def lpc_ref(signal, order):
""" Return the order + 1 LPC coefficients
for the signal. This is just for reference, as it is using
the direct inversion of the toeplitz matrix, which is really slow"""
if signal.ndim > 1:
print "Warning, not tested for rank > 1"
p = order + 1
r = autocorr_oneside_nofft(signal, order) / signal.shape[0]
return _N.concatenate(([1.], _N.dot(inv(toeplitz(r[:-1])), -r[1:])))
def lpc2(signal, order, axis = -1):
""" Returns ar coeff, err and k coeff"""
sz = signal.shape[axis]
if order >= sz:
raise RuntimeError("order should be strictly smaller than the length of signal")
# rank 1
if signal.ndim < 2:
if signal.dtype == _N.float64:
coeff = _N.zeros((order+1), _N.float64)
kcoeff = _N.zeros((order), _N.float64)
err = _N.zeros((1), _N.float64)
st = _lpc.dbl_lpc(signal, signal.size, order, coeff, kcoeff, err)
elif signal.dtype == _N.float32:
coeff = _N.zeros((order+1), _N.float32)
kcoeff = _N.zeros((order), _N.float32)
err = _N.zeros((1), _N.float32)
st = _lpc.flt_lpc(signal, signal.size, order, coeff, kcoeff, err)
else:
raise TypeError("Sorry, only float32 and float64 supported")
if not (st == 0):
raise RuntimeError("Error while using levinson algo, returns err is %d", st)
return coeff, err, kcoeff
# rank 2
elif signal.ndim == 2:
# Compute biased autocorrelation up to lag = order
bias = 1. / sz
corr = bias * autocorr_oneside_nofft(signal, order, axis)
if axis % 2 == 0:
# we transpose to have a major row order
icorr = corr.T
icorr = _N.require(icorr, requirements = 'C')
at = _N.zeros((icorr.shape[0], order+1), icorr.dtype)
kt = _N.zeros((icorr.shape[0], order), icorr.dtype)
et = _N.zeros(icorr.shape[0], icorr.dtype)
if icorr.dtype == _N.float64:
_lpc.dbl_levinson2d(icorr, icorr.shape[0], icorr.shape[1], at, et, kt)
elif icorr.dtype == _N.float32:
_lpc.flt_levinson2d(icorr, icorr.shape[0], icorr.shape[1], at, et, kt)
else:
raise TypeError("Only float32 and float64 supported")
return at.T, et.T, kt.T
elif axis % 2 == 1:
icorr = _N.require(corr, requirements = 'C')
at = _N.zeros((icorr.shape[0], order+1), icorr.dtype)
kt = _N.zeros((icorr.shape[0], order), icorr.dtype)
et = _N.zeros(icorr.shape[0], icorr.dtype)
if icorr.dtype == _N.float64:
_lpc.dbl_levinson2d(icorr, icorr.shape[0], icorr.shape[1], at, et, kt)
elif icorr.dtype == _N.float32:
_lpc.flt_levinson2d(icorr, icorr.shape[0], icorr.shape[1], at, et, kt)
else:
raise TypeError("Only float32 and float64 supported")
return at, et, kt
else:
raise RuntimeError("This should not happen, this is a bug")
else:
raise RuntimeError("Sorry, only rank <= 2 supported for now")
