def estimate_time_constant(y, p=2, sn=None, lags=5, fudge_factor=1.):
"""
Estimate AR model parameters through the autocovariance function
Parameters
----------
y : array, shape (T,)
One dimensional array containing the fluorescence intensities with
one entry per time-bin.
p : positive integer
order of AR system
sn : float
sn standard deviation, estimated if not provided.
lags : positive integer
number of additional lags where he autocovariance is computed
fudge_factor : float (0< fudge_factor <= 1)
shrinkage factor to reduce bias
Returns
-------
g : estimated coefficients of the AR process
"""
if sn is None:
sn = GetSn(y)
lags += p
xc = axcov(y, lags)
xc = xc[:, np.newaxis]
A = scipy.linalg.toeplitz(xc[lags + np.arange(lags)],
xc[lags + np.arange(p)]) - sn**2 * np.eye(lags, p)
g = np.linalg.lstsq(A, xc[lags + 1:])[0]
gr = np.roots(np.concatenate([np.array([1]), -g.flatten()]))
gr = (gr + gr.conjugate()) / 2.
gr[gr > 1] = 0.95 + np.random.normal(0, 0.01, np.sum(gr > 1))
gr[gr < 0] = 0.15 + np.random.normal(0, 0.01, np.sum(gr < 0))
g = np.poly(fudge_factor * gr)
g = -g[1:]
return g.flatten()
python类poly()的实例源码
def test_poly(self):
assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]),
[1, -3, -2, 6])
# From matlab docs
A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
assert_array_almost_equal(np.poly(A), [1, -6, -72, -27])
# Should produce real output for perfect conjugates
assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j])))
assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j,
1-2j, 1.+3.5j, 1-3.5j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j])))
assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j])))
assert_(np.isrealobj(np.poly([1j, -1j])))
assert_(np.isrealobj(np.poly([1, -1])))
assert_(np.iscomplexobj(np.poly([1j, -1.0000001j])))
np.random.seed(42)
a = np.random.randn(100) + 1j*np.random.randn(100)
assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a))))))
def test_objects(self):
from decimal import Decimal
p = np.poly1d([Decimal('4.0'), Decimal('3.0'), Decimal('2.0')])
p2 = p * Decimal('1.333333333333333')
assert_(p2[1] == Decimal("3.9999999999999990"))
p2 = p.deriv()
assert_(p2[1] == Decimal('8.0'))
p2 = p.integ()
assert_(p2[3] == Decimal("1.333333333333333333333333333"))
assert_(p2[2] == Decimal('1.5'))
assert_(np.issubdtype(p2.coeffs.dtype, np.object_))
p = np.poly([Decimal(1), Decimal(2)])
assert_equal(np.poly([Decimal(1), Decimal(2)]),
[1, Decimal(-3), Decimal(2)])
def test_zero_dims(self):
try:
np.poly(np.zeros((0, 0)))
except ValueError:
pass
def test_poly_int_overflow(self):
"""
Regression test for gh-5096.
"""
v = np.arange(1, 21)
assert_almost_equal(np.poly(v), np.poly(np.diag(v)))
def lsf_to_lpc(all_lsf):
if len(all_lsf.shape) < 2:
all_lsf = all_lsf[None]
order = all_lsf.shape[1]
all_lpc = np.zeros((len(all_lsf), order + 1))
for i in range(len(all_lsf)):
lsf = all_lsf[i]
zeros = np.exp(1j * lsf)
sum_zeros = zeros[::2]
diff_zeros = zeros[1::2]
sum_zeros = np.hstack((sum_zeros, np.conj(sum_zeros)))
diff_zeros = np.hstack((diff_zeros, np.conj(diff_zeros)))
sum_filt = np.poly(sum_zeros)
diff_filt = np.poly(diff_zeros)
if order % 2 != 0:
deconv_diff = sg.convolve(diff_filt, [1, 0, -1])
deconv_sum = sum_filt
else:
deconv_diff = sg.convolve(diff_filt, [1, -1])
deconv_sum = sg.convolve(sum_filt, [1, 1])
lpc = .5 * (deconv_sum + deconv_diff)
# Last coefficient is 0 and not returned
all_lpc[i] = lpc[:-1]
return np.squeeze(all_lpc)
def lsf_to_lpc(all_lsf):
if len(all_lsf.shape) < 2:
all_lsf = all_lsf[None]
order = all_lsf.shape[1]
all_lpc = np.zeros((len(all_lsf), order + 1))
for i in range(len(all_lsf)):
lsf = all_lsf[i]
zeros = np.exp(1j * lsf)
sum_zeros = zeros[::2]
diff_zeros = zeros[1::2]
sum_zeros = np.hstack((sum_zeros, np.conj(sum_zeros)))
diff_zeros = np.hstack((diff_zeros, np.conj(diff_zeros)))
sum_filt = np.poly(sum_zeros)
diff_filt = np.poly(diff_zeros)
if order % 2 != 0:
deconv_diff = sg.convolve(diff_filt, [1, 0, -1])
deconv_sum = sum_filt
else:
deconv_diff = sg.convolve(diff_filt, [1, -1])
deconv_sum = sg.convolve(sum_filt, [1, 1])
lpc = .5 * (deconv_sum + deconv_diff)
# Last coefficient is 0 and not returned
all_lpc[i] = lpc[:-1]
return np.squeeze(all_lpc)
def test_objects(self):
from decimal import Decimal
p = np.poly1d([Decimal('4.0'), Decimal('3.0'), Decimal('2.0')])
p2 = p * Decimal('1.333333333333333')
assert_(p2[1] == Decimal("3.9999999999999990"))
p2 = p.deriv()
assert_(p2[1] == Decimal('8.0'))
p2 = p.integ()
assert_(p2[3] == Decimal("1.333333333333333333333333333"))
assert_(p2[2] == Decimal('1.5'))
assert_(np.issubdtype(p2.coeffs.dtype, np.object_))
p = np.poly([Decimal(1), Decimal(2)])
assert_equal(np.poly([Decimal(1), Decimal(2)]),
[1, Decimal(-3), Decimal(2)])
def test_zero_dims(self):
try:
np.poly(np.zeros((0, 0)))
except ValueError:
pass
def test_poly_int_overflow(self):
"""
Regression test for gh-5096.
"""
v = np.arange(1, 21)
assert_almost_equal(np.poly(v), np.poly(np.diag(v)))
def estimate_time_constant(fluor, p = 2, sn = None, lags = 5, fudge_factor = 1.):
"""
Estimate AR model parameters through the autocovariance function
Inputs
----------
fluor : nparray
One dimensional array containing the fluorescence intensities with
one entry per time-bin.
p : positive integer
order of AR system
sn : float
noise standard deviation, estimated if not provided.
lags : positive integer
number of additional lags where he autocovariance is computed
fudge_factor : float (0< fudge_factor <= 1)
shrinkage factor to reduce bias
Return
-----------
g : estimated coefficients of the AR process
"""
if sn is None:
sn = GetSn(fluor)
lags += p
xc = axcov(fluor,lags)
xc = xc[:,np.newaxis]
A = scipy.linalg.toeplitz(xc[lags+np.arange(lags)],xc[lags+np.arange(p)]) - sn**2*np.eye(lags,p)
g = np.linalg.lstsq(A,xc[lags+1:])[0]
gr = np.roots(np.concatenate([np.array([1]),-g.flatten()]))
gr = (gr+gr.conjugate())/2.
gr[gr>1] = 0.95 + np.random.normal(0,0.01,np.sum(gr>1))
gr[gr<0] = 0.15 + np.random.normal(0,0.01,np.sum(gr<0))
g = np.poly(fudge_factor*gr)
g = -g[1:]
return g.flatten()
def test_objects(self):
from decimal import Decimal
p = np.poly1d([Decimal('4.0'), Decimal('3.0'), Decimal('2.0')])
p2 = p * Decimal('1.333333333333333')
assert_(p2[1] == Decimal("3.9999999999999990"))
p2 = p.deriv()
assert_(p2[1] == Decimal('8.0'))
p2 = p.integ()
assert_(p2[3] == Decimal("1.333333333333333333333333333"))
assert_(p2[2] == Decimal('1.5'))
assert_(np.issubdtype(p2.coeffs.dtype, np.object_))
p = np.poly([Decimal(1), Decimal(2)])
assert_equal(np.poly([Decimal(1), Decimal(2)]),
[1, Decimal(-3), Decimal(2)])
def test_zero_dims(self):
try:
np.poly(np.zeros((0, 0)))
except ValueError:
pass
def test_poly_int_overflow(self):
"""
Regression test for gh-5096.
"""
v = np.arange(1, 21)
assert_almost_equal(np.poly(v), np.poly(np.diag(v)))
def z_coeff(Poles,Zeros,fs,g,fg,fo = 'none'):
if fg == np.inf:
fg = fs/2
if fo == 'none':
beta = 1.0
else:
beta = f_warp(fo,fs)/fo
a = np.poly(z_from_f(beta*np.array(Poles),fs))
b = np.poly(z_from_f(beta*np.array(Zeros),fs))
gain = 10.**(g/20.)/abs(Fz_at_f(beta*np.array(Poles),beta*np.array(Zeros),fg,fs))
return (a,b*gain)
def test_objects(self):
from decimal import Decimal
p = np.poly1d([Decimal('4.0'), Decimal('3.0'), Decimal('2.0')])
p2 = p * Decimal('1.333333333333333')
assert_(p2[1] == Decimal("3.9999999999999990"))
p2 = p.deriv()
assert_(p2[1] == Decimal('8.0'))
p2 = p.integ()
assert_(p2[3] == Decimal("1.333333333333333333333333333"))
assert_(p2[2] == Decimal('1.5'))
assert_(np.issubdtype(p2.coeffs.dtype, np.object_))
p = np.poly([Decimal(1), Decimal(2)])
assert_equal(np.poly([Decimal(1), Decimal(2)]),
[1, Decimal(-3), Decimal(2)])
def test_zero_dims(self):
try:
np.poly(np.zeros((0, 0)))
except ValueError:
pass
def test_poly_int_overflow(self):
"""
Regression test for gh-5096.
"""
v = np.arange(1, 21)
assert_almost_equal(np.poly(v), np.poly(np.diag(v)))
def test_objects(self):
from decimal import Decimal
p = np.poly1d([Decimal('4.0'), Decimal('3.0'), Decimal('2.0')])
p2 = p * Decimal('1.333333333333333')
assert_(p2[1] == Decimal("3.9999999999999990"))
p2 = p.deriv()
assert_(p2[1] == Decimal('8.0'))
p2 = p.integ()
assert_(p2[3] == Decimal("1.333333333333333333333333333"))
assert_(p2[2] == Decimal('1.5'))
assert_(np.issubdtype(p2.coeffs.dtype, np.object_))
p = np.poly([Decimal(1), Decimal(2)])
assert_equal(np.poly([Decimal(1), Decimal(2)]),
[1, Decimal(-3), Decimal(2)])
def test_zero_dims(self):
try:
np.poly(np.zeros((0, 0)))
except ValueError:
pass
def test_poly_int_overflow(self):
"""
Regression test for gh-5096.
"""
v = np.arange(1, 21)
assert_almost_equal(np.poly(v), np.poly(np.diag(v)))
def lsf_to_lpc(all_lsf):
if len(all_lsf.shape) < 2:
all_lsf = all_lsf[None]
order = all_lsf.shape[1]
all_lpc = np.zeros((len(all_lsf), order + 1))
for i in range(len(all_lsf)):
lsf = all_lsf[i]
zeros = np.exp(1j * lsf)
sum_zeros = zeros[::2]
diff_zeros = zeros[1::2]
sum_zeros = np.hstack((sum_zeros, np.conj(sum_zeros)))
diff_zeros = np.hstack((diff_zeros, np.conj(diff_zeros)))
sum_filt = np.poly(sum_zeros)
diff_filt = np.poly(diff_zeros)
if order % 2 != 0:
deconv_diff = sg.convolve(diff_filt, [1, 0, -1])
deconv_sum = sum_filt
else:
deconv_diff = sg.convolve(diff_filt, [1, -1])
deconv_sum = sg.convolve(sum_filt, [1, 1])
lpc = .5 * (deconv_sum + deconv_diff)
# Last coefficient is 0 and not returned
all_lpc[i] = lpc[:-1]
return np.squeeze(all_lpc)
def lsf_to_lpc(all_lsf):
if len(all_lsf.shape) < 2:
all_lsf = all_lsf[None]
order = all_lsf.shape[1]
all_lpc = np.zeros((len(all_lsf), order + 1))
for i in range(len(all_lsf)):
lsf = all_lsf[i]
zeros = np.exp(1j * lsf)
sum_zeros = zeros[::2]
diff_zeros = zeros[1::2]
sum_zeros = np.hstack((sum_zeros, np.conj(sum_zeros)))
diff_zeros = np.hstack((diff_zeros, np.conj(diff_zeros)))
sum_filt = np.poly(sum_zeros)
diff_filt = np.poly(diff_zeros)
if order % 2 != 0:
deconv_diff = sg.convolve(diff_filt, [1, 0, -1])
deconv_sum = sum_filt
else:
deconv_diff = sg.convolve(diff_filt, [1, -1])
deconv_sum = sg.convolve(sum_filt, [1, 1])
lpc = .5 * (deconv_sum + deconv_diff)
# Last coefficient is 0 and not returned
all_lpc[i] = lpc[:-1]
return np.squeeze(all_lpc)
def lsf_to_lpc(all_lsf):
if len(all_lsf.shape) < 2:
all_lsf = all_lsf[None]
order = all_lsf.shape[1]
all_lpc = np.zeros((len(all_lsf), order + 1))
for i in range(len(all_lsf)):
lsf = all_lsf[i]
zeros = np.exp(1j * lsf)
sum_zeros = zeros[::2]
diff_zeros = zeros[1::2]
sum_zeros = np.hstack((sum_zeros, np.conj(sum_zeros)))
diff_zeros = np.hstack((diff_zeros, np.conj(diff_zeros)))
sum_filt = np.poly(sum_zeros)
diff_filt = np.poly(diff_zeros)
if order % 2 != 0:
deconv_diff = sg.convolve(diff_filt, [1, 0, -1])
deconv_sum = sum_filt
else:
deconv_diff = sg.convolve(diff_filt, [1, -1])
deconv_sum = sg.convolve(sum_filt, [1, 1])
lpc = .5 * (deconv_sum + deconv_diff)
# Last coefficient is 0 and not returned
all_lpc[i] = lpc[:-1]
return np.squeeze(all_lpc)
def lsf_to_lpc(all_lsf):
if len(all_lsf.shape) < 2:
all_lsf = all_lsf[None]
order = all_lsf.shape[1]
all_lpc = np.zeros((len(all_lsf), order + 1))
for i in range(len(all_lsf)):
lsf = all_lsf[i]
zeros = np.exp(1j * lsf)
sum_zeros = zeros[::2]
diff_zeros = zeros[1::2]
sum_zeros = np.hstack((sum_zeros, np.conj(sum_zeros)))
diff_zeros = np.hstack((diff_zeros, np.conj(diff_zeros)))
sum_filt = np.poly(sum_zeros)
diff_filt = np.poly(diff_zeros)
if order % 2 != 0:
deconv_diff = sg.convolve(diff_filt, [1, 0, -1])
deconv_sum = sum_filt
else:
deconv_diff = sg.convolve(diff_filt, [1, -1])
deconv_sum = sg.convolve(sum_filt, [1, 1])
lpc = .5 * (deconv_sum + deconv_diff)
# Last coefficient is 0 and not returned
all_lpc[i] = lpc[:-1]
return np.squeeze(all_lpc)
def lsf_to_lpc(all_lsf):
if len(all_lsf.shape) < 2:
all_lsf = all_lsf[None]
order = all_lsf.shape[1]
all_lpc = np.zeros((len(all_lsf), order + 1))
for i in range(len(all_lsf)):
lsf = all_lsf[i]
zeros = np.exp(1j * lsf)
sum_zeros = zeros[::2]
diff_zeros = zeros[1::2]
sum_zeros = np.hstack((sum_zeros, np.conj(sum_zeros)))
diff_zeros = np.hstack((diff_zeros, np.conj(diff_zeros)))
sum_filt = np.poly(sum_zeros)
diff_filt = np.poly(diff_zeros)
if order % 2 != 0:
deconv_diff = sg.convolve(diff_filt, [1, 0, -1])
deconv_sum = sum_filt
else:
deconv_diff = sg.convolve(diff_filt, [1, -1])
deconv_sum = sg.convolve(sum_filt, [1, 1])
lpc = .5 * (deconv_sum + deconv_diff)
# Last coefficient is 0 and not returned
all_lpc[i] = lpc[:-1]
return np.squeeze(all_lpc)
def lsf_to_lpc(all_lsf):
if len(all_lsf.shape) < 2:
all_lsf = all_lsf[None]
order = all_lsf.shape[1]
all_lpc = np.zeros((len(all_lsf), order + 1))
for i in range(len(all_lsf)):
lsf = all_lsf[i]
zeros = np.exp(1j * lsf)
sum_zeros = zeros[::2]
diff_zeros = zeros[1::2]
sum_zeros = np.hstack((sum_zeros, np.conj(sum_zeros)))
diff_zeros = np.hstack((diff_zeros, np.conj(diff_zeros)))
sum_filt = np.poly(sum_zeros)
diff_filt = np.poly(diff_zeros)
if order % 2 != 0:
deconv_diff = sg.convolve(diff_filt, [1, 0, -1])
deconv_sum = sum_filt
else:
deconv_diff = sg.convolve(diff_filt, [1, -1])
deconv_sum = sg.convolve(sum_filt, [1, 1])
lpc = .5 * (deconv_sum + deconv_diff)
# Last coefficient is 0 and not returned
all_lpc[i] = lpc[:-1]
return np.squeeze(all_lpc)
def lsf_to_lpc(all_lsf):
if len(all_lsf.shape) < 2:
all_lsf = all_lsf[None]
order = all_lsf.shape[1]
all_lpc = np.zeros((len(all_lsf), order + 1))
for i in range(len(all_lsf)):
lsf = all_lsf[i]
zeros = np.exp(1j * lsf)
sum_zeros = zeros[::2]
diff_zeros = zeros[1::2]
sum_zeros = np.hstack((sum_zeros, np.conj(sum_zeros)))
diff_zeros = np.hstack((diff_zeros, np.conj(diff_zeros)))
sum_filt = np.poly(sum_zeros)
diff_filt = np.poly(diff_zeros)
if order % 2 != 0:
deconv_diff = sg.convolve(diff_filt, [1, 0, -1])
deconv_sum = sum_filt
else:
deconv_diff = sg.convolve(diff_filt, [1, -1])
deconv_sum = sg.convolve(sum_filt, [1, 1])
lpc = .5 * (deconv_sum + deconv_diff)
# Last coefficient is 0 and not returned
all_lpc[i] = lpc[:-1]
return np.squeeze(all_lpc)
def lsf_to_lpc(all_lsf):
if len(all_lsf.shape) < 2:
all_lsf = all_lsf[None]
order = all_lsf.shape[1]
all_lpc = np.zeros((len(all_lsf), order + 1))
for i in range(len(all_lsf)):
lsf = all_lsf[i]
zeros = np.exp(1j * lsf)
sum_zeros = zeros[::2]
diff_zeros = zeros[1::2]
sum_zeros = np.hstack((sum_zeros, np.conj(sum_zeros)))
diff_zeros = np.hstack((diff_zeros, np.conj(diff_zeros)))
sum_filt = np.poly(sum_zeros)
diff_filt = np.poly(diff_zeros)
if order % 2 != 0:
deconv_diff = sg.convolve(diff_filt, [1, 0, -1])
deconv_sum = sum_filt
else:
deconv_diff = sg.convolve(diff_filt, [1, -1])
deconv_sum = sg.convolve(sum_filt, [1, 1])
lpc = .5 * (deconv_sum + deconv_diff)
# Last coefficient is 0 and not returned
all_lpc[i] = lpc[:-1]
return np.squeeze(all_lpc)
def lsf_to_lpc(all_lsf):
if len(all_lsf.shape) < 2:
all_lsf = all_lsf[None]
order = all_lsf.shape[1]
all_lpc = np.zeros((len(all_lsf), order + 1))
for i in range(len(all_lsf)):
lsf = all_lsf[i]
zeros = np.exp(1j * lsf)
sum_zeros = zeros[::2]
diff_zeros = zeros[1::2]
sum_zeros = np.hstack((sum_zeros, np.conj(sum_zeros)))
diff_zeros = np.hstack((diff_zeros, np.conj(diff_zeros)))
sum_filt = np.poly(sum_zeros)
diff_filt = np.poly(diff_zeros)
if order % 2 != 0:
deconv_diff = sg.convolve(diff_filt, [1, 0, -1])
deconv_sum = sum_filt
else:
deconv_diff = sg.convolve(diff_filt, [1, -1])
deconv_sum = sg.convolve(sum_filt, [1, 1])
lpc = .5 * (deconv_sum + deconv_diff)
# Last coefficient is 0 and not returned
all_lpc[i] = lpc[:-1]
return np.squeeze(all_lpc)
