Data Tables
MCGpy provides Data Tabels to store sensor information and time-varying current dipole activities. Every table is built as an astropy.table object. Then, users can easily modify and deal with elements.
This page shows two common examples when users encounter the situations Data Tables are required.
The Channel
from mcgpy.channel import (ChannelConfig, ChannelActive)
Channel module consists of ChannelConfig and ChannelActive classes. Each one has a function to read sensor information from the ini configuration file and from frame files, respectively.
The ChannelConfig
Magnetocardiography (MCG) data analysis is performed by not only time-varying magnetic signals also field distributions. In the case of reading a frame file that only included time-series datasets without sensor information, this configuration file is significant.
The syntax of a sensor configuration ini file is follows as below:
[Label]
1 = label_1
2 = label_2
3 = label_3
[Positions]
1 = 10.000, 10.000, 0.000
2 = 10.000, 15.000, 0.000
3 = 10.000, 20.000, 0.000
[Directions]
1 = 1.0000, 0.0000, 0.0000
2 = 1.0000, 0.0000, 0.0000
3 = 1.0000, 0.0000, 0.0000
There are examples of how to read sensor information from an ini file.
>>> from mcgpy.channel import ChannelConfig
>>> ini_path = '~/test/config/file.ini'
>>> config = ChannelConfig(ini_path)
>>> config.get('label') # get the label list of sensors
number label
int64 str3
1 label_1
2 label_2
3 label_3
>>> config.get('positions') # get the position list of sensors
number positions
int64 float64
1 (10., 10., 0.)
2 (10., 15., 0.)
3 (10., 20., 0.)
>>> config.get('directions') # get the directions list of sensors
number positions
int64 float64
1 (1., 0., 0.)
2 (1., 0., 0.)
3 (1., 0., 0.)
The ChannelActive
This class has a function to extract channel numbers and labels from a frame file. It will be useful when users want to read a single time series by using TimeSeires class, but the channel's number or label is unknown.
There are examples of how to obtain sensor's numbers and labels.
>>> from mcgpy.channel import ChannelActive
>>> kdf_path = '~/test/raw/file.kdf'
>>> ch_info = ChannelActive(kdf_path)
>>> ch_info.get_table() # obtain channel number and label as the table
number label
int64 str3
1 label_1
2 label_2
4 label_4
10 label_10
11 label_11
.
.
>>> ch_info.get_number() # obtain channel numbers as the list
[1,2,4,10,11,...]
>>> ch_info.get_label() # obtain channel labels as the list
['label_1','label_2','label_4','label_10','label_11',...]
FieldMap
Although a contour map, like the magnetic field map at a certain time of the MCG dataset, is a scalar dataset, vector distribution map can be obtained by calculating the gradient on each point or cell. However, it is hard to organize them into a simple matrix, for vectors are represented by their magnitude and direction on the coordinate system. Therefore, current arrows are stored as the table.
There are two examples of current arrows and the field pole arrow with arrows() and pole() methods, respectively.
>>> from mcgpy.timeseries import TimeSeriesArray
>>> from mcgpy.numeric import FieldMap
>>> hdf_path = '~/test/raw/file.hdf5'
>>> epoch_dataset = TimeSeriesArray(hdf_path).at(1126259462)
>>> fieldmap = FieldMap(epoch_dataset)
>>>
>>> fieldmap.arrows() # get the arrow information
<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
-150.0 .. -200.0 -149.94211658840587 .. -200.03288365171088 ... 2.221373259886304e-11 29.600970874872083
-125.0 .. -200.0 -124.91940414235607 .. -200.02704428456494 ... 2.8366900929366782e-11 18.549397931354616
-100.0 .. -200.0 -99.89696839768818 .. -200.0119855517868 ... 3.461143192185526e-11 6.6353309283519035
-75.0 .. -200.0 -74.88433270277011 .. -199.98755720788955 ... 3.881855053338731e-11 -6.139923914333862
-50.0 .. -200.0 -49.889719068935264 .. -199.95851881256343 ... 3.931563281813583e-11 -20.61334185009448
-25.0 .. -200.0 -24.9155668122923 .. -199.93326542522965 ... 3.591127477829506e-11 -38.322252464628534
0.0 .. -200.0 0.04408996224175425 .. -199.92005756492776 ... 3.0463215301695057e-11 -61.1223250722071
25.0 .. -200.0 25.001835485662816 .. -199.9225877322705 ... 2.583819229946889e-11 -88.6417413684232
50.0 .. -200.0 49.96991258695252 .. -199.93810160528204 ... 2.2965006249010528e-11 -115.92334087152777
75.0 .. -200.0 74.9541161228128 .. -199.95957421993253 ... 2.040522328867382e-11 -138.61848327288334
100.0 .. -200.0 99.95293929798622 .. -199.97980020168612 ... 1.708866117585782e-11 -156.76960759013545
125.0 .. -200.0 124.96067815838208 .. -199.99438059625945 ... 1.3254220582293216e-11 -171.86704490238282
150.0 .. -200.0 149.97092812140502 .. -200.00248245828382 ... 9.736009223719278e-12 175.11933206321967
175.0 .. -200.0 174.97966097140406 .. -200.0057125561094 ... 7.0493374498980925e-12 164.31171513458463
200.0 .. -200.0 199.9858278213182 .. -200.0064025381238 ... 5.1891614352136636e-12 155.68806577787495
-200.0 .. -175.0 -199.9726012419791 .. -175.04417989031938 ... 1.7346726656009182e-11 58.194318496544845
-175.0 .. -175.0 -174.9583988725216 .. -175.04787719748776 ... 2.1164052170242602e-11 49.01219357925647
-150.0 .. -175.0 -149.93676757496698 .. -175.05311803401713 ... 2.7556127478919716e-11 40.0317451629414
-125.0 .. -175.0 -124.90737495749306 .. -175.04961837992377 ... 3.5062430106783925e-11 28.177542997558902
-100.0 .. -175.0 -99.87718012282345 .. -175.0295212715798 ... 4.214978904192725e-11 13.515360878046911
-75.0 .. -175.0 -74.85943540653658 .. -174.99118187226603 ... 4.699581020580927e-11 -3.5896683259775
-50.0 .. -175.0 -49.86485581273417 .. -174.9429017578023 ... 4.895458854215875e-11 -22.90399415696121
-25.0 .. -175.0 -24.89667631665922 .. -174.90016994081816 ... 4.794069555181633e-11 -44.01478462512318
... ... ... ... ...
0.0 .. 175.0 0.07743500183678054 .. 175.09306260204872 ... 4.0397108392210966e-11 -50.2370272415893
25.0 .. 175.0 25.11189772222917 .. 175.0592833569958 ... 4.2254515413214435e-11 -27.914620313512447
50.0 .. 175.0 50.12573678520827 .. 175.0096456040758 ... 4.2079132212031285e-11 -4.3867204510172275
75.0 .. 175.0 75.11504974124594 .. 174.96153559546528 ... 4.0478507799496806e-11 18.486249167324814
100.0 .. 175.0 100.08694560984694 .. 174.9298363514549 ... 3.728040947215361e-11 38.902947609216355
125.0 .. 175.0 125.05360115526018 .. 174.92024033402313 ... 3.206575057234722e-11 56.09758303427284
150.0 .. 175.0 150.02675608962414 .. 174.92803464260268 ... 2.5619376170673633e-11 69.60530662164686
175.0 .. 175.0 175.0107066474462 .. 174.9430195552396 ... 1.934597441954906e-11 79.35818512826286
200.0 .. 175.0 200.002906073179 .. 174.95097442538363 ... 1.638757315935333e-11 86.60766607817193
-200.0 .. 200.0 -200.01155889402082 .. 200.01316750821366 ... 5.846464663878313e-12 -131.2777801258076
-175.0 .. 200.0 -175.0128621295698 .. 200.01543073999264 ... 6.703084974422652e-12 -129.81253789854145
-150.0 .. 200.0 -150.01438911685415 .. 200.02106395130272 ... 8.5120262139557e-12 -124.33759811870262
-125.0 .. 200.0 -125.0153614001027 .. 200.02934881121996 ... 1.1053453674788519e-11 -117.62791613116748
-100.0 .. 200.0 -100.01355813627043 .. 200.04069891222193 ... 1.4314154440939591e-11 -108.42459945643391
-75.0 .. 200.0 -75.00537432798211 .. 200.0540082963793 ... 1.8110498677621346e-11 -95.68275534099766
-50.0 .. 200.0 -49.98716612990017 .. 200.06572349358694 ... 2.2344823891764376e-11 -78.95085205033362
-25.0 .. 200.0 -24.958154659842595 .. 200.07009678941574 ... 2.7240626323285054e-11 -59.164270775606454
0.0 .. 200.0 0.07619143087919736 .. 200.06168679773657 ... 3.271153433695371e-11 -38.994637415530526
25.0 .. 200.0 25.104596948781673 .. 200.0393128530757 ... 3.728570357026443e-11 -20.59877355025309
50.0 .. 200.0 50.11613243871938 .. 200.00817271974248 ... 3.884692170218242e-11 -4.025504126985765
75.0 .. 200.0 75.10754271744837 .. 199.97753283972654 ... 3.665959766644338e-11 11.800161349500426
100.0 .. 200.0 100.08413795019683 .. 199.95575231286674 ... 3.172076809054885e-11 27.73963624735183
125.0 .. 200.0 125.0557107946247 .. 199.94650619235952 ... 2.577185699540861e-11 43.8369853598751
150.0 .. 200.0 150.03160623352406 .. 199.94799613017943 ... 2.0306171586517033e-11 58.71014847987613
175.0 .. 200.0 175.01578781947123 .. 199.9553078158115 ... 1.581603696309956e-11 70.54388429868378
200.0 .. 200.0 200.00702971238024 .. 199.95973253247462 ... 1.3639664136883761e-11 80.0973567603393
>>>
>>> fieldmap.pole() # get the field pole information
time min coordinate [2] max coordinate [2] vector distance angle ratio
s mm deg
---- ------------------ ------------------ ----------- ------------------ ------------------ -----------------
1126259462 50.0 .. 100.0 -75.0 .. -75.0 (-125-175j) 215.05813167606567 125.53767779197439 1.349543578763341
Accosicated classes
Note that in addition to the TimeSeires associated classes listed below.
| Classes | Description |
|---|---|
| TimeSeires | Dealing with a single time-series of a MCG dataset |
| TimeSeriesArray | Dealing with a multi-channel time-series array of a MCG dataset |
| Channel | Listing the channel information from a configuration file or a raw frame file |
| FieldMap | Calculate a lead field matrix and the current-dipole information |