ada.data_containers.generic

ada.data_containers.generic#

Classes#

GenericData

A class for storing and handling actigraphic data, both raw and epoched. Generic format compatible with various manufacturers and algorithms.

Module Contents#

class GenericData(data, metadata, fs, channel_names, epoching_method_metadata=None, scoring_method_metadata=None)[source]#

A class for storing and handling actigraphic data, both raw and epoched. Generic format compatible with various manufacturers and algorithms.

Parameters:

  • data (numpy.ndarray)

  • metadata (dict)

  • fs (float)

  • channel_names (list)

  • epoching_method_metadata (dict | None)

  • scoring_method_metadata (dict | None)

cut_by_dates(start_date, end_date)#

Create new object with the data cut by given dates.

Parameters:

  • start_date (str) – ISO-formated date of outputa data beginning.

  • end_date (str | None) – ISO-formated date of output data end. If None, last sample of output data will be last sample of input data.

Returns:

Object containing the cutted data of the same type as input data.

Return type:

_ActiData

cut_by_samples(start_sample, end_sample)[source]#

Create new object with the data cut by given indexes.

Parameters:

  • start_sample (int) – First sample of output data.

  • end_sample (int) – Sample after the last sample of output data.

Returns:

Object containing the cutted data.

Return type:

GenericData

cut_by_timestamp(start_ts, end_ts)[source]#

Create new object with the data cut by given timestamps.

Parameters:

  • start_ts (float) – Unix timestamp of output data beginning.

  • end_ts (float | None) – Unix timestamp of output data end. If None, last sample of output data will be last sample of input data.

Returns:

Object containing the cutted data.

Return type:

GenericData

export(path)[source]#

Exports object data to the generic format. All metadata are preserved in the file.

Parameters:

path (str) – Path to the .ada file.

static from_nongeneric(acti_data)[source]#

Converts any other actigraphic data object usable to the generic format. Usefull for standarization.

Parameters:

acti_data (_ActiData) – Actigraphic data object, either Raw or Epoched.

Raises:

ValueError – If the provided object is not actigraphic data.

Returns:

GenericData object containing the data from original object.

Return type:

GenericData

static load_file(path)[source]#

Loading file saved in the generic format provided by this package.

Parameters:

path (str) – Path to the .ada file.

Returns:

Object containing data.

Return type:

GenericData

static preview_metadata(path)[source]#

Preview of metadata without loading the file.

Parameters:

path (str) – Path to the file

Raises:

ValueError – Wrong format

Returns:

Metadata and channel names.

Return type:

tuple[dict, list[str]]

trim(time_from_start, time_from_end)#

Removes points from the recording beginning and end by given times.

Parameters:

  • time_from_start (str | None) – Time in HH:MM:SS.sss from the recording beginning. Microseconds can be ommited. If None, data is returned from first sample.

  • time_from_end (str | None) – Time in HH:MM:SS.sss from the recording end. Microseconds can be ommited. If None, data is returned to the end sample.

Returns:

Trimmed data container of the same type as input one.

Return type:

_ActiData

ActiData#
property channel_names: list[str]#

List of names for the channels stored in the data field.

Return type:

list[str]

property data: numpy.ndarray#

Actigraphic data in format (n_channels, n_samples).

Return type:

numpy.ndarray

property dynamic_range: float#

Dynamic range (in g) of the digital converter.

Return type:

float

property epoching_method_metadata: dict | None#

Metadata asssociated with the epoching method and its parameters. Returns None if data is not epoched.

Return type:

dict | None

property first_sample_timestamp: float#

Unix timestamp of first sample.

Return type:

float

property fs: float#

Sampling frequency of the data (not the same as recording frequency for epoched data).

Return type:

float

property id: str#

ID of the recording set during device configuration.

Return type:

str

property last_sample_timestamp: float#

Unix timestamp of last sample.

Return type:

float

property metadata: dict#

Metadata associated with the raw recording and the device.

Return type:

dict

property scoring_method_metadata: dict | None#

Metadata asssociated with the scoring algorithm and its parameters. Returns None if data is not scored.

Return type:

dict | None

property stationary_variance: float#

Variance of actigraph laying still (transducer noise).

Return type:

float

property timestamp: numpy.ndarray#

Unix timestamp of the data, relative to the recording beginning.

Return type:

numpy.ndarray

property to_score: numpy.ndarray#

Data to be scored by scoring algorithms.

Return type:

numpy.ndarray

property x: numpy.ndarray#

Data from x axis, raises Error if data is not 3-axial.

Return type:

numpy.ndarray

property y: numpy.ndarray#

Data from y axis, raises Error if data is not 3-axial.

Return type:

numpy.ndarray

property z: numpy.ndarray#

Data from z axis, raises Error if data is not 3-axial.

Return type:

numpy.ndarray

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