Merge branch 'release/0.3.8'

pyphi/__init__.py

@@ -57,7 +57,7 @@
 """
 
 __title__ = 'pyphi'
-__version__ = '0.3.7'
+__version__ = '0.3.8'
 __description__ = 'Python library for computing integrated information.',
 __author__ = 'Will Mayner'
 __author_email__ = 'wmayner@gmail.com'

pyphi/compute.py

@@ -330,16 +330,17 @@ def time_annotated(big_mip, small_phi_time=0.0):
     else:
         # Sequentially loop over all partitions, holding only two BigMips in
         # memory at once.
-        min_phi, min_mip = float('inf'), _null_mip(subsystem)
+        min_mip = _null_mip(subsystem)
+        min_mip.phi = float('inf')
         for i, partition in enumerate(bipartitions):
             new_mip = _evaluate_partition(
                 subsystem, partition, unpartitioned_constellation)
             log.debug("Finished {} of {} partitions.".format(
                 i + 1, len(bipartitions)))
-            if new_mip.phi < min_phi:
-                min_phi, min_mip = new_mip.phi, new_mip
-            # Stop as soon as we find a MIP with effectively 0 phi.
-            if min_phi < constants.EPSILON:
+            if new_mip < min_mip:
+                min_mip = new_mip
+            # Short-circuit as soon as we find a MIP with effectively 0 phi.
+            if not min_mip:
                 break
         return time_annotated(min_mip, small_phi_time)
 

pyphi/models.py

@@ -545,7 +545,7 @@ def __eq__(self, other):
     def __bool__(self):
         """A BigMip is truthy if it is not reducible; i.e. if it has a
         significant amount of |big_phi|."""
-        return self.phi > constants.EPSILON
+        return self.phi >= constants.EPSILON
 
     def __hash__(self):
         return hash((self.phi, self.unpartitioned_constellation,
@@ -558,9 +558,7 @@ def __hash__(self):
     def __lt__(self, other):
         if _phi_eq(self, other):
             if len(self.subsystem) == len(other.subsystem):
-                # Compare actual Phi values up to maximum precision, for
-                # more determinism in things like max and min
-                return self.phi < other.phi
+                return False
             else:
                 return len(self.subsystem) < len(other.subsystem)
         else:
@@ -569,9 +567,7 @@ def __lt__(self, other):
     def __gt__(self, other):
         if _phi_eq(self, other):
             if len(self.subsystem) == len(other.subsystem):
-                # Compare actual Phi values up to maximum precision, for
-                # more determinism in things like max and min
-                return self.phi > other.phi
+                return False
             else:
                 return len(self.subsystem) > len(other.subsystem)
         else:

test/test_big_phi.py

@@ -156,7 +156,7 @@
         (0, 1): 0.34811,
         (2, 3): 0.34811,
     },
-    'cut': models.Cut(severed=(1, 3), intact=(0, 2))
+    'cut': models.Cut(severed=(0, 2), intact=(1, 3))
 }
 
 
@@ -423,35 +423,50 @@ def test_rule152_complexes_no_caching(rule152):
         assert main.cut == result['cut']
 
 
-def test_big_mip_micro(micro_s, flushcache, restore_fs_cache):
+def test_big_mip_micro_parallel(micro_s, flushcache, restore_fs_cache):
+    flushcache()
+
+    initial = config.PARALLEL_CUT_EVALUATION
+    config.PARALLEL_CUT_EVALUATION = True
+
+    mip = compute.big_mip(micro_s)
+    check_mip(mip, micro_answer)
+
+    config.PARALLEL_CUT_EVALUATION = initial
+
+
+def test_big_mip_micro_sequential(micro_s, flushcache, restore_fs_cache):
     flushcache()
+
+    initial = config.PARALLEL_CUT_EVALUATION
+    config.PARALLEL_CUT_EVALUATION = False
+
     mip = compute.big_mip(micro_s)
     check_mip(mip, micro_answer)
 
+    config.PARALLEL_CUT_EVALUATION = initial
+
 
 @pytest.mark.filter
 def test_big_mip_macro(macro_s, flushcache, restore_fs_cache):
     flushcache()
     mip = compute.big_mip(macro_s)
     check_mip(mip, macro_answer)
 
+
 def test_strongly_connected():
-    # A disconnected matrix
+    # A disconnected matrix.
     cm1 = np.array([[0, 0, 1],
                     [0, 1, 0],
                     [1, 0, 0]])
-    # A strongly connected matrix
+    # A strongly connected matrix.
     cm2 = np.array([[0, 1, 0],
                     [0, 0, 1],
                     [1, 0, 0]])
-    # A weakly connected matrix
+    # A weakly connected matrix.
     cm3 = np.array([[0, 1, 0],
                     [0, 0, 1],
                     [0, 1, 0]])
     assert connected_components(csr_matrix(cm1), connection='strong')[0] > 1
     assert connected_components(csr_matrix(cm2), connection='strong')[0] == 1
     assert connected_components(csr_matrix(cm3), connection='strong')[0] > 1
-
-
-
-