Added beta distribution for interpersonal distances. #254

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
+from cara.monte_carlo.sampleable import LogNormal,LogCustomKernel,CustomKernel,Uniform, Beta
 
 
 sqrt2pi = np.sqrt(2.*np.pi)
@@ -202,5 +202,5 @@ short_range_expiration_distributions = {
 }
 
 
-# Fit from Fig 8 a) "stand-stand" in https://www.mdpi.com/1660-4601/17/4/1445/htm
-short_range_distances = LogNormal(-0.269359136417347, 0.4728300188814934)
+# Derived from Fig 8 a) "stand-stand" in https://www.mdpi.com/1660-4601/17/4/1445/htm
+short_range_distances = Beta(alpha=1.4342766632654418, beta=27.49916410927064)

cara/monte_carlo/sampleable.py

@@ -2,6 +2,7 @@ import typing
 
 import numpy as np
 from sklearn.neighbors import KernelDensity # type: ignore
+from scipy.stats import beta
 
 import cara.models
 
@@ -130,6 +131,22 @@ 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):
+        # these are resp. the alpha and beta of the underlying distribution
+        self.alpha = alpha
+        self.beta = beta
+
+    def generate_samples(self, size: int) -> float_array_size_n:
+        return beta.rvs(a = self.alpha, b = self.beta, loc=0.5, scale=7, 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, 15.24806213],
-        [10.6, 15.24806213],
-        [11.0, 15.24806213],
+        [10.5, 7.491771],
+        [10.6, 7.491771],
+        [11.0, 7.491771],
         [12.0, 0.],
     ]
 )