stisim.utils module

STIsim utilities

class stisim.utils.TimeSeries(t=None, vals=None, units: str | None = None, assumption: float | None = None, sigma: float | None = None)

Bases: object

Class to store time-series data

Internally values are stored as lists rather than numpy arrays because insert/remove operations on lists tend to be faster (and working with sparse data is a key role of TimeSeries objects). Note that methods like interpolate() return numpy arrays, so the output types from such functions should generally match up with what is required by the calling function.

Parameters:

• t – Optionally specify a scalar, list, or array of time values

• vals – Optionally specify a scalar, list, or array of values (must be same size as t)

• units – Optionally specify units (as a string)

• assumption – Optionally specify a scalar assumption

• sigma – Optionally specify a scalar uncertainty

t

Sorted array of time points. Normally interacted with via methods like insert()

vals

Time-specific values - indices correspond to self.t

units

The units of the quantity

assumption

The time-independent scalar assumption

sigma

Uncertainty value, assumed to be a standard deviation

copy()

Return a copy of the TimeSeries

Returns:

An independent copy of the TimeSeries

property has_data: bool

Check if any data has been provided

Returns:

True if any data has been entered (assumption or time-specific)

property has_time_data: bool

Check if time-specific data has been provided

Unlike has_data, this will return False if only an assumption has been entered

Returns:

True if any time-specific data has been entered

insert(t, v) → None

Insert a value or list of at a particular time

If the value already exists in the TimeSeries, it will be overwritten/updated. The arrays are internally sorted by time value, and this order will be maintained.

Parameters:

• t – Time value to insert or update. If None, the value will be assigned to the assumption

• v – Value to insert. If None, this function will return immediately without doing anything

get(t) → float

Retrieve value at a particular time

This function will automatically retrieve the value of the assumption if no time specific values have been provided, or if any time specific values are provided, will return the value entered at that time. If time specific values have been entered and the requested time is not explicitly present, an error will be raised.

This function may be deprecated in future because generally it is more useful to either call TimeSeries.interpolate() if interested in getting values at arbitrary times, or TimeSeries.get_arrays() if interested in retrieving values that have been entered.

Parameters:

t – A time value. If None, will return assumption regardless of whether time data has been entered or not

Returns:

The value at the corresponding time. Returns None if the value no value present

get_arrays()

Return arrays with the contents of this TimeSeries

The TimeSeries instance may have time values, or may simply have an assumption. If obtaining raw arrays is desired, this function will return arrays with values extracted from the appropriate attribute of the TimeSeries. However, in general, it is usually .interpolate() that is desired, rather than .get_arrays()

Returns:

Tuple with two arrays - the first item is times (with a single NaN if the TimeSeries only has an assumption) and the second item is values

remove(t) → None

Remove single time point

Parameters:

t – Time value to remove. Set to None to remove the assumption

remove_before(t_remove) → None

Remove times from start

Parameters:

tval – Remove times up to but not including this time

remove_after(t_remove) → None

Remove times from start

Parameters:

tval – Remove times up to but not including this time

remove_between(t_remove) → None

Remove a range of times

Note that the endpoints are not included

Parameters:

t_remove – two element iterable e.g. array, with [min,max] times

interpolate(t2: array, method='linear', **kwargs) → array

Return interpolated values

This method returns interpolated values from the time series at time points t2 according to a given interpolation method. There are 4 possibilities for the method

• ‘linear’ - normal linear interpolation (with constant, zero-gradient extrapolation)

• ‘pchip’ - legacy interpolation with some curvature between points (with constant, zero-gradient extrapolation)

• ‘previous’ - stepped interpolation, maintain value until the next timepoint is reached (with constant, zero-gradient extrapolation)

• Interpolation class or generator function

That final option allows the use of arbitrary interpolation methods. The underlying call will be:

c = method(t1, v1, **kwargs)
return c(t2)

so for example, if you wanted to use the base Scipy pchip method with no extrapolation, then could pass in:

TimeSeries.interpolate(...,method=scipy.interpolate.PchipInterpolator)

Note that the following special behaviours apply:

• If there is no data at all, this function will return np.nan for all requested time points

• If only an assumption exists, this assumption will be returned for all requested time points

• Otherwise, arrays will be formed with all finite time values

  • If no finite time values remain, an error will be raised (in general, a TimeSeries should not store such values anyway)

  • If only one finite time value remains, then that value will be returned for all requested time points

  • Otherwise, the specified interpolation method will be used

Parameters:

• t2 – float, list, or array, with times

• method – A string ‘linear’, ‘pchip’ or ‘previous’ OR a callable item that returns an Interpolator

Returns:

array the same length as t2, with interpolated values

sample(constant=True)

Return a sampled copy of the TimeSeries

This method returns a copy of the TimeSeries in which the values have been perturbed based on the uncertainty value.

Parameters:

constant – If True, time series will be perturbed by a single constant offset. If False, an different perturbation will be applied to each time specific value independently.

Returns:

A copied TimeSeries with perturbed values

stisim.utils.make_init_prev_fn(module, sim, uids, data=None, active=False)

Initialize prevalence by sex and risk group

stisim.utils.finalize_results(sim, modules_to_drop=None)

stisim.utils.shrink_calib(calib, n_results=100)