d^)EQB&nFWV81JTO!GZEQt<%EW=ND%pU4hm(r*`-a zSFP>2Ub_oje}1vXVlR)&IRwEQ?0=o>XWPrX)fTvNOKAP+4vz^iuHj6-xp$pWu1VXa zzV=$z#=ErLN6rB;4JTt%_TI!@Ob5J~7$fcrLHXI5*NTs^Lxd*~rkuyti@?^o!lH0m z*n3}U&le&tnf5Xfv6?gLaL`vtvB`^*EkJMYjg6a|2cacC5QwB8TR?(!%D50t-%q0{C}%_VC;oonJFRd3{q^+8sN@5PKD60B zL;2B0eLWStyzmJArpDHVF0q>&-GSonE8y7wKTn_jak_)_Uka`aQ3Bnq7uXIeW*>n- z))urOL;x-kk{NB^L>qEe0Dl*2TN}IAhTvIbcG)DRLy)cV5R)CFab<_>>Rp~@49fUq z!)OaCBLCt*t6a?BM}tEkQwkM_t<5WwGLjZ94j#|GK;dx|u9ZxVqv5N7iQB&Iz*)g} zIA4fm0iRqB4SYZ@8Ex3rBQF{)+5*ZeZ8AX{A(Z{}1_6F+vnpu7-G6%&+rdU^&<0{e 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