feat: add stacrs post

content/posts/2024-09-20-stacrs-v0.1.md

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++++
+title = "Python API for stac-rs v0.1"
+slug = "stacrs-python-v0.1"
+date = 2024-09-20
+
+[taxonomies]
+tags = ["python", "stac"]
+categories = ["releases"]
++++
+
+[stac-rs](https://github.com/stac-utils/stac-rs) has a simple, no-dependency Python API installable from PyPI:
+
+```shell
+pip install stacrs
+```
+
+The API documentation is available [here](https://stac-utils.github.io/stac-rs/python/api/).
+This Python API is intended to complement, not replace, existing Python packages such as [pystac](https://pystac.readthedocs.io) and [pystac-client](https://pystac-client.readthedocs.io) by providing functionality that those packages don't have.
+
+## stac-geoparquet
+
+[stac-geoparquet](https://github.com/stac-utils/stac-geoparquet) is a relatively new specification that describes how to store STAC items in [GeoParquet](https://geoparquet.org/).
+The specification repo also contains a reference implementation, mostly written in Python.
+**stac-geoparquet** was added to **stac-rs** in [v0.9.0](https://github.com/stac-utils/stac-rs/releases/tag/stac-v0.9.0), and v0.1 of the Python package includes this capability:
+
+```python
+import stacrs
+
+item_collection = stacrs.read("items.parquet")
+# do some work
+stacrs.write("items.parquet", item_collection)
+```
+
+Note that these items are read as JSON dictionaries, which might not be the most efficient way to work with the data depending on your use case.
+
+## Object store
+
+**stacrs** supports reading and writing from blog storage via [object_store](https://docs.rs/object_store):
+
+```python
+options = [("aws_access_key_id", "...")]
+item = stacrs.read("s3://bucket/item.json", options=options)
+```
+
+Currently, credentials are provided via `options`, which can be a little awkward — we'd like to make that experience better in the future (e.g. via [environment variables](https://github.com/stac-utils/stac-rs/issues/414)).
+
+## Searching into files
+
+A common workflow for data analysis is to search a STAC API for a large number of items, save those items to a file (e.g. **stac-geoparquet**) then iteratively work with those items for more focused queries.
+**stacrs** supports that workflow with `search_to`:
+
+```python
+stacrs.search_to("items.parquet",
+    "https://landsatlook.usgs.gov/stac-server",
+    collections=["landsat-c2l2-sr"],
+    intersects={"type": "Point", "coordinates": [-105.119, 40.173]},
+    sortby="-properties.datetime",
+    max_items=1000,
+)
+items = stacrs.read("items.parquet")
+# Do some work
+```
+
+## pystac compatability
+
+If **pystac** exists on the system, `stacrs.pystac` provides functions that take and return pystac objects:
+
+```python
+from pystac import ItemCollection
+
+item_collection = stacrs.pystac.read("items.json")
+assert isinstance(item_collection, ItemCollection)
+```
+
+## Get in touch
+
+If you're using the **stacs** Python package, or you'd like to use it, get in touch!
+I'm on [Bluesky](https://bsky.app/profile/gadom.ski), or ask anything in the [Github Discussions](https://github.com/stac-utils/stac-rs/discussions).