def display_page(current_path, href):
if current_path is None:
return [None]
pc = PerfCounter('Page %s' % current_path)
pc.display('start')
if current_path.endswith('/') and len(current_path) > 1:
current_path = current_path.lstrip('/')
page_or_class = all_pages.get(current_path)
if not page_or_class:
return [html.H2('Sivua ei löydy')]
if isinstance(page_or_class, Page):
page = page_or_class
elif issubclass(page_or_class, Page):
page = page_or_class()
else:
return html.H2('Sisäinen virhe')
ret = [
page.render(),
dcc.Store(id=page.make_id('path-store'), data=page.path)
]
pc.display('finished')
return [ret]
def display_page(current_path):
pc = PerfCounter('Page %s' % current_path)
pc.display('start')
if current_path.endswith('/') and len(current_path) > 1:
current_path = current_path.lstrip('/')
current_page = None
for page_path, page in all_pages.items():
if current_path == page_path:
current_page = page
break
if not current_page:
return [html.Div()]
ret = current_page.render()
pc.display('finished')
return [ret]
def initialize_app(app):
from utils.perf import PerfCounter
app.layout = generate_layout
page_contents = []
pc = PerfCounter('pages')
pc.display('Rendering all')
# Generate all pages for checking input and output callbacks
pages = []
for page in all_pages.values():
if isinstance(page, Page):
pass
elif issubclass(page, Page):
page = page()
else:
raise Exception('Invalid page: %s' % page)
pc.display(page.path)
page_contents.append(page.render())
page_contents.append(dcc.Store(id=page.make_id('path-store')))
pages.append(page)
pc.display('done')
global _all_page_contents
_all_page_contents = page_contents
register_callbacks(app, pages)
def wrap_calc_func(*args, **kwargs):
pc = PerfCounter('%s.%s' % (func.__module__, func.__name__))
pc.display('enter')
hash_data = _get_func_hash_data(func)
cache_key = _calculate_cache_key(hash_data)
assert 'variables' not in kwargs
assert 'datasets' not in kwargs
if not args and not kwargs:
should_cache_func = True
else:
should_cache_func = False
print('not caching func %s.%s' % (func.__module__, func.__name__))
if should_cache_func:
ret = cache.get(cache_key)
if ret is not None: # calcfuncs must not return None
pc.display('cache hit')
return ret
if variables is not None:
kwargs['variables'] = {x: get_variable(y) for x, y in variables.items()}
if datasets is not None:
datasets_to_load = set(list(datasets.values())) - set(_dataset_cache.keys())
if datasets_to_load:
loaded_datasets = []
for dataset_name in datasets_to_load:
ds_pc = PerfCounter('dataset %s' % dataset_name)
df = load_datasets(dataset_name)
ds_pc.display('loaded')
loaded_datasets.append(df)
del ds_pc
for dataset_name, dataset in zip(datasets_to_load, loaded_datasets):
_dataset_cache[dataset_name] = dataset
kwargs['datasets'] = {ds_name: _dataset_cache[ds_url] for ds_name, ds_url in datasets.items()}
ret = func(*args, **kwargs)
pc.display('func ret')
if should_cache_func:
assert ret is not None
cache.set(cache_key, ret, timeout=600)
return ret
def wrap_calc_func(*args, **kwargs):
should_profile = os.environ.get('PROFILE_CALC', '').lower() in ('1', 'true', 'yes')
only_if_in_cache = kwargs.pop('only_if_in_cache', False)
skip_cache = kwargs.pop('skip_cache', False)
var_store = kwargs.pop('variable_store', None)
if should_profile:
pc = PerfCounter('%s.%s' % (func.__module__, func.__name__))
pc.display('enter')
hash_data = _get_func_hash_data(func, None)
cache_key = _calculate_cache_key(func, hash_data, var_store=var_store)
assert 'variables' not in kwargs
assert 'datasets' not in kwargs
unknown_kwargs = set(kwargs.keys()) - set(['step_callback'])
if not args and not unknown_kwargs and not skip_cache:
should_cache_func = True
else:
should_cache_func = False
if should_cache_func:
ret = cache.get(cache_key)
if ret is not None: # calcfuncs must not return None
if should_profile:
pc.display('cache hit (%s)' % cache_key)
return ret
if only_if_in_cache:
if should_profile:
pc.display('cache miss so leaving as requested (%s)' % cache_key)
return None
if variables is not None:
kwargs['variables'] = {x: get_variable(y, var_store=var_store) for x, y in variables.items()}
if datasets is not None:
datasets_to_load = set(list(datasets.values())) - set(_dataset_cache.keys())
if datasets_to_load:
loaded_datasets = []
for dataset_name in datasets_to_load:
if should_profile:
ds_pc = PerfCounter('dataset %s' % dataset_name)
df = load_datasets(dataset_name)
if should_profile:
ds_pc.display('loaded')
del ds_pc
loaded_datasets.append(df)
for dataset_name, dataset in zip(datasets_to_load, loaded_datasets):
_dataset_cache[dataset_name] = dataset
kwargs['datasets'] = {ds_name: _dataset_cache[ds_url] for ds_name, ds_url in datasets.items()}
ret = func(*args, **kwargs)
if should_profile:
pc.display('func ret')
if should_cache_func:
assert ret is not None
cache.set(cache_key, ret, timeout=3600)
return ret
def simulate_individuals(variables, step_callback=None):
pc = PerfCounter()
df = get_population_for_area().sum(axis=1)
ages = df.index.values
counts = df.values
avg_contacts_per_day = get_physical_contacts_for_country()
hc_cap = (variables['hospital_beds'], variables['icu_units'])
max_age = max(ages)
age_counts = np.array(np.zeros(max_age + 1, dtype=np.int32))
for age, count in zip(ages, counts):
age_counts[age] = count
people = create_population(age_counts)
avg_contacts = np.array(avg_contacts_per_day.values, dtype=np.float32)
assert avg_contacts.size == max_age + 1
pop = Population(age_counts, avg_contacts)
hc = HealthcareSystem(hc_cap[0], hc_cap[1])
sevvar = variables['p_severe']
sev_arr = np.ndarray((len(sevvar), 2), dtype=np.float32)
for idx, (age, sev) in enumerate(sevvar):
sev_arr[idx] = (age, sev / 100)
disease = Disease(
p_infection=variables['p_infection'] / 100,
p_asymptomatic=variables['p_asymptomatic'] / 100,
p_severe=sev_arr,
p_critical=variables['p_critical'] / 100,
p_hospital_death=variables['p_hospital_death'] / 100,
p_icu_death=variables['p_icu_death'] / 100,
p_hospital_death_no_beds=variables['p_hospital_death_no_beds'] / 100,
p_icu_death_no_beds=variables['p_icu_death_no_beds'] / 100,
)
context = Context(pop,
people,
hc,
disease,
start_date=variables['start_date'])
start_date = date.fromisoformat(variables['start_date'])
ivs = nb.typed.List()
for iv in variables['interventions']:
iv_id = iv[0]
iv_date = iv[1]
if len(iv) > 2:
iv_value = iv[2]
else:
iv_value = None
# Extremely awkward, but Numba poses some limitations.
ivs.append(make_iv(context, iv_id, iv_date, value=iv_value))
context.interventions = ivs
pc.display('after init')
days = variables['simulation_days']
df = pd.DataFrame(columns=POP_ATTRS + STATE_ATTRS,
index=pd.date_range(start_date, periods=days))
for day in range(days):
state = context.generate_state()
rec = {attr: sum(getattr(state, attr)) for attr in POP_ATTRS}
rec['hospital_beds'] = state.available_hospital_beds
rec['icu_units'] = state.available_icu_units
rec['r'] = state.r
rec['exposed_per_day'] = state.exposed_per_day
rec['tests_run_per_day'] = state.tests_run_per_day
rec['sim_time_ms'] = pc.measure()
d = start_date + timedelta(days=day)
df.loc[d] = rec
if step_callback is not None:
ret = step_callback(df)
if not ret:
raise ExecutionInterrupted()
context.iterate()
return df