Grunnlag
Idea
Grunnlag is a Schema Provider for the Bergen Client accessing your Arnheim Framework
Prerequisites
Bergen (and in Conclusion Grunnlag) only works with a running Arnheim Instance (in your network or locally for debugging).
Usage
In order to initialize the Client you need to connect it as a Valid Application with your Arnheim Instance
from bergen import Bergen
client = Bergen(host="p-tnagerl-lab1",
port=8000,
client_id="APPLICATION_ID_FROM_ARNHEIM",
client_secret="APPLICATION_SECRET_FROM_ARNHEIM",
name="karl",
)
In your following code you can simple query your data according to the Schema of the Datapoint
Example 1:
from grunnlag.schema import Node
rep = Representation.objects.get(id=1)
print(rep.shape)
Access a Representation (Grunnlags Implementation of a 5 Dimensional Array e.g Image Stack, Time Series Photography) and display the dimensions
Example 2:
from grunnlag.schema import Representation, Sample
from bergen.query import TypedGQL
samples = TypedGQL("""
query {
samples(creator: 1){
id
representations(name: "initial", dims: ["x","y","z"]) {
id
store
}
}
}
""", Sample).run({})
for sample in samples:
print(sample.id)
for representation in sample.representations:
print(representation.data.shape)
Get all Samples and include the representations if they have the name "initial" and contains the required dimensions. (An automatically documented and browsable Schema can be found at your Arnheim Instance /graphql)
Example 3:
from grunnlag.schema import Representation, Sample
from bergen.query import TypedGQL
import xarray as xr
massive_array = xr.DataArray(da.zeros(1024,1024,100,40,4), dims=["x","y","z","t","c"])
rep = Representation.objects.from_xarray(massive_array, name="massive", sample=1)
The Grunnlag Implementation allows for upload of massive arrays do to its reliance on Xarray, dask, and zarr, combined with S3 Storage on the Backend. Client Data gets compresed and send over to the S3 Storage and automatically added to the system. (Permissions required!)
Example 4:
from grunnlag.schema import Representation, Sample
from bergen.query import TypedGQL
import xarray as xr
import napari
rep = Representation.objects.get(name="massive", sample=1)
with napari.gui_qt() as gui:
viewer = napari.view_image(rep.data.sel(c=0).data)
Combined with Napari that is able to handle dask arrays, data visualization of massive Datasets becomes a breeze as only required chunks are downloaded form the storage backend.
Testing and Documentation
So far Grunnlad does only provide limitedunit-tests and is in desperate need of documentation, please beware that you are using an Alpha-Version
Build with
Roadmap
This is considered pre-Alpha so pretty much everything is still on the roadmap
Deployment
Contact the Developer before you plan to deploy this App, it is NOT ready for public release
Versioning
There is not yet a working versioning profile in place, consider non-stable for every release
Authors
- **Johannes Roos ** - Initial work - jhnnsrs
See also the list of contributors who participated in this project.
License
Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
Acknowledgments
- EVERY single open-source project this library used (the list is too extensive so far)
1 maintainer
Metadata
1 maintainer
Release files for grunnlag 0.4.22
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