Add support

Author: trallard

.pytest_cache/v/cache/lastfailed

@@ -0,0 +1,4 @@
+{
+  "01_ProjectStructure.ipynb::Cell 1": true,
+  "01_ProjectStructure.ipynb::Cell 3": true
+}

.pytest_cache/v/cache/nodeids

@@ -0,0 +1,7 @@
+[
+  "01_ProjectStructure.ipynb::Cell 0",
+  "01_ProjectStructure.ipynb::Cell 1",
+  "01_ProjectStructure.ipynb::Cell 2",
+  "01_ProjectStructure.ipynb::Cell 3",
+  "01_ProjectStructure.ipynb::Cell 4"
+]

00_Setup.ipynb

@@ -30,6 +30,7 @@
     "- matplotlib\n",
     "- Jupyterlab (recommended)\n",
     "- Docker\n",
+    "- hypothesis\n",
     "\n",
     "Please follow the instructions below to get these software installed.\n",
     "\n",
@@ -453,7 +454,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python3"
   },

01_ProjectStructure.ipynb

@@ -209,11 +209,76 @@
     "    return HTML(styles)\n",
     "css_styling()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<generator object <genexpr> at 0x0000019833DAAD00>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print (i for i in range(6))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x19836140438>]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "x = range(5)\n",
+    "plt.plot(x, x, color='red' )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python3"
   },
@@ -228,6 +293,35 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.6.5"
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
   }
  },
  "nbformat": 4,

CITATION.cff

@@ -5,6 +5,6 @@ authors:
     given-names: Tania
     orcid: https://orcid.org/0000-0003-4925-7248
 title: 101 on reproducible workflows with Python 
-version: 1.0
+version: 1.1
 doi: 10.5281/zenodo.1241112
 date-released: 2018-05-04