def group_stars(pts_thres, mag_thres, vert_thres, all_groups):
'''
For each defined group, find the points that correspond to it.
'''
tot_sols = element_count(all_groups, pts_thres)
milestones, c = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100], 0
neighbors = [[] for _ in range(len(all_groups))]
neig_mags = [[] for _ in range(len(all_groups))]
# For each group defined.
for i, g in enumerate(all_groups):
# For each point in each group.
for vert_pt in g:
# For each point above threshold.
for j, p in enumerate(pts_thres):
c += 1
verts = vert_thres[j]
if verts == vert_pt:
neighbors[i].append(p)
neig_mags[i].append(mag_thres[j])
# Print percentage done.
milestones = print_perc(c, tot_sols, milestones)
return neighbors, neig_mags
def filt_density(f_name, fr_area, x_mr, y_mr, mag_mr, cent_rad, vor_flag,
area_frac_range, dens_frac, mean_filt_area):
'''
Apply density filter.
'''
save_to_log(f_name, 'Obtaining groups densities', 'a')
tot_sols = len(cent_rad)
milestones = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
# Frame's density.
fr_dens_area = len(x_mr) / fr_area
# Obtain integrated magnitude for each defined circle.
dens_accp_groups, dens_rej_groups = [], []
for c_x, c_y, r in cent_rad:
# Count stars within this circle, using stars that passed the
# magnitude filter.
clust_mags, N = [], 0
for i, (x, y) in enumerate(zip(*[x_mr, y_mr])):
if in_radius(c_x, c_y, r, x, y):
# This is used later on in the I/A filter function.
clust_mags.append(mag_mr[i])
N += 1
# Obtain density for this cluster, normalized to 1. for the frame's
# density.
clust_dens = (float(N) / (np.pi * (r ** 2))) / fr_dens_area
# If the overdensity has a density larger than a given fraction of
# the frame's density, keep (j=0). Else, discard (j=1).
if clust_dens > dens_frac:
dens_accp_groups.append([c_x, c_y, r, clust_dens, clust_mags])
else:
dens_rej_groups.append([c_x, c_y, r, clust_dens, clust_mags])
# Print percentage done.
milestones = print_perc(i, tot_sols, milestones)
# If the center coords and radii were read from file, obtain the min and
# max fraction of mean Voronoi area for these clusters.
if vor_flag != 'voronoi':
all_fracs = []
# Obtain, for each accepted cluster, its area/N divided by the mean
# Voronoi area value.
for g in dens_accp_groups:
cl_dens = (1. / fr_dens_area) * (1. / g[3])
all_fracs.append(cl_dens / mean_filt_area)
area_frac_range = [min(all_fracs), max(all_fracs)]
save_to_log(f_name, 'Obtained area range: [{:.2f}, {:.2f}]'.format(
*area_frac_range), 'a')
return dens_accp_groups, dens_rej_groups, area_frac_range
def consolidate(sets):
# http://rosettacode.org/wiki/Set_consolidation#Python:_Iterative
setlist = [s for s in sets if s]
tot_sols = len(setlist)
milestones = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
for i, s1 in enumerate(setlist):
if s1:
for s2 in setlist[i+1:]:
intersection = s1.intersection(s2)
if intersection:
s2.update(s1)
s1.clear()
s1 = s2
# Print percentage done.
milestones = print_perc(i, tot_sols, milestones)
return [s for s in setlist if s]
