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Add network friendly functions example #42

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171 changes: 171 additions & 0 deletions sea-level-rise-s3.ipynb
Original file line number Diff line number Diff line change
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{
"cells": [
{
"cell_type": "markdown",
"id": "8f552380-cc0a-4fb8-b4b3-276a6564b239",
"metadata": {},
"source": [
"# Analyzing Sea Level Rise in the Cloud with Coiled and Earthaccess\n",
"\n",
"_This notebook was adapted from [this NASA Earthdata Cloud Cookbook example](https://nasa-openscapes.github.io/earthdata-cloud-cookbook/tutorials/Sea_Level_Rise.html)_"
]
},
{
"cell_type": "markdown",
"id": "4a30aadb-41d4-4c08-a6fa-5ea60c6bd695",
"metadata": {},
"source": [
"## Get data files with `earthaccess`"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d2d48b80-6e72-4ad0-a71d-915d96f1ce03",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"%%time\n",
"\n",
"import earthaccess\n",
"\n",
"# Retrieve data files for the dataset I'm interested in\n",
"granules = earthaccess.search_data(\n",
" short_name=\"SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL2205\",\n",
" temporal=(\"2000\", \"2019\"),\n",
")"
]
},
{
"cell_type": "markdown",
"id": "64df6beb-e1e9-4691-b6ec-18dfa6803f53",
"metadata": {},
"source": [
"## Define processing function"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f5cb0793-5273-4ede-9b8d-080a79ab8228",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import coiled\n",
"import fsspec\n",
"import xarray as xr\n",
"\n",
"@coiled.function(region=\"us-west-2\") # Same region as data\n",
"def process(granule, fs):\n",
" files = []\n",
" for file in granule.data_links(\"direct\"):\n",
" ds = xr.open_dataset(fs.open(file))\n",
" ds = ds.sel(Latitude=slice(23, 50), Longitude=slice(270, 330))\n",
" ds = ds.SLA.where((ds.SLA >= 0) & (ds.SLA < 10))\n",
" # Write processed data to S3\n",
" outfile = f\"s3://openscapes-scratch/agu-2023/{os.path.basename(file)}\"\n",
" with fsspec.open(f\"simplecache::{outfile}\", mode=\"wb\") as out:\n",
" ds.to_netcdf(out, engine=\"h5netcdf\")\n",
" files.append(outfile)\n",
" return files\n",
"\n",
"\n",
"@coiled.function(region=\"us-west-2\") # Same region as data\n",
"def plot(files):\n",
" fs = fsspec.filesystem(\"s3\")\n",
" ds = xr.open_mfdataset(\n",
" [fs.open(f, mode=\"rb\") for f in files],\n",
" concat_dim=\"Time\",\n",
" combine=\"nested\",\n",
" parallel=True,\n",
" )\n",
" return ds.SLA.std(\"Time\").plot(x=\"Longitude\", y=\"Latitude\", figsize=(14, 6))"
]
},
{
"cell_type": "markdown",
"id": "fd853bd7-244e-4fbb-9cee-5e7921d082b1",
"metadata": {},
"source": [
"## Process Granules"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "abde80ff-a03d-4bfd-975a-17fcda39ce90",
"metadata": {},
"outputs": [],
"source": [
"fs = earthaccess.get_s3fs_session(results=granules)"
]
},
{
"cell_type": "markdown",
"id": "86988643-b344-43c5-9ad7-51c17c24b4d7",
"metadata": {},
"source": [
"### Single file"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b268d9e1-b056-444e-9b99-1171e2f5b075",
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"\n",
"files = process(granules[0], fs=fs)\n",
"plot(files);"
]
},
{
"cell_type": "markdown",
"id": "cbe32079-17f9-45a8-9601-c0fe21980183",
"metadata": {},
"source": [
"### All files in parallel"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6ea9c91a-7ab6-40aa-bf6c-a75bb2afdb04",
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"\n",
"files = process.map(granules, fs=fs)\n",
"files = sum(files, []) # Flatten list of files\n",
"plot(files);"
]
}
],
"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": "3.10.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}