# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
"""A generic data structure with entries and variables.
The concept of dataset is a key element for machine learning, post-processing, data
analysis, ...
A :class:`.Dataset` is a pandas
`MultiIndex DataFrame <https://pandas.pydata.org/docs/user_guide/advanced.html>`_
storing series of data
representing the values of multidimensional features
belonging to different groups of features.
A :class:`.Dataset` can be set
either from a file (:meth:`~.Dataset.from_csv` and :meth:`~.Dataset.from_txt`)
or from a NumPy array (:meth:`~.Dataset.from_array`),
and can be enriched from a group of variables (:meth:`~.Dataset.add_group`)
or from a single variable (:meth:`~.Dataset.add_variable`).
An :class:`.AbstractFullCache` or an :class:`.OptimizationProblem`
can also be exported to a :class:`.Dataset`
using the methods :meth:`.AbstractFullCache.to_dataset`
and :meth:`.OptimizationProblem.to_dataset`.
"""
from __future__ import annotations
import logging
from numbers import Number
from pathlib import Path
from typing import Any
from typing import Callable
from typing import ClassVar
from typing import Final
from typing import Iterable
from typing import Union
from docstring_inheritance import GoogleDocstringInheritanceMeta
from numpy import arange
from numpy import array
from numpy import atleast_1d
from numpy import int64 as np_int64
from numpy import isin
from numpy import ndarray
from numpy import newaxis
from numpy import setdiff1d
from pandas import DataFrame
from pandas import MultiIndex
from pandas import read_csv
from pandas.core.frame import Axes
from pandas.core.frame import Dtype
from gemseo.utils.string_tools import repr_variable
LOGGER = logging.getLogger(__name__)
StrColumnType = Union[str, Iterable[str]]
IndexType = Union[str, int, Iterable[Union[str, int]]]
DataType = Union[ndarray, Iterable[Any], Any]
ComponentType = Union[int, Iterable[int]]
[docs]class Dataset(DataFrame, metaclass=GoogleDocstringInheritanceMeta):
"""A generic data structure with entries and variables.
A variable is defined by a name and a number of components.
For instance,
the variable ``"x"`` can have ``2`` components:
``0`` and ``1``.
Or the variable ``y`` can have ``4`` components:
``"a"``, ``"b"``, ``"c"`` and ``"d"``.
A variable belongs to a group of variables (default: :attr:`.DEFAULT_GROUP`).
Two variables can have the same name;
only the tuple ``(group_name, variable_name)`` is unique
and is therefore called a *variable identifier*.
Based on a set of variable identifiers,
:class:`.Dataset` is a collection of entries
corresponding to a set of variable identifiers.
An entry corresponds to an index of the :class:`.Dataset`.
A :class:`.Dataset` is a special pandas DataFrame
with the multi-index ``(group_name, variable_name, component)``.
It must be built from the methods
:meth:`.add_variable`, :meth:`.add_group`,
:meth:`.from_array`,
:meth:`.from_txt` and :meth:`.from_csv`.
Miscellaneous information that is not specific to an entry of the dataset
can be stored in the dictionary :attr:`.misc`,
as ``dataset.misc["year"] = 2023``.
Notes:
The columns of a data structure
(NumPy array, ``DataFrame``, :class:`.Dataset`, ...)
are called *features*.
The features of a :class:`.Dataset` include
all the components of all the variables of all the groups.
Warnings:
A :class:`.Dataset` behaves like any multi-index DataFrame
but its instantiation using the constructor ``dataset = Dataset(data, ...)``
can lead to some inconsistencies
(multi-index levels, index values, dtypes, ...).
Hence, the construction from the dedicated methods is recommended,
e.g. ``dataset = Dataset(); dataset.add_variable("x", data)``.
See Also:
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html
"""
name: str
"""The name of the dataset."""
misc: dict[str, Any]
"""Miscellaneous information specific to the dataset, and not to an entry."""
_COLUMN_LEVEL_NAMES: Final[tuple[str, str, str]] = (
"GROUP",
"VARIABLE",
"COMPONENT",
)
"""The names of the column levels of the multi-index DataFrame."""
__GROUP_LEVEL: Final[int] = 0
"""The group level in the multi-index column."""
__VARIABLE_LEVEL: Final[int] = 1
"""The variable level in the multi-index column."""
__COMPONENT_LEVEL: Final[int] = 2
"""The component level in the multi-index column."""
PARAMETER_GROUP: Final[str] = "parameters"
"""The group name for the parameters."""
GRADIENT_GROUP: Final[str] = "gradients"
"""The group name for the gradients."""
DEFAULT_GROUP: ClassVar[str] = PARAMETER_GROUP
"""The default group name for the variables."""
DEFAULT_VARIABLE_NAME: ClassVar[str] = "x"
"""The default name for the variable set with :meth:`add_group`."""
# DataFrame._metadata lists the normal properties
# which will be passed to manipulation results,
# e.g. dataset.get_view(group_names=group_names).name
# normal properties
_metadata: ClassVar[list[str]] = ["name", "misc"]
def __init__(
self,
data: ndarray | Iterable | dict | DataFrame | None = None,
index: Axes | None = None,
columns: Axes | None = None,
dtype: Dtype | None = None,
copy: bool | None = None,
*,
dataset_name: str = "",
) -> None:
"""
Args:
dataset_name: The name of the dataset.
""" # noqa: D205, D212, D415
if data is None and index is None and columns is None:
columns = MultiIndex(
levels=[[], [], []],
codes=[[], [], []],
names=self._COLUMN_LEVEL_NAMES,
)
super().__init__(
data=data, index=index, columns=columns, dtype=dtype, copy=copy
)
self.name = dataset_name or self.__class__.__name__
self.misc = {}
@property
def _constructor(self) -> type[Dataset]:
return Dataset
@property
def group_names(self) -> list[str]:
"""The names of the groups of variables in alphabetical order."""
return sorted(self.columns.levels[self.__GROUP_LEVEL].unique())
@property
def variable_names(self) -> list[str]:
"""The names of the variables in alphabetical order."""
return sorted(self.columns.levels[self.__VARIABLE_LEVEL].unique())
@property
def variable_identifiers(self) -> list[tuple[str, str]]:
"""The variable identifiers.
A variable identifier is the tuple ``(group_name, variable_name)``.
Notes:
A variable name can belong to more than one group
while a variable identifier is unique
as a group name is unique.
"""
return sorted(self.columns.droplevel(self.__COMPONENT_LEVEL).unique())
@property
def variable_names_to_n_components(self) -> dict[str, int]:
"""The names of the variables bound to their number of components."""
return {
variable_name: len(
self.get_view(variable_names=variable_name).columns.get_level_values(
self.__COMPONENT_LEVEL
)
)
for variable_name in self.variable_names
}
@property
def group_names_to_n_components(self) -> dict[str, int]:
"""The names of the groups bound to their number of components."""
return {
group_name: len(
self.get_view(group_names=group_name).columns.get_level_values(
self.__COMPONENT_LEVEL
)
)
for group_name in self.group_names
}
[docs] def get_group_names(self, variable_name: str) -> list[str]:
"""Return the names of the groups that contain a variable.
Args:
variable_name: The name of the variable.
Returns:
The names of the groups that contain the variable.
"""
# TODO: remove Try/Except when using exclusively Pandas>=2.0
try:
return sorted(
self.get_view(variable_names=variable_name)
.columns.get_level_values(self.__GROUP_LEVEL)
.unique()
)
except KeyError:
return []
[docs] def get_variable_names(self, group_name: str) -> list[str]:
"""Return the names of the variables contained in a group.
Args:
group_name: The name of the group.
Notes:
Assure compatibility pandas 1 and 2
by returning an empty list if KeyError is raised.
Returns:
The names of the variables contained in the group.
"""
# TODO: remove Try/Except when using exclusively Pandas>=2.0
try:
return sorted(
self.get_view(group_names=group_name)
.columns.get_level_values(self.__VARIABLE_LEVEL)
.unique()
)
except KeyError:
return []
[docs] def get_variable_components(self, group_name: str, variable_name: str) -> list[int]:
"""Return the components of a given variable.
Args:
group_name: The name of the group.
variable_name: The name of the variable.
Notes:
Assure compatibility pandas 1 and 2
by returning an empty list if KeyError is raised.
Returns:
The components of the variables.
"""
# TODO: remove Try/Except when using exclusively Pandas>=2.0
try:
return (
self.get_view(group_name, variable_name)
.columns.get_level_values(self.__COMPONENT_LEVEL)
.tolist()
)
except KeyError:
return []
[docs] def get_normalized(
self,
excluded_variable_names: StrColumnType = (),
excluded_group_names: StrColumnType = (),
use_min_max: bool = True,
center: bool = False,
scale: bool = False,
) -> Dataset:
r"""Return a normalized copy of the dataset.
Args:
excluded_variable_names: The names of the variables not to be normalized.
If empty, normalize all the variables.
excluded_group_names: The names of the groups not to be normalized.
If empty, normalize all the groups.
use_min_max: Whether to use the geometric normalization
:math:`(x-\min(x))/(\max(x)-\min(x))`.
center: Whether to center the variables so that they have a zero mean.
scale: Whether to scale the variables so that they have a unit variance.
Returns:
A normalized dataset.
"""
output_dataset = self.copy()
variables_to_normalize = setdiff1d(self.variable_names, excluded_variable_names)
groups_to_normalize = setdiff1d(self.group_names, excluded_group_names)
data = output_dataset.get_view(
group_names=groups_to_normalize, variable_names=variables_to_normalize
).to_numpy()
if use_min_max:
data = (data - data.min(axis=0)) / (data.max(axis=0) - data.min(axis=0))
if center:
data -= data.mean(axis=0)
if scale:
data /= data.std(axis=0)
output_dataset.update_data(
data,
group_names=groups_to_normalize,
variable_names=variables_to_normalize,
)
return output_dataset
[docs] def update_data(
self,
data: DataType,
group_names: StrColumnType = (),
variable_names: StrColumnType = (),
components: ComponentType = (),
indices: IndexType = (),
) -> None:
"""Replace the data of some existing indices and columns by a deepcopy of ``data``.
Args:
data: The new data to be inserted in the dataset.
group_names: The name(s) of the group(s) corresponding to these data.
If empty, consider all the groups.
variable_names: The name(s) of the variables(s) corresponding to these data.
If empty, consider all the variables of the considered groups.
components: The component(s) corresponding to these data.
If empty, consider all the components of the considered variables.
indices: The index (indices) of the dataset into which these data is to be inserted.
If empty, consider all the indices.
Notes:
Changing the type of data can turn a view into a copy.
"""
# Using Multi-index may lead to inconsistency between the slices and the result.
# Setting the sortorder to 0 seem to solve this inconsistency.
# We set this parameter only for extracting data,
# and set it to its previous value.
original_sortorder = self.columns.sortorder
self.columns.sortorder = 0
self.loc[
self.__to_slice_or_list(indices),
(
self.__to_slice_or_list(group_names),
self.__to_slice_or_list(variable_names),
self.__to_slice_or_list(components),
),
] = data
self.columns.sortorder = original_sortorder
[docs] def add_variable(
self,
variable_name: str,
data: DataType,
group_name: str = DEFAULT_GROUP,
components: ComponentType = (),
) -> None:
"""Add the data related to a variable.
If the variable does not exist, it is added.
If the variable already exists, non-existing components can be added, when specified.
It is impossible to add components that have already been added.
Args:
variable_name: The name of the variable.
data: The data.
group_name: The name of the group related to this variable.
components: The component(s) considered.
If empty, use ``[0, ..., n_features]``.
Warnings:
The shape of ``data`` must be consistent
with the number of entries of the dataset.
If the dataset is empty,
the number of entries will be deducted from ``data``.
Notes:
The data can be passed as:
- an array shaped as ``(n_entries, n_features)``,
- an array shaped as ``(1, n_features)``
that will be reshaped as ``(n_entries, n_features)``
by replicating the original array ``n_entries`` times,
- an array shaped as ``(n_entries,)``
that will be reshaped as ``(n_entries, 1)``,
- a scalar that will be converted into an array
shaped as ``(n_entries, 1)`` if ``components`` is empty
or ``(n_entries, n_features)``
where ``n_features`` will be deducted from ``components``.
Raises:
ValueError: If the group already has the added components
of the variable named ``variable_name``.
"""
n_components = components if isinstance(components, int) else len(components)
data = self.__force_to_2d_array(data, n_components)
if components:
components = atleast_1d(components).astype(np_int64)
else:
components = arange(data.shape[1], dtype=np_int64)
self.__check_existence_variable_components(
group_name, variable_name, components
)
self.__check_data_shape_consistency(
data, len(self) or data.shape[0], len(components)
)
self.__transform_multi_index_column_to_single_index_column()
apply_scalar_to_all_entries = len(data) == 1 and not self.empty
for component, data_column in zip(components, data.T):
if apply_scalar_to_all_entries:
value = data_column[0]
else:
value = data_column
self[(group_name, variable_name, component)] = value
self.__transform_single_index_column_to_multi_index_column()
self._reindex()
def __check_existence_variable_components(
self, group_name, variable_name, components
):
"""Check if the variable components are already in the dataset."""
if isin(
components, self.get_variable_components(group_name, variable_name)
).any():
raise ValueError(
f"The group {repr(group_name)} "
f"has already a variable {repr(variable_name)} defined."
)
@staticmethod
def __force_to_2d_array(
data: DataType, n_components: int = 0
) -> ndarray[(Any, Any), Any]:
"""Force data to be a 2D NumPy array.
Args:
data: A scalar, or an array shaped as ``(n,)`` or ``(n, n_components)``.
n_components: Only used when ``data`` is a scalar.
In that case,
the returned array is constant and shaped as ``(1, n_components)``.
Returns:
A 2D NumPy array of shape ``(n, n_components)``.
"""
if isinstance(data, Number) and n_components:
return array([[data] * n_components])
data = atleast_1d(data)
if data.ndim == 1:
return data[:, newaxis]
return data
[docs] def add_group(
self,
group_name: str,
data: DataType,
variable_names: StrColumnType = (),
variable_names_to_n_components: dict[str, int] | None = None,
) -> None:
"""Add the data related to a new group.
Args:
group_name: The name of the group.
data: The data.
variable_names: The names of the variables.
If empty, use :attr:`.DEFAULT_VARIABLE_NAME`.
variable_names_to_n_components: The number of components of the variables.
If ``variable_names`` is empty,
this argument is not considered.
If ``None``,
assume that all the variables have a single component.
Raises:
ValueError: If the group already exists.
"""
if group_name in self.group_names:
raise ValueError(f"The group {repr(group_name)} is already defined.")
data = self.__force_to_2d_array(data)
n_rows, n_columns = data.shape
if not variable_names:
variables = [(self.DEFAULT_VARIABLE_NAME, i) for i in range(n_columns)]
else:
variables = []
variable_names_to_n_components = variable_names_to_n_components or {}
for variable in atleast_1d(variable_names):
n_components = variable_names_to_n_components.get(variable, 1)
variables.extend([(variable, i) for i in range(n_components)])
self.__check_data_shape_consistency(data, n_rows, len(variables))
for (variable_name, component), data_column in zip(variables, data.T):
self.add_variable(
variable_name,
data_column[:, newaxis],
group_name=group_name,
components=component,
)
@staticmethod
def __check_data_shape_consistency(
data: ndarray[(Any, Any), float], n_rows: int, n_columns: int
) -> None:
"""Check the consistency of a data array with numbers of components and indices.
Args:
data: The data to be checked.
n_rows: The expected number of rows.
n_columns: The expected number of columns.
Raises:
ValueError: When the shape of the data is inconsistent.
"""
if data.shape not in ((n_rows, n_columns), (1, n_columns)):
raise ValueError(
"The data shape "
f"must be ({n_rows}, {n_columns}) or (1, {n_columns}); "
f"got {data.shape} instead."
)
[docs] def get_view(
self,
group_names: StrColumnType = (),
variable_names: StrColumnType = (),
components: ComponentType = (),
indices: IndexType = (),
) -> Dataset:
"""Return a specific group of rows and columns of the dataset.
Args:
group_names: The name(s) of the group(s).
If empty, consider all the groups.
variable_names: The name(s) of the variables(s).
If empty, consider all the variables of the considered groups.
components: The component(s).
If empty, consider all the components of the considered variables.
indices: The index (indices) of the dataset
into which these data is to be inserted.
If empty, consider all the indices.
Notes:
The order asked by the user is preserved for the returned Dataset.
See also :meth:`.loc`.
Returns:
The specific group of rows and columns of the dataset.
"""
# Using Multi-index may lead to inconsistency between the slices and the result.
# Setting the sortorder to 0 seem to solve this inconsistency.
# We set this parameter only for extracting data,
# and then set it to its previous value.
original_sortorder = self.columns.sortorder
self.columns.sortorder = 0
data = self.loc[
self.__to_slice_or_list(indices),
(
self.__to_slice_or_list(group_names),
self.__to_slice_or_list(variable_names),
self.__to_slice_or_list(components),
),
]
self.columns.sortorder = original_sortorder
return data
@staticmethod
def __to_slice_or_list(obj: Any) -> slice | list[Any]:
"""Convert an object to a ``slice`` or a ``list``.
Args:
obj: The object.
Returns:
The object as a ``slice`` or a ``list``.
"""
if isinstance(obj, slice):
return obj
if not isinstance(obj, ndarray) and obj != 0 and not obj:
return slice(None)
return atleast_1d(obj).tolist()
[docs] def rename_group(self, group_name: str, new_group_name: str) -> None:
"""Rename a group.
Args:
group_name: The group to rename.
new_group_name: The new group name.
"""
self.rename(columns={group_name: new_group_name}, level=0, inplace=True)
[docs] def rename_variable(
self,
variable_name: str,
new_variable_name: str,
group_name: str = "",
) -> None:
"""Rename a variable.
Args:
variable_name: The name of the variable.
new_variable_name: The new name of the variable.
group_name: The group of the variable.
If empty,
change the name of all the variables matching ``variable_name``.
"""
if group_name:
components = self.get_variable_components(group_name, variable_name)
self.__transform_multi_index_column_to_single_index_column()
self.rename(
columns={
(group_name, variable_name, component): (
group_name,
new_variable_name,
component,
)
for component in components
},
inplace=True,
)
self.__transform_single_index_column_to_multi_index_column()
else:
self.rename(
columns={variable_name: new_variable_name},
level=self.__VARIABLE_LEVEL,
inplace=True,
)
def __transform_multi_index_column_to_single_index_column(self) -> None:
"""Transform the multi-index columns into tuple columns."""
self.columns = self.columns.to_numpy()
def __transform_single_index_column_to_multi_index_column(self) -> None:
"""Transform the tuple columns into multi-index columns."""
self.columns = MultiIndex.from_tuples(
self.columns, names=self._COLUMN_LEVEL_NAMES
)
[docs] @classmethod
def from_array(
cls,
data: DataType,
variable_names: StrColumnType = (),
variable_names_to_n_components: dict[str, int] | None = None,
variable_names_to_group_names: dict[str, str] | None = None,
) -> Dataset:
"""Create a dataset from a NumPy array.
Args:
data: The data to be stored, with the shape (n_entries, n_components).
variable_names: The names of the variables.
If empty, use default names.
variable_names_to_n_components: The number of components of the variables.
If ``None``,
assume that all the variables have a single component.
variable_names_to_group_names: The groups of the variables.
If ``None``,
use :attr:`.Dataset.DEFAULT_GROUP` for all the variables.
Returns:
A dataset built from the NumPy array.
"""
if variable_names:
variable_to_group = variable_names_to_group_names or {}
variable_to_n_component = variable_names_to_n_components or {}
else:
_, n_total_components = data.shape
variable_names = [
"_".join([cls.DEFAULT_VARIABLE_NAME, str(component)])
for component in arange(n_total_components)
]
# Do not consider groups nor n_components.
variable_to_group = {}
variable_to_n_component = {}
columns = []
for variable in variable_names:
group = variable_to_group.get(variable, cls.DEFAULT_GROUP)
n_components = variable_to_n_component.get(variable, 1)
columns.extend(
[
(group, variable, component)
for component in arange(n_components, dtype=np_int64)
]
)
index = MultiIndex.from_tuples(columns, names=cls._COLUMN_LEVEL_NAMES)
dataset = cls(data, columns=index)
dataset._reindex()
return dataset
[docs] @classmethod
def from_txt(
cls,
file_path: Path | str,
variable_names: Iterable[str] = (),
variable_names_to_n_components: dict[str, int] | None = None,
variable_names_to_group_names: dict[str, str] | None = None,
delimiter: str = ",",
header: bool = True,
) -> Dataset:
"""Create a dataset from a text file.
See Also:
If the file contains multi-index information
and not just an array,
the :meth:`.from_csv` method is better suited.
Args:
file_path: The path to the file containing the data.
variable_names: The names of the variables.
If empty and ``header`` is ``True``,
read the names from the first line of the file.
If empty and ``header`` is ``False``,
use default names
based on the patterns the :attr:`.DEFAULT_NAMES`
associated with the different groups.
variable_names_to_n_components: The number of components of the variables.
If ``None``,
assume that all the variables have a single component.
variable_names_to_group_names: The groups of the variables.
If ``None``,
use :attr:`.DEFAULT_GROUP` for all the variables.
delimiter: The field delimiter.
header: Whether to read the names of the variables
on the first line of the file.
Returns:
A dataset built from the text file.
"""
header = "infer" if header else None
return cls.from_array(
read_csv(file_path, delimiter=delimiter, header=header).to_numpy(),
variable_names,
variable_names_to_n_components,
variable_names_to_group_names,
)
[docs] @classmethod
def from_csv(cls, file_path: Path | str, delimiter: str = ",") -> Dataset:
"""Set the dataset from a CSV file.
The first three rows contain the values of the multi-index
``(column_name, variable_name, component)``.
See Also:
If the file does not contain multi-index information
and not just an array,
the method :meth:`.from_txt` is better suited.
Args:
file_path: The path to the file containing the data.
delimiter: The field delimiter.
Returns:
A dataset built from the CSV file.
"""
dataframe = read_csv(file_path, delimiter=delimiter, header=[0, 1, 2])
dataframe.columns = dataframe.columns.set_levels(
dataframe.columns.levels[cls.__COMPONENT_LEVEL].astype(np_int64),
level=cls.__COMPONENT_LEVEL,
)
dataset = cls(dataframe)
dataset.columns = dataset.columns.set_names(cls._COLUMN_LEVEL_NAMES)
dataset._reindex()
return dataset
[docs] def get_columns(
self, variable_names: Iterable[str] = (), as_tuple: bool = False
) -> list[str]:
"""Return the columns based on variable names.
Args:
variable_names: The names of the variables.
If empty, use all the variables.
as_tuple: Whether the variable identifiers are returned as tuples.
Returns:
The columns,
either as a variable identifier ``(group_name, variable_name, component)``
or as a variable component name ``"variable_name[component]"``
(or ``"variable_name"`` if the dimension of the variable is 1).
"""
columns = self.get_view(variable_names=variable_names).columns.to_list()
if as_tuple:
return columns
else:
return [
repr_variable(
column[self.__VARIABLE_LEVEL],
column[self.__COMPONENT_LEVEL],
size=len(
self.get_variable_components(
column[self.__GROUP_LEVEL], column[self.__VARIABLE_LEVEL]
)
),
)
for column in columns
]
def _reindex(self) -> None:
"""Reindex the dataframe."""
[docs] def to_dict_of_arrays(
self, by_group: bool = True
) -> dict[str, ndarray | dict[str, ndarray]]:
"""Convert the dataset into a dictionary of NumPy arrays.
Args:
by_group: Whether the data are returned as
``{group_name: {variable_name: variable_value}}``.
Otherwise,
the data are returned either as ``{variable_name: variable_value}``
if only one group contains the variable ``variable_name``
or as ``{f"{group_name}:{variable_name}": variable_value}``
if at least two groups contain the variable ``variable_name``.
Returns:
The dataset expressed as a dictionary of NumPy arrays.
"""
if by_group:
return {
group_name: {
variable_name: self.get_view(group_name, variable_name).to_numpy()
for variable_name in self.get_variable_names(group_name)
}
for group_name in self.group_names
}
dict_of_arrays = {}
for group_name, variable_name in self.variable_identifiers:
if len(self.get_group_names(variable_name)) == 1:
name = variable_name
else:
name = f"{group_name}:{variable_name}"
dict_of_arrays[name] = self.get_view(
group_names=group_name, variable_names=variable_name
).to_numpy()
return dict_of_arrays
