def plotPredictError():
filePart0 = ["F:\\one\\predict\\GM/wc", "F:\\one\\final\\inmin/workload"]
wrFile = WRFile()
filePart1 = [".xlsx", "inmin.xlsx"]
x = []
x_label = []
k = 0
for i in range(53, 61):
predict = wrFile.readDataFromExcel(filePath=filePart0[0] + str(i) +
filePart1[0])
data = wrFile.readDataFromExcel(filePath=filePart0[1] + str(i) +
filePart1[1])
dis = (predict - data) / data * 100
over = []
under = []
for j in range(len(dis)):
if dis[j] > 0:
over.append(dis[j])
else:
under.append(-dis[j])
#x.insert(k,over)
k += 1
x.insert(k, under)
x_label.append(str(i) + "-o")
#x_label.append(str(i)+"-u")
plt.boxplot(x=x,
labels=(53, 54, 55, 56, 57, 58, 59, 60),
notch=True,
patch_artist=True)
plt.grid(True)
plt.title("analyze the under-prediction error of Grey Model")
def learnChangeRate(self,day):
wrFile = WRFile()
day = int(day)
windows = 3
part0 = "F:\\one\\predict\\traindata/workload"
part1 = "inmin.xlsx"
if day <=53:
data = wrFile.readDataFromExcel(part0+str(53)+part1)
self.changeRate = np.zeros(len(data)-1)
elif day>53:
if (day-windows)<53:
windows = day-53+1
else:
windows+=1
cr = np.zeros(1440-1)
#print("day is",day,"windows is",windows)
for i in range(1,windows):
data = wrFile.readDataFromExcel(part0+str(day-i)+part1)
cr += np.diff(data)/data[:len(data)-1]
self.changeRate = cr/(windows-1)
#print("windows is",windows-1)
#plt.plot(np.arange(len(self.changeRate)),self.changeRate)
def lowPearson():
filePart0 = [
"F:\\one\\predict\\GM/wc", "F:\\one\\final\\inmin/workload",
"F:\\one\\final\inmin\\knots/workload"
]
wrFile = WRFile()
filePart1 = [".xlsx", "inmin.xlsx", "inmin_knots.xlsx"]
Hours = [[15, 17, 19, 21], [15, 17, 21], [17], [14, 22], [14, 22],
[17, 19, 22], [14, 15, 16, 20, 22], [14, 16, 21]]
CRinLowP = []
PRinLowP = []
x = []
DatainLowP = np.zeros(60 * 8 * 24)
PredinLowP = np.zeros(60 * 8 * 24)
for i in range(53, 61):
day = i - 53
predict = wrFile.readDataFromExcel(filePath=filePart0[0] + str(i) +
filePart1[0])
data = wrFile.readDataFromExcel(filePath=filePart0[1] + str(i) +
filePart1[1])
CR = np.diff(data) / data[:len(data) - 1] * 100 #计算负载的变化率
PR = (predict - data) / data * 100 #计算预测精度
PR = PR[1:]
#定位到指定时刻
h = 0
while h < len(Hours[day]):
time = (Hours[day])[h] * 60
CRinLowP.extend(CR[time:time + 60])
PRinLowP.extend(PR[time:time + 60])
#DatainLowP[time:time+60] = data[time:time+60]
#PredinLowP[day*60*24+time:day*60*24+time+60] = predict[time:time+60]
for minu in range(60):
x.append(str(i) + "-" + str((Hours[day])[h]) + ":" + str(minu))
h += 1
plt.plot(np.arange(len(CRinLowP)), CRinLowP, "m-")
plt.plot(np.arange(len(PRinLowP)), PRinLowP, "g*")
#plt.plot(x,DatainLowP)
#plt.plot(x,PredinLowP)
plt.legend(["CR", "PR"])
plt.grid(True)
plt.title(
"changement rate of workloads and prediction precision within low Pearson"
)
return PredinLowP
'''
def analyzePandR():
filePart0 = [
"F:\\one\\predict\\GM/wc", "F:\\one\\final\\inmin/workload",
"F:\\one\\final\inmin\\knots/workload"
]
wrFile = WRFile()
filePart1 = [".xlsx", "inmin.xlsx", "inmin_knots.xlsx"]
p_coefficient = []
for i in range(53, 61):
predict = wrFile.readDataFromExcel(filePath=filePart0[0] + str(i) +
filePart1[0])
#result = TestGreyModel(periods = 4 ,filePath = filePart0[2]+str(i)+filePart1[2])
data = wrFile.readDataFromExcel(filePath=filePart0[1] + str(i) +
filePart1[1])
print("day ", i)
p_coefficient.extend(analyzeChangeRateAndPrecition(data, predict))
def anaylzeDelayDistribution():
wrFile = WRFile()
#process_type = "SQ"
filePath_head = "D:\\cloudsim\\log\\"+process_type+"_q"+"/"+process_type+"_q"
QL_delay_result =[]
for QL in [55,60,66,70,75]:#以文件为单位进行分析
delay_result = np.zeros(6)
filePath = filePath_head+str(QL)+"/Cloudlet/"+process_type+"53cloudlet.xlsx"
print(filePath)
delay = wrFile.readDataFromExcel(filePath = filePath,sheet_name = "sheet",min_cols = 9,max_cols = 9)
for element in delay: #分析每个请求的延迟情况
if element==-1:
continue
elif element<=0.11:
delay_result[0]+=1
elif element<=0.22:
delay_result[1]+=1
elif element<=0.33:
delay_result[2]+=1
elif element<=0.44:
delay_result[3]+=1
elif element<=0.55:
delay_result[4]+=1
else:
delay_result[5]+=1
delay_result[0] = round(1.0*delay_result[0]/len(delay),3)
delay_result[1] = round(1.0*delay_result[1]/len(delay),3)
delay_result[2] = round(1.0*delay_result[2]/len(delay),3)
delay_result[3] = round(1.0*delay_result[3]/len(delay),3)
delay_result[4] = round(1.0*delay_result[4]/len(delay),3)
delay_result[5] = round(1.0*delay_result[5]/len(delay),3)
delay_result = delay_result.tolist()
QL_delay_result.append(delay_result)
return QL_delay_result
def caculateSumOfSeperateDelay(filePath, cols):
wrFile = WRFile()
data = np.array(
wrFile.readDataFromExcel(filePath=filePath,
min_cols=cols,
max_cols=cols))
data_sum = np.sum(data)
return data_sum
def __init__(self):
filePath = "F:/test/workload.xlsx"
wrFile = WRFile()
self.workload = wrFile.readDataFromExcel(filePath=filePath,
sheet_name="1")
data = self.evaluateBurst()
data = data / np.max(data)
wrFile.writeDataIntoExcel(data=data, filePath="F:/test/avgsampEn.xlsx")
def analyzeCompareResult(q):
wrFile = WRFile()
#fileKind = "F:\\data\\experiment/Delay_SQ_q"
fileKind = "F:\\data\\experiment/Delay_q"
x = wrFile.readDataFromExcel(filePath=fileKind + str(q) + ".xlsx",
min_cols=1,
max_cols=1)
y = wrFile.readDataFromExcel(filePath=fileKind + str(q) + ".xlsx",
min_cols=2,
max_cols=2)
z = wrFile.readDataFromExcel(filePath=fileKind + str(q) + ".xlsx",
min_cols=3,
max_cols=3)
a = wrFile.readDataFromExcel(filePath=fileKind + str(q) + ".xlsx",
min_cols=5,
max_cols=5)
#plot4DSeperate(x,y,z,a,q)
PlotData().plot4D(x, y, z, a, q)
def analyzePredictionPrecision():
objFileName = "svr_rbf"
precision = []# 列表头分别为 ratio,max,mean
wrFile = WRFile()
objFilePath = "F:\\FIFA\\predict\\"+objFileName+"/precision_over.xlsx"
predict = wrFile.readDataFromExcel(objFilePath,min_cols = 1,max_cols = 3)
predict = predict.reshape(3,8)
result = [np.average(predict[0]),np.average(predict[1]),np.average(predict[2])]
wrFile.writeDataIntoExcel(data = result,filePath = "F:\\FIFA\\predict\\"+objFileName+"/precision_over_evaluate.xlsx" )
def useCubicSplineFitData():
#读取数据
wrFile = WRFile()
filePath = "F:/one/final/spline/workload51inmin_knots.xlsx"
yaxis = np.array(wrFile.readDataFromExcel(filePath))
period = 5
#使用cubic spline进行拟合
data_volume = len(yaxis)
xaxis = np.arange(0,data_volume,1)*period
s = UnivariateSpline(x = xaxis,y = yaxis)
#使用spline的拟合结果对每分钟的并发量进行预测，并获取残差
y = wrFile.readDataFromExcel(filePath = "F:/one/final/inmin/workload51inmin.xlsx")
xnew = np.arange(0,len(y),1)
ynew = s(xnew)
residual = ynew-y
plt.plot(xnew,residual)
def plotData(method):
wrFile = WRFile()
data_filePath = "F:\\FIFA\\predict\\traindata/day53_60inmin.xlsx"
data = wrFile.readDataFromExcel(filePath = data_filePath)
predict_filePath = "F:\\FIFA\\predict\\"+method+"/wc53_60.xlsx"
if method=="lr":
method = "LinearRegression"
elif method=="gbdt":
method = "Gradient Boosting Decision Tree"
elif method=="svr_lr":
method = "Support Vector Regression-linear"
elif method=="svr_rbf":
method = "Support Vector Regression-rbf"
predict = np.floor(np.array(wrFile.readDataFromExcel(filePath = predict_filePath)))
plt.plot(np.arange(len(data)),data,"m",LineWidth=2)
plt.plot(np.arange(len(predict)),predict,"g",LineWidth=2)
plt.title("prediction results of "+method,fontsize = 20)
plt.xlabel("time(minute)",fontsize= 18)
plt.ylabel("workload(times)",fontsize=18)
plt.legend(["real","predict"],loc = "upper left",fontsize=18)
def TestGreyModel(periods, filePath):
grey = GreyForecastModel()
wrFile = WRFile()
data = wrFile.readDataFromExcel(filePath=filePath)
predict = []
predict[0:periods - 1] = data[0:periods - 1]
for i in range(periods - 1, len(data)):
#we set n as 4
x = data[i - (periods - 1):i + 1]
#print(x)
predict.append(grey.predictValue(x))
return [data, predict]
def divideSpace(q):
#以数据为中心，然后把每个点定位到一个立方体中。立方体用一个三维数组表示。
better = np.zeros(q**3).reshape(q, q, q)
worse = np.zeros(q**3).reshape(q, q, q)
equal = np.zeros(q**3).reshape(q, q, q)
wrFile = WRFile()
#用正方体左下角的点代替整个正方体
fileKind = "F:\\data\\experiment/Delay_q"
#fileKind = "F:\\data\\experiment/Delay_q"
x = wrFile.readDataFromExcel(filePath=fileKind + str(q) + ".xlsx",
min_cols=1,
max_cols=1)
y = wrFile.readDataFromExcel(filePath=fileKind + str(q) + ".xlsx",
min_cols=2,
max_cols=2)
z = wrFile.readDataFromExcel(filePath=fileKind + str(q) + ".xlsx",
min_cols=3,
max_cols=3)
a = wrFile.readDataFromExcel(filePath=fileKind + str(q) + ".xlsx",
min_cols=5,
max_cols=5)
for i in range(len(x)):
c = [x[i], y[i], z[i]]
c = moveDown(c)
result = a[i]
if result == 0:
equal[c[0]][c[1]][c[2]] += 1
elif result == 1:
better[c[0]][c[1]][c[2]] += 1
else:
worse[c[0]][c[1]][c[2]] += 1
#print("better is",better)
#print("worse is",worse)
#print("equal is",equal)
analyzeResult(q, better, worse)
def analyzePrecision():
fileList = ["RGM/","FGM/","FFGM/","GM/","MGM/","MRGM/"]
part0 = "F:\\one\\predict\\"
part1 = ".xlsx"
part2 = "F:\\one\\predict\\traindata/workload"
part3 = "inmin.xlsx"
wrFile = WRFile()
k = 0
title = ["MAE","EVS"]
rows = 9
cols = 3
while k<6:
result = []
for day in range(53,61):
data = wrFile.readDataFromExcel(filePath = part2+str(day)+part3)
predict = wrFile.readDataFromExcel(filePath = part0+fileList[k]+"wc"+str(day)+part1)
MAE = mean_absolute_error(y_true = data, y_pred = predict)
EVS =explained_variance_score(y_true = data, y_pred = predict)
result.append({"MAE":MAE,"EVS":EVS})
wrFile.writeDictIntoTable(data = result,filePath= part0+fileList[k]+"precision.docx" ,title = title ,cols = cols,rows = rows)
k+=1
def analyzeStats():
qList = [2, 3, 4, 5, 6]
wrFile = WRFile()
result = []
for q in qList:
fileName = "F:\data\experiment/Delay_SQ_q" + str(q) + ".xlsx"
data = wrFile.readDataFromExcel(filePath=fileName,
sheet_name="1",
min_cols=4,
max_cols=4)
r = getStatisticAttribute(data)
result.append(r)
#print("ATBM is",result)
wrFile.writeDataIntoExcel(
result, filePath="F:\data\experiment/Delay_SQ_stats.xlsx")
def splitOnlineData():
shopName = ["京东商城", "天猫商城", "亚马逊商城", "淘宝商城"]
#读出所有数据，然后都存放到
# 1.生成日期数据
wrFile = WRFile()
data = wrFile.readDataFromExcel(filePath="F:\\data\\orginal\\online.xlsx",
sheet_name="online",
cols=3)
#2. 对数据进行切片
jingdong = []
tmall = []
amazon = []
taobao = []
for i in range(len(data)):
count = i % 4
if count == 0:
if data[i] > 0:
jingdong.append(data[i])
else:
jingdong.append(data[i - 4])
elif count == 1:
if data[i] > 0:
tmall.append(data[i])
else:
tmall.append(data[i - 4])
elif count == 2:
if data[i] > 0:
amazon.append(data[i])
else:
amazon.append(data[i - 4])
elif count == 3:
if data[i] > 0:
taobao.append(data[i])
else:
taobao.append(data[i - 4])
#3.将数据写入excel
fileRoot = "F:\\data\\online/"
filesuffix = ".xlsx"
wrFile.writeDataIntoExcel(data=jingdong,
filePath=fileRoot + "jingdong" + filesuffix)
wrFile.writeDataIntoExcel(data=tmall,
filePath=fileRoot + "tmall" + filesuffix)
wrFile.writeDataIntoExcel(data=amazon,
filePath=fileRoot + "amazon" + filesuffix)
wrFile.writeDataIntoExcel(data=taobao,
filePath=fileRoot + "taobao" + filesuffix)
def testGM():
wrFile = WRFile()
part0 = "F:\\one\\predict\\traindata/workload"
part1 = "inmin.xlsx"
data = wrFile.readDataFromExcel(filePath=part0 + str(53) + part1, cols=1)
data_start = 1020
data_end = 1050
burst = [
62521, 62039, 61101, 66726, 64129, 61820, 63928, 63368, 61212, 60820,
59900, 62070, 62238, 61918, 61982, 64066, 65818, 63337, 65027, 64501,
68320, 80460, 63368, 61212, 60820, 59900, 62070, 62521, 62039, 61101,
66726
]
x = np.arange(0, len(burst))
#使用GM进行预测
periods = 5
GM = ModifiedGreyForecastModel(periods=periods)
start = data_start
pre_list = []
start = 1
for i in range(periods, len(burst)):
x_0 = burst[start:i]
pre = GM.predictGMValue(x_0)
pre_list.append(pre)
start += 1
plt.plot(x, burst, "b-*")
plt.plot(np.arange(periods, periods + len(pre_list)), pre_list, "r-*")
plt.legend(["real", "predict"])
plt.xlabel("time in minutes")
plt.ylabel("workloads")
plt.title("Prediction With GM")
c = moveDown(c)
result = a[i]
if result == 0:
equal[c[0]][c[1]][c[2]] += 1
elif result == 1:
better[c[0]][c[1]][c[2]] += 1
else:
worse[c[0]][c[1]][c[2]] += 1
#print("better is",better)
#print("worse is",worse)
#print("equal is",equal)
analyzeResult(q, better, worse)
#divideSpace(q)
#getStatisticAttribute(q)
#analyzeStats()
wrFile = WRFile()
data = wrFile.readDataFromExcel(
filePath="D:\\cloudsim\\log\\workload1/taobao.xlsx",
min_cols=1,
max_cols=1,
sheet_name="1")
data = np.floor((np.array(data) / 100))
#print(data)
wrFile.writeDataIntoExcel(
data=data, filePath="D:\\cloudsim\\log\\workload1/deplete_taobao.xlsx")
print(np.percentile(np.array(data), 80))
def analyzeReadData():
filePath = "F:/test/workload63_69.xlsx"
wrFile = WRFile()
wrFile.readDataFromExcel(filePath=filePath, sheet_name="1")
def test(q):
wrFile = WRFile()
ATBM_file = "F:\data\experiment/Seperate_Delay_ATBM_q" + str(q) + ".xlsx"
SQ_file = "F:\data\experiment/Seperate_Delay_SQ_q" + str(q) + ".xlsx"
'''atbm = wrFile.readDataFromExcel2(filePath = ATBM_file)
sq = wrFile.readDataFromExcel2(filePath = SQ_file)
for i in range(len(atbm)):
atbm_data = atbm[i]
sq_data = sq[i]
print(atbm_data)
print(sq_data)
print(np.sum(atbm_data[3:])-np.sum(sq_data[3:]))
if np.sum(atbm_data[3:])!= np.sum(sq_data[3:]):
print(atbm_data[:3])
'''
wrFile = WRFile()
taobao = wrFile.readDataFromExcel(
filePath="D:\\cloudsim\\log\\workload1/taobao.xlsx")
FIFA = wrFile.readDataFromExcel(
filePath="F:\\FIFA\\predict\\traindata\\inmin/workload53inmin.xlsx")
plt.plot(np.arange(len(FIFA)), FIFA, "k")
plt.legend([u'并发量'], fontsize=18, loc="upper left")
plt.xlabel(u"时间/分钟", fontsize=18)
plt.ylabel(u"并发量/次", fontsize=18)
plt.grid(True)
plt.title(u"1998年世界杯期间5月17日的用户并发量数据")
#plotDelayDistribution(q)
#q = 6
#plotDelayDistribution(q)
def compareModel():
fileStart = "F:\\one\\final\\inmin/workload"
fileEnd = "inmin.xlsx"
wrFile = WRFile()
#fft = FFTPredict()
#noBurst_pre = fft.FFTofNoBurst(data51 , data52 )
#WFD_pre = fft.FFTofWFD(data51 , data52 )
periods = 10
for i in range(53, 61):
result = TestGreyModel(periods, filePath=fileStart + str(i) + fileEnd)
predict = result[1]
print(i)
wrFile.writeDataIntoExcel(data=predict,
filePath="F:/one/predict/GM/wc" + str(i) +
".xlsx")
#compareModel()
wrFile = WRFile()
#predict = wrFile.readDataFromExcel("F:\\one\\predict\\GM/wc53_60.xlsx")
data = wrFile.readDataFromExcel(
"F:\\one\\predict\\traindata/workload54inmin.xlsx")
predict = wrFile.readDataFromExcel("F:\\one\\predict\\GM/wc54.xlsx")
plt.plot(np.arange(len(data)), data)
plt.plot(np.arange(len(predict)), predict)
plt.legend(["r-w", "p-w"])
plt.title("prediction result for day 54 by GM ")
def getData(self,filePath):
wrFile = WRFile()
data = wrFile.readDataFromExcel(filePath,cols=1)
return data
amazon.append(data[i - 4])
elif count == 3:
if data[i] > 0:
taobao.append(data[i])
else:
taobao.append(data[i - 4])
#3.将数据写入excel
fileRoot = "F:\\data\\online/"
filesuffix = ".xlsx"
wrFile.writeDataIntoExcel(data=jingdong,
filePath=fileRoot + "jingdong" + filesuffix)
wrFile.writeDataIntoExcel(data=tmall,
filePath=fileRoot + "tmall" + filesuffix)
wrFile.writeDataIntoExcel(data=amazon,
filePath=fileRoot + "amazon" + filesuffix)
wrFile.writeDataIntoExcel(data=taobao,
filePath=fileRoot + "taobao" + filesuffix)
wrFile = WRFile()
fileName = "taobao"
data = wrFile.readDataFromExcel(filePath="F:\\data\\online/" + fileName +
".xlsx",
sheet_name="Sheet1",
cols=2)
pre = wrFile.readDataFromExcel(filePath="F:\\data\\online\\predict/exp.xlsx",
sheet_name="Sheet1",
cols=3)
plt.plot(np.arange(len(data)), data)
plt.plot(np.arange(len(pre)), pre)
plt.legend(["r", "p"])
self._delayTime = np.copy(self.time)
for i in range(0,self.windows):
temp_requests+=self.workload[i]
temp = (temp_requests-self.c0)/r-(self.time[i]-self.t0)
if temp<0:
temp = 0
self._delayTime[i] += temp
def GiveQComputRB(self,q):
pass
#*************创建测试数据集********************
wrFile = WRFile()
filePath = "F:/result.xlsx"
cols = 1
data = wrFile.readDataFromExcel(filePath,cols,sheet_name = 2)
#data[6] = 200
time = np.arange(0,len(data))
result = np.vstack([data,time])
r = 400
b = 450
print("r is ",r,"b is" ,b)
#*************测试请求切分效果***********************
lager = LagerRequest(result,r =r)
q = 50
#请求达到后，队列的存储情况
rrange = lager.computeRRange()
sample = np.linspace(rrange[0],rrange[1],2)
b_result = []
delay_result[4]+=1
else:
delay_result[5]+=1
delay_result[0] = round(1.0*delay_result[0]/len(delay),3)
delay_result[1] = round(1.0*delay_result[1]/len(delay),3)
delay_result[2] = round(1.0*delay_result[2]/len(delay),3)
delay_result[3] = round(1.0*delay_result[3]/len(delay),3)
delay_result[4] = round(1.0*delay_result[4]/len(delay),3)
delay_result[5] = round(1.0*delay_result[5]/len(delay),3)
delay_result = delay_result.tolist()
QL_delay_result.append(delay_result)
return QL_delay_result
'''wrFile = WRFile()
process_type = "ATBM"
data = anaylzeDelayDistribution()
wrFile.writeDataIntoExcel(data = data,filePath ="D:\\cloudsim\\log\\"+process_type+"_q"+"/"+process_type+"_delay_distribution.xlsx" )
'''
wrFile = WRFile()
predict_type="lr"
data = wrFile.readDataFromExcel(filePath="F:\\FIFA\\final\\inmin/workload53_60inmin.xlsx")
predict = wrFile.readDataFromExcel(filePath="F:\\FIFA\\predict\\"+predict_type+"/wc53_60.xlsx")
plt.plot(np.arange(len(data)),data,"k")
plt.plot(np.arange(len(data)),predict,"wo")
plt.legend(["real","predict"],fontsize=16,loc="upper left")
plt.title("prediction results of "+predict_type,fontsize=20)
plt.xlabel("time(minute)",fontsize=18)
plt.xticks(fontsize = 16)
plt.ylabel("workloads",fontsize=18)
plt.yticks(fontsize = 16)
plt.savefig("E:\\"+ u"日常工作"+"\\"+u"下一代计算机技术"+"\\"+u"重要照片"+"\\"+u"各类算法的预测结果"+"/"+predict_type+".jpeg")
def testReadData():
wrFile = WRFile()
data = wrFile.readDataFromExcel(filePath="F:/test/workload.xlsx")
data = np.array(data)
@author: User
"""
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.append("D:/anaconda/project/utils")
from utils import WRFile
from scipy.stats.stats import pearsonr
import scipy.fftpack as fft
import pandas as pd
'''设置初始的数据值'''
fileStart = "F:\\one\\final\\inmin/knots/workload"
fileEnd = "inmin_knots.xlsx"
wrFile = WRFile()
data51 = wrFile.readDataFromExcel(filePath=fileStart + "51" + fileEnd)
data52 = wrFile.readDataFromExcel(filePath=fileStart + "52" + fileEnd)
data53 = wrFile.readDataFromExcel(filePath=fileStart + "53" + fileEnd)
data54 = wrFile.readDataFromExcel(filePath=fileStart + "54" + fileEnd)
data_volume = len(data51)
x = np.arange(0, data_volume)
def useAverage(dataA=data51, dataB=data52, dataC=data53):
predict = (dataA + dataB) / 2
#plt.plot(x,predict)
#plt.plot(x,dataC)
#plt.legend(["p_w","r_w"])
#plt.title("use average of dataA and dataB")
presicion = pearsonr(predict.real, dataC)
return [predict, presicion]
def inc_dec(self, begin, slope, fluctuation, amount):
middle = amount / 2
data0 = begin + slope * np.arange(middle)
data1 = np.max(data0) - slope * np.arange(middle, amount)
data = np.append(data0, data1) + np.random.randint(
-1 * fluctuation, fluctuation + 1, amount)
return data
def dec_inc(self, begin, slope, fluctuation, amount):
middle = amount / 2
data0 = begin - slope * np.arange(middle)
data1 = np.min(data0) + slope * np.arange(middle, amount)
data = np.append(data0, data1) + np.random.randint(
-1 * fluctuation, fluctuation + 1, amount)
return data
wrFile = WRFile()
file_List = [
"stationary", "inc", "dec", "flat_dec", "flat_inc", "inc_dec", "dec_inc"
]
data = wrFile.readDataFromExcel(
filePath="D:\\cloudsim\\log\\workload1/synthetic/" + file_List[0] +
".xlsx")
plt.plot(np.arange(len(data)), data, "k")
plt.title(u"平稳型并发量数据")
plt.xlabel(u"时间", fontsize=18)
plt.ylabel(u"并发量数量", fontsize=18)
plt.ylim(0, 60)
plt.legend(["并发量"], loc='upper left')
def __init__(self, filePath):
wrFile = WRFile()
self.data = numpy.array(
wrFile.readDataFromExcel(filePath=filePath, cols=1))
# -*- coding: utf-8 -*-
"""
Created on Wed Oct 26 11:22:48 2016
@author: User
"""
import matplotlib.pyplot as plt
from utils import WRFile
wrFile = WRFile()
filePath = "F:/lab/source/data/library/1.xlsx"
cols = 1
original_data = wrFile.readDataFromExcel(filePath=filePath,
cols=cols,
sheet_name="1")
print(original_data)
#把数据以100为单位进行切分
'''segement = -1
segement_data = []
for i in range(len(original_data)):
if i%500==0:
segement = segement+1
if(segement!=1):
wrFile.writeDataIntoExcel(data = segement_data,filePath = filePath,cols = segement,sheet_index = 1)
segement_data.clear()
print(segement)
segement_data.append(original_data[i])
'''