From 97c20b0d2a62a1c9ae943d49a530d78a81bae9ad Mon Sep 17 00:00:00 2001
From: Paul Tomasula <ptomasula@limno.com>
Date: Thu, 23 May 2024 11:21:46 -0400
Subject: [PATCH] Add use cases reference
Related issue #17
Prior to the use cases repository, we had a handful of example notebooks. Now that these have been converted to use cases, we can remove them. This commit adds a link to the use cases in the readme and deletes the examples from the repo.
---
.../examples/EDR_API_demo.ipynb | 112 -
README.md | 5 +
examples/AsyncProcessDemo.ipynb | 446 --
examples/EDR_API_demo.ipynb | 3612 -----------------
4 files changed, 5 insertions(+), 4170 deletions(-)
delete mode 100644 .virtual_documents/examples/EDR_API_demo.ipynb
delete mode 100644 examples/AsyncProcessDemo.ipynb
delete mode 100644 examples/EDR_API_demo.ipynb
diff --git a/.virtual_documents/examples/EDR_API_demo.ipynb b/.virtual_documents/examples/EDR_API_demo.ipynb
deleted file mode 100644
index ec1e86e8..00000000
--- a/.virtual_documents/examples/EDR_API_demo.ipynb
+++ /dev/null
@@ -1,112 +0,0 @@
-import requests
-from shapely.geometry import Point, Polygon
-import IPython.display
-
-
-URL_ROOT = 'http://localhost:5002'
-COLLECTION_NAME = 'PRISM'
-EDR_QUERY = 'position' # 'cube'
-
-
-request_url = f'{URL_ROOT}/collections/{COLLECTION_NAME}/{EDR_QUERY}'
-
-
-get_ipython().run_cell_magic("time", "", """prism = requests.get(
- request_url,
- params={
- 'coords': Point(-100, 40),
- }
-)""")
-
-
-prism.json()
-
-
-del prism
-
-
-IPython.display.IFrame(src=f'{URL_ROOT}',width=1000,height=300,)
-
-
-get_ipython().run_cell_magic("time", "", """prism_temp = requests.get(
- request_url,
- params={
- 'coords': Point(-100, 40),
- 'parameter-name': 'tmn,tmx',
- 'datetime': '2020-06-01'
- }
-)""")
-
-
-prism_temp_json = prism_temp.json()
-print("Maximum temperature in June 2020:", prism_temp_json['ranges']['tmx']['values'][0])
-print("Minimum temperature in June 2020:", prism_temp_json['ranges']['tmn']['values'][0])
-
-
-get_ipython().run_cell_magic("time", "", """prism_ppt = requests.get(
- request_url,
- params={
- 'coords': Point(-100, 40),
- 'parameter-name': 'ppt',
- 'datetime': '2000-01-01/2010-12-31'
- }
-)""")
-
-
-prism_ppt.json()['domain']['axes']['time']
-
-
-get_ipython().run_cell_magic("time", "", """prism_ppt_ub = requests.get(
- request_url,
- params={
- 'coords': Point(-100, 40),
- 'parameter-name': 'ppt',
- 'datetime': '2015-01-01/..'
- }
-)""")
-
-
-prism_ppt_ub.json()['domain']['axes']['time']
-
-
-import xarray as xr
-import fsspec
-
-
-data = 's3://rsignellbucket2/nhgf/sample_data/prism_v2.zarr'
-storage_options = {
- 'anon': True,
- 'requester_pays': False,
- 'client_kwargs': {'endpoint_url': 'https://renc.osn.xsede.org'}}
-
-
-get_ipython().run_cell_magic("time", "", """ds = xr.open_zarr(
- fsspec.get_mapper(
- data,
- **storage_options)
- )""")
-
-
-get_ipython().run_cell_magic("time", "", """single_point = ds.sel(lat=40, lon=-100, method='nearest').compute()""")
-
-
-get_ipython().run_cell_magic("time", "", """multi_variable = ds[['tmn', 'tmx']].sel(
- lat=40,
- lon=-100,
- time='2020-06-01',
- method='nearest').compute()""")
-
-
-get_ipython().run_cell_magic("time", "", """bounded_time = ds[['ppt']].sel(
- lat=40,
- lon=-100,
- method='nearest').sel(
- time=slice('2000-01-01', '2010-12-31')).compute()""")
-
-
-get_ipython().run_cell_magic("time", "", """end = ds.time.max().values
-unbounded_time = ds[['ppt']].sel(
- lat=40,
- lon=-100,
- method='nearest').sel(
- time=slice('2015-01-01', end)).compute()""")
diff --git a/README.md b/README.md
index 2d4f524c..354c8c7d 100644
--- a/README.md
+++ b/README.md
@@ -9,11 +9,16 @@
[set up]: /readmes/SETUP.md
[plugin development]: /readmes/PLUGINS.md
[testing]: /readmes/TESTING.md
+[use cases]: https://water.usgs.gov/catalog/usecases/
# USGS Pygeoapi Server Build Integration
This repository houses USGS's [pygeoapi] builder and integration system. It serves as the source control for the [pygeoapi configuration] files and contains the tools to generate USGS specific images for a set of projects that depend on pygeoapi. New projects can be built on top of the [pygeoapi plugin] architecture and have the hosting containers managed through this repository.
+# Examples of Services Use Cases
+
+There are a number of documented use cases and examples provided in a separate repository. Reference these [use cases] for examples of how to utilize some of the services that a built using this repository.
+
# User Workflows
This section is intended to document some workflows for users of the services provided by these containers.
diff --git a/examples/AsyncProcessDemo.ipynb b/examples/AsyncProcessDemo.ipynb
deleted file mode 100644
index 93f207ad..00000000
--- a/examples/AsyncProcessDemo.ipynb
+++ /dev/null
@@ -1,446 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "b415feb8-a205-4a34-aae7-698f4d0ccd4c",
- "metadata": {},
- "source": [
- "## Asynchronous Processing and Job Manager Demonstration"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "21e435da-becf-42cf-897e-9ab31b949b8c",
- "metadata": {},
- "source": [
- "#### Summary of Work\n",
- "\n",
- "The pygeoapi package provides an implementation of [OGC API – Processes specification](https://docs.ogc.org/is/18-062r2/18-062r2.html). By default, pygeoapi provides synchronous job execution with support for asynchronous process execution through a ‘manager’. The work demonstrated in this notebook focused on modifying this specific build pipeline to install the necessary dependencies and properly configure pygeoapi to support asynchronous process execution.\n",
- "\n",
- "#### Summary of changes\n",
- "\n",
- "* Updated conda environment, nox specification, and docker containers to install and configure necessary dependencies for asynchronous processing.\n",
- "* Validation and verification of asynchronous process and job request patterns. Includes a correction to implementation to support case insensitive headers (PR 1256 https://github.com/geopython/pygeoapi/pull/1256).\n",
- "* Modified technology stack to starlette and uvicorn for asynchronous http request processing. This is not strictly necessary for asynchronous processing support but offers significant performance enhancements. \n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "de9796a8-b765-4701-af2c-c7c84138f3b1",
- "metadata": {},
- "source": [
- "### 0. Imports. Constants, Utilities "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "facial-porter",
- "metadata": {},
- "outputs": [],
- "source": [
- "import requests\n",
- "import IPython.display\n",
- "from collections import deque\n",
- "import re\n",
- "import time"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "compatible-adoption",
- "metadata": {},
- "outputs": [],
- "source": [
- "URL_ROOT = 'http://localhost:5002/api/gdp/pygeoapi' #Note Perfix/Context is still not working correctly\n",
- "PROCESS_NAME = 'GDP-ProcessTest'\n",
- "\n",
- "HEADERS_ASYNC = {'prefer' : 'respond-async'}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0fc00d04-cbf1-446b-8e3c-799afde9e726",
- "metadata": {},
- "outputs": [],
- "source": [
- "def get_response_job(response:requests.Response) -> str:\n",
- " try:\n",
- " return re.sub('.*\\/jobs\\/','',response.headers['location'])\n",
- " except KeyError:\n",
- " return ''"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "86e5a098-6996-4634-80c1-22c36e0cef04",
- "metadata": {},
- "outputs": [],
- "source": [
- "def prettify_response(response:requests.Response) -> None:\n",
- " print(f'Status: {response.status_code}')\n",
- " print(f\"Date: {response.headers['date']}\")\n",
- " print(f\"Job Id: {get_response_job(response)}\")\n",
- " print(f'Body: {response.json()}')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "04955145-f522-49ea-b438-19fdb43a161c",
- "metadata": {
- "tags": []
- },
- "source": [
- "### 1. Quick tour of the UI "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d7077d2f-14aa-464c-8a73-9203453cc24a",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "IPython.display.IFrame(src=URL_ROOT,width=1200,height=500,)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1583c29d-f3c4-4ac6-ba9d-76ec3ba5c917",
- "metadata": {
- "tags": []
- },
- "source": [
- "### 2. Structure and patterns for a process request "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4ca80cbe-8d19-4c99-8ed5-0a73591c2c6e",
- "metadata": {},
- "source": [
- "* METHOD: POST\n",
- "* HEADERS: More detail when we get to async example but for now know that they are used \n",
- "* URL: {root}/processes/{process_name}/execution\n",
- "* BODY: {'inputs' : dict}"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7e713d85-8d87-47f3-a840-341ff63bf8b7",
- "metadata": {},
- "source": [
- "### 3. Synchronous Example "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "11eccfdc-eba5-40c8-b2f8-39ea57dbf5d2",
- "metadata": {},
- "outputs": [],
- "source": [
- "url = f'{URL_ROOT}/processes/{PROCESS_NAME}/execution'\n",
- "headers = {}\n",
- "body = {'inputs' : {'process_time' : 3}}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "298bfeb6-9f75-4cad-9da8-3283c279b1a6",
- "metadata": {},
- "outputs": [],
- "source": [
- "%%time\n",
- "response = requests.post(url, headers=headers, json=body)\n",
- "prettify_response(response)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e2fd10a1-8a45-4634-a04f-0cf38be905e6",
- "metadata": {},
- "source": [
- "### 4. A second look at job log"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7b3f20f8-8585-4231-9f2c-1e2bf546b861",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "IPython.display.IFrame(src=f'{URL_ROOT}/jobs',width=1200,height=270,)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "23286543-d8e8-4ee8-966e-a18323aefe97",
- "metadata": {},
- "source": [
- "### 5. Let's try that Asynchronously"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a34d98c0-2a1c-4672-8161-4c4ec8845c12",
- "metadata": {
- "tags": []
- },
- "source": [
- "#### 5.1 Queue up a job"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e79efc43-3260-4404-9741-7d5675c8be41",
- "metadata": {},
- "outputs": [],
- "source": [
- "url = f'{URL_ROOT}/processes/{PROCESS_NAME}/execution'\n",
- "headers = HEADERS_ASYNC\n",
- "body = {'inputs' : {'process_time' : 55}}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0360c44d-0c35-4b6b-ae3f-4e996e00d15d",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(HEADERS_ASYNC)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a5808401-0bb7-4578-8e7c-b2d6b4ded1cb",
- "metadata": {},
- "source": [
- "Note this pattern comes from the [OGC-API Processes specification](https://docs.ogc.org/is/18-062r2/18-062r2.html#toc32)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f934087e-378a-47b2-bdd3-d4af93e88420",
- "metadata": {},
- "outputs": [],
- "source": [
- "%%time\n",
- "#queue job\n",
- "response_job = requests.post(url, headers=headers, json=body)\n",
- "prettify_response(response_job)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ecaafe45-8fac-4601-95ab-8539e7afb59e",
- "metadata": {},
- "source": [
- "#### 5.2 Verify it is in the job log"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fb27858a-68f8-46b8-9a97-b8006f26fd82",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "IPython.display.IFrame(src=f'{URL_ROOT}/jobs',width=1100,height=270,)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "78892d25-a83e-4100-a986-3ba3a2b4a1be",
- "metadata": {},
- "source": [
- "#### 5.3 Check the job programmatically"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4cd05e27-8334-4073-909a-fc120c47293b",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "#check status of job\n",
- "job_id = get_response_job(response_job)\n",
- "url = f'{URL_ROOT}/jobs/{job_id}/'\n",
- "response_status = requests.get(url, json=body)\n",
- "print(f\"Current job status: {response_status.json()['status']}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "39c68020-44b0-4b88-aabf-db6943bc45eb",
- "metadata": {},
- "source": [
- "#### 5.4 Retrieve data "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8eb66a28-3841-4b4d-a5f7-ec618bc22aa8",
- "metadata": {},
- "outputs": [],
- "source": [
- "job_id = get_response_job(response_job)\n",
- "url = f'{URL_ROOT}/jobs/{job_id}/results?f=json'\n",
- "response_results = requests.get(url, json=body)\n",
- "print(f\"{response_results.json()}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d1575652-f7c8-4baa-af4d-3c5c4801c4b0",
- "metadata": {},
- "source": [
- "### 6. Compare Synchronous and Asynchronous "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e88fad14-712d-47a6-b6bd-fa6db32424c7",
- "metadata": {},
- "outputs": [],
- "source": [
- "url = f'{URL_ROOT}/processes/{PROCESS_NAME}/execution'\n",
- "inputs = [\n",
- " {'inputs' : {'process_time':'5'}},\n",
- " {'inputs' : {'process_time':'2'}},\n",
- " {'inputs' : {'process_time':'3'}},\n",
- " {'inputs' : {'process_time':'4'}},\n",
- " {'inputs' : {'process_time':'5'}},\n",
- "]"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4466e9c6-b804-4e0a-b390-5b7db2cb0a45",
- "metadata": {},
- "source": [
- "#### 6.1. Synchronous"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f8ce232b-3579-4c51-9cf2-566a16620408",
- "metadata": {},
- "outputs": [],
- "source": [
- "%%time\n",
- "results : dict[str:dict] = {}\n",
- "for body in inputs:\n",
- " response = requests.post(url, json=body)\n",
- " job_id = get_response_job(response)\n",
- " print(f'Completed job {job_id}')\n",
- " results[job_id] = response.json()\n",
- "\n",
- "print('\\n',results)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "deca17fc-7a10-469a-9522-49cb95d90222",
- "metadata": {},
- "source": [
- "#### 6.2 Asynchronous"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b3ed682f-d213-41ed-86ae-60f3ef889f3b",
- "metadata": {},
- "outputs": [],
- "source": [
- "%%time\n",
- "url = f'{URL_ROOT}/processes/{PROCESS_NAME}/execution'\n",
- "results : dict[str:dict] = {}\n",
- "headers = HEADERS_ASYNC\n",
- "job_queue : deque = deque()\n",
- "\n",
- "#queue jobs\n",
- "for body in inputs:\n",
- " response = requests.post(url, headers=headers, json=body)\n",
- " if response.status_code != 201:\n",
- " print(f'Issue queue job with inputs ${body}')\n",
- " print(response.status_code)\n",
- " print(response.reason)\n",
- " continue\n",
- " \n",
- " job_id = get_response_job(response)\n",
- " print(f'Queued job_id {job_id}')\n",
- " job_queue.append(job_id)\n",
- "\n",
- "#check jobs based on queue order and pull data when ready\n",
- "while len(job_queue) > 0:\n",
- " time.sleep(0.1)\n",
- " job = job_queue.popleft()\n",
- " url = f'{URL_ROOT}/jobs/{job}/'\n",
- " response_status = requests.get(url, json=body)\n",
- " if response_status.json().get('status') == 'successful':\n",
- " url = f'{URL_ROOT}/jobs/{job}/results?f=json'\n",
- " response_results = requests.get(url, json=body)\n",
- " results[job] = response_results.json()\n",
- " continue\n",
- " \n",
- " job_queue.append(job)\n",
- " \n",
- "print('\\n', results)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "442e7f19-fdf5-41d2-9add-1394f3001128",
- "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": "3.9.16"
- },
- "toc-autonumbering": false,
- "toc-showcode": false,
- "toc-showmarkdowntxt": false
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/EDR_API_demo.ipynb b/examples/EDR_API_demo.ipynb
deleted file mode 100644
index bc0ae401..00000000
--- a/examples/EDR_API_demo.ipynb
+++ /dev/null
@@ -1,3612 +0,0 @@
-{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Environmental Data Retrieval OGC API Demonstration\n",
- "\n",
- "## Summary of Work\n",
- "\n",
- "The OGC EDR API provides a family of lightweight query interfaces to access spatio-temporal data resources. We have enhanced `pygeoapi`'s EDR provider to allow USGS to serve datasets stored on the cloud on [Amazon S3](https://aws.amazon.com/pm/serv-s3/?trk=fecf68c9-3874-4ae2-a7ed-72b6d19c8034&sc_channel=ps&ef_id=CjwKCAjwsvujBhAXEiwA_UXnAIED5jlxDoB-uf55SCPR5kFNlulhowdy3Vf45RE5GMAQ1b5kTvEgpxoCgNsQAvD_BwE:G:s&s_kwcid=AL!4422!3!536397034501!e!!g!!amazon%20s3%20cloud%20storage!11204620052!112938566834) or the [Open Storage Network](https://www.openstoragenetwork.org/) pod housed at the Massachussetts Green High Performance Computing Center by leveraging [`fsspec`](https://filesystem-spec.readthedocs.io/en/latest/). This enhancement is made in [PR #1235](https://github.com/geopython/pygeoapi/pull/1235), which is pending review from Tom Kralidis. \n",
- "\n",
- "\n",
- "Along the way, we made the following speed enhancements and bug fixes:\n",
- "1. [PR #1238](https://github.com/geopython/pygeoapi/pull/1238): Original implementation lost all benefits of lazy loading, causing the build process and queries to take a long time (>10 min) or fail. \n",
- "2. [PR #1247](https://github.com/geopython/pygeoapi/pull/1247): Fix time querying for flexibility with different datetime formats, allow bounded and unbounded time intervals, and align allowable queries with the Swagger UI documentation for datetimes."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Adding EDR Collections from Cloud Storage to Config File\n",
- "\n",
- "Add `storage_options` and associated parameters to the config file, as shown below for USGS's PRISM dataset: \n",
- "\n",
- "```yaml\n",
- " providers:\n",
- " - type: edr\n",
- " name: xarray-edr\n",
- " data: s3://rsignellbucket2/nhgf/sample_data/prism_v2.zarr\n",
- " format:\n",
- " name: zarr\n",
- " mimetype: application/zip\n",
- " storage_options:\n",
- " anon: true\n",
- " requester_pays: false\n",
- " client_kwargs:\n",
- " endpoint_url: https://renc.osn.xsede.org\n",
- "```"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## EDR Query Demonstration"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Packages"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import requests\n",
- "from shapely.geometry import Point, Polygon\n",
- "import IPython.display"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Context\n",
- "In this demonstration, we'll query the PRISM dataset, which is monthly data from January 1895 to December 2020 for the continental United States, with the following dimensions:\n",
- "* `lat`: 621\n",
- "* `lon`: 1,405\n",
- "* `time`: 1,512\n",
- "\n",
- "It includes the following variables:\n",
- "* `ppt`: Mean monthly precipitation (mm/month)\n",
- "* `tmn`: Minimum monthly temperature (deg C)\n",
- "* `tmx`: Maximum monthly temperature (deg C)\n",
- "\n",
- "### Example Queries\n",
- "Define the request URL (using `localhost`) for the demo:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "URL_ROOT = 'http://localhost:5002'\n",
- "COLLECTION_NAME = 'PRISM'\n",
- "EDR_QUERY = 'position' # 'cube'\n",
- "\n",
- "\n",
- "request_url = f'{URL_ROOT}/collections/{COLLECTION_NAME}/{EDR_QUERY}'"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### All Variables, Single Point\n",
- "**Don't Run Live**\n",
- "Start by querying all data at a single point\n",
- "\n",
- "##### With Code"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: total: 31.2 ms\n",
- "Wall time: 1min 21s\n"
- ]
- }
- ],
- "source": [
- "%%time\n",
- "prism = requests.get(\n",
- " request_url,\n",
- " params={\n",
- " 'coords': Point(-100, 40),\n",
- " }\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "data": {
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- "{'type': 'Coverage',\n",
- " 'domain': {'type': 'Domain',\n",
- " 'domainType': 'Grid',\n",
- " 'axes': {'x': {'start': -100.0, 'stop': -100.0, 'num': 1},\n",
- " 'y': {'start': 40.0, 'stop': 40.0, 'num': 1},\n",
- " 'time': {'start': '1895-01-01T00:00:00.000000000',\n",
- " 'stop': '2020-12-01T00:00:00.000000000',\n",
- " 'num': 1512}},\n",
- " 'referencing': [{'coordinates': ['x', 'y'],\n",
- " 'system': {'type': 'GeographicCRS',\n",
- " 'id': 'http://www.opengis.net/def/crs/OGC/1.3/CRS84'}}]},\n",
- " 'parameters': {'ppt': {'type': 'Parameter',\n",
- " 'description': 'Mean monthly precipitation',\n",
- " 'unit': {'symbol': 'mm/month'},\n",
- " 'observedProperty': {'id': 'ppt',\n",
- " 'label': {'en': 'Mean monthly precipitation'}}},\n",
- " 'tmn': {'type': 'Parameter',\n",
- " 'description': 'Minimum monthly temperature',\n",
- " 'unit': {'symbol': 'degC'},\n",
- " 'observedProperty': {'id': 'tmn',\n",
- " 'label': {'en': 'Minimum monthly temperature'}}},\n",
- " 'tmx': {'type': 'Parameter',\n",
- " 'description': 'Maximum monthly temperature',\n",
- " 'unit': {'symbol': 'degC'},\n",
- " 'observedProperty': {'id': 'tmx',\n",
- " 'label': {'en': 'Maximum monthly temperature'}}}},\n",
- " 'ranges': {'ppt': {'type': 'NdArray',\n",
- " 'dataType': 'float32',\n",
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- " 31.92999839782715,\n",
- " 31.969999313354492,\n",
- " 33.130001068115234,\n",
- " 26.90999984741211,\n",
- " 22.15999984741211,\n",
- " 12.630000114440918,\n",
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- " 17.78999900817871,\n",
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- " 23.28999900817871,\n",
- " 30.279998779296875,\n",
- " 31.079999923706055,\n",
- " 31.15999984741211,\n",
- " 27.059999465942383,\n",
- " 18.469999313354492,\n",
- " 6.75,\n",
- " 0.5099999904632568,\n",
- " 3.879999876022339,\n",
- " 7.409999847412109,\n",
- " 11.50999927520752,\n",
- " 16.869998931884766,\n",
- " 22.939998626708984,\n",
- " 31.219999313354492,\n",
- " 32.48999786376953,\n",
- " 34.04999923706055,\n",
- " 23.44999885559082,\n",
- " 21.34000015258789,\n",
- " 9.569999694824219,\n",
- " 2.429999828338623,\n",
- " 0.5199999809265137,\n",
- " 11.149999618530273,\n",
- " 15.389999389648438,\n",
- " 20.149999618530273,\n",
- " 26.189998626708984,\n",
- " 30.049999237060547,\n",
- " 36.849998474121094,\n",
- " 30.01999855041504,\n",
- " 26.469999313354492,\n",
- " 22.350000381469727,\n",
- " 11.75,\n",
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- " 3.109999895095825,\n",
- " 8.389999389648438,\n",
- " 18.229999542236328,\n",
- " 25.329999923706055,\n",
- " 28.68000030517578,\n",
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- " 33.43000030517578,\n",
- " 26.049999237060547,\n",
- " 24.100000381469727,\n",
- " 10.079999923706055,\n",
- " 5.480000019073486,\n",
- " 4.819999694824219,\n",
- " 13.279999732971191,\n",
- " 13.449999809265137,\n",
- " 19.469999313354492,\n",
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- " 17.350000381469727,\n",
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- " 12.50999927520752,\n",
- " 14.359999656677246,\n",
- " 20.760000228881836,\n",
- " 25.53999900817871,\n",
- " 31.549999237060547,\n",
- " 35.11000061035156,\n",
- " 30.670000076293945,\n",
- " 28.65999984741211,\n",
- " 21.68000030517578,\n",
- " 12.1899995803833,\n",
- " 6.839999675750732,\n",
- " -2.009999990463257,\n",
- " -1.0,\n",
- " 12.109999656677246,\n",
- " 19.719999313354492,\n",
- " ...]}}}"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "prism.json()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "del prism"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "##### With the UI"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- " <iframe\n",
- " width=\"1000\"\n",
- " height=\"300\"\n",
- " src=\"http://localhost:5002\"\n",
- " frameborder=\"0\"\n",
- " allowfullscreen\n",
- " \n",
- " ></iframe>\n",
- " "
- ],
- "text/plain": [
- "<IPython.lib.display.IFrame at 0x1a8dcaea260>"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "IPython.display.IFrame(src=f'{URL_ROOT}',width=1000,height=300,)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Multiple Variables, Single Timestep\n",
- "Query minimum and maximum temperature in June 2020\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: total: 0 ns\n",
- "Wall time: 9.05 s\n"
- ]
- }
- ],
- "source": [
- "%%time\n",
- "prism_temp = requests.get(\n",
- " request_url,\n",
- " params={\n",
- " 'coords': Point(-100, 40),\n",
- " 'parameter-name': 'tmn,tmx',\n",
- " 'datetime': '2020-06-01'\n",
- " }\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Maximum temperature in June 2020: 32.470001220703125\n",
- "Minimum temperature in June 2020: 16.540000915527344\n"
- ]
- }
- ],
- "source": [
- "prism_temp_json = prism_temp.json()\n",
- "print(\"Maximum temperature in June 2020:\", prism_temp_json['ranges']['tmx']['values'][0])\n",
- "print(\"Minimum temperature in June 2020:\", prism_temp_json['ranges']['tmn']['values'][0])"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Single Variable, Bounded Time Range\n",
- "\n",
- "Query precipitation data at a single point between 2000 and 2010"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: total: 0 ns\n",
- "Wall time: 8.77 s\n"
- ]
- }
- ],
- "source": [
- "%%time\n",
- "prism_ppt = requests.get(\n",
- " request_url,\n",
- " params={\n",
- " 'coords': Point(-100, 40),\n",
- " 'parameter-name': 'ppt',\n",
- " 'datetime': '2000-01-01/2010-12-31'\n",
- " }\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'start': '2000-01-01T00:00:00.000000000',\n",
- " 'stop': '2010-12-01T00:00:00.000000000',\n",
- " 'num': 132}"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "prism_ppt.json()['domain']['axes']['time']"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Single Variable, Unbounded Time Interval\n",
- "Query precipitation at a single point from 2015 onward"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: total: 15.6 ms\n",
- "Wall time: 8.31 s\n"
- ]
- }
- ],
- "source": [
- "%%time\n",
- "prism_ppt_ub = requests.get(\n",
- " request_url,\n",
- " params={\n",
- " 'coords': Point(-100, 40),\n",
- " 'parameter-name': 'ppt',\n",
- " 'datetime': '2015-01-01/..'\n",
- " }\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'start': '2015-01-01T00:00:00.000000000',\n",
- " 'stop': '2020-12-01T00:00:00.000000000',\n",
- " 'num': 72}"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "prism_ppt_ub.json()['domain']['axes']['time']"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Upcoming Work\n",
- "\n",
- "1. `Querying projected datasets` (e.g., CONUS404, Alaska ET dataset): [`edr-crs-support`](https://github.com/LimnoTech/pygeoapi/tree/edr-crs-support) branch on LimnoTech's pygeoapi fork\n",
- " - Status: WIP but functional\n",
- " - Lift: Moderate\n",
- " - To Do: Clean up, add CI testing, work out transformation issues with USGS sample data\n",
- "2. `Fix cube query`\n",
- " - Status: Not Started\n",
- " - Level of Effort: Light\n",
- " - To Do: Code is looking for `bbox` in the wrong place\n",
- "4. `Fix Coords Query for Polygons / MultiPoint(?)`: It looks like position query is supposed to support polygons and multipoint, but fails. \n",
- " - Status: Not Started\n",
- " - Level of Effort: Light\n",
- " - To Do: add conditional to drop `method=nearest` with slice\n",
- " - General Question: Do we want this to handle a multi-point query with xarray? This is an explicit example in the Swagger docs, but fails and doesn't actually make sense to use for coverages\n",
- "5. `Optimization`: profile the EDR provider to make speed enhancements.\n",
- " - Status: Not Started\n",
- " - Level of Effort: Moderate"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Appendix: Time Comparison"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [],
- "source": [
- "import xarray as xr\n",
- "import fsspec"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [],
- "source": [
- "data = 's3://rsignellbucket2/nhgf/sample_data/prism_v2.zarr'\n",
- "storage_options = {\n",
- " 'anon': True,\n",
- " 'requester_pays': False,\n",
- " 'client_kwargs': {'endpoint_url': 'https://renc.osn.xsede.org'}}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: total: 3.61 s\n",
- "Wall time: 4.79 s\n"
- ]
- }
- ],
- "source": [
- "%%time\n",
- "ds = xr.open_zarr(\n",
- " fsspec.get_mapper(\n",
- " data,\n",
- " **storage_options)\n",
- " )"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: total: 44.2 s\n",
- "Wall time: 1min 2s\n"
- ]
- }
- ],
- "source": [
- "%%time\n",
- "single_point = ds.sel(lat=40, lon=-100, method='nearest').compute()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: total: 719 ms\n",
- "Wall time: 4.45 s\n"
- ]
- }
- ],
- "source": [
- "%%time\n",
- "multi_variable = ds[['tmn', 'tmx']].sel(\n",
- " lat=40,\n",
- " lon=-100,\n",
- " time='2020-06-01',\n",
- " method='nearest').compute()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: total: 2.73 s\n",
- "Wall time: 7.24 s\n"
- ]
- }
- ],
- "source": [
- "%%time\n",
- "bounded_time = ds[['ppt']].sel(\n",
- " lat=40,\n",
- " lon=-100,\n",
- " method='nearest').sel(\n",
- " time=slice('2000-01-01', '2010-12-31')).compute()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: total: 1.7 s\n",
- "Wall time: 6.91 s\n"
- ]
- }
- ],
- "source": [
- "%%time\n",
- "end = ds.time.max().values\n",
- "unbounded_time = ds[['ppt']].sel(\n",
- " lat=40,\n",
- " lon=-100,\n",
- " method='nearest').sel(\n",
- " time=slice('2015-01-01', end)).compute()"
- ]
- }
- ],
- "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": "3.8.13"
- },
- "toc-autonumbering": true
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
--
GitLab