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dhavidedhavide
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Updates Case Study notebooks to make sure collections work as needed.
1 parent fff2072 commit 17cc174

7 files changed

+487
-128
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‎04_Case_Studies/1a-Flooding-case-study.ipynb

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@@ -18,7 +18,11 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"from warnings import filterwarnings\n",
@@ -55,7 +59,11 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"# Study location\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"# Visualize location of area of study\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"%%time\n",
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"# The flooding event primarily happened during 04/30 - 05/02\n",
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"# We will search for data before and after the event\n",
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"start_date = datetime(year=2024, month=4, day=1)\n",
@@ -106,12 +123,8 @@
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"\n",
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"# Setup PySTAC client\n",
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"# POCLOUD refers to the PO DAAC cloud environment that hosts earth observation data\n",
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"catalog = Client.open(f'{STAC_URL}/POCLOUD/') \n",
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"\n",
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"# Setup PySTAC client\n",
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"provider_cat = Client.open(STAC_URL)\n",
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"catalog = Client.open(f'{STAC_URL}/POCLOUD/')\n",
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"collections = [\"OPERA_L3_DSWX-HLS_V1\"]\n",
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"collections = [\"OPERA_L3_DSWX-HLS_V1_1.0\"]\n",
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"\n",
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"search_opts = {\n",
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" 'bbox' : Point(*livingston_tx_lonlat).buffer(0.01).bounds, \n",
@@ -127,7 +140,11 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"def search_to_df(results, layer_name):\n",
@@ -150,16 +167,24 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"granules = search_to_df(results=results, layer_name='0_B01_WTR')"
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"%time granules = search_to_df(results=results, layer_name='0_B01_WTR')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"# We now filter the dataframe to restrict our results to a single tile_id\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"def urls_to_dataset(granule_dataframe):\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"dataset= urls_to_dataset(granules)"
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"%time dataset= urls_to_dataset(granules)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"dataset # Examine the attributes of the dataset object"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"# Define a colormap\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"img = dataset.hvplot.image(title = 'DSWx data for May 2024 Texas floods',\n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.3"
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"version": "3.12.4"
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}
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},
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"nbformat": 4,

‎04_Case_Studies/1b-BhakraNangal-Reservoir.ipynb

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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"from warnings import filterwarnings\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"bhakra_dam = (76.46, 31.42)\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"bhakra_dam_gv = gv.Points([bhakra_dam])\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"%%time\n",
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"# We will query the DSWx product record to understand variations in water levels in the reservoir\n",
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"start_date = datetime(year=2023, month=4, day=1)\n",
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"stop_date = datetime(year=2024, month=4, day=1)\n",
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"\n",
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"# Setup PySTAC client\n",
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"# POCLOUD refers to the PO DAAC cloud environment that hosts earth observation data\n",
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"catalog = Client.open(f'{STAC_URL}/POCLOUD/') \n",
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"\n",
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"# Setup PySTAC client\n",
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"provider_cat = Client.open(STAC_URL)\n",
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"catalog = Client.open(f'{STAC_URL}/POCLOUD/')\n",
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"collections = [\"OPERA_L3_DSWX-HLS_V1\"]\n",
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"collections = [\"OPERA_L3_DSWX-HLS_V1_1.0\"]\n",
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"\n",
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"# Setup search options\n",
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"opts = {\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"def filter_search_by_cc(results, cloud_threshold=10):\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"%%time\n",
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"# let's filter our results so that only scenes with less than 10% cloud cover are returned\n",
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"results = filter_search_by_cc(results)\n",
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"\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"# Load results into dataframe\n",
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{
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"cell_type": "code",
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"metadata": {},
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"dataset= urls_to_dataset(granules)"
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"%time dataset= urls_to_dataset(granules)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"# Define a colormap\n",
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{
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"cell_type": "code",
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"img = dataset.hvplot.image(title = 'Bhakra Nangal Dam, India - water extent over a year',\n",
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],
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"metadata": {
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"kernelspec": {
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"display_name": "climaterisk",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.3"
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"version": "3.12.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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"nbformat_minor": 4
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}

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