import numpy as np
from scipy.interpolate import pchip
[docs]def getcanonicalhrf(duration, tr):
# inputs
if duration == 0:
duration = 0.1
# obtain canonical response to a 0.1-s stimulus
hrf = basichrf()
# convolve to get the predicted response to the desired stimulus duration
trold = 0.1
hrf = np.convolve(
hrf,
np.ones(int(np.max([1, np.round(duration/trold)])))
)
sampler = np.asarray(np.arange(0, int((hrf.shape[0]-1)*trold), tr))
# resample to desired TR
hrf = pchip(
np.asarray(range(hrf.shape[0]))*trold,
hrf)(sampler)
# make the peak equal to one
hrf = hrf / np.max(hrf)
return hrf
[docs]def basichrf():
hrf = np.asarray([0,5.34e-06,3.55e-05,0.000104,0.00022,0.000388,0.00061,0.000886,0.00122,0.00159,0.00202,0.00249,0.003,0.00354,0.00411,0.00471,0.00533,0.00596,0.0066,0.00725,0.0079,0.00855,0.0092,0.00984,0.0105,0.0111,0.0117,0.0122,0.0128,0.0133,0.0138,0.0143,0.0148,0.0152,0.0156,0.016,0.0163,0.0166,0.0169,0.0171,0.0174,0.0176,0.0177,0.0179,0.018,0.0181,0.0181,0.0181,0.0182,0.0181,0.0181,0.018,0.018,0.0178,0.0177,0.0176,0.0174,0.0173,0.0171,0.0169,0.0167,0.0164,0.0162,0.0159,0.0157,0.0154,0.0151,0.0148,0.0146,0.0143,0.014,0.0136,0.0133,0.013,0.0127,0.0124,0.0121,0.0118,0.0114,0.0111,0.0108,0.0105,0.0102,0.00985,0.00954,0.00923,0.00893,0.00862,0.00832,0.00803,0.00773,0.00744,0.00716,0.00688,0.0066,0.00633,0.00607,0.00581,0.00555,0.0053,0.00505,0.00481,0.00458,0.00435,0.00412,0.0039,0.00369,0.00348,0.00328,0.00308,0.00289,0.0027,0.00252,0.00234,0.00217,0.002,0.00184,0.00168,0.00153,0.00139,0.00124,0.00111,0.000974,0.000846,0.000722,0.000603,0.000488,0.000377,0.000271,0.000168,6.96e-05,-2.51e-05,-0.000116,-0.000203,-0.000287,-0.000367,-0.000443,-0.000516,-0.000586,-0.000653,-0.000716,-0.000777,-0.000835,-0.00089,-0.000942,-0.000991,-0.00104,-0.00108,-0.00112,-0.00116,-0.0012,-0.00123,-0.00127,-0.0013,-0.00133,-0.00135,-0.00138,-0.0014,-0.00142,-0.00144,-0.00146,-0.00147,-0.00149,-0.0015,-0.00151,-0.00152,-0.00153,-0.00154,-0.00155,-0.00155,-0.00156,-0.00156,-0.00156,-0.00156,-0.00157,-0.00156,-0.00156,-0.00156,-0.00156,-0.00155,-0.00155,-0.00154,-0.00154,-0.00153,-0.00153,-0.00152,-0.00151,-0.0015,-0.00149,-0.00148,-0.00147,-0.00146,-0.00145,-0.00144,-0.00143,-0.00142,-0.00141,-0.0014,-0.00138,-0.00137,-0.00136,-0.00135,-0.00133,-0.00132,-0.00131,-0.00129,-0.00128,-0.00127,-0.00125,-0.00124,-0.00122,-0.00121,-0.0012,-0.00118,-0.00117,-0.00115,-0.00114,-0.00113,-0.00111,-0.0011,-0.00108,-0.00107,-0.00106,-0.00104,-0.00103,-0.00101,-0.001,-0.000987,-0.000973,-0.00096,-0.000947,-0.000933,-0.00092,-0.000907,-0.000894,-0.000881,-0.000868,-0.000855,-0.000843,-0.00083,-0.000818,-0.000805,-0.000793,-0.000781,-0.000769,-0.000758,-0.000746,-0.000734,-0.000723,-0.000711,-0.0007,-0.000689,-0.000678,-0.000667,-0.000657,-0.000646,-0.000636,-0.000625,-0.000615,-0.000605,-0.000595,-0.000585,-0.000576,-0.000566,-0.000557,-0.000547,-0.000538,-0.000529,-0.00052,-0.000511,-0.000503,-0.000494,-0.000486,-0.000477,-0.000469,-0.000461,-0.000453,-0.000445,-0.000438,-0.00043,-0.000422,-0.000415,-0.000408,-0.000401,-0.000393,-0.000387,-0.00038,-0.000373,-0.000366,-0.00036,-0.000353,-0.000347,-0.000341,-0.000335,-0.000329,-0.000323,-0.000317,-0.000311,-0.000305,-0.0003,-0.000294,-0.000289,-0.000284,-0.000278,-0.000273,-0.000268,-0.000263,-0.000258,-0.000254,-0.000249,-0.000244,-0.00024,-0.000235,-0.000231,-0.000226,-0.000222,-0.000218,-0.000214,-0.00021,-0.000206,-0.000202,-0.000198,-0.000194,-0.000191,-0.000187,-0.000184,-0.00018,-0.000177,-0.000173,-0.00017,-0.000167,-0.000163,-0.00016,-0.000157,-0.000154,-0.000151,-0.000148,-0.000145,-0.000143,-0.00014,-0.000137,-0.000134,-0.000132,-0.000129,-0.000127,-0.000124,-0.000122,-0.000119,-0.000117,-0.000115,-0.000113,-0.00011,-0.000108,-0.000106,-0.000104,-0.000102,-9.99e-05,-9.79e-05,-9.6e-05,-9.41e-05,-9.22e-05,-9.04e-05,-8.86e-05,-8.68e-05,-8.51e-05,-8.34e-05,-8.17e-05,-8.01e-05,-7.85e-05,-7.69e-05,-7.54e-05,-7.39e-05,-7.24e-05,-7.09e-05,-6.95e-05,-6.81e-05,-6.67e-05,-6.54e-05,-6.4e-05,-6.28e-05,-6.15e-05,-6.02e-05,-5.9e-05,-5.78e-05,-5.66e-05,-5.55e-05,-5.44e-05,-5.33e-05,-5.22e-05,-5.11e-05,-5.01e-05,-4.9e-05,-4.8e-05,-4.71e-05,-4.61e-05,-4.52e-05,-4.42e-05,-4.33e-05,-4.24e-05,-4.16e-05,-4.07e-05,-3.99e-05,-3.91e-05,-3.82e-05,-3.75e-05,-3.67e-05,-3.59e-05,-3.52e-05,-3.45e-05,-3.37e-05,-3.3e-05,-3.24e-05,-3.17e-05,-3.1e-05,-3.04e-05,-2.98e-05,-2.91e-05,-2.85e-05,-2.79e-05,-2.74e-05,-2.68e-05,-2.62e-05,-2.57e-05,-2.51e-05,-2.46e-05,-2.41e-05,-2.36e-05,-2.31e-05,-2.26e-05,-2.21e-05,-2.17e-05,-2.12e-05,-2.08e-05,-2.03e-05,-1.99e-05,-1.95e-05,-1.91e-05,-1.87e-05,-1.83e-05,-1.79e-05,-1.75e-05,-1.72e-05,-1.68e-05,-1.64e-05,-1.61e-05,-1.58e-05,-1.54e-05,-1.51e-05,-1.48e-05,-1.45e-05,-1.42e-05,-1.38e-05,-1.36e-05,-1.33e-05,-1.3e-05,-1.27e-05,-1.24e-05,-1.22e-05,-1.19e-05,-1.17e-05,-1.14e-05,-1.12e-05,-1.09e-05,-1.07e-05,-1.05e-05,-1.02e-05,-1e-05,-9.81e-06,-9.6e-06,-9.39e-06,-9.19e-06,-8.99e-06,-8.8e-06,-8.61e-06,-8.43e-06,-8.25e-06,-8.07e-06,-7.89e-06,-7.72e-06,-7.56e-06,-7.39e-06,-7.24e-06,-7.08e-06,-6.93e-06,-6.78e-06,-6.63e-06,-6.49e-06,-6.35e-06,-6.21e-06,-6.08e-06]).T
return hrf