def MACD(code, day1, day2, day3):
DIF = EMA(code, day1) - EMA(code, day2)
DEM = EMA(code, day3)
for i in range(1, len(DIF)):
DEM.iloc[i] = DIF.iloc[i] * 2 / (day3+1) + DEM.iloc[i-1] * (day3-1) / (day3+1)
OSC = (DIF - DEM) * 2
plt.bar(list(OSC.index), list(OSC['close']))
return OSC
def minToMaxIteration(minVM, maxVM, currentVM, machine_unit_power,machine_unit_price, predicted_arr, time_gap ):
yvalueset = []
predicted_arr_temp = predicted_arr[np.logical_not(np.isnan(predicted_arr))]
line2d= Line2D(np.arange(1,len(predicted_arr_temp),1), EMA.ema(predicted_arr_temp ,3))
#print("Max : %s" %max(line2d.get_xdata()))
sampling_distance = 0.2
for i in drange(min(line2d.get_xdata()), max(line2d.get_xdata()), sampling_distance):
#print(i-uptime)
z = getValue(line2d, i)
yvalueset.append(z[0])
predicted_arr2 = np.array(yvalueset)
#print("yvaluse : %s" %yvalueset)
tot_cost = 999999
#print(predicted_arr2)
time = sampling_distance * len(predicted_arr2)/60.0
# print(time)
index = minVM
violation_selected = 0
for i in range(minVM, maxVM+1):
precentage = violation_precentage(i,machine_unit_power,predicted_arr2)
cost = i*machine_unit_price*time + i*machine_unit_price*time*SLA_func(precentage)
print("VM: %d Cost: %d, Violation : %d" %(i, cost, precentage))
if i == minVM and precentage > 30:
i = currentVM
if cost < tot_cost :
tot_cost = cost
index = i
violation_selected = precentage
if precentage == 0:
break
return index, violation_selected
def MACD(hist_data, period):
count = hist_data['Open'].count()
data = pandas.DataFrame(
numpy.zeros(count * 6).reshape(count, 6),
index=hist_data.index,
columns=['EMA1', 'EMA2', 'DIF', 'DEA', 'MACD', 'MACD_s'])
data['EMA1'] = EMA.EMA(hist_data, EMA1_fast)['EMA']
data['EMA2'] = EMA.EMA(hist_data, EMA2_slow)['EMA']
data['DIF'] = data['EMA1'] - data['EMA2']
#replace 'Adj Close' with 'DIF' to reuse EMA function
hist_data['Adj Close'] = data['DIF']
data['DEA'] = EMA.EMA(hist_data, DEA_period)['EMA']
data['MACD'] = 2 * (data['DIF'] - data['DEA'])
for i in range(count):
data['MACD_s'][i] = 1 if data['DIF'][i] > data['DIF'][i - 1] else 0
return data
def minToMaxIteration(minVM, maxVM, currentVM, machine_unit_power,
machine_unit_price, predicted_arr, time_gap):
yvalueset = []
predicted_arr_temp = predicted_arr[np.logical_not(np.isnan(predicted_arr))]
line2d = Line2D(np.arange(1, len(predicted_arr_temp), 1),
EMA.ema(predicted_arr_temp, 3))
#print("Max : %s" %max(line2d.get_xdata()))
sampling_distance = 0.2
for i in drange(min(line2d.get_xdata()), max(line2d.get_xdata()),
sampling_distance):
#print(i-uptime)
z = getValue(line2d, i)
yvalueset.append(z[0])
predicted_arr2 = np.array(yvalueset)
#print("yvaluse : %s" %yvalueset)
tot_cost = 999999
#print(predicted_arr2)
time = sampling_distance * len(predicted_arr2) / 60.0
# print(time)
index = minVM
violation_selected = 0
for i in range(minVM, maxVM + 1):
precentage = violation_precentage(i, machine_unit_power,
predicted_arr2)
cost = i * machine_unit_price * time + i * machine_unit_price * time * SLA_func(
precentage)
print("VM: %d Cost: %d, Violation : %d" % (i, cost, precentage))
if i == minVM and precentage > 30:
i = currentVM
if cost < tot_cost:
tot_cost = cost
index = i
violation_selected = precentage
if precentage == 0:
break
return index, violation_selected
def calculate_stock_signal_new(hist_data):
count = hist_data['Open'].count()
all_data = hist_data.join(KDJ2.KDJ(hist_data, period)).join(
RSI2.RSI(hist_data,
period)).join(ForceIndex2.FI(hist_data, period)).join(
EMA.EMA(hist_data, period))
all_data = all_data.join(adj_close_signal_new(hist_data))
data = pandas.DataFrame(
numpy.zeros(hist_data['Open'].count() * 5).reshape(count, 5),
index=hist_data.index,
columns=['signal', 'price', 'buy', 'sell', 'profit'])
for i in range(count):
data['price'][i] = all_data['Adj Close'][i]
sum = all_data['KDJ_s'][i] + all_data['RSI_s'][i] + all_data['FI_s'][i]
if sum > 2:
if data['signal'].sum() > 0:
continue
data['signal'][i] = 1
data['buy'][i] = all_data['Adj Close'][i]
elif data['signal'].sum() > 0:
if all_data['FI_s'][i] > 0:
continue
elif (all_data['EMA_s'][i] + all_data['close_s'][i]) > 0:
continue
else:
data['sell'][i] = all_data['Adj Close'][i]
j = i - 1
while data['signal'][j] < 0.1:
j = j - 1
data['profit'][i] = (data['sell'][i] -
data['buy'][j]) / data['buy'][j]
data['signal'][i] = -1
return all_data.join(data)
def run(VM_parameter_unit, threshold_percentage, uptime, min_VM, shift, provider, prediction_type,
vm_price_per_hour, vm_init_data, actualFileName, scaleFileName, costFileName):
listVM = list()
open(scaleFileName, 'w').close()
open(costFileName, 'w').close()
scaleCSV = open(scaleFileName,"rw+")
costCSV = open(costFileName,"rw+")
scaleCSV.write("Time,VM Count\n" )
costCSV.write( "Time,Total Cost\n" )
#print(provider)
digix_cordinates = array.array('d')
digiy_cordinates = array.array('d')
x_coordinates = array.array('d')
y_coordinates = array.array('d')
digiy_coord_actual = array.array('d')
modely_coord_actual = array.array('d')
#Read vlaues while converting to VM_UNITS
ifile = open(actualFileName, "rb")
reader = csv.reader(ifile)
rownum = 0
for row in reader:
x_coordinates.append(float(row[0]))
y_coordinates.append(float(row[1])/(VM_parameter_unit))
rownum += 1
ifile.close()
##print(x_coordinates)
##print(y_coordinates)
# Regression
xdata = np.array(x_coordinates)
ydata = np.array(y_coordinates)
#Plot row data
rowdata = Line2D(xdata, ydata)
#plot regression line of data
#plt1.plot(xdata, quad(xdata,popt[0],popt[1],popt[2], popt[3]), '-')
#plot EMA
line2d = Line2D(xdata, EMA.ema(ydata ,3))
#plot stratos
line2d_stratos = Line2D(xdata[2:len(xdata)]+1,Stratos.Stratos(ydata))
#Initialize min_VMs
#VM = [23,42] #Todo fill with randoms
for j in range(0, min_VM):
# id = randint(1,999)
t = vm_init_data[j]#take this as arg?
vm = VM(initTime=-t, endTime=-1)
listVM.append(vm)
#Plot number of VMs required
vm_count = min_VM
yvalueset = []
if(provider == "default" and prediction_type == "reactive"):
for i in drange(uptime, max(xdata) - shift + uptime- 1, 0.1):
#print(i-uptime)
z = getValue(line2d, i - uptime)
yvalueset.append(z)
##print(z)
new_vm_count = math.ceil(z/threshold_percentage)
if new_vm_count < min_VM:
new_vm_count = min_VM
vm_change = int(math.ceil(new_vm_count - vm_count))
if vm_change > 0:
vm_count += startVMs(listVM, vm_change, i)
elif vm_change < 0:
vm_count -= removeVMs(listVM, -vm_change, provider, i)
digix_cordinates.append(i)
digiy_cordinates.append(new_vm_count)
digiy_coord_actual.append(vm_count)
scaleCSV.seek(0, 2)
scaleCSV.write("%.3f,%.3f\n" %(i, vm_count*VM_parameter_unit))
digixdata = np.array(digix_cordinates)
digiydata = np.array(digiy_cordinates)
actualydata = np.array(digiy_coord_actual)
lineAllocate = Line2D(digixdata, digiydata) #requirement
digi_line = Line2D(digixdata, actualydata) #actual
elif(provider == "default" and prediction_type == "stratos"):
request_count = 0
sampling_distance = 0.1
for i in drange(uptime, max(xdata) - shift + uptime -4, sampling_distance):
print(i)
line2d = line2d_stratos
z = getValue(line2d_stratos, i - uptime)
yvalueset.append(z)
##print(z)
new_vm_count = math.ceil(z/threshold_percentage)
if new_vm_count < min_VM:
new_vm_count = min_VM
vm_change = int(math.ceil(new_vm_count - vm_count))
if vm_change > 0:
vm_count += startVMs(listVM, vm_change, i)
request_count = 0
elif vm_change < 0:
request_count += 1
if request_count > 2.0/sampling_distance :
vm_count -= removeVMs(listVM, -vm_change, provider, i)
request_count = 0
digix_cordinates.append(i)
digiy_cordinates.append(new_vm_count)
digiy_coord_actual.append(vm_count)
scaleCSV.seek(0, 2)
scaleCSV.write("%.3f,%.3f\n" %(i, vm_count*VM_parameter_unit))
digixdata = np.array(digix_cordinates)
digiydata = np.array(digiy_cordinates)
actualydata = np.array(digiy_coord_actual)
lineAllocate = Line2D(digixdata, digiydata) #requirement
digi_line = Line2D(digixdata+4, actualydata) #actual
elif(provider == "aws" and prediction_type == "stratos"):
request_count = 0
sampling_distance = 0.1
for i in drange(uptime, max(xdata) - shift + uptime -4, 0.1):
print(i)
line2d = line2d_stratos
z = getValue(line2d, i - uptime)
yvalueset.append(z)
##print(z)
new_vm_count = math.ceil(z/threshold_percentage)
if new_vm_count < min_VM:
new_vm_count = min_VM
vm_change = int(math.ceil(new_vm_count - vm_count))
if vm_change > 0:
vm_count += startVMs(listVM, vm_change, i)
request_count = 0
elif vm_change < 0:
request_count += 1
if request_count > 2.0/sampling_distance:
vm_count -= removeVMs(listVM, -vm_change, provider, i)
digix_cordinates.append(i)
digiy_cordinates.append(new_vm_count)
digiy_coord_actual.append(vm_count)
scaleCSV.seek(0, 2)
scaleCSV.write("%.3f,%.3f\n" %(i, vm_count*VM_parameter_unit))
digixdata = np.array(digix_cordinates)
digiydata = np.array(digiy_cordinates)
actualydata = np.array(digiy_coord_actual)
lineAllocate = Line2D(digixdata, digiydata) #requirement
digi_line = Line2D(digixdata+4, actualydata) #actual
elif(provider == "aws" and prediction_type == "reactive"):
for i in drange(uptime, max(xdata) - shift + uptime- 1, 0.1):
#print(i-uptime)
z = getValue(line2d, i - uptime)
yvalueset.append(z)
##print(z)
new_vm_count = math.ceil(z/threshold_percentage)
if new_vm_count < min_VM:
new_vm_count = min_VM
vm_change = int(math.ceil(new_vm_count - vm_count))
if vm_change > 0:
vm_count += startVMs(listVM, vm_change, i)
elif vm_change < 0:
vm_count -= removeVMs(listVM, -vm_change, provider, i)
digix_cordinates.append(i)
digiy_cordinates.append(new_vm_count)
digiy_coord_actual.append(vm_count)
scaleCSV.seek(0, 2)
scaleCSV.write("%.3f,%.3f\n" %(i, vm_count*VM_parameter_unit))
digixdata = np.array(digix_cordinates)
digiydata = np.array(digiy_cordinates)
actualydata = np.array(digiy_coord_actual)
lineAllocate = Line2D(digixdata, digiydata) #requirement
digi_line = Line2D(digixdata, actualydata) #actual
elif(provider == "default" and prediction_type == "proactive"):
digix, digiy = CostModel.run(actualFileName)
for i in range(0, len(digix)):
new_vm_count = digiy[i]
if i != 0:
vm_change = int(math.ceil(digiy[i] - len([i for x in listVM if x.endTime == -1])))
else :
vm_change = int(math.ceil(digiy[0] - len([i for x in listVM if x.endTime == -1])))
if vm_change > 0:
vm_count += startVMs(listVM, vm_change, digix[i])
elif vm_change < 0:
vm_count -= removeVMs(listVM, -vm_change, provider, digix[i])
modely_coord_actual.append(vm_count)
scaleCSV.seek(0, 2)
scaleCSV.write("%.3f,%.3f\n" %(i, vm_count*VM_parameter_unit))
digixdata = np.array(digix)
digiydata = np.array(digiy)
lineAllocate = Line2D(digixdata, digiydata) #requirement
digi_line = Line2D(digixdata, modely_coord_actual) #actual
elif(provider == "aws" and prediction_type == "proactive"):
digix, digiy = CostModel.run(actualFileName)
#print(digix)
#print(digiy)
for i in range(0, len(digix)):
new_vm_count = digiy[i]
if i != 0:
vm_change = int(math.ceil(digiy[i] - len([i for x in listVM if x.endTime == -1])))
else :
vm_change = int(math.ceil(digiy[0] - len([i for x in listVM if x.endTime == -1])))
if vm_change > 0:
vm_count += startVMs(listVM, vm_change, digix[i])
elif vm_change < 0:
vm_count -= removeVMs(listVM, -vm_change, provider, digix[i])
modely_coord_actual.append(vm_count)
scaleCSV.seek(0, 2)
scaleCSV.write("%.3f,%.3f\n" %(i, vm_count*VM_parameter_unit))
digixdata = np.array(digix)
digiydata = np.array(digiy)
lineAllocate = Line2D(digixdata, digiydata) #requirement
digi_line = Line2D(digixdata, modely_coord_actual) #actual
#for vm in listVM:
#print("VM_id %3s: %5.1f - %5.1f = %5.1f%s" % (vm.id, vm.initTime, vm.endTime,(vm.endTime if vm.endTime > 0 else i) - vm.initTime, ("" if vm.endTime > 0 else " up")))
costValues = array.array('d')
for k in drange(0, max(xdata), 1):
cost = calculateAWSCost(listVM, k, vm_price_per_hour)
costValues.append(cost)
costCSV.seek(0, 2)
costCSV.write("%.3f,%.3f\n" %(k,cost))
costydata = np.array(costValues)
costxdata = np.arange(0, max(xdata), 1)
cost_line = Line2D(costxdata,costydata)
yvalues = np.array(yvalueset)
#e = mse(digiydata, yvalues)
start = max(min(line2d.get_xdata()), min(lineAllocate.get_xdata()))
end = min(max(line2d.get_xdata()), max(lineAllocate.get_xdata()));
#calculateViolation(predictLine=line2d, allocateline=lineAllocate, startTime= start , endTime= end)
return rowdata, line2d, digi_line,cost_line
def EMA_signal(hist_data):
data = EMA.EMA(hist_data, 13)
return 1 if data['EMA'][count - 1] > data['EMA'][count - 2] else 0
def run(VM_parameter_unit, threshold_percentage, uptime, min_VM, shift,
provider, prediction_type, vm_price_per_hour, vm_init_data,
actualFileName, scaleFileName, costFileName):
listVM = list()
open(scaleFileName, 'w').close()
open(costFileName, 'w').close()
scaleCSV = open(scaleFileName, "rw+")
costCSV = open(costFileName, "rw+")
scaleCSV.write("Time,VM Count\n")
costCSV.write("Time,Total Cost\n")
#print(provider)
digix_cordinates = array.array('d')
digiy_cordinates = array.array('d')
x_coordinates = array.array('d')
y_coordinates = array.array('d')
digiy_coord_actual = array.array('d')
modely_coord_actual = array.array('d')
#Read vlaues while converting to VM_UNITS
ifile = open(actualFileName, "rb")
reader = csv.reader(ifile)
rownum = 0
for row in reader:
x_coordinates.append(float(row[0]))
y_coordinates.append(float(row[1]) / (VM_parameter_unit))
rownum += 1
ifile.close()
##print(x_coordinates)
##print(y_coordinates)
# Regression
xdata = np.array(x_coordinates)
ydata = np.array(y_coordinates)
#Plot row data
rowdata = Line2D(xdata, ydata)
#plot regression line of data
#plt1.plot(xdata, quad(xdata,popt[0],popt[1],popt[2], popt[3]), '-')
#plot EMA
line2d = Line2D(xdata, EMA.ema(ydata, 3))
#plot stratos
line2d_stratos = Line2D(xdata[2:len(xdata)] + 1, Stratos.Stratos(ydata))
#Initialize min_VMs
#VM = [23,42] #Todo fill with randoms
for j in range(0, min_VM):
# id = randint(1,999)
t = vm_init_data[j] #take this as arg?
vm = VM(initTime=-t, endTime=-1)
listVM.append(vm)
#Plot number of VMs required
vm_count = min_VM
yvalueset = []
if (provider == "default" and prediction_type == "reactive"):
for i in drange(uptime, max(xdata) - shift + uptime - 1, 0.1):
#print(i-uptime)
z = getValue(line2d, i - uptime)
yvalueset.append(z)
##print(z)
new_vm_count = math.ceil(z / threshold_percentage)
if new_vm_count < min_VM:
new_vm_count = min_VM
vm_change = int(math.ceil(new_vm_count - vm_count))
if vm_change > 0:
vm_count += startVMs(listVM, vm_change, i)
elif vm_change < 0:
vm_count -= removeVMs(listVM, -vm_change, provider, i)
digix_cordinates.append(i)
digiy_cordinates.append(new_vm_count)
digiy_coord_actual.append(vm_count)
scaleCSV.seek(0, 2)
scaleCSV.write("%.3f,%.3f\n" % (i, vm_count * VM_parameter_unit))
digixdata = np.array(digix_cordinates)
digiydata = np.array(digiy_cordinates)
actualydata = np.array(digiy_coord_actual)
lineAllocate = Line2D(digixdata, digiydata) #requirement
digi_line = Line2D(digixdata, actualydata) #actual
elif (provider == "default" and prediction_type == "stratos"):
request_count = 0
sampling_distance = 0.1
for i in drange(uptime,
max(xdata) - shift + uptime - 4, sampling_distance):
print(i)
line2d = line2d_stratos
z = getValue(line2d_stratos, i - uptime)
yvalueset.append(z)
##print(z)
new_vm_count = math.ceil(z / threshold_percentage)
if new_vm_count < min_VM:
new_vm_count = min_VM
vm_change = int(math.ceil(new_vm_count - vm_count))
if vm_change > 0:
vm_count += startVMs(listVM, vm_change, i)
request_count = 0
elif vm_change < 0:
request_count += 1
if request_count > 2.0 / sampling_distance:
vm_count -= removeVMs(listVM, -vm_change, provider, i)
request_count = 0
digix_cordinates.append(i)
digiy_cordinates.append(new_vm_count)
digiy_coord_actual.append(vm_count)
scaleCSV.seek(0, 2)
scaleCSV.write("%.3f,%.3f\n" % (i, vm_count * VM_parameter_unit))
digixdata = np.array(digix_cordinates)
digiydata = np.array(digiy_cordinates)
actualydata = np.array(digiy_coord_actual)
lineAllocate = Line2D(digixdata, digiydata) #requirement
digi_line = Line2D(digixdata + 4, actualydata) #actual
elif (provider == "aws" and prediction_type == "stratos"):
request_count = 0
sampling_distance = 0.1
for i in drange(uptime, max(xdata) - shift + uptime - 4, 0.1):
print(i)
line2d = line2d_stratos
z = getValue(line2d, i - uptime)
yvalueset.append(z)
##print(z)
new_vm_count = math.ceil(z / threshold_percentage)
if new_vm_count < min_VM:
new_vm_count = min_VM
vm_change = int(math.ceil(new_vm_count - vm_count))
if vm_change > 0:
vm_count += startVMs(listVM, vm_change, i)
request_count = 0
elif vm_change < 0:
request_count += 1
if request_count > 2.0 / sampling_distance:
vm_count -= removeVMs(listVM, -vm_change, provider, i)
digix_cordinates.append(i)
digiy_cordinates.append(new_vm_count)
digiy_coord_actual.append(vm_count)
scaleCSV.seek(0, 2)
scaleCSV.write("%.3f,%.3f\n" % (i, vm_count * VM_parameter_unit))
digixdata = np.array(digix_cordinates)
digiydata = np.array(digiy_cordinates)
actualydata = np.array(digiy_coord_actual)
lineAllocate = Line2D(digixdata, digiydata) #requirement
digi_line = Line2D(digixdata + 4, actualydata) #actual
elif (provider == "aws" and prediction_type == "reactive"):
for i in drange(uptime, max(xdata) - shift + uptime - 1, 0.1):
#print(i-uptime)
z = getValue(line2d, i - uptime)
yvalueset.append(z)
##print(z)
new_vm_count = math.ceil(z / threshold_percentage)
if new_vm_count < min_VM:
new_vm_count = min_VM
vm_change = int(math.ceil(new_vm_count - vm_count))
if vm_change > 0:
vm_count += startVMs(listVM, vm_change, i)
elif vm_change < 0:
vm_count -= removeVMs(listVM, -vm_change, provider, i)
digix_cordinates.append(i)
digiy_cordinates.append(new_vm_count)
digiy_coord_actual.append(vm_count)
scaleCSV.seek(0, 2)
scaleCSV.write("%.3f,%.3f\n" % (i, vm_count * VM_parameter_unit))
digixdata = np.array(digix_cordinates)
digiydata = np.array(digiy_cordinates)
actualydata = np.array(digiy_coord_actual)
lineAllocate = Line2D(digixdata, digiydata) #requirement
digi_line = Line2D(digixdata, actualydata) #actual
elif (provider == "default" and prediction_type == "proactive"):
digix, digiy = CostModel.run(actualFileName)
for i in range(0, len(digix)):
new_vm_count = digiy[i]
if i != 0:
vm_change = int(
math.ceil(digiy[i] -
len([i for x in listVM if x.endTime == -1])))
else:
vm_change = int(
math.ceil(digiy[0] -
len([i for x in listVM if x.endTime == -1])))
if vm_change > 0:
vm_count += startVMs(listVM, vm_change, digix[i])
elif vm_change < 0:
vm_count -= removeVMs(listVM, -vm_change, provider, digix[i])
modely_coord_actual.append(vm_count)
scaleCSV.seek(0, 2)
scaleCSV.write("%.3f,%.3f\n" % (i, vm_count * VM_parameter_unit))
digixdata = np.array(digix)
digiydata = np.array(digiy)
lineAllocate = Line2D(digixdata, digiydata) #requirement
digi_line = Line2D(digixdata, modely_coord_actual) #actual
elif (provider == "aws" and prediction_type == "proactive"):
digix, digiy = CostModel.run(actualFileName)
#print(digix)
#print(digiy)
for i in range(0, len(digix)):
new_vm_count = digiy[i]
if i != 0:
vm_change = int(
math.ceil(digiy[i] -
len([i for x in listVM if x.endTime == -1])))
else:
vm_change = int(
math.ceil(digiy[0] -
len([i for x in listVM if x.endTime == -1])))
if vm_change > 0:
vm_count += startVMs(listVM, vm_change, digix[i])
elif vm_change < 0:
vm_count -= removeVMs(listVM, -vm_change, provider, digix[i])
modely_coord_actual.append(vm_count)
scaleCSV.seek(0, 2)
scaleCSV.write("%.3f,%.3f\n" % (i, vm_count * VM_parameter_unit))
digixdata = np.array(digix)
digiydata = np.array(digiy)
lineAllocate = Line2D(digixdata, digiydata) #requirement
digi_line = Line2D(digixdata, modely_coord_actual) #actual
#for vm in listVM:
#print("VM_id %3s: %5.1f - %5.1f = %5.1f%s" % (vm.id, vm.initTime, vm.endTime,(vm.endTime if vm.endTime > 0 else i) - vm.initTime, ("" if vm.endTime > 0 else " up")))
costValues = array.array('d')
for k in drange(0, max(xdata), 1):
cost = calculateAWSCost(listVM, k, vm_price_per_hour)
costValues.append(cost)
costCSV.seek(0, 2)
costCSV.write("%.3f,%.3f\n" % (k, cost))
costydata = np.array(costValues)
costxdata = np.arange(0, max(xdata), 1)
cost_line = Line2D(costxdata, costydata)
yvalues = np.array(yvalueset)
#e = mse(digiydata, yvalues)
start = max(min(line2d.get_xdata()), min(lineAllocate.get_xdata()))
end = min(max(line2d.get_xdata()), max(lineAllocate.get_xdata()))
#calculateViolation(predictLine=line2d, allocateline=lineAllocate, startTime= start , endTime= end)
return rowdata, line2d, digi_line, cost_line
