import h5py
import pandas as pd
import numpy as np
import os
import src.hdf5_vis as hdf5_vis
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def read_dataset_from_hdf5file(hdf5_file_path, dataset_path):
# Open the HDF5 file
with h5py.File(hdf5_file_path, 'r') as hdf:
# Load the dataset
dataset = hdf[dataset_path]
data = np.empty(dataset.shape, dtype=dataset.dtype)
dataset.read_direct(data)
df = pd.DataFrame(data)
for col_name in df.select_dtypes(exclude='number'):
df[col_name] = df[col_name].str.decode('utf-8') #apply(lambda x: x.decode('utf-8') if isinstance(x,bytes) else x)
## Extract metadata (attributes) and convert to a dictionary
#metadata = hdf5_vis.construct_attributes_dict(hdf[dataset_name].attrs)
## Create a one-row DataFrame with the metadata
#metadata_df = pd.DataFrame.from_dict(data, orient='columns')
return df
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def list_datasets_in_hdf5file(hdf5_file_path):
def get_datasets(name, obj, list_of_datasets):
if isinstance(obj,h5py.Dataset):
list_of_datasets.append(name)
#print(f'Adding dataset: {name}') #tail: {head} head: {tail}')
with h5py.File(hdf5_file_path,'r') as file:
list_of_datasets = []
file.visititems(lambda name, obj: get_datasets(name, obj, list_of_datasets))
dataset_df = pd.DataFrame({'dataset_name':list_of_datasets})
dataset_df['parent_instrument'] = dataset_df['dataset_name'].apply(lambda x: x.split('/')[-3])
dataset_df['parent_file'] = dataset_df['dataset_name'].apply(lambda x: x.split('/')[-2])
return dataset_df