class TestGaussianClass(unittest.TestCase):
def setUp(self):
self.gaussian = Gaussian(25, 2)
self.gaussian.read_data_file('numbers.txt')
def test_initialization(self):
self.assertEqual(self.gaussian.mean, 25, 'incorrect mean')
self.assertEqual(self.gaussian.stdev, 2,
'incorrect standard deviation')
def test_readdata(self):
self.assertEqual(self.gaussian.data, \
[1, 3, 99, 100, 120, 32, 330, 23, 76, 44, 31], 'data not read in correctly')
def test_meancalculation(self):
self.assertEqual(self.gaussian.calculate_mean(), \
sum(self.gaussian.data) / float(len(self.gaussian.data)), 'calculated mean not as expected')
def test_stdevcalculation(self):
self.assertEqual(round(self.gaussian.calculate_stdev(), 2), 92.87,
'sample standard deviation incorrect')
self.assertEqual(round(self.gaussian.calculate_stdev(0), 2), 88.55,
'population standard deviation incorrect')
def test_pdf(self):
self.assertEqual(round(self.gaussian.pdf(25), 5), 0.19947, \
'pdf function does not give expected result')
self.gaussian.calculate_mean()
self.gaussian.calculate_stdev()
self.assertEqual(round(self.gaussian.pdf(75), 5), 0.00429, \
'pdf function after calculating mean and stdev does not give expected result')
def test_add(self):
gaussian_one = Gaussian(25, 3)
gaussian_two = Gaussian(30, 4)
gaussian_sum = gaussian_one + gaussian_two
self.assertEqual(gaussian_sum.mean, 55)
self.assertEqual(gaussian_sum.stdev, 5)
from distributions import Gaussian
# initialize two gaussian distributions
gaussian_one = Gaussian(25, 3)
gaussian_two = Gaussian(30, 2)
# initialize a third gaussian distribution reading in a data efile
gaussian_three = Gaussian()
gaussian_three.read_data_file('numbers.txt')
gaussian_three.calculate_mean()
gaussian_three.calculate_stdev()
print(gaussian_one.mean)
print(gaussian_two.mean)
print(gaussian_one.stdev)
print(gaussian_two.stdev)
print(gaussian_three.mean)
print(gaussian_three.stdev)
# plot histogram of gaussian three
gaussian_three.plot_histogram_pdf()
# add gaussian_one and gaussian_two together
gaussian_one + gaussian_two
from distributions import Gaussian
g1 = Gaussian(25, 2)
g1.read_data_file('numbers.txt')
g1.plot_histogram()
