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 get_cent_rad(f_name, coords_flag, cr_file_cols, m_m_n, vor_flag, ra_cent,
dec_cent, acp_pts, acp_mags, pts_area, pts_vert,
mean_filt_area, area_frac_range):
'''
Assign a center and a radius for each overdensity/group identified.
Use an automatic algorithm based on a Voronoi diagram and grouping neighbor
stars, or a file with center coordinates and radii already stored.
'''
if vor_flag == 'voronoi':
# Apply maximum area filter.
pts_area_thres, mag_area_thres, vert_area_thres = area_filter(
acp_pts, acp_mags, pts_area, pts_vert, mean_filt_area,
area_frac_range)
save_to_log(f_name, "\nStars filtered by area range: {}".format(
len(pts_area_thres)), 'a')
save_to_log(f_name, 'Detect shared vertex', 'a')
all_groups = shared_vertex(vert_area_thres)
# This is a costly process.
save_to_log(f_name, 'Assign points to groups', 'a')
neighbors, neig_mags = group_stars(pts_area_thres, mag_area_thres,
vert_area_thres, all_groups)
save_to_log(f_name, 'Groups found: {}'.format(len(neighbors)), 'a')
# Keep only those groups with a higher number of members than
# min_neighbors.
pts_neighbors, mags_neighbors = neighbors_filter(neighbors, neig_mags,
m_m_n)
save_to_log(f_name,
'\nGroups filtered by number of members: {}'.format(
len(pts_neighbors)), 'a')
# Check if at least one group was defined with the minimum
# required number of neighbors.
if pts_neighbors:
# Obtain center and radius for each overdensity identified.
cent_rad = []
for g in pts_neighbors:
pts = np.array(zip(*g))
mean_pts = np.mean(pts, 0)
# Translate towards origin
pts -= mean_pts
result = make_circle(pts)
# Move back to correct position.
c_r = (result[0] + mean_pts[0], result[1] + mean_pts[1],
result[2])
cent_rad.append([c_r[0], c_r[1], c_r[2]])
else:
cent_rad = []
save_to_log(f_name, "No groups found with members in range {}".
format(m_m_n), 'a')
else:
# Get data from file.
cent_rad = get_cents_rad_file(coords_flag, vor_flag, cr_file_cols,
ra_cent, dec_cent)
pts_area_thres, mag_area_thres = [[0., 0.], [0., 0.]], [0.]
pts_neighbors, mags_neighbors = [[[0.], [0.]]], [0]
return cent_rad, pts_area_thres, mag_area_thres, pts_neighbors,\
mags_neighbors
