Surface NMR processing and inversion GUI
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harmonic.py 2.1KB

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  1. import numpy as np
  2. from scipy.optimize import least_squares
  3. def harmonic ( sN, f0, fs, nK, t ):
  4. """
  5. Performs inverse calculation of harmonics contaminating a signal.
  6. Args:
  7. sN = signal containing noise
  8. f0 = base frequency of the sinusoidal noise
  9. fs = sampling frequency
  10. nK = number of harmonics to calculate
  11. t = time samples
  12. """
  13. print("building matrix ")
  14. A = np.zeros( (len(t), 2*nK) )
  15. for irow, tt in enumerate(t):
  16. A[irow, 0::2] = np.cos( np.arange(nK)*2*np.pi*(f0/fs)*irow )
  17. A[irow, 1::2] = np.sin( np.arange(nK)*2*np.pi*(f0/fs)*irow )
  18. # brutal
  19. #for k, ik in enumerate( np.arange(0, 2*nK, 2) ):
  20. # A[irow, ik ] = np.cos( k*2*np.pi*(f0/fs)*irow )
  21. # A[irow, ik+1] = np.sin( k*2*np.pi*(f0/fs)*irow )
  22. v = np.linalg.lstsq(A, sN, rcond=None) #, rcond=1e-8)
  23. alpha = v[0][0::2]
  24. beta = v[0][1::2]
  25. amp = np.sqrt( alpha**2 + beta**2 )
  26. phase = np.arctan(- beta/alpha)
  27. h = np.zeros(len(t))
  28. for ik in range(nK):
  29. h += np.sqrt(alpha[ik]**2 + beta[ik]**2) * np.cos( 2.*np.pi*ik * (f0/fs) * np.arange(0, len(t), 1 ) + phase[ik] )
  30. #plt.matshow(A, aspect='auto')
  31. #plt.colorbar()
  32. #plt.figure()
  33. #plt.plot(alpha)
  34. #plt.plot(beta)
  35. #plt.plot(amp)
  36. #plt.figure()
  37. #plt.plot(h)
  38. #plt.title("modelled noise")
  39. return h
  40. if __name__ == "__main__":
  41. import matplotlib.pyplot as plt
  42. f0 = 60 # Hz
  43. delta = np.random.rand()
  44. fs = 50000 #1e4
  45. t = np.arange(0, 1, 1/fs)
  46. phi = .234
  47. A = 1.0
  48. nK = 20
  49. sN = A * np.sin( (delta+f0)*2*np.pi*t + phi ) + np.random.normal(0,.1,len(t))
  50. sNc = A * np.sin( (delta+f0)*2*np.pi*t + phi )
  51. h = harmonic(sN, f0, fs, nK, t)
  52. plt.figure()
  53. plt.plot(t, sN, label="sN")
  54. plt.plot(t, sN-h, label="sN-h")
  55. plt.plot(t, h, label='h')
  56. plt.title("true noise")
  57. plt.legend()
  58. plt.figure()
  59. plt.plot(t, sN-sNc, label='true noise')
  60. plt.plot(t, sN-h, label='harmonic removal')
  61. plt.legend()
  62. plt.title("true noise")
  63. plt.show()
  64. print("hello")