automatic import of python-grunnlag

3 files changed, 476 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
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--- a/.gitignore
+++ b/.gitignore
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+/grunnlag-0.4.22.tar.gz
diff --git a/python-grunnlag.spec b/python-grunnlag.spec
new file mode 100644
index 0000000..b364b79
--- /dev/null
+++ b/python-grunnlag.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-grunnlag
+Version:	0.4.22
+Release:	1
+Summary:	Basic Schema for interacting with Arnheim through Bergen
+License:	CC BY-NC 3.0
+URL:		https://github.com/jhnnsrs/grunnlag
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/4f/93/80aaee287d5bece4fd2b26318d43a19a3f4333b808cecc5d563eb0046898/grunnlag-0.4.22.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-bergen
+Requires:	python3-xarray
+Requires:	python3-zarr
+Requires:	python3-dask[dataframe,array]
+Requires:	python3-s3fs
+Requires:	python3-s3fs
+
+%description
+# 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
+
+```python
+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:
+```python
+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:
+```python
+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:
+```python
+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:
+```python
+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
+
+- [Arnheim](https://github.com/jhnnsrs/arnheim)
+- [Pydantic](https://github.com/jhnnsrs/arnheim)
+
+
+## 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](https://github.com/jhnnsrs)
+
+See also the list of [contributors](https://github.com/your/project/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)
+
+%package -n python3-grunnlag
+Summary:	Basic Schema for interacting with Arnheim through Bergen
+Provides:	python-grunnlag
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-grunnlag
+# 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
+
+```python
+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:
+```python
+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:
+```python
+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:
+```python
+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:
+```python
+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
+
+- [Arnheim](https://github.com/jhnnsrs/arnheim)
+- [Pydantic](https://github.com/jhnnsrs/arnheim)
+
+
+## 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](https://github.com/jhnnsrs)
+
+See also the list of [contributors](https://github.com/your/project/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)
+
+%package help
+Summary:	Development documents and examples for grunnlag
+Provides:	python3-grunnlag-doc
+%description help
+# 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
+
+```python
+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:
+```python
+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:
+```python
+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:
+```python
+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:
+```python
+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
+
+- [Arnheim](https://github.com/jhnnsrs/arnheim)
+- [Pydantic](https://github.com/jhnnsrs/arnheim)
+
+
+## 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](https://github.com/jhnnsrs)
+
+See also the list of [contributors](https://github.com/your/project/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)
+
+%prep
+%autosetup -n grunnlag-0.4.22
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-grunnlag -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.22-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..51ebe90
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+dbc5febad1c79a266112c742f90cf844  grunnlag-0.4.22.tar.gz