########                                                                                      #########
########          Randomizes Order of Treatments and exports them to a csv file               #########
#######################################################################################################
import random
import pandas as pd
from itertools import combinations
import itertools

#number_of_results = int(input("How many iterations?"))
number_of_results = 4
print(number_of_results)
treatment_list = [1,2,3,4]

def t_randomizer(n):
    combinations = []
    k = 0
    for i in itertools.permutations(range(1, n + 1)):
        if isinstance(i, (list, tuple)) and all(i[j] != i[j+1] - 1 for j in range(len(i)-1)):
            if k == 0 or i[0] != combinations[-1][0]:
                if all(i[j][:k] != combinations[-1][j][:k] for j in range(k)):
                    combinations.append(i)
                    k = min(k+1, n)
    return combinations


# def t_randomizer (lst, n):
#     output_list = []
#     for i in range(n):
#         one_iteration = random.sample(lst, len(lst))
#         if i == 0:
#             output_list.append(one_iteration)
#             #output_list = [element for sublist in output_list for element in sublist]
#             print(output_list)
#         else:
#             indicator = 0
#             while indicator == 0:
#                 one_iteration = random.sample(lst, len(lst))
#                 for iteration in range(n):
#                     for t in range(len(output_list)-1):
#                         print(f"op list t {output_list[t,iteration]} \nop list t+1 {output_list[t+1]} ")
#                         if t <=len(lst)-2:
#                             back_to_back_iter = [one_iteration[t], one_iteration[t+1]]
#                             back_to_back_op = [output_list[t], output_list[t+1]]
#                         else:
#                             back_to_back_iter = one_iteration[t]
#                             back_to_back_op = output_list[t]
#                         if one_iteration[t] == output_list[t] or back_to_back_iter == back_to_back_op:
#                             indicator = 0
#                 output_list.append(one_iteration)
#                 print(f"succesful iter: {one_iteration} \n\n")
#                 print(f"total list: {output_list} \n\n")
#                 indicator = 1
#     return output_list

randomized_treatment_orders = t_randomizer(4)

print(randomized_treatment_orders)

output_table = pd.DataFrame(randomized_treatment_orders, columns=['first','second','third','fourth'])

output_table.to_csv('random_treatment_iterations.csv',mode='w+')