def rotate(self, angle, scale=1.0, rotation_centre=None, recentre=True,
missing=0.0, interpolation='bicubic', interp_param=-0.5):
"""Returns a new rotated, rescaled and shifted map.
Parameters
---------
angle: float
The angle to rotate the image by (radians)
scale: float
A scale factor for the image, default is no scaling
rotation_centre: tuple
The point in the image to rotate around (Axis of rotation).
Default: Centre of the array
recentre: bool, or array-like
Move the centroid (axis of rotation) to the centre of the array
or recentre coords.
Default: True, recentre to the centre of the array.
missing: float
The numerical value to fill any missing points after rotation.
Default: 0.0
interpolation: {'nearest' | 'bilinear' | 'spline' | 'bicubic'}
Interpolation method to use in the transform.
Spline uses the
scipy.ndimage.interpolation.affline_transform routine.
nearest, bilinear and bicubic all replicate the IDL rot() function.
Default: 'bicubic'
interp_par: Int or Float
Optional parameter for controlling the interpolation.
Spline interpolation requires an integer value between 1 and 5 for
the degree of the spline fit.
Default: 3
BiCubic interpolation requires a flaot value between -1 and 0.
Default: 0.5
Other interpolation options ingore the argument.
Returns
-------
New rotated, rescaled, translated map
"""
#Interpolation parameter Sanity
assert interpolation in ['nearest','spline','bilinear','bicubic']
#Set defaults based on interpolation
if interp_param is None:
if interpolation is 'spline':
interp_param = 3
elif interpolation is 'bicubic':
interp_param = 0.5
else:
interp_param = 0 #Default value for nearest or bilinear
#Make sure recenter is a vector with shape (2,1)
if not isinstance(recentre, bool):
recentre = np.array(recentre).reshape(2,1)
#Define Size and centre of array
centre = (np.array(self.shape)-1)/2.0
#If rotation_centre is not set (None or False),
#set rotation_centre to the centre of the image.
if rotation_centre is None:
rotation_centre = centre
else:
#Else check rotation_centre is a vector with shape (2,1)
rotation_centre = np.array(rotation_centre).reshape(2,1)
#Recentre to the rotation_centre if recentre is True
if isinstance(recentre, bool):
#if rentre is False then this will be (0,0)
shift = np.array(rotation_centre) - np.array(centre)
else:
#Recentre to recentre vector otherwise
shift = np.array(recentre) - np.array(centre)
image = np.asarray(self).copy()
#Calulate the parameters for the affline_transform
c = np.cos(angle)
s = np.sin(angle)
mati = np.array([[c, s],[-s, c]]) / scale # res->orig
centre = np.array([centre]).transpose() # the centre of rotn
shift = np.array([shift]).transpose() # the shift
kpos = centre - np.dot(mati, (centre + shift))
# kpos and mati are the two transform constants, kpos is a 2x2 array
rsmat, offs = mati, np.squeeze((kpos[0,0], kpos[1,0]))
if interpolation == 'spline':
# This is the scipy call
data = scipy.ndimage.interpolation.affine_transform(image, rsmat,
offset=offs, order=interp_param, mode='constant',
cval=missing)
else:
#Use C extension Package
if not 'Crotate' in globals():
raise ValueError("You do not have the C extension sunpy.image.Crotate")
#Set up call parameters depending on interp type.
if interpolation == 'nearest':
interp_type = Crotate.NEAREST
elif interpolation == 'bilinear':
interp_type = Crotate.BILINEAR
elif interpolation == 'bicubic':
interp_type = Crotate.BICUBIC
#Make call to extension
data = Crotate.affine_transform(image,
rsmat, offset=offs,
kernel=interp_type, cubic=interp_param,
mode='constant', cval=missing)
#Return a new map
#Copy Header
header = self._original_header.copy()
# Create new map instance
new_map = self.__class__(data, header)
return new_map
print "c code"
print timeit.timeit("out_arr = Crotate.affine_transform(in_arr, rsmat, offset=offs, kernel=Crotate.BICUBIC, cubic=int_param, mode='constant', cval=missing)",
setup="from __main__ import *", number=100) /100.
print "cython wrapper"
print timeit.timeit("out_arr = aff_cythonwrap(in_arr, rsmat, offs, int_method, int_param, missing)",
setup="from __main__ import *", number=100) /100.
print "cython"
print timeit.timeit("out_arr = aff(in_arr, rsmat, offs, int_param, missing)",
setup="from __main__ import *", number=100) /100.
# print "python"
# print timeit.timeit("out_arr = aff_py(in_arr, rsmat, offs, int_method, int_param, missing)",
# setup="from __main__ import *", number=1) /1.
#
out_arr_C = Crotate.affine_transform(in_arr, rsmat, offset=offs, kernel=Crotate.BICUBIC, cubic=int_param, mode='constant', cval=missing)
out_arr_wr = aff_cythonwrap(in_arr, rsmat, offs, int_param, missing)
fig, ax = plt.subplots(1,3)
im1 = ax[0].imshow(out_arr_C - out_arr_wr, cmap=plt.get_cmap('Reds'), interpolation='none')
plt.colorbar(im1,ax=ax[0])
im2 = ax[1].imshow(out_arr_C, vmax =1, vmin=0, cmap=plt.get_cmap('Reds'), interpolation='none')
plt.colorbar(im2,ax=ax[1])
im3 = ax[2].imshow(out_arr_wr, vmax =1, vmin=0, cmap=plt.get_cmap('Reds'), interpolation='none')
plt.colorbar(im3,ax=ax[2])
plt.show()
#plt.imshow(out_arr)
#plt.show()
def rotate(
self,
angle=None,
rmatrix=None,
scale=1.0,
rotation_center=None,
recenter=True,
missing=0.0,
interpolation="bicubic",
interp_param=-0.5,
):
"""Returns a new rotated, rescaled and shifted map.
Parameters
----------
angle: float
The angle to rotate the image by (radians). Specify angle or matrix.
rmatrix: NxN
Linear transformation rotation matrix. Specify angle or matrix.
scale: float
A scale factor for the image, default is no scaling
rotation_center: tuple
The point in the image to rotate around (Axis of rotation).
Default: center of the array
recenter: bool, or array-like
Move the centroid (axis of rotation) to the center of the array
or recenter coords.
Default: True, recenter to the center of the array.
missing: float
The numerical value to fill any missing points after rotation.
Default: 0.0
interpolation: {'nearest' | 'bilinear' | 'spline' | 'bicubic'}
Interpolation method to use in the transform.
Spline uses the
scipy.ndimage.interpolation.affline_transform routine.
nearest, bilinear and bicubic all replicate the IDL rot() function.
Default: 'bicubic'
interp_par: Int or Float
Optional parameter for controlling the interpolation.
Spline interpolation requires an integer value between 1 and 5 for
the degree of the spline fit.
Default: 3
BiCubic interpolation requires a flaot value between -1 and 0.
Default: 0.5
Other interpolation options ingore the argument.
Returns
-------
New rotated, rescaled, translated map
Notes
-----
Apart from interpolation='spline' all other options use a compiled
C-API extension. If for some reason this is not compiled correctly this
routine will fall back upon the scipy implementation of order = 3.
For more infomation see:
http://sunpy.readthedocs.org/en/latest/guide/troubleshooting.html#crotate-warning
"""
assert angle is None or rmatrix is None
# Interpolation parameter Sanity
assert interpolation in ["nearest", "spline", "bilinear", "bicubic"]
# Set defaults based on interpolation
if interp_param is None:
if interpolation is "spline":
interp_param = 3
elif interpolation is "bicubic":
interp_param = 0.5
else:
interp_param = 0 # Default value for nearest or bilinear
# Make sure recenter is a vector with shape (2,1)
if not isinstance(recenter, bool):
recenter = np.array(recenter).reshape(2, 1)
# Define Size and center of array
center = (np.array(self.data.shape) - 1) / 2.0
# If rotation_center is not set (None or False),
# set rotation_center to the center of the image.
if rotation_center is None:
rotation_center = center
else:
# Else check rotation_center is a vector with shape (2,1)
rotation_center = np.array(rotation_center).reshape(2, 1)
# recenter to the rotation_center if recenter is True
if isinstance(recenter, bool):
# if rentre is False then this will be (0,0)
shift = np.array(rotation_center) - np.array(center)
else:
# recenter to recenter vector otherwise
shift = np.array(recenter) - np.array(center)
image = self.data.copy()
if not angle is None:
# Calulate the parameters for the affline_transform
c = np.cos(angle)
s = np.sin(angle)
mati = np.array([[c, s], [-s, c]]) / scale # res->orig
if not rmatrix is None:
mati = rmatrix / scale # res->orig
center = np.array([center]).transpose() # the center of rotn
shift = np.array([shift]).transpose() # the shift
kpos = center - np.dot(mati, (center + shift))
# kpos and mati are the two transform constants, kpos is a 2x2 array
rsmat, offs = mati, np.squeeze((kpos[0, 0], kpos[1, 0]))
if interpolation == "spline":
# This is the scipy call
data = scipy.ndimage.interpolation.affine_transform(
image, rsmat, offset=offs, order=interp_param, mode="constant", cval=missing
)
else:
# Use C extension Package
if not "Crotate" in globals():
warnings.warn(
"""The C extension sunpy.image.Crotate is not
installed, falling back to the interpolation='spline' of order=3""",
Warning,
)
data = scipy.ndimage.interpolation.affine_transform(
image, rsmat, offset=offs, order=3, mode="constant", cval=missing
)
else:
# Set up call parameters depending on interp type.
if interpolation == "nearest":
interp_type = Crotate.NEAREST
elif interpolation == "bilinear":
interp_type = Crotate.BILINEAR
elif interpolation == "bicubic":
interp_type = Crotate.BICUBIC
# Make call to extension
data = Crotate.affine_transform(
image, rsmat, offset=offs, kernel=interp_type, cubic=interp_param, mode="constant", cval=missing
)
# Return a new map
# Copy Header
new_map = deepcopy(self)
# Create new map instance
new_map.data = data
return new_map
def rotate(self,
angle=None,
rmatrix=None,
scale=1.0,
rotation_center=None,
recenter=True,
missing=0.0,
interpolation='bicubic',
interp_param=-0.5):
"""Returns a new rotated, rescaled and shifted map.
Parameters
----------
angle: float
The angle to rotate the image by (radians). Specify angle or matrix.
rmatrix: NxN
Linear transformation rotation matrix. Specify angle or matrix.
scale: float
A scale factor for the image, default is no scaling
rotation_center: tuple
The point in the image to rotate around (Axis of rotation).
Default: center of the array
recenter: bool, or array-like
Move the centroid (axis of rotation) to the center of the array
or recenter coords.
Default: True, recenter to the center of the array.
missing: float
The numerical value to fill any missing points after rotation.
Default: 0.0
interpolation: {'nearest' | 'bilinear' | 'spline' | 'bicubic'}
Interpolation method to use in the transform.
Spline uses the
scipy.ndimage.interpolation.affline_transform routine.
nearest, bilinear and bicubic all replicate the IDL rot() function.
Default: 'bicubic'
interp_par: Int or Float
Optional parameter for controlling the interpolation.
Spline interpolation requires an integer value between 1 and 5 for
the degree of the spline fit.
Default: 3
BiCubic interpolation requires a flaot value between -1 and 0.
Default: 0.5
Other interpolation options ingore the argument.
Returns
-------
New rotated, rescaled, translated map
Notes
-----
Apart from interpolation='spline' all other options use a compiled
C-API extension. If for some reason this is not compiled correctly this
routine will fall back upon the scipy implementation of order = 3.
For more infomation see:
http://sunpy.readthedocs.org/en/latest/guide/troubleshooting.html#crotate-warning
"""
assert angle is None or rmatrix is None
#Interpolation parameter Sanity
assert interpolation in ['nearest', 'spline', 'bilinear', 'bicubic']
#Set defaults based on interpolation
if interp_param is None:
if interpolation is 'spline':
interp_param = 3
elif interpolation is 'bicubic':
interp_param = 0.5
else:
interp_param = 0 #Default value for nearest or bilinear
#Make sure recenter is a vector with shape (2,1)
if not isinstance(recenter, bool):
recenter = np.array(recenter).reshape(2, 1)
#Define Size and center of array
center = (np.array(self.data.shape) - 1) / 2.0
#If rotation_center is not set (None or False),
#set rotation_center to the center of the image.
if rotation_center is None:
rotation_center = center
else:
#Else check rotation_center is a vector with shape (2,1)
rotation_center = np.array(rotation_center).reshape(2, 1)
#recenter to the rotation_center if recenter is True
if isinstance(recenter, bool):
#if rentre is False then this will be (0,0)
shift = np.array(rotation_center) - np.array(center)
else:
#recenter to recenter vector otherwise
shift = np.array(recenter) - np.array(center)
image = self.data.copy()
if not angle is None:
#Calulate the parameters for the affline_transform
c = np.cos(angle)
s = np.sin(angle)
mati = np.array([[c, s], [-s, c]]) / scale # res->orig
if not rmatrix is None:
mati = rmatrix / scale # res->orig
center = np.array([center]).transpose() # the center of rotn
shift = np.array([shift]).transpose() # the shift
kpos = center - np.dot(mati, (center + shift))
# kpos and mati are the two transform constants, kpos is a 2x2 array
rsmat, offs = mati, np.squeeze((kpos[0, 0], kpos[1, 0]))
if interpolation == 'spline':
# This is the scipy call
data = scipy.ndimage.interpolation.affine_transform(
image,
rsmat,
offset=offs,
order=interp_param,
mode='constant',
cval=missing)
else:
#Use C extension Package
if not 'Crotate' in globals():
warnings.warn(
"""The C extension sunpy.image.Crotate is not
installed, falling back to the interpolation='spline' of order=3""", Warning)
data = scipy.ndimage.interpolation.affine_transform(
image,
rsmat,
offset=offs,
order=3,
mode='constant',
cval=missing)
else:
#Set up call parameters depending on interp type.
if interpolation == 'nearest':
interp_type = Crotate.NEAREST
elif interpolation == 'bilinear':
interp_type = Crotate.BILINEAR
elif interpolation == 'bicubic':
interp_type = Crotate.BICUBIC
#Make call to extension
data = Crotate.affine_transform(image,
rsmat,
offset=offs,
kernel=interp_type,
cubic=interp_param,
mode='constant',
cval=missing)
#Return a new map
#Copy Header
new_map = deepcopy(self)
# Create new map instance
new_map.data = data
return new_map
def rotate(self, angle=None, rmatrix=None, scale=1.0,
rotation_center=(0, 0), recenter=False,
missing=0.0, interpolation='bicubic', interp_param=-0.5):
"""Returns a new rotated and rescaled map. Specify either a rotation
angle or a rotation matrix, but not both. If neither an angle or a
rotation matrix are specified, the map will be rotated by the rotation
angle in the metadata.
If the rotation is specified as an angle (either explicitly or
implicitly) and the metadata contains the CROTA2 keyword, that keyword
will be changed appropriately to account for the rotation.
Parameters
----------
angle: float
The angle (degrees) to rotate counterclockwise.
rmatrix: NxN
Linear transformation rotation matrix.
scale: float
A scale factor for the image, default is no scaling
rotation_center: tuple
The axis of rotation
Default: the origin in the data coordinate system
recenter: bool
If True, position the axis of rotation at the center of the new map
Default: False
missing: float
The numerical value to fill any missing points after rotation.
Default: 0.0
interpolation: {'nearest' | 'bilinear' | 'spline' | 'bicubic'}
Interpolation method to use in the transform.
Spline uses the
scipy.ndimage.interpolation.affline_transform routine.
nearest, bilinear and bicubic all replicate the IDL rot() function.
Default: 'bicubic'
interp_par: Int or Float
Optional parameter for controlling the interpolation.
Spline interpolation requires an integer value between 1 and 5 for
the degree of the spline fit.
Default: 3
BiCubic interpolation requires a flaot value between -1 and 0.
Default: 0.5
Other interpolation options ingore the argument.
Returns
-------
New rotated and rescaled map
Notes
-----
Apart from interpolation='spline' all other options use a compiled
C-API extension. If for some reason this is not compiled correctly this
routine will fall back upon the scipy implementation of order = 3.
For more infomation see:
http://sunpy.readthedocs.org/en/latest/guide/troubleshooting.html#crotate-warning
"""
assert angle is None or rmatrix is None
# Interpolation parameter sanity
assert interpolation in ['nearest', 'spline', 'bilinear', 'bicubic']
# Set defaults based on interpolation
if interp_param is None:
if interpolation is 'spline':
interp_param = 3
elif interpolation is 'bicubic':
interp_param = 0.5
else:
interp_param = 0 # Default value for nearest or bilinear
image = self.data.copy()
if angle is None and rmatrix is None:
angle = self.rotation_angle['y']
if not angle is None:
#Calulate the parameters for the affine_transform
c = np.cos(np.deg2rad(angle))
s = np.sin(np.deg2rad(angle))
rsmat = np.array([[c, -s], [s, c]]) / scale
if not rmatrix is None:
rsmat = np.asarray(rmatrix) / scale
# map_center is swapped compared to the x-y convention
map_center = (np.array(self.data.shape)-1)/2.0
# axis is swapped compared to the x-y convention
if recenter:
axis_x = self.data_to_pixel(rotation_center[0], 'x')
axis_y = self.data_to_pixel(rotation_center[1], 'y')
axis = (axis_y, axis_x)
else:
axis = map_center
# offs is swapped compared to the x-y convention
offs = axis - np.dot(rsmat, map_center)
if interpolation == 'spline':
# This is the scipy call
data = interp.affine_transform(image, rsmat, offset=offs,
order=interp_param, mode='constant',
cval=missing)
else:
#Use C extension Package
if not 'Crotate' in globals():
warnings.warn("""The C extension sunpy.image.Crotate is not
installed, falling back to the interpolation='spline' of order=3""" ,Warning)
data = interp.affine_transform(image, rsmat, offset=offs,
order=3, mode='constant',
cval=missing)
else:
#Set up call parameters depending on interp type.
if interpolation == 'nearest':
interp_type = Crotate.NEAREST
elif interpolation == 'bilinear':
interp_type = Crotate.BILINEAR
elif interpolation == 'bicubic':
interp_type = Crotate.BICUBIC
#Make call to extension
data = Crotate.affine_transform(image,
rsmat, offset=offs,
kernel=interp_type,
cubic=interp_param,
mode='constant',
cval=missing)
#Return a new map
#Copy Header
new_map = deepcopy(self)
# Create new map instance
new_map.data = data
if recenter:
new_center = rotation_center
else:
# Retrieve old coordinates for the center of the array
old_center = np.array(new_map.pixel_to_data(map_center[1], map_center[0]))
# Calculate new coordinates for the center of the array
new_center = rotation_center - np.dot(rsmat, rotation_center - old_center)
# Define a new reference pixel in the rotated space
new_map.meta['crval1'] = new_center[0]
new_map.meta['crval2'] = new_center[1]
new_map.meta['crpix1'] = map_center[1] + 1 # FITS counts pixels from 1
new_map.meta['crpix2'] = map_center[0] + 1 # FITS counts pixels from 1
if angle is not None and new_map.meta.get('crota2') is not None:
new_map.meta['crota2'] = new_map.rotation_angle['y'] - angle
return new_map
def rotate(self,
angle=None,
rmatrix=None,
scale=1.0,
rotation_center=(0, 0),
recenter=False,
missing=0.0,
interpolation='bicubic',
interp_param=-0.5):
"""Returns a new rotated and rescaled map. Specify either a rotation
angle or a rotation matrix, but not both. If neither an angle or a
rotation matrix are specified, the map will be rotated by the rotation
angle in the metadata.
If the rotation is specified as an angle (either explicitly or
implicitly) and the metadata contains the CROTA2 keyword, that keyword
will be changed appropriately to account for the rotation.
Parameters
----------
angle: float
The angle (degrees) to rotate counterclockwise.
rmatrix: NxN
Linear transformation rotation matrix.
scale: float
A scale factor for the image, default is no scaling
rotation_center: tuple
The axis of rotation
Default: the origin in the data coordinate system
recenter: bool
If True, position the axis of rotation at the center of the new map
Default: False
missing: float
The numerical value to fill any missing points after rotation.
Default: 0.0
interpolation: {'nearest' | 'bilinear' | 'spline' | 'bicubic'}
Interpolation method to use in the transform.
Spline uses the
scipy.ndimage.interpolation.affline_transform routine.
nearest, bilinear and bicubic all replicate the IDL rot() function.
Default: 'bicubic'
interp_par: Int or Float
Optional parameter for controlling the interpolation.
Spline interpolation requires an integer value between 1 and 5 for
the degree of the spline fit.
Default: 3
BiCubic interpolation requires a flaot value between -1 and 0.
Default: 0.5
Other interpolation options ingore the argument.
Returns
-------
New rotated and rescaled map
Notes
-----
Apart from interpolation='spline' all other options use a compiled
C-API extension. If for some reason this is not compiled correctly this
routine will fall back upon the scipy implementation of order = 3.
For more infomation see:
http://sunpy.readthedocs.org/en/latest/guide/troubleshooting.html#crotate-warning
"""
assert angle is None or rmatrix is None
# Interpolation parameter sanity
assert interpolation in ['nearest', 'spline', 'bilinear', 'bicubic']
# Set defaults based on interpolation
if interp_param is None:
if interpolation is 'spline':
interp_param = 3
elif interpolation is 'bicubic':
interp_param = 0.5
else:
interp_param = 0 # Default value for nearest or bilinear
image = self.data.copy()
if angle is None and rmatrix is None:
angle = self.rotation_angle['y']
if not angle is None:
#Calulate the parameters for the affine_transform
c = np.cos(np.deg2rad(angle))
s = np.sin(np.deg2rad(angle))
rsmat = np.array([[c, -s], [s, c]]) / scale
if not rmatrix is None:
rsmat = np.asarray(rmatrix) / scale
# map_center is swapped compared to the x-y convention
map_center = (np.array(self.data.shape) - 1) / 2.0
# axis is swapped compared to the x-y convention
if recenter:
axis_x = self.data_to_pixel(rotation_center[0], 'x')
axis_y = self.data_to_pixel(rotation_center[1], 'y')
axis = (axis_y, axis_x)
else:
axis = map_center
# offs is swapped compared to the x-y convention
offs = axis - np.dot(rsmat, map_center)
if interpolation == 'spline':
# This is the scipy call
data = interp.affine_transform(image,
rsmat,
offset=offs,
order=interp_param,
mode='constant',
cval=missing)
else:
#Use C extension Package
if not 'Crotate' in globals():
warnings.warn(
"""The C extension sunpy.image.Crotate is not
installed, falling back to the interpolation='spline' of order=3""", Warning)
data = interp.affine_transform(image,
rsmat,
offset=offs,
order=3,
mode='constant',
cval=missing)
else:
#Set up call parameters depending on interp type.
if interpolation == 'nearest':
interp_type = Crotate.NEAREST
elif interpolation == 'bilinear':
interp_type = Crotate.BILINEAR
elif interpolation == 'bicubic':
interp_type = Crotate.BICUBIC
#Make call to extension
data = Crotate.affine_transform(image,
rsmat,
offset=offs,
kernel=interp_type,
cubic=interp_param,
mode='constant',
cval=missing)
#Return a new map
#Copy Header
new_map = deepcopy(self)
# Create new map instance
new_map.data = data
if recenter:
new_center = rotation_center
else:
# Retrieve old coordinates for the center of the array
old_center = np.array(
new_map.pixel_to_data(map_center[1], map_center[0]))
# Calculate new coordinates for the center of the array
new_center = rotation_center - np.dot(rsmat,
rotation_center - old_center)
# Define a new reference pixel in the rotated space
new_map.meta['crval1'] = new_center[0]
new_map.meta['crval2'] = new_center[1]
new_map.meta['crpix1'] = map_center[1] + 1 # FITS counts pixels from 1
new_map.meta['crpix2'] = map_center[0] + 1 # FITS counts pixels from 1
if angle is not None and new_map.meta.get('crota2') is not None:
new_map.meta['crota2'] = new_map.rotation_angle['y'] - angle
return new_map