def add_missing_station(self, cnn, clusters, stations, stn_diff):
# this method does not update the labels because they are not used
# labels are only used to tie station
# find the station objects for the elements listed in stn_diff
stnobj_diff = [
s for s in stations
if s.NetworkCode + '.' + s.StationCode in stn_diff
]
tqdm.write(' -- Adding missing stations %s' % ' '.join(stn_diff))
if len(clusters['centroids']) == 1:
# single station network, just add the missing station
clusters['stations'][0] += stnobj_diff
clusters['labels'] = np.zeros(len(clusters['stations'][0]))
# because a station was added, delete the gamit_stats record to force reprocessing
cnn.delete('gamit_stats',
Project=self.name,
Year=self.date.year,
DOY=self.date.doy,
subnet=0)
cnn.delete('gamit_subnets',
Project=self.name,
Year=self.date.year,
DOY=self.date.doy,
subnet=0)
else:
for stn in stnobj_diff:
# find the closest centroid to this station
xyz = np.zeros((1, 3))
xyz[0] = np.array([stn.X, stn.Y, stn.Z])
sd = distance.cdist(xyz, clusters['centroids']) / 1e3
for i in range(len(clusters['centroids'])):
s = smallestN_indices(sd, len(clusters['centroids']))[i]
if sd[s[0], s[1]] <= MAX_DIST:
# can add the station to this sub-network
clusters['stations'][i].append(stn)
# because a station was added, delete the gamit_stats and subnets record to force reprocessing
cnn.delete('gamit_stats',
Project=self.name,
Year=self.date.year,
DOY=self.date.doy,
subnet=i + 1)
cnn.delete('gamit_subnets',
Project=self.name,
Year=self.date.year,
DOY=self.date.doy,
subnet=i + 1)
return clusters
def add_missing_station(self, cnn, clusters, add_station):
# this method does not update the labels because they are not used
# labels are only used to tie station
if len(clusters['centroids']) == 1:
# single station network, just add the missing station
clusters['stations'][0].append(add_station)
clusters['labels'] = np.zeros(len(clusters['stations'][0]))
# because a station was added, delete the gamit_stats record to force reprocessing
cnn.delete('gamit_stats', Project=self.name, Year=self.date.year, DOY=self.date.doy, subnet=0)
cnn.delete('gamit_subnets', Project=self.name, Year=self.date.year, DOY=self.date.doy, subnet=0)
tqdm.write(' -- %s was not originally in the processing, will be added to sub-network %s00'
% (add_station.netstn, self.org))
else:
# find the closest centroid to this station
xyz = np.zeros((1, 3))
xyz[0] = np.array([add_station.X, add_station.Y, add_station.Z])
sd = distance.cdist(xyz, clusters['centroids']) / 1e3
for i in range(len(clusters['centroids'])):
s = smallestN_indices(sd, len(clusters['centroids']))[i]
if sd[s[0], s[1]] <= MAX_DIST:
# can add the station to this sub-network
clusters['stations'][i].append(add_station)
# because a station was added, delete the gamit_stats and subnets record to force reprocessing
cnn.delete('gamit_stats', Project=self.name, Year=self.date.year, DOY=self.date.doy,
subnet=i + 1)
cnn.delete('gamit_subnets', Project=self.name, Year=self.date.year, DOY=self.date.doy,
subnet=i + 1)
tqdm.write(' -- %s was not originally in the processing, will be added to sub-network %s%02i'
% (add_station.netstn, self.org, i + 1))
break
return clusters
def tie_subnetworks(points, clusters, stations):
# get centroids and lables
centroids = clusters['centroids']
labels = clusters['labels']
# calculate distance between centroids
dist = distance.cdist(centroids, centroids) / 1e3
# variable to keep track of the number of ties for each subnetwork
ties = np.zeros((centroids.shape[0], centroids.shape[0]))
# make distance to self centroid infinite
dist[dist == 0] = np.inf
ties_vector = [[] for _ in range(len(centroids))]
for c in range(len(centroids)):
# get the closest three centroids to find the closest subnetworks
neighbors = np.argsort(dist[:, c])
# check that there are less than 3 ties
if sum(ties[c, :]) < 3:
# for each neighbor
for n in neighbors:
# only enter if not already tied AND ties of n < 3, unless ties c < 2 AND n != c
if ties[n, c] == 0 and (np.sum(ties[n, :]) < 3 or np.sum(ties[c, :]) < 2) and n != c:
# to link to this neighbor, it has to have less then 3 ties. Otherwise continue to next
# print 'working on net ' + str(c) + ' - ' + str(n)
# get all stations from current subnet and dist to each station of neighbor n
sd = distance.cdist(points[labels == c], points[labels == n]) / 1e3
# find the 4 closest stations
tie_c = []
tie_n = []
for i in range(len(sd)):
s = smallestN_indices(sd, len(sd))[i]
# ONLY ALLOW TIES BETWEEN MIN_DIST AND MAX_DIST. Also, tie stations should be > MIN_DIST
# from stations on the other subnetwork
if MIN_DIST <= sd[s[0], s[1]] <= MAX_DIST and np.all(sd[s[0], :] > MIN_DIST) \
and np.all(sd[:, s[1]] > MIN_DIST):
# print ' pair: ' + str([st for la, st in zip(labels.tolist(), stations)
# if la == n][s[1]])\
# + ' - ' + str([st for la, st in zip(labels.tolist(),
# stations) if la == c][s[0]]) + ' ( %.1f' % sd[s[0], s[1]] + ' km)'
# station objects:
# find the stations that have labels == (c, n) and from that subset, find the row (c)
# and col (n) obtained by smallestN_indices
tie_c += [[st for la, st in zip(labels.tolist(), stations) if la == n][s[1]]]
tie_n += [[st for la, st in zip(labels.tolist(), stations) if la == c][s[0]]]
# make these distances infinite to avoid selecting again
sd[s[0], :] = np.inf
sd[:, s[1]] = np.inf
if len(tie_c) == 4:
break
# if successfully added 3 or more tie stations, then declared it tied
if len(tie_c) >= 3:
# add a tie to c and n subnets
ties[n, c] += 1
ties[c, n] += 1
ties_vector[c] += tie_c
ties_vector[n] += tie_n
# print ties
else:
# print ' no suitable ties found between these two networks'
pass
if sum(ties[c, :]) >= 3:
# if current subnet already has 3 ties, continue to next one
break
# return the vector with the station objects representing the ties
return ties_vector