def spline_coeff(lat, time):
time = time[~np.isnan(lat)]
time_diff = time[1:]-time[:-1]
counter = 0
indeces_remotion = np.array([])
for i in range(0, time_diff.shape[0]):
if time_diff[i] > 60:
if counter < 5:
slack = np.arange(i - counter, i + 1)
indeces_remotion = np.append(indeces_remotion, slack)
counter = 0
else:
counter += 1
time = np.delete(time, indeces_remotion)
lat = lat[~np.isnan(lat)]
lat = np.delete(lat, indeces_remotion)
if lat.shape[0] > 5:
save_data = optimal_knots(lat, time)
t2 = time[save_data[1:-1]]
try2 = LSQUnivariateSpline(time, lat, t2, k=3)
spl_coeff = try2.get_coeffs()
knots = np.r_[(time[save_data[0]],) * (3 + 1), t2, (time[save_data[-1]-1],) * (3 + 1)]
return [spl_coeff, knots] # in this case returns spline coeff and knots
else:
return ["not enough data", lat] # in this case returns original data
def calc_coeffs(self):
coeff = []
for v, (g, kdata) in enumerate(zip(self.basegrid, self.knot_data)):
ranges = [(slice(r[0], r[1], t[0] * 1j) if kd.used() else slice(
(r[1] - r[0]) / 2.0, r[1], 1j))
for r, t, kd in zip(self.ranges, g[0], kdata)]
fnc = self.calc_grid(ranges, g[1], g[2])[v]
for n in range(len(ranges)):
kd = self.knot_data[v, n]
if not kd.used():
continue
s = list(fnc.shape)
s[n] = kd.num_coeffs()
out = np.empty(s)
s[n] = 1
x = np.mgrid[ranges[n]]
for i in np.ndindex(tuple(s)):
i = list(i)
i[n] = slice(None)
ss = LSQUnivariateSpline(x,
fnc[i],
kd.get_knots(),
bbox=kd.get_bbox(),
k=kd.get_order() - 1)
out[i] = ss.get_coeffs()
fnc = out
coeff.append(fnc)
return coeff
def b_ink(x, fnca, k):
"return t, bcoef"
ca = []
for fnc in fnca:
t = [None] * len(x)
for n in range(len(x)):
cf = np.empty_like(fnc)
s = list(fnc.shape)
s[n] = 1
xx = x[n]
xxe = xx[-1]
xxe += (xxe - xx[-2]) * 0.1
kk = k[n]
start = kk // 2
end = -(kk - start)
knots = xx[start:end].copy()
if kk % 2:
knots[:-1] += knots[1:]
knots[-1] += xx[end]
knots *= 0.5
for idx in np.ndindex(tuple(s)):
idx = list(idx)
idx[n] = slice(None)
#ss = InterpolatedUnivariateSpline(x[n], fnc[idx], bbox=(xx[0],xxe), k=k[n]-1)
ss = LSQUnivariateSpline(xx,
fnc[idx],
knots,
bbox=(xx[0], xxe),
k=kk - 1)
cf[idx] = ss.get_coeffs()
t[n] = np.pad(knots, kk, 'constant', constant_values=(xx[0], xxe))
fnc = cf
ca.append(fnc)
return t, ca
def b_ink(x, fnca, k):
"return t, bcoef"
ca = []
for fnc in fnca:
t = [None]*len(x)
for n in range(len(x)):
cf = np.empty_like(fnc)
s = list(fnc.shape)
s[n] = 1
xx = x[n]
xxe = xx[-1]
xxe += (xxe-xx[-2]) * 0.1
kk = k[n]
start = kk // 2
end = -(kk - start)
knots = xx[start:end].copy()
if kk % 2:
knots[:-1] += knots[1:]
knots[-1] += xx[end]
knots *= 0.5
for idx in np.ndindex(tuple(s)):
idx = list(idx)
idx[n] = slice(None)
#ss = InterpolatedUnivariateSpline(x[n], fnc[idx], bbox=(xx[0],xxe), k=k[n]-1)
ss = LSQUnivariateSpline(xx, fnc[idx], knots, bbox=(xx[0],xxe), k=kk-1)
cf[idx] = ss.get_coeffs()
t[n] = np.pad(knots, kk, 'constant', constant_values=(xx[0],xxe))
fnc = cf
ca.append(fnc)
return t, ca
def calc_coeffs(self):
coeff = []
for v, (g, kdata) in enumerate(zip(self.basegrid, self.knot_data)):
ranges = [(slice(r[0], r[1], t[0]*1j) if kd.used() else slice((r[1]-r[0])/2.0,r[1],1j)) for r, t, kd in zip(self.ranges,g[0],kdata)]
fnc = self.calc_grid(ranges, g[1], g[2])[v]
for n in range(len(ranges)):
kd = self.knot_data[v,n]
if not kd.used():
continue
s = list(fnc.shape)
s[n] = kd.num_coeffs()
out = np.empty(s)
s[n] = 1
x = np.mgrid[ranges[n]]
for i in np.ndindex(tuple(s)):
i = list(i)
i[n] = slice(None)
ss = LSQUnivariateSpline(x, fnc[i], kd.get_knots(), bbox=kd.get_bbox(), k=kd.get_order()-1)
out[i] = ss.get_coeffs()
fnc = out
coeff.append(fnc)
return coeff
def fit_cubic(self):
"""Fit (x,y) arrays using a cubic Spline approximation.
It uses LSQUnivariateSpline from scipy.interpolate
which uses order-1 knots.
"""
x = self.x
y = self.y
xmin,xmax = x.min(),x.max()
npieces = self.order
rn = (xmax - xmin)/npieces
tn = [(xmin+rn*k) for k in range(1,npieces)]
zu = LSQUnivariateSpline(x,y,tn)
self.coeff = zu.get_coeffs()
self.fx = lambda x: x
self.evfunc = lambda p,x: zu(x)
self.ier = None
return
m = (xmin <= t) & (t <= xmax)
m1 = ni.maximum_filter1d(m, 3)
t1 = t[m1]
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]):
m = (xmin <= t) & (t <= xmax)
m1 = ni.maximum_filter1d(m, 3)
t1 = t[m1]
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]):
y1 = np.concatenate(
(np.zeros(shape=len(arg1) // 10), y1, np.zeros(shape=len(arg1) // 10)))
s1 = psf_ref['sig_y'][i].flatten()[arg1]
s1 = np.concatenate((np.average(s1) * np.ones(shape=len(arg1) // 10), s1,
np.average(s1) * np.ones(shape=len(arg1) // 10)))
ym1 = median_filter(y1, size=len(y1) // 20)
res1 = y1 - ym1
rms1 = rmsd(y1, ym1)
mask1 = abs(res1) > 3.0 * rms1
x2 = x1[~mask1]
y2 = y1[~mask1]
s2 = s1[~mask1]
ya2 = uniform_filter1d(y2, size=len(y2) // 20)
splines2 = LSQUnivariateSpline(x2, y2, t=knots, bbox=[xb, xe], k=deg2)
tck_coeffs1 = np.append(tck_coeffs1, [splines2.get_coeffs()], axis=0)
ax1 = plt.Axes(
fig, [subfig_left[flag1 % 3], subfig_bottom[flag1 // 3], 0.25, 0.13])
fig.add_axes(ax1)
ax1.errorbar(x1[mask1],
y1[mask1],
yerr=s1[mask1],
fmt='r.',
ms=6,
zorder=0,
alpha=0.5)
ax1.errorbar(x2,
y2,
yerr=s2,
fmt='.',
