Surface NMR processing and inversion GUI
Vous ne pouvez pas sélectionner plus de 25 sujets Les noms de sujets doivent commencer par une lettre ou un nombre, peuvent contenir des tirets ('-') et peuvent comporter jusqu'à 35 caractères.

1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768
  1. ##########################################################
  2. #
  3. #
  4. #
  5. #
  6. #
  7. #from scipy import mean
  8. import numpy
  9. def pca(A, remove=[]):
  10. ''' The input matrix A should be a 2D numpy array in column-major
  11. order. Each column is a dataset and PCA will be applied across
  12. columns
  13. '''
  14. #nrow = len(A[:,0])
  15. #ncol = len(A[0,:])
  16. nrow,ncol = numpy.shape(A)
  17. # Cast into matrix type for easier math
  18. A = numpy.matrix(A)
  19. # Allocate Covariance Matrix
  20. covMatrix = numpy.matrix(numpy.zeros((nrow, nrow)))
  21. # Compute Means of each row. Each column must be normalised
  22. meanArray = []
  23. for i in range(nrow):
  24. meanArray.append(numpy.mean(A[i].tolist()[0]) )
  25. A[i] -= meanArray[i]
  26. meanArray = numpy.array(meanArray)
  27. # Generate Covariance Matrix
  28. covMatrix = numpy.cov(A)
  29. # Compute Eigen Values, Eigen Vectors
  30. eigs = numpy.linalg.eig(covMatrix)
  31. K = eigs[1].T
  32. #print K
  33. #print "Eigen Values"
  34. #print eigs[0]
  35. # Zero requested components
  36. for i in remove:
  37. K[i] = numpy.zeros(len(K))
  38. # Make Transform Matrices
  39. transMatrix = K*A
  40. # Return Necssary Stuff
  41. return numpy.array(transMatrix), K, meanArray
  42. #return K, A, meanArray
  43. #def invpca(K, A, means):
  44. def invpca(transMatrix, K, means):
  45. '''Converts a PCA rotated dataset back to normal. Input parameters
  46. are the components to discard in re-creation.
  47. '''
  48. K = numpy.matrix(K)
  49. # Transform and Untransform the data
  50. #transMatrix = K*A
  51. untransMatrix = K.T*transMatrix
  52. # Correct for normalisation
  53. for i in range(len(transMatrix)):
  54. untransMatrix[i] += means[i]
  55. return untransMatrix