{"library":"multiscale-spatial-image","title":"multiscale-spatial-image","description":"Generate a multiscale, chunked, multi-dimensional spatial image data structure that can be serialized to OME-NGFF. Version 2.1.0 is the latest, supports Python >=3.11, and uses xarray DataTree (instead of the archived datatree package).","language":"python","status":"active","last_verified":"Sat May 09","install":{"commands":["pip install multiscale-spatial-image"],"cli":null},"imports":["from multiscale_spatial_image import MultiscaleSpatialImage","from multiscale_spatial_image import to_multiscale"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import numpy as np\nimport xarray as xr\nfrom multiscale_spatial_image import MultiscaleSpatialImage, to_multiscale\n\n# Create a simple 2D spatial image (channels, y, x)\ndata = np.random.rand(3, 256, 256)\nxarr = xr.DataArray(\n    data,\n    dims=(\"c\", \"y\", \"x\"),\n    coords={\"c\": [\"R\", \"G\", \"B\"], \"y\": range(256), \"x\": range(256)},\n    name=\"image\"\n)\n\n# Generate multiscale representation (2 scales)\nms_image = to_multiscale(xarr, scale_factors=[2], chunks=64)\n\nprint(ms_image)\nprint(ms_image.scales)  # List of scale keys, e.g., [\"scale0\", \"scale1\"]\n\n# Save to OME-NGFF (Zarr) store\nms_image.to_zarr(\"output.zarr\")","lang":"python","description":"Quickstart: create a multiscale spatial image from an xarray DataArray and export to OME-NGFF.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}