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 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 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 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 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 _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 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 _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 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, 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)
