mth5.timeseries package

Submodules

mth5.timeseries.channel_ts module

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: object

Note

Assumes equally spaced samples from the start time.

The time series is stored in an xarray.Dataset that has coordinates of time and is a 1-D array labeled ‘data’. The xarray.Dataset can 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.metadata therefore any type of interpolation, resampling, groupby, etc can be done using xarray methods.

There are 3 metadata classes that hold important metadata

  • mth5.metadata.Station holds information about the station

  • mth5.metadata.Run holds 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

DESCRIPTION

Return type

TYPE

property channel_type

Channel Type

property component
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 (TYPE) – DESCRIPTION

  • end (TYPE) – DESCRIPTION

Returns

DESCRIPTION

Return type

TYPE

property has_data

check to see if there is an index in the time series

property n_samples

number of samples

plot()[source]

Simple plot of the data

Returns

figure object

Return type

matplotlib.figure

remove_instrument_response(**kwargs)[source]

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

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(dec_factor=1, inplace=False)[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 run_metadata

station metadata

property sample_interval

Sample interval = 1 / sample_rate

Returns

DESCRIPTION

Return type

TYPE

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.Trace object. 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.DataArray object of the channel timeseries this way metadata from the metadata class is updated upon return.

Returns

Returns a xarray.DataArray object 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
mth5.timeseries.channel_ts.make_dt_coordinates(start_time, sample_rate, n_samples, logger)[source]

get the date time index from the data

Parameters

  • start_time (string) – start time in time format

  • sample_rate (float) – sample rate in samples per seconds

  • n_samples (int) – number of samples in time series

  • logger (”logging.logger) – logger class object

Returns

date-time index

mth5.timeseries.run_ts module

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: object

holds 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.DataArray or mth5.timeseries.ChannelTS object.

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.DataArray or mth5.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

mth5.timeseries.RunTS

property channels

List of channel names in dataset

property dataset

xarray.Dataset

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.stream which is a list of obspy.core.Trace objects.

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

has_data()[source]

check to see if there is data

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

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.ChannelTS objects) – list of xarrays

  • align_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

to_obspy_stream()[source]

convert time series to an obspy.core.Stream which is like a list of obspy.core.Trace objects.

Returns

An Obspy Stream object from the time series data

Return type

obspy.core.Stream

validate_metadata()[source]

Check to make sure that the metadata matches what is in the data set.

updates metadata from the data.

Check the start and end times, channels recorded

mth5.timeseries.ts_filters module

time series filters

class mth5.timeseries.ts_filters.RemoveInstrumentResponse(ts, time_array, sample_interval, channel_response_filter, **kwargs)[source]

Bases: object

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

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

static get_window(window, window_params, size)[source]

Get window from scipy.signal

Parameters

  • window (string) – name of the window

  • window_params (dictionary) – dictionary of window parameters

  • size (integer) – number of points in the window

Returns

window function

Return type

class:scipy.signal

remove_instrument_response(operation='divide')[source]

Remove instrument response following the recipe provided

Returns

calibrated 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

Module contents

class mth5.timeseries.ChannelTS(channel_type='auxiliary', data=None, channel_metadata=None, station_metadata=None, run_metadata=None, survey_metadata=None, **kwargs)[source]

Bases: object

Note

Assumes equally spaced samples from the start time.

The time series is stored in an xarray.Dataset that has coordinates of time and is a 1-D array labeled ‘data’. The xarray.Dataset can 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.metadata therefore any type of interpolation, resampling, groupby, etc can be done using xarray methods.

There are 3 metadata classes that hold important metadata

  • mth5.metadata.Station holds information about the station

  • mth5.metadata.Run holds 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

DESCRIPTION

Return type

TYPE

property channel_type

Channel Type

property component
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 (TYPE) – DESCRIPTION

  • end (TYPE) – DESCRIPTION

Returns

DESCRIPTION

Return type

TYPE

property has_data

check to see if there is an index in the time series

property n_samples

number of samples

plot()[source]

Simple plot of the data

Returns

figure object

Return type

matplotlib.figure

remove_instrument_response(**kwargs)[source]

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

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(dec_factor=1, inplace=False)[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 run_metadata

station metadata

property sample_interval

Sample interval = 1 / sample_rate

Returns

DESCRIPTION

Return type

TYPE

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.Trace object. 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.DataArray object of the channel timeseries this way metadata from the metadata class is updated upon return.

Returns

Returns a xarray.DataArray object 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
class mth5.timeseries.RunTS(array_list=None, run_metadata=None, station_metadata=None, survey_metadata=None)[source]

Bases: object

holds 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.DataArray or mth5.timeseries.ChannelTS object.

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.DataArray or mth5.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

mth5.timeseries.RunTS

property channels

List of channel names in dataset

property dataset

xarray.Dataset

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.stream which is a list of obspy.core.Trace objects.

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

has_data()[source]

check to see if there is data

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

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.ChannelTS objects) – list of xarrays

  • align_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

to_obspy_stream()[source]

convert time series to an obspy.core.Stream which is like a list of obspy.core.Trace objects.

Returns

An Obspy Stream object from the time series data

Return type

obspy.core.Stream

validate_metadata()[source]

Check to make sure that the metadata matches what is in the data set.

updates metadata from the data.

Check the start and end times, channels recorded