Reconstructing the period sensitivity plots

how_to_interpret_sensitivity.py

#    return scores, found_periods, faps, alias_scores
scores = np.load(f"output_denser_W7_0_20211010.npy")
    
# periods:
periods_denser_raw = np.logspace(np.log10(1/24.5), np.log10(24.5), 60)
extra_periods = (1/4, 1/3, 1/2, 1, 2, 3, 4)
periods_denser = np.sort(np.append(periods_denser_raw, np.array(extra_periods)))
# 
# [ 0.04081633  0.04549088  0.05070078  0.05650736  0.06297895  0.07019171
#   0.07823051  0.08718997  0.09717553  0.1083047   0.12070845  0.13453276
#   0.14994032  0.16711245  0.18625124  0.20758193  0.23135555  0.25
#   0.25785188  0.28738274  0.32029566  0.33333333  0.35697798  0.3978614
#   0.44342705  0.49421117  0.5         0.55081142  0.61389389  0.68420097
#   0.76256007  0.84989336  0.94722862  1.          1.05571134  1.1766182
#   1.3113721   1.46155888  1.62894601  1.8155034   2.          2.02342654
#   2.25516238  2.5134381   2.80129322  3.          3.12211536  3.47968011
#   3.87819549  4.          4.32235141  4.81737493  5.36909173  5.98399469
#   6.6693203   7.43313381  8.28442417  9.23320978 10.29065642 11.46920867
#  12.7827363  14.24669759 15.87832114 17.69680874 19.72356124 21.98243049
#  24.5       ]
# amplitudes: 
amplitudes_denser = [0.001, 0.003, 0.005, 0.0075, 0.0085, 0.01, 0.0125, 0.015, 0.0175, 0.02, 0.025, 0.03]