import matplotlib.pyplot as plt
import matplotlib.transforms as mtransforms
import numpy as np

def fakeBootStrapper(n):
    '''
    This is just a placeholder for the user's method of
    bootstrapping the median and its confidence intervals.

    Returns an arbitrary median and confidence intervals
    packed into a tuple
    '''
    if n == 1:
        med = 0.1
        CI = (-0.25, 0.25)
    else:
        med = 0.2
        CI = (-0.35, 0.50)

    return med, CI



np.random.seed(2)
inc = 0.1
e1 = np.random.normal(0, 1, size=(500,))
e2 = np.random.normal(0, 1, size=(500,))
e3 = np.random.normal(0, 1 + inc, size=(500,))
e4 = np.random.normal(0, 1 + 2*inc, size=(500,))

treatments = [e1,e2,e3,e4]
med1, CI1 = fakeBootStrapper(1)
med2, CI2 = fakeBootStrapper(2)
medians = [None, None, med1, med2]
conf_intervals = [None, None, CI1, CI2]

fig = plt.figure()
ax = fig.add_subplot(111)
pos = np.array(range(len(treatments)))+1
bp = ax.boxplot(treatments, sym='k+', positions=pos,
                notch=1, bootstrap=5000,
                usermedians=medians,
                conf_intervals=conf_intervals)

ax.set_xlabel('treatment')
ax.set_ylabel('response')
plt.setp(bp['whiskers'], color='k',  linestyle='-' )
plt.setp(bp['fliers'], markersize=3.0)
plt.show()
