FieldMap

class mcgpy.numeric.FieldMap(data, sourcegrid_width=240, sourcegrid_height=-40, sourcegrid_interval=16, sensorgrid_width=400, sensorgrid_height=40, sensorgrid_interval=25, baseline=50, axis='z', conduct_model='horizontal', eigenvalues=10, **kwargs)

Calculate magnetic field maps on the sersor plane.

Parameters

  • data : mcgpy.timeseriesarra.TimeSeriesArray

    MCG dataset 1) at the certain time point 2) between certain duration

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

    width of source grid, default value is 240 [mm], but it depends on device properties

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

    height of source grid, default value os -40 [mm], but it depends on device properties

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

    interval of cells on source grid, default value is 16 [mm], but it depends on device properties

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

    width of sensor grid, default value is 400 [mm], but it depends on device properties

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

    height of sensor grid, defualt value is 40 [mm], but it depends on device properties

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

    interval of cells on sensor grid, default value is 25 [mm], but it depends on device properties

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

    length of baseline (Z-axis length of sensor), defualt value is 50 [mm], but it depends on device properties

  • axis : {x, y, z}, optional

    axis of magnetic vectors on sensor grid, default value is z

  • conduct_model : {spherical, horizontal, free}, optional

    conduct model about sources, default value is horizontal

  • eigenvalues : int, optional

    the number of eigenvalues to get quasi-inverser lead field matrix, default value is 10

Warning

Grid related parameters and sensor’s baseline depend on device properties. If they did not match the instrument obtained MCG signals, it is possible to get inaccuracy results.

Raises

  • TypeError

    if input data type is not mcgpy.timeseriesarray.TimeSeriesArray

Return : mcgpy.numerical.FieldMap

  • if the dimension of input dataset is one

    2D-array, amplitude of given direction of magnetic vector on sensor plane

  • if the dimension of input dataset is two

    3D-array, amplitude of given direction of magnetic vectors on sensor plane

Examples

>>> from mcgpy.timeseriesarray import TimeSeriesArray
>>> from mcgpy.numeric import LeadField
>>> dataset = TimeSeriesArray("~/test/raw/file/path.hdf5")
>>> epoch_dataset = dataset.at(1126259462)
>>> LeadField(epoch_dataset)
[[−1.2890067, −1.4466162, −1.6401107, …, 0.17438883, 0.30280878, 0.38030763], [−1.4262029, −1.6297339, −1.9009002, …, 0.4317918, 0.53622291, 0.58182473], 
[−1.5804608, −1.8479563, −2.234106, …, 0.84135491, 0.86858131, 0.84406456], …, [−0.76191537, −0.75202421, −0.66326362, …, 2.2549908, 1.8901924, 1.6201124]]1×10−15T
>>>
>>> duration_dataset =  dataset.crop(1126259462, 1126259470)
>>> LeadField(epoch_dataset)
[[[−1.2890067, −1.4466162, −1.6401107, …, 0.17438883, 0.30280878, 0.38030763], ..., [−1.4262029, −1.6297339, −1.9009002, …, 0.4317918, 0.53622291, 0.58182473]], 
...
[[−1.5804608, −1.8479563, −2.234106, …, 0.84135491, 0.86858131, 0.84406456], ..., [−0.76191537, −0.75202421, −0.66326362, …, 2.2549908, 1.8901924, 1.6201124]]]1×10−15T

Properties Summary

Properties Discription
X X-axis meshgrid
Y Y-axis mechgrid
datetime The data time at the point of data recording i.e., ‘2020-02-02 02:02:02.00000'
dt The inderval bwteen time points of time-axis
duration Data recording duration
sample_rate Data sample frequency
t0 The first data point of time-axis
times The time-axis coordinate
Note

Properties of the mcgpy.numeric.FieldMap class only provide reading metadata to prevent incorrect value use.

Note

dt, diration, sample_rate, and times are None if the input dataset is one-diemtional. This is because Field Map is calculated by magnetic field values at the epoch.

Methods Summary

Methods Discription
arrows() Calculate current vectors on the sensor plane and make table
currentmax() Find the maximum current dipole and make table
currents(normalize) Calculate the pseudo-current distribution
pole() Calculate a field current vector on the sensor plane and make table

Properties Documentation

X

X-axis meshgrid.

Here is an example:

>>> from mcgpy.timeseriesarray import TimeSeriesArray
>>> from mcgpy.numeric import LeadField
>>> dataset = TimeSeriesArray("~/test/raw/file/path.hdf5")
>>> Bz = LeadField(dataset)
>>> print(Bz.X)
<Quantity [[−200, −175, −150, …, 150, 175, 200], [−200, −175, −150, …, 150, 175, 200], [−200, −175, −150, …, 150, 175, 200], …, [−200, −175, −150, …, 150, 175, 200], [−200, −175, −150, …, 150, 175, 200], [−200, −175, −150, …, 150, 175, 200]]mm >

Y

Y-axis mechgrid.

Here is an example:

>>> from mcgpy.timeseriesarray import TimeSeriesArray
>>> from mcgpy.numeric import LeadField
>>> dataset = TimeSeriesArray("~/test/raw/file/path.hdf5")
>>> Bz = LeadField(dataset)
>>> print(Bz.Y)
<Quantity [[−200, −200, −200, …, −200, −200, −200], [−175, −175, −175, …, −175, −175, −175], [−150, −150, −150, …, −150, −150, −150], …, [150, 150, 150, …, 150, 150, 150], [175, 175, 175, …, 175, 175, 175], [200, 200, 200, …, 200, 200, 200]]mm >

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.timeseriesarray import TimeSeriesArray
>>> from mcgpy.numeric import LeadField
>>> dataset = TimeSeriesArray("~/test/raw/file/path.hdf5").crop(1643734922, 1643734930)
>>> Bz = LeadField(dataset)
>>> print(Bz.datetime)
'2022-02-02 02:02:02'

dt

The inderval bwteen time points of time-axis.

Here is an example:

>>> from mcgpy.timeseriesarray import TimeSeriesArray
>>> from mcgpy.numeric import LeadField
>>> dataset = TimeSeriesArray("~/test/raw/file/path.hdf5").crop(1643734922, 1643734930)
>>> Bz = LeadField(dataset)
>>> print(Bz.dt)
<Quantity 0.0009765625 s >

duration

Data recording duration.

Here is an example:

>>> from mcgpy.timeseriesarray import TimeSeriesArray
>>> from mcgpy.numeric import LeadField
>>> dataset = TimeSeriesArray("~/test/raw/file/path.hdf5").crop(1643734922, 1643734930)
>>> Bz = LeadField(dataset)
>>> print(Bz.duration)
<Quantity 8 s>

sample_rate

Data sample frequency.

Here is an example:

>>> from mcgpy.timeseriesarray import TimeSeriesArray
>>> from mcgpy.numeric import LeadField
>>> dataset = TimeSeriesArray("~/test/raw/file/path.hdf5").crop(1643734922, 1643734930)
>>> Bz = LeadField(dataset)
>>> print(Bz.sample_rate)
<Quantity 1024. Hz>

t0

The first data point of time-axis.

Here is an example:

>>> from mcgpy.timeseriesarray import TimeSeriesArray
>>> from mcgpy.numeric import LeadField
>>> dataset = TimeSeriesArray("~/test/raw/file/path.hdf5").crop(1643734922, 1643734930)
>>> Bz = LeadField(dataset)
>>> print(Bz.t0)
<Quantity 1.64373492e+09 s>

times

The time-axis coordinate.

Here is an example:

>>> from mcgpy.timeseriesarray import TimeSeriesArray
>>> from mcgpy.numeric import LeadField
>>> dataset = TimeSeriesArray("~/test/raw/file/path.hdf5").crop(1643734922, 1643734930)
>>> Bz = LeadField(dataset)
>>> print(Bz.times)
<Quantity [1.64373492e+09, 1.64373492e+09, 1.64373492e+09, ...,1.64373492e+09, 1.64373492e+09] s>

Methods Documentation

arrows(normalize)

def mcgpy.numeric.FieldMap.arrows(normalize)

Calculate current vectors on the sensor plane and make table.

Parameters

  • normalize: boolean, optional

    this option normalizes the length of each current arrow vector. it might be helpful when the current arrow map is plotted.

    default value is False

Return

  • if the dimension of input dataset is one : astropy.table.QTable

    table contains current arrows on sensor plane: tail coordinate, head coordinate, vector, distance, and angle

  • if the dimension of input dataset is two : astropy.table.QTable

    dictionanty consists of a table at each time

Examples
>>> from mcgpy.timeseriesarray import TimeSeriesArray
>>> from mcgpy.numeric import LeadField
>>> dataset = TimeSeriesArray("~/test/raw/file/path.hdf5")
>>> epoch_dataset = dataset.at(1643734922)
>>> LeadField(epoch_dataset).arrows(normalize=Ture)
<QTable length=289>
    tail [2]                      head [2]                  ...        distance               angle       
                                                            ...          A m                   deg        
    float64                       float64                   ...        float64               float64      
---------------- ------------------------------------------ ... ---------------------- -------------------
-200.0 .. -200.0 -199.97231886896392 .. -200.03173392832403 ...  1.405140770179355e-11   48.90219319940143
-175.0 .. -200.0 -174.95987290696857 .. -200.03277605720874 ... 1.7288536738689093e-11   39.24216178913423
.
.
.
>>> duration_dataset =  dataset.crop(1643734922, 1643734930)
>>> LeadField(epoch_dataset)
{1126259462:
<QTable length=289>
    tail [2]                      head [2]                  ...        distance               angle       
                                                            ...          A m                   deg        
    float64                       float64                   ...        float64               float64      
---------------- ------------------------------------------ ... ---------------------- -------------------
-200.0 .. -200.0 -199.97231886896392 .. -200.03173392832403 ...  1.405140770179355e-11   48.90219319940143
-175.0 .. -200.0 -174.95987290696857 .. -200.03277605720874 ... 1.7288536738689093e-11   39.24216178913423
.
.
.
,...}

currentmax()

def mcgpy.numeric.FieldMap.currentmax()

Find the maximum current dipole and make table

Return

  • if the dimension of input dataset is one : astropy.table.QTable

    table contains a maximum current dipole on sensor plane: position, vector, distances, angle

  • if the dimension of input dataset is two : astropy.table.QTable

    table contains a maximum current dipole on sensor plane during at each time: position, vector, distances, angle

Examples
>>> from mcgpy.timeseriesarray import TimeSeriesArray
>>> from mcgpy.numeric import LeadField
>>> dataset = TimeSeriesArray("~/test/raw/file/path.hdf5")
>>> epoch_dataset = dataset.at(1126259462)
>>> LeadField(epoch_dataset).currentmax()
    time     position [2]                 vector                        distance              angle      
     s                                                                    A m                  deg       
------------ ------------ -------------------------------------- ---------------------- -----------------
1638784409.0 50.0 .. 25.0 (543.2066276916597-874.8684876011514j) 1.0297904208943057e-06 58.16381709148949
.
.
.

currents()

def mcgpy.numeric.FieldMap.currents()

Calculate the pseudo-current distribution

Return

  • if the dimension of input dataset is one : astropy.table.QTable

    2D-array of the pseudo-current distribution

  • if the dimension of input dataset is two : astropy.table.QTable

    3D-array of the pseudo-current distribution

Examples
>>> from mcgpy.timeseriesarray import TimeSeriesArray
>>> from mcgpy.numeric import LeadField
>>> dataset = TimeSeriesArray("~/test/raw/file/path.hdf5")
>>> epoch_dataset = dataset.at(1643734922)
>>> LeadField(epoch_dataset).currents()
[[5.80895955e-08 6.82033252e-08 8.38246186e-08 ...
  1.15054266e-07 1.04146993e-07 9.39165794e-08]
 [7.09999483e-08 8.75171891e-08 1.14528064e-07 ...
  1.44635222e-07 1.21560702e-07 1.04386546e-07]
 [1.00648263e-07 1.33912668e-07 1.90724137e-07 ...
  1.92781156e-07 1.53434507e-07 1.24748848e-07]
 ...
 [1.03979718e-07 1.27879891e-07 1.61285186e-07 ...
  1.89137749e-07 1.54725988e-07 1.27253331e-07]
 [8.71076169e-08 9.96315932e-08 1.19166692e-07 ...
  1.35433846e-07 1.16556350e-07 9.96535657e-08]
 [7.68162949e-08 8.32770941e-08 9.37287466e-08 ...
  1.11205662e-07 9.82416776e-08 8.55364650e-08]] A m

pole()

def mcgpy.numeric.FieldMap.pole()

Calculate a field current vector on the sensor plane and make table.

Return

  • if the dimension of input dataset is one : astropy.table.QTable

    table contains a field current arrow on sensor plane: minimum coordinates, maximum coordinate, vector, distance, anngle, and ratio

  • if the dimension of input dataset is two : astropy.table.QTable

    table contains ontains a field current arrows on sensor plane during at each time: minimum coordinates, maximum coordinate, vector, distance, anngle, and ratio

Examples
>>> from mcgpy.timeseriesarray import TimeSeriesArray
>>> from mcgpy.numeric import LeadField
>>> dataset = TimeSeriesArray("~/test/raw/file/path.hdf5")
>>> epoch_dataset = dataset.at(1643734922)
>>> LeadField(epoch_dataset).pole()
time min coordinate [2] max coordinate [2]    vector        distance            angle              ratio      
 s                                                             mm                deg                          
---- ------------------ ------------------ ----------- ------------------ ------------------ -----------------
1643734922      50.0 .. 100.0     -75.0 .. -75.0 (-125-175j) 215.05813167606567 125.53767779197439 1.349543578763341

>>>
>>> duration_dataset =  dataset.crop(1643734922, 1643734930)
>>> LeadField(epoch_dataset)
time min coordinate [2] max coordinate [2]    vector        distance            angle              ratio      
 s                                                             mm                deg                          
---- ------------------ ------------------ ----------- ------------------ ------------------ -----------------
1643734922      50.0 .. 100.0     -75.0 .. -75.0 (-125-175j) 215.05813167606567 125.53767779197439 1.349543578763341
1643734922.2929687      50.0 .. 100.0     -75.0 .. -75.0 (-125-175j) 215.05813167606567 125.53767779197439 1.349543578763341
'''