nemos.pytrees.FeaturePytree

class nemos.pytrees.FeaturePytree(**kwargs)

Bases: UserDict

Pytree to represent GLM features.

This object is essentially a dictionary with strings as its keys and n-dimensional array-like objects as its values. FeaturePytree objects can only have a depth of 1, and we allow joint slicing.

This is intended to be used with jax.tree_util.tree_map and similar functionality.

Attributes

ndim	Force slicing only on the time axis.
shape	Return the sample points, i.e. the shared dimension.

__init__(**kwargs)

Methods

__init__(**kwargs)
clear()
copy()
fromkeys(iterable[, value])
get(k[,d])
items()
keys()
pop(k[,d])	If key is not found, d is returned if given, otherwise KeyError is raised.
popitem()	as a 2-tuple; but raise KeyError if D is empty.
setdefault(k[,d])
tree_flatten()	Flatten data.
tree_unflatten(aux_data, children)	Unflatten the data dict and return the class.
update([E, ]**F)	If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v
values()

__eq__(other)

Equality over a tree of arrays.

__getitem__(key)

Getitem using data dictionary keys.

__len__()

Return the number of sample points.

__repr__()

Represent a FeaturePytree.

__setitem__(key, value)

Check arrays and set the values.

clear() → None.  Remove all items from D.

copy()

classmethod fromkeys(iterable, value=None)

get(k[, d]) → D[k] if k in D, else d.  d defaults to None.

items() → a set-like object providing a view on D's items

keys() → a set-like object providing a view on D's keys

property ndim

Force slicing only on the time axis.

pop(k[, d]) → v, remove specified key and return the corresponding value.

If key is not found, d is returned if given, otherwise KeyError is raised.

popitem() → (k, v), remove and return some (key, value) pair

as a 2-tuple; but raise KeyError if D is empty.

setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D

property shape

Return the sample points, i.e. the shared dimension.

tree_flatten()

Flatten data.

classmethod tree_unflatten(aux_data, children)

Unflatten the data dict and return the class.

update([E, ]**F) → None.  Update D from mapping/iterable E and F.

If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

values() → an object providing a view on D's values