pysesa.spectral module¶
Calculate spectral statistics of a Nx3 point cloud
Syntax¶
You call the function like this:
data = pysesa.spectral.spec(points, nbin, res, proctype, lentype, taper, method).getdata()
or:
lengths = pysesa.spectral.spec(points, nbin, res, proctype, lentype, taper, method).getlengths()
or:
psdparams= pysesa.spectral.spec(points, nbin, res, proctype, lentype, taper, method).getstats()
or:
lengthscale = pysesa.spectral.spec(points, nbin, res, proctype, lentype, taper, method).getlengthscale()
or:
moments = pysesa.spectral.spec(points, nbin, res, proctype, lentype, taper, method).getmoments()
Parameters¶
- points : ndarray
- Nx3 point cloud
Other Parameters¶
- nbin : int, optional [default = 20]
- number of bins for power spectral binning
- res : float, optional [default = 0.05]
- spatial grid resolution to create a grid
- proctype : int, optional [default = 1, no spectral smoothing]
proctype type: 1, no spectral smoothing
2, spectrum smoothed with Gaussian
- lentype : int, optional [default = 1, l<0.5]
lengthscale type: 1, l<0.5
2, l<1/e
3, l<0
- taper : int, optional [default = Hanning]
flag for taper type: 1, Hanning (Hann)
2, Hamming
3, Blackman
4, Bartlett
- method : str, optional [default = ‘nearest’]
- gridding type
Returns [requested through .getdata()]¶
- self.data: list
slope = slope of regression line through log-log 1D power spectral density
intercept = intercept of regression line through log-log 1D power spectral density
r_value = correlation of regression through log-log 1D power spectral density
p_value = probability that slope of regression through log-log 1D power spectral density is not zero
std_err = standard error of regression through log-log 1D power spectral density
d = fractal dimension
l = integral lengthscale
wmax = peak wavelength
wmean = mean wavelength
rms1 = RMS amplitude from power spectral density
rms2 = RMS amplitude from bin averaged power spectral density
Z = zero-crossings per unit length
E = extreme per unit length
sigma = RMS amplitude
T0_1 = average spatial period (m_0/m_1)
T0_2 = average spatial period (m_0/m_2)^0.5
sw1 = spectral width
sw2 = spectral width (normalised radius of gyration)
m0 = zeroth moment of spectrum
m1 = first moment of spectrum
m2 = second moment of spectrum
m3 = third moment of spectrum
m4 = fourth moment of spectrum
phi = effective slope (degrees)
Returns [requested through .getpsdparams()]¶
- self.psdparams: list
slope = slope of regression line through log-log 1D power spectral density
intercept = intercept of regression line through log-log 1D power spectral density
r_value = correlation of regression through log-log 1D power spectral density
p_value = probability that slope of regression through log-log 1D power spectral density is not zero
std_err = standard error of regression through log-log 1D power spectral density
d = fractal dimension
Returns [requested through .getlengths()]¶
- self.lengths: list
wmax = peak wavelength
wmean = mean wavelength
rms1 = RMS amplitude from power spectral density
rms2 = RMS amplitude from bin averaged power spectral density
Returns [requested through .getlengthscale()]¶
- self.lengthscale: float
- l = integral lengthscale
Returns [requested through .getmoments()]¶
- self.moments: list
Z = zero-crossings per unit length
E = extreme per unit length sigma = RMS amplitude
T0_1 = average spatial period (m_0/m_1)
T0_2 = average spatial period (m_0/m_2)^0.5
sw1 = spectral width
sw2 = spectral width (normalised radius of gyration)
m0 = zeroth moment of spectrum
m1 = first moment of spectrum
m2 = second moment of spectrum
m3 = third moment of spectrum
m4 = fourth moment of spectrum
