fix matplotlib reference and add numpy one

paper/literature.bib

@@ -45,7 +45,7 @@ @article{matplotlib
 }
 
 @softmisc{matplotlibzenodo,
-  author    = {The Matplotlib Development Team},
+  author    = {{The Matplotlib Development Team}},
   title     = {Matplotlib: Visualization with Python},
   keywords  = {software},
   month     = may,
@@ -189,3 +189,25 @@ @article{RN147
   year    = {2019},
   type    = {Journal Article}
 }
+
+@article{harris2020array,
+	title         = {Array programming with {NumPy}},
+	author        = {Harris, C. R. and Millman, K. J. and 
+	{van der Walt}, S. J. and Gommers, R. and Virtanen, P. and
+	Cournapeau, D. and Wieser, E. and Taylor, J. and 
+	Berg, S. and Smith, N. J. and Kern, R. and Picus, M.
+	and Hoyer, S. and {van Kerkwijk}, M. H. and 
+	Brett, M. and Haldane, M. and del R{\'{i}}o, J. F. and Wiebe, M. and Peterson, P. and 
+	G{\'{e}}rard-Marchant, P. and Sheppard, K. and Reddy, T. and
+	Weckesser, W. and Abbasi, H. and Gohlke, C. and
+	Oliphant, T. E.},
+	year          = {2020},
+	month         = sep,
+	journal       = {Nature},
+	volume        = {585},
+	number        = {7825},
+	pages         = {357--362},
+	doi           = {10.1038/s41586-020-2649-2},
+	publisher     = {Springer Science and Business Media {LLC}},
+	url           = {https://doi.org/10.1038/s41586-020-2649-2}
+}

paper/paper.md

@@ -73,7 +73,7 @@ Since the inference data is stored alongside graphs and report sheets, users may
 Several stages of validation were employed to prove the suitability of $\texttt{PeakPerformance}$ for chromatographic peak data analysis.
 The goals were to showcase the efficacy of $\texttt{PeakPerformance}$ utilizing noisy synthetic data, to investigate cases where a peak could reasonably be fit with either of the single peak models, and finally to use experimental data to compare results obtained with $\texttt{PeakPerformance}$ to those from the commercial vendor software Sciex MultiQuant.
 
-For the first test, 500 random data sets were generated with the NumPy random module by drawing from the normal distributions detailed in Table 1 except for the mean parameter which was held constant at a value of 6.
+For the first test, 500 random data sets were generated with the NumPy random module [@harris2020array] by drawing from the normal distributions detailed in Table 1 except for the mean parameter which was held constant at a value of 6.
 Subsequently, normally distributed random noise ($\mathcal{N}(0, 0.6)$ or $\mathcal{N}(0, 1.2)$ for data sets with the tag "higher noise") was added to each data point.
 The amount of data points per time was chosen based on an LC-MS/MS method routinely utilized by the authors and accordingly set to one data point per 1.8 s.