# -*- coding: utf-8 -*-
"""
"""

import PyHum

def dotest():

   humfile = 'E:\Sturgeon\\2015Substrate\Humminbird\BeenUploaded\R00216.DAT'
   sonpath = 'E:\Sturgeon\\2015Substrate\Humminbird\BeenUploaded\R00216'

   doplot = 1 #yes

   # reading specific settings
   cs2cs_args = "epsg:32100" #Montana
   bedpick = 1 # auto bed pick
   c = 1450 # speed of sound fresh water
   t = 0.108 # length of transducer
   draft = 0.3 # draft in metres
   flip_lr = 0 # flip port and starboard
   model = 998 # humminbird model
   cog = 1 # GPS course-over-ground used for heading
   calc_bearing = 1 #no
   filt_bearing = 0 #no
   chunk = 65 # distance, 65m
   #chunk = 'p1000' # pings, 1000
   #chunk = 'h10' # heading deviation, 10 deg
          
   # correction specific settings
   maxW = 1000 # rms output wattage
   dofilt = 0 # 1 = apply a phase preserving filter (WARNING!! takes a very long time for large scans)
   correct_withwater = 0 # don't retain water column in radiometric correction (1 = retains water column for radiomatric corrections)
   ph = 7.0 # acidity on the pH scale
   temp = 10.0 # water temperature in degrees Celsius
   salinity = 0.0

   # for shadow removal
   shadowmask = 0 #automatic shadow removal

   # for texture calcs
   win = 100 # pixel window
   shift = 10 # pixel shift
   density = win/2 
   numclasses = 4 # number of discrete classes for contouring and k-means
   maxscale = 20 # Max scale as inverse fraction of data length (for wavelet analysis)
   notes = 4 # Notes per octave (for wavelet analysis)

   # for mapping
   dogrid = 1 # yes
   res = 0.2 # grid resolution in metres
   # if res==99, the program will automatically calc res from the spatial res of the scans
   mode = 1 # gridding mode (simple nearest neighbour)
   #mode = 2 # gridding mode (inverse distance weighted nearest neighbour)
   #mode = 3 # gridding mode (gaussian weighted nearest neighbour)
   dowrite = 0 #disable writing of point cloud data to file

   nn = 128 #number of nearest neighbours for gridding (used if mode > 1)
   influence = 1 #Radius of influence used in gridding. Cut off distance in meters 
   numstdevs = 5 #Threshold number of standard deviations in sidescan intensity per grid cell up to which to accept 

   # for downward-looking echosounder echogram (e1-e2) analysis
   beam = 20.0
   transfreq = 200.0 # frequency (kHz) of downward looking echosounder
   integ = 5
   numclusters = 3 # number of acoustic classes to group observations

   ## read data in SON files into PyHum memory mapped format (.dat)
   PyHum.read(humfile, sonpath, cs2cs_args, c, draft, doplot, t, bedpick, flip_lr, model, calc_bearing, filt_bearing, cog, chunk)

   ## correct scans and remove water column
   PyHum.correct(humfile, sonpath, maxW, doplot, dofilt, correct_withwater, ph, temp, salinity)

   ## remove acoustic shadows (caused by distal acoustic attenuation or sound hitting shallows or shoreline)
   #PyHum.rmshadows(humfile, sonpath, win, shadowmask, doplot)

   ## Calculate texture lengthscale maps using the method of Buscombe et al. (2015)
   #PyHum.texture(humfile, sonpath, win, shift, doplot, density, numclasses, maxscale, notes)

   ## grid and map the scans
   PyHum.map(humfile, sonpath, cs2cs_args, dogrid, res, dowrite, mode, nn, influence, numstdevs)

   #res = 0.5 # grid resolution in metres
   #numstdevs = 5
   
   ## grid and map the texture lengthscale maps
   #PyHum.map_texture(humfile, sonpath, cs2cs_args, dogrid, res, mode, nn, influence, numstdevs)

   ## calculate and map the e1 and e2 acoustic coefficients from the downward-looking sonar
   #PyHum.e1e2(humfile, sonpath, cs2cs_args, ph, temp, salinity, beam, transfreq, integ, numclusters, doplot)


if __name__ == '__main__':
   dotest()