Networkx v2 (#133)

* Add nbstripout

* update to networkx v2 API

* install ffmpeg from native ubuntu repo

* comment out xvfb

* change python to 3.8

* switch to py37

* manual listing of notebooks to execute

.gitattributes

@@ -0,0 +1,2 @@
+*.ipynb filter=nbstripout
+*.ipynb diff=ipynb

.gitignore

@@ -5,7 +5,7 @@ datasets/divvy_2013/*
 datasets/amazon_reviews/*.json
 network
 __pycache__/*
-.ipynb_checkpoints/*
+*.ipynb_checkpoints/*
 *.pyc
 custom/__pycache__/*
 .DS_Store

.travis.yml

@@ -1,20 +1,19 @@
-# This is necessary for pyjanitor as matplotlib is involved.
-before_script:
-  - "export DISPLAY=:99.0"
-  - "sh -e /etc/init.d/xvfb start"
-  - sleep 5 # give xvfb some time to start
+# before_script:
+#   - "export DISPLAY=:99.0"
+#   - "sh -e /etc/init.d/xvfb start"
+#   - sleep 5 # give xvfb some time to start
 
 language: python
 matrix:
   include:
     - python: 3.5  # we don't actually use this
-      env: PYTHON_VERSION=3.6
+      env: PYTHON_VERSION=3.7
 
 before_install:
   # Ensure that ffmpeg is installed
   # Inspired from here: https://gist.github.com/kevinGodell/bd52cd74c8c2d66fb69f108ef223c356
   # However, I also changed the PPA based on this URL: https://tecadmin.net/install-ffmpeg-on-linux/
-  - if [ "$TRAVIS_OS_NAME" == "linux" ]; then sudo add-apt-repository -y ppa:jonathonf/ffmpeg-3; fi
+  # - if [ "$TRAVIS_OS_NAME" == "linux" ]; then sudo add-apt-repository -y ppa:jonathonf/ffmpeg-3; fi
   - if [ "$TRAVIS_OS_NAME" == "linux" ]; then sudo apt-get update; fi
   - if [ "$TRAVIS_OS_NAME" == "linux" ]; then sudo apt-get install -y ffmpeg; fi
   - if [ "$TRAVIS_OS_NAME" == "osx" ]; then brew update; fi
@@ -44,12 +43,19 @@ install:
 script:
   # Your test script goes here
   # - command -v jupyter
-  - |
-    for nb in $(ls *-instructor.ipynb); do
-        jupyter nbconvert --config nbconvert_config.py --execute $nb
+  # - |
+  #   for nb in $(ls *-instructor.ipynb); do
+  #       jupyter nbconvert --config nbconvert_config.py --execute $nb
 
-    done
-  # - jupyter nbconvert --config nbconvert_config.py --execute 2-networkx-basics-instructor.ipynb
+  #   done
+  - jupyter nbconvert --config nbconvert_config.py --execute 2-networkx-basics-instructor.ipynb
+  - jupyter nbconvert --config nbconvert_config.py --execute 3-hubs-and-paths-instructor.ipynb
+  - jupyter nbconvert --config nbconvert_config.py --execute 4-cliques-triangles-structures-instructor.ipynb
+  - jupyter nbconvert --config nbconvert_config.py --execute 5-graph-input-output-instructor.ipynb
+  - jupyter nbconvert --config nbconvert_config.py --execute 6-bipartite-graphs-instructor.ipynb
+  - jupyter nbconvert --config nbconvert_config.py --execute 7-game-of-thrones-case-study-instructor.ipynb
+  - jupyter nbconvert --config nbconvert_config.py --execute bonus-1-network-statistical-inference-instructor.ipynb
+  - jupyter nbconvert --config nbconvert_config.py --execute bonus-3-matrices-instructor.ipynb
 
 after_success:
   - bash <(curl -s https://codecov.io/bash)

0-pre-tutorial-exercises.ipynb

@@ -11,14 +11,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2017-07-08T12:07:28.341024-04:00",
-     "start_time": "2017-07-08T12:07:28.328856Z"
-    },
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# 1. Basic Python data structures\n",
@@ -51,27 +45,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2017-07-08T12:07:28.468914-04:00",
-     "start_time": "2017-07-08T12:07:28.343869Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "UnboundLocalError",
-     "evalue": "local variable '______' referenced before assignment",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mUnboundLocalError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-2-d7bd54c0478d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Test your result below.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfind_persons_with_surname\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Lee'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfind_persons_with_surname\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Elmer'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-1-5332cbff266c>\u001b[0m in \u001b[0;36mfind_persons_with_surname\u001b[0;34m(persons, query_surname)\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0m______\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m______\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0m___________\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m__________\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m             \u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m________\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mUnboundLocalError\u001b[0m: local variable '______' referenced before assignment"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Test your result below.\n",
     "results = find_persons_with_surname(names, 'Lee')\n",
@@ -84,9 +60,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
@@ -161,10 +135,6 @@
     "_Feature"
    ],
    "window_display": false
-  },
-  "widgets": {
-   "state": {},
-   "version": "1.1.1"
   }
  },
  "nbformat": 4,

1-introduction.ipynb

@@ -36,28 +36,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2017-07-11T16:59:15.527287Z",
-     "start_time": "2017-07-11T16:59:15.501751Z"
-    },
     "slideshow": {
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'line': 1, 'name': 'raindrops on roses'}]"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "my_fav_things = []\n",
     "my_fav_things.append({'name': 'raindrops on roses', 'line': 1})\n",
@@ -245,9 +230,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
@@ -323,10 +306,6 @@
     "_Feature"
    ],
    "window_display": false
-  },
-  "widgets": {
-   "state": {},
-   "version": "1.1.1"
   }
  },
  "nbformat": 4,