'''
Defines the Sim class, Covasim's core class.
'''
#%% Imports
import numpy as np
import pandas as pd
import sciris as sc
from . import utils as cvu
from . import misc as cvm
from . import base as cvb
from . import defaults as cvd
from . import parameters as cvpar
from . import population as cvpop
from . import plotting as cvplt
from . import interventions as cvi
from . import immunity as cvimm
from . import analysis as cva
from .settings import options as cvo
# Almost everything in this file is contained in the Sim class
__all__ = ['Sim', 'diff_sims', 'demo', 'AlreadyRunError']
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class Sim(cvb.BaseSim):
'''
The Sim class handles the running of the simulation: the creation of the
population and the dynamics of the epidemic. This class handles the mechanics
of the actual simulation, while BaseSim takes care of housekeeping (saving,
loading, exporting, etc.). Please see the BaseSim class for additional methods.
Args:
pars (dict): parameters to modify from their default values
datafile (str/df): filename of (Excel, CSV) data file to load, or a pandas dataframe of the data
datacols (list): list of column names of the data to load
label (str): the name of the simulation (useful to distinguish in batch runs)
simfile (str): the filename for this simulation, if it's saved
popfile (str): if supplied, load the population from this file
people (varies): if supplied, use these pre-generated people (as a dict, SynthPop, or People object) instead of loading or generating new ones
version (str): if supplied, use default parameters from this version of Covasim instead of the latest
kwargs (dict): additional parameters; passed to ``cv.make_pars()``
**Examples**::
sim = cv.Sim()
sim = cv.Sim(pop_size=10e3, datafile='my_data.xlsx', label='Sim with data')
'''
def __init__(self, pars=None, datafile=None, label=None, simfile=None,
popfile=None, people=None, version=None, **kwargs):
# Set attributes
self.label = label # The label/name of the simulation
self.created = None # The datetime the sim was created
self.simfile = simfile # The filename of the sim
self.datafile = datafile # The name of the data file
self.popfile = popfile # The population file
self.data = None # The actual data
self.popdict = people # The population dictionary
self.people = None # Initialize these here so methods that check their length can see they're empty
self.t = None # The current time in the simulation (during execution); outside of sim.step(), its value corresponds to next timestep to be computed
self.results = {} # For storing results
self.summary = None # For storing a summary of the results
self.initialized = False # Whether or not initialization is complete
self.complete = False # Whether a simulation has completed running
self.results_ready = False # Whether or not results are ready
self._default_ver = version # Default version of parameters used
self._legacy_trans = None # Whether to use the legacy transmission calculation method (slower; for reproducing earlier results)
self._orig_pars = None # Store original parameters to optionally restore at the end of the simulation
# Make default parameters (using values from parameters.py)
default_pars = cvpar.make_pars(version=version) # Start with default pars
super().__init__(default_pars) # Initialize and set the parameters as attributes
# Now update everything
self.set_metadata(simfile) # Set the simulation date and filename
self.update_pars(pars, **kwargs) # Update the parameters, if provided
self.load_data(datafile) # Load the data, if provided
return
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def load_data(self, datafile=None, verbose=None, **kwargs):
''' Load the data to calibrate against, if provided '''
if verbose is None:
verbose = self['verbose']
self.datafile = datafile # Store this
if datafile is not None: # If a data file is provided, load it
self.data = cvm.load_data(datafile=datafile, verbose=verbose, start_day=self['start_day'], **kwargs)
return
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def initialize(self, reset=False, init_infections=True, **kwargs):
'''
Perform all initializations on the sim.
This includes validating the parameters, setting the random number seed,
creating the results structure, initializing the people, validating the
layer parameters (which requires the people), and initializing the interventions.
Note: to create a population to save for later use, use ``init_infections=False``.
This will then create a fresh People object which other sims can finish
initializing.
Args:
reset (bool): whether or not to reset people even if they already exist
init_infections (bool): whether to initialize infections (default true so sim is ready, but can't reuse people then)
kwargs (dict): passed to init_people
'''
self.t = 0 # The current time index
self.validate_pars() # Ensure parameters have valid values
self.set_seed() # Reset the random seed before the population is created
self.init_variants() # Initialize the variants
self.init_immunity() # initialize information about immunity (if use_waning=True)
self.init_results() # After initializing the variant, create the results structure
self.init_people(reset=reset, init_infections=init_infections, **kwargs) # Create all the people (the heaviest step)
self.init_interventions() # Initialize the interventions...
self.init_analyzers() # ...and the analyzers...
self.validate_layer_pars() # Once the population is initialized, validate the layer parameters again
self.set_seed() # Reset the random seed again so the random number stream is consistent
self.initialized = True
self.complete = False
self.results_ready = False
return self
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def layer_keys(self):
'''
Attempt to retrieve the current layer keys, in the following order: from
the people object (for an initialized sim), from the popdict (for one in
the process of being initialized), from the beta_layer parameter (for an
uninitialized sim), or by assuming a default (if none of the above are
available).
'''
try:
keys = list(self['beta_layer'].keys()) # Get keys from beta_layer since the "most required" layer parameter
except: # pragma: no cover
keys = []
return keys
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def reset_layer_pars(self, layer_keys=None, force=False):
'''
Reset the parameters to match the population.
Args:
layer_keys (list): override the default layer keys (use stored keys by default)
force (bool): reset the parameters even if they already exist
'''
if layer_keys is None:
if self.people is not None: # If people exist
layer_keys = self.people.contacts.keys()
elif self.popdict is not None:
layer_keys = self.popdict['layer_keys']
cvpar.reset_layer_pars(self.pars, layer_keys=layer_keys, force=force)
return
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def validate_layer_pars(self):
'''
Handle layer parameters, since they need to be validated after the population
creation, rather than before.
'''
# First, try to figure out what the layer keys should be and perform basic type checking
layer_keys = self.layer_keys()
layer_pars = cvpar.layer_pars # The names of the parameters that are specified by layer
for lp in layer_pars:
val = self[lp]
if sc.isnumber(val): # It's a scalar instead of a dict, assume it's all contacts
self[lp] = {k:val for k in layer_keys}
# Handle key mismatches
for lp in layer_pars:
lp_keys = set(self.pars[lp].keys())
if not lp_keys == set(layer_keys):
errormsg = 'At least one layer parameter is inconsistent with the layer keys; all parameters must have the same keys:'
errormsg += f'\nsim.layer_keys() = {layer_keys}'
for lp2 in layer_pars: # Fail on first error, but re-loop to list all of them
errormsg += f'\n{lp2} = ' + ', '.join(self.pars[lp2].keys())
raise sc.KeyNotFoundError(errormsg)
# Handle mismatches with the population
if self.people is not None:
pop_keys = set(self.people.contacts.keys())
if pop_keys != set(layer_keys): # pragma: no cover
if not len(pop_keys):
errormsg = f'Your population does not have any layer keys, but your simulation does {layer_keys}. If you called cv.People() directly, you probably need cv.make_people() instead.'
raise sc.KeyNotFoundError(errormsg)
else:
errormsg = f'Please update your parameter keys {layer_keys} to match population keys {pop_keys}. You may find sim.reset_layer_pars() helpful.'
raise sc.KeyNotFoundError(errormsg)
return
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def validate_pars(self, validate_layers=True):
'''
Some parameters can take multiple types; this makes them consistent.
Args:
validate_layers (bool): whether to validate layer parameters as well via validate_layer_pars() -- usually yes, except during initialization
'''
# Handle population size
pop_size = self.pars.get('pop_size')
scaled_pop = self.pars.get('scaled_pop')
pop_scale = self.pars.get('pop_scale')
if scaled_pop is not None: # If scaled_pop is supplied, try to use it
if pop_scale in [None, 1.0]: # Normal case, recalculate population scale
self['pop_scale'] = scaled_pop/pop_size
else: # Special case, recalculate number of agents
self['pop_size'] = int(scaled_pop/pop_scale)
# Handle types
for key in ['pop_size', 'pop_infected']:
try:
self[key] = int(self[key])
except Exception as E:
errormsg = f'Could not convert {key}={self[key]} of {type(self[key])} to integer'
raise ValueError(errormsg) from E
# Handle start day
start_day = self['start_day'] # Shorten
if start_day in [None, 0]: # Use default start day
start_day = '2020-03-01'
self['start_day'] = sc.date(start_day)
# Handle end day and n_days
end_day = self['end_day']
n_days = self['n_days']
if end_day:
self['end_day'] = sc.date(end_day)
n_days = sc.daydiff(self['start_day'], self['end_day'])
if n_days <= 0:
errormsg = f"Number of days must be >0, but you supplied start={str(self['start_day'])} and end={str(self['end_day'])}, which gives n_days={n_days}"
raise ValueError(errormsg)
else:
self['n_days'] = int(n_days)
else:
if n_days:
self['n_days'] = int(n_days)
self['end_day'] = self.date(n_days) # Convert from the number of days to the end day
else:
errormsg = f'You must supply one of n_days and end_day, not "{n_days}" and "{end_day}"'
raise ValueError(errormsg)
# Handle population data
popdata_choices = ['random', 'hybrid', 'clustered', 'synthpops']
choice = self['pop_type']
if choice and choice not in popdata_choices: # pragma: no cover
choicestr = ', '.join(popdata_choices)
errormsg = f'Population type "{choice}" not available; choices are: {choicestr}'
raise ValueError(errormsg)
# Handle interventions, analyzers, and variants
for key in ['interventions', 'analyzers', 'variants']: # Ensure all of them are lists
self[key] = sc.dcp(sc.tolist(self[key], keepnone=False)) # All of these have initialize functions that run into issues if they're reused
for i,interv in enumerate(self['interventions']):
if isinstance(interv, dict): # It's a dictionary representation of an intervention
self['interventions'][i] = cvi.InterventionDict(**interv)
self['variant_map'] = {int(k):v for k,v in self['variant_map'].items()} # Ensure keys are ints, not strings of ints if loaded from JSON
# Optionally handle layer parameters
if validate_layers:
self.validate_layer_pars()
# Handle versioning
if self._legacy_trans is None:
default_ver = self._default_ver if self._default_ver else self.version
self._legacy_trans = sc.compareversions(default_ver, '<3.1.1') # Handle regression
# Handle verbose
if self['verbose'] == 'brief':
self['verbose'] = -1
if not sc.isnumber(self['verbose']): # pragma: no cover
errormsg = f'Verbose argument should be either "brief", -1, or a float, not {type(self["verbose"])} "{self["verbose"]}"'
raise ValueError(errormsg)
return
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def init_results(self):
'''
Create the main results structure.
We differentiate between flows, stocks, and cumulative results
The prefix "new" is used for flow variables, i.e. counting new events (infections/deaths/recoveries) on each timestep
The prefix "n" is used for stock variables, i.e. counting the total number in any given state (sus/inf/rec/etc) on any particular timestep
The prefix "cum" is used for cumulative variables, i.e. counting the total number that have ever been in a given state at some point in the sim
Note that, by definition, n_dead is the same as cum_deaths and n_recovered is the same as cum_recoveries, so we only define the cumulative versions
'''
def init_res(*args, **kwargs):
''' Initialize a single result object '''
output = cvb.Result(*args, **kwargs, npts=self.npts)
return output
dcols = cvd.get_default_colors() # Get default colors
# Flows and cumulative flows
for key,label in cvd.result_flows.items():
self.results[f'cum_{key}'] = init_res(f'Cumulative {label}', color=dcols[key]) # Cumulative variables -- e.g. "Cumulative infections"
for key,label in cvd.result_flows.items(): # Repeat to keep all the cumulative keys together
self.results[f'new_{key}'] = init_res(f'Number of new {label}', color=dcols[key]) # Flow variables -- e.g. "Number of new infections"
# Stock variables
for key,label in cvd.result_stocks.items():
self.results[f'n_{key}'] = init_res(label, color=dcols[key])
# Other variables
self.results['n_imports'] = init_res('Number of imported infections', scale=True)
self.results['n_alive'] = init_res('Number alive', scale=True)
self.results['n_naive'] = init_res('Number never infected', scale=True)
self.results['n_preinfectious'] = init_res('Number preinfectious', scale=True, color=dcols.exposed)
self.results['n_removed'] = init_res('Number removed', scale=True, color=dcols.recovered)
self.results['prevalence'] = init_res('Prevalence', scale=False)
self.results['incidence'] = init_res('Incidence', scale=False)
self.results['r_eff'] = init_res('Effective reproduction number', scale=False)
self.results['doubling_time'] = init_res('Doubling time', scale=False)
self.results['test_yield'] = init_res('Testing yield', scale=False)
self.results['rel_test_yield'] = init_res('Relative testing yield', scale=False)
self.results['frac_vaccinated'] = init_res('Proportion vaccinated', scale=False)
self.results['pop_nabs'] = init_res('Population nab levels', scale=False, color=dcols.pop_nabs)
self.results['pop_protection'] = init_res('Population immunity protection', scale=False, color=dcols.pop_protection)
self.results['pop_symp_protection'] = init_res('Population symptomatic protection', scale=False, color=dcols.pop_symp_protection)
# Handle variants
nv = self['n_variants']
self.results['variant'] = {}
self.results['variant']['prevalence_by_variant'] = init_res('Prevalence by variant', scale=False, n_variants=nv)
self.results['variant']['incidence_by_variant'] = init_res('Incidence by variant', scale=False, n_variants=nv)
for key,label in cvd.result_flows_by_variant.items():
self.results['variant'][f'cum_{key}'] = init_res(f'Cumulative {label}', color=dcols[key], n_variants=nv) # Cumulative variables -- e.g. "Cumulative infections"
for key,label in cvd.result_flows_by_variant.items():
self.results['variant'][f'new_{key}'] = init_res(f'Number of new {label}', color=dcols[key], n_variants=nv) # Flow variables -- e.g. "Number of new infections"
for key,label in cvd.result_stocks_by_variant.items():
self.results['variant'][f'n_{key}'] = init_res(label, color=dcols[key], n_variants=nv)
# Populate the rest of the results
if self['rescale']:
scale = 1
else:
scale = self['pop_scale']
self.rescale_vec = scale*np.ones(self.npts) # Not included in the results, but used to scale them
self.results['date'] = self.datevec
self.results['t'] = self.tvec
self.results_ready = False
return
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def load_population(self, popfile=None, init_people=True, **kwargs):
'''
Load the population dictionary from file -- typically done automatically
as part of ``sim.initialize()``.
Supports loading either saved population dictionaries (popdicts, file ending
.pop by convention), or ready-to-go People objects (file ending .ppl by
convention). Either object an also be supplied directly. Once a population
file is loaded, it is removed from the Sim object.
Args:
popfile (str or obj): if a string, name of the file; otherwise, the popdict or People object to load
init_people (bool): whether to immediately convert the loaded popdict into an initialized People object
kwargs (dict): passed to ``sim.init_people()``
'''
# Set the file path if not is provided
if popfile is None and self.popfile is not None:
popfile = self.popfile
# Load the population into the popdict
self.popdict = cvm.load(popfile)
if self['verbose']:
print(f'Loading population from {popfile}')
if init_people:
self.init_people(**kwargs)
return
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def init_people(self, popdict=None, init_infections=False, reset=False, verbose=None, **kwargs):
'''
Create the people.
Use ``init_infections=False`` for creating a fresh People object for use
in future simulations
Args:
popdict (any): pre-generated people of various formats
init_infections (bool): whether to initialize infections (default false when called directly)
reset (bool): whether to regenerate the people even if they already exist
verbose (int): detail to print
kwargs (dict): passed to cv.make_people()
'''
# Handle inputs
if verbose is None:
verbose = self['verbose']
if popdict is not None:
self.popdict = popdict
if verbose > 0:
resetstr= ''
if self.people:
resetstr = ' (resetting people)' if reset else ' (warning: not resetting sim.people)'
print(f'Initializing sim{resetstr} with {self["pop_size"]:0n} people for {self["n_days"]} days')
if self.popfile and self.popdict is None: # If there's a popdict, we initialize it via cvpop.make_people()
self.load_population(init_people=False)
# Actually make the people
self.people = cvpop.make_people(self, reset=reset, verbose=verbose, **kwargs)
self.people.initialize(sim_pars=self.pars) # Fully initialize the people
self.reset_layer_pars(force=False) # Ensure that layer keys match the loaded population
if init_infections:
self.init_infections(verbose=verbose)
return self
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def init_interventions(self):
''' Initialize and validate the interventions '''
# Initialization
if self._orig_pars and 'interventions' in self._orig_pars:
self['interventions'] = self._orig_pars.pop('interventions') # Restore
for i,intervention in enumerate(self['interventions']):
if isinstance(intervention, cvi.Intervention):
intervention.initialize(self)
# Validation
trace_ind = np.nan # Index of the tracing intervention(s)
test_ind = np.nan # Index of the tracing intervention(s)
for i,intervention in enumerate(self['interventions']):
if isinstance(intervention, (cvi.contact_tracing)):
trace_ind = np.fmin(trace_ind, i) # Find the earliest-scheduled tracing intervention
elif isinstance(intervention, (cvi.test_num, cvi.test_prob)):
test_ind = np.fmax(test_ind, i) # Find the latest-scheduled testing intervention
if not np.isnan(trace_ind): # pragma: no cover
warnmsg = ''
if np.isnan(test_ind):
warnmsg = 'Note: you have defined a contact tracing intervention but no testing intervention was found. Unless this is intentional, please define at least one testing intervention.'
elif trace_ind < test_ind:
warnmsg = f'Note: contact tracing (index {trace_ind:.0f}) is scheduled before testing ({test_ind:.0f}); this creates a 1-day delay. Unless this is intentional, please reorder the interentions.'
if warnmsg:
cvm.warn(warnmsg)
return
def finalize_interventions(self):
for intervention in self['interventions']:
if isinstance(intervention, cvi.Intervention):
intervention.finalize(self)
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def init_analyzers(self):
''' Initialize the analyzers '''
if self._orig_pars and 'analyzers' in self._orig_pars:
self['analyzers'] = self._orig_pars.pop('analyzers') # Restore
for analyzer in self['analyzers']:
if isinstance(analyzer, cva.Analyzer):
analyzer.initialize(self)
return
def finalize_analyzers(self):
for analyzer in self['analyzers']:
if isinstance(analyzer, cva.Analyzer):
analyzer.finalize(self)
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def init_variants(self):
''' Initialize the variants '''
if self._orig_pars and 'variants' in self._orig_pars:
self['variants'] = self._orig_pars.pop('variants') # Restore
for i,variant in enumerate(self['variants']):
if isinstance(variant, cvimm.variant):
if not variant.initialized:
variant.initialize(self)
else: # pragma: no cover
errormsg = f'Variant {i} ({variant}) is not a cv.variant object; please create using cv.variant()'
raise TypeError(errormsg)
len_pars = len(self['variant_pars'])
len_map = len(self['variant_map'])
assert len_pars == len_map, f"variant_pars and variant_map must be the same length, but they're not: {len_pars} ≠ {len_map}"
self['n_variants'] = len_pars # Each variant has an entry in variant_pars
return
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def init_immunity(self, create=False):
''' Initialize immunity matrices and precompute nab waning for each variant '''
if self['use_waning']:
cvimm.init_immunity(self, create=create)
return
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def init_infections(self, force=False, verbose=None):
'''
Initialize prior immunity and seed infections.
Args:
force (bool): initialize prior infections even if already initialized
'''
if verbose is None:
verbose = self['verbose']
# If anyone is non-naive, don't re-initialize
if self.people.count_not('naive') == 0 or force: # Everyone is naive
# Handle anyone who isn't susceptible
if self['frac_susceptible'] < 1:
inds = cvu.choose(self['pop_size'], np.round((1-self['frac_susceptible'])*self['pop_size']))
self.people.make_nonnaive(inds=inds)
# Create the seed infections
if self['pop_infected']:
inds = cvu.choose(self['pop_size'], self['pop_infected'])
self.people.infect(inds=inds, layer='seed_infection') # Not counted by results since flows are re-initialized during the step
elif verbose:
print(f'People already initialized with {self.people.count_not("naive")} people non-naive and {self.people.count("exposed")} exposed; not reinitializing')
return
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def rescale(self):
''' Dynamically rescale the population -- used during step() '''
if self['rescale']:
pop_scale = self['pop_scale']
current_scale = self.rescale_vec[self.t]
if current_scale < pop_scale: # We have room to rescale
not_naive_inds = self.people.false('naive') # Find everyone not naive
n_not_naive = len(not_naive_inds) # Number of people who are not naive
n_people = self['pop_size'] # Number of people overall
current_ratio = n_not_naive/n_people # Current proportion not naive
threshold = self['rescale_threshold'] # Threshold to trigger rescaling
if current_ratio > threshold: # Check if we've reached point when we want to rescale
max_ratio = pop_scale/current_scale # We don't want to exceed the total population size
proposed_ratio = max(current_ratio/threshold, self['rescale_factor']) # The proposed ratio to rescale: the rescale factor, unless we've exceeded it
scaling_ratio = min(proposed_ratio, max_ratio) # We don't want to scale by more than the maximum ratio
self.rescale_vec[self.t:] *= scaling_ratio # Update the rescaling factor from here on
n = int(round(n_not_naive*(1.0-1.0/scaling_ratio))) # For example, rescaling by 2 gives n = 0.5*not_naive_inds
choices = cvu.choose(max_n=n_not_naive, n=n) # Choose who to make naive again
new_naive_inds = not_naive_inds[choices] # Convert these back into indices for people
self.people.make_naive(new_naive_inds) # Make people naive again
return
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def step(self):
'''
Step the simulation forward in time. Usually, the user would use sim.run()
rather than calling sim.step() directly.
'''
# Set the time and if we have reached the end of the simulation, then do nothing
if self.complete:
raise AlreadyRunError('Simulation already complete (call sim.initialize() to re-run)')
t = self.t
# If it's the first timestep, infect people
if t == 0:
self.init_infections(verbose=False)
# Perform initial operations
self.rescale() # Check if we need to rescale
people = self.people # Shorten this for later use
people.update_states_pre(t=t) # Update the state of everyone and count the flows
contacts = people.update_contacts() # Compute new contacts
hosp_max = people.count('severe') > self['n_beds_hosp'] if self['n_beds_hosp'] is not None else False # Check for acute bed constraint
icu_max = people.count('critical') > self['n_beds_icu'] if self['n_beds_icu'] is not None else False # Check for ICU bed constraint
# Randomly infect some people (imported infections)
if self['n_imports']:
n_imports = cvu.poisson(self['n_imports']/self.rescale_vec[self.t]) # Imported cases
if n_imports>0:
importation_inds = cvu.choose(max_n=self['pop_size'], n=n_imports)
people.infect(inds=importation_inds, hosp_max=hosp_max, icu_max=icu_max, layer='importation')
self.results['n_imports'][t] += n_imports
# Add variants
for variant in self['variants']:
if isinstance(variant, cvimm.variant):
variant.apply(self)
# Apply interventions
for i,intervention in enumerate(self['interventions']):
intervention(self) # If it's a function, call it directly
people.update_states_post() # Check for state changes after interventions
# Compute viral loads
frac_time = cvd.default_float(self['viral_dist']['frac_time'])
load_ratio = cvd.default_float(self['viral_dist']['load_ratio'])
high_cap = cvd.default_float(self['viral_dist']['high_cap'])
date_inf = people.date_infectious
date_rec = people.date_recovered
date_dead = people.date_dead
viral_load = cvu.compute_viral_load(t, date_inf, date_rec, date_dead, frac_time, load_ratio, high_cap)
# Shorten useful parameters
nv = self['n_variants'] # Shorten number of variants
sus = people.susceptible
symp = people.symptomatic
diag = people.diagnosed
quar = people.quarantined
iso = people.isolated
prel_trans = people.rel_trans
prel_sus = people.rel_sus
# Iterate through n_variants to calculate infections
for variant in range(nv):
# Deal with variant parameters
asymp_factor = self['asymp_factor']
variant_label = self.pars['variant_map'][variant]
beta = cvd.default_float(self['beta'] * self['rel_beta'] * self['variant_pars'][variant_label]['rel_beta'])
inf_variant = people.infectious * (people.infectious_variant == variant)
if ~inf_variant.any():
continue
for lkey, layer in contacts.items():
p1 = layer['p1']
p2 = layer['p2']
betas = layer['beta']
# Compute relative transmission and susceptibility
sus_imm = people.sus_imm[variant,:]
iso_factor = cvd.default_float(self['iso_factor'][lkey])
quar_factor = cvd.default_float(self['quar_factor'][lkey])
beta_layer = cvd.default_float(self['beta_layer'][lkey])
rel_trans, rel_sus = cvu.compute_trans_sus(prel_trans, prel_sus, inf_variant, sus, beta_layer, viral_load, symp, iso, quar, asymp_factor, iso_factor, quar_factor, sus_imm)
# Calculate actual transmission
pairs = [[p1,p2]] if not self._legacy_trans else [[p1,p2], [p2,p1]] # Support slower legacy method of calculation, but by default skip this loop
for p1,p2 in pairs:
source_inds, target_inds = cvu.compute_infections(beta, p1, p2, betas, rel_trans, rel_sus, legacy=self._legacy_trans) # Calculate transmission!
people.infect(inds=target_inds, hosp_max=hosp_max, icu_max=icu_max, source=source_inds, layer=lkey, variant=variant) # Actually infect people
# Update counts for this time step: stocks
for key in cvd.result_stocks.keys():
self.results[f'n_{key}'][t] = people.count(key)
for key in cvd.result_stocks_by_variant.keys():
for variant in range(nv):
self.results['variant'][f'n_{key}'][variant, t] = people.count_by_variant(key, variant)
# Update counts for this time step: flows
for key,count in people.flows.items():
self.results[key][t] += count
for key,count in people.flows_variant.items():
for variant in range(nv):
self.results['variant'][key][variant][t] += count[variant]
# Update nab and immunity for this time step
if self['use_waning']:
has_nabs = cvu.true(people.peak_nab)
if len(has_nabs):
cvimm.update_nab(people, inds=has_nabs)
inds_alive = cvu.false(people.dead)
self.results['pop_nabs'][t] = np.sum(people.nab[inds_alive[cvu.true(people.nab[inds_alive])]])/len(inds_alive)
self.results['pop_protection'][t] = np.nanmean(people.sus_imm)
self.results['pop_symp_protection'][t] = np.nanmean(people.symp_imm)
# Apply analyzers -- same syntax as interventions
for i,analyzer in enumerate(self['analyzers']):
analyzer(self)
# Tidy up
self.t += 1
if self.t == self.npts:
self.complete = True
return
[docs]
def run(self, do_plot=False, until=None, restore_pars=True, reset_seed=True, verbose=None):
'''
Run the simulation.
Args:
do_plot (bool): whether to plot
until (int/str): day or date to run until
restore_pars (bool): whether to make a copy of the parameters before the run and restore it after, so runs are repeatable
reset_seed (bool): whether to reset the random number stream immediately before run
verbose (float): level of detail to print, e.g. -1 = one-line output, 0 = no output, 0.1 = print every 10th day, 1 = print every day
Returns:
A pointer to the sim object (with results modified in-place)
'''
# Initialization steps -- start the timer, initialize the sim and the seed, and check that the sim hasn't been run
T = sc.timer()
if not self.initialized:
self.initialize()
self._orig_pars = sc.dcp(self.pars) # Create a copy of the parameters, to restore after the run, in case they are dynamically modified
if verbose is None:
verbose = self['verbose']
if reset_seed:
# Reset the RNG. If the simulation is newly created, then the RNG will be reset by sim.initialize() so the use case
# for resetting the seed here is if the simulation has been partially run, and changing the seed is required
self.set_seed()
# Check for AlreadyRun errors
errormsg = None
until = self.npts if until is None else self.day(until)
if until > self.npts:
errormsg = f'Requested to run until t={until} but the simulation end is t={self.npts}'
if self.t >= until: # NB. At the start, self.t is None so this check must occur after initialization
errormsg = f'Simulation is currently at t={self.t}, requested to run until t={until} which has already been reached'
if self.complete:
errormsg = 'Simulation is already complete (call sim.initialize() to re-run)'
if self.people.t not in [self.t, self.t-1]: # Depending on how the sim stopped, either of these states are possible
errormsg = f'The simulation has been run independently from the people (t={self.t}, people.t={self.people.t}): if this is intentional, manually set sim.people.t = sim.t. Remember to save the people object before running the sim.'
if errormsg:
raise AlreadyRunError(errormsg)
# Main simulation loop
while self.t < until:
# Check if we were asked to stop
elapsed = T.toc(output=True)
if self['timelimit'] and elapsed > self['timelimit']:
sc.printv(f"Time limit ({self['timelimit']} s) exceeded; call sim.finalize() to compute results if desired", 1, verbose)
return
elif self['stopping_func'] and self['stopping_func'](self):
sc.printv("Stopping function terminated the simulation; call sim.finalize() to compute results if desired", 1, verbose)
return
# Print progress
if verbose:
simlabel = f'"{self.label}": ' if self.label else ''
string = f' Running {simlabel}{self.datevec[self.t]} ({self.t:2.0f}/{self.pars["n_days"]}) ({elapsed:0.2f} s) '
if verbose >= 2:
sc.heading(string)
elif verbose>0:
if not (self.t % int(1.0/verbose)):
sc.progressbar(self.t+1, self.npts, label=string, length=20, newline=True)
# Do the heavy lifting -- actually run the model!
self.step()
# If simulation reached the end, finalize the results
if self.complete:
self.finalize(verbose=verbose, restore_pars=restore_pars)
sc.printv(f'Run finished after {elapsed:0.2f} s.\n', 1, verbose)
return self
[docs]
def finalize(self, verbose=None, restore_pars=True):
''' Compute final results '''
if self.results_ready:
# Because the results are rescaled in-place, finalizing the sim cannot be run more than once or
# otherwise the scale factor will be applied multiple times
raise AlreadyRunError('Simulation has already been finalized')
# Scale the results
for reskey in self.result_keys():
if self.results[reskey].scale: # Scale the result dynamically
self.results[reskey].values *= self.rescale_vec
for reskey in self.result_keys('variant'):
if self.results['variant'][reskey].scale: # Scale the result dynamically
self.results['variant'][reskey].values = np.einsum('ij,j->ij', self.results['variant'][reskey].values, self.rescale_vec)
# Calculate cumulative results
for key in cvd.result_flows.keys():
self.results[f'cum_{key}'][:] = np.cumsum(self.results[f'new_{key}'][:], axis=0)
for key in cvd.result_flows_by_variant.keys():
for variant in range(self['n_variants']):
self.results['variant'][f'cum_{key}'][variant, :] = np.cumsum(self.results['variant'][f'new_{key}'][variant, :], axis=0)
for res in [self.results['cum_infections'], self.results['variant']['cum_infections_by_variant']]: # Include initially infected people
res.values += self['pop_infected']*self.rescale_vec[0]
# Finalize interventions and analyzers
self.finalize_interventions()
self.finalize_analyzers()
# Final settings
self.results_ready = True # Set this first so self.summary() knows to print the results
self.t -= 1 # During the run, this keeps track of the next step; restore this be the final day of the sim
# Perform calculations on results
self.compute_results(verbose=verbose) # Calculate the rest of the results
self.results = sc.objdict(self.results) # Convert results to a odicts/objdict to allow e.g. sim.results.diagnoses
if restore_pars and self._orig_pars:
preserved = ['analyzers', 'interventions']
orig_pars_keys = list(self._orig_pars.keys()) # Get a list of keys so we can iterate over them
for key in orig_pars_keys:
if key not in preserved:
self.pars[key] = self._orig_pars.pop(key) # Restore everything except for the analyzers and interventions
# Optionally print summary output
if verbose: # Verbose is any non-zero value
if verbose>0: # Verbose is any positive number
self.summarize() # Print medium-length summary of the sim
else:
self.brief() # Print brief summary of the sim
return
[docs]
def compute_results(self, verbose=None):
''' Perform final calculations on the results '''
self.compute_states()
self.compute_yield()
self.compute_doubling()
self.compute_r_eff()
self.compute_summary()
return
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def compute_states(self):
'''
Compute prevalence, incidence, and other states. Prevalence is the current
number of infected people divided by the number of people who are alive.
Incidence is the number of new infections per day divided by the susceptible
population. Also calculates the number of people alive, the number preinfectious,
the number removed, and recalculates susceptibles to handle scaling.
'''
res = self.results
count_recov = 1-self['use_waning'] # If waning is on, don't count recovered people as removed
self.results['n_alive'][:] = self.scaled_pop_size - res['cum_deaths'][:] # Number of people still alive
self.results['n_naive'][:] = self.scaled_pop_size - res['cum_deaths'][:] - res['n_recovered'][:] - res['n_exposed'][:] # Number of people naive
self.results['n_susceptible'][:] = res['n_alive'][:] - res['n_exposed'][:] - count_recov*res['cum_recoveries'][:] # Recalculate the number of susceptible people, not agents
self.results['n_preinfectious'][:] = res['n_exposed'][:] - res['n_infectious'][:] # Calculate the number not yet infectious: exposed minus infectious
self.results['n_removed'][:] = count_recov*res['cum_recoveries'][:] + res['cum_deaths'][:] # Calculate the number removed: recovered + dead
self.results['prevalence'][:] = res['n_exposed'][:]/res['n_alive'][:] # Calculate the prevalence
self.results['incidence'][:] = res['new_infections'][:]/res['n_susceptible'][:] # Calculate the incidence
self.results['frac_vaccinated'][:] = res['n_vaccinated'][:]/res['n_alive'][:] # Calculate the fraction vaccinated
self.results['variant']['incidence_by_variant'][:] = np.einsum('ji,i->ji',res['variant']['new_infections_by_variant'][:], 1/res['n_susceptible'][:]) # Calculate the incidence
self.results['variant']['prevalence_by_variant'][:] = np.einsum('ji,i->ji',res['variant']['new_infections_by_variant'][:], 1/res['n_alive'][:]) # Calculate the prevalence
return
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def compute_yield(self):
'''
Compute test yield -- number of positive tests divided by the total number
of tests, also called test positivity rate. Relative yield is with respect
to prevalence: i.e., how the yield compares to what the yield would be from
choosing a person at random from the population.
'''
# Absolute yield
res = self.results
inds = cvu.true(res['new_tests'][:]) # Pull out non-zero numbers of tests
self.results['test_yield'][inds] = res['new_diagnoses'][inds]/res['new_tests'][inds] # Calculate the yield
# Relative yield
inds = cvu.true(res['n_infectious'][:]) # To avoid divide by zero if no one is infectious
denom = res['n_infectious'][inds] / (res['n_alive'][inds] - res['cum_diagnoses'][inds]) # Alive + undiagnosed people might test; infectious people will test positive
self.results['rel_test_yield'][inds] = self.results['test_yield'][inds]/denom # Calculate the relative yield
return
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def compute_doubling(self, window=3, max_doubling_time=30):
'''
Calculate doubling time using exponential approximation -- a more detailed
approach is in utils.py. Compares infections at time t to infections at time
t-window, and uses that to compute the doubling time. For example, if there are
100 cumulative infections on day 12 and 200 infections on day 19, doubling
time is 7 days.
Args:
window (float): the size of the window used (larger values are more accurate but less precise)
max_doubling_time (float): doubling time could be infinite, so this places a bound on it
Returns:
doubling_time (array): the doubling time results array
'''
cum_infections = self.results['cum_infections'].values
infections_now = cum_infections[window:]
infections_prev = cum_infections[:-window]
use = (infections_prev > 0) & (infections_now > infections_prev)
doubling_time = window * np.log(2) / np.log(infections_now[use] / infections_prev[use])
self.results['doubling_time'][:] = np.nan
self.results['doubling_time'][window:][use] = np.minimum(doubling_time, max_doubling_time)
return self.results['doubling_time'].values
[docs]
def compute_r_eff(self, method='daily', smoothing=2, window=7):
'''
Effective reproduction number based on number of people each person infected.
Args:
method (str): 'daily' uses daily infections, 'infectious' counts from the date infectious, 'outcome' counts from the date recovered/dead
smoothing (int): the number of steps to smooth over for the 'daily' method
window (int): the size of the window used for 'infectious' and 'outcome' calculations (larger values are more accurate but less precise)
Returns:
r_eff (array): the r_eff results array
'''
# Initialize arrays to hold sources and targets infected each day
sources = np.zeros(self.npts)
targets = np.zeros(self.npts)
window = int(window)
# Default method -- calculate the daily infections
if method == 'daily':
# Find the dates that everyone became infectious and recovered, and hence calculate infectious duration
recov_inds = self.people.defined('date_recovered')
dead_inds = self.people.defined('date_dead')
date_recov = self.people.date_recovered[recov_inds]
date_dead = self.people.date_dead[dead_inds]
date_outcome = np.concatenate((date_recov, date_dead))
inds = np.concatenate((recov_inds, dead_inds))
date_inf = self.people.date_infectious[inds]
if len(date_outcome):
mean_inf = date_outcome.mean() - date_inf.mean()
else:
warnmsg ='There were no infections during the simulation'
cvm.warn(warnmsg)
mean_inf = 0 # Doesn't matter since r_eff is 0
# Calculate R_eff as the mean infectious duration times the number of new infectious divided by the number of infectious people on a given day
new_infections = self.results['new_infections'].values - self.results['n_imports'].values
n_infectious = self.results['n_infectious'].values
raw_values = mean_inf*np.divide(new_infections, n_infectious, out=np.zeros(self.npts), where=n_infectious>0)
# Handle smoothing, including with too-short arrays
len_raw = len(raw_values) # Calculate the number of raw values
dur_pars = self['dur'][0] if isinstance(self['dur'], list) else self['dur'] # Note: does not take variants into account
if len_raw >= 3: # Can't smooth arrays shorter than this since the default smoothing kernel has length 3
initial_period = dur_pars['exp2inf']['par1'] + dur_pars['asym2rec']['par1'] # Approximate the duration of the seed infections for averaging
initial_period = int(min(len_raw, initial_period)) # Ensure we don't have too many points
for ind in range(initial_period): # Loop over each of the initial inds
raw_values[ind] = raw_values[ind:initial_period].mean() # Replace these values with their average
values = sc.smooth(raw_values, smoothing)
values[:smoothing] = raw_values[:smoothing] # To avoid numerical effects, replace the beginning and end with the original
values[-smoothing:] = raw_values[-smoothing:]
else:
values = raw_values
# Alternate (traditional) method -- count from the date of infection or outcome
elif method in ['infectious', 'outcome']:
# Store a mapping from each source to their date
source_dates = {}
for t in self.tvec:
# Sources are easy -- count up the arrays for all the people who became infections on that day
if method == 'infectious':
inds = cvu.true(t == self.people.date_infectious) # Find people who became infectious on this timestep
elif method == 'outcome':
recov_inds = cvu.true(t == self.people.date_recovered) # Find people who recovered on this timestep
dead_inds = cvu.true(t == self.people.date_dead) # Find people who died on this timestep
inds = np.concatenate((recov_inds, dead_inds))
sources[t] = len(inds)
# Create the mapping from sources to dates
for ind in inds:
source_dates[ind] = t
# Targets are hard -- loop over the transmission tree
for transdict in self.people.infection_log:
source = transdict['source']
if source is not None and source in source_dates: # Skip seed infections and people with e.g. recovery after the end of the sim
source_date = source_dates[source]
targets[source_date] += 1
# for ind in inds:
# targets[t] += len(self.people.transtree.targets[ind])
# Populate the array -- to avoid divide-by-zero, skip indices that are 0
r_eff = np.divide(targets, sources, out=np.full(self.npts, np.nan), where=sources > 0)
# Use stored weights calculate the moving average over the window of timesteps, n
num = np.nancumsum(r_eff * sources)
num[window:] = num[window:] - num[:-window]
den = np.cumsum(sources)
den[window:] = den[window:] - den[:-window]
values = np.divide(num, den, out=np.full(self.npts, np.nan), where=den > 0)
# Method not recognized
else: # pragma: no cover
errormsg = f'Method must be "daily", "infectious", or "outcome", not "{method}"'
raise ValueError(errormsg)
# Set the values and return
self.results['r_eff'].values[:] = values
return self.results['r_eff'].values
[docs]
def compute_gen_time(self):
'''
Calculate the generation time (or serial interval). There are two
ways to do this calculation. The 'true' interval (exposure time to
exposure time) or 'clinical' (symptom onset to symptom onset).
Returns:
gen_time (dict): the generation time results
'''
intervals1 = np.zeros(len(self.people))
intervals2 = np.zeros(len(self.people))
pos1 = 0
pos2 = 0
date_exposed = self.people.date_exposed
date_symptomatic = self.people.date_symptomatic
for infection in self.people.infection_log:
if infection['source'] is not None:
source_ind = infection['source']
target_ind = infection['target']
intervals1[pos1] = date_exposed[target_ind] - date_exposed[source_ind]
pos1 += 1
if np.isfinite(date_symptomatic[source_ind]) and np.isfinite(date_symptomatic[target_ind]):
intervals2[pos2] = date_symptomatic[target_ind] - date_symptomatic[source_ind]
pos2 += 1
self.results['gen_time'] = {
'true': np.mean(intervals1[:pos1]),
'true_std': np.std(intervals1[:pos1]),
'clinical': np.mean(intervals2[:pos2]),
'clinical_std': np.std(intervals2[:pos2])}
return self.results['gen_time']
[docs]
def compute_summary(self, full=None, t=None, update=True, output=False, require_run=False):
'''
Compute the summary dict and string for the sim. Used internally; see
sim.summarize() for the user version.
Args:
full (bool): whether or not to print all results (by default, only cumulative)
t (int/str): day or date to compute summary for (by default, the last point)
update (bool): whether to update the stored sim.summary
output (bool): whether to return the summary
require_run (bool): whether to raise an exception if simulations have not been run yet
'''
if t is None:
t = self.day(self.t)
# Compute the summary
if require_run and not self.results_ready:
errormsg = 'Simulation not yet run'
raise RuntimeError(errormsg)
summary = sc.objdict()
for key in self.result_keys():
summary[key] = self.results[key][t]
# Update the stored state
if update:
self.summary = summary
# Optionally return
if output:
return summary
else:
return
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def summarize(self, full=False, t=None, sep=None, output=False):
'''
Print a medium-length summary of the simulation, drawing from the last time
point in the simulation by default. Called by default at the end of a sim run.
See also sim.disp() (detailed output) and sim.brief() (short output).
Args:
full (bool): whether or not to print all results (by default, only cumulative)
t (int/str): day or date to compute summary for (by default, the last point)
sep (str): thousands separator (default ',')
output (bool): whether to return the summary instead of printing it
**Examples**::
sim = cv.Sim(label='Example sim', verbose=0) # Set to run silently
sim.run() # Run the sim
sim.summarize() # Print medium-length summary of the sim
sim.summarize(t=24, full=True) # Print a "slice" of all sim results on day 24
'''
# Compute the summary
summary = self.compute_summary(full=full, t=t, update=False, output=True)
# Construct the output string
if sep is None: sep = cvo.sep # Default separator
labelstr = f' "{self.label}"' if self.label else ''
string = f'Simulation{labelstr} summary:\n'
for key in self.result_keys():
if full or key.startswith('cum_'):
val = np.round(summary[key])
string += f' {val:10,.0f} {self.results[key].name.lower()}\n'.replace(',', sep) # Use replace since it's more flexible
# Print or return string
if not output:
print(string)
else:
return string
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def disp(self, output=False):
'''
Display a verbose description of a sim. See also sim.summarize() (medium
length output) and sim.brief() (short output).
Args:
output (bool): if true, return a string instead of printing output
**Example**::
sim = cv.Sim(label='Example sim', verbose=0) # Set to run silently
sim.run() # Run the sim
sim.disp() # Displays detailed output
'''
string = self._disp()
if not output:
print(string)
else:
return string
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def brief(self, output=False):
'''
Print a one-line description of a sim. See also sim.disp() (detailed output)
and sim.summarize() (medium length output). The symbol "⚙" is used to show
infections, and "☠" is used to show deaths.
Args:
output (bool): if true, return a string instead of printing output
**Example**::
sim = cv.Sim(label='Example sim', verbose=0) # Set to run silently
sim.run() # Run the sim
sim.brief() # Prints one-line output
'''
string = self._brief()
if not output:
print(string)
else:
return string
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def compute_fit(self, *args, **kwargs):
'''
Compute the fit between the model and the data. See cv.Fit() for more
information.
Args:
args (list): passed to cv.Fit()
kwargs (dict): passed to cv.Fit()
Returns:
A Fit object
**Example**::
sim = cv.Sim(datafile='data.csv')
sim.run()
fit = sim.compute_fit()
fit.plot()
'''
if not self.results_ready:
errormsg = 'Cannot compute fit since results are not ready yet -- did you run the sim?'
raise RuntimeError(errormsg)
self.fit = cva.Fit(self, *args, **kwargs)
return self.fit
[docs]
def calibrate(self, calib_pars, **kwargs):
'''
Automatically calibrate the simulation, returning a Calibration object
(a type of analyzer). See the documentation on that class for more information.
Args:
calib_pars (dict): a dictionary of the parameters to calibrate of the format dict(key1=[best, low, high])
kwargs (dict): passed to cv.Calibration()
Returns:
A Calibration object
**Example**::
sim = cv.Sim(datafile='data.csv')
calib_pars = dict(beta=[0.015, 0.010, 0.020])
calib = sim.calibrate(calib_pars, n_trials=50)
calib.plot()
'''
calib = cva.Calibration(sim=self, calib_pars=calib_pars, **kwargs)
calib.calibrate()
return calib
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def make_age_histogram(self, *args, output=True, **kwargs):
'''
Calculate the age histograms of infections, deaths, diagnoses, etc. See
cv.age_histogram() for more information. This can be used alternatively
to supplying the age histogram as an analyzer to the sim. If used this
way, it can only record the final time point since the states of each
person are not saved during the sim.
Args:
output (bool): whether or not to return the age histogram; if not, store in sim.results
args (list): passed to cv.age_histogram()
kwargs (dict): passed to cv.age_histogram()
**Example**::
sim = cv.Sim()
sim.run()
agehist = sim.make_age_histogram()
agehist.plot()
'''
if not self.results_ready:
errormsg = 'Cannot make age histogram since results are not ready yet -- did you run the sim?'
raise RuntimeError(errormsg)
agehist = cva.age_histogram(sim=self, *args, **kwargs)
if output:
return agehist
else: # pragma: no cover
self.results.agehist = agehist
return
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def make_transtree(self, *args, output=True, **kwargs):
'''
Create a TransTree (transmission tree) object, for analyzing the pattern
of transmissions in the simulation. See cv.TransTree() for more information.
Args:
output (bool): whether or not to return the TransTree; if not, store in sim.results
args (list): passed to cv.TransTree()
kwargs (dict): passed to cv.TransTree()
**Example**::
sim = cv.Sim()
sim.run()
tt = sim.make_transtree()
'''
if not self.results_ready:
errormsg = 'Cannot compute transmission tree since results are not ready yet -- did you run the sim?'
raise RuntimeError(errormsg)
tt = cva.TransTree(self, *args, **kwargs)
if output:
return tt
else: # pragma: no cover
self.results.transtree = tt
return
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def plot(self, *args, **kwargs):
'''
Plot the results of a single simulation.
Args:
to_plot (dict): Dict of results to plot; see get_default_plots() for structure
do_save (bool): Whether or not to save the figure
fig_path (str): Path to save the figure
fig_args (dict): Dictionary of kwargs to be passed to ``pl.figure()``
plot_args (dict): Dictionary of kwargs to be passed to ``pl.plot()``
scatter_args (dict): Dictionary of kwargs to be passed to ``pl.scatter()``
axis_args (dict): Dictionary of kwargs to be passed to ``pl.subplots_adjust()``
legend_args (dict): Dictionary of kwargs to be passed to ``pl.legend()``; if show_legend=False, do not show
date_args (dict): Control how the x-axis (dates) are shown (see below for explanation)
show_args (dict): Control which "extras" get shown: uncertainty bounds, data, interventions, ticks, the legend; additionally, "outer" will show the axes only on the outer plots
style_args (dict): Dictionary of kwargs to be passed to Matplotlib; options are dpi, font, fontsize, plus any valid key in ``pl.rcParams``
n_cols (int): Number of columns of subpanels to use for subplot
font_size (int): Size of the font
font_family (str): Font face
grid (bool): Whether or not to plot gridlines
commaticks (bool): Plot y-axis with commas rather than scientific notation
setylim (bool): Reset the y limit to start at 0
log_scale (bool): Whether or not to plot the y-axis with a log scale; if a list, panels to show as log
do_show (bool): Whether or not to show the figure
colors (dict): Custom color for each result, must be a dictionary with one entry per result key in to_plot
sep_figs (bool): Whether to show separate figures for different results instead of subplots
fig (fig): Handle of existing figure to plot into
ax (axes): Axes instance to plot into
kwargs (dict): Parsed among figure, plot, scatter, date, and other settings (will raise an error if not recognized)
The optional dictionary "date_args" allows several settings for controlling
how the x-axis of plots are shown, if this axis is dates. These options are:
- ``as_dates``: whether to format them as dates (else, format them as days since the start)
- ``dateformat``: string format for the date (if not provided, choose based on timeframe)
- ``rotation``: whether to rotate labels
- ``start``: the first day to plot
- ``end``: the last day to plot
- ``outer``: only show the date labels on the outer (bottom) plots
The ``show_args`` dictionary allows several other formatting options, such as:
- ``tight``: use tight layout for the figure (default false)
- ``maximize``: try to make the figure full screen (default false)
- ``outer``: only show outermost (bottom) date labels (default false)
Date, show, and other arguments can also be passed directly, e.g. ``sim.plot(tight=True)``.
For additional style options, see ``cv.options.with_style()``, which is the
final refuge of arguments that are not picked up by any of the other parsers,
e.g. ``sim.plot(**{'ytick.direction':'in'})``.
Returns:
fig: Figure handle
**Examples**::
sim = cv.Sim().run()
sim.plot() # Default plotting
sim.plot('overview') # Show overview
sim.plot('overview', maximize=True, outer=True, rotation=15) # Make some modifications to make plots easier to see
sim.plot(style='seaborn-whitegrid') # Use a built-in Matplotlib style
sim.plot(style='simple', font='Rosario', dpi=200) # Use the other house style with several customizations
| New in version 2.1.0: argument passing, date_args, and mpl_args
| New in version 3.1.2: updated date arguments; mpl_args renamed style_args
'''
fig = cvplt.plot_sim(sim=self, *args, **kwargs)
return fig
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def plot_result(self, key, *args, **kwargs):
'''
Simple method to plot a single result. Useful for results that aren't
standard outputs. See sim.plot() for explanation of other arguments.
Args:
key (str): the key of the result to plot
Returns:
fig: Figure handle
**Example**::
sim = cv.Sim().run()
sim.plot_result('r_eff')
'''
fig = cvplt.plot_result(sim=self, key=key, *args, **kwargs)
return fig
[docs]
def diff_sims(sim1, sim2, skip_key_diffs=False, skip=None, output=False, die=False):
'''
Compute the difference of the summaries of two simulations, and print any
values which differ.
Args:
sim1 (sim/dict): either a simulation object or the sim.summary dictionary
sim2 (sim/dict): ditto
skip_key_diffs (bool): whether to skip keys that don't match between sims
skip (list): a list of values to skip
output (bool): whether to return the output as a string (otherwise print)
die (bool): whether to raise an exception if the sims don't match
require_run (bool): require that the simulations have been run
**Example**::
s1 = cv.Sim(beta=0.01)
s2 = cv.Sim(beta=0.02)
s1.run()
s2.run()
cv.diff_sims(s1, s2)
'''
if isinstance(sim1, Sim):
sim1 = sim1.compute_summary(update=False, output=True, require_run=True)
if isinstance(sim2, Sim):
sim2 = sim2.compute_summary(update=False, output=True, require_run=True)
for sim in [sim1, sim2]:
if not isinstance(sim, dict): # pragma: no cover
errormsg = f'Cannot compare object of type {type(sim)}, must be a sim or a sim.summary dict'
raise TypeError(errormsg)
# Compare keys
keymatchmsg = ''
sim1_keys = set(sim1.keys())
sim2_keys = set(sim2.keys())
if sim1_keys != sim2_keys and not skip_key_diffs: # pragma: no cover
keymatchmsg = "Keys don't match!\n"
missing = list(sim1_keys - sim2_keys)
extra = list(sim2_keys - sim1_keys)
if missing:
keymatchmsg += f' Missing sim1 keys: {missing}\ns'
if extra:
keymatchmsg += f' Extra sim2 keys: {extra}\n'
# Compare values
valmatchmsg = ''
mismatches = {}
skip = sc.tolist(skip)
for key in sim2.keys(): # To ensure order
if key in sim1_keys and key not in skip: # If a key is missing, don't count it as a mismatch
sim1_val = sim1[key]
sim2_val = sim2[key]
both_nan = sc.isnumber(sim1_val, isnan=True) and sc.isnumber(sim2_val, isnan=True)
if not np.isclose(sim1_val,sim2_val) and not both_nan:
mismatches[key] = {'sim1': sim1_val, 'sim2': sim2_val}
if len(mismatches):
valmatchmsg = '\nThe following values differ between the two simulations:\n'
df = pd.DataFrame.from_dict(mismatches).transpose()
diff = []
ratio = []
change = []
small_change = 1e-3 # Define a small change, e.g. a rounding error
for mdict in mismatches.values():
old = mdict['sim1']
new = mdict['sim2']
numeric = sc.isnumber(sim1_val) and sc.isnumber(sim2_val)
if numeric and old>0:
this_diff = new - old
this_ratio = new/old
abs_ratio = max(this_ratio, 1.0/this_ratio)
# Set the character to use
if abs_ratio<small_change:
change_char = '≈'
elif new > old:
change_char = '↑'
elif new < old:
change_char = '↓'
else:
errormsg = f'Could not determine relationship between sim1={old} and sim2={new}'
raise ValueError(errormsg)
# Set how many repeats it should have
repeats = 1
if abs_ratio >= 1.1:
repeats = 2
if abs_ratio >= 2:
repeats = 3
if abs_ratio >= 10:
repeats = 4
this_change = change_char*repeats
else: # pragma: no cover
this_diff = np.nan
this_ratio = np.nan
this_change = 'N/A'
diff.append(this_diff)
ratio.append(this_ratio)
change.append(this_change)
df['diff'] = diff
df['ratio'] = ratio
for col in ['sim1', 'sim2', 'diff', 'ratio']:
df[col] = df[col].round(decimals=3)
df['change'] = change
valmatchmsg += str(df)
# Raise an error if mismatches were found
mismatchmsg = keymatchmsg + valmatchmsg
if mismatchmsg: # pragma: no cover
if die:
raise ValueError(mismatchmsg)
elif output:
return mismatchmsg
else:
print(mismatchmsg)
else:
if not output:
print('Sims match')
return
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def demo(preset=None, to_plot=None, scens=None, run_args=None, plot_args=None, **kwargs):
'''
Shortcut for ``cv.Sim().run().plot()``.
Args:
preset (str): use a preset run configuration; currently the only option is "full"
to_plot (str): what to plot
scens (dict): dictionary of scenarios to run as a multisim, if preset='full'
kwargs (dict): passed to Sim()
run_args (dict): passed to sim.run()
plot_args (dict): passed to sim.plot()
**Examples**::
cv.demo() # Simplest example
cv.demo('full') # Full example
cv.demo('full', overview=True) # Plot all results
cv.demo(beta=0.020, run_args={'verbose':0}, plot_args={'to_plot':'overview'}) # Pass in custom values
'''
from . import interventions as cvi # To avoid circular imports
from . import run as cvr
run_args = sc.mergedicts(run_args)
plot_args = sc.mergedicts(plot_args)
if to_plot:
plot_args = sc.mergedicts(plot_args, {'to_plot':to_plot})
if not preset:
sim = Sim(**kwargs)
sim.run(**run_args)
sim.plot(**plot_args)
return sim
elif preset == 'full':
# Define interventions
cb = cvi.change_beta(days=40, changes=0.5)
tp = cvi.test_prob(start_day=20, symp_prob=0.1, asymp_prob=0.01)
ct = cvi.contact_tracing(trace_probs=0.3, start_day=50)
# Define the parameters
pars = dict(
pop_size = 20e3, # Population size
pop_infected = 100, # Number of initial infections -- use more for increased robustness
pop_type = 'hybrid', # Population to use -- "hybrid" is random with household, school,and work structure
n_days = 60, # Number of days to simulate
verbose = 0, # Don't print details of the run
rand_seed = 2, # Set a non-default seed
interventions = [cb, tp, ct], # Include the most common interventions
)
pars = sc.mergedicts(pars, kwargs)
if scens is None:
scens = ('beta', {'Low beta':0.012, 'Medium beta':0.016, 'High beta':0.020})
scenpar = scens[0]
scenval = scens[1]
# Run the simulations
sims = [Sim(pars, **{scenpar:val}, label=label) for label,val in scenval.items()]
msim = cvr.MultiSim(sims)
msim.run(**run_args)
msim.plot(**plot_args)
msim.median()
msim.plot(**plot_args)
return msim
else:
errormsg = f'Could not understand preset argument "{preset}"; must be None or "full"'
raise NotImplementedError(errormsg)
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class AlreadyRunError(RuntimeError):
'''
This error is raised if a simulation is run in such a way that no timesteps
will be taken. This error is a distinct type so that it can be safely caught
and ignored if required, but it is anticipated that most of the time, calling
sim.run() and not taking any timesteps, would be an inadvertent error.
'''
pass
