rsvsim.base module

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.

class rsvsim.base.ParsObj(pars)[source]

Bases: rsvsim.base.FlexPretty

A class based around performing operations on a dict.

update_pars(pars=None, create=False)[source]

Update internal dict with new pars.

  • 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 rsvsim.base.Result(name=None, ntspts=None, scale=True, color=None, n_genotype=0)[source]

Bases: object

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

  • 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)


import rsvsim as rsv
r1 = rsv.Result(name='test1', npts=10)
r1[:5] = 20
property ntspts
class rsvsim.base.BaseSim(*args, **kwargs)[source]

Bases: rsvsim.base.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.

update_pars(pars=None, create=False, **kwargs)[source]

Ensure that metaparameters get used properly before being updated


Set the metadata for the simulation – creation time and filename

set_seed(seed=- 1)[source]

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


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

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 ntspts

Count number of time-step points

property tvec

Create a time vector

property tsvec

Create a time step time vector

property datevec

Create a vector of dates


Array of datetime instances containing the date associated with each day

property tsdatevec

Create a vector of dates associated with each timestep


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

day(day, *args)[source]

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


day (str, date, int, or list) – convert any of these objects to a day relative to the simulation’s start day


the day(s) in simulation time

Return type

days (int or str)

Example:'2020-04-05') # Returns 35
date(ind, *args, dateformat=None, as_date=False)[source]

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, which provides a partly overlapping set of date conversion features.

  • 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


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

Return type

dates (str, Date, or list)


sim = cv.Sim() # Returns '2020-04-04'[34, 54]) # Returns ['2020-04-04', '2020-04-24'][34, '2020-04-24']) # Returns ['2020-04-04', '2020-04-24'], 54, as_date=True) # Returns [, 4, 4),, 4, 24)]

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.


Returns a deep copy of the sim

export_results(for_json=True, filename=None, indent=2, *args, **kwargs)[source]

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.

  • 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()


dictionary representation of the results

Return type

resdict (dict)

export_pars(filename=None, indent=2, *args, **kwargs)[source]

Return parameters for JSON export – see also to_json().

This method is required so that interventions can specify their JSON-friendly representation.

  • 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()


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)[source]

Export results and parameters as JSON.

  • 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()


A unicode string containing a JSON representation of the results, or writes the JSON file to disk


json = sim.to_json()
sim.to_json('summary.json', keys='summary')

Export results to a pandas dataframe


date_index (bool) – if True, use the date as the index

to_excel(filename=None, skip_pars=None)[source]

Export parameters and results as Excel format

  • filename (str) – if None, return string; else, write to file

  • skip_pars (list) – if provided, a custom list parameters to exclude


An sc.Spreadsheet with an Excel file, or writes the file to disk

shrink(skip_attrs=None, in_place=True)[source]

“Shrinks” the simulation by removing the people, and returns a copy of the “shrunken” simulation. Used to reduce the memory required for saved files.


skip_attrs (list) – a list of attributes to skip in order to perform the shrinking; default “people”


a Sim object with the listed attributes removed

Return type

shrunken (Sim)

save(filename=None, keep_people=None, skip_attrs=None, **kwargs)[source]

Save to disk as a gzipped pickle.

  • filename (str or None) – the name or path of the file to save to; if None, uses stored

  • kwargs – passed to sc.makefilepath()


the validated absolute path to the saved file

Return type

filename (str)

Example: # Saves to a .sim file with the date and time of creation by default
static load(filename, *args, **kwargs)[source]

Load from disk from a gzipped pickle.

  • filename (str) – the name or path of the file to load from

  • kwargs – passed to cv.load()


the loaded simulation object

Return type

sim (Sim)


sim = cv.Sim.load('my-simulation.sim')
get_interventions(label=None, partial=False, as_inds=False)[source]

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).

  • 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


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
get_intervention(label=None, partial=False, first=False, die=True)[source]

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.

  • 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


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)
get_analyzers(label=None, partial=False, as_inds=False)[source]

Same as get_interventions(), but for analyzers.

get_analyzer(label=None, partial=False, first=False, die=True)[source]

Same as get_intervention(), but for analyzers.

class rsvsim.base.BasePeople[source]

Bases: rsvsim.base.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.


Lock the people object to prevent keys from being added


Unlock the people object to allow keys to be added


Print a summary of the people – same as brief

set(key, value, die=True)[source]

Ensure sizes and dtypes match


Convenience method – key can be string or list of strings


Return indices matching the condition


Return indices not matching the condition


Return indices of people who are not-nan


Return indices of people who are nan


Count the number of people for a given key

count_by_genotype(key, genotype)[source]

Count the number of people for a given key


Count the number of people who do not have a property for a given key


Re-link the parameters stored in the people object to the sim containing it, and perform some basic validation.


Returns keys for all properties of the people object


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


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


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


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


Get the available contact keys – try contacts first, then beta_layer


The indices of each people array

validate(die=True, verbose=False)[source]

Convert to a Pandas dataframe


Return as numpy array


Method to create person from the people


Return all people as a list

from_people(people, resize=True)[source]

Convert a list of people back into a People object


Convert all people to a networkx MultiDiGraph, including all properties of the people (nodes) and contacts (edges).


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)

Initialize the contacts dataframe with the correct columns and data types

add_contacts(contacts, lkey=None, beta=None)[source]

Add new contacts to the array. See also contacts.add_layer().


Parse a list of people with a list of contacts per person and turn it into an edge list.

static remove_duplicates(df)[source]

Sort the dataframe and remove duplicates – note, not extensively tested

class rsvsim.base.Person(pars=None, uid=None, age=- 1, sex=- 1, contacts=None)[source]

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 rsvsim.base.FlexDict[source]

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[source]
values() → an object providing a view on D’s values[source]
items() → a set-like object providing a view on D’s items[source]
class rsvsim.base.Contacts(layer_keys=None)[source]

Bases: rsvsim.base.FlexDict

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


Small method to add one or more layers to the contacts. Layers should be provided as keyword arguments.


hospitals_layer = cv.Layer(label='hosp')

Remove the layer(s) from the contacts.



Note: while included here for convenience, this operation is equivalent to simply popping the key from the contacts dictionary.


Convert all layers to a networkx MultiDiGraph


import networkx as nx
sim = cv.Sim(pop_size=50, pop_type='hybrid').run()
G = sim.people.contacts.to_graph()
class rsvsim.base.Layer(label=None, **kwargs)[source]

Bases: rsvsim.base.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.

  • 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).


# 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)
property members

Return sorted array of all members


Return the keys for the layer’s meta information – i.e., p1, p2, beta


Check the integrity of the layer: right types, right lengths


“Pop” the specified indices from the edgelist and return them as a dict. Returns in the right format to be used with layer.append().


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


Append contacts to the current layer.


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


Convert to dataframe

from_df(df, keys=None)[source]

Convert from a dataframe


Convert to a networkx DiGraph


import networkx as nx
sim = cv.Sim(pop_size=20, pop_type='hybrid').run()
G = sim.people.contacts['h'].to_graph()
find_contacts(inds, as_array=True)[source]

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.

  • inds (array) – indices of people whose contacts to return

  • as_array (bool) – if true, return as sorted array (otherwise, return as unsorted set)


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}

update(people, frac=1.0)[source]

Regenerate contacts on each timestep.

This method gets called if the layer appears in['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).

  • 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