def generateGlobalMatrix( date_from, date_to, list_all_station):
global trend_curve
list_station = list_all_station
#print list_station
meteo_size = len(list_station)
list_mean = []
for i in xrange(0,meteo_size):
#print list_station[i][0]
item = getTempOfAAtationFromTo( list_station[i][3],date_from, date_to)
list_all = zip(*item)
#3 for temp, 4 for humid, 5 for rain
list_temp = list_all[3]
#print list_temp
list_data.append(list_temp)
mean_item = (list_all[1][0],list_all[2][0], sum(list_all[3])/len(list_all[3]))
print mean_item
list_mean.append(mean_item)
f.write('MEAN: ' + str(list_mean) + '\n')
trend_curve = curveFitting(list_mean)
print trend_curve
#plotSurface(trend_curve, list_mean)
#print len(list_mean)
#print list_data[0]
#G = np.zeros([meteo_size, meteo_size])
for x in xrange(0,meteo_size):#x 0->97 is Meteo_id 1->98
for y in xrange(0,meteo_size):#0->97 is Meteo_id 1->98
#gen covariance for [x][y]
a = list_data[x]
b = list_data[y]
#cov =np.cov(a,b)[0][1]
semi = semivariance( a, b)
cov = covariance(a,b)
#print 'a: ', a
#print 'b: ',b
#print 'cov a b = ', str(cov)
#print covariance(a,b)
G[x][y]= semi
G_COV[x][y] = cov
'''
f.write('G_SEMI ----------------------------------------- \n')
for item in G:
f.write(str(item) + '\n')
f.write('G_COV ----------------------------------------- \n')
for item in G_COV:
f.write(str(item) + '\n')
'''
#print G[0:5,0:5]
return trend_curve
import numpy as np import matplotlib.pyplot as plt from get_data import getTempOfAAtationFromTo import os.path id1 = 15 id2 = 17 list_value = [] list_time =[] for x in xrange(2004,2015): print x date_from = str(x) + '-01-01' date_to = str(x) + '-12-30' list_1 = getTempOfAAtationFromTo(id1, date_from, date_to) list_2 = getTempOfAAtationFromTo(id2, date_from, date_to) size1= len(list_1) size2= len(list_2) print size1,' ', size2 if size1 == 0: continue list_1_all = zip(*list_1) list_1_temp = list_1_all[3] list_1_humid = list_1_all[4] list_1_rain = list_1_all[5] list_2_all = zip(*list_2) list_2_temp = list_2_all[3] list_2_humid = list_2_all[4] list_2_rain = list_2_all[5] #print list_1_temp #print list_1_humid
#date_to = '2015-01-01' # for all pairs date_from= '2011-01-01' date_to = '2011-01-20' x1 = np.linspace(0.0, 5.0) x2 = np.linspace(0.0, 2.0) y1 = np.cos(2 * np.pi * x1) * np.exp(-x1) y2 = np.cos(2 * np.pi * x2) query_date = '2011-01-02' list = getObservationList(query_date) size = len(list)# 98 Tram item0 = list[0] list_index = [] list_station_all = [] #98 tram for x in xrange(1,size+1): list_station_all.append(getTempOfAAtationFromTo(str(x), date_from, date_to)) #list_index.append(x) #list_h.append(haversine(item0[2], item0[1], list[x][2], list[x][1])) list_corr_tuple = [] list_value = [] list_h = [] 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]
