"""
This module provides routines for the co-alignment of images and
`~sunpy.map.mapsequence.MapSequence` objects through both template matching and
corrections due to solar rotation.
"""
import warnings
from copy import deepcopy
import astropy.units as u
import numpy as np
import sunpy.map
from scipy.ndimage import shift
from skimage.feature import match_template
from sunpy.map.mapbase import GenericMap
from sunpy.physics.differential_rotation import solar_rotate_coordinate
from sunpy.util.exceptions import SunpyUserWarning
__all__ = [
"match_template_to_layer",
"apply_shifts",
"mapsequence_coalign_by_match_template",
"calculate_match_template_shift",
"calculate_solar_rotate_shift",
"mapsequence_coalign_by_rotation",
]
def _default_fmap_function(data):
"""
This function ensures that the data are floats.
It is the default data manipulation function for the coalignment
method.
"""
return np.float64(data)
def _calculate_shift(this_layer, template):
"""
Calculates the pixel shift required to put the template in the "best"
position on a layer.
Parameters
----------
this_layer : `numpy.ndarray`
A numpy array of size ``(ny, nx)``, where the first two dimensions are
spatial dimensions.
template : `numpy.ndarray`
A numpy array of size ``(N, M)`` where ``N < ny`` and ``M < nx``.
Returns
-------
`tuple`
Pixel shifts ``(yshift, xshift)`` relative to the offset of the template
to the input array.
"""
# Warn user if any NANs, Infs, etc are present in the layer or the template
_check_for_nonfinite_entries(this_layer, template)
# Calculate the correlation array matching the template to this layer
corr = match_template_to_layer(this_layer, template)
# Calculate the y and x shifts in pixels
return _find_best_match_location(corr)
@u.quantity_input
def _clip_edges(data, yclips: u.pix, xclips: u.pix):
"""
Clips off the "y" and "x" edges of a 2D array according to a list of pixel
values. This function is useful for removing data at the edge of 2d images
that may be affected by shifts from solar de- rotation and layer co-
registration, leaving an image unaffected by edge effects.
Parameters
----------
data : `numpy.ndarray`
A numpy array of shape ``(ny, nx)``.
yclips : `astropy.units.Quantity`
The amount to clip in the y-direction of the data. Has units of
pixels, and values should be whole non-negative numbers.
xclips : `astropy.units.Quantity`
The amount to clip in the x-direction of the data. Has units of
pixels, and values should be whole non-negative numbers.
Returns
-------
`numpy.ndarray`
A 2D image with edges clipped off according to ``yclips`` and ``xclips``
arrays.
"""
ny = data.shape[0]
nx = data.shape[1]
# The purpose of the int below is to ensure integer type since by default
# astropy quantities are converted to floats.
return data[int(yclips[0].value) : ny - int(yclips[1].value), int(xclips[0].value) : nx - int(xclips[1].value)]
@u.quantity_input
def _calculate_clipping(y: u.pix, x: u.pix):
"""
Return the upper and lower clipping values for the "y" and "x" directions.
Parameters
----------
y : `astropy.units.Quantity`
An array of pixel shifts in the y-direction for an image.
x : `astropy.units.Quantity`
An array of pixel shifts in the x-direction for an image.
Returns
-------
`tuple`
The tuple is of the form ``([y0, y1], [x0, x1])``.
The number of (integer) pixels that need to be clipped off at each
edge in an image. The first element in the tuple is a list that gives
the number of pixels to clip in the y-direction. The first element in
that list is the number of rows to clip at the lower edge of the image
in y. The clipped image has "clipping[0][0]" rows removed from its
lower edge when compared to the original image. The second element in
that list is the number of rows to clip at the upper edge of the image
in y. The clipped image has "clipping[0][1]" rows removed from its
upper edge when compared to the original image. The second element in
the "clipping" tuple applies similarly to the x-direction (image
columns). The parameters ``y0, y1, x0, x1`` have the type
`~astropy.units.Quantity`.
"""
return (
[_lower_clip(y.value), _upper_clip(y.value)] * u.pix,
[_lower_clip(x.value), _upper_clip(x.value)] * u.pix,
)
def _upper_clip(z):
"""
Find smallest integer bigger than all the positive entries in the input
array.
"""
zupper = 0
zcond = z >= 0
if np.any(zcond):
zupper = int(np.max(np.ceil(z[zcond])))
return zupper
def _lower_clip(z):
"""
Find smallest positive integer bigger than the absolute values of the
negative entries in the input array.
"""
zlower = 0
zcond = z <= 0
if np.any(zcond):
zlower = int(np.max(np.ceil(-z[zcond])))
return zlower
[docs]
def match_template_to_layer(layer, template):
"""
Calculate the correlation array that describes how well the template
matches the layer. All inputs are assumed to be numpy arrays.
Parameters
----------
layer : `numpy.ndarray`
A numpy array of size ``(ny, nx)``.
template : `numpy.ndarray`
A numpy array of size ``(N, M)`` where ``N < ny`` and ``M < nx``.
Returns
-------
`numpy.ndarray`
A correlation array between the layer and the template.
The values in the array range between 0 and 1.
"""
return match_template(layer, template)
def _find_best_match_location(corr):
"""
Calculate an estimate of the location of the peak of the correlation result
in image pixels.
Parameters
----------
corr : `numpy.ndarray`
A 2D correlation array.
Returns
-------
`~astropy.units.Quantity`
The shift amounts ``(y, x)`` in image pixels. Subpixel values are
possible.
"""
# Get the index of the maximum in the correlation function
ij = np.unravel_index(np.argmax(corr), corr.shape)
cor_max_x, cor_max_y = ij[::-1]
# Get the correlation function around the maximum
array_maximum = corr[
np.max([0, cor_max_y - 1]) : np.min([cor_max_y + 2, corr.shape[0] - 1]),
np.max([0, cor_max_x - 1]) : np.min([cor_max_x + 2, corr.shape[1] - 1]),
]
y_shift_maximum, x_shift_maximum = _get_correlation_shifts(array_maximum)
# Get shift relative to correlation array
y_shift_correlation_array = y_shift_maximum + cor_max_y * u.pix
x_shift_correlation_array = x_shift_maximum + cor_max_x * u.pix
return y_shift_correlation_array, x_shift_correlation_array
def _get_correlation_shifts(array):
"""
Estimate the location of the maximum of a fit to the input array. The
estimation in the "x" and "y" directions are done separately. The location
estimates can be used to implement subpixel shifts between two different
images.
Parameters
----------
array : `numpy.ndarray`
An array with at least one dimension that has three elements. The
input array is at most a 3x3 array of correlation values calculated
by matching a template to an image.
Returns
-------
`~astropy.units.Quantity`
The ``(y, x)`` location of the peak of a parabolic fit, in image pixels.
"""
# Check input shape
ny = array.shape[0]
nx = array.shape[1]
if nx > 3 or ny > 3:
raise ValueError("Input array dimension should not be greater than 3 in any dimension.")
# Find where the maximum of the input array is
ij = np.unravel_index(np.argmax(array), array.shape)
x_max_location, y_max_location = ij[::-1]
# Estimate the location of the parabolic peak if there is enough data.
# Otherwise, just return the location of the maximum in a particular
# direction.
if ny == 3:
y_location = _parabolic_turning_point(array[:, x_max_location])
else:
y_location = 1.0 * y_max_location
if nx == 3:
x_location = _parabolic_turning_point(array[y_max_location, :])
else:
x_location = 1.0 * x_max_location
return y_location * u.pix, x_location * u.pix
def _parabolic_turning_point(y):
"""
Find the location of the turning point for a parabola ``y(x) = ax^2 + bx +
c``, given input values ``y(-1), y(0), y(1)``. The maximum is located at
``x0 = -b / 2a``. Assumes that the input array represents an equally spaced
sampling at the locations ``y(-1), y(0) and y(1)``.
Parameters
----------
y : `numpy.ndarray`
A one dimensional numpy array of shape "3" with entries that sample the
parabola at "-1", "0", and "1".
Returns
-------
`float`
A float, the location of the parabola maximum.
"""
numerator = -0.5 * y.dot([-1, 0, 1])
denominator = y.dot([1, -2, 1])
return numerator / denominator
def _check_for_nonfinite_entries(layer_image, template_image):
"""
Issue a warning if there is any nonfinite entry in the layer or template
images.
Parameters
----------
layer_image : `numpy.ndarray`
A two-dimensional `numpy.ndarray`.
template_image : `numpy.ndarray`
A two-dimensional `numpy.ndarray`.
"""
if not np.all(np.isfinite(layer_image)):
warnings.warn(
"The layer image has nonfinite entries. "
"This could cause errors when calculating shift between two "
"images. Please make sure there are no infinity or "
"Not a Number values. For instance, replacing them with a "
"local mean.",
SunpyUserWarning,
stacklevel=3,
)
if not np.all(np.isfinite(template_image)):
warnings.warn(
"The template image has nonfinite entries. "
"This could cause errors when calculating shift between two "
"images. Please make sure there are no infinity or "
"Not a Number values. For instance, replacing them with a "
"local mean.",
SunpyUserWarning,
stacklevel=3,
)
[docs]
@u.quantity_input
def apply_shifts(mc, yshift: u.pix, xshift: u.pix, clip=True, **kwargs):
"""
Apply a set of pixel shifts to a `~sunpy.map.MapSequence`, and return a new
`~sunpy.map.MapSequence`.
Parameters
----------
mc : `sunpy.map.MapSequence`
A `~sunpy.map.MapSequence` of shape ``(ny, nx, nt)``, where ``nt`` is the number of
layers in the `~sunpy.map.MapSequence`. ``ny`` is the number of pixels in the
"y" direction, ``nx`` is the number of pixels in the "x" direction.
yshift : `~astropy.units.Quantity`
An array of pixel shifts in the y-direction for an image.
xshift : `~astropy.units.Quantity`
An array of pixel shifts in the x-direction for an image.
clip : `bool`, optional
If `True` (default), then clip off "x", "y" edges of the maps in the sequence that are
potentially affected by edges effects.
Notes
-----
All other keywords are passed to `scipy.ndimage.shift`.
Returns
-------
`sunpy.map.MapSequence`
A `~sunpy.map.MapSequence` of the same shape as the input. All layers in
the `~sunpy.map.MapSequence` have been shifted according the input shifts.
"""
# New mapsequence will be constructed from this list
new_mc = []
# Calculate the clipping
if clip:
yclips, xclips = _calculate_clipping(-yshift, -xshift)
# Shift the data and construct the mapsequence
for i, m in enumerate(mc):
shifted_data = shift(deepcopy(m.data), [yshift[i].value, xshift[i].value], **kwargs)
new_meta = deepcopy(m.meta)
# Clip if required. Use the submap function to return the appropriate
# portion of the data.
if clip:
shifted_data = _clip_edges(shifted_data, yclips, xclips)
new_meta["naxis1"] = shifted_data.shape[1]
new_meta["naxis2"] = shifted_data.shape[0]
# Add one to go from zero-based to one-based indexing
new_meta["crpix1"] = m.reference_pixel.x.value + 1 + xshift[i].value - xshift[0].value
new_meta["crpix2"] = m.reference_pixel.y.value + 1 + yshift[i].value - yshift[0].value
new_map = sunpy.map.Map(shifted_data, new_meta)
# Append to the list
new_mc.append(new_map)
return sunpy.map.Map(new_mc, sequence=True)
[docs]
def calculate_match_template_shift(mc, template=None, layer_index=0, func=_default_fmap_function):
"""
Calculate the arcsecond shifts necessary to co-register the layers in a
`~sunpy.map.MapSequence` according to a template taken from that
`~sunpy.map.MapSequence`.
When using this functionality, it is a good idea to check that the shifts
that were applied to were reasonable and expected. One way of checking this
is to animate the original `~sunpy.map.MapSequence`, animate the coaligned
`~sunpy.map.MapSequence`, and compare the differences you see to the
calculated shifts.
Parameters
----------
mc : `sunpy.map.MapSequence`
A `~sunpy.map.MapSequence` of shape ``(ny, nx, nt)``, where ``nt`` is the number of
layers in the `~sunpy.map.MapSequence`.
template : {`None` | `~sunpy.map.Map` | `numpy.ndarray`}, optional
The template used in the matching. If an ~numpy.ndarray` is passed,
the `numpy.ndarray` has to have two dimensions.
layer_index : `int`, optional
The template is assumed to refer to the map in the `~sunpy.map.MapSequence`
indexed by the value of "layer_index". Displacements of all maps in the
`~sunpy.map.MapSequence` are assumed to be relative to this layer. The
displacements of the template relative to this layer are therefore
``(0, 0)``.
func : function, optional
A function which is applied to the data values before the coalignment
method is applied. This can be useful in coalignment, because it is
sometimes better to co-align on a function of the data rather than the
data itself. The calculated shifts are applied to the original data.
Examples of useful functions to consider for EUV images are the
logarithm or the square root. The function is of the form
``func = F(data)``. The default function ensures that the data are
floats.
"""
# Size of the data
ny = mc.maps[layer_index].data.shape[0]
nx = mc.maps[layer_index].data.shape[1]
nt = len(mc.maps)
# Calculate a template. If no template is passed then define one
# from the index layer.
if template is None:
tplate = mc.maps[layer_index].data[int(ny / 4) : int(3 * ny / 4), int(nx / 4) : int(3 * nx / 4)]
elif isinstance(template, GenericMap):
tplate = template.data
elif isinstance(template, np.ndarray):
tplate = template
else:
raise ValueError("Invalid template.")
# Apply the function to the template
tplate = func(tplate)
# Storage for the pixel shift
xshift_keep = np.zeros(nt) * u.pix
yshift_keep = np.zeros_like(xshift_keep)
# Storage for the arcsecond shift
xshift_arcseconds = np.zeros(nt) * u.arcsec
yshift_arcseconds = np.zeros_like(xshift_arcseconds)
# Match the template and calculate shifts
for i, m in enumerate(mc.maps):
# Get the next 2-d data array
this_layer = func(m.data)
# Calculate the y and x shifts in pixels
yshift, xshift = _calculate_shift(this_layer, tplate)
# Keep shifts in pixels
yshift_keep[i] = yshift
xshift_keep[i] = xshift
# Calculate shifts relative to the template layer
yshift_keep = yshift_keep - yshift_keep[layer_index]
xshift_keep = xshift_keep - xshift_keep[layer_index]
for i, m in enumerate(mc.maps):
# Calculate the shifts required in physical units, which are
# presumed to be arcseconds.
xshift_arcseconds[i] = xshift_keep[i] * m.scale[0]
yshift_arcseconds[i] = yshift_keep[i] * m.scale[1]
return {"x": xshift_arcseconds, "y": yshift_arcseconds}
[docs]
def mapsequence_coalign_by_match_template(
mc,
template=None,
layer_index=0,
func=_default_fmap_function,
clip=True,
shift=None,
**kwargs,
):
"""
Co-register the layers in a `~sunpy.map.MapSequence` according to a
template taken from that `~sunpy.map.MapSequence`.
When using this functionality, it is a good
idea to check that the shifts that were applied were reasonable and
expected. One way of checking this is to animate the original
`~sunpy.map.MapSequence`, animate the coaligned `~sunpy.map.MapSequence`,
and compare the differences you see to the calculated shifts.
Currently this module provides image co-alignment by template matching.
and is partially inspired by the SSWIDL routine
`tr_get_disp.pro <http://www.heliodocs.com/php/xdoc_print.php?file=$SSW/trace/idl/util/tr_get_disp.pro>`__.
In this implementation, the template matching is handled via
`skimage.feature.match_template`.
Parameters
----------
mc : `sunpy.map.MapSequence`
A `~sunpy.map.MapSequence` of shape ``(ny, nx, nt)``, where ``nt`` is the number of
layers in the `~sunpy.map.MapSequence`.
template : {None | sunpy.map.Map | `numpy.ndarray`}, optional
The template used in the matching. If an `numpy.ndarray` is passed,
the `numpy.ndarray` has to have two dimensions.
layer_index : `int`, optional
The template is assumed to refer to the map in the `~sunpy.map.MapSequence`
indexed by the value of ``layer_index``. Displacements of all maps in the
`~sunpy.map.MapSequence` are assumed to be relative to this layer. The
displacements of the template relative to this layer are therefore
``(0, 0)``.
func : function, optional
A function which is applied to the data values before the coalignment
method is applied. This can be useful in coalignment, because it is
sometimes better to co-align on a function of the data rather than the
data itself. The calculated shifts are applied to the original data.
Examples of useful functions to consider for EUV images are the
logarithm or the square root. The function is of the form
``func = F(data)``. The default function ensures that the data are
floats.
clip : bool, optional
If True, then clip off x, y edges of the maps in the sequence that are
potentially affected by edges effects.
shift : dict, optional
A dictionary with two keys, 'x' and 'y'. Key 'x' is an astropy
quantities array of corresponding to the amount of shift in the
x-direction (in arcseconds, assuming the helio-projective
Cartesian coordinate system) that is applied to the input
`~sunpy.map.MapSequence`. Key 'y' is an `~astropy.units.Quantity` array
corresponding to the amount of shift in the y-direction (in arcseconds,
assuming the helio-projective Cartesian coordinate system) that is
applied to the input `~sunpy.map.MapSequence`. The number of elements in
each array must be the same as the number of maps in the
`~sunpy.map.MapSequence`. If a shift is passed in to the function, that
shift is applied to the input `~sunpy.map.MapSequence` and the template
matching algorithm is not used.
Notes
-----
The remaining keyword arguments are sent to `~sunkit_image.coalignment.apply_shifts`.
Returns
-------
`sunpy.map.MapSequence`
A `~sunpy.map.MapSequence` that has co-aligned by matching the template.
Examples
--------
>>> from sunkit_image.coalignment import mapsequence_coalign_by_match_template as mc_coalign
>>> coaligned_mc = mc_coalign(mc) # doctest: +SKIP
>>> coaligned_mc = mc_coalign(mc, layer_index=-1) # doctest: +SKIP
>>> coaligned_mc = mc_coalign(mc, clip=False) # doctest: +SKIP
>>> coaligned_mc = mc_coalign(mc, template=sunpy_map) # doctest: +SKIP
>>> coaligned_mc = mc_coalign(mc, template=two_dimensional_ndarray) # doctest: +SKIP
>>> coaligned_mc = mc_coalign(mc, func=np.log) # doctest: +SKIP
References
----------
* http://scribblethink.org/Work/nvisionInterface/nip.html
* J.P. Lewis, Fast Template Matching, Vision Interface 95, Canadian Image
Processing and Pattern Recognition Society, Quebec City, Canada, May 15-19,
1995, p. 120-123 http://www.scribblethink.org/Work/nvisionInterface/vi95_lewis.pdf.
"""
# Number of maps
nt = len(mc.maps)
# Storage for the pixel shifts and the shifts in arcseconds
xshift_keep = np.zeros(nt) * u.pix
yshift_keep = np.zeros_like(xshift_keep)
if shift is None:
shifts = calculate_match_template_shift(mc, template=template, layer_index=layer_index, func=func)
xshift_arcseconds = shifts["x"]
yshift_arcseconds = shifts["y"]
else:
xshift_arcseconds = shift["x"]
yshift_arcseconds = shift["y"]
# Calculate the pixel shifts
for i, m in enumerate(mc):
xshift_keep[i] = xshift_arcseconds[i] / m.scale[0]
yshift_keep[i] = yshift_arcseconds[i] / m.scale[1]
# Apply the shifts and return the coaligned mapsequence
return apply_shifts(mc, -yshift_keep, -xshift_keep, clip=clip, **kwargs)
[docs]
def calculate_solar_rotate_shift(mc, layer_index=0, **kwargs):
"""
Calculate the shift that must be applied to each map contained in a
mapsequence in order to compensate for solar rotation.
The center of the map is used to calculate the position of each mapsequence
layer. Shifts are calculated relative to a specified layer in the mapsequence.
When using this functionality, it is a good idea to check that the shifts
that were applied to were reasonable and expected. One way of checking this
is to animate the original mapsequence, animate the derotated mapsequence, and
compare the differences you see to the calculated shifts. An example use is
as follows. If you select data from the SDO cutout service, it is common to
not use the solar tracking implemented by this service. This is because (at
time of writing) the solar tracking implemented by that service moves the
image by single pixels at a time. This is not optimal for many use cases,
as it introduces artificial jumps in the data. So with solar tracking not
chosen, the selected area is like a window through which you can see the
Sun rotating underneath.
Parameters
----------
mc : `sunpy.map.MapSequence`
The input mapsequence.
layer_index : int
The index layer. Shifts are calculated relative to the time of
this layer.
``**kwargs``
These keywords are passed to the function
`sunpy.physics.differential_rotation.solar_rotate_coordinate`.
Returns
-------
`~astropy.units.Quantity`, ~astropy.units.Quantity`
The shifts relative to the index layer that can be applied
to the input mapsequence in order to compensate for solar rotation.
The shifts are given in arcseconds as understood in helioprojective
coordinates systems.
"""
nt = len(mc.maps)
xshift_arcseconds = np.zeros(nt) * u.arcsec
yshift_arcseconds = np.zeros_like(xshift_arcseconds)
rotate_to_this_layer = mc.maps[layer_index]
for i, m in enumerate(mc):
# The shift of the reference layer is always zero by definition.
if i == layer_index:
continue
# Calculate the rotation of the center of the map 'm' at its
# observation time to the observation time of the reference layer
# indicated by "layer_index".
new_coordinate = solar_rotate_coordinate(m.center, observer=rotate_to_this_layer.observer_coordinate, **kwargs)
xshift_arcseconds[i] = new_coordinate.Tx - rotate_to_this_layer.center.Tx
yshift_arcseconds[i] = new_coordinate.Ty - rotate_to_this_layer.center.Ty
return {"x": xshift_arcseconds, "y": yshift_arcseconds}
[docs]
def mapsequence_coalign_by_rotation(mc, layer_index=0, clip=True, shift=None, **kwargs):
"""
Move the layers in a mapsequence according to the input shifts.
If an input shift is not given, the shifts due to
solar rotation relative to an index layer is calculated and
applied. When using this functionality, it is a good idea to check
that the shifts that were applied to were reasonable and expected.
One way of checking this is to animate the original mapsequence, animate
the derotated mapsequence, and compare the differences you see to the
calculated shifts.
Parameters
----------
mc : `sunpy.map.MapSequence`
A mapsequence of shape (ny, nx, nt), where nt is the number of layers in
the mapsequence.
layer_index : int
Solar derotation shifts of all maps in the mapsequence are assumed
to be relative to the layer in the mapsequence indexed by ``layer_index``.
clip : bool
If True, then clip off x, y edges in the datasequence that are potentially
affected by edges effects.
``**kwargs``
These keywords are passed to
`~sunkit_image.coalignment.calculate_solar_rotate_shift`.
Returns
-------
`sunpy.map.MapSequence`
The results of the shifts applied to the input mapsequence.
Examples
--------
>>> import sunpy.data.sample # doctest: +REMOTE_DATA
>>> from sunkit_image.coalignment import mapsequence_coalign_by_rotation
>>> map1 = sunpy.map.Map(sunpy.data.sample.AIA_171_IMAGE) # doctest: +REMOTE_DATA
>>> map2 = sunpy.map.Map(sunpy.data.sample.EIT_195_IMAGE) # doctest: +REMOTE_DATA
>>> mc = sunpy.map.Map([map1, map2], sequence=True) # doctest: +REMOTE_DATA
>>> derotated_mc = mapsequence_coalign_by_rotation(mc) # doctest: +SKIP
>>> derotated_mc = mapsequence_coalign_by_rotation(mc, layer_index=-1) # doctest: +SKIP
>>> derotated_mc = mapsequence_coalign_by_rotation(mc, clip=False) # doctest: +SKIP
"""
nt = len(mc.maps)
xshift_keep = np.zeros(nt) * u.pix
yshift_keep = np.zeros_like(xshift_keep)
if shift is None:
shift = calculate_solar_rotate_shift(mc, layer_index=layer_index, **kwargs)
xshift_arcseconds = shift["x"]
yshift_arcseconds = shift["y"]
for i, m in enumerate(mc):
xshift_keep[i] = xshift_arcseconds[i] / m.scale[0]
yshift_keep[i] = yshift_arcseconds[i] / m.scale[1]
return apply_shifts(mc, yshift_keep, xshift_keep, clip=clip)
