def dataRefresh(self):
"""数据更新按钮事件，重新获取数据并处理，并更新页面显示数据"""
getData.init()
getData.get_data()
dataProcess()
# 取得最新更新日期
with open('cache.txt') as f:
last = f.readlines()[0]
df = pd.read_csv('data/total.csv')
earlyData = df.iloc[len(df) - 2, :] # 前一天数据
lastData = df.iloc[len(df) - 1, :] # 最新数据
# 最新现存确诊
lastRemain = lastData[1] - lastData[2] - lastData[3] + lastData[4] - lastData[5] - lastData[6]
# 前一天现存确诊
earlyRemain = earlyData[1] - earlyData[2] - earlyData[3] + earlyData[4] - earlyData[5] - earlyData[6]
# 更新显示数据
self.lastRemainLabel.config(text=f'现存确诊：{lastRemain}')
self.lastDateLabel.config(text=f'最新数据更新日期：{last}')
self.lastConfirmLabel.config(text=f'确诊：{lastData[1] + lastData[4]}')
self.lastCuredLabel.config(text=f'治愈：{lastData[2] + lastData[5]}')
self.lastDeadLabel.config(text=f'死亡：{lastData[3] + lastData[6]}')
if lastRemain - earlyRemain > 0:
self.remainIncrease.config(text=f'现存确诊较昨日增加：{lastRemain - earlyRemain}')
else:
self.remainIncrease.config(text=f'现存确诊较昨日减少：{earlyRemain - lastRemain}')
self.ConfirmIncrease.config(text=f'确诊较昨日增加：{lastData[1] + lastData[4] - earlyData[1] - earlyData[4]}')
self.CuredIncrease.config(text=f'治愈较昨日增加：{lastData[2] + lastData[5] - earlyData[2] - earlyData[5]}')
self.DeadIncrease.config(text=f'死亡较昨日增加：{lastData[3] + lastData[6] - earlyData[3] - earlyData[6]}')
def main():
# One spark session to join them all
conf = SparkConf()
conf.set('spark.executorEnv.PGHOST', os.environ['PGHOST'])
conf.set('spark.executorEnv.PGUSER', os.environ['PGUSER'])
conf.set('spark.executorEnv.PGPASSWORD', os.environ['PGPASSWORD'])
spark = SparkSession.builder \
.appName("timeJoin") \
.config(conf=conf) \
.getOrCreate()
spark.sparkContext.addPyFile("postgres.py")
spark.sparkContext.addPyFile("globalVar.py")
spark.sparkContext.addPyFile("getTaxiFields.py")
spark.sparkContext.addPyFile("datetimeTools.py")
spark.sparkContext.addPyFile("appendWeatherData.py")
spark.sparkContext.addPyFile("dataProcessing.py")
# Years ond months of interest: n-years back from current year
nOfYears = glb('nOfPassYears')
currYear = datetime.now().year
yearList = [str(cnt + currYear - nOfYears + 1) for cnt in range(nOfYears)]
months = [str(val + 1).zfill(2) for val in range(12)]
# Create an object for every taxi data file
# Make sure to remove object if file does not exist
ptr = 0
dataObj = []
for yr in yearList:
for mn in months:
dataObj.append(dataProcess(yr, mn))
if not dataObj[ptr].hasData():
del dataObj[ptr]
else:
ptr = ptr + 1
# Start calling methods in dataProcessing.py
for dProp in dataObj:
dProp.readData(spark) # Read data
dProp.addTimestamp() # Convert string to timestamp
dProp.addWthrStationID() # Add weather station ID
dProp.joinTables(spark) # Main join process
dProp.writeToPostgres('yellow') # Write to DB with prefix 'yellow'
#dProp.printCheck()
spark.stop()
import pandas as pd import numpy as np from dataProcessing import loadData, dataProcess from plotting import TrainingPlot from Models import createLSTMModel train, test, submission, items, itemCategory, shops = loadData() dataset = dataProcess(train, test) # split into training set and test set X = np.expand_dims(dataset.values[:, :-1], axis=2) y = dataset.values[:, -1:] X_test = np.expand_dims(dataset.values[:, 1:], axis=2) # normalised_X = dataScaling(X) # normalised_Y = dataScaling(y) # normalised_X = torch.FloatTensor(normalised_X).view(-1) # normalised_Y = torch.FloatTensor(normalised_Y).view(-1) # timestep = 30 # normalisedTrainData_timebased = createTimeSeries(normalised_X, normalised_Y, timestep) # print(normalisedTrainData_timebased[:5]) filename = 'output/training_plot.jpg' plot_losses = TrainingPlot() model = createLSTMModel()
key = best_left_word_dict[key][0]
return result
def saveResult(self,result_list,path):
for i in range(len(result_list)):
with open(path,'a') as f:
f.write(result_list[i])
f.write('\n')
if __name__ == '__main__':
sentence_list = dataProcessing.dataProcess().getTestData()
for sentence in sentence_list:
print(Segment.sentenceCut(sentence))
_, words_dict = dataProcessing.dataProcess().readWordsDict(
config.wordList_1998_path)
words_pair_dict = dataProcessing.dataProcess().readWordsPairDict(
config.wordPairList_path_1998)
result_list = []
sentence = '欢乐热闹的气氛已悄悄降临'
print(Segment.getChineseSegment(sentence,words_dict,words_pair_dict))
for sentences in sentence_list:
sentence_cut = Segment.sentenceCut(sentences)