PySESA - a Python framework for Spatially Explicit Spectral Analysis
PySESA is an open-source project dedicated to provide a generic Python framework for spatially explicit statistical analyses of point clouds and other geospatial data, in the spatial and frequency domains, for use in the geosciences
The program is detailed in: Buscombe, D. (2016) "spatially explicit spectral analysis of point clouds and geospatial data", computers and geosciences 86, 92-108, 10.1016/j.cageo.2015.10.004.
This software is in the public domain because it contains materials that originally came from the United States Geological Survey, an agency of the United States Department of Interior. For more information, see the official USGS copyright policy at ttp://www.usgs.gov/visual-id/credit_usgs.html#copyright Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. government.
| Author | Daniel Buscombe |
|---|
| Grand Canyon Monitoring and Research Center
| United States Geological Survey
| Flagstaff, AZ 86001
| Now at Northern ARizona University. daniel.buscombe@nau.edu
Version: 2 | Revision: Nov, 2019
For latest code version please visit:
https://github.com/dbuscombe-usgs
This function is part of pysesa software This software is in the public domain because it contains materials that originally came from the United States Geological Survey, an agency of the United States Department of Interior. For more information, see the official USGS copyright policy at
http://www.usgs.gov/visual-id/credit_usgs.html#copyright
Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. government.
GNU Lesser General Public License, Version 3
http://www.gnu.org/copyleft/lesser.html
Clone the repo from github:
git clone git@github.com:dbuscombe-usgs/pysesa.git
change directory:
cd pysesa
Create a conda environment that contains all the libraries necessary to run pysesa
conda env create -f conda_env/owg.yml
Activate the environment:
conda activate pysesa
Compile the cython modules in place. Modules are already provided for python 3.7 on 64-bit Windows 10. Otherwise, you will need to recompile
python setup.py build_ext --inplace
A test can be carried out by running the supplied script:
python -c "import pysesa; pysesa.test()"
which carries out the following operations:
# general settings
infile = os.path.expanduser("~")+os.sep+'pysesa_test'+os.sep+'example_100000pts.xyz'
out = 0.5 #m output grid
detrend = 4 #ODR plane
proctype = 1 #Processing spectral parameters (no smoothing)
mxpts = 1024 # max pts per window
res = 0.05 #cm internal grid resolution
nbin = 20 #number of bins for spectral binning
lentype = 1 # l<0.5
taper = 1 # Hann taper
prc_overlap = 100 # 100% overlap between successive windows
minpts = 64 # min pts per window
pysesa.process(infile, out, detrend, proctype, mxpts, res, nbin, lentype, minpts, taper, prc_overlap)
Please use github issues
The programs in this package are as follows:
- read: read a 3-column space, comma or tab delimited text file
- partition: partition a Nx3 point cloud into M windows of nx3 points with specified spacing between centroids of adjacent windows and with specified overlap between windows.
- detrend: returns detrended amplitudes of a Nx3 point cloud
- spatial: calculate spatial statistics of a Nx3 point cloud
- RunningStats: called by \spatial} to compute sigma, skewness and kurtosis
- lengthscale: calculates the integral lengthscale of a Nx3 point cloud
- spectral: calculate spectral statistics of a Nx3 point cloud
- process: allows control of inputs to all modules (full workflow)
- write: write program outputs to a comma delimited text file
- test: program testing suite
These are all command-line/modular programs which take a number of input (some required, some optional). Please see the individual files for a comprehensive list of input options
'''
Custom fast (up to 3.5x faster than numpy's genfromtxt) txt file to numpy array
accepts comma, tab or space delimited files of 3 columns: x, y, and amplitude
Syntax
----------
pts = pysesa_read.txtread(infile)
Parameters
----------
infile : str
3-column ASCII file containing Nx3 point cloud
Returns
----------
data: ndarray
Nx3 point cloud, 32 bit precision
'''
'''
Partition a Nx3 point cloud into M windows of nx3 points
with specified spacing between centroids of adjacent windows
and with specified overlap between windows.
Implemented using a binary search tree for fast nearest neighbour
point check with boundary pruning
Syntax
----------
nr_pts = pysesa.partition(toproc, out, res, mxpts, minpts, prc_overlap).getdata()
Parameters
----------
toproc : ndarray
Nx3 point cloud
Other Parameters
----------
out : float, *optional* [default = 0.5]
output grid resolution
res : float, *optional* [default = 0.05]
spatial grid resolution to create a grid for the boundary pruning
mxpts : float, *optional* [default = 1024]
maximum number of points allowed in a window
minpts : float, *optional* [default = 16]
minimum number of points allowed in a window
prc_overlap : float, *optional" [default = 0]
percentage overlap between windows
Returns
----------
self.data: list
list of M ndarrays, each containing n indices
of original point cloud, toproc, to partition space
to create M windows
'''
'''
Detrend a Nx3 point cloud by specified method
Syntax
----------
detrended_pts = pysesa.detrend(points, proctype, res, method).getdata()
Parameters
----------
points : ndarray
Nx3 point cloud
proctype : int
type of detrending.
1 = remove mean
2 = remove Ordinary least squares plane
3 = remove Robust linear model plane
4 = remove Orthogonal Distance Regression plane
Other Parameters
----------
res : float, *optional* [default = 0.05]
for proctype==4 only
spatial grid resolution to create a grid
method : str, *optional* [default = 'nearest']
for proctype==4 only
gridding type
Returns
----------
self.data: ndarray
Nx3 detrended point cloud
'''
'''
Calculate spatial statistics of a Nx3 point cloud
Syntax
----------
stats = pysesa.spatial(points).getdata()
centroids = pysesa.spatial(points).getcentroid()
stats = pysesa.spatial(points).getstats()
Parameters
----------
points : ndarray
Nx3 point cloud
Returns [requested through .getdata()]
----------
self.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 [requested through .getcentroid()]
----------
self.centroid: list
1x3 point cloud centroid [x,y,z]
Returns [requested through .getstats()]
----------
self.stats: list
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
'''
'''
Calculates the integral lengthscale of a Nx3 point cloud
using 1 of 3 available methods
and also returns the tapered 2D grid of 3D pointcloud for spectral analysis
Syntax
----------
im = pysesa.lengthscale(points, res, lentype, taper, method).getdata()
lengthscale = pysesa.lengthscale(points, res, lentype, taper, method).getlengthscale()
Parameters
----------
points : ndarray
Nx3 point cloud
Other Parameters
----------
res : float, *optional* [default = 0.05]
spatial grid resolution to create a grid
lentype : int, *optional* [default = 0, 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: ndarray
tapered 2D grid of 3D pointcloud
Returns [requested through .getlengthscale()]
----------
self.lengthscale: float
integral lengthscale
'''
'''
Calculate spectral statistics of a Nx3 point cloud
Syntax
----------
data = pysesa.spectral.spec(points, nbin, res, proctype, lentype, taper, method).getdata()
lengths = pysesa.spectral.spec(points, nbin, res, proctype, lentype, taper, method).getlengths()
psdparams= pysesa.spectral.spec(points, nbin, res, proctype, lentype, taper, method).getstats()
lengthscale = pysesa.spectral.spec(points, nbin, res, proctype, lentype, taper, method).getlengthscale()
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
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
phi = effective slope (degrees)
'''
'''
Calculate spectral and spatial statistics of a Nx3 point cloud
Syntax
----------
() = 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)
'''
'''
Custom fast numpy array to comma-delimited ASCII txt file
Syntax
----------
() = pysesa.write.txtwrite(infile, towrite)
Parameters
----------
outfile : str
name of file to write to
towrite : ndarray
ndarray containing Nx3 point cloud
Other Parameters
----------
header : str, *optional* [default = None]
header string
Returns
----------
None
'''