# Read multiple datasets from same Group in h5 file using h5py

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datasetsusinggroup
90%

HDF5 is one answer, It’s a powerful binary data format with no upper limit on the file size

Example_snippet/controller/utility/_datasets.js/ import numpy as np import h5py. . .
import numpy as np
import h5py
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Suppose someone has sent you a HDF5 file, mytestfile,hdf5

Example_snippet/controller/utility/_datasets.js/ conda install h5py . . .
conda install h5py
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How to use HDF5 python library, How to use HDF5 python library , How to cite phono3py ,The physical unit is W/m-K, Each tensor element is the sum of tensor elements on the members of $${}^*\mathbf{k}$$, i

Example_snippet/controller/utility/_datasets.js/ In [8]: g = f['gamma'][30] In. . .
In[8]: g = f['gamma'][30]

In[9]: import numpy as np

In[10]: g = np.where(g > 0, g, -1)

In[11]: lifetime = np.where(g > 0, 1.0 / (2 * 2 * np.pi * g), 0)
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You are have the right idea, But, you don't need to loop on range(len(data

Example_snippet/controller/utility/_datasets.js/ import h5py filename = '../Res. . .
import h5py
filename = '../Results/someFileName.h5'
data = h5py.File(filename, 'r')
for group in data.keys():
print(group)
for dset in data.[group] keys():
print(dset)
ds_data = data[group][dset] # returns HDF5 dataset object
print(ds_data)
print(ds_data.shape, ds_data.dtype)
arr = data[group][dset][: ] # adding[: ] returns a numpy array
print(arr.shape, arr.dtype)
print(arr)