Ligand binding mode clustering with PyEMMA

other-materials/README.md

@@ -5,4 +5,5 @@ This contains material that is useful as background or in support of content els
 It may be crosslinked from course content, etc.
 
 ## Manifest
-- `python-intro`: Directory containing introductory material on Python; see `python-intro/README.md`
+- `python-intro`: Directory containing introductory material on Python; see [`python-intro/README.md`](python-intro/README.md)
+- `solvation-free-energies`: Directory containing example materials for hydration free energy calculations with Yank; see [`solvation-free-energies/README.md`][solvation-free-energies/README.md]

other-materials/python-intro/Intro_1.ipynb

@@ -6,6 +6,15 @@
    "source": [
     "# A brief, basic introduction to Python for scientific computing - Chapter 1\n",
     "\n",
+    "## Background/prerequisites\n",
+    "This is part of a brief introduction to Python; please find links to the other chapters and authorship information [here](https://github.com/MobleyLab/drug-computing/blob/master/other-materials/python-intro/README.md) on GitHub. This is the first chapter in this content.\n",
+    "\n",
+    "For best results with these notebooks, we recommend using the [Table of Contents nbextension](https://github.com/ipython-contrib/jupyter_contrib_nbextensions/tree/master/src/jupyter_contrib_nbextensions/nbextensions/toc2) which will provide you with a \"Navigate\" menu in the top menu bar which, if dragged out, will allow you to easily jump between sections in these notebooks. To install, in your command prompt, use:\n",
+    "* `conda install -c conda-forge jupyter_contrib_nbextensions`\n",
+    "* `jupyter contrib nbextension install --user`\n",
+    "* Open `jupyter notebook` and click the `nbextensions` button to enable `Table of Contents`. \n",
+    "(See the [jupyter nbextensions documentation](https://github.com/ipython-contrib/jupyter_contrib_nbextensions) for more information on using these.)\n",
+    "\n",
     "## Introduction/Overview\n",
     "\n",
     "Python is an extremely usable, high-level programming language that is quickly becoming a standard in scientific computing.  It is open source, completely standardized across different platforms (Windows / MacOS / Linux), immensely flexible, and easy to use and learn.  Programs written in Python are highly readable and often much shorter than comparable programs written in other languages like C or Fortran.  Moreover, Python comes pre-loaded with standard modules that provide a huge array of functions and algorithms, for tasks like parsing text data, manipulating and finding files on disk, reading/writing compressed files, and downloading data from web servers.  Python is also capable of all of the complex techniques that advanced programmers expect, like object orientation.\n",
@@ -77,7 +86,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "Python 3.6.2 |Anaconda custom (x86_64)| (default, Sep 21 2017, 18:29:43)\n",
@@ -96,7 +107,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     ">>> 1"
@@ -106,6 +119,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "### This notebook is its own interpreter\n",
+    "\n",
     "Here, in this Jupyter notebook, each code cell effectively functions as part of an interactive interpreter, so you will see very similar behavior (with some additional bells and whistles) compared to what you would see in the commmand line on your own computer.\n",
     "\n",
     "As we will show later, Python can also read scripts, or files that are pre-written lists of commands to execute in sequence.  With the exception that output after each line is suppressed when reading from a file, there is no difference in the way Python treats commands entered interactively, in Jupyter notebooks, and in scripts; the latter are simply read in as if they were typed at the interactive prompt or in a Jupyter notebook (except that Jupyter notebooks also provide some special commands).  This gives us a powerful way to test out commands in your programs by entering them interactively while writing code.\n",
@@ -116,12 +131,21 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     ">>> 1 #I just entered the number 1"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Breaking long commands"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -132,7 +156,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     ">>> 1.243 + (3.42839 - 4.394834) * 2.1 \\\n",
@@ -150,7 +176,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     ">>> (1.243 + (3.42839 - 4.394834) * 2.1 \n",
@@ -191,13 +219,17 @@
    "source": [
     "Avoid using this notation in programs that you write, as it will make your code more dense and less legible.\n",
     "\n",
+    "### Help in Python\n",
+    "\n",
     "There is a generic help function in Python that will tell you about almost everything.  For example, it will tell you what the proper arguments for a function are:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     ">>> help(sum)"
@@ -217,7 +249,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     ">>> Var = 1\n",
@@ -227,7 +261,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     ">>> Var"
@@ -236,7 +272,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     ">>> var"
@@ -246,6 +284,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "### Exiting interactive Python\n",
+    "\n",
     "To exit the Python interactive prompt on your computer (not relevant for Jupyter notebooks, as here), we need to use an end-of-file character.  Under Windows, this corresponds to the Ctrl-Z key combination; in Linux, it corresponds to Ctrl-D.  Alternatively, one can use the exit() function:"
    ]
   },
@@ -278,6 +318,16 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.6.2"
+  },
+  "toc": {
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": "block",
+   "toc_window_display": false
   }
  },
  "nbformat": 4,