covasim.base module¶

Base classes for Covasim. 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.

class covasim.base.ParsObj(pars)¶

Bases: sciris.sc_utils.prettyobj

A class based around performing operations on a self.pars dict.

update_pars(pars=None, create=False)¶

Update internal dict with new pars.

Parameters

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

class covasim.base.Result(name=None, npts=None, scale='dynamic', color=None)¶

Bases: object

Stores a single result – by default, acts like an array.

Parameters

name (str) – name of this result, e.g. new_infections
npts (int) – if values is None, precreate it to be of this length
scale (str) – whether or not the value scales by population size; options are “dynamic”, “static”, or False
color (str/arr) – default color for plotting (hex or RGB notation)

Example:

import covasim as cv
r1 = cv.Result(name='test1', npts=10)
r1[:5] = 20
print(r1.values)

property npts¶

class covasim.base.BaseSim(*args, **kwargs)¶

Bases: covasim.base.ParsObj

The BaseSim class handles the running of the simulation: the number of people, number of time points, and the parameters of the simulation.

set_seed(seed=- 1)¶

Set the seed for the random number stream from the stored or supplied value

Parameters: seed (None or int) – if no argument, use current seed; if None, randomize; otherwise, use and store supplied seed
Returns: None

property n¶: Count the number of people – if it fails, assume none

property scaled_pop_size¶: Get the total population size, i.e. the number of agents times the scale factor – if it fails, assume none

property npts¶: Count the number of time points

property tvec¶: Create a time vector

property datevec¶

Create a vector of dates

Returns: Array of datetime instances containing the date associated with each simulation time step

day(day, *args)¶

Convert a string, date/datetime object, or int to a day (int).

Parameters: day (str, date, int, or list) – convert any of these objects to a day relative to the simulation’s start day
Returns: the day(s) in simulation time
Return type: days (int or str)

Example:

sim.day('2020-04-05') # Returns 35

date(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.

Parameters

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

the date(s) corresponding to the simulation day(s)

Return type

dates (str, Date, or list)

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)]

result_keys()¶: Get the actual results objects, not other things stored in sim.results

copy()¶: Returns a deep copy of the sim

export_results(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.

Parameters

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

dictionary representation of the results

Return type

resdict (dict)

export_pars(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.

Parameters

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

a dictionary containing all the parameter values

Return type

pardict (dict)

to_json(filename=None, keys=None, tostring=False, indent=2, verbose=False, *args, **kwargs)¶

Export results as JSON.

Parameters

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')

to_excel(filename=None)¶

Export results as XLSX

Parameters: filename (str) – if None, return string; else, write to file
Returns: An sc.Spreadsheet with an Excel file, or writes the file to disk

shrink(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.

Parameters: skip_attrs (list) – a list of attributes to skip in order to perform the shrinking; default “people”
Returns: a Sim object with the listed attributes removed
Return type: shrunken_sim (Sim)

save(filename=None, keep_people=None, skip_attrs=None, **kwargs)¶

Save to disk as a gzipped pickle.

Parameters

filename (str or None) – the name or path of the file to save to; if None, uses stored
kwargs – passed to sc.makefilepath()

Returns

the validated absolute path to the saved file

Return type

filename (str)

Example:

sim.save() # Saves to a .sim file with the date and time of creation by default

static load(filename, *args, **kwargs)¶

Load from disk from a gzipped pickle.

Parameters

filename (str) – the name or path of the file to load from
kwargs – passed to cv.load()

Returns

the loaded simulation object

Return type

sim (Sim)

Example:

sim = cv.Sim.load('my-simulation.sim')

class covasim.base.BasePeople(pars=None, **kwargs)¶

Bases: sciris.sc_utils.prettyobj

A class to handle all the boilerplate for people – note that everything interesting happens in the People class.

Parameters: pars (dict) – a dictionary with, at minimum, keys ‘pop_size’ and ‘n_days’

set(key, value, die=True)¶: Ensure sizes and dtypes match

get(key)¶: Convenience method – key can be string or list of strings

true(key)¶: Return indices matching the condition

false(key)¶: Return indices not matching the condition

defined(key)¶: Return indices of people who are not-nan

not_defined(key)¶: Return indices of people who are nan

count(key)¶: Count the number of people for a given key

count_not(key)¶: Count the number of people who do not have a property for a given key

set_pars(pars)¶: Very simple method to re-link the parameters stored in the people object to the sim containing it: included simply for the sake of being explicit.

keys()¶: Returns keys for all properties of the people object

person_keys()¶: Returns keys specific to a person (e.g., their age)

state_keys()¶: Returns keys for different states of a person (e.g., symptomatic)

date_keys()¶: Returns keys for different event dates (e.g., date a person became symptomatic)

dur_keys()¶: Returns keys for different durations (e.g., the duration from exposed to infectious)

layer_keys()¶: Get the available contact keys – try contacts first, then beta_layer

indices()¶: The indices of each people array

validate(die=True, verbose=False)¶

to_df()¶: Convert to a Pandas dataframe

to_arr()¶: Return as numpy array

person(ind)¶: Method to create person from the people

to_people()¶: Return all people as a list

from_people(people, resize=True)¶: Convert a list of people back into a People object

init_contacts(reset=False)¶: Initialize the contacts dataframe with the correct columns and data types

add_contacts(contacts, lkey=None, beta=None)¶: Add new contacts to the array

make_edgelist(contacts)¶: Parse a list of people with a list of contacts per person and turn it into an edge list.

static remove_duplicates(df)¶: Sort the dataframe and remove duplicates – note, not extensively tested

class covasim.base.Person(pars=None, uid=None, age=- 1, sex=- 1, contacts=None)¶

Bases: sciris.sc_utils.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.

class covasim.base.FlexDict¶

Bases: 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.

keys() → a set-like object providing a view on D’s keys¶

values() → an object providing a view on D’s values¶

items() → a set-like object providing a view on D’s items¶

class covasim.base.Contacts(layer_keys=None)¶

Bases: covasim.base.FlexDict

A simple (for now) class for storing different contact layers.

class covasim.base.Layer(**kwargs)¶

Bases: covasim.base.FlexDict

A small class holding a single layer of contacts

meta_keys()¶: Return the keys for the layer’s meta information – i.e., p1, p2, beta

validate()¶: Check the integrity of the layer: right types, right lengths

pop_inds(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().

Parameters: inds (int, array, slice) – the indices to be removed

append(contacts)¶

Append contacts to the current layer.

Parameters: contacts (dict) – a dictionary of arrays with keys p1,p2,beta, as returned from layer.pop_inds()

to_df()¶: Convert to dataframe

from_df(df)¶: Convert from a dataframe

find_contacts(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.

Parameters

inds (array) – indices of people whose contacts to return
as_array (bool) – if true, return as sorted array (otherwise, return as unsorted set)

Returns

a set of indices for pairing partners

Return type

contact_inds (array)

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}