Updated steps 1, 2, and 3 for the Dask tutorial.

frontiersi · Jul 15, 2022 · ee1fa81 · ee1fa81
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diff --git a/Step1.ipynb b/Step1.ipynb
@@ -0,0 +1,121 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e3eb5d08-8515-4cfb-9ea4-f45c12914980",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# loading packages \n",
+    "import datacube \n",
+    "import matplotlib.pyplot as plt\n",
+    "from deafrica_tools.dask import create_local_dask_cluster\n",
+    "from deafrica_tools.plotting import rgb, display_map"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75ff9a1c-7d8f-4c40-95bf-101bf24c277d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# connecting to the datacube \n",
+    "dc = datacube.Datacube(app='Step1')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46d79dd1-c90e-409d-bdad-81e6d45675c9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# standard load function for data\n",
+    "data = dc.load(product='gm_s2_annual',\n",
+    "              measurements=['red','green', 'blue', 'nir'],\n",
+    "              x=(31.90, 32.00),\n",
+    "              y=(30.49, 30.40),\n",
+    "              time=('2020-01-01', '2020-12-31'))\n",
+    "\n",
+    "# return data \n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f77b995c-62c3-413f-a115-99212c813cf2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# lazy loading data through dask chunks parameter\n",
+    "lazy_data = dc.load(product='gm_s2_annual',\n",
+    "                   measurements=['red','green', 'blue', 'nir'],\n",
+    "                   x=(31.90, 32.00),\n",
+    "                   y=(30.49, 30.40),\n",
+    "                   time=('2020-01-01', '2020-12-31'),\n",
+    "                   dask_chunks={'time':1,'x':500, 'y':500})\n",
+    "# return data\n",
+    "lazy_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "59b47fac-97aa-4f59-aaf0-312a4a6a457f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# View a rgb (true colour) image of study area\n",
+    "rgb(lazy_data, bands=['red', 'green', 'blue'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4db3c37d-adc5-4507-b169-c89e2da87cb7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# formatting the figure \n",
+    "fig , axis = plt.subplots(1, 1, figsize=(6, 6))\n",
+    "axis.set_aspect('equal')\n",
+    "\n",
+    "lazy_data.red.plot(ax= axis)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "905a0368-e01a-4424-848b-1f1172430ed9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Step2.ipynb b/Step2.ipynb
@@ -0,0 +1,160 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea78b033-efa5-4675-99f4-a70f0bbe3b3b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# loading packages \n",
+    "import datacube \n",
+    "import matplotlib.pyplot as plt\n",
+    "from deafrica_tools.dask import create_local_dask_cluster\n",
+    "from deafrica_tools.plotting import rgb, display_map"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e075b5a-4937-40ed-9e99-271f71dce937",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# connecting to the datacube \n",
+    "dc = datacube.Datacube(app='Step2')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "904dc011-c2fc-4ff5-bb68-ab06c9519da1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# lazy loading data through dask chunks parameter\n",
+    "lazy_data = dc.load(product='gm_s2_annual',\n",
+    "                   measurements=['blue','green','red','nir'],\n",
+    "                   x=(31.90, 32.00),\n",
+    "                   y=(30.49, 30.40),\n",
+    "                   time=('2021-01-01', '2021-12-31'),\n",
+    "                   dask_chunks={'time':1,'x':500, 'y':500})\n",
+    "# return data\n",
+    "lazy_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d39bab90-7b26-4e5a-9e24-96399467d2ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# visualise the task graph\n",
+    "lazy_data.red.data.visualize()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c9f12c41-9b1e-47e0-ae11-6fb697e8c547",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# requesting small portion of red band \n",
+    "extract_from_red = lazy_data.red[:, 100:200, 100:200]\n",
+    "extract_from_red.data.visualize()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72a9cf6c-f871-44a5-b86b-7bf24c294b83",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calcualte NDVI using red and nir bands from array\n",
+    "band_diff = lazy_data.nir - lazy_data.red\n",
+    "band_sum = lazy_data.nir + lazy_data.red\n",
+    "\n",
+    "# added ndvi dask array to the lazy_data dataset\n",
+    "lazy_data['ndvi'] = band_diff / band_sum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e7697ac-9041-4139-a123-f99c9a94e880",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# return the dataset \n",
+    "lazy_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1d7c9f04-b722-4d2d-8f28-aa08ca6a252d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lazy_data.ndvi.data.visualize()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a891d1b-e84b-4c73-91c2-d4725295a84f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ndvi_compute = lazy_data.ndvi.compute()\n",
+    "ndvi_compute"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80fd0b17-615c-4097-a92e-791bdada40ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# formatting the figure \n",
+    "fig , axis = plt.subplots(1, 1, figsize=(6, 6))\n",
+    "axis.set_aspect('equal')\n",
+    "\n",
+    "ndvi_compute.plot(ax= axis, cmap='RdYlGn', vmin=0, vmax=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6d8a58c-68a7-484d-8d58-34a4abfa034a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}