def manual_mask_cleanup(self, catalog, filt):
"""Masks regions that were observed to have artefacts upon visual inspection"""
val = np.zeros(len(catalog))
col = Column(val, name='IN_MANUAL_MASK',
description="In a manual mask region", dtype=int)
catalog.add_column(col)
mask_tab = Table.read(self.params.manual_mask_file, format='ascii.basic')
q, = np.where((self.params.seg_id == mask_tab['SEG']) & (filt == mask_tab['FILTER']))
if len(q) == 0:
return catalog
else:
for m in q:
x_vertices = np.array([mask_tab['AX'][m], mask_tab['BX'][m], mask_tab['CX'][m], mask_tab['DX'][m]])
y_vertices = np.array([mask_tab['AY'][m], mask_tab['BY'][m], mask_tab['CY'][m], mask_tab['DY'][m]])
Xs = np.array([catalog['XMIN_IMAGE'], catalog['XMAX_IMAGE']],
dtype=int)
Ys = np.array([catalog['YMIN_IMAGE'], catalog['YMAX_IMAGE']],
dtype=int)
bottom_pixels = [[(x, Ys[0][i]) for x in range(Xs[0][i],Xs[1][i])]for i in range(len(catalog))]
top_pixels = [[(x, Ys[1][i]) for x in range(Xs[0][i], Xs[1][i])]for i in range(len(catalog))]
left_pixels = [[(Xs[0][i], y) for y in range(Ys[0][i], Ys[1][i])]for i in range(len(catalog))]
right_pixels = [[(Xs[1][i], y) for y in range(Ys[0][i], Ys[1][i])]for i in range(len(catalog))]
for i in range(len(catalog)):
pixels = bottom_pixels[i] + left_pixels[i] + top_pixels[i] + right_pixels[i]
bools = [fn.inpoly(pixel[0], pixel[1], x_vertices, y_vertices) for pixel in pixels]
if max(bools) == 1:
catalog['IN_MANUAL_MASK'][i] = 1
return catalog
def manual_mask_cleanup(self, catalog, filt):
"""masks regions that were observed to have artefacts upon visual inspection
"""
val = np.zeros(len(catalog))
col = Column(val,
name='IN_MANUAL_MASK',
description="In a manual mask region",
dtype=int)
catalog.add_column(col)
mask_tab = Table.read(self.params.manual_mask_file,
format='ascii.basic')
q, = np.where((self.params.seg_id == mask_tab['SEG'])
& (filt == mask_tab['FILTER']))
if len(q) == 0:
return catalog
else:
for m in q:
x_vertices = np.array([
mask_tab['AX'][m], mask_tab['BX'][m], mask_tab['CX'][m],
mask_tab['DX'][m]
])
y_vertices = np.array([
mask_tab['AY'][m], mask_tab['BY'][m], mask_tab['CY'][m],
mask_tab['DY'][m]
])
Xs = np.array([catalog['XMIN_IMAGE'], catalog['XMAX_IMAGE']],
dtype=int)
Ys = np.array([catalog['YMIN_IMAGE'], catalog['YMAX_IMAGE']],
dtype=int)
bottom_pixels = [[(x, Ys[0][i])
for x in range(Xs[0][i], Xs[1][i])]
for i in range(len(catalog))]
top_pixels = [[(x, Ys[1][i])
for x in range(Xs[0][i], Xs[1][i])]
for i in range(len(catalog))]
left_pixels = [[(Xs[0][i], y)
for y in range(Ys[0][i], Ys[1][i])]
for i in range(len(catalog))]
right_pixels = [[(Xs[1][i], y)
for y in range(Ys[0][i], Ys[1][i])]
for i in range(len(catalog))]
for i in range(len(catalog)):
pixels = bottom_pixels[i] + left_pixels[i] + top_pixels[
i] + right_pixels[i]
bools = [
fn.inpoly(pixel[0], pixel[1], x_vertices, y_vertices)
for pixel in pixels
] # for j in range(len(x_vertices.T))]
if max(bools) == 1:
catalog['IN_MANUAL_MASK'][i] = 1
return catalog
def diffraction_mask_cleanup(self, catalog,
diff_spike_params,
mag_cutoff=19.0):
"""Masks objects within diffraction spikes of saturated stars."""
m = diff_spike_params[0] # slope
b = diff_spike_params[1] # intercept
w = diff_spike_params[2] * 0.5 # width
theta = diff_spike_params[3] # angle
val = np.zeros(len(catalog))
col = Column(val, name='IN_DIFF_MASK',
description="Close to saturated star", dtype=int)
catalog.add_column(col)
print "Identifying saturated stars"
cond1 = catalog['MAG_CORR'] < mag_cutoff
cond2 = catalog['IS_STAR'] == 1
q, = np.where(cond1 & cond2)
# exit if no saturated stars present
if len(q) == 0:
print 'No saturated objects found'
return catalog
x0 = catalog['X_IMAGE'][q]
y0 = catalog['Y_IMAGE'][q]
r = np.mean([catalog['A_IMAGE'][q], catalog['B_IMAGE'][q]]) * 5
flux = catalog['FLUX_AUTO'][q]
l = m * flux + b
x_vertices = np.array([x0-w,x0-w,x0+w,x0+w,x0+r,x0+l,x0+l,x0+r,x0+w,x0+w,x0-w,x0-w,x0-r,x0-l,x0-l,x0-r])
y_vertices = np.array([y0+r,y0+l,y0+l,y0+r,y0+w,y0+w,y0-w,y0-w,y0-r,y0-l,y0-l,y0-r,y0-w,y0-w,y0+w,y0+w])
(x_vertices, y_vertices) = fn.rotate_table(x_vertices, y_vertices, x0, y0, theta)
catalog['IN_DIFF_MASK'][q] = 1
print "Identify objects in diffraction spike"
Xs = np.array([catalog['XMIN_IMAGE'], catalog['XMAX_IMAGE']],
dtype=int)
Ys = np.array([catalog['YMIN_IMAGE'], catalog['YMAX_IMAGE']],
dtype=int)
bottom_pixels = [[(x, Ys[0][i]) for x in range(Xs[0][i],Xs[1][i])]for i in range(len(catalog))]
top_pixels = [[(x, Ys[1][i]) for x in range(Xs[0][i], Xs[1][i])]for i in range(len(catalog))]
left_pixels = [[(Xs[0][i], y) for y in range(Ys[0][i], Ys[1][i])]for i in range(len(catalog))]
right_pixels = [[(Xs[1][i], y) for y in range(Ys[0][i], Ys[1][i])]for i in range(len(catalog))]
for i in range(len(catalog)):
pixels = bottom_pixels[i] + left_pixels[i] + top_pixels[i] + right_pixels[i]
bools = [fn.inpoly(pixel[0], pixel[1], x_vertices.T[j], y_vertices.T[j]) for pixel in pixels for j in range(len(x_vertices.T))]
if max(bools) == 1:
catalog['IN_DIFF_MASK'][i] = 1
return catalog
def diffraction_mask_cleanup(self,
catalog,
diff_spike_params,
mag_cutoff=19.0):
"""Masks objects within diffraction spikes of saturated stars."""
m = diff_spike_params[0] # slope
b = diff_spike_params[1] # intercept
w = diff_spike_params[2] * 0.5 # width
theta = diff_spike_params[3] # angle
val = np.zeros(len(catalog))
col = Column(val,
name='IN_DIFF_MASK',
description="Close to saturated star",
dtype=int)
catalog.add_column(col)
print "Identifying saturated stars"
cond1 = catalog['MAG_CORR'] < mag_cutoff
cond2 = catalog['IS_STAR'] == 1
q, = np.where(cond1 & cond2)
# exit if no saturated stars present
if len(q) == 0:
print 'No saturated objects found'
return catalog
x0 = catalog['X_IMAGE'][q]
y0 = catalog['Y_IMAGE'][q]
r = np.mean([catalog['A_IMAGE'][q], catalog['B_IMAGE'][q]]) * 5
flux = catalog['FLUX_AUTO'][q]
l = m * flux + b
x_vertices = np.array([
x0 - w, x0 - w, x0 + w, x0 + w, x0 + r, x0 + l, x0 + l, x0 + r,
x0 + w, x0 + w, x0 - w, x0 - w, x0 - r, x0 - l, x0 - l, x0 - r
])
y_vertices = np.array([
y0 + r, y0 + l, y0 + l, y0 + r, y0 + w, y0 + w, y0 - w, y0 - w,
y0 - r, y0 - l, y0 - l, y0 - r, y0 - w, y0 - w, y0 + w, y0 + w
])
(x_vertices, y_vertices) = fn.rotate_table(x_vertices, y_vertices, x0,
y0, theta)
catalog['IN_DIFF_MASK'][q] = 1
print "Identify objects in diffraction spike"
Xs = np.array([catalog['XMIN_IMAGE'], catalog['XMAX_IMAGE']],
dtype=int)
Ys = np.array([catalog['YMIN_IMAGE'], catalog['YMAX_IMAGE']],
dtype=int)
bottom_pixels = [[(x, Ys[0][i]) for x in range(Xs[0][i], Xs[1][i])]
for i in range(len(catalog))]
top_pixels = [[(x, Ys[1][i]) for x in range(Xs[0][i], Xs[1][i])]
for i in range(len(catalog))]
left_pixels = [[(Xs[0][i], y) for y in range(Ys[0][i], Ys[1][i])]
for i in range(len(catalog))]
right_pixels = [[(Xs[1][i], y) for y in range(Ys[0][i], Ys[1][i])]
for i in range(len(catalog))]
for i in range(len(catalog)):
pixels = bottom_pixels[i] + left_pixels[i] + top_pixels[
i] + right_pixels[i]
bools = [
fn.inpoly(pixel[0], pixel[1], x_vertices.T[j], y_vertices.T[j])
for pixel in pixels for j in range(len(x_vertices.T))
]
if max(bools) == 1:
catalog['IN_DIFF_MASK'][i] = 1
return catalog