Merge branch 'mr_small_corrections' into 'master'

small tweaks

See merge request !1

main.tex

@@ -15,7 +15,7 @@
 \definecolor{mygray}{rgb}{0.5,0.5,0.5}
 \definecolor{mymauve}{rgb}{0.58,0,0.82}
 
-\lstset{ 
+\lstset{
   backgroundcolor=\color{white},   % choose the background color; you must add \usepackage{color} or \usepackage{xcolor}; should come as last argument
   basicstyle=\footnotesize,        % the size of the fonts that are used for the code
   breakatwhitespace=false,         % sets if automatic breaks should only happen at whitespace
@@ -126,7 +126,7 @@ another object is needed:
 \begin{itemize}
 \item $\langle \mathcal H' \mathcal H'^\dagger \rangle_{\mathcal P (d|s)}$.
 \end{itemize}
-Note, that for Gaussian, Poissonian and Bernoulli likelihoods this term doesn't
+Note that for Gaussian, Poissonian and Bernoulli likelihoods this term doesn't
 need to be calculated and implemented because NIFTy computes it automatically.
 
 That's it. The rest of this paper explains what these formulae mean and how to
@@ -137,7 +137,7 @@ If the likelihood is Gaussian
 \begin{align*}
 \mathcal H(d|s) \propto (d-R(s))^\dagger N^{-1}(d-R(s))
 \end{align*}
-or Poissonian 
+or Poissonian
 \begin{align*}
 \mathcal H(d|s) \propto - \log (R(s))^\dagger d+\sum_i R(s)_i,
 \end{align*}
@@ -150,7 +150,7 @@ NIFTy needs:
 
 
 \section*{Even more specific}
-Since NIFTy is implemented in python and is based on numpy let us be as specific
+Since NIFTy is implemented in Python and is based on numpy let us be as specific
 as possible and talk about numpy arrays. In the end, $s$ and $d$ will be numpy
 arrays.
 
@@ -183,7 +183,7 @@ import numpy as np
 # Store the shape of s in 'shp'.
 # Store the function which implements the response in 'R'.
 
-response_out = R(np.ones(shp)) 
+response_out = R(np.ones(shp))
 if response_out.shape == d.shape:
     print('Yay!')
 else:
@@ -270,19 +270,20 @@ linear operator like the \texttt{FieldZeroPadder}, copy it and adopt it to your
 needs. The method \texttt{apply()} takes an instance of \texttt{Field} (which is
 esentially a numpy array accompanied by a domain) and returns one as well.
 
-Some tipps exclusively for you:
+Some tips exclusively for you:
 \begin{itemize}
 \item Please have a look at the method \texttt{weight()} of \texttt{Field}. With
   it, you can easily multiply the field values with or divide by the volume of
   each pixel.
 \item The methods \texttt{from\_global\_data()} and \texttt{to\_global\_data()}
   convert a Field to a numpy array and vice versa.
-\item Be aware of the fact that NIFTy fields are immuatable. As soon as you
-  pass a numpy array into a NIFTy field with \texttt{from\_global\_data()} the
-  \enquote{lock} flag will be set and the numpy array is immuatable
+\item Be aware of the fact that NIFTy fields are immutable. As soon as you
+  pass a numpy array into a NIFTy field with \texttt{from\_global\_data()}, the
+  \enquote{lock} flag will be set and the numpy array is immutable
   afterwards. \texttt{to\_global\_data()} returns an object reference to this
   numpy array which is locked and cannot be modified. If you want to modify
-  it, you may want to copy it with \texttt{to\_global\_data().copy()}.
+  it, you may want to obtain an unlocked copy via
+  \texttt{to\_global\_data\_rw()}.
 \end{itemize}
 
 The point is: You need to fiddle around until the following test passes: