Ejemplo n.º 1
0
def plot_period(list_h,list_value, area_id):
	list_period_value= []
	list_period_h = []
	d = 10
	size_h = len(list_h)
	for x in xrange(0,int(max(list_h)//10)):
		mean = 0
		total = 0
		for y in xrange(0,size_h):
			if (list_h[y] > d*x) and (list_h[y] < d*(x+1)):
				total = total +1
				mean = mean + list_value[y]
		if total >0:
			list_period_value.append(mean/total)
			list_period_h.append(d*x)
	plt.subplot(3, 2, 1)
	plt.plot(list_h, list_value, 'r.')
	plt.title('A tale of x subplots')
	plt.ylabel('Semi-variogram')

	plt.subplot(3, 2, 2)
	plt.plot(list_period_h, list_period_value, 'r.')
	#plt.plot(list_h, list_value, 'r.')
	plt.xlabel('Distance (km)')
	plt.ylabel('Semi-variogram PERIOD')

	#spherical
  	a0,c0, mse_sph = opt('spherical', list_period_h, list_period_value)
  	#a0,c0, mse_sph = opt('spherical', list_h, list_value)
  	print 'a0=',a0, ' c0=', c0, ' mse_sph = ', mse_sph
	plt.subplot(3, 2, 5)
	list_spherical = []
	list_h = sorted(list_h)
	for h in list_h :
		list_spherical.append(spherical( h, a0, c0))
		#if spherical( h, a0, c0) != c0:
		#	print spherical( h, a0, c0)
	plt.plot(list_h, list_spherical, 'r-')
	plt.xlabel('Distance (km)')
	plt.ylabel('Semi-variogram- SPHERICAL')

	#plt.savefig(os.path.splitext(os.path.basename('temp_semi_all_station_thang_2'))[0] + '.png')
	sph = [a0, c0]
	global_data[area_id] = sph
	if LOG:
		plt.show()
Ejemplo n.º 2
0
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
	K1 = np.zeros([neighbor, neighbor])
	K = np.zeros([neighbor, neighbor])
	k = np.zeros([neighbor, 1])
	k_semi = np.zeros([neighbor, 1])
	T = []# list of temp of neighbor station at interpolate time
	string = ''
	i = 0 
	for  item in list_neighbor:
		if LOG:
			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
		if LOG:
			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)
		k_semi[i][0] = spherical(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
	if LOG:
		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)
	if LOG:
		f.write('ori-weight: \n' + str(weight) +' \n')
	if sum(weight) == 0:
		if LOG:
			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_semi)
	f.write('derivation: ' + str(derivation[0][0])+' \n')	
	#time.sleep(2)
	return krige[0], derivation[0][0]