Module reference¶

devicely.EmpaticaReader¶

Empatica E4 is a wearable device that offers real-time physiological data acquisition such as blood volume pulse, electrodermal activity (EDA), heart rate, interbeat intervals, 3-axis acceleration and skin temperature.

class devicely.empatica.EmpaticaReader(path)¶

Read, timeshift and write data generated by Empatica E4.

start_times¶

Contain the timestamp of the first measurement for all measured signals (BVP, ACC, etc.).

Type: dict

sample_freqs¶

Contain the sampling frequencies of all measured signals in Hz.

Type: dict ]

IBI¶

Contain inter-beat interval data. The column “seconds_since_start” is the time in seconds between the start of measurements and the column “IBI” is the duration in seconds between consecutive beats.

Type: pandas.DataFrame

ACC¶

Contain the data measured with the onboard MEMS type 3-axis accelerometer, indexed by time of measurement.

Type: pandas.DataFrame

BVP¶

Contain blood volume pulse data, indexed by time of measurement.

Type: pandas.DataFrame

EDA¶

Contain data captured from the electrodermal activity sensor, indexed by time of measurement.

Type: pandas.DataFrame

HR¶

Contain heart rate data, indexed by time of measurement.

Type: pandas.DataFrame

TEMP¶

Contain temperature data, indexed by time of measurement.

Type: pandas.DataFrame

data¶

Joined dataframe of the ACC, BVP, EDA, HR and TEMP dataframes (see above). May contain NaN values because sampling frequencies differ across signals.

Type: pandas.DataFrame

timeshift(shift='random')¶

Timeshift all time related columns as well as the starting_times dict.

Parameters

shift (None/'random', pd.Timestamp or pd.Timedelta) –

If shift is not specified, shifts the data by a random time interval between one month and two years to the past.

If shift is a timdelta, adds that timedelta to all time-related attributes.

If shift is a timestamp, shifts the data such that the earliest entry has that timestamp. The remaining values will mantain the same time difference to the first entry.

write(dir_path)¶

Write the signal dataframes back to individual csv files formatted the same way as they were read.

Parameters

path (str) –

Path of the directory in which the csv files are created.

If the directory exists, the csv files are written using writing mode ‘w’ ignoring other files in the directory.

If the directory doe not exist, it will be created.

devicely.SpacelabsReader¶

Spacelabs (SL 90217) is an oscillometric blood pressure (BP) monitor which can be used to automatically track a person’s BP in specificed time intervals.

class devicely.spacelabs.SpacelabsReader(path)¶

Read, timeshift, deidentify and write data generated by Spacelabs(SL90217).

data¶

DataFrame with the values that were read from the abp file.

Type: DataFrame

subject¶

Contain the sujbect’s id. Can be changed for deidentification.

Type: str

valid_measurements¶

Contain the number of valid measurements in the abp file.

Type: str

metadata¶

The measurements’ metadata. Read from the xml at the bottom of the abp file. Can be erased for deidentification.

Type: dict

deidentify(subject_id=None)¶

Deidentify the data by removing the original XML metadata and subject id.

Parameters: subject_id (str, optional) – New subject id to be written in the deidentified file, by default None.

timeshift(shift='random')¶

Timeshift the data by shifting all time related columns.

Parameters

shift (None/'random', pd.Timestamp or pd.Timedelta) –

If shift is not specified, shifts the data by a random time interval between one month and two years to the past.

If shift is a timdelta, shifts the data by that timedelta.

If shift is a timestamp, shifts the data such that the earliest entry has that timestamp. The remaining values will mantain the same time difference to the first entry.

write(path)¶

Write the signals and metadata to the writing path in the same format as it was read.

Parameters: path (str) – Path to writing file. Writing mode: ‘w’. Use the file extension ‘abp’ to keep the SpaceLabs standard.

devicely.FarosReader¶

1-lead ECG monitor FarosTM 180 from Bittium is a one channel ECG monitor with sampling frequency up to 1000 Hz and a 3D acceleration sampling up to 100Hz.

class devicely.faros.FarosReader(path)¶

Read, timeshift and write data generated by Bittium Faros devices.

start_time¶

Start time of all measurements.

Type: pandas.Timestamp

sample_freqs¶

Sampling frequencies of all signals in Hz.

Type: dict

units¶

Units of all signals

Type: dict

ECG¶

ECG signal, indexed by timestamp.

Type: pandas.Series

ACC¶

Three ACC axes, indexed by timestamp.

Type: pandas.DataFrame

Marker¶

Markers, indexed by timestamp.

Type: pandas.Series

HRV¶

HRV signal, indexed by timestamp.

Type: pandas.Series

data¶

Contain all signals (ECG, ACC, Marker, HRV) indexed by timestamp. Since the signals have different sampling frequencies, many values will be NaN.

Type: DataFrame

join_dataframes()¶: Join the individual signal dataframes by timestamp. The resulting dataframe is saved in the attribute reader.data.

timeshift(shift='random')¶

Timeshift the data by shifting all time related values (i.e. start_time and data.index).

Parameters

shift (None/'random', pd.Timestamp or pd.Timedelta) –

If shift is not specified, shifts the data by a random time interval between one month and two years to the past.

If shift is a timdelta, shifts the data by that timedelta.

If shift is a timestamp, shifts the data such that the earliest entry has that timestamp. The remaining values will mantain the same time difference to the first entry.

write(path, file_format='directory')¶

Write the data either to an EDF file or to several files into a new directory. Because of the special structure of EDF files writing to EDF is only possible for readers that have been created from an EDF file and without any changes to the ACC, ECG, Marker, HRV and sample_freqs attributes. Because we want you to be able to modify the signals, you can write the data back to a directory of individual files. Writing to a directory is the preferred method and works in all cases.

Parameters

path (str) – Name of the file or directory to write the data to.
file_format ({'directory', 'edf'}, default 'directory') – Format of the written data.

devicely.EverionReader¶

” Biovotion Everion is a wearable device used for the continuous monitoring of vital signs. Currently, it measures the following vital signs: heart rate, blood pulse wave, heart rate variability, activity, SPO2, blood perfusion, respiration rate, steps, energy expenditure, skin temperature, EDA / galvanic skin response (GSR), barometric pressure and sleep.

class devicely.everion.EverionReader(path, signal_tags=[6, 7, 11, 12, 15, 19, 20, 21, 118, 119], sensor_tags=[80, 81, 82, 83, 84, 85, 86], feature_tags=[14])¶

Read, timeshift and write data generated by Biovotion Everion. You can find example data here. Each measurement has a tag which is an information about the type of measurement. By default, only some of the tags are read for signals, sensors and features. You can find these tags via self.default_signal_tags, self.default_sensor_tags and self.default_feature_tags. You can find out all available tags and what they mean by accessing the attributes EverionReader.SIGNAL_TAGS, EverionReader.SENSOR_TAGS and EverionReader.FEATURE_TAGS.

raw dataframes

These dataframes are accessible via the attributes aggregates, analytics_events, attributes_dailys, everion_events, features, sensors and signals. Each dataframe corresponds to a file in the reading path.

Type: dataframes

data¶

Joined version of the raw dataframes (aggregates, analytics_events, attributes_dailys, everion_events, features, sensors and signals).

Type: DataFrame

SIGNAL_TAGS¶

Signal tag numbers and their meaning.

Type: dict

SENSOR_TAGS¶

Sensor tag numbers and their meaning.

Type: dict

FEATURE_TAGS¶

Feature tag numbers and their meaning.

Type: dict

default_signal_tags¶

Subset of tags that are read by default for signals.

Type: list

default_sensor_tags¶

Subset of tags that are read by default for sensors.

Type: list

default_feature_tags¶

Subset of tags that are read by default for features.

Type: list

timeshift(shift='random')¶

Timeshift the data by shifting all time related columns in the joined dataframe (data) and raw dataframes (aggregates, analytics_events, attributes_dailys, everion_events, features, sensors, signals).

Parameters

shift (None/'random', pd.Timestamp or pd.Timedelta) –

If shift is not specified, shifts the data by a random time interval between one month and two years to the past.

If shift is a timdelta, shifts the data by that timedelta.

If shift is a timestamp, shifts the data such that the earliest entry has that timestamp. The remaining values will mantain the same time difference to the first entry.

write(path)¶

Write the raw dataframes back to individual files in the same format as they were read. Thus, the resulting directory can be read again using an EverionReader.

Parameters: path (str) – Path to the writing directory.

devicely.ShimmerReader¶

Shimmer Consensys GSR is a device that is used to collect sensor data in real time and it contains sensors such as GSR / EDA, photoplethysmography (PPG), 3-axis accelerometer, 3-axis gyroscope, 3-axis magnetometer & integrated altimeter.

class devicely.shimmer_plus.ShimmerPlusReader(path)¶

Read, timeshift and write data generated by Shimmer Consensys GSR.

data¶

DataFrame with the read signals. The column ‘Shimmer_40AC_Timestamp_Unix_CAL’ contains the timestamps of measurement.

Type: DataFrame

units¶

A unit for each column in the dataframe.

Type: pandas.Series

timeshift(shift='random')¶

Timeshift the data by shifting all time related columns.

Parameters

shift (None/'random', pd.Timestamp or pd.Timedelta) –

If shift is not specified, shifts the data by a random time interval between one month and two years to the past.

If shift is a timdelta, shifts the data by that timedelta.

If shift is a timestamp, shifts the data such that the earliest entry has that timestamp. The remaining values will mantain the same time difference to the first entry.

write(path)¶

Write the DataFrame and units to the writing path in the same format as it was read.

Parameters: path (str) – Path to writing csv file. Writing mode: ‘w’.

devicely.MuseReader¶

Module to process Muse data from the mind monitor application

class devicely.muse.MuseReader(path)¶

Parses, timeshifts and writes data generated by the Muse S headband using the Mind Monitor application.

data¶

dataframe of the read data

Type: DataFrame

timeshift(shift='random')¶

Shifts the index column ‘TimeStamp’.

Parameters

shift (None/'random', pd.Timestamp or pd.Timedelta) –

If shift is not specified, shifts the data by a random time interval between one month and two years to the past.

If shift is a timdelta, shifts the data by that timedelta.

If shift is a timestamp, shifts the data such that the earliest entry is at that timestamp and the remaining values keep the same time distance to the first entry.

write(path)¶

Writes the dataframe back into a csv file.

Parameters: path (str) – path to the write file.

devicely.TimeStampReader¶

TimeStamp for Android allows you to record the timestamp of an event at the time it occurs. It also allows you to create specific tags such as “Running” or “Walking” and timestamp those specific activities.

class devicely.time_stamp.TimeStampReader(path)¶

Read, timeshift and write data generated by the Android app TimeStamp

data¶

DataFrame with datetime index and ‘tag’ column.

Type: DataFrame

timeshift(shift='random')¶

Timeshift the data by shifting the index.

Parameters

shift (None/'random', pd.Timestamp or pd.Timedelta) –

If shift is not specified, shifts the ‘time’ column by a random time interval between one month and two years to the past.

If shift is a timdelta, shift the index by that timedelta.

If shift is a timestamp, shifts the data such that the earliest entry has that timestamp. The remaining values will mantain the same time difference to the first entry.

write(path)¶

Write the DataFrame stored in ‘data’ to ‘path’ in the same format as it was read.

Parameters: path (str) – Path to writing csv. Writing mode: ‘w’