From 68d495ee8e8aeac26a7e523a984002f792719bea Mon Sep 17 00:00:00 2001
From: Luis Aleixo <luis.aleixo@cern.ch>
Date: Fri, 28 Jan 2022 11:18:47 +0100
Subject: [PATCH] Move toronto temperatures into test_monte_carlo_full_models
 where it is used

---
 cara/data/__init__.py                      | 20 ----------------
 cara/tests/test_monte_carlo_full_models.py | 27 +++++++++++++++++++++-
 2 files changed, 26 insertions(+), 21 deletions(-)

diff --git a/cara/data/__init__.py b/cara/data/__init__.py
index 6f1ddb0b..31195ba7 100644
--- a/cara/data/__init__.py
+++ b/cara/data/__init__.py
@@ -22,10 +22,6 @@ geneva_coordinates = (46.204391, 6.143158)
 local_hourly_temperatures_celsius_per_hour = get_hourly_temperatures_celsius_per_hour(
     geneva_coordinates)
 
-# Load the weather data (temperature in kelvin) for Toronto.
-toronto_coordinates = (43.667, 79.400)
-toronto_hourly_temperatures_celsius_per_hour = get_hourly_temperatures_celsius_per_hour(
-    toronto_coordinates)
 
 # Geneva hourly temperatures as piecewise constant function (in Kelvin).
 GenevaTemperatures_hourly = {
@@ -38,25 +34,9 @@ GenevaTemperatures_hourly = {
     for month, temperatures in local_hourly_temperatures_celsius_per_hour.items()
 }
 
-# Toronto hourly temperatures as piecewise constant function (in Kelvin).
-TorontoTemperatures_hourly = {
-    month: models.PiecewiseConstant(
-        # NOTE:  It is important that the time type is float, not np.float, in
-        # order to allow hashability (for caching).
-        tuple(float(time) for time in range(25)),
-        tuple(273.15 + np.array(temperatures)),
-    )
-    for month, temperatures in toronto_hourly_temperatures_celsius_per_hour.items()
-}
 
 # Same Geneva temperatures on a finer temperature mesh (every 6 minutes).
 GenevaTemperatures = {
     month: GenevaTemperatures_hourly[month].refine(refine_factor=10)
     for month, temperatures in local_hourly_temperatures_celsius_per_hour.items()
 }
-
-# Same Toronto temperatures on a finer temperature mesh (every 6 minutes).
-TorontoTemperatures = {
-    month: TorontoTemperatures_hourly[month].refine(refine_factor=10)
-    for month, temperatures in toronto_hourly_temperatures_celsius_per_hour.items()
-}
diff --git a/cara/tests/test_monte_carlo_full_models.py b/cara/tests/test_monte_carlo_full_models.py
index e5c96b82..407ea37d 100644
--- a/cara/tests/test_monte_carlo_full_models.py
+++ b/cara/tests/test_monte_carlo_full_models.py
@@ -12,6 +12,31 @@ np.random.seed(2000)
 SAMPLE_SIZE = 250000
 TOLERANCE = 0.05
 
+# Load the weather data (temperature in kelvin) for Toronto.
+toronto_coordinates = (43.667, 79.400)
+toronto_hourly_temperatures_celsius_per_hour = data.get_hourly_temperatures_celsius_per_hour(
+    toronto_coordinates)
+
+
+# Toronto hourly temperatures as piecewise constant function (in Kelvin).
+TorontoTemperatures_hourly = {
+    month: models.PiecewiseConstant(
+        # NOTE:  It is important that the time type is float, not np.float, in
+        # order to allow hashability (for caching).
+        tuple(float(time) for time in range(25)),
+        tuple(273.15 + np.array(temperatures)),
+    )
+    for month, temperatures in toronto_hourly_temperatures_celsius_per_hour.items()
+}
+
+
+# Same Toronto temperatures on a finer temperature mesh (every 6 minutes).
+TorontoTemperatures = {
+    month: TorontoTemperatures_hourly[month].refine(refine_factor=10)
+    for month, temperatures in toronto_hourly_temperatures_celsius_per_hour.items()
+}
+
+
 # references values for infection_probability and expected new cases
 # in the following tests, were obtained from the feature/mc branch
 
@@ -69,7 +94,7 @@ def classroom_mc():
                 models.SlidingWindow(
                     active=models.PeriodicInterval(period=120, duration=120),
                     inside_temp=models.PiecewiseConstant((0., 24.), (293,)),
-                    outside_temp=data.TorontoTemperatures['Dec'],
+                    outside_temp=TorontoTemperatures['Dec'],
                     window_height=1.6,
                     opening_length=0.2,
                 ),