GaitDatasetFromData#

class mobgap.data.GaitDatasetFromData( _data: dict[tuple[Hashable, ...] | Hashable, dict[str, DataFrame]], _sampling_rate_hz: float | dict[tuple[Hashable, ...] | Hashable, float], _participant_metadata: dict[tuple[Hashable, ...] | Hashable, ParticipantMetadata] | None = None, _recording_metadata: dict[tuple[Hashable, ...] | Hashable, RecordingMetadata] | None = None, *, single_sensor_name: str = 'LowerBack', index_cols: list[str] | str | None = None, groupby_cols: list[str] | str | None = None, subset_index: DataFrame | None = None, )[source]#

Create dataset from data that is already loaded.

This is useful for testing and running pipelines on individual one-off datasets.

Parameters:

_data: The IMU data, as a dictionary of dictionaries of dataframes. The first level of the dictionary is the group label (e.g. participant id), the second level is the sensor label (e.g. “LowerBack”). The first level keys are turned into the index of the dataset. If you want to have multiple columns in the dataset index, use a tuple as the key. To customize the names of the index columns, use the index_cols parameter.
_sampling_rate_hz: The sampling rate of the IMU data in Hz. If you have different sampling rates for different groups, you can pass a dictionary with the group label as key and the sampling rate as value.
_participant_metadata: Metadata for each group. The keys of the dictionary are expected to be the same as the keys of the _data dictionary. The content of the metadata is theoretically not restricted, but if to use all pipelines, it should be at least have all keys available in ParticipantMetadata.
_recording_metadata: Recording metadata for each group. The keys of the dictionary are expected to be the same as the keys of the _data dictionary. The content of the metadata is theoretically not restricted, but if to use all pipelines, it should be at least have all keys available in RecordingMetadata.
single_sensor_name: The name of the sensor that is considered the “single sensor”. The data of this sensor is available via the data_ss attribute. The name should be a valid key in the data attribute.
index_cols: The name of the index columns. If your data keys are tuples, you can pass a list of strings to name the index columns.
groupby_cols: Columns to group the data by. See Dataset for details.
subset_index: The selected subset of the index. See Dataset for details.

Attributes:

data: The raw IMU data of all available sensors. This is a dictionary with the sensor name as key and the data as value.
data_ss: The IMU data of the “single sensor”. Compared to data, this is only just the data of a single sensor. Which sensor is considered the “single sensor” might be different for each dataset. Most datasets use a configuration of single_sensor_name=... to allow the user to select the sensor.
sampling_rate_hz: The sampling rate of the IMU data in Hz.
participant_metadata: General participant metadata. Contains at least the keys listed in ParticipantMetadata.
recording_metadata: General recording metadata. Contains at least the keys listed in RecordingMetadata.

See also

Dataset: For details about the groupby_cols and subset_index parameters.

Notes

To avoid creating copies of your data, this dataset will shallow copy the data and participant metadata when ~tpcp.clone is called on a dataset instance. This happens a couple of times in tpcp internal code, so we felt the need to reduce the memory footprint of this dataset. Just keep in mind that this means that if you modify the data or metadata of a dataset, you will also modify the original data and metadata. But, you should not modify the data or metadata of a dataset anyway, so that should be fine.

Methods

`UNITS`()	Representation of units IMU units in gait datasets.
`as_attrs`()	Return a version of the Dataset class that can be subclassed using `attrs` defined classes.
`as_dataclass`()	Return a version of the Dataset class that can be subclassed using dataclasses.
`assert_is_single`(groupby_cols, property_name)	Raise error if index does contain more than one group/row with the given groupby settings.
`assert_is_single_group`(property_name)	Raise error if index does contain more than one group/row.
`clone`()	Create a new instance of the class with all parameters copied over.
`create_index`()	Create the full index for the dataset.
`create_string_group_labels`(label_cols)	Generate a list of string labels for each group/row in the dataset.
`get_params`([deep])	Get parameters for this algorithm.
`get_subset`(*[, group_labels, index, bool_map])	Get a subset of the dataset.
`groupby`(groupby_cols)	Return a copy of the dataset grouped by the specified columns.
`index_as_tuples`()	Get all datapoint labels of the dataset (i.e. a list of the rows of the index as named tuples).
`is_single`(groupby_cols)	Return True if index contains only one row/group with the given groupby settings.
`is_single_group`()	Return True if index contains only one group.
`iter_level`(level)	Return generator object containing a subset for every category from the selected level.
`set_params`(**params)	Set the parameters of this Algorithm.

create_group_labels

__init__( _data: dict[tuple[Hashable, ...] | Hashable, dict[str, DataFrame]], _sampling_rate_hz: float | dict[tuple[Hashable, ...] | Hashable, float], _participant_metadata: dict[tuple[Hashable, ...] | Hashable, ParticipantMetadata] | None = None, _recording_metadata: dict[tuple[Hashable, ...] | Hashable, RecordingMetadata] | None = None, *, single_sensor_name: str = 'LowerBack', index_cols: list[str] | str | None = None, groupby_cols: list[str] | str | None = None, subset_index: DataFrame | None = None, ) → None[source]#

class UNITS[source]#

Representation of units IMU units in gait datasets.

Parameters:

acc: acceleration unit, default = ms^-2
gyr: gyroscope unit, default = deg/s
mag: magnetometer unit, default = uT

classmethod as_attrs()[source]#

Return a version of the Dataset class that can be subclassed using attrs defined classes.

Note, this requires attrs to be installed!

classmethod as_dataclass()[source]#: Return a version of the Dataset class that can be subclassed using dataclasses.

assert_is_single( groupby_cols: list[str] | str | None, property_name, ) → None[source]#

Raise error if index does contain more than one group/row with the given groupby settings.

This should be used when implementing access to data values, which can only be accessed when only a single trail/participant/etc. exist in the dataset.

Parameters:

groupby_cols: None (no grouping) or a valid subset of the columns available in the dataset index.
property_name: Name of the property this check is used in. Used to format the error message.

assert_is_single_group(property_name) → None[source]#

Raise error if index does contain more than one group/row.

Note that this is different from assert_is_single as it is aware of the current grouping. Instead of checking that a certain combination of columns is left in the dataset, it checks that only a single group exists with the already selected grouping as defined by self.groupby_cols.

Parameters:

property_name: Name of the property this check is used in. Used to format the error message.

clone() → Self[source]#

Create a new instance of the class with all parameters copied over.

This will create a new instance of the class itself and all nested objects

create_index() → DataFrame[source]#

Create the full index for the dataset.

This needs to be implemented by the subclass.

Warning

Make absolutely sure that the dataframe you return is deterministic and does not change between runs! This can lead to some nasty bugs! We try to catch them internally, but it is not always possible. As tips, avoid reliance on random numbers and make sure that the order is not depend on things like file system order, when creating an index by scanning a directory. Particularly nasty are cases when using non-sorted container like set, that sometimes maintain their order, but sometimes don’t. At the very least, we recommend to sort the final dataframe you return in create_index.

create_string_group_labels(label_cols: str | list[str]) → list[str][source]#

Generate a list of string labels for each group/row in the dataset.

Note

This has a different use case than the dataset-wide groupby. Using groupby reduces the effective size of the dataset to the number of groups. This method produces a group label for each group/row that is already in the dataset, without changing the dataset.

The output of this method can be used in combination with GroupKFold as the group label.

Parameters:

label_cols: The columns that should be included in the label. If the dataset is already grouped, this must be a subset of self.groupby_cols.

get_params(deep: bool = True) → dict[str, Any][source]#

Get parameters for this algorithm.

Parameters:

deep: Only relevant if object contains nested algorithm objects. If this is the case and deep is True, the params of these nested objects are included in the output using a prefix like nested_object_name__ (Note the two “_” at the end)

Returns:

params: Parameter names mapped to their values.

get_subset(

*,

group_labels: list[tuple[str, ...]] | None = None,

index: DataFrame | None = None,

bool_map: Sequence[bool] | None = None,

**kwargs: list[str] | str,

) → Self[source]#

Get a subset of the dataset.

Note

All arguments are mutable exclusive!

Parameters:

group_labels: A valid row locator or slice that can be passed to self.grouped_index.loc[locator, :]. This basically needs to be a subset of self.group_labels. Note that this is the only indexer that works on the grouped index. All other indexers work on the pure index.
index: pd.DataFrame that is a valid subset of the current dataset index.
bool_map: bool-map that is used to index the current index-dataframe. The list must be of same length as the number of rows in the index.
**kwargs: The key must be the name of an index column. The value is a list containing strings that correspond to the categories that should be kept. For examples see above.

Returns:

subset: New dataset object filtered by specified parameters.

property group: GroupLabelT#: Get the current group label. Deprecated, use group_label instead.

property group_label: GroupLabelT#

Get the current group label.

The group is defined by the current groupby settings.

Note, this attribute can only be used, if there is just a single group. This will return a named tuple. The tuple will contain only one entry if there is only a single groupby column or column in the index. The elements of the named tuple will have the same names as the groupby columns and will be in the same order.

property group_labels: list[GroupLabelT]#

Get all group labels of the dataset based on the set groupby level.

This will return a list of named tuples. The tuples will contain only one entry if there is only one groupby level or index column.

The elements of the named tuples will have the same names as the groupby columns and will be in the same order.

Note, that if one of the groupby levels/index columns is not a valid Python attribute name (e.g. in contains spaces or starts with a number), the named tuple will not contain the correct column name! For more information see the documentation of the rename parameter of collections.namedtuple.

For some examples and additional explanation see this example.

groupby( groupby_cols: list[str] | str | None, ) → Self[source]#

Return a copy of the dataset grouped by the specified columns.

This does not change the order of the rows of the dataset index.

Each unique group represents a single data point in the resulting dataset.

Parameters:

groupby_cols: None (no grouping) or a valid subset of the columns available in the dataset index.

property grouped_index: DataFrame#: Return the index with the groupby columns set as multiindex.

property groups: list[GroupLabelT]#: Get the current group labels. Deprecated, use group_labels instead.

property index: DataFrame#: Get index.

index_as_tuples() → list[GroupLabelT][source]#: Get all datapoint labels of the dataset (i.e. a list of the rows of the index as named tuples).

property index_is_unchanged: bool#

Returns True if the index is the same as the one created by create_index.

This can be used to check, if the index represents a subset or the actual full index. Note, that this is independent of the groupby_cols setting.

Note

Under the hood this uses the attrs functionality of pandas to store a hash of the original index on the dataframe. If the index is modified or a new index is created, this property does either not exist anymore or the content is modified.

is_single(groupby_cols: list[str] | str | None) → bool[source]#

Return True if index contains only one row/group with the given groupby settings.

If groupby_cols=None this checks if there is only a single row left. If you want to check if there is only a single group within the current grouping, use is_single_group instead.

Parameters:

groupby_cols: None (no grouping) or a valid subset of the columns available in the dataset index.

is_single_group() → bool[source]#: Return True if index contains only one group.

iter_level( level: str, ) → Iterator[Self][source]#

Return generator object containing a subset for every category from the selected level.

Parameters:

level: Optional str that sets the level which shall be used for iterating. This must be one of the columns names of the index.

Returns:

subset: New dataset object containing only one category in the specified level.

set_params(**params: Any) → Self[source]#

Set the parameters of this Algorithm.

To set parameters of nested objects use nested_object_name__para_name=.

property shape: tuple[int]#

Get the shape of the dataset.

This only reports a single dimension. This is equal to the number of rows in the index, if self.groupby_cols=None. Otherwise, it is equal to the number of unique groups.

Examples using `mobgap.data.GaitDatasetFromData`#

Custom Data and Datasets

GaitDatasetFromData#

Examples using mobgap.data.GaitDatasetFromData#

Examples using `mobgap.data.GaitDatasetFromData`#