Author: bugman
Date: Wed Nov 25 18:38:37 2015
New Revision: 28118
URL: http://svn.gna.org/viewcvs/relax?rev=28118&view=rev
Log:
Improved the printouts from the relax library principle component analysis.
This is in the pca_analysis() function of the lib.structure.pca module.
Modified:
trunk/lib/structure/pca.py
Modified: trunk/lib/structure/pca.py
URL:
http://svn.gna.org/viewcvs/relax/trunk/lib/structure/pca.py?rev=28118&r1=28117&r2=28118&view=diff
==============================================================================
--- trunk/lib/structure/pca.py (original)
+++ trunk/lib/structure/pca.py Wed Nov 25 18:38:37 2015
@@ -103,6 +103,7 @@
# Perform an eigenvalue decomposition of the covariance matrix.
if algorithm == 'eigen':
+ text = 'eigenvalues'
values, vectors = eigh(covariance_matrix)
# Sort the values and vectors.
@@ -112,6 +113,7 @@
# Perform a singular value decomposition of the covariance matrix.
elif algorithm == 'svd':
+ text = 'singular values'
vectors, values, V = svd(covariance_matrix)
# Invalid algorithm.
@@ -119,9 +121,9 @@
raise RelaxError("The '%s' algorithm is unknown. It should be either
'eigen' or 'svd'." % algorithm)
# Printout.
- print("\nThe eigenvalues/singular values are:")
+ print("\nThe %s in Angstrom are:" % text)
for i in range(num_modes):
- print("Mode %i: %10.5f" % (i+1, values[i]))
+ print("Mode %i: %15.5f" % (i+1, values[i]))
# Calculate the projection for each structure.
proj = zeros((num_modes, M), float64)
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