topostats.filters
=================

.. py:module:: topostats.filters

.. autoapi-nested-parse::

   Contains filter functions that take a 2D array representing an image as an input, as well as necessary parameters,
   and return a 2D array of the same size representing the filtered image.

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Attributes
----------

.. autoapisummary::

   topostats.filters.LOGGER


Classes
-------

.. autoapisummary::

   topostats.filters.Filters


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

.. py:data:: LOGGER

.. py:class:: Filters(image: numpy.ndarray, filename: str, pixel_to_nm_scaling: float, threshold_method: str = 'otsu', otsu_threshold_multiplier: float = 1.7, threshold_std_dev: dict = None, threshold_absolute: dict = None, gaussian_size: float = None, gaussian_mode: str = 'nearest', quiet: bool = False)

   
   Class for filtering scans.
















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   .. py:attribute:: filename


   .. py:attribute:: pixel_to_nm_scaling


   .. py:attribute:: gaussian_size


   .. py:attribute:: gaussian_mode


   .. py:attribute:: threshold_method


   .. py:attribute:: otsu_threshold_multiplier


   .. py:attribute:: threshold_std_dev


   .. py:attribute:: threshold_absolute


   .. py:attribute:: images


   .. py:attribute:: thresholds
      :value: None



   .. py:attribute:: medians


   .. py:attribute:: results


   .. py:method:: median_flatten(image: numpy.ndarray, mask: numpy.ndarray = None, img_name: str = None) -> numpy.ndarray

      
      Uses the method of median differences to flatten the rows of an image, aligning the rows and centering the
      median around zero. When used with a mask, this has the effect of centering the background data on zero.
      Note this function does not handle scars.

      :param image: 2-D image of the data to align the rows of.
      :type image: np.ndarray
      :param mask: Boolean array of points to mask out (ignore).
      :type mask: np.ndarray
      :param img_name: Name of the image (to be able to print information in the console).
      :type img_name: str

      :returns: Returns a copy of the input image with rows aligned
      :rtype: np.ndarray















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   .. py:method:: remove_tilt(image: numpy.ndarray, mask: numpy.ndarray = None, img_name: str = None)

      
      Removes planar tilt from an image (linear in 2D space). It uses a linear fit of the medians
      of the rows and columns to determine the linear slants in x and y directions and then subtracts the fit from the columns.

      :param image: 2-D image of the data to remove the planar tilt from.
      :type image: np.ndarray
      :param mask: Boolean array of points to mask out (ignore).
      :type mask: np.ndarray
      :param img_name: Name of the image (to be able to print information in the console).
      :type img_name: str

      :returns: Returns a copy of the input image with the planar tilt removed
      :rtype: np.ndarray















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   .. py:method:: remove_quadratic(image: numpy.ndarray, mask: numpy.ndarray = None, img_name: str = None)

      
      Removes the quadratic bowing that can be seen in some large-scale AFM images. It uses a simple quadratic fit
      on the medians of the columns of the image and then subtracts the calculated quadratic from the columns.

      :param image: 2-D image of the data to remove the quadratic from.
      :type image: np.ndarray
      :param mask: Boolean array of points to mask out (ignore).
      :type mask: np.ndarray
      :param img_name: Name of the image (to be able to print information in the console).
      :type img_name: str

      :returns: Returns a copy of the input image with the quadratic bowing removed
      :rtype: np.ndarray















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   .. py:method:: calc_diff(array: numpy.ndarray) -> numpy.ndarray
      :staticmethod:


      
      Calculate the difference of an array.
















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   .. py:method:: calc_gradient(array: numpy.ndarray, shape: int) -> numpy.ndarray

      
      Calculate the gradient of an array.
















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   .. py:method:: average_background(image: numpy.ndarray, mask: numpy.ndarray = None) -> numpy.ndarray

      
      Zero the background by subtracting the non-masked mean from all pixels.

      :param image: Numpy array representing image.
      :type image: np.array
      :param mask: Mask of the array, should have the same dimensions as image.
      :type mask: np.array

      :returns: Numpy array of image zero averaged.
      :rtype: np.ndarray















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   .. py:method:: gaussian_filter(image: numpy.ndarray, **kwargs) -> numpy.array

      
      Apply Gaussian filter to an image.

      :param image: Numpy array representing image.
      :type image: np.array

      :returns: Numpy array of gaussian blurred image.
      :rtype: np.array















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   .. py:method:: filter_image() -> None

      
      Process a single image, filtering, finding grains and calculating their statistics.

      .. rubric:: Example

      from topostats.io import LoadScan
      from topostats.topotracing import Filter, process_scan

      load_scan = LoadScan("minicircle.spm", channel="Height")
      load_scan.get_data()
      filter = Filter(image=load_scan.image,
      ...             pixel_to_nm_scaling=load_scan.pixel_to_nm_scaling,
      ...             filename=load_scan.filename,
      ...             threshold_method='otsu')
      filter.filter_image()















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