PlasmaCalcs.plotting.xarray_timelines.IndexableCycler

class PlasmaCalcs.plotting.xarray_timelines.IndexableCycler(left: Cycler[K, V] | Iterable[dict[K, V]] | None, right: Cycler[K, V] | None = None, op: Any = None)

Bases: Cycler

Cycler which can be indexed by integers.

Uses infinite indexing, i.e. result will be the same as the i’th value from itertools.cycle().

[EFF] uses i % len(self), so the indexing time complexity doesn’t scale with i.

__init__(left: Cycler[K, V] | Iterable[dict[K, V]] | None, right: Cycler[K, V] | None = None, op: Any = None)

Semi-private init.

Do not use this directly, use cycler function instead.

Methods

__init__(left[, right, op])
by_key()
change_key(old, new)
concat(right)
simplify()

Attributes

keys

by_key() → dict[K, list[V]]

Values by key.

This returns the transposed values of the cycler. Iterating

over a Cycler yields dicts with a single value for each key,

this method returns a dict of list which are the values

for the given key.

The returned value can be used to create an equivalent Cycler

using only +.

Returns

——-

transposedict

dict of lists of the values for each key.

change_key(old: K, new: K) → None

Change a key in this cycler to a new name.

Modification is performed in-place.

Does nothing if the old key is the same as the new key.

Raises a ValueError if the new key is already a key.

Raises a KeyError if the old key isn’t a key.

concat(right: Cycler[K, U]) → Cycler[K, V | U]

Concatenate Cyclers, as if chained using itertools.chain.

The keys must match exactly.

Examples

——–

>>> num = cycler(‘a’, range(3))

>>> let = cycler(‘a’, ‘abc’)

>>> num.concat(let)

cycler(‘a’, [0, 1, 2, ‘a’, ‘b’, ‘c’])

Returns

——-

Cycler

The concatenated cycler.

property keys: set[K]

The keys this Cycler knows about.

simplify() → Cycler[K, V]

Simplify the cycler into a sum (but no products) of cyclers.

Returns

——-

simple : Cycler