def circular_annulus_footprint(radius_inner, radius_outer, dtype=np.int):
"""
Create a circular annulus footprint.
A pixel is considered to be entirely in or out of the footprint
depending on whether its center is in or out of the footprint. The
size of the output array is the minimal bounding box for the
footprint.
Parameters
----------
radius_inner : int
The inner radius of the circular annulus.
radius_outer : int
The outer radius of the circular annulus.
dtype : data-type, optional
The data type of the output `~numpy.ndarray`.
Returns
-------
footprint : `~numpy.ndarray`
A footprint where array elements are 1 within the footprint and
0 otherwise.
Examples
--------
>>> from astroimtools import circular_footprint
>>> circular_annulus_footprint(1, 2)
array([[0, 0, 1, 0, 0],
[0, 1, 0, 1, 0],
[1, 0, 0, 0, 1],
[0, 1, 0, 1, 0],
[0, 0, 1, 0, 0]])
"""
if radius_inner > radius_outer:
raise ValueError('radius_outer must be >= radius_inner')
size = (radius_outer * 2) + 1
y, x = np.mgrid[0:size, 0:size]
circle_outer = Ellipse2D(1,
radius_outer,
radius_outer,
radius_outer,
radius_outer,
theta=0)(x, y)
circle_inner = Ellipse2D(1.,
radius_outer,
radius_outer,
radius_inner,
radius_inner,
theta=0)(x, y)
return np.asarray(circle_outer - circle_inner, dtype=dtype)
def ellipse_region(cube, center, a, ecc, theta, annulus = False):
''' Function that returns a mask that defines the elements that lie
within an ellipsoidal region. The ellipse can also be annular.
Inputs:
Cube: The data array
center: Tuple of the central coordinates (x_0, y_0) (spaxels)
a: The semi-major axis length (spaxels).
ecc: The eccentricity of the ellipse (0<ecc<1)
theta: The rotation angle of the ellipse from vertical (degrees)
rotating clockwise.
Optional Inputs:
annulus: False if just simple ellipse, otherwise is the INNER
annular radius (spaxels)
'''
# Define angle, eccentricity term, and semi-minor axis
an = Angle(theta, 'deg')
e = np.sqrt(1 - (ecc**2))
b = a * e
# Create outer ellipse
ell_region = Ellipse2D(amplitude=10, x_0 = center[0], y_0 = center[1],\
a=a, b=b, theta=an.radian)
x,y = np.mgrid[0:cube.shape[1], 0:cube.shape[2]]
ell_good = ell_region(x,y)
if annulus:
# Define inner ellipse parameters and then create inner mask
a2 = annulus
b2 = a2 * e
ell_region_inner = Ellipse2D(amplitude=10, x_0 = center[0],\
y_0 = center[1], a=a2, b=b2, theta=an.radian)
# Set region of outer mask and within inner ellipse to zero, leaving
# an annular elliptical region
ell_inner_good = ell_region_inner(x,y)
ell_good[ell_inner_good > 0] = 0
#fig, ax = mpl.subplots(figsize = (12,7))
#mpl.imshow(np.nansum(cube, axis = 0), origin = 'lower', interpolation = None)
#mpl.imshow(ell_good, alpha=0.2, origin = 'lower', interpolation = None)
#mpl.show()
# Return the mask
return np.array(ell_good, dtype = bool)
def intensity_props(data, blob, min_rad=4):
'''
Return the mean and std for the elliptical region in the given data.
Parameters
----------
data : LowerDimensionalObject or SpectralCube
Data to estimate the background from.
blob : numpy.array
Contains the properties of the region.
'''
y, x, major, minor, pa = blob[:5]
inner_ellipse = \
Ellipse2D(True, x, y, max(min_rad, 0.75 * major),
max(min_rad, 0.75 * minor), pa)
yy, xx = np.mgrid[:data.shape[-2], :data.shape[-1]]
ellip_mask = inner_ellipse(xx, yy).astype(bool)
vals = data[ellip_mask]
mean, sig = robust_skewed_std(vals)
return mean, sig
def test_ellipse_extent():
# Test this properly bounds the ellipse
imshape = (100, 100)
coords = y, x = np.indices(imshape)
amplitude = 1
x0 = 50
y0 = 50
a = 30
b = 10
theta = np.pi / 4
model = Ellipse2D(amplitude, x0, y0, a, b, theta)
dx, dy = ellipse_extent(a, b, theta)
limits = ((y0 - dy, y0 + dy), (x0 - dx, x0 + dx))
model.bounding_box = limits
actual = model.render(coords=coords)
expected = model(x, y)
# Check that the full ellipse is captured
np.testing.assert_allclose(expected, actual, atol=0, rtol=1)
# Check the bounding_box isn't too large
limits = np.array(limits).flatten()
for i in [0, 1]:
s = actual.sum(axis=i)
diff = np.abs(limits[2 * i] - np.where(s > 0)[0][0])
assert diff < 1
def add_holes(shape, nholes=100, rad_max=30, rad_min=10, hole_level=None,
return_info=False, max_corr=0.5, max_trials=500, max_eccent=2.):
ylim, xlim = shape
array = np.ones(shape) * 255
yy, xx = np.mgrid[-ylim/2:ylim/2, -xlim/2:xlim/2]
params = np.empty((0, 5))
ct = 0
trial = 0
while True:
xcenter = np.random.random_integers(-(xlim/2-rad_max),
high=xlim/2-rad_max)
ycenter = np.random.random_integers(-(ylim/2-rad_max),
high=ylim/2-rad_max)
major = np.random.uniform(rad_min, rad_max)
minor = np.random.uniform(major / float(max_eccent), major)
pa = np.random.uniform(0, np.pi)
blob = np.array([ycenter, xcenter, major, minor, pa])
if ct != 0:
corrs = np.array([overlap_metric(par, blob, return_corr=True)
for par in params])
if np.any(corrs > max_corr):
trial += 1
continue
if hole_level is None:
# Choose random hole bkg levels
bkg = np.random.random_integers(0, 230)
else:
bkg = hole_level
ellip = Ellipse2D(True, xcenter, ycenter, major, minor, pa)
array[ellip(yy, xx).astype(bool)] = bkg
params = np.append(params, blob[np.newaxis, :], axis=0)
ct += 1
trial += 1
if ct == nholes:
break
if trial == max_trials:
Warning("Only found %i valid regions in %i trials." %
(ct, max_trials))
break
if return_info:
return array, params
return array
def circular_annulus_footprint(radius_inner, radius_outer, dtype=np.int):
if radius_inner > radius_outer:
raise ValueError('radius_outer must be >= radius_inner')
size = (radius_outer * 2) + 1
y, x = np.mgrid[0:size, 0:size]
circle_outer = Ellipse2D(1,
radius_outer,
radius_outer,
radius_outer,
radius_outer,
theta=0)(x, y)
circle_inner = Ellipse2D(1.,
radius_outer,
radius_outer,
radius_inner,
radius_inner,
theta=0)(x, y)
return np.asarray(circle_outer - circle_inner, dtype=dtype)
def fitEllipse(cc,axesLength,angle):
x0, y0 = cc[0],cc[1]
a, b = axesLength[0],axesLength[1]
theta = Angle(angle, 'deg')
e = Ellipse2D(amplitude=100., x_0=x0, y_0=y0, a=a, b=b, theta=theta.radian)
y, x = np.mgrid[0:1000, 0:1000]
center = PixCoord(x=x0, y=y0)
reg = EllipsePixelRegion(center=center, width=2*a, height=2*b, angle=theta)
patch = reg.as_artist(facecolor='none', edgecolor='red', lw=2)
return reg
def test_half_ellipse_in_fraction_rotate():
blob = [70., 30., 20., 10., np.pi / 4.]
ellip = Ellipse2D(True, blob[1], blob[0], blob[2], blob[3], blob[4])
yy, xx = np.mgrid[:101, :1011]
mask = ellip(yy, xx).astype(bool)
np.testing.assert_allclose(fraction_in_mask(blob, mask), 1.0, rtol=0.01)
def circular_annulus_footprint(radius_inner, radius_outer, dtype=np.int):
"""
Create a circular annulus footprint.
A pixel is considered to be entirely in or out of the footprint
depending on whether its center is in or out of the footprint. The
size of the output array is the minimal bounding box for the
footprint.
Parameters
----------
radius_inner : int
The inner radius of the circular annulus.
radius_outer : int
The outer radius of the circular annulus.
dtype : data-type, optional
The data type of the output `~numpy.ndarray`.
Returns
-------
footprint : `~numpy.ndarray`
A footprint where array elements are 1 within the footprint and
0 otherwise.
"""
size = (radius_outer * 2) + 1
y, x = np.mgrid[0:size, 0:size]
circle_outer = Ellipse2D(1,
radius_outer,
radius_outer,
radius_outer,
radius_outer,
theta=0)(x, y)
circle_inner = Ellipse2D(1.,
radius_outer,
radius_outer,
radius_inner,
radius_inner,
theta=0)(x, y)
return np.asarray(circle_outer - circle_inner, dtype=dtype)
def ellipse_in_array(params, shape):
'''
Test if the entire ellipse is within the given shape.
'''
yext, xext = Ellipse2D(True, params[1], params[0], params[2], params[3],
params[4]).bounding_box
bottom_corner = np.array([floor_int(yext[0]), floor_int(xext[0])])
top_corner = np.array([ceil_int(yext[1]), ceil_int(xext[1])])
return in_array(bottom_corner, shape) and in_array(top_corner, shape)
def elliptical_footprint(a, b, theta=0, dtype=np.int):
"""
Create an elliptical footprint.
A pixel is considered to be entirely in or out of the footprint
depending on whether its center is in or out of the footprint. The
size of the output array is the minimal bounding box for the
footprint.
Parameters
----------
a : int
The semimajor axis.
b : int
The semiminor axis.
theta : float, optional
The rotation angle in radians of the semimajor axis. The angle
is measured counterclockwise from the positive x axis.
dtype : data-type, optional
The data type of the output `~numpy.ndarray`.
Returns
-------
footprint : `~numpy.ndarray`
A footprint where array elements are 1 within the footprint and
0 otherwise.
Examples
--------
>>> import numpy as np
>>> from astroimtools import elliptical_footprint
>>> elliptical_footprint(3, 1, theta=np.pi/4.)
array([[1, 1, 0, 0, 0],
[1, 1, 1, 0, 0],
[0, 1, 1, 1, 0],
[0, 0, 1, 1, 1],
[0, 0, 0, 1, 1]])
"""
if b > a:
raise ValueError('a must be >= b')
size = (a * 2) + 1
y, x = np.mgrid[0:size, 0:size]
ellipse = Ellipse2D(1, a, a, a, b, theta=theta)(x, y)
# crop to minimal bounding box
yi, xi = ellipse.nonzero()
idx = (slice(min(yi), max(yi) + 1), slice(min(xi), max(xi) + 1))
return np.asarray(ellipse[idx], dtype=dtype)
def shape_from_blob_moments(blob, response, expand_factor=np.sqrt(2)):
'''
Use blob properties to define a region, then correct its shape by
using the response surface
'''
yy, xx = np.mgrid[:response.shape[0], :response.shape[1]]
mask = Ellipse2D.evaluate(yy, xx, True, blob[0],
blob[1], expand_factor * blob[2],
expand_factor * blob[3], blob[4]).astype(np.int)
resp = response.copy()
# We don't care about the negative values here, so set to 0
resp[resp < 0.0] = 0.0
props = regionprops(mask, intensity_image=resp)[0]
# Now use the moments to refine the shape
wprops = weighted_props(props)
y, x, major, minor, pa = wprops
new_mask = Ellipse2D.evaluate(yy, xx, True, y,
x, major,
minor, pa).astype(bool)
# Find the maximum value in the response
max_resp = np.max(response[new_mask])
# print(blob)
# print((y, x, major, minor, pa, max_resp))
# import matplotlib.pyplot as p
# p.imshow(response, origin='lower', cmap='afmhot')
# p.contour(mask, colors='r')
# p.contour(new_mask, colors='g')
# p.draw()
# import time; time.sleep(0.1)
# raw_input("?")
# p.clf()
return np.array([y, x, major, minor, pa, max_resp])
def shape_from_blob_moments(blob, response, expand_factor=np.sqrt(2)):
'''
Use blob properties to define a region, then correct its shape by
using the response surface
'''
yy, xx = np.mgrid[:response.shape[0], :response.shape[1]]
mask = Ellipse2D.evaluate(yy, xx, True, blob[0], blob[1],
expand_factor * blob[2], expand_factor * blob[3],
blob[4]).astype(np.int)
resp = response.copy()
# We don't care about the negative values here, so set to 0
resp[resp < 0.0] = 0.0
props = regionprops(mask, intensity_image=resp)[0]
# Now use the moments to refine the shape
wprops = weighted_props(props)
y, x, major, minor, pa = wprops
new_mask = Ellipse2D.evaluate(yy, xx, True, y, x, major, minor,
pa).astype(bool)
# Find the maximum value in the response
max_resp = np.max(response[new_mask])
# print(blob)
# print((y, x, major, minor, pa, max_resp))
# import matplotlib.pyplot as p
# p.imshow(response, origin='lower', cmap='afmhot')
# p.contour(mask, colors='r')
# p.contour(new_mask, colors='g')
# p.draw()
# import time; time.sleep(0.1)
# raw_input("?")
# p.clf()
return np.array([y, x, major, minor, pa, max_resp])
def __init__(self, stddev_maj, stddev_min, position_angle, support_scaling=1,
**kwargs):
self._model = Ellipse2D(1. / (np.pi * stddev_maj * stddev_min), 0, 0,
stddev_maj, stddev_min, position_angle)
try:
from astropy.modeling.utils import ellipse_extent
except ImportError:
raise NotImplementedError("EllipticalTophat2DKernel requires"
" astropy 1.1b1 or greater.")
max_extent = \
np.max(ellipse_extent(stddev_maj, stddev_min, position_angle))
self._default_size = \
_round_up_to_odd_integer(support_scaling * 2 * max_extent)
super(EllipticalTophat2DKernel, self).__init__(**kwargs)
self._truncation = 0
def fp_wat_cal(wat_map,
angle,
fp_row,
fp_col,
a=27.3,
b=5,
window_row=101,
window_col=101):
'''
obtain the statistic corresponding to the footprint region of the image
wat_map: 2-d numpy.array, water map
angle: degree(unit), direction of the ellipse
fp_row, fp_col: location of the footprint.
a,b: the length corresponding to the semimajor axis and semiminor axis respectively.
window_row,window_col: the sub-region size of water map with the centered footprint.
'''
row_map, col_map = wat_map.shape
center_row = round((window_row - 1) / 2)
center_col = round((window_col - 1) / 2)
if col_map - fp_col < center_col + 1:
wat_map = np.pad(wat_map, ((0, 0), (0, center_col + 1)),
constant_values=0)
if row_map - fp_row < center_row + 1:
wat_map = np.pad(wat_map, ((0, center_row + 1), (0, 0)),
constant_values=0)
wat_map_window = wat_map[fp_row - center_row:fp_row + center_row + 1,
fp_col - center_col:fp_col + center_col + 1]
theta = Angle(angle, 'deg')
y, x = np.mgrid[0:window_row, 0:window_col]
e = Ellipse2D(amplitude=1.,
x_0=center_col,
y_0=center_row,
a=a,
b=b,
theta=theta.radian)
mask = e(x, y)
## from botton-letf coordinate to up-left(image col-row) coordinate
mask = np.flip(mask, axis=0)
values = wat_map_window[mask == 1]
wat_percent = np.sum(values) / values.shape
return wat_percent
def elliptical_footprint(a, b, theta=0, dtype=np.int):
"""
Create an elliptical footprint.
A pixel is considered to be entirely in or out of the footprint
depending on whether its center is in or out of the footprint. The
size of the output array is the minimal bounding box for the
footprint.
Parameters
----------
a : int
The semimajor axis.
b : int
The semiminor axis.
theta : float, optional
The angle in radians of the semimajor axis. The angle is
measured counterclockwise from the positive x axis.
dtype : data-type, optional
The data type of the output `~numpy.ndarray`.
Returns
-------
footprint : `~numpy.ndarray`
A footprint where array elements are 1 within the footprint and
0 otherwise.
"""
size = (a * 2) + 1
y, x = np.mgrid[0:size, 0:size]
ellipse = Ellipse2D(1, a, a, a, b, theta=theta)(x, y)
# crop to minimal bounding box
yi, xi = ellipse.nonzero()
idx = (slice(min(yi), max(yi) + 1), slice(min(xi), max(xi) + 1))
return np.asarray(ellipse[idx], dtype=dtype)
def fp_mask(img, a, b, center_row, center_col, angle):
'''
obtain the ellipse mask of the image
img: 2-d numpy.array;
a,b: the length corresponding to the semimajor axis and semiminor axis respectively.
center_row,center_col: the center location
'''
if len(img.shape) == 3:
img_row, img_col, _ = img.shape
else:
img_row, img_col = img.shape
theta = Angle(angle, 'deg')
y, x = np.mgrid[0:img_row, 0:img_col]
e = Ellipse2D(amplitude=1.,
x_0=center_col,
y_0=center_row,
a=a,
b=b,
theta=theta.radian)
mask = e(x, y)
## from botton-letf coordinate to up-left(image col-row) coordinate
mask = np.flip(mask, axis=0)
return mask
def elliptical_annulus_footprint(a_inner,
a_outer,
b_inner,
theta=0,
dtype=np.int):
"""
Create an elliptical annulus footprint.
A pixel is considered to be entirely in or out of the footprint
depending on whether its center is in or out of the footprint. The
size of the output array is the minimal bounding box for the
footprint.
Parameters
----------
a_inner : int
The inner semimajor axis.
a_outer : int
The outer semimajor axis.
b_inner : int
The inner semiminor axis. The outer semiminor axis is
calculated using the same axis ratio as the semimajor axis:
.. math::
b_{outer} = b_{inner} \\left( \\frac{a_{outer}}{a_{inner}}
\\right)
theta : float, optional
The rotation angle in radians of the semimajor axis. The angle
is measured counterclockwise from the positive x axis.
dtype : data-type, optional
The data type of the output `~numpy.ndarray`.
Returns
-------
footprint : `~numpy.ndarray`
A footprint where array elements are 1 within the footprint and
0 otherwise.
Examples
--------
>>> import numpy as np
>>> from astroimtools import elliptical_annulus_footprint
>>> elliptical_annulus_footprint(2, 4, 1, theta=np.pi/4.)
array([[0, 1, 1, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 0],
[1, 1, 0, 0, 1, 0, 0],
[0, 1, 0, 0, 0, 1, 0],
[0, 0, 1, 0, 0, 1, 1],
[0, 0, 0, 1, 1, 1, 1],
[0, 0, 0, 0, 1, 1, 0]])
"""
if a_inner > a_outer:
raise ValueError('a_outer must be >= a_inner')
if b_inner > a_inner:
raise ValueError('a_inner must be >= b_inner')
size = (a_outer * 2) + 1
y, x = np.mgrid[0:size, 0:size]
b_outer = b_inner * (a_outer / a_inner)
ellipse_outer = Ellipse2D(1,
a_outer,
a_outer,
a_outer,
b_outer,
theta=theta)(x, y)
ellipse_inner = Ellipse2D(1,
a_outer,
a_outer,
a_inner,
b_inner,
theta=theta)(x, y)
annulus = ellipse_outer - ellipse_inner
# crop to minimal bounding box
yi, xi = annulus.nonzero()
idx = (slice(min(yi), max(yi) + 1), slice(min(xi), max(xi) + 1))
return np.asarray(annulus[idx], dtype=dtype)
import numpy as np
from astropy.io import fits
from astropy.modeling.models import Ellipse2D
(x_0, y_0) = (1000., 2500.) # Center of ellipse, pixel coords
(a, b) = (500., 300.) # Major and minor axes of ellipse, pixel coords
theta = np.pi/4 # Orientation of ellipse, radians
ellipse = Ellipse2D(amplitude=1., x_0=x_0, y_0=y_0,
a=a, b=b, theta=theta)
img_filename = 'elp1m008-fl05-20170101-0119-e91.fits' # Example image I had access to
with fits.open(img_filename) as hdul:
data = hdul[0].data
y, x = np.mgrid[0:data.shape[0], 0:data.shape[1]]
ellipse_mask = ellipse(x, y).astype(bool)
cropped_data = np.where(ellipse_mask, data, np.nan)
hdul[0].data = cropped_data
hdul.writeto('cropped_image.fits')
def find_bubble_edges(array,
blob,
max_extent=1.0,
edge_mask=None,
nsig_thresh=1,
value_thresh=None,
radius=None,
return_mask=False,
min_pixels=16,
filter_size=4,
verbose=False,
min_radius_frac=0.0,
try_local_bkg=True,
**kwargs):
'''
Expand/contract to match the contours in the data.
Parameters
----------
array : 2D numpy.ndarray or spectral_cube.LowerDimensionalObject
Data used to define the region boundaries.
max_extent : float, optional
Multiplied by the major radius to set how far should be searched
when searching for the boundary.
nsig_thresh : float, optional
Number of times sigma above the mean to set the boundary intensity
requirement. This is used whenever the local background is higher
than the given `value_thresh`.
value_thresh : float, optional
When given, sets the minimum intensity for defining a bubble edge.
The natural choice is a few times the noise level in the cube.
radius : float, optional
Give an optional radius to use instead of the major radius defined
for the bubble.
kwargs : passed to profile.profile_line.
Returns
-------
extent_coords : np.ndarray
Array with the positions of edges.
'''
if try_local_bkg:
mean, std = intensity_props(array, blob)
background_thresh = mean + nsig_thresh * std
# Define a suitable background based on the intensity within the
# elliptical region
if value_thresh is None:
value_thresh = background_thresh
else:
# If value_thresh is higher use it. Otherwise use the bkg.
if value_thresh < background_thresh:
value_thresh = background_thresh
# Set the number of theta to be ~ the perimeter.
y, x, major, minor, pa = blob[:5]
y = int(np.round(y, decimals=0))
x = int(np.round(x, decimals=0))
# If the center is on the edge of the array, subtract one to
# index correctly
if y == array.shape[0]:
y -= 1
if x == array.shape[1]:
x -= 1
# Use the ellipse model to define a bounding box for the mask.
bbox = Ellipse2D(True, 0.0, 0.0, major * max_extent, minor * max_extent,
pa).bounding_box
y_range = ceil_int(bbox[0][1] - bbox[0][0] + 1 + filter_size)
x_range = ceil_int(bbox[1][1] - bbox[1][0] + 1 + filter_size)
shell_thetas = []
yy, xx = np.mgrid[-int(y_range / 2):int(y_range / 2) + 1,
-int(x_range / 2):int(x_range / 2) + 1]
if edge_mask is not None:
arr = edge_mask[max(0, y - int(y_range / 2)):y + int(y_range / 2) + 1,
max(0, x - int(x_range / 2)):x + int(x_range / 2) + 1]
else:
arr = array[max(0, y - int(y_range / 2)):y + int(y_range / 2) + 1,
max(0, x - int(x_range / 2)):x + int(x_range / 2) + 1]
# Adjust meshes if they exceed the array shape
x_min = -min(0, x - int(x_range / 2))
x_max = xx.shape[1] - max(0, x + int(x_range / 2) - array.shape[1] + 1)
y_min = -min(0, y - int(y_range / 2))
y_max = yy.shape[0] - max(0, y + int(y_range / 2) - array.shape[0] + 1)
offset = (max(0, int(y - (y_range / 2))), max(0, int(x - (x_range / 2))))
yy = yy[y_min:y_max, x_min:x_max]
xx = xx[y_min:y_max, x_min:x_max]
dist_arr = np.sqrt(yy**2 + xx**2)
if edge_mask is not None:
smooth_mask = arr
else:
smooth_mask = \
smooth_edges(arr <= value_thresh, filter_size, min_pixels)
region_mask = \
Ellipse2D(True, 0.0, 0.0, major * max_extent, minor * max_extent,
pa)(xx, yy).astype(bool)
region_mask = nd.binary_dilation(region_mask, eight_conn, iterations=2)
# The bubble center must fall within a valid region
mid_pt = np.where(dist_arr == 0.0)
if len(mid_pt[0]) == 0:
middle_fail = True
else:
local_center = zip(*np.where(dist_arr == 0.0))[0]
middle_fail = False
# _make_bubble_mask(smooth_mask, local_center)
# If the center is not contained within a bubble region, return
# empties.
bad_case = not smooth_mask.any() or smooth_mask.all() or \
(smooth_mask * region_mask).all() or middle_fail
if bad_case:
if return_mask:
return np.array([]), 0.0, 0.0, value_thresh, smooth_mask
return np.array([]), 0.0, 0.0, value_thresh
orig_perim = find_contours(region_mask, 0, fully_connected='high')[0]
# new_perim = find_contours(smooth_mask, 0, fully_connected='high')
coords = []
extent_mask = np.zeros_like(region_mask)
# for perim in new_perim:
# perim = perim.astype(np.int)
# good_pts = \
# np.array([pos for pos, pt in enumerate(perim)
# if region_mask[pt[0], pt[1]]])
# if not good_pts.any():
# continue
# # Now split into sections
# from utils import consec_split
# split_pts = consec_split(good_pts)
# # Remove the duplicated end point if it was initially connected
# if len(split_pts) > 1:
# # Join these if the end pts initially matched
# if split_pts[0][0] == split_pts[-1][-1]:
# split_pts[0] = np.append(split_pts[0],
# split_pts[-1][::-1])
# split_pts.pop(-1)
# for split in split_pts:
# coords.append(perim[split])
# extent_mask[perim[good_pts][:, 0], perim[good_pts][:, 1]] = True
# Based on the curvature of the shell, only fit points whose
# orientation matches the assumed centre.
# incoord, outcoord = shell_orientation(coords, local_center,
# verbose=False)
# Now only keep the points that are not blocked from the centre pixel
for pt in orig_perim:
theta = np.arctan2(pt[0] - local_center[0], pt[1] - local_center[1])
num_pts = int(
np.round(np.hypot(pt[0] - local_center[0],
pt[1] - local_center[1]),
decimals=0))
ys = np.round(np.linspace(local_center[0], pt[0], num_pts),
decimals=0).astype(np.int)
xs = np.round(np.linspace(local_center[1], pt[1], num_pts),
decimals=0).astype(np.int)
not_on_edge = np.logical_and(ys < smooth_mask.shape[0],
xs < smooth_mask.shape[1])
ys = ys[not_on_edge]
xs = xs[not_on_edge]
dist = np.sqrt((ys - local_center[0])**2 + (xs - local_center[1])**2)
prof = smooth_mask[ys, xs]
prof = prof[dist >= min_radius_frac * minor]
ys = ys[dist >= min_radius_frac * minor]
xs = xs[dist >= min_radius_frac * minor]
# Look for the first 0 and ignore all others past it
zeros = np.where(prof == 0)[0]
# If none, move on
if not zeros.any():
continue
edge = zeros[0]
extent_mask[ys[edge], xs[edge]] = True
coords.append((ys[edge], xs[edge]))
shell_thetas.append(theta)
# Calculate the fraction of the region associated with a shell
shell_frac = len(shell_thetas) / float(len(orig_perim))
shell_thetas = np.array(shell_thetas)
coords = np.array(coords)
# Use the theta values to find the standard deviation i.e. how
# dispersed the shell locations are. Assumes a circle, but we only
# consider moderately elongated ellipses, so the statistics approx.
# hold.
theta_var = np.sqrt(circvar(shell_thetas * u.rad)).value
extent_coords = \
np.vstack([pt + off for pt, off in
zip(np.where(extent_mask), offset)]).T
if verbose:
print("Shell fraction : " + str(shell_frac))
print("Angular Std. : " + str(theta_var))
import matplotlib.pyplot as p
true_region_mask = \
Ellipse2D(True, 0.0, 0.0, major, minor,
pa)(xx, yy).astype(bool)
ax = p.subplot(121)
ax.imshow(arr, origin='lower', interpolation='nearest')
ax.contour(smooth_mask, colors='b')
ax.contour(region_mask, colors='r')
ax.contour(true_region_mask, colors='g')
if len(coords) > 0:
p.plot(coords[:, 1], coords[:, 0], 'bD')
p.plot(local_center[1], local_center[0], 'gD')
ax2 = p.subplot(122)
ax2.imshow(extent_mask, origin='lower', interpolation='nearest')
p.draw()
raw_input("?")
p.clf()
if return_mask:
return extent_coords, shell_frac, theta_var, value_thresh, \
extent_mask
return extent_coords, shell_frac, theta_var, value_thresh
def _ellipse_overlap(blob1, blob2, grid_space=0.5, return_corr=False):
'''
Ellipse intersection are difficult. But counting common pixels is not!
This routine creates arrays up-sampled from the original pixel scale to
better estimate the overlap fraction.
'''
ellip1_area = np.pi * blob1[2] * blob1[3]
ellip2_area = np.pi * blob2[2] * blob2[3]
if ellip1_area >= ellip2_area:
large_blob = blob1
small_blob = blob2
large_ellip_area = ellip1_area
small_ellip_area = ellip2_area
else:
large_blob = blob2
small_blob = blob1
large_ellip_area = ellip2_area
small_ellip_area = ellip1_area
bound1 = Ellipse2D(True, 0.0, 0.0, large_blob[2], large_blob[3],
large_blob[4]).bounding_box
bound2 = Ellipse2D(True, small_blob[1]-large_blob[1],
small_blob[0]-large_blob[0],
small_blob[2], small_blob[3],
small_blob[4]).bounding_box
# If there is no overlap in the bounding boxes, there is no overlap
if bound1[0][1] <= bound2[0][0] or bound1[1][1] <= bound2[1][0]:
return 0.0
# Now check if the smaller one is completely inside the larger
low_in_large = bound1[0][0] <= bound2[0][0] and \
bound1[0][1] >= bound2[0][1]
high_in_large = bound1[1][0] <= bound2[1][0] and \
bound1[1][1] >= bound2[1][1]
if low_in_large and high_in_large:
if return_corr:
# Aover / sqrt(A1 * A2) = A1 / sqrt(A1 * A2) = sqrt(A1/A2)
return np.sqrt(small_ellip_area / large_ellip_area)
return 1.0
# Only evaluate the overlap between the two.
ybounds = (max(bound1[0][0], bound2[0][0]),
min(bound1[0][1], bound2[0][1]))
xbounds = (max(bound1[1][0], bound2[1][0]),
min(bound1[1][1], bound2[1][1]))
yy, xx = \
np.meshgrid(np.arange(ybounds[0] - grid_space, ybounds[1] + grid_space,
grid_space),
np.arange(xbounds[0] - grid_space, xbounds[1] + grid_space,
grid_space))
ellip1 = Ellipse2D.evaluate(xx, yy, True, 0.0,
0.0, large_blob[2],
large_blob[3], large_blob[4])
ellip2 = Ellipse2D.evaluate(xx, yy, True,
small_blob[1]-large_blob[1],
small_blob[0]-large_blob[0],
small_blob[2], small_blob[3],
small_blob[4])
overlap_area = np.sum(np.logical_and(ellip1, ellip2)) * grid_space ** 2
if return_corr:
return overlap_area / np.sqrt(small_ellip_area * large_ellip_area)
return overlap_area / small_ellip_area
def _ellipse_overlap(blob1, blob2, grid_space=0.5, return_corr=False):
'''
Ellipse intersection are difficult. But counting common pixels is not!
This routine creates arrays up-sampled from the original pixel scale to
better estimate the overlap fraction.
'''
ellip1_area = np.pi * blob1[2] * blob1[3]
ellip2_area = np.pi * blob2[2] * blob2[3]
if ellip1_area >= ellip2_area:
large_blob = blob1
small_blob = blob2
large_ellip_area = ellip1_area
small_ellip_area = ellip2_area
else:
large_blob = blob2
small_blob = blob1
large_ellip_area = ellip2_area
small_ellip_area = ellip1_area
bound1 = Ellipse2D(True, 0.0, 0.0, large_blob[2], large_blob[3],
large_blob[4]).bounding_box
bound2 = Ellipse2D(True, small_blob[1] - large_blob[1],
small_blob[0] - large_blob[0], small_blob[2],
small_blob[3], small_blob[4]).bounding_box
# If there is no overlap in the bounding boxes, there is no overlap
if bound1[0][1] <= bound2[0][0] or bound1[1][1] <= bound2[1][0]:
return 0.0
# Now check if the smaller one is completely inside the larger
low_in_large = bound1[0][0] <= bound2[0][0] and \
bound1[0][1] >= bound2[0][1]
high_in_large = bound1[1][0] <= bound2[1][0] and \
bound1[1][1] >= bound2[1][1]
if low_in_large and high_in_large:
if return_corr:
# Aover / sqrt(A1 * A2) = A1 / sqrt(A1 * A2) = sqrt(A1/A2)
return np.sqrt(small_ellip_area / large_ellip_area)
return 1.0
# Only evaluate the overlap between the two.
ybounds = (max(bound1[0][0], bound2[0][0]), min(bound1[0][1],
bound2[0][1]))
xbounds = (max(bound1[1][0], bound2[1][0]), min(bound1[1][1],
bound2[1][1]))
yy, xx = \
np.meshgrid(np.arange(ybounds[0] - grid_space, ybounds[1] + grid_space,
grid_space),
np.arange(xbounds[0] - grid_space, xbounds[1] + grid_space,
grid_space))
ellip1 = Ellipse2D.evaluate(xx, yy, True, 0.0, 0.0, large_blob[2],
large_blob[3], large_blob[4])
ellip2 = Ellipse2D.evaluate(xx, yy, True, small_blob[1] - large_blob[1],
small_blob[0] - large_blob[0], small_blob[2],
small_blob[3], small_blob[4])
overlap_area = np.sum(np.logical_and(ellip1, ellip2)) * grid_space**2
if return_corr:
return overlap_area / np.sqrt(small_ellip_area * large_ellip_area)
return overlap_area / small_ellip_area
def galaxy_mask(header,leda):
ra, dec, theta, diam, ba = leda
sz = (header["NAXIS1"], header["NAXIS2"])
outmsk = np.zeros((sz[0],sz[1]),dtype=bool)
w = WCS(header)
##CCD corners (these are actually in the header for DECam)
xc = [0,0,sz[0]-1,sz[0]-1]
yc = [0,sz[1]-1,0,sz[1]-1]
xcen = (sz[0]-1)/2
ycen = (sz[1]-1)/2
c = w.pixel_to_world(xcen,ycen)
sep = c.separation(w.pixel_to_world(xc, yc))
racen = c.ra.degree
deccen = c.dec.degree
dac = np.max(sep.degree)
coordleda = SkyCoord(ra,dec,frame='icrs',unit='deg')
dangle = c.separation(coordleda).degree
keep = dangle < dac + diam
#filter out galaxies with too large angle displace to consider
if sum(keep) == 0:
return outmsk
relevant = w.world_to_pixel(coordleda[keep])
with warnings.catch_warnings():
warnings.simplefilter('ignore', category=(AstropyWarning,RuntimeWarning))
wperm = Wcsprm(header=header.tostring().encode('utf-8'))
cd = wperm.cd #* wperm.cdelt[np.mod(np.linspace(0,3,4,dtype=int),2).reshape(2,2)]
pscl = np.mean(np.sqrt(np.sum(cd**2,axis=1)))*3600 #convert back to arcsec from deg for interp
msz = np.ceil(3600*diam[keep]/pscl).astype(int) #this is the mask size in the pixel scale
msz += np.mod(msz,2) == 0 #adjust even size to be odd so that the center means something
mh = (msz-1)//2 #I think this -1 is necessary becase we are all odd by construction
ix = np.round(relevant[0]).astype(int)
iy = np.round(relevant[1]).astype(int)
#largest circle approx must fall in ccd
insideim = ((ix+mh) >= 0) & ((ix-mh) < sz[0]) & ((iy+mh) >= 0) & (iy-mh < sz[1])
nim = sum(insideim)
#filter out galaxy with no overlap
if nim == 0:
return outmsk
rainl = coordleda[keep][insideim].ra.degree
decinl = coordleda[keep][insideim].dec.degree
thetal = np.deg2rad(theta[keep][insideim])
print("Masked Galaxies: %d" % nim)
for i in range(0,nim):
un, ue = tan_unit_vectors(rainl[i],decinl[i],racen,deccen)
ueofn = un*np.cos(thetal[i])+ue*np.sin(thetal[i])
angle = np.rad2deg(np.arctan2(ueofn[1],ueofn[0]))
ixn = ix[insideim][i]
iyn = iy[insideim][i]
mhn = mh[insideim][i]
mszn = msz[insideim][i]
e = Ellipse2D(x_0=mhn, y_0=mhn, a=mhn, b=ba[keep][insideim][i]*mhn,
theta=np.deg2rad(angle))
y, x = np.mgrid[0:mszn, 0:mszn]
smsk = (e(x, y) == 1)
outmsk[np.clip(ixn-mhn,a_min=0,a_max=None):np.clip(ixn+mhn+1,a_min=None,a_max=sz[0]),
np.clip(iyn-mhn,a_min=0,a_max=None):np.clip(iyn+mhn+1,a_min=None,a_max=sz[1])] |= smsk[
np.clip(mhn-ixn,a_min=0,a_max=None):np.clip(sz[0]-ixn+mhn,a_min=None,a_max=mszn),
np.clip(mhn-iyn,a_min=0,a_max=None):np.clip(sz[1]-iyn+mhn,a_min=None,a_max=mszn)]
return outmsk.T #revist need for transpose
