Experiment¶
We have covered some basics of MT metadata, lets put it together into an Experiment.
from mt_metadata.timeseries import (
Auxiliary,
Electric,
Experiment,
Magnetic,
Run,
Station,
Survey,
)
from mt_metadata.timeseries.filters import CoefficientFilterexperiment = Experiment()
# set a common start and end time
start = "2020-01-01T00:00:00+00:00"
end = "2021-01-01T12:00:00+00:00"
kwargs = {"time_period.start": start, "time_period.end": end}
cf1 = CoefficientFilter(name="filter_01", gain=10)
cf2 = CoefficientFilter(name="filter_02", gain=100)
ch1_kwargs = {
"time_period.start": start,
"time_period.end": end,
"filter.name": ["filter_01"],
"comments": "run_ids: mt01a, mt01b",
}
ch2_kwargs = {
"time_period.start": start,
"time_period.end": end,
"filter.name": ["filter_02"],
"comments": "run_ids: mt02a, mt02b",
}
for survey in ["One", "Two"]:
survey_obj = Survey(id=survey)
survey_obj.fdsn.network = "EM"
survey_obj.acquired_by.author = "M T"
survey_obj.project_lead.author = "T M"
if survey in ["One"]:
survey_obj.filters[cf1.name] = cf1
else:
survey_obj.filters[cf2.name] = cf2
for station in ["mt01", "mt02"]:
station_obj = Station(id=station, **kwargs)
for run in ["mt01a", "mt01b"]:
run_obj = Run(id=run, **kwargs)
if survey in ["One"]:
for ch in ["ex", "ey"]:
ch_obj = Electric(component=ch, **ch1_kwargs)
ch_obj.dipole_length = 100
run_obj.channels.append(ch_obj)
for ch in ["hx", "hy", "hz"]:
ch_obj = Magnetic(component=ch, **ch1_kwargs)
run_obj.channels.append(ch_obj)
for ch in ["temperature"]:
ch_obj = Auxiliary(component="tx", **ch1_kwargs)
ch_obj.type = ch
ch_obj.measured_azimuth = 0
run_obj.channels.append(ch_obj)
else:
for ch in ["ex", "ey"]:
ch_obj = Electric(component=ch, **ch2_kwargs)
ch_obj.dipole_length = 100
run_obj.channels.append(ch_obj)
for ch in ["hx", "hy", "hz"]:
ch_obj = Magnetic(component=ch, **ch2_kwargs)
run_obj.channels.append(ch_obj)
for ch in ["temperature"]:
ch_obj = Auxiliary(component="ty", **ch2_kwargs)
ch_obj.type = ch
ch_obj.measured_azimuth = 90
run_obj.channels.append(ch_obj)
run_obj.update_time_period()
station_obj.runs.append(run_obj)
station_obj.update_time_period()
survey_obj.stations.append(station_obj)
survey_obj.update_time_period()
experiment.surveys.append(survey_obj)experimentExperiment Contents
--------------------
Number of Surveys: 2
Survey ID: One
Number of Stations: 2
Number of Filters: 1
--------------------
Filter Name: filter_01
Filter Type: coefficient
--------------------
Station ID: mt01
Number of Runs: 2
--------------------
Run ID: mt01a
Number of Channels: 6
Recorded Channels: ex, ey, hx, hy, hz, tx
Start: 2020-01-01T00:00:00+00:00
End: 2021-01-01T12:00:00+00:00
--------------------
Run ID: mt01b
Number of Channels: 6
Recorded Channels: ex, ey, hx, hy, hz, tx
Start: 2020-01-01T00:00:00+00:00
End: 2021-01-01T12:00:00+00:00
--------------------
Station ID: mt02
Number of Runs: 2
--------------------
Run ID: mt01a
Number of Channels: 6
Recorded Channels: ex, ey, hx, hy, hz, tx
Start: 2020-01-01T00:00:00+00:00
End: 2021-01-01T12:00:00+00:00
--------------------
Run ID: mt01b
Number of Channels: 6
Recorded Channels: ex, ey, hx, hy, hz, tx
Start: 2020-01-01T00:00:00+00:00
End: 2021-01-01T12:00:00+00:00
--------------------
Survey ID: Two
Number of Stations: 2
Number of Filters: 1
--------------------
Filter Name: filter_02
Filter Type: coefficient
--------------------
Station ID: mt01
Number of Runs: 2
--------------------
Run ID: mt01a
Number of Channels: 6
Recorded Channels: ex, ey, hx, hy, hz, ty
Start: 2020-01-01T00:00:00+00:00
End: 2021-01-01T12:00:00+00:00
--------------------
Run ID: mt01b
Number of Channels: 6
Recorded Channels: ex, ey, hx, hy, hz, ty
Start: 2020-01-01T00:00:00+00:00
End: 2021-01-01T12:00:00+00:00
--------------------
Station ID: mt02
Number of Runs: 2
--------------------
Run ID: mt01a
Number of Channels: 6
Recorded Channels: ex, ey, hx, hy, hz, ty
Start: 2020-01-01T00:00:00+00:00
End: 2021-01-01T12:00:00+00:00
--------------------
Run ID: mt01b
Number of Channels: 6
Recorded Channels: ex, ey, hx, hy, hz, ty
Start: 2020-01-01T00:00:00+00:00
End: 2021-01-01T12:00:00+00:00
--------------------To JSON¶
We can write this to a JSON file, these can get long, but there are readers out there that make it easy to search.
experiment.to_json(fn="test.json")To XML¶
We can write an XML file if you really want to.
xml_element = experiment.to_xml(fn="test.xml")StationXML¶
FDSN uses StationXML to archive which can sometimes be a pain to translate to. mt_metadata provides translation tools to and from StationXML. The StationXML format that is output has been extensively adjusted to fit both MT and FDSN standards, so if possible these tools should be used to create StationXML metadata files from MT Experiments.
For the StationXML side Obspy is used to be sure the format is correct.
Before we do the conversion we need to be sure that some important metadata are included in experiment.
The translations are in mt_metadata.timeseries.stationxml
from mt_metadata.timeseries.stationxml import xml_inventory_mt_experiment
translator = xml_inventory_mt_experiment.XMLInventoryMTExperiment()inventory = translator.mt_to_xml(experiment, stationxml_fn="test_stationxml.xml")The created StationXML can be the StationXML you use for archiving.