first running version

adaptive_umbrella.py

@@ -76,55 +76,49 @@ class UmbrellaRunner():
         smaller E_max"""
         selection = np.where((pmf <= E_max) & (pmf >= 0))
         zipped = list(zip(*selection))
+
         return zipped
 
-    def _get_new_frames(self, pmf, root_frames):
+    def _generate_neighbor_list(self, root):
-        """ returns a dict of all frames surrounding the root_frames
+        """ builds a list of all direct neighbors of the root coordinate """
-        that have not an assigned energy yet, as well as their corresponding root
+        dimens = len(root)
-        frame in the format {new_frame1: root_frame1, new_frame2: root_frame2} """
+        coords = []
-
+        for i in range(dimens):
-        def generate_neighbor_list(root, coords=[]):
+            if len(coords) == 0:
-            """ recursively builds a list of all direct neighbors of the root coordinate """
+                new_coords = []
-            if len(coords) > 0 and len(coords[0]) == len(root):
+                new_coords.append([root[i]-1])
-                return [tuple(x) for x in coords]
+                new_coords.append([root[i]])
-            elif len(coords) == 0:
+                new_coords.append([root[i]+1])
-                coords.append([root[0]-1])
+                coords = new_coords
-                coords.append([root[0]])
-                coords.append([root[0]+1])
-                return generate_neighbor_list(root, coords)
             else:
                 new_coords = []
                 for coord in coords:
-                    dimen = len(coord)
+                    new_coords.append(coord + [root[i] - 1])
+                    new_coords.append(coord + [root[i]])
+                    new_coords.append(coord + [root[i] + 1])
+                coords = new_coords
+        return [tuple(x) for x in coords]
 
-                    new_coord = deepcopy(coord)
-                    new_coord.append(root[dimen]-1)
-                    new_coords.append(new_coord)
 
-                    new_coord = deepcopy(coord)
+    def _is_in_pmf(self, frame):
-                    new_coord.append(root[dimen])
-                    new_coords.append(new_coord)
-
-                    new_coord = deepcopy(coord)
-                    new_coord.append(root[dimen]+1)
-                    new_coords.append(new_coord)
-                return generate_neighbor_list(root, new_coords)
-
-        def in_pmf(frame):
         num_dimens = len(self.pmf.shape)
         for dimen in range(num_dimens):
             if frame[dimen] < 0 or frame[dimen] >= self.pmf.shape[dimen]:
                 return False
         return True
 
+    def _get_new_frames(self, pmf, root_frames):
+        """ returns a dict of all frames surrounding the root_frames
+        that have not an assigned energy yet, as well as their corresponding root
+        frame in the format {new_frame1: root_frame1, new_frame2: root_frame2} """
 
         # find all neighboring frames and create a dict that associates them to the root frame with lowest energy
         new_frames = {}
         for frame in root_frames:
-            neighbors = generate_neighbor_list(frame)
+            neighbors = self._generate_neighbor_list(frame)
 
             # remove neighbors that are not inside the pmf
-            neighbors = [n for n in neighbors if in_pmf(n)]
+            neighbors = [n for n in neighbors if self._is_in_pmf(n)]
 
             # for each neighbor, check if its already in the list and compare root frame energy
             for n in neighbors:
@@ -166,6 +160,11 @@ class UmbrellaRunner():
                 print("Max iterations reached ({})".format(self.max_iterations))
                 return
 
+            # stop if the pmf is sampled 
+            if len(np.where(self.pmf < 0)) == 0:
+                print("Every window of the PMF appears to be sampled.")
+                return
+            
             print("~~~~~~~~~~~~~~~ Iteration {}/{} ~~~~~~~~~~~~~~~~".format(self.num_iterations, self.max_iterations))
             lambdas = dict([(self._get_lambdas_for_index(x), self._get_lambdas_for_index(y)) for x,y in new_frames.items()])
 
@@ -394,28 +393,64 @@ class UmbrellaRunnerTest(unittest.TestCase):
         self.assertEquals(1, len(root_frames))
         self.assertEquals((0, 2, 2), root_frames[0])
 
-    def test_get_new_frames(self):
+    def test_generate_neighbor_list(self):
+        runner = UmbrellaRunner()
+        root = (1, 3)
+        neighbors = runner._generate_neighbor_list(root)
+        expected_neighbors = [
+            (0, 2),
+            (0, 3),
+            (0, 4),
+            (1, 2),
+            (1, 3),
+            (1, 4),
+            (2, 2),
+            (2, 3),
+            (2, 4),
+        ]
+        self.assertEquals(neighbors, expected_neighbors)
+
+    def test_generate_neighbor_list2(self):
+        runner = UmbrellaRunner()
+        root = (3, 1)
+        neighbors = runner._generate_neighbor_list(root)
+        expected_neighbors = [
+            (2, 0),
+            (2, 1),
+            (2, 2),
+            (3, 0),
+            (3, 1),
+            (3, 2),
+            (4, 0),
+            (4, 1),
+            (4, 2),
+        ]
+        self.assertEquals(neighbors, expected_neighbors)
+        pass
+
+    def test_generate_neighbor_list_3d(self):
+        runner = UmbrellaRunner()
+        root = (2, 2, 2)
+        neighbors = runner._generate_neighbor_list(root)
+        expected_neighbors = []
+        for x in [-1, 0, 1]:
+            for y in [-1, 0, 1]:
+                for z in [-1, 0, 1]:
+                    expected_neighbors.append((root[0]+x, root[1]+y, root[2]+z))
+        self.assertEquals(len(neighbors), len(expected_neighbors))
+
+
+    def test_is_in_pmf(self):
         runner = UmbrellaRunner()
         runner.cvs = np.array([
-            (-3, 3, 1),
+            (-1, 1, 1),
-            (0, 4, 1)
+            (-1, 1, 1),
+            (-1, 1, 1)
         ])
         runner.pmf = runner._init_pmf()
-        runner.pmf[0, 3] = 5
+        self.assertFalse(runner._is_in_pmf((-1, 0, 0)))
-        runner.pmf[0, 2] = 2
+        self.assertFalse(runner._is_in_pmf((0, 0, 3)))
-        runner.pmf[0, 4] = 2
+        self.assertTrue(runner._is_in_pmf((0, 0, 0)))
-        root_frames = [(0, 3), (0, 2), (0, 4)]
-        new_frames = runner._get_new_frames(runner.pmf, root_frames)
-
-        expected_new_frames = {
-            (0, 1): (0, 2),
-            (1, 1): (0, 2),
-            (1, 2): (0, 2),
-            (1, 3): (0, 2),
-            (1, 4): (0, 4)
-        }
-        self.assertEquals(len(expected_new_frames.keys()), len(new_frames.keys()))
-        self.assertDictEqual(expected_new_frames, new_frames)
 
     def test_get_new_frames_3d(self):
         runner = UmbrellaRunner()
@@ -439,6 +474,35 @@ class UmbrellaRunnerTest(unittest.TestCase):
         self.assertEquals(26, len(new_frames.keys()))
         self.assertDictEqual(expected_new_frames, new_frames)
 
+    def test_get_new_frames(self):
+        runner = UmbrellaRunner()
+        runner.cvs = np.array([
+            (-2, 2, 1),
+            (-2, 2, 1),
+        ])
+        runner.pmf = np.array([
+            [-1, -1, -1, -1, -1],
+            [-1, 34, 32, 26, -1],
+            [-1, 40, 42, 51, -1],
+            [-1, 28, 41, 37, -1],
+            [-1, -1, -1, -1, -1]])
+        root_frames = [(1, 3), (3, 1)]
+        expected_new_frames = {
+            (0, 2): (1, 3),
+            (0, 3): (1, 3),
+            (0, 4): (1, 3),
+            (1, 4): (1, 3),
+            (2, 4): (1, 3),
+            (2, 0): (3, 1),
+            (3, 0): (3, 1),
+            (4, 0): (3, 1),
+            (4, 1): (3, 1),
+            (4, 2): (3, 1),
+        }
+        new_frames = runner._get_new_frames(runner.pmf, root_frames)
+        self.assertDictEqual(expected_new_frames, new_frames)
+
+
 
 if __name__ == '__main__':
     unittest.main()

example.py

@@ -13,12 +13,21 @@ class MyUmbrellaRunner(WHAM2DRunner):
         pmf_to_plot = deepcopy(self.pmf.T)
         pmf_to_plot[pmf_to_plot < 0] = None
         plt.figure()
-        plt.imshow(pmf_to_plot, origin="lower", cmap='jet')
+        plt.imshow(pmf_to_plot, origin="bottom", cmap='jet')
+        ticks = [(x,x) for x in [-3, -2, -1, 0, 1, 2, 3]]
+        tick_positions = [ self._get_index_for_lambdas(x)[0] for x in ticks ]
+        tick_labels = [ str(x[0]) for x in ticks ]
+        
+        plt.xticks(tick_positions, tick_labels)
+        plt.yticks(tick_positions, tick_labels)
+        
+
         cb = plt.colorbar(pad=0.1)
         cb.set_label("kJ/mol")
         plt.savefig(filename)
         os.system("cp {} {}".format(filename, "pmf_current.pdf"))
 
+
     def simulate_frames(self, lambdas, frames):
         print("{} new simulations:".format(len(lambdas)))
         counter = 0
@@ -35,17 +44,16 @@ class MyUmbrellaRunner(WHAM2DRunner):
             # print("Running {}".format(command))
             os.system(command)
 
-
 runner = MyUmbrellaRunner()
 runner.WHAM_EXEC = "/opt/wham/wham-2d/wham-2d"
 runner.cvs = np.array([
-    (-3.1, 3.1, 0.1),
+    (-3, 3, 0.2),
-    (-3.1, 3.1, 0.1),
+    (-3, 3, 0.2),
 ])
-runner.cvs_init = (1, -1.4)
+runner.cvs_init = (1.4, -1.4)
 runner.E_min = 10
-runner.E_max = 200
+runner.E_max = 100
 runner.E_incr = 10
-runner.max_iterations = 25
+runner.max_iterations = 100
 
 runner.run()