Xarray Data Structures - an fNIRS example

This example illustrates the usage of xarray-based data structures for calculating the Beer-Lambert transformation.

# This cells setups the environment when executed in Google Colab.
try:
    import google.colab
    !curl -s https://raw.githubusercontent.com/ibs-lab/cedalion/dev/scripts/colab_setup.py -o colab_setup.py
    # Select branch with --branch "branch name" (default is "dev")
    %run colab_setup.py
except ImportError:
    pass

import cedalion
import cedalion.nirs
import cedalion.xrutils
import cedalion.xrutils as xrutils
from cedalion.data import get_fingertapping_snirf_path
import numpy as np
import xarray as xr
import pint
import matplotlib.pyplot as p
import scipy.signal
import os.path
xr.set_options(display_max_rows=3, display_values_threshold=50)
np.set_printoptions(precision=4)

Loading raw CW-NIRS data from a SNIRF file

This notebook uses a finger-tapping dataset in BIDS layout provided by Rob Luke. It can can be downloaded via cedalion.data.

Load amplitude data from the snirf file.

path_to_snirf_file = get_fingertapping_snirf_path()

recordings = cedalion.io.read_snirf(path_to_snirf_file)
rec = recordings[0] # there is only one NirsElement in this snirf file...
amp = rec["amp"]    # ... which holds amplitude data

# restrict to first 60 seconds and fill in missing units
amp = amp.sel(time=amp.time < 60)
amp = amp.pint.dequantify().pint.quantify("V")
geo3d = rec.geo3d

recordings

[<Recording |  timeseries: ['amp'],  masks: [],  stim: ['1.0', '15.0', '2.0', '3.0'],  aux_ts: [],  aux_obj: []>]

Amplitude data

display(amp.round(4))

<xarray.DataArray (channel: 28, wavelength: 2, time: 469)> Size: 210kB
<Quantity([[[0.0914 0.091  0.091  ... 0.0903 0.0902 0.0899]
  [0.1857 0.1864 0.1837 ... 0.1849 0.185  0.1847]]

 [[0.2275 0.2297 0.2261 ... 0.2241 0.2243 0.2257]
  [0.6355 0.6377 0.6298 ... 0.6223 0.6237 0.6272]]

 [[0.1065 0.1066 0.1053 ... 0.1065 0.1062 0.1056]
  [0.2755 0.2762 0.2727 ... 0.2737 0.2742 0.276 ]]

 ...

 [[0.2028 0.1997 0.2005 ... 0.1998 0.2007 0.2026]
  [0.4666 0.4554 0.4562 ... 0.4482 0.4511 0.4541]]

 [[0.4885 0.4802 0.4818 ... 0.5005 0.5036 0.5045]
  [0.8458 0.826  0.826  ... 0.8386 0.8441 0.8475]]

 [[0.6305 0.6284 0.6287 ... 0.6373 0.638  0.6392]
  [1.2286 1.2206 1.219  ... 1.2232 1.2259 1.2278]]], 'volt')>
Coordinates: (3/6)
  * time        (time) float64 4kB 0.0 0.128 0.256 0.384 ... 59.65 59.78 59.9
    samples     (time) int64 4kB 0 1 2 3 4 5 6 7 ... 462 463 464 465 466 467 468
    ...          ...
  * wavelength  (wavelength) float64 16B 760.0 850.0
Attributes:
    data_type_group:  unprocessed raw

xarray.DataArray

• channel: 28
• wavelength: 2
• time: 469

• [V] 0.0914 0.091 0.091 0.0904 0.0909 ... 1.223 1.222 1.223 1.226 1.228

  Magnitude

  [[[0.0914 0.091 0.091 ... 0.0903 0.0902 0.0899] [0.1857 0.1864 0.1837 ... 0.1849 0.185 0.1847]] [[0.2275 0.2297 0.2261 ... 0.2241 0.2243 0.2257] [0.6355 0.6377 0.6298 ... 0.6223 0.6237 0.6272]] [[0.1065 0.1066 0.1053 ... 0.1065 0.1062 0.1056] [0.2755 0.2762 0.2727 ... 0.2737 0.2742 0.276]] ... [[0.2028 0.1997 0.2005 ... 0.1998 0.2007 0.2026] [0.4666 0.4554 0.4562 ... 0.4482 0.4511 0.4541]] [[0.4885 0.4802 0.4818 ... 0.5005 0.5036 0.5045] [0.8458 0.826 0.826 ... 0.8386 0.8441 0.8475]] [[0.6305 0.6284 0.6287 ... 0.6373 0.638 0.6392] [1.2286 1.2206 1.219 ... 1.2232 1.2259 1.2278]]]

  Units

  volt

• Coordinates: (6)
  • time
    (time)
    float64
    0.0 0.128 0.256 ... 59.78 59.9

    units :

    second

    array([ 0.   ,  0.128,  0.256, ..., 59.648, 59.776, 59.904], shape=(469,))

  • samples
    (time)
    int64
    0 1 2 3 4 5 ... 464 465 466 467 468

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,
            13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,
            26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,
            39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,
            52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,
            65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,
            78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,
            91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,
           104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,
           117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,
           130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,
           143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,
           156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,
           169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,
           195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207,
           208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220,
           221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,
           234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246,
           247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259,
           260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272,
           273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285,
           286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298,
           299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311,
           312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324,
           325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337,
           338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350,
           351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363,
           364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376,
           377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389,
           390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402,
           403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415,
           416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428,
           429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441,
           442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454,
           455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467,
           468])

  • channel
    (channel)
    object
    'S1D1' 'S1D2' ... 'S8D8' 'S8D16'

    array(['S1D1', 'S1D2', 'S1D3', 'S1D9', 'S2D1', 'S2D3', 'S2D4', 'S2D10', 'S3D2',
           'S3D3', 'S3D11', 'S4D3', 'S4D4', 'S4D12', 'S5D5', 'S5D6', 'S5D7',
           'S5D13', 'S6D5', 'S6D7', 'S6D8', 'S6D14', 'S7D6', 'S7D7', 'S7D15',
           'S8D7', 'S8D8', 'S8D16'], dtype=object)

  • source
    (channel)
    object
    'S1' 'S1' 'S1' ... 'S8' 'S8' 'S8'

    array([np.str_('S1'), np.str_('S1'), np.str_('S1'), np.str_('S1'),
           np.str_('S2'), np.str_('S2'), np.str_('S2'), np.str_('S2'),
           np.str_('S3'), np.str_('S3'), np.str_('S3'), np.str_('S4'),
           np.str_('S4'), np.str_('S4'), np.str_('S5'), np.str_('S5'),
           np.str_('S5'), np.str_('S5'), np.str_('S6'), np.str_('S6'),
           np.str_('S6'), np.str_('S6'), np.str_('S7'), np.str_('S7'),
           np.str_('S7'), np.str_('S8'), np.str_('S8'), np.str_('S8')],
          dtype=object)

  • detector
    (channel)
    object
    'D1' 'D2' 'D3' ... 'D7' 'D8' 'D16'

    array([np.str_('D1'), np.str_('D2'), np.str_('D3'), np.str_('D9'),
           np.str_('D1'), np.str_('D3'), np.str_('D4'), np.str_('D10'),
           np.str_('D2'), np.str_('D3'), np.str_('D11'), np.str_('D3'),
           np.str_('D4'), np.str_('D12'), np.str_('D5'), np.str_('D6'),
           np.str_('D7'), np.str_('D13'), np.str_('D5'), np.str_('D7'),
           np.str_('D8'), np.str_('D14'), np.str_('D6'), np.str_('D7'),
           np.str_('D15'), np.str_('D7'), np.str_('D8'), np.str_('D16')],
          dtype=object)

  • wavelength
    (wavelength)
    float64
    760.0 850.0

    array([760., 850.])

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(Index([   0.0,  0.128,  0.256,  0.384,  0.512,   0.64,  0.768,  0.896,  1.024,
            1.152,
           ...
           58.752,  58.88, 59.008, 59.136, 59.264, 59.392,  59.52, 59.648, 59.776,
           59.904],
          dtype='float64', name='time', length=469))

  • channel
    PandasIndex

    PandasIndex(Index(['S1D1', 'S1D2', 'S1D3', 'S1D9', 'S2D1', 'S2D3', 'S2D4', 'S2D10', 'S3D2',
           'S3D3', 'S3D11', 'S4D3', 'S4D4', 'S4D12', 'S5D5', 'S5D6', 'S5D7',
           'S5D13', 'S6D5', 'S6D7', 'S6D8', 'S6D14', 'S7D6', 'S7D7', 'S7D15',
           'S8D7', 'S8D8', 'S8D16'],
          dtype='object', name='channel'))

  • wavelength
    PandasIndex

    PandasIndex(Index([760.0, 850.0], dtype='float64', name='wavelength'))

• Attributes: (1)

  data_type_group :

  unprocessed raw

Montage information

The geo3d DataArray maps labels to 3D positions, thus storing the location of optodes and landmarks.

display_labels = ["S1", "S2", "D1", "D2", "NASION"] # for brevity show only these
geo3d.round(5).sel(label=display_labels)

<xarray.DataArray (label: 5, pos: 3)> Size: 120B
<Quantity([[-0.0416  0.0268  0.1299]
 [-0.0648  0.0581  0.0908]
 [-0.0376  0.0632  0.1157]
 [-0.0413 -0.0118  0.1349]
 [ 0.      0.114  -0.    ]], 'meter')>
Coordinates:
  * label    (label) <U6 120B 'S1' 'S2' 'D1' 'D2' 'NASION'
    type     (label) object 40B PointType.SOURCE ... PointType.LANDMARK
Dimensions without coordinates: pos

xarray.DataArray

• label: 5
• pos: 3

• [m] -0.04161 0.0268 0.1299 -0.06477 0.05814 ... 0.1349 0.0 0.114 -0.0

  Magnitude	[[-0.04161 0.0268 0.1299] [-0.06477 0.05814 0.09084] [-0.03762 0.06323 0.11573] [-0.04134 -0.01178 0.13495] [0.0 0.11405 -0.0]]
  Units	meter

• Coordinates: (2)
  • label
    (label)
    <U6
    'S1' 'S2' 'D1' 'D2' 'NASION'

    array(['S1', 'S2', 'D1', 'D2', 'NASION'], dtype='<U6')

  • type
    (label)
    object
    PointType.SOURCE ... PointType.L...

    array([<PointType.SOURCE: 1>, <PointType.SOURCE: 1>, <PointType.DETECTOR: 2>,
           <PointType.DETECTOR: 2>, <PointType.LANDMARK: 3>], dtype=object)

• Indexes: (1)
  • label
    PandasIndex

    PandasIndex(Index(['S1', 'S2', 'D1', 'D2', 'NASION'], dtype='object', name='label'))

• Attributes: (0)

To obtain channel distances, we can lookup amp’s source and detector coordinates in geo3d, subtract these and calculate the vector norm.

dists = xrutils.norm(geo3d.loc[amp.source] - geo3d.loc[amp.detector], dim="pos")
display(dists.round(3))

<xarray.DataArray (channel: 28)> Size: 224B
<Quantity([0.039 0.039 0.041 0.008 0.037 0.038 0.037 0.007 0.04  0.037 0.008 0.041
 0.034 0.008 0.039 0.039 0.041 0.008 0.037 0.037 0.037 0.008 0.04  0.037
 0.007 0.041 0.033 0.008], 'meter')>
Coordinates:
  * channel   (channel) object 224B 'S1D1' 'S1D2' 'S1D3' ... 'S8D8' 'S8D16'
    source    (channel) object 224B 'S1' 'S1' 'S1' 'S1' ... 'S7' 'S8' 'S8' 'S8'
    detector  (channel) object 224B 'D1' 'D2' 'D3' 'D9' ... 'D7' 'D8' 'D16'

xarray.DataArray

• channel: 28

• [m] 0.039 0.039 0.041 0.008 0.037 ... 0.037 0.007 0.041 0.033 0.008

  Magnitude	[0.039 0.039 0.041 0.008 0.037 0.038 0.037 0.007 0.04 0.037 0.008 0.041 0.034 0.008 0.039 0.039 0.041 0.008 0.037 0.037 0.037 0.008 0.04 0.037 0.007 0.041 0.033 0.008]
  Units	meter

• Coordinates: (3)
  • channel
    (channel)
    object
    'S1D1' 'S1D2' ... 'S8D8' 'S8D16'

    array(['S1D1', 'S1D2', 'S1D3', 'S1D9', 'S2D1', 'S2D3', 'S2D4', 'S2D10', 'S3D2',
           'S3D3', 'S3D11', 'S4D3', 'S4D4', 'S4D12', 'S5D5', 'S5D6', 'S5D7',
           'S5D13', 'S6D5', 'S6D7', 'S6D8', 'S6D14', 'S7D6', 'S7D7', 'S7D15',
           'S8D7', 'S8D8', 'S8D16'], dtype=object)

  • source
    (channel)
    object
    'S1' 'S1' 'S1' ... 'S8' 'S8' 'S8'

    array([np.str_('S1'), np.str_('S1'), np.str_('S1'), np.str_('S1'),
           np.str_('S2'), np.str_('S2'), np.str_('S2'), np.str_('S2'),
           np.str_('S3'), np.str_('S3'), np.str_('S3'), np.str_('S4'),
           np.str_('S4'), np.str_('S4'), np.str_('S5'), np.str_('S5'),
           np.str_('S5'), np.str_('S5'), np.str_('S6'), np.str_('S6'),
           np.str_('S6'), np.str_('S6'), np.str_('S7'), np.str_('S7'),
           np.str_('S7'), np.str_('S8'), np.str_('S8'), np.str_('S8')],
          dtype=object)

  • detector
    (channel)
    object
    'D1' 'D2' 'D3' ... 'D7' 'D8' 'D16'

    array([np.str_('D1'), np.str_('D2'), np.str_('D3'), np.str_('D9'),
           np.str_('D1'), np.str_('D3'), np.str_('D4'), np.str_('D10'),
           np.str_('D2'), np.str_('D3'), np.str_('D11'), np.str_('D3'),
           np.str_('D4'), np.str_('D12'), np.str_('D5'), np.str_('D6'),
           np.str_('D7'), np.str_('D13'), np.str_('D5'), np.str_('D7'),
           np.str_('D8'), np.str_('D14'), np.str_('D6'), np.str_('D7'),
           np.str_('D15'), np.str_('D7'), np.str_('D8'), np.str_('D16')],
          dtype=object)

• Indexes: (1)
  • channel
    PandasIndex

    PandasIndex(Index(['S1D1', 'S1D2', 'S1D3', 'S1D9', 'S2D1', 'S2D3', 'S2D4', 'S2D10', 'S3D2',
           'S3D3', 'S3D11', 'S4D3', 'S4D4', 'S4D12', 'S5D5', 'S5D6', 'S5D7',
           'S5D13', 'S6D5', 'S6D7', 'S6D8', 'S6D14', 'S7D6', 'S7D7', 'S7D15',
           'S8D7', 'S8D8', 'S8D16'],
          dtype='object', name='channel'))

• Attributes: (0)

Beer-Lambert transformation

Beer-Lambert transformation

The modified Beer-Lambert law (mBLL) relates changes in optical density (OD) at two wavelengths to changes in oxy- and deoxyhaemoglobin (HbO / HbR) concentration:

\[\Delta c = (\mathbf{E} \cdot ext{DPF} \cdot d)^{-1} \Delta ext{OD}\]

where:

• \(\mathbf{E}\) is the extinction coefficient matrix — tabulated absorptivity of HbO and HbR at each wavelength (unit: 1/(mm·mM)).

• \(d\) is the source–detector distance (mm), derived from optode positions in geo3d.

• DPF (differential path length factor) corrects for the longer effective optical path of diffusely scattered photons. A value of 6 is a common approximation for adult scalp tissue in the 700–900 nm range.

Below we assemble these quantities manually to illustrate xarray’s matrix inversion and broadcasting. Cedalion’s convenience functions cedalion.nirs.cw.int2od() and cedalion.nirs.cw.od2conc() wrap the same steps.

dpf = xr.DataArray([6., 6.], dims="wavelength", coords={"wavelength" : [760., 850.]})

E = cedalion.nirs.get_extinction_coefficients("prahl", amp.wavelength)
Einv = cedalion.xrutils.pinv(E)
display(Einv.round(4))

<xarray.DataArray (wavelength: 2, chromo: 2)> Size: 32B
<Quantity([[-0.0024  0.0037]
 [ 0.0055 -0.0021]], 'millimeter * molar')>
Coordinates:
  * chromo      (chromo) <U3 24B 'HbO' 'HbR'
  * wavelength  (wavelength) float64 16B 760.0 850.0

xarray.DataArray

• wavelength: 2
• chromo: 2

• [mm·M] -0.0024 0.0037 0.0055 -0.0021

  Magnitude	[[-0.0024 0.0037] [0.0055 -0.0021]]
  Units	millimeter molar

• Coordinates: (2)
  • chromo
    (chromo)
    <U3
    'HbO' 'HbR'

    array(['HbO', 'HbR'], dtype='<U3')

  • wavelength
    (wavelength)
    float64
    760.0 850.0

    array([760., 850.])

• Indexes: (2)
  • chromo
    PandasIndex

    PandasIndex(Index(['HbO', 'HbR'], dtype='object', name='chromo'))

  • wavelength
    PandasIndex

    PandasIndex(Index([760.0, 850.0], dtype='float64', name='wavelength'))

• Attributes: (0)

optical_density =  -np.log( amp / amp.mean("time"))

conc = Einv @ (optical_density / ( dists * dpf))

display(conc.pint.to("micromolar").round(4))

<xarray.DataArray (chromo: 2, channel: 28, time: 469)> Size: 210kB
<Quantity([[[-0.0538 -0.1835  0.1612 ... -0.0725 -0.1064 -0.0939]
  [-0.1971 -0.1772 -0.0504 ...  0.1388  0.0943  0.0257]
  [ 0.0052 -0.0338  0.1269 ...  0.1558  0.0805 -0.1094]
  ...
  [-0.4716 -0.084  -0.0781 ...  0.2843  0.1805  0.1309]
  [-0.6011 -0.1653 -0.124  ... -0.0753 -0.1786 -0.2662]
  [-1.1848 -0.5717 -0.392  ... -0.076  -0.2826 -0.3658]]

 [[-0.168  -0.0692 -0.2042 ... -0.0249  0.0083  0.0364]
  [-0.0429 -0.1653 -0.0254 ...  0.0097  0.0158 -0.0304]
  [-0.0471 -0.0485  0.0284 ... -0.1066 -0.0428  0.0851]
  ...
  [ 0.0045  0.0366 -0.0132 ... -0.1122 -0.1236 -0.2155]
  [ 0.3211  0.3949  0.3327 ... -0.2165 -0.264  -0.2554]
  [ 0.6814  0.6478  0.554  ... -0.4091 -0.3963 -0.487 ]]], 'micromolar')>
Coordinates: (3/6)
  * chromo    (chromo) <U3 24B 'HbO' 'HbR'
  * time      (time) float64 4kB 0.0 0.128 0.256 0.384 ... 59.65 59.78 59.9
    ...        ...
    detector  (channel) object 224B 'D1' 'D2' 'D3' 'D9' ... 'D7' 'D8' 'D16'

xarray.DataArray

• chromo: 2
• channel: 28
• time: 469

• [µM] -0.0538 -0.1835 0.1612 0.0041 ... -0.4089 -0.4091 -0.3963 -0.487

  Magnitude

  [[[-0.0538 -0.1835 0.1612 ... -0.0725 -0.1064 -0.0939] [-0.1971 -0.1772 -0.0504 ... 0.1388 0.0943 0.0257] [0.0052 -0.0338 0.1269 ... 0.1558 0.0805 -0.1094] ... [-0.4716 -0.084 -0.0781 ... 0.2843 0.1805 0.1309] [-0.6011 -0.1653 -0.124 ... -0.0753 -0.1786 -0.2662] [-1.1848 -0.5717 -0.392 ... -0.076 -0.2826 -0.3658]] [[-0.168 -0.0692 -0.2042 ... -0.0249 0.0083 0.0364] [-0.0429 -0.1653 -0.0254 ... 0.0097 0.0158 -0.0304] [-0.0471 -0.0485 0.0284 ... -0.1066 -0.0428 0.0851] ... [0.0045 0.0366 -0.0132 ... -0.1122 -0.1236 -0.2155] [0.3211 0.3949 0.3327 ... -0.2165 -0.264 -0.2554] [0.6814 0.6478 0.554 ... -0.4091 -0.3963 -0.487]]]

  Units

  micromolar

• Coordinates: (6)
  • chromo
    (chromo)
    <U3
    'HbO' 'HbR'

    array(['HbO', 'HbR'], dtype='<U3')

  • time
    (time)
    float64
    0.0 0.128 0.256 ... 59.78 59.9

    array([ 0.   ,  0.128,  0.256, ..., 59.648, 59.776, 59.904], shape=(469,))

  • samples
    (time)
    int64
    0 1 2 3 4 5 ... 464 465 466 467 468

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,
            13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,
            26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,
            39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,
            52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,
            65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,
            78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,
            91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,
           104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,
           117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,
           130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,
           143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,
           156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,
           169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,
           195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207,
           208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220,
           221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,
           234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246,
           247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259,
           260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272,
           273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285,
           286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298,
           299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311,
           312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324,
           325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337,
           338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350,
           351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363,
           364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376,
           377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389,
           390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402,
           403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415,
           416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428,
           429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441,
           442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454,
           455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467,
           468])

  • channel
    (channel)
    object
    'S1D1' 'S1D2' ... 'S8D8' 'S8D16'

    array(['S1D1', 'S1D2', 'S1D3', 'S1D9', 'S2D1', 'S2D3', 'S2D4', 'S2D10', 'S3D2',
           'S3D3', 'S3D11', 'S4D3', 'S4D4', 'S4D12', 'S5D5', 'S5D6', 'S5D7',
           'S5D13', 'S6D5', 'S6D7', 'S6D8', 'S6D14', 'S7D6', 'S7D7', 'S7D15',
           'S8D7', 'S8D8', 'S8D16'], dtype=object)

  • source
    (channel)
    object
    'S1' 'S1' 'S1' ... 'S8' 'S8' 'S8'

    array([np.str_('S1'), np.str_('S1'), np.str_('S1'), np.str_('S1'),
           np.str_('S2'), np.str_('S2'), np.str_('S2'), np.str_('S2'),
           np.str_('S3'), np.str_('S3'), np.str_('S3'), np.str_('S4'),
           np.str_('S4'), np.str_('S4'), np.str_('S5'), np.str_('S5'),
           np.str_('S5'), np.str_('S5'), np.str_('S6'), np.str_('S6'),
           np.str_('S6'), np.str_('S6'), np.str_('S7'), np.str_('S7'),
           np.str_('S7'), np.str_('S8'), np.str_('S8'), np.str_('S8')],
          dtype=object)

  • detector
    (channel)
    object
    'D1' 'D2' 'D3' ... 'D7' 'D8' 'D16'

    array([np.str_('D1'), np.str_('D2'), np.str_('D3'), np.str_('D9'),
           np.str_('D1'), np.str_('D3'), np.str_('D4'), np.str_('D10'),
           np.str_('D2'), np.str_('D3'), np.str_('D11'), np.str_('D3'),
           np.str_('D4'), np.str_('D12'), np.str_('D5'), np.str_('D6'),
           np.str_('D7'), np.str_('D13'), np.str_('D5'), np.str_('D7'),
           np.str_('D8'), np.str_('D14'), np.str_('D6'), np.str_('D7'),
           np.str_('D15'), np.str_('D7'), np.str_('D8'), np.str_('D16')],
          dtype=object)

• Indexes: (3)
  • chromo
    PandasIndex

    PandasIndex(Index(['HbO', 'HbR'], dtype='object', name='chromo'))

  • time
    PandasIndex

    PandasIndex(Index([   0.0,  0.128,  0.256,  0.384,  0.512,   0.64,  0.768,  0.896,  1.024,
            1.152,
           ...
           58.752,  58.88, 59.008, 59.136, 59.264, 59.392,  59.52, 59.648, 59.776,
           59.904],
          dtype='float64', name='time', length=469))

  • channel
    PandasIndex

    PandasIndex(Index(['S1D1', 'S1D2', 'S1D3', 'S1D9', 'S2D1', 'S2D3', 'S2D4', 'S2D10', 'S3D2',
           'S3D3', 'S3D11', 'S4D3', 'S4D4', 'S4D12', 'S5D5', 'S5D6', 'S5D7',
           'S5D13', 'S6D5', 'S6D7', 'S6D8', 'S6D14', 'S7D6', 'S7D7', 'S7D15',
           'S8D7', 'S8D8', 'S8D16'],
          dtype='object', name='channel'))

• Attributes: (0)

f,ax = p.subplots(1,1, figsize=(12,4))
ax.plot( conc.time, conc.sel(channel="S1D1", chromo="HbO").pint.to("micromolar"), "r-", label="HbO")
ax.plot( conc.time, conc.sel(channel="S1D1", chromo="HbR").pint.to("micromolar"), "b-", label="HbR")
p.legend()
ax.set_xlabel("time / s")
ax.set_ylabel("$\Delta c$ / $\mu M$");

References

cedalion.bib.dump_to_notebook()

Methods used

[1]	Luke2021	cedalion.data.get_fingertapping_snirf_path	Robert Luke and David McAlpine. fNIRS Finger Tapping Data in BIDS Format. September 2021. doi:10.5281/zenodo.5529797.
[2]	Tucker2022	cedalion.io.snirf.read_snirf	Stephen Tucker, Jay Dubb, Sreekanth Kura, Alexander von Lühmann, Robert Franke, Jörn M. Horschig, Samuel Powell, Robert Oostenveld, Michael Lührs, Édouard Delaire, Zahra M. Aghajan, Hanseok Yun, Meryem A. Yücel, Qianqian Fang, Theodore J. Huppert, Blaise deB. Frederick, Luca Pollonini, David A. Boas, and Robert Luke. Introduction to the shared near infrared spectroscopy format. Neurophotonics, 10(1):013507, 2022. doi:10.1117/1.NPh.10.1.013507.
[3]	Prahl1998	cedalion.nirs.common.get_extinction_coefficients	Scott A. Prahl. Optical absorption of hemoglobin. Oregon Medical Laser Center, online resource, 1998. URL: https://omlc.org/spectra/hemoglobin/.