我们从Python开源项目中，提取了以下35个代码示例，用于说明如何使用numpy.poly()。
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()
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 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 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)
