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)
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 test_geos1():
"""do second test posted to bug report"""
n_fill = 10
theta1_high_fill = np.full(n_fill, 5.)
theta1_low_fill = np.full(n_fill, -65.)
phi1_high_fill = np.linspace(50. - 360., 50. - 1., n_fill)
phi1_low_fill = phi1_high_fill[::-1]
theta1s = np.hstack(
[theta1_high_fill, theta1_low_fill, theta1_high_fill[0]])
phi1s = np.hstack([phi1_high_fill, phi1_low_fill, phi1_high_fill[0]])
phi_in1 = 0.
theta_in1 = 70.
bounding_xyz1 = np.array(sgv.radec_to_vector(phi1s, theta1s,
degrees=True)).T
inside_xyz1 = np.array(
sgv.radec_to_vector(phi_in1, theta_in1, degrees=True))
sp_poly1 = SphericalPolygon(bounding_xyz1, inside=inside_xyz1)
theta2_high_fill = np.full(n_fill, -30.)
theta2_low_fill = np.full(n_fill, -85.)
phi2_high_fill = np.linspace(65 - 360., 55. - 10., n_fill)
phi2_low_fill = np.linspace(5 - 360., 5. - 10., n_fill)[::-1]
theta2s = np.hstack(
[theta2_high_fill, theta2_low_fill, theta2_high_fill[0]])
phi2s = np.hstack([phi2_high_fill, phi2_low_fill, phi2_high_fill[0]])
phi_in2 = 0.
theta_in2 = 70.
bounding_xyz2 = np.array(sgv.radec_to_vector(phi2s, theta2s,
degrees=True)).T
inside_xyz2 = np.array(
sgv.radec_to_vector(phi_in2, theta_in2, degrees=True))
sp_poly2 = SphericalPolygon(bounding_xyz2, inside=inside_xyz2)
int_poly = sp_poly2.intersection(sp_poly1)
theta_out = -50. * np.pi / 180.
phi_out = 0.
outside_xyz = np.array(
sgv.radec_to_vector(phi_out, theta_out, degrees=False))
theta_in3 = 70. * np.pi / 180.
phi_in3 = 0.
inside_xyz3 = np.array(
sgv.radec_to_vector(phi_in3, theta_in3, degrees=False))
print("area int, area 1,area 2: ", int_poly.area(), sp_poly1.area(),
sp_poly2.area())
print("Should be True , True : ",
int_poly.area() <= sp_poly1.area(),
int_poly.area() <= sp_poly2.area())
assert int_poly.area() <= sp_poly1.area()
assert int_poly.area() <= sp_poly2.area()
assert not int_poly.contains_point(outside_xyz)
assert not sp_poly1.contains_point(outside_xyz)
assert not sp_poly2.contains_point(outside_xyz)
print("Should be True ,True ,True : ",
int_poly.contains_point(inside_xyz1),
sp_poly1.contains_point(inside_xyz1),
sp_poly2.contains_point(inside_xyz1))
assert int_poly.contains_point(inside_xyz1)
assert sp_poly1.contains_point(inside_xyz1)
assert sp_poly2.contains_point(inside_xyz1)
print("Should be True ,True ,True : ",
int_poly.contains_point(inside_xyz2),
sp_poly1.contains_point(inside_xyz2),
sp_poly2.contains_point(inside_xyz2))
assert int_poly.contains_point(inside_xyz2)
assert sp_poly1.contains_point(inside_xyz2)
assert sp_poly2.contains_point(inside_xyz2)
print("Should be True ,True ,True : ",
int_poly.contains_point(inside_xyz3),
sp_poly1.contains_point(inside_xyz3),
sp_poly2.contains_point(inside_xyz3))
assert int_poly.contains_point(inside_xyz3)
assert sp_poly1.contains_point(inside_xyz3)
assert sp_poly2.contains_point(inside_xyz3)
inside_res = list(int_poly.inside)
for itr in range(0, len(inside_res)):
print("Should be True ,True ,True : ",
int_poly.contains_point(inside_res[itr]),
sp_poly1.contains_point(inside_res[itr]),
sp_poly2.contains_point(inside_res[itr]))
assert int_poly.contains_point(inside_res[itr])
assert sp_poly1.contains_point(inside_res[itr])
assert sp_poly2.contains_point(inside_res[itr])
def analyze_protein(protein):
prot = protein[0]
chain = protein[1]
print("Downloading {}{}.".format(prot, chain))
cmd = "wget https://files.rcsb.org/download/{}.pdb".format(prot)
proc = Popen(shlex.split(cmd), stdout=DEVNULL, stderr=STDOUT)
proc.communicate()
print("Extracting coordinates.")
cmd = "./pdb_to_xyz.R {}.pdb --chain-id={} -o {}.txt".format(
prot, chain, prot)
proc = Popen(shlex.split(cmd), stdout=DEVNULL, stderr=STDOUT)
proc.communicate()
print("Removing pdb file.")
proc = Popen(["rm", "{}.pdb".format(prot)], stdout=DEVNULL, stderr=STDOUT)
proc.communicate()
print("Analyzing protein")
for proj in projections:
cmd = "./polynomial_invariant {}.txt --names-db=internal --arrow --nb-projections={} --output-diagram={}{}_gl_{}.txt".format(
prot, proj, prot, chain, proj)
proc = Popen(shlex.split(cmd), stdout=DEVNULL, stderr=STDOUT)
proc.communicate()
name = "{}{}_gl_{}.txt".format(prot, chain, proj)
origins, origin_types = [], []
file = open(name, "r")
next(file)
me = 0
checked = []
vertex_to_index_list = dict()
edges_to_add = []
for l in file:
line = l.strip('\n').split('\t')
if line[3] != 'UNKNOWN':
if '*' not in line[3]:
origin_types.append(line[3])
origins.append(
[float(line[0]),
float(line[1]),
float(line[2])])
if line[3] not in checked:
vertex_to_index_list[repr(line[3])] = me
me += 1
origins = numpy.array(origins)
voro = SphericalVoronoi(origins)
voro.sort_vertices_of_regions()
for i in range(len(voro.regions)):
for j in range(i + 1, len(voro.regions)):
intersect = list(set(voro.regions[i]) & set(voro.regions[j]))
if intersect:
permissable = set(
[1, len(voro.regions[i]),
len(voro.regions[j])])
index_A = [voro.regions[i].index(x) for x in intersect]
diff_A = [
abs(j - i) for i, j in zip(index_A[:-1], index_A[1:])
]
index_B = [voro.regions[j].index(x) for x in intersect]
diff_B = [
abs(j - i) for i, j in zip(index_B[:-1], index_B[1:])
]
# print ("intersect",intersect,"voro[i]",voro.regions[i],"voro[j]",voro.regions[j])
# print ("index_A",index_A,"diff_A",diff_A,"index_B",index_B,"diff_B",diff_B)
# print ("perm_A",permissable & set(diff_A),"perm_B", permissable & set(diff_B))
if permissable & set(diff_A) and permissable & set(diff_B):
for k in range(int(len(intersect) / 2)):
edges_to_add.append(
(vertex_to_index_list[repr(origin_types[i])],
vertex_to_index_list[repr(origin_types[j])]))
# Comment lines 155 - 165 and uncomment lines 169-170 to go to previous version.
# for k in range(int(len(intersect)/2)):
# edges_to_add.append((vertex_to_index_list[repr(origin_types[i])],vertex_to_index_list[repr(origin_types[j])]))
# print ("len",len(voro.vertices))
print("Creating graph.")
g = igraph.Graph(len(origin_types), directed=False)
origin_types = [
x.split("|")[0].strip('m').strip('s') for x in origin_types
]
g.vs["label"] = [x for x in origin_types]
g.add_edges(edges_to_add)
all_connections = []
for edge in g.es:
src = g.vs[edge.source]['label']
tgt = g.vs[edge.target]['label']
if (src == '3_1' and tgt != '3_1'):
all_connections.append((src, tgt))
elif (src != '3_1' and tgt == '3_1'):
all_connections.append((tgt, src))
proj_errors = 0
error_pairs = []
for pair in all_connections:
if theoretical_values[pair[0]][pair[1]] != 1:
proj_errors += 1
error_pairs.append(pair)
try:
errors = round(proj_errors / len(all_connections), 4)
except ZeroDivisionError:
errors = 'NaN'
edges_between = []
for edge in g.es:
src = g.vs[edge.source]['label']
tgt = g.vs[edge.target]['label']
if (src == '3_1' and tgt == '2_1'):
edges_between.append((src, tgt))
elif (src == '2_1' and tgt == '3_1'):
edges_between.append((tgt, src))
if len(edges_between) > 0:
try:
interface = round(len(edges_between) / len(all_connections), 4)
except ZeroDivisionError:
interface = 'NaN'
else:
interface = 'NaN'
area_31, area_21 = [], []
all_area_31, all_area_21 = [], []
# voro_areas = voro.calculate_areas()
print("voro_areas: ", voro_areas)
for i in range(len(voro.regions)):
voronoi_cell = numpy.asarray(
[list(voro.vertices[x]) for x in voro.regions[i]])
if origin_types[i] == '3_1':
sph_pol = SphericalPolygon(voronoi_cell)
area_31.append(sph_pol.area())
elif origin_types[i] == '2_1':
sph_pol = SphericalPolygon(voronoi_cell)
area_21.append(sph_pol.area())
all_area_31 = numpy.sum(area_31)
all_area_21 = numpy.sum(area_21)
all_graphs.append({
'n': str(proj),
'Error': errors,
'Interface': interface,
'Protein': prot + chain,
'Spectrum': len(set(origin_types)),
'Error Pairs': set(error_pairs),
'Full Spectrum': set(origin_types),
'Area 3_1': all_area_31,
'Area 2_1': all_area_21,
'Area 2_1/3_1': all_area_21 / all_area_31
})
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