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| 1 | +{ |
| 2 | + "cells": [ |
| 3 | + { |
| 4 | + "cell_type": "markdown", |
| 5 | + "id": "dca900b4-b5ef-479d-847c-2c43bcf8e497", |
| 6 | + "metadata": {}, |
| 7 | + "source": [ |
| 8 | + "# Read in data from the S3 bucket" |
| 9 | + ] |
| 10 | + }, |
| 11 | + { |
| 12 | + "cell_type": "markdown", |
| 13 | + "id": "bd5d50d2-2af2-4f50-9935-bda6ddebe7b2", |
| 14 | + "metadata": {}, |
| 15 | + "source": [ |
| 16 | + "---" |
| 17 | + ] |
| 18 | + }, |
| 19 | + { |
| 20 | + "cell_type": "markdown", |
| 21 | + "id": "4c7d47c8-1be7-4654-85dc-d88cf8979333", |
| 22 | + "metadata": {}, |
| 23 | + "source": [ |
| 24 | + "## Imports" |
| 25 | + ] |
| 26 | + }, |
| 27 | + { |
| 28 | + "cell_type": "code", |
| 29 | + "execution_count": null, |
| 30 | + "id": "f7604cb4-59ec-4068-adeb-b79aadb50db7", |
| 31 | + "metadata": {}, |
| 32 | + "outputs": [], |
| 33 | + "source": [ |
| 34 | + "import xarray as xr\n", |
| 35 | + "import s3fs" |
| 36 | + ] |
| 37 | + }, |
| 38 | + { |
| 39 | + "cell_type": "markdown", |
| 40 | + "id": "69df2c27-18d0-47d5-86da-e231e39ef789", |
| 41 | + "metadata": {}, |
| 42 | + "source": [ |
| 43 | + "## Loading our data into xarray\n", |
| 44 | + "First, let's open a single file.\n", |
| 45 | + "\n", |
| 46 | + "Our data is stored in the cloud on Jetstream2. We'll load in one of the NetCDF files, recast it from an s3 `fsspec` object into something that `xarray` can open, and then open and examine the dataset." |
| 47 | + ] |
| 48 | + }, |
| 49 | + { |
| 50 | + "cell_type": "code", |
| 51 | + "execution_count": null, |
| 52 | + "id": "ed825e8d-407d-4b72-93fc-85ef1de1b815", |
| 53 | + "metadata": {}, |
| 54 | + "outputs": [], |
| 55 | + "source": [ |
| 56 | + "jetstream_url = 'https://js2.jetstream-cloud.org:8001/'\n", |
| 57 | + "\n", |
| 58 | + "s3 = s3fs.S3FileSystem(anon=True, client_kwargs=dict(endpoint_url=jetstream_url))\n", |
| 59 | + "\n", |
| 60 | + "s3path = 's3://pythia/ml-hurricane-intensity/final_proc_5yr_6h.nc'\n", |
| 61 | + "\n", |
| 62 | + "# Open all files from folder\n", |
| 63 | + "s3file = s3.open(s3path)\n", |
| 64 | + "\n", |
| 65 | + "# Open with xarray\n", |
| 66 | + "ds = xr.open_dataset(s3file)\n" |
| 67 | + ] |
| 68 | + }, |
| 69 | + { |
| 70 | + "cell_type": "code", |
| 71 | + "execution_count": null, |
| 72 | + "id": "31a8cc52-b063-49a9-ab4a-967097f5a9b6", |
| 73 | + "metadata": {}, |
| 74 | + "outputs": [], |
| 75 | + "source": [ |
| 76 | + "ds" |
| 77 | + ] |
| 78 | + } |
| 79 | + ], |
| 80 | + "metadata": { |
| 81 | + "kernelspec": { |
| 82 | + "display_name": "Local Turing Environment (Testing Only!)", |
| 83 | + "language": "python", |
| 84 | + "name": "local" |
| 85 | + }, |
| 86 | + "language_info": { |
| 87 | + "codemirror_mode": { |
| 88 | + "name": "ipython", |
| 89 | + "version": 3 |
| 90 | + }, |
| 91 | + "file_extension": ".py", |
| 92 | + "mimetype": "text/x-python", |
| 93 | + "name": "python", |
| 94 | + "nbconvert_exporter": "python", |
| 95 | + "pygments_lexer": "ipython3", |
| 96 | + "version": "3.13.5" |
| 97 | + } |
| 98 | + }, |
| 99 | + "nbformat": 4, |
| 100 | + "nbformat_minor": 5 |
| 101 | +} |
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