File size: 2,095 Bytes
a35137b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 | import dataclasses
from enum import Enum
from typing import List, Optional, Tuple
from barista.data.dataframe_wrapper import DataframeWrapper
@dataclasses.dataclass
class MetadataSpatialGroupRow:
dataset: str
subject: str
session: str
subject_session: str
name: str # name/identifier of the spatial grouping
n_effective_components: int
max_elements_for_component: (
Tuple # tuple of size n_effective_components (or larger)
)
padding_indices: Tuple # tuple of size n_effective_components (or larger)
group_components: List # list of len number of channels -- List tuples that contains group info for each channel, useful for spatial encoding
group_ids: List # list of len number of channels -- List of int specifying which group each channel belongs to, useful for spatial masking
class SpatialGroupingName(Enum):
COORDS = "coords"
DESTRIEUX = "destrieux"
LOBES = "lobes"
class MetadataSpatialGroups(DataframeWrapper):
def _get_spatial_grouping_index(
self, subject_session: str, name: str
) -> Optional[int]:
indices = self.get_indices_matching_cols_values(
["subject_session", "name"], [subject_session, name]
)
if len(indices) == 0:
return None
assert (
len(indices) == 1
), f"More than one results for spatial grouping '{name}' for '{subject_session}'"
return indices[0]
def get_spatial_grouping(
self, subject_session: str, name: str
) -> MetadataSpatialGroupRow:
idx = self._get_spatial_grouping_index(subject_session, name)
if idx is None:
return None
a = self._df.iloc[idx].to_dict()
if "uniq_group_components" in a:
del a["uniq_group_components"]
return MetadataSpatialGroupRow(**a)
def remove_spatial_group(self, subject_session: str, name: str) -> int:
idx = self._get_spatial_grouping_index(subject_session, name)
if idx is None:
return 0
return self.drop_rows_based_on_indices([idx])
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