Merge pull request planetlabs#260 from planetlabs/add_output_coastal_erosion

Add outputs to coastal erosion workshop and a note about Matplotlib's low resolution bug.

Author: kevinlacaille

jupyter-notebooks/coastal-erosion-example/1_rasterio_firstlook.ipynb

@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -103,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -113,9 +113,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<open DatasetReader name='20170831_172754_101c_3b_Visual.tif' mode='r'>\n"
+     ]
+    }
+   ],
    "source": [
     "# Open our image\n",
     "satdat = rasterio.open(image_file)\n",
@@ -126,9 +134,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20170831_172754_101c_3b_Visual.tif\n",
+      "3\n"
+     ]
+    }
+   ],
    "source": [
     "# let's look at some basic information about this geoTIFF:\n",
     "\n",
@@ -148,9 +165,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1, 2, 3)\n"
+     ]
+    }
+   ],
    "source": [
     "# And provides a sequence of band indexes.  These are one indexing, not zero indexing like Numpy arrays.\n",
     "print(satdat.indexes)"
@@ -165,7 +190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -188,19 +213,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "uint8\n"
+     ]
+    }
+   ],
    "source": [
     "# each band is stored as a numpy array, and its values are a numpy data type\n",
     "print(blue.dtype)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "width: 8337, height: 3922\n"
+     ]
+    }
+   ],
    "source": [
     "# using the blue band as an example, examine the width & height of the image (in pixels)\n",
     "\n",
@@ -227,7 +268,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,

jupyter-notebooks/coastal-erosion-example/2_rasterbands.ipynb

@@ -71,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "1f17f977",
    "metadata": {},
    "outputs": [],
@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "6963b7d3",
    "metadata": {},
    "outputs": [],
@@ -104,10 +104,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "86a94b70",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Define a new figure\n",
     "fig = plt.figure()\n",
@@ -168,7 +181,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,