RF: cost2thresh calls cost2thresh2 to minimize code duplication, added tests

Author: CindeeM

brainx/tests/test_util.py

@@ -3,7 +3,7 @@
 #-----------------------------------------------------------------------------
 # Imports
 #-----------------------------------------------------------------------------
-
+import unittest
 
 # Third party
 import nose.tools as nt
@@ -55,6 +55,34 @@ def test_cost_size():
     n_nodes = 5
     npt.assert_warns(DeprecationWarning, util.cost_size, n_nodes)
 
+class TestCost2Thresh(unittest.TestCase):
+    def setUp(self):
+        nnodes, nsub, nblocks, nsubblocks = 45, 20, 6, 2
+        prng = np.random.RandomState(42)
+        self.data_5d = prng.random_sample((nsubblocks, nblocks, 
+                nsub, nnodes, nnodes))
+        ind = np.triu_indices(nnodes, k=1)
+        nedges = (np.empty((nnodes, nnodes))[ind]).shape[0]
+        costs, _, _ = util.cost_size(nnodes)
+        self.costs = costs
+        self.lookup = np.zeros((nsubblocks, nblocks, nsub,2, nedges))
+        bigcost =np.tile(costs[1:], nblocks*nsubblocks*nsub)
+        bigcost.shape = (nsubblocks, nblocks, nsub, nedges)
+        self.lookup[:,:,:,1,:] = bigcost
+        for sblock in range(nsubblocks):
+            for block in range(nblocks):
+                for sid in range(nsub):
+                    tmp = data_5d[sblock, block, sid]
+                    self.lookup[sblock,block,sid,0,:] = tmp[ind]
+        
+        def test_cost2thresh2(self):
+            thr = util.cost2thresh2(self.costs[100], 0,0,0,self.lookup)
+            npt.assert_almost_equal(thr, 0.24929222914887494, decimal=7)
+
+        def test_cost2thresh(self):
+            lookup = self.lookup[0].squeeze()
+            thr = util.cost2thresh(self.costs[100],0,0,0,lookup)
+            npt.assert_almost_equal(thr, 0.24929222914887494, decimal=7)
 
 def test_apply_cost():
     corr_mat = np.array([[0.0, 0.5, 0.3, 0.2, 0.1],

brainx/util.py

@@ -606,6 +606,7 @@ def cost2thresh(cost, sub, bl, lk, idc=[], costlist=[]):
     be registered.
 
     """
+    return cost2thresh2(cost, sub, bl, axis0=None, lk=lk, last = None, idc=idc,costlist = costlist)
     # For this subject and block, find the indices corresponding to this cost.
     # Note there may be more than one such index.  There will be no such
     # indices if cost is not a value in the array.
@@ -632,7 +633,8 @@ def cost2thresh(cost, sub, bl, lk, idc=[], costlist=[]):
     return th
 
 
-def cost2thresh2(cost, sub, sc, c, lk, last = None, idc = [], costlist=[]):
+def cost2thresh2(cost, sub, axis1, axis0, lk, 
+        last = None, idc = [], costlist=[]):
     """A definition for loading the lookup table and finding the threshold 
     associated with a particular cost for a particular subject in a 
     particular block of data
@@ -663,7 +665,7 @@ def cost2thresh2(cost, sub, sc, c, lk, last = None, idc = [], costlist=[]):
     threshold : float
         threshold value for this cost"""
 
-    subject_lookup = slice_data(lk, sub, c, subcond=sc) 
+    subject_lookup = slice_data(lk, sub, axis0, subcond=axis1) 
     index = np.where(subject_lookup[1] == cost)
     threshold = subject_lookup[0][ind]
 
@@ -676,7 +678,7 @@ def cost2thresh2(cost, sub, sc, c, lk, last = None, idc = [], costlist=[]):
     elif len(threshold) < 1:
         idc = idc-1
         newcost = costlist[idc]
-        threshold = cost2thresh2(newcost, sub, sc, c, lk, 
+        threshold = cost2thresh2(newcost, sub, axis1, axis0, lk, 
                                  idc=idc, costlist = costlist) 
         print(' '.join(['Subject %s does not have cost at %s'%(sub, cost),
                         'index 1: %s, index 2: %s'%(c, sc),