def _find_melting_layer_from_v(v_prof: np.ndarray,
width_prof: Union[np.ndarray, None],
height: np.ndarray) -> Union[np.ndarray, None]:
v = np.copy(v_prof[:-1])
v_diff = np.diff(v_prof)
v[v_diff < 0] = 0
v[v_diff > 0] = 1
n_increasing = utils.cumsumr(v)
try:
top = int(np.argmax(n_increasing))
base = np.where(n_increasing[:top] == 0)[0][-1]
except IndexError:
return None
if width_prof is not None:
conditions = [
width_prof[base] - width_prof[top] > 0.2,
v_prof[top] - v_prof[base] > 0.5,
50 < (height[top] - height[base]) < 1000,
v_prof[base] < -2,
]
else:
conditions = [
v_prof[top] - v_prof[base] > 2,
50 < (height[top] - height[base]) < 1000,
v_prof[base] < -2,
]
if all(conditions):
base = int(round(top - (top - base) / 2))
return np.arange(base, top)
return None
def calc_adiabatic_lwc(lwc_change_rate: np.ndarray, dheight: float) -> np.ndarray:
"""Calculates adiabatic liquid water content (g/m3).
Args:
lwc_change_rate: Liquid water content change rate (g/m3/m) calculated at the base of each
cloud and filled to that cloud.
dheight: Median difference of the height vector (m).
Returns:
Liquid water content (g/m3).
"""
is_liquid = lwc_change_rate != 0
ind_from_base = utils.cumsumr(is_liquid, axis=1)
return ind_from_base * dheight * lwc_change_rate
def _mask_low_values_above_consequent_negatives(
data: np.ndarray,
n_negatives: int = 5,
threshold: float = 8e-6,
n_gates: int = 95,
n_skip_lowest: int = 5,
) -> np.ndarray:
negative_data = data[:, n_skip_lowest:n_gates + n_skip_lowest] < 0
n_consequent_negatives = utils.cumsumr(negative_data, axis=1)
time_indices, alt_indices = np.where(
n_consequent_negatives > n_negatives)
alt_indices += n_skip_lowest
for time_ind, alt_ind in zip(time_indices, alt_indices):
profile = data[time_ind, alt_ind:]
profile[profile < threshold] = ma.masked
cleaned_time_indices = np.unique(time_indices)
logging.info(
f"Cleaned {len(cleaned_time_indices)} profiles with negative filter"
)
return cleaned_time_indices
def test_cumsumr_4():
x = np.array([[0, 1, 1, 0], [0, 5, 0, 0]])
res = np.array([[0, 1, 1, 0], [0, 6, 0, 0]])
assert_array_almost_equal(utils.cumsumr(x), res)
def test_cumsumr_2():
x = np.array([[0, 1, 1, 0], [0, 5, 0, 0]])
res = np.array([[0, 1, 2, 0], [0, 5, 0, 0]])
assert_array_almost_equal(utils.cumsumr(x, axis=1), res)
def test_cumsumr_1():
x = np.array([0, 1, 2, 0, 1, 1])
res = np.array([0, 1, 3, 0, 1, 2])
assert_array_almost_equal(utils.cumsumr(x), res)
