neuralib.model.rastermap.core.RasterOptions

class neuralib.model.rastermap.core.RasterOptions

Bases: TypedDict

Run Rastermap model options. Refer to the rastermap.rastermap.setting_info()

__init__(*args, **kwargs)

Methods

__init__(*args, **kwargs)
clear()
copy()
fromkeys([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(k[,d])	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.
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()

Attributes

n_clusters	Number of clusters created from data before upsampling and creating embedding (any number above 150 will be very slow due to NP-hard sorting problem)
n_PCs	Number of PCs to use during optimization
time_lag_window	Number of time points into the future to compute cross-correlation, useful for sequence finding
locality	How local should the algorithm be -- set to 1.0 for highly local + sequence finding
n_splits	Recluster and sort n_splits times (increases local neighborhood preservation)
time_bin	Binning of data in time before PCA is computed
grid_upsample	How much to upsample clusters
mean_time	Whether to project out the mean over data samples at each timepoint, usually good to keep on to find structure
verbose	Whether to output progress during optimization
verbose_sorting	Output progress in travelling salesman
start_time
end_time

n_clusters: int

Number of clusters created from data before upsampling and creating embedding (any number above 150 will be very slow due to NP-hard sorting problem)

n_PCs: int

Number of PCs to use during optimization

time_lag_window: float

Number of time points into the future to compute cross-correlation, useful for sequence finding

locality: float

How local should the algorithm be – set to 1.0 for highly local + sequence finding

n_splits: int

Recluster and sort n_splits times (increases local neighborhood preservation)

time_bin: int

Binning of data in time before PCA is computed

grid_upsample: int

How much to upsample clusters

mean_time: bool

Whether to project out the mean over data samples at each timepoint, usually good to keep on to find structure

verbose: bool

Whether to output progress during optimization

__init__(*args, **kwargs)

__new__(**kwargs)

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

copy() → a shallow copy of D

fromkeys(value=None, /)

Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, /)

Return the value for key if key is in the dictionary, else default.

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

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

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

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.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

setdefault(key, default=None, /)

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None.  Update D from dict/iterable E and 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() → an object providing a view on D's values

verbose_sorting: bool

Output progress in travelling salesman

start_time: int

end_time: int