siibra.features.tabular.marmoset_cortical_profile

Classes

DFWithMeta	Two-dimensional, size-mutable, potentially heterogeneous tabular data.
MarmosetCalbindinDensityProfile	Represents a table of different measures anchored to a brain location.

Module Contents

class siibra.features.tabular.marmoset_cortical_profile.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.marmoset_cortical_profile.MarmosetCalbindinDensityProfile(url: str, anchor: siibra.features.anchor.AnatomicalAnchor, datasets: list = [], prerelease: bool = False, id: str = None, region: str | siibra.core.region.Region = None, connector: siibra.retrieval.repositories.RepositoryConnector = None, decode_func: Callable = None)

Represents a table of different measures anchored to a brain location.

Columns represent different types of values, while rows represent different samples. The number of columns might thus be interpreted as the feature dimension.

As an example, receptor fingerprints use rows to represent different neurotransmitter receptors, and separate columns for the mean and standard deviations measure across multiple tissue samples.

class CorticalProfileConnector(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.

classmethod decode_meta(spec)

plot(*args, backend='matplotlib', **kwargs)

Plot the profile.

Parameters:

• backend (str) – “matplotlib”, “plotly”, or others supported by pandas DataFrame plotting backend.

• **kwargs – Keyword arguments are passed on to the plot command. ‘layercolor’ can be used to specify a color for cortical layer shading.

DESCRIPTION = 'Marmoset Calbindin Cell Density Profile'

property data

Returns a matrix as a pandas dataframe.

pd.DataFrame

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

file

property key

region = None

region_code