{ "cells": [ { "cell_type": "markdown", "id": "23831a0e-7b09-4548-9179-dff7f85111b6", "metadata": {}, "source": [ "
\n", "
\n", "
\n", "\n", "
\n", "

\n", " earthaccess and the cloud, the force awakens\n", "

\n", "

\n", " Luis A. López
\n", " NSIDC, NASA Openscapes\n", "

\n", "

\n", " NASA ESDS Tech Spotlight, Feb 2024\n", "

\n", "
\n", "
\n", "\n", "**Art**: **Allison Horts**" ] }, { "cell_type": "markdown", "id": "a0fe0d34-c41d-4de3-babc-a018900fa0d6", "metadata": {}, "source": [ "Notes:\n", "\n", "> * Disclaimer: Opinions expressed here are solely my own and do not express the views or opinions of my employer.\n", ">\n", "> * This notebook runs out of a local experimental environment that has not been pushed to the main earthaccess repository and is not expected to run as is until these changes are there.\n", ">\n", "> * This notebook focuses on the Pangeo ecosystem, as Python is the dominant language is safe to assume that we have plenty of researchers using this technologies.\n", "> \n", "> * This will be a pop-culture driven presentation, expect memes and millennial sense of humor." ] }, { "cell_type": "markdown", "id": "9cda10db-bbc3-409b-bc7c-5fda9ed1571b", "metadata": {}, "source": [ "## 1. In the cloud era, what can earthaccess do for us?\n", "\n", "---\n", "
\n", "\n", "* ### Access remote files, automatically handling authentication and serialization.\n", " * #### NASA Openscapes [cookbook examples](https://nasa-openscapes.github.io/earthdata-cloud-cookbook/tutorials/Observing_Seasonal_Ag_Changes.html)\n", "* ### Generate an on-the-fly Zarr compatible cache with Kerchunk!\n", " * #### Presented at the [Dask demo day for August/2023](https://youtu.be/MepdrbDjYPc?t=984)\n", "* ### Smart Access ◀\n", " * ### Sneak peak today, *more details at SciPy 2024!* \n", "* ### Scale out workflows with Dask ◀\n", " * ### [Processing Terabyte-Scale NASA Cloud Datasets with Coiled](https://blog.coiled.io/blog/processing-terabyte-scale-nasa-cloud-datasets-with-coiled.html)\n", "\n", "--- \n", "
\n", "\n", "
\n", "\n", "
\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "1350657b-7c0f-423b-8805-59e063d99a5c", "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", " var py_version = '3.3.4'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", " var reloading = false;\n", " var Bokeh = root.Bokeh;\n", "\n", " if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_failed_load = false;\n", " }\n", "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) {\n", " if (callback != null)\n", " callback();\n", " });\n", " } finally {\n", " delete root._bokeh_onload_callbacks;\n", " }\n", " console.debug(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", " if (css_urls == 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\n", "" ] }, "metadata": { "application/vnd.holoviews_exec.v0+json": { "id": "p1002" } }, "output_type": "display_data" } ], "source": [ "import earthaccess\n", "\n", "import hvplot.xarray\n", "import xarray as xr\n", "import time\n", "\n", "auth = earthaccess.login()" ] }, { "cell_type": "code", "execution_count": 94, "id": "716dae68-36e6-47d9-bbe5-33b1c6612bb2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Granules found: 20\n" ] } ], "source": [ "datasets = [\"ATL03\"]\n", "\n", "results = earthaccess.search_data(\n", " short_name=datasets,\n", " cloud_hosted=True,\n", " temporal=(\"2022-01\", \"2023-10\"),\n", " bounding_box=(34.44,31.52,34.48,31.54),\n", " count=10\n", ")" ] }, { "cell_type": "code", "execution_count": 95, "id": "4dee6684-8c5f-4c39-b251-724734a20f24", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
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Data: ATL03_20220808183613_07311602_006_01.h5

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Size: 663.88 MB

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Cloud Hosted: True

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\n", " " ], "text/plain": [ "Collection: {'EntryTitle': 'ATLAS/ICESat-2 L2A Global Geolocated Photon Data V006'}\n", "Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': 30.52795, 'Latitude': 59.5424}, {'Longitude': 30.30746, 'Latitude': 59.5308}, {'Longitude': 30.56149, 'Latitude': 58.25762}, {'Longitude': 31.13017, 'Latitude': 55.16358}, {'Longitude': 31.55882, 'Latitude': 52.61709}, {'Longitude': 31.94924, 'Latitude': 50.13897}, {'Longitude': 32.58842, 'Latitude': 45.72077}, {'Longitude': 33.14276, 'Latitude': 41.56439}, {'Longitude': 33.63922, 'Latitude': 37.5987}, {'Longitude': 34.04775, 'Latitude': 34.18401}, {'Longitude': 34.78772, 'Latitude': 27.64814}, {'Longitude': 34.86486, 'Latitude': 26.94847}, {'Longitude': 34.99077, 'Latitude': 26.95949}, {'Longitude': 34.91437, 'Latitude': 27.65923}, {'Longitude': 34.18329, 'Latitude': 34.19532}, {'Longitude': 33.78073, 'Latitude': 37.60982}, {'Longitude': 33.29258, 'Latitude': 41.57551}, {'Longitude': 32.74893, 'Latitude': 45.73193}, {'Longitude': 32.12403, 'Latitude': 50.15024}, {'Longitude': 31.74331, 'Latitude': 52.62832}, {'Longitude': 31.32622, 'Latitude': 55.17493}, {'Longitude': 30.77427, 'Latitude': 58.26925}, {'Longitude': 30.52795, 'Latitude': 59.5424}]}}]}}}\n", "Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2022-08-08T18:36:11.782Z', 'EndingDateTime': '2022-08-08T18:44:42.495Z'}}\n", "Size(MB): 663.8765316009521\n", "Data: ['https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-protected/ATLAS/ATL03/006/2022/08/08/ATL03_20220808183613_07311602_006_01.h5']" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results[3]" ] }, { "cell_type": "code", "execution_count": 96, "id": "eb60263d-16e3-4b77-9f6f-afdc16ce478e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('349.38 MB', 'ATL03_20220208031618_07311402_006_01.h5'),\n", " ('663.88 MB', 'ATL03_20220808183613_07311602_006_01.h5'),\n", " ('665.11 MB', 'ATL03_20221107141555_07311702_006_01.h5'),\n", " ('779.02 MB', 'ATL03_20220509225606_07311502_006_01.h5'),\n", " ('800.61 MB', 'ATL03_20230807011436_07312002_006_01.h5'),\n", " ('1115.44 MB', 'ATL03_20220926042045_00831706_006_01.h5'),\n", " ('1584.02 MB', 'ATL03_20230625151938_00832006_006_01.h5'),\n", " ('2042.96 MB', 'ATL03_20220828054448_10281606_006_01.h5'),\n", " ('2634.99 MB', 'ATL03_20230508053526_07311902_006_01.h5'),\n", " ('3586.49 MB', 'ATL03_20220529100448_10281506_006_01.h5')]" ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ordered = sorted(results, key=lambda d: d.size())\n", "[(f\"{round(g.size(),2)} MB\", g[\"meta\"][\"native-id\"]) for g in ordered]" ] }, { "cell_type": "markdown", "id": "7fbb57d5-1a11-47d5-b274-c1ed5f0e3bbe", "metadata": {}, "source": [ "## 2. What \"cloud native workflows\" means?\n", "
\n", "\n", "* ### When we run cloud native workflows, we don't replicate the archives. e.g. COGs or Zarr. Instead, this is what usually happens:\n", "\n", " * ```python\n", " import xarray as xr\n", " \n", " # We open our data and we live happily ever after\n", " ds = xr.open_dataset(some_zarr_store, engine=\"zarr\")\n", " \n", " ```\n", "
\n", "\n", "* #### Most mission data is not in COGs or Zarr but fear not, **earthaccess** FTW!.\n", "\n", "\n", "> [Closed Platforms vs. Open Architectures for Cloud-Native Earth System Analytics](https://medium.com/pangeo/closed-platforms-vs-open-architectures-for-cloud-native-earth-system-analytics-1ad88708ebb6)\n", "> \n", "> [HDF in the Cloud](https://matthewrocklin.com/blog/work/2018/02/06/hdf-in-the-cloud)\n", "\n", "\n", "\n", "
\n", "Credit: Open Architecture for scalable cloud-based data analytics. From Abernathey, Ryan (2020): Data Access Modes in Science.
Figure. https://doi.org/10.6084/m9.figshare.11987466.v1\n", "" ] }, { "cell_type": "code", "execution_count": 5, "id": "1fe4beef-c0b9-49c5-9578-9b45ebe22287", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Opening 1 granules, approx size: 0.34 GB\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e542e7e4c06d4cfa98d48a6ba0d2020d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "QUEUEING TASKS | : 0%| | 0/1 [00:00\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:         (delta_time: 3376706, ds_surf_type: 5)\n",
       "Coordinates:\n",
       "  * delta_time      (delta_time) datetime64[ns] 2022-02-08T03:16:18.010370560...\n",
       "    lat_ph          (delta_time) float64 ...\n",
       "    lon_ph          (delta_time) float64 ...\n",
       "Dimensions without coordinates: ds_surf_type\n",
       "Data variables:\n",
       "    dist_ph_across  (delta_time) float32 ...\n",
       "    dist_ph_along   (delta_time) float32 ...\n",
       "    h_ph            (delta_time) float32 ...\n",
       "    pce_mframe_cnt  (delta_time) uint32 ...\n",
       "    ph_id_channel   (delta_time) uint8 ...\n",
       "    ph_id_count     (delta_time) uint8 ...\n",
       "    ph_id_pulse     (delta_time) uint8 ...\n",
       "    quality_ph      (delta_time) int8 ...\n",
       "    signal_conf_ph  (delta_time, ds_surf_type) int8 ...\n",
       "    weight_ph       (delta_time) uint8 ...\n",
       "Attributes:\n",
       "    Description:  Contains arrays of the parameters for each received photon.\n",
       "    data_rate:    Data are stored at the photon detection rate.
" ], "text/plain": [ "\n", "Dimensions: (delta_time: 3376706, ds_surf_type: 5)\n", "Coordinates:\n", " * delta_time (delta_time) datetime64[ns] 2022-02-08T03:16:18.010370560...\n", " lat_ph (delta_time) float64 ...\n", " lon_ph (delta_time) float64 ...\n", "Dimensions without coordinates: ds_surf_type\n", "Data variables:\n", " dist_ph_across (delta_time) float32 ...\n", " dist_ph_along (delta_time) float32 ...\n", " h_ph (delta_time) float32 ...\n", " pce_mframe_cnt (delta_time) uint32 ...\n", " ph_id_channel (delta_time) uint8 ...\n", " ph_id_count (delta_time) uint8 ...\n", " ph_id_pulse (delta_time) uint8 ...\n", " quality_ph (delta_time) int8 ...\n", " signal_conf_ph (delta_time, ds_surf_type) int8 ...\n", " weight_ph (delta_time) uint8 ...\n", "Attributes:\n", " Description: Contains arrays of the parameters for each received photon.\n", " data_rate: Data are stored at the photon detection rate." ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "\n", "start_time = time.time()\n", "ds_normal = xr.open_dataset(\n", " fo_normal,\n", " group=\"/gt1l/heights\",\n", " engine=\"h5netcdf\")\n", "t_normal = round(time.time() - start_time, 2)\n", "\n", "print(f\"Opening the data (out-of-region) with xarray took {t_normal} seconds\")\n", "ds_normal" ] }, { "cell_type": "markdown", "id": "4ea96ea7-5dcb-4122-9de9-01e4b69c4208", "metadata": {}, "source": [ "## 3. Let's use the force.\n", "\n", "
\n", "\n", "
" ] }, { "cell_type": "code", "execution_count": 10, "id": "122f83fa-ad3a-4c2c-afc2-79bdb9c1a734", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Opening 1 granules, approx size: 0.34 GB\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5e51b827e71042e59b923dc0315eda2c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "QUEUEING TASKS | : 0%| | 0/1 [00:00\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:         (delta_time: 3376706, ds_surf_type: 5)\n",
       "Coordinates:\n",
       "  * delta_time      (delta_time) datetime64[ns] 2022-02-08T03:16:18.010370560...\n",
       "    lat_ph          (delta_time) float64 ...\n",
       "    lon_ph          (delta_time) float64 ...\n",
       "Dimensions without coordinates: ds_surf_type\n",
       "Data variables:\n",
       "    dist_ph_across  (delta_time) float32 ...\n",
       "    dist_ph_along   (delta_time) float32 ...\n",
       "    h_ph            (delta_time) float32 ...\n",
       "    pce_mframe_cnt  (delta_time) uint32 ...\n",
       "    ph_id_channel   (delta_time) uint8 ...\n",
       "    ph_id_count     (delta_time) uint8 ...\n",
       "    ph_id_pulse     (delta_time) uint8 ...\n",
       "    quality_ph      (delta_time) int8 ...\n",
       "    signal_conf_ph  (delta_time, ds_surf_type) int8 ...\n",
       "    weight_ph       (delta_time) uint8 ...\n",
       "Attributes:\n",
       "    Description:  Contains arrays of the parameters for each received photon.\n",
       "    data_rate:    Data are stored at the photon detection rate.
" ], "text/plain": [ "\n", "Dimensions: (delta_time: 3376706, ds_surf_type: 5)\n", "Coordinates:\n", " * delta_time (delta_time) datetime64[ns] 2022-02-08T03:16:18.010370560...\n", " lat_ph (delta_time) float64 ...\n", " lon_ph (delta_time) float64 ...\n", "Dimensions without coordinates: ds_surf_type\n", "Data variables:\n", " dist_ph_across (delta_time) float32 ...\n", " dist_ph_along (delta_time) float32 ...\n", " h_ph (delta_time) float32 ...\n", " pce_mframe_cnt (delta_time) uint32 ...\n", " ph_id_channel (delta_time) uint8 ...\n", " ph_id_count (delta_time) uint8 ...\n", " ph_id_pulse (delta_time) uint8 ...\n", " quality_ph (delta_time) int8 ...\n", " signal_conf_ph (delta_time, ds_surf_type) int8 ...\n", " weight_ph (delta_time) uint8 ...\n", "Attributes:\n", " Description: Contains arrays of the parameters for each received photon.\n", " data_rate: Data are stored at the photon detection rate." ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "start_time = time.time()\n", "\n", "ds_smart = xr.open_dataset(\n", " fo_smart,\n", " group=\"/gt1l/heights\",\n", " engine=\"h5netcdf\")\n", "\n", "t_smart = round(time.time() - start_time, 2)\n", "print(f\"Opening the data (out-of-region) with xarray took {t_smart} seconds with earthacess smart open\")\n", "\n", "ds_smart" ] }, { "cell_type": "code", "execution_count": 12, "id": "8d8073f2-1bae-4bb8-a774-f936f31e3ff2", "metadata": {}, "outputs": [], "source": [ "mean_normal = ds_normal.h_ph.mean()\n", "mean_smart = ds_smart.h_ph.mean()\n", "\n", "assert mean_normal == mean_smart" ] }, { "cell_type": "markdown", "id": "6002132a-bef5-4eb9-9b88-4f87c0ff7c86", "metadata": {}, "source": [ "## 4. Recap and the importance of optimized access patterns.\n", "\n", "* ### We accessed one granule of mission data with earthaccess and xarray.\n", " * #### The first time we opened it as usual, it takes forever...\n", " * #### The second time we used experimental earthaccess' \"smart open\" capabilities.\n", " \n", "* ### Now let's see some I/O stats" ] }, { "cell_type": "code", "execution_count": 92, "id": "c57de1e9-6f5b-4dc5-8992-8b62c3b8684d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "1 data granule\n", "Size: 349.38 MB\n", "\n", "Egress:\n", " without earthaccess: 3199.29 MB \n", " with earthaccess : 112.0 MB\n", "\n", "Time to science:\n", " without earthaccess: 15.9 minutes \n", " with earthaccess : 0.52 minutes\n", "\n" ] } ], "source": [ "print(f\"\"\"\n", "1 data granule\n", "Size: {round(ordered[0].size(), 2)} MB\n", "\n", "Egress:\n", " without earthaccess: {round(fo_normal.cache.total_requested_bytes / 1024**2, 2)} MB \n", " with earthaccess : {round(fo_smart.cache.total_requested_bytes / 1024**2, 2)} MB\n", "\n", "Time to science:\n", " without earthaccess: {round(t_normal/60, 2)} minutes \n", " with earthaccess : {round(t_smart/60, 2)} minutes\n", "\"\"\")" ] }, { "cell_type": "markdown", "id": "59f4f92f-618d-470b-929a-86cb9bd4bf76", "metadata": {}, "source": [ "> Bibliography:\n", "> * [Patrick Quinn's Cloud-Optimized Format Study](https://ntrs.nasa.gov/api/citations/20200001178/downloads/20200001178.pdf)\n", "> * [ Aleksandar Jelenak: Toward Cloud-Optimized HDF5 Files](https://ntrs.nasa.gov/citations/20220019334)" ] }, { "cell_type": "markdown", "id": "e0ce0528-f6b2-4801-9764-3e84084e6c0f", "metadata": {}, "source": [ "## 5. Why is this important?\n", "\n", "
\n", "\n", "
\n", "\n", "Source: [2023 NASA ESDS Program Highlights](https://www.earthdata.nasa.gov/esds/esds-highlights/2023-esds-highlights)\n", "\n", "
\n", "\n", "\n", " \n", "* #### As data in the cloud grows, we need to provide researchers with the means to do cloud native workflows, **the simple way!**\n", "* #### Cloud native workflows could get expensive, **earthaccess can help!**\n", "\n", "
" ] }, { "cell_type": "markdown", "id": "ed192214-fcb8-413c-8b4b-7160c4eac8c4", "metadata": {}, "source": [ "# 6. Cloud compute with *earthaccess*, from [terabyte](https://blog.coiled.io/blog/processing-terabyte-scale-nasa-cloud-datasets-with-coiled.html) to petabyte scale analysis.\n", "\n", "### We are going to answer a scientific question: \"How cold do the Great Lakes get in December?\"\n", "\n", "* #### Local execution: 10 years, 2 days around Santa's visit. ~ 10GB of HDF\n", "* #### Cloud execution: 20 years, all December. ~ 0.25 TB of HDF\n", "\n", "* #### We'll use NASA's [GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1)](https://podaac.jpl.nasa.gov/dataset/MUR-JPL-L4-GLOB-v4.1)\n", "\n", "
\n", "
\n", "\n", "#### Now you're probably asking, what AWS integration is he going to use? Lambda? Step Functions? Perhaps a novel SQS-powered pipeline...\n", "\n", "
\n", "\n", "
\n", "\n", "
\n" ] }, { "cell_type": "markdown", "id": "1620b56c-0daf-4691-a600-a3f7ec499126", "metadata": { "jupyter": { "source_hidden": true } }, "source": [ "\n", "> A Little background story\n", "\n", "
\n", "
\n", "\n", "

\n", " Me and others talking to Nobel Prize winner Paul Romer, very approchable guy who happens to be a Pythonista. Paris, 2023.\n", "

\n", "
\n", "\n", "
\n", "\n", "* After talking to Paul Romer, I met some Dask core maintainers and started to chat about our projects. They left NVIDIA to start a company called Coiled.\n", "* They(Coiled) were looking for a simple package to handle NASA APIs for a climate startup, I was looking for ways to unlock the real potential of earthaccess.\n", "* A colaboration from academia and industry led to improvements in *earthaccess* \n" ] }, { "cell_type": "code", "execution_count": 24, "id": "cd14bad9-142a-4e15-8c6b-ef4d0b1ac481", "metadata": {}, "outputs": [], "source": [ "%%capture\n", "import coiled\n", "import dask.bag as db\n", "import numpy as np\n", "\n", "december_ts = []\n", "total_size = 0\n", "total_granules = 0\n", "\n", "for year in range(2013, 2023):\n", " print(f\"Searching for year {year}: \")\n", " results = earthaccess.search_data(\n", " short_name=\"MUR-JPL-L4-GLOB-v4.1\",\n", " # temporal=(f\"{year}-12-01\", f\"{year}-12-31\"),\n", " temporal=(f\"{year}-12-24\", f\"{year}-12-25\"),\n", " )\n", " total_size += round(sum([g.size() for g in results]), 2)\n", " total_granules += len(results)\n", " december_ts.append({f\"{year}\": results})" ] }, { "cell_type": "code", "execution_count": 25, "id": "0052b978-1832-421a-b432-0fb204108567", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "granules: 20, total size: 9.45GB\n" ] } ], "source": [ "print(f\"granules: {total_granules}, total size: {round(total_size/1024, 2)}GB\")" ] }, { "cell_type": "markdown", "id": "cdbbd87d-d943-4dd3-aebf-68bf46f79dc2", "metadata": {}, "source": [ "
\n", "

\n", " “Unfortunately, no one can be told what cloud compute at scale is.
\n", " You have to see it for yourself.”
\n", " — Luis López\n", "

\n", "\n", "
\n" ] }, { "cell_type": "code", "execution_count": 80, "id": "240a8f5e-82ac-42a2-9c15-4654bcca7265", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "@coiled.function(\n", " name=\"clusty\",\n", " region=\"us-west-2\", # Run in the same region as data\n", " environ=earthaccess.auth_environ(), # Forward Earthdata auth to cloud VMs\n", " arm=True, # Use ARM-based instances \n", " vm_type=\"x2gd.medium\",\n", " local=True\n", ")\n", "def process(data):\n", " granules, params= data\n", " results = []\n", " io_stats = {\n", " \"total\": 0,\n", " \"hits\": 0,\n", " \"miss\": 0\n", " }\n", " bbox = params[\"bbox\"]\n", " variables = params[\"variables\"]\n", " agg = params[\"agg\"]\n", "\n", " def aggr_func(dataset, op):\n", " \"\"\"Apply different aggregations to an xarray Dataset.\"\"\"\n", " supported_ops = {\n", " 'mean': lambda ds: ds.mean(\"time\"), 'median': lambda ds: ds.median(\"time\"), 'min': lambda ds: ds.min(\"time\"),\n", " 'max': lambda ds: ds.max(\"time\"), 'std': lambda ds: ds.std(\"time\")}\n", " try:\n", " func = supported_ops[op]\n", " return func(dataset)\n", " except KeyError:\n", " raise ValueError(f'\"{op}\" aggregation operation not supported')\n", " \n", " earthaccess.login()\n", "\n", " fileset = earthaccess.open(granules, smart=True)\n", " min_lon, min_lat, max_lon, max_lat = bbox\n", " for file in fileset:\n", " ds = xr.open_dataset(file)\n", " ds = ds.sel(lon=slice(min_lon, max_lon), lat=slice(min_lat, max_lat))\n", " # this is particular to this dataset but can be parametrized\n", " cond = (ds.sea_ice_fraction < 0.15) | np.isnan(ds.sea_ice_fraction)\n", " result = ds.where(cond)\n", " results.append(result[variables])\n", " # This is only to keep track of IO\n", " io_stats[\"total\"] += file.cache.total_requested_bytes\n", " io_stats[\"hits\"] += file.cache.hit_count\n", " io_stats[\"miss\"] += file.cache.miss_count\n", " cube = xr.concat(results, dim=\"time\")\n", " if agg:\n", " cube_redux = aggr_func(cube, agg)\n", " cube_redux[\"time\"] = cube.time.min().values\n", " return (cube_redux, io_stats)\n", " return (cube[variables], io_stats)" ] }, { "cell_type": "markdown", "id": "beb051d9-a256-4c3f-8e98-925906eb049e", "metadata": {}, "source": [ "### Remote code execution, or local code execution... our choice." ] }, { "cell_type": "code", "execution_count": 82, "id": "3330d9b1-e5d1-484f-8b68-3552f345f883", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Step 3: Run function on each file in parallel\n", "params = {\n", " \"bbox\" : (-94.93, 40.26, -73.83, 48.95),\n", " \"variables\": [\"analysed_sst\"],\n", " \"agg\": \"mean\"\n", "}\n", "\n", "data = list(map(lambda e: (list(e.values())[0], params), december_ts))\n", "\n", "dask_bag = db.from_sequence(data)\n", "c = dask_bag.map(process)\n", "c.visualize()" ] }, { "cell_type": "code", "execution_count": 83, "id": "445a6982-b83e-435e-a6ac-cca640f363f4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 28.9 s, sys: 5.62 s, total: 34.6 s\n", "Wall time: 2min 37s\n" ] }, { "data": { "text/plain": [ "{'miss': 252, 'hits': 1968, 'total': 1008.0}" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "computation = process.map(data)\n", "dds, iostats = zip(*computation)\n", "\n", "# This is only to consolidate the i/o stats\n", "agg = {key:sum(i[key] for i in iostats if key in i) \n", " for key in set(a for l in iostats for a in l.keys())}\n", "\n", "agg[\"total\"] = agg[\"total\"] / (1024*1024) # MB\n", "agg" ] }, { "cell_type": "code", "execution_count": 84, "id": "b3325042-c8e9-407a-98a6-7368dcad735c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:       (time: 10, lat: 870, lon: 2111)\n",
       "Coordinates:\n",
       "  * lat           (lat) float32 40.26 40.27 40.28 40.29 ... 48.93 48.94 48.95\n",
       "  * lon           (lon) float32 -94.93 -94.92 -94.91 ... -73.85 -73.84 -73.83\n",
       "  * time          (time) datetime64[ns] 2013-12-24T09:00:00 ... 2022-12-24T09...\n",
       "Data variables:\n",
       "    analysed_sst  (time, lat, lon) float32 nan nan nan nan ... nan nan nan nan
" ], "text/plain": [ "\n", "Dimensions: (time: 10, lat: 870, lon: 2111)\n", "Coordinates:\n", " * lat (lat) float32 40.26 40.27 40.28 40.29 ... 48.93 48.94 48.95\n", " * lon (lon) float32 -94.93 -94.92 -94.91 ... -73.85 -73.84 -73.83\n", " * time (time) datetime64[ns] 2013-12-24T09:00:00 ... 2022-12-24T09...\n", "Data variables:\n", " analysed_sst (time, lat, lon) float32 nan nan nan nan ... nan nan nan nan" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds = xr.concat(dds, dim=\"time\")\n", "\n", "# transform to celcius\n", "ds[\"analysed_sst\"] = ds[\"analysed_sst\"] - 273.15\n", "ds.analysed_sst.attrs['units'] = 'degC'\n", "\n", "ds" ] }, { "cell_type": "code", "execution_count": 85, "id": "bc82c37a-4183-48ff-bd45-f53e9c0be2c5", "metadata": {}, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.holoviews_exec.v0+json": "", "text/html": [ "
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\n", "" ], "text/plain": [ ":DynamicMap [time]\n", " :Overlay\n", " .Polygons.I :Polygons [lon,lat] (analysed_sst)\n", " .Coastline.I :Feature [Longitude,Latitude]" ] }, "execution_count": 85, "metadata": { "application/vnd.holoviews_exec.v0+json": { "id": "p118215" } }, "output_type": "execute_result" } ], "source": [ "hvplot.output(widget_location=\"bottom\")\n", "ds.analysed_sst.hvplot.contourf(\n", " groupby=\"time\",\n", " cmap=\"viridis\",\n", " x=\"lon\", y=\"lat\",\n", " levels=10,\n", " coastline=True)" ] }, { "cell_type": "code", "execution_count": 86, "id": "cab212fc-72d1-4896-b5d3-b183da01908c", "metadata": {}, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.holoviews_exec.v0+json": "", "text/html": [ "
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\n", "" ], "text/plain": [ ":NdOverlay [lat]\n", " :Distribution [analysed_sst] (Density)" ] }, "execution_count": 86, "metadata": { "application/vnd.holoviews_exec.v0+json": { "id": "p121801" } }, "output_type": "execute_result" } ], "source": [ "ds.isel(time=0).sel(lat=[42,44,46,48]).hvplot.kde(\"analysed_sst\", by='lat', alpha=0.5)" ] }, { "cell_type": "code", "execution_count": 89, "id": "8db40265-5a51-417e-9c57-e2b4c8b3dcf5", "metadata": {}, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.holoviews_exec.v0+json": "", "text/html": [ "
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\n", "" ], "text/plain": [ ":NdOverlay [lat]\n", " :Distribution [analysed_sst] (Density)" ] }, "execution_count": 89, "metadata": { "application/vnd.holoviews_exec.v0+json": { "id": "p122233" } }, "output_type": "execute_result" } ], "source": [ "ds.isel(time=3).sel(lat=[42,44,46,48]).hvplot.kde(\"analysed_sst\", by='lat', alpha=0.5)" ] }, { "cell_type": "code", "execution_count": 103, "id": "ee44669f-76fc-4e41-8a54-a5fb3914f247", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total egress: 0.98gb\n" ] } ], "source": [ "print(f\"total egress: {round(agg['total']/1024, 2)}gb\")" ] }, { "cell_type": "markdown", "id": "9701f5d8-10e7-4ef0-9e87-a600b51fbc53", "metadata": { "jp-MarkdownHeadingCollapsed": true }, "source": [ "
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\n", " Cloud computing with earthaccess\n", "

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\n", " Thanks to the earthaccess community and the projects that have supported this work!\n", "

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