ArrayDict

class torch_brain.data.ArrayDict(**kwargs)

Bases: object

A dictionary of arrays that share the same first dimension. The number of dimensions for each array can be different, but they need to be at least 1-dimensional.

Parameters:

**kwargs (ndarray | list | tuple | _SupportsArray) – arrays that shares the same first dimension.

Example

>>> from torch_brain.data import ArrayDict
>>> import numpy as np

>>> units = ArrayDict(
...     unit_id=["unit01", "unit02"],
...     brain_region=["M1", "M1"],
...     waveform_mean=np.random.rand(2, 48),
... )

>>> units
ArrayDict(
  unit_id=[2],
  brain_region=[2],
  waveform_mean=[2, 48]
)

keys()

Returns a list of all array attribute names.

Return type:

list[str]

select_by_mask(mask)

Index all arrays with a boolean mask and return a copy.

Parameters:

mask (ndarray) – Boolean array used for masking. The mask needs to be 1-dimensional, and of equal length as the object itself.

Example

>>> from torch_brain.data import ArrayDict
>>> import numpy as np

>>> units = ArrayDict(
...     unit_id=["unit01", "unit02"],
...     brain_region=["M1", "M1"],
...     waveform_mean=np.random.rand(2, 48),
... )

>>> units_subset = units.select_by_mask(np.array([True, False]))
>>> units_subset
ArrayDict(
  unit_id=[1],
  brain_region=[1],
  waveform_mean=[1, 48]
)

classmethod from_dataframe(df, unsigned_to_long=True, **kwargs)

Creates an ArrayDict object from a pandas DataFrame.

The columns in the DataFrame are converted to arrays when possible, otherwise they will be skipped.

Parameters:

• df – DataFrame.

• unsigned_to_long – If True, automatically converts unsigned integers to int64. Defaults to True.

to_hdf5(file)

Saves the data object to an HDF5 file.

Parameters:

file – HDF5 file.

import h5py
from torch_brain.data import ArrayDict

data = ArrayDict(
    unit_id=["unit01", "unit02"],
    brain_region=["M1", "M1"],
    waveform_mean=np.zeros((2, 48)),
)

with h5py.File("data.h5", "w") as f:
    data.to_hdf5(f)

classmethod from_hdf5(file)

Loads the data object from an HDF5 file.

Parameters:

file – HDF5 file.

Note

This method will load all data in memory, if you would like to use lazy loading, call LazyArrayDict.from_hdf5() instead.

import h5py
from torch_brain.data import ArrayDict

with h5py.File("data.h5", "r") as f:
    data = ArrayDict.from_hdf5(f)

materialize()

Materializes the data object, i.e., loads into memory all of the data that is still referenced in the HDF5 file.

Return type:

ArrayDict