Merge pull request #31 from TUW-GEO/unit2

Unit2

unit_02/06_backscatter-variability_exercise.ipynb → unit_02/06_homework_exercise.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Exercise: Dielectric Properties of Natural Media\n",
+    "# Homework Exercise: Dielectric Properties of Natural Media\n",
     "\n",
     "## RGB Composite\n",
     "Not only radar reflectivity vary according to different land cover classes. The backscattered signal also depends on the change of the imagined natural media over time. We may want to capture in one single image how different land covers comparably change across the same time range. A way to do achieve that is by generating a RGB composite. But what is a RGB composite? It all comes down to combining three image bands into one picture by setting each band as either Red, Green or Blue channel.\n",
@@ -41,11 +41,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_path = Path('~/shared/datasets/rs/sentinel-1/neusiedler').expanduser()\n",
+    "data_path = Path(\"~/shared/datasets/rs/sentinel-1/neusiedler\").expanduser()\n",
     "\n",
     "\n",
     "def _preprocess(x, bbox):\n",
-    "    '''\n",
+    "    \"\"\"\n",
     "    Preprocess file.\n",
     "\n",
     "    Parameters\n",
@@ -57,21 +57,19 @@
     "    Returns\n",
     "    -------\n",
     "    xarray.Dataset\n",
-    "    '''\n",
-    "    file = x.encoding['source']\n",
+    "    \"\"\"\n",
+    "    file = x.encoding[\"source\"]\n",
     "\n",
     "    with rasterio.open(file) as src:\n",
-    "        scale_factor = pd.to_numeric(src.tags().get('scale_factor'))\n",
-    "        time_value = pd.to_datetime(src.tags().get('time_begin'))\n",
+    "        scale_factor = pd.to_numeric(src.tags().get(\"scale_factor\"))\n",
+    "        time_value = pd.to_datetime(src.tags().get(\"time_begin\"))\n",
     "\n",
     "        x = x / scale_factor\n",
     "        x = x.assign_coords(time=time_value).expand_dims(\"time\")\n",
     "\n",
     "    x = x.rio.clip_box(*bbox, crs=\"EPSG:4326\")\n",
     "\n",
-    "    return x.rename(\n",
-    "        {\"band_data\": \"sig0\"}\n",
-    "    ).squeeze(\"band\").drop_vars(\"band\")\n",
+    "    return x.rename({\"band_data\": \"sig0\"}).squeeze(\"band\").drop_vars(\"band\")\n",
     "\n",
     "\n",
     "bbox = box(16.6, 47.7, 16.75, 47.8)\n",
@@ -80,11 +78,10 @@
     "sig0_ds = xr.open_mfdataset(\n",
     "    data_path.glob(\"*.tif\"),\n",
     "    engine=\"rasterio\",\n",
-    "    combine='nested',\n",
+    "    combine=\"nested\",\n",
     "    concat_dim=\"time\",\n",
-    "    preprocess=partial_func\n",
-    "    ).\\\n",
-    "    sortby(\"time\")\n",
+    "    preprocess=partial_func,\n",
+    ").sortby(\"time\")\n",
     "\n",
     "sig0_da = sig0_ds.sig0\n",
     "sig0_da"
@@ -94,7 +91,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Select the dates of interest\n",
+    "## Select the Dates of Interest\n",
     "\n",
     "To create an RGB composite, we need to choose three distinct dates. It's recommended to select dates that are spaced out to capture noticeable changes over time. Pay attention to the season as well, as vegetation and other environmental factors often change significantly throughout the year.\n",
     "\n",
@@ -144,14 +141,29 @@
     "\n",
     "\n",
     "def normalize(val, min, max):\n",
+    "    \"\"\"\n",
+    "    Min-max normalize value\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    val: float\n",
+    "        target\n",
+    "    min: float\n",
+    "        minimum value of range\n",
+    "    max: float\n",
+    "        maximum value of range\n",
+    "    Returns\n",
+    "    -------\n",
+    "    float\n",
+    "    \"\"\"\n",
+    "\n",
     "    return (val - min) / (max - min)\n",
     "\n",
     "\n",
     "normalized_ds = normalize(linear_ds, min_value, max_robust).clip(min=0, max=1)\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(8, 5))\n",
-    "normalized_ds.isel(time=0).plot.imshow()\n",
-    "ax.set_aspect('equal')\n",
+    "normalized_ds.isel(time=0).plot().axes.set_aspect(\"equal\")\n",
     "plt.tight_layout()"
    ]
   },
@@ -172,17 +184,16 @@
    "source": [
     "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
     "\n",
-    "normalized_ds.isel(time=0).plot(ax=axes[0], cmap='Reds')\n",
-    "normalized_ds.isel(time=1).plot(ax=axes[1], cmap='Greens')\n",
-    "normalized_ds.isel(time=2).plot(ax=axes[2], cmap='Blues')\n",
+    "normalized_ds.isel(time=0).plot(ax=axes[0], cmap=\"Reds\")\n",
+    "normalized_ds.isel(time=1).plot(ax=axes[1], cmap=\"Greens\")\n",
+    "normalized_ds.isel(time=2).plot(ax=axes[2], cmap=\"Blues\")\n",
     "\n",
-    "axes[0].set_aspect('equal')\n",
-    "axes[1].set_aspect('equal')\n",
-    "axes[2].set_aspect('equal')\n",
+    "axes[0].set_aspect(\"equal\")\n",
+    "axes[1].set_aspect(\"equal\")\n",
+    "axes[2].set_aspect(\"equal\")\n",
     "\n",
     "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
+    "plt.tight_layout()"
    ]
   },
   {
@@ -201,11 +212,8 @@
    "outputs": [],
    "source": [
     "fig, ax = plt.subplots(figsize=(8, 5))\n",
-    "normalized_ds.plot.imshow()\n",
-    "ax.set_aspect('equal')\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "plt.show()"
+    "normalized_ds.plot().axes.set_aspect(\"equal\")\n",
+    "plt.tight_layout()"
    ]
   }
  ],