def test_02_01_median():
'''A median filter larger than the image = median of image'''
np.random.seed(0)
img = np.random.uniform(size=(9, 9))
result = median_filter(img, 20, np.ones((9, 9), bool))
np.testing.assert_equal(result[0, 0], np.median(img))
assert (np.all(result == np.median(img)))
def test_02_01_median():
'''A median filter larger than the image = median of image'''
np.random.seed(0)
img = np.random.uniform(size=(9, 9))
result = median_filter(img, 20, np.ones((9, 9), bool))
np.testing.assert_equal(result[0, 0], np.median(img))
assert (np.all(result == np.median(img)))
def test_03_01_shape():
'''Make sure the median filter is the expected octagonal shape'''
radius = 5
a_2 = int(radius / 2.414213)
i, j = np.mgrid[-10:11, -10:11]
octagon = np.ones((21, 21), bool)
#
# constrain the octagon mask to be the points that are on
# the correct side of the 8 edges
#
octagon[i < -radius] = False
octagon[i > radius] = False
octagon[j < -radius] = False
octagon[j > radius] = False
octagon[i + j < -radius - a_2] = False
octagon[j - i > radius + a_2] = False
octagon[i + j > radius + a_2] = False
octagon[i - j > radius + a_2] = False
np.random.seed(0)
img = np.random.uniform(size=(21, 21))
result = median_filter(img, radius, np.ones((21, 21), bool))
sorted = img[octagon]
sorted.sort()
min_acceptable = sorted[len(sorted) / 2 - 1]
max_acceptable = sorted[len(sorted) / 2 + 1]
assert (result[10, 10] >= min_acceptable)
assert (result[10, 10] <= max_acceptable)
def test_03_01_shape():
'''Make sure the median filter is the expected octagonal shape'''
radius = 5
a_2 = int(radius / 2.414213)
i, j = np.mgrid[-10:11, -10:11]
octagon = np.ones((21, 21), bool)
#
# constrain the octagon mask to be the points that are on
# the correct side of the 8 edges
#
octagon[i < -radius] = False
octagon[i > radius] = False
octagon[j < -radius] = False
octagon[j > radius] = False
octagon[i + j < -radius - a_2] = False
octagon[j - i > radius + a_2] = False
octagon[i + j > radius + a_2] = False
octagon[i - j > radius + a_2] = False
np.random.seed(0)
img = np.random.uniform(size=(21, 21))
result = median_filter(img, radius, np.ones((21, 21), bool))
sorted = img[octagon]
sorted.sort()
min_acceptable = sorted[len(sorted) / 2 - 1]
max_acceptable = sorted[len(sorted) / 2 + 1]
assert (result[10, 10] >= min_acceptable)
assert (result[10, 10] <= max_acceptable)
def test_01_01_mask():
'''The median filter, masking a single value'''
img = np.zeros((10, 10))
img[5, 5] = 1
mask = np.ones((10, 10), bool)
mask[5, 5] = False
result = median_filter(img, 3, mask)
assert (np.all(result[mask] == 0))
np.testing.assert_equal(result[5, 5], 1)
def test_01_01_mask():
'''The median filter, masking a single value'''
img = np.zeros((10, 10))
img[5, 5] = 1
mask = np.ones((10, 10), bool)
mask[5, 5] = False
result = median_filter(img, 3, mask)
assert (np.all(result[mask] == 0))
np.testing.assert_equal(result[5, 5], 1)
def test_02_02_median_bigger():
'''Use an image of more than 255 values to test approximation'''
np.random.seed(0)
img = np.random.uniform(size=(20, 20))
result = median_filter(img, 40, np.ones((20, 20), bool))
sorted = np.ravel(img)
sorted.sort()
min_acceptable = sorted[198]
max_acceptable = sorted[202]
assert (np.all(result >= min_acceptable))
assert (np.all(result <= max_acceptable))
def test_02_02_median_bigger():
'''Use an image of more than 255 values to test approximation'''
np.random.seed(0)
img = np.random.uniform(size=(20, 20))
result = median_filter(img, 40, np.ones((20, 20), bool))
sorted = np.ravel(img)
sorted.sort()
min_acceptable = sorted[198]
max_acceptable = sorted[202]
assert (np.all(result >= min_acceptable))
assert (np.all(result <= max_acceptable))
def test_04_01_half_masked():
'''Make sure that the median filter can handle large masked areas.'''
img = np.ones((20, 20))
mask = np.ones((20, 20), bool)
mask[10:, :] = False
img[~mask] = 2
img[1, 1] = 0 # to prevent short circuit for uniform data.
result = median_filter(img, 5, mask)
# in partial coverage areas, the result should be only
# from the masked pixels
assert (np.all(result[:14, :] == 1))
# in zero coverage areas, the result should be the lowest
# value in the valid area
assert (np.all(result[15:, :] == np.min(img[mask])))
def test_04_01_half_masked():
'''Make sure that the median filter can handle large masked areas.'''
img = np.ones((20, 20))
mask = np.ones((20, 20), bool)
mask[10:, :] = False
img[~ mask] = 2
img[1, 1] = 0 # to prevent short circuit for uniform data.
result = median_filter(img, 5, mask)
# in partial coverage areas, the result should be only
# from the masked pixels
assert (np.all(result[:14, :] == 1))
# in zero coverage areas, the result should be the lowest
# value in the valid area
assert (np.all(result[15:, :] == np.min(img[mask])))
def main( argv=None ):
# default values
pn = '/media/data/data/OTHER/RS2 Agulhas and Lion/RS2_FQA_1xQGSS20101218_173930_00000005/'
xOff=0
yOff=0
xS=None
yS=None
xBufScale=None
yBufScale=None
s = 'abs'
if argv is None:
argv = sys.argv
if argv is None:
print ( "Please specify the path to the RS2 folder! \n" + \
"See USAGE for more details \n")
return Usage()
# Parse arguments
try:
opts, args = getopt.getopt(argv,"hi:o:",["pn=","xoff="])
except getopt.GetoptError:
print 'readRS2.py -pn <inputfile> ...'
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print 'readRS2.py -pn <inputfile> ...'
sys.exit()
elif opt in ("-pn", "--pn"):
pn = arg
elif opt in ("-xoff", "--xoff"):
xOff = arg
elif opt in ("-yoff", "--yoff"):
yOff = arg
elif opt in ("-xS", "--xS"):
xS = arg
elif opt in ("-yS", "--yS"):
yS = arg
elif opt in ("-xScale", "--xScale"):
xBufScale = arg
elif opt in ("-yScale", "--yScale"):
yBufScale = arg
print "Path to the RS2 file:\n", pn
if core_ct < 4:
# calibrate the scene
print "Calibrating"
currtime = time()
calib = calibRS2(pn, xOff, yOff, xS, yS, xBufScale, yBufScale, 'sigma0')
# get the incidence angle
print "Incidence Angle and Lats/Lons"
IncidenceAngle = calib.incidence_angle()
IncidenceAngle = rad2deg(IncidenceAngle)
# get the lats/lons
(lat, lon, line, pixel) = calib.xml2geo()
print "Filtering"
# 1. Remove speckle noise (using Lee-Wiener filter)
calib.speckle_filter('wiener', 7)
print 'Serial: time elapsed:', time() - currtime
elif core_ct >= 4:
# calibrate the scene
print "Calibrating"
currtime = time()
calibPar = calibRS2par(pn, xOff, yOff, xS, yS, xBufScale, yBufScale, s)
# get the incidence angle
print "Incidence Angle and Lats/Lons"
IncidenceAngle = calibPar.incidence_angle()
IncidenceAngle = rad2deg(IncidenceAngle)
# get the lats/lons
(lat, lon, line, pixel) = calibPar.xml2geo()
print "Filtering"
# 1. Remove speckle noise (using Lee-Wiener filter)
calibPar.speckle_filter('wiener', 7)
print 'Parallel: time elapsed: %f' % ( time() - currtime )
# export to geotiff
# calibPar.save_tiff()
print "Some Sigma calculations..."
SigmaHHwnr = calibPar.SigmaHHwnr
SigmaVVwnr = calibPar.SigmaVVwnr
# SigmaHVwnr = calibPar.SigmaHVwnr
# Free up some memory
del calibPar.SigmaHHwnr, calibPar.SigmaVVwnr, calibPar.SigmaHVwnr
# # Calculate some wind
# # testing that with sar=-0.387 wind speed is 10m/s
# w = rcs2wind(sar=-0.387*ones((1,1)), cmdv=4, windir=0, theta=20*ones((1,1)))
# print "Testing CMOD4 passed, Wind =", w
#
# sar=10*log10(SigmaVVwnr[::10,::10])
## sar=10*log10(SigmaVVwnr)
# w = rcs2wind(sar, cmdv=4, windir=130, theta=33*ones(sar.shape))
# print "Mean CMOD4 Wind =", w.mean()
# calculate the conjugate
SigmaVVConjHH = calibPar.S_VV*conj(calibPar.S_HH)
SigmaVVConjHHWnr = wiener(SigmaVVConjHH, mysize=(7,7), noise=None)
# all sigmas in linear units
SigmaVVConjHHdelta = SigmaVVConjHHWnr/(SigmaVVwnr - SigmaHHwnr)
# # Check that imagenary part is much less then the real one
# SigmaVVConjHHr = real(SigmaVVConjHH)
# SigmaVVConjHHi = imag(SigmaVVConjHH)
# SigmaVVConjHHr.mean()
# SigmaVVConjHHi.mean()
# Free up some memory
del SigmaVVConjHH, calibPar.S_HH, calibPar.S_VV
# 2. Find Poolarization ratio PR in dB and delta - linear
# delta - is a Bragg component, which describes wind field impact
# PR = 10*log10(SigmaHHwnr) - 10*log10(SigmaVVwnr)
# # PR must not be > 0, be careful with masking
#P[P>0] = 0
PR = SigmaHHwnr/SigmaVVwnr
delta = SigmaVVwnr - SigmaHHwnr
# # Masking Land from HV polarization
# maskedLand = SigmaHVwnr>0.003
# 3. Linear fit of Sigma and delta: Sigma = A*delta
# A = sum(SigmaVVwnr[~maskedLand]*delta[~maskedLand])/sum(delta[~maskedLand]^2);
# B = sum(SigmaHHwnr[~maskedLand]*delta[~maskedLand])/sum(delta[~maskedLand]^2);
# imposing from theory
A = linspace(2.3,1.8,IncidenceAngle.size) # dependence on inc angle
A = tile(A, (line[-1,0]+1, 1))
# 4. Calculate SigmaVVwb = SigmaVVwb === SigmaHHwb
# substracting wind field variability contribution, the rest is impact of
# current and film on the image
SigmaWb = SigmaVVwnr - ( A*delta )
# Checking that SigmaWb from VV and HH are the same
# SigmaVVwb = SigmaVVwnr - ( A*delta )
# SigmaHHwb = SigmaHHwnr - ( B*delta )
# SigmaWb = SigmaVVwb
# mean(SigmaHHwb(:)./SigmaVVwb(:))
# clear SigmaVVwb SigmaHHwb
# Cropping the oil slicks
## проверить почему переворот изображения!!!
# from imcrop import imzoom
# print ( "Cropping...")
# plt.figure()
# plt.imshow(SigmaWb)
# plt.clim(0,0.03)
# plt.gray()
# plt.show()
# pts = imzoom()
ptsCropSouth = array([[ 3200, 4550],
[ 3800, 5100]])
oilCropSouth = imcrop(ptsCropSouth, delta)
ptsCropNorth = array([[ 3400, 0],
[ 4900, 1650]])
oilCropNorth = imcrop(ptsCropNorth, delta)
# reduce the speckle noise
oilN = wiener(oilCropNorth, mysize=(3,3), noise=None)
oilS = wiener(oilCropSouth, mysize=(3,3), noise=None)
# use median filter to smooth
oilN = median_filter(oilN, radius=37)
oilS = median_filter(oilS, radius=37)
# find the global threshold
global_threshN = threshold_otsu(oilN)
global_threshS = threshold_otsu(oilS)
# create a binary mask
markersN = ones(oilN.shape, dtype=uint)
markersS = ones(oilS.shape, dtype=uint)
markersN[oilN < global_threshN*0.84] = 0
markersS[oilS < global_threshS*0.84] = 0
# Find the relation of PR in/out-side the Slicks
oilCropNorthPR = imcrop(ptsCropNorth, PR)
oilCropPN = oilCropNorthPR[markersN==0].mean()/oilCropNorthPR[markersS==1].mean()
oilCropSouthP = imcrop(ptsCropSouth, PR)
oilCropPS = oilCropSouthP[markersS==0].mean()/oilCropSouthP[markersS==1].mean()
# Checking that calculations are OK
oilCropNorthKVV = imcrop(ptsCropNorth, SigmaVVwnr)
KVV = oilCropNorthKVV[markersN==0].mean()/oilCropNorthKVV[markersN==1].mean()
oilCropNorthKHH = imcrop(ptsCropNorth, SigmaHHwnr)
KHH = oilCropNorthKHH[markersN==0].mean()/oilCropNorthKHH[markersN==1].mean()
PSlick = KHH/KVV*oilCropNorthKHH[markersN==1].mean()/oilCropNorthKVV[markersN==1].mean()
print "Polarization ratio Out/In-side Slick = \
\n %0.2f for Northern \n %0.2f for Southern" \
%(1/oilCropPN, 1/oilCropPS)
oilCropNorthVV = imcrop(ptsCropNorth, SigmaVVwnr)
oilCropNorthDelta = imcrop(ptsCropNorth, delta)
oilCropNorthSigmaWb = imcrop(ptsCropNorth, SigmaWb)
print ( "Plotting on a map... \nUsing default NSPER Basemap \n")
# setup nsper basemap
# Lat/Lon coords of image corners
ll_lat = lat.min()
ur_lat = lat.max()
ll_lon = lon.min()
ur_lon = lon.max()
cent_lat = lat.mean()
cent_lon = lon.mean()
m = Basemap(llcrnrlat=ll_lat, urcrnrlat=ur_lat,\
llcrnrlon=ll_lon, urcrnrlon=ur_lon, \
resolution='f', projection='nsper', \
satellite_height=798000, \
lat_0=cent_lat,lon_0=cent_lon)
# m = Basemap(llcrnrlat=30, urcrnrlat=50,\
# llcrnrlon=1, urcrnrlon=20, \
# resolution='l', projection='nsper', \
# satellite_height=798000, \
# lat_0=cent_lat,lon_0=cent_lon)
print ( "Plotting figures... \n")
scale = 8 # setting scale factor to resize in 1/scale times
label1 = 'Sigma VV [dB]'
label2 = 'Sigma HH [dB]'
label11 = 'Sigma VV [linear units]'
label22 = 'Sigma HH [linear units]'
label3 = 'PR [dB]'
label33 = 'PR [linear units]'
label4 = 'PD [linear units]'
label5 = 'Wb contribution [linear units]'
label6 = 'SigmaVVConjHH [linear units]'
label7 = 'SigmaVVConjHHdelta [linear units]'
# label8 = 'Wind CMOD4 [m/s]'
# import plotRS2
# reload(plotRS2)
# from plotRS2 import plotRS2
# import mpl_util
# reload(mpl_util)
#
# import gmtColormap
# reload(gmtColormap)
#
# import createMapsEtopo1
# reload(createMapsEtopo1)
# from createMapsEtopo1 import makeMap
#
# import g1sst
# reload(g1sst)
# from g1sst import g1sst
# plotRS2(10*log10(SigmaVVwnr), lat, lon, pixel, line, \
# scale=scale, m=m, clm=(-25,-5), label=label1)
# makeMap(ll_lon, ur_lon, \
# ll_lat, ur_lat, m, name=label1, contour='land')
# plt.close('all')
#
# plotRS2(10*log10(SigmaHHwnr), lat, lon, pixel, line, \
# scale=scale, m=m, clm=(-25,-5), label=label2)
# makeMap(ll_lon, ur_lon, \
# ll_lat, ur_lat, m, name=label2, contour='land')
# plt.close('all')
# plotRS2(PR, lat, lon, pixel, line, \
# scale=scale, m=m, clm=(-2.7,0), label=label3)
# makeMap(ll_lon, ur_lon, \
# ll_lat, ur_lat, m, name=label3, contour='land')
# plt.close('all')
# Coords to plot Figure number
x, y = m(3.15, 42.15)
# Figure 1 a - VV
plotRS2(SigmaVVwnr, lat, lon, pixel, line, \
scale=scale, m=m, clm=(0,0.05), label=label11)
plt.text(x,y,"a",bbox=dict(facecolor='w', alpha=0.5),stretch='expanded',fontsize=27)
makeMap(ll_lon, ur_lon, \
ll_lat, ur_lat, m, name=label11, contour='land')
plt.close('all')
a = label11.split(' ')[0] # split the name if it has spaces
system("rm " + "/home/mag/" + a + ".tiff")
system("mv " + "/home/mag/" + a + "_ETOPO1.tiff" + " " + "/home/mag/Figure1a_.tiff")
system("convert -compress lzw /home/mag/Figure1a_.tiff /home/mag/Figure1a_.tiff")
# Figure 1 b - HH
plotRS2(SigmaHHwnr, lat, lon, pixel, line, \
scale=scale, m=m, clm=(0,0.05), label=label22)
plt.text(x,y,"b",bbox=dict(facecolor='w', alpha=0.5),stretch='expanded',fontsize=27)
makeMap(ll_lon, ur_lon, \
ll_lat, ur_lat, m, name=label22, contour='land')
plt.close('all')
a = label22.split(' ')[0] # split the name if it has spaces
system("rm " + "/home/mag/" + a + ".tiff")
system("mv " + "/home/mag/" + a + "_ETOPO1.tiff" + " " + "/home/mag/Figure1b_.tiff")
system("convert -compress lzw /home/mag/Figure1b_.tiff /home/mag/Figure1b_.tiff")
# Figure 1 c - polarization ratio (PR) (always<1)
plotRS2(PR, lat, lon, pixel, line, \
scale=scale, m=m, clm=(0.6,0.9), label=label33)
plt.text(x,y,"c",bbox=dict(facecolor='w', alpha=0.5),stretch='expanded',fontsize=27)
makeMap(ll_lon, ur_lon, \
ll_lat, ur_lat, m, name=label3, contour='land')
plt.close('all')
a = label33.split(' ')[0] # split the name if it has spaces
system("rm " + "/home/mag/" + a + ".tiff")
system("mv " + "/home/mag/" + a + "_ETOPO1.tiff" + " " + "/home/mag/Figure1c_.tiff")
system("convert -compress lzw /home/mag/Figure1c_.tiff /home/mag/Figure1c_.tiff")
# Figure 1 d - polarization difference (PD) (always>0)
plotRS2(delta, lat, lon, pixel, line, \
scale=scale, m=m, clm=(0,0.02), label=label4)
plt.text(x,y,"d",bbox=dict(facecolor='w', alpha=0.5),stretch='expanded',fontsize=27)
makeMap(ll_lon, ur_lon, \
ll_lat, ur_lat, m, name=label4, contour='land')
plt.close('all')
a = label4.split(' ')[0] # split the name if it has spaces
system("rm " + "/home/mag/" + a + ".tiff")
system("mv " + "/home/mag/" + a + "_ETOPO1.tiff" + " " + "/home/mag/Figure1d_.tiff")
system("convert -compress lzw /home/mag/Figure1d_.tiff /home/mag/Figure1d_.tiff")
# Figure 4 - wave breaking contribution
plotRS2(SigmaWb, lat, lon, pixel, line, \
scale=scale, m=m, clm=(0,0.03), label=label5)
# plt.text(x,y,"d",bbox=dict(facecolor='w', alpha=0.5),stretch='expanded',fontsize=27)
makeMap(ll_lon, ur_lon, \
ll_lat, ur_lat, m, name=label5, contour='land')
plt.close('all')
a = label5.split(' ')[0] # split the name if it has spaces
system("rm " + "/home/mag/" + a + ".tiff")
system("mv " + "/home/mag/" + a + "_ETOPO1.tiff" + " " + "/home/mag/Figure4.tiff")
system("convert -compress lzw /home/mag/Figure4.tiff /home/mag/Figure4.tiff")
# Figure 3
# Plot cropped Current
ptsCropCurrent = array([[ 970, 3061], [2756, 5401]])
CurrentVV = imcrop(ptsCropCurrent, SigmaVVwnr)
CurrentPD = imcrop(ptsCropCurrent, delta)
CurrentPR = imcrop(ptsCropCurrent, PR)
CurrentWb = imcrop(ptsCropCurrent, SigmaWb)
# Transect Current Front
# pts = array([[1400, 4300], [2100, 3600]])
# PDtrans = transect.transect(delta, pts, 30)[1]
# PRtrans = transect.transect(PR, pts, 30)[1]
# VVtrans = transect.transect(SigmaVVwnr, pts, 30)[1]
# SigmaWbtrans = transect.transect(SigmaWb, pts, 30)[1]
# Пока нет возможности сделать преобразование коорлинат, чтобы при помощи
# imcrop находить координаты сечения исходного изображения в вырезанном.
# Поэтому пока делаем сечение в вырезанной области, а в функцию plotTrnsImage
# передаём исходные изображения и координаты для вырезания.
pts = array([[ 650, 1700], [1230, 1000]])
PDtrans = transect.transect(CurrentPD, pts, 30)[1]
PRtrans = transect.transect(CurrentPR, pts, 30)[1]
VVtrans = transect.transect(CurrentVV, pts, 30)[1]
SigmaWbtrans = transect.transect(CurrentWb, pts, 30)[1]
# setup nsper basemap
# Lat/Lon coords of image corners
lat_new, lon_new = intCrpLL(lat, lon, pixel, line, scale=1, ptsCrop=ptsCropCurrent)
ll_lat = lat_new.min()
ur_lat = lat_new.max()
ll_lon = lon_new.min()
ur_lon = lon_new.max()
cent_lat = lat_new.mean()
cent_lon = lon_new.mean()
m = Basemap(llcrnrlat=ll_lat, urcrnrlat=ur_lat,\
llcrnrlon=ll_lon, urcrnrlon=ur_lon, \
resolution='c', projection='nsper', \
satellite_height=798000, \
lat_0=cent_lat,lon_0=cent_lon)
# Get the length of the transect. Input coords in (row,col)
trnsLength = distancelib.getDistancePx([pts[0,1], pts[0,0]], [pts[1,1], pts[1,0]], lat_new, lon_new, CurrentVV)
dist = linspace(0, trnsLength, len(VVtrans))
print "Exporting transect to .mat file"
pn = '/home/mag/Documents/MATLAB/Leon_Agulhas_RS2/'
expFn = 'CurrentFrontTrns/CurrentFrontTrns.mat'
if not path.isfile(pn + expFn):
print "Exporting to:\n" , pn + expFn
savemat(pn + expFn, mdict={ \
'dist':dist, \
'VVtrans':VVtrans, \
'PRtrans':PRtrans, \
'PDtrans':PDtrans, \
'Wbtrans':SigmaWbtrans, \
}, do_compression=False)
# Figure 3 a - VVtrans Image
transect.plotTrnsImage(VVtrans, pts, lat, lon, SigmaVVwnr, pixel, line, \
pn='/home/mag/', \
label=label11, \
clm=(0.01,0.05), ptsCrop=ptsCropCurrent, m=m, scale=1)
plt.close('all')
transect.plotTrns(VVtrans, pts, lat_new, lon_new, CurrentVV, \
pn='/home/mag/', \
label=label11)
# Figure 3 b - VVtrans
plt.close('all')
system("convert -compress lzw /home/mag/" + label11.split(' ')[0] + "-with-trns.tiff /home/mag/Figure3a.tiff")
system("convert -compress lzw /home/mag/" + label11.split(' ')[0] + "-trns.tiff /home/mag/Figure3b.tiff")
# Figure 3 c - PRtrans Image
transect.plotTrnsImage(PRtrans, pts, lat, lon, PR, pixel, line, \
pn='/home/mag/', \
label=label33, \
clm=(0.65,0.9), ptsCrop=ptsCropCurrent, m=m, scale=1)
plt.close('all')
# Figure 3 d - PRtrans
transect.plotTrns(PRtrans, pts, lat_new, lon_new, CurrentPR, \
pn='/home/mag/', \
label=label33)
plt.close('all')
system("convert -compress lzw /home/mag/" + label33.split(' ')[0] + "-with-trns.tiff /home/mag/Figure3c.tiff")
system("convert -compress lzw /home/mag/" + label33.split(' ')[0] + "-trns.tiff /home/mag/Figure3d.tiff")
# Figure 3 e - PDtrans Image
transect.plotTrnsImage(PDtrans, pts, lat, lon, delta, pixel, line, \
pn='/home/mag/', \
label=label4, \
clm=(0.,0.02), ptsCrop=ptsCropCurrent, m=m, scale=1)
plt.close('all')
# Figure 3 f - PRtrans
transect.plotTrns(PDtrans, pts, lat_new, lon_new, CurrentPD, \
pn='/home/mag/', \
label=label4)
plt.close('all')
system("convert -compress lzw /home/mag/" + label4.split(' ')[0] + "-with-trns.tiff /home/mag/Figure3e.tiff")
system("convert -compress lzw /home/mag/" + label4.split(' ')[0] + "-trns.tiff /home/mag/Figure3f.tiff")
# Figure 3 g - PDtrans Image
transect.plotTrnsImage(SigmaWbtrans, pts, lat, lon, SigmaWb, pixel, line, \
pn='/home/mag/', \
label=label5, \
clm=(0.,0.03), ptsCrop=ptsCropCurrent, m=m, scale=1)
plt.close('all')
# Figure 3 h - PRtrans
transect.plotTrns(SigmaWbtrans, pts, lat_new, lon_new, CurrentWb, \
pn='/home/mag/', \
label=label5)
plt.close('all')
system("convert -compress lzw /home/mag/" + label5.split(' ')[0] + "-with-trns.tiff /home/mag/Figure3g.tiff")
system("convert -compress lzw /home/mag/" + label5.split(' ')[0] + "-trns.tiff /home/mag/Figure3h.tiff")
# Figure Transects as subplot
plt.figure(figsize=(7, 5))
plt.subplot(411)
plt.plot(dist, VVtrans)
plt.text(dist[15],(VVtrans.max()+VVtrans.min())/1.51, \
label11, \
bbox=dict(facecolor='w'),stretch='expanded',fontsize=27)
plt.grid()
plt.subplot(412)
plt.plot(dist, PRtrans)
plt.text(dist[15],(PRtrans.max()+PRtrans.min())/1.77, \
label33, \
bbox=dict(facecolor='w'),stretch='expanded',fontsize=27)
plt.grid()
plt.subplot(413)
plt.plot(dist, PDtrans)
plt.text(dist[15],(PDtrans.max()+PDtrans.min())/1.34, \
label4, \
bbox=dict(facecolor='w'),stretch='expanded',fontsize=27)
plt.grid()
plt.subplot(414)
plt.text(dist[15],(SigmaWbtrans.max()+SigmaWbtrans.min())/1.27, \
label5, \
bbox=dict(facecolor='w'),stretch='expanded',fontsize=27)
plt.plot(dist, SigmaWbtrans)
plt.grid()
plt.xlabel('Distance [km]')
#
#
# plt.title(label)
# plt.axis('tight')
# plt.text(dist[15],(trn.max()+trn.min())/2,"A",bbox=dict(facecolor='w', alpha=0.7),stretch='expanded',fontsize=27)
# plt.text(dist[-55],(trn.max()+trn.min())/2,"B",bbox=dict(facecolor='w', alpha=0.7),stretch='expanded',fontsize=27)
# if fign is not None:
# plt.text(dist.max()*0.9,trn.max()-(trn.max()-trn.min())*0.1,fign,bbox=dict(facecolor='w', alpha=1),stretch='expanded',fontsize=27)
## mng = plt.get_current_fig_manager()
## mng.resize(1920,1080)
# plt.draw()
# a = label.split(' ')[0] # split the name if it has spaces
# plt.savefig(pn+a+'-trns.tiff', facecolor='w', edgecolor='w', \
# dpi=300, bbox_inches="tight", pad_inches=0.1)
#
#
#
# Figure 5
# Plot cropped Northern Oil Spill
lat_new, lon_new = intCrpLL(lat, lon, pixel, line, scale=1, ptsCrop=ptsCropNorth)
ll_lat = lat_new.min()
ur_lat = lat_new.max()
ll_lon = lon_new.min()
ur_lon = lon_new.max()
cent_lat = lat_new.mean()
cent_lon = lon_new.mean()
m = Basemap(llcrnrlat=ll_lat, urcrnrlat=ur_lat,\
llcrnrlon=ll_lon, urcrnrlon=ur_lon, \
resolution='c', projection='nsper', \
satellite_height=798000, \
lat_0=cent_lat,lon_0=cent_lon)
# Transect North Oil Slick
pts = array([[613, 851], [833, 1033]])
# [1] returns only Mean Transect
PDtrans = transect.transect(oilCropNorthDelta, pts, 30)[1]
PRtrans = transect.transect(oilCropNorthPR, pts, 30)[1]
VVtrans = transect.transect(oilCropNorthVV, pts, 30)[1]
SigmaWbtrans = transect.transect(oilCropNorthSigmaWb, pts, 30)[1]
# Get the length of the transect. Input coords in (row,col)
trnsLength = distancelib.getDistancePx([pts[0,1], pts[0,0]], [pts[1,1], pts[1,0]], lat_new, lon_new, oilCropNorthVV)
dist = linspace(0, trnsLength, len(VVtrans))
print "Exporting transect to .mat file"
pn = '/home/mag/Documents/MATLAB/Leon_Agulhas_RS2/'
expFn = 'OilSlickTrns/OilSlickTrns.mat'
if not path.isfile(pn + expFn):
print "Exporting to:\n" , pn + expFn
savemat(pn + expFn, mdict={ \
'dist':dist, \
'VVtrans':VVtrans, \
'PRtrans':PRtrans, \
'PDtrans':PDtrans, \
'Wbtrans':SigmaWbtrans, \
}, do_compression=False)
# Figure 5 a - VVtrans Image
transect.plotTrnsImage(VVtrans, pts, lat, lon, SigmaVVwnr, pixel, line, \
pn='/home/mag/', \
label=label11, \
clm=(0.01,0.05), ptsCrop=ptsCropNorth, m=m, scale=1, fign='a')
plt.close('all')
# Figure 5 b - VVtrans
transect.plotTrns(VVtrans, pts, lat_new, lon_new, oilCropNorthVV, \
pn='/home/mag/', \
label=label11, fign='b')
plt.close('all')
system("convert -compress lzw /home/mag/" + label11.split(' ')[0] + "-with-trns.tiff /home/mag/Figure5a.tiff")
system("convert -compress lzw /home/mag/" + label11.split(' ')[0] + "-trns.tiff /home/mag/Figure5b.tiff")
# Figure 5 c - PRtrans Image
transect.plotTrnsImage(PRtrans, pts, lat, lon, PR, pixel, line, \
pn='/home/mag/', \
label=label33, \
clm=(0.65,0.9), ptsCrop=ptsCropNorth, m=m, scale=1)
plt.close('all')
# Figure 5 d - PRtrans
transect.plotTrns(PRtrans, pts, lat_new, lon_new, oilCropNorthPR, \
pn='/home/mag/', \
label=label33)
plt.close('all')
system("convert -compress lzw /home/mag/" + label33.split(' ')[0] + "-with-trns.tiff /home/mag/Figure5c.tiff")
system("convert -compress lzw /home/mag/" + label33.split(' ')[0] + "-trns.tiff /home/mag/Figure5d.tiff")
# Figure 5 e - PDtrans Image
transect.plotTrnsImage(PDtrans, pts, lat, lon, delta, pixel, line, \
pn='/home/mag/', \
label=label4, \
clm=(0.,0.02), ptsCrop=ptsCropNorth, m=m, scale=1)
plt.close('all')
# Figure 5 f - PRtrans
transect.plotTrns(PDtrans, pts, lat_new, lon_new, oilCropNorthDelta, \
pn='/home/mag/', \
label=label4)
plt.close('all')
system("convert -compress lzw /home/mag/" + label4.split(' ')[0] + "-with-trns.tiff /home/mag/Figure5e.tiff")
system("convert -compress lzw /home/mag/" + label4.split(' ')[0] + "-trns.tiff /home/mag/Figure5f.tiff")
# Figure 5 g - PDtrans Image
transect.plotTrnsImage(SigmaWbtrans, pts, lat, lon, SigmaWb, pixel, line, \
pn='/home/mag/', \
label=label5, \
clm=(0.,0.03), ptsCrop=ptsCropNorth, m=m, scale=1,)
plt.close('all')
# Figure 5 h - PRtrans
transect.plotTrns(SigmaWbtrans, pts, lat_new, lon_new, oilCropNorthSigmaWb, \
pn='/home/mag/', \
label=label5)
plt.close('all')
system("convert -compress lzw /home/mag/" + label5.split(' ')[0] + "-with-trns.tiff /home/mag/Figure5g.tiff")
system("convert -compress lzw /home/mag/" + label5.split(' ')[0] + "-trns.tiff /home/mag/Figure5h.tiff")
# Clean up
system("rm " + "/home/mag/*trns.tiff")
def test_00_02_all_but_one_masked():
mask = np.zeros((10, 10), bool)
mask[5, 5] = True
median_filter(np.zeros((10, 10)), 3, mask)
def test_wrong_shape():
img = np.empty((10, 10, 3))
median_filter(img)
def test_00_01_all_masked():
'''Test a completely masked image
Regression test of IMG-1029'''
result = median_filter(np.zeros((10, 10)), 3, np.zeros((10, 10), bool))
assert (np.all(result == 0))
def test_insufficient_size():
img = (np.random.random((20, 20)) * 255).astype(np.uint8)
median_filter(img, radius=1)
def test_00_02_all_but_one_masked():
mask = np.zeros((10, 10), bool)
mask[5, 5] = True
median_filter(np.zeros((10, 10)), 3, mask)
def getNeighbourKernel(segmentKernelSize, rotation, springCost, rotationCost):
pass
def getNeighbourSuggestion(modelList, I):
pass
gs = np.load("dev/gs.npy")
#Edgekernel
K = np.ones((4, 6))
K[:2] = -1
#Segment kernel
R = makeSegmentKernel((28, 61), edge=2)
#Vertical features
V = np.abs(ndimage.convolve(gs.astype(np.float), K.T))
V = ndimage.binary_erosion(
np.logical_and(V < np.mean(V) * 15, V > np.mean(V) * 1.5))
#Horizontal features
H = filter.median_filter(
ndimage.binary_erosion(ndimage.convolve(gs.astype(np.float), K) > 0), 3)
#Composite features
D = ndimage.convolve((V + H).astype(np.float), R)
from skimage.filter import threshold_otsu from skimage.filter import median_filter ptsCropSouth = array([[3000, 4650], [3700, 5200]]) oilCropSouth = imcrop(ptsCropSouth, delta) ptsCropNorth = array([[3400, 0], [4900, 1650]]) oilCropNorth = imcrop(ptsCropNorth, delta) image = oilCropNorth # reduce the speckle noise image = wiener(image, mysize=(3, 3), noise=None) # use median filter to smooth image = median_filter(image, radius=37) # find the global threshold global_thresh = threshold_otsu(image) # create a binary mask markers = ones(image.shape, dtype=uint) markers[image < global_thresh * 0.84] = 0 oilCropNorthMeanP = P[markers == 1] oilCropNorthMeanP = oilCropNorthMeanP.mean() ## display results #plt.figure() #plt.subplot(131) #plt.imshow(image, cmap=plt.cm.gray)
from skimage.filter import median_filter
ptsCropSouth = array([[ 3000, 4650],
[ 3700, 5200]])
oilCropSouth = imcrop(ptsCropSouth, delta)
ptsCropNorth = array([[ 3400, 0],
[ 4900, 1650]])
oilCropNorth = imcrop(ptsCropNorth, delta)
image = oilCropNorth
# reduce the speckle noise
image = wiener(image, mysize=(3,3), noise=None)
# use median filter to smooth
image = median_filter(image, radius=37)
# find the global threshold
global_thresh = threshold_otsu(image)
# create a binary mask
markers = ones(image.shape, dtype=uint)
markers[image < global_thresh*0.84] = 0
oilCropNorthMeanP = P[markers==1]
oilCropNorthMeanP = oilCropNorthMeanP.mean()
## display results
#plt.figure()
#plt.subplot(131)
#plt.imshow(image, cmap=plt.cm.gray)
"""
This example compares several denoising filters available in scikit-image:
a Gaussian filter, a median filter, and total variation denoising.
"""
import matplotlib.pyplot as plt
from skimage import data
from skimage import filter
from scipy import ndimage
coins = data.coins()
gaussian_filter_coins = ndimage.gaussian_filter(coins, sigma=2)
med_filter_coins = filter.median_filter(coins)
tv_filter_coins = filter.tv_denoise(coins, weight=0.1)
plt.figure(figsize=(16, 4))
plt.subplot(141)
plt.imshow(coins[10:80, 300:370], cmap="gray", interpolation="nearest")
plt.axis("off")
plt.title("Image")
plt.subplot(142)
plt.imshow(gaussian_filter_coins[10:80, 300:370], cmap="gray", interpolation="nearest")
plt.axis("off")
plt.title("Gaussian filter")
plt.subplot(143)
plt.imshow(med_filter_coins[10:80, 300:370], cmap="gray", interpolation="nearest")
plt.axis("off")
plt.title("Median filter")
plt.subplot(144)
plt.imshow(tv_filter_coins[10:80, 300:370], cmap="gray", interpolation="nearest")
plt.axis("off")
def test_default_values():
img = (np.random.random((20, 20)) * 255).astype(np.uint8)
mask = np.ones((20, 20), dtype=np.uint8)
result1 = median_filter(img, radius=2, mask=mask, percent=50)
result2 = median_filter(img)
np.testing.assert_array_equal(result1, result2)
def test_insufficient_size():
img = (np.random.random((20, 20)) * 255).astype(np.uint8)
median_filter(img, radius=1)
def test_00_01_all_masked():
'''Test a completely masked image
Regression test of IMG-1029'''
result = median_filter(np.zeros((10, 10)), 3, np.zeros((10, 10), bool))
assert (np.all(result == 0))
def main( argv=None ):
# default values
pn = '/media/data/data/OTHER/RS2 Agulhas and Lion/RS2_FQA_1xQGSS20101218_173930_00000005/'
xOff=0
yOff=0
xS=None
yS=None
xBufScale=None
yBufScale=None
s = 'abs'
if argv is None:
argv = sys.argv
if argv is None:
print ( "Please specify the path to the RS2 folder! \n" + \
"See USAGE for more details \n")
return Usage()
# Parse arguments
try:
opts, args = getopt.getopt(argv,"hi:o:",["pn=","xoff="])
except getopt.GetoptError:
print 'readRS2.py -pn <inputfile> ...'
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print 'readRS2.py -pn <inputfile> ...'
sys.exit()
elif opt in ("-pn", "--pn"):
pn = arg
elif opt in ("-xoff", "--xoff"):
xOff = arg
elif opt in ("-yoff", "--yoff"):
yOff = arg
elif opt in ("-xS", "--xS"):
xS = arg
elif opt in ("-yS", "--yS"):
yS = arg
elif opt in ("-xScale", "--xScale"):
xBufScale = arg
elif opt in ("-yScale", "--yScale"):
yBufScale = arg
print "Path to the RS2 file:\n", pn
if core_ct < 4:
# calibrate the scene
print "Calibrating"
currtime = time()
calib = calibRS2(pn, xOff, yOff, xS, yS, xBufScale, yBufScale, 'sigma0')
# get the incidence angle
print "Incidence Angle and Lats/Lons"
IncidenceAngle = calib.incidence_angle()
IncidenceAngle = rad2deg(IncidenceAngle)
# get the lats/lons
(lat, lon, line, pixel) = calib.xml2geo()
print "Filtering"
# 1. Remove speckle noise (using Lee-Wiener filter)
calib.speckle_filter('wiener', 7)
print 'Serial: time elapsed:', time() - currtime
elif core_ct >= 4:
# calibrate the scene
print "Calibrating"
currtime = time()
calibPar = calibRS2par(pn, xOff, yOff, xS, yS, xBufScale, yBufScale, s)
# get the incidence angle
print "Incidence Angle and Lats/Lons"
IncidenceAngle = calibPar.incidence_angle()
IncidenceAngle = rad2deg(IncidenceAngle)
# get the lats/lons
(lat, lon, line, pixel) = calibPar.xml2geo()
print "Filtering"
# 1. Remove speckle noise (using Lee-Wiener filter)
calibPar.speckle_filter('wiener', 7)
print 'Parallel: time elapsed: %f' % ( time() - currtime )
# export to geotiff
# calibPar.save_tiff()
print "Some Sigma calculations..."
SigmaHHwnr = calibPar.SigmaHHwnr
SigmaVVwnr = calibPar.SigmaVVwnr
# SigmaHVwnr = calibPar.SigmaHVwnr
# Free up some memory
del calibPar.SigmaHHwnr, calibPar.SigmaVVwnr, calibPar.SigmaHVwnr
# # Calculate some wind
# # testing that with sar=-0.387 wind speed is 10m/s
# w = rcs2wind(sar=-0.387*ones((1,1)), cmdv=4, windir=0, theta=20*ones((1,1)))
# print "Testing CMOD4 passed, Wind =", w
#
# sar=10*log10(SigmaVVwnr[::10,::10])
## sar=10*log10(SigmaVVwnr)
# w = rcs2wind(sar, cmdv=4, windir=130, theta=33*ones(sar.shape))
# print "Mean CMOD4 Wind =", w.mean()
# calculate the conjugate
SigmaVVConjHH = calibPar.S_VV*conj(calibPar.S_HH)
SigmaVVConjHHWnr = wiener(SigmaVVConjHH, mysize=(7,7), noise=None)
# all sigmas in linear units
SigmaVVConjHHdelta = SigmaVVConjHHWnr/(SigmaVVwnr - SigmaHHwnr)
# # Check that imagenary part is much less then the real one
# SigmaVVConjHHr = real(SigmaVVConjHH)
# SigmaVVConjHHi = imag(SigmaVVConjHH)
# SigmaVVConjHHr.mean()
# SigmaVVConjHHi.mean()
# Free up some memory
del SigmaVVConjHH, calibPar.S_HH, calibPar.S_VV
# 2. Find Poolarization ratio PR in dB and delta - linear
# delta - is a Bragg component, which describes wind field impact
# PR = 10*log10(SigmaHHwnr) - 10*log10(SigmaVVwnr)
# # PR must not be > 0, be careful with masking
#P[P>0] = 0
PR = SigmaHHwnr/SigmaVVwnr
delta = SigmaVVwnr - SigmaHHwnr
# # Masking Land from HV polarization
# maskedLand = SigmaHVwnr>0.003
# 3. Linear fit of Sigma and delta: Sigma = A*delta
# A = sum(SigmaVVwnr[~maskedLand]*delta[~maskedLand])/sum(delta[~maskedLand]^2);
# B = sum(SigmaHHwnr[~maskedLand]*delta[~maskedLand])/sum(delta[~maskedLand]^2);
# imposing from theory
A = linspace(2.3,1.8,IncidenceAngle.size) # dependence on inc angle
A = tile(A, (line[-1,0]+1, 1))
# 4. Calculate SigmaVVwb = SigmaVVwb === SigmaHHwb
# substracting wind field variability contribution, the rest is impact of
# current and film on the image
SigmaWb = SigmaVVwnr - ( A*delta )
# Checking that SigmaWb from VV and HH are the same
# SigmaVVwb = SigmaVVwnr - ( A*delta )
# SigmaHHwb = SigmaHHwnr - ( B*delta )
# SigmaWb = SigmaVVwb
# mean(SigmaHHwb(:)./SigmaVVwb(:))
# clear SigmaVVwb SigmaHHwb
# Cropping the oil slicks
## проверить почему переворот изображения!!!
# from imcrop import imzoom
# print ( "Cropping...")
# plt.figure()
# plt.imshow(SigmaWb)
# plt.clim(0,0.03)
# plt.gray()
# plt.show()
# pts = imzoom()
ptsCropSouth = array([[ 3200, 4550],
[ 3800, 5100]])
oilCropSouth = imcrop(ptsCropSouth, delta)
ptsCropNorth = array([[ 3400, 0],
[ 4900, 1650]])
oilCropNorth = imcrop(ptsCropNorth, delta)
# reduce the speckle noise
oilN = wiener(oilCropNorth, mysize=(3,3), noise=None)
oilS = wiener(oilCropSouth, mysize=(3,3), noise=None)
# use median filter to smooth
oilN = median_filter(oilN, radius=37)
oilS = median_filter(oilS, radius=37)
# find the global threshold
global_threshN = threshold_otsu(oilN)
global_threshS = threshold_otsu(oilS)
# create a binary mask
markersN = ones(oilN.shape, dtype=uint)
markersS = ones(oilS.shape, dtype=uint)
markersN[oilN < global_threshN*0.84] = 0
markersS[oilS < global_threshS*0.84] = 0
# Find the relation of PR in/out-side the Slicks
oilCropNorthPR = imcrop(ptsCropNorth, PR)
oilCropPN = oilCropNorthPR[markersN==0].mean()/oilCropNorthPR[markersS==1].mean()
oilCropSouthP = imcrop(ptsCropSouth, PR)
oilCropPS = oilCropSouthP[markersS==0].mean()/oilCropSouthP[markersS==1].mean()
# Checking that calculations are OK
oilCropNorthKVV = imcrop(ptsCropNorth, SigmaVVwnr)
KVV = oilCropNorthKVV[markersN==0].mean()/oilCropNorthKVV[markersN==1].mean()
oilCropNorthKHH = imcrop(ptsCropNorth, SigmaHHwnr)
KHH = oilCropNorthKHH[markersN==0].mean()/oilCropNorthKHH[markersN==1].mean()
PSlick = KHH/KVV*oilCropNorthKHH[markersN==1].mean()/oilCropNorthKVV[markersN==1].mean()
print "Polarization ratio Out/In-side Slick = \
\n %0.2f for Northern \n %0.2f for Southern" \
%(1/oilCropPN, 1/oilCropPS)
oilCropNorthVV = imcrop(ptsCropNorth, SigmaVVwnr)
oilCropNorthDelta = imcrop(ptsCropNorth, delta)
oilCropNorthSigmaWb = imcrop(ptsCropNorth, SigmaWb)
print ( "Plotting on a map... \nUsing default NSPER Basemap \n")
# setup nsper basemap
# Lat/Lon coords of image corners
ll_lat = lat.min()
ur_lat = lat.max()
ll_lon = lon.min()
ur_lon = lon.max()
cent_lat = lat.mean()
cent_lon = lon.mean()
m = Basemap(llcrnrlat=ll_lat, urcrnrlat=ur_lat,\
llcrnrlon=ll_lon, urcrnrlon=ur_lon, \
resolution='f', projection='nsper', \
satellite_height=798000, \
lat_0=cent_lat,lon_0=cent_lon)
# m = Basemap(llcrnrlat=30, urcrnrlat=50,\
# llcrnrlon=1, urcrnrlon=20, \
# resolution='l', projection='nsper', \
# satellite_height=798000, \
# lat_0=cent_lat,lon_0=cent_lon)
print ( "Plotting figures... \n")
scale = 8 # setting scale factor to resize in 1/scale times
label1 = 'Sigma VV [dB]'
label2 = 'Sigma HH [dB]'
label11 = 'Sigma VV [linear units]'
label22 = 'Sigma HH [linear units]'
label3 = 'PR [dB]'
label33 = 'PR [linear units]'
label4 = 'PD [linear units]'
label5 = 'Wb contribution [linear units]'
label6 = 'SigmaVVConjHH [linear units]'
label7 = 'SigmaVVConjHHdelta [linear units]'
# label8 = 'Wind CMOD4 [m/s]'
# import plotRS2
# reload(plotRS2)
# from plotRS2 import plotRS2
# import mpl_util
# reload(mpl_util)
#
# import gmtColormap
# reload(gmtColormap)
#
# import createMapsEtopo1
# reload(createMapsEtopo1)
# from createMapsEtopo1 import makeMap
#
# import g1sst
# reload(g1sst)
# from g1sst import g1sst
# plotRS2(10*log10(SigmaVVwnr), lat, lon, pixel, line, \
# scale=scale, m=m, clm=(-25,-5), label=label1)
# makeMap(ll_lon, ur_lon, \
# ll_lat, ur_lat, m, name=label1, contour='land')
# plt.close('all')
#
# plotRS2(10*log10(SigmaHHwnr), lat, lon, pixel, line, \
# scale=scale, m=m, clm=(-25,-5), label=label2)
# makeMap(ll_lon, ur_lon, \
# ll_lat, ur_lat, m, name=label2, contour='land')
# plt.close('all')
# plotRS2(PR, lat, lon, pixel, line, \
# scale=scale, m=m, clm=(-2.7,0), label=label3)
# makeMap(ll_lon, ur_lon, \
# ll_lat, ur_lat, m, name=label3, contour='land')
# plt.close('all')
# Coords to plot Figure number
x, y = m(3.15, 42.15)
# Figure 1 a - VV
plotRS2(SigmaVVwnr, lat, lon, pixel, line, \
scale=scale, m=m, clm=(0,0.05), label=label11)
plt.text(x,y,"a",bbox=dict(facecolor='w', alpha=0.5),stretch='expanded',fontsize=27)
makeMap(ll_lon, ur_lon, \
ll_lat, ur_lat, m, name=label11, contour='land')
plt.close('all')
a = label11.split(' ')[0] # split the name if it has spaces
system("rm " + "/home/mag/" + a + ".tiff")
system("mv " + "/home/mag/" + a + "_ETOPO1.tiff" + " " + "/home/mag/Figure1a_.tiff")
system("convert -compress lzw /home/mag/Figure1a_.tiff /home/mag/Figure1a_.tiff")
# Figure 1 b - HH
plotRS2(SigmaHHwnr, lat, lon, pixel, line, \
scale=scale, m=m, clm=(0,0.05), label=label22)
plt.text(x,y,"b",bbox=dict(facecolor='w', alpha=0.5),stretch='expanded',fontsize=27)
makeMap(ll_lon, ur_lon, \
ll_lat, ur_lat, m, name=label22, contour='land')
plt.close('all')
a = label22.split(' ')[0] # split the name if it has spaces
system("rm " + "/home/mag/" + a + ".tiff")
system("mv " + "/home/mag/" + a + "_ETOPO1.tiff" + " " + "/home/mag/Figure1b_.tiff")
system("convert -compress lzw /home/mag/Figure1b_.tiff /home/mag/Figure1b_.tiff")
# Figure 1 c - polarization ratio (PR) (always<1)
plotRS2(PR, lat, lon, pixel, line, \
scale=scale, m=m, clm=(0.6,0.9), label=label33)
plt.text(x,y,"c",bbox=dict(facecolor='w', alpha=0.5),stretch='expanded',fontsize=27)
makeMap(ll_lon, ur_lon, \
ll_lat, ur_lat, m, name=label3, contour='land')
plt.close('all')
a = label33.split(' ')[0] # split the name if it has spaces
system("rm " + "/home/mag/" + a + ".tiff")
system("mv " + "/home/mag/" + a + "_ETOPO1.tiff" + " " + "/home/mag/Figure1c_.tiff")
system("convert -compress lzw /home/mag/Figure1c_.tiff /home/mag/Figure1c_.tiff")
# Figure 1 d - polarization difference (PD) (always>0)
plotRS2(delta, lat, lon, pixel, line, \
scale=scale, m=m, clm=(0,0.02), label=label4)
plt.text(x,y,"d",bbox=dict(facecolor='w', alpha=0.5),stretch='expanded',fontsize=27)
makeMap(ll_lon, ur_lon, \
ll_lat, ur_lat, m, name=label4, contour='land')
plt.close('all')
a = label4.split(' ')[0] # split the name if it has spaces
system("rm " + "/home/mag/" + a + ".tiff")
system("mv " + "/home/mag/" + a + "_ETOPO1.tiff" + " " + "/home/mag/Figure1d_.tiff")
system("convert -compress lzw /home/mag/Figure1d_.tiff /home/mag/Figure1d_.tiff")
# Figure 4 - wave breaking contribution
plotRS2(SigmaWb, lat, lon, pixel, line, \
scale=scale, m=m, clm=(0,0.03), label=label5)
plt.text(x,y,"d",bbox=dict(facecolor='w', alpha=0.5),stretch='expanded',fontsize=27)
makeMap(ll_lon, ur_lon, \
ll_lat, ur_lat, m, name=label5, contour='land')
plt.close('all')
a = label5.split(' ')[0] # split the name if it has spaces
system("rm " + "/home/mag/" + a + ".tiff")
system("mv " + "/home/mag/" + a + "_ETOPO1.tiff" + " " + "/home/mag/Figure4.tiff")
system("convert -compress lzw /home/mag/Figure4.tiff /home/mag/Figure4.tiff")
# Figure 3
# Plot cropped Current
ptsCropCurrent = array([[ 970, 3061], [2756, 5401]])
CurrentVV = imcrop(ptsCropCurrent, SigmaVVwnr)
CurrentPD = imcrop(ptsCropCurrent, delta)
CurrentPR = imcrop(ptsCropCurrent, PR)
CurrentWb = imcrop(ptsCropCurrent, SigmaWb)
# Transect Current Front
# pts = array([[1400, 4300], [2100, 3600]])
# PDtrans = transect.transect(delta, pts, 30)[1]
# PRtrans = transect.transect(PR, pts, 30)[1]
# VVtrans = transect.transect(SigmaVVwnr, pts, 30)[1]
# SigmaWbtrans = transect.transect(SigmaWb, pts, 30)[1]
# Пока нет возможности сделать преобразование коорлинат, чтобы при помощи
# imcrop находить координаты сечения исходного изображения в вырезанном.
# Поэтому пока делаем сечение в вырезанной области, а в функцию plotTrnsImage
# передаём исходные изображения и координаты для вырезания.
pts = array([[ 650, 1700], [1230, 1000]])
PDtrans = transect.transect(CurrentPD, pts, 30)[1]
PRtrans = transect.transect(CurrentPR, pts, 30)[1]
VVtrans = transect.transect(CurrentVV, pts, 30)[1]
SigmaWbtrans = transect.transect(CurrentWb, pts, 30)[1]
# setup nsper basemap
# Lat/Lon coords of image corners
lat_new, lon_new = intCrpLL(lat, lon, pixel, line, scale=1, ptsCrop=ptsCropCurrent)
ll_lat = lat_new.min()
ur_lat = lat_new.max()
ll_lon = lon_new.min()
ur_lon = lon_new.max()
cent_lat = lat_new.mean()
cent_lon = lon_new.mean()
m = Basemap(llcrnrlat=ll_lat, urcrnrlat=ur_lat,\
llcrnrlon=ll_lon, urcrnrlon=ur_lon, \
resolution='c', projection='nsper', \
satellite_height=798000, \
lat_0=cent_lat,lon_0=cent_lon)
# Figure 3 a - VVtrans Image
transect.plotTrnsImage(VVtrans, pts, lat, lon, SigmaVVwnr, pixel, line, \
pn='/home/mag/', \
label=label11, \
clm=(0.01,0.05), ptsCrop=ptsCropCurrent, m=m, scale=1, fign='a')
plt.close('all')
# Figure 3 b - VVtrans
transect.plotTrns(VVtrans, pts, lat_new, lon_new, CurrentVV, \
pn='/home/mag/', \
label=label11, fign='b')
plt.close('all')
system("convert -compress lzw /home/mag/" + label11.split(' ')[0] + "-with-trns.tiff /home/mag/Figure3a.tiff")
system("convert -compress lzw /home/mag/" + label11.split(' ')[0] + "-trns.tiff /home/mag/Figure3b.tiff")
# Figure 3 c - PRtrans Image
transect.plotTrnsImage(PRtrans, pts, lat, lon, PR, pixel, line, \
pn='/home/mag/', \
label=label33, \
clm=(0.65,0.9), ptsCrop=ptsCropCurrent, m=m, scale=1, fign='c')
plt.close('all')
# Figure 3 d - PRtrans
transect.plotTrns(PRtrans, pts, lat_new, lon_new, CurrentPR, \
pn='/home/mag/', \
label=label33, fign='d')
plt.close('all')
system("convert -compress lzw /home/mag/" + label33.split(' ')[0] + "-with-trns.tiff /home/mag/Figure3c.tiff")
system("convert -compress lzw /home/mag/" + label33.split(' ')[0] + "-trns.tiff /home/mag/Figure3d.tiff")
# Figure 3 e - PDtrans Image
transect.plotTrnsImage(PDtrans, pts, lat, lon, delta, pixel, line, \
pn='/home/mag/', \
label=label4, \
clm=(0.,0.02), ptsCrop=ptsCropCurrent, m=m, scale=1, fign='e')
plt.close('all')
# Figure 3 f - PRtrans
transect.plotTrns(PDtrans, pts, lat_new, lon_new, CurrentPD, \
pn='/home/mag/', \
label=label4, fign='f')
plt.close('all')
system("convert -compress lzw /home/mag/" + label4.split(' ')[0] + "-with-trns.tiff /home/mag/Figure3e.tiff")
system("convert -compress lzw /home/mag/" + label4.split(' ')[0] + "-trns.tiff /home/mag/Figure3f.tiff")
# Figure 3 g - PDtrans Image
transect.plotTrnsImage(SigmaWbtrans, pts, lat, lon, SigmaWb, pixel, line, \
pn='/home/mag/', \
label=label5, \
clm=(0.,0.03), ptsCrop=ptsCropCurrent, m=m, scale=1, fign='g')
plt.close('all')
# Figure 3 h - PRtrans
transect.plotTrns(SigmaWbtrans, pts, lat_new, lon_new, CurrentWb, \
pn='/home/mag/', \
label=label5, fign='h')
plt.close('all')
system("convert -compress lzw /home/mag/" + label5.split(' ')[0] + "-with-trns.tiff /home/mag/Figure3g.tiff")
system("convert -compress lzw /home/mag/" + label5.split(' ')[0] + "-trns.tiff /home/mag/Figure3h.tiff")
# Figure 5
# Plot cropped Northern Oil Spill
lat_new, lon_new = intCrpLL(lat, lon, pixel, line, scale=1, ptsCrop=ptsCropNorth)
ll_lat = lat_new.min()
ur_lat = lat_new.max()
ll_lon = lon_new.min()
ur_lon = lon_new.max()
cent_lat = lat_new.mean()
cent_lon = lon_new.mean()
m = Basemap(llcrnrlat=ll_lat, urcrnrlat=ur_lat,\
llcrnrlon=ll_lon, urcrnrlon=ur_lon, \
resolution='c', projection='nsper', \
satellite_height=798000, \
lat_0=cent_lat,lon_0=cent_lon)
# Transect North Oil Slick
pts = array([[613, 851], [833, 1033]])
# [1] returns only Mean Transect
PDtrans = transect.transect(oilCropNorthDelta, pts, 30)[1]
PRtrans = transect.transect(oilCropNorthPR, pts, 30)[1]
VVtrans = transect.transect(oilCropNorthVV, pts, 30)[1]
SigmaWbtrans = transect.transect(oilCropNorthSigmaWb, pts, 30)[1]
# Figure 5 a - VVtrans Image
transect.plotTrnsImage(VVtrans, pts, lat, lon, SigmaVVwnr, pixel, line, \
pn='/home/mag/', \
label=label11, \
clm=(0.01,0.05), ptsCrop=ptsCropNorth, m=m, scale=1, fign='a')
plt.close('all')
# Figure 5 b - VVtrans
transect.plotTrns(VVtrans, pts, lat_new, lon_new, oilCropNorthVV, \
pn='/home/mag/', \
label=label11, fign='b')
plt.close('all')
system("convert -compress lzw /home/mag/" + label11.split(' ')[0] + "-with-trns.tiff /home/mag/Figure5a.tiff")
system("convert -compress lzw /home/mag/" + label11.split(' ')[0] + "-trns.tiff /home/mag/Figure5b.tiff")
# Figure 5 c - PRtrans Image
transect.plotTrnsImage(PRtrans, pts, lat, lon, PR, pixel, line, \
pn='/home/mag/', \
label=label33, \
clm=(0.65,0.9), ptsCrop=ptsCropNorth, m=m, scale=1, fign='c')
plt.close('all')
# Figure 5 d - PRtrans
transect.plotTrns(PRtrans, pts, lat_new, lon_new, oilCropNorthPR, \
pn='/home/mag/', \
label=label33, fign='d')
plt.close('all')
system("convert -compress lzw /home/mag/" + label33.split(' ')[0] + "-with-trns.tiff /home/mag/Figure5c.tiff")
system("convert -compress lzw /home/mag/" + label33.split(' ')[0] + "-trns.tiff /home/mag/Figure5d.tiff")
# Figure 5 e - PDtrans Image
transect.plotTrnsImage(PDtrans, pts, lat, lon, delta, pixel, line, \
pn='/home/mag/', \
label=label4, \
clm=(0.,0.02), ptsCrop=ptsCropNorth, m=m, scale=1, fign='e')
plt.close('all')
# Figure 5 f - PRtrans
transect.plotTrns(PDtrans, pts, lat_new, lon_new, oilCropNorthDelta, \
pn='/home/mag/', \
label=label4, fign='f')
plt.close('all')
system("convert -compress lzw /home/mag/" + label4.split(' ')[0] + "-with-trns.tiff /home/mag/Figure5e.tiff")
system("convert -compress lzw /home/mag/" + label4.split(' ')[0] + "-trns.tiff /home/mag/Figure5f.tiff")
# Figure 5 g - PDtrans Image
transect.plotTrnsImage(SigmaWbtrans, pts, lat, lon, SigmaWb, pixel, line, \
pn='/home/mag/', \
label=label5, \
clm=(0.,0.03), ptsCrop=ptsCropNorth, m=m, scale=1, fign='g')
plt.close('all')
# Figure 5 h - PRtrans
transect.plotTrns(SigmaWbtrans, pts, lat_new, lon_new, oilCropNorthSigmaWb, \
pn='/home/mag/', \
label=label5, fign='h')
plt.close('all')
system("convert -compress lzw /home/mag/" + label5.split(' ')[0] + "-with-trns.tiff /home/mag/Figure5g.tiff")
system("convert -compress lzw /home/mag/" + label5.split(' ')[0] + "-trns.tiff /home/mag/Figure5h.tiff")
# Clean up
system("rm " + "/home/mag/*trns.tiff")
def ctmf_med(image, radius):
return median_filter(image=image, radius=radius)
def main( argv=None ):
# default values
pn = '/media/data/data/OTHER/RS2 Agulhas and Lion/RS2_FQA_1xQGSS20101218_173930_00000005/'
xOff=0
yOff=0
xS=None
yS=None
xBufScale=None
yBufScale=None
s = 'abs'
if argv is None:
argv = sys.argv
if argv is None:
print ( "Please specify the path to the RS2 folder! \n" + \
"See USAGE for more details \n")
return Usage()
# Parse arguments
try:
opts, args = getopt.getopt(argv,"hi:o:",["pn=","xoff="])
except getopt.GetoptError:
print 'readRS2.py -pn <inputfile> ...'
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print 'readRS2.py -pn <inputfile> ...'
sys.exit()
elif opt in ("-pn", "--pn"):
pn = arg
elif opt in ("-xoff", "--xoff"):
xOff = arg
elif opt in ("-yoff", "--yoff"):
yOff = arg
elif opt in ("-xS", "--xS"):
xS = arg
elif opt in ("-yS", "--yS"):
yS = arg
elif opt in ("-xScale", "--xScale"):
xBufScale = arg
elif opt in ("-yScale", "--yScale"):
yBufScale = arg
print "Path to the RS2 file:\n", pn
if core_ct < 4:
# calibrate the scene
print "Calibrating"
currtime = time()
calib = calibRS2(pn, xOff, yOff, xS, yS, xBufScale, yBufScale, 'sigma0')
# get the incidence angle
print "Incidence Angle and Lats/Lons"
IncidenceAngle = calib.incidence_angle()
IncidenceAngle = rad2deg(IncidenceAngle)
# get the lats/lons
(lat, lon, line, pixel) = calib.xml2geo()
print "Filtering"
# 1. Remove speckle noise (using Lee-Wiener filter)
calib.speckle_filter('wiener', 7)
print 'Serial: time elapsed:', time() - currtime
elif core_ct >= 4:
# calibrate the scene
print "Calibrating"
currtime = time()
calibPar = calibRS2par(pn, xOff, yOff, xS, yS, xBufScale, yBufScale, s)
# get the incidence angle
print "Incidence Angle and Lats/Lons"
IncidenceAngle = calibPar.incidence_angle()
IncidenceAngle = rad2deg(IncidenceAngle)
# get the lats/lons
(lat, lon, line, pixel) = calibPar.xml2geo()
print "Filtering"
# 1. Remove speckle noise (using Lee-Wiener filter)
calibPar.speckle_filter('wiener', 7)
print 'Parallel: time elapsed: %f' % ( time() - currtime )
# export to geotiff
# calibPar.save_tiff()
print "Some Sigma calculations..."
SigmaHHwnr = calibPar.SigmaHHwnr
SigmaVVwnr = calibPar.SigmaVVwnr
# SigmaHVwnr = calibPar.SigmaHVwnr
# Free up some memory
del calibPar.SigmaHHwnr, calibPar.SigmaVVwnr, calibPar.SigmaHVwnr
# # Calculate some wind
# # testing that with sar=-0.387 wind speed is 10m/s
# w = rcs2wind(sar=-0.387*ones((1,1)), cmdv=4, windir=0, theta=20*ones((1,1)))
# print "Testing CMOD4 passed, Wind =", w
#
# sar=10*log10(SigmaVVwnr[::10,::10])
## sar=10*log10(SigmaVVwnr)
# w = rcs2wind(sar, cmdv=4, windir=130, theta=33*ones(sar.shape))
# print "Mean CMOD4 Wind =", w.mean()
# calculate the conjugate
SigmaVVConjHH = calibPar.S_VV*conj(calibPar.S_HH)
SigmaVVConjHHWnr = wiener(SigmaVVConjHH, mysize=(7,7), noise=None)
# all sigmas in linear units
SigmaVVConjHHdelta = SigmaVVConjHHWnr/(SigmaVVwnr - SigmaHHwnr)
# # Check that imagenary part is much less then the real one
# SigmaVVConjHHr = real(SigmaVVConjHH)
# SigmaVVConjHHi = imag(SigmaVVConjHH)
# SigmaVVConjHHr.mean()
# SigmaVVConjHHi.mean()
# Free up some memory
del SigmaVVConjHH, calibPar.S_HH, calibPar.S_VV
# 2. Find Poolarization ratio PR in dB and delta - linear
# delta - is a Bragg component, which describes wind field impact
# PR = 10*log10(SigmaHHwnr) - 10*log10(SigmaVVwnr)
# # PR must not be > 0, be careful with masking
#P[P>0] = 0
PR = SigmaHHwnr/SigmaVVwnr
delta = SigmaVVwnr - SigmaHHwnr
# # Masking Land from HV polarization
# maskedLand = SigmaHVwnr>0.003
# 3. Linear fit of Sigma and delta: Sigma = A*delta
# A = sum(SigmaVVwnr[~maskedLand]*delta[~maskedLand])/sum(delta[~maskedLand]^2);
# B = sum(SigmaHHwnr[~maskedLand]*delta[~maskedLand])/sum(delta[~maskedLand]^2);
# imposing from theory
A = linspace(2.3,1.8,IncidenceAngle.size) # dependence on inc angle
A = tile(A, (line[-1,0]+1, 1))
# 4. Calculate SigmaVVwb = SigmaVVwb === SigmaHHwb
# substracting wind field variability contribution, the rest is impact of
# current and film on the image
SigmaWb = SigmaVVwnr - ( A*delta )
# Checking that SigmaWb from VV and HH are the same
# SigmaVVwb = SigmaVVwnr - ( A*delta )
# SigmaHHwb = SigmaHHwnr - ( B*delta )
# SigmaWb = SigmaVVwb
# mean(SigmaHHwb(:)./SigmaVVwb(:))
# clear SigmaVVwb SigmaHHwb
# Cropping the oil slicks
# проверить почему переворот изображения!!!
# print ( "Cropping...")
# plt.figure()
# plt.imshow(delta)
# plt.clim(-0.01,0.03)
# plt.gray()
# plt.show()
# pts = imzoom()
ptsCropSouth = array([[ 3200, 4550],
[ 3800, 5100]])
oilCropSouth = imcrop(ptsCropSouth, delta)
ptsCropNorth = array([[ 3400, 0],
[ 4900, 1650]])
oilCropNorth = imcrop(ptsCropNorth, delta)
# reduce the speckle noise
oilN = wiener(oilCropNorth, mysize=(3,3), noise=None)
oilS = wiener(oilCropSouth, mysize=(3,3), noise=None)
# use median filter to smooth
oilN = median_filter(oilN, radius=37)
oilS = median_filter(oilS, radius=37)
# find the global threshold
global_threshN = threshold_otsu(oilN)
global_threshS = threshold_otsu(oilS)
# create a binary mask
markersN = ones(oilN.shape, dtype=uint)
markersS = ones(oilS.shape, dtype=uint)
markersN[oilN < global_threshN*0.84] = 0
markersS[oilS < global_threshS*0.84] = 0
# Find the relation of PR in/out-side the Slicks
oilCropNorthPR = imcrop(ptsCropNorth, PR)
oilCropPN = oilCropNorthPR[markersN==0].mean()/oilCropNorthPR[markersS==1].mean()
oilCropSouthP = imcrop(ptsCropSouth, PR)
oilCropPS = oilCropSouthP[markersS==0].mean()/oilCropSouthP[markersS==1].mean()
# Checking that calculations are OK
oilCropNorthKVV = imcrop(ptsCropNorth, SigmaVVwnr)
KVV = oilCropNorthKVV[markersN==0].mean()/oilCropNorthKVV[markersN==1].mean()
oilCropNorthKHH = imcrop(ptsCropNorth, SigmaHHwnr)
KHH = oilCropNorthKHH[markersN==0].mean()/oilCropNorthKHH[markersN==1].mean()
PSlick = KHH/KVV*oilCropNorthKHH[markersN==1].mean()/oilCropNorthKVV[markersN==1].mean()
print "Polarization ratio Out/In-side Slick = \
\n %0.2f for Northern \n %0.2f for Southern" \
%(1/oilCropPN, 1/oilCropPS)
oilCropNorthVV = imcrop(ptsCropNorth, SigmaVVwnr)
oilCropNorthDelta = imcrop(ptsCropNorth, delta)
oilCropNorthSigmaWb = imcrop(ptsCropNorth, SigmaWb)
# Interpolating lat/lon to image size for future pcolormesh
from numpy import linspace, arange, round, floor, ceil
from scipy.interpolate import interp2d
def interpLL(l, pixel, line, gx, gy):
"""
Interpolating lat/lon to image size for future pcolormesh
"""
fl = interp2d(pixel[0,:], line[:,0], l, kind='linear')
l_new = fl(gx, gy)
return l_new
ptsCrop = ptsCropNorth
RasterXSize = round((-ptsCrop[0,0]+ptsCrop[-1,0]))
RasterYSize = round((-ptsCrop[0,-1]+ptsCrop[-1,-1]))
gx = linspace(ptsCrop[0,0], ptsCrop[-1,0], RasterXSize+1)
gy = linspace(ptsCrop[0,-1], ptsCrop[-1,-1], RasterYSize+1)
RasterXSize = round(pixel[0,-1])
RasterYSize = round(line[-1,0])
gx = linspace(0, pixel[0,-1], RasterXSize+1)
gy = linspace(0, line[-1,0], RasterYSize+1)
lat_new = interpLL(lat, pixel, line, gx, gy)
lon_new = interpLL(lon, pixel, line, gx, gy)
# save to MAT file
from scipy.io import savemat
print "Exporting"
pn = '/home/mag/Documents/MATLAB/Leon_Agulhas_RS2/'
expFn = 'OilSlickTrns/OilSlickTrns.mat'
if not path.isfile(pn + expFn):
print "Exporting to:\n" , pn + expFn
savemat(pn + expFn, mdict={ \
'VV':oilCropNorthVV, \
'PD':oilCropNorthDelta, \
'SigmaWb':oilCropNorthSigmaWb, \
'PR':oilCropNorthPR, \
'lat':lat_new, 'lon':lon_new, \
}, do_compression=False)
expFn = 'CurrentFrontTrns/CurrentFrontTrns.mat'
if not path.isfile(pn + expFn):
print "Exporting to:\n" , pn + expFn
savemat(pn + expFn, mdict={ \
'PD':delta, \
'PR':PR, \
'VV':SigmaVVwnr, \
'SigmaWb':SigmaWb, \
'lat':lat_new, 'lon':lon_new, \
}, do_compression=False)
import h5py
pn = '/home/mag/Documents/MATLAB/Leon_Agulhas_RS2/'
expFn = 'CurrentFrontTrns/CurrentFrontTrns.mat'
expFn = 'OilSlickTrns/OilSlickTrns.mat'
f = h5py.File(pn + expFn)
SigmaWb = f["SigmaWb"]
SigmaWb = flipud(rot90(array(SigmaWb)))
lat = f["lat"]
lat = flipud(rot90(array(lat)))
lon = f["lon"]
lon = flipud(rot90(array(lon)))
y = f["tWb"]['transx']
y = array(y)
x = f["tWb"]['transy']
x = array(x)
pts = zeros((2,2))
pts[:,0] = x
pts[:,1] = y
# transec_init = f["tWb"]['transec_init']
# transec_init = array(transec_init)
import distancelib
reload(distancelib)
# Get pixel resolution in Col/Row direction
PxResCol, PxResRow = distancelib.getPixelResolution(lat, lon, SigmaWb)
# Get the length of the transect. Input coords in (row,col)
trnsLength = distancelib.getDistancePx([pts[0,1], pts[0,0]], [pts[1,1], pts[1,0]], lat, lon, SigmaWb)
import transect
reload(transect)
transect.plotTrns(SigmaWbtrans, pts, lat, lon, SigmaWb, pn)
# Making transect
plt.figure()
plt.imshow(oilCropNorthDelta)
plt.gray()
plt.clim(0, 0.02)
plt.colorbar()
pts = transect.imagePts()
plt.savefig('/home/mag/PDtrns.png', facecolor='w', edgecolor='w', \
dpi=100, bbox_inches="tight", pad_inches=1.75)
# Transect North Oil Slick
pts = array([[610, 871], [822, 1007]])
# [1] returns only Mean Transect
PDtrans = transect.transect(oilCropNorthDelta, pts, 30)[1]
PRtrans = transect.transect(oilCropNorthPR, pts, 30)[1]
VVtrans = transect.transect(oilCropNorthVV, pts, 30)[1]
SigmaWbtrans = transect.transect(oilCropNorthSigmaWb, pts, 30)[1]
# PDtrans = transect.transect(oilCropNorthDelta, pts, 100)[0]
plt.figure()
plt.imshow(CurrentWb)
plt.gray()
plt.clim(0, 0.03)
plt.colorbar()
pts = transect.imagePts()
plt.savefig('/home/mag/PDtrns.png', facecolor='w', edgecolor='w', \
dpi=100, bbox_inches="tight", pad_inches=1.75)
# Transect Current Front
pts = array([[1207., 4318.], [2473., 3763.]])
PDtrans = transect.transect(delta, pts, 30)[1]
PRtrans = transect.transect(PR, pts, 30)[1]
VVtrans = transect.transect(SigmaVVwnr, pts, 30)[1]
SigmaWbtrans = transect.transect(SigmaWb, pts, 30, method='cubic')[1]
# use median filter to smooth
from scipy.ndimage import median_filter as mflt
PRtransSmthd = mflt(PRtrans, size=21, mode='nearest')
PDtransSmthd = mflt(PDtrans, size=21, mode='nearest')
VVtransSmthd = mflt(VVtrans, size=21, mode='nearest')
SigmaWbtransSmthd = mflt(SigmaWbtrans, size=21, mode='nearest')
plt.figure()
# plt.imshow(SigmaWb)
plt.imshow(SigmaWb)
plt.gray()
plt.clim(0, 0.02)
plt.colorbar()
ax = plt.gca().get_xbound()
ay = plt.gca().get_ybound()
x = pts[:, 0]
y = pts[:, 1]
plt.plot(x, y, '-o')
plt.text(x[0],y[0],"A",bbox=dict(facecolor='w', alpha=0.7),stretch='expanded',fontsize=21)
plt.text(x[1],y[1],"B",bbox=dict(facecolor='w', alpha=0.7),stretch='expanded',fontsize=21)
plt.gca().set_xbound(ax)
plt.gca().set_ybound(ay)
plt.draw()
# window = gtk.Window()
# screen = window.get_screen()
# width = screen.get_width()
# height = screen.get_height()
# mng = plt.get_current_fig_manager()
# mng.resize(width, height)
plt.savefig('/home/mag/PDtrns.png', facecolor='w', edgecolor='w', \
dpi=100, bbox_inches="tight", pad_inches=1.75)
plt.figure()
plt.plot(PDtransSmthd)
plt.grid()
# window = gtk.Window()
# screen = window.get_screen()
# width = screen.get_width()
# height = screen.get_height()
# mng = plt.get_current_fig_manager()
# mng.resize(width, height)
plt.savefig('/home/mag/PDtrnsLine.png', facecolor='w', edgecolor='w', \
dpi=100, bbox_inches="tight", pad_inches=1.75)
plt.figure()
plt.plot(PRtransSmthd)
plt.grid()
# window = gtk.Window()
# screen = window.get_screen()
# width = screen.get_width()
# height = screen.get_height()
# mng = plt.get_current_fig_manager()
# mng.resize(width, height)
plt.savefig('/home/mag/PRtransLine.png', facecolor='w', edgecolor='w', \
dpi=100, bbox_inches="tight", pad_inches=1.75)
plt.figure()
plt.plot(VVtransSmthd)
plt.grid()
# window = gtk.Window()
# screen = window.get_screen()
# width = screen.get_width()
# height = screen.get_height()
# mng = plt.get_current_fig_manager()
# mng.resize(width, height)
plt.savefig('/home/mag/VVtransLine.png', facecolor='w', edgecolor='w', \
dpi=100, bbox_inches="tight", pad_inches=1.75)
plt.figure()
plt.plot(SigmaWbtransSmthd)
plt.grid()
# window = gtk.Window()
# screen = window.get_screen()
# width = screen.get_width()
# height = screen.get_height()
# mng = plt.get_current_fig_manager()
# mng.resize(width, height)
plt.savefig('/home/mag/SigmaWbtransLine.png', facecolor='w', edgecolor='w', \
dpi=100, bbox_inches="tight", pad_inches=1.75)
plt.close('all')
print ( "Plotting on a map... \nUsing default NSPER Basemap \n")
# setup nsper basemap
# Lat/Lon coords of image corners
ll_lat = lat.min()
ur_lat = lat.max()
ll_lon = lon.min()
ur_lon = lon.max()
cent_lat = lat.mean()
cent_lon = lon.mean()
m = Basemap(llcrnrlat=ll_lat, urcrnrlat=ur_lat,\
llcrnrlon=ll_lon, urcrnrlon=ur_lon, \
resolution='f', projection='nsper', \
satellite_height=798000, \
lat_0=cent_lat,lon_0=cent_lon)
# m = Basemap(llcrnrlat=30, urcrnrlat=50,\
# llcrnrlon=1, urcrnrlon=20, \
# resolution='l', projection='nsper', \
# satellite_height=798000, \
# lat_0=cent_lat,lon_0=cent_lon)
print ( "Plotting figures... \n")
scale = 8 # setting scale factor to resize in 1/scale times
label1 = 'Sigma VV [dB]'
label2 = 'Sigma HH [dB]'
label11 = 'Sigma VV [linear units]'
label22 = 'Sigma HH [linear units]'
label3 = 'PR [dB]'
label33 = 'PR [linear units]'
label4 = 'PD [linear units]'
label5 = 'Wb contribution [linear units]'
label6 = 'SigmaVVConjHH [linear units]'
label7 = 'SigmaVVConjHHdelta [linear units]'
# label8 = 'Wind CMOD4 [m/s]'
import plotRS2
reload(plotRS2)
from plotRS2 import plotRS2
# import mpl_util
# reload(mpl_util)
#
# import gmtColormap
# reload(gmtColormap)
#
# import createMapsEtopo1
# reload(createMapsEtopo1)
# from createMapsEtopo1 import makeMap
#
# import g1sst
# reload(g1sst)
# from g1sst import g1sst
# plotRS2(10*log10(SigmaVVwnr), lat, lon, pixel, line, \
# scale=scale, m=m, clm=(-25,-5), label=label1)
# makeMap(ll_lon, ur_lon, \
# ll_lat, ur_lat, m, name=label1, contour='land')
# plt.close('all')
#
# plotRS2(10*log10(SigmaHHwnr), lat, lon, pixel, line, \
# scale=scale, m=m, clm=(-25,-5), label=label2)
# makeMap(ll_lon, ur_lon, \
# ll_lat, ur_lat, m, name=label2, contour='land')
# plt.close('all')
# plotRS2(PR, lat, lon, pixel, line, \
# scale=scale, m=m, clm=(-2.7,0), label=label3)
# makeMap(ll_lon, ur_lon, \
# ll_lat, ur_lat, m, name=label3, contour='land')
# plt.close('all')
# Coords to plot Figure number
x, y = m(3.15, 42.15)
# Figure 1 a - VV
plotRS2(SigmaVVwnr, lat, lon, pixel, line, \
scale=scale, m=m, clm=(0,0.05), label=label11)
plt.text(x,y,"a",bbox=dict(facecolor='w', alpha=0.5),stretch='expanded',fontsize=27)
makeMap(ll_lon, ur_lon, \
ll_lat, ur_lat, m, name=label11, contour='land')
plt.close('all')
a = label11.split(' ')[0] # split the name if it has spaces
system("rm " + "/home/mag/" + a + ".tiff")
system("mv " + "/home/mag/" + a + "_ETOPO1.tiff" + " " + "/home/mag/Figure1a.tiff")
system("convert -compress lzw /home/mag/Figure1a.tiff /home/mag/Figure1a.tiff")
# Figure 1 b - HH
plotRS2(SigmaHHwnr, lat, lon, pixel, line, \
scale=scale, m=m, clm=(0,0.05), label=label22)
plt.text(x,y,"b",bbox=dict(facecolor='w', alpha=0.5),stretch='expanded',fontsize=27)
makeMap(ll_lon, ur_lon, \
ll_lat, ur_lat, m, name=label22, contour='land')
plt.close('all')
a = label22.split(' ')[0] # split the name if it has spaces
system("rm " + "/home/mag/" + a + ".tiff")
system("mv " + "/home/mag/" + a + "_ETOPO1.tiff" + " " + "/home/mag/Figure1b.tiff")
system("convert -compress lzw /home/mag/Figure1b.tiff /home/mag/Figure1b.tiff")
# Figure 1 c - polarization ratio (PR) (always<1)
plotRS2(PR, lat, lon, pixel, line, \
scale=scale, m=m, clm=(0.4,1), label=label33)
plt.text(x,y,"c",bbox=dict(facecolor='w', alpha=0.5),stretch='expanded',fontsize=27)
makeMap(ll_lon, ur_lon, \
ll_lat, ur_lat, m, name=label3, contour='land')
plt.close('all')
a = label33.split(' ')[0] # split the name if it has spaces
system("rm " + "/home/mag/" + a + ".tiff")
system("mv " + "/home/mag/" + a + "_ETOPO1.tiff" + " " + "/home/mag/Figure1c.tiff")
system("convert -compress lzw /home/mag/Figure1c.tiff /home/mag/Figure1c.tiff")
# Figure 1 d - polarization difference (PD) (always>0)
plotRS2(delta, lat, lon, pixel, line, \
scale=scale, m=m, clm=(0,0.02), label=label4)
plt.text(x,y,"d",bbox=dict(facecolor='w', alpha=0.5),stretch='expanded',fontsize=27)
makeMap(ll_lon, ur_lon, \
ll_lat, ur_lat, m, name=label4, contour='land')
plt.close('all')
a = label4.split(' ')[0] # split the name if it has spaces
system("rm " + "/home/mag/" + a + ".tiff")
system("mv " + "/home/mag/" + a + "_ETOPO1.tiff" + " " + "/home/mag/Figure1d.tiff")
system("convert -compress lzw /home/mag/Figure1d.tiff /home/mag/Figure1d.tiff")
# Figure 4 - wave breaking contribution
plotRS2(SigmaWb, lat, lon, pixel, line, \
scale=scale, m=m, clm=(0,0.03), label=label5)
plt.text(x,y,"d",bbox=dict(facecolor='w', alpha=0.5),stretch='expanded',fontsize=27)
makeMap(ll_lon, ur_lon, \
ll_lat, ur_lat, m, name=label5, contour='land')
plt.close('all')
a = label5.split(' ')[0] # split the name if it has spaces
system("rm " + "/home/mag/" + a + ".tiff")
system("mv " + "/home/mag/" + a + "_ETOPO1.tiff" + " " + "/home/mag/Figure4.tiff")
system("convert -compress lzw /home/mag/Figure4.tiff /home/mag/Figure4.tiff")
import transect
reload(transect)
transect.plotTrns(SigmaWbtrans, pts, lat, lon, SigmaWb, pn)
# Figure 3
# Plot cropped Oil Spills
# Northern
# setup nsper basemap
# Lat/Lon coords of image corners
lat_new, lon_new = intCrpLL(lat, lon, pixel, line, scale=1, ptsCrop=ptsCropNorth)
ll_lat = lat_new.min()
ur_lat = lat_new.max()
ll_lon = lon_new.min()
ur_lon = lon_new.max()
cent_lat = lat_new.mean()
cent_lon = lon_new.mean()
m = Basemap(llcrnrlat=ll_lat, urcrnrlat=ur_lat,\
llcrnrlon=ll_lon, urcrnrlon=ur_lon, \
resolution='c', projection='nsper', \
satellite_height=798000, \
lat_0=cent_lat,lon_0=cent_lon)
# Coords to plot Figure number
xSl, ySl = m(3.473, 42.173)
# Figure 3 a - VV
plotRS2(SigmaVVwnr, lat, lon, pixel, line, \
scale=1, m=m, clm=(0,0.05), label=label11, ptsCrop=ptsCropNorth)
plt.text(xSl,ySl,"a",bbox=dict(facecolor='w', alpha=0.5),stretch='expanded',fontsize=27)
plt.savefig('/home/mag/Figure3a.tiff', facecolor='w', edgecolor='w', \
dpi=300, bbox_inches="tight", pad_inches=1.75)
plt.close('all')
# Figure 3 b - PR
plotRS2(PR, lat, lon, pixel, line, \
scale=1, m=m, clm=(0.5,1), label=label33, ptsCrop=ptsCropNorth)
plt.text(xSl,ySl,"b",bbox=dict(facecolor='w', alpha=0.5),stretch='expanded',fontsize=27)
plt.savefig('/home/mag/Figure3b.tiff', facecolor='w', edgecolor='w', \
dpi=300, bbox_inches="tight", pad_inches=1.75)
plt.close('all')
# # Figure 3 c - PD (always>0)
# plotRS2(delta, lat, lon, pixel, line, \
# scale=1, m=m, clm=(0,0.03), label=label4, ptsCrop=ptsCropNorth)
# plt.text(xSl,ySl,"c",bbox=dict(facecolor='w', alpha=0.5),stretch='expanded',fontsize=27)
# plt.savefig('/home/mag/Figure3c.tiff', facecolor='w', edgecolor='w', \
# dpi=300, bbox_inches="tight", pad_inches=1.75)
# plt.close('all')
system("convert -compress lzw /home/mag/Figure3a.tiff /home/mag/Figure3a.tiff")
system("convert -compress lzw /home/mag/Figure3b.tiff /home/mag/Figure3b.tiff")
# system("convert -compress lzw /home/mag/Figure3c.tiff /home/mag/Figure3c.tiff")
# # Southern
# # setup nsper basemap
# # Lat/Lon coords of image corners
# lat_new, lon_new = intCrpLL(lat, lon, pixel, line, scale=1, ptsCrop=ptsCropSouth)
# ll_lat = lat_new.min()
# ur_lat = lat_new.max()
# ll_lon = lon_new.min()
# ur_lon = lon_new.max()
# cent_lat = lat_new.mean()
# cent_lon = lon_new.mean()
# m = Basemap(llcrnrlat=ll_lat, urcrnrlat=ur_lat,\
# llcrnrlon=ll_lon, urcrnrlon=ur_lon, \
# resolution='c', projection='nsper', \
# satellite_height=798000, \
# lat_0=cent_lat,lon_0=cent_lon)
# # Coords to plot Figure number
# xSl, ySl = m(3.491, 41.973)
# # Figure 3 d - VV
# plotRS2(SigmaVVwnr, lat, lon, pixel, line, \
# scale=1, m=m, clm=(0,0.05), label=label11, ptsCrop=ptsCropSouth)
# plt.text(xSl,ySl,"d",bbox=dict(facecolor='w', alpha=0.5),stretch='expanded',fontsize=27)
# plt.savefig('/home/mag/Figure3d.tiff', facecolor='w', edgecolor='w', \
# dpi=300, bbox_inches="tight", pad_inches=1.75)
# plt.close('all')
# # Figure 3 e - PR (always<1)
# plotRS2(PR, lat, lon, pixel, line, \
# scale=1, m=m, clm=(0.4,1), label=label33, ptsCrop=ptsCropSouth)
# plt.text(xSl,ySl,"e",bbox=dict(facecolor='w', alpha=0.5),stretch='expanded',fontsize=27)
# plt.savefig('/home/mag/Figure3e.tiff', facecolor='w', edgecolor='w', \
# dpi=300, bbox_inches="tight", pad_inches=1.75)
# plt.close('all')
# # Figure 3 f - PD (always>0)
# plotRS2(delta, lat, lon, pixel, line, \
# scale=1, m=m, clm=(0,0.03), label=label4, ptsCrop=ptsCropSouth)
# plt.text(xSl,ySl,"f",bbox=dict(facecolor='w', alpha=0.5),stretch='expanded',fontsize=27)
# plt.savefig('/home/mag/Figure3f.tiff', facecolor='w', edgecolor='w', \
# dpi=300, bbox_inches="tight", pad_inches=1.75)
# plt.close('all')
# system("convert -compress lzw /home/mag/Figure3d.tiff /home/mag/Figure3d.tiff")
# system("convert -compress lzw /home/mag/Figure3e.tiff /home/mag/Figure3e.tiff")
# system("convert -compress lzw /home/mag/Figure3f.tiff /home/mag/Figure3f.tiff")
import transect
reload(transect)
transect.plotTrns(PRtrans, pts, lat, lon, PR, pixel, line, pn='/home/mag/', \
label='PR [linear units]', clm=(0.5,1), \
ptsCrop=ptsCropNorth, m=m, scale=1)
def test_default_values():
img = (np.random.random((20, 20)) * 255).astype(np.uint8)
mask = np.ones((20, 20), dtype=np.uint8)
result1 = median_filter(img, radius=2, mask=mask, percent=50)
result2 = median_filter(img)
np.testing.assert_array_equal(result1, result2)
def test_00_00_zeros():
'''The median filter on an array of all zeros should be zero'''
result = median_filter(np.zeros((10, 10)), 3, np.ones((10, 10), bool))
assert np.all(result == 0)
def test_wrong_shape():
img = np.empty((10, 10, 3))
median_filter(img)
"""
This example compares several denoising filters available in scikit-image:
a Gaussian filter, a median filter, and total variation denoising.
"""
import matplotlib.pyplot as plt
from skimage import data
from skimage import filter
from scipy import ndimage
coins = data.coins()
gaussian_filter_coins = ndimage.gaussian_filter(coins, sigma=2)
med_filter_coins = filter.median_filter(coins)
tv_filter_coins = filter.tv_denoise(coins, weight=0.1)
plt.figure(figsize=(16, 4))
plt.subplot(141)
plt.imshow(coins[10:80, 300:370], cmap='gray', interpolation='nearest')
plt.axis('off')
plt.title('Image')
plt.subplot(142)
plt.imshow(gaussian_filter_coins[10:80, 300:370],
cmap='gray',
interpolation='nearest')
plt.axis('off')
plt.title('Gaussian filter')
plt.subplot(143)
plt.imshow(med_filter_coins[10:80, 300:370],
cmap='gray',
interpolation='nearest')
plt.axis('off')
def test_00_00_zeros():
'''The median filter on an array of all zeros should be zero'''
result = median_filter(np.zeros((10, 10)), 3, np.ones((10, 10), bool))
assert np.all(result == 0)
def ctmf_med(image, radius):
return median_filter(image=image, radius=radius)
def main( argv=None ):
# default values
#~ pn = '/media/data/data/OTHER/RS2 Agulhas and Lion/RS2_FQA_1xQGSS20101218_173930_00000005/'
pn = '/media/data/data/OTHER/RS2 Agulhas and Lion/RS2_SQA_1xQGSS20091224_164846_00000004/'
#~ pn = '/media/data/data/OTHER/WhiteSea2012/RS-2/RS2_FQA_1xQGSS20120729_144551_00000005xQ12_16bxx_24139_FC556/'
xOff=0
yOff=0
xS=None
yS=None
xBufScale=None
yBufScale=None
s = 'abs'
if argv is None:
argv = sys.argv
if argv is None:
print ( "Please specify the path to the RS2 folder! \n" + \
"See USAGE for more details \n")
return Usage()
# Parse arguments
try:
opts, args = getopt.getopt(argv,"hi:o:",["pn=","xoff="])
except getopt.GetoptError:
print 'readRS2.py -pn <inputfile> ...'
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print 'readRS2.py -pn <inputfile> ...'
sys.exit()
elif opt in ("-pn", "--pn"):
pn = arg
elif opt in ("-xoff", "--xoff"):
xOff = arg
elif opt in ("-yoff", "--yoff"):
yOff = arg
elif opt in ("-xS", "--xS"):
xS = arg
elif opt in ("-yS", "--yS"):
yS = arg
elif opt in ("-xScale", "--xScale"):
xBufScale = arg
elif opt in ("-yScale", "--yScale"):
yBufScale = arg
print "Path to the RS2 file:\n", pn
if core_ct < 4:
# calibrate the scene
print "Calibrating"
currtime = time()
calib = calibRS2(pn, xOff, yOff, xS, yS, xBufScale, yBufScale, 'sigma0')
# get the incidence angle
print "Incidence Angle and Lats/Lons"
IncidenceAngle = calib.incidence_angle()
IncidenceAngle = rad2deg(IncidenceAngle)
# get the lats/lons
(lat, lon, line, pixel) = calib.xml2geo()
print "Filtering"
# 1. Remove speckle noise (using Lee-Wiener filter)
calib.speckle_filter('wiener', 7)
print 'Serial: time elapsed:', time() - currtime
elif core_ct >= 4:
# calibrate the scene
print "Calibrating"
currtime = time()
calibPar = calibRS2par(pn, xOff, yOff, xS, yS, xBufScale, yBufScale, s)
# get the incidence angle
print "Incidence Angle and Lats/Lons"
IncidenceAngle = calibPar.incidence_angle()
IncidenceAngle = rad2deg(IncidenceAngle)
# get the lats/lons
(lat, lon, line, pixel) = calibPar.xml2geo()
print "Filtering"
# 1. Remove speckle noise (using Lee-Wiener filter)
calibPar.speckle_filter('wiener', 7)
print 'Parallel: time elapsed: %f' % ( time() - currtime )
# export to geotiff
# calibPar.save_tiff()
print "Some Sigma calculations..."
SigmaHHwnr = calibPar.SigmaHHwnr
SigmaVVwnr = calibPar.SigmaVVwnr
# SigmaHVwnr = calibPar.SigmaHVwnr
# Free up some memory
del calibPar.SigmaHHwnr, calibPar.SigmaVVwnr, calibPar.SigmaHVwnr
# # Calculate some wind
# # testing that with sar=-0.387 wind speed is 10m/s
# w = rcs2wind(sar=-0.387*ones((1,1)), cmdv=4, windir=0, theta=20*ones((1,1)))
# print "Testing CMOD4 passed, Wind =", w
#
# sar=10*log10(SigmaVVwnr[::10,::10])
## sar=10*log10(SigmaVVwnr)
# w = rcs2wind(sar, cmdv=4, windir=130, theta=33*ones(sar.shape))
# print "Mean CMOD4 Wind =", w.mean()
# calculate the conjugate
SigmaVVConjHH = calibPar.S_VV*conj(calibPar.S_HH)
SigmaVVConjHHWnr = wiener(SigmaVVConjHH, mysize=(7,7), noise=None)
# all sigmas in linear units
SigmaVVConjHHdelta = SigmaVVConjHHWnr/(SigmaVVwnr - SigmaHHwnr)
# # Check that imagenary part is much less then the real one
# SigmaVVConjHHr = real(SigmaVVConjHH)
# SigmaVVConjHHi = imag(SigmaVVConjHH)
# SigmaVVConjHHr.mean()
# SigmaVVConjHHi.mean()
# Free up some memory
del SigmaVVConjHH, calibPar.S_HH, calibPar.S_VV
# 2. Find Poolarization ratio PR in dB and delta - linear
# delta - is a Bragg component, which describes wind field impact
# PR = 10*log10(SigmaHHwnr) - 10*log10(SigmaVVwnr)
# # PR must not be > 0, be careful with masking
#P[P>0] = 0
PR = SigmaHHwnr/SigmaVVwnr
delta = SigmaVVwnr - SigmaHHwnr
# # Masking Land from HV polarization
# maskedLand = SigmaHVwnr>0.003
# 3. Linear fit of Sigma and delta: Sigma = A*delta
# A = sum(SigmaVVwnr[~maskedLand]*delta[~maskedLand])/sum(delta[~maskedLand]^2);
# B = sum(SigmaHHwnr[~maskedLand]*delta[~maskedLand])/sum(delta[~maskedLand]^2);
# imposing from theory
A = linspace(2.3,1.8,IncidenceAngle.size) # dependence on inc angle
A = tile(A, (line[-1,0]+1, 1))
# 4. Calculate SigmaVVwb = SigmaVVwb === SigmaHHwb
# substracting wind field variability contribution, the rest is impact of
# current and film on the image
SigmaWb = SigmaVVwnr - ( A*delta )
# Checking that SigmaWb from VV and HH are the same
# SigmaVVwb = SigmaVVwnr - ( A*delta )
# SigmaHHwb = SigmaHHwnr - ( B*delta )
# SigmaWb = SigmaVVwb
# mean(SigmaHHwb(:)./SigmaVVwb(:))
# clear SigmaVVwb SigmaHHwb
# Cropping the oil slicks
## проверить почему переворот изображения!!!
# from imcrop import imzoom
# print ( "Cropping...")
# plt.figure()
# plt.imshow(SigmaWb)
# plt.clim(0,0.03)
# plt.gray()
# plt.show()
# pts = imzoom()
ptsCropSouth = array([[ 3200, 4550],
[ 3800, 5100]])
oilCropSouth = imcrop(ptsCropSouth, delta)
ptsCropNorth = array([[ 3400, 0],
[ 4900, 1650]])
oilCropNorth = imcrop(ptsCropNorth, delta)
# reduce the speckle noise
oilN = wiener(oilCropNorth, mysize=(3,3), noise=None)
oilS = wiener(oilCropSouth, mysize=(3,3), noise=None)
# use median filter to smooth
oilN = median_filter(oilN, radius=37)
oilS = median_filter(oilS, radius=37)
# find the global threshold
global_threshN = threshold_otsu(oilN)
global_threshS = threshold_otsu(oilS)
# create a binary mask
markersN = ones(oilN.shape, dtype=uint)
markersS = ones(oilS.shape, dtype=uint)
markersN[oilN < global_threshN*0.84] = 0
markersS[oilS < global_threshS*0.84] = 0
# Find the relation of PR in/out-side the Slicks
oilCropNorthPR = imcrop(ptsCropNorth, PR)
oilCropPN = oilCropNorthPR[markersN==0].mean()/oilCropNorthPR[markersS==1].mean()
oilCropSouthP = imcrop(ptsCropSouth, PR)
oilCropPS = oilCropSouthP[markersS==0].mean()/oilCropSouthP[markersS==1].mean()
# Checking that calculations are OK
oilCropNorthKVV = imcrop(ptsCropNorth, SigmaVVwnr)
KVV = oilCropNorthKVV[markersN==0].mean()/oilCropNorthKVV[markersN==1].mean()
oilCropNorthKHH = imcrop(ptsCropNorth, SigmaHHwnr)
KHH = oilCropNorthKHH[markersN==0].mean()/oilCropNorthKHH[markersN==1].mean()
PSlick = KHH/KVV*oilCropNorthKHH[markersN==1].mean()/oilCropNorthKVV[markersN==1].mean()
print "Polarization ratio Out/In-side Slick = \
\n %0.2f for Northern \n %0.2f for Southern" \
%(1/oilCropPN, 1/oilCropPS)
oilCropNorthVV = imcrop(ptsCropNorth, SigmaVVwnr)
oilCropNorthDelta = imcrop(ptsCropNorth, delta)
oilCropNorthSigmaWb = imcrop(ptsCropNorth, SigmaWb)
print ( "Plotting on a map... \nUsing default NSPER Basemap \n")
# setup nsper basemap
# Lat/Lon coords of image corners
ll_lat = lat.min()
ur_lat = lat.max()
ll_lon = lon.min()
ur_lon = lon.max()
cent_lat = lat.mean()
cent_lon = lon.mean()
m = Basemap(llcrnrlat=ll_lat, urcrnrlat=ur_lat,\
llcrnrlon=ll_lon, urcrnrlon=ur_lon, \
resolution='f', projection='nsper', \
satellite_height=798000, \
lat_0=cent_lat,lon_0=cent_lon)
# m = Basemap(llcrnrlat=30, urcrnrlat=50,\
# llcrnrlon=1, urcrnrlon=20, \
# resolution='l', projection='nsper', \
# satellite_height=798000, \
# lat_0=cent_lat,lon_0=cent_lon)
print ( "Plotting figures... \n")
scale = 8 # setting scale factor to resize in 1/scale times
label1 = 'Sigma VV [dB]'
label2 = 'Sigma HH [dB]'
label11 = 'Sigma VV [linear units]'
label22 = 'Sigma HH [linear units]'
label3 = 'PR [dB]'
label33 = 'PR [linear units]'
label4 = 'PD [linear units]'
label5 = 'Wb contribution [linear units]'
label6 = 'SigmaVVConjHH [linear units]'
label7 = 'SigmaVVConjHHdelta [linear units]'
# label8 = 'Wind CMOD4 [m/s]'
# import plotRS2
# reload(plotRS2)
# from plotRS2 import plotRS2
# import mpl_util
# reload(mpl_util)
#
# import gmtColormap
# reload(gmtColormap)
#
# import createMapsEtopo1
# reload(createMapsEtopo1)
# from createMapsEtopo1 import makeMap
#
# import g1sst
# reload(g1sst)
# from g1sst import g1sst
# plotRS2(10*log10(SigmaVVwnr), lat, lon, pixel, line, \
# scale=scale, m=m, clm=(-25,-5), label=label1)
# makeMap(ll_lon, ur_lon, \
# ll_lat, ur_lat, m, name=label1, contour='land')
# plt.close('all')
#
# plotRS2(10*log10(SigmaHHwnr), lat, lon, pixel, line, \
# scale=scale, m=m, clm=(-25,-5), label=label2)
# makeMap(ll_lon, ur_lon, \
# ll_lat, ur_lat, m, name=label2, contour='land')
# plt.close('all')
# plotRS2(PR, lat, lon, pixel, line, \
# scale=scale, m=m, clm=(-2.7,0), label=label3)
# makeMap(ll_lon, ur_lon, \
# ll_lat, ur_lat, m, name=label3, contour='land')
# plt.close('all')
# Coords to plot Figure number
#~ x, y = m(3.15, 42.15) # for Lion
x, y = m(27.97, -32.81) # for Agulhas
#~ x, y = m(27.97, -32.81) # for WhiteSea Gorlo
# Figure 1 a - VV
plotRS2(SigmaVVwnr, lat, lon, pixel, line, \
scale=scale, m=m, clm=(0,0.1), label=label11)
plt.text(x,y,"a",bbox=dict(facecolor='w', alpha=1),stretch='expanded',fontsize=27)
makeMap(ll_lon, ur_lon, \
ll_lat, ur_lat, m, name=label11, contour='land')
plt.close('all')
a = label11.split(' ')[0] # split the name if it has spaces
system("rm " + "/home/mag/" + a + ".tiff")
system("mv " + "/home/mag/" + a + "_ETOPO1.tiff" + " " + "/home/mag/Figure1a_.tiff")
system("convert -compress lzw /home/mag/Figure1a_.tiff /home/mag/Figure1a_.tiff")
# Figure 1 b - HH
plotRS2(SigmaHHwnr, lat, lon, pixel, line, \
scale=scale, m=m, clm=(0,0.1), label=label22)
plt.text(x,y,"b",bbox=dict(facecolor='w', alpha=1),stretch='expanded',fontsize=27)
makeMap(ll_lon, ur_lon, \
ll_lat, ur_lat, m, name=label22, contour='land')
plt.close('all')
a = label22.split(' ')[0] # split the name if it has spaces
system("rm " + "/home/mag/" + a + ".tiff")
system("mv " + "/home/mag/" + a + "_ETOPO1.tiff" + " " + "/home/mag/Figure1b_.tiff")
system("convert -compress lzw /home/mag/Figure1b_.tiff /home/mag/Figure1b_.tiff")
# Figure 1 c - polarization ratio (PR) (always<1)
plotRS2(PR, lat, lon, pixel, line, \
scale=scale, m=m, clm=(0.65,0.95), label=label33)
plt.text(x,y,"c",bbox=dict(facecolor='w', alpha=1),stretch='expanded',fontsize=27)
makeMap(ll_lon, ur_lon, \
ll_lat, ur_lat, m, name=label3, contour='land')
plt.close('all')
a = label33.split(' ')[0] # split the name if it has spaces
system("rm " + "/home/mag/" + a + ".tiff")
system("mv " + "/home/mag/" + a + "_ETOPO1.tiff" + " " + "/home/mag/Figure1c_.tiff")
system("convert -compress lzw /home/mag/Figure1c_.tiff /home/mag/Figure1c_.tiff")
# Figure 1 d - polarization difference (PD) (always>0)
plotRS2(delta, lat, lon, pixel, line, \
scale=scale, m=m, clm=(0,0.025), label=label4)
plt.text(x,y,"d",bbox=dict(facecolor='w', alpha=1),stretch='expanded',fontsize=27)
makeMap(ll_lon, ur_lon, \
ll_lat, ur_lat, m, name=label4, contour='land')
plt.close('all')
a = label4.split(' ')[0] # split the name if it has spaces
system("rm " + "/home/mag/" + a + ".tiff")
system("mv " + "/home/mag/" + a + "_ETOPO1.tiff" + " " + "/home/mag/Figure1d_.tiff")
system("convert -compress lzw /home/mag/Figure1d_.tiff /home/mag/Figure1d_.tiff")
# Figure 4 - wave breaking contribution
plotRS2(SigmaWb, lat, lon, pixel, line, \
scale=scale, m=m, clm=(0,0.08), label=label5)
# plt.text(x,y,"d",bbox=dict(facecolor='w', alpha=1),stretch='expanded',fontsize=27)
makeMap(ll_lon, ur_lon, \
ll_lat, ur_lat, m, name=label5, contour='land')
plt.close('all')
a = label5.split(' ')[0] # split the name if it has spaces
system("rm " + "/home/mag/" + a + ".tiff")
system("mv " + "/home/mag/" + a + "_ETOPO1.tiff" + " " + "/home/mag/Figure4.tiff")
system("convert -compress lzw /home/mag/Figure4.tiff /home/mag/Figure4.tiff")
