capitalise python everywhere

paper/paper.md

@@ -87,9 +87,9 @@ Unfortunately, despite active academic research in the last decade, no efficient
 
 Besides the aforementioned packages, few open access tools have been developed for the mining of temporal networks, with the existing solutions focusing on specific sub-problems within the space. Those which have attempted to generalise to all temporal network analysis are either actively under development, but too preliminary to use in production, or have been abandoned due to lack of funding or changing research goals. 
 
-As examples of these three categories: Pathpy is a Python package for the analysis of time series data on networks, but focuses on extracting and analysing time-respecting paths [@hackl2021analysis]. Similarly, DyNetX [@DyNetX], a pure python library relying on networkX, focuses on temporal slicing and the computation of time-respecting paths. The recently released Reticula offers a range of methods developed in C++ with a Python interface [@badie2023reticula]. Phasik [@lucas2023phasik;@lucas2023inferring], written in Python, focuses on inferring phases from temporal network data. EvolvingGraphs.jl [@zhang2015dynamic], RecallGraph [@RecallGraph] and Chronograph [@Chronograph] all saw significant work before development was halted indefinitely.
+As examples of these three categories: Pathpy is a Python package for the analysis of time series data on networks, but focuses on extracting and analysing time-respecting paths [@hackl2021analysis]. Similarly, DyNetX [@DyNetX], a pure Python library relying on networkX, focuses on temporal slicing and the computation of time-respecting paths. The recently released Reticula offers a range of methods developed in C++ with a Python interface [@badie2023reticula]. Phasik [@lucas2023phasik;@lucas2023inferring], written in Python, focuses on inferring phases from temporal network data. EvolvingGraphs.jl [@zhang2015dynamic], RecallGraph [@RecallGraph] and Chronograph [@Chronograph] all saw significant work before development was halted indefinitely.
 
-Raphtory is a valuable addition to this ecosystem for the following reasons. Originally developed in Scala [@steer2020raphtory], its current core is entirely written in Rust. This is to ensure fast and memory-efficient computation that a pure python implementation could not achieve, and to handle the sheer volume of temporal network data, which often dwarfs that of an equivalent static network.
+Raphtory is a valuable addition to this ecosystem for the following reasons. Originally developed in Scala [@steer2020raphtory], its current core is entirely written in Rust. This is to ensure fast and memory-efficient computation that a pure Python implementation could not achieve, and to handle the sheer volume of temporal network data, which often dwarfs that of an equivalent static network.
 
 The library provides an expressive Python interface for interoperability with other data science tools, as well as simpler and more maintainable code. In addition, the library is built with a focus on scalability, as it relies on efficient data structures that can be used to extract different views of large temporal graphs. This avoids the creation of multiple graph objects that is not feasible with large datasets. The use of these new features is supported by well-documented APIs and tutorials, guiding the user from data loading through to analysis.