#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Definition of RSA Dataset class and TemporalDataset
@author: baihan, jdiedrichsen, bpeters, adkipnis
"""
from __future__ import annotations
from typing import List, Optional
from warnings import warn
from copy import deepcopy
import numpy as np
from pandas import DataFrame
from rsatoolbox.data.ops import merge_datasets
from rsatoolbox.util.data_utils import get_unique_unsorted
from rsatoolbox.util.data_utils import get_unique_inverse
from rsatoolbox.util.descriptor_utils import check_descriptor_length_error
from rsatoolbox.util.descriptor_utils import subset_descriptor
from rsatoolbox.util.descriptor_utils import num_index
from rsatoolbox.util.descriptor_utils import format_descriptor
from rsatoolbox.util.descriptor_utils import parse_input_descriptor
from rsatoolbox.util.descriptor_utils import desc_eq
from rsatoolbox.io.hdf5 import read_dict_hdf5
from rsatoolbox.io.pkl import read_dict_pkl
from rsatoolbox.data.base import DatasetBase
[docs]class Dataset(DatasetBase):
"""
Dataset class is a standard version of DatasetBase.
It contains one data set - or multiple data sets with the same structure
"""
def __eq__(self, other: Dataset) -> bool:
"""Test for equality
This magic method gets called when you compare two
Datasets objects: `ds1 == ds2`.
True if the objects are of the same type, and
measurements and descriptors are equal.
Args:
other (Dataset): The second Dataset to compare to
Returns:
bool: True if the objects' properties are equal
"""
return all([
isinstance(other, Dataset),
np.all(self.measurements == other.measurements),
self.descriptors == other.descriptors,
desc_eq(self.obs_descriptors, other.obs_descriptors),
desc_eq(self.channel_descriptors, other.channel_descriptors),
])
[docs] def copy(self) -> Dataset:
"""Return a copy of this object, with all properties
equal to the original's
Returns:
Dataset: Value copy
"""
return Dataset(
measurements=self.measurements.copy(),
descriptors=deepcopy(self.descriptors),
obs_descriptors=deepcopy(self.obs_descriptors),
channel_descriptors=deepcopy(self.channel_descriptors)
)
[docs] def split_obs(self, by):
""" Returns a list Datasets splited by obs
Args:
by(String): the descriptor by which the splitting is made
Returns:
list of Datasets, split by the selected obs_descriptor
"""
unique_values, inverse = get_unique_inverse(self.obs_descriptors[by])
dataset_list = []
for i_v, _ in enumerate(unique_values):
selection = np.where(inverse == i_v)[0]
measurements = self.measurements[selection, :]
descriptors = self.descriptors.copy()
obs_descriptors = subset_descriptor(
self.obs_descriptors, selection)
channel_descriptors = self.channel_descriptors
dataset = Dataset(measurements=measurements,
descriptors=descriptors,
obs_descriptors=obs_descriptors,
channel_descriptors=channel_descriptors,
check_dims=False)
dataset_list.append(dataset)
return dataset_list
[docs] def split_channel(self, by):
""" Returns a list Datasets splited by channels
Args:
by(String): the descriptor by which the split is done
Returns:
list of Datasets, split by the selected channel_descriptor
"""
unique_values, inverse = get_unique_inverse(self.channel_descriptors[by])
dataset_list = []
for i_v, v in enumerate(unique_values):
selection = np.where(inverse == i_v)[0]
measurements = self.measurements[:, selection]
descriptors = self.descriptors.copy()
descriptors[by] = v
obs_descriptors = self.obs_descriptors
channel_descriptors = subset_descriptor(
self.channel_descriptors, selection)
dataset = Dataset(measurements=measurements,
descriptors=descriptors,
obs_descriptors=obs_descriptors,
channel_descriptors=channel_descriptors,
check_dims=False)
dataset_list.append(dataset)
return dataset_list
[docs] def subset_obs(self, by, value):
""" Returns a subsetted Dataset defined by certain obs value
Args:
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
"""
selection = num_index(self.obs_descriptors[by], value)
measurements = self.measurements[selection, :]
descriptors = self.descriptors
obs_descriptors = subset_descriptor(
self.obs_descriptors, selection)
channel_descriptors = self.channel_descriptors
dataset = Dataset(measurements=measurements,
descriptors=descriptors,
obs_descriptors=obs_descriptors,
channel_descriptors=channel_descriptors)
return dataset
[docs] def subset_channel(self, by, value):
""" Returns a subsetted Dataset defined by certain channel value
Args:
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
"""
selection = num_index(self.channel_descriptors[by], value)
measurements = self.measurements[:, selection]
descriptors = self.descriptors
obs_descriptors = self.obs_descriptors
channel_descriptors = subset_descriptor(
self.channel_descriptors, selection)
dataset = Dataset(measurements=measurements,
descriptors=descriptors,
obs_descriptors=obs_descriptors,
channel_descriptors=channel_descriptors)
return dataset
[docs] def sort_by(self, by):
""" sorts the dataset by a given observation descriptor
Args:
by(String): the descriptor by which the dataset shall be sorted
Returns:
---
"""
desc = self.obs_descriptors[by]
order = np.argsort(desc, kind='stable')
self.measurements = self.measurements[order]
self.obs_descriptors = subset_descriptor(self.obs_descriptors, order)
[docs] def get_measurements(self):
"Getter function for measurements"
return self.measurements.copy()
[docs] def get_measurements_tensor(self, 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.
Args:
by(String):
the descriptor by which the splitting is made
Returns:
measurements_tensor (numpy.ndarray):
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"
"""
assert by in self.obs_descriptors.keys(), \
"third dimension not in obs_descriptors"
unique_values = get_unique_unsorted(self.obs_descriptors[by])
measurements_list = []
for v in unique_values:
selection = np.array([desc == v
for desc in self.obs_descriptors[by]])
measurements_subset = self.measurements[selection, :]
measurements_list.append(measurements_subset)
measurements_tensor = np.stack(measurements_list, axis=0)
measurements_tensor = np.swapaxes(measurements_tensor, 1, 2)
return measurements_tensor, unique_values
[docs] def odd_even_split(self, 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.
Args:
obs_desc (str):
Observation descriptor, basis for partitioning (must contained
in keys of dataset.obs_descriptors)
Returns:
odd_split (Dataset):
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
"""
assert obs_desc in self.obs_descriptors.keys(), \
"obs_desc must be contained in keys of dataset.obs_descriptors"
ds_part = self.split_obs(obs_desc)
odd_list = ds_part[0::2]
even_list = ds_part[1::2]
odd_split = merge_datasets(odd_list)
even_split = merge_datasets(even_list)
return odd_split, even_split
[docs] def nested_odd_even_split(self, 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.
Args:
l1_obs_desc (str):
Observation descriptor, basis for level 1 partitioning
(must contained in keys of dataset.obs_descriptors)
Returns:
odd_split (Dataset):
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
"""
assert l1_obs_desc and l2_obs_desc in self.obs_descriptors.keys(), \
"observation descriptors must be contained in keys " \
+ "of dataset.obs_descriptors"
ds_part = self.split_obs(l1_obs_desc)
odd_list = []
even_list = []
for partition in ds_part:
odd_split, even_split = partition.odd_even_split(l2_obs_desc)
odd_list.append(odd_split)
even_list.append(even_split)
odd_split = merge_datasets(odd_list)
even_split = merge_datasets(even_list)
return odd_split, even_split
[docs] @staticmethod
def from_df(df: DataFrame,
channels: Optional[List] = None,
channel_descriptor: Optional[str] = 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.
Args:
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: Dataset representing the data from the DataFrame
"""
if channels is None:
channels = [c for (c, t) in df.dtypes.items() if 'float' in str(t)]
if channel_descriptor is None:
channel_descriptor = 'name'
descriptors = set(df.columns).difference(channels)
ds_descriptors, obs_descriptors = dict(), dict()
for desc in descriptors:
if df[desc].unique().size == 1:
ds_descriptors[desc] = df[desc][0]
else:
obs_descriptors[desc] = list(df[desc])
return Dataset(
measurements=df[channels].values,
descriptors=ds_descriptors,
obs_descriptors=obs_descriptors,
channel_descriptors={channel_descriptor: channels}
)
[docs] def to_df(self, channel_descriptor: Optional[str] = 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.
Args:
channel_descriptor: Which channel descriptor to use to
label the data columns in the Dataframe. Defaults to the
first channel descriptor.
Returns:
DataFrame: A pandas DataFrame representing the Dataset
"""
desc = channel_descriptor or list(self.channel_descriptors.keys())[0]
ch_names = self.channel_descriptors[desc]
df = DataFrame(self.measurements, columns=ch_names)
all_descriptors = {**self.obs_descriptors, **self.descriptors}
for dname, dval in all_descriptors.items():
df[dname] = dval
return df
[docs]class TemporalDataset(Dataset):
"""
TemporalDataset for spatio-temporal datasets
Args:
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
"""
def __init__(self, measurements, descriptors=None,
obs_descriptors=None, channel_descriptors=None,
time_descriptors=None, check_dims=True):
if measurements.ndim != 3:
raise AttributeError(
"measurements must be in dimension n_obs x n_channel x time")
self.measurements = measurements
self.n_obs, self.n_channel, self.n_time = self.measurements.shape
if time_descriptors is None:
time_descriptors = {'time': np.arange(self.n_time)}
elif 'time' not in time_descriptors:
time_descriptors['time'] = np.arange(self.n_time)
raise Warning(
"there was no 'time' provided in dictionary time_descriptors"
"\n'time' will be set to (0, 1, ..., n_time-1)")
if check_dims:
check_descriptor_length_error(obs_descriptors,
"obs_descriptors",
self.n_obs
)
check_descriptor_length_error(channel_descriptors,
"channel_descriptors",
self.n_channel
)
check_descriptor_length_error(time_descriptors,
"time_descriptors",
self.n_time
)
self.descriptors = parse_input_descriptor(descriptors)
self.obs_descriptors = parse_input_descriptor(obs_descriptors)
self.channel_descriptors = parse_input_descriptor(channel_descriptors)
self.time_descriptors = parse_input_descriptor(time_descriptors)
def __eq__(self, other: TemporalDataset) -> bool:
return all([
isinstance(other, TemporalDataset),
np.all(self.measurements == other.measurements),
self.descriptors == other.descriptors,
desc_eq(self.obs_descriptors, other.obs_descriptors),
desc_eq(self.channel_descriptors, other.channel_descriptors),
desc_eq(self.time_descriptors, other.time_descriptors)
])
def __str__(self):
"""
defines the output of print
"""
string_desc = format_descriptor(self.descriptors)
string_obs_desc = format_descriptor(self.obs_descriptors)
string_channel_desc = format_descriptor(self.channel_descriptors)
string_time_desc = format_descriptor(self.time_descriptors)
if self.measurements.shape[0] > 5:
measurements = self.measurements[:5, :, :]
else:
measurements = self.measurements
return (f'rsatoolbox.data.{self.__class__.__name__}\n'
f'measurements = \n{measurements}\n...\n\n'
f'descriptors: \n{string_desc}\n\n'
f'obs_descriptors: \n{string_obs_desc}\n\n'
f'channel_descriptors: \n{string_channel_desc}\n'
f'time_descriptors: \n{string_time_desc}\n'
)
[docs] def copy(self) -> TemporalDataset:
"""Return a copy of this object, with all properties
equal to the original's
Returns:
Dataset: Value copy
"""
return TemporalDataset(
measurements=self.measurements.copy(),
descriptors=deepcopy(self.descriptors),
obs_descriptors=deepcopy(self.obs_descriptors),
channel_descriptors=deepcopy(self.channel_descriptors),
time_descriptors=deepcopy(self.time_descriptors)
)
[docs] def split_obs(self, by):
""" Returns a list TemporalDataset splited by obs
Args:
by(String): the descriptor by which the splitting is made
Returns:
list of TemporalDataset, splitted by the selected obs_descriptor
"""
unique_values, inverse = get_unique_inverse(self.obs_descriptors[by])
dataset_list = []
for i_v, _ in enumerate(unique_values):
selection = np.where(inverse == i_v)[0]
measurements = self.measurements[selection, :, :]
descriptors = self.descriptors
obs_descriptors = subset_descriptor(
self.obs_descriptors, selection)
channel_descriptors = self.channel_descriptors
time_descriptors = self.time_descriptors
dataset = TemporalDataset(
measurements=measurements,
descriptors=descriptors,
obs_descriptors=obs_descriptors,
channel_descriptors=channel_descriptors,
time_descriptors=time_descriptors,
check_dims=False)
dataset_list.append(dataset)
return dataset_list
[docs] def split_channel(self, by):
""" Returns a list of TemporalDataset split by channels
Args:
by(String): the descriptor by which the splitting is made
Returns:
list of TemporalDataset,
split by the selected channel_descriptor
"""
unique_values, inverse = get_unique_inverse(self.channel_descriptors[by])
dataset_list = []
for i_v, v in enumerate(unique_values):
selection = np.where(inverse == i_v)[0]
measurements = self.measurements[:, selection, :]
descriptors = self.descriptors.copy()
descriptors[by] = v
obs_descriptors = self.obs_descriptors
channel_descriptors = subset_descriptor(
self.channel_descriptors, selection)
time_descriptors = self.time_descriptors
dataset = TemporalDataset(
measurements=measurements,
descriptors=descriptors,
obs_descriptors=obs_descriptors,
channel_descriptors=channel_descriptors,
time_descriptors=time_descriptors,
check_dims=False)
dataset_list.append(dataset)
return dataset_list
[docs] def split_time(self, by):
""" Returns a list TemporalDataset splited by time
Args:
by(String): the descriptor by which the splitting is made
Returns:
list of TemporalDataset, splitted by the selected time_descriptor
"""
time = get_unique_unsorted(self.time_descriptors[by])
dataset_list = []
for v in time:
selection = [i for i, val in enumerate(self.time_descriptors[by])
if val == v]
measurements = self.measurements[:, :, selection]
descriptors = self.descriptors
obs_descriptors = self.obs_descriptors
channel_descriptors = self.channel_descriptors
time_descriptors = subset_descriptor(
self.time_descriptors, selection)
dataset = TemporalDataset(
measurements=measurements,
descriptors=descriptors,
obs_descriptors=obs_descriptors,
channel_descriptors=channel_descriptors,
time_descriptors=time_descriptors,
check_dims=False)
dataset_list.append(dataset)
return dataset_list
[docs] def bin_time(self, by, bins):
""" Returns an object TemporalDataset with time-binned data.
Args:
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.
"""
time = self.time_descriptors[by]
n_bins = len(bins)
binned_measurements = np.zeros((self.n_obs, self.n_channel, n_bins))
binned_time = np.zeros(n_bins)
for t in range(n_bins):
t_idx = np.isin(time, bins[t])
binned_measurements[:, :, t] = np.mean(
self.measurements[:, :, t_idx], axis=2)
binned_time[t] = np.mean(time[t_idx])
time_descriptors = self.time_descriptors.copy()
time_descriptors[by] = binned_time
# adding the bins as an additional descriptor currently
# does not work because of check_descriptor_length which transforms
# it into a numpy.array.
# time_descriptors['bins'] = [x for x in bins]
time_descriptors['bins'] = [
np.array2string(x, precision=2, separator=',')
for x in bins]
dataset = TemporalDataset(
measurements=binned_measurements,
descriptors=self.descriptors,
obs_descriptors=self.obs_descriptors,
channel_descriptors=self.channel_descriptors,
time_descriptors=time_descriptors)
return dataset
[docs] def subset_obs(self, by, value):
""" Returns a subsetted TemporalDataset defined by certain obs value
Args:
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
"""
selection = num_index(self.obs_descriptors[by], value)
measurements = self.measurements[selection, :, :]
descriptors = self.descriptors
obs_descriptors = subset_descriptor(
self.obs_descriptors, selection)
channel_descriptors = self.channel_descriptors
time_descriptors = self.time_descriptors
dataset = TemporalDataset(
measurements=measurements,
descriptors=descriptors,
obs_descriptors=obs_descriptors,
channel_descriptors=channel_descriptors,
time_descriptors=time_descriptors)
return dataset
[docs] def subset_channel(self, by, value):
""" Returns a subsetted TemporalDataset defined by
a certain channel descriptor value
Args:
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
"""
selection = num_index(self.channel_descriptors[by], value)
measurements = self.measurements[:, selection]
descriptors = self.descriptors
obs_descriptors = self.obs_descriptors
channel_descriptors = subset_descriptor(
self.channel_descriptors, selection)
time_descriptors = self.time_descriptors
dataset = TemporalDataset(
measurements=measurements,
descriptors=descriptors,
obs_descriptors=obs_descriptors,
channel_descriptors=channel_descriptors,
time_descriptors=time_descriptors)
return dataset
[docs] def subset_time(self, by, t_from, t_to):
""" Returns a subsetted TemporalDataset
with time between t_from and t_to
Args:
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 = get_unique_unsorted(self.time_descriptors[by])
sel_time = [t for t in time if t_from <= t <= t_to]
selection = num_index(self.time_descriptors[by], sel_time)
measurements = self.measurements[:, :, selection]
descriptors = self.descriptors
obs_descriptors = self.obs_descriptors
channel_descriptors = self.channel_descriptors
time_descriptors = subset_descriptor(
self.time_descriptors, selection)
dataset = TemporalDataset(
measurements=measurements,
descriptors=descriptors,
obs_descriptors=obs_descriptors,
channel_descriptors=channel_descriptors,
time_descriptors=time_descriptors)
return dataset
[docs] def sort_by(self, by):
""" sorts the dataset by a given observation descriptor
Args:
by(String): the descriptor by which the dataset shall be sorted
Returns:
---
"""
desc = self.obs_descriptors[by]
order = np.argsort(desc)
self.measurements = self.measurements[order]
self.obs_descriptors = subset_descriptor(self.obs_descriptors, order)
[docs] def time_as_channels(self) -> Dataset:
"""Converts this to a standard Dataset "long format",
where timepoints are represented as additional channels.
Args:
by (str): the descriptor which indicates the time dimension in
the time_descriptor.
Returns:
Dataset
"""
n_obs, n_chans, n_tps = self.measurements.shape
old_chn_des = self.channel_descriptors
chn_des = {k: np.repeat(v, n_tps) for (k, v) in old_chn_des.items()}
for k, v in self.time_descriptors.items():
chn_des[k] = np.tile(v, n_chans)
return Dataset(
measurements=self.measurements.reshape(n_obs, -1),
descriptors=deepcopy(self.descriptors),
obs_descriptors=deepcopy(self.obs_descriptors),
channel_descriptors=chn_des
)
[docs] def time_as_observations(self, by='time') -> Dataset:
"""Converts this to a standard Dataset "long format",
where timepoints are represented as additional observations.
Args:
by (str): the descriptor which indicates the time dimension in
the time_descriptor.
Returns:
Dataset
"""
time = get_unique_unsorted(self.time_descriptors[by])
descriptors = self.descriptors
channel_descriptors = self.channel_descriptors.copy()
measurements = np.empty([0, self.n_channel])
obs_descriptors = dict.fromkeys(self.obs_descriptors, [])
for key in self.time_descriptors:
obs_descriptors[key] = np.array([])
for v in time:
selection = [i for i, val in enumerate(self.time_descriptors[by])
if val == v]
measurements = np.concatenate((
measurements, self.measurements[:, :, selection].squeeze()),
axis=0)
for key in self.obs_descriptors:
obs_descriptors[key] = np.concatenate((
obs_descriptors[key], self.obs_descriptors[key].copy()),
axis=0)
for key in self.time_descriptors:
obs_descriptors[key] = np.concatenate((
obs_descriptors[key], np.repeat(
[self.time_descriptors[key][s]
for s in selection],
self.n_obs)),
axis=0)
dataset = Dataset(measurements=measurements,
descriptors=descriptors,
obs_descriptors=obs_descriptors,
channel_descriptors=channel_descriptors)
return dataset
[docs] def convert_to_dataset(self, by):
""" converts to Dataset long format.
time dimension is absorbed into observation dimension
Deprecated: Use `TemporalDataset.time_as_observations()` instead.
Args:
by(String): the descriptor which indicates the time dimension in
the time_descriptor
Returns:
Dataset
"""
warn('Deprecated: [TemporalDataset.convert_to_dataset()]. Replace by '
'[TemporalDataset.time_as_observations()]', DeprecationWarning)
return self.time_as_observations(by)
[docs] def to_dict(self):
""" Generates a dictionary which contains the information to
recreate the TemporalDataset object. Used for saving to disc
Returns:
data_dict(dict): dictionary with TemporalDataset information
"""
data_dict = {}
data_dict['measurements'] = self.measurements
data_dict['descriptors'] = self.descriptors
data_dict['obs_descriptors'] = self.obs_descriptors
data_dict['channel_descriptors'] = self.channel_descriptors
data_dict['time_descriptors'] = self.channel_descriptors
data_dict['type'] = type(self).__name__
return data_dict
[docs]def load_dataset(filename, file_type=None):
""" loads a Dataset object from disc
Args:
filename(String): path to file to load
"""
if file_type is None:
if isinstance(filename, str):
if filename[-4:] == '.pkl':
file_type = 'pkl'
elif filename[-3:] == '.h5' or filename[-4:] == 'hdf5':
file_type = 'hdf5'
if file_type == 'hdf5':
data_dict = read_dict_hdf5(filename)
elif file_type == 'pkl':
data_dict = read_dict_pkl(filename)
else:
raise ValueError('filetype not understood')
return dataset_from_dict(data_dict)
[docs]def dataset_from_dict(data_dict):
""" regenerates a Dataset object from the dictionary representation
Currently this function works for Dataset, DatasetBase,
and TemporalDataset objects
Args:
data_dict(dict): the dictionary representation
Returns:
data(Dataset): the regenerated Dataset
"""
if data_dict['type'] == 'Dataset':
data = Dataset(
data_dict['measurements'],
descriptors=data_dict['descriptors'],
obs_descriptors=data_dict['obs_descriptors'],
channel_descriptors=data_dict['channel_descriptors'])
elif data_dict['type'] == 'DatasetBase':
data = DatasetBase(
data_dict['measurements'],
descriptors=data_dict['descriptors'],
obs_descriptors=data_dict['obs_descriptors'],
channel_descriptors=data_dict['channel_descriptors'])
elif data_dict['type'] == 'TemporalDataset':
data = TemporalDataset(
data_dict['measurements'],
descriptors=data_dict['descriptors'],
obs_descriptors=data_dict['obs_descriptors'],
channel_descriptors=data_dict['channel_descriptors'],
time_descriptors=data_dict['time_descriptors'])
else:
raise ValueError('type of Dataset not recognized')
return data
[docs]def 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.
Args:
dataset_list (list):
List containing rsatoolbox datasets
Returns:
merged_dataset (Dataset):
rsatoolbox dataset created from all datasets in dataset_list
"""
warn('Deprecated: [rsatoolbox.data.dataset.merge_subsets()]. Replace by '
'[rsatoolbox.data.ops.merge_datasets()]', DeprecationWarning)
return merge_datasets(dataset_list)