pyuncertainnumber.pba.intervals¶

Submodules¶

Classes¶

Interval

Interval is the main class

Functions¶

`lo`(→ Union[float, numpy.ndarray])	Return the left endpoint of an Interval object.
`hi`(→ Union[float, numpy.ndarray])	Return the right endpoint of an Interval object.
`mid`(→ Union[float, numpy.ndarray])	Return the midpoint of an Interval.
`rad`(→ Union[float, numpy.ndarray])	Return the radius of an Interval object.
`width`(→ Union[float, numpy.ndarray])	Return the width of an Interval object.
`contain`(x, y)
`intersect`(x, y)
`straddle_zero`(→ bool)
`split_interval`(x[, y])
`subintervalise`(...)
`intervalise`(...)	This function casts an array-like structure into an Interval structure.
`sizeit`(→ pyuncertainnumber.pba.intervals.number.Interval)	Takes an unsized scalar interval and turns it in to a sized one.
`uniform_endpoints`([n, left_bound, right_bound, kind, ...])	Draws endpoints from a uniform distribution.

Package Contents¶

class pyuncertainnumber.pba.intervals.Interval(lo: float | numpy.ndarray, hi: float | numpy.ndarray | None = None, do_heavy_checks: bool = True)¶

Bases: pyuncertainnumber.pba.mixins.NominalValueMixin

Interval is the main class

_lo¶

_hi = None¶

run_heavy_checks()¶: Run heavy checks on the interval object

__repr__()¶

__str__()¶

__len__()¶

__iter__()¶

__contains__(item)¶

Check if an item is enclosed within the interval.

Example

>>> i = Interval(1,3)
>>> 2 in i
True
>>> 4 in i
False

__next__()¶

__getitem__(i: int | slice)¶

to_numpy() → numpy.ndarray¶: transform interval objects to numpy arrays

to_pbox()¶

lhs_sample(n) → numpy.ndarray¶

LHS sampling within the interval

Parameters:: n – number of samples

endpoints_lhs_sample(n) → numpy.ndarray¶

LHS sampling within the interval plus the endpoints

Parameters:: n – number of samples

plot(ax=None, **kwargs)¶

display()¶

is_degenerate() → bool¶: Check if the interval is degenerate (i.e., has zero width).

_compute_nominal_value()¶

ravel()¶

Return a flattened (1D) interval object for multi-dimensional intervals

Example

>>> A = np.random.rand(200, 200, 2)
>>> i = pba.intervalise(A)
>>> print(i.shape)
>>> i2 = i.ravel()
>>> print(i2.shape)

property lo: numpy.ndarray | float¶

property hi: numpy.ndarray | float¶

property left¶

property right¶

property width¶

property rad¶: half width

property mid¶

property unsized¶

property val¶: seemingly equivalent to self.to_numpy()

property scalar¶: Check if the interval is wide sense scalar

Note

wide sense: I(1,2) and I([1],[2]) are both scalars

property is_scalar¶: Check if the interval is a strict-sense scalar

Note

strict sense: I(1,2) is a scalar, but I([1],[2]) is not

property shape¶

property ndim¶

__neg__()¶

__pos__()¶

__add__(other)¶

__radd__(left)¶

__sub__(other)¶

__rsub__(left)¶

__mul__(other)¶

__rmul__(left)¶

__truediv__(other)¶

__rtruediv__(left)¶

__pow__(other)¶

__lt__(other)¶

__rlt__(left)¶

__gt__(other)¶

__rgt__(left)¶

__le__(other)¶

__rle__(left)¶

__ge__(other)¶

__rge__(left)¶

__eq__(other)¶

__ne__(other)¶

__array_ufunc__(ufunc, method, *inputs, **kwargs)¶

abs()¶

sqrt()¶

exp()¶

log()¶

sin()¶

cos()¶

tan()¶

classmethod from_meanform(x, half_width)¶

save_json(filename: str, comment: str = None, save_dir: str | pathlib.Path = '.') → None¶

Save the interval object to a JSON5 file.

Parameters:

filename (str) – The name of the file (without extension) to save the interval object to.
comment (str, optional) – A comment to include at the top of the file.
save_dir (str | Path, optional) – Directory where the file should be saved. Defaults to current directory.

Note

The file is saved with a .json5 extension.

Example

>>> a.save_json("interval_data", comment="This is interval data", save_dir="results/")

pyuncertainnumber.pba.intervals.lo(x: Interval) → float | numpy.ndarray¶

Return the left endpoint of an Interval object.