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: 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

ChannelTS

has_data()[source]: check to see if there is an index in the time series

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

mth5.timeseries.ChannelTS

property n_samples: number of samples

plot()[source]

Simple plot of the data

Returns: figure object
Return type: matplotlib.figure

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.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: Time series as a numpy array

welch_spectra(window_length=4096, **kwargs)[source]

get welch spectra

Parameters

window_length (TYPE) – DESCRIPTION
**kwargs –
DESCRIPTION

Returns

DESCRIPTION

Return type

TYPE

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

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.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

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

mth5.timeseries.RunTS

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.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):

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

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: 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

ChannelTS

has_data()[source]: check to see if there is an index in the time series

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

mth5.timeseries.ChannelTS

property n_samples: number of samples

plot()[source]

Simple plot of the data

Returns: figure object
Return type: matplotlib.figure

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.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: Time series as a numpy array

welch_spectra(window_length=4096, **kwargs)[source]

get welch spectra

Parameters

window_length (TYPE) – DESCRIPTION
**kwargs –
DESCRIPTION

Returns

DESCRIPTION

Return type

TYPE

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

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.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

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

mth5.timeseries.RunTS

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.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