def test_rastermetadata_cache(self):
# GDAL helper functions for generating VRT
landsat = Landsat(self.metadata_set)
# get a numpy.ndarray from bands for specified epl_grpc
# 'nir', 'swir1', 'swir2'
band_numbers = [Band.NIR, Band.SWIR1, Band.SWIR2]
scaleParams = [[0.0, 40000.0], [0.0, 40000.0], [0.0, 40000.0]]
nda = landsat.fetch_imagery_array(
band_numbers,
scaleParams,
polygon_boundary_wkb=self.taos_shape.wkb,
spatial_resolution_m=120)
self.assertIsNotNone(nda)
self.assertEqual((902, 648, 3), nda.shape)
band_numbers = [Band.RED, Band.BLUE, Band.GREEN]
nda = landsat.fetch_imagery_array(
band_numbers,
scaleParams,
polygon_boundary_wkb=self.taos_shape.wkb,
spatial_resolution_m=120)
self.assertIsNotNone(nda)
self.assertEqual((902, 648, 3), nda.shape)
band_numbers = [Band.RED, Band.BLUE, Band.GREEN]
nda = landsat.fetch_imagery_array(band_numbers,
scaleParams,
spatial_resolution_m=120)
self.assertIsNotNone(nda)
self.assertNotEqual((902, 648, 3), nda.shape)
def test_ndvi_taos(self):
code = """import numpy as np
def ndvi_numpy(in_ar, out_ar, xoff, yoff, xsize, ysize, raster_xsize, raster_ysize, buf_radius, gt, **kwargs):
with np.errstate(divide = 'ignore', invalid = 'ignore'):
factor = float(kwargs['factor'])
output = np.divide((in_ar[1] - in_ar[0]), (in_ar[1] + in_ar[0]))
output[np.isnan(output)] = 0.0
# shift range from -1.0-1.0 to 0.0-2.0
output += 1.0
# scale up from 0.0-2.0 to 0 to 255 by multiplying by 255/2
# https://stackoverflow.com/a/1735122/445372
output *= factor/2.0
# https://stackoverflow.com/a/10622758/445372
# in place type conversion
out_ar[:] = output.astype(np.int16, copy=False)"""
code2 = """import numpy as np
def ndvi_numpy2(in_ar, out_ar, xoff, yoff, xsize, ysize, raster_xsize, raster_ysize, buf_radius, gt, **kwargs):
with np.errstate(divide = 'ignore', invalid = 'ignore'):
output = (in_ar[1] - in_ar[0]) / (in_ar[1] + in_ar[0])
output[np.isnan(output)] = 0.0
out_ar[:] = output"""
landsat = Landsat(self.metadata_set)
scale_params = [[0, DataType.UINT16.range_max, -1.0, 1.0]]
pixel_function_details = FunctionDetails(
name="ndvi_numpy",
band_definitions=[Band.RED, Band.NIR],
code=code,
arguments={"factor": DataType.UINT16.range_max},
data_type=DataType.UINT16)
nda = landsat.fetch_imagery_array(
[pixel_function_details],
scale_params=scale_params,
polygon_boundary_wkb=self.taos_shape.wkb,
output_type=DataType.FLOAT32)
self.assertIsNotNone(nda)
self.assertGreaterEqual(1.0, nda.max())
self.assertLessEqual(-1.0, nda.min())
pixel_function_details = FunctionDetails(
name="ndvi_numpy2",
band_definitions=[Band.RED, Band.NIR],
code=code2,
data_type=DataType.FLOAT32)
nda2 = landsat.fetch_imagery_array(
[pixel_function_details],
polygon_boundary_wkb=self.taos_shape.wkb,
output_type=DataType.FLOAT32)
self.assertIsNotNone(nda2)
self.assertGreaterEqual(1.0, nda2.max())
self.assertLessEqual(-1.0, nda2.min())
def test_file_creation(self):
landsat = Landsat(self.metadata_set[0])
# get a numpy.ndarray from bands for specified epl_grpc
band_numbers = [Band.RED, Band.GREEN, Band.BLUE]
scale_params = [[0.0, 65535], [0.0, 65535], [0.0, 65535]]
file_name = landsat.fetch_file(band_numbers,
scale_params,
self.taos_shape.wkb,
spatial_resolution_m=480)
self.assertGreater(len(file_name), 0)
def test_cutline(self):
# GDAL helper functions for generating VRT
landsat = Landsat(self.metadata_set[0])
# get a numpy.ndarray from bands for specified epl_grpc
band_numbers = [Band.RED, Band.GREEN, Band.BLUE]
scale_params = [[0.0, 65535], [0.0, 65535], [0.0, 65535]]
nda = landsat.fetch_imagery_array(band_numbers,
scale_params,
self.taos_shape.wkb,
spatial_resolution_m=480)
self.assertIsNotNone(nda)
def test_mosaic(self):
# GDAL helper functions for generating VRT
landsat = Landsat(self.metadata_set)
# get a numpy.ndarray from bands for specified epl_grpc
band_numbers = [Band.RED, Band.GREEN, Band.BLUE]
scale_params = [[0.0, 65535], [0.0, 65535], [0.0, 65535]]
nda = landsat.fetch_imagery_array(
band_numbers,
scale_params,
envelope_boundary=self.taos_shape.bounds)
self.assertIsNotNone(nda)
self.assertEqual((1804, 1295, 3), nda.shape)
def test_vrt_with_alpha(self):
landsat = Landsat(self.metadata_set)
# get a numpy.ndarray from bands for specified epl_grpc
band_numbers = [Band.RED, Band.GREEN, Band.BLUE, Band.ALPHA]
scaleParams = [[0.0, 40000], [0.0, 40000], [0.0, 40000]]
nda = landsat.fetch_imagery_array(
band_numbers,
scaleParams,
envelope_boundary=self.taos_shape.bounds,
output_type=DataType.UINT16,
spatial_resolution_m=120)
self.assertIsNotNone(nda)
def test_mosaic_cutline(self):
# GDAL helper functions for generating VRT
landsat = Landsat(self.metadata_set)
# get a numpy.ndarray from bands for specified epl_grpc
# 'nir', 'swir1', 'swir2'
band_numbers = [Band.NIR, Band.SWIR1, Band.SWIR2]
scaleParams = [[0.0, 40000.0], [0.0, 40000.0], [0.0, 40000.0]]
nda = landsat.fetch_imagery_array(
band_numbers,
scaleParams,
polygon_boundary_wkb=self.taos_shape.wkb)
self.assertIsNotNone(nda)
self.assertEqual((1804, 1295, 3), nda.shape)
def test_ndarray(self):
# sql_filters = ['scene_id="LC80270312016188LGN00"']
landsat_filters = LandsatQueryFilters()
landsat_filters.scene_id.set_value("LC80270312016188LGN00")
metadata_rows = self.metadata_service.search(
SpacecraftID.UNKNOWN_SPACECRAFT, data_filters=landsat_filters)
metadata_set = list(metadata_rows)
landsat = Landsat(metadata_set)
data = landsat.fetch_imagery_array(
band_definitions=[Band.RED, Band.GREEN, Band.BLUE, Band.ALPHA],
spatial_resolution_m=960)
self.assertEqual(data.shape, (249, 245, 4))
self.assertEqual(data.dtype, np.uint8)
def test_datatypes(self):
landsat = Landsat(self.metadata_set)
# get a numpy.ndarray from bands for specified epl_grpc
band_numbers = [Band.RED, Band.GREEN, Band.BLUE]
scaleParams = [[0.0, 40000], [0.0, 40000], [0.0, 40000]]
for data_type in DataType:
if data_type == DataType.CFLOAT32 or data_type == DataType.CFLOAT64 or data_type == DataType.UNKNOWN_GDAL:
continue
nda = landsat.fetch_imagery_array(
band_numbers,
scaleParams,
envelope_boundary=self.taos_shape.bounds,
output_type=data_type,
spatial_resolution_m=240)
self.assertIsNotNone(nda)
self.assertGreaterEqual(data_type.range_max, nda.max())
self.assertLessEqual(data_type.range_min, nda.min())
def test_band_enum(self):
self.assertTrue(True)
d_start = date(2016, 7, 20)
d_end = date(2016, 7, 28)
landsat_filters = LandsatQueryFilters()
landsat_filters.scene_id.set_value("LC80390332016208LGN00")
landsat_filters.acquired.set_range(start=d_start, end=d_end)
rows = self.metadata_service.search(SpacecraftID.LANDSAT_8,
limit=1,
data_filters=landsat_filters)
rows = list(rows)
metadata = rows[0]
landsat = Landsat(metadata)
scale_params = [[0.0, 65535], [0.0, 65535], [0.0, 65535]]
# nda = landsat.__get_ndarray(band_numbers, metadata, scale_params)
nda = landsat.fetch_imagery_array([Band.RED, Band.GREEN, Band.BLUE],
scale_params=scale_params,
spatial_resolution_m=240)
self.assertIsNotNone(nda)
nda2 = landsat.fetch_imagery_array([4, 3, 2],
scale_params=scale_params,
spatial_resolution_m=240)
np.testing.assert_almost_equal(nda, nda2)
def test_pixel_function_vrt_1(self):
metadata_service = MetadataService()
# TODO figure out what's wrong here for AWS
self.assertTrue(True)
utah_box = (-112.66342163085938, 37.738141282210385, -111.79824829101562, 38.44821130413263)
d_start = date(2016, 7, 20)
d_end = date(2016, 7, 28)
landsat_filters = LandsatQueryFilters()
landsat_filters.cloud_cover.set_value(5)
landsat_filters.acquired.set_range(start=d_start, end=d_end)
landsat_filters.aoi.set_bounds(*utah_box)
rows = metadata_service.search(SpacecraftID.LANDSAT_8,
limit=10,
data_filters=landsat_filters)
rows = list(rows)
self.assertEqual(len(rows), 2)
#
# # metadata_row = ['LC80390332016208LGN00', '', 'LANDSAT_8', 'OLI_TIRS', '2016-07-26',
# # '2016-07-26T18:14:46.9465460Z', 'PRE', 'N/A', 'L1T', 39, 33, 1.69,
# # 39.96962, 37.81744, -115.27267, -112.56732, 1070517542,
# # 'gs://gcp-public-data-landsat/LC08/PRE/039/033/LC80390332016208LGN00']
metadata = rows[0]
# GDAL helper functions for generating VRT
landsat = Landsat([metadata])
# get a numpy.ndarray from bands for specified epl_grpc
band_numbers = [4, 3, 2]
scale_params = [[0.0, 65535], [0.0, 65535], [0.0, 65535]]
nda = landsat.fetch_imagery_array(band_numbers, scale_params)
self.assertEqual(nda.shape, (3861, 3786, 3))