def test_normalize_to_100g(self):
"""Normalize a row of weight based measurements (in grams) to 100g"""
flour = [1.0]
milk = [3.75]
egg = [1.01]
butter = [0.16]
columns = zip(flour, milk, egg, butter)
new_columns = list(normalize_to_100g(columns))
self.assertEquals(len(new_columns), 1)
self.assertEquals(len(new_columns[0]), 4)
self.assertAlmostEquals(new_columns[0][0], 16.9, 1)
self.assertAlmostEquals(new_columns[0][1], 63.32, 1)
self.assertAlmostEquals(new_columns[0][2], 17.05, 1)
self.assertAlmostEquals(new_columns[0][3], 2.73, 1)
def calculate_statistics(raw_data, ingredients, zero_columns):
"""Calculate mean, confidence interval and minimum sample size for each
ingredient.
"""
raw_data = list(normalize_to_100g(raw_data))
data = numpy.array(raw_data).transpose()
if zero_columns is not None and len(zero_columns) > 0:
filter_map = create_zero_filter(ingredients, zero_columns)
data = filter_zeros(data, filter_map)
_, _, defaults = calculate_variables(data)
data = apply_defaults(raw_data, defaults, filter_map)
return calculate_statistics(data, ingredients, None)
else:
intervals, std_deviations, means = calculate_variables(data)
return Statistics(ingredients, intervals, std_deviations, means)
def normalize(ingredients, columns):
"""Tests were originally written with the axis swapped, so
we use zip to swap back here so that the tests don't need
to change"""
columns = to_grams(ingredients, columns)
return zip(*normalize_to_100g(columns))
