Note
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Define an electrophysiological dataset using Xarray#
This example illustrates how to define a dataset using Xarray. If you don’t know this library, we can simplify by saying that it provides containers that accept arrays but you can also labelize your dimensions. Another way of seeing it, pandas is mostly made for tables (i.e 2D arrays) while Xarray provide almost the same functionalities but for multi-dimensional arrays.
import numpy as np
import pandas as pd
from xarray import DataArray
from frites.dataset import DatasetEphy
from frites import set_mpl_style
import matplotlib.pyplot as plt
set_mpl_style()
Create artificial data#
We start by creating some random data for several subjects. To do that, each subject is going have a 3 dimensional array of shape (n_epochs, n_channels, n_times). Then, all of the arrays are grouped together in a list of length (n_subjects,)
n_subjects = 5
n_epochs = 10
n_channels = 5
n_times = 100
sf = 512
x, ch = [], []
for k in range(n_subjects):
# generate single subject data
x_suj = np.random.rand(n_epochs, n_channels, n_times)
# generate some random channel names
ch_suj = np.array([f"ch_{r}" for r in range(n_channels)])
# concatenate in a list
x.append(x_suj)
ch.append(ch_suj)
# finally lets create a time vector
times = np.arange(n_times) / sf
epochs = np.arange(n_epochs)
Xarray conversion to DataArray#
Here, we convert the NumPy arrays to xarray.DataArray
x_xr = []
for k in range(n_subjects):
# DataArray conversion
arr_xr = DataArray(x[k], dims=('epochs', 'channels', 'times'),
coords=(epochs, ch[k], times))
# finally, replace it in the original list
x_xr.append(arr_xr)
print(x_xr[0])
<xarray.DataArray (epochs: 10, channels: 5, times: 100)>
array([[[4.30008581e-01, 4.15619327e-01, 8.97145491e-01, ...,
8.55757189e-01, 7.59507621e-01, 6.01747707e-01],
[4.02573102e-01, 1.97828239e-01, 5.37139008e-01, ...,
7.22666646e-01, 9.64066936e-02, 9.28621072e-02],
[4.49673129e-01, 2.16219113e-03, 7.54797779e-01, ...,
5.84479083e-02, 7.13079108e-01, 9.06147441e-01],
[5.51751917e-02, 6.86199079e-01, 5.83072825e-01, ...,
4.32877614e-01, 3.04843236e-01, 9.90082998e-02],
[7.10332224e-01, 1.66541040e-01, 4.79279965e-01, ...,
4.02772711e-01, 5.79732869e-01, 4.55569592e-01]],
[[4.42653653e-02, 6.25589536e-01, 5.42576396e-01, ...,
3.63802376e-01, 3.60585380e-01, 1.48582786e-01],
[1.69032542e-01, 7.05612502e-01, 2.68142230e-01, ...,
1.87627439e-01, 3.43634152e-01, 9.07068918e-01],
[1.33953239e-01, 7.25917385e-02, 9.79412500e-01, ...,
8.84689139e-04, 1.68531356e-01, 6.95919895e-01],
[9.74397033e-01, 5.62802608e-01, 8.99226653e-01, ...,
6.07992950e-01, 9.96455361e-01, 5.26676247e-01],
[3.18727564e-01, 5.45024319e-01, 8.47879462e-01, ...,
...
7.42579340e-01, 6.94069768e-01, 1.63560913e-02],
[2.71233272e-01, 2.42853502e-01, 4.28530018e-01, ...,
3.37794144e-02, 8.39410105e-02, 3.97082427e-01],
[7.11755955e-01, 8.27479920e-01, 4.23932500e-01, ...,
5.32398555e-01, 4.05543934e-02, 1.77295659e-01],
[4.75732969e-01, 5.30331741e-02, 4.49995004e-01, ...,
8.18620214e-02, 6.39047089e-01, 8.78680437e-01],
[1.23442276e-01, 5.43527659e-01, 2.42839586e-02, ...,
5.09528262e-01, 7.31521327e-01, 9.86438075e-01]],
[[1.81666507e-02, 1.13056264e-01, 4.09512525e-01, ...,
1.36661335e-01, 1.56284832e-01, 9.49422564e-01],
[6.60434418e-02, 3.53363148e-01, 5.15162443e-01, ...,
9.11941959e-01, 2.97756640e-01, 1.33746518e-01],
[3.54040444e-01, 3.04852266e-01, 1.96269968e-01, ...,
7.63964864e-01, 6.96044342e-01, 2.63218272e-01],
[8.28104642e-01, 4.62469735e-01, 6.10613807e-01, ...,
4.81611330e-01, 4.72313149e-01, 2.53627297e-01],
[2.44161830e-01, 4.55406466e-01, 2.79696627e-01, ...,
9.38663277e-01, 3.84363188e-01, 7.05822369e-01]]])
Coordinates:
* epochs (epochs) int64 0 1 2 3 4 5 6 7 8 9
* channels (channels) <U4 'ch_0' 'ch_1' 'ch_2' 'ch_3' 'ch_4'
* times (times) float64 0.0 0.001953 0.003906 ... 0.1895 0.1914 0.1934
Build the dataset#
Finally, we pass the data to the frites.dataset.DatasetEphy class
in order to create the dataset
# here, we specify to the DatasetEphy class that the roi dimension is actually
# called 'channels' in the DataArray and the times dimension is called 'times'
dt = DatasetEphy(x_xr, roi='channels', times='times')
print(dt)
print('Time vector : ', dt.times)
print('ROI\n: ', dt.df_rs)
<xarray.DataArray 'subject_0' (trials: 10, roi: 5, times: 100)>
array([[[4.30008581e-01, 4.15619327e-01, 8.97145491e-01, ...,
8.55757189e-01, 7.59507621e-01, 6.01747707e-01],
[4.02573102e-01, 1.97828239e-01, 5.37139008e-01, ...,
7.22666646e-01, 9.64066936e-02, 9.28621072e-02],
[4.49673129e-01, 2.16219113e-03, 7.54797779e-01, ...,
5.84479083e-02, 7.13079108e-01, 9.06147441e-01],
[5.51751917e-02, 6.86199079e-01, 5.83072825e-01, ...,
4.32877614e-01, 3.04843236e-01, 9.90082998e-02],
[7.10332224e-01, 1.66541040e-01, 4.79279965e-01, ...,
4.02772711e-01, 5.79732869e-01, 4.55569592e-01]],
[[4.42653653e-02, 6.25589536e-01, 5.42576396e-01, ...,
3.63802376e-01, 3.60585380e-01, 1.48582786e-01],
[1.69032542e-01, 7.05612502e-01, 2.68142230e-01, ...,
1.87627439e-01, 3.43634152e-01, 9.07068918e-01],
[1.33953239e-01, 7.25917385e-02, 9.79412500e-01, ...,
8.84689139e-04, 1.68531356e-01, 6.95919895e-01],
[9.74397033e-01, 5.62802608e-01, 8.99226653e-01, ...,
6.07992950e-01, 9.96455361e-01, 5.26676247e-01],
[3.18727564e-01, 5.45024319e-01, 8.47879462e-01, ...,
...
7.42579340e-01, 6.94069768e-01, 1.63560913e-02],
[2.71233272e-01, 2.42853502e-01, 4.28530018e-01, ...,
3.37794144e-02, 8.39410105e-02, 3.97082427e-01],
[7.11755955e-01, 8.27479920e-01, 4.23932500e-01, ...,
5.32398555e-01, 4.05543934e-02, 1.77295659e-01],
[4.75732969e-01, 5.30331741e-02, 4.49995004e-01, ...,
8.18620214e-02, 6.39047089e-01, 8.78680437e-01],
[1.23442276e-01, 5.43527659e-01, 2.42839586e-02, ...,
5.09528262e-01, 7.31521327e-01, 9.86438075e-01]],
[[1.81666507e-02, 1.13056264e-01, 4.09512525e-01, ...,
1.36661335e-01, 1.56284832e-01, 9.49422564e-01],
[6.60434418e-02, 3.53363148e-01, 5.15162443e-01, ...,
9.11941959e-01, 2.97756640e-01, 1.33746518e-01],
[3.54040444e-01, 3.04852266e-01, 1.96269968e-01, ...,
7.63964864e-01, 6.96044342e-01, 2.63218272e-01],
[8.28104642e-01, 4.62469735e-01, 6.10613807e-01, ...,
4.81611330e-01, 4.72313149e-01, 2.53627297e-01],
[2.44161830e-01, 4.55406466e-01, 2.79696627e-01, ...,
9.38663277e-01, 3.84363188e-01, 7.05822369e-01]]])
Coordinates:
* trials (trials) int64 0 1 2 3 4 5 6 7 8 9
* roi (roi) <U4 'ch_0' 'ch_1' 'ch_2' 'ch_3' 'ch_4'
agg_ch (roi) int64 0 0 0 0 0
* times (times) float64 0.0 0.001953 0.003906 ... 0.1895 0.1914 0.1934
subject (trials) int64 0 0 0 0 0 0 0 0 0 0
Attributes:
__version__: 0.4.5
modality: electrophysiology
dtype: SubjectEphy
y_dtype: none
z_dtype: none
mi_type: none
mi_repr: none
sfreq: 512.0
agg_ch: 1
multivariate: 0
<xarray.DataArray 'subject_1' (trials: 10, roi: 5, times: 100)>
array([[[0.11928999, 0.52618525, 0.1269329 , ..., 0.43542025,
0.01777982, 0.86024913],
[0.04195896, 0.76269211, 0.07286539, ..., 0.47600079,
0.7127555 , 0.8185346 ],
[0.75214272, 0.39270629, 0.57258749, ..., 0.85279663,
0.27334737, 0.5835292 ],
[0.31117365, 0.80379896, 0.39240379, ..., 0.56765811,
0.65457514, 0.79160714],
[0.84410288, 0.05874125, 0.35772784, ..., 0.24846727,
0.86705291, 0.11126956]],
[[0.56571765, 0.31572435, 0.00228454, ..., 0.65429404,
0.78269457, 0.91066937],
[0.68297909, 0.51570707, 0.15465983, ..., 0.99604268,
0.77917695, 0.85680089],
[0.36828647, 0.16288634, 0.31779514, ..., 0.45992761,
0.96891307, 0.94530682],
[0.67823772, 0.75737861, 0.9682205 , ..., 0.2577613 ,
0.26592108, 0.3656392 ],
[0.68707087, 0.40793532, 0.85120588, ..., 0.42044553,
...
0.99267846, 0.87307804],
[0.3715323 , 0.33442063, 0.61211934, ..., 0.81183621,
0.26675029, 0.05376418],
[0.07408775, 0.46635269, 0.84369904, ..., 0.75604235,
0.12462772, 0.62275252],
[0.52300034, 0.52183708, 0.71067454, ..., 0.55096305,
0.30933686, 0.10933034],
[0.48531365, 0.50578056, 0.68892731, ..., 0.62669821,
0.28354695, 0.63785965]],
[[0.11784489, 0.27841905, 0.86564685, ..., 0.93935605,
0.57069434, 0.87304802],
[0.27614432, 0.05458362, 0.20360945, ..., 0.59174349,
0.27716257, 0.62411018],
[0.26398572, 0.8130729 , 0.11523605, ..., 0.50230037,
0.55251583, 0.43443558],
[0.4158952 , 0.95302152, 0.91040658, ..., 0.83558555,
0.4134908 , 0.95575555],
[0.35729684, 0.82486333, 0.71743575, ..., 0.83589958,
0.64293541, 0.07556001]]])
Coordinates:
* trials (trials) int64 0 1 2 3 4 5 6 7 8 9
* roi (roi) <U4 'ch_0' 'ch_1' 'ch_2' 'ch_3' 'ch_4'
agg_ch (roi) int64 0 0 0 0 0
* times (times) float64 0.0 0.001953 0.003906 ... 0.1895 0.1914 0.1934
subject (trials) int64 1 1 1 1 1 1 1 1 1 1
Attributes:
__version__: 0.4.5
modality: electrophysiology
dtype: SubjectEphy
y_dtype: none
z_dtype: none
mi_type: none
mi_repr: none
sfreq: 512.0
agg_ch: 1
multivariate: 0
<xarray.DataArray 'subject_2' (trials: 10, roi: 5, times: 100)>
array([[[0.91948695, 0.87979538, 0.79281291, ..., 0.82263166,
0.92349852, 0.64741517],
[0.74029803, 0.68089148, 0.23847544, ..., 0.96049495,
0.91139698, 0.0852222 ],
[0.06195706, 0.50751781, 0.02717379, ..., 0.96101482,
0.67725388, 0.98765984],
[0.27896102, 0.34642859, 0.12684912, ..., 0.78485668,
0.36077026, 0.23479245],
[0.78552317, 0.1281671 , 0.80580504, ..., 0.61991236,
0.08161449, 0.07320001]],
[[0.29114134, 0.26134147, 0.68072373, ..., 0.79293835,
0.76346626, 0.6600864 ],
[0.2380412 , 0.55981534, 0.82065207, ..., 0.63367644,
0.10015319, 0.35899084],
[0.37397522, 0.83304634, 0.52126711, ..., 0.20324482,
0.37542584, 0.997347 ],
[0.83454822, 0.16800315, 0.64732877, ..., 0.18370469,
0.0258599 , 0.4788021 ],
[0.61807283, 0.6953434 , 0.30728251, ..., 0.19432944,
...
0.9224637 , 0.63125157],
[0.55708516, 0.87784986, 0.37966677, ..., 0.9632567 ,
0.21915866, 0.00457697],
[0.49752683, 0.17981057, 0.57013578, ..., 0.45255511,
0.725804 , 0.1114034 ],
[0.83133338, 0.09896551, 0.11999966, ..., 0.28314694,
0.58339661, 0.33079066],
[0.11909597, 0.34758649, 0.10550889, ..., 0.97560088,
0.5777897 , 0.42748875]],
[[0.03267569, 0.61950707, 0.64991809, ..., 0.71811551,
0.3099583 , 0.52742885],
[0.11269014, 0.44404203, 0.07050745, ..., 0.73059958,
0.21967989, 0.03593816],
[0.64056715, 0.26810605, 0.19728428, ..., 0.77406934,
0.8526119 , 0.90081977],
[0.77515231, 0.86759694, 0.63609906, ..., 0.79314501,
0.9176867 , 0.37723888],
[0.31401674, 0.41066661, 0.4381768 , ..., 0.41852247,
0.31735248, 0.79469672]]])
Coordinates:
* trials (trials) int64 0 1 2 3 4 5 6 7 8 9
* roi (roi) <U4 'ch_0' 'ch_1' 'ch_2' 'ch_3' 'ch_4'
agg_ch (roi) int64 0 0 0 0 0
* times (times) float64 0.0 0.001953 0.003906 ... 0.1895 0.1914 0.1934
subject (trials) int64 2 2 2 2 2 2 2 2 2 2
Attributes:
__version__: 0.4.5
modality: electrophysiology
dtype: SubjectEphy
y_dtype: none
z_dtype: none
mi_type: none
mi_repr: none
sfreq: 512.0
agg_ch: 1
multivariate: 0
<xarray.DataArray 'subject_3' (trials: 10, roi: 5, times: 100)>
array([[[0.43004538, 0.65614089, 0.13284507, ..., 0.16816806,
0.33979741, 0.37546796],
[0.70255806, 0.78549951, 0.08647421, ..., 0.36036255,
0.00245529, 0.55037426],
[0.50360901, 0.84320302, 0.69687019, ..., 0.08969652,
0.95284688, 0.39686039],
[0.18588025, 0.25937333, 0.22517591, ..., 0.08456359,
0.29593196, 0.97353069],
[0.64624918, 0.05814698, 0.21607716, ..., 0.70918007,
0.76225478, 0.66491777]],
[[0.46265872, 0.35913096, 0.16493715, ..., 0.84188051,
0.93837826, 0.86643623],
[0.95134456, 0.09031157, 0.75450837, ..., 0.06485132,
0.2687475 , 0.96743844],
[0.17654793, 0.44472352, 0.91355439, ..., 0.58816824,
0.02522253, 0.28955094],
[0.7819593 , 0.7743504 , 0.94428168, ..., 0.77961091,
0.65580837, 0.55473179],
[0.63224248, 0.40365546, 0.74860973, ..., 0.75569197,
...
0.34429112, 0.02805752],
[0.7131015 , 0.04315035, 0.87821316, ..., 0.61443466,
0.81192817, 0.54860817],
[0.79701442, 0.70784575, 0.36757337, ..., 0.91409791,
0.00200909, 0.71946715],
[0.37660624, 0.60205987, 0.90814293, ..., 0.87080868,
0.1424463 , 0.90913818],
[0.94780456, 0.63795864, 0.14539284, ..., 0.53113956,
0.82708304, 0.8529294 ]],
[[0.57654435, 0.03810384, 0.86381878, ..., 0.58978081,
0.50967147, 0.62782527],
[0.40155199, 0.88181222, 0.09689691, ..., 0.81137492,
0.37524963, 0.91177733],
[0.4087309 , 0.98467908, 0.94713449, ..., 0.68061051,
0.6589184 , 0.03143535],
[0.58335109, 0.61492765, 0.72611378, ..., 0.52935148,
0.67717479, 0.08073243],
[0.4694373 , 0.71561275, 0.29705791, ..., 0.51541419,
0.2558448 , 0.17006528]]])
Coordinates:
* trials (trials) int64 0 1 2 3 4 5 6 7 8 9
* roi (roi) <U4 'ch_0' 'ch_1' 'ch_2' 'ch_3' 'ch_4'
agg_ch (roi) int64 0 0 0 0 0
* times (times) float64 0.0 0.001953 0.003906 ... 0.1895 0.1914 0.1934
subject (trials) int64 3 3 3 3 3 3 3 3 3 3
Attributes:
__version__: 0.4.5
modality: electrophysiology
dtype: SubjectEphy
y_dtype: none
z_dtype: none
mi_type: none
mi_repr: none
sfreq: 512.0
agg_ch: 1
multivariate: 0
<xarray.DataArray 'subject_4' (trials: 10, roi: 5, times: 100)>
array([[[0.81060687, 0.06395865, 0.93834251, ..., 0.38458425,
0.33503571, 0.35213913],
[0.9728364 , 0.22333399, 0.51167532, ..., 0.23949269,
0.12319338, 0.67722901],
[0.65227237, 0.63854027, 0.20061244, ..., 0.32318281,
0.96644307, 0.37664041],
[0.25948739, 0.00694006, 0.29042107, ..., 0.78536757,
0.37503725, 0.72966275],
[0.32831335, 0.71420023, 0.89184502, ..., 0.2454904 ,
0.04527967, 0.35549081]],
[[0.67116019, 0.88330395, 0.79450572, ..., 0.67184612,
0.05460093, 0.14406935],
[0.38628614, 0.42851805, 0.74168145, ..., 0.08240887,
0.9857575 , 0.77432535],
[0.14615664, 0.37525907, 0.46117555, ..., 0.85307356,
0.85050297, 0.34925627],
[0.30121031, 0.92883483, 0.04915563, ..., 0.94363192,
0.83600807, 0.97264693],
[0.61594377, 0.56711937, 0.07108376, ..., 0.32221322,
...
0.40183786, 0.17538522],
[0.79461588, 0.94227847, 0.24801076, ..., 0.74271852,
0.52351185, 0.31655994],
[0.51511617, 0.16578703, 0.51492142, ..., 0.65666036,
0.51381209, 0.21136769],
[0.62106923, 0.90611925, 0.58828558, ..., 0.56307889,
0.6740868 , 0.49002678],
[0.10904393, 0.19411827, 0.21737293, ..., 0.13419232,
0.62989886, 0.19977215]],
[[0.52818457, 0.61406642, 0.48828433, ..., 0.99444816,
0.40827414, 0.95696391],
[0.94988824, 0.86420457, 0.64538879, ..., 0.59625007,
0.10451909, 0.97635364],
[0.48001973, 0.03687578, 0.80113744, ..., 0.98693827,
0.80515945, 0.2371893 ],
[0.98858328, 0.8495341 , 0.486449 , ..., 0.54092701,
0.9243252 , 0.06330291],
[0.66754356, 0.48554682, 0.72044128, ..., 0.31882968,
0.90538804, 0.72165753]]])
Coordinates:
* trials (trials) int64 0 1 2 3 4 5 6 7 8 9
* roi (roi) <U4 'ch_0' 'ch_1' 'ch_2' 'ch_3' 'ch_4'
agg_ch (roi) int64 0 0 0 0 0
* times (times) float64 0.0 0.001953 0.003906 ... 0.1895 0.1914 0.1934
subject (trials) int64 4 4 4 4 4 4 4 4 4 4
Attributes:
__version__: 0.4.5
modality: electrophysiology
dtype: SubjectEphy
y_dtype: none
z_dtype: none
mi_type: none
mi_repr: none
sfreq: 512.0
agg_ch: 1
multivariate: 0
Time vector : [0. 0.00195312 0.00390625 0.00585938 0.0078125 0.00976562
0.01171875 0.01367188 0.015625 0.01757812 0.01953125 0.02148438
0.0234375 0.02539062 0.02734375 0.02929688 0.03125 0.03320312
0.03515625 0.03710938 0.0390625 0.04101562 0.04296875 0.04492188
0.046875 0.04882812 0.05078125 0.05273438 0.0546875 0.05664062
0.05859375 0.06054688 0.0625 0.06445312 0.06640625 0.06835938
0.0703125 0.07226562 0.07421875 0.07617188 0.078125 0.08007812
0.08203125 0.08398438 0.0859375 0.08789062 0.08984375 0.09179688
0.09375 0.09570312 0.09765625 0.09960938 0.1015625 0.10351562
0.10546875 0.10742188 0.109375 0.11132812 0.11328125 0.11523438
0.1171875 0.11914062 0.12109375 0.12304688 0.125 0.12695312
0.12890625 0.13085938 0.1328125 0.13476562 0.13671875 0.13867188
0.140625 0.14257812 0.14453125 0.14648438 0.1484375 0.15039062
0.15234375 0.15429688 0.15625 0.15820312 0.16015625 0.16210938
0.1640625 0.16601562 0.16796875 0.16992188 0.171875 0.17382812
0.17578125 0.17773438 0.1796875 0.18164062 0.18359375 0.18554688
0.1875 0.18945312 0.19140625 0.19335938]
ROI
: #subjects subjects keep
roi
ch_0 5 [0, 1, 2, 3, 4] True
ch_1 5 [0, 1, 2, 3, 4] True
ch_2 5 [0, 1, 2, 3, 4] True
ch_3 5 [0, 1, 2, 3, 4] True
ch_4 5 [0, 1, 2, 3, 4] True
MultiIndex support#
DataArray also supports multi-indexing of a single dimension.
# create a continuous regressor (prediction error, delta P etc.)
dp = np.random.uniform(-1, 1, (n_epochs,))
# create a discret variable (e.g experimental conditions)
cond = np.array([0] * 5 + [1] * 5)
# now, create a multi-index using pandas
midx = pd.MultiIndex.from_arrays((dp, cond), names=('dp', 'blocks'))
# convert again the original arrays but this time, the epoch dimension is going
# to be a multi-index
x_xr = []
for k in range(n_subjects):
# DataArray conversion
arr_xr = DataArray(x[k], dims=('epochs', 'channels', 'times'),
coords=(midx, ch[k], times))
# finally, replace it in the original list
x_xr.append(arr_xr)
print(x_xr[0])
# finally, when you create your dataset you can also specify the y and z inputs
# by providing their names in the DataArray
dt = DatasetEphy(x_xr, roi='channels', times='times', y='dp', z='blocks')
print(dt)
<xarray.DataArray (epochs: 10, channels: 5, times: 100)>
array([[[4.30008581e-01, 4.15619327e-01, 8.97145491e-01, ...,
8.55757189e-01, 7.59507621e-01, 6.01747707e-01],
[4.02573102e-01, 1.97828239e-01, 5.37139008e-01, ...,
7.22666646e-01, 9.64066936e-02, 9.28621072e-02],
[4.49673129e-01, 2.16219113e-03, 7.54797779e-01, ...,
5.84479083e-02, 7.13079108e-01, 9.06147441e-01],
[5.51751917e-02, 6.86199079e-01, 5.83072825e-01, ...,
4.32877614e-01, 3.04843236e-01, 9.90082998e-02],
[7.10332224e-01, 1.66541040e-01, 4.79279965e-01, ...,
4.02772711e-01, 5.79732869e-01, 4.55569592e-01]],
[[4.42653653e-02, 6.25589536e-01, 5.42576396e-01, ...,
3.63802376e-01, 3.60585380e-01, 1.48582786e-01],
[1.69032542e-01, 7.05612502e-01, 2.68142230e-01, ...,
1.87627439e-01, 3.43634152e-01, 9.07068918e-01],
[1.33953239e-01, 7.25917385e-02, 9.79412500e-01, ...,
8.84689139e-04, 1.68531356e-01, 6.95919895e-01],
[9.74397033e-01, 5.62802608e-01, 8.99226653e-01, ...,
6.07992950e-01, 9.96455361e-01, 5.26676247e-01],
[3.18727564e-01, 5.45024319e-01, 8.47879462e-01, ...,
...
7.42579340e-01, 6.94069768e-01, 1.63560913e-02],
[2.71233272e-01, 2.42853502e-01, 4.28530018e-01, ...,
3.37794144e-02, 8.39410105e-02, 3.97082427e-01],
[7.11755955e-01, 8.27479920e-01, 4.23932500e-01, ...,
5.32398555e-01, 4.05543934e-02, 1.77295659e-01],
[4.75732969e-01, 5.30331741e-02, 4.49995004e-01, ...,
8.18620214e-02, 6.39047089e-01, 8.78680437e-01],
[1.23442276e-01, 5.43527659e-01, 2.42839586e-02, ...,
5.09528262e-01, 7.31521327e-01, 9.86438075e-01]],
[[1.81666507e-02, 1.13056264e-01, 4.09512525e-01, ...,
1.36661335e-01, 1.56284832e-01, 9.49422564e-01],
[6.60434418e-02, 3.53363148e-01, 5.15162443e-01, ...,
9.11941959e-01, 2.97756640e-01, 1.33746518e-01],
[3.54040444e-01, 3.04852266e-01, 1.96269968e-01, ...,
7.63964864e-01, 6.96044342e-01, 2.63218272e-01],
[8.28104642e-01, 4.62469735e-01, 6.10613807e-01, ...,
4.81611330e-01, 4.72313149e-01, 2.53627297e-01],
[2.44161830e-01, 4.55406466e-01, 2.79696627e-01, ...,
9.38663277e-01, 3.84363188e-01, 7.05822369e-01]]])
Coordinates:
* epochs (epochs) object MultiIndex
* dp (epochs) float64 0.3657 -0.9241 -0.7556 ... 0.4543 0.3517 -0.8852
* blocks (epochs) int64 0 0 0 0 0 1 1 1 1 1
* channels (channels) <U4 'ch_0' 'ch_1' 'ch_2' 'ch_3' 'ch_4'
* times (times) float64 0.0 0.001953 0.003906 ... 0.1895 0.1914 0.1934
<xarray.DataArray 'subject_0' (trials: 10, roi: 5, times: 100)>
array([[[4.30008581e-01, 4.15619327e-01, 8.97145491e-01, ...,
8.55757189e-01, 7.59507621e-01, 6.01747707e-01],
[4.02573102e-01, 1.97828239e-01, 5.37139008e-01, ...,
7.22666646e-01, 9.64066936e-02, 9.28621072e-02],
[4.49673129e-01, 2.16219113e-03, 7.54797779e-01, ...,
5.84479083e-02, 7.13079108e-01, 9.06147441e-01],
[5.51751917e-02, 6.86199079e-01, 5.83072825e-01, ...,
4.32877614e-01, 3.04843236e-01, 9.90082998e-02],
[7.10332224e-01, 1.66541040e-01, 4.79279965e-01, ...,
4.02772711e-01, 5.79732869e-01, 4.55569592e-01]],
[[4.42653653e-02, 6.25589536e-01, 5.42576396e-01, ...,
3.63802376e-01, 3.60585380e-01, 1.48582786e-01],
[1.69032542e-01, 7.05612502e-01, 2.68142230e-01, ...,
1.87627439e-01, 3.43634152e-01, 9.07068918e-01],
[1.33953239e-01, 7.25917385e-02, 9.79412500e-01, ...,
8.84689139e-04, 1.68531356e-01, 6.95919895e-01],
[9.74397033e-01, 5.62802608e-01, 8.99226653e-01, ...,
6.07992950e-01, 9.96455361e-01, 5.26676247e-01],
[3.18727564e-01, 5.45024319e-01, 8.47879462e-01, ...,
...
7.42579340e-01, 6.94069768e-01, 1.63560913e-02],
[2.71233272e-01, 2.42853502e-01, 4.28530018e-01, ...,
3.37794144e-02, 8.39410105e-02, 3.97082427e-01],
[7.11755955e-01, 8.27479920e-01, 4.23932500e-01, ...,
5.32398555e-01, 4.05543934e-02, 1.77295659e-01],
[4.75732969e-01, 5.30331741e-02, 4.49995004e-01, ...,
8.18620214e-02, 6.39047089e-01, 8.78680437e-01],
[1.23442276e-01, 5.43527659e-01, 2.42839586e-02, ...,
5.09528262e-01, 7.31521327e-01, 9.86438075e-01]],
[[1.81666507e-02, 1.13056264e-01, 4.09512525e-01, ...,
1.36661335e-01, 1.56284832e-01, 9.49422564e-01],
[6.60434418e-02, 3.53363148e-01, 5.15162443e-01, ...,
9.11941959e-01, 2.97756640e-01, 1.33746518e-01],
[3.54040444e-01, 3.04852266e-01, 1.96269968e-01, ...,
7.63964864e-01, 6.96044342e-01, 2.63218272e-01],
[8.28104642e-01, 4.62469735e-01, 6.10613807e-01, ...,
4.81611330e-01, 4.72313149e-01, 2.53627297e-01],
[2.44161830e-01, 4.55406466e-01, 2.79696627e-01, ...,
9.38663277e-01, 3.84363188e-01, 7.05822369e-01]]])
Coordinates:
* trials (trials) int64 0 1 2 3 4 5 6 7 8 9
y (trials) float64 0.3657 -0.9241 -0.7556 ... 0.4543 0.3517 -0.8852
z (trials) int64 0 0 0 0 0 1 1 1 1 1
* roi (roi) <U4 'ch_0' 'ch_1' 'ch_2' 'ch_3' 'ch_4'
agg_ch (roi) int64 0 0 0 0 0
* times (times) float64 0.0 0.001953 0.003906 ... 0.1895 0.1914 0.1934
subject (trials) int64 0 0 0 0 0 0 0 0 0 0
Attributes:
__version__: 0.4.5
modality: electrophysiology
dtype: SubjectEphy
y_dtype: float
z_dtype: int
mi_type: ccd
mi_repr: I(x; y (continuous)) | z (discret)
sfreq: 512.0
agg_ch: 1
multivariate: 0
<xarray.DataArray 'subject_1' (trials: 10, roi: 5, times: 100)>
array([[[0.11928999, 0.52618525, 0.1269329 , ..., 0.43542025,
0.01777982, 0.86024913],
[0.04195896, 0.76269211, 0.07286539, ..., 0.47600079,
0.7127555 , 0.8185346 ],
[0.75214272, 0.39270629, 0.57258749, ..., 0.85279663,
0.27334737, 0.5835292 ],
[0.31117365, 0.80379896, 0.39240379, ..., 0.56765811,
0.65457514, 0.79160714],
[0.84410288, 0.05874125, 0.35772784, ..., 0.24846727,
0.86705291, 0.11126956]],
[[0.56571765, 0.31572435, 0.00228454, ..., 0.65429404,
0.78269457, 0.91066937],
[0.68297909, 0.51570707, 0.15465983, ..., 0.99604268,
0.77917695, 0.85680089],
[0.36828647, 0.16288634, 0.31779514, ..., 0.45992761,
0.96891307, 0.94530682],
[0.67823772, 0.75737861, 0.9682205 , ..., 0.2577613 ,
0.26592108, 0.3656392 ],
[0.68707087, 0.40793532, 0.85120588, ..., 0.42044553,
...
0.99267846, 0.87307804],
[0.3715323 , 0.33442063, 0.61211934, ..., 0.81183621,
0.26675029, 0.05376418],
[0.07408775, 0.46635269, 0.84369904, ..., 0.75604235,
0.12462772, 0.62275252],
[0.52300034, 0.52183708, 0.71067454, ..., 0.55096305,
0.30933686, 0.10933034],
[0.48531365, 0.50578056, 0.68892731, ..., 0.62669821,
0.28354695, 0.63785965]],
[[0.11784489, 0.27841905, 0.86564685, ..., 0.93935605,
0.57069434, 0.87304802],
[0.27614432, 0.05458362, 0.20360945, ..., 0.59174349,
0.27716257, 0.62411018],
[0.26398572, 0.8130729 , 0.11523605, ..., 0.50230037,
0.55251583, 0.43443558],
[0.4158952 , 0.95302152, 0.91040658, ..., 0.83558555,
0.4134908 , 0.95575555],
[0.35729684, 0.82486333, 0.71743575, ..., 0.83589958,
0.64293541, 0.07556001]]])
Coordinates:
* trials (trials) int64 0 1 2 3 4 5 6 7 8 9
y (trials) float64 0.3657 -0.9241 -0.7556 ... 0.4543 0.3517 -0.8852
z (trials) int64 0 0 0 0 0 1 1 1 1 1
* roi (roi) <U4 'ch_0' 'ch_1' 'ch_2' 'ch_3' 'ch_4'
agg_ch (roi) int64 0 0 0 0 0
* times (times) float64 0.0 0.001953 0.003906 ... 0.1895 0.1914 0.1934
subject (trials) int64 1 1 1 1 1 1 1 1 1 1
Attributes:
__version__: 0.4.5
modality: electrophysiology
dtype: SubjectEphy
y_dtype: float
z_dtype: int
mi_type: ccd
mi_repr: I(x; y (continuous)) | z (discret)
sfreq: 512.0
agg_ch: 1
multivariate: 0
<xarray.DataArray 'subject_2' (trials: 10, roi: 5, times: 100)>
array([[[0.91948695, 0.87979538, 0.79281291, ..., 0.82263166,
0.92349852, 0.64741517],
[0.74029803, 0.68089148, 0.23847544, ..., 0.96049495,
0.91139698, 0.0852222 ],
[0.06195706, 0.50751781, 0.02717379, ..., 0.96101482,
0.67725388, 0.98765984],
[0.27896102, 0.34642859, 0.12684912, ..., 0.78485668,
0.36077026, 0.23479245],
[0.78552317, 0.1281671 , 0.80580504, ..., 0.61991236,
0.08161449, 0.07320001]],
[[0.29114134, 0.26134147, 0.68072373, ..., 0.79293835,
0.76346626, 0.6600864 ],
[0.2380412 , 0.55981534, 0.82065207, ..., 0.63367644,
0.10015319, 0.35899084],
[0.37397522, 0.83304634, 0.52126711, ..., 0.20324482,
0.37542584, 0.997347 ],
[0.83454822, 0.16800315, 0.64732877, ..., 0.18370469,
0.0258599 , 0.4788021 ],
[0.61807283, 0.6953434 , 0.30728251, ..., 0.19432944,
...
0.9224637 , 0.63125157],
[0.55708516, 0.87784986, 0.37966677, ..., 0.9632567 ,
0.21915866, 0.00457697],
[0.49752683, 0.17981057, 0.57013578, ..., 0.45255511,
0.725804 , 0.1114034 ],
[0.83133338, 0.09896551, 0.11999966, ..., 0.28314694,
0.58339661, 0.33079066],
[0.11909597, 0.34758649, 0.10550889, ..., 0.97560088,
0.5777897 , 0.42748875]],
[[0.03267569, 0.61950707, 0.64991809, ..., 0.71811551,
0.3099583 , 0.52742885],
[0.11269014, 0.44404203, 0.07050745, ..., 0.73059958,
0.21967989, 0.03593816],
[0.64056715, 0.26810605, 0.19728428, ..., 0.77406934,
0.8526119 , 0.90081977],
[0.77515231, 0.86759694, 0.63609906, ..., 0.79314501,
0.9176867 , 0.37723888],
[0.31401674, 0.41066661, 0.4381768 , ..., 0.41852247,
0.31735248, 0.79469672]]])
Coordinates:
* trials (trials) int64 0 1 2 3 4 5 6 7 8 9
y (trials) float64 0.3657 -0.9241 -0.7556 ... 0.4543 0.3517 -0.8852
z (trials) int64 0 0 0 0 0 1 1 1 1 1
* roi (roi) <U4 'ch_0' 'ch_1' 'ch_2' 'ch_3' 'ch_4'
agg_ch (roi) int64 0 0 0 0 0
* times (times) float64 0.0 0.001953 0.003906 ... 0.1895 0.1914 0.1934
subject (trials) int64 2 2 2 2 2 2 2 2 2 2
Attributes:
__version__: 0.4.5
modality: electrophysiology
dtype: SubjectEphy
y_dtype: float
z_dtype: int
mi_type: ccd
mi_repr: I(x; y (continuous)) | z (discret)
sfreq: 512.0
agg_ch: 1
multivariate: 0
<xarray.DataArray 'subject_3' (trials: 10, roi: 5, times: 100)>
array([[[0.43004538, 0.65614089, 0.13284507, ..., 0.16816806,
0.33979741, 0.37546796],
[0.70255806, 0.78549951, 0.08647421, ..., 0.36036255,
0.00245529, 0.55037426],
[0.50360901, 0.84320302, 0.69687019, ..., 0.08969652,
0.95284688, 0.39686039],
[0.18588025, 0.25937333, 0.22517591, ..., 0.08456359,
0.29593196, 0.97353069],
[0.64624918, 0.05814698, 0.21607716, ..., 0.70918007,
0.76225478, 0.66491777]],
[[0.46265872, 0.35913096, 0.16493715, ..., 0.84188051,
0.93837826, 0.86643623],
[0.95134456, 0.09031157, 0.75450837, ..., 0.06485132,
0.2687475 , 0.96743844],
[0.17654793, 0.44472352, 0.91355439, ..., 0.58816824,
0.02522253, 0.28955094],
[0.7819593 , 0.7743504 , 0.94428168, ..., 0.77961091,
0.65580837, 0.55473179],
[0.63224248, 0.40365546, 0.74860973, ..., 0.75569197,
...
0.34429112, 0.02805752],
[0.7131015 , 0.04315035, 0.87821316, ..., 0.61443466,
0.81192817, 0.54860817],
[0.79701442, 0.70784575, 0.36757337, ..., 0.91409791,
0.00200909, 0.71946715],
[0.37660624, 0.60205987, 0.90814293, ..., 0.87080868,
0.1424463 , 0.90913818],
[0.94780456, 0.63795864, 0.14539284, ..., 0.53113956,
0.82708304, 0.8529294 ]],
[[0.57654435, 0.03810384, 0.86381878, ..., 0.58978081,
0.50967147, 0.62782527],
[0.40155199, 0.88181222, 0.09689691, ..., 0.81137492,
0.37524963, 0.91177733],
[0.4087309 , 0.98467908, 0.94713449, ..., 0.68061051,
0.6589184 , 0.03143535],
[0.58335109, 0.61492765, 0.72611378, ..., 0.52935148,
0.67717479, 0.08073243],
[0.4694373 , 0.71561275, 0.29705791, ..., 0.51541419,
0.2558448 , 0.17006528]]])
Coordinates:
* trials (trials) int64 0 1 2 3 4 5 6 7 8 9
y (trials) float64 0.3657 -0.9241 -0.7556 ... 0.4543 0.3517 -0.8852
z (trials) int64 0 0 0 0 0 1 1 1 1 1
* roi (roi) <U4 'ch_0' 'ch_1' 'ch_2' 'ch_3' 'ch_4'
agg_ch (roi) int64 0 0 0 0 0
* times (times) float64 0.0 0.001953 0.003906 ... 0.1895 0.1914 0.1934
subject (trials) int64 3 3 3 3 3 3 3 3 3 3
Attributes:
__version__: 0.4.5
modality: electrophysiology
dtype: SubjectEphy
y_dtype: float
z_dtype: int
mi_type: ccd
mi_repr: I(x; y (continuous)) | z (discret)
sfreq: 512.0
agg_ch: 1
multivariate: 0
<xarray.DataArray 'subject_4' (trials: 10, roi: 5, times: 100)>
array([[[0.81060687, 0.06395865, 0.93834251, ..., 0.38458425,
0.33503571, 0.35213913],
[0.9728364 , 0.22333399, 0.51167532, ..., 0.23949269,
0.12319338, 0.67722901],
[0.65227237, 0.63854027, 0.20061244, ..., 0.32318281,
0.96644307, 0.37664041],
[0.25948739, 0.00694006, 0.29042107, ..., 0.78536757,
0.37503725, 0.72966275],
[0.32831335, 0.71420023, 0.89184502, ..., 0.2454904 ,
0.04527967, 0.35549081]],
[[0.67116019, 0.88330395, 0.79450572, ..., 0.67184612,
0.05460093, 0.14406935],
[0.38628614, 0.42851805, 0.74168145, ..., 0.08240887,
0.9857575 , 0.77432535],
[0.14615664, 0.37525907, 0.46117555, ..., 0.85307356,
0.85050297, 0.34925627],
[0.30121031, 0.92883483, 0.04915563, ..., 0.94363192,
0.83600807, 0.97264693],
[0.61594377, 0.56711937, 0.07108376, ..., 0.32221322,
...
0.40183786, 0.17538522],
[0.79461588, 0.94227847, 0.24801076, ..., 0.74271852,
0.52351185, 0.31655994],
[0.51511617, 0.16578703, 0.51492142, ..., 0.65666036,
0.51381209, 0.21136769],
[0.62106923, 0.90611925, 0.58828558, ..., 0.56307889,
0.6740868 , 0.49002678],
[0.10904393, 0.19411827, 0.21737293, ..., 0.13419232,
0.62989886, 0.19977215]],
[[0.52818457, 0.61406642, 0.48828433, ..., 0.99444816,
0.40827414, 0.95696391],
[0.94988824, 0.86420457, 0.64538879, ..., 0.59625007,
0.10451909, 0.97635364],
[0.48001973, 0.03687578, 0.80113744, ..., 0.98693827,
0.80515945, 0.2371893 ],
[0.98858328, 0.8495341 , 0.486449 , ..., 0.54092701,
0.9243252 , 0.06330291],
[0.66754356, 0.48554682, 0.72044128, ..., 0.31882968,
0.90538804, 0.72165753]]])
Coordinates:
* trials (trials) int64 0 1 2 3 4 5 6 7 8 9
y (trials) float64 0.3657 -0.9241 -0.7556 ... 0.4543 0.3517 -0.8852
z (trials) int64 0 0 0 0 0 1 1 1 1 1
* roi (roi) <U4 'ch_0' 'ch_1' 'ch_2' 'ch_3' 'ch_4'
agg_ch (roi) int64 0 0 0 0 0
* times (times) float64 0.0 0.001953 0.003906 ... 0.1895 0.1914 0.1934
subject (trials) int64 4 4 4 4 4 4 4 4 4 4
Attributes:
__version__: 0.4.5
modality: electrophysiology
dtype: SubjectEphy
y_dtype: float
z_dtype: int
mi_type: ccd
mi_repr: I(x; y (continuous)) | z (discret)
sfreq: 512.0
agg_ch: 1
multivariate: 0
Total running time of the script: (0 minutes 0.820 seconds)
Estimated memory usage: 9 MB