From f3e82620a714626776db19d38536353037996e74 Mon Sep 17 00:00:00 2001
From: Luis Aleixo <luis.aleixo@cern.ch>
Date: Tue, 11 Jan 2022 18:51:09 +0100
Subject: [PATCH] Removed quick guide reference for specific event

---
 cara/apps/templates/base/calculator.form.html.j2 | 2 --
 cara/models.py                                   | 3 ---
 cara/tests/models/test_exposure_model.py         | 4 ++--
 3 files changed, 2 insertions(+), 7 deletions(-)

diff --git a/cara/apps/templates/base/calculator.form.html.j2 b/cara/apps/templates/base/calculator.form.html.j2
index 3b0a8cf7..023ecb33 100644
--- a/cara/apps/templates/base/calculator.form.html.j2
+++ b/cara/apps/templates/base/calculator.form.html.j2
@@ -557,8 +557,6 @@
         <li>Level 3 = 95 m<sup>3</sup>/h (silent).</li>
     </ul>
   </ul>
-  <b>Specific event:</b><br>
-  If the simulation refers to a Specific event at a given time (e.g. meeting or conference) indicate the population of the given location and the 7-day average of new reported cases.</p>
   <b>Activity types:</b><br>
   The type of activity applies to both the infected and exposed persons:
   <ul>
diff --git a/cara/models.py b/cara/models.py
index e995e5d8..4fd27b07 100644
--- a/cara/models.py
+++ b/cara/models.py
@@ -1166,9 +1166,6 @@ class ExposureModel:
         AB = 5
         return sct.binom.pmf(x, event, self.probability_random_individual(cases, population, AB))
 
-    def infection_probability(self) -> _VectorisedFloat:
-        exposure = self.exposure()
-
     def deposited_exposure(self) -> _VectorisedFloat:
         """
         The number of virus per m^3 deposited on the respiratory tract.
diff --git a/cara/tests/models/test_exposure_model.py b/cara/tests/models/test_exposure_model.py
index 7dcd81de..1b687151 100644
--- a/cara/tests/models/test_exposure_model.py
+++ b/cara/tests/models/test_exposure_model.py
@@ -225,9 +225,9 @@ def test_infectious_dose_vectorisation():
 @pytest.mark.parametrize(
     "population, cm, pop, cases, specific_event_probability",[
     [populations[1], known_concentrations(lambda t: 36.),
-     100000, 68, 3.24287],
+     100000, 68, 2.24124],
     [populations[0], known_concentrations(lambda t: 36.),
-     100000, 68, 3.067046],
+     100000, 68, 1.875652],
 
     ])
 def test_specific_event_probability(population, cm,