Toolkit for processing sparse 4D STEM image data.
Singularity instructions may be found here.
Build instructions may be found here.
frompathlibimportPathimportstempy.ioasstioimportstempy.imageasstimimportmatplotlib.pyplotaspltimportnumpyasnpdata_path=Path('/mnt/hdd1/2020.06.03')
files=sorted(data_path.glob('data_scan0*015_*.data')) # raw data filessReader=stio.reader(files, stio.FileVersion.VERSION5)
events=stim.electron_count(sReader, np.zeros((576,576),background_threshold_n_sigma=4.0))
stio.save_electron_counts('/mnt/hdd1/data_scan15_electrons.h5', events)
# Now create a bright field STEM image from the data by summing pixels # 0 to 110 radiallybf=stim.create_stem_images(events, 0, 35)
# Create a summed diffraction pattern of the entire datasetdp=stim.calculate_sum_sparse(events.data,
events.frame_dimensions)
fg, ax=plt.subplots(1,2)
ax[0].imshow(bf[0,:,:])
ax[1].imshow(dp)
Data output after counting is in a stempy.io.sparse_array.SparseArray format. These can also be saved as an HDF5 file and loaded from a properly formatted HDF5 file. Here is an example of loading a sparse data set and summing all diffraction patterns. See the examples for more details.
importstempy.ioasstioimportmatplotlib.pyplotaspltsa=stio.SparseArray.from_hdf5('data_scan5_electrons.h5')
dp=sa.sum(axis=(0, 1))
plt.figure()
plt.imshow(dp)Build repo and set PYTHONPATH:
export PYTHONPATH=<build dir>/lib/Interact with raw data
importstempy.ioasstior=stio.reader('/data/4dstem/smallScanningDiffraction/data0000.dat')
b=r.read()
print(b.header.images_in_block)
# 32print(b.header.image_numbers)
# [1, 33, 65, 97, 129, 161, 193, 225, 257, 289, 321, 353, 385, 417, 449, 481, 513, 545, 577, 609, 641, 673, 705, 737, 769, 801, 833, 865, 897, 929, 961, 993]print(b.data)
# array([[ 0, 0, 0, ..., 0, 0, 0],# [ 0, 0, 0, ..., 0, 0, 0],# [ 0, 0, 0, ..., 0, 0, 0],# ...,# [ 932, 1017, 976, ..., 984, 834, 1031],# [ 928, 1081, 1100, ..., 1020, 985, 969],# [ 989, 940, 1045, ..., 1010, 959, 887]], dtype=uint16)