def cutFiles(arg):
if len(arg) == 1:
print("Nothing to do!!! Exiting...")
return (0)
file1 = arg[0]
# Open file1, get projection and pixsize
dst1 = gdal.Open(file1)
p1 = dst1.GetProjection()
# Find the largest pixel size of all scenes
pixSize = getPixSize(arg[0])
for x in range(len(arg) - 1):
tmp = getPixSize(arg[x + 1])
pixSize = max(pixSize, tmp)
# Make sure that UTM projections match
ptr = p1.find("UTM zone ")
if ptr != -1:
(zone1, hemi) = [
t(s) for t, s in zip((int, str),
re.search("(\d+)(.)", p1[ptr:]).groups())
]
for x in range(len(arg) - 1):
file2 = arg[x + 1]
# Open up file2, get projection
dst2 = gdal.Open(file2)
p2 = dst2.GetProjection()
# Cut the UTM zone out of projection2
ptr = p2.find("UTM zone ")
zone2 = re.search("(\d+)", p2[ptr:]).groups()
zone2 = int(zone2[0])
if zone1 != zone2:
print("Projections don't match... Reprojecting %s" % file2)
if hemi == "N":
proj = ('EPSG:326%02d' % int(zone1))
else:
proj = ('EPSG:327%02d' % int(zone1))
print(" reprojecting post image")
print(" proj is %s" % proj)
name = file2.replace(".tif", "_reproj.tif")
gdal.Warp(name, file2, dstSRS=proj, xRes=pixSize, yRes=pixSize)
arg[x + 1] = name
# Find the overlap between all scenes
coords = getCorners(arg[0])
for x in range(len(arg) - 1):
coords = getOverlap(coords, arg[x + 1])
# Check to make sure there was some overlap
print("Clipping coordinates: {}".format(coords))
diff1 = (coords[2] - coords[0]) / pixSize
diff2 = (coords[3] - coords[1]) / pixSize * -1.0
print("Found overlap size of {}x{}".format(int(diff1), int(diff2)))
if diff1 < 1 or diff2 < 1:
print("ERROR: There was no overlap between scenes")
exit(1)
# Finally, clip all scenes to the overlap region at the largest pixel size
lst = list(coords)
tmp = lst[3]
lst[3] = lst[1]
lst[1] = tmp
coords = tuple(lst)
print("Pixsize : x = {} y = {}".format(pixSize, -1 * pixSize))
for x in range(len(arg)):
file1 = arg[x]
file1_new = file1.replace('.tif', '_clip.tif')
print(" clipping file {} to create file {}".format(
file1, file1_new))
# dst_d1 = gdal.Translate(file1_new,file1,projWin=coords,xRes=pixSize,yRes=pixSize,creationOptions = ['COMPRESS=LZW'])
gdal.Warp(file1_new,
file1,
outputBounds=coords,
xRes=pixSize,
yRes=-1 * pixSize,
creationOptions=['COMPRESS=LZW'])
os.chdir(company.parameters['output_directory'])
# Export 2D proportions matrix as map
output_name = 'DD_' + company.name + '_map' + '.tif'
output_raster = gdal.GetDriverByName('GTiff').Create(output_name,
ncols, nrows, 1, gdal.GDT_Float32)
output_raster.SetGeoTransform(geotransform) # Specify file coordinates
srs = osr.SpatialReference() # Establish coordinate encoding
srs.ImportFromEPSG(4326) # Specify WGS84 lat/long
output_raster.SetProjection(srs.ExportToWkt()) # Exports the coordinate system to the file
output_raster.GetRasterBand(1).WriteArray(DD_output) # Writes my array to the raster
output_raster = None
# project the raster
iras = gdal.Open(output_name)
prj_output_name = 'DD_' + company.name + '_map' +'_prj' + '.tif'
gdal.Warp(prj_output_name,iras,dstSRS = "EPSG:"+str(epsg))
# Export 2D MCB matrix as map
output_name = 'MCB_' + company.name + '_map' + '.tif'
output_raster = gdal.GetDriverByName('GTiff').Create('output_name,
ncols, nrows, 1, gdal.GDT_Float32)
output_raster.SetGeoTransform(geotransform) # Specify file coordinates
srs = osr.SpatialReference() # Establish coordinate encoding
srs.ImportFromEPSG(4326) # Specify WGS84 lat/long
output_raster.SetProjection(srs.ExportToWkt()) # Exports the coordinate system to the file
output_raster.GetRasterBand(1).WriteArray(MCB_output) # Writes my array to the raster
output_raster = None
# project the raster
iras = gdal.Open(output_name)
prj_output_name = 'MCB_' + company.name + '_map' +'_prj' + '.tif'
product_request.save_data()
for i in [10, 20, 60]:
path = 'A:\lab4\S2A_MSIL2A_20190821T085601_N0213_R007_T36UUA_20190821T115206.SAFE\GRANULE\L2A_T36UUA_A021740_20190821T085815\IMG_DATA\R{}m\\'.format(
i)
in1 = glob.glob(str(path) + ('*B02_{}m.jp2').format(i))
in2 = glob.glob(str(path) + '*B03_{}m.jp2'.format(i))
in3 = glob.glob(str(path) + '*B04_{}m.jp2'.format(i))
in4 = glob.glob(str(path) + '*_*_*8*_{}m.jp2'.format(i))
gm.main([
'', '-separate', '-o', 'AR{}.tif'.format(i), in1[0], in2[0], in3[0],
in4[0]
])
for i in [1, 2, 6]:
gdal.Warp('12AR{}0.tif'.format(i),
'1AR{}0.tif'.format(i),
dstSRS="EPSG:4326")
gdal.Warp('final.tif', [
'proek_AR10.tif', 'proek_AR20.tif', 'proek_AR60.tif', 'proek_BR10.tif',
'proek_BR20.tif', 'proek_BR60.tif'
])
gdal.Warp('WrapedImg.tif',
'AllInOne.tif',
format='GTiff',
cutlineDSName='Kyiv_regions.shp',
cutlineLayer='extent',
cropToCutline=True,
dstNodata=0)
def convert_files(s1aFlag, proj=None, res=30):
makeKMZ("filt_topophase.unw.geo", "unw")
shutil.move("unw.kmz", "colorized_unw.kmz")
makeKMZ("filt_topophase.flat.geo", "col")
shutil.move("col.kmz", "color.kmz")
gcsname = "tmp_gcs.tif"
# Create the phase image
if proj is None:
gdal.Translate("phase.tif",
"filt_topophase.unw.geo",
bandList=[2],
creationOptions=['COMPRESS=PACKBITS'])
shutil.copy("phase.tif", gcsname)
else:
print("Creating tmp.tif")
gdal.Translate("tmp.tif",
"filt_topophase.unw.geo.vrt",
bandList=[2],
creationOptions=['COMPRESS=PACKBITS'])
print("phase.tif")
gdal.Warp("phase.tif",
"tmp.tif",
dstSRS=proj,
xRes=res,
yRes=res,
resampleAlg="cubic",
dstNodata=0,
creationOptions=['COMPRESS=LZW'])
print("mv tmp.tif {}".format(gcsname))
shutil.copy("tmp.tif", gcsname)
# os.remove("tmp.tif")
print("Creating browse image colorized_unw.png")
create_browse("unw.png", "colorized_unw.png",
"colorized_unw.png.aux.xml", gcsname, proj, 1024)
create_browse("unw.png", "colorized_unw_large.png",
"colorized_unw_large.png.aux.xml", gcsname, proj, 2048)
print("Creating browse image color.png")
create_browse("col.png", "color.png", "color.png.aux.xml", gcsname,
proj, 1024)
print("Creating browse image color_large.png")
create_browse("col.png", "color_large.png", "color_large.png.aux.xml",
gcsname, proj, 2048)
# Create the amplitude image
if proj is None:
gdal.Translate("amp.tif",
"filt_topophase.unw.geo",
bandList=[1],
creationOptions=['COMPRESS=PACKBITS'])
else:
gdal.Translate("tmp.tif",
"filt_topophase.unw.geo.vrt",
bandList=[1],
creationOptions=['COMPRESS=PACKBITS'])
gdal.Warp("amp.tif",
"tmp.tif",
dstSRS=proj,
xRes=res,
yRes=res,
resampleAlg="cubic",
dstNodata=0,
creationOptions=['COMPRESS=LZW'])
os.remove("tmp.tif")
# Create the coherence image
if proj is None:
gdal.Translate("coherence.tif",
"phsig.cor.geo",
creationOptions=['COMPRESS=PACKBITS'])
else:
gdal.Translate("tmp.tif",
"phsig.cor.geo.vrt",
creationOptions=['COMPRESS=PACKBITS'])
gdal.Warp("coherence.tif",
"tmp.tif",
dstSRS=proj,
xRes=res,
yRes=res,
resampleAlg="cubic",
dstNodata=0,
creationOptions=['COMPRESS=LZW'])
os.remove("tmp.tif")
temp2.SetGeoTransform(temp1.GetGeoTransform())
temp2.SetProjection(temp1.GetProjection())
temp2_band = temp2.GetRasterBand(1)
gdal.ComputeProximity(temp1_band, temp2_band, ["VALUES=1"])
temp2_band.FlushCache()
# Clip to region area
warp_options = gdal.WarpOptions(
outputBounds=match_ds.rio.bounds(),
creationOptions=["COMPRESS=LZW"],
outputType=gdal.GDT_Int16,
dstNodata=NODATA_VALUE,
cutlineDSName=region.get("path"),
)
dst_fn = os.path.join(output_folder, f"{feature.get('name')}_proximity.tif")
temp3 = gdal.Warp(dst_fn, temp2, options=warp_options)
stack.append(temp3.ReadAsArray())
stack = np.stack(stack)
stack = np.ma.array(stack, mask=(stack == NODATA_VALUE))
arr = stack.min(axis=0)
arr = arr.filled(NODATA_VALUE)
combined_fn = os.path.join(output_folder, "comb_proximity.tif")
with rasterio.open(
combined_fn,
"w",
driver="GTiff",
width=match_ds.rio.width,
height=match_ds.rio.height,
count=1,
def match_netcdf_to_data(src_filename,match_filename,dst_filename,year,\
country_code=None,shpfile=None,force=False,\
nodata=-300,frmat='GTiff',verbose=False):
'''
see :
https://stackoverflow.com/questions/10454316/
how-to-project-and-resample-a-grid-to-match-another-grid-with-gdal-python
'''
'''
Get the projection, geotransform and dataset
size that we want to match to
'''
if verbose: print(f'getting info from match file {match_filename}')
match_ds = gdal.Open(match_filename, gdalconst.GA_ReadOnly)
match_proj = match_ds.GetProjection()
match_geotrans = match_ds.GetGeoTransform()
wide = match_ds.RasterXSize
high = match_ds.RasterYSize
# close the file -- we dont need it any more
del match_ds
'''
access information from source
'''
if verbose: print(f'getting info from source netcdf file {src_filename}')
try:
src_dataname = 'NETCDF:"' + src_filename + '":t2m'
src = gdal.Open(src_dataname, gdalconst.GA_ReadOnly)
except:
if verbose: print('failed')
return (None)
# get meta data
meta = gdal.Open(src_filename, gdalconst.GA_ReadOnly).GetMetadata()
extent = [match_geotrans[0],match_geotrans[0]+match_geotrans[1]*wide,\
match_geotrans[3]+match_geotrans[5]*high,match_geotrans[3]]
# get time info
timer = np.array([(datetime(1900,1,1) + timedelta(days=float(i)/24.)) \
for i in meta['NETCDF_DIM_time_VALUES'][1:-1].split(',')])
if (not Path(dst_filename).exists()) or force:
'''
Get geotrans, proj, data type and number of bands
from source dataset
'''
band1 = src.GetRasterBand(1)
src_geotrans = src.GetGeoTransform()
src_proj = src.GetProjection()
nbands = src.RasterCount
src_format = band1.DataType
dst = gdal.GetDriverByName('MEM').Create(\
'', wide, high, \
nbands, src_format)
dst.SetGeoTransform(match_geotrans)
dst.SetProjection(match_proj)
if verbose: print(f'reprojecting ...')
# Output / destination
_ = gdal.ReprojectImage(src, dst, \
src_proj, \
match_proj,\
gdalconst.GRA_Bilinear )
if verbose: print(f'cropping to {country_code:s} ...')
done = gdal.Warp(dst_filename,
dst,
format=frmat,
dstNodata=nodata,
cutlineDSName=shpfile,
cutlineWhere=f"FIPS='{country_code:s}'",
cropToCutline=True)
del dst
return (timer, dst_filename, extent)
def warp(src, dst, dtype="Float32", template=None, overwrite=False,
compress=None, **kwargs):
"""
Warp a raster to a new geometry.
Parameters
----------
src : str
Path to source raster file.
dst : str
Path to target raster file.
dtype : str | gdal object
GDAL data type. Can be a string or a gdal type object (e.g.
gdal.GDT_Float32, "GDT_Float32", "float32"). Available GDAL data types
and descriptions can be found in the GDAL_TYPES dictionary.
template : str
Path to a raster file with desired target raster geometry, crs,
resolution, and extent values. This will overwrite other arguments
provided for these parameters. Template-derived arguments will
overwrite **kwargs.
overwrite : boolean
compress : str
A compression technique. Available options are "DEFLATE", "JPEG",
"LZW"
**kwargs
Any available key word arguments for gdalwarp. Available options
and descriptions can be found using gdal_options("warp").
Returns
-------
None.
Example:
warp(src="/Users/twillia2/Box/WETO 1.2/data/rasters/agcounty_product.tif",
dst="/Users/twillia2/Box/WETO 1.2/data/rasters/test.tif",
template="/Users/twillia2/Box/WETO 1.2/data/rasters/albers/acre/cost_codes_ac.tif",
dstSRS="epsg:102008")
"""
# Create progress callback - these behave differently by module
def warp_progress(percent, message, unknown):
"""A progress callback that recreates the gdal printouts."""
# We don't need the message or unknown objects
del message, unknown
# Between numeric printouts we need three dots
dots = [[str(i) + d for d in ["2", "5", "8"]] for i in range(10)]
dots = [int(l) for sl in dots for l in sl]
# If divisible by ten, print the number
if percent % 10 == 0 and percent != 0:
print("{}".format(percent), end="")
# If one of three numbers between multiples of 10, print a dot
elif percent in dots:
print(".", end="")
return 1
# Overwrite existing file
if os.path.exists(dst):
if overwrite:
if os.path.isfile(dst):
os.remove(dst)
else:
shutil.rmtree(dst)
else:
print(dst + " exists, use overwrite=True to replace this file.")
return
# Specifying data types shouldn't be so difficult
if isinstance(dtype, str):
dtype = dtype.lower().replace("gdt_", "")
try:
dtype = GDAL_TYPEMAP[dtype]["type"]
except KeyError:
print("\n'" + dtype + "' is not an available data type. "
"Choose a value from this list:")
print(str(list(GDAL_TYPEMAP.keys())))
# Create a spatial reference object
spatial_ref = osr.SpatialReference()
# If a template is provided, use its geometry for target figures
if template:
temp = gdal.Open(template)
spatial_ref.ImportFromWkt(temp.GetProjection())
srs = spatial_ref.ExportToProj4()
width = temp.RasterXSize # consider using these warp options
height = temp.RasterYSize
transform = temp.GetGeoTransform()
xmin, xres, xrot, ymax, yrot, yres = transform
xs = [xmin + xres * i for i in range(width)]
ys = [ymax + yres * i for i in range(height)]
xmax = max(xs) + 0.5*xres
ymax = ymax + 0.5*xres
ymin = min(ys)
extent = [xmin, ymin, xmax, ymax]
kwargs["dstSRS"] = srs
kwargs["outputBounds"] = extent
kwargs["xRes"] = transform[1]
kwargs["yRes"] = transform[-1] # careful here
kwargs["outputType"] = dtype
elif not kwargs:
print("No warp options provided.")
gdal_options("warp")
return
# Get source srs
source = gdal.Open(src)
spatial_ref.ImportFromWkt(source.GetProjection())
srs = spatial_ref.ExportToProj4()
kwargs["srcSRS"] = srs
# Use the progress callback
kwargs["callback"] = gdal_progress
# Compress
if compress:
kwargs["creationOptions"] = ["COMPRESS=" + compress]
# Check Options: https://gdal.org/python/osgeo.gdal-module.html#WarpOptions
ops = gdal_options("warp", **kwargs)
# Call
print("Processing " + dst + " :")
ds = gdal.Warp(dst, src, options=ops)
del ds
parser = argparse.ArgumentParser(description=
('This script merge separate field zones into large raster file'))
parser.add_argument('-i', required=True, metavar='input folder',
help='Input folder with separate zones')
parser.add_argument('-o', required=True, metavar='output raster',
help='Output raster')
if (len(sys.argv) == 1) :
parser.print_usage()
exit(0)
args = parser.parse_args()
input_folder = args.i
output_merged_raster = args.o
if not os.path.exists(input_folder):
print("ERROR: path doesn't exist: " + input_folder)
exit(1)
os.chdir(input_folder)
zone_list = [os.path.join(input_folder,f) for f in glob.glob('*.tif')]
gdal.Warp(output_merged_raster,
zone_list,
format = 'GTiff',
srcNodata=0,
dstNodata=0
)
input = 'NPP_21616_151230010535.tif'
#output =
ds = gdal.Open(input)
if ds is None:
print 'Unable to open INPUT.tif'
sys.exit(1)
#num = ds.RasterCount
#print 'Number of bands:', num
#if num > 1:
# print 'Leave one band'
# ds = gdal.Translate('output.tif', ds, bandList = [1])
# input = 'output.tif'
#print input
#ds = gdal.Open('output.tif')
#srcband = ds.GetRasterBand(1)
#print srcband
#(min,max) = srcband.ComputeRasterMinMax()
#print 'MIN', min, 'MAX', max
#if min == 0:
print '0'
dstDS = gdal.Warp('outputWARP.tif', ds, dstAlpha=True, srcNodata=0)
#else:
# print '255'
# dstDS = gdal.Warp('outputWARP.tif', ds, dstAlpha=True, srcNodata=255)
def test_gdalwarp_lib_128():
mem_ds = gdal.GetDriverByName('MEM').Create('', 1177, 4719)
rpc = [
"HEIGHT_OFF=109",
"LINE_NUM_COEFF=-0.001245683 -0.09427649 -1.006342 -1.954469e-05 0.001033926 2.020534e-08 -3.845472e-07 -0.002075817 0.0005520694 0 -4.642442e-06 -3.271793e-06 2.705977e-05 -7.634384e-07 -2.132832e-05 -3.248862e-05 -8.17894e-06 -3.678094e-07 2.002032e-06 3.693162e-08",
"LONG_OFF=7.1477",
"SAMP_DEN_COEFF=1 0.01415176 -0.003715018 -0.001205632 -0.0007738299 4.057763e-05 -1.649126e-05 0.0001453584 0.0001628194 -7.354731e-05 4.821444e-07 -4.927701e-06 -1.942371e-05 -2.817499e-06 1.946396e-06 3.04243e-06 2.362282e-07 -2.5371e-07 -1.36993e-07 1.132432e-07",
"LINE_SCALE=2360",
"SAMP_NUM_COEFF=0.04337163 1.775948 -0.87108 0.007425391 0.01783631 0.0004057179 -0.000184695 -0.04257537 -0.01127869 -1.531228e-06 1.017961e-05 0.000572344 -0.0002941 -0.0001301705 -0.0003289546 5.394918e-05 6.388447e-05 -4.038289e-06 -7.525785e-06 -5.431241e-07",
"LONG_SCALE=0.8383", "SAMP_SCALE=593", "SAMP_OFF=589",
"LAT_SCALE=1.4127", "LAT_OFF=33.8992", "LINE_OFF=2359",
"LINE_DEN_COEFF=1 0.0007273139 -0.0006006867 -4.272095e-07 2.578717e-05 4.718479e-06 -2.116976e-06 -1.347805e-05 -2.209958e-05 8.131258e-06 -7.290143e-08 5.105109e-08 -7.353388e-07 0 2.131142e-06 9.697701e-08 1.237039e-08 7.153246e-08 6.758015e-08 5.811124e-08",
"HEIGHT_SCALE=96.3"
]
mem_ds.SetMetadata(rpc, "RPC")
mem_ds.GetRasterBand(1).Fill(255)
cutlineDSName = '/vsimem/test_gdalwarp_lib_128.json'
cutline_ds = ogr.GetDriverByName('GeoJSON').CreateDataSource(cutlineDSName)
cutline_lyr = cutline_ds.CreateLayer('cutline')
f = ogr.Feature(cutline_lyr.GetLayerDefn())
f.SetGeometry(
ogr.CreateGeometryFromWkt(
'POLYGON ((7.2151 32.51930,7.214316 32.58116,7.216043 32.59476,7.21666 32.5193,7.2151 32.51930))'
))
cutline_lyr.CreateFeature(f)
f = None
cutline_lyr = None
cutline_ds = None
# Default is GDALWARP_DENSIFY_CUTLINE=YES
ds = gdal.Warp(
'',
mem_ds,
format='MEM',
cutlineDSName=cutlineDSName,
dstSRS='EPSG:4326',
outputBounds=[7.2, 32.52, 7.217, 32.59],
xRes=0.000226555,
yRes=0.000226555,
transformerOptions=['RPC_DEM=data/test_gdalwarp_lib_128_dem.tif'])
cs = ds.GetRasterBand(1).Checksum()
if cs != 4248:
gdaltest.post_reason('bad checksum')
print(cs)
return 'fail'
# Below steps depend on GEOS
if not ogrtest.have_geos():
gdal.Unlink(cutlineDSName)
return 'success'
gdal.SetConfigOption('GDALWARP_DENSIFY_CUTLINE', 'ONLY_IF_INVALID')
ds = gdal.Warp(
'',
mem_ds,
format='MEM',
cutlineDSName=cutlineDSName,
dstSRS='EPSG:4326',
outputBounds=[7.2, 32.52, 7.217, 32.59],
xRes=0.000226555,
yRes=0.000226555,
transformerOptions=['RPC_DEM=data/test_gdalwarp_lib_128_dem.tif'])
gdal.SetConfigOption('GDALWARP_DENSIFY_CUTLINE', None)
cs = ds.GetRasterBand(1).Checksum()
if cs != 4248:
gdaltest.post_reason('bad checksum')
print(cs)
return 'fail'
gdal.SetConfigOption('GDALWARP_DENSIFY_CUTLINE', 'NO')
with gdaltest.error_handler():
ds = gdal.Warp(
'',
mem_ds,
format='MEM',
cutlineDSName=cutlineDSName,
dstSRS='EPSG:4326',
outputBounds=[7.2, 32.52, 7.217, 32.59],
xRes=0.000226555,
yRes=0.000226555,
transformerOptions=['RPC_DEM=data/test_gdalwarp_lib_128_dem.tif'])
gdal.SetConfigOption('GDALWARP_DENSIFY_CUTLINE', None)
if ds is not None:
gdaltest.post_reason('expected none return')
return 'fail'
gdal.Unlink(cutlineDSName)
return 'success'
work_folder += "40" + "/"
source_folder = "../uploads"
sentinel_file = source_folder + "/citra/"
shp_file = source_folder + "/area/"
clipped_file = ""
grid_file = ""
raster_tahun_tanam = ""
kode_area = "18"
null_value = -9999
format_file = "GTiff"
pixel_size = 10.0
vector_path = "D:/Indra/Tesis/Ngoprek6/Basemap/Best Agro/SPLIT"
sentinel_file = "C:/xampp/htdocs/pkt/uploads/citra/18/S2B_MSIL2A_20190822_bestagro B1_super_resolved.tif"
for id in range(1, 177, 1):
shp_file = vector_path + "/id_" + str(id) + ".gpkg"
clipped_file = vector_path + "/id_" + str(id) + ".tif"
warp_opts = gdal.WarpOptions(
format=format_file,
cutlineDSName=shp_file,
cropToCutline=True,
dstNodata=null_value,
xRes=pixel_size,
yRes=pixel_size,
)
gdal.Warp(clipped_file, sentinel_file, options=warp_opts)
print("DONE!")
def calculate(self, process_path):
qgs = QgsApplication([], False)
qgs.initQgis()
Processing.initialize()
QgsApplication.processingRegistry().addProvider(QgsNativeAlgorithms())
gdal.AllRegister()
#for alg in QgsApplication.processingRegistry().algorithms():
# print(alg.id(), "->", alg.displayName())
# read raster
#inputRaster = input_mdt
# read maximum depth
#max_depth = max_depth
# read distance
#distance = distance
# minimum size
#size = min_size
#outPath2 = output_path
#process_path = "/home/rodrigo/data/d/process"
layer_raster = QgsRasterLayer(self.input_mdt, os.path.basename(self.input_mdt), "gdal")
data_mdt = layer_raster.dataProvider()
extent_raster = data_mdt.extent()
xmin_raster = extent_raster.xMinimum()
xmax_raster = extent_raster.xMaximum()
ymin_raster = extent_raster.yMinimum()
ymax_raster = extent_raster.yMaximum()
extent_raster_str = str(xmin_raster) + "," + str(xmax_raster) + "," + str(ymin_raster) + "," + str(ymax_raster)
cellSize = layer_raster.rasterUnitsPerPixelX()
#stream = QFileInfo(QgsApplication.qgisUserDatabaseFilePath()).path() + "/stream.tif"
stream = process_path + "/stream.tif"
Processing.runAlgorithm("grass7:r.watershed",{
'elevation': self.input_mdt,
'depression': None,
'flow': None,
'disturbed_land': None,
'blocking': None,
'threshold': self.size,
'max_slope_length': None,
'convergence': 5,
'memory': 300,
'-s': False,
'-m': False,
'-4': False,
'-a': False,
'-b': False,
'accumulation': None,
'drainage': None,
'basin': None,
'stream': stream,
'half_basin': None,
'length_slope': None,
'slope_steepness': None,
'tci': None,
'spi': None,
'GRASS_REGION_PARAMETER': extent_raster_str + '[EPSG:3763]',
'GRASS_REGION_CELLSIZE_PARAMETER': cellSize,
'GRASS_RASTER_FORMAT_OPT': '',
'GRASS_RASTER_FORMAT_META': ''
})
# condition stream > 1 to have the lines with value 1
#stream_ones = QFileInfo(QgsApplication.qgisUserDatabaseFilePath()).path() + "/stream_ones.tif"
stream_ones = process_path + "/stream_ones.tif"
Processing.runAlgorithm("grass7:r.mapcalc.simple",{
'a': str(stream),
'b': None,
'c': None,
'd': None,
'e': None,
'f': None,
'expression': 'A>1',
'output': stream_ones,
'GRASS_REGION_PARAMETER': None,
'GRASS_REGION_CELLSIZE_PARAMETER': 0,
'GRASS_RASTER_FORMAT_OPT': '',
'GRASS_RASTER_FORMAT_META': ''
})
# raster distance
#raster_distance = QFileInfo(QgsApplication.qgisUserDatabaseFilePath()).path() + "/raster_distance.tif"
raster_distance = process_path + "/raster_distance.tif"
#Processing.runAlgorithm("saga:proximitygrid", None, str(stream_ones_str), 3, str(raster_distance), None, None)
#Processing.runAlgorithm("saga:proximityraster", {
# 'FEATURES': str(stream_ones),
# 'DISTANCE': str(raster_distance), 'DIRECTION': 'TEMPORARY_OUTPUT', 'ALLOCATION': 'TEMPORARY_OUTPUT'})
Processing.runAlgorithm("grass7:r.grow.distance", {
'input': str(stream_ones),
'metric': 0,
'-m': False,
'-': False,
'distance': str(raster_distance),
'value': 'TEMPORARY_OUTPUT',
'GRASS_REGION_PARAMETER': None,
'GRASS_REGION_CELLSIZE_PARAMETER': 0,
'GRASS_RASTER_FORMAT_OPT': '',
'GRASS_RASTER_FORMAT_META': ''
})
# condition distance >= 200, always maximum depth meters
#dist_major_200 = QFileInfo(QgsApplication.qgisUserDatabaseFilePath()).path() + "/dist_major_200.tif"
dist_major_200 = process_path + "/dist_major_200.tif"
Processing.runAlgorithm("grass7:r.mapcalc.simple", {
'a': str(raster_distance),
'b': None,
'c': None,
'd': None,
'e': None,
'f': None,
'expression': "A>="+str(self.distance),
'output': dist_major_200,
'GRASS_REGION_PARAMETER': None,
'GRASS_REGION_CELLSIZE_PARAMETER': 0,
'GRASS_RASTER_FORMAT_OPT': '',
'GRASS_RASTER_FORMAT_META': ''
})
#dist_multiplication = QFileInfo(QgsApplication.qgisUserDatabaseFilePath()).path() + "/dist_multiplication.tif"
dist_multiplication = process_path + "/dist_multiplication.tif"
Processing.runAlgorithm("grass7:r.mapcalc.simple", {
'a': str(dist_major_200),
'b': None,
'c': None,
'd': None,
'e': None,
'f': None,
'expression': "A*"+str(self.max_depth),
'output': dist_multiplication,
'GRASS_REGION_PARAMETER': None,
'GRASS_REGION_CELLSIZE_PARAMETER': 0,
'GRASS_RASTER_FORMAT_OPT': '',
'GRASS_RASTER_FORMAT_META': ''
})
# condition distance < 200, inteprolation between 0 and maximum depth
#dist_minor_200 = QFileInfo(QgsApplication.qgisUserDatabaseFilePath()).path() + "/dist_minor_200.tif"
dist_minor_200 = process_path + "/dist_minor_200.tif"
Processing.runAlgorithm("grass7:r.mapcalc.simple", {
'a': str(raster_distance),
'b': None,
'c': None,
'd': None,
'e': None,
'f': None,
'expression': "A<"+str(self.distance),
'output': dist_minor_200,
'GRASS_REGION_PARAMETER': None,
'GRASS_REGION_CELLSIZE_PARAMETER': 0,
'GRASS_RASTER_FORMAT_OPT': '',
'GRASS_RASTER_FORMAT_META': ''
})
# multiplication by the raster distance
#dist_multiplication_dist = QFileInfo(QgsApplication.qgisUserDatabaseFilePath()).path() + "/dist_multiplication_dist.tif"
dist_multiplication_dist = process_path + "/dist_multiplication_dist.tif"
#Processing.runAlgorithm("grass7:r.mapcalc.simple",
# {'a': str(dist_minor_200),
# 'b': str(dist_major_200),
# 'c': None, 'd': None, 'e': None, 'f': None,
# 'expression': 'A*B',
# 'output': dist_multiplication_dist, 'GRASS_REGION_PARAMETER': None,
# 'GRASS_REGION_CELLSIZE_PARAMETER': 0, 'GRASS_RASTER_FORMAT_OPT': '',
# 'GRASS_RASTER_FORMAT_META': ''})
Processing.runAlgorithm("grass7:r.mapcalc.simple", {
'a': str(dist_minor_200),
'b': str(raster_distance),
'c': None,
'd': None,
'e': None,
'f': None,
'expression': 'A*B',
'output': dist_multiplication_dist,
'GRASS_REGION_PARAMETER': None,
'GRASS_REGION_CELLSIZE_PARAMETER': 0,
'GRASS_RASTER_FORMAT_OPT': '',
'GRASS_RASTER_FORMAT_META': ''
})
# interpolation between 0 and distance
#interpolation_dist = QFileInfo(QgsApplication.qgisUserDatabaseFilePath()).path() + "/interpolation_dist.tif"
interpolation_dist = process_path + "/interpolation_dist.tif"
Processing.runAlgorithm("grass7:r.mapcalc.simple", {
'a': str(dist_multiplication_dist),
'b': None,
'c': None,
'd': None,
'e': None,
'f': None,
'expression': "A*"+str(self.max_depth)+"/"+str(self.distance),
'output': interpolation_dist,
'GRASS_REGION_PARAMETER': None,
'GRASS_REGION_CELLSIZE_PARAMETER': 0,
'GRASS_RASTER_FORMAT_OPT': '',
'GRASS_RASTER_FORMAT_META': ''
})
# depth surface = sum of two conditions
#depth_surface = QFileInfo(QgsApplication.qgisUserDatabaseFilePath()).path() + "/depth_surface.tif"
depth_surface = process_path + "/depth_surface.tif"
Processing.runAlgorithm("grass7:r.mapcalc.simple", {
'a': str(dist_multiplication),
'b': str(dist_multiplication_dist),
'c': None,
'd': None,
'e': None,
'f': None,
'expression': 'A+B',
'output': depth_surface,
'GRASS_REGION_PARAMETER': None,
'GRASS_REGION_CELLSIZE_PARAMETER': 0,
'GRASS_RASTER_FORMAT_OPT': '',
'GRASS_RASTER_FORMAT_META': ''
})
# indexes for topography
"""rattings_lista = []
for linha in ratings:
for coluna in linha:
rattings_lista = rattings_lista + [str(coluna)]
string = ","
intervalos = string.join(rattings_lista)
results = list(map(float, rattings_lista))
print(results)"""
#Processing.runAlgorithm("saga:reclassifyvalues",{'INPUT': depth_surface, 'METHOD':2, 'OLD':0, 'NEW':1, 'SOPERATOR':0, 'MIN':0, 'MAX':1,
# 'RNEW':2, 'ROPERATOR':0, 'RETAB':results, 'TOPERATOR':0, 'NODATAOPT':True, 'NODATA':0,
# 'OTHEROPT':True, 'OTHERS':0, 'RESULT':outPath2})
result = process_path + "/result.tif"
Processing.runAlgorithm("native:reclassifybytable", {
'INPUT_RASTER': str(depth_surface),
'RASTER_BAND': 1,
'TABLE': self.rattings,
'NO_DATA': -9999,
'RANGE_BOUNDARIES': 0,
'NODATA_FOR_MISSING': False,
'DATA_TYPE': 5,
'OUTPUT': result
})
out_raster = gdal.Open(result)
gdal.Warp(self.output_file, out_raster, dstSRS="EPSG:3857")
def rectify(project_path,
img_fname,
img_rectified_fname,
eo,
ground_height,
sensor_width,
focal_length,
gsd='auto'):
"""
In order to generate individual ortho-image, this function rectifies a given drone image on a reference plane.
:param img_fname:
:param img_rectified_fname:
:param eo:
:param project_path:
:param ground_height: Ground height in m
:param sensor_width: Width of the sensor in mm
:param gsd: GSD in m. If not specified, it will automatically determine gsd.
:return File name of rectified image, boundary polygon in WKT string
"""
img_path = os.path.join(project_path, img_fname)
start_time = time.time()
print('Read the image - ' + img_fname)
image = cv2.imread(img_path)
# 0. Extract EXIF data from a image
# focal_length, orientation = getExif(img_path) # unit: m
orientation = 0
# 1. Restore the image based on orientation information
restored_image = restoreOrientation(image, orientation)
image_rows = restored_image.shape[0]
image_cols = restored_image.shape[1]
pixel_size = sensor_width / image_cols # unit: mm/px
pixel_size = pixel_size / 1000 # unit: m/px
end_time = time.time()
print("--- %s seconds ---" % (time.time() - start_time))
read_time = end_time - start_time
print('Read EOP - ' + img_fname)
print('Northing | Easting | Height | Omega | Phi | Kappa')
converted_eo = convertCoordinateSystem(eo)
print(converted_eo)
R = Rot3D(converted_eo)
# 2. Extract a projected boundary of the image
bbox = boundary(restored_image, converted_eo, R, ground_height, pixel_size,
focal_length)
print("--- %s seconds ---" % (time.time() - start_time))
if gsd == 'auto':
gsd = (pixel_size *
(converted_eo[2] - ground_height)) / focal_length # unit: m/px
# Boundary size
boundary_cols = int((bbox[1, 0] - bbox[0, 0]) / gsd)
boundary_rows = int((bbox[3, 0] - bbox[2, 0]) / gsd)
print('projectedCoord')
start_time = time.time()
proj_coords = projectedCoord(bbox, boundary_rows, boundary_cols, gsd,
converted_eo, ground_height)
print("--- %s seconds ---" % (time.time() - start_time))
# Image size
image_size = np.reshape(restored_image.shape[0:2], (2, 1))
print('backProjection')
start_time = time.time()
backProj_coords = backProjection(proj_coords, R, focal_length, pixel_size,
image_size)
print("--- %s seconds ---" % (time.time() - start_time))
print('resample')
start_time = time.time()
b, g, r, a = resample(backProj_coords, boundary_rows, boundary_cols, image)
print("--- %s seconds ---" % (time.time() - start_time))
print('Save the image in GeoTiff')
start_time = time.time()
img_rectified_fname_kctm = img_rectified_fname.split('.')[0] + '_kctm.tif'
dst = os.path.join(project_path, img_rectified_fname_kctm)
createGeoTiff(b, g, r, a, bbox, gsd, boundary_rows, boundary_cols, dst)
# GDAL warp to reproject from EPSG:5186 to EPSG:4326
gdal.Warp(os.path.join(project_path, img_rectified_fname),
gdal.Open(os.path.join(project_path, img_rectified_fname_kctm)),
format='GTiff',
srcSRS='EPSG:5186',
dstSRS='EPSG:4326')
# Remove orthoimage georeferenced as EPSG:5186
os.remove(os.path.join(project_path, img_rectified_fname_kctm))
print("--- %s seconds ---" % (time.time() - start_time))
print('*** Processing time per each image')
print("--- %s seconds ---" % (time.time() - start_time + read_time))
bbox_wkt = export_bbox_to_wkt(bbox)
return bbox_wkt
def main_clip_raster(path_img: str,
path_geom: str,
pad: int,
res: int,
path_out: str = None,
attr: str = None,
no_split=False,
add_mask=False):
bn_img = os.path.splitext(os.path.basename(path_img))[0]
bn_geom = os.path.splitext(os.path.basename(path_geom))[0]
#
idx_pref = bn_geom
if path_out is None:
path_out = os.path.splitext(path_img)[0]
if no_split:
idx_pref = 'clip'
else:
idx_pref = None
#
geom = gp.read_file(path_geom)
geom = __reproject_to_img(geom, path_img)
geom_bboxes = get_geom_bboxes(geom,
pad,
idx_def=idx_pref,
attr=attr,
no_split=no_split)
#
num = len(geom_bboxes)
for xi, x in enumerate(geom_bboxes):
g = x['geom']
bbox_xy = x['bbox']
fidx = x['idx']
if fidx is not None:
pout_img = path_out + '_' + fidx + '.tif'
pout_msk = path_out + '_' + fidx + '_msk.tif'
else:
pout_img = path_out + '.tif'
pout_msk = path_out + '_msk.tif'
if os.path.isfile(pout_img):
logging.warning(f'\t!!! output file exist, skip ... [{pout_img}]')
if add_mask:
logging.info('\t\t({}/{}) (1) (rasterize-mask) #plgn={}'.format(
xi, num, len(g)))
ds_msk = rasterize_geom_mask(geom_xy=g, bbox_xy=bbox_xy, res=res)
logging.info(
'\t\t({}/{}) (2) (crop-image) size-m = {:0.2f}x{:0.2f}, res={:0.2f} (m/pix)'
.format(xi, num, bbox_xy[1][0] - bbox_xy[0][0],
bbox_xy[1][1] - bbox_xy[0][1], res))
ds_img = gdal.Warp('',
path_img,
format='MEM',
outputBounds=np.array(bbox_xy).reshape(-1).tolist(),
xRes=res,
yRes=res,
resampleAlg='bilinear')
pout_img_tmp = generate_tmp_path(pout_img)
pout_msk_tmp = generate_tmp_path(pout_msk)
if add_mask:
logging.info('\t({}/{}) mask -> ({})'.format(xi, num, pout_msk))
gdal.Translate(pout_msk_tmp,
ds_msk,
creationOptions=['COMPRESS=LZW', 'NBITS=1'])
shutil.move(pout_msk_tmp, pout_msk)
logging.info('\t({}/{}) crop-img -> ({})'.format(xi, num, pout_img))
gdal.Translate(pout_img_tmp, ds_img, creationOptions=['COMPRESS=LZW'])
shutil.move(pout_img_tmp, pout_img)
def calculation(output_directory, inputs_raster_selection,
inputs_parameter_selection):
"""
Main function
"""
now = datetime.datetime.now()
data = dict(output_directory=output_directory,
inputs_raster_selection=inputs_raster_selection,
inputs_parameter_selection=inputs_parameter_selection)
with open(f"/tmp/req_wind_{now:%y-%m-%d_%H%M%S}.json", "w") as jsn:
json.dump(data, jsn)
# list of error messages
# TODO: to be fixed according to CREM format
messages = []
res = {}
res["name"] = CM_NAME
res["graphics"] = []
res["indicator"] = []
# retrieve the inputs all input defined in the signature
w_in = {
"res_hub":
float(inputs_parameter_selection["res_hub"]),
"height":
float(inputs_parameter_selection["height"]),
"setup_costs":
int(inputs_parameter_selection["setup_costs"]),
"tot_cost_year":
(float(inputs_parameter_selection["maintenance_percentage"]) / 100 *
int(inputs_parameter_selection["setup_costs"])),
"financing_years":
int(inputs_parameter_selection["financing_years"]),
"peak_power":
float(inputs_parameter_selection["peak_power"]),
}
discount_rate = float(inputs_parameter_selection["discount_rate"])
print(f"w_in={w_in}")
# retrieve the inputs layes
# ds = gdal.Open(inputs_raster_selection["output_wind_speed"])
print(f"inputs_raster_selection={inputs_raster_selection}")
print(f"inputs_raster_selection.keys()={inputs_raster_selection.keys()}")
ds = gdal.Warp(
"warp_test.tif",
inputs_raster_selection["output_wind_speed"],
outputType=gdal.GDT_Float32,
xRes=w_in["res_hub"],
yRes=w_in["res_hub"],
dstNodata=0,
)
plant_raster = ds.ReadAsArray()
potential = ds.ReadAsArray()
potential = np.nan_to_num(potential)
plant_raster = np.nan_to_num(plant_raster)
plant_raster[plant_raster > 0] = 1
# TODO: set peak power and swept area from a list of turbines
wind_plant = wind.WindPlant(
id_plant="Wind",
peak_power=w_in["peak_power"],
height=w_in["height"],
model="Enercon E48 800",
)
wind_plant.area = w_in["res_hub"] * w_in["res_hub"]
wind_plant.n_plants = plant_raster.sum()
wind_plant.prof = None
print(f"wind_plant.n_plants: {wind_plant.n_plants}")
if wind_plant.n_plants > 0:
wind_plant.raw = False
wind_plant.mean = None
wind_plant.lat, wind_plant.lon = rr.get_lat_long(ds, potential)
try:
wind_plant.prof = wind_plant.profile()
wind_plant.energy_production = wind_plant.prof.sum()["electricity"]
wind_plant.resolution = ["Hours", "hourly"]
"""
run_source(kind, pl, data_in,
most_suitable,
n_plant_raster,
discount_rate,
)
"""
try:
res = run_source(
"Wind",
wind_plant,
w_in,
potential,
plant_raster,
discount_rate,
res,
)
except Exception as exc:
print(f"FAILED to execute run_source function due to {exc}")
except Exception:
messages.append((
"Not able to reach the RenewableNinja website to retrieve the hourly values",
"-",
"-",
))
else:
res["indicator"].append(
dict(
unit="",
name=
"Not suitable pixels have been identified in the area selected, please select another area",
value=0,
), )
print("Not suitable pixels have been identified.")
for msgtxt, msgval, msgunt in messages:
res["indicator"].append({
"unit": msgunt,
"name": "WARNING: " + msgtxt,
"value": msgval
})
print("Wind computation completed")
return res
def smoothTaData(self, ALEXIgeodict):
ALEXILatRes = ALEXIgeodict['ALEXI_LatRes']
ALEXILonRes = ALEXIgeodict['ALEXI_LonRes']
sceneID = self.sceneID
scene = self.scene
outFN = os.path.join(self.resultsBase, scene,
'%s_Ta.tif' % sceneID[:-5])
inProj4 = '+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs'
# =======================convert fine TA to coarse resolution=========
outfile = os.path.join(self.resultsBase, scene, 'Ta_DisALEXI.tif')
coarseFile = os.path.join(self.resultsBase, scene, 'TaCoarse.tif')
coarse2fineFile = os.path.join(self.resultsBase, scene,
'TaCoarse2Fine.tif')
if not os.path.exists(outFN):
print 'get->Ta'
# get mask from Landsat LAI
ls = GeoTIFF(outfile)
sceneDir = os.path.join(self.satscene_path, 'CF_MASK')
maskFN = os.path.join(sceneDir, '%s_Mask.tif' % sceneID)
g = gdal.Open(maskFN, GA_ReadOnly)
cfmask = g.ReadAsArray()
g = None
# =============find Average Ta====================================== COMMENTED FOR TESTING
in_ds = gdal.Open(outfile)
coarseds = gdal.Translate(coarseFile,
in_ds,
options=gdal.TranslateOptions(
resampleAlg='average',
xRes=400,
yRes=400))
fineds = gdal.Warp(outFN,
coarseds,
options=gdal.WarpOptions(resampleAlg='average',
height=ls.nrow,
width=ls.ncol))
coarseds = None
# ========smooth Ta data========================================
ta = fineds.ReadAsArray()
fineRes = ls.Lat[1, 0] - ls.Lat[0, 0]
coarseRes = ALEXILatRes
course2fineRatio = coarseRes**2 / fineRes**2
rid2 = int(np.sqrt(course2fineRatio))
# gauss_kernal = Gaussian2DKernel(rid2)
box_kernal = Box2DKernel(rid2)
ta = convolve_fft(ta, box_kernal, allow_huge=True)
fineds.GetRasterBand(1).WriteArray(ta)
fineds = None
ulx = ls.ulx
uly = ls.uly
delx = ls.delx
dely = -ls.dely
fineRes = ls.Lat[1, 0] - ls.Lat[0, 0]
coarseRes = ALEXILatRes
inUL = [ulx, uly]
inRes = [delx, dely]
# Ta = interp_ta(ta,coarseRes,fineRes)-273.16
# Ta = ta - 273.16 # FOR TESTING!!
Ta = ta
outFormat = gdal.GDT_Float32
writeArray2Tiff(Ta, inRes, inUL, ls.proj4, outFN, outFormat)
os.remove(coarseFile)
def compare_modis(self):
"""
Compare the annual map obtained with gap filling approach to the Modis annual map.
"""
modis_snowserie = str(self.params.get("modis_snow_map"))
modis_datefile = self.params.get("modis_snow_map_dates")
self.modis_annual_snow_map = op.join(self.path_tmp,
"modis_annual_snowmap.tif")
modis_dates = read_list_from_file(modis_datefile)
modis_start_index = None
modis_stop_index = None
for i in range(0, len(modis_dates)):
tmp_date = str_to_datetime(modis_dates[i], "%Y,%m,%d")
if tmp_date == self.date_start:
modis_start_index = i
if tmp_date == self.date_stop:
modis_stop_index = i
# generate the summary map
band_index = range(modis_start_index + 1, modis_stop_index + 2)
expression = "+".join(
["(im1b" + str(i) + "==200?1:0)" for i in band_index])
if not op.exists(self.modis_annual_snow_map):
bandMathApp = band_math([modis_snowserie],
self.modis_annual_snow_map, expression,
self.ram, otb.ImagePixelType_uint16)
bandMathApp.ExecuteAndWriteOutput()
bandMathApp = None
shutil.copy2(self.modis_annual_snow_map, self.path_out)
# Compute intersection of the raster footprint
intersection, srs = get_raster_intersection(self.annual_snow_map,
self.modis_annual_snow_map)
# Export intersection as shapefile
intersection_shapefile = op.join(self.path_tmp, "intersection.shp")
write_poly_to_shapefile(intersection, intersection_shapefile, srs)
# Crop to intersection S2 map
s2_cropped = self.annual_snow_map.replace(".tif", "_cropped.tif")
gdal.Warp(s2_cropped,
self.annual_snow_map,
format='GTiff',
cutlineDSName=intersection_shapefile,
cropToCutline=True,
dstNodata=-1,
outputType=gdal.GDT_Int16)
shutil.copy2(s2_cropped, self.path_out)
# Crop to intersection MODIS map
modis_cropped = self.modis_annual_snow_map.replace(
".tif", "_cropped.tif")
gdal.Warp(modis_cropped,
self.modis_annual_snow_map,
format='GTiff',
cutlineDSName=intersection_shapefile,
cropToCutline=True,
dstNodata=-1,
outputType=gdal.GDT_Int16)
shutil.copy2(modis_cropped, self.path_out)
# Crop to intersection DEM
dem_cropped = op.join(self.path_tmp, "dem_cropped.tif")
gdal.Warp(dem_cropped,
self.dem,
format='GTiff',
cutlineDSName=intersection_shapefile,
cropToCutline=True,
dstNodata=-1,
outputType=gdal.GDT_Int16)
shutil.copy2(dem_cropped, self.path_out)
# Reproject the DEM onto MODIS footprint
dem_cropped_reprojected = op.join(self.path_tmp,
"dem_cropped_reprojected.tif")
super_impose_app = super_impose(modis_cropped, dem_cropped,
dem_cropped_reprojected, "bco", -1,
self.ram, otb.ImagePixelType_int16)
super_impose_app.ExecuteAndWriteOutput()
super_impose_app = None
shutil.copy2(dem_cropped_reprojected, self.path_out)
compute_annual_stats(s2_cropped, dem_cropped, modis_cropped,
dem_cropped_reprojected, self.path_out,
"intersection")
for i in archivos:
print('Archivo:' + i)
data, xmin, ymin, xmax, ymax, nx, ny = G16.extraeNetCDFL2(i)
#print data, xmin, ymin, xmax, ymax, nx, ny
G16.creaTiff(data, xmin, ymin, xmax, ymax, nx, ny)
ds = gdal.Open('tmp.tif')
gdal.Translate('tmp_rec.tif',ds,options = gdal.TranslateOptions(projWin=coor))
ds = gdal.Open('tmp_rec.tif')
gdal.Warp('tmp_rec_4326.tif',ds,options=gdal.WarpOptions(dstSRS='EPSG:4326',dstNodata=-9999.000))
ds = gdal.Open('tmp_rec_4326.tif')
gdal.Translate('tmp_rec_4326_rec.tif',ds,options = gdal.TranslateOptions(projWin=coor2))
ds = gdal.Open('tmp_rec_4326_rec.tif')
data = ds.ReadAsArray()
data = data - 273.15
data[data == -9999.0] = np.nan
#data[data >= 0.0] = np.nan
#np.flipud(data)
def calculate(self, process_path):
qgs = QgsApplication([], False)
qgs.initQgis()
Processing.initialize()
QgsApplication.processingRegistry().addProvider(QgsNativeAlgorithms())
gdal.AllRegister()
#for alg in QgsApplication.processingRegistry().algorithms():
# print(alg.id(), "->", alg.displayName())
# read mdt data
inputRaster = self.input_mdt
process_path = process_path
outPath2 = self.output_file
gdalRaster = gdal.Open(str(inputRaster))
x = gdalRaster.RasterXSize
y = gdalRaster.RasterYSize
geo = gdalRaster.GetGeoTransform()
minx = geo[0]
maxy = geo[3]
maxx = minx + geo[1]*x
miny = maxy + geo[5]*y
#extent_raster = str(minx) + "," + str(maxx) + "," + str(miny) + "," + str(maxy)
#pixelSize = geo[1]
band_mdt = gdalRaster.GetRasterBand(1)
#data_mdt = band_mdt.ReadAsArray(0, 0, x, y)
Processing.initialize()
# mdt_interp = QFileInfo(QgsApplication.qgisUserDatabaseFilePath()).path() + "/mdt_interp"
# Processing.runAlgorithm("grass7:r.surf.idw", None, inputRaster, 12, False, extent_raster, pixelSize, mdt_interp)
# mdt = mdt_interp + "." + "tif"
#
# gdalMDT = gdal.Open(str(mdt_interp) + "." + "tif")
# x_mdt = gdalMDT.RasterXSize
# y_mdt = gdalMDT.RasterYSize
# geo_mdt = gdalMDT.GetGeoTransform()
# band_mdt = gdalMDT.GetRasterBand(1)
# data_mdt = band_mdt.ReadAsArray(0,0,x_mdt,y_mdt)
# coeficients a and b of the regression lines, y = ax + b, used for mean monthly precipitation, y(mm), as a function of altitude, x(m)
# a = 0.99
# b = 542.22
# precip_mul = numpy.multiply(data_mdt,a)
# precipitat = precip_mul + b
# precipitation = numpy.array(precipitat)
# recharge = numpy.multiply(precipitation, 0.15)
recharge_without_rec = process_path + "recharge_without_rec"
#Processing.runAlgorithm("gdal:rastercalculator",{
# 'INPUT_A': inputRaster,
# 'BAND_A': 1,
# 'INPUT_B': None,
# 'BAND_B': -1,
# 'INPUT_C': None,
# 'BAND_C': -1,
# 'INPUT_D': None,
# 'BAND_D': -1,
# 'INPUT_E': None,
# 'BAND_E': -1,
# 'INPUT_F': None,
# 'BAND_F': -1,
# 'FORMULA': '(A*0.99+542.22)*0.15',
# 'NO_DATA': None,
# 'RTYPE': 6,
# 'EXTRA': '',
# 'OPTIONS': '',
# 'OUTPUT':recharge_without_rec
#})
Processing.runAlgorithm("grass7:r.mapcalc.simple", {
'a': str(inputRaster),
'b': None,
'c': None,
'd': None,
'e': None,
'f': None,
'expression': "((A*0.99)+542.22)*0.15",
'output': recharge_without_rec,
'GRASS_REGION_PARAMETER': None,
'GRASS_REGION_CELLSIZE_PARAMETER': 0,
'GRASS_RASTER_FORMAT_OPT': '',
'GRASS_RASTER_FORMAT_META': ''
})
# Create an output imagedriver with the multiplication result
# driver2 = gdal.GetDriverByName( "GTiff" )
# outData2 = driver2.Create(str(recharge_without_rec+'.'+'tif'), x,y,1, gdal.GDT_Float32)
# outData2.GetRasterBand(1).WriteArray(recharge)
# outData2.SetGeoTransform(geo)
#outData2 = None
recharge_without_rec_file = gdal.Open(recharge_without_rec)
recharge_without_rec_rep = process_path + "recharge_without_rec_rep"
gdal.Warp(recharge_without_rec_rep, recharge_without_rec_file, dstSRS="EPSG:3763")
#Processing.runAlgorithm("gdal:assignprojection",
# {'INPUT': recharge_without_rec,
# 'CRS': QgsCoordinateReferenceSystem('EPSG:3763')})
# indexes for topography for the two methods
#numberRows = int(self.tableWidget.rowCount())
#numberColumns = int(self.tableWidget.columnCount())
#classes = ''
#lista = []
#for i in range(0,numberRows):
# for j in range(0,numberColumns):
# self.line = self.tableWidget.item(i,j)
# lista = lista + [str(self.line.text())]
# string = ","
# intervalos = string.join(lista)
#results = list(map(int, lista))
#QMessageBox.about(self, 'teste', str(results))
Processing.initialize()
result = process_path + "/result.tif"
Processing.runAlgorithm("native:reclassifybytable", {
'INPUT_RASTER': recharge_without_rec_rep,
'RASTER_BAND': 1, 'TABLE': self.rattings,
'NO_DATA': -9999, 'RANGE_BOUNDARIES': 0, 'NODATA_FOR_MISSING': False, 'DATA_TYPE': 5,
'OUTPUT': result})
# add result into canvas
#file_info_norm = QFileInfo(str(outPath2))
# QMessageBox.about(self, "teste", str(file_info_norm))
#rlayer_new_norm = QgsRasterLayer(outPath2, file_info_norm.fileName(), 'gdal')
# QMessageBox.about(self, "teste", str(rlayer_new_norm))
#QgsProject.instance().addMapLayer(rlayer_new_norm)
#self.iface.canvas.setExtent(rlayer_new_norm.extent())
# set the map canvas layer set
#self.iface.canvas.setLayers([rlayer_new_norm])
# add result into canvas
# file_info_recharge = QFileInfo(outPath2)
# if file_info_recharge.exists():
# layer_name_recharge = file_info_recharge.baseName()
# else:
# return False
# rlayer_new_recharge = QgsRasterLayer(outPath2, layer_name_recharge)
# if rlayer_new_recharge.isValid():
# QgsMapLayerRegistry.instance().addMapLayer(rlayer_new_recharge)
# layer_prec_recharge = QgsMapCanvasLayer(rlayer_new_recharge)
# layerList_recharge = [layer_prec_recharge]
# extent_recharge = self.iface.canvas.setExtent(rlayer_new_recharge.extent())
# self.iface.canvas.setLayerSet(layerList_recharge)
# self.iface.canvas.setVisible(True)
# return True
# else:
# return False
#QMessageBox.information(self, self.tr( "Finished" ), self.tr( "Net Recharge completed." ) )
out_raster = gdal.Open(result)
gdal.Warp(outPath2, out_raster, dstSRS="EPSG:3857")
intersectionWkt = footprint
else:
request = ogr.CreateGeometryFromWkt(requestWkt)
intersection = footprint.Intersection(request)
intersectionWkt = intersection.ExportToWkt()
print 'Intersection WKT of ' + request.ExportToWkt(
) + ' with ' + footprint.ExportToWkt() + ' is ' + intersectionWkt
#quit()
directory = os.path.dirname(filename)
print "Path is " + directory
csvFile = open(directory + '/cutline.csv', 'w')
csvFile.write('ID, WKT\n')
csvFile.write('1, "' + intersectionWkt + '"\n')
csvFile.close()
prjFile = open(directory + '/cutline.prj', 'w')
prjFile.write('EPSG:4326')
prjFile.close()
tmp_ds = gdal.Warp('temp',
filename,
format='MEM',
cutlineDSName=directory + '/cutline.csv',
cropToCutline=True,
dstSRS='EPSG:4326')
gdal.Translate(directory + '/out.tiff', tmp_ds, format='GTiff')
# , photometric = 'RGB')
def clip(ds, outLayer, no_data=None, rect_file=None,
enlarge=10, save_cache=False, ext=None, new=True):
# Open the data source and read in the extent
t = ds.GetGeoTransform()
x_min, x_max, y_min, y_max = outLayer.GetExtent()
ulX, ulY = geo2imagexy(ds, x_min, y_min)
lrX, lrY = geo2imagexy(ds, x_max, y_max)
clip_range = [min(ulX, lrX), min(ulY, lrY),
abs(ulX - lrX) + 1, abs(ulY - lrY) + 1]
ul_lon = t[0] + t[1] * clip_range[0] + t[2] * clip_range[1]
ul_lat = t[3] + t[4] * clip_range[0] + t[5] * clip_range[1]
lr_lon = t[0] + t[1] * (clip_range[0] + clip_range[2]) + \
t[2] * (clip_range[1] + clip_range[3])
lr_lat = t[3] + t[4] * (clip_range[0] + clip_range[2]) + \
t[5] * (clip_range[1] + clip_range[3])
bound = [min(ul_lon, lr_lon), min(ul_lat, lr_lat),
max(ul_lon, lr_lon), max(ul_lat, lr_lat)]
if save_cache:
cache_dir = 'cache'
if not os.path.exists(cache_dir):
os.mkdir(cache_dir)
if ext is None:
poly_file = rep_file(cache_dir, 'poly.tif')
burn_file = rep_file(cache_dir, 'burn.tif')
elif new:
poly_file = rep_file(cache_dir, str(ext) + '_poly.tif')
burn_file = rep_file(cache_dir, str(ext) + '_burn.tif')
else:
poly_file = os.path.join(cache_dir, str(ext) + '_poly.tif')
burn_file = os.path.join(cache_dir, str(ext) + '_burn.tif')
else:
poly_file = '/vsimem/_poly.tif'
burn_file = '/vsimem/_burn.tif'
# set no data
if ds.GetRasterBand(1).GetNoDataValue() is not None:
no_data = ds.GetRasterBand(1).GetNoDataValue()
if no_data is None:
raise(ValueError("no_data must be initialed"))
# create temp bool in_poly tif
has_old = os.path.exists(burn_file) and not new
# clip with rectangle
if rect_file is None:
rect_file = '/vsimem/_rect.tif'
option = gdal.WarpOptions(multithread=True, options=CONFIG,
creationOptions=CREATION, outputBounds=bound,
dstSRS=outLayer.GetSpatialRef(), dstNodata=no_data,
xRes=t[1], yRes=t[5], srcNodata=no_data,
resampleAlg=gdal.GRA_NearestNeighbour)
rect = gdal.Warp(rect_file, ds, options=option)
if not has_old:
# set geotransform
trans = list(rect.GetGeoTransform())
trans[1] = trans[1] / enlarge
trans[5] = trans[5] / enlarge
# set SpatialReference
srs = outLayer.GetSpatialRef()
zeros_tif(poly_file, int(clip_range[2] * enlarge),
int(clip_range[3] * enlarge), 1,
gdal.GDT_Byte, trans, srs, no_data=2)
poly_ds = gdal.Open(poly_file, gdal.GA_Update)
# Rasterize
gdal.RasterizeLayer(poly_ds, [1], outLayer, burn_values=[
1], options=['ALL_TOUCHED=TRUE'])
poly_ds = None
option = gdal.WarpOptions(multithread=True, options=CONFIG,
creationOptions=CREATION,
xRes=rect.GetGeoTransform()[1],
yRes=rect.GetGeoTransform()[5],
resampleAlg=gdal.GRA_Average,
outputType=gdal.GDT_Float32)
burn_ds = gdal.Warp(burn_file, poly_file, options=option)
burn_ds.GetRasterBand(1).SetNoDataValue(0)
# return bool matrix in polygon
burn_band = burn_ds.GetRasterBand(1)
burn_data = burn_band.ReadAsArray()
else:
burn_ds = gdal.Open(burn_file)
burn_band = burn_ds.GetRasterBand(1)
burn_data = burn_band.ReadAsArray()
# change rect
for c in range(1, rect.RasterCount + 1):
rect_band = rect.GetRasterBand(c)
if rect_band.GetNoDataValue() is None:
rect_band.SetNoDataValue(no_data)
block_write(rect, [rect_band, burn_band], rect_band, map_burn)
burn_ds = None
poly_ds = None
return rect, burn_data
def run(
url=None,
obstmp=None,
clipshpfn=None,
inputs=None,
tmpdir=None,
models=None,
nocleanup=False,
**args,
):
"""
Load and prepare the data required for the change detection algorithms
and then pass this data to the algorithm. Use `args` to parametrise.
"""
log("# Loading models")
loaded_models = []
for m in models:
config = deepcopy(m)
name = config.pop("name")
outfn = config.pop("output")
driver = config.pop("driver")
inputs = config.pop("inputs")
log(f"Model: {name} -> {outfn}")
try:
model = get_model(name, **config)
if model.verbose:
model.log = log
except IncorrectChecksumError as e:
warning(f"Model has an incorrect SHA256 checksum, exiting...")
sys.exit(1)
loaded_models.append(model)
log("# Retrieving NRT observation details")
url = normalise_url(url)
log(f"Obs. URL: {url}")
obswkt = get_bounds(url)
obsdate = parse_obsdate(url)
log(f"Obs. Date: {obsdate}")
log(f"Obs. WKT: {wktfmt(obswkt)}")
source = DEASentinel2()
# Determine the minimal set of bands required across all models
def name(x):
if isinstance(x, int):
return source.bands[x]
else:
return x
bands = {band: False for band in source.bands}
for model in loaded_models:
try:
required = [name(b) for b in model.required_bands]
except AttributeError:
required = source.bands
for band in required:
bands[band] = True
bands = [k for k, v in bands.items() if v is True]
log(f"Req.Bands: {','.join(bands)}")
if len(bands) == 0:
# get at least one band
bands = ["B02"]
obsgeo, obsprj, obsdata, mask = source.get_observations(
url, bands_required=bands)
ysize, xsize = mask.shape
obspoly = polygon_from_geobox(obsgeo, xsize, ysize)
log(f"# Preparing ancillary data")
log(f"Writing mask to mask.tif")
driver = gdal.GetDriverByName("GTiff")
fd = driver.Create("mask.tif", xsize, ysize, 1, gdal.GDT_Byte)
fd.SetGeoTransform(obsgeo)
fd.SetProjection(obsprj)
ob = fd.GetRasterBand(1)
ob.WriteArray(mask)
ob.SetNoDataValue(0)
del fd
ysize, xsize, psize = obsdata.shape
log(f"Writing observation data to {obstmp}. Data has {psize} bands.")
driver = gdal.GetDriverByName("GTiff")
fd = driver.Create(obstmp, xsize, ysize, psize, gdal.GDT_Float32)
fd.SetGeoTransform(obsgeo)
fd.SetProjection(obsprj)
for i in range(fd.RasterCount):
ob = fd.GetRasterBand(i + 1)
ob.WriteArray(obsdata[:, :, i])
ob.SetNoDataValue(np.nan)
ob.SetDescription(bands[i])
del fd
log(f"Determining ancillary files required")
outputs = []
inputfns = []
for model in models:
name = model["name"]
log(f"Checking '{name}' model")
output = model["output"]
ips = model["inputs"]
for ip in ips:
fn = ip["filename"]
if fn not in outputs:
inputfns.append(fn)
outputs.append(output)
log(f"# Warping and clipping ancillary data")
# Get the unique inputs
inputfns = [*{*inputfns}]
if len(inputfns) > 0:
log("Determining clip area from NRT observation")
clipshpfn = generate_clip_shape_from(obstmp, clipshpfn)
if not clipshpfn.startswith("/vsimem"):
log(f"Proj: {obsprj}")
log(f"Saving clipping area to disk as '{clipshpfn}'")
else:
log(f"No ancillary datas are required!")
datamap = {}
obssr = osr.SpatialReference()
obssr.ImportFromProj4(obsprj)
for afn in inputfns:
ofn = f"{tmpdir}/{uuid.uuid4()}"
fd = gdal.Open(afn)
geo = fd.GetGeoTransform()
prj = fd.GetProjection()
insr = osr.SpatialReference()
insr.ImportFromProj4(prj)
insr_to_obssr = osr.CoordinateTransformation(insr, obssr)
poly = polygon_from_geobox(geo, fd.RasterXSize, fd.RasterYSize)
poly.Transform(insr_to_obssr)
if not poly.Intersects(obspoly):
raise InputDataError(
f"Input data '{afn}' does not intersect observation.")
log(f"Clipping and warping input '{afn}' to '{ofn}'")
fd = gdal.Warp(ofn,
fd,
cutlineDSName=clipshpfn,
cropToCutline=True,
dstSRS=obsprj)
datamap[afn] = ofn
# Get processing configuration parameters
tilewidth = args.pop("tilewidth", None)
obsscale = args.pop("obsscale", None)
# Scale observation data
if obsscale is not None:
log(f"Scaling observation data by {obsscale}")
obsdata *= float(obsscale)
log("# Applying loaded models to data")
for model, m in zip(loaded_models, models):
config = deepcopy(m)
name = config.pop("name")
log(f"@Running '{name}' model")
# Update model config based on new information from observation
config["obsurl"] = url
config["obswkt"] = obswkt
config["obsdate"] = obsdate
config["geo"] = obsgeo
config["prj"] = obsprj
config["bands"] = bands # possibly reduced set of bands
log("Observation data:")
log(f" data min: {np.nanmin(obsdata)} max: {np.nanmax(obsdata)}")
log(f" pixel resolution: {obsgeo[1]:.4f} x {obsgeo[5]:.4f}")
model.update(**config)
# Prepare all the appropriate ancillary data sets and pass the
# observation data as the last one in the list.
datas = [mask.copy()]
outfn = m["output"]
inputfns = m["inputs"]
log("Loading model inputs:")
for ip in inputfns:
fn = datamap[ip["filename"]]
fd = gdal.Open(fn)
geo = fd.GetGeoTransform()
# First assume bands are the same as source
ipbands = source.bands
try:
# Then see if they are overwritten in config
ipbands = ip["bands"]
except KeyError:
# If that fails, try to get bandnames from file
fbands = []
for i in range(fd.RasterCount):
rb = fd.GetRasterBand(i + 1)
desc = rb.GetDescription()
if len(desc) > 0:
fbands.append(desc)
if len(fbands) == fd.RasterCount:
ipbands = fbands
log(f" - path: {ip['filename']}")
log(f" bands: {','.join(ipbands)}")
notreq = set(source.bands) - set(bands)
toload = [b for b in ipbands if b not in notreq]
bandidx = [
i + 1 for i, b in zip(range(fd.RasterCount), ipbands)
if b not in notreq
]
log(f" loading: {','.join(toload)}")
nbands = len(bandidx)
data = np.empty((ysize, xsize, nbands), dtype=np.float32)
for i, bi in enumerate(bandidx):
band = fd.GetRasterBand(bi)
band.ReadAsArray(
buf_type=gdal.GDT_Float32,
buf_xsize=xsize,
buf_ysize=ysize,
buf_obj=data[:, :, i],
)
nodata = fd.GetRasterBand(1).GetNoDataValue()
data[data == nodata] = np.nan
nnan = np.count_nonzero(np.isnan(data))
nval = xsize * ysize * nbands
pnan = nnan / nval
if pnan > 0.9:
warning(f"clipped input '{afn}' has more than 90% no data")
scale = ip.pop("scale", None)
if scale is not None:
log(f" scaling: {scale}")
data *= scale
log(f" data min: {np.nanmin(data)} max: {np.nanmax(data)}")
log(f" pixel resolution: {geo[1]:.4f} x {geo[5]:.4f}")
datas.append(data)
datas.append(obsdata)
log(f"Output: {outfn}")
log("Running model predictions")
# The model is responsible for saving its prediction to disk (or memory
# using /vsimem) as it is best placed to make a decision on the format, etc.
# A simple model only needs to implement the `predict` method but can also
# implement `predict_and_save` if more control of writing output is needed.
if tilewidth:
model = TiledPrediction(model, int(tilewidth))
model.predict_and_save(outfn, *datas)
datamap[outfn] = outfn
log("Finished running predictions")
if nocleanup:
return
log("# Cleaning up")
for k, fn in datamap.items():
log(f"Removing {fn}")
gdal.Unlink(fn)
def reproject_raster(img,
resAlg,
out,
ref=None,
dstSRS=None,
srcNoData=None,
dstNodata=None):
"""
:param img:
:param resAlg:
:param out:
:param ref:
:param dstSRS:
:param srcNoData:
:param dstNodata:
:return:
"""
GDAL_resAlg = {
"near": gdalconst.GRA_NearestNeighbour,
"bilinear": gdalconst.GRA_Bilinear,
"average": gdalconst.GRA_Average,
"cubic": gdalconst.GRA_Cubic,
"mode": gdalconst.GRA_Mode
}
# check for new CRS
if dstSRS is None:
dstSRS = img.GetProjection()
if ref is not None:
src_proj = img.GetProjection()
src_trans = img.GetGeoTransform()
ref_proj = ref.GetProjection()
ref_trans = ref.GetGeoTransform()
xdim = ref.RasterXSize
ydim = ref.RasterYSize
bands = img.RasterCount
# check datatype:
if resAlg == 'near':
dtype = img.GetRasterBand(1).DataType
else:
dtype = 6 # Float32
resAlg = GDAL_resAlg[resAlg]
dst = gdal.GetDriverByName('GTiff').Create(
out, xdim, ydim, bands, dtype, options=['COMPRESS=DEFLATE'])
dst.SetGeoTransform(ref_trans)
dst.SetProjection(ref_proj)
# check NoData value:
if srcNoData is not None:
img.GetRasterBand(1).SetNoDataValue(srcNoData)
if dstNodata is not None:
dst.GetRasterBand(1).SetNoDataValue(dstNodata)
dst.GetRasterBand(1).Fill(dstNodata)
gdal.ReprojectImage(img, dst, src_proj, ref_proj, resAlg)
else:
gdal.ReprojectImage(img, dst, src_proj, ref_proj, resAlg)
del dst
return gdal.Open(out)
else:
# check NoData value:
if dstNodata is not None:
fil = gdal.Warp(out,
img,
dstSRS=dstSRS,
resampleAlg=resAlg,
dstNodata=dstNodata)
else:
fil = gdal.Warp(out, img, dstSRS=dstSRS, resampleAlg=resAlg)
del fil
return gdal.Open(out)
# p is of the form
# VNL_v2_npp_2020_global_vcmslcfg_c202102150000.median_masked.tif.gz,
year = p.name[11:15]
template = f"data/azml/conus_hls_median_{year}.vrt"
cutline_layer = "data/azml/conus.geojson"
output_file = f"/vsiaz/hls/viirs_{year}.tif"
input_ds = gdal.Open("/vsigzip/" + str(p))
with rio.open(template) as t:
bounds = list(t.bounds)
crs = t.crs
# Crop the dataset to CONUS so calculations can be done in memory
cropped_ds = gdal.Warp(
"",
input_ds,
format="VRT",
cutlineDSName=cutline_layer,
cropToCutline=True,
)
# Normalize values and convert to integer for smaller disk size
values = cropped_ds.ReadAsArray()
# Some very small negatives
values[values < 0] = 0
# Dave: sqrt of max normalized values. Here we use 5k as a "theoretical"
# max among all years (given max among 2013, 2016, and 2020 as ~3k).
# Then multiply by 10000 to save as integer
values = np.sqrt(values / 5000.0) * 10000.0
output_ds = gdal_array.OpenArray(np.int16(values))
output_band = output_ds.GetRasterBand(1)
output_band.SetScale(0.0001)
from osgeo import gdal
input_cutline = sys.argv[1]
input_raster = sys.argv[2]
output_raster = sys.argv[3]
ds = gdal.Warp(output_raster,
input_raster,
format = 'GTiff',
cutlineDSName = input_cutline,
cutlineLayer = 'extent',
dstNodata = 0)
ds = None
def makeNDVIandEVI(state_1, rsb01, rsb02, rsb03, names, NdviPath, EviPath):
areas = {
'HuangHuaiHai': [482000, 3189000, 1574000, 4662000],
'ChangJiangZhongXiaYou': [-704000, 2596000, 1574000, 3841000],
'DongBei': [1033000, 4267000, 2207000, 5922000]
}
outputBounds = [-704000, 2596000, 2207000, 5922000]
if (state_1 is None):
print("state_1 is None")
return
if (rsb01 is None):
return
if (rsb02 is None):
return
if (rsb03 is None):
return
#state1是对遥感的像元质量检测，去掉没有云的
state1 = gdal_array.BandReadAsArray(state_1.GetRasterBand(1))
b3 = gdal_array.BandReadAsArray(rsb03.GetRasterBand(1))
b2 = gdal_array.BandReadAsArray(rsb02.GetRasterBand(1))
b1 = gdal_array.BandReadAsArray(rsb01.GetRasterBand(1))
## NDVI
ndvi = ((b2 - b1) * 1.0) / ((b2 + b1) * 1.0)
ndvi[numpy.isnan(ndvi)] = -9999
ndvi[numpy.isinf(ndvi)] = -9999
ndvi[ndvi > 1] = -9999
ndvi[ndvi < -1] = -9999
nodata = rsb02.GetRasterBand(1).GetNoDataValue()
ndvi[b2 == nodata] = -9999
nodata = rsb01.GetRasterBand(1).GetNoDataValue()
ndvi[b1 == nodata] = -9999
try:
state1_10 = state1 << 10
for i in range(0, 3600):
for j in range(0, 3600):
if state1_10[i][j] == 0:
ndvi[2 * i][2 * j] = -9999
ndvi[2 * i][2 * j + 1] = -9999
ndvi[2 * i + 1][2 * j] = -9999
ndvi[2 * i + 1][2 * j + 1] = -9999
except Exception as e:
print(e)
outNdvi = gdal_array.SaveArray(ndvi, "fgf", format="MEM", prototype=rsb01)
NdviFileName = 'MOD09GA.%s.%s.%s.tif' % (names[1], names[3], 'ndvi')
NdviFile = os.path.join(NdviPath, NdviFileName)
gdal.Warp(
NdviFile,
outNdvi,
outputBounds=outputBounds,
xRes=1000,
yRes=1000,
srcNodata=-9999,
dstNodata=-9999,
outputType=gdal.GDT_Float32,
dstSRS=
"+proj=aea +ellps=WGS84 +datum=WGS84 +lon_0=105 +lat_1=25 +lat_2=47 +units=m +"
)
#only huanghuaihai
areaNdviFileName = 'MOD09GA.%s.%s.%s.tif' % (names[1], names[3],
'huanghuaihai.ndvi')
areaNdviFile = os.path.join(NdviPath, areaNdviFileName)
gdal.Warp(areaNdviFile,
NdviFile,
outputBounds=[482000, 3189000, 1574000, 4662000])
#only dongbei
'''
def load_file(self, filename, env):
ds = gdal.Open(filename, gdal.GA_ReadOnly)
if not ds:
raise ValidationError(_("GDAL library was unable to open the file."))
if ds.RasterCount not in (3, 4):
raise ValidationError(_("Only RGB and RGBA rasters are supported."))
dsdriver = ds.GetDriver()
dsproj = ds.GetProjection()
dsgtran = ds.GetGeoTransform()
if dsdriver.ShortName not in SUPPORTED_DRIVERS:
raise ValidationError(
_("Raster has format '%(format)s', however only following formats are supported: %(all_formats)s.") # NOQA: E501
% dict(format=dsdriver.ShortName, all_formats=", ".join(SUPPORTED_DRIVERS))
)
if not dsproj or not dsgtran:
raise ValidationError(_("Raster files without projection info are not supported."))
data_type = None
alpha_band = None
has_nodata = None
for bidx in range(1, ds.RasterCount + 1):
band = ds.GetRasterBand(bidx)
if data_type is None:
data_type = band.DataType
elif data_type != band.DataType:
raise ValidationError(_("Complex data types are not supported."))
if band.GetRasterColorInterpretation() == gdal.GCI_AlphaBand:
assert alpha_band is None, "Multiple alpha bands found!"
alpha_band = bidx
else:
has_nodata = (has_nodata is None or has_nodata) and (
band.GetNoDataValue() is not None)
src_osr = osr.SpatialReference()
src_osr.ImportFromWkt(dsproj)
dst_osr = self.resource.srs.to_osr()
reproject = not src_osr.IsSame(dst_osr)
info = gdal.Info(filename, format='json')
geom = Geometry.from_geojson(info['wgs84Extent'])
self.footprint = ga.elements.WKBElement(bytearray(geom.wkb), srid=4326)
self.fileobj = env.file_storage.fileobj(component='raster_mosaic')
dst_file = env.raster_mosaic.workdir_filename(self.fileobj, makedirs=True)
co = ['COMPRESS=DEFLATE', 'TILED=YES', 'BIGTIFF=YES']
if reproject:
gdal.Warp(
dst_file,
filename,
options=gdal.WarpOptions(
format='GTiff',
dstSRS='EPSG:%d' % self.resource.srs.id,
dstAlpha=not has_nodata and alpha_band is None,
creationOptions=co,
),
)
else:
gdal.Translate(
dst_file,
filename,
options=gdal.TranslateOptions(
format='GTiff',
creationOptions=co
)
)
self.build_overview()
def btn_calculate(self):
if not self.folder_output.text():
QtGui.QMessageBox.critical(None, self.tr("Error"),
self.tr("Choose an output folder where the output will be saved."), None)
return
# Note that the super class has several tests in it - if they fail it
# returns False, which would mean this function should stop execution
# as well.
ret = super(DlgReportingSDG, self).btn_calculate()
if not ret:
return
layer_traj = [l for l in self.layer_traj_list if l.name() == self.layer_traj.currentText()][0]
layer_state = [l for l in self.layer_state_list if l.name() == self.layer_state.currentText()][0]
layer_perf = [l for l in self.layer_perf_list if l.name() == self.layer_perf.currentText()][0]
layer_lc = [l for l in self.layer_lc_list if l.name() == self.layer_lc.currentText()][0]
# Check that all of the layers have the same coordinate system and TODO
# are in 4326.
if layer_traj.crs() != layer_state.crs():
QtGui.QMessageBox.critical(None, self.tr("Error"),
self.tr("Coordinate systems of trajectory layer and state layer do not match."), None)
return
if layer_traj.crs() != layer_perf.crs():
QtGui.QMessageBox.critical(None, self.tr("Error"),
self.tr("Coordinate systems of trajectory layer and performance layer do not match."), None)
return
if layer_traj.crs() != layer_lc.crs():
QtGui.QMessageBox.critical(None, self.tr("Error"),
self.tr("Coordinate systems of trajectory layer and land cover layer do not match."), None)
return
# Resample the land cover data to match the resolutions of the other
# layers:
log('Reprojecting land cover...')
ds_lc = reproject_dataset(layer_lc.dataProvider().dataSourceUri(),
layer_traj.dataProvider().dataSourceUri(),
layer_traj.rasterUnitsPerPixelX(),
layer_lc.crs().toWkt())
log('crs: {}'.format(layer_lc.crs().toWkt()))
temp_lc_file = tempfile.NamedTemporaryFile(suffix='.tif').name
# ds_lc = gdal.Translate(temp_lc_file,
# layer_lc.dataProvider().dataSourceUri(),
# format='GTiff',
# outputType=gdal.GDT_Int16,
# xRes=layer_traj.rasterUnitsPerPixelX,
# yRes=layer_traj.rasterUnitsPerPixelY,
# outputSRS=layer_lc.crs().toWkt(),
# resampleAlg=gdal.GRA_Mode)
log('Reprojection of land cover finished.')
# Check that all of the layers have the same resolution
def res(layer):
return (round(layer.rasterUnitsPerPixelX(), 10), round(layer.rasterUnitsPerPixelY(), 10))
if res(layer_traj) != res(layer_state):
QtGui.QMessageBox.critical(None, self.tr("Error"),
self.tr("Resolutions of trajectory layer and state layer do not match."), None)
return
if res(layer_traj) != res(layer_perf):
QtGui.QMessageBox.critical(None, self.tr("Error"),
self.tr("Resolutions of trajectory layer and performance layer do not match."), None)
return
# Check that all of the layers cover the area of interest
if not self.aoi.within(QgsGeometry.fromRect(layer_traj.extent())):
QtGui.QMessageBox.critical(None, self.tr("Error"),
self.tr("Area of interest is not entirely within the trajectory layer."), None)
return
if not self.aoi.within(QgsGeometry.fromRect(layer_state.extent())):
QtGui.QMessageBox.critical(None, self.tr("Error"),
self.tr("Area of interest is not entirely within the state layer."), None)
return
if not self.aoi.within(QgsGeometry.fromRect(layer_perf.extent())):
QtGui.QMessageBox.critical(None, self.tr("Error"),
self.tr("Area of interest is not entirely within the performance layer."), None)
return
if not self.aoi.within(QgsGeometry.fromRect(layer_lc.extent())):
QtGui.QMessageBox.critical(None, self.tr("Error"),
self.tr("Area of interest is not entirely within the land cover layer."), None)
return
log('Combining degradation layers...')
ds_traj = gdal.Open(layer_traj.dataProvider().dataSourceUri())
ds_state = gdal.Open(layer_state.dataProvider().dataSourceUri())
ds_perf = gdal.Open(layer_perf.dataProvider().dataSourceUri())
# Note trajectory significance is band 2
traj_band = ds_traj.GetRasterBand(2)
block_sizes = traj_band.GetBlockSize()
x_block_size = block_sizes[0]
y_block_size = block_sizes[1]
xsize = traj_band.XSize
ysize = traj_band.YSize
driver = gdal.GetDriverByName("GTiff")
temp_deg_file = tempfile.NamedTemporaryFile(suffix='.tif').name
dst_ds = driver.Create(temp_deg_file, xsize, ysize, 1, gdal.GDT_Int16, ['COMPRESS=LZW'])
lc_traj = ds_lc.GetGeoTransform()
dst_ds.SetGeoTransform(lc_traj)
dst_srs = osr.SpatialReference()
dst_srs.ImportFromWkt(ds_traj.GetProjectionRef())
dst_ds.SetProjection(dst_srs.ExportToWkt())
state_band = ds_state.GetRasterBand(1)
perf_band = ds_perf.GetRasterBand(1)
lc_band = ds_lc.GetRasterBand(1)
log('Traj size: {}, {}'.format(traj_band.XSize, traj_band.YSize))
log('State size: {}, {}'.format(state_band.XSize, state_band.YSize))
log('Perf size: {}, {}'.format(perf_band.XSize, perf_band.YSize))
log('LC size: {}, {}'.format(lc_band.XSize, lc_band.YSize))
xsize = traj_band.XSize
ysize = traj_band.YSize
blocks = 0
for y in xrange(0, ysize, y_block_size):
if y + y_block_size < ysize:
rows = y_block_size
else:
rows = ysize - y
for x in xrange(0, xsize, x_block_size):
if x + x_block_size < xsize:
cols = x_block_size
else:
cols = xsize - x
deg = traj_band.ReadAsArray(x, y, cols, rows)
state_array = state_band.ReadAsArray(x, y, cols, rows)
perf_array = perf_band.ReadAsArray(x, y, cols, rows)
lc_array = lc_band.ReadAsArray(x, y, cols, rows)
# log('type lc_deg: {}'.format(type(lc_deg)))
# log('type lc_array: {}'.format(type(lc_array)))
deg[lc_array == -1] = -1
deg[(state_array == -1) & (perf_array == -1)] = -1
dst_ds.GetRasterBand(1).WriteArray(deg, x, y)
del deg
blocks += 1
dst_ds = None
ds_traj = None
ds_state = None
ds_perf = None
ds_lc = None
log('Degradation layers combined.')
# Use 'processing' to clip and crop
mask_layer = QgsVectorLayer("Polygon?crs=epsg:4326", "mask", "memory")
mask_pr = mask_layer.dataProvider()
fet = QgsFeature()
fet.setGeometry(self.aoi)
mask_pr.addFeatures([fet])
mask_layer_file = tempfile.NamedTemporaryFile(suffix='.shp').name
QgsVectorFileWriter.writeAsVectorFormat(mask_layer, mask_layer_file, "CP1250", None, "ESRI Shapefile")
out_file = os.path.join(self.folder_output.text(), 'sdg_15_3_degradation.tif')
gdal.Warp(out_file, temp_deg_file, format='GTiff', cutlineDSName=mask_layer_file, cropToCutline=True, dstNodata=9999)
# Load the file add it to the map, and style it
style_sdg_ld(out_file)
# Calculate area degraded, improved, etc.
deg_equal_area_tempfile = tempfile.NamedTemporaryFile(suffix='.shp').name
ds_equal_area = gdal.Warp(deg_equal_area_tempfile, out_file, dstSRS='EPSG:54009')
deg_gt = ds_equal_area.GetGeoTransform()
res_x = deg_gt[1]
res_y = -deg_gt[5]
deg_array = ds_equal_area.GetRasterBand(1).ReadAsArray()
area_deg = np.sum(deg_array == -1) * res_x * res_y / 1e6
area_stable = np.sum(deg_array == 0) * res_x * res_y / 1e6
area_imp = np.sum(deg_array == 1) * res_x * res_y / 1e6
area_water = np.sum(deg_array == 2) * res_x * res_y / 1e6
area_urban = np.sum(deg_array == 3) * res_x * res_y / 1e6
header = ("Area Degraded", "Area Stable", "Area Improved", "Water Area", "Urban Area")
values = (area_deg, area_stable, area_imp, area_water, area_urban)
out_file_csv = os.path.join(self.folder_output.text(), 'sdg_15_3_degradation.csv')
with open(out_file_csv, 'wb') as fh:
writer = csv.writer(fh, delimiter=',')
for row in zip(header, values):
writer.writerow(row)
log('Area deg: {}, stable: {}, imp: {}, water: {}, urban: {}'.format(area_deg, area_stable, area_imp, area_water, area_urban))
self.close()
utm_dir = 'data_UTM/input/'
make_directory(utm_dir)
make_directory(utm_dir + 'temperature_degC/')
make_directory(utm_dir + 'precipitation_mm/')
make_directory(utm_dir + 'solar_radiation/')
make_directory(utm_dir + 'month_prcp_day/')
make_directory(utm_dir + 'snow_gm2/')
make_directory(utm_dir + 'vegetation_percent_cover/')
make_directory(utm_dir + 'wind_speed_monthly_clipped/')
make_directory(utm_dir + 'soil/')
dem_file_name = 'dem_aoi.tif'
dem_file_path = os.path.join(data_dir, dem_file_name)
dem_utm_path = os.path.join(utm_dir, dem_file_name)
gdal.Warp(dem_utm_path, dem_file_path, srcSRS='EPSG:4326', dstSRS='EPSG:32648')
dem_arr = read_raster_as_array(dem_utm_path)
dem_arr[dem_arr == 0] = np.nan
RasterSave(dem_arr, dem_utm_path, dem_utm_path)
raindays = [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 3.0, 3.0, 1.0, 0.0, 0.0, 0.0]
for k in range(0, 12):
num = str(k + 1)
if k + 1 < 10:
num = '0' + str(k + 1)
temp_file_path = 'temperature_degC/' + 'wc2.0_30s_tave_' + num + '.tif'
temp_in_path = os.path.join(data_dir, temp_file_path)
scaled_temp = os.path.join(scaled_dir, temp_file_path)
def project(self):
print('Generating projected output file: ' + self.output_file_name)
gdal.Warp(self.output_file_name, self.output_file, dstSRS='EPSG:4326')