def chlor_a(im_fname, out_dir, alg_name):
# Load image
im_dset = gdal.Open(im_fname, gdalconst.GA_ReadOnly)
im_wl = pml_utils_io.get_ATCOR_image_wavelength(im_dset)
# Process data
if alg_name=='git':
# Gitelson Chl-a
# Select bands
git_bands = [675.,695.,730.]
sel_band_ind,sel_band_val = pml_utils_io.select_bands(im_wl,git_bands)
# Load bands
im_data = pml_utils_io.get_image_raster(im_dset,sel_band_ind)
# Interpolate Gitelson bands
ip_fun = scipy.interpolate.interp1d(sel_band_val,im_data,axis=2,kind='linear',bounds_error=False,fill_value=-9999.)
interp_im_data = ip_fun(git_bands)
# Gitelson calculation for Rrs
chla = pml_utils_wq.gitelson(interp_im_data[:,:,0], interp_im_data[:,:,1], interp_im_data[:,:,2])
elif alg_name=='oc4':
# OC4 Chl-a
# Select bands
oc4_bands = [443.,489.,510.,555.]
sel_band_ind,sel_band_val = pml_utils_io.select_bands(im_wl,oc4_bands)
# Load bands
im_data = pml_utils_io.get_image_raster(im_dset,sel_band_ind)
# Interpolate OC4 bands
ip_fun = scipy.interpolate.interp1d(sel_band_val,im_data,axis=2,kind='linear',bounds_error=False,fill_value=-9999.)
interp_im_data = ip_fun(oc4_bands)
# OC4 calculation
chla = pml_utils_wq.oc4v6(interp_im_data[:,:,0], interp_im_data[:,:,1], interp_im_data[:,:,2], interp_im_data[:,:,3])
else:
print "ERROR: unknown name for chl-a algorithm ($s)"%alg_name
raise
# Apply water mask
water_mask = pml_utils_wq.water_mask(im_dset,im_wl)
chla[water_mask] = 0.
# ------------------------------------------------------------------------------------------
# Write down results
out_fname = os.path.join(out_dir,'chl_'+alg_name+'_'+os.path.basename(im_fname))
pml_utils_io.write_envi_image(out_fname,chla,im_dset)
def water_mask(im_dset,im_wl): water_threshold = 800. #water_threshold = 0.08 # Select bands bands = [875.] #bands = [772.] sel_band_ind,sel_band_val = pml_utils_io.select_bands(im_wl,bands) #pdb.set_trace() # Load bands im_data = pml_utils_io.get_image_raster(im_dset,[sel_band_ind[0]]) water_mask = np.logical_or( im_data[:,:,0]>water_threshold , im_data[:,:,0]<=0 ) return water_mask
def nechad_tsm(im_dset,im_wl): # Select bands tsm_bands = [710.] sel_band_ind,sel_band_val = pml_utils_io.select_bands(im_wl,tsm_bands) # Load bands im_data = pml_utils_io.get_image_raster(im_dset,sel_band_ind) # Interpolate bands ip_fun = scipy.interpolate.interp1d(sel_band_val,im_data,axis=2,kind='linear',bounds_error=False,fill_value=-9999.) interp_im_data = ip_fun(tsm_bands) # Calculation Rw710 = interp_im_data[:,:,0]*0.0001 #Rw710 = interp_im_data[:,:,0]*1. Ap = 561.94 Bp = 1.23 Cp = 0.1892 S = Ap*Rw710*(1-Rw710/Cp)+Bp return S
def iop(im_dset,im_wl,date_time,lat,lon,water_mask):
model_dir = os.path.dirname( os.path.realpath(__file__) ) + '/iop_model/'
# ------------------------------------------
# set model dir as your current dir
current_dir = os.getcwd()
os.chdir(model_dir)
# Select bands
bands = [412.,443.,490.,510.,555.,670.]
sel_band_ind,sel_band_val = pml_utils_io.select_bands(im_wl,bands)
# Load bands
im_data = pml_utils_io.get_image_raster(im_dset,sel_band_ind)
# Interpolate IOP bands
ip_fun = scipy.interpolate.interp1d(sel_band_val,im_data,axis=2,kind='linear',bounds_error=False,fill_value=-9999.)
interp_im_data = ip_fun(bands)
# Rw calculation
Rw = interp_im_data*0.0001
# Calculate sun angle
# sun_theta = `sun_angle $lat $lon $year $jday[1] $gmt`
jday = julian(date_time.day,date_time.month,date_time.year)
cmd_str = model_dir+'sun_angle ' + \
str(lat) + ' ' + str(lon) + ' ' + str(date_time.year) + ' ' + str(jday) + ' ' + str(date_time.hour + date_time.minute/60.)
sun_theta_str = commands.getoutput(cmd_str)
res = []
no_data_res = ['0.0']*24
for i in xrange(Rw.shape[0]):
for j in xrange(Rw.shape[1]):
if water_mask[i,j]:
# In case of land or no data
res.append(no_data_res)
else:
# Calculate iop output without noise
cmd_str = model_dir+'pml_iop_model_test' + \
' -config '+model_dir+'config/pml_acc.cfg' + \
' -sun_theta ' + sun_theta_str + \
' -sen_theta ' + '0.83053' + \
' -dphi '+ '1.57' + \
' -rhow'
for r in xrange(Rw.shape[2]): cmd_str = cmd_str+' '+str(Rw[i,j,r])
# process and store IOP model output
r = commands.getoutput(cmd_str)
rp = string.strip(r,' \t\n')
res.append(re.split('\s+',rp))
print "%i/%i"%(i,Rw.shape[0])
ares = np.array(res,dtype=float)
a = ares[:,:6]
ady = ares[:,6:12]
ap = ares[:,12:18]
bbp = ares[:,18:24]
a_ = a.reshape((Rw.shape[0],Rw.shape[1],6))
ady_ = ady.reshape((Rw.shape[0],Rw.shape[1],6))
ap_ = ap.reshape((Rw.shape[0],Rw.shape[1],6))
bbp_ = bbp.reshape((Rw.shape[0],Rw.shape[1],6))
# return to the original working directory
os.chdir(current_dir)
return (a_,ady_,ap_,bbp_)