Merge branch 'th_rev' of github.com:markovmodel/pyemma_tutorials into th_rev

Author: thempel

manuscript/manuscript.tex

@@ -399,7 +399,7 @@ \subsection{Dimensionality reduction}
 Discrete jumps between the minima can be observed by visualizing the transformation of the first trajectory into these ICs (Fig.~\ref{fig:io-to-tica}d).
 We thus assume that our TICA-transformed backbone torsion features describe one or more metastable processes.
 
-We demonstrate how to apply TICA, suggest how to interpret the projected coordinates and compare the results to other dimension reduction techniques in Notebook (02).
+We demonstrate how to apply TICA, suggest how to interpret the projected coordinates, and compare the results to other dimension reduction techniques in Notebook (02).
 
 \begin{figure}
 \includegraphics{figure_3}
@@ -568,7 +568,7 @@ \subsection{Connecting the MSM with experimental data}
 We see that the predicted relaxation signal has a much larger amplitude for the nonequilibrium initialization,
 making it more likely to be experimentally measurable.
 
-Besides a detailed demonstration of the above, Notebook (06) demonstrates how to compute J-couplings and dynamic fingerprints from MSMs.
+In addition to a detailed demonstration of the above, Notebook (06) demonstrates how to compute J-couplings and dynamic fingerprints from MSMs.
 
 \subsection{Summary}
 
@@ -597,7 +597,7 @@ \subsection{Modeling large systems}
 we explain how to deal with those in the tutorials (Notebook 00 and 02).
 
 More details on how to model complex systems with the techniques presented here are described, e.g., by~\cite{plattner_protein_2015,plattner_complete_2017}.
-We further demonstrate the symptoms of difficult data situations and how to deal with them in Notebook (08).
+We further examine some symptoms that may indicate problematic or difficult datasets, and demonstrate how to deal with them in Notebook (08).
 
 \subsection{Advanced Methods}
 

notebooks/02-dimension-reduction-and-discretization.ipynb

@@ -388,7 +388,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Following the previous example, we perform a $k$-means ($100$ centers, stride of $5$) and a regspace clustering ($0.3$ radians center distance) on the full two-dimensional data set and visualize the obtained centers. In [Notebook 03 ➜ 📓](03-msm-estimation-and-validation.ipynb), we show the effect of different numbers of cluster centers."
+    "Following the previous example, we perform a $k$-means ($100$ centers, stride of $5$) and a regspace clustering ($0.3$ radians center distance) on the full two-dimensional data set and visualize the obtained centers. In [Notebook 03 ➜ 📓](03-msm-estimation-and-validation.ipynb), we show the effect of different numbers of cluster centers on MSM estimation."
    ]
   },
   {

notebooks/03-msm-estimation-and-validation.ipynb

@@ -241,7 +241,7 @@
     "Please note though that this ITS convergence analysis is based on the assumption that $200$ $k$-means centers are sufficient to discretize the dynamics.\n",
     "In order to study the influence of the clustering on the ITS convergence,\n",
     "we repeat the clustering and ITS convergence analysis for various number of cluster centers.\n",
-    "For the sake of simplicity, we will restrict ourselves to the $k$-means algorithm; different techniques are presented in [Notebook 02 ➜ 📓](02-dimension-reduction-and-discretization.ipynb)."
+    "For the sake of simplicity, we will restrict ourselves to the $k$-means algorithm; alternative clustering methods are presented in [Notebook 02 ➜ 📓](02-dimension-reduction-and-discretization.ipynb)."
    ]
   },
   {

notebooks/08-common-problems.ipynb

@@ -781,7 +781,7 @@
     "the TICA lag time was deliberately chosen way too high.\n",
     "That's easy to fix.\n",
     "\n",
-    "Let's now have a look at how the metastable trajectories should look like for a decent model such as the one estimated in [Notebook 05 ➜ 📓](05-pcca-tpt.ipynb).\n",
+    "Let's now have a look at how the metastable trajectories should look for a decent model such as the one estimated in [Notebook 05 ➜ 📓](05-pcca-tpt.ipynb).\n",
     "We will take the same input data,\n",
     "do a TICA transform with a realistic lag time of $10$ ps,\n",
     "and coarse grain into $2$ metastable states in order to compare with the example above."