HDF5 Data Format Introduction

Structure of hdf5

Key Features of HDF5:

1. Hierarchical Structure: HDF5 files are organized like a file system, with “groups” that act like directories and “datasets” that act like files. This allows for complex, hierarchical data storage.
2. Efficient Storage: HDF5 is optimized for storing and retrieving large datasets. It uses compression techniques (like GZIP or SZIP) to reduce file size without losing data.
3. Cross-platform Compatibility: The format is portable across different platforms and operating systems, meaning that HDF5 files can be used on Windows, macOS, Linux, etc.
4. Self-describing Format: HDF5 files include metadata that describe the contents of the file. This makes it easy to understand the data structure without additional documentation.
5. Multidimensional Data: HDF5 supports storing complex, multidimensional data (such as arrays, tables, images, etc.).
6. Supports Many Data Types: It can store data in various types, such as integers, floats, strings, and more.

/root                (Group)
    /experiment1     (Group)
        /data        (Dataset)
        /info        (Dataset)
    /experiment2     (Group)
        /data        (Dataset)
        /info        (Dataset)

Use Cases:

• Scientific Data: For example, storing results from simulations, satellite data, or genome sequences.
• Machine Learning: Large training datasets can be stored in HDF5 format for efficient access during training.
• Image Storage: Storing large collections of images or medical imaging data (e.g., MRI scans).

Show all Names of Groups and Data

import h5py

# Open the file in read mode
with h5py.File('file1.h5', 'r') as f:
    def print_hdf5_structure(group, indent=0):
        for key in group.keys():
            item = group[key]
            print("  " * indent + f"{key}: {type(item)}")
            if isinstance(item, h5py.Group):
                print_hdf5_structure(item, indent + 1)

    # Print structure from the root
    print_hdf5_structure(f)

How to Merge Multiple hdf5 Files

import h5py
import numpy as np

# Function to recursively copy/merge the structure and data from source_group to target_group
def copy_and_merge(source_group, target_group):
    for key in source_group.keys():
        item = source_group[key]
        # If the item is a group, we create the same group in the target and copy its contents
        if isinstance(item, h5py.Group):
            if key not in target_group:
                target_group.create_group(key)
            copy_and_merge(item, target_group[key])  # Recursive call to merge the group's contents
        # If the item is a dataset, we merge it
        elif isinstance(item, h5py.Dataset):
            # If the dataset doesn't exist in the target file, copy it
            if key not in target_group:
                target_group.create_dataset(key, data=item[:])
            # If the dataset exists, concatenate the data along the first axis
            else:
                existing_data = target_group[key][:]
                new_data = item[:]
                # Concatenate datasets along the first axis
                merged_data = np.concatenate((existing_data, new_data), axis=0)
                # Delete the old dataset and replace it with the merged one
                del target_group[key]
                target_group.create_dataset(key, data=merged_data)

# Function to merge multiple HDF5 files and save the result to a new file
def merge_multiple_hdf5(files, output_file):
    # Create a new HDF5 file to store the merged result
    with h5py.File(output_file, 'w') as target_file:
        for file in files:
            with h5py.File(file, 'r') as source_file:
                # Merge the contents of each source file into the target file
                copy_and_merge(source_file, target_file)
        print(f"All files have been merged into {output_file}")

# List of HDF5 files to be merged
files_to_merge = ['file1.h5', 'file2.h5', 'file3.h5']  # Add as many files as needed
# Specify the output file where the merged data will be saved
output_file = 'merged_output.h5'
# Merge the files and save the result
merge_multiple_hdf5(files_to_merge, output_file)

Explanation of the Code:

1. copy_and_merge function remains the same, recursively merging groups and datasets from the source to the target.
2. merge_multiple_hdf5 function:
   • Accepts a list of HDF5 files (files) and an output_file name.
   • It creates a new HDF5 file (output_file) in write mode ('w').
   • It loops through each file in the list, opens it in read mode ('r'), and calls the copy_and_merge function to copy the contents into the newly created file.
   • After all files are merged, it saves the result as output_file.

!!! note Key Points:
- Each dataset is merged by concatenating along the first axis. If you need to merge along a different axis or have more complex merging rules, we can adjust the code.
- Make sure the datasets you’re merging are compatible (same dimensionality along non-concatenated axes).

Change the Group Names

To rename a group in an HDF5 file using h5py, you can’t directly change the group’s name. Instead, you can copy the group to a new group with the desired name, and then delete the original group.

Here’s how you can rename the group “4skj” to “4skj_10086”:

Step-by-Step Code:

import h5py
import shutil

# Function to rename a group in an HDF5 file
def rename_group(hdf5_file, old_group_name, new_group_name):
    # Open the file in read/write mode
    with h5py.File(hdf5_file, 'r+') as f:
        # Check if the group exists
        if old_group_name in f:
            # Copy the old group to the new group
            f.copy(old_group_name, new_group_name)
            # Delete the old group
            del f[old_group_name]
            print(f"Group '{old_group_name}' has been renamed to '{new_group_name}'")
        else:
            print(f"Group '{old_group_name}' does not exist in the file.")

# Rename the group in the HDF5 file
hdf5_file = 'file1.h5'  # Replace with your actual file path
old_group_name = '4skj'  # Original group name
new_group_name = '4skj_10086'  # New group name

rename_group(hdf5_file, old_group_name, new_group_name)

1. Check if the group exists: The script checks if the group "4skj" exists in the HDF5 file.
2. Copy the group: It uses the f.copy() function to copy the group and its contents to a new group with the desired name ("4skj_10086").
3. Delete the old group: After copying, the original group is deleted with del f[old_group_name].
4. Save changes: Since the file is opened in 'r+' mode (read/write), all changes are saved automatically.