rsatoolbox.data.dataset module

Definition of RSA Dataset class and TemporalDataset

@author: baihan, jdiedrichsen, bpeters, adkipnis

class rsatoolbox.data.dataset.Dataset(measurements, descriptors=None, obs_descriptors=None, channel_descriptors=None, check_dims=True)

Bases: DatasetBase

Dataset class is a standard version of DatasetBase. It contains one data set - or multiple data sets with the same structure

copy() → Dataset

Return a copy of this object, with all properties equal to the original’s

Returns:

Value copy

Return type:

Dataset

static from_df(df: DataFrame, channels: List | None = None, channel_descriptor: str | None = None) → Dataset

Create a Dataset from a Pandas DataFrame

Float columns are interpreted as channels, and their names stored as a channel descriptor “name”. Columns of any other datatype will be interpreted as observation descriptors, unless they have the same value throughout, in which case they will be interpreted as Dataset descriptor.

Parameters:

• df (DataFrame) – a long-format DataFrame

• channels (list) – list of column names to interpret as channels. By default all float columns are considered channels.

• channel_descriptor (str) – Name of the channel descriptor to create on the Dataset which contains the column names. Default is “name”.

Returns:

Dataset representing the data from the DataFrame

Return type:

Dataset

get_measurements()

Getter function for measurements

get_measurements_tensor(by)

Returns a tensor version of the measurements array, split by an observation descriptor. This procedure will keep the order of measurements the same as it is in the dataset.

Parameters:

by (String) – the descriptor by which the splitting is made

Returns:

n_obs_rest x n_channel x n_obs_by 3d-array, where n_obs_by is are the unique values that the obs_descriptor “by” takes, and n_obs_rest is the remaining number of observations per unique instance of “by”

Return type:

measurements_tensor (numpy.ndarray)

nested_odd_even_split(l1_obs_desc, l2_obs_desc)

Nested version of odd_even_split, where dataset is first partitioned according to the l1_obs_desc and each partition is again partitioned according to the l2_obs_desc (after which the actual oe-split occurs).

Useful for balancing, especially if the order of your measurements is inconsistent, or if the two descriptors are not orthogonalized. It’s advised to apply .sort_by(l2_obs_desc) to the output of this function.

Parameters:

l1_obs_desc (str) – Observation descriptor, basis for level 1 partitioning (must contained in keys of dataset.obs_descriptors)

Returns:

subset of the Dataset with odd list-indices after partitioning

according to obs_desc

even_split (Dataset):

subset of the Dataset with even list-indices after partitioning according to obs_desc

Return type:

odd_split (Dataset)

odd_even_split(obs_desc)

Perform a simple odd-even split on an rsatoolbox dataset. It will be partitioned into n different datasets, where n is the number of distinct values on dataset.obs_descriptors[obs_desc]. The resulting list will be split into odd and even (index) subset. The datasets contained in these subsets will then be merged.

Parameters:

obs_desc (str) – Observation descriptor, basis for partitioning (must contained in keys of dataset.obs_descriptors)

Returns:

subset of the Dataset with odd list-indices after partitioning

according to obs_desc

even_split (Dataset):

subset of the Dataset with even list-indices after partitioning according to obs_desc

Return type:

odd_split (Dataset)

sort_by(by)

sorts the dataset by a given observation descriptor

Parameters:

by (String) – the descriptor by which the dataset shall be sorted

Returns:

—

split_channel(by)

Returns a list Datasets splited by channels

Parameters:

by (String) – the descriptor by which the split is done

Returns:

list of Datasets, split by the selected channel_descriptor

split_obs(by)

Returns a list Datasets splited by obs

Parameters:

by (String) – the descriptor by which the splitting is made

Returns:

list of Datasets, split by the selected obs_descriptor

subset_channel(by, value)

Returns a subsetted Dataset defined by certain channel value

Parameters:

• by (String) – the descriptor by which the subset selection is made from channel dimension

• value – the value by which the subset selection is made from channel dimension

Returns:

Dataset, with subset defined by the selected channel_descriptor

subset_obs(by, value)

Returns a subsetted Dataset defined by certain obs value

Parameters:

• by (String) – the descriptor by which the subset selection is made from obs dimension

• value – the value by which the subset selection is made from obs dimension

Returns:

Dataset, with subset defined by the selected obs_descriptor

to_df(channel_descriptor: str | None = None) → DataFrame

returns a Pandas DataFrame representing this Dataset

Channels, observation descriptors and Dataset descriptors make up the columns. Rows represent observations.

Note that channel descriptors beyond the one used for the column names will not be represented.

Parameters:

channel_descriptor – Which channel descriptor to use to label the data columns in the Dataframe. Defaults to the first channel descriptor.

Returns:

A pandas DataFrame representing the Dataset

Return type:

DataFrame

class rsatoolbox.data.dataset.TemporalDataset(measurements, descriptors=None, obs_descriptors=None, channel_descriptors=None, time_descriptors=None, check_dims=True)

Bases: Dataset

TemporalDataset for spatio-temporal datasets

Parameters:

• measurements (numpy.ndarray) – n_obs x n_channel x time 3d-array,

• descriptors (dict) – descriptors (metadata)

• obs_descriptors (dict) – observation descriptors (all are array-like with shape = (n_obs,…))

• channel_descriptors (dict) – channel descriptors (all are array-like with shape = (n_channel,…))

• time_descriptors (dict) –

  time descriptors (alls are array-like with shape= (n_time,…))

  time_descriptors needs to contain one key ‘time’ that specifies the time-coordinate. if None is provided, ‘time’ is set as (0, 1, …, n_time-1)

Returns:

dataset object

bin_time(by, bins)

Returns an object TemporalDataset with time-binned data.

Parameters:

bins (array-like) – list of bins, with bins[i] containing the vector of time-points for the i-th bin

Returns:

a single TemporalDataset object

Data is averaged within time-bins. ‘time’ descriptor is set to the average of the binned time-points.

convert_to_dataset(by)

converts to Dataset long format.

time dimension is absorbed into observation dimension

Deprecated: Use TemporalDataset.time_as_observations() instead.

Parameters:

by (String) – the descriptor which indicates the time dimension in the time_descriptor

Returns:

Dataset

copy() → TemporalDataset

Return a copy of this object, with all properties equal to the original’s

Returns:

Value copy

Return type:

Dataset

sort_by(by)

sorts the dataset by a given observation descriptor

Parameters:

by (String) – the descriptor by which the dataset shall be sorted

Returns:

—

split_channel(by)

Returns a list of TemporalDataset split by channels

Parameters:

by (String) – the descriptor by which the splitting is made

Returns:

list of TemporalDataset,

split by the selected channel_descriptor

split_obs(by)

Returns a list TemporalDataset splited by obs

Parameters:

by (String) – the descriptor by which the splitting is made

Returns:

list of TemporalDataset, splitted by the selected obs_descriptor

split_time(by)

Returns a list TemporalDataset splited by time

Parameters:

by (String) – the descriptor by which the splitting is made

Returns:

list of TemporalDataset, splitted by the selected time_descriptor

subset_channel(by, value)

Returns a subsetted TemporalDataset defined by a certain channel descriptor value

Parameters:

• by (String) – the descriptor by which the subset selection is made from channel dimension

• value – the value by which the subset selection is made from channel dimension

Returns:

TemporalDataset, with subset defined by the selected channel_descriptor

subset_obs(by, value)

Returns a subsetted TemporalDataset defined by certain obs value

Parameters:

• by (String) – the descriptor by which the subset selection is made from obs dimension

• value – the value by which the subset selection is made from obs dimension

Returns:

TemporalDataset, with subset defined by the selected obs_descriptor

subset_time(by, t_from, t_to)

Returns a subsetted TemporalDataset with time between t_from and t_to

Parameters:

• by (String) – the descriptor by which the subset selection is made from channel dimension

• t_from – time-point from which onwards data should be subsetted

• t_to – time-point until which data should be subsetted

Returns:

TemporalDataset

with subset defined by the selected time_descriptor

time_as_channels() → Dataset

Converts this to a standard Dataset “long format”, where timepoints are represented as additional channels.

Parameters:

by (str) – the descriptor which indicates the time dimension in the time_descriptor.

Returns:

Dataset

time_as_observations(by='time') → Dataset

Converts this to a standard Dataset “long format”, where timepoints are represented as additional observations.

Parameters:

by (str) – the descriptor which indicates the time dimension in the time_descriptor.

Returns:

Dataset

to_dict()

Generates a dictionary which contains the information to recreate the TemporalDataset object. Used for saving to disc

Returns:

dictionary with TemporalDataset information

Return type:

data_dict(dict)

rsatoolbox.data.dataset.dataset_from_dict(data_dict)

regenerates a Dataset object from the dictionary representation

Currently this function works for Dataset, DatasetBase, and TemporalDataset objects

Parameters:

data_dict (dict) – the dictionary representation

Returns:

the regenerated Dataset

Return type:

data(Dataset)

rsatoolbox.data.dataset.load_dataset(filename, file_type=None)

loads a Dataset object from disc

Parameters:

filename (String) – path to file to load

rsatoolbox.data.dataset.merge_subsets(dataset_list)

Generate a dataset object from a list of smaller dataset objects (e.g., as generated by the subset_* methods). Assumes that descriptors, channel descriptors and number of channels per observation match.

Deprecated. Use rsatoolbox.data.ops.merge_datasets instead.

Parameters:

dataset_list (list) – List containing rsatoolbox datasets

Returns:

rsatoolbox dataset created from all datasets in dataset_list

Return type:

merged_dataset (Dataset)