How can I run hierarchical clustering on a correlation matrix in scipy/numpy? I have a matrix of 100 rows by 9 columns, and I'd like to hierarchically clustering by correlations of each entry across the 9 conditions. I'd like to use 1-pearson correlation as the distances for clustering. Assuming I have a numpy array "X" that contains the 100 x 9 matrix, how can I do this?

I tried using hcluster, based on this example:

Y=pdist(X, 'seuclidean')
Z=linkage(Y, 'single')
dendrogram(Z, color_threshold=0)

however, pdist is not what I want since that's euclidean distance. Any ideas?

thanks.