mistrasdta-hdf5 0.1.6

Streaming parser and HDF5 exporter for Mistras DTA acoustic emission data

MistrasDTA-hdf5

Python tools for streaming ingestion of Mistras DTA acoustic emission data with incremental HDF5 export, designed for use in modern Python applications and data pipelines.

This project is a fork of MistrasDTA by Daniel A. Cogswell. It preserves the original binary parsing logic while introducing a generator-based streaming API and native HDF5 output.

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Background

Mistras DTA files contain binary-encoded acoustic emission (AE) hit summaries, waveforms, and hardware metadata. The file structure is described in Appendix II of the Mistras User Manual.

The original MistrasDTA package focuses on reading entire files into memory and returning NumPy record arrays.
MistrasDTA-hdf5 is optimized instead for:

• large DTA files
• incremental / streaming processing
• integration into Python applications and services
• persistent, schema-stable HDF5 storage

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Key differences from upstream MistrasDTA

Compared to the original project, this fork provides:

• Streaming parser (read_bin_stream) using Python generators
• Incremental processing of hits, waveforms, and metadata
• Waveforms as scaled NumPy arrays (not raw byte blobs)
• Native HDF5 export with chunked, append-only datasets
• Clear separation of:
  • hit data
  • waveform data
  • waveform metadata
  • hardware and test metadata

The original read_bin() API is intentionally not preserved to avoid implicit full-file loading and memory coupling.

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Installation

Install from PyPI:

pip install mistrasdta-hdf5[hdf5]

where [hdf5] is an optional dependency (required only for output to h5 files).

Usage

• Streaming parse of a DTA file

from mistrasdta_hdf5 import read_bin_stream

for tag, obj in read_bin_stream("cluster.DTA"):
    if tag == "rec":
        # acoustic emission hit
        print(obj["RTOT"], obj["ENER"])
    elif tag == "wfm":
        # waveform
        print(obj["CID"], obj["WAVEFORM"].shape)
    elif tag == "meta":
        # metadata (hardware, test start time, ...)
        print(obj)

• Stream directly to HDF5

from mistrasdta_hdf5 import stream_to_h5

stream_to_h5(
    dta_path="cluster.DTA",
    h5_path="cluster.h5",
    skip_wfm=False,
    chunk=10000,
)

The resulting HDF5 file contains:

/hits/RTOT
/hits/CID
/hits/<parametric fields>

/waveforms
/waveforms_meta/CID
/waveforms_meta/SRATE
/waveforms_meta/TDLY

/file attributes:
  test_start_time
  hardware configuration

License and attribution

This project is released under the MIT License.

Original work:

Copyright © 2019–2024 Daniel A. Cogswell

Modifications and extensions:

Copyright © 2026 Robert Farla

See LICENSE for full details.

Disclaimer

This project is not affiliated with, endorsed by, or supported by Mistras. It is an independent, community-driven tool provided “as is”.