{ "cells": [ { "cell_type": "markdown", "id": "d36845bd-4c34-4703-ada7-edb5f55ba1de", "metadata": {}, "source": [ "## S3 Access for cloud enabled datasets : h5 example\n", "\n", "If a given dataset is in the cloud we have a set of libraries to access them. If the data is in a cloud friendly format we can efficiently load only what we need. If not we may need to read entire files.\n", "\n", "Dependencies:\n", "\n", "* [Valid `.netrc`](https://github.com/NASA-Openscapes/2021-Cloud-Hackathon/blob/main/tutorials/04_NASA_Earthdata_Authentication.ipynb) file in home directory\n", "* Running this code in AWS **us-west-2** (Like we are in the Openscapes hub)\n", "\n", "Glossary\n", "\n", "* Collection:\n", "* Granule:\n", "* S3: \n", "* S3 bucket:\n", "\n", "Workflow\n", "* Searched for a cloud-hosted dataset stored in hdf5 files (not in this notenook: no easy way to do this with CMR)\n", "* Getting credentials for DAAC that hosts the data\n", "* Search for granule with specified parameters\n", "* Setting up query to CMR and explore meta data to find direct links\n", "* Get urls of the data\n", "* Open 1 granule (file) on S3 to explore the data\n", "* Explored the data file hierarchy and chose a variable of interest\n", "* get the variables we want out of the h5 file\n", "* Make plots\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "54f325cc-792d-49a7-8a66-6622ebba7e77", "metadata": {}, "outputs": [], "source": [ "import requests\n", "from pprint import pprint\n", "from pathlib import Path\n", "import s3fs\n", "import fsspec\n", "\n", "# added this two for the h5 example\n", "import numpy\n", "import h5py\n", "\n", "import xarray as xr\n", "\n", "import matplotlib.pyplot as plt\n", "import cartopy.crs as ccrs" ] }, { "cell_type": "code", "execution_count": 2, "id": "1826a8d6-2f80-4a77-8813-4a3a091c8001", "metadata": {}, "outputs": [], "source": [ "# setting the endpoints and credentials\n", "# Here we know we want a certain dataset and know the concept ID\n", "\n", "# This endpoint is specific to daac, if we want cloud data from a different DAAC\n", "# we need to change it. See: https://raw.githubusercontent.com/betolink/earthdata/main/earthdata/daac.py \n", "s3_cred_endpoint = 'https://data.ornldaac.earthdata.nasa.gov/s3credentials'\n", "\n", "cmr_search_url = 'https://cmr.earthdata.nasa.gov/search'\n", "cmr_granule_url = f'{cmr_search_url}/{\"granules\"}'\n", "\n", "\n", "def get_temp_creds():\n", " temp_creds_url = s3_cred_endpoint\n", " return requests.get(temp_creds_url).json()\n", "temp_creds_req = get_temp_creds()\n", "\n", "s3_fs = s3fs.S3FileSystem(\n", " key=temp_creds_req['accessKeyId'],\n", " secret=temp_creds_req['secretAccessKey'],\n", " token=temp_creds_req['sessionToken'],\n", " client_kwargs={'region_name':'us-west-2'},\n", ")" ] }, { "cell_type": "code", "execution_count": 3, "id": "1dc7aeb6-bba0-4a1a-91aa-3b989461c7e4", "metadata": {}, "outputs": [], "source": [ "# Search for granule with specified parameters\n", "\n", "# In this case we know the dataset id in advance but we could use CMR to look for one\n", "concept_id = 'C2114031882-ORNL_CLOUD'\n", "# Bounding Box spatial parameter in decimal degree 'W,S,E,N' format.\n", "# If the dataset is global the bbox is just ignored because it will match anything.\n", "bounding_box = '-105,21,-125,32'\n", "# Each date in yyyy-MM-ddTHH:mm:ssZ format; date range in start,end format\n", "temporal = '2019-06-22T00:00:00Z,2019-06-23T23:59:59Z'" ] }, { "cell_type": "code", "execution_count": 4, "id": "0ac89aea-1c90-4a97-bac0-9fe3cc9f9d05", "metadata": {}, "outputs": [], "source": [ "# setting up query to CMR and explore meta data to find direct links\n", "\n", "\n", "response = requests.get(cmr_granule_url, \n", " params={\n", " 'concept_id': concept_id,\n", " 'temporal': temporal,\n", " 'bounding_box': bounding_box,\n", " 'page_size': 200,\n", " },\n", " headers={\n", " 'Accept': 'application/json'\n", " }\n", " )\n", "\n", "# These are the metadata records for each granule, this is where we get our links to the data\n", "# If the data is in the cloud the link prefix will start with s3://\n", "granules = response.json()['feed']['entry']\n", "#urls = []\n", "\n", "#can uncomment to find links you need that points to the s3 data\n", "#for granule in granules:\n", "# print(granule['links'])\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "ed3ad6c4-63ee-45f3-9d45-5669036ef5f7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['s3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019173011100_O02969_T02656_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019173024347_O02970_T01081_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019173041633_O02971_T01082_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019173054919_O02972_T05352_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019173072205_O02973_T05353_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019173085451_O02974_T02508_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019173102737_O02975_T03779_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019173120023_O02976_T00934_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019173133309_O02977_T00935_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019173150556_O02978_T05205_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019173181128_O02980_T02361_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019173194414_O02981_T00786_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019173211700_O02982_T00787_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019173224946_O02983_T05057_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019174002232_O02984_T02212_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019174015518_O02985_T03483_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019174032805_O02986_T03484_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019174050051_O02987_T00639_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019174063337_O02988_T04909_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019174080623_O02989_T02064_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019174093909_O02990_T02065_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019174111155_O02991_T03336_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019174124441_O02992_T03337_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019174141727_O02993_T00492_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019174172300_O02995_T03187_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019174185546_O02996_T03188_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019174202832_O02997_T00343_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019174220118_O02998_T00344_02_001_01.h5',\n", " 's3://ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density/data/GEDI04_A_2019174233404_O02999_T04614_02_001_01.h5']\n" ] } ], "source": [ "# we knew we need the files that end with s3# from looking at the links\n", "# Get urls of the data\n", "\n", "urls = []\n", "for granule in granules:\n", " for link in granule['links']:\n", " if link['rel'].endswith('/s3#'):\n", " urls.append(link['href'])\n", " break\n", "pprint(urls)" ] }, { "cell_type": "code", "execution_count": 6, "id": "7a60481d-507e-44a1-b472-99b31a5183c4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2 µs, sys: 0 ns, total: 2 µs\n", "Wall time: 6.91 µs\n" ] }, { "data": { "text/html": [ "
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<xarray.Dataset>\n",
       "Dimensions:  ()\n",
       "Data variables:\n",
       "    *empty*\n",
       "Attributes:\n",
       "    short_name:  GEDI_L4A
" ], "text/plain": [ "\n", "Dimensions: ()\n", "Data variables:\n", " *empty*\n", "Attributes:\n", " short_name: GEDI_L4A" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Open 1 granule (file) on S3 to explore the data\n", "%time\n", "file = s3_fs.open(urls[0])\n", "ds = xr.open_dataset(file)\n", "ds" ] }, { "cell_type": "markdown", "id": "bee4914e-14bc-4f2c-956e-18289ef27d03", "metadata": {}, "source": [ "From looking at ds, we can see it is a GEDI level4 dataset. We will look into the dataset more to find what data is in it. " ] }, { "cell_type": "code", "execution_count": 7, "id": "07895fee-6ef2-4fc8-985a-ef17b670694a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Explored the data file hierarchy and chose a variable of interest\n", "\n", "dataset=h5py.File(file,'r')\n", "dataset.keys()" ] }, { "cell_type": "code", "execution_count": 8, "id": "980c9e3f-b0fc-461f-9cb5-397be02ccc01", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#now lets see what is in beam0001 by doing the same thing\n", "beam1=dataset['BEAM0001']\n", "beam1.keys()" ] }, { "cell_type": "code", "execution_count": 10, "id": "cde2306f-5969-4742-a67d-1280e73fab01", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "agbd data: \n", "agbd data attributes: ['coordinates', 'description', 'long_name', 'units']\n" ] } ], "source": [ "#look at a agbd to understand more about the data\n", "\n", "print(\"agbd data: {}\".format(beam1['agbd']))\n", "print(\"agbd data attributes: {}\".format(list(beam1['agbd'].attrs)))\n", "\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "1cc9f511-ca73-4d3b-9dee-05fabba04fde", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "geolocation data: \n", "geolocation data attributes: []\n" ] } ], "source": [ "# look at a geolocation to understand more about the data (didn't look promising)\n", "\n", "print(\"geolocation data: {}\".format(beam1['geolocation']))\n", "print(\"geolocation data attributes: {}\".format(list(beam1['geolocation'].attrs)))\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "82e45c12-fe7c-4beb-8b22-f5d6421f9090", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "lat data: \n", "lat data attributes: ['coordinates', 'description', 'long_name', 'source', 'units', 'valid_range']\n" ] } ], "source": [ "# look at a lat_lowest mode to understand more about the data\n", "\n", "print(\"lat data: {}\".format(beam1['lat_lowestmode']))\n", "print(\"lat data attributes: {}\".format(list(beam1['lat_lowestmode'].attrs)))" ] }, { "cell_type": "code", "execution_count": 13, "id": "e6583602-4d39-4724-80b6-54144bb80507", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Unit: degrees\n" ] } ], "source": [ "# now check out its units\n", "print(\"Unit: {}\".format(beam1['lat_lowestmode'].attrs['units']))" ] }, { "cell_type": "markdown", "id": "6a1c4dab-b791-44c0-8896-6b91e9384b6a", "metadata": {}, "source": [ "Now we are going to get the variables we want out of the h5 file" ] }, { "cell_type": "code", "execution_count": 14, "id": "5a92bc0e-c07c-40ee-9786-29acbfb6dda2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset>\n",
       "Dimensions:         (segment: 364708)\n",
       "Dimensions without coordinates: segment\n",
       "Data variables:\n",
       "    lat_lowestmode  (segment) float64 -43.12 -43.12 -43.12 ... -51.81 -51.81\n",
       "    lon_lowestmode  (segment) float64 0.1851 0.1857 0.1863 ... -63.85 -63.85\n",
       "    agbd            (segment) float32 -9.999e+03 -9.999e+03 ... -9.999e+03
" ], "text/plain": [ "\n", "Dimensions: (segment: 364708)\n", "Dimensions without coordinates: segment\n", "Data variables:\n", " lat_lowestmode (segment) float64 -43.12 -43.12 -43.12 ... -51.81 -51.81\n", " lon_lowestmode (segment) float64 0.1851 0.1857 0.1863 ... -63.85 -63.85\n", " agbd (segment) float32 -9.999e+03 -9.999e+03 ... -9.999e+03" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "variable_names = [\n", " '/BEAM0001/lat_lowestmode',\n", " '/BEAM0001/lon_lowestmode',\n", " '/BEAM0001/agbd'\n", " ]\n", "\n", "\n", "\n", "with h5py.File(file, 'r') as h5:\n", " data_vars = {}\n", " for varname in variable_names:\n", " var = h5[varname]\n", " name = varname.split('/')[-1]\n", " # Convert attributes\n", " attrs = {}\n", " for k, v in var.attrs.items():\n", " if k != 'DIMENSION_LIST':\n", " if isinstance(v, bytes):\n", " attrs[k] = v.decode('utf-8')\n", " else:\n", " attrs[k] = v\n", " data = var[:]\n", " if '_FillValue' in attrs:\n", " data = np.where(data < attrs['_FillValue'], data, np.nan)\n", " data_vars[name] = (['segment'], data, attrs)\n", " gedi_ds = xr.Dataset(data_vars)\n", " \n", "gedi_ds" ] }, { "cell_type": "code", "execution_count": 15, "id": "fddb288d-64bf-4ceb-a0ad-33b98f9fbe23", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#plot agbd from the gedi dataset\n", "gedi_ds.agbd.plot() ;" ] }, { "cell_type": "code", "execution_count": 16, "id": "7f4c535d-ccfe-403c-a9cd-8000287c70c0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#plot location of data and values\n", "\n", "map_proj = ccrs.PlateCarree()\n", "\n", "fig = plt.figure(figsize=(10,5))\n", "ax = fig.add_subplot(projection=map_proj)\n", "ax.coastlines()\n", "\n", "\n", "\n", "# Plot IS2 surface height \n", "gedi_img = ax.scatter(gedi_ds.lon_lowestmode, gedi_ds.lat_lowestmode,\n", " c=gedi_ds.agbd, \n", " vmax=2000, # Set max height to plot\n", " cmap='Reds', alpha=0.6, s=2\n", " )\n", "\n", "# Add colorbars\n", "\n", "fig.colorbar(gedi_img, label='GEDI agbd (m)')\n" ] } ], "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": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 5 }