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a better approach to dev
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‎sunkit_image/new_coalignment_api_dev/coalignment_interface.py‎

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import warnings
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import astropy.units as u
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import numpy as np
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import sunpy.map
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from skimage.feature import match_template
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from sunpy.util.exceptions import SunpyUserWarning
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############################ Coalignment Interface begins #################################
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@u.quantity_input
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def _clip_edges(data, yclips: u.pix, xclips: u.pix):
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"""
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Clips off the "y" and "x" edges of a 2D array according to a list of pixel
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values. This function is useful for removing data at the edge of 2d images
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that may be affected by shifts from solar de- rotation and layer co-
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registration, leaving an image unaffected by edge effects.
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Parameters
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----------
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data : `numpy.ndarray`
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A numpy array of shape ``(ny, nx)``.
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yclips : `astropy.units.Quantity`
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The amount to clip in the y-direction of the data. Has units of
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pixels, and values should be whole non-negative numbers.
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xclips : `astropy.units.Quantity`
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The amount to clip in the x-direction of the data. Has units of
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pixels, and values should be whole non-negative numbers.
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Returns
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-------
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`numpy.ndarray`
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A 2D image with edges clipped off according to ``yclips`` and ``xclips``
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arrays.
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"""
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ny = data.shape[0]
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nx = data.shape[1]
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# The purpose of the int below is to ensure integer type since by default
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# astropy quantities are converted to floats.
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return data[int(yclips[0].value) : ny - int(yclips[1].value), int(xclips[0].value) : nx - int(xclips[1].value)]
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@u.quantity_input
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def _calculate_clipping(y: u.pix, x: u.pix):
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"""
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Return the upper and lower clipping values for the "y" and "x" directions.
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Parameters
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----------
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y : `astropy.units.Quantity`
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An array of pixel shifts in the y-direction for an image.
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x : `astropy.units.Quantity`
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An array of pixel shifts in the x-direction for an image.
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Returns
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-------
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`tuple`
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The tuple is of the form ``([y0, y1], [x0, x1])``.
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The number of (integer) pixels that need to be clipped off at each
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edge in an image. The first element in the tuple is a list that gives
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the number of pixels to clip in the y-direction. The first element in
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that list is the number of rows to clip at the lower edge of the image
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in y. The clipped image has "clipping[0][0]" rows removed from its
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lower edge when compared to the original image. The second element in
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that list is the number of rows to clip at the upper edge of the image
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in y. The clipped image has "clipping[0][1]" rows removed from its
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upper edge when compared to the original image. The second element in
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the "clipping" tuple applies similarly to the x-direction (image
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columns). The parameters ``y0, y1, x0, x1`` have the type
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`~astropy.units.Quantity`.
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"""
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return (
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[_lower_clip(y.value), _upper_clip(y.value)] * u.pix,
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[_lower_clip(x.value), _upper_clip(x.value)] * u.pix,
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)
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def _upper_clip(z):
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"""
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Find smallest integer bigger than all the positive entries in the input
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array.
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"""
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zupper = 0
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zcond = z >= 0
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if np.any(zcond):
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zupper = int(np.max(np.ceil(z[zcond])))
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return zupper
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def _lower_clip(z):
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"""
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Find smallest positive integer bigger than the absolute values of the
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negative entries in the input array.
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"""
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zlower = 0
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zcond = z <= 0
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if np.any(zcond):
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zlower = int(np.max(np.ceil(-z[zcond])))
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return zlower
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def convert_array_to_map(array_obj, map_obj):
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"""
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Convert a 2D numpy array to a sunpy Map object using the header of a given
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map object.
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Parameters
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----------
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array_obj : `numpy.ndarray`
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The 2D numpy array to be converted.
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map_obj : `sunpy.map.Map`
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The map object whose header is to be used for the new map.
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Returns
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-------
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`sunpy.map.Map`
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A new sunpy map object with the data from `array_obj` and the header from `map_obj`.
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"""
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header = map_obj.meta.copy()
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header["crpix1"] -= array_obj.shape[1] / 2.0 - map_obj.data.shape[1] / 2.0
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header["crpix2"] -= array_obj.shape[0] / 2.0 - map_obj.data.shape[0] / 2.0
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return sunpy.map.Map(array_obj, header)
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def coalignment_interface(method, input_map, template_map, handle_nan=None):
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"""
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Interface for performing image coalignment using a specified method.
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Parameters
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----------
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method : str
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The name of the registered coalignment method to use.
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input_map : `sunpy.map.Map`
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The input map to be coaligned.
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template_map : `sunpy.map.Map`
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The template map to which the input map is to be coaligned.
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handle_nan : callable, optional
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Function to handle NaN values in the input and template arrays.
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Returns
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-------
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`sunpy.map.Map`
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The coaligned input map.
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Raises
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------
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ValueError
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If the specified method is not registered.
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"""
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if method not in registered_methods:
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msg = f"Method {method} is not a registered method. Please register before using."
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raise ValueError(msg)
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input_array = np.float64(input_map.data)
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template_array = np.float64(template_map.data)
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# Warn user if any NANs, Infs, etc are present in the input or the template array
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if not np.all(np.isfinite(input_array)):
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if not handle_nan:
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warnings.warn(
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"The layer image has nonfinite entries. "
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"This could cause errors when calculating shift between two "
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"images. Please make sure there are no infinity or "
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"Not a Number values. For instance, replacing them with a "
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"local mean.",
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SunpyUserWarning,
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stacklevel=3,
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)
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else:
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input_array = handle_nan(input_array)
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if not np.all(np.isfinite(template_array)):
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if not handle_nan:
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warnings.warn(
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"The template image has nonfinite entries. "
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"This could cause errors when calculating shift between two "
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"images. Please make sure there are no infinity or "
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"Not a Number values. For instance, replacing them with a "
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"local mean.",
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SunpyUserWarning,
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stacklevel=3,
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)
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else:
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template_array = handle_nan(template_array)
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shifts = registered_methods[method](input_array, template_array)
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# Calculate the clipping required
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yclips, xclips = _calculate_clipping(shifts["x"] * u.pix, shifts["y"] * u.pix)
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# Clip 'em
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coaligned_input_array = _clip_edges(input_array, yclips, xclips)
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return convert_array_to_map(coaligned_input_array, input_map)
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######################################## Coalignment interface ends ##################

‎sunkit_image/new_coalignment_api_dev/defining_method.py‎

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import warnings
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import astropy.units as u
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import numpy as np
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import sunpy.map
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from skimage.feature import match_template
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from sunpy.util.exceptions import SunpyUserWarning
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######################################## Defining a method ###########################
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def _parabolic_turning_point(y):
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"""
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Calculate the turning point of a parabola given three points.
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Parameters
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----------
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y : `numpy.ndarray`
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An array of three points defining the parabola.
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Returns
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-------
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float
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The x-coordinate of the turning point.
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"""
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numerator = -0.5 * y.dot([-1, 0, 1])
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denominator = y.dot([1, -2, 1])
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return numerator / denominator
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def _get_correlation_shifts(array):
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"""
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Calculate the shifts in x and y directions based on the correlation array.
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Parameters
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----------
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array : `numpy.ndarray`
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A 2D array representing the correlation values.
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Returns
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-------
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tuple
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The shifts in y and x directions.
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Raises
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------
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ValueError
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If the input array dimensions are greater than 3 in any direction.
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"""
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ny, nx = array.shape
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if nx > 3 or ny > 3:
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msg = "Input array dimension should not be greater than 3 in any dimension."
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raise ValueError(msg)
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ij = np.unravel_index(np.argmax(array), array.shape)
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x_max_location, y_max_location = ij[::-1]
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y_location = _parabolic_turning_point(array[:, x_max_location]) if ny == 3 else 1.0 * y_max_location
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x_location = _parabolic_turning_point(array[y_max_location, :]) if nx == 3 else 1.0 * x_max_location
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return y_location, x_location
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def _find_best_match_location(corr):
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"""
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Find the best match location in the correlation array.
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Parameters
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----------
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corr : `numpy.ndarray`
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The correlation array.
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Returns
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-------
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tuple
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The best match location in the y and x directions.
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"""
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ij = np.unravel_index(np.argmax(corr), corr.shape)
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cor_max_x, cor_max_y = ij[::-1]
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array_maximum = corr[
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max(0, cor_max_y - 1) : min(cor_max_y + 2, corr.shape[0] - 1),
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max(0, cor_max_x - 1) : min(cor_max_x + 2, corr.shape[1] - 1),
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]
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y_shift_maximum, x_shift_maximum = _get_correlation_shifts(array_maximum)
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y_shift_correlation_array = y_shift_maximum + cor_max_y
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x_shift_correlation_array = x_shift_maximum + cor_max_x
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return y_shift_correlation_array, x_shift_correlation_array
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def match_template_coalign(input_array, template_array):
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"""
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Perform coalignment by matching the template array to the input array.
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Parameters
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----------
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input_array : `numpy.ndarray`
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The input 2D array to be coaligned.
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template_array : `numpy.ndarray`
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The template 2D array to align to.
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Returns
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-------
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dict
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A dictionary containing the shifts in x and y directions.
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"""
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corr = match_template(input_array, template_array)
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# Find the best match location
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y_shift, x_shift = _find_best_match_location(corr)
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# Apply the shift to get the coaligned input array
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return {"x": x_shift, "y": y_shift}
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################################ Registering the defined method ########################
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register_coalignment_method("match_template", match_template_coalign)

‎sunkit_image/new_coalignment_api_dev/register_method.py‎

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import warnings
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import astropy.units as u
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import numpy as np
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import sunpy.map
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from skimage.feature import match_template
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from sunpy.util.exceptions import SunpyUserWarning
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## This dictionary will be further replaced in a more appropriate location, once the decorator structure is in place.
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registered_methods = {}
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def register_coalignment_method(name, method):
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"""
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Registers a coalignment method to be used by the coalignment interface.
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Parameters
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----------
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name : str
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The name of the coalignment method.
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method : callable
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The function implementing the coalignment method.
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"""
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registered_methods[name] = method

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