Merge pull request #179 from markovmodel/soledits

More linking between notebook 00/06, other minor fixes.

Author: psolsson

notebooks/00-pentapeptide-showcase.ipynb

@@ -1002,15 +1002,16 @@
     "We find that the committor is constant within the metastable sets defined above.\n",
     "Transition regions can be identified by committor values $\\approx 0.5$.\n",
     "\n",
-    "## Computing experimental observables [➜ 📓](07-expectations-and-observables.ipynb)\n",
+    "## Computing experimental observables [➜ 📓](06-expectations-and-observables.ipynb)\n",
     "\n",
     "Having thoroughly constructed, validated, and analyzed our MSM,\n",
     "we may want to take the next step and compare our model to experimental data.\n",
     "PyEMMA enables computation of stationary as well as dynamic experimental observables;\n",
     "below we give give some examples of this.\n",
     "We will make use of some external library functionality provided by MDTraj <a id=\"ref-9\" href=\"#cite-mdtraj\">mcgibbon-15</a>.\n",
     "\n",
-    "Theoretical backgrounds can be found here:\n",
+    "[Notebook 06](06-expectations-and-observables.ipynb) includes a brief summary of the theory used in below.\n",
+    "More in-depth descriptions of the theory and their applications to various data can be found in the following references:\n",
     "- <a id=\"ref-10\" href=\"#cite-simon-amm\">olsson-17</a>\n",
     "- <a id=\"ref-11\" href=\"#cite-noe-fingerprints\">noe-11</a>\n",
     "- <a id=\"ref-12\" href=\"#cite-simon-mech-mod-nmr\">olsson-16</a>\n",
@@ -1182,7 +1183,7 @@
    "metadata": {},
    "source": [
     "As for the stationary expectation (ensemble averages) considered above,\n",
-    "we can use our pre-computed SASA vector to compute the auto-correlation function of trypotophan flourescene using the MSM `correlation()` method:"
+    "we can use our pre-computed SASA vector to compute the auto-correlation function of trypotophan flourescene using the MSM `correlation()` method (See [Notebook 06](06-expectations-and-observables.ipynb#Dynamic/kinetic-experimental-observables) for details):"
    ]
   },
   {
@@ -1234,7 +1235,7 @@
     "Note the scale on the $y$-axis: this amplitude is likely too small to be experimentally measurable considering experimental uncertainty.\n",
     "\n",
     "However, using more advanced experimental setups such as stopped flow, T-jump, P-jump, and others,\n",
-    "we can prepare our ensemble in a non-equilibrium initial condition.\n",
+    "we can prepare our ensemble in a non-equilibrium initial condition. (See [Notebook 06](06-expectations-and-observables.ipynb#Dynamic/kinetic-experimental-observables) for details)\n",
     "\n",
     "Let us say we can experimentally prepare a sample to only be in metastable state $\\mathcal{S}_1$.\n",
     "In this case, the initial condition will be given by the metastable distribution of metastable state $\\mathcal{S}_1$, $p_0$.\n",
@@ -1322,7 +1323,7 @@
     "\n",
     "## Hidden Markov models [➜ 📓](07-hidden-markov-state-models.ipynb)\n",
     "\n",
-    "Another way of approaching metastable dynamics is with hidden Markov models (HMMs) <a id=\"ref-14\" href=\"#cite-hmm-baum-welch-alg\">baum-1970</a>.\n",
+    "Another way of approaching metastable dynamics is with hidden Markov models (HMMs) <a id=\"ref-14\" href=\"#cite-noe-proj-hid-msm\">noe-15</a>.\n",
     "HMMs model the dynamics between so-called hidden states which we initiate from the metastable states found by PCCA++.\n",
     "The estimation is less prone to discretization errors as we do not assume Markovianity in the space of our cluster centers.\n",
     "It further provides a natural coarse graining into a given number of hidden states,\n",
@@ -1757,7 +1758,7 @@
     "\n",
     "<a id=\"cite-noe-dy-neut-scatt\"/><sup><a href=#ref-13>[^]</a></sup>Benjamin Lindner and Zheng Yi and Jan-Hendrik Prinz and Jeremy C. Smith and Frank Noé. 2013. _Dynamic neutron scattering from conformational dynamics. I. Theory and Markov models_. [URL](https://doi.org/10.1063/1.4824070)\n",
     "\n",
-    "<a id=\"cite-hmm-baum-welch-alg\"/><sup><a href=#ref-14>[^]</a></sup>Leonard E. Baum and Ted Petrie and George Soules and Norman Weiss. 1970. _A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains_. [URL](http://www.jstor.org/stable/2239727)\n",
+    "<a id=\"cite-noe-proj-hid-msm\"/><sup><a href=#ref-14>[^]</a></sup>Frank Noé and Hao Wu and Jan-Hendrik Prinz and Nuria Plattner. 2013. _Projected and hidden Markov models for calculating kinetics and metastable states of complex molecules_. [URL](https://doi.org/10.1063/1.4828816)\n",
     "\n"
    ]
   }
@@ -1778,7 +1779,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.6"
   },
   "toc": {
    "base_numbering": 1,

notebooks/06-expectations-and-observables.ipynb

@@ -715,6 +715,13 @@
     "We here compute the auto-correlation function of the distance between the amide and alpha protons."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,