siibra.features.tabular.interareal_connectivity

Classes

DFWithMeta	Two-dimensional, size-mutable, potentially heterogeneous tabular data.
InterarealConnectivityMatrix	Parcellation-averaged connectivity, providing one or more matrices of a

Functions

name_to_code(name)

Module Contents

class siibra.features.tabular.interareal_connectivity.DFWithMeta(data=None, index: pandas._typing.Axes | None = None, columns: pandas._typing.Axes | None = None, dtype: pandas._typing.Dtype | None = None, copy: bool | None = None)

Two-dimensional, size-mutable, potentially heterogeneous tabular data.

Data structure also contains labeled axes (rows and columns). Arithmetic operations align on both row and column labels. Can be thought of as a dict-like container for Series objects. The primary pandas data structure.

Parameters:

• data (ndarray (structured or homogeneous), Iterable, dict, or DataFrame) –

  Dict can contain Series, arrays, constants, dataclass or list-like objects. If data is a dict, column order follows insertion-order. If a dict contains Series which have an index defined, it is aligned by its index. This alignment also occurs if data is a Series or a DataFrame itself. Alignment is done on Series/DataFrame inputs.

  If data is a list of dicts, column order follows insertion-order.

• index (Index or array-like) – Index to use for resulting frame. Will default to RangeIndex if no indexing information part of input data and no index provided.

• columns (Index or array-like) – Column labels to use for resulting frame when data does not have them, defaulting to RangeIndex(0, 1, 2, …, n). If data contains column labels, will perform column selection instead.

• dtype (dtype, default None) – Data type to force. Only a single dtype is allowed. If None, infer.

• copy (bool or None, default None) –

  Copy data from inputs. For dict data, the default of None behaves like copy=True. For DataFrame or 2d ndarray input, the default of None behaves like copy=False. If data is a dict containing one or more Series (possibly of different dtypes), copy=False will ensure that these inputs are not copied.

  Changed in version 1.3.0.

See also

DataFrame.from_records

Constructor from tuples, also record arrays.

DataFrame.from_dict

From dicts of Series, arrays, or dicts.

read_csv

Read a comma-separated values (csv) file into DataFrame.

read_table

Read general delimited file into DataFrame.

read_clipboard

Read text from clipboard into DataFrame.

Notes

Please reference the User Guide for more information.

Examples

Constructing DataFrame from a dictionary.

>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df
   col1  col2
0     1     3
1     2     4

Notice that the inferred dtype is int64.

>>> df.dtypes
col1    int64
col2    int64
dtype: object

To enforce a single dtype:

>>> df = pd.DataFrame(data=d, dtype=np.int8)
>>> df.dtypes
col1    int8
col2    int8
dtype: object

Constructing DataFrame from a dictionary including Series:

>>> d = {'col1': [0, 1, 2, 3], 'col2': pd.Series([2, 3], index=[2, 3])}
>>> pd.DataFrame(data=d, index=[0, 1, 2, 3])
   col1  col2
0     0   NaN
1     1   NaN
2     2   2.0
3     3   3.0

Constructing DataFrame from numpy ndarray:

>>> df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
...                    columns=['a', 'b', 'c'])
>>> df2
   a  b  c
0  1  2  3
1  4  5  6
2  7  8  9

Constructing DataFrame from a numpy ndarray that has labeled columns:

>>> data = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],
...                 dtype=[("a", "i4"), ("b", "i4"), ("c", "i4")])
>>> df3 = pd.DataFrame(data, columns=['c', 'a'])
...
>>> df3
   c  a
0  3  1
1  6  4
2  9  7

Constructing DataFrame from dataclass:

>>> from dataclasses import make_dataclass
>>> Point = make_dataclass("Point", [("x", int), ("y", int)])
>>> pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])
   x  y
0  0  0
1  0  3
2  2  3

Constructing DataFrame from Series/DataFrame:

>>> ser = pd.Series([1, 2, 3], index=["a", "b", "c"])
>>> df = pd.DataFrame(data=ser, index=["a", "c"])
>>> df
   0
a  1
c  3

>>> df1 = pd.DataFrame([1, 2, 3], index=["a", "b", "c"], columns=["x"])
>>> df2 = pd.DataFrame(data=df1, index=["a", "c"])
>>> df2
   x
a  1
c  3

class siibra.features.tabular.interareal_connectivity.InterarealConnectivityMatrix(cohort: str, modality: str, regions: list, connector: siibra.retrieval.repositories.RepositoryConnector, decode_func: Callable, files: Dict[str, str], anchor: siibra.features.anchor.AnatomicalAnchor, description: str = '', datasets: list = [], prerelease: bool = False, id: str = None)

Parcellation-averaged connectivity, providing one or more matrices of a given modality for a given parcellation.

class ConnectivityConnector(url: str)

Base class for repository connectors.

class ZipFileLoader(zipfile, filename, decode_func, meta=None)

Loads a file from the zip archive, but mimics the behaviour of cached http requests used in other connectors.

property cached

cachefile

property data

filename

func

meta = None

zipfile

get_loader(filename, folder='', decode_func=None)

Get a lazy loader for a file, for loading data only once loader.data is accessed.

compute_centroids(space)

Computes the list of centroid coordinates corresponding to matrix rows, in the given reference space.

Parameters:

space (Space, str) –

Return type:

list[tuple(float, float, float)]

classmethod decode_meta(spec)

get_matrix(subject: str = None)

Returns a matrix as a pandas dataframe.

Parameters:

subject (str, default: None) – Name of the subject (see ConnectivityMatrix.subjects for available names). If None, the mean is taken in case of multiple available matrices.

Returns:

A square matrix with region names as the column and row names.

Return type:

pd.DataFrame

get_profile(region: str | siibra.core.region.Region, subject: str = None, min_connectivity: float = 0, max_rows: int = None, direction: Literal['column', 'row'] = 'column')

Extract a regional profile from the matrix, to obtain a tabular data feature with the connectivity as the single column. Rows are be sorted by descending connection strength.

Parameters:

• region (str, Region) –

• subject (str, default: None) –

• min_connectivity (float, default: 0) – Regions with connectivity less than this value are discarded.

• max_rows (int, default: None) – Max number of regions with highest connectivity.

• direction (str, default: 'column') – Choose the direction of profile extraction particularly for non-symmetric matrices. (‘column’ or ‘row’)

plot(subject: str = None, regions: str = None, logscale: bool = False, *args, backend='nilearn', **kwargs)

Plots the heatmap of the connectivity matrix using nilearn.plotting.

Parameters:

• subject (str) – Name of the subject (see ConnectivityMatrix.subjects for available names). If “mean” or None is given, the mean is taken in case of multiple available matrices.

• regions (list[str]) – Display the matrix only for selected regions. By default, shows all the regions. It can only be a subset of regions of the feature.

• logscale (bool) – Display the data in log10 scale

• backend (str) – “nilearn” or “plotly”

• **kwargs – Can take all the arguments nilearn.plotting.plot_matrix can take. See the doc at https://nilearn.github.io/stable/modules/generated/nilearn.plotting.plot_matrix.html

plot_matrix(subject: str = None, regions: List[str] = None, logscale: bool = False, *args, backend='nilearn', **kwargs)

Plots the heatmap of the connectivity matrix using nilearn.plotting.

Parameters:

• subject (str) – Name of the subject (see ConnectivityMatrix.subjects for available names). If “mean” or None is given, the mean is taken in case of multiple available matrices.

• regions (list[str]) – Display the matrix only for selected regions. By default, shows all the regions. It can only be a subset of regions of the feature.

• logscale (bool) – Display the data in log10 scale

• backend (str) – “nilearn” or “plotly”

• **kwargs – Can take all the arguments nilearn.plotting.plot_matrix can take. See the doc at https://nilearn.github.io/stable/modules/generated/nilearn.plotting.plot_matrix.html

plot_profile(region: str | siibra.core.region.Region, subject: str = None, min_connectivity: float = 0, max_rows: int = None, direction: Literal['column', 'row'] = 'column', logscale: bool = False, *args, backend='matplotlib', **kwargs)

cohort

property data

property name

Returns a short human-readable name of this feature.

regions

property subjects

Returns the subject identifiers for which matrices are available.

siibra.features.tabular.interareal_connectivity.name_to_code(name)