spl = LSQUnivariateSpline(x1, y1,
t1[1:-1], bbox = [t1[0], t1[-1]])
spl = LSQUnivariateSpline(x1, y1,
t2[1:-3], bbox = [t2[0], t2[-3]])
coeffs = np.empty(len(t)+2, dtype="f")
coeffs.fill(np.nan)
if np.all(m1):
coeffs[:] = spl.get_coeffs()
else:
coeffs[1:-1][m1] = spl.get_coeffs()[1:-1]
coeffs_list.append([coeffs])
print len(spl.get_knots()), len(spl.get_coeffs())
plot(x1, y1)
plot(xx, spl(xx))
aa = np.concatenate(coeffs_list, axis=0)
aaf = np.empty_like(aa)
tt = np.arange(aa.shape[0])
for ix in range(aa.shape[-1]):
y_data = aa[:,ix]
my = np.isfinite(y_data)
p = np.polyfit(tt[my], y_data[my], 4)
p = polyfitr.polyfitr(tt[my], y_data[my], 4, 2,
fev=100, w=None, diag=False, clip='both',
spl = LSQUnivariateSpline(x1, y1,
t1[1:-1], bbox = [t1[0], t1[-1]])
spl = LSQUnivariateSpline(x1, y1,
t2[1:-3], bbox = [t2[0], t2[-3]])
coeffs = np.empty(len(t)+2, dtype="f")
coeffs.fill(np.nan)
if np.all(m1):
coeffs[:] = spl.get_coeffs()
else:
coeffs[1:-1][m1] = spl.get_coeffs()[1:-1]
coeffs_list.append([coeffs])
print len(spl.get_knots()), len(spl.get_coeffs())
plot(x1, y1)
plot(xx, spl(xx))
aa = np.concatenate(coeffs_list, axis=0)
aaf = np.empty_like(aa)
tt = np.arange(aa.shape[0])
for ix in range(aa.shape[-1]):
y_data = aa[:,ix]
my = np.isfinite(y_data)
p = np.polyfit(tt[my], y_data[my], 4)
p = polyfitr.polyfitr(tt[my], y_data[my], 4, 2,
fev=100, w=None, diag=False, clip='both',
from scipy.interpolate import LSQUnivariateSpline, UnivariateSpline import matplotlib.pyplot as plt x = np.linspace(-3, 3, 50) y = np.exp(-x**2) + 0.1 * np.random.randn(50) # Fit a smoothing spline with a pre-defined internal knots: t = [-1, 0, 1] spl = LSQUnivariateSpline(x, y, t) xs = np.linspace(-3, 3, 1000) plt.plot(x, y, 'ro', ms=5) plt.plot(xs, spl(xs), 'g-', lw=3) plt.show() # Check the knot vector: spl.get_knots() # array([-3., -1., 0., 1., 3.]) # Constructing lsq spline using the knots from another spline: x = np.arange(10) s = UnivariateSpline(x, x, s=0) s.get_knots() # array([ 0., 2., 3., 4., 5., 6., 7., 9.]) knt = s.get_knots() s1 = LSQUnivariateSpline(x, x, knt[1:-1]) # Chop 1st and last knot s1.get_knots() # array([ 0., 2., 3., 4., 5., 6., 7., 9.])
