Removed beta distribution and added custom distribution for the interpersonal distances distribution

cara/monte_carlo/data.py

@@ -5,7 +5,7 @@ import numpy as np
 from scipy import special as sp
 
 import cara.monte_carlo as mc
-from cara.monte_carlo.sampleable import LogNormal,LogCustomKernel,CustomKernel,Uniform, Beta
+from cara.monte_carlo.sampleable import LogNormal,LogCustomKernel,CustomKernel,Uniform, Custom
 
 
 sqrt2pi = np.sqrt(2.*np.pi)
@@ -203,4 +203,8 @@ short_range_expiration_distributions = {
 
 
 # Derived from Fig 8 a) "stand-stand" in https://www.mdpi.com/1660-4601/17/4/1445/htm
-short_range_distances = Beta(alpha=0.588715, beta=1.50214, loc=0.5, scale=1.5)
+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)

cara/monte_carlo/sampleable.py

@@ -2,7 +2,6 @@ import typing
 
 import numpy as np
 from sklearn.neighbors import KernelDensity # type: ignore
-from scipy.stats import beta
 
 import cara.models
 
@@ -131,24 +130,6 @@ class LogCustomKernel(SampleableDistribution):
         return 10 ** kde_model.sample(n_samples=size)[:, 0]
 
 
-class Beta(SampleableDistribution):
-    """
-    Defines a Beta distribution parameterized by two positive shape parameters, 
-    denoted by alpha (α) and beta (β), that appear as exponents of the random 
-    variable and control the shape of the distribution.
-    """
-
-    def __init__(self, alpha: float, beta: float, loc: float, scale: float):
-        # these are resp. the alpha and beta of the underlying distribution
-        self.alpha = alpha
-        self.beta = beta
-        self.loc = loc
-        self.scale = scale
-
-    def generate_samples(self, size: int) -> float_array_size_n:
-        return beta.rvs(a = self.alpha, b = self.beta, loc=self.loc, scale=self.scale, size=size)
-
-
 _VectorisedFloatOrSampleable = typing.Union[
     SampleableDistribution, cara.models._VectorisedFloat,
 ]

cara/tests/models/test_short_range_model.py

@@ -72,9 +72,9 @@ def test_dilution_factor(activity, expected_dilution):
 @pytest.mark.parametrize(
     "time, expected_short_range_concentration", [
         [8.5, 0.],
-        [10.5, 8.037883241318065],
-        [10.6, 8.037883241318065],
-        [11.0, 8.037883241318065],
+        [10.5, 5.401601371244907],
+        [10.6, 5.401601371244907],
+        [11.0, 5.401601371244907],
         [12.0, 0.],
     ]
 )