rsvsim.run module

Functions and classes for running multiple rsvsim runs.

rsvsim.run.make_metapars()

Create default metaparameters for a Scenarios run

class rsvsim.run.MultiSim(sims=None, base_sim=None, label=None, initialize=False, **kwargs)

Bases: rsvsim.base.FlexPretty

Class for running multiple copies of a simulation. The parameter n_runs controls how many copies of the simulation there will be, if a list of sims is not provided. This is the main class that’s used to run multiple versions of a simulation (e.g., with different random seeds).

Parameters

• sims (Sim/list) – a single sim or a list of sims

• base_sim (Sim) – the sim used for shared properties; if not supplied, the first of the sims provided

• label (str) – the name of the multisim

• initialize (bool) – whether or not to initialize the sims (otherwise, initialize them during run)

• kwargs (dict) – stored in run_args and passed to run()

Returns

a MultiSim object

Return type

msim

Examples:

sim = cv.Sim() # Create the sim
msim = cv.MultiSim(sim, n_runs=5) # Create the multisim
msim.run() # Run them in parallel
msim.combine() # Combine into one sim
msim.plot() # Plot results

sim = cv.Sim() # Create the sim
msim = cv.MultiSim(sim, n_runs=11, noise=0.1, keep_people=True) # Set up a multisim with noise
msim.run() # Run
msim.reduce() # Compute statistics
msim.plot() # Plot

sims = [cv.Sim(beta=0.015*(1+0.02*i)) for i in range(5)] # Create sims
for sim in sims: sim.run() # Run sims in serial
msim = cv.MultiSim(sims) # Convert to multisim
msim.plot() # Plot as single sim

result_keys()

Attempt to retrieve the results keys from the base sim

init_sims(**kwargs)

Initialize the sims, but don’t actually run them. Syntax is the same as MultiSim.run(). Note: in most cases you can just call run() directly, there is no need to call this separately.

Parameters

kwargs (dict) – passed to multi_run()

run(reduce=False, combine=False, **kwargs)

Run the actual sims

Parameters

• reduce (bool) – whether or not to reduce after running (see reduce())

• combine (bool) – whether or not to combine after running (see combine(), not compatible with reduce)

• kwargs (dict) – passed to multi_run(); use run_args to pass arguments to sim.run()

Returns

None (modifies MultiSim object in place)

Examples:

msim.run()
msim.run(run_args=dict(until='2020-0601', restore_pars=False))

shrink(**kwargs)

Not to be confused with reduce(), this shrinks each sim in the msim; see sim.shrink() for more information.

Parameters

kwargs (dict) – passed to sim.shrink() for each sim

reset()

Undo a combine() or reduce() by resetting the base sim, which, and results

reduce(quantiles=None, use_mean=False, bounds=None, output=False)

Combine multiple sims into a single sim statistically: by default, use the median value and the 10th and 90th percentiles for the lower and upper bounds. If use_mean=True, then use the mean and ±2 standard deviations for lower and upper bounds.

Parameters

• quantiles (dict) – the quantiles to use, e.g. [0.1, 0.9] or {‘low : ‘0.1, ‘high’ : 0.9}

• use_mean (bool) – whether to use the mean instead of the median

• bounds (float) – if use_mean=True, the multiplier on the standard deviation for upper and lower bounds (default 2)

• output (bool) – whether to return the “reduced” sim (in any case, modify the multisim in-place)

Example:

msim = cv.MultiSim(cv.Sim())
msim.run()
msim.reduce()
msim.summarize()

mean(bounds=None, **kwargs)

Alias for reduce(use_mean=True). See reduce() for full description.

Parameters

• bounds (float) – multiplier on the standard deviation for the upper and lower bounds (default, 2)

• kwargs (dict) – passed to reduce()

median(quantiles=None, **kwargs)

Alias for reduce(use_mean=False). See reduce() for full description.

Parameters

• quantiles (list or dict) – upper and lower quantiles (default, 0.1 and 0.9)

• kwargs (dict) – passed to reduce()

combine(output=False)

Combine multiple sims into a single sim with scaled results.

Example:

msim = cv.MultiSim(cv.Sim())
msim.run()
msim.combine()
msim.summarize()

compare(t=None, sim_inds=None, output=False, do_plot=False, **kwargs)

Create a dataframe compare sims at a single point in time.

Parameters

• t (int/str) – the day (or date) to do the comparison; default, the end

• sim_inds (list) – list of integers of which sims to include (default: all)

• output (bool) – whether or not to return the comparison as a dataframe

• do_plot (bool) – whether or not to plot the comparison (see also plot_compare())

• kwargs (dict) – passed to plot_compare()

Returns

a dataframe comparison

Return type

df (dataframe)

plot(to_plot=None, inds=None, plot_sims=False, color_by_sim=None, max_sims=5, colors=None, labels=None, alpha_range=None, plot_args=None, show_args=None, **kwargs)

Plot all the sims – arguments passed to Sim.plot(). The behavior depends on whether or not combine() or reduce() has been called. If so, this function by default plots only the combined/reduced sim (which you can override with plot_sims=True). Otherwise, it plots a separate line for each sim.

Note that this function is complex because it aims to capture the flexibility of both sim.plot() and scens.plot(). By default, if combine() or reduce() has been used, it will resemble sim.plot(); otherwise, it will resemble scens.plot(). This can be changed via color_by_sim, together with the other options.

Parameters

• to_plot (list) – list or dict of which results to plot; see cv.get_default_plots() for structure

• inds (list) – if not combined or reduced, the indices of the simulations to plot (if None, plot all)

• plot_sims (bool) – whether to plot individual sims, even if combine() or reduce() has been used

• color_by_sim (bool) – if True, set colors based on the simulation type; otherwise, color by result type; True implies a scenario-style plotting, False implies sim-style plotting

• max_sims (int) – maximum number of sims to use with color-by-sim; can be overridden by other options

• colors (list) – if supplied, override default colors for color_by_sim

• labels (list) – if supplied, override default labels for color_by_sim

• alpha_range (list) – a 2-element list/tuple/array providing the range of alpha values to use to distinguish the lines

• plot_args (dict) – passed to sim.plot()

• show_args (dict) – passed to sim.plot()

• kwargs (dict) – passed to sim.plot()

Returns

Figure handle

Return type

fig

Examples:

sim = cv.Sim()
msim = cv.MultiSim(sim)
msim.run()
msim.plot() # Plots individual sims
msim.reduce()
msim.plot() # Plots the combined sim

plot_result(key, colors=None, labels=None, *args, **kwargs)

Convenience method for plotting – arguments passed to sim.plot_result()

plot_compare(t=- 1, sim_inds=None, log_scale=True, **kwargs)

Plot a comparison between sims, using bars to show different values for each result. For an explanation of other available arguments, see Sim.plot().

Parameters

• t (int) – index of results, passed to compare()

• sim_inds (list) – which sims to include, passed to compare()

• log_scale (bool) – whether to plot with a logarithmic x-axis

• kwargs (dict) – standard plotting arguments, see Sim.plot() for explanation

Returns

Figure handle

Return type

fig

save(filename=None, keep_people=False, **kwargs)

Save to disk as a gzipped pickle. Load with cv.load(filename) or cv.MultiSim.load(filename).

Parameters

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

• keep_people (bool) – whether or not to store the population in the Sim objects (NB, very large)

• kwargs (dict) – passed to sc.makefilepath()

Returns

the validated absolute path to the saved file

Return type

scenfile (str)

Example:

msim.save() # Saves to an .msim file

static load(msimfile, *args, **kwargs)

Load from disk from a gzipped pickle.

Parameters

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

• kwargs – passed to cv.load()

Returns

the loaded MultiSim object

Return type

msim (MultiSim)

Example:

msim = cv.MultiSim.load('my-multisim.msim')

static merge(*args, base=False)

Convenience method for merging two MultiSim objects.

Parameters

• args (MultiSim) – the MultiSims to merge (either a list, or separate)

• base (bool) – if True, make a new list of sims from the multisim’s two base sims; otherwise, merge the multisim’s lists of sims

Returns

a new MultiSim object

Return type

msim (MultiSim)

Examples:

mm1 = cv.MultiSim.merge(msim1, msim2, base=True) mm2 = cv.MultiSim.merge([m1, m2, m3, m4], base=False)

split(inds=None, chunks=None)

Convenience method for splitting one MultiSim into several. You can specify either individual indices of simulations to extract, via inds, or consecutive chunks of indices, via chunks. If this function is called on a merged MultiSim, the chunks can be retrieved automatically and no arguments are necessary.

Parameters

• inds (list) – a list of lists of indices, with each list turned into a MultiSim

• chunks (int or list) – if an int, split the MultiSim into chunks of that length; if a list return chunks of that many sims

Returns

A list of MultiSim objects

Examples:

m1 = cv.MultiSim(cv.Sim(label='sim1'), initialize=True)
m2 = cv.MultiSim(cv.Sim(label='sim2'), initialize=True)
m3 = cv.MultiSim.merge(m1, m2)
m3.run()
m1b, m2b = m3.split()

msim = cv.MultiSim(cv.Sim(), n_runs=6)
msim.run()
m1, m2 = msim.split(inds=[[0,2,4], [1,3,5]])
mlist1 = msim.split(chunks=[2,4]) # Equivalent to inds=[[0,1], [2,3,4,5]]
mlist2 = msim.split(chunks=3) # Equivalent to inds=[[0,1,2], [3,4,5]]

disp(output=False)

Display a verbose description of a multisim. See also multisim.summarize() (medium length output) and multisim.brief() (short output).

Parameters

output (bool) – if true, return a string instead of printing output

Example:

msim = cv.MultiSim(cv.Sim(verbose=0), label='Example multisim')
msim.run()
msim.disp() # Displays detailed output

summarize(output=False)

Print a moderate length summary of the MultiSim. See also multisim.disp() (detailed output) and multisim.brief() (short output).

Parameters

output (bool) – if true, return a string instead of printing output

Example:

msim = cv.MultiSim(cv.Sim(verbose=0), label='Example multisim')
msim.run()
msim.summarize() # Prints moderate length output

brief(output=False)

Print a compact representation of the multisim. See also multisim.disp() (detailed output) and multisim.summarize() (medium length output).

Parameters

output (bool) – if true, return a string instead of printing output

Example:

msim = cv.MultiSim(cv.Sim(verbose=0), label='Example multisim')
msim.run()
msim.brief() # Prints one-line output

to_json(*args, **kwargs)

Shortcut for base_sim.to_json()

to_excel(*args, **kwargs)

Shortcut for base_sim.to_excel()

class rsvsim.run.Scenarios(sim=None, metapars=None, scenarios=None, basepars=None, scenfile=None, label=None)

Bases: rsvsim.base.ParsObj

Class for running multiple sets of multiple simulations – e.g., scenarios. Note that most users are recommended to use MultiSim rather than Scenarios, as it gives more control over run options. Scenarios should be used primarily for quick investigations. See the examples folder for example usage.

Parameters

• sim (Sim) – if supplied, use a pre-created simulation as the basis for the scenarios

• metapars (dict) – meta-parameters for the run, e.g. number of runs; see make_metapars() for structure

• scenarios (dict) – a dictionary defining the scenarios; see examples folder for examples; see below for baseline

• basepars (dict) – a dictionary of sim parameters to be used for the basis of the scenarios (not required if sim is provided)

• scenfile (str) – a filename for saving (defaults to the creation date)

• label (str) – the name of the scenarios

Example:

scens = cv.Scenarios()

Returns

a Scenarios object

Return type

scens

result_keys(which='all')

Attempt to retrieve the results keys from the base sim

run(debug=False, keep_people=False, verbose=None, **kwargs)

Run the specified scenarios.

Parameters

• debug (bool) – if True, runs a single run instead of multiple, which makes debugging easier

• verbose (int) – level of detail to print, passed to sim.run()

• kwargs (dict) – passed to multi_run() and thence to sim.run()

Returns

None (modifies Scenarios object in place)

compare(t=None, output=False)

Print out a comparison of each scenario.

Parameters

• t (int/str) – the day (or date) to do the comparison; default, the end

• output (bool) – if true, return the dataframe instead of printing output

Example:

scenarios = {'base': {'name':'Base','pars': {}}, 'beta': {'name':'Beta', 'pars': {'beta': 0.020}}}
scens = cv.Scenarios(scenarios=scenarios, label='Example scenarios')
scens.run()
scens.compare(t=30) # Prints comparison for day 30

plot(*args, **kwargs)

Plot the results of a scenario. For an explanation of available arguments, see Sim.plot().

Returns

Figure handle

Return type

fig

Example:

scens = cv.Scenarios()
scens.run()
scens.plot()

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

Export results as JSON.

Parameters

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

Returns

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

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

save(scenfile=None, keep_sims=True, keep_people=False, **kwargs)

Save to disk as a gzipped pickle.

Parameters

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

• keep_sims (bool) – whether or not to store the actual Sim objects in the Scenarios object

• keep_people (bool) – whether or not to store the population in the Sim objects (NB, very large)

• kwargs (dict) – passed to makefilepath()

Returns

the validated absolute path to the saved file

Return type

scenfile (str)

Example:

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

static load(scenfile, *args, **kwargs)

Load from disk from a gzipped pickle.

Parameters

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

• kwargs – passed to cv.load()

Returns

the loaded scenarios object

Return type

scens (Scenarios)

Example:

scens = cv.Scenarios.load('my-scenarios.scens')

disp(output=False)

Display a verbose description of the scenarios. See also scenarios.summarize() (medium length output) and scenarios.brief() (short output).

Parameters

output (bool) – if true, return a string instead of printing output

Example:

scens = cv.Scenarios(cv.Sim(), label='Example scenarios')
scens.run(verbose=0) # Run silently
scens.disp() # Displays detailed output

summarize(output=False)

Print a moderate length summary of the scenarios. See also scenarios.disp() (detailed output) and scenarios.brief() (short output).

Parameters

output (bool) – if true, return a string instead of printing output

Example:

scens = cv.Scenarios(cv.Sim(), label='Example scenarios')
scens.run(verbose=0) # Run silently
scens.summarize() # Prints moderate length output

brief(output=False)

Print a compact representation of the scenarios. See also scenarios.disp() (detailed output) and scenarios.summarize() (medium length output).

Parameters

output (bool) – if true, return a string instead of printing output

Example:

scens = cv.Scenarios(label='Example scenarios')
scens.run()
scens.brief() # Prints one-line output

rsvsim.run.single_run(sim, ind=0, reseed=True, noise=0.0, noisepar=None, keep_people=False, run_args=None, sim_args=None, verbose=None, do_run=True, **kwargs)

Convenience function to perform a single simulation run. Mostly used for parallelization, but can also be used directly.

Parameters

• sim (Sim) – the sim instance to be run

• ind (int) – the index of this sim

• reseed (bool) – whether or not to generate a fresh seed for each run

• noise (float) – the amount of noise to add to each run

• noisepar (str) – the name of the parameter to add noise to

• keep_people (bool) – whether to keep the people after the sim run

• run_args (dict) – arguments passed to sim.run()

• sim_args (dict) – extra parameters to pass to the sim, e.g. ‘n_infected’

• verbose (int) – detail to print

• do_run (bool) – whether to actually run the sim (if not, just initialize it)

• kwargs (dict) – also passed to the sim

Returns

a single sim object with results

Return type

sim (Sim)

Example:

import rsvsim as cv
sim = cv.Sim() # Create a default simulation
sim = cv.single_run(sim) # Run it, equivalent(ish) to sim.run()

rsvsim.run.multi_run(sim, n_runs=4, reseed=True, noise=0.0, noisepar=None, iterpars=None, combine=False, keep_people=None, run_args=None, sim_args=None, par_args=None, do_run=True, parallel=True, n_cpus=None, verbose=None, **kwargs)

For running multiple runs in parallel. If the first argument is a list of sims, exactly these will be run and most other arguments will be ignored.

Parameters

• sim (Sim) – the sim instance to be run, or a list of sims.

• n_runs (int) – the number of parallel runs

• reseed (bool) – whether or not to generate a fresh seed for each run

• noise (float) – the amount of noise to add to each run

• noisepar (str) – the name of the parameter to add noise to

• iterpars (dict) – any other parameters to iterate over the runs; see sc.parallelize() for syntax

• combine (bool) – whether or not to combine all results into one sim, rather than return multiple sim objects

• keep_people (bool) – whether to keep the people after the sim run (default false)

• run_args (dict) – arguments passed to sim.run()

• sim_args (dict) – extra parameters to pass to the sim

• par_args (dict) – arguments passed to sc.parallelize()

• do_run (bool) – whether to actually run the sim (if not, just initialize it)

• parallel (bool) – whether to run in parallel using multiprocessing (else, just run in a loop)

• n_cpus (int) – the number of CPUs to run on (if blank, set automatically; otherwise, passed to par_args)

• verbose (int) – detail to print

• kwargs (dict) – also passed to the sim

Returns

If combine is True, a single sim object with the combined results from each sim. Otherwise, a list of sim objects (default).

Example:

import rsvsim as cv
sim = cv.Sim()
sims = cv.multi_run(sim, n_runs=6, noise=0.2)