Mortgage DEMO

# quote from 'introduction to computation and programming     
# using Python, revised, MIT press 
import pylab

def findPayment(loan, r, m):
    """Assumes: loan and r are floats, m an int
       Returns the monthly payment for a mortgage of size
       loan at a monthly rate of r for m months"""
    return loan*((r*(1+r)**m)/((1+r)**m - 1))
    
class Mortgage(object):
    """Abstract class for building different kinds of mortgage"""
    def __init__(self, loan, annRate, months):
        """Create a new mortgage"""
        self.loan = loan
        self.rate = annRate/12.0
        self.months = months
        self.paid = [0.0]
        self.owed = [loan]
        self.payment = findPayment(loan, self.rate, months)
        self.legend = None #description of mortgage
        
    def makePayment(self):
        """Make a payment"""
        self.paid.append(self.payment)
        reduction = self.payment - self.owed[-1]*self.rate
        self.owed.append(self.owed[-1] - reduction)
        
    def getTotalPaid(self):
        """Return the total amount paid so far"""
        return sum(self.paid)
        
    def __str__(self):
        return self.legend
        
    def plotPayments(self, style):
        pylab.plot(self.paid[1:], style, label = self.legend)
        
    def plotBalance(self, style):
        pylab.plot(self.owed, style, label = self.legend)
        
    def plotTotPd(self, style):
        """Plot the cumulative total of the payments made"""
        totPd = [self.paid[0]]
        for i in range(1, len(self.paid)):
            totPd.append(totPd[-1] + self.paid[i])
        pylab.plot(totPd, style, label = self.legend)
        
    def plotNet(self, style):
        """Plot an approximation to the total cost of the mortgage
           over time by plotting the cash expended minus the equity
           acquired by paying off part of the loan"""
        totPd = [self.paid[0]]
        for i in range(1, len(self.paid)):
            totPd.append(totPd[-1] + self.paid[i])
        #Equity acquired through payments is amount of original loan
        #  paid to date, which is amount of load minus what is still owed"""
        equityAcquired = pylab.array([self.loan]*len(self.owed))
        equityAcquired -= pylab.array(self.owed)
        net = pylab.array(totPd) - equityAcquired
        pylab.plot(net, style, label = self.legend)
    
class Fixed(Mortgage):
    def __init__(self, loan, r, months):
        Mortgage.__init__(self, loan, r, months)
        self.legend = 'Fixed, ' + str(r*100) + '%'
        
class FixedWithPts(Mortgage):
    def __init__(self, loan, r, months, pts):
        Mortgage.__init__(self, loan, r, months)
        self.pts = pts
        self.paid = [loan*(pts/100.0)]
        self.legend = 'Fixed, ' + str(r*100) + '%, '\
                      + str(pts) + ' points'
                      
class TwoRate(Mortgage):
    def __init__(self, loan, r, months, teaserRate, teaserMonths):
        Mortgage.__init__(self, loan, teaserRate, months)
        self.teaserMonths = teaserMonths
        self.nextRate = r/12.0
        self.legend = str(teaserRate*100)\
                      + '% for ' + str(self.teaserMonths)\
                      + ' months, then ' + str(r*100) + '%'
                      
    def makePayment(self):
        if len(self.paid) == self.teaserMonths + 1:
            self.rate = self.nextRate
            self.payment = findPayment(self.owed[-1], self.rate,
                                       self.months - self.teaserMonths)
        
        Mortgage.makePayment(self)

def plotMortgages(morts, amt):
    styles = ['b-', 'b-.', 'b:']
    #Give names to figure numbers
    payments = 0
    cost = 1
    balance = 2
    netCost = 3
    
    pylab.figure(payments)
    pylab.title('Monthly Payments of Different $' + str(amt) + ' Mortgage')
    pylab.xlabel('Months')
    pylab.ylabel('Monthly Payments')
    
    pylab.figure(cost)
    pylab.title('Cash Outlay of Different $' + str(amt) + ' Mortgage')
    pylab.xlabel('Months')
    pylab.ylabel('Total Payments')
    
    pylab.figure(balance)
    pylab.title('Balance Remaining of %' + str(amt) + ' Mortgage')
    pylab.xlabel('Months')
    pylab.ylabel('Remaining Loan Balance of $')
    
    pylab.figure(netCost)
    pylab.title('Net Cost of $' + str(amt) + ' Mortgage')
    pylab.xlabel('Months')
    pylab.ylabel('Payments - Eqauity $')
    
    for i in range(len(morts)):
        pylab.figure(payments)
        morts[i].plotPayments(styles[i])
        
        pylab.figure(cost)
        morts[i].plotTotPd(styles[i])
        
        pylab.figure(balance)
        morts[i].plotBalance(styles[i])
        
        pylab.figure(netCost)
        morts[i].plotNet(styles[i])
        
    pylab.figure(payments)
    pylab.legend(loc = 'upper center')
    pylab.figure(cost)
    pylab.legend(loc = 'best')
    pylab.figure(balance)
    pylab.legend(loc = 'best')
    pylab.figure(netCost)
    pylab.legend(loc = 'best')
    
    
def compareMortgages(amt, years, fixedRate, pts, ptsRate,
                     varRate1, varRate2, varMonths):
    totMonths = years * 12
    fixed1 = Fixed(amt, fixedRate, totMonths)
    fixed2 = FixedWithPts(amt, ptsRate, totMonths, pts)
    twoRate = TwoRate(amt, varRate2, totMonths, varRate1, varMonths)
    morts = [fixed1, fixed2, twoRate]
    for m in range(totMonths):
        for mort in morts:
            mort.makePayment()
    plotMortgages(morts, amt)  
    
compareMortgages(amt=200000, years=30, fixedRate=0.07,
                 pts=3.25, ptsRate=0.05,
                 varRate1=0.045, varRate2=0.095, varMonths=48)
pylab.show()




内容概要:本文探讨了在MATLAB/SimuLink环境中进行三相STATCOM(静态同步补偿器)无功补偿的技术方法及其仿真过程。首先介绍了STATCOM作为无功功率补偿装置的工作原理,即通过调节交流电压的幅值和相位来实现对无功功率的有效管理。接着详细描述了在MATLAB/SimuLink平台下构建三相STATCOM仿真模型的具体步骤,包括创建新模型、添加电源和负载、搭建主电路、加入控制模块以及完成整个电路的连接。然后阐述了如何通过对STATCOM输出电压和电流的精确调控达到无功补偿的目的,并展示了具体的仿真结果分析方法,如读取仿真数据、提取关键参数、绘制无功功率变化曲线等。最后指出,这种技术可以显著提升电力系统的稳定性与电能质量,展望了STATCOM在未来的发展潜力。 适合人群:电气工程专业学生、从事电力系统相关工作的技术人员、希望深入了解无功补偿技术的研究人员。 使用场景及目标:适用于想要掌握MATLAB/SimuLink软件操作技能的人群,特别是那些专注于电力电子领域的从业者;旨在帮助他们学会建立复杂的电力系统仿真模型,以便更好地理解STATCOM的工作机制,进而优化实际项目中的无功补偿方案。 其他说明:文中提供的实例代码可以帮助读者直观地了解如何从零开始构建一个完整的三相STATCOM仿真环境,并通过图形化的方式展示无功补偿的效果,便于进一步的学习与研究。
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