def eeg_autocorr_fftw(icaact_, srate_, pct_data=100, test=False):
"""
assume channels,frames,epochs
"""
srate = int(srate_)
if icaact_.ndim == 3:
nchannels = icaact_.shape[0]
nframes = icaact_.shape[1]
nepochs = icaact_.shape[2]
else:
nchannels = icaact_.shape[0]
nframes = icaact_.shape[1]
nepochs = 1
nfft = next_power_of_2(2 * nframes - 1)
icaact = icaact_ #np.reshape(icaact_,(nchannels,nframes,nepochs),order='F')
if test:
icaact = loadmat('data/icaact.mat')['icaact2']
# % calc autocorrelation
# fftw('planner', 'hybrid');
ncomp = icaact.shape[0]
ac = np.zeros((ncomp, nfft))
for it in np.arange(ncomp):
X = np.fft.fft(icaact[it, :, :], nfft, axis=0)
ac[it, :] = np.mean(np.square(np.abs(X)), axis=1)
ac = np.real(np.fft.ifft(ac, axis=1))
if nframes < srate:
ac = np.concatenate(
[ac[:, :nframes],
np.zeros((ac.shape[0], srate - nframes + 1))],
axis=-1)
else:
ac = ac[:, :srate + 1]
# % normalize by 0-tap autocorrelation
ac = ac[:, :(srate + 1)] / ac[:, 0][:, None]
# % resample to 1 second at 100 samples/sec
frac = fractions.Fraction.from_float(100 / 256).limit_denominator()
p = frac.numerator
q = frac.denominator
desired_length = int((np.ceil(ac.shape[1] * p / q)).astype(int))
resample = sx.resample_poly(ac, desired_length, ac.shape[1], axis=-1)
resample = resample[:, 1:]
return resample
import numpy as np
from iclabelpy.iclabelpy import ICL_feature_extractor, iclabel
from iclabelpy.utils import topoplotFast, reref
import scipy.io as sio
if __name__ == '__main__':
import mne
import numpy as np
import scipy.io as sio
from iclabelpy.mat import loadmat
data = loadmat('Y:\code\iclabelpy\data\sub-010317_PREP_clean_ICA.mat')
ica = loadmat('Y:\code\iclabelpy\data\ica.mat')
print('ok')
flag_autocorr = True
EEG = data['EEG']
mixing = ica['icawinv']
demixing = ica['icaweights'] @ ica['icasphere']
#ICL_feature_extractor(EEG, flag_autocorr)
ncomp = mixing.shape[1]
nchannels = mixing.shape[0]
average_reference = False
if not average_reference:
data = reref(EEG['data'])
# Rereference ICA Matrix
newICAchaninds = reref(mixing)
icaact = EEG['icaact']
assert np.all(np.isreal(icaact)) == True # Check Activations are real
def eeg_rpsd(icaact, srate, demixing, nfreqs=None, pct_data=None, test=False):
"""
assume channels,frames,epochs
"""
if icaact.ndim == 3:
nchannels = icaact.shape[0]
nframes = icaact.shape[1]
nepochs = icaact.shape[2]
else:
nchannels = icaact.shape[0]
nframes = icaact.shape[1]
nepochs = 1
# % clean input cutoff freq
nyquist = int(np.floor(srate / 2).astype(int))
if nfreqs is None:
nfreqs = nyquist
elif nfreqs > nyquist:
nfreqs = nyquist
if pct_data is None:
pct_data = 100
# % setup constants
ncomp = demixing.shape[0]
n_points = np.min([nframes, srate]).astype(int)
window = np.hamming(n_points)
cutoff = np.floor(nframes / n_points) * n_points
index = np.array([
x + np.arange(0, n_points)
for x in np.arange(0, cutoff - n_points + 1, n_points / 2)
]).astype(int).T
n_seg = index.shape[1] * nepochs
rng = np.random.RandomState(seed=0)
seq = [
5489, 1301868182, 2938499221, 2950281878, 1875628136, 751856242,
944701696, 2243192071, 694061057, 219885934, 2066767472, 3182869408,
485472502, 2336857883, 1071588843, 3418470598, 951210697, 3693558366,
2923482051, 1793174584, 2982310801, 1586906132, 1951078751, 1808158765,
1733897588, 431328322, 4202539044, 530658942, 1714810322, 3025256284,
3342585396, 1937033938, 2640572511, 1654299090, 3692403553, 4233871309,
3497650794, 862629010, 2943236032, 2426458545, 1603307207, 1133453895,
3099196360, 2208657629, 2747653927, 931059398, 761573964, 3157853227,
785880413, 730313442, 124945756, 2937117055, 3295982469, 1724353043,
3021675344, 3884886417, 4010150098, 4056961966, 699635835, 2681338818,
1339167484, 720757518, 2800161476, 2376097373, 1532957371, 3902664099,
1238982754, 3725394514, 3449176889, 3570962471, 4287636090, 4087307012,
3603343627, 202242161, 2995682783, 1620962684, 3704723357, 371613603,
2814834333, 2111005706, 624778151, 2094172212, 4284947003, 1211977835,
991917094, 1570449747, 2962370480, 1259410321, 170182696, 146300961,
2836829791, 619452428, 2723670296, 1881399711, 1161269684, 1675188680,
4132175277, 780088327, 3409462821, 1036518241, 1834958505, 3048448173,
161811569, 618488316, 44795092, 3918322701, 1924681712, 3239478144,
383254043, 4042306580, 2146983041, 3992780527, 3518029708, 3545545436,
3901231469, 1896136409, 2028528556, 2339662006, 501326714, 2060962201,
2502746480, 561575027, 581893337, 3393774360, 1778912547, 3626131687,
2175155826, 319853231, 986875531, 819755096, 2915734330, 2688355739,
3482074849, 2736559, 2296975761, 1029741190, 2876812646, 690154749,
579200347, 4027461746, 1285330465, 2701024045, 4117700889, 759495121,
3332270341, 2313004527, 2277067795, 4131855432, 2722057515, 1264804546,
3848622725, 2211267957, 4100593547, 959123777, 2130745407, 3194437393,
486673947, 1377371204, 17472727, 352317554, 3955548058, 159652094,
1232063192, 3835177280, 49423123, 3083993636, 733092, 2120519771,
2573409834, 1112952433, 3239502554, 761045320, 1087580692, 2540165110,
641058802, 1792435497, 2261799288, 1579184083, 627146892, 2165744623,
2200142389, 2167590760, 2381418376, 1793358889, 3081659520, 1663384067,
2009658756, 2689600308, 739136266, 2304581039, 3529067263, 591360555,
525209271, 3131882996, 294230224, 2076220115, 3113580446, 1245621585,
1386885462, 3203270426, 123512128, 12350217, 354956375, 4282398238,
3356876605, 3888857667, 157639694, 2616064085, 1563068963, 2762125883,
4045394511, 4180452559, 3294769488, 1684529556, 1002945951, 3181438866,
22506664, 691783457, 2685221343, 171579916, 3878728600, 2475806724,
2030324028, 3331164912, 1708711359, 1970023127, 2859691344, 2588476477,
2748146879, 136111222, 2967685492, 909517429, 2835297809, 3206906216,
3186870716, 341264097, 2542035121, 3353277068, 548223577, 3170936588,
1678403446, 297435620, 2337555430, 466603495, 1132321815, 1208589219,
696392160, 894244439, 2562678859, 470224582, 3306867480, 201364898,
2075966438, 1767227936, 2929737987, 3674877796, 2654196643, 3692734598,
3528895099, 2796780123, 3048728353, 842329300, 191554730, 2922459673,
3489020079, 3979110629, 1022523848, 2202932467, 3583655201, 3565113719,
587085778, 4176046313, 3013713762, 950944241, 396426791, 3784844662,
3477431613, 3594592395, 2782043838, 3392093507, 3106564952, 2829419931,
1358665591, 2206918825, 3170783123, 31522386, 2988194168, 1782249537,
1105080928, 843500134, 1225290080, 1521001832, 3605886097, 2802786495,
2728923319, 3996284304, 903417639, 1171249804, 1020374987, 2824535874,
423621996, 1988534473, 2493544470, 1008604435, 1756003503, 1488867287,
1386808992, 732088248, 1780630732, 2482101014, 976561178, 1543448953,
2602866064, 2021139923, 1952599828, 2360242564, 2117959962, 2753061860,
2388623612, 4138193781, 2962920654, 2284970429, 766920861, 3457264692,
2879611383, 815055854, 2332929068, 1254853997, 3740375268, 3799380844,
4091048725, 2006331129, 1982546212, 686850534, 1907447564, 2682801776,
2780821066, 998290361, 1342433871, 4195430425, 607905174, 3902331779,
2454067926, 1708133115, 1170874362, 2008609376, 3260320415, 2211196135,
433538229, 2728786374, 2189520818, 262554063, 1182318347, 3710237267,
1221022450, 715966018, 2417068910, 2591870721, 2870691989, 3418190842,
4238214053, 1540704231, 1575580968, 2095917976, 4078310857, 2313532447,
2110690783, 4056346629, 4061784526, 1123218514, 551538993, 597148360,
4120175196, 3581618160, 3181170517, 422862282, 3227524138, 1713114790,
662317149, 1230418732, 928171837, 1324564878, 1928816105, 1786535431,
2878099422, 3290185549, 539474248, 1657512683, 552370646, 1671741683,
3655312128, 1552739510, 2605208763, 1441755014, 181878989, 3124053868,
1447103986, 3183906156, 1728556020, 3502241336, 3055466967, 1013272474,
818402132, 1715099063, 2900113506, 397254517, 4194863039, 1009068739,
232864647, 2540223708, 2608288560, 2415367765, 478404847, 3455100648,
3182600021, 2115988978, 434269567, 4117179324, 3461774077, 887256537,
3545801025, 286388911, 3451742129, 1981164769, 786667016, 3310123729,
3097811076, 2224235657, 2959658883, 3370969234, 2514770915, 3345656436,
2677010851, 2206236470, 271648054, 2342188545, 4292848611, 3646533909,
3754009956, 3803931226, 4160647125, 1477814055, 4043852216, 1876372354,
3133294443, 3871104810, 3177020907, 2074304428, 3479393793, 759562891,
164128153, 1839069216, 2114162633, 3989947309, 3611054956, 1333547922,
835429831, 494987340, 171987910, 1252001001, 370809172, 3508925425,
2535703112, 1276855041, 1922855120, 835673414, 3030664304, 613287117,
171219893, 3423096126, 3376881639, 2287770315, 1658692645, 1262815245,
3957234326, 1168096164, 2968737525, 2655813712, 2132313144, 3976047964,
326516571, 353088456, 3679188938, 3205649712, 2654036126, 1249024881,
880166166, 691800469, 2229503665, 1673458056, 4032208375, 1851778863,
2563757330, 376742205, 1794655231, 340247333, 1505873033, 396524441,
879666767, 3335579166, 3260764261, 3335999539, 506221798, 4214658741,
975887814, 2080536343, 3360539560, 571586418, 138896374, 4234352651,
2737620262, 3928362291, 1516365296, 38056726, 3599462320, 3585007266,
3850961033, 471667319, 1536883193, 2310166751, 1861637689, 2530999841,
4139843801, 2710569485, 827578615, 2012334720, 2907369459, 3029312804,
2820112398, 1965028045, 35518606, 2478379033, 643747771, 1924139484,
4123405127, 3811735531, 3429660832, 3285177704, 1948416081, 1311525291,
1183517742, 1739192232, 3979815115, 2567840007, 4116821529, 213304419,
4125718577, 1473064925, 2442436592, 1893310111, 4195361916, 3747569474,
828465101, 2991227658, 750582866, 1205170309, 1409813056, 678418130,
1171531016, 3821236156, 354504587, 4202874632, 3882511497, 1893248677,
1903078632, 26340130, 2069166240, 3657122492, 3725758099, 831344905,
811453383, 3447711422, 2434543565, 4166886888, 3358210805, 4142984013,
2988152326, 3527824853, 982082992, 2809155763, 190157081, 3340214818,
2365432395, 2548636180, 2894533366, 3474657421, 2372634704, 2845748389,
43024175, 2774226648, 1987702864, 3186502468, 453610222, 4204736567,
1392892630, 2471323686, 2470534280, 3541393095, 4269885866, 3909911300,
759132955, 1482612480, 667715263, 1795580598, 2337923983, 3390586366,
581426223, 1515718634, 476374295, 705213300, 363062054, 2084697697,
2407503428, 2292957699, 2426213835, 2199989172, 1987356470, 4026755612,
2147252133, 270400031, 1367820199, 2369854699, 2844269403, 79981964,
624
]
pos = 623
seq = np.array(seq[:-1])
# subset = randperm(n_seg, ceil(n_seg * pct_data / 100)); % need to improve this
k = np.ceil(n_seg * pct_data / 100).astype(
int) #by default k = n_seg since pctdata is not set by default
rng.set_state(('MT19937', seq, 624, 0, 0.0))
subset = rng.permutation(np.arange(0, n_seg))
if test:
subset = np.array([
124, 100, 175, 36, 9, 93, 92, 176, 55, 120, 171, 147, 97, 122, 52,
191, 166, 161, 149, 136, 141, 48, 167, 47, 134, 229, 114, 150, 108,
177, 37, 49, 104, 130, 187, 148, 151, 219, 81, 164, 172, 225, 217,
10, 212, 65, 99, 3, 7, 218, 220, 129, 1, 158, 183, 105, 132, 4,
146, 230, 197, 173, 43, 163, 213, 28, 165, 89, 83, 222, 76, 109,
206, 72, 145, 110, 203, 20, 86, 45, 139, 181, 34, 111, 195, 30, 21,
196, 143, 96, 22, 56, 31, 84, 231, 33, 224, 170, 85, 182, 228, 199,
126, 64, 131, 115, 67, 82, 14, 205, 127, 40, 69, 77, 209, 178, 180,
12, 189, 138, 50, 142, 119, 211, 117, 57, 162, 186, 91, 44, 210, 2,
61, 87, 60, 79, 75, 184, 51, 154, 59, 133, 174, 159, 95, 66, 155,
135, 190, 193, 53, 118, 18, 38, 29, 140, 156, 121, 168, 201, 116,
169, 152, 200, 88, 204, 32, 112, 80, 58, 128, 35, 25, 24, 17, 153,
13, 106, 192, 39, 103, 41, 216, 113, 101, 70, 185, 11, 123, 137,
78, 98, 27, 73, 207, 5, 74, 90, 63, 16, 54, 179, 157, 102, 226,
223, 62, 46, 26, 202, 23, 6, 107, 198, 94, 208, 8, 215, 68, 144,
125, 188, 227, 221, 15, 42, 71, 214, 19, 160, 194
]) - 1 #for test
# i tried but no luck
# % calculate windowed spectrums
psdmed = np.zeros((ncomp, nfreqs))
icaact2 = icaact #np.reshape(icaact,(nchannels,nframes,nepochs),order='F')
if test:
icaact2 = loadmat('data/icaact.mat')['icaact2']
for it in np.arange(ncomp):
shape_ = tuple(
np.concatenate(
(np.array([1]),
np.array(index.shape) * np.array([1, nepochs]))).tolist())
temp = np.reshape(icaact2[it, index, :], shape_, order='F')
temp = temp[:, :, subset] * window[None, :, None]
#https://stackoverflow.com/questions/23544889/converting-bsxfun-with-times-to-numpy , https://stackoverflow.com/questions/16229823/how-to-multiply-numpy-2d-array-with-numpy-1d-array
temp = np.fft.fft(temp, n_points, axis=1)
temp = temp * np.conj(temp)
temp = np.real(temp)
temp = temp[:, np.arange(1, nfreqs + 1), :] * 2 / (
srate * np.sum(np.square(window)))
if nfreqs == nyquist:
temp[:, -1, :] = temp[:, -1, :] / 2
psdmed[it, :] = 20 * np.log10(np.median(temp, 2))
return psdmed
from iclabelpy.utils import mergesimpts,gdatav4,mergepoints2D, multimergesimpts
from iclabelpy.mat import loadmat
import numpy as np
import matplotlib.pyplot as plt
if __name__ == '__main__':
data = loadmat(r'Y:\code\iclabelpy\data\values.mat')
locfile = loadmat(r'Y:\code\iclabelpy\data\locfile.mat')
plotrad = loadmat(r'Y:\code\iclabelpy\data\plotrad.mat')
temptopo = loadmat(r'Y:\code\iclabelpy\data\temptopo.mat')
labels = loadmat(r'Y:\code\iclabelpy\data\labels.mat')
tmpeloc = loadmat(r'Y:\code\iclabelpy\data\tmpeloc.mat')
th = loadmat(r'Y:\code\iclabelpy\data\Th.mat')
rd = loadmat(r'Y:\code\iclabelpy\data\Rd.mat')
indices = loadmat(r'Y:\code\iclabelpy\data\indices.mat')
gdataIN =loadmat(r'Y:\code\iclabelpy\data\gdataIN.mat')
inty = gdataIN['varargin'][0]
intx = gdataIN['varargin'][1]
intValues = gdataIN['varargin'][2]
yi = gdataIN['varargin'][3]
x i = gdataIN['varargin'][4]
vq = gdatav4(inty,intx,intValues,yi,xi)
# x,y,v = mergepoints2D(data['x'],data['y'],data['v'])
# plt.imshow(vq)
# plt.colorbar()
# plt.show()
# x = [[1,2], [1.06,2], [1.1, 2], [1.1, 2.03]]
# multimergesimpts(x,[0.05,0.1],'','')
print('ok')
from iclabelpy.utils import mergesimpts, gdatav4, mergepoints2D, multimergesimpts
from iclabelpy.mat import loadmat
import numpy as np
import matplotlib.pyplot as plt
if __name__ == '__main__':
gdataIN = loadmat(r'Y:\code\iclabelpy\data\gdataIN.mat')
inty = gdataIN['varargin'][0]
intx = gdataIN['varargin'][1]
intValues = gdataIN['varargin'][2]
yi = gdataIN['varargin'][3]
xi = gdataIN['varargin'][4]
gdataOUT = loadmat(r'Y:\code\iclabelpy\data\gdataOUT.mat')
Xi = gdataOUT['sa']['Xi']
Yi = gdataOUT['sa']['Yi']
Zi = gdataOUT['sa']['Zi']
xq, yq, vq = gdatav4(inty, intx, intValues, yi, xi)
# x,y,v = mergepoints2D(data['x'],data['y'],data['v'])
plt.imshow(vq)
plt.colorbar()
plt.show()
# x = [[1,2], [1.06,2], [1.1, 2], [1.1, 2.03]]
# multimergesimpts(x,[0.05,0.1],'','')
print('ok')
from iclabelpy.utils import mergesimpts, mergesimpts_, gdatav4, mergepoints2D, multimergesimpts
from iclabelpy.mat import loadmat
import numpy as np
import matplotlib.pyplot as plt
if __name__ == '__main__':
x = [1, 1.1, 1.05]
mergesimpts_(x, 0.1, 'unique', '')
# x = list(np.arange(0,10,0.5))
# mergesimpts(x,1,mode='')
# x=[-0.147921891353479,0.147921891353479,-0.122450697428586,0.122721402649397,-0.245187423015011,0.245166992432308,-0.367658551026300,0.367658551026300,-0.117632487188521,0.117632487188521,-0.234883896620009,0.234883896620009,-0.351164911943686,0.351164911943686,0,-0.102262955834933,0.102173367580941,-0.203418205392725,0.203150481352913,-0.301299618644754,0.301082429428487,-0.392349849052477,0.392107755545381,-0.117632487188521,0.117632487188521,-0.234883896620009,0.234883896620009,-0.351164911943686,0.351164911943686,-0.145728126853333,0.145728126853333,0,-0.145728126853333,0.145728126853333,5.70486668071449*1e-17,-0.102262955834933,0.102173367580941,-0.203418205392725,0.203150481352913,-0.301299618644754,0.301082429428487,-0.392349849052477,0.392107755545381,-0.147921891353479,0.147921891353479,-0.281533912238001,0.281291034975421,4.27783685248524*1e-17,2.85280864407292*1e-17,-0.490196078431373,0.490196078431373,-0.464959285410511,0.464803892788837]
# a = mergesimpts(x,3.814697265625003*1e-6,'')
data = loadmat('Y:\code\iclabelpy\data\gdatav4.mat')
data2 = loadmat('Y:\code\iclabelpy\data\mergesimpoints.mat')
vq = gdatav4(data2['x'], data2['y'], data2['v'], data['xq'], data['yq'])
x, y, v = mergepoints2D(data['x'], data['y'], data['v'])
plt.imshow(vq)
plt.colorbar()
plt.show()
x = [[1, 2], [1.06, 2], [1.1, 2], [1.1, 2.03]]
multimergesimpts(x, [0.05, 0.1], '', '')
print('ok')