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import h5py
import numpy as np
from rsciio._docstrings import FILENAME_DOC, LAZY_DOC, RETURNS_DOC
def file_reader(filename, lazy=False):
"""
Read a Delmic hdf5 hyperspectral image.
Parameters
----------
%s
%s
%s
"""
hdf = h5py.File(filename, "r")
Acquisition2 = hdf.get("Acquisition2")
Acquisition2_ImageData = Acquisition2.get("ImageData")
Acquisition2_ImageData_Image = Acquisition2_ImageData.get("Image")
Acquisition2_ImageData_DimensionScaleC = Acquisition2_ImageData.get(
"DimensionScaleC"
)
Acquisition2_ImageData_DimensionScaleX = Acquisition2_ImageData.get(
"DimensionScaleX"
)
Acquisition2_ImageData_DimensionScaleY = Acquisition2_ImageData.get(
"DimensionScaleY"
)
DATA = Acquisition2_ImageData_Image[:, 0, 0, :, :]
data = DATA.transpose()
del DATA
axes = [
{
"name": "X",
"size": data.shape[0],
"offset": 0,
"scale": float(np.array(Acquisition2_ImageData_DimensionScaleX)),
"units": "µm",
"navigate": True,
},
{
"name": "Y",
"size": data.shape[1],
"offset": 0,
"scale": float(np.array(Acquisition2_ImageData_DimensionScaleY)),
"units": "µm",
"navigate": True,
},
{
"name": "Wavelength",
"axis": np.array(Acquisition2_ImageData_DimensionScaleC),
"units": "nm",
"navigate": False,
},
]
metadata = {"signal": {"signal_type": "", "quantity": "Intensity (counts)"}}
original_metadata = dict(DimensionScaleX="182", DimensionScaleY="132")
spim = {
"data": data,
"axes": axes,
"metadata": metadata,
"original_metadata": original_metadata,
}
return [
spim,
]
file_reader.__doc__ %= (FILENAME_DOC, LAZY_DOC, RETURNS_DOC)
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