def test_arrayframe_add(self):
temp_path = hb.temp('.tif', 'testing_arrayframe_add', True)
hb.add(self.global_1deg_raster_path, self.global_1deg_raster_path,
temp_path)
temp_path = hb.temp('.tif', 'testing_arrayframe_add', True)
af1 = hb.ArrayFrame(self.global_1deg_raster_path)
hb.add(af1, af1, temp_path)
def test_read_1d_npy_chunk(self):
path = r"C:\temp\_20180327_144427_046hh9\allocate_all_sectors\bau\sector_change_lists.npy"
r = np.random.randint(2, 9, 200)
temp_path = hb.temp('.npy', remove_at_exit=True)
hb.save_array_as_npy(r, temp_path)
output = hb.read_1d_npy_chunk(temp_path, 3, 8)
self.assertTrue(sum(r[3:3 + 8]) == sum(output))
def test_extract_features_in_shapefile_by_attribute(self):
input_shp_uri = 'data/two_poly_wgs84_aoi.shp'
output_shp_uri = hb.temp('.shp', remove_at_exit=True)
column_name = 'adm1_code'
column_filter = 'NLD-903'
hb.extract_features_in_shapefile_by_attribute(input_shp_uri,
output_shp_uri,
column_name,
column_filter)
def arrayframe_load_and_save():
input_array = np.arange(0, 18, 1).reshape((3, 6))
input_uri = hb.temp('.tif', remove_at_exit=False)
geotransform = hb.calc_cylindrical_geotransform_from_array(input_array)
# projection = hb.get_wkt_from_epsg_code(hb.common_epsg_codes_by_name['plate_carree'])
projection = 'plate_carree'
hb.save_array_as_geotiff(input_array,
input_uri,
geotransform_override=geotransform,
projection_override=projection)
hb.ArrayFrame(input_uri)
def test_arrayframe_load_and_save(self):
input_array = np.arange(0, 18, 1).reshape((3, 6))
input_uri = hb.temp('.tif', remove_at_exit=True)
geotransform = hb.calc_cylindrical_geotransform_from_array(input_array)
# projection = hb.get_wkt_from_epsg_code(hb.common_epsg_codes_by_name['plate_carree'])
projection = 'wgs84'
ndv = 255
data_type = 1
hb.save_array_as_geotiff(input_array,
input_uri,
geotransform_override=geotransform,
projection_override=projection,
ndv=ndv,
data_type=data_type)
hb.ArrayFrame(input_uri)
def test_reclassify_int_array_by_dict_to_ints(self):
a = np.random.randint(1, 7, (32, 32)).astype(np.float)
rules = {
2: 23,
# 3: 23,
# 4: 24,
# 5: 25,
# 6: 26,
# 7: 27,
}
temp_path = hb.temp('.tif', remove_at_exit=True)
b = hb.reclassify(a, rules, temp_path)
self.assertIsInstance(b, np.ndarray)
def get_tile_names_and_degrees_from_aoi(shapefile_uri, tile_increment):
"""Get a list of strings representing tile names under the nsew-degree structure ie
['n10w90', 'n10w85', 'n15w90', 'n15w85', 'n20w90', 'n20w85']
"""
temp_uri = hb.temp(ext='.shp', remove_at_exit=True)
print('shapefile_uri', shapefile_uri)
hb.reproject_datasource_uri(shapefile_uri, hb.wgs_84_wkt, temp_uri)
tile_names = []
bb = hb.get_datasource_bounding_box(temp_uri)
degrees = [0,0,0,0]
degrees[0] = hb.round_to_nearest_containing_increment(bb[1], tile_increment, 'up')
degrees[1] = hb.round_to_nearest_containing_increment(bb[0], tile_increment, 'down')
degrees[2] = hb.round_to_nearest_containing_increment(bb[3], tile_increment, 'down')
degrees[3] = hb.round_to_nearest_containing_increment(bb[2], tile_increment, 'up')
ns_degree_increments = list(range(degrees[2], degrees[0] + tile_increment, tile_increment))
ew_degree_increments = list(range(degrees[1], degrees[3] + tile_increment, tile_increment))
for c1, ns in enumerate(ns_degree_increments):
for c2, ew in enumerate(ew_degree_increments):
to_append = ''
if ns < 0:
to_append += 's' + str(ns).replace('-', '').zfill(2)
else:
to_append += 'n' + str(ns).replace('-', '').zfill(2)
if ew < 0:
to_append += 'w' + str(ew).replace('-', '').zfill(3)
else:
to_append += 'e' + str(ew).replace('-', '').zfill(3)
tile_names.append(to_append)
return tile_names, degrees
def clip_hydrosheds_dem_from_aoi(output_uri, aoi_uri, match_uri):
hydrosheds_dir = os.path.join(hb.BULK_DATA_DIR, 'hydrosheds/3s/hydrologically_conditioned_dem')
temp_uri = hb.temp('.tif', remove_at_exit=True)
merge_hydrosheds_by_aoi(hydrosheds_dir, temp_uri, aoi_uri)
hb.clip_dataset_uri(temp_uri, aoi_uri, output_uri, assert_datasets_projected=False)
def __truediv__(self, after):
def op(left, right):
return left + right
output_path = hb.temp(filename_start='div', remove_at_exit=False)
return hb.raster_calculator_af_flex([self.path, after.path], op, output_path)
def __sub__(self, after):
def op(left, right):
return left - right
output_path = hb.temp(filename_start='sub', remove_at_exit=True)
return hb.raster_calculator_af_flex([self.path, after.path], op, output_path)
def test_create_vector_from_raster_extents(self):
extent_path = hb.temp('.shp', remove_at_exit=True)
hb.create_vector_from_raster_extents(self.global_5m_raster_path,
extent_path)
self.assertTrue(os.path.exists(extent_path))
def test_resample_arrayframe(self):
temp_path = hb.temp('.tif', 'temp_test_resample_array', True)
hb.resample(self.global_5m_raster_path, temp_path, 12)
import hazelbean as hb
global_random_floats_15m_32bit_path = os.path.join(hb.TEST_DATA_DIR, 'global_random_floats_15m_32bit.tif')
two_poly_eckert_iv_aoi_path = os.path.join(hb.TEST_DATA_DIR, 'two_poly_eckert_iv_aoi.shp')
two_poly_wgs84_aoi_path = os.path.join(hb.TEST_DATA_DIR, 'two_poly_wgs84_aoi.shp')
a = hb.as_array(global_random_floats_15m_32bit_path)
# Old clip method for reference
# hb.clip_dataset_uri(global_random_floats_15m_32bit_path, two_poly_wgs84_aoi_path, hb.temp('.tif', 'clip1', False, 'tests'))
base_raster_path_list = [global_random_floats_15m_32bit_path]
target_raster_path_list = [hb.temp('.tif', 'clip1', False, 'tests')]
resample_method_list = ['bilinear']
target_pixel_size = hb.get_raster_info(global_random_floats_15m_32bit_path)['pixel_size']
bounding_box_mode = 'intersection'
base_vector_path_list = [two_poly_wgs84_aoi_path]
raster_align_index = 0
hb.align_and_resize_raster_stack(
base_raster_path_list, target_raster_path_list, resample_method_list,
target_pixel_size, bounding_box_mode, base_vector_path_list=base_vector_path_list, all_touched=True,
raster_align_index=raster_align_index,
gtiff_creation_options=hb.DEFAULT_GTIFF_CREATION_OPTIONS)
hb.clip_raster_by_vector(global_random_floats_15m_32bit_path, hb.temp('.tif', 'clip2', False, 'tests'), two_poly_wgs84_aoi_path)
def create_physical_suitability():
global p
L.info('Creating physical suitability layer from base data.')
# physical suitability calculations, though for speed it's included as a base datum.
dem_unaligned_path = hb.temp(
'.tif', folder=p.workspace_dir,
remove_at_exit=True) #hb.temp('.tif', remove_at_exit=True)
stats_to_calculate = ['TRI']
hb.clip_hydrosheds_dem_from_aoi(dem_unaligned_path,
p.area_of_interest_path,
p.match_float_path)
hb.calculate_topographic_stats_from_dem(
dem_unaligned_path,
p.physical_suitability_dir,
stats_to_calculate=stats_to_calculate,
output_suffix='unaligned')
dem_path = os.path.join(p.physical_suitability_dir, 'dem.tif')
hb.align_dataset_to_match(dem_unaligned_path,
p.match_float_path,
dem_path,
aoi_uri=p.area_of_interest_path)
for stat in stats_to_calculate:
stat_unaligned_path = os.path.join(p.physical_suitability_dir,
stat + '_unaligned.tif')
hb.delete_path_at_exit(stat_unaligned_path)
stat_path = os.path.join(p.physical_suitability_dir, stat + '.tif')
hb.align_dataset_to_match(stat_unaligned_path,
p.match_float_path,
stat_path,
resample_method='bilinear',
align_to_match=True,
aoi_uri=p.area_of_interest_path)
soc_path = os.path.join(p.physical_suitability_dir, 'soc.tif')
hb.align_dataset_to_match(p.base_data_soc_path,
p.match_int_path,
soc_path,
aoi_uri=p.area_of_interest_path,
output_data_type=7)
tri_path = os.path.join(p.physical_suitability_dir, 'tri.tif')
hb.align_dataset_to_match(p.base_data_tri_path,
p.match_int_path,
tri_path,
aoi_uri=p.area_of_interest_path,
output_data_type=7)
# TODOO Create cythonized array_sum_product()
p.physical_suitability_path = os.path.join(p.physical_suitability_dir,
'physical_suitability.tif')
soc_array = hb.as_array(soc_path)
tri_array = hb.as_array(tri_path)
physical_suitability_array = np.log(soc_array) - np.log(tri_array)
# p.global_physical_suitability_path = os.path.join(p.model_base_data_dir, 'physical_suitability_compressed.tif')
p.clipped_physical_suitability_path = os.path.join(
p.cur_dir, 'physical_suitability.tif')
if p.run_this and p.run_this_zone:
# hb.clip_raster_by_vector(p.global_physical_suitability_path, p.physical_suitability_path, p.coarse_res_aoi_path, all_touched=True)
hb.clip_while_aligning_to_coarser(
p.physical_suitability_path,
p.clipped_physical_suitability_path,
p.area_of_interest_path,
p.current_change_map_paths[0],
resample_method='nearest',
all_touched=True,
verbose=True,
ensure_fits=True,
gtiff_creation_options=hb.DEFAULT_GTIFF_CREATION_OPTIONS)
p.current_physical_suitability_path = p.clipped_physical_suitability_path # NOTE awkward naming
# hb.clip_dataset_uri(p.global_physical_suitability_path, p.coarse_res_aoi_path, p.physical_suitability_path, False, all_touched=False)
physical_suitability_array = hb.as_array(
p.current_physical_suitability_path)
p.match_float_path = p.current_physical_suitability_path
np.seterr(divide='ignore', invalid='ignore')
physical_suitability_array = np.where(
physical_suitability_array > -1000, physical_suitability_array, 0)
physical_suitability_array = np.where(
physical_suitability_array < 100000000, physical_suitability_array,
0)
hb.save_array_as_geotiff(physical_suitability_array,
p.current_physical_suitability_path,
p.match_float_path,
compress=True)
def test_describe(self):
a = np.random.rand(5, 5)
tmp_path = hb.temp('.npy', remove_at_exit=True)
hb.save_array_as_npy(a, tmp_path)
hb.describe(tmp_path, surpress_print=True, surpress_logger=True)
