def _merge_layers(rows,
cols,
geotransform,
spatialreference,
array1,
array2,
min1,
max1,
min2,
max2,
nodata1=None,
nodata2=None):
path = create_tmp_filename('', ".tif")
# find the indexes of the arrays
index1 = (array1 > min1) & (array1 <= max1) & (array1 != nodata1)
index2 = (array2 > min2) & (array2 <= max2) & (array2 != nodata2)
# merge array indexes
compound_index = index1 & index2
del index1, index2
# create a new raster
output_raster = gdal.GetDriverByName('GTiff').Create(
path, rows, cols, 1, gdal.GDT_Int16) # Open the file
output_raster.SetGeoTransform(geotransform)
srs = SpatialReference(wkt=spatialreference)
output_raster.SetProjection(srs.ExportToWkt())
# create raster from the compound_index of the two rasters
# TODO: the reshape slows the operation, use matrixes
output_raster.GetRasterBand(1).WriteArray(
compound_index.reshape(cols, rows))
return path
def gdal_translate(src_filename,
dst_filename,
dst_format="GTiff",
bands=None,
nodata=None,
projection=None,
options=None):
"""
Convert a raster image with the specified arguments (as if running from commandline)
:param argstring: command line arguments as string
:return: Result of gdal_translate process (success or failure)
"""
from osgeo import gdal
from osr import SpatialReference
if not options:
options = []
# Open existing dataset, subsetting bands if necessary
if bands:
tmp_file = src_filename + ".sub"
gdal_band_subset(src_filename, bands, tmp_file)
src_ds = gdal.Open(tmp_file)
else:
src_ds = gdal.Open(src_filename)
try:
#Open output format driver, see gdal_translate --formats for list
driver = gdal.GetDriverByName(dst_format)
#Output to new format
dst_ds = driver.CreateCopy(dst_filename, src_ds, 0, options)
if projection:
srs = SpatialReference()
srs.SetWellKnownGeogCS(projection)
dst_ds.SetProjection(srs.ExportToWkt())
if nodata is not None:
band = dst_ds.GetRasterBand(1)
band.SetNoDataValue(nodata)
finally:
#Properly close the datasets to flush to disk
dst_ds = None
src_ds = None
band = None
if bands and tmp_file:
os.remove(tmp_file)
def calc_elevation_models(self):
for item in range(len(self.data_lst)):
file_name = self.data_lst[item].strip(".xyz")
self.outfile = os.path.join(self.outfolder, file_name + ".tif")
x, y, z = np.loadtxt(self.data_lst[item], skiprows=1, unpack=True)
xmin, xmax, ymin, ymax = [min(x), max(x), min(y), max(y)]
#size of the grid
nx = (int(xmax - xmin + 1))
ny = (int(ymax - ymin + 1))
# Generate a regular grid to interpolate the data.
xi = np.linspace(xmin, xmax, nx)
yi = np.linspace(ymin, ymax, ny)
xi, yi = np.meshgrid(xi, yi)
# adding the z - values
zi = il.griddata((x, y), z, (xi, yi),
method='nearest') # linear, cubic, nearest
#--------------- Write to GeoTIFF ------------------------
nrows, ncols = np.shape(zi)
xres = (xmax - xmin) / float(ncols)
yres = (ymax - ymin) / float(nrows)
geotransform = (xmin, xres, 0, ymin, 0, yres)
self.outDS = gdal.GetDriverByName('GTiff').Create(
self.outfile, ncols, nrows, 1, gdal.GDT_Float32,
['TFW=YES', 'COMPRESS=PACKBITS'])
self.outDS.SetGeoTransform(geotransform) # Specify its coordinates
srs = SpatialReference() # Establish its coordinate encoding
srs.ImportFromEPSG(self.epsg) # WGS 84 / UTM zone 32N
self.outDS.SetProjection(
srs.ExportToWkt()) # Exports the coordinate system to the file
self.outDS.GetRasterBand(1).WriteArray(
zi) # Writes my array to the raster
#self.outDS.FlushCache()
self.data_out_lst.append(self.outfile)
self.outDS = None
zi = None
x, y, z = None, None, None
nrows, ncols = None, None
return [self.message, self.data_out_lst]