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import unittest
from unittest.mock import patch
import numpy as np
from samgis_core.utilities.utilities import hash_calculate
from samgis_lisa_on_cuda.io import raster_helpers
def get_three_channels(size=5, param1=1000, param2=3, param3=-88):
arr_base = np.arange(size*size).reshape(size, size) / size**2
channel_0 = arr_base * param1
channel_1 = arr_base * param2
channel_2 = arr_base * param3
return channel_0, channel_1, channel_2
def helper_bell(size=10, param1=0.1, param2=2):
x = np.linspace(-size, size, num=size**2)
y = np.linspace(-size, size, num=size**2)
x, y = np.meshgrid(x, y)
return np.exp(-param1 * x ** param2 - param1 * y ** param2)
arr_5x5x5 = np.arange(125).reshape((5, 5, 5)) / 25
arr = np.arange(25).resize((5, 5))
channel0, channel1, channel2 = get_three_channels()
z = helper_bell()
slope_z_cellsize3, curvature_z_cellsize3 = raster_helpers.get_slope_curvature(z, slope_cellsize=3)
class Test(unittest.TestCase):
def test_get_rgb_prediction_image_real(self):
output = raster_helpers.get_rgb_prediction_image(z, slope_cellsize=61, invert_image=True)
hash_output = hash_calculate(output)
assert hash_output == b'QpQ9yxgCLw9cf3klNFKNFXIDHaSkuiZxkbpeQApR8pA='
output = raster_helpers.get_rgb_prediction_image(z, slope_cellsize=61, invert_image=False)
hash_output = hash_calculate(output)
assert hash_output == b'Y+iXO9w/sKzNVOw2rBh2JrVGJUFRqaa8/0F9hpevmLs='
@patch.object(raster_helpers, "get_slope_curvature")
@patch.object(raster_helpers, "normalize_array_list")
@patch.object(raster_helpers, "get_rgb_image")
def test_get_rgb_prediction_image_mocked(self, get_rgb_image_mocked, normalize_array_list, get_slope_curvature):
local_arr = np.array(z * 100, dtype=np.uint8)
get_slope_curvature.return_value = slope_z_cellsize3, curvature_z_cellsize3
normalize_array_list.side_effect = None
get_rgb_image_mocked.return_value = np.bitwise_not(local_arr)
output = raster_helpers.get_rgb_prediction_image(local_arr, slope_cellsize=61, invert_image=True)
hash_output = hash_calculate(output)
assert hash_output == b'BPIyVH64RgVunj42EuQAx4/v59Va8ZAjcMnuiGNqTT0='
get_rgb_image_mocked.return_value = local_arr
output = raster_helpers.get_rgb_prediction_image(local_arr, slope_cellsize=61, invert_image=False)
hash_output = hash_calculate(output)
assert hash_output == b'XX54sdLQQUrhkUHT6ikQZYSloMYDSfh/AGITDq6jnRM='
@patch.object(raster_helpers, "get_slope_curvature")
def test_get_rgb_prediction_image_value_error(self, get_slope_curvature):
msg = "this is a value error"
get_slope_curvature.side_effect = ValueError(msg)
with self.assertRaises(ValueError):
try:
raster_helpers.get_rgb_prediction_image(arr, slope_cellsize=3)
except ValueError as ve:
self.assertEqual(str(ve), msg)
raise ve
def test_get_rgb_image(self):
output = raster_helpers.get_rgb_image(channel0, channel1, channel2, invert_image=True)
hash_output = hash_calculate(output)
assert hash_output == b'YVnRWla5Ptfet6reSfM+OEIsGytLkeso6X+CRs34YHk='
output = raster_helpers.get_rgb_image(channel0, channel1, channel2, invert_image=False)
hash_output = hash_calculate(output)
assert hash_output == b'LC/kIZGUZULSrwwSXCeP1My2spTZdW9D7LH+tltwERs='
def test_get_rgb_image_value_error_1(self):
with self.assertRaises(ValueError):
try:
raster_helpers.get_rgb_image(arr_5x5x5, arr_5x5x5, arr_5x5x5, invert_image=True)
except ValueError as ve:
self.assertEqual(f"arr_size, wrong type:{type(arr_5x5x5)} or arr_size:{arr_5x5x5.shape}.", str(ve))
raise ve
def test_get_rgb_image_value_error2(self):
arr_0 = np.arange(25).reshape((5, 5))
arr_1 = np.arange(4).reshape((2, 2))
with self.assertRaises(ValueError):
try:
raster_helpers.get_rgb_image(arr_0, arr_1, channel2, invert_image=True)
except ValueError as ve:
self.assertEqual('could not broadcast input array from shape (2,2) into shape (5,5)', str(ve))
raise ve
def test_get_slope_curvature(self):
slope_output, curvature_output = raster_helpers.get_slope_curvature(z, slope_cellsize=3)
hash_curvature = hash_calculate(curvature_output)
hash_slope = hash_calculate(slope_output)
assert hash_curvature == b'LAL9JFOjJP9D6X4X3fVCpnitx9VPM9drS5YMHwMZ3iE='
assert hash_slope == b'IYf6x4G0lmR47j6HRS5kUYWdtmimhLz2nak8py75nwc='
def test_get_slope_curvature_value_error(self):
from samgis_lisa_on_cuda.io import raster_helpers
with self.assertRaises(ValueError):
try:
raster_helpers.get_slope_curvature(np.array(1), slope_cellsize=3)
except ValueError as ve:
self.assertEqual('not enough values to unpack (expected 2, got 0)', str(ve))
raise ve
def test_calculate_slope(self):
slope_output = raster_helpers.calculate_slope(z, cell_size=3)
hash_output = hash_calculate(slope_output)
assert hash_output == b'IYf6x4G0lmR47j6HRS5kUYWdtmimhLz2nak8py75nwc='
def test_calculate_slope_value_error(self):
with self.assertRaises(ValueError):
try:
raster_helpers.calculate_slope(np.array(1), cell_size=3)
except ValueError as ve:
self.assertEqual('not enough values to unpack (expected 2, got 0)', str(ve))
raise ve
def test_normalize_array(self):
def check_ndarrays_almost_equal(cls, arr1, arr2, places, check_type="float", check_ndiff=1):
count_abs_diff = 0
for list00, list01 in zip(arr1.tolist(), arr2.tolist()):
for el00, el01 in zip(list00, list01):
ndiff = abs(el00 - el01)
if el00 != el01:
count_abs_diff += 1
if check_type == "float":
cls.assertAlmostEqual(el00, el01, places=places)
cls.assertTrue(ndiff < check_ndiff)
print("count_abs_diff:", count_abs_diff)
normalized_array = raster_helpers.normalize_array(z)
hash_output = hash_calculate(normalized_array)
assert hash_output == b'MPkQwiiQa5NxL7LDvCS9V143YUEJT/Qh1aNEKc/Ehvo='
mult_variable = 3.423
test_array_input = np.arange(256).reshape((16, 16))
test_array_output = raster_helpers.normalize_array(test_array_input * mult_variable)
check_ndarrays_almost_equal(self, test_array_output, test_array_input, places=8)
test_array_output1 = raster_helpers.normalize_array(test_array_input * mult_variable, high=128, norm_type="int")
o = np.arange(256).reshape((16, 16)) / 2
expected_array_output1 = o.astype(int)
check_ndarrays_almost_equal(
self, test_array_output1, expected_array_output1, places=2, check_type="int", check_ndiff=2)
@patch.object(np, "nanmin")
@patch.object(np, "nanmax")
def test_normalize_array_floating_point_error_mocked(self, nanmax_mocked, nanmin_mocked):
nanmax_mocked.return_value = 100
nanmin_mocked.return_value = 100
with self.assertRaises(ValueError):
try:
raster_helpers.normalize_array(
np.arange(25).reshape((5, 5))
)
except ValueError as ve:
self.assertEqual(
"normalize_array:::h_arr_max:100,h_min_arr:100,fe:divide by zero encountered in divide.",
str(ve)
)
raise ve
@patch.object(np, "nanmin")
@patch.object(np, "nanmax")
def test_normalize_array_exception_error_mocked(self, nanmax_mocked, nanmin_mocked):
nanmax_mocked.return_value = 100
nanmin_mocked.return_value = np.NaN
with self.assertRaises(ValueError):
try:
raster_helpers.normalize_array(
np.arange(25).reshape((5, 5))
)
except ValueError as ve:
self.assertEqual("cannot convert float NaN to integer", str(ve))
raise ve
def test_normalize_array_value_error(self):
with self.assertRaises(ValueError):
try:
raster_helpers.normalize_array(
np.zeros((5, 5))
)
except ValueError as ve:
self.assertEqual(
"normalize_array::empty array '',h_min_arr:0.0,h_arr_max:0.0,h_diff:0.0, " 'dtype:float64.',
str(ve)
)
raise ve
def test_normalize_array_list(self):
normalized_array = raster_helpers.normalize_array_list([channel0, channel1, channel2])
hash_output = hash_calculate(normalized_array)
assert hash_output == b'+6IbhIpyb3vPElTgqqPkQdIR0umf4uFP2c7t5IaBVvI='
test_norm_list_output2 = raster_helpers.normalize_array_list(
[channel0, channel1, channel2], exaggerations_list=[2.0, 3.0, 5.0])
hash_variable2 = hash_calculate(test_norm_list_output2)
assert hash_variable2 == b'yYCYWCKO3i8NYsWk/wgYOzSRRLSLUprEs7mChJkdL+A='
def test_normalize_array_list_value_error(self):
with self.assertRaises(ValueError):
try:
raster_helpers.normalize_array_list([])
except ValueError as ve:
self.assertEqual("input list can't be empty:[].", str(ve))
raise ve
def test_check_empty_array(self):
a = np.zeros((10, 10))
b = np.ones((10, 10))
c = np.ones((10, 10)) * 2
d = np.zeros((10, 10))
d[1, 1] = np.nan
e = np.ones((10, 10)) * 3
e[1, 1] = np.nan
self.assertTrue(raster_helpers.check_empty_array(a, 999))
self.assertTrue(raster_helpers.check_empty_array(b, 0))
self.assertTrue(raster_helpers.check_empty_array(c, 2))
self.assertTrue(raster_helpers.check_empty_array(d, 0))
self.assertTrue(raster_helpers.check_empty_array(e, 3))
self.assertFalse(raster_helpers.check_empty_array(z, 3))
def test_get_nextzen_terrain_rgb_formula(self):
output = raster_helpers.get_nextzen_terrain_rgb_formula(channel0, channel1, channel2)
hash_output = hash_calculate(output)
assert hash_output == b'3KJ81YKmQRdccRZARbByfwo1iMVLj8xxz9mfsWki/qA='
def test_get_mapbox__terrain_rgb_formula(self):
output = raster_helpers.get_mapbox__terrain_rgb_formula(channel0, channel1, channel2)
hash_output = hash_calculate(output)
assert hash_output == b'RU7CcoKoR3Fkh5LE+m48DHRVUy/vGq6UgfOFUMXx07M='
def test_get_raster_terrain_rgb_like(self):
from samgis_lisa_on_cuda.utilities.type_hints import XYZTerrainProvidersNames
arr_input = raster_helpers.get_rgb_image(channel0, channel1, channel2, invert_image=True)
output_nextzen = raster_helpers.get_raster_terrain_rgb_like(
arr_input, XYZTerrainProvidersNames.NEXTZEN_TERRAIN_TILES_NAME)
hash_nextzen = hash_calculate(output_nextzen)
assert hash_nextzen == b'+o2OTJliJkkBoqiAIGnhJ4s0xoLQ4MxHOvevLhNxysE='
output_mapbox = raster_helpers.get_raster_terrain_rgb_like(
arr_input, XYZTerrainProvidersNames.MAPBOX_TERRAIN_TILES_NAME)
hash_mapbox = hash_calculate(output_mapbox)
assert hash_mapbox == b'zWmekyKrpnmHnuDACnveCJl+o4GuhtHJmGlRDVwsce4='
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