From a6e013c5600224138817a7adf58d5ca5633b35a0 Mon Sep 17 00:00:00 2001 From: asteiker Date: Sun, 15 Sep 2024 22:39:56 +0000 Subject: [PATCH 1/9] start outline of IS2 Harmony tututorial --- _quarto.yml | 4 +- tutorials/IS2_Harmony.ipynb | 257 ++++++++++++++++++++++++++++++++++++ tutorials/index.qmd | 5 +- 3 files changed, 262 insertions(+), 4 deletions(-) create mode 100644 tutorials/IS2_Harmony.ipynb diff --git a/_quarto.yml b/_quarto.yml index 203371c5..98b8585b 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -100,8 +100,8 @@ website: href: tutorials/Above_Ground_Biomass_Density.ipynb - text: "Agriculture Observations" href: tutorials/Observing_Seasonal_Ag_Changes.ipynb - - text: "ICESat-2" - href: external/IS2_cloud_Landsat_integration.ipynb + - text: "ICESat-2 Subsetting" + href: tutorials/IS2_Harmony.ipynb - text: "Cloud & Non-cloud" href: tutorials/04_On-Prem_Cloud.ipynb - text: "`earthaccess` Python library" diff --git a/tutorials/IS2_Harmony.ipynb b/tutorials/IS2_Harmony.ipynb new file mode 100644 index 00000000..fe19caf7 --- /dev/null +++ b/tutorials/IS2_Harmony.ipynb @@ -0,0 +1,257 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "54e9415f-f1de-4957-8580-252df5813eb8", + "metadata": {}, + "source": [ + "---\n", + "title: \"Subsetting ICESat-2 Data Using NASA Harmony\"\n", + "aliases:\n", + " - /tutorials/IS2_Harmony.html\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "b35db394-c8ba-4263-b7ba-0c1e8f8453b0", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "We will find, request, and open subsetted ICESat-2 data using NASA Harmony. Add blub on what Harmony is, and link to Harmony docs." + ] + }, + { + "cell_type": "markdown", + "id": "c43422fe-ae4a-40ea-9edf-11fa30596165", + "metadata": {}, + "source": [ + "## Learning Objectives\n", + "\n", + "1. Conceptualize data subsetting services provided by Harmony.\n", + "2. Discover what service options exist for a given ICESat-2 dataset.\n", + "3. Utilize the `harmony-py` library to request subsetted ATL03 data over _insert area_ during _insert date_\n", + "5. Read Harmony subsetted outputs directly into xarray." + ] + }, + { + "cell_type": "markdown", + "id": "c8a95244-4d35-4cde-b711-7d2e0ec8d580", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "First we’ll import python packages and set up the authentication needed for requesting ICESat-2 subsets. \n", + "\n", + "Data in NASA’s Earthdata Cloud, including the subsetted data processed by Harmony, reside in Amazon Web Services (AWS) Simple Storage Service (S3) buckets. Access is provided via temporary credentials; this free access is limited to requests made within the US West (Oregon) (code: us-west-2) AWS region. While this compute location is required for direct S3 access, all data in Earthdata Cloud are still freely available via download.\n", + "\n", + "This tutorial will demonstrate both download and direct s3 access methods. For the latter method to work, you will need to be running this notebook in the AWS us-west-2 region. \n", + "\n", + "### Import Required Packages" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9d8e9be7-c7ab-4e87-baae-0d516e1d9a37", + "metadata": {}, + "outputs": [], + "source": [ + "# Earthdata Login Authentication\n", + "import earthaccess \n", + "\n", + "# Harmony services\n", + "from harmony import BBox, Client, Collection, Request, LinkType\n", + "import datetime as dt\n", + "from pprint import pprint\n", + "import s3fs\n", + "import xarray as xr" + ] + }, + { + "cell_type": "markdown", + "id": "f522e1a5-ea40-444f-b1a5-ce4338c58d75", + "metadata": {}, + "source": [ + "## Discover service options for a given data set\n", + "\n", + "Add capabilities endpoint guidance here. Adopt from https://github.com/nasa/harmony-py/blob/main/examples/collection_capabilities.ipynb " + ] + }, + { + "cell_type": "markdown", + "id": "43316940-28e2-4e8f-898e-7302cafc82ca", + "metadata": {}, + "source": [ + "## Login to NASA Earthdata\n", + "\n", + "An [Earthdata Login](https://urs.earthdata.nasa.gov) account is required to access data from the NASA Earthdata system. Before requesting a subset of ICESat-2 data, we first need to set up our Earthdata Login authentication.\n", + "\n", + "The `earthaccess.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 us to enter our username and password. We use a `.netrc` strategy. A `.netrc` file is a text file located in our home directory that contains login information for remote machines. If we don't have a `.netrc` file, login can create one for us.\n", + "\n", + "`earthaccess.login(strategy='interactive', persist=True)`" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "800bf1d0-ba1a-45cf-bdf6-2af2f3707256", + "metadata": {}, + "outputs": [], + "source": [ + "auth = earthaccess.login()" + ] + }, + { + "cell_type": "markdown", + "id": "258e04a7-4b96-4652-b523-9bbdef9f7f06", + "metadata": {}, + "source": [ + "## Using `harmony-py` to subset data\n", + "\n", + "[`harmony-py`](https://github.com/nasa/harmony-py) provides a pip installable Python alternative to directly using Harmony’s RESTful API to make it easier to request data and service options, especially when interacting within a Python Jupyter Notebook environment." + ] + }, + { + "cell_type": "markdown", + "id": "319cd4c7-0b3c-4571-97d2-02c344aba418", + "metadata": {}, + "source": [ + "### Create A Subset Request\n", + "\n", + "Here we’ll create a request for a spatial subset of data.\n", + "\n", + "First, we need to create a Harmony Client, which is what we will interact with to submit and inspect a data request to Harmony, as well as to retrieve results.\n", + "\n", + "When creating the Client, we need to provide Earthdata Login credentials. This basic line below assumes that we have a .netrc available. See the Earthdata Cloud Cookbook appendix for more information on Earthdata Login and netrc setup." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "4f2c2fcb-f79e-4da4-9c73-db065d995cd5", + "metadata": {}, + "outputs": [], + "source": [ + "harmony_client = Client()" + ] + }, + { + "cell_type": "markdown", + "id": "c1b953ba-e8cd-486f-8132-6cdbb69b656c", + "metadata": {}, + "source": [ + "See the [harmony-py](https://harmony-py.readthedocs.io/en/latest/) documentation for details on how to construct your request.\n", + "\n", + "UPDATE example below to use ICESat-2:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5bb08390-ddb0-4356-905e-c0a3244b9247", + "metadata": {}, + "outputs": [], + "source": [ + "sst_short_name=\"MUR-JPL-L4-GLOB-v4.1\"\n", + "\n", + "request = Request(\n", + " collection=Collection(id=sst_short_name),\n", + " shape=geojson_path,\n", + " temporal={\n", + " 'start': dt.datetime(2021, 8, 1, 1),\n", + " 'stop': dt.datetime(2021, 8, 1, 2) \n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "34e01d68-0fd5-47c9-8757-d3fdc760dcf9", + "metadata": {}, + "source": [ + "### Submit a subset request" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c1c312bc-a986-4ebd-951f-c0d11208d19f", + "metadata": {}, + "outputs": [], + "source": [ + "job_id = harmony_client.submit(request)\n", + "job_id" + ] + }, + { + "cell_type": "markdown", + "id": "53982496-f3a6-4c46-a61f-f164157e119a", + "metadata": {}, + "source": [ + "### Check request status" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9b253c1e-da1e-4d14-883d-7fbd87834133", + "metadata": {}, + "outputs": [], + "source": [ + "harmony_client.wait_for_processing(job_id, show_progress=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be41beda-a445-419f-9af2-080fb7e7b1d3", + "metadata": {}, + "outputs": [], + "source": [ + "data = harmony_client.result_json(job_id)\n", + "pprint(data)" + ] + }, + { + "cell_type": "markdown", + "id": "421391bc-f996-4dbb-a69d-ac7d40d67243", + "metadata": {}, + "source": [ + "### Access data\n", + "\n", + "We will demonstrate both download and direct s3 access options below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8860630c-2050-4256-88c2-f1879456a9a1", + "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": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/index.qmd b/tutorials/index.qmd index 22defca3..fa850a99 100644 --- a/tutorials/index.qmd +++ b/tutorials/index.qmd @@ -32,8 +32,9 @@ The following tutorials are meant to present example workflows with NASA Earthda ### **Observing Seasonality in Agricultural Areas** - [Tutorial](Observing_Seasonal_Ag_Changes.ipynb) *Working with HLS Landsat and Sentinel-2 data products in the cloud* -### **ICESat-2 and Landsat cloud access and data integration** - [Tutorial](../external/IS2_cloud_Landsat_integration.ipynb) -*Accessing multiple large datasets in the cloud* +### **Subsetting ICESat-2 Data Using NASA Harmony** - [Tutorial] +(IS2_Harmony.ipynb) +*Requesting spatial and temporal subsets of ICESat-2 L2A and L3A data* ### **Pairing Cloud and non-Cloud Data** - [Tutorial](04_On-Prem_Cloud.ipynb) *Accessing and harmonizing data located within and outside of the NASA Earthdata Cloud* From 71616affacd8cfaeb31f6c91f755c0687c2a3b6f Mon Sep 17 00:00:00 2001 From: asteiker Date: Tue, 17 Sep 2024 23:17:58 +0000 Subject: [PATCH 2/9] outline complete with example code running end to end --- tutorials/IS2_Harmony.ipynb | 715 +++++++++++++++++++++++++++++++++--- 1 file changed, 667 insertions(+), 48 deletions(-) diff --git a/tutorials/IS2_Harmony.ipynb b/tutorials/IS2_Harmony.ipynb index fe19caf7..c5a72739 100644 --- a/tutorials/IS2_Harmony.ipynb +++ b/tutorials/IS2_Harmony.ipynb @@ -19,7 +19,12 @@ "source": [ "## Summary\n", "\n", - "We will find, request, and open subsetted ICESat-2 data using NASA Harmony. Add blub on what Harmony is, and link to Harmony docs." + "We will find, request, and open subsetted ICESat-2 data using NASA Harmony. \n", + "\n", + "TODO:\n", + "Add blurb on what Harmony is, and link to Harmony docs:\n", + "Documentation: https://harmony.earthdata.nasa.gov/docs\n", + "harmony-py: https://github.com/nasa/harmony-py" ] }, { @@ -31,8 +36,9 @@ "\n", "1. Conceptualize data subsetting services provided by Harmony.\n", "2. Discover what service options exist for a given ICESat-2 dataset.\n", - "3. Utilize the `harmony-py` library to request subsetted ATL03 data over _insert area_ during _insert date_\n", - "5. Read Harmony subsetted outputs directly into xarray." + "3. Utilize the `harmony-py` library to request subsetted ATL03 data over TODO: _insert area_ during _insert date_\n", + "4. Access ATL03 subsetted outputs via direct download or by streaming data directly in the cloud. \n", + "5. Read ATL03 subsetted outputs directly into xarray." ] }, { @@ -53,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 19, "id": "9d8e9be7-c7ab-4e87-baae-0d516e1d9a37", "metadata": {}, "outputs": [], @@ -62,23 +68,15 @@ "import earthaccess \n", "\n", "# Harmony services\n", - "from harmony import BBox, Client, Collection, Request, LinkType\n", + "from harmony import BBox, Client, Collection, Request, LinkType, CapabilitiesRequest, Environment \n", + "#Remove Environment module when we are ready to switch to production. Including this for UAT notebook testing.\n", + "import json\n", "import datetime as dt\n", "from pprint import pprint\n", "import s3fs\n", "import xarray as xr" ] }, - { - "cell_type": "markdown", - "id": "f522e1a5-ea40-444f-b1a5-ce4338c58d75", - "metadata": {}, - "source": [ - "## Discover service options for a given data set\n", - "\n", - "Add capabilities endpoint guidance here. Adopt from https://github.com/nasa/harmony-py/blob/main/examples/collection_capabilities.ipynb " - ] - }, { "cell_type": "markdown", "id": "43316940-28e2-4e8f-898e-7302cafc82ca", @@ -95,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 27, "id": "800bf1d0-ba1a-45cf-bdf6-2af2f3707256", "metadata": {}, "outputs": [], @@ -105,36 +103,91 @@ }, { "cell_type": "markdown", - "id": "258e04a7-4b96-4652-b523-9bbdef9f7f06", + "id": "f522e1a5-ea40-444f-b1a5-ce4338c58d75", "metadata": {}, "source": [ - "## Using `harmony-py` to subset data\n", + "## Discover service options for a given data set\n", "\n", - "[`harmony-py`](https://github.com/nasa/harmony-py) provides a pip installable Python alternative to directly using Harmony’s RESTful API to make it easier to request data and service options, especially when interacting within a Python Jupyter Notebook environment." + "TODO: Add capabilities endpoint guidance here. Adopt from https://github.com/nasa/harmony-py/blob/main/examples/collection_capabilities.ipynb. See also API documentation: https://harmony.earthdata.nasa.gov/docs#get-harmony-capabilities-for-the-provided-collection\n", + "\n", + "First, we need to create a Harmony Client, which is what we will interact with to submit and inspect a data request to Harmony, as well as to retrieve results." ] }, { - "cell_type": "markdown", - "id": "319cd4c7-0b3c-4571-97d2-02c344aba418", + "cell_type": "code", + "execution_count": 36, + "id": "bc59afbe-e218-4240-8315-5f73a51e6125", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"conceptId\": \"C1256407609-NSIDC_CUAT\",\n", + " \"shortName\": \"ATL03\",\n", + " \"variableSubset\": false,\n", + " \"bboxSubset\": true,\n", + " \"shapeSubset\": true,\n", + " \"concatenate\": false,\n", + " \"reproject\": false,\n", + " \"outputFormats\": [\n", + " \"application/x-hdf\"\n", + " ],\n", + " \"services\": [\n", + " {\n", + " \"name\": \"sds/trajectory-subsetter\",\n", + " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/S1242315633-EEDTEST\",\n", + " \"capabilities\": {\n", + " \"subsetting\": {\n", + " \"temporal\": true,\n", + " \"bbox\": true,\n", + " \"shape\": true,\n", + " \"variable\": true\n", + " },\n", + " \"output_formats\": [\n", + " \"application/x-hdf\"\n", + " ]\n", + " }\n", + " }\n", + " ],\n", + " \"variables\": [],\n", + " \"capabilitiesVersion\": \"2\"\n", + "}\n" + ] + } + ], "source": [ - "### Create A Subset Request\n", + "harmony_client = Client(env=Environment.UAT)\n", + "#Including UAT environment for testing purposes. Update when ready to move to production:\n", + "#harmony_client = Client()\n", "\n", - "Here we’ll create a request for a spatial subset of data.\n", + "capabilities_request = CapabilitiesRequest(collection_id='C1256407609-NSIDC_CUAT')\n", + "#Including a specific collection id for testing. Update to use short_name when ready to move to production:\n", + "#capabilities_request = CapabilitiesRequest(short_name='ATL03')\n", "\n", - "First, we need to create a Harmony Client, which is what we will interact with to submit and inspect a data request to Harmony, as well as to retrieve results.\n", + "capabilities = harmony_client.submit(capabilities_request)\n", + "print(json.dumps(capabilities, indent=2))" + ] + }, + { + "cell_type": "markdown", + "id": "258e04a7-4b96-4652-b523-9bbdef9f7f06", + "metadata": {}, + "source": [ + "## Using `harmony-py` to subset data\n", "\n", - "When creating the Client, we need to provide Earthdata Login credentials. This basic line below assumes that we have a .netrc available. See the Earthdata Cloud Cookbook appendix for more information on Earthdata Login and netrc setup." + "[`harmony-py`](https://github.com/nasa/harmony-py) provides a pip installable Python alternative to directly using Harmony’s RESTful API to make it easier to request data and service options, especially when interacting within a Python Jupyter Notebook environment." ] }, { - "cell_type": "code", - "execution_count": 4, - "id": "4f2c2fcb-f79e-4da4-9c73-db065d995cd5", + "cell_type": "markdown", + "id": "319cd4c7-0b3c-4571-97d2-02c344aba418", "metadata": {}, - "outputs": [], "source": [ - "harmony_client = Client()" + "### Create A Subset Request\n", + "\n", + "Here we’ll create a request for a spatial subset of data." ] }, { @@ -142,27 +195,27 @@ "id": "c1b953ba-e8cd-486f-8132-6cdbb69b656c", "metadata": {}, "source": [ - "See the [harmony-py](https://harmony-py.readthedocs.io/en/latest/) documentation for details on how to construct your request.\n", - "\n", - "UPDATE example below to use ICESat-2:" + "See the [harmony-py](https://harmony-py.readthedocs.io/en/latest/) documentation for details on how to construct your request." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "id": "5bb08390-ddb0-4356-905e-c0a3244b9247", "metadata": {}, "outputs": [], "source": [ - "sst_short_name=\"MUR-JPL-L4-GLOB-v4.1\"\n", + "collection_id='C1256407609-NSIDC_CUAT'\n", + "#Including a specific collection id for testing. Update to use short_name when ready to move to production:\n", + "# short_name = 'ATL03'\n", "\n", "request = Request(\n", - " collection=Collection(id=sst_short_name),\n", - " shape=geojson_path,\n", - " temporal={\n", - " 'start': dt.datetime(2021, 8, 1, 1),\n", - " 'stop': dt.datetime(2021, 8, 1, 2) \n", - " },\n", + " collection=Collection(id=collection_id),\n", + " spatial=BBox(-105.5,40,-105,40.005),\n", + " temporal={\n", + " 'start': dt.datetime(2020, 4, 27),\n", + " 'stop': dt.datetime(2020, 4, 28)\n", + " }\n", ")" ] }, @@ -176,10 +229,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "id": "c1c312bc-a986-4ebd-951f-c0d11208d19f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'451ae86b-c911-4910-a3c3-d2557a910bdb'" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "job_id = harmony_client.submit(request)\n", "job_id" @@ -195,20 +259,60 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "id": "9b253c1e-da1e-4d14-883d-7fbd87834133", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " [ Processing: 100% ] |###################################################| [|]\n" + ] + } + ], "source": [ "harmony_client.wait_for_processing(job_id, show_progress=True)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "id": "be41beda-a445-419f-9af2-080fb7e7b1d3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'createdAt': '2024-09-17T23:08:38.357Z',\n", + " 'dataExpiration': '2024-10-17T23:08:38.357Z',\n", + " 'jobID': '451ae86b-c911-4910-a3c3-d2557a910bdb',\n", + " 'links': [{'href': 'https://harmony.uat.earthdata.nasa.gov/stac/451ae86b-c911-4910-a3c3-d2557a910bdb/',\n", + " 'rel': 'stac-catalog-json',\n", + " 'title': 'STAC catalog',\n", + " 'type': 'application/json'},\n", + " {'bbox': [-108.28738, 26.94838, -103.60569, 59.54235],\n", + " 'href': 'https://harmony.uat.earthdata.nasa.gov/service-results/harmony-uat-staging/public/451ae86b-c911-4910-a3c3-d2557a910bdb/4834058/ATL03_20200427193622_04930702_006_02_subsetted.h5',\n", + " 'rel': 'data',\n", + " 'temporal': {'end': '2020-04-27T19:44:52.680Z',\n", + " 'start': '2020-04-27T19:36:22.028Z'},\n", + " 'title': 'ATL03_20200427193622_04930702_006_02_subsetted.h5',\n", + " 'type': 'application/x-hdf5'},\n", + " {'href': 'https://harmony.uat.earthdata.nasa.gov/jobs/451ae86b-c911-4910-a3c3-d2557a910bdb?linktype=https&page=1&limit=2000',\n", + " 'rel': 'self',\n", + " 'title': 'The current page',\n", + " 'type': 'application/json'}],\n", + " 'message': 'The job has completed successfully',\n", + " 'numInputGranules': 1,\n", + " 'progress': 100,\n", + " 'request': 'https://harmony.uat.earthdata.nasa.gov/C1256407609-NSIDC_CUAT/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat(40%3A40.005)&subset=lon(-105.5%3A-105)&subset=time(%222020-04-27T00%3A00%3A00%22%3A%222020-04-28T00%3A00%3A00%22)',\n", + " 'status': 'successful',\n", + " 'updatedAt': '2024-09-17T23:10:27.792Z',\n", + " 'username': 'amy.steiker'}\n" + ] + } + ], "source": [ "data = harmony_client.result_json(job_id)\n", "pprint(data)" @@ -224,11 +328,526 @@ "We will demonstrate both download and direct s3 access options below." ] }, + { + "cell_type": "markdown", + "id": "aec849b9-f97e-4d76-96ae-382a379ece23", + "metadata": {}, + "source": [ + "#### Download Harmony Results\n", + "\n", + "TODO: The followin code block may need updating. It is based on a harmony-py tutorial: https://github.com/nasa/harmony-py/blob/main/examples/basic.ipynb" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, + "id": "9778c3dd-00e8-45a7-b8dd-69a53ce29c48", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Downloading results:\n", + "ATL03_20200427193622_04930702_006_02_subsetted.h5\n", + "ATL03_20200427193622_04930702_006_02_subsetted.h5\n", + "\n", + "Done downloading.\n" + ] + } + ], + "source": [ + "print('\\nDownloading results:')\n", + "futures = harmony_client.download_all(job_id)\n", + "\n", + "for f in futures:\n", + " print(f.result()) # f.result() is a filename, in this case\n", + "\n", + "print('\\nDone downloading.')" + ] + }, + { + "cell_type": "markdown", + "id": "690ed2a6-3307-414c-9f2f-0aa2716e2bb6", + "metadata": {}, + "source": [ + "#### Direct S3 Access of Harmony Results\n", + "\n", + "You must be running this notebook in the AWS us-west-2 region in order for the following code to run." + ] + }, + { + "cell_type": "code", + "execution_count": 47, "id": "8860630c-2050-4256-88c2-f1879456a9a1", "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 158kB\n",
+       "Dimensions:         (delta_time: 3153, ds_surf_type: 5)\n",
+       "Coordinates:\n",
+       "  * delta_time      (delta_time) datetime64[ns] 25kB 2020-04-27T19:39:45.8497...\n",
+       "    lat_ph          (delta_time) float64 25kB ...\n",
+       "    lon_ph          (delta_time) float64 25kB ...\n",
+       "Dimensions without coordinates: ds_surf_type\n",
+       "Data variables:\n",
+       "    dist_ph_across  (delta_time) float32 13kB ...\n",
+       "    dist_ph_along   (delta_time) float32 13kB ...\n",
+       "    h_ph            (delta_time) float32 13kB ...\n",
+       "    pce_mframe_cnt  (delta_time) uint32 13kB ...\n",
+       "    ph_id_channel   (delta_time) uint8 3kB ...\n",
+       "    ph_id_count     (delta_time) uint8 3kB ...\n",
+       "    ph_id_pulse     (delta_time) uint8 3kB ...\n",
+       "    quality_ph      (delta_time) int8 3kB ...\n",
+       "    signal_conf_ph  (delta_time, ds_surf_type) int8 16kB ...\n",
+       "    weight_ph       (delta_time) uint8 3kB ...\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": [ + " Size: 158kB\n", + "Dimensions: (delta_time: 3153, ds_surf_type: 5)\n", + "Coordinates:\n", + " * delta_time (delta_time) datetime64[ns] 25kB 2020-04-27T19:39:45.8497...\n", + " lat_ph (delta_time) float64 25kB ...\n", + " lon_ph (delta_time) float64 25kB ...\n", + "Dimensions without coordinates: ds_surf_type\n", + "Data variables:\n", + " dist_ph_across (delta_time) float32 13kB ...\n", + " dist_ph_along (delta_time) float32 13kB ...\n", + " h_ph (delta_time) float32 13kB ...\n", + " pce_mframe_cnt (delta_time) uint32 13kB ...\n", + " ph_id_channel (delta_time) uint8 3kB ...\n", + " ph_id_count (delta_time) uint8 3kB ...\n", + " ph_id_pulse (delta_time) uint8 3kB ...\n", + " quality_ph (delta_time) int8 3kB ...\n", + " signal_conf_ph (delta_time, ds_surf_type) int8 16kB ...\n", + " weight_ph (delta_time) uint8 3kB ...\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": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results = harmony_client.result_urls(job_id, link_type=LinkType.s3)\n", + "urls = list(results)\n", + "url = urls[0]\n", + "\n", + "creds = harmony_client.aws_credentials()\n", + "\n", + "s3_fs = s3fs.S3FileSystem(\n", + " key=creds['aws_access_key_id'],\n", + " secret=creds['aws_secret_access_key'],\n", + " token=creds['aws_session_token'],\n", + " client_kwargs={'region_name':'us-west-2'},\n", + ")\n", + "\n", + "f = s3_fs.open(url, mode='rb')\n", + "ds = xr.open_dataset(f, group='gt1l/heights')\n", + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7fc2b566-1b4a-4fd0-a271-ecabb41524bd", + "metadata": {}, "outputs": [], "source": [] } From 968c100fafcdc1ce1039ac0fb46ecb0b1570c51f Mon Sep 17 00:00:00 2001 From: Andy Barrett Date: Tue, 24 Sep 2024 18:42:39 +0000 Subject: [PATCH 3/9] update summary and learning objectives --- tutorials/IS2_Harmony.ipynb | 23 +++++++++++++---------- 1 file changed, 13 insertions(+), 10 deletions(-) diff --git a/tutorials/IS2_Harmony.ipynb b/tutorials/IS2_Harmony.ipynb index c5a72739..867094de 100644 --- a/tutorials/IS2_Harmony.ipynb +++ b/tutorials/IS2_Harmony.ipynb @@ -19,12 +19,13 @@ "source": [ "## Summary\n", "\n", - "We will find, request, and open subsetted ICESat-2 data using NASA Harmony. \n", + "In this tutorial we will use NASA's Earthdata Harmony to subset and access ICESat-2 data with the `harmony-py` Python library. We will use the ATL03 Geolocated Photon Dataset as an example. However, Harmony can be used to subset other ICESat-2 datasets.\n", "\n", - "TODO:\n", - "Add blurb on what Harmony is, and link to Harmony docs:\n", - "Documentation: https://harmony.earthdata.nasa.gov/docs\n", - "harmony-py: https://github.com/nasa/harmony-py" + "**What is Harmony?** Harmony provides access (_is it a service?_) to a set of services that can be used to subset, reproject and reformat NASA datasets. Data can be subsetted for a geographic region, a temporal range and by variable. Data can be \"reprojected\" from it's native coordinate reference system (CRS) to the coordinate reference system relevant to your analysis. And data can be reformatted from it's native file format to a format that is more relevant for your application. These services are collectively called _transformation services_. However, not all services are available for all datasets. You will learn how to discover which services are available for your dataset.\n", + "\n", + "Data transformed by Harmony services are staged on NASA Amazon Web Services (AWS) S3 buckets or on user-owned AWS S3 buckets. Data in NASA S3 buckets are accessed using signed URLs or temporary access credentials. This data can be downloaded to your local machine or you can access the data directlyif you are working on a AWS cloud instances, such as a Jupyter Hub, in AWS `us-west-2`. \n", + "\n", + "_Add links or provide background to terminology_" ] }, { @@ -34,11 +35,12 @@ "source": [ "## Learning Objectives\n", "\n", - "1. Conceptualize data subsetting services provided by Harmony.\n", - "2. Discover what service options exist for a given ICESat-2 dataset.\n", - "3. Utilize the `harmony-py` library to request subsetted ATL03 data over TODO: _insert area_ during _insert date_\n", - "4. Access ATL03 subsetted outputs via direct download or by streaming data directly in the cloud. \n", - "5. Read ATL03 subsetted outputs directly into xarray." + "In this tutorial you will learn how to:\n", + "\n", + "1. discover Harmony service options ICESat-2 datasets;\n", + "3. use the `harmony-py` library to perform a spatial and temporal subset ATL03;\n", + "4. download ATL03 subsetted data to a local machine;\n", + "5. access and load ATL03 subsetted data directly into xarray." ] }, { @@ -48,6 +50,7 @@ "source": [ "## Prerequisites\n", "\n", + "\n", "First we’ll import python packages and set up the authentication needed for requesting ICESat-2 subsets. \n", "\n", "Data in NASA’s Earthdata Cloud, including the subsetted data processed by Harmony, reside in Amazon Web Services (AWS) Simple Storage Service (S3) buckets. Access is provided via temporary credentials; this free access is limited to requests made within the US West (Oregon) (code: us-west-2) AWS region. While this compute location is required for direct S3 access, all data in Earthdata Cloud are still freely available via download.\n", From cf1ea39584f3ac1a6be88a6f457692eb54d47eff Mon Sep 17 00:00:00 2001 From: Andy Barrett Date: Fri, 25 Oct 2024 20:46:46 +0000 Subject: [PATCH 4/9] update IS2 harmony notebook --- glossary.qmd | 17 +- tutorials/IS2_Harmony.ipynb | 5391 ++++++++++++++++- .../images/atl03_ground_track_and_bbox.png | Bin 0 -> 660495 bytes tutorials/make_atl03_figure.ipynb | 319 + 4 files changed, 5478 insertions(+), 249 deletions(-) create mode 100644 tutorials/images/atl03_ground_track_and_bbox.png create mode 100644 tutorials/make_atl03_figure.ipynb diff --git a/glossary.qmd b/glossary.qmd index b854a979..ed3383c0 100644 --- a/glossary.qmd +++ b/glossary.qmd @@ -1,7 +1,18 @@ --- -title: "Glossary, Cheatsheets, & Guides" -aliases: - - /glossary.html +title: 'Glossary, Cheatsheets, & Guides' +aliases: + - /glossary.html +jupyter: + jupytext: + text_representation: + extension: .qmd + format_name: quarto + format_version: '1.0' + jupytext_version: 1.16.1 + kernelspec: + display_name: Python 3 (ipykernel) + language: python + name: python3 --- ## Glossary diff --git a/tutorials/IS2_Harmony.ipynb b/tutorials/IS2_Harmony.ipynb index 867094de..27baf0a0 100644 --- a/tutorials/IS2_Harmony.ipynb +++ b/tutorials/IS2_Harmony.ipynb @@ -14,18 +14,38 @@ }, { "cell_type": "markdown", - "id": "b35db394-c8ba-4263-b7ba-0c1e8f8453b0", + "id": "c25e52f7-dae8-4246-a6e7-56606f50c343", "metadata": {}, + "source": [ + "## Aim for November 6 publication\n", + "\n", + "## TODO by Monday 28 October\n", + "- check authentication\n", + "- fix no granules found error\n", + "- add option to get https link\n", + "- prune imports" + ] + }, + { + "cell_type": "markdown", + "id": "b35db394-c8ba-4263-b7ba-0c1e8f8453b0", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, "source": [ "## Summary\n", "\n", - "In this tutorial we will use NASA's Earthdata Harmony to subset and access ICESat-2 data with the `harmony-py` Python library. We will use the ATL03 Geolocated Photon Dataset as an example. However, Harmony can be used to subset other ICESat-2 datasets.\n", + "In this tutorial, we will use NASA's Earthdata Harmony Services to subset and access ICESat-2 data with the `harmony-py` Python library. Harmony can be used to subset _any of the_(?) ICESat-2 data products. In this tutorial, we will subset the ATL03 Geolocated Photon Dataset as an example.\n", + "\n", + "**What is Harmony?** Harmony provides access (_is it a service?_) to a set of services that can subset, reproject and reformat NASA datasets. Data can be subsetted for a geographic region, a temporal range and by variable. Data can be \"reprojected\" from it's native coordinate reference system (CRS) to the coordinate reference system relevant to your analysis. And data can be reformatted from it's native file format to a format that is more relevant for your application. These services are collectively called _transformation services_. However, not all services are available for all datasets. You will learn how to discover which services are available for your dataset.\n", "\n", - "**What is Harmony?** Harmony provides access (_is it a service?_) to a set of services that can be used to subset, reproject and reformat NASA datasets. Data can be subsetted for a geographic region, a temporal range and by variable. Data can be \"reprojected\" from it's native coordinate reference system (CRS) to the coordinate reference system relevant to your analysis. And data can be reformatted from it's native file format to a format that is more relevant for your application. These services are collectively called _transformation services_. However, not all services are available for all datasets. You will learn how to discover which services are available for your dataset.\n", + "Data transformed by Harmony services are staged on NASA Amazon Web Services (AWS) S3 buckets or on user-owned AWS S3 buckets. Data in NASA S3 buckets are accessed using signed URLs or temporary access credentials. This data can be downloaded to your local machine or you can access the data directly if you are working in an AWS cloud instance, such as a Jupyter Hub, in AWS `us-west-2`. \n", "\n", - "Data transformed by Harmony services are staged on NASA Amazon Web Services (AWS) S3 buckets or on user-owned AWS S3 buckets. Data in NASA S3 buckets are accessed using signed URLs or temporary access credentials. This data can be downloaded to your local machine or you can access the data directlyif you are working on a AWS cloud instances, such as a Jupyter Hub, in AWS `us-west-2`. \n", + "_Add links or provide background to terminology_\n", "\n", - "_Add links or provide background to terminology_" + "_Stuff for later, maybe_\n", + "Data in NASA’s Earthdata Cloud, including the subsetted data processed by Harmony, reside in Amazon Web Services (AWS) Simple Storage Service (S3) buckets. Access is provided via temporary credentials; this free access is limited to requests made within the US West (Oregon) (code: us-west-2) AWS region. While this compute location is required for direct S3 access, all data in Earthdata Cloud are still freely available via download.\n", + "s-west-2 region. " ] }, { @@ -38,9 +58,9 @@ "In this tutorial you will learn how to:\n", "\n", "1. discover Harmony service options ICESat-2 datasets;\n", - "3. use the `harmony-py` library to perform a spatial and temporal subset ATL03;\n", - "4. download ATL03 subsetted data to a local machine;\n", - "5. access and load ATL03 subsetted data directly into xarray." + "3. use the `harmony-py` library to subset ATL03 granules for a bounding box and time range;\n", + "4. download the subsetted ATL03 to your local machine;\n", + "5. load the subsetted ATL03 data directly into xarray." ] }, { @@ -50,34 +70,86 @@ "source": [ "## Prerequisites\n", "\n", + "This tutorial has been designed to run in an AWS cloud compute instance in AWS region `us-west-2`. However, if you want run it from your laptop or workstation, everything except Step-4, direct access, should work just fine. _Also get https link_\n", "\n", - "First we’ll import python packages and set up the authentication needed for requesting ICESat-2 subsets. \n", - "\n", - "Data in NASA’s Earthdata Cloud, including the subsetted data processed by Harmony, reside in Amazon Web Services (AWS) Simple Storage Service (S3) buckets. Access is provided via temporary credentials; this free access is limited to requests made within the US West (Oregon) (code: us-west-2) AWS region. While this compute location is required for direct S3 access, all data in Earthdata Cloud are still freely available via download.\n", - "\n", - "This tutorial will demonstrate both download and direct s3 access methods. For the latter method to work, you will need to be running this notebook in the AWS us-west-2 region. \n", + "An [Earthdata Login](https://urs.earthdata.nasa.gov) account is required to access data from the NASA Earthdata system. Before requesting a subset of ICESat-2 data, we first need to set up our Earthdata Login authentication." + ] + }, + { + "cell_type": "markdown", + "id": "6968a5b2-c32b-446d-bce0-ab1cb57de62d", + "metadata": {}, + "source": [ + "## Tutorial" + ] + }, + { + "cell_type": "markdown", + "id": "f89bf6e8-cb9a-4be8-96aa-7c61ae1a2d99", + "metadata": {}, + "source": [ + "### Step 1: import required packages\n", "\n", - "### Import Required Packages" + "We'll be using `earthaccess`, `harmony-py` and `xarray`, along with some Python standard libraries. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "156ec5b1-75d3-41a7-8735-58e587bb704f", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "# Update harmony-py to 0.4.15\n", + "%pip install -U harmony-py" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 2, "id": "9d8e9be7-c7ab-4e87-baae-0d516e1d9a37", "metadata": {}, "outputs": [], "source": [ + "# Harmony services\n", + "# `Environment` not required when in production\n", + "from harmony import BBox, Client, Collection, Request, LinkType, CapabilitiesRequest, Environment \n", + "\n", "# Earthdata Login Authentication\n", "import earthaccess \n", "\n", - "# Harmony services\n", - "from harmony import BBox, Client, Collection, Request, LinkType, CapabilitiesRequest, Environment \n", + "# Load the data\n", + "import xarray as xr\n", + "\n", "#Remove Environment module when we are ready to switch to production. Including this for UAT notebook testing.\n", + "import netrc\n", "import json\n", "import datetime as dt\n", "from pprint import pprint\n", - "import s3fs\n", - "import xarray as xr" + "import s3fs\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cdf0d4c7-08a0-4fe8-b6da-2fdf383653e0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'0.5.0'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import harmony\n", + "harmony.__version__" ] }, { @@ -85,57 +157,126 @@ "id": "43316940-28e2-4e8f-898e-7302cafc82ca", "metadata": {}, "source": [ - "## Login to NASA Earthdata\n", + "### Step 2: login to NASA Earthdata\n", + "\n", + "We need to log in to NASA Earthdata to use Harmony services. We'll use `earthaccess.login()` to do this.\n", "\n", - "An [Earthdata Login](https://urs.earthdata.nasa.gov) account is required to access data from the NASA Earthdata system. Before requesting a subset of ICESat-2 data, we first need to set up our Earthdata Login authentication.\n", + "`earthaccess.login()` will search for you Earthdata Login credentials stored as environment variables or in a `.netrc` file. See the [`earthaccess` documentation](_link to docs_) for more details. If no environment variables or `.netrc` file is found, we will be prompted for a _username_ and _password_.\n", "\n", - "The `earthaccess.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 us to enter our username and password. We use a `.netrc` strategy. A `.netrc` file is a text file located in our home directory that contains login information for remote machines. If we don't have a `.netrc` file, login can create one for us.\n", + "`earthaccess.login()` can be used to create a `.netrc` file with the following command. This command will prompt for a _username_ and _password_ and save these to a `.netrc` file located in the `home` directory.\n", "\n", "`earthaccess.login(strategy='interactive', persist=True)`" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 4, "id": "800bf1d0-ba1a-45cf-bdf6-2af2f3707256", "metadata": {}, "outputs": [], "source": [ - "auth = earthaccess.login()" + "auth = earthaccess.login() # " + ] + }, + { + "cell_type": "markdown", + "id": "86f8544a-658e-478d-903f-87bb0f76495c", + "metadata": {}, + "source": [ + "### Step 3: start a Harmony client\n", + "\n", + "Once we have logged in we need to start a Harmony client. A Harmony client is the interface to Harmony services and is used to submit requests and retrieve the results of those requests.\n", + "\n", + "We only need to start the client once. That client can then be used to discover what options are available for a dataset, request subsetting, check on the status of that request and retrieve the results." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "94650df1-161f-499d-a5be-4eff06a55e3a", + "metadata": {}, + "outputs": [], + "source": [ + "# Including UAT environment for testing purposes. `env=Environment.UAT` can be deleted when in production\n", + "harmony_client = Client(auth=(\"apbarret\", \"FurryM0nk3y?\"), env=Environment.UAT)" ] }, { + "attachments": {}, "cell_type": "markdown", - "id": "f522e1a5-ea40-444f-b1a5-ce4338c58d75", + "id": "d79e241d-74a1-446f-85d9-e84e1adce707", "metadata": {}, "source": [ - "## Discover service options for a given data set\n", + "### Step 4: discover service options for ICESAT-2\n", "\n", - "TODO: Add capabilities endpoint guidance here. Adopt from https://github.com/nasa/harmony-py/blob/main/examples/collection_capabilities.ipynb. See also API documentation: https://harmony.earthdata.nasa.gov/docs#get-harmony-capabilities-for-the-provided-collection\n", + "The first thing we want to know is what service options Harmony has for the ICESat-2 dataset. We discover service options for a dataset by submitting a `CapabilitiesRequest`. \n", "\n", - "First, we need to create a Harmony Client, which is what we will interact with to submit and inspect a data request to Harmony, as well as to retrieve results." + "A `CapabilitiesRequest` takes a single argument that is either the _Collection ID_ (also known as the _Concept ID_) or the _Short Name_ for the dataset. A _Concept ID_ is a unique identifier for a dataset. For ATL03 Version 6, it is C2596864127-NSIDC_CPRD. For ICESat-2 products, the _Short Name_ is the familiar product name starting with ATL; e.g. ATL03.\n", + "\n", + "Submitting requests to Harmony will follow the same pattern, whether we are discovering capabilities or subsetting data. We first create a request and then submit that request." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "21a1f815-b187-4059-a4ef-0337721da3e8", + "metadata": {}, + "outputs": [], + "source": [ + "short_name = \"ATL03\"\n", + "# capabilities_request = CapabilitiesRequest(collection_id='C1256407609-NSIDC_CUAT')\n", + "capabilities_request = CapabilitiesRequest(short_name=short_name)" + ] + }, + { + "cell_type": "markdown", + "id": "a7ac0899-c629-48fc-93b5-2926ac2988b9", + "metadata": {}, + "source": [ + "We then submit the request using the request method of the Harmony client." ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 7, "id": "bc59afbe-e218-4240-8315-5f73a51e6125", "metadata": {}, + "outputs": [], + "source": [ + "capabilities = harmony_client.submit(capabilities_request)" + ] + }, + { + "cell_type": "markdown", + "id": "f6e984f6-7758-496a-af24-12ca87b58bb6", + "metadata": {}, + "source": [ + "The result is returned as a [JSON](https://en.wikipedia.org/wiki/JSON) string. This string is human readable text but the format makes it hard to read the contents. We can use the `json` library to _decode_ the string and print it in a more reader-friendly format." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "20a8a6c4-095a-4aeb-8fc6-6ff96db182c7", + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", - " \"conceptId\": \"C1256407609-NSIDC_CUAT\",\n", + " \"conceptId\": \"C1234714691-EEDTEST\",\n", " \"shortName\": \"ATL03\",\n", - " \"variableSubset\": false,\n", + " \"variableSubset\": true,\n", " \"bboxSubset\": true,\n", " \"shapeSubset\": true,\n", - " \"concatenate\": false,\n", + " \"concatenate\": true,\n", " \"reproject\": false,\n", " \"outputFormats\": [\n", - " \"application/x-hdf\"\n", + " \"application/x-hdf\",\n", + " \"application/x-zarr\"\n", " ],\n", " \"services\": [\n", " {\n", @@ -152,237 +293,4877 @@ " \"application/x-hdf\"\n", " ]\n", " }\n", + " },\n", + " {\n", + " \"name\": \"harmony/netcdf-to-zarr\",\n", + " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/S1237980031-EEDTEST\",\n", + " \"capabilities\": {\n", + " \"concatenation\": true,\n", + " \"concatenate_by_default\": false,\n", + " \"subsetting\": {\n", + " \"variable\": false\n", + " },\n", + " \"output_formats\": [\n", + " \"application/x-zarr\"\n", + " ]\n", + " }\n", " }\n", " ],\n", - " \"variables\": [],\n", - " \"capabilitiesVersion\": \"2\"\n", - "}\n" - ] + " \"variables\": [\n", + " {\n", + " \"name\": \"/ancillary_data/altimetry/atl03_pad\",\n", + " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795788-EEDTEST\"\n", + " },\n", + " {\n", + " \"name\": \"/ancillary_data/altimetry/band_tol\",\n", + " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795774-EEDTEST\"\n", + " },\n", + " {\n", + " \"name\": \"/ancillary_data/altimetry/min_full_sat\",\n", + " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795768-EEDTEST\"\n", + " },\n", + " {\n", + " \"name\": \"/ancillary_data/altimetry/min_near_sat\",\n", + " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795781-EEDTEST\"\n", + " },\n", + " {\n", + " \"name\": \"/ancillary_data/altimetry/min_sat_h\",\n", + " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795777-EEDTEST\"\n", + " },\n", + " {\n", + " \"name\": \"/ancillary_data/altimetry/min_scan_s\",\n", + " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795786-EEDTEST\"\n", + " },\n", + " {\n", + " \"name\": \"/ancillary_data/altimetry/ph_sat_flag\",\n", + " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795776-EEDTEST\"\n", + " },\n", + " {\n", + " \"name\": \"/ancillary_data/altimetry/ph_sat_lb\",\n", + " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795784-EEDTEST\"\n", + " },\n", + " {\n", + " \"name\": \"/ancillary_data/altimetry/ph_sat_ub\",\n", + " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795772-EEDTEST\"\n", + " },\n", + " {\n", + " \"name\": \"/ancillary_data/altimetry/podppd_pad\",\n", + " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795770-EEDTEST\"\n", + " },\n", + " {\n", + " \"name\": \"/ancillary_data/altimetry/scan_settle_s\",\n", + " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795780-EEDTEST\"\n", + " },\n", + " {\n", + " \"name\": \"/ancillary_data/atlas_engineering/det_ab_flag\",\n", + " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795544-EEDTEST\"\n", + " },\n", + " {\n", + " \"name\": \"/ancillary_data/atlas_engineering/ds_gt\",\n", + " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795542-EEDTEST\"\n", + " },\n", + " {\n", + " \"name\": \"/ancillary_data/atlas_engineering/ds_stat\",\n", + " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795548-EEDTEST\"\n", + " },\n", + " {\n", + " 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\"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794882-EEDTEST\"\n", + " },\n", + " {\n", + " \"name\": \"/quality_assessment/gt3r/qa_perc_signal_conf_ph_low\",\n", + " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794884-EEDTEST\"\n", + " },\n", + " {\n", + " \"name\": \"/quality_assessment/gt3r/qa_perc_signal_conf_ph_med\",\n", + " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794886-EEDTEST\"\n", + " },\n", + " {\n", + " \"name\": \"/quality_assessment/gt3r/qa_perc_surf_type\",\n", + " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794888-EEDTEST\"\n", + " },\n", + " {\n", + " \"name\": \"/quality_assessment/gt3r/qa_total_signal_conf_ph_high\",\n", + " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794890-EEDTEST\"\n", + " },\n", + " {\n", + " \"name\": \"/quality_assessment/gt3r/qa_total_signal_conf_ph_low\",\n", + " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794892-EEDTEST\"\n", + " },\n", + " {\n", + " \"name\": \"/quality_assessment/gt3r/qa_total_signal_conf_ph_med\",\n", + " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794894-EEDTEST\"\n", + " },\n", + " {\n", + " \"name\": \"/quality_assessment/qa_granule_fail_reason\",\n", + " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794808-EEDTEST\"\n", + " },\n", + " {\n", + " \"name\": \"/quality_assessment/qa_granule_pass_fail\",\n", + " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794805-EEDTEST\"\n", + " },\n", + " {\n", + " \"name\": \"ds_surf_type\",\n", + " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794224-EEDTEST\"\n", + " },\n", + " {\n", + " \"name\": \"ds_xyz\",\n", + " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794222-EEDTEST\"\n", + " }\n", + " ],\n", + " \"capabilitiesVersion\": \"2\"\n", + "}\n" + ] + } + ], + "source": [ + "print(json.dumps(capabilities, indent=2))" + ] + }, + { + "cell_type": "markdown", + "id": "3e48c57a-7914-4df0-be72-7129afe9371d", + "metadata": {}, + "source": [ + "The JSON response contains a list of transformation options available for each dataset: marked as `true` if available and `false` if not. A list of Harmony service _endpoints_ and capabilities associated with the transformation options. And a list of variables in the dataset if variable subsetting is available.\n", + "\n", + "For ATL03, you can see that only subsetting bounding box (`bboxSubset`) and subsetting by Shapefile (`shapeSubset`) are marked `true`. Variable subsetting (`variableSubset`), concatenation (`concatenate`) and reprojection (`reproject`) are all `false`. Bounding box and Shapefile subsetting are performed by the Trajectory Subsetter. This subsetting routine outputs an HDF5 file." + ] + }, + { + "cell_type": "markdown", + "id": "258e04a7-4b96-4652-b523-9bbdef9f7f06", + "metadata": {}, + "source": [ + "### Step 5: subset ICESat-2 ATL03 file\n", + "\n", + "Now that we know what subsetting options are available for for the ATL03 dataset, we can request a subsetted dataset. " + ] + }, + { + "cell_type": "markdown", + "id": "319cd4c7-0b3c-4571-97d2-02c344aba418", + "metadata": {}, + "source": [ + "#### Create A Subset Request\n", + "\n", + "we create a simple request for subset of ATL03 for a bounding box over the northern Colorado Front Range and for the 2020-04-27 to 2020-05-28 period.\n", + "\n", + "![Bounding Box and ATL03 Ground Track for northern Colorado](images/atl03_ground_track_and_bbox.png)" + ] + }, + { + "cell_type": "markdown", + "id": "c1b953ba-e8cd-486f-8132-6cdbb69b656c", + "metadata": {}, + "source": [ + "See the [harmony-py](https://harmony-py.readthedocs.io/en/latest/) documentation for details on how to construct your request." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5bb08390-ddb0-4356-905e-c0a3244b9247", + "metadata": {}, + "outputs": [], + "source": [ + "# Use collection_id='C1256407609-NSIDC_CUAT' for testing. Update to use short_name when ready to move to production:\n", + "# short_name = 'ATL03'\n", + "\n", + "# Use earthaccess to check for granules\n", + "request = Request(\n", + " collection = Collection(id='C1256407609-NSIDC_CUAT'), #capabilities[\"conceptId\"]),\n", + " spatial=BBox(-105.5,40,-105,41.),\n", + " temporal={\n", + " 'start': dt.datetime(2020, 4, 27),\n", + " 'stop': dt.datetime(2020, 5, 28)\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "34e01d68-0fd5-47c9-8757-d3fdc760dcf9", + "metadata": {}, + "source": [ + "#### Submit the request\n", + "\n", + "The request is submitted in the same way as the Capabilities request. This starts the subsetting process, which may take a while depending on the size of the request. Submitting request returns a Job ID, which is a unique identifier for your request that is used to track the progress of the request and to access the results.\n", + "\n", + "If the request involves a lot of files (more than 300), Harmony will only process the first 300 files. See section on large jobs on how to work around this restriction. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "c1c312bc-a986-4ebd-951f-c0d11208d19f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'0f383131-ee48-419a-aef9-2d2b71f920e3'" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "job_id = harmony_client.submit(request)\n", + "job_id" + ] + }, + { + "cell_type": "markdown", + "id": "53982496-f3a6-4c46-a61f-f164157e119a", + "metadata": {}, + "source": [ + "### Step 6: check the status of the request\n", + "\n", + "Subsetting is performed in the cloud. For small jobs, the subsetting process can be monitored with a progress bar by submitting the `job_id` to the `wait_for_processing` method of the Harmony client." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "9b253c1e-da1e-4d14-883d-7fbd87834133", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " [ Processing: 100% ] |###################################################| [|]\n" + ] + } + ], + "source": [ + "harmony_client.wait_for_processing(job_id, show_progress=True)" + ] + }, + { + "cell_type": "markdown", + "id": "ff9542f8-2e64-457c-a98b-4e1ba0e757ea", + "metadata": {}, + "source": [ + "Once the subsetting has finished, information about the job can be accessed as a JSON file" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "be41beda-a445-419f-9af2-080fb7e7b1d3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'createdAt': '2024-10-25T19:41:24.048Z',\n", + " 'dataExpiration': '2024-11-24T19:41:24.048Z',\n", + " 'jobID': '0f383131-ee48-419a-aef9-2d2b71f920e3',\n", + " 'labels': [],\n", + " 'links': [{'href': 'https://harmony.uat.earthdata.nasa.gov/stac/0f383131-ee48-419a-aef9-2d2b71f920e3/',\n", + " 'rel': 'stac-catalog-json',\n", + " 'title': 'STAC catalog',\n", + " 'type': 'application/json'},\n", + " {'bbox': [-108.28738, 26.94838, -103.60569, 59.54235],\n", + " 'href': 'https://harmony.uat.earthdata.nasa.gov/service-results/harmony-uat-staging/public/0f383131-ee48-419a-aef9-2d2b71f920e3/4882744/ATL03_20200427193622_04930702_006_02_subsetted.h5',\n", + " 'rel': 'data',\n", + " 'temporal': {'end': '2020-04-27T19:44:52.680Z',\n", + " 'start': '2020-04-27T19:36:22.028Z'},\n", + " 'title': 'ATL03_20200427193622_04930702_006_02_subsetted.h5',\n", + " 'type': 'application/x-hdf5'},\n", + " {'href': 'https://harmony.uat.earthdata.nasa.gov/jobs/0f383131-ee48-419a-aef9-2d2b71f920e3?linktype=https&page=1&limit=2000',\n", + " 'rel': 'self',\n", + " 'title': 'The current page',\n", + " 'type': 'application/json'}],\n", + " 'message': 'The job has completed successfully',\n", + " 'numInputGranules': 1,\n", + " 'progress': 100,\n", + " 'request': 'https://harmony.uat.earthdata.nasa.gov/C1256407609-NSIDC_CUAT/ogc-api-coverages/1.0.0/collections/parameter_vars/coverage/rangeset?forceAsync=true&subset=lat(40%3A41.0)&subset=lon(-105.5%3A-105)&subset=time(%222020-04-27T00%3A00%3A00%22%3A%222020-05-28T00%3A00%3A00%22)&variable=all',\n", + " 'status': 'successful',\n", + " 'updatedAt': '2024-10-25T19:44:11.633Z',\n", + " 'username': 'apbarret'}\n" + ] + } + ], + "source": [ + "job_summary = harmony_client.result_json(job_id)\n", + "pprint(job_summary)" + ] + }, + { + "cell_type": "markdown", + "id": "421391bc-f996-4dbb-a69d-ac7d40d67243", + "metadata": {}, + "source": [ + "### Step 7: access the subsetted data\n", + "\n", + "The subsetted files can be accessed by downloading the files to a local machine, such as a laptop or desktop workstation, or by _streaming_ the data. We will use both access methods in the two examples below.\n", + "\n", + "Results are staged for 30 days Explain where these are stored!" + ] + }, + { + "cell_type": "markdown", + "id": "120e006a-071e-4bd9-8115-ccd1cc5aac52", + "metadata": {}, + "source": [ + "#### Download a single file\n", + "\n", + "The _download_ method takes a url to a single subsetted file. The `directory` keyword is used to specify a download path. The default is the current working directory (`.`). Setting `overwrite` to False avoids downloading the same file twice. If you need to download the file again, then set `overwrite=True`.\n", + "\n", + ":::{note}\n", + "The `download` and `download_all` method are [_asynchronous_](https://en.wikipedia.org/wiki/Asynchrony_(computer_programming)), so that downloading each file is performed independently. Once the downloads are completed, the filepaths for the downloaded file are accessed using the `result` method.\n", + ":::" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e8f110a-4aa3-4bec-9ac3-65eef9f0e4aa", + "metadata": {}, + "outputs": [], + "source": [ + "url = list(harmony_client.result_urls(job_id))[0] # Get the data url of the first file\n", + "filepath = harmony_client.download(url, directory=\".\", overwrite=False).result()\n", + "print(filepath)" + ] + }, + { + "cell_type": "markdown", + "id": "4876c8ac-c822-4bae-b4fd-462026330040", + "metadata": {}, + "source": [ + "#### Download all files\n", + "\n", + "The `download_all` method can use the _job-id_ or the _result-json_, which contains result urls. \n", + "\n", + "As with `download`, the download directory path on the local machine can be specified with the `directory` keyword. To save downloading the same file, the `overwrite` keyword can be set to False. " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "951581ed-03a3-4aa2-b147-ed2054b75905", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['./4882744_ATL03_20200427193622_04930702_006_02_subsetted.h5']\n" + ] + } + ], + "source": [ + "futures = harmony_client.download_all(job_id, directory=\".\", overwrite=False)\n", + "filelist = [f.result() for f in futures] # get filepaths\n", + "print(filelist)" + ] + }, + { + "cell_type": "markdown", + "id": "acba9b5d-d4f6-4287-ac7d-3e7e1c70740b", + "metadata": {}, + "source": [ + "#### Load a file into an `xarray` dataset\n", + "\n", + "The simplest way to load ICESat-2 data is to use `xarray`.\n", + "\n", + ":::{note}\n", + "You could also load the data into a `geopandas.GeoDataframe`\n", + ":::" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "ae13153c-2968-44c1-a668-0f72f07a7d8c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 92MB\n",
+       "Dimensions:         (delta_time: 1848993, ds_surf_type: 5)\n",
+       "Coordinates:\n",
+       "  * delta_time      (delta_time) datetime64[ns] 15MB 2020-04-27T19:39:45.8497...\n",
+       "    lat_ph          (delta_time) float64 15MB ...\n",
+       "    lon_ph          (delta_time) float64 15MB ...\n",
+       "Dimensions without coordinates: ds_surf_type\n",
+       "Data variables:\n",
+       "    dist_ph_across  (delta_time) float32 7MB ...\n",
+       "    dist_ph_along   (delta_time) float32 7MB ...\n",
+       "    h_ph            (delta_time) float32 7MB ...\n",
+       "    pce_mframe_cnt  (delta_time) uint32 7MB ...\n",
+       "    ph_id_channel   (delta_time) uint8 2MB ...\n",
+       "    ph_id_count     (delta_time) uint8 2MB ...\n",
+       "    ph_id_pulse     (delta_time) uint8 2MB ...\n",
+       "    quality_ph      (delta_time) int8 2MB ...\n",
+       "    signal_conf_ph  (delta_time, ds_surf_type) int8 9MB ...\n",
+       "    weight_ph       (delta_time) uint8 2MB ...\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": [ + " Size: 92MB\n", + "Dimensions: (delta_time: 1848993, ds_surf_type: 5)\n", + "Coordinates:\n", + " * delta_time (delta_time) datetime64[ns] 15MB 2020-04-27T19:39:45.8497...\n", + " lat_ph (delta_time) float64 15MB ...\n", + " lon_ph (delta_time) float64 15MB ...\n", + "Dimensions without coordinates: ds_surf_type\n", + "Data variables:\n", + " dist_ph_across (delta_time) float32 7MB ...\n", + " dist_ph_along (delta_time) float32 7MB ...\n", + " h_ph (delta_time) float32 7MB ...\n", + " pce_mframe_cnt (delta_time) uint32 7MB ...\n", + " ph_id_channel (delta_time) uint8 2MB ...\n", + " ph_id_count (delta_time) uint8 2MB ...\n", + " ph_id_pulse (delta_time) uint8 2MB ...\n", + " quality_ph (delta_time) int8 2MB ...\n", + " signal_conf_ph (delta_time, ds_surf_type) int8 9MB ...\n", + " weight_ph (delta_time) uint8 2MB ...\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": 33, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "harmony_client = Client(env=Environment.UAT)\n", - "#Including UAT environment for testing purposes. Update when ready to move to production:\n", - "#harmony_client = Client()\n", - "\n", - "capabilities_request = CapabilitiesRequest(collection_id='C1256407609-NSIDC_CUAT')\n", - "#Including a specific collection id for testing. Update to use short_name when ready to move to production:\n", - "#capabilities_request = CapabilitiesRequest(short_name='ATL03')\n", - "\n", - "capabilities = harmony_client.submit(capabilities_request)\n", - "print(json.dumps(capabilities, indent=2))" - ] - }, - { - "cell_type": "markdown", - "id": "258e04a7-4b96-4652-b523-9bbdef9f7f06", - "metadata": {}, - "source": [ - "## Using `harmony-py` to subset data\n", - "\n", - "[`harmony-py`](https://github.com/nasa/harmony-py) provides a pip installable Python alternative to directly using Harmony’s RESTful API to make it easier to request data and service options, especially when interacting within a Python Jupyter Notebook environment." + "ds = xr.open_dataset(filelist[0], group=\"gt1l/heights\")\n", + "ds" ] }, { "cell_type": "markdown", - "id": "319cd4c7-0b3c-4571-97d2-02c344aba418", + "id": "690ed2a6-3307-414c-9f2f-0aa2716e2bb6", "metadata": {}, "source": [ - "### Create A Subset Request\n", + "#### Direct S3 Access of Harmony Results\n", "\n", - "Here we’ll create a request for a spatial subset of data." - ] - }, - { - "cell_type": "markdown", - "id": "c1b953ba-e8cd-486f-8132-6cdbb69b656c", - "metadata": {}, - "source": [ - "See the [harmony-py](https://harmony-py.readthedocs.io/en/latest/) documentation for details on how to construct your request." - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "5bb08390-ddb0-4356-905e-c0a3244b9247", - "metadata": {}, - "outputs": [], - "source": [ - "collection_id='C1256407609-NSIDC_CUAT'\n", - "#Including a specific collection id for testing. Update to use short_name when ready to move to production:\n", - "# short_name = 'ATL03'\n", + "If you are working in the AWS `us-west-2` region (the same region as NASA Earthdata Cloud) you can _stream_ the data using direct S3 access.\n", "\n", - "request = Request(\n", - " collection=Collection(id=collection_id),\n", - " spatial=BBox(-105.5,40,-105,40.005),\n", - " temporal={\n", - " 'start': dt.datetime(2020, 4, 27),\n", - " 'stop': dt.datetime(2020, 4, 28)\n", - " }\n", - ")" + ":::{warning}\n", + "You must be running this notebook in the AWS us-west-2 region to run the following code cells.\n", + ":::\n", + "\n" ] }, { "cell_type": "markdown", - "id": "34e01d68-0fd5-47c9-8757-d3fdc760dcf9", + "id": "75096fba-1367-4bd4-a45d-c19c8449662d", "metadata": {}, "source": [ - "### Submit a subset request" + "We need to get the url for the data in the S3 bucket. We can do this using `result_urls`, as we did for `download` but we set `link_type=LinkType.s3` to specify we want the S3 url." ] }, { "cell_type": "code", - "execution_count": 41, - "id": "c1c312bc-a986-4ebd-951f-c0d11208d19f", + "execution_count": 27, + "id": "a8d8a29d-91df-4872-9d9c-6a116bc92774", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'451ae86b-c911-4910-a3c3-d2557a910bdb'" + "['s3://harmony-uat-staging/public/0f383131-ee48-419a-aef9-2d2b71f920e3/4882744/ATL03_20200427193622_04930702_006_02_subsetted.h5']" ] }, - "execution_count": 41, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "job_id = harmony_client.submit(request)\n", - "job_id" + "urls = list(harmony_client.result_urls(job_id, link_type=LinkType.s3)) # result_urls returns a generator possible issue to return list\n", + "urls" ] }, { "cell_type": "markdown", - "id": "53982496-f3a6-4c46-a61f-f164157e119a", + "id": "815db398-723b-4278-ae30-2ad5f9764912", "metadata": {}, "source": [ - "### Check request status" + "We need AWS credentials to access the S3 bucket with the results. These are returned using the `aws_credentials` method.\n", + "\n", + "_what about if we use our own bucket?_" ] }, { "cell_type": "code", - "execution_count": 42, - "id": "9b253c1e-da1e-4d14-883d-7fbd87834133", + "execution_count": 28, + "id": "d58df790-d40c-410c-a295-22d6d39dfd5b", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " [ Processing: 100% ] |###################################################| [|]\n" - ] - } - ], + "outputs": [], "source": [ - "harmony_client.wait_for_processing(job_id, show_progress=True)" + "creds = harmony_client.aws_credentials()" ] }, { - "cell_type": "code", - "execution_count": 43, - "id": "be41beda-a445-419f-9af2-080fb7e7b1d3", + "cell_type": "markdown", + "id": "7d20cc12-b670-4091-9ae9-82fdbc2ded07", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'createdAt': '2024-09-17T23:08:38.357Z',\n", - " 'dataExpiration': '2024-10-17T23:08:38.357Z',\n", - " 'jobID': '451ae86b-c911-4910-a3c3-d2557a910bdb',\n", - " 'links': [{'href': 'https://harmony.uat.earthdata.nasa.gov/stac/451ae86b-c911-4910-a3c3-d2557a910bdb/',\n", - " 'rel': 'stac-catalog-json',\n", - " 'title': 'STAC catalog',\n", - " 'type': 'application/json'},\n", - " {'bbox': [-108.28738, 26.94838, -103.60569, 59.54235],\n", - " 'href': 'https://harmony.uat.earthdata.nasa.gov/service-results/harmony-uat-staging/public/451ae86b-c911-4910-a3c3-d2557a910bdb/4834058/ATL03_20200427193622_04930702_006_02_subsetted.h5',\n", - " 'rel': 'data',\n", - " 'temporal': {'end': '2020-04-27T19:44:52.680Z',\n", - " 'start': '2020-04-27T19:36:22.028Z'},\n", - " 'title': 'ATL03_20200427193622_04930702_006_02_subsetted.h5',\n", - " 'type': 'application/x-hdf5'},\n", - " {'href': 'https://harmony.uat.earthdata.nasa.gov/jobs/451ae86b-c911-4910-a3c3-d2557a910bdb?linktype=https&page=1&limit=2000',\n", - " 'rel': 'self',\n", - " 'title': 'The current page',\n", - " 'type': 'application/json'}],\n", - " 'message': 'The job has completed successfully',\n", - " 'numInputGranules': 1,\n", - " 'progress': 100,\n", - " 'request': 'https://harmony.uat.earthdata.nasa.gov/C1256407609-NSIDC_CUAT/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat(40%3A40.005)&subset=lon(-105.5%3A-105)&subset=time(%222020-04-27T00%3A00%3A00%22%3A%222020-04-28T00%3A00%3A00%22)',\n", - " 'status': 'successful',\n", - " 'updatedAt': '2024-09-17T23:10:27.792Z',\n", - " 'username': 'amy.steiker'}\n" - ] - } - ], "source": [ - "data = harmony_client.result_json(job_id)\n", - "pprint(data)" + "We then create a virtual file system that allows us to access the S3 bucket. We pass the credentials to authenticate." ] }, { - "cell_type": "markdown", - "id": "421391bc-f996-4dbb-a69d-ac7d40d67243", + "cell_type": "code", + "execution_count": 29, + "id": "73bc9a2d-b009-4cf9-8a47-531a308833b0", "metadata": {}, + "outputs": [], "source": [ - "### Access data\n", - "\n", - "We will demonstrate both download and direct s3 access options below." + "s3_fs = s3fs.S3FileSystem(\n", + " key=creds['aws_access_key_id'],\n", + " secret=creds['aws_secret_access_key'],\n", + " token=creds['aws_session_token'],\n", + " client_kwargs={'region_name':'us-west-2'},\n", + ")" ] }, { "cell_type": "markdown", - "id": "aec849b9-f97e-4d76-96ae-382a379ece23", + "id": "71e965bc-3950-47a9-ad8f-2e98c83a09c5", "metadata": {}, "source": [ - "#### Download Harmony Results\n", + "We then open the S3 url as a _file-like_ object.\n", "\n", - "TODO: The followin code block may need updating. It is based on a harmony-py tutorial: https://github.com/nasa/harmony-py/blob/main/examples/basic.ipynb" + ":::{note}\n", + "A _file-like_ object is just what it sounds like, an _object_ - a collection of bytes in memory - that is recognized as a file by applications.\n", + ":::" ] }, { "cell_type": "code", - "execution_count": 44, - "id": "9778c3dd-00e8-45a7-b8dd-69a53ce29c48", + "execution_count": 30, + "id": "f29584a0-357a-4f1a-8a3b-ab72fc20d6e4", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Downloading results:\n", - "ATL03_20200427193622_04930702_006_02_subsetted.h5\n", - "ATL03_20200427193622_04930702_006_02_subsetted.h5\n", - "\n", - "Done downloading.\n" - ] - } - ], + "outputs": [], "source": [ - "print('\\nDownloading results:')\n", - "futures = harmony_client.download_all(job_id)\n", - "\n", - "for f in futures:\n", - " print(f.result()) # f.result() is a filename, in this case\n", - "\n", - "print('\\nDone downloading.')" + "f = [s3_fs.open(url, mode='rb') for url in urls]" ] }, { "cell_type": "markdown", - "id": "690ed2a6-3307-414c-9f2f-0aa2716e2bb6", + "id": "cb377464-7b37-495c-bb0e-7c00af35ae51", "metadata": {}, "source": [ - "#### Direct S3 Access of Harmony Results\n", - "\n", - "You must be running this notebook in the AWS us-west-2 region in order for the following code to run." + "We can then open one of the files using `xarray`. " ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 31, "id": "8860630c-2050-4256-88c2-f1879456a9a1", "metadata": {}, "outputs": [ @@ -752,30 +5533,30 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
<xarray.Dataset> Size: 158kB\n",
-       "Dimensions:         (delta_time: 3153, ds_surf_type: 5)\n",
+       "
<xarray.Dataset> Size: 92MB\n",
+       "Dimensions:         (delta_time: 1848993, ds_surf_type: 5)\n",
        "Coordinates:\n",
-       "  * delta_time      (delta_time) datetime64[ns] 25kB 2020-04-27T19:39:45.8497...\n",
-       "    lat_ph          (delta_time) float64 25kB ...\n",
-       "    lon_ph          (delta_time) float64 25kB ...\n",
+       "  * delta_time      (delta_time) datetime64[ns] 15MB 2020-04-27T19:39:45.8497...\n",
+       "    lat_ph          (delta_time) float64 15MB ...\n",
+       "    lon_ph          (delta_time) float64 15MB ...\n",
        "Dimensions without coordinates: ds_surf_type\n",
        "Data variables:\n",
-       "    dist_ph_across  (delta_time) float32 13kB ...\n",
-       "    dist_ph_along   (delta_time) float32 13kB ...\n",
-       "    h_ph            (delta_time) float32 13kB ...\n",
-       "    pce_mframe_cnt  (delta_time) uint32 13kB ...\n",
-       "    ph_id_channel   (delta_time) uint8 3kB ...\n",
-       "    ph_id_count     (delta_time) uint8 3kB ...\n",
-       "    ph_id_pulse     (delta_time) uint8 3kB ...\n",
-       "    quality_ph      (delta_time) int8 3kB ...\n",
-       "    signal_conf_ph  (delta_time, ds_surf_type) int8 16kB ...\n",
-       "    weight_ph       (delta_time) uint8 3kB ...\n",
+       "    dist_ph_across  (delta_time) float32 7MB ...\n",
+       "    dist_ph_along   (delta_time) float32 7MB ...\n",
+       "    h_ph            (delta_time) float32 7MB ...\n",
+       "    pce_mframe_cnt  (delta_time) uint32 7MB ...\n",
+       "    ph_id_channel   (delta_time) uint8 2MB ...\n",
+       "    ph_id_count     (delta_time) uint8 2MB ...\n",
+       "    ph_id_pulse     (delta_time) uint8 2MB ...\n",
+       "    quality_ph      (delta_time) int8 2MB ...\n",
+       "    signal_conf_ph  (delta_time, ds_surf_type) int8 9MB ...\n",
+       "    weight_ph       (delta_time) uint8 2MB ...\n",
        "Attributes:\n",
        "    Description:  Contains arrays of the parameters for each received photon.\n",
-       "    data_rate:    Data are stored at the photon detection rate.
  • Description :
    Contains arrays of the parameters for each received photon.
    data_rate :
    Data are stored at the photon detection rate.
    • " ], "text/plain": [ - " Size: 158kB\n", - "Dimensions: (delta_time: 3153, ds_surf_type: 5)\n", + " Size: 92MB\n", + "Dimensions: (delta_time: 1848993, ds_surf_type: 5)\n", "Coordinates:\n", - " * delta_time (delta_time) datetime64[ns] 25kB 2020-04-27T19:39:45.8497...\n", - " lat_ph (delta_time) float64 25kB ...\n", - " lon_ph (delta_time) float64 25kB ...\n", + " * delta_time (delta_time) datetime64[ns] 15MB 2020-04-27T19:39:45.8497...\n", + " lat_ph (delta_time) float64 15MB ...\n", + " lon_ph (delta_time) float64 15MB ...\n", "Dimensions without coordinates: ds_surf_type\n", "Data variables:\n", - " dist_ph_across (delta_time) float32 13kB ...\n", - " dist_ph_along (delta_time) float32 13kB ...\n", - " h_ph (delta_time) float32 13kB ...\n", - " pce_mframe_cnt (delta_time) uint32 13kB ...\n", - " ph_id_channel (delta_time) uint8 3kB ...\n", - " ph_id_count (delta_time) uint8 3kB ...\n", - " ph_id_pulse (delta_time) uint8 3kB ...\n", - " quality_ph (delta_time) int8 3kB ...\n", - " signal_conf_ph (delta_time, ds_surf_type) int8 16kB ...\n", - " weight_ph (delta_time) uint8 3kB ...\n", + " dist_ph_across (delta_time) float32 7MB ...\n", + " dist_ph_along (delta_time) float32 7MB ...\n", + " h_ph (delta_time) float32 7MB ...\n", + " pce_mframe_cnt (delta_time) uint32 7MB ...\n", + " ph_id_channel (delta_time) uint8 2MB ...\n", + " ph_id_count (delta_time) uint8 2MB ...\n", + " ph_id_pulse (delta_time) uint8 2MB ...\n", + " quality_ph (delta_time) int8 2MB ...\n", + " signal_conf_ph (delta_time, ds_surf_type) int8 9MB ...\n", + " weight_ph (delta_time) uint8 2MB ...\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": 47, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "results = harmony_client.result_urls(job_id, link_type=LinkType.s3)\n", - "urls = list(results)\n", - "url = urls[0]\n", + "ds = xr.open_dataset(f[0], group='gt1l/heights')\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "id": "ba0d2015-f342-4987-aa16-e0b67b6e35b3", + "metadata": {}, + "source": [ + "## Handling large requests\n", "\n", - "creds = harmony_client.aws_credentials()\n", + "Need to explain large-jobs and previews, pausing\n", "\n", - "s3_fs = s3fs.S3FileSystem(\n", - " key=creds['aws_access_key_id'],\n", - " secret=creds['aws_secret_access_key'],\n", - " token=creds['aws_session_token'],\n", - " client_kwargs={'region_name':'us-west-2'},\n", - ")\n", + "https://github.com/nasa/harmony-py/blob/main/examples/job_pause_resume.ipynb\n", "\n", - "f = s3_fs.open(url, mode='rb')\n", - "ds = xr.open_dataset(f, group='gt1l/heights')\n", - "ds" + "By default Harmony will only process first 300 granules\n", + "\n", + "https://harmony.earthdata.nasa.gov/docs#skipping-preview" + ] + }, + { + "cell_type": "markdown", + "id": "bdbaa61e-7ecb-4826-9986-1893784d98c7", + "metadata": {}, + "source": [ + "## Some useful tools\n", + "\n", + "`harmony-py` is an interface to the Harmony RESTful API. RESTful API send requests and receive responses via HTTPS; the same protocol that serves web pages. Requests are sent to service endpoints, which is a URL (e.g. `https://service.endpoint.com/type_of_service`). Ouery parameters that modify a request can be appended a key-value pairs after a `?`. Each key-value pair is separated by an &. For example:\n", + "\n", + "```\n", + "https://service.endpoint.com/type_of_service?param1=value1¶m2=value2\n", + "```\n", + "\n", + "This url could be entered into a web browser.\n", + "\n", + "The Harmony client has a `request_as_url` method that returns the request created by `CapabilitiesRequest` or `Request`" ] }, { "cell_type": "code", - "execution_count": null, - "id": "7fc2b566-1b4a-4fd0-a271-ecabb41524bd", + "execution_count": 10, + "id": "8edb5214-427e-41a7-8f8a-a66da9cb6020", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'https://harmony.uat.earthdata.nasa.gov/capabilities?shortname=ATL03'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "harmony_client.request_as_url(capabilities_request)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8eff95b3-c77a-4647-ab02-f6d81f434a00", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'https://harmony.uat.earthdata.nasa.gov/C1256407609-NSIDC_CUAT/ogc-api-coverages/1.0.0/collections/parameter_vars/coverage/rangeset?forceAsync=true&subset=lat%2840%3A41.0%29&subset=lon%28-105.5%3A-105%29&subset=time%28%222020-04-27T00%3A00%3A00%22%3A%222020-05-28T00%3A00%3A00%22%29&variable=all'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "harmony_client.request_as_url(request)" + ] + }, + { + "cell_type": "markdown", + "id": "5a4604c4-80bb-4666-be54-57564ca427dd", + "metadata": {}, + "source": [ + "Try pasting the URL in the output cell below into a web browser to see the response." + ] + }, + { + "cell_type": "markdown", + "id": "ef3140c9-4285-47af-9fe8-98a411043cdb", + "metadata": {}, + "source": [ + "Harmony requests can also be sent with `curl`, a library and command line tool for transfering data using various network protocols, including HTTPS. The `request_as_curl` method can be used to generate a curl command. This can be helpful if you want to automate a process outside of a Jupyter Notebook or for testing requests." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "356149e4-5da4-4c94-962a-663621e36c78", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"curl -X GET -H 'Accept: */*' -H 'Accept-Encoding: gzip, deflate, br' -H 'Authorization: *****' -H 'Connection: keep-alive' -H 'Cookie: uat_urs_user_already_logged=yes; state=s%3A7893b9a533c0a171525bc82a3693d9b5.XXQF2YV1GMxMt5SI%2BEQy05yxJC8KLMzUQRbpfAi1MqY; token=*****; _urs_uat-gui_session=fb95333993ef92e65ff1fe8efd4e7449' -H 'User-Agent: CPython/3.10.14 Linux/5.10.224-212.876.amzn2.x86_64 harmony-py/0.5.0 python-requests/2.32.3' 'https://harmony.uat.earthdata.nasa.gov/capabilities?shortname=ATL03'\"" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "harmony_client.request_as_curl(capabilities_request)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "40d1655b-6b36-485d-8188-70b6d2e28361", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'curl -X POST -H \\'Accept: */*\\' -H \\'Accept-Encoding: gzip, deflate, br\\' -H \\'Authorization: *****\\' -H \\'Connection: keep-alive\\' -H \\'Content-Length: 563\\' -H \\'Content-Type: multipart/form-data; boundary=fa4d368a0e27040358124f0e030b456b\\' -H \\'Cookie: uat_urs_user_already_logged=yes; state=s%3A7893b9a533c0a171525bc82a3693d9b5.XXQF2YV1GMxMt5SI%2BEQy05yxJC8KLMzUQRbpfAi1MqY; token=*****; _urs_uat-gui_session=fb95333993ef92e65ff1fe8efd4e7449\\' -H \\'User-Agent: CPython/3.10.14 Linux/5.10.224-212.876.amzn2.x86_64 harmony-py/0.5.0 python-requests/2.32.3\\' -d \\'--fa4d368a0e27040358124f0e030b456b\\r\\nContent-Disposition: form-data; name=\"forceAsync\"\\r\\n\\r\\ntrue\\r\\n--fa4d368a0e27040358124f0e030b456b\\r\\nContent-Disposition: form-data; name=\"subset\"\\r\\n\\r\\nlat(40:41.0)\\r\\n--fa4d368a0e27040358124f0e030b456b\\r\\nContent-Disposition: form-data; name=\"subset\"\\r\\n\\r\\nlon(-105.5:-105)\\r\\n--fa4d368a0e27040358124f0e030b456b\\r\\nContent-Disposition: form-data; name=\"subset\"\\r\\n\\r\\ntime(\"2020-04-27T00:00:00\":\"2020-05-28T00:00:00\")\\r\\n--fa4d368a0e27040358124f0e030b456b\\r\\nContent-Disposition: form-data; name=\"variable\"\\r\\n\\r\\nall\\r\\n--fa4d368a0e27040358124f0e030b456b--\\r\\n\\' https://harmony.uat.earthdata.nasa.gov/C1256407609-NSIDC_CUAT/ogc-api-coverages/1.0.0/collections/parameter_vars/coverage/rangeset'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "harmony_client.request_as_curl(request)" + ] + }, + { + "cell_type": "markdown", + "id": "09b9bbce-cfee-40c3-8fb6-f7844d34ad15", "metadata": {}, - "outputs": [], "source": [] } ], diff --git a/tutorials/images/atl03_ground_track_and_bbox.png 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zc4=2U)BBsq>fMhHUiZI9i;LiaAb3IoIVd^VkQ-0iYGTQB^eC;E!l&Q#h2wNx89-_} z2(O4!gB0U$StTE8G|vEU`1h(og&85CWQDoReF9gq&@8|2ZOUhq_U@E~gRNW;N{afs z12z>3$@(W{5segfVi}@5A)du`y8_Ig-;d?!1))Z^eex_+t*;Sg1t5&q)*fTU?G$;i zm&`(|c1%?!-bevHF~kIaJxb;h;MA?F*9Ada^K%}Qy1nFawg7wtAMy%*VgM>e62NY+ zww>@-g_akqeha@H4{!CtDV@zSL*;z_YU!MikyO|@G`(IvnI1d5n*lEcS5M2VUIir? zD<{}Hyfb|dsuJL?w;OUYwtFjB zw1_&dmaRAT1A_Sx4w7Gn4F0Jt(}$=l!N_pchMJtSCj1Wn0%?|MRa()`VT12ABW95BakhpFRHLX8(QP=zlX4|LZcbdGbntY5lL){m)Bz|8*(=1.5 in /srv/conda/envs/notebook/lib/python3.10/site-packages (from python-dateutil->owslib) (1.16.0)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in /srv/conda/envs/notebook/lib/python3.10/site-packages (from requests->owslib) (3.3.2)\n", + "Requirement already satisfied: idna<4,>=2.5 in /srv/conda/envs/notebook/lib/python3.10/site-packages (from requests->owslib) (3.6)\n", + "Requirement already satisfied: urllib3<3,>=1.21.1 in /srv/conda/envs/notebook/lib/python3.10/site-packages (from requests->owslib) (1.26.18)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /srv/conda/envs/notebook/lib/python3.10/site-packages (from requests->owslib) (2024.2.2)\n", + "Using cached OWSLib-0.32.0-py2.py3-none-any.whl (240 kB)\n", + "Installing collected packages: owslib\n", + "Successfully installed owslib-0.32.0\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "pip install owslib" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "e03b12b9-084a-4030-8f20-9ebcc8916e71", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as mpatches\n", + "\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "\n", + "from cartopy.io import srtm, PostprocessedRasterSource, LocatedImage\n", + "from cartopy.io.srtm import SRTM3Source, SRTM1Source\n", + "\n", + "import cartopy.io.img_tiles as cimgt\n", + "\n", + "from owslib.wmts import WebMapTileService\n", + "\n", + "from shapely import box, Polygon\n", + "\n", + "import earthaccess" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "6f380e4f-f4c2-40a9-b3c2-51ad47ca49c0", + "metadata": {}, + "outputs": [], + "source": [ + "proj = ccrs.AlbersEqualArea(central_latitude=23., central_longitude=-96., standard_parallels=[29.5, 45.5])\n", + "extent = [-7096284.71, 7105582.09, 92407.95, 4785334.02]\n", + "colorado_extent = [-110., -102., 37., 41.]\n", + "\n", + "bbox = box(-105.5,40,-105,41.)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "6bfccfee-ef47-4cd8-8a04-d15ffb8d0bf6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Granules found: 1\n" + ] + } + ], + "source": [ + "result = earthaccess.search_data(\n", + " short_name=\"ATL03\",\n", + " bounding_box=(-105.5,40,-105,41.),\n", + " temporal=(\"2020-04-27\",\"2020-05-28\"),\n", + " version=\"006\",\n", + " cloud_hosted=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "eff65800-989c-4490-a56b-3f392e1d5766", + "metadata": {}, + "outputs": [], + "source": [ + "boundary = result[0][\"umm\"][\"SpatialExtent\"][\"HorizontalSpatialDomain\"][\"Geometry\"][\"GPolygons\"][0][\"Boundary\"][\"Points\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "08d22add-80f8-4931-8b49-a319c43b8944", + "metadata": {}, + "outputs": [], + "source": [ + "boundary_poly = Polygon([point.values() for point in boundary])" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "bdfaf9f4-bed6-4972-bbc9-90374fdb52af", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/srv/conda/envs/notebook/lib/python3.10/site-packages/owslib/wmts.py:860: FutureWarning: The behavior of this method will change in future versions. Use specific 'len(elem)' or 'elem is not None' test instead.\n", + " if current and current.text == 'true':\n", + "/srv/conda/envs/notebook/lib/python3.10/site-packages/owslib/wmts.py:657: RuntimeWarning: TileMatrixLimits with tileMatrix \"1\" already exists\n", + " warnings.warn(msg, RuntimeWarning)\n", + "/srv/conda/envs/notebook/lib/python3.10/site-packages/owslib/wmts.py:657: RuntimeWarning: TileMatrixLimits with tileMatrix \"2\" already exists\n", + " warnings.warn(msg, RuntimeWarning)\n", + "/srv/conda/envs/notebook/lib/python3.10/site-packages/owslib/wmts.py:657: RuntimeWarning: TileMatrixLimits with tileMatrix \"3\" already exists\n", + " warnings.warn(msg, RuntimeWarning)\n", + "/srv/conda/envs/notebook/lib/python3.10/site-packages/owslib/wmts.py:657: RuntimeWarning: TileMatrixLimits with tileMatrix \"4\" already exists\n", + " warnings.warn(msg, RuntimeWarning)\n", + "/srv/conda/envs/notebook/lib/python3.10/site-packages/owslib/wmts.py:657: RuntimeWarning: TileMatrixLimits with tileMatrix \"5\" already exists\n", + " warnings.warn(msg, RuntimeWarning)\n", + "/srv/conda/envs/notebook/lib/python3.10/site-packages/owslib/wmts.py:657: RuntimeWarning: TileMatrixLimits with tileMatrix \"6\" already exists\n", + " warnings.warn(msg, RuntimeWarning)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "

    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "url = 'https://gibs.earthdata.nasa.gov/wmts/epsg4326/best/wmts.cgi'\n", + "wmts = WebMapTileService(url)\n", + "\n", + "layers = ['BlueMarble_ShadedRelief']\n", + "date_str = '2020-05-15'\n", + "\n", + "# Create a feature for States/Admin 1 regions at 1:50m from Natural Earth\n", + "states_provinces = cfeature.NaturalEarthFeature(\n", + " category='cultural',\n", + " name='admin_1_states_provinces_lines',\n", + " scale='50m',\n", + " facecolor='none',\n", + " edgecolor='0.7')\n", + "\n", + "populated_places = cfeature.NaturalEarthFeature(category='cultural', \n", + " name='populated_places',\n", + " scale='10m')\n", + "\n", + "fig = plt.figure(figsize=(8,5))\n", + "ax = fig.add_subplot(projection=proj)\n", + "ax.add_feature(states_provinces) #, edgecolor='0.7')\n", + "\n", + "ax.set_extent(colorado_extent, ccrs.Geodetic())\n", + "\n", + "ax.add_geometries([bbox], ccrs.PlateCarree(), edgecolor=None, facecolor=\"cyan\", alpha=0.5, label=\"Area of Interest\")\n", + "ax.add_geometries([boundary_poly], ccrs.PlateCarree(), edgecolor=None, facecolor=\"lightgreen\", alpha=0.5, label=\"ATL03 Ground Track\")\n", + "\n", + "ax.add_wmts(wmts, layers[0], wmts_kwargs={'time': date_str})\n", + "\n", + "aoi = mpatches.Patch(color='cyan', alpha=0.5, label='Area of Interest')\n", + "footprint = mpatches.Patch(color=\"lightgreen\", alpha=0.5, label=\"ATL03 Ground Track\")\n", + "ax.legend(handles=[aoi, footprint], loc='lower left')\n", + "\n", + "marker = [\".\", \"*\"]\n", + "annotate = [False, True]\n", + "color = ['k', 'k']\n", + "size = [30, 80]\n", + "fontsize = []\n", + "\n", + "add_cities(gdf.groupby(\"plot_code\"), marker=marker, annotate=annotate, color=color, markersize=size)\n", + "\n", + "fig.tight_layout()\n", + "\n", + "fig.savefig(\"images/atl03_ground_track_and_bbox.png\")" + ] + }, + { + "cell_type": "markdown", + "id": "73e0e293-c1f4-4db5-a9ec-fdab27474f7e", + "metadata": {}, + "source": [ + "## Load and subset Populated Places from Natural Earth" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "53c43196-c947-43cf-98f8-70b32a0371f5", + "metadata": {}, + "outputs": [], + "source": [ + "import cartopy.io.shapereader as shpr\n", + "import geopandas as gpd\n", + "\n", + "fname = shpr.natural_earth(resolution='10m', category='cultural', name='populated_places')\n", + "\n", + "gdf = gpd.read_file(fname).to_crs(ax.projection.proj4_init) # load and convert to CRS" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "0c59a41c-ecdb-4522-a1f2-ff167739b0e0", + "metadata": {}, + "outputs": [], + "source": [ + "gdf = gdf[gdf.geometry.within(box(*[ax.get_extent()[i] for i in [0,2,1,3]]))]" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "45b1545d-11f2-4e05-81e0-2d62fb904f4b", + "metadata": {}, + "outputs": [], + "source": [ + "gdf[\"plot_code\"] = gdf[\"FEATURECLA\"].isin(['Admin-1 capital', 'Admin-1 region capital', 'Admin-0 region capital', \n", + " 'Admin-0 capital', 'Admin-0 capital alt'])" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "7b76f5c2-bdd3-4e36-8b75-75550a797d1e", + "metadata": {}, + "outputs": [], + "source": [ + "def add_cities(grouper, ax=None, marker=None, annotate=None, color=None, markersize=None, fontsize=None):\n", + " \"\"\"Adds cities and lebels by group to a plot\"\"\"\n", + "\n", + " if not ax:\n", + " ax = plt.gca()\n", + "\n", + " ngroup = grouper.ngroups\n", + " if not marker:\n", + " marker = [marker] * ngroup # Create an array of markers\n", + " if not annotate:\n", + " annotate = [annotate] * ngroup\n", + " if not color:\n", + " color = [color] * ngroup\n", + " if not markersize:\n", + " markersize = [markersize] * ngroup\n", + " if not fontsize:\n", + " fontsize = [fontsize] * ngroup\n", + "\n", + " for (name, group), mrkr, ann, c, ms, fs in zip(gdf.groupby(\"plot_code\"), marker, annotate, color, markersize, fontsize):\n", + " group.plot(ax=ax, marker=mrkr, color=c, markersize=ms)\n", + "\n", + " if ann:\n", + " for x, y, label in zip(group.geometry.x, group.geometry.y, group.NAME):\n", + " ax.annotate(label, xy=(x, y), xytext=(3, 3), textcoords=\"offset points\", fontsize=fs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "996bd2ca-7d7c-4477-af66-738896566399", + "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": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 869390e0499b140e0652c1d6c5782e0e1144b0f4 Mon Sep 17 00:00:00 2001 From: Andy Barrett Date: Thu, 31 Oct 2024 20:30:40 +0000 Subject: [PATCH 5/9] update text and add large order guidance --- tutorials/IS2_Harmony.ipynb | 4505 +++-------------------------------- 1 file changed, 293 insertions(+), 4212 deletions(-) diff --git a/tutorials/IS2_Harmony.ipynb b/tutorials/IS2_Harmony.ipynb index 27baf0a0..75c03e29 100644 --- a/tutorials/IS2_Harmony.ipynb +++ b/tutorials/IS2_Harmony.ipynb @@ -21,7 +21,6 @@ "\n", "## TODO by Monday 28 October\n", "- check authentication\n", - "- fix no granules found error\n", "- add option to get https link\n", "- prune imports" ] @@ -95,19 +94,7 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "156ec5b1-75d3-41a7-8735-58e587bb704f", - "metadata": {}, - "outputs": [], - "source": [ - "%%capture\n", - "# Update harmony-py to 0.4.15\n", - "%pip install -U harmony-py" - ] - }, - { - "cell_type": "code", - "execution_count": 2, + "execution_count": 20, "id": "9d8e9be7-c7ab-4e87-baae-0d516e1d9a37", "metadata": {}, "outputs": [], @@ -127,78 +114,40 @@ "import json\n", "import datetime as dt\n", "from pprint import pprint\n", - "import s3fs\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "cdf0d4c7-08a0-4fe8-b6da-2fdf383653e0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'0.5.0'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import harmony\n", - "harmony.__version__" + "import s3fs\n", + "\n", + "from IPython.display import display, JSON" ] }, { "cell_type": "markdown", - "id": "43316940-28e2-4e8f-898e-7302cafc82ca", + "id": "86f8544a-658e-478d-903f-87bb0f76495c", "metadata": {}, "source": [ - "### Step 2: login to NASA Earthdata\n", - "\n", - "We need to log in to NASA Earthdata to use Harmony services. We'll use `earthaccess.login()` to do this.\n", + "### Step 2: start a Harmony client\n", "\n", - "`earthaccess.login()` will search for you Earthdata Login credentials stored as environment variables or in a `.netrc` file. See the [`earthaccess` documentation](_link to docs_) for more details. If no environment variables or `.netrc` file is found, we will be prompted for a _username_ and _password_.\n", + "A Harmony client is the interface to Harmony services and is used to submit requests and retrieve the results of those requests.\n", "\n", - "`earthaccess.login()` can be used to create a `.netrc` file with the following command. This command will prompt for a _username_ and _password_ and save these to a `.netrc` file located in the `home` directory.\n", + "To create a Harmony Client we need to supply our Earthdata Login credentials. We will use `earthaccess.login()` to do this.\n", "\n", - "`earthaccess.login(strategy='interactive', persist=True)`" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "800bf1d0-ba1a-45cf-bdf6-2af2f3707256", - "metadata": {}, - "outputs": [], - "source": [ - "auth = earthaccess.login() # " - ] - }, - { - "cell_type": "markdown", - "id": "86f8544a-658e-478d-903f-87bb0f76495c", - "metadata": {}, - "source": [ - "### Step 3: start a Harmony client\n", + ":::{note}\n", + "`earthaccess.login()` will search for you Earthdata Login credentials stored as environment variables or in a `.netrc` file. If no environment variables or `.netrc` file is found, we will be prompted for a _username_ and _password_.\n", "\n", - "Once we have logged in we need to start a Harmony client. A Harmony client is the interface to Harmony services and is used to submit requests and retrieve the results of those requests.\n", + "See the [`earthaccess` documentation](https://earthaccess.readthedocs.io/en/latest/user_guide/authenticate/) for more details.\n", + ":::\n", "\n", "We only need to start the client once. That client can then be used to discover what options are available for a dataset, request subsetting, check on the status of that request and retrieve the results." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "id": "94650df1-161f-499d-a5be-4eff06a55e3a", "metadata": {}, "outputs": [], "source": [ - "# Including UAT environment for testing purposes. `env=Environment.UAT` can be deleted when in production\n", - "harmony_client = Client(auth=(\"apbarret\", \"FurryM0nk3y?\"), env=Environment.UAT)" + "# auth = earthaccess.login()\n", + "harmony_client = Client() #auth=(auth.username, auth.password))" ] }, { @@ -207,7 +156,7 @@ "id": "d79e241d-74a1-446f-85d9-e84e1adce707", "metadata": {}, "source": [ - "### Step 4: discover service options for ICESAT-2\n", + "### Step 3: discover service options for ICESAT-2\n", "\n", "The first thing we want to know is what service options Harmony has for the ICESat-2 dataset. We discover service options for a dataset by submitting a `CapabilitiesRequest`. \n", "\n", @@ -218,13 +167,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "id": "21a1f815-b187-4059-a4ef-0337721da3e8", "metadata": {}, "outputs": [], "source": [ "short_name = \"ATL03\"\n", - "# capabilities_request = CapabilitiesRequest(collection_id='C1256407609-NSIDC_CUAT')\n", "capabilities_request = CapabilitiesRequest(short_name=short_name)" ] }, @@ -238,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "id": "bc59afbe-e218-4240-8315-5f73a51e6125", "metadata": {}, "outputs": [], @@ -251,4085 +199,72 @@ "id": "f6e984f6-7758-496a-af24-12ca87b58bb6", "metadata": {}, "source": [ - "The result is returned as a [JSON](https://en.wikipedia.org/wiki/JSON) string. This string is human readable text but the format makes it hard to read the contents. We can use the `json` library to _decode_ the string and print it in a more reader-friendly format." + "The result is returned as a [JSON](https://en.wikipedia.org/wiki/JSON) string. This string is human readable text but the format makes it hard to read the contents. We can use the `json` library to _decode_ the string and print it in a more reader-friendly format.\n", + "\n", + ":::{warning}\n", + "The JSON returned by capabilities can be very long if variable subsetting is an option and data\n", + "files contains lots of variables. However, information about subsetting service options should be in the first few lines of output.\n", + ":::" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 21, "id": "20a8a6c4-095a-4aeb-8fc6-6ff96db182c7", "metadata": { "scrolled": true }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"conceptId\": \"C1234714691-EEDTEST\",\n", - " \"shortName\": \"ATL03\",\n", - " \"variableSubset\": true,\n", - " \"bboxSubset\": true,\n", - " \"shapeSubset\": true,\n", - " \"concatenate\": true,\n", - " \"reproject\": false,\n", - " \"outputFormats\": [\n", - " \"application/x-hdf\",\n", - " \"application/x-zarr\"\n", - " ],\n", - " \"services\": [\n", - " {\n", - " \"name\": \"sds/trajectory-subsetter\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/S1242315633-EEDTEST\",\n", - " \"capabilities\": {\n", - " \"subsetting\": {\n", - " \"temporal\": true,\n", - " \"bbox\": true,\n", - " \"shape\": true,\n", - " \"variable\": true\n", - " },\n", - " \"output_formats\": [\n", - " \"application/x-hdf\"\n", - " ]\n", - " }\n", - " },\n", - " {\n", - " \"name\": \"harmony/netcdf-to-zarr\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/S1237980031-EEDTEST\",\n", - " \"capabilities\": {\n", - " \"concatenation\": true,\n", - " \"concatenate_by_default\": false,\n", - " \"subsetting\": {\n", - " \"variable\": false\n", - " },\n", - " \"output_formats\": [\n", - " \"application/x-zarr\"\n", - " ]\n", - " }\n", - " }\n", - " ],\n", - " \"variables\": [\n", - " {\n", - " \"name\": \"/ancillary_data/altimetry/atl03_pad\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795788-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/altimetry/band_tol\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795774-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/altimetry/min_full_sat\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795768-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/altimetry/min_near_sat\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795781-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/altimetry/min_sat_h\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795777-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/altimetry/min_scan_s\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795786-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/altimetry/ph_sat_flag\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795776-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/altimetry/ph_sat_lb\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795784-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/altimetry/ph_sat_ub\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795772-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/altimetry/podppd_pad\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795770-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/altimetry/scan_settle_s\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795780-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/atlas_engineering/det_ab_flag\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795544-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/atlas_engineering/ds_gt\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795542-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/atlas_engineering/ds_stat\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795548-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/atlas_engineering/hvpc_ab_flag\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795556-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/atlas_engineering/laser_12_flag\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795550-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/atlas_engineering/lrs_ab_flag\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795546-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/atlas_engineering/pdu_ab_flag\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795552-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/atlas_engineering/ph_uncorrelated_error\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795554-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/atlas_engineering/receiver/rx_bckgrd_sensitivity\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795572-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/atlas_engineering/receiver/rx_return_sensitivity\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795574-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/atlas_engineering/spd_ab_flag\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795538-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/atlas_engineering/tams_ab_flag\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795540-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/atlas_engineering/transmit/tx_pulse_distribution\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795558-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/atlas_engineering/transmit/tx_pulse_energy\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795560-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/atlas_engineering/transmit/tx_pulse_skew_est\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795562-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/atlas_engineering/transmit/tx_pulse_thresh_lower\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795564-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/atlas_engineering/transmit/tx_pulse_thresh_upper\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795566-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/atlas_engineering/transmit/tx_pulse_width_lower\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795568-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/atlas_engineering/transmit/tx_pulse_width_upper\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795570-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/atlas_sdp_gps_epoch\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795324-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time/cal42_product\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795418-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time/gt1l/dead_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795430-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time/gt1l/sigma\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795432-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time/gt1r/dead_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795422-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time/gt1r/sigma\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795424-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time/gt2l/dead_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795438-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time/gt2l/sigma\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795440-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time/gt2r/dead_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795426-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time/gt2r/sigma\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795428-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time/gt3l/dead_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795434-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time/gt3l/sigma\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795436-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time/gt3r/dead_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795442-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time/gt3r/sigma\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795444-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time/side\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795420-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time/temperature\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795416-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time_radiometric_signal_loss/cal34_product\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795366-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time_radiometric_signal_loss/gt1l/dead_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795368-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time_radiometric_signal_loss/gt1l/rad_corr\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795372-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time_radiometric_signal_loss/gt1l/strength\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795370-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time_radiometric_signal_loss/gt1l/width\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795374-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time_radiometric_signal_loss/gt1r/dead_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795384-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time_radiometric_signal_loss/gt1r/rad_corr\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795388-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time_radiometric_signal_loss/gt1r/strength\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795386-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time_radiometric_signal_loss/gt1r/width\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795390-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time_radiometric_signal_loss/gt2l/dead_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795376-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time_radiometric_signal_loss/gt2l/rad_corr\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795380-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time_radiometric_signal_loss/gt2l/strength\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795378-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time_radiometric_signal_loss/gt2l/width\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795382-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time_radiometric_signal_loss/gt2r/dead_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795392-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time_radiometric_signal_loss/gt2r/rad_corr\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795396-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time_radiometric_signal_loss/gt2r/strength\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795394-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time_radiometric_signal_loss/gt2r/width\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795398-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time_radiometric_signal_loss/gt3l/dead_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795400-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time_radiometric_signal_loss/gt3l/rad_corr\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795404-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time_radiometric_signal_loss/gt3l/strength\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795402-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time_radiometric_signal_loss/gt3l/width\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795406-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time_radiometric_signal_loss/gt3r/dead_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795408-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time_radiometric_signal_loss/gt3r/rad_corr\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795412-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time_radiometric_signal_loss/gt3r/strength\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795410-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/dead_time_radiometric_signal_loss/gt3r/width\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795414-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/ds_channel\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795364-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/first_photon_bias/cal19_product\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795446-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/first_photon_bias/gt1l/dead_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795448-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/first_photon_bias/gt1l/ffb_corr\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795452-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/first_photon_bias/gt1l/strength\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795450-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/first_photon_bias/gt1l/width\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795454-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/calibrations/first_photon_bias/gt1r/dead_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795456-EEDTEST\"\n", - 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" },\n", - " {\n", - " \"name\": \"/ancillary_data/gt1r/signal_find_input/e_m\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795584-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt1r/signal_find_input/e_m_mult\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795600-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt1r/signal_find_input/htspanmin\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795628-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt1r/signal_find_input/lslant_flag\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795596-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt1r/signal_find_input/min_fit_time_fact\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795610-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt1r/signal_find_input/n_delta_z1\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795608-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt1r/signal_find_input/n_delta_z2\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795598-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt1r/signal_find_input/nbin_min\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795626-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt1r/signal_find_input/nphot_min\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795590-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt1r/signal_find_input/nslw\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795620-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt1r/signal_find_input/nslw_v\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795588-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt1r/signal_find_input/out_edit_flag\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795632-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt1r/signal_find_input/pc_bckgrd_flag\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795612-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt1r/signal_find_input/r\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795638-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt1r/signal_find_input/r2\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795604-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt1r/signal_find_input/sig_find_t_inc\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795576-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt1r/signal_find_input/snrlow\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795586-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt1r/signal_find_input/snrmed\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795606-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt1r/signal_find_input/t_gap_big\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795614-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/addpad_flag\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795902-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/alpha_inc\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795908-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/alpha_max\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795856-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/delta_t_gap_min\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795912-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/delta_t_lin_fit\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795914-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/delta_t_max\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795872-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/delta_t_min\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795880-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/delta_z_bg\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795858-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/delta_zmax2\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795896-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/delta_zmin\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795870-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/e_a\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795893-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/e_linfit_edit\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795860-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/e_linfit_slant\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795900-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/e_m\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795862-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/e_m_mult\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795878-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/htspanmin\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795906-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/lslant_flag\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795874-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/min_fit_time_fact\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795888-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/n_delta_z1\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795886-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/n_delta_z2\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795876-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/nbin_min\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795904-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/nphot_min\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795868-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/nslw\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795898-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/nslw_v\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795866-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/out_edit_flag\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795910-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/pc_bckgrd_flag\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795890-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/r\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795916-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/r2\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795882-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/sig_find_t_inc\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795854-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/snrlow\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795864-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2l/signal_find_input/snrmed\",\n", - 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" },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2r/signal_find_input/delta_t_lin_fit\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795700-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2r/signal_find_input/delta_t_max\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795658-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2r/signal_find_input/delta_t_min\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795666-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2r/signal_find_input/delta_z_bg\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795644-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2r/signal_find_input/delta_zmax2\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795681-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/ancillary_data/gt2r/signal_find_input/delta_zmin\",\n", - 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" \"name\": \"/gt2l/geophys_corr/tide_earth\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795222-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/geophys_corr/tide_earth_free2mean\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795230-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/geophys_corr/tide_equilibrium\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795234-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/geophys_corr/tide_load\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795228-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/geophys_corr/tide_oc_pole\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795216-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/geophys_corr/tide_ocean\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795220-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/geophys_corr/tide_pole\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795240-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/heights/delta_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795310-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/heights/dist_ph_across\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795314-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/heights/dist_ph_along\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795298-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/heights/h_ph\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795304-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/heights/lat_ph\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795296-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/heights/lon_ph\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795308-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/heights/pce_mframe_cnt\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795312-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/heights/ph_id_channel\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795306-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/heights/ph_id_count\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795294-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/heights/ph_id_pulse\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795302-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/heights/quality_ph\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795292-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/heights/signal_conf_ph\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795300-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/signal_find_output/inlandwater/bckgrd_mean\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795282-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/signal_find_output/inlandwater/bckgrd_sigma\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795284-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/signal_find_output/inlandwater/delta_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795286-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/signal_find_output/inlandwater/t_pc_delta\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795288-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/signal_find_output/inlandwater/z_pc_delta\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795290-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/signal_find_output/land/bckgrd_mean\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795271-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/signal_find_output/land/bckgrd_sigma\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795275-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/signal_find_output/land/delta_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795274-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/signal_find_output/land/t_pc_delta\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795278-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/signal_find_output/land/z_pc_delta\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795279-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/signal_find_output/land_ice/bckgrd_mean\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795262-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/signal_find_output/land_ice/bckgrd_sigma\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795264-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/signal_find_output/land_ice/delta_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795266-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/signal_find_output/land_ice/t_pc_delta\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795268-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/signal_find_output/land_ice/z_pc_delta\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795270-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/signal_find_output/ocean/bckgrd_mean\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795242-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/signal_find_output/ocean/bckgrd_sigma\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795244-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/signal_find_output/ocean/delta_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795246-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/signal_find_output/ocean/t_pc_delta\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795247-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/signal_find_output/ocean/z_pc_delta\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795250-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/signal_find_output/sea_ice/bckgrd_mean\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795252-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/signal_find_output/sea_ice/bckgrd_sigma\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795254-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/signal_find_output/sea_ice/delta_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795256-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/signal_find_output/sea_ice/t_pc_delta\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795258-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2l/signal_find_output/sea_ice/z_pc_delta\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795260-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/bckgrd_atlas/bckgrd_counts\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794436-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/bckgrd_atlas/bckgrd_counts_reduced\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794429-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/bckgrd_atlas/bckgrd_hist_top\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794426-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/bckgrd_atlas/bckgrd_int_height\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794414-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/bckgrd_atlas/bckgrd_int_height_reduced\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794424-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/bckgrd_atlas/bckgrd_rate\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794418-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/bckgrd_atlas/delta_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794432-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/bckgrd_atlas/pce_mframe_cnt\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794433-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/bckgrd_atlas/tlm_height_band1\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794416-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/bckgrd_atlas/tlm_height_band2\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794428-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/bckgrd_atlas/tlm_top_band1\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794420-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/bckgrd_atlas/tlm_top_band2\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794421-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/geolocation/altitude_sc\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794504-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/geolocation/bounce_time_offset\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794498-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/geolocation/delta_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794500-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/geolocation/full_sat_fract\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794488-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/geolocation/near_sat_fract\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794492-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/geolocation/neutat_delay_derivative\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794444-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/geolocation/neutat_delay_total\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794509-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/geolocation/neutat_ht\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794460-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/geolocation/ph_index_beg\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794461-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/geolocation/pitch\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794437-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/geolocation/podppd_flag\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794484-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/geolocation/range_bias_corr\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794446-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/geolocation/ref_azimuth\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794454-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/geolocation/ref_elev\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794458-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/geolocation/reference_photon_index\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794502-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/geolocation/reference_photon_lat\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794494-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/geolocation/reference_photon_lon\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794448-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/geolocation/roll\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794474-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/geolocation/segment_dist_x\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794505-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/geolocation/segment_id\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794490-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/geolocation/segment_length\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794442-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/geolocation/segment_ph_cnt\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794440-EEDTEST\"\n", - 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" {\n", - " \"name\": \"/gt2r/geophys_corr/tide_oc_pole\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794508-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/geophys_corr/tide_ocean\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794512-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/geophys_corr/tide_pole\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794534-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/heights/delta_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794604-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/heights/dist_ph_across\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794608-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt2r/heights/dist_ph_along\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794592-EEDTEST\"\n", - " },\n", - " {\n", - 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" \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795110-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3l/heights/ph_id_count\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795098-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3l/heights/ph_id_pulse\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795106-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3l/heights/quality_ph\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795095-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3l/heights/signal_conf_ph\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795104-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3l/signal_find_output/inlandwater/bckgrd_mean\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795086-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3l/signal_find_output/inlandwater/bckgrd_sigma\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795088-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3l/signal_find_output/inlandwater/delta_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795090-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3l/signal_find_output/inlandwater/t_pc_delta\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795091-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3l/signal_find_output/inlandwater/z_pc_delta\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795094-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3l/signal_find_output/land/bckgrd_mean\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795046-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3l/signal_find_output/land/bckgrd_sigma\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795048-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3l/signal_find_output/land/delta_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795050-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3l/signal_find_output/land/t_pc_delta\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795052-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3l/signal_find_output/land/z_pc_delta\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795054-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3l/signal_find_output/land_ice/bckgrd_mean\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795075-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3l/signal_find_output/land_ice/bckgrd_sigma\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795078-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3l/signal_find_output/land_ice/delta_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245795079-EEDTEST\"\n", - 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" },\n", - " {\n", - " \"name\": \"/gt3r/geolocation/segment_dist_x\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796102-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geolocation/segment_id\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796086-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geolocation/segment_length\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796038-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geolocation/segment_ph_cnt\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796036-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geolocation/sigma_across\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796046-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geolocation/sigma_along\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796068-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geolocation/sigma_h\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796078-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geolocation/sigma_lat\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796064-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geolocation/sigma_lon\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796048-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geolocation/solar_azimuth\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796060-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geolocation/solar_elevation\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796094-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geolocation/surf_type\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796062-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geolocation/tx_pulse_energy\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796074-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geolocation/tx_pulse_skew_est\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796072-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geolocation/tx_pulse_width_lower\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796066-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geolocation/tx_pulse_width_upper\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796076-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geolocation/velocity_sc\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796082-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geolocation/yaw\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796052-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geophys_corr/dac\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796116-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geophys_corr/delta_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796128-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geophys_corr/dem_flag\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796108-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geophys_corr/dem_h\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796121-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geophys_corr/geoid\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796125-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geophys_corr/geoid_free2mean\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796114-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geophys_corr/tide_earth\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796112-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geophys_corr/tide_earth_free2mean\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796120-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geophys_corr/tide_equilibrium\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796124-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geophys_corr/tide_load\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796117-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geophys_corr/tide_oc_pole\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796106-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geophys_corr/tide_ocean\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796110-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/geophys_corr/tide_pole\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796129-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/heights/delta_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796200-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/heights/dist_ph_across\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796204-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/heights/dist_ph_along\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796188-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/heights/h_ph\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796194-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/heights/lat_ph\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796186-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/heights/lon_ph\",\n", - 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" {\n", - " \"name\": \"/gt3r/signal_find_output/land_ice/bckgrd_sigma\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796154-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/signal_find_output/land_ice/delta_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796156-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/signal_find_output/land_ice/t_pc_delta\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796158-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/signal_find_output/land_ice/z_pc_delta\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796160-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/signal_find_output/ocean/bckgrd_mean\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796132-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/signal_find_output/ocean/bckgrd_sigma\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796134-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/signal_find_output/ocean/delta_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796136-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/signal_find_output/ocean/t_pc_delta\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796138-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/signal_find_output/ocean/z_pc_delta\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796140-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/signal_find_output/sea_ice/bckgrd_mean\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796142-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/signal_find_output/sea_ice/bckgrd_sigma\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796144-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/gt3r/signal_find_output/sea_ice/delta_time\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245796146-EEDTEST\"\n", - 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" },\n", - " {\n", - " \"name\": \"/quality_assessment/gt1l/qa_perc_signal_conf_ph_med\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794844-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt1l/qa_perc_surf_type\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794846-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt1l/qa_total_signal_conf_ph_high\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794848-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt1l/qa_total_signal_conf_ph_low\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794850-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt1l/qa_total_signal_conf_ph_med\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794852-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt1r/qa_perc_signal_conf_ph_high\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794812-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt1r/qa_perc_signal_conf_ph_low\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794814-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt1r/qa_perc_signal_conf_ph_med\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794816-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt1r/qa_perc_surf_type\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794818-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt1r/qa_total_signal_conf_ph_high\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794820-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt1r/qa_total_signal_conf_ph_low\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794822-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt1r/qa_total_signal_conf_ph_med\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794824-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt2l/qa_perc_signal_conf_ph_high\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794868-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt2l/qa_perc_signal_conf_ph_low\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794870-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt2l/qa_perc_signal_conf_ph_med\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794872-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt2l/qa_perc_surf_type\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794874-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt2l/qa_total_signal_conf_ph_high\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794876-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt2l/qa_total_signal_conf_ph_low\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794878-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt2l/qa_total_signal_conf_ph_med\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794880-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt2r/qa_perc_signal_conf_ph_high\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794826-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt2r/qa_perc_signal_conf_ph_low\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794828-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt2r/qa_perc_signal_conf_ph_med\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794830-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt2r/qa_perc_surf_type\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794832-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt2r/qa_total_signal_conf_ph_high\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794834-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt2r/qa_total_signal_conf_ph_low\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794836-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt2r/qa_total_signal_conf_ph_med\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794838-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt3l/qa_perc_signal_conf_ph_high\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794854-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt3l/qa_perc_signal_conf_ph_low\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794856-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt3l/qa_perc_signal_conf_ph_med\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794858-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt3l/qa_perc_surf_type\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794860-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt3l/qa_total_signal_conf_ph_high\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794862-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt3l/qa_total_signal_conf_ph_low\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794864-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt3l/qa_total_signal_conf_ph_med\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794866-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt3r/qa_perc_signal_conf_ph_high\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794882-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt3r/qa_perc_signal_conf_ph_low\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794884-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt3r/qa_perc_signal_conf_ph_med\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794886-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt3r/qa_perc_surf_type\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794888-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt3r/qa_total_signal_conf_ph_high\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794890-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt3r/qa_total_signal_conf_ph_low\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794892-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/gt3r/qa_total_signal_conf_ph_med\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794894-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/qa_granule_fail_reason\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794808-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"/quality_assessment/qa_granule_pass_fail\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794805-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"ds_surf_type\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794224-EEDTEST\"\n", - " },\n", - " {\n", - " \"name\": \"ds_xyz\",\n", - " \"href\": \"https://cmr.uat.earthdata.nasa.gov/search/concepts/V1245794222-EEDTEST\"\n", - " }\n", - " ],\n", - " \"capabilitiesVersion\": \"2\"\n", - "}\n" - ] + "data": { + "application/json": { + "bboxSubset": true, + "capabilitiesVersion": "2", + "concatenate": false, + "conceptId": "C2596864127-NSIDC_CPRD", + "outputFormats": [ + "application/x-hdf" + ], + "reproject": false, + "services": [ + { + "capabilities": { + "output_formats": [ + "application/x-hdf" + ], + "subsetting": { + "bbox": true, + "shape": true, + "temporal": true, + "variable": true + } + }, + "href": "https://cmr.earthdata.nasa.gov/search/concepts/S2836723123-XYZ_PROV", + "name": "sds/trajectory-subsetter" + } + ], + "shapeSubset": true, + "shortName": "ATL03", + "variableSubset": false, + "variables": [] + }, + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": { + "application/json": { + "expanded": false, + "root": "root" + } + }, + "output_type": "execute_result" } ], "source": [ - "print(json.dumps(capabilities, indent=2))" + "# print(json.dumps(capabilities, indent=2))\n", + "JSON(capabilities)" ] }, { @@ -4347,7 +282,7 @@ "id": "258e04a7-4b96-4652-b523-9bbdef9f7f06", "metadata": {}, "source": [ - "### Step 5: subset ICESat-2 ATL03 file\n", + "### Step 4: subset ICESat-2 ATL03 file\n", "\n", "Now that we know what subsetting options are available for for the ATL03 dataset, we can request a subsetted dataset. " ] @@ -4359,7 +294,7 @@ "source": [ "#### Create A Subset Request\n", "\n", - "we create a simple request for subset of ATL03 for a bounding box over the northern Colorado Front Range and for the 2020-04-27 to 2020-05-28 period.\n", + "we will create a simple request for subset of ATL03 for a bounding box over the northern Colorado Front Range and for the 2020-04-27 to 2020-05-28 period.\n", "\n", "![Bounding Box and ATL03 Ground Track for northern Colorado](images/atl03_ground_track_and_bbox.png)" ] @@ -4369,7 +304,11 @@ "id": "c1b953ba-e8cd-486f-8132-6cdbb69b656c", "metadata": {}, "source": [ - "See the [harmony-py](https://harmony-py.readthedocs.io/en/latest/) documentation for details on how to construct your request." + "The spatial extent to subset is passed as a `BBox` object that defines the _minimum longitude_, _minimum latitude_, _maximum longitude_ and _maximum latitude_ in decimal degrees.\n", + "\n", + "The temporal range to subset is passed to Harmony as a dictionary with `\"start\"` and `\"stop\"` keys, and dates supplied as `datetime` objects. \n", + "\n", + "Currently, ICESat-2 data only allows spatial and temporal subsetting so we only demonstrate these capabilities. For further information on constructing a `Request` objects see the [harmony-py](https://harmony-py.readthedocs.io/en/latest/) documentation." ] }, { @@ -4379,12 +318,8 @@ "metadata": {}, "outputs": [], "source": [ - "# Use collection_id='C1256407609-NSIDC_CUAT' for testing. Update to use short_name when ready to move to production:\n", - "# short_name = 'ATL03'\n", - "\n", - "# Use earthaccess to check for granules\n", "request = Request(\n", - " collection = Collection(id='C1256407609-NSIDC_CUAT'), #capabilities[\"conceptId\"]),\n", + " collection = Collection(id=capabilities[\"conceptId\"]),\n", " spatial=BBox(-105.5,40,-105,41.),\n", " temporal={\n", " 'start': dt.datetime(2020, 4, 27),\n", @@ -4402,22 +337,22 @@ "\n", "The request is submitted in the same way as the Capabilities request. This starts the subsetting process, which may take a while depending on the size of the request. Submitting request returns a Job ID, which is a unique identifier for your request that is used to track the progress of the request and to access the results.\n", "\n", - "If the request involves a lot of files (more than 300), Harmony will only process the first 300 files. See section on large jobs on how to work around this restriction. " + "If the request involves a lot of files (more than 300), Harmony will only process the first 300 files. See section on [Handling Large Jobs](https://openscapes.2i2c.cloud/user/andypbarrett/lab/workspaces/auto-e/tree/earthdata-cloud-cookbook/tutorials/IS2_Harmony.ipynb#handling-large-requests) on how to work around this restriction. " ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "id": "c1c312bc-a986-4ebd-951f-c0d11208d19f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'0f383131-ee48-419a-aef9-2d2b71f920e3'" + "'2dd7cf22-a902-4eb8-8c0e-8d8cdd48b27c'" ] }, - "execution_count": 15, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -4432,14 +367,14 @@ "id": "53982496-f3a6-4c46-a61f-f164157e119a", "metadata": {}, "source": [ - "### Step 6: check the status of the request\n", + "### Step 5: check the status of the request\n", "\n", "Subsetting is performed in the cloud. For small jobs, the subsetting process can be monitored with a progress bar by submitting the `job_id` to the `wait_for_processing` method of the Harmony client." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 8, "id": "9b253c1e-da1e-4d14-883d-7fbd87834133", "metadata": {}, "outputs": [ @@ -4465,46 +400,73 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 22, "id": "be41beda-a445-419f-9af2-080fb7e7b1d3", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'createdAt': '2024-10-25T19:41:24.048Z',\n", - " 'dataExpiration': '2024-11-24T19:41:24.048Z',\n", - " 'jobID': '0f383131-ee48-419a-aef9-2d2b71f920e3',\n", - " 'labels': [],\n", - " 'links': [{'href': 'https://harmony.uat.earthdata.nasa.gov/stac/0f383131-ee48-419a-aef9-2d2b71f920e3/',\n", - " 'rel': 'stac-catalog-json',\n", - " 'title': 'STAC catalog',\n", - " 'type': 'application/json'},\n", - " {'bbox': [-108.28738, 26.94838, -103.60569, 59.54235],\n", - " 'href': 'https://harmony.uat.earthdata.nasa.gov/service-results/harmony-uat-staging/public/0f383131-ee48-419a-aef9-2d2b71f920e3/4882744/ATL03_20200427193622_04930702_006_02_subsetted.h5',\n", - " 'rel': 'data',\n", - " 'temporal': {'end': '2020-04-27T19:44:52.680Z',\n", - " 'start': '2020-04-27T19:36:22.028Z'},\n", - " 'title': 'ATL03_20200427193622_04930702_006_02_subsetted.h5',\n", - " 'type': 'application/x-hdf5'},\n", - " {'href': 'https://harmony.uat.earthdata.nasa.gov/jobs/0f383131-ee48-419a-aef9-2d2b71f920e3?linktype=https&page=1&limit=2000',\n", - " 'rel': 'self',\n", - " 'title': 'The current page',\n", - " 'type': 'application/json'}],\n", - " 'message': 'The job has completed successfully',\n", - " 'numInputGranules': 1,\n", - " 'progress': 100,\n", - " 'request': 'https://harmony.uat.earthdata.nasa.gov/C1256407609-NSIDC_CUAT/ogc-api-coverages/1.0.0/collections/parameter_vars/coverage/rangeset?forceAsync=true&subset=lat(40%3A41.0)&subset=lon(-105.5%3A-105)&subset=time(%222020-04-27T00%3A00%3A00%22%3A%222020-05-28T00%3A00%3A00%22)&variable=all',\n", - " 'status': 'successful',\n", - " 'updatedAt': '2024-10-25T19:44:11.633Z',\n", - " 'username': 'apbarret'}\n" - ] + "data": { + "application/json": { + "createdAt": "2024-10-29T20:46:15.371Z", + "dataExpiration": "2024-11-28T20:46:15.371Z", + "jobID": "2dd7cf22-a902-4eb8-8c0e-8d8cdd48b27c", + "labels": [], + "links": [ + { + "href": "https://harmony.earthdata.nasa.gov/stac/2dd7cf22-a902-4eb8-8c0e-8d8cdd48b27c/", + "rel": "stac-catalog-json", + "title": "STAC catalog", + "type": "application/json" + }, + { + "bbox": [ + -108.28738, + 26.94838, + -103.60569, + 59.54235 + ], + "href": "https://harmony.earthdata.nasa.gov/service-results/harmony-prod-staging/public/2dd7cf22-a902-4eb8-8c0e-8d8cdd48b27c/79898885/ATL03_20200427193622_04930702_006_02_subsetted.h5", + "rel": "data", + "temporal": { + "end": "2020-04-27T19:44:52.680Z", + "start": "2020-04-27T19:36:22.028Z" + }, + "title": "ATL03_20200427193622_04930702_006_02_subsetted.h5", + "type": "application/x-hdf5" + }, + { + "href": "https://harmony.earthdata.nasa.gov/jobs/2dd7cf22-a902-4eb8-8c0e-8d8cdd48b27c?linktype=https&page=1&limit=2000", + "rel": "self", + "title": "The current page", + "type": "application/json" + } + ], + "message": "The job has completed successfully", + "numInputGranules": 1, + "progress": 100, + "request": "https://harmony.earthdata.nasa.gov/C2596864127-NSIDC_CPRD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat(40%3A41.0)&subset=lon(-105.5%3A-105)&subset=time(%222020-04-27T00%3A00%3A00%22%3A%222020-05-28T00%3A00%3A00%22)", + "status": "successful", + "updatedAt": "2024-10-29T20:48:52.599Z", + "username": "apbarret" + }, + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": { + "application/json": { + "expanded": false, + "root": "root" + } + }, + "output_type": "execute_result" } ], "source": [ "job_summary = harmony_client.result_json(job_id)\n", - "pprint(job_summary)" + "# pprint(job_summary)\n", + "JSON(job_summary)" ] }, { @@ -4512,7 +474,7 @@ "id": "421391bc-f996-4dbb-a69d-ac7d40d67243", "metadata": {}, "source": [ - "### Step 7: access the subsetted data\n", + "### Step 6: access the subsetted data\n", "\n", "The subsetted files can be accessed by downloading the files to a local machine, such as a laptop or desktop workstation, or by _streaming_ the data. We will use both access methods in the two examples below.\n", "\n", @@ -4535,10 +497,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "2e8f110a-4aa3-4bec-9ac3-65eef9f0e4aa", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "./ATL03_20200427193622_04930702_006_02_subsetted.h5\n", + "./ATL03_20200427193622_04930702_006_02_subsetted.h5\n", + "./ATL03_20200427193622_04930702_006_02_subsetted.h5\n" + ] + } + ], "source": [ "url = list(harmony_client.result_urls(job_id))[0] # Get the data url of the first file\n", "filepath = harmony_client.download(url, directory=\".\", overwrite=False).result()\n", @@ -4559,7 +531,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 12, "id": "951581ed-03a3-4aa2-b147-ed2054b75905", "metadata": {}, "outputs": [ @@ -4567,7 +539,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "['./4882744_ATL03_20200427193622_04930702_006_02_subsetted.h5']\n" + "['./ATL03_20200427193622_04930702_006_02_subsetted.h5']\n" ] } ], @@ -4593,7 +565,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 13, "id": "ae13153c-2968-44c1-a668-0f72f07a7d8c", "metadata": {}, "outputs": [ @@ -4964,7 +936,7 @@ " fill: currentColor;\n", "}\n", "
    <xarray.Dataset> Size: 92MB\n",
-       "Dimensions:         (delta_time: 1848993, ds_surf_type: 5)\n",
+       "Dimensions:         (delta_time: 1848992, ds_surf_type: 5)\n",
        "Coordinates:\n",
        "  * delta_time      (delta_time) datetime64[ns] 15MB 2020-04-27T19:39:45.8497...\n",
        "    lat_ph          (delta_time) float64 15MB ...\n",
@@ -4983,10 +955,10 @@
        "    weight_ph       (delta_time) uint8 2MB ...\n",
        "Attributes:\n",
        "    Description:  Contains arrays of the parameters for each received photon.\n",
-       "    data_rate:    Data are stored at the photon detection rate.
  • Description :
    Contains arrays of the parameters for each received photon.
    data_rate :
    Data are stored at the photon detection rate.
    • " ], "text/plain": [ " Size: 92MB\n", - "Dimensions: (delta_time: 1848993, ds_surf_type: 5)\n", + "Dimensions: (delta_time: 1848992, ds_surf_type: 5)\n", "Coordinates:\n", " * delta_time (delta_time) datetime64[ns] 15MB 2020-04-27T19:39:45.8497...\n", " lat_ph (delta_time) float64 15MB ...\n", @@ -5033,7 +1005,7 @@ " data_rate: Data are stored at the photon detection rate." ] }, - "execution_count": 33, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -5068,17 +1040,17 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 14, "id": "a8d8a29d-91df-4872-9d9c-6a116bc92774", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['s3://harmony-uat-staging/public/0f383131-ee48-419a-aef9-2d2b71f920e3/4882744/ATL03_20200427193622_04930702_006_02_subsetted.h5']" + "['s3://harmony-prod-staging/public/2dd7cf22-a902-4eb8-8c0e-8d8cdd48b27c/79898885/ATL03_20200427193622_04930702_006_02_subsetted.h5']" ] }, - "execution_count": 27, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -5100,7 +1072,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 15, "id": "d58df790-d40c-410c-a295-22d6d39dfd5b", "metadata": {}, "outputs": [], @@ -5118,7 +1090,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 16, "id": "73bc9a2d-b009-4cf9-8a47-531a308833b0", "metadata": {}, "outputs": [], @@ -5145,7 +1117,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 17, "id": "f29584a0-357a-4f1a-8a3b-ab72fc20d6e4", "metadata": {}, "outputs": [], @@ -5163,7 +1135,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 18, "id": "8860630c-2050-4256-88c2-f1879456a9a1", "metadata": {}, "outputs": [ @@ -5534,7 +1506,7 @@ " fill: currentColor;\n", "}\n", "
    <xarray.Dataset> Size: 92MB\n",
-       "Dimensions:         (delta_time: 1848993, ds_surf_type: 5)\n",
+       "Dimensions:         (delta_time: 1848992, ds_surf_type: 5)\n",
        "Coordinates:\n",
        "  * delta_time      (delta_time) datetime64[ns] 15MB 2020-04-27T19:39:45.8497...\n",
        "    lat_ph          (delta_time) float64 15MB ...\n",
@@ -5553,10 +1525,10 @@
        "    weight_ph       (delta_time) uint8 2MB ...\n",
        "Attributes:\n",
        "    Description:  Contains arrays of the parameters for each received photon.\n",
-       "    data_rate:    Data are stored at the photon detection rate.
  • Description :
    Contains arrays of the parameters for each received photon.
    data_rate :
    Data are stored at the photon detection rate.
    • " ], "text/plain": [ " Size: 92MB\n", - "Dimensions: (delta_time: 1848993, ds_surf_type: 5)\n", + "Dimensions: (delta_time: 1848992, ds_surf_type: 5)\n", "Coordinates:\n", " * delta_time (delta_time) datetime64[ns] 15MB 2020-04-27T19:39:45.8497...\n", " lat_ph (delta_time) float64 15MB ...\n", @@ -5603,7 +1575,7 @@ " data_rate: Data are stored at the photon detection rate." ] }, - "execution_count": 31, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -5613,12 +1585,20 @@ "ds" ] }, + { + "cell_type": "markdown", + "id": "43f8125f-8e99-4fe9-a7cb-62a10295b21d", + "metadata": {}, + "source": [ + "## Appendices" + ] + }, { "cell_type": "markdown", "id": "ba0d2015-f342-4987-aa16-e0b67b6e35b3", "metadata": {}, "source": [ - "## Handling large requests\n", + "### A. Handling large requests\n", "\n", "Need to explain large-jobs and previews, pausing\n", "\n", @@ -5629,14 +1609,115 @@ "https://harmony.earthdata.nasa.gov/docs#skipping-preview" ] }, + { + "cell_type": "code", + "execution_count": 23, + "id": "7ca0b14a-7f6d-40f4-8eeb-b7f871a851da", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'status': 'running',\n", + " 'message': 'The job is being processed',\n", + " 'progress': 0,\n", + " 'created_at': datetime.datetime(2024, 10, 29, 22, 14, 21, 784000, tzinfo=tzlocal()),\n", + " 'updated_at': datetime.datetime(2024, 10, 29, 22, 14, 21, 784000, tzinfo=tzlocal()),\n", + " 'created_at_local': '2024-10-29T22:14:21+00:00',\n", + " 'updated_at_local': '2024-10-29T22:14:21+00:00',\n", + " 'request': 'https://harmony.earthdata.nasa.gov/C2596864127-NSIDC_CPRD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat(40%3A41.0)&subset=lon(-105.5%3A-105)',\n", + " 'num_input_granules': 134,\n", + " 'data_expiration': datetime.datetime(2024, 11, 28, 22, 14, 21, 784000, tzinfo=tzlocal()),\n", + " 'data_expiration_local': '2024-11-28T22:14:21+00:00'}" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "request = Request(\n", + " collection = Collection(id=capabilities[\"conceptId\"]),\n", + " spatial=BBox(-105.5,40,-105,41.),\n", + " # temporal={\n", + " # 'start': dt.datetime(2020, 4, 27),\n", + " # 'stop': dt.datetime(2020, 5, 28)\n", + " # }\n", + ")\n", + "job_id = harmony_client.submit(request)\n", + "harmony_client.status(job_id)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "00b36a4a-d673-45d5-b0a4-9c7bba958852", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " [ Processing: 80% ] |######################################## | [/]\n", + "Job is running with errors.\n", + " [ Processing: 100% ] |###################################################| [|]\n" + ] + } + ], + "source": [ + "harmony_client.wait_for_processing(job_id, show_progress=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "6710674d-d124-4b9d-9a39-265b65baa749", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'status': 'complete_with_errors',\n", + " 'message': 'The job has completed with errors. See the errors field for more details',\n", + " 'progress': 100,\n", + " 'created_at': datetime.datetime(2024, 10, 29, 22, 14, 21, 784000, tzinfo=tzlocal()),\n", + " 'updated_at': datetime.datetime(2024, 10, 29, 22, 29, 32, 444000, tzinfo=tzlocal()),\n", + " 'created_at_local': '2024-10-29T22:14:21+00:00',\n", + " 'updated_at_local': '2024-10-29T22:29:32+00:00',\n", + " 'request': 'https://harmony.earthdata.nasa.gov/C2596864127-NSIDC_CPRD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat(40%3A41.0)&subset=lon(-105.5%3A-105)',\n", + " 'num_input_granules': 134,\n", + " 'data_expiration': datetime.datetime(2024, 11, 28, 22, 14, 21, 784000, tzinfo=tzlocal()),\n", + " 'data_expiration_local': '2024-11-28T22:14:21+00:00',\n", + " 'errors': [{'url': 'https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-protected/ATLAS/ATL03/006/2023/08/20/ATL03_20230820094935_09352002_006_02.h5',\n", + " 'message': 'WorkItem failed: sds/trajectory-subsetter:0.1.21: L2 Trajectory Subsetter failed with error: No data found that matched subset constraints.'},\n", + " {'url': 'https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-protected/ATLAS/ATL03/006/2020/08/25/ATL03_20200825135211_09350802_006_01.h5',\n", + " 'message': 'WorkItem failed: sds/trajectory-subsetter:0.1.21: L2 Trajectory Subsetter failed with error: No data found that matched subset constraints.'},\n", + " {'url': 'https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-protected/ATLAS/ATL03/006/2018/11/28/ATL03_20181128201334_09350102_006_02.h5',\n", + " 'message': 'WorkItem failed: sds/trajectory-subsetter:0.1.21: L2 Trajectory Subsetter failed with error: No data found that matched subset constraints.'},\n", + " {'url': 'https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-protected/ATLAS/ATL03/006/2022/11/20/ATL03_20221120225059_09351702_006_02.h5',\n", + " 'message': 'WorkItem failed: sds/trajectory-subsetter:0.1.21: L2 Trajectory Subsetter failed with error: No data found that matched subset constraints.'},\n", + " {'url': 'https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-protected/ATLAS/ATL03/006/2021/02/21/ATL03_20210221172745_09121006_006_01.h5',\n", + " 'message': 'WorkItem failed: sds/trajectory-subsetter:0.1.21: L2 Trajectory Subsetter failed with error: No data found that matched subset constraints.'}]}" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "harmony_client.status(job_id)" + ] + }, { "cell_type": "markdown", "id": "bdbaa61e-7ecb-4826-9986-1893784d98c7", "metadata": {}, "source": [ - "## Some useful tools\n", + "### B. Translating requests\n", "\n", - "`harmony-py` is an interface to the Harmony RESTful API. RESTful API send requests and receive responses via HTTPS; the same protocol that serves web pages. Requests are sent to service endpoints, which is a URL (e.g. `https://service.endpoint.com/type_of_service`). Ouery parameters that modify a request can be appended a key-value pairs after a `?`. Each key-value pair is separated by an &. For example:\n", + "`harmony-py` is an interface to the Harmony RESTful API. RESTful API send requests and receive responses via HTTPS; the same protocol that serves web pages. Requests are sent to service endpoints, which is a URL (e.g. `https://service.endpoint.com/type_of_service`). Query parameters that modify a request can be appended a key-value pairs after a `?`. Each key-value pair is separated by an &. For example:\n", "\n", "```\n", "https://service.endpoint.com/type_of_service?param1=value1¶m2=value2\n", From 1909905316ed8c062ff7c681584e3f0cdd4df4b6 Mon Sep 17 00:00:00 2001 From: asteiker Date: Fri, 1 Nov 2024 21:47:01 +0000 Subject: [PATCH 6/9] review comments and minor updates --- tutorials/IS2_Harmony.ipynb | 1254 +++++++++-------------------------- 1 file changed, 317 insertions(+), 937 deletions(-) diff --git a/tutorials/IS2_Harmony.ipynb b/tutorials/IS2_Harmony.ipynb index 75c03e29..a851591f 100644 --- a/tutorials/IS2_Harmony.ipynb +++ b/tutorials/IS2_Harmony.ipynb @@ -34,17 +34,18 @@ "source": [ "## Summary\n", "\n", - "In this tutorial, we will use NASA's Earthdata Harmony Services to subset and access ICESat-2 data with the `harmony-py` Python library. Harmony can be used to subset _any of the_(?) ICESat-2 data products. In this tutorial, we will subset the ATL03 Geolocated Photon Dataset as an example.\n", + "In this tutorial, we will use NASA's Earthdata Harmony Services to subset and access ICESat-2 data with the `harmony-py` Python library. Harmony can be used to subset Level-2 and Level-3A ICESat-2 data products. In this tutorial, we will subset the [ATL03 Global Geolocated Photon Data](https://nsidc.org/data/atl03/versions/6) product as an example.\n", "\n", - "**What is Harmony?** Harmony provides access (_is it a service?_) to a set of services that can subset, reproject and reformat NASA datasets. Data can be subsetted for a geographic region, a temporal range and by variable. Data can be \"reprojected\" from it's native coordinate reference system (CRS) to the coordinate reference system relevant to your analysis. And data can be reformatted from it's native file format to a format that is more relevant for your application. These services are collectively called _transformation services_. However, not all services are available for all datasets. You will learn how to discover which services are available for your dataset.\n", + "**What is Harmony?** [Harmony](https://harmony.earthdata.nasa.gov/) is a service that allows you to customize many NASA datasets, including the ability to subset, reproject and reformat files. Data can be subsetted for a geographic region, a temporal range and by variable. Data can be \"reprojected\" from its native coordinate reference system (CRS) to the coordinate reference system relevant to your analysis. And data can be reformatted from its native file format to a format that is more relevant for your application. These services are collectively called _transformation services_. However, not all services are available for all datasets. You will learn how to discover which services are available for a given dataset.\n", "\n", "Data transformed by Harmony services are staged on NASA Amazon Web Services (AWS) S3 buckets or on user-owned AWS S3 buckets. Data in NASA S3 buckets are accessed using signed URLs or temporary access credentials. This data can be downloaded to your local machine or you can access the data directly if you are working in an AWS cloud instance, such as a Jupyter Hub, in AWS `us-west-2`. \n", "\n", "_Add links or provide background to terminology_\n", + "_AS: I added a few links above. I think this is sufficient but feel free to add other references if you'd like_\n", "\n", "_Stuff for later, maybe_\n", - "Data in NASA’s Earthdata Cloud, including the subsetted data processed by Harmony, reside in Amazon Web Services (AWS) Simple Storage Service (S3) buckets. Access is provided via temporary credentials; this free access is limited to requests made within the US West (Oregon) (code: us-west-2) AWS region. While this compute location is required for direct S3 access, all data in Earthdata Cloud are still freely available via download.\n", - "s-west-2 region. " + "_AS: I don't think this additional info is needed here but it's a great explanation. I wonder if it ought to be incorporated into NSIDC's cloud access guide: https://nsidc.org/data/user-resources/help-center/nasa-earthdata-cloud-data-access-guide\n", + "Data in NASA’s Earthdata Cloud, including the subsetted data processed by Harmony, reside in Amazon Web Services (AWS) Simple Storage Service (S3) buckets. Access is provided via temporary credentials; this free access is limited to requests made within the US West (Oregon) (code: us-west-2) AWS region. While this compute location is required for direct S3 access, all data in Earthdata Cloud are still freely available via download." ] }, { @@ -56,7 +57,7 @@ "\n", "In this tutorial you will learn how to:\n", "\n", - "1. discover Harmony service options ICESat-2 datasets;\n", + "1. discover Harmony service options for ICESat-2 datasets;\n", "3. use the `harmony-py` library to subset ATL03 granules for a bounding box and time range;\n", "4. download the subsetted ATL03 to your local machine;\n", "5. load the subsetted ATL03 data directly into xarray." @@ -69,9 +70,9 @@ "source": [ "## Prerequisites\n", "\n", - "This tutorial has been designed to run in an AWS cloud compute instance in AWS region `us-west-2`. However, if you want run it from your laptop or workstation, everything except Step-4, direct access, should work just fine. _Also get https link_\n", + "This tutorial has been designed to run in an AWS cloud compute instance in AWS region `us-west-2`. However, if you want run it from your laptop or workstation, everything except Step-4, direct access, should work just fine. _Also get https link_ _AS: What does that comment reference?_ \n", "\n", - "An [Earthdata Login](https://urs.earthdata.nasa.gov) account is required to access data from the NASA Earthdata system. Before requesting a subset of ICESat-2 data, we first need to set up our Earthdata Login authentication." + "An [Earthdata Login](https://urs.earthdata.nasa.gov) account is required to access data from the NASA Earthdata system. Before requesting a subset of ICESat-2 data, we first need to set up our Earthdata Login authentication, shown below." ] }, { @@ -94,14 +95,13 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "id": "9d8e9be7-c7ab-4e87-baae-0d516e1d9a37", "metadata": {}, "outputs": [], "source": [ "# Harmony services\n", - "# `Environment` not required when in production\n", - "from harmony import BBox, Client, Collection, Request, LinkType, CapabilitiesRequest, Environment \n", + "from harmony import BBox, Client, Collection, Request, LinkType, CapabilitiesRequest \n", "\n", "# Earthdata Login Authentication\n", "import earthaccess \n", @@ -109,11 +109,9 @@ "# Load the data\n", "import xarray as xr\n", "\n", - "#Remove Environment module when we are ready to switch to production. Including this for UAT notebook testing.\n", "import netrc\n", "import json\n", "import datetime as dt\n", - "from pprint import pprint\n", "import s3fs\n", "\n", "from IPython.display import display, JSON" @@ -136,18 +134,20 @@ "See the [`earthaccess` documentation](https://earthaccess.readthedocs.io/en/latest/user_guide/authenticate/) for more details.\n", ":::\n", "\n", - "We only need to start the client once. That client can then be used to discover what options are available for a dataset, request subsetting, check on the status of that request and retrieve the results." + "We only need to start the client once. That client can then be used to discover what options are available for a dataset, request subsetting, check on the status of that request and retrieve the results.\n", + "\n", + "_AS ANDY: I removed the comment on the auth line. I'm not sure if earthaccess is even needed if a .netrc exists, as I believe `Client()` is also looking for a netrc. Please update if I am misinterpreting_ " ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 19, "id": "94650df1-161f-499d-a5be-4eff06a55e3a", "metadata": {}, "outputs": [], "source": [ - "# auth = earthaccess.login()\n", - "harmony_client = Client() #auth=(auth.username, auth.password))" + "auth = earthaccess.login()\n", + "harmony_client = Client() " ] }, { @@ -167,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 20, "id": "21a1f815-b187-4059-a4ef-0337721da3e8", "metadata": {}, "outputs": [], @@ -186,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 21, "id": "bc59afbe-e218-4240-8315-5f73a51e6125", "metadata": {}, "outputs": [], @@ -209,7 +209,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "id": "20a8a6c4-095a-4aeb-8fc6-6ff96db182c7", "metadata": { "scrolled": true @@ -252,7 +252,7 @@ "" ] }, - "execution_count": 21, + "execution_count": 22, "metadata": { "application/json": { "expanded": false, @@ -263,7 +263,6 @@ } ], "source": [ - "# print(json.dumps(capabilities, indent=2))\n", "JSON(capabilities)" ] }, @@ -274,7 +273,7 @@ "source": [ "The JSON response contains a list of transformation options available for each dataset: marked as `true` if available and `false` if not. A list of Harmony service _endpoints_ and capabilities associated with the transformation options. And a list of variables in the dataset if variable subsetting is available.\n", "\n", - "For ATL03, you can see that only subsetting bounding box (`bboxSubset`) and subsetting by Shapefile (`shapeSubset`) are marked `true`. Variable subsetting (`variableSubset`), concatenation (`concatenate`) and reprojection (`reproject`) are all `false`. Bounding box and Shapefile subsetting are performed by the Trajectory Subsetter. This subsetting routine outputs an HDF5 file." + "For ATL03, you can see that only subsetting bounding box (`bboxSubset`) and subsetting by Shapefile (`shapeSubset`) are marked `true`. Variable subsetting (`variableSubset`), concatenation (`concatenate`) and reprojection (`reproject`) are all `false`. Bounding box and Shapefile subsetting, as well as temporal subsetting, are performed by the Trajectory Subsetter. This subsetting routine outputs an HDF5 file." ] }, { @@ -284,7 +283,7 @@ "source": [ "### Step 4: subset ICESat-2 ATL03 file\n", "\n", - "Now that we know what subsetting options are available for for the ATL03 dataset, we can request a subsetted dataset. " + "Now that we know what subsetting options are available for the ATL03 dataset, we can request a subsetted dataset. " ] }, { @@ -313,7 +312,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 23, "id": "5bb08390-ddb0-4356-905e-c0a3244b9247", "metadata": {}, "outputs": [], @@ -337,22 +336,24 @@ "\n", "The request is submitted in the same way as the Capabilities request. This starts the subsetting process, which may take a while depending on the size of the request. Submitting request returns a Job ID, which is a unique identifier for your request that is used to track the progress of the request and to access the results.\n", "\n", - "If the request involves a lot of files (more than 300), Harmony will only process the first 300 files. See section on [Handling Large Jobs](https://openscapes.2i2c.cloud/user/andypbarrett/lab/workspaces/auto-e/tree/earthdata-cloud-cookbook/tutorials/IS2_Harmony.ipynb#handling-large-requests) on how to work around this restriction. " + "If the request involves a lot of files (more than 300), Harmony will only process the first 300 files. See section below on [Handling Large Jobs](https://openscapes.2i2c.cloud/user/andypbarrett/lab/workspaces/auto-e/tree/earthdata-cloud-cookbook/tutorials/IS2_Harmony.ipynb#handling-large-requests) on how to work around this restriction. \n", + "\n", + "_AS: ANDY: The link above is broken for me; it looks like it's pointing to your local jupyter instance. Maybe we could just leave this unlinked for now._" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 24, "id": "c1c312bc-a986-4ebd-951f-c0d11208d19f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'2dd7cf22-a902-4eb8-8c0e-8d8cdd48b27c'" + "'16ef9962-7be4-4434-891a-963189aa7854'" ] }, - "execution_count": 7, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -374,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 25, "id": "9b253c1e-da1e-4d14-883d-7fbd87834133", "metadata": {}, "outputs": [ @@ -400,20 +401,20 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 26, "id": "be41beda-a445-419f-9af2-080fb7e7b1d3", "metadata": {}, "outputs": [ { "data": { "application/json": { - "createdAt": "2024-10-29T20:46:15.371Z", - "dataExpiration": "2024-11-28T20:46:15.371Z", - "jobID": "2dd7cf22-a902-4eb8-8c0e-8d8cdd48b27c", + "createdAt": "2024-11-01T21:37:52.381Z", + "dataExpiration": "2024-12-01T21:37:52.381Z", + "jobID": "16ef9962-7be4-4434-891a-963189aa7854", "labels": [], "links": [ { - "href": "https://harmony.earthdata.nasa.gov/stac/2dd7cf22-a902-4eb8-8c0e-8d8cdd48b27c/", + "href": "https://harmony.earthdata.nasa.gov/stac/16ef9962-7be4-4434-891a-963189aa7854/", "rel": "stac-catalog-json", "title": "STAC catalog", "type": "application/json" @@ -425,7 +426,7 @@ -103.60569, 59.54235 ], - "href": "https://harmony.earthdata.nasa.gov/service-results/harmony-prod-staging/public/2dd7cf22-a902-4eb8-8c0e-8d8cdd48b27c/79898885/ATL03_20200427193622_04930702_006_02_subsetted.h5", + "href": "https://harmony.earthdata.nasa.gov/service-results/harmony-prod-staging/public/16ef9962-7be4-4434-891a-963189aa7854/80019316/ATL03_20200427193622_04930702_006_02_subsetted.h5", "rel": "data", "temporal": { "end": "2020-04-27T19:44:52.680Z", @@ -435,7 +436,7 @@ "type": "application/x-hdf5" }, { - "href": "https://harmony.earthdata.nasa.gov/jobs/2dd7cf22-a902-4eb8-8c0e-8d8cdd48b27c?linktype=https&page=1&limit=2000", + "href": "https://harmony.earthdata.nasa.gov/jobs/16ef9962-7be4-4434-891a-963189aa7854?linktype=https&page=1&limit=2000", "rel": "self", "title": "The current page", "type": "application/json" @@ -446,14 +447,14 @@ "progress": 100, "request": "https://harmony.earthdata.nasa.gov/C2596864127-NSIDC_CPRD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat(40%3A41.0)&subset=lon(-105.5%3A-105)&subset=time(%222020-04-27T00%3A00%3A00%22%3A%222020-05-28T00%3A00%3A00%22)", "status": "successful", - "updatedAt": "2024-10-29T20:48:52.599Z", - "username": "apbarret" + "updatedAt": "2024-11-01T21:40:25.144Z", + "username": "amy.steiker" }, "text/plain": [ "" ] }, - "execution_count": 22, + "execution_count": 26, "metadata": { "application/json": { "expanded": false, @@ -465,7 +466,6 @@ ], "source": [ "job_summary = harmony_client.result_json(job_id)\n", - "# pprint(job_summary)\n", "JSON(job_summary)" ] }, @@ -478,7 +478,9 @@ "\n", "The subsetted files can be accessed by downloading the files to a local machine, such as a laptop or desktop workstation, or by _streaming_ the data. We will use both access methods in the two examples below.\n", "\n", - "Results are staged for 30 days Explain where these are stored!" + "Results are staged for 30 days in the Harmony s3 bucket.\n", + "\n", + "_AS: ANDY, you had a note to explain the staging location. I updated the sentence above but I don't think this really needs any additional explanation. You have already explained S3 in the initial summary. The key point here is that the data are only available for 30 days._" ] }, { @@ -497,7 +499,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 27, "id": "2e8f110a-4aa3-4bec-9ac3-65eef9f0e4aa", "metadata": {}, "outputs": [ @@ -505,8 +507,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "./ATL03_20200427193622_04930702_006_02_subsetted.h5\n", - "./ATL03_20200427193622_04930702_006_02_subsetted.h5\n", + "./ATL03_20200427193622_04930702_006_02_subsetted.h5\n" + ] + }, + { + "data": { + "text/plain": [ + "'./ATL03_20200427193622_04930702_006_02_subsetted.h5'" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "./ATL03_20200427193622_04930702_006_02_subsetted.h5\n" ] } @@ -514,7 +531,7 @@ "source": [ "url = list(harmony_client.result_urls(job_id))[0] # Get the data url of the first file\n", "filepath = harmony_client.download(url, directory=\".\", overwrite=False).result()\n", - "print(filepath)" + "filepath" ] }, { @@ -531,22 +548,46 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 28, "id": "951581ed-03a3-4aa2-b147-ed2054b75905", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "['./ATL03_20200427193622_04930702_006_02_subsetted.h5']\n" - ] + "data": { + "text/plain": [ + "['./ATL03_20200427193622_04930702_006_02_subsetted.h5']" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "futures = harmony_client.download_all(job_id, directory=\".\", overwrite=False)\n", "filelist = [f.result() for f in futures] # get filepaths\n", - "print(filelist)" + "filelist" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "203a3c23-4bdc-47ee-a874-b361296d584b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'./ATL03_20200427193622_04930702_006_02_subsetted.h5'" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "filelist[0]" ] }, { @@ -565,449 +606,25 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 33, "id": "ae13153c-2968-44c1-a668-0f72f07a7d8c", "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
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    <xarray.Dataset> Size: 92MB\n",
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    " - ], - "text/plain": [ - " Size: 92MB\n", - "Dimensions: (delta_time: 1848992, ds_surf_type: 5)\n", - "Coordinates:\n", - " * delta_time (delta_time) datetime64[ns] 15MB 2020-04-27T19:39:45.8497...\n", - " lat_ph (delta_time) float64 15MB ...\n", - " lon_ph (delta_time) float64 15MB ...\n", - "Dimensions without coordinates: ds_surf_type\n", - "Data variables:\n", - " dist_ph_across (delta_time) float32 7MB ...\n", - " dist_ph_along (delta_time) float32 7MB ...\n", - " h_ph (delta_time) float32 7MB ...\n", - " pce_mframe_cnt (delta_time) uint32 7MB ...\n", - " ph_id_channel (delta_time) uint8 2MB ...\n", - " ph_id_count (delta_time) uint8 2MB ...\n", - " ph_id_pulse (delta_time) uint8 2MB ...\n", - " quality_ph (delta_time) int8 2MB ...\n", - " signal_conf_ph (delta_time, ds_surf_type) int8 9MB ...\n", - " weight_ph (delta_time) uint8 2MB ...\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": 13, - "metadata": {}, - "output_type": "execute_result" + "ename": "ValueError", + "evalue": "numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[33], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[43mxr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilelist\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgt1l/heights\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m ds\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/backends/api.py:573\u001b[0m, in \u001b[0;36mopen_dataset\u001b[0;34m(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, inline_array, chunked_array_type, from_array_kwargs, backend_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 561\u001b[0m decoders \u001b[38;5;241m=\u001b[39m _resolve_decoders_kwargs(\n\u001b[1;32m 562\u001b[0m decode_cf,\n\u001b[1;32m 563\u001b[0m open_backend_dataset_parameters\u001b[38;5;241m=\u001b[39mbackend\u001b[38;5;241m.\u001b[39mopen_dataset_parameters,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 569\u001b[0m decode_coords\u001b[38;5;241m=\u001b[39mdecode_coords,\n\u001b[1;32m 570\u001b[0m )\n\u001b[1;32m 572\u001b[0m overwrite_encoded_chunks \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moverwrite_encoded_chunks\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m--> 573\u001b[0m backend_ds \u001b[38;5;241m=\u001b[39m \u001b[43mbackend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen_dataset\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 574\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 575\u001b[0m \u001b[43m \u001b[49m\u001b[43mdrop_variables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdrop_variables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 576\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdecoders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 577\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 578\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 579\u001b[0m ds \u001b[38;5;241m=\u001b[39m _dataset_from_backend_dataset(\n\u001b[1;32m 580\u001b[0m backend_ds,\n\u001b[1;32m 581\u001b[0m filename_or_obj,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 591\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 592\u001b[0m )\n\u001b[1;32m 593\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:646\u001b[0m, in \u001b[0;36mNetCDF4BackendEntrypoint.open_dataset\u001b[0;34m(self, filename_or_obj, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, use_cftime, decode_timedelta, group, mode, format, clobber, diskless, persist, lock, autoclose)\u001b[0m\n\u001b[1;32m 625\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mopen_dataset\u001b[39m( \u001b[38;5;66;03m# type: ignore[override] # allow LSP violation, not supporting **kwargs\u001b[39;00m\n\u001b[1;32m 626\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 627\u001b[0m filename_or_obj: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m os\u001b[38;5;241m.\u001b[39mPathLike[Any] \u001b[38;5;241m|\u001b[39m BufferedIOBase \u001b[38;5;241m|\u001b[39m AbstractDataStore,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 643\u001b[0m autoclose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 644\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Dataset:\n\u001b[1;32m 645\u001b[0m filename_or_obj \u001b[38;5;241m=\u001b[39m _normalize_path(filename_or_obj)\n\u001b[0;32m--> 646\u001b[0m store \u001b[38;5;241m=\u001b[39m \u001b[43mNetCDF4DataStore\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 647\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 648\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 649\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 650\u001b[0m \u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 651\u001b[0m \u001b[43m \u001b[49m\u001b[43mclobber\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclobber\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 652\u001b[0m \u001b[43m \u001b[49m\u001b[43mdiskless\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdiskless\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 653\u001b[0m \u001b[43m \u001b[49m\u001b[43mpersist\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpersist\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 654\u001b[0m \u001b[43m \u001b[49m\u001b[43mlock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlock\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 655\u001b[0m \u001b[43m \u001b[49m\u001b[43mautoclose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautoclose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 656\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 658\u001b[0m store_entrypoint \u001b[38;5;241m=\u001b[39m StoreBackendEntrypoint()\n\u001b[1;32m 659\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m close_on_error(store):\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:376\u001b[0m, in \u001b[0;36mNetCDF4DataStore.open\u001b[0;34m(cls, filename, mode, format, group, clobber, diskless, persist, lock, lock_maker, autoclose)\u001b[0m\n\u001b[1;32m 362\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m 363\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mopen\u001b[39m(\n\u001b[1;32m 364\u001b[0m \u001b[38;5;28mcls\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 374\u001b[0m autoclose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 375\u001b[0m ):\n\u001b[0;32m--> 376\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnetCDF4\u001b[39;00m\n\u001b[1;32m 378\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(filename, os\u001b[38;5;241m.\u001b[39mPathLike):\n\u001b[1;32m 379\u001b[0m filename \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mfspath(filename)\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/netCDF4/__init__.py:3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# init for netCDF4. package\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# Docstring comes from extension module _netCDF4.\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_netCDF4\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Need explicit imports for names beginning with underscores\u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_netCDF4\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;18m__doc__\u001b[39m\n", + "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:1\u001b[0m, in \u001b[0;36minit netCDF4._netCDF4\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject" + ] } ], "source": [ @@ -1040,17 +657,17 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 34, "id": "a8d8a29d-91df-4872-9d9c-6a116bc92774", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['s3://harmony-prod-staging/public/2dd7cf22-a902-4eb8-8c0e-8d8cdd48b27c/79898885/ATL03_20200427193622_04930702_006_02_subsetted.h5']" + "['s3://harmony-prod-staging/public/16ef9962-7be4-4434-891a-963189aa7854/80019316/ATL03_20200427193622_04930702_006_02_subsetted.h5']" ] }, - "execution_count": 14, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -1067,12 +684,13 @@ "source": [ "We need AWS credentials to access the S3 bucket with the results. These are returned using the `aws_credentials` method.\n", "\n", - "_what about if we use our own bucket?_" + "_what about if we use our own bucket?_\n", + "_AS: Andy, why don't we leave this guidance out for the first iterationa and add a new Issue as a future enhancement. I don't think this is necessary for our intial publication and it could take some time to learn and document in the tutorial. But agree it's a great addition to include (in the future)_ " ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 35, "id": "d58df790-d40c-410c-a295-22d6d39dfd5b", "metadata": {}, "outputs": [], @@ -1090,7 +708,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 36, "id": "73bc9a2d-b009-4cf9-8a47-531a308833b0", "metadata": {}, "outputs": [], @@ -1117,7 +735,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 37, "id": "f29584a0-357a-4f1a-8a3b-ab72fc20d6e4", "metadata": {}, "outputs": [], @@ -1135,449 +753,209 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 38, "id": "8860630c-2050-4256-88c2-f1879456a9a1", "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset> Size: 92MB\n",
-       "Dimensions:         (delta_time: 1848992, ds_surf_type: 5)\n",
-       "Coordinates:\n",
-       "  * delta_time      (delta_time) datetime64[ns] 15MB 2020-04-27T19:39:45.8497...\n",
-       "    lat_ph          (delta_time) float64 15MB ...\n",
-       "    lon_ph          (delta_time) float64 15MB ...\n",
-       "Dimensions without coordinates: ds_surf_type\n",
-       "Data variables:\n",
-       "    dist_ph_across  (delta_time) float32 7MB ...\n",
-       "    dist_ph_along   (delta_time) float32 7MB ...\n",
-       "    h_ph            (delta_time) float32 7MB ...\n",
-       "    pce_mframe_cnt  (delta_time) uint32 7MB ...\n",
-       "    ph_id_channel   (delta_time) uint8 2MB ...\n",
-       "    ph_id_count     (delta_time) uint8 2MB ...\n",
-       "    ph_id_pulse     (delta_time) uint8 2MB ...\n",
-       "    quality_ph      (delta_time) int8 2MB ...\n",
-       "    signal_conf_ph  (delta_time, ds_surf_type) int8 9MB ...\n",
-       "    weight_ph       (delta_time) uint8 2MB ...\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": [ - " Size: 92MB\n", - "Dimensions: (delta_time: 1848992, ds_surf_type: 5)\n", - "Coordinates:\n", - " * delta_time (delta_time) datetime64[ns] 15MB 2020-04-27T19:39:45.8497...\n", - " lat_ph (delta_time) float64 15MB ...\n", - " lon_ph (delta_time) float64 15MB ...\n", - "Dimensions without coordinates: ds_surf_type\n", - "Data variables:\n", - " dist_ph_across (delta_time) float32 7MB ...\n", - " dist_ph_along (delta_time) float32 7MB ...\n", - " h_ph (delta_time) float32 7MB ...\n", - " pce_mframe_cnt (delta_time) uint32 7MB ...\n", - " ph_id_channel (delta_time) uint8 2MB ...\n", - " ph_id_count (delta_time) uint8 2MB ...\n", - " ph_id_pulse (delta_time) uint8 2MB ...\n", - " quality_ph (delta_time) int8 2MB ...\n", - " signal_conf_ph (delta_time, ds_surf_type) int8 9MB ...\n", - " weight_ph (delta_time) uint8 2MB ...\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": 18, - "metadata": {}, - "output_type": "execute_result" + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "A module that was compiled using NumPy 1.x cannot be run in\n", + "NumPy 2.1.2 as it may crash. To support both 1.x and 2.x\n", + "versions of NumPy, modules must be compiled with NumPy 2.0.\n", + "Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.\n", + "\n", + "If you are a user of the module, the easiest solution will be to\n", + "downgrade to 'numpy<2' or try to upgrade the affected module.\n", + "We expect that some modules will need time to support NumPy 2.\n", + "\n", + "Traceback (most recent call last): File \"/srv/conda/envs/notebook/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\n", + " return _run_code(code, main_globals, None,\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/runpy.py\", line 86, in _run_code\n", + " exec(code, run_globals)\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/ipykernel_launcher.py\", line 18, in \n", + " app.launch_new_instance()\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/traitlets/config/application.py\", line 1075, in launch_instance\n", + " app.start()\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/ipykernel/kernelapp.py\", line 739, in start\n", + " self.io_loop.start()\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/tornado/platform/asyncio.py\", line 205, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/asyncio/base_events.py\", line 603, in run_forever\n", + " self._run_once()\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/asyncio/base_events.py\", line 1909, in _run_once\n", + " handle._run()\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/asyncio/events.py\", line 80, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 545, in dispatch_queue\n", + " await self.process_one()\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 534, in process_one\n", + " await dispatch(*args)\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 437, in dispatch_shell\n", + " await result\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/ipykernel/ipkernel.py\", line 359, in execute_request\n", + " await super().execute_request(stream, ident, parent)\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 778, in execute_request\n", + " reply_content = await reply_content\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/ipykernel/ipkernel.py\", line 446, in do_execute\n", + " res = shell.run_cell(\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/ipykernel/zmqshell.py\", line 549, in run_cell\n", + " return super().run_cell(*args, **kwargs)\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3075, in run_cell\n", + " result = self._run_cell(\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3130, in _run_cell\n", + " result = runner(coro)\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/core/async_helpers.py\", line 129, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3334, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3517, in run_ast_nodes\n", + " if await self.run_code(code, result, async_=asy):\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3577, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"/tmp/ipykernel_791/3576303952.py\", line 2, in \n", + " ds\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/core/displayhook.py\", line 268, in __call__\n", + " format_dict, md_dict = self.compute_format_data(result)\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/core/displayhook.py\", line 157, in compute_format_data\n", + " return self.shell.display_formatter.format(result)\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/core/formatters.py\", line 182, in format\n", + " data = formatter(obj)\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/decorator.py\", line 232, in fun\n", + " return caller(func, *(extras + args), **kw)\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/core/formatters.py\", line 226, in catch_format_error\n", + " r = method(self, *args, **kwargs)\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/core/formatters.py\", line 711, in __call__\n", + " printer.pretty(obj)\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/lib/pretty.py\", line 411, in pretty\n", + " return _repr_pprint(obj, self, cycle)\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/lib/pretty.py\", line 779, in _repr_pprint\n", + " output = repr(obj)\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/dataset.py\", line 2540, in __repr__\n", + " return formatting.dataset_repr(self)\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/reprlib.py\", line 21, in wrapper\n", + " result = user_function(self)\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting.py\", line 724, in dataset_repr\n", + " summary.append(coords_repr(ds.coords, col_width=col_width, max_rows=max_rows))\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting.py\", line 434, in coords_repr\n", + " return _mapping_repr(\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting.py\", line 407, in _mapping_repr\n", + " summary += [\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting.py\", line 408, in \n", + " summarizer(k, v, col_width, **summarizer_kwargs[k])\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting.py\", line 344, in summarize_variable\n", + " values_str = inline_variable_array_repr(variable, values_width)\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting.py\", line 303, in inline_variable_array_repr\n", + " dask_array_type = array_type(\"dask\")\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/namedarray/pycompat.py\", line 83, in array_type\n", + " return _get_cached_duck_array_module(mod).type\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/namedarray/pycompat.py\", line 74, in _get_cached_duck_array_module\n", + " duckmod = DuckArrayModule(mod)\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/namedarray/pycompat.py\", line 43, in __init__\n", + " duck_array_type = (import_module(\"dask.array\").Array,)\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/importlib/__init__.py\", line 126, in import_module\n", + " return _bootstrap._gcd_import(name[level:], package, level)\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/dask/array/__init__.py\", line 4, in \n", + " from dask.array import backends, fft, lib, linalg, ma, overlap, random\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/dask/array/backends.py\", line 8, in \n", + " from dask.array.core import Array\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/dask/array/core.py\", line 37, in \n", + " from dask.array.chunk_types import is_valid_array_chunk, is_valid_chunk_type\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/dask/array/chunk_types.py\", line 131, in \n", + " import scipy.sparse\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/sparse/__init__.py\", line 295, in \n", + " from ._csr import *\n", + " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/sparse/_csr.py\", line 11, in \n", + " from ._sparsetools import (csr_tocsc, csr_tobsr, csr_count_blocks,\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "_ARRAY_API not found", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;31mAttributeError\u001b[0m: _ARRAY_API not found" + ] + }, + { + "ename": "ValueError", + "evalue": "numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/core/formatters.py:711\u001b[0m, in \u001b[0;36mPlainTextFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 704\u001b[0m stream \u001b[38;5;241m=\u001b[39m StringIO()\n\u001b[1;32m 705\u001b[0m printer \u001b[38;5;241m=\u001b[39m pretty\u001b[38;5;241m.\u001b[39mRepresentationPrinter(stream, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose,\n\u001b[1;32m 706\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_width, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnewline,\n\u001b[1;32m 707\u001b[0m max_seq_length\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_seq_length,\n\u001b[1;32m 708\u001b[0m singleton_pprinters\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msingleton_printers,\n\u001b[1;32m 709\u001b[0m type_pprinters\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtype_printers,\n\u001b[1;32m 710\u001b[0m deferred_pprinters\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdeferred_printers)\n\u001b[0;32m--> 711\u001b[0m \u001b[43mprinter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpretty\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 712\u001b[0m printer\u001b[38;5;241m.\u001b[39mflush()\n\u001b[1;32m 713\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m stream\u001b[38;5;241m.\u001b[39mgetvalue()\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/lib/pretty.py:411\u001b[0m, in \u001b[0;36mRepresentationPrinter.pretty\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 408\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m meth(obj, \u001b[38;5;28mself\u001b[39m, cycle)\n\u001b[1;32m 409\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mobject\u001b[39m \\\n\u001b[1;32m 410\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__dict__\u001b[39m\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__repr__\u001b[39m\u001b[38;5;124m'\u001b[39m)):\n\u001b[0;32m--> 411\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_repr_pprint\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcycle\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 413\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _default_pprint(obj, \u001b[38;5;28mself\u001b[39m, cycle)\n\u001b[1;32m 414\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/lib/pretty.py:779\u001b[0m, in \u001b[0;36m_repr_pprint\u001b[0;34m(obj, p, cycle)\u001b[0m\n\u001b[1;32m 777\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"A pprint that just redirects to the normal repr function.\"\"\"\u001b[39;00m\n\u001b[1;32m 778\u001b[0m \u001b[38;5;66;03m# Find newlines and replace them with p.break_()\u001b[39;00m\n\u001b[0;32m--> 779\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mrepr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 780\u001b[0m lines \u001b[38;5;241m=\u001b[39m output\u001b[38;5;241m.\u001b[39msplitlines()\n\u001b[1;32m 781\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m p\u001b[38;5;241m.\u001b[39mgroup():\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/dataset.py:2540\u001b[0m, in \u001b[0;36mDataset.__repr__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 2539\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__repr__\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mstr\u001b[39m:\n\u001b[0;32m-> 2540\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mformatting\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset_repr\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/reprlib.py:21\u001b[0m, in \u001b[0;36mrecursive_repr..decorating_function..wrapper\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 19\u001b[0m repr_running\u001b[38;5;241m.\u001b[39madd(key)\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 21\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43muser_function\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 23\u001b[0m repr_running\u001b[38;5;241m.\u001b[39mdiscard(key)\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting.py:724\u001b[0m, in \u001b[0;36mdataset_repr\u001b[0;34m(ds)\u001b[0m\n\u001b[1;32m 721\u001b[0m summary\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdims_start\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdims_values\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 723\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ds\u001b[38;5;241m.\u001b[39mcoords:\n\u001b[0;32m--> 724\u001b[0m summary\u001b[38;5;241m.\u001b[39mappend(\u001b[43mcoords_repr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcoords\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcol_width\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcol_width\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_rows\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_rows\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 726\u001b[0m unindexed_dims_str \u001b[38;5;241m=\u001b[39m unindexed_dims_repr(ds\u001b[38;5;241m.\u001b[39mdims, ds\u001b[38;5;241m.\u001b[39mcoords, max_rows\u001b[38;5;241m=\u001b[39mmax_rows)\n\u001b[1;32m 727\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m unindexed_dims_str:\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting.py:434\u001b[0m, in \u001b[0;36mcoords_repr\u001b[0;34m(coords, col_width, max_rows)\u001b[0m\n\u001b[1;32m 432\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m col_width \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 433\u001b[0m col_width \u001b[38;5;241m=\u001b[39m _calculate_col_width(coords)\n\u001b[0;32m--> 434\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_mapping_repr\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 435\u001b[0m \u001b[43m \u001b[49m\u001b[43mcoords\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 436\u001b[0m \u001b[43m \u001b[49m\u001b[43mtitle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mCoordinates\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 437\u001b[0m \u001b[43m \u001b[49m\u001b[43msummarizer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msummarize_variable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 438\u001b[0m \u001b[43m 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title, summarizer, expand_option_name, col_width, max_rows, indexes)\u001b[0m\n\u001b[1;32m 402\u001b[0m summary \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 403\u001b[0m summarizer(k, mapping[k], col_width, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39msummarizer_kwargs[k])\n\u001b[1;32m 404\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m keys[\u001b[38;5;241m-\u001b[39mlast_rows:]\n\u001b[1;32m 405\u001b[0m ]\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 407\u001b[0m summary \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 408\u001b[0m summarizer(k, v, col_width, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39msummarizer_kwargs[k])\n\u001b[1;32m 409\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m mapping\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m 410\u001b[0m ]\n\u001b[1;32m 411\u001b[0m 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\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfirst_col\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mdims_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mvariable\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mnbytes_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 343\u001b[0m values_width \u001b[38;5;241m=\u001b[39m max_width \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mlen\u001b[39m(front_str)\n\u001b[0;32m--> 344\u001b[0m values_str \u001b[38;5;241m=\u001b[39m \u001b[43minline_variable_array_repr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalues_width\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 346\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m front_str \u001b[38;5;241m+\u001b[39m values_str\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting.py:303\u001b[0m, in \u001b[0;36minline_variable_array_repr\u001b[0;34m(var, max_width)\u001b[0m\n\u001b[1;32m 301\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m var\u001b[38;5;241m.\u001b[39m_in_memory:\n\u001b[1;32m 302\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m format_array_flat(var, max_width)\n\u001b[0;32m--> 303\u001b[0m dask_array_type \u001b[38;5;241m=\u001b[39m \u001b[43marray_type\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdask\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 304\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(var\u001b[38;5;241m.\u001b[39m_data, dask_array_type):\n\u001b[1;32m 305\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m inline_dask_repr(var\u001b[38;5;241m.\u001b[39mdata)\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/namedarray/pycompat.py:83\u001b[0m, in \u001b[0;36marray_type\u001b[0;34m(mod)\u001b[0m\n\u001b[1;32m 81\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21marray_type\u001b[39m(mod: ModType) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DuckArrayTypes:\n\u001b[1;32m 82\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Quick wrapper to get the array class of the module.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 83\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_get_cached_duck_array_module\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmod\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mtype\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/namedarray/pycompat.py:74\u001b[0m, in \u001b[0;36m_get_cached_duck_array_module\u001b[0;34m(mod)\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_cached_duck_array_module\u001b[39m(mod: ModType) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DuckArrayModule:\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mod \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m _cached_duck_array_modules:\n\u001b[0;32m---> 74\u001b[0m duckmod \u001b[38;5;241m=\u001b[39m \u001b[43mDuckArrayModule\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmod\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 75\u001b[0m _cached_duck_array_modules[mod] \u001b[38;5;241m=\u001b[39m duckmod\n\u001b[1;32m 76\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m duckmod\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/namedarray/pycompat.py:43\u001b[0m, in \u001b[0;36mDuckArrayModule.__init__\u001b[0;34m(self, mod)\u001b[0m\n\u001b[1;32m 40\u001b[0m duck_array_version \u001b[38;5;241m=\u001b[39m Version(duck_array_module\u001b[38;5;241m.\u001b[39m__version__)\n\u001b[1;32m 42\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mod \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdask\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m---> 43\u001b[0m duck_array_type \u001b[38;5;241m=\u001b[39m (\u001b[43mimport_module\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdask.array\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mArray,)\n\u001b[1;32m 44\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m mod \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpint\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 45\u001b[0m duck_array_type \u001b[38;5;241m=\u001b[39m (duck_array_module\u001b[38;5;241m.\u001b[39mQuantity,)\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/importlib/__init__.py:126\u001b[0m, in \u001b[0;36mimport_module\u001b[0;34m(name, package)\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m 125\u001b[0m level \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m--> 126\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_bootstrap\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_gcd_import\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m[\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpackage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m:1050\u001b[0m, in \u001b[0;36m_gcd_import\u001b[0;34m(name, package, level)\u001b[0m\n", + "File \u001b[0;32m:1027\u001b[0m, in \u001b[0;36m_find_and_load\u001b[0;34m(name, import_)\u001b[0m\n", + "File \u001b[0;32m:1006\u001b[0m, in \u001b[0;36m_find_and_load_unlocked\u001b[0;34m(name, import_)\u001b[0m\n", + "File \u001b[0;32m:688\u001b[0m, in \u001b[0;36m_load_unlocked\u001b[0;34m(spec)\u001b[0m\n", + "File \u001b[0;32m:883\u001b[0m, in \u001b[0;36mexec_module\u001b[0;34m(self, module)\u001b[0m\n", + "File \u001b[0;32m:241\u001b[0m, in \u001b[0;36m_call_with_frames_removed\u001b[0;34m(f, *args, **kwds)\u001b[0m\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/dask/array/__init__.py:4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m__future__\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m annotations\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdask\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01marray\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backends, fft, lib, linalg, ma, overlap, random\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdask\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01marray\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mblockwise\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m atop, blockwise\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdask\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01marray\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mchunk_types\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m register_chunk_type\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/dask/array/fft.py:11\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfftpack\u001b[39;00m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[1;32m 13\u001b[0m scipy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/fftpack/__init__.py:93\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124;03m=========================================================\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124;03mLegacy discrete Fourier transforms (:mod:`scipy.fftpack`)\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 77\u001b[0m \n\u001b[1;32m 78\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 81\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfft\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifft\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfftn\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifftn\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrfft\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mirfft\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 82\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfft2\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifft2\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 83\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdiff\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 90\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdct\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124midct\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdst\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124midst\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdctn\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124midctn\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdstn\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124midstn\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 91\u001b[0m ]\n\u001b[0;32m---> 93\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_basic\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_pseudo_diffs\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_helper\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/fftpack/_basic.py:8\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Created by Pearu Peterson, August,September 2002\u001b[39;00m\n\u001b[1;32m 5\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfft\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifft\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfftn\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifftn\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrfft\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mirfft\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 6\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfft2\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifft2\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfft\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _pocketfft\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_helper\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _good_shape\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfft\u001b[39m(x, n\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, overwrite_x\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/fft/__init__.py:90\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_basic\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 86\u001b[0m fft, ifft, fft2, ifft2, fftn, ifftn,\n\u001b[1;32m 87\u001b[0m rfft, irfft, rfft2, irfft2, rfftn, irfftn,\n\u001b[1;32m 88\u001b[0m hfft, ihfft, hfft2, ihfft2, hfftn, ihfftn)\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_realtransforms\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m dct, idct, dst, idst, dctn, idctn, dstn, idstn\n\u001b[0;32m---> 90\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_fftlog\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m fht, ifht, fhtoffset\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_helper\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m next_fast_len, fftfreq, rfftfreq, fftshift, ifftshift\n\u001b[1;32m 92\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_backend\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (set_backend, skip_backend, set_global_backend,\n\u001b[1;32m 93\u001b[0m register_backend)\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/fft/_fftlog.py:10\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_basic\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _dispatch\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_lib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01muarray\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Dispatchable\n\u001b[0;32m---> 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_fftlog_backend\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m fhtoffset\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 13\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfht\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifht\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfhtoffset\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/fft/_fftlog_backend.py:4\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m warn\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_basic\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m rfft, irfft\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mspecial\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m loggamma, poch\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_lib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_array_api\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m array_namespace, copy\n\u001b[1;32m 8\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfht\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifht\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfhtoffset\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/special/__init__.py:777\u001b[0m\n\u001b[1;32m 773\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m\n\u001b[1;32m 775\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_sf_error\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SpecialFunctionWarning, SpecialFunctionError\n\u001b[0;32m--> 777\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _ufuncs\n\u001b[1;32m 778\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_ufuncs\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 780\u001b[0m \u001b[38;5;66;03m# Replace some function definitions from _ufuncs to add Array API support\u001b[39;00m\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/special/_ufuncs.pyx:1\u001b[0m, in \u001b[0;36minit scipy.special._ufuncs\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject" + ] + }, + { + "ename": "ValueError", + "evalue": "numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/core/formatters.py:347\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 345\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n\u001b[1;32m 346\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 347\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 348\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 349\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/dataset.py:2545\u001b[0m, in \u001b[0;36mDataset._repr_html_\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 2543\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m OPTIONS[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdisplay_style\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 2544\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m
    \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mescape(\u001b[38;5;28mrepr\u001b[39m(\u001b[38;5;28mself\u001b[39m))\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m
    \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 2545\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mformatting_html\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset_repr\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting_html.py:337\u001b[0m, in \u001b[0;36mdataset_repr\u001b[0;34m(ds)\u001b[0m\n\u001b[1;32m 331\u001b[0m obj_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxarray.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(ds)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 333\u001b[0m header_components \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m
    \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mescape(obj_type)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m
    \u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 335\u001b[0m sections \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 336\u001b[0m dim_section(ds),\n\u001b[0;32m--> 337\u001b[0m \u001b[43mcoord_section\u001b[49m\u001b[43m(\u001b[49m\u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcoords\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 338\u001b[0m datavar_section(ds\u001b[38;5;241m.\u001b[39mdata_vars),\n\u001b[1;32m 339\u001b[0m index_section(_get_indexes_dict(ds\u001b[38;5;241m.\u001b[39mxindexes)),\n\u001b[1;32m 340\u001b[0m attr_section(ds\u001b[38;5;241m.\u001b[39mattrs),\n\u001b[1;32m 341\u001b[0m ]\n\u001b[1;32m 343\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _obj_repr(ds, header_components, sections)\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting_html.py:200\u001b[0m, in \u001b[0;36m_mapping_section\u001b[0;34m(mapping, name, details_func, max_items_collapse, expand_option_name, enabled)\u001b[0m\n\u001b[1;32m 193\u001b[0m expanded \u001b[38;5;241m=\u001b[39m _get_boolean_with_default(\n\u001b[1;32m 194\u001b[0m expand_option_name, n_items \u001b[38;5;241m<\u001b[39m max_items_collapse\n\u001b[1;32m 195\u001b[0m )\n\u001b[1;32m 196\u001b[0m collapsed \u001b[38;5;241m=\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m expanded\n\u001b[1;32m 198\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m collapsible_section(\n\u001b[1;32m 199\u001b[0m name,\n\u001b[0;32m--> 200\u001b[0m details\u001b[38;5;241m=\u001b[39m\u001b[43mdetails_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmapping\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 201\u001b[0m n_items\u001b[38;5;241m=\u001b[39mn_items,\n\u001b[1;32m 202\u001b[0m enabled\u001b[38;5;241m=\u001b[39menabled,\n\u001b[1;32m 203\u001b[0m collapsed\u001b[38;5;241m=\u001b[39mcollapsed,\n\u001b[1;32m 204\u001b[0m )\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting_html.py:116\u001b[0m, in \u001b[0;36msummarize_coords\u001b[0;34m(variables)\u001b[0m\n\u001b[1;32m 114\u001b[0m li_items \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 115\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m variables\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m--> 116\u001b[0m li_content \u001b[38;5;241m=\u001b[39m \u001b[43msummarize_variable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mis_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxindexes\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 117\u001b[0m li_items\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m
  • \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mli_content\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m
    • \u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 119\u001b[0m vars_li \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(li_items)\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting_html.py:89\u001b[0m, in \u001b[0;36msummarize_variable\u001b[0;34m(name, var, is_index, dtype)\u001b[0m\n\u001b[1;32m 86\u001b[0m data_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata-\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(uuid\u001b[38;5;241m.\u001b[39muuid4())\n\u001b[1;32m 87\u001b[0m disabled \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(var\u001b[38;5;241m.\u001b[39mattrs) \u001b[38;5;28;01melse\u001b[39;00m 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dask_array_type \u001b[38;5;241m=\u001b[39m \u001b[43marray_type\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdask\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 304\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(var\u001b[38;5;241m.\u001b[39m_data, dask_array_type):\n\u001b[1;32m 305\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m inline_dask_repr(var\u001b[38;5;241m.\u001b[39mdata)\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/namedarray/pycompat.py:83\u001b[0m, in \u001b[0;36marray_type\u001b[0;34m(mod)\u001b[0m\n\u001b[1;32m 81\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21marray_type\u001b[39m(mod: ModType) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DuckArrayTypes:\n\u001b[1;32m 82\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Quick wrapper to get the array class of the module.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 83\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_get_cached_duck_array_module\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmod\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mtype\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/namedarray/pycompat.py:74\u001b[0m, in \u001b[0;36m_get_cached_duck_array_module\u001b[0;34m(mod)\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_cached_duck_array_module\u001b[39m(mod: ModType) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DuckArrayModule:\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mod \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m _cached_duck_array_modules:\n\u001b[0;32m---> 74\u001b[0m duckmod \u001b[38;5;241m=\u001b[39m \u001b[43mDuckArrayModule\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmod\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 75\u001b[0m _cached_duck_array_modules[mod] \u001b[38;5;241m=\u001b[39m duckmod\n\u001b[1;32m 76\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m duckmod\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/namedarray/pycompat.py:43\u001b[0m, in \u001b[0;36mDuckArrayModule.__init__\u001b[0;34m(self, mod)\u001b[0m\n\u001b[1;32m 40\u001b[0m duck_array_version \u001b[38;5;241m=\u001b[39m Version(duck_array_module\u001b[38;5;241m.\u001b[39m__version__)\n\u001b[1;32m 42\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mod \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdask\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m---> 43\u001b[0m duck_array_type \u001b[38;5;241m=\u001b[39m (\u001b[43mimport_module\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdask.array\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mArray,)\n\u001b[1;32m 44\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m mod \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpint\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 45\u001b[0m duck_array_type \u001b[38;5;241m=\u001b[39m (duck_array_module\u001b[38;5;241m.\u001b[39mQuantity,)\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/importlib/__init__.py:126\u001b[0m, in \u001b[0;36mimport_module\u001b[0;34m(name, package)\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m 125\u001b[0m level \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m--> 126\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_bootstrap\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_gcd_import\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m[\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpackage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m:1050\u001b[0m, in \u001b[0;36m_gcd_import\u001b[0;34m(name, package, level)\u001b[0m\n", + "File \u001b[0;32m:1027\u001b[0m, in \u001b[0;36m_find_and_load\u001b[0;34m(name, import_)\u001b[0m\n", + "File \u001b[0;32m:1006\u001b[0m, in \u001b[0;36m_find_and_load_unlocked\u001b[0;34m(name, import_)\u001b[0m\n", + "File \u001b[0;32m:688\u001b[0m, in \u001b[0;36m_load_unlocked\u001b[0;34m(spec)\u001b[0m\n", + "File \u001b[0;32m:883\u001b[0m, in \u001b[0;36mexec_module\u001b[0;34m(self, module)\u001b[0m\n", + "File \u001b[0;32m:241\u001b[0m, in \u001b[0;36m_call_with_frames_removed\u001b[0;34m(f, *args, **kwds)\u001b[0m\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/dask/array/__init__.py:4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m__future__\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m annotations\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdask\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01marray\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backends, fft, lib, linalg, ma, overlap, random\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdask\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01marray\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mblockwise\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m atop, blockwise\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdask\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01marray\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mchunk_types\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m register_chunk_type\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/dask/array/fft.py:11\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfftpack\u001b[39;00m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[1;32m 13\u001b[0m scipy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/fftpack/__init__.py:93\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124;03m=========================================================\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124;03mLegacy discrete Fourier transforms (:mod:`scipy.fftpack`)\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 77\u001b[0m \n\u001b[1;32m 78\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 81\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfft\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifft\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfftn\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifftn\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrfft\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mirfft\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 82\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfft2\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifft2\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 83\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdiff\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 90\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdct\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124midct\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdst\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124midst\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdctn\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124midctn\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdstn\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124midstn\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 91\u001b[0m ]\n\u001b[0;32m---> 93\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_basic\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_pseudo_diffs\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_helper\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/fftpack/_basic.py:8\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Created by Pearu Peterson, August,September 2002\u001b[39;00m\n\u001b[1;32m 5\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfft\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifft\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfftn\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifftn\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrfft\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mirfft\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 6\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfft2\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifft2\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfft\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _pocketfft\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_helper\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _good_shape\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfft\u001b[39m(x, n\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, overwrite_x\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/fft/__init__.py:90\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_basic\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 86\u001b[0m fft, ifft, fft2, ifft2, fftn, ifftn,\n\u001b[1;32m 87\u001b[0m rfft, irfft, rfft2, irfft2, rfftn, irfftn,\n\u001b[1;32m 88\u001b[0m hfft, ihfft, hfft2, ihfft2, hfftn, ihfftn)\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_realtransforms\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m dct, idct, dst, idst, dctn, idctn, dstn, idstn\n\u001b[0;32m---> 90\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_fftlog\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m fht, ifht, fhtoffset\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_helper\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m next_fast_len, fftfreq, rfftfreq, fftshift, ifftshift\n\u001b[1;32m 92\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_backend\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (set_backend, skip_backend, set_global_backend,\n\u001b[1;32m 93\u001b[0m register_backend)\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/fft/_fftlog.py:10\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_basic\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _dispatch\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_lib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01muarray\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Dispatchable\n\u001b[0;32m---> 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_fftlog_backend\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m fhtoffset\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 13\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfht\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifht\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfhtoffset\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/fft/_fftlog_backend.py:4\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m warn\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_basic\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m rfft, irfft\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mspecial\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m loggamma, poch\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_lib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_array_api\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m array_namespace, copy\n\u001b[1;32m 8\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfht\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifht\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfhtoffset\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/special/__init__.py:777\u001b[0m\n\u001b[1;32m 773\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m\n\u001b[1;32m 775\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_sf_error\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SpecialFunctionWarning, SpecialFunctionError\n\u001b[0;32m--> 777\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _ufuncs\n\u001b[1;32m 778\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_ufuncs\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 780\u001b[0m \u001b[38;5;66;03m# Replace some function definitions from _ufuncs to add Array API support\u001b[39;00m\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/special/_ufuncs.pyx:1\u001b[0m, in \u001b[0;36minit scipy.special._ufuncs\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject" + ] } ], "source": [ @@ -1598,6 +976,8 @@ "id": "ba0d2015-f342-4987-aa16-e0b67b6e35b3", "metadata": {}, "source": [ + "_AS Andy: We could also remove this entirely for the first iteration and just point to harmony-py docs above when you first reference the 300 limit. This could be a future enhancement to add more examples or use cases as an Appendix._\n", + "\n", "### A. Handling large requests\n", "\n", "Need to explain large-jobs and previews, pausing\n", From 493e04765c8c8c5d17a1ca1ab201f97b5fd497a4 Mon Sep 17 00:00:00 2001 From: Andy Barrett Date: Tue, 5 Nov 2024 19:19:03 +0000 Subject: [PATCH 7/9] address AS comments and suggestions --- tutorials/IS2_Harmony.ipynb | 1348 ++++++++++++++++++++++++----------- 1 file changed, 941 insertions(+), 407 deletions(-) diff --git a/tutorials/IS2_Harmony.ipynb b/tutorials/IS2_Harmony.ipynb index a851591f..b20c42fe 100644 --- a/tutorials/IS2_Harmony.ipynb +++ b/tutorials/IS2_Harmony.ipynb @@ -34,18 +34,11 @@ "source": [ "## Summary\n", "\n", - "In this tutorial, we will use NASA's Earthdata Harmony Services to subset and access ICESat-2 data with the `harmony-py` Python library. Harmony can be used to subset Level-2 and Level-3A ICESat-2 data products. In this tutorial, we will subset the [ATL03 Global Geolocated Photon Data](https://nsidc.org/data/atl03/versions/6) product as an example.\n", + "In this tutorial, we will use NASA's Earthdata Harmony Services to subset and access ICESat-2 data using the `harmony-py` Python library. Harmony can be used to subset Level-2 and Level-3A ICESat-2 data products. In this tutorial, we will subset the [ATL03 Global Geolocated Photon Data](https://nsidc.org/data/atl03/versions/6) product as an example.\n", "\n", "**What is Harmony?** [Harmony](https://harmony.earthdata.nasa.gov/) is a service that allows you to customize many NASA datasets, including the ability to subset, reproject and reformat files. Data can be subsetted for a geographic region, a temporal range and by variable. Data can be \"reprojected\" from its native coordinate reference system (CRS) to the coordinate reference system relevant to your analysis. And data can be reformatted from its native file format to a format that is more relevant for your application. These services are collectively called _transformation services_. However, not all services are available for all datasets. You will learn how to discover which services are available for a given dataset.\n", "\n", - "Data transformed by Harmony services are staged on NASA Amazon Web Services (AWS) S3 buckets or on user-owned AWS S3 buckets. Data in NASA S3 buckets are accessed using signed URLs or temporary access credentials. This data can be downloaded to your local machine or you can access the data directly if you are working in an AWS cloud instance, such as a Jupyter Hub, in AWS `us-west-2`. \n", - "\n", - "_Add links or provide background to terminology_\n", - "_AS: I added a few links above. I think this is sufficient but feel free to add other references if you'd like_\n", - "\n", - "_Stuff for later, maybe_\n", - "_AS: I don't think this additional info is needed here but it's a great explanation. I wonder if it ought to be incorporated into NSIDC's cloud access guide: https://nsidc.org/data/user-resources/help-center/nasa-earthdata-cloud-data-access-guide\n", - "Data in NASA’s Earthdata Cloud, including the subsetted data processed by Harmony, reside in Amazon Web Services (AWS) Simple Storage Service (S3) buckets. Access is provided via temporary credentials; this free access is limited to requests made within the US West (Oregon) (code: us-west-2) AWS region. While this compute location is required for direct S3 access, all data in Earthdata Cloud are still freely available via download." + "Data transformed by Harmony services are staged on NASA Amazon Web Services (AWS) S3 buckets or on user-owned AWS S3 buckets. Data in NASA S3 buckets are accessed using signed URLs or temporary access credentials. This data can be downloaded to your local machine or you can access the data directly if you are working in an AWS cloud instance, such as a Jupyter Hub, in AWS `us-west-2`. " ] }, { @@ -95,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 1, "id": "9d8e9be7-c7ab-4e87-baae-0d516e1d9a37", "metadata": {}, "outputs": [], @@ -124,30 +117,27 @@ "source": [ "### Step 2: start a Harmony client\n", "\n", - "A Harmony client is the interface to Harmony services and is used to submit requests and retrieve the results of those requests.\n", - "\n", - "To create a Harmony Client we need to supply our Earthdata Login credentials. We will use `earthaccess.login()` to do this.\n", + "A Harmony [`Client`](https://harmony-py.readthedocs.io/en/latest/api.html#harmony.Client) is the interface to Harmony services and is used to submit requests and retrieve the results of those requests. We only need to start the client once. That client can then be used to discover what options are available for a dataset, request subsetting, check on the status of that request and retrieve the results.\n", "\n", - ":::{note}\n", - "`earthaccess.login()` will search for you Earthdata Login credentials stored as environment variables or in a `.netrc` file. If no environment variables or `.netrc` file is found, we will be prompted for a _username_ and _password_.\n", - "\n", - "See the [`earthaccess` documentation](https://earthaccess.readthedocs.io/en/latest/user_guide/authenticate/) for more details.\n", - ":::\n", + "We need to supply our Earthdata Login credentials to use Harmony services. `hamony.Client` will use EDL_USERNAME & EDL_PASSWORD environment variables or the credentials in our `.netrc` file, if one is available. If we don't have either of these set up, we can enter our credentials directly.\n", "\n", - "We only need to start the client once. That client can then be used to discover what options are available for a dataset, request subsetting, check on the status of that request and retrieve the results.\n", + "```\n", + "harmony_client = Client(auth=('edl_username','edl_password'))\n", + "```\n", "\n", - "_AS ANDY: I removed the comment on the auth line. I'm not sure if earthaccess is even needed if a .netrc exists, as I believe `Client()` is also looking for a netrc. Please update if I am misinterpreting_ " + ":::{note}\n", + "If you don't have environment variables or a `.netrc` file setup, replace the command in the following code cell with the one above, and use your actual username and password.\n", + ":::" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 2, "id": "94650df1-161f-499d-a5be-4eff06a55e3a", "metadata": {}, "outputs": [], "source": [ - "auth = earthaccess.login()\n", - "harmony_client = Client() " + "harmony_client = Client()" ] }, { @@ -167,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 3, "id": "21a1f815-b187-4059-a4ef-0337721da3e8", "metadata": {}, "outputs": [], @@ -186,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 4, "id": "bc59afbe-e218-4240-8315-5f73a51e6125", "metadata": {}, "outputs": [], @@ -209,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 5, "id": "20a8a6c4-095a-4aeb-8fc6-6ff96db182c7", "metadata": { "scrolled": true @@ -252,7 +242,7 @@ "" ] }, - "execution_count": 22, + "execution_count": 5, "metadata": { "application/json": { "expanded": false, @@ -312,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 6, "id": "5bb08390-ddb0-4356-905e-c0a3244b9247", "metadata": {}, "outputs": [], @@ -334,26 +324,24 @@ "source": [ "#### Submit the request\n", "\n", - "The request is submitted in the same way as the Capabilities request. This starts the subsetting process, which may take a while depending on the size of the request. Submitting request returns a Job ID, which is a unique identifier for your request that is used to track the progress of the request and to access the results.\n", + "The request is submitted in the same way as the Capabilities request. This starts the subsetting process, which may take a while depending on the size of the request. Submitting the request returns a Job ID, which is a unique identifier for your request that is used to track the progress of the request and to access the results.\n", "\n", - "If the request involves a lot of files (more than 300), Harmony will only process the first 300 files. See section below on [Handling Large Jobs](https://openscapes.2i2c.cloud/user/andypbarrett/lab/workspaces/auto-e/tree/earthdata-cloud-cookbook/tutorials/IS2_Harmony.ipynb#handling-large-requests) on how to work around this restriction. \n", - "\n", - "_AS: ANDY: The link above is broken for me; it looks like it's pointing to your local jupyter instance. Maybe we could just leave this unlinked for now._" + "If the request involves a lot of files (more than 300), Harmony will only process the first 300 files. See section below on Handling Large Jobs on how to work around this restriction." ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 7, "id": "c1c312bc-a986-4ebd-951f-c0d11208d19f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'16ef9962-7be4-4434-891a-963189aa7854'" + "'4e07cf87-d846-4f59-8ea0-d1c52477b377'" ] }, - "execution_count": 24, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -375,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 8, "id": "9b253c1e-da1e-4d14-883d-7fbd87834133", "metadata": {}, "outputs": [ @@ -401,20 +389,20 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 9, "id": "be41beda-a445-419f-9af2-080fb7e7b1d3", "metadata": {}, "outputs": [ { "data": { "application/json": { - "createdAt": "2024-11-01T21:37:52.381Z", - "dataExpiration": "2024-12-01T21:37:52.381Z", - "jobID": "16ef9962-7be4-4434-891a-963189aa7854", + "createdAt": "2024-11-05T18:54:20.607Z", + "dataExpiration": "2024-12-05T18:54:20.607Z", + "jobID": "4e07cf87-d846-4f59-8ea0-d1c52477b377", "labels": [], "links": [ { - "href": "https://harmony.earthdata.nasa.gov/stac/16ef9962-7be4-4434-891a-963189aa7854/", + "href": "https://harmony.earthdata.nasa.gov/stac/4e07cf87-d846-4f59-8ea0-d1c52477b377/", "rel": "stac-catalog-json", "title": "STAC catalog", "type": "application/json" @@ -426,7 +414,7 @@ -103.60569, 59.54235 ], - "href": "https://harmony.earthdata.nasa.gov/service-results/harmony-prod-staging/public/16ef9962-7be4-4434-891a-963189aa7854/80019316/ATL03_20200427193622_04930702_006_02_subsetted.h5", + "href": "https://harmony.earthdata.nasa.gov/service-results/harmony-prod-staging/public/4e07cf87-d846-4f59-8ea0-d1c52477b377/80698796/ATL03_20200427193622_04930702_006_02_subsetted.h5", "rel": "data", "temporal": { "end": "2020-04-27T19:44:52.680Z", @@ -436,7 +424,7 @@ "type": "application/x-hdf5" }, { - "href": "https://harmony.earthdata.nasa.gov/jobs/16ef9962-7be4-4434-891a-963189aa7854?linktype=https&page=1&limit=2000", + "href": "https://harmony.earthdata.nasa.gov/jobs/4e07cf87-d846-4f59-8ea0-d1c52477b377?linktype=https&page=1&limit=2000", "rel": "self", "title": "The current page", "type": "application/json" @@ -447,14 +435,14 @@ "progress": 100, "request": "https://harmony.earthdata.nasa.gov/C2596864127-NSIDC_CPRD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat(40%3A41.0)&subset=lon(-105.5%3A-105)&subset=time(%222020-04-27T00%3A00%3A00%22%3A%222020-05-28T00%3A00%3A00%22)", "status": "successful", - "updatedAt": "2024-11-01T21:40:25.144Z", - "username": "amy.steiker" + "updatedAt": "2024-11-05T18:58:09.109Z", + "username": "apbarret" }, "text/plain": [ "" ] }, - "execution_count": 26, + "execution_count": 9, "metadata": { "application/json": { "expanded": false, @@ -476,11 +464,9 @@ "source": [ "### Step 6: access the subsetted data\n", "\n", - "The subsetted files can be accessed by downloading the files to a local machine, such as a laptop or desktop workstation, or by _streaming_ the data. We will use both access methods in the two examples below.\n", - "\n", - "Results are staged for 30 days in the Harmony s3 bucket.\n", + "The subsetted files can be accessed by downloading the files to a local machine, such as a laptop or desktop workstation, or by _streaming_ the data. We will use both access methods in the two examples below. \n", "\n", - "_AS: ANDY, you had a note to explain the staging location. I updated the sentence above but I don't think this really needs any additional explanation. You have already explained S3 in the initial summary. The key point here is that the data are only available for 30 days._" + "Results are staged for 30 days in the Harmony s3 bucket." ] }, { @@ -499,33 +485,19 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 13, "id": "2e8f110a-4aa3-4bec-9ac3-65eef9f0e4aa", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "./ATL03_20200427193622_04930702_006_02_subsetted.h5\n" - ] - }, { "data": { "text/plain": [ "'./ATL03_20200427193622_04930702_006_02_subsetted.h5'" ] }, - "execution_count": 27, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "./ATL03_20200427193622_04930702_006_02_subsetted.h5\n" - ] } ], "source": [ @@ -543,22 +515,24 @@ "\n", "The `download_all` method can use the _job-id_ or the _result-json_, which contains result urls. \n", "\n", - "As with `download`, the download directory path on the local machine can be specified with the `directory` keyword. To save downloading the same file, the `overwrite` keyword can be set to False. " + "As with `download`, the download directory path on the local machine can be specified with the `directory` keyword. To save downloading the same file, the `overwrite` keyword can be set to False.\n", + "\n", + "The paths fo the files are returned as a list." ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 16, "id": "951581ed-03a3-4aa2-b147-ed2054b75905", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['./ATL03_20200427193622_04930702_006_02_subsetted.h5']" + "1" ] }, - "execution_count": 28, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -566,12 +540,20 @@ "source": [ "futures = harmony_client.download_all(job_id, directory=\".\", overwrite=False)\n", "filelist = [f.result() for f in futures] # get filepaths\n", - "filelist" + "len(filelist)" + ] + }, + { + "cell_type": "markdown", + "id": "443960f2-3983-4fa6-ae00-6e3bd7dee39e", + "metadata": {}, + "source": [ + "In this case, there is only one file download. Individual filepaths can be accessed using the normal methods for Python lists." ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 15, "id": "203a3c23-4bdc-47ee-a874-b361296d584b", "metadata": {}, "outputs": [ @@ -581,7 +563,7 @@ "'./ATL03_20200427193622_04930702_006_02_subsetted.h5'" ] }, - "execution_count": 32, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -606,25 +588,449 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 17, "id": "ae13153c-2968-44c1-a668-0f72f07a7d8c", "metadata": {}, "outputs": [ { - "ename": "ValueError", - "evalue": "numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[33], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[43mxr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilelist\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgt1l/heights\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m ds\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/backends/api.py:573\u001b[0m, in \u001b[0;36mopen_dataset\u001b[0;34m(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, inline_array, chunked_array_type, from_array_kwargs, backend_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 561\u001b[0m decoders \u001b[38;5;241m=\u001b[39m _resolve_decoders_kwargs(\n\u001b[1;32m 562\u001b[0m decode_cf,\n\u001b[1;32m 563\u001b[0m open_backend_dataset_parameters\u001b[38;5;241m=\u001b[39mbackend\u001b[38;5;241m.\u001b[39mopen_dataset_parameters,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 569\u001b[0m decode_coords\u001b[38;5;241m=\u001b[39mdecode_coords,\n\u001b[1;32m 570\u001b[0m )\n\u001b[1;32m 572\u001b[0m overwrite_encoded_chunks \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moverwrite_encoded_chunks\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m--> 573\u001b[0m backend_ds \u001b[38;5;241m=\u001b[39m \u001b[43mbackend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen_dataset\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 574\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 575\u001b[0m \u001b[43m \u001b[49m\u001b[43mdrop_variables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdrop_variables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 576\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdecoders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 577\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 578\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 579\u001b[0m ds \u001b[38;5;241m=\u001b[39m _dataset_from_backend_dataset(\n\u001b[1;32m 580\u001b[0m backend_ds,\n\u001b[1;32m 581\u001b[0m filename_or_obj,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 591\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 592\u001b[0m )\n\u001b[1;32m 593\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:646\u001b[0m, in \u001b[0;36mNetCDF4BackendEntrypoint.open_dataset\u001b[0;34m(self, filename_or_obj, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, use_cftime, decode_timedelta, group, mode, format, clobber, diskless, persist, lock, autoclose)\u001b[0m\n\u001b[1;32m 625\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mopen_dataset\u001b[39m( \u001b[38;5;66;03m# type: ignore[override] # allow LSP violation, not supporting **kwargs\u001b[39;00m\n\u001b[1;32m 626\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 627\u001b[0m filename_or_obj: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m os\u001b[38;5;241m.\u001b[39mPathLike[Any] \u001b[38;5;241m|\u001b[39m BufferedIOBase \u001b[38;5;241m|\u001b[39m AbstractDataStore,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 643\u001b[0m autoclose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 644\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Dataset:\n\u001b[1;32m 645\u001b[0m filename_or_obj \u001b[38;5;241m=\u001b[39m _normalize_path(filename_or_obj)\n\u001b[0;32m--> 646\u001b[0m store \u001b[38;5;241m=\u001b[39m \u001b[43mNetCDF4DataStore\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 647\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 648\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 649\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 650\u001b[0m \u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 651\u001b[0m \u001b[43m \u001b[49m\u001b[43mclobber\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclobber\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 652\u001b[0m \u001b[43m \u001b[49m\u001b[43mdiskless\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdiskless\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 653\u001b[0m \u001b[43m \u001b[49m\u001b[43mpersist\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpersist\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 654\u001b[0m \u001b[43m \u001b[49m\u001b[43mlock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlock\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 655\u001b[0m \u001b[43m \u001b[49m\u001b[43mautoclose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautoclose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 656\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 658\u001b[0m store_entrypoint \u001b[38;5;241m=\u001b[39m StoreBackendEntrypoint()\n\u001b[1;32m 659\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m close_on_error(store):\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:376\u001b[0m, in \u001b[0;36mNetCDF4DataStore.open\u001b[0;34m(cls, filename, mode, format, group, clobber, diskless, persist, lock, lock_maker, autoclose)\u001b[0m\n\u001b[1;32m 362\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m 363\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mopen\u001b[39m(\n\u001b[1;32m 364\u001b[0m \u001b[38;5;28mcls\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 374\u001b[0m autoclose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 375\u001b[0m ):\n\u001b[0;32m--> 376\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnetCDF4\u001b[39;00m\n\u001b[1;32m 378\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(filename, os\u001b[38;5;241m.\u001b[39mPathLike):\n\u001b[1;32m 379\u001b[0m filename \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mfspath(filename)\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/netCDF4/__init__.py:3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# init for netCDF4. package\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# Docstring comes from extension module _netCDF4.\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_netCDF4\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Need explicit imports for names beginning with underscores\u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_netCDF4\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;18m__doc__\u001b[39m\n", - "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:1\u001b[0m, in \u001b[0;36minit netCDF4._netCDF4\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject" - ] + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset> Size: 92MB\n",
+       "Dimensions:         (delta_time: 1848992, ds_surf_type: 5)\n",
+       "Coordinates:\n",
+       "  * delta_time      (delta_time) datetime64[ns] 15MB 2020-04-27T19:39:45.8497...\n",
+       "    lat_ph          (delta_time) float64 15MB ...\n",
+       "    lon_ph          (delta_time) float64 15MB ...\n",
+       "Dimensions without coordinates: ds_surf_type\n",
+       "Data variables:\n",
+       "    dist_ph_across  (delta_time) float32 7MB ...\n",
+       "    dist_ph_along   (delta_time) float32 7MB ...\n",
+       "    h_ph            (delta_time) float32 7MB ...\n",
+       "    pce_mframe_cnt  (delta_time) uint32 7MB ...\n",
+       "    ph_id_channel   (delta_time) uint8 2MB ...\n",
+       "    ph_id_count     (delta_time) uint8 2MB ...\n",
+       "    ph_id_pulse     (delta_time) uint8 2MB ...\n",
+       "    quality_ph      (delta_time) int8 2MB ...\n",
+       "    signal_conf_ph  (delta_time, ds_surf_type) int8 9MB ...\n",
+       "    weight_ph       (delta_time) uint8 2MB ...\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": [ + " Size: 92MB\n", + "Dimensions: (delta_time: 1848992, ds_surf_type: 5)\n", + "Coordinates:\n", + " * delta_time (delta_time) datetime64[ns] 15MB 2020-04-27T19:39:45.8497...\n", + " lat_ph (delta_time) float64 15MB ...\n", + " lon_ph (delta_time) float64 15MB ...\n", + "Dimensions without coordinates: ds_surf_type\n", + "Data variables:\n", + " dist_ph_across (delta_time) float32 7MB ...\n", + " dist_ph_along (delta_time) float32 7MB ...\n", + " h_ph (delta_time) float32 7MB ...\n", + " pce_mframe_cnt (delta_time) uint32 7MB ...\n", + " ph_id_channel (delta_time) uint8 2MB ...\n", + " ph_id_count (delta_time) uint8 2MB ...\n", + " ph_id_pulse (delta_time) uint8 2MB ...\n", + " quality_ph (delta_time) int8 2MB ...\n", + " signal_conf_ph (delta_time, ds_surf_type) int8 9MB ...\n", + " weight_ph (delta_time) uint8 2MB ...\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": 17, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -657,17 +1063,17 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 18, "id": "a8d8a29d-91df-4872-9d9c-6a116bc92774", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['s3://harmony-prod-staging/public/16ef9962-7be4-4434-891a-963189aa7854/80019316/ATL03_20200427193622_04930702_006_02_subsetted.h5']" + "['s3://harmony-prod-staging/public/4e07cf87-d846-4f59-8ea0-d1c52477b377/80698796/ATL03_20200427193622_04930702_006_02_subsetted.h5']" ] }, - "execution_count": 34, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -682,15 +1088,12 @@ "id": "815db398-723b-4278-ae30-2ad5f9764912", "metadata": {}, "source": [ - "We need AWS credentials to access the S3 bucket with the results. These are returned using the `aws_credentials` method.\n", - "\n", - "_what about if we use our own bucket?_\n", - "_AS: Andy, why don't we leave this guidance out for the first iterationa and add a new Issue as a future enhancement. I don't think this is necessary for our intial publication and it could take some time to learn and document in the tutorial. But agree it's a great addition to include (in the future)_ " + "We need AWS credentials to access the S3 bucket with the results. These are returned using the `aws_credentials` method." ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 19, "id": "d58df790-d40c-410c-a295-22d6d39dfd5b", "metadata": {}, "outputs": [], @@ -708,7 +1111,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 20, "id": "73bc9a2d-b009-4cf9-8a47-531a308833b0", "metadata": {}, "outputs": [], @@ -735,7 +1138,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 21, "id": "f29584a0-357a-4f1a-8a3b-ab72fc20d6e4", "metadata": {}, "outputs": [], @@ -753,209 +1156,449 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 22, "id": "8860630c-2050-4256-88c2-f1879456a9a1", "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A module that was compiled using NumPy 1.x cannot be run in\n", - "NumPy 2.1.2 as it may crash. To support both 1.x and 2.x\n", - "versions of NumPy, modules must be compiled with NumPy 2.0.\n", - "Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.\n", - "\n", - "If you are a user of the module, the easiest solution will be to\n", - "downgrade to 'numpy<2' or try to upgrade the affected module.\n", - "We expect that some modules will need time to support NumPy 2.\n", - "\n", - "Traceback (most recent call last): File \"/srv/conda/envs/notebook/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\n", - " return _run_code(code, main_globals, None,\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/runpy.py\", line 86, in _run_code\n", - " exec(code, run_globals)\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/ipykernel_launcher.py\", line 18, in \n", - " app.launch_new_instance()\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/traitlets/config/application.py\", line 1075, in launch_instance\n", - " app.start()\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/ipykernel/kernelapp.py\", line 739, in start\n", - " self.io_loop.start()\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/tornado/platform/asyncio.py\", line 205, in start\n", - " self.asyncio_loop.run_forever()\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/asyncio/base_events.py\", line 603, in run_forever\n", - " self._run_once()\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/asyncio/base_events.py\", line 1909, in _run_once\n", - " handle._run()\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/asyncio/events.py\", line 80, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 545, in dispatch_queue\n", - " await self.process_one()\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 534, in process_one\n", - " await dispatch(*args)\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 437, in dispatch_shell\n", - " await result\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/ipykernel/ipkernel.py\", line 359, in execute_request\n", - " await super().execute_request(stream, ident, parent)\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 778, in execute_request\n", - " reply_content = await reply_content\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/ipykernel/ipkernel.py\", line 446, in do_execute\n", - " res = shell.run_cell(\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/ipykernel/zmqshell.py\", line 549, in run_cell\n", - " return super().run_cell(*args, **kwargs)\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3075, in run_cell\n", - " result = self._run_cell(\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3130, in _run_cell\n", - " result = runner(coro)\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/core/async_helpers.py\", line 129, in _pseudo_sync_runner\n", - " coro.send(None)\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3334, in run_cell_async\n", - " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3517, in run_ast_nodes\n", - " if await self.run_code(code, result, async_=asy):\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3577, in run_code\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " File \"/tmp/ipykernel_791/3576303952.py\", line 2, in \n", - " ds\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/core/displayhook.py\", line 268, in __call__\n", - " format_dict, md_dict = self.compute_format_data(result)\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/core/displayhook.py\", line 157, in compute_format_data\n", - " return self.shell.display_formatter.format(result)\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/core/formatters.py\", line 182, in format\n", - " data = formatter(obj)\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/decorator.py\", line 232, in fun\n", - " return caller(func, *(extras + args), **kw)\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/core/formatters.py\", line 226, in catch_format_error\n", - " r = method(self, *args, **kwargs)\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/core/formatters.py\", line 711, in __call__\n", - " printer.pretty(obj)\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/lib/pretty.py\", line 411, in pretty\n", - " return _repr_pprint(obj, self, cycle)\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/lib/pretty.py\", line 779, in _repr_pprint\n", - " output = repr(obj)\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/dataset.py\", line 2540, in __repr__\n", - " return formatting.dataset_repr(self)\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/reprlib.py\", line 21, in wrapper\n", - " result = user_function(self)\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting.py\", line 724, in dataset_repr\n", - " summary.append(coords_repr(ds.coords, col_width=col_width, max_rows=max_rows))\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting.py\", line 434, in coords_repr\n", - " return _mapping_repr(\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting.py\", line 407, in _mapping_repr\n", - " summary += [\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting.py\", line 408, in \n", - " summarizer(k, v, col_width, **summarizer_kwargs[k])\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting.py\", line 344, in summarize_variable\n", - " values_str = inline_variable_array_repr(variable, values_width)\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting.py\", line 303, in inline_variable_array_repr\n", - " dask_array_type = array_type(\"dask\")\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/namedarray/pycompat.py\", line 83, in array_type\n", - " return _get_cached_duck_array_module(mod).type\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/namedarray/pycompat.py\", line 74, in _get_cached_duck_array_module\n", - " duckmod = DuckArrayModule(mod)\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/namedarray/pycompat.py\", line 43, in __init__\n", - " duck_array_type = (import_module(\"dask.array\").Array,)\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/importlib/__init__.py\", line 126, in import_module\n", - " return _bootstrap._gcd_import(name[level:], package, level)\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/dask/array/__init__.py\", line 4, in \n", - " from dask.array import backends, fft, lib, linalg, ma, overlap, random\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/dask/array/backends.py\", line 8, in \n", - " from dask.array.core import Array\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/dask/array/core.py\", line 37, in \n", - " from dask.array.chunk_types import is_valid_array_chunk, is_valid_chunk_type\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/dask/array/chunk_types.py\", line 131, in \n", - " import scipy.sparse\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/sparse/__init__.py\", line 295, in \n", - " from ._csr import *\n", - " File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/sparse/_csr.py\", line 11, in \n", - " from ._sparsetools import (csr_tocsc, csr_tobsr, csr_count_blocks,\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "_ARRAY_API not found", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;31mAttributeError\u001b[0m: _ARRAY_API not found" - ] - }, - { - "ename": "ValueError", - "evalue": "numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/core/formatters.py:711\u001b[0m, in \u001b[0;36mPlainTextFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 704\u001b[0m stream \u001b[38;5;241m=\u001b[39m StringIO()\n\u001b[1;32m 705\u001b[0m printer \u001b[38;5;241m=\u001b[39m pretty\u001b[38;5;241m.\u001b[39mRepresentationPrinter(stream, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose,\n\u001b[1;32m 706\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_width, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnewline,\n\u001b[1;32m 707\u001b[0m max_seq_length\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_seq_length,\n\u001b[1;32m 708\u001b[0m singleton_pprinters\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msingleton_printers,\n\u001b[1;32m 709\u001b[0m type_pprinters\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtype_printers,\n\u001b[1;32m 710\u001b[0m deferred_pprinters\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdeferred_printers)\n\u001b[0;32m--> 711\u001b[0m \u001b[43mprinter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpretty\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 712\u001b[0m printer\u001b[38;5;241m.\u001b[39mflush()\n\u001b[1;32m 713\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m stream\u001b[38;5;241m.\u001b[39mgetvalue()\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/lib/pretty.py:411\u001b[0m, in \u001b[0;36mRepresentationPrinter.pretty\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 408\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m meth(obj, \u001b[38;5;28mself\u001b[39m, cycle)\n\u001b[1;32m 409\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mobject\u001b[39m \\\n\u001b[1;32m 410\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__dict__\u001b[39m\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__repr__\u001b[39m\u001b[38;5;124m'\u001b[39m)):\n\u001b[0;32m--> 411\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_repr_pprint\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcycle\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 413\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _default_pprint(obj, \u001b[38;5;28mself\u001b[39m, cycle)\n\u001b[1;32m 414\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/lib/pretty.py:779\u001b[0m, in \u001b[0;36m_repr_pprint\u001b[0;34m(obj, p, cycle)\u001b[0m\n\u001b[1;32m 777\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"A pprint that just redirects to the normal repr function.\"\"\"\u001b[39;00m\n\u001b[1;32m 778\u001b[0m \u001b[38;5;66;03m# Find newlines and replace them with p.break_()\u001b[39;00m\n\u001b[0;32m--> 779\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mrepr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 780\u001b[0m lines \u001b[38;5;241m=\u001b[39m output\u001b[38;5;241m.\u001b[39msplitlines()\n\u001b[1;32m 781\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m p\u001b[38;5;241m.\u001b[39mgroup():\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/dataset.py:2540\u001b[0m, in \u001b[0;36mDataset.__repr__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 2539\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__repr__\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mstr\u001b[39m:\n\u001b[0;32m-> 2540\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mformatting\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset_repr\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/reprlib.py:21\u001b[0m, in \u001b[0;36mrecursive_repr..decorating_function..wrapper\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 19\u001b[0m repr_running\u001b[38;5;241m.\u001b[39madd(key)\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 21\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43muser_function\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 23\u001b[0m repr_running\u001b[38;5;241m.\u001b[39mdiscard(key)\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting.py:724\u001b[0m, in \u001b[0;36mdataset_repr\u001b[0;34m(ds)\u001b[0m\n\u001b[1;32m 721\u001b[0m summary\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdims_start\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdims_values\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 723\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ds\u001b[38;5;241m.\u001b[39mcoords:\n\u001b[0;32m--> 724\u001b[0m summary\u001b[38;5;241m.\u001b[39mappend(\u001b[43mcoords_repr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcoords\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcol_width\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcol_width\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_rows\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_rows\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 726\u001b[0m unindexed_dims_str \u001b[38;5;241m=\u001b[39m unindexed_dims_repr(ds\u001b[38;5;241m.\u001b[39mdims, ds\u001b[38;5;241m.\u001b[39mcoords, max_rows\u001b[38;5;241m=\u001b[39mmax_rows)\n\u001b[1;32m 727\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m unindexed_dims_str:\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting.py:434\u001b[0m, in \u001b[0;36mcoords_repr\u001b[0;34m(coords, col_width, max_rows)\u001b[0m\n\u001b[1;32m 432\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m col_width \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 433\u001b[0m col_width \u001b[38;5;241m=\u001b[39m _calculate_col_width(coords)\n\u001b[0;32m--> 434\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_mapping_repr\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 435\u001b[0m \u001b[43m \u001b[49m\u001b[43mcoords\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 436\u001b[0m \u001b[43m \u001b[49m\u001b[43mtitle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mCoordinates\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 437\u001b[0m \u001b[43m \u001b[49m\u001b[43msummarizer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msummarize_variable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 438\u001b[0m \u001b[43m \u001b[49m\u001b[43mexpand_option_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdisplay_expand_coords\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 439\u001b[0m \u001b[43m \u001b[49m\u001b[43mcol_width\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcol_width\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 440\u001b[0m \u001b[43m \u001b[49m\u001b[43mindexes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcoords\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxindexes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 441\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_rows\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_rows\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 442\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting.py:407\u001b[0m, in \u001b[0;36m_mapping_repr\u001b[0;34m(mapping, title, summarizer, expand_option_name, col_width, max_rows, indexes)\u001b[0m\n\u001b[1;32m 402\u001b[0m summary \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 403\u001b[0m summarizer(k, mapping[k], col_width, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39msummarizer_kwargs[k])\n\u001b[1;32m 404\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m keys[\u001b[38;5;241m-\u001b[39mlast_rows:]\n\u001b[1;32m 405\u001b[0m ]\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 407\u001b[0m summary \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 408\u001b[0m summarizer(k, v, col_width, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39msummarizer_kwargs[k])\n\u001b[1;32m 409\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m mapping\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m 410\u001b[0m ]\n\u001b[1;32m 411\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 412\u001b[0m summary \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [EMPTY_REPR]\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting.py:408\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 402\u001b[0m summary \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 403\u001b[0m summarizer(k, mapping[k], col_width, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39msummarizer_kwargs[k])\n\u001b[1;32m 404\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m keys[\u001b[38;5;241m-\u001b[39mlast_rows:]\n\u001b[1;32m 405\u001b[0m ]\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 407\u001b[0m summary \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m--> 408\u001b[0m \u001b[43msummarizer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcol_width\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msummarizer_kwargs\u001b[49m\u001b[43m[\u001b[49m\u001b[43mk\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 409\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m mapping\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m 410\u001b[0m ]\n\u001b[1;32m 411\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 412\u001b[0m summary \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [EMPTY_REPR]\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting.py:344\u001b[0m, in \u001b[0;36msummarize_variable\u001b[0;34m(name, var, col_width, max_width, is_index)\u001b[0m\n\u001b[1;32m 341\u001b[0m front_str \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfirst_col\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mdims_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mvariable\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mnbytes_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 343\u001b[0m values_width \u001b[38;5;241m=\u001b[39m max_width \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mlen\u001b[39m(front_str)\n\u001b[0;32m--> 344\u001b[0m values_str \u001b[38;5;241m=\u001b[39m \u001b[43minline_variable_array_repr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalues_width\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 346\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m front_str \u001b[38;5;241m+\u001b[39m values_str\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting.py:303\u001b[0m, in \u001b[0;36minline_variable_array_repr\u001b[0;34m(var, max_width)\u001b[0m\n\u001b[1;32m 301\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m var\u001b[38;5;241m.\u001b[39m_in_memory:\n\u001b[1;32m 302\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m format_array_flat(var, max_width)\n\u001b[0;32m--> 303\u001b[0m dask_array_type \u001b[38;5;241m=\u001b[39m \u001b[43marray_type\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdask\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 304\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(var\u001b[38;5;241m.\u001b[39m_data, dask_array_type):\n\u001b[1;32m 305\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m inline_dask_repr(var\u001b[38;5;241m.\u001b[39mdata)\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/namedarray/pycompat.py:83\u001b[0m, in \u001b[0;36marray_type\u001b[0;34m(mod)\u001b[0m\n\u001b[1;32m 81\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21marray_type\u001b[39m(mod: ModType) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DuckArrayTypes:\n\u001b[1;32m 82\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Quick wrapper to get the array class of the module.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 83\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_get_cached_duck_array_module\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmod\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mtype\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/namedarray/pycompat.py:74\u001b[0m, in \u001b[0;36m_get_cached_duck_array_module\u001b[0;34m(mod)\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_cached_duck_array_module\u001b[39m(mod: ModType) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DuckArrayModule:\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mod \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m _cached_duck_array_modules:\n\u001b[0;32m---> 74\u001b[0m duckmod \u001b[38;5;241m=\u001b[39m \u001b[43mDuckArrayModule\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmod\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 75\u001b[0m _cached_duck_array_modules[mod] \u001b[38;5;241m=\u001b[39m duckmod\n\u001b[1;32m 76\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m duckmod\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/namedarray/pycompat.py:43\u001b[0m, in \u001b[0;36mDuckArrayModule.__init__\u001b[0;34m(self, mod)\u001b[0m\n\u001b[1;32m 40\u001b[0m duck_array_version \u001b[38;5;241m=\u001b[39m Version(duck_array_module\u001b[38;5;241m.\u001b[39m__version__)\n\u001b[1;32m 42\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mod \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdask\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m---> 43\u001b[0m duck_array_type \u001b[38;5;241m=\u001b[39m (\u001b[43mimport_module\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdask.array\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mArray,)\n\u001b[1;32m 44\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m mod \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpint\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 45\u001b[0m duck_array_type \u001b[38;5;241m=\u001b[39m (duck_array_module\u001b[38;5;241m.\u001b[39mQuantity,)\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/importlib/__init__.py:126\u001b[0m, in \u001b[0;36mimport_module\u001b[0;34m(name, package)\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m 125\u001b[0m level \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m--> 126\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_bootstrap\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_gcd_import\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m[\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpackage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m:1050\u001b[0m, in \u001b[0;36m_gcd_import\u001b[0;34m(name, package, level)\u001b[0m\n", - "File \u001b[0;32m:1027\u001b[0m, in \u001b[0;36m_find_and_load\u001b[0;34m(name, import_)\u001b[0m\n", - "File \u001b[0;32m:1006\u001b[0m, in \u001b[0;36m_find_and_load_unlocked\u001b[0;34m(name, import_)\u001b[0m\n", - "File \u001b[0;32m:688\u001b[0m, in \u001b[0;36m_load_unlocked\u001b[0;34m(spec)\u001b[0m\n", - "File \u001b[0;32m:883\u001b[0m, in \u001b[0;36mexec_module\u001b[0;34m(self, module)\u001b[0m\n", - "File \u001b[0;32m:241\u001b[0m, in \u001b[0;36m_call_with_frames_removed\u001b[0;34m(f, *args, **kwds)\u001b[0m\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/dask/array/__init__.py:4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m__future__\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m annotations\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdask\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01marray\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backends, fft, lib, linalg, ma, overlap, random\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdask\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01marray\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mblockwise\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m atop, blockwise\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdask\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01marray\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mchunk_types\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m register_chunk_type\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/dask/array/fft.py:11\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfftpack\u001b[39;00m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[1;32m 13\u001b[0m scipy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/fftpack/__init__.py:93\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124;03m=========================================================\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124;03mLegacy discrete Fourier transforms (:mod:`scipy.fftpack`)\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 77\u001b[0m \n\u001b[1;32m 78\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 81\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfft\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifft\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfftn\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifftn\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrfft\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mirfft\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 82\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfft2\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifft2\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 83\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdiff\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 90\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdct\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124midct\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdst\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124midst\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdctn\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124midctn\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdstn\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124midstn\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 91\u001b[0m ]\n\u001b[0;32m---> 93\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_basic\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_pseudo_diffs\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_helper\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/fftpack/_basic.py:8\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Created by Pearu Peterson, August,September 2002\u001b[39;00m\n\u001b[1;32m 5\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfft\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifft\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfftn\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifftn\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrfft\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mirfft\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 6\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfft2\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifft2\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfft\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _pocketfft\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_helper\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _good_shape\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfft\u001b[39m(x, n\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, overwrite_x\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/fft/__init__.py:90\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_basic\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 86\u001b[0m fft, ifft, fft2, ifft2, fftn, ifftn,\n\u001b[1;32m 87\u001b[0m rfft, irfft, rfft2, irfft2, rfftn, irfftn,\n\u001b[1;32m 88\u001b[0m hfft, ihfft, hfft2, ihfft2, hfftn, ihfftn)\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_realtransforms\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m dct, idct, dst, idst, dctn, idctn, dstn, idstn\n\u001b[0;32m---> 90\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_fftlog\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m fht, ifht, fhtoffset\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_helper\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m next_fast_len, fftfreq, rfftfreq, fftshift, ifftshift\n\u001b[1;32m 92\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_backend\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (set_backend, skip_backend, set_global_backend,\n\u001b[1;32m 93\u001b[0m register_backend)\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/fft/_fftlog.py:10\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_basic\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _dispatch\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_lib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01muarray\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Dispatchable\n\u001b[0;32m---> 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_fftlog_backend\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m fhtoffset\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 13\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfht\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifht\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfhtoffset\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/fft/_fftlog_backend.py:4\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m warn\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_basic\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m rfft, irfft\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mspecial\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m loggamma, poch\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_lib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_array_api\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m array_namespace, copy\n\u001b[1;32m 8\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfht\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifht\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfhtoffset\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/special/__init__.py:777\u001b[0m\n\u001b[1;32m 773\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m\n\u001b[1;32m 775\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_sf_error\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SpecialFunctionWarning, SpecialFunctionError\n\u001b[0;32m--> 777\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _ufuncs\n\u001b[1;32m 778\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_ufuncs\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 780\u001b[0m \u001b[38;5;66;03m# Replace some function definitions from _ufuncs to add Array API support\u001b[39;00m\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/special/_ufuncs.pyx:1\u001b[0m, in \u001b[0;36minit scipy.special._ufuncs\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject" - ] - }, - { - "ename": "ValueError", - "evalue": "numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/IPython/core/formatters.py:347\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 345\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n\u001b[1;32m 346\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 347\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 348\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 349\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/dataset.py:2545\u001b[0m, in \u001b[0;36mDataset._repr_html_\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 2543\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m OPTIONS[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdisplay_style\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 2544\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m
    \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mescape(\u001b[38;5;28mrepr\u001b[39m(\u001b[38;5;28mself\u001b[39m))\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m
    \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 2545\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mformatting_html\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset_repr\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting_html.py:337\u001b[0m, in \u001b[0;36mdataset_repr\u001b[0;34m(ds)\u001b[0m\n\u001b[1;32m 331\u001b[0m obj_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxarray.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(ds)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 333\u001b[0m header_components \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m
    \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mescape(obj_type)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m
    \u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 335\u001b[0m sections \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 336\u001b[0m dim_section(ds),\n\u001b[0;32m--> 337\u001b[0m \u001b[43mcoord_section\u001b[49m\u001b[43m(\u001b[49m\u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcoords\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 338\u001b[0m datavar_section(ds\u001b[38;5;241m.\u001b[39mdata_vars),\n\u001b[1;32m 339\u001b[0m index_section(_get_indexes_dict(ds\u001b[38;5;241m.\u001b[39mxindexes)),\n\u001b[1;32m 340\u001b[0m attr_section(ds\u001b[38;5;241m.\u001b[39mattrs),\n\u001b[1;32m 341\u001b[0m ]\n\u001b[1;32m 343\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _obj_repr(ds, header_components, sections)\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting_html.py:200\u001b[0m, in \u001b[0;36m_mapping_section\u001b[0;34m(mapping, name, details_func, max_items_collapse, expand_option_name, enabled)\u001b[0m\n\u001b[1;32m 193\u001b[0m expanded \u001b[38;5;241m=\u001b[39m _get_boolean_with_default(\n\u001b[1;32m 194\u001b[0m expand_option_name, n_items \u001b[38;5;241m<\u001b[39m max_items_collapse\n\u001b[1;32m 195\u001b[0m )\n\u001b[1;32m 196\u001b[0m collapsed \u001b[38;5;241m=\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m expanded\n\u001b[1;32m 198\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m collapsible_section(\n\u001b[1;32m 199\u001b[0m name,\n\u001b[0;32m--> 200\u001b[0m details\u001b[38;5;241m=\u001b[39m\u001b[43mdetails_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmapping\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 201\u001b[0m n_items\u001b[38;5;241m=\u001b[39mn_items,\n\u001b[1;32m 202\u001b[0m enabled\u001b[38;5;241m=\u001b[39menabled,\n\u001b[1;32m 203\u001b[0m collapsed\u001b[38;5;241m=\u001b[39mcollapsed,\n\u001b[1;32m 204\u001b[0m )\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/formatting_html.py:116\u001b[0m, in \u001b[0;36msummarize_coords\u001b[0;34m(variables)\u001b[0m\n\u001b[1;32m 114\u001b[0m li_items \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 115\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m variables\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m--> 116\u001b[0m li_content \u001b[38;5;241m=\u001b[39m \u001b[43msummarize_variable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mis_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxindexes\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 117\u001b[0m li_items\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m
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"File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/dask/array/fft.py:11\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfftpack\u001b[39;00m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[1;32m 13\u001b[0m scipy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/fftpack/__init__.py:93\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124;03m=========================================================\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124;03mLegacy discrete Fourier transforms (:mod:`scipy.fftpack`)\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 77\u001b[0m \n\u001b[1;32m 78\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 81\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfft\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifft\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfftn\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifftn\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrfft\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mirfft\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 82\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfft2\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifft2\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 83\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdiff\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 90\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdct\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124midct\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdst\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124midst\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdctn\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124midctn\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdstn\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124midstn\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 91\u001b[0m ]\n\u001b[0;32m---> 93\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_basic\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_pseudo_diffs\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_helper\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/fftpack/_basic.py:8\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Created by Pearu Peterson, August,September 2002\u001b[39;00m\n\u001b[1;32m 5\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfft\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifft\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfftn\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifftn\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrfft\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mirfft\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 6\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfft2\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifft2\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfft\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _pocketfft\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_helper\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _good_shape\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfft\u001b[39m(x, n\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, overwrite_x\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/fft/__init__.py:90\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_basic\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 86\u001b[0m fft, ifft, fft2, ifft2, fftn, ifftn,\n\u001b[1;32m 87\u001b[0m rfft, irfft, rfft2, irfft2, rfftn, irfftn,\n\u001b[1;32m 88\u001b[0m hfft, ihfft, hfft2, ihfft2, hfftn, ihfftn)\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_realtransforms\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m dct, idct, dst, idst, dctn, idctn, dstn, idstn\n\u001b[0;32m---> 90\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_fftlog\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m fht, ifht, fhtoffset\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_helper\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m next_fast_len, fftfreq, rfftfreq, fftshift, ifftshift\n\u001b[1;32m 92\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_backend\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (set_backend, skip_backend, set_global_backend,\n\u001b[1;32m 93\u001b[0m register_backend)\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/fft/_fftlog.py:10\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_basic\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _dispatch\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_lib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01muarray\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Dispatchable\n\u001b[0;32m---> 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_fftlog_backend\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m fhtoffset\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 13\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfht\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifht\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfhtoffset\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/fft/_fftlog_backend.py:4\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m warn\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_basic\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m rfft, irfft\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mspecial\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m loggamma, poch\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_lib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_array_api\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m array_namespace, copy\n\u001b[1;32m 8\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfht\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mifht\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfhtoffset\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/special/__init__.py:777\u001b[0m\n\u001b[1;32m 773\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m\n\u001b[1;32m 775\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_sf_error\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SpecialFunctionWarning, SpecialFunctionError\n\u001b[0;32m--> 777\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _ufuncs\n\u001b[1;32m 778\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_ufuncs\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 780\u001b[0m \u001b[38;5;66;03m# Replace some function definitions from _ufuncs to add Array API support\u001b[39;00m\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/scipy/special/_ufuncs.pyx:1\u001b[0m, in \u001b[0;36minit scipy.special._ufuncs\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject" - ] + "data": { + "text/html": [ + "
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    <xarray.Dataset> Size: 92MB\n",
+       "Dimensions:         (delta_time: 1848993, ds_surf_type: 5)\n",
+       "Coordinates:\n",
+       "  * delta_time      (delta_time) datetime64[ns] 15MB 2020-04-27T19:39:45.8497...\n",
+       "    lat_ph          (delta_time) float64 15MB ...\n",
+       "    lon_ph          (delta_time) float64 15MB ...\n",
+       "Dimensions without coordinates: ds_surf_type\n",
+       "Data variables:\n",
+       "    dist_ph_across  (delta_time) float32 7MB ...\n",
+       "    dist_ph_along   (delta_time) float32 7MB ...\n",
+       "    h_ph            (delta_time) float32 7MB ...\n",
+       "    pce_mframe_cnt  (delta_time) uint32 7MB ...\n",
+       "    ph_id_channel   (delta_time) uint8 2MB ...\n",
+       "    ph_id_count     (delta_time) uint8 2MB ...\n",
+       "    ph_id_pulse     (delta_time) uint8 2MB ...\n",
+       "    quality_ph      (delta_time) int8 2MB ...\n",
+       "    signal_conf_ph  (delta_time, ds_surf_type) int8 9MB ...\n",
+       "    weight_ph       (delta_time) uint8 2MB ...\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": [ + " Size: 92MB\n", + "Dimensions: (delta_time: 1848993, ds_surf_type: 5)\n", + "Coordinates:\n", + " * delta_time (delta_time) datetime64[ns] 15MB 2020-04-27T19:39:45.8497...\n", + " lat_ph (delta_time) float64 15MB ...\n", + " lon_ph (delta_time) float64 15MB ...\n", + "Dimensions without coordinates: ds_surf_type\n", + "Data variables:\n", + " dist_ph_across (delta_time) float32 7MB ...\n", + " dist_ph_along (delta_time) float32 7MB ...\n", + " h_ph (delta_time) float32 7MB ...\n", + " pce_mframe_cnt (delta_time) uint32 7MB ...\n", + " ph_id_channel (delta_time) uint8 2MB ...\n", + " ph_id_count (delta_time) uint8 2MB ...\n", + " ph_id_pulse (delta_time) uint8 2MB ...\n", + " quality_ph (delta_time) int8 2MB ...\n", + " signal_conf_ph (delta_time, ds_surf_type) int8 9MB ...\n", + " weight_ph (delta_time) uint8 2MB ...\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": 22, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -976,118 +1619,9 @@ "id": "ba0d2015-f342-4987-aa16-e0b67b6e35b3", "metadata": {}, "source": [ - "_AS Andy: We could also remove this entirely for the first iteration and just point to harmony-py docs above when you first reference the 300 limit. This could be a future enhancement to add more examples or use cases as an Appendix._\n", - "\n", "### A. Handling large requests\n", "\n", - "Need to explain large-jobs and previews, pausing\n", - "\n", - "https://github.com/nasa/harmony-py/blob/main/examples/job_pause_resume.ipynb\n", - "\n", - "By default Harmony will only process first 300 granules\n", - "\n", - "https://harmony.earthdata.nasa.gov/docs#skipping-preview" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "7ca0b14a-7f6d-40f4-8eeb-b7f871a851da", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'status': 'running',\n", - " 'message': 'The job is being processed',\n", - " 'progress': 0,\n", - " 'created_at': datetime.datetime(2024, 10, 29, 22, 14, 21, 784000, tzinfo=tzlocal()),\n", - " 'updated_at': datetime.datetime(2024, 10, 29, 22, 14, 21, 784000, tzinfo=tzlocal()),\n", - " 'created_at_local': '2024-10-29T22:14:21+00:00',\n", - " 'updated_at_local': '2024-10-29T22:14:21+00:00',\n", - " 'request': 'https://harmony.earthdata.nasa.gov/C2596864127-NSIDC_CPRD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat(40%3A41.0)&subset=lon(-105.5%3A-105)',\n", - " 'num_input_granules': 134,\n", - " 'data_expiration': datetime.datetime(2024, 11, 28, 22, 14, 21, 784000, tzinfo=tzlocal()),\n", - " 'data_expiration_local': '2024-11-28T22:14:21+00:00'}" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "request = Request(\n", - " collection = Collection(id=capabilities[\"conceptId\"]),\n", - " spatial=BBox(-105.5,40,-105,41.),\n", - " # temporal={\n", - " # 'start': dt.datetime(2020, 4, 27),\n", - " # 'stop': dt.datetime(2020, 5, 28)\n", - " # }\n", - ")\n", - "job_id = harmony_client.submit(request)\n", - "harmony_client.status(job_id)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "00b36a4a-d673-45d5-b0a4-9c7bba958852", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " [ Processing: 80% ] |######################################## | [/]\n", - "Job is running with errors.\n", - " [ Processing: 100% ] |###################################################| [|]\n" - ] - } - ], - "source": [ - "harmony_client.wait_for_processing(job_id, show_progress=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "6710674d-d124-4b9d-9a39-265b65baa749", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'status': 'complete_with_errors',\n", - " 'message': 'The job has completed with errors. See the errors field for more details',\n", - " 'progress': 100,\n", - " 'created_at': datetime.datetime(2024, 10, 29, 22, 14, 21, 784000, tzinfo=tzlocal()),\n", - " 'updated_at': datetime.datetime(2024, 10, 29, 22, 29, 32, 444000, tzinfo=tzlocal()),\n", - " 'created_at_local': '2024-10-29T22:14:21+00:00',\n", - " 'updated_at_local': '2024-10-29T22:29:32+00:00',\n", - " 'request': 'https://harmony.earthdata.nasa.gov/C2596864127-NSIDC_CPRD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat(40%3A41.0)&subset=lon(-105.5%3A-105)',\n", - " 'num_input_granules': 134,\n", - " 'data_expiration': datetime.datetime(2024, 11, 28, 22, 14, 21, 784000, tzinfo=tzlocal()),\n", - " 'data_expiration_local': '2024-11-28T22:14:21+00:00',\n", - " 'errors': [{'url': 'https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-protected/ATLAS/ATL03/006/2023/08/20/ATL03_20230820094935_09352002_006_02.h5',\n", - " 'message': 'WorkItem failed: sds/trajectory-subsetter:0.1.21: L2 Trajectory Subsetter failed with error: No data found that matched subset constraints.'},\n", - " {'url': 'https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-protected/ATLAS/ATL03/006/2020/08/25/ATL03_20200825135211_09350802_006_01.h5',\n", - " 'message': 'WorkItem failed: sds/trajectory-subsetter:0.1.21: L2 Trajectory Subsetter failed with error: No data found that matched subset constraints.'},\n", - " {'url': 'https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-protected/ATLAS/ATL03/006/2018/11/28/ATL03_20181128201334_09350102_006_02.h5',\n", - " 'message': 'WorkItem failed: sds/trajectory-subsetter:0.1.21: L2 Trajectory Subsetter failed with error: No data found that matched subset constraints.'},\n", - " {'url': 'https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-protected/ATLAS/ATL03/006/2022/11/20/ATL03_20221120225059_09351702_006_02.h5',\n", - " 'message': 'WorkItem failed: sds/trajectory-subsetter:0.1.21: L2 Trajectory Subsetter failed with error: No data found that matched subset constraints.'},\n", - " {'url': 'https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-protected/ATLAS/ATL03/006/2021/02/21/ATL03_20210221172745_09121006_006_01.h5',\n", - " 'message': 'WorkItem failed: sds/trajectory-subsetter:0.1.21: L2 Trajectory Subsetter failed with error: No data found that matched subset constraints.'}]}" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "harmony_client.status(job_id)" + "By default Harmony will only process first 300 granules for large requests. This allows users to check that results look correct. Once we are satified that our request is correct, the job can be resumed. See this [notebook](https://github.com/nasa/harmony-py/blob/main/examples/job_pause_resume.ipynb) for examples." ] }, { From 0251a61dcb774932df2b75dfadce3f141edb0108 Mon Sep 17 00:00:00 2001 From: Julia Stewart Lowndes Date: Thu, 7 Nov 2024 10:54:30 -0800 Subject: [PATCH 8/9] deleting glossary additions (of unknown origin); this is only md text, no need for jupyter specs --- _freeze/glossary/execute-results/html.json | 11 +++++++++++ glossary.qmd | 11 ----------- 2 files changed, 11 insertions(+), 11 deletions(-) create mode 100644 _freeze/glossary/execute-results/html.json diff --git a/_freeze/glossary/execute-results/html.json b/_freeze/glossary/execute-results/html.json new file mode 100644 index 00000000..3b43b166 --- /dev/null +++ b/_freeze/glossary/execute-results/html.json @@ -0,0 +1,11 @@ +{ + "hash": "254aa4be2cc71c914ef6fdd5eb6e8f1b", + "result": { + "markdown": "---\ntitle: 'Glossary, Cheatsheets, & Guides'\naliases:\n - /glossary.html\n---\n\n## Glossary\n\nA new paradigm brings new terminology you will encounter while you learn about cloud data access and computing. \n\nThis handy cheatsheet, **Cloud Terminology 101**, defines commonly used cloud computing terms and phrases.\n\n\n\n[Published Google Slide](https://docs.google.com/presentation/d/e/2PACX-1vSryqtYkNSuw7wgg8m5Vdi1oaBRN9gIIO-LXlaOWMzHJFc8v9nPeXAbKkKtcbS2n8BOwPBaJjPFy-uv/pub?start=false&loop=false&delayms=3000){target=\"_blank\"}\n\nMore specific to working with NASA Earthdata in the cloud, this **Workflow Terminology Cheatsheet** defines terms associated with NASA Earthdata, cloud-optimized data, and some open software science tools you may encounter. A full list of NASA Eathdata-specific terms are found in the [NASA Earthdata Glossary](https://earthdata.nasa.gov/learn/user-resources/glossary).\n\n\n\n[Published Google Slide](https://docs.google.com/presentation/d/e/2PACX-1vR7Iap03ogOenimB9aoMy0rxB2J2VWcNhXlE_pIabKaCj9VoNNdxuc6IK0A_WjJVEEYZtmzkMn4GHTY/pub?start=false&loop=false&delayms=3000){target=\"_blank\"}\n\n\n## Cheatsheets & Guides\n\nHow are all of these terms and concepts related? And how can you begin to put them to work?\n\nHere are some cheatsheets and guides to help visualize what working with NASA Earthdata in the cloud can look like, and how to get started.\n\n_All slides and cheatsheets are available for re-use and remix! Let us know what you think! We welcome your input so we can continue to improve and update these guides. Slides are credited for each deck; Cheatsheets development have been led by Catalina Oaida Taglialatela and Cassie Nickles (PO.DAAC) in Spring 2022._\n\n([Internal link](https://docs.google.com/presentation/d/1PIWlAHkfn17txYAzPT06RyFL9qvekK33Gw0TH4MsBxQ/edit){target=\"_blank\"}).\n\n### What is the NASA Earthdata Cloud?\n\nNASA Earthdata Cloud is the NASA archive of Earth observations and is hosted in Amazon Web Services (AWS) cloud with DAAC tools and services built for use \"next to the data.\" The NASA DAACs (data centers) are currently transitioning to this cloud-based environment. The cloud offers a scalable and effective way to address storage, network, and data movement concerns while offering a tremendous amount of flexibility to the user. Particularly if working with large data volumes, data access and processing would be more efficient if workflows are taking place in the cloud, avoiding having to download large data volumes. Data download will continue to be freely available to users, from the Earthdata Cloud archive.\n\n\n\n[Published Google Slide](https://docs.google.com/presentation/d/e/2PACX-1vS2qii_wY_hzDzamTqCWCU2SfLNG-Zh68uVzklwE1EJLyPi-xV1jdrSZKA_035D2nYIQnuHfE77gbFA/pub?start=false&loop=false&delayms=3000){target=\"_blank\"}\n\n### Cloud Access Pathways\n\nThree pathway examples, to interact and access data (and services) from and within the NASA Earthdata Cloud, are illustrated in the diagram. Green arrows and icons indicate working locally, after downloading data to your local machine, servers, or compute/storage space. Orange arrows and icons highlight a workflow within the cloud, setting up your own AWS EC2 cloud instance, or virtual machine, in the cloud next to the data. Blue arrows and icons also indicate a within the cloud workflow, through shareable cloud environments such as Binder or JupyterHub set up in an AWS cloud region. Note that each of these may have a range of cost models. EOSDIS data are being stored in the *us-west-2* region of AWS cloud; we recommend setting up your cloud computing environment in the same region as the data for free and easy in-cloud access.\n\n\n\n\n[Published Google Slide](https://docs.google.com/presentation/d/e/2PACX-1vTFrXw0UQGB5cv6apCyv6VYDmfw0ZW_1seD6h61LmhfeqGmNdon-aU_Lo8IkE06z3ms0bD-sj831j5i/pub?start=false&loop=false&delayms=3000){target=\"_blank\"}\n\nA note on *costing*: What is free and what do I have to budget for, now that data is archived in the cloud?\n\n- Downloading data from the Earthdata Cloud archive in AWS, to your local computer environment or local storage (e.g. servers) is and will continue to be free for the user.\n- Accessing the data directly in the cloud (from *us-west-2* S3 region) is free. Users will need a NASA Earthdata Login account and AWS credentials to access, but there is no cost associated with these authentication steps, which are in place for security reasons.\n- Accessing data in the cloud via EOSDIS or DAAC cloud-based tools and services such as the CMR API, Harmony API, OPenDAP API (from *us-west-2* S3 region) is free to the user. Having the tools and services \"next to the data\" in the cloud enables DAACs to support data reduction and transformation, more efficiently, on behalf of the user, so users only access the data they need.\n- Cloud computing environments (i.e. virtual machines in the cloud) for working with data in the cloud (beyond direct or via services provided access) such as data analysis or running models with the data, is user responsibility, and should be considered in budgeting. I.e. User would need to set up a cloud compute environment (such as an EC2 instance or JupyterLab) and are responsible for any storage and computing costs.\n - This means that even though direct data access in the cloud is free to the user, they would first need to have a cloud computing environment/machine to execute the data access step from, and then continue their analysis.\n - Depending on whether that cloud environment is provided by the user themselves, user's institution, community hubs like Pangeo or NASA Openscapes JupyterLab sandbox, this element of the workflow may require user accountability, budgeting and user financial maintenance.\n\n### Getting Started Roadmap\n\n#### Cloud Workflow\n\nThe following is a conceptual roadmap for users getting started with NASA Earth Observations cloud-archived data using an in-cloud workflow (i.e. bringing user code into the cloud, avoiding data download and performing data workflows \"next to the data\").\n\n\n\n[Published Google Slide](https://docs.google.com/presentation/d/e/2PACX-1vR1ReEM5OSyjGqdOu6C_6KFCtiCL6cF_Y4-06uZnCs_PAiMyp4cwEpaA4iRsroLL39J1hSw27Co1-bX/pub?start=false&loop=false&delayms=3000){target=\"_blank\"}\n\n#### Local Workflow\n\nThe following is a conceptual roadmap for users getting started with NASA Earth Observations cloud-archived data using a local machine (e.g. laptop) workflow, as data storage and computational work.\n\n\n\n[Published Google Slide](https://docs.google.com/presentation/d/e/2PACX-1vSpmQBM-BfRMdtut9qT2oc4uxxTDFpulXMCN6h_9TviDlujn-MZJ4gwX8ilHK1qg1hgvwrt_JXwlaVF/pub?start=false&loop=false&delayms=3000){target=\"_blank\"}\n\n### Tools & Services Roadmap\n\nBelow is a practical guide for learning about and selecting helpful tools or services for a given use case, focusing on how to find and access NASA Earthdata Cloud-archived data from local compute environment (e.g. laptop) or from a cloud computing workspace, with accompanying example tutorials. Once you follow your desired pathway, click on the respective blue notebook icon to get to the example tutorial. Note: these pathways are not exhaustive, there are many ways to accomplish these common steps, but these are some of our recommendations.\n\n\n\n[Published Google Slide](https://docs.google.com/presentation/d/e/2PACX-1vQZluRH_3t4UsGM-g0LntY9txqQpx3Nr_AJ_D0IQjdrXMadE694Acsef9uCUANHPafsT16WdpQ8daFZ/pub?start=false&loop=false&delayms=3000){target=\"_blank\"}\n\n\n### Workflow Cheatsheet\n\nThe following is a practical reference guide with links to tutorials and informational websites for users who are starting to take the conceptual pieces and explore and implement in their own workflows.\n\n\n\n[Published Google Slide](https://docs.google.com/presentation/d/e/2PACX-1vRR8Vi1K_c82ztwuNcZPMnD3CYZBtMo-NGPngpjh80UlrV3he6Y_mdtt5azNhdfN-lzQQrrqvBzDQSM/pub?start=false&loop=false&delayms=3000){target=\"_blank\"}\n\n\n## Slides\nSelected presentations about working with NASA Earthdata on the Cloud; for all presentations see [nasa-openscapes.github.io > slides](https://nasa-openscapes.github.io/about.html#slides).\n\n### [NASA Earthdata Cloud: Myths, Truths, Questions](https://docs.google.com/presentation/d/1rmzodGujlOQxy4ocl7g2BEqVxMOQoIS_Fw48rifvh0M/edit?usp=sharing)\n\nby Amy Steiker, Kate Heightley (NSIDC) September 7, 2022\n\n### [NSIDC DAAC User Working Group](https://docs.google.com/presentation/d/1Wtlgt-wUx8HgeWjoxvvz4BwknLlKDmGP2F-pSuI5utQ/edit?usp=sharing){target=\"_blank\"}\n\nby Andrew Barrett, Amy Steiker, Walt Meier, Jennie Roebuck, Mikala Beig, Luis Lopez, (NSIDC) May 20, 2022.\n\n### [NASA Earthdata Cloud & The Cloud Paradigm](https://docs.google.com/presentation/d/1D-d3VQDG-KlRt6gnyfj5aN3yVo1FBsbYTaQInRB4eMw/edit#slide=id.p){target=\"_blank\"}\n\nby Aaron Friesz (LP DAAC), April 2022.\n\n", + "supporting": [ + "glossary_files" + ], + "filters": [], + "includes": {} + } +} \ No newline at end of file diff --git a/glossary.qmd b/glossary.qmd index ed3383c0..42b3bc7a 100644 --- a/glossary.qmd +++ b/glossary.qmd @@ -2,17 +2,6 @@ title: 'Glossary, Cheatsheets, & Guides' aliases: - /glossary.html -jupyter: - jupytext: - text_representation: - extension: .qmd - format_name: quarto - format_version: '1.0' - jupytext_version: 1.16.1 - kernelspec: - display_name: Python 3 (ipykernel) - language: python - name: python3 --- ## Glossary From 2f4cce98d90145f94f7c8ed097b7ab7abb67ae14 Mon Sep 17 00:00:00 2001 From: asteiker Date: Thu, 7 Nov 2024 23:39:58 +0000 Subject: [PATCH 9/9] update intro and steps to reframe and generalize --- _quarto.yml | 2 +- tutorials/IS2_Harmony.ipynb | 23 +++++------------------ 2 files changed, 6 insertions(+), 19 deletions(-) diff --git a/_quarto.yml b/_quarto.yml index 98b8585b..03e197f5 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -100,7 +100,7 @@ website: href: tutorials/Above_Ground_Biomass_Density.ipynb - text: "Agriculture Observations" href: tutorials/Observing_Seasonal_Ag_Changes.ipynb - - text: "ICESat-2 Subsetting" + - text: "Subsetting Data with Harmony" href: tutorials/IS2_Harmony.ipynb - text: "Cloud & Non-cloud" href: tutorials/04_On-Prem_Cloud.ipynb diff --git a/tutorials/IS2_Harmony.ipynb b/tutorials/IS2_Harmony.ipynb index b20c42fe..df04dcbc 100644 --- a/tutorials/IS2_Harmony.ipynb +++ b/tutorials/IS2_Harmony.ipynb @@ -12,19 +12,6 @@ "---" ] }, - { - "cell_type": "markdown", - "id": "c25e52f7-dae8-4246-a6e7-56606f50c343", - "metadata": {}, - "source": [ - "## Aim for November 6 publication\n", - "\n", - "## TODO by Monday 28 October\n", - "- check authentication\n", - "- add option to get https link\n", - "- prune imports" - ] - }, { "cell_type": "markdown", "id": "b35db394-c8ba-4263-b7ba-0c1e8f8453b0", @@ -34,7 +21,7 @@ "source": [ "## Summary\n", "\n", - "In this tutorial, we will use NASA's Earthdata Harmony Services to subset and access ICESat-2 data using the `harmony-py` Python library. Harmony can be used to subset Level-2 and Level-3A ICESat-2 data products. In this tutorial, we will subset the [ATL03 Global Geolocated Photon Data](https://nsidc.org/data/atl03/versions/6) product as an example.\n", + "In this tutorial, we will use NASA's Earthdata Harmony Services to subset and access data using the `harmony-py` Python library. We will subset the [ATL03 Global Geolocated Photon Data](https://nsidc.org/data/atl03/versions/6) product from the [ICESat-2 mission](https://nsidc.org/data/icesat-2) as an example. Please see [Demonstration for working with TEMPO data via Harmony-py](https://nasa.github.io/ASDC_Data_and_User_Services/TEMPO/tempo_with_harmony-py_clean.html) as a complimentary tutorial highlighting Harmony services for the [TEMPO mission](https://asdc.larc.nasa.gov/project/TEMPO). \n", "\n", "**What is Harmony?** [Harmony](https://harmony.earthdata.nasa.gov/) is a service that allows you to customize many NASA datasets, including the ability to subset, reproject and reformat files. Data can be subsetted for a geographic region, a temporal range and by variable. Data can be \"reprojected\" from its native coordinate reference system (CRS) to the coordinate reference system relevant to your analysis. And data can be reformatted from its native file format to a format that is more relevant for your application. These services are collectively called _transformation services_. However, not all services are available for all datasets. You will learn how to discover which services are available for a given dataset.\n", "\n", @@ -50,7 +37,7 @@ "\n", "In this tutorial you will learn how to:\n", "\n", - "1. discover Harmony service options for ICESat-2 datasets;\n", + "1. discover Harmony service options for a given dataset;\n", "3. use the `harmony-py` library to subset ATL03 granules for a bounding box and time range;\n", "4. download the subsetted ATL03 to your local machine;\n", "5. load the subsetted ATL03 data directly into xarray." @@ -63,7 +50,7 @@ "source": [ "## Prerequisites\n", "\n", - "This tutorial has been designed to run in an AWS cloud compute instance in AWS region `us-west-2`. However, if you want run it from your laptop or workstation, everything except Step-4, direct access, should work just fine. _Also get https link_ _AS: What does that comment reference?_ \n", + "This tutorial has been designed to run in an AWS cloud compute instance in AWS region `us-west-2`. However, if you want run it from your laptop or workstation, everything except Step-4, direct access, should work just fine.\n", "\n", "An [Earthdata Login](https://urs.earthdata.nasa.gov) account is required to access data from the NASA Earthdata system. Before requesting a subset of ICESat-2 data, we first need to set up our Earthdata Login authentication, shown below." ] @@ -146,7 +133,7 @@ "id": "d79e241d-74a1-446f-85d9-e84e1adce707", "metadata": {}, "source": [ - "### Step 3: discover service options for ICESAT-2\n", + "### Step 3: discover service options for a given dataset\n", "\n", "The first thing we want to know is what service options Harmony has for the ICESat-2 dataset. We discover service options for a dataset by submitting a `CapabilitiesRequest`. \n", "\n", @@ -271,7 +258,7 @@ "id": "258e04a7-4b96-4652-b523-9bbdef9f7f06", "metadata": {}, "source": [ - "### Step 4: subset ICESat-2 ATL03 file\n", + "### Step 4: subset data\n", "\n", "Now that we know what subsetting options are available for the ATL03 dataset, we can request a subsetted dataset. " ]