# prefect-planetary-computer
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Visit the full docs [here](https://giorgiobasile.github.io/prefect-planetary-computer) to see additional examples and the API reference.
Prefect integrations with the Microsoft [Planetary Computer](https://planetarycomputer.microsoft.com/) (PC).
<a href="https://planetarycomputer.microsoft.com/"><img src="https://camo.githubusercontent.com/55810ac9ab5a7f4bb66d532d6c6afd26ef926a0c2436d506a91bb439a4983194/68747470733a2f2f6169346564617461736574737075626c69636173736574732e626c6f622e636f72652e77696e646f77732e6e65742f6173736574732f616f645f696d616765732f706c616e65746172795f636f6d70757465725f6865616465725f383030772e706e67" class="pc-banner" height=100/></a>
## Overview
This collection includes a [Credentials Block 🔑](https://github.com/giorgiobasile/prefect-planetary-computer/) to store and retrieve a PC subscription key and Jupyter Hub token, with convenience methods to easily interact with the PC [Data Catalog 🌍](https://planetarycomputer.microsoft.com/catalog) and [Dask Gateway 🚀](https://planetarycomputer.microsoft.com/docs/quickstarts/scale-with-dask/) server.
For more information about:
- using Azure services with Prefect and the Planetary Computer, check out the [`prefect-azure`](https://github.com/PrefectHQ/prefect-azure/) collection.
- the integration between Prefect and Dask, check out the [`prefect-dask`](https://github.com/PrefectHQ/prefect-dask/) collection.
- taking advantage of the Planetary Computer data catalog and compute resources, check out the [Planetary Computer documentation](https://planetarycomputer.microsoft.com/docs/).
## Resources
For more tips on how to use tasks and flows in a Collection, check out [Using Collections](https://docs.prefect.io/collections/usage/)!
### Installation
Install `prefect-planetary-computer` with `pip`:
```bash
pip install prefect-planetary-computer
```
Requires an installation of Python 3.8+.
We recommend using a Python virtual environment manager such as pipenv, conda or virtualenv.
These tasks are designed to work with Prefect 2.0. For more information about how to use Prefect, please refer to the [Prefect documentation](https://docs.prefect.io/).
### Usage
!!! note
* The following Examples are adapted from [Planetary Computer - Scale with Dask](https://planetarycomputer.microsoft.com/docs/quickstarts/scale-with-dask/).
- Require the following additional packages:
```
pip install xarray zarr adlfs netcdf4 prefect_azure
```
- Make sure to share the same python dependencies - in particular `dask` and `distributed` - between the flow execution environment, the Dask Scheduler and Workers, [as explained in the Dask docs](https://docs.dask.org/en/stable/deployment-considerations.html#consistent-software-environments).
#### Computing Dask Collections
Dask collection computations, such as Dask DataFrames, can be supported from within a Prefect task by creating a Dask Gateway cluster using the credentials block within the main flow or task itself.
```python
# Prefect tasks are executed using the default ConcurrentTaskRunner
# Dask Collections tasks are executed on a new temporary Dask cluster
import xarray as xr
from prefect import flow, task, get_run_logger
from prefect_planetary_computer import PlanetaryComputerCredentials
from prefect_azure import AzureBlobStorageCredentials
from prefect_azure.blob_storage import blob_storage_upload
pc_credentials = PlanetaryComputerCredentials.load("PC_BLOCK_NAME")
bs_credentials = AzureBlobStorageCredentials.load("BS_BLOCK_NAME")
@task
def compute_mean(asset):
logger = get_run_logger()
with pc_credentials.new_gateway_cluster(
name="test-cluster",
image="pangeo/pangeo-notebook:latest"
) as cluster:
cluster.adapt(minimum=2, maximum=10)
client = cluster.get_client()
ds = xr.open_zarr(
asset.href,
**asset.extra_fields["xarray:open_kwargs"],
storage_options=asset.extra_fields["xarray:storage_options"]
)
logger.info(f"Daymet dataset info\n: {ds}")
timeseries = ds["tmin"].mean(dim=["x", "y"]).compute()
logger.info(f"Mean timeseries info\n: {timeseries}")
return timeseries
@flow
def pc_dask_flow():
# get a configured PySTAC client
catalog = pc_credentials.get_stac_catalog()
# compute the minimum daily temperature averaged over all of Hawaii,
# using the Daymet dataset
asset = catalog.get_collection("daymet-daily-hi").assets["zarr-abfs"]
prefect_future = compute_mean.submit(asset)
timeseries = prefect_future.result()
# save NetCDF timeseries file
timeseries.to_netcdf("timeseries.nc")
# upload to 'my-container' blob storage container
with open("timeseries.nc", "rb") as f:
blob = blob_storage_upload(
data=f.read(),
container="my-container",
blob="timeseries.nc",
blob_storage_credentials=bs_credentials,
overwrite=False,
)
# return the blob name of the uploaded timeseries object
return blob
pc_dask_flow()
```
#### Using the Dask Task Runner
Prefect's [`prefect_dask.DaskTaskRunner`](https://prefecthq.github.io/prefect-dask/task_runners/#prefect_dask.task_runners.DaskTaskRunner) automatically instatiates a temporary Dask cluster at flow execution time, enabling submission of both Prefect and Dask Collections tasks.
!!! warning
- [`prefect-dask`](https://prefecthq.github.io/prefect-dask/) requires:
```
distributed==2022.2.0; python_version < '3.8'
distributed>=2022.5.0,<=2023.3.1
```
- It requires [less configuration](https://discourse.prefect.io/t/how-can-i-resolve-this-error-prefecthttpstatuserror-on-prefect-2-3-1-dasktaskrunner/1541/4) on the Dask Workers side when using Prefect Cloud, you can [get started](https://docs.prefect.io/2.11.3/cloud/cloud-quickstart/) for free.
```python
# Both Prefect tasks and Dask Collections task are executed
# on a new temporary Dask cluster
import xarray as xr
from prefect import flow, task, get_run_logger
from prefect_planetary_computer import PlanetaryComputerCredentials
from prefect_azure import AzureBlobStorageCredentials
from prefect_azure.blob_storage import blob_storage_upload
from prefect_dask import get_dask_client
pc_credentials = PlanetaryComputerCredentials.load("PC_BLOCK_NAME")
bs_credentials = AzureBlobStorageCredentials.load("BS_BLOCK_NAME")
pc_runner = pc_credentials.get_dask_task_runner(
cluster_kwargs={
"image": "pangeo/pangeo-notebook:latest",
},
adapt_kwargs={'minimum': 1, 'maximum': 10, 'active': True}
)
@task
def compute_mean(asset):
logger = get_run_logger()
with get_dask_client() as client:
ds = xr.open_zarr(
asset.hr
**asset.extra_fields["xarray:open_kwargs"],
storage_options=asset.extra_fields["xarray:storage_options"]
)
logger.info(f"Daymet dataset info\n: {ds}")
timeseries = ds["tmin"].mean(dim=["x", "y"]).compute()
logger.info(f"Mean timeseries info\n: {timeseries}")
return timeseries
@flow(task_runner=pc_runner)
def pc_dask_flow():
# get a configured PySTAC client
catalog = pc_credentials.get_stac_catalog()
# compute the minimum daily temperature averaged over all of Hawaii,
# using the Daymet dataset
asset = catalog.get_collection("daymet-daily-hi").assets["zarr-abfs"]
mean_task = compute_mean.submit(asset)
timeseries = mean_task.result()
# save NetCDF timeseries file
timeseries.to_netcdf("timeseries.nc")
# upload to 'my-container' blob storage container
with open("timeseries.nc", "rb") as f:
blob = blob_storage_upload(
data=f.read(),
container="my-container",
blob="timeseries.nc",
blob_storage_credentials=bs_credentials,
overwrite=False,
)
# return the blob name of the uploaded timeseries object
return blob
pc_dask_flow()
```
### Feedback
If you encounter any bugs while using `prefect-planetary-computer`, feel free to open an issue in the [prefect-planetary-computer](https://github.com/giorgiobasile/prefect-planetary-computer) repository.
If you have any questions or issues while using `prefect-planetary-computer`, you can find help in either the [Prefect Discourse forum](https://discourse.prefect.io/) or the [Prefect Slack community](https://prefect.io/slack).
Feel free to star or watch [`prefect-planetary-computer`](https://github.com/giorgiobasile/prefect-planetary-computer) for updates too!
### Contributing
If you'd like to help contribute to fix an issue or add a feature to `prefect-planetary-computer`, please [propose changes through a pull request from a fork of the repository](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request-from-a-fork).
Here are the steps:
1. [Fork the repository](https://docs.github.com/en/get-started/quickstart/fork-a-repo#forking-a-repository)
2. [Clone the forked repository](https://docs.github.com/en/get-started/quickstart/fork-a-repo#cloning-your-forked-repository)
3. Install the repository and its dependencies:
```
pip install -e ".[dev]"
```
4. Make desired changes
5. Add tests
6. Insert an entry to [CHANGELOG.md](https://github.com/giorgiobasile/prefect-planetary-computer/blob/main/CHANGELOG.md)
7. Install `pre-commit` to perform quality checks prior to commit:
```
pre-commit install
```
8. `git commit`, `git push`, and create a pull request
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"description": "# prefect-planetary-computer\n\n<p align=\"center\">\n <!--- Insert a cover image here -->\n <!--- <br> -->\n <a href=\"https://pypi.python.org/pypi/prefect-planetary-computer/\" alt=\"PyPI version\">\n <img alt=\"PyPI\" src=\"https://img.shields.io/pypi/v/prefect-planetary-computer?color=0052FF&labelColor=090422\"></a>\n <a href=\"https://github.com/giorgiobasile/prefect-planetary-computer/\" alt=\"Stars\">\n <img src=\"https://img.shields.io/github/stars/giorgiobasile/prefect-planetary-computer?color=0052FF&labelColor=090422\" /></a>\n <a href=\"https://pypistats.org/packages/prefect-planetary-computer/\" alt=\"Downloads\">\n <img src=\"https://img.shields.io/pypi/dm/prefect-planetary-computer?color=0052FF&labelColor=090422\" /></a>\n <a href=\"https://github.com/giorgiobasile/prefect-planetary-computer/pulse\" alt=\"Activity\">\n <img src=\"https://img.shields.io/github/commit-activity/m/giorgiobasile/prefect-planetary-computer?color=0052FF&labelColor=090422\" /></a>\n <br>\n <a href=\"https://prefect-community.slack.com\" alt=\"Slack\">\n <img src=\"https://img.shields.io/badge/slack-join_community-red.svg?color=0052FF&labelColor=090422&logo=slack\" /></a>\n <a href=\"https://discourse.prefect.io/\" alt=\"Discourse\">\n <img src=\"https://img.shields.io/badge/discourse-browse_forum-red.svg?color=0052FF&labelColor=090422&logo=discourse\" /></a>\n</p>\n\nVisit the full docs [here](https://giorgiobasile.github.io/prefect-planetary-computer) to see additional examples and the API reference.\n\nPrefect integrations with the Microsoft [Planetary Computer](https://planetarycomputer.microsoft.com/) (PC).\n\n<a href=\"https://planetarycomputer.microsoft.com/\"><img src=\"https://camo.githubusercontent.com/55810ac9ab5a7f4bb66d532d6c6afd26ef926a0c2436d506a91bb439a4983194/68747470733a2f2f6169346564617461736574737075626c69636173736574732e626c6f622e636f72652e77696e646f77732e6e65742f6173736574732f616f645f696d616765732f706c616e65746172795f636f6d70757465725f6865616465725f383030772e706e67\" class=\"pc-banner\" height=100/></a>\n\n## Overview\n\nThis collection includes a [Credentials Block \ud83d\udd11](https://github.com/giorgiobasile/prefect-planetary-computer/) to store and retrieve a PC subscription key and Jupyter Hub token, with convenience methods to easily interact with the PC [Data Catalog \ud83c\udf0d](https://planetarycomputer.microsoft.com/catalog) and [Dask Gateway \ud83d\ude80](https://planetarycomputer.microsoft.com/docs/quickstarts/scale-with-dask/) server.\n\nFor more information about:\n\n- using Azure services with Prefect and the Planetary Computer, check out the [`prefect-azure`](https://github.com/PrefectHQ/prefect-azure/) collection.\n- the integration between Prefect and Dask, check out the [`prefect-dask`](https://github.com/PrefectHQ/prefect-dask/) collection.\n- taking advantage of the Planetary Computer data catalog and compute resources, check out the [Planetary Computer documentation](https://planetarycomputer.microsoft.com/docs/).\n\n## Resources\n\nFor more tips on how to use tasks and flows in a Collection, check out [Using Collections](https://docs.prefect.io/collections/usage/)!\n\n### Installation\n\nInstall `prefect-planetary-computer` with `pip`:\n\n```bash\npip install prefect-planetary-computer\n```\n\nRequires an installation of Python 3.8+.\n\nWe recommend using a Python virtual environment manager such as pipenv, conda or virtualenv.\n\nThese tasks are designed to work with Prefect 2.0. For more information about how to use Prefect, please refer to the [Prefect documentation](https://docs.prefect.io/).\n\n### Usage\n\n!!! note\n * The following Examples are adapted from [Planetary Computer - Scale with Dask](https://planetarycomputer.microsoft.com/docs/quickstarts/scale-with-dask/).\n\n - Require the following additional packages:\n ```\n pip install xarray zarr adlfs netcdf4 prefect_azure\n ```\n - Make sure to share the same python dependencies - in particular `dask` and `distributed` - between the flow execution environment, the Dask Scheduler and Workers, [as explained in the Dask docs](https://docs.dask.org/en/stable/deployment-considerations.html#consistent-software-environments).\n\n#### Computing Dask Collections\n\nDask collection computations, such as Dask DataFrames, can be supported from within a Prefect task by creating a Dask Gateway cluster using the credentials block within the main flow or task itself.\n\n\n```python\n# Prefect tasks are executed using the default ConcurrentTaskRunner\n# Dask Collections tasks are executed on a new temporary Dask cluster \n\nimport xarray as xr\n\nfrom prefect import flow, task, get_run_logger\nfrom prefect_planetary_computer import PlanetaryComputerCredentials\n\nfrom prefect_azure import AzureBlobStorageCredentials\nfrom prefect_azure.blob_storage import blob_storage_upload\n\npc_credentials = PlanetaryComputerCredentials.load(\"PC_BLOCK_NAME\")\nbs_credentials = AzureBlobStorageCredentials.load(\"BS_BLOCK_NAME\")\n\n@task\ndef compute_mean(asset):\n logger = get_run_logger()\n\n with pc_credentials.new_gateway_cluster(\n name=\"test-cluster\",\n image=\"pangeo/pangeo-notebook:latest\"\n ) as cluster:\n\n cluster.adapt(minimum=2, maximum=10)\n client = cluster.get_client()\n\n ds = xr.open_zarr(\n asset.href,\n **asset.extra_fields[\"xarray:open_kwargs\"],\n storage_options=asset.extra_fields[\"xarray:storage_options\"]\n )\n logger.info(f\"Daymet dataset info\\n: {ds}\")\n \n timeseries = ds[\"tmin\"].mean(dim=[\"x\", \"y\"]).compute()\n logger.info(f\"Mean timeseries info\\n: {timeseries}\")\n\n return timeseries\n\n@flow\ndef pc_dask_flow():\n\n # get a configured PySTAC client\n catalog = pc_credentials.get_stac_catalog()\n\n # compute the minimum daily temperature averaged over all of Hawaii, \n # using the Daymet dataset\n asset = catalog.get_collection(\"daymet-daily-hi\").assets[\"zarr-abfs\"]\n prefect_future = compute_mean.submit(asset)\n timeseries = prefect_future.result()\n\n # save NetCDF timeseries file\n timeseries.to_netcdf(\"timeseries.nc\")\n\n # upload to 'my-container' blob storage container\n with open(\"timeseries.nc\", \"rb\") as f:\n blob = blob_storage_upload(\n data=f.read(),\n container=\"my-container\",\n blob=\"timeseries.nc\",\n blob_storage_credentials=bs_credentials,\n overwrite=False,\n )\n\n # return the blob name of the uploaded timeseries object\n return blob\n\npc_dask_flow()\n```\n\n#### Using the Dask Task Runner\n\nPrefect's [`prefect_dask.DaskTaskRunner`](https://prefecthq.github.io/prefect-dask/task_runners/#prefect_dask.task_runners.DaskTaskRunner) automatically instatiates a temporary Dask cluster at flow execution time, enabling submission of both Prefect and Dask Collections tasks.\n\n!!! warning\n - [`prefect-dask`](https://prefecthq.github.io/prefect-dask/) requires:\n ```\n distributed==2022.2.0; python_version < '3.8'\n distributed>=2022.5.0,<=2023.3.1\n ```\n - It requires [less configuration](https://discourse.prefect.io/t/how-can-i-resolve-this-error-prefecthttpstatuserror-on-prefect-2-3-1-dasktaskrunner/1541/4) on the Dask Workers side when using Prefect Cloud, you can [get started](https://docs.prefect.io/2.11.3/cloud/cloud-quickstart/) for free.\n\n```python\n# Both Prefect tasks and Dask Collections task are executed\n# on a new temporary Dask cluster \nimport xarray as xr\n\nfrom prefect import flow, task, get_run_logger\nfrom prefect_planetary_computer import PlanetaryComputerCredentials\n\nfrom prefect_azure import AzureBlobStorageCredentials\nfrom prefect_azure.blob_storage import blob_storage_upload\n\nfrom prefect_dask import get_dask_client \n\npc_credentials = PlanetaryComputerCredentials.load(\"PC_BLOCK_NAME\")\nbs_credentials = AzureBlobStorageCredentials.load(\"BS_BLOCK_NAME\")\n\npc_runner = pc_credentials.get_dask_task_runner(\n cluster_kwargs={\n \"image\": \"pangeo/pangeo-notebook:latest\",\n },\n adapt_kwargs={'minimum': 1, 'maximum': 10, 'active': True}\n)\n\n@task\ndef compute_mean(asset):\n logger = get_run_logger()\n\n with get_dask_client() as client:\n ds = xr.open_zarr(\n asset.hr\n **asset.extra_fields[\"xarray:open_kwargs\"],\n storage_options=asset.extra_fields[\"xarray:storage_options\"]\n )\n logger.info(f\"Daymet dataset info\\n: {ds}\")\n\n timeseries = ds[\"tmin\"].mean(dim=[\"x\", \"y\"]).compute()\n logger.info(f\"Mean timeseries info\\n: {timeseries}\")\n\n return timeseries\n\n@flow(task_runner=pc_runner)\ndef pc_dask_flow():\n \n # get a configured PySTAC client\n catalog = pc_credentials.get_stac_catalog()\n\n # compute the minimum daily temperature averaged over all of Hawaii, \n # using the Daymet dataset\n asset = catalog.get_collection(\"daymet-daily-hi\").assets[\"zarr-abfs\"]\n\n mean_task = compute_mean.submit(asset)\n timeseries = mean_task.result()\n\n # save NetCDF timeseries file\n timeseries.to_netcdf(\"timeseries.nc\")\n\n # upload to 'my-container' blob storage container\n with open(\"timeseries.nc\", \"rb\") as f:\n blob = blob_storage_upload(\n data=f.read(),\n container=\"my-container\",\n blob=\"timeseries.nc\",\n blob_storage_credentials=bs_credentials,\n overwrite=False,\n )\n\n # return the blob name of the uploaded timeseries object\n return blob\n\npc_dask_flow()\n```\n\n### Feedback\n\nIf you encounter any bugs while using `prefect-planetary-computer`, feel free to open an issue in the [prefect-planetary-computer](https://github.com/giorgiobasile/prefect-planetary-computer) repository.\n\nIf you have any questions or issues while using `prefect-planetary-computer`, you can find help in either the [Prefect Discourse forum](https://discourse.prefect.io/) or the [Prefect Slack community](https://prefect.io/slack).\n\nFeel free to star or watch [`prefect-planetary-computer`](https://github.com/giorgiobasile/prefect-planetary-computer) for updates too!\n\n### Contributing\n\nIf you'd like to help contribute to fix an issue or add a feature to `prefect-planetary-computer`, please [propose changes through a pull request from a fork of the repository](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request-from-a-fork).\n\nHere are the steps:\n\n1. [Fork the repository](https://docs.github.com/en/get-started/quickstart/fork-a-repo#forking-a-repository)\n2. [Clone the forked repository](https://docs.github.com/en/get-started/quickstart/fork-a-repo#cloning-your-forked-repository)\n3. Install the repository and its dependencies:\n```\npip install -e \".[dev]\"\n```\n4. Make desired changes\n5. Add tests\n6. Insert an entry to [CHANGELOG.md](https://github.com/giorgiobasile/prefect-planetary-computer/blob/main/CHANGELOG.md)\n7. Install `pre-commit` to perform quality checks prior to commit:\n```\npre-commit install\n```\n8. `git commit`, `git push`, and create a pull request\n",
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