ada.io.geneactiv#

Module for reading and cutting GeneActive data.

Classes#

`GeneActivMVM`	A class for storing and handling epoched data generated by GeneActiv-style MVM method.
`RawGeneActiv`	A class for storing and handling raw data generated by GeneActiv.

Module Contents#

class GeneActivMVM[source]#

A class for storing and handling epoched data generated by GeneActiv-style MVM method.

cut_by_dates(start_date, end_date=None)[source]#

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.

Return type:

GeneActivMVM

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:

GeneActivMVM

cut_by_timestamp(start_ts, end_ts=None)[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:

GeneActivMVM

export(path)[source]#

Export object to .csv file fully resembling the manufacturer .csv format.

Parameters:: path (str) – Path to .csv file to which data will be exported.

static load_file(path)[source]#

Loading .csv file with epoched data generated by the GeneActiv software or the MVM method.

Parameters:: path (str) – Path .csv file.
Raises:: ValueError – Unsupported file format otherwise.
Returns:: Object containing data.
Return type:: GeneActivMVM

static preview_metadata(path)[source]#

Preview metadata without loading the whole file.

Parameters:: path (str) – Path to a file
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 button: numpy.ndarray#

Sum of button values (1=pressed) over epochs.

Return type:: numpy.ndarray

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 epoching_method_metadata: dict#

Metadata asssociated with the epoching method and its parameters.

Return type:: dict

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 lux: numpy.ndarray#

Epoch-wise mean lightmeter values in lux.

Return type:: numpy.ndarray

property metadata: dict#

Metadata associated with the raw recording and the device.

Return type:: dict

property mvm: numpy.ndarray#

Mean vector magnitude calculated as sum of vlens in the epoch.

Return type:: numpy.ndarray

property peak_lux: numpy.ndarray#

Maximum lightmeter value for each epoch (in lux).

Return type:: numpy.ndarray

property temperature: numpy.ndarray#

Epoch-wise mean recorded temperature in Celsius degrees.

Return type:: numpy.ndarray

property timestamp: numpy.ndarray#

Timestamp of the epoch’s end.

Return type:: numpy.ndarray

property to_score: numpy.ndarray#

Data to be scored by scoring algorithms.

Return type:: numpy.ndarray

property x: numpy.ndarray#

Mean acceleration (in g) along x axis.

Return type:: numpy.ndarray

property x_std: numpy.ndarray#

Epoch-wise standard deviation along x axis.

Return type:: numpy.ndarray

property y: numpy.ndarray#

Epoch-wise mean acceleration (in g) along y axis.

Return type:: numpy.ndarray

property y_std: numpy.ndarray#

Epoch-wise standard deviation along y axis.

Return type:: numpy.ndarray

property z: numpy.ndarray#

Epoch-wise mean acceleration (in g) along z axis.

Return type:: numpy.ndarray

property z_std: numpy.ndarray#

Epoch-wise standard deviation along z axis.

Return type:: numpy.ndarray

class RawGeneActiv[source]#

A class for storing and handling raw data generated by GeneActiv.

cut_by_dates(start_date, end_date)[source]#

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.

Return type:

RawGeneActiv

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:

RawGeneActiv

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:

RawGeneActiv

export(path)[source]#

Export object to .csv file fully resembling the manufacturer .csv format.

Parameters:: path (str) – Path to .csv file to which data will be exported.

static load_file(path)[source]#

Loading file generated by the GeneActiv device.

Parameters:: path (str) – Path to .bin or .csv file.
Raises:: ValueError – Unsupported file format otherwise.
Returns:: Object containing data.
Return type:: RawGeneActiv

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 button: numpy.ndarray#

Button values (1=pressed).

Return type:: numpy.ndarray

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 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 lux: numpy.ndarray#

Lightmeter values in lux.

Return type:: numpy.ndarray

property metadata: dict#

Metadata associated with the raw recording and the device.

Return type:: dict

property stationary_variance: float#

Variance of actigraph laying still (transducer noise).

Return type:: float

property temperature: numpy.ndarray#

Recorded temperature in Celsius degrees.

Return type:: numpy.ndarray

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. Here equal to vlen.

Return type:: numpy.ndarray

property vlen: numpy.ndarray#

Mean vector magnitude (in g) calculated as |sqrt(x**2 + y**2 + z**2) - 1|.

Return type:: numpy.ndarray

property x: numpy.ndarray#

Acceleration (in g) along x axis.

Return type:: numpy.ndarray

property y: numpy.ndarray#

Acceleration (in g) along y axis.

Return type:: numpy.ndarray

property z: numpy.ndarray#

Acceleration (in g) along z axis.

Return type:: numpy.ndarray