qualia_core.preprocessing package

Submodules

• qualia_core.preprocessing.BandPassFilter module
  • BandPassFilter
• qualia_core.preprocessing.Class2BinMatrix module
  • Class2BinMatrix
• qualia_core.preprocessing.CopySet module
  • CopySet
    • CopySet.import_data()
• qualia_core.preprocessing.DatamodelConverter module
  • DatamodelConverter
    • DatamodelConverter.import_data()
• qualia_core.preprocessing.DatasetSplitter module
  • DatasetSplitter
    • DatasetSplitter.import_data()
• qualia_core.preprocessing.DatasetSplitterBySubjects module
  • DatasetSplitterBySubjects
    • DatasetSplitterBySubjects.import_data()
• qualia_core.preprocessing.MFCC module
  • MFCC
• qualia_core.preprocessing.Normalize module
  • Normalize
    • Normalize.z_score()
    • Normalize.min_max()
    • Normalize.methods
• qualia_core.preprocessing.Preprocessing module
  • Preprocessing
    • Preprocessing.import_data()
• qualia_core.preprocessing.PrintHARDataModelSummary module
  • PrintHARDataModelSummary
    • PrintHARDataModelSummary.log
• qualia_core.preprocessing.RemoveActivity module
  • RemoveActivity
• qualia_core.preprocessing.RemoveSensor module
  • RemoveSensor
• qualia_core.preprocessing.Reshape2DTo1D module
  • Reshape2DTo1D
• qualia_core.preprocessing.VisualizeActivities module
• qualia_core.preprocessing.VisualizeWindows module
• qualia_core.preprocessing.Window module
  • Window

Module contents

class qualia_core.preprocessing.BandPassFilter(f1: float, f2: float, fs: float, sensorkind, dimension: str)

Bases: Preprocessing

Band pass filter Requires fixed sampling frequency, do an interpolation before.

class qualia_core.preprocessing.Class2BinMatrix(classes: int = None)

Bases: Preprocessing

Warning: must be applied after Window

class qualia_core.preprocessing.CopySet(source: str = 'train', dest: str = 'test', ratio: float = 0.1)

Bases: Preprocessing[RawDataModel, RawDataModel]

import_data(dataset: Dataset[Any]) → Dataset[Any]

class qualia_core.preprocessing.DatamodelConverter

Bases: Preprocessing[HARDataModel, RawDataModel]

import_data(dataset: Dataset[Any]) → Dataset[Any]

class qualia_core.preprocessing.DatasetSplitterBySubjects(source_subjects: list[Any], dest_subjects: list[Any], source: str = 'train', dest: str = 'test')

Bases: Preprocessing[RawDataModel, RawDataModel]

Warning: must be applied after Window to get correct split and randomization of windows.

import_data(dataset: Dataset[Any]) → Dataset[Any]

class qualia_core.preprocessing.DatasetSplitter(source: str = 'train', dest: str = 'test', ratio: float = 0.1)

Bases: Preprocessing[RawDataModel, RawDataModel]

import_data(dataset: Dataset[Any]) → Dataset[Any]

class qualia_core.preprocessing.MFCC(sample_rate: int, n_mfcc: int, dct_type: int = 2, norm: str = 'ortho', log_mels=False, melkwargs: dict = {}, chunks: int = 4, dims: int = 1)

Bases: Preprocessing

class qualia_core.preprocessing.Normalize(method: str = 'z-score', axis: tuple[int, ...] = (0,), debug: bool = False)

Bases: Preprocessing[RawDataModel, RawDataModel]

z_score(dataset: RawDataModel) → RawDataModel

min_max(datamodel: RawDataModel) → RawDataModel

methods: ClassVar[dict[str, Callable[[Self, RawDataModel], RawDataModel]]] = {'min-max': <function Normalize.min_max>, 'z-score': <function Normalize.z_score>}

class qualia_core.preprocessing.PrintHARDataModelSummary

Bases: Preprocessing

log = []

class qualia_core.preprocessing.RemoveActivity(activities, noconvert: bool = False)

Bases: Preprocessing

class qualia_core.preprocessing.RemoveSensor(sensorkinds, noconvert: bool = False)

Bases: Preprocessing

class qualia_core.preprocessing.Reshape2DTo1D

Bases: Preprocessing

class qualia_core.preprocessing.Window(size: int = 0, stride: int = 1, no_overlapping_labels: bool = True, unique_label_per_window: bool = True)

Bases: Preprocessing

Warning: Must be applied before Class2BinMatrix and DatasetSplitter