def statistics_func(raster_folder, raster_filename, output_folder, statistical_functions, statistical_args):
start_time = datetime.now()
input_raster = os.path.join(raster_folder, raster_filename)
hdr_file = "" # input_raster + ".hdr" # only used for ENVI stacks
outname = os.path.join(output_folder, raster_filename)
if outname.find(".tif") != -1:
outname = outname[0:len(outname) - 4]
# arr: full size numpy array 3D XxYxZ 200x300x100
arr = preprocessing.rio_array(input_raster, hdr_file=hdr_file)
# activate to get list of dates from .hdr file (.hdr file needs to be specified above)
# dates = arr[1]
for i, func in enumerate(statistical_functions):
# creating results with calling wanted algorithm in parallel_apply_along_axis for quick runtime
result = apply_along_axis.parallel_apply_along_axis(func1d=func, arr=arr[0], axis=0, cores=mp.cpu_count(),
**statistical_args[i])
# selecting dtype based on result
dtype = type(result[0][0])
func_name_end = str(func).find(" at")
func_name_start = 10
func_name = str(func)[func_name_start:func_name_end]
# exporting result to new raster
export_arr.functions_out_array(outname=outname + "_" + func_name + str(i), arr=result, input_file=input_raster,
dtype=dtype)
# print time to this point
statistics_time = datetime.now()
print("breakpoint-time = ", statistics_time - start_time, "Hr:min:sec")
def filter_func(raster_folder, raster_filename, output_folder, filter_functions, filter_args):
start_time = datetime.now()
input_raster = os.path.join(raster_folder, raster_filename)
hdr_file = "" # only used for ENVI stacks
outname = os.path.join(output_folder, raster_filename)
if outname.find(".tif") != -1:
outname = outname[0:len(outname) - 4]
# arr: full size numpy array 3D XxYxZ 200x300x100
arr = preprocessing.rio_array(input_raster, hdr_file=hdr_file)
# activate to get list of dates from .hdr file (.hdr file needs to be specified above)
# dates = arr[1]
for i, func in enumerate(filter_functions):
kernel_size = str(filter_args[i]['kernel'])
filtered_arr = apply_along_axis.parallel_apply_along_axis(func1d=func, arr=arr[0], axis=0, cores=mp.cpu_count(),
**filter_args[i])
filtered_arr = np.rollaxis(filtered_arr, 2)
filtered_arr = np.rollaxis(filtered_arr, 1)
filtered_arr = np.rollaxis(filtered_arr, 2)
dtype = type(filtered_arr[0][0][0])
func_name_end = str(func).find(" at")
func_name_start = 10
func_name = str(func)[func_name_start:func_name_end]
# exporting result to new raster
export_arr.functions_out_array(outname=outname + "_" + func_name + str(kernel_size), arr=filtered_arr,
input_file=input_raster, dtype=dtype)
# print time to this point
filter_time = datetime.now()
print("filter-time = ", filter_time - start_time, "Hr:min:sec")
def main():
################################### INPUT ########################################
# Input Folder Marlin:
raster_folder = "C:/Users/marli/Desktop/GEO402_Testdaten/Input_Files/Raster/"
#shape_folder = "C:/Users/marli/Desktop/GEO402_Testdaten/Input_Files/Shapes/"
# Input Folder Jonas:
# raster_folder = "C:/Users/jz199/Documents/Studium/Master/1. Semester\Vorlesungsmitschriften/GEO402 - Ableitung von Landoberflächenparametern/Subset/"
# Input file name
raster_filename = "S1_A_VH_agulhas_full_study_site_50m"
#raster_filename = "SubsetVH.tif"
# shape_filename = "threshold_VH.shp"
################################### OUTPUT ########################################
# Output Folder Marlin:
output_folder = "C:/Users/marli/Desktop/GEO402_Testdaten/AAA_output/"
# Output Folder Jonas:
# output_folder = "C:/Users/jz199/Documents/Studium/Master/1. Semester\Vorlesungsmitschriften/GEO419 - Pythonprogrammierung Habermeyer/GEO402_Output/"
# Output File Name:
output_file = raster_filename + "cleaned.tif"
###################### NO USER INPUT BEYOND THIS POINT ###############################
input_raster = raster_folder + raster_filename
#input_shape = shape_folder + shape_filename
outname = output_folder + output_file
# arr: full size numpy array 3D XxYxZ 200x300x100
arr = preprocessing.rio_array(input_raster)
#shp = preprocessing.fiona_shape(shape_path = input_shape)
#shapes = [feature["geometry"] for feature in shp]
#shapes1 = [shapes[0]]
#burn_dates = preprocessing.fiona_burn_date(input_shape)
#print(burn_dates)
#time_series_length = arr.shape[0]
#print(time_series_length)
#mask_raster.raster_mask_func(raster=input_raster, shape=shapes, output_folder=output_folder)
#result = tes_local_2.mask_raster_test(outname)
#export_arr.out_array(outname=outname, arr=result, input_file=input_raster, dtype="float32")
#result = apply_along_axis.parallel_apply_along_axis(func1d=function.slope_minimum, arr=arr, axis=0, cores=mp.cpu_count())
result = arr
#dtype = type(result[0][0])
export_arr.cleaned_out_array(outname=outname, arr=result, input_file=input_raster, dtype="float32")
end_time = datetime.now()
print("end-time = ", end_time-start_time, "Hr:min:sec")
def main():
################################### INPUT ########################################
# Input Folder Marlin:
raster_folder = "C:/Users/marli/Desktop/GEO402_Testdaten/Input_Files/Raster/"
# Input Folder Jonas:
# raster_folder = "C:/Users/jz199/Documents/Studium/Master/1. Semester\Vorlesungsmitschriften/GEO402 - Ableitung von Landoberflächenparametern/Subset/"
# Input file name
raster_filename = "S1_A_VH_agulhas_full_study_site_50m"
# raster_filename = "SubsetVH.tif"
################################### OUTPUT ########################################
# Output Folder Marlin:
output_folder = "C:/Users/marli/Desktop/GEO402_Testdaten/AAA_output/"
# Output Folder Jonas:
# output_folder = "C:/Users/jz199/Documents/Studium/Master/1. Semester\Vorlesungsmitschriften/GEO419 - Pythonprogrammierung Habermeyer/GEO402_Output/"
# Output File Name:
# output_file = raster_filename[0:len(raster_filename)-4] + "_median_filtered3.tif"
output_file = raster_filename + "_median_test2.tif"
#simple_edge_detection
###################### NO USER INPUT BEYOND THIS POINT ###############################
input_raster = raster_folder + raster_filename
hdr_file = input_raster + ".hdr"
outname = output_folder + output_file
# arr: full size numpy array 3D XxYxZ 200x300x100
arr = preprocessing.rio_array(input_raster, hdr_file=hdr_file)
dates = arr[1]
#print(dates)
# creating filtered array depending on set filter function
filtered_arr = apply_along_axis.parallel_apply_along_axis(func1d=filter_functions.median_filter11, arr=arr[0], axis=0, cores=mp.cpu_count())
filtered_arr = np.rollaxis(filtered_arr, 2)
filtered_arr = np.rollaxis(filtered_arr, 1)
filtered_arr = np.rollaxis(filtered_arr, 2)
dtype = type(filtered_arr[0][0][0])
#print(type(dtype)
#if type(dtype) == np.float64:
# print("lalala")
# --> change float64 to float32
export_arr.functions_out_array(outname=outname, arr=filtered_arr, dates=dates, input_file=input_raster, dtype=dtype)
# creating results with calling wanted algorithm in parallel_apply_along_axis for quick runtime
#result = apply_along_axis.parallel_apply_along_axis(func1d=function.find_peaks, arr=arr[0], axis=0, cores=mp.cpu_count())
# selecting dtype based on result
dtype = np.int32 #type(result[0][0])
# dtype = type(filtered_arr[0][0])
# float64 to float32
# exporting result to new raster
#export_arr.functions_out_array(outname=outname, arr=result, dates=dates, input_file=input_raster, dtype=dtype)
# export_arr.functions_out_array(outname=outname, arr=filtered_arr, input_file=input_raster, dtype=dtype)
end_time = datetime.now()
print("end-time = ", end_time-start_time, "Hr:min:sec")