def calc_sigma_units(time_vector: np.ndarray,
range_los: np.ndarray) -> Tuple[float, float]:
"""Calculates Gaussian peak std parameters.
The amount of smoothing is hard coded. This function calculates
how many steps in time and height corresponds to this smoothing.
Args:
time_vector: 1D vector (fraction hour).
range_los: 1D vector (m).
Returns:
tuple: Two element tuple containing number of steps in time and height to achieve wanted
smoothing.
"""
if len(time_vector) == 0 or np.max(time_vector) > 24:
raise ValueError("Invalid time vector")
minutes_in_hour = 60
sigma_minutes = 2
sigma_metres = 5
time_step = utils.mdiff(time_vector) * minutes_in_hour
alt_step = utils.mdiff(range_los)
x_std = sigma_minutes / time_step
y_std = sigma_metres / alt_step
return x_std, y_std
def __init__(self, categorize_file):
super().__init__(categorize_file)
self.mie = self._read_mie_lut()
self.dheight = utils.mdiff(self.getvar('height'))
self.z = self._convert_z_units()
self.beta = self.getvar('beta')
self.v = self.getvar('v')
def __init__(self, categorize_file: str):
super().__init__(categorize_file)
self.mie = self._read_mie_lut()
self.dheight = utils.mdiff(self.getvar("height"))
self.z = self._convert_z_units()
self.beta = self.getvar("beta")
self.v = self.getvar("v")
def __init__(self, categorize_file):
super().__init__(categorize_file)
self.lwp = self.getvar('lwp')
self.lwp_error = self.getvar('lwp_error')
self.is_rain = self.getvar('is_rain')
self.dheight = utils.mdiff(self.getvar('height'))
self.atmosphere = self._get_atmosphere(categorize_file)
self.categorize_bits = CategorizeBits(categorize_file)
def __init__(self, categorize_file: str):
super().__init__(categorize_file)
self.lwp = self.getvar("lwp")
self.lwp[self.lwp < 0] = 0
self.lwp_error = self.getvar("lwp_error")
self.is_rain = get_is_rain(categorize_file)
self.dheight = utils.mdiff(self.getvar("height"))
self.atmosphere = self._get_atmosphere(categorize_file)
self.categorize_bits = CategorizeBits(categorize_file)
def _number_of_independent_pulses() -> float:
seconds_in_hour = 3600
dwell_time = utils.mdiff(self.time) * seconds_in_hour
return (
dwell_time
* self.radar_frequency
* 1e9
* 4
* np.sqrt(math.pi)
* self.data["width"][:]
/ 3e8
)
def _calc_sigma_units(time, range_instru):
"""Calculates Gaussian peak std parameters.
The amount of smoothing is hard coded. This function calculates
how many steps in time and height corresponds to this smoothing.
Args:
time (ndarray): 1D vector (fraction hour).
range_instru (ndarray): 1D vector (m).
Returns:
tuple: Two element tuple containing number of steps in time and height
to achieve wanted smoothing.
"""
minutes_in_hour = 60
sigma_minutes = 2
sigma_metres = 5
time_step = utils.mdiff(time) * minutes_in_hour
alt_step = utils.mdiff(range_instru)
x_std = sigma_minutes / time_step
y_std = sigma_metres / alt_step
return x_std, y_std
def test_mdiff(input, output):
assert utils.mdiff(input) == output
def _calc_range(self):
"""Assumes 'range' means the upper limit of range gate."""
ceilo_range = self._getvar('range')
return ceilo_range - utils.mdiff(ceilo_range) / 2
def _estimate_snr_gain(time_sparse: np.ndarray,
time_dense: np.ndarray) -> float:
"""Returns factor for SNR (dB) increase when data is binned."""
binning_ratio = utils.mdiff(time_sparse) / utils.mdiff(time_dense)
return np.sqrt(binning_ratio)
def __init__(self, model: Model, classification: ClassificationResult):
self._dheight = utils.mdiff(model.height)
self._model = model.data_dense
self._liquid_in_pixel = utils.isbit(classification.category_bits, 0)
self.classification = classification
def _number_of_pulses():
"""Returns number of independent pulses."""
seconds_in_hour = 3600
dwell_time = utils.mdiff(self.time) * seconds_in_hour
return (dwell_time * self.radar_frequency * 1e9 * 4 *
np.sqrt(math.pi) * self.data['width'][:] / 3e8)
