def calculateOneStation(id): item0 = list[id] station_0 = list_station_all[id] for x in xrange(1,size): print x item = list_station_all[x] leng = len(item) #print leng #cov =np.cov(station_0,item)[0][1] #correlate = np.corrcoef(station_0, item)[0,1] semi = semivariance(station_0, item) #list_corr_tuple.append((id, x, correlate)) list_corr_tuple.append((id, x, semi)) #print correlate #correlate = corr(station_0, item) #value = 0 #for y in xrange(0, leng): # value = value + abs(station_0[y] - item[y]) #value = value/leng #print value list_index.append(x) list_value.append(semi) list_h.append(haversine(item0[2], item0[1], list[x][2], list[x][1]))
def calculateOneStation(id):
item0 = list[id]
station_0 = list_station_all[id]
for x in xrange(1, size):
item = list_station_all[x]
correlate = np.corrcoef(station_0, item)[0, 1]
list_index.append(x)
list_value.append(correlate)
list_h.append(haversine(item0[2], item0[1], list[x][2], list[x][1]))
def generateDistanceMatrix(list_sta_by_area): #l = getAllObservation() l = list_sta_by_area meteo_size = len(l) D = np.zeros([meteo_size, meteo_size]) for x in xrange(0,meteo_size):#0->97 item1 = l[x] for y in xrange(0,meteo_size):#0->97 item2 = l[y] d = haversine(item1[2], item1[1], item2[2], item2[1]) D[x][y]= round(d) #print x, ' ', y, ' ', D[x][y] #time.sleep(0.5) #print d print D[0:10,0:10] return D
def calculateOneStation(id): item0 = list[id] station_0 = list_station_all[id] for x in xrange(1,size): item = list_station_all[x] correlate = np.corrcoef(station_0, item)[0,1] # print x # item = list_station_all[x] # leng = len(item) # print leng # value = 0 # for y in xrange(0, leng): # value = value + abs(station_0[y] - item[y]) # value = value/leng # print value list_index.append(x) list_value.append(correlate) list_h.append(haversine(item0[2], item0[1], list[x][2], list[x][1]))
def krigeOne(point, neighbor, list_all_station, list_data, G, D):
#print 'KRIGE ONE'
list_station = list_all_station
#Cal distances from current point to all others
item0 = (0, point[0], point[1])
meteo_size = len(list_station)
list_d = []
#cal all distance from point to others stations
for i in xrange(0,meteo_size):
#Cal distance one-one
item1 = list_station[i]
d = haversine(item0[2], item0[1] ,item1[2], item1[1])
newitem = (item1[0], d, item1[3], item1[1], item1[2])#id, distance, temp, lat, lon
list_d.append(newitem)
#Sort by distance, get n first nearest neighbors
sorted_list = sorted(list_d, key=itemgetter(1))
list_neighbor = sorted_list[:neighbor]
#print list_neighbor
'''
#Get areaid for each station
list_neighbor_with_areaid = []
for item in list_neighbor:
item1 = list_station[item[0]]
new_item = (item[0], item[1], getAreaIdFromPoint([item1[1], item1[2]]))
list_neighbor_with_areaid.append(new_item)
'''
K1 = np.zeros([neighbor, neighbor])
K = np.zeros([neighbor, neighbor])
k = np.zeros([neighbor, 1])
T = []# list of temp of neighbor station at interpolate time
string = ''
i = 0
for item in list_neighbor:
f.write('getting T\n')
#Nhiet do 1 thang cua tram item
#print len(list_data)
t = list_data[item[0]-1]
trendValue = getTrendValue(trend_curve, item[3], item[4])
#print trend_curve, trendValue
f.write(str(t) + '\n')
t_size = len(t)
T.append(t[t_size - 1] - trendValue)
j = 0
string = string + str(item[0])
#print item
#Area key = 0, because of we have only one model for all
a0, c0 = global_data[0][0], global_data[0][1]
h = item[1]#distance
k[i][0] = semiToCov( h, a0, c0)
for sub_item in list_neighbor:
string = string+' ' + str(sub_item[0])
#print i, j
K1[i][j] = G_COV[item[0]-1][sub_item[0]-1]
K [j][i] = K1[i][j]#Inverse of K1
j = j + 1
string = string + '\n'
i = i + 1
f.write('T: ' + str(T) + '\n')
f.write('neighbors list: \n' + string + '\n')
#print string
#print k
#print K
f.write('K: \n' + str(K) +' \n')
f.write('k: \n' + str(k) +' \n')
weight = np.dot(K,k)
f.write('ori-weight: \n' + str(weight) +' \n')
if sum(weight) == 0:
f.write('NOT RELATED\n')
weight[:] = 1 #assign the same weight for all elements
weight = weight/sum(weight)
krige = np.dot(T, weight)
f.write('nor-weight: \n' + str(weight) +' \n')
f.write('T: \n' + str(T)+' \n')
f.write('krige: ' + str(krige)+' \n')
#print krige
#print krige[0],' ',
#print k
weight = inverseMatrix(weight)
derivation = np.dot(weight, k)
f.write('derivation: ' + str(derivation[0][0])+' \n')
#time.sleep(2)
return krige[0], derivation[0][0]
'''