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Merge pull request #230 from ImperialCollegeLondon/paper-corrections
Paper corrections
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paper/paper.md

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orchid: 0000-0003-1641-3371
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affiliation: "1, 2"
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affiliations:
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- name: Department of Mechanical Engineering, Imperial College London, UK
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- name: Department of Mechanical Engineering, Imperial College London, United Kingdom
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index: 1
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- name: The Faraday Institution, UK
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- name: The Faraday Institution, United Kingdom
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- name: Research Computing Service, ICT, Imperial College London, UK
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- name: Research Computing Service, ICT, Imperial College London, United Kingdom
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bibliography: paper.bib
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@@ -67,7 +67,7 @@ Once imported into a `PyProBE` **Procedure**, individual cycles and steps can be
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\autoref{fig:result flowchart} shows how `PyProBE` **RawData** and **Result** objects can be used. **RawData** DataFrames contain only the columns of the `PyProBE` standardised format, while **Result** DataFrames contain any data columns produced from further analysis.The data stored in `PyProBE` **Result** object can be returned as a `NumPy` [@harris2020array] array or `Polars` dataframe for further manipulation. They can also be immediately visualised with built-in plotting methods for `matplotlib` [@Hunter:2007] and `hvplot` [@hvplot2024], or passed to an included wrapper for `seaborn` [@Waskom2021].
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The analysis module contains classes and functions which, when passed a **Result** object, enable additional functionality. The steps to smooth voltage data before differentiation are described \autoref{fig:analysis flowchart}.
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The analysis module contains classes and functions which, when passed a **Result** object, enable additional functionality. The steps to smooth voltage data before differentiation are described in \autoref{fig:analysis flowchart}.
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![The workflow for smoothing and differentiating voltage data in `PyProBE`. The complete code can be seen in the "Differentiating voltage data" example.\label{fig:analysis flowchart}](images/Analysis_flowchart.jpg)
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