Serialization

pytme provides utilities for serializing and deserializing template matching results using pickle and HDF5 formats. The serialization module offers a unified interface that automatically handles format selection based on file extensions, supports compression, and efficiently manages numpy memmaps.

import numpy as np
from tme.serialization import serialize, deserialize

# Serialize data to pickle format
data = [np.random.rand(100, 100), {"scores": [0.9, 0.8, 0.7]}]
serialize(data, "results.pickle")

# Deserialize from pickle
loaded_data = deserialize("results.pickle")

# Serialize to pickle with compression
serialize(data, "results.pickle.gz")

# Serialize to HDF5 with compression (default is lzf)
serialize(data, "results.h5", compression="lzf")

# Lazy loading for HDF5 (returns HDF5Loader instance)
loader = deserialize("results.h5")
first_item = loader[0]

Both formats are accessible by changing the extension of --output-file in match_template.

Which format is better depends on your workflow priorities. If you want to explore different parameters in postprocess, use HDF5 as its lazy loading capabilities speeds up postprocessing. Use pickle or gzipped pickles for maximum compression and smaller file sizes.

Core Functions

serialize	Serialize data to file.
deserialize	Deserialize data from file.

HDF5 Utilities

HDF5Loader

Lazy loader for HDF5 files with random access.