pingu_98/cara
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Move toronto temperatures into test_monte_carlo_full_models where it is used

Browse source
This commit is contained in:
Luis Aleixo 2022-01-28 11:18:47 +01:00
parent 7af1abcde6
commit 68d495ee8e
2 changed files with 26 additions and 21 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

20
cara/data/__init__.py
View file

@ -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()
}

27
cara/tests/test_monte_carlo_full_models.py
View file

@ -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,
),