Source code for covasim.sim

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']

[docs] 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 = 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 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
[docs] 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 = cvm.load_data(datafile=datafile, verbose=verbose, start_day=self['start_day'], **kwargs) return
[docs] 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
[docs] 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
[docs] 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(, layer_keys=layer_keys, force=force) return
[docs] 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([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([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
[docs] 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 ='pop_size') scaled_pop ='scaled_pop') pop_scale ='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'] = # Handle end day and n_days end_day = self['end_day'] n_days = self['n_days'] if end_day: self['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'] = # 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
[docs] 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, 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
[docs] 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
[docs] 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( # 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
[docs] 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)
[docs] 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)
[docs] 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
[docs] 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
[docs] 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
[docs] 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
[docs] def step(self): ''' Step the simulation forward in time. Usually, the user would use 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 =['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([inds_alive[cvu.true([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( # 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 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}/{["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:[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
[docs] 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
[docs] 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
[docs] def compute_doubling(self, window=3, max_doubling_time=30): ''' Calculate doubling time using exponential approximation -- a more detailed approach is in 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 = # 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
[docs] 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 # 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
[docs] 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 # Run the sim sim.disp() # Displays detailed output ''' string = self._disp() if not output: print(string) else: return string
[docs] 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 # Run the sim sim.brief() # Prints one-line output ''' string = self._brief() if not output: print(string) else: return string
[docs] 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') 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) = cva.Fit(self, *args, **kwargs) return
[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
[docs] 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() 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
[docs] 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() 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
[docs] 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
[docs] 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) 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] if key in sim1 else 'not present' sim2_val = sim2[key] if key in sim2 else 'not present' both_nan = sc.isnumber(sim1_val, isnan=True) and sc.isnumber(sim2_val, isnan=True) if 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
[docs] 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 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)**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)**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)
[docs] 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 and not taking any timesteps, would be an inadvertent error. ''' pass