smash.PrcpIndicesResult

class smash.PrcpIndicesResult

Represents the precipitation indices result.

See also

Model.prcp_indices

Compute precipitations indices of the Model.

Notes

This class is essentially a subclass of dict with attribute accessors and an additional method PrcpIndicesResult.to_numpy.

Examples

>>> setup, mesh = smash.load_dataset("cance")
>>> model = smash.Model(setup, mesh)
>>> prcpind = model.prcp_indices()

Convert the result to a numpy.ndarray:

>>> prcpind_tonumpy = prcpind.to_numpy()
>>> prcpind_tonumpy
array([[[nan, nan, nan, nan],
        [nan, nan, nan, nan],
        [nan, nan, nan, nan],
        ...,
        [nan, nan, nan, nan],
        [nan, nan, nan, nan],
        [nan, nan, nan, nan]]], dtype=float32)

>>> prcpind_tonumpy.shape
(3, 1440, 4)

Attributes:

stdnumpy.ndarray

The precipitation spatial standard deviation.

d1numpy.ndarray

The first scaled moment [Zoccatelli et al., 2011].

d2numpy.ndarray

The second scaled moment [Zoccatelli et al., 2011].

vgnumpy.ndarray

The vertical gap [Emmanuel et al., 2015].

Methods

clear()
copy()
fromkeys(iterable[, value])	Create a new dictionary with keys from iterable and values set to value.
get(key[, default])	Return the value for key if key is in the dictionary, else default.
items()
keys()
pop(key[, default])	If the key is not found, return the default if given; otherwise, raise a KeyError.
popitem(/)	Remove and return a (key, value) pair as a 2-tuple.
setdefault(key[, default])	Insert key with a value of default if key is not in the dictionary.
to_numpy([axis])	Convert the PrcpIndicesResult object to a numpy.ndarray.
update([E, ]**F)	If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values()