def show_area()

in workshop/3-Experimentation/src/plot.py [0:0]

• 24 lines of code
• 13 McCabe index (conditional complexity)

def show_area(ax, d_hat, stderr, sig_level, area_type='power'):
    """Fill between upper significance boundary and distribution for
    alternative hypothesis
    """
    left, right = confidence_interval(sample_mean=0, sample_std=stderr,
                                      sig_level=sig_level)
    x = np.linspace(-12 * stderr, 12 * stderr, 1000)
    null = ab_dist(stderr, 'control')
    alternative = ab_dist(stderr, d_hat, 'test')

    # if area_type is power
    # Fill between upper significance boundary and distribution for alternative
    # hypothesis
    if area_type == 'power':
        ax.fill_between(x, 0, alternative.pdf(x), color='green', alpha='0.25',
                        where=(x > right))
        ax.text(-3 * stderr, null.pdf(0),
                'power = {0:.3f}'.format(1 - alternative.cdf(right)),
                fontsize=12, ha='right', color='k')

    # if area_type is alpha
    # Fill between upper significance boundary and distribution for null
    # hypothesis
    if area_type == 'alpha':
        ax.fill_between(x, 0, null.pdf(x), color='green', alpha='0.25',
                        where=(x > right))
        ax.text(-3 * stderr, null.pdf(0),
                'alpha = {0:.3f}'.format(1 - null.cdf(right)),
                fontsize=12, ha='right', color='k')

    # if area_type is beta
    # Fill between distribution for alternative hypothesis and upper
    # significance boundary
    if area_type == 'beta':
        ax.fill_between(x, 0, alternative.pdf(x), color='green', alpha='0.25',
                        where=(x < right))
        ax.text(-3 * stderr, null.pdf(0),
                'beta = {0:.3f}'.format(alternative.cdf(right)),
                fontsize=12, ha='right', color='k')