TimeSeriesArray

class mcgpy.timeseriesarray.TimeSeriesArray(source, config=None, positions=None, directions=None, unit=None, t0=None, sample_rate=None, times=None, **kwargs)

Make a multi-channel time-series array with metadata

Parameters

• source : str, list, np.ndarray, astropy.units.Quantity, mcgpy.io.Array

  it can take multi-type data formats

• config : str, conditional

  if the input source is the KDF file path, this parameter is essential

• positions : list, np.ndarray, astropy.units.Quantity, optional

  sensor positions

  • if the input source is the KDF file path and sensor configuration file path is also given, this parameter will be ignored
  • if the input source is the HDF file path, this parameter will be ignored
  • if the input source is user defined data array and will not use numerical classes, mcgpy.numeric.LeadField and mcgpy.numeric.FieldMap, this parameter is optional

• directions : list, np.ndarray, astropy.units.Quantity, optional

  sensor positions

  • if the input source is the KDF file path and sensor configuration file path is also given, this parameter will be ignored
  • if the input source is the HDF file path, this parameter will be ignored
  • if the input source is user defined data array and will not use numerical classes, mcgpy.numeric.LeadField and mcgpy.numeric.FieldMap, this parameter is optional

• unit : astropy.units.Quantity, optional

  an unit of data,

  default unit is femto tesla, 10E-15 T, if the input source is not KDF or HDF5 file path

• t0 : int, float, astropy.units.Quantity, optional

  start time of time-series,

  default value is 0 s, if the input source is not KDF or HDF5 file path

• sample_rate : int, float, astropy.units.Quantity, optional

  signal sample frequency,

  default value is 1 Hz, if the input source is not KDF or HDF5 file path

• times : list, np.ndarray, astropy.units.Quantity, optional

  time xindex,

  default value is made by data size, t0 and sample_rate, if the input source is not KDF or HDF5 file path

Return

• if the input source is the path of a KDF or HDF5 file, read whole dataset and return it with metadata.

• if the input source is the data array, read a data of given channel number or label, and return it.

  return the time-series array with metadata

Examples

>>> from mcgpy.timeseries import TimeSeriesArray
>>> dataset = TimeSeriesArray("~/test/raw/file/path.hdf5")
>>> dataset
[[136.26814, 156.5814, …, −67.710876, 33.009052], 
[455.35564, 413.03635, …, 60.093403, 143.03923],
…, 
[−1409.2326, −1286.5067,  …, −2374.959, −2407.968], 
[−1499.7959, −1494.7176,  …,  −1954.3052, −1994.9317]]1×10−15T
>>> dataset.directions
array([[1., 0., 0.],
      [1., 0., 0.],
       ...,
      [0., 1., 0.],
      [0., 1., 0.]])
    

Properties Summary

Methods Summary

Properties Documentation

biosemi

Identification code i.e., National Hospital, National Reaserch, …

Here is an example:

>>> from mcgpy.timeserie import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> print(dataset.biosemi)
'MCGpy schoool'

channels

Channel information table of numbers and labels

Here is an example:

>>> from mcgpy.timeserie import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> print(dataset.channels)
<QTable length=8>
number label
int64   str3
------ -----
     1    label_1
     2    label_2
     3    label_3
     4    label_4
     5    label_5
     6    label_6
     7    label_7
     8    label_8

datetime

The data time at the point of data recording_ i.e., ‘2020-02-02 02:02:02.00000'_

Here is an example:

>>> from mcgpy.timeserie import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> print(dataset.datetime)
'2020-02-02 02:02:02'

direction

The sensor direction for measuring a magnetic field i.e., [1.,0.,0.] if mcgpy.timeseriesarray.TimeSeriesArray is one-dimentaional.

Here is an example:

>>> from mcgpy.timeserie import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> ch1 = dateset.read(number=1)
>>> print(ch1.direction)
array([1, 0, 0])

directions

The sensor direction for measuring a magnetic field i.e., [[1.,0.,0.],[1.,0.,0.],…,[0.,1.,0.]] if mcgpy.timeseriesarray.TimeSeriesArray is two-dimentaional.

Here is an example:

>>> from mcgpy.timeserie import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> print(dataset.directions)
array([[1., 0., 0.],
       [1., 0., 0.],
       [1., 0., 0.],
       [1., 0., 0.],
       [0., 1., 0.],
       [0., 1., 0.],
       [0., 1., 0.],
       [0., 1., 0.]]

duration

Data recording duration

Here is an example:

>>> from mcgpy.timeserie import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> print(dataset.duration)
<Quantity 10. s>

label

Label of a channel magnetic field if mcgpy.timeseriesarray.TimeSeriesArray is one-dimentaional.

Here is an example:

>>> from mcgpy.timeserie import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> ch1 = dateset.read(number=1)
>>> print(ch1.label)
'label_1'

labels

Labels of channel magnetic fields if mcgpy.timeseriesarray.TimeSeriesArray is two-dimentaional.

Here is an example:

>>> from mcgpy.timeserie import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> print(dataset.labels)
array(['label_1', 'label_2', 'label_3', 'label_4', 'label_5', 'label_6', 'label_7', 'label_8'], dtype='<U3')

note

It might be included with the patient information or medical options.

Here is an example:

>>> from mcgpy.timeserie import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> print(dataset.note)
{'encoded info': 'a7F76ae32B2566A8F165_22221223',
 'opinion': 'Healthy',
 'patient number': '0000'}

number

Number of a channel magnetic field if mcgpy.timeseriesarray.TimeSeriesArray is one-dimentaional.

Here is an example:

>>> from mcgpy.timeserie import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> ch1 = dateset.read(number=1)
>>> print(ch1.number)
1

numbers

Numbers of channel magnetic fields if mcgpy.timeseriesarray.TimeSeriesArray is two-dimentaional.

Here is an example:

>>> from mcgpy.timeserie import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> print(dataset.numbers)
array([ 1,  2,  3,  4,  5,  6,  7,  8])

position

The sensor direction for measuring a magnetic field if mcgpy.timeseriesarray.TimeSeriesArray is one-dimentaional.

Here is an example:

>>> from mcgpy.timeserie import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> ch1 = dateset.read(number=1)
>>> print(ch1.position)
array([0, 0, 0])

positions

The sensor directions for measuring magnetic fields if mcgpy.timeseriesarray.TimeSeriesArray is two-dimentaional.

Here is an example:

>>> from mcgpy.timeserie import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> print(dataset.positions)
array([[  10.  ,  0.,    0.   ],
       [  -10. ,  0.,    0.   ],
       [  0.   ,  10.,   0.   ],
       [  0.   , -10.,   0.   ],
       [  10.  ,  10 ,   0.   ],
       [  -10. , -10.,   0.   ],
       [  10.  , -10.,   0.   ],
       [  -10. ,  10.,   0.   ]])

sample_rate

Data sample frequency.

Here is an example:

>>> from mcgpy.timeserie import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> print(dataset.sampe_rate)
<Quantity 1024 Hz>

t0

The first data point of time-axis.

Here is an example:

>>> from mcgpy.timeserie import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> print(dataset.t0)
<Quantity 2082875272. s>

times

The time-axis coordinate.

Here is an example:

>>> from mcgpy.timeserie import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> print(dataset.times)
<Quantity [1.08287527e+09, 1.08287527e+09, ...,1.08287528e+09] s>

unit

The physical unit of the data.

Here is an example:

>>> from mcgpy.timeserie import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> print(dataset.unit)
Unit("1e-15 T")

Methods Documentation

argmax()

def mcgpy.timeseriesarray.TimeSeriesArray.argmax()

Find the epoch of the maximum value

Return

• if the dataset is one-dimensional, return will be a timestamp of the maximum value : astropy.table.Quantity

• if the dataset is two-dimensional, return will be timestamps of the maximum values for each channel : astropy.table.Quantity in list

Examples

>>> from mcgpy.timeseries import TimeSeriesArray
>>> data = TimeSeriesArray("~/test/raw/file/path.hdf5").to_rms()
>>> data.max()
4480.30971×10−15T
>>> data.argmax()
11.3447265625 s

argmin()

def mcgpy.timeseriesarray.TimeSeriesArray.argmin()

Find the epoch of the minimum value

Return

• if the dataset is one-dimensional, return will be a timestamp of the minimum value : astropy.table.Quantity

• if the dataset is two-dimensional, return will be timestamps of the minimum values for each channel : astropy.table.Quantity in list

Examples

>>> from mcgpy.timeseries import TimeSeriesArray
>>> data = TimeSeriesArray("~/test/raw/file/path.hdf5").to_rms()
>>> data.min()
53.7786021×10−15T
>>> data.argmin()
10 s

area(start, end, **kwargs)

def mcgpy.timeseriesarray.TimeSeriesArray.area(start, end, **kwargs)

Calculate the area between start and end timestamps.

Parameters

• start : int, float, astropy.units.Quantity

  start timestamp

• end : int, float, astropy.units.Quantity

  end timestamp

Return : mcgpy.timeseries.TimeSeriesArray

• if the dataset is one-dimensional, return the area of signal between start and end timestamps

• if the dateset is two-dimensional, return the area of signal between start and end timestamps for each channel

Examples

>>> from mcgpy.timeseries import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> dataset.area(2082875272,2082875274)
[0.24546295, 0.46202301, ..., 1.409447, 2.6042676](𝑈𝑛𝑖𝑡𝑛𝑜𝑡𝑖𝑛𝑖𝑡𝑖𝑎𝑙𝑖𝑠𝑒𝑑)

asd(fftlength=None, overlap=0, window='hann', average='median', **kwargs)

def mcgpy.timeseriesarray.TimeSeriesArray.asd(fftlength=None, overlap=0, window='hann', average='median', **kwargs)

Calculate the acceleration spectral density, ASD

Parameters

• fftlength : int, float, optional

  number of seconds for dividing the time window into equal bins, if None type value is given, it will be the size of signal

• overlap : int, float, optional

  number of seconds of overlap between FFTs, default value is 0

• window : str

  Desired window to use. If window is a string or tuple, it is passed to get_window to generate the window values, which are DFT-even by default. See get_window for a list of windows and required parameters. If window is array_like it will be used directly as the window and its length must be nperseg. Defaults to a Hann window.

  See more detailed explanation in scipy.signal.welch

• average : { "mean", "median" }, optional

  Method to use when averaging periodograms. Defaults to ‘mean'.

  See more detailed explanation in scipy.signal.welch

Return : mcgpy.series.FrequencySeries

• if the dataset is one-dimensional, return asd frequency-series

• if the dateset is two-dimensional, return asd frequency-series for each channel

Examples

>>> from mcgpy.timeseries import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> dataset.asd(2,1)
[[16.390747, 62.977136, 149.35229, …, 1.8138222, 1.4555211, 0.93069027], 
[24.143041, 113.88421, 261.50669, …, 1.7955032, 1.8442637, 0.72403336], 
…, 
[12.476006, 44.834316, 56.699417, …, 1.6649341, 1.9098386, 1.0112418], 
[153.54132, 400.77368, 220.70556, …, 1.955279, 1.9509556, 0.80185085]]1×10−15THz1/2

at(epoch, **kwargs)

def mcgpy.timeseriesarray.TimeSeriesArray.at(epoch, **kwargs)

Peak up the value/values at an input time.

Parameters

• epoch : int, float, astropy.units.Quantity

  timestamp user wants to get the value

Return : mcgpy.timeseries.TimeSeriesArray

• if the dataset is one-dimensional, return the value at the given time

• if the dateset is two-dimensional, return the values for each channel at the given time

Examples

>>> from mcgpy.timeseries import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> dataset.at(2082875272)
[136.26814, 455.35564, ..., −1499.7959, −2495.1458]1×10−15T

bandpass(lfreq, hfreq, order=4, **kwargs)

def mcgpy.timeseriesarray.TimeSeriesArray.bandpass(lfreq, hfreq, order=4, flattening=True, **kwargs)

Apply the bandpass filter to the dataset.

Parameters

• lfreq : int, float, astropy.units.Quantity

  the low cutoff frequencies

• hfreq : int, float, astropy.units.Quantity

  the high cutoff frequencies

• sample_rate : int, float, astropy.units.Quantity

  sample rate of ditital signal

• order : int, optional

  the order of the filter, default value is 4

• flattening : Boonlean, optional

  signal flattening option, defaule value is True

Return : mcgpy.timeseries.TimeSeriesArray

filted dataset

Examples

>>> from mcgpy.timeseries import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> dataset.bandpass(0.1, 200)
    [[5.8798634, 35.303578, 95.930749, …, 72.160133, 27.332395, 18.921922], 
     [19.648239, 113.21599, 288.53974, …, 214.44662, 209.56741, 173.21204], 
     …, 
     [−64.715018, −378.69255, −987.28802, …, −195.79194, −150.83508, −97.942407], 
     [−107.66359, −631.4016, −1649.1429, …, −1785.628, −1803.213, −1788.7173]]1×10−15T

crop(start, end, **kwargs)

def mcgpy.timeseriesarray.TimeSeriesArray.crop(start, end, **kwargs)

Slice the time-series between start and end times.

Parameters

• start : int, float, astropy.units.Quantity

start timestamp

• end : int, float, astropy.units.Quantity

end timestamp

Return : mcgpy.timeseries.TimeSeriesArray

• if the dataset is one-dimensional, return sliced time-series array

• if the dateset is two-dimensional, return sliced time-series for each channel array

Examples

>>> from mcgpy.timeseries import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> dataset.crop(2082875272,2082875273)
[[136.26814, 156.5814, 177.74105, …, 10.156631, −3.3855438, 5.9247017], 
 [455.35564, 413.03635, 386.79838, …, 394.41586, 352.94294, 360.56042], 
 …, 
 [−1499.7959, −1494.7176, −1477.7899, …, −1535.3441, −1401.6151, −1513.3381], 
 [−2495.1458, −2518.8446, −2456.212, …, −2870.9412, −2869.2484, −2865.0165]]1×10−15T

exclude(numbers=None, labels=None, **kwargs)

def mcgpy.timeseriesarray.TimeSeriesArray.exclude(numbers=None, labels=None, **kwargs)

Except the channel data from the dataset.

Parameters

• numbers : list, tuple, np.ndarray, conditional

  the number list of what user wants to remove channels from the dataset

• labels : list, tuple, np.ndarray, conditional

  the label list of what user wants to remove channels from the dataset

Return : mcgpy.timeseries.TimeSeriesArray

the dataset except for the given channel list

Examples

>>> from mcgpy.timeseries import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> dataset.exclude(numbers=(1,2))
[[485.82554, 424.03936, 330.93691, …, 534.91592, 451.9701, 475.66891], 
 [214.13565, 215.82842, 265.76519, …, 275.92182, 276.76821, 246.29831], 
 …, 
 [−1499.7959, −1494.7176, −1477.7899, …, −1985.6215, −1954.3052, −1994.9317], 
 [−2495.1458, −2518.8446, −2456.212, …, −1951.766, −1929.76, −1776.5641]]1×10−15T

fft()

def mcgpy.timeseriesarray.TimeSeriesArray.fft()

Calculate the fast Fourier transform, FFT.

Return : mcgpy.series.FrequencySeries

• if the dataset is one-dimensional, return fft frequency-series

• if the dateset is two-dimensional, return fft frequency-series for each channel

Examples

>>> from mcgpy.timeseries import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> dataset.fft()
[[92.382265, 16.48454, 57.167697, …, 0.05992157, 0.27365534, 0.2273498], 
 [56.252973, 42.362682, 35.193567, …, 0.13228269, 0.017953475, 0.036300067], 
 …,  
 [1741.4278, 104.75867, 48.947051, …, 0.1638588, 0.14912128, 0.073103484], 
 [1630.1735, 103.73168, 145.30199, …, 0.10946647, 0.048501745, 0.073445149]]1×10−15T

find_peaks(height_amp=0.85, threshold=None, distance=None, prominence=None, width=1, wlen=None, rel_height=0.5, plateau_size=None, **kwargs)

def mcgpy.timeseries.TimeSeriesArray.find_peaks(height_amp=0.85, threshold=None, distance=None, prominence=None, width=1, wlen=None, rel_height=0.5, plateau_size=None, **kwargs)

Find peaks inside a signal based on peak properties.

Parameters : ini, float, str

• height_amp : float, optional

  Used for determining maximum height in sample.

• threshold : number or ndarray or sequence, optional

  Required threshold of peaks, the vertical distance to its neighboring samples. Either a number, None, an array matching x or a 2-element sequence of the former. The first element is always interpreted as the minimal and the second, if supplied, as the maximal required threshold.

• distance : number, optional

  Required minimal horizontal distance (>= 1) in samples between neighbouring peaks. Smaller peaks are removed first until the condition is fulfilled for all remaining peaks.

• prominence : number or ndarray or sequence, optional

  Required prominence of peaks. Either a number, None, an array matching x or a 2-element sequence of the former. The first element is always interpreted as the minimal and the second, if supplied, as the maximal required prominence.

• width : number or ndarray or sequence, optional

  Required width of peaks in samples. Either a number, None, an array matching x or a 2-element sequence of the former. The first element is always interpreted as the minimal and the second, if supplied, as the maximal required width.

• wlen : int, optional

  Used for calculation of the peaks prominences, thus it is only used if one of the arguments prominence or width is given. See argument wlen in peak_prominences for a full description of its effects.

• rel_height : float, optional

  Used for calculation of the peaks width, thus it is only used if width is given. See argument rel_height in peak_widths for a full description of its effects.

• plateau_size : number or ndarray or sequence, optional

  Required size of the flat top of peaks in samples. Either a number, None, an array matching x or a 2-element sequence of the former. The first element is always interpreted as the minimal and the second, if supplied as the maximal required plateau size.

Return : mcgpy.timeseries.TimeSeriesArray

times of peaks in dataset that satisfy all given conditions

Examples

>>> from mcgpy.timeseries import TimeSeriesArray
>>> data = TimeSeriesArray("~/test/raw/file/path.hdf5").to_rms()
>>> data.find_peaks()
[1.11948764e+09 1.11948764e+09 1.11948764e+09 1.11948764e+09
 1.11948764e+09 1.11948764e+09 1.11948764e+09 1.11948765e+09
 1.11948765e+09 1.11948765e+09 1.11948765e+09 1.11948765e+09
 1.11948765e+09 1.11948765e+09 1.11948765e+09 1.11948766e+09
 1.11948766e+09 1.11948766e+09 1.11948766e+09 1.11948766e+09
 1.11948766e+09 1.11948766e+09] s

flattened(lam, p, niter, **kwargs)

def mcgpy.timeseriesarray.TimeSeriesArray.flattened(lam=None, p=1e-1, niter=10, **kwargs)

Flattened a wave form by an Asymmetric Least Squares Smoothing

Parameters

• lam : int

• p : float

• niter : int

Return : mcgpy.series.TimeSeriesArray

baseline corrected signal or signals

Examples

>>> from mcgpy.timeseries import TimeSeriesArray
>>> data = TimeSeriesArray("~/test/raw/file/path.hdf5")
>>> data.flattened()
[[−106.09462, −86.757371, …,−44.093128, −34.719921], [−101.92919, −147.60086, …,  −10.727882, −15.01086], 
 …, 
 [−26.580124, 33.935216,  …, 0.5097395, 0.65614824], 
 [37.148019, 35.133146, …, 22.03233, 31.360074]]1×10−15T

highpass(hfreq, order=2, **kwargs)

def mcgpy.timeseriesarray.TimeSeriesArray.highpass(hfreq, order=2, flattening=True, **kwargs)

Apply the highpass filter to the dataset.

Parameters

• hfreq : int, float, astropy.units.Quantity

  the cutoff frequencies

• sample_rate : int, float, astropy.units.Quantity

  sample rate of ditital signal

• order : int, optional

  the order of the filter, default value is 2

• flattening : Boonlean, optional

  signal flattening option, defaule value is True

Return : mcgpy.timeseries.TimeSeriesArray

filted dataset

Examples

>>> from mcgpy.timeseries import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> dataset.highpass(1)
[[135.67819, 154.72616, 174.44649, …, −63.927263, 8.7490505, 108.60693], 
 [453.38425, 407.31395, 377.63813, …, 83.275947, 71.758854, 153.03796], 
 …, 
 [−1493.3028, −1475.2884, −1445.5762, …, 5.3016175, 36.47909, −4.2455184], 
 [−2484.3434, −2486.3819, −2402.3519, …, −128.50984, −103.82009, 51.282091]]1×10−15T

integral(start, end, **kwargs)

def mcgpy.timeseriesarray.TimeSeriesArray.integral(start, end, **kwargs)

Calculate the integrated area between start and end timestamps.

Parameters

• start : int, float, astropy.units.Quantity

  start timestamp

• end : int, float, astropy.units.Quantity

  end timestamp

Return :mcgpy.timeseries.TimeSeriesArray

• if the dataset is one-dimensional, return the integrated area between start and end timestamps

• if the dateset is two-dimensional, return the integrated area between start and end timestamps for each channel

Examples

>>> from mcgpy.timeseries import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> dataset.intergral(2082875272,2082875276)
[0.057181275, 0.18835077, ..., −1.409359, −2.5957822](𝑈𝑛𝑖𝑡𝑛𝑜𝑡𝑖𝑛𝑖𝑡𝑖𝑎𝑙𝑖𝑠𝑒𝑑)

lowpass(lfreq, order=2, **kwargs)

def mcgpy.timeseriesarray.TimeSeriesArray.lowpass(lfreq, order=2, flattening=True, **kwargs)

Apply the lowpass filter to the dataset.

Parameters

• lfreq : int, float, astropy.units.Quantity

  the cutoff frequencies

• sample_rate : int, float, astropy.units.Quantity

  sample rate of ditital signal

• order : int, optional

  the order of the filter, default value is 2

• flattening : Boonlean, optional

  signal flattening option, defaule value is True

Return : mcgpy.timeseries.TimeSeriesArray

filted dataset

Examples

>>> from mcgpy.timeseries import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> dataset.lowpass(300)
[[27.122792, 96.382285, 158.37537, …, −114.89549, −122.86079, −74.991527], 
 [90.633926, 300.13861, 435.25997, …, 125.62341, 87.47646, 76.55231], 
 …, 
 [−298.51918, −1015.2932, −1538.4497, …, −2075.8569, −2023.2914, −1972.1399], 
 [−496.63348, −1695.4982, −2574.3753, …, −1975.7265, −1958.595, −1906.0884]]1×10−15T

notch(freq, Q=30, **kwargs)

def mcgpy.timeseriesarray.TimeSeriesArray.notch(freq, Q=30, flattening=True, **kwargs)

Apply the notch/bandstop filter to the dataset.

Parameters

• freq : int, float, astropy.units.Quantity

  the cutoff frequencies

• sample_rate : int, float, astropy.units.Quantity

  sample rate of ditital signal

• Q : int, optional

  the Q-factor of the filter, default value is 30

• flattening : Boonlean, optional

  signal flattening option, defaule value is True

Return : mcgpy.timeseries.TimeSeriesArray

filted dataset

Examples

>>> from mcgpy.timeseries import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> dataset.notch(60)
[[135.4371, 154.08522, 173.6796, …, −134.07926, −57.510631, 44.525834], 
 [452.57862, 405.36713, 375.73626, …, 58.700064, 45.456142, 126.85806], 
 …, 
 [−1490.6493, −1468.6386, −1438.5928, …, −1970.1784, −1950.0809, −2002.2954], 
 [−2479.929, −2475.262, −2390.6521, …, −1948.1221, −1940.4119, −1800.9872]]1×10−15T

offset_correction_at(epoch, **kwargs)

def mcgpy.timeseriesarray.TimeSeriesArray.offset_correction_at(epoch, **kwargs)

Offset correction by the value at the given timestamp, each signal offset will be subtracted from the value at the given timestamp.

Parameters

• epoch : int, float, astropy.units.Quantity

  timestamp user wants to get the value

Return : mcgpy.timeseries.TimeSeriesArray

offset corrected dataset for each channel based on the value of the input timestamp

Examples

>>> from mcgpy.timeseries import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> dataset.offset_correction_at(2082875273.397583)
[[165.04526, 185.35852, 206.51817, …, −111.72295, −38.933754, 61.786175], 
 [27.084351, −15.234947, −41.472912, …, −358.02126, −368.17789, −285.23207], 
 …, 
 [−11.849403, −6.7710876, 10.156631, …, −497.67494, −466.35866, −506.98519], 
 [323.31944, 299.62063, 362.25319, …, 866.69922, 888.70525, 1041.9011]]1×10−15T

psd(fftlength=None, overlap=0, window='hann', average='median', **kwargs)

def mcgpy.timeseriesarray.TimeSeriesArray.psd(fftlength=None, overlap=0, window='hann', average='median', **kwargs)

Calculate the power spectral density, PSD.

Parameters

• fftlength : int, float, optional

  number of seconds for dividing the time window into equal bins, if None type value is given, it will be the size of signal

• overlap : int, float, optional

  number of seconds of overlap between FFTs, default value is 0

• window : str

  Desired window to use. If window is a string or tuple, it is passed to get_window to generate the window values, which are DFT-even by default. See get_window for a list of windows and required parameters. If window is array_like it will be used directly as the window and its length must be nperseg. Defaults to a Hann window.

  See more detailed explanation in scipy.signal.welch

• average : { "mean", "median" }, optional

  Method to use when averaging periodograms. Defaults to ‘mean'.

  See more detailed explanation in scipy.signal.welch

Return : mcgpy.series.FrequencySeries

• if the dataset is one-dimensional, return psd frequency-series

• if the dateset is two-dimensional, return psd frequency-series for each channel

Examples

>>> from mcgpy.timeseries import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> dataset.psd(2,1)
[[16.390747, 62.977136, 149.35229, …, 1.8138222, 1.4555211, 0.93069027], 
 [582.88645, 12969.614, 68385.748, …, 3.2238317, 3.4013085, 0.5242243], 
 …, 
 [155.65072, 2010.1159, 3214.8239, …, 2.7720054, 3.6474833, 1.02261], 
 [23574.937, 160619.55, 48710.946, …, 3.823116, 3.8062277, 0.64296479]]1×10−30T2Hz

read(number=None, label=None, **kwargs)

def mcgpy.timeseriesarray.TimeSeriesArray.read(number=None, label=None, **kwargs)

Read one channel data from the dataset.

Parameters

• number : int, conditional

  number of a channel, while label parameter is None

• label : str, conditional

  label of a channel, while number parameter is None

Return : mcgpy.timeseries.TimeSeries

a single channel time-series data

Examples

>>> from mcgpy.timeseries import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> dataset.read(number=1)
[136.26814, 156.5814, …, −67.710876, 33.009052]1×10−15T

rms(stride=1, **kwargs)

def mcgpy.timeseriesarray.TimeSeriesArray.rms(stride=1, **kwargs)

Get the rms dataset by a given stride.

Parameters

• stride : int, float, astropy.units.Quantity, optional

  sliding step for rms calculation

Return : mcgpy.timeseries.TimeSeriesArray

• if the dataset is one-dimensional, return rms series

• if the dateset is two-dimensional, return rsm series for each channel

Examples

>>> from mcgpy.timeseries import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> dataset.rms()
[[397.35058, 475.13264, 335.78015, …, 375.31408, 345.16582, 385.43835],
[667.53699, 810.86134, 459.06969, …, 595.80191, 563.2746, 617.1745], 
…, 
[1497.2229, 1485.2231, 1497.0368, …, 2169.8838, 2085.6369, 2026.235], 
[2499.4694, 2532.6463, 2630.5988, …, 2015.9665, 666.48291, 1291.5833]]1×10−15T

smooth(window_len=20, window='hamming')

def mcgpy.timeseries.TimeSeriesArray.smooth(window_len=20, window='hamming')

smooth the data using a window with requested size.

This method is based on the convolution of a scaled window with the signal. The signal is prepared by introducing reflected copies of the signal (with the window size) in both ends so that transient parts are minimized in the begining and end part of the output signal.

Parameters

• window_len : int, optional

  the dimension of the smoothing window; should be an odd integer

• window : str, optional

  the type of window from flat, hanning, hamming, bartlett, blackman

  flat window will produce a moving average smoothing.

Return : mcgpy.timeseries.TimeSeries

rms series

Examples

>>> from mcgpy.timeseries import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5').to_rms()
   >>> data.smooth()
    [0.05400055 0.05393543 0.05380835 ... 0.02055647 0.02056301 0.02056301] 1e-15 T

to_avg()

def mcgpy.timeseriesarray.TimeSeriesArray.to_abg()

Calculate an average of channel signals.

Raises

• TypeError

  if the dataset was one-dimensional

Return : mcgpy.timeseries.TimeSeriesArray

an average of channel signals, 1D-array

Examples

>>> from mcgpy.timeseries import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> dataset.to_avg()
[−308.02403, −316.00424, …, −549.95943, −541.59633]1×10−15T

to_rms()

def mcgpy.timeseriesarray.TimeSeriesArray.to_rms()

Calculate the rms for all channels.

Raises

• TypeError

  if the dataset was one-dimensional

Return : mcgpy.timeseries.TimeSeriesArray

rms time-series, 1D-array

Examples

>>> from mcgpy.timeseries import TimeSeriesArray
>>> dataset = TimeSeriesArray('~/test/data/file.hdf5')
>>> dataset.to_rms()
[1881.8758, 1874.3042, …, 1929.9437, 1915.6712]1×10−15T