Update paper.md

add Numpy ref

paper.md

@@ -33,7 +33,7 @@ Studying the coordinated cell and tissue movements within confluent cell layers
 
 To date, Optical Flow and Particle Image Velocimetry (PIV) analysis of microscopy data has primarily relied on different plugins for ImageJ, such as [PIV analyser](https://imagej.net/PIV_analyser), or on MATLAB scripts [@Vig2016]. However, these methods are limited in their accessibility, analysis capacity, metadata handling, and data visualization capabilities. Optical Flow and PIV are commonly used in the fields of fluid dynamics [@Taylor2010] and computer vision [@Bradski2000], and several open source frameworks exist to service these communities, e.g. [openPIV](http://www.openpiv.net/), [JPIV](https://eguvep.github.io/jpiv/index.html) and [OpenCV](https://opencv.org/) [@Bradski2000]. A corresponding framework for bioimaging and cell biology applications has so far been lacking.
 
-Cellocity is an Python-based bioimage analysis tool for quantifying cell and tissue dynamics. It has been developed as a flexible framework for researchers interested in investigating dynamic behaviors within confluent cell layers, and to address the Optical Flow/PIV needs unique to the microscopy community. Cellocity allows users to test and evaluate a diverse set of preprocessing steps, analysis algorithms, packages, and parameters on their experimental data. It provides a uniform programming interface to work with the many aspects in a cell dynamics analysis pipeline, from reading and preprocessing raw microscopy data, to creating flow visualizations and figures.
+Cellocity is an Python-based bioimage analysis tool for quantifying cell and tissue dynamics. It has been developed as a flexible framework for researchers interested in investigating dynamic behaviors within confluent cell layers, and to address the Optical Flow/PIV needs unique to the microscopy community. Cellocity allows users to test and evaluate a diverse set of preprocessing steps, analysis algorithms, packages, and parameters on their experimental data. It provides a uniform programming interface to work with the many aspects in a cell dynamics analysis pipeline, from reading and preprocessing raw microscopy data, to creating flow visualizations and figures with the help of [Matplotlib](https://matplotlib.org/) [@Hunter2007]. [Numpy](https://numpy.org/) [@vanderWalt2011] is used extensively by Cellocity for array processing. 
 
 One unique feature of microscopy data is that the spatial resolution (pixel size) can be known to a high degree of accuracy, and together with frame time-stamps this can be used to calculate accurate flow velocities. A major problem when performing preprocessing of time lapse microscopy data, for example through temporal median filtering, is that such operations sometimes can change the time interval between frames and/or the pixel size. Cellocity is by design keeping track of and recalculating time and space units during operations, so as to not report back erroneous output data to the user. It is possible to configure Cellocity to give output in different units, such as µm/min or µm/h, depending on the time scale of the experiment being analyzed. Moreover, Cellocity can calculate derived parameters, including the i) Instantaneous Order Parameter [@Malinverno2017], ii) Alignment Index [@Malinverno2017], and/or iii) 5-sigma correlation length [@Laang2018], and thereby provides the user with a comprehensive tool box to describe confluent cell layer dynamics phenomena.