MTH5 is written to make read/writing an .mth5 file easier.


MTH5 is comprehensively logged, therefore if any problems arise you can always check the mth5_debug.log and the mth5_error.log, which will be written to your current working directory.

Each MTH5 file has default groups. A ‘group’ is basically like a folder that can contain other groups or datasets. These are:

  • Survey –> The master or root group of the HDF5 file

  • Filters –> Holds all filters and filter information

  • Reports –> Holds any reports relevant to the survey

  • Standards –> A summary of metadata standards used

  • Stations –> Holds all the stations an subsequent data

Each group also has a summary table to make it easier to search and access different parts of the file. Each entry in the table will have an HDF5 reference that can be directly used to get the appropriate group or dataset without using the path.

Opening and Closing Files

To open a new .mth5 file:

>>> from mth5 import mth5
>>> mth5_obj = mth5.MTH5()
>>>"path/to/file.mth5", mode="w")

To open an exiting .mth5 file:

>>> from mth5 import mth5
>>> mth5_obj = mth5.MTH5()
>>>"path/to/file.mth5", mode="a")


If ‘w’ is used for the mode, it will overwrite any file of the same name, so be careful you don’t overwrite any files. Using ‘a’ for the mode is safer as this will open an existing file of the same name and will give you write privilages.

To close a file:

>>> mth5_obj.close_mth5()
2020-06-26T15:01:05 - mth5.mth5.MTH5.close_mth5 - INFO - Flushed and
closed example_02.mth5


Once a MTH5 file is closed any data contained within cannot be accessed. All groups are weakly referenced, therefore once the file closes the group can no longer access the HDF5 group and you will get a similar message as below. This is to remove any lingering references to the HDF5 file which will be important for parallel computing.

>>> 2020-06-26T15:21:47 - mth5.groups.Station.__str__ - WARNING - MTH5 file is closed and cannot be accessed. MTH5 file is closed and cannot be accessed.

A MTH5 object is represented by the file structure and can be displayed at anytime from the command line.

>>> mth5_obj
        |- Group: Survey
                |- Group: Filters
                        --> Dataset: Summary
                |- Group: Reports
                        --> Dataset: Summary
                |- Group: Standards
                        --> Dataset: Summary
                |- Group: Stations
                        |- Group: MT001
                                --> Dataset: Summary
                        --> Dataset: Summary

This file does not contain a lot of stations, but this can get verbose if there are a lot of stations and filters. If you want to check what stations are in the current file.

>>> mth5_obj.station_list
['Summary', 'MT001']

Each group has a property attribute with an appropriate container including convenience methods. Each group has a property attribute called group_list that lists all groups the next level down.

See also

mth5.groups and mth5.metadata for more information.


Each group object has a container called metadata that holds the appropriate metadata (mth5.metadata) data according to the standards defined at MT Metadata Standards. The exceptions are the HDF5 file object which has metadata that describes the file type and is not part of the standards, and the stations_group, which is just a container to hold a collection of stations.

Input metadata will be validated against the standards and if it does not conform will throw an error.

The basic Python type used to store metadata is a dictionary, but there are three ways to input/output the metadata, dictionary, JSON, and XML. Many people have their own way of storing metadata so this should accommodate most everyone. If you store your metadata as JSON or XML you will need to read in the file first and input the appropriate element to the metadata.

Setting Attributes

Metadata can be input either manually by setting the appropriate attribute:

>>> existing_station = mth5_obj.get_station('MT001')
>>> existing_station.metadata.archive_id = 'MT010'


Currently, if you change any metadata attribute you will need to mannually update the attribute in the HDF5 group:

>>> existing_station.write_metadata()

Metadata Help

To get help with any metadata attribute you can use:

>>> existing_station.metadata.attribute_information('archive_id')

alias: [] description: station name that is archived {a-z;A-Z;0-9} example: MT201 options: [] required: True style: alpha numeric type: string units: None

If no argument is given information for all metadata attributes will be printed.

Creating New Attributes

If you want to add new standard attributes to the metadata you can do this through :function:`mth5.metadata.Base.add_base_attribute method`

>>> extra = {'type': str,
...          'style': 'controlled vocabulary',
...          'required': False,
...          'units': 'celsius',
...          'description': 'local temperature',
...          'alias': ['temp'],
...          'options': [ 'ambient', 'air', 'other'],
...          'example': 'ambient'}
>>> existing_station.metadata.add_base_attribute('temperature', 'ambient', extra)

Dictionary Input/Output

You can input a dictionary of attributes


The dictionary must be of the form {‘level’: {‘key’: ‘value’}}, where ‘level’ is either [ ‘survey’ | ‘station’ | ‘run’ | ‘channel’ | ‘filter’ ]

>>> meta_dict = {'station': {'archive_id': 'MT010'}}
>>> existing_station.metadata.from_dict(meta_dict)
>>> exiting_station.metadata.to_dict()
{'station': OrderedDict([('', None),
      ('acquired_by.comments', None),
      ('archive_id', 'MT010'),
      ('channel_layout', 'X'),
      ('channels_recorded', ['Hx', 'Hy', 'Hz', 'Ex', 'Ey']),
      ('comments', None),
      ('data_type', 'BB, LP'),
      ('geographic_name', 'Beachy Keen, FL, USA'),
      ('hdf5_reference', '<HDF5 object reference>'),
      ('id', 'FL001'),
       'Declination obtained from the instrument GNSS NMEA sequence'),
      ('location.declination.model', 'Unknown'),
      ('location.declination.value', -4.1),
      ('location.elevation', 0.0),
      ('location.latitude', 29.7203555),
      ('location.longitude', -83.4854715),
      ('mth5_type', 'Station'),
      ('orientation.method', 'compass'),
      ('orientation.reference_frame', 'geographic'),
      ('provenance.comments', None),
      ('provenance.creation_time', '2020-05-29T21:08:40+00:00'),
      ('provenance.log', None),
      ('', 'Anna Kelbert, USGS'),
      ('', 'mth5_metadata.m'),
      ('', '2020-05-29'),
      ('', 'Anna Kelbert, USGS'),
      ('', ''),
       'USGS Geomagnetism Program'),
      ('time_period.end', '2015-01-29T16:18:14+00:00'),
      ('time_period.start', '2015-01-08T19:49:15+00:00')])}

JSON Input/Output

JSON input is as a string, therefore you will need to read the file first.

>>> json_string = '{"station": {"archive_id": "MT010"}}
>>> existing_station.metadata.from_json(json_string)
>>> print(existing_station.metadata.to_json(nested=True))
        "station": {
                "acquired_by": {
                        "author": null,
                        "comments": null
                "archive_id": "FL001",
                "channel_layout": "X",
                "channels_recorded": [
                "comments": null,
                "data_type": "BB, LP",
                "geographic_name": "Beachy Keen, FL, USA",
                "hdf5_reference": "<HDF5 object reference>",
                "id": "MT010",
                "location": {
                        "latitude": 29.7203555,
                        "longitude": -83.4854715,
                        "elevation": 0.0,
                        "declination": {
                                "comments": "Declination obtained from the instrument GNSS NMEA sequence",
                                "model": "Unknown",
                                "value": -4.1
                "mth5_type": "Station",
                "orientation": {
                        "method": "compass",
                        "reference_frame": "geographic"
                "provenance": {
                        "creation_time": "2020-05-29T21:08:40+00:00",
                        "comments": null,
                        "log": null,
                        "software": {
                                "author": "Anna Kelbert, USGS",
                                "version": "2020-05-29",
                                "name": "mth5_metadata.m"
                        "submitter": {
                                "author": "Anna Kelbert, USGS",
                                "organization": "USGS Geomagnetism Program",
                                "email": ""
                "time_period": {
                        "end": "2015-01-29T16:18:14+00:00",
                        "start": "2015-01-08T19:49:15+00:00"

XML Input/Output

You can input as a XML element following the form previously mentioned. If you store your metadata in XML files you will need to read the and input the appropriate element into the metadata.

>>> from xml.etree import cElementTree as et
>>> root = et.Element('station')
>>> et.SubElement(root, 'archive_id', {'text': 'MT010'})
>>> existing_station.from_xml(root)
>>> print(existing_station.to_xml(string=True)
<?xml version="1.0" ?>
        <data_type>BB, LP</data_type>
        <geographic_name>Beachy Keen, FL, USA</geographic_name>
        <hdf5_reference type="h5py_reference">&lt;HDF5 object reference&gt;</hdf5_reference>
                <latitude type="float" units="degrees">29.7203555</latitude>
                <longitude type="float" units="degrees">-83.4854715</longitude>
                <elevation type="float" units="degrees">0.0</elevation>
                        <comments>Declination obtained from the instrument GNSS NMEA sequence</comments>
                        <value type="float" units="degrees">-4.1</value>
                        <author>Anna Kelbert, USGS</author>
                        <author>Anna Kelbert, USGS</author>
                        <organization>USGS Geomagnetism Program</organization>

See also

mth5.metadata for more information.


There are some gotchas or things you should understand when using HDF5 files as well as MTH5


Compression can slow down making a MTH5 file, so you should understand the compression parameters. See <>`__ and <>`__ for more information.

Compression is set in MTH5 when you instatiate an MTH5 object

>>> m = MTH5(shuffle=None, fletcher32=None, compression=None, compression_opts=None)

The compression parameters will be validated using mth5.helpers.validate_compression

Datasets can use chunks, which by default is set to True, which lets h5py pick the most efficient way to chunk the data.

Lossless compression filters

GZIP filter ("gzip")

Available with every installation of HDF5, so it’s best where portability is required. Good compression, moderate speed. compression_opts sets the compression level and may be an integer from 0 to 9, default is 4.

LZF filter ("lzf")

Available with every installation of h5py (C source code also available). Low to moderate compression, very fast. No options.

SZIP filter ("szip")

Patent-encumbered filter used in the NASA community. Not available with all installations of HDF5 due to legal reasons. Consult the HDF5 docs for filter options.


Logging is great, but can have dramatic effects on performance, mainly because I’m new to logging and probably haven’t written them most efficiently. By default the logging level is set to INFO. This seems to run as you might expect with slight overhead. If you change the logging level to DEBUG expect a slow down. You should only do this if you are a developer or are curious as to why something looks weird.