From 5accd9c86c61da28b6d8456b50f7a36677f506d7 Mon Sep 17 00:00:00 2001 From: asteiker Date: Wed, 29 Nov 2023 00:09:53 +0000 Subject: [PATCH 01/13] tutorial outline --- book/tutorials/NASA-Earthdata-Cloud.ipynb | 121 ++++++++++++++++++++++ 1 file changed, 121 insertions(+) create mode 100644 book/tutorials/NASA-Earthdata-Cloud.ipynb diff --git a/book/tutorials/NASA-Earthdata-Cloud.ipynb b/book/tutorials/NASA-Earthdata-Cloud.ipynb new file mode 100644 index 0000000..2ef8fc9 --- /dev/null +++ b/book/tutorials/NASA-Earthdata-Cloud.ipynb @@ -0,0 +1,121 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7fd4844a-aee8-4a9c-b22a-02688a8067f9", + "metadata": { + "user_expressions": [] + }, + "source": [ + "# NASA Earthdata Cloud and data access using earthaccess and icepyx\n", + "\n", + "TODOs / Questions:\n", + "* Do we want to break this into multiple notebooks/ md files?\n", + "* Where does this live within the existing CryoCloud book?\n", + "\n", + "## 1. Tutorial Overview\n", + "___\n", + "\n", + "### Introduction\n", + "This tutorial is designed for the \"[Cloud Computing and Open-Source Scientific Software for Cryosphere Communities](https://agu.confex.com/agu/fm23/meetingapp.cgi/Session/193477)\" Learning Workshop at the 2023 AGU Fall Meeting.\n", + "\n", + "### Credits\n", + "\n", + "\n", + "### Learning Objectives\n", + "\n", + "### Prerequisites" + ] + }, + { + "cell_type": "markdown", + "id": "b651cd5d-5e8f-4747-88fd-c05b736c554b", + "metadata": {}, + "source": [ + "## 2. Introduction to NASA Earthdata and NASA Earthdata Cloud" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9e0e96c6-ede7-4b4b-88b0-fcbaf8050a1f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "cda28d37-7a59-4571-ad2f-62c91f324e38", + "metadata": {}, + "source": [ + "## 3. Cloud Computing Considerations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b3be0ba0-01f7-4753-a984-5d9e04b02e68", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "ca5df483-a6a1-4680-be2f-d5497a47b077", + "metadata": { + "tags": [] + }, + "source": [ + "## 4. Introduction to ICESat-2\n", + "\n", + "* Scientific use case leveraging cloud computing\n", + "* Consider Andy's slides from Coiled Community call: https://github.com/andypbarrett/openscapes_community_chat_coiled" + ] + }, + { + "cell_type": "markdown", + "id": "08f25ec2-348f-4855-9aa3-97cc64f9bffa", + "metadata": {}, + "source": [ + "## 5. Search and access data using `earthaccess` " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c965d59f-5e6b-45cf-a63b-694c94e95d0a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "7cc66d51-9b34-440d-9edc-617499c3cec9", + "metadata": {}, + "source": [ + "## 6. Using `icepyx` to search and access ICESat-2 data" + ] + } + ], + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 1ad5ac5f5b0bf68ec37419a02791798e61de7d3f Mon Sep 17 00:00:00 2001 From: asteiker Date: Wed, 29 Nov 2023 22:59:31 +0000 Subject: [PATCH 02/13] broke out tutorials into separate notebooks --- ...loud.ipynb => Intro-Earthdata-Cloud.ipynb} | 30 ++++---- book/tutorials/earthaccess.ipynb | 71 +++++++++++++++++++ book/tutorials/icepyx.ipynb | 71 +++++++++++++++++++ 3 files changed, 157 insertions(+), 15 deletions(-) rename book/tutorials/{NASA-Earthdata-Cloud.ipynb => Intro-Earthdata-Cloud.ipynb} (86%) create mode 100644 book/tutorials/earthaccess.ipynb create mode 100644 book/tutorials/icepyx.ipynb diff --git a/book/tutorials/NASA-Earthdata-Cloud.ipynb b/book/tutorials/Intro-Earthdata-Cloud.ipynb similarity index 86% rename from book/tutorials/NASA-Earthdata-Cloud.ipynb rename to book/tutorials/Intro-Earthdata-Cloud.ipynb index 2ef8fc9..faaa68f 100644 --- a/book/tutorials/NASA-Earthdata-Cloud.ipynb +++ b/book/tutorials/Intro-Earthdata-Cloud.ipynb @@ -7,10 +7,10 @@ "user_expressions": [] }, "source": [ - "# NASA Earthdata Cloud and data access using earthaccess and icepyx\n", + "# NASA Earthdata Cloud and data access using earthaccess and icepyx: \n", + "## Introduction to NASA Earthdata and NASA Earthdata Cloud\n", "\n", "TODOs / Questions:\n", - "* Do we want to break this into multiple notebooks/ md files?\n", "* Where does this live within the existing CryoCloud book?\n", "\n", "## 1. Tutorial Overview\n", @@ -30,9 +30,11 @@ { "cell_type": "markdown", "id": "b651cd5d-5e8f-4747-88fd-c05b736c554b", - "metadata": {}, + "metadata": { + "user_expressions": [] + }, "source": [ - "## 2. Introduction to NASA Earthdata and NASA Earthdata Cloud" + "## 2. NASA Earthdata Cloud" ] }, { @@ -46,7 +48,9 @@ { "cell_type": "markdown", "id": "cda28d37-7a59-4571-ad2f-62c91f324e38", - "metadata": {}, + "metadata": { + "user_expressions": [] + }, "source": [ "## 3. Cloud Computing Considerations" ] @@ -63,7 +67,8 @@ "cell_type": "markdown", "id": "ca5df483-a6a1-4680-be2f-d5497a47b077", "metadata": { - "tags": [] + "tags": [], + "user_expressions": [] }, "source": [ "## 4. Introduction to ICESat-2\n", @@ -72,14 +77,6 @@ "* Consider Andy's slides from Coiled Community call: https://github.com/andypbarrett/openscapes_community_chat_coiled" ] }, - { - "cell_type": "markdown", - "id": "08f25ec2-348f-4855-9aa3-97cc64f9bffa", - "metadata": {}, - "source": [ - "## 5. Search and access data using `earthaccess` " - ] - }, { "cell_type": "code", "execution_count": null, @@ -91,7 +88,10 @@ { "cell_type": "markdown", "id": "7cc66d51-9b34-440d-9edc-617499c3cec9", - "metadata": {}, + "metadata": { + "tags": [], + "user_expressions": [] + }, "source": [ "## 6. Using `icepyx` to search and access ICESat-2 data" ] diff --git a/book/tutorials/earthaccess.ipynb b/book/tutorials/earthaccess.ipynb new file mode 100644 index 0000000..1ca5a79 --- /dev/null +++ b/book/tutorials/earthaccess.ipynb @@ -0,0 +1,71 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7fd4844a-aee8-4a9c-b22a-02688a8067f9", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "# NASA Earthdata Cloud and data access using earthaccess and icepyx:\n", + "## Introduction to the `earthaccess` python library\n", + "\n", + "TODOs / Questions:\n", + "* Where does this live within the existing CryoCloud book?\n", + "\n", + "## 1. Tutorial Overview\n", + "___\n", + "\n", + "### Introduction\n", + "This tutorial is designed for the \"[Cloud Computing and Open-Source Scientific Software for Cryosphere Communities](https://agu.confex.com/agu/fm23/meetingapp.cgi/Session/193477)\" Learning Workshop at the 2023 AGU Fall Meeting.\n", + "\n", + "### Credits\n", + "\n", + "\n", + "### Learning Objectives\n", + "\n", + "### Prerequisites" + ] + }, + { + "cell_type": "markdown", + "id": "08f25ec2-348f-4855-9aa3-97cc64f9bffa", + "metadata": { + "user_expressions": [] + }, + "source": [ + "## 2. Search and access data using `earthaccess` " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c965d59f-5e6b-45cf-a63b-694c94e95d0a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/book/tutorials/icepyx.ipynb b/book/tutorials/icepyx.ipynb new file mode 100644 index 0000000..3b3e078 --- /dev/null +++ b/book/tutorials/icepyx.ipynb @@ -0,0 +1,71 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7fd4844a-aee8-4a9c-b22a-02688a8067f9", + "metadata": { + "user_expressions": [] + }, + "source": [ + "# NASA Earthdata Cloud and data access using earthaccess and icepyx:\n", + "## Using the `icepyx` python library to access ICESat-2 data\n", + "\n", + "TODOs / Questions:\n", + "* Where does this live within the existing CryoCloud book?\n", + "\n", + "## 1. Tutorial Overview\n", + "___\n", + "\n", + "### Introduction\n", + "This tutorial is designed for the \"[Cloud Computing and Open-Source Scientific Software for Cryosphere Communities](https://agu.confex.com/agu/fm23/meetingapp.cgi/Session/193477)\" Learning Workshop at the 2023 AGU Fall Meeting.\n", + "\n", + "### Credits\n", + "\n", + "\n", + "### Learning Objectives\n", + "\n", + "### Prerequisites" + ] + }, + { + "cell_type": "markdown", + "id": "7cc66d51-9b34-440d-9edc-617499c3cec9", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "## 2. Using `icepyx` to search and access ICESat-2 data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d070bb18-ff3e-49c3-9cae-d933c871984d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 85b8d8e6f6a7124160ec2c8489c255328060263f Mon Sep 17 00:00:00 2001 From: asteiker Date: Sun, 3 Dec 2023 19:22:34 +0000 Subject: [PATCH 03/13] reorg and intro content build --- book/tutorials/Intro-Earthdata-Cloud.ipynb | 121 ------------------ .../Intro-Earthdata-Cloud.md | 76 +++++++++++ .../earthaccess.ipynb | 0 .../icepyx.ipynb | 0 .../images/NSIDC-DAAC.png | Bin 0 -> 165570 bytes .../images/shiklomanov-cloud-paradigm.png | Bin 0 -> 197226 bytes 6 files changed, 76 insertions(+), 121 deletions(-) delete mode 100644 book/tutorials/Intro-Earthdata-Cloud.ipynb create mode 100644 book/tutorials/NASA-Earthdata-Cloud-Access/Intro-Earthdata-Cloud.md rename book/tutorials/{ => NASA-Earthdata-Cloud-Access}/earthaccess.ipynb (100%) rename book/tutorials/{ => NASA-Earthdata-Cloud-Access}/icepyx.ipynb (100%) create mode 100644 book/tutorials/NASA-Earthdata-Cloud-Access/images/NSIDC-DAAC.png create mode 100644 book/tutorials/NASA-Earthdata-Cloud-Access/images/shiklomanov-cloud-paradigm.png diff --git a/book/tutorials/Intro-Earthdata-Cloud.ipynb b/book/tutorials/Intro-Earthdata-Cloud.ipynb deleted file mode 100644 index faaa68f..0000000 --- a/book/tutorials/Intro-Earthdata-Cloud.ipynb +++ /dev/null @@ -1,121 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "7fd4844a-aee8-4a9c-b22a-02688a8067f9", - "metadata": { - "user_expressions": [] - }, - "source": [ - "# NASA Earthdata Cloud and data access using earthaccess and icepyx: \n", - "## Introduction to NASA Earthdata and NASA Earthdata Cloud\n", - "\n", - "TODOs / Questions:\n", - "* Where does this live within the existing CryoCloud book?\n", - "\n", - "## 1. Tutorial Overview\n", - "___\n", - "\n", - "### Introduction\n", - "This tutorial is designed for the \"[Cloud Computing and Open-Source Scientific Software for Cryosphere Communities](https://agu.confex.com/agu/fm23/meetingapp.cgi/Session/193477)\" Learning Workshop at the 2023 AGU Fall Meeting.\n", - "\n", - "### Credits\n", - "\n", - "\n", - "### Learning Objectives\n", - "\n", - "### Prerequisites" - ] - }, - { - "cell_type": "markdown", - "id": "b651cd5d-5e8f-4747-88fd-c05b736c554b", - "metadata": { - "user_expressions": [] - }, - "source": [ - "## 2. NASA Earthdata Cloud" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9e0e96c6-ede7-4b4b-88b0-fcbaf8050a1f", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "cda28d37-7a59-4571-ad2f-62c91f324e38", - "metadata": { - "user_expressions": [] - }, - "source": [ - "## 3. Cloud Computing Considerations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b3be0ba0-01f7-4753-a984-5d9e04b02e68", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "ca5df483-a6a1-4680-be2f-d5497a47b077", - "metadata": { - "tags": [], - "user_expressions": [] - }, - "source": [ - "## 4. Introduction to ICESat-2\n", - "\n", - "* Scientific use case leveraging cloud computing\n", - "* Consider Andy's slides from Coiled Community call: https://github.com/andypbarrett/openscapes_community_chat_coiled" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c965d59f-5e6b-45cf-a63b-694c94e95d0a", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "7cc66d51-9b34-440d-9edc-617499c3cec9", - "metadata": { - "tags": [], - "user_expressions": [] - }, - "source": [ - "## 6. Using `icepyx` to search and access ICESat-2 data" - ] - } - ], - "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": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/book/tutorials/NASA-Earthdata-Cloud-Access/Intro-Earthdata-Cloud.md b/book/tutorials/NASA-Earthdata-Cloud-Access/Intro-Earthdata-Cloud.md new file mode 100644 index 0000000..eb811ea --- /dev/null +++ b/book/tutorials/NASA-Earthdata-Cloud-Access/Intro-Earthdata-Cloud.md @@ -0,0 +1,76 @@ +# NASA Earthdata Cloud and data access using earthaccess and icepyx: +## Introduction to NASA Earthdata and NASA Earthdata Cloud + +TODOs / Questions: +* Cost considerations + +# Learning Outcomes + +The purpose of this overview is to introduce the data search and access options provided within the Earthdata Cloud, along with an introduction to NASA's ICESat-2 Mission. + +## Prerequisites + +None + +## Credits + +This guide was adapted from the following tutorials: +* [Data Discovery and Access: Overview](https://icesat-2-2023.hackweek.io/tutorials/data-access-and-format/overview.html) by Andy Barrett, NSIDC DAAC +* [Using icepyx to access ICESat-2 data](Using icepyx to access ICESat-2 data) by Rachel Wegener, University of Maryland + +## Modes of Data Access + +In the past, most of our scientific data analysis workflows have started with searching for data and then downloading that data to a local machine; whether that is the hard drive of your laptop or workstation, or some shared storage device hosted by your institution or research group. This can be a time consuming process if the volume of data is large, even with fast internet. It also requires that you have sufficient disk-space. If you want to work with data from different geoscience domains, you may have to download data from several data centers. + +
+
+ Earthdata Cloud Transition. Credit: Alexey Shiklomanov, NASA ESDIS +
+
+ +However, a change is a-foot. New modes of data access are starting to becoming available. Driven by the growth in the volume of data from future satellite missions, the archiving and distribution of NASA data is in a [state of transition](https://www.earthdata.nasa.gov/eosdis/cloud-evolution). Over the next few years, all NASA data will be migrated to the NASA Earthdata Cloud, a cloud-hosted data store that will have all NASA datasets in one place. This not only offers new modes of accessing NASA data but also offers new ways of working with this data. As with Google Docs or Sheets, data in these "files" is not just stored in the cloud but compute resources offered by cloud providers allow you to process and analyze the data in the cloud. When you edit your Google Doc or Sheet, you are working in the cloud not on your computer. All you need is a web browser; you can work with these files on your laptop, tablet or even your phone. If you choose to share these documents with others, they can actively collaborate with you on the same document also in the cloud. For large geoscience datasets, this means you can _skip the download_ and take your _analysis to the data_. + +## NASA Earthdata Cloud at the NSIDC DAAC + +During this transition period, data will remain freely available from the NASA DAACs (Distributed Active Archive Centers) that have archived and distributed data for over 20 years; and will support data in cloud-hosted storage known as the Earthdata Cloud as data sets are migrated. + +
+
+ NSIDC DAAC Intro +
+
+ +The NSIDC DAAC now offers all [ICESat-2](https://nsidc.org/data/icesat-2) and [ICESat/GLAS](https://nsidc.org/data/icesat) data sets in the cloud. A listing of all NSIDC DAAC cloud-hosted data can be found [here](https://nsidc.org/data/earthdata-cloud/data). + +### More on Cloud Computing + +"The Cloud" is a somewhat nebulous term (pun intended). In general, the cloud is a network of remote servers that run software and services that are accessed over the internet. There is a growing number of commercial cloud providers (Google Cloud Services, Amazon Web Services, Microsoft Azure). NASA has contracted with Amazon Web Services (AWS) to host data using the AWS Simple Storage Service (S3). AWS offers a large number of services in addition to S3 storage. A key service is Amazon Elastic Compute Cloud (Amazon EC2). This is the service that is _under-the-hood_ of the CryoCloud JupyterHub you are using during today's workshop. When you start a JupyterHub, an EC2 _instance_ is started. You can think of an EC2 _instance_ as a remote computer. + +AWS has the concept of a region, which is a cluster of data centers. These data centers house the servers that run S3 and EC2 instances. NASA Earthdata Cloud is hosted in the `us-west-2` region. This is important because if your EC2 instance is in the same region as the Earthdata Cloud S3 storage, you can access data in S3 directly in a way that is analogous to accessing a file on your laptop's or workstation's hard drive. This is one of the key advantages of working in the cloud; you can do analysis where the data is stored without having to download the data to a local machine. + +### Cost Considerations + +The notion of _analysis in place_, or the concept of bringing your compute, or processing, to the data, provides several advantages over the more traditional download method: You no longer need to move data from its archived location, and you only pay for the compute needed to do your analysis. A few key points about cost: + +* Cost to access: As long as you are performing your processing in the same location as where the data are located in Earthdata Cloud, then the cost to access the data is completely free. The CryoCloud is running in the same `us-west-2` region as where the NASA Earthdata Cloud data are stored. +* Cost to compute: Just like your laptop costs money up front that provides you with certain CPU and memory, the compute resources needed to run your analyses do cost money. This can be thought of as the difference between an upfront cost like purchasing a laptop to process data locally versus something you can pay for as you go. There is a cost associated with the EC2 instance mentioned above, paid for by CryoCloud. +* Cost to store: With _analysis in place_, the data are being streamed directly from its native location in the cloud, so storage is not needed. However you may wish to store analysis outputs or other data using your own S3 bucket which does incur a cost. + +### When To Cloud + +## Introduction to ICESat-2 + +* Scientific use case leveraging cloud computing +* Consider Andy's slides from Coiled Community call: https://github.com/andypbarrett/openscapes_community_chat_coiled + + +Extras: + + +Earthdata Cloud is a NASA Earth Science Data Systems program that enables new methods of data analysis and distribution, while preserving most existing analysis and distribution methods. Having data in the cloud enables efficient use of large amounts of data collections and collaborative work with these data. + +Data products, or "collections", from NASA Earthdata are available from Earth Observing System Data and Information System (EOSDIS) Distributed Active Archive Centers (DAACs) that are in the process of moving data holdings to a cloud platform. The Earthdata Cloud is hosted in Amazon Web Services (AWS), with tools and services co-located next to the data. + +As far as quick background on those missions: these are both altimetry-focused missions that describe elevations of sea ice, land ice, forest canopies, water height, urban areas, and more. + +ICESat-2 data sets were some of the first data to be migrated to the cloud. 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oh)s>~C8u+zgQDu*%4Z0gUFBj^^>J!wHyLwPI+sY9-7qB@O4YR`1E{^qsjrdy z+3-5NO^#ygw7<>BhZ2wK=}fI};v0lA?`_teARZ_cp4n)-s5|THiOI`XyTZ&9MxXf3 zz3P#oDv^2dw0cUQFE#z8Siv6K035ck`K96cM|1A@OC%eks{4X|)(gZHd&2M)!W!XP zuGZgSXJX))PdTVM!Q<{4bpb<|N>5YsS*O>x2ST*u!L?;KSk|8DD%*1b(Q9zXGAdQ0wZuD&Wm{dY{Yb5M zw9#Bug&qD3&T7K=Qu_%)r=vWJ^7z~js({H1PqGd@-#(1t;JDW{a?RRpw;Is!4)p3( zn1jd60^LG}Gxi^7j|YCjove!-eHSuQK5;rPVZUfu*HLzo!V)=PhwC`39 zR#P~C-Gqt72xhW{tNykKn)!q&k7M1;RT#0-@I6LLj6rD-Jj%w&S?v3>f_%;4Hyk8Z zZ90M4b+%+T$m_H`H|!g%eeiAjBg&^@mQl8)4H0k!k{lE*7KL5qtxVvdr}`Jf37otm z_A43gv%ZLjs<4w$ZWhoB7z{@BXp1p*rKCWp+CAFsgKbeERhLaqz4zA8pW?;>{@R({ z0aKLG?jZ)QNp3ZpMxWEdH%U=r003~sNFQp=>m%eCcp&@f>-bzRrBh7MVbV#&iw7ea zZz}PtGp9 Date: Sun, 3 Dec 2023 21:44:18 +0000 Subject: [PATCH 04/13] intro draft --- .../Intro-Earthdata-Cloud.md | 61 ++++++++++++------- 1 file changed, 40 insertions(+), 21 deletions(-) diff --git a/book/tutorials/NASA-Earthdata-Cloud-Access/Intro-Earthdata-Cloud.md b/book/tutorials/NASA-Earthdata-Cloud-Access/Intro-Earthdata-Cloud.md index eb811ea..9b8e94a 100644 --- a/book/tutorials/NASA-Earthdata-Cloud-Access/Intro-Earthdata-Cloud.md +++ b/book/tutorials/NASA-Earthdata-Cloud-Access/Intro-Earthdata-Cloud.md @@ -1,24 +1,21 @@ -# NASA Earthdata Cloud and data access using earthaccess and icepyx: -## Introduction to NASA Earthdata and NASA Earthdata Cloud +# Introduction to NASA Earthdata Cloud and ICESat-2 -TODOs / Questions: -* Cost considerations - -# Learning Outcomes +### Learning Outcomes The purpose of this overview is to introduce the data search and access options provided within the Earthdata Cloud, along with an introduction to NASA's ICESat-2 Mission. -## Prerequisites +### Prerequisites None -## Credits +### Credits This guide was adapted from the following tutorials: * [Data Discovery and Access: Overview](https://icesat-2-2023.hackweek.io/tutorials/data-access-and-format/overview.html) by Andy Barrett, NSIDC DAAC * [Using icepyx to access ICESat-2 data](Using icepyx to access ICESat-2 data) by Rachel Wegener, University of Maryland +* [Data Strategies for Future Us](https://nsidc.github.io/data_strategies_for_future_us/data_strategies_slides#/workflow-solutions-3) by Andy Barrett, NSIDC DAAC -## Modes of Data Access +## 1. Modes of Data Access In the past, most of our scientific data analysis workflows have started with searching for data and then downloading that data to a local machine; whether that is the hard drive of your laptop or workstation, or some shared storage device hosted by your institution or research group. This can be a time consuming process if the volume of data is large, even with fast internet. It also requires that you have sufficient disk-space. If you want to work with data from different geoscience domains, you may have to download data from several data centers. @@ -28,9 +25,12 @@ In the past, most of our scientific data analysis workflows have started with se +Figure credit: Alexey Shiklomanov, NASA ESDIS, from [The future of NASA Earth Science in the commercial cloud: +Challenges and opportunities](https://docs.google.com/presentation/d/12mh_8WU9lsrPviBO_MBv2blbjRufXoQmqCB4XGyxQ90/edit?pli=1) + However, a change is a-foot. New modes of data access are starting to becoming available. Driven by the growth in the volume of data from future satellite missions, the archiving and distribution of NASA data is in a [state of transition](https://www.earthdata.nasa.gov/eosdis/cloud-evolution). Over the next few years, all NASA data will be migrated to the NASA Earthdata Cloud, a cloud-hosted data store that will have all NASA datasets in one place. This not only offers new modes of accessing NASA data but also offers new ways of working with this data. As with Google Docs or Sheets, data in these "files" is not just stored in the cloud but compute resources offered by cloud providers allow you to process and analyze the data in the cloud. When you edit your Google Doc or Sheet, you are working in the cloud not on your computer. All you need is a web browser; you can work with these files on your laptop, tablet or even your phone. If you choose to share these documents with others, they can actively collaborate with you on the same document also in the cloud. For large geoscience datasets, this means you can _skip the download_ and take your _analysis to the data_. -## NASA Earthdata Cloud at the NSIDC DAAC +## 2. NASA Earthdata Cloud During this transition period, data will remain freely available from the NASA DAACs (Distributed Active Archive Centers) that have archived and distributed data for over 20 years; and will support data in cloud-hosted storage known as the Earthdata Cloud as data sets are migrated. @@ -40,9 +40,9 @@ During this transition period, data will remain freely available from the NASA D -The NSIDC DAAC now offers all [ICESat-2](https://nsidc.org/data/icesat-2) and [ICESat/GLAS](https://nsidc.org/data/icesat) data sets in the cloud. A listing of all NSIDC DAAC cloud-hosted data can be found [here](https://nsidc.org/data/earthdata-cloud/data). +The NSIDC DAAC now offers all [ICESat-2](https://nsidc.org/data/icesat-2) and [ICESat/GLAS](https://nsidc.org/data/icesat) data sets in the cloud. A listing of all NSIDC DAAC cloud-hosted data can be found [here](https://nsidc.org/data/earthdata-cloud/data). More details on ICESat-2 below. -### More on Cloud Computing +### Earthdata Cloud Computing Basics "The Cloud" is a somewhat nebulous term (pun intended). In general, the cloud is a network of remote servers that run software and services that are accessed over the internet. There is a growing number of commercial cloud providers (Google Cloud Services, Amazon Web Services, Microsoft Azure). NASA has contracted with Amazon Web Services (AWS) to host data using the AWS Simple Storage Service (S3). AWS offers a large number of services in addition to S3 storage. A key service is Amazon Elastic Compute Cloud (Amazon EC2). This is the service that is _under-the-hood_ of the CryoCloud JupyterHub you are using during today's workshop. When you start a JupyterHub, an EC2 _instance_ is started. You can think of an EC2 _instance_ as a remote computer. @@ -52,25 +52,44 @@ AWS has the concept of a region, which is a cluster of data centers. These data The notion of _analysis in place_, or the concept of bringing your compute, or processing, to the data, provides several advantages over the more traditional download method: You no longer need to move data from its archived location, and you only pay for the compute needed to do your analysis. A few key points about cost: -* Cost to access: As long as you are performing your processing in the same location as where the data are located in Earthdata Cloud, then the cost to access the data is completely free. The CryoCloud is running in the same `us-west-2` region as where the NASA Earthdata Cloud data are stored. +* Cost to access: As long as you are performing your processing in the same location as where the data are located in Earthdata Cloud, then the cost to access the data is completely free. CryoCloud is running in the same `us-west-2` region as where the NASA Earthdata Cloud data are stored. * Cost to compute: Just like your laptop costs money up front that provides you with certain CPU and memory, the compute resources needed to run your analyses do cost money. This can be thought of as the difference between an upfront cost like purchasing a laptop to process data locally versus something you can pay for as you go. There is a cost associated with the EC2 instance mentioned above, paid for by CryoCloud. * Cost to store: With _analysis in place_, the data are being streamed directly from its native location in the cloud, so storage is not needed. However you may wish to store analysis outputs or other data using your own S3 bucket which does incur a cost. ### When To Cloud -## Introduction to ICESat-2 +Migrating to a cloud-based data analysis workflow can often have a steep learning curve and feel overwhelming. There are times when Cloud is effective and times when the download model may still be more appropriate. Here are a few key questions to ask yourself: + +* What is the data volume? +* How long will it take to download? +* Can you store all that data (cost and space)? +* Do you have the computing power for processing? +* Does your team need a common computing environment? +* Do you need to share data at each step or just an end product? + + +## 3. Introduction to ICESat-2 + +![IS2](https://icesat-2.gsfc.nasa.gov/sites/default/files/MissionLogo_0.png) + +ICESat-2 carries a satellite lidar instrument, ATLAS. Lidar is an active remote sensing technique in which pulses of light are emitted and the return time is used to measure distance. The available ICESat-2 data products range from sea ice freeboard to land elevation to cloud backscatter characteristics. A list of availble products can be found [here](https://icesat-2.gsfc.nasa.gov/science/data-products). + +### Data Collection + +ICESat-2 measures data along 3 strong/weak beam pairs, resulting in 3 strong beams and 3 weak beams. The strong and weak beams are calibrated such that the weak beams have more sensitivity to viewing very bright surfaces (Ex. ice), while the strong beams are able to view surfaces with lower reflectances (Ex. water). The beams are designated in each data product as `gt1l`, `gt1r`, `gt2l`, `gt2r`, `gt3l`, and `gt3r`, where `gt` stands for "ground track", the number refers to the photon emitter, and the `l` and `r` indicate "left" or "right" beam of the pair. Which of these designations is strong or weak depends on the orientation of the satellite (forwards, `sc_orient==1`; backwards, `sc_orient==0`). A helpful table of which beams are strong/weak can be found on p131 of the [ATL03 Algorithm Theoretical Basis Document](https://icesat-2.gsfc.nasa.gov/sites/default/files/page_files/ICESat2_ATL03_ATBD_r006.pdf). The ATLAS spot number (values 1-6) is based on the ground track designation (`gt1l` etc.) and spacecraft orientation and, once determined, can be used to consistently identify strong (Spots 1, 3, and 5) and weak (Spots 2, 4, and 6) beams. -* Scientific use case leveraging cloud computing -* Consider Andy's slides from Coiled Community call: https://github.com/andypbarrett/openscapes_community_chat_coiled +![Tracks](https://ars.els-cdn.com/content/image/1-s2.0-S0034425718305066-gr1.jpg) +Photo: Neuenschwander et. al. 2019, Remote Sens. Env. [DOI](https://doi.org/10.1016/j.rse.2018.11.005) -Extras: +### Counting Photons +The ICESat-2 lidar collects at the single photon level, different from most commercial lidar systems. A lot of additional photons get returned as solar background noise, and removing these unwanted photons is a key part of the algorithms that produce the higher level data products. -Earthdata Cloud is a NASA Earth Science Data Systems program that enables new methods of data analysis and distribution, while preserving most existing analysis and distribution methods. Having data in the cloud enables efficient use of large amounts of data collections and collaborative work with these data. + -Data products, or "collections", from NASA Earthdata are available from Earth Observing System Data and Information System (EOSDIS) Distributed Active Archive Centers (DAACs) that are in the process of moving data holdings to a cloud platform. The Earthdata Cloud is hosted in Amazon Web Services (AWS), with tools and services co-located next to the data. +> _Fig. 2. Results from signal finding methods for simulated ATLAS data. Black points show raw point cloud data as ingested from ATL03 product. Blue points overlaid in each plot show which photons each method identified as signal. Top panel reflects the signal photons as identified on the ATL03 data product (medium and high confidence signal photons). Bottom panel reflects the signal photons identified from the ATL08 DRAGANN method._ (Neuenschwander & Pitts, 2019) -As far as quick background on those missions: these are both altimetry-focused missions that describe elevations of sea ice, land ice, forest canopies, water height, urban areas, and more. +Photo: Neuenschwander et. al. 2019, Remote Sens. Env. [DOI](https://doi.org/10.1016/j.rse.2018.11.005) -ICESat-2 data sets were some of the first data to be migrated to the cloud. All Level-2 (e.g. ATL03 and beyond) ICESat-2 datasets are available in Earthdata Cloud. +To aggregate all these photons into more manegable chunks, many of the Level-3B products such as [ATL08](https://nsidc.org/data/atl08) consolidate the photons into variable segment lengths. \ No newline at end of file From f65a80151b553d0cc3a47643c6f75165b0adee9d Mon Sep 17 00:00:00 2001 From: asteiker Date: Sun, 3 Dec 2023 21:48:22 +0000 Subject: [PATCH 05/13] add image and fix typos --- .../Intro-Earthdata-Cloud.md | 4 ++-- .../images/ATL08_signalphotons.jpg | Bin 0 -> 152525 bytes 2 files changed, 2 insertions(+), 2 deletions(-) create mode 100644 book/tutorials/NASA-Earthdata-Cloud-Access/images/ATL08_signalphotons.jpg diff --git a/book/tutorials/NASA-Earthdata-Cloud-Access/Intro-Earthdata-Cloud.md b/book/tutorials/NASA-Earthdata-Cloud-Access/Intro-Earthdata-Cloud.md index 9b8e94a..7d85ac6 100644 --- a/book/tutorials/NASA-Earthdata-Cloud-Access/Intro-Earthdata-Cloud.md +++ b/book/tutorials/NASA-Earthdata-Cloud-Access/Intro-Earthdata-Cloud.md @@ -12,7 +12,7 @@ None This guide was adapted from the following tutorials: * [Data Discovery and Access: Overview](https://icesat-2-2023.hackweek.io/tutorials/data-access-and-format/overview.html) by Andy Barrett, NSIDC DAAC -* [Using icepyx to access ICESat-2 data](Using icepyx to access ICESat-2 data) by Rachel Wegener, University of Maryland +* [Using icepyx to access ICESat-2 data](https://nasa-openscapes.github.io/2023-ssc/tutorials/data-access/icepyx.html) by Rachel Wegener, University of Maryland * [Data Strategies for Future Us](https://nsidc.github.io/data_strategies_for_future_us/data_strategies_slides#/workflow-solutions-3) by Andy Barrett, NSIDC DAAC ## 1. Modes of Data Access @@ -56,7 +56,7 @@ The notion of _analysis in place_, or the concept of bringing your compute, or p * Cost to compute: Just like your laptop costs money up front that provides you with certain CPU and memory, the compute resources needed to run your analyses do cost money. This can be thought of as the difference between an upfront cost like purchasing a laptop to process data locally versus something you can pay for as you go. There is a cost associated with the EC2 instance mentioned above, paid for by CryoCloud. * Cost to store: With _analysis in place_, the data are being streamed directly from its native location in the cloud, so storage is not needed. However you may wish to store analysis outputs or other data using your own S3 bucket which does incur a cost. -### When To Cloud +### "When To Cloud" Migrating to a cloud-based data analysis workflow can often have a steep learning curve and feel overwhelming. There are times when Cloud is effective and times when the download model may still be more appropriate. 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Intro-Earthdata-Cloud.md} | 40 +- .../2. earthdata_search.md | 61 + .../3. earthaccess.ipynb | 1936 +++++++++++++++++ .../{icepyx.ipynb => 4. icepyx.ipynb} | 44 +- .../earthaccess.ipynb | 71 - .../Screenshot_EDSC_S3_links_credentials.png | Bin 0 -> 653461 bytes ...reenshot_EDSC_Searching_Cloud_Datasets.png | Bin 0 -> 1077848 bytes ...eenshot_EDSC_getting_s3_links_workflow.png | Bin 0 -> 1683478 bytes .../images/atl06_example_plot.png | Bin 0 -> 59356 bytes 9 files changed, 2075 insertions(+), 77 deletions(-) rename book/tutorials/NASA-Earthdata-Cloud-Access/{Intro-Earthdata-Cloud.md => 1. Intro-Earthdata-Cloud.md} (79%) create mode 100644 book/tutorials/NASA-Earthdata-Cloud-Access/2. earthdata_search.md create mode 100644 book/tutorials/NASA-Earthdata-Cloud-Access/3. earthaccess.ipynb rename book/tutorials/NASA-Earthdata-Cloud-Access/{icepyx.ipynb => 4. icepyx.ipynb} (50%) delete mode 100644 book/tutorials/NASA-Earthdata-Cloud-Access/earthaccess.ipynb create mode 100644 book/tutorials/NASA-Earthdata-Cloud-Access/images/Screenshot_EDSC_S3_links_credentials.png create mode 100644 book/tutorials/NASA-Earthdata-Cloud-Access/images/Screenshot_EDSC_Searching_Cloud_Datasets.png create mode 100644 book/tutorials/NASA-Earthdata-Cloud-Access/images/Screenshot_EDSC_getting_s3_links_workflow.png create mode 100644 book/tutorials/NASA-Earthdata-Cloud-Access/images/atl06_example_plot.png diff --git a/book/tutorials/NASA-Earthdata-Cloud-Access/Intro-Earthdata-Cloud.md b/book/tutorials/NASA-Earthdata-Cloud-Access/1. Intro-Earthdata-Cloud.md similarity index 79% rename from book/tutorials/NASA-Earthdata-Cloud-Access/Intro-Earthdata-Cloud.md rename to book/tutorials/NASA-Earthdata-Cloud-Access/1. Intro-Earthdata-Cloud.md index 7d85ac6..f780de6 100644 --- a/book/tutorials/NASA-Earthdata-Cloud-Access/Intro-Earthdata-Cloud.md +++ b/book/tutorials/NASA-Earthdata-Cloud-Access/1. Intro-Earthdata-Cloud.md @@ -14,6 +14,7 @@ This guide was adapted from the following tutorials: * [Data Discovery and Access: Overview](https://icesat-2-2023.hackweek.io/tutorials/data-access-and-format/overview.html) by Andy Barrett, NSIDC DAAC * [Using icepyx to access ICESat-2 data](https://nasa-openscapes.github.io/2023-ssc/tutorials/data-access/icepyx.html) by Rachel Wegener, University of Maryland * [Data Strategies for Future Us](https://nsidc.github.io/data_strategies_for_future_us/data_strategies_slides#/workflow-solutions-3) by Andy Barrett, NSIDC DAAC +* [Accessing and working with ICESat-2 data in the cloud](https://nasa-openscapes.github.io/2023-ssc/tutorials/data-access/) by Rachel Wegener, University of Maryland; Luis Lopez, NSIDC DAAC; and Amy Steiker, NSIDC DAAC. ## 1. Modes of Data Access @@ -72,7 +73,16 @@ Migrating to a cloud-based data analysis workflow can often have a steep learnin ![IS2](https://icesat-2.gsfc.nasa.gov/sites/default/files/MissionLogo_0.png) -ICESat-2 carries a satellite lidar instrument, ATLAS. Lidar is an active remote sensing technique in which pulses of light are emitted and the return time is used to measure distance. The available ICESat-2 data products range from sea ice freeboard to land elevation to cloud backscatter characteristics. A list of availble products can be found [here](https://icesat-2.gsfc.nasa.gov/science/data-products). +ICESat-2 carries a satellite lidar instrument, ATLAS. Lidar is an active remote sensing technique in which pulses of light are emitted and the return time is used to measure distance. The available ICESat-2 data products range from sea ice freeboard to land elevation to cloud backscatter characteristics. A list of available products can be found [here](https://icesat-2.gsfc.nasa.gov/science/data-products). + +![IS2-Product-Tree](https://nsidc.org/sites/default/files/styles/article_image/public/images/Other/icesat2_graphic_2023_update_final.png.webp) + +More key features of ICESat-2: + +* Height determined using round-trip travel time of laser light (photon counting lidar) +* 10,000 laser pulses released per second, split into 3 weak/strong beam pairs at a wavelength of 532 nanometers (bright green on the visible spectrum). +* Measurements taken every 70 cm along the satellite’s ground track, roughly 11 m wide footprint. +* The number of photons that returns to the telescope depends on surface reflectivity and cloud cover (which obscures ATLAS’s view of Earth). As such, the spatial resolution of signal photons varies. ### Data Collection @@ -92,4 +102,30 @@ The ICESat-2 lidar collects at the single photon level, different from most comm Photo: Neuenschwander et. al. 2019, Remote Sens. Env. [DOI](https://doi.org/10.1016/j.rse.2018.11.005) -To aggregate all these photons into more manegable chunks, many of the Level-3B products such as [ATL08](https://nsidc.org/data/atl08) consolidate the photons into variable segment lengths. \ No newline at end of file +To aggregate all these photons into more manegable chunks, many of the Level-3B products such as [ATL08](https://nsidc.org/data/atl08) consolidate the photons into variable segment lengths. + + +## 4. Navigating ICESat-2 Tool & Access options + +This table provides an overview of the capabilities across the tools and services highlighted in today's workshop: + +```{table} Data Access Method and Tools +:name: data-access-overview-table + +| | `icepyx` | `earthaccess` | Sliderule | OpenAltimetry | NASA Earthdata Search | NSIDC data product pages | +|:--- |:---:|:---:|:---:|:---:|:---:|:---:| +| Filter Spatially using: | | | | | | | +| Interactive map widget | | | x | x | x | x | +| Bounding Box | x | x | x | x | x | x | +| Polygon | x | x | x | | x | x | +| GeoJSON or Shapefile | x | | x | | x | x | +| Filter by time and date | x | x | x | x | x | x | +| Preview data | x | x | | x | x | x | +| Download data from DAAC | x | x | | x | x | x | +| Access cloud-hosted data | x | x | x | | x | | +| All ICESat-2 data | x | x | | | x | x | +| Subset (spatially, temporally, by variable) | x | | x | x | _x_ | | +| Load data by direct-access | x | x | x | | | | +| Process and analyze data | | | x | | | | +| Plot data with built-in methods | x | | x | x | | | +``` \ No newline at end of file diff --git a/book/tutorials/NASA-Earthdata-Cloud-Access/2. earthdata_search.md b/book/tutorials/NASA-Earthdata-Cloud-Access/2. earthdata_search.md new file mode 100644 index 0000000..ded06d0 --- /dev/null +++ b/book/tutorials/NASA-Earthdata-Cloud-Access/2. earthdata_search.md @@ -0,0 +1,61 @@ +# Using NASA Earthdata Search to Discover Cloud-Hosted Data + +## Learning Objective + +In this tutorial you will learn how to: + +- discover cloud-hosted datasets using NASA Earthdata Search. +- get AWS S3 credentials so you can access this data +- get the S3 links to data granules + +## Prerequisites + +- an Earthdata Login + +## Overview + +NASA Earthdata Search is a web-based tool to discover, filter, visualize and access all of NASA's Earth science data, both in Earthdata Cloud and archived at the NASA DAACs. It is a useful first step in data discovery, especially if you are not sure what data is available for your research problem. + +This tutorials is based on the NSIDC [NASA Earthdata Cloud Access Guide](https://nsidc.org/data/user-resources/help-center/nasa-earthdata-cloud-data-access-guide). Take a look at this access guide if you want more information and also to learn how to use command line tools to download cloud-hosted data from an S3 bucket. + + +## Searching for data and S3 links using Earthdata Search + +### Search for Data + +Step 1. Go to https://search.earthdata.nasa.gov and log in using your Earthdata Login credentials by clicking on the Earthdata Login button in the top-right corner. + +Step 2. Check the **Available in Earthdata Cloud** box in the **Filter Collections** side-bar on the left of the page (Box 1 on the screenshot below). The Matching Collections will appear in the results box. All datasets in Earthdata Cloud have a badge showing a cloud symbol and "Earthdata Cloud" next to them. To narrow the search, we will filter by datasets supported by NSIDC, by typing NSIDC in the search box (Box 2 on the screen shot below). If you wanted, you could narrow the search further using spatial and temporal filters, or any of the other filters in the filter collections box. + +

+
+ Screenshot of Search for Cloud Datasets in Earthdata Search +
+
+ +Step 3. You can now select the dataset you want by clicking on that dataset. The Search Results box now contains granules that match you search. The location of these granules is shown on the map. The search can be refined using spatial and temporal filters or you can select individual granules using the "+" symbol on each granule search result. Once you have the data you want, click the **Download All** (Box 1 in the screenshot below). In the sidebar that appears, select **Direct Download** (Box 2 in the screenshot below). Then select **Download Data**. + +
+
+ Screenshot of getting S3 links +
+
+ +### Getting S3 links and AWS S3 Credentials + +Step 4. A Download Status window will appear (this may take a short amount of time) similar to the one shown below. You will see a tab for **AWS S3 Access** (Box 1 in the screenshot below). Select this tab. A list of S3 links starting with `s3://` will be in the box below. You can save them to a text file or copy them to your clipboard using the **Save** and **Copy** buttons (Box 2 in the screenshot below). Or you can copy each link separately by hovering over a link and clicking the clipboard icon (Box 3). + +Step 5. To access data in Earthdata Cloud, you need AWS S3 credentials; “accessKeyId”, “secretAccessKey”, and “sessionToken”. These are temporary credentials that last for one hour. To get them click on the **Get AWS S3 Credentials** (Box 4 in the screenshot below). This will open a new page that contains the three credentials. + +
+
+ Screenshot of S3 links and credentials +
+
+ +### Using links and credentials from the command line + +Now that you have the S3 links and credentials you can download the data using the [AWS Command Line Interface (CLI)](https://aws.amazon.com/cli/). This is shown in the [NASA Earthdata Cloud Access Guide](https://nsidc.org/data/user-resources/help-center/nasa-earthdata-cloud-data-access-guide). + +[!IMPORTANT] +As of writing the AWS CLI is not installed on CryoCloud. diff --git a/book/tutorials/NASA-Earthdata-Cloud-Access/3. earthaccess.ipynb b/book/tutorials/NASA-Earthdata-Cloud-Access/3. earthaccess.ipynb new file mode 100644 index 0000000..b1d1a71 --- /dev/null +++ b/book/tutorials/NASA-Earthdata-Cloud-Access/3. earthaccess.ipynb @@ -0,0 +1,1936 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7fd4844a-aee8-4a9c-b22a-02688a8067f9", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "# Introduction to the `earthaccess` python library\n", + "\n", + "## 1. Tutorial Overview\n", + "\n", + "This tutorial is designed for the \"[Cloud Computing and Open-Source Scientific Software for Cryosphere Communities](https://agu.confex.com/agu/fm23/meetingapp.cgi/Session/193477)\" Learning Workshop at the 2023 AGU Fall Meeting.\n", + "\n", + "This notebook demonstrates how to search for, directly access, and work with cloud-hosted ICESat-2 Land Ice Height (ATL06) granules from an Amazon Compute Cloud (EC2) instance using the `earthaccess` package. Data in the \"NASA Earthdata Cloud\" are stored in Amazon Web Services (AWS) Simple Storage Service (S3) Buckets. **Direct Access** is an efficient way to work with data stored in an S3 Bucket when you are working in the cloud. Cloud-hosted granules can be opened and loaded into memory without the need to download them first. This allows you take advantage of the scalability and power of cloud computing. \n", + "\n", + "As an example data collection, we use ICESat-2 Land Ice Height (ATL06) over the Juneau Icefield, AK, for March 2003. ICESat-2 data granules, including ATL06, are stored in HDF5 format. We demonstrate how to open an HDF5 granule and access data variables using `xarray`. Land Ice Heights are then plotted using `hvplot`. \n", + "\n", + "
\n", + "
\n", + " Example plot using data downloaded in tutorial\n", + "
ATL06 Land Ice Heights for the margin of the Juneau Ice Field
\n", + "
\n", + "
\n", + "\n", + "We use `earthaccess`, a package developed by Luis Lopez (NSIDC developer) to allow easy search of the NASA Common Metadata Repository (CMR) and download of NASA data collections. It can be used for programmatic search and access for both _DAAC-hosted_ and _cloud-hosted_ data. It manages authenticating using Earthdata Login credentials which are then used to obtain the S3 tokens that are needed for S3 direct access. `earthaccess` can be used to find and access both DAAC-hosted and cloud-hosted data in just **three** lines of code. See [https://github.com/nsidc/earthaccess](https://github.com/nsidc/earthaccess).\n", + "\n", + "### Learning Objectives\n", + "\n", + "In this tutorial you will learn: \n", + "1. how to use `earthaccess` to search for ICESat-2 data using spatial and temporal filters and explore the search results; \n", + "2. how to open data granules using direct access to the ICESat-2 S3 bucket; \n", + "3. how to load a HDF5 group into an `xarray.Dataset`; \n", + "4. how visualize the land ice heights using `hvplot`. \n", + "\n", + "## Prerequisites\n", + "\n", + "The workflow described in this tutorial forms the initial steps of an _Analysis in Place_ workflow that would be run on a AWS cloud compute resource. You will need:\n", + "\n", + "1. a JupyterHub, such as CryoHub, or AWS EC2 instance in the us-west-2 region.\n", + "3. a NASA Earthdata Login. If you need to register for an Earthdata Login see the [Getting an Earthdata Login](https://icesat-2-2023.hackweek.io/preliminary/checklist/earthdata.html#getting-an-earthdata-login) section of the ICESat-2 Hackweek 2023 Jupyter Book.\n", + "4. A `.netrc` file, that contains your Earthdata Login credentials, in your home directory. See [Configure Programmatic Access to NASA Servers](https://icesat-2-2023.hackweek.io/preliminary/checklist/earthdata.html#configure-programmatic-access-to-nasa-servers) to create a `.netrc` file.\n", + "\n", + "## Credits\n", + "\n", + "This notebook is based on an [NSIDC Data Tutorial](https://github.com/nsidc/NSIDC-Data-Tutorials) originally created by Luis Lopez and Mikala Beig, NSIDC, modified by Andy Barrett, NSIDC, and updated by Jennifer Roebuck, NSIDC." + ] + }, + { + "cell_type": "markdown", + "id": "afb08333-e7f5-4c78-98c4-949313b6d481", + "metadata": { + "user_expressions": [] + }, + "source": [ + "## Computing Environment\n", + "\n", + "The tutorial uses `python` and requires the following packages:\n", + "- `earthaccess`, which enables Earthdata Login authentication and retrieves AWS credentials; enables collection and granule searches; and S3 access;\n", + "- `xarray`, used to load data;\n", + "- `hvplot`, used to visualize land ice height data.\n", + "\n", + "We are going to import the whole `earthaccess` package.\n", + "\n", + "We will also import the whole `xarray` package but use a standard short name `xr`, using the `import as ` syntax. We could use anything for a short name but `xr` is an accepted standard that most `xarray` users are familiar with.\n", + "\n", + "We only need the `xarray` module from `hvplot` so we import that using the `import .` syntax." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c965d59f-5e6b-45cf-a63b-694c94e95d0a", + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + " var py_version = '3.2.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n", + " var reloading = false;\n", + " var Bokeh = root.Bokeh;\n", + " var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", + "\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n", + " 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times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " Bokeh = root.Bokeh;\n", + " bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " if (!reloading && (!bokeh_loaded || is_dev)) {\n", + "\troot.Bokeh = undefined;\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + "\trun_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n var py_version = '3.2.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n var reloading = false;\n var Bokeh = root.Bokeh;\n var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\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 == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n run_callbacks();\n return null;\n }\n if (!reloading) {\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n })\n require([\"jspanel-modal\"], function() {\n\ton_load()\n })\n require([\"jspanel-tooltip\"], function() {\n\ton_load()\n })\n require([\"jspanel-hint\"], function() {\n\ton_load()\n })\n require([\"jspanel-layout\"], function() {\n\ton_load()\n })\n require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n })\n require([\"jspanel-dock\"], function() {\n\ton_load()\n })\n require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n })\n require([\"notyf\"], function() {\n\ton_load()\n })\n root._bokeh_is_loading = css_urls.length + 9;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n var existing_stylesheets = []\n var links = document.getElementsByTagName('link')\n for (var i = 0; i < links.length; i++) {\n var link = links[i]\n if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n }\n }\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } var existing_scripts = []\n var scripts = document.getElementsByTagName('script')\n for (var i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n\texisting_scripts.push(script.src)\n }\n }\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (var i = 0; i < js_modules.length; i++) {\n var url = js_modules[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n var url = js_exports[name];\n if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.holoviz.org/panel/1.2.3/dist/panel.min.js\"];\n var js_modules = [];\n var js_exports = {};\n var css_urls = [];\n var inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && 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autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", + " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", + "}\n", + "\n", + "\n", + " function JupyterCommManager() {\n", + " }\n", + "\n", + " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n", + " comm.onMsg = msg_handler;\n", + " });\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " console.log(message)\n", + " var content = {data: message.data, comm_id};\n", + " var buffers = []\n", + " for (var buffer of message.buffers || []) {\n", + " buffers.push(new DataView(buffer))\n", + " }\n", + " var metadata = message.metadata || {};\n", + " var msg = {content, buffers, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " })\n", + " }\n", + " }\n", + "\n", + " JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n", + " if (comm_id in window.PyViz.comms) {\n", + " return window.PyViz.comms[comm_id];\n", + " } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n", + " if (msg_handler) {\n", + " comm.on_msg(msg_handler);\n", + " }\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n", + " comm.open();\n", + " if (msg_handler) {\n", + " comm.onMsg = msg_handler;\n", + " }\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " var comm_promise = google.colab.kernel.comms.open(comm_id)\n", + " comm_promise.then((comm) => {\n", + " window.PyViz.comms[comm_id] = comm;\n", + " if (msg_handler) {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " var content = {data: message.data};\n", + " var metadata = message.metadata || {comm_id};\n", + " var msg = {content, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " }) \n", + " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n", + " return comm_promise.then((comm) => {\n", + " comm.send(data, metadata, buffers, disposeOnDone);\n", + " });\n", + " };\n", + " var comm = {\n", + " send: sendClosure\n", + " };\n", + " }\n", + " window.PyViz.comms[comm_id] = comm;\n", + " return comm;\n", + " }\n", + " window.PyViz.comm_manager = new JupyterCommManager();\n", + " \n", + "\n", + "\n", + "var JS_MIME_TYPE = 'application/javascript';\n", + "var HTML_MIME_TYPE = 'text/html';\n", + "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n", + "var CLASS_NAME = 'output';\n", + "\n", + "/**\n", + " * Render data to the DOM node\n", + " */\n", + "function render(props, node) {\n", + " var div = document.createElement(\"div\");\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(div);\n", + " node.appendChild(script);\n", + "}\n", + "\n", + "/**\n", + " * Handle when a new output is added\n", + " */\n", + "function handle_add_output(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = 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"# For searching and accessing NASA data\n", + "import earthaccess\n", + "\n", + "# For reading data, analysis and plotting\n", + "import xarray as xr\n", + "import hvplot.xarray\n", + "\n", + "import pprint # For nice printing of python objects" + ] + }, + { + "cell_type": "markdown", + "id": "cf13e8ff-a256-4108-bc6c-5e20fc8f321c", + "metadata": { + "user_expressions": [] + }, + "source": [ + "## Authenticate\n", + "\n", + "The first step is to get the correct authentication to access _cloud-hosted_ ICESat-2 data. This is all done through Earthdata Login. The `login` method also gets the correct AWS credentials.\n", + "\n", + "Login requires your Earthdata Login username and password. The `login` method will automatically search for these credentials as environment variables or in a `.netrc` file, and if those aren't available it will prompt you to enter your username and password. We use a `.netrc` strategy here. A `.netrc` file is a text file located in our home directory that contains login information for remote machines. If you don't have a `.netrc` file, `login` can create one for you.\n", + "\n", + "```\n", + "earthaccess.login(strategy='interactive', persist=True)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "dd332b9e-0aca-4c47-9d4e-8d92a48b2e42", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EARTHDATA_USERNAME and EARTHDATA_PASSWORD are not set in the current environment, try setting them or use a different strategy (netrc, interactive)\n", + "No .netrc found in /home/jovyan\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your Earthdata Login username: amy.steiker\n", + "Enter your Earthdata password: ········\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You're now authenticated with NASA Earthdata Login\n", + "Using token with expiration date: 01/26/2024\n", + "Using user provided credentials for EDL\n" + ] + } + ], + "source": [ + "auth = earthaccess.login()" + ] + }, + { + "cell_type": "markdown", + "id": "fcd4d20c-71bb-4a49-85c1-d43e7e9eeb3e", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "## Search for ICESat-2 Collections\n", + "\n", + "`earthaccess` leverages the Common Metadata Repository (CMR) API to search for collections and granules. [Earthdata Search](https://search.earthdata.nasa.gov/search) also uses the CMR API.\n", + "\n", + "We can use the `search_datasets` method to search for ICESat-2 collections by setting `keyword=\"ICESat-2\"` The argument passed to `keyword` can be any string and can include wildcard characters `?` or `*`.\n", + "\n", + "```{note}\n", + "To see a full list of search parameters you can type `earthaccess.search_datasets?`. Using `?` after a python object displays the `docstring` for that object.\n", + "```\n", + "\n", + "A count of the number of data collections (Datasets) found is given." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "4b6131f7-0f3c-4227-9301-618f364dcec6", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Datasets found: 89\n" + ] + } + ], + "source": [ + "query = earthaccess.search_datasets(\n", + " keyword=\"ICESat-2\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "bfd55ecf-6245-4caa-bd7e-903374086827", + "metadata": { + "user_expressions": [] + }, + "source": [ + "In this case, there are 69 datasets that have the keyword ICESat-2. \n", + "\n", + "`search_datasets` returns a python list of `DataCollection` objects. We can view metadata for each collection in long form by passing a `DataCollection` object to print or as a summary using the `summary` method for the `DataCollection` object. Here, I use the `pprint` function to _Pretty Print_ each object." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f54b13d9", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{ 'concept-id': 'C2559919423-NSIDC_ECS',\n", + " 'file-type': \"[{'FormatType': 'Native', 'Format': 'HDF5', \"\n", + " \"'FormatDescription': 'HTTPS'}]\",\n", + " 'get-data': [ 'https://n5eil01u.ecs.nsidc.org/ATLAS/ATL03.006/',\n", + " 'https://search.earthdata.nasa.gov/search?q=ATL03+V006',\n", + " 'http://openaltimetry.org/',\n", + " 'https://nsidc.org/data/data-access-tool/ATL03/versions/6/'],\n", + " 'short-name': 'ATL03',\n", + " 'version': '006'}\n", + "\n", + "{ 'cloud-info': { 'Region': 'us-west-2',\n", + " 'S3BucketAndObjectPrefixNames': [ 'nsidc-cumulus-prod-protected/ATLAS/ATL03/006',\n", + " 'nsidc-cumulus-prod-public/ATLAS/ATL03/006'],\n", + " 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{ 'Region': 'us-west-2',\n", + " 'S3BucketAndObjectPrefixNames': [ 'nsidc-cumulus-prod-protected/ATLAS/ATL03/005',\n", + " 'nsidc-cumulus-prod-public/ATLAS/ATL03/005'],\n", + " 'S3CredentialsAPIDocumentationURL': 'https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME',\n", + " 'S3CredentialsAPIEndpoint': 'https://data.nsidc.earthdatacloud.nasa.gov/s3credentials'},\n", + " 'concept-id': 'C2153572325-NSIDC_CPRD',\n", + " 'file-type': \"[{'FormatType': 'Native', 'Format': 'HDF5', \"\n", + " \"'FormatDescription': 'HTTPS'}]\",\n", + " 'get-data': ['https://search.earthdata.nasa.gov/search?q=ATL03+V005'],\n", + " 'short-name': 'ATL03',\n", + " 'version': '005'}\n", + "\n", + "{ 'concept-id': 'C2564427300-NSIDC_ECS',\n", + " 'file-type': \"[{'FormatType': 'Native', 'Format': 'HDF5', \"\n", + " \"'FormatDescription': 'HTTPS'}]\",\n", + " 'get-data': [ 'https://n5eil01u.ecs.nsidc.org/ATLAS/ATL06.006/',\n", + " 'https://search.earthdata.nasa.gov/search?q=ATL06+V006',\n", + " 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\"'FormatDescription': 'HTTPS'}]\",\n", + " 'get-data': [ 'https://n5eil01u.ecs.nsidc.org/ATLAS/ATL06.005/',\n", + " 'https://search.earthdata.nasa.gov/search?q=ATL06+V005',\n", + " 'https://openaltimetry.org/',\n", + " 'https://nsidc.org/data/data-access-tool/ATL06/versions/5/'],\n", + " 'short-name': 'ATL06',\n", + " 'version': '005'}\n", + "\n", + "{ 'cloud-info': { 'Region': 'us-west-2',\n", + " 'S3BucketAndObjectPrefixNames': [ 'nsidc-cumulus-prod-protected/ATLAS/ATL06/005',\n", + " 'nsidc-cumulus-prod-public/ATLAS/ATL06/005'],\n", + " 'S3CredentialsAPIDocumentationURL': 'https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME',\n", + " 'S3CredentialsAPIEndpoint': 'https://data.nsidc.earthdatacloud.nasa.gov/s3credentials'},\n", + " 'concept-id': 'C2153572614-NSIDC_CPRD',\n", + " 'file-type': \"[{'FormatType': 'Native', 'Format': 'HDF5', \"\n", + " \"'FormatDescription': 'HTTPS'}]\",\n", + " 'get-data': ['https://search.earthdata.nasa.gov/search?q=ATL06+V005'],\n", + " 'short-name': 'ATL06',\n", + " 'version': '005'}\n", + "\n", + "{ 'concept-id': 'C2565090645-NSIDC_ECS',\n", + " 'file-type': \"[{'FormatType': 'Native', 'Format': 'HDF5', \"\n", + " \"'FormatDescription': 'HTTPS'}]\",\n", + " 'get-data': [ 'https://n5eil01u.ecs.nsidc.org/ATLAS/ATL08.006/',\n", + " 'https://search.earthdata.nasa.gov/search?q=ATL08+V006',\n", + " 'https://openaltimetry.org/',\n", + " 'https://nsidc.org/data/data-access-tool/ATL08/versions/6/'],\n", + " 'short-name': 'ATL08',\n", + " 'version': '006'}\n", + "\n", + "{ 'cloud-info': { 'Region': 'us-west-2',\n", + " 'S3BucketAndObjectPrefixNames': [ 'nsidc-cumulus-prod-protected/ATLAS/ATL08/006',\n", + " 'nsidc-cumulus-prod-public/ATLAS/ATL08/006'],\n", + " 'S3CredentialsAPIDocumentationURL': 'https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME',\n", + " 'S3CredentialsAPIEndpoint': 'https://data.nsidc.earthdatacloud.nasa.gov/s3credentials'},\n", + " 'concept-id': 'C2613553260-NSIDC_CPRD',\n", + " 'file-type': \"[{'FormatType': 'Native', 'Format': 'HDF5', \"\n", + " \"'FormatDescription': 'HTTPS'}]\",\n", + " 'get-data': ['https://search.earthdata.nasa.gov/search?q=ATL08+V006'],\n", + " 'short-name': 'ATL08',\n", + " 'version': '006'}\n", + "\n" + ] + } + ], + "source": [ + "for collection in query[:10]:\n", + " pprint.pprint(collection.summary(), sort_dicts=True, indent=4)\n", + " print('') # Add a space between collections for readability" + ] + }, + { + "cell_type": "markdown", + "id": "b7e7fbf6-44f3-46df-99c6-5a138c2e7b11", + "metadata": { + "user_expressions": [] + }, + "source": [ + "For each collection, `summary` returns a subset of fields from the collection metadata and Unified Metadata Model (UMM) entry.\n", + "\n", + "- `concept-id` is an unique identifier for the collection that is composed of a alphanumeric code and the provider-id for the DAAC.\n", + "- `short-name` is the name of the dataset that appears on the dataset set landing page. For ICESat-2, `ShortNames` are generally how different products are referred to.\n", + "- `version` is the version of each collection.\n", + "- `file-type` gives information about the file format of the collection files.\n", + "- `get-data` is a collection of URL that can be used to access data, dataset landing pages, and tools. \n", + "\n", + "For _cloud-hosted_ data, there is additional information about the location of the S3 bucket that holds the data and where to get credentials to access the S3 buckets. In general, you don't need to worry about this information because `earthaccess` handles S3 credentials for you. Nevertheless it may be useful for troubleshooting. \n", + "\n", + "```{note}\n", + "In Python, all data are represented by _objects_. These _objects_ contain both data and methods (think functions) that operate on the data. `earthaccess` includes `DataCollection` and `DataGranule` objects that contain data about collections and granules returned by `search_datasets` and `search_data` respectively. If you are familiar with Python, you will see that the data in each `DataCollection` object is organized as a hierarchy of python dictionaries, lists and strings. So if you know the dictionary key for the metadata entry you want you can get that metadata using standard dictionary methods. For example, to get the dataset short name from the example below, you could just use `collection['meta']['concept-id']`. However, in this example the `concept-id' method for the DataCollection object returns the same information. Take a look at https://github.com/nsidc/earthaccess/blob/main/earthaccess/results.py#L80 to see how this is done.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "82f2d885-ea84-44fe-b2d8-cf139562fc70", + "metadata": { + "user_expressions": [] + }, + "source": [ + "For the ICESat-2 search results the provider-id is `NSIDC_ECS` and `NSIDC_CPRD`. `NSIDC_ECS` is for collections archived at the NSIDC DAAC and `NSIDC_CPRD` is for the _cloud-hosted_ collections. \n", + "\n", + "For ICESat-2 `short-name` refers to the following products. \n", + "\n", + "| ShortName | Product Description |\n", + "|:-----------:|:---------------------|\n", + "| ATL03 | ATLAS/ICESat-2 L2A Global Geolocated Photon Data |\n", + "| ATL06 | ATLAS/ICESat-2 L3A Land Ice Height |\n", + "| ATL07 | ATLAS/ICESat-2 L3A Sea Ice Height |\n", + "| ATL08 | ATLAS/ICESat-2 L3A Land and Vegetation Height |\n", + "| ATL09 | ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics |\n", + "| ATL10 | ATLAS/ICESat-2 L3A Sea Ice Freeboard |\n", + "| ATL11 | ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series |\n", + "| ATL12 | ATLAS/ICESat-2 L3A Ocean Surface Height |\n", + "| ATL13 | ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data |" + ] + }, + { + "cell_type": "markdown", + "id": "5329bbf6-4530-4dfd-88dd-153a1fb5d862", + "metadata": { + "user_expressions": [] + }, + "source": [ + "### Search for cloud-hosted data\n", + "\n", + "If you only want to search for data in the cloud, you can set `cloud_hosted=True`. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "322d78c3", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Datasets found: 40\n" + ] + } + ], + "source": [ + "Query = earthaccess.search_datasets(\n", + " keyword = 'ICESat-2',\n", + " cloud_hosted = True,\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "904e589e-4524-4aaa-9c91-8f464c0ccb96", + "metadata": { + "user_expressions": [] + }, + "source": [ + "## Search a data set using spatial and temporal filters \n", + "\n", + "Once, you have identified the dataset you want to work with, you can use the `search_data` method to search a data set with spatial and temporal filters. As an example, we'll search for ATL06 granules over the Juneau Icefield, AK, for March and April 2020.\n", + "\n", + "Either `concept-id` or `short-name` can be used to search for granules from a particular dataset. If you use `short-name` you also need to set `version`. If you use `concept-id`, this is all that is required because `concept-id` is unique. \n", + "\n", + "The temporal range is identified with standard date strings, and latitude-longitude corners of a bounding box is specified. Polygons and points, as well as shapefiles can also be specified.\n", + "\n", + "This will display the number of granules that match our search. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5fba5c34", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Granules found: 4\n" + ] + } + ], + "source": [ + "results = earthaccess.search_data(\n", + " short_name = 'ATL06',\n", + " version = '006',\n", + " cloud_hosted = True,\n", + " bounding_box = (-134.7,58.9,-133.9,59.2),\n", + " temporal = ('2020-03-01','2020-04-30'),\n", + " count = 100\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "89147064-a839-4900-beea-9cc5c228ce04", + "metadata": { + "user_expressions": [] + }, + "source": [ + "We'll get metadata for these 4 granules and display it. The rendered metadata shows a download link, granule size and two images of the data.\n", + "\n", + "The download link is `https` and can be used download the granule to your local machine. This is similar to downloading _DAAC-hosted_ data but in this case the data are coming from the Earthdata Cloud. For NASA data in the Earthdata Cloud, there is no charge to the user for egress from AWS Cloud servers. This is not the case for other data in the cloud." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a04370d3", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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+       "Dimensions:                (delta_time: 24471)\n",
+       "Coordinates:\n",
+       "  * delta_time             (delta_time) datetime64[ns] 2020-03-12T23:40:48.24...\n",
+       "    latitude               (delta_time) float64 ...\n",
+       "    longitude              (delta_time) float64 ...\n",
+       "Data variables:\n",
+       "    atl06_quality_summary  (delta_time) int8 ...\n",
+       "    h_li                   (delta_time) float32 ...\n",
+       "    h_li_sigma             (delta_time) float32 ...\n",
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+       "    Description:  The land_ice_height group contains the primary set of deriv...\n",
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" + ], + "text/plain": [ + "\n", + "Dimensions: (delta_time: 24471)\n", + "Coordinates:\n", + " * delta_time (delta_time) datetime64[ns] 2020-03-12T23:40:48.24...\n", + " latitude (delta_time) float64 ...\n", + " longitude (delta_time) float64 ...\n", + "Data variables:\n", + " atl06_quality_summary (delta_time) int8 ...\n", + " h_li (delta_time) float32 ...\n", + " h_li_sigma (delta_time) float32 ...\n", + " segment_id (delta_time) float64 ...\n", + " sigma_geo_h (delta_time) float32 ...\n", + "Attributes:\n", + " Description: The land_ice_height group contains the primary set of deriv...\n", + " data_rate: Data within this group are sparse. Data values are provide..." + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds" + ] + }, + { + "cell_type": "markdown", + "id": "0a261535-692b-4e3a-af1f-e7316b7f9084", + "metadata": { + "user_expressions": [] + }, + "source": [ + "`hvplot` is an interactive plotting tool that is useful for exploring data." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "be7386c3", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
\n", + "
\n", + "
\n", + "" + ], + "text/plain": [ + ":Scatter [longitude] (h_li)" + ] + }, + "execution_count": 11, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "p1002" + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "ds['h_li'].hvplot(kind='scatter', s=2)" + ] + }, + { + "cell_type": "markdown", + "id": "e9b656e1-9a42-4656-bd59-79256053f1c8", + "metadata": { + "user_expressions": [] + }, + "source": [ + "## Additional resources\n", + "\n", + "For general information about NSIDC DAAC data in the Earthdata Cloud: \n", + "\n", + "[FAQs About NSIDC DAAC's Earthdata Cloud Migration](https://nsidc.org/data/user-resources/help-center/faqs-about-nsidc-daacs-earthdata-cloud-migration)\n", + "\n", + "[NASA Earthdata Cloud Data Access Guide](https://nsidc.org/data/user-resources/help-center/nasa-earthdata-cloud-data-access-guide)\n", + "\n", + "Additional tutorials and How Tos:\n", + "\n", + "[NASA Earthdata Cloud Cookbook](https://nasa-openscapes.github.io/earthdata-cloud-cookbook/)" + ] + } + ], + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/book/tutorials/NASA-Earthdata-Cloud-Access/icepyx.ipynb b/book/tutorials/NASA-Earthdata-Cloud-Access/4. icepyx.ipynb similarity index 50% rename from book/tutorials/NASA-Earthdata-Cloud-Access/icepyx.ipynb rename to book/tutorials/NASA-Earthdata-Cloud-Access/4. icepyx.ipynb index 3b3e078..63603e3 100644 --- a/book/tutorials/NASA-Earthdata-Cloud-Access/icepyx.ipynb +++ b/book/tutorials/NASA-Earthdata-Cloud-Access/4. icepyx.ipynb @@ -10,9 +10,6 @@ "# NASA Earthdata Cloud and data access using earthaccess and icepyx:\n", "## Using the `icepyx` python library to access ICESat-2 data\n", "\n", - "TODOs / Questions:\n", - "* Where does this live within the existing CryoCloud book?\n", - "\n", "## 1. Tutorial Overview\n", "___\n", "\n", @@ -27,6 +24,45 @@ "### Prerequisites" ] }, + { + "cell_type": "markdown", + "id": "ad66ecf4-e2e7-43e6-b763-dec2f715e118", + "metadata": { + "user_expressions": [] + }, + "source": [ + "## What is `icepyx`?" + ] + }, + { + "cell_type": "markdown", + "id": "b10b4cdb-9503-482b-9c3a-e580a5af5b4a", + "metadata": { + "user_expressions": [] + }, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "id": "95c70c55-5327-4846-a7e8-22fd89a1bde6", + "metadata": { + "user_expressions": [] + }, + "source": [ + "icepyx is a community and software library for searching, downloading, and reading ICESat-2 data. While opening data should be straightforward, there are some oddities in navigating the highly nested organization and hundreds of variables of the ICESat-2 data. icepyx provides tools to help with those oddities.\n", + "\n", + "### Fitting icepyx into the data access package landscape\n", + "\n", + "For ICESat-2 data, the icepyx package can:\n", + "- search for available data granules (data files)\n", + "- order and download data\n", + "- order a subset of data: clipped in space, time, containing fewer variables, or a few other options provided by NSIDC\n", + "- provides functionality to search through the available data variables\n", + "- read ICESat-2 data into xarray DataArrays, including merging data from multiple files" + ] + }, { "cell_type": "markdown", "id": "7cc66d51-9b34-440d-9edc-617499c3cec9", @@ -35,7 +71,7 @@ "user_expressions": [] }, "source": [ - "## 2. Using `icepyx` to search and access ICESat-2 data" + "## Using `icepyx` to search and access ICESat-2 data" ] }, { diff --git a/book/tutorials/NASA-Earthdata-Cloud-Access/earthaccess.ipynb b/book/tutorials/NASA-Earthdata-Cloud-Access/earthaccess.ipynb deleted file mode 100644 index 1ca5a79..0000000 --- a/book/tutorials/NASA-Earthdata-Cloud-Access/earthaccess.ipynb +++ /dev/null @@ -1,71 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "7fd4844a-aee8-4a9c-b22a-02688a8067f9", - "metadata": { - "tags": [], - "user_expressions": [] - }, - "source": [ - "# NASA Earthdata Cloud and data access using earthaccess and icepyx:\n", - "## Introduction to the `earthaccess` python library\n", - "\n", - "TODOs / Questions:\n", - "* Where does this live within the existing CryoCloud book?\n", - "\n", - "## 1. Tutorial Overview\n", - "___\n", - "\n", - "### Introduction\n", - "This tutorial is designed for the \"[Cloud Computing and Open-Source Scientific Software for Cryosphere Communities](https://agu.confex.com/agu/fm23/meetingapp.cgi/Session/193477)\" Learning Workshop at the 2023 AGU Fall Meeting.\n", - "\n", - "### Credits\n", - "\n", - "\n", - "### Learning Objectives\n", - "\n", - "### Prerequisites" - ] - }, - { - "cell_type": "markdown", - "id": "08f25ec2-348f-4855-9aa3-97cc64f9bffa", - "metadata": { - "user_expressions": [] - }, - "source": [ - "## 2. Search and access data using `earthaccess` " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c965d59f-5e6b-45cf-a63b-694c94e95d0a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/book/tutorials/NASA-Earthdata-Cloud-Access/images/Screenshot_EDSC_S3_links_credentials.png b/book/tutorials/NASA-Earthdata-Cloud-Access/images/Screenshot_EDSC_S3_links_credentials.png new file mode 100644 index 0000000000000000000000000000000000000000..cc9bef2ced96a35531ed6c3b1301c45207059a1d GIT binary patch literal 653461 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