biopsykit.utils.dtypes module¶

A couple of helper functions that ease the use of the typical biopsykit data formats.

biopsykit.utils.dtypes.Acc1dDataFrame = biopsykit.utils.dtypes._Acc1dDataFrame | pandas.core.frame.DataFrame¶

DataFrame containing 1-d acceleration data.

The dataframe is expected to have one of the following column sets:

[“acc”]: one level column index
[“acc_norm”]: one level column index

biopsykit.utils.dtypes.Acc3dDataFrame = biopsykit.utils.dtypes._Acc3dDataFrame | pandas.core.frame.DataFrame¶

DataFrame containing 3-d acceleration data.

The dataframe is expected to have one of the following column sets:

[“acc_x”, “acc_y”, “acc_z”]: one level column index
[(“acc”, “x”), (“acc”, “y”), (“acc”, “z”)]: two-level column index, first level specifying the channel (acceleration), second level specifying the axes

biopsykit.utils.dtypes.BPointDataFrame = biopsykit.utils.dtypes._BPointDataFrame | pandas.core.frame.DataFrame¶

DataFrame containing B-Point locations extracted from ICG data.

The dataframe is expected to have at least the following columns:

b_point_sample: The sample index of the B-point in the ICG signal

Optionally, the dataframe can contain additional columns, such as:

nan_reason: Reason why the B-point was set to NaN (e.g., “c_point_nan”, “no_zero_crossing”)

biopsykit.utils.dtypes.BiomarkerRawDataFrame = biopsykit.utils.dtypes._BiomarkerRawDataFrame | pandas.core.frame.DataFrame¶

DataFrame containing raw biomarker data in a standardized format.

Data needs to be in long-format and must have a pandas.MultiIndex with index level names:

subject: subject ID; can be number or string
sample: saliva sample ID; can be number or string

Additionally, the following index levels can be added to identify saliva values, such as:

condition: subject condition during the study (e.g., “Control” vs. “Condition”)
day: day ID, if saliva samples were collected over multiple days
night: night ID, if saliva samples were collected over multiple night
…

biopsykit.utils.dtypes.CPointDataFrame = biopsykit.utils.dtypes._CPointDataFrame | pandas.core.frame.DataFrame¶

DataFrame containing C-Point locations extracted from ICG data.

The dataframe is expected to have at least the following columns:

c_point_sample: The sample index of the C-point in the ICG signal

Optionally, the dataframe can contain additional columns, such as:

nan_reason: Reason why the C-point was set to NaN (e.g., “no_local_maxima”)

biopsykit.utils.dtypes.CodebookDataFrame = biopsykit.utils.dtypes._CodebookDataFrame | pandas.core.frame.DataFrame¶

DataFrame representing a codebook which encodes numerical and categorical values in a standardized format.

A CodebookDataFrame has an index level named variable. The column names are the numerical values (0, 1, …), the dataframe entries then represent the mapping of numerical value to categorical value for the variable.

biopsykit.utils.dtypes.EcgRawDataFrame = biopsykit.utils.dtypes._EcgRawDataFrame | pandas.core.frame.DataFrame¶

DataFrame containing raw ECG data of one subject.

The dataframe is expected to have the following columns:

ecg: Raw ECG signal

biopsykit.utils.dtypes.EcgResultDataFrame = biopsykit.utils.dtypes._EcgResultDataFrame | pandas.core.frame.DataFrame¶

DataFrame containing processed ECG data of one subject.

The dataframe is expected to have the following columns:

ECG_Raw: Raw ECG signal
ECG_Clean: Cleaned (filtered) ECG signal
ECG_Quality: ECG signal quality indicator in the range of [0, 1]
ECG_R_Peaks: 1.0 where R peak was detected in the ECG signal, 0.0 else
R_Peak_Outlier: 1.0 when a detected R peak was classified as outlier, 0.0 else
Heart_Rate: Computed Heart rate interpolated to signal length

biopsykit.utils.dtypes.Gyr1dDataFrame = biopsykit.utils.dtypes._Gyr1dDataFrame | pandas.core.frame.DataFrame¶

DataFrame containing 1-d gyroscope data.

The dataframe is expected to have one of the following column sets:

[“gyr”]: one level column index
[“gyr_norm”]: one level column index

biopsykit.utils.dtypes.Gyr3dDataFrame = biopsykit.utils.dtypes._Gyr3dDataFrame | pandas.core.frame.DataFrame¶

DataFrame containing 3-d gyroscope data.

The dataframe is expected to have one of the following column sets:

[“gyr_x”, “gyr_y”, “gyr_z”]: one level column index
[(“gyr”, “x”), (“gyr”, “y”), (“gyr”, “z”)]: two-level column index, first level specifying the channel (gyroscope), second level specifying the axes

biopsykit.utils.dtypes.HeartRateDataFrame = biopsykit.utils.dtypes._HeartRateDataFrame | pandas.core.frame.DataFrame¶

DataFrame containing heart rate time series data of one subject.

The dataframe is expected to have the following columns:

Heart_Rate: Heart rate data. Can either be instantaneous heart rate or resampled heart rate

biopsykit.utils.dtypes.HeartRatePhaseDict¶

Dictionary containing time-series heart rate data of one single subject split into different phases.

A HeartRatePhaseDict is a dictionary with the following format:

{ “phase_1” : hr_dataframe, “phase_2” : hr_dataframe, … }

Each hr_dataframe is a DataFrame with the following format:

time Index: pandas.DatetimeIndex with heart rate sample timestamps
Heart_Rate Column: heart rate values

biopsykit.utils.dtypes.HeartRateStudyDataDict¶

Dictionary with heart rate data from multiple phases collected during a psychological protocol.

A HeartRateStudyDataDict is a nested dictionary with time-series heart rate data from multiple phases, each phase containing data from different subjects. It is expected to have the level order phase, subject:

{

“phase_1” : { “subject1” : hr_dataframe, “subject2” : hr_dataframe, … },
“phase_2” : { “subject1” : hr_dataframe, “subject2” : hr_dataframe, … },
…

}

Each hr_dataframe is a DataFrame with the following format:

time Index: pandas.DatetimeIndex with heart rate sample timestamps
Heart_Rate Column: heart rate values

This dict results from rearranging a HeartRateSubjectDataDict by calling rearrange_subject_data_dict().

biopsykit.utils.dtypes.HeartRateSubjectDataDict = dict[str, dict[str, biopsykit.utils.dtypes._HeartRateDataFrame | pandas.core.frame.DataFrame]] | dict[str, dict[str, dict[str, biopsykit.utils.dtypes._HeartRateDataFrame | pandas.core.frame.DataFrame]]]¶

Dictionary with time-series heart rate data from multiple subjects collected during a psychological protocol.

A HeartRateSubjectDataDict is a nested dictionary with time-series heart rate data from multiple subjects, each containing data from different phases. It is expected to have the level order subject, phase:

{

“subject1” : { “phase_1” : hr_dataframe, “phase_2” : hr_dataframe, … },
“subject2” : { “phase_1” : hr_dataframe, “phase_2” : hr_dataframe, … },
…

}

Each hr_dataframe is a DataFrame with the following format:

time Index: pandas.DatetimeIndex with heart rate sample timestamps
Heart_Rate Column: heart rate values

This dictionary can, for instance, be rearranged to a HeartRateStudyDataDict, where the level order is reversed: phase, subject.

biopsykit.utils.dtypes.HeartbeatSegmentationDataFrame = biopsykit.utils.dtypes._HeartbeatSegmentationDataFrame | pandas.core.frame.DataFrame¶

DataFrame containing results of heartbeat segmentation.

The dataframe is expected to have at least the following columns:

start_sample: Start sample of segmented heartbeat
end_sample: End sample of segmented heartbeat
r_peak_sample: R-peak sample of segmented heartbeat

biopsykit.utils.dtypes.IcgRawDataFrame = biopsykit.utils.dtypes._IcgRawDataFrame | pandas.core.frame.DataFrame¶

DataFrame containing raw ICG data of one subject.

The dataframe is expected to have one of the following columns:

icg: Raw ICG signal
icg_der: Derivative of the ICG signal

biopsykit.utils.dtypes.ImuDataFrame = biopsykit.utils.dtypes._ImuDataFrame | pandas.core.frame.DataFrame¶

DataFrame containing 6-d inertial measurement (IMU) (acceleration and gyroscope) data.

Hence, an ImuDataFrame must both be a AccDataFrame and a GyrDataFrame.

The dataframe is expected to have one of the following column sets:

[“acc_x”, “acc_y”, “acc_z”, “gyr_x”, “gyr_y”, “gyr_z”]: one level column index
[(“acc”, “x”), (“acc”, “y”), (“acc”, “z”), (“gyr”, “x”), (“gyr”, “y”), (“gyr”, “z”)]: two-level column index, first level specifying the channel (acceleration and gyroscope), second level specifying the axes

biopsykit.utils.dtypes.MeanSeDataFrame = biopsykit.utils.dtypes._MeanSeDataFrame | pandas.core.frame.DataFrame¶

DataFrame containing mean and standard error of time-series data in a standardized format.

The resulting dataframe must at least the two columns mean and se. It can have additional index levels, such as phase, subphase or condition.

biopsykit.utils.dtypes.MergedStudyDataDict¶

Dictionary with merged time-series data of multiple subjects, split into different phases.

A MergedStudyDataDict is a dictionary with the following format:

{

“phase_1” : merged_dataframe,
“phase_2” : merged_dataframe,
…

}

This dict results from merging the inner dictionary into one dataframe by calling merge_study_data_dict().

Note

Merging the inner dictionaries requires that the dataframes of all subjects have same length within each phase.

Each merged_dataframe is a DataFrame with the following format:

Index: time. Name of index level: time
Columns: time series data per subject, each subject has its own column. Name of the column index level: subject

biopsykit.utils.dtypes.PepResultDataFrame = biopsykit.utils.dtypes._PepResultDataFrame | pandas.core.frame.DataFrame¶

DataFrame containing results of PEP extraction.

The dataframe is expected to have at least the following columns:

heartbeat_start_sample: Start sample of segmented heartbeat
heartbeat_end_sample: End sample of segmented heartbeat
r_peak_sample: R-peak sample of segmented heartbeat
q_peak_sample: Q-peak sample of segmented heartbeat
b_point_sample: B-point sample of segmented heartbeat
pep_sample: Pre-ejection period (PEP) in samples
pep_ms: Pre-ejection period (PEP) in milliseconds

Additionally, the dataframe can contain the following columns:

rr_interval_sample: RR interval between the previous and the current heartbeat in samples
rr_interval_ms: RR interval between the previous and the current heartbeat in milliseconds
heart_rate_bpm: Heart rate in beats per minute, derived from RR interval
nan_reason: Reason why the PEP was set to NaN (e.g., “r_peak_nan”, “no_zero_crossing”)

biopsykit.utils.dtypes.PhaseDict¶

Dictionary containing general time-series data of one single subject split into different phases.

A PhaseDict is a dictionary with the following format:

{ “phase_1” : dataframe, “phase_2” : dataframe, … }

Each dataframe is a DataFrame with the following format:

Index: pandas.DatetimeIndex with timestamps, name of index level: time

biopsykit.utils.dtypes.QPeakDataFrame = biopsykit.utils.dtypes._QPeakDataFrame | pandas.core.frame.DataFrame¶

DataFrame containing Q-peak locations extracted from ECG data.

The dataframe is expected to have at least the following columns:

q_peak_sample: The sample index of the Q-peak in the ECG signal

Optionally, the dataframe can contain additional columns, such as:

nan_reason: Reason why the Q-peak was set to NaN (e.g., “r_peak_nan”, “no_zero_crossing”)

biopsykit.utils.dtypes.RPeakDataFrame = biopsykit.utils.dtypes._RPeakDataFrame | pandas.core.frame.DataFrame¶

DataFrame containing R-peak locations of one subject extracted from ECG data.

The dataframe is expected to have the following columns:

R_Peak_Quality: Signal quality indicator (of the raw ECG signal) in the range of [0, 1]
R_Peak_Idx: Array index of detected R peak in the raw ECG signal
RR_Interval: Interval between the current and the successive R peak in seconds
R_Peak_Outlier: 1.0 when a detected R peak was classified as outlier, 0.0 else

biopsykit.utils.dtypes.SalivaFeatureDataFrame = biopsykit.utils.dtypes._SalivaFeatureDataFrame | pandas.core.frame.DataFrame¶

DataFrame containing feature computed from saliva data in a standardized format.

The resulting dataframe must at least have a subject index level and all column names need to begin with the saliva marker type (e.g. “cortisol”), followed by the feature name, separated by underscore ‘_’ Additionally, the name of the column index needs to be saliva_feature.

biopsykit.utils.dtypes.SalivaMeanSeDataFrame = biopsykit.utils.dtypes._SalivaMeanSeDataFrame | pandas.core.frame.DataFrame¶

DataFrame containing mean and standard error of saliva samples in a standardized format.

The resulting dataframe must at least have a sample index level and the two columns mean and se. It can have additional index levels, such as condition or time.

biopsykit.utils.dtypes.SalivaRawDataFrame = biopsykit.utils.dtypes._BiomarkerRawDataFrame | pandas.core.frame.DataFrame¶

DataFrame containing raw saliva data in a standardized format.

Data needs to be in long-format and must have a pandas.MultiIndex with index level names:

subject: subject ID; can be number or string
sample: saliva sample ID; can be number or string

Additionally, the following index levels can be added to identify saliva values, such as:

condition: subject condition during the study (e.g., “Control” vs. “Condition”)
day: day ID, if saliva samples were collected over multiple days
night: night ID, if saliva samples were collected over multiple night
…

biopsykit.utils.dtypes.SleepEndpointDataFrame = biopsykit.utils.dtypes._SleepEndpointDataFrame | pandas.core.frame.DataFrame¶

DataFrame containing sleep endpoints in a standardized format.

The resulting dataframe must at least have a date index level, and, optionally, further index levels like night.

The columns defining the sleep endpoints should follow a standardized naming convention, regardless of the origin (IMU sensor, sleep mattress, psg, etc.).

Required are the columns:

sleep_onset: Sleep Onset, i.e., time of falling asleep, in absolute time
wake_onset: Wake Onset, i.e., time of awakening, in absolute time
total_sleep_duration: Total sleep duration, i.e., time between Sleep Onset and Wake Onset, in minutes

The following columns are common, but not required:

total_duration: Total recording time, in minutes
net_sleep_duration: Net duration spent sleeping, in minutes
bed_interval_start: Bed Interval Start, i.e, time when participant went to bed, in absolute time
bed_interval_end: Bed Interval End, i.e, time when participant left bed, in absolute time
sleep_efficiency: Sleep Efficiency, defined as the ratio between net sleep duration and sleep duration in percent
sleep_onset_latency: Sleep Onset Latency, i.e., time in bed needed to fall asleep, in minutes
getup_latency: Get Up Latency, i.e., time in bed after awakening until getting up, in minutes
wake_after_sleep_onset: Wake After Sleep Onset (WASO), i.e., total time awake after falling asleep, in minutes
number_wake_bouts: Total number of wake bouts

The following columns are further possible:

total_time_light_sleep: Total time of light sleep, in minutes
total_time_deep_sleep: Total time of deep sleep, in minutes
total_time_rem_sleep: Total time of REM sleep, in minutes
total_time_awake: Total time of being awake, in minutes
count_snoring_episodes: Total number of snoring episodes
total_time_snoring: Total time of snoring, in minutes
heart_rate_avg: Average heart rate during recording, in bpm
heart_rate_min: Minimum heart rate during recording, in bpm
heart_rate_max: Maximum heart rate during recording, in bpm

biopsykit.utils.dtypes.SleepEndpointDict¶

Dictionary containing sleep endpoints in a standardized format.

The dict entries represent the sleep endpoints and should follow a standardized naming convention, regardless of the origin (IMU sensor, sleep mattress, psg, etc.).

Required are the entries:

sleep_onset: Sleep Onset, i.e., time of falling asleep, in absolute time
wake_onset: Wake Onset, i.e., time of awakening, in absolute time
total_sleep_duration: Total sleep duration, i.e., time between Sleep Onset and Wake Onset, in minutes

The following entries are common, but not required:

total_duration: Total recording time, in minutes
net_sleep_duration: Net duration spent sleeping, in minutes
bed_interval_start: Bed Interval Start, i.e, time when participant went to bed, in absolute time
bed_interval_end: Bed Interval End, i.e, time when participant left bed, in absolute time
sleep_efficiency: Sleep Efficiency, defined as the ratio between net sleep duration and sleep duration in percent
sleep_onset_latency: Sleep Onset Latency, i.e., time in bed needed to fall asleep, in minutes
getup_latency: Get Up Latency, i.e., time in bed after awakening until getting up, in minutes
wake_after_sleep_onset: Wake After Sleep Onset (WASO), i.e., total time awake after falling asleep, in minutes
sleep_bouts: List with start and end times of sleep bouts
wake_bouts: List with start and end times of wake bouts
number_wake_bouts: Total number of wake bouts

The following entries are, for instance, further possible:

total_time_light_sleep: Total time of light sleep, in minutes
total_time_deep_sleep: Total time of deep sleep, in minutes
total_time_rem_sleep: Total time of REM sleep, in minutes
total_time_awake: Total time of being awake, in minutes
count_snoring_episodes: Total number of snoring episodes
total_time_snoring: Total time of snoring, in minutes
heart_rate_avg: Average heart rate during recording, in bpm
heart_rate_min: Minimum heart rate during recording, in bpm
heart_rate_max: Maximum heart rate during recording, in bpm

biopsykit.utils.dtypes.SleepWakeDataFrame = biopsykit.utils.dtypes._SleepWakeDataFrame | pandas.core.frame.DataFrame¶

DataFrame containing sleep/wake predictions.

The dataframe is expected to have at least the following column(s):

[“sleep_wake”]: sleep/wake predictions where 1 indicates sleep and 0 indicates wake

biopsykit.utils.dtypes.StudyDataDict¶

Dictionary with data from multiple phases collected during a psychological protocol.

A StudyDataDict is a nested dictionary with time-series data from multiple phases, each phase containing data from different subjects. It is expected to have the level order phase, subject:

{

“phase_1” : { “subject1” : dataframe, “subject2” : dataframe, … },
“phase_2” : { “subject1” : dataframe, “subject2” : dataframe, … },
…

}

This dict results from rearranging a biopsykit.utils.dtypes.SubjectDataDict by calling rearrange_subject_data_dict().

biopsykit.utils.dtypes.SubjectConditionDataFrame = biopsykit.utils.dtypes._SubjectConditionDataFrame | pandas.core.frame.DataFrame¶

DataFrame containing subject IDs and condition assignment in a standardized format.

A SubjectConditionDataFrame has an index with subject IDs named subject and a column with the condition assignment named condition.

biopsykit.utils.dtypes.SubjectConditionDict¶

Dictionary containing subject IDs and condition assignment in a standardized format.

A SubjectConditionDict contains conditions as dictionary keys and a collection of subject IDs (list, numpy array, pandas Index) as dictionary values.

biopsykit.utils.dtypes.SubjectDataDict¶

Dictionary representing time-series data from multiple subjects collected during a psychological protocol.

A SubjectDataDict is a nested dictionary with time-series data from multiple subjects, each containing data from different phases. It is expected to have the level order subject, phase:

{

“subject1” : { “phase_1” : dataframe, “phase_2” : dataframe, … },
“subject2” : { “phase_1” : dataframe, “phase_2” : dataframe, … },
…

}

This dictionary can, for instance, be rearranged to a biopsykit.utils.dtypes.StudyDataDict, where the level order is reversed: phase, subject.

biopsykit.utils.dtypes.is_acc1d_dataframe(data, raise_exception=True)[source]¶

Check whether dataframe is a Acc1dDataFrame.

Parameters

data (DataFrame) – data to check if it is a Acc1dDataFrame
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a Acc1dDataFrame
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a Acc1dDataFrame

Return type

bool | None

See also

Acc1dDataFrame: dataframe format

biopsykit.utils.dtypes.is_acc3d_dataframe(data, raise_exception=True)[source]¶

Check whether dataframe is a Acc3dDataFrame.

Parameters

data (DataFrame) – data to check if it is a Acc3dDataFrame
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a Acc3dDataFrame
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a Acc3dDataFrame

Return type

bool | None

See also

Acc3dDataFrame: dataframe format

biopsykit.utils.dtypes.is_b_point_dataframe(data, raise_exception=True)[source]¶

Check whether dataframe is a BPointDataFrame.

Parameters

data (DataFrame) – data to check if it is a BPointDataFrame
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a BPointDataFrame
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a BPointDataFrame

Return type

bool | None

See also

BPointDataFrame: dataframe format

biopsykit.utils.dtypes.is_biomarker_raw_dataframe(data, biomarker_type, raise_exception=True)[source]¶

Check whether dataframe is a SalivaRawDataFrame.

Parameters

data (DataFrame) – data to check if it is a SalivaRawDataFrame
saliva_type (str or list of str) – type of saliva data (or list of saliva types) in the dataframe, e.g., “cortisol” or “amylase”
raise_exception (bool, optional) – whether to raise an exception or return a bool value
biomarker_type (str | list[str]) –

Returns

True if data is a SalivaRawDataFrame`
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a SalivaRawDataFrame

Return type

bool | None

See also

SalivaRawDataFrame: dataframe format

biopsykit.utils.dtypes.is_c_point_dataframe(data, raise_exception=True)[source]¶

Check whether dataframe is a CPointDataFrame.

Parameters

data (DataFrame) – data to check if it is a CPointDataFrame
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a CPointDataFrame
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a CPointDataFrame

Return type

bool | None

See also

CPointDataFrame: dataframe format

biopsykit.utils.dtypes.is_codebook_dataframe(data, raise_exception=True)[source]¶

Check whether dataframe is a CodebookDataFrame.

Parameters

data (DataFrame) – data to check if it is a CodebookDataFrame
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a CodebookDataFrame
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a CodebookDataFrame

Return type

bool | None

See also

CodebookDataFrame: dataframe format

biopsykit.utils.dtypes.is_ecg_raw_dataframe(data, raise_exception=True)[source]¶

Check whether dataframe is a EcgRawDataFrame.

Parameters

data (DataFrame) – data to check if it is a EcgRawDataFrame
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a EcgRawDataFrame
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a EcgRawDataFrame

Return type

bool | None

See also

EcgRawDataFrame: dataframe format

biopsykit.utils.dtypes.is_ecg_result_dataframe(data, raise_exception=True)[source]¶

Check whether dataframe is a EcgResultDataFrame.

Parameters

data (DataFrame) – data to check if it is a EcgResultDataFrame
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a EcgResultDataFrame
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a EcgResultDataFrame

Return type

bool | None

See also

EcgResultDataFrame: dataframe format

biopsykit.utils.dtypes.is_gyr1d_dataframe(data, raise_exception=True)[source]¶

Check whether dataframe is a Gyr1dDataFrame.

Parameters

data (DataFrame) – data to check if it is a Gyr1dDataFrame
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a Gyr1dDataFrame
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a Gyr1dDataFrame

Return type

bool | None

See also

Gyr1dDataFrame: dataframe format

biopsykit.utils.dtypes.is_gyr3d_dataframe(data, raise_exception=True)[source]¶

Check whether dataframe is a Gyr3dDataFrame.

Parameters

data (DataFrame) – data to check if it is a Gyr3dDataFrame
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a Gyr3dDataFrame
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a Gyr3dDataFrame

Return type

bool | None

See also

Gyr3dDataFrame: dataframe format

biopsykit.utils.dtypes.is_heart_rate_dataframe(data, raise_exception=True)[source]¶

Check whether dataframe is a HeartRateDataFrame.

Parameters

data (DataFrame) – data to check if it is a HeartRateDataFrame
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a HeartRateDataFrame
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a HeartRateDataFrame

Return type

bool | None

See also

HeartRateDataFrame: dataframe format

biopsykit.utils.dtypes.is_heartbeat_segmentation_dataframe(data, raise_exception=True)[source]¶

Check whether dataframe is a HeartbeatSegmentationDataFrame.

Parameters

data (DataFrame) – data to check if it is a HeartbeatSegmentationDataFrame
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a HeartbeatSegmentationDataFrame
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a HeartbeatSegmentationDataFrame

Return type

bool | None

See also

HeartbeatSegmentationDataFrame: dataframe format

biopsykit.utils.dtypes.is_hr_phase_dict(data, raise_exception=True)[source]¶

Check whether a dict is a HeartRatePhaseDict.

Parameters

data (dict) – dict to check
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a HeartRatePhaseDict
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a HeartRatePhaseDict

Return type

bool | None

See also

HeartRatePhaseDict: dictionary format

biopsykit.utils.dtypes.is_hr_subject_data_dict(data, raise_exception=True)[source]¶

Check whether a dict is a HeartRateSubjectDataDict.

Parameters

data (dict) – dict to check
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a HeartRateSubjectDataDict
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a HeartRateSubjectDataDict

Return type

bool | None

See also

HeartRateSubjectDataDict: dictionary format

biopsykit.utils.dtypes.is_icg_raw_dataframe(data, raise_exception=True)[source]¶

Check whether dataframe is a IcgRawDataFrame.

Parameters

data (DataFrame) – data to check if it is a IcgRawDataFrame
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a IcgRawDataFrame
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a IcgRawDataFrame

Return type

bool | None

See also

IcgRawDataFrame: dataframe format

biopsykit.utils.dtypes.is_imu_dataframe(data, raise_exception=True)[source]¶

Check whether dataframe is a ImuDataFrame.

Parameters

data (DataFrame) – data to check if it is a ImuDataFrame
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a ImuDataFrame
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a ImuDataFrame

Return type

bool | None

See also

ImuDataFrame: dataframe format

biopsykit.utils.dtypes.is_mean_se_dataframe(data, raise_exception=True)[source]¶

Check whether dataframe is a MeanSeDataFrame.

Parameters

data (DataFrame) – data to check if it is a MeanSeDataFrame
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a MeanSeDataFrame
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a MeanSeDataFrame

Return type

bool | None

See also

MeanSeDataFrame: dataframe format

biopsykit.utils.dtypes.is_merged_study_data_dict(data, raise_exception=True)[source]¶

Check whether a dict is a MergedStudyDataDict.

Parameters

data (dict) – dict to check
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a MergedStudyDataDict
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a MergedStudyDataDict

Return type

bool | None

See also

MergedStudyDataDict: dictionary format

biopsykit.utils.dtypes.is_pep_result_dataframe(data, raise_exception=True)[source]¶

Check whether dataframe is a PepResultDataFrame.

Parameters

data (DataFrame) – data to check if it is a PepResultDataFrame
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a PepResultDataFrame
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a PepResultDataFrame

Return type

bool | None

See also

PepResultDataFrame: dataframe format

biopsykit.utils.dtypes.is_phase_dict(data, raise_exception=True)[source]¶

Check whether a dict is a PhaseDict.

Parameters

data (dict) – dict to check
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a PhaseDict
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a PhaseDict

Return type

bool | None

See also

PhaseDict: dictionary format

biopsykit.utils.dtypes.is_q_peak_dataframe(data, raise_exception=True)[source]¶

Check whether dataframe is a QPeakDataFrame.

Parameters

data (DataFrame) – data to check if it is a QPeakDataFrame
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a QPeakDataFrame
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a QPeakDataFrame

Return type

bool | None

See also

QPeakDataFrame: dataframe format

biopsykit.utils.dtypes.is_r_peak_dataframe(data, raise_exception=True)[source]¶

Check whether dataframe is a RPeakDataFrame.

Parameters

data (DataFrame) – data to check if it is a RPeakDataFrame
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a RPeakDataFrame`
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a RPeakDataFrame

Return type

bool | None

See also

RPeakDataFrame: dataframe format

biopsykit.utils.dtypes.is_saliva_feature_dataframe(data, saliva_type, raise_exception=True)[source]¶

Check whether dataframe is a SalivaFeatureDataFrame.

Parameters

data (DataFrame) – data to check if it is a SalivaFeatureDataFrame
saliva_type (str or list of str) – type of saliva data in the dataframe, e.g., “cortisol” or “amylase”
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a SalivaFeatureDataFrame
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a SalivaFeatureDataFrame

Return type

bool | None

See also

SalivaFeatureDataFrame: dataframe format

biopsykit.utils.dtypes.is_saliva_mean_se_dataframe(data, raise_exception=True)[source]¶

Check whether dataframe is a SalivaMeanSeDataFrame.

Parameters

data (DataFrame) – data to check if it is a SalivaMeanSeDataFrame
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a SalivaMeanSeDataFrame`
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a SalivaMeanSeDataFrame

Return type

bool | None

See also

SalivaMeanSeDataFrame: dataframe format

biopsykit.utils.dtypes.is_saliva_raw_dataframe(data, saliva_type, raise_exception=True)[source]¶

Check whether dataframe is a SalivaRawDataFrame.

Parameters

data (DataFrame) – data to check if it is a SalivaRawDataFrame
saliva_type (str or list of str) – type of saliva data (or list of saliva types) in the dataframe, e.g., “cortisol” or “amylase”
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a SalivaRawDataFrame`
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a SalivaRawDataFrame

Return type

bool | None

See also

SalivaRawDataFrame: dataframe format

biopsykit.utils.dtypes.is_sleep_endpoint_dataframe(data, raise_exception=True)[source]¶

Check whether dataframe is a SleepEndpointDataFrame.

Parameters

data (DataFrame) – data to check if it is a SleepEndpointDataFrame
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a SleepEndpointDataFrame
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a SleepEndpointDataFrame

Return type

bool | None

See also

SleepEndpointDataFrame: dataframe format

biopsykit.utils.dtypes.is_sleep_endpoint_dict(data, raise_exception=True)[source]¶

Check whether dictionary is a SleepEndpointDict.

Parameters

data (dict) – data to check if it is a SleepEndpointDict
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a SleepEndpointDict
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a SleepEndpointDict

Return type

bool | None

See also

SleepEndpointDict: dictionary format

biopsykit.utils.dtypes.is_sleep_wake_dataframe(data, raise_exception=True)[source]¶

Check whether dataframe is a SleepWakeDataFrame.

Parameters

data (DataFrame) – data to check if it is a SleepWakeDataFrame
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a SleepWakeDataFrame
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a SleepWakeDataFrame

Return type

bool | None

See also

SleepWakeDataFrame: dataframe format

biopsykit.utils.dtypes.is_study_data_dict(data, raise_exception=True)[source]¶

Check whether a dict is a StudyDataDict.

Parameters

data (dict) – dict to check
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a StudyDataDict
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a StudyDataDict

Return type

bool | None

See also

StudyDataDict: dictionary format

biopsykit.utils.dtypes.is_subject_condition_dataframe(data, raise_exception=True)[source]¶

Check whether dataframe is a SubjectConditionDataFrame.

Parameters

data (DataFrame) – data to check if it is a SubjectConditionDataFrame
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a SubjectConditionDataFrame
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a SubjectConditionDataFrame

Return type

bool | None

See also

SubjectConditionDataFrame: dataframe format

biopsykit.utils.dtypes.is_subject_condition_dict(data, raise_exception=True)[source]¶

Check whether dataframe is a SubjectConditionDict.

Parameters

data (dict) – dict to check if it is a SubjectConditionDict
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a SubjectConditionDict
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a SubjectConditionDict

Return type

bool | None

See also

SubjectConditionDict: dictionary format

biopsykit.utils.dtypes.is_subject_data_dict(data, raise_exception=True)[source]¶

Check whether a dict is a SubjectDataDict.

Parameters

data (dict) – dict to check
raise_exception (bool, optional) – whether to raise an exception or return a bool value

Returns

True if data is a SubjectDataDict`
False otherwise (if raise_exception is False)

Raises

ValidationError – if raise_exception is True and data is not a SubjectDataDict

Return type

bool | None

See also

SubjectDataDict: dictionary format

biopsykit.utils.dataframe_handling module biopsykit.utils.exceptions module