import os, sys
import numpy as np
from pprint import pprint

np_raw_results_csv = np.genfromtxt(
    "np_raw_results.csv",
    delimiter=",",
    dtype=[
        ("img_test_path", "U50"),
        ("diff_gray", "f8"),
        ("diff_rgb", "f8"),
        ("sift_match", "f8"),
        ("diff_contour", "f8"),
        ("d_cld_full", "f8"),
        ("d_cld_up", "f8"),
        ("d_cld_down", "f8"),
        ("d_cld_left", "f8"),
        ("d_cld_right", "f8"),
    ],
)


np_raw_results = np.array([list(row) for row in np_raw_results_csv])
pprint(np_raw_results)


weight_diff_gray = 0.5
weight_diff_rgb = 0.5
weight_diff_sift = 0
weight_diff_contour = 0
weight_diff_cld = 0

weight_diff_u = 0
weight_diff_d = 0
weight_diff_l = 0
weight_diff_r = 0


min_values = np.min(np_raw_results[:, 1:].astype(np.float64), axis=0)
max_values = np.max(np_raw_results[:, 1:].astype(np.float64), axis=0)
normalized_array = (np_raw_results[:, 1:].astype(np.float64) - min_values) / (max_values - min_values)
np_raw_results = np.concatenate((np_raw_results, normalized_array), axis=1)

start_col = 9
np_raw_results[:, start_col + 1] = np_raw_results[:, start_col + 1].astype(np.float64) * weight_diff_gray

np_raw_results[:, start_col + 2] = np_raw_results[:, start_col + 2].astype(np.float64) * weight_diff_rgb
np_raw_results[:, start_col + 3] = (1 - np_raw_results[:, start_col + 3].astype(np.float64)) * weight_diff_sift
np_raw_results[:, start_col + 4] = np_raw_results[:, start_col + 4].astype(np.float64) * weight_diff_contour
#
np_raw_results[:, start_col + 5] = np_raw_results[:, start_col + 5].astype(np.float64) * weight_diff_cld
np_raw_results[:, start_col + 6] = np_raw_results[:, start_col + 6].astype(np.float64) * weight_diff_u
np_raw_results[:, start_col + 7] = np_raw_results[:, start_col + 7].astype(np.float64) * weight_diff_d
np_raw_results[:, start_col + 8] = np_raw_results[:, start_col + 8].astype(np.float64) * weight_diff_l
np_raw_results[:, start_col + 9] = np_raw_results[:, start_col + 9].astype(np.float64) * weight_diff_r


diff_sums = np.sum(np_raw_results[:, 10:-1].astype(np.float64), axis=1)

np_raw_results = np.concatenate((np_raw_results, diff_sums[:, np.newaxis]), axis=1)
np_raw_results = np.concatenate(
    (np_raw_results, np.array([row[0].replace("image.orig/", "")[0] for row in np_raw_results])[:, np.newaxis]), axis=1
)
np_raw_results = np.concatenate((np_raw_results, np_raw_results[:, 0][:, np.newaxis]), axis=1)

np_raw_results_sorted = np_raw_results[np.argsort(np_raw_results[:, -3].astype(np.float64))]

raw_results = np_raw_results_sorted.tolist()

column_names = [
    "img_test_path",
    "diff_gray",
    "diff_rgb",
    "sift_match",
    "diff_contour",
    "d_cld_full",
    "d_cld_up",
    "d_cld_down",
    "d_cld_left",
    "d_cld_right",
    #
    "w_gray",
    "w_rgb",
    "w_sift_match",
    "w_contour",
    "w_cld_full",
    "w_cld_up",
    "w_cld_down",
    "w_cld_left",
    "w_cld_right",
    "w_sum",
    "category",
    "image",
]

np.savetxt(
    "np_raw_results_sorted.csv",
    np_raw_results_sorted,
    delimiter=",",
    fmt="%s",
    header=",".join(column_names),
    comments="",
)

# np.savetxt("np_raw_results_sorted.csv", np_raw_results_sorted, delimiter=",", fmt="%f")

print(np_raw_results)