RunTS Object¶
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.
To get a RunTS object from an MTH5:
run_group = mth5_object.get_run("station", "run_id", survey="survey")
run_ts_object = run_group.to_runts()%matplotlib inline
import numpy as np
from mth5.timeseries import ChannelTS, RunTS
from mt_metadata.timeseries import Electric, Magnetic, Auxiliary, Run, StationCreate 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.
channel_list = []
common_start = "2020-01-01T00:00:00"
sample_rate = 1.0
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")Create magnetic channels¶
for component in ["hx", "hy", "hz"]:
h_metadata = Magnetic(component=component)
h_metadata.time_period.start = common_start
h_metadata.sample_rate = sample_rate
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)
Create electric channels¶
for component in ["ex", "ey"]:
e_metadata = Electric(component=component)
e_metadata.time_period.start = common_start
e_metadata.sample_rate = sample_rate
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)Create auxiliary channel¶
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)
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.
run = RunTS(channel_list)runRunTS Summary:
Survey: 0
Station: mt001
Run: 001
Start: 2020-01-01T00:00:00+00:00
End: 2020-01-01T01:08:15+00:00
Sample Rate: 1.0
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.
run.plot()
Resample¶
Resampling is a common method when working with data. There are a few methods including
| Method | Description |
|---|---|
decimate | classic decimation: apply an anti-alias window then down sample by up to a factor of 8, if more are required repeat |
resample | no anti-alias filter just simply pick samples at the new sample rate |
resample_poly | upsampled, an FIR zero-phase low pass filter is applied then downsampled, efficient and most accurate preferred method |
resample_run = run.resample_poly(.25)
resample_run2024-09-25T14:56:14.759297-0700 | WARNING | mth5.timeseries.run_ts | validate_metadata | end time of dataset 2020-01-01T01:08:12+00:00 does not match metadata end 2020-01-01T01:08:15+00:00 updating metatdata value to 2020-01-01T01:08:12+00:00
2024-09-25T14:56:14.759297-0700 | WARNING | mth5.timeseries.run_ts | validate_metadata | sample rate of dataset 0.0 does not match metadata sample rate 0.25 updating metatdata value to 0.0
RunTS Summary:
Survey: 0
Station: mt001
Run: 001
Start: 2020-01-01T00:00:00+00:00
End: 2020-01-01T01:08:12+00:00
Sample Rate: 0.0
Components: ['hx', 'hy', 'hz', 'ex', 'ey', 'temperature']resample_run.plot()
Calibration¶
a RunTS object can also be calibrate, it is a convenience function that calls remove_instrument_response on each channel.
See Example 5 for a working example.
calibrated_run = run_ts_object.calibrate()