def initialize_landcover_parameters(landcover_file, landcover_initial_fields_file, dst_dir, landuse_shp):
"""generate initial landcover_init_param parameters"""
lc_data_items = read_data_items_from_txt(landcover_initial_fields_file)
# print(lc_data_items)
field_names = lc_data_items[0]
lu_id = -1
for i, v in enumerate(field_names):
if StringClass.string_match(v, 'LANDUSE_ID'):
lu_id = i
break
data_items = lc_data_items[1:]
replace_dicts = dict()
for item in data_items:
for i, v in enumerate(item):
if i != lu_id:
if field_names[i].upper() not in list(replace_dicts.keys()):
replace_dicts[field_names[i].upper()] = {float(item[lu_id]): float(v)}
else:
replace_dicts[field_names[i].upper()][float(item[lu_id])] = float(v)
# print(replace_dicts)
# Generate GTIFF
landcover_init_csv = r'D:\SEIMS\data\zts\data_prepare\spatial\test\landcover_init_csv.csv'
RasterUtilClass.landuse_cover_reclassify(landcover_file, landuse_shp, replace_dicts, landcover_init_csv)
print (landcover_init_csv)
# for item, v in list(replace_dicts.items()):
# filename = dst_dir + os.path.sep + item + '.tif'
# print(filename)
# RasterUtilClass.raster_reclassify(landcover_file, v, filename)
return list(replace_dicts['LANDCOVER'].values())
def reclassify_landcover_parameters(landuse_file, landcover_file,
landcover_initial_fields_file,
landcover_lookup_file, attr_names,
dst_dir, landuse_shp):
"""relassify landcover_init_param parameters"""
land_cover_codes = LanduseUtilClass.initialize_landcover_parameters(
landuse_file, landcover_initial_fields_file, dst_dir, landuse_shp)
attr_map = LanduseUtilClass.read_crop_lookup_table(
landcover_lookup_file)
n = len(attr_names)
replace_dicts = []
replace_dicts_attrn = dict()
dst_crop_tifs = []
for i in range(n):
cur_attr = attr_names[i]
cur_dict = dict()
dic = attr_map[cur_attr]
for code in land_cover_codes:
if MathClass.floatequal(code, DEFAULT_NODATA):
continue
if code not in list(cur_dict.keys()):
cur_dict[code] = dic.get(code)
replace_dicts_attrn[cur_attr] = cur_dict
replace_dicts.append(cur_dict)
dst_crop_tifs.append(dst_dir + os.path.sep + cur_attr + '.tif')
# print(replace_dicts)
# print(len(replace_dicts))
# print(dst_crop_tifs)
# print(len(dst_crop_tifs))
# Generate GTIFF
landcover_rec_csv = r'D:\SEIMS\data\zts\data_prepare\spatial\test\landcover_rec_csv.csv'
RasterUtilClass.landuse_cover_reclassify(landcover_file, landuse_shp,
replace_dicts_attrn,
landcover_rec_csv)
print(landcover_rec_csv)
def reclassify_landcover_parameters(landuse_file, landcover_file, landcover_initial_fields_file,
landcover_lookup_file, attr_names, dst_dir, landuse_shp):
"""relassify landcover_init_param parameters"""
land_cover_codes = LanduseUtilClass.initialize_landcover_parameters(
landuse_file, landcover_initial_fields_file, dst_dir, landuse_shp)
attr_map = LanduseUtilClass.read_crop_lookup_table(landcover_lookup_file)
n = len(attr_names)
replace_dicts = []
replace_dicts_attrn = dict()
dst_crop_tifs = []
for i in range(n):
cur_attr = attr_names[i]
cur_dict = dict()
dic = attr_map[cur_attr]
for code in land_cover_codes:
if MathClass.floatequal(code, DEFAULT_NODATA):
continue
if code not in list(cur_dict.keys()):
cur_dict[code] = dic.get(code)
replace_dicts_attrn[cur_attr] = cur_dict
replace_dicts.append(cur_dict)
dst_crop_tifs.append(dst_dir + os.path.sep + cur_attr + '.tif')
# print(replace_dicts)
# print(len(replace_dicts))
# print(dst_crop_tifs)
# print(len(dst_crop_tifs))
# Generate GTIFF
landcover_rec_csv = r'D:\SEIMS\data\zts\data_prepare\spatial\test\landcover_rec_csv.csv'
RasterUtilClass.landuse_cover_reclassify(landcover_file, landuse_shp, replace_dicts_attrn, landcover_rec_csv)
print (landcover_rec_csv)