def test_parameters():
param = Parameters(
current_hospitalized=100,
doubling_time=6.0,
known_infected=5000,
market_share=0.05,
relative_contact_rate=0.15,
susceptible=500000,
hospitalized=RateLos(0.05, 7),
icu=RateLos(0.02, 9),
ventilated=RateLos(0.01, 10),
n_days=60,
)
# test the Parameters
# hospitalized, icu, ventilated
assert param.rates == (0.05, 0.02, 0.01)
assert param.lengths_of_stay == (7, 9, 10)
assert param.infected == 40000.0
assert isinstance(param.infected, float) # based off note in models.py
# test the class-calculated attributes
assert param.detection_probability == 0.125
assert param.intrinsic_growth_rate == 0.12246204830937302
assert param.beta == 3.2961405355450555e-07
assert param.r_t == 2.307298374881539
assert param.r_naught == 2.7144686763312222
assert param.doubling_time_t == 7.764405988534983
# test the things n_days creates, which in turn tests sim_sir, sir, and get_dispositions
assert (len(param.susceptible_v) == len(param.infected_v) == len(
param.recovered_v) == param.n_days + 1 == 61)
assert param.susceptible_v[0] == 500000.0
assert round(param.susceptible_v[-1], 0) == 67202
assert round(param.infected_v[1], 0) == 43735
assert round(param.recovered_v[30], 0) == 224048
assert [d[0] for d in param.dispositions] == [100.0, 40.0, 20.0]
assert [round(d[-1], 0)
for d in param.dispositions] == [1182.0, 473.0, 236.0]
# change n_days, make sure it cascades
param.n_days = 2
assert (len(param.susceptible_v) == len(param.infected_v) == len(
param.recovered_v) == param.n_days + 1 == 3)
# test that admissions are being properly calculated (thanks @PhilMiller)
admissions = build_admissions_df(param)
cumulative_admissions = admissions.cumsum()
diff = cumulative_admissions["Hospitalized"][1:-1] - (
0.05 * 0.05 * (param.infected_v[1:-1] + param.recovered_v[1:-1]) - 100)
assert (diff.abs() < 0.1).all()
assert len(param.susceptible_v) == len(param.infected_v) == len(
param.recovered_v) == param.n_days + 1 == 3
def test_mitigation_statement():
st.cleanup()
expected_doubling = "outbreak **reduces the doubling time to 7.8** days"
display_header(st, MODEL, PARAM)
assert [s for s in st.render_store if expected_doubling in s]
# assert len((list(filter(lambda s: expected_doubling in s, st.render_store))))
st.cleanup()
expected_halving = "outbreak **halves the infections every 51.9** days"
halving_params = Parameters(
current_hospitalized=100,
doubling_time=6.0,
known_infected=5000,
market_share=0.05,
relative_contact_rate=0.7,
susceptible=500000,
hospitalized=RateLos(0.05, 7),
icu=RateLos(0.02, 9),
ventilated=RateLos(0.01, 10),
n_days=60,
)
halving_model = SimSirModel(halving_params)
display_header(st, halving_model, halving_params)
assert [s for s in st.render_store if expected_halving in s]
#assert len((list(filter(lambda s: expected_halving in s, st.render_store))))
st.cleanup()
def halving_param():
return Parameters(
current_date=datetime(year=2020, month=3, day=28),
current_hospitalized=100,
doubling_time=6.0,
market_share=0.05,
relative_contact_rate=0.7,
population=500000,
hospitalized=Disposition(0.05, 7),
icu=Disposition(0.02, 9),
ventilated=Disposition(0.01, 10),
n_days=60,
)
def halving_param():
return Parameters(
current_date=datetime(year=2020, month=3, day=28),
current_hospitalized=100,
doubling_time=6.0,
hospitalized=Disposition.create(rate=0.05, days=7),
icu=Disposition.create(rate=0.02, days=9),
infectious_days=14,
market_share=0.05,
mitigation_date=datetime(year=2020, month=3, day=28),
n_days=60,
population=500000,
recovered=0,
relative_contact_rate=0.7,
ventilated=Disposition.create(rate=0.01, days=10),
)
def DEFAULTS():
return Parameters(
region=Regions(
delaware=564696,
chester=519293,
montgomery=826075,
bucks=628341,
philly=1581000,
),
current_date=datetime(year=2020, month=3, day=28),
current_hospitalized=14,
date_first_hospitalized=datetime(year=2020, month=3, day=7),
doubling_time=4.0,
n_days=60,
market_share=0.15,
relative_contact_rate=0.3,
hospitalized=Disposition(0.025, 7),
icu=Disposition(0.0075, 9),
ventilated=Disposition(0.005, 10),
)
def defaults():
return Parameters(
region=Regions(
delaware=564696,
chester=519293,
montgomery=826075,
bucks=628341,
philly=1581000,
),
current_date=datetime(year=2020, month=3, day=28),
current_hospitalized=14,
date_first_hospitalized=datetime(year=2020, month=3, day=7),
doubling_time=4.0,
hospitalized=Disposition.create(rate=0.025, days=7),
icu=Disposition.create(rate=0.0075, days=9),
infectious_days=14,
n_days=60,
market_share=0.15,
mitigation_date=datetime(year=2020, month=3, day=28),
recovered=0,
relative_contact_rate=0.3,
ventilated=Disposition.create(rate=0.005, days=10),
)
import pandas as pd # type: ignore
import numpy as np # type: ignore
import altair as alt # type: ignore
from src.penn_chime.charts import new_admissions_chart, admitted_patients_chart
from src.penn_chime.models import sir, sim_sir
from src.penn_chime.parameters import Parameters
from src.penn_chime.presentation import display_header
from src.penn_chime.settings import DEFAULTS
from src.penn_chime.defaults import RateLos
PARAM = Parameters(current_hospitalized=100,
doubling_time=6.0,
known_infected=5000,
market_share=0.05,
relative_contact_rate=0.15,
susceptible=500000,
hospitalized=RateLos(0.05, 7),
icu=RateLos(0.02, 9),
ventilated=RateLos(0.01, 10),
n_days=60)
# set up
# we just want to verify that st _attempted_ to render the right stuff
# so we store the input, and make sure that it matches what we expect
class MockStreamlit:
def __init__(self):
self.render_store = []
self.markdown = self.just_store_instead_of_rendering
self.latex = self.just_store_instead_of_rendering
)
from src.penn_chime.presentation import display_header
EPSILON = 1.e-7
# The defaults in settings will change and break the tests
DEFAULTS = Parameters(
region=Regions(
delaware=564696,
chester=519293,
montgomery=826075,
bucks=628341,
philly=1581000,
),
current_date=datetime(year=2020, month=3, day=28),
current_hospitalized=14,
date_first_hospitalized=datetime(year=2020, month=3, day=7),
doubling_time=4.0,
n_days=60,
market_share=0.15,
relative_contact_rate=0.3,
hospitalized=RateDays(0.025, 7),
icu=RateDays(0.0075, 9),
ventilated=RateDays(0.005, 10),
)
PARAM = Parameters(
current_hospitalized=100,
doubling_time=6.0,
market_share=0.05,
relative_contact_rate=0.15,