Sleep Get

class src.openCHA.tasks.affect.sleep_get.SleepGet(*, name: str = 'affect_sleep_get', chat_name: str = 'AffectSleepGet', description: str = 'Returns the sleep data for a specific patient over a date or a period (if two dates are provided). This will return the detailed raw data and stores it in the datapipe.', dependencies: List[str] = [], inputs: List[str] = ["user ID in string. It can be refered as user, patient, individual, etc. Start with 'par_' following with a number (e.g., 'par_1').", 'start date of the sleep data in string with the following format: `%Y-%m-%d`', "end date of the sleep data in string with the following format: `%Y-%m-%d`. If there is no end date, the value should be an empty string (i.e., '')"], outputs: List[str] = ['returns an array of json objects which contains the following keys:\n**date (in milliseconds)**: epoch format\n**total_sleep_time (in minutes)**: is Total amount of sleep (a.k.a. sleep duration) registered during the sleep period.\n**awake_duration (in minutes)**: is the total amount of awake time registered during the sleep period.\n**light_sleep_duration (in minutes)**: is the total amount of light (N1 or N2) sleep registered during the sleep period.\n**rem_sleep_duration (in minutes)**: is the total amount of REM sleep registered during the sleep period.\n**deep_sleep_duration (in minutes)**: is the total amount of deep (N3) sleep registered during the sleep period.\n**sleep_onset_latency (in minutes)**: is detected latency from bedtime_start to the beginning of the firstfive minutes of persistent sleep.\n**midpoint_time_of_sleep (in minutes)**: is the time from the start of sleep to the midpoint of sleep. The midpoint ignores awake periods.\n**sleep_efficiency**: is the percentage of the sleep period spent asleep (100% * sleep duration / time in bed).\n**average_heart_rate**: is the average heart rate registered during the sleep period.\n**minimum_heart_rate**: is the lowest heart rate (5 minutes sliding average) registered during the sleep period.\n**rmssd is the average**: Root Mean Square of Successive Differences (RMSSD) registered during the sleep period.\n**average_breathing_rate**: is the average breathing rate registered during the sleep period.\n**temperature_variation**: is the skin temperature deviation from the long-term temperature average.'], datapipe: DataPipe = None, output_type: bool = True, return_direct: bool = False, file_name: str = 'sleep.csv', device_name: str = 'oura', local_dir: str = 'data/affect', columns_to_keep: List[str] = ['date', 'total', 'awake', 'light', 'rem', 'deep', 'onset_latency', 'midpoint_time', 'efficiency', 'hr_average', 'hr_lowest', 'rmssd', 'breath_average', 'temperature_delta'], columns_revised: List[str] = ['date', 'total_sleep_time', 'awake_duration', 'light_sleep_duration', 'rem_sleep_duration', 'deep_sleep_duration', 'sleep_onset_latency', 'midpoint_time_of_sleep', 'sleep_efficiency', 'average_heart_rate', 'minimum_heart_rate', 'rmssd', 'average_breathing_rate', 'temperature_variation'], variables_in_seconds: List[str] = ['total_sleep_time', 'awake_duration', 'light_sleep_duration', 'rem_sleep_duration', 'deep_sleep_duration', 'sleep_onset_latency', 'midpoint_time_of_sleep'])

Description:

This tasks gets sleep affect data for specific patient.

_execute(inputs: List[Any]) → str

Abstract method representing the execution of the task. You should implement this method based on your need. This method is called by the execute method that provides the parsed inputs to this method.

Parameters:

inputs (List[Any]) – Input data for the task.

Returns:

Result of the task execution.

Return type:

str

Raises:

NotImplementedError – Subclasses must implement the execute method.