def get_fov(self, radius, fermi_frame=False):
"""
Returns
-------
array of RA and DEC
"""
steps = 500
if fermi_frame:
fermi = self._center
poly = SphericalPolygon.from_cone(fermi.lon.value,
fermi.lat.value,
radius,
steps=steps)
else:
j2000 = self._center.icrs
poly = SphericalPolygon.from_cone(j2000.ra.value,
j2000.dec.value,
radius,
steps=steps)
# ra, dec
return [p for p in poly.to_radec()][0]
def get_fov(self, radius, fermi_frame=False):
"""
Returns
-------
array of RA and DEC
"""
steps = 500
if fermi_frame:
fermi = self._center
poly = SphericalPolygon.from_cone(fermi.lon.value,
fermi.lat.value,
radius,
steps=steps)
else:
j2000 = self._center.icrs
poly = SphericalPolygon.from_cone(j2000.ra.value,
j2000.dec.value,
radius,
steps=steps)
# ra, dec
return [p for p in poly.to_radec()][0]
def _update_bounding_polygon(self):
polygons = [im.polygon for im in self._images]
if len(polygons) == 0:
self._polygon = SphericalPolygon([])
self._radec = []
else:
self._polygon = SphericalPolygon.multi_union(polygons)
self._radec = list(self._polygon.to_radec())
def _update_bounding_polygon(self):
polygons = [im.polygon for im in self._images]
if len(polygons) == 0:
self._polygon = SphericalPolygon([])
self._radec = []
else:
self._polygon = SphericalPolygon.multi_union(polygons)
self._radec = list(self._polygon.to_radec())
def get_difference(poly1, poly2, pixels=None):
"""attempt to get the difference poly1-poly2 as poly1^~poly2
use a pixelation to attempt to find an outside point"""
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
m = Basemap(projection='moll', lon_0=0)
if pixels is None:
pixels = get_healpix_pixelation(4)
bounding_xyz = list(poly2.points)
#contained = np.zeros(pixels.shape[0],dtype==bool)
#for itr in range(0,len(bounding_xyz)):
# ra,dec = sgv.vector_to_radec(
# contained = contained | contains_points(bounding_xyz[itr],pixels)
contained = contains_points(pixels, poly2)
poly2_complement = None
#poly2_complement = poly2.invert_polygon()
#contained_c = contains_points(pixels,poly2_complement)
#assert not np.any(contained & contained_c)
first_false = 100 + np.argmin(contained[100:])
#print("orig 1",contains_points(pixels[first_false:first_false+1],poly1),poly1.area())
#print("orig 2",contains_points(pixels[first_false:first_false+1],poly2),poly2.area())
print(poly2)
colors = ['red', 'green', 'blue']
for itr in range(0, len(bounding_xyz)):
first_false = 100 + itr + np.argmin(contained[100 + itr:])
theta_in = pixels[first_false, 0]
phi_in = pixels[first_false, 1]
inside_xyz = np.asarray(
sgv.radec_to_vector(phi_in, theta_in - np.pi / 2., degrees=False))
loc_poly = SphericalPolygon(bounding_xyz[itr].copy(),
inside_xyz.copy())
loc_poly.draw(m, color=colors[itr])
cont = contains_points(pixels[first_false:first_false + 1], loc_poly)
print("loc contains: ", cont)
if poly2_complement is None:
poly2_complement = deepcopy(loc_poly)
else:
poly2_complement = deepcopy(
poly2_complement.intersection(loc_poly))
cont_comp = contains_points(pixels[first_false:first_false + 1],
poly2_complement)
print("comp contains: ", cont_comp)
print("test: ",
np.all(contains_points(pixels[contained], poly2_complement)))
print("test: ",
np.any(contains_points(pixels[~contained], poly2_complement)))
print("insp: ", list(poly2_complement.inside))
#print("comp ",itr,contains_points(pixels[first_false:first_false+1],poly2_complement),poly2_complement.area(),poly2_complement.is_clockwise())
#print("inside c ",list(poly2_complement.inside))
#print("vert c ",list(poly2_complement.points))
#for itr2 in range(0,len(list(poly2_complement.inside))):
# print("loc cont inside c ",loc_poly.contains_point(list(poly2_complement.inside)[itr2]))
#print(poly1.area(),poly2.area(),poly2_complement.area())
plt.show()
return poly1.intersection(poly2_complement)
def get_earth_point_with_time(self,t): earth_radius = 6371. * u.km if self.sc_pos_f is not None and self.time_band is not None: x_f, y_f, z_f = self.sc_pos_f try: n = len(t) t = np.array(t) tband = t.max() - t.min() tband_sl = self.time_band[1] - self.time_band[0] if tband <= tband_sl + 2: t[t <= self.time_band[0]] = self.time_band[0] + 0.00001 t[t >= self.time_band[1]] = self.time_band[1] - 0.00001 else: t[t <= self.time_band[0]] = np.nan t[t >= self.time_band[1]] = np.nan x = x_f(t) * self.pos_unit y = y_f(t) * self.pos_unit z = z_f(t) * self.pos_unit earth_point_list = [] for i in range(n): position = cartesian_to_spherical(x[i],y[i],z[i]) xyz_position = SkyCoord(position[2].deg,position[1].deg,frame='icrs',unit='deg') fermi_radius = np.sqrt(x[i]**2 + y[i]**2 + z[i]**2) radius_deg = np.rad2deg(np.arcsin((earth_radius / fermi_radius).to(u.dimensionless_unscaled)).value) poly = SphericalPolygon.from_cone(position[2].deg,position[1].deg,radius_deg,steps=180) x_,y_ = np.array(list(poly.to_radec())[0]) earth_point_list.append([xyz_position,radius_deg,x_,y_]) return earth_point_list except (TypeError): if (t<=self.time_band[0]): if (self.time_band[0]-t<=1): t = self.time_band[0]+0.00001 else: t = np.nan if t>=self.time_band[1]: if (t-self.time_band[0]<=1): t = self.time_band[1]-0.00001 else: t = np.nan x = x_f(t) * self.pos_unit y = y_f(t) * self.pos_unit z = z_f(t) * self.pos_unit position = cartesian_to_spherical(x,y,z) xyz_position = SkyCoord(position[2].deg,position[1].deg,frame='icrs',unit='deg') fermi_radius = np.sqrt(x**2 + y**2 + z**2) radius_deg = np.rad2deg(np.arcsin((earth_radius / fermi_radius).to(u.dimensionless_unscaled)).value) poly = SphericalPolygon.from_cone(position[2].deg,position[1].deg,radius_deg,steps=180) x_,y_ = np.array(list(poly.to_radec())[0]) return xyz_position,radius_deg,x_,y_ else: return None
def build_polygon(self, member='total'):
if self.edges_ra is None:
self.get_edges_sky(member=member)
self.polygon = SphericalPolygon.from_radec(self.edges_ra,
self.edges_dec,
self.meta_wcs.wcs.crval)
def get_earth_point(self, index=None):
if self.sc_pos is not None:
earth_point_list = []
if index is not None:
sc_pos_list = self.sc_pos[index]
else:
sc_pos_list = self.sc_pos
for sc_pos in sc_pos_list:
position = cartesian_to_spherical(-sc_pos[0], -sc_pos[1],
-sc_pos[2])
xyz_position = SkyCoord(position[2].deg,
position[1].deg,
frame='icrs',
unit='deg')
earth_radius = 6371. * u.km
fermi_radius = np.sqrt((sc_pos**2).sum())
radius_deg = np.rad2deg(
np.arcsin((earth_radius / fermi_radius).to(
u.dimensionless_unscaled)).value)
poly = SphericalPolygon.from_cone(position[2].deg,
position[1].deg,
radius_deg,
steps=100)
x, y = [p for p in poly.to_radec()][0]
earth_point_list.append([xyz_position, radius_deg, x, y])
return earth_point_list
else:
print('No satellite position!')
return None
def sky_cone(ra_c, dec_c, theta, steps=50, include_center=True):
"""
Get ra and dec coordinates of a cone on the sky.
Parameters
----------
ra_c, dec_c: float
Center of cone in degrees.
theta: astropy Quantity, float, or int
Angular radius of cone. Must be in arcsec
if not a Quantity object.
steps: int, optional
Number of steps in the cone.
include_center: bool, optional
If True, include center point in cone.
Returns
-------
ra, dec: ndarry
Coordinates of cone.
"""
if isinstance(theta, float) or isinstance(theta, int):
theta = theta * u.Unit('arcsec')
cone = SphericalPolygon.from_cone(ra_c,
dec_c,
theta.to('deg').value,
steps=steps)
ra, dec = list(cone.to_lonlat())[0]
ra = np.mod(ra - 360., 360.0)
if include_center:
ra = np.concatenate([ra, [ra_c]])
dec = np.concatenate([dec, [dec_c]])
return ra, dec
def build_polygon(self):
if self.edges_ra is None:
self.get_edges_sky()
self.polygon = SphericalPolygon.from_radec(self.edges_ra,
self.edges_dec,
self.meta_wcs.wcs.crval)
def build_polygon(self):
inner_pix = self.wcs.pixel_to_world_values(2, 2)
# define polygon on the sky
self.polygon = SphericalPolygon.from_radec(self.corners[:, 0],
self.corners[:,
1], inner_pix)
def getWcsRegion(imageRow):
region = None
stcs = imageRow["regionSTCS"]
if (stcs is not None):
a = stcs.split(' ')
if len(a) == 10:
ras = [
float(a[2]),
float(a[4]),
float(a[6]),
float(a[8]),
float(a[2])
]
decs = [
float(a[3]),
float(a[5]),
float(a[7]),
float(a[9]),
float(a[3])
]
print ras
print decs
region = SphericalPolygon.from_radec(ras, decs)
return region
def get_fov(conter,radius = 10.): fov_point_list = [] for conter_i in conter: poly = SphericalPolygon.from_cone(conter_i.ra.value,conter_i.dec.value,radius,steps=100) x,y = [p for p in poly.to_radec()][0] fov_point_list.append([radius,x,y]) return fov_point_list
def get_fov(self,conter,radius = 10.): fov_point_list = [] for conter_i in conter: poly = SphericalPolygon.from_cone(conter_i.ra.value,conter_i.dec.value,radius,steps=180) x,y = np.array(list(poly.to_radec())[0]) fov_point_list.append([radius,x,y]) return fov_point_list
def contains_point(self, point):
steps = 300
j2000 = self.center.icrs
poly = SphericalPolygon.from_cone(j2000.ra.value,
j2000.dec.value,
self.radius,
steps=steps)
return poly.contains_point(point.cartesian.xyz.value)
def set_members(self, mlist, polygon):
if mlist is None:
member_list = []
elif isinstance(mlist, list):
member_list = mlist
if [1 for m in mlist if not isinstance(m, SkyLineMember)]:
raise ValueError("The 'mlist' argument must be either "
"a single 'SkyLineMember' object or a "
"Python list of 'SkyLineMember' objects.")
elif isinstance(mlist, SkyLineMember):
member_list = [mlist]
else:
raise ValueError("The 'mlist' argument must be either "
"a single 'SkyLineMember' object or a "
"Python list of 'SkyLineMember' objects.")
self._members = []
# Not using set to preserve order
n = len(member_list)
if n == 0:
if polygon is None:
self.polygon = SphericalPolygon([])
else:
self.polygon = deepcopy(polygon)
self._id = ''
self._is_mf_mosaic = False
elif n == 1:
assert isinstance(member_list[0], SkyLineMember)
if polygon is None:
self.polygon = deepcopy(member_list[0].polygon)
else:
self.polygon = deepcopy(polygon)
self._id = member_list[0].id
self._members.append(member_list[0])
self._is_mf_mosaic = False
else:
assert isinstance(member_list[0], SkyLineMember)
if polygon is None:
mpol = deepcopy(member_list[0].polygon)
else:
mpol = deepcopy(polygon)
self._members.append(member_list[0])
for m in member_list[1:]:
# Report corrupted members list instead of skipping
assert isinstance(m, SkyLineMember)
if m not in self._members:
self._members.append(m)
if polygon is None:
mpol = mpol.union(m.polygon)
self.polygon = mpol
self._update_mosaic_flag_id()
def get_poly(theta_vertices, phi_vertices, theta_in, phi_in):
"""get the SphericalPolygon object for the geometry"""
bounding_theta = theta_vertices - np.pi / 2. #to radec
bounding_phi = phi_vertices
bounding_xyz = np.asarray(
sgv.radec_to_vector(bounding_phi, bounding_theta, degrees=False)).T
inside_xyz = np.asarray(
sgv.radec_to_vector(phi_in, theta_in - np.pi / 2., degrees=False))
sp_poly = SphericalPolygon(bounding_xyz, inside=inside_xyz)
return sp_poly
def get_fov(self,radius): '''这里是计算出探头标注''' if radius >= 60: steps = 500 elif radius >= 30: steps = 300 else: steps = 250 j2000 = self.center.icrs poly = SphericalPolygon.from_cone(j2000.ra.value,j2000.dec.value,radius,steps = steps) re = [p for p in poly.to_radec()][0] return re
def makeQueryRegion(ra, dec, size):
region = None
if size[1] is None:
# Then we have a radius, not a box.
radius = size[0]
region = SphericalPolygon.from_cone(ra, dec, radius)
else:
# Make a box.
radius = None
# Figure out this stupid shape later...
return region
def getWcsRegion(imageRow):
region = None
stcs = imageRow["regionSTCS"]
if stcs is not None:
a = stcs.split(" ")
if len(a) == 10:
ras = [float(a[2]), float(a[4]), float(a[6]), float(a[8]), float(a[2])]
decs = [float(a[3]), float(a[5]), float(a[7]), float(a[9]), float(a[3])]
print ras
print decs
region = SphericalPolygon.from_radec(ras, decs)
return region
def makeQueryRegion(ra, dec, size):
region = None
if size[1] is None:
# Then we have a radius, not a box.
radius = size[0]
region = SphericalPolygon.from_cone(ra, dec, radius)
else:
# Make a box.
radius = None
# Figure out this stupid shape later...
return region
def get_fov(self, radius):
if radius >= 60:
steps = 5000 ## could be modified to speed up the plotting
elif radius >= 30:
steps = 400 ## could be modified to speed up the plotting
else:
steps = 100 ## could be modified to speed up the plotting
j2000 = self.center.icrs
poly = SphericalPolygon.from_cone(j2000.ra.value,
j2000.dec.value,
radius,
steps=steps)
re = [p for p in poly.to_radec()][0]
return re
def run(self, dataSlice, slicePoint=None):
# RA and Dec from dataSlice doesn't necessarily match healpix
RA = dataSlice['fieldRA'][0]
Dec = dataSlice['fieldDec'][0]
# Get RA and Dec from slicer
RA = slicePoint['ra']
Dec = slicePoint['dec']
nside = slicePoint['nside']
# get the boundaries from HealPix
bounding = hp.boundaries(nside, slicePoint['sid'], step=1).T
inside_val = np.asarray(
sgv.radec_to_vector(slicePoint['ra'],
slicePoint['dec'],
degrees=False))
test_pointing = SphericalPolygon(bounding, inside_val)
overlap_area = [
test_pointing.intersection(survey_polygon).area()
for survey_polygon in survey_list[self.region_name]
]
total = sum(overlap_area)
healpix_area = test_pointing.area()
return min(total, 4 * np.pi - total)
def plot_earth(self, t, satellite, **kwargs):
if 'facecolor' not in kwargs:
kwargs['facecolor'] = '#90d7ec'
if 'edgecolor' not in kwargs:
kwargs['edgecolor'] = '#90d7ec'
try:
met, ra, dec, radius = satellite.get_earth_point(t)
for i in range(len(met)):
poly = SphericalPolygon.from_cone(ra[i],
dec[i],
radius[i],
steps=180)
x_, y_ = np.array(list(poly.to_radec())[0])
earth = self.Polygon(list(zip(x_, y_))[::-1], **kwargs)
self.ax.add_patch(earth)
except (TypeError):
met, ra, dec, radius = satellite.get_earth_point(t)
poly = SphericalPolygon.from_cone(ra, dec, radius, steps=180)
x_, y_ = np.array(list(poly.to_radec())[0])
earth = self.Polygon(list(zip(x_, y_))[::-1], **kwargs)
self.ax.add_patch(earth)
def _draw_members(self, map, **kwargs):
"""
Draw individual extensions in members.
Useful for debugging.
Parameters
----------
map : Basemap axes object
**kwargs : Any plot arguments to pass to basemap
"""
wcs_list = self._indv_mem_wcslist()
for wcs in wcs_list:
poly = SphericalPolygon.from_wcs(wcs)
poly.draw(map, **kwargs)
def _draw_members(self, map, **kwargs):
"""
Draw individual extensions in members.
Useful for debugging.
Parameters
----------
map : Basemap axes object
**kwargs : Any plot arguments to pass to basemap
"""
wcs_list = self._indv_mem_wcslist()
for wcs in wcs_list:
poly = SphericalPolygon.from_wcs(wcs)
poly.draw(map, **kwargs)
def get_good_detector_centers(self, source=None):
good_detector_centers = []
good_detector_index = []
if source is not None:
for index, center in enumerate(self.detectors.center_all):
steps = 250
j2000 = center.icrs
poly = SphericalPolygon.from_cone(j2000.ra.value,
j2000.dec.value,
self.radius,
steps=steps)
if (poly.contains_point(source.cartesian.xyz.value)):
good_detector_centers.append(center)
good_detector_index.append(index)
else:
print('No source! return []')
return good_detector_index, good_detector_centers
def get_fov(self, radius):
if radius >= 60:
steps = 500
elif (radius >= 30):
steps = 300
else:
steps = 250
j2000 = self.center_all.icrs
c = []
for index, center in enumerate(j2000):
print(str(index))
poly = SphericalPolygon.from_cone(center.ra.value,
center.dec.value,
radius,
steps=steps)
re = [p for p in poly.to_radec()][0]
#print(re)
c.append(re)
return c
def quad_to_poly(ra, dec, **kwargs):
p = SphericalPolygon.from_radec(ra, dec, degrees=True)
points = p.polygons[0]._points
_ra, _dec = [], []
for A, B in zip(points[0:-1], points[1:]):
length = great_circle_arc.length(A, B, degrees=True)
if not np.isfinite(length):
length = 2
interpolated = great_circle_arc.interpolate(A, B, length * 4)
lon, lat = vector.vector_to_lonlat(interpolated[:, 0],
interpolated[:, 1],
interpolated[:, 2],
degrees=True)
for lon0, lat0, lon1, lat1 in zip(lon[0:-1], lat[0:-1], lon[1:],
lat[1:]):
_ra.append(lon0)
_dec.append(lat0)
_ra.append(lon1)
_dec.append(lat1)
return PolyCollection([np.c_[_ra, _dec]], **kwargs)
def true_axial_analysis(df_row, traj_def, euler_def, path_fnc, add_axial_rot_fnc):
traj = df_row[traj_def]
start_idx = df_row['up_down_analysis'].max_run_up_start_idx
end_idx = df_row['up_down_analysis'].max_run_up_end_idx
orient = np.rad2deg(rgetattr(traj, euler_def))
true_axial = np.rad2deg(getattr(traj, 'true_axial_rot'))
apparent_orient_diff = orient[end_idx, 2] - orient[start_idx, 2]
true_axial_diff = true_axial[end_idx] - true_axial[start_idx]
add_axial_rot = add_axial_rot_fnc(orient, start_idx, end_idx)
long, lat = path_fnc(orient, start_idx, end_idx)
# compute the area
mid_ix = int((start_idx + end_idx) / 2)
sp = SphericalPolygon.from_lonlat(long, lat, center=(orient[mid_ix, 0], orient[mid_ix, 1]/2))
area = np.rad2deg(sp.area())
# if the actual path and the "euler" path cross each other the spherical_geometry polygon incorrectly estimates the
# area
while area > 180:
area -= 180
return apparent_orient_diff, true_axial_diff, area, add_axial_rot, sp.is_clockwise()
def _calc_sky_orig(self, overlap=None, delta=True):
"""
Compute sky background value.
Parameters
----------
overlap : SkyImage, SkyGroup, SphericalPolygon, list of tuples, \
None, optional
Another `SkyImage`, `SkyGroup`,
:py:class:`spherical_geometry.polygons.SphericalPolygon`, or
a list of tuples of (RA, DEC) of vertices of a spherical
polygon. This parameter is used to indicate that sky statistics
should computed only in the region of intersection of *this*
image with the polygon indicated by `overlap`. When `overlap` is
`None`, sky statistics will be computed over the entire image.
delta : bool, optional
Should this function return absolute sky value or the difference
between the computed value and the value of the sky stored in the
`sky` property.
Returns
-------
skyval : float, None
Computed sky value (absolute or relative to the `sky` attribute).
If there are no valid data to perform this computations (e.g.,
because this image does not overlap with the image indicated by
`overlap`), `skyval` will be set to `None`.
npix : int
Number of pixels used to compute sky statistics.
polyarea : float
Area (in srad) of the polygon that bounds data used to compute
sky statistics.
"""
if overlap is None:
if self.mask is None:
data = self.image
else:
data = self.image[self.mask]
polyarea = self.poly_area
else:
fill_mask = np.zeros(self.image.shape, dtype=bool)
if isinstance(overlap, (SkyImage, SkyGroup, SphericalPolygon)):
intersection = self.intersection(overlap)
polyarea = np.fabs(intersection.area())
radec = intersection.to_radec()
else: # assume a list of (ra, dec) tuples:
radec = []
polyarea = 0.0
for r, d in overlap:
poly = SphericalPolygon.from_radec(r, d)
polyarea1 = np.fabs(poly.area())
if polyarea1 == 0.0 or len(r) < 4:
continue
polyarea += polyarea1
radec.append(self.intersection(poly).to_radec())
if polyarea == 0.0:
return (None, 0, 0.0)
for ra, dec in radec:
if len(ra) < 4:
continue
# set pixels in 'fill_mask' that are inside a polygon to True:
x, y = self.wcs_inv(ra, dec)
poly_vert = list(zip(*[x, y]))
polygon = region.Polygon(True, poly_vert)
fill_mask = polygon.scan(fill_mask)
if self.mask is not None:
fill_mask &= self.mask
data = self.image[fill_mask]
if data.size < 1:
return (None, 0, 0.0)
# Calculate sky
try:
skyval, npix = self._skystat(data)
except ValueError:
return (None, 0, 0.0)
if delta:
skyval -= self._sky
return skyval, npix, polyarea
def __init__(self,
image,
wcs_fwd,
wcs_inv,
pix_area=1.0,
convf=1.0,
mask=None,
id=None,
skystat=None,
stepsize=None,
meta=None):
""" Initializes the SkyImage object.
Parameters
----------
image : numpy.ndarray
A 2D array of image data.
wcs_fwd : function
"forward" pixel-to-world transformation function.
wcs_inv : function
"inverse" world-to-pixel transformation function.
pix_area : float, optional
Average pixel's sky area.
convf : float, optional
Conversion factor that when multiplied to `image` data converts
the data to "uniform" (across multiple images) surface
brightness units.
.. note::
The functionality to support this conversion is not yet
implemented and at this moment `convf` is ignored.
mask : numpy.ndarray
A 2D array that indicates
what pixels in the input `image` should be used for sky
computations (``1``) and which pixels should **not** be used
for sky computations (``0``).
id : anything
The value of this parameter is simple stored within the `SkyImage`
object. While it can be of any type, it is prefereble that `id` be
of a type with nice string representation.
skystat : callable, None, optional
A callable object that takes a either a 2D image (2D
`numpy.ndarray`) or a list of pixel values (a Nx1 array) and
returns a tuple of two values: some statistics (e.g., mean,
median, etc.) and number of pixels/values from the input image
used in computing that statistics.
When `skystat` is not set, `SkyImage` will use
:py:class:`~jwst_pipeline.skymatch.skystatistics.SkyStats` object
to perform sky statistics on image data.
stepsize : int, None, optional
Spacing between vertices of the image's bounding polygon. Default
value of `None` creates bounding polygons with four vertices
corresponding to the corners of the image.
meta : dict, None, optional
A dictionary of various items to be stored within the `SkyImage`
object.
"""
self.image = image
self.convf = convf
self.meta = meta
self._id = id
self._pix_area = pix_area
# WCS
self.wcs_fwd = wcs_fwd
self.wcs_inv = wcs_inv
# initial sky value:
self._sky = 0.0
# check that mask has the same shape as image:
if mask is None:
self.mask = None
else:
if image is None:
raise ValueError("'mask' must be None when 'image' is None")
self.mask = np.asanyarray(mask, dtype=np.bool)
if self.mask.shape != image.shape:
raise ValueError("'mask' must have the same shape as 'image'.")
# create spherical polygon bounding the image
if image is None or wcs_fwd is None or wcs_inv is None:
self._radec = [(np.array([]), np.array([]))]
self._polygon = SphericalPolygon([])
self._poly_area = 0.0
else:
self.calc_bounding_polygon(stepsize)
# set sky statistics function (NOTE: it must return statistics and
# the number of pixels used after clipping)
if skystat is None:
self.set_builtin_skystat()
else:
self.skystat = skystat
class SkyImage:
"""
Container that holds information about properties of a *single*
image such as:
* image data;
* WCS of the chip image;
* bounding spherical polygon;
* id;
* pixel area;
* sky background value;
* sky statistics parameters;
* mask associated image data indicating "good" (1) data.
"""
def __init__(self,
image,
wcs_fwd,
wcs_inv,
pix_area=1.0,
convf=1.0,
mask=None,
id=None,
skystat=None,
stepsize=None,
meta=None):
""" Initializes the SkyImage object.
Parameters
----------
image : numpy.ndarray
A 2D array of image data.
wcs_fwd : function
"forward" pixel-to-world transformation function.
wcs_inv : function
"inverse" world-to-pixel transformation function.
pix_area : float, optional
Average pixel's sky area.
convf : float, optional
Conversion factor that when multiplied to `image` data converts
the data to "uniform" (across multiple images) surface
brightness units.
.. note::
The functionality to support this conversion is not yet
implemented and at this moment `convf` is ignored.
mask : numpy.ndarray
A 2D array that indicates
what pixels in the input `image` should be used for sky
computations (``1``) and which pixels should **not** be used
for sky computations (``0``).
id : anything
The value of this parameter is simple stored within the `SkyImage`
object. While it can be of any type, it is prefereble that `id` be
of a type with nice string representation.
skystat : callable, None, optional
A callable object that takes a either a 2D image (2D
`numpy.ndarray`) or a list of pixel values (a Nx1 array) and
returns a tuple of two values: some statistics (e.g., mean,
median, etc.) and number of pixels/values from the input image
used in computing that statistics.
When `skystat` is not set, `SkyImage` will use
:py:class:`~jwst_pipeline.skymatch.skystatistics.SkyStats` object
to perform sky statistics on image data.
stepsize : int, None, optional
Spacing between vertices of the image's bounding polygon. Default
value of `None` creates bounding polygons with four vertices
corresponding to the corners of the image.
meta : dict, None, optional
A dictionary of various items to be stored within the `SkyImage`
object.
"""
self.image = image
self.convf = convf
self.meta = meta
self._id = id
self._pix_area = pix_area
# WCS
self.wcs_fwd = wcs_fwd
self.wcs_inv = wcs_inv
# initial sky value:
self._sky = 0.0
# check that mask has the same shape as image:
if mask is None:
self.mask = None
else:
if image is None:
raise ValueError("'mask' must be None when 'image' is None")
self.mask = np.asanyarray(mask, dtype=np.bool)
if self.mask.shape != image.shape:
raise ValueError("'mask' must have the same shape as 'image'.")
# create spherical polygon bounding the image
if image is None or wcs_fwd is None or wcs_inv is None:
self._radec = [(np.array([]), np.array([]))]
self._polygon = SphericalPolygon([])
self._poly_area = 0.0
else:
self.calc_bounding_polygon(stepsize)
# set sky statistics function (NOTE: it must return statistics and
# the number of pixels used after clipping)
if skystat is None:
self.set_builtin_skystat()
else:
self.skystat = skystat
@property
def id(self):
""" Set or get `SkyImage`'s `id`.
While `id` can be of any type, it is prefereble that `id` be
of a type with nice string representation.
"""
return self._id
@id.setter
def id(self, id):
self._id = id
@property
def pix_area(self):
""" Set or get mean pixel area.
"""
return self._pix_area
@pix_area.setter
def pix_area(self, pix_area):
self._pix_area = pix_area
@property
def poly_area(self):
""" Get bounding polygon area in srad units.
"""
return self._poly_area
@property
def sky(self):
""" Sky background value. See `calc_sky` for more details.
"""
return self._sky
@sky.setter
def sky(self, sky):
self._sky = sky
@property
def radec(self):
"""
Get RA and DEC of the verteces of the bounding polygon as a
`~numpy.ndarray` of shape (N, 2) where N is the number of verteces + 1.
"""
return self._radec
@property
def polygon(self):
""" Get image's bounding polygon.
"""
return self._polygon
def intersection(self, skyimage):
"""
Compute intersection of this `SkyImage` object and another
`SkyImage`, `SkyGroup`, or
:py:class:`~spherical_geometry.polygon.SphericalPolygon`
object.
Parameters
----------
skyimage : SkyImage, SkyGroup, SphericalPolygon
Another object that should be intersected with this `SkyImage`.
Returns
-------
polygon : SphericalPolygon
A :py:class:`~spherical_geometry.polygon.SphericalPolygon` that is
the intersection of this `SkyImage` and `skyimage`.
"""
if isinstance(skyimage, (SkyImage, SkyGroup)):
return self._polygon.intersection(skyimage.polygon)
else:
return self._polygon.intersection(skyimage)
def calc_bounding_polygon(self, stepsize=None):
""" Compute image's bounding polygon.
Parameters
----------
stepsize : int, None, optional
Indicates the maximum separation between two adjacent vertices
of the bounding polygon along each side of the image. Corners
of the image are included automatically. If `stepsize` is `None`,
bounding polygon will contain only vertices of the image.
"""
ny, nx = self.image.shape
if stepsize is None:
nintx = 2
ninty = 2
else:
nintx = max(2, int(np.ceil((nx + 1.0) / stepsize)))
ninty = max(2, int(np.ceil((ny + 1.0) / stepsize)))
xs = np.linspace(-0.5, nx - 0.5, nintx, dtype=np.float)
ys = np.linspace(-0.5, ny - 0.5, ninty, dtype=np.float)[1:-1]
nptx = xs.size
npty = ys.size
npts = 2 * (nptx + npty)
borderx = np.empty((npts + 1, ), dtype=np.float)
bordery = np.empty((npts + 1, ), dtype=np.float)
# "bottom" points:
borderx[:nptx] = xs
bordery[:nptx] = -0.5
# "right"
sl = np.s_[nptx:nptx + npty]
borderx[sl] = nx - 0.5
bordery[sl] = ys
# "top"
sl = np.s_[nptx + npty:2 * nptx + npty]
borderx[sl] = xs[::-1]
bordery[sl] = ny - 0.5
# "left"
sl = np.s_[2 * nptx + npty:-1]
borderx[sl] = -0.5
bordery[sl] = ys[::-1]
# close polygon:
borderx[-1] = borderx[0]
bordery[-1] = bordery[0]
ra, dec = self.wcs_fwd(borderx, bordery, with_bounding_box=False)
# TODO: for strange reasons, occasionally ra[0] != ra[-1] and/or
# dec[0] != dec[-1] (even though we close the polygon in the
# previous two lines). Then SphericalPolygon fails because
# points are not closed. Threfore we force it to be closed:
ra[-1] = ra[0]
dec[-1] = dec[0]
self._radec = [(ra, dec)]
self._polygon = SphericalPolygon.from_radec(ra, dec)
self._poly_area = np.fabs(self._polygon.area())
@property
def skystat(self):
""" Stores/retrieves a callable object that takes a either a 2D image
(2D `numpy.ndarray`) or a list of pixel values (a Nx1 array) and
returns a tuple of two values: some statistics
(e.g., mean, median, etc.) and number of pixels/values from the input
image used in computing that statistics.
When `skystat` is not set, `SkyImage` will use
:py:class:`~jwst_pipeline.skymatch.skystatistics.SkyStats` object
to perform sky statistics on image data.
"""
return self._skystat
@skystat.setter
def skystat(self, skystat):
self._skystat = skystat
def set_builtin_skystat(self,
skystat='median',
lower=None,
upper=None,
nclip=5,
lsigma=4.0,
usigma=4.0,
binwidth=0.1):
"""
Replace already set `skystat` with a "built-in" version of a
statistics callable object used to measure sky background.
See :py:class:`~jwst_pipeline.skymatch.skystatistics.SkyStats` for the
parameter description.
"""
self._skystat = SkyStats(skystat=skystat,
lower=lower,
upper=upper,
nclip=nclip,
lsig=lsigma,
usig=usigma,
binwidth=binwidth)
# TODO: due to a bug in the sphere package, see
# https://github.com/spacetelescope/sphere/issues/74
# intersections with polygons formed as union does not work.
# For this reason I re-implement 'calc_sky' below with
# a workaround for the bug.
# The original implementation (now called ``_calc_sky_orig``
# should replace current 'calc_sky' once the bug is fixed.
#
def calc_sky(self, overlap=None, delta=True):
"""
Compute sky background value.
Parameters
----------
overlap : SkyImage, SkyGroup, SphericalPolygon, list of tuples, \
None, optional
Another `SkyImage`, `SkyGroup`,
:py:class:`spherical_geometry.polygons.SphericalPolygon`, or
a list of tuples of (RA, DEC) of vertices of a spherical
polygon. This parameter is used to indicate that sky statistics
should computed only in the region of intersection of *this*
image with the polygon indicated by `overlap`. When `overlap` is
`None`, sky statistics will be computed over the entire image.
delta : bool, optional
Should this function return absolute sky value or the difference
between the computed value and the value of the sky stored in the
`sky` property.
Returns
-------
skyval : float, None
Computed sky value (absolute or relative to the `sky` attribute).
If there are no valid data to perform this computations (e.g.,
because this image does not overlap with the image indicated by
`overlap`), `skyval` will be set to `None`.
npix : int
Number of pixels used to compute sky statistics.
polyarea : float
Area (in srad) of the polygon that bounds data used to compute
sky statistics.
"""
if overlap is None:
if self.mask is None:
data = self.image
else:
data = self.image[self.mask]
polyarea = self.poly_area
else:
fill_mask = np.zeros(self.image.shape, dtype=bool)
if isinstance(overlap, SkyImage):
intersection = self.intersection(overlap)
polyarea = np.fabs(intersection.area())
radec = list(intersection.to_radec())
elif isinstance(overlap, SkyGroup):
radec = []
polyarea = 0.0
for im in overlap:
intersection = self.intersection(im)
polyarea1 = np.fabs(intersection.area())
if polyarea1 == 0.0:
continue
polyarea += polyarea1
radec += list(intersection.to_radec())
elif isinstance(overlap, SphericalPolygon):
radec = []
polyarea = 0.0
for p in overlap._polygons:
intersection = self.intersection(SphericalPolygon([p]))
polyarea1 = np.fabs(intersection.area())
if polyarea1 == 0.0:
continue
polyarea += polyarea1
radec += list(intersection.to_radec())
else: # assume a list of (ra, dec) tuples:
radec = []
polyarea = 0.0
for r, d in overlap:
poly = SphericalPolygon.from_radec(r, d)
polyarea1 = np.fabs(poly.area())
if polyarea1 == 0.0 or len(r) < 4:
continue
polyarea += polyarea1
radec.append(self.intersection(poly).to_radec())
if polyarea == 0.0:
return (None, 0, 0.0)
for ra, dec in radec:
if len(ra) < 4:
continue
# set pixels in 'fill_mask' that are inside a polygon to True:
x, y = self.wcs_inv(ra, dec)
poly_vert = list(zip(*[x, y]))
polygon = region.Polygon(True, poly_vert)
fill_mask = polygon.scan(fill_mask)
if self.mask is not None:
fill_mask &= self.mask
data = self.image[fill_mask]
if data.size < 1:
return (None, 0, 0.0)
# Calculate sky
try:
skyval, npix = self._skystat(data)
except ValueError:
return (None, 0, 0.0)
if delta:
skyval -= self._sky
return skyval, npix, polyarea
def _calc_sky_orig(self, overlap=None, delta=True):
"""
Compute sky background value.
Parameters
----------
overlap : SkyImage, SkyGroup, SphericalPolygon, list of tuples, \
None, optional
Another `SkyImage`, `SkyGroup`,
:py:class:`spherical_geometry.polygons.SphericalPolygon`, or
a list of tuples of (RA, DEC) of vertices of a spherical
polygon. This parameter is used to indicate that sky statistics
should computed only in the region of intersection of *this*
image with the polygon indicated by `overlap`. When `overlap` is
`None`, sky statistics will be computed over the entire image.
delta : bool, optional
Should this function return absolute sky value or the difference
between the computed value and the value of the sky stored in the
`sky` property.
Returns
-------
skyval : float, None
Computed sky value (absolute or relative to the `sky` attribute).
If there are no valid data to perform this computations (e.g.,
because this image does not overlap with the image indicated by
`overlap`), `skyval` will be set to `None`.
npix : int
Number of pixels used to compute sky statistics.
polyarea : float
Area (in srad) of the polygon that bounds data used to compute
sky statistics.
"""
if overlap is None:
if self.mask is None:
data = self.image
else:
data = self.image[self.mask]
polyarea = self.poly_area
else:
fill_mask = np.zeros(self.image.shape, dtype=bool)
if isinstance(overlap, (SkyImage, SkyGroup, SphericalPolygon)):
intersection = self.intersection(overlap)
polyarea = np.fabs(intersection.area())
radec = intersection.to_radec()
else: # assume a list of (ra, dec) tuples:
radec = []
polyarea = 0.0
for r, d in overlap:
poly = SphericalPolygon.from_radec(r, d)
polyarea1 = np.fabs(poly.area())
if polyarea1 == 0.0 or len(r) < 4:
continue
polyarea += polyarea1
radec.append(self.intersection(poly).to_radec())
if polyarea == 0.0:
return (None, 0, 0.0)
for ra, dec in radec:
if len(ra) < 4:
continue
# set pixels in 'fill_mask' that are inside a polygon to True:
x, y = self.wcs_inv(ra, dec)
poly_vert = list(zip(*[x, y]))
polygon = region.Polygon(True, poly_vert)
fill_mask = polygon.scan(fill_mask)
if self.mask is not None:
fill_mask &= self.mask
data = self.image[fill_mask]
if data.size < 1:
return (None, 0, 0.0)
# Calculate sky
try:
skyval, npix = self._skystat(data)
except ValueError:
return (None, 0, 0.0)
if delta:
skyval -= self._sky
return skyval, npix, polyarea
def copy(self):
"""
Return a shallow copy of the `SkyImage` object.
"""
si = SkyImage(image=None,
wcs_fwd=self.wcs_fwd,
wcs_inv=self.wcs_inv,
pix_area=self.pix_area,
convf=self.convf,
mask=None,
id=self.id,
stepsize=None,
meta=self.meta)
si.image = self.image
si.mask = self.mask
si._radec = self._radec
si._polygon = self._polygon
si._poly_area = self._poly_area
si.sky = self.sky
return si
def calc_bounding_polygon(self, stepsize=None):
""" Compute image's bounding polygon.
Parameters
----------
stepsize : int, None, optional
Indicates the maximum separation between two adjacent vertices
of the bounding polygon along each side of the image. Corners
of the image are included automatically. If `stepsize` is `None`,
bounding polygon will contain only vertices of the image.
"""
ny, nx = self.image.shape
if stepsize is None:
nintx = 2
ninty = 2
else:
nintx = max(2, int(np.ceil((nx + 1.0) / stepsize)))
ninty = max(2, int(np.ceil((ny + 1.0) / stepsize)))
xs = np.linspace(-0.5, nx - 0.5, nintx, dtype=np.float)
ys = np.linspace(-0.5, ny - 0.5, ninty, dtype=np.float)[1:-1]
nptx = xs.size
npty = ys.size
npts = 2 * (nptx + npty)
borderx = np.empty((npts + 1, ), dtype=np.float)
bordery = np.empty((npts + 1, ), dtype=np.float)
# "bottom" points:
borderx[:nptx] = xs
bordery[:nptx] = -0.5
# "right"
sl = np.s_[nptx:nptx + npty]
borderx[sl] = nx - 0.5
bordery[sl] = ys
# "top"
sl = np.s_[nptx + npty:2 * nptx + npty]
borderx[sl] = xs[::-1]
bordery[sl] = ny - 0.5
# "left"
sl = np.s_[2 * nptx + npty:-1]
borderx[sl] = -0.5
bordery[sl] = ys[::-1]
# close polygon:
borderx[-1] = borderx[0]
bordery[-1] = bordery[0]
ra, dec = self.wcs_fwd(borderx, bordery, with_bounding_box=False)
# TODO: for strange reasons, occasionally ra[0] != ra[-1] and/or
# dec[0] != dec[-1] (even though we close the polygon in the
# previous two lines). Then SphericalPolygon fails because
# points are not closed. Threfore we force it to be closed:
ra[-1] = ra[0]
dec[-1] = dec[0]
self._radec = [(ra, dec)]
self._polygon = SphericalPolygon.from_radec(ra, dec)
self._poly_area = np.fabs(self._polygon.area())
class SkyImage:
"""
Container that holds information about properties of a *single*
image such as:
* image data;
* WCS of the chip image;
* bounding spherical polygon;
* id;
* pixel area;
* sky background value;
* sky statistics parameters;
* mask associated image data indicating "good" (1) data.
"""
def __init__(self, image, wcs_fwd, wcs_inv, pix_area=1.0, convf=1.0,
mask=None, id=None, skystat=None, stepsize=None, meta=None):
""" Initializes the SkyImage object.
Parameters
----------
image : numpy.ndarray
A 2D array of image data.
wcs_fwd : function
"forward" pixel-to-world transformation function.
wcs_inv : function
"inverse" world-to-pixel transformation function.
pix_area : float, optional
Average pixel's sky area.
convf : float, optional
Conversion factor that when multiplied to `image` data converts
the data to "uniform" (across multiple images) surface
brightness units.
.. note::
The functionality to support this conversion is not yet
implemented and at this moment `convf` is ignored.
mask : numpy.ndarray
A 2D array that indicates
what pixels in the input `image` should be used for sky
computations (``1``) and which pixels should **not** be used
for sky computations (``0``).
id : anything
The value of this parameter is simple stored within the `SkyImage`
object. While it can be of any type, it is prefereble that `id` be
of a type with nice string representation.
skystat : callable, None, optional
A callable object that takes a either a 2D image (2D
`numpy.ndarray`) or a list of pixel values (a Nx1 array) and
returns a tuple of two values: some statistics (e.g., mean,
median, etc.) and number of pixels/values from the input image
used in computing that statistics.
When `skystat` is not set, `SkyImage` will use
:py:class:`~jwst_pipeline.skymatch.skystatistics.SkyStats` object
to perform sky statistics on image data.
stepsize : int, None, optional
Spacing between vertices of the image's bounding polygon. Default
value of `None` creates bounding polygons with four vertices
corresponding to the corners of the image.
meta : dict, None, optional
A dictionary of various items to be stored within the `SkyImage`
object.
"""
self.image = image
self.convf = convf
self.meta = meta
self._id = id
self._pix_area = pix_area
# WCS
self.wcs_fwd = wcs_fwd
self.wcs_inv = wcs_inv
# initial sky value:
self._sky = 0.0
self._sky_is_valid = False
# check that mask has the same shape as image:
if mask is None:
self.mask = None
else:
if image is None:
raise ValueError("'mask' must be None when 'image' is None")
self.mask = np.asanyarray(mask, dtype=np.bool)
if self.mask.shape != image.shape:
raise ValueError("'mask' must have the same shape as 'image'.")
# create spherical polygon bounding the image
if image is None or wcs_fwd is None or wcs_inv is None:
self._radec = [(np.array([]), np.array([]))]
self._polygon = SphericalPolygon([])
self._poly_area = 0.0
else:
self.calc_bounding_polygon(stepsize)
# set sky statistics function (NOTE: it must return statistics and
# the number of pixels used after clipping)
if skystat is None:
self.set_builtin_skystat()
else:
self.skystat = skystat
@property
def id(self):
""" Set or get `SkyImage`'s `id`.
While `id` can be of any type, it is prefereble that `id` be
of a type with nice string representation.
"""
return self._id
@id.setter
def id(self, id):
self._id = id
@property
def pix_area(self):
""" Set or get mean pixel area.
"""
return self._pix_area
@pix_area.setter
def pix_area(self, pix_area):
self._pix_area = pix_area
@property
def poly_area(self):
""" Get bounding polygon area in srad units.
"""
return self._poly_area
@property
def sky(self):
""" Sky background value. See `calc_sky` for more details.
"""
return self._sky
@sky.setter
def sky(self, sky):
self._sky = sky
@property
def is_sky_valid(self):
"""
Indicates whether sky value was successfully computed.
Must be set externally.
"""
return self._sky_is_valid
@is_sky_valid.setter
def is_sky_valid(self, valid):
self._sky_is_valid = valid
@property
def radec(self):
"""
Get RA and DEC of the verteces of the bounding polygon as a
`~numpy.ndarray` of shape (N, 2) where N is the number of verteces + 1.
"""
return self._radec
@property
def polygon(self):
""" Get image's bounding polygon.
"""
return self._polygon
def intersection(self, skyimage):
"""
Compute intersection of this `SkyImage` object and another
`SkyImage`, `SkyGroup`, or
:py:class:`~spherical_geometry.polygon.SphericalPolygon`
object.
Parameters
----------
skyimage : SkyImage, SkyGroup, SphericalPolygon
Another object that should be intersected with this `SkyImage`.
Returns
-------
polygon : SphericalPolygon
A :py:class:`~spherical_geometry.polygon.SphericalPolygon` that is
the intersection of this `SkyImage` and `skyimage`.
"""
if isinstance(skyimage, (SkyImage, SkyGroup)):
return self._polygon.intersection(skyimage.polygon)
else:
return self._polygon.intersection(skyimage)
def calc_bounding_polygon(self, stepsize=None):
""" Compute image's bounding polygon.
Parameters
----------
stepsize : int, None, optional
Indicates the maximum separation between two adjacent vertices
of the bounding polygon along each side of the image. Corners
of the image are included automatically. If `stepsize` is `None`,
bounding polygon will contain only vertices of the image.
"""
ny, nx = self.image.shape
if stepsize is None:
nintx = 2
ninty = 2
else:
nintx = max(2, int(np.ceil((nx + 1.0) / stepsize)))
ninty = max(2, int(np.ceil((ny + 1.0) / stepsize)))
xs = np.linspace(-0.5, nx - 0.5, nintx, dtype=np.float)
ys = np.linspace(-0.5, ny - 0.5, ninty, dtype=np.float)[1:-1]
nptx = xs.size
npty = ys.size
npts = 2 * (nptx + npty)
borderx = np.empty((npts + 1,), dtype=np.float)
bordery = np.empty((npts + 1,), dtype=np.float)
# "bottom" points:
borderx[:nptx] = xs
bordery[:nptx] = -0.5
# "right"
sl = np.s_[nptx:nptx + npty]
borderx[sl] = nx - 0.5
bordery[sl] = ys
# "top"
sl = np.s_[nptx + npty:2 * nptx + npty]
borderx[sl] = xs[::-1]
bordery[sl] = ny - 0.5
# "left"
sl = np.s_[2 * nptx + npty:-1]
borderx[sl] = -0.5
bordery[sl] = ys[::-1]
# close polygon:
borderx[-1] = borderx[0]
bordery[-1] = bordery[0]
ra, dec = self.wcs_fwd(borderx, bordery, with_bounding_box=False)
# TODO: for strange reasons, occasionally ra[0] != ra[-1] and/or
# dec[0] != dec[-1] (even though we close the polygon in the
# previous two lines). Then SphericalPolygon fails because
# points are not closed. Threfore we force it to be closed:
ra[-1] = ra[0]
dec[-1] = dec[0]
self._radec = [(ra, dec)]
self._polygon = SphericalPolygon.from_radec(ra, dec)
self._poly_area = np.fabs(self._polygon.area())
@property
def skystat(self):
""" Stores/retrieves a callable object that takes a either a 2D image
(2D `numpy.ndarray`) or a list of pixel values (a Nx1 array) and
returns a tuple of two values: some statistics
(e.g., mean, median, etc.) and number of pixels/values from the input
image used in computing that statistics.
When `skystat` is not set, `SkyImage` will use
:py:class:`~jwst_pipeline.skymatch.skystatistics.SkyStats` object
to perform sky statistics on image data.
"""
return self._skystat
@skystat.setter
def skystat(self, skystat):
self._skystat = skystat
def set_builtin_skystat(self, skystat='median', lower=None, upper=None,
nclip=5, lsigma=4.0, usigma=4.0, binwidth=0.1):
"""
Replace already set `skystat` with a "built-in" version of a
statistics callable object used to measure sky background.
See :py:class:`~jwst_pipeline.skymatch.skystatistics.SkyStats` for the
parameter description.
"""
self._skystat = SkyStats(
skystat=skystat,
lower=lower,
upper=upper,
nclip=nclip,
lsig=lsigma,
usig=usigma,
binwidth=binwidth
)
# TODO: due to a bug in the sphere package, see
# https://github.com/spacetelescope/sphere/issues/74
# intersections with polygons formed as union does not work.
# For this reason I re-implement 'calc_sky' below with
# a workaround for the bug.
# The original implementation (now called ``_calc_sky_orig``
# should replace current 'calc_sky' once the bug is fixed.
#
def calc_sky(self, overlap=None, delta=True):
"""
Compute sky background value.
Parameters
----------
overlap : SkyImage, SkyGroup, SphericalPolygon, list of tuples, \
None, optional
Another `SkyImage`, `SkyGroup`,
:py:class:`spherical_geometry.polygons.SphericalPolygon`, or
a list of tuples of (RA, DEC) of vertices of a spherical
polygon. This parameter is used to indicate that sky statistics
should computed only in the region of intersection of *this*
image with the polygon indicated by `overlap`. When `overlap` is
`None`, sky statistics will be computed over the entire image.
delta : bool, optional
Should this function return absolute sky value or the difference
between the computed value and the value of the sky stored in the
`sky` property.
Returns
-------
skyval : float, None
Computed sky value (absolute or relative to the `sky` attribute).
If there are no valid data to perform this computations (e.g.,
because this image does not overlap with the image indicated by
`overlap`), `skyval` will be set to `None`.
npix : int
Number of pixels used to compute sky statistics.
polyarea : float
Area (in srad) of the polygon that bounds data used to compute
sky statistics.
"""
if overlap is None:
if self.mask is None:
data = self.image
else:
data = self.image[self.mask]
polyarea = self.poly_area
else:
fill_mask = np.zeros(self.image.shape, dtype=bool)
if isinstance(overlap, SkyImage):
intersection = self.intersection(overlap)
polyarea = np.fabs(intersection.area())
radec = list(intersection.to_radec())
elif isinstance(overlap, SkyGroup):
radec = []
polyarea = 0.0
for im in overlap:
intersection = self.intersection(im)
polyarea1 = np.fabs(intersection.area())
if polyarea1 == 0.0:
continue
polyarea += polyarea1
radec += list(intersection.to_radec())
elif isinstance(overlap, SphericalPolygon):
radec = []
polyarea = 0.0
for p in overlap._polygons:
intersection = self.intersection(SphericalPolygon([p]))
polyarea1 = np.fabs(intersection.area())
if polyarea1 == 0.0:
continue
polyarea += polyarea1
radec += list(intersection.to_radec())
else: # assume a list of (ra, dec) tuples:
radec = []
polyarea = 0.0
for r, d in overlap:
poly = SphericalPolygon.from_radec(r, d)
polyarea1 = np.fabs(poly.area())
if polyarea1 == 0.0 or len(r) < 4:
continue
polyarea += polyarea1
radec.append(self.intersection(poly).to_radec())
if polyarea == 0.0:
return (None, 0, 0.0)
for ra, dec in radec:
if len(ra) < 4:
continue
# set pixels in 'fill_mask' that are inside a polygon to True:
x, y = self.wcs_inv(ra, dec)
poly_vert = list(zip(*[x, y]))
polygon = region.Polygon(True, poly_vert)
fill_mask = polygon.scan(fill_mask)
if self.mask is not None:
fill_mask &= self.mask
data = self.image[fill_mask]
if data.size < 1:
return (None, 0, 0.0)
# Calculate sky
try:
skyval, npix = self._skystat(data)
except ValueError:
return (None, 0, 0.0)
if delta:
skyval -= self._sky
return skyval, npix, polyarea
def _calc_sky_orig(self, overlap=None, delta=True):
"""
Compute sky background value.
Parameters
----------
overlap : SkyImage, SkyGroup, SphericalPolygon, list of tuples, \
None, optional
Another `SkyImage`, `SkyGroup`,
:py:class:`spherical_geometry.polygons.SphericalPolygon`, or
a list of tuples of (RA, DEC) of vertices of a spherical
polygon. This parameter is used to indicate that sky statistics
should computed only in the region of intersection of *this*
image with the polygon indicated by `overlap`. When `overlap` is
`None`, sky statistics will be computed over the entire image.
delta : bool, optional
Should this function return absolute sky value or the difference
between the computed value and the value of the sky stored in the
`sky` property.
Returns
-------
skyval : float, None
Computed sky value (absolute or relative to the `sky` attribute).
If there are no valid data to perform this computations (e.g.,
because this image does not overlap with the image indicated by
`overlap`), `skyval` will be set to `None`.
npix : int
Number of pixels used to compute sky statistics.
polyarea : float
Area (in srad) of the polygon that bounds data used to compute
sky statistics.
"""
if overlap is None:
if self.mask is None:
data = self.image
else:
data = self.image[self.mask]
polyarea = self.poly_area
else:
fill_mask = np.zeros(self.image.shape, dtype=bool)
if isinstance(overlap, (SkyImage, SkyGroup, SphericalPolygon)):
intersection = self.intersection(overlap)
polyarea = np.fabs(intersection.area())
radec = intersection.to_radec()
else: # assume a list of (ra, dec) tuples:
radec = []
polyarea = 0.0
for r, d in overlap:
poly = SphericalPolygon.from_radec(r, d)
polyarea1 = np.fabs(poly.area())
if polyarea1 == 0.0 or len(r) < 4:
continue
polyarea += polyarea1
radec.append(self.intersection(poly).to_radec())
if polyarea == 0.0:
return (None, 0, 0.0)
for ra, dec in radec:
if len(ra) < 4:
continue
# set pixels in 'fill_mask' that are inside a polygon to True:
x, y = self.wcs_inv(ra, dec)
poly_vert = list(zip(*[x, y]))
polygon = region.Polygon(True, poly_vert)
fill_mask = polygon.scan(fill_mask)
if self.mask is not None:
fill_mask &= self.mask
data = self.image[fill_mask]
if data.size < 1:
return (None, 0, 0.0)
# Calculate sky
try:
skyval, npix = self._skystat(data)
except ValueError:
return (None, 0, 0.0)
if delta:
skyval -= self._sky
return skyval, npix, polyarea
def copy(self):
"""
Return a shallow copy of the `SkyImage` object.
"""
si = SkyImage(
image=None,
wcs_fwd=self.wcs_fwd,
wcs_inv=self.wcs_inv,
pix_area=self.pix_area,
convf=self.convf,
mask=None,
id=self.id,
stepsize=None,
meta=self.meta
)
si.image = self.image
si.mask = self.mask
si._radec = self._radec
si._polygon = self._polygon
si._poly_area = self._poly_area
si.sky = self.sky
return si
def _skycoord_to_spherical_polygon(skycoord):
return SphericalPolygon.from_radec(skycoord.spherical.lon.degree,
skycoord.spherical.lat.degree,
degrees=True)
def _calc_sky_orig(self, overlap=None, delta=True):
"""
Compute sky background value.
Parameters
----------
overlap : SkyImage, SkyGroup, SphericalPolygon, list of tuples, \
None, optional
Another `SkyImage`, `SkyGroup`,
:py:class:`spherical_geometry.polygons.SphericalPolygon`, or
a list of tuples of (RA, DEC) of vertices of a spherical
polygon. This parameter is used to indicate that sky statistics
should computed only in the region of intersection of *this*
image with the polygon indicated by `overlap`. When `overlap` is
`None`, sky statistics will be computed over the entire image.
delta : bool, optional
Should this function return absolute sky value or the difference
between the computed value and the value of the sky stored in the
`sky` property.
Returns
-------
skyval : float, None
Computed sky value (absolute or relative to the `sky` attribute).
If there are no valid data to perform this computations (e.g.,
because this image does not overlap with the image indicated by
`overlap`), `skyval` will be set to `None`.
npix : int
Number of pixels used to compute sky statistics.
polyarea : float
Area (in srad) of the polygon that bounds data used to compute
sky statistics.
"""
if overlap is None:
if self.mask is None:
data = self.image
else:
data = self.image[self.mask]
polyarea = self.poly_area
else:
fill_mask = np.zeros(self.image.shape, dtype=bool)
if isinstance(overlap, (SkyImage, SkyGroup, SphericalPolygon)):
intersection = self.intersection(overlap)
polyarea = np.fabs(intersection.area())
radec = intersection.to_radec()
else: # assume a list of (ra, dec) tuples:
radec = []
polyarea = 0.0
for r, d in overlap:
poly = SphericalPolygon.from_radec(r, d)
polyarea1 = np.fabs(poly.area())
if polyarea1 == 0.0 or len(r) < 4:
continue
polyarea += polyarea1
radec.append(self.intersection(poly).to_radec())
if polyarea == 0.0:
return (None, 0, 0.0)
for ra, dec in radec:
if len(ra) < 4:
continue
# set pixels in 'fill_mask' that are inside a polygon to True:
x, y = self.wcs_inv(ra, dec)
poly_vert = list(zip(*[x, y]))
polygon = region.Polygon(True, poly_vert)
fill_mask = polygon.scan(fill_mask)
if self.mask is not None:
fill_mask &= self.mask
data = self.image[fill_mask]
if data.size < 1:
return (None, 0, 0.0)
# Calculate sky
try:
skyval, npix = self._skystat(data)
except ValueError:
return (None, 0, 0.0)
if delta:
skyval -= self._sky
return skyval, npix, polyarea
def __init__(self, ra, dec, inside=None, max_depth=10):
ra = ra.to(u.rad).value
dec = dec.to(u.rad).value
# Check if the vertices form a closed polygon
if ra[0] != ra[-1] or dec[0] != dec[-1]:
# If not, append the first vertex to ``vertices``
ra = np.append(ra, ra[0])
dec = np.append(dec, dec[0])
vertices = SkyCoord(ra=ra, dec=dec, unit="rad", frame="icrs")
if inside:
# Convert it to (x, y, z) cartesian coordinates on the sphere
inside = (inside.icrs.ra.rad, inside.icrs.dec.rad)
self.polygon = SphericalPolygon.from_lonlat(lon=ra, lat=dec, center=inside, degrees=False)
start_depth, ipixels = self._get_starting_depth()
end_depth = max_depth
# When the start depth returned is > to the depth requested
# For that specific case, we only do one iteration at start_depth
# Thus the MOC will contain the partially intersecting cells with the
# contained ones at start_depth
# And we degrade the MOC to the max_depth
self.degrade_to_max_depth = False
if start_depth > end_depth:
end_depth = start_depth
self.degrade_to_max_depth = True
self.ipix_d = {str(order): [] for order in range(start_depth, end_depth + 1)}
## Iterative version of the algorithm: seems a bit faster than the recursive one
for depth in range(start_depth, end_depth + 1):
# Define a HEALPix at the current depth
hp = HEALPix(nside=(1 << depth), order='nested', frame=ICRS())
# Get the lon and lat of the corners of the pixels
# intersecting the polygon
lon, lat = hp.boundaries_lonlat(ipixels, step=1)
lon = lon.to(u.rad).value
lat = lat.to(u.rad).value
# closes the lon and lat array so that their first and last value matches
lon = self._closes_numpy_2d_array(lon)
lat = self._closes_numpy_2d_array(lat)
num_ipix_inter_poly = ipixels.shape[0]
# Define a 3d numpy array containing the corners coordinates of the intersecting pixels
# The first dim is the num of ipixels
# The second is the number of coordinates (5 as it defines the closed polygon of a HEALPix cell)
# The last is of size 2 (lon and lat)
shapes = np.vstack((lon.ravel(), lat.ravel())).T.reshape(num_ipix_inter_poly, 5, -1)
ipix_in_polygon_l = []
ipix_inter_polygon_l = []
for i in range(num_ipix_inter_poly):
shape = shapes[i]
# Definition of a SphericalPolygon from the border coordinates of a HEALPix cell
ipix_shape = SphericalPolygon.from_radec(lon=shape[:, 0], lat=shape[:, 1], degrees=False)
ipix = ipixels[i]
if self.polygon.intersects_poly(ipix_shape):
# If we are at the max depth then we direcly add to the MOC the intersecting ipixels
if depth == end_depth:
ipix_in_polygon_l.append(ipix)
else:
# Check whether polygon contains ipix or not
if self.polygon_contains_ipix(ipix_shape):
ipix_in_polygon_l.append(ipix)
else:
# The ipix is just intersecting without being contained in the polygon
# We split it in its 4 children
child_ipix = ipix << 2
ipix_inter_polygon_l.extend([child_ipix,
child_ipix + 1,
child_ipix + 2,
child_ipix + 3])
self.ipix_d.update({str(depth): ipix_in_polygon_l})
ipixels = np.asarray(ipix_inter_polygon_l)
def calc_bounding_polygon(self, stepsize=None):
""" Compute image's bounding polygon.
Parameters
----------
stepsize : int, None, optional
Indicates the maximum separation between two adjacent vertices
of the bounding polygon along each side of the image. Corners
of the image are included automatically. If `stepsize` is `None`,
bounding polygon will contain only vertices of the image.
"""
ny, nx = self.image.shape
if stepsize is None:
nintx = 2
ninty = 2
else:
nintx = max(2, int(np.ceil((nx + 1.0) / stepsize)))
ninty = max(2, int(np.ceil((ny + 1.0) / stepsize)))
xs = np.linspace(-0.5, nx - 0.5, nintx, dtype=np.float)
ys = np.linspace(-0.5, ny - 0.5, ninty, dtype=np.float)[1:-1]
nptx = xs.size
npty = ys.size
npts = 2 * (nptx + npty)
borderx = np.empty((npts + 1,), dtype=np.float)
bordery = np.empty((npts + 1,), dtype=np.float)
# "bottom" points:
borderx[:nptx] = xs
bordery[:nptx] = -0.5
# "right"
sl = np.s_[nptx:nptx + npty]
borderx[sl] = nx - 0.5
bordery[sl] = ys
# "top"
sl = np.s_[nptx + npty:2 * nptx + npty]
borderx[sl] = xs[::-1]
bordery[sl] = ny - 0.5
# "left"
sl = np.s_[2 * nptx + npty:-1]
borderx[sl] = -0.5
bordery[sl] = ys[::-1]
# close polygon:
borderx[-1] = borderx[0]
bordery[-1] = bordery[0]
ra, dec = self.wcs_fwd(borderx, bordery, with_bounding_box=False)
# TODO: for strange reasons, occasionally ra[0] != ra[-1] and/or
# dec[0] != dec[-1] (even though we close the polygon in the
# previous two lines). Then SphericalPolygon fails because
# points are not closed. Threfore we force it to be closed:
ra[-1] = ra[0]
dec[-1] = dec[0]
self._radec = [(ra, dec)]
self._polygon = SphericalPolygon.from_radec(ra, dec)
self._poly_area = np.fabs(self._polygon.area())
def __init__(self, image, ext, dq_bits=0, dqimage=None, dqext=None,
usermask=None, usermask_ext=None):
"""
Parameters
----------
image : ImageRef
An :py:class:`~stsci.skypac.utils.ImageRef` object that refers
to an open FITS file
ext : tuple, int, str
Extension specification in the `image` the `SkyLineMember`
object will be associated with.
An int `ext` specifies extension number. A tuple in the form
(str, int) specifies extension name and number. A string `ext`
specifies extension name and the extension version is assumed
to be 1. See documentation for `astropy.io.fits.getData`
for examples.
dq_bits : int, None (Default = 0)
Integer sum of all the DQ bit values from the
input `image`'s DQ array that should be considered "good"
when building masks for sky computations. For example,
if pixels in the DQ array can be combinations of 1, 2, 4,
and 8 flags and one wants to consider DQ "defects" having
flags 2 and 4 as being acceptable for sky computations,
then `dq_bits` should be set to 2+4=6. Then a DQ pixel
having values 2,4, or 6 will be considered a good pixel,
while a DQ pixel with a value, e.g., 1+2=3, 4+8=12, etc.
will be flagged as a "bad" pixel.
| Default value (0) will make *all* non-zero
pixels in the DQ mask to be considered "bad" pixels,
and the corresponding image pixels will not be used
for sky computations.
| Set `dq_bits` to `None` to turn off the use of
image's DQ array for sky computations.
.. note::
DQ masks (if used), *will be* combined with user masks
specified by the `usermask` parameter.
dqimage : ImageRef
An :py:class:`~stsci.skypac.utils.ImageRef` object that refers
to an open FITS file that has DQ data of the input `image`.
.. note::
When DQ data are located in the same FITS file as the
science image data (e.g., HST/ACS, HST/WFC3, etc.),
`dqimage` may point to the
same :py:class:`~stsci.skypac.utils.ImageRef` object.
In this case the reference count of the
\ :py:class:`~stsci.skypac.utils.ImageRef` object must be
increased adequately.
dqext : tuple, int, str
Extension specification of the `dqimage` that contains
`image`'s DQ information. See help for `ext` for more
details on acceptable formats for this parameter.
usermask : ImageRef
An :py:class:`~stsci.skypac.utils.ImageRef` object that refers
to an open FITS file that has user mask data that indicate
what pixels in the input `image` should be used for sky
computations (``1``) and which pixels should **not** be used
for sky computations (``0``).
usermask_ext : tuple, int, str
Extension specification of the `usermask` mask file that
contains user's mask data that should be associated with
the input `image` and `ext`. See help for `ext` for more
details on acceptable formats for this parameter.
"""
assert(hasattr(self.__class__, '_initialized') and \
self.__class__._initialized)
self._reset()
# check that input images and extensions are valid --
# either integers or tuples of strings and integers, e.g., ('sci',1):
_check_valid_imgext(image, 'image', ext, 'ext', can_img_be_None=False)
if dq_bits is not None:
if dqimage is None:
dq_bits = 0
else:
_check_valid_imgext(dqimage, 'dqimage', dqext, 'dqext')
_check_valid_imgext(usermask, 'usermask', usermask_ext,'usermask_ext')
# get telescope, instrument, and detector info:
self.telescope, self.instrument, self.detector = get_instrument_info(
image, ext)
# check dq_bits:
if dq_bits is not None and not isinstance(dq_bits,int):
if image: dqimage.release()
if usermask: usermask.release()
if dqimage: dqimage.release()
raise TypeError("Argument 'dq_bits' must be either an integer or None.")
# buld mask:
self._buildMask(image.original_fname, ext, dq_bits,
dqimage, dqext, usermask, usermask_ext)
if dqimage: dqimage.release()
if usermask: usermask.release()
# save file, user mask, and DQ extension info:
self._fname = image.original_fname
self._basefname = basename(self._fname)
self._image = image
self._ext = ext
self._can_free_image = image.can_reload_data and self.optimize != 'speed'
# check extension and create a string representation:
try:
extstr = ext2str(ext)
except ValueError:
raise ValueError("Unexpected extension type \'{}\' for file {}.".\
format(ext,self._basefname))
self._id = "{:s}[{:s}]".format(self._basefname, extstr)
# extract WCS for bounding-box computation
try:
if hasattr(image.hdu[ext], 'wcs'):
self._wcs = image.hdu[ext].wcs
else:
if self.telescope in supported_telescopes:
self._wcs = wcsutil.HSTWCS(image.hdu, ext)
else:
self._wcs = pywcs.WCS(image.hdu[ext].header, image.hdu)
if self._wcs is None:
raise Exception("Invalid WCS.")
except:
msg = "Unable to obtain WCS information for the file {:s}." \
.format(self._id)
self._ml.error(msg)
self._ml.flush()
self._release_all()
raise
# determine pixel scale:
self._get_pixel_scale()
# see if image data are in counts or count-rate
# and compute count(-rate) to flux (per arcsec^2) conversion factor:
self._brightness_conv_from_hdu(image.hdu, self._idcscale)
# process Sky user's keyword and its value:
self._init_skyuser(image.hdu[ext].header)
# Set polygon to be the bounding box of the chip:
self._polygon = SphericalPolygon.from_wcs(self.wcs, steps=1)
class SkyGroup:
"""
Holds multiple :py:class:`SkyImage` objects whose sky background values
must be adjusted together.
`SkyGroup` provides methods for obtaining bounding polygon of the group
of :py:class:`SkyImage` objects and to compute sky value of the group.
"""
def __init__(self, images, id=None, sky=0.0):
if isinstance(images, SkyImage):
self._images = [images]
elif hasattr(images, '__iter__'):
self._images = []
for im in images:
if not isinstance(im, SkyImage):
raise TypeError("Each element of the 'images' parameter "
"must be an 'SkyImage' object.")
self._images.append(im)
else:
raise TypeError(
"Parameter 'images' must be either a single "
"'SkyImage' object or a list of 'SkyImage' objects")
self._id = id
self._update_bounding_polygon()
self._sky = sky
for im in self._images:
im.sky += sky
@property
def id(self):
""" Set or get `SkyImage`'s `id`.
While `id` can be of any type, it is prefereble that `id` be
of a type with nice string representation.
"""
return self._id
@id.setter
def id(self, id):
self._id = id
@property
def sky(self):
""" Sky background value. See `calc_sky` for more details.
"""
return self._sky
@sky.setter
def sky(self, sky):
delta_sky = sky - self._sky
self._sky = sky
for im in self._images:
im.sky += delta_sky
@property
def radec(self):
"""
Get RA and DEC of the verteces of the bounding polygon as a
`~numpy.ndarray` of shape (N, 2) where N is the number of verteces + 1.
"""
return self._radec
@property
def polygon(self):
""" Get image's bounding polygon.
"""
return self._polygon
def intersection(self, skyimage):
"""
Compute intersection of this `SkyImage` object and another
`SkyImage`, `SkyGroup`, or
:py:class:`~spherical_geometry.polygon.SphericalPolygon`
object.
Parameters
----------
skyimage : SkyImage, SkyGroup, SphericalPolygon
Another object that should be intersected with this `SkyImage`.
Returns
-------
polygon : SphericalPolygon
A :py:class:`~spherical_geometry.polygon.SphericalPolygon` that is
the intersection of this `SkyImage` and `skyimage`.
"""
if isinstance(skyimage, (SkyImage, SkyGroup)):
return self._polygon.intersection(skyimage.polygon)
else:
return self._polygon.intersection(skyimage)
def _update_bounding_polygon(self):
polygons = [im.polygon for im in self._images]
if len(polygons) == 0:
self._polygon = SphericalPolygon([])
self._radec = []
else:
self._polygon = SphericalPolygon.multi_union(polygons)
self._radec = list(self._polygon.to_radec())
def __len__(self):
return len(self._images)
def __getitem__(self, idx):
return self._images[idx]
def __setitem__(self, idx, value):
if not isinstance(value, SkyImage):
raise TypeError("Item must be of 'SkyImage' type")
value.sky += self._sky
self._images[idx] = value
self._update_bounding_polygon()
def __delitem__(self, idx):
del self._images[idx]
if len(self._images) == 0:
self._sky = 0.0
self._id = None
self._update_bounding_polygon()
def __iter__(self):
for image in self._images:
yield image
def insert(self, idx, value):
"""Inserts a `SkyImage` into the group.
"""
if not isinstance(value, SkyImage):
raise TypeError("Item must be of 'SkyImage' type")
value.sky += self._sky
self._images.insert(idx, value)
self._update_bounding_polygon()
def append(self, value):
"""Appends a `SkyImage` to the group.
"""
if not isinstance(value, SkyImage):
raise TypeError("Item must be of 'SkyImage' type")
value.sky += self._sky
self._images.append(value)
self._update_bounding_polygon()
def calc_sky(self, overlap=None, delta=True):
"""
Compute sky background value.
Parameters
----------
overlap : SkyImage, SkyGroup, SphericalPolygon, list of tuples, \
None, optional
Another `SkyImage`, `SkyGroup`,
:py:class:`spherical_geometry.polygons.SphericalPolygon`, or
a list of tuples of (RA, DEC) of vertices of a spherical
polygon. This parameter is used to indicate that sky statistics
should computed only in the region of intersection of *this*
image with the polygon indicated by `overlap`. When `overlap` is
`None`, sky statistics will be computed over the entire image.
delta : bool, optional
Should this function return absolute sky value or the difference
between the computed value and the value of the sky stored in the
`sky` property.
Returns
-------
skyval : float, None
Computed sky value (absolute or relative to the `sky` attribute).
If there are no valid data to perform this computations (e.g.,
because this image does not overlap with the image indicated by
`overlap`), `skyval` will be set to `None`.
npix : int
Number of pixels used to compute sky statistics.
polyarea : float
Area (in srad) of the polygon that bounds data used to compute
sky statistics.
"""
if len(self._images) == 0:
return (None, 0, 0.0)
wght = 0
area = 0.0
if overlap is None:
# compute minimum sky across all images in the group:
wsky = None
for image in self._images:
# make sure all images have the same background:
image.background = self._sky
sky, npix, imarea = image.calc_sky(overlap=None, delta=delta)
if sky is None:
continue
if wsky is None or wsky > sky:
wsky = sky
wght = npix
area = imarea
return (wsky, wght, area)
################################################
## compute weighted sky in various overlaps: ##
################################################
wsky = 0.0
for image in self._images:
# make sure all images have the same background:
image.background = self._sky
sky, npix, area1 = image.calc_sky(overlap=overlap, delta=delta)
area += area1
if sky is not None and npix > 0:
pix_area = npix * image.pix_area
wsky += sky * pix_area
wght += pix_area
if wght == 0.0 or area == 0.0:
return (None, wght, area)
else:
return (wsky / wght, wght, area)
class SkyGroup:
"""
Holds multiple :py:class:`SkyImage` objects whose sky background values
must be adjusted together.
`SkyGroup` provides methods for obtaining bounding polygon of the group
of :py:class:`SkyImage` objects and to compute sky value of the group.
"""
def __init__(self, images, id=None, sky=0.0):
if isinstance(images, SkyImage):
self._images = [images]
elif hasattr(images, '__iter__'):
self._images = []
for im in images:
if not isinstance(im, SkyImage):
raise TypeError("Each element of the 'images' parameter "
"must be an 'SkyImage' object.")
self._images.append(im)
else:
raise TypeError("Parameter 'images' must be either a single "
"'SkyImage' object or a list of 'SkyImage' objects")
self._id = id
self._update_bounding_polygon()
self._sky = sky
for im in self._images:
im.sky += sky
@property
def id(self):
""" Set or get `SkyImage`'s `id`.
While `id` can be of any type, it is prefereble that `id` be
of a type with nice string representation.
"""
return self._id
@id.setter
def id(self, id):
self._id = id
@property
def sky(self):
""" Sky background value. See `calc_sky` for more details.
"""
return self._sky
@sky.setter
def sky(self, sky):
delta_sky = sky - self._sky
self._sky = sky
for im in self._images:
im.sky += delta_sky
@property
def radec(self):
"""
Get RA and DEC of the verteces of the bounding polygon as a
`~numpy.ndarray` of shape (N, 2) where N is the number of verteces + 1.
"""
return self._radec
@property
def polygon(self):
""" Get image's bounding polygon.
"""
return self._polygon
def intersection(self, skyimage):
"""
Compute intersection of this `SkyImage` object and another
`SkyImage`, `SkyGroup`, or
:py:class:`~spherical_geometry.polygon.SphericalPolygon`
object.
Parameters
----------
skyimage : SkyImage, SkyGroup, SphericalPolygon
Another object that should be intersected with this `SkyImage`.
Returns
-------
polygon : SphericalPolygon
A :py:class:`~spherical_geometry.polygon.SphericalPolygon` that is
the intersection of this `SkyImage` and `skyimage`.
"""
if isinstance(skyimage, (SkyImage, SkyGroup)):
return self._polygon.intersection(skyimage.polygon)
else:
return self._polygon.intersection(skyimage)
def _update_bounding_polygon(self):
polygons = [im.polygon for im in self._images]
if len(polygons) == 0:
self._polygon = SphericalPolygon([])
self._radec = []
else:
self._polygon = SphericalPolygon.multi_union(polygons)
self._radec = list(self._polygon.to_radec())
def __len__(self):
return len(self._images)
def __getitem__(self, idx):
return self._images[idx]
def __setitem__(self, idx, value):
if not isinstance(value, SkyImage):
raise TypeError("Item must be of 'SkyImage' type")
value.sky += self._sky
self._images[idx] = value
self._update_bounding_polygon()
def __delitem__(self, idx):
del self._images[idx]
if len(self._images) == 0:
self._sky = 0.0
self._id = None
self._update_bounding_polygon()
def __iter__(self):
for image in self._images:
yield image
def insert(self, idx, value):
"""Inserts a `SkyImage` into the group.
"""
if not isinstance(value, SkyImage):
raise TypeError("Item must be of 'SkyImage' type")
value.sky += self._sky
self._images.insert(idx, value)
self._update_bounding_polygon()
def append(self, value):
"""Appends a `SkyImage` to the group.
"""
if not isinstance(value, SkyImage):
raise TypeError("Item must be of 'SkyImage' type")
value.sky += self._sky
self._images.append(value)
self._update_bounding_polygon()
def calc_sky(self, overlap=None, delta=True):
"""
Compute sky background value.
Parameters
----------
overlap : SkyImage, SkyGroup, SphericalPolygon, list of tuples, \
None, optional
Another `SkyImage`, `SkyGroup`,
:py:class:`spherical_geometry.polygons.SphericalPolygon`, or
a list of tuples of (RA, DEC) of vertices of a spherical
polygon. This parameter is used to indicate that sky statistics
should computed only in the region of intersection of *this*
image with the polygon indicated by `overlap`. When `overlap` is
`None`, sky statistics will be computed over the entire image.
delta : bool, optional
Should this function return absolute sky value or the difference
between the computed value and the value of the sky stored in the
`sky` property.
Returns
-------
skyval : float, None
Computed sky value (absolute or relative to the `sky` attribute).
If there are no valid data to perform this computations (e.g.,
because this image does not overlap with the image indicated by
`overlap`), `skyval` will be set to `None`.
npix : int
Number of pixels used to compute sky statistics.
polyarea : float
Area (in srad) of the polygon that bounds data used to compute
sky statistics.
"""
if len(self._images) == 0:
return (None, 0, 0.0)
wght = 0
area = 0.0
if overlap is None:
# compute minimum sky across all images in the group:
wsky = None
for image in self._images:
# make sure all images have the same background:
image.background = self._sky
sky, npix, imarea = image.calc_sky(overlap=None, delta=delta)
if sky is None:
continue
if wsky is None or wsky > sky:
wsky = sky
wght = npix
area = imarea
return (wsky, wght, area)
################################################
## compute weighted sky in various overlaps: ##
################################################
wsky = 0.0
for image in self._images:
# make sure all images have the same background:
image.background = self._sky
sky, npix, area1 = image.calc_sky(overlap=overlap, delta=delta)
area += area1
if sky is not None and npix > 0:
pix_area = npix * image.pix_area
wsky += sky * pix_area
wght += pix_area
if wght == 0.0 or area == 0.0:
return (None, wght, area)
else:
return (wsky / wght, wght, area)
def __init__(self, image, wcs_fwd, wcs_inv, pix_area=1.0, convf=1.0,
mask=None, id=None, skystat=None, stepsize=None, meta=None):
""" Initializes the SkyImage object.
Parameters
----------
image : numpy.ndarray
A 2D array of image data.
wcs_fwd : function
"forward" pixel-to-world transformation function.
wcs_inv : function
"inverse" world-to-pixel transformation function.
pix_area : float, optional
Average pixel's sky area.
convf : float, optional
Conversion factor that when multiplied to `image` data converts
the data to "uniform" (across multiple images) surface
brightness units.
.. note::
The functionality to support this conversion is not yet
implemented and at this moment `convf` is ignored.
mask : numpy.ndarray
A 2D array that indicates
what pixels in the input `image` should be used for sky
computations (``1``) and which pixels should **not** be used
for sky computations (``0``).
id : anything
The value of this parameter is simple stored within the `SkyImage`
object. While it can be of any type, it is prefereble that `id` be
of a type with nice string representation.
skystat : callable, None, optional
A callable object that takes a either a 2D image (2D
`numpy.ndarray`) or a list of pixel values (a Nx1 array) and
returns a tuple of two values: some statistics (e.g., mean,
median, etc.) and number of pixels/values from the input image
used in computing that statistics.
When `skystat` is not set, `SkyImage` will use
:py:class:`~jwst_pipeline.skymatch.skystatistics.SkyStats` object
to perform sky statistics on image data.
stepsize : int, None, optional
Spacing between vertices of the image's bounding polygon. Default
value of `None` creates bounding polygons with four vertices
corresponding to the corners of the image.
meta : dict, None, optional
A dictionary of various items to be stored within the `SkyImage`
object.
"""
self.image = image
self.convf = convf
self.meta = meta
self._id = id
self._pix_area = pix_area
# WCS
self.wcs_fwd = wcs_fwd
self.wcs_inv = wcs_inv
# initial sky value:
self._sky = 0.0
self._sky_is_valid = False
# check that mask has the same shape as image:
if mask is None:
self.mask = None
else:
if image is None:
raise ValueError("'mask' must be None when 'image' is None")
self.mask = np.asanyarray(mask, dtype=np.bool)
if self.mask.shape != image.shape:
raise ValueError("'mask' must have the same shape as 'image'.")
# create spherical polygon bounding the image
if image is None or wcs_fwd is None or wcs_inv is None:
self._radec = [(np.array([]), np.array([]))]
self._polygon = SphericalPolygon([])
self._poly_area = 0.0
else:
self.calc_bounding_polygon(stepsize)
# set sky statistics function (NOTE: it must return statistics and
# the number of pixels used after clipping)
if skystat is None:
self.set_builtin_skystat()
else:
self.skystat = skystat