def extract_corrections(xds_path):
""" Extract details for geometrical corrections and a mask of untrusted detector regions.
Save a .png file of the latter; assumes folder ./checks exists. """
# generate boolean mask in shape of detector from INIT.LP, with False indicating untrusted pixel
filename = xds_path + "INIT.LP"
mask = np.ones(system['shape'], dtype = bool)
with open(filename, "r") as f:
content = f.readlines()
# mask untrusted rows and columns of detector
corners = np.asarray([s.strip('\n').split()[-6:-2] for s in content if "UNTRUSTED_RECTANGLE" in s], dtype=float).astype(int)
for corner in corners:
if corner[0] == 0:
for i in range(corner[2], corner[3]):
mask[i] = False
if corner[2] == 0:
for i in range(corner[0], corner[1]):
mask[:,i] = False
# mask shadow of beamstop arm if it's marked as an untrusted region by XDS
if any("UNTRUSTED_QUADRILATERAL" in s for s in content):
vertices = np.asarray([s.strip('\n').split()[-10:-2] for s in content if "UNTRUSTED_QUADRILATERAL" in s][0], dtype=int)
bbPath = mplPath.Path(np.array([[vertices[0], vertices[1]],
[vertices[2], vertices[3]],
[vertices[4], vertices[5]],
[vertices[6], vertices[7]],]))
for x in range(mask.shape[1]):
for y in range(mask.shape[0]):
if bbPath.contains_point((x, y)):
mask[y, x] = False
# mask beamstop if marked as an untrusted region by XDS
ebounds = [s.strip('\n').split()[1:5] for s in content if "UNTRUSTED_ELLIPSE" in s][0]
ebounds = np.array([float(eb) for eb in ebounds])
xwidth, ywidth = ebounds[1] - ebounds[0], ebounds[3] - ebounds[2]
center = np.array([int(xwidth/2.0 + ebounds[0]), int(ywidth/2.0 + ebounds[2])])
e = Ellipse(xy=center, width=xwidth, height=ywidth)
for xpoint in np.arange(ebounds[0]-5, ebounds[1]+5):
for ypoint in np.arange(ebounds[2]-5, ebounds[3]+5):
if e.contains_point((xpoint, ypoint)):
mask[int(ypoint), int(xpoint)] = False
system['mask'] = mask
# extract parameters for parallax correction
if 'parallax' in system['corrections']:
filename = xds_path + "XYCORR.LP"
with open(filename, "r") as f:
content = f.readlines()
# attentuation coefficient, mu, in units of m^{-1}; thickness, t0, in units of meters
system['mu'] = 1000*float([s.strip('\n').split()[-1] for s in content if "SILICON" in s][0])
system['t0'] = 0.001*float([s.strip('\n').split()[-1] for s in content if "SENSOR_THICKNESS" in s][0])
# extract polarization fraction and check that plane normal is aligned along y
if 'polarization' in system['corrections']:
filename = xds_path + "CORRECT.LP"
with open(filename, "r") as f:
content = f.readlines()
system['pf'] = float([s.strip('\n').split()[-1] for s in content if "FRACTION_OF_POLARIZATION" in s][0])
pol_axis = np.asarray([s.strip('\n').split()[1:] for s in content if "POLARIZATION_PLANE_NORMAL" in s][0], dtype=float)
try:
assert pol_axis[0]==0 and pol_axis[1]==1 and pol_axis[2]==0
except:
"Polarization axis unexpected and flipped relative to Indexer.py"
return
ax.text(x - 15, y, len(pixels[0]) - i, c='k',
fontsize=15) #shadow
e = Ellipse((x, y),
l,
w,
angle=pa - 90,
linewidth=2,
edgecolor='k',
facecolor='none')
es.append(e)
matches = [[] for e in es]
for i, e in enumerate(es):
#print(e.contains_points(list(zip(*ca_pixels))))
for j, (x, y) in enumerate(zip(*ca_pixels)):
if e.contains_point([x, y]):
matches[i].append(j)
if debug:
ax.add_patch(e)
matches = np.array(matches)
##### Remove duplicate matches
len_matches = [len(l) for l in matches]
len_matches_ranked = np.argsort(len_matches)
c = Counter(list(pinklib.postprocessing.flatten(matches)))
new_matches = [[] for e in es]
dups = []
for i, m in enumerate(matches[len_matches_ranked]):
for mm in m:
if c[mm] < 2:
logging.debug("Plotting footprint: %s"%opts.footprint)
proj = safe_proj(m,perim['ra'],perim['dec'])
m.plot(*proj,**ft_kwargs)
if opts.footprint == 'des':
# Plot the SN fields
logging.debug("Plotting supernova fields.")
# This does the projection correctly, but fails at boundary
sn_kwargs = dict(facecolor='none',edgecolor=ft_kwargs['color'],zorder=exp_zorder-1)
# Check that point inside boundary
fact = 0.99
boundary = Ellipse((m.rmajor,m.rminor),2*(fact*m.rmajor),2*(fact*m.rminor))
for v in SN.values():
if not boundary.contains_point(m(*v)): continue
m.tissot(v[0],v[1],1.0,100,**sn_kwargs)
# The SN labels
sntxt_kwargs = dict(zorder=exp_zorder-1,fontsize=12,
bbox=dict(boxstyle='round,pad=0',fc='w',ec='none',
alpha=0.25))
for k,v in SN_LABELS.items():
ax.annotate(k,m(*v),**sntxt_kwargs)
# Annotate with some information
logging.debug("Adding info text.")
bbox_props = dict(boxstyle='round', facecolor='white')
textstr= "%s %s\n"%("UTC:",utc.strftime('%Y-%m-%d %H:%M:%S'))
textstr+="%s %i (%s)\n"%("Exposure:",expnum[idx],band[idx])
textstr+="%s %i\n"%("NExp:",opts.numexp)
