def MergeSpectrumData(points):
#---------------------------------------------------
# gather all 2D spectrum data in exploitable arrays
#---------------------------------------------------
# get array size
k, Spectra, SpectraStd = [point.Spectrum.WaveSpectrum.k for point in points], [point.Spectrum.WaveSpectrum.Spectrum for point in points], \
[point.Spectrum.WaveSpectrum.StandardDeviation for point in points]
# store and save data
fname = os.getcwd() + '/Output/Bathymetry/WaveSpectrum.out'
data = CL.SpectrumProcessedData(np.asarray(k), np.asarray(Spectra),
np.asarray(SpectraStd))
data.pickle(fname)
return k, Spectra
def ComputeSpectrum(SubsetParameters, ComputingParameters, Subsets):
#--------------------------------------------------------------------------
# Estimate Subsets Spectra and directions and determine their mean values
#--------------------------------------------------------------------------
WaveSpectra, Directions, ImageSpectra = [], [], []
for i in range(SubsetParameters.BoxNb):
subset = Subsets[i]
# Compute wave related spectrum
if ComputingParameters.SpectrumParameters.WaveSpectrumParameters.SpectrumType in {
'waves', 'Waves'
}:
# use a multiple of wave signal
SpectrumComputedData = SubsetSpectrum(ComputingParameters, subset)
elif ComputingParameters.SpectrumParameters.WaveSpectrumParameters.SpectrumType in {
'radial', 'Radial'
}:
# use the radial integration of the Image Spectrum
SpectrumComputedData = RadialSpectrum(ComputingParameters, subset)
else:
sys.exit("Method for Spectrum Estimation not defined")
# store data
Directions.append(SpectrumComputedData.WaveDirection)
WaveSpectra.append(SpectrumComputedData.WaveSpectrum)
ImageSpectra.append(SpectrumComputedData.ImageSpectrum)
# compute mean directions
direction = np.nanmean(Directions)
# compute mean Spectrum
K = np.zeros((SubsetParameters.BoxNb, WaveSpectra[0].k.shape[0]))
WSP = np.zeros((SubsetParameters.BoxNb, WaveSpectra[0].Spectrum.shape[0]))
for i in range(SubsetParameters.BoxNb):
K[i, :] = WaveSpectra[i].k
WSP[i, :] = WaveSpectra[i].Spectrum
k = np.mean(K, axis=0)
spectrum = np.mean(WSP, axis=0)
spectrumstd = np.std(WSP, axis=0)
"""
fig, ax = plt.subplots(1)
for i in range(SubsetParameters.BoxNb):
ax.plot(K[i,:], WSP[i,:])
plt.show()
"""
# compute peak wavelength
PeakWavelength = WavelengthEstimate(ComputingParameters, k, spectrum)
# store processed data
Spectrum = CL.SpectrumProcessedData(k, spectrum, spectrumstd,
PeakWavelength)
# post-processing data for figures and statistics
SubscenesSpectrum = CL.SpectrumComputedData(Directions, WaveSpectra,
ImageSpectra, Subsets)
# store mean data
ComputedSpectrumData = CL.SpectrumComputedData(direction, Spectrum)
return ComputedSpectrumData, SubscenesSpectrum