Source code for rsvsim.base

'''
Base classes for rsvsim. These classes handle a lot of the boilerplate of the
People and Sim classes (e.g. loading, saving, key lookups, etc.), so those classes
can be focused on the disease-specific functionality.
'''

import numpy as np
import pandas as pd
import sciris as sc
import datetime as dt
from . import version as cvv
from . import utils as cvu
from . import misc as cvm
from . import defaults as cvd
from . import parameters as cvpar

# Specify all externally visible classes this file defines
__all__ = ['ParsObj', 'Result', 'BaseSim', 'BasePeople', 'Person', 'FlexDict', 'Contacts', 'Layer']


#%% Define simulation classes

class FlexPretty(sc.prettyobj):
    '''
    A class that supports multiple different display options: namely obj.brief()
    for a one-line description and obj.disp() for a full description.
    '''

    def __repr__(self):
        ''' Use brief repr by default '''
        try:
            string = self._brief()
        except Exception as E:
            string = sc.objectid(self)
            string += f'Warning, something went wrong printing object:\n{str(E)}'
        return string

    def _disp(self):
        ''' Verbose output -- use Sciris' pretty repr by default '''
        return sc.prepr(self)

    def disp(self, output=False):
        ''' Print or output verbose representation of the object '''
        string = self._disp()
        if not output:
            print(string)
        else:
            return string

    def _brief(self):
        ''' Brief output -- use a one-line output, a la Python's default '''
        return sc.objectid(self)

    def brief(self, output=False):
        ''' Print or output a brief representation of the object '''
        string = self._brief()
        if not output:
            print(string)
        else:
            return string


[docs]class ParsObj(FlexPretty): ''' A class based around performing operations on a self.pars dict. ''' def __init__(self, pars): self.update_pars(pars, create=True) return def __getitem__(self, key): ''' Allow sim['par_name'] instead of sim.pars['par_name'] ''' try: return self.pars[key] except: all_keys = '\n'.join(list(self.pars.keys())) errormsg = f'Key "{key}" not found; available keys:\n{all_keys}' raise sc.KeyNotFoundError(errormsg) def __setitem__(self, key, value): ''' Ditto ''' if key in self.pars: self.pars[key] = value else: all_keys = '\n'.join(list(self.pars.keys())) errormsg = f'Key "{key}" not found; available keys:\n{all_keys}' raise sc.KeyNotFoundError(errormsg) return
[docs] def update_pars(self, pars=None, create=False): ''' Update internal dict with new pars. Args: pars (dict): the parameters to update (if None, do nothing) create (bool): if create is False, then raise a KeyNotFoundError if the key does not already exist ''' if pars is not None: if not isinstance(pars, dict): raise TypeError(f'The pars object must be a dict; you supplied a {type(pars)}') if not hasattr(self, 'pars'): self.pars = pars if not create: available_keys = list(self.pars.keys()) mismatches = [key for key in pars.keys() if key not in available_keys] if len(mismatches): errormsg = f'Key(s) {mismatches} not found; available keys are {available_keys}' raise sc.KeyNotFoundError(errormsg) self.pars.update(pars) return
[docs]class Result(object): ''' Stores a single result -- by default, acts like an array. Args: name (str): name of this result, e.g. new_infections npts (int): if values is None, precreate it to be of this length scale (bool): whether or not the value scales by population scale factor color (str/arr): default color for plotting (hex or RGB notation) n_genotype (int): the number of genotype the result is for (0 for results not by genotype) **Example**:: import rsvsim as rsv r1 = rsv.Result(name='test1', npts=10) r1[:5] = 20 print(r1.values) ''' def __init__(self, name=None, ntspts=None, scale=True, color=None, n_genotype=0): self.name = name # Name of this result self.scale = scale # Whether or not to scale the result by the scale factor if color is None: color = cvd.get_default_colors()['default'] self.color = color # Default color if ntspts is None: ntspts = 0 ntspts = int(ntspts) if n_genotype>0: self.values = np.zeros((n_genotype, ntspts), dtype=cvd.result_float) else: self.values = np.zeros(ntspts, dtype=cvd.result_float) self.low = None self.high = None return def __repr__(self): ''' Use pretty repr, like sc.prettyobj, but displaying full values ''' output = sc.prepr(self, skip=['values', 'low', 'high'], use_repr=False) output += 'values:\n' + repr(self.values) if self.low is not None: output += '\nlow:\n' + repr(self.low) if self.high is not None: output += '\nhigh:\n' + repr(self.high) return output def __getitem__(self, key): ''' To allow e.g. result['high'] instead of result.high, and result[5] instead of result.values[5] ''' if isinstance(key, str): output = getattr(self, key) else: output = self.values.__getitem__(key) return output def __setitem__(self, key, value): ''' To allow e.g. result[:] = 1 instead of result.values[:] = 1 ''' if isinstance(key, str): setattr(self, key, value) else: self.values.__setitem__(key, value) return def __len__(self): ''' To allow len(result) instead of len(result.values) ''' return len(self.values) @property def ntspts(self): return len(self.values)
def set_metadata(obj): ''' Set standard metadata for an object ''' obj.created = sc.now() obj.version = cvv.__version__ obj.git_info = cvm.git_info() return
[docs]class BaseSim(ParsObj): ''' The BaseSim class stores various methods useful for the Sim that are not directly related to simulating the epidemic. It is not used outside of the Sim object, so the separation of methods into the BaseSim and Sim classes is purely to keep each one of manageable size. ''' def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # Initialize and set the parameters as attributes return def _disp(self): ''' Print a verbose display of the sim object. Used by repr(). See sim.disp() for the user version. Equivalent to sc.prettyobj(). ''' return sc.prepr(self) def _brief(self): ''' Return a one-line description of a sim -- used internally and by repr(); see sim.brief() for the user version. ''' # Try to get a detailed description of the sim... try: if self.results_ready: infections = self.summary['cum_infections'] deaths = self.summary['cum_deaths'] results = f'{infections:n}⚙, {deaths:n}☠' else: results = 'not run' # Set label string labelstr = f'"{self.label}"' if self.label else '<no label>' start = sc.date(self['start_day'], as_date=False) if self['end_day']: end = sc.date(self['end_day'], as_date=False) else: end = sc.date(self['n_days'], start_date=start) pop_size = self['pop_size'] pop_type = self['pop_type'] string = f'Sim({labelstr}; {start} to {end}; pop: {pop_size:n} {pop_type}; epi: {results})' # ...but if anything goes wrong, return the default with a warning except Exception as E: # pragma: no cover string = sc.objectid(self) string += f'Warning, sim appears to be malformed; use sim.disp() for details:\n{str(E)}' return string
[docs] def update_pars(self, pars=None, create=False, **kwargs): ''' Ensure that metaparameters get used properly before being updated ''' # Merge everything together pars = sc.mergedicts(pars, kwargs) if pars: # Define aliases mapping = dict( n_agents = 'pop_size', init_infected = 'pop_infected', ) for key1,key2 in mapping.items(): if key1 in pars: pars[key2] = pars.pop(key1) # Handle other special parameters if pars.get('pop_type'): cvpar.reset_layer_pars(pars, force=False) if pars.get('prog_by_age'): pars['prognoses'] = cvpar.get_prognoses() # Reset prognoses if pars.get('timestep'): pars['tspy'] = np.round(cvd.dpy / pars['timestep']) # Call update_pars() for ParsObj super().update_pars(pars=pars, create=create) return
[docs] def set_metadata(self, simfile): ''' Set the metadata for the simulation -- creation time and filename ''' set_metadata(self) if simfile is None: datestr = sc.getdate(obj=self.created, dateformat='%Y-%b-%d_%H.%M.%S') self.simfile = f'rsvsim_{datestr}.sim' return
[docs] def set_seed(self, seed=-1): ''' Set the seed for the random number stream from the stored or supplied value Args: seed (None or int): if no argument, use current seed; if None, randomize; otherwise, use and store supplied seed ''' # Unless no seed is supplied, reset it if seed != -1: self['rand_seed'] = seed cvu.set_seed(self['rand_seed']) return
@property def n(self): ''' Count the number of people -- if it fails, assume none ''' try: # By default, the length of the people dict return len(self.people) except: # pragma: no cover # If it's None or missing return 0 @property def scaled_pop_size(self): ''' Get the total population size, i.e. the number of agents times the scale factor -- if it fails, assume none ''' try: return self['pop_size']*self['pop_scale'] except: # pragma: no cover # If it's None or missing return 0 @property def npts(self): ''' Count the number of time points ''' try: return int(self['n_days'] + 1) except: # pragma: no cover return 0 @property def ntspts(self): ''' Count number of time-step points''' try: return int((self['n_days']/self['timestep'])+1) except: return 0 @property def tvec(self): ''' Create a time vector ''' try: return np.arange(self.npts) except: # pragma: no cover return np.array([]) @property def tsvec(self): ''' Create a time step time vector''' try: return np.arange(self.ntspts) except: return np.array([]) @property def datevec(self): ''' Create a vector of dates Returns: Array of `datetime` instances containing the date associated with each day ''' try: return self['start_day'] + self.tvec * dt.timedelta(days=1) except: # pragma: no cover return np.array([]) @property def tsdatevec(self): ''' Create a vector of dates associated with each timestep Returns: Array of `datetime` instances containing the date associated with each simulation time step ''' try: return self['start_day'] + self.tvec * dt.timedelta(days=self['timestep']) except: # pragma: no cover return np.array([])
[docs] def day(self, day, *args): ''' Convert a string, date/datetime object, or int to a day (int). Args: day (str, date, int, or list): convert any of these objects to a day relative to the simulation's start day Returns: days (int or str): the day(s) in simulation time **Example**:: sim.day('2020-04-05') # Returns 35 ''' return sc.day(day, *args, start_day=self['start_day'])
[docs] def date(self, ind, *args, dateformat=None, as_date=False): ''' Convert one or more integer days of simulation time to a date/list of dates -- by default returns a string, or returns a datetime Date object if as_date is True. See also cv.date(), which provides a partly overlapping set of date conversion features. Args: ind (int, list, or array): the index day(s) in simulation time (NB: strings and date objects are accepted, and will be passed unchanged) args (list): additional day(s) dateformat (str): the format to return the date in as_date (bool): whether to return as a datetime date instead of a string Returns: dates (str, Date, or list): the date(s) corresponding to the simulation day(s) **Examples**:: sim = cv.Sim() sim.date(34) # Returns '2020-04-04' sim.date([34, 54]) # Returns ['2020-04-04', '2020-04-24'] sim.date([34, '2020-04-24']) # Returns ['2020-04-04', '2020-04-24'] sim.date(34, 54, as_date=True) # Returns [datetime.date(2020, 4, 4), datetime.date(2020, 4, 24)] ''' # Handle inputs if not isinstance(ind, list): # If it's a number, string, or dateobj, convert it to a list ind = sc.promotetolist(ind) ind.extend(args) if dateformat is None: dateformat = '%Y-%m-%d' # Do the conversion dates = [] for raw in ind: if sc.isnumber(raw): date_obj = sc.date(self['start_day'], as_date=True) + dt.timedelta(days=int(raw)) else: date_obj = sc.date(raw, as_date=True) if as_date: dates.append(date_obj) else: dates.append(date_obj.strftime(dateformat)) # Return a string rather than a list if only one provided if len(ind)==1: dates = dates[0] return dates
[docs] def result_keys(self, which='main'): ''' Get the actual results objects, not other things stored in sim.results. If which is 'main', return only the main results keys. If 'genotype', return only genotype keys. If 'all', return all keys. ''' keys = [] choices = ['main', 'genotype', 'all'] if which in ['main', 'all']: keys += [key for key,res in self.results.items() if isinstance(res, Result)] if which in ['genotype', 'all'] and 'genotype' in self.results: keys += [key for key,res in self.results['genotype'].items() if isinstance(res, Result)] if which not in choices: # pragma: no cover errormsg = f'Choice "which" not available; choices are: {sc.strjoin(choices)}' raise ValueError(errormsg) return keys
[docs] def copy(self): ''' Returns a deep copy of the sim ''' return sc.dcp(self)
[docs] def export_results(self, for_json=True, filename=None, indent=2, *args, **kwargs): ''' Convert results to dict -- see also to_json(). The results written to Excel must have a regular table shape, whereas for the JSON output, arbitrary data shapes are supported. Args: for_json (bool): if False, only data associated with Result objects will be included in the converted output filename (str): filename to save to; if None, do not save indent (int): indent (int): if writing to file, how many indents to use per nested level args (list): passed to savejson() kwargs (dict): passed to savejson() Returns: resdict (dict): dictionary representation of the results ''' if not self.results_ready: # pragma: no cover errormsg = 'Please run the sim before exporting the results' raise RuntimeError(errormsg) resdict = {} resdict['t'] = self.results['t'] # Assume that there is a key for time if for_json: resdict['timeseries_keys'] = self.result_keys() for key,res in self.results.items(): if isinstance(res, Result): resdict[key] = res.values if res.low is not None: resdict[key+'_low'] = res.low if res.high is not None: resdict[key+'_high'] = res.high elif for_json: if key == 'date': resdict[key] = [str(d) for d in res] # Convert dates to strings else: resdict[key] = res if filename is not None: sc.savejson(filename=filename, obj=resdict, indent=indent, *args, **kwargs) return resdict
[docs] def export_pars(self, filename=None, indent=2, *args, **kwargs): ''' Return parameters for JSON export -- see also to_json(). This method is required so that interventions can specify their JSON-friendly representation. Args: filename (str): filename to save to; if None, do not save indent (int): indent (int): if writing to file, how many indents to use per nested level args (list): passed to savejson() kwargs (dict): passed to savejson() Returns: pardict (dict): a dictionary containing all the parameter values ''' pardict = {} for key in self.pars.keys(): if key == 'interventions': pardict[key] = [intervention.to_json() for intervention in self.pars[key]] elif key == 'start_day': pardict[key] = str(self.pars[key]) else: pardict[key] = self.pars[key] if filename is not None: sc.savejson(filename=filename, obj=pardict, indent=indent, *args, **kwargs) return pardict
[docs] def to_json(self, filename=None, keys=None, tostring=False, indent=2, verbose=False, *args, **kwargs): ''' Export results and parameters as JSON. Args: filename (str): if None, return string; else, write to file keys (str or list): attributes to write to json (default: results, parameters, and summary) tostring (bool): if not writing to file, whether to write to string (alternative is sanitized dictionary) indent (int): if writing to file, how many indents to use per nested level verbose (bool): detail to print args (list): passed to savejson() kwargs (dict): passed to savejson() Returns: A unicode string containing a JSON representation of the results, or writes the JSON file to disk **Examples**:: json = sim.to_json() sim.to_json('results.json') sim.to_json('summary.json', keys='summary') ''' # Handle keys if keys is None: keys = ['results', 'pars', 'summary'] keys = sc.promotetolist(keys) # Convert to JSON-compatible format d = {} for key in keys: if key == 'results': resdict = self.export_results(for_json=True) d['results'] = resdict elif key in ['pars', 'parameters']: pardict = self.export_pars() d['parameters'] = pardict elif key == 'summary': d['summary'] = dict(sc.dcp(self.summary)) else: # pragma: no cover try: d[key] = sc.sanitizejson(getattr(self, key)) except Exception as E: errormsg = f'Could not convert "{key}" to JSON: {str(E)}; continuing...' print(errormsg) if filename is None: output = sc.jsonify(d, tostring=tostring, indent=indent, verbose=verbose, *args, **kwargs) else: output = sc.savejson(filename=filename, obj=d, indent=indent, *args, **kwargs) return output
[docs] def to_df(self, date_index=False): ''' Export results to a pandas dataframe Args: date_index (bool): if True, use the date as the index ''' resdict = self.export_results(for_json=False) df = pd.DataFrame.from_dict(resdict) df['date'] = self.datevec new_columns = ['t','date'] + df.columns[1:-1].tolist() # Get column order df = df.reindex(columns=new_columns) # Reorder so 't' and 'date' are first if date_index: df = df.set_index('date') return df
[docs] def to_excel(self, filename=None, skip_pars=None): ''' Export parameters and results as Excel format Args: filename (str): if None, return string; else, write to file skip_pars (list): if provided, a custom list parameters to exclude Returns: An sc.Spreadsheet with an Excel file, or writes the file to disk ''' if skip_pars is None: skip_pars = ['genotype_map', 'vaccine_map'] # These include non-string keys so fail at sc.flattendict() # Export results result_df = self.to_df(date_index=True) # Export parameters pars = {k:v for k,v in self.pars.items() if k not in skip_pars} par_df = pd.DataFrame.from_dict(sc.flattendict(pars, sep='_'), orient='index', columns=['Value']) par_df.index.name = 'Parameter' # Convert to spreadsheet spreadsheet = sc.Spreadsheet() spreadsheet.freshbytes() with pd.ExcelWriter(spreadsheet.bytes, engine='xlsxwriter') as writer: result_df.to_excel(writer, sheet_name='Results') par_df.to_excel(writer, sheet_name='Parameters') spreadsheet.load() if filename is None: output = spreadsheet else: output = spreadsheet.save(filename) return output
[docs] def shrink(self, skip_attrs=None, in_place=True): ''' "Shrinks" the simulation by removing the people, and returns a copy of the "shrunken" simulation. Used to reduce the memory required for saved files. Args: skip_attrs (list): a list of attributes to skip in order to perform the shrinking; default "people" Returns: shrunken (Sim): a Sim object with the listed attributes removed ''' # By default, skip people (~90% of memory), the popdict (which is usually empty anyway), and _orig_pars (which is just a backup) if skip_attrs is None: skip_attrs = ['popdict', 'people', '_orig_pars'] # Create the new object, and copy original dict, skipping the skipped attributes if in_place: shrunken = self for attr in skip_attrs: setattr(self, attr, None) return else: shrunken = object.__new__(self.__class__) shrunken.__dict__ = {k:(v if k not in skip_attrs else None) for k,v in self.__dict__.items()} # Shrink interventions and analyzers, with a lot of checking along the way if hasattr(shrunken, 'pars'): # In case the user removes this for key in ['interventions', 'analyzers']: ia_list = self.pars[key] # List of interventions or analyzers self.pars[key] = [ia.shrink(in_place=in_place) for ia in ia_list] # Actually shrink, and re-store # Don't return if in place if in_place: return else: return shrunken
[docs] def save(self, filename=None, keep_people=None, skip_attrs=None, **kwargs): ''' Save to disk as a gzipped pickle. Args: filename (str or None): the name or path of the file to save to; if None, uses stored kwargs: passed to sc.makefilepath() Returns: filename (str): the validated absolute path to the saved file **Example**:: sim.save() # Saves to a .sim file with the date and time of creation by default ''' # Set keep_people based on whether or not we're in the middle of a run if keep_people is None: if self.initialized and not self.results_ready: keep_people = True else: keep_people = False # Handle the filename if filename is None: filename = self.simfile filename = sc.makefilepath(filename=filename, **kwargs) self.filename = filename # Store the actual saved filename # Handle the shrinkage and save if skip_attrs or not keep_people: obj = self.shrink(skip_attrs=skip_attrs, in_place=False) else: obj = self cvm.save(filename=filename, obj=obj) return filename
[docs] @staticmethod def load(filename, *args, **kwargs): ''' Load from disk from a gzipped pickle. Args: filename (str): the name or path of the file to load from kwargs: passed to cv.load() Returns: sim (Sim): the loaded simulation object **Example**:: sim = cv.Sim.load('my-simulation.sim') ''' sim = cvm.load(filename, *args, **kwargs) if not isinstance(sim, BaseSim): # pragma: no cover errormsg = f'Cannot load object of {type(sim)} as a Sim object' raise TypeError(errormsg) return sim
def _get_ia(self, which, label=None, partial=False, as_list=False, as_inds=False, die=True, first=False): ''' Helper method for get_interventions() and get_analyzers(); see get_interventions() docstring ''' # Handle inputs if which not in ['interventions', 'analyzers']: # pragma: no cover errormsg = f'This method is only defined for interventions and analyzers, not "{which}"' raise ValueError(errormsg) ia_list = self.pars[which] # List of interventions or analyzers n_ia = len(ia_list) # Number of interventions/analyzers if label == 'summary': # Print a summary of the interventions df = pd.DataFrame(columns=['ind', 'label', 'type']) for ind,ia_obj in enumerate(ia_list): df = df.append(dict(ind=ind, label=str(ia_obj.label), type=type(ia_obj)), ignore_index=True) print(f'Summary of {which}:') print(df) return else: # Standard usage case position = 0 if first else -1 # Choose either the first or last element if label is None: # Get all interventions if no label is supplied, e.g. sim.get_interventions() label = np.arange(n_ia) if isinstance(label, np.ndarray): # Allow arrays to be provided label = label.tolist() labels = sc.promotetolist(label) # Calculate the matches matches = [] match_inds = [] for label in labels: if sc.isnumber(label): matches.append(ia_list[label]) # This will raise an exception if an invalid index is given label = n_ia + label if label<0 else label # Convert to a positive number match_inds.append(label) elif sc.isstring(label) or isinstance(label, type): for ind,ia_obj in enumerate(ia_list): if sc.isstring(label) and ia_obj.label == label or (partial and (label in str(ia_obj.label))): matches.append(ia_obj) match_inds.append(ind) elif isinstance(label, type) and isinstance(ia_obj, label): matches.append(ia_obj) match_inds.append(ind) else: # pragma: no cover errormsg = f'Could not interpret label type "{type(label)}": should be str, int, list, or {which} class' raise TypeError(errormsg) # Parse the output options if as_inds: output = match_inds elif as_list: # Used by get_interventions() output = matches else: if len(matches) == 0: # pragma: no cover if die: errormsg = f'No {which} matching "{label}" were found' raise ValueError(errormsg) else: output = None else: output = matches[position] # Return either the first or last match (usually), used by get_intervention() return output
[docs] def get_interventions(self, label=None, partial=False, as_inds=False): ''' Find the matching intervention(s) by label, index, or type. If None, return all interventions. If the label provided is "summary", then print a summary of the interventions (index, label, type). Args: label (str, int, Intervention, list): the label, index, or type of intervention to get; if a list, iterate over one of those types partial (bool): if true, return partial matches (e.g. 'beta' will match all beta interventions) as_inds (bool): if true, return matching indices instead of the actual interventions **Examples**:: tp = cv.test_prob(symp_prob=0.1) cb1 = cv.change_beta(days=5, changes=0.3, label='NPI') cb2 = cv.change_beta(days=10, changes=0.3, label='Masks') sim = cv.Sim(interventions=[tp, cb1, cb2]) cb1, cb2 = sim.get_interventions(cv.change_beta) tp, cb2 = sim.get_interventions([0,2]) ind = sim.get_interventions(cv.change_beta, as_inds=True) # Returns [1,2] sim.get_interventions('summary') # Prints a summary ''' return self._get_ia('interventions', label=label, partial=partial, as_inds=as_inds, as_list=True)
[docs] def get_intervention(self, label=None, partial=False, first=False, die=True): ''' Like get_interventions(), find the matching intervention(s) by label, index, or type. If more than one intervention matches, return the last by default. If no label is provided, return the last intervention in the list. Args: label (str, int, Intervention, list): the label, index, or type of intervention to get; if a list, iterate over one of those types partial (bool): if true, return partial matches (e.g. 'beta' will match all beta interventions) first (bool): if true, return first matching intervention (otherwise, return last) die (bool): whether to raise an exception if no intervention is found **Examples**:: tp = cv.test_prob(symp_prob=0.1) cb = cv.change_beta(days=5, changes=0.3, label='NPI') sim = cv.Sim(interventions=[tp, cb]) cb = sim.get_intervention('NPI') cb = sim.get_intervention('NP', partial=True) cb = sim.get_intervention(cv.change_beta) cb = sim.get_intervention(1) cb = sim.get_intervention() tp = sim.get_intervention(first=True) ''' return self._get_ia('interventions', label=label, partial=partial, first=first, die=die, as_inds=False, as_list=False)
[docs] def get_analyzers(self, label=None, partial=False, as_inds=False): ''' Same as get_interventions(), but for analyzers. ''' return self._get_ia('analyzers', label=label, partial=partial, as_list=True, as_inds=as_inds)
[docs] def get_analyzer(self, label=None, partial=False, first=False, die=True): ''' Same as get_intervention(), but for analyzers. ''' return self._get_ia('analyzers', label=label, partial=partial, first=first, die=die, as_inds=False, as_list=False)
#%% Define people classes
[docs]class BasePeople(FlexPretty): ''' A class to handle all the boilerplate for people -- note that as with the BaseSim vs Sim classes, everything interesting happens in the People class, whereas this class exists to handle the less interesting implementation details. ''' def __getitem__(self, key): ''' Allow people['attr'] instead of getattr(people, 'attr') If the key is an integer, alias `people.person()` to return a `Person` instance ''' try: return self.__dict__[key] except: # pragma: no cover if isinstance(key, int): return self.person(key) else: errormsg = f'Key "{key}" is not a valid attribute of people' raise AttributeError(errormsg) def __setitem__(self, key, value): ''' Ditto ''' if self._lock and key not in self.__dict__: # pragma: no cover errormsg = f'Key "{key}" is not a current attribute of people, and the people object is locked; see people.unlock()' raise AttributeError(errormsg) self.__dict__[key] = value return
[docs] def lock(self): ''' Lock the people object to prevent keys from being added ''' self._lock = True return
[docs] def unlock(self): ''' Unlock the people object to allow keys to be added ''' self._lock = False return
def __len__(self): ''' This is just a scalar, but validate() and _resize_arrays() make sure it's right ''' return int(self.pars['pop_size']) def __iter__(self): ''' Iterate over people ''' for i in range(len(self)): yield self[i] def __add__(self, people2): ''' Combine two people arrays ''' newpeople = sc.dcp(self) keys = list(self.keys()) for key in keys: npval = newpeople[key] p2val = people2[key] if npval.ndim == 1: newpeople.set(key, np.concatenate([npval, p2val], axis=0), die=False) # Allow size mismatch elif npval.ndim == 2: newpeople.set(key, np.concatenate([npval, p2val], axis=1), die=False) else: errormsg = f'Not sure how to combine arrays of {npval.ndim} dimensions for {key}' raise NotImplementedError(errormsg) # Validate newpeople.pars['pop_size'] += people2.pars['pop_size'] newpeople.validate() # Reassign UIDs so they're unique newpeople.set('uid', np.arange(len(newpeople))) return newpeople def __radd__(self, people2): ''' Allows sum() to work correctly ''' if not people2: return self else: return self.__add__(people2) def _brief(self): ''' Return a one-line description of the people -- used internally and by repr(); see people.brief() for the user version. ''' try: layerstr = ', '.join([str(k) for k in self.layer_keys()]) string = f'People(n={len(self):0n}; layers: {layerstr})' except Exception as E: # pragma: no cover string = sc.objectid(self) string += f'Warning, multisim appears to be malformed:\n{str(E)}' return string
[docs] def summarize(self, output=False): ''' Print a summary of the people -- same as brief ''' return self.brief(output=output)
[docs] def set(self, key, value, die=True): ''' Ensure sizes and dtypes match ''' current = self[key] value = np.array(value, dtype=self._dtypes[key]) # Ensure it's the right type if die and len(value) != len(current): # pragma: no cover errormsg = f'Length of new array does not match current ({len(value)} vs. {len(current)})' raise IndexError(errormsg) self[key] = value return
[docs] def get(self, key): ''' Convenience method -- key can be string or list of strings ''' if isinstance(key, str): return self[key] elif isinstance(key, list): arr = np.zeros((len(self), len(key))) for k,ky in enumerate(key): arr[:,k] = self[ky] return arr
[docs] def true(self, key): ''' Return indices matching the condition ''' return self[key].nonzero()[0]
[docs] def false(self, key): ''' Return indices not matching the condition ''' return (~self[key]).nonzero()[0]
[docs] def defined(self, key): ''' Return indices of people who are not-nan ''' return (~np.isnan(self[key])).nonzero()[0]
[docs] def undefined(self, key): ''' Return indices of people who are nan ''' return np.isnan(self[key]).nonzero()[0]
[docs] def count(self, key): ''' Count the number of people for a given key ''' return np.count_nonzero(self[key])
[docs] def count_by_genotype(self, key, genotype): ''' Count the number of people for a given key ''' return np.count_nonzero(self[key][genotype,:])
[docs] def count_not(self, key): ''' Count the number of people who do not have a property for a given key ''' return len(self[key]) - self.count(key)
[docs] def set_pars(self, pars=None): ''' Re-link the parameters stored in the people object to the sim containing it, and perform some basic validation. ''' if pars is None: pars = {} elif sc.isnumber(pars): # Interpret as a population size pars = {'pop_size':pars} # Ensure it's a dictionary orig_pars = self.__dict__.get('pars') # Get the current parameters using dict's get method pars = sc.mergedicts(orig_pars, pars) if 'pop_size' not in pars: errormsg = f'The parameter "pop_size" must be included in a population; keys supplied were:\n{sc.newlinejoin(pars.keys())}' raise sc.KeyNotFoundError(errormsg) pars['pop_size'] = int(pars['pop_size']) pars.setdefault('n_genotypes', 2) pars.setdefault('location', None) self.pars = pars # Actually store the pars return
[docs] def keys(self): ''' Returns keys for all properties of the people object ''' return self.meta.all_states[:]
[docs] def person_keys(self): ''' Returns keys specific to a person (e.g., their age) ''' return self.meta.person[:]
[docs] def state_keys(self): ''' Returns keys for different states of a person (e.g., symptomatic) ''' return self.meta.states[:]
[docs] def date_keys(self): ''' Returns keys for different event dates (e.g., date a person became symptomatic) ''' return self.meta.dates[:]
[docs] def dur_keys(self): ''' Returns keys for different durations (e.g., the duration from exposed to infectious) ''' return self.meta.durs[:]
[docs] def layer_keys(self): ''' Get the available contact keys -- try contacts first, then beta_layer ''' try: keys = list(self.contacts.keys()) except: # If not fully initialized try: keys = list(self.pars['beta_layer'].keys()) except: # pragma: no cover # If not even partially initialized keys = [] return keys
[docs] def indices(self): ''' The indices of each people array ''' return np.arange(len(self))
[docs] def validate(self, die=True, verbose=False): # Check that the keys match contact_layer_keys = set(self.contacts.keys()) layer_keys = set(self.layer_keys()) if contact_layer_keys != layer_keys: errormsg = f'Parameters layers {layer_keys} are not consistent with contact layers {contact_layer_keys}' raise ValueError(errormsg) # Check that the length of each array is consistent expected_len = len(self) expected_genotype = self.pars['n_genotypes'] for key in self.keys(): if self[key].ndim == 1: actual_len = len(self[key]) else: # If it's 2D, genotype need to be checked separately actual_genotype, actual_len = self[key].shape if actual_genotype != expected_genotype: if verbose: print(f'Resizing "{key}" from {actual_genotype} to {expected_genotype}') self._resize_arrays(keys=key, new_size=(expected_genotype, expected_len)) if actual_len != expected_len: # pragma: no cover if die: errormsg = f'Length of key "{key}" did not match population size ({actual_len} vs. {expected_len})' raise IndexError(errormsg) else: if verbose: print(f'Resizing "{key}" from {actual_len} to {expected_len}') self._resize_arrays(keys=key) # Check that the layers are valid for layer in self.contacts.values(): layer.validate() return
def _resize_arrays(self, new_size=None, keys=None): ''' Resize arrays if any mismatches are found ''' # Handle None or tuple input (representing genotype and pop_size) if new_size is None: new_size = len(self) pop_size = new_size if not isinstance(new_size, tuple) else new_size[1] self.pars['pop_size'] = pop_size # Reset sizes if keys is None: keys = self.keys() keys = sc.promotetolist(keys) for key in keys: self[key].resize(new_size, refcheck=False) # Don't worry about cross-references to the arrays return
[docs] def to_df(self): ''' Convert to a Pandas dataframe ''' df = pd.DataFrame.from_dict({key:self[key] for key in self.keys()}) return df
[docs] def to_arr(self): ''' Return as numpy array ''' arr = np.empty((len(self), len(self.keys())), dtype=cvd.default_float) for k,key in enumerate(self.keys()): if key == 'uid': arr[:,k] = np.arange(len(self)) else: arr[:,k] = self[key] return arr
[docs] def person(self, ind): ''' Method to create person from the people ''' p = Person() for key in self.meta.all_states: data = self[key] if data.ndim == 1: val = data[ind] elif data.ndim == 2: val = data[:,ind] else: errormsg = f'Cannot extract data from {key}: unexpected dimensionality ({data.ndim})' raise ValueError(errormsg) setattr(p, key, val) contacts = {} for lkey, layer in self.contacts.items(): contacts[lkey] = layer.find_contacts(ind) p.contacts = contacts return p
[docs] def to_people(self): ''' Return all people as a list ''' return list(self)
[docs] def from_people(self, people, resize=True): ''' Convert a list of people back into a People object ''' # Handle population size pop_size = len(people) if resize: self._resize_arrays(new_size=pop_size) # Iterate over people -- slow! for p,person in enumerate(people): for key in self.keys(): self[key][p] = getattr(person, key) return
[docs] def to_people_dict(self): people = dict() alive_inds = cvu.false(self.dead) for ind in alive_inds: if not isinstance(ind, np.ndarray): ind_contact = sc.promotetoarray(ind) if ind_contact.dtype != np.int64: # pragma: no cover # This is int64 since indices often come from cv.true(), which returns int64 ind_contact = np.array(ind_contact, dtype=np.int64) people[ind] = sc.objdict( age=self.age[ind], sex=self.sex[ind], contacts=sc.objdict( H=cvu.find_contacts(self.contacts['h']['p1'], self.contacts['h']['p2'], ind_contact), S=cvu.find_contacts(self.contacts['s']['p1'], self.contacts['s']['p2'], ind_contact), C=cvu.find_contacts(self.contacts['c']['p1'], self.contacts['c']['p2'], ind_contact) ), scid=self.scid[ind] if not np.isnan(self.scid[ind]) else None, hhid=self.hhid[ind], sc_type=list(self.pars['school_mapping'].keys())[int(self.sc_type[ind])] if not np.isnan(self.sc_type[ind]) else None, sc_student=self.sc_student[ind] if not np.isnan(self.sc_student[ind]) else None ) return people
[docs] def to_graph(self): # pragma: no cover ''' Convert all people to a networkx MultiDiGraph, including all properties of the people (nodes) and contacts (edges). **Example**:: import rsvsim as rsv import networkx as nx sim = rsv.Sim(pop_size=50, pop_type='hybrid', contacts=dict(h=3, s=10, w=10, c=5)).run() G = sim.people.to_graph() nodes = G.nodes(data=True) edges = G.edges(keys=True) node_colors = [n['age'] for i,n in nodes] layer_map = dict(h='#37b', s='#e11', w='#4a4', c='#a49') edge_colors = [layer_map[G[i][j][k]['layer']] for i,j,k in edges] edge_weights = [G[i][j][k]['beta']*5 for i,j,k in edges] nx.draw(G, node_color=node_colors, edge_color=edge_colors, width=edge_weights, alpha=0.5) ''' import networkx as nx # Copy data from people into graph G = self.contacts.to_graph() for key in self.keys(): data = {k:v for k,v in enumerate(self[key])} nx.set_node_attributes(G, data, name=key) # Include global layer weights for u,v,k in G.edges(keys=True): edge = G[u][v][k] edge['beta'] *= self.pars['beta_layer'][edge['layer']] return G
[docs] def init_contacts(self, reset=False): ''' Initialize the contacts dataframe with the correct columns and data types ''' # Create the contacts dictionary contacts = Contacts(layer_keys=self.layer_keys()) if self.contacts is None or reset: # Reset all self.contacts = contacts else: # Only replace specified keys for key,layer in contacts.items(): self.contacts[key] = layer return
[docs] def add_contacts(self, contacts, lkey=None, beta=None): ''' Add new contacts to the array. See also contacts.add_layer(). ''' # If no layer key is supplied and it can't be worked out from defaults, use the first layer if lkey is None: lkey = self.layer_keys()[0] # Validate the supplied contacts if isinstance(contacts, Contacts): new_contacts = contacts elif isinstance(contacts, Layer): new_contacts = {} new_contacts[lkey] = contacts elif sc.checktype(contacts, 'array'): new_contacts = {} new_contacts[lkey] = pd.DataFrame(data=contacts) elif isinstance(contacts, dict): new_contacts = {} new_contacts[lkey] = pd.DataFrame.from_dict(contacts) elif isinstance(contacts, list): # Assume it's a list of contacts by person, not an edgelist new_contacts = self.make_edgelist(contacts) # Assume contains key info else: # pragma: no cover errormsg = f'Cannot understand contacts of type {type(contacts)}; expecting dataframe, array, or dict' raise TypeError(errormsg) # Ensure the columns are right and add values if supplied for lkey, new_layer in new_contacts.items(): n = len(new_layer['p1']) if 'beta' not in new_layer.keys() or len(new_layer['beta']) != n: if beta is None: beta = 1.0 beta = cvd.default_float(beta) new_layer['beta'] = np.ones(n, dtype=cvd.default_float)*beta # Create the layer if it doesn't yet exist if lkey not in self.contacts: self.contacts[lkey] = Layer(label=lkey) # Actually include them, and update properties if supplied for col in self.contacts[lkey].keys(): # Loop over the supplied columns self.contacts[lkey][col] = np.concatenate([self.contacts[lkey][col], new_layer[col]]) self.contacts[lkey].validate() return
[docs] def make_edgelist(self, contacts): ''' Parse a list of people with a list of contacts per person and turn it into an edge list. ''' # Handle layer keys lkeys = self.layer_keys() if len(contacts): contact_keys = contacts[0].keys() # Pull out the keys of this contact list lkeys += [key for key in contact_keys if key not in lkeys] # Extend the layer keys # Initialize the new contacts new_contacts = Contacts(layer_keys=lkeys) for lkey in lkeys: new_contacts[lkey]['p1'] = [] # Person 1 of the contact pair new_contacts[lkey]['p2'] = [] # Person 2 of the contact pair # Populate the new contacts for p,cdict in enumerate(contacts): for lkey,p_contacts in cdict.items(): n = len(p_contacts) # Number of contacts new_contacts[lkey]['p1'].extend([p]*n) # e.g. [4, 4, 4, 4] new_contacts[lkey]['p2'].extend(p_contacts) # e.g. [243, 4538, 7,19] # Turn into a dataframe for lkey in lkeys: new_layer = Layer(label=lkey) for ckey,value in new_contacts[lkey].items(): new_layer[ckey] = np.array(value, dtype=new_layer.meta[ckey]) new_contacts[lkey] = new_layer return new_contacts
[docs] @staticmethod def remove_duplicates(df): ''' Sort the dataframe and remove duplicates -- note, not extensively tested ''' p1 = df[['p1', 'p2']].values.min(1) # Reassign p1 to be the lower-valued of the two contacts p2 = df[['p1', 'p2']].values.max(1) # Reassign p2 to be the higher-valued of the two contacts df['p1'] = p1 df['p2'] = p2 df.sort_values(['p1', 'p2'], inplace=True) # Sort by p1, then by p2 df.drop_duplicates(['p1', 'p2'], inplace=True) # Remove duplicates df = df[df['p1'] != df['p2']] # Remove self connections df.reset_index(inplace=True, drop=True) return df
[docs]class Person(sc.prettyobj): ''' Class for a single person. Note: this is largely deprecated since sim.people is now based on arrays rather than being a list of people. ''' def __init__(self, pars=None, uid=None, age=-1, sex=-1, contacts=None): self.uid = uid # This person's unique identifier self.age = cvd.default_float(age) # Age of the person (in years) self.sex = cvd.default_int(sex) # Female (0) or male (1) self.contacts = contacts # Contacts # self.infected = [] #: Record the UIDs of all people this person infected # self.infected_by = None #: Store the UID of the person who caused the infection. If None but person is infected, then it was an externally seeded infection return
[docs]class FlexDict(dict): ''' A dict that allows more flexible element access: in addition to obj['a'], also allow obj[0]. Lightweight implementation of the Sciris odict class. ''' def __getitem__(self, key): ''' Lightweight odict -- allow indexing by number, with low performance ''' try: return super().__getitem__(key) except KeyError as KE: try: # Assume it's an integer dictkey = self.keys()[key] return self[dictkey] except: raise sc.KeyNotFoundError(KE) # Raise the original error
[docs] def keys(self): return list(super().keys())
[docs] def values(self): return list(super().values())
[docs] def items(self): return list(super().items())
[docs]class Contacts(FlexDict): ''' A simple (for now) class for storing different contact layers. ''' def __init__(self, layer_keys=None): if layer_keys is not None: for lkey in layer_keys: self[lkey] = Layer(label=lkey) return def __repr__(self): ''' Use slightly customized repr''' keys_str = ', '.join([str(k) for k in self.keys()]) output = f'Contacts({keys_str})\n' for key in self.keys(): output += f'\n"{key}": ' output += self[key].__repr__() + '\n' return output def __len__(self): ''' The length of the contacts is the length of all the layers ''' output = 0 for key in self.keys(): try: output += len(self[key]) except: # pragma: no cover pass return output
[docs] def add_layer(self, **kwargs): ''' Small method to add one or more layers to the contacts. Layers should be provided as keyword arguments. **Example**:: hospitals_layer = cv.Layer(label='hosp') sim.people.contacts.add_layer(hospitals=hospitals_layer) ''' for lkey,layer in kwargs.items(): layer.validate() self[lkey] = layer return
[docs] def pop_layer(self, *args): ''' Remove the layer(s) from the contacts. **Example**:: sim.people.contacts.pop_layer('hospitals') Note: while included here for convenience, this operation is equivalent to simply popping the key from the contacts dictionary. ''' for lkey in args: self.pop(lkey) return
[docs] def to_graph(self): # pragma: no cover ''' Convert all layers to a networkx MultiDiGraph **Example**:: import networkx as nx sim = cv.Sim(pop_size=50, pop_type='hybrid').run() G = sim.people.contacts.to_graph() nx.draw(G) ''' import networkx as nx H = nx.MultiDiGraph() for lkey,layer in self.items(): G = layer.to_graph() H = nx.compose(H, nx.MultiDiGraph(G)) return H
[docs]class Layer(FlexDict): ''' A small class holding a single layer of contact edges (connections) between people. The input is typically three arrays: person 1 of the connection, person 2 of the connection, and the weight of the connection. Connections are undirected; each person is both a source and sink. This class is usually not invoked directly by the user, but instead is called as part of the population creation. Args: p1 (array): an array of N connections, representing people on one side of the connection p2 (array): an array of people on the other side of the connection beta (array): an array of weights for each connection label (str): the name of the layer (optional) kwargs (dict): other keys copied directly into the layer Note that all arguments (except for label) must be arrays of the same length, although not all have to be supplied at the time of creation (they must all be the same at the time of initialization, though, or else validation will fail). **Examples**:: # Generate an average of 10 contacts for 1000 people n = 10_000 n_people = 1000 p1 = np.random.randint(n_people, size=n) p2 = np.random.randint(n_people, size=n) beta = np.ones(n) layer = cv.Layer(p1=p1, p2=p2, beta=beta, label='rand') # Convert one layer to another with extra columns index = np.arange(n) self_conn = p1 == p2 layer2 = cv.Layer(**layer, index=index, self_conn=self_conn, label=layer.label) ''' def __init__(self, label=None, **kwargs): self.meta = { 'p1': cvd.default_int, # Person 1 'p2': cvd.default_int, # Person 2 'beta': cvd.default_float, # Default transmissibility for this contact type } self.basekey = 'p1' # Assign a base key for calculating lengths and performing other operations self.label = label # Initialize the keys of the layers for key,dtype in self.meta.items(): self[key] = np.empty((0,), dtype=dtype) # Set data, if provided for key,value in kwargs.items(): self[key] = np.array(value, dtype=self.meta.get(key)) return def __len__(self): try: return len(self[self.basekey]) except: # pragma: no cover return 0 def __repr__(self): ''' Convert to a dataframe for printing ''' namestr = self.__class__.__name__ labelstr = f'"{self.label}"' if self.label else '<no label>' keys_str = ', '.join(self.keys()) output = f'{namestr}({labelstr}, {keys_str})\n' # e.g. Layer("h", p1, p2, beta) output += self.to_df().__repr__() return output def __contains__(self, item): """ Check if a person is present in a layer Args: item: Person index Returns: True if person index appears in any interactions """ return (item in self['p1']) or (item in self['p2']) @property def members(self): """ Return sorted array of all members """ return np.unique([self['p1'], self['p2']])
[docs] def meta_keys(self): ''' Return the keys for the layer's meta information -- i.e., p1, p2, beta ''' return self.meta.keys()
[docs] def validate(self): ''' Check the integrity of the layer: right types, right lengths ''' n = len(self[self.basekey]) for key,dtype in self.meta.items(): if dtype: actual = self[key].dtype expected = dtype if actual != expected: errormsg = f'Expecting dtype "{expected}" for layer key "{key}"; got "{actual}"' raise TypeError(errormsg) actual_n = len(self[key]) if n != actual_n: errormsg = f'Expecting length {n} for layer key "{key}"; got {actual_n}' raise TypeError(errormsg) return
[docs] def pop_inds(self, inds): ''' "Pop" the specified indices from the edgelist and return them as a dict. Returns in the right format to be used with layer.append(). Args: inds (int, array, slice): the indices to be removed ''' output = {} for key in self.meta_keys(): output[key] = self[key][inds] # Copy to the output object self[key] = np.delete(self[key], inds) # Remove from the original return output
[docs] def append(self, contacts): ''' Append contacts to the current layer. Args: contacts (dict): a dictionary of arrays with keys p1,p2,beta, as returned from layer.pop_inds() ''' for key in self.keys(): new_arr = contacts[key] n_curr = len(self[key]) # Current number of contacts n_new = len(new_arr) # New contacts to add n_total = n_curr + n_new # New size self[key] = np.resize(self[key], n_total) # Resize to make room, preserving dtype self[key][n_curr:] = new_arr # Copy contacts into the layer return
[docs] def to_df(self): ''' Convert to dataframe ''' df = pd.DataFrame.from_dict(self) return df
[docs] def from_df(self, df, keys=None): ''' Convert from a dataframe ''' if keys is None: keys = self.meta_keys() for key in keys: self[key] = df[key].to_numpy() return self
[docs] def to_graph(self): # pragma: no cover ''' Convert to a networkx DiGraph **Example**:: import networkx as nx sim = cv.Sim(pop_size=20, pop_type='hybrid').run() G = sim.people.contacts['h'].to_graph() nx.draw(G) ''' import networkx as nx data = [np.array(self[k], dtype=dtype).tolist() for k,dtype in [('p1', int), ('p2', int), ('beta', float)]] G = nx.DiGraph() G.add_weighted_edges_from(zip(*data), weight='beta') nx.set_edge_attributes(G, self.label, name='layer') return G
[docs] def find_contacts(self, inds, as_array=True): """ Find all contacts of the specified people For some purposes (e.g. contact tracing) it's necessary to find all of the contacts associated with a subset of the people in this layer. Since contacts are bidirectional it's necessary to check both P1 and P2 for the target indices. The return type is a Set so that there is no duplication of indices (otherwise if the Layer has explicit symmetric interactions, they could appear multiple times). This is also for performance so that the calling code doesn't need to perform its own unique() operation. Note that this cannot be used for cases where multiple connections count differently than a single infection, e.g. exposure risk. Args: inds (array): indices of people whose contacts to return as_array (bool): if true, return as sorted array (otherwise, return as unsorted set) Returns: contact_inds (array): a set of indices for pairing partners Example: If there were a layer with - P1 = [1,2,3,4] - P2 = [2,3,1,4] Then find_contacts([1,3]) would return {1,2,3} """ # Check types if not isinstance(inds, np.ndarray): inds = sc.promotetoarray(inds) if inds.dtype != np.int64: # pragma: no cover # This is int64 since indices often come from cv.true(), which returns int64 inds = np.array(inds, dtype=np.int64) # Find the contacts contact_inds = cvu.find_contacts(self['p1'], self['p2'], inds) if as_array: contact_inds = np.fromiter(contact_inds, dtype=cvd.default_int) contact_inds.sort() # Sorting ensures that the results are reproducible for a given seed as well as being identical to previous versions of RSVsim return contact_inds
[docs] def update(self, people, frac=1.0): ''' Regenerate contacts on each timestep. This method gets called if the layer appears in ``sim.pars['dynam_layer']``. The Layer implements the update procedure so that derived classes can customize the update e.g. implementing over-dispersion/other distributions, random clusters, etc. Typically, this method also takes in the ``people`` object so that the update can depend on person attributes that may change over time (e.g. changing contacts for people that are severe/critical). Args: people (People): the RSVSim People object, which is usually used to make new contacts frac (float): the fraction of contacts to update on each timestep ''' # Choose how many contacts to make pop_size = len(people) # Total number of people n_contacts = len(self) # Total number of contacts n_new = int(np.round(n_contacts*frac)) # Since these get looped over in both directions later inds = cvu.choose(n_contacts, n_new) # Create the contacts, not skipping self-connections self['p1'][inds] = np.array(cvu.choose_r(max_n=pop_size, n=n_new), dtype=cvd.default_int) # Choose with replacement self['p2'][inds] = np.array(cvu.choose_r(max_n=pop_size, n=n_new), dtype=cvd.default_int) self['beta'][inds] = np.ones(n_new, dtype=cvd.default_float) return