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.
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class
rsvsim.base.
ParsObj
(pars)[source]¶ Bases:
rsvsim.base.FlexPretty
A class based around performing operations on a self.pars dict.
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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.
- 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 (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)
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property
ntspts
¶
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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.
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update_pars
(pars=None, create=False, **kwargs)[source]¶ Ensure that metaparameters get used properly before being updated
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set_seed
(seed=- 1)[source]¶ 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
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property
n
¶ Count the number of people – if it fails, assume none
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property
scaled_pop_size
¶ Get the total population size, i.e. the number of agents times the scale factor – if it fails, assume none
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property
npts
¶ Count the number of time points
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property
ntspts
¶ Count number of time-step points
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property
tvec
¶ Create a time vector
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property
tsvec
¶ Create a time step time vector
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property
datevec
¶ Create a vector of dates
- Returns
Array of datetime instances containing the date associated with each day
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property
tsdatevec
¶ Create a vector of dates associated with each timestep
- Returns
Array of datetime instances containing the date associated with each simulation time step
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day
(day, *args)[source]¶ 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
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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 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)]
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result_keys
(which='main')[source]¶ 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.
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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.
- 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)
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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.
- 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)
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to_json
(filename=None, keys=None, tostring=False, indent=2, verbose=False, *args, **kwargs)[source]¶ Export results and parameters 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')
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to_df
(date_index=False)[source]¶ Export results to a pandas dataframe
- Parameters
date_index (bool) – if True, use the date as the index
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to_excel
(filename=None, skip_pars=None)[source]¶ Export parameters and results as Excel format
- Parameters
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
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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.
- 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)
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save
(filename=None, keep_people=None, skip_attrs=None, **kwargs)[source]¶ 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
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static
load
(filename, *args, **kwargs)[source]¶ 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')
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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).
- Parameters
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
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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.
- Parameters
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)
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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.
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set_pars
(pars=None)[source]¶ Re-link the parameters stored in the people object to the sim containing it, and perform some basic validation.
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date_keys
()[source]¶ Returns keys for different event dates (e.g., date a person became symptomatic)
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dur_keys
()[source]¶ Returns keys for different durations (e.g., the duration from exposed to infectious)
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to_graph
()[source]¶ 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)
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init_contacts
(reset=False)[source]¶ Initialize the contacts dataframe with the correct columns and data types
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add_contacts
(contacts, lkey=None, beta=None)[source]¶ Add new contacts to the array. See also contacts.add_layer().
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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.
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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.
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class
rsvsim.base.
Contacts
(layer_keys=None)[source]¶ Bases:
rsvsim.base.FlexDict
A simple (for now) class for storing different contact layers.
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add_layer
(**kwargs)[source]¶ 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)
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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.
- Parameters
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)
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property
members
¶ Return sorted array of all members
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pop_inds
(inds)[source]¶ “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
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append
(contacts)[source]¶ Append contacts to the current layer.
- Parameters
contacts (dict) – a dictionary of arrays with keys p1,p2,beta, as returned from layer.pop_inds()
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to_graph
()[source]¶ 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)
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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.
- 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}
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update
(people, frac=1.0)[source]¶ 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).- Parameters
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