topostats.filters#
Module for filtering 2D Numpy arrays.
Attributes#
Classes#
Class for filtering scans. |
Functions#
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Mask data that should not be included in flattening. |
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Obtain thresholds for masking data points. |
Module Contents#
- topostats.filters.LOGGER_NAME = 'topostats'#
- topostats.filters.get_mask(image: numpy.typing.NDArray, thresholds: dict, img_name: str = None) numpy.typing.NDArray [source]#
Mask data that should not be included in flattening.
- Parameters:
image (npt.NDArray) – 2D Numpy array of the image to have a mask derived for.
thresholds (dict) – Dictionary of thresholds, at a bare minimum must have key ‘below’ with an associated value, second key is to have an ‘above’ threshold.
img_name (str) – Image name that is being masked.
- Returns:
2D Numpy boolean array of points to mask.
- Return type:
npt.NDArray
- topostats.filters.get_thresholds(image: numpy.typing.NDArray, threshold_method: str, otsu_threshold_multiplier: float = None, threshold_std_dev: dict = None, absolute: dict = None, **kwargs) dict [source]#
Obtain thresholds for masking data points.
- Parameters:
image (npt.NDArray) – 2D Numpy array of image to be masked.
threshold_method (str) – Method for thresholding, ‘otsu’, ‘std_dev’ or ‘absolute’ are valid options.
otsu_threshold_multiplier (float) – Scaling value for Otsu threshold.
threshold_std_dev (dict) – Dict of above and below thresholds for the standard deviation method.
absolute (tuple) – Dict of below and above thresholds.
**kwargs – Dictionary passed to ‘topostats.threshold(**kwargs)’.
- Returns:
Dictionary of thresholds, contains keys ‘below’ and optionally ‘above’.
- Return type:
Dict
- topostats.filters.LOGGER#
- class topostats.filters.Filters(image: numpy.typing.NDArray, filename: str, pixel_to_nm_scaling: float, row_alignment_quantile: float = 0.5, 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', remove_scars: dict = None)[source]#
Class for filtering scans.
- Parameters:
image (npt.NDArray) – The raw image from the Atomic Force Microscopy machine.
filename (str) – The filename (used in logging only).
pixel_to_nm_scaling (float) – Value for converting pixels to nanometers.
row_alignment_quantile (float) – Quantile (0.0 to 1.0) to be used to determine the average background for the image below values may improve flattening of large features.
threshold_method (str) – Method for thresholding, default ‘otsu’, valid options ‘otsu’, ‘std_dev’ and ‘absolute’.
otsu_threshold_multiplier (float) – Value for scaling the derived Otsu threshold.
threshold_std_dev (dict) – If using the ‘std_dev’ threshold method. Dictionary that contains above and below threshold values for the number of standard deviations from the mean to threshold.
threshold_absolute (dict) – If using the ‘absolute’ threshold method. Dictionary that contains above and below absolute threshold values for flattening.
gaussian_size (float) – If using the ‘absolute’ threshold method. Dictionary that contains above and below absolute threshold values for flattening.
gaussian_mode (str) – Method passed to ‘skimage.filters.gaussian(mode = gaussian_mode)’.
remove_scars (dict) – Dictionary containing configuration parameters for the scar removal function.
- median_flatten(image: numpy.typing.NDArray, mask: numpy.typing.NDArray = None, row_alignment_quantile: float = 0.5) numpy.typing.NDArray [source]#
Flatten images using median differences.
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.
- Parameters:
image (npt.NDArray) – 2-D image of the data to align the rows of.
mask (npt.NDArray) – Boolean array of points to mask (ignore).
row_alignment_quantile (float) – Quantile (in the range 0.0 to 1.0) used for defining the average background.
- Returns:
Copy of the input image with rows aligned.
- Return type:
npt.NDArray
- remove_tilt(image: numpy.typing.NDArray, mask: numpy.typing.NDArray = None) numpy.typing.NDArray [source]#
Remove the planar tilt from an image (linear in 2D spaces).
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.
- Parameters:
image (npt.NDArray) – 2-D image of the data to remove the planar tilt from.
mask (npt.NDArray) – Boolean array of points to mask (ignore).
- Returns:
Numpy array of image with tilt removed.
- Return type:
npt.NDArray
- remove_nonlinear_polynomial(image: numpy.typing.NDArray, mask: numpy.typing.NDArray | None = None) numpy.typing.NDArray [source]#
Fit and remove a “saddle” shaped nonlinear polynomial from the image.
“Saddles” with the form a + b * x * y - c * x - d * y from the supplied image. AFM images sometimes contain a “saddle” shape trend to their background, and so to remove them we fit a nonlinear polynomial of x and y and then subtract the fit from the image.
If these trends are not removed, then the image will not flatten properly and will leave opposite diagonal corners raised or lowered.
- Parameters:
image (npt.NDArray) – 2-D numpy height-map array of floats with a polynomial trend to remove.
mask (npt.NDArray, optional) – 2-D Numpy boolean array used to mask any points in the image that are deemed not to be part of the height-map’s background data.
- Returns:
Image with the polynomial trend subtracted.
- Return type:
npt.NDArray
- remove_quadratic(image: numpy.typing.NDArray, mask: numpy.typing.NDArray = None) numpy.typing.NDArray [source]#
Remove the quadratic bowing that can be seen in some large-scale AFM images.
Use a simple quadratic fit on the medians of the columns of the image and then subtracts the calculated quadratic from the columns.
- Parameters:
image (npt.NDArray) – 2-D image of the data to remove the quadratic from.
mask (npt.NDArray) – Boolean array of points to mask (ignore).
- Returns:
Image with the quadratic bowing removed.
- Return type:
npt.NDArray
- static calc_diff(array: numpy.typing.NDArray) numpy.typing.NDArray [source]#
Calculate the difference between the last and first rows of a 2-D array.
- Parameters:
array (npt.NDArray) – A Numpy array.
- Returns:
An array of the difference between the last and first rows of an array.
- Return type:
npt.NDArray
- calc_gradient(array: numpy.typing.NDArray, shape: int) numpy.typing.NDArray [source]#
Calculate the gradient of an array.
- Parameters:
array (npt.NDArray) – Array for gradient to be calculated.
shape (int) – Shape of the array.
- Returns:
Gradient across the array.
- Return type:
npt.NDArray
- average_background(image: numpy.typing.NDArray, mask: numpy.typing.NDArray = None) numpy.typing.NDArray [source]#
Zero the background by subtracting the non-masked mean from all pixels.
- Parameters:
image (npt.NDArray) – Numpy array representing the image.
mask (npt.NDArray) – Mask of the array, should have the same dimensions as image.
- Returns:
Numpy array of image zero averaged.
- Return type:
npt.NDArray
- gaussian_filter(image: numpy.typing.NDArray, **kwargs) numpy.typing.NDArray [source]#
Apply Gaussian filter to an image.
- Parameters:
image (npt.NDArray) – Numpy array representing the image.
**kwargs – Keyword arguments passed on to the skimage.filters.gaussian() function.
- Returns:
Numpy array that represent the image after Gaussian filtering.
- Return type:
npt.NDArray
- filter_image() None [source]#
Process a single image, filtering, finding grains and calculating their statistics.
- Returns:
Does not return anything.
- Return type:
None
Examples
from topostats.io import LoadScan from topostats.topotracing import Filter, process_scan
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()