r24902 - /trunk/lib/dispersion/matrix_exponential.py -- August 01, 2014 - 18:09

Author: tlinnet
Date: Fri Aug  1 18:09:23 2014
New Revision: 24902

URL: http://svn.gna.org/viewcvs/relax?rev=24902&view=rev
Log:
Created function to create the helper index numpy array, to help figuring out 
the indices to store in the exchange data matrix.

This is for striding through data and store the data correct in the data 
matrix.

This is for a special situation where numpy version is < 1.8, and where the 
numpy.linalg.eig() function can only
be performed on square matrices, and not on higher dimensional data.

For this situation, it is necessary to stride through the data.

Modified:
    trunk/lib/dispersion/matrix_exponential.py

Modified: trunk/lib/dispersion/matrix_exponential.py
URL: 
http://svn.gna.org/viewcvs/relax/trunk/lib/dispersion/matrix_exponential.py?rev=24902&r1=24901&r2=24902&view=diff
==============================================================================
--- trunk/lib/dispersion/matrix_exponential.py  (original)
+++ trunk/lib/dispersion/matrix_exponential.py  Fri Aug  1 18:09:23 2014
@@ -28,6 +28,50 @@
 from numpy.linalg import eig, inv
 
 
+def create_index(NE=None, NS=None, NM=None, NO=None, ND=None):
+    """Method to create the helper index numpy array, to help figuring out 
the indices to store in the exchange data matrix.
+
+    @keyword NE:  The total number of experiment types.
+    @type NE:     None or int
+    @keyword NS:  The total number of spins of the spin cluster.
+    @type NS:     int
+    @keyword NM:  The total number of magnetic field strengths.
+    @type NM:     int
+    @keyword NO:  The total number of spin-lock offsets.
+    @type NO:     int
+    @keyword ND:  The total number of dispersion points (either the 
spin-lock field strength or the nu_CPMG frequency).
+    @type ND:     int
+    @return:      The numpy array for containing index indices for storing 
in the strided exchange data matrix.
+    @rtype:       numpy int array of rank [NE][NS][NM][NO][ND][5] or 
[NS][NM][NO][ND][4].
+    """
+
+    # Make array to store index.
+    if NE != None:
+        index = zeros([NE, NS, NM, NO, ND, 5], int16)
+
+    else:
+        # Make array to store index.
+        index = zeros([NS, NM, NO, ND, 4], int16)
+
+    # Make indices for storing in data matrix.
+    if NE != None:
+        for ei in range(NE):
+            for si in range(NS):
+                for mi in range(NM):
+                    for oi in range(NO):
+                        for di in range(ND):
+                            index[ei, si, mi, oi, di] = ei, si, mi, oi, di
+
+    else:
+        for si in range(NS):
+            for mi in range(NM):
+                for oi in range(NO):
+                    for di in range(ND):
+                        index[si, mi, oi, di] = si, mi, oi, di
+
+    return index
+
+
 def create_index_rank_NE_NS_NM_NO_ND_x_x(data):
     """ Method to create the helper index matrix, to help figuring out the 
index to store in the data matrix. """