What does rc
mean in rcParams
?
runtime configuration.
PyLab 绘图能力的完整用户指南参见 https://matplotlib.org/users/index.html
完整的颜色和线型标识符列表,参见 http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot
rcParams 中有很多设置项目,完整的列表参见 http://matplotlib.org/users/customizing.html
如果你不想花费精力对这些参数进行单独设置,可以使用一个预定义的样式表,具体介绍参见 http://matplotlib.org/users/style_sheets.html#style-sheets
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 mortgages """
def __init__(self, loan, annRate, months):
""" Assumes: loan and annRate are floats, months an int
Creates a new mortgage of size loan, duration months, and annual rate annRate """
# Here are three instance initial values
self.loan = loan
self.rate = annRate / 12 # Is it an common agreement that annual rate is 12 times of monthly rate?
self.months = months
# Here are two lists extended monthly
self.paid = [0.0] # At the first day of loan you don't need to pay loan. Note this list is extended monthly
self.outstanding = [loan] # the m th month 本金额 remained to be paid
# Here is the monthly payment for a mortgage of size 'loan' at monthly rate of 'self.rate' for 'months' months
self.payment = findPayment (loan, self.rate, months)
self.legend = None # description of certain kind of mortgage
def makePayment (self):
""" Make a monthly payment"""
# Calculate monthly payments then append them to two lists:
# monthly payments and monthly outstanding loan balance
self.paid.append (self.payment)
# reduction used to reduce the loan balance = monthly payment - the amount of interest due on the outstanding loan balance
reduction = self.payment - self.outstanding [-1] * self.rate
self.outstanding.append (self.outstanding [-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.outstanding, style, label = self.legend)
def plotTotPd(self, style):
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):
totPd = [self.paid[0]]
for i in range(1, len(self.paid)):
totPd.append(totPd[-1] + self.paid[i])
equityAcquired = np.array([self.loan] * \
len(self.outstanding))
equityAcquired = equityAcquired - \
np.array(self.outstanding)
net = np.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.teaserRate = teaserRate
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.outstanding[-1],
self.rate,
self.months - self.teaserMonths)
Mortgage.makePayment(self)
def plotMortgages(morts, amt):
def labelPlot(figure, title, xLabel, yLabel):
pylab.figure(figure)
pylab.title(title)
pylab.xlabel(xLabel)
pylab.ylabel(yLabel)
pylab.legend(loc = 'best')
styles = ['k-', 'k-.', 'k:']
#Give names to figure numbers
payments, cost, balance, netCost = 0, 1, 2, 3
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])
labelPlot(payments, 'Monthly Payments of $' + str(amt) +
' Mortgages', 'Months', 'Monthly Payments')
pylab.show(payments)
labelPlot(cost, 'Cash Outlay of $' + str(amt) +
' Mortgages', 'Months', 'Total Payments')
pylab.show(cost)
labelPlot(balance, 'Balance Remaining of $' + str(amt) +
' Mortgages', 'Months', 'Remaining Loan Balance of $')
pylab.show(balance)
labelPlot(netCost, 'Net Cost of $' + str(amt) + ' Mortgages',
'Months', 'Payments - Equity $')
pylab.show(balance)
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)
---
title: 11 PLOTTING AND MORE ABOUT CLASSES
subtitle: 
author: John Qu
date: 2020-06-12
slug: plotting-and-more-about-classes
tags:
- 
categories:
- MIT600
- ICPP booknotes
typora-root-url: ../../static
show_toc: yes
output: html_notebook
---
### How to use `matplotlib.pyplot` in `*.Rmd` file?

Initiate a virtual environment.

```commandline
pip install virtualenv
```



Have a test.

```{python}
import matplotlib.pyplot as pylab
import numpy as np
pylab.figure(1) #create figure 1 
pylab.plot([1,2,3,4], [1,7,3,5]) #draw on figure 1 
pylab.show() #show figure on screen
```

### What does `rc` mean in `rcParams`?

runtime configuration.



PyLab 绘图能力的完整用户指南参见
https://matplotlib.org/users/index.html

完整的颜色和线型标识符列表，参见
http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot

rcParams 中有很多设置项目，完整的列表参见
http://matplotlib.org/users/customizing.html

如果你不想花费精力对这些参数进行单独设置，可以使用一个预定义的样式表，具体介绍参见
http://matplotlib.org/users/style_sheets.html#style-sheets

```{python}
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 mortgages """

    def __init__(self, loan, annRate, months):
        """ Assumes: loan and annRate are floats, months an int 
            Creates a new mortgage of size loan, duration months, and annual rate annRate """
        # Here are three instance initial values
        self.loan = loan
        self.rate = annRate / 12 # Is it an common agreement that annual rate is 12 times of monthly rate?
        self.months = months
        # Here are two lists extended monthly
        self.paid = [0.0] # At the first day of loan you don't need to pay loan. Note this list is extended monthly
        self.outstanding = [loan] # the m th month 本金额 remained to be paid
        # Here is the monthly payment for a mortgage of size 'loan' at monthly rate of 'self.rate' for 'months' months
        self.payment = findPayment (loan, self.rate, months) 
        self.legend = None  # description of certain kind of mortgage

    def makePayment (self):
        """ Make a monthly payment"""
        # Calculate monthly payments then append them to two lists:
        # monthly payments and monthly outstanding loan balance
        self.paid.append (self.payment) 
        # reduction used to reduce the loan balance = monthly payment - the amount of interest due on the outstanding loan balance
        reduction = self.payment - self.outstanding [-1] * self.rate 
        self.outstanding.append (self.outstanding [-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.outstanding, style, label = self.legend)
        
    def plotTotPd(self, style):
        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):
        totPd = [self.paid[0]]
        for i in range(1, len(self.paid)):
            totPd.append(totPd[-1] + self.paid[i]) 
        equityAcquired = np.array([self.loan] * \
                         len(self.outstanding)) 
        equityAcquired = equityAcquired - \
                         np.array(self.outstanding) 
        net = np.array(totPd) - equityAcquired
        pylab.plot(net, style, label = self.legend)
```


```{python}
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.teaserRate = teaserRate 
        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.outstanding[-1], 
                                       self.rate, 
                                       self.months - self.teaserMonths)
        Mortgage.makePayment(self)
```



```{python}
def plotMortgages(morts, amt):

    def labelPlot(figure, title, xLabel, yLabel):
        pylab.figure(figure) 
        pylab.title(title) 
        pylab.xlabel(xLabel) 
        pylab.ylabel(yLabel) 
        pylab.legend(loc = 'best')
        
    styles = ['k-', 'k-.', 'k:']
    #Give names to figure numbers
    payments, cost, balance, netCost = 0, 1, 2, 3 
    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])
    labelPlot(payments, 'Monthly Payments of $' + str(amt) +
              ' Mortgages', 'Months', 'Monthly Payments')
    pylab.show(payments)
    labelPlot(cost, 'Cash Outlay of $' + str(amt) +
              ' Mortgages', 'Months', 'Total Payments') 
    pylab.show(cost)
    labelPlot(balance, 'Balance Remaining of $' + str(amt) +
              ' Mortgages', 'Months', 'Remaining Loan Balance of $') 
    pylab.show(balance)
    labelPlot(netCost, 'Net Cost of $' + str(amt) + ' Mortgages',
              'Months', 'Payments - Equity $')
    pylab.show(balance)          
```




```{python}
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)
```



```{python}
compareMortgages(amt=200000, years=30, fixedRate=0.07, 
                 pts = 3.25, ptsRate=0.05, 
                 varRate1=0.045, varRate2=0.095, varMonths=48)
```