'/Users/Apple/Desktop/first_chain_code_results/9_ses_set6_10run/rr_data80.txt'
)
rr_data45 = numpy.loadtxt(
'/Users/Apple/Desktop/first_chain_code_results/9_ses_set6_10run/rr_data81.txt'
)
rr_data46 = numpy.loadtxt(
'/Users/Apple/Desktop/first_chain_code_results/9_ses_set6_10run/rr_data82.txt'
)
rr_data47 = numpy.loadtxt(
'/Users/Apple/Desktop/first_chain_code_results/9_ses_set6_10run/rr_data83.txt'
)
######################################### 3
concat_data = concatenate_func(rr_data39, rr_data40, rr_data41, rr_data42,
rr_data43, rr_data44, rr_data45, rr_data46,
rr_data47)
print(concat_data.shape)
file = open('concat_data.txt', "w")
numpy.savetxt('concat_data.txt', concat_data, fmt='%.18e')
################################### 4
num_train_examp = 0.9
x_train, x_test = make_train_and_test_concat_data(concat_data, num_train_examp)
################################### 5
critical_times_set = find_critical_times(rr_data39, rr_data40, rr_data41,
'/Users/Apple/Desktop/first_chain_code_results/9_ses_set2_10run/rr_data32.txt'
)
rr_data33 = numpy.loadtxt(
'/Users/Apple/Desktop/first_chain_code_results/9_ses_set2_10run/rr_data33.txt'
)
rr_data34 = numpy.loadtxt(
'/Users/Apple/Desktop/first_chain_code_results/9_ses_set2_10run/rr_data34.txt'
)
rr_data35 = numpy.loadtxt(
'/Users/Apple/Desktop/first_chain_code_results/9_ses_set2_10run/rr_data35.txt'
)
######################################### 3
concat_data = concatenate_func(rr_data27, rr_data28, rr_data29, rr_data30,
rr_data31, rr_data32, rr_data33, rr_data34,
rr_data35)
print(concat_data.shape)
file = open('concat_data.txt', "w")
numpy.savetxt('concat_data.txt', concat_data, fmt='%.18e')
##3temp
target_voxel_ind = 3122
my_theta_mean = numpy.loadtxt(
"/Users/Apple/Desktop/first_chain_code_results/9_ses_set2_10run/zero_initialize-random_train_examp/my_theta_mean.txt"
)
c_back0 = numpy.zeros(concat_data.shape)
c_back0 = concat_data