centre = d['cop1']
else:
centre = d['cop2']
cop = d['cops']
distances = np.array([physUtils.distance(centre, pos) for pos in cop])
if max(distances) < 5.0:
print("distance criteria failed")
continue
distmask = np.array([distance < 5.0 and distance > 1.5 for distance in
distances])
distances = distances[distmask]
cop = cop[distmask]
directions = np.array([physUtils.sphericalCoordinates(pos - centre) for
pos in cop])
fitdata = clustering.precomputeDistances(directions)
meansep = np.mean([min(x[x>0]) for x in fitdata])
neighbours= NearestNeighbors(n_neighbors=5,
metric='precomputed').fit(fitdata)
# neighbours= NearestNeighbors(n_neighbors=5,
# metric='precomputed').fit(np.divide(fitdata,
# meansep))
k_distances, indices = neighbours.kneighbors(n_neighbors=4, return_distance=True)
four_distances = k_distances[:,3]
plt.plot(range(0, len(four_distances)), sorted(four_distances,
reverse=True), alpha=0.1, c='k')
centre = bestLG['cop1']
else:
MWindex = bestLG['ind2']
centre = bestLG['cop2']
cop = bestLG['cops']
distances = np.array([physUtils.distance(centre, pos) for pos in cop])
distmask = np.array(
[distance < maxdist and distance > mindist for distance in distances])
distances = distances[distmask]
relvel = (bestLG['vels'] - bestLG['vels'][MWindex])[distmask]
cop = cop[distmask]
directions = np.array(
[physUtils.sphericalCoordinates(pos - centre) for pos in cop])
radvel = np.array([
physUtils.velocityComponents(relvel[j], cop[j] - centre)[0]
for j in range(len(cop))
])
fitdata = clustering.precomputeDistances(directions)
db = DBSCAN(
eps=eps,
min_samples=ms,
metric='precomputed',
).fit(fitdata)
labels = db.labels_
unique_labels = set(labels)
clusters = len(unique_labels) - (1 if -1 in labels else 0)
if d['mass1'] < d['mass2']:
centre = d['cop1']
else:
centre = d['cop2']
cop = d['cops']
relvel = d['vels'] - d['centrevel']
distances = np.array([physUtils.distance(centre, pos) for pos in cop])
distmask = np.array(
[distance > 1.5 and distance < 5.0 for distance in distances])
distances = distances[distmask]
directions = np.array([
physUtils.sphericalCoordinates(pos - centre) for pos in cop[distmask]
])
mass = d['masses'][distmask]
radvel = np.array([
physUtils.velocityComponents(relvel[j], cop[j] - centre)[0]
for j in range(len(cop[distmask]))
])
fit = findBestHubbleflow(distances, radvel)[0]
for j in range(len(radvel)):
radvel[j] = radvel[j] - (fit[0] * distances[j] + fit[1])
rc('font', **{'family': 'serif', 'serif': ['Palatino']})
rc('text', usetex=True)