Create groups in a HDF5 file using H5Py

Overview:

HDF5 is a specification and format for creating hierarchical data from very large data sources.
In HDF5 the data is organized in a file. The file object acts as the / (root) group of the hierarchy. Similar to the UNIX file system, in HDF5 the datasets and their groups are organized as an inverted tree.
Several groups can be created under the / (root) group. A group can have one or more datasets and other groups (Self referencing links are possible as well.)
A group at any level below the / (root) group in a HDF5 file is created using the method create_group() on a h5py File instance;
The members in the group can be accessed using
- Dictionary notation – parentobject[“childname”], parentobject [“level1/leveln/childname”]
- parentobject.get(“childname”)
Datasets inside the groups can be created using
- parentobject[“datasetname”]
- parentobject.create_dataset()
A group’s parent is given by the attribute, parent of the group.

Example:

# Example Python program that creates a hierarchy of groups

# and datasets in a HDF5 file using h5py

import h5py

import random

import numpy.random

# Create a HDF5 file

hierarchicalFileName = "Hierarchical.hdf5";

hierarchicalFile = h5py.File(hierarchicalFileName, "w");

# Create a group under root

grp1 = hierarchicalFile.create_group("Group1");

grp2 = grp1.create_group("Group2");

grp3 = grp2.create_group("Group3");

# Use POSIX path to create a hierarchy of group under root

grp4 = hierarchicalFile.create_group("/GroupA/GroupB/GroupC");

# Use dictionary notation to create a dataset inside a group

grp4["D1"] = 1;

# Create another dataset inside the same group

datasetShape = (10,2);

d2 = grp4.create_dataset("D2", datasetShape);

# Print the groups

print(hierarchicalFile["/"]);

print(grp1);

print(grp2);

print(grp3);

print(grp4);

print(grp4["D1"]);

print(grp4["D1"].parent);

print(grp4["D2"]);

print(grp4["D2"].parent);

# Add value to the D2 dataset

for a in range(0, d2.shape[0]):

for b in range(0, d2.shape[1]):

d2[a, b] = numpy.random.uniform(1, 1000, 1)[0];

print("Data from d2:");

# Read value from the dataset

for x in range(0, 10):

for y in range(0, 2):

print(d2[x, y]);

Output:

Data from d2:

833.7155

16.872583

120.88334

745.5382

209.70691

331.48315

923.9745

869.833

809.82605

138.74417

523.5276

99.866295

276.7765

137.3819

673.0125

218.77142

832.29254

283.78696

302.44574

148.8316