def write(self, rasterpath = None):
""" writes the chunk_bundle to its rasterpath (allows new filepath input) """
if not rasterpath:
rasterpath = self.rasterpath
# write with metadata using dnppy and arcpy.
if self.metadata and not self.force_scv:
raster.from_numpy()
# write without metadata using simple CV
else: pass
return
def write(self, rasterpath):
"""
writes the chunk_bundle to its rasterpath """
# write with metadata using dnppy and arcpy.
if self.metadata and not self.force_scv:
from dnppy import raster
raster.from_numpy(self._assemble_chunks(), self.metadata,
rasterpath)
# write without metadata using simple CV
else:
pass
return
def write(self, rasterpath):
"""
writes the chunk_bundle to its rasterpath """
# write with metadata using dnppy and arcpy.
if self.metadata and not self.force_scv:
from dnppy import raster
raster.from_numpy(self._assemble_chunks(), self.metadata, rasterpath)
# write without metadata using simple CV
else: pass
return
def write(self, rasterpath=None):
""" writes the chunk_bundle to its rasterpath (allows new filepath input) """
if not rasterpath:
rasterpath = self.rasterpath
# write with metadata using dnppy and arcpy.
if self.metadata and not self.force_scv:
raster.from_numpy()
# write without metadata using simple CV
else:
pass
return
def decibel_convert(filename):
"""
Converts the input UAVSAR .grd file into units of decibels. Note
that a .hdr file must be created and accompany the .grd/.inc files for this to work
:param filename: the full file path string for the .grd data file
:return outname: filepath to output file created by this function.
"""
#arcpy.CheckOutExtension("Spatial")
inRaster, meta = raster.to_numpy(filename)
dB_raster = 10 * numpy.log10(inRaster)
outname = filename.replace(".grd", "_dB.tif")
raster.from_numpy(dB_raster, meta, outname)
return outname
def decibel_convert(filename):
"""
Converts the input UAVSAR .grd file into units of decibels. Note
that a .hdr file must be created and accompany the .grd/.inc files for this to work
:param filename: the full file path string for the .grd data file
:return outname: filepath to output file created by this function.
"""
#arcpy.CheckOutExtension("Spatial")
inRaster, meta = raster.to_numpy(filename)
dB_raster = 10 * numpy.log10(inRaster)
outname = filename.replace(".grd", "_dB.tif")
raster.from_numpy(dB_raster, meta, outname)
return outname
def gap_fill_temporal(rasterlist, outdir = False):
"""
This function is designed to input a time sequence of rasters with partial voids and
output a copy of each input image with every pixel equal to the last good value taken.
This function will step forward in time through each raster and fill voids from the values
of previous rasters. The resulting output image will contain all the data that was in the
original image, with the voids filled with older data. A second output image will be
generated where the pixel values are equal to the age of each pixel in the image. So
if a void was filled with data thats 5 days old, the "age" raster will have a value of
"5" at that location.
Inputs:
rasterlist a list of filepaths for rasters with which to fill gaps
*the first item in this list will be the base raster
**values will be filled based on the list's ordering
(e.g. gap in raster 1 will first attempt to take the corresponding
value from raster 2, then 3 if raster 2 also contains a gap there)
outdir the path to the desired output folder
*optional - left "False" by default
**if left "False", the output tiff will be place in the same folder
as the first input raster
"""
#enforce the list of rasters to ensure it's sanitized
rasterlist = core.enf_filelist(rasterlist)
#create an empty list to store output arrays in
arr_list = []
#convert each raster in the input list to an array, and save its data to the list
for i, raster in enumerate(rasterlist):
item = rasterlist[i]
item_arr = to_numpy(item)
arr_list.append(item_arr[0].data)
#convert the list to a numpy array
arr_list = np.array(arr_list)
#set the lists of ranges of values for each dimension of the output array
xrange = range(0, np.shape(arr_list)[2])
yrange = range(0, np.shape(arr_list)[1])
zrange = range(0, np.shape(arr_list)[0])
#pull out the first array to be edited
new_arr = arr_list[0]
#loop through each x, y value
#if the first array's value at each location is "Not A Number",
#attempt to fill it with the corresponding value from the next array
#if no array has a corresponding value, it will be left as a "nan"
for i in yrange:
for j in xrange:
if np.isnan(new_arr[i,j]) == True:
x = 1
while x <= zrange[-1]:
if np.isnan(arr_list[x,i,j]) == False:
new_arr[i,j] = arr_list[x,i,j]
break
x = x + 1
#separate the filename from the first input array
inname = os.path.splitext(rasterlist[0])[0]
#create an output name
if outdir:
outdir = os.path.abspath(outdir)
name = "{0}_gapfilled.tif".format(os.path.split(inname)[1])
outname = os.path.join(outdir, name)
else:
outname = "{0}_gapfilled.tif".format(inname)
#convert the edited array to a tiff
from_numpy(new_arr, item_arr[1], outname, "NoData")
return outname
