From b6bb878fc267b9418304ae9b46d811decf40897f Mon Sep 17 00:00:00 2001
From: Nicolas Mounet <nicolas.mounet@cern.ch>
Date: Tue, 11 May 2021 20:00:40 +0200
Subject: [PATCH] Adding predefined Activity distributions

---
 cara/models.py | 39 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 39 insertions(+)

diff --git a/cara/models.py b/cara/models.py
index 291dcfc1..1fa3fbe7 100644
--- a/cara/models.py
+++ b/cara/models.py
@@ -37,6 +37,7 @@ import typing
 
 import numpy as np
 from scipy.interpolate import interp1d
+import scipy.stats as sct
 
 if not typing.TYPE_CHECKING:
     from memoization import cached
@@ -55,6 +56,18 @@ _VectorisedFloat = typing.Union[float, np.ndarray]
 _VectorisedInt = typing.Union[int, np.ndarray]
 
 
+def _lognormal_distribution(csi: float, lamb: float, samples: int) -> np.ndarray:
+    """
+    Generates a number of samples from a specified lognormal distribution
+    :param csi: parameter used to specify the lognormal distribution
+    :param lamb: parameter used to specify the lognormal distribution
+    :param samples: number of samples to be generated
+    :return: numpy-array containing the samples
+    """
+    sf_norm = sct.norm.sf(np.random.normal(size=samples))
+    return sct.lognorm.isf(sf_norm, csi, loc=0, scale=np.exp(lamb))
+
+
 @dataclass(frozen=True)
 class Room:
     #: The total volume of the room
@@ -532,6 +545,9 @@ class Activity:
     #: Pre-populated examples of activities.
     types: typing.ClassVar[typing.Dict[str, "Activity"]]
 
+    #: Pre-defined examples of activity distributions.
+    distributions: typing.ClassVar[typing.Dict[str, typing.Callable[[int], "Activity"]]]
+
 
 Activity.types = {
     'Seated': Activity(0.51, 0.51),
@@ -541,6 +557,29 @@ Activity.types = {
     'Heavy exercise': Activity(3.30, 3.30),
 }
 
+def _generate_activity_distribution(csi: float, lamb: float, samples: int):
+    return Activity(
+        _lognormal_distribution(csi, lamb, samples),
+        _lognormal_distribution(csi, lamb, samples),
+    )
+
+Activity.distributions = {
+    'Seated': lambda n: _generate_activity_distribution(
+                            0.10498338229297108, -0.6872121723362303, n),
+
+    'Standing': lambda n: _generate_activity_distribution(
+                            0.09373162411398223, -0.5742377578494785, n),
+
+    'Light activity': lambda n: _generate_activity_distribution(
+                            0.09435378091059601, 0.21380242785625422, n),
+
+    'Moderate activity': lambda n: _generate_activity_distribution(
+                            0.1894616357138137, 0.551771330362601, n),
+
+    'Heavy exercise': lambda n: _generate_activity_distribution(
+                            0.21744554768657565, 1.1644665696723049, n),
+}
+
 
 @dataclass(frozen=True)
 class Population: