Update docs image and add code that produced the image.

Author: simonrp84

Examples/Complex_SEVIRI.ipynb

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    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4fd53d4",
+   "metadata": {},
+   "source": [
+    "## Note\n",
+    "#### The code below produces the example SEVIRI image shown in the `pycoxmunk` documentation.\n",
+    "\n",
+    "It applies the land / sea mask to the data, showing SEVIRI data over land and the Cox-Munk reflectance over sea."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f571cc5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the RGB composite for the land component\n",
+    "pcm.scn.load(['natural_color'])\n",
+    "land_b1 = pcm.scn['natural_color'].data[0, :, :]\n",
+    "land_b2 = pcm.scn['natural_color'].data[1, :, :]\n",
+    "land_b3 = pcm.scn['natural_color'].data[2, :, :]\n",
+    "\n",
+    "# The sea component from Cox-Munk\n",
+    "sea_b1 = pcm.scn['cox_munk_refl_VIS006'].data\n",
+    "sea_b2 = pcm.scn['cox_munk_refl_VIS008'].data\n",
+    "sea_b3 = pcm.scn['cox_munk_refl_IR_016'].data\n",
+    "\n",
+    "# Apply the land / sea mask\n",
+    "lsm = pcm.pixmask.mask\n",
+    "out_b1 = np.where(lsm == 1, land_b1, sea_b1)\n",
+    "out_b2 = np.where(lsm == 1, land_b2, sea_b2)\n",
+    "out_b3 = np.where(lsm == 1, land_b3, sea_b3)\n",
+    "\n",
+    "# Apply the result to the Scene and save to disk.\n",
+    "pcm.scn['natural_color'].data = np.moveaxis(np.dstack((out_b3, out_b2, out_b1)), 2, 0)\n",
+    "pcm.scn.save_dataset('natural_color', base_dir='D:/sat_data/SEV/out2/', fill_value=0, writer='simple_image')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "273caf29",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
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