def test_find_liquid():
class Obs:
def __init__(self):
self.height = np.arange(6) * 100
self.time = np.arange(3)
self.lwp = ma.array(range(3))
self.beta = ma.array([[1e-8, 1e-8, 2e-6, 1e-3, 5e-6, 1e-8],
[1e-8, 1e-8, 1e-5, 1e-3, 1e-5, 1e-8],
[1e-8, 1e-8, 1e-5, 1e-3, 1e-5, 1e-8]],
mask=[[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]])
bases = np.array([[0, 0, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0],
[0, 0, 1, 0, 0, 0]])
tops = np.array([[0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0]])
is_liquid = np.array([[0, 0, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0],
[0, 0, 1, 1, 1, 0]])
obs = Obs()
result = droplet.find_liquid(obs)
assert_array_equal(bases, result['bases'])
assert_array_equal(tops, result['tops'])
assert_array_equal(is_liquid, result['presence'])
def classify_measurements(data: dict) -> ClassificationResult:
"""Classifies radar/lidar observations.
This function classifies atmospheric scatterers from the input data.
The input data needs to be averaged or interpolated to the common
time / height grid before calling this function.
Args:
data: Containing :class:`Radar`, :class:`Lidar`, :class:`Model` and :class:`Mwr` instances.
Returns:
A :class:`ClassificationResult` instance.
References:
The Cloudnet classification scheme is based on methodology proposed by
Hogan R. and O'Connor E., 2004, https://bit.ly/2Yjz9DZ and its
proprietary Matlab implementation.
Notes:
Some of the individual classification methods are changed in this Python
implementation compared to the original Cloudnet methodology.
Especially methods classifying insects, melting layer and liquid droplets.
"""
obs = ClassData(data)
bits: List[np.ndarray] = [np.array([])] * 6
liquid = droplet.find_liquid(obs)
bits[3] = melting.find_melting_layer(obs)
bits[2] = freezing.find_freezing_region(obs, bits[3])
bits[0] = droplet.correct_liquid_top(obs, liquid, bits[2], limit=500)
bits[5] = insects.find_insects(obs, bits[3], bits[0])
bits[1] = falling.find_falling_hydrometeors(obs, bits[0], bits[5])
bits, filtered_ice = _filter_falling(bits)
for _ in range(5):
bits[3] = _fix_undetected_melting_layer(bits)
bits = _filter_insects(bits)
bits[4] = _find_aerosols(obs, bits[1], bits[0])
bits[4][filtered_ice] = False
return ClassificationResult(
_bits_to_integer(bits), obs.is_rain, obs.is_clutter, liquid["bases"], obs.rain_rate
)