def mosaic(demlist, outname, byteorder=1, gammapar=True):
nodata = str(raster.Raster(demlist[0]).nodata)
run(
dissolve([
"gdalwarp", "-q", "-of", "ENVI", "-srcnodata", nodata,
"-dstnodata", nodata, demlist, outname
]))
if byteorder == 1:
swap(outname, outname + "_swap")
for item in [outname, outname + ".hdr", outname + ".aux.xml"]:
os.remove(item)
os.rename(outname + "_swap", outname)
os.rename(outname + "_swap.hdr", outname + ".hdr")
if gammapar:
dempar(outname)
# Kennaugh matrix derivation from scattering matrix elements
# module of software gammaGUI
# John Truckenbrodt 2015
##############################################################
import os
from ancillary import grouping, dissolve
path_log = os.path.join(os.getcwd(), "LOG/LAT/")
if not os.path.exists(path_log):
os.makedirs(path_log)
# create list of scene tuple objects
tuples = grouping()
print "#############################################"
print "creating kennaugh decomposition..."
for scene in tuples:
print scene.main
items = [
getattr(scene, x) if hasattr(scene, x) else "-"
for x in ["t11", "t22", "t33", "t12", "t13", "t23"]
]
cmd = dissolve(
["KENNAUGH_MATRIX", items,
os.path.join(scene.main, scene.basename)])
print cmd
print "...done"
print "#############################################"
path_log = os.path.join(os.getcwd(), "LOG/LAT/")
if not os.path.exists(path_log):
os.makedirs(path_log)
# create list of scene tuple objects
tuples = grouping()
counter = 0
for tag in [
"HH", "VV", "HV", "VH", "pauli_alpha", "pauli_beta", "pauli_gamma",
"ctd_1_mag", "ctd_2_mag", "ctd_3_mag", "fdd_pd", "fdd_ps", "fdd_pv"
]:
# collect processing candidates
processlist = dissolve(
[[getattr(scene, x) for x in scene.index if re.search(tag, x)]
for scene in tuples])
if tnorm:
processlist = [
x for x in processlist
if re.search("_norm_geo$", x) and not os.path.isfile(x + "_tfilt")
]
else:
processlist = [
x for x in processlist if re.search("_geo$", x) and "norm" not in x
and not os.path.isfile(x + "_tfilt")
]
if len(processlist) > 1:
counter += 1
nodata = ["-dstnodata", nodata]
format = ["-of", "ENVI"]
resampling = ["-r", resampling]
# find all files matching the defined pattern(s)
items = finder(path_in, pattern.split(", "), regex=True if reg == "True" else False)
if len(items) > 0:
path_out = os.path.dirname(file_out)
if not os.path.exists(path_out):
os.makedirs(path_out)
print "the following files will be stacked to file {0}:".format(file_out)
for item in items:
print item
decision = raw_input("proceed (y/n)?: ")
if decision == "y":
vrt = file_out+".vrt"
sp.check_call(dissolve(["gdalbuildvrt", "-q", "-overwrite", "-separate", vrt, items]))
sp.check_call(dissolve(["gdalwarp", "-q", "-overwrite", resampling, format, nodata, extent, targetres, vrt, file_out]))
os.remove(vrt)
# add band names (basenames of the original files without extension) to the envi header file
par = HDRobject(file_out+".hdr")
par.band_names = [os.path.splitext(os.path.basename(x))[0] for x in items]
hdr(par)
else:
print "command aborted"
else:
print "no files matching the defined pattern(s) found"
run(["gc_map_fine", lut_rough, samples_dem, diffpar, lut_fine, 1], path_out, path_log)
for item in [lut_rough, sim_map, sim_rdc]:
os.remove(item)
else:
# read additional parameters
samples_dem = ReadPar(dem_sub + ".par").width
samples_mli = ReadPar(masterpar).range_samples
# perform topographic normalization
if tnorm:
print "----------"
# define regular expressions for images to be normalized (mli, fd3c decomp, cloude decomp)
normtags = ["_mli$", "fdd_p[dsv]$", "ctd_[123]_mag$"]
# create a list containing the actual image names including their absolute directory paths (only if the corresponding normalized processing result does not yet exist)
normlist = dissolve([getattr(scene, x) for x in scene.index for tag in normtags if re.search(tag, x)])
normlist = [x for x in normlist if not os.path.isfile(x+"_norm")]
if len(normlist) > 0:
print "topographic normalization\n----------"
for image in normlist:
print os.path.basename(image)
topo_base = os.path.join(path_out, os.path.basename(image))
pix_gamma = topo_base+"_pix_gamma"
pix_gamma0ratio = topo_base+"_pix_gamma0ratio"
ellip_pix_gamma0 = topo_base+"_ellip_pix_gamma0"
parfile = image+".par" if re.search("_mli$", image) else getattr(scene, "HH_mli")+".par"
# refined pixel area estimation
run(["pixel_area", parfile, dem_sub+".par", dem_sub, lut_fine, ls_map, inc, "-", pix_gamma], path_out, path_log)
run(["radcal_MLI", image, parfile, "-", "temp", "-", "-", "-", 2, "-", "-", ellip_pix_gamma0], path_out, path_log)
width_mli = ReadPar(parfile).range_samples
run(["ratio", ellip_pix_gamma0, pix_gamma, pix_gamma0ratio, width_mli, 1, 1, 0], path_out, path_log)