:py:mod:`vamtoolbox.util.data`
==============================

.. py:module:: vamtoolbox.util.data


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


Functions
~~~~~~~~~

.. autoapisummary::

   vamtoolbox.util.data.sigmoid
   vamtoolbox.util.data.clipToCircle
   vamtoolbox.util.data.filterTargetOSMO
   vamtoolbox.util.data.filterTargetBCLP
   vamtoolbox.util.data.filterTarget
   vamtoolbox.util.data.filterSinogram
   vamtoolbox.util.data._get_fourier_filter
   vamtoolbox.util.data.histogramEqualization
   vamtoolbox.util.data.discretize



Attributes
~~~~~~~~~~

.. autoapisummary::

   vamtoolbox.util.data.fftmodule
   vamtoolbox.util.data.fft


.. py:data:: fftmodule

   

.. py:data:: fft

   

.. py:function:: sigmoid(x, g)


.. py:function:: clipToCircle(x: numpy.ndarray)

   Sets all data outside the inscribed circle to zero

   :param x: square array to be modified
   :type x: np.ndarray

   :returns: input array with all data outside the inscribed circle set to zero
   :rtype: x


.. py:function:: filterTargetOSMO(x: numpy.ndarray, filter_name: str)

   :param x:
   :type x: np.ndarray
   :param filter_name: type of filter to apply to target, options: "ram-lak", "shepp-logan", "cosine", "hamming", "hanning", "none"
   :type filter_name: str

   :returns: **x_filtered** -- direct output of filtering in frequency space
   :rtype: np.ndarray


.. py:function:: filterTargetBCLP(real_space_array: numpy.ndarray, filter_name: str)

   :param real_space_array:
   :type real_space_array: np.ndarray
   :param filter_name: type of filter to apply to target, options: "ram-lak", "shepp-logan", "cosine", "hamming", "hanning", "none"
   :type filter_name: str

   :returns: **x_filtered** -- direct output of filtering in frequency space
   :rtype: np.ndarray


.. py:function:: filterTarget(real_space_array: numpy.ndarray, filter_name: str)

   :param real_space_array:
   :type real_space_array: np.ndarray
   :param filter_name: type of filter to apply to target, options: "ram-lak", "shepp-logan", "cosine", "hamming", "hanning", "none"
   :type filter_name: str

   :returns: **x_filtered** -- direct output of filtering in frequency space
   :rtype: np.ndarray


.. py:function:: filterSinogram(sinogram: numpy.ndarray, filter_name: str)

   Filters a set of sinogram for a 2D or 3D target

   :param sinogram: input sinogram
   :type sinogram: np.ndarray
   :param filter_name: type of filter to apply to sinogram, options: "ram-lak", "shepp-logan", "cosine", "hamming", "hanning", "ram-lak_freq", "none"
   :type filter_name: str

   :returns: **sinogram_filt** -- filtered sinogram
   :rtype: np.ndarray


.. py:function:: _get_fourier_filter(size: int, filter_name: str)

   Construct the Fourier filter.
   This computation lessens artifacts and removes a small bias as
   explained in [1], Chap 3. Equation 61.

   :param size: filter size. Must be even.
   :type size: int
   :param filter_name: Filter used in frequency domain filtering. Filters available:
                       ram-lak, shepp-logan, cosine, hamming, hanning. Assign None to use
                       no filter.
   :type filter_name: str

   :returns: **fourier_filter** -- The computed Fourier filter.
   :rtype: np.ndarray

   .. rubric:: References

   .. [1] AC Kak, M Slaney, "Principles of Computerized Tomographic
          Imaging", IEEE Press 1988.


.. py:function:: histogramEqualization(x: numpy.ndarray, bit_depth: int, output_dtype: numpy.dtype = float)


.. py:function:: discretize(x: numpy.ndarray, bit_depth: int, range: list, output_dtype: numpy.dtype = float)

   Digitizes a variable to requested bit depth and output data type

   :param x: array to digitize
   :type x: nd.ndarray
   :param bit_depth: bit depth of output, 2^bit_depth number of bins
   :type bit_depth: int
   :param range: [min,max] values to discretize within
   :type range: list
   :param output_dtype: data type of resulting digitized array
   :type output_dtype: np.dtype (optional)