pysesa.pysesa module

Calculate spectral and spatial statistics of a Nx3 point cloud

Syntax

You call the function like this:

() = pysesa.process(infile, out, detrend, proctype, mxpts, res, nbin, lentype, minpts, taper, prc_overlap)

Parameters

infile : str
ASCII file containing an Nx3 point cloud in 3 columns

Other Parameters

out : float, optional [default = 0.5]
output grid resolution
detrend : int, optional [default = 4]

type of detrending.

1 = remove mean

2 = remove Ordinary least squares plane

3 = remove Robust linear model plane

4 = remove Orthogonal Distance Regression plane

5 = remove Savitsky-Golay digital filter, order 1

proctype : int, optional [default = 1, no spectral smoothing]

proctype type: 1 = spectral only, no spectral smoothing

2 = spectral only, spectrum smoothed with Gaussian

3 = spatial only

4 = spatial + spectrum, no spectral smoothing

5 = spatial + spectrum smoothed with Gaussian

mxpts : float, optional [default = 1024]
maximum number of points allowed in a window
res : float, optional [default = 0.05]
spatial grid resolution to create a grid
nbin : int, optional [default = 20]
number of bins for power spectral binning
lentype : int, optional [default = 1, l<0.5]

lengthscale type: 1 = l<0.5

2 = l<1/e

3 = l<0

minpts : float, optional [default = 16]
minimum number of points allowed in a window
taper : int, optional [default = Hanning]
flag for taper type: 1 = Hanning (Hann) 2 = Hamming 3 = Blackman 4 = Bartlett
prc_overlap : float, *optional” [default = 0]
percentage overlap between windows

Returns [proctype = 1 or proctype = 2]

data: list

x = centroid in horizontal coordinate

y = centroid in laterial coordinate

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 [proctype = 3]

data: list

x = centroid in horizontal coordinate

y = centroid in laterial coordinate

z_mean = centroid in amplitude

z_max = max amplitude

z_min = min amplitude

z_range = range in amplitude

sigma = standard deviation of amplitudes

skewness = skewness of amplitudes

kurtosis = skewness of amplitudes

n = number of 3D coordinates

Returns [proctype = 4 or proctype = 4]

data: list

x = centroid in horizontal coordinate

y = centroid in laterial coordinate

z_mean = centroid in amplitude

z_max = max amplitude

z_min = min amplitude

z_range = range in amplitude

sigma = standard deviation of amplitudes

skewness = skewness of amplitudes

kurtosis = skewness of amplitudes

n = number of 3D coordinates

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)

_images/pysesa_colour.jpg