Sim

class Sim(pars=None, sim_pars=None, fp_pars=None, contra_pars=None, edu_pars=None, fp_module=None, contraception_module=None, empowerment_module=None, education_module=None, label=None, people=None, demographics=None, diseases=None, networks=None, interventions=None, analyzers=None, connectors=None, copy_inputs=True, **kwargs)

Bases: Sim

The Sim class handles the running of the simulation. It extends the Starim Sim class, so all Starsim Sim methods are available to FPsims.

When a Sim is initialized, it triggers the creation of the population. Methods related to creating, initializing, and updating people can be found in the People class.

Parameters:

• pars (dict) – parameters to modify from their default values

• location (str) – name of the location (country) to look for data file to load

• label (str) – the name of the simulation (useful to distinguish in batch runs)

• track_children (bool) – whether to track links between mothers and their children (slow, so disabled by default)

• kwargs (dict) – additional parameters; passed to fp.make_pars()

Examples:

sim = fp.Sim()
sim = fp.Sim(n_agents=10e3, location='senegal', label='My small Senegal sim')

Attributes

dt	Get the current module timestep
label	Get the sim label from the parameters, if available.
module_dict	Return a dictionary of all Module instances; see sim.module_list for the list version
module_list	Return a list of all Module instances (stored in standard places) in the Sim; see sim.module_dict for the dict version
now	Shortcut to self.t.now()
ti	Get the current module timestep
timevec	Shortcut to self.t.timevec
ty
y

Methods

static remap_pars(pars)

Remap the parameters to the new names. This is useful for backwards compatibility.

separate_pars(pars=None, sim_pars=None, fp_pars=None, contra_pars=None, edu_pars=None, sim_kwargs=None, **kwargs)

Separate the parameters into simulation and fp-specific parameters.

init(force=False)

Fully initialize the Sim with modules, people and result storage

init_results()

Initialize the results dictionary. This is called at the start of the simulation.

update_mortality()

Update infant and maternal mortality for the sim’s current year. Update general mortality trend as this uses a spline interpolation instead of an array.

plot(key=None, to_plot=None, xlims=None, ylims=None, do_save=None, do_show=True, filename='fpsim.png', style=None, fig=None, fig_args=None, fig_kw=None, plot_kw=None, alpha=None, is_jupyter=False, plot_args=None, axis_args=None, fill_args=None, label=None, new_fig=True, colors=None)

Plot the results – can supply arguments for both the figure and the plots.

Parameters:

• key (str/dict) – Alias for to_plot

• to_plot (str/dict) – What to plot (e.g. ‘default’ or ‘cpr’), or a dictionary of result:label pairs

• xlims (list/dict) – passed to pl.xlim() (use [None, None] for default)

• ylims (list/dict) – passed to pl.ylim()

• do_save (bool) – Whether or not to save the figure. If a string, save to that filename.

• do_show (bool) – Whether to show the plots at the end

• filename (str) – If a figure is saved, use this filename

• style (bool) – Custom style arguments

• fig_args (dict) – Passed to pl.figure() (plus nrows and ncols for overriding defaults)

• plot_args (dict) – Passed to pl.plot()

• axis_args (dict) – Passed to pl.subplots_adjust()

• fill_args (dict) – Passed to pl.fill_between())

• label (str) – Label to override default

• new_fig (bool) – Whether to create a new figure (true unless part of a multisim)

• colors (list/dict) – Colors for plots with multiple lines

plot_age_first_birth(do_show=None, do_save=None, fig_args=None, filename='first_birth_age.png')

Plot age at first birth

Parameters:

• fig_args (dict) – arguments to pass to pl.figure()

• do_show (bool) – whether the user wants to show the output plot (default: true)

• do_save (bool) – whether the user wants to save the plot to filepath (default: false)

• filename (str) – the name of the path to output the plot

format_method_df(method_list=None, timeseries=False)

Outputs a dataframe for method mix plotting for either a single year or a timeseries

Parameters:

• method_list (list) – list of proportions where each index is equal to the integer value of the corresponding method

• timeseries (boolean) – if true, provides a dataframe with data from every year, otherwise a method_list is required for the year

Returns:

pandas.DataFrame with columns [“Percentage”, “Method”, “Sim”, “Seed”] and optionally “Year” if timeseries

list_available_results()

Pretty print availbale results keys, sorted alphabetically