implemented a function that will be used to provide a generic n-th order, nth-dimension error approximation function

lib/baker/__init__.py

@@ -3,6 +3,7 @@ from baker.tools import smblog
 import numpy as np
 import sys
 
+import itertools
 from tools import smberror
 
 def get_phis(X, R):
@@ -305,3 +306,30 @@ def run_baker_3D(X, R, S):
             }
 
   return answer
+
+def _boxings(n, k):
+  """\
+    source for this function:
+    http://old.nabble.com/Simple-combinatorics-with-Numpy-td20086915.html
+    http://old.nabble.com/Re:-Simple-combinatorics-with-Numpy-p20099736.html
+  """
+  seq, i = [n] * k + [0], k
+  while i:
+    yield tuple(seq[i] - seq[i+1] for i in xrange(k))
+    i = seq.index(0) - 1
+    seq[i:k] = [seq[i] - 1] * (k-i)
+
+def _samples_ur(items, k, offset = 0):
+  """Returns k unordered samples (with replacement) from items."""
+  n = len(items)
+  for sample in _boxings(k, n):
+    selections = [[items[i]]*count for i,count in enumerate(sample)]
+    yield tuple([x + offset  for sel in selections for x in sel])
+
+def pattern(power, phicount, offset = 0):
+  smblog.debug("(power = %s, phicount = %s)" % (power, phicount))
+  r = []
+  for i in _samples_ur(range(1, phicount + 1), power, offset):
+    if not len(set(i)) == 1:
+      r.append(i)
+  return r