def read(humfile, sonpath, cs2cs_args="epsg:26949", c=1450.0, draft=0.3, doplot=1, t=0.108, bedpick=1, flip_lr=0, model=998, calc_bearing = 0, filt_bearing = 0, chunk='d100'): #cog = 1,
'''
Read a .DAT and associated set of .SON files recorded by a Humminbird(R)
instrument.
Parse the data into a set of memory mapped files that will
subsequently be used by the other functions of the PyHum module.
Export time-series data and metadata in other formats.
Create a kml file for visualising boat track
Syntax
----------
[] = PyHum.read(humfile, sonpath, cs2cs_args, c, draft, doplot, t, bedpick, flip_lr, chunksize, model, calc_bearing, filt_bearing, chunk)
Parameters
------------
humfile : str
path to the .DAT file
sonpath : str
path where the *.SON files are
cs2cs_args : int, *optional* [Default="epsg:26949"]
arguments to create coordinates in a projected coordinate system
this argument gets given to pyproj to turn wgs84 (lat/lon) coordinates
into any projection supported by the proj.4 libraries
c : float, *optional* [Default=1450.0]
speed of sound in water (m/s). Defaults to a value of freshwater
draft : float, *optional* [Default=0.3]
draft from water surface to transducer face (m)
doplot : float, *optional* [Default=1]
if 1, plots will be made
t : float, *optional* [Default=0.108]
length of transducer array (m).
Default value is that of the 998 series Humminbird(R)
bedpick : int, *optional* [Default=1]
if 1, bedpicking with be carried out automatically
if 0, user will be prompted to pick the bed location on screen
flip_lr : int, *optional* [Default=0]
if 1, port and starboard scans will be flipped
(for situations where the transducer is flipped 180 degrees)
model: int, *optional* [Default=998]
A 3 or 4 number code indicating the model number
Examples: 998, 997, 1198, 1199
calc_bearing : float, *optional* [Default=0]
if 1, bearing will be calculated from coordinates
filt_bearing : float, *optional* [Default=0]
if 1, bearing will be filtered
chunk : str, *optional* [Default='d100' (distance, 100 m)]
letter, followed by a number.
There are the following letter options:
'd' - parse chunks based on distance, then number which is distance in m
'p' - parse chunks based on number of pings, then number which is number of pings
'h' - parse chunks based on change in heading, then number which is the change in heading in degrees
'1' - process just 1 chunk
Returns
---------
sonpath+base+'_data_port.dat': memory-mapped file
contains the raw echogram from the port side
sidescan sonar (where present)
sonpath+base+'_data_port.dat': memory-mapped file
contains the raw echogram from the starboard side
sidescan sonar (where present)
sonpath+base+'_data_dwnhi.dat': memory-mapped file
contains the raw echogram from the high-frequency
echosounder (where present)
sonpath+base+'_data_dwnlow.dat': memory-mapped file
contains the raw echogram from the low-frequency
echosounder (where present)
sonpath+base+"trackline.kml": google-earth kml file
contains the trackline of the vessel during data
acquisition
sonpath+base+'rawdat.csv': comma separated value file
contains time-series data. columns corresponding to
longitude
latitude
easting (m)
northing (m)
depth to bed (m)
alongtrack cumulative distance (m)
vessel heading (deg.)
sonpath+base+'meta.mat': .mat file
matlab format file containing a dictionary object
holding metadata information. Fields are:
e : ndarray, easting (m)
n : ndarray, northing (m)
es : ndarray, low-pass filtered easting (m)
ns : ndarray, low-pass filtered northing (m)
lat : ndarray, latitude
lon : ndarray, longitude
shape_port : tuple, shape of port scans in memory mapped file
shape_star : tuple, shape of starboard scans in memory mapped file
shape_hi : tuple, shape of high-freq. scans in memory mapped file
shape_low : tuple, shape of low-freq. scans in memory mapped file
dep_m : ndarray, depth to bed (m)
dist_m : ndarray, distance along track (m)
heading : ndarray, heading of vessel (deg. N)
pix_m: float, size of 1 pixel in across-track dimension (m)
bed : ndarray, depth to bed (m)
c : float, speed of sound in water (m/s)
t : length of sidescan transducer array (m)
spd : ndarray, vessel speed (m/s)
time_s : ndarray, time elapsed (s)
caltime : ndarray, unix epoch time (s)
'''
# prompt user to supply file if no input file given
if not humfile:
print 'An input file is required!!!!!!'
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
humfile = askopenfilename(filetypes=[("DAT files","*.DAT")])
# prompt user to supply directory if no input sonpath is given
if not sonpath:
print 'A *.SON directory is required!!!!!!'
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
sonpath = askdirectory()
# print given arguments to screen and convert data type where necessary
if humfile:
print 'Input file is %s' % (humfile)
if sonpath:
print 'Son files are in %s' % (sonpath)
if cs2cs_args:
print 'cs2cs arguments are %s' % (cs2cs_args)
if draft:
draft = float(draft)
print 'Draft: %s' % (str(draft))
if c:
c = float(c)
print 'Celerity of sound: %s m/s' % (str(c))
if doplot:
doplot = int(doplot)
if doplot==0:
print "Plots will not be made"
if flip_lr:
flip_lr = int(flip_lr)
if flip_lr==1:
print "Port and starboard will be flipped"
if t:
t = np.asarray(t,float)
print 'Transducer length is %s m' % (str(t))
if bedpick:
bedpick = np.asarray(bedpick,int)
if bedpick==1:
print 'Bed picking is auto'
elif bedpick==0:
print 'Bed picking is manual'
else:
print 'User will be prompted per chunk about bed picking method'
if chunk:
chunk = str(chunk)
if chunk[0]=='d':
chunkmode=1
chunkval = int(chunk[1:])
print 'Chunks based on distance of %s m' % (str(chunkval))
elif chunk[0]=='p':
chunkmode=2
chunkval = int(chunk[1:])
print 'Chunks based on %s pings' % (str(chunkval))
elif chunk[0]=='h':
chunkmode=3
chunkval = int(chunk[1:])
print 'Chunks based on heading devation of %s degrees' % (str(chunkval))
elif chunk[0]=='1':
chunkmode=4
chunkval = 1
print 'Only 1 chunk will be produced'
else:
print "Chunk mode not understood - should be 'd', 'p', or 'h' - using defaults"
chunkmode=1
chunkval = 100
print 'Chunks based on distance of %s m' % (str(chunkval))
if model:
model = int(model)
print "Data is from the %s series" % (str(model))
# if cog:
# cog = int(cog)
# if cog==1:
# print "Heading based on course-over-ground"
if calc_bearing:
calc_bearing = int(calc_bearing)
if calc_bearing==1:
print "Bearing will be calculated from coordinates"
if filt_bearing:
filt_bearing = int(filt_bearing)
if filt_bearing==1:
print "Bearing will be filtered"
## for debugging
#humfile = r"test.DAT"; sonpath = "test_data"
#cs2cs_args = "epsg:26949"; doplot = 1; draft = 0
#c=1450; bedpick=1; fliplr=1; chunk = 'd100'
#model=998; cog=1; calc_bearing=0; filt_bearing=0
f = 455
try:
print "Checking the epsg code you have chosen for compatibility with Basemap ... "
from mpl_toolkits.basemap import Basemap
m = Basemap(projection='merc', epsg=cs2cs_args.split(':')[1],
resolution = 'i', llcrnrlon=10, llcrnrlat=10, urcrnrlon=30, urcrnrlat=30)
del m
print "... epsg code compatible"
except:
print "Error: the epsg code you have chosen is not compatible with Basemap"
print "please choose a different epsg code (http://spatialreference.org/)"
print "program will now close"
sys.exit()
# start timer
if os.name=='posix': # true if linux/mac or cygwin on windows
start = time.time()
else: # windows
start = time.clock()
# if son path name supplied has no separator at end, put one on
if sonpath[-1]!=os.sep:
sonpath = sonpath + os.sep
# get the SON files from this directory
sonfiles = glob.glob(sonpath+'*.SON')
if not sonfiles:
sonfiles = glob.glob(os.getcwd()+os.sep+sonpath+'*.SON')
base = humfile.split('.DAT') # get base of file name for output
base = base[0].split(os.sep)[-1]
# remove underscores, negatives and spaces from basename
base = humutils.strip_base(base)
print "WARNING: Because files have to be read in byte by byte,"
print "this could take a very long time ..."
#reading each sonfile in parallel should be faster ...
try:
o = Parallel(n_jobs = np.min([len(sonfiles), cpu_count()]), verbose=0)(delayed(getscans)(sonfiles[k], humfile, c, model, cs2cs_args) for k in xrange(len(sonfiles)))
X, Y, A, B = zip(*o)
for k in xrange(len(Y)):
if Y[k] == 'sidescan_port':
dat = A[k] #data.gethumdat()
metadat = B[k] #data.getmetadata()
if flip_lr==0:
data_port = X[k].astype('int16')
else:
data_star = X[k].astype('int16')
elif Y[k] == 'sidescan_starboard':
if flip_lr==0:
data_star = X[k].astype('int16')
else:
data_port = X[k].astype('int16')
elif Y[k] == 'down_lowfreq':
data_dwnlow = X[k].astype('int16')
elif Y[k] == 'down_highfreq':
data_dwnhi = X[k].astype('int16')
del X, Y, A, B, o
old_pyread = 0
if 'data_port' not in locals():
data_port = ''
print "portside scan not available"
if 'data_star' not in locals():
data_star = ''
print "starboardside scan not available"
if 'data_dwnhi' not in locals():
data_dwnlow = ''
print "high-frq. downward scan not available"
if 'data_dwnlow' not in locals():
data_dwnlow = ''
print "low-frq. downward scan not available"
except: # revert back to older version if paralleleised version fails
print "something went wrong with the parallelised version of pyread ..."
import pyread
data = pyread.pyread(sonfiles, humfile, c, model, cs2cs_args)
dat = data.gethumdat()
metadat = data.getmetadata()
old_pyread = 1
nrec = len(metadat['n'])
metadat['instr_heading'] = metadat['heading'][:nrec]
#metadat['heading'] = humutils.get_bearing(calc_bearing, filt_bearing, cog, metadat['lat'], metadat['lon'], metadat['instr_heading'])
try:
es = humutils.runningMeanFast(metadat['e'][:nrec],len(metadat['e'][:nrec])/100)
ns = humutils.runningMeanFast(metadat['n'][:nrec],len(metadat['n'][:nrec])/100)
except:
es = metadat['e'][:nrec]
ns = metadat['n'][:nrec]
metadat['es'] = es
metadat['ns'] = ns
try:
trans = pyproj.Proj(init=cs2cs_args)
except:
trans = pyproj.Proj(cs2cs_args.lstrip(), inverse=True)
lon, lat = trans(es, ns, inverse=True)
metadat['lon'] = lon
metadat['lat'] = lat
metadat['heading'] = humutils.get_bearing(calc_bearing, filt_bearing, metadat['lat'], metadat['lon'], metadat['instr_heading']) #cog
dist_m = humutils.get_dist(lat, lon)
metadat['dist_m'] = dist_m
if calc_bearing==1: # recalculate speed, m/s
ds=np.gradient(np.squeeze(metadat['time_s']))
dx=np.gradient(np.squeeze(metadat['dist_m']))
metadat['spd'] = dx[:nrec]/ds[:nrec]
# theta at 3dB in the horizontal
theta3dB = np.arcsin(c/(t*(f*1000)))
#resolution of 1 sidescan pixel to nadir
ft = (np.pi/2)*(1/theta3dB) #/ (f/455)
dep_m = humutils.get_depth(metadat['dep_m'][:nrec])
if old_pyread == 1: #older pyread version
# port scan
try:
if flip_lr==0:
data_port = data.getportscans().astype('int16')
else:
data_port = data.getstarscans().astype('int16')
except:
data_port = ''
print "portside scan not available"
if data_port!='':
Zt, ind_port = makechunks_scan(chunkmode, chunkval, metadat, data_port, 0)
del data_port
## create memory mapped file for Z
shape_port = io.set_mmap_data(sonpath, base, '_data_port.dat', 'int16', Zt)
##we are only going to access the portion of memory required
port_fp = io.get_mmap_data(sonpath, base, '_data_port.dat', 'int16', shape_port)
if old_pyread == 1: #older pyread version
# starboard scan
try:
if flip_lr==0:
data_star = data.getstarscans().astype('int16')
else:
data_star = data.getportscans().astype('int16')
except:
data_star = ''
print "starboardside scan not available"
if data_star!='':
Zt, ind_star = makechunks_scan(chunkmode, chunkval, metadat, data_star, 1)
del data_star
# create memory mapped file for Z
shape_star = io.set_mmap_data(sonpath, base, '_data_star.dat', 'int16', Zt)
star_fp = io.get_mmap_data(sonpath, base, '_data_star.dat', 'int16', shape_star)
if 'star_fp' in locals() and 'port_fp' in locals():
# check that port and starboard are same size
# and trim if not
if np.shape(star_fp)!=np.shape(port_fp):
print "port and starboard scans are different sizes ... rectifying"
if np.shape(port_fp[0])[1] > np.shape(star_fp[0])[1]:
tmp = port_fp.copy()
tmp2 = np.empty_like(star_fp)
for k in xrange(len(tmp)):
tmp2[k] = tmp[k][:,:np.shape(star_fp[k])[1]]
del tmp
# create memory mapped file for Z
shape_port = io.set_mmap_data(sonpath, base, '_data_port2.dat', 'int16', tmp2)
#shape_star = shape_port.copy()
shape_star = tuple(np.asarray(shape_port).copy())
##we are only going to access the portion of memory required
port_fp = io.get_mmap_data(sonpath, base, '_data_port2.dat', 'int16', shape_port)
ind_port = list(ind_port)
ind_port[-1] = np.shape(star_fp[0])[1]
ind_port = tuple(ind_port)
elif np.shape(port_fp[0])[1] < np.shape(star_fp[0])[1]:
tmp = star_fp.copy()
tmp2 = np.empty_like(port_fp)
for k in xrange(len(tmp)):
tmp2[k] = tmp[k][:,:np.shape(port_fp[k])[1]]
del tmp
# create memory mapped file for Z
shape_port = io.set_mmap_data(sonpath, base, '_data_star2.dat', 'int16', tmp2)
#shape_star = shape_port.copy()
shape_star = tuple(np.asarray(shape_port).copy())
#we are only going to access the portion of memory required
star_fp = io.get_mmap_data(sonpath, base, '_data_star2.dat', 'int16', shape_star)
ind_star = list(ind_star)
ind_star[-1] = np.shape(port_fp[0])[1]
ind_star = tuple(ind_star)
if old_pyread == 1: #older pyread version
# low-freq. sonar
try:
data_dwnlow = data.getlowscans().astype('int16')
except:
data_dwnlow = ''
print "low-freq. scan not available"
if data_dwnlow!='':
Zt, ind_low = makechunks_scan(chunkmode, chunkval, metadat, data_dwnlow, 2)
del data_dwnlow
# create memory mapped file for Z
shape_low = io.set_mmap_data(sonpath, base, '_data_dwnlow.dat', 'int16', Zt)
##we are only going to access the portion of memory required
dwnlow_fp = io.get_mmap_data(sonpath, base, '_data_dwnlow.dat', 'int16', shape_low)
if old_pyread == 1: #older pyread version
# hi-freq. sonar
try:
data_dwnhi = data.gethiscans().astype('int16')
except:
data_dwnhi = ''
print "high-freq. scan not available"
if data_dwnhi!='':
Zt, ind_hi = makechunks_scan(chunkmode, chunkval, metadat, data_dwnhi, 3)
del data_dwnhi
# create memory mapped file for Z
shape_hi = io.set_mmap_data(sonpath, base, '_data_dwnhi.dat', 'int16', Zt)
dwnhi_fp = io.get_mmap_data(sonpath, base, '_data_dwnhi.dat', 'int16', shape_hi)
if 'dwnhi_fp' in locals() and 'dwnlow_fp' in locals():
# check that low and high are same size
# and trim if not
if (np.shape(dwnhi_fp)!=np.shape(dwnlow_fp)) and (chunkmode!=4):
print "dwnhi and dwnlow are different sizes ... rectifying"
if np.shape(dwnhi_fp[0])[1] > np.shape(dwnlow_fp[0])[1]:
tmp = dwnhi_fp.copy()
tmp2 = np.empty_like(dwnlow_fp)
for k in xrange(len(tmp)):
tmp2[k] = tmp[k][:,:np.shape(dwnlow_fp[k])[1]]
del tmp
# create memory mapped file for Z
shape_low = io.set_mmap_data(sonpath, base, '_data_dwnhi2.dat', 'int16', tmp2)
#shape_hi = shape_low.copy()
shape_hi = tuple(np.asarray(shape_low).copy())
##we are only going to access the portion of memory required
dwnhi_fp = io.get_mmap_data(sonpath, base, '_data_dwnhi2.dat', 'int16', shape_hi)
ind_hi = list(ind_hi)
ind_hi[-1] = np.shape(dwnlow_fp[0])[1]
ind_hi = tuple(ind_hi)
elif np.shape(dwnhi_fp[0])[1] < np.shape(dwnlow_fp[0])[1]:
tmp = dwnlow_fp.copy()
tmp2 = np.empty_like(dwnhi_fp)
for k in xrange(len(tmp)):
tmp2[k] = tmp[k][:,:np.shape(dwnhi_fp[k])[1]]
del tmp
# create memory mapped file for Z
shape_low = io.set_mmap_data(sonpath, base, '_data_dwnlow2.dat', 'int16', tmp2)
#shape_hi = shape_low.copy()
shape_hi = tuple(np.asarray(shape_low).copy())
##we are only going to access the portion of memory required
dwnlow_fp = io.get_mmap_data(sonpath, base, '_data_dwnlow2.dat', 'int16', shape_low)
ind_low = list(ind_low)
ind_low[-1] = np.shape(dwnhi_fp[0])[1]
ind_low = tuple(ind_low)
if old_pyread == 1: #older pyread version
del data
if ('shape_port' in locals()) and (chunkmode!=4):
metadat['shape_port'] = shape_port
nrec = metadat['shape_port'][0] * metadat['shape_port'][2]
elif ('shape_port' in locals()) and (chunkmode==4):
metadat['shape_port'] = shape_port
nrec = metadat['shape_port'][1]
else:
metadat['shape_port'] = ''
if ('shape_star' in locals()) and (chunkmode!=4):
metadat['shape_star'] = shape_star
nrec = metadat['shape_star'][0] * metadat['shape_star'][2]
elif ('shape_star' in locals()) and (chunkmode==4):
metadat['shape_star'] = shape_star
nrec = metadat['shape_star'][1]
else:
metadat['shape_star'] = ''
if ('shape_hi' in locals()) and (chunkmode!=4):
metadat['shape_hi'] = shape_hi
#nrec = metadat['shape_hi'][0] * metadat['shape_hi'][2] * 2
elif ('shape_hi' in locals()) and (chunkmode==4):
metadat['shape_hi'] = shape_hi
else:
metadat['shape_hi'] = ''
if ('shape_low' in locals()) and (chunkmode!=4):
metadat['shape_low'] = shape_low
#nrec = metadat['shape_low'][0] * metadat['shape_low'][2] * 2
elif ('shape_low' in locals()) and (chunkmode==4):
metadat['shape_low'] = shape_low
else:
metadat['shape_low'] = ''
#make kml boat trackline
humutils.make_trackline(lon,lat, sonpath, base)
if 'port_fp' in locals() and 'star_fp' in locals():
#if not os.path.isfile(os.path.normpath(os.path.join(sonpath,base+'meta.mat'))):
if 2>1:
if bedpick == 1: # auto
x, bed = humutils.auto_bedpick(ft, dep_m, chunkmode, port_fp, c)
if len(dist_m)<len(bed):
dist_m = np.append(dist_m,dist_m[-1]*np.ones(len(bed)-len(dist_m)))
if doplot==1:
if chunkmode!=4:
for k in xrange(len(star_fp)):
plot_2bedpicks(port_fp[k], star_fp[k], bed[ind_port[-1]*k:ind_port[-1]*(k+1)], dist_m[ind_port[-1]*k:ind_port[-1]*(k+1)], x[ind_port[-1]*k:ind_port[-1]*(k+1)], ft, shape_port, sonpath, k, chunkmode)
else:
plot_2bedpicks(port_fp, star_fp, bed, dist_m, x, ft, shape_port, sonpath, 0, chunkmode)
# 'real' bed is estimated to be the minimum of the two
bed = np.min(np.vstack((bed[:nrec],np.squeeze(x[:nrec]))),axis=0)
bed = humutils.runningMeanFast(bed, 3)
elif bedpick>1: # user prompt
x, bed = humutils.auto_bedpick(ft, dep_m, chunkmode, port_fp, c)
if len(dist_m)<len(bed):
dist_m = np.append(dist_m,dist_m[-1]*np.ones(len(bed)-len(dist_m)))
# 'real' bed is estimated to be the minimum of the two
bed = np.min(np.vstack((bed[:nrec],np.squeeze(x[:nrec]))),axis=0)
bed = humutils.runningMeanFast(bed, 3)
# manually intervene
fig = plt.figure()
ax = plt.gca()
if chunkmode !=4:
im = ax.imshow(np.hstack(port_fp), cmap = 'gray', origin = 'upper')
else:
im = ax.imshow(port_fp, cmap = 'gray', origin = 'upper')
plt.plot(bed,'r')
plt.axis('normal'); plt.axis('tight')
pts1 = plt.ginput(n=300, timeout=30) # it will wait for 200 clicks or 60 seconds
x1=map(lambda x: x[0],pts1) # map applies the function passed as
y1=map(lambda x: x[1],pts1) # first parameter to each element of pts
plt.close()
del fig
if x1 != []: # if x1 is not empty
tree = KDTree(zip(np.arange(1,len(bed)), bed))
try:
dist, inds = tree.query(zip(x1, y1), k = 100, eps=5, n_jobs=-1)
except:
dist, inds = tree.query(zip(x1, y1), k = 100, eps=5)
b = np.interp(inds,x1,y1)
bed2 = bed.copy()
bed2[inds] = b
bed = bed2
if doplot==1:
if chunkmode!=4:
for k in xrange(len(star_fp)):
plot_2bedpicks(port_fp[k], star_fp[k], bed[ind_port[-1]*k:ind_port[-1]*(k+1)], dist_m[ind_port[-1]*k:ind_port[-1]*(k+1)], x[ind_port[-1]*k:ind_port[-1]*(k+1)], ft, shape_port, sonpath, k, chunkmode)
else:
plot_2bedpicks(port_fp, star_fp, bed, dist_m, x, ft, shape_port, sonpath, 0, chunkmode)
else: #manual
beds=[]
if chunkmode!=4:
for k in xrange(len(port_fp)):
raw_input("Bed picking "+str(k+1)+" of "+str(len(port_fp))+", are you ready? 30 seconds. Press Enter to continue...")
bed={}
fig = plt.figure()
ax = plt.gca()
im = ax.imshow(port_fp[k], cmap = 'gray', origin = 'upper')
pts1 = plt.ginput(n=300, timeout=30) # it will wait for 200 clicks or 60 seconds
x1=map(lambda x: x[0],pts1) # map applies the function passed as
y1=map(lambda x: x[1],pts1) # first parameter to each element of pts
bed = np.interp(np.r_[:ind_port[-1]],x1,y1)
plt.close()
del fig
beds.append(bed)
extent = np.shape(port_fp[k])[0]
bed = np.asarray(np.hstack(beds),'float')
else:
raw_input("Bed picking - are you ready? 30 seconds. Press Enter to continue...")
bed={}
fig = plt.figure()
ax = plt.gca()
im = ax.imshow(port_fp, cmap = 'gray', origin = 'upper')
pts1 = plt.ginput(n=300, timeout=30) # it will wait for 200 clicks or 60 seconds
x1=map(lambda x: x[0],pts1) # map applies the function passed as
y1=map(lambda x: x[1],pts1) # first parameter to each element of pts
bed = np.interp(np.r_[:ind_port[-1]],x1,y1)
plt.close()
del fig
beds.append(bed)
extent = np.shape(port_fp)[1]
bed = np.asarray(np.hstack(beds),'float')
# now revise the depth in metres
dep_m = (1/ft)*bed
if doplot==1:
if chunkmode!=4:
for k in xrange(len(star_fp)):
plot_bedpick(port_fp[k], star_fp[k], (1/ft)*bed[ind_port[-1]*k:ind_port[-1]*(k+1)], dist_m[ind_port[-1]*k:ind_port[-1]*(k+1)], ft, shape_port, sonpath, k, chunkmode)
else:
plot_bedpick(port_fp, star_fp, (1/ft)*bed, dist_m, ft, shape_port, sonpath, 0, chunkmode)
metadat['bed'] = bed[:nrec]
else:
metadat['bed'] = dep_m[:nrec]*ft
metadat['heading'] = metadat['heading'][:nrec]
metadat['lon'] = lon[:nrec]
metadat['lat'] = lat[:nrec]
metadat['dist_m'] = dist_m[:nrec]
metadat['dep_m'] = dep_m[:nrec]
metadat['pix_m'] = 1/ft
metadat['bed'] = metadat['bed'][:nrec]
metadat['c'] = c
metadat['t'] = t
metadat['f'] = f
metadat['spd'] = metadat['spd'][:nrec]
metadat['time_s'] = metadat['time_s'][:nrec]
metadat['e'] = metadat['e'][:nrec]
metadat['n'] = metadat['n'][:nrec]
metadat['es'] = metadat['es'][:nrec]
metadat['ns'] = metadat['ns'][:nrec]
metadat['caltime'] = metadat['caltime'][:nrec]
savemat(os.path.normpath(os.path.join(sonpath,base+'meta.mat')), metadat ,oned_as='row')
f = open(os.path.normpath(os.path.join(sonpath,base+'rawdat.csv')), 'wt')
writer = csv.writer(f)
writer.writerow( ('longitude', 'latitude', 'easting', 'northing', 'depth (m)', 'distance (m)', 'instr. heading (deg)', 'heading (deg.)' ) )
for i in range(0, nrec):
writer.writerow(( float(lon[i]),float(lat[i]),float(es[i]),float(ns[i]),float(dep_m[i]),float(dist_m[i]), float(metadat['instr_heading'][i]), float(metadat['heading'][i]) ))
f.close()
del lat, lon, dep_m #, dist_m
if doplot==1:
plot_pos(sonpath, metadat, es, ns)
if 'dwnlow_fp' in locals():
plot_dwnlow(dwnlow_fp, chunkmode, sonpath)
if 'dwnhi_fp' in locals():
plot_dwnhi(dwnhi_fp, chunkmode, sonpath)
if os.name=='posix': # true if linux/mac
elapsed = (time.time() - start)
else: # windows
elapsed = (time.clock() - start)
print "Processing took ", elapsed , "seconds to analyse"
print "Done!"
def texture(humfile,
sonpath,
win=100,
shift=10,
doplot=1,
density=50,
numclasses=4,
maxscale=20,
notes=4):
'''
Create a texture lengthscale map using the algorithm detailed by Buscombe et al. (2015)
This textural lengthscale is not a direct measure of grain size. Rather, it is a statistical
representation that integrates over many attributes of bed texture, of which grain size is the most important.
The technique is a physically based means to identify regions of texture within a sidescan echogram,
and could provide a basis for objective, automated riverbed sediment classification.
Syntax
----------
[] = PyHum.texture(humfile, sonpath, win, shift, doplot, density, numclasses, maxscale, notes)
Parameters
----------
humfile : str
path to the .DAT file
sonpath : str
path where the *.SON files are
win : int, *optional* [Default=100]
pixel in pixels of the moving window
shift : int, *optional* [Default=10]
shift in pixels for moving window operation
doplot : int, *optional* [Default=1]
if 1, make plots, otherwise do not make plots
density : int, *optional* [Default=win/2]
echogram will be sampled every 'density' pixels
numclasses : int, *optional* [Default=4]
number of 'k means' that the texture lengthscale will be segmented into
maxscale : int, *optional* [Default=20]
Max scale as inverse fraction of data length for wavelet analysis
notes : int, *optional* [Default=100]
notes per octave for wavelet analysis
Returns
-------
sonpath+base+'_data_class.dat': memory-mapped file
contains the texture lengthscale map
sonpath+base+'_data_kclass.dat': memory-mapped file
contains the k-means segmented texture lengthscale map
References
----------
.. [1] Buscombe, D., Grams, P.E., and Smith, S.M.C., 2015, Automated riverbed sediment
classification using low-cost sidescan sonar. Journal of Hydraulic Engineering 10.1061/(ASCE)HY.1943-7900.0001079, 06015019.
'''
# prompt user to supply file if no input file given
if not humfile:
print 'An input file is required!!!!!!'
Tk().withdraw(
) # we don't want a full GUI, so keep the root window from appearing
humfile = askopenfilename(filetypes=[("DAT files", "*.DAT")])
# prompt user to supply directory if no input sonpath is given
if not sonpath:
print 'A *.SON directory is required!!!!!!'
Tk().withdraw(
) # we don't want a full GUI, so keep the root window from appearing
sonpath = askdirectory()
# print given arguments to screen and convert data type where necessary
if humfile:
print 'Input file is %s' % (humfile)
if sonpath:
print 'Sonar file path is %s' % (sonpath)
if win:
win = np.asarray(win, int)
print 'Window is %s square pixels' % (str(win))
if shift:
shift = np.asarray(shift, int)
print 'Min shift is %s pixels' % (str(shift))
if density:
density = np.asarray(density, int)
print 'Image will be sampled every %s pixels' % (str(density))
if numclasses:
numclasses = np.asarray(numclasses, int)
print 'Number of sediment classes: %s' % (str(numclasses))
if maxscale:
maxscale = np.asarray(maxscale, int)
print 'Max scale as inverse fraction of data length: %s' % (
str(maxscale))
if notes:
notes = np.asarray(notes, int)
print 'Notes per octave: %s' % (str(notes))
if doplot:
doplot = int(doplot)
if doplot == 0:
print "Plots will not be made"
print '[Default] Number of processors is %s' % (str(cpu_count()))
########################################################
########################################################
# start timer
if os.name == 'posix': # true if linux/mac or cygwin on windows
start = time.time()
else: # windows
start = time.clock()
# if son path name supplied has no separator at end, put one on
if sonpath[-1] != os.sep:
sonpath = sonpath + os.sep
base = humfile.split('.DAT') # get base of file name for output
base = base[0].split(os.sep)[-1]
# remove underscores, negatives and spaces from basename
base = humutils.strip_base(base)
meta = loadmat(os.path.normpath(os.path.join(sonpath, base + 'meta.mat')))
ft = 1 / loadmat(sonpath + base + 'meta.mat')['pix_m']
#pix_m = np.squeeze(meta['pix_m'])
#dep_m = np.squeeze(meta['dep_m'])
dist_m = np.squeeze(meta['dist_m'])
### port
print "processing port side ..."
# load memory mapped scan ... port
shape_port = np.squeeze(meta['shape_port'])
if shape_port != '':
if os.path.isfile(
os.path.normpath(
os.path.join(sonpath, base + '_data_port_lar.dat'))):
port_fp = io.get_mmap_data(sonpath, base, '_data_port_lar.dat',
'float32', tuple(shape_port))
else:
port_fp = io.get_mmap_data(sonpath, base, '_data_port_la.dat',
'float32', tuple(shape_port))
port_fp2 = io.get_mmap_data(sonpath, base, '_data_port_l.dat',
'float32', tuple(shape_port))
### star
print "processing starboard side ..."
# load memory mapped scan ... port
shape_star = np.squeeze(loadmat(sonpath + base + 'meta.mat')['shape_star'])
if shape_star != '':
if os.path.isfile(
os.path.normpath(
os.path.join(sonpath, base + '_data_star_lar.dat'))):
star_fp = io.get_mmap_data(sonpath, base, '_data_star_lar.dat',
'float32', tuple(shape_star))
else:
star_fp = io.get_mmap_data(sonpath, base, '_data_star_la.dat',
'float32', tuple(shape_star))
star_fp2 = io.get_mmap_data(sonpath, base, '_data_star_l.dat',
'float32', tuple(shape_star))
if len(shape_star) > 2:
shape = shape_port.copy()
shape[1] = shape_port[1] + shape_star[1]
else:
shape = []
shape.append(1)
shape.append(shape_port[0])
shape.append(shape_port[1])
shape[1] = shape_port[0] + shape_star[0]
# create memory mapped file for Sp
with open(
os.path.normpath(os.path.join(sonpath, base + '_data_class.dat')),
'w+') as ff:
fp = np.memmap(ff, dtype='float32', mode='w+', shape=tuple(shape))
if len(shape_star) > 2:
#SRT = []
for p in xrange(len(port_fp)):
Z, ind = humutils.sliding_window_sliced(
np.vstack((np.flipud(port_fp[p]), star_fp[p])), density,
(win, win), (shift, shift))
Snn = get_srt(Z, ind, maxscale, notes, win) #, density)
# replace nans using infilling algorithm
rn = replace_nans.RN(Snn.astype('float64'), 1000, 0.01, 2,
'localmean')
Snn = rn.getdata()
del rn
Ny, Nx = np.shape(np.vstack((np.flipud(port_fp[p]), star_fp[p])))
Snn = median_filter(Snn, (int(Nx / 100), int(Ny / 100)))
Sp = humutils.im_resize(Snn, Nx, Ny)
del Snn
Sp[np.isnan(np.vstack(
(np.flipud(port_fp[p]), star_fp[p])))] = np.nan
Sp[np.isnan(np.vstack(
(np.flipud(port_fp2[p]), star_fp2[p])))] = np.nan
#extent = shape_port[1]
#Zdist = dist_m[shape_port[-1]*p:shape_port[-1]*(p+1)]
#yvec = np.linspace(pix_m,extent*pix_m,extent)
#d = dep_m[shape_port[-1]*p:shape_port[-1]*(p+1)]
R_fp = io.get_mmap_data(sonpath, base, '_data_range.dat',
'float32', tuple(shape_star))
R = np.vstack((np.flipud(R_fp[0]), R_fp[0]))
R = R / np.max(R)
#R[R>0.8] = np.nan
rn = replace_nans.RN(R.astype('float64'), 1000, 0.01, 2,
'localmean')
R = rn.getdata()
del rn
Sp = (Sp**2) * np.cos(R) / shift**2
fp[p] = Sp.astype('float32')
del Sp
del fp # flush data to file
class_fp = io.get_mmap_data(sonpath, base, '_data_class.dat',
'float32', tuple(shape))
else:
Z, ind = humutils.sliding_window_sliced(
np.vstack((np.flipud(port_fp), star_fp)), density, (win, win),
(shift, shift))
Snn = get_srt(Z, ind, maxscale, notes, win) #, density)
# replace nans using infilling algorithm
rn = replace_nans.RN(Snn.astype('float64'), 1000, 0.01, 2, 'localmean')
Snn = rn.getdata()
del rn
Ny, Nx = np.shape(np.vstack((np.flipud(port_fp), star_fp)))
Snn = median_filter(Snn, (int(Nx / 100), int(Ny / 100)))
Sp = humutils.im_resize(Snn, Nx, Ny)
del Snn
Sp[np.isnan(np.vstack((np.flipud(port_fp), star_fp)))] = np.nan
Sp[np.isnan(np.vstack((np.flipud(port_fp2), star_fp2)))] = np.nan
#extent = shape_port[0]
#Zdist = dist_m
#yvec = np.linspace(pix_m,extent*pix_m,extent)
#d = dep_m
R_fp = io.get_mmap_data(sonpath, base, '_data_range.dat', 'float32',
tuple(shape_star))
R = np.vstack((np.flipud(R_fp), R_fp))
R = R / np.max(R)
#R[R>0.8] = np.nan
rn = replace_nans.RN(R.astype('float64'), 1000, 0.01, 2, 'localmean')
R = rn.getdata()
del rn
Sp = (Sp**2) * np.cos(R) / shift**2
with open(
os.path.normpath(
os.path.join(sonpath, base + '_data_class.dat')),
'w+') as ff:
np.save(ff, np.squeeze(Sp).astype('float32'))
with open(
os.path.normpath(
os.path.join(sonpath, base + '_data_class.dat')),
'r') as ff:
class_fp = np.load(ff)
del Sp
dist_m = np.squeeze(loadmat(sonpath + base + 'meta.mat')['dist_m'])
########################################################
if doplot == 1:
if len(shape_star) > 2:
for p in xrange(len(star_fp)):
plot_class(dist_m, shape_port, port_fp[p], star_fp[p],
class_fp[p], ft, humfile, sonpath, base, p)
else:
plot_class(dist_m, shape_port, port_fp, star_fp, class_fp, ft,
humfile, sonpath, base, 0)
if len(shape_star) > 2:
for p in xrange(len(star_fp)):
plot_contours(dist_m, shape_port, class_fp[p], ft, humfile,
sonpath, base, numclasses, p)
else:
plot_contours(dist_m, shape_port, class_fp, ft, humfile, sonpath,
base, numclasses, 0)
#######################################################
# k-means
if len(shape_star) > 2:
with open(
os.path.normpath(
os.path.join(sonpath, base + '_data_kclass.dat')),
'w+') as ff:
fp = np.memmap(ff, dtype='float32', mode='w+', shape=tuple(shape))
for p in xrange(len(port_fp)):
wc = get_kclass(class_fp[p].copy(), numclasses)
fp[p] = wc.astype('float32')
del wc
del fp
kclass_fp = io.get_mmap_data(sonpath, base, '_data_kclass.dat',
'float32', tuple(shape))
else:
wc = get_kclass(class_fp.copy(), numclasses)
with open(
os.path.normpath(
os.path.join(sonpath, base + '_data_kclass.dat')),
'w+') as ff:
np.save(ff, np.squeeze(wc).astype('float32'))
del wc
with open(
os.path.normpath(
os.path.join(sonpath, base + '_data_kclass.dat')),
'r') as ff:
kclass_fp = np.load(ff)
########################################################
if doplot == 1:
if len(shape_star) > 2:
for p in xrange(len(star_fp)):
plot_kmeans(dist_m, shape_port, port_fp[p], star_fp[p],
kclass_fp[p], ft, humfile, sonpath, base, p)
else:
plot_kmeans(dist_m, shape_port, port_fp, star_fp, kclass_fp, ft,
humfile, sonpath, base, 0)
if os.name == 'posix': # true if linux/mac
elapsed = (time.time() - start)
else: # windows
elapsed = (time.clock() - start)
print "Processing took ", elapsed, "seconds to analyse"
print "Done!"
def e1e2(humfile, sonpath, cs2cs_args, ph, temp, salinity, beam, transfreq, integ, numclusters, doplot):
'''
Analysis of first (e1, 'roughness') and second (e2, 'hardness') echo returns from the high-frequency downward looking echosounder
Generates generalised acoustic parameters
for the purposes of point classification of submerged substrates/vegetation
Accounts for the absorption of sound in water
Does a basic k-means cluster of e1 and e2 coefficients into specified number of 'acoustic classes'
based on code by Barb Fagetter ([email protected])
Syntax
----------
[] = PyHum.e1e2(humfile, sonpath, cs2cs_args, ph, temp, salinity, beam, transfreq, integ, numclusters, doplot)
Parameters
----------
humfile : str
path to the .DAT file
sonpath : str
path where the *.SON files are
cs2cs_args : int, *optional* [Default="epsg:26949"]
arguments to create coordinates in a projected coordinate system
this argument gets given to pyproj to turn wgs84 (lat/lon) coordinates
into any projection supported by the proj.4 libraries
ph : float, *optional* [Default=7.0]
water acidity in pH
temp : float, *optional* [Default=10.0]
water temperature in degrees Celsius
salinity : float, *optional* [Default=0.0]
salinity of water in parts per thousand
beam : float, *optional* [Default=20.0]
beam width in degrees
transfreq : float, *optional* [Default=200.0]
transducer frequency in kHz
integ : int, *optional* [Default=5]
number of pings over which to integrate
numclusters : int, *optional* [Default=3]
number of acoustic classes to classify all the data into
doplot : int, *optional* [Default=1]
1 = make plots, otherwise do not
Returns
-------
sonpath+base+'rough_and_hard'+str(p)+'.csv' : csv file
contains the following fields: 'longitude', 'latitude', 'easting', 'northing', 'depth',
'roughness', 'hardness', 'average roughness', 'average hardness','k-mean label'
of the pth chunk
'average' implies average over 'integ' successive pings
The following are returned if doplot==1:
sonpath+'e1e2_scan'+str(p).png : png image file
png image file showing the downward echosounder echogram overlain with the locations of the start and
end of the first and second echo region envelope
sonpath+'e1e2_kmeans'+str(p).png: png image file
png image file showing 1) (left) volume scattering coefficient 1 versus volume scattering coefficient 2, colour-coded
by k-means acoustic class, and
2) (right) e1 versus e2, colour-coded
by k-means acoustic class
sonpath+'rgh_hard_kmeans'+str(p).png : png image file
png image file showing scatter plot of easting versus northing colour-coded by k-means acoustic class
sonpath+'map_rgh'+str(p).png : png image file
png image file showing scatter plot of 'roughness' (e1) overlying an aerial image pulled from an ESRI image server
sonpath+'map_hard'+str(p).png : png image file
png image file showing scatter plot of 'hardness' (e2) overlying an aerial image pulled from an ESRI image server
sonpath,'Rough'+str(p).png : png image file
png image overlay associated with the kml file, sonpath,'Hard'+str(p).kml
sonpath,'Rough'+str(p).kml : kml file
kml overlay for showing roughness scatter plot (sonpath,'Rough'+str(p).png)
sonpath,'Hard'+str(p).png : png image file
png image overlay associated with the kml file, sonpath,'Hard'+str(p).kml
sonpath,'Hard'+str(p).kml : kml file
kml overlay for showing harness scatter plot (sonpath,'Hard'+str(p).png)
'''
# prompt user to supply file if no input file given
if not humfile:
print('An input file is required!!!!!!')
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
inputfile = askopenfilename(filetypes=[("DAT files","*.DAT")])
# prompt user to supply directory if no input sonpath is given
if not sonpath:
print('A *.SON directory is required!!!!!!')
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
sonpath = askdirectory()
# print given arguments to screen and convert data type where necessary
if humfile:
print('Input file is %s' % (humfile))
if sonpath:
print('Sonar file path is %s' % (sonpath))
if cs2cs_args:
print('cs2cs arguments are %s' % (cs2cs_args))
if beam:
beam = np.asarray(beam,float)
print('Beam is %s deg' % (str(beam)))
if salinity:
salinity = np.asarray(salinity,float)
print('Salinity is %s ppt' % (str(salinity)))
if ph:
ph = np.asarray(ph,float)
print('pH is %s' % (str(ph)))
if temp:
temp = np.asarray(temp,float)
print('Temperature is %s' % (str(temp)))
if transfreq:
transfreq = np.asarray(transfreq,float)
print('Dwnward sonar freq. is %s' % (str(transfreq)))
if integ:
integ = np.asarray(integ,int)
print('number of records for integration is %s' % (str(integ)))
if numclusters:
numclusters = np.asarray(numclusters,int)
print('number of returned acoustic clusters is %s' % (str(numclusters)))
if doplot:
doplot = int(doplot)
if doplot==0:
print("Plots will not be made")
# if son path name supplied has no separator at end, put one on
if sonpath[-1]!=os.sep:
sonpath = sonpath + os.sep
base = humfile.split('.DAT') # get base of file name for output
base = base[0].split(os.sep)[-1]
# remove underscores, negatives and spaces from basename
base = humutils.strip_base(base)
meta = loadmat(os.path.normpath(os.path.join(sonpath,base+'meta.mat')))
beamwidth = beam*(np.sqrt(0.5))
equivbeam = (5.78/(np.power(1.6,2)))*(np.power((np.sin((beamwidth*np.pi)/(2*180))),2))
meta = loadmat(sonpath+base+'meta.mat')
c = np.squeeze(meta['c'])
t = np.squeeze(meta['t'])
f = np.squeeze(meta['f'])
maxW = np.squeeze(meta['maxW'])
lat = np.squeeze(meta['lat'])
lon = np.squeeze(meta['lon'])
es = np.squeeze(meta['e'])
ns = np.squeeze(meta['n'])
dep = np.squeeze(meta['dep_m'])
#del meta
# load memory mapped scans
shape_hi= np.squeeze(meta['shape_hi'])
if shape_hi!='':
try:
#dwnhi_fp = np.memmap(sonpath+base+'_data_dwnhi.dat', dtype='int16', mode='r', shape=tuple(shape_hi))
with open(os.path.normpath(os.path.join(sonpath,base+'_data_dwnhi.dat')), 'r') as ff:
dwnhi_fp = np.memmap(ff, dtype='int16', mode='r', shape=tuple(shape_hi))
except:
shape_lo= np.squeeze(meta['shape_low'])
#dwnhi_fp = np.memmap(sonpath+base+'_data_dwnhi.dat', dtype='int16', mode='r', shape=tuple(shape_lo))
with open(os.path.normpath(os.path.join(sonpath,base+'_data_dwnhi.dat')), 'r') as ff:
dwnhi_fp = np.memmap(ff, dtype='int16', mode='r', shape=tuple(shape_lo))
if 'dwnhi_fp' in locals():
theta3dB = np.arcsin(c/(t*(f*1000))) # *(180/pi) # to see in degs
ft = (np.pi/2)*(1/theta3dB)
bed = ft*dep
if len(shape_hi)>2:
i = np.linspace(1,shape_hi[0]*shape_hi[2], len(bed))
#np.shape(beam_data)[1],len(bed))
#bedi = np.interp(np.linspace(1,np.shape(beam_data)[1],np.shape(beam_data)[1]), i, bed)
bedi = np.interp(np.linspace(1,shape_hi[0]*shape_hi[2],shape_hi[0]*shape_hi[2]), i, bed)
ei = np.interp(np.linspace(1,shape_hi[0]*shape_hi[2],shape_hi[0]*shape_hi[2]), i, es)
ni = np.interp(np.linspace(1,shape_hi[0]*shape_hi[2],shape_hi[0]*shape_hi[2]), i, ns)
lati = np.interp(np.linspace(1,shape_hi[0]*shape_hi[2],shape_hi[0]*shape_hi[2]), i, lat)
loni = np.interp(np.linspace(1,shape_hi[0]*shape_hi[2],shape_hi[0]*shape_hi[2]), i, lon)
del i
else:
i = np.linspace(1,shape_hi[1], len(bed))
#np.shape(beam_data)[1],len(bed))
#bedi = np.interp(np.linspace(1,np.shape(beam_data)[1],np.shape(beam_data)[1]), i, bed)
bedi = np.interp(np.linspace(1,shape_hi[1],shape_hi[1]), i, bed)
ei = np.interp(np.linspace(1,shape_hi[1],shape_hi[1]), i, es)
ni = np.interp(np.linspace(1,shape_hi[1],shape_hi[1]), i, ns)
lati = np.interp(np.linspace(1,shape_hi[1],shape_hi[1]), i, lat)
loni = np.interp(np.linspace(1,shape_hi[1],shape_hi[1]), i, lon)
del i
bedi = np.asarray(bedi,'int')
depi = ((1/ft)*bedi)
# near-field region
nf = int(ft*(1000*(0.105**2)*f/(4*1500)))
#absorption = calcAb(c, ph, salinity, temp, np.asarray(depi), transfreq)
absorption = water_atten(np.asarray(depi), transfreq, c, ph, temp, salinity)
if len(shape_hi)>2:
for p in range(len(dwnhi_fp)):
#make an index of every other record
ind = range(0,np.shape(dwnhi_fp[p])[1])
Zdepi = depi[shape_hi[2]*p:shape_hi[2]*(p+1)]
Zabsorp = absorption[shape_hi[2]*p:shape_hi[2]*(p+1)]
Zlat = lati[shape_hi[2]*p:shape_hi[2]*(p+1)]
Zlon = loni[shape_hi[2]*p:shape_hi[2]*(p+1)]
Zes = ei[shape_hi[2]*p:shape_hi[2]*(p+1)]
Zns = ni[shape_hi[2]*p:shape_hi[2]*(p+1)]
try: #parallel processing with all available cores
w = Parallel(n_jobs=-1, verbose=0)(delayed(get_rgh_hrd)(dwnhi_fp[p][:,i],Zdepi[i],Zabsorp[i],c,nf,transfreq,equivbeam,maxW,pi,ft) for i in ind)
except: #fall back to serial
w = Parallel(n_jobs=1, verbose=0)(delayed(get_rgh_hrd)(dwnhi_fp[p][:,i],Zdepi[i],Zabsorp[i],c,nf,transfreq,equivbeam,maxW,pi,ft) for i in ind)
rough, hard, sv_e1, sv_e2, e1a, e1b, e2a, e2b = zip(*w)
rough = np.array(rough,'float')
rough[rough==0.0] = np.nan
hard = np.array(hard,'float')
hard[hard==0.0] = np.nan
sv_e1 = np.array(sv_e1,'float')
sv_e1[sv_e1==0.0] = np.nan
sv_e2 = np.array(sv_e2,'float')
sv_e2[sv_e2==0.0] = np.nan
try:
nans, y= humutils.nan_helper(rough)
rough[nans]= np.interp(y(nans), y(~nans), rough[~nans])
except:
pass
try:
nans, y= humutils.nan_helper(hard)
hard[nans]= np.interp(y(nans), y(~nans), hard[~nans])
except:
pass
try:
nans, y= humutils.nan_helper(sv_e1)
sv_e1[nans]= np.interp(y(nans), y(~nans), sv_e1[~nans])
except:
pass
try:
nans, y= humutils.nan_helper(sv_e2)
sv_e2[nans]= np.interp(y(nans), y(~nans), sv_e2[~nans])
except:
pass
data = np.column_stack([sv_e1, sv_e2])
k_means = MiniBatchKMeans(numclusters)
# fit the model
k_means.fit(data)
values = k_means.cluster_centers_.squeeze()
labels = k_means.labels_
hardav = humutils.runningMeanFast(hard,integ)
roughav = humutils.runningMeanFast(rough,integ)
#f = open(sonpath+base+'rough_and_hard'+str(p)+'.csv', 'wt')
f = open(os.path.normpath(os.path.join(sonpath,base+'rough_and_hard'+str(p)+'.csv')), 'wt')
writer = csv.writer(f)
writer.writerow( ('longitude', 'latitude', 'easting', 'northing', 'depth', 'roughness', 'hardness', 'average roughness', 'average hardness','k-mean label') )
for i in range(0, len(rough)):
writer.writerow(( float(Zlon[i]),float(Zlat[i]),float(Zes[i]),float(Zns[i]),float(Zdepi[i]),float(rough[i]),float(hard[i]),float(roughav[i]),float(hardav[i]), labels[i].astype(int) ))
f.close()
if doplot==1:
try:
fig = plt.figure()
plt.imshow(dwnhi_fp[p], cmap='gray')
plt.plot(e1a,'r');
plt.plot(e1b,'y');
plt.plot(e2a,'c');
plt.plot(e2b,'m');
plt.axis('tight')
#plt.show()
custom_save(sonpath,'e1e2_scan'+str(p))
del fig
except:
print("plot could not be produced")
if doplot==1:
try:
fig = plt.figure()
fig.subplots_adjust(wspace = 0.4, hspace=0.4)
plt.subplot(221)
plt.plot(sv_e1[labels==0],sv_e2[labels==0],'ko');
plt.plot(sv_e1[labels==1],sv_e2[labels==1],'ro');
plt.plot(sv_e1[labels==2],sv_e2[labels==2],'bo');
plt.xlabel('SV1'); plt.ylabel('SV2')
plt.xlim(0,1); plt.ylim(0,1)
plt.subplot(222)
plt.plot(rough[labels==0],hard[labels==0],'ko');
plt.plot(rough[labels==1],hard[labels==1],'ro');
plt.plot(rough[labels==2],hard[labels==2],'bo');
plt.xlabel('E1'); plt.ylabel('E2')
plt.xlim(1,8); plt.ylim(1,8)
#plt.show()
custom_save(sonpath,'e1e2_kmeans'+str(p))
del fig
except:
print("plot could not be produced")
if doplot==1:
try:
fig = plt.figure()
s=plt.scatter(Zes[labels==0],Zns[labels==0],marker='o',c='k', s=10, linewidth=0, vmin=0, vmax=8);
s=plt.scatter(Zes[labels==1],Zns[labels==1],marker='o',c='r', s=10, linewidth=0, vmin=0, vmax=8);
s=plt.scatter(Zes[labels==2],Zns[labels==2],marker='o',c='b', s=10, linewidth=0, vmin=0, vmax=8);
custom_save(sonpath,'rgh_hard_kmeans'+str(p))
del fig
except:
print("plot could not be produced")
if doplot==1:
try:
print("drawing and printing map ...")
fig = plt.figure(frameon=False)
#fig.subplots_adjust(wspace = 0.4, hspace=0.4)
map = Basemap(projection='merc', epsg=cs2cs_args.split(':')[1], #epsg=26949,
resolution = 'i', #h #f
llcrnrlon=np.min(Zlon)-0.0001, llcrnrlat=np.min(Zlat)-0.0001,
urcrnrlon=np.max(Zlon)+0.0001, urcrnrlat=np.max(Zlat)+0.0001)
# draw point cloud
x,y = map.projtran(Zlon, Zlat)
cs = map.scatter(x.flatten(), y.flatten(), 1, rough.flatten(), linewidth=0, vmin=0, vmax=8)
try:
map.arcgisimage(server='http://server.arcgisonline.com/ArcGIS', service='ESRI_Imagery_World_2D', xpixels=1000, ypixels=None, dpi=300)
except:
map.arcgisimage(server='http://server.arcgisonline.com/ArcGIS', service='World_Imagery', xpixels=1000, ypixels=None, dpi=300)
cbar = map.colorbar(cs,location='bottom',pad="5%")
cbar.set_label('E1')
cbar.set_ticks([0,2,4,6,8])
custom_save(sonpath,'map_rgh'+str(p))
del fig
except:
print("plot could not be produced")
if doplot==1:
try:
fig = plt.figure()
#fig.subplots_adjust(wspace = 0.4, hspace=0.4)
map = Basemap(projection='merc', epsg=cs2cs_args.split(':')[1],
resolution = 'i', #h #f
llcrnrlon=np.min(Zlon)-0.0001, llcrnrlat=np.min(Zlat)-0.0001,
urcrnrlon=np.max(Zlon)+0.0001, urcrnrlat=np.max(Zlat)+0.0001)
# draw point cloud
x,y = map.projtran(Zlon, Zlat)
cs = map.scatter(x.flatten(), y.flatten(), 1, hard.flatten(), linewidth=0, vmin=0, vmax=8)
try:
map.arcgisimage(server='http://server.arcgisonline.com/ArcGIS', service='ESRI_Imagery_World_2D', xpixels=1000, ypixels=None, dpi=300)
except:
map.arcgisimage(server='http://server.arcgisonline.com/ArcGIS', service='World_Imagery', xpixels=1000, ypixels=None, dpi=300)
cbar = map.colorbar(cs,location='bottom',pad="5%")
cbar.set_label('E2')
cbar.set_ticks([0,2,4,6,8])
custom_save(sonpath,'map_hard'+str(p))
del fig
except:
print("plot could not be produced")
if doplot==1:
try:
print("drawing and printing map ...")
fig = plt.figure(frameon=False)
map = Basemap(projection='merc', epsg=cs2cs_args.split(':')[1], #26949,
resolution = 'i', #h #f
llcrnrlon=np.min(Zlon)-0.001, llcrnrlat=np.min(Zlat)-0.001,
urcrnrlon=np.max(Zlon)+0.001, urcrnrlat=np.max(Zlat)+0.001)
ax = plt.Axes(fig, [0., 0., 1., 1.], )
ax.set_axis_off()
fig.add_axes(ax)
## draw point cloud
x,y = map.projtran(Zlon, Zlat)
map.scatter(x.flatten(), y.flatten(), 1, rough.flatten(), linewidth = '0', vmin=0, vmax=8)
custom_save(sonpath,'Rough'+str(p))
del fig
kml = simplekml.Kml()
ground = kml.newgroundoverlay(name='GroundOverlay')
ground.icon.href = 'Rough'+str(p)+'.png'
ground.latlonbox.north = np.min(Zlat)-0.001
ground.latlonbox.south = np.max(Zlat)+0.001
ground.latlonbox.east = np.max(Zlon)+0.001
ground.latlonbox.west = np.min(Zlon)-0.001
ground.latlonbox.rotation = 0
#kml.save(sonpath+'Rough'+str(p)+'.kml')
kml.save(os.path.normpath(os.path.join(sonpath,'Rough'+str(p)+'.kml')))
except:
print("plot could not be produced")
if doplot==1:
try:
print("drawing and printing map ...")
fig = plt.figure(frameon=False)
map = Basemap(projection='merc', epsg=cs2cs_args.split(':')[1], #26949,
resolution = 'i', #h #f
llcrnrlon=np.min(Zlon)-0.001, llcrnrlat=np.min(Zlat)-0.001,
urcrnrlon=np.max(Zlon)+0.001, urcrnrlat=np.max(Zlat)+0.001)
ax = plt.Axes(fig, [0., 0., 1., 1.], )
ax.set_axis_off()
fig.add_axes(ax)
## draw point cloud
x,y = map.projtran(Zlon, Zlat)
map.scatter(x.flatten(), y.flatten(), 1, hard.flatten(), linewidth = '0', vmin=0, vmax=8)
custom_save(sonpath,'Hard'+str(p))
del fig
kml = simplekml.Kml()
ground = kml.newgroundoverlay(name='GroundOverlay')
ground.icon.href = 'Hard'+str(p)+'.png'
ground.latlonbox.north = np.min(Zlat)-0.001
ground.latlonbox.south = np.max(Zlat)+0.001
ground.latlonbox.east = np.max(Zlon)+0.001
ground.latlonbox.west = np.min(Zlon)-0.001
ground.latlonbox.rotation = 0
#kml.save(sonpath+'Hard'+str(p)+'.kml')
kml.save(os.path.normpath(os.path.join(sonpath,'Hard'+str(p)+'.kml')))
except:
print("plot could not be produced")
else:
if 2 > 1: # need to tiday all this up later!!
#make an index of every other record
ind = range(0,np.shape(dwnhi_fp)[1])
Zdepi = depi
Zabsorp = absorption
Zlat = lati
Zlon = loni
Zes = ei
Zns = ni
try: #parallel processing with all available cores
w = Parallel(n_jobs=-1, verbose=0)(delayed(get_rgh_hrd)(dwnhi_fp[:,i],Zdepi[i],Zabsorp[i],c,nf,transfreq,equivbeam,maxW,pi,ft) for i in ind)
except: #fall back to serial
w = Parallel(n_jobs=1, verbose=0)(delayed(get_rgh_hrd)(dwnhi_fp[:,i],Zdepi[i],Zabsorp[i],c,nf,transfreq,equivbeam,maxW,pi,ft) for i in ind)
rough, hard, sv_e1, sv_e2, e1a, e1b, e2a, e2b = zip(*w)
rough = np.array(rough,'float')
rough[rough==0.0] = np.nan
hard = np.array(hard,'float')
hard[hard==0.0] = np.nan
sv_e1 = np.array(sv_e1,'float')
sv_e1[sv_e1==0.0] = np.nan
sv_e2 = np.array(sv_e2,'float')
sv_e2[sv_e2==0.0] = np.nan
try:
nans, y= humutils.nan_helper(rough)
rough[nans]= np.interp(y(nans), y(~nans), rough[~nans])
except:
pass
try:
nans, y= humutils.nan_helper(hard)
hard[nans]= np.interp(y(nans), y(~nans), hard[~nans])
except:
pass
try:
nans, y= humutils.nan_helper(sv_e1)
sv_e1[nans]= np.interp(y(nans), y(~nans), sv_e1[~nans])
except:
pass
try:
nans, y= humutils.nan_helper(sv_e2)
sv_e2[nans]= np.interp(y(nans), y(~nans), sv_e2[~nans])
except:
pass
data = np.column_stack([sv_e1, sv_e2])
k_means = MiniBatchKMeans(numclusters)
# fit the model
k_means.fit(data)
values = k_means.cluster_centers_.squeeze()
labels = k_means.labels_
hardav = humutils.runningMeanFast(hard,integ)
roughav = humutils.runningMeanFast(rough,integ)
#f = open(sonpath+base+'rough_and_hard'+str(p)+'.csv', 'wt')
f = open(os.path.normpath(os.path.join(sonpath,base+'rough_and_hard'+str(0)+'.csv')), 'wt')
writer = csv.writer(f)
writer.writerow( ('longitude', 'latitude', 'easting', 'northing', 'depth', 'roughness', 'hardness', 'average roughness', 'average hardness','k-mean label') )
for i in range(0, len(rough)):
writer.writerow(( float(Zlon[i]),float(Zlat[i]),float(Zes[i]),float(Zns[i]),float(Zdepi[i]),float(rough[i]),float(hard[i]),float(roughav[i]),float(hardav[i]), labels[i].astype(int) ))
f.close()
if doplot==1:
try:
fig = plt.figure()
plt.imshow(dwnhi_fp, cmap='gray')
plt.plot(e1a,'r');
plt.plot(e1b,'y');
plt.plot(e2a,'c');
plt.plot(e2b,'m');
plt.axis('tight')
#plt.show()
custom_save(sonpath,'e1e2_scan'+str(0))
del fig
except:
print("plot could not be produced")
if doplot==1:
try:
fig = plt.figure()
fig.subplots_adjust(wspace = 0.4, hspace=0.4)
plt.subplot(221)
plt.plot(sv_e1[labels==0],sv_e2[labels==0],'ko');
plt.plot(sv_e1[labels==1],sv_e2[labels==1],'ro');
plt.plot(sv_e1[labels==2],sv_e2[labels==2],'bo');
plt.xlabel('SV1'); plt.ylabel('SV2')
plt.xlim(0,1); plt.ylim(0,1)
plt.subplot(222)
plt.plot(rough[labels==0],hard[labels==0],'ko');
plt.plot(rough[labels==1],hard[labels==1],'ro');
plt.plot(rough[labels==2],hard[labels==2],'bo');
plt.xlabel('E1'); plt.ylabel('E2')
plt.xlim(1,8); plt.ylim(1,8)
#plt.show()
custom_save(sonpath,'e1e2_kmeans'+str(0))
del fig
except:
print("plot could not be produced")
if doplot==1:
try:
fig = plt.figure()
s=plt.scatter(Zes[labels==0],Zns[labels==0],marker='o',c='k', s=10, linewidth=0, vmin=0, vmax=8);
s=plt.scatter(Zes[labels==1],Zns[labels==1],marker='o',c='r', s=10, linewidth=0, vmin=0, vmax=8);
s=plt.scatter(Zes[labels==2],Zns[labels==2],marker='o',c='b', s=10, linewidth=0, vmin=0, vmax=8);
custom_save(sonpath,'rgh_hard_kmeans'+str(0))
del fig
except:
print("plot could not be produced")
if doplot==1:
try:
print("drawing and printing map ...")
fig = plt.figure(frameon=False)
#fig.subplots_adjust(wspace = 0.4, hspace=0.4)
map = Basemap(projection='merc', epsg=cs2cs_args.split(':')[1], #epsg=26949,
resolution = 'i', #h #f
llcrnrlon=np.min(Zlon)-0.0001, llcrnrlat=np.min(Zlat)-0.0001,
urcrnrlon=np.max(Zlon)+0.0001, urcrnrlat=np.max(Zlat)+0.0001)
# draw point cloud
x,y = map.projtran(Zlon, Zlat)
cs = map.scatter(x.flatten(), y.flatten(), 1, rough.flatten(), linewidth=0, vmin=0, vmax=8)
try:
map.arcgisimage(server='http://server.arcgisonline.com/ArcGIS', service='ESRI_Imagery_World_2D', xpixels=1000, ypixels=None, dpi=300)
except:
map.arcgisimage(server='http://server.arcgisonline.com/ArcGIS', service='World_Imagery', xpixels=1000, ypixels=None, dpi=300)
cbar = map.colorbar(cs,location='bottom',pad="5%")
cbar.set_label('E1')
cbar.set_ticks([0,2,4,6,8])
custom_save(sonpath,'map_rgh'+str(0))
del fig
except:
print("plot could not be produced")
if doplot==1:
try:
fig = plt.figure()
#fig.subplots_adjust(wspace = 0.4, hspace=0.4)
map = Basemap(projection='merc', epsg=cs2cs_args.split(':')[1],
resolution = 'i', #h #f
llcrnrlon=np.min(Zlon)-0.0001, llcrnrlat=np.min(Zlat)-0.0001,
urcrnrlon=np.max(Zlon)+0.0001, urcrnrlat=np.max(Zlat)+0.0001)
# draw point cloud
x,y = map.projtran(Zlon, Zlat)
cs = map.scatter(x.flatten(), y.flatten(), 1, hard.flatten(), linewidth=0, vmin=0, vmax=8)
try:
map.arcgisimage(server='http://server.arcgisonline.com/ArcGIS', service='ESRI_Imagery_World_2D', xpixels=1000, ypixels=None, dpi=300)
except:
map.arcgisimage(server='http://server.arcgisonline.com/ArcGIS', service='World_Imagery', xpixels=1000, ypixels=None, dpi=300)
cbar = map.colorbar(cs,location='bottom',pad="5%")
cbar.set_label('E2')
cbar.set_ticks([0,2,4,6,8])
custom_save(sonpath,'map_hard'+str(0))
del fig
except:
print("plot could not be produced")
if doplot==1:
try:
print("drawing and printing map ...")
fig = plt.figure(frameon=False)
map = Basemap(projection='merc', epsg=cs2cs_args.split(':')[1], #26949,
resolution = 'i', #h #f
llcrnrlon=np.min(Zlon)-0.001, llcrnrlat=np.min(Zlat)-0.001,
urcrnrlon=np.max(Zlon)+0.001, urcrnrlat=np.max(Zlat)+0.001)
ax = plt.Axes(fig, [0., 0., 1., 1.], )
ax.set_axis_off()
fig.add_axes(ax)
## draw point cloud
x,y = map.projtran(Zlon, Zlat)
map.scatter(x.flatten(), y.flatten(), 1, rough.flatten(), linewidth = '0', vmin=0, vmax=8)
custom_save(sonpath,'Rough'+str(0))
del fig
kml = simplekml.Kml()
ground = kml.newgroundoverlay(name='GroundOverlay')
ground.icon.href = 'Rough'+str(0)+'.png'
ground.latlonbox.north = np.min(Zlat)-0.001
ground.latlonbox.south = np.max(Zlat)+0.001
ground.latlonbox.east = np.max(Zlon)+0.001
ground.latlonbox.west = np.min(Zlon)-0.001
ground.latlonbox.rotation = 0
#kml.save(sonpath+'Rough'+str(p)+'.kml')
kml.save(os.path.normpath(os.path.join(sonpath,'Rough'+str(0)+'.kml')))
except:
print("plot could not be produced")
if doplot==1:
try:
print("drawing and printing map ...")
fig = plt.figure(frameon=False)
map = Basemap(projection='merc', epsg=cs2cs_args.split(':')[1], #26949,
resolution = 'i', #h #f
llcrnrlon=np.min(Zlon)-0.001, llcrnrlat=np.min(Zlat)-0.001,
urcrnrlon=np.max(Zlon)+0.001, urcrnrlat=np.max(Zlat)+0.001)
ax = plt.Axes(fig, [0., 0., 1., 1.], )
ax.set_axis_off()
fig.add_axes(ax)
## draw point cloud
x,y = map.projtran(Zlon, Zlat)
map.scatter(x.flatten(), y.flatten(), 1, hard.flatten(), linewidth = '0', vmin=0, vmax=8)
custom_save(sonpath,'Hard'+str(0))
del fig
kml = simplekml.Kml()
ground = kml.newgroundoverlay(name='GroundOverlay')
ground.icon.href = 'Hard'+str(0)+'.png'
ground.latlonbox.north = np.min(Zlat)-0.001
ground.latlonbox.south = np.max(Zlat)+0.001
ground.latlonbox.east = np.max(Zlon)+0.001
ground.latlonbox.west = np.min(Zlon)-0.001
ground.latlonbox.rotation = 0
#kml.save(sonpath+'Hard'+str(p)+'.kml')
kml.save(os.path.normpath(os.path.join(sonpath,'Hard'+str(0)+'.kml')))
except:
print("plot could not be produced")
else:
print("high-frequency downward echosounder data not available")
def texture_slic(humfile, sonpath, doplot=1, numclasses=4, maxscale=20, notes=4):
'''
Create a texture lengthscale map using the algorithm detailed by Buscombe et al. (2015)
This textural lengthscale is not a direct measure of grain size. Rather, it is a statistical
representation that integrates over many attributes of bed texture, of which grain size is the most important.
The technique is a physically based means to identify regions of texture within a sidescan echogram,
and could provide a basis for objective, automated riverbed sediment classification.
Syntax
----------
[] = PyHum.texture(humfile, sonpath, doplot, numclasses, maxscale, notes)
Parameters
----------
humfile : str
path to the .DAT file
sonpath : str
path where the *.SON files are
doplot : int, *optional* [Default=1]
if 1, make plots, otherwise do not make plots
numclasses : int, *optional* [Default=4]
number of 'k means' that the texture lengthscale will be segmented into
maxscale : int, *optional* [Default=20]
Max scale as inverse fraction of data length for wavelet analysis
notes : int, *optional* [Default=100]
notes per octave for wavelet analysis
Returns
-------
sonpath+base+'_data_class.dat': memory-mapped file
contains the texture lengthscale map
sonpath+base+'_data_kclass.dat': memory-mapped file
contains the k-means segmented texture lengthscale map
References
----------
.. [1] Buscombe, D., Grams, P.E., and Smith, S.M.C., 2015, Automated riverbed sediment
classification using low-cost sidescan sonar. Journal of Hydraulic Engineering 10.1061/(ASCE)HY.1943-7900.0001079, 06015019.
'''
# prompt user to supply file if no input file given
if not humfile:
print('An input file is required!!!!!!')
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
humfile = askopenfilename(filetypes=[("DAT files","*.DAT")])
# prompt user to supply directory if no input sonpath is given
if not sonpath:
print('A *.SON directory is required!!!!!!')
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
sonpath = askdirectory()
# print given arguments to screen and convert data type where necessary
if humfile:
print('Input file is %s' % (humfile))
if sonpath:
print('Sonar file path is %s' % (sonpath))
if numclasses:
numclasses = np.asarray(numclasses,int)
print('Number of sediment classes: %s' % (str(numclasses)))
if maxscale:
maxscale = np.asarray(maxscale,int)
print('Max scale as inverse fraction of data length: %s' % (str(maxscale)))
if notes:
notes = np.asarray(notes,int)
print('Notes per octave: %s' % (str(notes)))
if doplot:
doplot = int(doplot)
if doplot==0:
print("Plots will not be made")
print('[Default] Number of processors is %s' % (str(cpu_count())))
########################################################
########################################################
# start timer
if os.name=='posix': # true if linux/mac or cygwin on windows
start = time.time()
else: # windows
start = time.clock()
# if son path name supplied has no separator at end, put one on
if sonpath[-1]!=os.sep:
sonpath = sonpath + os.sep
base = humfile.split('.DAT') # get base of file name for output
base = base[0].split(os.sep)[-1]
# remove underscores, negatives and spaces from basename
base = humutils.strip_base(base)
meta = loadmat(os.path.normpath(os.path.join(sonpath,base+'meta.mat')))
ft = 1/loadmat(sonpath+base+'meta.mat')['pix_m']
#pix_m = np.squeeze(meta['pix_m'])
#dep_m = np.squeeze(meta['dep_m'])
dist_m = np.squeeze(meta['dist_m'])
### port
print("processing port side ...")
# load memory mapped scan ... port
shape_port = np.squeeze(meta['shape_port'])
if shape_port!='':
if os.path.isfile(os.path.normpath(os.path.join(sonpath,base+'_data_port_lar.dat'))):
port_fp = io.get_mmap_data(sonpath, base, '_data_port_lar.dat', 'float32', tuple(shape_port))
else:
port_fp = io.get_mmap_data(sonpath, base, '_data_port_la.dat', 'float32', tuple(shape_port))
#port_fp2 = io.get_mmap_data(sonpath, base, '_data_port_l.dat', 'float32', tuple(shape_port))
### star
print("processing starboard side ...")
# load memory mapped scan ... port
shape_star = np.squeeze(loadmat(sonpath+base+'meta.mat')['shape_star'])
if shape_star!='':
if os.path.isfile(os.path.normpath(os.path.join(sonpath,base+'_data_star_lar.dat'))):
star_fp = io.get_mmap_data(sonpath, base, '_data_star_lar.dat', 'float32', tuple(shape_star))
else:
star_fp = io.get_mmap_data(sonpath, base, '_data_star_la.dat', 'float32', tuple(shape_star))
#star_fp2 = io.get_mmap_data(sonpath, base, '_data_star_l.dat', 'float32', tuple(shape_star))
if len(shape_star)>2:
shape = shape_port.copy()
shape[1] = shape_port[1] + shape_star[1]
else:
shape = []
shape.append(1)
shape.append(shape_port[0])
shape.append(shape_port[1])
shape[1] = shape_port[0] + shape_star[0]
#work on the entire scan
#im = humutils.rescale(np.vstack((np.flipud(np.hstack(port_fp)), np.hstack(star_fp))),0,1)
im = np.vstack((np.flipud(np.hstack(port_fp)), np.hstack(star_fp)))
im[np.isnan(im)] = 0
im = humutils.rescale(im,0,1)
#get SLIC superpixels
segments_slic = slic(im, n_segments=int(im.shape[0]/10), compactness=.1)
#pre-allocate texture lengthscale array
tl = np.zeros(im.shape, dtype = "float64")
#cycle through each segment and compute tl
for k in np.unique(segments_slic):
mask = np.zeros(im.shape[:2], dtype = "uint8")
mask[segments_slic == k] = 255
cmask, cim = crop_toseg(mask, im)
tl[segments_slic == k] = parallel_me(cim, maxscale, notes, np.shape(cim)[0])
R_fp = io.get_mmap_data(sonpath, base, '_data_range.dat', 'float32', tuple(shape_star))
R = np.vstack((np.flipud(np.hstack(R_fp)), np.hstack(R_fp)))
R = R/np.max(R)
#correct for range and scale
tl = tl * np.cos(R) * (1/ft)
tl[im==0] = np.nan
tl[np.isnan(im)] = np.nan
# create memory mapped file for Sp
with open(os.path.normpath(os.path.join(sonpath,base+'_data_class.dat')), 'w+') as ff:
fp = np.memmap(ff, dtype='float32', mode='w+', shape=tuple(shape))
counter = 0
if len(shape_star)>2:
for p in range(len(port_fp)):
if p==0:
n,m = np.shape(np.vstack((np.flipud(port_fp[p]), star_fp[p])))
else:
n,m = np.shape(np.vstack((np.flipud(port_fp[p]), star_fp[p])))
Sp = tl[:n, counter:counter+m]
counter = counter+m
fp[p] = Sp.astype('float32')
del Sp
del fp # flush data to file
class_fp = io.get_mmap_data(sonpath, base, '_data_class.dat', 'float32', tuple(shape))
else:
with open(os.path.normpath(os.path.join(sonpath,base+'_data_class.dat')), 'w+') as ff:
np.save(ff, np.squeeze(Sp).astype('float32'))
with open(os.path.normpath(os.path.join(sonpath,base+'_data_class.dat')), 'r') as ff:
class_fp = np.load(ff)
dist_m = np.squeeze(loadmat(sonpath+base+'meta.mat')['dist_m'])
########################################################
if doplot==1:
if len(shape_star)>2:
for p in range(len(star_fp)):
plot_class(dist_m, shape_port, port_fp[p], star_fp[p], class_fp[p], ft, humfile, sonpath, base, p)
else:
plot_class(dist_m, shape_port, port_fp, star_fp, class_fp, ft, humfile, sonpath, base, 0)
if len(shape_star)>2:
for p in range(len(star_fp)):
plot_contours(dist_m, shape_port, class_fp[p], ft, humfile, sonpath, base, numclasses, p)
else:
plot_contours(dist_m, shape_port, class_fp, ft, humfile, sonpath, base, numclasses, 0)
#######################################################
# k-means
if len(shape_star)>2:
with open(os.path.normpath(os.path.join(sonpath,base+'_data_kclass.dat')), 'w+') as ff:
fp = np.memmap(ff, dtype='float32', mode='w+', shape=tuple(shape))
for p in range(len(port_fp)):
wc = get_kclass(class_fp[p].copy(), numclasses)
fp[p] = wc.astype('float32')
del wc
del fp
kclass_fp = io.get_mmap_data(sonpath, base, '_data_kclass.dat', 'float32', tuple(shape))
else:
wc = get_kclass(class_fp.copy(), numclasses)
with open(os.path.normpath(os.path.join(sonpath,base+'_data_kclass.dat')), 'w+') as ff:
np.save(ff, np.squeeze(wc).astype('float32'))
del wc
with open(os.path.normpath(os.path.join(sonpath,base+'_data_kclass.dat')), 'r') as ff:
kclass_fp = np.load(ff)
########################################################
if doplot==1:
if len(shape_star)>2:
for p in range(len(star_fp)):
plot_kmeans(dist_m, shape_port, port_fp[p], star_fp[p], kclass_fp[p], ft, humfile, sonpath, base, p)
else:
plot_kmeans(dist_m, shape_port, port_fp, star_fp, kclass_fp, ft, humfile, sonpath, base, 0)
if os.name=='posix': # true if linux/mac
elapsed = (time.time() - start)
else: # windows
elapsed = (time.clock() - start)
print("Processing took "+str(elapsed)+"seconds to analyse")
print("Done!")
def map_texture(humfile, sonpath, cs2cs_args = "epsg:26949", res = 0.5, mode=3, nn = 64, numstdevs=5): #influence = 10,
'''
Create plots of the texture lengthscale maps made in PyHum.texture module
using the algorithm detailed by Buscombe et al. (2015)
This textural lengthscale is not a direct measure of grain size. Rather, it is a statistical
representation that integrates over many attributes of bed texture, of which grain size is the most important.
The technique is a physically based means to identify regions of texture within a sidescan echogram,
and could provide a basis for objective, automated riverbed sediment classification.
Syntax
----------
[] = PyHum.map_texture(humfile, sonpath, cs2cs_args, res, mode, nn, numstdevs)
Parameters
----------
humfile : str
path to the .DAT file
sonpath : str
path where the *.SON files are
cs2cs_args : int, *optional* [Default="epsg:26949"]
arguments to create coordinates in a projected coordinate system
this argument gets given to pyproj to turn wgs84 (lat/lon) coordinates
into any projection supported by the proj.4 libraries
res : float, *optional* [Default=0.5]
grid resolution of output gridded texture map
mode: int, *optional* [Default=3]
gridding mode. 1 = nearest neighbour
2 = inverse weighted nearest neighbour
3 = Gaussian weighted nearest neighbour
nn: int, *optional* [Default=64]
number of nearest neighbours for gridding (used if mode > 1)
numstdevs: int, *optional* [Default = 4]
Threshold number of standard deviations in texture lengthscale per grid cell up to which to accept
Returns
-------
sonpath+'x_y_class'+str(p)+'.asc' : text file
contains the point cloud of easting, northing, and texture lengthscales
of the pth chunk
sonpath+'class_GroundOverlay'+str(p)+'.kml': kml file
contains gridded (or point cloud) texture lengthscale map for importing into google earth
of the pth chunk
sonpath+'class_map'+str(p)+'.png' :
image overlay associated with the kml file
sonpath+'class_map_imagery'+str(p)+'.png' : png image file
gridded (or point cloud) texture lengthscale map
overlain onto an image pulled from esri image server
References
----------
.. [1] Buscombe, D., Grams, P.E., and Smith, S.M.C., 2015, Automated riverbed sediment
classification using low-cost sidescan sonar. Journal of Hydraulic Engineering 10.1061/(ASCE)HY.1943-7900.0001079, 06015019.
'''
# prompt user to supply file if no input file given
if not humfile:
print 'An input file is required!!!!!!'
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
humfile = askopenfilename(filetypes=[("DAT files","*.DAT")])
# prompt user to supply directory if no input sonpath is given
if not sonpath:
print 'A *.SON directory is required!!!!!!'
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
sonpath = askdirectory()
# print given arguments to screen and convert data type where necessary
if humfile:
print 'Input file is %s' % (humfile)
if sonpath:
print 'Sonar file path is %s' % (sonpath)
if cs2cs_args:
print 'cs2cs arguments are %s' % (cs2cs_args)
if res:
res = np.asarray(res,float)
print 'Gridding resolution: %s' % (str(res))
if mode:
mode = int(mode)
print 'Mode for gridding: %s' % (str(mode))
if nn:
nn = int(nn)
print 'Number of nearest neighbours for gridding: %s' % (str(nn))
#if influence:
# influence = int(influence)
# print 'Radius of influence for gridding: %s (m)' % (str(influence))
if numstdevs:
numstdevs = int(numstdevs)
print 'Threshold number of standard deviations in texture lengthscale per grid cell up to which to accept: %s' % (str(numstdevs))
# start timer
if os.name=='posix': # true if linux/mac or cygwin on windows
start = time.time()
else: # windows
start = time.clock()
trans = pyproj.Proj(init=cs2cs_args)
# if son path name supplied has no separator at end, put one on
if sonpath[-1]!=os.sep:
sonpath = sonpath + os.sep
base = humfile.split('.DAT') # get base of file name for output
base = base[0].split(os.sep)[-1]
# remove underscores, negatives and spaces from basename
base = humutils.strip_base(base)
meta = loadmat(os.path.normpath(os.path.join(sonpath,base+'meta.mat')))
esi = np.squeeze(meta['e'])
nsi = np.squeeze(meta['n'])
pix_m = np.squeeze(meta['pix_m'])
dep_m = np.squeeze(meta['dep_m'])
c = np.squeeze(meta['c'])
#dist_m = np.squeeze(meta['dist_m'])
theta = np.squeeze(meta['heading'])/(180/np.pi)
# load memory mapped scans
shape_port = np.squeeze(meta['shape_port'])
if shape_port!='':
if os.path.isfile(os.path.normpath(os.path.join(sonpath,base+'_data_port_lar.dat'))):
port_fp = io.get_mmap_data(sonpath, base, '_data_port_lar.dat', 'float32', tuple(shape_port))
else:
port_fp = io.get_mmap_data(sonpath, base, '_data_port_la.dat', 'float32', tuple(shape_port))
shape_star = np.squeeze(meta['shape_star'])
if shape_star!='':
if os.path.isfile(os.path.normpath(os.path.join(sonpath,base+'_data_star_lar.dat'))):
star_fp = io.get_mmap_data(sonpath, base, '_data_star_lar.dat', 'float32', tuple(shape_star))
else:
star_fp = io.get_mmap_data(sonpath, base, '_data_star_la.dat', 'float32', tuple(shape_star))
if len(shape_star)>2:
shape = shape_port.copy()
shape[1] = shape_port[1] + shape_star[1]
class_fp = io.get_mmap_data(sonpath, base, '_data_class.dat', 'float32', tuple(shape))
#with open(os.path.normpath(os.path.join(sonpath,base+'_data_class.dat')), 'r') as ff:
# class_fp = np.memmap(ff, dtype='float32', mode='r', shape=tuple(shape))
else:
with open(os.path.normpath(os.path.join(sonpath,base+'_data_class.dat')), 'r') as ff:
class_fp = np.load(ff)
tvg = ((8.5*10**-5)+(3/76923)+((8.5*10**-5)/4))*c
dist_tvg = ((np.tan(np.radians(25)))*dep_m)-(tvg)
if len(shape_star)>2:
for p in xrange(len(class_fp)):
e = esi[shape_port[-1]*p:shape_port[-1]*(p+1)]
n = nsi[shape_port[-1]*p:shape_port[-1]*(p+1)]
t = theta[shape_port[-1]*p:shape_port[-1]*(p+1)]
d = dist_tvg[shape_port[-1]*p:shape_port[-1]*(p+1)]
len_n = len(n)
merge = class_fp[p].copy()
merge[np.isnan(merge)] = 0
merge[np.isnan(np.vstack((np.flipud(port_fp[p]),star_fp[p])))] = 0
extent = shape_port[1]
R1 = merge[extent:,:]
R2 = np.flipud(merge[:extent,:])
merge = np.vstack((R2,R1))
del R1, R2
# get number pixels in scan line
extent = int(np.shape(merge)[0]/2)
yvec = np.linspace(pix_m,extent*pix_m,extent)
X, Y = getXY(e,n,yvec,d,t,extent)
merge[merge==0] = np.nan
if len(merge.flatten()) != len(X):
merge = merge[:,:len_n]
merge = merge.T.flatten()
index = np.where(np.logical_not(np.isnan(merge)))[0]
X, Y, merge = trim_xys(X, Y, merge, index)
X = X.astype('float32')
Y = Y.astype('float32')
merge = merge.astype('float32')
# write raw bs to file
outfile = os.path.normpath(os.path.join(sonpath,'x_y_class'+str(p)+'.asc'))
with open(outfile, 'w') as f:
np.savetxt(f, np.hstack((humutils.ascol(X),humutils.ascol(Y), humutils.ascol(merge))), delimiter=' ', fmt="%8.6f %8.6f %8.6f")
humlon, humlat = trans(X, Y, inverse=True)
#if dogrid==1:
orig_def, targ_def, grid_x, grid_y, res, shape = get_griddefs(np.min(X), np.max(X), np.min(Y), np.max(Y), res, humlon, humlat, trans)
grid_x = grid_x.astype('float32')
grid_y = grid_y.astype('float32')
sigmas = 1 #m
eps = 2
dat, res = get_grid(mode, orig_def, targ_def, merge, res*10, np.min(X), np.max(X), np.min(Y), np.max(Y), res, nn, sigmas, eps, shape, numstdevs, trans, humlon, humlat)
del merge
dat[dat==0] = np.nan
dat[np.isinf(dat)] = np.nan
datm = np.ma.masked_invalid(dat)
del dat
glon, glat = trans(grid_x, grid_y, inverse=True)
del grid_x, grid_y
vmin=np.nanmin(datm)+0.1
vmax=np.nanmax(datm)-0.1
if vmin > vmax:
vmin=np.nanmin(datm)
vmax=np.nanmax(datm)
print_map(cs2cs_args, glon, glat, datm, sonpath, p, vmin=vmin, vmax=vmax)
else: #just 1 chunk
e = esi
n = nsi
t = theta
d = dist_tvg
len_n = len(n)
merge = class_fp.copy()
merge[np.isnan(merge)] = 0
merge[np.isnan(np.vstack((np.flipud(port_fp),star_fp)))] = 0
extent = shape_port[0]
R1 = merge[extent:,:]
R2 = np.flipud(merge[:extent,:])
merge = np.vstack((R2,R1))
del R1, R2
# get number pixels in scan line
extent = int(np.shape(merge)[0]/2)
yvec = np.linspace(pix_m,extent*pix_m,extent)
X, Y = getXY(e,n,yvec,d,t,extent)
merge[merge==0] = np.nan
if len(merge.flatten()) != len(X):
merge = merge[:,:len_n]
merge = merge.T.flatten()
index = np.where(np.logical_not(np.isnan(merge)))[0]
X, Y, merge = trim_xys(X, Y, merge, index)
# write raw bs to file
outfile = os.path.normpath(os.path.join(sonpath,'x_y_class'+str(0)+'.asc'))
with open(outfile, 'w') as f:
np.savetxt(f, np.hstack((humutils.ascol(X),humutils.ascol(Y), humutils.ascol(merge))), delimiter=' ', fmt="%8.6f %8.6f %8.6f")
humlon, humlat = trans(X, Y, inverse=True)
#if dogrid==1:
if 2>1:
orig_def, targ_def, grid_x, grid_y, res, shape = get_griddefs(np.min(X), np.max(X), np.min(Y), np.max(Y), res, humlon, humlat, trans)
## create mask for where the data is not
tree = KDTree(np.c_[X.flatten(),Y.flatten()])
if pykdtree==1:
dist, _ = tree.query(np.c_[grid_x.ravel(), grid_y.ravel()], k=1)
else:
try:
dist, _ = tree.query(np.c_[grid_x.ravel(), grid_y.ravel()], k=1, n_jobs=cpu_count())
except:
#print ".... update your scipy installation to use faster kd-tree queries"
dist, _ = tree.query(np.c_[grid_x.ravel(), grid_y.ravel()], k=1)
dist = dist.reshape(grid_x.shape)
sigmas = 1 #m
eps = 2
dat, res = get_grid(mode, orig_def, targ_def, merge, res*10, np.min(X), np.max(X), np.min(Y), np.max(Y), res, nn, sigmas, eps, shape, numstdevs, trans, humlon, humlat)
del merge
#if dogrid==1:
if 2>1:
dat[dat==0] = np.nan
dat[np.isinf(dat)] = np.nan
dat[dist>res*2] = np.nan
del dist
datm = np.ma.masked_invalid(dat)
glon, glat = trans(grid_x, grid_y, inverse=True)
del grid_x, grid_y
vmin=np.nanmin(datm)+0.1
vmax=np.nanmax(datm)-0.1
if vmin > vmax:
vmin=np.nanmin(datm)
vmax=np.nanmax(datm)
Parallel(n_jobs = 2, verbose=0)(delayed(doplots)(k, humlon, humlat, cs2cs_args, glon, glat, datm, sonpath, 0, vmin=vmin, vmax=vmax) for k in xrange(2))
#print_map(cs2cs_args, glon, glat, datm, sonpath, 0, vmin=vmin, vmax=vmax)
#print_contour_map(cs2cs_args, humlon, humlat, glon, glat, datm, sonpath, 0, vmin=vmin, vmax=vmax)
if os.name=='posix': # true if linux/mac
elapsed = (time.time() - start)
else: # windows
elapsed = (time.clock() - start)
print "Processing took ", elapsed , "seconds to analyse"
print "Done!"
def texture2(humfile, sonpath, win, doplot, numclasses):
'''
Create a texture lengthscale map using the algorithm detailed by Buscombe et al. (2015)
This textural lengthscale is not a direct measure of grain size. Rather, it is a statistical
representation that integrates over many attributes of bed texture, of which grain size is the most important.
The technique is a physically based means to identify regions of texture within a sidescan echogram,
and could provide a basis for objective, automated riverbed sediment classification.
Syntax
----------
[] = PyHum.texture(humfile, sonpath, win, doplot, numclasses)
Parameters
----------
humfile : str
path to the .DAT file
sonpath : str
path where the *.SON files are
win : int, *optional* [Default=10]
pixel in pixels of the moving window
doplot : int, *optional* [Default=1]
if 1, make plots, otherwise do not make plots
numclasses : int, *optional* [Default=4]
number of 'k means' that the texture lengthscale will be segmented into
Returns
-------
sonpath+base+'_data_class.dat': memory-mapped file
contains the texture lengthscale map
sonpath+base+'_data_kclass.dat': memory-mapped file
contains the k-means segmented texture lengthscale map
References
----------
.. [1] Buscombe, D., Grams, P.E., and Smith, S.M.C., 2015, Automated riverbed sediment
classification using low-cost sidescan sonar. Journal of Hydraulic Engineering 10.1061/(ASCE)HY.1943-7900.0001079, 06015019.
'''
# prompt user to supply file if no input file given
if not humfile:
print('An input file is required!!!!!!')
Tk().withdraw(
) # we don't want a full GUI, so keep the root window from appearing
humfile = askopenfilename(filetypes=[("DAT files", "*.DAT")])
# prompt user to supply directory if no input sonpath is given
if not sonpath:
print('A *.SON directory is required!!!!!!')
Tk().withdraw(
) # we don't want a full GUI, so keep the root window from appearing
sonpath = askdirectory()
# print given arguments to screen and convert data type where necessary
if humfile:
print('Input file is %s' % (humfile))
if sonpath:
print('Sonar file path is %s' % (sonpath))
if win:
win = np.asarray(win, int)
print('Window is %s square pixels' % (str(win)))
if numclasses:
numclasses = np.asarray(numclasses, int)
print('Number of sediment classes: %s' % (str(numclasses)))
if doplot:
doplot = int(doplot)
if doplot == 0:
print("Plots will not be made")
print('[Default] Number of processors is %s' % (str(cpu_count())))
########################################################
########################################################
# start timer
if os.name == 'posix': # true if linux/mac or cygwin on windows
start = time.time()
else: # windows
start = time.clock()
# if son path name supplied has no separator at end, put one on
if sonpath[-1] != os.sep:
sonpath = sonpath + os.sep
base = humfile.split('.DAT') # get base of file name for output
base = base[0].split(os.sep)[-1]
# remove underscores, negatives and spaces from basename
base = humutils.strip_base(base)
meta = loadmat(os.path.normpath(os.path.join(sonpath, base + 'meta.mat')))
ft = 1 / loadmat(sonpath + base + 'meta.mat')['pix_m']
#pix_m = np.squeeze(meta['pix_m'])
#dep_m = np.squeeze(meta['dep_m'])
dist_m = np.squeeze(meta['dist_m'])
### port
print("processing port side ...")
# load memory mapped scan ... port
shape_port = np.squeeze(meta['shape_port'])
if shape_port != '':
if os.path.isfile(
os.path.normpath(
os.path.join(sonpath, base + '_data_port_lar.dat'))):
port_fp = io.get_mmap_data(sonpath, base, '_data_port_lar.dat',
'float32', tuple(shape_port))
else:
port_fp = io.get_mmap_data(sonpath, base, '_data_port_la.dat',
'float32', tuple(shape_port))
port_fp2 = io.get_mmap_data(sonpath, base, '_data_port_l.dat',
'float32', tuple(shape_port))
### star
print("processing starboard side ...")
# load memory mapped scan ... port
shape_star = np.squeeze(loadmat(sonpath + base + 'meta.mat')['shape_star'])
if shape_star != '':
if os.path.isfile(
os.path.normpath(
os.path.join(sonpath, base + '_data_star_lar.dat'))):
star_fp = io.get_mmap_data(sonpath, base, '_data_star_lar.dat',
'float32', tuple(shape_star))
else:
star_fp = io.get_mmap_data(sonpath, base, '_data_star_la.dat',
'float32', tuple(shape_star))
star_fp2 = io.get_mmap_data(sonpath, base, '_data_star_l.dat',
'float32', tuple(shape_star))
if len(shape_star) > 2:
shape = shape_port.copy()
shape[1] = shape_port[1] + shape_star[1]
else:
shape = []
shape.append(1)
shape.append(shape_port[0])
shape.append(shape_port[1])
shape[1] = shape_port[0] + shape_star[0]
# create memory mapped file for Sp
#with open(os.path.normpath(os.path.join(sonpath,base+'_data_class.dat')), 'w+') as ff:
# fp = np.memmap(ff, dtype='float32', mode='w+', shape=tuple(shape))
fp = np.zeros(tuple(shape), dtype='float32')
if len(shape_star) > 2:
for p in range(len(port_fp)):
merge = np.vstack((np.flipud(port_fp[p]), star_fp[p]))
merge = denoise_tv_chambolle(merge.copy(),
weight=2,
multichannel=False).astype('float32')
Snn = std_convoluted(merge, win)[1]
del merge
try:
Snn = medfilt2d(Snn, (win + 1, win + 1))
except:
Snn = medfilt2d(Snn, (win, win))
Snn[np.isnan(np.vstack(
(np.flipud(port_fp[p]), star_fp[p])))] = np.nan
Snn[np.isnan(np.vstack(
(np.flipud(port_fp2[p]), star_fp2[p])))] = np.nan
R_fp = io.get_mmap_data(sonpath, base, '_data_range.dat',
'float32', tuple(shape_star))
R = np.vstack((np.flipud(R_fp[0]), R_fp[0]))
R = R / np.max(R)
rn = replace_nans.RN(R.astype('float64'), 1000, 0.01, 2,
'localmean')
R = rn.getdata()
del rn
Sp = (Snn**2) * np.cos(np.deg2rad(R)) / win ##**2
fp[p] = Sp.astype('float32')
del Sp
#del fp # flush data to file
shape = io.set_mmap_data(sonpath, base, '_data_class.dat', 'float32',
np.squeeze(fp))
del fp
class_fp = io.get_mmap_data(sonpath, base, '_data_class.dat',
'float32', tuple(shape))
else:
merge = np.vstack((np.flipud(port_fp), star_fp))
merge = denoise_tv_chambolle(merge.copy(),
weight=2,
multichannel=False).astype('float32')
Snn = std_convoluted(merge, win)[1]
del merge
try:
Snn = medfilt2d(Snn, (win + 1, win + 1))
except:
Snn = medfilt2d(Snn, (win, win))
Snn[np.isnan(np.vstack((np.flipud(port_fp), star_fp)))] = np.nan
Snn[np.isnan(np.vstack((np.flipud(port_fp2), star_fp2)))] = np.nan
R_fp = io.get_mmap_data(sonpath, base, '_data_range.dat', 'float32',
tuple(shape_star))
R = np.vstack((np.flipud(R_fp), R_fp))
R = R / np.max(R)
rn = replace_nans.RN(R.astype('float64'), 1000, 0.01, 2, 'localmean')
R = rn.getdata()
del rn
Sp = (Snn**2) * np.cos(np.deg2rad(R)) / win ##**2
shape = io.set_mmap_data(sonpath, base, '_data_class.dat', 'float32',
np.squeeze(Sp))
#with open(os.path.normpath(os.path.join(sonpath,base+'_data_class.dat')), 'w+') as ff:
# np.save(ff, np.squeeze(Sp).astype('float32'))
#with open(os.path.normpath(os.path.join(sonpath,base+'_data_class.dat')), 'r') as ff:
# class_fp = np.load(ff)
#del Sp
class_fp = io.get_mmap_data(sonpath, base, '_data_class.dat',
'float32', tuple(shape))
dist_m = np.squeeze(loadmat(sonpath + base + 'meta.mat')['dist_m'])
########################################################
if doplot == 1:
if len(shape_star) > 2:
for p in range(len(star_fp)):
plot_class(dist_m, shape_port, port_fp[p], star_fp[p],
class_fp[p], ft, humfile, sonpath, base, p)
else:
plot_class(dist_m, shape_port, port_fp, star_fp, class_fp, ft,
humfile, sonpath, base, 0)
if len(shape_star) > 2:
for p in range(len(star_fp)):
plot_contours(dist_m, shape_port, class_fp[p], ft, humfile,
sonpath, base, numclasses, p)
else:
plot_contours(dist_m, shape_port, port_fp, star_fp, class_fp, ft,
humfile, sonpath, base, numclasses, 0)
#######################################################
# k-means
if len(shape_star) > 2:
with open(
os.path.normpath(
os.path.join(sonpath, base + '_data_kclass.dat')),
'w+') as ff:
fp = np.memmap(ff, dtype='float32', mode='w+', shape=tuple(shape))
for p in range(len(port_fp)):
wc = get_kclass(class_fp[p].copy(), numclasses)
fp[p] = wc.astype('float32')
del wc
del fp
kclass_fp = io.get_mmap_data(sonpath, base, '_data_kclass.dat',
'float32', tuple(shape))
else:
wc = get_kclass(class_fp.copy(), numclasses)
with open(
os.path.normpath(
os.path.join(sonpath, base + '_data_kclass.dat')),
'w+') as ff:
np.save(ff, np.squeeze(wc).astype('float32'))
del wc
with open(
os.path.normpath(
os.path.join(sonpath, base + '_data_kclass.dat')),
'r') as ff:
kclass_fp = np.load(ff)
########################################################
if doplot == 1:
if len(shape_star) > 2:
for p in range(len(star_fp)):
plot_kmeans(dist_m, shape_port, port_fp[p], star_fp[p],
kclass_fp[p], ft, humfile, sonpath, base, p)
else:
plot_kmeans(dist_m, shape_port, port_fp, star_fp, kclass_fp, ft,
humfile, sonpath, base, 0)
if os.name == 'posix': # true if linux/mac
elapsed = (time.time() - start)
else: # windows
elapsed = (time.clock() - start)
print("Processing took " + str(elapsed) + "seconds to analyse")
print("Done!")
print("===================================================")
def rmshadows(humfile, sonpath, win=31, shadowmask=0, doplot=1):
'''
Remove dark shadows in scans caused by shallows, shorelines, and attenuation of acoustics with distance
Manual or automated processing options available
Works on the radiometrically corrected outputs of the correct module
Syntax
----------
[] = PyHum.rmshadows(humfile, sonpath, win, shadowmask, doplot)
Parameters
----------
humfile : str
path to the .DAT file
sonpath : str
path where the *.SON files are
win : int, *optional* [Default=100]
window size (pixels) for the automated shadow removal algorithm
shadowmask : int, *optional* [Default=0]
1 = do manual shadow masking, otherwise do automatic shadow masking
doplot : int, *optional* [Default=1]
1 = make plots, otherwise do not
Returns
-------
sonpath+base+'_data_star_la.dat': memory-mapped file
contains the starboard scan with water column removed and
radiometrically corrected, and shadows removed
sonpath+base+'_data_port_la.dat': memory-mapped file
contains the portside scan with water column removed and
radiometrically corrected, and shadows removed
'''
# prompt user to supply file if no input file given
if not humfile:
print 'An input file is required!!!!!!'
Tk().withdraw(
) # we don't want a full GUI, so keep the root window from appearing
humfile = askopenfilename(filetypes=[("DAT files", "*.DAT")])
# prompt user to supply directory if no input sonpath is given
if not sonpath:
print 'A *.SON directory is required!!!!!!'
Tk().withdraw(
) # we don't want a full GUI, so keep the root window from appearing
sonpath = askdirectory()
# print given arguments to screen and convert data type where necessary
if humfile:
print 'Input file is %s' % (humfile)
if sonpath:
print 'Sonar file path is %s' % (sonpath)
if win:
win = np.asarray(win, int)
print 'Window is %s square pixels' % (str(win))
if shadowmask:
shadowmask = np.asarray(shadowmask, int)
if shadowmask == 1:
print 'Shadow masking is manual'
else:
print 'Shadow masking is auto'
if doplot:
doplot = int(doplot)
if doplot == 0:
print "Plots will not be made"
# start timer
if os.name == 'posix': # true if linux/mac or cygwin on windows
start = time.time()
else: # windows
start = time.clock()
# if son path name supplied has no separator at end, put one on
if sonpath[-1] != os.sep:
sonpath = sonpath + os.sep
base = humfile.split('.DAT') # get base of file name for output
base = base[0].split(os.sep)[-1]
base = humutils.strip_base(base)
meta = loadmat(os.path.normpath(os.path.join(sonpath, base + 'meta.mat')))
# load memory mapped scans
shape_port = np.squeeze(meta['shape_port'])
if shape_port != '':
#port_fp = np.memmap(sonpath+base+'_data_port_la.dat', dtype='float32', mode='r', shape=tuple(shape_port))
with open(
os.path.normpath(
os.path.join(sonpath, base + '_data_port_la.dat')),
'r') as ff:
port_fp = np.memmap(ff,
dtype='float32',
mode='r',
shape=tuple(shape_port))
shape_star = np.squeeze(meta['shape_star'])
if shape_star != '':
#star_fp = np.memmap(sonpath+base+'_data_star_la.dat', dtype='float32', mode='r', shape=tuple(shape_star))
with open(
os.path.normpath(
os.path.join(sonpath, base + '_data_star_la.dat')),
'r') as ff:
star_fp = np.memmap(ff,
dtype='float32',
mode='r',
shape=tuple(shape_star))
dist_m = np.squeeze(meta['dist_m'])
ft = 1 / (meta['pix_m'])
extent = shape_star[1]
if shadowmask == 1: #manual
Zt = []
if len(np.shape(star_fp)) > 2:
for p in xrange(len(star_fp)):
raw_input(
"Shore picking (starboard), are you ready? 30 seconds. Press Enter to continue..."
)
shoreline_star = {}
fig = plt.figure()
ax = plt.gca()
ax.imshow(star_fp[p], cmap='gray') #, origin = 'upper') #im =
plt.axis('normal')
plt.axis('tight')
pts1 = plt.ginput(
n=300,
timeout=30) # it will wait for 200 clicks or 30 seconds
x1 = map(lambda x: x[0],
pts1) # map applies the function passed as
y1 = map(lambda x: x[1],
pts1) # first parameter to each element of pts
shoreline_star = np.interp(np.r_[:np.shape(star_fp[p])[1]], x1,
y1)
plt.close()
del fig
star_mg = star_fp[p].copy()
shoreline_star = np.asarray(shoreline_star, 'int')
# shift proportionally depending on where the bed is
for k in xrange(np.shape(star_mg)[1]):
star_mg[shoreline_star[k]:, k] = np.nan
del shoreline_star
Zt.append(star_mg)
else:
raw_input(
"Shore picking (starboard), are you ready? 30 seconds. Press Enter to continue..."
)
shoreline_star = {}
fig = plt.figure()
ax = plt.gca()
ax.imshow(star_fp, cmap='gray') #, origin = 'upper') #im =
plt.axis('normal')
plt.axis('tight')
pts1 = plt.ginput(
n=300, timeout=30) # it will wait for 200 clicks or 30 seconds
x1 = map(lambda x: x[0],
pts1) # map applies the function passed as
y1 = map(lambda x: x[1],
pts1) # first parameter to each element of pts
shoreline_star = np.interp(np.r_[:np.shape(star_fp)[1]], x1, y1)
plt.close()
del fig
star_mg = star_fp.copy()
shoreline_star = np.asarray(shoreline_star, 'int')
# shift proportionally depending on where the bed is
for k in xrange(np.shape(star_mg)[1]):
star_mg[shoreline_star[k]:, k] = np.nan
del shoreline_star
Zt.append(star_mg)
## create memory mapped file for Z
#p = np.memmap(sonpath+base+'_data_star_la.dat', dtype='float32', mode='w+', shape=np.shape(Zt))
#fp[:] = Zt[:]
#del fp
Zt = np.squeeze(Zt)
# create memory mapped file for Zs
#fp = np.memmap(sonpath+base+'_data_star_lar.dat', dtype='float32', mode='w+', shape=np.shape(Zs))
with open(
os.path.normpath(
os.path.join(sonpath, base + '_data_star_lar.dat')),
'w+') as ff:
fp = np.memmap(ff, dtype='float32', mode='w+', shape=np.shape(Zt))
fp[:] = Zt[:]
del fp
del Zt
#shutil.move(os.path.normpath(os.path.join(sonpath,base+'_data_star_lar.dat')), os.path.normpath(os.path.join(sonpath,base+'_data_star_la.dat')))
Zt = []
if len(np.shape(star_fp)) > 2:
for p in xrange(len(port_fp)):
raw_input(
"Shore picking (port), are you ready? 30 seconds. Press Enter to continue..."
)
shoreline_port = {}
fig = plt.figure()
ax = plt.gca()
ax.imshow(port_fp[p], cmap='gray') #, origin = 'upper') #im =
plt.axis('normal')
plt.axis('tight')
pts1 = plt.ginput(
n=300,
timeout=30) # it will wait for 200 clicks or 30 seconds
x1 = map(lambda x: x[0],
pts1) # map applies the function passed as
y1 = map(lambda x: x[1],
pts1) # first parameter to each element of pts
shoreline_port = np.interp(np.r_[:np.shape(port_fp[p])[1]], x1,
y1)
plt.close()
del fig
port_mg = port_fp[p].copy()
shoreline_port = np.asarray(shoreline_port, 'int')
# shift proportionally depending on where the bed is
for k in xrange(np.shape(port_mg)[1]):
port_mg[shoreline_port[k]:, k] = np.nan
del shoreline_port
Zt.append(port_mg)
else:
raw_input(
"Shore picking (port), are you ready? 30 seconds. Press Enter to continue..."
)
shoreline_port = {}
fig = plt.figure()
ax = plt.gca()
ax.imshow(port_fp, cmap='gray') #, origin = 'upper') #im =
plt.axis('normal')
plt.axis('tight')
pts1 = plt.ginput(
n=300, timeout=30) # it will wait for 200 clicks or 30 seconds
x1 = map(lambda x: x[0],
pts1) # map applies the function passed as
y1 = map(lambda x: x[1],
pts1) # first parameter to each element of pts
shoreline_port = np.interp(np.r_[:np.shape(port_fp)[1]], x1, y1)
plt.close()
del fig
port_mg = port_fp.copy()
shoreline_port = np.asarray(shoreline_port, 'int')
# shift proportionally depending on where the bed is
for k in xrange(np.shape(port_mg)[1]):
port_mg[shoreline_port[k]:, k] = np.nan
del shoreline_port
Zt.append(port_mg)
Zt = np.squeeze(Zt)
## create memory mapped file for Z
#fp = np.memmap(sonpath+base+'_data_port_la.dat', dtype='float32', mode='w+', shape=np.shape(Zt))
#fp[:] = Zt[:]
#del fp
# create memory mapped file for Zp
#fp = np.memmap(sonpath+base+'_data_port_lar.dat', dtype='float32', mode='w+', shape=np.shape(Zp))
with open(
os.path.normpath(
os.path.join(sonpath, base + '_data_port_lar.dat')),
'w+') as ff:
fp = np.memmap(ff, dtype='float32', mode='w+', shape=np.shape(Zt))
fp[:] = Zt[:]
del fp
del Zt
#shutil.move(os.path.normpath(os.path.join(sonpath,base+'_data_port_lar.dat')), os.path.normpath(os.path.join(sonpath,base+'_data_port_la.dat')))
else: #auto
#win = 31
Zs = []
Zp = []
if len(np.shape(star_fp)) > 2:
for p in xrange(len(star_fp)):
merge = np.vstack((np.flipud(port_fp[p]), star_fp[p]))
merge = np.asarray(merge, 'float64')
merge_mask = np.vstack((np.flipud(port_fp[p]), star_fp[p]))
merge[merge_mask == 0] = 0
del merge_mask
mask = np.asarray(merge != 0,
'int8') # only 8bit precision needed
merge[np.isnan(merge)] = 0
#Z,ind = humutils.sliding_window(merge,(win,win),(win/2,win/2))
Z, ind = humutils.sliding_window(merge, (win, win), (win, win))
#zmean = np.reshape(zmean, ( ind[0], ind[1] ) )
Ny, Nx = np.shape(merge)
#zmean[np.isnan(zmean)] = 0
try: #parallel processing with all available cores
w = Parallel(n_jobs=-1,
verbose=0)(delayed(parallel_me)(Z[k])
for k in xrange(len(Z)))
except: #fall back to serial
w = Parallel(n_jobs=1,
verbose=0)(delayed(parallel_me)(Z[k])
for k in xrange(len(Z)))
zmean = np.reshape(w, (ind[0], ind[1]))
del w
M = humutils.im_resize(zmean, Nx, Ny)
M[mask == 0] = 0
del zmean
bw = M > 0.5
del M
# erode and dilate to remove splotches of no data
bw2 = binary_dilation(binary_erosion(bw,
structure=np.ones(
(3, 3))),
structure=np.ones((13, 13)))
#bw2 = binary_dilation(binary_erosion(bw,structure=np.ones((win/4,win/4))), structure=np.ones((win/4,win/4)))
##bw2 = binary_erosion(bw,structure=np.ones((win*2,win*2)))
## fill holes
bw2 = binary_fill_holes(bw2, structure=np.ones(
(win, win))).astype(int)
merge2 = grey_erosion(merge, structure=np.ones((win, win)))
#del bw
#bw2 = np.asarray(bw2!=0,'int8') # we only need 8 bit precision
bw2 = np.asarray(bw != 0,
'int8') # we only need 8 bit precision
del bw
merge[bw2 == 1] = 0 #blank out bad data
merge[merge2 == np.min(merge2)] = 0 #blank out bad data
del merge2
## do plots of merged scans
if doplot == 1:
Zdist = dist_m[shape_port[-1] * p:shape_port[-1] * (p + 1)]
fig = plt.figure()
plt.imshow(merge,
cmap='gray',
extent=[
min(Zdist),
max(Zdist), -extent * (1 / ft),
extent * (1 / ft)
])
plt.ylabel('Range (m)'), plt.xlabel(
'Distance along track (m)')
plt.axis('normal')
plt.axis('tight')
custom_save(sonpath,
'merge_corrected_rmshadow_scan' + str(p))
del fig
Zp.append(np.flipud(merge[:shape_port[1], :]))
Zs.append(merge[shape_port[1]:, :])
del merge, bw2
else:
merge = np.vstack((np.flipud(port_fp), star_fp))
merge = np.asarray(merge, 'float64')
merge_mask = np.vstack((np.flipud(port_fp), star_fp))
merge[merge_mask == 0] = 0
del merge_mask
mask = np.asarray(merge != 0, 'int8') # only 8bit precision needed
merge[np.isnan(merge)] = 0
#Z,ind = humutils.sliding_window(merge,(win,win),(win/2,win/2))
Z, ind = humutils.sliding_window(merge, (win, win), (win, win))
#zmean = np.reshape(zmean, ( ind[0], ind[1] ) )
Ny, Nx = np.shape(merge)
#zmean[np.isnan(zmean)] = 0
try: #parallel processing with all available cores
w = Parallel(n_jobs=-1, verbose=0)(delayed(parallel_me)(Z[k])
for k in xrange(len(Z)))
except: #fall back to serial
w = Parallel(n_jobs=1, verbose=0)(delayed(parallel_me)(Z[k])
for k in xrange(len(Z)))
zmean = np.reshape(w, (ind[0], ind[1]))
del w
M = humutils.im_resize(zmean, Nx, Ny)
M[mask == 0] = 0
del zmean
bw = M > 0.5
del M
# erode and dilate to remove splotches of no data
bw2 = binary_dilation(binary_erosion(bw, structure=np.ones(
(3, 3))),
structure=np.ones((13, 13)))
#bw2 = binary_dilation(binary_erosion(bw,structure=np.ones((win/4,win/4))), structure=np.ones((win/4,win/4)))
##bw2 = binary_erosion(bw,structure=np.ones((win*2,win*2)))
## fill holes
bw2 = binary_fill_holes(bw2, structure=np.ones(
(win, win))).astype(int)
merge2 = grey_erosion(merge, structure=np.ones((win, win)))
#del bw
#bw2 = np.asarray(bw2!=0,'int8') # we only need 8 bit precision
bw2 = np.asarray(bw != 0, 'int8') # we only need 8 bit precision
del bw
merge[bw2 == 1] = 0 #blank out bad data
merge[merge2 == np.min(merge2)] = 0 #blank out bad data
del merge2
# erode and dilate to remove splotches of no data
#bw2 = binary_dilation(binary_erosion(bw,structure=np.ones((3,3))), structure=np.ones((13,13)))
#bw2 = binary_dilation(binary_erosion(bw,structure=np.ones((win,win))), structure=np.ones((win*2,win*2)))
#bw2 = binary_erosion(bw,structure=np.ones((win,win)))
# fill holes
#bw2 = binary_fill_holes(bw2, structure=np.ones((3,3))).astype(int)
#del bw
#bw2 = np.asarray(bw2!=0,'int8') # we only need 8 bit precision
#merge[bw2==1] = 0 #blank out bad data
## do plots of merged scans
if doplot == 1:
Zdist = dist_m
fig = plt.figure()
plt.imshow(merge,
cmap='gray',
extent=[
min(Zdist),
max(Zdist), -extent * (1 / ft),
extent * (1 / ft)
])
plt.ylabel('Range (m)'), plt.xlabel('Distance along track (m)')
plt.axis('normal')
plt.axis('tight')
custom_save(sonpath, 'merge_corrected_rmshadow_scan' + str(0))
del fig
Zp.append(np.flipud(merge[:shape_port[0], :]))
Zs.append(merge[shape_port[0]:, :])
del merge, bw2
Zp = np.squeeze(Zp)
Zs = np.squeeze(Zs)
# create memory mapped file for Zp
#fp = np.memmap(sonpath+base+'_data_port_lar.dat', dtype='float32', mode='w+', shape=np.shape(Zp))
#with open(sonpath+base+'_data_port_lar.dat', 'w+') as f:
with open(
os.path.normpath(
os.path.join(sonpath, base + '_data_port_lar.dat')),
'w+') as ff:
fp = np.memmap(ff, dtype='float32', mode='w+', shape=np.shape(Zp))
fp[:] = Zp[:]
del fp
del Zp
#shutil.move(sonpath+base+'_data_port_lar.dat', sonpath+base+'_data_port_la.dat')
#shutil.move(os.path.normpath(os.path.join(sonpath,base+'_data_port_lar.dat')), os.path.normpath(os.path.join(sonpath,base+'_data_port_la.dat')))
# create memory mapped file for Zs
#fp = np.memmap(sonpath+base+'_data_star_lar.dat', dtype='float32', mode='w+', shape=np.shape(Zs))
#with open(sonpath+base+'_data_star_lar.dat', 'w+') as f:
with open(
os.path.normpath(
os.path.join(sonpath, base + '_data_star_lar.dat')),
'w+') as ff:
fp = np.memmap(ff, dtype='float32', mode='w+', shape=np.shape(Zs))
fp[:] = Zs[:]
del fp
del Zs
#shutil.move(sonpath+base+'_data_star_lar.dat', sonpath+base+'_data_star_la.dat')
#shutil.move(os.path.normpath(os.path.join(sonpath,base+'_data_star_lar.dat')), os.path.normpath(os.path.join(sonpath,base+'_data_star_la.dat')))
if os.name == 'posix': # true if linux/mac
elapsed = (time.time() - start)
else: # windows
elapsed = (time.clock() - start)
print "Processing took ", elapsed, "seconds to analyse"
print "Done!"
def mosaic_texture(humfile,
sonpath,
cs2cs_args="epsg:26949",
res=99,
nn=5,
weight=1):
'''
Create mosaics of the spatially referenced sidescan echograms
Syntax
----------
[] = PyHum.mosaic_texture(humfile, sonpath, cs2cs_args, res, nn, weight)
Parameters
----------
humfile : str
path to the .DAT file
sonpath : str
path where the *.SON files are
cs2cs_args : int, *optional* [Default="epsg:26949"]
arguments to create coordinates in a projected coordinate system
this argument gets given to pyproj to turn wgs84 (lat/lon) coordinates
into any projection supported by the proj.4 libraries
res : float, *optional* [Default=0]
grid resolution of output gridded texture map
if res=99, res will be determined automatically from the spatial resolution of 1 pixel
nn: int, *optional* [Default=5]
number of nearest neighbours for gridding
weight: int, *optional* [Default=1]
specifies the type of pixel weighting in the gridding process
weight = 1, based on grazing angle and inverse distance weighting
weight = 2, based on grazing angle only
weight = 3, inverse distance weighting only
weight = 4, no weighting
Returns
-------
sonpath+'GroundOverlay.kml': kml file
contains gridded (or point cloud) sidescan intensity map for importing into google earth
of the pth chunk
sonpath+'map.png' :
image overlay associated with the kml file
'''
# prompt user to supply file if no input file given
if not humfile:
print('An input file is required!!!!!!')
Tk().withdraw(
) # we don't want a full GUI, so keep the root window from appearing
humfile = askopenfilename(filetypes=[("DAT files", "*.DAT")])
# prompt user to supply directory if no input sonpath is given
if not sonpath:
print('A *.SON directory is required!!!!!!')
Tk().withdraw(
) # we don't want a full GUI, so keep the root window from appearing
sonpath = askdirectory()
# print given arguments to screen and convert data type where necessary
if humfile:
print('Input file is %s' % (humfile))
if sonpath:
print('Sonar file path is %s' % (sonpath))
if cs2cs_args:
print('cs2cs arguments are %s' % (cs2cs_args))
if res:
res = np.asarray(res, float)
print('Gridding resolution: %s' % (str(res)))
if nn:
nn = int(nn)
print('Number of nearest neighbours for gridding: %s' % (str(nn)))
if weight:
weight = int(weight)
print('Weighting for gridding: %s' % (str(weight)))
##nn = 5 #number of nearest neighbours in gridding
##noisefloor=10 # noise threshold in dB W
# start timer
if os.name == 'posix': # true if linux/mac or cygwin on windows
start = time.time()
else: # windows
start = time.clock()
trans = pyproj.Proj(init=cs2cs_args)
# if son path name supplied has no separator at end, put one on
if sonpath[-1] != os.sep:
sonpath = sonpath + os.sep
base = humfile.split('.DAT') # get base of file name for output
base = base[0].split(os.sep)[-1]
# remove underscores, negatives and spaces from basename
base = humutils.strip_base(base)
meta = loadmat(os.path.normpath(os.path.join(sonpath, base + 'meta.mat')))
esi = np.squeeze(meta['e'])
nsi = np.squeeze(meta['n'])
theta = np.squeeze(meta['heading']) / (180 / np.pi)
# load memory mapped scans
shape_port = np.squeeze(meta['shape_port'])
if shape_port != '':
if os.path.isfile(
os.path.normpath(
os.path.join(sonpath, base + '_data_port_lar.dat'))):
port_fp = io.get_mmap_data(sonpath, base, '_data_port_lar.dat',
'float32', tuple(shape_port))
else:
port_fp = io.get_mmap_data(sonpath, base, '_data_port_la.dat',
'float32', tuple(shape_port))
shape_star = np.squeeze(meta['shape_star'])
if shape_star != '':
if os.path.isfile(
os.path.normpath(
os.path.join(sonpath, base + '_data_star_lar.dat'))):
star_fp = io.get_mmap_data(sonpath, base, '_data_star_lar.dat',
'float32', tuple(shape_star))
else:
star_fp = io.get_mmap_data(sonpath, base, '_data_star_la.dat',
'float32', tuple(shape_star))
# time varying gain
tvg = ((8.5 * 10**-5) + (3 / 76923) + ((8.5 * 10**-5) / 4)) * meta['c']
# depth correction
dist_tvg = np.squeeze((
(np.tan(np.radians(25))) * np.squeeze(meta['dep_m'])) - (tvg))
# read in range data
R_fp = io.get_mmap_data(sonpath, base, '_data_range.dat', 'float32',
tuple(shape_star))
dx = np.arcsin(meta['c'] / (1000 * meta['t'] * meta['f']))
pix_m = meta['pix_m']
c = meta['c']
if not os.path.isfile(os.path.normpath(os.path.join(sonpath,
base + "S.p"))):
#if 2 > 1:
inputfiles = []
if len(shape_star) > 2:
for p in range(len(star_fp)):
e = esi[shape_port[-1] * p:shape_port[-1] * (p + 1)]
n = nsi[shape_port[-1] * p:shape_port[-1] * (p + 1)]
t = theta[shape_port[-1] * p:shape_port[-1] * (p + 1)]
d = dist_tvg[shape_port[-1] * p:shape_port[-1] * (p + 1)]
dat_port = port_fp[p]
dat_star = star_fp[p]
data_R = R_fp[p]
print("writing chunk %s " % (str(p)))
write_points(e, n, t, d, dat_port, dat_star, data_R, pix_m,
res, cs2cs_args, sonpath, p, c, dx)
inputfiles.append(
os.path.normpath(
os.path.join(sonpath, 'x_y_class' + str(p) + '.asc')))
else:
p = 0
print("writing chunk %s " % (str(p)))
write_points(esi, nsi, theta, dist_tvg, port_fp, star_fp, R_fp,
meta['pix_m'], res, cs2cs_args, sonpath, 0, c, dx)
inputfiles.append(
os.path.normpath(
os.path.join(sonpath, 'x_y_class' + str(p) + '.asc')))
#trans = pyproj.Proj(init=cs2cs_args)
# D, R, h, t
print("reading points from %s files" % (str(len(inputfiles))))
X, Y, S, D, R, h, t, i = getxys(inputfiles)
print("%s points read from %s files" %
(str(len(S)), str(len(inputfiles))))
# remove values where sidescan intensity is zero
ind = np.where(np.logical_not(S == 0))[0]
X = X[ind]
Y = Y[ind]
S = S[ind]
D = D[ind]
R = R[ind]
h = h[ind]
t = t[ind]
i = i[ind]
del ind
# save to file for temporary storage
pickle.dump(
S, open(os.path.normpath(os.path.join(sonpath, base + "S.p")),
"wb"))
del S
pickle.dump(
D, open(os.path.normpath(os.path.join(sonpath, base + "D.p")),
"wb"))
del D
pickle.dump(
t, open(os.path.normpath(os.path.join(sonpath, base + "t.p")),
"wb"))
del t
pickle.dump(
i, open(os.path.normpath(os.path.join(sonpath, base + "i.p")),
"wb"))
del i
pickle.dump(
X, open(os.path.normpath(os.path.join(sonpath, base + "X.p")),
"wb"))
del X
pickle.dump(
Y, open(os.path.normpath(os.path.join(sonpath, base + "Y.p")),
"wb"))
del Y
pickle.dump(
R, open(os.path.normpath(os.path.join(sonpath, base + "R.p")),
"wb"))
pickle.dump(
h, open(os.path.normpath(os.path.join(sonpath, base + "h.p")),
"wb"))
#grazing angle
g = np.arctan(R.flatten(), h.flatten())
pickle.dump(
g, open(os.path.normpath(os.path.join(sonpath, base + "g.p")),
"wb"))
del g, R, h
print("creating grids ...")
if res == 0:
res = 99
if res == 99:
#### prepare grids
R = pickle.load(
open(os.path.normpath(os.path.join(sonpath, base + "R.p")), "rb"))
## actual along-track resolution is this: dx times dy = Af
tmp = R * dx * (c * 0.007 / 2)
del R
resg = np.min(tmp[tmp > 0])
del tmp
else:
resg = res
X = pickle.load(
open(os.path.normpath(os.path.join(sonpath, base + "X.p")), "rb"))
Y = pickle.load(
open(os.path.normpath(os.path.join(sonpath, base + "Y.p")), "rb"))
humlon, humlat = trans(X, Y, inverse=True)
grid_x, grid_y = np.meshgrid(np.arange(np.min(X), np.max(X), resg),
np.arange(np.min(Y), np.max(Y), resg))
shape = np.shape(grid_x)
tree = KDTree(zip(X.flatten(), Y.flatten()))
del X, Y
print("mosaicking ...")
#k nearest neighbour
try:
dist, inds = tree.query(zip(grid_x.flatten(), grid_y.flatten()),
k=nn,
n_jobs=-1)
except:
#print ".... update your scipy installation to use faster kd-tree"
dist, inds = tree.query(zip(grid_x.flatten(), grid_y.flatten()), k=nn)
#del grid_x, grid_y
if weight == 1:
g = pickle.load(
open(os.path.normpath(os.path.join(sonpath, base + "g.p")), "rb"))
w = g[inds] + 1.0 / dist**2
del g
elif weight == 2:
g = pickle.load(
open(os.path.normpath(os.path.join(sonpath, base + "g.p")), "rb"))
w = g[inds]
del g
elif weight == 3:
w = 1.0 / dist**2
elif weight == 4:
w = 1.0
#g = pickle.load( open( os.path.normpath(os.path.join(sonpath,base+"g.p")), "rb" ) )
#w = g[inds] + 1.0 / dist**2
#del g
if weight < 4:
w[np.isinf(w)] = 1
w[np.isnan(w)] = 1
w[w > 10000] = 10000
w[w <= 0] = 1
# load in sidescan intensity
S = pickle.load(
open(os.path.normpath(os.path.join(sonpath, base + "S.p")), "rb"))
# filter out noise pixels
S[S < noisefloor] = np.nan
if nn == 1:
Sdat_g = (w * S.flatten()[inds]).reshape(shape)
del w
dist = dist.reshape(shape)
else:
if weight < 4:
Sdat_g = (np.nansum(w * S.flatten()[inds], axis=1) /
np.nansum(w, axis=1)).reshape(shape)
else:
Sdat_g = (np.nansum(S.flatten()[inds], axis=1)).reshape(shape)
del w
dist = np.nanmean(dist, axis=1).reshape(shape)
del S
Sdat_g[dist > 1] = np.nan
Sdat_g[Sdat_g < noisefloor] = np.nan
dat = Sdat_g.copy()
dat[dist > 1] = 0
dat2 = replace_nans.RN(dat.astype('float64'), 1000, 0.01, 2,
'localmean').getdata()
dat2[dat == 0] = np.nan
del dat
dat2[dat2 < noisefloor] = np.nan
Sdat_g = dat2.copy()
del dat2
Sdat_g[Sdat_g == 0] = np.nan
Sdat_g[np.isinf(Sdat_g)] = np.nan
Sdat_gm = np.ma.masked_invalid(Sdat_g)
del Sdat_g
glon, glat = trans(grid_x, grid_y, inverse=True)
del grid_x, grid_y
# =========================================================
print("creating kmz file ...")
## new way to create kml file
pixels = 1024 * 10
fig, ax = humutils.gearth_fig(llcrnrlon=glon.min(),
llcrnrlat=glat.min(),
urcrnrlon=glon.max(),
urcrnrlat=glat.max(),
pixels=pixels)
cs = ax.pcolormesh(glon, glat, Sdat_gm)
ax.set_axis_off()
fig.savefig(os.path.normpath(os.path.join(sonpath, 'class_overlay1.png')),
transparent=True,
format='png')
fig = plt.figure(figsize=(1.0, 4.0), facecolor=None, frameon=False)
ax = fig.add_axes([0.0, 0.05, 0.2, 0.9])
cb = fig.colorbar(cs, cax=ax)
cb.set_label('Texture lengthscale [m]',
rotation=-90,
color='k',
labelpad=20)
fig.savefig(os.path.normpath(os.path.join(sonpath, 'class_legend.png')),
transparent=False,
format='png')
humutils.make_kml(
llcrnrlon=glon.min(),
llcrnrlat=glat.min(),
urcrnrlon=glon.max(),
urcrnrlat=glat.max(),
figs=[os.path.normpath(os.path.join(sonpath, 'class_overlay1.png'))],
colorbar=os.path.normpath(os.path.join(sonpath, 'class_legend.png')),
kmzfile=os.path.normpath(
os.path.join(sonpath, 'class_GroundOverlay.kmz')),
name='Sidescan Intensity')
# =========================================================
print("drawing and printing map ...")
fig = plt.figure(frameon=False)
map = Basemap(
projection='merc',
epsg=cs2cs_args.split(':')[1],
resolution='i', #h #f
llcrnrlon=np.min(humlon) - 0.001,
llcrnrlat=np.min(humlat) - 0.001,
urcrnrlon=np.max(humlon) + 0.001,
urcrnrlat=np.max(humlat) + 0.001)
gx, gy = map.projtran(glon, glat)
try:
map.arcgisimage(server='http://server.arcgisonline.com/ArcGIS',
service='ESRI_Imagery_World_2D',
xpixels=1000,
ypixels=None,
dpi=300)
except:
map.arcgisimage(server='http://server.arcgisonline.com/ArcGIS',
service='World_Imagery',
xpixels=1000,
ypixels=None,
dpi=300)
#finally:
# print "error: map could not be created..."
ax = plt.Axes(
fig,
[0., 0., 1., 1.],
)
ax.set_axis_off()
fig.add_axes(ax)
if Sdat_gm.size > 25000000:
print(
"matrix size > 25,000,000 - decimating by factor of 5 for display")
map.pcolormesh(gx[::5, ::5],
gy[::5, ::5],
Sdat_gm[::5, ::5],
vmin=np.nanmin(Sdat_gm),
vmax=np.nanmax(Sdat_gm))
else:
map.pcolormesh(gx,
gy,
Sdat_gm,
vmin=np.nanmin(Sdat_gm),
vmax=np.nanmax(Sdat_gm))
custom_save2(sonpath, 'class_map_imagery')
del fig
if os.name == 'posix': # true if linux/mac
elapsed = (time.time() - start)
else: # windows
elapsed = (time.clock() - start)
print("Processing took " + str(elapsed) + "seconds to analyse")
print("Done!")
def rmshadows(humfile, sonpath, win, shadowmask, doplot, dissim, correl, contrast, energy, mn):
'''
Remove dark shadows in scans caused by shallows, shorelines, and attenuation of acoustics with distance
Manual or automated processing options available
Works on the radiometrically corrected outputs of the correct module
Syntax
----------
[] = PyHum.rmshadows(humfile, sonpath, win, shadowmask, doplot)
Parameters
----------
humfile : str
path to the .DAT file
sonpath : str
path where the *.SON files are
win : int, *optional* [Default=100]
window size (pixels) for the automated shadow removal algorithm
shadowmask : int, *optional* [Default=0]
1 = do manual shadow masking, otherwise do automatic shadow masking
doplot : int, *optional* [Default=1]
1 = make plots, otherwise do not
Returns
-------
sonpath+base+'_data_star_la.dat': memory-mapped file
contains the starboard scan with water column removed and
radiometrically corrected, and shadows removed
sonpath+base+'_data_port_la.dat': memory-mapped file
contains the portside scan with water column removed and
radiometrically corrected, and shadows removed
'''
# prompt user to supply file if no input file given
if not humfile:
print('An input file is required!!!!!!')
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
humfile = askopenfilename(filetypes=[("DAT files","*.DAT")])
# prompt user to supply directory if no input sonpath is given
if not sonpath:
print('A *.SON directory is required!!!!!!')
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
sonpath = askdirectory()
# print given arguments to screen and convert data type where necessary
if humfile:
print('Input file is %s' % (humfile))
if sonpath:
print('Sonar file path is %s' % (sonpath))
if win:
win = np.asarray(win,int)
print('Window is %s square pixels' % (str(win)))
if shadowmask:
shadowmask = np.asarray(shadowmask,int)
if shadowmask==1:
print('Shadow masking is manual')
else:
print('Shadow masking is auto')
if doplot:
doplot = int(doplot)
if doplot==0:
print("Plots will not be made")
if dissim:
dissim = np.asarray(dissim,int)
print('Threshold dissimilarity (shadow is <) is %s' % (str(dissim)))
if correl:
correl = np.asarray(correl,int)
print('Threshold correlation (shadow is <) is %s' % (str(correl)))
if contrast:
contrast = np.asarray(contrast,int)
print('Threshold contrast (shadow is <) is %s' % (str(contrast)))
if energy:
energy = np.asarray(energy,int)
print('Threshold energy (shadow is >) is %s' % (str(energy)))
if mn:
mn = np.asarray(mn,int)
print('Threshold mean intensity (shadow is <) is %s' % (str(mn)))
# start timer
if os.name=='posix': # true if linux/mac or cygwin on windows
start = time.time()
else: # windows
start = time.clock()
# if son path name supplied has no separator at end, put one on
if sonpath[-1]!=os.sep:
sonpath = sonpath + os.sep
base = humfile.split('.DAT') # get base of file name for output
base = base[0].split(os.sep)[-1]
base = humutils.strip_base(base)
meta = loadmat(os.path.normpath(os.path.join(sonpath,base+'meta.mat')))
# load memory mapped scans
shape_port = np.squeeze(meta['shape_port'])
if shape_port!='':
#port_fp = np.memmap(sonpath+base+'_data_port_la.dat', dtype='float32', mode='r', shape=tuple(shape_port))
with open(os.path.normpath(os.path.join(sonpath,base+'_data_port_la.dat')), 'r') as ff:
port_fp = np.memmap(ff, dtype='float32', mode='r', shape=tuple(shape_port))
shape_star = np.squeeze(meta['shape_star'])
if shape_star!='':
#star_fp = np.memmap(sonpath+base+'_data_star_la.dat', dtype='float32', mode='r', shape=tuple(shape_star))
with open(os.path.normpath(os.path.join(sonpath,base+'_data_star_la.dat')), 'r') as ff:
star_fp = np.memmap(ff, dtype='float32', mode='r', shape=tuple(shape_star))
dist_m = np.squeeze(meta['dist_m'])
ft = 1/(meta['pix_m'])
extent = shape_star[1]
if shadowmask==1: #manual
Zt = []
if len(np.shape(star_fp))>2:
for p in range(len(star_fp)):
raw_input("Shore picking "+str(p+1)+" of "+str(len(star_fp))+" (starboard), are you ready? 60 seconds. Press Enter to continue...")
shoreline_star={}
fig = plt.figure()
ax = plt.gca()
ax.imshow(star_fp[p], cmap = 'gray') #, origin = 'upper') #im =
plt.axis('normal'); plt.axis('tight')
pts1 = plt.ginput(n=300, timeout=75) # it will wait for 200 clicks or 75 seconds
x1=map(lambda x: x[0],pts1) # map applies the function passed as
y1=map(lambda x: x[1],pts1) # first parameter to each element of pts
shoreline_star = np.interp(np.r_[:np.shape(star_fp[p])[1]],x1,y1)
plt.close()
del fig
star_mg = star_fp[p].copy()
shoreline_star = np.asarray(shoreline_star,'int')
# shift proportionally depending on where the bed is
for k in range(np.shape(star_mg)[1]):
star_mg[shoreline_star[k]:,k] = np.nan
del shoreline_star
Zt.append(star_mg)
else:
raw_input("Shore picking "+str(len(star_fp))+" of "+str(len(star_fp))+" (starboard), are you ready? 60 seconds. Press Enter to continue...")
shoreline_star={}
fig = plt.figure()
ax = plt.gca()
ax.imshow(star_fp, cmap = 'gray') #, origin = 'upper') #im =
plt.axis('normal'); plt.axis('tight')
pts1 = plt.ginput(n=300, timeout=75) # it will wait for 200 clicks or 75 seconds
x1=map(lambda x: x[0],pts1) # map applies the function passed as
y1=map(lambda x: x[1],pts1) # first parameter to each element of pts
shoreline_star = np.interp(np.r_[:np.shape(star_fp)[1]],x1,y1)
plt.close()
del fig
star_mg = star_fp.copy()
shoreline_star = np.asarray(shoreline_star,'int')
# shift proportionally depending on where the bed is
for k in range(np.shape(star_mg)[1]):
star_mg[shoreline_star[k]:,k] = np.nan
del shoreline_star
Zt.append(star_mg)
## create memory mapped file for Z
#p = np.memmap(sonpath+base+'_data_star_la.dat', dtype='float32', mode='w+', shape=np.shape(Zt))
#fp[:] = Zt[:]
#del fp
Zt = np.squeeze(Zt)
# create memory mapped file for Zs
#fp = np.memmap(sonpath+base+'_data_star_lar.dat', dtype='float32', mode='w+', shape=np.shape(Zs))
with open(os.path.normpath(os.path.join(sonpath,base+'_data_star_lar.dat')), 'w+') as ff:
fp = np.memmap(ff, dtype='float32', mode='w+', shape=np.shape(Zt))
fp[:] = Zt[:]
del fp
del Zt
#shutil.move(os.path.normpath(os.path.join(sonpath,base+'_data_star_lar.dat')), os.path.normpath(os.path.join(sonpath,base+'_data_star_la.dat')))
Zt = []
if len(np.shape(star_fp))>2:
for p in range(len(port_fp)):
raw_input("Shore picking "+str(p+1)+" of "+str(len(port_fp))+" (port), are you ready? 60 seconds. Press Enter to continue...")
shoreline_port={}
fig = plt.figure()
ax = plt.gca()
ax.imshow(port_fp[p], cmap = 'gray') #, origin = 'upper') #im =
plt.axis('normal'); plt.axis('tight')
pts1 = plt.ginput(n=300, timeout=75) # it will wait for 200 clicks or 75 seconds
x1=map(lambda x: x[0],pts1) # map applies the function passed as
y1=map(lambda x: x[1],pts1) # first parameter to each element of pts
shoreline_port = np.interp(np.r_[:np.shape(port_fp[p])[1]],x1,y1)
plt.close()
del fig
port_mg = port_fp[p].copy()
shoreline_port = np.asarray(shoreline_port,'int')
# shift proportionally depending on where the bed is
for k in range(np.shape(port_mg)[1]):
port_mg[shoreline_port[k]:,k] = np.nan
del shoreline_port
Zt.append(port_mg)
else:
raw_input("Shore picking "+str(len(port_fp))+" of "+str(len(port_fp))+" (port), are you ready? 60 seconds. Press Enter to continue...")
shoreline_port={}
fig = plt.figure()
ax = plt.gca()
ax.imshow(port_fp, cmap = 'gray') #, origin = 'upper') #im =
plt.axis('normal'); plt.axis('tight')
pts1 = plt.ginput(n=300, timeout=75) # it will wait for 200 clicks or 75 seconds
x1=map(lambda x: x[0],pts1) # map applies the function passed as
y1=map(lambda x: x[1],pts1) # first parameter to each element of pts
shoreline_port = np.interp(np.r_[:np.shape(port_fp)[1]],x1,y1)
plt.close()
del fig
port_mg = port_fp.copy()
shoreline_port = np.asarray(shoreline_port,'int')
# shift proportionally depending on where the bed is
for k in range(np.shape(port_mg)[1]):
port_mg[shoreline_port[k]:,k] = np.nan
del shoreline_port
Zt.append(port_mg)
Zt = np.squeeze(Zt)
## create memory mapped file for Z
#fp = np.memmap(sonpath+base+'_data_port_la.dat', dtype='float32', mode='w+', shape=np.shape(Zt))
#fp[:] = Zt[:]
#del fp
# create memory mapped file for Zp
#fp = np.memmap(sonpath+base+'_data_port_lar.dat', dtype='float32', mode='w+', shape=np.shape(Zp))
with open(os.path.normpath(os.path.join(sonpath,base+'_data_port_lar.dat')), 'w+') as ff:
fp = np.memmap(ff, dtype='float32', mode='w+', shape=np.shape(Zt))
fp[:] = Zt[:]
del fp
del Zt
#shutil.move(os.path.normpath(os.path.join(sonpath,base+'_data_port_lar.dat')), os.path.normpath(os.path.join(sonpath,base+'_data_port_la.dat')))
else: #auto
Zs = []; Zp = []
if len(np.shape(star_fp))>2:
for p in range(len(star_fp)):
merge = np.vstack((np.flipud(port_fp[p]),star_fp[p]))
merge = np.asarray(merge, 'float64')
merge_mask = np.vstack((np.flipud(port_fp[p]),star_fp[p]))
merge[merge_mask==0] = 0
del merge_mask
mask = np.asarray(merge!=0,'int8') # only 8bit precision needed
merge[np.isnan(merge)] = 0
#Z,ind = humutils.sliding_window(merge,(win,win),(win/2,win/2))
Z,ind = humutils.sliding_window(merge,(win,win),(win,win))
#zmean = np.reshape(zmean, ( ind[0], ind[1] ) )
Ny, Nx = np.shape(merge)
#zmean[np.isnan(zmean)] = 0
try: #parallel processing with all available cores
w = Parallel(n_jobs = -1, verbose=0)(delayed(parallel_me)(Z[k], dissim, correl, contrast, energy, mn) for k in range(len(Z)))
except: #fall back to serial
w = Parallel(n_jobs = 1, verbose=0)(delayed(parallel_me)(Z[k], dissim, correl, contrast, energy, mn) for k in range(len(Z)))
zmean = np.reshape(w , ( ind[0], ind[1] ) )
del w
M = humutils.im_resize(zmean,Nx,Ny)
M[mask==0] = 0
del zmean
bw = M>0.5
del M
# erode and dilate to remove splotches of no data
bw2 = binary_dilation(binary_erosion(bw,structure=np.ones((3,3))), structure=np.ones((13,13)))
#bw2 = binary_dilation(binary_erosion(bw,structure=np.ones((win/4,win/4))), structure=np.ones((win/4,win/4)))
##bw2 = binary_erosion(bw,structure=np.ones((win*2,win*2)))
## fill holes
bw2 = binary_fill_holes(bw2, structure=np.ones((win,win))).astype(int)
merge2 = grey_erosion(merge,structure=np.ones((win,win)))
#del bw
#bw2 = np.asarray(bw2!=0,'int8') # we only need 8 bit precision
bw2 = np.asarray(bw!=0,'int8') # we only need 8 bit precision
del bw
merge[bw2==1] = 0 #blank out bad data
merge[merge2==np.min(merge2)] = 0 #blank out bad data
del merge2
## do plots of merged scans
if doplot==1:
Zdist = dist_m[shape_port[-1]*p:shape_port[-1]*(p+1)]
fig = plt.figure()
plt.imshow(merge, cmap='gray', extent=[min(Zdist), max(Zdist), -extent*(1/ft), extent*(1/ft)])
plt.ylabel('Range (m)'), plt.xlabel('Distance along track (m)')
plt.axis('normal'); plt.axis('tight')
custom_save(sonpath,'merge_corrected_rmshadow_scan'+str(p))
del fig
Zp.append(np.flipud(merge[:shape_port[1],:]))
Zs.append(merge[shape_port[1]:,:])
del merge, bw2
else:
merge = np.vstack((np.flipud(port_fp),star_fp))
merge = np.asarray(merge, 'float64')
merge_mask = np.vstack((np.flipud(port_fp),star_fp))
merge[merge_mask==0] = 0
del merge_mask
mask = np.asarray(merge!=0,'int8') # only 8bit precision needed
merge[np.isnan(merge)] = 0
#Z,ind = humutils.sliding_window(merge,(win,win),(win/2,win/2))
Z,ind = humutils.sliding_window(merge,(win,win),(win,win))
#zmean = np.reshape(zmean, ( ind[0], ind[1] ) )
Ny, Nx = np.shape(merge)
#zmean[np.isnan(zmean)] = 0
try: #parallel processing with all available cores
w = Parallel(n_jobs = -1, verbose=0)(delayed(parallel_me)(Z[k], dissim, correl, contrast, energy, mn) for k in range(len(Z)))
except: #fall back to serial
w = Parallel(n_jobs = 1, verbose=0)(delayed(parallel_me)(Z[k], dissim, correl, contrast, energy, mn) for k in range(len(Z)))
zmean = np.reshape(w , ( ind[0], ind[1] ) )
del w
M = humutils.im_resize(zmean,Nx,Ny)
M[mask==0] = 0
del zmean
bw = M>0.5
del M
# erode and dilate to remove splotches of no data
bw2 = binary_dilation(binary_erosion(bw,structure=np.ones((3,3))), structure=np.ones((13,13)))
#bw2 = binary_dilation(binary_erosion(bw,structure=np.ones((win/4,win/4))), structure=np.ones((win/4,win/4)))
##bw2 = binary_erosion(bw,structure=np.ones((win*2,win*2)))
## fill holes
bw2 = binary_fill_holes(bw2, structure=np.ones((win,win))).astype(int)
merge2 = grey_erosion(merge,structure=np.ones((win,win)))
#del bw
#bw2 = np.asarray(bw2!=0,'int8') # we only need 8 bit precision
bw2 = np.asarray(bw!=0,'int8') # we only need 8 bit precision
del bw
merge[bw2==1] = 0 #blank out bad data
merge[merge2==np.min(merge2)] = 0 #blank out bad data
del merge2
# erode and dilate to remove splotches of no data
#bw2 = binary_dilation(binary_erosion(bw,structure=np.ones((3,3))), structure=np.ones((13,13)))
#bw2 = binary_dilation(binary_erosion(bw,structure=np.ones((win,win))), structure=np.ones((win*2,win*2)))
#bw2 = binary_erosion(bw,structure=np.ones((win,win)))
# fill holes
#bw2 = binary_fill_holes(bw2, structure=np.ones((3,3))).astype(int)
#del bw
#bw2 = np.asarray(bw2!=0,'int8') # we only need 8 bit precision
#merge[bw2==1] = 0 #blank out bad data
## do plots of merged scans
if doplot==1:
Zdist = dist_m
fig = plt.figure()
plt.imshow(merge, cmap='gray', extent=[min(Zdist), max(Zdist), -extent*(1/ft), extent*(1/ft)])
plt.ylabel('Range (m)'), plt.xlabel('Distance along track (m)')
plt.axis('normal'); plt.axis('tight')
custom_save(sonpath,'merge_corrected_rmshadow_scan'+str(0))
del fig
Zp.append(np.flipud(merge[:shape_port[0],:]))
Zs.append(merge[shape_port[0]:,:])
del merge, bw2
Zp = np.squeeze(Zp)
Zs = np.squeeze(Zs)
# create memory mapped file for Zp
#fp = np.memmap(sonpath+base+'_data_port_lar.dat', dtype='float32', mode='w+', shape=np.shape(Zp))
#with open(sonpath+base+'_data_port_lar.dat', 'w+') as f:
with open(os.path.normpath(os.path.join(sonpath,base+'_data_port_lar.dat')), 'w+') as ff:
fp = np.memmap(ff, dtype='float32', mode='w+', shape=np.shape(Zp))
fp[:] = Zp[:]
del fp
del Zp
#shutil.move(sonpath+base+'_data_port_lar.dat', sonpath+base+'_data_port_la.dat')
#shutil.move(os.path.normpath(os.path.join(sonpath,base+'_data_port_lar.dat')), os.path.normpath(os.path.join(sonpath,base+'_data_port_la.dat')))
# create memory mapped file for Zs
#fp = np.memmap(sonpath+base+'_data_star_lar.dat', dtype='float32', mode='w+', shape=np.shape(Zs))
#with open(sonpath+base+'_data_star_lar.dat', 'w+') as f:
with open(os.path.normpath(os.path.join(sonpath,base+'_data_star_lar.dat')), 'w+') as ff:
fp = np.memmap(ff, dtype='float32', mode='w+', shape=np.shape(Zs))
fp[:] = Zs[:]
del fp
del Zs
#shutil.move(sonpath+base+'_data_star_lar.dat', sonpath+base+'_data_star_la.dat')
#shutil.move(os.path.normpath(os.path.join(sonpath,base+'_data_star_lar.dat')), os.path.normpath(os.path.join(sonpath,base+'_data_star_la.dat')))
if os.name=='posix': # true if linux/mac
elapsed = (time.time() - start)
else: # windows
elapsed = (time.clock() - start)
print("Processing took "+str(elapsed)+ "seconds to analyse")
print("Done!")
print("===================================================")
def e1e2(humfile, sonpath, cs2cs_args="epsg:26949", ph=7.0, temp=10.0, salinity=0.0, beam=20.0, transfreq=200.0, integ=5, numclusters=3, doplot=1):
'''
Analysis of first (e1, 'roughness') and second (e2, 'hardness') echo returns from the high-frequency downward looking echosounder
Generates generalised acoustic parameters
for the purposes of point classification of submerged substrates/vegetation
Accounts for the absorption of sound in water
Does a basic k-means cluster of e1 and e2 coefficients into specified number of 'acoustic classes'
based on code by Barb Fagetter ([email protected])
Syntax
----------
[] = PyHum.e1e2(humfile, sonpath, cs2cs_args, ph, temp, salinity, beam, transfreq, integ, numclusters, doplot)
Parameters
----------
humfile : str
path to the .DAT file
sonpath : str
path where the *.SON files are
cs2cs_args : int, *optional* [Default="epsg:26949"]
arguments to create coordinates in a projected coordinate system
this argument gets given to pyproj to turn wgs84 (lat/lon) coordinates
into any projection supported by the proj.4 libraries
ph : float, *optional* [Default=7.0]
water acidity in pH
temp : float, *optional* [Default=10.0]
water temperature in degrees Celsius
salinity : float, *optional* [Default=0.0]
salinity of water in parts per thousand
beam : float, *optional* [Default=20.0]
beam width in degrees
transfreq : float, *optional* [Default=200.0]
transducer frequency in kHz
integ : int, *optional* [Default=5]
number of pings over which to integrate
numclusters : int, *optional* [Default=3]
number of acoustic classes to classify all the data into
doplot : int, *optional* [Default=1]
1 = make plots, otherwise do not
Returns
-------
sonpath+base+'rough_and_hard'+str(p)+'.csv' : csv file
contains the following fields: 'longitude', 'latitude', 'easting', 'northing', 'depth',
'roughness', 'hardness', 'average roughness', 'average hardness','k-mean label'
of the pth chunk
'average' implies average over 'integ' successive pings
The following are returned if doplot==1:
sonpath+'e1e2_scan'+str(p).png : png image file
png image file showing the downward echosounder echogram overlain with the locations of the start and
end of the first and second echo region envelope
sonpath+'e1e2_kmeans'+str(p).png: png image file
png image file showing 1) (left) volume scattering coefficient 1 versus volume scattering coefficient 2, colour-coded
by k-means acoustic class, and
2) (right) e1 versus e2, colour-coded
by k-means acoustic class
sonpath+'rgh_hard_kmeans'+str(p).png : png image file
png image file showing scatter plot of easting versus northing colour-coded by k-means acoustic class
sonpath+'map_rgh'+str(p).png : png image file
png image file showing scatter plot of 'roughness' (e1) overlying an aerial image pulled from an ESRI image server
sonpath+'map_hard'+str(p).png : png image file
png image file showing scatter plot of 'hardness' (e2) overlying an aerial image pulled from an ESRI image server
sonpath,'Rough'+str(p).png : png image file
png image overlay associated with the kml file, sonpath,'Hard'+str(p).kml
sonpath,'Rough'+str(p).kml : kml file
kml overlay for showing roughness scatter plot (sonpath,'Rough'+str(p).png)
sonpath,'Hard'+str(p).png : png image file
png image overlay associated with the kml file, sonpath,'Hard'+str(p).kml
sonpath,'Hard'+str(p).kml : kml file
kml overlay for showing harness scatter plot (sonpath,'Hard'+str(p).png)
'''
# prompt user to supply file if no input file given
if not humfile:
print 'An input file is required!!!!!!'
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
inputfile = askopenfilename(filetypes=[("DAT files","*.DAT")])
# prompt user to supply directory if no input sonpath is given
if not sonpath:
print 'A *.SON directory is required!!!!!!'
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
sonpath = askdirectory()
# print given arguments to screen and convert data type where necessary
if humfile:
print 'Input file is %s' % (humfile)
if sonpath:
print 'Sonar file path is %s' % (sonpath)
if cs2cs_args:
print 'cs2cs arguments are %s' % (cs2cs_args)
if beam:
beam = np.asarray(beam,float)
print 'Beam is %s deg' % (str(beam))
if salinity:
salinity = np.asarray(salinity,float)
print 'Salinity is %s ppt' % (str(salinity))
if ph:
ph = np.asarray(ph,float)
print 'pH is %s' % (str(ph))
if temp:
temp = np.asarray(temp,float)
print 'Temperature is %s' % (str(temp))
if transfreq:
transfreq = np.asarray(transfreq,float)
print 'Dwnward sonar freq. is %s' % (str(transfreq))
if integ:
integ = np.asarray(integ,int)
print 'number of records for integration is %s' % (str(integ))
if numclusters:
numclusters = np.asarray(numclusters,int)
print 'number of returned acoustic clusters is %s' % (str(numclusters))
if doplot:
doplot = int(doplot)
if doplot==0:
print "Plots will not be made"
# if son path name supplied has no separator at end, put one on
if sonpath[-1]!=os.sep:
sonpath = sonpath + os.sep
base = humfile.split('.DAT') # get base of file name for output
base = base[0].split(os.sep)[-1]
# remove underscores, negatives and spaces from basename
base = humutils.strip_base(base)
meta = loadmat(os.path.normpath(os.path.join(sonpath,base+'meta.mat')))
beamwidth = beam*(np.sqrt(0.5))
equivbeam = (5.78/(np.power(1.6,2)))*(np.power((np.sin((beamwidth*np.pi)/(2*180))),2))
meta = loadmat(sonpath+base+'meta.mat')
c = np.squeeze(meta['c'])
t = np.squeeze(meta['t'])
f = np.squeeze(meta['f'])
maxW = np.squeeze(meta['maxW'])
lat = np.squeeze(meta['lat'])
lon = np.squeeze(meta['lon'])
es = np.squeeze(meta['e'])
ns = np.squeeze(meta['n'])
dep = np.squeeze(meta['dep_m'])
#del meta
# load memory mapped scans
shape_hi= np.squeeze(meta['shape_hi'])
if shape_hi!='':
try:
#dwnhi_fp = np.memmap(sonpath+base+'_data_dwnhi.dat', dtype='int16', mode='r', shape=tuple(shape_hi))
with open(os.path.normpath(os.path.join(sonpath,base+'_data_dwnhi.dat')), 'r') as ff:
dwnhi_fp = np.memmap(ff, dtype='int16', mode='r', shape=tuple(shape_hi))
except:
shape_lo= np.squeeze(meta['shape_low'])
#dwnhi_fp = np.memmap(sonpath+base+'_data_dwnhi.dat', dtype='int16', mode='r', shape=tuple(shape_lo))
with open(os.path.normpath(os.path.join(sonpath,base+'_data_dwnhi.dat')), 'r') as ff:
dwnhi_fp = np.memmap(ff, dtype='int16', mode='r', shape=tuple(shape_lo))
if 'dwnhi_fp' in locals():
theta3dB = np.arcsin(c/(t*(f*1000))) # *(180/pi) # to see in degs
ft = (np.pi/2)*(1/theta3dB)
bed = ft*dep
if len(shape_hi)>2:
i = np.linspace(1,shape_hi[0]*shape_hi[2], len(bed))
#np.shape(beam_data)[1],len(bed))
#bedi = np.interp(np.linspace(1,np.shape(beam_data)[1],np.shape(beam_data)[1]), i, bed)
bedi = np.interp(np.linspace(1,shape_hi[0]*shape_hi[2],shape_hi[0]*shape_hi[2]), i, bed)
ei = np.interp(np.linspace(1,shape_hi[0]*shape_hi[2],shape_hi[0]*shape_hi[2]), i, es)
ni = np.interp(np.linspace(1,shape_hi[0]*shape_hi[2],shape_hi[0]*shape_hi[2]), i, ns)
lati = np.interp(np.linspace(1,shape_hi[0]*shape_hi[2],shape_hi[0]*shape_hi[2]), i, lat)
loni = np.interp(np.linspace(1,shape_hi[0]*shape_hi[2],shape_hi[0]*shape_hi[2]), i, lon)
del i
else:
i = np.linspace(1,shape_hi[1], len(bed))
#np.shape(beam_data)[1],len(bed))
#bedi = np.interp(np.linspace(1,np.shape(beam_data)[1],np.shape(beam_data)[1]), i, bed)
bedi = np.interp(np.linspace(1,shape_hi[1],shape_hi[1]), i, bed)
ei = np.interp(np.linspace(1,shape_hi[1],shape_hi[1]), i, es)
ni = np.interp(np.linspace(1,shape_hi[1],shape_hi[1]), i, ns)
lati = np.interp(np.linspace(1,shape_hi[1],shape_hi[1]), i, lat)
loni = np.interp(np.linspace(1,shape_hi[1],shape_hi[1]), i, lon)
del i
bedi = np.asarray(bedi,'int')
depi = ((1/ft)*bedi)
# near-field region
nf = int(ft*(1000*(0.105**2)*f/(4*1500)))
#absorption = calcAb(c, ph, salinity, temp, np.asarray(depi), transfreq)
absorption = water_atten(np.asarray(depi), transfreq, c, ph, temp, salinity)
if len(shape_hi)>2:
for p in xrange(len(dwnhi_fp)):
#make an index of every other record
ind = range(0,np.shape(dwnhi_fp[p])[1])
Zdepi = depi[shape_hi[2]*p:shape_hi[2]*(p+1)]
Zabsorp = absorption[shape_hi[2]*p:shape_hi[2]*(p+1)]
Zlat = lati[shape_hi[2]*p:shape_hi[2]*(p+1)]
Zlon = loni[shape_hi[2]*p:shape_hi[2]*(p+1)]
Zes = ei[shape_hi[2]*p:shape_hi[2]*(p+1)]
Zns = ni[shape_hi[2]*p:shape_hi[2]*(p+1)]
try: #parallel processing with all available cores
w = Parallel(n_jobs=-1, verbose=0)(delayed(get_rgh_hrd)(dwnhi_fp[p][:,i],Zdepi[i],Zabsorp[i],c,nf,transfreq,equivbeam,maxW,pi,ft) for i in ind)
except: #fall back to serial
w = Parallel(n_jobs=1, verbose=0)(delayed(get_rgh_hrd)(dwnhi_fp[p][:,i],Zdepi[i],Zabsorp[i],c,nf,transfreq,equivbeam,maxW,pi,ft) for i in ind)
rough, hard, sv_e1, sv_e2, e1a, e1b, e2a, e2b = zip(*w)
rough = np.array(rough,'float')
rough[rough==0.0] = np.nan
hard = np.array(hard,'float')
hard[hard==0.0] = np.nan
sv_e1 = np.array(sv_e1,'float')
sv_e1[sv_e1==0.0] = np.nan
sv_e2 = np.array(sv_e2,'float')
sv_e2[sv_e2==0.0] = np.nan
try:
nans, y= humutils.nan_helper(rough)
rough[nans]= np.interp(y(nans), y(~nans), rough[~nans])
except:
pass
try:
nans, y= humutils.nan_helper(hard)
hard[nans]= np.interp(y(nans), y(~nans), hard[~nans])
except:
pass
try:
nans, y= humutils.nan_helper(sv_e1)
sv_e1[nans]= np.interp(y(nans), y(~nans), sv_e1[~nans])
except:
pass
try:
nans, y= humutils.nan_helper(sv_e2)
sv_e2[nans]= np.interp(y(nans), y(~nans), sv_e2[~nans])
except:
pass
data = np.column_stack([sv_e1, sv_e2])
k_means = MiniBatchKMeans(numclusters)
# fit the model
k_means.fit(data)
values = k_means.cluster_centers_.squeeze()
labels = k_means.labels_
hardav = humutils.runningMeanFast(hard,integ)
roughav = humutils.runningMeanFast(rough,integ)
#f = open(sonpath+base+'rough_and_hard'+str(p)+'.csv', 'wt')
f = open(os.path.normpath(os.path.join(sonpath,base+'rough_and_hard'+str(p)+'.csv')), 'wt')
writer = csv.writer(f)
writer.writerow( ('longitude', 'latitude', 'easting', 'northing', 'depth', 'roughness', 'hardness', 'average roughness', 'average hardness','k-mean label') )
for i in range(0, len(rough)):
writer.writerow(( float(Zlon[i]),float(Zlat[i]),float(Zes[i]),float(Zns[i]),float(Zdepi[i]),float(rough[i]),float(hard[i]),float(roughav[i]),float(hardav[i]), labels[i].astype(int) ))
f.close()
if doplot==1:
try:
fig = plt.figure()
plt.imshow(dwnhi_fp[p], cmap='gray')
plt.plot(e1a,'r');
plt.plot(e1b,'y');
plt.plot(e2a,'c');
plt.plot(e2b,'m');
plt.axis('tight')
#plt.show()
custom_save(sonpath,'e1e2_scan'+str(p))
del fig
except:
print "plot could not be produced"
if doplot==1:
try:
fig = plt.figure()
fig.subplots_adjust(wspace = 0.4, hspace=0.4)
plt.subplot(221)
plt.plot(sv_e1[labels==0],sv_e2[labels==0],'ko');
plt.plot(sv_e1[labels==1],sv_e2[labels==1],'ro');
plt.plot(sv_e1[labels==2],sv_e2[labels==2],'bo');
plt.xlabel('SV1'); plt.ylabel('SV2')
plt.xlim(0,1); plt.ylim(0,1)
plt.subplot(222)
plt.plot(rough[labels==0],hard[labels==0],'ko');
plt.plot(rough[labels==1],hard[labels==1],'ro');
plt.plot(rough[labels==2],hard[labels==2],'bo');
plt.xlabel('E1'); plt.ylabel('E2')
plt.xlim(1,8); plt.ylim(1,8)
#plt.show()
custom_save(sonpath,'e1e2_kmeans'+str(p))
del fig
except:
print "plot could not be produced"
if doplot==1:
try:
fig = plt.figure()
s=plt.scatter(Zes[labels==0],Zns[labels==0],marker='o',c='k', s=10, linewidth=0, vmin=0, vmax=8);
s=plt.scatter(Zes[labels==1],Zns[labels==1],marker='o',c='r', s=10, linewidth=0, vmin=0, vmax=8);
s=plt.scatter(Zes[labels==2],Zns[labels==2],marker='o',c='b', s=10, linewidth=0, vmin=0, vmax=8);
custom_save(sonpath,'rgh_hard_kmeans'+str(p))
del fig
except:
print "plot could not be produced"
if doplot==1:
try:
print "drawing and printing map ..."
fig = plt.figure(frameon=False)
#fig.subplots_adjust(wspace = 0.4, hspace=0.4)
map = Basemap(projection='merc', epsg=cs2cs_args.split(':')[1], #epsg=26949,
resolution = 'i', #h #f
llcrnrlon=np.min(Zlon)-0.0001, llcrnrlat=np.min(Zlat)-0.0001,
urcrnrlon=np.max(Zlon)+0.0001, urcrnrlat=np.max(Zlat)+0.0001)
# draw point cloud
x,y = map.projtran(Zlon, Zlat)
cs = map.scatter(x.flatten(), y.flatten(), 1, rough.flatten(), linewidth=0, vmin=0, vmax=8)
try:
map.arcgisimage(server='http://server.arcgisonline.com/ArcGIS', service='ESRI_Imagery_World_2D', xpixels=1000, ypixels=None, dpi=300)
except:
map.arcgisimage(server='http://server.arcgisonline.com/ArcGIS', service='World_Imagery', xpixels=1000, ypixels=None, dpi=300)
cbar = map.colorbar(cs,location='bottom',pad="5%")
cbar.set_label('E1')
cbar.set_ticks([0,2,4,6,8])
custom_save(sonpath,'map_rgh'+str(p))
del fig
except:
print "plot could not be produced"
if doplot==1:
try:
fig = plt.figure()
#fig.subplots_adjust(wspace = 0.4, hspace=0.4)
map = Basemap(projection='merc', epsg=cs2cs_args.split(':')[1],
resolution = 'i', #h #f
llcrnrlon=np.min(Zlon)-0.0001, llcrnrlat=np.min(Zlat)-0.0001,
urcrnrlon=np.max(Zlon)+0.0001, urcrnrlat=np.max(Zlat)+0.0001)
# draw point cloud
x,y = map.projtran(Zlon, Zlat)
cs = map.scatter(x.flatten(), y.flatten(), 1, hard.flatten(), linewidth=0, vmin=0, vmax=8)
try:
map.arcgisimage(server='http://server.arcgisonline.com/ArcGIS', service='ESRI_Imagery_World_2D', xpixels=1000, ypixels=None, dpi=300)
except:
map.arcgisimage(server='http://server.arcgisonline.com/ArcGIS', service='World_Imagery', xpixels=1000, ypixels=None, dpi=300)
cbar = map.colorbar(cs,location='bottom',pad="5%")
cbar.set_label('E2')
cbar.set_ticks([0,2,4,6,8])
custom_save(sonpath,'map_hard'+str(p))
del fig
except:
print "plot could not be produced"
if doplot==1:
try:
print "drawing and printing map ..."
fig = plt.figure(frameon=False)
map = Basemap(projection='merc', epsg=cs2cs_args.split(':')[1], #26949,
resolution = 'i', #h #f
llcrnrlon=np.min(Zlon)-0.001, llcrnrlat=np.min(Zlat)-0.001,
urcrnrlon=np.max(Zlon)+0.001, urcrnrlat=np.max(Zlat)+0.001)
ax = plt.Axes(fig, [0., 0., 1., 1.], )
ax.set_axis_off()
fig.add_axes(ax)
## draw point cloud
x,y = map.projtran(Zlon, Zlat)
map.scatter(x.flatten(), y.flatten(), 1, rough.flatten(), linewidth = '0', vmin=0, vmax=8)
custom_save(sonpath,'Rough'+str(p))
del fig
kml = simplekml.Kml()
ground = kml.newgroundoverlay(name='GroundOverlay')
ground.icon.href = 'Rough'+str(p)+'.png'
ground.latlonbox.north = np.min(Zlat)-0.001
ground.latlonbox.south = np.max(Zlat)+0.001
ground.latlonbox.east = np.max(Zlon)+0.001
ground.latlonbox.west = np.min(Zlon)-0.001
ground.latlonbox.rotation = 0
#kml.save(sonpath+'Rough'+str(p)+'.kml')
kml.save(os.path.normpath(os.path.join(sonpath,'Rough'+str(p)+'.kml')))
except:
print "plot could not be produced"
if doplot==1:
try:
print "drawing and printing map ..."
fig = plt.figure(frameon=False)
map = Basemap(projection='merc', epsg=cs2cs_args.split(':')[1], #26949,
resolution = 'i', #h #f
llcrnrlon=np.min(Zlon)-0.001, llcrnrlat=np.min(Zlat)-0.001,
urcrnrlon=np.max(Zlon)+0.001, urcrnrlat=np.max(Zlat)+0.001)
ax = plt.Axes(fig, [0., 0., 1., 1.], )
ax.set_axis_off()
fig.add_axes(ax)
## draw point cloud
x,y = map.projtran(Zlon, Zlat)
map.scatter(x.flatten(), y.flatten(), 1, hard.flatten(), linewidth = '0', vmin=0, vmax=8)
custom_save(sonpath,'Hard'+str(p))
del fig
kml = simplekml.Kml()
ground = kml.newgroundoverlay(name='GroundOverlay')
ground.icon.href = 'Hard'+str(p)+'.png'
ground.latlonbox.north = np.min(Zlat)-0.001
ground.latlonbox.south = np.max(Zlat)+0.001
ground.latlonbox.east = np.max(Zlon)+0.001
ground.latlonbox.west = np.min(Zlon)-0.001
ground.latlonbox.rotation = 0
#kml.save(sonpath+'Hard'+str(p)+'.kml')
kml.save(os.path.normpath(os.path.join(sonpath,'Hard'+str(p)+'.kml')))
except:
print "plot could not be produced"
else:
if 2 > 1: # need to tiday all this up later!!
#make an index of every other record
ind = range(0,np.shape(dwnhi_fp)[1])
Zdepi = depi
Zabsorp = absorption
Zlat = lati
Zlon = loni
Zes = ei
Zns = ni
try: #parallel processing with all available cores
w = Parallel(n_jobs=-1, verbose=0)(delayed(get_rgh_hrd)(dwnhi_fp[:,i],Zdepi[i],Zabsorp[i],c,nf,transfreq,equivbeam,maxW,pi,ft) for i in ind)
except: #fall back to serial
w = Parallel(n_jobs=1, verbose=0)(delayed(get_rgh_hrd)(dwnhi_fp[:,i],Zdepi[i],Zabsorp[i],c,nf,transfreq,equivbeam,maxW,pi,ft) for i in ind)
rough, hard, sv_e1, sv_e2, e1a, e1b, e2a, e2b = zip(*w)
rough = np.array(rough,'float')
rough[rough==0.0] = np.nan
hard = np.array(hard,'float')
hard[hard==0.0] = np.nan
sv_e1 = np.array(sv_e1,'float')
sv_e1[sv_e1==0.0] = np.nan
sv_e2 = np.array(sv_e2,'float')
sv_e2[sv_e2==0.0] = np.nan
try:
nans, y= humutils.nan_helper(rough)
rough[nans]= np.interp(y(nans), y(~nans), rough[~nans])
except:
pass
try:
nans, y= humutils.nan_helper(hard)
hard[nans]= np.interp(y(nans), y(~nans), hard[~nans])
except:
pass
try:
nans, y= humutils.nan_helper(sv_e1)
sv_e1[nans]= np.interp(y(nans), y(~nans), sv_e1[~nans])
except:
pass
try:
nans, y= humutils.nan_helper(sv_e2)
sv_e2[nans]= np.interp(y(nans), y(~nans), sv_e2[~nans])
except:
pass
data = np.column_stack([sv_e1, sv_e2])
k_means = MiniBatchKMeans(numclusters)
# fit the model
k_means.fit(data)
values = k_means.cluster_centers_.squeeze()
labels = k_means.labels_
hardav = humutils.runningMeanFast(hard,integ)
roughav = humutils.runningMeanFast(rough,integ)
#f = open(sonpath+base+'rough_and_hard'+str(p)+'.csv', 'wt')
f = open(os.path.normpath(os.path.join(sonpath,base+'rough_and_hard'+str(0)+'.csv')), 'wt')
writer = csv.writer(f)
writer.writerow( ('longitude', 'latitude', 'easting', 'northing', 'depth', 'roughness', 'hardness', 'average roughness', 'average hardness','k-mean label') )
for i in range(0, len(rough)):
writer.writerow(( float(Zlon[i]),float(Zlat[i]),float(Zes[i]),float(Zns[i]),float(Zdepi[i]),float(rough[i]),float(hard[i]),float(roughav[i]),float(hardav[i]), labels[i].astype(int) ))
f.close()
if doplot==1:
try:
fig = plt.figure()
plt.imshow(dwnhi_fp, cmap='gray')
plt.plot(e1a,'r');
plt.plot(e1b,'y');
plt.plot(e2a,'c');
plt.plot(e2b,'m');
plt.axis('tight')
#plt.show()
custom_save(sonpath,'e1e2_scan'+str(0))
del fig
except:
print "plot could not be produced"
if doplot==1:
try:
fig = plt.figure()
fig.subplots_adjust(wspace = 0.4, hspace=0.4)
plt.subplot(221)
plt.plot(sv_e1[labels==0],sv_e2[labels==0],'ko');
plt.plot(sv_e1[labels==1],sv_e2[labels==1],'ro');
plt.plot(sv_e1[labels==2],sv_e2[labels==2],'bo');
plt.xlabel('SV1'); plt.ylabel('SV2')
plt.xlim(0,1); plt.ylim(0,1)
plt.subplot(222)
plt.plot(rough[labels==0],hard[labels==0],'ko');
plt.plot(rough[labels==1],hard[labels==1],'ro');
plt.plot(rough[labels==2],hard[labels==2],'bo');
plt.xlabel('E1'); plt.ylabel('E2')
plt.xlim(1,8); plt.ylim(1,8)
#plt.show()
custom_save(sonpath,'e1e2_kmeans'+str(0))
del fig
except:
print "plot could not be produced"
if doplot==1:
try:
fig = plt.figure()
s=plt.scatter(Zes[labels==0],Zns[labels==0],marker='o',c='k', s=10, linewidth=0, vmin=0, vmax=8);
s=plt.scatter(Zes[labels==1],Zns[labels==1],marker='o',c='r', s=10, linewidth=0, vmin=0, vmax=8);
s=plt.scatter(Zes[labels==2],Zns[labels==2],marker='o',c='b', s=10, linewidth=0, vmin=0, vmax=8);
custom_save(sonpath,'rgh_hard_kmeans'+str(0))
del fig
except:
print "plot could not be produced"
if doplot==1:
try:
print "drawing and printing map ..."
fig = plt.figure(frameon=False)
#fig.subplots_adjust(wspace = 0.4, hspace=0.4)
map = Basemap(projection='merc', epsg=cs2cs_args.split(':')[1], #epsg=26949,
resolution = 'i', #h #f
llcrnrlon=np.min(Zlon)-0.0001, llcrnrlat=np.min(Zlat)-0.0001,
urcrnrlon=np.max(Zlon)+0.0001, urcrnrlat=np.max(Zlat)+0.0001)
# draw point cloud
x,y = map.projtran(Zlon, Zlat)
cs = map.scatter(x.flatten(), y.flatten(), 1, rough.flatten(), linewidth=0, vmin=0, vmax=8)
try:
map.arcgisimage(server='http://server.arcgisonline.com/ArcGIS', service='ESRI_Imagery_World_2D', xpixels=1000, ypixels=None, dpi=300)
except:
map.arcgisimage(server='http://server.arcgisonline.com/ArcGIS', service='World_Imagery', xpixels=1000, ypixels=None, dpi=300)
cbar = map.colorbar(cs,location='bottom',pad="5%")
cbar.set_label('E1')
cbar.set_ticks([0,2,4,6,8])
custom_save(sonpath,'map_rgh'+str(0))
del fig
except:
print "plot could not be produced"
if doplot==1:
try:
fig = plt.figure()
#fig.subplots_adjust(wspace = 0.4, hspace=0.4)
map = Basemap(projection='merc', epsg=cs2cs_args.split(':')[1],
resolution = 'i', #h #f
llcrnrlon=np.min(Zlon)-0.0001, llcrnrlat=np.min(Zlat)-0.0001,
urcrnrlon=np.max(Zlon)+0.0001, urcrnrlat=np.max(Zlat)+0.0001)
# draw point cloud
x,y = map.projtran(Zlon, Zlat)
cs = map.scatter(x.flatten(), y.flatten(), 1, hard.flatten(), linewidth=0, vmin=0, vmax=8)
try:
map.arcgisimage(server='http://server.arcgisonline.com/ArcGIS', service='ESRI_Imagery_World_2D', xpixels=1000, ypixels=None, dpi=300)
except:
map.arcgisimage(server='http://server.arcgisonline.com/ArcGIS', service='World_Imagery', xpixels=1000, ypixels=None, dpi=300)
cbar = map.colorbar(cs,location='bottom',pad="5%")
cbar.set_label('E2')
cbar.set_ticks([0,2,4,6,8])
custom_save(sonpath,'map_hard'+str(0))
del fig
except:
print "plot could not be produced"
if doplot==1:
try:
print "drawing and printing map ..."
fig = plt.figure(frameon=False)
map = Basemap(projection='merc', epsg=cs2cs_args.split(':')[1], #26949,
resolution = 'i', #h #f
llcrnrlon=np.min(Zlon)-0.001, llcrnrlat=np.min(Zlat)-0.001,
urcrnrlon=np.max(Zlon)+0.001, urcrnrlat=np.max(Zlat)+0.001)
ax = plt.Axes(fig, [0., 0., 1., 1.], )
ax.set_axis_off()
fig.add_axes(ax)
## draw point cloud
x,y = map.projtran(Zlon, Zlat)
map.scatter(x.flatten(), y.flatten(), 1, rough.flatten(), linewidth = '0', vmin=0, vmax=8)
custom_save(sonpath,'Rough'+str(0))
del fig
kml = simplekml.Kml()
ground = kml.newgroundoverlay(name='GroundOverlay')
ground.icon.href = 'Rough'+str(0)+'.png'
ground.latlonbox.north = np.min(Zlat)-0.001
ground.latlonbox.south = np.max(Zlat)+0.001
ground.latlonbox.east = np.max(Zlon)+0.001
ground.latlonbox.west = np.min(Zlon)-0.001
ground.latlonbox.rotation = 0
#kml.save(sonpath+'Rough'+str(p)+'.kml')
kml.save(os.path.normpath(os.path.join(sonpath,'Rough'+str(0)+'.kml')))
except:
print "plot could not be produced"
if doplot==1:
try:
print "drawing and printing map ..."
fig = plt.figure(frameon=False)
map = Basemap(projection='merc', epsg=cs2cs_args.split(':')[1], #26949,
resolution = 'i', #h #f
llcrnrlon=np.min(Zlon)-0.001, llcrnrlat=np.min(Zlat)-0.001,
urcrnrlon=np.max(Zlon)+0.001, urcrnrlat=np.max(Zlat)+0.001)
ax = plt.Axes(fig, [0., 0., 1., 1.], )
ax.set_axis_off()
fig.add_axes(ax)
## draw point cloud
x,y = map.projtran(Zlon, Zlat)
map.scatter(x.flatten(), y.flatten(), 1, hard.flatten(), linewidth = '0', vmin=0, vmax=8)
custom_save(sonpath,'Hard'+str(0))
del fig
kml = simplekml.Kml()
ground = kml.newgroundoverlay(name='GroundOverlay')
ground.icon.href = 'Hard'+str(0)+'.png'
ground.latlonbox.north = np.min(Zlat)-0.001
ground.latlonbox.south = np.max(Zlat)+0.001
ground.latlonbox.east = np.max(Zlon)+0.001
ground.latlonbox.west = np.min(Zlon)-0.001
ground.latlonbox.rotation = 0
#kml.save(sonpath+'Hard'+str(p)+'.kml')
kml.save(os.path.normpath(os.path.join(sonpath,'Hard'+str(0)+'.kml')))
except:
print "plot could not be produced"
else:
print "high-frequency downward echosounder data not available"
def correct(humfile, sonpath, maxW, doplot, dofilt, correct_withwater, ph, temp, salinity, dconcfile):
'''
Remove water column and carry out some rudimentary radiometric corrections,
accounting for directivity and attenuation with range
Syntax
----------
[] = PyHum.correct(humfile, sonpath, maxW, doplot, correct_withwater, ph, temp, salinity, dconcfile)
Parameters
----------
humfile : str
path to the .DAT file
sonpath : str
path where the *.SON files are
maxW : int, *optional* [Default=1000]
maximum transducer power
doplot : int, *optional* [Default=1]
1 = make plots, otherwise do not
dofilt : int, *optional* [Default=0]
1 = apply a phase preserving filter to the scans
correct_withwater : int, *optional* [Default=0]
1 = apply radiometric correction but don't remove water column from scans
ph : float, *optional* [Default=7.0]
water acidity in pH
temp : float, *optional* [Default=10.0]
water temperature in degrees Celsius
salinity : float, *optional* [Default=0.0]
salinity of water in parts per thousand
dconcfile : str, *optional* [Default=None]
file path of a text file containing sediment concentration data
this file must contain the following fields separated by spaces:
size (microns) conc (mg/L) dens (kg/m3)
with one row per grain size, for example:
30 1700 2200
100 15 2650
Returns
-------
sonpath+base+'_data_star_l.dat': memory-mapped file
contains the starboard scan with water column removed
sonpath+base+'_data_port_l.dat': memory-mapped file
contains the portside scan with water column removed
sonpath+base+'_data_star_la.dat': memory-mapped file
contains the starboard scan with water column removed and
radiometrically corrected
sonpath+base+'_data_port_la.dat': memory-mapped file
contains the portside scan with water column removed and
radiometrically corrected
sonpath+base+'_data_range.dat': memory-mapped file
contains the cosine of the range which is used to correct
for attenuation with range
sonpath+base+'_data_dwnlow_l.dat': memory-mapped file
contains the low freq. downward scan with water column removed
sonpath+base+'_data_dwnhi_l.dat': memory-mapped file
contains the high freq. downward scan with water column removed
sonpath+base+'_data_dwnlow_la.dat': memory-mapped file
contains the low freq. downward scan with water column removed and
radiometrically corrected
sonpath+base+'_data_dwnhi_la.dat': memory-mapped file
contains the high freq. downward scan with water column removed and
radiometrically corrected
if correct_withwater == 1:
sonpath+base+'_data_star_lw.dat': memory-mapped file
contains the starboard scan with water column retained and
radiometrically corrected
sonpath+base+'_data_port_lw.dat': memory-mapped file
contains the portside scan with water column retained and
radiometrically corrected
'''
# prompt user to supply file if no input file given
if not humfile:
print('An input file is required!!!!!!')
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
inputfile = askopenfilename(filetypes=[("DAT files","*.DAT")])
# prompt user to supply directory if no input sonpath is given
if not sonpath:
print('A *.SON directory is required!!!!!!')
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
sonpath = askdirectory()
# print given arguments to screen and convert data type where necessary
if humfile:
print('Input file is %s' % (humfile))
if sonpath:
print('Sonar file path is %s' % (sonpath))
if maxW:
maxW = np.asarray(maxW,float)
print('Max. transducer power is %s W' % (str(maxW)))
if doplot:
doplot = int(doplot)
if doplot==0:
print("Plots will not be made")
if dofilt:
dofilt = int(dofilt)
if dofilt==0:
print("Phase preserving filter will not be applied")
else:
print("Phase preserving filter will be applied")
if correct_withwater:
correct_withwater = int(correct_withwater)
if correct_withwater==1:
print("Correction will be applied without removing water column")
if salinity:
salinity = np.asarray(salinity,float)
print('Salinity is %s ppt' % (str(salinity)))
if ph:
ph = np.asarray(ph,float)
print('pH is %s' % (str(ph)))
if temp:
temp = np.asarray(temp,float)
print('Temperature is %s' % (str(temp)))
if dconcfile is not None:
try:
print('Suspended sediment size/conc. file is %s' % (dconcfile))
dconc = np.genfromtxt(dconcfile).T
conc = dconc[1]
dens = dconc[2]
d = dconc[0]
except:
pass
#================================
# start timer
if os.name=='posix': # true if linux/mac or cygwin on windows
start = time.time()
else: # windows
start = time.clock()
# if son path name supplied has no separator at end, put one on
if sonpath[-1]!=os.sep:
sonpath = sonpath + os.sep
base = humfile.split('.DAT') # get base of file name for output
base = base[0].split(os.sep)[-1]
# remove underscores, negatives and spaces from basename
base = humutils.strip_base(base)
# add wattage to metadata dict
meta = loadmat(os.path.normpath(os.path.join(sonpath,base+'meta.mat')))
dep_m = meta['dep_m'][0]
pix_m = meta['pix_m'][0]
meta['maxW'] = maxW
savemat(os.path.normpath(os.path.join(sonpath,base+'meta.mat')), meta ,oned_as='row')
bed = np.squeeze(meta['bed'])
ft = 1/(meta['pix_m'])
dist_m = np.squeeze(meta['dist_m'])
try:
if dconcfile is not None:
# sediment attenuation
alpha = sed_atten(meta['f'],conc,dens,d,meta['c'])
else:
alpha = 0
except:
alpha = 0
# load memory mapped scans
shape_port = np.squeeze(meta['shape_port'])
if shape_port!='':
if os.path.isfile(os.path.normpath(os.path.join(sonpath,base+'_data_port2.dat'))):
port_fp = io.get_mmap_data(sonpath, base, '_data_port2.dat', 'int16', tuple(shape_port))
else:
port_fp = io.get_mmap_data(sonpath, base, '_data_port.dat', 'int16', tuple(shape_port))
shape_star = np.squeeze(meta['shape_star'])
if shape_star!='':
if os.path.isfile(os.path.normpath(os.path.join(sonpath,base+'_data_star2.dat'))):
star_fp = io.get_mmap_data(sonpath, base, '_data_star2.dat', 'int16', tuple(shape_star))
else:
star_fp = io.get_mmap_data(sonpath, base, '_data_star.dat', 'int16', tuple(shape_star))
if len(shape_star)==2:
extent = shape_star[0]
else:
extent = shape_star[1] #np.shape(data_port)[0]
bed = np.asarray(bed,'int')+int(0.25*ft)
# calculate in dB
######### star
Zt, R, A = remove_water(star_fp, bed, shape_star, dep_m, pix_m, 1, maxW)
Zt = np.squeeze(Zt)
# create memory mapped file for Z)
shape_star = io.set_mmap_data(sonpath, base, '_data_star_l.dat', 'float32', Zt)
del Zt
A = np.squeeze(A)
# create memory mapped file for A
shape_A = io.set_mmap_data(sonpath, base, '_data_incidentangle.dat', 'float32', A)
del A
R = np.squeeze(R)
R[np.isnan(R)] = 0
try:
alpha_w = water_atten(R,meta['f'],meta['c'], ph, temp, salinity)
except:
alpha_w = 1e-5
# compute transmission losses
TL = (40 * np.log10(R) + alpha_w + (2*alpha)*R/1000)/255
del alpha_w
# create memory mapped file for R
shape_R = io.set_mmap_data(sonpath, base, '_data_range.dat', 'float32', R)
del R
TL[np.isnan(TL)] = 0
TL[TL<0] = 0
shape_TL = io.set_mmap_data(sonpath, base, '_data_TL.dat', 'float32', TL)
del TL
A_fp = io.get_mmap_data(sonpath, base, '_data_incidentangle.dat', 'float32', shape_star)
TL_fp = io.get_mmap_data(sonpath, base, '_data_TL.dat', 'float32', shape_star)
R_fp = io.get_mmap_data(sonpath, base, '_data_range.dat', 'float32', shape_star)
if correct_withwater == 1:
Zt = correct_scans(star_fp, A_fp, TL_fp, dofilt)
# create memory mapped file for Z)
shape_star = io.set_mmap_data(sonpath, base, '_data_star_lw.dat', 'float32', Zt)
#we are only going to access the portion of memory required
star_fp = io.get_mmap_data(sonpath, base, '_data_star_l.dat', 'float32', shape_star)
##Zt = correct_scans(star_fp, A_fp, TL_fp, dofilt)
#phi=1.69
alpha=59 # vertical beam width at 3db
theta=35 #opening angle theta
# lambertian correction
Zt = correct_scans_lambertian(star_fp, A_fp, TL_fp, R_fp, meta['c'], meta['f'], theta, alpha)
Zt = np.squeeze(Zt)
avg = np.nanmedian(Zt,axis=0)
##avg = median_filter(avg,int(len(avg)/10))
Zt2 = Zt-avg + np.nanmean(avg)
Zt2 = Zt2 + np.abs(np.nanmin(Zt2))
try:
Zt2 = median_filter(Zt2, (3,3))
except:
pass
##Zt2 = np.empty(np.shape(Zt))
##for kk in range(np.shape(Zt)[1]):
## Zt2[:,kk] = (Zt[:,kk] - avg) + np.nanmean(avg)
##Zt2[Zt<=0] = np.nan
##Zt2[Zt2<=0] = np.nan
del Zt
# create memory mapped file for Z
shape_star = io.set_mmap_data(sonpath, base, '_data_star_la.dat', 'float32', Zt2)
del Zt2
#we are only going to access the portion of memory required
star_fp = io.get_mmap_data(sonpath, base, '_data_star_la.dat', 'float32', shape_star)
######### port
if correct_withwater == 1:
Zt = correct_scans(port_fp, A_fp, TL, dofilt)
# create memory mapped file for Z)
shape_port = io.set_mmap_data(sonpath, base, '_data_port_lw.dat', 'float32', Zt)
Zt = remove_water(port_fp, bed, shape_port, dep_m, pix_m, 0, maxW)
Zt = np.squeeze(Zt)
# create memory mapped file for Z
shape_port = io.set_mmap_data(sonpath, base, '_data_port_l.dat', 'float32', Zt)
#we are only going to access the portion of memory required
port_fp = io.get_mmap_data(sonpath, base, '_data_port_l.dat', 'float32', shape_port)
##Zt = correct_scans(port_fp, A_fp, TL_fp, dofilt)
# lambertian correction
Zt = correct_scans_lambertian(port_fp, A_fp, TL_fp, R_fp, meta['c'], meta['f'], theta, alpha)
Zt = np.squeeze(Zt)
Zt2 = Zt-avg + np.nanmean(avg)
Zt2 = Zt2 + np.abs(np.nanmin(Zt2))
##Zt2 = np.empty(np.shape(Zt))
##for kk in range(np.shape(Zt)[1]):
## Zt2[:,kk] = (Zt[:,kk] - avg) + np.nanmean(avg)
##Zt2[Zt<=0] = np.nan
##Zt2[Zt2<=0] = np.nan
del Zt
# create memory mapped file for Z
shape_port = io.set_mmap_data(sonpath, base, '_data_port_la.dat', 'float32', Zt2)
del Zt2
#we are only going to access the portion of memory required
port_fp = io.get_mmap_data(sonpath, base, '_data_port_la.dat', 'float32', shape_port)
## do plots of merged scans
if doplot==1:
if correct_withwater == 1:
port_fpw = io.get_mmap_data(sonpath, base, '_data_port_lw.dat', 'float32', shape_port)
star_fpw = io.get_mmap_data(sonpath, base, '_data_star_lw.dat', 'float32', shape_star)
if len(np.shape(star_fpw))>2:
for p in range(len(star_fpw)):
plot_merged_scans(port_fpw[p], star_fpw[p], dist_m, shape_port, ft, sonpath, p)
else:
plot_merged_scans(port_fpw, star_fpw, dist_m, shape_port, ft, sonpath, 0)
else:
if len(np.shape(star_fp))>2:
for p in range(len(star_fp)):
plot_merged_scans(port_fp[p], star_fp[p], dist_m, shape_port, ft, sonpath, p)
else:
plot_merged_scans(port_fp, star_fp, dist_m, shape_port, ft, sonpath, 0)
# load memory mapped scans
shape_low = np.squeeze(meta['shape_low'])
shape_hi = np.squeeze(meta['shape_hi'])
if shape_low!='':
if os.path.isfile(os.path.normpath(os.path.join(sonpath,base+'_data_dwnlow2.dat'))):
try:
low_fp = io.get_mmap_data(sonpath, base, '_data_dwnlow2.dat', 'int16', tuple(shape_low))
except:
low_fp = io.get_mmap_data(sonpath, base, '_data_dwnlow.dat', 'int16', tuple(shape_low))
finally:
low_fp = io.get_mmap_data(sonpath, base, '_data_dwnlow.dat', 'int16', tuple(shape_hi))
#if 'shape_hi' in locals():
# low_fp = io.get_mmap_data(sonpath, base, '_data_dwnlow2.dat', 'int16', tuple(shape_hi))
else:
try:
low_fp = io.get_mmap_data(sonpath, base, '_data_dwnlow.dat', 'int16', tuple(shape_low))
except:
if 'shape_hi' in locals():
low_fp = io.get_mmap_data(sonpath, base, '_data_dwnlow.dat', 'int16', tuple(shape_hi))
shape_hi = np.squeeze(meta['shape_hi'])
if shape_hi!='':
if os.path.isfile(os.path.normpath(os.path.join(sonpath,base+'_data_dwnhi2.dat'))):
try:
hi_fp = io.get_mmap_data(sonpath, base, '_data_dwnhi2.dat', 'int16', tuple(shape_hi))
except:
hi_fp = io.get_mmap_data(sonpath, base, '_data_dwnhi.dat', 'int16', tuple(shape_hi))
finally:
hi_fp = io.get_mmap_data(sonpath, base, '_data_dwnhi.dat', 'int16', tuple(shape_low))
#if 'shape_low' in locals():
# hi_fp = io.get_mmap_data(sonpath, base, '_data_dwnhi2.dat', 'int16', tuple(shape_low))
else:
try:
hi_fp = io.get_mmap_data(sonpath, base, '_data_dwnhi.dat', 'int16', tuple(shape_hi))
except:
if 'shape_low' in locals():
hi_fp = io.get_mmap_data(sonpath, base, '_data_dwnhi.dat', 'int16', tuple(shape_low))
if 'low_fp' in locals():
######### low
Zt = remove_water(low_fp, bed, shape_low, dep_m, pix_m, 0, maxW)
Zt = np.squeeze(Zt)
# create memory mapped file for Z
shape_low = io.set_mmap_data(sonpath, base, '_data_dwnlow_l.dat', 'float32', Zt)
del Zt
#we are only going to access the portion of memory required
low_fp = io.get_mmap_data(sonpath, base, '_data_dwnlow_l.dat', 'float32', shape_low)
Zt = correct_scans2(low_fp, TL_fp)
# create memory mapped file for Z
shape_low = io.set_mmap_data(sonpath, base, '_data_dwnlow_la.dat', 'float32', Zt)
del Zt
#we are only going to access the lowion of memory required
low_fp = io.get_mmap_data(sonpath, base, '_data_dwnlow_la.dat', 'float32', shape_low)
if doplot==1:
if len(np.shape(low_fp))>2:
for p in range(len(low_fp)):
plot_dwnlow_scans(low_fp[p], dist_m, shape_low, ft, sonpath, p)
else:
plot_dwnlow_scans(low_fp, dist_m, shape_low, ft, sonpath, 0)
if 'hi_fp' in locals():
######### hi
Zt = remove_water(hi_fp, bed, shape_hi, dep_m, pix_m, 0, maxW)
Zt = np.squeeze(Zt)
# create memory mapped file for Z
shape_hi = io.set_mmap_data(sonpath, base, '_data_dwnhi_l.dat', 'float32', Zt)
del Zt
#we are only going to access the portion of memory required
hi_fp = io.get_mmap_data(sonpath, base, '_data_dwnhi_l.dat', 'float32', shape_hi)
Zt = correct_scans2(hi_fp, TL_fp)
# create memory mapped file for Z
shape_hi = io.set_mmap_data(sonpath, base, '_data_dwnhi_la.dat', 'float32', Zt)
del Zt
#we are only going to access the hiion of memory required
hi_fp = io.get_mmap_data(sonpath, base, '_data_dwnhi_la.dat', 'float32', shape_hi)
if doplot==1:
if len(np.shape(hi_fp))>2:
for p in range(len(hi_fp)):
plot_dwnhi_scans(hi_fp[p], dist_m, shape_hi, ft, sonpath, p)
else:
plot_dwnhi_scans(hi_fp, dist_m, shape_hi, ft, sonpath, 0)
if os.name=='posix': # true if linux/mac
elapsed = (time.time() - start)
else: # windows
elapsed = (time.clock() - start)
print("Processing took "+ str(elapsed) + "seconds to analyse")
print("Done!")
print("===================================================")
def texture(humfile, sonpath, win, shift, doplot, density, numclasses, maxscale, notes):
'''
Create a texture lengthscale map using the algorithm detailed by Buscombe et al. (2015)
This textural lengthscale is not a direct measure of grain size. Rather, it is a statistical
representation that integrates over many attributes of bed texture, of which grain size is the most important.
The technique is a physically based means to identify regions of texture within a sidescan echogram,
and could provide a basis for objective, automated riverbed sediment classification.
Syntax
----------
[] = PyHum.texture(humfile, sonpath, win, shift, doplot, density, numclasses, maxscale, notes)
Parameters
----------
humfile : str
path to the .DAT file
sonpath : str
path where the *.SON files are
win : int, *optional* [Default=100]
pixel in pixels of the moving window
shift : int, *optional* [Default=10]
shift in pixels for moving window operation
doplot : int, *optional* [Default=1]
if 1, make plots, otherwise do not make plots
density : int, *optional* [Default=win/2]
echogram will be sampled every 'density' pixels
numclasses : int, *optional* [Default=4]
number of 'k means' that the texture lengthscale will be segmented into
maxscale : int, *optional* [Default=20]
Max scale as inverse fraction of data length for wavelet analysis
notes : int, *optional* [Default=100]
notes per octave for wavelet analysis
Returns
-------
sonpath+base+'_data_class.dat': memory-mapped file
contains the texture lengthscale map
sonpath+base+'_data_kclass.dat': memory-mapped file
contains the k-means segmented texture lengthscale map
References
----------
.. [1] Buscombe, D., Grams, P.E., and Smith, S.M.C., 2015, Automated riverbed sediment
classification using low-cost sidescan sonar. Journal of Hydraulic Engineering 10.1061/(ASCE)HY.1943-7900.0001079, 06015019.
'''
# prompt user to supply file if no input file given
if not humfile:
print('An input file is required!!!!!!')
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
humfile = askopenfilename(filetypes=[("DAT files","*.DAT")])
# prompt user to supply directory if no input sonpath is given
if not sonpath:
print('A *.SON directory is required!!!!!!')
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
sonpath = askdirectory()
# print given arguments to screen and convert data type where necessary
if humfile:
print('Input file is %s' % (humfile))
if sonpath:
print('Sonar file path is %s' % (sonpath))
if win:
win = np.asarray(win,int)
print('Window is %s square pixels' % (str(win)))
if shift:
shift = np.asarray(shift,int)
print('Min shift is %s pixels' % (str(shift)))
if density:
density = np.asarray(density,int)
print('Image will be sampled every %s pixels' % (str(density)))
if numclasses:
numclasses = np.asarray(numclasses,int)
print('Number of sediment classes: %s' % (str(numclasses)))
if maxscale:
maxscale = np.asarray(maxscale,int)
print('Max scale as inverse fraction of data length: %s' % (str(maxscale)))
if notes:
notes = np.asarray(notes,int)
print('Notes per octave: %s' % (str(notes)))
if doplot:
doplot = int(doplot)
if doplot==0:
print("Plots will not be made")
print('[Default] Number of processors is %s' % (str(cpu_count())))
########################################################
########################################################
# start timer
if os.name=='posix': # true if linux/mac or cygwin on windows
start = time.time()
else: # windows
start = time.clock()
# if son path name supplied has no separator at end, put one on
if sonpath[-1]!=os.sep:
sonpath = sonpath + os.sep
base = humfile.split('.DAT') # get base of file name for output
base = base[0].split(os.sep)[-1]
# remove underscores, negatives and spaces from basename
base = humutils.strip_base(base)
meta = loadmat(os.path.normpath(os.path.join(sonpath,base+'meta.mat')))
ft = 1/loadmat(sonpath+base+'meta.mat')['pix_m']
#pix_m = np.squeeze(meta['pix_m'])
#dep_m = np.squeeze(meta['dep_m'])
dist_m = np.squeeze(meta['dist_m'])
### port
print("processing port side ...")
# load memory mapped scan ... port
shape_port = np.squeeze(meta['shape_port'])
if shape_port!='':
if os.path.isfile(os.path.normpath(os.path.join(sonpath,base+'_data_port_lar.dat'))):
port_fp = io.get_mmap_data(sonpath, base, '_data_port_lar.dat', 'float32', tuple(shape_port))
else:
port_fp = io.get_mmap_data(sonpath, base, '_data_port_la.dat', 'float32', tuple(shape_port))
port_fp2 = io.get_mmap_data(sonpath, base, '_data_port_l.dat', 'float32', tuple(shape_port))
### star
print("processing starboard side ...")
# load memory mapped scan ... port
shape_star = np.squeeze(loadmat(sonpath+base+'meta.mat')['shape_star'])
if shape_star!='':
if os.path.isfile(os.path.normpath(os.path.join(sonpath,base+'_data_star_lar.dat'))):
star_fp = io.get_mmap_data(sonpath, base, '_data_star_lar.dat', 'float32', tuple(shape_star))
else:
star_fp = io.get_mmap_data(sonpath, base, '_data_star_la.dat', 'float32', tuple(shape_star))
star_fp2 = io.get_mmap_data(sonpath, base, '_data_star_l.dat', 'float32', tuple(shape_star))
if len(shape_star)>2:
shape = shape_port.copy()
shape[1] = shape_port[1] + shape_star[1]
else:
shape = []
shape.append(1)
shape.append(shape_port[0])
shape.append(shape_port[1])
shape[1] = shape_port[0] + shape_star[0]
# create memory mapped file for Sp
with open(os.path.normpath(os.path.join(sonpath,base+'_data_class.dat')), 'w+') as ff:
fp = np.memmap(ff, dtype='float32', mode='w+', shape=tuple(shape))
if len(shape_star)>2:
#SRT = []
for p in range(len(port_fp)):
Z,ind = humutils.sliding_window_sliced(np.vstack((np.flipud(port_fp[p]), star_fp[p])), density, (win,win),(shift,shift))
Snn = get_srt(Z,ind,maxscale, notes, win) #, density)
# replace nans using infilling algorithm
rn = replace_nans.RN(Snn.astype('float64'),1000,0.01,2,'localmean')
Snn = rn.getdata()
del rn
Ny, Nx = np.shape( np.vstack((np.flipud(port_fp[p]), star_fp[p])) )
Snn = median_filter(Snn,(int(Nx/100),int(Ny/100)))
Sp = humutils.im_resize(Snn,Nx,Ny)
del Snn
Sp[np.isnan(np.vstack((np.flipud(port_fp[p]), star_fp[p])))] = np.nan
Sp[np.isnan(np.vstack((np.flipud(port_fp2[p]), star_fp2[p])))] = np.nan
#extent = shape_port[1]
#Zdist = dist_m[shape_port[-1]*p:shape_port[-1]*(p+1)]
#yvec = np.linspace(pix_m,extent*pix_m,extent)
#d = dep_m[shape_port[-1]*p:shape_port[-1]*(p+1)]
R_fp = io.get_mmap_data(sonpath, base, '_data_range.dat', 'float32', tuple(shape_star))
R = np.vstack((np.flipud(R_fp[0]),R_fp[0]))
R = R/np.max(R)
#R[R>0.8] = np.nan
rn = replace_nans.RN(R.astype('float64'),1000,0.01,2,'localmean')
R = rn.getdata()
del rn
Sp = (Sp**2) * np.cos(np.deg2rad(R)) /shift ##**2
fp[p] = Sp.astype('float32')
del Sp
del fp # flush data to file
class_fp = io.get_mmap_data(sonpath, base, '_data_class.dat', 'float32', tuple(shape))
else:
Z,ind = humutils.sliding_window_sliced(np.vstack((np.flipud(port_fp), star_fp)), density, (win,win),(shift,shift))
Snn = get_srt(Z,ind,maxscale, notes, win) #, density)
# replace nans using infilling algorithm
rn = replace_nans.RN(Snn.astype('float64'),1000,0.01,2,'localmean')
Snn = rn.getdata()
del rn
Ny, Nx = np.shape( np.vstack((np.flipud(port_fp), star_fp)) )
Snn = median_filter(Snn,(int(Nx/100),int(Ny/100)))
Sp = humutils.im_resize(Snn,Nx,Ny)
del Snn
Sp[np.isnan(np.vstack((np.flipud(port_fp), star_fp)))] = np.nan
Sp[np.isnan(np.vstack((np.flipud(port_fp2), star_fp2)))] = np.nan
#extent = shape_port[0]
#Zdist = dist_m
#yvec = np.linspace(pix_m,extent*pix_m,extent)
#d = dep_m
R_fp = io.get_mmap_data(sonpath, base, '_data_range.dat', 'float32', tuple(shape_star))
R = np.vstack((np.flipud(R_fp),R_fp))
R = R/np.max(R)
#R[R>0.8] = np.nan
rn = replace_nans.RN(R.astype('float64'),1000,0.01,2,'localmean')
R = rn.getdata()
del rn
Sp = (Sp**2) * np.cos(np.deg2rad(R)) / shift ##**2
with open(os.path.normpath(os.path.join(sonpath,base+'_data_class.dat')), 'w+') as ff:
np.save(ff, np.squeeze(Sp).astype('float32'))
with open(os.path.normpath(os.path.join(sonpath,base+'_data_class.dat')), 'r') as ff:
class_fp = np.load(ff)
del Sp
dist_m = np.squeeze(loadmat(sonpath+base+'meta.mat')['dist_m'])
########################################################
if doplot==1:
if len(shape_star)>2:
for p in range(len(star_fp)):
plot_class(dist_m, shape_port, port_fp[p], star_fp[p], class_fp[p], ft, humfile, sonpath, base, p)
else:
plot_class(dist_m, shape_port, port_fp, star_fp, class_fp, ft, humfile, sonpath, base, 0)
if len(shape_star)>2:
for p in range(len(star_fp)):
plot_contours(dist_m, shape_port, class_fp[p], ft, humfile, sonpath, base, numclasses, p)
else:
plot_contours(dist_m, shape_port, class_fp, ft, humfile, sonpath, base, numclasses, 0)
#######################################################
# k-means
if len(shape_star)>2:
with open(os.path.normpath(os.path.join(sonpath,base+'_data_kclass.dat')), 'w+') as ff:
fp = np.memmap(ff, dtype='float32', mode='w+', shape=tuple(shape))
for p in range(len(port_fp)):
wc = get_kclass(class_fp[p].copy(), numclasses)
fp[p] = wc.astype('float32')
del wc
del fp
kclass_fp = io.get_mmap_data(sonpath, base, '_data_kclass.dat', 'float32', tuple(shape))
else:
wc = get_kclass(class_fp.copy(), numclasses)
with open(os.path.normpath(os.path.join(sonpath,base+'_data_kclass.dat')), 'w+') as ff:
np.save(ff, np.squeeze(wc).astype('float32'))
del wc
with open(os.path.normpath(os.path.join(sonpath,base+'_data_kclass.dat')), 'r') as ff:
kclass_fp = np.load(ff)
########################################################
if doplot==1:
if len(shape_star)>2:
for p in range(len(star_fp)):
plot_kmeans(dist_m, shape_port, port_fp[p], star_fp[p], kclass_fp[p], ft, humfile, sonpath, base, p)
else:
plot_kmeans(dist_m, shape_port, port_fp, star_fp, kclass_fp, ft, humfile, sonpath, base, 0)
if os.name=='posix': # true if linux/mac
elapsed = (time.time() - start)
else: # windows
elapsed = (time.clock() - start)
print("Processing took "+ str(elapsed)+ "seconds to analyse")
print("Done!")
print("===================================================")
def texture_slic(humfile,
sonpath,
doplot=1,
numclasses=4,
maxscale=20,
notes=4):
'''
Create a texture lengthscale map using the algorithm detailed by Buscombe et al. (2015)
This textural lengthscale is not a direct measure of grain size. Rather, it is a statistical
representation that integrates over many attributes of bed texture, of which grain size is the most important.
The technique is a physically based means to identify regions of texture within a sidescan echogram,
and could provide a basis for objective, automated riverbed sediment classification.
Syntax
----------
[] = PyHum.texture(humfile, sonpath, doplot, numclasses, maxscale, notes)
Parameters
----------
humfile : str
path to the .DAT file
sonpath : str
path where the *.SON files are
doplot : int, *optional* [Default=1]
if 1, make plots, otherwise do not make plots
numclasses : int, *optional* [Default=4]
number of 'k means' that the texture lengthscale will be segmented into
maxscale : int, *optional* [Default=20]
Max scale as inverse fraction of data length for wavelet analysis
notes : int, *optional* [Default=100]
notes per octave for wavelet analysis
Returns
-------
sonpath+base+'_data_class.dat': memory-mapped file
contains the texture lengthscale map
sonpath+base+'_data_kclass.dat': memory-mapped file
contains the k-means segmented texture lengthscale map
References
----------
.. [1] Buscombe, D., Grams, P.E., and Smith, S.M.C., 2015, Automated riverbed sediment
classification using low-cost sidescan sonar. Journal of Hydraulic Engineering 10.1061/(ASCE)HY.1943-7900.0001079, 06015019.
'''
# prompt user to supply file if no input file given
if not humfile:
print('An input file is required!!!!!!')
Tk().withdraw(
) # we don't want a full GUI, so keep the root window from appearing
humfile = askopenfilename(filetypes=[("DAT files", "*.DAT")])
# prompt user to supply directory if no input sonpath is given
if not sonpath:
print('A *.SON directory is required!!!!!!')
Tk().withdraw(
) # we don't want a full GUI, so keep the root window from appearing
sonpath = askdirectory()
# print given arguments to screen and convert data type where necessary
if humfile:
print('Input file is %s' % (humfile))
if sonpath:
print('Sonar file path is %s' % (sonpath))
if numclasses:
numclasses = np.asarray(numclasses, int)
print('Number of sediment classes: %s' % (str(numclasses)))
if maxscale:
maxscale = np.asarray(maxscale, int)
print('Max scale as inverse fraction of data length: %s' %
(str(maxscale)))
if notes:
notes = np.asarray(notes, int)
print('Notes per octave: %s' % (str(notes)))
if doplot:
doplot = int(doplot)
if doplot == 0:
print("Plots will not be made")
print('[Default] Number of processors is %s' % (str(cpu_count())))
########################################################
########################################################
# start timer
if os.name == 'posix': # true if linux/mac or cygwin on windows
start = time.time()
else: # windows
start = time.clock()
# if son path name supplied has no separator at end, put one on
if sonpath[-1] != os.sep:
sonpath = sonpath + os.sep
base = humfile.split('.DAT') # get base of file name for output
base = base[0].split(os.sep)[-1]
# remove underscores, negatives and spaces from basename
base = humutils.strip_base(base)
meta = loadmat(os.path.normpath(os.path.join(sonpath, base + 'meta.mat')))
ft = 1 / loadmat(sonpath + base + 'meta.mat')['pix_m']
#pix_m = np.squeeze(meta['pix_m'])
#dep_m = np.squeeze(meta['dep_m'])
dist_m = np.squeeze(meta['dist_m'])
### port
print("processing port side ...")
# load memory mapped scan ... port
shape_port = np.squeeze(meta['shape_port'])
if shape_port != '':
if os.path.isfile(
os.path.normpath(
os.path.join(sonpath, base + '_data_port_lar.dat'))):
port_fp = io.get_mmap_data(sonpath, base, '_data_port_lar.dat',
'float32', tuple(shape_port))
else:
port_fp = io.get_mmap_data(sonpath, base, '_data_port_la.dat',
'float32', tuple(shape_port))
#port_fp2 = io.get_mmap_data(sonpath, base, '_data_port_l.dat', 'float32', tuple(shape_port))
### star
print("processing starboard side ...")
# load memory mapped scan ... port
shape_star = np.squeeze(loadmat(sonpath + base + 'meta.mat')['shape_star'])
if shape_star != '':
if os.path.isfile(
os.path.normpath(
os.path.join(sonpath, base + '_data_star_lar.dat'))):
star_fp = io.get_mmap_data(sonpath, base, '_data_star_lar.dat',
'float32', tuple(shape_star))
else:
star_fp = io.get_mmap_data(sonpath, base, '_data_star_la.dat',
'float32', tuple(shape_star))
#star_fp2 = io.get_mmap_data(sonpath, base, '_data_star_l.dat', 'float32', tuple(shape_star))
if len(shape_star) > 2:
shape = shape_port.copy()
shape[1] = shape_port[1] + shape_star[1]
else:
shape = []
shape.append(1)
shape.append(shape_port[0])
shape.append(shape_port[1])
shape[1] = shape_port[0] + shape_star[0]
#work on the entire scan
#im = humutils.rescale(np.vstack((np.flipud(np.hstack(port_fp)), np.hstack(star_fp))),0,1)
im = np.vstack((np.flipud(np.hstack(port_fp)), np.hstack(star_fp)))
im[np.isnan(im)] = 0
im = humutils.rescale(im, 0, 1)
#get SLIC superpixels
segments_slic = slic(im, n_segments=int(im.shape[0] / 10), compactness=.1)
#pre-allocate texture lengthscale array
tl = np.zeros(im.shape, dtype="float64")
#cycle through each segment and compute tl
for k in np.unique(segments_slic):
mask = np.zeros(im.shape[:2], dtype="uint8")
mask[segments_slic == k] = 255
cmask, cim = crop_toseg(mask, im)
tl[segments_slic == k] = parallel_me(cim, maxscale, notes,
np.shape(cim)[0])
R_fp = io.get_mmap_data(sonpath, base, '_data_range.dat', 'float32',
tuple(shape_star))
R = np.vstack((np.flipud(np.hstack(R_fp)), np.hstack(R_fp)))
R = R / np.max(R)
#correct for range and scale
tl = tl * np.cos(R) * (1 / ft)
tl[im == 0] = np.nan
tl[np.isnan(im)] = np.nan
# create memory mapped file for Sp
with open(
os.path.normpath(os.path.join(sonpath, base + '_data_class.dat')),
'w+') as ff:
fp = np.memmap(ff, dtype='float32', mode='w+', shape=tuple(shape))
counter = 0
if len(shape_star) > 2:
for p in range(len(port_fp)):
if p == 0:
n, m = np.shape(np.vstack((np.flipud(port_fp[p]), star_fp[p])))
else:
n, m = np.shape(np.vstack((np.flipud(port_fp[p]), star_fp[p])))
Sp = tl[:n, counter:counter + m]
counter = counter + m
fp[p] = Sp.astype('float32')
del Sp
del fp # flush data to file
class_fp = io.get_mmap_data(sonpath, base, '_data_class.dat',
'float32', tuple(shape))
else:
with open(
os.path.normpath(
os.path.join(sonpath, base + '_data_class.dat')),
'w+') as ff:
np.save(ff, np.squeeze(Sp).astype('float32'))
with open(
os.path.normpath(
os.path.join(sonpath, base + '_data_class.dat')),
'r') as ff:
class_fp = np.load(ff)
dist_m = np.squeeze(loadmat(sonpath + base + 'meta.mat')['dist_m'])
########################################################
if doplot == 1:
if len(shape_star) > 2:
for p in range(len(star_fp)):
plot_class(dist_m, shape_port, port_fp[p], star_fp[p],
class_fp[p], ft, humfile, sonpath, base, p)
else:
plot_class(dist_m, shape_port, port_fp, star_fp, class_fp, ft,
humfile, sonpath, base, 0)
if len(shape_star) > 2:
for p in range(len(star_fp)):
plot_contours(dist_m, shape_port, class_fp[p], ft, humfile,
sonpath, base, numclasses, p)
else:
plot_contours(dist_m, shape_port, class_fp, ft, humfile, sonpath,
base, numclasses, 0)
#######################################################
# k-means
if len(shape_star) > 2:
with open(
os.path.normpath(
os.path.join(sonpath, base + '_data_kclass.dat')),
'w+') as ff:
fp = np.memmap(ff, dtype='float32', mode='w+', shape=tuple(shape))
for p in range(len(port_fp)):
wc = get_kclass(class_fp[p].copy(), numclasses)
fp[p] = wc.astype('float32')
del wc
del fp
kclass_fp = io.get_mmap_data(sonpath, base, '_data_kclass.dat',
'float32', tuple(shape))
else:
wc = get_kclass(class_fp.copy(), numclasses)
with open(
os.path.normpath(
os.path.join(sonpath, base + '_data_kclass.dat')),
'w+') as ff:
np.save(ff, np.squeeze(wc).astype('float32'))
del wc
with open(
os.path.normpath(
os.path.join(sonpath, base + '_data_kclass.dat')),
'r') as ff:
kclass_fp = np.load(ff)
########################################################
if doplot == 1:
if len(shape_star) > 2:
for p in range(len(star_fp)):
plot_kmeans(dist_m, shape_port, port_fp[p], star_fp[p],
kclass_fp[p], ft, humfile, sonpath, base, p)
else:
plot_kmeans(dist_m, shape_port, port_fp, star_fp, kclass_fp, ft,
humfile, sonpath, base, 0)
if os.name == 'posix': # true if linux/mac
elapsed = (time.time() - start)
else: # windows
elapsed = (time.clock() - start)
print("Processing took " + str(elapsed) + "seconds to analyse")
print("Done!")
def mosaic_texture(humfile, sonpath, cs2cs_args = "epsg:26949", res = 99, nn = 5, weight = 1):
'''
Create mosaics of the spatially referenced sidescan echograms
Syntax
----------
[] = PyHum.mosaic_texture(humfile, sonpath, cs2cs_args, res, nn, weight)
Parameters
----------
humfile : str
path to the .DAT file
sonpath : str
path where the *.SON files are
cs2cs_args : int, *optional* [Default="epsg:26949"]
arguments to create coordinates in a projected coordinate system
this argument gets given to pyproj to turn wgs84 (lat/lon) coordinates
into any projection supported by the proj.4 libraries
res : float, *optional* [Default=0]
grid resolution of output gridded texture map
if res=99, res will be determined automatically from the spatial resolution of 1 pixel
nn: int, *optional* [Default=5]
number of nearest neighbours for gridding
weight: int, *optional* [Default=1]
specifies the type of pixel weighting in the gridding process
weight = 1, based on grazing angle and inverse distance weighting
weight = 2, based on grazing angle only
weight = 3, inverse distance weighting only
weight = 4, no weighting
Returns
-------
sonpath+'GroundOverlay.kml': kml file
contains gridded (or point cloud) sidescan intensity map for importing into google earth
of the pth chunk
sonpath+'map.png' :
image overlay associated with the kml file
'''
# prompt user to supply file if no input file given
if not humfile:
print('An input file is required!!!!!!')
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
humfile = askopenfilename(filetypes=[("DAT files","*.DAT")])
# prompt user to supply directory if no input sonpath is given
if not sonpath:
print('A *.SON directory is required!!!!!!')
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
sonpath = askdirectory()
# print given arguments to screen and convert data type where necessary
if humfile:
print('Input file is %s' % (humfile))
if sonpath:
print('Sonar file path is %s' % (sonpath))
if cs2cs_args:
print('cs2cs arguments are %s' % (cs2cs_args))
if res:
res = np.asarray(res,float)
print('Gridding resolution: %s' % (str(res)))
if nn:
nn = int(nn)
print('Number of nearest neighbours for gridding: %s' % (str(nn)))
if weight:
weight = int(weight)
print('Weighting for gridding: %s' % (str(weight)))
##nn = 5 #number of nearest neighbours in gridding
##noisefloor=10 # noise threshold in dB W
# start timer
if os.name=='posix': # true if linux/mac or cygwin on windows
start = time.time()
else: # windows
start = time.clock()
trans = pyproj.Proj(init=cs2cs_args)
# if son path name supplied has no separator at end, put one on
if sonpath[-1]!=os.sep:
sonpath = sonpath + os.sep
base = humfile.split('.DAT') # get base of file name for output
base = base[0].split(os.sep)[-1]
# remove underscores, negatives and spaces from basename
base = humutils.strip_base(base)
meta = loadmat(os.path.normpath(os.path.join(sonpath,base+'meta.mat')))
esi = np.squeeze(meta['e'])
nsi = np.squeeze(meta['n'])
theta = np.squeeze(meta['heading'])/(180/np.pi)
# load memory mapped scans
shape_port = np.squeeze(meta['shape_port'])
if shape_port!='':
if os.path.isfile(os.path.normpath(os.path.join(sonpath,base+'_data_port_lar.dat'))):
port_fp = io.get_mmap_data(sonpath, base, '_data_port_lar.dat', 'float32', tuple(shape_port))
else:
port_fp = io.get_mmap_data(sonpath, base, '_data_port_la.dat', 'float32', tuple(shape_port))
shape_star = np.squeeze(meta['shape_star'])
if shape_star!='':
if os.path.isfile(os.path.normpath(os.path.join(sonpath,base+'_data_star_lar.dat'))):
star_fp = io.get_mmap_data(sonpath, base, '_data_star_lar.dat', 'float32', tuple(shape_star))
else:
star_fp = io.get_mmap_data(sonpath, base, '_data_star_la.dat', 'float32', tuple(shape_star))
# time varying gain
tvg = ((8.5*10**-5)+(3/76923)+((8.5*10**-5)/4))*meta['c']
# depth correction
dist_tvg = np.squeeze(((np.tan(np.radians(25)))*np.squeeze(meta['dep_m']))-(tvg))
# read in range data
R_fp = io.get_mmap_data(sonpath, base, '_data_range.dat', 'float32', tuple(shape_star))
dx = np.arcsin(meta['c']/(1000*meta['t']*meta['f']))
pix_m = meta['pix_m']
c = meta['c']
if not os.path.isfile( os.path.normpath(os.path.join(sonpath,base+"S.p")) ):
#if 2 > 1:
inputfiles = []
if len(shape_star)>2:
for p in range(len(star_fp)):
e = esi[shape_port[-1]*p:shape_port[-1]*(p+1)]
n = nsi[shape_port[-1]*p:shape_port[-1]*(p+1)]
t = theta[shape_port[-1]*p:shape_port[-1]*(p+1)]
d = dist_tvg[shape_port[-1]*p:shape_port[-1]*(p+1)]
dat_port = port_fp[p]
dat_star = star_fp[p]
data_R = R_fp[p]
print("writing chunk %s " % (str(p)))
write_points(e, n, t, d, dat_port, dat_star, data_R, pix_m, res, cs2cs_args, sonpath, p, c, dx)
inputfiles.append(os.path.normpath(os.path.join(sonpath,'x_y_class'+str(p)+'.asc')))
else:
p=0
print("writing chunk %s " % (str(p)))
write_points(esi, nsi, theta, dist_tvg, port_fp, star_fp, R_fp, meta['pix_m'], res, cs2cs_args, sonpath, 0, c, dx)
inputfiles.append(os.path.normpath(os.path.join(sonpath,'x_y_class'+str(p)+'.asc')))
#trans = pyproj.Proj(init=cs2cs_args)
# D, R, h, t
print("reading points from %s files" % (str(len(inputfiles))))
X,Y,S,D,R,h,t,i = getxys(inputfiles)
print("%s points read from %s files" % (str(len(S)), str(len(inputfiles))))
# remove values where sidescan intensity is zero
ind = np.where(np.logical_not(S==0))[0]
X = X[ind]; Y = Y[ind]
S = S[ind]; D = D[ind]
R = R[ind]; h = h[ind]
t = t[ind]; i = i[ind]
del ind
# save to file for temporary storage
pickle.dump( S, open( os.path.normpath(os.path.join(sonpath,base+"S.p")), "wb" ) ); del S
pickle.dump( D, open( os.path.normpath(os.path.join(sonpath,base+"D.p")), "wb" ) ); del D
pickle.dump( t, open( os.path.normpath(os.path.join(sonpath,base+"t.p")), "wb" ) ); del t
pickle.dump( i, open( os.path.normpath(os.path.join(sonpath,base+"i.p")), "wb" ) ); del i
pickle.dump( X, open( os.path.normpath(os.path.join(sonpath,base+"X.p")), "wb" ) ); del X
pickle.dump( Y, open( os.path.normpath(os.path.join(sonpath,base+"Y.p")), "wb" ) ); del Y
pickle.dump( R, open( os.path.normpath(os.path.join(sonpath,base+"R.p")), "wb" ) );
pickle.dump( h, open( os.path.normpath(os.path.join(sonpath,base+"h.p")), "wb" ) );
#grazing angle
g = np.arctan(R.flatten(),h.flatten())
pickle.dump( g, open( os.path.normpath(os.path.join(sonpath,base+"g.p")), "wb" ) ); del g, R, h
print("creating grids ...")
if res==0:
res=99
if res==99:
#### prepare grids
R = pickle.load( open( os.path.normpath(os.path.join(sonpath,base+"R.p")), "rb" ) )
## actual along-track resolution is this: dx times dy = Af
tmp = R * dx * (c*0.007 / 2)
del R
resg = np.min(tmp[tmp>0])
del tmp
else:
resg = res
X = pickle.load( open( os.path.normpath(os.path.join(sonpath,base+"X.p")), "rb" ) )
Y = pickle.load( open( os.path.normpath(os.path.join(sonpath,base+"Y.p")), "rb" ) )
humlon, humlat = trans(X, Y, inverse=True)
grid_x, grid_y = np.meshgrid( np.arange(np.min(X), np.max(X), resg), np.arange(np.min(Y), np.max(Y), resg) )
shape = np.shape(grid_x)
tree = KDTree(zip(X.flatten(), Y.flatten()))
del X, Y
print("mosaicking ...")
#k nearest neighbour
try:
dist, inds = tree.query(zip(grid_x.flatten(), grid_y.flatten()), k = nn, n_jobs=-1)
except:
#print ".... update your scipy installation to use faster kd-tree"
dist, inds = tree.query(zip(grid_x.flatten(), grid_y.flatten()), k = nn)
#del grid_x, grid_y
if weight==1:
g = pickle.load( open( os.path.normpath(os.path.join(sonpath,base+"g.p")), "rb" ) )
w = g[inds] + 1.0 / dist**2
del g
elif weight==2:
g = pickle.load( open( os.path.normpath(os.path.join(sonpath,base+"g.p")), "rb" ) )
w = g[inds]
del g
elif weight==3:
w = 1.0 / dist**2
elif weight==4:
w = 1.0
#g = pickle.load( open( os.path.normpath(os.path.join(sonpath,base+"g.p")), "rb" ) )
#w = g[inds] + 1.0 / dist**2
#del g
if weight < 4:
w[np.isinf(w)]=1
w[np.isnan(w)]=1
w[w>10000]=10000
w[w<=0]=1
# load in sidescan intensity
S = pickle.load( open( os.path.normpath(os.path.join(sonpath,base+"S.p")), "rb" ) )
# filter out noise pixels
S[S<noisefloor] = np.nan
if nn==1:
Sdat_g = (w * S.flatten()[inds]).reshape(shape)
del w
dist = dist.reshape(shape)
else:
if weight < 4:
Sdat_g = (np.nansum(w * S.flatten()[inds], axis=1) / np.nansum(w, axis=1)).reshape(shape)
else:
Sdat_g = (np.nansum(S.flatten()[inds], axis=1)).reshape(shape)
del w
dist = np.nanmean(dist,axis=1).reshape(shape)
del S
Sdat_g[dist>1] = np.nan
Sdat_g[Sdat_g<noisefloor] = np.nan
dat = Sdat_g.copy()
dat[dist>1] = 0
dat2 = replace_nans.RN(dat.astype('float64'),1000,0.01,2,'localmean').getdata()
dat2[dat==0] = np.nan
del dat
dat2[dat2<noisefloor] = np.nan
Sdat_g = dat2.copy()
del dat2
Sdat_g[Sdat_g==0] = np.nan
Sdat_g[np.isinf(Sdat_g)] = np.nan
Sdat_gm = np.ma.masked_invalid(Sdat_g)
del Sdat_g
glon, glat = trans(grid_x, grid_y, inverse=True)
del grid_x, grid_y
# =========================================================
print("creating kmz file ...")
## new way to create kml file
pixels = 1024 * 10
fig, ax = humutils.gearth_fig(llcrnrlon=glon.min(),
llcrnrlat=glat.min(),
urcrnrlon=glon.max(),
urcrnrlat=glat.max(),
pixels=pixels)
cs = ax.pcolormesh(glon, glat, Sdat_gm)
ax.set_axis_off()
fig.savefig(os.path.normpath(os.path.join(sonpath,'class_overlay1.png')), transparent=True, format='png')
fig = plt.figure(figsize=(1.0, 4.0), facecolor=None, frameon=False)
ax = fig.add_axes([0.0, 0.05, 0.2, 0.9])
cb = fig.colorbar(cs, cax=ax)
cb.set_label('Texture lengthscale [m]', rotation=-90, color='k', labelpad=20)
fig.savefig(os.path.normpath(os.path.join(sonpath,'class_legend.png')), transparent=False, format='png')
humutils.make_kml(llcrnrlon=glon.min(), llcrnrlat=glat.min(),
urcrnrlon=glon.max(), urcrnrlat=glat.max(),
figs=[os.path.normpath(os.path.join(sonpath,'class_overlay1.png'))],
colorbar=os.path.normpath(os.path.join(sonpath,'class_legend.png')),
kmzfile=os.path.normpath(os.path.join(sonpath,'class_GroundOverlay.kmz')),
name='Sidescan Intensity')
# =========================================================
print("drawing and printing map ...")
fig = plt.figure(frameon=False)
map = Basemap(projection='merc', epsg=cs2cs_args.split(':')[1],
resolution = 'i', #h #f
llcrnrlon=np.min(humlon)-0.001, llcrnrlat=np.min(humlat)-0.001,
urcrnrlon=np.max(humlon)+0.001, urcrnrlat=np.max(humlat)+0.001)
gx,gy = map.projtran(glon, glat)
try:
map.arcgisimage(server='http://server.arcgisonline.com/ArcGIS', service='ESRI_Imagery_World_2D', xpixels=1000, ypixels=None, dpi=300)
except:
map.arcgisimage(server='http://server.arcgisonline.com/ArcGIS', service='World_Imagery', xpixels=1000, ypixels=None, dpi=300)
#finally:
# print "error: map could not be created..."
ax = plt.Axes(fig, [0., 0., 1., 1.], )
ax.set_axis_off()
fig.add_axes(ax)
if Sdat_gm.size > 25000000:
print("matrix size > 25,000,000 - decimating by factor of 5 for display")
map.pcolormesh(gx[::5,::5], gy[::5,::5], Sdat_gm[::5,::5], vmin=np.nanmin(Sdat_gm), vmax=np.nanmax(Sdat_gm))
else:
map.pcolormesh(gx, gy, Sdat_gm, vmin=np.nanmin(Sdat_gm), vmax=np.nanmax(Sdat_gm))
custom_save2(sonpath,'class_map_imagery')
del fig
if os.name=='posix': # true if linux/mac
elapsed = (time.time() - start)
else: # windows
elapsed = (time.clock() - start)
print("Processing took "+str(elapsed)+"seconds to analyse")
print("Done!")
def map_texture(humfile, sonpath, cs2cs_args, res, mode, nn, numstdevs): #influence = 10,
'''
Create plots of the texture lengthscale maps made in PyHum.texture module
using the algorithm detailed by Buscombe et al. (2015)
This textural lengthscale is not a direct measure of grain size. Rather, it is a statistical
representation that integrates over many attributes of bed texture, of which grain size is the most important.
The technique is a physically based means to identify regions of texture within a sidescan echogram,
and could provide a basis for objective, automated riverbed sediment classification.
Syntax
----------
[] = PyHum.map_texture(humfile, sonpath, cs2cs_args, res, mode, nn, numstdevs)
Parameters
----------
humfile : str
path to the .DAT file
sonpath : str
path where the *.SON files are
cs2cs_args : int, *optional* [Default="epsg:26949"]
arguments to create coordinates in a projected coordinate system
this argument gets given to pyproj to turn wgs84 (lat/lon) coordinates
into any projection supported by the proj.4 libraries
res : float, *optional* [Default=0.5]
grid resolution of output gridded texture map
mode: int, *optional* [Default=3]
gridding mode. 1 = nearest neighbour
2 = inverse weighted nearest neighbour
3 = Gaussian weighted nearest neighbour
nn: int, *optional* [Default=64]
number of nearest neighbours for gridding (used if mode > 1)
numstdevs: int, *optional* [Default = 4]
Threshold number of standard deviations in texture lengthscale per grid cell up to which to accept
Returns
-------
sonpath+'x_y_class'+str(p)+'.asc' : text file
contains the point cloud of easting, northing, and texture lengthscales
of the pth chunk
sonpath+'class_GroundOverlay'+str(p)+'.kml': kml file
contains gridded (or point cloud) texture lengthscale map for importing into google earth
of the pth chunk
sonpath+'class_map'+str(p)+'.png' :
image overlay associated with the kml file
sonpath+'class_map_imagery'+str(p)+'.png' : png image file
gridded (or point cloud) texture lengthscale map
overlain onto an image pulled from esri image server
References
----------
.. [1] Buscombe, D., Grams, P.E., and Smith, S.M.C., 2015, Automated riverbed sediment
classification using low-cost sidescan sonar. Journal of Hydraulic Engineering 10.1061/(ASCE)HY.1943-7900.0001079, 06015019.
'''
# prompt user to supply file if no input file given
if not humfile:
print('An input file is required!!!!!!')
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
humfile = askopenfilename(filetypes=[("DAT files","*.DAT")])
# prompt user to supply directory if no input sonpath is given
if not sonpath:
print('A *.SON directory is required!!!!!!')
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
sonpath = askdirectory()
# print given arguments to screen and convert data type where necessary
if humfile:
print('Input file is %s' % (humfile))
if sonpath:
print('Sonar file path is %s' % (sonpath))
if cs2cs_args:
print('cs2cs arguments are %s' % (cs2cs_args))
if res:
res = np.asarray(res,float)
print('Gridding resolution: %s' % (str(res)))
if mode:
mode = int(mode)
print('Mode for gridding: %s' % (str(mode)))
if nn:
nn = int(nn)
print('Number of nearest neighbours for gridding: %s' % (str(nn)))
#if influence:
# influence = int(influence)
# print 'Radius of influence for gridding: %s (m)' % (str(influence))
if numstdevs:
numstdevs = int(numstdevs)
print('Threshold number of standard deviations in texture lengthscale per grid cell up to which to accept: %s' % (str(numstdevs)))
# start timer
if os.name=='posix': # true if linux/mac or cygwin on windows
start = time.time()
else: # windows
start = time.clock()
trans = pyproj.Proj(init=cs2cs_args)
# if son path name supplied has no separator at end, put one on
if sonpath[-1]!=os.sep:
sonpath = sonpath + os.sep
base = humfile.split('.DAT') # get base of file name for output
base = base[0].split(os.sep)[-1]
# remove underscores, negatives and spaces from basename
base = humutils.strip_base(base)
meta = loadmat(os.path.normpath(os.path.join(sonpath,base+'meta.mat')))
esi = np.squeeze(meta['e'])
nsi = np.squeeze(meta['n'])
pix_m = np.squeeze(meta['pix_m'])*1.1
dep_m = np.squeeze(meta['dep_m'])
c = np.squeeze(meta['c'])
#dist_m = np.squeeze(meta['dist_m'])
theta = np.squeeze(meta['heading'])/(180/np.pi)
# load memory mapped scans
shape_port = np.squeeze(meta['shape_port'])
if shape_port!='':
if os.path.isfile(os.path.normpath(os.path.join(sonpath,base+'_data_port_lar.dat'))):
port_fp = io.get_mmap_data(sonpath, base, '_data_port_lar.dat', 'float32', tuple(shape_port))
else:
port_fp = io.get_mmap_data(sonpath, base, '_data_port_la.dat', 'float32', tuple(shape_port))
shape_star = np.squeeze(meta['shape_star'])
if shape_star!='':
if os.path.isfile(os.path.normpath(os.path.join(sonpath,base+'_data_star_lar.dat'))):
star_fp = io.get_mmap_data(sonpath, base, '_data_star_lar.dat', 'float32', tuple(shape_star))
else:
star_fp = io.get_mmap_data(sonpath, base, '_data_star_la.dat', 'float32', tuple(shape_star))
if len(shape_star)>2:
shape = shape_port.copy()
shape[1] = shape_port[1] + shape_star[1]
class_fp = io.get_mmap_data(sonpath, base, '_data_class.dat', 'float32', tuple(shape))
#with open(os.path.normpath(os.path.join(sonpath,base+'_data_class.dat')), 'r') as ff:
# class_fp = np.memmap(ff, dtype='float32', mode='r', shape=tuple(shape))
else:
with open(os.path.normpath(os.path.join(sonpath,base+'_data_class.dat')), 'r') as ff:
class_fp = np.load(ff)
tvg = ((8.5*10**-5)+(3/76923)+((8.5*10**-5)/4))*c
dist_tvg = ((np.tan(np.radians(25)))*dep_m)-(tvg)
if len(shape_star)>2:
for p in range(len(class_fp)):
e = esi[shape_port[-1]*p:shape_port[-1]*(p+1)]
n = nsi[shape_port[-1]*p:shape_port[-1]*(p+1)]
t = theta[shape_port[-1]*p:shape_port[-1]*(p+1)]
d = dist_tvg[shape_port[-1]*p:shape_port[-1]*(p+1)]
len_n = len(n)
merge = class_fp[p].copy()
merge[np.isnan(merge)] = 0
merge[np.isnan(np.vstack((np.flipud(port_fp[p]),star_fp[p])))] = 0
extent = shape_port[1]
R1 = merge[extent:,:]
R2 = np.flipud(merge[:extent,:])
merge = np.vstack((R2,R1))
del R1, R2
# get number pixels in scan line
extent = int(np.shape(merge)[0]/2)
yvec = np.linspace(pix_m,extent*pix_m,extent)
X, Y = getXY(e,n,yvec,d,t,extent)
merge[merge==0] = np.nan
if len(merge.flatten()) != len(X):
merge = merge[:,:len_n]
merge = merge.T.flatten()
index = np.where(np.logical_not(np.isnan(merge)))[0]
X, Y, merge = trim_xys(X, Y, merge, index)
X = X.astype('float32')
Y = Y.astype('float32')
merge = merge.astype('float32')
# write raw bs to file
outfile = os.path.normpath(os.path.join(sonpath,'x_y_class'+str(p)+'.asc'))
with open(outfile, 'w') as f:
np.savetxt(f, np.hstack((humutils.ascol(X),humutils.ascol(Y), humutils.ascol(merge))), delimiter=' ', fmt="%8.6f %8.6f %8.6f")
humlon, humlat = trans(X, Y, inverse=True)
#if dogrid==1:
orig_def, targ_def, grid_x, grid_y, res, shape = get_griddefs(np.min(X), np.max(X), np.min(Y), np.max(Y), res, humlon, humlat, trans)
grid_x = grid_x.astype('float32')
grid_y = grid_y.astype('float32')
sigmas = 1 #m
eps = 2
dat, res = get_grid(mode, orig_def, targ_def, merge, res*10, np.min(X), np.max(X), np.min(Y), np.max(Y), res, nn, sigmas, eps, shape, numstdevs, trans, humlon, humlat)
del merge
dat[dat==0] = np.nan
dat[np.isinf(dat)] = np.nan
datm = np.ma.masked_invalid(dat)
del dat
glon, glat = trans(grid_x, grid_y, inverse=True)
del grid_x, grid_y
vmin=np.nanmin(datm)+0.1
vmax=np.nanmax(datm)-0.1
if vmin > vmax:
vmin=np.nanmin(datm)
vmax=np.nanmax(datm)
print_map(cs2cs_args, glon, glat, datm, sonpath, p, vmin=vmin, vmax=vmax)
else: #just 1 chunk
e = esi
n = nsi
t = theta
d = dist_tvg
len_n = len(n)
merge = class_fp.copy()
merge[np.isnan(merge)] = 0
merge[np.isnan(np.vstack((np.flipud(port_fp),star_fp)))] = 0
extent = shape_port[0]
R1 = merge[extent:,:]
R2 = np.flipud(merge[:extent,:])
merge = np.vstack((R2,R1))
del R1, R2
# get number pixels in scan line
extent = int(np.shape(merge)[0]/2)
yvec = np.linspace(pix_m,extent*pix_m,extent)
X, Y = getXY(e,n,yvec,d,t,extent)
merge[merge==0] = np.nan
if len(merge.flatten()) != len(X):
merge = merge[:,:len_n]
merge = merge.T.flatten()
index = np.where(np.logical_not(np.isnan(merge)))[0]
X, Y, merge = trim_xys(X, Y, merge, index)
# write raw bs to file
outfile = os.path.normpath(os.path.join(sonpath,'x_y_class'+str(0)+'.asc'))
with open(outfile, 'w') as f:
np.savetxt(f, np.hstack((humutils.ascol(X),humutils.ascol(Y), humutils.ascol(merge))), delimiter=' ', fmt="%8.6f %8.6f %8.6f")
humlon, humlat = trans(X, Y, inverse=True)
#if dogrid==1:
if 2>1:
orig_def, targ_def, grid_x, grid_y, res, shape = get_griddefs(np.min(X), np.max(X), np.min(Y), np.max(Y), res, humlon, humlat, trans)
## create mask for where the data is not
tree = KDTree(np.c_[X.flatten(),Y.flatten()])
if pykdtree==1:
dist, _ = tree.query(np.c_[grid_x.ravel(), grid_y.ravel()], k=1)
else:
try:
dist, _ = tree.query(np.c_[grid_x.ravel(), grid_y.ravel()], k=1, n_jobs=cpu_count())
except:
#print ".... update your scipy installation to use faster kd-tree queries"
dist, _ = tree.query(np.c_[grid_x.ravel(), grid_y.ravel()], k=1)
dist = dist.reshape(grid_x.shape)
sigmas = 1 #m
eps = 2
dat, res = get_grid(mode, orig_def, targ_def, merge, res*10, np.min(X), np.max(X), np.min(Y), np.max(Y), res, nn, sigmas, eps, shape, numstdevs, trans, humlon, humlat)
del merge
#if dogrid==1:
if 2>1:
dat[dat==0] = np.nan
dat[np.isinf(dat)] = np.nan
dat[dist>res*2] = np.nan
del dist
datm = np.ma.masked_invalid(dat)
glon, glat = trans(grid_x, grid_y, inverse=True)
del grid_x, grid_y
vmin=np.nanmin(datm)+0.1
vmax=np.nanmax(datm)-0.1
if vmin > vmax:
vmin=np.nanmin(datm)
vmax=np.nanmax(datm)
Parallel(n_jobs = 2, verbose=0)(delayed(doplots)(k, humlon, humlat, cs2cs_args, glon, glat, datm, sonpath, 0, vmin=vmin, vmax=vmax) for k in range(2))
#print_map(cs2cs_args, glon, glat, datm, sonpath, 0, vmin=vmin, vmax=vmax)
#print_contour_map(cs2cs_args, humlon, humlat, glon, glat, datm, sonpath, 0, vmin=vmin, vmax=vmax)
if os.name=='posix': # true if linux/mac
elapsed = (time.time() - start)
else: # windows
elapsed = (time.clock() - start)
print("Processing took "+str(elapsed)+"seconds to analyse")
print("Done!")
print("===================================================")
def map(humfile, sonpath, cs2cs_args, res, mode, nn, numstdevs, use_uncorrected, scalemax): #dogrid = 1, influence = 1, dowrite = 0,
'''
Create plots of the spatially referenced sidescan echograms
Syntax
----------
[] = PyHum.map(humfile, sonpath, cs2cs_args, res, mode, nn, numstdevs)
Parameters
----------
humfile : str
path to the .DAT file
sonpath : str
path where the *.SON files are
cs2cs_args : int, *optional* [Default="epsg:26949"]
arguments to create coordinates in a projected coordinate system
this argument gets given to pyproj to turn wgs84 (lat/lon) coordinates
into any projection supported by the proj.4 libraries
res : float, *optional* [Default=0]
grid resolution of output gridded texture map
if res=0, res will be determined automatically from the spatial resolution of 1 pixel
mode: int, *optional* [Default=3]
gridding mode. 1 = nearest neighbour
2 = inverse weighted nearest neighbour
3 = Gaussian weighted nearest neighbour
nn: int, *optional* [Default=64]
number of nearest neighbours for gridding (used if mode > 1)
numstdevs: int, *optional* [Default = 4]
Threshold number of standard deviations in sidescan intensity per grid cell up to which to accept
Returns
-------
sonpath+'x_y_ss_raw'+str(p)+'.asc' : text file
contains the point cloud of easting, northing, and sidescan intensity
of the pth chunk
sonpath+'GroundOverlay'+str(p)+'.kml': kml file
contains gridded (or point cloud) sidescan intensity map for importing into google earth
of the pth chunk
sonpath+'map'+str(p)+'.png' :
image overlay associated with the kml file
'''
# prompt user to supply file if no input file given
if not humfile:
print('An input file is required!!!!!!')
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
humfile = askopenfilename(filetypes=[("DAT files","*.DAT")])
# prompt user to supply directory if no input sonpath is given
if not sonpath:
print('A *.SON directory is required!!!!!!')
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
sonpath = askdirectory()
# print given arguments to screen and convert data type where necessary
if humfile:
print('Input file is %s' % (humfile))
if sonpath:
print('Sonar file path is %s' % (sonpath))
if cs2cs_args:
print('cs2cs arguments are %s' % (cs2cs_args))
if res:
res = np.asarray(res,float)
print('Gridding resolution: %s' % (str(res)))
if mode:
mode = int(mode)
print('Mode for gridding: %s' % (str(mode)))
if nn:
nn = int(nn)
print('Number of nearest neighbours for gridding: %s' % (str(nn)))
if numstdevs:
numstdevs = int(numstdevs)
print('Threshold number of standard deviations in sidescan intensity per grid cell up to which to accept: %s' % (str(numstdevs)))
if use_uncorrected:
use_uncorrected = int(use_uncorrected)
if use_uncorrected==1:
print("Radiometrically uncorrected scans will be used")
# start timer
if os.name=='posix': # true if linux/mac or cygwin on windows
start = time.time()
else: # windows
start = time.clock()
#trans = pyproj.Proj(init=cs2cs_args)
# if son path name supplied has no separator at end, put one on
if sonpath[-1]!=os.sep:
sonpath = sonpath + os.sep
base = humfile.split('.DAT') # get base of file name for output
base = base[0].split(os.sep)[-1]
# remove underscores, negatives and spaces from basename
base = humutils.strip_base(base)
meta = loadmat(os.path.normpath(os.path.join(sonpath,base+'meta.mat')))
esi = np.squeeze(meta['e'])
nsi = np.squeeze(meta['n'])
theta = np.squeeze(meta['heading'])/(180/np.pi)
# load memory mapped scans
shape_port = np.squeeze(meta['shape_port'])
shape_star = np.squeeze(meta['shape_star'])
if use_uncorrected == 1:
print("using uncorrected scans")
if shape_port!='':
port_fp = io.get_mmap_data(sonpath, base, '_data_port_l.dat', 'float32', tuple(shape_port))
if shape_port!='':
star_fp = io.get_mmap_data(sonpath, base, '_data_star_l.dat', 'float32', tuple(shape_star))
else:
if shape_port!='':
if os.path.isfile(os.path.normpath(os.path.join(sonpath,base+'_data_port_lar.dat'))):
port_fp = io.get_mmap_data(sonpath, base, '_data_port_lar.dat', 'float32', tuple(shape_port))
else:
port_fp = io.get_mmap_data(sonpath, base, '_data_port_la.dat', 'float32', tuple(shape_port))
if shape_star!='':
if os.path.isfile(os.path.normpath(os.path.join(sonpath,base+'_data_star_lar.dat'))):
star_fp = io.get_mmap_data(sonpath, base, '_data_star_lar.dat', 'float32', tuple(shape_star))
else:
star_fp = io.get_mmap_data(sonpath, base, '_data_star_la.dat', 'float32', tuple(shape_star))
# time varying gain
tvg = ((8.5*10**-5)+(3/76923)+((8.5*10**-5)/4))*meta['c']
# depth correction
dist_tvg = np.squeeze(((np.tan(np.radians(25)))*np.squeeze(meta['dep_m']))-(tvg))
# read in range data
R_fp = io.get_mmap_data(sonpath, base, '_data_range.dat', 'float32', tuple(shape_star))
# for debugging/testing
# p=0
# e = esi[shape_port[-1]*p:shape_port[-1]*(p+1)]
# n = nsi[shape_port[-1]*p:shape_port[-1]*(p+1)]
# t = theta[shape_port[-1]*p:shape_port[-1]*(p+1)]
# d = dist_tvg[shape_port[-1]*p:shape_port[-1]*(p+1)]
# dat_port = port_fp[p]
# dat_star = star_fp[p]
# data_R = R_fp[p]
# dx=np.arcsin(meta['c']/(1000*meta['t']*meta['f']))
# e = esi;# del esi
# n = nsi; #del nsi
# t = theta;# del theta
# d = dist_tvg;# del dist_tvg
# dat_port = port_fp;# del port_fp
# dat_star = star_fp; #del star_fp
# data_R = R_fp; #del R_fp
dx = np.arcsin(meta['c']/(1000*meta['t']*meta['f']))
pix_m = meta['pix_m']*1.1
c = meta['c']
if res==0:
res=99
print("Number of chunks for mapping: %s" % (len(star_fp)))
if len(shape_star)>2:
for p in range(len(star_fp)):
try:
print("progress: " + str(p) + " / " + str(len(star_fp)))
res = make_map(esi[shape_port[-1]*p:shape_port[-1]*(p+1)], nsi[shape_port[-1]*p:shape_port[-1]*(p+1)], theta[shape_port[-1]*p:shape_port[-1]*(p+1)], dist_tvg[shape_port[-1]*p:shape_port[-1]*(p+1)], port_fp[p], star_fp[p], R_fp[p], meta['pix_m'], res, cs2cs_args, sonpath, p, mode, nn, numstdevs, meta['c'], np.arcsin(meta['c']/(1000*meta['t']*meta['f'])), use_uncorrected, scalemax) #dogrid, influence, dowrite,
print("grid resolution is %s" % (str(res)))
except:
print("error on chunk "+str(p))
else:
res = make_map(esi, nsi, theta, dist_tvg, port_fp, star_fp, R_fp, meta['pix_m'], res, cs2cs_args, sonpath, 0, mode, nn, numstdevs, meta['c'], np.arcsin(meta['c']/(1000*meta['t']*meta['f'])), use_uncorrected, scalemax) #dogrid, influence,dowrite,
if os.name=='posix': # true if linux/mac
elapsed = (time.time() - start)
else: # windows
elapsed = (time.clock() - start)
print("Processing took "+str(elapsed)+"seconds to analyse")
print("Done!")
print("===================================================")
def correct(humfile,
sonpath,
maxW=1000,
doplot=1,
dofilt=0,
correct_withwater=0,
ph=7,
temp=10,
salinity=0,
dconcfile=None):
'''
Remove water column and carry out some rudimentary radiometric corrections,
accounting for directivity and attenuation with range
Syntax
----------
[] = PyHum.correct(humfile, sonpath, maxW, doplot, correct_withwater, ph, temp, salinity, dconcfile)
Parameters
----------
humfile : str
path to the .DAT file
sonpath : str
path where the *.SON files are
maxW : int, *optional* [Default=1000]
maximum transducer power
doplot : int, *optional* [Default=1]
1 = make plots, otherwise do not
dofilt : int, *optional* [Default=0]
1 = apply a phase preserving filter to the scans
correct_withwater : int, *optional* [Default=0]
1 = apply radiometric correction but don't remove water column from scans
ph : float, *optional* [Default=7.0]
water acidity in pH
temp : float, *optional* [Default=10.0]
water temperature in degrees Celsius
salinity : float, *optional* [Default=0.0]
salinity of water in parts per thousand
dconcfile : str, *optional* [Default=None]
file path of a text file containing sediment concentration data
this file must contain the following fields separated by spaces:
size (microns) conc (mg/L) dens (kg/m3)
with one row per grain size, for example:
30 1700 2200
100 15 2650
Returns
-------
sonpath+base+'_data_star_l.dat': memory-mapped file
contains the starboard scan with water column removed
sonpath+base+'_data_port_l.dat': memory-mapped file
contains the portside scan with water column removed
sonpath+base+'_data_star_la.dat': memory-mapped file
contains the starboard scan with water column removed and
radiometrically corrected
sonpath+base+'_data_port_la.dat': memory-mapped file
contains the portside scan with water column removed and
radiometrically corrected
sonpath+base+'_data_range.dat': memory-mapped file
contains the cosine of the range which is used to correct
for attenuation with range
sonpath+base+'_data_dwnlow_l.dat': memory-mapped file
contains the low freq. downward scan with water column removed
sonpath+base+'_data_dwnhi_l.dat': memory-mapped file
contains the high freq. downward scan with water column removed
sonpath+base+'_data_dwnlow_la.dat': memory-mapped file
contains the low freq. downward scan with water column removed and
radiometrically corrected
sonpath+base+'_data_dwnhi_la.dat': memory-mapped file
contains the high freq. downward scan with water column removed and
radiometrically corrected
if correct_withwater == 1:
sonpath+base+'_data_star_lw.dat': memory-mapped file
contains the starboard scan with water column retained and
radiometrically corrected
sonpath+base+'_data_port_lw.dat': memory-mapped file
contains the portside scan with water column retained and
radiometrically corrected
'''
# prompt user to supply file if no input file given
if not humfile:
print 'An input file is required!!!!!!'
Tk().withdraw(
) # we don't want a full GUI, so keep the root window from appearing
inputfile = askopenfilename(filetypes=[("DAT files", "*.DAT")])
# prompt user to supply directory if no input sonpath is given
if not sonpath:
print 'A *.SON directory is required!!!!!!'
Tk().withdraw(
) # we don't want a full GUI, so keep the root window from appearing
sonpath = askdirectory()
# print given arguments to screen and convert data type where necessary
if humfile:
print 'Input file is %s' % (humfile)
if sonpath:
print 'Sonar file path is %s' % (sonpath)
if maxW:
maxW = np.asarray(maxW, float)
print 'Max. transducer power is %s W' % (str(maxW))
if doplot:
doplot = int(doplot)
if doplot == 0:
print "Plots will not be made"
if dofilt:
dofilt = int(dofilt)
if dofilt == 0:
print "Phase preserving filter will not be applied"
else:
print "Phase preserving filter will be applied"
if correct_withwater:
correct_withwater = int(correct_withwater)
if correct_withwater == 1:
print "Correction will be applied without removing water column"
if salinity:
salinity = np.asarray(salinity, float)
print 'Salinity is %s ppt' % (str(salinity))
if ph:
ph = np.asarray(ph, float)
print 'pH is %s' % (str(ph))
if temp:
temp = np.asarray(temp, float)
print 'Temperature is %s' % (str(temp))
if dconcfile is not None:
try:
print 'Suspended sediment size/conc. file is %s' % (dconcfile)
dconc = np.genfromtxt(dconcfile).T
conc = dconc[1]
dens = dconc[2]
d = dconc[0]
except:
pass
#================================
# start timer
if os.name == 'posix': # true if linux/mac or cygwin on windows
start = time.time()
else: # windows
start = time.clock()
# if son path name supplied has no separator at end, put one on
if sonpath[-1] != os.sep:
sonpath = sonpath + os.sep
base = humfile.split('.DAT') # get base of file name for output
base = base[0].split(os.sep)[-1]
# remove underscores, negatives and spaces from basename
base = humutils.strip_base(base)
# add wattage to metadata dict
meta = loadmat(os.path.normpath(os.path.join(sonpath, base + 'meta.mat')))
dep_m = meta['dep_m'][0]
pix_m = meta['pix_m'][0]
meta['maxW'] = maxW
savemat(os.path.normpath(os.path.join(sonpath, base + 'meta.mat')),
meta,
oned_as='row')
bed = np.squeeze(meta['bed'])
ft = 1 / (meta['pix_m'])
dist_m = np.squeeze(meta['dist_m'])
try:
if dconcfile is not None:
# sediment attenuation
alpha = sed_atten(meta['f'], conc, dens, d, meta['c'])
else:
alpha = 0
except:
alpha = 0
# load memory mapped scans
shape_port = np.squeeze(meta['shape_port'])
if shape_port != '':
if os.path.isfile(
os.path.normpath(
os.path.join(sonpath, base + '_data_port2.dat'))):
port_fp = io.get_mmap_data(sonpath, base, '_data_port2.dat',
'int16', tuple(shape_port))
else:
port_fp = io.get_mmap_data(sonpath, base, '_data_port.dat',
'int16', tuple(shape_port))
shape_star = np.squeeze(meta['shape_star'])
if shape_star != '':
if os.path.isfile(
os.path.normpath(
os.path.join(sonpath, base + '_data_star2.dat'))):
star_fp = io.get_mmap_data(sonpath, base, '_data_star2.dat',
'int16', tuple(shape_star))
else:
star_fp = io.get_mmap_data(sonpath, base, '_data_star.dat',
'int16', tuple(shape_star))
if len(shape_star) == 2:
extent = shape_star[0]
else:
extent = shape_star[1] #np.shape(data_port)[0]
bed = np.asarray(bed, 'int') + int(0.25 * ft)
# calculate in dB
######### star
Zt, R, A = remove_water(star_fp, bed, shape_star, dep_m, pix_m, 1, maxW)
Zt = np.squeeze(Zt)
# create memory mapped file for Z)
shape_star = io.set_mmap_data(sonpath, base, '_data_star_l.dat', 'float32',
Zt)
del Zt
A = np.squeeze(A)
# create memory mapped file for A
shape_A = io.set_mmap_data(sonpath, base, '_data_incidentangle.dat',
'float32', A)
del A
R = np.squeeze(R)
R[np.isnan(R)] = 0
try:
alpha_w = water_atten(R, meta['f'], meta['c'], ph, temp, salinity)
except:
alpha_w = 1e-5
# compute transmission losses
TL = (40 * np.log10(R) + alpha_w + (2 * alpha) * R / 1000) / 255
del alpha_w
# create memory mapped file for R
shape_R = io.set_mmap_data(sonpath, base, '_data_range.dat', 'float32', R)
del R
TL[np.isnan(TL)] = 0
TL[TL < 0] = 0
shape_TL = io.set_mmap_data(sonpath, base, '_data_TL.dat', 'float32', TL)
del TL
A_fp = io.get_mmap_data(sonpath, base, '_data_incidentangle.dat',
'float32', shape_star)
TL_fp = io.get_mmap_data(sonpath, base, '_data_TL.dat', 'float32',
shape_star)
R_fp = io.get_mmap_data(sonpath, base, '_data_range.dat', 'float32',
shape_star)
if correct_withwater == 1:
Zt = correct_scans(star_fp, A_fp, TL_fp, dofilt)
# create memory mapped file for Z)
shape_star = io.set_mmap_data(sonpath, base, '_data_star_lw.dat',
'float32', Zt)
#we are only going to access the portion of memory required
star_fp = io.get_mmap_data(sonpath, base, '_data_star_l.dat', 'float32',
shape_star)
##Zt = correct_scans(star_fp, A_fp, TL_fp, dofilt)
#phi=1.69
alpha = 59 # vertical beam width at 3db
theta = 35 #opening angle theta
# lambertian correction
Zt = correct_scans_lambertian(star_fp, A_fp, TL_fp, R_fp, meta['c'],
meta['f'], theta, alpha)
Zt = np.squeeze(Zt)
avg = np.nanmedian(Zt, axis=1)
Zt2 = np.empty(np.shape(Zt))
for kk in xrange(np.shape(Zt)[1]):
Zt2[:, kk] = (Zt[:, kk] - avg) + np.nanmean(avg)
Zt2[Zt <= 0] = np.nan
Zt2[Zt2 <= 0] = np.nan
del Zt
# create memory mapped file for Z
shape_star = io.set_mmap_data(sonpath, base, '_data_star_la.dat',
'float32', Zt2)
del Zt2
#we are only going to access the portion of memory required
star_fp = io.get_mmap_data(sonpath, base, '_data_star_la.dat', 'float32',
shape_star)
######### port
if correct_withwater == 1:
Zt = correct_scans(port_fp, A_fp, TL, dofilt)
# create memory mapped file for Z)
shape_port = io.set_mmap_data(sonpath, base, '_data_port_lw.dat',
'float32', Zt)
Zt = remove_water(port_fp, bed, shape_port, dep_m, pix_m, 0, maxW)
Zt = np.squeeze(Zt)
# create memory mapped file for Z
shape_port = io.set_mmap_data(sonpath, base, '_data_port_l.dat', 'float32',
Zt)
#we are only going to access the portion of memory required
port_fp = io.get_mmap_data(sonpath, base, '_data_port_l.dat', 'float32',
shape_port)
##Zt = correct_scans(port_fp, A_fp, TL_fp, dofilt)
# lambertian correction
Zt = correct_scans_lambertian(port_fp, A_fp, TL_fp, R_fp, meta['c'],
meta['f'], theta, alpha)
Zt = np.squeeze(Zt)
Zt2 = np.empty(np.shape(Zt))
for kk in xrange(np.shape(Zt)[1]):
Zt2[:, kk] = (Zt[:, kk] - avg) + np.nanmean(avg)
Zt2[Zt <= 0] = np.nan
Zt2[Zt2 <= 0] = np.nan
del Zt
# create memory mapped file for Z
shape_port = io.set_mmap_data(sonpath, base, '_data_port_la.dat',
'float32', Zt2)
del Zt2
#we are only going to access the portion of memory required
port_fp = io.get_mmap_data(sonpath, base, '_data_port_la.dat', 'float32',
shape_port)
## do plots of merged scans
if doplot == 1:
if correct_withwater == 1:
port_fpw = io.get_mmap_data(sonpath, base, '_data_port_lw.dat',
'float32', shape_port)
star_fpw = io.get_mmap_data(sonpath, base, '_data_star_lw.dat',
'float32', shape_star)
if len(np.shape(star_fpw)) > 2:
for p in xrange(len(star_fpw)):
plot_merged_scans(port_fpw[p], star_fpw[p], dist_m,
shape_port, ft, sonpath, p)
else:
plot_merged_scans(port_fpw, star_fpw, dist_m, shape_port, ft,
sonpath, 0)
else:
if len(np.shape(star_fp)) > 2:
for p in xrange(len(star_fp)):
plot_merged_scans(port_fp[p], star_fp[p], dist_m,
shape_port, ft, sonpath, p)
else:
plot_merged_scans(port_fp, star_fp, dist_m, shape_port, ft,
sonpath, 0)
# load memory mapped scans
shape_low = np.squeeze(meta['shape_low'])
shape_hi = np.squeeze(meta['shape_hi'])
if shape_low != '':
if os.path.isfile(
os.path.normpath(
os.path.join(sonpath, base + '_data_dwnlow2.dat'))):
try:
low_fp = io.get_mmap_data(sonpath, base, '_data_dwnlow2.dat',
'int16', tuple(shape_low))
except:
low_fp = io.get_mmap_data(sonpath, base, '_data_dwnlow.dat',
'int16', tuple(shape_low))
finally:
low_fp = io.get_mmap_data(sonpath, base, '_data_dwnlow.dat',
'int16', tuple(shape_hi))
#if 'shape_hi' in locals():
# low_fp = io.get_mmap_data(sonpath, base, '_data_dwnlow2.dat', 'int16', tuple(shape_hi))
else:
try:
low_fp = io.get_mmap_data(sonpath, base, '_data_dwnlow.dat',
'int16', tuple(shape_low))
except:
if 'shape_hi' in locals():
low_fp = io.get_mmap_data(sonpath, base,
'_data_dwnlow.dat', 'int16',
tuple(shape_hi))
shape_hi = np.squeeze(meta['shape_hi'])
if shape_hi != '':
if os.path.isfile(
os.path.normpath(
os.path.join(sonpath, base + '_data_dwnhi2.dat'))):
try:
hi_fp = io.get_mmap_data(sonpath, base, '_data_dwnhi2.dat',
'int16', tuple(shape_hi))
except:
hi_fp = io.get_mmap_data(sonpath, base, '_data_dwnhi.dat',
'int16', tuple(shape_hi))
finally:
hi_fp = io.get_mmap_data(sonpath, base, '_data_dwnhi.dat',
'int16', tuple(shape_low))
#if 'shape_low' in locals():
# hi_fp = io.get_mmap_data(sonpath, base, '_data_dwnhi2.dat', 'int16', tuple(shape_low))
else:
try:
hi_fp = io.get_mmap_data(sonpath, base, '_data_dwnhi.dat',
'int16', tuple(shape_hi))
except:
if 'shape_low' in locals():
hi_fp = io.get_mmap_data(sonpath, base, '_data_dwnhi.dat',
'int16', tuple(shape_low))
if 'low_fp' in locals():
######### low
Zt = remove_water(low_fp, bed, shape_low, dep_m, pix_m, 0, maxW)
Zt = np.squeeze(Zt)
# create memory mapped file for Z
shape_low = io.set_mmap_data(sonpath, base, '_data_dwnlow_l.dat',
'float32', Zt)
del Zt
#we are only going to access the portion of memory required
low_fp = io.get_mmap_data(sonpath, base, '_data_dwnlow_l.dat',
'float32', shape_low)
Zt = correct_scans2(low_fp, TL_fp)
# create memory mapped file for Z
shape_low = io.set_mmap_data(sonpath, base, '_data_dwnlow_la.dat',
'float32', Zt)
del Zt
#we are only going to access the lowion of memory required
low_fp = io.get_mmap_data(sonpath, base, '_data_dwnlow_la.dat',
'float32', shape_low)
if doplot == 1:
if len(np.shape(low_fp)) > 2:
for p in xrange(len(low_fp)):
plot_dwnlow_scans(low_fp[p], dist_m, shape_low, ft,
sonpath, p)
else:
plot_dwnlow_scans(low_fp, dist_m, shape_low, ft, sonpath, 0)
if 'hi_fp' in locals():
######### hi
Zt = remove_water(hi_fp, bed, shape_hi, dep_m, pix_m, 0, maxW)
Zt = np.squeeze(Zt)
# create memory mapped file for Z
shape_hi = io.set_mmap_data(sonpath, base, '_data_dwnhi_l.dat',
'float32', Zt)
del Zt
#we are only going to access the portion of memory required
hi_fp = io.get_mmap_data(sonpath, base, '_data_dwnhi_l.dat', 'float32',
shape_hi)
Zt = correct_scans2(hi_fp, TL_fp)
# create memory mapped file for Z
shape_hi = io.set_mmap_data(sonpath, base, '_data_dwnhi_la.dat',
'float32', Zt)
del Zt
#we are only going to access the hiion of memory required
hi_fp = io.get_mmap_data(sonpath, base, '_data_dwnhi_la.dat',
'float32', shape_hi)
if doplot == 1:
if len(np.shape(hi_fp)) > 2:
for p in xrange(len(hi_fp)):
plot_dwnhi_scans(hi_fp[p], dist_m, shape_hi, ft, sonpath,
p)
else:
plot_dwnhi_scans(hi_fp, dist_m, shape_hi, ft, sonpath, 0)
if os.name == 'posix': # true if linux/mac
elapsed = (time.time() - start)
else: # windows
elapsed = (time.clock() - start)
print "Processing took ", elapsed, "seconds to analyse"
print "Done!"
# modify paths below
if __name__ == '__main__':
humfile = r"C:\workspace\Reach_4a\2014_09\R01767\R01767.DAT"
sonpath = r"C:\workspace\Reach_4a\2014_09\R01767"
# if son path name supplied has no separator at end, put one on
if sonpath[-1] != os.sep:
sonpath = sonpath + os.sep
base = humfile.split('.DAT') # get base of file name for output
base = base[0].split(os.sep)[-1]
# remove underscores, negatives and spaces from basename
base = humutils.strip_base(base)
meta = loadmat(os.path.normpath(os.path.join(sonpath, base + 'meta.mat')))
### port
print "processing port side ..."
# load memory mapped scan ... port
shape_port = np.squeeze(meta['shape_port'])
if shape_port != '':
if os.path.isfile(
os.path.normpath(
os.path.join(sonpath, base + '_data_port_lar.dat'))):
port_fp = io.get_mmap_data(sonpath, base, '_data_port_lar.dat',
'float32', tuple(shape_port))
else:
def read(humfile, sonpath, cs2cs_args, c, draft, doplot, t, bedpick, flip_lr, model, calc_bearing, filt_bearing, chunk): #cog = 1,
'''
Read a .DAT and associated set of .SON files recorded by a Humminbird(R)
instrument.
Parse the data into a set of memory mapped files that will
subsequently be used by the other functions of the PyHum module.
Export time-series data and metadata in other formats.
Create a kml file for visualising boat track
Syntax
----------
[] = PyHum.read(humfile, sonpath, cs2cs_args, c, draft, doplot, t, bedpick, flip_lr, chunksize, model, calc_bearing, filt_bearing, chunk)
Parameters
------------
humfile : str
path to the .DAT file
sonpath : str
path where the *.SON files are
cs2cs_args : int, *optional* [Default="epsg:26949"]
arguments to create coordinates in a projected coordinate system
this argument gets given to pyproj to turn wgs84 (lat/lon) coordinates
into any projection supported by the proj.4 libraries
c : float, *optional* [Default=1450.0]
speed of sound in water (m/s). Defaults to a value of freshwater
draft : float, *optional* [Default=0.3]
draft from water surface to transducer face (m)
doplot : float, *optional* [Default=1]
if 1, plots will be made
t : float, *optional* [Default=0.108]
length of transducer array (m).
Default value is that of the 998 series Humminbird(R)
bedpick : int, *optional* [Default=1]
if 1, bedpicking with be carried out automatically
if 0, user will be prompted to pick the bed location on screen
flip_lr : int, *optional* [Default=0]
if 1, port and starboard scans will be flipped
(for situations where the transducer is flipped 180 degrees)
model: int, *optional* [Default=998]
A 3 or 4 number code indicating the model number
Examples: 998, 997, 1198, 1199
calc_bearing : float, *optional* [Default=0]
if 1, bearing will be calculated from coordinates
filt_bearing : float, *optional* [Default=0]
if 1, bearing will be filtered
chunk : str, *optional* [Default='d100' (distance, 100 m)]
letter, followed by a number.
There are the following letter options:
'd' - parse chunks based on distance, then number which is distance in m
'p' - parse chunks based on number of pings, then number which is number of pings
'h' - parse chunks based on change in heading, then number which is the change in heading in degrees
'1' - process just 1 chunk
Returns
---------
sonpath+base+'_data_port.dat': memory-mapped file
contains the raw echogram from the port side
sidescan sonar (where present)
sonpath+base+'_data_port.dat': memory-mapped file
contains the raw echogram from the starboard side
sidescan sonar (where present)
sonpath+base+'_data_dwnhi.dat': memory-mapped file
contains the raw echogram from the high-frequency
echosounder (where present)
sonpath+base+'_data_dwnlow.dat': memory-mapped file
contains the raw echogram from the low-frequency
echosounder (where present)
sonpath+base+"trackline.kml": google-earth kml file
contains the trackline of the vessel during data
acquisition
sonpath+base+'rawdat.csv': comma separated value file
contains time-series data. columns corresponding to
longitude
latitude
easting (m)
northing (m)
depth to bed (m)
alongtrack cumulative distance (m)
vessel heading (deg.)
sonpath+base+'meta.mat': .mat file
matlab format file containing a dictionary object
holding metadata information. Fields are:
e : ndarray, easting (m)
n : ndarray, northing (m)
es : ndarray, low-pass filtered easting (m)
ns : ndarray, low-pass filtered northing (m)
lat : ndarray, latitude
lon : ndarray, longitude
shape_port : tuple, shape of port scans in memory mapped file
shape_star : tuple, shape of starboard scans in memory mapped file
shape_hi : tuple, shape of high-freq. scans in memory mapped file
shape_low : tuple, shape of low-freq. scans in memory mapped file
dep_m : ndarray, depth to bed (m)
dist_m : ndarray, distance along track (m)
heading : ndarray, heading of vessel (deg. N)
pix_m: float, size of 1 pixel in across-track dimension (m)
bed : ndarray, depth to bed (m)
c : float, speed of sound in water (m/s)
t : length of sidescan transducer array (m)
spd : ndarray, vessel speed (m/s)
time_s : ndarray, time elapsed (s)
caltime : ndarray, unix epoch time (s)
'''
# prompt user to supply file if no input file given
if not humfile:
print('An input file is required!!!!!!')
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
humfile = askopenfilename(filetypes=[("DAT files","*.DAT")])
# prompt user to supply directory if no input sonpath is given
if not sonpath:
print('A *.SON directory is required!!!!!!')
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
sonpath = askdirectory()
# print given arguments to screen and convert data type where necessary
if humfile:
print('Input file is %s' % (humfile))
if sonpath:
print('Son files are in %s' % (sonpath))
if cs2cs_args:
print('cs2cs arguments are %s' % (cs2cs_args))
if draft:
draft = float(draft)
print('Draft: %s' % (str(draft)))
if c:
c = float(c)
print('Celerity of sound: %s m/s' % (str(c)))
if doplot:
doplot = int(doplot)
if doplot==0:
print("Plots will not be made")
if flip_lr:
flip_lr = int(flip_lr)
if flip_lr==1:
print("Port and starboard will be flipped")
if t:
t = np.asarray(t,float)
print('Transducer length is %s m' % (str(t)))
if bedpick:
bedpick = np.asarray(bedpick,int)
if bedpick==1:
print('Bed picking is auto')
elif bedpick==0:
print('Bed picking is manual')
else:
print('User will be prompted per chunk about bed picking method')
if chunk:
chunk = str(chunk)
if chunk[0]=='d':
chunkmode=1
chunkval = int(chunk[1:])
print('Chunks based on distance of %s m' % (str(chunkval)))
elif chunk[0]=='p':
chunkmode=2
chunkval = int(chunk[1:])
print('Chunks based on %s pings' % (str(chunkval)))
elif chunk[0]=='h':
chunkmode=3
chunkval = int(chunk[1:])
print('Chunks based on heading devation of %s degrees' % (str(chunkval)))
elif chunk[0]=='1':
chunkmode=4
chunkval = 1
print('Only 1 chunk will be produced')
else:
print("Chunk mode not understood - should be 'd', 'p', or 'h' - using defaults")
chunkmode=1
chunkval = 100
print('Chunks based on distance of %s m' % (str(chunkval)))
if model:
try:
model = int(model)
print("Data is from the %s series" % (str(model)))
except:
if model=='onix':
model=0
print("Data is from the ONIX series")
elif model=='helix':
model=1
print("Data is from the HELIX series")
elif model=='mega':
model=2
print("Data is from the MEGA series")
# if cog:
# cog = int(cog)
# if cog==1:
# print "Heading based on course-over-ground"
if calc_bearing:
calc_bearing = int(calc_bearing)
if calc_bearing==1:
print("Bearing will be calculated from coordinates")
if filt_bearing:
filt_bearing = int(filt_bearing)
if filt_bearing==1:
print("Bearing will be filtered")
## for debugging
#humfile = r"test.DAT"; sonpath = "test_data"
#cs2cs_args = "epsg:26949"; doplot = 1; draft = 0
#c=1450; bedpick=1; fliplr=1; chunk = 'd100'
#model=998; cog=1; calc_bearing=0; filt_bearing=0
#if model==2:
# f = 1000
#else:
f = 455
try:
print("Checking the epsg code you have chosen for compatibility with Basemap ... ")
from mpl_toolkits.basemap import Basemap
m = Basemap(projection='merc', epsg=cs2cs_args.split(':')[1],
resolution = 'i', llcrnrlon=10, llcrnrlat=10, urcrnrlon=30, urcrnrlat=30)
del m
print("... epsg code compatible")
except (ValueError):
print("Error: the epsg code you have chosen is not compatible with Basemap")
print("please choose a different epsg code (http://spatialreference.org/)")
print("program will now close")
sys.exit()
# start timer
if os.name=='posix': # true if linux/mac or cygwin on windows
start = time.time()
else: # windows
start = time.clock()
# if son path name supplied has no separator at end, put one on
if sonpath[-1]!=os.sep:
sonpath = sonpath + os.sep
# get the SON files from this directory
sonfiles = glob.glob(sonpath+'*.SON')
if not sonfiles:
sonfiles = glob.glob(os.getcwd()+os.sep+sonpath+'*.SON')
base = humfile.split('.DAT') # get base of file name for output
base = base[0].split(os.sep)[-1]
# remove underscores, negatives and spaces from basename
base = humutils.strip_base(base)
print("WARNING: Because files have to be read in byte by byte,")
print("this could take a very long time ...")
#reading each sonfile in parallel should be faster ...
try:
o = Parallel(n_jobs = np.min([len(sonfiles), cpu_count()]), verbose=0)(delayed(getscans)(sonfiles[k], humfile, c, model, cs2cs_args) for k in range(len(sonfiles)))
X, Y, A, B = zip(*o)
for k in range(len(Y)):
if Y[k] == 'sidescan_port':
dat = A[k] #data.gethumdat()
metadat = B[k] #data.getmetadata()
if flip_lr==0:
data_port = X[k].astype('int16')
else:
data_star = X[k].astype('int16')
elif Y[k] == 'sidescan_starboard':
if flip_lr==0:
data_star = X[k].astype('int16')
else:
data_port = X[k].astype('int16')
elif Y[k] == 'down_lowfreq':
data_dwnlow = X[k].astype('int16')
elif Y[k] == 'down_highfreq':
data_dwnhi = X[k].astype('int16')
elif Y[k] == 'down_vhighfreq': #hopefully this only applies to mega systems
data_dwnhi = X[k].astype('int16')
del X, Y, A, B, o
old_pyread = 0
if 'data_port' not in locals():
data_port = ''
print("portside scan not available")
if 'data_star' not in locals():
data_star = ''
print("starboardside scan not available")
if 'data_dwnhi' not in locals():
data_dwnlow = ''
print("high-frq. downward scan not available")
if 'data_dwnlow' not in locals():
data_dwnlow = ''
print("low-frq. downward scan not available")
except: # revert back to older version if paralleleised version fails
print("something went wrong with the parallelised version of pyread ...")
try:
import pyread
except:
from . import pyread
data = pyread.pyread(sonfiles, humfile, c, model, cs2cs_args)
dat = data.gethumdat()
metadat = data.getmetadata()
old_pyread = 1
nrec = len(metadat['n'])
metadat['instr_heading'] = metadat['heading'][:nrec]
#metadat['heading'] = humutils.get_bearing(calc_bearing, filt_bearing, cog, metadat['lat'], metadat['lon'], metadat['instr_heading'])
try:
es = humutils.runningMeanFast(metadat['e'][:nrec],len(metadat['e'][:nrec])/100)
ns = humutils.runningMeanFast(metadat['n'][:nrec],len(metadat['n'][:nrec])/100)
except:
es = metadat['e'][:nrec]
ns = metadat['n'][:nrec]
metadat['es'] = es
metadat['ns'] = ns
try:
trans = pyproj.Proj(init=cs2cs_args)
except:
trans = pyproj.Proj(cs2cs_args.lstrip(), inverse=True)
lon, lat = trans(es, ns, inverse=True)
metadat['lon'] = lon
metadat['lat'] = lat
metadat['heading'] = humutils.get_bearing(calc_bearing, filt_bearing, metadat['lat'], metadat['lon'], metadat['instr_heading']) #cog
dist_m = humutils.get_dist(lat, lon)
metadat['dist_m'] = dist_m
if calc_bearing==1: # recalculate speed, m/s
ds=np.gradient(np.squeeze(metadat['time_s']))
dx=np.gradient(np.squeeze(metadat['dist_m']))
metadat['spd'] = dx[:nrec]/ds[:nrec]
# theta at 3dB in the horizontal
theta3dB = np.arcsin(c/(t*(f*1000)))
#resolution of 1 sidescan pixel to nadir
ft = (np.pi/2)*(1/theta3dB) #/ (f/455)
dep_m = humutils.get_depth(metadat['dep_m'][:nrec])
if old_pyread == 1: #older pyread version
# port scan
try:
if flip_lr==0:
data_port = data.getportscans().astype('int16')
else:
data_port = data.getstarscans().astype('int16')
except:
data_port = ''
print("portside scan not available")
if data_port!='':
Zt, ind_port = makechunks_scan(chunkmode, chunkval, metadat, data_port, 0)
del data_port
## create memory mapped file for Z
shape_port = io.set_mmap_data(sonpath, base, '_data_port.dat', 'int16', Zt)
##we are only going to access the portion of memory required
port_fp = io.get_mmap_data(sonpath, base, '_data_port.dat', 'int16', shape_port)
if old_pyread == 1: #older pyread version
# starboard scan
try:
if flip_lr==0:
data_star = data.getstarscans().astype('int16')
else:
data_star = data.getportscans().astype('int16')
except:
data_star = ''
print("starboardside scan not available")
if data_star!='':
Zt, ind_star = makechunks_scan(chunkmode, chunkval, metadat, data_star, 1)
del data_star
# create memory mapped file for Z
shape_star = io.set_mmap_data(sonpath, base, '_data_star.dat', 'int16', Zt)
star_fp = io.get_mmap_data(sonpath, base, '_data_star.dat', 'int16', shape_star)
if 'star_fp' in locals() and 'port_fp' in locals():
# check that port and starboard are same size
# and trim if not
if np.shape(star_fp)!=np.shape(port_fp):
print("port and starboard scans are different sizes ... rectifying")
if np.shape(port_fp[0])[1] > np.shape(star_fp[0])[1]:
tmp = port_fp.copy()
tmp2 = np.empty_like(star_fp)
for k in range(len(tmp)):
tmp2[k] = tmp[k][:,:np.shape(star_fp[k])[1]]
del tmp
# create memory mapped file for Z
shape_port = io.set_mmap_data(sonpath, base, '_data_port2.dat', 'int16', tmp2)
#shape_star = shape_port.copy()
shape_star = tuple(np.asarray(shape_port).copy())
##we are only going to access the portion of memory required
port_fp = io.get_mmap_data(sonpath, base, '_data_port2.dat', 'int16', shape_port)
ind_port = list(ind_port)
ind_port[-1] = np.shape(star_fp[0])[1]
ind_port = tuple(ind_port)
elif np.shape(port_fp[0])[1] < np.shape(star_fp[0])[1]:
tmp = star_fp.copy()
tmp2 = np.empty_like(port_fp)
for k in range(len(tmp)):
tmp2[k] = tmp[k][:,:np.shape(port_fp[k])[1]]
del tmp
# create memory mapped file for Z
shape_port = io.set_mmap_data(sonpath, base, '_data_star2.dat', 'int16', tmp2)
#shape_star = shape_port.copy()
shape_star = tuple(np.asarray(shape_port).copy())
#we are only going to access the portion of memory required
star_fp = io.get_mmap_data(sonpath, base, '_data_star2.dat', 'int16', shape_star)
ind_star = list(ind_star)
ind_star[-1] = np.shape(port_fp[0])[1]
ind_star = tuple(ind_star)
if old_pyread == 1: #older pyread version
# low-freq. sonar
try:
data_dwnlow = data.getlowscans().astype('int16')
except:
data_dwnlow = ''
print("low-freq. scan not available")
if data_dwnlow!='':
Zt, ind_low = makechunks_scan(chunkmode, chunkval, metadat, data_dwnlow, 2)
del data_dwnlow
# create memory mapped file for Z
shape_low = io.set_mmap_data(sonpath, base, '_data_dwnlow.dat', 'int16', Zt)
##we are only going to access the portion of memory required
dwnlow_fp = io.get_mmap_data(sonpath, base, '_data_dwnlow.dat', 'int16', shape_low)
if old_pyread == 1: #older pyread version
# hi-freq. sonar
try:
data_dwnhi = data.gethiscans().astype('int16')
except:
data_dwnhi = ''
print("high-freq. scan not available")
if data_dwnhi!='':
Zt, ind_hi = makechunks_scan(chunkmode, chunkval, metadat, data_dwnhi, 3)
del data_dwnhi
# create memory mapped file for Z
shape_hi = io.set_mmap_data(sonpath, base, '_data_dwnhi.dat', 'int16', Zt)
dwnhi_fp = io.get_mmap_data(sonpath, base, '_data_dwnhi.dat', 'int16', shape_hi)
if 'dwnhi_fp' in locals() and 'dwnlow_fp' in locals():
# check that low and high are same size
# and trim if not
if (np.shape(dwnhi_fp)!=np.shape(dwnlow_fp)) and (chunkmode!=4):
print("dwnhi and dwnlow are different sizes ... rectifying")
if np.shape(dwnhi_fp[0])[1] > np.shape(dwnlow_fp[0])[1]:
tmp = dwnhi_fp.copy()
tmp2 = np.empty_like(dwnlow_fp)
for k in range(len(tmp)):
tmp2[k] = tmp[k][:,:np.shape(dwnlow_fp[k])[1]]
del tmp
# create memory mapped file for Z
shape_low = io.set_mmap_data(sonpath, base, '_data_dwnhi2.dat', 'int16', tmp2)
#shape_hi = shape_low.copy()
shape_hi = tuple(np.asarray(shape_low).copy())
##we are only going to access the portion of memory required
dwnhi_fp = io.get_mmap_data(sonpath, base, '_data_dwnhi2.dat', 'int16', shape_hi)
ind_hi = list(ind_hi)
ind_hi[-1] = np.shape(dwnlow_fp[0])[1]
ind_hi = tuple(ind_hi)
elif np.shape(dwnhi_fp[0])[1] < np.shape(dwnlow_fp[0])[1]:
tmp = dwnlow_fp.copy()
tmp2 = np.empty_like(dwnhi_fp)
for k in range(len(tmp)):
tmp2[k] = tmp[k][:,:np.shape(dwnhi_fp[k])[1]]
del tmp
# create memory mapped file for Z
shape_low = io.set_mmap_data(sonpath, base, '_data_dwnlow2.dat', 'int16', tmp2)
#shape_hi = shape_low.copy()
shape_hi = tuple(np.asarray(shape_low).copy())
##we are only going to access the portion of memory required
dwnlow_fp = io.get_mmap_data(sonpath, base, '_data_dwnlow2.dat', 'int16', shape_low)
ind_low = list(ind_low)
ind_low[-1] = np.shape(dwnhi_fp[0])[1]
ind_low = tuple(ind_low)
if old_pyread == 1: #older pyread version
del data
if ('shape_port' in locals()) and (chunkmode!=4):
metadat['shape_port'] = shape_port
nrec = metadat['shape_port'][0] * metadat['shape_port'][2]
elif ('shape_port' in locals()) and (chunkmode==4):
metadat['shape_port'] = shape_port
nrec = metadat['shape_port'][1]
else:
metadat['shape_port'] = ''
if ('shape_star' in locals()) and (chunkmode!=4):
metadat['shape_star'] = shape_star
nrec = metadat['shape_star'][0] * metadat['shape_star'][2]
elif ('shape_star' in locals()) and (chunkmode==4):
metadat['shape_star'] = shape_star
nrec = metadat['shape_star'][1]
else:
metadat['shape_star'] = ''
if ('shape_hi' in locals()) and (chunkmode!=4):
metadat['shape_hi'] = shape_hi
#nrec = metadat['shape_hi'][0] * metadat['shape_hi'][2] * 2
elif ('shape_hi' in locals()) and (chunkmode==4):
metadat['shape_hi'] = shape_hi
else:
metadat['shape_hi'] = ''
if ('shape_low' in locals()) and (chunkmode!=4):
metadat['shape_low'] = shape_low
#nrec = metadat['shape_low'][0] * metadat['shape_low'][2] * 2
elif ('shape_low' in locals()) and (chunkmode==4):
metadat['shape_low'] = shape_low
else:
metadat['shape_low'] = ''
#make kml boat trackline
humutils.make_trackline(lon,lat, sonpath, base)
if 'port_fp' in locals() and 'star_fp' in locals():
#if not os.path.isfile(os.path.normpath(os.path.join(sonpath,base+'meta.mat'))):
if 2>1:
if bedpick == 1: # auto
x, bed = humutils.auto_bedpick(ft, dep_m, chunkmode, port_fp, c)
if len(dist_m)<len(bed):
dist_m = np.append(dist_m,dist_m[-1]*np.ones(len(bed)-len(dist_m)))
if doplot==1:
if chunkmode!=4:
for k in range(len(star_fp)):
plot_2bedpicks(port_fp[k], star_fp[k], bed[ind_port[-1]*k:ind_port[-1]*(k+1)], dist_m[ind_port[-1]*k:ind_port[-1]*(k+1)], x[ind_port[-1]*k:ind_port[-1]*(k+1)], ft, shape_port, sonpath, k, chunkmode)
else:
plot_2bedpicks(port_fp, star_fp, bed, dist_m, x, ft, shape_port, sonpath, 0, chunkmode)
# 'real' bed is estimated to be the minimum of the two
bed = np.min(np.vstack((bed[:nrec],np.squeeze(x[:nrec]))),axis=0)
bed = humutils.runningMeanFast(bed, 3)
elif bedpick>1: # user prompt
x, bed = humutils.auto_bedpick(ft, dep_m, chunkmode, port_fp, c)
if len(dist_m)<len(bed):
dist_m = np.append(dist_m,dist_m[-1]*np.ones(len(bed)-len(dist_m)))
# 'real' bed is estimated to be the minimum of the two
bed = np.min(np.vstack((bed[:nrec],np.squeeze(x[:nrec]))),axis=0)
bed = humutils.runningMeanFast(bed, 3)
# manually intervene
fig = plt.figure()
ax = plt.gca()
if chunkmode !=4:
im = ax.imshow(np.hstack(port_fp), cmap = 'gray', origin = 'upper')
else:
im = ax.imshow(port_fp, cmap = 'gray', origin = 'upper')
plt.plot(bed,'r')
plt.axis('normal'); plt.axis('tight')
pts1 = plt.ginput(n=300, timeout=30) # it will wait for 200 clicks or 60 seconds
x1=map(lambda x: x[0],pts1) # map applies the function passed as
y1=map(lambda x: x[1],pts1) # first parameter to each element of pts
plt.close()
del fig
if x1 != []: # if x1 is not empty
tree = KDTree(zip(np.arange(1,len(bed)), bed))
try:
dist, inds = tree.query(zip(x1, y1), k = 100, eps=5, n_jobs=-1)
except:
dist, inds = tree.query(zip(x1, y1), k = 100, eps=5)
b = np.interp(inds,x1,y1)
bed2 = bed.copy()
bed2[inds] = b
bed = bed2
if doplot==1:
if chunkmode!=4:
for k in range(len(star_fp)):
plot_2bedpicks(port_fp[k], star_fp[k], bed[ind_port[-1]*k:ind_port[-1]*(k+1)], dist_m[ind_port[-1]*k:ind_port[-1]*(k+1)], x[ind_port[-1]*k:ind_port[-1]*(k+1)], ft, shape_port, sonpath, k, chunkmode)
else:
plot_2bedpicks(port_fp, star_fp, bed, dist_m, x, ft, shape_port, sonpath, 0, chunkmode)
else: #manual
beds=[]
if chunkmode!=4:
for k in range(len(port_fp)):
raw_input("Bed picking "+str(k+1)+" of "+str(len(port_fp))+", are you ready? 30 seconds. Press Enter to continue...")
bed={}
fig = plt.figure()
ax = plt.gca()
im = ax.imshow(port_fp[k], cmap = 'gray', origin = 'upper')
pts1 = plt.ginput(n=300, timeout=30) # it will wait for 200 clicks or 60 seconds
x1=map(lambda x: x[0],pts1) # map applies the function passed as
y1=map(lambda x: x[1],pts1) # first parameter to each element of pts
bed = np.interp(np.r_[:ind_port[-1]],x1,y1)
plt.close()
del fig
beds.append(bed)
extent = np.shape(port_fp[k])[0]
bed = np.asarray(np.hstack(beds),'float')
else:
raw_input("Bed picking - are you ready? 30 seconds. Press Enter to continue...")
bed={}
fig = plt.figure()
ax = plt.gca()
im = ax.imshow(port_fp, cmap = 'gray', origin = 'upper')
pts1 = plt.ginput(n=300, timeout=30) # it will wait for 200 clicks or 60 seconds
x1=map(lambda x: x[0],pts1) # map applies the function passed as
y1=map(lambda x: x[1],pts1) # first parameter to each element of pts
bed = np.interp(np.r_[:ind_port[-1]],x1,y1)
plt.close()
del fig
beds.append(bed)
extent = np.shape(port_fp)[1]
bed = np.asarray(np.hstack(beds),'float')
# now revise the depth in metres
dep_m = (1/ft)*bed
if doplot==1:
if chunkmode!=4:
for k in range(len(star_fp)):
plot_bedpick(port_fp[k], star_fp[k], (1/ft)*bed[ind_port[-1]*k:ind_port[-1]*(k+1)], dist_m[ind_port[-1]*k:ind_port[-1]*(k+1)], ft, shape_port, sonpath, k, chunkmode)
else:
plot_bedpick(port_fp, star_fp, (1/ft)*bed, dist_m, ft, shape_port, sonpath, 0, chunkmode)
metadat['bed'] = bed[:nrec]
else:
metadat['bed'] = dep_m[:nrec]*ft
metadat['heading'] = metadat['heading'][:nrec]
metadat['lon'] = lon[:nrec]
metadat['lat'] = lat[:nrec]
metadat['dist_m'] = dist_m[:nrec]
metadat['dep_m'] = dep_m[:nrec]
metadat['pix_m'] = 1/ft
metadat['bed'] = metadat['bed'][:nrec]
metadat['c'] = c
metadat['t'] = t
if model==2:
metadat['f'] = f*2
else:
metadat['f'] = f
metadat['spd'] = metadat['spd'][:nrec]
metadat['time_s'] = metadat['time_s'][:nrec]
metadat['e'] = metadat['e'][:nrec]
metadat['n'] = metadat['n'][:nrec]
metadat['es'] = metadat['es'][:nrec]
metadat['ns'] = metadat['ns'][:nrec]
try:
metadat['caltime'] = metadat['caltime'][:nrec]
except:
metadat['caltime'] = metadat['caltime']
savemat(os.path.normpath(os.path.join(sonpath,base+'meta.mat')), metadat ,oned_as='row')
f = open(os.path.normpath(os.path.join(sonpath,base+'rawdat.csv')), 'wt')
writer = csv.writer(f)
writer.writerow( ('longitude', 'latitude', 'easting', 'northing', 'depth (m)', 'distance (m)', 'instr. heading (deg)', 'heading (deg.)' ) )
for i in range(0, nrec):
writer.writerow(( float(lon[i]),float(lat[i]),float(es[i]),float(ns[i]),float(dep_m[i]),float(dist_m[i]), float(metadat['instr_heading'][i]), float(metadat['heading'][i]) ))
f.close()
del lat, lon, dep_m #, dist_m
if doplot==1:
plot_pos(sonpath, metadat, es, ns)
if 'dwnlow_fp' in locals():
plot_dwnlow(dwnlow_fp, chunkmode, sonpath)
if 'dwnhi_fp' in locals():
plot_dwnhi(dwnhi_fp, chunkmode, sonpath)
if os.name=='posix': # true if linux/mac
elapsed = (time.time() - start)
else: # windows
elapsed = (time.clock() - start)
print("Processing took "+ str(elapsed) + "seconds to analyse")
print("Done!")
print("===================================================")
