def draw_distribution_graph(column, name, mean, median, mode):
column.sort()
graph.hist(column, bins=100, density=True, alpha=constant.SCATTER_ALPHA, color=constant.SCATTER_COLOUR)
x = np.linspace(graph.xlim()[0], graph.xlim()[1], 100)
graph.xlabel(name)
# distributions
p = norm.pdf(x, mean, MathsUtil.arr_standard_deviation(column, mean))
graph.plot(x, p, color=constant.DISTRIBUTION_MEAN_COLOUR)
p = norm.pdf(x, median, MathsUtil.arr_standard_deviation(column, median))
graph.plot(x, p, color=constant.DISTRIBUTION_MEDIAN_COLOUR)
p = norm.pdf(x, mode, MathsUtil.arr_standard_deviation(column, mode))
graph.plot(x, p, color=constant.DISTRIBUTION_MODE_COLOUR)
# averages
graph.axvline(mean, color=constant.DISTRIBUTION_MEAN_COLOUR,
linestyle=constant.DISTRIBUTION_MEAN_LINE_STYLE)
graph.axvline(median, color=constant.DISTRIBUTION_MEDIAN_COLOUR,
linestyle=constant.DISTRIBUTION_MEDIAN_LINE_STYLE)
graph.axvline(mode, color=constant.DISTRIBUTION_MODE_COLOUR,
linestyle=constant.DISTRIBUTION_MODE_LINE_STYLE)
# title
graph.legend({"Mean": mean, "Median": median, "Mode": mode})
mean_title = f"mean={mean:.2f}, variance={MathsUtil.arr_variance(column, mean):.2f} std={MathsUtil.arr_standard_deviation(column, mean):.2f}"
median_title = f"median={median:.2f}, variance={MathsUtil.arr_variance(column, median):.2f} std={MathsUtil.arr_standard_deviation(column, median):.2f}"
mode_title = f"mode={mode:.2f}, variance={MathsUtil.arr_variance(column, mode):.2f} std={MathsUtil.arr_standard_deviation(column, mode):.2f}"
graph.title(f"Distribution: {mean_title}\n{median_title}\n{mode_title}")
def print_deviation_calculation(average_type, rows, average):
variance = MathsUtil.arr_variance(rows, average)
standard_deviation = MathsUtil.arr_standard_deviation(rows, average)
print(f"For {average_type}: The Standard Deviation is {standard_deviation:.2f}, The Variance is {variance:.2f} and the {average_type} in {average:.2f}")
def test_arr_standard_deviation(self):
self.assertEqual(MathsUtil.arr_standard_deviation([3, 9], MathsUtil.arr_mean([3, 9])), 3)
