Metadata for magnetotelluric data
Project description
mt_metadata version 1.0.3
Standard MT metadata
Description
MT Metadata is a project led by IRIS-PASSCAL MT Software working group and USGS to develop tools that standardize magnetotelluric metadata, well, at least create tools for standards that are generally accepted. This include the two main types of magnetotelluric data
-
Time Series
- Structured as:
- Experiment -> Survey -> Station -> Run -> Channel
- Supports translation to/from StationXML
- Structured as:
-
Transfer Functions
- Supports (will support) to/from:
- EDI (most common format)
- ZMM (Egberts EMTF output)
- JFILE (BIRRP output)
- EMTFXML (Kelbert's format)
- AVG (Zonge output)
- Supports (will support) to/from:
Most people will be using the transfer functions, but a lot of that metadata comes from the time series metadata. This module supports both and has tried to make them more or less seamless to reduce complication.
- Version: 1.0.3
- Free software: MIT license
- Documentation: https://mt-metadata.readthedocs.io.
- Examples: Click the
Binderbadge above and Jupyter Notebook examples are in mt_metadata/examples/notebooks and docs/source/notebooks - Suggested Citation: Peacock, J. R., Kappler, K., Ronan, T., Heagy, L., Kelbert, A., Frassetto, A. (2022) MTH5: An archive and exchangeable data format for magnetotelluric time series data, Computers & Geoscience, 162, doi:10.1016/j.cageo.2022.105102
- IPDS: IP-138156
Installation
From Source
git clone https://github.com/kujaku11/mt_metadata.git
pip install .
You can add the flag -e if you want to install the source repository in an editable state.
PIP
pip install mt_metadata
You can install with optional packages by appending
[option_name]to the package name during thepipinstall command. E.g:
pip install mt_metadata[obspy]or
pip install .[obspy]if building from source.
Conda
conda install mt_metadata
Standards
Each metadata keyword has an associated standard that goes with it. These are stored internally in JSON file. The JSON files are read in when the package is loaded to initialize the standards. Each keyword is described by:
-
type - How the value should be represented based on very basic types
- string
- number (float or integer)
- boolean
-
required - A boolean (True or False) denoting whether the metadata key word required to represent the data.
-
style - How the value should be represented within the type. For instance is the value a controlled string where there are only a few options, or is the value a controlled naming convention where only a 5 character alpha-numeric string is allowed. The styles are
- Alpha Numeric a string with alphabetic and numberic characters
- Free Form a free form string
- Controlled Vocabulary only certain values are allowed according to options
- Date a date and/or time string in ISO format
- Number a float or integer
- Boolean the value can only be True or False
-
units - Units of the value
-
description - Full description of what the metadata key is meant to convey.
-
options - Any options of a Controlled Vocabulary style.
-
alias - Any aliases that may represent the same metadata key.
-
example - An example value to inform the user.
All input values are internally validated according to the definition providing a robust way to standardize metadata.
Each metadata object is based on a Base class that has methods:
- to/from_json
- to/from_xml
- to_from_dict
- attribute_information
And each object has a doc string that describes the standard:
| Metadata Key | Description | Example |
|---|---|---|
| key | description of what the key describes | example value |
| Required: False | ||
| Units: None | ||
| Type: string | ||
| Style: controlled vocabulary |
The time series module is more mature than the transfer function module at the moment, and this is still a work in progress.
Example
from mt_metadata import timeseries
x = timeseries.Instrument()
Help
help(x)
+----------------------------------------------+-----------------------------------------------+----------------+
| **Metadata Key** | **Description** | **Example** |
+==============================================+===============================================+================+
| **id** | instrument ID number can be serial number or | mt01 |
| | a designated ID | |
| Required: True | | |
| | | |
| Units: None | | |
| | | |
| Type: string | | |
| | | |
| Style: free form | | |
+----------------------------------------------+-----------------------------------------------+----------------+
| **manufacturer** | who manufactured the instrument | mt gurus |
| | | |
| Required: True | | |
| | | |
| Units: None | | |
| | | |
| Type: string | | |
| | | |
| Style: free form | | |
+----------------------------------------------+-----------------------------------------------+----------------+
| **type** | instrument type | broadband |
| | | 32-bit |
| Required: True | | |
| | | |
| Units: None | | |
| | | |
| Type: string | | |
| | | |
| Style: free form | | |
+----------------------------------------------+-----------------------------------------------+----------------+
| **model** | model version of the instrument | falcon5 |
| | | |
| Required: False | | |
| | | |
| Units: None | | |
| | | |
| Type: string | | |
| | | |
| Style: free form | | |
+----------------------------------------------+-----------------------------------------------+----------------+
Fill in metadata
x.model = "falcon 5"
x.type = "broadband 32-bit"
x.manufacturer = "MT Gurus"
x.id = "f176"
to JSON
print(x.to_json())
{
"instrument": {
"id": "f176",
"manufacturer": "MT Gurus",
"model": "falcon 5",
"type": "broadband 32-bit"
}
}
to XML
print(x.to_xml(string=True))
<?xml version="1.0" ?>
<instrument>
<id>f176</id>
<manufacturer>MT Gurus</manufacturer>
<model>falcon 5</model>
<type>broadband 32-bit</type>
</instrument>
Credits
This project is in cooperation with the Incorporated Research Institutes of Seismology, the U.S. Geological Survey, and other collaborators. Facilities of the IRIS Consortium are supported by the National Science Foundation’s Seismological Facilities for the Advancement of Geoscience (SAGE) Award under Cooperative Support Agreement EAR-1851048. USGS is partially funded through the Community for Data Integration and IMAGe through the Minerals Resources Program.
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