config.py

import numpy as np
import scipy

l                                = 0.09218/2                                # dist b.w. 2 sides of hexagon
f_s_orig                         = 16000                                    # sampling frequency
c                                = scipy.constants.speed_of_sound           # speed of sound              
user                             = "2"
setting                          = "2"
file_path                        = f"./data/user{user}_setting{setting}/*.csv"



mic_locs_1 = np.array([
          [0.5, 0.5],
          [0  , 3],
          [1.5, 0.5],
          [1.5, 3.5],
          [2, 1.5],
          [0, 1.5],
          [3, 3],
          [3.5, 0]])
mic_locs_2 = np.array([
          [0.5, 2],
          [1.25  , 4.25],
          [0, 3.5],
          [2.75, 1.25],
          [0,    0],
          [1.5,  0.5],
          [2,    3],
          [1.5,  2]])
mic_locs_3 = np.array([
          [3.5, 1.5],
          [5.5  , 0],
          [0.5, 3],
          [1, 0],
          [2,    2],
          [0,  0],
          [4.5, 3.5],
          [4.5, 0]])
mic_locs_4 = np.array([
          [0.5, 2.0],
          [1.25, 4.25],
          [0.0, 3.5],
          [2.75, 1.25],
          [0.0, 0.0],
          [1.5, 0.5],
          [2.0, 3.0],
          [1.5, 2.0],
  
          ])
mic_locs_5 = np.array([
          [0.5, 0.5],
          [0,   3],
          [1.5, 0.5],
          [1.5, 3.5],
          [2.0, 1.5],
          [0.0, 1.5],
          [3.0, 3.0],
          [3.5, 0.0],
  
          ])
mic_locs_6 = np.array([
          [0.0, 1.5],
          [2.5, 4.5],
          [0.0, 0.0],
          [5.0, 0.0],
          [2.5, 1.5],
          [0.0, 3.0],
          [2.5, 0.0],
          [5.25, 2.5],
  
          ])

mic_locs_all                     = np.array([mic_locs_1, mic_locs_2, mic_locs_3, mic_locs_4, mic_locs_5, mic_locs_6])
mic_locs                         = mic_locs_all[int(setting) - 1]
actual_x, actual_y               = (3,1)      if setting == "1" else (4,1)

mic_angles                       = (60 * (1 - np.arange(6))) * np.pi / 180

resample_factor                  = 4
board_width                      = 6
board_height                     = 4
use_smoothing                    = False
smoothing_window_len             = 41
smoothing_order                  = 2
aoa_step_angle                   = 1
aoa_correlate_using_product      = True
probs_flatten_using_product      = False
step_x                           = 0.01
step_y                           = 0.01
f_s                              = f_s_orig * resample_factor
thresh                           = 0.9

final.ipynb

final.py