def cnvlTcOld(idxPrc,<EOL>aryPrfTcChunk,<EOL>varTr,<EOL>varNumVol,<EOL>queOut):
<EOL>vecHrf = funcHrf(varNumVol, varTr)<EOL>tplInpShp = aryPrfTcChunk.shape<EOL>aryPrfTcChunk = aryPrfTcChunk.reshape((-<NUM_LIT:1>, aryPrfTcChunk.shape[-<NUM_LIT:1>]))<EOL>aryConv = np.zeros(np.shape(aryPrfTcChunk))<EOL>for idxTc, vecTc in enumerate(aryPrfTcChunk):<EOL><INDENT>vecTc = np.append(vecTc, np.zeros(<NUM_LIT:100>))<EOL>vecHrf = np.append(vecHrf, np.zeros(<NUM_LIT:100>))<EOL>aryConv[idxTc, :] = np.convolve(vecTc, vecHrf,<EOL>mode='<STR_LIT>')[:varNumVol]<EOL><DEDENT>tplOutShp = tplInpShp[:-<NUM_LIT:1>] + (<NUM_LIT:1>, ) + (tplInpShp[-<NUM_LIT:1>], )<EOL>lstOut = [idxPrc,<EOL>aryConv.reshape(tplOutShp)]<EOL>queOut.put(lstOut)<EOL>
Old version:
Convolution of time courses with one canonical HRF model.