def ampoffset(time, data):
pt, pv = peaks.conservative_peaks(time, data, pktype="peak")
tt, tv = peaks.conservative_peaks(time, data, pktype="trough")
tspl = UnivariateSpline(tt, tv, k=4, s=0)
pspl = UnivariateSpline(pt, pv, k=4, s=0)
off = tspl(time) + 0.5 * (pspl(time) - tspl(time))
amp = 0.5 * (pspl(time) - tspl(time))
return amp, off
def get_peaks(time,
data,
pktype="peak",
refine=True,
refine_pktype="peak",
Npk=5,
polyorder=3,
splorder=3,
minidx=200,
peak_method="spline"):
if pktype == "joined":
pt, pv = peak.joined_peaks(time,
data,
refine=refine,
refine_pktype=refine_pktype,
Npk=Npk,
polyorder=polyorder,
splorder=splorder,
minidx=minidx,
peak_method=peak_method)
joined = True
else:
pt, pv = peak.conservative_peaks(time,
data,
refine=refine,
refine_pktype=refine_pktype,
Npk=Npk,
polyorder=polyorder,
splorder=splorder,
minidx=minidx,
peak_method=peak_method)
joined = False
return pt, pv, joined
def resonant_trajectory(t, x1, x2, t_left, t_right, Npts=10000, refinex1=False,
t_res=None):
if refinex1:
pt, pv, p_ref, pv_unref = pk.conservative_peaks(t, x1, returnrefined=True)
x1spl = UnivariateSpline(t, p_ref, k=4, s=0)
else:
x1spl = UnivariateSpline(t, x1, k=4, s=0)
x2spl = UnivariateSpline(t, x2, k=4, s=0)
t_traj = np.linspace(t_left, t_right, Npts)
x1_traj = x1spl(t_traj)
x2_traj = x2spl(t_traj)
return [x1_traj, x2_traj]
def even_sampled_omega(orbpath,
ptsperorbit=1000,
method="savgol",
return_cos=False):
rraw = loadfile(orbpath)
rmagraw = np.sqrt(rraw[:, 1]**2 + rraw[:, 2]**2 + rraw[:, 3]**2)
pt, pv = peaks.conservative_peaks(rraw[:, 0], rmagraw, refine=False)
norbits = len(pt)
print "Norbits: ", norbits
npts = norbits * ptsperorbit
return even_sampled_omega_npts(orbpath,
npts,
method=method,
return_cos=return_cos)
def omega_theta(thtime, theta, pktime, pkdata, N=1, minidx=200, refine=True):
#Njoined = N*2
#Njoined = N
pkt, pkv = peaks.conservative_peaks(pktime,
pkdata,
refine=True,
minidx=minidx)
chi = monotonic_acos(thtime, theta, tnew=pkt, unwrap=True)
t0s, tfs, dT, tmids = window_ranges(pkt, N)
dChi = np.array(chi[N:] - chi[:-N])
omths = dChi / dT
outarr = np.column_stack((tmids, omths))
return outarr
def orbits_on_resonance(t, r, t_left, t_right, refine=True):
pt, pv = pk.conservative_peaks(t, r, refine=refine, minidx=200)
argl = np.argmin(np.abs(pt - t_left))
argr = np.argmin(np.abs(pt - t_right))
return argr-argl