WIP: Took a pass an filling out the notebooks

notebooks/1_overview.ipynb

@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -65,7 +65,7 @@
        "'module://ipykernel.pylab.backend_inline'"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -79,7 +79,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We told matplotlib to use the ipython inline backend when we typed `%matplotlib inline`"
+    "We told matplotlib to use the ipython inline backend when we typed `%matplotlib inline`\n",
+    "\n",
+    "the `inline` backend results in static, non-interactive images.  Later is this tutorial we will cover how to use interactive backends in the notebook."
    ]
   },
   {
@@ -101,6 +103,16 @@
    "execution_count": 3,
    "metadata": {},
    "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
     {
      "data": {
       "text/plain": [
@@ -112,8 +124,7 @@
     }
    ],
    "source": [
-    "\n",
-    "plt.figure()\n"
+    "plt.figure()"
    ]
   },
   {
@@ -132,42 +143,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "(<Figure size 432x288 with 1 Axes>,\n",
-       " <matplotlib.axes._subplots.AxesSubplot at 0x7f00817ef908>)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {
+      "image/png": {
+       "height": 252,
+       "width": 380
+      },
       "needs_background": "light"
      },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.subplots()"
+    "fig, ax = plt.subplots()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If your figure looks \"fuzzy\" it is likely you have a hi-dpi (aka 'retnia' display), try running\n",
+    "\n",
+    "```ipython\n",
+    "%config InlineBackend.figure_format = 'retina'  # tell IPython to use hi-dpi pngs\n",
+    "```   \n",
+    "And the re-rendering your figure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": []
   }
  ],
@@ -187,7 +201,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.9.0a0"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
@@ -238,5 +252,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }