import numpy as np
from numpy import save
import os


# AU = ["AU01", "AU02", "AU04", "AU05", "AU06", "AU07", "AU09","AU10","AU12","AU14","AU15","AU17","AU20","AU23",
#       "AU25","AU26","AU45","AU01_c","AU02_c","AU04_c","AU05_c","AU06_c","AU07_c","AU09_c","AU10_c","AU12_c","AU14_c","AU15_c","AU17_c",
#       "AU20_c","AU23_c","AU25_c","AU26_c","AU28_c","AU45_c"]

reference_au = ["File", "AU01", "AU02", "AU04", "AU05", "AU06", "AU07", "AU09","AU10","AU12","AU14","AU15","AU17","AU20","AU23",
      "AU25","AU26", "AU45"]


# finding reference values
ref_au = {}
ref = []

#  get all offset data
data_all = np.load(f'/home/valapil/Project/ForkCausal_Adithya/processed_data.npy')
# data = np.concatenate(data_all[:3], data_all[4:])
data = np.delete(data_all, 2,1)
activation = []


unique = np.unique(data[1:, 0])

for i in unique:
    same_rows = data[data[:, 0] == i]
    mean = np.mean(same_rows[:, 2:19].astype(np.float64), axis=0)
    std = np.std(same_rows[:, 2:19].astype(np.float64), axis=0)
    ref_au = mean + (0.5 * std)
    # ref_au.append(mean + (0.5 * std))
    ref = np.hstack((i, ref_au))
    reference_au = np.row_stack((reference_au, ref))

# folder where each offset data is stored
folder = '/home/valapil/Project/ForkCausal_Adithya/data'
csv_files = [file for file in os.listdir(folder) if file.endswith('.npy')]



for file in csv_files:
    EXP = ["Happiness upper", "Happiness lower", "Surprise upper", "Surprise lower", "Disgust lower", "Fear upper",
            "Fear lower", "Sadness upper", "Sadness lower", "Anger upper", "Anger lower"]

    # Get data
    file_path = os.path.join(folder,file)
    # data = np.genfromtxt(file_path, delimiter=',', skip_header=1)
    data = np.load(file_path)

    # find the particular person
    base_name, extension = os.path.splitext(file)
    new_name = base_name.split('.')[0]
    f1 = new_name[:-2]
    selected_columns = data

    index = np.where(reference_au[1:, 0] == f1)[0]

    # AU activation
    change = np.subtract(selected_columns, reference_au[index+1, 1:].astype(np.float16))
    activation = np.where(change >= 0, 1, 0)

    expression_labels = np.zeros((activation.shape[0], 11), dtype=int)

    # Define expressions based on specific AUs
    expression_labels[:, 0] = np.any(activation[:, [4]], axis=1)  # AU06 is 4th au in order. [4] to get results with []
    expression_labels[:, 1] = np.all(activation[:, [8, 14]], axis=1)  # AU12 and AU25
    expression_labels[:, 2] = np.all(activation[:, [0, 1, 3]], axis=1)  # AU01, AU02, AU05
    expression_labels[:, 3] = np.any(activation[:, [15]], axis=1)  # AU26
    expression_labels[:, 4] = np.all(activation[:, [6, 7, 14]], axis=1)  # AU09, AU10, AU25
    expression_labels[:, 5] = np.all(activation[:, [0, 1, 2, 3]], axis=1)  # AU01, AU02, AU04, AU05
    expression_labels[:, 6] = np.all(activation[:, [12, 14]], axis=1)  # AU20, AU25
    expression_labels[:, 7] = np.all(activation[:, [0, 2]], axis=1)  # AU01, AU04
    expression_labels[:, 8] = np.all(activation[:, [10, 11]], axis=1)  # AU15, AU17
    expression_labels[:, 9] = np.all(activation[:, [2, 3, 5]], axis=1)  # AU04, AU05, AU07
    expression_labels[:, 10] = np.all(activation[:, [11, 13]], axis=1)  # AU17, AU23

    EXP = np.row_stack((EXP, expression_labels))

    # Save the result to a new NumPy file
    # Each file has expressions for each frame based on activation of AU in that frame
    np.save(f'/home/valapil/Project/ForkCausal_Adithya/test/{new_name}.npy', EXP)