def getEigenvaluePCA(a, b, c, dim, decim=2):
	"""
	Function to estimate the eigenvalues of the PCA 
	for every points into a given sphere of radius dim/2
	------
	INPUT:
		@a, @b, @c: numpy arrays with the coordinates
		@dim	  : float - diameter of interest of the neighborhood ball
		@decim	: integer - decimation factor to lower the time processing.
	-----
	OUTPUT:
		@comp	 : array of array with the 3 eigenvalues
	
	EXAMPLE: 
		[[0.68693784 0.31106007 0.0020021 ]
		 [0.75319626 0.24592732 0.00087642]
		 [0.78309201 0.21595601 0.00095197]
		 ...]
	
	"""
	from sklearn.decomposition import PCA
	from scipy import spatial
	
	
	
	print('Data are decimed by a factor of ', decim)
	# Remove the mean of each vector
	a -= np.mean(a, axis=0)
	b -= np.mean(b, axis=0)
	c -= np.mean(c, axis=0)	
	# Concat the coordinates arrays into a single matrix.
	Y = np.c_[a[::decim], b[::decim], c[::decim]]
	print("shape of Y " ,np.shape(Y))

	# Estimate the distance of one point with every neighbour around
	dist = spatial.distance.squareform(spatial.distance.pdist(Y))	
	

	
	n_samples = Y.shape[0]		   # number of points in Y
	
	print('  Treating distance of', dim, 'm')
	comp = []						# Initialize the eigenvalues components array

	# Loop over every points to have a look on its neighbour.
	#   1. We consider a sphere of radius dim/2 and we only consider 
	#				the neighbours inside this sphere
	#   2. We create a subsample with these points
	#   3. Using PCA scikit-learn function, we estimate the 3 eigenvalues of these points
	#   4. We concatenate these 3 eigenvalues in a single array which will be return at the end.
	for kk in range(0,n_samples):
		pts = np.where((dist[kk,:]<=dim/2))  # Mask of values corresp. to the criteria
											 

		if np.size(pts)<3:			 # Check if the number of neighbour is sufficient	 
			print("Revise dims: ")
			print(pts)
			print(dist)
		else:
			Ytmp = Y[pts,:]			 # Apply the criteria on the dataset
			Ytmp = Ytmp[0,:,:]		  # Reduce the depth of the array	   
										# (not necessary depending on the data structure used)
										# Equivalent to Ytmp = np.squeeze(Ytmp)

			pca = PCA(n_components=3)   # Instantiate the PCA object 
			pca.fit(Ytmp)
			eigenvalues = pca.explained_variance_ratio_
			#sval = pca.singular_values_
			#print('------')
			#print( eigenvalues) 
			#print( sval**2 / np.sum(sval**2) )
			comp.append(eigenvalues)	# Append the 3 eigenvalues to "comp" array
	comp = np.array(comp)
	return comp