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)