| Name | xarray-ome-ngff JSON |
| Version |
2.2.0
JSON |
| download |
| home_page | None |
| Summary | xarray and ome-ngff |
| upload_time | 2024-05-09 20:45:51 |
| maintainer | None |
| docs_url | None |
| author | Davis Vann Bennett |
| requires_python | <4.0,>=3.9 |
| license | None |
| keywords |
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| VCS |
|
| bugtrack_url |
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| requirements |
No requirements were recorded.
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No Travis.
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| coveralls test coverage |
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|
# xarray-ome-ngff
Integrating [Xarray](https://docs.xarray.dev/en/stable/) with [OME-NGFF](https://ngff.openmicroscopy.org/).
## Help
See [documentation](https://janeliascicomp.github.io/xarray-ome-ngff/) for more details
## Usage
### Read OME-NGFF data
```python
import zarr
from xarray_ome_ngff import read_multiscale_group, DaskArrayWrapper
group = zarr.open_group("https://uk1s3.embassy.ebi.ac.uk/idr/zarr/v0.4/idr0062A/6001240.zarr")
# this ensures that we create a Dask array, which gives us lazy loading
array_wrapper = DaskArrayWrapper(chunks=10)
arrays = read_multiscale_group(group, array_wrapper=array_wrapper)
print(arrays)
"""
{'0': <xarray.DataArray 'array-bb42996937dbff7600e0481e2b1572cc' (c: 2, z: 236,
y: 275, x: 271)>
dask.array<array, shape=(2, 236, 275, 271), dtype=uint16, chunksize=(2, 10, 10, 10), chunktype=numpy.ndarray>
Coordinates:
* c (c) float64 0.0 1.0
* z (z) float64 0.0 0.5002 1.0 1.501 2.001 ... 116.0 116.5 117.0 117.5
* y (y) float64 0.0 0.3604 0.7208 1.081 ... 97.67 98.03 98.39 98.75
* x (x) float64 0.0 0.3604 0.7208 1.081 ... 96.23 96.59 96.95 97.31, '1': <xarray.DataArray 'array-2bfe6d4a6d289444ca93aa84fcb36342' (c: 2, z: 236,
y: 137, x: 135)>
dask.array<array, shape=(2, 236, 137, 135), dtype=uint16, chunksize=(2, 10, 10, 10), chunktype=numpy.ndarray>
Coordinates:
* c (c) float64 0.0 1.0
* z (z) float64 0.0 0.5002 1.0 1.501 2.001 ... 116.0 116.5 117.0 117.5
* y (y) float64 0.0 0.7208 1.442 2.162 ... 95.87 96.59 97.31 98.03
* x (x) float64 0.0 0.7208 1.442 2.162 ... 94.42 95.15 95.87 96.59, '2': <xarray.DataArray 'array-80c5fc67c0c57909c0a050656a5ab630' (c: 2, z: 236,
y: 68, x: 67)>
dask.array<array, shape=(2, 236, 68, 67), dtype=uint16, chunksize=(2, 10, 10, 10), chunktype=numpy.ndarray>
Coordinates:
* c (c) float64 0.0 1.0
* z (z) float64 0.0 0.5002 1.0 1.501 2.001 ... 116.0 116.5 117.0 117.5
* y (y) float64 0.0 1.442 2.883 4.325 5.766 ... 92.26 93.7 95.15 96.59
* x (x) float64 0.0 1.442 2.883 4.325 5.766 ... 90.82 92.26 93.7 95.15}
"""
```
### Create OME-NGFF data
```python
import numpy as np
from xarray import DataArray
from xarray_ome_ngff import create_multiscale_group
from zarr import MemoryStore
base_array = DataArray(
np.zeros((10,10), dtype='uint8'),
coords={
'x': DataArray(np.arange(-5,5) * 3, dims=('x',), attrs={'units': 'meter'}),
'y': DataArray(np.arange(-10, 0) * 3, dims=('y',), attrs={'units': 'meter'})
})
# create a little multiscale pyramid
arrays = {
's0': base_array,
's1': base_array.coarsen({'x': 2, 'y': 2}, boundary='trim').mean().astype(base_array.dtype)
}
# This example uses in-memory storage, but you can use a
# different store class from `zarr`
store = MemoryStore()
group = create_multiscale_group(store=store, path='my_group', arrays=arrays)
print(group.attrs.asdict())
"""
{
'multiscales': (
{
'version': '0.4',
'name': None,
'type': None,
'metadata': None,
'datasets': (
{
'path': 's0',
'coordinateTransformations': (
{'type': 'scale', 'scale': (3.0, 3.0)},
{'type': 'translation', 'translation': (-15.0, -30.0)},
),
},
{
'path': 's1',
'coordinateTransformations': (
{'type': 'scale', 'scale': (6.0, 6.0)},
{'type': 'translation', 'translation': (-13.5, -28.5)},
),
},
),
'axes': (
{'name': 'x', 'type': 'space', 'unit': 'meter'},
{'name': 'y', 'type': 'space', 'unit': 'meter'},
),
'coordinateTransformations': None,
},
)
}
"""
# check that the arrays are there
print(tuple(group.arrays()))
"""
(('s0', <zarr.core.Array '/my_group/s0' (10, 10) uint8>), ('s1', <zarr.core.Array '/my_group/s1' (5, 5) uint8>))
"""
# write data to the arrays
for path, array in arrays.items():
group[path][:] = array.data
```
Raw data
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"description": "# xarray-ome-ngff\n\nIntegrating [Xarray](https://docs.xarray.dev/en/stable/) with [OME-NGFF](https://ngff.openmicroscopy.org/).\n\n## Help\nSee [documentation](https://janeliascicomp.github.io/xarray-ome-ngff/) for more details\n\n## Usage\n\n### Read OME-NGFF data\n\n```python\nimport zarr\nfrom xarray_ome_ngff import read_multiscale_group, DaskArrayWrapper\ngroup = zarr.open_group(\"https://uk1s3.embassy.ebi.ac.uk/idr/zarr/v0.4/idr0062A/6001240.zarr\")\n\n# this ensures that we create a Dask array, which gives us lazy loading\narray_wrapper = DaskArrayWrapper(chunks=10)\narrays = read_multiscale_group(group, array_wrapper=array_wrapper)\nprint(arrays)\n\"\"\"\n{'0': <xarray.DataArray 'array-bb42996937dbff7600e0481e2b1572cc' (c: 2, z: 236,\n y: 275, x: 271)>\ndask.array<array, shape=(2, 236, 275, 271), dtype=uint16, chunksize=(2, 10, 10, 10), chunktype=numpy.ndarray>\nCoordinates:\n * c (c) float64 0.0 1.0\n * z (z) float64 0.0 0.5002 1.0 1.501 2.001 ... 116.0 116.5 117.0 117.5\n * y (y) float64 0.0 0.3604 0.7208 1.081 ... 97.67 98.03 98.39 98.75\n * x (x) float64 0.0 0.3604 0.7208 1.081 ... 96.23 96.59 96.95 97.31, '1': <xarray.DataArray 'array-2bfe6d4a6d289444ca93aa84fcb36342' (c: 2, z: 236,\n y: 137, x: 135)>\ndask.array<array, shape=(2, 236, 137, 135), dtype=uint16, chunksize=(2, 10, 10, 10), chunktype=numpy.ndarray>\nCoordinates:\n * c (c) float64 0.0 1.0\n * z (z) float64 0.0 0.5002 1.0 1.501 2.001 ... 116.0 116.5 117.0 117.5\n * y (y) float64 0.0 0.7208 1.442 2.162 ... 95.87 96.59 97.31 98.03\n * x (x) float64 0.0 0.7208 1.442 2.162 ... 94.42 95.15 95.87 96.59, '2': <xarray.DataArray 'array-80c5fc67c0c57909c0a050656a5ab630' (c: 2, z: 236,\n y: 68, x: 67)>\ndask.array<array, shape=(2, 236, 68, 67), dtype=uint16, chunksize=(2, 10, 10, 10), chunktype=numpy.ndarray>\nCoordinates:\n * c (c) float64 0.0 1.0\n * z (z) float64 0.0 0.5002 1.0 1.501 2.001 ... 116.0 116.5 117.0 117.5\n * y (y) float64 0.0 1.442 2.883 4.325 5.766 ... 92.26 93.7 95.15 96.59\n * x (x) float64 0.0 1.442 2.883 4.325 5.766 ... 90.82 92.26 93.7 95.15}\n\"\"\"\n```\n\n### Create OME-NGFF data\n\n```python\nimport numpy as np\nfrom xarray import DataArray\nfrom xarray_ome_ngff import create_multiscale_group\nfrom zarr import MemoryStore\n\nbase_array = DataArray(\n np.zeros((10,10), dtype='uint8'),\n coords={\n 'x': DataArray(np.arange(-5,5) * 3, dims=('x',), attrs={'units': 'meter'}),\n 'y': DataArray(np.arange(-10, 0) * 3, dims=('y',), attrs={'units': 'meter'})\n })\n\n# create a little multiscale pyramid\narrays = {\n 's0': base_array,\n 's1': base_array.coarsen({'x': 2, 'y': 2}, boundary='trim').mean().astype(base_array.dtype)\n}\n\n# This example uses in-memory storage, but you can use a \n# different store class from `zarr`\nstore = MemoryStore()\n\ngroup = create_multiscale_group(store=store, path='my_group', arrays=arrays)\nprint(group.attrs.asdict())\n\"\"\"\n{\n 'multiscales': (\n {\n 'version': '0.4',\n 'name': None,\n 'type': None,\n 'metadata': None,\n 'datasets': (\n {\n 'path': 's0',\n 'coordinateTransformations': (\n {'type': 'scale', 'scale': (3.0, 3.0)},\n {'type': 'translation', 'translation': (-15.0, -30.0)},\n ),\n },\n {\n 'path': 's1',\n 'coordinateTransformations': (\n {'type': 'scale', 'scale': (6.0, 6.0)},\n {'type': 'translation', 'translation': (-13.5, -28.5)},\n ),\n },\n ),\n 'axes': (\n {'name': 'x', 'type': 'space', 'unit': 'meter'},\n {'name': 'y', 'type': 'space', 'unit': 'meter'},\n ),\n 'coordinateTransformations': None,\n },\n )\n}\n\"\"\"\n\n# check that the arrays are there\nprint(tuple(group.arrays()))\n\"\"\"\n(('s0', <zarr.core.Array '/my_group/s0' (10, 10) uint8>), ('s1', <zarr.core.Array '/my_group/s1' (5, 5) uint8>))\n\"\"\"\n\n# write data to the arrays\nfor path, array in arrays.items():\n group[path][:] = array.data\n```",
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