Note
Click here to download the full example code
Reading time data¶
Resistics can quickly read a single continuous recording using the quick reading functionality. This can be useful for inspecting the metadata and having a look at the data when in the field.
The dataset in this example has been provided for use by the SAMTEX consortium. For more information, please refer to [Jones2009]. Additional details about the dataset can be found at https://www.mtnet.info/data/kap03/kap03.html.
from pathlib import Path
import seedir as sd
import plotly
import resistics.letsgo as letsgo
Define the data path. This is dependent on where the data is stored.
time_data_path = Path("..", "..", "data", "time", "quick", "kap123")
sd.seedir(str(time_data_path), style="emoji")
Out:
📁 kap123/
├─📄 data.npy
└─📄 metadata.json
Quickly read the time series data and inspect the metadata
time_data = letsgo.quick_read(time_data_path)
time_data.metadata.summary()
Out:
{
'file_info': {
'created_on_local': '2021-07-07T22:25:45.320529',
'created_on_utc': '2021-07-07T21:25:45.320529',
'version': '1.0.0a0'
},
'fs': 0.2,
'chans': ['Hx', 'Hy', 'Hz', 'Ex', 'Ey'],
'n_chans': 5,
'n_samples': 361512,
'first_time': '2003-11-10 15:00:00.000000_000000_000000_000000',
'last_time': '2003-12-01 13:05:55.000000_000000_000000_000000',
'system': '',
'serial': '',
'wgs84_latitude': -999.0,
'wgs84_longitude': -999.0,
'easting': -999.0,
'northing': -999.0,
'elevation': -999.0,
'chans_metadata': {
'Hx': {
'name': 'Hx',
'data_files': ['data.npy'],
'chan_type': 'magnetic',
'chan_source': None,
'sensor': '',
'serial': '',
'gain1': 1.0,
'gain2': 1.0,
'scaling': 1.0,
'chopper': False,
'dipole_dist': 1.0,
'sensor_calibration_file': '',
'instrument_calibration_file': ''
},
'Hy': {
'name': 'Hy',
'data_files': ['data.npy'],
'chan_type': 'magnetic',
'chan_source': None,
'sensor': '',
'serial': '',
'gain1': 1.0,
'gain2': 1.0,
'scaling': 1.0,
'chopper': False,
'dipole_dist': 1.0,
'sensor_calibration_file': '',
'instrument_calibration_file': ''
},
'Hz': {
'name': 'Hz',
'data_files': ['data.npy'],
'chan_type': 'magnetic',
'chan_source': None,
'sensor': '',
'serial': '',
'gain1': 1.0,
'gain2': 1.0,
'scaling': 1.0,
'chopper': False,
'dipole_dist': 1.0,
'sensor_calibration_file': '',
'instrument_calibration_file': ''
},
'Ex': {
'name': 'Ex',
'data_files': ['data.npy'],
'chan_type': 'electric',
'chan_source': None,
'sensor': '',
'serial': '',
'gain1': 1.0,
'gain2': 1.0,
'scaling': 1.0,
'chopper': False,
'dipole_dist': 1.0,
'sensor_calibration_file': '',
'instrument_calibration_file': ''
},
'Ey': {
'name': 'Ey',
'data_files': ['data.npy'],
'chan_type': 'electric',
'chan_source': None,
'sensor': '',
'serial': '',
'gain1': 1.0,
'gain2': 1.0,
'scaling': 1.0,
'chopper': False,
'dipole_dist': 1.0,
'sensor_calibration_file': '',
'instrument_calibration_file': ''
}
},
'history': {
'records': [
{
'time_local': '2021-10-14T22:19:02.731137',
'time_utc': '2021-10-14T22:19:02.731136',
'creator': {
'name': 'TimeReaderNumpy',
'apply_scalings': True,
'extension': '.npy'
},
'messages': [
'Reading raw data from ../../data/time/quick/kap123',
'Sampling frequency 0.2 Hz',
'From sample, time: 0, 2003-11-10 15:00:00',
'To sample, time: 361511, 2003-12-01 13:05:55'
],
'record_type': 'process'
}
]
}
}
Take a subsection of the data and inspect the metadata for the subsection
time_data_sub = time_data.subsection("2003-11-20 12:00:00", "2003-11-21 00:00:00")
time_data_sub.metadata.summary()
Out:
{
'file_info': {
'created_on_local': '2021-07-07T22:25:45.320529',
'created_on_utc': '2021-07-07T21:25:45.320529',
'version': '1.0.0a0'
},
'fs': 0.2,
'chans': ['Hx', 'Hy', 'Hz', 'Ex', 'Ey'],
'n_chans': 5,
'n_samples': 8641,
'first_time': '2003-11-20 12:00:00.000000_000000_000000_000000',
'last_time': '2003-11-21 00:00:00.000000_000000_000000_000000',
'system': '',
'serial': '',
'wgs84_latitude': -999.0,
'wgs84_longitude': -999.0,
'easting': -999.0,
'northing': -999.0,
'elevation': -999.0,
'chans_metadata': {
'Hx': {
'name': 'Hx',
'data_files': ['data.npy'],
'chan_type': 'magnetic',
'chan_source': None,
'sensor': '',
'serial': '',
'gain1': 1.0,
'gain2': 1.0,
'scaling': 1.0,
'chopper': False,
'dipole_dist': 1.0,
'sensor_calibration_file': '',
'instrument_calibration_file': ''
},
'Hy': {
'name': 'Hy',
'data_files': ['data.npy'],
'chan_type': 'magnetic',
'chan_source': None,
'sensor': '',
'serial': '',
'gain1': 1.0,
'gain2': 1.0,
'scaling': 1.0,
'chopper': False,
'dipole_dist': 1.0,
'sensor_calibration_file': '',
'instrument_calibration_file': ''
},
'Hz': {
'name': 'Hz',
'data_files': ['data.npy'],
'chan_type': 'magnetic',
'chan_source': None,
'sensor': '',
'serial': '',
'gain1': 1.0,
'gain2': 1.0,
'scaling': 1.0,
'chopper': False,
'dipole_dist': 1.0,
'sensor_calibration_file': '',
'instrument_calibration_file': ''
},
'Ex': {
'name': 'Ex',
'data_files': ['data.npy'],
'chan_type': 'electric',
'chan_source': None,
'sensor': '',
'serial': '',
'gain1': 1.0,
'gain2': 1.0,
'scaling': 1.0,
'chopper': False,
'dipole_dist': 1.0,
'sensor_calibration_file': '',
'instrument_calibration_file': ''
},
'Ey': {
'name': 'Ey',
'data_files': ['data.npy'],
'chan_type': 'electric',
'chan_source': None,
'sensor': '',
'serial': '',
'gain1': 1.0,
'gain2': 1.0,
'scaling': 1.0,
'chopper': False,
'dipole_dist': 1.0,
'sensor_calibration_file': '',
'instrument_calibration_file': ''
}
},
'history': {
'records': [
{
'time_local': '2021-10-14T22:19:02.731137',
'time_utc': '2021-10-14T22:19:02.731136',
'creator': {
'name': 'TimeReaderNumpy',
'apply_scalings': True,
'extension': '.npy'
},
'messages': [
'Reading raw data from ../../data/time/quick/kap123',
'Sampling frequency 0.2 Hz',
'From sample, time: 0, 2003-11-10 15:00:00',
'To sample, time: 361511, 2003-12-01 13:05:55'
],
'record_type': 'process'
},
{
'time_local': '2021-10-14T22:19:02.748221',
'time_utc': '2021-10-14T22:19:02.748220',
'creator': {
'name': 'Subsection',
'from_time': '2003-11-20 12:00:00',
'to_time': '2003-11-21 00:00:00'
},
'messages': [
'Subection from sample 170640 to 179280',
'Adjusted times 2003-11-20 12:00:00 to 2003-11-21 00:00:00'
],
'record_type': 'process'
}
]
}
}
Plot the full time data with LTTB downsampling and a subsection without any downsampling. Comparing the downsampled and original data, there is clearly some loss but the LTTB downsampled data does a reasonable job capaturing the main features whilst showing a greater amount of data.
fig = time_data.plot(max_pts=1_000)
fig = time_data_sub.plot(
fig, chans=time_data.metadata.chans, color="red", legend="Subsection", max_pts=None
)
fig.update_layout(height=700)
plotly.io.show(fig)
Total running time of the script: ( 0 minutes 5.855 seconds)