import numpy as np                                                              ## Does some shit
import matplotlib.pyplot as plt                                                 ## Does some more shit

def probability():
    global bad_batches                                                          ## Set as global variable so can be used outside the procedure
    bad_batches = 0                                                             ## Reset bad_batches counter to 0
    batch = [0]*(150-bad)+[1]*bad                                               ## Create list 'batch' with 'bad' 1's and (150-'bad') 0's
    for _ in range(0,10000):                                                    ## Loop 10000 times for each 'bad' value
        np.random.shuffle(batch)                                                ## Shuffle batch
        if sum(batch[:20]) > 2:                                                 ## If more than two samples are bad, the sum of the random 20 samples will be greater than 2
            bad_batches += 1                                                    ## Increase the bad_batches counter by one
    print("bad = {}...........probability = {}%".format(bad,bad_batches/100))   ## Output 'bad' value and probability of the batch being rejected (%)

######################### Below is for the probability against 'bad' graph #########################

badvalues = list(range(0,151))                                                  ## Set list of 'bad' values 0 to 150
probabilities = []                                                              ## Reset y-axis data list

for bad in badvalues:                                                           ## Loop 150 times, with 'bad' starting at 0 and increasing by 1 each cycle
    probability()                                                               ## Run probability procedure for current 'bad' value
    probabilities.append(bad_batches/100)                                       ## Append probability to y-axis values list

plt.xlabel('"bad" value')                                                       ## Self explanatory
plt.ylabel('probability of lot being rejected (%)')                             ## Self explanatory
plt.plot(badvalues,probabilities)                                               ## Plot graph probability against 'bad' value