dont use very

Author: marscher

notebooks/01-data-io-and-featurization.ipynb

@@ -1081,7 +1081,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.6"
   },
   "toc": {
    "base_numbering": 1,

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

@@ -147,10 +147,10 @@
     "because in the initial phase you want to explore the phase space as much as possible.\n",
     "The downside of placing states in areas of low density is that we will have poor statistics on these states. \n",
     "\n",
-    "Another advantage of regular space clustering is that it is very fast in comparison to $k$-means:\n",
+    "Another advantage of regular space clustering is that it is fast in comparison to $k$-means:\n",
     "regspace clustering runs in linear time while $k$-means is superpolynomial in time.\n",
     "\n",
-    "⚠️ For very large datasets we also offer a mini batch version of $k$-means which has the same semantics as the original method but trains the centers on subsets of your data.\n",
+    "⚠️ For large datasets we also offer a mini batch version of $k$-means which has the same semantics as the original method but trains the centers on subsets of your data.\n",
     "This tutorial does not cover this case, but you should keep in mind that $k$-means requires your low dimensional space to fit into your main memory.\n",
     "\n",
     "The main result of a discretization for Markov modeling, however,\n",
@@ -347,7 +347,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this very simple example, we clearly see a significant correlation between the $y$ component of the input data and the first independent component.\n",
+    "In this simple example, we clearly see a significant correlation between the $y$ component of the input data and the first independent component.\n",
     "\n",
     "## Case 2: low-dimensional molecular dynamics data (alanine dipeptide)\n",
     "\n",
@@ -596,7 +596,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This is not very helpful as it only shows that some of our $x, y, z$-coordinates correlate with the TICA components.\n",
+    "This is not helpful as it only shows that some of our $x, y, z$-coordinates correlate with the TICA components.\n",
     "Since we rather expect the slow processes to happen in backbone torsion space, this comes to no surprise. \n",
     "\n",
     "To understand what the TICs really mean, let us do a more systematic approach and scan through some angular features.\n",
@@ -1259,7 +1259,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.6"
   },
   "toc": {
    "base_numbering": 1,

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

@@ -617,7 +617,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.6"
   },
   "toc": {
    "base_numbering": 1,