Update README.md

Author: mengaldo

README.md

@@ -44,18 +44,18 @@
 
 
 ## Description
-**PySPOD** is a Python package that implements the so-called **Spectral Proper Orthgonal Decomposition** whose name was first conied by (picard-&-delville-2000), and goes back to the original work by [(Lumley 1970)](#lumley-1970). The implementation proposed here follows the original contributions by [(Towne et al. 2018)](#towne-et-al.-2018), [(Schmidt & Towne 2019)](#schmidt-&-towne-2019).
+**PySPOD** is a Python package that implements the so-called **Spectral Proper Orthgonal Decomposition** whose name was first conied by (picard-&-delville-2000), and goes back to the original work by [(Lumley 1970)](#lumley-1970). The implementation proposed here follows the original contributions by [(Towne et al. 2018)](#towne-et-al-2018), [(Schmidt and Towne 2019)](#schmidt-and-towne-2019).
 
-**Spectral Proper Orthgonal Decomposition (SPOD)** has been extensively used in the past few years to identify spatio-temporal coherent pattern in a variety of datasets, mainly in the fluidmechanics and climate communities. In fluidmechanics it was applied to jets (Schmidt et al. 2017), wakes [(Araya et al. 2017)](#araya-et-al.-2017), and boundary layers [(Tutkun & George 2017)](#tutkun-&-george-2017), among others, while in weather and climate it was applied to ECMWF reanalysis datasets under the name Spectral Empirical Orthogonal Function, or SEOF, [(Schmidt et al. 2019)](#schmidt-et-al.-2019).
+**Spectral Proper Orthgonal Decomposition (SPOD)** has been extensively used in the past few years to identify spatio-temporal coherent pattern in a variety of datasets, mainly in the fluidmechanics and climate communities. In fluidmechanics it was applied to jets [(Schmidt et al. 2017)](#schmidt-et-al-2017), wakes [(Araya et al. 2017)](#araya-et-al-2017), and boundary layers [(Tutkun and George 2017)](#tutkun-and-george-2017), among others, while in weather and climate it was applied to ECMWF reanalysis datasets under the name Spectral Empirical Orthogonal Function, or SEOF, [(Schmidt et al. 2019)](#schmidt-et-al-2019).
 
-The SPOD approach targets statistically stationary problems and involves the decomposition of the cross-spectral density tensor. This means that the SPOD leads to a set of spatial modes that oscillate in time at a single frequency and that optimally capture the variance of an ensemble of stochastic data [(Towne et al. 2018)](#towne-et-al.-2018). Therefore, given a dataset that is statistically stationary, one is able to capture the optimal spatio-temporal coherent structures that explain the variance in the dataset. 
+The SPOD approach targets statistically stationary problems and involves the decomposition of the cross-spectral density tensor. This means that the SPOD leads to a set of spatial modes that oscillate in time at a single frequency and that optimally capture the variance of an ensemble of stochastic data [(Towne et al. 2018)](#towne-et-al-2018). Therefore, given a dataset that is statistically stationary, one is able to capture the optimal spatio-temporal coherent structures that explain the variance in the dataset. 
 
-This can help identifying relations to multiple variables or understanding the reduced order behavior of a given phenomenon of interest and represent a powerful tool for the data-driven analysis of nonlinear dynamical systems. The SPOD approach shares some relationships with the dynamic mode decomposition (DMD), and the resolvent analysis,  [(Towne et al. 2018)](#Towne-et-al.-2018), that are also widely used approaches for the data-driven analysis of nonlinear systems. SPOD can be used for both experimental and simulation data, and a general description of its key parameters can be found in [(Schmidt & Colonius 2020)](#schmidt-&-colonius-2020).  
+This can help identifying relations to multiple variables or understanding the reduced order behavior of a given phenomenon of interest and represent a powerful tool for the data-driven analysis of nonlinear dynamical systems. The SPOD approach shares some relationships with the dynamic mode decomposition (DMD), and the resolvent analysis,  [(Towne et al. 2018)](#Towne-et-al-2018), that are also widely used approaches for the data-driven analysis of nonlinear systems. SPOD can be used for both experimental and simulation data, and a general description of its key parameters can be found in [(Schmidt and Colonius 2020)](#schmidt-and-colonius-2020).  
 
 In this package we implement three version of SPOD 
 - SPOD_low_storage: that is intended for large RAM machines or small datasets
 - SPOD_low_ram: that is intended for small RAM machines or large datasets, and 
-- SPOD_streaming: that is the algorithm presented in [(Schmidt & Towne 2019)](schmidt-&-towne-2019), and it is intended for large datasets.
+- SPOD_streaming: that is the algorithm presented in [(Schmidt and Towne 2019)](schmidt-and-towne-2019), and it is intended for large datasets.
 
 To see how to use the **PySPOD** package and its user-friendly interface, you can look at the [**Tutorials**](tutorials/README.md). 
 
@@ -208,39 +208,39 @@ IF you want to run tests locally, you can do so by:
 *Stochastic Tools in Turbulence.*
 [[DOI](https://www.elsevier.com/books/stochastic-tools-in-turbulence/lumey/978-0-12-395772-6?aaref=https%3A%2F%2Fwww.google.com)]
 
-#### (Picard & Delville 2000) 
+#### (Picard and Delville 2000) 
 *Pressure velocity coupling in a subsonic round jet.*
 [[DOI](https://www.sciencedirect.com/science/article/abs/pii/S0142727X00000217)]
 
-#### (Tutkun & George 2017)
+#### (Tutkun and George 2017)
 *Lumley decomposition of turbulent boundary layer at high Reynolds numbers.*
 [[DOI](https://aip.scitation.org/doi/10.1063/1.4974746)]
 
-#### (Schmidt et al. 2017) 
+#### (Schmidt et al 2017) 
 *Wavepackets and trapped acoustic modes in a turbulent jet: coherent structure eduction and global stability.*
 [[DOI](https://doi.org/10.1017/jfm.2017.407)]
 
-#### (Araya et al. 2017)
+#### (Araya et al 2017)
 *Transition to bluff-body dynamics in the wake of vertical-axis wind turbines.*
 [[DOI]( https://doi.org/10.1017/jfm.2016.862)]
 
-#### (Taira et al. 2017) 
+#### (Taira et al 2017) 
 *Modal analysis of fluid flows: An overview.*
 [[DOI](https://doi.org/10.2514/1.J056060)]
 
-#### (Towne et al. 2018)
+#### (Towne et al 2018)
 *Spectral proper orthogonal decomposition and its relationship to dynamic mode decomposition and resolvent analysis.*
 [[DOI]( https://doi.org/10.1017/jfm.2018.283)]
 
 #### (Schmidt and Towne 2019)
 *An efficient streaming algorithm for spectral proper orthogonal decomposition.*
 [[DOI](https://doi.org/10.1016/j.cpc.2018.11.009)]
 
-#### (Schmidt et al. 2019)
+#### (Schmidt et al 2019)
 *Spectral empirical orthogonal function analysis of weather and climate data.*
 [[DOI](https://doi.org/10.1175/MWR-D-18-0337.1)]
 
-#### (Schmidt & Colonius 2020)
+#### (Schmidt and Colonius 2020)
 *Guide to spectral proper orthogonal decomposition.*
 [[DOI](https://doi.org/10.2514/1.J058809)]