def get_chime_model(parameters):
'''
instantiates and returns CHIME model using parameters
'''
if not isinstance(parameters, Parameters):
t = type(parameters)
raise TypeError(f'parameters must be a Parameters object not {t}')
return Sir(parameters)
def test_model_first_hosp_fit(param):
param.date_first_hospitalized = param.current_date - timedelta(days=43)
param.doubling_time = None
my_model = Sir(param)
assert abs(my_model.intrinsic_growth_rate - 0.123) / 0.123 < 0.01
assert abs(my_model.beta - 4.21501347256401e-07) < EPSILON
assert abs(my_model.r_t - 2.32) / 2.32 < 0.01
assert abs(my_model.r_naught - 2.72) / 2.72 < 0.01
assert abs(my_model.doubling_time_t - 7.71) / 7.71 < 0.01
def fixture_penn_chime_setup() -> Tuple[Parameters, Sir]:
"""Initializes penn_chime parameters and SIR model
"""
p = Parameters(
current_hospitalized=69,
date_first_hospitalized=date(2020, 3, 7),
doubling_time=None,
hospitalized=Disposition.create(days=7, rate=0.025),
icu=Disposition.create(days=9, rate=0.0075),
infectious_days=14,
market_share=0.15,
n_days=100,
population=3600000,
recovered=0,
relative_contact_rate=0.3,
ventilated=Disposition.create(days=10, rate=0.005),
)
return p, Sir(p)
def handle_model_change(i, sidebar_data, lock_zoom_clicks,
graphs_relayout_data):
model = {}
pars = None
result = []
viz_kwargs = {}
if sidebar_data:
pars = parameters_deserializer(sidebar_data["parameters"])
model = Sir(pars)
vis = i.components.get("visualizations", None) if i else None
vis_content = vis.content if vis else None
viz_kwargs = dict(labels=pars.labels,
table_mod=7,
content=vis_content)
result.extend(i.components["intro"].build(model, pars))
for df_key in ["admits_df", "census_df", "sim_sir_w_date_df"]:
df = None
if model:
df = model.__dict__.get(df_key, None)
result.extend(prepare_visualization_group(df, **viz_kwargs))
figures = [result[1], result[4], result[7]]
for n_clicks, relayout_data, figure in zip(lock_zoom_clicks,
graphs_relayout_data,
figures):
if relayout_data:
if n_clicks == None or n_clicks % 2 == 0:
# Set plot_data figure coordinates
if "xaxis.range[0]" in relayout_data:
figure["layout"]["xaxis"]["range"] = [
relayout_data["xaxis.range[0]"],
relayout_data["xaxis.range[1]"]
]
if "yaxis.range[0]" in relayout_data:
figure["layout"]["yaxis"]["range"] = [
relayout_data["yaxis.range[0]"],
relayout_data["yaxis.range[1]"]
]
return result
def handle_model_change(i, sidebar_data):
model = {}
pars = None
result = []
viz_kwargs = {}
if sidebar_data:
pars = parameters_deserializer(sidebar_data["parameters"])
model = Sir(pars)
vis = i.components.get("visualizations", None) if i else None
vis_content = vis.content if vis else None
viz_kwargs = dict(labels=pars.labels,
table_mod=7,
max_y_axis=pars.max_y_axis,
content=vis_content)
result.extend(i.components["intro"].build(model, pars))
for df_key in ["admits_df", "census_df", "sim_sir_w_date_df"]:
df = None
if model:
df = model.__dict__.get(df_key, None)
result.extend(prepare_visualization_group(df, **viz_kwargs))
return result
def sim_chime(scenario: str,
p: Parameters,
intrinsic_growth_rate: float = None,
initial_doubling_time: float = None,
admits_df: Optional = None,
rcr_policies_df: Optional = None):
"""
Run one CHIME simulation
:param scenario: identifier for scenario
:param p: CHIME parameters
:param intrinsic_growth_rate: Can optionally specify to override CHIME fitting/estimation
:param initial_doubling_time: Can optionally specify to override CHIME fitting/estimation
:param admits_df: Experimental - not currently using
:param rcr_policies_df: Dataframe of dates and rcr values
:return:
"""
# Run the model
if rcr_policies_df is None:
is_dynamic_rcr = False
# Just call penn_chime version of Sir
m = Sir(p)
else:
# Using dynamic rcrs, need to call customized version of Sir
is_dynamic_rcr = True
m = SirPlus(p, intrinsic_growth_rate, initial_doubling_time, admits_df,
rcr_policies_df)
# Gather results
input_params_dict = vars(p)
results = gather_sim_results(m, scenario, input_params_dict,
is_dynamic_rcr)
# Return model object and results
return m, results
def halving_model(halving_param):
return Sir(halving_param)
def model(param):
return Sir(param)