Fix bugs in adaptivity computation (#80)

* Fix bugs in global adaptivity

* Fix formatting and also a bug in the local adaptivity

* Set coarse tolerance to sys.float_info.min if all the similarity distances are zero

micro_manager/adaptivity/adaptivity.py

@@ -5,6 +5,7 @@
 import numpy as np
 from math import exp
 from typing import Callable
+from warnings import warn
 
 
 class AdaptivityCalculator:
@@ -84,7 +85,13 @@ def _update_active_sims(
         _is_sim_active : numpy array
             Updated 1D array having state (active or inactive) of each micro simulation
         """
-        self._coarse_tol = self._coarse_const * self._refine_const * np.amax(similarity_dists)
+        max_similarity_dist = np.amax(similarity_dists)
+
+        if max_similarity_dist == 0.0:
+            warn("All similarity distances are zero, probably because all the data for adaptivity is the same. Coarsening tolerance will be manually set to minimum float number.")
+            self._coarse_tol = sys.float_info.min
+        else:
+            self._coarse_tol = self._coarse_const * self._refine_const * max_similarity_dist
 
         _is_sim_active = np.copy(is_sim_active)  # Input is_sim_active is not longer used after this point
 

micro_manager/adaptivity/global_adaptivity.py

@@ -105,11 +105,15 @@ def compute_adaptivity(
         is_sim_active = self._update_active_sims(similarity_dists, is_sim_active_nm1)
 
         is_sim_active, sim_is_associated_to = self._update_inactive_sims(
-            similarity_dists, is_sim_active_nm1, sim_is_associated_to_nm1, micro_sims)
+            similarity_dists, is_sim_active, sim_is_associated_to_nm1, micro_sims)
+
+        print("sim_is_associated_to: {}".format(sim_is_associated_to))
 
         sim_is_associated_to = self._associate_inactive_to_active(
             similarity_dists, is_sim_active, sim_is_associated_to)
 
+        print("sim_is_associated_to: {}".format(sim_is_associated_to))
+
         self._logger.info(
             "{} active simulations, {} inactive simulations".format(
                 np.count_nonzero(

micro_manager/adaptivity/local_adaptivity.py

@@ -61,7 +61,7 @@ def compute_adaptivity(
         is_sim_active = self._update_active_sims(similarity_dists, is_sim_active_nm1)
 
         is_sim_active, sim_is_associated_to = self._update_inactive_sims(
-            similarity_dists, is_sim_active_nm1, sim_is_associated_to_nm1, micro_sims)
+            similarity_dists, is_sim_active, sim_is_associated_to_nm1, micro_sims)
 
         sim_is_associated_to = self._associate_inactive_to_active(
             similarity_dists, is_sim_active, sim_is_associated_to)

tests/unit/test_adaptivity_parallel.py

@@ -74,6 +74,63 @@ def get_state(self):
         elif self._rank == 1:
             self.assertTrue(np.array_equal([2, 2, 2], dummy_micro_sims[1].get_state()))
 
+    def test_update_all_active_sims_global_adaptivity(self):
+        """
+        Test functionality to calculate adaptivity when all simulations are active, for a global adaptivity setting.
+        Run this test in parallel using MPI with 2 ranks.
+        """
+        if self._rank == 0:
+            global_ids = [0, 1, 2]
+            data_for_adaptivity = {"data1": [1.0, 1.0, 1.0], "data2": [13.0, 13.0, 13.0]}
+        elif self._rank == 1:
+            global_ids = [3, 4]
+            data_for_adaptivity = {"data1": [1.0, 1.0], "data2": [13.0, 13.0]}
+
+        similarity_dists = np.zeros((5, 5))
+        is_sim_active = np.array([True, True, True, True, True])
+        sim_is_associated_to = [-2, -2, -2, -2, -2]
+        expected_is_sim_active = np.array([False, False, False, False, True])
+        expected_sim_is_associated_to = [4, 4, 4, 4, -2]
+
+        configurator = MagicMock()
+        configurator.get_adaptivity_hist_param = MagicMock(return_value=0.1)
+        configurator.get_adaptivity_refining_const = MagicMock(return_value=0.05)
+        configurator.get_adaptivity_coarsening_const = MagicMock(return_value=0.2)
+        configurator.get_adaptivity_similarity_measure = MagicMock(return_value="L2rel")
+        adaptivity_controller = GlobalAdaptivityCalculator(
+            configurator,
+            MagicMock(),
+            5,
+            global_ids,
+            rank=self._rank,
+            comm=self._comm)
+
+        adaptivity_controller._adaptivity_data_names = {"data1": "scalar", "data2": "scalar"}
+
+        class MicroSimulation():
+            def __init__(self, global_id) -> None:
+                self._global_id = global_id
+                self._state = [global_id] * 3
+
+            def get_global_id(self):
+                return self._global_id
+
+            def set_state(self, state):
+                self._state = state
+
+            def get_state(self):
+                return self._state.copy()
+
+        dummy_micro_sims = []
+        for i in global_ids:
+            dummy_micro_sims.append(MicroSimulation(i))
+
+        _, is_sim_active, sim_is_associated_to = adaptivity_controller.compute_adaptivity(
+            0.1, dummy_micro_sims, similarity_dists, is_sim_active, sim_is_associated_to, data_for_adaptivity)
+
+        self.assertTrue(np.array_equal(expected_is_sim_active, is_sim_active))
+        self.assertTrue(np.array_equal(expected_sim_is_associated_to, sim_is_associated_to))
+
     def test_communicate_micro_output(self):
         """
         Test functionality to communicate micro output from active sims to their associated inactive sims, for a global adaptivity setting.