nenupy.schedule.constraints.PeriodicCnst

class nenupy.schedule.constraints.PeriodicCnst(start_time, periodicity, tolerance, weight=1)

Bases: ScheduleConstraint

Constraint to force a block to be scheduled within periodical time intervals in the schedule.

Examples

>>> from nenupy.schedule.constraints import PeriodicCnst
>>> from astropy.time import Time, TimeDelta

>>> start = Time("2025-12-01 00:00:00")
>>> p = TimeDelta(2, format="jd")
>>> tol = TimeDelta(0.3, format="jd")
>>> cnt = PeriodicCnst(
        start_time=start,
        periodicity=p,
        tolerance=tol
    )

>>> times = start + np.arange(300) * TimeDelta(1800, format="sec")
>>> score = cnt(times)
>>> fig = plt.figure(figsize=(10, 5))
>>> ax = fig.add_subplot()

>>> for i in range(4):
>>>     middle = start + i * p
>>>     ax.axvline(middle.datetime, linestyle=":", color="black")
>>>     ax.axvspan((middle - tol).datetime, (middle + tol).datetime, color="tab:orange", alpha=0.5)

>>> ax.plot(times[:-1].datetime, np.ones(times[:-1].size), marker=".", linestyle="")
>>> mask = ~ np.isnan(score)
>>> ax.plot(times[:-1].datetime[mask], np.ones(times[:-1].size)[mask], marker=".", linestyle="")
>>> ax.set_xlabel("Time")

__call__(*arg)

Evaluate the constraint against the schedule properties.

Returns:

The constraint score evaluated at each time step.

Return type:

ndarray

Raises:

AttributeError – If the constraint does not have an _evaluate() method.

__init__(start_time, periodicity, tolerance, weight=1)

Return an instance of PeriodicCnst.

Parameters:

• start_time (Time) – Center of the first time interval.

• periodicity (TimeDelta) – Time duration before the next repetition of the time interval.

• tolerance (TimeDelta) – half-width of each time interval.

• weight (float, optional) – Constraint weight used to ponderate constraints with respect to one another, by default 1

Methods

__init__(start_time, periodicity, tolerance)	Return an instance of PeriodicCnst.
get_score(indices)	Return the constraint score evaluated on a schedule indices.
plot([fig, ax])	Plot the constraint's score previously evaluated.

Attributes

weight

Relative weight associated to the constraint.

get_score(indices)

Return the constraint score evaluated on a schedule indices. The constraint must have been evaluated first.

Parameters:

indices (ndarray) – Time indices on which the constraint score should be computed. The indices must match the array stored in score.

Returns:

The constraint score evaluated as its average on indices.

Return type:

float

plot(fig=None, ax=None, **kwargs)

Plot the constraint’s score previously evaluated.

Parameters:

• fig (Figure) – Figure instance to re-use for plotting purposes, by default None (i.e., a new Figure will be created)

• ax (Axes) – Ax instance to re-use for plotting purposes, by default None (i.e., a new Axes will be created)

• figsize ([float, float]) – Size of the figure, by default (10, 5)

• figname (str) – Name of the figure to be saved, by default '' (i.e., the figure is only displayed)

• marker (str) – Plot marker type (see matplotlib.pyplot.plot()), by default '.'

• linestyle (str) – Plot line style (see matplotlib.pyplot.plot()), by default ':'

• linewidth (int) – Plot line width (see matplotlib.pyplot.plot()), by default 1

property weight

Relative weight associated to the constraint. The constraint score of an observation block within a given schedule is ultimately taken at the weighted mean among all the constraints associated to this observaiton request.