import re
import numpy as np
import pandas as pd

def isaligned(key1, key2):
# alignment rule
rule = [['temp', 'tempreture'], ['humidity', 'rh']]
if key1 == key2: return True
tmp = [key1, key2]
tmp.sort()
if tmp in rule: return True
return False

def featInData(feat, dataset):
for itm in dataset:
   if isaligned(feat, itm): return True, itm
return False, ''

# the confidence of correctness of website 1 and website2
def weatherfuse(data1, data2, confidence1, confidence2):
fusedata = {}
i = 0
j = 0
for feat1 in data1:
   for feat2 in data2:
         if isaligned(feat1, feat2):
            fusedata[feat1] = []
         else:
            fusedata[feat1] = []
for feat2 in data2:
   if not featInData(feat2, data1):
         fusedata[feat2] = []
while i != len(data1) and j != len(data2):
   time1 = int(data1['time'][i].split(':')[0]) * 60 + int(data1['time'][i].split(':')[1])
   time2 = int(data2['time'][j].split(':')[0]) * 60 + int(data2['time'][j].split(':')[1])
   onedata = []
   # fuse data1 and data2 using (data1*conf1+data2*conf2)/(conf1+conf2) if their time stamps are the same
   if time1 == time2:
         for feat in fusedata:
            if feat == 'time': fusedata[feat].append(data1[feat][i]); continue
            flag, alignfeat = featInData(feat, data2)
            if feat in data1 and flag:
               fusedata[feat].append(
                     round((float(data1[feat][i]) * confidence1 + float(data2[alignfeat][j]) * confidence2) / (
                        confidence1 + confidence2), 2))
            elif feat in data1:
               fusedata[feat].append(data1[feat][i])
            else:
               fusedata[feat].append(data2[feat][j])

Project 1 - Search Purpose: One claim is that for sufficiently lar...