review cw [ci skip]

Author: marscher

manuscript/manuscript.tex

@@ -241,8 +241,6 @@ \subsection{The PyEMMA workflow}
 We present the results obtained in this notebook, thereby providing an example of how results generated using PyEMMA can be integrated into research publications.
 The figures that will be displayed in the following are created in the showcase notebook (00) and can be easily reproduced.
 
-\subsection{Feature selection}
-
 In the workflow there are multiple hyper parameters to be chosen by the modeler. In our approach we try to optimize a 
 parameter at the current stage of the pipeline and continue to the next stage, once a good choice was found. This 
 requires the researcher to understand the consequences of non optimal deciscions for the final result. For instance

notebooks/00-pentapeptide-showcase.ipynb

@@ -231,7 +231,11 @@
     "\n",
     "### TICA\n",
     "\n",
-    "The goal of the next step is to find a function that maps the usually high-dimensional input space into some lower dimensional space that captures the important dynamics. The recommended way of doing so is a time-lagged independent component analysis (TICA), <a id=\"ref-4\" href=\"#cite-tica2\">molgedey-94</a>, <a id=\"ref-5\" href=\"#cite-tica\">perez-hernandez-13</a>. We perform TICA (with kinetic map scaling) using the lag time obtained from the VAMP-2 score. The other values are kept at their default parameters, which will use as many dimensions in order to preserve $95\\%$ of the kinetic variance. By kinetic map scaling we ensure that Euclidean distances in the projected space approximate kinetic distances, which is a desirable property for clustering in this space.\n",
+    "The goal of the next step is to find a function that maps the usually high-dimensional input space into some lower dimensional space that captures the important dynamics. The recommended way of doing so is a time-lagged independent component analysis (TICA), <a id=\"ref-4\" href=\"#cite-tica2\">molgedey-94</a>, <a id=\"ref-5\" href=\"#cite-tica\">perez-hernandez-13</a>. We perform TICA (with kinetic map scaling) using the lag time obtained from the VAMP-2 score.\n",
+    "\n",
+    "By using the tica() functions default parameters, we will use as many dimensions in order to preserve $95\\%$ of the kinetic variance. By default, tica also applies a kinetic map scaling.\n",
+    "This scaling ensures that Euclidean distances in the projected space approximate kinetic distances,\n",
+    "which is beneficial during the subsequent discretization.\n",
     "\n",
     "Please note that the general `PyEMMA` API is consistant for all estimators. By calling the TICA estimator with the data (`tica = pyemma.coordinates.tica(torsions_data)`), the estimation is done and an estimator instance returned (`tica`); this object contains all the information about the specific transformation. For small systems, we can access the transformed data by calling `tica.get_output()`. For large systems, we recommend to pass the `tica` object itself into the subsequent stages, e.g., clustering."
    ]