def test_radiance_wrong_shape():
band = np.array([[0, 0, 0], [0, 1, 1], [1, 0, 1]]).astype('float32')
# wrong ML shape
with pytest.raises(ValueError):
radiance.radiance(band, np.array([[1, 2, 3], [4, 5, 6]]), -0.00000001)
# wrong AL shape
with pytest.raises(ValueError):
radiance.radiance(band, 35.1, np.array([1, 3]))
def test_radiance():
band = np.array([[0, 0, 0], [0, 1, 1], [1, 0, 1]]).astype('float32')
ML = 0.2
AL = -0.1
assert np.array_equal(
radiance.radiance(band, ML, AL),
np.array([[0., 0., 0.], [0., 0.1, 0.1], [0.1, 0.,
0.1]]).astype(np.float32))
def test_calculate_radiance2(test_data):
tif, tif_meta, tif_output, tif_shape, tif_output_meta, mtl = test_data
M = toa_utils._load_mtl_key(
mtl,
['L1_METADATA_FILE', 'RADIOMETRIC_RESCALING', 'RADIANCE_MULT_BAND_'],
5)
A = toa_utils._load_mtl_key(
mtl,
['L1_METADATA_FILE', 'RADIOMETRIC_RESCALING', 'RADIANCE_ADD_BAND_'], 5)
toa = toa_utils.rescale(radiance.radiance(tif, M, A), 55000, np.uint16)
assert toa.dtype == np.uint16
assert np.all(toa) < 1.5
assert np.all(toa) >= 0.0
def brightness_temp(img, ML, AL, K1, K2, src_nodata=0):
"""Calculate brightness temperature of Landsat 8
as outlined here: http://landsat.usgs.gov/Landsat8_Using_Product.php
T = K2 / np.log((K1 / L) + 1)
and
L = ML * Q + AL
where:
T = At-satellite brightness temperature (degrees kelvin)
L = TOA spectral radiance (Watts / (m2 * srad * mm))
ML = Band-specific multiplicative rescaling factor from the metadata
(RADIANCE_MULT_BAND_x, where x is the band number)
AL = Band-specific additive rescaling factor from the metadata
(RADIANCE_ADD_BAND_x, where x is the band number)
Q = Quantized and calibrated standard product pixel values (DN)
(ndarray img)
K1 = Band-specific thermal conversion constant from the metadata
(K1_CONSTANT_BAND_x, where x is the thermal band number)
K2 = Band-specific thermal conversion constant from the metadata
(K1_CONSTANT_BAND_x, where x is the thermal band number)
Parameters
-----------
img: ndarray
array of input pixels
ML: float
multiplicative rescaling factor from scene metadata
AL: float
additive rescaling factor from scene metadata
K1: float
thermal conversion constant from scene metadata
K2: float
thermal conversion constant from scene metadata
Returns
--------
ndarray:
float32 ndarray with shape == input shape
"""
L = radiance.radiance(img, ML, AL, src_nodata=0)
L[img == src_nodata] = np.NaN
T = K2 / np.log((K1 / L) + 1)
return T
def test_calculate_radiance(test_data):
tif, tif_meta, tif_output, tif_shape, tif_output_meta, mtl = test_data
M = toa_utils._load_mtl_key(
mtl,
['L1_METADATA_FILE', 'RADIOMETRIC_RESCALING', 'RADIANCE_MULT_BAND_'],
5)
A = toa_utils._load_mtl_key(
mtl,
['L1_METADATA_FILE', 'RADIOMETRIC_RESCALING', 'RADIANCE_ADD_BAND_'], 5)
assert isinstance(M, float)
toa = radiance.radiance(tif, M, A)
toa_rescaled = toa_utils.rescale(toa, 255, np.uint8)
scale = float(np.iinfo(np.uint16).max) / float(np.iinfo(np.uint8).max)
tif_out_rescaled = np.clip((tif_output / scale), 0,
np.iinfo(np.uint8).max).astype(np.uint8)
assert toa_rescaled.dtype == np.uint8
assert np.min(tif_out_rescaled) == np.min(toa_rescaled)
assert int(np.max(tif_out_rescaled)) == int(np.max(toa_rescaled))
def test_radiance_wrong_type():
band = np.array([[9931., 9872., 9939.], [0., 5000., 100.],
[10000.1, 0., 100002.]]).astype('float32')
with pytest.raises(TypeError):
radiance.radiance(band, '45sldf', -0.1, 65.0)