h5py

import h5py import numpy as np import os file_path = 'my_data.h5' # Create a new HDF5 file (mode 'w' will overwrite if exists) with h5py.File(file_path, 'w') as f: # Create a group (like a directory) group = f.create_group('my_group') # Create a dataset within the group (like a NumPy array) data = np.arange(100).reshape(10, 10) dset = group.create_dataset('dataset_1', data=data) # Add attributes to the dataset (metadata) dset.attrs['units'] = 'arbitrary' dset.attrs['description'] = 'Sample 2D integer array' # You can also create datasets directly at the root level f.create_dataset('another_dataset', data=np.random.rand(5)) print(f"File '{file_path}' created successfully.") # Read data from the HDF5 file with h5py.File(file_path, 'r') as f: # List all top-level objects print(f"\nKeys in file: {list(f.keys())}") # Access a group group_read = f['my_group'] print(f"Keys in 'my_group': {list(group_read.keys())}") # Access a dataset dset_read = group_read['dataset_1'] # Read data into memory (using array-style slicing for the whole dataset) read_data = dset_read[()] print(f"\nShape of read_data: {read_data.shape}") print(f"First 5 elements of read_data: {read_data.flatten()[:5]}") # Access attributes print(f"Units attribute: {dset_read.attrs['units']}") # Read a slice of the data slice_data = dset_read[0:5, 0:5] print(f"Slice (0:5, 0:5) of dataset_1:\n{slice_data}") # Clean up the created file os.remove(file_path)