Python programming exam
svenkata
Assigment061.docxSHIVAKIRAN RAO PAMULAPARTHI VENKATA
ID: 244705
Assignment - 6
Exercise 6.1
import matplotlib.pyplot as plt
import numpy as np
x = []
y = []
infile = open('write_cml_function.txt', 'r')
for line in infile:
coords = line.split()
x.append(float(coords[0]))
y.append(float(coords[1]))
infile.close()
x, y = np.array(x), np.array(y)
print 'Minimum x value = %f' % x.min()
print 'Maximum x value = %f' % x.max()
print 'Minimum y value = %f' % y.min()
print 'Maximum y value = %f' % y.max()
plt.plot(x, y, color='#053061', linewidth=1.5)
plt.xlabel('x')
plt.ylabel('y')
plt.show()
Console:
Exercise 6.2
Code :
import numpy as np
import matplotlib.pyplot as plt
import datetime as dt
def get_city_codes(citylist_filename):
infile = open(citylist_filename, 'r')
for i in range(260):
infile.readline() # skip everything before content
city_filenames = {}
while True:
line = infile.readline()
if not line: # exit loop at end of file
break
if line.find('mso-list') != -1: # check line contains country name
city = line.split('<b>')[1][:-7]
infile.readline() # skip a line
line = infile.readline() # read in next line
city_filename = line.split(
'>')[1][:line.split('>')[1].find('.txt') + 4]
city_filenames[city] = city_filename
infile.close()
return city_filenames
def load_city_data(city, city_filenames, subfolder='city_temp'):
filepath = subfolder + '/' + city_filenames[city]
# load data and create datetime object array to store dates
month, day, year, temperature = np.loadtxt(filepath, unpack=True)
temperature = np.loadtxt(filepath, usecols=(3,), dtype=float)
date = [dt.datetime(y, m, d) for y, m, d in
zip(year.astype(int), month.astype(int), day.astype(int))]
date = np.asarray(date)
# store in dictionary (filtering out entries with missing data)
valid = temperature != -99
city_data = {'name': city,
'date': date[valid],
'temperature': temperature[valid]}
return city_data
def plot_city_data(*city_data_dicts):
cities = []
for city_data in city_data_dicts:
cities.append(city_data['name'])
plt.plot_date(city_data['date'], city_data['temperature'], '.')
plt.ylabel('Temperature (C)')
plt.xlabel('Date')
plt.ylim([0, 110])
plt.legend(cities)
plt.show()
city_filenames = get_city_codes('city_temp/citylistWorld.htm')
Buenos_Aires_data = load_city_data('Buenos Aires', city_filenames)
London_data = load_city_data('London', city_filenames)
Reykjavik_data = load_city_data('Reykjavik', city_filenames)
Beirut_data = load_city_data('Beirut', city_filenames)
plot_city_data(Buenos_Aires_data, London_data, Reykjavik_data, Beirut_data)
Console:
Exercise 6.8.
Code :
def load_constants(constantsfile):
constants = {}
infile = open(constantsfile, 'r')
lines = infile.readlines()[2:]
for line in lines:
title = line[:27].strip()
value = float(line[27:].split()[0])
constants[title] = value
infile.close()
return constants
fundamental_constants = load_constants('constants.txt')
print '------------------------------------'
for constant, value in fundamental_constants.iteritems():
print '%-24s %g' % (constant, value)
print '------------------------------------'
Exercise 6.10
def read_densities_v1(filename):
infile = open(filename, 'r')
densities = {}
for line in infile:
words = line.split()
density = float(words[-1])
if len(words[:-1]) == 2:
substance = words[0] + ' ' + words[1]
else:
substance = words[0]
densities[substance] = density
infile.close()
return densities
def read_densities_v2(filename):
infile = open(filename, 'r')
densities = {}
for line in infile:
words = line.split()
density = float(words[-1])
substance = ' '.join(words[:-1])
densities[substance] = density
infile.close()
return densities
def read_densities_v3(filename):
infile = open(filename, 'r')
densities = {}
for line in infile:
words = line.split()
density = float(words[-1])
substance = line[:12].strip()
densities[substance] = density
infile.close()
return densities
f = 'densities.dat'
print read_densities_v1(f) == read_densities_v2(f) == read_densities_v3(f)
Exercise 6.12.
Code
infile = open('stars.dat', 'r')
data = {}
for line in infile.readlines()[1:]:
words = line.split()
name = ' '.join(words[:-3])
luminosity = float(words[-1])
data[name] = luminosity
print '='*31
print '%-20s %10s' % ('Star', 'Luminosity')
print '-'*31
for star, luminosity in data.iteritems():
print '%-20s %10.5f' % (star, luminosity)
print '='*31
Console :
===============================
Star Luminosity
-------------------------------
Wolf 359 0.00002
Sun 1.00000
Alpha Centauri C 0.00006
Alpha Centauri B 0.45000
Alpha Centauri A 1.56000
Luyten 726-8 A 0.00006
Sirius B 0.00300
Sirius A 23.60000
Luyten 726-8 B 0.00004
BD +36 degrees 2147 0.00600
Barnard's Star 0.00050
Ross 154 0.00050
===============================
Exercise 6.13
Code:
infile = open('stars.dat', 'r')
data = {}
for line in infile.readlines()[1:]:
words = line.split()
name = ' '.join(words[:-3])
if words[-3] == '-':
distance = '-'
apparent_brightness = '-'
else:
distance = float(words[-3])
apparent_brightness = float(words[-2])
luminosity = float(words[-1])
data[name] = {'distance': distance,
'apparent brightness': apparent_brightness,
'luminosity': luminosity}
print '='*68
print '%-20s %12s %18s %15s' % ('Star', 'Distance',
'App. brightness', 'Luminosity')
print '-'*68
for star in data:
if star == 'Sun':
print '%-20s %12s %18s %15.5f' % (star,
data[star]['distance'],
data[star]['apparent brightness'],
data[star]['luminosity'])
else:
print '%-20s %12f %18f %15.5f' % (star,
data[star]['distance'],
data[star]['apparent brightness'],
data[star]['luminosity'])
print '='*68
console :
====================================================================
Star Distance App. brightness Luminosity
--------------------------------------------------------------------
Wolf 359 7.700000 0.000001 0.00002
Sun - - 1.00000
Alpha Centauri C 4.200000 0.000010 0.00006
Alpha Centauri B 4.300000 0.077000 0.45000
Alpha Centauri A 4.300000 0.260000 1.56000
Luyten 726-8 A 8.400000 0.000003 0.00006
Sirius B 8.600000 0.001000 0.00300
Sirius A 8.600000 1.000000 23.60000
Luyten 726-8 B 8.400000 0.000002 0.00004
BD +36 degrees 2147 8.200000 0.000300 0.00600
Barnard's Star 6.000000 0.000040 0.00050
Ross 154 9.400000 0.000020 0.00050
====================================================================
Exercise 6.18
Code :
def read_file(filename):
infile = open(filename, 'r')
infile.readline() # read column headings
dates = []
prices = []
for line in infile:
columns = line.split(',')
date = columns[0]
date = date[:-3] # skip day of month
price = columns[-1]
dates.append(date)
prices.append(float(price))
infile.close()
dates.reverse()
prices.reverse()
return dates, prices
def load_files(filelist):
dates = {}
prices = {}
for filename in filelist:
name = filename[:-4] # read company name from 'name.csv' string
d, p = read_file(filename)
dates[name] = d
prices[name] = p
data = {'prices': prices, 'dates': dates}
return data
def normalise_prices(prices):
norm_prices = {}
for company in prices:
norm_price = prices[company][0]
norm_prices[company] = \
[p / norm_price for p in prices[company]]
return norm_prices
filelist = ['Apple.csv', 'Google.csv', 'Microsoft.csv',
'Nokia.csv', 'Sony.csv']
data = load_files(filelist)
data['norm prices'] = normalise_prices(data['prices'])
# Let the "x" values in the plot just be the indices
companies = []
for company in data['norm prices']:
plt.plot(data['norm prices'][company], linewidth=1.5)
companies.append(company)
plt.legend(companies, loc=2)
plt.ylabel('Stock price')
plt.xlabel('Time (months from Jan 1 2005)')
plt.title('Major corporation stock price variation - 2005-2014')
plt.show()
console:
Exercise 6.22
import numpy as np
import matplotlib.pyplot as plt
import datetime as dt
def get_city_codes(citylist_filename):
infile = open(citylist_filename, 'r')
for i in range(260):
infile.readline() # skip everything before content
city_filenames = {}
while True:
line = infile.readline()
if not line: # exit loop at end of file
break
if line.find('mso-list') != -1: # check line contains country name
city = line.split('<b>')[1][:-7]
infile.readline() # skip a line
line = infile.readline() # read in next line
city_filename = line.split(
'>')[1][:line.split('>')[1].find('.txt') + 4]
city_filenames[city] = city_filename
infile.close()
return city_filenames
def load_city_data(city, city_filenames, subfolder='city_temp'):
filepath = subfolder + '/' + city_filenames[city]
# load data and create datetime object array to store dates
month, day, year, temperature = np.loadtxt(filepath, unpack=True)
temperature = np.loadtxt(filepath, usecols=(3,), dtype=float)
date = [dt.datetime(y, m, d) for y, m, d in
zip(year.astype(int), month.astype(int), day.astype(int))]
date = np.asarray(date)
# store in dictionary (filtering out entries with missing data)
valid = temperature != -99
city_data = {'name': city,
'date': date[valid],
'temperature': temperature[valid]}
return city_data
def plot_city_data(*city_data_dicts):
cities = []
for city_data in city_data_dicts:
cities.append(city_data['name'])
plt.plot_date(city_data['date'], city_data['temperature'],
plt.ylabel('Temperature (C)')
plt.xlabel('Date')
plt.ylim([0, 110])
plt.legend(cities)
plt.show()
city_filenames = get_city_codes('city_temp/citylistWorld.htm')
Buenos_Aires_data = load_city_data('Buenos Aires', city_filenames)
London_data = load_city_data('London', city_filenames)
Reykjavik_data = load_city_data('Reykjavik', city_filenames)
Beirut_data = load_city_data('Beirut', city_filenames)
plot_city_data(Buenos_Aires_data, London_data, Reykjavik_data, Beirut_data)
Exercise 6.23
print ÔHello, World!Õ
