def band_averaged_psd_by_constit(tin, t, e, elor, coef, opt):
# Band-averaged (ba) spectral densities at each constituent freq.
constits = coef.aux.frq
if opt.equi:
e_ = e
if len(tin) > len(t):
e_ = complex_interp(tin, t, e)
ba = band_psd(tin, e_, constits, equi=True)
else:
ba = band_psd(t, e, constits, equi=False, frqosamp=opt.lsfrqosmp)
# power [ (e units)^2 ] from spectral density [ (e units)^2 / cph ]
df = 1 / (elor * 24) # inverse of record length in hours
ba.Puu *= df
Puu = np.zeros_like(constits)
Pvv = Puv = None
if opt.twodim:
ba.Pvv *= df
ba.Puv *= df
Pvv = np.zeros_like(constits)
Puv = np.zeros_like(constits)
for i, (lo, hi) in enumerate(ba.fbnd):
inside = (constits >= lo) & (constits <= hi)
Puu[inside] = ba.Puu[i]
if opt.twodim:
Pvv[inside] = ba.Pvv[i]
Puv[inside] = ba.Puv[i]
return Puu, Pvv, Puv
def band_averaged_psd_by_constit(tin, t, e, elor, coef, opt):
# Band-averaged (ba) spectral densities at each constituent freq.
constits = coef.aux.frq
if opt.equi:
e_ = e
if len(tin) > len(t):
e_ = complex_interp(tin, t, e)
ba = band_psd(tin, e_, constits, equi=True)
else:
ba = band_psd(t, e, constits,
equi=False, frqosamp=opt.lsfrqosmp)
# power [ (e units)^2 ] from spectral density [ (e units)^2 / cph ]
df = 1 / (elor * 24) # inverse of record length in hours
ba.Puu *= df
Puu = np.zeros_like(constits)
Pvv = Puv = None
if opt.twodim:
ba.Pvv *= df
ba.Puv *= df
Pvv = np.zeros_like(constits)
Puv = np.zeros_like(constits)
for i, (lo, hi) in enumerate(ba.fbnd):
inside = (constits >= lo) & (constits <= hi)
Puu[inside] = ba.Puu[i]
if opt.twodim:
Pvv[inside] = ba.Pvv[i]
Puv[inside] = ba.Puv[i]
return Puu, Pvv, Puv