Welcome to MTH5’s documentation!
MTH5
MTH5 is an HDF5 data container for magnetotelluric time series data, but could be extended to other data types. This package provides tools for reading/writing/manipulating MTH5 files.
MTH5 uses h5py to interact with the HDF5 file, xarray to interact with the data in a nice way, and all metadata use mt_metadata.
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.
Version: 0.3.1
Free software: MIT license
Documentation: https://mth5.readthedocs.io.
Examples: Click the Binder badge above and Jupyter Notebook examples are in docs/examples/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
Features
Read and write HDF5 files formated for magnetotelluric time series.
From MTH5 a user can create an MTH5 file, get/add/remove stations, runs, channels and filters and all associated metadata.
Data is contained as an xarray which can house the data and metadata together, and data is indexed by time.
Readers for some data types are included as plugins, namely
Instrument
File Types
NIMS
[.bin, .bnn]
LEMI424
[.txt]
USGS ASCII
[.asc, .gzip]
Zonge ZEN
[.z3d]
Phoenix
[.td_*, .bin]
miniseed
[.mseed]
Introduction
The goal of MTH5 is to provide a self describing heirarchical data format for working, sharing, and archiving. MTH5 was cooperatively developed with community input and follows logically how magnetotelluric data are collected. This module provides open-source tools to interact with an MTH5 file.
The metadata follows the standards proposed by the IRIS-PASSCAL MT Software working group and documented in MT Metadata Standards Note: If you would like to comment or contribute checkout Issues or Slack.
MTH5 Format
The basic format of MTH5 is illustrated below, where metadata is attached at each level.
MTH5 File Version 0.1.0
MTH5 file version 0.1.0 was the original file version where Survey was the highest level of the file. This has some limitations in that only one Survey could be saved in a single file, but if you have mulitple Surveys that you would like to store we need to add a higher level Experiment.
Important: Some MTH5 0.1.0 files have already been archived on ScienceBase and has been used as the working format for Aurora and is here for reference. Moving forward the new format will be 0.2.0 as described below.
MTH5 File Version 0.2.0
MTH5 file version 0.2.0 has Experiment as the top level. This allows for multiple Surveys to be included in a single file and therefore allows for more flexibility. For example if you would like to remote reference stations in a local survey with stations from a different survey collected at the same time you can have all those surveys and stations in the same file and make it easier for processing.
Hint
MTH5 is comprehensively logged, therefore if any problems arise you can always check the mth5_debug.log (if you are in debug mode, change the mode in the mth5.__init__) and the mth5_error.log, which will be written to your current working directory.
Examples
Make a simple MTH5 with one station, 2 runs, and 2 channels (version 0.2.0)
from mth5.mth5 import MTH5
mth5_object = MTH5()
mth5_object.open_mth5(r"/home/mt/example_mth5.h5", "a")
# add a survey
survey_group = mth5_object.add_survey("example")
# add a station with metadata
station_group = m.add_station("mt001", survey="example")
station_group = survey_group.stations_group.add_station("mt002")
station_group.metadata.location.latitude = "40:05:01"
station_group.metadata.location.longitude = -122.3432
station_group.metadata.location.elevation = 403.1
station_group.metadata.acquired_by.author = "me"
station_group.metadata.orientation.reference_frame = "geomagnetic"
# IMPORTANT: Must always use the write_metadata method when metadata is updated.
station_group.write_metadata()
# add runs
run_01 = m.add_run("mt002", "001", survey="example")
run_02 = station_group.add_run("002")
# add channels
ex = m.add_channel("mt002", "001", "ex", "electric", None, survey="example")
hy = run_01.add_channel("hy", "magnetic", None)
print(mth5_object)
/:
====================
|- Group: Experiment
--------------------
|- Group: Reports
-----------------
|- Group: Standards
-------------------
--> Dataset: summary
......................
|- Group: Surveys
-----------------
|- Group: example
-----------------
|- Group: Filters
-----------------
|- Group: coefficient
---------------------
|- Group: fap
-------------
|- Group: fir
-------------
|- Group: time_delay
--------------------
|- Group: zpk
-------------
|- Group: Reports
-----------------
|- Group: Standards
-------------------
--> Dataset: summary
......................
|- Group: Stations
------------------
|- Group: mt001
---------------
|- Group: mt002
---------------
|- Group: 001
-------------
--> Dataset: ex
.................
--> Dataset: hy
.................
|- Group: 002
-------------
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.
Installation
Stable release
To install MTH5, run this command in your terminal:
$ pip install mth5
This is the preferred method to install MTH5, as it will always install the most recent stable release.
If you don’t have pip installed, this Python installation guide can guide you through the process.
Conda Forge
To install MTH5 from Conda run this command:
$ conda config --add channels conda-forge
$ conda config --set channel_priority strict
$ conda install mth5
This should be the same as installing with pip that pulls from PyPi and should work better if you are using an Anaconda environment.
From sources
The sources for MTH5 can be downloaded from the Github repo.
You can either clone the public repository:
$ git clone git://github.com/kujaku11/mth5
Or download the tarball:
$ curl -OJL https://github.com/kujaku11/mth5/tarball/master
Once you have a copy of the source, you can install it with:
$ python setup.py install
Or you can install it in editing mode and be able to adjust the code as needed:
$ python setup.py -e install
Contributing
Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.
You can contribute in many ways:
Types of Contributions
Report Bugs
Report bugs at https://github.com/kujaku11/mth5/issues.
If you are reporting a bug, please include:
Your operating system name and version.
Any details about your local setup that might be helpful in troubleshooting.
Detailed steps to reproduce the bug.
Fix Bugs
Look through the GitHub issues for bugs. Anything tagged with “bug” and “help wanted” is open to whoever wants to implement it.
Implement Features
Look through the GitHub issues for features. Anything tagged with “enhancement” and “help wanted” is open to whoever wants to implement it.
Write Documentation
MTH5 could always use more documentation, whether as part of the official MTH5 docs, in docstrings, or even on the web in blog posts, articles, and such.
Submit Feedback
The best way to send feedback is to file an issue at https://github.com/kujaku11/mth5/issues.
If you are proposing a feature:
Explain in detail how it would work.
Keep the scope as narrow as possible, to make it easier to implement.
Remember that this is a volunteer-driven project, and that contributions are welcome :)
Get Started!
Ready to contribute? Here’s how to set up mth5 for local development.
Fork the mth5 repo on GitHub.
Clone your fork locally:
$ git clone git@github.com:your_name_here/mth5.git
Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development:
$ mkvirtualenv mth5 $ cd mth5/ $ python setup.py developCreate a branch for local development:
$ git checkout -b name-of-your-bugfix-or-feature
Now you can make your changes locally.
When you’re done making changes, check that your changes pass flake8 and the tests, including testing other Python versions with tox:
$ flake8 mth5 tests $ python setup.py test or pytest $ toxTo get flake8 and tox, just pip install them into your virtualenv.
Commit your changes and push your branch to GitHub:
$ git add . $ git commit -m "Your detailed description of your changes." $ git push origin name-of-your-bugfix-or-featureSubmit a pull request through the GitHub website.
Pull Request Guidelines
Before you submit a pull request, check that it meets these guidelines:
The pull request should include tests.
If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst.
The pull request should work for Python 3.5, 3.6, 3.7 and 3.8, and for PyPy. Check the Actions report to make sure that the tests pass for all supported Python versions.
Tips
To run a subset of tests:
$ pytest tests.test_mth5
Deploying
A reminder for the maintainers on how to deploy. Make sure all your changes are committed (including an entry in HISTORY.rst). Then run:
$ bump2version patch # possible: major / minor / patch
$ git push
$ git push --tags
GitActions will then deploy to PyPI if tests pass.
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.
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
Development Lead
Jared Peacock <jpeacock@usgs.gov>
Contributors
Karl Kappler
Lindsey Heagy
Tim Ronan
Anna Kelbert
Laura Keyson
History
0.1.0 (2021-06-30)
First release on PyPI.
0.2.0 (2021-10-31)
Updated the structure of MTH5 to have
Experimentas the top levelUpdated tests
Backwards compatibility works
Updated Docs
0.2.5 (2022-04-07)
fixed bugs
Added TransferFunctions and TransferFunction groups at the station level that can now hold transfer functions
Added channel_summary and tf_summary tables that are updated upon close if the file is in ‘w’ mode
Updated tests
Updated documentation
Note: tests for make_mth5 from IRIS are currently not working as there has been some reorganization with data at the DMC
0.2.6 (2022-07-01)
Added calibration functions
minor bug fixes
updated tests
updated documentation
0.2.7 (2022-09-14)
Rebased IO module to make a module for each data logger type
Updated tests
Updated documentation
Factored make_mth5
0.3.0 (2022-09-25)
change default initialize_mth5 to use append mode, issue #92 by @kkappler in #94
Fix issue 105 by @kkappler in PR #106
adding in parallel mth5 tutorial by @nre900 in #110
adding in new tutorial and modifications to mth5_in_parallel.ipynb by @nre900 in issue #112
Add phoenix reader by @kujaku11 in PR #103
Remove response by @kujaku11 in PR #100
0.3.1 (2023-01-18)
Speed up station and survey validataion by
Tutorial updates by @nre900
remove kwarg specifying default value
update initialize_mth5
Have a single metadata object for ChannelTS and RunTS
Use h5 Paths to get groups and datasets
Bump wheel from 0.33.6 to 0.38.1
GOTCHAS
There are some gotchas or things you should understand when using HDF5 files as well as MTH5
Compression
Compression can slow down making a MTH5 file, so you should understand the compression parameters. See H5 Compression and DataSets 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_optssets 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
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.
Conventions
Some conventions that have been implemented:
All metadata names are lower case and _ separated.
Survey Names
Survey names are not standardized yet, but will be in the future. Survey names are commonly a string of up to 10 characters, though there is no limit. Characters representing the project or geographic location are commonly used. For example a survey in Long Valley could be lv, a survey of the continental US could be conus, a NSF funded project on imaging magma under Mount St. Helens (iMUSH) could be imush.
Station Names
Stations names are not standardized yet, but will be in the future. Station names are often 4-6 characters. Convention is a 2 or 3 character survey representation followed by a 2-4 digit number representing the station number.
For example a survey in California could begin with ca and station 1 would be ca001.
Now that 3-D grids are more common people name lines or rows with a letter or number. For instance the EarthScope/MT Array across the US uses {state}{line}{number}. So a station could be can10 for station 10 on the N easting line in California.
Future suggestion is:
{survey_name}{line}{row}{number}
Run Names
Run names have not been standardized yet but will be in the future. There are 2 conventions, one to use alphabetic run names like run a and the other is numeric like run 001.
The disadvantage of alphabetic names is they can be limited by the alphabet if the number of runs gets large. Alphabetic names are common in long period experiments where you have a handful of runs when you need to change batteries or fix cables.
Numeric names are more flexible and will probably be the standard in the future. For broadband experiments where multiple sampling rates are used there can be lots of runs. For example if you have a continuous band sampling at 50 samples/second with 5 second bursts of 1000 samples/second every minute then you are going to have 60 short runs per hour. Keeping track of these can be tedious so a numeric counter would be the easiest.
Future suggestions would be a 4 digit string, maybe include the sample rate which could be a character similar to seismic FDSN standards.
{sample_rate}{run_number}
Channel Names
Channel names have not been standardized yet but will be in the future. Direction indicators are often given as:
x: northing direction or strike direction
y: easting direction or across strike direction
z: vertical direction
For MT we have 2 main types of channels electric and magnetic.
Electric channel names often start with an e and are followed by a directional indicator, like ex for an electric channel in the northing direction.
Magnetic channel names often start with an h or b followed by a directional indicator, like hz for a vertical magnetic channel.
Extending this to other data types we have Auxiliary channels which could be any other type of geophysical measurement, like temperature or battery voltage. Suggest using the full name at the moment, maybe in the future we will have measurment codes like seismic FDSN, but they can be cryptic.
In the future channel names will likely be standardized as:
{channel_type}{channel_direction}{channel_number}
to allow for flexibility to other methods like IP and DC surveys.
Usage v0.1.0
Important
This usage describes using MTH5 version 0.1.0 where Survey is the top level of the MTH5 file structure. The current version is 0.2.0. Some version 0.1.0 files have been archived and this is here for reference.
Basic Groups
Each MTH5 file has default groups. A ‘group’ is basically like a folder that can contain other groups or datasets. These are:
SurveyGroup: The master or root group of the HDF5 file
FiltersGroup: Holds all filters and filter information
ZPKGroup: Holds pole zero filters
FAPGroup: Holds frequency look up table filters
FIRGroup: Holds finite impulse response filters
CoefficientGroup: Holds coefficient filters
TimeDelayGroup: Holds time delay filters
Reports: Holds any reports relevant to the survey
StandardsGroup: A summary of metadata standards used
StationsGroup: Holds all the stations an subsequent data
StationGroup: Holds a single station
RunGroup: Holds a single run
ChannelDataset: Holds a single channel
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.
See also
Example
Example of Working with a Version 0.1.0 MTH5 File
[1]:
from mth5.mth5 import MTH5
2022-03-24T18:12:02 [line 157] numexpr.utils._init_num_threads - INFO: NumExpr defaulting to 8 threads.
2022-03-24 18:12:04,296 [line 135] mth5.setup_logger - INFO: Logging file can be found C:\Users\jpeacock\Documents\GitHub\mth5\logs\mth5_debug.log
Initialize an MTH5 object with file version 0.1.0
[2]:
m = MTH5(file_version="0.1.0")
Have a look at the attributes of the file
[3]:
m.file_attributes
[3]:
{'file.type': 'MTH5',
'file.version': '0.1.0',
'file.access.platform': 'Windows-10-10.0.19041-SP0',
'file.access.time': '2022-03-25T01:12:04.433011+00:00',
'mth5.software.version': '0.2.4',
'mth5.software.name': 'mth5',
'data_level': 1}
Here are the data set options
[4]:
m.dataset_options
[4]:
{'compression': 'gzip',
'compression_opts': 9,
'shuffle': True,
'fletcher32': True}
The file is currently not open yet
[5]:
m
[5]:
HDF5 file is closed and cannot be accessed.
Open a new file
We will open the file in mode w here, which will overwrite the file if it already exists. If you don’t want to do that or are unsure if a file already exists the safest option is using mode a.
Its strongly encouraged that if you are making an MTH5 file, even if you want to open it up afterwards that you use with.
with MTH5(**kwargs) as m:
m.open_mth5(filename)
#pack MTH5
Using this style of pseudocode your MTH5 file will be made in a safe way if anything goes wrong in the packing. Using the with statement will automatically flush and close the MTH5 upon exiting the with statement, that includes any errors encountered.
Here we are just showing an example and how to interogate an MTH5 file.
[6]:
m.open_mth5(r"example.h5", "w")
2022-03-24 18:12:04,481 [line 604] mth5.mth5.MTH5.open_mth5 - WARNING: example.h5 will be overwritten in 'w' mode
2022-03-24 18:12:04,787 [line 687] mth5.mth5.MTH5._initialize_file - INFO: Initialized MTH5 0.1.0 file example.h5 in mode w
Now that we have initiated a file, let’s see what’s in an empty file.
[7]:
m
[7]:
/:
====================
|- Group: Survey
----------------
|- Group: Filters
-----------------
|- Group: coefficient
---------------------
|- Group: fap
-------------
|- Group: fir
-------------
|- Group: time_delay
--------------------
|- Group: zpk
-------------
|- Group: Reports
-----------------
|- Group: Standards
-------------------
--> Dataset: summary
......................
|- Group: Stations
------------------
--> Dataset: channel_summary
..............................
--> Dataset: tf_summary
.........................
We can see that there are default groups that are initiated by default. And here are the methods an MTH5 object contains. You can open/close an MTH5 file; add/remove station, run, channel; read from an mt_metadata.timeseries.Experiment object to fill the metadata and structure before adding data and create an mt_metadata.timeseries.Experiment object for archiving.
[8]:
print("\n".join(sorted([func for func in dir(m) if callable(getattr(m, func)) and not func.startswith("_")])))
2022-03-24 18:12:04,823 [line 470] mth5.mth5.MTH5.experiment_group - INFO: File version 0.1.0 does not have an Experiment Group
2022-03-24 18:12:04,833 [line 497] mth5.mth5.MTH5.surveys_group - INFO: File version 0.1.0 does not have a survey_group, try surveys_group
add_channel
add_run
add_station
add_survey
add_transfer_function
close_mth5
from_experiment
from_reference
get_channel
get_run
get_station
get_survey
get_transfer_function
h5_is_read
h5_is_write
has_group
open_mth5
remove_channel
remove_run
remove_station
remove_survey
remove_transfer_function
to_experiment
validate_file
Add a station
Here we will add a station called mt001. This will return a StationGroup object.
[9]:
station_group = m.add_station("mt001")
Add some metadata to this station like location, who acquired it, and the reference frame in which the data were collected.
[10]:
station_group.metadata.location.latitude = "40:05:01"
station_group.metadata.location.longitude = -122.3432
station_group.metadata.location.elevation = 403.1
station_group.metadata.acquired_by.author = "me"
station_group.metadata.orientation.reference_frame = "geomagnetic"
# IMPORTANT: Must always use the write_metadata method when metadata is updated.
station_group.write_metadata()
[11]:
station_group.metadata
[11]:
{
"station": {
"acquired_by.author": "me",
"channels_recorded": [],
"data_type": "BBMT",
"geographic_name": null,
"hdf5_reference": "<HDF5 object reference>",
"id": "mt001",
"location.declination.model": "WMM",
"location.declination.value": 0.0,
"location.elevation": 403.1,
"location.latitude": 40.08361111111111,
"location.longitude": -122.3432,
"mth5_type": "Station",
"orientation.method": null,
"orientation.reference_frame": "geomagnetic",
"provenance.creation_time": "1980-01-01T00:00:00+00:00",
"provenance.software.author": "none",
"provenance.software.name": null,
"provenance.software.version": null,
"provenance.submitter.email": null,
"provenance.submitter.organization": null,
"run_list": [],
"time_period.end": "1980-01-01T00:00:00+00:00",
"time_period.start": "1980-01-01T00:00:00+00:00"
}
}
We can now add a run to the new station. We can do this in 2 ways, one directly from the m the MTH5 object, or from the newly created station_group
[12]:
run_01 = m.add_run("mt001", "001")
run_02 = station_group.add_run("002")
[13]:
station_group
[13]:
/Survey/Stations/mt001:
====================
|- Group: 001
-------------
|- Group: 002
-------------
|- Group: Transfer_Functions
----------------------------
Again we can do this in 2 ways: directly from the m the MTH5 object, or from the newly created run_01 or run_02 group. There are only 3 types of channels electric, magnetic, and auxiliary and this needs to be specified when a channel is initiated. We will initate the channel with data=None, which will create an empty data set.
[14]:
ex = m.add_channel("mt001", "001", "ex", "electric", None)
hy = run_01.add_channel("hy", "magnetic", None)
[15]:
run_01
[15]:
/Survey/Stations/mt001/001:
====================
--> Dataset: ex
.................
--> Dataset: hy
.................
Now, let’s see what the contents are of this file
[16]:
m
[16]:
/:
====================
|- Group: Survey
----------------
|- Group: Filters
-----------------
|- Group: coefficient
---------------------
|- Group: fap
-------------
|- Group: fir
-------------
|- Group: time_delay
--------------------
|- Group: zpk
-------------
|- Group: Reports
-----------------
|- Group: Standards
-------------------
--> Dataset: summary
......................
|- Group: Stations
------------------
|- Group: mt001
---------------
|- Group: 001
-------------
--> Dataset: ex
.................
--> Dataset: hy
.................
|- Group: 002
-------------
|- Group: Transfer_Functions
----------------------------
--> Dataset: channel_summary
..............................
--> Dataset: tf_summary
.........................
Channel Summary
This is a summary of all channels in the file, this can take a long time to build if the data file is large.
[17]:
%time
m.channel_summary.clear_table()
m.channel_summary.summarize()
ch_df = m.channel_summary.to_dataframe()
ch_df
Wall time: 0 ns
[17]:
| survey | station | run | latitude | longitude | elevation | component | start | end | n_samples | sample_rate | measurement_type | azimuth | tilt | units | hdf5_reference | run_hdf5_reference | station_hdf5_reference | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | none | mt001 | 001 | 40.083611 | -122.3432 | 403.1 | ex | 1980-01-01 00:00:00+00:00 | 1980-01-01 00:00:00+00:00 | 1 | 0.0 | electric | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 1 | none | mt001 | 001 | 40.083611 | -122.3432 | 403.1 | hy | 1980-01-01 00:00:00+00:00 | 1980-01-01 00:00:00+00:00 | 1 | 0.0 | magnetic | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
Close MTH5 file
This part is important, be sure to close the file in order to save any changes. This function flushes metadata and data to the HDF5 file and then closes it. Note that once a file is closed all groups lose their link to the file and cannot retrieve any data.
[18]:
m.close_mth5()
station_group
2022-03-24 18:12:05,104 [line 747] mth5.mth5.MTH5.close_mth5 - INFO: Flushing and closing example.h5
2022-03-24 18:12:05,110 [line 113] mth5.groups.base.Station.__str__ - WARNING: MTH5 file is closed and cannot be accessed.
[18]:
MTH5 file is closed and cannot be accessed.
Usage v0.2.0
Important
This usage describes using MTH5 version 0.2.0 where Survey is the top level of the MTH5 file structure. The current version is 0.2.0. Some version 0.1.0 files have been archived and this is here for reference.
Basic Groups
Each MTH5 file has default groups. A ‘group’ is basically like a folder that can contain other groups or datasets. These are:
- ExperimentGroup: The master or root group of the HDF5 file
ReportsGroup: Holds any reports relevant to the experiment
StandardsGroup: A summary of metadata standards used
SurveysGroup: Holds the surveys in an experiment
SurveyGroup: The master or root group of the HDF5 file
FiltersGroup: Holds all filters and filter information
ZPKGroup: Holds pole zero filters
FAPGroup: Holds frequency look up table filters
FIRGroup: Holds finite impulse response filters
CoefficientGroup: Holds coefficient filters
TimeDelayGroup: Holds time delay filters
Reports: Holds any reports relevant to the survey
StationsGroup: Holds all the stations an subsequent data
StationGroup: Holds a single station
RunGroup: Holds a single run
ChannelDataset: Holds a single channel
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.
See also
Example
Example of Working with a Version 0.2.0 MTH5 File
[1]:
from mth5.mth5 import MTH5
2022-04-12T21:21:46 [line 157] numexpr.utils._init_num_threads - INFO: NumExpr defaulting to 8 threads.
2022-04-12 21:21:47,999 [line 135] mth5.setup_logger - INFO: Logging file can be found C:\Users\jpeacock\Documents\GitHub\mth5\logs\mth5_debug.log
Initialize an MTH5 object with file version 0.2.0
[2]:
m = MTH5(file_version="0.2.0")
Have a look at the attributes of the file
[3]:
m.file_attributes
[3]:
{'file.type': 'MTH5',
'file.version': '0.2.0',
'file.access.platform': 'Windows-10-10.0.19041-SP0',
'file.access.time': '2022-04-13T04:21:53.788393+00:00',
'mth5.software.version': '0.2.5',
'mth5.software.name': 'mth5',
'data_level': 1}
Here are the data set options
[4]:
m.dataset_options
[4]:
{'compression': 'gzip',
'compression_opts': 9,
'shuffle': True,
'fletcher32': True}
The file is currently not open yet
[5]:
m
[5]:
HDF5 file is closed and cannot be accessed.
Open a new file
We will open the file in mode w here, which will overwrite the file if it already exists. If you don’t want to do that or are unsure if a file already exists the safest option is using mode a.
Its strongly encouraged that if you are making an MTH5 file, even if you want to open it up afterwards that you use with.
with MTH5(**kwargs) as m:
m.open_mth5(filename)
#pack MTH5
Using this style of pseudocode your MTH5 file will be made in a safe way if anything goes wrong in the packing. Using the with statement will automatically flush and close the MTH5 upon exiting the with statement, that includes any errors encountered.
Here we are just showing an example and how to interogate an MTH5 file.
[6]:
m.open_mth5(r"example.h5", "w")
2022-04-12 21:22:02,707 [line 591] mth5.mth5.MTH5.open_mth5 - WARNING: example.h5 will be overwritten in 'w' mode
2022-04-12 21:22:03,000 [line 656] mth5.mth5.MTH5._initialize_file - INFO: Initialized MTH5 0.2.0 file example.h5 in mode w
Now that we have initiated a file, let’s see what’s in an empty file.
[20]:
m
[20]:
/:
====================
|- Group: Experiment
--------------------
|- Group: Reports
-----------------
|- Group: Standards
-------------------
--> Dataset: summary
......................
|- Group: Surveys
-----------------
|- Group: example
-----------------
|- Group: Filters
-----------------
|- Group: coefficient
---------------------
|- Group: fap
-------------
|- Group: fir
-------------
|- Group: time_delay
--------------------
|- Group: zpk
-------------
|- Group: Reports
-----------------
|- Group: Standards
-------------------
--> Dataset: summary
......................
|- Group: Stations
------------------
|- Group: mt001
---------------
|- Group: Transfer_Functions
----------------------------
|- Group: mt002
---------------
|- Group: 001
-------------
--> Dataset: ex
.................
--> Dataset: hy
.................
|- Group: 002
-------------
|- Group: Transfer_Functions
----------------------------
--> Dataset: channel_summary
..............................
--> Dataset: tf_summary
.........................
We can see that there are default groups that are initiated by default. And here are the methods an MTH5 object contains. You can open/close an MTH5 file; add/remove station, run, channel; read from an mt_metadata.timeseries.Experiment object to fill the metadata and structure before adding data and create an mt_metadata.timeseries.Experiment object for archiving.
[8]:
print("\n".join(sorted([func for func in dir(m) if callable(getattr(m, func)) and not func.startswith("_")])))
2022-04-12 21:22:11,930 [line 519] mth5.mth5.MTH5.filters_group - INFO: File version 0.2.0 does not have a FiltersGroup at the experiment level
2022-04-12 21:22:11,932 [line 541] mth5.mth5.MTH5.stations_group - INFO: File version 0.2.0 does not have a Stations. try surveys_group.
2022-04-12 21:22:11,934 [line 479] mth5.mth5.MTH5.survey_group - INFO: File version 0.2.0 does not have a survey_group, try surveys_group
add_channel
add_run
add_station
add_survey
add_transfer_function
close_mth5
from_experiment
from_reference
get_channel
get_run
get_station
get_survey
get_transfer_function
h5_is_read
h5_is_write
has_group
open_mth5
remove_channel
remove_run
remove_station
remove_survey
remove_transfer_function
to_experiment
validate_file
Add a Survey
The first step is to add a survey, here we will add the survey example. This will return a SurveyGroup object which will commonly be the main group we work with.
[9]:
survey_group = m.add_survey("example")
Add a station
Here we will add a station called mt001. This will return a StationGroup object. We can add a station 2 ways: one directy from the MTH5 object m or from the newly created survey_group. Note if we add it from m then we need to include the survey name example.
[10]:
station_group = m.add_station("mt001", survey="example")
station_group = survey_group.stations_group.add_station("mt002")
Add some metadata to this station like location, who acquired it, and the reference frame in which the data were collected.
[11]:
station_group.metadata.location.latitude = "40:05:01"
station_group.metadata.location.longitude = -122.3432
station_group.metadata.location.elevation = 403.1
station_group.metadata.acquired_by.author = "me"
station_group.metadata.orientation.reference_frame = "geomagnetic"
# IMPORTANT: Must always use the write_metadata method when metadata is updated.
station_group.write_metadata()
[12]:
station_group.metadata
[12]:
{
"station": {
"acquired_by.name": "me",
"channels_recorded": [],
"data_type": "BBMT",
"geographic_name": null,
"hdf5_reference": "<HDF5 object reference>",
"id": "mt002",
"location.declination.model": "WMM",
"location.declination.value": 0.0,
"location.elevation": 403.1,
"location.latitude": 40.08361111111111,
"location.longitude": -122.3432,
"mth5_type": "Station",
"orientation.method": null,
"orientation.reference_frame": "geomagnetic",
"provenance.creation_time": "1980-01-01T00:00:00+00:00",
"provenance.software.author": "none",
"provenance.software.name": null,
"provenance.software.version": null,
"provenance.submitter.email": null,
"provenance.submitter.organization": null,
"run_list": [],
"time_period.end": "1980-01-01T00:00:00+00:00",
"time_period.start": "1980-01-01T00:00:00+00:00"
}
}
We can now add a run to the new station. We can do this in 2 ways, one directly from the m the MTH5 object, or from the newly created station_group
[13]:
run_01 = m.add_run("mt002", "001", survey="example")
run_02 = station_group.add_run("002")
[14]:
station_group
[14]:
/Experiment/Surveys/example/Stations/mt002:
====================
|- Group: 001
-------------
|- Group: 002
-------------
|- Group: Transfer_Functions
----------------------------
Again we can do this in 2 ways: directly from the m the MTH5 object, or from the newly created run_01 or run_02 group. There are only 3 types of channels electric, magnetic, and auxiliary and this needs to be specified when a channel is initiated. We will initate the channel with data=None, which will create an empty data set.
[15]:
ex = m.add_channel("mt002", "001", "ex", "electric", None, survey="example")
hy = run_01.add_channel("hy", "magnetic", None)
[16]:
hy
[16]:
Channel Magnetic:
-------------------
component: hy
data type: magnetic
data format: int32
data shape: (1,)
start: 1980-01-01T00:00:00+00:00
end: 1980-01-01T00:00:00+00:00
sample rate: 0.0
Now, let’s see what the contents are of this file
[17]:
m
[17]:
/:
====================
|- Group: Experiment
--------------------
|- Group: Reports
-----------------
|- Group: Standards
-------------------
--> Dataset: summary
......................
|- Group: Surveys
-----------------
|- Group: example
-----------------
|- Group: Filters
-----------------
|- Group: coefficient
---------------------
|- Group: fap
-------------
|- Group: fir
-------------
|- Group: time_delay
--------------------
|- Group: zpk
-------------
|- Group: Reports
-----------------
|- Group: Standards
-------------------
--> Dataset: summary
......................
|- Group: Stations
------------------
|- Group: mt001
---------------
|- Group: Transfer_Functions
----------------------------
|- Group: mt002
---------------
|- Group: 001
-------------
--> Dataset: ex
.................
--> Dataset: hy
.................
|- Group: 002
-------------
|- Group: Transfer_Functions
----------------------------
--> Dataset: channel_summary
..............................
--> Dataset: tf_summary
.........................
Channel Summary
We can have a look at the what channels are in this file. This can take a long time if you have lots of data. This returns a pandas.DataFrame object and can therefore be queried with the standard Pandas methods.
Note: the number of samples is 1 even though we did not add any data. This is because we initialize the dataset to be extendable and it needs at least 1 dimension to be initialized. We set the max shape to be (1, None) which means it can be extended to an arbitrary shape.
[18]:
%time
m.channel_summary.clear_table()
m.channel_summary.summarize()
ch_df = m.channel_summary.to_dataframe()
ch_df
Wall time: 0 ns
[18]:
| survey | station | run | latitude | longitude | elevation | component | start | end | n_samples | sample_rate | measurement_type | azimuth | tilt | units | hdf5_reference | run_hdf5_reference | station_hdf5_reference | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | example | mt002 | 001 | 40.083611 | -122.3432 | 403.1 | ex | 1980-01-01 00:00:00+00:00 | 1980-01-01 00:00:00+00:00 | 1 | 0.0 | electric | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 1 | example | mt002 | 001 | 40.083611 | -122.3432 | 403.1 | hy | 1980-01-01 00:00:00+00:00 | 1980-01-01 00:00:00+00:00 | 1 | 0.0 | magnetic | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
Access channel through HDF5 Reference
The channel summary table contains a column labeled hdf5_reference, this is an interal HDF5 reference that can be used directly to access that specific group or dataset. A method is provided in MTH5 to use this reference and return the proper group object. Here we will request to get the first channel in the table
[19]:
h5_reference = ch_df.iloc[0].hdf5_reference
ex = m.from_reference(h5_reference)
ex
[19]:
Channel Electric:
-------------------
component: ex
data type: electric
data format: int32
data shape: (1,)
start: 1980-01-01T00:00:00+00:00
end: 1980-01-01T00:00:00+00:00
sample rate: 0.0
Close MTH5 file
This part is important, be sure to close the file in order to save any changes. This function flushes metadata and data to the HDF5 file and then closes it. Note that once a file is closed all groups lose their link to the file and cannot retrieve any data.
[ ]:
m.close_mth5()
station_group
MTH5 Groups
Each group within an MTH5 file has a corresponding group object that provides convenience functions to interact with the HDF5 file and add, get, and remove groups or datasets to that group. For example a station has a corresponding mth5.groups.StationGroup object that provides functions to add, get, and remove a run. These are meant to make it easier for the user to interact with the HDF5 file without too much trouble.
v0.1.0 Groups
SurveyGroup: The master or root group of the HDF5 file
FiltersGroup: Holds all filters and filter information
ZPKGroup: Holds pole zero filters
FAPGroup: Holds frequency look up table filters
FIRGroup: Holds finite impulse response filters
CoefficientGroup: Holds coefficient filters
TimeDelayGroup: Holds time delay filters
Reports: Holds any reports relevant to the survey
StandardsGroup: A summary of metadata standards used
StationsGroup: Holds all the stations an subsequent data
StationGroup: Holds a single station
RunGroup: Holds a single run
ChannelDataset: Holds a single channel
v0.2.0 Groups
ExperimentGroup: The master or root group of the HDF5 file
ReportsGroup: Holds any reports relevant to the experiment
StandardsGroup: A summary of metadata standards used
SurveysGroup: Holds the surveys in an experiment
SurveyGroup: The master or root group of the HDF5 file
FiltersGroup: Holds all filters and filter information
ZPKGroup: Holds pole zero filters
FAPGroup: Holds frequency look up table filters
FIRGroup: Holds finite impulse response filters
CoefficientGroup: Holds coefficient filters
TimeDelayGroup: Holds time delay filters
Reports: Holds any reports relevant to the survey
StationsGroup: Holds all the stations an subsequent data
StationGroup: Holds a single station
RunGroup: Holds a single run
ChannelDataset: Holds a single channel
Note
Each group contains a weak reference to the HDF5 group, this way when a file is closed there are no lingering references to a closed HDF5 file.
Base Groups
Each group inherits mth5.groups.base.BaseGroup which provides basic methods for the group. This includes setting up the proper metadata and group structure. The __str__ and __repr__ methods are overwritten to show the structure of the group.
A groups_list property is provided that lists all the subgroups in the group.
Group Descriptions
There are 2 survey containers mth5.groups.MasterSurveysGroup and mth5.groups.SurveyGroup.
Master Surveys Group
mth5.groups.MasterSurveysGroup is an umbrella container that holds a collection of mth5.groups.SurveyGroup objects and contains a summary table that summarizes all surveys within the survey. Use mth5.groups.MasterSurveysGroup to add/get/remove surveys.
No metadata currently accompanies mth5.groups.MasterSurveysGroup.
Add/get will return a mth5.groups.SurveyGroup. If adding a survey that has the same name as an existing survey the mth5.groups.SurveyGroup returned will be of the existing survey and no survey will be added. Change the name or update the existing staiton. If getting a survey that does not exist a mth5.utils.exceptions.MTH5Error will be raised.
Add/Get/Remove Survey
Add/get/remove surveys can be done from the mth5.MTH5.surveys_group which is a mth5.groups.MasterSurveysGroup object.
>>> # v0.1.0 does not have a surveys_group
>>> # v0.2.0
>>> surveys = mth5_obj.surveys_group
>>> type(surveys)
mth5.groups.MasterSurveysGroup
From the surveys_group you can add/remove/get a survey.
To add a survey:
>>> new_survey = surveys.add_survey('survey_01')
>>> print(type(new_survey))
mth5.groups.SurveyGroup
To get an existing survey:
>>> existing_survey = surveys.get_survey('MT001')
To remove an existing survey:
>>> surveys.remove_survey('MT002')
>>> surveys.group_list
['MT001']
Summary Table
This table includes all channels within the Survey. This can be quite large and can take a long time to build.
Column |
Description |
|---|---|
survey |
Survey ID for channel |
station |
Station ID for channel |
run |
Run ID for channel |
latitude |
Survey latitude (decimal degrees) |
longitude |
Survey longitude (decimal degrees) |
elevation |
Survey elevation (meters) |
component |
Channel component |
start |
Start time for the channel (ISO format) |
end |
End time for the channel (ISO format) |
n_samples |
Number of samples in the channel |
sample_rate |
Channel sample rate |
measurement_type |
Channel measurement type |
azimuth |
Channel azimuth (degrees from N) |
tilt |
Channel tilt (degrees from horizontal) |
units |
Channel units |
hdf5_reference |
HDF5 internal reference |
Survey Group
A single survey is contained within a mth5.groups.SurveyGroup object, which has the appropriate metadata for a single survey. mth5.groups.SurveyGroup contains all the runs for that survey.
Survey Metadata
Metadata is accessed through the metadata property, which is a mt_metadata.timeseries.Survey object.
>>> type(new_survey.metadata)
mt_metadata.timeseries.Survey
>>> new_survey.metadata
{
"survey": {
"citation_dataset.doi": null,
"citation_journal.doi": null,
"country": null,
"datum": null,
"geographic_name": null,
"id": null,
"name": null,
"northwest_corner.latitude": 0.0,
"northwest_corner.longitude": 0.0,
"project": null,
"project_lead.email": null,
"project_lead.organization": null,
"release_license": "CC-0",
"southeast_corner.latitude": 0.0,
"southeast_corner.longitude": 0.0,
"summary": null,
"time_period.end_date": "1980-01-01",
"time_period.start_date": "1980-01-01"
}
}
See also
mth5.groups.SurveyGroup and mt_metadata.timeseries.Survey
There are 2 station containers mth5.groups.MasterStationsGroup and mth5.groups.StationGroup.
Master Stations Group
mth5.groups.MasterStationsGroup is an umbrella container that holds a collection of mth5.groups.StationGroup objects and contains a summary table that summarizes all stations within the survey. Use mth5.groups.MasterStationsGroup to add/get/remove stations.
No metadata currently accompanies mth5.groups.MasterStationsGroup.
Add/get will return a mth5.groups.StationGroup. If adding a station that has the same name as an existing station the mth5.groups.StationGroup returned will be of the existing station and no station will be added. Change the name or update the existing staiton. If getting a station that does not exist a mth5.utils.exceptions.MTH5Error will be raised.
Add/Get/Remove Station
Add/get/remove stations can be done from the mth5.MTH5.stations_group which is a mth5.groups.MasterStationsGroup object.
>>> # v0.1.0
>>> stations = mth5_obj.stations_group
>>> # v0.2.0
>>> stations = mth5_obj.get_survey("example").stations_group
>>> type(stations)
mth5.groups.MasterStationsGroup
>>> stations
/Survey/Stations:
====================
|- Group: MT001
---------------
|- Group: MT001a
----------------
--> Dataset: Ex
.................
--> Dataset: Ey
.................
--> Dataset: Hx
.................
--> Dataset: Hy
.................
--> Dataset: Hz
.................
From the stations_group you can add/remove/get a station.
To add a station:
>>> new_station = stations.add_station('MT002')
>>> print(type(new_station))
mth5.groups.StationGroup
>>> new_station
/Survey/Stations/MT002:
====================
To get an existing station:
>>> existing_station = stations.get_station('MT001')
To remove an existing station:
>>> stations.remove_station('MT002')
>>> stations.group_list
['MT001']
Summary Table
Column |
Description |
|---|---|
archive_id |
Station archive name |
start |
Start time of the station (ISO format) |
end |
End time of the station (ISO format) |
components |
All components measured by the station |
measurement_type |
All measurement types collected by the station |
location.latitude |
Station latitude (decimal degrees) |
location.longitude |
Station longitude (decimal degrees) |
location.elevation |
Station elevation (meters) |
hdf5_reference |
Internal HDF5 reference |
Station Group
A single station is contained within a mth5.groups.StationGroup object, which has the appropriate metadata for a single station. mth5.groups.StationGroup contains all the runs for that station.
Summary Table
The summary table in mth5.groups.StationGroup summarizes all runs for that station.
Column |
Description |
|---|---|
id |
Run ID |
start |
Start time of the run (ISO format) |
end |
End time of the run (ISO format) |
components |
All components measured for that run |
measurement_type |
Type of measurement for that run |
sample_rate |
Sample rate of the run (samples/second) |
hdf5_reference |
Internal HDF5 reference |
Station Metadata
Metadata is accessed through the metadata property, which is a mt_metadata.timeseries.Station object.
>>> type(new_station.metadata)
mt_metadata.timeseries.Station
>>> new_station.metadata
{
"station": {
"acquired_by.author": null,
"acquired_by.comments": null,
"archive_id": "FL001",
"channel_layout": "X",
"channels_recorded": [
"Hx",
"Hy",
"Hz",
"Ex",
"Ey"
],
"comments": null,
"data_type": "BB, LP",
"geographic_name": "Beachy Keen, FL, USA",
"hdf5_reference": "<HDF5 object reference>",
"id": "FL001",
"location.declination.comments": "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": null,
"provenance.creation_time": "2020-05-29T21:08:40+00:00",
"provenance.log": null,
"provenance.software.author": "Anna Kelbert, USGS",
"provenance.software.name": "mth5_metadata.m",
"provenance.software.version": "2020-05-29",
"provenance.submitter.author": "Anna Kelbert, USGS",
"provenance.submitter.email": "akelbert@usgs.gov",
"provenance.submitter.organization": "USGS Geomagnetism Program",
"time_period.end": "2015-01-29T16:18:14+00:00",
"time_period.start": "2015-01-08T19:49:15+00:00"
}
}
See also
mth5.groups.StationGroup and mt_metadata.timeseries.Station
Runs
A run is a collection of channels that recorded at similar start and end times at the same sample rate for a given station. A run is contained within a mth5.groups.RunGroup object. A run is the next level down from a station.
The main way to add/remove/get a run object is through a mth5.groups.StationGroup object
Accessing through StationGroup
You can get a mth5.groups.StationGroup using
>>> # v0.1.0
>>> new_station = mth5_obj.add_station('MT003')
>>> # v0.2.0
>>> new_station = mth5_obj.add_station("MT003", survey="example")
>>> # if you don't already have a run name one can be assigned based on existing runs
>>> new_run_name = new_station.make_run_name()
>>> new_run = new_station.add_run(new_run_name)
>>> new_run.metadata.id
'001'
Similar methods for get/remove a run
>>> existing_run = new_station.get_run('MT003a')
>>> new_station.remove_run('MT003a')
Summary Table
The summary table summarizes all channels for that run.
Column |
Description |
|---|---|
component |
Component name |
start |
Start time of the channel (ISO format) |
end |
End time of the channel (ISO format0 |
n_samples |
Number of samples for the channel |
measurement_type |
Measuremnt type of the channel |
units |
Units of the channel data |
hdf5_reference |
HDF5 internal reference |
Metadata
Metadata is accessed through the metadata property, which is a mth5.metadata.Run object.
>>> type(new_run)
mth5.metadata.Run
>>> new_run.metadata
{
"run": {
"acquired_by.author": "BB",
"acquired_by.comments": "it's cold in florida",
"channels_recorded_auxiliary": null,
"channels_recorded_electric": null,
"channels_recorded_magnetic": null,
"comments": null,
"data_logger.firmware.author": "Barry Narod",
"data_logger.firmware.name": null,
"data_logger.firmware.version": null,
"data_logger.id": "1305-1",
"data_logger.manufacturer": "Barry Narod",
"data_logger.model": "NIMS",
"data_logger.power_source.comments": "voltage measurements not recorded",
"data_logger.power_source.id": null,
"data_logger.power_source.type": "battery",
"data_logger.power_source.voltage.end": null,
"data_logger.power_source.voltage.start": null,
"data_logger.timing_system.comments": null,
"data_logger.timing_system.drift": 0.0,
"data_logger.timing_system.type": "GPS",
"data_logger.timing_system.uncertainty": 1.0,
"data_logger.type": null,
"data_type": "BB, LP",
"hdf5_reference": "<HDF5 object reference>",
"id": "MT003a",
"metadata_by.author": "Anna Kelbert; Paul Bedrosian",
"metadata_by.comments": "Paul Bedrosian: Ey, electrode dug up",
"mth5_type": "Run",
"provenance.comments": null,
"provenance.log": null,
"sample_rate": 8.0,
"time_period.end": "2015-01-19T14:54:54+00:00",
"time_period.start": "2015-01-08T19:49:15+00:00"
}
}
See also
mth5.groups.RunGroup and mt_metadata.timeseries.Run for more information.
Time Series Objects
All datasets at the Channel level are represented by mth5.timeseries.ChannelTS objects. The mth5.timeseries.ChannelTS are based on xarray.DataArray objects. This way memory usage is minimal because xarray is lazy and only uses what is called for. Another benefit is that metadata can directly accompany the data. Currently the model is that all metadata are input into a mth5.metadata.Base object to be validated first and then the xarray.DataArray can be updated. This is not automated at this point so the user just needs to use the function update_xarray_metadata when metadata values are changed. Another advantage of using xarray is that the time series data are indexed by time making it easier to align, trim, extract, sort, etc.
All run datasets are represented by mth5.timeseries.RunTS objects, which are based on xarray.DataSet which is a collection of xarray.DataArray objects. The benefits of using xarray are that many of the methods such as aligning, indexing, sorting are already developed and are robust. Therefore the useability is easier without more coding.
Another reason why xarray was picked as the basis for representing the data is that it works seamlessly with other programs like Dask for parallel computing, and plotting tools like hvplot.
Examples
ChannelTS
A ChannelTS object is a container for a single channel. The data are stored in an xarray.DataArray and indexed by time according to the metadata provided. Here we will make a simple electric channel and look at how to interogate it.
[1]:
%matplotlib inline
import numpy as np
from mth5.timeseries import ChannelTS
from mt_metadata.timeseries import Electric, Run, Station
Here create some metadata, the keys are the time_period.start and the sample_rate.
[2]:
ex_metadata = Electric()
ex_metadata.time_period.start = "2020-01-01T00:00:00"
ex_metadata.sample_rate = 8.0
ex_metadata.component = "ex"
ex_metadata.dipole_length = 100.
ex_metadata.units = "millivolts"
Create Station and Run metadata
[3]:
station_metadata = Station(id="mt001")
run_metadata = Run(id="001")
Create “realistic” data
[4]:
n_samples = 4096
t = np.arange(n_samples)
data = np.sum([np.cos(2*np.pi*w*t + phi) for w, phi in zip(np.logspace(-3, 3, 20), np.random.rand(20))], axis=0)
[5]:
ex = ChannelTS(channel_type="electric",
data=data,
channel_metadata=ex_metadata,
run_metadata=run_metadata,
station_metadata=station_metadata)
[6]:
ex
[6]:
Channel Summary:
Survey: 0
Station: mt001
Run: 001
Channel Type: Electric
Component: ex
Sample Rate: 8.0
Start: 2020-01-01T00:00:00+00:00
End: 2020-01-01T00:08:31.875000+00:00
N Samples: 4096
Get a slice of the data
Here we will provide a start time of the slice and the number of samples that we want the slice to be
[7]:
ex_slice = ex.get_slice("2020-01-01T00:00:00", n_samples=256)
[8]:
ex_slice
[8]:
Channel Summary:
Survey: 0
Station: mt001
Run: 001
Channel Type: Electric
Component: ex
Sample Rate: 8.0
Start: 2020-01-01T00:00:00+00:00
End: 2020-01-01T00:00:31.875000+00:00
N Samples: 256
Plot the data
This is a work in progress, but this can be done through the xarray container.
[9]:
ex_slice.plot()
[9]:
[<matplotlib.lines.Line2D at 0x293bb639ca0>]
Convert to an xarray
We can convert the ChannelTS object to an xarray.DataArray which could be easier to use.
[10]:
ex_xarray = ex.to_xarray()
[11]:
ex_xarray
[11]:
<xarray.DataArray 'ex' (time: 4096)>
array([16.43704101, 2.7440483 , 4.62460915, ..., -3.61015321,
-0.73752295, 1.85945637])
Coordinates:
* time (time) datetime64[ns] 2020-01-01 ... 2020-01-01T00:08:31.875000
Attributes:
channel_number: 0
component: ex
data_quality.rating.value: 0
dipole_length: 100.0
filter.applied: [False]
filter.name: []
measurement_azimuth: 0.0
measurement_tilt: 0.0
negative.elevation: 0.0
negative.id: None
negative.latitude: 0.0
negative.longitude: 0.0
negative.manufacturer: None
negative.type: None
positive.elevation: 0.0
positive.id: None
positive.latitude: 0.0
positive.longitude: 0.0
positive.manufacturer: None
positive.type: None
sample_rate: 8.0
time_period.end: 2020-01-01T00:08:31.875000+00:00
time_period.start: 2020-01-01T00:00:00+00:00
type: electric
units: millivolts
station.id: mt001
run.id: 001[12]:
ex.plot()
ex_xarray.plot()
[12]:
[<matplotlib.lines.Line2D at 0x293bbe13190>]
Convert to an Obspy.Trace object
The ChannelTS object can be converted to an obspy.Trace object. This can be useful when dealing with data received from a mainly seismic archive like IRIS. This can also be useful for using some tools provided by Obspy.
Note there is a loss of information when doing this because an obspy.Trace is based on miniSEED data formats which has minimal metadata.
[13]:
ex.station_metadata.fdsn.id = "mt001"
ex_trace = ex.to_obspy_trace()
[14]:
ex_trace
[14]:
.mt001..MQN | 2020-01-01T00:00:00.000000Z - 2020-01-01T00:08:31.875000Z | 8.0 Hz, 4096 samples
Convert from an Obspy.Trace object
We can reverse that and convert an obspy.Trace into a ChannelTS. Again useful when dealing with seismic dominated archives.
[15]:
ex_from_trace = ChannelTS()
ex_from_trace.from_obspy_trace(ex_trace)
[16]:
ex_from_trace
[16]:
Channel Summary:
Survey: 0
Station: mt001
Run: sr8_001
Channel Type: Electric
Component: ex
Sample Rate: 8.0
Start: 2020-01-01T00:00:00+00:00
End: 2020-01-01T00:08:31.875000+00:00
N Samples: 4096
[17]:
ex
[17]:
Channel Summary:
Survey: 0
Station: mt001
Run: 001
Channel Type: Electric
Component: ex
Sample Rate: 8.0
Start: 2020-01-01T00:00:00+00:00
End: 2020-01-01T00:08:31.875000+00:00
N Samples: 4096
On comparison you can see the loss of metadata information.
Calibrate
ChannelTS.remove_instrument_response is supplied just for this.See Also: Make Data From IRIS examples for working examples.
[18]:
help(ex.remove_instrument_response)
Help on method remove_instrument_response in module mth5.timeseries.channel_ts:
remove_instrument_response(**kwargs) method of mth5.timeseries.channel_ts.ChannelTS instance
Remove instrument response from the given channel response filter
The order of operations is important (if applied):
1) detrend
2) zero mean
3) zero pad
4) time window
5) frequency window
6) remove response
7) undo time window
8) bandpass
**kwargs**
:param plot: to plot the calibration process [ False | True ]
:type plot: boolean, default True
:param detrend: Remove linar trend of the time series
:type detrend: boolean, default True
:param zero_mean: Remove the mean of the time series
:type zero_mean: boolean, default True
:param zero_pad: pad the time series to the next power of 2 for efficiency
:type zero_pad: boolean, default True
:param t_window: Time domain windown name see `scipy.signal.windows` for options
:type t_window: string, default None
:param t_window_params: Time domain window parameters, parameters can be
found in `scipy.signal.windows`
:type t_window_params: dictionary
:param f_window: Frequency domain windown name see `scipy.signal.windows` for options
:type f_window: string, defualt None
:param f_window_params: Frequency window parameters, parameters can be
found in `scipy.signal.windows`
:type f_window_params: dictionary
:param bandpass: bandpass freequency and order {"low":, "high":, "order":,}
:type bandpass: dictionary
XArray Accessor for Decimate and Filtering
A common practice when working with time series would be decimating or downsampling the data. xarray has some builtins for resampling, however these do not apply a filter prior to downsampling and has alias issues. We have added some utilities for decimation and filtering following the package xr-scipy. When MTH5 is initiated a sps_filters accessor to xarray.DataArray and xarray.Dataset which includes some filtering
methods as well as decimation and resampling. Therefore for access to these methods use DataArray.sps_filters.decimate or Dataset.sps_filters.decimate.
Methods include
Name |
Function |
|---|---|
|
low pass filter the data |
|
high pass filter the data |
|
band pass filter the data |
|
filter out frequencies using a band stop (notch filter) |
|
simulates |
|
uses |
|
uses |
Note: In future versions of MTH5 filters will be added to ChannelTS.
DecimateHere we will decimate to a new sample rate of 1 sample per second.
[19]:
decimated_ex = ex.decimate(1)
[20]:
ex.plot()
decimated_ex.plot()
[20]:
[<matplotlib.lines.Line2D at 0x293bd0293a0>]
resample_polyresample_poly is more accurate for real signals because there is no innate assumption of periodicity. As you can see in the decimate case there are edge effects. Have a look at the scipy documentation for more information. In the plot below, you’ll notice no edge effects and similar numbers as decimate.
Note: Strongly suggest using ChannelTS.resample_poly for downsampling data.
[28]:
resample_poly_ex = ex.resample_poly(1)
[29]:
ex.plot()
decimated_ex.plot()
resample_poly_ex.plot()
[29]:
[<matplotlib.lines.Line2D at 0x293bf9291f0>]
Merge Channels
A common step in working with time series would be to combine different segments of collected for the same channel. If the sample rates are the same you can use channel_01 + channel_02. This should also work if the sample rates are not the same, though you should use ChannelTS.merge if the sample rates are not the same. The channels combined must have the same component. There are two builtin methods to combine channels those are + and merge().
added_channel = cnahhel_01 + channel_02merged_channel = channel_01.merge(channel_02)
Both methods use xarray.combine_by_coords([ch1, rch2], combine_attrs='override'. The combine_by_coords method simply concatenates along similar dimensions and cares nothing of a monotonix dimension variable. Therefore, xarray.DataArray.reindex is used to create a monotonically increasing time series. Any gaps are filled interpolated using a 1-D interpolation. The default method is slinear which is probably the most useful for processing time series. If you want more control over
the interpolation method use ChannelTS.merge([ch1, ch2, ...], gap_method=interpolation_method. For more information on interpolation methods see Scipy.interpolate.interp1d. Similarly if you want more control on how the datasets are merged use xarray tools.
Adding channels together does 2 at a time so if you are adding multiple channels together channel_01 + channel_02 + channel_03 ... its better to use merge. Also if you want more control on how the channels are merged and how time gaps are interpolated use merge.
[21]:
ex2 = ex.copy()
ex2.start = "2020-01-01T00:08:45"
[22]:
added_channel = ex + ex2
[23]:
added_channel
[23]:
Channel Summary:
Survey: 0
Station: mt001
Run: 001
Channel Type: Electric
Component: ex
Sample Rate: 8.0
Start: 2020-01-01T00:00:00+00:00
End: 2020-01-01T00:17:16.875000+00:00
N Samples: 8296
[24]:
added_channel.plot()
[24]:
[<matplotlib.lines.Line2D at 0x293bd05e340>]
If channels have different sample rates or you want to combine channels and resample to a lower sample rate, use the keyword argument new_sample_rate.
[25]:
merged_channel = ex.merge(ex2, new_sample_rate=1)
[26]:
merged_channel
[26]:
Channel Summary:
Survey: 0
Station: mt001
Run: 001
Channel Type: Electric
Component: ex
Sample Rate: 1.0
Start: 2020-01-01T00:00:00+00:00
End: 2020-01-01T00:17:16+00:00
N Samples: 1037
[27]:
added_channel.plot()
merged_channel.plot()
[27]:
[<matplotlib.lines.Line2D at 0x293bd512340>]
RunTS
mth5.timeseries.RunTS is a container to hold multiple synchronous channels of the same sampling rate. The data is contained in an xarray.DataSet which is a collection of ChannelTS.to_xarray() objects.
[1]:
%matplotlib inline
import numpy as np
from mth5.timeseries import ChannelTS, RunTS
from mt_metadata.timeseries import Electric, Magnetic, Auxiliary, Run, Station
Create a Run
We will create a common run that has all 5 channels of an MT measurement (Hx, Hy, Hz, Ex, Ey) plus an auxiliary channel. We will make individual channels first and then add them into a RunTS object.
[2]:
channel_list = []
common_start = "2020-01-01T00:00:00"
sample_rate = 8.0
n_samples = 4096
t = np.arange(n_samples)
data = np.sum([np.cos(2*np.pi*w*t + phi) for w, phi in zip(np.logspace(-3, 3, 20), np.random.rand(20))], axis=0)
station_metadata = Station(id="mt001")
run_metadata = Run(id="001")
magnetic channels[3]:
for component in ["hx", "hy", "hz"]:
h_metadata = Magnetic(component=component)
h_metadata.time_period.start = common_start
h_metadata.sample_rate = sample_rate
if component in ["hy"]:
h_metadata.measurement_azimuth = 90
h_channel = ChannelTS(
channel_type="magnetic",
data=data,
channel_metadata=h_metadata,
run_metadata=run_metadata,
station_metadata=station_metadata)
channel_list.append(h_channel)
electric channels[4]:
for component in ["ex", "ey"]:
e_metadata = Electric(component=component)
e_metadata.time_period.start = common_start
e_metadata.sample_rate = sample_rate
if component in ["ey"]:
e_metadata.measurement_azimuth = 90
e_channel = ChannelTS(
channel_type="electric",
data=data,
channel_metadata=e_metadata,
run_metadata=run_metadata,
station_metadata=station_metadata)
channel_list.append(e_channel)
auxiliary channel[5]:
aux_metadata = Auxiliary(component="temperature")
aux_metadata.time_period.start = common_start
aux_metadata.sample_rate = sample_rate
aux_channel = ChannelTS(
channel_type="auxiliary",
data=np.random.rand(n_samples) * 30,
channel_metadata=aux_metadata,
run_metadata=run_metadata,
station_metadata=station_metadata)
aux_channel.channel_metadata.type = "temperature"
channel_list.append(aux_channel)
Create RunTS object
Now that we have made individual channels we can make a RunTS object by inputing a list of ChannelTS objects.
Note: This can also be a list of xarray.DataArray objects formated like a channel.
[6]:
run = RunTS(channel_list)
[7]:
run
[7]:
RunTS Summary:
Survey: 0
Station: mt001
Run: 001
Start: 2020-01-01T00:00:00+00:00
End: 2020-01-01T00:08:31.875000+00:00
Sample Rate: 8.0
Components: ['hx', 'hy', 'hz', 'ex', 'ey', 'temperature']
Plot Run
Again this is a hack at the moment, we are working on a better visualization, but this works for now.
[8]:
run.plot()
[8]:
Decimate and Filters
A common practice when working with time series would be decimating or downsampling the data. xarray has some builtins for resampling, however these do not apply a filter prior to downsampling and has alias issues. We have added some utilities for decimation and filtering following the package xr-scipy. When MTH5 is initiated a sps_filters accessor to xarray.DataArray and xarray.Dataset which includes some filtering
methods as well as decimation and resampling. Therefore for access to these methods use DataArray.sps_filters.decimate or Dataset.sps_filters.decimate.
Methods include
Name |
Function |
|---|---|
|
low pass filter the data |
|
high pass filter the data |
|
band pass filter the data |
|
filter out frequencies using a band stop (notch filter) |
|
simulates |
|
uses |
|
uses |
Note: In future versions of MTH5 filters will be added to RunTS.
DecimateHere we will decimate to a new sample rate of 1 sample per second.
[9]:
decimated_run = run.decimate(1)
2023-09-27T16:30:24.906668-0700 | WARNING | mth5.timeseries.run_ts | validate_metadata | end time of dataset 2020-01-01T00:08:31+00:00 does not match metadata end 2020-01-01T00:08:31.875000+00:00 updating metatdata value to 2020-01-01T00:08:31+00:00
resample_polyresample_poly is more accurate for real signals because there is no innate assumption of periodicity. As you can see in the decimate case there are edge effects. Have a look at the scipy documentation for more information. In the plot below, you’ll notice no edge effects and similar numbers as decimate.
Note: Strongly suggest using RunTS.resample_poly for downsampling data.
[10]:
resample_poly_run = run.resample_poly(1)
2023-09-27T16:30:25.076013-0700 | WARNING | mth5.timeseries.run_ts | validate_metadata | end time of dataset 2020-01-01T00:08:31+00:00 does not match metadata end 2020-01-01T00:08:31.875000+00:00 updating metatdata value to 2020-01-01T00:08:31+00:00
[12]:
decimated_run.plot()
resample_poly_run.plot()
[12]:
Merge Runs
It can be benificial to add runs together to create a longer time series. To combine runs each run must have the same sample rate. There are 2 builtin options for combining runs
added_run = run_01 + run_02merged_run = run_01.merge(run_02)
Both methods use xarray.combine_by_coords([run1, run2], combine_attrs='override'. The combine_by_coords method simply concatenates along similar dimensions and cares nothing of a monotonix dimension variable. Therefore, xarray.DataSet.reindex is used to create a monotonically increasing time series. Any gaps are filled interpolated using a 1-D interpolation. The default method is slinear which is probably the most useful for processing time series. If you want more control over
the interpolation method use RunTS.merge([run1, run2, ...], gap_method=interpolation_method. For more information on interpolation methods see Scipy.interpolate.interp1d. Similarly if you want more control on how the datasets are merged use xarray tools.
+Using run_01 + run_02 will combine and interpolate onto a new monotonic time index. Any gaps will be interpolated linearly interpolated. If channels are different Nan’s will be placed where channels do not overlap. If you have hz in run_01 but not run_02 then Nan’s will be place in hz for the time period of run_02.
[14]:
for ch in channel_list:
ch.start = "2020-01-01T00:08:45"
run_02 = RunTS(channel_list)
[15]:
added_run = run + run_02
2023-09-27T16:32:27.726365-0700 | WARNING | mth5.timeseries.run_ts | validate_metadata | end time of dataset 2020-01-01T00:17:16.875000+00:00 does not match metadata end 2020-01-01T00:08:31.875000+00:00 updating metatdata value to 2020-01-01T00:17:16.875000+00:00
2023-09-27T16:32:27.726365-0700 | WARNING | mth5.timeseries.run_ts | validate_metadata | sample rate of dataset 8.0 does not match metadata sample rate 1.0 updating metatdata value to 8.0
[16]:
added_run
[16]:
RunTS Summary:
Survey: 0
Station: mt001
Run: 001
Start: 2020-01-01T00:00:00+00:00
End: 2020-01-01T00:17:16.875000+00:00
Sample Rate: 8.0
Components: ['hx', 'hy', 'hz', 'ex', 'ey', 'temperature']
Plotting you can see the linear interpolation in the gap of time between the runs. Here it makes a spike because the first point in the time series is a spike.
[17]:
added_run.plot()
[17]:
Using merge is a little more flexible and many runs can be merged together at the same time. Note if you use run_01 + run_02 + run_03 it only combines 2 at a time, which can be inefficient if multiple runs are combined. RunTS.merge provides the option of combining multiple runs and also decimating to lower sample rate with the keyword new_sample_rate.
When using merge not all runs need to have the same sample rate, but you need to set new_sample_rate to the common sample rate for the combined runs. This will decimate the data by first applying an iir filter to remove aliasing and then downsample to the desired sample rate. This mimics scipy.signal.decimate.
To change how gaps are interpolated change the gap_method parameter. See (scipy.interpolate.interp1d)[https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html]
Options are
‘linear’
‘nearest’
‘nearest-up’
‘zero’
‘slinear’ (default)
‘quadratic’
‘cubic’
‘previous’
‘next’
[18]:
merged_run = run.merge(run_02, new_sample_rate=1, gap_method="slinear")
2023-09-27T16:32:29.163041-0700 | WARNING | mth5.timeseries.run_ts | validate_metadata | end time of dataset 2020-01-01T00:08:31+00:00 does not match metadata end 2020-01-01T00:08:31.875000+00:00 updating metatdata value to 2020-01-01T00:08:31+00:00
2023-09-27T16:32:29.316779-0700 | WARNING | mth5.timeseries.run_ts | validate_metadata | end time of dataset 2020-01-01T00:17:16+00:00 does not match metadata end 2020-01-01T00:17:16.875000+00:00 updating metatdata value to 2020-01-01T00:17:16+00:00
2023-09-27T16:32:29.499618-0700 | WARNING | mth5.timeseries.run_ts | validate_metadata | end time of dataset 2020-01-01T00:17:16+00:00 does not match metadata end 2020-01-01T00:08:31.875000+00:00 updating metatdata value to 2020-01-01T00:17:16+00:00
[19]:
merged_run
[19]:
RunTS Summary:
Survey: 0
Station: mt001
Run: 001
Start: 2020-01-01T00:00:00+00:00
End: 2020-01-01T00:17:16+00:00
Sample Rate: 1.0
Components: ['hx', 'hy', 'hz', 'ex', 'ey', 'temperature']
Plot the merged runs downsampled to 1 second, and the linear interpolation is more obvious.
[20]:
merged_run.plot()
[20]:
To/From Obspy.Stream
When data is downloaded from an FDSN client or data is collected as miniseed Obspy is used to contain the data as a Stream object. Transformation between RunTS and Stream is supported.
[21]:
stream = run.to_obspy_stream()
[22]:
stream
[22]:
6 Trace(s) in Stream:
.mt001..MFN | 2020-01-01T00:00:00.000000Z - 2020-01-01T00:08:31.875000Z | 8.0 Hz, 4096 samples
.mt001..MFE | 2020-01-01T00:00:00.000000Z - 2020-01-01T00:08:31.875000Z | 8.0 Hz, 4096 samples
.mt001..MFZ | 2020-01-01T00:00:00.000000Z - 2020-01-01T00:08:31.875000Z | 8.0 Hz, 4096 samples
.mt001..MQN | 2020-01-01T00:00:00.000000Z - 2020-01-01T00:08:31.875000Z | 8.0 Hz, 4096 samples
.mt001..MQE | 2020-01-01T00:00:00.000000Z - 2020-01-01T00:08:31.875000Z | 8.0 Hz, 4096 samples
.mt001..MKN | 2020-01-01T00:00:00.000000Z - 2020-01-01T00:08:31.875000Z | 8.0 Hz, 4096 samples
[23]:
new_run = RunTS()
new_run.from_obspy_stream(stream)
[24]:
new_run
[24]:
RunTS Summary:
Survey: 0
Station: mt001
Run: sr8_001
Start: 2020-01-01T00:00:00+00:00
End: 2020-01-01T00:08:31.875000+00:00
Sample Rate: 8.0
Components: ['hx', 'hy', 'hz', 'ex', 'ey', 'temperaturex']
[ ]:
Filters
This section includes information about filters, which can be a very painful process and somewhat obtuse.
Filters are basically anything that manipulates the time series data into something else. This includes scaling, removing instrument response, removing trends, notch filtering, passband filtering, etc. We want to work with data that is in physical units, but the data are often collected in digital counts. To transform between the two we need to apply or unapply certain filters. When going from physical units to digital counts we are applying filters, channel_metadata.filter.applied = [True],
whereas going from counts to physical units we are unapplying the filters channel_metadata.filter.applied = [False]. This may seem backwards, but this is the way most archived data is thought of. Then any filter applied to physical units for the purposes of cleaning the data are channel_metadata.filter.applied = [True].
Note: currently there are not tools to convert digital counts to physical units, this is a work in progress. Nevertheless all the information is there for you to do the transformation.
Supported filters in mt_metadata.timeseries.filters are:
Filter |
Description |
|---|---|
CoefficientFilter |
A coefficient filter scales the data by a given factor, a real value. |
FIRFilter |
A finite impulse response filter is commonly an anti-alias filter and is represented as a 1-D array of real valued coefficients. |
PoleZeroFilter |
A pole-zero filter is often an type of bandpass filter. It is represented as symmetric complex poles and zeros with a scale factor |
TimeDelayFilter |
A time delay filter delays the data by a real valued time delay. A negative value is a delay and a positive value is a prediction. |
FrequencyResponseTableFilter |
A frequency, amplitude, phase look-up table, commonly in physical units and degrees. This is commonly how manufacturers provide instrument responses. |
ChannelResponseFilter |
A comprehensive representation of all filters. Contains a list of all filters, should be the most commonly used filter object as it can compute the total response, and estimate the passband and normalization frequency. |
Filter Base
All filters inherit from a FilterBase class, which has attributes and methods common to all filters.
Units
The two common to all filters are units_in and units_out. These attributes are key as they describes how the filters are transforming the data. The units should be SI units and given as all lowercase full names. For example Volts would be volts and V/m would be volts per meter. All units are represented in short form.
Complex Response
A method is provided to compute the complex response, this is slightly different for each filter and the method overwritten by the different filters.
Pass Band
A method is also provided to compute the “pass band” of a given filter. This is the band where the response is flat, the estimation is only approximate and should be used with caution. It works well for simple filters, but more complex filters, it tries to pick the longest segment with a flat response. CoefficientFilter and TimeDelayFilter objects have an infinite pass band. FrequencyResponseTableFilter estimate the pass band within the given frequencies, unless told otherwise. If
the frequency range is out of the range of the calibration frequencies extrapolation is applied. This should also be done with caution.
[1]:
%matplotlib inline
import numpy as np
# make a general frequency array to calculate complex response
# note that these are linear frequencies and the complex response uses angular frequencies
frequencies = np.logspace(-5, 5, 500)
Coefficient Filter
A coefficient filter is relatively simple. It includes a scale factor represented as gain.
[2]:
from mt_metadata.timeseries.filters import CoefficientFilter
[3]:
cf = CoefficientFilter(units_in="volts", units_out="V", gain=100.0, name="example_coefficient_filter")
cf
[3]:
{
"coefficient_filter": {
"calibration_date": "1980-01-01",
"gain": 100.0,
"name": "example_coefficient_filter",
"type": "coefficient",
"units_in": "V",
"units_out": "V"
}
}
[4]:
cf.plot_response(frequencies, x_units="frequency")
FIR Filter
An finite impulse response filter is commonly used as an anti-alias filter, and is represented as a 1-D array of real valued coefficients.
[5]:
from mt_metadata.timeseries.filters import FIRFilter
[6]:
fir = FIRFilter(
units_in="volts",
units_out="volts",
name="example_fir",
decimation_input_sample_rate=32000,
gain=0.999904,
symmetry="EVEN",
decimation_factor=16,
)
fir.coefficients = [
1.0828314e-06, 1.7808272e-06, 3.2410387e-06, 5.4627321e-06, 8.682945e-06, 1.3240843e-05, 1.9565294e-05,
2.8185128e-05, 3.9656901e-05, 5.4688699e-05, 7.4153548e-05, 9.8989171e-05, 0.00013036761, 0.00016954952,
0.00021798223, 0.00027731725, 0.00034936491, 0.00043613836, 0.00053984317, 0.0006628664, 0.00080777059,
0.00097733398, 0.0011744311, 0.0014021378, 0.0016635987, 0.0019620692, 0.0023008469, 0.0026832493,
0.0031125348, 0.0035918986, 0.0041243695, 0.0047127693, 0.0053596641, 0.0060672448, 0.0068373145,
0.0076711699, 0.0085695535, 0.0095325625, 0.010559602, 0.01164928, 0.012799387, 0.014006814, 0.015267504,
0.016576445, 0.017927598, 0.019313928, 0.020727372, 0.022158878, 0.023598416, 0.025035053, 0.026456987,
0.027851671, 0.029205887, 0.030505868, 0.031737458, 0.032886244, 0.03393773, 0.034877509, 0.035691477,
0.036365997, 0.036888145, 0.037245877, 0.03742826, 0.037425674, 0.03723, 0.036834806, 0.036235519,
0.035429578, 0.034416564, 0.033198304, 0.031778947, 0.030165028, 0.028365461, 0.026391543, 0.024256891,
0.021977346, 0.019570865, 0.017057346, 0.014458441, 0.011797323, 0.009098433, 0.0063871844, 0.0036896705,
0.0010323179, -0.0015584482, -0.0040565561, -0.0064366534, -0.0086744577, -0.010747112, -0.012633516,
-0.014314661, -0.0157739, -0.01699724, -0.017973563, -0.018694809, -0.019156145, -0.01935605, -0.019296378,
-0.018982368, -0.018422581, -0.017628808, -0.016615927, -0.015401681, -0.014006456, -0.012452973,
-0.010765962, -0.0089718029, -0.0070981304, -0.0051734182, -0.0032265538, -0.0012863991, 0.0006186511,
0.0024610918, 0.0042147399, 0.0058551184, 0.0073598339, 0.0087089101, 0.0098850802, 0.01087404, 0.011664648,
0.012249067, 0.012622863, 0.012785035, 0.012737988, 0.012487462, 0.01204238, 0.011414674, 0.010619033,
0.0096726287, 0.0085947802, 0.007406604, 0.006130626, 0.0047903718, 0.0034099557, 0.002013647, 0.00062545808,
-0.0007312728, -0.0020342721, -0.0032627005, -0.0043974896, -0.0054216445, -0.0063205026, -0.0070819431,
-0.0076965573, -0.0081577515, -0.0084618134, -0.0086079109, -0.0085980454, -0.0084369555, -0.0081319623,
-0.0076927822, -0.0071312929, -0.0064612622, -0.005698049, -0.0048582871, -0.0039595389, -0.0030199524,
-0.0020579058, -0.0010916605, -0.00013902181, 0.00078298012, 0.0016583927, 0.0024726097, 0.0032126096,
0.0038671608, 0.0044269804, 0.0048848582, 0.005235733, 0.0054767267, 0.0056071337, 0.0056283739,
0.0055438946, 0.0053590466, 0.0050809216, 0.0047181565, 0.0042807218, 0.0037796798, 0.0032269375,
0.0026349833, 0.0020166242, 0.001384723, 0.00075194426, 0.00013050952, -0.00046802824, -0.0010329896,
-0.0015547526, -0.0020249044, -0.0024363673, -0.0027834927, -0.0030621202, -0.0032696081, -0.0034048304,
-0.003468141, -0.0034613123, -0.003387446, -0.0032508562, -0.003056939, -0.0028120177, -0.0025231787,
-0.0021980959, -0.0018448512, -0.001471751, -0.0010871479, -0.00069926586, -0.00031603608, 5.5052958e-05,
0.00040709358, 0.00073387625, 0.0010299878, 0.0012908906, 0.0015129783, 0.0016936094, 0.0018311206,
0.0019248178, 0.0019749457, 0.0019826426, 0.0019498726, 0.0018793481, 0.001774437, 0.0016390601,
0.0014775817, 0.0012946966, 0.0010953132, 0.00088443852, 0.00066706788, 0.00044807568, 0.00023212004,
2.3551687e-05, -0.00017366471, -0.00035601447, -0.00052048819, -0.00066461961, -0.0007865114, -0.00088484585,
-0.00095888576, -0.0010084547, -0.0010339168, -0.0010361352, -0.0010164281, -0.00097651512, -0.0009184563,
-0.00084458862, -0.00075745879, -0.00065975531, -0.0005542388, -0.00044368068, -0.00033079527, -0.00021818698,
-0.0001082966, -3.3548122e-06, 9.4652831e-05, 0.00018401834, 0.00026333242, 0.00033149892, 0.00038773834,
0.00043159194, 0.00046290434, 0.0004818163, 0.0004887383, 0.00048432534, 0.00046944799, 0.00044515711,
0.0004126484, 0.0003732258, 0.00032826542, 0.0002791743, 0.00022736372, 0.0001742071, 0.00012101705,
6.9016627e-05, 1.9314915e-05, -2.7108661e-05, -6.9420195e-05, -0.00010694123, -0.00013915499, -0.00016570302,
-0.00018639189, -0.00020117435, -0.00021015058, -0.00021354953, -0.00021171506, -0.00020509114, -0.00019420317,
-0.00017963824, -0.00016202785, -0.00014203126, -0.00012030972, -9.7522447e-05, -7.4297881e-05, -5.1229126e-05,
-2.8859566e-05, -7.6714587e-06, 1.191924e-05, 2.9567975e-05, 4.5006156e-05, 5.8043028e-05, 6.8557049e-05,
7.6507284e-05, 8.1911501e-05, 8.4854546e-05, 8.5473977e-05, 8.3953237e-05, 8.0514517e-05, 7.5410928e-05,
6.8914269e-05, 6.1308667e-05, 5.2886291e-05, 4.3926615e-05, 3.4708195e-05, 2.5484322e-05, 1.6489239e-05,
7.9309229e-06, -1.335887e-08, -7.1985955e-06, -1.3510014e-05, -1.8867066e-05, -2.32245e-05, -2.6558888e-05,
-2.8887769e-05, -3.0242758e-05, -3.0684769e-05, -3.0291432e-05, -2.9154804e-05, -2.7376906e-05, -2.5070442e-05,
-2.2347936e-05, -1.9321938e-05, -1.6109379e-05, -1.2808429e-05, -9.5234091e-06, -6.3382245e-06,
-3.3291028e-06, -5.5987789e-07, 1.9187619e-06, 4.072373e-06, 5.8743913e-06, 7.312286e-06, 8.3905516e-06,
9.10975e-06, 9.4979405e-06, 9.5751602e-06, 9.3762619e-06, 8.9382884e-06, 8.301693e-06, 7.5045982e-06,
6.592431e-06, 5.6033018e-06, 4.5713236e-06, 3.5404175e-06, 2.5302468e-06, 1.5771827e-06, 6.9930724e-07,
-8.6047464e-08, -7.6676685e-07, -1.3332562e-06, -1.7875625e-06, -2.1266892e-06, -2.35267e-06, -2.4824365e-06,
-2.5098916e-06, -2.4598471e-06, -2.335345e-06, -2.1523615e-06, -1.9251499e-06, -1.6707684e-06, -1.3952398e-06,
-1.1173763e-06, -8.4543007e-07, -5.7948262e-07, -3.444687e-07, -1.2505329e-07, 6.1743521e-08, 2.1873758e-07,
3.4424812e-07, 4.3748074e-07, 4.931357e-07, 5.2551894e-07, 5.344753e-07, 5.136161e-07, 4.9029785e-07,
4.3492003e-07, 3.8198567e-07, 3.2236682e-07, 2.6023093e-07, 1.9363162e-07, 1.3382508e-07, 6.8672463e-08,
2.1443693e-08, -1.9671351e-09, -4.7522178e-08, -6.2719053e-08, -1.0190665e-07, -1.2015286e-07, -1.103657e-07,
-1.0294882e-07, -1.1965994e-07, -1.3612285e-07, -1.463918e-07, -1.4752351e-07, 3.9802276e-07]
[7]:
fir.plot_response(frequencies, x_units="frequency", pb_tol=.5)
print(f"Pass Band frequency range estimation: {fir.pass_band(frequencies, tol=.5)}")
Pass Band frequency range estimation: [1.00000000e-05 1.18071285e+01]
Pole Zero Filter
A pole-zero filter is a mathematical way to represent a filter using complex valued poles and zeros and a scaling factor. The advantage of the pole-zero representation is that arbitrary frequency ranges can be computed without having to extrapolate.
[8]:
from mt_metadata.timeseries.filters import PoleZeroFilter
[9]:
pz = PoleZeroFilter(units_in="volts", units_out="nanotesla", name="example_zpk_response")
pz.poles = [(-6.283185+10.882477j), (-6.283185-10.882477j), (-12.566371+0j)]
pz.zeros = []
pz.normalization_factor = 2002.269
pz
[9]:
{
"pole_zero_filter": {
"calibration_date": "1980-01-01",
"gain": 1.0,
"name": "example_zpk_response",
"normalization_factor": 2002.269,
"poles": {
"real": [
-6.283185,
-6.283185,
-12.566371
],
"imag": [
10.882477,
-10.882477,
0.0
]
},
"type": "zpk",
"units_in": "V",
"units_out": "nT",
"zeros": {
"real": [],
"imag": []
}
}
}
[10]:
pz.plot_response(frequencies, x_units="frequency", pb_tol=1e-2)
print(f"Pass Band frequency range estimation: {pz.pass_band(frequencies, tol=1e-2)}")
Pass Band frequency range estimation: [1.00000000e-05 5.36363132e-01]
Time Delay Filter
A time delay filter are often incorporated in the AD converter that controls mulitple channels and creates a small time delay as it shifts between the channels. A positive value predicts time and a negative value delays time. For causality the value should always be negative. The delay is givne in seconds.
[11]:
from mt_metadata.timeseries.filters import TimeDelayFilter
[12]:
td = TimeDelayFilter(units_in="volts", units_out="volts", name="example_time_delay", delay=-.25)
td.pass_band(frequencies)
[12]:
array([1.e-05, 1.e+05])
[13]:
td.plot_response(frequencies, x_units="frequency")
FrequencyResponseTableFilter
Commonly to calibrate the frequency response of an instrument a manufacturer will run a calibration taking measurements at discrete frequencies and then supplying a table of frequency, amplitude, and phase for the users. This is more accurate than pole-zero representation but has limitations in that extrapolation needs to be applied to frequencies outside of the calibrated frequencies and interpolation between calibrated frequencies.
Note: phase is assumed to be in radians.
[14]:
from mt_metadata.timeseries.filters import FrequencyResponseTableFilter
[15]:
fap = FrequencyResponseTableFilter(units_in="volts", units_out="nanotesla", name="example_fap")
fap.frequencies = [
1.95312000e-03, 2.76214000e-03, 3.90625000e-03,
5.52427000e-03, 7.81250000e-03, 1.10485000e-02,
1.56250000e-02, 2.20971000e-02, 3.12500000e-02,
4.41942000e-02, 6.25000000e-02, 8.83883000e-02,
1.25000000e-01, 1.76780000e-01, 2.50000000e-01,
3.53550000e-01, 5.00000000e-01, 7.07110000e-01,
1.00000000e+00, 1.41420000e+00, 2.00000000e+00,
2.82840000e+00, 4.00000000e+00, 5.65690000e+00,
8.00000000e+00, 1.13140000e+01, 1.60000000e+01,
2.26270000e+01, 3.20000000e+01, 4.52550000e+01,
6.40000000e+01, 9.05100000e+01, 1.28000000e+02,
1.81020000e+02, 2.56000000e+02, 3.62040000e+02,
5.12000000e+02, 7.24080000e+02, 1.02400000e+03,
1.44820000e+03, 2.04800000e+03, 2.89630000e+03,
4.09600000e+03, 5.79260000e+03, 8.19200000e+03,
1.15850000e+04]
fap.amplitudes = [
1.59009000e-03, 3.07497000e-03, 5.52793000e-03,
9.47448000e-03, 1.54565000e-02, 2.49498000e-02,
3.96462000e-02, 7.87192000e-02, 1.57134000e-01,
3.09639000e-01, 5.94224000e-01, 1.12698000e+00,
2.01092000e+00, 3.33953000e+00, 5.00280000e+00,
6.62396000e+00, 7.97545000e+00, 8.82872000e+00,
9.36883000e+00, 9.64102000e+00, 9.79664000e+00,
9.87183000e+00, 9.90666000e+00, 9.92845000e+00,
9.93559000e+00, 9.93982000e+00, 9.94300000e+00,
9.93546000e+00, 9.93002000e+00, 9.90873000e+00,
9.86383000e+00, 9.78129000e+00, 9.61814000e+00,
9.26461000e+00, 8.60175000e+00, 7.18337000e+00,
4.46123000e+00, -8.72600000e-01, -5.15684000e+00,
-2.95111000e+00, -9.28512000e-01, -2.49850000e-01,
-5.75682000e-02, -1.34293000e-02, -1.02708000e-03,
1.09577000e-03]
fap.phases = [
7.60824000e-02, 1.09174000e-01, 1.56106000e-01,
2.22371000e-01, 3.12020000e-01, 4.41080000e-01,
6.23548000e-01, 8.77188000e-01, 1.23360000e+00,
1.71519000e+00, 2.35172000e+00, 3.13360000e+00,
3.98940000e+00, 4.67269000e+00, 4.96593000e+00,
4.65875000e+00, 3.95441000e+00, 3.11098000e+00,
2.30960000e+00, 1.68210000e+00, 1.17928000e+00,
8.20015000e-01, 5.36474000e-01, 3.26955000e-01,
1.48051000e-01, -8.24275000e-03, -1.66064000e-01,
-3.48852000e-01, -5.66625000e-01, -8.62435000e-01,
-1.25347000e+00, -1.81065000e+00, -2.55245000e+00,
-3.61512000e+00, -5.00185000e+00, -6.86158000e+00,
-8.78698000e+00, -9.08920000e+00, -4.22925000e+00,
2.15533000e-01, 6.00661000e-01, 3.12368000e-01,
1.31660000e-01, 5.01553000e-02, 1.87239000e-02,
6.68243000e-03]
[16]:
from matplotlib import pyplot as plt
[17]:
fap.plot_response(frequencies, x_units="frequency", unwrap=True, pb_tol=1E-2, interpolation_method="slinear")
print(f"Pass Band frequency range estimation: {fap.pass_band(fap.frequencies, tol=1e-2, interpolation_method='slinear')}")
2023-09-27T16:22:20.641040-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating, use values outside calibration frequencies with caution
2023-09-27T16:22:20.641040-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating, use values outside calibration frequencies with caution
Pass Band frequency range estimation: [ 2. 181.02]
IMPORTANT: As you can see above extrapolation is unstable and will have change the data in unknown ways. If you need to extrapolate past the calibrated frequencies try to extrapolate using a different method than the one provided. Try using a different interpolation method, or a different curve fitting algorithm.
ChannelResponseFilter
These individual filters are useful, but the more practical use is combining all filters into a single filter and calculating the total response. The mt_metadata.timeseries.filters.ChannelResponseFilter is provide for this purpose.
[18]:
from mt_metadata.timeseries.filters import ChannelResponseFilter
[19]:
channel_response = ChannelResponseFilter()
channel_response.filters_list = [fir, td, fap]
channel_response.frequencies = frequencies
[20]:
channel_response.plot_response(x_units="frequency", pb_tol=1e-1, include_delay=True)
2023-09-27T16:22:21.020759-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating, use values outside calibration frequencies with caution
2023-09-27T16:22:21.042704-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating, use values outside calibration frequencies with caution
2023-09-27T16:22:21.055112-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating, use values outside calibration frequencies with caution
2023-09-27T16:22:21.076144-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating, use values outside calibration frequencies with caution
2023-09-27T16:22:21.092895-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating, use values outside calibration frequencies with caution
Estimate Total Sensitivity
If you want to do a quick calibration you can divide the time series by the total sensitivity and it will be accurate within the pass band.
[21]:
print(f"Normalization Frequency: {channel_response.normalization_frequency} Hz")
print(f"Pass Band: {channel_response.pass_band} Hz")
print(f"Total Sensitivity: {channel_response.compute_instrument_sensitivity(sig_figs=3)}")
2023-09-27T16:22:21.571180-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating, use values outside calibration frequencies with caution
2023-09-27T16:22:21.593486-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating, use values outside calibration frequencies with caution
Normalization Frequency: 1.097 Hz
2023-09-27T16:22:21.609817-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating, use values outside calibration frequencies with caution
Pass Band: [ 0.1018629 11.80712847] Hz
2023-09-27T16:22:21.629781-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating, use values outside calibration frequencies with caution
2023-09-27T16:22:21.648280-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating, use values outside calibration frequencies with caution
2023-09-27T16:22:21.661262-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating, use values outside calibration frequencies with caution
2023-09-27T16:22:21.681687-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating, use values outside calibration frequencies with caution
2023-09-27T16:22:21.705189-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating, use values outside calibration frequencies with caution
2023-09-27T16:22:21.725088-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating, use values outside calibration frequencies with caution
Total Sensitivity: 9.405
Check if units are proper between filters
The output units of the first filter must be the same units as the input units for the second filter. This is done when the filter list is set. Here you can see that the unit order is not correct.
[22]:
channel_response = ChannelResponseFilter()
channel_response.filters_list = [cf, fap, pz]
2023-09-27T16:22:21.747735-0700 | ERROR | mt_metadata.timeseries.filters.channel_response_filter | _check_consistency_of_units | Unit consistency is incorrect. The input units for example_zpk_response should be nT not V
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~\AppData\Local\Temp\1\ipykernel_19688\4206849502.py in <cell line: 2>()
1 channel_response = ChannelResponseFilter()
----> 2 channel_response.filters_list = [cf, fap, pz]
~\OneDrive - DOI\Documents\GitHub\mt_metadata\mt_metadata\base\metadata.py in __setattr__(self, name, value)
360
361 if name in skip_list:
--> 362 super().__setattr__(name, value)
363 return
364 if not name.startswith("_"):
~\OneDrive - DOI\Documents\GitHub\mt_metadata\mt_metadata\timeseries\filters\channel_response_filter.py in filters_list(self, filters_list)
70 """set the filters list and validate the list"""
71 self._filters_list = self._validate_filters_list(filters_list)
---> 72 self._check_consistency_of_units()
73
74 @property
~\OneDrive - DOI\Documents\GitHub\mt_metadata\mt_metadata\timeseries\filters\channel_response_filter.py in _check_consistency_of_units(self)
338 )
339 self.logger.error(msg)
--> 340 raise ValueError(msg)
341 previous_units = mt_filter.units_out
342
ValueError: Unit consistency is incorrect. The input units for example_zpk_response should be nT not V
Remove Instrument Response
This is an example notebook for removing the instrument response to calibrate the data into physical units.
Note: The workflow in this notebook is all incorporated into a class mth5.timeseris.ts_filters.RemoveInstrumentResponse and has been incorporated into mth5.timeseries.ChannelTS as mth5.timeseries.ChannelTS.remove_instrument_response and mth5.timeseries.RunTS as mth5.timeseries.RunTS.calibrate. An example is shown at the bottom of the notebook.
All the filters are represented as mt_metadata.timeseries.filters objects. More information about these can be found in mt_metadata filters.
This example will read in an existing MTH5 that has one station CAS04, one run a, and 2 channels ex and hx.
[9]:
from mth5.mth5 import MTH5
%matplotlib widget
Open the MTH5 file
This has 2 stations in it CAS04 and NVR08 both MTArray/Earthscope stations. Each only has one run. You can make this MTH5 file using make_mth5_driver_v0.2.0.ipnb
[3]:
m = MTH5()
m.open_mth5(r"8P_CAS04_NVR08.h5")
Have a look at the channel summary. This is a table of all the channels available in the MTH5 file.
[4]:
m.channel_summary.summarize()
ch_df = m.channel_summary.to_dataframe()
ch_df
[4]:
| survey | station | run | latitude | longitude | elevation | component | start | end | n_samples | sample_rate | measurement_type | azimuth | tilt | units | hdf5_reference | run_hdf5_reference | station_hdf5_reference | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | CONUS South | CAS04 | CAS04a | 37.633351 | -121.468382 | 329.387 | ex | 1980-01-01 00:00:00+00:00 | 1980-01-01 00:00:00+00:00 | 1 | 0.0 | electric | 13.2 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 1 | CONUS South | CAS04 | CAS04a | 37.633351 | -121.468382 | 329.387 | ey | 1980-01-01 00:00:00+00:00 | 1980-01-01 00:00:00+00:00 | 1 | 0.0 | electric | 103.2 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 2 | CONUS South | CAS04 | CAS04a | 37.633351 | -121.468382 | 329.387 | hx | 1980-01-01 00:00:00+00:00 | 1980-01-01 00:00:00+00:00 | 1 | 0.0 | magnetic | 13.2 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 3 | CONUS South | CAS04 | CAS04a | 37.633351 | -121.468382 | 329.387 | hy | 1980-01-01 00:00:00+00:00 | 1980-01-01 00:00:00+00:00 | 1 | 0.0 | magnetic | 103.2 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 4 | CONUS South | CAS04 | CAS04a | 37.633351 | -121.468382 | 329.387 | hz | 1980-01-01 00:00:00+00:00 | 1980-01-01 00:00:00+00:00 | 1 | 0.0 | magnetic | 13.2 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 65 | CONUS South | NVR08 | c | 38.326630 | -118.082382 | 1375.425 | ex | 2020-06-14 18:00:44+00:00 | 2020-06-24 15:55:47+00:00 | 856503 | 1.0 | electric | 12.6 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 66 | CONUS South | NVR08 | c | 38.326630 | -118.082382 | 1375.425 | ey | 2020-06-14 18:00:44+00:00 | 2020-06-24 15:55:47+00:00 | 856503 | 1.0 | electric | 102.6 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 67 | CONUS South | NVR08 | c | 38.326630 | -118.082382 | 1375.425 | hx | 2020-06-14 18:00:44+00:00 | 2020-06-24 15:55:47+00:00 | 856503 | 1.0 | magnetic | 12.6 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 68 | CONUS South | NVR08 | c | 38.326630 | -118.082382 | 1375.425 | hy | 2020-06-14 18:00:44+00:00 | 2020-06-24 15:55:47+00:00 | 856503 | 1.0 | magnetic | 102.6 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 69 | CONUS South | NVR08 | c | 38.326630 | -118.082382 | 1375.425 | hz | 2020-06-14 18:00:44+00:00 | 2020-06-24 15:55:47+00:00 | 856503 | 1.0 | magnetic | 0.0 | 90.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
70 rows × 18 columns
Get observatory data
[16]:
import json
frn_json = json.loads('{"type":"Timeseries","metadata":{"intermagnet":{"imo":{"iaga_code":"FRN","name":"Fresno","coordinates":[240.282,37.091,331.0]},"reported_orientation":"H","sensor_orientation":"HDZF","data_type":"adjusted","sampling_period":1,"digital_sampling_rate":0.01},"status":200,"generated":"2022-04-11T23:46:00Z","url":null},"times":["2020-06-02T19:00:00.000Z","2020-06-02T19:00:01.000Z","2020-06-02T19:00:02.000Z","2020-06-02T19:00:03.000Z","2020-06-02T19:00:04.000Z","2020-06-02T19:00:05.000Z","2020-06-02T19:00:06.000Z","2020-06-02T19:00:07.000Z","2020-06-02T19:00:08.000Z","2020-06-02T19:00:09.000Z","2020-06-02T19:00:10.000Z","2020-06-02T19:00:11.000Z","2020-06-02T19:00:12.000Z","2020-06-02T19:00:13.000Z","2020-06-02T19:00:14.000Z","2020-06-02T19:00:15.000Z","2020-06-02T19:00:16.000Z","2020-06-02T19:00:17.000Z","2020-06-02T19:00:18.000Z","2020-06-02T19:00:19.000Z","2020-06-02T19:00:20.000Z","2020-06-02T19:00:21.000Z","2020-06-02T19:00:22.000Z","2020-06-02T19:00:23.000Z","2020-06-02T19:00:24.000Z","2020-06-02T19:00:25.000Z","2020-06-02T19:00:26.000Z","2020-06-02T19:00:27.000Z","2020-06-02T19:00:28.000Z","2020-06-02T19:00:29.000Z","2020-06-02T19:00:30.000Z","2020-06-02T19:00:31.000Z","2020-06-02T19:00:32.000Z","2020-06-02T19:00:33.000Z","2020-06-02T19:00:34.000Z","2020-06-02T19:00:35.000Z","2020-06-02T19:00:36.000Z","2020-06-02T19:00:37.000Z","2020-06-02T19:00:38.000Z","2020-06-02T19:00:39.000Z","2020-06-02T19:00:40.000Z","2020-06-02T19:00:41.000Z","2020-06-02T19:00:42.000Z","2020-06-02T19:00:43.000Z","2020-06-02T19:00:44.000Z","2020-06-02T19:00:45.000Z","2020-06-02T19:00:46.000Z","2020-06-02T19:00:47.000Z","2020-06-02T19:00:48.000Z","2020-06-02T19:00:49.000Z","2020-06-02T19:00:50.000Z","2020-06-02T19:00:51.000Z","2020-06-02T19:00:52.000Z","2020-06-02T19:00:53.000Z","2020-06-02T19:00:54.000Z","2020-06-02T19:00:55.000Z","2020-06-02T19:00:56.000Z","2020-06-02T19:00:57.000Z","2020-06-02T19:00:58.000Z","2020-06-02T19:00:59.000Z","2020-06-02T19:01:00.000Z","2020-06-02T19:01:01.000Z","2020-06-02T19:01:02.000Z","2020-06-02T19:01:03.000Z","2020-06-02T19:01:04.000Z","2020-06-02T19:01:05.000Z","2020-06-02T19:01:06.000Z","2020-06-02T19:01:07.000Z","2020-06-02T19:01:08.000Z","2020-06-02T19:01:09.000Z","2020-06-02T19:01:10.000Z","2020-06-02T19:01:11.000Z","2020-06-02T19:01:12.000Z","2020-06-02T19:01:13.000Z","2020-06-02T19:01:14.000Z","2020-06-02T19:01:15.000Z","2020-06-02T19:01:16.000Z","2020-06-02T19:01:17.000Z","2020-06-02T19:01:18.000Z","2020-06-02T19:01:19.000Z","2020-06-02T19:01:20.000Z","2020-06-02T19:01:21.000Z","2020-06-02T19:01:22.000Z","2020-06-02T19:01:23.000Z","2020-06-02T19:01:24.000Z","2020-06-02T19:01:25.000Z","2020-06-02T19:01:26.000Z","2020-06-02T19:01:27.000Z","2020-06-02T19:01:28.000Z","2020-06-02T19:01:29.000Z","2020-06-02T19:01:30.000Z","2020-06-02T19:01:31.000Z","2020-06-02T19:01:32.000Z","2020-06-02T19:01:33.000Z","2020-06-02T19:01:34.000Z","2020-06-02T19:01:35.000Z","2020-06-02T19:01:36.000Z","2020-06-02T19:01:37.000Z","2020-06-02T19:01:38.000Z","2020-06-02T19:01:39.000Z","2020-06-02T19:01:40.000Z","2020-06-02T19:01:41.000Z","2020-06-02T19:01:42.000Z","2020-06-02T19:01:43.000Z","2020-06-02T19:01:44.000Z","2020-06-02T19:01:45.000Z","2020-06-02T19:01:46.000Z","2020-06-02T19:01:47.000Z","2020-06-02T19:01:48.000Z","2020-06-02T19:01:49.000Z","2020-06-02T19:01:50.000Z","2020-06-02T19:01:51.000Z","2020-06-02T19:01:52.000Z","2020-06-02T19:01:53.000Z","2020-06-02T19:01:54.000Z","2020-06-02T19:01:55.000Z","2020-06-02T19:01:56.000Z","2020-06-02T19:01:57.000Z","2020-06-02T19:01:58.000Z","2020-06-02T19:01:59.000Z","2020-06-02T19:02:00.000Z","2020-06-02T19:02:01.000Z","2020-06-02T19:02:02.000Z","2020-06-02T19:02:03.000Z","2020-06-02T19:02:04.000Z","2020-06-02T19:02:05.000Z","2020-06-02T19:02:06.000Z","2020-06-02T19:02:07.000Z","2020-06-02T19:02:08.000Z","2020-06-02T19:02:09.000Z","2020-06-02T19:02:10.000Z","2020-06-02T19:02:11.000Z","2020-06-02T19:02:12.000Z","2020-06-02T19:02:13.000Z","2020-06-02T19:02:14.000Z","2020-06-02T19:02:15.000Z","2020-06-02T19:02:16.000Z","2020-06-02T19:02:17.000Z","2020-06-02T19:02:18.000Z","2020-06-02T19:02:19.000Z","2020-06-02T19:02:20.000Z","2020-06-02T19:02:21.000Z","2020-06-02T19:02:22.000Z","2020-06-02T19:02:23.000Z","2020-06-02T19:02:24.000Z","2020-06-02T19:02:25.000Z","2020-06-02T19:02:26.000Z","2020-06-02T19:02:27.000Z","2020-06-02T19:02:28.000Z","2020-06-02T19:02:29.000Z","2020-06-02T19:02:30.000Z","2020-06-02T19:02:31.000Z","2020-06-02T19:02:32.000Z","2020-06-02T19:02:33.000Z","2020-06-02T19:02:34.000Z","2020-06-02T19:02:35.000Z","2020-06-02T19:02:36.000Z","2020-06-02T19:02:37.000Z","2020-06-02T19:02:38.000Z","2020-06-02T19:02:39.000Z","2020-06-02T19:02:40.000Z","2020-06-02T19:02:41.000Z","2020-06-02T19:02:42.000Z","2020-06-02T19:02:43.000Z","2020-06-02T19:02:44.000Z","2020-06-02T19:02:45.000Z","2020-06-02T19:02:46.000Z","2020-06-02T19:02:47.000Z","2020-06-02T19:02:48.000Z","2020-06-02T19:02:49.000Z","2020-06-02T19:02:50.000Z","2020-06-02T19:02:51.000Z","2020-06-02T19:02:52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Get EX channel
Here we will get an EX channel, it will return a channel_dataset object.
Change ex to hx to look at the magnetic channels
[5]:
ex = m.get_channel("CAS04", "a", "hx", survey="CONUS_South")
ChannelTS object
Convert the ex channel to a ChannelTS object which is based on an xarray.DataArray and has methods to working with the data. More information can be found Time Series Objects
[6]:
exts = ex.to_channel_ts()
exts = exts.get_slice(start=exts.start, n_samples=4096)
Channel Response Filter
With the ChannelTS object comes the ChannelResponseFilter object. This includes all the filters that need to be applied to convert to physical units. Here we print out what the channel_response_filter attribute includes.
[7]:
exts.channel_response_filter
[7]:
Filters Included:
=========================
pole_zero_filter:
calibration_date = 1980-01-01
comments = NIMS magnetic field 3 pole Butterworth 0.5 low pass (analog)
gain = 1.0
name = magnetic_butterworth_low_pass
normalization_factor = 2002.26936395594
poles = [ -6.283185+10.882477j -6.283185-10.882477j -12.566371 +0.j ]
type = zpk
units_in = nT
units_out = V
zeros = []
--------------------
coefficient_filter:
calibration_date = 1980-01-01
comments = analog to digital conversion (magnetic)
gain = 100.0
name = magnetic_analog_to_digital
type = coefficient
units_in = V
units_out = count
--------------------
time_delay_filter:
calibration_date = 1980-01-01
comments = time offset in seconds (digital)
delay = -0.192
gain = 1.0
name = hx_time_offset
type = time delay
units_in = count
units_out = count
--------------------
Plot the channel response
The gray areas are the pass band of the filters and the vertical black line is the normalization frequency. These are estimated values.
[8]:
exts.channel_response_filter.plot_response(x_units="frequency")
Remove Instrument Response
Here we will attempt to remove the instrument response to calibrate the data in physical units. Create a few helper functions
zero_padwill pad an input array to a power of 2.
[20]:
calibrated_ex = exts.remove_instrument_response(plot=True)
[18]:
from matplotlib import pyplot as plt
import numpy as np
from scipy import signal
[29]:
# plot normalized version of original and calibrated
fig = plt.figure(2, figsize=[5,4])
fig.clf()
ax_original = fig.add_subplot(2, 1, 1)
original_ts = exts.ts.copy()
original_ts = original_ts - original_ts.mean()
original_ts = original_ts / original_ts.max()
frn_ts = np.array(frn_json["values"][0]["values"], dtype=float)
frn_ts = signal.detrend(frn_ts, type="linear")
frn_ts = frn_ts - frn_ts.mean()
#frn_ts = frn_ts / frn_ts.max()
l1, = ax_original.twinx().plot(exts._ts.time, original_ts, color=(0, 0, 0))
l2, = ax_original.plot(exts._ts.time, calibrated_ex.ts, color=(.75, .1, .1))
l3, = ax_original.plot(np.array(frn_json["times"], dtype=np.datetime64), frn_ts, color=(.2, .1, .75))
ax_original.set_ylabel("nT")
ax_original.set_xlim((exts._ts.time[0], exts._ts.time[-1]))
ax_original_fft = fig.add_subplot(2, 1, 2)
f = np.fft.rfftfreq(original_ts.size, d=1)
original_fft = abs(np.fft.rfft(original_ts))
calibrated = abs(np.fft.rfft(calibrated_ex.ts))
response = abs(exts.channel_response_filter.complex_response(f))[::-1]
response[-1] = response[-2]
frn_spectra = abs(np.fft.rfft(frn_ts))
f_frn = np.fft.rfftfreq(frn_ts.size, d=1)
l1, = ax_original_fft.loglog(f, original_fft / original_fft.max(), color=(0, 0, 0))
l2, = ax_original_fft.loglog(f, calibrated / calibrated.max(), color=(.75, .1, .2))
l3, = ax_original_fft.loglog(f, response / response.max(), color=(.1, .75, .2))
l4, = ax_original_fft.loglog(f_frn, frn_spectra / frn_spectra.max(), color=(.2, .1, .75))
ax_original_fft.set_ylim((1E-6, 10))
fig.legend(
[l1, l2, l4, l3],
["Original", "Calibrated", "Observatory", "Response"],
ncol=4,
loc="upper center",
)
fig.canvas.toolbar_visible = True
fig.canvas.header_visible = True
fig.canvas.resizable = True
fig.tight_layout()
plt.show()
C:\Users\jpeacock\AppData\Local\Temp\11\ipykernel_9464\1879432517.py:14: DeprecationWarning: parsing timezone aware datetimes is deprecated; this will raise an error in the future
l3, = ax_original.plot(np.array(frn_json["times"], dtype=np.datetime64), frn_ts, color=(.2, .1, .75))
[ ]:
#m.close_mth5()
[ ]:
File Readers
Basics
The file readers have been setup to be like plugins, well hacked so it is setup like plugins. Further work needs to be done to fully set it up as Python plugins, but for now it works.
There is a generic reader that is loaded when MTH5 is imported called read_file. It will pick the correct reader based on the file extension or if the extension is ambiguous the user can input the specific file type.
>>> import mth5
>>> run_obj = mth5.read_file(r"/home/mt_data/mt001.bin")
>>> run_obj = mth5.read_file(r"/home/mt_data/mt001.bin", file_type='nims')
Supported Data Types
The following file types are supported:
File Structure
MTH5 Key
File Types
Returns
NIMS
nims
[.bin, .bnn]
RunTS
LEMI424
lemi
[.txt]
RunTS
USGS ASCII
usgs_ascii
[.asc, .gzip]
RunTS
Zonge Z3D
zen
[.z3d]
ChannelTS
Phoenix
phoenix
[.td_*, .bin]
ChannelTS
As you can see, NIMS, USGS ASCII, LEMI424 will return a mth5.timeseries.RunTS object and Zonge Z3D and Phoenix returns a mth5.timeseries.ChannelTS object. The return type depends on the structure of the file. Long period instruments usually record each channel in a single block of data, so all channels are in a single file. Whereas, broadband instruments record each channel as a single file. It might make sense in to return the same data type, but for now this is the way it is. Also returned are any extra metadata that might not belong to a channel or run. Specifically, most files have information about location and some other metadata about the station that could be helpful in filling out metadata for the station.
Collection
To further make intake easier, collections classes have been developed for each data type. For example LEMI424 files are typically 1 day long, but that does not necessarily mean that is the defines a single run. A run may incorporate multiple days, thus multiple files. The role of the LEMICollection object is to organize the files, in a logical way and assign a run ID to runs defined as a continuous block of data that may spand multiple files. See the next section for further examples.
Examples of Reading in Data
Note
In these examples the data are stored locally and will not run if you don’t have the data. We are working on setting up a repository to store data. But if you have data you can easily swap out the directory path.
Make an MTH5 from LEMI data
This notebook provides an example of how to read in LEMI (.TXT) files into an MTH5.
[1]:
from mth5.mth5 import MTH5
from mth5.io.lemi import LEMICollection
from mth5 import read_file
2022-09-07 18:07:09,774 [line 135] mth5.setup_logger - INFO: Logging file can be found C:\Users\jpeacock\OneDrive - DOI\Documents\GitHub\mth5\logs\mth5_debug.log
LEMI Collection
We will use the LEMICollection to assemble the .txt files into a logical order by schedule action or run. The output LEMI files include all data for each channel.
IMPORTANT: LEMICollection assumes the given file path is for a single station.
Metadata: we need to input the station_id and the survey_id to provide minimal metadata when making an MTH5 fild.
The LEMICollection.get_runs() will return a two level ordered dictionary (OrderedDict). The first level is keyed by station ID. These objects are in turn ordered dictionaries by run ID. Therefore you can loop over stations and runs.
Note: n_samples is an estimate based on file size not the data. To get an accurate number you should read in the full file.
[2]:
zc = LEMICollection(r"c:\Users\jpeacock\OneDrive - DOI\mt\lemi\DATA0110")
zc.station_id = "mt001"
zc.survey_id = "test"
runs = zc.get_runs(sample_rates=[1])
print(f"Found {len(runs)} station with {len(runs[list(runs.keys())[0]])} runs")
Found 1 station with 5 runs
[3]:
for run_id, run_df in runs[zc.station_id].items():
display(run_df)
| survey | station | run | start | end | channel_id | component | fn | sample_rate | file_size | n_samples | sequence_number | instrument_id | calibration_fn | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | test | mt001 | sr1_0001 | 2020-09-30 20:21:00+00:00 | 2020-09-30 20:28:15+00:00 | 1 | temperature_e,temperature_h,e1,e2,bx,by,bz | c:\Users\jpeacock\OneDrive - DOI\mt\lemi\DATA0... | 1.0 | 66272 | 436 | 0 | LEMI424 | None |
| survey | station | run | start | end | channel_id | component | fn | sample_rate | file_size | n_samples | sequence_number | instrument_id | calibration_fn | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | test | mt001 | sr1_0002 | 2020-09-30 20:29:00+00:00 | 2020-09-30 20:42:16+00:00 | 1 | temperature_e,temperature_h,e1,e2,bx,by,bz | c:\Users\jpeacock\OneDrive - DOI\mt\lemi\DATA0... | 1.0 | 121144 | 797 | 0 | LEMI424 | None |
| survey | station | run | start | end | channel_id | component | fn | sample_rate | file_size | n_samples | sequence_number | instrument_id | calibration_fn | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | test | mt001 | sr1_0003 | 2020-09-30 20:54:00+00:00 | 2020-09-30 21:11:01+00:00 | 1 | temperature_e,temperature_h,e1,e2,bx,by,bz | c:\Users\jpeacock\OneDrive - DOI\mt\lemi\DATA0... | 1.0 | 155344 | 1022 | 0 | LEMI424 | None |
| survey | station | run | start | end | channel_id | component | fn | sample_rate | file_size | n_samples | sequence_number | instrument_id | calibration_fn | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3 | test | mt001 | sr1_0004 | 2020-09-30 21:12:00+00:00 | 2020-09-30 21:13:45+00:00 | 1 | temperature_e,temperature_h,e1,e2,bx,by,bz | c:\Users\jpeacock\OneDrive - DOI\mt\lemi\DATA0... | 1.0 | 16112 | 106 | 0 | LEMI424 | None |
| survey | station | run | start | end | channel_id | component | fn | sample_rate | file_size | n_samples | sequence_number | instrument_id | calibration_fn | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | test | mt001 | sr1_0005 | 2020-09-30 21:14:00+00:00 | 2020-09-30 23:59:59+00:00 | 1 | temperature_e,temperature_h,e1,e2,bx,by,bz | c:\Users\jpeacock\OneDrive - DOI\mt\lemi\DATA0... | 1.0 | 1513920 | 9960 | 0 | LEMI424 | None |
| 5 | test | mt001 | sr1_0005 | 2020-10-01 00:00:00+00:00 | 2020-10-01 23:59:59+00:00 | 1 | temperature_e,temperature_h,e1,e2,bx,by,bz | c:\Users\jpeacock\OneDrive - DOI\mt\lemi\DATA0... | 1.0 | 13132800 | 86400 | 0 | LEMI424 | None |
| 6 | test | mt001 | sr1_0005 | 2020-10-02 00:00:00+00:00 | 2020-10-02 23:59:59+00:00 | 1 | temperature_e,temperature_h,e1,e2,bx,by,bz | c:\Users\jpeacock\OneDrive - DOI\mt\lemi\DATA0... | 1.0 | 13132800 | 86400 | 0 | LEMI424 | None |
| 7 | test | mt001 | sr1_0005 | 2020-10-03 00:00:00+00:00 | 2020-10-03 23:59:59+00:00 | 1 | temperature_e,temperature_h,e1,e2,bx,by,bz | c:\Users\jpeacock\OneDrive - DOI\mt\lemi\DATA0... | 1.0 | 13132800 | 86400 | 0 | LEMI424 | None |
| 8 | test | mt001 | sr1_0005 | 2020-10-04 00:00:00+00:00 | 2020-10-04 23:59:59+00:00 | 1 | temperature_e,temperature_h,e1,e2,bx,by,bz | c:\Users\jpeacock\OneDrive - DOI\mt\lemi\DATA0... | 1.0 | 13132800 | 86400 | 0 | LEMI424 | None |
| 9 | test | mt001 | sr1_0005 | 2020-10-05 00:00:00+00:00 | 2020-10-05 23:59:59+00:00 | 1 | temperature_e,temperature_h,e1,e2,bx,by,bz | c:\Users\jpeacock\OneDrive - DOI\mt\lemi\DATA0... | 1.0 | 13132801 | 86400 | 0 | LEMI424 | None |
| 10 | test | mt001 | sr1_0005 | 2020-10-06 00:00:00+00:00 | 2020-10-06 23:59:59+00:00 | 1 | temperature_e,temperature_h,e1,e2,bx,by,bz | c:\Users\jpeacock\OneDrive - DOI\mt\lemi\DATA0... | 1.0 | 13132800 | 86400 | 0 | LEMI424 | None |
| 11 | test | mt001 | sr1_0005 | 2020-10-07 00:00:00+00:00 | 2020-10-07 14:19:46+00:00 | 1 | temperature_e,temperature_h,e1,e2,bx,by,bz | c:\Users\jpeacock\OneDrive - DOI\mt\lemi\DATA0... | 1.0 | 7841224 | 51587 | 0 | LEMI424 | None |
Now that we have a logical collection of files, lets load them into an MTH5. We will simply loop of the stations, runs, and channels in the ordered dictionary.
There are a few things that to keep in mind:
The LEMI raw files come with very little metadata, so as a user you will have to manually input most of it.
The output files from a LEMI are already calibrated into units of nT and mV/km (I think), therefore there are no filter to apply to calibrate the data.
Since this is a MTH5 file version 0.2.0 the filters are in the
survey_groupso add them there.
[4]:
m = MTH5()
m.open_mth5(zc.file_path.joinpath("from_lemi.h5"))
2022-09-07 18:07:12,663 [line 663] mth5.mth5.MTH5._initialize_file - INFO: Initialized MTH5 0.2.0 file c:\Users\jpeacock\OneDrive - DOI\mt\lemi\DATA0110\from_lemi.h5 in mode a
[5]:
survey_group = m.add_survey(zc.survey_id)
[6]:
%%time
for station_id in runs.keys():
station_group = survey_group.stations_group.add_station(station_id)
for run_id, run_df in runs[station_id].items():
run_group = station_group.add_run(run_id)
run_ts = read_file(run_df.fn.to_list())
run_group.from_runts(run_ts)
station_group.metadata.update(run_ts.station_metadata)
station_group.write_metadata()
Wall time: 42 s
[7]:
%%time
station_group.validate_station_metadata()
station_group.write_metadata()
survey_group.update_survey_metadata()
survey_group.write_metadata()
Wall time: 27.3 s
Have a look at the MTH5 structure and make sure it looks correct.
[8]:
m
[8]:
/:
====================
|- Group: Experiment
--------------------
|- Group: Reports
-----------------
|- Group: Standards
-------------------
--> Dataset: summary
......................
|- Group: Surveys
-----------------
|- Group: test
--------------
|- Group: Filters
-----------------
|- Group: coefficient
---------------------
|- Group: fap
-------------
|- Group: fir
-------------
|- Group: time_delay
--------------------
|- Group: zpk
-------------
|- Group: Reports
-----------------
|- Group: Standards
-------------------
--> Dataset: summary
......................
|- Group: Stations
------------------
|- Group: mt001
---------------
|- Group: Transfer_Functions
----------------------------
|- Group: sr1_0001
------------------
--> Dataset: bx
.................
--> Dataset: by
.................
--> Dataset: bz
.................
--> Dataset: e1
.................
--> Dataset: e2
.................
--> Dataset: temperature_e
............................
--> Dataset: temperature_h
............................
|- Group: sr1_0002
------------------
--> Dataset: bx
.................
--> Dataset: by
.................
--> Dataset: bz
.................
--> Dataset: e1
.................
--> Dataset: e2
.................
--> Dataset: temperature_e
............................
--> Dataset: temperature_h
............................
|- Group: sr1_0003
------------------
--> Dataset: bx
.................
--> Dataset: by
.................
--> Dataset: bz
.................
--> Dataset: e1
.................
--> Dataset: e2
.................
--> Dataset: temperature_e
............................
--> Dataset: temperature_h
............................
|- Group: sr1_0004
------------------
--> Dataset: bx
.................
--> Dataset: by
.................
--> Dataset: bz
.................
--> Dataset: e1
.................
--> Dataset: e2
.................
--> Dataset: temperature_e
............................
--> Dataset: temperature_h
............................
|- Group: sr1_0005
------------------
--> Dataset: bx
.................
--> Dataset: by
.................
--> Dataset: bz
.................
--> Dataset: e1
.................
--> Dataset: e2
.................
--> Dataset: temperature_e
............................
--> Dataset: temperature_h
............................
--> Dataset: channel_summary
..............................
--> Dataset: tf_summary
.........................
Channel Summary
Have a look at the channel summary and make sure everything looks good.
[9]:
m.channel_summary.summarize()
m.channel_summary.to_dataframe()
[9]:
| survey | station | run | latitude | longitude | elevation | component | start | end | n_samples | sample_rate | measurement_type | azimuth | tilt | units | hdf5_reference | run_hdf5_reference | station_hdf5_reference | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | bx | 2020-09-30 20:21:00+00:00 | 2020-09-30 20:28:16+00:00 | 436 | 1.0 | magnetic | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 1 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | by | 2020-09-30 20:21:00+00:00 | 2020-09-30 20:28:16+00:00 | 436 | 1.0 | magnetic | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 2 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | bz | 2020-09-30 20:21:00+00:00 | 2020-09-30 20:28:16+00:00 | 436 | 1.0 | magnetic | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 3 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | e1 | 2020-09-30 20:21:00+00:00 | 2020-09-30 20:28:16+00:00 | 436 | 1.0 | electric | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 4 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | e2 | 2020-09-30 20:21:00+00:00 | 2020-09-30 20:28:16+00:00 | 436 | 1.0 | electric | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 5 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | temperature_e | 2020-09-30 20:21:00+00:00 | 2020-09-30 20:28:16+00:00 | 436 | 1.0 | auxiliary | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 6 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | temperature_h | 2020-09-30 20:21:00+00:00 | 2020-09-30 20:28:16+00:00 | 436 | 1.0 | auxiliary | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 7 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | bx | 2020-09-30 20:29:00+00:00 | 2020-09-30 20:42:17+00:00 | 797 | 1.0 | magnetic | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 8 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | by | 2020-09-30 20:29:00+00:00 | 2020-09-30 20:42:17+00:00 | 797 | 1.0 | magnetic | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 9 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | bz | 2020-09-30 20:29:00+00:00 | 2020-09-30 20:42:17+00:00 | 797 | 1.0 | magnetic | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 10 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | e1 | 2020-09-30 20:29:00+00:00 | 2020-09-30 20:42:17+00:00 | 797 | 1.0 | electric | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 11 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | e2 | 2020-09-30 20:29:00+00:00 | 2020-09-30 20:42:17+00:00 | 797 | 1.0 | electric | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 12 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | temperature_e | 2020-09-30 20:29:00+00:00 | 2020-09-30 20:42:17+00:00 | 797 | 1.0 | auxiliary | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 13 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | temperature_h | 2020-09-30 20:29:00+00:00 | 2020-09-30 20:42:17+00:00 | 797 | 1.0 | auxiliary | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 14 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | bx | 2020-09-30 20:54:00+00:00 | 2020-09-30 21:11:02+00:00 | 1022 | 1.0 | magnetic | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 15 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | by | 2020-09-30 20:54:00+00:00 | 2020-09-30 21:11:02+00:00 | 1022 | 1.0 | magnetic | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 16 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | bz | 2020-09-30 20:54:00+00:00 | 2020-09-30 21:11:02+00:00 | 1022 | 1.0 | magnetic | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 17 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | e1 | 2020-09-30 20:54:00+00:00 | 2020-09-30 21:11:02+00:00 | 1022 | 1.0 | electric | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 18 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | e2 | 2020-09-30 20:54:00+00:00 | 2020-09-30 21:11:02+00:00 | 1022 | 1.0 | electric | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 19 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | temperature_e | 2020-09-30 20:54:00+00:00 | 2020-09-30 21:11:02+00:00 | 1022 | 1.0 | auxiliary | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 20 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | temperature_h | 2020-09-30 20:54:00+00:00 | 2020-09-30 21:11:02+00:00 | 1022 | 1.0 | auxiliary | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 21 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | bx | 2020-09-30 21:12:00+00:00 | 2020-09-30 21:13:46+00:00 | 106 | 1.0 | magnetic | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 22 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | by | 2020-09-30 21:12:00+00:00 | 2020-09-30 21:13:46+00:00 | 106 | 1.0 | magnetic | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 23 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | bz | 2020-09-30 21:12:00+00:00 | 2020-09-30 21:13:46+00:00 | 106 | 1.0 | magnetic | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 24 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | e1 | 2020-09-30 21:12:00+00:00 | 2020-09-30 21:13:46+00:00 | 106 | 1.0 | electric | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 25 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | e2 | 2020-09-30 21:12:00+00:00 | 2020-09-30 21:13:46+00:00 | 106 | 1.0 | electric | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 26 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | temperature_e | 2020-09-30 21:12:00+00:00 | 2020-09-30 21:13:46+00:00 | 106 | 1.0 | auxiliary | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 27 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | temperature_h | 2020-09-30 21:12:00+00:00 | 2020-09-30 21:13:46+00:00 | 106 | 1.0 | auxiliary | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 28 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | bx | 2020-09-30 21:14:00+00:00 | 2020-10-07 17:05:47+00:00 | 589907 | 1.0 | magnetic | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 29 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | by | 2020-09-30 21:14:00+00:00 | 2020-10-07 17:05:47+00:00 | 589907 | 1.0 | magnetic | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 30 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | bz | 2020-09-30 21:14:00+00:00 | 2020-10-07 17:05:47+00:00 | 589907 | 1.0 | magnetic | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 31 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | e1 | 2020-09-30 21:14:00+00:00 | 2020-10-07 17:05:47+00:00 | 589907 | 1.0 | electric | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 32 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | e2 | 2020-09-30 21:14:00+00:00 | 2020-10-07 17:05:47+00:00 | 589907 | 1.0 | electric | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 33 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | temperature_e | 2020-09-30 21:14:00+00:00 | 2020-10-07 17:05:47+00:00 | 589907 | 1.0 | auxiliary | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 34 | test | mt001 | a | 34.080655 | -107.214079 | 2202.8 | temperature_h | 2020-09-30 21:14:00+00:00 | 2020-10-07 17:05:47+00:00 | 589907 | 1.0 | auxiliary | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
This is important, you should close the file after you are done using it. Otherwise bad things can happen if you try to open it with another program or Python interpreter.
[10]:
m.close_mth5()
2022-09-07 18:08:23,271 [line 744] mth5.mth5.MTH5.close_mth5 - INFO: Flushing and closing c:\Users\jpeacock\OneDrive - DOI\mt\lemi\DATA0110\from_lemi.h5
Make an MTH5 from NIMS data
This notebook provides an example of how to read in NIMS (.BIN) files into an MTH5. NIMS files represent a single run.
[1]:
from mth5.mth5 import MTH5
from mth5.io.nims import NIMSCollection
from mth5 import read_file
2022-09-07 13:02:52,583 [line 135] mth5.setup_logger - INFO: Logging file can be found C:\Users\jpeacock\OneDrive - DOI\Documents\GitHub\mth5\logs\mth5_debug.log
NIMS Collection
We will use the NIMSCollection to assemble the .bin files into a logical order by run. The output NIMS files include all data for each channel for a single run. Therefore the collection is relatively simple.
Metadata: we need to input the survey_id to provide minimal metadata when making an MTH5 file.
The NIMSCollection.get_runs() will return a two level ordered dictionary (OrderedDict). The first level is keyed by station ID. These objects are in turn ordered dictionaries by run ID. Therefore you can loop over stations and runs.
Note: n_samples and end are estimates based on file size not the data. To get an accurate number you should read in the full file.
[2]:
nc = NIMSCollection(r"c:\Users\jpeacock\OneDrive - DOI\mt\nims")
nc.survey_id = "test"
runs = nc.get_runs(sample_rates=[8])
print(f"Found {len(runs)} station with {len(runs[list(runs.keys())[0]])} runs")
2022-09-07 13:02:53,062 [line 123] mth5.io.nims.header.NIMS.read_header - INFO: Reading NIMS file c:\Users\jpeacock\OneDrive - DOI\mt\nims\mnp300a.BIN
2022-09-07 13:02:53,081 [line 242] mth5.io.nims.header.NIMS.end_time - WARNING: Estimating end time from n_samples
2022-09-07 13:02:53,086 [line 123] mth5.io.nims.header.NIMS.read_header - INFO: Reading NIMS file c:\Users\jpeacock\OneDrive - DOI\mt\nims\mnp300b.BIN
2022-09-07 13:02:53,099 [line 242] mth5.io.nims.header.NIMS.end_time - WARNING: Estimating end time from n_samples
Found 1 station with 2 runs
[3]:
for run_id, run_df in runs["mnp300"].items():
display(run_df)
| survey | station | run | start | end | channel_id | component | fn | sample_rate | file_size | n_samples | sequence_number | instrument_id | calibration_fn | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | test | mnp300 | mnp300a | 2019-09-26 18:29:29+00:00 | 2019-10-01 15:03:23+00:00 | 1 | hx,hy,hz,ex,ey,temperature | c:\Users\jpeacock\OneDrive - DOI\mt\nims\mnp30... | 8 | 54972155 | 3357078 | 1 | NIMS | None |
| survey | station | run | start | end | channel_id | component | fn | sample_rate | file_size | n_samples | sequence_number | instrument_id | calibration_fn | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | test | mnp300 | mnp300b | 2019-10-01 16:16:42+00:00 | 2019-10-03 22:55:52+00:00 | 1 | hx,hy,hz,ex,ey,temperature | c:\Users\jpeacock\OneDrive - DOI\mt\nims\mnp30... | 8 | 25774314 | 1574003 | 2 | NIMS | None |
Now that we have a logical collection of files, lets load them into an MTH5. We will simply loop of the stations, runs, and channels in the ordered dictionary.
There are a few things that to keep in mind:
The LEMI raw files come with very little metadata, so as a user you will have to manually input most of it.
The output files from a LEMI are already calibrated into units of nT and mV/km (I think), therefore there are no filter to apply to calibrate the data.
Since this is a MTH5 file version 0.2.0 the filters are in the
survey_groupso add them there.
TODO:
- make sure filters get propagated throught to mth5
- think about run names
[4]:
calibrate = True
m = MTH5()
if calibrate:
m.data_level = 2
m.open_mth5(nc.file_path.joinpath("from_nims.h5"))
2022-09-07 13:02:53,674 [line 663] mth5.mth5.MTH5._initialize_file - INFO: Initialized MTH5 0.2.0 file c:\Users\jpeacock\OneDrive - DOI\mt\nims\from_nims.h5 in mode a
[5]:
survey_group = m.add_survey(nc.survey_id)
[6]:
%%time
for station_id in runs.keys():
station_group = survey_group.stations_group.add_station(station_id)
for run_id, run_df in runs[station_id].items():
run_group = station_group.add_run(run_id)
run_ts = read_file(run_df.fn.unique()[0])
if calibrate:
run_ts = run_ts.calibrate()
run_group.from_runts(run_ts)
station_group.metadata.update(run_ts.station_metadata)
station_group.write_metadata()
2022-09-07 13:02:54,234 [line 123] mth5.io.nims.header.NIMS.read_header - INFO: Reading NIMS file c:\Users\jpeacock\OneDrive - DOI\mt\nims\mnp300a.BIN
2022-09-07 13:02:54,649 [line 927] mth5.io.nims.header.NIMS.read_nims - WARNING: odd number of bytes 54971209, not even blocks cutting down the data by 72 bits
2022-09-07 13:02:55,667 [line 1019] mth5.io.nims.header.NIMS.read_nims - INFO: Reading took 1.43 seconds
2022-09-07 13:02:56,835 [line 288] mth5.timeseries.run_ts.RunTS.validate_metadata - WARNING: end time of dataset 2019-10-01T15:07:08+00:00 does not match metadata end 2019-10-01T15:07:07.875000+00:00 updating metatdata value to 2019-10-01T15:07:08+00:00
2022-09-07 13:02:57,909 [line 288] mth5.timeseries.run_ts.RunTS.validate_metadata - WARNING: end time of dataset 2019-10-01T15:07:08+00:00 does not match metadata end 2019-10-01T15:07:07.875000+00:00 updating metatdata value to 2019-10-01T15:07:08+00:00
2022-09-07 13:03:07,154 [line 123] mth5.io.nims.header.NIMS.read_header - INFO: Reading NIMS file c:\Users\jpeacock\OneDrive - DOI\mt\nims\mnp300b.BIN
2022-09-07 13:03:07,769 [line 1019] mth5.io.nims.header.NIMS.read_nims - INFO: Reading took 0.61 seconds
2022-09-07 13:03:08,620 [line 288] mth5.timeseries.run_ts.RunTS.validate_metadata - WARNING: end time of dataset 2019-10-03T23:01:04+00:00 does not match metadata end 2019-10-03T23:01:03.875000+00:00 updating metatdata value to 2019-10-03T23:01:04+00:00
2022-09-07 13:03:09,250 [line 288] mth5.timeseries.run_ts.RunTS.validate_metadata - WARNING: end time of dataset 2019-10-03T23:01:04+00:00 does not match metadata end 2019-10-03T23:01:03.875000+00:00 updating metatdata value to 2019-10-03T23:01:04+00:00
Wall time: 26.9 s
[7]:
%%time
station_group.validate_station_metadata()
station_group.write_metadata()
survey_group.update_survey_metadata()
survey_group.write_metadata()
Wall time: 7.68 s
Have a look at the MTH5 structure and make sure it looks correct.
[8]:
m
[8]:
/:
====================
|- Group: Experiment
--------------------
|- Group: Reports
-----------------
|- Group: Standards
-------------------
--> Dataset: summary
......................
|- Group: Surveys
-----------------
|- Group: test
--------------
|- Group: Filters
-----------------
|- Group: coefficient
---------------------
|- Group: dipole_101.00
-----------------------
|- Group: dipole_106.00
-----------------------
|- Group: dipole_109.00
-----------------------
|- Group: e_analog_to_digital
-----------------------------
|- Group: h_analog_to_digital
-----------------------------
|- Group: to_mt_units
---------------------
|- Group: fap
-------------
|- Group: fir
-------------
|- Group: time_delay
--------------------
|- Group: ex_time_offset
------------------------
|- Group: ey_time_offset
------------------------
|- Group: hx_time_offset
------------------------
|- Group: hy_time_offset
------------------------
|- Group: hz_time_offset
------------------------
|- Group: zpk
-------------
|- Group: nims_1_pole_butterworth
---------------------------------
--> Dataset: poles
....................
--> Dataset: zeros
....................
|- Group: nims_3_pole_butterworth
---------------------------------
--> Dataset: poles
....................
--> Dataset: zeros
....................
|- Group: nims_5_pole_butterworth
---------------------------------
--> Dataset: poles
....................
--> Dataset: zeros
....................
|- Group: Reports
-----------------
|- Group: Standards
-------------------
--> Dataset: summary
......................
|- Group: Stations
------------------
|- Group: mnp300
----------------
|- Group: Transfer_Functions
----------------------------
|- Group: mnp300a
-----------------
--> Dataset: ex
.................
--> Dataset: ey
.................
--> Dataset: hx
.................
--> Dataset: hy
.................
--> Dataset: hz
.................
--> Dataset: temperature
..........................
|- Group: mnp300b
-----------------
--> Dataset: ex
.................
--> Dataset: ey
.................
--> Dataset: hx
.................
--> Dataset: hy
.................
--> Dataset: hz
.................
--> Dataset: temperature
..........................
--> Dataset: channel_summary
..............................
--> Dataset: tf_summary
.........................
Channel Summary
Have a look at the channel summary and make sure everything looks good.
[9]:
m.channel_summary.summarize()
m.channel_summary.to_dataframe()
[9]:
| survey | station | run | latitude | longitude | elevation | component | start | end | n_samples | sample_rate | measurement_type | azimuth | tilt | units | hdf5_reference | run_hdf5_reference | station_hdf5_reference | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | test | mnp300 | mnp300a | 34.726823 | -115.735015 | 940.0 | ex | 2019-09-26 18:33:21+00:00 | 2019-10-01 15:07:08+00:00 | 3357016 | 8.0 | electric | 0.0 | 0.0 | count | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 1 | test | mnp300 | mnp300a | 34.726823 | -115.735015 | 940.0 | ey | 2019-09-26 18:33:21+00:00 | 2019-10-01 15:07:08+00:00 | 3357016 | 8.0 | electric | 90.0 | 0.0 | count | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 2 | test | mnp300 | mnp300a | 34.726823 | -115.735015 | 940.0 | hx | 2019-09-26 18:33:21+00:00 | 2019-10-01 15:07:08+00:00 | 3357016 | 8.0 | magnetic | 0.0 | 0.0 | count | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 3 | test | mnp300 | mnp300a | 34.726823 | -115.735015 | 940.0 | hy | 2019-09-26 18:33:21+00:00 | 2019-10-01 15:07:08+00:00 | 3357016 | 8.0 | magnetic | 90.0 | 0.0 | count | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 4 | test | mnp300 | mnp300a | 34.726823 | -115.735015 | 940.0 | hz | 2019-09-26 18:33:21+00:00 | 2019-10-01 15:07:08+00:00 | 3357016 | 8.0 | magnetic | 0.0 | 90.0 | count | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 5 | test | mnp300 | mnp300a | 34.726823 | -115.735015 | 940.0 | temperature | 2019-09-26 18:33:21+00:00 | 2019-10-01 15:07:08+00:00 | 3357016 | 8.0 | auxiliary | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 6 | test | mnp300 | mnp300b | 34.726823 | -115.735015 | 940.0 | ex | 2019-10-01 16:22:01+00:00 | 2019-10-03 23:01:04+00:00 | 1573944 | 8.0 | electric | 0.0 | 0.0 | count | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 7 | test | mnp300 | mnp300b | 34.726823 | -115.735015 | 940.0 | ey | 2019-10-01 16:22:01+00:00 | 2019-10-03 23:01:04+00:00 | 1573944 | 8.0 | electric | 90.0 | 0.0 | count | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 8 | test | mnp300 | mnp300b | 34.726823 | -115.735015 | 940.0 | hx | 2019-10-01 16:22:01+00:00 | 2019-10-03 23:01:04+00:00 | 1573944 | 8.0 | magnetic | 0.0 | 0.0 | count | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 9 | test | mnp300 | mnp300b | 34.726823 | -115.735015 | 940.0 | hy | 2019-10-01 16:22:01+00:00 | 2019-10-03 23:01:04+00:00 | 1573944 | 8.0 | magnetic | 90.0 | 0.0 | count | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 10 | test | mnp300 | mnp300b | 34.726823 | -115.735015 | 940.0 | hz | 2019-10-01 16:22:01+00:00 | 2019-10-03 23:01:04+00:00 | 1573944 | 8.0 | magnetic | 0.0 | 90.0 | count | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 11 | test | mnp300 | mnp300b | 34.726823 | -115.735015 | 940.0 | temperature | 2019-10-01 16:22:01+00:00 | 2019-10-03 23:01:04+00:00 | 1573944 | 8.0 | auxiliary | 0.0 | 0.0 | none | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
This is important, you should close the file after you are done using it. Otherwise bad things can happen if you try to open it with another program or Python interpreter.
[10]:
m.close_mth5()
2022-09-07 13:03:29,078 [line 744] mth5.mth5.MTH5.close_mth5 - INFO: Flushing and closing c:\Users\jpeacock\OneDrive - DOI\mt\nims\from_nims.h5
[11]:
run_ts.plot()
[ ]:
Make an MTH5 from Phoenix Data
This example demonstrates how to read Phoenix data into an MTH5 file. The data comes from example data in PhoenixGeoPy. Here I downloaded those data into a local folder on my computer by forking the main branch.
Imports
[1]:
from pathlib import Path
from mth5.mth5 import MTH5
from mth5 import read_file
from mth5.io.phoenix import ReceiverMetadataJSON, PhoenixCollection
2022-08-31 13:19:07,431 [line 135] mth5.setup_logger - INFO: Logging file can be found C:\Users\jpeacock\OneDrive - DOI\Documents\GitHub\mth5\logs\mth5_debug.log
Data Directory
Specify the station directory. Phoenix files place each channel in a folder under the station directory named by the channel number. There is also a recmeta.json file that has metadata output by the receiver that can be useful. In the PhoenixGeopPy/sample_data there are 2 folders one for native data, these are .bin files which are the raw data in counts sampled at 24k. There is also a folder for segmented files, these files are calibrated to millivolts and decimated or segmented data
according to the recording configuration. Most of the time you would use the segmented files?
[2]:
station_dir = Path(r"c:\Users\jpeacock\OneDrive - DOI\mt\phoenix_example_data\10291_2019-09-06-015630")
File Collection
We’ve developed a collection dataframe to help sort out which files are which and which files can be grouped together into runs. Continous runs will be given a single run name and segmented data will have sequential run names. Both will have the pattern sr{sample_rate}_#### for the run.id.
Here PhoenixCollection.get_runs returns a two level ordered dictionary (OrderedDict). The first level is keyed by station ID. These objects are in turn ordered dictionaries by run ID. Therefore you can loop over stations and runs.
The data logger or receiver will output a JSON file that contains useful metadata that is missing from the data files. The recmeta.json file can be read into an object with methods to translate to mt_metadata objects. This is read in by PhoenixCollection and is in the attribute receiver_metadata.
[3]:
phx_collection = PhoenixCollection(file_path=station_dir)
run_dict = phx_collection.get_runs(sample_rates=[150, 24000])
Initiate MTH5
First initiate an MTH5 file, can use the receiver metadata to fill in some Survey metadata
[4]:
m = MTH5()
m.open_mth5(station_dir.joinpath("mth5_from_phoenix.h5"), "w")
2022-08-31 13:19:32,755 [line 596] mth5.mth5.MTH5.open_mth5 - WARNING: mth5_from_phoenix.h5 will be overwritten in 'w' mode
2022-08-31 13:19:33,267 [line 663] mth5.mth5.MTH5._initialize_file - INFO: Initialized MTH5 0.2.0 file c:\Users\jpeacock\OneDrive - DOI\mt\phoenix_example_data\10291_2019-09-06-015630\mth5_from_phoenix.h5 in mode w
[5]:
survey_metadata = phx_collection.receiver_metadata.survey_metadata
survey_group = m.add_survey(survey_metadata.id)
Loop through Stations, Runs, and Channels
Using the run_dict value output by the PhoenixCollection.get_runs we can simply loop through the runs without knowing if the data are continous or discontinuous, the read_file will take care of that.
Users should note the Phoenix file structure. Inside the folder are files with extensions of .td_24k and td_150.
.td_24kare usually bursts of a few seconds of data sampled at 24k samples per second to get high frequency information. The returned object is amth5.timeseries.ChannelTS.td_150is data continuously sampled at 150 samples per second. These files usually have a set length, commonly an hour. The returned object is amth5.timeseries.ChannelTS.
[6]:
%%time
for station_id, station_dict in run_dict.items():
station_metadata = phx_collection.receiver_metadata.station_metadata
station_group = survey_group.stations_group.add_station(
station_metadata.id,
station_metadata=station_metadata
)
for run_id, run_df in station_dict.items():
run_metadata = phx_collection.receiver_metadata.run_metadata
run_metadata.id = run_id
run_metadata.sample_rate = float(run_df.sample_rate.unique()[0])
run_group = station_group.add_run(run_metadata.id, run_metadata=run_metadata)
for row in run_df.itertuples():
ch_ts = read_file(row.fn, **{"channel_map":phx_collection.receiver_metadata.channel_map})
ch_metadata = phx_collection.receiver_metadata.get_ch_metadata(
ch_ts.channel_metadata.channel_number
)
# need to update the time period and sample rate as estimated from the data not the metadata
ch_metadata.sample_rate = ch_ts.sample_rate
ch_metadata.time_period.update(ch_ts.channel_metadata.time_period)
ch_ts.channel_metadata.update(ch_metadata)
# add channel to the run group
ch_dataset = run_group.from_channel_ts(ch_ts)
Wall time: 3min 57s
Need to update the metadata to account for added stations, runs, and channels.
[7]:
%%time
station_group.validate_station_metadata()
station_group.write_metadata()
survey_group.update_survey_metadata()
survey_group.write_metadata()
Wall time: 9min 19s
[8]:
m.channel_summary.summarize()
m.channel_summary.to_dataframe()
[8]:
| survey | station | run | latitude | longitude | elevation | component | start | end | n_samples | sample_rate | measurement_type | azimuth | tilt | units | hdf5_reference | run_hdf5_reference | station_hdf5_reference | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | UofS | REMOTE | sr150_0001 | 50.979666 | -106.794989 | 559.472154 | e1 | 2019-09-05 18:56:30+00:00 | 2019-09-06 07:15:38+00:00 | 6652200 | 150.0 | electric | 0.0 | 0.0 | millivolts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 1 | UofS | REMOTE | sr150_0001 | 50.979666 | -106.794989 | 559.472154 | e2 | 2019-09-05 18:56:30+00:00 | 2019-09-06 07:15:38+00:00 | 6652200 | 150.0 | electric | 0.0 | 0.0 | millivolts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 2 | UofS | REMOTE | sr150_0001 | 50.979666 | -106.794989 | 559.472154 | h1 | 2019-09-05 18:56:30+00:00 | 2019-09-06 07:15:38+00:00 | 6652200 | 150.0 | magnetic | 0.0 | 0.0 | millivolts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 3 | UofS | REMOTE | sr150_0001 | 50.979666 | -106.794989 | 559.472154 | h2 | 2019-09-05 18:56:30+00:00 | 2019-09-06 07:15:38+00:00 | 6652200 | 150.0 | magnetic | 0.0 | 0.0 | millivolts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 4 | UofS | REMOTE | sr150_0001 | 50.979666 | -106.794989 | 559.472154 | h3 | 2019-09-05 18:56:30+00:00 | 2019-09-06 07:15:38+00:00 | 6652200 | 150.0 | magnetic | 0.0 | 0.0 | millivolts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 995 | UofS | REMOTE | sr24k_0124 | 50.979666 | -106.794989 | 559.472154 | h2 | 2019-09-06 14:14:31+00:00 | 2019-09-06 14:14:33+00:00 | 48000 | 24000.0 | magnetic | 0.0 | 0.0 | millivolts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 996 | UofS | REMOTE | sr24k_0124 | 50.979666 | -106.794989 | 559.472154 | h3 | 2019-09-06 14:14:31+00:00 | 2019-09-06 14:14:33+00:00 | 48000 | 24000.0 | magnetic | 0.0 | 0.0 | millivolts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 997 | UofS | REMOTE | sr24k_0124 | 50.979666 | -106.794989 | 559.472154 | h4 | 2019-09-06 14:14:31+00:00 | 2019-09-06 14:14:33+00:00 | 48000 | 24000.0 | magnetic | 0.0 | 0.0 | millivolts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 998 | UofS | REMOTE | sr24k_0124 | 50.979666 | -106.794989 | 559.472154 | h5 | 2019-09-06 14:14:31+00:00 | 2019-09-06 14:14:33+00:00 | 48000 | 24000.0 | magnetic | 0.0 | 0.0 | millivolts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 999 | UofS | REMOTE | sr24k_0124 | 50.979666 | -106.794989 | 559.472154 | h6 | 2019-09-06 14:14:31+00:00 | 2019-09-06 14:14:33+00:00 | 48000 | 24000.0 | magnetic | 0.0 | 0.0 | millivolts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
1000 rows × 18 columns
[9]:
m.close_mth5()
2022-08-31 13:32:58,389 [line 744] mth5.mth5.MTH5.close_mth5 - INFO: Flushing and closing c:\Users\jpeacock\OneDrive - DOI\mt\phoenix_example_data\10291_2019-09-06-015630\mth5_from_phoenix.h5
Make an MTH5 from ZEN data
This notebook provides an example of how to read in ZEN (.Z3D) files into an MTH5.
[1]:
from mth5.mth5 import MTH5
from mth5.io.zen import Z3DCollection
from mth5 import read_file
2022-09-07 18:20:17,973 [line 135] mth5.setup_logger - INFO: Logging file can be found C:\Users\jpeacock\OneDrive - DOI\Documents\GitHub\mth5\logs\mth5_debug.log
Z3D Collection
We will use the Z3DCollection to assemble the .z3d files into a logical order by schedule action or run.
Note: n_samples is an estimate based on file size not the data. To get an accurate number you should read in the full file. Same with start and end. start is based on the schedule start time which is usually 2 seconds earlier than the data start because of instrument buffer while chaning sampling rates. end is based on file size and sample rate.
The Z3DCollection.get_runs() will return a two level ordered dictionary (OrderedDict). The first level is keyed by station ID. These objects are in turn ordered dictionaries by run ID. Therefore you can loop over stations and runs.
[2]:
zc = Z3DCollection(r"c:\Users\jpeacock\OneDrive - DOI\mt\example_z3d_data")
runs = zc.get_runs(sample_rates=[4096, 256])
print(f"Found {len(runs)} station with {len(runs[list(runs.keys())[0]])} runs")
Found 1 station with 2 runs
[3]:
runs["100"]["sr4096_0001"]
[3]:
| survey | station | run | start | end | channel_id | component | fn | sample_rate | file_size | n_samples | sequence_number | instrument_id | calibration_fn | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5 | 100 | sr4096_0001 | 2022-05-17 12:59:57+00:00 | 2022-05-17 13:09:53.349854+00:00 | 4 | ex | c:\Users\jpeacock\OneDrive - DOI\mt\example_z3... | 4096.0 | 9641572 | 2442649 | 1 | ZEN_024 | None | |
| 6 | 100 | sr4096_0001 | 2022-05-17 12:59:57+00:00 | 2022-05-17 13:09:53.351807+00:00 | 5 | ey | c:\Users\jpeacock\OneDrive - DOI\mt\example_z3... | 4096.0 | 9641604 | 2442657 | 1 | ZEN_024 | None | |
| 7 | 100 | sr4096_0001 | 2022-05-17 12:59:57+00:00 | 2022-05-17 13:09:53.348877+00:00 | 1 | hx | c:\Users\jpeacock\OneDrive - DOI\mt\example_z3... | 4096.0 | 9644628 | 2442645 | 1 | ZEN_024 | None | |
| 8 | 100 | sr4096_0001 | 2022-05-17 12:59:57+00:00 | 2022-05-17 13:09:53.351318+00:00 | 2 | hy | c:\Users\jpeacock\OneDrive - DOI\mt\example_z3... | 4096.0 | 9644156 | 2442655 | 1 | ZEN_024 | None | |
| 9 | 100 | sr4096_0001 | 2022-05-17 12:59:57+00:00 | 2022-05-17 13:09:53.351562+00:00 | 3 | hz | c:\Users\jpeacock\OneDrive - DOI\mt\example_z3... | 4096.0 | 9644160 | 2442656 | 1 | ZEN_024 | None |
Now that we have a logical collection of files, lets load them into an MTH5. We will simply loop of the stations, runs, and channels in the ordered dictionary.
There are a few things that we need to keep track of.
The station metadata pulled directly from the Z3D files can be input into the station metadata, be sure to use the
write_metadatamethod to write the metadata to the MTH5.The Z3D files have the coil response and zen response embedded in the file, so we can put those into the appropriate filter container in MTH5. This is important for calibrating later.
Since this is a MTH5 file version 0.2.0 the filters are in the
survey_groupso add them there.If you want to calibrate the data set calibrate to
True.
[4]:
calibrate = True
m = MTH5()
if calibrate:
m.data_level = 2
m.open_mth5(zc.file_path.joinpath("from_z3d.h5"))
2022-09-07 18:20:18,860 [line 663] mth5.mth5.MTH5._initialize_file - INFO: Initialized MTH5 0.2.0 file c:\Users\jpeacock\OneDrive - DOI\mt\example_z3d_data\from_z3d.h5 in mode a
[5]:
survey_group = m.add_survey("test")
[6]:
%%time
for station_id in runs.keys():
station_group = survey_group.stations_group.add_station(station_id)
station_group.metadata.update(zc.station_metadata_dict[station_id])
station_group.write_metadata()
for run_id, run_df in runs[station_id].items():
run_group = station_group.add_run(run_id)
for row in run_df.itertuples():
ch_ts = read_file(row.fn)
# NOTE: this is where the calibration occurs
if calibrate:
ch_ts = ch_ts.remove_instrument_response()
run_group.from_channel_ts(ch_ts)
2022-09-07 18:20:22,178 [line 221] mt_metadata.base.metadata.frequency_response_table_filter.complex_response - WARNING: Extrapolating, use values outside calibration frequencies with caution
2022-09-07 18:20:23,720 [line 221] mt_metadata.base.metadata.frequency_response_table_filter.complex_response - WARNING: Extrapolating, use values outside calibration frequencies with caution
2022-09-07 18:20:25,140 [line 221] mt_metadata.base.metadata.frequency_response_table_filter.complex_response - WARNING: Extrapolating, use values outside calibration frequencies with caution
2022-09-07 18:20:29,176 [line 221] mt_metadata.base.metadata.frequency_response_table_filter.complex_response - WARNING: Extrapolating, use values outside calibration frequencies with caution
2022-09-07 18:20:30,853 [line 221] mt_metadata.base.metadata.frequency_response_table_filter.complex_response - WARNING: Extrapolating, use values outside calibration frequencies with caution
2022-09-07 18:20:32,481 [line 221] mt_metadata.base.metadata.frequency_response_table_filter.complex_response - WARNING: Extrapolating, use values outside calibration frequencies with caution
Wall time: 14 s
[7]:
%%time
station_group.validate_station_metadata()
station_group.write_metadata()
survey_group.update_survey_metadata()
survey_group.write_metadata()
Wall time: 5.76 s
Have a look at the MTH5 structure and make sure it looks correct.
[8]:
m
[8]:
/:
====================
|- Group: Experiment
--------------------
|- Group: Reports
-----------------
|- Group: Standards
-------------------
--> Dataset: summary
......................
|- Group: Surveys
-----------------
|- Group: test
--------------
|- Group: Filters
-----------------
|- Group: coefficient
---------------------
|- Group: dipole_55.00m
-----------------------
|- Group: dipole_56.00m
-----------------------
|- Group: zen_counts2mv
-----------------------
|- Group: fap
-------------
|- Group: ant4_2314_response
----------------------------
--> Dataset: fap_table
........................
|- Group: ant4_2324_response
----------------------------
--> Dataset: fap_table
........................
|- Group: ant4_2334_response
----------------------------
--> Dataset: fap_table
........................
|- Group: fir
-------------
|- Group: time_delay
--------------------
|- Group: zpk
-------------
|- Group: Reports
-----------------
|- Group: Standards
-------------------
--> Dataset: summary
......................
|- Group: Stations
------------------
|- Group: 100
-------------
|- Group: Transfer_Functions
----------------------------
|- Group: sr256_0002
--------------------
--> Dataset: ex
.................
--> Dataset: ey
.................
--> Dataset: hx
.................
--> Dataset: hy
.................
--> Dataset: hz
.................
|- Group: sr4096_0001
---------------------
--> Dataset: ex
.................
--> Dataset: ey
.................
--> Dataset: hx
.................
--> Dataset: hy
.................
--> Dataset: hz
.................
--> Dataset: channel_summary
..............................
--> Dataset: tf_summary
.........................
Channel Summary
Have a look at the channel summary and make sure everything looks good.
[9]:
m.channel_summary.summarize()
m.channel_summary.to_dataframe()
[9]:
| survey | station | run | latitude | longitude | elevation | component | start | end | n_samples | sample_rate | measurement_type | azimuth | tilt | units | hdf5_reference | run_hdf5_reference | station_hdf5_reference | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | test | 100 | sr256_0002 | 40.497576 | -116.821188 | 1456.7 | ex | 2022-05-17 13:09:58+00:00 | 2022-05-17 15:54:42+00:00 | 2530304 | 256.0 | electric | 0.0 | 0.0 | count | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 1 | test | 100 | sr256_0002 | 40.497576 | -116.821188 | 1456.7 | ey | 2022-05-17 13:09:58+00:00 | 2022-05-17 15:54:42+00:00 | 2530304 | 256.0 | electric | 0.0 | 0.0 | count | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 2 | test | 100 | sr256_0002 | 40.497576 | -116.821188 | 1456.7 | hx | 2022-05-17 13:09:58+00:00 | 2022-05-17 15:54:42+00:00 | 2530304 | 256.0 | magnetic | 0.0 | 0.0 | count | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 3 | test | 100 | sr256_0002 | 40.497576 | -116.821188 | 1456.7 | hy | 2022-05-17 13:09:58+00:00 | 2022-05-17 15:54:42+00:00 | 2530304 | 256.0 | magnetic | 90.0 | 0.0 | count | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 4 | test | 100 | sr256_0002 | 40.497576 | -116.821188 | 1456.7 | hz | 2022-05-17 13:09:58+00:00 | 2022-05-17 15:54:42+00:00 | 2530304 | 256.0 | magnetic | 0.0 | 0.0 | count | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 5 | test | 100 | sr4096_0001 | 40.497576 | -116.821188 | 1456.7 | ex | 2022-05-17 12:59:58+00:00 | 2022-05-17 13:09:41.997559+00:00 | 2392054 | 4096.0 | electric | 0.0 | 0.0 | count | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 6 | test | 100 | sr4096_0001 | 40.497576 | -116.821188 | 1456.7 | ey | 2022-05-17 12:59:58+00:00 | 2022-05-17 13:09:41.999023+00:00 | 2392060 | 4096.0 | electric | 0.0 | 0.0 | count | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 7 | test | 100 | sr4096_0001 | 40.497576 | -116.821188 | 1456.7 | hx | 2022-05-17 12:59:58+00:00 | 2022-05-17 13:09:41.996094+00:00 | 2392048 | 4096.0 | magnetic | 0.0 | 0.0 | count | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 8 | test | 100 | sr4096_0001 | 40.497576 | -116.821188 | 1456.7 | hy | 2022-05-17 12:59:58+00:00 | 2022-05-17 13:09:41.997070+00:00 | 2392052 | 4096.0 | magnetic | 90.0 | 0.0 | count | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 9 | test | 100 | sr4096_0001 | 40.497576 | -116.821188 | 1456.7 | hz | 2022-05-17 12:59:58+00:00 | 2022-05-17 13:09:41.998779+00:00 | 2392059 | 4096.0 | magnetic | 0.0 | 0.0 | count | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
This is important, you should close the file after you are done using it. Otherwise bad things can happen if you try to open it with another program or Python interpreter.
[10]:
m.close_mth5()
2022-09-07 18:20:39,445 [line 744] mth5.mth5.MTH5.close_mth5 - INFO: Flushing and closing c:\Users\jpeacock\OneDrive - DOI\mt\example_z3d_data\from_z3d.h5
Adding Plugins
Everyone has their own file structure and therefore there will need to be various readers for the different data formats. If you have a data format that isn’t supported adding a reader would be a welcomed contribution. To keep things somewhat uniform here are some guidelines to add a reader.
Reader Structure
The reader should be setup with having a class that contains the metadata which is inherited to a class that holds the data. This makes things a little easier to separate and read. It helps if the metadata has similar names as the standards but don’t have to be it just means you have to do some translation.
It helps if you have properties, if attributes are not appropriate, for important information that is passed onto mth5.timeseries.ChannelTS or mth5.timeseries.RunTS.
from mth5.timeseries import ChannelTS, RunTS
class MyFileMetadata:
""" Read in metadata into appropriate objects """
def __init__(self, fn):
self.fn = fn
self.start = None
self.end = None
self.sample_rate = None
def read_metadata():
""" function to read in metadata and fill attribute values """
pass
class MyFile(MyFileMetadata):
""" inheret metadata and read data """
def __init__(self, fn):
self.fn = fn
self.data = None
super().__init__()
@property
def station_metadata(self):
""" Any station metadata within the file """
station_meta_dict = {}
station_meta_dict['location.latitude'] = self.latitude
return {'Station': station_meta_dict}
@property
def run_metadata(self):
""" Any run metadata within the file """
run_meta_dict = {}
run_meta_dict['id'] = f"{self.station}a"
return {'Run': run_meta_dict}
@property
def channel_metadata(self):
""" channel metadata filled from information in the file """
channel_meta_dict = {}
channel_meta_dict['time_period.start'] = self.start
channel_meta_dict['time_period.end'] = self.end
channel_meta_dict['sample_rate'] = self.sample_rate
return {'Electric': channel_meta_dict}
@property
def ex(self):
""" ex convenience property """
# if a pandas dataframe or numpy structured array
return timeseries.ChannelTS('electric',
data=self.data['ex'],
channel_metadata=self.channel_metadata,
station_metadata=self.station_metadata,
run_metadata=self.run_metadata)
def read_my_file(self):
""" read in data """
# suggest reading into a data type like numpy, pandas, xarray
# xarray is the main object used for time series data in mth5
return RunTS([self.ex, self.ey, self.hx, self.hy, self.hx])
def read_my_file(fn):
""" the helper function to read the file """
new_obj = MyFile(fn)
return new_obj.read_my_file()
See also
mth5.io.zen and mth5.io.nims for working examples.
Once you have come up a reader you can add it to the reader module. You just need to add a file name and associated file types.
In the dictionary in mth5.reader ‘readers’ add a line like:
"my_file": {"file_types": ["dat", "data"], "reader": my_file.read_my_file},
Then you can see if your reader works
>>> import mth5
>>> run = mth5.read_file(r"/home/mt_data/test.dat", file_type='my_file')
Collections Structure
If you add a reader, you should also add a collection class that can sort your given file structure into runs. This should inherit mth5.io.collection.Collection.
import pandas as pd
from mth5.io import Collection
from mth5.io.my_file import MyFileReader
class MyCollection(Collection):
def __init__(self, **kwargs):
super()__init__(self, **kwargs)
self.file_ext = "my_file_extension"
def to_dataframe(self, sample_rates, run_name_zeros=4, calibration_path=None):
"""
Create a :class:`pandas.DataFrame` from my_file_type files. This should
be specific enough to your file structure and generic enough to plug in.
This method should only read the metadata from the files, and not open
the entire file.
:param sample_rates: sample rate to get, will always be 1 for LEMI data
defaults to [1]
:type sample_rates: int or list, optional
:param run_name_zeros: number of zeros to assing to the run name,
defaults to 4
:type run_name_zeros: int, optional
:param calibration_path: path to calibration files, defaults to None
:type calibration_path: string or Path, optional
:return: Dataframe with information of each TXT file in the given
directory.
:rtype: :class:`pandas.DataFrame`
"""
entries = []
for fn in self.get_files(self.file_ext):
my_file_obj = MyFileReader(fn)
n_samples = int(my_file_obj.n_samples)
my_file_obj.read_metadata()
entry = {}
entry["survey"] = self.survey_metadata.id
entry["station"] = self.station_metadata.id
entry["run"] = None
entry["start"] = my_file_obj.start.isoformat()
entry["end"] = my_file_obj.end.isoformat()
entry["channel_id"] = my_file_obj.channel_metadata.id
entry["component"] = my_file_obj.channel_metadata.component
entry["fn"] = fn
entry["sample_rate"] = my_file_obj.sample_rate
entry["file_size"] = my_file_obj.file_size
entry["n_samples"] = my_file_obj.n_samples
entry["sequence_number"] = 0
entry["instrument_id"] = "MyInstrument"
entry["calibration_fn"] = None
entries.append(entry)
# make pandas dataframe and set data types
df = self._sort_df(
self._set_df_dtypes(pd.DataFrame(entries)), run_name_zeros
)
return df
def assign_run_names(self, df, zeros=4):
"""
Assign run names based on the file structure. Remember
a run is defined as a continuously recorded block of
data. So if your file structure splits up files during
a run make sure there is logic to assign the same run ID
to those files that are in the same run.
Below is an example of testing the start time of one file
against the end time of the next file.
Run names are assigned as sr{sample_rate}_{run_number:0{zeros}}.
:param df: Dataframe with the appropriate columns
:type df: :class:`pandas.DataFrame`
:param zeros: number of zeros in run name, defaults to 4
:type zeros: int, optional
:return: Dataframe with run names
:rtype: :class:`pandas.DataFrame`
"""
count = 1
for row in df.itertuples():
if row.Index == 0:
df.loc[row.Index, "run"] = f"sr1_{count:0{zeros}}"
previous_end = row.end
else:
if (
row.start - previous_end
).total_seconds() / row.sample_rate == row.sample_rate:
df.loc[row.Index, "run"] = f"sr1_{count:0{zeros}}"
else:
count += 1
df.loc[row.Index, "run"] = f"sr1_{count:0{zeros}}"
previous_end = row.end
return df
Transfer Functions
There exists the ability to store transfer functions in an MTH5 for a comprehensive representation of data. The transfer functions are stored at the Station level in a group called TransferFunctionsGroup. Within this group station transfer functions are stored as a single group for each transfer function TransferFunctionGroup. Each TransferFunctionGroup has a data set for each statistical estimate provided within the transfer function. Currently supported estimates are
Estimate |
Description |
Shape |
Data Type |
|---|---|---|---|
transfer_function |
Full transfer function |
n_periods x n_inputs x n_outputs |
complex |
transfer_function_error |
Full transfer function error estimation |
n_periods x n_inputs x n_outputs |
real |
impedance |
Only horizontal components, traditional impedance tensor |
n_periods x 2 x 2 |
complex |
impedance_error |
Only horizontal components, traditional impedance tensor error |
n_periods x 2 x 2 |
real |
tipper |
horizontal and vertical magnetic transfer function, Tipper |
n_periods x 1 x 2 |
complex |
tipper_error |
horizontal and vertical magnetic transfer function, Tipper error |
n_periods x 1 x 2 |
real |
inverse_signal_power |
covariance of input channels (sources) |
n_periods x n_inputs x n_inputs |
complex |
residual_covariance |
covariance of output channels (responses) |
n_periods x n_outputs x n_outputs |
complex |
Note: There are plans to add phase tensor and resitivity/phase estimations in the future.
This examples demonstrates how transfer functions can be added to an MTH5 file.
TF object
The TF object comes from mt_metadata.transfer_functions.core.TF and is meant to be the common container for transfer functions. It has readers for:
EDI
EMFT XML
Z-files (EMTF output)
J-files (BIRRP output)
AVG files (Zonge output)
The TF object has two important metadata objects survey_metadata and station_metadata. Metadata from the other files are translated into these containers and translated back when writing a file.
The statistical estimates are stored as xarray.Datasets that have coordinates of period, input_channels, output_channels. This way the transfer function can be generalized. impedance and tipper are stored in transfer_function and TF provides convenience functions to access impedance and tipper and associated errors. Variances are stored as covariances for input channels (inverse_signal_power) and output channels (residual_covariance) when possible,
and the transfer_function_error is stored as well.
Note: There are future plans to include phase tensor and resistivity/phase representation as well.
[1]:
from mth5.mth5 import MTH5
from mt_metadata import TF_XML, TF_EDI_SPECTRA, TF_ZMM, TF_EDI_CGG
from mt_metadata.transfer_functions.core import TF
2023-04-21 10:31:36,442 [line 141] mth5.setup_logger - INFO: Logging file can be found C:\Users\jpeacock\OneDrive - DOI\Documents\GitHub\mth5\logs\mth5_debug.log
[2]:
m = MTH5(file_version="0.2.0")
m.open_mth5(r"transfer_function_example.h5", "w")
2023-04-21 10:31:42,346 [line 672] mth5.mth5.MTH5._initialize_file - INFO: Initialized MTH5 0.2.0 file transfer_function_example.h5 in mode w
Read in Transfer functions
TF_XML: An example of EMTF XML format, the preferred format for archiving
TF_EDI_SPECTRA: An example of an EDI file stored as spectra
TF_EDI_CGG: An example of an output file from a contractor
TF_ZMM: An example of an output file from EMFT
[3]:
tf1 = TF(TF_XML)
tf1.read_tf_file()
tf2 = TF(TF_EDI_SPECTRA)
tf2.read_tf_file()
tf3 = TF(TF_EDI_CGG)
tf3.read_tf_file()
tf4 = TF(TF_ZMM)
tf4.read_tf_file()
Add TF_XML to the MTH5
When we add a transfer function to an MTH5, it looks for the survey.id and station.id, if it doesn’t find any then they are created. If information is provided on which runs were processed and channels used those are filled in as well.
Note: If you have multiple transfer functions for a given station be sure to rename the file, for an EDI this is in HEADER under the attribute DATAID. Name it something like station_sample_rate or station_runs
[4]:
tf_group_01 = m.add_transfer_function(tf1)
tf_group_01
2023-04-21 10:31:49,566 [line 1054] mth5.mth5.MTH5.get_survey - WARNING: /Experiment/Surveys/CONUS_South does not exist, check survey_list for existing names.
[4]:
/Experiment/Surveys/CONUS_South/Stations/NMX20/Transfer_Functions/NMX20:
====================
--> Dataset: inverse_signal_power
...................................
--> Dataset: period
.....................
--> Dataset: residual_covariance
..................................
--> Dataset: transfer_function
................................
--> Dataset: transfer_function_error
......................................
[5]:
est = tf_group_01.get_estimate("transfer_function")
tf_group_01.has_estimate("covariance")
[5]:
True
Have a look at what was added to the MTH5
Note that an EMTF XML has comprehensive metadata which can be used to populate the MTH5 as necessary, including Runs and Channels
Add an example EDI
Here the survey is not specified therefore we need to fill that information in ourselves otherwise an error is raised, see below.
[6]:
#tf_group_02 = m.add_transfer_function(tf2)
Here we give the survey the id unknown_survey. Also note that because the data are stored as spectra in the EDI we can calculate the inverse_signal_power and residual_covariance.
[6]:
tf2.survey_metadata.id = "unknown_survey"
tf_group_02 = m.add_transfer_function(tf2)
print(tf_group_02.has_estimate("covariance"))
2023-04-21 10:32:07,195 [line 1054] mth5.mth5.MTH5.get_survey - WARNING: /Experiment/Surveys/unknown_survey does not exist, check survey_list for existing names.
True
Add typical EDI file
This file only has impedance and tipper and minimal metadata, which are converted into a full transfer function for storage.
[7]:
tf3.survey_metadata.id = "unknown_survey"
tf_group_03 = m.add_transfer_function(tf3)
tf_group_03.has_estimate("covariance")
2023-04-21 10:32:11,616 [line 222] mth5.groups.base.TransferFunction.get_estimate - ERROR: residual_covariance does not exist, check groups_list for existing names
[7]:
False
Add an output from EMTF
A ZMM file contains the full covariance and transfer functions but has minimal metadata
[8]:
print(tf4)
Station: 300
--------------------------------------------------
Survey: None
Project: None
Acquired by: None
Acquired date: 1980-01-01
Latitude: 34.727
Longitude: -115.735
Elevation: 0.000
Declination:
Value: 13.1
Model: WMM
Coordinate System: geographic
Impedance: True
Tipper: True
N Periods: 38
Period Range:
Min: 1.16364E+00 s
Max: 1.09227E+04 s
Frequency Range:
Min: 9.15527E-05 Hz
Max: 8.59372E-01 Hz
[9]:
tf4.survey_metadata.id = "unknown_survey"
tf_group_04 = m.add_transfer_function(tf4)
tf_group_04.has_estimate("impedance")
[9]:
True
Have a look at the MTH5 file
Everything has been filled in now in the MTH5 including metadata about runs and channels
[10]:
m
[10]:
/:
====================
|- Group: Experiment
--------------------
|- Group: Reports
-----------------
|- Group: Standards
-------------------
--> Dataset: summary
......................
|- Group: Surveys
-----------------
|- Group: CONUS_South
---------------------
|- Group: Filters
-----------------
|- Group: coefficient
---------------------
|- Group: fap
-------------
|- Group: fir
-------------
|- Group: time_delay
--------------------
|- Group: zpk
-------------
|- Group: Reports
-----------------
|- Group: Standards
-------------------
--> Dataset: summary
......................
|- Group: Stations
------------------
|- Group: NMX20
---------------
|- Group: NMX20a
----------------
--> Dataset: ex
.................
--> Dataset: ey
.................
--> Dataset: hx
.................
--> Dataset: hy
.................
--> Dataset: hz
.................
|- Group: NMX20b
----------------
--> Dataset: ex
.................
--> Dataset: ey
.................
--> Dataset: hx
.................
--> Dataset: hy
.................
--> Dataset: hz
.................
|- Group: Transfer_Functions
----------------------------
|- Group: NMX20
---------------
--> Dataset: inverse_signal_power
...................................
--> Dataset: period
.....................
--> Dataset: residual_covariance
..................................
--> Dataset: transfer_function
................................
--> Dataset: transfer_function_error
......................................
|- Group: unknown_survey
------------------------
|- Group: Filters
-----------------
|- Group: coefficient
---------------------
|- Group: fap
-------------
|- Group: fir
-------------
|- Group: time_delay
--------------------
|- Group: zpk
-------------
|- Group: Reports
-----------------
|- Group: Standards
-------------------
--> Dataset: summary
......................
|- Group: Stations
------------------
|- Group: 300
-------------
|- Group: 300a
--------------
--> Dataset: ex
.................
--> Dataset: ey
.................
--> Dataset: hx
.................
--> Dataset: hy
.................
--> Dataset: hz
.................
|- Group: Transfer_Functions
----------------------------
|- Group: 300
-------------
--> Dataset: inverse_signal_power
...................................
--> Dataset: period
.....................
--> Dataset: residual_covariance
..................................
--> Dataset: transfer_function
................................
--> Dataset: transfer_function_error
......................................
|- Group: SAGE_2005
-------------------
|- Group: SAGE_2005a
--------------------
--> Dataset: ex
.................
--> Dataset: ey
.................
--> Dataset: hx
.................
--> Dataset: hy
.................
--> Dataset: hz
.................
|- Group: Transfer_Functions
----------------------------
|- Group: SAGE_2005
-------------------
--> Dataset: inverse_signal_power
...................................
--> Dataset: period
.....................
--> Dataset: residual_covariance
..................................
--> Dataset: transfer_function
................................
--> Dataset: transfer_function_error
......................................
|- Group: unknown_survey_001
----------------------------
|- Group: Filters
-----------------
|- Group: coefficient
---------------------
|- Group: fap
-------------
|- Group: fir
-------------
|- Group: time_delay
--------------------
|- Group: zpk
-------------
|- Group: Reports
-----------------
|- Group: Standards
-------------------
--> Dataset: summary
......................
|- Group: Stations
------------------
|- Group: TEST01
----------------
|- Group: TEST01a
-----------------
--> Dataset: ex
.................
--> Dataset: ey
.................
--> Dataset: hx
.................
--> Dataset: hy
.................
--> Dataset: hz
.................
|- Group: Transfer_Functions
----------------------------
|- Group: TEST01
----------------
--> Dataset: period
.....................
--> Dataset: transfer_function
................................
--> Dataset: transfer_function_error
......................................
--> Dataset: channel_summary
..............................
--> Dataset: tf_summary
.........................
Get a transfer function object from MTH5
To retrieve a transfer function from the MTH5 file a convenience function m.get_transfer_function is supplied. You only need to know the station.id, tf.id, and the survey.id. Here the tf.id is the same as the station.id.
[11]:
tf1_h5 = m.get_transfer_function(tf1.station_metadata.id, tf1.tf_id, tf1.survey_metadata.id)
[12]:
print(tf1)
print(tf1_h5)
Station: NMX20
--------------------------------------------------
Survey: CONUS South
Project: USMTArray
Acquired by: National Geoelectromagnetic Facility
Acquired date: 2020-09-20
Latitude: 34.471
Longitude: -108.712
Elevation: 1940.050
Declination:
Value: 9.09
Model: WMM
Coordinate System: geographic
Impedance: True
Tipper: True
N Periods: 33
Period Range:
Min: 4.65455E+00 s
Max: 2.91271E+04 s
Frequency Range:
Min: 3.43323E-05 Hz
Max: 2.14844E-01 Hz
Station: NMX20
--------------------------------------------------
Survey: CONUS South
Project: USMTArray
Acquired by: National Geoelectromagnetic Facility
Acquired date: 2020-09-20
Latitude: 34.471
Longitude: -108.712
Elevation: 1940.050
Declination:
Value: 9.09
Model: WMM
Coordinate System: geographic
Impedance: True
Tipper: True
N Periods: 33
Period Range:
Min: 4.65455E+00 s
Max: 2.91271E+04 s
Frequency Range:
Min: 3.43323E-05 Hz
Max: 2.14844E-01 Hz
Summarize what transfer functions are in the file
In the MTH5 file there is a property called tf_summary, this provides an array that can be converted to a pandas.DataFrame of the transfer functions within the file. There is a column called hdf5_reference which can be used to get the transfer function directly. This table is updated when the file is close, so when you open the file next it should be up to date. This table should be read only, if you want to change metadata, you should do it directly in the transfer function object.
[13]:
m.tf_summary.clear_table()
m.tf_summary.summarize()
tf_df = m.tf_summary.to_dataframe()
tf_df
[13]:
| station | survey | latitude | longitude | elevation | tf_id | units | has_impedance | has_tipper | has_covariance | period_min | period_max | hdf5_reference | station_hdf5_reference | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NMX20 | CONUS_South | 34.470528 | -108.712288 | 1940.05 | NMX20 | none | True | True | True | 4.654550 | 29127.110000 | <HDF5 object reference> | <HDF5 object reference> |
| 1 | 300 | unknown_survey | 34.727000 | -115.735000 | 0.00 | 300 | none | True | True | True | 1.163640 | 10922.666990 | <HDF5 object reference> | <HDF5 object reference> |
| 2 | SAGE_2005 | unknown_survey | 35.550000 | -106.283333 | 0.00 | SAGE_2005 | none | True | True | True | 0.004196 | 209.731544 | <HDF5 object reference> | <HDF5 object reference> |
| 3 | TEST01 | unknown_survey_001 | -30.930285 | 127.229230 | 175.27 | TEST01 | none | True | True | False | 0.001212 | 1211.527490 | <HDF5 object reference> | <HDF5 object reference> |
Get TF from reference
[14]:
tf_object = m.from_reference(tf_df.iloc[0].hdf5_reference)
print(tf_object)
Station: NMX20
--------------------------------------------------
Survey: CONUS South
Project: USMTArray
Acquired by: National Geoelectromagnetic Facility
Acquired date: 2020-09-20
Latitude: 34.471
Longitude: -108.712
Elevation: 1940.050
Declination:
Value: 9.09
Model: WMM
Coordinate System: geographic
Impedance: True
Tipper: True
N Periods: 33
Period Range:
Min: 4.65455E+00 s
Max: 2.91271E+04 s
Frequency Range:
Min: 3.43323E-05 Hz
Max: 2.14844E-01 Hz
MTpy
To analyze, plot, prepare input files one should look to use MTpy. Note: MTpy version 2.0 will use MTH5 as the storage mechanism and TF to read/write files.
[15]:
m.close_mth5()
2023-04-21 10:32:58,441 [line 753] mth5.mth5.MTH5.close_mth5 - INFO: Flushing and closing transfer_function_example.h5
[ ]:
Examples
Make MTH5 from IRIS Data Managment Center v0.1.0
This example demonstrates how to build an MTH5 from data archived at IRIS, it could work with any MT data stored at an FDSN data center (probably).
We will use the mth5.clients.FDSN class to build the file. There is also second way using the more generic mth5.clients.MakeMTH5 class, which will be highlighted below.
Note: this example assumes that data availability (Network, Station, Channel, Start, End) are all previously known. If you do not know the data that you want to download use IRIS tools to get data availability.
[1]:
from pathlib import Path
import numpy as np
import pandas as pd
from mth5.mth5 import MTH5
from mth5.clients import FDSN
from matplotlib import pyplot as plt
%matplotlib widget
2023-03-23 14:32:18,703 [line 135] mth5.setup_logger - INFO: Logging file can be found C:\Users\jpeacock\OneDrive - DOI\Documents\GitHub\mth5\logs\mth5_debug.log
Set the path to save files to as the current working directory
[2]:
default_path = Path().cwd()
Initialize a MakeMTH5 object
Here, we are setting the MTH5 file version to 0.1.0 so that we can only have one survey in a single file. Also, setting the client to “IRIS”. Here, we are using obspy.clients tools for the request. Here are the available FDSN clients.
Note: Only the “IRIS” client has been tested.
[3]:
fdsn_object = FDSN(mth5_version='0.1.0')
fdsn_object.client = "IRIS"
Make the data inquiry as a DataFrame
There are a few ways to make the inquiry to request data.
Make a DataFrame by hand. Here we will make a list of entries and then create a DataFrame with the proper column names
You can create a CSV file with a row for each entry. There are some formatting that you need to be aware of. That is the column names and making sure that date-times are YYYY-MM-DDThh:mm:ss
Column Name |
Description |
|---|---|
network |
|
station |
|
location |
|
channel |
|
start |
Start time (YYYY-MM-DDThh:mm:ss) UTC |
end |
End time (YYYY-MM-DDThh:mm:ss) UTC |
[6]:
channels = ["LFE", "LFN", "LFZ", "LQE", "LQN"]
CAS04 = ["8P", "CAS04", '2020-06-02T19:00:00', '2020-07-13T19:00:00']
request_list = []
for entry in [CAS04]:
for channel in channels:
request_list.append(
[entry[0], entry[1], "", channel, entry[2], entry[3]]
)
# Turn list into dataframe
request_df = pd.DataFrame(request_list, columns=fdsn_object.request_columns)
request_df
[6]:
| network | station | location | channel | start | end | |
|---|---|---|---|---|---|---|
| 0 | 8P | CAS04 | LFE | 2020-06-02T19:00:00 | 2020-07-13T19:00:00 | |
| 1 | 8P | CAS04 | LFN | 2020-06-02T19:00:00 | 2020-07-13T19:00:00 | |
| 2 | 8P | CAS04 | LFZ | 2020-06-02T19:00:00 | 2020-07-13T19:00:00 | |
| 3 | 8P | CAS04 | LQE | 2020-06-02T19:00:00 | 2020-07-13T19:00:00 | |
| 4 | 8P | CAS04 | LQN | 2020-06-02T19:00:00 | 2020-07-13T19:00:00 |
Save the request as a CSV
Its helpful to be able to save the request as a CSV and modify it and use it later. A CSV can be input as a request to MakeMTH5
[7]:
request_df.to_csv(default_path.joinpath("fdsn_request.csv"))
Get only the metadata from IRIS
It can be helpful to make sure that your request is what you would expect. For that you can request only the metadata from IRIS. The request is quick and light so shouldn’t need to worry about the speed. This returns a StationXML file and is loaded into an obspy.Inventory object.
[8]:
inventory, data = fdsn_object.get_inventory_from_df(request_df, data=False)
Have a look at the Inventory to make sure it contains what is requested.
[9]:
inventory
[9]:
Inventory created at 2023-03-23T21:32:59.642492Z
Created by: ObsPy 1.3.0
https://www.obspy.org
Sending institution: MTH5
Contains:
Networks (1):
8P
Stations (1):
8P.CAS04 (Corral Hollow, CA, USA)
Channels (5):
8P.CAS04..LFZ, 8P.CAS04..LFN, 8P.CAS04..LFE, 8P.CAS04..LQN,
8P.CAS04..LQE
Make an MTH5 from a request
Now that we’ve created a request, and made sure that its what we expect, we can make an MTH5 file. The input can be either the DataFrame or the CSV file.
We are going to time it just to get an indication how long it might take. Should take about 4 minutes.
Note: we are setting interact=False. If you want to just to keep the file open to interogat it set interact=True.
Make an MTH5 using MakeMTH5
Another way to make a file is using the mth5.clients.MakeMTH5 class, which is more generic than FDSN, but doesn’t have as many methods. The MakeMTH5 class is meant to be a convienence method for the various clients.
from mth5.clients import MakeMTH5
make_mth5_object = MakeMTH5(mth5_version='0.1.0', interact=False)
mth5_filename = make_mth5_object.from_fdsn_client(request_df, client="IRIS")
[8]:
%%time
mth5_filename = fdsn_object.make_mth5_from_fdsn_client(request_df, interact=False)
print(f"Created {mth5_filename}")
2022-09-12 17:00:43,438 [line 605] mth5.mth5.MTH5.open_mth5 - WARNING: 8P_CAS04.h5 will be overwritten in 'w' mode
2022-09-12 17:00:43,981 [line 672] mth5.mth5.MTH5._initialize_file - INFO: Initialized MTH5 0.1.0 file C:\Users\jpeacock\OneDrive - DOI\Documents\GitHub\mth5\docs\examples\notebooks\8P_CAS04.h5 in mode w
2022-09-12 17:00:52,792 [line 120] mt_metadata.base.metadata.station.add_run - WARNING: Run a is being overwritten with current information
2022-09-12T17:00:53 [line 134] obspy_stages.create_filter_from_stage - INFO: Converting PoleZerosResponseStage electric_si_units to a CoefficientFilter
2022-09-12T17:00:53 [line 134] obspy_stages.create_filter_from_stage - INFO: Converting PoleZerosResponseStage electric_dipole_92.000 to a CoefficientFilter
2022-09-12T17:00:53 [line 134] obspy_stages.create_filter_from_stage - INFO: Converting PoleZerosResponseStage electric_si_units to a CoefficientFilter
2022-09-12T17:00:53 [line 134] obspy_stages.create_filter_from_stage - INFO: Converting PoleZerosResponseStage electric_dipole_92.000 to a CoefficientFilter
2022-09-12T17:00:53 [line 134] obspy_stages.create_filter_from_stage - INFO: Converting PoleZerosResponseStage electric_si_units to a CoefficientFilter
2022-09-12T17:00:53 [line 134] obspy_stages.create_filter_from_stage - INFO: Converting PoleZerosResponseStage electric_dipole_92.000 to a CoefficientFilter
2022-09-12T17:00:53 [line 134] obspy_stages.create_filter_from_stage - INFO: Converting PoleZerosResponseStage electric_si_units to a CoefficientFilter
2022-09-12T17:00:53 [line 134] obspy_stages.create_filter_from_stage - INFO: Converting PoleZerosResponseStage electric_dipole_92.000 to a CoefficientFilter
2022-09-12T17:00:53 [line 134] obspy_stages.create_filter_from_stage - INFO: Converting PoleZerosResponseStage electric_si_units to a CoefficientFilter
2022-09-12T17:00:53 [line 134] obspy_stages.create_filter_from_stage - INFO: Converting PoleZerosResponseStage electric_dipole_92.000 to a CoefficientFilter
2022-09-12 17:00:56,417 [line 784] mth5.groups.base.Station.add_run - INFO: run a already exists, returning existing group.
2022-09-12 17:00:56,963 [line 272] mth5.timeseries.run_ts.RunTS.validate_metadata - WARNING: start time of dataset 2020-06-02T19:00:00+00:00 does not match metadata start 2020-06-02T18:41:43+00:00 updating metatdata value to 2020-06-02T19:00:00+00:00
2022-09-12 17:00:56,963 [line 286] mth5.timeseries.run_ts.RunTS.validate_metadata - WARNING: end time of dataset 2020-06-02T22:07:47+00:00 does not match metadata end 2020-06-02T22:07:46+00:00 updating metatdata value to 2020-06-02T22:07:47+00:00
2022-09-12 17:01:09,192 [line 784] mth5.groups.base.Station.add_run - INFO: run b already exists, returning existing group.
2022-09-12 17:01:09,740 [line 286] mth5.timeseries.run_ts.RunTS.validate_metadata - WARNING: end time of dataset 2020-06-12T17:52:24+00:00 does not match metadata end 2020-06-12T17:52:23+00:00 updating metatdata value to 2020-06-12T17:52:24+00:00
2022-09-12 17:01:22,183 [line 784] mth5.groups.base.Station.add_run - INFO: run c already exists, returning existing group.
2022-09-12 17:01:22,937 [line 286] mth5.timeseries.run_ts.RunTS.validate_metadata - WARNING: end time of dataset 2020-07-01T17:33:00+00:00 does not match metadata end 2020-07-01T17:32:59+00:00 updating metatdata value to 2020-07-01T17:33:00+00:00
2022-09-12 17:01:38,198 [line 784] mth5.groups.base.Station.add_run - INFO: run d already exists, returning existing group.
2022-09-12 17:01:38,778 [line 286] mth5.timeseries.run_ts.RunTS.validate_metadata - WARNING: end time of dataset 2020-07-13T19:00:01+00:00 does not match metadata end 2020-07-13T21:46:12+00:00 updating metatdata value to 2020-07-13T19:00:01+00:00
2022-09-12 17:01:49,581 [line 753] mth5.mth5.MTH5.close_mth5 - INFO: Flushing and closing C:\Users\jpeacock\OneDrive - DOI\Documents\GitHub\mth5\docs\examples\notebooks\8P_CAS04.h5
Created C:\Users\jpeacock\OneDrive - DOI\Documents\GitHub\mth5\docs\examples\notebooks\8P_CAS04.h5
Wall time: 1min 6s
[9]:
# open file already created
mth5_object = MTH5()
mth5_object.open_mth5(mth5_filename)
Have a look at the contents of the created file
[10]:
mth5_object
[10]:
/:
====================
|- Group: Survey
----------------
|- Group: Filters
-----------------
|- Group: coefficient
---------------------
|- Group: electric_analog_to_digital
------------------------------------
|- Group: electric_dipole_92.000
--------------------------------
|- Group: electric_si_units
---------------------------
|- Group: magnetic_analog_to_digital
------------------------------------
|- Group: fap
-------------
|- Group: fir
-------------
|- Group: time_delay
--------------------
|- Group: electric_time_offset
------------------------------
|- Group: hx_time_offset
------------------------
|- Group: hy_time_offset
------------------------
|- Group: hz_time_offset
------------------------
|- Group: zpk
-------------
|- Group: electric_butterworth_high_pass
----------------------------------------
--> Dataset: poles
....................
--> Dataset: zeros
....................
|- Group: electric_butterworth_low_pass
---------------------------------------
--> Dataset: poles
....................
--> Dataset: zeros
....................
|- Group: magnetic_butterworth_low_pass
---------------------------------------
--> Dataset: poles
....................
--> Dataset: zeros
....................
|- Group: Reports
-----------------
|- Group: Standards
-------------------
--> Dataset: summary
......................
|- Group: Stations
------------------
|- Group: CAS04
---------------
|- Group: Transfer_Functions
----------------------------
|- Group: a
-----------
--> Dataset: ex
.................
--> Dataset: ey
.................
--> Dataset: hx
.................
--> Dataset: hy
.................
--> Dataset: hz
.................
|- Group: b
-----------
--> Dataset: ex
.................
--> Dataset: ey
.................
--> Dataset: hx
.................
--> Dataset: hy
.................
--> Dataset: hz
.................
|- Group: c
-----------
--> Dataset: ex
.................
--> Dataset: ey
.................
--> Dataset: hx
.................
--> Dataset: hy
.................
--> Dataset: hz
.................
|- Group: d
-----------
--> Dataset: ex
.................
--> Dataset: ey
.................
--> Dataset: hx
.................
--> Dataset: hy
.................
--> Dataset: hz
.................
--> Dataset: channel_summary
..............................
--> Dataset: tf_summary
.........................
Channel Summary
A convenience table is supplied with an MTH5 file. This table provides some information about each channel that is present in the file. It also provides columns hdf5_reference, run_hdf5_reference, and station_hdf5_reference, these are internal references within an HDF5 file and can be used to directly access a group or dataset by using mth5_object.from_reference method.
Note: When a MTH5 file is close the table is resummarized so when you open the file next the channel_summary will be up to date. Same with the tf_summary.
[11]:
mth5_object.channel_summary.clear_table()
mth5_object.channel_summary.summarize()
ch_df = mth5_object.channel_summary.to_dataframe()
ch_df
[11]:
| survey | station | run | latitude | longitude | elevation | component | start | end | n_samples | sample_rate | measurement_type | azimuth | tilt | units | hdf5_reference | run_hdf5_reference | station_hdf5_reference | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | CONUS South | CAS04 | a | 37.633351 | -121.468382 | 329.3875 | ex | 2020-06-02 19:00:00+00:00 | 2020-06-02 22:07:47+00:00 | 11267 | 1.0 | electric | 13.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 1 | CONUS South | CAS04 | a | 37.633351 | -121.468382 | 329.3875 | ey | 2020-06-02 19:00:00+00:00 | 2020-06-02 22:07:47+00:00 | 11267 | 1.0 | electric | 103.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 2 | CONUS South | CAS04 | a | 37.633351 | -121.468382 | 329.3875 | hx | 2020-06-02 19:00:00+00:00 | 2020-06-02 22:07:47+00:00 | 11267 | 1.0 | magnetic | 13.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 3 | CONUS South | CAS04 | a | 37.633351 | -121.468382 | 329.3875 | hy | 2020-06-02 19:00:00+00:00 | 2020-06-02 22:07:47+00:00 | 11267 | 1.0 | magnetic | 103.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 4 | CONUS South | CAS04 | a | 37.633351 | -121.468382 | 329.3875 | hz | 2020-06-02 19:00:00+00:00 | 2020-06-02 22:07:47+00:00 | 11267 | 1.0 | magnetic | 0.0 | 90.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 5 | CONUS South | CAS04 | b | 37.633351 | -121.468382 | 329.3875 | ex | 2020-06-02 22:24:55+00:00 | 2020-06-12 17:52:24+00:00 | 847649 | 1.0 | electric | 13.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 6 | CONUS South | CAS04 | b | 37.633351 | -121.468382 | 329.3875 | ey | 2020-06-02 22:24:55+00:00 | 2020-06-12 17:52:24+00:00 | 847649 | 1.0 | electric | 103.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 7 | CONUS South | CAS04 | b | 37.633351 | -121.468382 | 329.3875 | hx | 2020-06-02 22:24:55+00:00 | 2020-06-12 17:52:24+00:00 | 847649 | 1.0 | magnetic | 13.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 8 | CONUS South | CAS04 | b | 37.633351 | -121.468382 | 329.3875 | hy | 2020-06-02 22:24:55+00:00 | 2020-06-12 17:52:24+00:00 | 847649 | 1.0 | magnetic | 103.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 9 | CONUS South | CAS04 | b | 37.633351 | -121.468382 | 329.3875 | hz | 2020-06-02 22:24:55+00:00 | 2020-06-12 17:52:24+00:00 | 847649 | 1.0 | magnetic | 0.0 | 90.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 10 | CONUS South | CAS04 | c | 37.633351 | -121.468382 | 329.3875 | ex | 2020-06-12 18:32:17+00:00 | 2020-07-01 17:33:00+00:00 | 1638043 | 1.0 | electric | 13.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 11 | CONUS South | CAS04 | c | 37.633351 | -121.468382 | 329.3875 | ey | 2020-06-12 18:32:17+00:00 | 2020-07-01 17:33:00+00:00 | 1638043 | 1.0 | electric | 103.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 12 | CONUS South | CAS04 | c | 37.633351 | -121.468382 | 329.3875 | hx | 2020-06-12 18:32:17+00:00 | 2020-07-01 17:33:00+00:00 | 1638043 | 1.0 | magnetic | 13.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 13 | CONUS South | CAS04 | c | 37.633351 | -121.468382 | 329.3875 | hy | 2020-06-12 18:32:17+00:00 | 2020-07-01 17:33:00+00:00 | 1638043 | 1.0 | magnetic | 103.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 14 | CONUS South | CAS04 | c | 37.633351 | -121.468382 | 329.3875 | hz | 2020-06-12 18:32:17+00:00 | 2020-07-01 17:33:00+00:00 | 1638043 | 1.0 | magnetic | 0.0 | 90.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 15 | CONUS South | CAS04 | d | 37.633351 | -121.468382 | 329.3875 | ex | 2020-07-01 19:36:55+00:00 | 2020-07-13 19:00:01+00:00 | 1034586 | 1.0 | electric | 13.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 16 | CONUS South | CAS04 | d | 37.633351 | -121.468382 | 329.3875 | ey | 2020-07-01 19:36:55+00:00 | 2020-07-13 19:00:01+00:00 | 1034586 | 1.0 | electric | 103.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 17 | CONUS South | CAS04 | d | 37.633351 | -121.468382 | 329.3875 | hx | 2020-07-01 19:36:55+00:00 | 2020-07-13 19:00:01+00:00 | 1034586 | 1.0 | magnetic | 13.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 18 | CONUS South | CAS04 | d | 37.633351 | -121.468382 | 329.3875 | hy | 2020-07-01 19:36:55+00:00 | 2020-07-13 19:00:01+00:00 | 1034586 | 1.0 | magnetic | 103.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 19 | CONUS South | CAS04 | d | 37.633351 | -121.468382 | 329.3875 | hz | 2020-07-01 19:36:55+00:00 | 2020-07-13 19:00:01+00:00 | 1034586 | 1.0 | magnetic | 0.0 | 90.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
Have a look at a station
Lets grab one station CAS04 and have a look at its metadata and contents. Here we will grab it from the mth5_object.
[12]:
cas04 = mth5_object.get_station("CAS04")
cas04.metadata
[12]:
{
"station": {
"acquired_by.name": null,
"channels_recorded": [],
"data_type": "MT",
"fdsn.id": "CAS04",
"geographic_name": "Corral Hollow, CA, USA",
"hdf5_reference": "<HDF5 object reference>",
"id": "CAS04",
"location.declination.comments": "igrf.m by Drew Compston",
"location.declination.model": "IGRF-13",
"location.declination.value": 13.1745887285666,
"location.elevation": 329.3875,
"location.latitude": 37.633351,
"location.longitude": -121.468382,
"mth5_type": "Station",
"orientation.method": "compass",
"orientation.reference_frame": "geographic",
"provenance.creation_time": "1980-01-01T00:00:00+00:00",
"provenance.software.author": "Anna Kelbert, USGS",
"provenance.software.name": "mth5_metadata.m",
"provenance.software.version": "2022-03-31",
"provenance.submitter.email": null,
"provenance.submitter.organization": null,
"run_list": [
"a",
"b",
"c",
"d"
],
"time_period.end": "2020-07-13T21:46:12+00:00",
"time_period.start": "2020-06-02T18:41:43+00:00"
}
}
Changing Metadata
If you want to change the metadata of any group, be sure to use the write_metadata method. Here’s an example:
[13]:
cas04.metadata.location.declination.value = -13.5
cas04.write_metadata()
print(cas04.metadata.location.declination)
declination:
comments = igrf.m by Drew Compston
model = IGRF-13
value = -13.5
Have a look at a single channel
Let’s pick out a channel and interogate it. There are a couple ways 1. Get a channel the first will be from the hdf5_reference [demonstrated here] 2. Get a channel from mth5_object 3. Get a station first then get a channel
[14]:
ex = mth5_object.from_reference(ch_df.iloc[0].hdf5_reference).to_channel_ts()
print(ex)
Channel Summary:
Station: CAS04
Run: a
Channel Type: electric
Component: ex
Sample Rate: 1.0
Start: 2020-06-02T19:00:00+00:00
End: 2020-06-02T22:07:47+00:00
N Samples: 11267
[15]:
ex.channel_metadata
[15]:
{
"electric": {
"channel_number": 0,
"comments": "run_ids: [c,b,a]",
"component": "ex",
"data_quality.rating.value": 0,
"dipole_length": 92.0,
"filter.applied": [
false
],
"filter.name": [
"electric_si_units",
"electric_dipole_92.000",
"electric_butterworth_low_pass",
"electric_butterworth_high_pass",
"electric_analog_to_digital",
"electric_time_offset"
],
"hdf5_reference": "<HDF5 object reference>",
"measurement_azimuth": 13.2,
"measurement_tilt": 0.0,
"mth5_type": "Electric",
"negative.elevation": 329.4,
"negative.id": "200406D",
"negative.latitude": 37.633351,
"negative.longitude": -121.468382,
"negative.manufacturer": "Oregon State University",
"negative.model": "Pb-PbCl2 kaolin gel Petiau 2 chamber type",
"negative.type": "electrode",
"positive.elevation": 329.4,
"positive.id": "200406B",
"positive.latitude": 37.633351,
"positive.longitude": -121.468382,
"positive.manufacturer": "Oregon State University",
"positive.model": "Pb-PbCl2 kaolin gel Petiau 2 chamber type",
"positive.type": "electrode",
"sample_rate": 1.0,
"time_period.end": "2020-06-02T22:07:47+00:00",
"time_period.start": "2020-06-02T19:00:00+00:00",
"type": "electric",
"units": "digital counts"
}
}
Calibrate time series data
Most data loggers output data in digital counts. Then a series of filters that represent the various instrument responses are applied to get the data into physical units. The data can then be analyzed and processed. Commonly this is done during the processing step, but it is important to be able to look at time series data in physical units. Here we provide a remove_instrument_response method in the ChananelTS object. Here’s an example:
[16]:
print(ex.channel_response_filter)
ex.channel_response_filter.plot_response(np.logspace(-4, 1, 50))
Filters Included:
=========================
coefficient_filter:
calibration_date = 1980-01-01
comments = practical to SI unit conversion
gain = 1e-06
name = electric_si_units
type = coefficient
units_in = mV/km
units_out = V/m
--------------------
coefficient_filter:
calibration_date = 1980-01-01
comments = electric dipole for electric field
gain = 92.0
name = electric_dipole_92.000
type = coefficient
units_in = V/m
units_out = V
--------------------
pole_zero_filter:
calibration_date = 1980-01-01
comments = NIMS electric field 5 pole Butterworth 0.5 low pass (analog)
gain = 1.0
name = electric_butterworth_low_pass
normalization_factor = 313383.493219835
poles = [ -3.883009+11.951875j -3.883009-11.951875j -10.166194 +7.386513j
-10.166194 -7.386513j -12.566371 +0.j ]
type = zpk
units_in = V
units_out = V
zeros = []
--------------------
pole_zero_filter:
calibration_date = 1980-01-01
comments = NIMS electric field 1 pole Butterworth high pass (analog)
gain = 1.0
name = electric_butterworth_high_pass
normalization_factor = 1.00000000378188
poles = [-0.000167+0.j]
type = zpk
units_in = V
units_out = V
zeros = [0.+0.j]
--------------------
coefficient_filter:
calibration_date = 1980-01-01
comments = analog to digital conversion (electric)
gain = 409600000.0
name = electric_analog_to_digital
type = coefficient
units_in = V
units_out = count
--------------------
time_delay_filter:
calibration_date = 1980-01-01
comments = time offset in seconds (digital)
delay = -0.285
gain = 1.0
name = electric_time_offset
type = time delay
units_in = count
units_out = count
--------------------
[17]:
ex.remove_instrument_response(plot=True)
C:\Users\jpeacock\OneDrive - DOI\Documents\GitHub\mth5\mth5\timeseries\ts_filters.py:500: UserWarning: Attempted to set non-positive left xlim on a log-scaled axis.
Invalid limit will be ignored.
ax2.set_xlim((f[0], f[-1]))
[17]:
Channel Summary:
Station: CAS04
Run: a
Channel Type: electric
Component: ex
Sample Rate: 1.0
Start: 2020-06-02T19:00:00+00:00
End: 2020-06-02T22:07:47+00:00
N Samples: 11267
Have a look at a run
Let’s pick out a run, take a slice of it, and interogate it. There are a couple ways 1. Get a run the first will be from the run_hdf5_reference [demonstrated here] 2. Get a run from mth5_object 3. Get a station first then get a run
[18]:
run_from_reference = mth5_object.from_reference(ch_df.iloc[0].run_hdf5_reference).to_runts(start=ch_df.iloc[0].start.isoformat(), n_samples=360)
print(run_from_reference)
RunTS Summary:
Station: CAS04
Run: a
Start: 2020-06-02T19:00:00+00:00
End: 2020-06-02T19:06:01+00:00
Sample Rate: 1.0
Components: ['ex', 'ey', 'hx', 'hy', 'hz']
[19]:
run_from_reference.plot()
Calibrate Run
[20]:
calibrated_run = run_from_reference.calibrate()
calibrated_run.plot()
2022-09-12 17:02:02,653 [line 286] mth5.timeseries.run_ts.RunTS.validate_metadata - WARNING: end time of dataset 2020-06-02T19:06:01+00:00 does not match metadata end 2020-06-02T22:07:47+00:00 updating metatdata value to 2020-06-02T19:06:01+00:00
Load Transfer Functions
You can download the transfer functions for CAS04 and NVR08 from IRIS SPUD EMTF. This has already been done as EMTF XML format and will be loaded here.
[21]:
cas04_tf = r"USMTArray.CAS04.2020.xml"
[22]:
from mt_metadata.transfer_functions.core import TF
[23]:
for tf_fn in [cas04_tf]:
tf_obj = TF(tf_fn)
tf_obj.read_tf_file()
mth5_object.add_transfer_function(tf_obj)
Have a look at the transfer function summary
[24]:
mth5_object.tf_summary.summarize()
tf_df = mth5_object.tf_summary.to_dataframe()
tf_df
[24]:
| station | survey | latitude | longitude | elevation | tf_id | units | has_impedance | has_tipper | has_covariance | period_min | period_max | hdf5_reference | station_hdf5_reference | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | CAS04 | CONUS_South | 37.633351 | -121.468382 | 329.387 | CAS04 | none | True | True | True | 4.65455 | 29127.11 | <HDF5 object reference> | <HDF5 object reference> |
Plot the transfer functions using MTpy
Note: This currently works on branch mtpy/v2_plots
[25]:
from mtpy import MTCollection
2022-09-12T17:02:07 [line 121] mtpy.utils.mtpy_decorator._check_gdal_data - INFO: GDAL_DATA is set to: C:\Users\jpeacock\Anaconda3\envs\em\Library\share\gdal
2022-09-12 17:02:07,362 [line 133] matplotlib.get_mtpy_logger - INFO: Logging file can be found C:\Users\jpeacock\OneDrive - DOI\Documents\GitHub\mtpy\logs\matplotlib_warn.log
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
~\AppData\Local\Temp\11\ipykernel_6372\2507741362.py in <cell line: 1>()
----> 1 from mtpy import MTCollection
ImportError: cannot import name 'MTCollection' from 'mtpy' (C:\Users\jpeacock\OneDrive - DOI\Documents\GitHub\mtpy\mtpy\__init__.py)
[ ]:
mc = MTCollection()
mc.open_collection(r"8P_CAS04_NVR08")
[ ]:
pmr = mc.plot_mt_response(["CAS04", "NVR08"], plot_style="1")
Plot Station locations
Here we can plot station locations for all stations in the file, or we can give it a bounding box. If you have internet access a basemap will be plotted using Contextily.
[ ]:
st = mc.plot_stations(pad=.9, fig_num=5, fig_size=[6, 4])
[ ]:
st.fig.get_axes()[0].set_xlim((-121.9, -117.75))
st.fig.get_axes()[0].set_ylim((37.35, 38.5))
st.update_plot()
[ ]:
mth5_object.close_mth5()
[ ]:
Make MTH5 from IRIS Data Managment Center v0.2.0
This example demonstrates how to build an MTH5 from data archived at IRIS, it could work with any MT data stored at an FDSN data center (probably).
We will use the mth5.clients.FDSN class to build the file. There is also second way using the more generic mth5.clients.MakeMTH5 class, which will be highlighted below.
Note: this example assumes that data availability (Network, Station, Channel, Start, End) are all previously known. If you do not know the data that you want to download use IRIS tools to get data availability.
[1]:
from pathlib import Path
import numpy as np
import pandas as pd
from mth5.mth5 import MTH5
from mth5.clients.make_mth5 import FDSN
from matplotlib import pyplot as plt
%matplotlib widget
Set the path to save files to as the current working directory
[2]:
default_path = Path().cwd()
Initialize a MakeMTH5 object
Here, we are setting the MTH5 file version to 0.2.0 so that we can have multiple surveys in a single file. Also, setting the client to “IRIS”. Here, we are using obspy.clients tools for the request. Here are the available FDSN clients.
Note: Only the “IRIS” client has been tested.
[3]:
fdsn_object = FDSN(mth5_version='0.2.0')
fdsn_object.client = "IRIS"
Make the data inquiry as a DataFrame
There are a few ways to make the inquiry to request data.
Make a DataFrame by hand. Here we will make a list of entries and then create a DataFrame with the proper column names
You can create a CSV file with a row for each entry. There are some formatting that you need to be aware of. That is the column names and making sure that date-times are YYYY-MM-DDThh:mm:ss
Column Name |
Description |
|---|---|
network |
|
station |
|
location |
|
channel |
|
start |
Start time (YYYY-MM-DDThh:mm:ss) UTC |
end |
End time (YYYY-MM-DDThh:mm:ss) UTC |
[4]:
channels = ["LFE", "LFN", "LFZ", "LQE", "LQN"]
CAS04 = ["8P", "CAS04", '2020-06-02T19:00:00', '2020-07-13T19:00:00']
NVR08 = ["8P", "NVR08", '2020-06-02T19:00:00', '2020-07-13T19:00:00']
request_list = []
for entry in [CAS04, NVR08]:
for channel in channels:
request_list.append(
[entry[0], entry[1], "", channel, entry[2], entry[3]]
)
# Turn list into dataframe
request_df = pd.DataFrame(request_list, columns=fdsn_object.request_columns)
request_df
[4]:
| network | station | location | channel | start | end | |
|---|---|---|---|---|---|---|
| 0 | 8P | CAS04 | LFE | 2020-06-02T19:00:00 | 2020-07-13T19:00:00 | |
| 1 | 8P | CAS04 | LFN | 2020-06-02T19:00:00 | 2020-07-13T19:00:00 | |
| 2 | 8P | CAS04 | LFZ | 2020-06-02T19:00:00 | 2020-07-13T19:00:00 | |
| 3 | 8P | CAS04 | LQE | 2020-06-02T19:00:00 | 2020-07-13T19:00:00 | |
| 4 | 8P | CAS04 | LQN | 2020-06-02T19:00:00 | 2020-07-13T19:00:00 | |
| 5 | 8P | NVR08 | LFE | 2020-06-02T19:00:00 | 2020-07-13T19:00:00 | |
| 6 | 8P | NVR08 | LFN | 2020-06-02T19:00:00 | 2020-07-13T19:00:00 | |
| 7 | 8P | NVR08 | LFZ | 2020-06-02T19:00:00 | 2020-07-13T19:00:00 | |
| 8 | 8P | NVR08 | LQE | 2020-06-02T19:00:00 | 2020-07-13T19:00:00 | |
| 9 | 8P | NVR08 | LQN | 2020-06-02T19:00:00 | 2020-07-13T19:00:00 |
Save the request as a CSV
Its helpful to be able to save the request as a CSV and modify it and use it later. A CSV can be input as a request to MakeMTH5
[5]:
request_df.to_csv(default_path.joinpath("fdsn_request.csv"))
Get only the metadata from IRIS
It can be helpful to make sure that your request is what you would expect. For that you can request only the metadata from IRIS. The request is quick and light so shouldn’t need to worry about the speed. This returns a StationXML file and is loaded into an obspy.Inventory object.
[6]:
inventory, data = fdsn_object.get_inventory_from_df(request_df, data=False)
Have a look at the Inventory to make sure it contains what is requested.
[7]:
inventory
[7]:
Inventory created at 2023-07-22T21:45:37.964795Z
Created by: ObsPy 1.4.0
https://www.obspy.org
Sending institution: MTH5
Contains:
Networks (1):
8P
Stations (2):
8P.CAS04 (Corral Hollow, CA, USA)
8P.NVR08 (Rhodes Salt Marsh, NV, USA)
Channels (10):
8P.CAS04..LFZ, 8P.CAS04..LFN, 8P.CAS04..LFE, 8P.CAS04..LQN,
8P.CAS04..LQE, 8P.NVR08..LFZ, 8P.NVR08..LFN, 8P.NVR08..LFE,
8P.NVR08..LQN, 8P.NVR08..LQE
Make an MTH5 from a request
Now that we’ve created a request, and made sure that its what we expect, we can make an MTH5 file. The input can be either the DataFrame or the CSV file.
We are going to time it just to get an indication how long it might take. Should take about 4 minutes.
Note: we are setting interact=False. If you want to just to keep the file open to interogat it set interact=True.
Make an MTH5 using MakeMTH5
Another way to make a file is using the mth5.clients.MakeMTH5 class, which is more generic than FDSN, but doesn’t have as many methods. The MakeMTH5 class is meant to be a convienence method for the various clients.
from mth5.clients import MakeMTH5
make_mth5_object = MakeMTH5(mth5_version='0.2.0', interact=False)
mth5_filename = make_mth5_object.from_fdsn_client(request_df, client="IRIS")
[8]:
%%time
mth5_object = fdsn_object.make_mth5_from_fdsn_client(request_df, interact=False)
print(f"Created {mth5_object}")
2023-07-22T14:46:04 [line 677] mth5.mth5.MTH5._initialize_file - INFO: Initialized MTH5 0.2.0 file /home/kkappler/software/irismt/mth5/docs/examples/notebooks/8P_CAS04_NVR08.h5 in mode w
2023-07-22T14:47:23.232455-0700 | INFO | mt_metadata.timeseries.filters.obspy_stages | create_filter_from_stage | Converting PoleZerosResponseStage electric_si_units to a CoefficientFilter.
2023-07-22T14:47:23.240907-0700 | INFO | mt_metadata.timeseries.filters.obspy_stages | create_filter_from_stage | Converting PoleZerosResponseStage electric_dipole_92.000 to a CoefficientFilter.
2023-07-22T14:47:23.286284-0700 | INFO | mt_metadata.timeseries.filters.obspy_stages | create_filter_from_stage | Converting PoleZerosResponseStage electric_si_units to a CoefficientFilter.
2023-07-22T14:47:23.295961-0700 | INFO | mt_metadata.timeseries.filters.obspy_stages | create_filter_from_stage | Converting PoleZerosResponseStage electric_dipole_92.000 to a CoefficientFilter.
2023-07-22T14:47:23.447693-0700 | INFO | mt_metadata.timeseries.filters.obspy_stages | create_filter_from_stage | Converting PoleZerosResponseStage electric_si_units to a CoefficientFilter.
2023-07-22T14:47:23.455765-0700 | INFO | mt_metadata.timeseries.filters.obspy_stages | create_filter_from_stage | Converting PoleZerosResponseStage electric_dipole_94.000 to a CoefficientFilter.
2023-07-22T14:47:23.504153-0700 | INFO | mt_metadata.timeseries.filters.obspy_stages | create_filter_from_stage | Converting PoleZerosResponseStage electric_si_units to a CoefficientFilter.
2023-07-22T14:47:23.512332-0700 | INFO | mt_metadata.timeseries.filters.obspy_stages | create_filter_from_stage | Converting PoleZerosResponseStage electric_dipole_94.000 to a CoefficientFilter.
2023-07-22T14:47:24 [line 311] mth5.groups.base.Station._add_group - INFO: RunGroup a already exists, returning existing group.
2023-07-22T14:47:24 [line 664] mth5.timeseries.run_ts.RunTS.validate_metadata - WARNING: start time of dataset 2020-06-02T19:00:00+00:00 does not match metadata start 2020-06-02T18:41:43+00:00 updating metatdata value to 2020-06-02T19:00:00+00:00
2023-07-22T14:47:24 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id a
2023-07-22T14:47:24 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id a
2023-07-22T14:47:25 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id a
2023-07-22T14:47:25 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id a
2023-07-22T14:47:25 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id a
2023-07-22T14:47:25 [line 311] mth5.groups.base.Station._add_group - INFO: RunGroup b already exists, returning existing group.
2023-07-22T14:47:25 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id b
2023-07-22T14:47:26 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id b
2023-07-22T14:47:26 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id b
2023-07-22T14:47:26 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id b
2023-07-22T14:47:26 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id b
2023-07-22T14:47:26 [line 311] mth5.groups.base.Station._add_group - INFO: RunGroup c already exists, returning existing group.
2023-07-22T14:47:26 [line 936] mth5.timeseries.run_ts.RunTS.from_obspy_stream - WARNING: could not find ey
2023-07-22T14:47:27 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id c
2023-07-22T14:47:27 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id c
2023-07-22T14:47:27 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id c
2023-07-22T14:47:27 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id c
2023-07-22T14:47:27 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id c
2023-07-22T14:47:27 [line 311] mth5.groups.base.Station._add_group - INFO: RunGroup d already exists, returning existing group.
2023-07-22T14:47:27 [line 936] mth5.timeseries.run_ts.RunTS.from_obspy_stream - WARNING: could not find ex
2023-07-22T14:47:27 [line 936] mth5.timeseries.run_ts.RunTS.from_obspy_stream - WARNING: could not find ey
2023-07-22T14:47:28 [line 678] mth5.timeseries.run_ts.RunTS.validate_metadata - WARNING: end time of dataset 2020-07-13T19:00:00+00:00 does not match metadata end 2020-07-13T21:46:12+00:00 updating metatdata value to 2020-07-13T19:00:00+00:00
2023-07-22T14:47:28 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id d
2023-07-22T14:47:28 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id d
2023-07-22T14:47:28 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id d
2023-07-22T14:47:28 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id d
2023-07-22T14:47:28 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id d
2023-07-22T14:47:28 [line 311] mth5.groups.base.Station._add_group - INFO: RunGroup a already exists, returning existing group.
2023-07-22T14:47:29 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id a
2023-07-22T14:47:29 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id a
2023-07-22T14:47:29 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id a
2023-07-22T14:47:29 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id a
2023-07-22T14:47:29 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id a
2023-07-22T14:47:29 [line 311] mth5.groups.base.Station._add_group - INFO: RunGroup b already exists, returning existing group.
2023-07-22T14:47:30 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id b
2023-07-22T14:47:30 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id b
2023-07-22T14:47:30 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id b
2023-07-22T14:47:30 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id b
2023-07-22T14:47:30 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id b
2023-07-22T14:47:31 [line 311] mth5.groups.base.Station._add_group - INFO: RunGroup c already exists, returning existing group.
2023-07-22T14:47:31 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id c
2023-07-22T14:47:31 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id c
2023-07-22T14:47:31 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id c
2023-07-22T14:47:31 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id c
2023-07-22T14:47:32 [line 623] mth5.groups.base.Run.from_runts - WARNING: Channel run.id sr1_001 != group run.id c
2023-07-22T14:47:32 [line 758] mth5.mth5.MTH5.close_mth5 - INFO: Flushing and closing /home/kkappler/software/irismt/mth5/docs/examples/notebooks/8P_CAS04_NVR08.h5
Created /home/kkappler/software/irismt/mth5/docs/examples/notebooks/8P_CAS04_NVR08.h5
CPU times: user 9.73 s, sys: 279 ms, total: 10 s
Wall time: 1min 28s
[9]:
# open file already created
mth5_object = MTH5()
mth5_object.open_mth5("8P_CAS04_NVR08.h5")
Have a look at the contents of the created file
[10]:
mth5_object
[10]:
/:
====================
|- Group: Experiment
--------------------
|- Group: Reports
-----------------
|- Group: Standards
-------------------
--> Dataset: summary
......................
|- Group: Surveys
-----------------
|- Group: CONUS_South
---------------------
|- Group: Filters
-----------------
|- Group: coefficient
---------------------
|- Group: electric_analog_to_digital
------------------------------------
|- Group: electric_dipole_92.000
--------------------------------
|- Group: electric_dipole_94.000
--------------------------------
|- Group: electric_si_units
---------------------------
|- Group: magnetic_analog_to_digital
------------------------------------
|- Group: fap
-------------
|- Group: fir
-------------
|- Group: time_delay
--------------------
|- Group: electric_time_offset
------------------------------
|- Group: hx_time_offset
------------------------
|- Group: hy_time_offset
------------------------
|- Group: hz_time_offset
------------------------
|- Group: zpk
-------------
|- Group: electric_butterworth_high_pass
----------------------------------------
--> Dataset: poles
....................
--> Dataset: zeros
....................
|- Group: electric_butterworth_low_pass
---------------------------------------
--> Dataset: poles
....................
--> Dataset: zeros
....................
|- Group: magnetic_butterworth_low_pass
---------------------------------------
--> Dataset: poles
....................
--> Dataset: zeros
....................
|- Group: Reports
-----------------
|- Group: Standards
-------------------
--> Dataset: summary
......................
|- Group: Stations
------------------
|- Group: CAS04
---------------
|- Group: Fourier_Coefficients
------------------------------
|- Group: Transfer_Functions
----------------------------
|- Group: a
-----------
--> Dataset: ex
.................
--> Dataset: ey
.................
--> Dataset: hx
.................
--> Dataset: hy
.................
--> Dataset: hz
.................
|- Group: b
-----------
--> Dataset: ex
.................
--> Dataset: ey
.................
--> Dataset: hx
.................
--> Dataset: hy
.................
--> Dataset: hz
.................
|- Group: c
-----------
--> Dataset: ex
.................
--> Dataset: ey
.................
--> Dataset: hx
.................
--> Dataset: hy
.................
--> Dataset: hz
.................
|- Group: d
-----------
--> Dataset: ex
.................
--> Dataset: ey
.................
--> Dataset: hx
.................
--> Dataset: hy
.................
--> Dataset: hz
.................
|- Group: NVR08
---------------
|- Group: Fourier_Coefficients
------------------------------
|- Group: Transfer_Functions
----------------------------
|- Group: a
-----------
--> Dataset: ex
.................
--> Dataset: ey
.................
--> Dataset: hx
.................
--> Dataset: hy
.................
--> Dataset: hz
.................
|- Group: b
-----------
--> Dataset: ex
.................
--> Dataset: ey
.................
--> Dataset: hx
.................
--> Dataset: hy
.................
--> Dataset: hz
.................
|- Group: c
-----------
--> Dataset: ex
.................
--> Dataset: ey
.................
--> Dataset: hx
.................
--> Dataset: hy
.................
--> Dataset: hz
.................
--> Dataset: channel_summary
..............................
--> Dataset: tf_summary
.........................
Channel Summary
A convenience table is supplied with an MTH5 file. This table provides some information about each channel that is present in the file. It also provides columns hdf5_reference, run_hdf5_reference, and station_hdf5_reference, these are internal references within an HDF5 file and can be used to directly access a group or dataset by using mth5_object.from_reference method.
Note: When a MTH5 file is close the table is resummarized so when you open the file next the channel_summary will be up to date. Same with the tf_summary.
[11]:
mth5_object.channel_summary.clear_table()
mth5_object.channel_summary.summarize()
ch_df = mth5_object.channel_summary.to_dataframe()
ch_df
[11]:
| survey | station | run | latitude | longitude | elevation | component | start | end | n_samples | sample_rate | measurement_type | azimuth | tilt | units | hdf5_reference | run_hdf5_reference | station_hdf5_reference | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | CONUS South | CAS04 | a | 37.633351 | -121.468382 | 329.3875 | ex | 2020-06-02 19:00:00+00:00 | 2020-06-02 22:07:46+00:00 | 11267 | 1.0 | electric | 13.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 1 | CONUS South | CAS04 | a | 37.633351 | -121.468382 | 329.3875 | ey | 2020-06-02 19:00:00+00:00 | 2020-06-02 22:07:46+00:00 | 11267 | 1.0 | electric | 103.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 2 | CONUS South | CAS04 | a | 37.633351 | -121.468382 | 329.3875 | hx | 2020-06-02 19:00:00+00:00 | 2020-06-02 22:07:46+00:00 | 11267 | 1.0 | magnetic | 13.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 3 | CONUS South | CAS04 | a | 37.633351 | -121.468382 | 329.3875 | hy | 2020-06-02 19:00:00+00:00 | 2020-06-02 22:07:46+00:00 | 11267 | 1.0 | magnetic | 103.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 4 | CONUS South | CAS04 | a | 37.633351 | -121.468382 | 329.3875 | hz | 2020-06-02 19:00:00+00:00 | 2020-06-02 22:07:46+00:00 | 11267 | 1.0 | magnetic | 0.0 | 90.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 5 | CONUS South | CAS04 | b | 37.633351 | -121.468382 | 329.3875 | ex | 2020-06-02 22:24:55+00:00 | 2020-06-12 17:52:23+00:00 | 847649 | 1.0 | electric | 13.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 6 | CONUS South | CAS04 | b | 37.633351 | -121.468382 | 329.3875 | ey | 2020-06-02 22:24:55+00:00 | 2020-06-12 17:52:23+00:00 | 847649 | 1.0 | electric | 103.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 7 | CONUS South | CAS04 | b | 37.633351 | -121.468382 | 329.3875 | hx | 2020-06-02 22:24:55+00:00 | 2020-06-12 17:52:23+00:00 | 847649 | 1.0 | magnetic | 13.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 8 | CONUS South | CAS04 | b | 37.633351 | -121.468382 | 329.3875 | hy | 2020-06-02 22:24:55+00:00 | 2020-06-12 17:52:23+00:00 | 847649 | 1.0 | magnetic | 103.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 9 | CONUS South | CAS04 | b | 37.633351 | -121.468382 | 329.3875 | hz | 2020-06-02 22:24:55+00:00 | 2020-06-12 17:52:23+00:00 | 847649 | 1.0 | magnetic | 0.0 | 90.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 10 | CONUS South | CAS04 | c | 37.633351 | -121.468382 | 329.3875 | ex | 2020-06-12 18:32:17+00:00 | 2020-07-01 17:32:59+00:00 | 1638043 | 1.0 | electric | 13.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 11 | CONUS South | CAS04 | c | 37.633351 | -121.468382 | 329.3875 | ey | 2020-06-12 18:32:17+00:00 | 2020-07-01 17:32:59+00:00 | 1638043 | 1.0 | electric | 0.0 | 0.0 | counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 12 | CONUS South | CAS04 | c | 37.633351 | -121.468382 | 329.3875 | hx | 2020-06-12 18:32:17+00:00 | 2020-07-01 17:32:59+00:00 | 1638043 | 1.0 | magnetic | 13.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 13 | CONUS South | CAS04 | c | 37.633351 | -121.468382 | 329.3875 | hy | 2020-06-12 18:32:17+00:00 | 2020-07-01 17:32:59+00:00 | 1638043 | 1.0 | magnetic | 103.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 14 | CONUS South | CAS04 | c | 37.633351 | -121.468382 | 329.3875 | hz | 2020-06-12 18:32:17+00:00 | 2020-07-01 17:32:59+00:00 | 1638043 | 1.0 | magnetic | 0.0 | 90.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 15 | CONUS South | CAS04 | d | 37.633351 | -121.468382 | 329.3875 | ex | 2020-07-01 19:36:55+00:00 | 2020-07-13 19:00:00+00:00 | 1034586 | 1.0 | electric | 0.0 | 0.0 | counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 16 | CONUS South | CAS04 | d | 37.633351 | -121.468382 | 329.3875 | ey | 2020-07-01 19:36:55+00:00 | 2020-07-13 19:00:00+00:00 | 1034586 | 1.0 | electric | 0.0 | 0.0 | counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 17 | CONUS South | CAS04 | d | 37.633351 | -121.468382 | 329.3875 | hx | 2020-07-01 19:36:55+00:00 | 2020-07-13 19:00:00+00:00 | 1034586 | 1.0 | magnetic | 13.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 18 | CONUS South | CAS04 | d | 37.633351 | -121.468382 | 329.3875 | hy | 2020-07-01 19:36:55+00:00 | 2020-07-13 19:00:00+00:00 | 1034586 | 1.0 | magnetic | 103.2 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 19 | CONUS South | CAS04 | d | 37.633351 | -121.468382 | 329.3875 | hz | 2020-07-01 19:36:55+00:00 | 2020-07-13 19:00:00+00:00 | 1034586 | 1.0 | magnetic | 0.0 | 90.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 20 | CONUS South | NVR08 | a | 38.326630 | -118.082382 | 1375.4250 | ex | 2020-06-03 19:10:11+00:00 | 2020-06-03 19:57:51+00:00 | 2861 | 1.0 | electric | 12.6 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 21 | CONUS South | NVR08 | a | 38.326630 | -118.082382 | 1375.4250 | ey | 2020-06-03 19:10:11+00:00 | 2020-06-03 19:57:51+00:00 | 2861 | 1.0 | electric | 102.6 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 22 | CONUS South | NVR08 | a | 38.326630 | -118.082382 | 1375.4250 | hx | 2020-06-03 19:10:11+00:00 | 2020-06-03 19:57:51+00:00 | 2861 | 1.0 | magnetic | 12.6 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 23 | CONUS South | NVR08 | a | 38.326630 | -118.082382 | 1375.4250 | hy | 2020-06-03 19:10:11+00:00 | 2020-06-03 19:57:51+00:00 | 2861 | 1.0 | magnetic | 102.6 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 24 | CONUS South | NVR08 | a | 38.326630 | -118.082382 | 1375.4250 | hz | 2020-06-03 19:10:11+00:00 | 2020-06-03 19:57:51+00:00 | 2861 | 1.0 | magnetic | 0.0 | 90.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 25 | CONUS South | NVR08 | b | 38.326630 | -118.082382 | 1375.4250 | ex | 2020-06-03 20:14:13+00:00 | 2020-06-14 16:56:02+00:00 | 938510 | 1.0 | electric | 12.6 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 26 | CONUS South | NVR08 | b | 38.326630 | -118.082382 | 1375.4250 | ey | 2020-06-03 20:14:13+00:00 | 2020-06-14 16:56:02+00:00 | 938510 | 1.0 | electric | 102.6 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 27 | CONUS South | NVR08 | b | 38.326630 | -118.082382 | 1375.4250 | hx | 2020-06-03 20:14:13+00:00 | 2020-06-14 16:56:02+00:00 | 938510 | 1.0 | magnetic | 12.6 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 28 | CONUS South | NVR08 | b | 38.326630 | -118.082382 | 1375.4250 | hy | 2020-06-03 20:14:13+00:00 | 2020-06-14 16:56:02+00:00 | 938510 | 1.0 | magnetic | 102.6 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 29 | CONUS South | NVR08 | b | 38.326630 | -118.082382 | 1375.4250 | hz | 2020-06-03 20:14:13+00:00 | 2020-06-14 16:56:02+00:00 | 938510 | 1.0 | magnetic | 0.0 | 90.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 30 | CONUS South | NVR08 | c | 38.326630 | -118.082382 | 1375.4250 | ex | 2020-06-14 18:00:44+00:00 | 2020-06-24 15:55:46+00:00 | 856503 | 1.0 | electric | 12.6 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 31 | CONUS South | NVR08 | c | 38.326630 | -118.082382 | 1375.4250 | ey | 2020-06-14 18:00:44+00:00 | 2020-06-24 15:55:46+00:00 | 856503 | 1.0 | electric | 102.6 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 32 | CONUS South | NVR08 | c | 38.326630 | -118.082382 | 1375.4250 | hx | 2020-06-14 18:00:44+00:00 | 2020-06-24 15:55:46+00:00 | 856503 | 1.0 | magnetic | 12.6 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 33 | CONUS South | NVR08 | c | 38.326630 | -118.082382 | 1375.4250 | hy | 2020-06-14 18:00:44+00:00 | 2020-06-24 15:55:46+00:00 | 856503 | 1.0 | magnetic | 102.6 | 0.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 34 | CONUS South | NVR08 | c | 38.326630 | -118.082382 | 1375.4250 | hz | 2020-06-14 18:00:44+00:00 | 2020-06-24 15:55:46+00:00 | 856503 | 1.0 | magnetic | 0.0 | 90.0 | digital counts | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
Check Filters
[15]:
ch_df["n_filters"] = -1
for i_row, row in ch_df.iterrows():
channel = mth5_object.get_channel(row.station, row.run, row.component, row.survey)
n_filters = len(channel.channel_response_filter.filters_list)
ch_df.n_filters.iat[i_row] = n_filters
Take a look at the dataframe below, inspecting to see if there are any rows with n_filters=0, this would be unexpected for data drawn from an FSDN archive.
[16]:
ch_df[["station", "run", "component", "start", "end", "n_filters"]]
[16]:
| station | run | component | start | end | n_filters | |
|---|---|---|---|---|---|---|
| 0 | CAS04 | a | ex | 2020-06-02 19:00:00+00:00 | 2020-06-02 22:07:46+00:00 | 6 |
| 1 | CAS04 | a | ey | 2020-06-02 19:00:00+00:00 | 2020-06-02 22:07:46+00:00 | 6 |
| 2 | CAS04 | a | hx | 2020-06-02 19:00:00+00:00 | 2020-06-02 22:07:46+00:00 | 3 |
| 3 | CAS04 | a | hy | 2020-06-02 19:00:00+00:00 | 2020-06-02 22:07:46+00:00 | 3 |
| 4 | CAS04 | a | hz | 2020-06-02 19:00:00+00:00 | 2020-06-02 22:07:46+00:00 | 3 |
| 5 | CAS04 | b | ex | 2020-06-02 22:24:55+00:00 | 2020-06-12 17:52:23+00:00 | 6 |
| 6 | CAS04 | b | ey | 2020-06-02 22:24:55+00:00 | 2020-06-12 17:52:23+00:00 | 6 |
| 7 | CAS04 | b | hx | 2020-06-02 22:24:55+00:00 | 2020-06-12 17:52:23+00:00 | 3 |
| 8 | CAS04 | b | hy | 2020-06-02 22:24:55+00:00 | 2020-06-12 17:52:23+00:00 | 3 |
| 9 | CAS04 | b | hz | 2020-06-02 22:24:55+00:00 | 2020-06-12 17:52:23+00:00 | 3 |
| 10 | CAS04 | c | ex | 2020-06-12 18:32:17+00:00 | 2020-07-01 17:32:59+00:00 | 6 |
| 11 | CAS04 | c | ey | 2020-06-12 18:32:17+00:00 | 2020-07-01 17:32:59+00:00 | 0 |
| 12 | CAS04 | c | hx | 2020-06-12 18:32:17+00:00 | 2020-07-01 17:32:59+00:00 | 3 |
| 13 | CAS04 | c | hy | 2020-06-12 18:32:17+00:00 | 2020-07-01 17:32:59+00:00 | 3 |
| 14 | CAS04 | c | hz | 2020-06-12 18:32:17+00:00 | 2020-07-01 17:32:59+00:00 | 3 |
| 15 | CAS04 | d | ex | 2020-07-01 19:36:55+00:00 | 2020-07-13 19:00:00+00:00 | 0 |
| 16 | CAS04 | d | ey | 2020-07-01 19:36:55+00:00 | 2020-07-13 19:00:00+00:00 | 0 |
| 17 | CAS04 | d | hx | 2020-07-01 19:36:55+00:00 | 2020-07-13 19:00:00+00:00 | 3 |
| 18 | CAS04 | d | hy | 2020-07-01 19:36:55+00:00 | 2020-07-13 19:00:00+00:00 | 3 |
| 19 | CAS04 | d | hz | 2020-07-01 19:36:55+00:00 | 2020-07-13 19:00:00+00:00 | 3 |
| 20 | NVR08 | a | ex | 2020-06-03 19:10:11+00:00 | 2020-06-03 19:57:51+00:00 | 6 |
| 21 | NVR08 | a | ey | 2020-06-03 19:10:11+00:00 | 2020-06-03 19:57:51+00:00 | 6 |
| 22 | NVR08 | a | hx | 2020-06-03 19:10:11+00:00 | 2020-06-03 19:57:51+00:00 | 3 |
| 23 | NVR08 | a | hy | 2020-06-03 19:10:11+00:00 | 2020-06-03 19:57:51+00:00 | 3 |
| 24 | NVR08 | a | hz | 2020-06-03 19:10:11+00:00 | 2020-06-03 19:57:51+00:00 | 3 |
| 25 | NVR08 | b | ex | 2020-06-03 20:14:13+00:00 | 2020-06-14 16:56:02+00:00 | 6 |
| 26 | NVR08 | b | ey | 2020-06-03 20:14:13+00:00 | 2020-06-14 16:56:02+00:00 | 6 |
| 27 | NVR08 | b | hx | 2020-06-03 20:14:13+00:00 | 2020-06-14 16:56:02+00:00 | 3 |
| 28 | NVR08 | b | hy | 2020-06-03 20:14:13+00:00 | 2020-06-14 16:56:02+00:00 | 3 |
| 29 | NVR08 | b | hz | 2020-06-03 20:14:13+00:00 | 2020-06-14 16:56:02+00:00 | 3 |
| 30 | NVR08 | c | ex | 2020-06-14 18:00:44+00:00 | 2020-06-24 15:55:46+00:00 | 6 |
| 31 | NVR08 | c | ey | 2020-06-14 18:00:44+00:00 | 2020-06-24 15:55:46+00:00 | 6 |
| 32 | NVR08 | c | hx | 2020-06-14 18:00:44+00:00 | 2020-06-24 15:55:46+00:00 | 3 |
| 33 | NVR08 | c | hy | 2020-06-14 18:00:44+00:00 | 2020-06-24 15:55:46+00:00 | 3 |
| 34 | NVR08 | c | hz | 2020-06-14 18:00:44+00:00 | 2020-06-24 15:55:46+00:00 | 3 |
Have a look at a station
Lets grab one station CAS04 and have a look at its metadata and contents. Here we will grab it from the mth5_object.
[17]:
cas04 = mth5_object.get_station("CAS04", survey="CONUS_South")
cas04.metadata
[17]:
{
"station": {
"acquired_by.name": null,
"channels_recorded": [
"ex",
"ey",
"hx",
"hy",
"hz"
],
"data_type": "MT",
"fdsn.id": "CAS04",
"geographic_name": "Corral Hollow, CA, USA",
"hdf5_reference": "<HDF5 object reference>",
"id": "CAS04",
"location.declination.comments": "igrf.m by Drew Compston",
"location.declination.model": "IGRF-13",
"location.declination.value": 13.1745887285666,
"location.elevation": 329.3875,
"location.latitude": 37.633351,
"location.longitude": -121.468382,
"mth5_type": "Station",
"orientation.method": "compass",
"orientation.reference_frame": "geographic",
"provenance.creation_time": "1980-01-01T00:00:00+00:00",
"provenance.software.author": "Anna Kelbert, USGS",
"provenance.software.name": "mth5_metadata.m",
"provenance.software.version": "2022-03-31",
"provenance.submitter.email": null,
"provenance.submitter.organization": null,
"release_license": "CC0-1.0",
"run_list": [
"a",
"b",
"c",
"d"
],
"time_period.end": "2020-07-13T21:46:12+00:00",
"time_period.start": "2020-06-02T18:41:43+00:00"
}
}
Changing Metadata
If you want to change the metadata of any group, be sure to use the write_metadata method. Here’s an example:
[18]:
cas04.metadata.location.declination.value = -13.5
cas04.write_metadata()
print(cas04.metadata.location.declination)
declination:
comments = igrf.m by Drew Compston
model = IGRF-13
value = -13.5
Have a look at a single channel
Let’s pick out a channel and interogate it. There are a couple ways 1. Get a channel the first will be from the hdf5_reference [demonstrated here] 2. Get a channel from mth5_object 3. Get a station first then get a channel
[19]:
ex = mth5_object.from_reference(ch_df.iloc[0].hdf5_reference).to_channel_ts()
print(ex)
Channel Summary:
Survey: CONUS South
Station: CAS04
Run: a
Channel Type: Electric
Component: ex
Sample Rate: 1.0
Start: 2020-06-02T19:00:00+00:00
End: 2020-06-02T22:07:46+00:00
N Samples: 11267
[20]:
ex.channel_metadata
[20]:
{
"electric": {
"channel_number": 0,
"comments": "run_ids: [c,b,a]",
"component": "ex",
"data_quality.rating.value": 0,
"dipole_length": 92.0,
"filter.applied": [
false,
false,
false,
false,
false,
false
],
"filter.name": [
"electric_si_units",
"electric_dipole_92.000",
"electric_butterworth_low_pass",
"electric_butterworth_high_pass",
"electric_analog_to_digital",
"electric_time_offset"
],
"hdf5_reference": "<HDF5 object reference>",
"measurement_azimuth": 13.2,
"measurement_tilt": 0.0,
"mth5_type": "Electric",
"negative.elevation": 329.4,
"negative.id": "200406D",
"negative.latitude": 37.633351,
"negative.longitude": -121.468382,
"negative.manufacturer": "Oregon State University",
"negative.model": "Pb-PbCl2 kaolin gel Petiau 2 chamber type",
"negative.type": "electrode",
"positive.elevation": 329.4,
"positive.id": "200406B",
"positive.latitude": 37.633351,
"positive.longitude": -121.468382,
"positive.manufacturer": "Oregon State University",
"positive.model": "Pb-PbCl2 kaolin gel Petiau 2 chamber type",
"positive.type": "electrode",
"sample_rate": 1.0,
"time_period.end": "2020-06-02T22:07:46+00:00",
"time_period.start": "2020-06-02T19:00:00+00:00",
"type": "electric",
"units": "digital counts"
}
}
Calibrate time series data
Most data loggers output data in digital counts. Then a series of filters that represent the various instrument responses are applied to get the data into physical units. The data can then be analyzed and processed. Commonly this is done during the processing step, but it is important to be able to look at time series data in physical units. Here we provide a remove_instrument_response method in the ChananelTS object. Here’s an example:
[21]:
print(ex.channel_response_filter)
ex.channel_response_filter.plot_response(np.logspace(-4, 1, 50))
Filters Included:
=========================
coefficient_filter:
calibration_date = 1980-01-01
comments = practical to SI unit conversion
gain = 1e-06
name = electric_si_units
type = coefficient
units_in = mV/km
units_out = V/m
--------------------
coefficient_filter:
calibration_date = 1980-01-01
comments = electric dipole for electric field
gain = 92.0
name = electric_dipole_92.000
type = coefficient
units_in = V/m
units_out = V
--------------------
pole_zero_filter:
calibration_date = 1980-01-01
comments = NIMS electric field 5 pole Butterworth 0.5 low pass (analog)
gain = 1.0
name = electric_butterworth_low_pass
normalization_factor = 313383.493219835
poles = [ -3.883009+11.951875j -3.883009-11.951875j -10.166194 +7.386513j
-10.166194 -7.386513j -12.566371 +0.j ]
type = zpk
units_in = V
units_out = V
zeros = []
--------------------
pole_zero_filter:
calibration_date = 1980-01-01
comments = NIMS electric field 1 pole Butterworth high pass (analog)
gain = 1.0
name = electric_butterworth_high_pass
normalization_factor = 1.00000000378188
poles = [-0.000167+0.j]
type = zpk
units_in = V
units_out = V
zeros = [0.+0.j]
--------------------
coefficient_filter:
calibration_date = 1980-01-01
comments = analog to digital conversion (electric)
gain = 409600000.0
name = electric_analog_to_digital
type = coefficient
units_in = V
units_out = count
--------------------
time_delay_filter:
calibration_date = 1980-01-01
comments = time offset in seconds (digital)
delay = -0.285
gain = 1.0
name = electric_time_offset
type = time delay
units_in = count
units_out = count
--------------------
[22]:
ex.remove_instrument_response(plot=True)
/home/kkappler/software/irismt/mth5/mth5/timeseries/ts_filters.py:498: UserWarning: Attempt to set non-positive xlim on a log-scaled axis will be ignored.
ax2.set_xlim((f[0], f[-1]))
[22]:
Channel Summary:
Survey: CONUS South
Station: CAS04
Run: a
Channel Type: Electric
Component: ex
Sample Rate: 1.0
Start: 2020-06-02T19:00:00+00:00
End: 2020-06-02T22:07:46+00:00
N Samples: 11267
Have a look at a run
Let’s pick out a run, take a slice of it, and interogate it. There are a couple ways 1. Get a run the first will be from the run_hdf5_reference [demonstrated here] 2. Get a run from mth5_object 3. Get a station first then get a run
[23]:
run_from_reference = mth5_object.from_reference(ch_df.iloc[0].run_hdf5_reference).to_runts(start=ch_df.iloc[0].start.isoformat(), n_samples=360)
print(run_from_reference)
2023-07-22T14:50:42 [line 678] mth5.timeseries.run_ts.RunTS.validate_metadata - WARNING: end time of dataset 2020-06-02T19:05:59+00:00 does not match metadata end 2020-06-02T22:07:46+00:00 updating metatdata value to 2020-06-02T19:05:59+00:00
RunTS Summary:
Survey: CONUS South
Station: CAS04
Run: a
Start: 2020-06-02T19:00:00+00:00
End: 2020-06-02T19:05:59+00:00
Sample Rate: 1.0
Components: ['ex', 'ey', 'hx', 'hy', 'hz']
[24]:
run_from_reference.plot()
Calibrate Run
[25]:
calibrated_run = run_from_reference.calibrate()
calibrated_run.plot()
Load Transfer Functions
You can download the transfer functions for CAS04 and NVR08 from IRIS SPUD EMTF. This has already been done as EMTF XML format and will be loaded here.
[26]:
cas04_tf = r"USMTArray.CAS04.2020.xml"
nvr08_tf = r"USMTArray.NVR08.2020.xml"
[27]:
from mt_metadata.transfer_functions.core import TF
[28]:
for tf_fn in [cas04_tf, nvr08_tf]:
tf_obj = TF(tf_fn)
tf_obj.read_tf_file()
mth5_object.add_transfer_function(tf_obj)
Have a look at the transfer function summary
[24]:
mth5_object.tf_summary.summarize()
tf_df = mth5_object.tf_summary.to_dataframe()
tf_df
[24]:
| station | survey | latitude | longitude | elevation | tf_id | units | has_impedance | has_tipper | has_covariance | period_min | period_max | hdf5_reference | station_hdf5_reference | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | CAS04 | CONUS_South | 37.633351 | -121.468382 | 329.387 | CAS04 | none | True | True | True | 4.65455 | 29127.11 | <HDF5 object reference> | <HDF5 object reference> |
| 1 | NVR08 | CONUS_South | 38.326630 | -118.082382 | 1375.425 | NVR08 | none | True | True | True | 4.65455 | 29127.11 | <HDF5 object reference> | <HDF5 object reference> |
Plot the transfer functions using MTpy
Note: This currently works on branch mtpy/v2_plots
[25]:
from mtpy import MTCollection
2022-09-12T15:31:25 [line 121] mtpy.utils.mtpy_decorator._check_gdal_data - INFO: GDAL_DATA is set to: C:\Users\jpeacock\Anaconda3\envs\em\Library\share\gdal
2022-09-12 15:31:25,580 [line 133] matplotlib.get_mtpy_logger - INFO: Logging file can be found C:\Users\jpeacock\OneDrive - DOI\Documents\GitHub\mtpy\logs\matplotlib_warn.log
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
~\AppData\Local\Temp\11\ipykernel_21912\2507741362.py in <cell line: 1>()
----> 1 from mtpy import MTCollection
ImportError: cannot import name 'MTCollection' from 'mtpy' (C:\Users\jpeacock\OneDrive - DOI\Documents\GitHub\mtpy\mtpy\__init__.py)
[ ]:
mc = MTCollection()
mc.open_collection(r"8P_CAS04_NVR08")
[ ]:
pmr = mc.plot_mt_response(["CAS04", "NVR08"], plot_style="1")
Plot Station locations
Here we can plot station locations for all stations in the file, or we can give it a bounding box. If you have internet access a basemap will be plotted using Contextily.
[ ]:
st = mc.plot_stations(pad=.9, fig_num=5, fig_size=[6, 4])
[ ]:
st.fig.get_axes()[0].set_xlim((-121.9, -117.75))
st.fig.get_axes()[0].set_ylim((37.35, 38.5))
st.update_plot()
[ ]:
mth5_object.close_mth5()
[ ]:
Build MTH5 from USGS Geomagnetic data
Its common to look at observatory data for geomagnetic storms or to use as a remote reference. The USGS provides geomagnetic observatory data for observatories in North America. In the future this will be expanded to the various other observatories using well developed packages like geomagpy.
You will need to know ahead of time what observatories you would like to download data from, dates, and type of data. There are no wildcards. See USGS Geomagnetic webservices for more information on allowed options.
Here we will download 2 days of data from 2 different observatories for the x and y components of calibrated data (‘adjusted’).
[1]:
import pandas as pd
from mth5.clients import MakeMTH5
2023-03-23 15:44:33,968 [line 135] mth5.setup_logger - INFO: Logging file can be found C:\Users\jpeacock\OneDrive - DOI\Documents\GitHub\mth5\logs\mth5_debug.log
Create a request DataFrame
The request input is in the form of a pandas.DataFrame with the following columns
Column |
Description |
Options |
|---|---|---|
observatory |
Observatory code |
BDT, BOU, TST, BRW, BRT, BSL, CMO, CMT, DED, DHT, FRD, FRN, GUA, HON, NEW, SHU, SIT, SJG, TUC, USGS, BLC, BRD, CBB, EUA, FCC, IQA, MEA, OTT, RES, SNK, STJ, VIC, YKC, HAD, HER, KAK |
type |
The type of data to download |
variation, adjusted, quasi-definitive, definitivevariation, adjusted (default), quasi-definitive, definitive |
elements |
Components or elements of the geomagnetic data to download, should be a list |
D, DIST, DST, E, E-E, E-N, F, G, H, SQ, SV, UK1, UK2, UK3, UK4, X, Y, ZD, DIST, DST, E, E-E, E-N, F, G, H, SQ, SV, UK1, UK2, UK3, UK4, X, Y, Z |
sampling_period |
Sampling period of data to download in seconds |
1, 60, 3600 |
start |
Start time (YYYY-MM-DDThh:mm:ss) in UTC time |
|
end |
End time (YYYY-MM-DDThh:mm:ss) in UTC time |
[2]:
request_df = pd.DataFrame(
{
"observatory": ["frn", "frn", "ott", "ott"],
"type": ["adjusted"] * 4,
"elements": [["x", "y"]] * 4,
"sampling_period": [1] * 4,
"start": [
"2022-01-01T00:00:00",
"2022-01-03T00:00:00",
"2022-01-01T00:00:00",
"2022-01-03T00:00:00",
],
"end": [
"2022-01-02T00:00:00",
"2022-01-04T00:00:00",
"2022-01-02T00:00:00",
"2022-01-04T00:00:00",
],
}
)
[3]:
request_df
[3]:
| observatory | type | elements | sampling_period | start | end | |
|---|---|---|---|---|---|---|
| 0 | frn | adjusted | [x, y] | 1 | 2022-01-01T00:00:00 | 2022-01-02T00:00:00 |
| 1 | frn | adjusted | [x, y] | 1 | 2022-01-03T00:00:00 | 2022-01-04T00:00:00 |
| 2 | ott | adjusted | [x, y] | 1 | 2022-01-01T00:00:00 | 2022-01-02T00:00:00 |
| 3 | ott | adjusted | [x, y] | 1 | 2022-01-03T00:00:00 | 2022-01-04T00:00:00 |
Adding Run ID
When the request is input automatically run names will be assigned to different windows of time by f"sp{sampling_period}_{count:03}". So the first run is sp1_001, alternatively you can add a run column and name them as you like.
Create MTH5
Once the request is complete get the data. The file name will be created automatically as usgs_geomag_{list of observatories}_{list of elements}.h5
[4]:
make_mth5_object = MakeMTH5(mth5_version="0.2.0", interact=True)
mth5_object = make_mth5_object.from_usgs_geomag(request_df)
2023-03-23 15:44:34,903 [line 674] mth5.mth5.MTH5._initialize_file - INFO: Initialized MTH5 0.2.0 file C:\Users\jpeacock\OneDrive - DOI\Documents\GitHub\mth5\docs\examples\notebooks\usgs_geomag_frn_ott_xy.h5 in mode a
2023-03-23 15:44:43,406 [line 311] mth5.groups.base.MasterStation.add_station - INFO: Station Fresno already exists, returning existing group.
2023-03-23 15:44:48,843 [line 311] mth5.groups.base.MasterStation.add_station - INFO: Station Ottowa already exists, returning existing group.
Check to make sure everything was downloaded properly
[8]:
mth5_object.channel_summary.summarize()
mth5_object.channel_summary.to_dataframe()
[8]:
| survey | station | run | latitude | longitude | elevation | component | start | end | n_samples | sample_rate | measurement_type | azimuth | tilt | units | hdf5_reference | run_hdf5_reference | station_hdf5_reference | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | USGS-GEOMAG | Fresno | sp1_001 | 37.091 | -119.718 | 331.0 | hx | 2022-01-01 00:00:00+00:00 | 2022-01-02 00:00:00+00:00 | 86401 | 1.0 | magnetic | 0.0 | 0.0 | nanotesla | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 1 | USGS-GEOMAG | Fresno | sp1_001 | 37.091 | -119.718 | 331.0 | hy | 2022-01-01 00:00:00+00:00 | 2022-01-02 00:00:00+00:00 | 86401 | 1.0 | magnetic | 90.0 | 0.0 | nanotesla | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 2 | USGS-GEOMAG | Fresno | sp1_002 | 37.091 | -119.718 | 331.0 | hx | 2022-01-03 00:00:00+00:00 | 2022-01-04 00:00:00+00:00 | 86401 | 1.0 | magnetic | 0.0 | 0.0 | nanotesla | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 3 | USGS-GEOMAG | Fresno | sp1_002 | 37.091 | -119.718 | 331.0 | hy | 2022-01-03 00:00:00+00:00 | 2022-01-04 00:00:00+00:00 | 86401 | 1.0 | magnetic | 90.0 | 0.0 | nanotesla | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 4 | USGS-GEOMAG | Ottowa | sp1_001 | 45.400 | -75.500 | 0.0 | hx | 2022-01-01 00:00:00+00:00 | 2022-01-02 00:00:00+00:00 | 86401 | 1.0 | magnetic | 0.0 | 0.0 | nanotesla | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 5 | USGS-GEOMAG | Ottowa | sp1_001 | 45.400 | -75.500 | 0.0 | hy | 2022-01-01 00:00:00+00:00 | 2022-01-02 00:00:00+00:00 | 86401 | 1.0 | magnetic | 90.0 | 0.0 | nanotesla | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 6 | USGS-GEOMAG | Ottowa | sp1_002 | 45.400 | -75.500 | 0.0 | hx | 2022-01-03 00:00:00+00:00 | 2022-01-04 00:00:00+00:00 | 86401 | 1.0 | magnetic | 0.0 | 0.0 | nanotesla | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
| 7 | USGS-GEOMAG | Ottowa | sp1_002 | 45.400 | -75.500 | 0.0 | hy | 2022-01-03 00:00:00+00:00 | 2022-01-04 00:00:00+00:00 | 86401 | 1.0 | magnetic | 90.0 | 0.0 | nanotesla | <HDF5 object reference> | <HDF5 object reference> | <HDF5 object reference> |
Have a look at a run
[12]:
run = mth5_object.get_run("Fresno", "sp1_001", "USGS-GEOMAG")
[13]:
run_ts = run.to_runts()
run_ts.plot()
Close the MTH5 file
IMPORTANT: Be sure to close the file, otherwise bad things can happen.
[14]:
mth5_object.close_mth5()
2023-03-23 15:48:55,374 [line 755] mth5.mth5.MTH5.close_mth5 - INFO: Flushing and closing C:\Users\jpeacock\OneDrive - DOI\Documents\GitHub\mth5\docs\examples\notebooks\usgs_geomag_frn_ott_xy.h5
[ ]:
Parallel HDF5/MTH5
Creating an mth5 file using Parallel HDF5
This tutorial will examine how to build an mth5 file and write MT time series data for each station in a survey in parallel by utilising h5py’s Parallel HDF5 functionality. Parallel HDF5 allows users to open files across multiple parallel processes by utilising the Messaging Passing Interface (MPI) standard in mpi4py.
Note that in order to use Parallel HDF5, both HDF5 and h5py must be compiled with MPI support turned on.
For this example, we will be converting Earth Data Logger (ASCII) time series data from 93 stations of the AusLAMP Musgraves Province survey (see https://dx.doi.org/10.25914/5eaa30d63bd17). The ASCII time series were concatenated per run for each station and the total volume of time series data was 106 GB. 90 of the 93 stations were a single run - stations SA246, SA299 and SA324-2 had multiple runs.
This example was tested on the National Computational Infrastructure’s Gadi HPC system. Gadi is Australia’s most powerful supercomputer, a highly parallel cluster comprising more than 200,000 processor cores on ten different types of compute nodes. The example also makes use of the NCI-geophysics 2022.06 module which contains Parallel HDF5.
Building an mth5 skeleton
To build our mth5 file requires a two step process: 1. create an mth5 skeleton 2. populate the skeleton with the time series data in parallel using Parallel HDF5.
Let’s start by building the mth5 skeleton script which requires the following libraries:
[ ]:
from mth5.mth5 import MTH5
import numpy as np
from os import path
import os
import glob
import time
from mt_metadata.utils.mttime import MTime
startTime = time.time()
Next we can define our working directories and file paths which will need to be changed for your use case:
[ ]:
### directory on Gadi file system that contains merged ASCII time series per station run
work_dir = '/g/data/.../.../merged_data_all'
### full path to the concatenated Earth Data Logger ASCII time series files
full_path_to_files = sorted(glob.glob(work_dir+"/*"))
### directory on Gadi to write final mth5 file to
mth5_test_dir = '/g/data/.../.../mth5_outdir'
### name of the mth5 file
hdf5_filename = 'example_mth5_file.h5'
### full path to the mth5 file
h5_fn = mth5_test_dir+'/'+hdf5_filename
Now we can define: 1. the Earth Data Logger channels 2. the stations in our survey 3. the survey name 4. the run number for stations with a single run
[ ]:
### define raw time series data channels from Earth Data Logger
raw_data_channels = ['EX','EY','BX','BY','BZ','TP','ambientTemperature']
### define stations to go into mth5 file
stations_all = ['SA225-2','SA227', 'SA242', 'SA243', 'SA245',
'SA247', 'SA248', 'SA249', 'SA250', 'SA251',
'SA252', 'SA26W-2', 'SA270', 'SA271', 'SA272',
'SA273', 'SA274-2', 'SA274', 'SA275', 'SA276',
'SA277', 'SA293-2', 'SA294', 'SA295', 'SA296',
'SA297', 'SA298', 'SA300', 'SA301', 'SA319',
'SA320', 'SA320-2', 'SA321', 'SA322', 'SA323',
'SA324', 'SA325-2', 'SA325', 'SA326N', 'SA326S',
'SA344', 'SA344-2', 'SA345', 'SA346', 'SA347',
'SA348', 'SA349', 'SA350', 'SA351', ### 49 single run SA stations
'WA10', 'WA13', 'WA14', 'WA15', 'WA26',
'WA27', 'WA29', 'WA30', 'WA31', 'WA42',
'WA43', 'WA44', 'WA45', 'WA46', 'WA47',
'WA54', 'WA55', 'WA56', 'WA57', 'WA58',
'WA60', 'WA61', 'WA62', 'WA63', 'WA64',
'WA65', 'WA66', 'WA67', 'WA68', 'WA69',
'WA70', 'WA71', 'WA72', 'WA73', 'WA74',
'WA75', 'WANT19', 'WANT38', 'WANT45', 'WASA302',
'WASA327', ### 41 single run WA stations
'SA246', 'SA299', 'SA324-2'] ### 3 stations with multiple runs
### define survey name and run number (for stations with a single run)
survey_name = "AusLAMP_Musgraves"
run_number = "001"
We will define some functions that will be used to create our MTH5 skeleton:
[ ]:
def make_mth5_dir(mth5_output_directory):
### creates mth5 output directory if it doesn't already exist
try:
os.makedirs(mth5_output_directory)
except FileExistsError:
# directory already exists
print('directory already exists!')
pass
def remove_existing_mth5_file(mth5_file):
### removes existing mth5 file if it exists
if path.exists(mth5_file):
os.unlink(mth5_file)
print("INFO: Removed existing file {mth5_file}")
else:
print("File does not exist")
def channel_line_count(channels):
### counts lines in electromagnetic ASCII files
EX = [file for file in channels if file.endswith('EX')]
EY = [file for file in channels if file.endswith('EY')]
BX = [file for file in channels if file.endswith('BX')]
BY = [file for file in channels if file.endswith('BY')]
BZ = [file for file in channels if file.endswith('BZ')]
count_ex = sum(1 for line in open(EX[0]))
count_ey = sum(1 for line in open(EY[0]))
count_bx = sum(1 for line in open(BX[0]))
count_by = sum(1 for line in open(BY[0]))
count_bz = sum(1 for line in open(BZ[0]))
return count_ex, count_ey, count_bx, count_by, count_bz
def create_mth5_group_station_run_channel(station):
### creates the mth5 gropus, stations, runs and channels for the mth5 skeleton
add_station = m.add_station(station, survey=survey_name)
channels = []
for file in full_path_to_files:
if station in file:
channels.append(file)
else:
continue
### for stations with a single run:
if len(channels) == len(raw_data_channels):
add_run = m.add_run(station, run_number, survey=survey_name)
count_ex,count_ey,count_bx,count_by,count_bz = channel_line_count(channels)
ex_zeros = np.zeros(count_ex,)
ey_zeros = np.zeros(count_ey,)
bx_zeros = np.zeros(count_bx,)
by_zeros = np.zeros(count_by,)
bz_zeros = np.zeros(count_bz,)
ex = m.add_channel(station, run_number, "ex", "electric", ex_zeros, survey=survey_name)
ey = m.add_channel(station, run_number, "ey", "electric", ey_zeros, survey=survey_name)
bx = m.add_channel(station, run_number, "bx", "magnetic", bx_zeros, survey=survey_name)
by = m.add_channel(station, run_number, "by", "magnetic", by_zeros, survey=survey_name)
bz = m.add_channel(station, run_number, "bz", "magnetic", bz_zeros, survey=survey_name)
#######################################################################################
# Note: At the time of writing this example, the resizing of datasets caused h5py #
# parallel to fail when running using the mpio driver. A workaround was to create #
# 'zeros' arrays of size count_<xx> (see above). # #
# #
# ex = m.add_channel(station, run_number, "ex", "electric", None, survey=survey_name) #
# ey = m.add_channel(station, run_number, "ey", "electric", None, survey=survey_name) #
# bx = m.add_channel(station, run_number, "bx", "magnetic", None, survey=survey_name) #
# by = m.add_channel(station, run_number, "by", "magnetic", None, survey=survey_name) #
# bz = m.add_channel(station, run_number, "bz", "magnetic", None, survey=survey_name) #
# #
# ex.hdf5_dataset.resize((count_ex,)) #
# ey.hdf5_dataset.resize((count_ey,)) #
# bx.hdf5_dataset.resize((count_bx,)) #
# by.hdf5_dataset.resize((count_by,)) #
# bz.hdf5_dataset.resize((count_bz,)) #
#######################################################################################
### for stations with multiple runs:
elif len(channels) > len(raw_data_channels):
sort_files = sorted(channels)
number_of_channels = len(raw_data_channels)
split_lists = [sort_files[x:x+number_of_channels] for x in range(0, len(sort_files), number_of_channels)]
for i,group in enumerate(split_lists):
mrun_number = i+1
run = "00%i" % mrun_number
add_run = m.add_run(station, run, survey=survey_name)
count_ex,count_ey,count_bx,count_by,count_bz = channel_line_count(group)
ex_zeros = np.zeros(count_ex,)
ey_zeros = np.zeros(count_ey,)
bx_zeros = np.zeros(count_bx,)
by_zeros = np.zeros(count_by,)
bz_zeros = np.zeros(count_bz,)
ex = m.add_channel(station, run, "ex", "electric", ex_zeros, survey=survey_name)
ey = m.add_channel(station, run, "ey", "electric", ey_zeros, survey=survey_name)
bx = m.add_channel(station, run, "bx", "magnetic", bx_zeros, survey=survey_name)
by = m.add_channel(station, run, "by", "magnetic", by_zeros, survey=survey_name)
bz = m.add_channel(station, run, "bz", "magnetic", bz_zeros, survey=survey_name)
elif len(channels) < len(raw_data_channels):
print('you are likely missing some channels')
print(station)
else:
print('something has gone wrong')
The final step is to create our mth5 skeleton. Note that the Parallel HDF5 version used in this example does not support compression, so compression was turned off when generating the MTH5 skeleton:
[ ]:
### create mth5 directory (if it doesn't already exist)
make_mth5_dir(mth5_test_dir)
### remove any existing mth5 file in our mth5 directory
remove_existing_mth5_file(h5_fn)
start = MTime()
start.now()
### ensure compression is turned off
m = MTH5(file_version='0.2.0',shuffle=None,fletcher32=None,compression=None,compression_opts=None)
### open mth5 file in write mode
m.open_mth5(h5_fn, "w")
### add survey group
survey_group = m.add_survey(survey_name)
### create station, run and channel groups for all stations in our survey
for station in sorted(stations_all):
create_mth5_group_station_run_channel(station)
m.close_mth5()
### print total time to run our mth5 skeleton script
print('The script took {0} seconds !'.format(time.time()-startTime))
Putting this all together into a Python script (mth5_skeleton.py):
[ ]:
from mth5.mth5 import MTH5
import numpy as np
from os import path
import os
import glob
import time
from mt_metadata.utils.mttime import MTime
startTime = time.time()
### define working directories and file paths
work_dir = '/g/data/.../.../merged_data_all'
mth5_test_dir = '/g/data/.../.../mth5_outdir'
hdf5_filename = 'example_mth5_file.h5'
h5_fn = mth5_test_dir+'/'+hdf5_filename
full_path_to_files = sorted(glob.glob(work_dir+"/*"))
### define raw time series data channels
raw_data_channels = ['EX','EY','BX','BY','BZ','TP','ambientTemperature']
### define stations to go into mth5 file
stations_all = ['SA225-2','SA227', 'SA242', 'SA243', 'SA245',
'SA247', 'SA248', 'SA249', 'SA250', 'SA251',
'SA252', 'SA26W-2', 'SA270', 'SA271', 'SA272',
'SA273', 'SA274-2', 'SA274', 'SA275', 'SA276',
'SA277', 'SA293-2', 'SA294', 'SA295', 'SA296',
'SA297', 'SA298', 'SA300', 'SA301', 'SA319',
'SA320', 'SA320-2', 'SA321', 'SA322', 'SA323',
'SA324', 'SA325-2', 'SA325', 'SA326N', 'SA326S',
'SA344', 'SA344-2', 'SA345', 'SA346', 'SA347',
'SA348', 'SA349', 'SA350', 'SA351', ### 49 single run SA stations
'WA10', 'WA13', 'WA14', 'WA15', 'WA26',
'WA27', 'WA29', 'WA30', 'WA31', 'WA42',
'WA43', 'WA44', 'WA45', 'WA46', 'WA47',
'WA54', 'WA55', 'WA56', 'WA57', 'WA58',
'WA60', 'WA61', 'WA62', 'WA63', 'WA64',
'WA65', 'WA66', 'WA67', 'WA68', 'WA69',
'WA70', 'WA71', 'WA72', 'WA73', 'WA74',
'WA75', 'WANT19', 'WANT38', 'WANT45', 'WASA302',
'WASA327', ### 41 single run WA stations
'SA246', 'SA299', 'SA324-2'] ### 3 stations with multiple runs
### define survey name and run number (for stations with a single run)
survey_name = "AusLAMP_Musgraves"
run_number = "001"
### define functions
def make_mth5_dir(mth5_output_directory):
### creates mth5 output directory if it doesn't already exist
try:
os.makedirs(mth5_output_directory)
except FileExistsError:
# directory already exists
print('directory already exists!')
pass
def remove_existing_mth5_file(mth5_file):
### removes existing mth5 file if it exists
if path.exists(mth5_file):
os.unlink(mth5_file)
print("INFO: Removed existing file {mth5_file}")
else:
print("File does not exist")
def channel_line_count(channels):
### counts lines in electromagnetic ASCII files
EX = [file for file in channels if file.endswith('EX')]
EY = [file for file in channels if file.endswith('EY')]
BX = [file for file in channels if file.endswith('BX')]
BY = [file for file in channels if file.endswith('BY')]
BZ = [file for file in channels if file.endswith('BZ')]
count_ex = sum(1 for line in open(EX[0]))
count_ey = sum(1 for line in open(EY[0]))
count_bx = sum(1 for line in open(BX[0]))
count_by = sum(1 for line in open(BY[0]))
count_bz = sum(1 for line in open(BZ[0]))
return count_ex, count_ey, count_bx, count_by, count_bz
def create_mth5_group_station_run_channel(station):
### creates the mth5 gropus, stations, runs and channels for the mth5 skeleton
add_station = m.add_station(station, survey=survey_name)
channels = []
for file in full_path_to_files:
if station in file:
channels.append(file)
else:
continue
### for stations with a single run:
if len(channels) == len(raw_data_channels):
add_run = m.add_run(station, run_number, survey=survey_name)
count_ex,count_ey,count_bx,count_by,count_bz = channel_line_count(channels)
ex_zeros = np.zeros(count_ex,)
ey_zeros = np.zeros(count_ey,)
bx_zeros = np.zeros(count_bx,)
by_zeros = np.zeros(count_by,)
bz_zeros = np.zeros(count_bz,)
ex = m.add_channel(station, run_number, "ex", "electric", ex_zeros, survey=survey_name)
ey = m.add_channel(station, run_number, "ey", "electric", ey_zeros, survey=survey_name)
bx = m.add_channel(station, run_number, "bx", "magnetic", bx_zeros, survey=survey_name)
by = m.add_channel(station, run_number, "by", "magnetic", by_zeros, survey=survey_name)
bz = m.add_channel(station, run_number, "bz", "magnetic", bz_zeros, survey=survey_name)
#######################################################################################
# Note: At the time of writing this example, the resizing of datasets caused h5py #
# parallel to fail when running using the mpio driver. A workaround was to create #
# 'zeros' arrays of size count_<xx> (see above). # #
# #
# ex = m.add_channel(station, run_number, "ex", "electric", None, survey=survey_name) #
# ey = m.add_channel(station, run_number, "ey", "electric", None, survey=survey_name) #
# bx = m.add_channel(station, run_number, "bx", "magnetic", None, survey=survey_name) #
# by = m.add_channel(station, run_number, "by", "magnetic", None, survey=survey_name) #
# bz = m.add_channel(station, run_number, "bz", "magnetic", None, survey=survey_name) #
# #
# ex.hdf5_dataset.resize((count_ex,)) #
# ey.hdf5_dataset.resize((count_ey,)) #
# bx.hdf5_dataset.resize((count_bx,)) #
# by.hdf5_dataset.resize((count_by,)) #
# bz.hdf5_dataset.resize((count_bz,)) #
#######################################################################################
### for stations with multiple runs:
elif len(channels) > len(raw_data_channels):
sort_files = sorted(channels)
number_of_channels = len(raw_data_channels)
split_lists = [sort_files[x:x+number_of_channels] for x in range(0, len(sort_files), number_of_channels)]
for i,group in enumerate(split_lists):
mrun_number = i+1
run = "00%i" % mrun_number
add_run = m.add_run(station, run, survey=survey_name)
count_ex,count_ey,count_bx,count_by,count_bz = channel_line_count(group)
ex_zeros = np.zeros(count_ex,)
ey_zeros = np.zeros(count_ey,)
bx_zeros = np.zeros(count_bx,)
by_zeros = np.zeros(count_by,)
bz_zeros = np.zeros(count_bz,)
ex = m.add_channel(station, run, "ex", "electric", ex_zeros, survey=survey_name)
ey = m.add_channel(station, run, "ey", "electric", ey_zeros, survey=survey_name)
bx = m.add_channel(station, run, "bx", "magnetic", bx_zeros, survey=survey_name)
by = m.add_channel(station, run, "by", "magnetic", by_zeros, survey=survey_name)
bz = m.add_channel(station, run, "bz", "magnetic", bz_zeros, survey=survey_name)
elif len(channels) < len(raw_data_channels):
print('you are likely missing some channels')
print(station)
else:
print('something has gone wrong')
### create mth5 file skeleton
make_mth5_dir(mth5_test_dir)
remove_existing_mth5_file(h5_fn)
start = MTime()
start.now()
m = MTH5(file_version='0.2.0',shuffle=None,fletcher32=None,compression=None,compression_opts=None)
m.open_mth5(h5_fn, "w")
survey_group = m.add_survey(survey_name)
for station in sorted(stations_all):
create_mth5_group_station_run_channel(station)
m.close_mth5()
### print total time to run script
print('The script took {0} seconds !'.format(time.time()-startTime))
For the mth5 skeleton, we have populated a single mth5 file with all station groups in our survey. No actual time series data has been added yet (this will happen in parallel in the next steps). It is important to note that this process is a collective process that modifies the structure of an HDF5 file and all processes must participate. It is for this reason that mth5_skeleton.py can not be run in parallel when generating a single mth5 file for all stations (see
Collective versus independent operations in https://docs.h5py.org/en/stable/mpi.html).
The following is a summary of how long mth5_skeleton.py took to run for the AusLAMP Musgraves Province survey.
run on: Gadi
number of stations: 93
number of runs: 101
NCPUs used: 1
memory used: 30GB
walltime used: 1264 seconds (21m04s)
CPU time used: 1162 seconds (19m22s)
service units: 5.27
Populating our mth5 skeleton with time series data in parallel
Now that we have created our mth5 skeleton, it’s time to populate the time time series data from each station in our survey using Parallel HDF5.
First we will import the required Python libraries. The important libraries are mpi4py and h5py.
[ ]:
from mpi4py import MPI
import h5py
import os, psutil
import glob
import numpy as np
import shutil
import sys
from os import path
import time
startTime = time.time()
Next we will define the MPI communicator, rank and size:
[ ]:
comm = MPI.COMM_WORLD
rank = MPI.COMM_WORLD.rank
size = MPI.COMM_WORLD.size
Once again, we will define our: 1. working directories and file paths 2. Earth Data Logger channels 3. survey stations
[ ]:
### directory on Gadi file system that contains merged ASCII time series per station per run
work_dir = '/g/data/.../.../merged_data_all'
### directory to write final mth5 file
mth5_test_dir = '/g/data/.../.../mth5_outdir'
### name of mth5 file
hdf5_filename = 'example_mth5_file.h5'
### full path to mth5 file
h5_fn = mth5_test_dir+'/'+hdf5_filename
### full path to concatenated Earth Data Logger ASCII time series files
full_path_files = sorted(glob.glob(work_dir+"/*"))
### raw data channels
raw_data_channels = ['EX','EY','BX','BY','BZ','TP','ambientTemperature']
### MT stations to go into mth5 file
stations_all = ['SA225-2','SA227', 'SA242', 'SA243', 'SA245',
'SA247', 'SA248', 'SA249', 'SA250', 'SA251',
'SA252', 'SA26W-2', 'SA270', 'SA271', 'SA272',
'SA273', 'SA274-2', 'SA274', 'SA275', 'SA276',
'SA277', 'SA293-2', 'SA294', 'SA295', 'SA296',
'SA297', 'SA298', 'SA300', 'SA301', 'SA319',
'SA320', 'SA320-2', 'SA321', 'SA322', 'SA323',
'SA324', 'SA325-2', 'SA325', 'SA326N', 'SA326S',
'SA344', 'SA344-2', 'SA345', 'SA346', 'SA347',
'SA348', 'SA349', 'SA350', 'SA351', ### 49 single run SA stations
'WA10', 'WA13', 'WA14', 'WA15', 'WA26',
'WA27', 'WA29', 'WA30', 'WA31', 'WA42',
'WA43', 'WA44', 'WA45', 'WA46', 'WA47',
'WA54', 'WA55', 'WA56', 'WA57', 'WA58',
'WA60', 'WA61', 'WA62', 'WA63', 'WA64',
'WA65', 'WA66', 'WA67', 'WA68', 'WA69',
'WA70', 'WA71', 'WA72', 'WA73', 'WA74',
'WA75', 'WANT19', 'WANT38', 'WANT45', 'WASA302'
'WASA327', ### 41 single run WA stations
'SA246', 'SA299', 'SA324-2'] ### 3 stations with multiple runs
Let’s define the functions that will be used to populate our MTH5 skeleton with our Earth Data Logger time series data:
[ ]:
def channel_data_extraction(channels):
### extracts electromagnetic time series data from concatenated (per run) Earth Data Logger ASCII time series files
EX = [file for file in channels if file.endswith('EX')]
EY = [file for file in channels if file.endswith('EY')]
BX = [file for file in channels if file.endswith('BX')]
BY = [file for file in channels if file.endswith('BY')]
BZ = [file for file in channels if file.endswith('BZ')]
with open(EX[0], 'r') as file:
EX1 = file.read().splitlines()
ex = np.array(EX1).astype(np.int32)
with open(EY[0], 'r') as file:
EY1 = file.read().splitlines()
ey = np.array(EY1).astype(np.int32)
with open(BX[0], 'r') as file:
BX1 = file.read().splitlines()
bx = np.array(BX1).astype(np.int32)
with open(BY[0], 'r') as file:
BY1 = file.read().splitlines()
by = np.array(BY1).astype(np.int32)
with open(BZ[0], 'r') as file:
BZ1 = file.read().splitlines()
bz = np.array(BZ1).astype(np.int32)
return ex, ey, bx, by, bz
def write_channels(list_of_stations,full_path_to_files):
### writes time series data to the mth5 skeleton file in an embarrisingly parallel way - that is,
### each rank is dedicated to writing data from a single station.
for i,station in enumerate(sorted(list_of_stations)):
if i%size!=rank:
continue
channels = []
for file in full_path_to_files:
if station in file:
channels.append(file)
else:
continue
### for stations with a single run:
if len(channels) == len(raw_data_channels):
run = '001'
station_group = 'Experiment/Surveys/AusLAMP_Musgraves/Stations/'
site_run_path = station_group+station+'/'+run
channel_ex_path = site_run_path+'/'+'ex'
channel_ey_path = site_run_path+'/'+'ey'
channel_bx_path = site_run_path+'/'+'bx'
channel_by_path = site_run_path+'/'+'by'
channel_bz_path = site_run_path+'/'+'bz'
channel_ex = f[channel_ex_path]
channel_ey = f[channel_ey_path]
channel_bx = f[channel_bx_path]
channel_by = f[channel_by_path]
channel_bz = f[channel_bz_path]
ex,ey,bx,by,bz = channel_data_extraction(channels)
channel_ex[:] = ex
channel_ey[:] = ey
channel_bx[:] = bx
channel_by[:] = by
channel_bz[:] = bz
process = psutil.Process(os.getpid())
print('this run took %d MB of memory' % (process.memory_info().rss / 1024 ** 2))
print("Station number %d (%s) being done by processor %d of %d" % (i, station, rank, size))
### for stations with multiple runs:
elif len(channels) > len(raw_data_channels):
sort_files = sorted(channels)
number_of_channels = len(raw_data_channels)
split_lists = [sort_files[x:x+number_of_channels] for x in range(0, len(sort_files), number_of_channels)]
for i,group in enumerate(split_lists):
mrun_number = i+1
run = "00%i" % mrun_number
station_group = 'Experiment/Surveys/AusLAMP_Musgraves/Stations/'
site_run_path = station_group+station+'/'+run
channel_ex_path = site_run_path+'/'+'ex'
channel_ey_path = site_run_path+'/'+'ey'
channel_bx_path = site_run_path+'/'+'bx'
channel_by_path = site_run_path+'/'+'by'
channel_bz_path = site_run_path+'/'+'bz'
channel_ex = f[channel_ex_path]
channel_ey = f[channel_ey_path]
channel_bx = f[channel_bx_path]
channel_by = f[channel_by_path]
channel_bz = f[channel_bz_path]
ex,ey,bx,by,bz = channel_data_extraction(group)
channel_ex[:] = ex
channel_ey[:] = ey
channel_bx[:] = bx
channel_by[:] = by
channel_bz[:] = bz
process = psutil.Process(os.getpid())
print('this run took %d MB of memory' % (process.memory_info().rss / 1024 ** 2))
print("Station number %d (%s) being done by processor %d of %d" % (i, station, rank, size))
elif len(channels) < len(raw_data_channels):
print('you are likely missing some channels')
else:
print('something has gone wrong')
Finally, we need to open our MTH5 skeleton in append mode utilising the mpio driver. We can then write our time series channels for all our stations in parallel and close the file:
[ ]:
### write to mth5 file in parallel using the mpio driver
with h5py.File(h5_fn,'a',driver='mpio',comm=MPI.COMM_WORLD) as f:
write_channels(stations_all,full_path_files)
f.close()
### print total time for script to run
if rank==0:
print('The script took {0} seconds !'.format(time.time()-startTime))
Putting this all together into a Python script (mth5_muscle.py):
[ ]:
from mpi4py import MPI
import h5py
import os, psutil
import glob
import numpy as np
import shutil
import sys
from os import path
import time
startTime = time.time()
### define MPI comm, rank and size
comm = MPI.COMM_WORLD
rank = MPI.COMM_WORLD.rank
size = MPI.COMM_WORLD.size
### define working directories, hdf5 file name and the full path to the ASCII MT time series files
work_dir = '/g/data/.../.../merged_data_all'
mth5_test_dir = '/g/data/.../.../mth5_outdir'
hdf5_filename = 'example_mth5_file.h5'
h5_fn = mth5_test_dir+'/'+hdf5_filename
full_path_files = sorted(glob.glob(work_dir+"/*"))
### define raw data channels
raw_data_channels = ['EX','EY','BX','BY','BZ','TP','ambientTemperature']
### define MT stations to go into mth5 file
stations_all = ['SA225-2','SA227', 'SA242', 'SA243', 'SA245',
'SA247', 'SA248', 'SA249', 'SA250', 'SA251',
'SA252', 'SA26W-2', 'SA270', 'SA271', 'SA272',
'SA273', 'SA274-2', 'SA274', 'SA275', 'SA276',
'SA277', 'SA293-2', 'SA294', 'SA295', 'SA296',
'SA297', 'SA298', 'SA300', 'SA301', 'SA319',
'SA320', 'SA320-2', 'SA321', 'SA322', 'SA323',
'SA324', 'SA325-2', 'SA325', 'SA326N', 'SA326S',
'SA344', 'SA344-2', 'SA345', 'SA346', 'SA347',
'SA348', 'SA349', 'SA350', 'SA351', ### 49 single run SA stations
'WA10', 'WA13', 'WA14', 'WA15', 'WA26',
'WA27', 'WA29', 'WA30', 'WA31', 'WA42',
'WA43', 'WA44', 'WA45', 'WA46', 'WA47',
'WA54', 'WA55', 'WA56', 'WA57', 'WA58',
'WA60', 'WA61', 'WA62', 'WA63', 'WA64',
'WA65', 'WA66', 'WA67', 'WA68', 'WA69',
'WA70', 'WA71', 'WA72', 'WA73', 'WA74',
'WA75', 'WANT19', 'WANT38', 'WANT45', 'WASA302'
'WASA327', ### 41 single run WA stations
'SA246', 'SA299', 'SA324-2'] ### 3 stations with multiple runs
### define functions
def channel_data_extraction(channels):
EX = [file for file in channels if file.endswith('EX')]
EY = [file for file in channels if file.endswith('EY')]
BX = [file for file in channels if file.endswith('BX')]
BY = [file for file in channels if file.endswith('BY')]
BZ = [file for file in channels if file.endswith('BZ')]
with open(EX[0], 'r') as file:
EX1 = file.read().splitlines()
ex = np.array(EX1).astype(np.int32)
with open(EY[0], 'r') as file:
EY1 = file.read().splitlines()
ey = np.array(EY1).astype(np.int32)
with open(BX[0], 'r') as file:
BX1 = file.read().splitlines()
bx = np.array(BX1).astype(np.int32)
with open(BY[0], 'r') as file:
BY1 = file.read().splitlines()
by = np.array(BY1).astype(np.int32)
with open(BZ[0], 'r') as file:
BZ1 = file.read().splitlines()
bz = np.array(BZ1).astype(np.int32)
return ex, ey, bx, by, bz
def write_channels(list_of_stations,full_path_to_files):
for i,station in enumerate(sorted(list_of_stations)):
if i%size!=rank:
continue
channels = []
for file in full_path_to_files:
if station in file:
channels.append(file)
else:
continue
if len(channels) == len(raw_data_channels):
run = '001'
station_group = 'Experiment/Surveys/AusLAMP_Musgraves/Stations/'
site_run_path = station_group+station+'/'+run
channel_ex_path = site_run_path+'/'+'ex'
channel_ey_path = site_run_path+'/'+'ey'
channel_bx_path = site_run_path+'/'+'bx'
channel_by_path = site_run_path+'/'+'by'
channel_bz_path = site_run_path+'/'+'bz'
channel_ex = f[channel_ex_path]
channel_ey = f[channel_ey_path]
channel_bx = f[channel_bx_path]
channel_by = f[channel_by_path]
channel_bz = f[channel_bz_path]
ex,ey,bx,by,bz = channel_data_extraction(channels)
channel_ex[:] = ex
channel_ey[:] = ey
channel_bx[:] = bx
channel_by[:] = by
channel_bz[:] = bz
process = psutil.Process(os.getpid())
print('this run took %d MB of memory' % (process.memory_info().rss / 1024 ** 2))
print("Station number %d (%s) being done by processor %d of %d" % (i, station, rank, size))
elif len(channels) > len(raw_data_channels):
sort_files = sorted(channels)
number_of_channels = len(raw_data_channels)
split_lists = [sort_files[x:x+number_of_channels] for x in range(0, len(sort_files), number_of_channels)]
for i,group in enumerate(split_lists):
mrun_number = i+1
run = "00%i" % mrun_number
station_group = 'Experiment/Surveys/AusLAMP_Musgraves/Stations/'
site_run_path = station_group+station+'/'+run
channel_ex_path = site_run_path+'/'+'ex'
channel_ey_path = site_run_path+'/'+'ey'
channel_bx_path = site_run_path+'/'+'bx'
channel_by_path = site_run_path+'/'+'by'
channel_bz_path = site_run_path+'/'+'bz'
channel_ex = f[channel_ex_path]
channel_ey = f[channel_ey_path]
channel_bx = f[channel_bx_path]
channel_by = f[channel_by_path]
channel_bz = f[channel_bz_path]
ex,ey,bx,by,bz = channel_data_extraction(group)
channel_ex[:] = ex
channel_ey[:] = ey
channel_bx[:] = bx
channel_by[:] = by
channel_bz[:] = bz
process = psutil.Process(os.getpid())
print('this run took %d MB of memory' % (process.memory_info().rss / 1024 ** 2))
print("Station number %d (%s) being done by processor %d of %d" % (i, station, rank, size))
elif len(channels) < len(raw_data_channels):
print('you are likely missing some channels')
else:
print('something has gone wrong')
### write to mth5 file in parallel using the mpio driver
with h5py.File(h5_fn,'a',driver='mpio',comm=MPI.COMM_WORLD) as f:
write_channels(stations_all,full_path_files)
f.close()
### print total time for script to run
if rank==0:
print('The script took {0} seconds !'.format(time.time()-startTime))
Now that we have created our mth5_muscle.py script, we next need to create a job submission script to submit to the Gadi PBSPro scheduler. The job submission script specifys the queue to use and the duration/resources needed for the job.
#!/bin/bash
#PBS -N mth5_mpi4py
#PBS -q hugemem
#PBS -P fp0
#PBS -l walltime=0:30:00
#PBS -l ncpus=96
#PBS -l mem=1000GB
#PBS -l jobfs=10GB
#PBS -l storage=gdata/fp0+gdata/my80+gdata/lm70+gdata/up99
module use /g/data/up99/modulefiles
module load NCI-geophys/22.06
cd ${PBS_O_WORKDIR}
mpirun -np $PBS_NCPUS python3 mth5_muscle.py > ./pbs_job_logs/$PBS_JOBID.log
For our jobscript above (mth5_muscle.sh), we have requested: 1. to use the hugemem queue 2. 96 CPUs (2 nodes) 3. 1 TB of memory 4. half an hour of walltime
We also need to define the NCI project codes used in mth5_muscle.py. The project code up99 is required to make use of the NCI-geophys/22.06 module that contains Parallel HDF5.
To submit this jobscript (mth5_muscle.sh) on Gadi:
$ qsub mth5_muscle.sh
This job will process the 93 stations in our survey using 96 MPI ranks (one station per rank).
The following is a summary of how long mth5_muscle.py took to run for the AusLAMP Musgraves Province survey.
run on: Gadi
number of stations: 93
number of runs: 101
NCPUs used: 96 (2 nodes)
memory used: 736 GB
walltime used: 836 seconds (13m56s)
CPU time used: 48570 seconds (13h29m30s)
service units: 66.88
Concluding remarks
Generating one mth5 file for many stations can take a significant amount of time if no parallelism is introduced. For the Musgraves example above, if using the mth5 library alone it would have taken approximately 14 hours to generate our final mth5 file. By utilising Parallel HDF5 we have managed to reduce this time to approximately 35 minutes.
For the “all stations in a single mth5 file” model, the bottleneck lies in generating the mth5 skeleton as this can’t be done in parallel. The authors have created the tutorial mth5_in_parallel_one_file_per_station that shows how to generate a single mth5 file per station in parallel, which yields much quicker results than the “all stations in a single mth5 file” model.
This example only dealt with the writing of MT time series data and did not consider mt_metadata. The mth5_in_parallel_one_file_per_station tutorial demonstrates how one could add mt_metadata into their mth5 automations.
Creating one mth5 file per station in parallel
In this tutorial, we will demonstrate how to generate one mth5 file per station (with associated mt_metadata) from a survey in parallel on HPC.
For this example, we will be converting Earth Data Logger (ASCII) time series data from 93 stations of the AusLAMP Musgraves Province survey (see https://dx.doi.org/10.25914/5eaa30d63bd17). The ASCII time series were concatenated per run for each station and the total volume of time series data was 106 GB. 90 of the 93 stations were a single run - stations SA246, SA299 and SA324-2 had multiple runs.
This example was tested on the National Computational Infrastructure’s Gadi HPC system. Gadi is Australia’s most powerful supercomputer, a highly parallel cluster comprising more than 200,000 processor cores on ten different types of compute nodes. The example also makes use of the NCI-geophysics 2022.06 module which contains the Python libraries used in this tutorial.
Building our mth5_onefileperstation.py code
In the mth5_in_parallel tutorial, we built a single mth5 file containing all stations in our survey. In that example, we had to construct two codes: 1. mth5_skeleton.py which created the mth5 file structure without adding in any time series data. This code could not be run in parallel as the processes involved were collective (see Collective versus independent operations in
https://docs.h5py.org/en/stable/mpi.html). 2. mth5_muscle.py which added in the timeseries data for each station in parallel (i.e. one station per node).
For this example, we will be creating a single file per station with associated mt_metadata. As a result, we only need to create one code as the generation of each file is independent (i.e., the different mpi ranks do not need to communicate with each other). The authors use mpi4py for this tutorial, but note that other libraries such as Dask or multiprocessing could also be used.
Our mth5_onefileperstation.py code requires the following Python libraries:
[ ]:
from mpi4py import MPI
import h5py
from mth5.mth5 import MTH5
import numpy as np
from os import path
import os, psutil
import glob
import nc_time_axis
import time
from mt_metadata import timeseries as metadata
from mt_metadata.utils.mttime import MTime
import json
startTime = time.time()
As we are using mpi4py, we will need to define the MPI communicator, rank and size:
[ ]:
comm = MPI.COMM_WORLD
rank = MPI.COMM_WORLD.rank
size = MPI.COMM_WORLD.size
Next, we will define our: 1. working directories and file paths 2. Earth Data Logger channels 3. survey stations 4. survey name and run number (for stations with a single run)
[ ]:
### define working directories and file paths
work_dir = '/g/data/.../.../merged_data_all'
mth5_output_directory = '/g/data/.../.../mth5_outdir_single_file_per_station'
full_path_to_mth5_files = sorted(glob.glob(mth5_output_directory+"/*"))
full_path_to_ascii_files = sorted(glob.glob(work_dir+"/*"))
mt_metadata_dir = '/g/data/.../.../mt_metadata_json'
full_path_to_mt_metadata = sorted(glob.glob(mt_metadata_dir+"/*"))
survey_file_name = 'survey.json'
survey_json = mt_metadata_dir+'/'+survey_file_name
### define raw time series data channels
raw_data_channels = ['EX','EY','BX','BY','BZ','TP','ambientTemperature']
### define the stations in our survey
stations_all = ['SA225-2','SA227', 'SA242', 'SA243', 'SA245',
'SA247', 'SA248', 'SA249', 'SA250', 'SA251',
'SA252', 'SA26W-2', 'SA270', 'SA271', 'SA272',
'SA273', 'SA274-2', 'SA274', 'SA275', 'SA276',
'SA277', 'SA293-2', 'SA294', 'SA295', 'SA296',
'SA297', 'SA298', 'SA300', 'SA301', 'SA319',
'SA320', 'SA320-2', 'SA321', 'SA322', 'SA323',
'SA324', 'SA325-2', 'SA325', 'SA326N', 'SA326S',
'SA344', 'SA344-2', 'SA345', 'SA346', 'SA347',
'SA348', 'SA349', 'SA350', 'SA351', ### 49 single run SA stations
'WA10', 'WA13', 'WA14', 'WA15', 'WA26',
'WA27', 'WA29', 'WA30', 'WA31', 'WA42',
'WA43', 'WA44', 'WA45', 'WA46', 'WA47',
'WA54', 'WA55', 'WA56', 'WA57', 'WA58',
'WA60', 'WA61', 'WA62', 'WA63', 'WA64',
'WA65', 'WA66', 'WA67', 'WA68', 'WA69',
'WA70', 'WA71', 'WA72', 'WA73', 'WA74',
'WA75', 'WANT19', 'WANT38', 'WANT45', 'WASA302',
'WASA327', ### 41 single run WA stations
'SA246', 'SA299', 'SA324-2'] ### 3 stations with multiple runs
### define the survey name and run number (for stations with a single run)
survey_name = "AusLAMP_Musgraves"
run_number = "001"
We will also be adding mt_metadata into our mth5 files. For this example, a single json file was created per run and the contents of each json file looked something like this:
{
"electric_ex": {
"channel_number": 0,
"component": null,
"data_quality.rating.value": 0,
"dipole_length": null,
"filter.applied": [
false
],
"filter.name": [],
"measurement_azimuth": 0.0,
"measurement_tilt": 0.0,
"negative.elevation": 0.0,
"negative.id": null,
"negative.latitude": 0.0,
"negative.longitude": 0.0,
"negative.manufacturer": null,
"negative.type": null,
"positive.elevation": 0.0,
"positive.id": null,
"positive.latitude": 0.0,
"positive.longitude": 0.0,
"positive.manufacturer": null,
"positive.type": null,
"sample_rate": 10.0,
"time_period.end": "1980-01-01T00:00:00+00:00",
"time_period.start": "1980-01-01T00:00:00+00:00",
"type": "electric",
"units": null
},
"electric_ey": {
"channel_number": 0,
"component": null,
"data_quality.rating.value": 0,
"dipole_length": null,
"filter.applied": [
false
],
"filter.name": [],
"measurement_azimuth": 0.0,
"measurement_tilt": 0.0,
"negative.elevation": 0.0,
"negative.id": null,
"negative.latitude": 0.0,
"negative.longitude": 0.0,
"negative.manufacturer": null,
"negative.type": null,
"positive.elevation": 0.0,
"positive.id": null,
"positive.latitude": 0.0,
"positive.longitude": 0.0,
"positive.manufacturer": null,
"positive.type": null,
"sample_rate": 10.0,
"time_period.end": "1980-01-01T00:00:00+00:00",
"time_period.start": "1980-01-01T00:00:00+00:00",
"type": "electric",
"units": null
},
"magnetic_bx": {
"channel_number": 0,
"component": null,
"data_quality.rating.value": 0,
"filter.applied": [
false
],
"filter.name": [],
"location.elevation": 0.0,
"location.latitude": 0.0,
"location.longitude": 0.0,
"measurement_azimuth": 0.0,
"measurement_tilt": 0.0,
"sample_rate": 10.0,
"sensor.id": null,
"sensor.manufacturer": null,
"sensor.type": null,
"time_period.end": "1980-01-01T00:00:00+00:00",
"time_period.start": "1980-01-01T00:00:00+00:00",
"type": "magnetic",
"units": null
},
"magnetic_by": {
"channel_number": 0,
"component": null,
"data_quality.rating.value": 0,
"filter.applied": [
false
],
"filter.name": [],
"location.elevation": 0.0,
"location.latitude": 0.0,
"location.longitude": 0.0,
"measurement_azimuth": 0.0,
"measurement_tilt": 0.0,
"sample_rate": 10.0,
"sensor.id": null,
"sensor.manufacturer": null,
"sensor.type": null,
"time_period.end": "1980-01-01T00:00:00+00:00",
"time_period.start": "1980-01-01T00:00:00+00:00",
"type": "magnetic",
"units": null
},
"magnetic_bz": {
"channel_number": 0,
"component": null,
"data_quality.rating.value": 0,
"filter.applied": [
false
],
"filter.name": [],
"location.elevation": 0.0,
"location.latitude": 0.0,
"location.longitude": 0.0,
"measurement_azimuth": 0.0,
"measurement_tilt": 0.0,
"sample_rate": 10.0,
"sensor.id": null,
"sensor.manufacturer": null,
"sensor.type": null,
"time_period.end": "1980-01-01T00:00:00+00:00",
"time_period.start": "1980-01-01T00:00:00+00:00",
"type": "magnetic",
"units": null
},
"run": {
"channels_recorded_auxiliary": [],
"channels_recorded_electric": [],
"channels_recorded_magnetic": [],
"data_logger.firmware.author": null,
"data_logger.firmware.name": null,
"data_logger.firmware.version": null,
"data_logger.id": null,
"data_logger.manufacturer": null,
"data_logger.timing_system.drift": 0.0,
"data_logger.timing_system.type": "GPS",
"data_logger.timing_system.uncertainty": 0.0,
"data_logger.type": null,
"data_type": "LPMT",
"id": null,
"sample_rate": 10.0,
"time_period.end": "1980-01-01T00:00:00+00:00",
"time_period.start": "1980-01-01T00:00:00+00:00"
},
"station": {
"acquired_by.name": null,
"channels_recorded": [],
"data_type": "LPMT",
"geographic_name": null,
"id": null,
"location.declination.model": "WMM",
"location.declination.value": 0.0,
"location.elevation": 0.0,
"location.latitude": 0.0,
"location.longitude": 0.0,
"orientation.method": null,
"orientation.reference_frame": "geographic",
"provenance.creation_time": "1980-01-01T00:00:00+00:00",
"provenance.software.author": "none",
"provenance.software.name": null,
"provenance.software.version": null,
"provenance.submitter.email": null,
"provenance.submitter.organization": null,
"run_list": [],
"time_period.end": "1980-01-01T00:00:00+00:00",
"time_period.start": "1980-01-01T00:00:00+00:00"
},
"survey": {
"citation_dataset.doi": null,
"citation_journal.doi": null,
"country": null,
"datum": "WGS84",
"geographic_name": null,
"id": null,
"name": null,
"northwest_corner.latitude": 0.0,
"northwest_corner.longitude": 0.0,
"project": null,
"project_lead.email": null,
"project_lead.organization": null,
"release_license": "CC-0",
"southeast_corner.latitude": 0.0,
"southeast_corner.longitude": 0.0,
"summary": null,
"time_period.end_date": "1980-01-01",
"time_period.start_date": "1980-01-01"
},
}
Note that the mt_metadata in these json files were synthetic and were mainly used to show how mt_metadata could be added into our automation.
We will now define functions that will be used to create our MTH5 files:
[ ]:
def make_mth5_dir(mth5_output_directory):
### creates the mth5 output directory if it doesn't already exist
try:
os.makedirs(mth5_output_directory)
except FileExistsError:
# directory already exists
print('directory already exists!')
pass
def remove_existing_mth5_files(mth5_files):
### removes existing mth5 files if they exists
for mth5_file in mth5_files:
if path.exists(mth5_file):
os.unlink(mth5_file)
print("INFO: Removed existing file {}".format(mth5_file))
else:
print("File does not exist")
def channel_data_extraction(channels):
### extracts electromagnetic time series data from concatenated (per run) Earth Data Logger
### ASCII time series files
EX = [file for file in channels if file.endswith('EX')]
EY = [file for file in channels if file.endswith('EY')]
BX = [file for file in channels if file.endswith('BX')]
BY = [file for file in channels if file.endswith('BY')]
BZ = [file for file in channels if file.endswith('BZ')]
with open(EX[0], 'r') as file:
EX1 = file.read().splitlines()
ex_ts = np.array(EX1).astype(np.int32)
with open(EY[0], 'r') as file:
EY1 = file.read().splitlines()
ey_ts = np.array(EY1).astype(np.int32)
with open(BX[0], 'r') as file:
BX1 = file.read().splitlines()
bx_ts = np.array(BX1).astype(np.int32)
with open(BY[0], 'r') as file:
BY1 = file.read().splitlines()
by_ts = np.array(BY1).astype(np.int32)
with open(BZ[0], 'r') as file:
BZ1 = file.read().splitlines()
bz_ts = np.array(BZ1).astype(np.int32)
return ex_ts, ey_ts, bx_ts, by_ts, bz_ts
def create_mth5_group_station_run_channel(station,mt_metadata):
### creates the mth5 gropus, stations, runs and channels for each mth5 file
### and populates mt_metadata from the relevant json file
with open(mt_metadata[0], 'r') as json_file:
json_load = json.load(json_file)
station_dict = json_load['station']
add_station = m.add_station(station, survey=survey_name)
add_station.metadata.from_dict(station_dict)
add_station.write_metadata()
channels = []
for file in full_path_to_ascii_files:
if station in file:
channels.append(file)
else:
continue
### for stations with a single run:
if len(channels) == len(raw_data_channels):
run_dict = json_load['run']
ex_dict = json_load['electric_ex']
ey_dict = json_load['electric_ey']
bx_dict = json_load['magnetic_bx']
by_dict = json_load['magnetic_by']
bz_dict = json_load['magnetic_bz']
add_run = m.add_run(station, run_number, survey=survey_name)
add_run.metadata.from_dict(run_dict)
add_run.write_metadata()
ex_ts,ey_ts,bx_ts,by_ts,bz_ts = channel_data_extraction(channels)
ex = m.add_channel(station, run_number, "ex", "electric", ex_ts, survey=survey_name)
ey = m.add_channel(station, run_number, "ey", "electric", ey_ts, survey=survey_name)
bx = m.add_channel(station, run_number, "bx", "magnetic", bx_ts, survey=survey_name)
by = m.add_channel(station, run_number, "by", "magnetic", by_ts, survey=survey_name)
bz = m.add_channel(station, run_number, "bz", "magnetic", bz_ts, survey=survey_name)
ex.metadata.from_dict(ex_dict)
ex.write_metadata()
ey.metadata.from_dict(ey_dict)
ey.write_metadata()
bx.metadata.from_dict(bx_dict)
bx.write_metadata()
by.metadata.from_dict(by_dict)
by.write_metadata()
bz.metadata.from_dict(bz_dict)
bz.write_metadata()
### for stations with multiple runs:
elif len(channels) > len(raw_data_channels):
sort_files = sorted(channels)
number_of_channels = len(raw_data_channels)
split_lists = [sort_files[x:x+number_of_channels] for x in range(0, len(sort_files), number_of_channels)]
mt_metadata_files = sorted(mt_metadata)
for i, (group,mt_meta) in enumerate(zip(split_lists,mt_metadata_files)):
mrun_number = i+1
run = "00%i" % mrun_number
with open(mt_meta, 'r') as json_file:
json_load = json.load(json_file)
run_dict = json_load['run']
ex_dict = json_load['electric_ex']
ey_dict = json_load['electric_ey']
bx_dict = json_load['magnetic_bx']
by_dict = json_load['magnetic_by']
bz_dict = json_load['magnetic_bz']
add_run = m.add_run(station, run, survey=survey_name)
add_run.metadata.from_dict(run_dict)
add_run.write_metadata()
ex_ts,ey_ts,bx_ts,by_ts,bz_ts = channel_data_extraction(channels)
ex = m.add_channel(station, run, "ex", "electric", ex_ts, survey=survey_name)
ey = m.add_channel(station, run, "ey", "electric", ey_ts, survey=survey_name)
bx = m.add_channel(station, run, "bx", "magnetic", bx_ts, survey=survey_name)
by = m.add_channel(station, run, "by", "magnetic", by_ts, survey=survey_name)
bz = m.add_channel(station, run, "bz", "magnetic", bz_ts, survey=survey_name)
ex.metadata.from_dict(ex_dict)
ex.write_metadata()
ey.metadata.from_dict(ey_dict)
ey.write_metadata()
bx.metadata.from_dict(bx_dict)
bx.write_metadata()
by.metadata.from_dict(by_dict)
by.write_metadata()
bz.metadata.from_dict(bz_dict)
bz.write_metadata()
elif len(channels) < len(raw_data_channels):
print('you are likely missing some channels')
print(station)
else:
print('something has gone wrong')
The final step is to create our mth5 files per station. For this we will be creating a single mth5 file per MPI rank for each station in our survey. As each process on each rank is independent, we can run all processes in parallel. Additionally, we can add compression to each file as we are not using the Parallel HDF5 library (which doesn’t support compression) that was used in the mth5_in_parallel tutorial. For this example, we will use “gzip” level 4 compression.
[ ]:
### create mth5 output directory and remove existing files
if rank==0:
make_mth5_dir(mth5_output_directory)
remove_existing_mth5_files(full_path_to_mth5_files)
comm.Barrier()
### create a single mth5 file per station with compression and associated mt_metadata
for i,station in enumerate(sorted(stations_all)):
if i%size!=rank:
continue
m = MTH5(file_version='0.2.0',shuffle=None,fletcher32=None,compression="gzip",compression_opts=4)
hdf5_filename = '{}.h5'.format(station)
h5_fn = mth5_output_directory+'/'+hdf5_filename
m.open_mth5(h5_fn, "w")
survey_group = m.add_survey(survey_name)
with open(survey_json, 'r') as json_file:
json_load = json.load(json_file)
survey_dict = json_load['survey']
survey_group.metadata.from_dict(survey_dict)
survey_group.write_metadata()
mt_metadata_file_name = '{}.json'.format(station)
mt_metadata = [file for file in full_path_to_mt_metadata if file.endswith(mt_metadata_file_name)]
create_mth5_group_station_run_channel(station,mt_metadata)
m.close_mth5()
comm.Barrier()
### print total time to run script
if rank==0:
print('The script took {0} seconds !'.format(time.time()-startTime))
Putting this altogether into a Python script (mth5_onefileperstation.py):
[ ]:
from mpi4py import MPI
import h5py
from mth5.mth5 import MTH5
import numpy as np
from os import path
import os, psutil
import glob
import nc_time_axis
import time
from mt_metadata import timeseries as metadata
from mt_metadata.utils.mttime import MTime
import json
startTime = time.time()
### define MPI comm, rank and size
comm = MPI.COMM_WORLD
rank = MPI.COMM_WORLD.rank
size = MPI.COMM_WORLD.size
### define working directories and file paths
work_dir = '/g/data/.../.../merged_data_all'
mth5_output_directory = '/g/data/.../.../mth5_outdir_single_file_per_station'
full_path_to_mth5_files = sorted(glob.glob(mth5_output_directory+"/*"))
full_path_to_ascii_files = sorted(glob.glob(work_dir+"/*"))
mt_metadata_dir = '/g/data/.../.../mt_metadata_json'
full_path_to_mt_metadata = sorted(glob.glob(mt_metadata_dir+"/*"))
survey_file_name = 'survey.json'
survey_json = mt_metadata_dir+'/'+survey_file_name
### define raw time series data channels
raw_data_channels = ['EX','EY','BX','BY','BZ','TP','ambientTemperature']
### define stations to go into mth5 file
stations_all = ['SA225-2','SA227', 'SA242', 'SA243', 'SA245',
'SA247', 'SA248', 'SA249', 'SA250', 'SA251',
'SA252', 'SA26W-2', 'SA270', 'SA271', 'SA272',
'SA273', 'SA274-2', 'SA274', 'SA275', 'SA276',
'SA277', 'SA293-2', 'SA294', 'SA295', 'SA296',
'SA297', 'SA298', 'SA300', 'SA301', 'SA319',
'SA320', 'SA320-2', 'SA321', 'SA322', 'SA323',
'SA324', 'SA325-2', 'SA325', 'SA326N', 'SA326S',
'SA344', 'SA344-2', 'SA345', 'SA346', 'SA347',
'SA348', 'SA349', 'SA350', 'SA351', ### 49 single run SA stations
'WA10', 'WA13', 'WA14', 'WA15', 'WA26',
'WA27', 'WA29', 'WA30', 'WA31', 'WA42',
'WA43', 'WA44', 'WA45', 'WA46', 'WA47',
'WA54', 'WA55', 'WA56', 'WA57', 'WA58',
'WA60', 'WA61', 'WA62', 'WA63', 'WA64',
'WA65', 'WA66', 'WA67', 'WA68', 'WA69',
'WA70', 'WA71', 'WA72', 'WA73', 'WA74',
'WA75', 'WANT19', 'WANT38', 'WANT45', 'WASA302',
'WASA327', ### 41 single run WA stations
'SA246', 'SA299', 'SA324-2'] ### 3 stations with multiple runs
### define survey name and run number (for stations with a single run)
survey_name = "AusLAMP_Musgraves"
run_number = "001"
### define functions
def make_mth5_dir(mth5_output_directory):
### creates mth5 output directory if it doesn't already exist
try:
os.makedirs(mth5_output_directory)
except FileExistsError:
# directory already exists
print('directory already exists!')
pass
def remove_existing_mth5_files(mth5_files):
### removes existing mth5 files if they exists
for mth5_file in mth5_files:
if path.exists(mth5_file):
os.unlink(mth5_file)
print("INFO: Removed existing file {}".format(mth5_file))
else:
print("File does not exist")
def channel_data_extraction(channels):
### extracts electromagnetic time series data from concatenated (per run) Earth Data Logger ASCII time series files
EX = [file for file in channels if file.endswith('EX')]
EY = [file for file in channels if file.endswith('EY')]
BX = [file for file in channels if file.endswith('BX')]
BY = [file for file in channels if file.endswith('BY')]
BZ = [file for file in channels if file.endswith('BZ')]
with open(EX[0], 'r') as file:
EX1 = file.read().splitlines()
ex_ts = np.array(EX1).astype(np.int32)
with open(EY[0], 'r') as file:
EY1 = file.read().splitlines()
ey_ts = np.array(EY1).astype(np.int32)
with open(BX[0], 'r') as file:
BX1 = file.read().splitlines()
bx_ts = np.array(BX1).astype(np.int32)
with open(BY[0], 'r') as file:
BY1 = file.read().splitlines()
by_ts = np.array(BY1).astype(np.int32)
with open(BZ[0], 'r') as file:
BZ1 = file.read().splitlines()
bz_ts = np.array(BZ1).astype(np.int32)
return ex_ts, ey_ts, bx_ts, by_ts, bz_ts
def create_mth5_group_station_run_channel(station,mt_metadata):
### creates the mth5 gropus, stations, runs and channels for each mth5 file
### and populates mt_metadata from the relevant json file
with open(mt_metadata[0], 'r') as json_file:
json_load = json.load(json_file)
station_dict = json_load['station']
add_station = m.add_station(station, survey=survey_name)
add_station.metadata.from_dict(station_dict)
add_station.write_metadata()
channels = []
for file in full_path_to_ascii_files:
if station in file:
channels.append(file)
else:
continue
### for stations with a single run:
if len(channels) == len(raw_data_channels):
run_dict = json_load['run']
ex_dict = json_load['electric_ex']
ey_dict = json_load['electric_ey']
bx_dict = json_load['magnetic_bx']
by_dict = json_load['magnetic_by']
bz_dict = json_load['magnetic_bz']
add_run = m.add_run(station, run_number, survey=survey_name)
add_run.metadata.from_dict(run_dict)
add_run.write_metadata()
ex_ts,ey_ts,bx_ts,by_ts,bz_ts = channel_data_extraction(channels)
ex = m.add_channel(station, run_number, "ex", "electric", ex_ts, survey=survey_name)
ey = m.add_channel(station, run_number, "ey", "electric", ey_ts, survey=survey_name)
bx = m.add_channel(station, run_number, "bx", "magnetic", bx_ts, survey=survey_name)
by = m.add_channel(station, run_number, "by", "magnetic", by_ts, survey=survey_name)
bz = m.add_channel(station, run_number, "bz", "magnetic", bz_ts, survey=survey_name)
ex.metadata.from_dict(ex_dict)
ex.write_metadata()
ey.metadata.from_dict(ey_dict)
ey.write_metadata()
bx.metadata.from_dict(bx_dict)
bx.write_metadata()
by.metadata.from_dict(by_dict)
by.write_metadata()
bz.metadata.from_dict(bz_dict)
bz.write_metadata()
### for stations with multiple runs:
elif len(channels) > len(raw_data_channels):
sort_files = sorted(channels)
number_of_channels = len(raw_data_channels)
split_lists = [sort_files[x:x+number_of_channels] for x in range(0, len(sort_files), number_of_channels)]
mt_metadata_files = sorted(mt_metadata)
for i, (group,mt_meta) in enumerate(zip(split_lists,mt_metadata_files)):
mrun_number = i+1
run = "00%i" % mrun_number
with open(mt_meta, 'r') as json_file:
json_load = json.load(json_file)
run_dict = json_load['run']
ex_dict = json_load['electric_ex']
ey_dict = json_load['electric_ey']
bx_dict = json_load['magnetic_bx']
by_dict = json_load['magnetic_by']
bz_dict = json_load['magnetic_bz']
add_run = m.add_run(station, run, survey=survey_name)
add_run.metadata.from_dict(run_dict)
add_run.write_metadata()
ex_ts,ey_ts,bx_ts,by_ts,bz_ts = channel_data_extraction(channels)
ex = m.add_channel(station, run, "ex", "electric", ex_ts, survey=survey_name)
ey = m.add_channel(station, run, "ey", "electric", ey_ts, survey=survey_name)
bx = m.add_channel(station, run, "bx", "magnetic", bx_ts, survey=survey_name)
by = m.add_channel(station, run, "by", "magnetic", by_ts, survey=survey_name)
bz = m.add_channel(station, run, "bz", "magnetic", bz_ts, survey=survey_name)
ex.metadata.from_dict(ex_dict)
ex.write_metadata()
ey.metadata.from_dict(ey_dict)
ey.write_metadata()
bx.metadata.from_dict(bx_dict)
bx.write_metadata()
by.metadata.from_dict(by_dict)
by.write_metadata()
bz.metadata.from_dict(bz_dict)
bz.write_metadata()
elif len(channels) < len(raw_data_channels):
print('you are likely missing some channels')
print(station)
else:
print('something has gone wrong')
### create mth5 output directory and remove existing files
if rank==0:
make_mth5_dir(mth5_output_directory)
remove_existing_mth5_files(full_path_to_mth5_files)
comm.Barrier()
### create a single mth5 file per station with compression and associated mt_metadata
for i,station in enumerate(sorted(stations_all)):
if i%size!=rank:
continue
m = MTH5(file_version='0.2.0',shuffle=None,fletcher32=None,compression="gzip",compression_opts=4)
hdf5_filename = '{}.h5'.format(station)
h5_fn = mth5_output_directory+'/'+hdf5_filename
m.open_mth5(h5_fn, "w")
survey_group = m.add_survey(survey_name)
with open(survey_json, 'r') as json_file:
json_load = json.load(json_file)
survey_dict = json_load['survey']
survey_group.metadata.from_dict(survey_dict)
survey_group.write_metadata()
mt_metadata_file_name = '{}.json'.format(station)
mt_metadata = [file for file in full_path_to_mt_metadata if file.endswith(mt_metadata_file_name)]
create_mth5_group_station_run_channel(station,mt_metadata)
m.close_mth5()
comm.Barrier()
### print total time to run script
if rank==0:
print('The script took {0} seconds !'.format(time.time()-startTime))
Now that we have created our mth5_onefileperstation.py script, we next need to make a job submission script to submit to the Gadi PBSPro scheduler. The job submission script specifys the queue to use and the duration/resources needed for the job.
#!/bin/bash
#PBS -N mth5_onefileperstation
#PBS -q hugemem
#PBS -P fp0
#PBS -l walltime=0:05:00
#PBS -l ncpus=96
#PBS -l mem=900GB
#PBS -l jobfs=10GB
#PBS -l storage=gdata/fp0+gdata/my80+gdata/lm70+gdata/up99
module use /g/data/up99/modulefiles
module load NCI-geophys/22.06
cd ${PBS_O_WORKDIR}
mpirun -np $PBS_NCPUS python3 mth5_onefileperstation.py > ./pbs_job_logs/$PBS_JOBID.log
For our jobscript above (mth5_onefileperstation.sh), we have requested: 1. to use the hugemem queue 2. 96 CPUs (2 nodes) 3. 900GB of memory 4. 5 minutes of walltime
We also need to define the NCI project codes used in mth5_onefileperstation.py. The project code up99 is required to make use of the NCI-geophys/22.06 module that contains the Python libraries used in this tutorial.
To submit this jobscript (mth5_onefileperstation.sh) on Gadi:
$ qsub mth5_muscle.sh
This job will process the 93 stations in our survey using 96 MPI ranks (one station per rank) and creates the following 93 mth5 files:
SA225-2.h5 SA252.h5 SA293-2.h5 SA320.h5 SA344.h5 WA15.h5 WA47.h5 WA65.h5 WANT19.h5
SA227.h5 SA26W-2.h5 SA294.h5 SA321.h5 SA345.h5 WA26.h5 WA54.h5 WA66.h5 WANT38.h5
SA242.h5 SA270.h5 SA295.h5 SA322.h5 SA346.h5 WA27.h5 WA55.h5 WA67.h5 WANT45.h5
SA243.h5 SA271.h5 SA296.h5 SA323.h5 SA347.h5 WA29.h5 WA56.h5 WA68.h5 WASA302.h5
SA245.h5 SA272.h5 SA297.h5 SA324-2.h5 SA348.h5 WA30.h5 WA57.h5 WA69.h5 WASA327.h5
SA246.h5 SA273.h5 SA298.h5 SA324.h5 SA349.h5 WA31.h5 WA58.h5 WA70.h5
SA247.h5 SA274-2.h5 SA299.h5 SA325-2.h5 SA350.h5 WA42.h5 WA60.h5 WA71.h5
SA248.h5 SA274.h5 SA300.h5 SA325.h5 SA351.h5 WA43.h5 WA61.h5 WA72.h5
SA249.h5 SA275.h5 SA301.h5 SA326N.h5 WA10.h5 WA44.h5 WA62.h5 WA73.h5
SA250.h5 SA276.h5 SA319.h5 SA326S.h5 WA13.h5 WA45.h5 WA63.h5 WA74.h5
SA251.h5 SA277.h5 SA320-2.h5 SA344-2.h5 WA14.h5 WA46.h5 WA64.h5 WA75.h5
Below is a summary of how long mth5_onefileperstation.py took to run for the AusLAMP Musgraves Province survey:
run on: Gadi
number of stations: 93
number of runs: 101
NCPUs used: 96 (2 nodes)
memory used: 791 GB
walltime used: 215 seconds (3m35s)
CPU time used: 48570 seconds (5h06m07s)
service units: 17.20
Concluding remarks
We have generated 93 mth5 files for the 93 stations from the Musgraves survey in 3 minutes and 35 seconds using 2 nodes (96 CPUs) on Gadi. For comparison, if we only used one CPU it would have taken approximately 5 hours to create these files.
In our mth5_in_parallel example, we generated a single mth5 file with all stations and this took approximately 35 minutes of walltime (or ~14 hours of CPU time). The “single file per station” model was much quicker as it did not require the creation of an mth5 skeleton as each process (rank) was run independently.
We were able to introduce compression into our mth5_onefileperstation.py code and the total volume of the 93 mth5 files was 14 GB. The mth5_in_parallel.ipynb tutorial was following the “all stations in a single mth5 file” model and as our version of Parallel HDF5 did not support compression, the total volume of the single mth5 file was 106 GB.
MT Metadata
See mt_metadata documentation for more information on magnetotelluric time series and transfer function metadata standards.
mth5
mth5 package
Subpackages
mth5.clients package
This module provides helper functions to make MTH5 file from various clients
Supported Clients include:
FDSN (through Obspy)
Science Base (TODO)
NCI - Australia (TODO)
Updated on Wed Aug 25 19:57:00 2021
@author: jpeacock + tronan
- class mth5.clients.make_mth5.MakeMTH5(mth5_version='0.2.0', interact=False, save_path=None, **kwargs)[source]
Bases:
object- from_fdsn_client(request_df, client='IRIS', **kwargs)[source]
Pull data from an FDSN archive like IRIS. Uses Obspy.Clients.
- Parameters
request_df (
pandas.DataFrame) –DataFrame with columns
’network’ –> FDSN Network code
’station’ –> FDSN Station code
’location’ –> FDSN Location code
’channel’ –> FDSN Channel code
’start’ –> Start time YYYY-MM-DDThh:mm:ss
’end’ –> End time YYYY-MM-DDThh:mm:ss
client (string, optional) – FDSN client name, defaults to “IRIS”
interact (bool) – Boolean to keep the created MTH5 file open or not
- Raises
AttributeError – If the input DataFrame is not properly formatted an Attribute Error will be raised.
ValueError – If the values of the DataFrame are not correct a ValueError will be raised.
- Returns
MTH5 file name
- Return type
pathlib.Path
Note
If any of the column values are blank, then any value will
searched for. For example if you leave ‘station’ blank, any station within the given start and end time will be returned.
- from_usgs_geomag(request_df, **kwargs)[source]
Download geomagnetic observatory data from USGS webservices into an MTH5 using a request dataframe or csv file.
observatory: Geogmangetic observatory ID
type: type of data to get ‘adjusted’
start: start date time to request UTC
end: end date time to request UTC
elements: components to get
sampling_period: samples between measurements in seconds
- Parameters
request_df (
pandas.DataFrame, str or Path if csv file) –DataFrame with columns
’observatory’ –> Observatory code
’type’ –> data type [ ‘variation’ | ‘adjusted’ | ‘quasi-definitive’ | ‘definitive’ ]
’elements’ –> Elements to get [D, DIST, DST, E, E-E, E-N, F, G, H, SQ, SV, UK1, UK2, UK3, UK4, X, Y, Z]
’sampling_period’ –> sample period [ 1 | 60 | 3600 ]
’start’ –> Start time YYYY-MM-DDThh:mm:ss
’end’ –> End time YYYY-MM-DDThh:mm:ss
- Returns
if interact is True an MTH5 object is returned otherwise the path to the file is returned
- Return type
Path or
mth5.mth5.MTH5
- from_zen(data_path, sample_rates=[4096, 1024, 256], calibration_path=None, survey_id=None, combine=True, **kwargs)[source]
Create an MTH5 from zen data.
- Parameters
data_path (TYPE) – DESCRIPTION
sample_rates (TYPE, optional) – DESCRIPTION, defaults to [4096, 1024, 256]
save_path (TYPE, optional) – DESCRIPTION, defaults to None
calibration_path (TYPE, optional) – DESCRIPTION, defaults to None
- Returns
DESCRIPTION
- Return type
TYPE
- class mth5.clients.FDSN(client='IRIS', mth5_version='0.2.0', **kwargs)[source]
Bases:
object- build_network_dict(df, client)[source]
Build out a dictionary of networks, keyed by network_id, start_time. We could return this dict and use it as an auxilliary variable, but it seems easier to just add a column to the df.
- Parameters
df (pd.DataFrame) – This is a “request_df”
- build_station_dict(df, client, networks_dict)[source]
Given the {network-id, starttime}-keyed dict of networks, we build a station layer below this
- Parameters
df –
networks_dict –
- get_df_from_inventory(inventory)[source]
Create an data frame from an inventory object
- Parameters
inventory (
obspy.Inventory) – inventory object- Returns
dataframe in proper format
- Return type
pandas.DataFrame
- get_inventory_from_df(df, client=None, data=True)[source]
20230806: The nested for looping here can make debugging complex, as well as lead to a lot of redundancies. I propose that we build out a dictionary of networks, keyed by network_id, start_time. It may actually be simpler to just add a column to the request_df that has the network_obj
networks = {} networks[network_id] = {} networks[network_id][start_time_1] = obspy_network_obj networks[network_id][start_time_2] = obspy_network_obj …
Then the role of “returned_network” can be replaced by accessing the appropriate element and the second for-loop can move up by a layer of indentation.
Will try to factor i Get an
obspy.Inventoryobject from apandas.DataFrame- Parameters
df (
pandas.DataFrame) –DataFrame with columns
’network’ –> FDSN Network code
’station’ –> FDSN Station code
’location’ –> FDSN Location code
’channel’ –> FDSN Channel code
’start’ –> Start time YYYY-MM-DDThh:mm:ss
’end’ –> End time YYYY-MM-DDThh:mm:ss
client (string) – FDSN client
data – True if you want data False if you want just metadata,
defaults to True :type data: boolean, optional :return: An inventory of metadata requested and data :rtype:
obspy.Inventoryandobspy.StreamNote
If any of the column values are blank, then any value will
searched for. For example if you leave ‘station’ blank, any station within the given start and end time will be returned.
- get_run_group(mth5_obj_or_survey, station_id, run_id)[source]
This method is key to merging wrangle_runs_into_containers_v1 and wrangle_runs_into_containers_v2. Because a v1 mth5 object can get a survey group with the same method as can a v2 survey_group
Thus we can replace run_group = m.stations_group.get_station(station_id).add_run(run_id) & run_group = survey_group.stations_group.get_station(station_id).add_run(run_id) with run_group = mth5_obj_or_survey.stations_group.get_station(station_id).add_run(run_id) :param mth5_obj_or_survey: :type mth5_obj_or_survey: mth5.mth5.MTH5 or mth5.groups.survey.SurveyGroup
- get_run_list_from_station_id(m, station_id, survey_id=None)[source]
ignored_groups created to address issue #153. This might be better placed closer to the core of mth5.
- Parameters
m –
station_id –
- Returns
run_list
- Return type
list of strings
- get_unique_networks_and_stations(df)[source]
Get unique lists of networks, stations, locations, and channels from a given data frame.
[{‘network’: FDSN code, “stations”: [list of stations for network]}]
- Parameters
df (
pandas.DataFrame) – request data frame- Returns
list of network dictionaries with
[{‘network’: FDSN code, “stations”: [list of stations for network]}] :rtype: list
- make_filename(df)[source]
Make a filename from a data frame that is networks and stations
- Parameters
df (
pandas.DataFrame) – request data frame- Returns
file name as network_01+stations_network_02+stations.h5
- Return type
string
- make_mth5_from_fdsn_client(df, path=None, client=None, interact=False)[source]
Make an MTH5 file from an FDSN data center
- Parameters
df (
pandas.DataFrame) –DataFrame with columns
’network’ –> FDSN Network code
’station’ –> FDSN Station code
’location’ –> FDSN Location code
’channel’ –> FDSN Channel code
’start’ –> Start time YYYY-MM-DDThh:mm:ss
’end’ –> End time YYYY-MM-DDThh:mm:ss
path (string or
pathlib.Path, optional) – Path to save MTH5 file to, defaults to Noneclient (string, optional) – FDSN client name, defaults to “IRIS”
- Raises
AttributeError – If the input DataFrame is not properly
formatted an Attribute Error will be raised. :raises ValueError: If the values of the DataFrame are not correct a ValueError will be raised. :return: MTH5 file name :rtype:
pathlib.PathNote
If any of the column values are blank, then any value will
searched for. For example if you leave ‘station’ blank, any station within the given start and end time will be returned.
- property run_list_ne_stream_intervals_message
note about not equal stream intervals
- run_timings_match_stream_timing(run_group, stream_start, stream_end)[source]
Checks start and end times in the run. Compares start and end times of runs to start and end times of traces. If True, will packs runs based on time spans.
- Parameters
run_group –
stream_start –
stream_end –
- stream_boundaries(streams)[source]
Identify start and end times of streams
- Parameters
streams (obspy.core.stream.Stream) –
- wrangle_runs_into_containers(m, station_id, survey_group=None)[source]
Note 1: There used to be two separate functions for this, but now there is one run_group_source is defined as either m or survey_group depending on v0.1.0 or 0.2.0
Note 2: If/elif/elif/else Logic: The strategy is to add the group first. This will get the already filled in metadata to update the run_ts_obj. Then get streams an add existing metadata.
- Parameters
m –
streams –
station_id –
survey_group –
- class mth5.clients.MakeMTH5(mth5_version='0.2.0', interact=False, save_path=None, **kwargs)[source]
Bases:
object- from_fdsn_client(request_df, client='IRIS', **kwargs)[source]
Pull data from an FDSN archive like IRIS. Uses Obspy.Clients.
- Parameters
request_df (
pandas.DataFrame) –DataFrame with columns
’network’ –> FDSN Network code
’station’ –> FDSN Station code
’location’ –> FDSN Location code
’channel’ –> FDSN Channel code
’start’ –> Start time YYYY-MM-DDThh:mm:ss
’end’ –> End time YYYY-MM-DDThh:mm:ss
client (string, optional) – FDSN client name, defaults to “IRIS”
interact (bool) – Boolean to keep the created MTH5 file open or not
- Raises
AttributeError – If the input DataFrame is not properly formatted an Attribute Error will be raised.
ValueError – If the values of the DataFrame are not correct a ValueError will be raised.
- Returns
MTH5 file name
- Return type
pathlib.Path
Note
If any of the column values are blank, then any value will
searched for. For example if you leave ‘station’ blank, any station within the given start and end time will be returned.
- from_usgs_geomag(request_df, **kwargs)[source]
Download geomagnetic observatory data from USGS webservices into an MTH5 using a request dataframe or csv file.
observatory: Geogmangetic observatory ID
type: type of data to get ‘adjusted’
start: start date time to request UTC
end: end date time to request UTC
elements: components to get
sampling_period: samples between measurements in seconds
- Parameters
request_df (
pandas.DataFrame, str or Path if csv file) –DataFrame with columns
’observatory’ –> Observatory code
’type’ –> data type [ ‘variation’ | ‘adjusted’ | ‘quasi-definitive’ | ‘definitive’ ]
’elements’ –> Elements to get [D, DIST, DST, E, E-E, E-N, F, G, H, SQ, SV, UK1, UK2, UK3, UK4, X, Y, Z]
’sampling_period’ –> sample period [ 1 | 60 | 3600 ]
’start’ –> Start time YYYY-MM-DDThh:mm:ss
’end’ –> End time YYYY-MM-DDThh:mm:ss
- Returns
if interact is True an MTH5 object is returned otherwise the path to the file is returned
- Return type
Path or
mth5.mth5.MTH5
- from_zen(data_path, sample_rates=[4096, 1024, 256], calibration_path=None, survey_id=None, combine=True, **kwargs)[source]
Create an MTH5 from zen data.
- Parameters
data_path (TYPE) – DESCRIPTION
sample_rates (TYPE, optional) – DESCRIPTION, defaults to [4096, 1024, 256]
save_path (TYPE, optional) – DESCRIPTION, defaults to None
calibration_path (TYPE, optional) – DESCRIPTION, defaults to None
- Returns
DESCRIPTION
- Return type
TYPE
- class mth5.clients.PhoenixClient(data_path, sample_rates=[130, 24000], save_path=None, calibration_path=None)[source]
Bases:
object- property calibration_path
Path to calibration data
- property data_path
Path to phoenix data
- make_mth5_from_phoenix(**kwargs)[source]
Make an MTH5 from Phoenix files. Split into runs, account for filters
- Parameters
data_path (TYPE, optional) – DESCRIPTION, defaults to None
sample_rates (TYPE, optional) – DESCRIPTION, defaults to None
save_path (TYPE, optional) – DESCRIPTION, defaults to None
- Returns
DESCRIPTION
- Return type
TYPE
- property sample_rates
sample rates to look for
- property save_path
Path to save mth5
- class mth5.clients.USGSGeomag(**kwargs)[source]
Bases:
object- add_run_id(request_df)[source]
Add run id to request df
- Parameters
request_df (
pandas.DataFrame) – request dataframe- Returns
add a run number to unique time windows for each observatory at each unique sampling period.
- Return type
pandas.DataFrame
- make_mth5_from_geomag(request_df)[source]
Download geomagnetic observatory data from USGS webservices into an MTH5 using a request dataframe or csv file.
- Parameters
request_df (
pandas.DataFrame, str or Path if csv file) –DataFrame with columns
’observatory’ –> Observatory code
’type’ –> data type [ ‘variation’ | ‘adjusted’ | ‘quasi-definitive’ | ‘definitive’ ]
’elements’ –> Elements to get [D, DIST, DST, E, E-E, E-N, F, G, H, SQ, SV, UK1, UK2, UK3, UK4, X, Y, Z]
’sampling_period’ –> sample period [ 1 | 60 | 3600 ]
’start’ –> Start time YYYY-MM-DDThh:mm:ss
’end’ –> End time YYYY-MM-DDThh:mm:ss
- Returns
if interact is True an MTH5 object is returned otherwise the path to the file is returned
- Return type
Path or
mth5.mth5.MTH5
- class mth5.clients.ZenClient(data_path, sample_rates=[4096, 1024, 256], save_path=None, calibration_path=None, **kwargs)[source]
Bases:
object- property calibration_path
Path to calibration data
- property data_path
Path to phoenix data
- get_survey(station_dict)[source]
get survey name from a dictionary of a single station of runs :param station_dict: DESCRIPTION :type station_dict: TYPE :return: DESCRIPTION :rtype: TYPE
- make_mth5_from_zen(survey_id=None, combine=True, **kwargs)[source]
Make an MTH5 from Phoenix files. Split into runs, account for filters
- Parameters
data_path (TYPE, optional) – DESCRIPTION, defaults to None
sample_rates (TYPE, optional) – DESCRIPTION, defaults to None
save_path (TYPE, optional) – DESCRIPTION, defaults to None
- Returns
DESCRIPTION
- Return type
TYPE
- property sample_rates
sample rates to look for
- property save_path
Path to save mth5
mth5.groups package
Created on Wed Jun 9 08:58:15 2021
- copyright
Jared Peacock (jpeacock@usgs.gov)
- license
MIT
- class mth5.groups.filter_groups.coefficient_filter_group.CoefficientGroup(group, **kwargs)[source]
Bases:
BaseGroupContainer for Coefficient type filters
- add_filter(name, coefficient_metadata)[source]
Add a coefficient Filter
- Parameters
name (TYPE) – DESCRIPTION
coefficient_metadata (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- property filter_dict
Dictionary of available coefficient filters
- Returns
DESCRIPTION
- Return type
TYPE
- from_object(coefficient_object)[source]
make a filter from a
mt_metadata.timeseries.filters.CoefficientFilter- Parameters
zpk_object (
mt_metadata.timeseries.filters.CoefficientFilter) – MT metadata Coefficient Filter
Created on Wed Jun 9 08:55:16 2021
- copyright
Jared Peacock (jpeacock@usgs.gov)
- license
MIT
- class mth5.groups.filter_groups.fap_filter_group.FAPGroup(group, **kwargs)[source]
Bases:
BaseGroupContainer for fap type filters
- add_filter(name, frequency, amplitude, phase, fap_metadata)[source]
create an HDF5 group/dataset from information given.
- Parameters
name (string) – name of the filter
frequency (list, np.ndarray) – frequency array in samples per second
amplitude (list, np.ndarray) – amplitude array in units of units out
phase (list, np.ndarray) – Phase in degrees
fap_metadata – other metadata for the filter see
mt_metadata.timeseries.filters.FrequencyResponseTableFilterfor details on entries :type fap_metadata: dictionary :return: DESCRIPTION :rtype: TYPE
- property filter_dict
Dictionary of available fap filters
- Returns
DESCRIPTION
- Return type
TYPE
- from_object(fap_object)[source]
make a filter from a
mt_metadata.timeseries.filters.PoleZeroFilter- Parameters
fap_object (
mt_metadata.timeseries.filters.PoleZeroFilter) – MT metadata PoleZeroFilter
- get_filter(name)[source]
Get a filter from the name
- Parameters
name (string) – name of the filter
- Returns
HDF5 group of the fap filter
Created on Wed Jun 9 08:55:16 2021
- copyright
Jared Peacock (jpeacock@usgs.gov)
- license
MIT
- class mth5.groups.filter_groups.fir_filter_group.FIRGroup(group, **kwargs)[source]
Bases:
BaseGroupContainer for fir type filters
- add_filter(name, coefficients, fir_metadata)[source]
create an HDF5 group/dataset from information given.
- Parameters
name (string) – Nane of the filter
poles (np.ndarray(dtype=complex)) – poles of the filter as complex numbers
zeros (np.ndarray(dtype=comples)) – zeros of the filter as complex numbers
fir_metadata – metadata dictionary see
mt_metadata.timeseries.filters.PoleZeroFilterfor details on entries :type fir_metadata: dictionary
- property filter_dict
Dictionary of available fir filters
- Returns
DESCRIPTION
- Return type
TYPE
- from_object(fir_object)[source]
make a filter from a
mt_metadata.timeseries.filters.PoleZeroFilter- Parameters
fir_object (
mt_metadata.timeseries.filters.PoleZeroFilter) – MT metadata PoleZeroFilter
Created on Wed Jun 9 09:01:55 2021
- copyright
Jared Peacock (jpeacock@usgs.gov)
- license
MIT
- class mth5.groups.filter_groups.time_delay_filter_group.TimeDelayGroup(group, **kwargs)[source]
Bases:
BaseGroupContainer for time_delay type filters
- add_filter(name, time_delay_metadata)[source]
create an HDF5 group/dataset from information given.
- Parameters
name (string) – Nane of the filter
poles (np.ndarray(dtype=complex)) – poles of the filter as complex numbers
zeros (np.ndarray(dtype=comples)) – zeros of the filter as complex numbers
time_delay_metadata – metadata dictionary see
mt_metadata.timeseries.filters.PoleZeroFilterfor details on entries :type time_delay_metadata: dictionary
- property filter_dict
Dictionary of available time_delay filters
- Returns
DESCRIPTION
- Return type
TYPE
- from_object(time_delay_object)[source]
make a filter from a
mt_metadata.timeseries.filters.PoleZeroFilter- Parameters
time_delay_object (
mt_metadata.timeseries.filters.PoleZeroFilter) – MT metadata PoleZeroFilter
Created on Wed Jun 9 08:55:16 2021
- copyright
Jared Peacock (jpeacock@usgs.gov)
- license
MIT
- class mth5.groups.filter_groups.zpk_filter_group.ZPKGroup(group, **kwargs)[source]
Bases:
BaseGroupContainer for ZPK type filters
- add_filter(name, poles, zeros, zpk_metadata)[source]
create an HDF5 group/dataset from information given.
- Parameters
name (string) – Nane of the filter
poles (np.ndarray(dtype=complex)) – poles of the filter as complex numbers
zeros (np.ndarray(dtype=comples)) – zeros of the filter as complex numbers
zpk_metadata – metadata dictionary see
mt_metadata.timeseries.filters.PoleZeroFilterfor details on entries :type zpk_metadata: dictionary
- property filter_dict
Dictionary of available ZPK filters
- Returns
DESCRIPTION
- Return type
TYPE
- from_object(zpk_object)[source]
make a filter from a
mt_metadata.timeseries.filters.PoleZeroFilter- Parameters
zpk_object (
mt_metadata.timeseries.filters.PoleZeroFilter) – MT metadata PoleZeroFilter
Import all Group objects
- class mth5.groups.filter_groups.CoefficientGroup(group, **kwargs)[source]
Bases:
BaseGroupContainer for Coefficient type filters
- add_filter(name, coefficient_metadata)[source]
Add a coefficient Filter
- Parameters
name (TYPE) – DESCRIPTION
coefficient_metadata (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- property filter_dict
Dictionary of available coefficient filters
- Returns
DESCRIPTION
- Return type
TYPE
- from_object(coefficient_object)[source]
make a filter from a
mt_metadata.timeseries.filters.CoefficientFilter- Parameters
zpk_object (
mt_metadata.timeseries.filters.CoefficientFilter) – MT metadata Coefficient Filter
- class mth5.groups.filter_groups.FAPGroup(group, **kwargs)[source]
Bases:
BaseGroupContainer for fap type filters
- add_filter(name, frequency, amplitude, phase, fap_metadata)[source]
create an HDF5 group/dataset from information given.
- Parameters
name (string) – name of the filter
frequency (list, np.ndarray) – frequency array in samples per second
amplitude (list, np.ndarray) – amplitude array in units of units out
phase (list, np.ndarray) – Phase in degrees
fap_metadata – other metadata for the filter see
mt_metadata.timeseries.filters.FrequencyResponseTableFilterfor details on entries :type fap_metadata: dictionary :return: DESCRIPTION :rtype: TYPE
- property filter_dict
Dictionary of available fap filters
- Returns
DESCRIPTION
- Return type
TYPE
- from_object(fap_object)[source]
make a filter from a
mt_metadata.timeseries.filters.PoleZeroFilter- Parameters
fap_object (
mt_metadata.timeseries.filters.PoleZeroFilter) – MT metadata PoleZeroFilter
- get_filter(name)[source]
Get a filter from the name
- Parameters
name (string) – name of the filter
- Returns
HDF5 group of the fap filter
- class mth5.groups.filter_groups.FIRGroup(group, **kwargs)[source]
Bases:
BaseGroupContainer for fir type filters
- add_filter(name, coefficients, fir_metadata)[source]
create an HDF5 group/dataset from information given.
- Parameters
name (string) – Nane of the filter
poles (np.ndarray(dtype=complex)) – poles of the filter as complex numbers
zeros (np.ndarray(dtype=comples)) – zeros of the filter as complex numbers
fir_metadata – metadata dictionary see
mt_metadata.timeseries.filters.PoleZeroFilterfor details on entries :type fir_metadata: dictionary
- property filter_dict
Dictionary of available fir filters
- Returns
DESCRIPTION
- Return type
TYPE
- from_object(fir_object)[source]
make a filter from a
mt_metadata.timeseries.filters.PoleZeroFilter- Parameters
fir_object (
mt_metadata.timeseries.filters.PoleZeroFilter) – MT metadata PoleZeroFilter
- class mth5.groups.filter_groups.TimeDelayGroup(group, **kwargs)[source]
Bases:
BaseGroupContainer for time_delay type filters
- add_filter(name, time_delay_metadata)[source]
create an HDF5 group/dataset from information given.
- Parameters
name (string) – Nane of the filter
poles (np.ndarray(dtype=complex)) – poles of the filter as complex numbers
zeros (np.ndarray(dtype=comples)) – zeros of the filter as complex numbers
time_delay_metadata – metadata dictionary see
mt_metadata.timeseries.filters.PoleZeroFilterfor details on entries :type time_delay_metadata: dictionary
- property filter_dict
Dictionary of available time_delay filters
- Returns
DESCRIPTION
- Return type
TYPE
- from_object(time_delay_object)[source]
make a filter from a
mt_metadata.timeseries.filters.PoleZeroFilter- Parameters
time_delay_object (
mt_metadata.timeseries.filters.PoleZeroFilter) – MT metadata PoleZeroFilter
- class mth5.groups.filter_groups.ZPKGroup(group, **kwargs)[source]
Bases:
BaseGroupContainer for ZPK type filters
- add_filter(name, poles, zeros, zpk_metadata)[source]
create an HDF5 group/dataset from information given.
- Parameters
name (string) – Nane of the filter
poles (np.ndarray(dtype=complex)) – poles of the filter as complex numbers
zeros (np.ndarray(dtype=comples)) – zeros of the filter as complex numbers
zpk_metadata – metadata dictionary see
mt_metadata.timeseries.filters.PoleZeroFilterfor details on entries :type zpk_metadata: dictionary
- property filter_dict
Dictionary of available ZPK filters
- Returns
DESCRIPTION
- Return type
TYPE
- from_object(zpk_object)[source]
make a filter from a
mt_metadata.timeseries.filters.PoleZeroFilter- Parameters
zpk_object (
mt_metadata.timeseries.filters.PoleZeroFilter) – MT metadata PoleZeroFilter
Contains all the base functions that will be used by group classes.
Created on Fri May 29 15:09:48 2020
- copyright
Jared Peacock (jpeacock@usgs.gov)
- license
MIT
- class mth5.groups.base.BaseGroup(group, group_metadata=None, **kwargs)[source]
Bases:
objectGeneric object that will have functionality for reading/writing groups, including attributes. To access the hdf5 group directly use the BaseGroup.hdf5_group property.
>>> base = BaseGroup(hdf5_group) >>> base.hdf5_group.ref <HDF5 Group Reference>
Note
All attributes should be input into the metadata object, that way all input will be validated against the metadata standards. If you change attributes in metadata object, you should run the BaseGroup.write_metadata method. This is a temporary solution working on an automatic updater if metadata is changed.
>>> base.metadata.existing_attribute = 'update_existing_attribute' >>> base.write_metadata()
If you want to add a new attribute this should be done using the metadata.add_base_attribute method.
>>> base.metadata.add_base_attribute('new_attribute', ... 'new_attribute_value', ... {'type':str, ... 'required':True, ... 'style':'free form', ... 'description': 'new attribute desc.', ... 'units':None, ... 'options':[], ... 'alias':[], ... 'example':'new attribute'})
Includes intializing functions that makes a summary table and writes metadata.
- property dataset_options
- property groups_list
- property metadata
Metadata for the Group based on mt_metadata.timeseries
Created on Thu Mar 10 09:02:16 2022
@author: jpeacock
- class mth5.groups.estimate_dataset.EstimateDataset(dataset, dataset_metadata=None, write_metadata=True, **kwargs)[source]
Bases:
objectHolds a statistical estimate
This will hold multi-dimensional statistical estimates for transfer functions.
- Parameters
dataset (h5py.Dataset) – hdf5 dataset
dataset_metadata – data set metadata see
mt_metadata.transfer_functions.tf.StatisticalEstimate,defaults to None
- Parameters
write_metadata (Boolean, optional) – True to write metadata, defaults to True
**kwargs –
DESCRIPTION
- Raises
MTH5Error – When an estimate is not present, or metadata name does not match the given name
- from_numpy(new_estimate)[source]
- Returns
a numpy structured array
- Return type
numpy.ndarray
Note
data is a builtin to numpy and cannot be used as a name
loads into RAM
- from_xarray(data)[source]
- Returns
an xarray DataArray with appropriate metadata and the appropriate coordinates base on the metadata.
- Return type
xarray.DataArray
Note
that metadta will not be validated if changed in an xarray.
loads from memory
- read_metadata()[source]
Read metadata from the HDF5 file into the metadata container, that way it can be validated.
- replace_dataset(new_data_array)[source]
replace the entire dataset with a new one, nothing left behind
- Parameters
new_data_array (
numpy.ndarray) – new data array
Created on Wed Dec 23 16:59:45 2020
- copyright
Jared Peacock (jpeacock@usgs.gov)
- license
MIT
- class mth5.groups.experiment.ExperimentGroup(group, **kwargs)[source]
Bases:
BaseGroupUtility class to hold general information about the experiment and accompanying metadata for an MT experiment.
To access the hdf5 group directly use ExperimentGroup.hdf5_group.
>>> experiment = ExperimentGroup(hdf5_group) >>> experiment.hdf5_group.ref <HDF5 Group Reference>
Note
All attributes should be input into the metadata object, that way all input will be validated against the metadata standards. If you change attributes in metadata object, you should run the ExperimentGroup.write_metadata() method. This is a temporary solution, working on an automatic updater if metadata is changed.
>>> experiment.metadata.existing_attribute = 'update_existing_attribute' >>> experiment.write_metadata()
If you want to add a new attribute this should be done using the metadata.add_base_attribute method.
>>> experiment.metadata.add_base_attribute('new_attribute', >>> ... 'new_attribute_value', >>> ... {'type':str, >>> ... 'required':True, >>> ... 'style':'free form', >>> ... 'description': 'new attribute desc.', >>> ... 'units':None, >>> ... 'options':[], >>> ... 'alias':[], >>> ... 'example':'new attribute
Tip
If you want ot add stations, reports, etc to the experiment this should be done from the MTH5 object. This is to avoid duplication, at least for now.
To look at what the structure of
/Experimentlooks like:>>> experiment /Experiment: ==================== |- Group: Surveys ----------------- |- Group: Reports ----------------- |- Group: Standards ------------------- |- Group: Stations ------------------
- property metadata
Overwrite get metadata to include station information
- property surveys_group
Created on Wed Dec 23 17:08:40 2020
Need to make a group for FAP and FIR filters.
- copyright
Jared Peacock (jpeacock@usgs.gov)
- license
MIT
- class mth5.groups.filters.FiltersGroup(group, **kwargs)[source]
Bases:
BaseGroupNot implemented yet
- add_filter(filter_object)[source]
Add a filter dataset based on type
- current types are:
zpk –> zeros, poles, gain
fap –> frequency look up table
time_delay –> time delay filter
coefficient –> coefficient filter
- Parameters
filter_object (
mt_metadata.timeseries.filters) – An MT metadata filter object
- property filter_dict
Created on Wed Dec 23 17:03:53 2020
- copyright
Jared Peacock (jpeacock@usgs.gov)
- license
MIT
Created on Wed Dec 23 17:05:33 2020
- copyright
Jared Peacock (jpeacock@usgs.gov)
- license
MIT
- class mth5.groups.standards.StandardsGroup(group, **kwargs)[source]
Bases:
BaseGroupThe StandardsGroup is a convenience group that stores the metadata standards that were used to make the current file. This is to help a user understand the metadata directly from the file and not have to look up documentation that might not be updated.
The metadata standards are stored in the summary table
/Survey/Standards/summary>>> standards = mth5_obj.standards_group >>> standards.summary_table index | attribute | type | required | style | units | description | options | alias | example --------------------------------------------------------------------------
- get_attribute_information(attribute_name)[source]
get information about an attribute
The attribute name should be in the summary table.
- Parameters
attribute_name (string) – attribute name
- Returns
prints a description of the attribute
- Raises
MTH5TableError – if attribute is not found
>>> standars = mth5_obj.standards_group >>> standards.get_attribute_information('survey.release_license') survey.release_license -------------------------- type : string required : True style : controlled vocabulary units : description : How the data can be used. The options are based on Creative Commons licenses. For details visit https://creativecommons.org/licenses/ options : CC-0,CC-BY,CC-BY-SA,CC-BY-ND,CC-BY-NC-SA,CC-BY-NC-ND alias : example : CC-0 default : CC-0
- initialize_group()[source]
Initialize the group by making a summary table that summarizes the metadata standards used to describe the data.
Also, write generic metadata information.
- property summary_table
Created on Wed Dec 23 16:59:45 2020
- copyright
Jared Peacock (jpeacock@usgs.gov)
- license
MIT
- class mth5.groups.survey.MasterSurveyGroup(group, **kwargs)[source]
Bases:
BaseGroupUtility class to hold information about the surveys within an experiment and accompanying metadata. This class is next level down from Experiment for stations
Experiment/Surveys. This class provides methods to add and get surveys.To access MasterSurveyGroup from an open MTH5 file:
>>> from mth5 import mth5 >>> mth5_obj = mth5.MTH5() >>> mth5_obj.open_mth5(r"/test.mth5", mode='a') >>> surveys = mth5_obj.surveys_group
To check what stations exist
>>> surveys.groups_list ['survey_01', 'survey_02']
To access the hdf5 group directly use SurveyGroup.hdf5_group.
>>> stations.hdf5_group.ref <HDF5 Group Reference>
Note
All attributes should be input into the metadata object, that way all input will be validated against the metadata standards. If you change attributes in metadata object, you should run the SurveyGroup.write_metadata() method. This is a temporary solution, working on an automatic updater if metadata is changed.
If you want to add a new attribute this should be done using the metadata.add_base_attribute method.
>>> stations.metadata.add_base_attribute('new_attribute', >>> ... 'new_attribute_value', >>> ... {'type':str, >>> ... 'required':True, >>> ... 'style':'free form', >>> ... 'description': 'new attribute desc.', >>> ... 'units':None, >>> ... 'options':[], >>> ... 'alias':[], >>> ... 'example':'new attribute
To add a survey:
>>> new_survey = surveys.add_survey('new_survey') >>> surveys Experiment/Surveys: ==================== |- Group: new_survey --------------------- |- Group: Filters ------------------ |- Group: Reports ----------------- |- Group: Standards ------------------- |- Group: Stations ------------------
Add a survey with metadata:
>>> from mth5.metadata import Survey >>> survey_metadata = Survey() >>> survey_metadata.id = 'MT004' >>> survey_metadata.time_period.start = '2020-01-01T12:30:00' >>> new_survey = surveys.add_survey('Test_01', survey_metadata) >>> # to look at the metadata >>> new_survey.metadata { "survey": { "acquired_by.author": null, "acquired_by.comments": null, "id": "MT004", ... } }
See also
mth5.metadata for details on how to add metadata from various files and python objects.
To remove a survey:
>>> surveys.remove_survey('new_survey') >>> surveys /Survey/Stations: ====================
Note
Deleting a survey is not as simple as del(survey). In HDF5 this does not free up memory, it simply removes the reference to that survey. The common way to get around this is to copy what you want into a new file, or overwrite the survey.
To get a survey:
>>> existing_survey = surveys.get_survey('existing_survey_name')
- add_survey(survey_name, survey_metadata=None)[source]
- Add a survey with metadata if given with the path:
/Survey/surveys/survey_name
If the survey already exists, will return that survey and nothing is added.
- Parameters
survey_name (string) – Name of the survey, should be the same as metadata.id
survey_metadata (
mth5.metadata.Station, optional) – Station metadata container, defaults to None
- Returns
A convenience class for the added survey
- Return type
mth5_groups.StationGroup- To add a survey
>>> new_survey = surveys.add_survey('new_survey') >>> surveys Experiment/Surveys: ==================== |- Group: new_survey --------------------- |- Group: Filters ------------------ |- Group: Reports ----------------- |- Group: Standards ------------------- |- Group: Stations ------------------
- Add a survey with metadata
>>> from mth5.metadata import Survey >>> survey_metadata = Survey() >>> survey_metadata.id = 'MT004' >>> survey_metadata.time_period.start = '2020-01-01T12:30:00' >>> new_survey = surveys.add_survey('Test_01', survey_metadata) >>> # to look at the metadata >>> new_survey.metadata { "survey": { "acquired_by.author": null, "acquired_by.comments": null, "id": "MT004", ... } }
See also
mth5.metadata for details on how to add metadata from various files and python objects.
- property channel_summary
Summary of all channels in the file.
- get_survey(survey_name)[source]
Get a survey with the same name as survey_name
- Parameters
survey_name (string) – existing survey name
- Returns
convenience survey class
- Return type
mth5.mth5_groups.surveyGroup- Raises
MTH5Error – if the survey name is not found.
- Example
>>> from mth5 import mth5 >>> mth5_obj = mth5.MTH5() >>> mth5_obj.open_mth5(r"/test.mth5", mode='a') >>> # one option >>> existing_survey = mth5_obj.get_survey('MT001') >>> # another option >>> existing_survey = mth5_obj.experiment_group.surveys_group.get_survey('MT001') MTH5Error: MT001 does not exist, check survey_list for existing names
- remove_survey(survey_name)[source]
Remove a survey from the file.
Note
Deleting a survey is not as simple as del(survey). In HDF5 this does not free up memory, it simply removes the reference to that survey. The common way to get around this is to copy what you want into a new file, or overwrite the survey.
- Parameters
survey_name (string) – existing survey name
- Example
>>> from mth5 import mth5 >>> mth5_obj = mth5.MTH5() >>> mth5_obj.open_mth5(r"/test.mth5", mode='a') >>> # one option >>> mth5_obj.remove_survey('MT001') >>> # another option >>> mth5_obj.surveys_group.remove_survey('MT001')
- class mth5.groups.survey.SurveyGroup(group, survey_metadata=None, **kwargs)[source]
Bases:
BaseGroupUtility class to holds general information about the survey and accompanying metadata for an MT survey.
To access the hdf5 group directly use SurveyGroup.hdf5_group.
>>> survey = SurveyGroup(hdf5_group) >>> survey.hdf5_group.ref <HDF5 Group Reference>
Note
All attributes should be input into the metadata object, that way all input will be validated against the metadata standards. If you change attributes in metadata object, you should run the SurveyGroup.write_metadata() method. This is a temporary solution, working on an automatic updater if metadata is changed.
>>> survey.metadata.existing_attribute = 'update_existing_attribute' >>> survey.write_metadata()
If you want to add a new attribute this should be done using the metadata.add_base_attribute method.
>>> survey.metadata.add_base_attribute('new_attribute', >>> ... 'new_attribute_value', >>> ... {'type':str, >>> ... 'required':True, >>> ... 'style':'free form', >>> ... 'description': 'new attribute desc.', >>> ... 'units':None, >>> ... 'options':[], >>> ... 'alias':[], >>> ... 'example':'new attribute
Tip
If you want ot add surveys, reports, etc to the survey this should be done from the MTH5 object. This is to avoid duplication, at least for now.
To look at what the structure of
/Surveylooks like:>>> survey /Survey: ==================== |- Group: Filters ----------------- --> Dataset: summary ----------------- |- Group: Reports ----------------- --> Dataset: summary ----------------- |- Group: Standards ------------------- --> Dataset: summary ----------------- |- Group: Stations ------------------ --> Dataset: summary -----------------
- property filters_group
Convenience property for /Survey/Filters group
- property metadata
Overwrite get metadata to include station information in the survey
- property reports_group
Convenience property for /Survey/Reports group
- property standards_group
Convenience property for /Survey/Standards group
- property stations_group
Created on Thu Mar 10 08:22:33 2022
@author: jpeacock
- class mth5.groups.transfer_function.TransferFunctionGroup(group, **kwargs)[source]
Bases:
BaseGroupObject to hold a single transfer function estimation
- add_statistical_estimate(estimate_name, estimate_data=None, estimate_metadata=None, max_shape=(None, None, None), chunks=True, **kwargs)[source]
Add a StatisticalEstimate
- Parameters
estimate (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- from_tf_object(tf_obj)[source]
Create data sets from a
mt_metadata.transfer_function.core.TFobject.- Parameters
tf_obj (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- property period
Get period from hdf5_group[“period”]
- Returns
DESCRIPTION
- Return type
TYPE
- class mth5.groups.transfer_function.TransferFunctionsGroup(group, **kwargs)[source]
Bases:
BaseGroupObject to hold transfer functions
The is the high level group, all transfer functions for the station are held here and each one will have its own TransferFunctionGroup.
This has add, get, remove_transfer_function.
- add_transfer_function(name, tf_object=None)[source]
Add a transfer function to the group
- Parameters
name (string) – name of the transfer function
tf_object (
mt_metadata.transfer_function.core.TF) – Transfer Function object
- Returns
DESCRIPTION
- Return type
TYPE
>>> from mth5.mth5 import MTH5 >>> m = MTH5() >>> m.open_mth5("example.h5", "a") >>> station_group = m.get_station("mt01", survey="test") >>> tf_group = station_group.transfer_functions_group >>> tf_group.add_transfer_function("mt01_4096", tf_object)
- get_tf_object(tf_id)[source]
This is the function you want to use to get a proper
mt_metadata.transfer_functions.core.TFobject with all the appropriate metadata.- Parameters
tf_id (string) – name of the transfer function to get
- Returns
Full transfer function with appropriate metadata
- Return type
mt_metadata.transfer_functions.core.TF
>>> from mth5.mth5 import MTH5 >>> m = MTH5() >>> m.open_mth5("example.h5", "a") >>> station_group = m.get_station("mt01", survey="test") >>> tf_group = station_group.transfer_functions_group >>> tf_object = tf_group.get_tf_object("mt01_4096")
- get_transfer_function(tf_id)[source]
Get transfer function from id
- Parameters
tf_id (string) – name of transfer function
- Returns
Transfer function group
- Return type
>>> from mth5.mth5 import MTH5 >>> m = MTH5() >>> m.open_mth5("example.h5", "a") >>> station_group = m.get_station("mt01", survey="test") >>> tf_group = station_group.transfer_functions_group.get_transfer_function("mt01_4096")
- remove_transfer_function(tf_id)[source]
Remove a transfer function from the group
- Parameters
tf_id (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
>>> from mth5.mth5 import MTH5 >>> m = MTH5() >>> m.open_mth5("example.h5", "a") >>> station_group = m.get_station("mt01", survey="test") >>> tf_group = station_group.transfer_functions_group >>> tf_group.remove_transfer_function("mt01_4096")
Import all Group objects
- class mth5.groups.AuxiliaryDataset(group, **kwargs)[source]
Bases:
ChannelDatasetHolds a channel dataset. This is a simple container for the data to make sure that the user has the flexibility to turn the channel into an object they want to deal with.
For now all the numpy type slicing can be used on hdf5_dataset
- Parameters
dataset (
h5py.Dataset) – dataset object for the channeldataset_metadata ([
mth5.metadata.Electric|mth5.metadata.Magnetic|mth5.metadata.Auxiliary], optional) – metadata container, defaults to None
- Raises
MTH5Error – If the dataset is not of the correct type
Utilities will be written to create some common objects like:
xarray.DataArray
pandas.DataFrame
zarr
dask.Array
The benefit of these other objects is that they can be indexed by time, and they have much more buit-in funcionality.
>>> from mth5 import mth5 >>> mth5_obj = mth5.MTH5() >>> mth5_obj.open_mth5(r"/test.mth5", mode='a') >>> run = mth5_obj.stations_group.get_station('MT001').get_run('MT001a') >>> channel = run.get_channel('Ex') >>> channel Channel Electric: ------------------- component: Ey data type: electric data format: float32 data shape: (4096,) start: 1980-01-01T00:00:00+00:00 end: 1980-01-01T00:00:01+00:00 sample rate: 4096
- class mth5.groups.BaseGroup(group, group_metadata=None, **kwargs)[source]
Bases:
objectGeneric object that will have functionality for reading/writing groups, including attributes. To access the hdf5 group directly use the BaseGroup.hdf5_group property.
>>> base = BaseGroup(hdf5_group) >>> base.hdf5_group.ref <HDF5 Group Reference>
Note
All attributes should be input into the metadata object, that way all input will be validated against the metadata standards. If you change attributes in metadata object, you should run the BaseGroup.write_metadata method. This is a temporary solution working on an automatic updater if metadata is changed.
>>> base.metadata.existing_attribute = 'update_existing_attribute' >>> base.write_metadata()
If you want to add a new attribute this should be done using the metadata.add_base_attribute method.
>>> base.metadata.add_base_attribute('new_attribute', ... 'new_attribute_value', ... {'type':str, ... 'required':True, ... 'style':'free form', ... 'description': 'new attribute desc.', ... 'units':None, ... 'options':[], ... 'alias':[], ... 'example':'new attribute'})
Includes intializing functions that makes a summary table and writes metadata.
- property dataset_options
- property groups_list
- property metadata
Metadata for the Group based on mt_metadata.timeseries
- class mth5.groups.ChannelDataset(dataset, dataset_metadata=None, write_metadata=True, **kwargs)[source]
Bases:
objectHolds a channel dataset. This is a simple container for the data to make sure that the user has the flexibility to turn the channel into an object they want to deal with.
For now all the numpy type slicing can be used on hdf5_dataset
- Parameters
dataset (
h5py.Dataset) – dataset object for the channeldataset_metadata ([
mth5.metadata.Electric|mth5.metadata.Magnetic|mth5.metadata.Auxiliary], optional) – metadata container, defaults to None
- Raises
MTH5Error – If the dataset is not of the correct type
Utilities will be written to create some common objects like:
xarray.DataArray
pandas.DataFrame
zarr
dask.Array
The benefit of these other objects is that they can be indexed by time, and they have much more buit-in funcionality.
>>> from mth5 import mth5 >>> mth5_obj = mth5.MTH5() >>> mth5_obj.open_mth5(r"/test.mth5", mode='a') >>> run = mth5_obj.stations_group.get_station('MT001').get_run('MT001a') >>> channel = run.get_channel('Ex') >>> channel Channel Electric: ------------------- component: Ey data type: electric data format: float32 data shape: (4096,) start: 1980-01-01T00:00:00+00:00 end: 1980-01-01T00:00:01+00:00 sample rate: 4096
- property channel_entry
channel entry that will go into a full channel summary of the entire survey
- property channel_response_filter
- property end
return end time based on the data
- extend_dataset(new_data_array, start_time, sample_rate, fill=None, max_gap_seconds=1, fill_window=10)[source]
Append data according to how the start time aligns with existing data. If the start time is before existing start time the data is prepended, similarly if the start time is near the end data will be appended.
If the start time is within the existing time range, existing data will be replace with the new data.
If there is a gap between start or end time of the new data with the existing data you can either fill the data with a constant value or an error will be raise depending on the value of fill.
- Parameters
new_data_array (
numpy.ndarray) – new data array with shape (npts, )start_time (string or
mth5.utils.mttime.MTime) – start time of the new data array in UTCsample_rate (float) – Sample rate of the new data array, must match existing sample rate
fill (string, None, float, integer) – If there is a data gap how do you want to fill the gap * None: will raise an
mth5.utils.exceptions.MTH5Error* ‘mean’: will fill with the mean of each data set within the fill window * ‘median’: will fill with the median of each data set within the fill window * value: can be an integer or float to fill the gap * ‘nan’: will fill the gap with NaNmax_gap_seconds (float or integer) – sets a maximum number of seconds the gap can be. Anything over this number will raise a
mth5.utils.exceptions.MTH5Error.fill_window (integer) – number of points from the end of each data set to estimate fill value from.
- Raises
mth5.utils.excptions.MTH5Errorif sample rate is not the same, or fill value is not understood,- Append Example
>>> ex = mth5_obj.get_channel('MT001', 'MT001a', 'Ex') >>> ex.n_samples 4096 >>> ex.end 2015-01-08T19:32:09.500000+00:00 >>> t = timeseries.ChannelTS('electric', ... data=2*np.cos(4 * np.pi * .05 * ... np.linspace(0,4096l num=4096) * ... .01), ... channel_metadata={'electric':{ ... 'component': 'ex', ... 'sample_rate': 8, ... 'time_period.start':(ex.end+(1)).iso_str}}) >>> ex.extend_dataset(t.ts, t.start, t.sample_rate, fill='median', ... max_gap_seconds=2) 2020-07-02T18:02:47 - mth5.groups.Electric.extend_dataset - INFO - filling data gap with 1.0385180759767025 >>> ex.n_samples 8200 >>> ex.end 2015-01-08T19:40:42.500000+00:00
- from_channel_ts(channel_ts_obj, how='replace', fill=None, max_gap_seconds=1, fill_window=10)[source]
fill data set from a
mth5.timeseries.ChannelTSobject.Will check for time alignement, and metadata.
- Parameters
channel_ts_obj (
mth5.timeseries.ChannelTS) – time series objecthow –
how the new array will be input to the existing dataset:
’replace’ -> replace the entire dataset nothing is left over.
’extend’ -> add onto the existing dataset, any overlapping values will be rewritten, if there are gaps between data sets those will be handled depending on the value of fill.
- param fill
If there is a data gap how do you want to fill the gap:
None -> will raise an
mth5.utils.exceptions.MTH5Error’mean’-> will fill with the mean of each data set within the fill window
’median’ -> will fill with the median of each data set within the fill window
value -> can be an integer or float to fill the gap
’nan’ -> will fill the gap with NaN
max_gap_seconds (float or integer) – sets a maximum number of seconds the gap can be. Anything over this number will raise a
mth5.utils.exceptions.MTH5Error.fill_window (integer) – number of points from the end of each data set to estimate fill value from.
- from_xarray(data_array, how='replace', fill=None, max_gap_seconds=1, fill_window=10)[source]
fill data set from a
xarray.DataArrayobject.Will check for time alignement, and metadata.
- Parameters
data_array_obj – Xarray data array
how –
how the new array will be input to the existing dataset:
’replace’ -> replace the entire dataset nothing is left over.
’extend’ -> add onto the existing dataset, any overlapping
values will be rewritten, if there are gaps between data sets those will be handled depending on the value of fill.
- param fill
If there is a data gap how do you want to fill the gap:
None -> will raise an
mth5.utils.exceptions.MTH5Error- ’mean’-> will fill with the mean of each data set within
the fill window
- ’median’ -> will fill with the median of each data set
within the fill window
value -> can be an integer or float to fill the gap
’nan’ -> will fill the gap with NaN
max_gap_seconds (float or integer) – sets a maximum number of seconds the gap can be. Anything over this number will raise a
mth5.utils.exceptions.MTH5Error.fill_window (integer) – number of points from the end of each data set to estimate fill value from.
- get_index_from_end_time(given_time)[source]
get the end index value. Add one to be inclusive of the found index.
- Parameters
given_time (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- get_index_from_time(given_time)[source]
get the appropriate index for a given time.
- Parameters
given_time (string or MTime) – time string
- Returns
index value
- Return type
int
- property n_samples
- read_metadata()[source]
Read metadata from the HDF5 file into the metadata container, that way it can be validated.
- replace_dataset(new_data_array)[source]
replace the entire dataset with a new one, nothing left behind
- Parameters
new_data_array (
numpy.ndarray) – new data array shape (npts, )
- property run_metadata
run metadata
- property sample_rate
- property start
- property station_metadata
station metadata
- property survey_id
shortcut to survey group
- property survey_metadata
survey metadata
- property time_index
Create a time index based on the metadata. This can help when asking for time windows from the data
- Returns
DESCRIPTION
- Return type
TYPE
- time_slice(start, end=None, n_samples=None, return_type='channel_ts')[source]
Get a time slice from the channel and return the appropriate type
numpy array with metadata
pandas.Dataframe with metadata
xarray.DataFrame with metadata
mth5.timeseries.ChannelTS‘default’dask.DataFrame with metadata ‘not yet’
- Parameters
start (string or
mth5.utils.mttime.MTime) – start time of the sliceend (string or
mth5.utils.mttime.MTime, optional) – end time of the slicen_samples (integer, optional) – number of samples to read in
- Returns
the correct container for the time series.
- Return type
[
xarray.DataArray|pandas.DataFrame|mth5.timeseries.ChannelTS|numpy.ndarray]- Raises
ValueError if both end_time and n_samples are None or given.
- Example with number of samples
>>> ex = mth5_obj.get_channel('FL001', 'FL001a', 'Ex') >>> ex_slice = ex.time_slice("2015-01-08T19:49:15", n_samples=4096) >>> ex_slice <xarray.DataArray (time: 4096)> array([0.93115046, 0.14233688, 0.87917119, ..., 0.26073634, 0.7137319 , 0.88154395]) Coordinates: * time (time) datetime64[ns] 2015-01-08T19:49:15 ... 2015-01-08T19:57:46.875000 Attributes: ac.end: None ac.start: None ... >>> type(ex_slice) mth5.timeseries.ChannelTS # plot the time series >>> ex_slice.ts.plot()
- Example with start and end time
>>> ex_slice = ex.time_slice("2015-01-08T19:49:15", ... end_time="2015-01-09T19:49:15")
- Raises Example
>>> ex_slice = ex.time_slice("2015-01-08T19:49:15", ... end_time="2015-01-09T19:49:15", ... n_samples=4096) ValueError: Must input either end_time or n_samples, not both.
- to_channel_ts()[source]
- Returns
a Timeseries with the appropriate time index and metadata
- Return type
loads from memory (nearly half the size of xarray alone, not sure why)
- to_dataframe()[source]
- Returns
a dataframe where data is stored in the ‘data’ column and attributes are stored in the experimental attrs attribute
- Return type
pandas.DataFrame
Note
that metadta will not be validated if changed in an xarray.
loads into RAM
- to_numpy()[source]
- Returns
a numpy structured array with 2 columns (time, channel_data)
- Return type
numpy.core.records
Note
data is a builtin to numpy and cannot be used as a name
loads into RAM
- class mth5.groups.ElectricDataset(group, **kwargs)[source]
Bases:
ChannelDatasetHolds a channel dataset. This is a simple container for the data to make sure that the user has the flexibility to turn the channel into an object they want to deal with.
For now all the numpy type slicing can be used on hdf5_dataset
- Parameters
dataset (
h5py.Dataset) – dataset object for the channeldataset_metadata ([
mth5.metadata.Electric|mth5.metadata.Magnetic|mth5.metadata.Auxiliary], optional) – metadata container, defaults to None
- Raises
MTH5Error – If the dataset is not of the correct type
Utilities will be written to create some common objects like:
xarray.DataArray
pandas.DataFrame
zarr
dask.Array
The benefit of these other objects is that they can be indexed by time, and they have much more buit-in funcionality.
>>> from mth5 import mth5 >>> mth5_obj = mth5.MTH5() >>> mth5_obj.open_mth5(r"/test.mth5", mode='a') >>> run = mth5_obj.stations_group.get_station('MT001').get_run('MT001a') >>> channel = run.get_channel('Ex') >>> channel Channel Electric: ------------------- component: Ey data type: electric data format: float32 data shape: (4096,) start: 1980-01-01T00:00:00+00:00 end: 1980-01-01T00:00:01+00:00 sample rate: 4096
- class mth5.groups.EstimateDataset(dataset, dataset_metadata=None, write_metadata=True, **kwargs)[source]
Bases:
objectHolds a statistical estimate
This will hold multi-dimensional statistical estimates for transfer functions.
- Parameters
dataset (h5py.Dataset) – hdf5 dataset
dataset_metadata – data set metadata see
mt_metadata.transfer_functions.tf.StatisticalEstimate,defaults to None
- Parameters
write_metadata (Boolean, optional) – True to write metadata, defaults to True
**kwargs –
DESCRIPTION
- Raises
MTH5Error – When an estimate is not present, or metadata name does not match the given name
- from_numpy(new_estimate)[source]
- Returns
a numpy structured array
- Return type
numpy.ndarray
Note
data is a builtin to numpy and cannot be used as a name
loads into RAM
- from_xarray(data)[source]
- Returns
an xarray DataArray with appropriate metadata and the appropriate coordinates base on the metadata.
- Return type
xarray.DataArray
Note
that metadta will not be validated if changed in an xarray.
loads from memory
- read_metadata()[source]
Read metadata from the HDF5 file into the metadata container, that way it can be validated.
- replace_dataset(new_data_array)[source]
replace the entire dataset with a new one, nothing left behind
- Parameters
new_data_array (
numpy.ndarray) – new data array
- class mth5.groups.ExperimentGroup(group, **kwargs)[source]
Bases:
BaseGroupUtility class to hold general information about the experiment and accompanying metadata for an MT experiment.
To access the hdf5 group directly use ExperimentGroup.hdf5_group.
>>> experiment = ExperimentGroup(hdf5_group) >>> experiment.hdf5_group.ref <HDF5 Group Reference>
Note
All attributes should be input into the metadata object, that way all input will be validated against the metadata standards. If you change attributes in metadata object, you should run the ExperimentGroup.write_metadata() method. This is a temporary solution, working on an automatic updater if metadata is changed.
>>> experiment.metadata.existing_attribute = 'update_existing_attribute' >>> experiment.write_metadata()
If you want to add a new attribute this should be done using the metadata.add_base_attribute method.
>>> experiment.metadata.add_base_attribute('new_attribute', >>> ... 'new_attribute_value', >>> ... {'type':str, >>> ... 'required':True, >>> ... 'style':'free form', >>> ... 'description': 'new attribute desc.', >>> ... 'units':None, >>> ... 'options':[], >>> ... 'alias':[], >>> ... 'example':'new attribute
Tip
If you want ot add stations, reports, etc to the experiment this should be done from the MTH5 object. This is to avoid duplication, at least for now.
To look at what the structure of
/Experimentlooks like:>>> experiment /Experiment: ==================== |- Group: Surveys ----------------- |- Group: Reports ----------------- |- Group: Standards ------------------- |- Group: Stations ------------------
- property metadata
Overwrite get metadata to include station information
- property surveys_group
- class mth5.groups.FCChannelDataset(dataset, dataset_metadata=None, write_metadata=True, **kwargs)[source]
Bases:
objectThis will hold multi-dimensional set of Fourier Coefficients
Columns
time
frequency [ integer as harmonic index or float ]
fc (complex)
weight_channel (maybe)
weight_band (maybe)
weight_time (maybe)
- - name
- - start time
- - end time
- - acquistion_sample_rate
- - decimated_sample rate
- - window_sample_rate
- Type
delta_t within the window
- - units
- - [optional] weights or masking
- - frequency method
- Type
integer * window length / delta_t of window
- Parameters
dataset (h5py.Dataset) – hdf5 dataset
dataset_metadata – data set metadata see
mt_metadata.transfer_functions.tf.StatisticalEstimate,defaults to None
- Parameters
write_metadata (Boolean, optional) – True to write metadata, defaults to True
**kwargs –
DESCRIPTION
- Raises
MTH5Error – When an estimate is not present, or metadata name does not match the given name
- property frequency
frequency array dictated by window size and sample rate
- Returns
DESCRIPTION
- Return type
TYPE
- from_numpy(new_estimate)[source]
- Returns
a numpy structured array
- Return type
numpy.ndarray
Note
data is a builtin to numpy and cannot be used as a name
loads into RAM
- from_xarray(data)[source]
- Returns
an xarray DataArray with appropriate metadata and the appropriate coordinates base on the metadata.
- Return type
xarray.DataArray
Note
that metadta will not be validated if changed in an xarray.
loads from memory
- property n_frequencies
number of frequencies (window size)
- property n_windows
number of time windows
- read_metadata()[source]
Read metadata from the HDF5 file into the metadata container, that way it can be validated.
- replace_dataset(new_data_array)[source]
replace the entire dataset with a new one, nothing left behind
- Parameters
new_data_array (
numpy.ndarray) – new data array
- property time
Time array that includes the start of each time window
- Returns
DESCRIPTION
- Return type
TYPE
- class mth5.groups.FCDecimationGroup(group, decimation_level_metadata=None, **kwargs)[source]
Bases:
BaseGroupHolds a single decimation level
Attributes
start time
end time
channels (list)
decimation factor
decimation level
decimation sample rate
method (FFT, wavelet, …)
anti alias filter
prewhitening type
extra_pre_fft_detrend_type
recoloring (True | False)
harmonics_kept (index values of harmonics kept (list) | ‘all’)
- window parameters
length
overlap
type
type parameters
window sample rate (method or property)
[optional] masking or weighting information
- add_channel(fc_name, fc_data=None, fc_metadata=None, max_shape=(None, None), chunks=True, **kwargs)[source]
Add a set of Fourier coefficients for a single channel at a single decimation level for a processing run.
time
frequency [ integer as harmonic index or float ]
fc (complex)
The input can be
a numpy array where the index values for the time, frequency, and
coefficients are supplied in channel_key, frequency_key, channel_key as integer values.
a numpy structured array, dataframe, or xarray dataset or dataarray
where the channel_key if not supplied is assumed to the same as the fc_name.
haven’t fully tested dataframe or xarray yet.
Weights should be a separate data set as a 1D array along with matching index as fcs.
weight_channel (maybe)
weight_band (maybe)
weight_time (maybe)
- Parameters
fc_datastet_name (TYPE) – DESCRIPTION
fc_dataset_metadata (TYPE, optional) – DESCRIPTION, defaults to None
- Returns
DESCRIPTION
- Return type
TYPE
- add_weights(weight_name, weight_data=None, weight_metadata=None, max_shape=(None, None, None), chunks=True, **kwargs)[source]
- property channel_summary
summary of channels in run :return: DESCRIPTION :rtype: TYPE
- from_dataframe(df, channel_key, time_key='time', frequency_key='frequency')[source]
assumes channel_key is the coefficient values
- Parameters
df (TYPE) – DESCRIPTION
channel_key (TYPE) – DESCRIPTION
time_key (TYPE, optional) – DESCRIPTION, defaults to “time”
frequency_key (TYPE, optional) – DESCRIPTION, defaults to “frequency”
- Returns
DESCRIPTION
- Return type
TYPE
- from_numpy_array(nd_array, ch_name)[source]
assumes shape of (n_frequencies, n_windows) or (n_channels, n_frequencies, n_windows)
- Parameters
nd_array (TYPE) – DESCRIPTION
ch_name – name of channel(s)
- Returns
DESCRIPTION
- Return type
TYPE
- from_xarray(data_array)[source]
can input a dataarray or dataset
- Parameters
data_array (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- get_channel(fc_name)[source]
get an fc dataset
- Parameters
fc_dataset_name (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- property metadata
Overwrite get metadata to include channel information in the runs
- remove_channel(fc_name)[source]
remove an fc dataset
- Parameters
fc_dataset_name (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- class mth5.groups.FCGroup(group, decimation_level_metadata=None, **kwargs)[source]
Bases:
BaseGroupHolds a set of Fourier Coefficients based on a single set of configuration parameters.
Note
Must be calibrated FCs. Otherwise weird things will happen, can always rerun the FC estimation if the metadata changes.
Metadata should include:
list of decimation levels
start time (earliest)
end time (latest)
method (fft, wavelet, …)
list of channels (all inclusive)
list of acquistion runs (maybe)
starting sample rate
- add_decimation_level(decimation_level_name, decimation_level_metadata=None)[source]
add a Decimation level
- Parameters
decimation_level_name (TYPE) – DESCRIPTION
decimation_level_metadata (TYPE, optional) – DESCRIPTION, defaults to None
- Returns
DESCRIPTION
- Return type
TYPE
- property decimation_level_summary
summary of channels in run :return: DESCRIPTION :rtype: TYPE
- get_decimation_level(decimation_level_name)[source]
Get a Decimation Level
- Parameters
decimation_level_name (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- property metadata
Overwrite get metadata to include channel information in the runs
- remove_decimation_level(decimation_level_name)[source]
Remove decimation level
- Parameters
decimation_level_name (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- supports_aurora_processing_config(processing_config, remote)[source]
This is an “all-or-nothing” check: Either every (valid) decimation level in the processing config is available or we will build all FCs.
- Parameters
processing_config (aurora.config.metadata.processing.Processing) –
remote (bool) –
- class mth5.groups.FiltersGroup(group, **kwargs)[source]
Bases:
BaseGroupNot implemented yet
- add_filter(filter_object)[source]
Add a filter dataset based on type
- current types are:
zpk –> zeros, poles, gain
fap –> frequency look up table
time_delay –> time delay filter
coefficient –> coefficient filter
- Parameters
filter_object (
mt_metadata.timeseries.filters) – An MT metadata filter object
- property filter_dict
- class mth5.groups.MagneticDataset(group, **kwargs)[source]
Bases:
ChannelDatasetHolds a channel dataset. This is a simple container for the data to make sure that the user has the flexibility to turn the channel into an object they want to deal with.
For now all the numpy type slicing can be used on hdf5_dataset
- Parameters
dataset (
h5py.Dataset) – dataset object for the channeldataset_metadata ([
mth5.metadata.Electric|mth5.metadata.Magnetic|mth5.metadata.Auxiliary], optional) – metadata container, defaults to None
- Raises
MTH5Error – If the dataset is not of the correct type
Utilities will be written to create some common objects like:
xarray.DataArray
pandas.DataFrame
zarr
dask.Array
The benefit of these other objects is that they can be indexed by time, and they have much more buit-in funcionality.
>>> from mth5 import mth5 >>> mth5_obj = mth5.MTH5() >>> mth5_obj.open_mth5(r"/test.mth5", mode='a') >>> run = mth5_obj.stations_group.get_station('MT001').get_run('MT001a') >>> channel = run.get_channel('Ex') >>> channel Channel Electric: ------------------- component: Ey data type: electric data format: float32 data shape: (4096,) start: 1980-01-01T00:00:00+00:00 end: 1980-01-01T00:00:01+00:00 sample rate: 4096
- class mth5.groups.MasterFCGroup(group, **kwargs)[source]
Bases:
BaseGroupMaster group to hold various Fourier coefficient estimations of time series data. No metadata needed as of yet.
- add_fc_group(fc_name, fc_metadata=None)[source]
Add a Fourier Coefficent group
- Parameters
fc_name (TYPE) – DESCRIPTION
fc_metadata (TYPE, optional) – DESCRIPTION, defaults to None
- Returns
DESCRIPTION
- Return type
TYPE
- property fc_summary
Summar of fourier coefficients
- Returns
DESCRIPTION
- Return type
TYPE
- class mth5.groups.MasterStationGroup(group, **kwargs)[source]
Bases:
BaseGroupUtility class to holds information about the stations within a survey and accompanying metadata. This class is next level down from Survey for stations
/Survey/Stations. This class provides methods to add and get stations. A summary table of all existing stations is also provided as a convenience look up table to make searching easier.To access MasterStationGroup from an open MTH5 file:
>>> from mth5 import mth5 >>> mth5_obj = mth5.MTH5() >>> mth5_obj.open_mth5(r"/test.mth5", mode='a') >>> stations = mth5_obj.stations_group
To check what stations exist
>>> stations.groups_list ['summary', 'MT001', 'MT002', 'MT003']
To access the hdf5 group directly use SurveyGroup.hdf5_group.
>>> stations.hdf5_group.ref <HDF5 Group Reference>
Note
All attributes should be input into the metadata object, that way all input will be validated against the metadata standards. If you change attributes in metadata object, you should run the SurveyGroup.write_metadata() method. This is a temporary solution, working on an automatic updater if metadata is changed.
>>> stations.metadata.existing_attribute = 'update_existing_attribute' >>> stations.write_metadata()
If you want to add a new attribute this should be done using the metadata.add_base_attribute method.
>>> stations.metadata.add_base_attribute('new_attribute', >>> ... 'new_attribute_value', >>> ... {'type':str, >>> ... 'required':True, >>> ... 'style':'free form', >>> ... 'description': 'new attribute desc.', >>> ... 'units':None, >>> ... 'options':[], >>> ... 'alias':[], >>> ... 'example':'new attribute
To add a station:
>>> new_station = stations.add_station('new_station') >>> stations /Survey/Stations: ==================== --> Dataset: summary ...................... |- Group: new_station --------------------- --> Dataset: summary ......................
Add a station with metadata:
>>> from mth5.metadata import Station >>> station_metadata = Station() >>> station_metadata.id = 'MT004' >>> station_metadata.time_period.start = '2020-01-01T12:30:00' >>> station_metadata.location.latitude = 40.000 >>> station_metadata.location.longitude = -120.000 >>> new_station = stations.add_station('Test_01', station_metadata) >>> # to look at the metadata >>> new_station.metadata { "station": { "acquired_by.author": null, "acquired_by.comments": null, "id": "MT004", ... } }
See also
mth5.metadata for details on how to add metadata from various files and python objects.
To remove a station:
>>> stations.remove_station('new_station') >>> stations /Survey/Stations: ==================== --> Dataset: summary ......................
Note
Deleting a station is not as simple as del(station). In HDF5 this does not free up memory, it simply removes the reference to that station. The common way to get around this is to copy what you want into a new file, or overwrite the station.
To get a station:
>>> existing_station = stations.get_station('existing_station_name') >>> existing_station /Survey/Stations/existing_station_name: ======================================= --> Dataset: summary ...................... |- Group: run_01 ---------------- --> Dataset: summary ...................... --> Dataset: Ex ...................... --> Dataset: Ey ...................... --> Dataset: Hx ...................... --> Dataset: Hy ...................... --> Dataset: Hz ......................
A summary table is provided to make searching easier. The table summarized all stations within a survey. To see what names are in the summary table:
>>> stations.station_summary
- add_station(station_name, station_metadata=None)[source]
- Add a station with metadata if given with the path:
/Survey/Stations/station_name
If the station already exists, will return that station and nothing is added.
- Parameters
station_name (string) – Name of the station, should be the same as metadata.id
station_metadata (
mth5.metadata.Station, optional) – Station metadata container, defaults to None
- Returns
A convenience class for the added station
- Return type
mth5_groups.StationGroup- Example
>>> from mth5 import mth5 >>> mth5_obj = mth5.MTH5() >>> mth5_obj.open_mth5(r"/test.mth5", mode='a') >>> # one option >>> stations = mth5_obj.stations_group >>> new_station = stations.add_station('MT001') >>> # another option >>> new_staiton = mth5_obj.stations_group.add_station('MT001')
- get_station(station_name)[source]
Get a station with the same name as station_name
- Parameters
station_name (string) – existing station name
- Returns
convenience station class
- Return type
mth5.mth5_groups.StationGroup- Raises
MTH5Error – if the station name is not found.
- Example
>>> from mth5 import mth5 >>> mth5_obj = mth5.MTH5() >>> mth5_obj.open_mth5(r"/test.mth5", mode='a') >>> # one option >>> stations = mth5_obj.stations_group >>> existing_station = stations.get_station('MT001') >>> # another option >>> existing_staiton = mth5_obj.stations_group.get_station('MT001') MTH5Error: MT001 does not exist, check station_list for existing names
- remove_station(station_name)[source]
Remove a station from the file.
Note
Deleting a station is not as simple as del(station). In HDF5 this does not free up memory, it simply removes the reference to that station. The common way to get around this is to copy what you want into a new file, or overwrite the station.
- Parameters
station_name (string) – existing station name
- Example
>>> from mth5 import mth5 >>> mth5_obj = mth5.MTH5() >>> mth5_obj.open_mth5(r"/test.mth5", mode='a') >>> # one option >>> stations = mth5_obj.stations_group >>> stations.remove_station('MT001') >>> # another option >>> mth5_obj.stations_group.remove_station('MT001')
- property station_summary
Summary of stations in the file
- Returns
DESCRIPTION
- Return type
TYPE
- class mth5.groups.MasterSurveyGroup(group, **kwargs)[source]
Bases:
BaseGroupUtility class to hold information about the surveys within an experiment and accompanying metadata. This class is next level down from Experiment for stations
Experiment/Surveys. This class provides methods to add and get surveys.To access MasterSurveyGroup from an open MTH5 file:
>>> from mth5 import mth5 >>> mth5_obj = mth5.MTH5() >>> mth5_obj.open_mth5(r"/test.mth5", mode='a') >>> surveys = mth5_obj.surveys_group
To check what stations exist
>>> surveys.groups_list ['survey_01', 'survey_02']
To access the hdf5 group directly use SurveyGroup.hdf5_group.
>>> stations.hdf5_group.ref <HDF5 Group Reference>
Note
All attributes should be input into the metadata object, that way all input will be validated against the metadata standards. If you change attributes in metadata object, you should run the SurveyGroup.write_metadata() method. This is a temporary solution, working on an automatic updater if metadata is changed.
If you want to add a new attribute this should be done using the metadata.add_base_attribute method.
>>> stations.metadata.add_base_attribute('new_attribute', >>> ... 'new_attribute_value', >>> ... {'type':str, >>> ... 'required':True, >>> ... 'style':'free form', >>> ... 'description': 'new attribute desc.', >>> ... 'units':None, >>> ... 'options':[], >>> ... 'alias':[], >>> ... 'example':'new attribute
To add a survey:
>>> new_survey = surveys.add_survey('new_survey') >>> surveys Experiment/Surveys: ==================== |- Group: new_survey --------------------- |- Group: Filters ------------------ |- Group: Reports ----------------- |- Group: Standards ------------------- |- Group: Stations ------------------
Add a survey with metadata:
>>> from mth5.metadata import Survey >>> survey_metadata = Survey() >>> survey_metadata.id = 'MT004' >>> survey_metadata.time_period.start = '2020-01-01T12:30:00' >>> new_survey = surveys.add_survey('Test_01', survey_metadata) >>> # to look at the metadata >>> new_survey.metadata { "survey": { "acquired_by.author": null, "acquired_by.comments": null, "id": "MT004", ... } }
See also
mth5.metadata for details on how to add metadata from various files and python objects.
To remove a survey:
>>> surveys.remove_survey('new_survey') >>> surveys /Survey/Stations: ====================
Note
Deleting a survey is not as simple as del(survey). In HDF5 this does not free up memory, it simply removes the reference to that survey. The common way to get around this is to copy what you want into a new file, or overwrite the survey.
To get a survey:
>>> existing_survey = surveys.get_survey('existing_survey_name')
- add_survey(survey_name, survey_metadata=None)[source]
- Add a survey with metadata if given with the path:
/Survey/surveys/survey_name
If the survey already exists, will return that survey and nothing is added.
- Parameters
survey_name (string) – Name of the survey, should be the same as metadata.id
survey_metadata (
mth5.metadata.Station, optional) – Station metadata container, defaults to None
- Returns
A convenience class for the added survey
- Return type
mth5_groups.StationGroup- To add a survey
>>> new_survey = surveys.add_survey('new_survey') >>> surveys Experiment/Surveys: ==================== |- Group: new_survey --------------------- |- Group: Filters ------------------ |- Group: Reports ----------------- |- Group: Standards ------------------- |- Group: Stations ------------------
- Add a survey with metadata
>>> from mth5.metadata import Survey >>> survey_metadata = Survey() >>> survey_metadata.id = 'MT004' >>> survey_metadata.time_period.start = '2020-01-01T12:30:00' >>> new_survey = surveys.add_survey('Test_01', survey_metadata) >>> # to look at the metadata >>> new_survey.metadata { "survey": { "acquired_by.author": null, "acquired_by.comments": null, "id": "MT004", ... } }
See also
mth5.metadata for details on how to add metadata from various files and python objects.
- property channel_summary
Summary of all channels in the file.
- get_survey(survey_name)[source]
Get a survey with the same name as survey_name
- Parameters
survey_name (string) – existing survey name
- Returns
convenience survey class
- Return type
mth5.mth5_groups.surveyGroup- Raises
MTH5Error – if the survey name is not found.
- Example
>>> from mth5 import mth5 >>> mth5_obj = mth5.MTH5() >>> mth5_obj.open_mth5(r"/test.mth5", mode='a') >>> # one option >>> existing_survey = mth5_obj.get_survey('MT001') >>> # another option >>> existing_survey = mth5_obj.experiment_group.surveys_group.get_survey('MT001') MTH5Error: MT001 does not exist, check survey_list for existing names
- remove_survey(survey_name)[source]
Remove a survey from the file.
Note
Deleting a survey is not as simple as del(survey). In HDF5 this does not free up memory, it simply removes the reference to that survey. The common way to get around this is to copy what you want into a new file, or overwrite the survey.
- Parameters
survey_name (string) – existing survey name
- Example
>>> from mth5 import mth5 >>> mth5_obj = mth5.MTH5() >>> mth5_obj.open_mth5(r"/test.mth5", mode='a') >>> # one option >>> mth5_obj.remove_survey('MT001') >>> # another option >>> mth5_obj.surveys_group.remove_survey('MT001')
- class mth5.groups.ReportsGroup(group, **kwargs)[source]
Bases:
BaseGroupNot sure how to handle this yet
- class mth5.groups.RunGroup(group, run_metadata=None, **kwargs)[source]
Bases:
BaseGroupRunGroup is a utility class to hold information about a single run and accompanying metadata. This class is the next level down from Stations –>
/Survey/Stations/station/station{a-z}.This class provides methods to add and get channels. A summary table of all existing channels in the run is also provided as a convenience look up table to make searching easier.
- Parameters
group (
h5py.Group) – HDF5 group for a station, should have a path/Survey/Stations/station_name/run_namestation_metadata (
mth5.metadata.Station, optional) – metadata container, defaults to None
- Access RunGroup from an open MTH5 file
>>> from mth5 import mth5 >>> mth5_obj = mth5.MTH5() >>> mth5_obj.open_mth5(r"/test.mth5", mode='a') >>> run = mth5_obj.stations_group.get_station('MT001').get_run('MT001a')
- Check what channels exist
>>> station.groups_list ['Ex', 'Ey', 'Hx', 'Hy']
To access the hdf5 group directly use RunGroup.hdf5_group
>>> station.hdf5_group.ref <HDF5 Group Reference>
Note
All attributes should be input into the metadata object, that way all input will be validated against the metadata standards. If you change attributes in metadata object, you should run the SurveyGroup.write_metadata() method. This is a temporary solution, working on an automatic updater if metadata is changed.
>>> run.metadata.existing_attribute = 'update_existing_attribute' >>> run.write_metadata()
If you want to add a new attribute this should be done using the metadata.add_base_attribute method.
>>> station.metadata.add_base_attribute('new_attribute', >>> ... 'new_attribute_value', >>> ... {'type':str, >>> ... 'required':True, >>> ... 'style':'free form', >>> ... 'description': 'new attribute desc.', >>> ... 'units':None, >>> ... 'options':[], >>> ... 'alias':[], >>> ... 'example':'new attribute
- Add a channel
>>> new_channel = run.add_channel('Ex', 'electric', >>> ... data=numpy.random.rand(4096)) >>> new_run /Survey/Stations/MT001/MT001a: ======================================= --> Dataset: summary ...................... --> Dataset: Ex ...................... --> Dataset: Ey ...................... --> Dataset: Hx ...................... --> Dataset: Hy ......................
- Add a channel with metadata
>>> from mth5.metadata import Electric >>> ex_metadata = Electric() >>> ex_metadata.time_period.start = '2020-01-01T12:30:00' >>> ex_metadata.time_period.end = '2020-01-03T16:30:00' >>> new_ex = run.add_channel('Ex', 'electric', >>> ... channel_metadata=ex_metadata) >>> # to look at the metadata >>> new_ex.metadata { "electric": { "ac.end": 1.2, "ac.start": 2.3, ... } }
See also
mth5.metadata for details on how to add metadata from various files and python objects.
- Remove a channel
>>> run.remove_channel('Ex') >>> station /Survey/Stations/MT001/MT001a: ======================================= --> Dataset: summary ...................... --> Dataset: Ey ...................... --> Dataset: Hx ...................... --> Dataset: Hy ......................
Note
Deleting a station is not as simple as del(station). In HDF5 this does not free up memory, it simply removes the reference to that station. The common way to get around this is to copy what you want into a new file, or overwrite the station.
- Get a channel
>>> existing_ex = stations.get_channel('Ex') >>> existing_ex Channel Electric: ------------------- data type: Ex data type: electric data format: float32 data shape: (4096,) start: 1980-01-01T00:00:00+00:00 end: 1980-01-01T00:32:+08:00 sample rate: 8
- Summary Table
A summary table is provided to make searching easier. The table summarized all stations within a survey. To see what names are in the summary table:
>>> run.summary_table.dtype.descr [('component', ('|S5', {'h5py_encoding': 'ascii'})), ('start', ('|S32', {'h5py_encoding': 'ascii'})), ('end', ('|S32', {'h5py_encoding': 'ascii'})), ('n_samples', '<i4'), ('measurement_type', ('|S12', {'h5py_encoding': 'ascii'})), ('units', ('|S25', {'h5py_encoding': 'ascii'})), ('hdf5_reference', ('|O', {'ref': h5py.h5r.Reference}))]
Note
When a run is added an entry is added to the summary table, where the information is pulled from the metadata.
>>> new_run.summary_table index | component | start | end | n_samples | measurement_type | units | hdf5_reference -------------------------------------------------------------------------- -------------
- add_channel(channel_name, channel_type, data, channel_dtype='int32', shape=None, max_shape=(None,), chunks=True, channel_metadata=None, **kwargs)[source]
add a channel to the run
- Parameters
channel_name (string) – name of the channel
channel_type (string) – [ electric | magnetic | auxiliary ]
shape (tuple, optional) – Set the shape of the array, uses the data if input. Can be useful if you are setting up the file. This will set the size of the dataset, whereas max_shape sets the max shape which ends up in different memory size. If you are not sure about the size of the array suggest using max_shape, but if you already know and want to start with an array of 0’s use shape, defaults to None
max_shape (tuple, optional) – Absolute max shape of the data to be stored, this means the data can be extended up to the given shape. If None is given then the data can be extended infinitely (or until memory runs out), defaults to (None,)
chunks (bool, optional) – Use chunked storage, defaults to True
channel_metadata ([
mth5.metadata.Electric|mth5.metadata.Magnetic|mth5.metadata.Auxiliary], optional) – metadata container, defaults to None**kwargs –
Key word arguments
- Raises
MTH5Error – If channel type is not correct
- Returns
DESCRIPTION
- Return type
TYPE
- Returns
Channel container
- Return type
[
mth5.mth5_groups.ElectricDatset|mth5.mth5_groups.MagneticDatset|mth5.mth5_groups.AuxiliaryDatset]
>>> new_channel = run.add_channel('Ex', 'electric', None) >>> new_channel Channel Electric: ------------------- component: None data type: electric data format: float32 data shape: (1,) start: 1980-01-01T00:00:00+00:00 end: 1980-01-01T00:00:00+00:00 sample rate: None
- property channel_summary
summary of channels in run :return: DESCRIPTION :rtype: TYPE
- from_channel_ts(channel_ts_obj)[source]
create a channel data set from a
mth5.timeseries.ChannelTSobject and update metadata.- Parameters
channel_ts_obj (
mth5.timeseries.ChannelTS) – a single time series object- Returns
new channel dataset
- Return type
:class:`mth5.groups.ChannelDataset
- from_runts(run_ts_obj, **kwargs)[source]
create channel datasets from a
mth5.timeseries.RunTSobject and update metadata.:parameter
mth5.timeseries.RunTSrun_ts_obj: Run object with all the appropriate channels and metadata.Will create a run group and appropriate channel datasets.
- get_channel(channel_name)[source]
Get a channel from an existing name. Returns the appropriate container.
- Parameters
channel_name (string) – name of the channel
- Returns
Channel container
- Return type
[
mth5.mth5_groups.ElectricDatset|mth5.mth5_groups.MagneticDatset|mth5.mth5_groups.AuxiliaryDatset]- Raises
MTH5Error – If no channel is found
- Example
>>> existing_channel = run.get_channel('Ex') MTH5Error: Ex does not exist, check groups_list for existing names'
>>> run.groups_list ['Ey', 'Hx', 'Hz']
>>> existing_channel = run.get_channel('Ey') >>> existing_channel Channel Electric: ------------------- component: Ey data type: electric data format: float32 data shape: (4096,) start: 1980-01-01T00:00:00+00:00 end: 1980-01-01T00:00:01+00:00 sample rate: 4096
- property metadata
Overwrite get metadata to include channel information in the runs
- remove_channel(channel_name)[source]
Remove a run from the station.
Note
Deleting a channel is not as simple as del(channel). In HDF5 this does not free up memory, it simply removes the reference to that channel. The common way to get around this is to copy what you want into a new file, or overwrite the channel.
- Parameters
station_name (string) – existing station name
- Example
>>> from mth5 import mth5 >>> mth5_obj = mth5.MTH5() >>> mth5_obj.open_mth5(r"/test.mth5", mode='a') >>> run = mth5_obj.stations_group.get_station('MT001').get_run('MT001a') >>> run.remove_channel('Ex')
- property station_metadata
station metadata
- property survey_metadata
survey metadata
- to_runts(start=None, end=None, n_samples=None)[source]
create a
mth5.timeseries.RunTSobject from channels of the run- Returns
DESCRIPTION
- Return type
TYPE
- class mth5.groups.StandardsGroup(group, **kwargs)[source]
Bases:
BaseGroupThe StandardsGroup is a convenience group that stores the metadata standards that were used to make the current file. This is to help a user understand the metadata directly from the file and not have to look up documentation that might not be updated.
The metadata standards are stored in the summary table
/Survey/Standards/summary>>> standards = mth5_obj.standards_group >>> standards.summary_table index | attribute | type | required | style | units | description | options | alias | example --------------------------------------------------------------------------
- get_attribute_information(attribute_name)[source]
get information about an attribute
The attribute name should be in the summary table.
- Parameters
attribute_name (string) – attribute name
- Returns
prints a description of the attribute
- Raises
MTH5TableError – if attribute is not found
>>> standars = mth5_obj.standards_group >>> standards.get_attribute_information('survey.release_license') survey.release_license -------------------------- type : string required : True style : controlled vocabulary units : description : How the data can be used. The options are based on Creative Commons licenses. For details visit https://creativecommons.org/licenses/ options : CC-0,CC-BY,CC-BY-SA,CC-BY-ND,CC-BY-NC-SA,CC-BY-NC-ND alias : example : CC-0 default : CC-0
- initialize_group()[source]
Initialize the group by making a summary table that summarizes the metadata standards used to describe the data.
Also, write generic metadata information.
- property summary_table
- class mth5.groups.StationGroup(group, station_metadata=None, **kwargs)[source]
Bases:
BaseGroupStationGroup is a utility class to hold information about a single station and accompanying metadata. This class is the next level down from Stations –>
/Survey/Stations/station_name.This class provides methods to add and get runs. A summary table of all existing runs in the station is also provided as a convenience look up table to make searching easier.
- Parameters
group (
h5py.Group) – HDF5 group for a station, should have a path/Survey/Stations/station_namestation_metadata (
mth5.metadata.Station, optional) – metadata container, defaults to None
- Usage
- Access StationGroup from an open MTH5 file
>>> from mth5 import mth5 >>> mth5_obj = mth5.MTH5() >>> mth5_obj.open_mth5(r"/test.mth5", mode='a') >>> station = mth5_obj.stations_group.get_station('MT001')
- Check what runs exist
>>> station.groups_list ['MT001a', 'MT001b', 'MT001c', 'MT001d']
To access the hdf5 group directly use StationGroup.hdf5_group.
>>> station.hdf5_group.ref <HDF5 Group Reference>
Note
All attributes should be input into the metadata object, that way all input will be validated against the metadata standards. If you change attributes in metadata object, you should run the SurveyGroup.write_metadata() method. This is a temporary solution, working on an automatic updater if metadata is changed.
>>> station.metadata.existing_attribute = 'update_existing_attribute' >>> station.write_metadata()
If you want to add a new attribute this should be done using the metadata.add_base_attribute method.
>>> station.metadata.add_base_attribute('new_attribute', >>> ... 'new_attribute_value', >>> ... {'type':str, >>> ... 'required':True, >>> ... 'style':'free form', >>> ... 'description': 'new attribute desc.', >>> ... 'units':None, >>> ... 'options':[], >>> ... 'alias':[], >>> ... 'example':'new attribute
- To add a run
>>> new_run = stations.add_run('MT001e') >>> new_run /Survey/Stations/Test_01: ========================= |- Group: MT001e ----------------- --> Dataset: summary ...................... --> Dataset: summary ......................
- Add a run with metadata
>>> from mth5.metadata import Run >>> run_metadata = Run() >>> run_metadata.time_period.start = '2020-01-01T12:30:00' >>> run_metadata.time_period.end = '2020-01-03T16:30:00' >>> run_metadata.location.latitude = 40.000 >>> run_metadata.location.longitude = -120.000 >>> new_run = runs.add_run('Test_01', run_metadata) >>> # to look at the metadata >>> new_run.metadata { "run": { "acquired_by.author": "new_user", "acquired_by.comments": "First time", "channels_recorded_auxiliary": ['T'], ... } }
See also
mth5.metadata for details on how to add metadata from various files and python objects.
- Remove a run
>>> station.remove_run('new_run') >>> station /Survey/Stations/Test_01: ========================= --> Dataset: summary ......................
Note
Deleting a station is not as simple as del(station). In HDF5 this does not free up memory, it simply removes the reference to that station. The common way to get around this is to copy what you want into a new file, or overwrite the station.
- Get a run
>>> existing_run = stations.get_station('existing_run') >>> existing_run /Survey/Stations/MT001/MT001a: ======================================= --> Dataset: summary ...................... --> Dataset: Ex ...................... --> Dataset: Ey ...................... --> Dataset: Hx ...................... --> Dataset: Hy ...................... --> Dataset: Hz ......................
- Summary Table
A summary table is provided to make searching easier. The table summarized all stations within a survey. To see what names are in the summary table:
>>> new_run.summary_table.dtype.descr [('id', ('|S20', {'h5py_encoding': 'ascii'})), ('start', ('|S32', {'h5py_encoding': 'ascii'})), ('end', ('|S32', {'h5py_encoding': 'ascii'})), ('components', ('|S100', {'h5py_encoding': 'ascii'})), ('measurement_type', ('|S12', {'h5py_encoding': 'ascii'})), ('sample_rate', '<f8'), ('hdf5_reference', ('|O', {'ref': h5py.h5r.Reference}))]
Note
When a run is added an entry is added to the summary table, where the information is pulled from the metadata.
>>> station.summary_table index | id | start | end | components | measurement_type | sample_rate | hdf5_reference -------------------------------------------------------------------------- -------------
- add_run(run_name, run_metadata=None)[source]
Add a run to a station.
- Parameters
run_name (string) – run name, should be id{a-z}
metadata (
mth5.metadata.Station, optional) – metadata container, defaults to None
need to be able to fill an entry in the summary table.
- property fourier_coefficients_group
Convinience method for /Station/Fourier_Coefficients
- get_run(run_name)[source]
get a run from run name
- Parameters
run_name (string) – existing run name
- Returns
Run object
- Return type
mth5.mth5_groups.RunGroup
>>> existing_run = station.get_run('MT001')
- locate_run(sample_rate, start)[source]
Locate a run based on sample rate and start time from the summary table
- Parameters
sample_rate (float) – sample rate in samples/seconds
start (string or
mth5.utils.mttime.MTime) – start time
- Returns
appropriate run name, None if not found
- Return type
string or None
- make_run_name(alphabet=False)[source]
Make a run name that will be the next alphabet letter extracted from the run list. Expects that all runs are labled as id{a-z}.
- Returns
metadata.id + next letter
- Return type
string
>>> station.metadata.id = 'MT001' >>> station.make_run_name() 'MT001a'
- property master_station_group
shortcut to master station group
- property metadata
Overwrite get metadata to include run information in the station
- property name
- remove_run(run_name)[source]
Remove a run from the station.
Note
Deleting a station is not as simple as del(station). In HDF5 this does not free up memory, it simply removes the reference to that station. The common way to get around this is to copy what you want into a new file, or overwrite the station.
- Parameters
station_name (string) – existing station name
- Example
>>> from mth5 import mth5 >>> mth5_obj = mth5.MTH5() >>> mth5_obj.open_mth5(r"/test.mth5", mode='a') >>> # one option >>> stations = mth5_obj.stations_group >>> stations.remove_station('MT001') >>> # another option >>> mth5_obj.stations_group.remove_station('MT001')
- property run_summary
Summary of runs in the station
- Returns
DESCRIPTION
- Return type
TYPE
- property survey_metadata
survey metadata
- property transfer_functions_group
Convinience method for /Station/Transfer_Functions
- class mth5.groups.SurveyGroup(group, survey_metadata=None, **kwargs)[source]
Bases:
BaseGroupUtility class to holds general information about the survey and accompanying metadata for an MT survey.
To access the hdf5 group directly use SurveyGroup.hdf5_group.
>>> survey = SurveyGroup(hdf5_group) >>> survey.hdf5_group.ref <HDF5 Group Reference>
Note
All attributes should be input into the metadata object, that way all input will be validated against the metadata standards. If you change attributes in metadata object, you should run the SurveyGroup.write_metadata() method. This is a temporary solution, working on an automatic updater if metadata is changed.
>>> survey.metadata.existing_attribute = 'update_existing_attribute' >>> survey.write_metadata()
If you want to add a new attribute this should be done using the metadata.add_base_attribute method.
>>> survey.metadata.add_base_attribute('new_attribute', >>> ... 'new_attribute_value', >>> ... {'type':str, >>> ... 'required':True, >>> ... 'style':'free form', >>> ... 'description': 'new attribute desc.', >>> ... 'units':None, >>> ... 'options':[], >>> ... 'alias':[], >>> ... 'example':'new attribute
Tip
If you want ot add surveys, reports, etc to the survey this should be done from the MTH5 object. This is to avoid duplication, at least for now.
To look at what the structure of
/Surveylooks like:>>> survey /Survey: ==================== |- Group: Filters ----------------- --> Dataset: summary ----------------- |- Group: Reports ----------------- --> Dataset: summary ----------------- |- Group: Standards ------------------- --> Dataset: summary ----------------- |- Group: Stations ------------------ --> Dataset: summary -----------------
- property filters_group
Convenience property for /Survey/Filters group
- property metadata
Overwrite get metadata to include station information in the survey
- property reports_group
Convenience property for /Survey/Reports group
- property standards_group
Convenience property for /Survey/Standards group
- property stations_group
- class mth5.groups.TransferFunctionGroup(group, **kwargs)[source]
Bases:
BaseGroupObject to hold a single transfer function estimation
- add_statistical_estimate(estimate_name, estimate_data=None, estimate_metadata=None, max_shape=(None, None, None), chunks=True, **kwargs)[source]
Add a StatisticalEstimate
- Parameters
estimate (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- from_tf_object(tf_obj)[source]
Create data sets from a
mt_metadata.transfer_function.core.TFobject.- Parameters
tf_obj (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- property period
Get period from hdf5_group[“period”]
- Returns
DESCRIPTION
- Return type
TYPE
- class mth5.groups.TransferFunctionsGroup(group, **kwargs)[source]
Bases:
BaseGroupObject to hold transfer functions
The is the high level group, all transfer functions for the station are held here and each one will have its own TransferFunctionGroup.
This has add, get, remove_transfer_function.
- add_transfer_function(name, tf_object=None)[source]
Add a transfer function to the group
- Parameters
name (string) – name of the transfer function
tf_object (
mt_metadata.transfer_function.core.TF) – Transfer Function object
- Returns
DESCRIPTION
- Return type
TYPE
>>> from mth5.mth5 import MTH5 >>> m = MTH5() >>> m.open_mth5("example.h5", "a") >>> station_group = m.get_station("mt01", survey="test") >>> tf_group = station_group.transfer_functions_group >>> tf_group.add_transfer_function("mt01_4096", tf_object)
- get_tf_object(tf_id)[source]
This is the function you want to use to get a proper
mt_metadata.transfer_functions.core.TFobject with all the appropriate metadata.- Parameters
tf_id (string) – name of the transfer function to get
- Returns
Full transfer function with appropriate metadata
- Return type
mt_metadata.transfer_functions.core.TF
>>> from mth5.mth5 import MTH5 >>> m = MTH5() >>> m.open_mth5("example.h5", "a") >>> station_group = m.get_station("mt01", survey="test") >>> tf_group = station_group.transfer_functions_group >>> tf_object = tf_group.get_tf_object("mt01_4096")
- get_transfer_function(tf_id)[source]
Get transfer function from id
- Parameters
tf_id (string) – name of transfer function
- Returns
Transfer function group
- Return type
>>> from mth5.mth5 import MTH5 >>> m = MTH5() >>> m.open_mth5("example.h5", "a") >>> station_group = m.get_station("mt01", survey="test") >>> tf_group = station_group.transfer_functions_group.get_transfer_function("mt01_4096")
- remove_transfer_function(tf_id)[source]
Remove a transfer function from the group
- Parameters
tf_id (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
>>> from mth5.mth5 import MTH5 >>> m = MTH5() >>> m.open_mth5("example.h5", "a") >>> station_group = m.get_station("mt01", survey="test") >>> tf_group = station_group.transfer_functions_group >>> tf_group.remove_transfer_function("mt01_4096")
mth5.io package
Created on Tue May 11 15:31:31 2021
- copyright
Jared Peacock (jpeacock@usgs.gov)
- license
MIT
- class mth5.io.lemi.lemi424.LEMI424(fn=None, **kwargs)[source]
Bases:
objectRead in a LEMI424 file, this is a place holder until IRIS finalizes their reader.
- Parameters
fn (
pathlib.Pathor string) – full path to LEMI424 filesample_rate (float) – sample rate of the file, default is 1.0
chunk_size (integer) – chunk size for pandas to use, does not change reading time much for a single day file. default is 8640
file_column_names (list of strings) – column names of the LEMI424 file
dtypes (dictionary with keys of column names and values of data types) – data types for each column
data_column_names (dictionary with keys of column names and values of data types) – same as file_column names with and added column for date, which is the combined date and time columns.
- LEMI424 File Column Names
year
month
day
hour
minute
second
bx
by
bz
temperature_e
temperature_h
e1
e2
e3
e4
battery
elevation
latitude
lat_hemisphere
longitude
lon_hemisphere
n_satellites
gps_fix
time_diff
- Data Column Names
date
bx
by
bz
temperature_e
temperature_h
e1
e2
e3
e4
battery
elevation
latitude
lat_hemisphere
longitude
lon_hemisphere
n_satellites
gps_fix
time_diff
- property data
Data represented as a
pandas.DataFramewith data_column names
- property elevation
median elevation where data have been collected in the LEMI424 file
- property end
end time of data collection in the LEMI424 file
- property file_size
size of file in bytes
- property fn
full path to LEMI424 file
- property gps_lock
has GPS lock
- property latitude
median latitude where data have been collected in the LEMI424 file
- property longitude
median longitude where data have been collected in the LEMI424 file
- property n_samples
number of samples in the file
- read(fn=None, fast=True)[source]
Read a LEMI424 file using pandas. The fast way will read in the first and last line to get the start and end time to make a time index. Then it will read in the data skipping parsing the date time columns. It will check to make sure the expected amount of points are correct. If not then it will read in the slower way which used the date time parser to ensure any time gaps are respected.
- Parameters
fn (string or
pathlib.Path, optional) – full path to file, defaults to None. Uses LEMI424.fn if not providedfast – read the fast way (True) or not (False)
- Returns
DESCRIPTION
- Return type
TYPE
- read_metadata()[source]
Read only first and last rows to get important metadata to use in the collection.
- property run_metadata
run metadata as
mt_metadata.timeseries.Run
- property start
start time of data collection in the LEMI424 file
- property station_metadata
station metadata as
mt_metadata.timeseries.Station
- to_run_ts(fn=None, e_channels=['e1', 'e2'])[source]
Create a
mth5.timeseries.RunTSobject from the data- Parameters
fn (string or
pathlib.Path, optional) – full path to file, defaults to None. Will use LEMI424.fn if None.e_channels (list of strings, optional) – columns for the electric channels to use, defaults to [“e1”, “e2”]
- Returns
RunTS object
- Return type
- mth5.io.lemi.lemi424.lemi_date_parser(year, month, day, hour, minute, second)[source]
convenience function to combine the date-time columns that are output by lemi into a single column
Assumes UTC
- Parameters
year (int) – year
month (int) – month
day (int) – day of the month
hour (int) – hour in 24 hr format
minute (int) – minutes in the hour
second (int) – seconds in the minute
- Returns
date time as a single column
- Return type
pandas.DateTime
- mth5.io.lemi.lemi424.lemi_hemisphere_parser(hemisphere)[source]
convert hemisphere into a value [-1, 1]. Assumes the prime meridian is 0.
- Parameters
hemisphere (string) – hemisphere string [ ‘N’ | ‘S’ | ‘E’ | ‘W’]
- Returns
unity with a sign for the given hemisphere
- Return type
signed integer
- mth5.io.lemi.lemi424.lemi_position_parser(position)[source]
convenience function to parse the location strings into a decimal float Uses the hemisphere for the sign.
Note
the format of the location is odd in that it is multiplied by 100 within the LEMI to provide a single floating point value that includes the degrees and decimal degrees –> {degrees}{degrees[mm.ss]}. For example 40.50166 would be represented as 4030.1.
- Parameters
position (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- mth5.io.lemi.lemi424.read_lemi424(fn, e_channels=['e1', 'e2'], fast=True)[source]
Read a LEMI 424 TXT file.
- Parameters
fn (string or Path) – input file name
e_channels – A list of electric channels to read,
defaults to [“e1”, “e2”] :type e_channels: list of strings, optional :return: A RunTS object with appropriate metadata :rtype:
mth5.timeseries.RunTS
Collection of TXT files combined into runs
Created on Wed Aug 31 10:32:44 2022
@author: jpeacock
- class mth5.io.lemi.lemi_collection.LEMICollection(file_path=None, **kwargs)[source]
Bases:
CollectionCollection of LEMI 424 files into runs based on start and end times. Will assign the run name as ‘sr1_{index:0{zeros}}’ –> ‘sr1_0001’ for zeros = 4.
- Parameters
file_path (string or :class`pathlib.Path`) – full path to single station LEMI424 directory
file_ext (string) – extension of LEMI424 files, default is ‘txt’
station_id (string) – station id
survey_id (string) – survey id
Note
This class assumes that the given file path contains a single LEMI station. If you want to do multiple stations merge the returned data frames.
Note
LEMI data comes with little metadata about the station or survey, therefore you should assign station_id and survey_id.
>>> from mth5.io.lemi import LEMICollection >>> lc = LEMICollection(r"/path/to/single/lemi/station") >>> lc.station_id = "mt001" >>> lc.survey_id = "test_survey" >>> run_dict = lc.get_runs(1)
- assign_run_names(df, zeros=4)[source]
Assign run names based on start and end times, checks if a file has the same start time as the last end time.
Run names are assigned as sr{sample_rate}_{run_number:0{zeros}}.
- Parameters
df (
pandas.DataFrame) – Dataframe with the appropriate columnszeros (int, optional) – number of zeros in run name, defaults to 4
- Returns
Dataframe with run names
- Return type
pandas.DataFrame
- to_dataframe(sample_rates=[1], run_name_zeros=4, calibration_path=None)[source]
Create a data frame of each TXT file in a given directory.
Note
This assumes the given directory contains a single station
- Parameters
sample_rates (int or list, optional) – sample rate to get, will always be 1 for LEMI data defaults to [1]
run_name_zeros (int, optional) – number of zeros to assing to the run name, defaults to 4
calibration_path (string or Path, optional) – path to calibration files, defaults to None
- Returns
Dataframe with information of each TXT file in the given directory.
- Return type
pandas.DataFrame- Example
>>> from mth5.io.lemi import LEMICollection >>> lc = LEMICollection("/path/to/single/lemi/station") >>> lemi_df = lc.to_dataframe()
- class mth5.io.lemi.LEMI424(fn=None, **kwargs)[source]
Bases:
objectRead in a LEMI424 file, this is a place holder until IRIS finalizes their reader.
- Parameters
fn (
pathlib.Pathor string) – full path to LEMI424 filesample_rate (float) – sample rate of the file, default is 1.0
chunk_size (integer) – chunk size for pandas to use, does not change reading time much for a single day file. default is 8640
file_column_names (list of strings) – column names of the LEMI424 file
dtypes (dictionary with keys of column names and values of data types) – data types for each column
data_column_names (dictionary with keys of column names and values of data types) – same as file_column names with and added column for date, which is the combined date and time columns.
- LEMI424 File Column Names
year
month
day
hour
minute
second
bx
by
bz
temperature_e
temperature_h
e1
e2
e3
e4
battery
elevation
latitude
lat_hemisphere
longitude
lon_hemisphere
n_satellites
gps_fix
time_diff
- Data Column Names
date
bx
by
bz
temperature_e
temperature_h
e1
e2
e3
e4
battery
elevation
latitude
lat_hemisphere
longitude
lon_hemisphere
n_satellites
gps_fix
time_diff
- property data
Data represented as a
pandas.DataFramewith data_column names
- property elevation
median elevation where data have been collected in the LEMI424 file
- property end
end time of data collection in the LEMI424 file
- property file_size
size of file in bytes
- property fn
full path to LEMI424 file
- property gps_lock
has GPS lock
- property latitude
median latitude where data have been collected in the LEMI424 file
- property longitude
median longitude where data have been collected in the LEMI424 file
- property n_samples
number of samples in the file
- read(fn=None, fast=True)[source]
Read a LEMI424 file using pandas. The fast way will read in the first and last line to get the start and end time to make a time index. Then it will read in the data skipping parsing the date time columns. It will check to make sure the expected amount of points are correct. If not then it will read in the slower way which used the date time parser to ensure any time gaps are respected.
- Parameters
fn (string or
pathlib.Path, optional) – full path to file, defaults to None. Uses LEMI424.fn if not providedfast – read the fast way (True) or not (False)
- Returns
DESCRIPTION
- Return type
TYPE
- read_metadata()[source]
Read only first and last rows to get important metadata to use in the collection.
- property run_metadata
run metadata as
mt_metadata.timeseries.Run
- property start
start time of data collection in the LEMI424 file
- property station_metadata
station metadata as
mt_metadata.timeseries.Station
- to_run_ts(fn=None, e_channels=['e1', 'e2'])[source]
Create a
mth5.timeseries.RunTSobject from the data- Parameters
fn (string or
pathlib.Path, optional) – full path to file, defaults to None. Will use LEMI424.fn if None.e_channels (list of strings, optional) – columns for the electric channels to use, defaults to [“e1”, “e2”]
- Returns
RunTS object
- Return type
- class mth5.io.lemi.LEMICollection(file_path=None, **kwargs)[source]
Bases:
CollectionCollection of LEMI 424 files into runs based on start and end times. Will assign the run name as ‘sr1_{index:0{zeros}}’ –> ‘sr1_0001’ for zeros = 4.
- Parameters
file_path (string or :class`pathlib.Path`) – full path to single station LEMI424 directory
file_ext (string) – extension of LEMI424 files, default is ‘txt’
station_id (string) – station id
survey_id (string) – survey id
Note
This class assumes that the given file path contains a single LEMI station. If you want to do multiple stations merge the returned data frames.
Note
LEMI data comes with little metadata about the station or survey, therefore you should assign station_id and survey_id.
>>> from mth5.io.lemi import LEMICollection >>> lc = LEMICollection(r"/path/to/single/lemi/station") >>> lc.station_id = "mt001" >>> lc.survey_id = "test_survey" >>> run_dict = lc.get_runs(1)
- assign_run_names(df, zeros=4)[source]
Assign run names based on start and end times, checks if a file has the same start time as the last end time.
Run names are assigned as sr{sample_rate}_{run_number:0{zeros}}.
- Parameters
df (
pandas.DataFrame) – Dataframe with the appropriate columnszeros (int, optional) – number of zeros in run name, defaults to 4
- Returns
Dataframe with run names
- Return type
pandas.DataFrame
- to_dataframe(sample_rates=[1], run_name_zeros=4, calibration_path=None)[source]
Create a data frame of each TXT file in a given directory.
Note
This assumes the given directory contains a single station
- Parameters
sample_rates (int or list, optional) – sample rate to get, will always be 1 for LEMI data defaults to [1]
run_name_zeros (int, optional) – number of zeros to assing to the run name, defaults to 4
calibration_path (string or Path, optional) – path to calibration files, defaults to None
- Returns
Dataframe with information of each TXT file in the given directory.
- Return type
pandas.DataFrame- Example
>>> from mth5.io.lemi import LEMICollection >>> lc = LEMICollection("/path/to/single/lemi/station") >>> lemi_df = lc.to_dataframe()
- mth5.io.lemi.read_lemi424(fn, e_channels=['e1', 'e2'], fast=True)[source]
Read a LEMI 424 TXT file.
- Parameters
fn (string or Path) – input file name
e_channels – A list of electric channels to read,
defaults to [“e1”, “e2”] :type e_channels: list of strings, optional :return: A RunTS object with appropriate metadata :rtype:
mth5.timeseries.RunTS
Created on Wed Sep 30 10:20:12 2020
- author
Jared Peacock
- license
MIT
- mth5.io.miniseed.miniseed.read_miniseed(fn)[source]
Read a miniseed file into a
mth5.timeseries.RunTSobject. Uses Obspy to read the miniseed.- Parameters
fn (string) – full path to the miniseed file
- Returns
RunTS object
- Return type
- mth5.io.miniseed.read_miniseed(fn)[source]
Read a miniseed file into a
mth5.timeseries.RunTSobject. Uses Obspy to read the miniseed.- Parameters
fn (string) – full path to the miniseed file
- Returns
RunTS object
- Return type
Created on Thu Sep 1 11:43:56 2022
@author: jpeacock
- class mth5.io.nims.gps.GPS(gps_string, index=0)[source]
Bases:
objectclass to parse GPS stamp from the NIMS
Depending on the type of Stamp different attributes will be filled.
GPRMC has full date and time information and declination GPGGA has elevation data
Note
GPGGA date is set to 1980-01-01 so that the time can be estimated. Should use GPRMC for accurate date/time information.
- property declination
geomagnetic declination in degrees from north
- property elevation
elevation in meters
- property fix
GPS fixed
- property gps_type
GPRMC or GPGGA
- property latitude
Latitude in decimal degrees, WGS84
- property longitude
Latitude in decimal degrees, WGS84
- parse_gps_string(gps_string)[source]
Parse a raw gps string from the NIMS and set appropriate attributes. GPS string will first be validated, then parsed.
- Parameters
gps_string (string) – raw GPS string to be parsed
- property time_stamp
return a datetime object of the time stamp
- validate_gps_list(gps_list)[source]
check to make sure the gps stamp is the correct format, checks each element for the proper format
- Parameters
gps_list (list) – a parsed gps string from a NIMS
- Raises
mth5.io.nims.GPSErrorif anything is wrong.
Created on Thu Sep 1 12:57:32 2022
@author: jpeacock
- class mth5.io.nims.header.NIMSHeader(fn=None)[source]
Bases:
objectclass to hold the NIMS header information.
A typical header looks like
''' >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>user field>>>>>>>>>>>>>>>>>>>>>>>>>>>> SITE NAME: Budwieser Spring STATE/PROVINCE: CA COUNTRY: USA >>> The following code in double quotes is REQUIRED to start the NIMS << >>> The next 3 lines contain values required for processing <<<<<<<<<<<< >>> The lines after that are optional <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< "300b" <-- 2CHAR EXPERIMENT CODE + 3 CHAR SITE CODE + RUN LETTER 1105-3; 1305-3 <-- SYSTEM BOX I.D.; MAG HEAD ID (if different) 106 0 <-- N-S Ex WIRE LENGTH (m); HEADING (deg E mag N) 109 90 <-- E-W Ey WIRE LENGTH (m); HEADING (deg E mag N) 1 <-- N ELECTRODE ID 3 <-- E ELECTRODE ID 2 <-- S ELECTRODE ID 4 <-- W ELECTRODE ID Cu <-- GROUND ELECTRODE INFO GPS INFO: 01/10/19 16:16:42 1616.7000 3443.6088 115.7350 W 946.6 OPERATOR: KP COMMENT: N/S CRS: .95/.96 DCV: 3.5 ACV:1 E/W CRS: .85/.86 DCV: 1.5 ACV: 1 Redeployed site for run b b/c possible animal disturbance '''
- property file_size
Size of the file
- property fn
Full path to NIMS file
- read_header(fn=None)[source]
read header information
- Parameters
fn (string or
pathlib.Path) – full path to file to read- Raises
mth5.io.nims.NIMSErrorif something is not right.
- property station
Station ID
deals with reading in NIMS DATA.BIN files
- This is a translation from Matlab codes written and edited by:
Anna Kelbert
Paul Bedrosian
Esteban Bowles-Martinez
Possibly others.
I’ve tested it against a version, and it matches. The data/GPS gaps I still don’t understand so for now the time series is just made continuous and the number of missing seconds is clipped from the end of the time series.
Note
this only works for 8Hz data for now
- copyright
Jared Peacock (jpeacock@usgs.gov)
- license
MIT
- class mth5.io.nims.nims.NIMS(fn=None)[source]
Bases:
NIMSHeaderNIMS Class will read in a NIMS DATA.BIN file.
A fast way to read the binary files are to first read in the GPS strings, the third byte in each block as a character and parse that into valid GPS stamps.
Then read in the entire data set as unsigned 8 bit integers and reshape the data to be n seconds x block size. Then parse that array into the status information and data.
I only have a limited amount of .BIN files to test so this will likely break if there are issues such as data gaps. This has been tested against the matlab program loadNIMS by Anna Kelbert and the match for all the .bin files I have. If something looks weird check it against that program.
Warning
Currently Only 8 Hz data is supported
- align_data(data_array, stamps)[source]
Need to match up the first good GPS stamp with the data
Do this by using the first GPS stamp and assuming that the time from the first time stamp to the start is the index value.
put the data into a pandas data frame that is indexed by time
- Parameters
data_array (array) – structure array with columns for each component [hx, hy, hz, ex, ey]
stamps (list) – list of GPS stamps [[status_index, [GPRMC, GPGGA]]]
- Returns
pandas DataFrame with colums of components and indexed by time initialized by the start time.
Note
Data gaps are squeezed cause not sure what a gap actually means.
- property box_temperature
data logger temperature, sampled at 1 second
- check_timing(stamps)[source]
make sure that there are the correct number of seconds in between the first and last GPS GPRMC stamps
- Parameters
stamps (list) – list of GPS stamps [[status_index, [GPRMC, GPGGA]]]
- Returns
[ True | False ] if data is valid or not.
- Returns
gap index locations
Note
currently it is assumed that if a data gap occurs the data can be squeezed to remove them. Probably a more elegant way of doing it.
- property declination
median elevation value from all the GPS stamps in decimal degrees WGS84
Only get from the first stamp within the sets
- property elevation
median elevation value from all the GPS stamps in decimal degrees WGS84
Only get from the first stamp within the sets
- property end_time
start time is the first good GPS time stamp minus the seconds to the beginning of the time series.
- property ex
EX
- property ex_metadata
- property ey
EY
- property ey_metadata
- find_sequence(data_array, block_sequence=None)[source]
find a sequence in a given array
- Parameters
data_array (array) – array of the data with shape [n, m] where n is the number of seconds recorded m is the block length for a given sampling rate.
block_sequence (list) – sequence pattern to locate default is [1, 131] the start of a data block.
- Returns
array of index locations where the sequence is found.
- get_channel_response(channel, dipole_length=1)[source]
Get the channel response for a given channel
- Parameters
channel (TYPE) – DESCRIPTION
dipole_length (TYPE, optional) – DESCRIPTION, defaults to 1
- Returns
DESCRIPTION
- Return type
TYPE
- get_stamps(nims_string)[source]
get a list of valid GPS strings and match synchronous GPRMC with GPGGA stamps if possible.
- Parameters
nims_string (str) – raw GPS string output by NIMS
- property hx
HX
- property hx_metadata
- property hy
HY
- property hy_metadata
- property hz
HZ
- property hz_metadata
- property latitude
median latitude value from all the GPS stamps in decimal degrees WGS84
Only get from the GPRMC stamp as they should be duplicates
- property longitude
median longitude value from all the GPS stamps in decimal degrees WGS84
Only get from the first stamp within the sets
- make_dt_index(start_time, sample_rate, stop_time=None, n_samples=None)[source]
make time index array
Note
date-time format should be YYYY-M-DDThh:mm:ss.ms UTC
- Parameters
start_time (string) – start time
end_time (string) – end time
sample_rate (float) – sample_rate in samples/second
- match_status_with_gps_stamps(status_array, gps_list)[source]
Match the index values from the status array with the index values of the GPS stamps. There appears to be a bit of wiggle room between when the lock is recorded and the stamp was actually recorded. This is typically 1 second and sometimes 2.
- Parameters
status_array (array) – array of status values from each data block
gps_list (list) – list of valid GPS stamps [[GPRMC, GPGGA], …]
Note
I think there is a 2 second gap between the lock and the first stamp character.
- property n_samples
- read_nims(fn=None)[source]
Read NIMS DATA.BIN file.
Read in the header information and stores those as attributes with the same names as in the header file.
Locate the beginning of the data blocks by looking for the first [1, 131, …] combo. Anything before that is cut out.
Make sure the data is a multiple of the block length, if the data is longer the extra bits are cut off.
Read in the GPS data (3rd byte of each block) as characters. Parses those into valid GPS stamps with appropriate index locations of where the ‘$’ was found.
Read in the data as unsigned 8-bit integers and reshape the array into [N, data_block_length]. Parse this array into the status information and the data.
Remove duplicate blocks, by removing the first of the duplicates as suggested by Anna and Paul.
Match the GPS locks from the status with valid GPS stamps.
Check to make sure that there is the correct number of seconds between the first and last GPS stamp. The extra seconds are cut off from the end of the time series. Not sure if this is the best way to accommodate gaps in the data.
Note
The data and information array returned have the duplicates removed and the sequence reset to be monotonic.
- Parameters
fn (str) – full path to DATA.BIN file
- Example
>>> from mth5.io import nims >>> n = nims.NIMS(r"/home/mt_data/nims/mt001.bin")
- remove_duplicates(info_array, data_array)[source]
remove duplicate blocks, removing the first duplicate as suggested by Paul and Anna. Checks to make sure that the mag data are identical for the duplicate blocks. Removes the blocks from the information and data arrays and returns the reduced arrays. This should sync up the timing of GPS stamps and index values.
- Parameters
info_array (np.array) – structured array of block information
data_array (np.array) – structured array of the data
- Returns
reduced information array
- Returns
reduced data array
- Returns
index of duplicates in raw data
- property run_metadata
Run metadata
- property start_time
start time is the first good GPS time stamp minus the seconds to the beginning of the time series.
- property station_metadata
Station metadata from nims file
Collection of TXT files combined into runs
Created on Wed Aug 31 10:32:44 2022
@author: jpeacock
- class mth5.io.nims.nims_collection.NIMSCollection(file_path=None, **kwargs)[source]
Bases:
CollectionCollection of NIMS files into runs.
>>> from mth5.io.nims import LEMICollection >>> lc = NIMSCollection(r"/path/to/single/lemi/station") >>> lc.station_id = "mt001" >>> lc.survey_id = "test_survey" >>> run_dict = lc.get_runs(1)
- assign_run_names(df, zeros=2)[source]
Assign run names assuming a row represents single station
Run names are assigned as sr{sample_rate}_{run_number:0{zeros}}.
- Parameters
df (
pandas.DataFrame) – Dataframe with the appropriate columnszeros (int, optional) – number of zeros in run name, defaults to 4
- Returns
Dataframe with run names
- Return type
pandas.DataFrame
- to_dataframe(sample_rates=[1], run_name_zeros=2, calibration_path=None)[source]
Create a data frame of each TXT file in a given directory.
Note
This assumes the given directory contains a single station
- Parameters
sample_rates (int or list, optional) – sample rate to get, will always be 1 for LEMI data defaults to [1]
run_name_zeros (int, optional) – number of zeros to assing to the run name, defaults to 4
calibration_path (string or Path, optional) – path to calibration files, defaults to None
- Returns
Dataframe with information of each TXT file in the given directory.
- Return type
pandas.DataFrame- Example
>>> from mth5.io.lemi import LEMICollection >>> lc = LEMICollection("/path/to/single/lemi/station") >>> lemi_df = lc.to_dataframe()
Created on Fri Sep 2 13:50:51 2022
@author: jpeacock
- class mth5.io.nims.response_filters.Response(system_id=None, **kwargs)[source]
Bases:
objectCommon NIMS response filters for electric and magnetic channels
- dipole_filter(length)[source]
Make a dipole filter
- Parameters
length (TYPE) – dipole length in meters
- Returns
DESCRIPTION
- Return type
TYPE
- property electric_conversion
electric channel conversion from counts to volts :return: DESCRIPTION :rtype: TYPE
- property electric_high_pass_hp
1-pole low pass for 1 hz instuments :return: DESCRIPTION :rtype: TYPE
- property electric_high_pass_pc
1-pole low pass filter for 8 hz instruments :return: DESCRIPTION :rtype: TYPE
- property electric_low_pass
5 pole electric low pass filter :return: DESCRIPTION :rtype: TYPE
- property electric_physical_units
DESCRIPTION :rtype: TYPE
- Type
return
- get_channel_response(channel, dipole_length=1)[source]
Get the full channel response filter :param channel: DESCRIPTION :type channel: TYPE :param dipole_length: DESCRIPTION, defaults to 1 :type dipole_length: TYPE, optional :return: DESCRIPTION :rtype: TYPE
- get_electric_high_pass(hardware='pc')[source]
get the electric high pass filter based on the hardware
- property magnetic_conversion
DESCRIPTION :rtype: TYPE
- Type
return
- property magnetic_low_pass
Low pass 3 pole filter
- Returns
DESCRIPTION
- Return type
TYPE
- class mth5.io.nims.GPS(gps_string, index=0)[source]
Bases:
objectclass to parse GPS stamp from the NIMS
Depending on the type of Stamp different attributes will be filled.
GPRMC has full date and time information and declination GPGGA has elevation data
Note
GPGGA date is set to 1980-01-01 so that the time can be estimated. Should use GPRMC for accurate date/time information.
- property declination
geomagnetic declination in degrees from north
- property elevation
elevation in meters
- property fix
GPS fixed
- property gps_type
GPRMC or GPGGA
- property latitude
Latitude in decimal degrees, WGS84
- property longitude
Latitude in decimal degrees, WGS84
- parse_gps_string(gps_string)[source]
Parse a raw gps string from the NIMS and set appropriate attributes. GPS string will first be validated, then parsed.
- Parameters
gps_string (string) – raw GPS string to be parsed
- property time_stamp
return a datetime object of the time stamp
- validate_gps_list(gps_list)[source]
check to make sure the gps stamp is the correct format, checks each element for the proper format
- Parameters
gps_list (list) – a parsed gps string from a NIMS
- Raises
mth5.io.nims.GPSErrorif anything is wrong.
- class mth5.io.nims.NIMS(fn=None)[source]
Bases:
NIMSHeaderNIMS Class will read in a NIMS DATA.BIN file.
A fast way to read the binary files are to first read in the GPS strings, the third byte in each block as a character and parse that into valid GPS stamps.
Then read in the entire data set as unsigned 8 bit integers and reshape the data to be n seconds x block size. Then parse that array into the status information and data.
I only have a limited amount of .BIN files to test so this will likely break if there are issues such as data gaps. This has been tested against the matlab program loadNIMS by Anna Kelbert and the match for all the .bin files I have. If something looks weird check it against that program.
Warning
Currently Only 8 Hz data is supported
- align_data(data_array, stamps)[source]
Need to match up the first good GPS stamp with the data
Do this by using the first GPS stamp and assuming that the time from the first time stamp to the start is the index value.
put the data into a pandas data frame that is indexed by time
- Parameters
data_array (array) – structure array with columns for each component [hx, hy, hz, ex, ey]
stamps (list) – list of GPS stamps [[status_index, [GPRMC, GPGGA]]]
- Returns
pandas DataFrame with colums of components and indexed by time initialized by the start time.
Note
Data gaps are squeezed cause not sure what a gap actually means.
- property box_temperature
data logger temperature, sampled at 1 second
- check_timing(stamps)[source]
make sure that there are the correct number of seconds in between the first and last GPS GPRMC stamps
- Parameters
stamps (list) – list of GPS stamps [[status_index, [GPRMC, GPGGA]]]
- Returns
[ True | False ] if data is valid or not.
- Returns
gap index locations
Note
currently it is assumed that if a data gap occurs the data can be squeezed to remove them. Probably a more elegant way of doing it.
- property declination
median elevation value from all the GPS stamps in decimal degrees WGS84
Only get from the first stamp within the sets
- property elevation
median elevation value from all the GPS stamps in decimal degrees WGS84
Only get from the first stamp within the sets
- property end_time
start time is the first good GPS time stamp minus the seconds to the beginning of the time series.
- property ex
EX
- property ex_metadata
- property ey
EY
- property ey_metadata
- find_sequence(data_array, block_sequence=None)[source]
find a sequence in a given array
- Parameters
data_array (array) – array of the data with shape [n, m] where n is the number of seconds recorded m is the block length for a given sampling rate.
block_sequence (list) – sequence pattern to locate default is [1, 131] the start of a data block.
- Returns
array of index locations where the sequence is found.
- get_channel_response(channel, dipole_length=1)[source]
Get the channel response for a given channel
- Parameters
channel (TYPE) – DESCRIPTION
dipole_length (TYPE, optional) – DESCRIPTION, defaults to 1
- Returns
DESCRIPTION
- Return type
TYPE
- get_stamps(nims_string)[source]
get a list of valid GPS strings and match synchronous GPRMC with GPGGA stamps if possible.
- Parameters
nims_string (str) – raw GPS string output by NIMS
- property hx
HX
- property hx_metadata
- property hy
HY
- property hy_metadata
- property hz
HZ
- property hz_metadata
- property latitude
median latitude value from all the GPS stamps in decimal degrees WGS84
Only get from the GPRMC stamp as they should be duplicates
- property longitude
median longitude value from all the GPS stamps in decimal degrees WGS84
Only get from the first stamp within the sets
- make_dt_index(start_time, sample_rate, stop_time=None, n_samples=None)[source]
make time index array
Note
date-time format should be YYYY-M-DDThh:mm:ss.ms UTC
- Parameters
start_time (string) – start time
end_time (string) – end time
sample_rate (float) – sample_rate in samples/second
- match_status_with_gps_stamps(status_array, gps_list)[source]
Match the index values from the status array with the index values of the GPS stamps. There appears to be a bit of wiggle room between when the lock is recorded and the stamp was actually recorded. This is typically 1 second and sometimes 2.
- Parameters
status_array (array) – array of status values from each data block
gps_list (list) – list of valid GPS stamps [[GPRMC, GPGGA], …]
Note
I think there is a 2 second gap between the lock and the first stamp character.
- property n_samples
- read_nims(fn=None)[source]
Read NIMS DATA.BIN file.
Read in the header information and stores those as attributes with the same names as in the header file.
Locate the beginning of the data blocks by looking for the first [1, 131, …] combo. Anything before that is cut out.
Make sure the data is a multiple of the block length, if the data is longer the extra bits are cut off.
Read in the GPS data (3rd byte of each block) as characters. Parses those into valid GPS stamps with appropriate index locations of where the ‘$’ was found.
Read in the data as unsigned 8-bit integers and reshape the array into [N, data_block_length]. Parse this array into the status information and the data.
Remove duplicate blocks, by removing the first of the duplicates as suggested by Anna and Paul.
Match the GPS locks from the status with valid GPS stamps.
Check to make sure that there is the correct number of seconds between the first and last GPS stamp. The extra seconds are cut off from the end of the time series. Not sure if this is the best way to accommodate gaps in the data.
Note
The data and information array returned have the duplicates removed and the sequence reset to be monotonic.
- Parameters
fn (str) – full path to DATA.BIN file
- Example
>>> from mth5.io import nims >>> n = nims.NIMS(r"/home/mt_data/nims/mt001.bin")
- remove_duplicates(info_array, data_array)[source]
remove duplicate blocks, removing the first duplicate as suggested by Paul and Anna. Checks to make sure that the mag data are identical for the duplicate blocks. Removes the blocks from the information and data arrays and returns the reduced arrays. This should sync up the timing of GPS stamps and index values.
- Parameters
info_array (np.array) – structured array of block information
data_array (np.array) – structured array of the data
- Returns
reduced information array
- Returns
reduced data array
- Returns
index of duplicates in raw data
- property run_metadata
Run metadata
- property start_time
start time is the first good GPS time stamp minus the seconds to the beginning of the time series.
- property station_metadata
Station metadata from nims file
- class mth5.io.nims.NIMSCollection(file_path=None, **kwargs)[source]
Bases:
CollectionCollection of NIMS files into runs.
>>> from mth5.io.nims import LEMICollection >>> lc = NIMSCollection(r"/path/to/single/lemi/station") >>> lc.station_id = "mt001" >>> lc.survey_id = "test_survey" >>> run_dict = lc.get_runs(1)
- assign_run_names(df, zeros=2)[source]
Assign run names assuming a row represents single station
Run names are assigned as sr{sample_rate}_{run_number:0{zeros}}.
- Parameters
df (
pandas.DataFrame) – Dataframe with the appropriate columnszeros (int, optional) – number of zeros in run name, defaults to 4
- Returns
Dataframe with run names
- Return type
pandas.DataFrame
- to_dataframe(sample_rates=[1], run_name_zeros=2, calibration_path=None)[source]
Create a data frame of each TXT file in a given directory.
Note
This assumes the given directory contains a single station
- Parameters
sample_rates (int or list, optional) – sample rate to get, will always be 1 for LEMI data defaults to [1]
run_name_zeros (int, optional) – number of zeros to assing to the run name, defaults to 4
calibration_path (string or Path, optional) – path to calibration files, defaults to None
- Returns
Dataframe with information of each TXT file in the given directory.
- Return type
pandas.DataFrame- Example
>>> from mth5.io.lemi import LEMICollection >>> lc = LEMICollection("/path/to/single/lemi/station") >>> lemi_df = lc.to_dataframe()
- class mth5.io.nims.NIMSHeader(fn=None)[source]
Bases:
objectclass to hold the NIMS header information.
A typical header looks like
''' >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>user field>>>>>>>>>>>>>>>>>>>>>>>>>>>> SITE NAME: Budwieser Spring STATE/PROVINCE: CA COUNTRY: USA >>> The following code in double quotes is REQUIRED to start the NIMS << >>> The next 3 lines contain values required for processing <<<<<<<<<<<< >>> The lines after that are optional <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< "300b" <-- 2CHAR EXPERIMENT CODE + 3 CHAR SITE CODE + RUN LETTER 1105-3; 1305-3 <-- SYSTEM BOX I.D.; MAG HEAD ID (if different) 106 0 <-- N-S Ex WIRE LENGTH (m); HEADING (deg E mag N) 109 90 <-- E-W Ey WIRE LENGTH (m); HEADING (deg E mag N) 1 <-- N ELECTRODE ID 3 <-- E ELECTRODE ID 2 <-- S ELECTRODE ID 4 <-- W ELECTRODE ID Cu <-- GROUND ELECTRODE INFO GPS INFO: 01/10/19 16:16:42 1616.7000 3443.6088 115.7350 W 946.6 OPERATOR: KP COMMENT: N/S CRS: .95/.96 DCV: 3.5 ACV:1 E/W CRS: .85/.86 DCV: 1.5 ACV: 1 Redeployed site for run b b/c possible animal disturbance '''
- property file_size
Size of the file
- property fn
Full path to NIMS file
- read_header(fn=None)[source]
read header information
- Parameters
fn (string or
pathlib.Path) – full path to file to read- Raises
mth5.io.nims.NIMSErrorif something is not right.
- property station
Station ID
- class mth5.io.nims.Response(system_id=None, **kwargs)[source]
Bases:
objectCommon NIMS response filters for electric and magnetic channels
- dipole_filter(length)[source]
Make a dipole filter
- Parameters
length (TYPE) – dipole length in meters
- Returns
DESCRIPTION
- Return type
TYPE
- property electric_conversion
electric channel conversion from counts to volts :return: DESCRIPTION :rtype: TYPE
- property electric_high_pass_hp
1-pole low pass for 1 hz instuments :return: DESCRIPTION :rtype: TYPE
- property electric_high_pass_pc
1-pole low pass filter for 8 hz instruments :return: DESCRIPTION :rtype: TYPE
- property electric_low_pass
5 pole electric low pass filter :return: DESCRIPTION :rtype: TYPE
- property electric_physical_units
DESCRIPTION :rtype: TYPE
- Type
return
- get_channel_response(channel, dipole_length=1)[source]
Get the full channel response filter :param channel: DESCRIPTION :type channel: TYPE :param dipole_length: DESCRIPTION, defaults to 1 :type dipole_length: TYPE, optional :return: DESCRIPTION :rtype: TYPE
- get_electric_high_pass(hardware='pc')[source]
get the electric high pass filter based on the hardware
- property magnetic_conversion
DESCRIPTION :rtype: TYPE
- Type
return
- property magnetic_low_pass
Low pass 3 pole filter
- Returns
DESCRIPTION
- Return type
TYPE
Module to read and parse native Phoenix Geophysics data formats of the MTU-5C Family.
This module implements Streamed readers for decimated continuos time series formats of the MTU-5C family.
- author
Jorge Torres-Solis
Revised 2022 by J. Peacock
- class mth5.io.phoenix.readers.contiguous.decimated_continuous_reader.DecimatedContinuousReader(path, num_files=1, report_hw_sat=False, **kwargs)[source]
Bases:
TSReaderBaseClass to create a streamer for continuous decimated time series, i.e. ‘td_150’, ‘td_30’
These files have no sub header information.
- read()[source]
Read in the full data from the file given.
- Returns
single channel data array
- Return type
numpy.ndarray
- read_sequence(start=0, end=None)[source]
Read a sequence of files
- Parameters
start (integer, optional) – starting index in the sequence, defaults to 0
end (integer, optional) – eneding index in the sequence to read, defaults to None
- Returns
data within the given sequence range
- Return type
numpy.ndarray
- property segment_end_time
estimate end time
The first sequence starts 1 second later than the set start time due to initiation within the data logger
- Returns
estimated end time from number of samples
- Return type
mt_metadata.utils.mttime.MTime
- property segment_start_time
estimate the segment start time based on sequence number
The first sequence starts 1 second later than the set start time due to initiation within the data logger
- Returns
start time of the recording
- Return type
mt_metadata.utils.mttime.MTime
- property sequence_end
- property sequence_start
- class mth5.io.phoenix.readers.contiguous.DecimatedContinuousReader(path, num_files=1, report_hw_sat=False, **kwargs)[source]
Bases:
TSReaderBaseClass to create a streamer for continuous decimated time series, i.e. ‘td_150’, ‘td_30’
These files have no sub header information.
- read()[source]
Read in the full data from the file given.
- Returns
single channel data array
- Return type
numpy.ndarray
- read_sequence(start=0, end=None)[source]
Read a sequence of files
- Parameters
start (integer, optional) – starting index in the sequence, defaults to 0
end (integer, optional) – eneding index in the sequence to read, defaults to None
- Returns
data within the given sequence range
- Return type
numpy.ndarray
- property segment_end_time
estimate end time
The first sequence starts 1 second later than the set start time due to initiation within the data logger
- Returns
estimated end time from number of samples
- Return type
mt_metadata.utils.mttime.MTime
- property segment_start_time
estimate the segment start time based on sequence number
The first sequence starts 1 second later than the set start time due to initiation within the data logger
- Returns
start time of the recording
- Return type
mt_metadata.utils.mttime.MTime
- property sequence_end
- property sequence_start
Module to read and parse native Phoenix Geophysics data formats of the MTU-5C Family.
This module implements Streamed readers for segmented-decimated time series formats of the MTU-5C family.
- author
Jorge Torres-Solis
Revised 2022 by J. Peacock
- class mth5.io.phoenix.readers.native.native_reader.NativeReader(path, num_files=1, scale_to=1, header_length=128, last_frame=0, ad_plus_minus_range=5.0, channel_type='E', report_hw_sat=False, **kwargs)[source]
Bases:
TSReaderBaseNative sampling rate ‘Raw’ time series reader class, these are the .bin files. They are formatted with a header of 128 bytes then frames of 64.
Each frame is 20 x 3 byte (24-bit) data point then a 4 byte footer.
- property npts_per_frame
- read()[source]
Read the full data file.
Note
This uses
numpy.lib.stride_tricks.as_stridedwhich can be unstable if the bytes are not the correct length. See notes by numpy.Got this solution from: https://stackoverflow.com/questions/12080279/how-do-i-create-a-numpy-dtype-that-includes-24-bit-integers?msclkid=3398046ecd6511ec9a37394f28c5aaba
- Returns
scaled data and footer
- Return type
tuple (data, footer)
- read_frames(num_frames)[source]
Read the given amount of frames from the data.
Note
that seek is not reset so if you iterate this the stream reads from the last tell.
- Parameters
num_frames (integer) – Number of frames to read
- Returns
Scaled data from the given number of frames
- Return type
np.ndarray(dtype=float)
- read_sequence(start=0, end=None)[source]
Read sequence of files into a single array
- Parameters
start (integer, optional) – sequence start, defaults to 0
end (integer, optional) – sequence end, defaults to None
- Returns
scaled data
- Return type
np.ndarray(dtype=float32)
- Returns
footer
- Return type
np.ndarray(dtype=int32)
- class mth5.io.phoenix.readers.native.NativeReader(path, num_files=1, scale_to=1, header_length=128, last_frame=0, ad_plus_minus_range=5.0, channel_type='E', report_hw_sat=False, **kwargs)[source]
Bases:
TSReaderBaseNative sampling rate ‘Raw’ time series reader class, these are the .bin files. They are formatted with a header of 128 bytes then frames of 64.
Each frame is 20 x 3 byte (24-bit) data point then a 4 byte footer.
- property npts_per_frame
- read()[source]
Read the full data file.
Note
This uses
numpy.lib.stride_tricks.as_stridedwhich can be unstable if the bytes are not the correct length. See notes by numpy.Got this solution from: https://stackoverflow.com/questions/12080279/how-do-i-create-a-numpy-dtype-that-includes-24-bit-integers?msclkid=3398046ecd6511ec9a37394f28c5aaba
- Returns
scaled data and footer
- Return type
tuple (data, footer)
- read_frames(num_frames)[source]
Read the given amount of frames from the data.
Note
that seek is not reset so if you iterate this the stream reads from the last tell.
- Parameters
num_frames (integer) – Number of frames to read
- Returns
Scaled data from the given number of frames
- Return type
np.ndarray(dtype=float)
- read_sequence(start=0, end=None)[source]
Read sequence of files into a single array
- Parameters
start (integer, optional) – sequence start, defaults to 0
end (integer, optional) – sequence end, defaults to None
- Returns
scaled data
- Return type
np.ndarray(dtype=float32)
- Returns
footer
- Return type
np.ndarray(dtype=int32)
Module to read and parse native Phoenix Geophysics data formats of the MTU-5C Family
- This module implements Streamed readers for segmented-decimated time series
formats of the MTU-5C family.
- author
Jorge Torres-Solis
Revised 2022 by J. Peacock
- class mth5.io.phoenix.readers.segmented.decimated_segmented_reader.DecimatedSegmentCollection(path, num_files=1, report_hw_sat=False, **kwargs)[source]
Bases:
TSReaderBaseClass to create a streamer for segmented decimated time series, i.e. ‘td_24k’
These files have a sub header
- class mth5.io.phoenix.readers.segmented.decimated_segmented_reader.DecimatedSegmentedReader(path, num_files=1, report_hw_sat=False, **kwargs)[source]
Bases:
TSReaderBaseClass to create a streamer for segmented decimated time series, i.e. ‘td_24k’
These files have a sub header
- class mth5.io.phoenix.readers.segmented.decimated_segmented_reader.Segment(stream, **kwargs)[source]
Bases:
SubHeaderA segment class to hold a single segment
- read_segment(metadata_only=False)[source]
Read the whole file in
- Returns
DESCRIPTION
- Return type
TYPE
- property segment_end_time
estimate end time
- Returns
DESCRIPTION
- Return type
TYPE
- property segment_start_time
estimate the segment start time based on sequence number
- Returns
DESCRIPTION
- Return type
TYPE
- class mth5.io.phoenix.readers.segmented.decimated_segmented_reader.SubHeader(**kwargs)[source]
Bases:
objectClass for subheader of segmented files
- property gps_time_stamp
GPS time stamp in UTC
- property missing_count
- property n_samples
- property saturation_count
- property value_max
- property value_mean
- property value_min
- class mth5.io.phoenix.readers.segmented.DecimatedSegmentedReader(path, num_files=1, report_hw_sat=False, **kwargs)[source]
Bases:
TSReaderBaseClass to create a streamer for segmented decimated time series, i.e. ‘td_24k’
These files have a sub header
Module to read and parse native Phoenix Geophysics data formats of the MTU-5C Family.
This module implements Streamed readers for segmented-decimated continuus-decimated and native sampling rate time series formats of the MTU-5C family.
- author
Jorge Torres-Solis
Revised 2022 by J. Peacock
- class mth5.io.phoenix.readers.base.TSReaderBase(path, num_files=1, header_length=128, report_hw_sat=False, **kwargs)[source]
Bases:
HeaderGeneric reader that all other readers will inherit
- property base_dir
parent directory of file :rtype:
pathlib.Path- Type
return
- property base_path
full path of file :rtype:
pathlib.Path- Type
return
- property channel_metadata
Channel metadata updated from recmeta
- Returns
DESCRIPTION
- Return type
TYPE
- property config_file_path
- property file_extension
file extension :rtype: string
- Type
return
- property file_name
name of the file :rtype: string
- Type
return
- property file_size
file size in bytes :rtype: integer
- Type
return
- get_channel_response_filter(rxcal_fn=None, scal_fn=None)[source]
Get the channel response filter
- Parameters
rxcal_fn (TYPE, optional) – DESCRIPTION, defaults to None
scal_fn (TYPE, optional) – DESCRIPTION, defaults to None
- Returns
DESCRIPTION
- Return type
TYPE
- get_config_object()[source]
Read a config file into an object.
- Returns
DESCRIPTION
- Return type
TYPE
- get_lowpass_filter_name()[source]
Get the lowpass filter used by the receiver pre-decimation.
- Returns
DESCRIPTION
- Return type
TYPE
- get_receiver_lowpass_filter(rxcal_fn)[source]
get reciever lowpass filter from the rxcal.json file
- Parameters
lp_name (TYPE) – DESCRIPTION
rxcal_fn (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- get_receiver_metadata_object()[source]
Read recmeta.json into an object
- Returns
DESCRIPTION
- Return type
TYPE
- get_sensor_filter(scal_fn)[source]
- Parameters
scal_fn (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- property instrument_id
instrument ID :rtype: string
- Type
return
- property max_samples
Max number of samples in a file which is:
(total number of bytes - header length) / frame size * n samples per frame
- Returns
max number of samples in a file
- Return type
int
- open_file_seq(file_seq_num=None)[source]
Open a file in the sequence given the sequence number :param file_seq_num: sequence number to open, defaults to None :type file_seq_num: integer, optional :return: [True] if next file is now open, [False] if it is not :rtype: boolean
- open_next()[source]
Open the next file in the sequence :return: [True] if next file is now open, [False] if it is not :rtype: boolean
- property recmeta_file_path
- property run_metadata
Run metadata updated from recmeta
- Returns
DESCRIPTION
- Return type
TYPE
- property seq
sequence number of the file :rtype: int
- Type
return
- property sequence_list
get all the files in the sequence sorted by sequence number
- property station_metadata
station metadata updated from recmeta
- Returns
DESCRIPTION
- Return type
TYPE
Adopted from TimeSeries reader, making all attributes properties for easier reading and testing.
Module to read and parse native Phoenix Geophysics data formats of the MTU-5C Family
This module implements Streamed readers for segmented-decimated continuus-decimated and native sampling rate time series formats of the MTU-5C family.
- author
Jorge Torres-Solis
Revised 2022 by J. Peacock
- class mth5.io.phoenix.readers.header.Header(**kwargs)[source]
Bases:
objectThe header is 128 bytes with a specific format. This reads in the 128 bytes and provides properties to read each attribute of the header in the correct way.
- property attenuator_gain
- property battery_voltage_v
- property board_model_main
- property board_model_revision
- property bytes_per_sample
- property ch_board_model
- property ch_board_serial
- property ch_firmware
- property channel_id
- property channel_main_gain
- property channel_type
- property decimation_node_id
- property detected_channel_type
- property file_sequence
- property file_type
- property file_version
- property frag_period
- property frame_rollover_count
- property frame_size
- property frame_size_bytes
- property future1
- property future2
- get_channel_metadata()[source]
translate metadata to channel metadata :return: DESCRIPTION :rtype: TYPE
- property gps_elevation
- property gps_horizontal_accuracy
In millimeters
- property gps_lat
- property gps_long
- property gps_vertical_accuracy
In millimeters
- property hardware_configuration
- property header_length
- property instrument_serial_number
- property instrument_type
- property intrinsic_circuitry_gain
This function will adjust the intrinsic circuitry gain based on the sensor range configuration in the configuration fingerprint
For this, we consider that for the Electric channel, calibration path, or H-legacy sensors all go through a 1/4 gain stage, and then they get a virtial x2 gain from Single-ended-diff before the A/D. In the case of newer sensors (differential) instead of a 1/4 gain stage, there is only a 1/2 gain stage
Therefore, in the E,cal and legacy sensor case the circuitry gain is 1/2, while for newer sensors it is 1
Note
Circuitry Gain not directly configurable by the user
- property lp_frequency
- property max_signal
- property min_signal
- property missing_frames
- property preamp_gain
- property recording_id
- property recording_start_time
The actual data recording starts 1 second after the set start time. This is caused by the data logger starting up and initializing filter. This is taken care of in the segment start time
See https://github.com/kujaku11/PhoenixGeoPy/tree/main/Docs for more information.
The time recorded is GPS time.
- Returns
DESCRIPTION
- Return type
TYPE
- property sample_rate
- property sample_rate_base
- property sample_rate_exp
- property saturated_frames
- property timing_flags
- property timing_sat_count
- property timing_stability
- property timing_status
- property total_circuitry_gain
- property total_selectable_gain
- class mth5.io.phoenix.readers.DecimatedContinuousReader(path, num_files=1, report_hw_sat=False, **kwargs)[source]
Bases:
TSReaderBaseClass to create a streamer for continuous decimated time series, i.e. ‘td_150’, ‘td_30’
These files have no sub header information.
- read()[source]
Read in the full data from the file given.
- Returns
single channel data array
- Return type
numpy.ndarray
- read_sequence(start=0, end=None)[source]
Read a sequence of files
- Parameters
start (integer, optional) – starting index in the sequence, defaults to 0
end (integer, optional) – eneding index in the sequence to read, defaults to None
- Returns
data within the given sequence range
- Return type
numpy.ndarray
- property segment_end_time
estimate end time
The first sequence starts 1 second later than the set start time due to initiation within the data logger
- Returns
estimated end time from number of samples
- Return type
mt_metadata.utils.mttime.MTime
- property segment_start_time
estimate the segment start time based on sequence number
The first sequence starts 1 second later than the set start time due to initiation within the data logger
- Returns
start time of the recording
- Return type
mt_metadata.utils.mttime.MTime
- property sequence_end
- property sequence_start
- class mth5.io.phoenix.readers.DecimatedSegmentedReader(path, num_files=1, report_hw_sat=False, **kwargs)[source]
Bases:
TSReaderBaseClass to create a streamer for segmented decimated time series, i.e. ‘td_24k’
These files have a sub header
- class mth5.io.phoenix.readers.Header(**kwargs)[source]
Bases:
objectThe header is 128 bytes with a specific format. This reads in the 128 bytes and provides properties to read each attribute of the header in the correct way.
- property attenuator_gain
- property battery_voltage_v
- property board_model_main
- property board_model_revision
- property bytes_per_sample
- property ch_board_model
- property ch_board_serial
- property ch_firmware
- property channel_id
- property channel_main_gain
- property channel_type
- property decimation_node_id
- property detected_channel_type
- property file_sequence
- property file_type
- property file_version
- property frag_period
- property frame_rollover_count
- property frame_size
- property frame_size_bytes
- property future1
- property future2
- get_channel_metadata()[source]
translate metadata to channel metadata :return: DESCRIPTION :rtype: TYPE
- property gps_elevation
- property gps_horizontal_accuracy
In millimeters
- property gps_lat
- property gps_long
- property gps_vertical_accuracy
In millimeters
- property hardware_configuration
- property header_length
- property instrument_serial_number
- property instrument_type
- property intrinsic_circuitry_gain
This function will adjust the intrinsic circuitry gain based on the sensor range configuration in the configuration fingerprint
For this, we consider that for the Electric channel, calibration path, or H-legacy sensors all go through a 1/4 gain stage, and then they get a virtial x2 gain from Single-ended-diff before the A/D. In the case of newer sensors (differential) instead of a 1/4 gain stage, there is only a 1/2 gain stage
Therefore, in the E,cal and legacy sensor case the circuitry gain is 1/2, while for newer sensors it is 1
Note
Circuitry Gain not directly configurable by the user
- property lp_frequency
- property max_signal
- property min_signal
- property missing_frames
- property preamp_gain
- property recording_id
- property recording_start_time
The actual data recording starts 1 second after the set start time. This is caused by the data logger starting up and initializing filter. This is taken care of in the segment start time
See https://github.com/kujaku11/PhoenixGeoPy/tree/main/Docs for more information.
The time recorded is GPS time.
- Returns
DESCRIPTION
- Return type
TYPE
- property sample_rate
- property sample_rate_base
- property sample_rate_exp
- property saturated_frames
- property timing_flags
- property timing_sat_count
- property timing_stability
- property timing_status
- property total_circuitry_gain
- property total_selectable_gain
- class mth5.io.phoenix.readers.NativeReader(path, num_files=1, scale_to=1, header_length=128, last_frame=0, ad_plus_minus_range=5.0, channel_type='E', report_hw_sat=False, **kwargs)[source]
Bases:
TSReaderBaseNative sampling rate ‘Raw’ time series reader class, these are the .bin files. They are formatted with a header of 128 bytes then frames of 64.
Each frame is 20 x 3 byte (24-bit) data point then a 4 byte footer.
- property npts_per_frame
- read()[source]
Read the full data file.
Note
This uses
numpy.lib.stride_tricks.as_stridedwhich can be unstable if the bytes are not the correct length. See notes by numpy.Got this solution from: https://stackoverflow.com/questions/12080279/how-do-i-create-a-numpy-dtype-that-includes-24-bit-integers?msclkid=3398046ecd6511ec9a37394f28c5aaba
- Returns
scaled data and footer
- Return type
tuple (data, footer)
- read_frames(num_frames)[source]
Read the given amount of frames from the data.
Note
that seek is not reset so if you iterate this the stream reads from the last tell.
- Parameters
num_frames (integer) – Number of frames to read
- Returns
Scaled data from the given number of frames
- Return type
np.ndarray(dtype=float)
- read_sequence(start=0, end=None)[source]
Read sequence of files into a single array
- Parameters
start (integer, optional) – sequence start, defaults to 0
end (integer, optional) – sequence end, defaults to None
- Returns
scaled data
- Return type
np.ndarray(dtype=float32)
- Returns
footer
- Return type
np.ndarray(dtype=int32)
- class mth5.io.phoenix.readers.PhoenixCalibration(cal_fn=None, **kwargs)[source]
Bases:
object- property base_filter_name
- property cal_fn
- property calibration_date
- get_filter(channel, filter_name)[source]
get the lowpass filter for the given channel and lowpass value
- Parameters
channel (TYPE) – DESCRIPTION
lp_name (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- get_filter_lp_name(channel, max_freq)[source]
get the filter name as
{instrument_model}_{instrument_type}_{inst_serial}_{channel}_{max_freq}_lp
- Parameters
channel (TYPE) – DESCRIPTION
max_freq (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- get_filter_sensor_name(sensor)[source]
get the filter name as
{instrument_model}_{instrument_type}_{inst_serial}_{sensor}
- Parameters
max_freq (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- class mth5.io.phoenix.readers.PhoenixConfig(fn=None, **kwargs)[source]
Bases:
objectA container for the config.json file used to control the recording
- property auto_power_enabled
- property config
- property empower_version
- property fn
- property mtc150_reset
- property network
- read(fn=None)[source]
read a config.json file that is in the Phoenix format
- Parameters
fn (TYPE, optional) – DESCRIPTION, defaults to None
- Returns
DESCRIPTION
- Return type
TYPE
- property receiver
- property schedule
- property surveyTechnique
- property timezone
- property timezone_offset
- property version
- class mth5.io.phoenix.readers.PhoenixReceiverMetadata(fn=None, **kwargs)[source]
Bases:
objectA container for the recmeta.json file used to control the recording
- property channel_map
- property e1_metadata
- property e2_metadata
- property fn
- property h1_metadata
- property h2_metadata
- property h3_metadata
- property h4_metadata
- property h5_metadata
- property h6_metadata
- property instrument_id
- property lp_filter_base_name
- read(fn=None)[source]
read a config.json file that is in the Phoenix format
- Parameters
fn (TYPE, optional) – DESCRIPTION, defaults to None
- Returns
DESCRIPTION
- Return type
TYPE
- property run_metadata
- property station_metadata
- property survey_metadata
- class mth5.io.phoenix.readers.TSReaderBase(path, num_files=1, header_length=128, report_hw_sat=False, **kwargs)[source]
Bases:
HeaderGeneric reader that all other readers will inherit
- property base_dir
parent directory of file :rtype:
pathlib.Path- Type
return
- property base_path
full path of file :rtype:
pathlib.Path- Type
return
- property channel_metadata
Channel metadata updated from recmeta
- Returns
DESCRIPTION
- Return type
TYPE
- property config_file_path
- property file_extension
file extension :rtype: string
- Type
return
- property file_name
name of the file :rtype: string
- Type
return
- property file_size
file size in bytes :rtype: integer
- Type
return
- get_channel_response_filter(rxcal_fn=None, scal_fn=None)[source]
Get the channel response filter
- Parameters
rxcal_fn (TYPE, optional) – DESCRIPTION, defaults to None
scal_fn (TYPE, optional) – DESCRIPTION, defaults to None
- Returns
DESCRIPTION
- Return type
TYPE
- get_config_object()[source]
Read a config file into an object.
- Returns
DESCRIPTION
- Return type
TYPE
- get_lowpass_filter_name()[source]
Get the lowpass filter used by the receiver pre-decimation.
- Returns
DESCRIPTION
- Return type
TYPE
- get_receiver_lowpass_filter(rxcal_fn)[source]
get reciever lowpass filter from the rxcal.json file
- Parameters
lp_name (TYPE) – DESCRIPTION
rxcal_fn (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- get_receiver_metadata_object()[source]
Read recmeta.json into an object
- Returns
DESCRIPTION
- Return type
TYPE
- get_sensor_filter(scal_fn)[source]
- Parameters
scal_fn (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- property instrument_id
instrument ID :rtype: string
- Type
return
- property max_samples
Max number of samples in a file which is:
(total number of bytes - header length) / frame size * n samples per frame
- Returns
max number of samples in a file
- Return type
int
- open_file_seq(file_seq_num=None)[source]
Open a file in the sequence given the sequence number :param file_seq_num: sequence number to open, defaults to None :type file_seq_num: integer, optional :return: [True] if next file is now open, [False] if it is not :rtype: boolean
- open_next()[source]
Open the next file in the sequence :return: [True] if next file is now open, [False] if it is not :rtype: boolean
- property recmeta_file_path
- property run_metadata
Run metadata updated from recmeta
- Returns
DESCRIPTION
- Return type
TYPE
- property seq
sequence number of the file :rtype: int
- Type
return
- property sequence_list
get all the files in the sequence sorted by sequence number
- property station_metadata
station metadata updated from recmeta
- Returns
DESCRIPTION
- Return type
TYPE
Phoenix file collection
Created on Thu Aug 4 16:48:47 2022
@author: jpeacock
- class mth5.io.phoenix.phoenix_collection.PhoenixCollection(file_path=None, **kwargs)[source]
Bases:
CollectionA class to collect the various files in a Phoenix file system and try to organize them into runs.
- assign_run_names(df, zeros=4)[source]
Assign run names by looping through start times.
For continous data a single run is assigned as long as the start and end times of each file align. If there is a break a new run name is assigned.
For segmented data a new run name is assigned to each segment
- Parameters
df (
pandas.DataFrame) – Dataframe returned by to_dataframe methodzeros (integer, optional) – Number of zeros in the run name, defaults to 4
- Returns
Dataframe with run names
- Return type
pandas.DataFrame
- get_runs(sample_rates, run_name_zeros=4, calibration_path=None)[source]
Get a list of runs contained within the given folder. First the dataframe will be developed from which the runs are extracted.
For continous data all you need is the first file in the sequence. The reader will read in the entire sequence.
For segmented data it will only read in the given segment, which is slightly different from the original reader.
- Parameters
sample_rates – list of sample rates to read, defaults to [150, 24000]
run_name_zeros (integer, optional) – Number of zeros in the run name, defaults to 4
- Returns
List of run dataframes with only the first block of files
- Return type
OrderedDict
- Example
>>> from mth5.io.phoenix import PhoenixCollection >>> phx_collection = PhoenixCollection(r"/path/to/station") >>> run_dict = phx_collection.get_runs(sample_rates=[150, 24000])
- to_dataframe(sample_rates=[150, 24000], run_name_zeros=4, calibration_path=None)[source]
Get a dataframe of all the files in a given directory with given columns. Loop over station folders.
- Parameters
sample_rates (list of integers, optional) – list of sample rates to read, defaults to [150, 24000]
run_name_zeros (integer, optional) – Number of zeros in the run name, defaults to 4
- Returns
Dataframe with each row representing a single file
- Return type
pandas.DataFrame
Created on Fri May 6 12:39:34 2022
@author: jpeacock
- class mth5.io.phoenix.PhoenixCollection(file_path=None, **kwargs)[source]
Bases:
CollectionA class to collect the various files in a Phoenix file system and try to organize them into runs.
- assign_run_names(df, zeros=4)[source]
Assign run names by looping through start times.
For continous data a single run is assigned as long as the start and end times of each file align. If there is a break a new run name is assigned.
For segmented data a new run name is assigned to each segment
- Parameters
df (
pandas.DataFrame) – Dataframe returned by to_dataframe methodzeros (integer, optional) – Number of zeros in the run name, defaults to 4
- Returns
Dataframe with run names
- Return type
pandas.DataFrame
- get_runs(sample_rates, run_name_zeros=4, calibration_path=None)[source]
Get a list of runs contained within the given folder. First the dataframe will be developed from which the runs are extracted.
For continous data all you need is the first file in the sequence. The reader will read in the entire sequence.
For segmented data it will only read in the given segment, which is slightly different from the original reader.
- Parameters
sample_rates – list of sample rates to read, defaults to [150, 24000]
run_name_zeros (integer, optional) – Number of zeros in the run name, defaults to 4
- Returns
List of run dataframes with only the first block of files
- Return type
OrderedDict
- Example
>>> from mth5.io.phoenix import PhoenixCollection >>> phx_collection = PhoenixCollection(r"/path/to/station") >>> run_dict = phx_collection.get_runs(sample_rates=[150, 24000])
- to_dataframe(sample_rates=[150, 24000], run_name_zeros=4, calibration_path=None)[source]
Get a dataframe of all the files in a given directory with given columns. Loop over station folders.
- Parameters
sample_rates (list of integers, optional) – list of sample rates to read, defaults to [150, 24000]
run_name_zeros (integer, optional) – Number of zeros in the run name, defaults to 4
- Returns
Dataframe with each row representing a single file
- Return type
pandas.DataFrame
- class mth5.io.phoenix.PhoenixConfig(fn=None, **kwargs)[source]
Bases:
objectA container for the config.json file used to control the recording
- property auto_power_enabled
- property config
- property empower_version
- property fn
- property mtc150_reset
- property network
- read(fn=None)[source]
read a config.json file that is in the Phoenix format
- Parameters
fn (TYPE, optional) – DESCRIPTION, defaults to None
- Returns
DESCRIPTION
- Return type
TYPE
- property receiver
- property schedule
- property surveyTechnique
- property timezone
- property timezone_offset
- property version
- class mth5.io.phoenix.PhoenixReceiverMetadata(fn=None, **kwargs)[source]
Bases:
objectA container for the recmeta.json file used to control the recording
- property channel_map
- property e1_metadata
- property e2_metadata
- property fn
- property h1_metadata
- property h2_metadata
- property h3_metadata
- property h4_metadata
- property h5_metadata
- property h6_metadata
- property instrument_id
- property lp_filter_base_name
- read(fn=None)[source]
read a config.json file that is in the Phoenix format
- Parameters
fn (TYPE, optional) – DESCRIPTION, defaults to None
- Returns
DESCRIPTION
- Return type
TYPE
- property run_metadata
- property station_metadata
- property survey_metadata
Created on Thu Aug 27 16:54:09 2020
- author
Jared Peacock
- license
MIT
- class mth5.io.usgs_ascii.usgs_ascii.USGSascii(fn=None, **kwargs)[source]
Bases:
AsciiMetadataRead and write USGS ascii formatted time series.
Attributes
Description
ts
Pandas dataframe holding the time series data
fn
Full path to .asc file
station_dir
Full path to station directory
meta_notes
Notes of how the station was collected
- Example
>>> zc = Z3DCollection() >>> fn_list = zc.get_time_blocks(z3d_path) >>> zm = USGSasc() >>> zm.SurveyID = 'iMUSH' >>> zm.get_z3d_db(fn_list[0]) >>> zm.read_mtft24_cfg() >>> zm.CoordinateSystem = 'Geomagnetic North' >>> zm.SurveyID = 'MT' >>> zm.write_asc_file(str_fmt='%15.7e') >>> zm.write_station_info_metadata()
- property ex
- property ey
- property hx
HX
- property hy
- property hz
- read(fn=None)[source]
Read in a USGS ascii file and fill attributes accordingly.
- Parameters
fn (string) – full path to .asc file to be read in
- write(save_fn=None, chunk_size=1024, str_fmt='%15.7e', full=True, compress=False, save_dir=None, compress_type='zip', convert_electrics=True)[source]
Write an ascii file in the USGS ascii format.
- Parameters
save_fn (string) – full path to file name to save the merged ascii to
chunck_size (int) – chunck size to write file in blocks, larger numbers are typically slower.
str_fmt (string) – format of the data as written
full (boolean [ True | False ]) – write out the complete file, mostly for testing.
compress (boolean [ True | False ]) – compress file
compress_type (boolean [ zip | gzip ]) – compress file using zip or gzip
- class mth5.io.usgs_ascii.AsciiMetadata(fn=None, **kwargs)[source]
Bases:
objectContainer for all the important metadata in a USGS ascii file.
Attributes
Description
survey_id
Survey name
site_id
Site name
run_id
Run number
site_latitude
Site latitude in decimal degrees WGS84
site_longitude
Site longitude in decimal degrees WGS84
site_elevation
Site elevation according to national map meters
start
Start time of station YYYY-MM-DDThh:mm:ss UTC
end
Stop time of station YYYY-MM-DDThh:mm:ss UTC
sample_rate
Sampling rate samples/second
n_samples
Number of samples
n_channels
Number of channels
coordinate_system
[ Geographic North | Geomagnetic North ]
chn_settings
Channel settings, see below
missing_data_flag
Missing data value
- Chn_settings
Keys
Description
ChnNum
site_id+channel number
ChnID
Component [ ex | ey | hx | hy | hz ]
InstrumentID
Data logger + sensor number
Azimuth
Setup angle of componet in degrees relative to coordinate_system
Dipole_Length
Dipole length in meters
- property elevation
get elevation from national map
- property end
- property file_size
- property fn
- get_component_info(comp)[source]
- Parameters
comp (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- property latitude
- property longitude
- property n_channels
- property n_samples
- read_metadata(fn=None, meta_lines=None)[source]
Read in a meta from the raw string or file. Populate all metadata as attributes.
- Parameters
fn (string) – full path to USGS ascii file
meta_lines (list) – lines of metadata to read
- property run_id
- property run_metadata
- property sample_rate
- property site_id
- property start
- property station_metadata
- property survey_id
- property survey_metadata
- class mth5.io.usgs_ascii.USGSascii(fn=None, **kwargs)[source]
Bases:
AsciiMetadataRead and write USGS ascii formatted time series.
Attributes
Description
ts
Pandas dataframe holding the time series data
fn
Full path to .asc file
station_dir
Full path to station directory
meta_notes
Notes of how the station was collected
- Example
>>> zc = Z3DCollection() >>> fn_list = zc.get_time_blocks(z3d_path) >>> zm = USGSasc() >>> zm.SurveyID = 'iMUSH' >>> zm.get_z3d_db(fn_list[0]) >>> zm.read_mtft24_cfg() >>> zm.CoordinateSystem = 'Geomagnetic North' >>> zm.SurveyID = 'MT' >>> zm.write_asc_file(str_fmt='%15.7e') >>> zm.write_station_info_metadata()
- property ex
- property ey
- property hx
HX
- property hy
- property hz
- read(fn=None)[source]
Read in a USGS ascii file and fill attributes accordingly.
- Parameters
fn (string) – full path to .asc file to be read in
- write(save_fn=None, chunk_size=1024, str_fmt='%15.7e', full=True, compress=False, save_dir=None, compress_type='zip', convert_electrics=True)[source]
Write an ascii file in the USGS ascii format.
- Parameters
save_fn (string) – full path to file name to save the merged ascii to
chunck_size (int) – chunck size to write file in blocks, larger numbers are typically slower.
str_fmt (string) – format of the data as written
full (boolean [ True | False ]) – write out the complete file, mostly for testing.
compress (boolean [ True | False ]) – compress file
compress_type (boolean [ zip | gzip ]) – compress file using zip or gzip
- class mth5.io.usgs_ascii.USGSasciiCollection(file_path=None, **kwargs)[source]
Bases:
CollectionCollection of USGS ASCII files.
>>> from mth5.io.usgs_ascii import USGSasciiCollection >>> lc = USGSasciiCollection(r"/path/to/ascii/files") >>> run_dict = lc.get_runs(1)
- assign_run_names(df, zeros=4)[source]
Assign run names based on start and end times, checks if a file has the same start time as the last end time.
Run names are assigned as sr{sample_rate}_{run_number:0{zeros}}. Only if the run name is not assigned already.
- Parameters
df (
pandas.DataFrame) – Dataframe with the appropriate columnszeros (int, optional) – number of zeros in run name, defaults to 4
- Returns
Dataframe with run names
- Return type
pandas.DataFrame
- to_dataframe(sample_rates=[4], run_name_zeros=4, calibration_path=None)[source]
Create a data frame of each TXT file in a given directory.
Note
If a run name is already present it will not be overwritten
- Parameters
sample_rates (int or list, optional) – sample rate to get, defaults to [4]
run_name_zeros (int, optional) – number of zeros to assing to the run name, defaults to 4
calibration_path (string or Path, optional) – path to calibration files, defaults to None
- Returns
Dataframe with information of each TXT file in the given directory.
- Return type
pandas.DataFrame- Example
>>> from mth5.io.usgs_ascii import USGSasciiCollection >>> lc = USGSasciiCollection("/path/to/ascii/files") >>> ascii_df = lc.to_dataframe()
Read an amtant.cal file provided by Zonge.
Apparently, the file includes the 6th and 8th harmonic of the given frequency, which is a fancy way of saying f x 6 and f x 8.
- class mth5.io.zen.coil_response.CoilResponse(calibration_file=None, angular_frequency=False)[source]
Bases:
object- property calibration_file
- file_exists()[source]
Check to make sure the file exists
- Returns
True if it does, False if it does not
- Return type
boolean
- get_coil_response_fap(coil_number, extrapolate=True)[source]
Read an amtant.cal file provided by Zonge.
Apparently, the file includes the 6th and 8th harmonic of the given frequency, which is a fancy way of saying f * 6 and f * 8.
- Parameters
coil_number (int or string) – ANT4 4 digit serial number
- Returns
Frequency look up table
- Return type
mt_metadata.timeseries.filters.FrequencyResponseTableFilter
- has_coil_number(coil_number)[source]
Test if coil number is in the antenna file
- Parameters
coil_number (int or string) – ANT4 serial number
- Returns
True if the coil is found, False if it is not
- Return type
boolean
- read_antenna_file(antenna_calibration_file=None)[source]
Read in the Antenna file to frequency, amplitude, phase of the proper harmonics (6, 8)
Note
Phase is measureed in milli-radians and will be converted
to radians.
- Parameters
antenna_calibration_file (string or Path) – path to antenna.cal file provided by Zonge
An object to hold Z3D file information to make processing easier.
Created on Sat Apr 4 12:40:40 2020
@author: peacock
- class mth5.io.zen.z3d_collection.Z3DCollection(file_path=None, **kwargs)[source]
Bases:
CollectionAn object to deal with a collection of Z3D files. Metadata and information are contained with in Pandas DataFrames for easy searching.
- get_calibrations(antenna_calibration_file)[source]
Get coil calibrations from the antenna.cal file
- Parameters
antenna_calibration_file (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- to_dataframe(sample_rates=[256, 4096], run_name_zeros=4, calibration_path=None)[source]
Get general z3d information and put information in a dataframe
- Parameters
z3d_fn_list (list) – List of files Paths to z3d files
- Returns
Dataframe of z3d information
- Return type
Pandas.DataFrame
- Example
>>> zc_obj = zc.Z3DCollection(r"/home/z3d_files") >>> z3d_fn_list = zc.get_z3d_fn_list() >>> z3d_df = zc.get_z3d_info(z3d_fn_list) >>> # write dataframe to a file to use later >>> z3d_df.to_csv(r"/home/z3d_files/z3d_info.csv")
Tools for reading and writing files for Zen and processing software
Tools for copying data from SD cards
Tools for copying schedules to SD cards
Created on Tue Jun 11 10:53:23 2013 Updated August 2020 (JP)
- copyright
Jared Peacock (jpeacock@usgs.gov)
- license
MIT
- class mth5.io.zen.z3d_header.Z3DHeader(fn=None, fid=None, **kwargs)[source]
Bases:
objectRead in the header information of a Z3D file and make each metadata entry an attirbute.
- Parameters
fn (string or
pathlib.Path) – full path to Z3D filefid (file) – file object ex. open(Z3Dfile, ‘rb’)
Attributes
Definition
_header_len
lenght of header in bits (512)
ad_gain
gain of channel
ad_rate
sampling rate in Hz
alt
altitude of the station (not reliable)
attenchannelsmask
not sure
box_number
ZEN box number
box_serial
ZEN box serial number
channel
channel number of the file
channelserial
serial number of the channel board
duty
duty cycle of the transmitter
fpga_buildnum
build number of one of the boards
gpsweek
GPS week
header_str
full header string
lat
latitude of station
logterminal
not sure
long
longitude of the station
main_hex_buildnum
build number of the ZEN box in hexidecimal
numsats
number of gps satelites
period
period of the transmitter
tx_duty
transmitter duty cycle
tx_freq
transmitter frequency
version
version of the firmware
- Example
>>> import mtpy.usgs.zen as zen >>> Z3Dfn = r"/home/mt/mt01/mt01_20150522_080000_256_EX.Z3D" >>> header_obj = zen.Z3DHeader() >>> header_obj.read_header()
- convert_value(key_string, value_string)[source]
convert the value to the appropriate units given the key
- property data_logger
Data logger name as ZEN{box_number}
- read_header(fn=None, fid=None)[source]
Read the header information into appropriate attributes
- Parameters
fn (string or
pathlib.Path) – full path to Z3D filefid (file) – file object ex. open(Z3Dfile, ‘rb’)
- Example
>>> import mtpy.usgs.zen as zen >>> Z3Dfn = r"/home/mt/mt01/mt01_20150522_080000_256_EX.Z3D" >>> header_obj = zen.Z3DHeader() >>> header_obj.read_header()
Created on Wed Aug 24 11:35:59 2022
@author: jpeacock
- class mth5.io.zen.z3d_metadata.Z3DMetadata(fn=None, fid=None, **kwargs)[source]
Bases:
objectWill read in the metadata information of a Z3D file and make each metadata entry an attirbute.The attributes are left in capitalization of the Z3D file.
- Parameters
fn (string or
pathlib.Path) – full path to Z3D filefid (file) – file object ex. open(Z3Dfile, ‘rb’)
Attributes
Definition
_header_length
length of header in bits (512)
_metadata_length
length of metadata blocks (512)
_schedule_metadata_len
length of schedule meta data (512)
board_cal
board calibration np.ndarray()
cal_ant
antenna calibration
cal_board
board calibration
cal_ver
calibration version
ch_azimuth
channel azimuth
ch_cmp
channel component
ch_length
channel length (or # of coil)
ch_number
channel number on the ZEN board
ch_xyz1
channel xyz location (not sure)
ch_xyz2
channel xyz location (not sure)
coil_cal
coil calibration np.ndarray (freq, amp, phase)
fid
file object
find_metadata
boolean of finding metadata
fn
full path to Z3D file
gdp_operator
operater of the survey
gdp_progver
program version
job_by
job preformed by
job_for
job for
job_name
job name
job_number
job number
m_tell
location in the file where the last metadata block was found.
rx_aspace
electrode spacing
rx_sspace
not sure
rx_xazimuth
x azimuth of electrode
rx_xyz0
not sure
rx_yazimuth
y azimuth of electrode
survey_type
type of survey
unit_length
length units (m)
- Example
>>> import mtpy.usgs.zen as zen >>> Z3Dfn = r"/home/mt/mt01/mt01_20150522_080000_256_EX.Z3D" >>> header_obj = zen.Z3DMetadata() >>> header_obj.read_metadata()
Created on Wed Aug 24 11:24:57 2022
@author: jpeacock
- class mth5.io.zen.z3d_schedule.Z3DSchedule(fn=None, fid=None, **kwargs)[source]
Bases:
objectWill read in the schedule information of a Z3D file and make each metadata entry an attirbute. The attributes are left in capitalization of the Z3D file.
- Parameters
fn (string or
pathlib.Path) – full path to Z3D filefid (file) – file object ex. open(Z3Dfile, ‘rb’)
Attributes
Definition
AutoGain
Auto gain for the channel
Comment
Any comments for the schedule
Date
Date of when the schedule action was started YYYY-MM-DD
Duty
Duty cycle of the transmitter
FFTStacks
FFT stacks from the transmitter
Filename
Name of the file that the ZEN gives it
Gain
Gain of the channel
Log
Log the data [ Y | N ]
NewFile
Create a new file [ Y | N ]
Period
Period of the transmitter
RadioOn
Turn on the radio [ Y | N ]
SR
Sampling Rate in Hz
SamplesPerAcq
Samples per aquisition for transmitter
Sleep
Set the box to sleep [ Y | N ]
Sync
Sync with GPS [ Y | N ]
Time
Time the schedule action started HH:MM:SS (GPS time)
_header_len
length of header in bits (512)
_schedule_metadata_len
length of schedule metadata in bits (512)
fid
file object of the file
fn
file name to read in
meta_string
string of the schedule
- Example
>>> import mtpy.usgs.zen as zen >>> Z3Dfn = r"/home/mt/mt01/mt01_20150522_080000_256_EX.Z3D" >>> header_obj = zen.Z3DSchedule() >>> header_obj.read_schedule()
Tools for reading and writing files for Zen and processing software
Tools for copying data from SD cards
Tools for copying schedules to SD cards
Created on Tue Jun 11 10:53:23 2013 Updated August 2020 (JP)
- copyright
Jared Peacock (jpeacock@usgs.gov)
- license
MIT
- class mth5.io.zen.zen.Z3D(fn=None, **kwargs)[source]
Bases:
objectDeals with the raw Z3D files output by zen. :param **fn**: full path to .Z3D file to be read in :type **fn**: string
Attributes
Description
Default Value
_block_len
length of data block to read in as chunks faster reading
65536
_counts_to_mv_conversion
conversion factor to convert counts to mv
9.53674316406e-10
_gps_bytes
number of bytes for a gps stamp
16
_gps_dtype
data type for a gps stamp
see below
_gps_epoch
starting date of GPS time format is a tuple
- (1980, 1, 6, 0,
0, 0, -1, -1, 0)
_gps_f0
first gps flag in raw binary
_gps_f1
second gps flag in raw binary
_gps_flag_0
first gps flag as an int32
2147483647
_gps_flag_1
second gps flag as an int32
-2147483648
_gps_stamp_length
bit length of gps stamp
64
_leap_seconds
leap seconds, difference between UTC time and GPS time. GPS time is ahead by this much
16
_week_len
week length in seconds
604800
df
sampling rate of the data
256
fn
Z3D file name
None
gps_flag
full gps flag
_gps_f0+_gps_f1
gps_stamps
np.ndarray of gps stamps
None
header
Z3DHeader object
Z3DHeader
metadata
Z3DMetadata
Z3DMetadata
schedule
Z3DSchedule
Z3DSchedule
time_series
np.ndarra(len_data)
None
units
units in which the data is in
counts
zen_schedule
time when zen was set to run
None
- gps_dtype is formated as np.dtype([(‘flag0’, np.int32),
(‘flag1’, np.int32), (‘time’, np.int32), (‘lat’, np.float64), (‘lon’, np.float64), (‘num_sat’, np.int32), (‘gps_sens’, np.int32), (‘temperature’, np.float32), (‘voltage’, np.float32), (‘num_fpga’, np.int32), (‘num_adc’, np.int32), (‘pps_count’, np.int32), (‘dac_tune’, np.int32), (‘block_len’, np.int32)])
- Example
>>> import mtpy.usgs.zen as zen >>> zt = zen.Zen3D(r"/home/mt/mt00/mt00_20150522_080000_256_EX.Z3D") >>> zt.read_z3d() >>> ------- Reading /home/mt/mt00/mt00_20150522_080000_256_EX.Z3D ----- --> Reading data took: 0.322 seconds Scheduled time was 2015-05-22,08:00:16 (GPS time) 1st good stamp was 2015-05-22,08:00:18 (GPS time) difference of 2.00 seconds found 6418 GPS time stamps found 1642752 data points >>> zt.plot_time_series()
- property azimuth
azimuth of instrument setup
- property channel_metadata
Channel metadata
- property channel_number
- property channel_response
- check_start_time()[source]
check to make sure the scheduled start time is similar to the first good gps stamp
- property coil_number
coil number
- property coil_response
Make the coile response into a FAP filter
Phase must be in radians
- property component
channel
- convert_mv_to_counts(data)[source]
convert millivolts to counts assuming no other scaling has been applied
- property counts2mv_filter
Create a counts2mv coefficient filter
Note
Needs to be 1/channel factor because we divided the instrument response from the data.
- property dipole_filter
- property dipole_length
dipole length
- property elevation
elevation in meters
- property end
- property file_size
- property fn
- get_UTC_date_time(gps_week, gps_time)[source]
get the actual date and time of measurement as UTC.
- Parameters
gps_week (int) – integer value of gps_week that the data was collected
gps_time (int) – number of seconds from beginning of gps_week
- Returns
mth5.utils.mttime.MTime
- get_gps_stamp_index(ts_data, old_version=False)[source]
locate the time stamps in a given time series.
Looks for gps_flag_0 first, if the file is newer, then makes sure the next value is gps_flag_1
- Returns
list of gps stamps indicies
- get_gps_time(gps_int, gps_week=0)[source]
from the gps integer get the time in seconds.
- Parameters
gps_int (int) – integer from the gps time stamp line
gps_week (int) – relative gps week, if the number of seconds is larger than a week then a week is subtracted from the seconds and computed from gps_week += 1
- Returns
gps_time as number of seconds from the beginning of the relative gps week.
- property latitude
latitude in decimal degrees
- property longitude
longitude in decimal degrees
- property n_samples
- read_z3d(z3d_fn=None)[source]
read in z3d file and populate attributes accordingly
Read in the entire file as chunks as np.int32.
Extract the gps stamps and convert accordingly. Check to make sure gps time stamps are 1 second apart and incrementing as well as checking the number of data points between stamps is the same as the sampling rate.
- Converts gps_stamps[‘time’] to seconds relative to header.gps_week
Note we skip the first two gps stamps because there is something wrong with the data there due to some type of buffering. Therefore the first GPS time is when the time series starts, so you will notice that gps_stamps[0][‘block_len’] = 0, this is because there is nothing previous to this time stamp and so the ‘block_len’ measures backwards from the corresponding time index.
Put the data chunks into Pandas data frame that is indexed by time
- Example
>>> from mth5.io import zen >>> z_obj = zen.Z3D(r"home/mt_data/zen/mt001.z3d") >>> z_obj.read_z3d()
- property run_metadata
Run metadata
- property sample_rate
sampling rate
- property start
- property station
station name
- property station_metadata
station metadta
- trim_data()[source]
apparently need to skip the first 2 seconds of data because of something to do with the SD buffer
This method will be deprecated after field testing
- validate_time_blocks()[source]
validate gps time stamps and make sure each block is the proper length
- property zen_response
Zen response, not sure the full calibration comes directly from the Z3D file, so skipping for now. Will have to read a Zen##.cal file to get the full calibration. This shouldn’t be a big issue cause it should roughly be the same for all channels and since the TF is computing the ratio they will cancel out. Though we should look more into this if just looking at calibrate time series.
- property zen_schedule
zen schedule data and time
- class mth5.io.zen.CoilResponse(calibration_file=None, angular_frequency=False)[source]
Bases:
object- property calibration_file
- file_exists()[source]
Check to make sure the file exists
- Returns
True if it does, False if it does not
- Return type
boolean
- get_coil_response_fap(coil_number, extrapolate=True)[source]
Read an amtant.cal file provided by Zonge.
Apparently, the file includes the 6th and 8th harmonic of the given frequency, which is a fancy way of saying f * 6 and f * 8.
- Parameters
coil_number (int or string) – ANT4 4 digit serial number
- Returns
Frequency look up table
- Return type
mt_metadata.timeseries.filters.FrequencyResponseTableFilter
- has_coil_number(coil_number)[source]
Test if coil number is in the antenna file
- Parameters
coil_number (int or string) – ANT4 serial number
- Returns
True if the coil is found, False if it is not
- Return type
boolean
- read_antenna_file(antenna_calibration_file=None)[source]
Read in the Antenna file to frequency, amplitude, phase of the proper harmonics (6, 8)
Note
Phase is measureed in milli-radians and will be converted
to radians.
- Parameters
antenna_calibration_file (string or Path) – path to antenna.cal file provided by Zonge
- class mth5.io.zen.Z3D(fn=None, **kwargs)[source]
Bases:
objectDeals with the raw Z3D files output by zen. :param **fn**: full path to .Z3D file to be read in :type **fn**: string
Attributes
Description
Default Value
_block_len
length of data block to read in as chunks faster reading
65536
_counts_to_mv_conversion
conversion factor to convert counts to mv
9.53674316406e-10
_gps_bytes
number of bytes for a gps stamp
16
_gps_dtype
data type for a gps stamp
see below
_gps_epoch
starting date of GPS time format is a tuple
- (1980, 1, 6, 0,
0, 0, -1, -1, 0)
_gps_f0
first gps flag in raw binary
_gps_f1
second gps flag in raw binary
_gps_flag_0
first gps flag as an int32
2147483647
_gps_flag_1
second gps flag as an int32
-2147483648
_gps_stamp_length
bit length of gps stamp
64
_leap_seconds
leap seconds, difference between UTC time and GPS time. GPS time is ahead by this much
16
_week_len
week length in seconds
604800
df
sampling rate of the data
256
fn
Z3D file name
None
gps_flag
full gps flag
_gps_f0+_gps_f1
gps_stamps
np.ndarray of gps stamps
None
header
Z3DHeader object
Z3DHeader
metadata
Z3DMetadata
Z3DMetadata
schedule
Z3DSchedule
Z3DSchedule
time_series
np.ndarra(len_data)
None
units
units in which the data is in
counts
zen_schedule
time when zen was set to run
None
- gps_dtype is formated as np.dtype([(‘flag0’, np.int32),
(‘flag1’, np.int32), (‘time’, np.int32), (‘lat’, np.float64), (‘lon’, np.float64), (‘num_sat’, np.int32), (‘gps_sens’, np.int32), (‘temperature’, np.float32), (‘voltage’, np.float32), (‘num_fpga’, np.int32), (‘num_adc’, np.int32), (‘pps_count’, np.int32), (‘dac_tune’, np.int32), (‘block_len’, np.int32)])
- Example
>>> import mtpy.usgs.zen as zen >>> zt = zen.Zen3D(r"/home/mt/mt00/mt00_20150522_080000_256_EX.Z3D") >>> zt.read_z3d() >>> ------- Reading /home/mt/mt00/mt00_20150522_080000_256_EX.Z3D ----- --> Reading data took: 0.322 seconds Scheduled time was 2015-05-22,08:00:16 (GPS time) 1st good stamp was 2015-05-22,08:00:18 (GPS time) difference of 2.00 seconds found 6418 GPS time stamps found 1642752 data points >>> zt.plot_time_series()
- property azimuth
azimuth of instrument setup
- property channel_metadata
Channel metadata
- property channel_number
- property channel_response
- check_start_time()[source]
check to make sure the scheduled start time is similar to the first good gps stamp
- property coil_number
coil number
- property coil_response
Make the coile response into a FAP filter
Phase must be in radians
- property component
channel
- convert_mv_to_counts(data)[source]
convert millivolts to counts assuming no other scaling has been applied
- property counts2mv_filter
Create a counts2mv coefficient filter
Note
Needs to be 1/channel factor because we divided the instrument response from the data.
- property dipole_filter
- property dipole_length
dipole length
- property elevation
elevation in meters
- property end
- property file_size
- property fn
- get_UTC_date_time(gps_week, gps_time)[source]
get the actual date and time of measurement as UTC.
- Parameters
gps_week (int) – integer value of gps_week that the data was collected
gps_time (int) – number of seconds from beginning of gps_week
- Returns
mth5.utils.mttime.MTime
- get_gps_stamp_index(ts_data, old_version=False)[source]
locate the time stamps in a given time series.
Looks for gps_flag_0 first, if the file is newer, then makes sure the next value is gps_flag_1
- Returns
list of gps stamps indicies
- get_gps_time(gps_int, gps_week=0)[source]
from the gps integer get the time in seconds.
- Parameters
gps_int (int) – integer from the gps time stamp line
gps_week (int) – relative gps week, if the number of seconds is larger than a week then a week is subtracted from the seconds and computed from gps_week += 1
- Returns
gps_time as number of seconds from the beginning of the relative gps week.
- property latitude
latitude in decimal degrees
- property longitude
longitude in decimal degrees
- property n_samples
- read_z3d(z3d_fn=None)[source]
read in z3d file and populate attributes accordingly
Read in the entire file as chunks as np.int32.
Extract the gps stamps and convert accordingly. Check to make sure gps time stamps are 1 second apart and incrementing as well as checking the number of data points between stamps is the same as the sampling rate.
- Converts gps_stamps[‘time’] to seconds relative to header.gps_week
Note we skip the first two gps stamps because there is something wrong with the data there due to some type of buffering. Therefore the first GPS time is when the time series starts, so you will notice that gps_stamps[0][‘block_len’] = 0, this is because there is nothing previous to this time stamp and so the ‘block_len’ measures backwards from the corresponding time index.
Put the data chunks into Pandas data frame that is indexed by time
- Example
>>> from mth5.io import zen >>> z_obj = zen.Z3D(r"home/mt_data/zen/mt001.z3d") >>> z_obj.read_z3d()
- property run_metadata
Run metadata
- property sample_rate
sampling rate
- property start
- property station
station name
- property station_metadata
station metadta
- trim_data()[source]
apparently need to skip the first 2 seconds of data because of something to do with the SD buffer
This method will be deprecated after field testing
- validate_time_blocks()[source]
validate gps time stamps and make sure each block is the proper length
- property zen_response
Zen response, not sure the full calibration comes directly from the Z3D file, so skipping for now. Will have to read a Zen##.cal file to get the full calibration. This shouldn’t be a big issue cause it should roughly be the same for all channels and since the TF is computing the ratio they will cancel out. Though we should look more into this if just looking at calibrate time series.
- property zen_schedule
zen schedule data and time
- class mth5.io.zen.Z3DCollection(file_path=None, **kwargs)[source]
Bases:
CollectionAn object to deal with a collection of Z3D files. Metadata and information are contained with in Pandas DataFrames for easy searching.
- get_calibrations(antenna_calibration_file)[source]
Get coil calibrations from the antenna.cal file
- Parameters
antenna_calibration_file (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- to_dataframe(sample_rates=[256, 4096], run_name_zeros=4, calibration_path=None)[source]
Get general z3d information and put information in a dataframe
- Parameters
z3d_fn_list (list) – List of files Paths to z3d files
- Returns
Dataframe of z3d information
- Return type
Pandas.DataFrame
- Example
>>> zc_obj = zc.Z3DCollection(r"/home/z3d_files") >>> z3d_fn_list = zc.get_z3d_fn_list() >>> z3d_df = zc.get_z3d_info(z3d_fn_list) >>> # write dataframe to a file to use later >>> z3d_df.to_csv(r"/home/z3d_files/z3d_info.csv")
- class mth5.io.zen.Z3DHeader(fn=None, fid=None, **kwargs)[source]
Bases:
objectRead in the header information of a Z3D file and make each metadata entry an attirbute.
- Parameters
fn (string or
pathlib.Path) – full path to Z3D filefid (file) – file object ex. open(Z3Dfile, ‘rb’)
Attributes
Definition
_header_len
lenght of header in bits (512)
ad_gain
gain of channel
ad_rate
sampling rate in Hz
alt
altitude of the station (not reliable)
attenchannelsmask
not sure
box_number
ZEN box number
box_serial
ZEN box serial number
channel
channel number of the file
channelserial
serial number of the channel board
duty
duty cycle of the transmitter
fpga_buildnum
build number of one of the boards
gpsweek
GPS week
header_str
full header string
lat
latitude of station
logterminal
not sure
long
longitude of the station
main_hex_buildnum
build number of the ZEN box in hexidecimal
numsats
number of gps satelites
period
period of the transmitter
tx_duty
transmitter duty cycle
tx_freq
transmitter frequency
version
version of the firmware
- Example
>>> import mtpy.usgs.zen as zen >>> Z3Dfn = r"/home/mt/mt01/mt01_20150522_080000_256_EX.Z3D" >>> header_obj = zen.Z3DHeader() >>> header_obj.read_header()
- convert_value(key_string, value_string)[source]
convert the value to the appropriate units given the key
- property data_logger
Data logger name as ZEN{box_number}
- read_header(fn=None, fid=None)[source]
Read the header information into appropriate attributes
- Parameters
fn (string or
pathlib.Path) – full path to Z3D filefid (file) – file object ex. open(Z3Dfile, ‘rb’)
- Example
>>> import mtpy.usgs.zen as zen >>> Z3Dfn = r"/home/mt/mt01/mt01_20150522_080000_256_EX.Z3D" >>> header_obj = zen.Z3DHeader() >>> header_obj.read_header()
- class mth5.io.zen.Z3DMetadata(fn=None, fid=None, **kwargs)[source]
Bases:
objectWill read in the metadata information of a Z3D file and make each metadata entry an attirbute.The attributes are left in capitalization of the Z3D file.
- Parameters
fn (string or
pathlib.Path) – full path to Z3D filefid (file) – file object ex. open(Z3Dfile, ‘rb’)
Attributes
Definition
_header_length
length of header in bits (512)
_metadata_length
length of metadata blocks (512)
_schedule_metadata_len
length of schedule meta data (512)
board_cal
board calibration np.ndarray()
cal_ant
antenna calibration
cal_board
board calibration
cal_ver
calibration version
ch_azimuth
channel azimuth
ch_cmp
channel component
ch_length
channel length (or # of coil)
ch_number
channel number on the ZEN board
ch_xyz1
channel xyz location (not sure)
ch_xyz2
channel xyz location (not sure)
coil_cal
coil calibration np.ndarray (freq, amp, phase)
fid
file object
find_metadata
boolean of finding metadata
fn
full path to Z3D file
gdp_operator
operater of the survey
gdp_progver
program version
job_by
job preformed by
job_for
job for
job_name
job name
job_number
job number
m_tell
location in the file where the last metadata block was found.
rx_aspace
electrode spacing
rx_sspace
not sure
rx_xazimuth
x azimuth of electrode
rx_xyz0
not sure
rx_yazimuth
y azimuth of electrode
survey_type
type of survey
unit_length
length units (m)
- Example
>>> import mtpy.usgs.zen as zen >>> Z3Dfn = r"/home/mt/mt01/mt01_20150522_080000_256_EX.Z3D" >>> header_obj = zen.Z3DMetadata() >>> header_obj.read_metadata()
- class mth5.io.zen.Z3DSchedule(fn=None, fid=None, **kwargs)[source]
Bases:
objectWill read in the schedule information of a Z3D file and make each metadata entry an attirbute. The attributes are left in capitalization of the Z3D file.
- Parameters
fn (string or
pathlib.Path) – full path to Z3D filefid (file) – file object ex. open(Z3Dfile, ‘rb’)
Attributes
Definition
AutoGain
Auto gain for the channel
Comment
Any comments for the schedule
Date
Date of when the schedule action was started YYYY-MM-DD
Duty
Duty cycle of the transmitter
FFTStacks
FFT stacks from the transmitter
Filename
Name of the file that the ZEN gives it
Gain
Gain of the channel
Log
Log the data [ Y | N ]
NewFile
Create a new file [ Y | N ]
Period
Period of the transmitter
RadioOn
Turn on the radio [ Y | N ]
SR
Sampling Rate in Hz
SamplesPerAcq
Samples per aquisition for transmitter
Sleep
Set the box to sleep [ Y | N ]
Sync
Sync with GPS [ Y | N ]
Time
Time the schedule action started HH:MM:SS (GPS time)
_header_len
length of header in bits (512)
_schedule_metadata_len
length of schedule metadata in bits (512)
fid
file object of the file
fn
file name to read in
meta_string
string of the schedule
- Example
>>> import mtpy.usgs.zen as zen >>> Z3Dfn = r"/home/mt/mt01/mt01_20150522_080000_256_EX.Z3D" >>> header_obj = zen.Z3DSchedule() >>> header_obj.read_schedule()
Phoenix file collection
Created on Thu Aug 4 16:48:47 2022
@author: jpeacock
- class mth5.io.collection.Collection(file_path=None, **kwargs)[source]
Bases:
objectA general collection class to keep track of files with methods to create runs and run ids.
- property file_path
Path object to file directory
- get_empty_entry_dict()[source]
- Returns
an empty dictionary with the proper keys for an entry into a dataframe
- Return type
dict
- get_files(extension)[source]
Get files with given extension. Uses Pathlib.Path.rglob, so it finds all files within the file_path by searching all sub-directories.
- Parameters
extension (string or list) – file extension(s)
- Returns
list of files in the file_path with the given extensions
- Return type
list of Path objects
- get_runs(sample_rates, run_name_zeros=4, calibration_path=None)[source]
Get a list of runs contained within the given folder. First the dataframe will be developed from which the runs are extracted.
For continous data all you need is the first file in the sequence. The reader will read in the entire sequence.
For segmented data it will only read in the given segment, which is slightly different from the original reader.
- Parameters
sample_rates – list of sample rates to read, defaults to [150, 24000]
run_name_zeros (integer, optional) – Number of zeros in the run name, defaults to 4
- Returns
List of run dataframes with only the first block of files
- Return type
collections.OrderedDict- Example
>>> from mth5.io.phoenix import PhoenixCollection >>> phx_collection = PhoenixCollection(r"/path/to/station") >>> run_dict = phx_collection.get_runs(sample_rates=[150, 24000])
- to_dataframe()[source]
Get a data frame of the file summary with column names:
survey: survey id
station: station id
run: run id
start: start time UTC
end: end time UTC
channel_id: channel id or list of channel id’s in file
component: channel component or list of components in file
fn: path to file
sample_rate: sample rate in samples per second
file_size: file size in bytes
n_samples: number of samples in file
sequence_number: sequence number of the file
instrument_id: instrument id
calibration_fn: calibration file
- Returns
summary table of file names,
- Return type
TYPE
This is a utility function to get the appropriate reader for a given file type and
return the appropriate object of mth5.timeseries
This setup to be like plugins but a hack cause I did not find the time to set this up properly as a true plugin.
If you are writing your own reader you need the following structure:
Class object that will read the given file
a reader function that is read_{file_type}, for instance read_nims
the return value is a
mth5.timeseries.MTTSormth5.timeseries.RunTSobject and any extra metadata in the form of a dictionary with keys as {level.attribute}.
class NewFile
def __init__(self, fn):
self.fn = fn
def read_header(self):
return header_information
def read_newfile(self):
ex, ey, hx, hy, hz = read_in_channels_as_MTTS
return RunTS([ex, ey, hx, hy, hz])
def read_newfile(fn):
new_file_obj = NewFile(fn)
run_obj = new_file_obj.read_newfile()
return run_obj, extra_metadata
Then add your reader to the reader dictionary so that those files can be read.
See also
Existing readers for some guidance found in mth5.io
Created on Wed Aug 26 10:32:45 2020
- author
Jared Peacock
- license
MIT
- mth5.io.reader.get_reader(extension)[source]
get the proper reader for file extension
- Parameters
extension (string) – file extension
- Returns
the correct function to read the file
- Return type
function
- mth5.io.reader.read_file(fn, file_type=None, **kwargs)[source]
This is the universal reader for MT time series. This will pick out the proper reader given the file type or extension. Keyworkd arguments will depend on the reader and file type.
- Parameters
fn (string or
pathlib.Path) – full path to filefile_type (string) – a specific file time if the extension is ambiguous.
- Returns
channel or run time series object
- Return type
mth5.timeseries.MTTSormth5.timeseries.RunTS
- class mth5.io.Collection(file_path=None, **kwargs)[source]
Bases:
objectA general collection class to keep track of files with methods to create runs and run ids.
- property file_path
Path object to file directory
- get_empty_entry_dict()[source]
- Returns
an empty dictionary with the proper keys for an entry into a dataframe
- Return type
dict
- get_files(extension)[source]
Get files with given extension. Uses Pathlib.Path.rglob, so it finds all files within the file_path by searching all sub-directories.
- Parameters
extension (string or list) – file extension(s)
- Returns
list of files in the file_path with the given extensions
- Return type
list of Path objects
- get_runs(sample_rates, run_name_zeros=4, calibration_path=None)[source]
Get a list of runs contained within the given folder. First the dataframe will be developed from which the runs are extracted.
For continous data all you need is the first file in the sequence. The reader will read in the entire sequence.
For segmented data it will only read in the given segment, which is slightly different from the original reader.
- Parameters
sample_rates – list of sample rates to read, defaults to [150, 24000]
run_name_zeros (integer, optional) – Number of zeros in the run name, defaults to 4
- Returns
List of run dataframes with only the first block of files
- Return type
collections.OrderedDict- Example
>>> from mth5.io.phoenix import PhoenixCollection >>> phx_collection = PhoenixCollection(r"/path/to/station") >>> run_dict = phx_collection.get_runs(sample_rates=[150, 24000])
- to_dataframe()[source]
Get a data frame of the file summary with column names:
survey: survey id
station: station id
run: run id
start: start time UTC
end: end time UTC
channel_id: channel id or list of channel id’s in file
component: channel component or list of components in file
fn: path to file
sample_rate: sample rate in samples per second
file_size: file size in bytes
n_samples: number of samples in file
sequence_number: sequence number of the file
instrument_id: instrument id
calibration_fn: calibration file
- Returns
summary table of file names,
- Return type
TYPE
- mth5.io.read_file(fn, file_type=None, **kwargs)[source]
This is the universal reader for MT time series. This will pick out the proper reader given the file type or extension. Keyworkd arguments will depend on the reader and file type.
- Parameters
fn (string or
pathlib.Path) – full path to filefile_type (string) – a specific file time if the extension is ambiguous.
- Returns
channel or run time series object
- Return type
mth5.timeseries.MTTSormth5.timeseries.RunTS
mth5.tables package
Created on Wed Mar 23 14:09:38 2022
@author: jpeacock
Created on Wed Dec 23 16:53:55 2020
- author
Jared Peacock
- license
MIT
- class mth5.tables.mth5_table.MTH5Table(hdf5_dataset)[source]
Bases:
objectUse the underlying NumPy basics, there are simple actions in this table, if a user wants to use something more sophisticated for querying they should try using a pandas table. In this case entries in the table are more difficult to change and datatypes need to be kept track of.
- add_row(row, index=None)[source]
Add a row to the table.
row must be of the same data type as the table
- Parameters
row (TYPE) – row entry for the table
index (integer, if None is given then the row is added to the end of the array) – index of row to add
- Returns
index of the row added
- Return type
integer
- clear_table()[source]
clear a table,
Basically delete the table and start over :return: DESCRIPTION :rtype: TYPE
- property dtype
- locate(column, value, test='eq')[source]
locate index where column is equal to value :param column: DESCRIPTION :type column: TYPE :param value: DESCRIPTION :type value: TYPE :type test: type of test to try * ‘eq’: equals * ‘lt’: less than * ‘le’: less than or equal to * ‘gt’: greater than * ‘ge’: greater than or equal to. * ‘be’: between or equal to * ‘bt’: between
If be or bt input value as a list of 2 values
- Returns
DESCRIPTION
- Return type
TYPE
- property nrows
- remove_row(index)[source]
Remove a row
Note
that there is not index value within the array, so the indexing is on the fly. A user should use the HDF5 reference instead of index number that is the safest and most robust method.
- Parameters
index (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
This isn’t as easy as just deleteing an element. Need to delete the element from the weakly referenced array and then set the summary table dataset to the new array.
So set to a null array for now until a more clever option is found.
- property shape
Created on Wed Mar 23 14:09:38 2022
@author: jpeacock
- class mth5.tables.ChannelSummaryTable(hdf5_dataset)[source]
Bases:
MTH5TableObject to hold the channel summary and provide some convenience functions like fill, to_dataframe …
- class mth5.tables.MTH5Table(hdf5_dataset)[source]
Bases:
objectUse the underlying NumPy basics, there are simple actions in this table, if a user wants to use something more sophisticated for querying they should try using a pandas table. In this case entries in the table are more difficult to change and datatypes need to be kept track of.
- add_row(row, index=None)[source]
Add a row to the table.
row must be of the same data type as the table
- Parameters
row (TYPE) – row entry for the table
index (integer, if None is given then the row is added to the end of the array) – index of row to add
- Returns
index of the row added
- Return type
integer
- clear_table()[source]
clear a table,
Basically delete the table and start over :return: DESCRIPTION :rtype: TYPE
- property dtype
- locate(column, value, test='eq')[source]
locate index where column is equal to value :param column: DESCRIPTION :type column: TYPE :param value: DESCRIPTION :type value: TYPE :type test: type of test to try * ‘eq’: equals * ‘lt’: less than * ‘le’: less than or equal to * ‘gt’: greater than * ‘ge’: greater than or equal to. * ‘be’: between or equal to * ‘bt’: between
If be or bt input value as a list of 2 values
- Returns
DESCRIPTION
- Return type
TYPE
- property nrows
- remove_row(index)[source]
Remove a row
Note
that there is not index value within the array, so the indexing is on the fly. A user should use the HDF5 reference instead of index number that is the safest and most robust method.
- Parameters
index (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
This isn’t as easy as just deleteing an element. Need to delete the element from the weakly referenced array and then set the summary table dataset to the new array.
So set to a null array for now until a more clever option is found.
- property shape
mth5.timeseries package
lists and arrays that goes on, seems easiest to convert all lists to strings and then convert them back if read in.
- copyright
Jared Peacock (jpeacock@usgs.gov)
- license
MIT
- class mth5.timeseries.channel_ts.ChannelTS(channel_type='auxiliary', data=None, channel_metadata=None, station_metadata=None, run_metadata=None, survey_metadata=None, **kwargs)[source]
Bases:
objectNote
Assumes equally spaced samples from the start time.
The time series is stored in an
xarray.Datasetthat has coordinates of time and is a 1-D array labeled ‘data’. Thexarray.Datasetcan be accessed and set from the ts. The data is stored in ‘ts.data’ and the time index is a coordinate of ts.The time coordinate is made from the start time, sample rate and number of samples. Currently, End time is a derived property and cannot be set.
Channel time series object is based on xarray and
mth5.metadatatherefore any type of interpolation, resampling, groupby, etc can be done using xarray methods.There are 3 metadata classes that hold important metadata
mth5.metadata.Stationholds information about the stationmth5.metadata.Runholds information about the run the channel
belongs to. * :class`mth5.metadata.Channel` holds information specific to the channel.
This way a single channel will hold all information needed to represent the channel.
- Rubric
>>> from mth5.timeseries import ChannelTS >>> ts_obj = ChannelTS('auxiliary') >>> ts_obj.sample_rate = 8 >>> ts_obj.start = '2020-01-01T12:00:00+00:00' >>> ts_obj.ts = range(4096) >>> ts_obj.station_metadata.id = 'MT001' >>> ts_obj.run_metadata.id = 'MT001a' >>> ts_obj.component = 'temperature' >>> print(ts_obj) Station = MT001 Run = MT001a Channel Type = auxiliary Component = temperature Sample Rate = 8.0 Start = 2020-01-01T12:00:00+00:00 End = 2020-01-01T12:08:31.875000+00:00 N Samples = 4096 >>> p = ts_obj.ts.plot()
- property channel_metadata
station metadata
- property channel_response_filter
Full channel response filter
- Returns
full channel response filter
- Return type
mt_metadata.timeseries.filters.ChannelResponseFilter
- property channel_type
Channel Type
- property component
- copy(data=True)[source]
Make a copy of the ChannelTS object with or without data.
- Parameters
data (boolean) – include data in the copy (True) or not (False)
- Returns
Copy of the channel
- Return type
:class:`mth5.timeseries.ChannelTS
- decimate(new_sample_rate, inplace=False, max_decimation=8)[source]
decimate the data by using scipy.signal.decimate
- Parameters
dec_factor (int) – decimation factor
refills ts.data with decimated data and replaces sample_rate
- property end
MTime object
- from_obspy_trace(obspy_trace)[source]
Fill data from an
obspy.core.Trace- Parameters
obspy_trace (obspy.core.trace) – Obspy trace object
- get_slice(start, end=None, n_samples=None)[source]
Get a slice from the time series given a start and end time.
Looks for >= start & <= end
Uses loc to be exact with milliseconds
- Parameters
start (string, MTime) – start time of the slice
end (string, MTime) – end time of the slice
n_samples (integer) – number of sample to get after start time
- Returns
slice of the channel requested
- Return type
- merge(other, gap_method='slinear', new_sample_rate=None, resample_method='poly')[source]
merg two channels or list of channels together in the following steps
xr.combine_by_coords([original, other])
compute monotonic time index
reindex(new_time_index, method=gap_method)
If you want a different method or more control use merge
- Parameters
other (
mth5.timeseries.ChannelTS) – Another channel- Raises
TypeError – If input is not a ChannelTS
ValueError – if the components are different
- Returns
Combined channel with monotonic time index and same metadata
- Return type
- property n_samples
number of samples
- plot_spectra(spectra_type='welch', window_length=4096, **kwargs)[source]
- Parameters
spectra_type (string, optional) – spectra type, defaults to “welch”
window_length (int, optional) – window length of the welch method should be a power of 2, defaults to 2 ** 12
**kwargs –
DESCRIPTION
- remove_instrument_response(**kwargs)[source]
Remove instrument response from the given channel response filter
The order of operations is important (if applied):
detrend
zero mean
zero pad
time window
frequency window
remove response
undo time window
bandpass
kwargs
- Parameters
plot (boolean, default True) – to plot the calibration process [ False | True ]
detrend (boolean, default True) – Remove linar trend of the time series
zero_mean (boolean, default True) – Remove the mean of the time series
zero_pad (boolean, default True) – pad the time series to the next power of 2 for efficiency
t_window (string, default None) – Time domain windown name see scipy.signal.windows for options
t_window_params – Time domain window parameters, parameters can be
found in scipy.signal.windows :type t_window_params: dictionary :param f_window: Frequency domain windown name see scipy.signal.windows for options :type f_window: string, defualt None :param f_window_params: Frequency window parameters, parameters can be found in scipy.signal.windows :type f_window_params: dictionary :param bandpass: bandpass freequency and order {“low”:, “high”:, “order”:,} :type bandpass: dictionary
- resample_poly(new_sample_rate, pad_type='mean', inplace=False)[source]
Use scipy.signal.resample_poly to resample data while using an FIR filter to remove aliasing.
- Parameters
new_sample_rate (TYPE) – DESCRIPTION
pad_type (TYPE, optional) – DESCRIPTION, defaults to “mean”
- Returns
DESCRIPTION
- Return type
TYPE
- property run_metadata
station metadata
- property sample_interval
Sample interval = 1 / sample_rate
- Returns
sample interval as time distance between time samples
- Return type
float
- property sample_rate
sample rate in samples/second
- property start
MTime object
- property station_metadata
station metadata
- property survey_metadata
survey metadata
- property time_index
time index as a numpy array dtype np.datetime[ns]
- Returns
array of the time index
- Return type
np.ndarray(dtype=np.datetime[ns])
- to_obspy_trace()[source]
Convert the time series to an
obspy.core.trace.Traceobject. This will be helpful for converting between data pulled from IRIS and data going into IRIS.- Returns
DESCRIPTION
- Return type
TYPE
- to_xarray()[source]
Returns a
xarray.DataArrayobject of the channel timeseries this way metadata from the metadata class is updated upon return.- Returns
Returns a
xarray.DataArrayobject of the channel timeseries
this way metadata from the metadata class is updated upon return. :rtype:
xarray.DataArray>>> import numpy as np >>> from mth5.timeseries import ChannelTS >>> ex = ChannelTS("electric") >>> ex.start = "2020-01-01T12:00:00" >>> ex.sample_rate = 16 >>> ex.ts = np.random.rand(4096)
- property ts
Time series as a numpy array
lists and arrays that goes on, seems easiest to convert all lists to strings and then convert them back if read in.
- copyright
Jared Peacock (jpeacock@usgs.gov)
- license
MIT
- class mth5.timeseries.run_ts.RunTS(array_list=None, run_metadata=None, station_metadata=None, survey_metadata=None)[source]
Bases:
objectholds all run ts in one aligned array
components –> {‘ex’: ex_xarray, ‘ey’: ey_xarray}
ToDo, have a single Survey object under the hood and properties to other metadata objects for get/set.
- add_channel(channel)[source]
Add a channel to the dataset, can be an
xarray.DataArrayormth5.timeseries.ChannelTSobject.Need to be sure that the coordinates and dimensions are the same as the existing dataset, namely coordinates are time, and dimensions are the same, if the dimesions are larger than the existing dataset then the added channel will be clipped to the dimensions of the existing dataset.
If the start time is not the same nan’s will be placed at locations where the timing does not match the current start time. This is a feature of xarray.
- Parameters
channel (
xarray.DataArrayormth5.timeseries.ChannelTS) – a channel xarray or ChannelTS to add to the run
- calibrate(**kwargs)[source]
Calibrate the data according to the filters in each channel.
- Returns
calibrated run
- Return type
- property channels
List of channel names in dataset
- copy(data=True)[source]
- Parameters
data (TYPE, optional) – DESCRIPTION, defaults to True
- Returns
DESCRIPTION
- Return type
TYPE
- property dataset
xarray.Dataset
- decimate(new_sample_rate, inplace=False, max_decimation=8)[source]
decimate data to new sample rate.
- Parameters
new_sample_rate (TYPE) – DESCRIPTION
inplace (TYPE, optional) – DESCRIPTION, defaults to False
- Returns
DESCRIPTION
- Return type
TYPE
- property end
End time UTC
- property filters
Dictionary of filters used by the channels
- from_obspy_stream(obspy_stream, run_metadata=None)[source]
Get a run from an
obspy.core.streamwhich is a list ofobspy.core.Traceobjects.- Parameters
obspy_stream (
obspy.core.Stream) – Obspy Stream object
- get_slice(start, end=None, n_samples=None)[source]
- Parameters
start (TYPE) – DESCRIPTION
end (TYPE, optional) – DESCRIPTION, defaults to None
n_samples (TYPE, optional) – DESCRIPTION, defaults to None
- Raises
ValueError – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- merge(other, gap_method='slinear', new_sample_rate=None, resample_method='poly')[source]
merg two runs or list of runs together in the following steps
xr.combine_by_coords([original, other])
compute monotonic time index
reindex(new_time_index, method=gap_method)
If you want a different method or more control use merge
- Parameters
other (
mth5.timeseries.RunTS) – Another run- Raises
TypeError – If input is not a RunTS
ValueError – if the components are different
- Returns
Combined run with monotonic time index and same metadata
- Return type
- plot(color_map={'ex': (1, 0.2, 0.2), 'ey': (1, 0.5, 0), 'hx': (0, 0.5, 1), 'hy': (0.5, 0.2, 1), 'hz': (0.2, 1, 1)}, channel_order=None)[source]
plot the time series probably slow for large data sets
- plot_spectra(spectra_type='welch', color_map={'ex': (1, 0.2, 0.2), 'ey': (1, 0.5, 0), 'hx': (0, 0.5, 1), 'hy': (0.5, 0.2, 1), 'hz': (0.2, 1, 1)}, **kwargs)[source]
Plot spectra using spectra type, only ‘welch’ is supported now.
- Parameters
spectra_type (string, optional) – spectra type, defaults to “welch”
color_map (dictionary, optional) – colors of channels, defaults to { “ex”: (1, 0.2, 0.2), “ey”: (1, 0.5, 0), “hx”: (0, 0.5, 1), “hy”: (0.5, 0.2, 1), “hz”: (0.2, 1, 1), }
**kwargs –
key words for the spectra type
- resample(new_sample_rate, inplace=False)[source]
Resample data to new sample rate. :param new_sample_rate: DESCRIPTION :type new_sample_rate: TYPE :param inplace: DESCRIPTION, defaults to False :type inplace: TYPE, optional :return: DESCRIPTION :rtype: TYPE
- resample_poly(new_sample_rate, pad_type='mean', inplace=False)[source]
Use scipy.signal.resample_poly to resample data while using an FIR filter to remove aliasing.
- Parameters
new_sample_rate (TYPE) – DESCRIPTION
pad_type (TYPE, optional) – DESCRIPTION, defaults to “mean”
- Returns
DESCRIPTION
- Return type
TYPE
- property run_metadata
station metadata
- property sample_interval
Sample interval = 1 / sample_rate :return: DESCRIPTION :rtype: TYPE
- property sample_rate
Sample rate, this is estimated by the mdeian difference between samples in time, if data is present. Otherwise return the metadata sample rate.
- set_dataset(array_list, align_type='outer')[source]
- Parameters
array_list (list of
mth5.timeseries.ChannelTSobjects) – list of xarraysalign_type (string) – how the different times will be aligned * ’outer’: use the union of object indexes * ’inner’: use the intersection of object indexes * ’left’: use indexes from the first object with each dimension * ’right’: use indexes from the last object with each dimension * ’exact’: instead of aligning, raise ValueError when indexes to be aligned are not equal * ’override’: if indexes are of same size, rewrite indexes to be those of the first object with that dimension. Indexes for the same dimension must have the same size in all objects.
- property start
Start time UTC
- property station_metadata
station metadata
- property summarize_metadata
Get a summary of all the metadata
- Returns
A summary of all channel metadata in one place
- Return type
dictionary
- property survey_metadata
survey metadata
time series filters
- class mth5.timeseries.ts_filters.RemoveInstrumentResponse(ts, time_array, sample_interval, channel_response_filter, **kwargs)[source]
Bases:
objectRemove instrument response from the given channel response filter
The order of operations is important (if applied):
detrend
zero mean
zero pad
time window
frequency window
remove response
undo time window
bandpass
- Parameters
ts (np.ndarray((N,) , dtype=float)) – time series data to remove response from
time_array (np.ndarray((N,) , dtype=np.datetime[ns])) – time index that corresponds to the time series
sample_interval (float) – seconds per sample (time interval between samples)
channel_response_filter – Channel response filter with all filters
included to convert from counts to physical units :type channel_response_filter: class:mt_metadata.timeseries.filters.ChannelResponseFilter`
kwargs
- Parameters
plot (boolean, default True) – to plot the calibration process [ False | True ]
detrend (boolean, default True) – Remove linar trend of the time series
zero_mean (boolean, default True) – Remove the mean of the time series
zero_pad (boolean, default True) – pad the time series to the next power of 2 for efficiency
t_window (string, default None) – Time domain windown name see scipy.signal.windows for options
t_window_params – Time domain window parameters, parameters can be
found in scipy.signal.windows :type t_window_params: dictionary :param f_window: Frequency domain windown name see scipy.signal.windows for options :type f_window: string, defualt None :param f_window_params: Frequency window parameters, parameters can be found in scipy.signal.windows :type f_window_params: dictionary :param bandpass: bandpass freequency and order {“low”:, “high”:, “order”:,} :type bandpass: dictionary
- apply_bandpass(ts)[source]
apply a bandpass filter to the calibrated data
- Parameters
ts (np.ndarray) – calibrated time series
- Returns
bandpassed time series
- Return type
np.ndarray
- apply_detrend(ts)[source]
Detrend time series using scipy.detrend(‘linear’)
- Parameters
ts (np.ndarray) – input time series
- Returns
detrended time series
- Return type
np.ndarray
- apply_f_window(data)[source]
Apply a frequency domain window. Get the available windows from scipy.signal.windows
Need to create a window twice the size of the input because we are only taking the rfft which gives just half the spectra and then take only half the window
- Parameters
data (np.ndarray) – input spectra
- Returns
windowed spectra
- Return type
np.ndarray
- apply_t_window(ts)[source]
Apply a window in the time domain. Get the available windows from scipy.signal.windows
- Parameters
ts (np.ndarray) – input time series
- Returns
windowed time series
- Return type
np.ndarray
- apply_zero_mean(ts)[source]
Remove the mean from the time series
- Parameters
ts (np.ndarray) – input time series
- Returns
zero mean time series
- Return type
np.ndarray
- apply_zero_pad(ts)[source]
zero pad to power of 2, at the end of the time series to make the FFT more efficient
- Parameters
ts (np.ndarray) – input time series
- Returns
zero padded time series
- Return type
np.ndarray
- mth5.timeseries.ts_filters.adaptive_notch_filter(bx, df=100, notches=[50, 100], notchradius=0.5, freqrad=0.9, rp=0.1, dbstop_limit=5.0)[source]
- Parameters
bx (np.ndarray) – time series to filter
df (float, optional) – sample rate in samples per second, defaults to 100
notches (list, optional) – list of frequencies to locate notches at in Hz, defaults to [50, 100]
notchradius (float, optional) – notch radius, defaults to 0.5
freqrad (float, optional) – radius to search for a peak at the notch frequency, defaults to 0.9
rp (float, optional) – ripple of Chebyshev type 1 filter, lower numbers means less ripples, defaults to 0.1
dbstop_limit (float, optional) – limits the difference between the peak at the notch and surrounding spectra. Any difference above dbstop_limit will be filtered, anything less will not, defaults to 5.0
- Returns
notch filtered data
- Return type
np.ndarray
- Returns
list of notch frequencies
- Return type
list
Example
>>> import RemovePeriodicNoise_Kate as rmp >>> # make a variable for the file to load in >>> fn = r"/home/MT/mt01_20130101_000000.BX" >>> # load in file, if the time series is not an ascii file >>> # might need to add keywords to np.loadtxt or use another >>> # method to read in the file >>> bx = np.loadtxt(fn) >>> # create a list of frequencies to filter out >>> freq_notches = [50, 150, 200] >>> # filter data >>> bx_filt, filt_lst = rmp.adaptiveNotchFilter(bx, df=100. >>> ... notches=freq_notches) >>> #save the filtered data into a file >>> np.savetxt(r"/home/MT/Filtered/mt01_20130101_000000.BX", bx_filt)
Notes
Most of the time the default parameters work well, the only thing you need to change is the notches and perhaps the radius. I would test it out with a few time series to find the optimum parameters. Then make a loop over all you time series data. Something like
>>> import os >>> dirpath = r"/home/MT" >>> #make a director to save filtered time series >>> save_path = r"/home/MT/Filtered" >>> if not os.path.exists(save_path): >>> os.mkdir(save_path) >>> for fn in os.listdir(dirpath): >>> bx = np.loadtxt(os.path.join(dirpath, fn) >>> bx_filt, filt_lst = rmp.adaptiveNotchFilter(bx, df=100. >>> ... notches=freq_notches) >>> np.savetxt(os.path.join(save_path, fn), bx_filt)
- mth5.timeseries.ts_filters.butter_bandpass(lowcut, highcut, fs, order=5)[source]
Butterworth bandpass filter using scipy.signal
- Parameters
lowcut (float) – low cut frequency in Hz
highcut (float) – high cut frequency in Hz
fs (float) – Sample rate
order (int, optional) – Butterworth order, defaults to 5
- Returns
SOS scipy.signal format
- Return type
scipy.signal.SOS?
- mth5.timeseries.ts_filters.butter_bandpass_filter(data, lowcut, highcut, fs, order=5)[source]
- Parameters
data (np.ndarray) – 1D time series data
lowcut (float) – low cut frequency in Hz
highcut (float) – high cut frequency in Hz
fs (float) – Sample rate
order (int, optional) – Butterworth order, defaults to 5
- Returns
filtered data
- Return type
np.ndarray
- mth5.timeseries.ts_filters.low_pass(data, low_pass_freq, cutoff_freq, sampling_rate)[source]
- Parameters
data (np.ndarray) – 1D time series data
low_pass_freq (float) – low pass frequency in Hz
cutoff_freq (float) – cut off frequency in Hz
sampling_rate (float) – Sample rate in samples per second
- Returns
lowpass filtered data
- Return type
np.ndarray
- mth5.timeseries.ts_filters.zero_pad(input_array, power=2, pad_fill=0)[source]
- Parameters
input_array (np.ndarray) – 1D array
power – base power to used to pad to, defaults to 2 which is optimal
for the FFT :type power: int, optional :param pad_fill: fill value for padded values, defaults to 0 :type pad_fill: float, optional :return: zero padded array :rtype: np.ndarray
- class mth5.timeseries.ChannelTS(channel_type='auxiliary', data=None, channel_metadata=None, station_metadata=None, run_metadata=None, survey_metadata=None, **kwargs)[source]
Bases:
objectNote
Assumes equally spaced samples from the start time.
The time series is stored in an
xarray.Datasetthat has coordinates of time and is a 1-D array labeled ‘data’. Thexarray.Datasetcan be accessed and set from the ts. The data is stored in ‘ts.data’ and the time index is a coordinate of ts.The time coordinate is made from the start time, sample rate and number of samples. Currently, End time is a derived property and cannot be set.
Channel time series object is based on xarray and
mth5.metadatatherefore any type of interpolation, resampling, groupby, etc can be done using xarray methods.There are 3 metadata classes that hold important metadata
mth5.metadata.Stationholds information about the stationmth5.metadata.Runholds information about the run the channel
belongs to. * :class`mth5.metadata.Channel` holds information specific to the channel.
This way a single channel will hold all information needed to represent the channel.
- Rubric
>>> from mth5.timeseries import ChannelTS >>> ts_obj = ChannelTS('auxiliary') >>> ts_obj.sample_rate = 8 >>> ts_obj.start = '2020-01-01T12:00:00+00:00' >>> ts_obj.ts = range(4096) >>> ts_obj.station_metadata.id = 'MT001' >>> ts_obj.run_metadata.id = 'MT001a' >>> ts_obj.component = 'temperature' >>> print(ts_obj) Station = MT001 Run = MT001a Channel Type = auxiliary Component = temperature Sample Rate = 8.0 Start = 2020-01-01T12:00:00+00:00 End = 2020-01-01T12:08:31.875000+00:00 N Samples = 4096 >>> p = ts_obj.ts.plot()
- property channel_metadata
station metadata
- property channel_response_filter
Full channel response filter
- Returns
full channel response filter
- Return type
mt_metadata.timeseries.filters.ChannelResponseFilter
- property channel_type
Channel Type
- property component
- copy(data=True)[source]
Make a copy of the ChannelTS object with or without data.
- Parameters
data (boolean) – include data in the copy (True) or not (False)
- Returns
Copy of the channel
- Return type
:class:`mth5.timeseries.ChannelTS
- decimate(new_sample_rate, inplace=False, max_decimation=8)[source]
decimate the data by using scipy.signal.decimate
- Parameters
dec_factor (int) – decimation factor
refills ts.data with decimated data and replaces sample_rate
- property end
MTime object
- from_obspy_trace(obspy_trace)[source]
Fill data from an
obspy.core.Trace- Parameters
obspy_trace (obspy.core.trace) – Obspy trace object
- get_slice(start, end=None, n_samples=None)[source]
Get a slice from the time series given a start and end time.
Looks for >= start & <= end
Uses loc to be exact with milliseconds
- Parameters
start (string, MTime) – start time of the slice
end (string, MTime) – end time of the slice
n_samples (integer) – number of sample to get after start time
- Returns
slice of the channel requested
- Return type
- merge(other, gap_method='slinear', new_sample_rate=None, resample_method='poly')[source]
merg two channels or list of channels together in the following steps
xr.combine_by_coords([original, other])
compute monotonic time index
reindex(new_time_index, method=gap_method)
If you want a different method or more control use merge
- Parameters
other (
mth5.timeseries.ChannelTS) – Another channel- Raises
TypeError – If input is not a ChannelTS
ValueError – if the components are different
- Returns
Combined channel with monotonic time index and same metadata
- Return type
- property n_samples
number of samples
- plot_spectra(spectra_type='welch', window_length=4096, **kwargs)[source]
- Parameters
spectra_type (string, optional) – spectra type, defaults to “welch”
window_length (int, optional) – window length of the welch method should be a power of 2, defaults to 2 ** 12
**kwargs –
DESCRIPTION
- remove_instrument_response(**kwargs)[source]
Remove instrument response from the given channel response filter
The order of operations is important (if applied):
detrend
zero mean
zero pad
time window
frequency window
remove response
undo time window
bandpass
kwargs
- Parameters
plot (boolean, default True) – to plot the calibration process [ False | True ]
detrend (boolean, default True) – Remove linar trend of the time series
zero_mean (boolean, default True) – Remove the mean of the time series
zero_pad (boolean, default True) – pad the time series to the next power of 2 for efficiency
t_window (string, default None) – Time domain windown name see scipy.signal.windows for options
t_window_params – Time domain window parameters, parameters can be
found in scipy.signal.windows :type t_window_params: dictionary :param f_window: Frequency domain windown name see scipy.signal.windows for options :type f_window: string, defualt None :param f_window_params: Frequency window parameters, parameters can be found in scipy.signal.windows :type f_window_params: dictionary :param bandpass: bandpass freequency and order {“low”:, “high”:, “order”:,} :type bandpass: dictionary
- resample_poly(new_sample_rate, pad_type='mean', inplace=False)[source]
Use scipy.signal.resample_poly to resample data while using an FIR filter to remove aliasing.
- Parameters
new_sample_rate (TYPE) – DESCRIPTION
pad_type (TYPE, optional) – DESCRIPTION, defaults to “mean”
- Returns
DESCRIPTION
- Return type
TYPE
- property run_metadata
station metadata
- property sample_interval
Sample interval = 1 / sample_rate
- Returns
sample interval as time distance between time samples
- Return type
float
- property sample_rate
sample rate in samples/second
- property start
MTime object
- property station_metadata
station metadata
- property survey_metadata
survey metadata
- property time_index
time index as a numpy array dtype np.datetime[ns]
- Returns
array of the time index
- Return type
np.ndarray(dtype=np.datetime[ns])
- to_obspy_trace()[source]
Convert the time series to an
obspy.core.trace.Traceobject. This will be helpful for converting between data pulled from IRIS and data going into IRIS.- Returns
DESCRIPTION
- Return type
TYPE
- to_xarray()[source]
Returns a
xarray.DataArrayobject of the channel timeseries this way metadata from the metadata class is updated upon return.- Returns
Returns a
xarray.DataArrayobject of the channel timeseries
this way metadata from the metadata class is updated upon return. :rtype:
xarray.DataArray>>> import numpy as np >>> from mth5.timeseries import ChannelTS >>> ex = ChannelTS("electric") >>> ex.start = "2020-01-01T12:00:00" >>> ex.sample_rate = 16 >>> ex.ts = np.random.rand(4096)
- property ts
Time series as a numpy array
- class mth5.timeseries.RunTS(array_list=None, run_metadata=None, station_metadata=None, survey_metadata=None)[source]
Bases:
objectholds all run ts in one aligned array
components –> {‘ex’: ex_xarray, ‘ey’: ey_xarray}
ToDo, have a single Survey object under the hood and properties to other metadata objects for get/set.
- add_channel(channel)[source]
Add a channel to the dataset, can be an
xarray.DataArrayormth5.timeseries.ChannelTSobject.Need to be sure that the coordinates and dimensions are the same as the existing dataset, namely coordinates are time, and dimensions are the same, if the dimesions are larger than the existing dataset then the added channel will be clipped to the dimensions of the existing dataset.
If the start time is not the same nan’s will be placed at locations where the timing does not match the current start time. This is a feature of xarray.
- Parameters
channel (
xarray.DataArrayormth5.timeseries.ChannelTS) – a channel xarray or ChannelTS to add to the run
- calibrate(**kwargs)[source]
Calibrate the data according to the filters in each channel.
- Returns
calibrated run
- Return type
- property channels
List of channel names in dataset
- copy(data=True)[source]
- Parameters
data (TYPE, optional) – DESCRIPTION, defaults to True
- Returns
DESCRIPTION
- Return type
TYPE
- property dataset
xarray.Dataset
- decimate(new_sample_rate, inplace=False, max_decimation=8)[source]
decimate data to new sample rate.
- Parameters
new_sample_rate (TYPE) – DESCRIPTION
inplace (TYPE, optional) – DESCRIPTION, defaults to False
- Returns
DESCRIPTION
- Return type
TYPE
- property end
End time UTC
- property filters
Dictionary of filters used by the channels
- from_obspy_stream(obspy_stream, run_metadata=None)[source]
Get a run from an
obspy.core.streamwhich is a list ofobspy.core.Traceobjects.- Parameters
obspy_stream (
obspy.core.Stream) – Obspy Stream object
- get_slice(start, end=None, n_samples=None)[source]
- Parameters
start (TYPE) – DESCRIPTION
end (TYPE, optional) – DESCRIPTION, defaults to None
n_samples (TYPE, optional) – DESCRIPTION, defaults to None
- Raises
ValueError – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- merge(other, gap_method='slinear', new_sample_rate=None, resample_method='poly')[source]
merg two runs or list of runs together in the following steps
xr.combine_by_coords([original, other])
compute monotonic time index
reindex(new_time_index, method=gap_method)
If you want a different method or more control use merge
- Parameters
other (
mth5.timeseries.RunTS) – Another run- Raises
TypeError – If input is not a RunTS
ValueError – if the components are different
- Returns
Combined run with monotonic time index and same metadata
- Return type
- plot(color_map={'ex': (1, 0.2, 0.2), 'ey': (1, 0.5, 0), 'hx': (0, 0.5, 1), 'hy': (0.5, 0.2, 1), 'hz': (0.2, 1, 1)}, channel_order=None)[source]
plot the time series probably slow for large data sets
- plot_spectra(spectra_type='welch', color_map={'ex': (1, 0.2, 0.2), 'ey': (1, 0.5, 0), 'hx': (0, 0.5, 1), 'hy': (0.5, 0.2, 1), 'hz': (0.2, 1, 1)}, **kwargs)[source]
Plot spectra using spectra type, only ‘welch’ is supported now.
- Parameters
spectra_type (string, optional) – spectra type, defaults to “welch”
color_map (dictionary, optional) – colors of channels, defaults to { “ex”: (1, 0.2, 0.2), “ey”: (1, 0.5, 0), “hx”: (0, 0.5, 1), “hy”: (0.5, 0.2, 1), “hz”: (0.2, 1, 1), }
**kwargs –
key words for the spectra type
- resample(new_sample_rate, inplace=False)[source]
Resample data to new sample rate. :param new_sample_rate: DESCRIPTION :type new_sample_rate: TYPE :param inplace: DESCRIPTION, defaults to False :type inplace: TYPE, optional :return: DESCRIPTION :rtype: TYPE
- resample_poly(new_sample_rate, pad_type='mean', inplace=False)[source]
Use scipy.signal.resample_poly to resample data while using an FIR filter to remove aliasing.
- Parameters
new_sample_rate (TYPE) – DESCRIPTION
pad_type (TYPE, optional) – DESCRIPTION, defaults to “mean”
- Returns
DESCRIPTION
- Return type
TYPE
- property run_metadata
station metadata
- property sample_interval
Sample interval = 1 / sample_rate :return: DESCRIPTION :rtype: TYPE
- property sample_rate
Sample rate, this is estimated by the mdeian difference between samples in time, if data is present. Otherwise return the metadata sample rate.
- set_dataset(array_list, align_type='outer')[source]
- Parameters
array_list (list of
mth5.timeseries.ChannelTSobjects) – list of xarraysalign_type (string) – how the different times will be aligned * ’outer’: use the union of object indexes * ’inner’: use the intersection of object indexes * ’left’: use indexes from the first object with each dimension * ’right’: use indexes from the last object with each dimension * ’exact’: instead of aligning, raise ValueError when indexes to be aligned are not equal * ’override’: if indexes are of same size, rewrite indexes to be those of the first object with that dimension. Indexes for the same dimension must have the same size in all objects.
- property start
Start time UTC
- property station_metadata
station metadata
- property summarize_metadata
Get a summary of all the metadata
- Returns
A summary of all channel metadata in one place
- Return type
dictionary
- property survey_metadata
survey metadata
mth5.utils package
Exceptions raised by MTH5
Created on Wed May 13 19:07:21 2020
@author: jpeacock
Tools for FDSN standards
Created on Wed Sep 30 11:47:01 2020
- author
Jared Peacock
- license
MIT
- mth5.utils.fdsn_tools.get_location_code(channel_obj)[source]
Get the location code given the components and channel number
- Parameters
channel_obj (
Channel) – Channel object- Returns
2 character location code
- Return type
string
- mth5.utils.fdsn_tools.get_measurement_code(measurement)[source]
get SEED sensor code given the measurement type
- Parameters
measurement (string) – measurement type, e.g. * temperature * electric * magnetic
- Returns
single character SEED sensor code, if the measurement type has not been defined yet Y is returned.
- Return type
string
- mth5.utils.fdsn_tools.get_orientation_code(azimuth, orientation='horizontal')[source]
Get orientation code given angle and orientation. This is a general code and the true azimuth is stored in channel
- Parameters
azimuth (float) – angel assuming 0 is north, 90 is east, 0 is vertical down
- Returns
single character SEED orientation code
- Return type
string
- mth5.utils.fdsn_tools.get_period_code(sample_rate)[source]
Get the SEED sampling rate code given a sample rate
- Parameters
sample_rate (float) – sample rate in samples per second
- Returns
single character SEED sampling code
- Return type
string
- mth5.utils.fdsn_tools.make_channel_code(channel_obj)[source]
Make the 3 character SEED channel code
- Parameters
channel_obj (
Channel) – Channel metadata- Returns
3 character channel code
- Type
string
- mth5.utils.helpers.get_compare_dict(input_dict)[source]
Helper function for removing 2 added attributes to metadata
hdf5_reference
mth5_type
- Parameters
input_dict (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- mth5.utils.helpers.initialize_mth5(h5_path, mode='a', file_version='0.1.0')[source]
mth5 initializer for the case of writting files.
- Parameters
h5_path (string or pathlib.Path) – path to file
mode (string) –
how to open the file, options are
”r”: read
”w”: write
”a”: append
- Returns
mth5 object
- Return type
mth5.MTH5
- mth5.utils.helpers.read_back_data(mth5_path, station_id, run_id, survey=None, close_mth5=True, return_objects=[])[source]
Testing helper function, used to confirm that the h5 file can be accessed and that the data size is as expected.
- Parameters
mth5_path (Path or string) – the full path the the mth5 that this method is going to try to read
station_id (string) – the label for the station, e.g. “PKD”
run_id (string) – The label for the run to read. e.g. “001”
survey (string) – The label for the survey associated with the run to read.
close_mth5 (bool) – Whether or not to close the mth5 object after reading
return_objects – List of strings. Specifies what, if anything to return.
Allowed values: [“run”, “run_ts”] :type return_objects: List of strings. :return: run object :rtype:
mth5.groups.RunGroup:return: run time series :rtype:mth5.timeseries.RunTS
Submodules
mth5.helpers module
Helper functions for HDF5
Created on Tue Jun 2 12:37:50 2020
- copyright
Jared Peacock (jpeacock@usgs.gov)
- license
MIT
- mth5.helpers.from_numpy_type(value)[source]
Need to make the attributes friendly with Numpy and HDF5.
For numbers and bool this is straight forward they are automatically mapped in h5py to a numpy type.
But for strings this can be a challenge, especially a list of strings.
HDF5 should only deal with ASCII characters or Unicode. No binary data is allowed.
- mth5.helpers.get_tree(parent)[source]
Simple function to recursively print the contents of an hdf5 group :param parent: HDF5 (sub-)tree to print :type parent:
h5py.Group
- mth5.helpers.to_numpy_type(value)[source]
Need to make the attributes friendly with Numpy and HDF5.
For numbers and bool this is straight forward they are automatically mapped in h5py to a numpy type.
But for strings this can be a challenge, especially a list of strings.
HDF5 should only deal with ASCII characters or Unicode. No binary data is allowed.
- mth5.helpers.validate_compression(compression, level)[source]
validate that the input compression is supported.
- Parameters
compression (string, [ 'lzf' | 'gzip' | 'szip' | None ]) – type of lossless compression
level (string for 'szip' or int for 'gzip') – compression level if supported
- Returns
compression type
- Return type
string
- Returns
compressiong level
- Return type
string for ‘szip’ or int for ‘gzip’
- Raises
ValueError if comporession or level are not supported
- Raises
TypeError if compression level is not a string
mth5.mth5 module
MTH5
MTH5 deals with reading and writing an MTH5 file, which are HDF5 files developed for magnetotelluric (MT) data. The code is based on h5py and therefor numpy. This is the simplest and we are not really dealing with large tables of data to warrant using pytables.
Created on Sun Dec 9 20:50:41 2018
- copyright
Jared Peacock (jpeacock@usgs.gov)
- license
MIT
- class mth5.mth5.MTH5(filename=None, compression='gzip', compression_opts=4, shuffle=True, fletcher32=True, data_level=1, file_version='0.2.0')[source]
Bases:
objectMTH5 is the main container for the HDF5 file format developed for MT data
It uses the metadata standards developled by the IRIS PASSCAL software group and defined in the metadata documentation.
MTH5 is built with h5py and therefore numpy. The structure follows the different levels of MT data collection:
For version 0.1.0:
Survey
Reports
Standards
Filters
Stations
Run
Channel
For version 0.2.0:
Experiment
Reports
Standards
Surveys
Reports
Standards
Filters
Stations
Run
-Channel
All timeseries data are stored as individual channels with the appropriate metadata defined for the given channel, i.e. electric, magnetic, auxiliary.
Each level is represented as a mth5 group class object which has methods to add, remove, and get a group from the level below. Each group has a metadata attribute that is the approprate metadata class object. For instance the SurveyGroup has an attribute metadata that is a
mth5.metadata.Surveyobject. Metadata is stored in the HDF5 group attributes as (key, value) pairs.All groups are represented by their structure tree and can be shown at any time from the command line.
Each level has a summary array of the contents of the levels below to hopefully make searching easier.
- Parameters
filename (string or
pathlib.Path) – name of the to be or existing filecompression –
compression type. Supported lossless compressions are
- ’lzf’ - Available with every installation of h5py
(C source code also available). Low to moderate compression, very fast. No options.
- ’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 3.
- ’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.
compression_opts (string or int depending on compression type) – compression options, see above
shuffle (boolean) – Block-oriented compressors like GZIP or LZF work better when presented with runs of similar values. Enabling the shuffle filter rearranges the bytes in the chunk and may improve compression ratio. No significant speed penalty, lossless.
fletcher32 (boolean) – Adds a checksum to each chunk to detect data corruption. Attempts to read corrupted chunks will fail with an error. No significant speed penalty. Obviously shouldn’t be used with lossy compression filters.
data_level (integer, defaults to 1) –
level the data are stored following levels defined by NASA ESDS
0 - Raw data
1 - Raw data with response information and full metadata
2 - Derived product, raw data has been manipulated
file_version (string, optional) – Version of the file [ ‘0.1.0’ | ‘0.2.0’ ], defaults to “0.2.0”
- Usage
Open a new file and show initialized file
>>> from mth5 import mth5 >>> mth5_obj = mth5.MTH5(file_version='0.1.0') >>> # Have a look at the dataset options >>> mth5.dataset_options {'compression': 'gzip', 'compression_opts': 3, 'shuffle': True, 'fletcher32': True} >>> mth5_obj.open_mth5(r"/home/mtdata/mt01.mth5", 'w') >>> mth5_obj /: ==================== |- Group: Survey ---------------- |- Group: Filters ----------------- --> Dataset: summary ...................... |- Group: Reports ----------------- --> Dataset: summary ...................... |- Group: Standards ------------------- --> Dataset: summary ...................... |- Group: Stations ------------------ --> Dataset: summary ......................
Add metadata for survey from a dictionary
>>> survey_dict = {'survey':{'acquired_by': 'me', 'archive_id': 'MTCND'}} >>> survey = mth5_obj.survey_group >>> survey.metadata.from_dict(survey_dict) >>> survey.metadata { "survey": { "acquired_by.author": "me", "acquired_by.comments": null, "archive_id": "MTCND" ...} }
Add a station from the convenience function
>>> station = mth5_obj.add_station('MT001') >>> mth5_obj /: ==================== |- Group: Survey ---------------- |- Group: Filters ----------------- --> Dataset: summary ...................... |- Group: Reports ----------------- --> Dataset: summary ...................... |- Group: Standards ------------------- --> Dataset: summary ...................... |- Group: Stations ------------------ |- Group: MT001 --------------- --> Dataset: summary ...................... --> Dataset: summary ...................... >>> station /Survey/Stations/MT001: ==================== --> Dataset: summary ......................
>>> data.schedule_01.ex[0:10] = np.nan >>> data.calibration_hx[...] = np.logspace(-4, 4, 20)
Note
if replacing an entire array with a new one you need to use […] otherwise the data will not be updated.
Warning
You can only replace entire arrays with arrays of the same size. Otherwise you need to delete the existing data and make a new dataset.
See also
- add_channel(station_name, run_name, channel_name, channel_type, data, channel_dtype='int32', max_shape=(None,), chunks=True, channel_metadata=None, survey=None)[source]
Convenience function to add a channel using
mth5.stations_group.get_station().get_run().add_channel()add a channel to a given run for a given station
- Parameters
station_name (string) – existing station name
run_name (string) – existing run name
channel_name (string) – name of the channel
channel_type (string) – [ electric | magnetic | auxiliary ]
channel_metadata ([
mth5.metadata.Electric|mth5.metadata.Magnetic|mth5.metadata.Auxiliary], optional) – metadata container, defaults to Nonesurvey (string) – existing survey name, needed for file version >= 0.2.0
- Raises
MTH5Error – If channel type is not correct
- Returns
Channel container
- Return type
[
mth5.mth5_groups.ElectricDatset|mth5.mth5_groups.MagneticDatset|mth5.mth5_groups.AuxiliaryDatset]- Example
>>> new_channel = mth5_obj.add_channel('MT001', 'MT001a''Ex', >>> ... 'electric', None) >>> new_channel Channel Electric: ------------------- component: None data type: electric data format: float32 data shape: (1,) start: 1980-01-01T00:00:00+00:00 end: 1980-01-01T00:00:00+00:00 sample rate: None
- add_run(station_name, run_name, run_metadata=None, survey=None)[source]
Convenience function to add a run using
Add a run to a given station.
- Parameters
run_name (string) – run name, should be archive_id{a-z}
survey (string) – existing survey name, needed for file version >= 0.2.0
metadata (
mth5.metadata.Station, optional) – metadata container, defaults to None
- Example
>>> new_run = mth5_obj.add_run('MT001', 'MT001a')
- add_station(station_name, station_metadata=None, survey=None)[source]
Convenience function to add a station using
mth5.stations_group.add_station- Add a station with metadata if given with the path [v0.1.0]:
/Survey/Stations/station_name- Add a station with metadata if given with the path [v0.2.0]:
Experiment/Surveys/survey/Stations/station_name
If the station already exists, will return that station and nothing is added.
- Parameters
station_name (string) – Name of the station, should be the same as metadata.archive_id
station_metadata (
mth5.metadata.Station, optional) – Station metadata container, defaults to Nonesurvey (string) – existing survey name, needed for file version >= 0.2.0
- Returns
A convenience class for the added station
- Return type
mth5_groups.StationGroup- Example
>>> new_staiton = mth5_obj.add_station('MT001')
- add_survey(survey_name, survey_metadata=None)[source]
- Add a survey with metadata if given with the path:
/Experiment/Surveys/survey_name
If the survey already exists, will return that survey and nothing is added.
- Parameters
survey_name (string) – Name of the survey, should be the same as metadata.id
survey_metadata (
mth5.metadata.survey, optional) – survey metadata container, defaults to None
- Returns
A convenience class for the added survey
- Return type
mth5_groups.SurveyGroup- Example
>>> from mth5 import mth5 >>> mth5_obj = mth5.MTH5() >>> mth5_obj.open_mth5(r"/test.mth5", mode='a') >>> # one option >>> new_survey = mth5_obj.add_survey('MT001') >>> # another option >>> new_station = mth5_obj.experiment_group.surveys_group.add_survey('MT001')
- add_transfer_function(tf_object)[source]
Add a transfer function :param tf_object: DESCRIPTION :type tf_object: TYPE :return: DESCRIPTION :rtype: TYPE
- property channel_summary
return a dataframe of channels
- property data_level
data level
- property dataset_options
summary of dataset options
- property experiment_group
Convenience property for /Experiment group
- property file_attributes
- property file_type
File Type should be MTH5
- property file_version
mth5 file version
- property filename
file name of the hdf5 file
- property filters_group
Convenience property for /Survey/Filters group
- from_experiment(experiment, survey_index=0, update=False)[source]
Fill out an MTH5 from a
mt_metadata.timeseries.Experimentobject given a survey_id- Parameters
experiment (
mt_metadata.timeseries.Experiment) – Experiment metadatasurvey_index (int, defaults to 0) – Index of the survey to write
- from_reference(h5_reference)[source]
Get an HDF5 group, dataset, etc from a reference
- Parameters
h5_reference (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- get_channel(station_name, run_name, channel_name, survey=None)[source]
Convenience function to get a channel using
mth5.stations_group.get_station().get_run().get_channel()Get a channel from an existing name. Returns the appropriate container.
- Parameters
station_name (string) – existing station name
run_name (string) – existing run name
channel_name (string) – name of the channel
survey (string) – existing survey name, needed for file version >= 0.2.0
- Returns
Channel container
- Return type
[
mth5.mth5_groups.ElectricDatset|mth5.mth5_groups.MagneticDatset|mth5.mth5_groups.AuxiliaryDatset]- Raises
MTH5Error – If no channel is found
- Example
>>> existing_channel = mth5_obj.get_channel(station_name, >>> ... run_name, >>> ... channel_name) >>> existing_channel Channel Electric: ------------------- component: Ex data type: electric data format: float32 data shape: (4096,) start: 1980-01-01T00:00:00+00:00 end: 1980-01-01T00:00:01+00:00 sample rate: 4096
- get_run(station_name, run_name, survey=None)[source]
Convenience function to get a run using
mth5.stations_group.get_station(station_name).get_run()get a run from run name for a given station
- Parameters
station_name (string) – existing station name
run_name (string) – existing run name
survey (string) – existing survey name, needed for file version >= 0.2.0
- Returns
Run object
- Return type
mth5.mth5_groups.RunGroup- Example
>>> existing_run = mth5_obj.get_run('MT001', 'MT001a')
- get_station(station_name, survey=None)[source]
Convenience function to get a station using
Get a station with the same name as station_name
- Parameters
station_name (string) – existing station name
survey (string) – existing survey name, needed for file version >= 0.2.0
- Returns
convenience station class
- Return type
mth5.mth5_groups.StationGroup- Raises
MTH5Error – if the station name is not found.
- Example
>>> existing_staiton = mth5_obj.get_station('MT001') MTH5Error: MT001 does not exist, check station_list for existing names
- get_survey(survey_name)[source]
Get a survey with the same name as survey_name
- Parameters
survey_name (string) – existing survey name
- Returns
convenience survey class
- Return type
mth5.mth5_groups.surveyGroup- Raises
MTH5Error – if the survey name is not found.
- Example
>>> from mth5 import mth5 >>> mth5_obj = mth5.MTH5() >>> mth5_obj.open_mth5(r"/test.mth5", mode='a') >>> # one option >>> existing_survey = mth5_obj.get_survey('MT001') >>> # another option >>> existing_staiton = mth5_obj.experiment_group.surveys_group.get_survey('MT001') MTH5Error: MT001 does not exist, check groups_list for existing names
- get_transfer_function(station_id, tf_id, survey=None)[source]
Get a transfer function
- Parameters
survey_id (TYPE) – DESCRIPTION
station_id (TYPE) – DESCRIPTION
tf_id (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- h5_is_read()[source]
check to see if the hdf5 file is open and readable
- Returns
True if readable, False if not
- Return type
Boolean
- open_mth5(filename=None, mode='a')[source]
open an mth5 file
- Returns
Survey Group
- Type
- Example
>>> from mth5 import mth5 >>> mth5_object = mth5.MTH5(file_version='0.1.0') >>> survey_object = mth5_object.open_mth5('Test.mth5', 'w')
>>> from mth5 import mth5 >>> mth5_object = mth5.MTH5() >>> survey_object = mth5_object.open_mth5('Test.mth5', 'w') >>> mth5_object.file_version '0.2.0'
- remove_channel(station_name, run_name, channel_name, survey=None)[source]
Convenience function to remove a channel using
mth5.stations_group.get_station().get_run().remove_channel()Remove a channel from a given run and station.
Note
Deleting a channel is not as simple as del(channel). In HDF5 this does not free up memory, it simply removes the reference to that channel. The common way to get around this is to copy what you want into a new file, or overwrite the channel.
- Parameters
station_name (string) – existing station name
run_name (string) – existing run name
channel_name (string) – existing station name
survey (string) – existing survey name, needed for file version >= 0.2.0
- Example
>>> mth5_obj.remove_channel('MT001', 'MT001a', 'Ex')
- remove_run(station_name, run_name, survey=None)[source]
Remove a run from the station.
Note
Deleting a run is not as simple as del(run). In HDF5 this does not free up memory, it simply removes the reference to that station. The common way to get around this is to copy what you want into a new file, or overwrite the run.
- Parameters
station_name (string) – existing station name
run_name (string) – existing run name
survey (string) – existing survey name, needed for file version >= 0.2.0
- Example
>>> mth5_obj.remove_station('MT001', 'MT001a')
- remove_station(station_name, survey=None)[source]
Convenience function to remove a station using
Remove a station from the file.
Note
Deleting a station is not as simple as del(station). In HDF5 this does not free up memory, it simply removes the reference to that station. The common way to get around this is to copy what you want into a new file, or overwrite the station.
- Parameters
station_name (string) – existing station name
survey (string) – existing survey name, needed for file version >= 0.2.0
- Example
>>> mth5_obj.remove_station('MT001')
- remove_survey(survey_name)[source]
Remove a survey from the file.
Note
Deleting a survey is not as simple as del(survey). In HDF5 this does not free up memory, it simply removes the reference to that survey. The common way to get around this is to copy what you want into a new file, or overwrite the survey.
- Parameters
survey_name (string) – existing survey name
- Example
>>> from mth5 import mth5 >>> mth5_obj = mth5.MTH5() >>> mth5_obj.open_mth5(r"/test.mth5", mode='a') >>> # one option >>> mth5_obj.remove_survey('MT001') >>> # another option >>> mth5_obj.experiment_group.surveys_group.remove_survey('MT001')
- remove_transfer_function(station_id, tf_id, survey=None)[source]
remove a transfer function
- Parameters
survey_id (TYPE) – DESCRIPTION
station_id (TYPE) – DESCRIPTION
tf_id (TYPE) – DESCRIPTION
- Returns
DESCRIPTION
- Return type
TYPE
- property reports_group
Convenience property for /Survey/Reports group
- property software_name
software name that wrote the file
- property standards_group
Convenience property for /Standards group
- property station_list
list of existing stations names
- property stations_group
Convenience property for /Survey/Stations group
- property survey_group
Convenience property for /Survey group
- property surveys_group
Convenience property for /Surveys group
- property tf_summary
return a dataframe of channels
Module contents
Top-level package for MTH5.