def main():
"""The application entry point"""
print 'EXERCISE 1A'
print '==========='
print 'This program takes a CSV file, asks you to select a row from that'
print 'file, and then computes the mean and standard deviation of the'
print 'values in that row.'
print
file_path = io.get_and_confirm_input('Enter csv file with values: ')
data = io.read_csv_file(file_path)
if not data:
raise RuntimeError('No data found in file {}'.format(file_path))
column = io.choose_from_list(
'Which column would you like to use:', data[0].keys())
if column not in data[0]:
raise RuntimeError('Invalid column {}'.format(column))
values = linked_list.LinkedList()
for each in data:
values.insert(each[column])
for each in values:
print each
print 'Mean: ', statistics.mean(values)
print 'Std Dev: ', statistics.standard_deviation(values)
def execute(self):
print '8A: Load CSV file and sort by selected column'
print
file_name = io.prompt_existant_file_name('CSV file to sort: ')
data = io.read_csv_file(file_name)
if not data:
print 'ERROR: File contains no data.'
sys.exit(1)
column_names = data[0].keys()
sort_column = io.choose_from_list('Column to sort on', column_names)
try:
for each in data:
each[sort_column] = float(each[sort_column])
except ValueError:
print 'ERROR: Column {} contains non-integer value.'.format(
sort_column)
sys.exit(1)
sorted_data = sorted(data, key=lambda item: item[sort_column])
table = display_table.DisplayTable(column_names)
for each in sorted_data:
table.add_row(each.values())
table.display()
def execute(self):
parser = argparse.ArgumentParser()
parser.add_argument(
'CSVFILE', help='path to csv file with historical data.')
parser.add_argument(
'ESTVAL', help='an estimated value')
args = parser.parse_args()
file_path = args.CSVFILE
estimated_value = float(args.ESTVAL)
csv_data = io.read_csv_file(file_path)
if not csv_data:
print 'ERROR: No data'
sys.exit(1)
columns = csv_data[0].keys()
x_column = io.choose_from_list('X Column:', columns)
y_column = io.choose_from_list('Y Column:', columns)
x_data = [float(each[x_column]) for each in csv_data if each[x_column]]
y_data = [float(each[y_column]) for each in csv_data if each[y_column]]
print
x_data, y_data = probe.trim_to_equal_length(x_data, y_data)
print 'X DATA: {}'.format(x_data)
print 'Y DATA: {}'.format(y_data)
print
beta_0 = statistics.beta_0(x_data, y_data)
print u'\u03B20: {}'.format(beta_0)
beta_1 = statistics.beta_1(x_data, y_data)
print u'\u03B21: {}'.format(beta_1)
integ = integration.Integrator(20, 0.000001)
tdist = statistics.make_t_distribution(len(x_data) - 2)
itdist = lambda x: integ.integrate_minus_infinity_to(tdist, x)
std_dev = (
statistics.standard_deviation_around_regression(x_data, y_data)
)
print "StdDev: ", std_dev
projection = beta_0 + beta_1 * estimated_value
print 'Projection: ', projection
print 't(70 percent): ', integration.approximate_inverse(itdist, 0.85)
print 't(90 percent): ', integration.approximate_inverse(itdist, 0.95)
range70 = statistics.prediction_range(
estimated_value, 0.85, x_data, y_data
)
range90 = statistics.prediction_range(
estimated_value, 0.95, x_data, y_data
)
print 'Range(70 percent) =', projection + range70, \
'UPI =', projection - range70, 'LPI =', range70
print 'Range(90 percent) =', projection + range90, \
'UPI =', projection - range90, 'LPI =', range90
def execute(self):
"""Prompt the user for a CSV file and column selection. Then, perform
chi-squared test on the data given.
"""
print 'PSP Exercise 9A'
print 'This program performs the chi-squared test on data given.'
print
file_path = self.get_file_name()
data = io.read_csv_file(file_path)
test_data = self.get_test_column(data)
q_val, p_val = chi_squared.ChiSquaredTest().execute(test_data)
print 'Q: ', q_val
print 'P: ', 1.0 - p_val
def execute(self):
"""Run the program"""
parser = argparse.ArgumentParser()
parser.add_argument('CSVFILE', help='path to csv file with data.')
args = parser.parse_args()
csv_data = io.read_csv_file(args.CSVFILE)
if not csv_data:
print 'ERROR: Invalid csv data file.'
sys.exit(1)
columns = csv_data[0].keys()
x_column = io.choose_from_list('X Column:', columns)
y_column = io.choose_from_list('Y Column:', columns)
x_data = [float(each[x_column]) for each in csv_data if each[x_column]]
y_data = [float(each[y_column]) for each in csv_data if each[x_column]]
print 'R:', statistics.correlation(x_data, y_data)
print 'T:', statistics.t_value(x_data, y_data)
print 'Significance:', statistics.significance(x_data, y_data)
def execute(self):
"""Calculate and display the relative size table report"""
data = io.read_csv_file(self.file_path)
if not data:
print "NO DATA"
return
if not all([self.are_valid_keys(each.keys()) for each in data]):
raise RuntimeError(
'Invalid data columns {}'.format(data[0].keys()))
normalized_data = self.get_normalized_data(data)
normalized_by_category = self.group_by_category(normalized_data)
results = {}
for category, items in normalized_by_category.iteritems():
if len(items) < 2:
results[category] = [items[0]] * 5
else:
results[category] = statistics.size_ranges(items)
self.print_table(results)
def from_csv_file(cls, filename):
"""Reads historical data from a CSV file.
Arguments:
filename(str): A file name
Returns:
HistoricalData: Historical data read from CSV file.
"""
data = io.read_csv_file(filename)
planned_sizes = collect("Planned A+M Size", data)
proxy_sizes = collect("Proxy Size Estimate", data)
actual_sizes = collect("Actual A+M Size", data)
planned_times = collect("Planned Time", data)
actual_times = collect("Actual Time", data)
return cls(
planned_sizes=planned_sizes,
proxy_sizes=proxy_sizes,
actual_sizes=actual_sizes,
planned_times=planned_times,
actual_times=actual_times,
)
def execute(self):
parser = argparse.ArgumentParser()
parser.add_argument(
'CSVFILE', help='path to csv file with historical data.')
args = parser.parse_args()
file_path = args.CSVFILE
csv_data = io.read_csv_file(file_path)
if not csv_data:
print 'ERROR: No data'
sys.exit(1)
columns = csv_data[0].keys()
x_column = io.choose_from_list('X Column:', columns)
y_column = io.choose_from_list('Y Column:', columns)
x_data = [float(each[x_column]) for each in csv_data]
y_data = [float(each[y_column]) for each in csv_data]
#x_data, y_data = statistics.remove_outliers(x_data, y_data)
print
print 'X DATA: {}'.format(x_data)
print 'Y DATA: {}'.format(y_data)
print
beta_0 = statistics.beta_0(x_data, y_data)
print u'\u03B20: {}'.format(beta_0)
warnings = statistics.beta_0_warnings(beta_0)
if warnings:
print 'WARNINGS:'
print '\n'.join(warnings)
print
beta_1 = statistics.beta_1(x_data, y_data)
print u'\u03B21: {}'.format(beta_1)
warnings = statistics.beta_1_warnings(beta_1)
if warnings:
print 'WARNINGS:'
print '\n'.join(warnings)
print
