infected data in monte carlo data file with helper methods
This commit is contained in:
Luis Aleixo
parent
ac050ee49c
commit
3cc90593c2
1 changed files with 304 additions and 81 deletions
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@ -6,12 +6,115 @@ from scipy import special as sp
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from scipy.stats import weibull_min
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import caimira.monte_carlo as mc
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from caimira.monte_carlo.sampleable import LogCustom, LogNormal,LogCustomKernel,CustomKernel,Uniform, Custom
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from caimira.monte_carlo.sampleable import LogCustom, LogNormal, Normal, LogCustomKernel, CustomKernel, Uniform, Custom
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from caimira.store.configuration import config
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sqrt2pi = np.sqrt(2.*np.pi)
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sqrt2 = np.sqrt(2.)
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def custom_distribution_lookup(dict: dict, key_part: str) -> typing.Any:
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"""
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Look up a custom distribution based on a partial key.
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Args:
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dict (dict): The root to search.
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key_part (str): The distribution key to match.
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Returns:
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str: The associated distribution.
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"""
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try:
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for key, value in dict.items():
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if (key_part in key):
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return value['associated_distribution']
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except KeyError:
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return f"Key '{key_part}' not found."
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def evaluate_reference(reference_variable: str) -> typing.Any:
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"""
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Evaluate a reference variable.
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Args:
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reference_variable (str): The variable to evaluate.
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Returns:
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Any: The evaluated value or an error message if the variable is not defined.
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"""
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try:
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return eval(reference_variable)
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except NameError:
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return f"Variable '{reference_variable}' is not defined."
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def evaluate_custom_distribution(dist: str, params: typing.Dict) -> typing.Any:
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"""
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Evaluate a custom distribution.
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Args:
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dist (str): The type of distribution.
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params (Dict): The parameters for the distribution.
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Returns:
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Any: The generated distribution.
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Raises:
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ValueError: If the distribution type is not recognized.
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"""
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if dist == 'Numpy Linear Space (linspace)':
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return np.linspace(params['start'], params['stop'], params['num'])
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elif dist == 'Numpy Normal Distribution (random.normal)':
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return Normal(params['mean_gaussian'], params['standard_deviation_gaussian'])
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elif dist == 'Numpy Log-normal Distribution (random.lognormal)':
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return LogNormal(params['mean_gaussian'], params['standard_deviation_gaussian'])
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elif dist == 'Numpy Uniform Distribution (random.uniform)':
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return Uniform(params['low'], params['high'])
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else:
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raise ValueError('Bad request - distribution not found.')
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def param_evaluation(root: typing.Dict, param: typing.Union[str, typing.Any]) -> typing.Any:
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"""
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Evaluate a parameter from a nested dictionary.
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Args:
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root (dict): The root dictionary.
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param (Union[str, Any]): The parameter to evaluate.
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Returns:
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Any: The evaluated value.
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Raises:
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TypeError: If the type of the parameter is not defined.
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"""
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value = root.get(param)
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if isinstance(value, str):
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if value.startswith('Ref:'):
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reference_variable = value.split(' - ')[1].strip()
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return evaluate_reference(reference_variable)
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elif value == 'Custom':
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custom_distribution: typing.Dict = custom_distribution_lookup(
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root, 'custom distribution')
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for d, p in custom_distribution.items():
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return evaluate_custom_distribution(d, p)
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elif isinstance(value, dict):
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dist: str = root[param]['associated_distribution']
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params: typing.Dict = root[param]['parameters']
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return evaluate_custom_distribution(dist, params)
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elif isinstance(value, float) or isinstance(value, int):
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return value
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else:
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raise TypeError('Bad request - type not allowed.')
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@dataclass(frozen=True)
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class BLOmodel:
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"""
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@ -34,25 +137,37 @@ class BLOmodel:
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#: cn (cm^-3) for resp. the B, L and O modes. Corresponds to the
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# total concentration of aerosols for each mode.
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cn: typing.Tuple[float, float, float] = (0.06, 0.2, 0.0010008)
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cn: typing.Tuple[float, float, float] = (
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config.BLOmodel['cn']['B'],
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config.BLOmodel['cn']['L'],
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config.BLOmodel['cn']['O']
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)
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# Mean of the underlying normal distributions (represents the log of a
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# diameter in microns), for resp. the B, L and O modes.
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mu: typing.Tuple[float, float, float] = (0.989541, 1.38629, 4.97673)
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mu: typing.Tuple[float, float, float] = (
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config.BLOmodel['mu']['B'],
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config.BLOmodel['mu']['L'],
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config.BLOmodel['mu']['O']
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)
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# Std deviation of the underlying normal distribution, for resp.
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# the B, L and O modes.
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sigma: typing.Tuple[float, float, float] = (0.262364, 0.506818, 0.585005)
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sigma: typing.Tuple[float, float, float] = (
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config.BLOmodel['sigma']['B'],
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config.BLOmodel['sigma']['L'],
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config.BLOmodel['sigma']['O']
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)
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def distribution(self, d):
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"""
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Returns the raw value of the probability distribution for a
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given diameter d (microns).
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"""
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return sum( (1 / d) * (A * cn / (sqrt2pi * sigma)) *
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np.exp(-(np.log(d) - mu) ** 2 / (2 * sigma ** 2))
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for A,cn,mu,sigma in zip(self.BLO_factors, self.cn,
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self.mu, self.sigma) )
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return sum((1 / d) * (A * cn / (sqrt2pi * sigma)) *
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np.exp(-(np.log(d) - mu) ** 2 / (2 * sigma ** 2))
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for A, cn, mu, sigma in zip(self.BLO_factors, self.cn,
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self.mu, self.sigma))
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def integrate(self, dmin, dmax):
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"""
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@ -60,7 +175,7 @@ class BLOmodel:
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probability distribution.
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"""
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result = 0.
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for A,cn,mu,sigma in zip(self.BLO_factors, self.cn, self.mu, self.sigma):
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for A, cn, mu, sigma in zip(self.BLO_factors, self.cn, self.mu, self.sigma):
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ymin = (np.log(dmin)-mu)/(sqrt2*sigma)
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ymax = (np.log(dmax)-mu)/(sqrt2*sigma)
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result += A * cn * (sp.erf(ymax)-sp.erf(ymin)) / 2.
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@ -69,35 +184,55 @@ class BLOmodel:
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# From https://doi.org/10.1101/2021.10.14.21264988 and references therein
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activity_distributions = {
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'Seated': mc.Activity(LogNormal(-0.6872121723362303, 0.10498338229297108),
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LogNormal(-0.6872121723362303, 0.10498338229297108)),
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'Seated': mc.Activity(
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inhalation_rate=param_evaluation(
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config.activity_distributions['Seated'], 'inhalation_rate'),
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exhalation_rate=param_evaluation(
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config.activity_distributions['Seated'], 'exhalation_rate'),
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),
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'Standing': mc.Activity(LogNormal(-0.5742377578494785, 0.09373162411398223),
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LogNormal(-0.5742377578494785, 0.09373162411398223)),
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'Standing': mc.Activity(
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inhalation_rate=param_evaluation(
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config.activity_distributions['Standing'], 'inhalation_rate'),
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exhalation_rate=param_evaluation(
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config.activity_distributions['Standing'], 'exhalation_rate'),
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),
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'Light activity': mc.Activity(LogNormal(0.21380242785625422,0.09435378091059601),
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LogNormal(0.21380242785625422,0.09435378091059601)),
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'Light activity': mc.Activity(
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inhalation_rate=param_evaluation(
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config.activity_distributions['Light activity'], 'inhalation_rate'),
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exhalation_rate=param_evaluation(
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config.activity_distributions['Light activity'], 'exhalation_rate'),
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),
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'Moderate activity': mc.Activity(LogNormal(0.551771330362601, 0.1894616357138137),
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LogNormal(0.551771330362601, 0.1894616357138137)),
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'Moderate activity': mc.Activity(
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inhalation_rate=param_evaluation(
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config.activity_distributions['Moderate activity'], 'inhalation_rate'),
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exhalation_rate=param_evaluation(
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config.activity_distributions['Moderate activity'], 'exhalation_rate'),
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),
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'Heavy exercise': mc.Activity(LogNormal(1.1644665696723049, 0.21744554768657565),
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LogNormal(1.1644665696723049, 0.21744554768657565)),
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'Heavy exercise': mc.Activity(
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inhalation_rate=param_evaluation(
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config.activity_distributions['Heavy exercise'], 'inhalation_rate'),
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exhalation_rate=param_evaluation(
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config.activity_distributions['Heavy exercise'], 'exhalation_rate'),
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),
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}
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# From https://doi.org/10.1101/2021.10.14.21264988 and references therein
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symptomatic_vl_frequencies = LogCustomKernel(
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np.array((2.46032, 2.67431, 2.85434, 3.06155, 3.25856, 3.47256, 3.66957, 3.85979, 4.09927, 4.27081,
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4.47631, 4.66653, 4.87204, 5.10302, 5.27456, 5.46478, 5.6533, 5.88428, 6.07281, 6.30549,
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6.48552, 6.64856, 6.85407, 7.10373, 7.30075, 7.47229, 7.66081, 7.85782, 8.05653, 8.27053,
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8.48453, 8.65607, 8.90573, 9.06878, 9.27429, 9.473, 9.66152, 9.87552)),
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4.47631, 4.66653, 4.87204, 5.10302, 5.27456, 5.46478, 5.6533, 5.88428, 6.07281, 6.30549,
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6.48552, 6.64856, 6.85407, 7.10373, 7.30075, 7.47229, 7.66081, 7.85782, 8.05653, 8.27053,
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8.48453, 8.65607, 8.90573, 9.06878, 9.27429, 9.473, 9.66152, 9.87552)),
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np.array((0.001206885, 0.007851618, 0.008078144, 0.01502491, 0.013258014, 0.018528495, 0.020053765,
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0.021896167, 0.022047184, 0.018604005, 0.01547796, 0.018075445, 0.021503523, 0.022349217,
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0.025097721, 0.032875078, 0.030594727, 0.032573045, 0.034717482, 0.034792991,
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0.033267721, 0.042887485, 0.036846816, 0.03876473, 0.045016819, 0.040063473, 0.04883754,
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0.043944602, 0.048142864, 0.041588741, 0.048762031, 0.027921732, 0.033871788,
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0.022122693, 0.016927718, 0.008833228, 0.00478598, 0.002807662)),
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0.021896167, 0.022047184, 0.018604005, 0.01547796, 0.018075445, 0.021503523, 0.022349217,
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0.025097721, 0.032875078, 0.030594727, 0.032573045, 0.034717482, 0.034792991,
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0.033267721, 0.042887485, 0.036846816, 0.03876473, 0.045016819, 0.040063473, 0.04883754,
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0.043944602, 0.048142864, 0.041588741, 0.048762031, 0.027921732, 0.033871788,
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0.022122693, 0.016927718, 0.008833228, 0.00478598, 0.002807662)),
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kernel_bandwidth=0.1
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)
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@ -105,61 +240,103 @@ symptomatic_vl_frequencies = LogCustomKernel(
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# Weibull distribution with a shape factor of 3.47 and a scale factor of 7.01.
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# From https://elifesciences.org/articles/65774 and first line of the figure in
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# https://iiif.elifesciences.org/lax:65774%2Felife-65774-fig4-figsupp3-v2.tif/full/1500,/0/default.jpg
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viral_load = np.linspace(weibull_min.ppf(0.01, c=3.47, scale=7.01),
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weibull_min.ppf(0.99, c=3.47, scale=7.01), 30)
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frequencies_pdf = weibull_min.pdf(viral_load, c=3.47, scale=7.01)
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covid_overal_vl_data = LogCustom(bounds=(2, 10),
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function=lambda d: np.interp(d, viral_load, frequencies_pdf, left=0., right=0.),
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max_function=0.2)
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viral_load = np.linspace(
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weibull_min.ppf(
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config.covid_overal_vl_data['start'],
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c=config.covid_overal_vl_data['shape_factor'],
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scale=config.covid_overal_vl_data['scale_factor']
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),
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weibull_min.ppf(
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config.covid_overal_vl_data['stop'],
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c=config.covid_overal_vl_data['shape_factor'],
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scale=config.covid_overal_vl_data['scale_factor']
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),
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int(config.covid_overal_vl_data['num'])
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)
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frequencies_pdf = weibull_min.pdf(
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viral_load,
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c=config.covid_overal_vl_data['shape_factor'],
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scale=config.covid_overal_vl_data['scale_factor']
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)
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covid_overal_vl_data = LogCustom(bounds=(config.covid_overal_vl_data['min_bound'], config.covid_overal_vl_data['max_bound']),
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function=lambda d: np.interp(d, viral_load, frequencies_pdf, config.covid_overal_vl_data[
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'interpolation_fp_left'], config.covid_overal_vl_data['interpolation_fp_right']),
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max_function=config.covid_overal_vl_data['max_function'])
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# Derived from data in doi.org/10.1016/j.ijid.2020.09.025 and
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# https://iosh.com/media/8432/aerosol-infection-risk-hospital-patient-care-full-report.pdf (page 60)
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viable_to_RNA_ratio_distribution = Uniform(0.01, 0.6)
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viable_to_RNA_ratio_distribution = Uniform(
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config.viable_to_RNA_ratio_distribution['low'], config.viable_to_RNA_ratio_distribution['high'])
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# From discussion with virologists
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infectious_dose_distribution = Uniform(10., 100.)
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infectious_dose_distribution = Uniform(
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config.infectious_dose_distribution['low'], config.infectious_dose_distribution['high'])
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# From https://doi.org/10.1101/2021.10.14.21264988 and refererences therein
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virus_distributions = {
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'SARS_CoV_2': mc.SARSCoV2(
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viral_load_in_sputum=covid_overal_vl_data,
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infectious_dose=infectious_dose_distribution,
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viable_to_RNA_ratio=viable_to_RNA_ratio_distribution,
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transmissibility_factor=1.,
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),
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viral_load_in_sputum=param_evaluation(
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config.virus_distributions['SARS_CoV_2'], 'viral_load_in_sputum'),
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infectious_dose=param_evaluation(
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config.virus_distributions['SARS_CoV_2'], 'infectious_dose'),
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viable_to_RNA_ratio=param_evaluation(
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config.virus_distributions['SARS_CoV_2'], 'viable_to_RNA_ratio'),
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transmissibility_factor=param_evaluation(
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config.virus_distributions['SARS_CoV_2'], 'transmissibility_factor'),
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),
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'SARS_CoV_2_ALPHA': mc.SARSCoV2(
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viral_load_in_sputum=covid_overal_vl_data,
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infectious_dose=infectious_dose_distribution,
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viable_to_RNA_ratio=viable_to_RNA_ratio_distribution,
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transmissibility_factor=0.78,
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),
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viral_load_in_sputum=param_evaluation(
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config.virus_distributions['SARS_CoV_2_ALPHA'], 'viral_load_in_sputum'),
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infectious_dose=param_evaluation(
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config.virus_distributions['SARS_CoV_2_ALPHA'], 'infectious_dose'),
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viable_to_RNA_ratio=param_evaluation(
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config.virus_distributions['SARS_CoV_2_ALPHA'], 'viable_to_RNA_ratio'),
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transmissibility_factor=param_evaluation(
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config.virus_distributions['SARS_CoV_2_ALPHA'], 'transmissibility_factor'),
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),
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'SARS_CoV_2_BETA': mc.SARSCoV2(
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viral_load_in_sputum=covid_overal_vl_data,
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infectious_dose=infectious_dose_distribution,
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viable_to_RNA_ratio=viable_to_RNA_ratio_distribution,
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transmissibility_factor=0.8,
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),
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viral_load_in_sputum=param_evaluation(
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config.virus_distributions['SARS_CoV_2_BETA'], 'viral_load_in_sputum'),
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infectious_dose=param_evaluation(
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config.virus_distributions['SARS_CoV_2_BETA'], 'infectious_dose'),
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viable_to_RNA_ratio=param_evaluation(
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config.virus_distributions['SARS_CoV_2_BETA'], 'viable_to_RNA_ratio'),
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transmissibility_factor=param_evaluation(
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config.virus_distributions['SARS_CoV_2_BETA'], 'transmissibility_factor'),
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),
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'SARS_CoV_2_GAMMA': mc.SARSCoV2(
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viral_load_in_sputum=covid_overal_vl_data,
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infectious_dose=infectious_dose_distribution,
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viable_to_RNA_ratio=viable_to_RNA_ratio_distribution,
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transmissibility_factor=0.72,
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),
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viral_load_in_sputum=param_evaluation(
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config.virus_distributions['SARS_CoV_2_GAMMA'], 'viral_load_in_sputum'),
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infectious_dose=param_evaluation(
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config.virus_distributions['SARS_CoV_2_GAMMA'], 'infectious_dose'),
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viable_to_RNA_ratio=param_evaluation(
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config.virus_distributions['SARS_CoV_2_GAMMA'], 'viable_to_RNA_ratio'),
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transmissibility_factor=param_evaluation(
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config.virus_distributions['SARS_CoV_2_GAMMA'], 'transmissibility_factor'),
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),
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'SARS_CoV_2_DELTA': mc.SARSCoV2(
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viral_load_in_sputum=covid_overal_vl_data,
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infectious_dose=infectious_dose_distribution,
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viable_to_RNA_ratio=viable_to_RNA_ratio_distribution,
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transmissibility_factor=0.51,
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),
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viral_load_in_sputum=param_evaluation(
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config.virus_distributions['SARS_CoV_2_DELTA'], 'viral_load_in_sputum'),
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infectious_dose=param_evaluation(
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config.virus_distributions['SARS_CoV_2_DELTA'], 'infectious_dose'),
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viable_to_RNA_ratio=param_evaluation(
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config.virus_distributions['SARS_CoV_2_DELTA'], 'viable_to_RNA_ratio'),
|
||||
transmissibility_factor=param_evaluation(
|
||||
config.virus_distributions['SARS_CoV_2_DELTA'], 'transmissibility_factor'),
|
||||
),
|
||||
'SARS_CoV_2_OMICRON': mc.SARSCoV2(
|
||||
viral_load_in_sputum=covid_overal_vl_data,
|
||||
infectious_dose=infectious_dose_distribution,
|
||||
viable_to_RNA_ratio=viable_to_RNA_ratio_distribution,
|
||||
transmissibility_factor=0.2,
|
||||
),
|
||||
viral_load_in_sputum=param_evaluation(
|
||||
config.virus_distributions['SARS_CoV_2_OMICRON'], 'viral_load_in_sputum'),
|
||||
infectious_dose=param_evaluation(
|
||||
config.virus_distributions['SARS_CoV_2_OMICRON'], 'infectious_dose'),
|
||||
viable_to_RNA_ratio=param_evaluation(
|
||||
config.virus_distributions['SARS_CoV_2_OMICRON'], 'viable_to_RNA_ratio'),
|
||||
transmissibility_factor=param_evaluation(
|
||||
config.virus_distributions['SARS_CoV_2_OMICRON'], 'transmissibility_factor'),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
|
|
@ -169,14 +346,33 @@ virus_distributions = {
|
|||
# https://doi.org/10.4209/aaqr.2020.08.0531
|
||||
# https://doi.org/10.1080/02786826.2021.1890687
|
||||
mask_distributions = {
|
||||
'Type I': mc.Mask(η_inhale=Uniform(0.25, 0.80)),
|
||||
'FFP2': mc.Mask(η_inhale=Uniform(0.83, 0.91)),
|
||||
'Cloth': mc.Mask(η_inhale=Uniform(0.05, 0.40), η_exhale=Uniform(0.20, 0.50)),
|
||||
'Type I': mc.Mask(
|
||||
η_inhale=param_evaluation(
|
||||
config.mask_distributions['Type I'], 'η_inhale'),
|
||||
η_exhale=param_evaluation(
|
||||
config.mask_distributions['Type I'], 'η_exhale')
|
||||
if config.mask_distributions['Type I']['Known filtration efficiency of masks when exhaling?'] == 'Yes' else None,
|
||||
),
|
||||
'FFP2': mc.Mask(
|
||||
η_inhale=param_evaluation(
|
||||
config.mask_distributions['FFP2'], 'η_inhale'),
|
||||
η_exhale=param_evaluation(
|
||||
config.mask_distributions['FFP2'], 'η_exhale')
|
||||
if config.mask_distributions['FFP2']['Known filtration efficiency of masks when exhaling?'] == 'Yes' else None,
|
||||
),
|
||||
'Cloth': mc.Mask(
|
||||
η_inhale=param_evaluation(
|
||||
config.mask_distributions['Cloth'], 'η_inhale'),
|
||||
η_exhale=param_evaluation(
|
||||
config.mask_distributions['Cloth'], 'η_exhale')
|
||||
if config.mask_distributions['Cloth']['Known filtration efficiency of masks when exhaling?'] == 'Yes' else None,
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
def expiration_distribution(
|
||||
BLO_factors,
|
||||
d_min=0.1,
|
||||
d_max=30.,
|
||||
) -> mc.Expiration:
|
||||
"""
|
||||
|
|
@ -187,40 +383,67 @@ def expiration_distribution(
|
|||
an historical choice based on previous implementations of the model
|
||||
(it limits the influence of the O-mode).
|
||||
"""
|
||||
dscan = np.linspace(0.1, d_max, 3000)
|
||||
dscan = np.linspace(d_min, d_max, 3000)
|
||||
return mc.Expiration(
|
||||
CustomKernel(
|
||||
dscan,
|
||||
BLOmodel(BLO_factors).distribution(dscan),
|
||||
kernel_bandwidth=0.1,
|
||||
),
|
||||
cn=BLOmodel(BLO_factors).integrate(0.1, d_max),
|
||||
cn=BLOmodel(BLO_factors).integrate(d_min, d_max),
|
||||
)
|
||||
|
||||
|
||||
expiration_BLO_factors = {
|
||||
'Breathing': (1., 0., 0.),
|
||||
'Speaking': (1., 1., 1.),
|
||||
'Singing': (1., 5., 5.),
|
||||
'Shouting': (1., 5., 5.),
|
||||
'Breathing': (
|
||||
param_evaluation(config.expiration_BLO_factors['Breathing'], 'B'),
|
||||
param_evaluation(config.expiration_BLO_factors['Breathing'], 'L'),
|
||||
param_evaluation(config.expiration_BLO_factors['Breathing'], 'O')
|
||||
),
|
||||
'Speaking': (
|
||||
param_evaluation(config.expiration_BLO_factors['Speaking'], 'B'),
|
||||
param_evaluation(config.expiration_BLO_factors['Speaking'], 'L'),
|
||||
param_evaluation(config.expiration_BLO_factors['Speaking'], 'O')
|
||||
),
|
||||
'Singing': (
|
||||
param_evaluation(config.expiration_BLO_factors['Singing'], 'B'),
|
||||
param_evaluation(config.expiration_BLO_factors['Singing'], 'L'),
|
||||
param_evaluation(config.expiration_BLO_factors['Singing'], 'O')
|
||||
),
|
||||
'Shouting': (
|
||||
param_evaluation(config.expiration_BLO_factors['Shouting'], 'B'),
|
||||
param_evaluation(config.expiration_BLO_factors['Shouting'], 'L'),
|
||||
param_evaluation(config.expiration_BLO_factors['Shouting'], 'O')
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
expiration_distributions = {
|
||||
exp_type: expiration_distribution(BLO_factors)
|
||||
exp_type: expiration_distribution(BLO_factors,
|
||||
d_min=param_evaluation(
|
||||
config.long_range_expiration_distributions, 'minimum_diameter'),
|
||||
d_max=param_evaluation(config.long_range_expiration_distributions, 'maximum_diameter'))
|
||||
for exp_type, BLO_factors in expiration_BLO_factors.items()
|
||||
}
|
||||
|
||||
|
||||
short_range_expiration_distributions = {
|
||||
exp_type: expiration_distribution(BLO_factors, d_max=100)
|
||||
exp_type: expiration_distribution(
|
||||
BLO_factors,
|
||||
d_min=param_evaluation(
|
||||
config.short_range_expiration_distributions, 'minimum_diameter'),
|
||||
d_max=param_evaluation(config.short_range_expiration_distributions, 'maximum_diameter'))
|
||||
for exp_type, BLO_factors in expiration_BLO_factors.items()
|
||||
}
|
||||
|
||||
|
||||
# Derived from Fig 8 a) "stand-stand" in https://www.mdpi.com/1660-4601/17/4/1445/htm
|
||||
distances = np.array((0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9,2))
|
||||
frequencies = np.array((0.0598036,0.0946154,0.1299152,0.1064905,0.1099066,0.0998209, 0.0845298,0.0479286,0.0406084,0.039795,0.0205997,0.0152316,0.0118155,0.0118155,0.018485,0.0205997))
|
||||
short_range_distances = Custom(bounds=(0.5,2.),
|
||||
function=lambda x: np.interp(x,distances,frequencies,left=0.,right=0.),
|
||||
max_function=0.13)
|
||||
distances = np.array((0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2,
|
||||
1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2))
|
||||
frequencies = np.array((0.0598036, 0.0946154, 0.1299152, 0.1064905, 0.1099066, 0.0998209, 0.0845298,
|
||||
0.0479286, 0.0406084, 0.039795, 0.0205997, 0.0152316, 0.0118155, 0.0118155, 0.018485, 0.0205997))
|
||||
short_range_distances = Custom(bounds=(param_evaluation(config.short_range_distances, 'minimum_distance'),
|
||||
param_evaluation(config.short_range_distances, 'maximum_distance')),
|
||||
function=lambda x: np.interp(
|
||||
x, distances, frequencies, left=0., right=0.),
|
||||
max_function=0.13)
|
||||
|
|
|
|||
Loading…
Reference in a new issue