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import numpy as np
import tables as tb
basedim = 4
file = "array4.h5"
# Open a new empty HDF5 file
fileh = tb.open_file(file, mode="w")
# Get the root group
group = fileh.root
# Set the type codes to test
dtypes = [np.int8, np.uint8, np.int16, int, np.float32, float]
for i, dtype in enumerate(dtypes, 1):
# Create an array of dtype, with incrementally bigger ranges
a = np.ones((basedim,) * i, dtype)
# Save it on the HDF5 file
dsetname = f"array_{a.dtype.char}"
hdfarray = fileh.create_array(group, dsetname, a, "Large array")
print(f"Created dataset: {hdfarray}")
# Create a new group
group = fileh.create_group(group, f"group{i}")
# Close the file
fileh.close()
# Open the previous HDF5 file in read-only mode
fileh = tb.open_file(file, mode="r")
# Get the root group
group = fileh.root
# Get the metadata on the previosly saved arrays
for i, dtype in enumerate(dtypes, 1):
# Create an array for later comparison
a = np.ones((basedim,) * i, dtype)
# Get the dset object hangin from group
dset = getattr(group, "array_" + a.dtype.char)
print(f"Info from dataset: {dset!r}")
# Read the actual data in array
b = dset.read()
print(f"Array b read from file. Shape ==> {b.shape}. Dtype ==> {b.dtype}")
# Test if the original and read arrays are equal
if np.allclose(a, b):
print("Good: Read array is equal to the original")
else:
print("Error: Read array and the original differs!")
# Iterate over the next group
group = getattr(group, f"group{i}")
# Close the file
fileh.close()
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