all_series, all_stations = ushcn_io.get_ushcn_data(params)
if not hasattr(params, 'stations'):
station_ids = sorted(random.sample(all_stations.keys(), params.nstns))
else:
station_ids = sorted(params.stations)
stations = dict(zip(station_ids, [all_stations[s] for s in station_ids]))
series_list = [all_series[station] for station in station_ids]
series = dict(zip([s.coop_id for s in series_list], series_list))
##
series_copy = copy.deepcopy(series)
##
n = Network(stations, series, name=params.project)
print n
##########################################################################
print "Analyzing geographic network neighborhoods"
all_neighbors = dict()
stations_list = n.stations.values()
for station in stations_list:
print station.coop_id
print "...computing neighbor distances"
neighbors = find_neighborhood(station, stations_list, **params)
all_neighbors[station.coop_id] = neighbors
all_series, all_stations = ushcn_io.get_ushcn_data(params)
if not params.stations:
station_ids = sorted(random.sample(all_stations.keys(), params.nstns))
else:
station_ids = sorted(params.stations)
stations = dict(zip(station_ids, [all_stations[s] for s in station_ids]))
series_list = [all_series[station] for station in station_ids]
series = dict(zip([s.coop_id for s in series_list], series_list))
##
series_copy = copy.deepcopy(series)
##
n = Network(stations, series, name=params.project)
print n
##########################################################################
## PRE PROCESS THE NETWORK
preprocess(n, **params)
##########################################################################
## BEGIN SPLITMERGE EXPERIMENTS ##if os.path.exists("corr_out"):
hom_params = dict(nstns=params.nstns,
numsrt=params.numsrt,
numcorr=params.numcorr,
beg_year=params.begyr,
end_year=params.endyr,
data_src=params.data_src,