run

run

Functions and classes for running multiple HPVsim runs.

Classes

Name	Description
MultiSim	Class for running multiple copies of a simulation. The parameter n_runs
Scenarios	Class for running multiple sets of multiple simulations – e.g., scenarios.
Sweep	Class for running parameter sweeps.

MultiSim

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

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

Name	Type	Description	Default
sims (Sim/list)		a single sim or a list of sims	required
base_sim (Sim)		the sim used for shared properties; if not supplied, the first of the sims provided	required
label (str)		the name of the multisim	required
initialize (bool)		whether or not to initialize the sims (otherwise, initialize them during run)	required
kwargs (dict)		stored in run_args and passed to run()	required

Returns

Name	Type	Description
msim		a MultiSim object

Examples::

sim = hpv.Sim() # Create the sim
msim = hpv.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 = hpv.Sim() # Create the sim
msim = hpv.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 = [hpv.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 = hpv.MultiSim(sims) # Convert to multisim
msim.plot() # Plot as single sim

Methods

Name	Description
brief	Print a compact representation of the multisim. See also multisim.disp()
combine	Combine multiple sims into a single sim with scaled results.
compare	Create a dataframe compare sims at a single point in time.
disp	Display a verbose description of a multisim. See also multisim.summarize()
init_sims	Initialize the sims, but don’t actually run them. Syntax is the same
load	Load from disk from a gzipped pickle.
mean	Alias for reduce(use_mean=True). See reduce() for full description.
median	Alias for reduce(use_mean=False). See reduce() for full description.
merge	Convenience method for merging two MultiSim objects.
plot	Plot all the sims – arguments passed to Sim.plot(). The
plot_compare	Plot a comparison between sims, using bars to show different values for
plot_result	Convenience method for plotting – arguments passed to sim.plot_result()
reduce	Combine multiple sims into a single sim statistically: by default, use
reset	Undo a combine() or reduce() by resetting the base sim, which, and results
result_keys	Attempt to retrieve the results keys from the base sim
run	Run the actual sims
save	Save to disk as a gzipped pickle. Load with hpv.load(filename) or
shrink	Not to be confused with reduce(), this shrinks each sim in the msim;
split	Convenience method for splitting one MultiSim into several. You can specify
summarize	Print a moderate length summary of the MultiSim. See also multisim.disp()
to_excel	Shortcut for base_sim.to_excel()
to_json	Shortcut for base_sim.to_json()

brief

run.MultiSim.brief(output=False)

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

Parameters

Name	Type	Description	Default
output	bool	if true, return a string instead of printing output	False

Example::

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

combine

run.MultiSim.combine(output=False)

Combine multiple sims into a single sim with scaled results.

Example::

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

compare

run.MultiSim.compare(
    t=None,
    sim_inds=None,
    output=False,
    do_plot=False,
    show_match=False,
    **kwargs,
)

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

Parameters

Name	Type	Description	Default
t (int/str)		the day (or date) to do the comparison; default, the end	required
sim_inds (list)		list of integers of which sims to include (default: all)	required
output (bool)		whether or not to return the comparison as a dataframe	required
do_plot (bool)		whether or not to plot the comparison (see also plot_compare())	required
show_match (bool)		whether to include a column for whether all sims match	required
kwargs (dict)		passed to plot_compare()	required

Returns

Name	Type	Description
df	dataframe	a dataframe comparison

disp

run.MultiSim.disp(output=False)

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

Parameters

Name	Type	Description	Default
output	bool	if true, return a string instead of printing output	False

Example::

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

init_sims

run.MultiSim.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

Name	Type	Description	Default
kwargs	dict	passed to multi_run()	{}

load

run.MultiSim.load(msimfile, *args, **kwargs)

Load from disk from a gzipped pickle.

Parameters

Name	Type	Description	Default
msimfile	str	the name or path of the file to load from	required
kwargs		passed to hpv.load()	{}

Returns

Name	Type	Description
msim	MultiSim	the loaded MultiSim object

Example::

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

mean

run.MultiSim.mean(bounds=None, **kwargs)

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

Parameters

Name	Type	Description	Default
bounds	float	multiplier on the standard deviation for the upper and lower bounds (default, 2)	None
kwargs	dict	passed to reduce()	{}

median

run.MultiSim.median(quantiles=None, **kwargs)

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

Parameters

Name	Type	Description	Default
quantiles	list or dict	upper and lower quantiles (default, 0.1 and 0.9)	None
kwargs	dict	passed to reduce()	{}

merge

run.MultiSim.merge(*args, base=False)

Convenience method for merging two MultiSim objects.

Parameters

Name	Type	Description	Default
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	False

Returns

Name	Type	Description
msim	MultiSim	a new MultiSim object

Examples:

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

plot

run.MultiSim.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

Name	Type	Description	Default
to_plot (list)		list or dict of which results to plot; see hpv.get_default_plots() for structure	required
inds (list)		if not combined or reduced, the indices of the simulations to plot (if None, plot all)	required
plot_sims (bool)		whether to plot individual sims, even if combine() or reduce() has been used	required
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	required
max_sims (int)		maximum number of sims to use with color-by-sim; can be overridden by other options	required
colors (list)		if supplied, override default colors for color_by_sim	required
labels (list)		if supplied, override default labels for color_by_sim	required
alpha_range (list)		a 2-element list/tuple/array providing the range of alpha values to use to distinguish the lines	required
plot_args (dict)		passed to sim.plot()	required
show_args (dict)		passed to sim.plot()	required
kwargs (dict)		passed to sim.plot()	required

Returns

Name	Type	Description
fig		Figure handle

Examples::

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

plot_compare

run.MultiSim.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

Name	Type	Description	Default
t (int)		index of results, passed to compare()	required
sim_inds (list)		which sims to include, passed to compare()	required
log_scale (bool)		whether to plot with a logarithmic x-axis	required
kwargs (dict)		standard plotting arguments, see Sim.plot() for explanation	required

Returns

Name	Type	Description
fig		Figure handle

plot_result

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

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

reduce

run.MultiSim.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

Name	Type	Description	Default
quantiles	dict	the quantiles to use, e.g. [0.1, 0.9] or {‘low : ’0.1, ’high’ : 0.9}	None
use_mean	bool	whether to use the mean instead of the median	False
bounds	float	if use_mean=True, the multiplier on the standard deviation for upper and lower bounds (default 2)	None
output	bool	whether to return the “reduced” sim (in any case, modify the multisim in-place)	False

Example::

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

reset

run.MultiSim.reset()

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

result_keys

run.MultiSim.result_keys()

Attempt to retrieve the results keys from the base sim

run

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

Run the actual sims

Parameters

Name	Type	Description	Default
reduce	bool	whether or not to reduce after running (see reduce())	False
combine	bool	whether or not to combine after running (see combine(), not compatible with reduce)	False
kwargs	dict	passed to multi_run(); use run_args to pass arguments to sim.run()	{}

Returns

Name	Type	Description
		None (modifies MultiSim object in place)

Examples::

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

save

run.MultiSim.save(filename=None, keep_people=False, **kwargs)

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

Parameters

Name	Type	Description	Default
filename (str)		the name or path of the file to save to; if None, uses default	required
keep_people (bool)		whether or not to store the population in the Sim objects (NB, very large)	required
kwargs (dict)		passed to sc.makefilepath()	required

Returns

Name	Type	Description
scenfile	str	the validated absolute path to the saved file

Example::

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

shrink

run.MultiSim.shrink(**kwargs)

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

Parameters

Name	Type	Description	Default
kwargs	dict	passed to sim.shrink() for each sim	{}

split

run.MultiSim.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

Name	Type	Description	Default
inds	list	a list of lists of indices, with each list turned into a MultiSim	None
chunks	int or list	if an int, split the MultiSim into that many chunks; if a list return chunks of that many sims	None

Returns

Name	Type	Description
		A list of MultiSim objects

Examples::

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

msim = hpv.MultiSim(hpv.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=2) # Equivalent to inds=[[0,1,2], [3,4,5]]

summarize

run.MultiSim.summarize(output=False)

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

Parameters

Name	Type	Description	Default
output	bool	if true, return a string instead of printing output	False

Example::

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

to_excel

run.MultiSim.to_excel(*args, **kwargs)

Shortcut for base_sim.to_excel()

to_json

run.MultiSim.to_json(*args, **kwargs)

Shortcut for base_sim.to_json()

Scenarios

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

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

Name	Type	Description	Default
sim (Sim)		if supplied, use a pre-created simulation as the basis for the scenarios	required
metapars (dict)		meta-parameters for the run, e.g. number of runs; see make_metapars() for structure	required
scenarios (dict)		a dictionary defining the scenarios; see examples folder for examples; see below for baseline	required
basepars (dict)		a dictionary of sim parameters to be used for the basis of the scenarios (not required if sim is provided)	required
scenfile (str)		a filename for saving (defaults to the creation date)	required
label (str)		the name of the scenarios	required

Example::

scens = hpv.Scenarios()

Returns

Name	Type	Description
scens		a Scenarios object

Methods

Name	Description
brief	Print a compact representation of the scenarios. See also scenarios.disp()
compare	Print out a comparison of each scenario.
disp	Display a verbose description of the scenarios. See also scenarios.summarize()
load	Load from disk from a gzipped pickle.
merge	Merge two or more scenarios to create a single scenario object
plot	Plot the results of a scenario. For an explanation of available arguments,
plot_age_results	Plot all age_results analyzers in a scenario together.
result_keys	Attempt to retrieve the results keys from the base sim
run	Run the specified scenarios.
save	Save to disk as a gzipped pickle.
summarize	Print a moderate length summary of the scenarios. See also scenarios.disp()
to_excel	Export results as XLSX
to_json	Export results as JSON.

brief

run.Scenarios.brief(output=False)

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

Parameters

Name	Type	Description	Default
output	bool	if true, return a string instead of printing output	False

Example::

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

compare

run.Scenarios.compare(t=None, output=False)

Print out a comparison of each scenario.

Parameters

Name	Type	Description	Default
t (int/str)		the day (or date) to do the comparison; default, the end	required
output (bool)		if true, return the dataframe instead of printing output	required

Example::

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

disp

run.Scenarios.disp(output=False)

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

Parameters

Name	Type	Description	Default
output	bool	if true, return a string instead of printing output	False

Example::

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

load

run.Scenarios.load(scenfile, *args, **kwargs)

Load from disk from a gzipped pickle.

Parameters

Name	Type	Description	Default
scenfile	str	the name or path of the file to load from	required
kwargs		passed to hpv.load()	{}

Returns

Name	Type	Description
scens	Scenarios	the loaded scenarios object

Example::

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

merge

run.Scenarios.merge(*args)

Merge two or more scenarios to create a single scenario object Args: args (Scenarios): the Scenarios to merge (either a list, or separate) Returns: scen (Scenarios): a new Scenario object

plot

run.Scenarios.plot(*args, **kwargs)

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

Returns

Name	Type	Description
fig		Figure handle

Example::

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

plot_age_results

run.Scenarios.plot_age_results(*args, **kwargs)

Plot all age_results analyzers in a scenario together. Returns: fig: Figure handle

Example::

az = hpv.age_results(timepoints=['2015', '2020'], results=['hpv_incidence', 'total_cancers'])
base_sim = hpv.Sim(analyzers=az)
scens = hpv.Scenarios(sim=base_sim)
scens.run()
scens.plot_age_results()

result_keys

run.Scenarios.result_keys(which='all')

Attempt to retrieve the results keys from the base sim

run

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

Run the specified scenarios.

Parameters

Name	Type	Description	Default
debug (bool)		if True, runs a single run instead of multiple, which makes debugging easier	required
verbose (int)		level of detail to print, passed to sim.run()	required
kwargs (dict)		passed to multi_run() and thence to sim.run()	required

Returns

Name	Type	Description
		None (modifies Scenarios object in place)

save

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

Save to disk as a gzipped pickle.

Parameters

Name	Type	Description	Default
scenfile (str)		the name or path of the file to save to; if None, uses stored	required
keep_sims (bool)		whether or not to store the actual Sim objects in the Scenarios object	required
keep_people (bool)		whether or not to store the population in the Sim objects (NB, very large)	required
kwargs (dict)		passed to makefilepath()	required

Returns

Name	Type	Description
scenfile	str	the validated absolute path to the saved file

Example::

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

summarize

run.Scenarios.summarize(output=False)

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

Parameters

Name	Type	Description	Default
output	bool	if true, return a string instead of printing output	False

Example::

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

to_excel

run.Scenarios.to_excel(filename=None)

Export results as XLSX

Parameters

Name	Type	Description	Default
filename	str	if None, return string; else, write to file	None

Returns

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

to_json

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

Export results as JSON.

Parameters

Name	Type	Description	Default
filename	str	if None, return string; else, write to file	None

Returns

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

Sweep

run.Sweep(
    base_sim=None,
    sweep_pars=None,
    sweep_vars=None,
    n_draws=4,
    initialize=False,
    label=None,
    **kwargs,
)

Class for running parameter sweeps.

Parameters

Name	Type	Description	Default
base_sim (Sim)		the sim used for shared properties	required
label (str)		the name of the multisim	required
kwargs (dict)		stored in run_args and passed to run()	required

Returns

Name	Type	Description
sweep		a Sweep object

Examples: sim = hpv.Sim() # Create the sim sweep = hpv.Sweep(sim, sweep_pars={‘beta’:[0.4, 0.8]}, n_runs=5) # Create the sweep sweep.run() # Run the sims in parallel sweep.plot() # Plot results

Methods

Name	Description
reduce	Create a dataframe holding reduced results
run	Run the sims

reduce

run.Sweep.reduce(sum_totals=True, from_year=None, to_year=None)

Create a dataframe holding reduced results

run

run.Sweep.run(
    shrink=True,
    reduce=True,
    sum_totals=True,
    from_year=None,
    to_year=None,
    **kwargs,
)

Run the sims Args: shrink (bool): whether or not to shrink after running kwargs (dict): passed to multi_run(); use run_args to pass arguments to sim.run()

Returns

Name	Type	Description
		None (modifies MultiSim object in place)

Examples:: sweep.run()

Functions

Name	Description
make_metapars	Create default metaparameters for a Scenarios run
multi_run	For running multiple runs in parallel. If the first argument is a list of sims,
parallel	A shortcut to hpv.MultiSim(), allowing the quick running of multiple simulations
single_run	Convenience function to perform a single simulation run. Mostly used for

make_metapars

run.make_metapars()

Create default metaparameters for a Scenarios run

multi_run

run.multi_run(
    sim,
    n_runs=4,
    reseed=None,
    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

Name	Type	Description	Default
sim (Sim)		the sim instance to be run, or a list of sims.	required
n_runs (int)		the number of parallel runs	required
reseed (bool)		whether or not to generate a fresh seed for each run (default: true for single, false for list of sims)	required
noise (float)		the amount of noise to add to each run	required
noisepar (str)		the name of the parameter to add noise to	required
iterpars (dict)		any other parameters to iterate over the runs; see sc.parallelize() for syntax	required
combine (bool)		whether or not to combine all results into one sim, rather than return multiple sim objects	required
keep_people (bool)		whether to keep the people after the sim run (default false)	required
run_args (dict)		arguments passed to sim.run()	required
sim_args (dict)		extra parameters to pass to the sim	required
par_args (dict)		arguments passed to sc.parallelize()	required
do_run (bool)		whether to actually run the sim (if not, just initialize it)	required
parallel (bool)		whether to run in parallel using multiprocessing (else, just run in a loop)	required
n_cpus (int)		the number of CPUs to run on (if blank, set automatically; otherwise, passed to par_args)	required
verbose (int)		detail to print	required
kwargs (dict)		also passed to the sim	required

Returns

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

Example::

import hpvsim as hpv
sim = hpv.Sim()
sims = hpv.multi_run(sim, n_runs=6, noise=0.2)

parallel

run.parallel(*args, **kwargs)

A shortcut to hpv.MultiSim(), allowing the quick running of multiple simulations at once.

Parameters

Name	Type	Description	Default
args	list	The simulations to run	()
kwargs	dict	passed to multi_run()	{}

Returns

Name	Type	Description
		A run MultiSim object.

Examples::

s1 = hpv.Sim(beta=0.01, label='Low')
s2 = hpv.Sim(beta=0.02, label='High')
hpv.parallel(s1, s2).plot()
msim = hpv.parallel([s1, s2], keep_people=True)

single_run

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

Name	Type	Description	Default
sim (Sim)		the sim instance to be run	required
ind (int)		the index of this sim	required
reseed (bool)		whether or not to generate a fresh seed for each run	required
noise (float)		the amount of noise to add to each run	required
noisepar (str)		the name of the parameter to add noise to	required
keep_people (bool)		whether to keep the people after the sim run	required
run_args (dict)		arguments passed to sim.run()	required
sim_args (dict)		extra parameters to pass to the sim, e.g. ‘n_infected’	required
verbose (int)		detail to print	required
do_run (bool)		whether to actually run the sim (if not, just initialize it)	required
kwargs (dict)		also passed to the sim	required

Returns

Name	Type	Description
sim	Sim	a single sim object with results

Example::

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