minerva.transforms.tfc
======================

.. py:module:: minerva.transforms.tfc


Classes
-------

.. autoapisummary::

   minerva.transforms.tfc.TFC_Transforms


Module Contents
---------------

.. py:class:: TFC_Transforms

   Bases: :py:obj:`minerva.transforms.transform._Transform`


   Transformations used in the TFC model.
   It consists of time and frequency domain data augmentation.


   .. py:method:: DataTransform_FD(sample)

      Weak and strong augmentations.
      Consists of jittering and adding or removing frequency components.

      Parameters
      ----------
      - sample: np.ndarray
          The input data to be augmented

      Returns
      -------
      - np.ndarray
          The augmented data




   .. py:method:: DataTransform_TD(sample, jitter_ratio = 0.8)

      Weak and strong augmentations.
      Consists of jittering and removing time components.

      Parameters
      ----------
      - sample: np.ndarray
          The input data to be augmented
      - jitter_ratio: float
          The ratio of the jittering transformation

      Returns
      -------
      - np.ndarray
          The augmented data




   .. py:method:: __call__(x)

      Method that applies the transformations to the input data.

      Parameters
      ----------
      - x: Union[np.ndarray, torch.Tensor]
          The input data to be transformed

      Returns
      -------
      - Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]
          A tuple with the original data, the transformed data in the time domain the frequency version of the data and the tranformed data in frequency domain



   .. py:method:: add_frequency(x, pertub_ratio = 0)

      function to add frequency components to the input data.

      Parameters
      ----------
      - x: np.ndarray
          The input data to be augmented
      - pertub_ratio: float
          The ratio of the frequency components to be added

      Returns
      -------
      - np.ndarray
          The data with added frequency components





   .. py:method:: jitter(x, sigma = 0.8)

      Add noise to the input data.

      Parameters
      ----------
      - x: np.ndarray
          The input data to be augmented
      - sigma: float
          The standard deviation of the noise

      Returns
      -------
      - np.ndarray
          The data with added noise





   .. py:method:: one_hot_encoding(X, n_values = None)

      One-hot encoding of the input data

      Parameters
      ----------
      - X: np.ndarray
          The input data to be encoded
      - n_values: int
          The number of classes in the data. If None, the number of classes is inferred from the data

      Returns
      -------
      - np.ndarray
          The one-hot encoded data



   .. py:method:: remove_frequency(x, maskout_ratio = 0)

      function to remove frequency components from the input data.

      Parameters
      ----------
      - x: np.ndarray
          The input data to be augmented
      - maskout_ratio: float
          The ratio of the frequency components to be removed

      Returns
      -------
      - np.ndarray
          The data with removed frequency components