From 9f6e500618be39281a04dd9426ca0a2fa9962bd8 Mon Sep 17 00:00:00 2001
From: Andrejh <andre.henriques@cern.ch>
Date: Fri, 19 Mar 2021 11:31:54 +0100
Subject: [PATCH] create present_qR_values

---
 cara/model_scenarios_publication.py | 28 ++++++++++++++++++++++++++++
 cara/montecarlo.py                  | 13 ++++++++++++-
 2 files changed, 40 insertions(+), 1 deletion(-)

diff --git a/cara/model_scenarios_publication.py b/cara/model_scenarios_publication.py
index 689da9f3..e78f2ac2 100644
--- a/cara/model_scenarios_publication.py
+++ b/cara/model_scenarios_publication.py
@@ -1,3 +1,31 @@
 from cara import models
 from cara.montecarlo import *
 
+######### Standard exposure models ###########
+exposure_models = [MCExposureModel(
+    concentration_model=MCConcentrationModel(
+        room=models.Room(volume=45),
+        ventilation=models.SlidingWindow(
+            active=models.PeriodicInterval(period=120, duration=120),
+            inside_temp=models.PiecewiseConstant((0, 24), (293,)),
+            outside_temp=models.PiecewiseConstant((0, 24), (283,)),
+            window_height=1.6, opening_length=0.2,
+        ),
+        infected=MCInfectedPopulation(
+            number=1,
+            presence=models.SpecificInterval(((0, 4), (5, 9))),
+            masked=False,
+            virus=MCVirus(halflife=1.1, qID=100),
+            expiratory_activity=a,
+            samples=2000000,
+            breathing_category=3,
+            expiratory_activity_weights=(0.7, 0.3, 0)
+        )
+    ),
+    exposed=models.Population(
+        number=2,
+        presence=models.SpecificInterval(((0, 4), (5, 9))),
+        activity=models.Activity.types['Seated'],
+        mask=models.Mask.types['Type I']
+    )
+) for a in (1, 2, 3)]
\ No newline at end of file
diff --git a/cara/montecarlo.py b/cara/montecarlo.py
index 912f3d4c..fca16db8 100644
--- a/cara/montecarlo.py
+++ b/cara/montecarlo.py
@@ -426,11 +426,22 @@ def print_qr_info(log_qr: np.ndarray) -> None:
     print(f"MEAN of log_10(qR) = {np.mean(log_qr)}\n"
           f"SD of log_10(qR) = {np.std(log_qr)}\n"
           f"MEAN of qR = {np.mean(qr_values)}\n"
-          f"SD of qR = {np.std(qr_values)}\n")
+          f"SD of qR = {np.std(qr_values)}\n"
+          f"Median of qR = {np.quantile(qr_values, 0.5)}\n")
 
     for quantile in (0.01, 0.05, 0.25, 0.50, 0.75, 0.95, 0.99):
         print(f"qR_{quantile} = {np.quantile(qr_values, quantile)}")
 
+def present_qR_values(model: MCConcentrationModel) -> None:
+    """
+    Displays a handful of key parameters and results of qR from a given MCConcentrationModel
+    :param model: The MCConcentrationModel representing the scenario to be presented
+    :return: Nothing, parameters are printed
+    """
+
+    qRs = (np.log10(model.infected.emission_rate_when_present()))
+
+    print_qr_info(np.asarray(qRs))
 
 def present_model(model: MCConcentrationModel, bins: int = 200,
                   title: str = 'Summary of $qR$ model parameters') -> None: