saschahofmann

My impression is that keeping the zarr encoding leads to a bunch of issues (see my issue above) or the current one. There also seems to be an issue with...

Ah yes that would be another way to do it. I agree maybe it doesn't warrant an extra step especially if the more common use case is to persist on...

For the test, I am not sure how to do it. I assume I could somehow get the cache map or whatever it is it's used for I tried to...

I am currently playing around with this ```python from pydantic import BaseModel from pydantic.main import ModelMetaclass from pydantic_sqlalchemy import sqlalchemy_to_pydantic class MetaOptions: def __init__(self, options): self.model = getattr(options, "model", None)...

@tiangolo Sorry for tagging you this shamelessly but is this something you would be interested in? Also the above class approach has the disadvantage that you cant specify additional fields...

Any success with this? I also tried to find whether pydantic has some capabilities around geospatial data. The closest I have found is https://pypi.org/project/geojson-pydantic/

I managed to parse the DB data to a json response by defining this ```python from pydantic_geojson import FeatureModel from geoalchemy2 import WKBElement from geoalchemy2.shape import to_shape from shapely import...

The docs seem pretty clear about this: Only the mercator projection is supported for custom layers. What I am wondering is whether there are plans to change that and what...

Do I understand this right that you had to define the projection yourself?

The example was using `[d for d in datasets]` with `datasets` from the original post. **But** I just ran my breaking example and it finished without no problem. I assume...