python module for spatially explicit spectral analysis
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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) "Computational considerations for spatially explicit spectral analysis of point clouds and geospatial data", 86, 92-108, 10.1016/j.cageo.2015.10.004.

For more information visit http://dbuscombe-usgs.github.io/pysesa/

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.

Contributing & Credits

Author Daniel Buscombe
    |  Grand Canyon Monitoring and Research Center
    | United States Geological Survey
    | Flagstaff, AZ 86001
    | dbuscombe@usgs.gov

Version: 0.0.19 | Revision: Oct, 2015

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.

License

GNU Lesser General Public License, Version 3

http://www.gnu.org/copyleft/lesser.html

Setup

Install

Automatic Installation from PyPI

The PyPI repository is here: https://pypi.python.org/pypi/pysesa

pip uninstall pysesa (removes previous installation)
pip install pysesa

Automatic Installation from github:

git clone git@github.com:dbuscombe-usgs/pysesa.git
cd pysesa
python setup.py install

or a local installation:

python setup.py install --user

or with admin privileges, e.g.:

sudo python setup.py install

Notes for Linux users

You could try before you install, using a virtual environment:

virtualenv venv
source venv/bin/activate
pip install numpy
pip install cython
pip install scipy
pip install joblib
pip install statsmodels
pip install matplotlib
pip install ift_nifty
pip install pysesa
pip install dask
pip install toolz
python -c "import pysesa; pysesa.test()"
deactivate (or source venv/bin/deactivate)

The results will live in "venv/lib/python2.7/site-packages/pysesa"

###Manual Installation

PYTHON LIBRARIES YOU MAY NEED TO INSTALL TO USE pysesa:

  1. Nifty (http://www.mpa-garching.mpg.de/ift/nifty/index.html)
  2. SciPy (http://www.scipy.org/scipylib/download.html)
  3. Numpy (http://www.scipy.org/scipylib/download.html)
  4. Matplotlib (http://matplotlib.org/downloads.html)
  5. cython (http://cython.org/)
  6. statsmodels (http://statsmodels.sourceforge.net/)
  7. mayavi, used in some pysesa::plot modules (http://docs.enthought.com/mayavi/mayavi/)

All of the above are available through pip (https://pypi.python.org/pypi/pip) and easy_install (https://pythonhosted.org/setuptools/easy_install.html)

###Installation on Amazon Linux EC-2 instance It's best to install numpy, scipy, cython and matplotlib through the OS package manager:

  sudo yum install gcc gcc-c++
  sudo yum install python27-numpy python27-Cython python27-scipy python27-matplotlib

Then pysesa using pip (which will install nifty, joblib and statsmodels):

  sudo pip install pysesa

Test

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)

Support

This is a new project written and maintained by Daniel Buscombe. Bugs are expected - please report them, I will fix them quickly. Feedback and suggestions for improvements are very welcome

Please download, try, report bugs, fork, modify, evaluate, discuss, collaborate. Please address all suggestions, comments and queries to: dbuscombe@usgs.gov. Thanks for stopping by!

Contents

The programs in this package are as follows:

  1. read: read a 3-column space, comma or tab delimited text file
  2. 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.
  3. detrend: returns detrended amplitudes of a Nx3 point cloud
  4. sgolay: returns the Savitsky-Golay digital filter of a 2D signal
  5. spatial: calculate spatial statistics of a Nx3 point cloud
  6. RunningStats: called by \spatial} to compute sigma, skewness and kurtosis
  7. lengthscale: calculates the integral lengthscale of a Nx3 point cloud
  8. spectral: calculate spectral statistics of a Nx3 point cloud
  9. process: allows control of inputs to all modules (full workflow)
  10. write: write program outputs to a comma delimited text file
  11. test: program testing suite
  12. plot: some plotting utilities for outputs in 2d and 3d

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

Read

'''
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

'''
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

'''
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
    5 = remove Savitsky-Golay digital filter, order 1

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

'''

Sgolay

'''
Create a Savitsky-Golay digital filter from a 2D signal
based on code from http://www.scipy.org/Cookbook/SavitzkyGolay

Syntax
----------
Z = pysesa.sgolay(z, window_size, order).getdata()

Parameters
----------
z : array_like, shape (N,)
  the 2D signal.
window_size : int
   the length of the window. Must be an odd integer number.
order : int
   the order of the polynomial used in the filtering.
   Must be less than `window_size` - 1.

Returns
-------
self.data : ndarray, shape (N)
   the smoothed signal.

References
----------
.. [1] A. Savitzky, M. J. E. Golay, Smoothing and Differentiation of
   Data by Simplified Least Squares Procedures. Analytical
   Chemistry, 1964, 36 (8), pp 1627-1639.
.. [2] Numerical Recipes 3rd Edition: The Art of Scientific Computing
   W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery
   Cambridge University Press ISBN-13: 9780521880688
'''

Spatial

'''
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

'''

Lengthscale

'''
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

'''

Spectral

'''
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
    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
    phi = effective slope (degrees)
    
'''

Process

'''
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)
'''

Write

'''
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

'''

Plot

'''
Initialise the pysesa plot class
Takes a file output from pysesa.process and allows a number
of different 2d or 3d plots of the outputs

Syntax
----------
p = pysesa.plot()
p = pysesa.plot('/home/my_pysesa_output_file.xyz')

Parameters
-----------
pysesa_file : str
pysesa::process output file

If no arguments given, it prompts you to choose a pysesa::process output file

Returns
----------
self : instance
  pysesa.plot instance

DATA functions
---------------
pc = p.get_pc() : ndarray
  NxM contents of pysesa_file

xyz = p.get_xyz() : ndarray        
  Nx3 contents of raw point cloud (the file processed by pysesa::process)

vars = p.parse_pc_vars() : dict
  NxM contents of pysesa_file parsed into dict object
  1 key per variable in p.get_pc()
  vars.keys() returns list of variables in dict

2D plotting functions (mayavi not required)
--------------------------------------------

p.grd_xyz()
------------- 
  produces 2d plot of the gridded [x,y,z] surface made from decimated point cloud,
  as returned by parse_pc_vars()

  Syntax
  ----------
  [] = p.grd_xyz(res, azimuth, altitude, zf, cmap, dpi, alpha, ticksize, labelsize)

  Parameters
  ------------
  p : instance
   pysesa.plot instance returned by pysesa::plot
       e.g. p = pysesa.plot()

  Optional Parameters
  --------------------
  res : float, *optional* [default = 0.1]
   grid resolution

  azimuth : float, *optional* [default = 315]
       Lighting azimuthal angle (in degrees, 0-360)
       for hillshade calculation, see:
       http://edndoc.esri.com/arcobjects/9.2/net/shared/geoprocessing/spatial_analyst_tools/how_hillshade_works.htm

  altitude : float, *optional* [default = 45]
       Lighting zenith angle (in degrees, 0-90)
       for hillshade calculation, see:
       http://edndoc.esri.com/arcobjects/9.2/net/shared/geoprocessing/spatial_analyst_tools/how_hillshade_works.htm

  zf : float, *optional* [default = 1]
       Vertical exaggeration factor
       1=no exaggeration, <1 minimizes, >1 exaggerates 

  cmap : str, *optional* [default = 'hot']
   colormap
       possible colormaps are documented here:
       http://matplotlib.org/examples/color/colormaps_reference.html
   
  dpi : int, *optional* [default = 300]
       figure resolution in dots per inch

  alpha : float, *optional* [default = 0.5]
       transparency, between 0.0 and 1.0

  ticksize : int, *optional* [default = 4]
       size of x, y, and z tick labels

  labelsize : int, *optional* [default = 6]
       size of x and y axes labels

p.grd_var()
------------      
  produces 2d plot of the gridded surface made from 1 output variable in p.parse_pc_vars()
  e.g. p.grd_var('sigma')

  Syntax
  ----------
  [] = p.grd_var(var, res, azimuth, altitude, zf, cmap, dpi, log_scale, smooth, filtsz, alpha, ticksize, labelsize)

  Parameters
  ------------
  p : instance
   pysesa.plot instance returned by pysesa::plot
       e.g. p = pysesa.plot()

  var : str
   name of variable in p.parse_pc_vars() that will be plotted
       e.g. p.grd_var('sigma')


  Optional Parameters
  --------------------
  res : float, *optional* [default = 0.1]
   grid resolution

  azimuth : float, *optional* [default = 315]
       Lighting azimuthal angle (in degrees, 0-360)
       for hillshade calculation, see:
       http://edndoc.esri.com/arcobjects/9.2/net/shared/geoprocessing/spatial_analyst_tools/how_hillshade_works.htm

  altitude : float, *optional* [default = 45]
       Lighting zenith angle (in degrees, 0-90)
       for hillshade calculation, see:
       http://edndoc.esri.com/arcobjects/9.2/net/shared/geoprocessing/spatial_analyst_tools/how_hillshade_works.htm

  zf : float, *optional* [default = 1]
       Vertical exaggeration factor
       1=no exaggeration, <1 minimizes, >1 exaggerates 

  cmap : str, *optional* [default = 'hot']
   colormap
       possible colormaps are documented here:
       http://matplotlib.org/examples/color/colormaps_reference.html
   
  dpi : int, *optional* [default = 300]
       figure resolution in dots per inch

  log_scale : bool, *optional* [default = False]
   if True, will log scale plotted dependent variable

  smooth : bool, *optional* [default = True]
   if True, will smooth plotted dependent variable
       with a median filter and window size specified by filtsz (below)

  filtsz : int, *optional* [default = 3]
   size of filter (pixels) if smooth==1

  alpha : float, *optional* [default = 0.5]
       transparency, between 0.0 and 1.0

  ticksize : int, *optional* [default = 4]
       size of x, y, and z tick labels

  labelsize : int, *optional* [default = 6]
       size of x and y axes labels


p.grd_vars()
-------------
  produces a 2d plot of the gridded surface made from each output variable in p.parse_pc_vars()

  Syntax
  ----------
  [] = p.grd_vars(res, azimuth, altitude, zf, cmap, dpi, log_scale, smooth, filtsz, alpha, ticksize, labelsize)

  Parameters
  ------------
  p : instance
   pysesa.plot instance returned by pysesa::plot
       e.g. p = pysesa.plot()

  Optional Parameters
  --------------------
  res : float, *optional* [default = 0.1]
   grid resolution

  azimuth : float, *optional* [default = 315]
       Lighting azimuthal angle (in degrees, 0-360)
       for hillshade calculation, see:
       http://edndoc.esri.com/arcobjects/9.2/net/shared/geoprocessing/spatial_analyst_tools/how_hillshade_works.htm

  altitude : float, *optional* [default = 45]
       Lighting zenith angle (in degrees, 0-90)
       for hillshade calculation, see:
       http://edndoc.esri.com/arcobjects/9.2/net/shared/geoprocessing/spatial_analyst_tools/how_hillshade_works.htm

  zf : float, *optional* [default = 1]
       Vertical exaggeration factor
       1=no exaggeration, <1 minimizes, >1 exaggerates 

  cmap : str, *optional* [default = 'hot']
   colormap
       possible colormaps are documented here:
       http://matplotlib.org/examples/color/colormaps_reference.html
   
  dpi : int, *optional* [default = 300]
       figure resolution in dots per inch

  log_scale : bool, *optional* [default = False]
   if True, will log scale plotted dependent variable

  smooth : bool, *optional* [default = True]
   if True, will smooth plotted dependent variable
       with a median filter and window size specified by filtsz (below)

  filtsz : int, *optional* [default = 3]
   size of filter (pixels) if smooth==1

  alpha : float, *optional* [default = 0.5]
       transparency, between 0.0 and 1.0

  ticksize : int, *optional* [default = 4]
       size of x, y, and z tick labels

  labelsize : int, *optional* [default = 6]
       size of x and y axes labels


3D plotting functions (mayavi not required)
--------------------------------------------

p.plt_xyz()
------------ 
  produces 3d plot of Nx3 contents of raw point cloud, as returned by p.get_xyz()

  Syntax
  ----------
  [] = p.plt_xyz(elev, azim, markersize, dpi, ticksize, labelsize)

  Parameters
  ------------
  p : instance
   pysesa.plot instance returned by pysesa::plot
       e.g. p = pysesa.plot()

  Optional Parameters
  --------------------
  elev : float, *optional* [default = 65]
       the elevation angle in the z plane

  azimuth : float, *optional* [default = -115]
       azimuth angle in the x,y plane 

  markersize : float, *optional* [default = 0.01]
       marker size in x and y axes units

  dpi : int, *optional* [default = 300]
       figure resolution in dots per inch

  ticksize : int, *optional* [default = 4]
       size of x, y, and z tick labels

  labelsize : int, *optional* [default = 6]
       size of x and y axes labels       


p.plt_xy_var()  
--------------- 
  produces 3d plot of 1 output variable in p.parse_pc_vars(), e.g. p.grd_var('sigma')

  Syntax
  ----------
  [] = p.plt_xy_var(var, log_scale, dpi, markersize, ticksize, labelsize, elev, azim)

  Parameters
  ------------
  p : instance
   pysesa.plot instance returned by pysesa::plot
       e.g. p = pysesa.plot()

  var : str
   name of variable in p.parse_pc_vars() that will be plotted
       e.g. p.grd_var('sigma')

  Optional Parameters
  --------------------
  log_scale : bool, *optional* [default = False]
   if True, will log scale plotted dependent variable

  dpi : int, *optional* [default = 300]
       figure resolution in dots per inch

  markersize : float, *optional* [default = 5]
       marker size in points^2
       http://matplotlib.org/mpl_toolkits/mplot3d/api.html#mpl_toolkits.mplot3d.axes3d.Axes3D.scatter

  ticksize : int, *optional* [default = 4]
       size of x, y, and z tick labels

  labelsize : int, *optional* [default = 6]
       size of x and y axes labels

  elev : float, *optional* [default = 65]
       the elevation angle in the z plane

  azimuth : float, *optional* [default = -115]
       azimuth angle in the x,y plane          


p.plt_xy_vars()
----------------
  produces a 3d plot of each output variable in p.parse_pc_vars() 

  Syntax
  ----------
  [] = p.plt_xy_vars(log_scale, dpi, markersize, ticksize, labelsize, elev, azim)

  Parameters
  ------------
  p : instance
   pysesa.plot instance returned by pysesa::plot
       e.g. p = pysesa.plot()

  Optional Parameters
  --------------------
  log_scale : bool, *optional* [default = False]
   if True, will log scale plotted dependent variable

  dpi : int, *optional* [default = 300]
       figure resolution in dots per inch

  markersize : float, *optional* [default = 5]
       marker size in points^2
       http://matplotlib.org/mpl_toolkits/mplot3d/api.html#mpl_toolkits.mplot3d.axes3d.Axes3D.scatter

  ticksize : int, *optional* [default = 4]
       size of x, y, and z tick labels

  labelsize : int, *optional* [default = 6]
       size of x and y axes labels

  elev : float, *optional* [default = 65]
       the elevation angle in the z plane

  azimuth : float, *optional* [default = -115]
       azimuth angle in the x,y plane        


3D plotting functions (requires mayavi) 
----------------------------------------

p.grd_xyz3d() 
-------------  
  produces 3d plot of the gridded surface made from the Nx3 contents of raw point cloud,
  as returned by p.get_xyz()

  Syntax
  ----------
  [] = p.grd_xyz3d(res, cmap, pitch, azimuth, distance, xsize, ysize)

  Parameters
  ------------
  p : instance
   pysesa.plot instance returned by pysesa::plot
       e.g. p = pysesa.plot()


  Optional Parameters
  --------------------
  res : float, *optional* [default = 0.1]
   grid resolution

  cmap : str, *optional* [default = 'hot']
   colormap
       possible colormaps are documented here:
       http://docs.enthought.com/mayavi/mayavi/mlab_changing_object_looks.html

  pitch : float, *optional* [default = 10]
   rotates the camera. see:
       http://docs.enthought.com/mayavi/mayavi/auto/mlab_camera.html?highlight=pitch#mayavi.mlab.pitch

  azimuth : float, *optional* [default = -200]
       The azimuthal angle (in degrees, 0-360)
   http://docs.enthought.com/mayavi/mayavi/auto/mlab_camera.html?highlight=view#mayavi.mlab.view

  distance : float or 'auto', *optional* [default = 50]
       A positive floating point number representing the distance from the focal point to place the camera.
       if ‘auto’ is passed, the distance is computed to have a best fit of objects in the frame.
   http://docs.enthought.com/mayavi/mayavi/auto/mlab_camera.html?highlight=view#mayavi.mlab.view

  xsize : int, *optional* [default = 2000]
   size (number of pixels) of output image in x dimension

  ysize : int, *optional* [default = 1000]
   size (number of pixels) of output image in y dimension

p.grd_var_3d()  
---------------
  produces 3d plot of the gridded surface made from 1 output variable in p.parse_pc_vars()

  Syntax
  ----------
  [] = p.grd_var_3d(var, res, cmap, pitch, azimuth, distance, log_scale, smooth, filtsz, xsize, ysize)

  Parameters
  ------------
  p : instance
   pysesa.plot instance returned by pysesa::plot
       e.g. p = pysesa.plot()

  var : str
   name of variable in p.parse_pc_vars() that will be plotted
       e.g. p.grd_var_3d('sigma')


  Optional Parameters
  --------------------
  res : float, *optional* [default = 0.1]
   grid resolution

  cmap : str, *optional* [default = 'hot']
   colormap
       possible colormaps are documented here:
       http://docs.enthought.com/mayavi/mayavi/mlab_changing_object_looks.html

  pitch : float, *optional* [default = 10]
   rotates the camera. see:
       http://docs.enthought.com/mayavi/mayavi/auto/mlab_camera.html?highlight=pitch#mayavi.mlab.pitch

  azimuth : float, *optional* [default = -200]
       The azimuthal angle (in degrees, 0-360)
   http://docs.enthought.com/mayavi/mayavi/auto/mlab_camera.html?highlight=view#mayavi.mlab.view

  distance : float or 'auto', *optional* [default = 50]
       A positive floating point number representing the distance from the focal point to place the camera.
       if ‘auto’ is passed, the distance is computed to have a best fit of objects in the frame.
   http://docs.enthought.com/mayavi/mayavi/auto/mlab_camera.html?highlight=view#mayavi.mlab.view

  log_scale : bool, *optional* [default = False]
   if True, will log scale plotted dependent variable

  smooth : bool, *optional* [default = True]
   if True, will smooth plotted dependent variable
       with a median filter and window size specified by filtsz (below)

  filtsz : int, *optional* [default = 3]
   size of filter (pixels) if smooth==1

  xsize : int, *optional* [default = 2000]
   size (number of pixels) of output image in x dimension

  ysize : int, *optional* [default = 1000]
   size (number of pixels) of output image in y dimension
      
p.grd_vars_3d() 
----------------
  produces a 3d plot of the gridded surface made from each output variable in p.parse_pc_vars()

  Syntax
  ----------
  [] = p.grd_vars_3d(res, cmap, pitch, azimuth, distance, log_scale, smooth, filtsz, xsize, ysize)

  Parameters
  ------------
  p : instance
   pysesa.plot instance returned by pysesa::plot
       e.g. p = pysesa.plot()

  Optional Parameters
  --------------------
  res : float, *optional* [default = 0.1]
   grid resolution

  cmap : str, *optional* [default = 'hot']
   colormap
       possible colormaps are documented here:
       http://docs.enthought.com/mayavi/mayavi/mlab_changing_object_looks.html

  pitch : float, *optional* [default = 10]
   rotates the camera. see:
       http://docs.enthought.com/mayavi/mayavi/auto/mlab_camera.html?highlight=pitch#mayavi.mlab.pitch

  azimuth : float, *optional* [default = -200]
       The azimuthal angle (in degrees, 0-360)
   http://docs.enthought.com/mayavi/mayavi/auto/mlab_camera.html?highlight=view#mayavi.mlab.view

  distance : float or 'auto', *optional* [default = 50]
       A positive floating point number representing the distance from the focal point to place the camera.
       if ‘auto’ is passed, the distance is computed to have a best fit of objects in the frame.
   http://docs.enthought.com/mayavi/mayavi/auto/mlab_camera.html?highlight=view#mayavi.mlab.view

  log_scale : bool, *optional* [default = False]
   if True, will log scale plotted dependent variable

  smooth : bool, *optional* [default = True]
   if True, will smooth plotted dependent variable
       with a median filter and window size specified by filtsz (below)

  filtsz : int, *optional* [default = 3]
   size of filter (pixels) if smooth==1

  xsize : int, *optional* [default = 2000]
   size (number of pixels) of output image in x dimension

  ysize : int, *optional* [default = 1000]
   size (number of pixels) of output image in y dimension

'''