tested seh non-timed and the example script for getting ancestors

examples/seh_tppu_unbinding/analysis/walker_lineage.py

@@ -8,8 +8,8 @@
 if sys.argv[1] == '-h' or sys.argv[1] == '--help':
     print("walker_lineage.py run_index walker_index output_DCD_path")
 else:
-    run_idx = sys.argv[1]
-    walker_idx = sys.argv[2]
+    run_idx = int(sys.argv[1])
+    walker_idx = int(sys.argv[2])
     dcd_path = sys.argv[3]
 
     outputs_dir = osp.realpath('../outputs')
@@ -22,15 +22,15 @@
 
     wepy_h5.open()
 
-    cycle_idx = wepy_h5.traj(run_idx, walker_idx)['positions'].shape[0]
+    cycle_idx = wepy_h5.traj(run_idx, walker_idx)['positions'].shape[0] - 1
 
     resampling_panel = wepy_h5.run_resampling_panel(run_idx)
 
-    parent_panel = WExplore1.DECISION.parent_panel(resampling_panel)
-    parent_table = WExplore1.DECISION.net_parent_panel(parent_panel)
+    parent_panel = WExplore1Resampler.DECISION.parent_panel(resampling_panel)
+    parent_table = WExplore1Resampler.DECISION.net_parent_table(parent_panel)
 
     lineage = ancestors(parent_table, cycle_idx, walker_idx)
 
-    mdj_traj = wepy_h5.trace_to_mdtraj(lineage)
+    mdj_traj = wepy_h5.run_trace_to_mdtraj(run_idx, lineage)
 
     mdj_traj.save_dcd(dcd_path)

examples/seh_tppu_unbinding/we.py

@@ -103,6 +103,10 @@
 # standard reporters
 from wepy.reporter.hdf5 import WepyHDF5Reporter
 
+# a reporter to show a dashboard in plaintext of current summarized
+# results of the simulation
+from wepy.reporter.dashboard import WExploreDashboardReporter
+
 ## PARAMETERS
 
 # OpenMM simulation parameters
@@ -367,6 +371,14 @@ def main(n_runs, n_cycles, steps, n_walkers, n_workers=1, debug_prints=False, se
                                      all_atoms_rep_freq=ALL_ATOMS_SAVE_FREQ
     )
 
+    dashboard_reporter = WExploreDashboardReporter('./outputs/wepy.dash.txt', mode='w',
+                                                   step_time=STEP_SIZE.value_in_unit(unit.second),
+                                                   max_n_regions=resampler.max_n_regions,
+                                                   max_region_sizes=resampler.max_region_sizes,
+                                                   bc_cutoff_distance=ubc.cutoff_distance)
+
+    reporters = [hdf5_reporter, dashboard_reporter]
+
     ## The work mapper
 
     # we use a mapper that uses GPUs
@@ -388,7 +400,7 @@ def main(n_runs, n_cycles, steps, n_walkers, n_workers=1, debug_prints=False, se
                           resampler=resampler,
                           boundary_conditions=ubc,
                           work_mapper=work_mapper,
-                          reporters=[hdf5_reporter])
+                          reporters=reporters)
 
 
     ### RUN the simulation

wepy/analysis/tree.py

@@ -28,9 +28,9 @@ def ancestors(parent_matrix, cycle_idx, walker_idx, ancestor_cycle=0):
 
     previous_walker = walker_idx
 
-    for cycle_idx in range(cycle_idx, ancestor_cycle, -1):
-            previous_walker = parent_matrix[cycle_idx][previous_walker]
-            previous_point = (cycle_idx - 1, previous_walker)
+    for curr_cycle_idx in range(cycle_idx, ancestor_cycle, -1):
+            previous_walker = parent_matrix[curr_cycle_idx][previous_walker]
+            previous_point = (curr_cycle_idx - 1, previous_walker)
             ancestors.insert(0, previous_point)
 
     return ancestors

wepy/reporter/dashboard.py

@@ -48,6 +48,7 @@ class WExploreDashboardReporter(FileReporter):
     Average Resampling Time: {avg_resampling_time}
     Average Cycle Time: {avg_cycle_time}
     Worker Avg. Segment Times:
+
 {worker_avg_segment_time}
 
 Warping Log:
@@ -350,6 +351,8 @@ def update_performance_values(self, cycle_idx, n_steps, worker_segment_times,
                                                                'worker_idx', 'segment_time'))
         # the aggregated table for the workers
         self.worker_agg_table = worker_df.groupby('worker_idx')[['segment_time']].aggregate(np.mean)
+        self.worker_agg_table.rename(columns={'segment_time' : 'avg_segment_time (s)'},
+                                     inplace=True)
 
         # log of the components times
         self.cycle_runner_times.append(cycle_runner_time)
@@ -419,13 +422,13 @@ def dashboard_string(self):
         branching_table_str = branching_table_df.to_string()
 
         # log of warp events
-        warp_table_colnames = ('walker_idx', 'weight', 'time')
+        warp_table_colnames = ('walker_idx', 'weight', 'time (s)')
         warp_table_df = pd.DataFrame(self.warp_records, columns=warp_table_colnames)
         warp_table_str = warp_table_df.to_string()
 
         # log of cycle times
-        cycle_table_colnames = ('cycle_time', 'runner_time', 'boundary_conditions_time',
-                                'resampling_time')
+        cycle_table_colnames = ('cycle_time (s)', 'runner_time (s)', 'boundary_conditions_time (s)',
+                                'resampling_time (s)')
         cycle_table_df = pd.DataFrame({'cycle_times' : self.cycle_compute_times,
                                        'runner_time' : self.cycle_runner_times,
                                        'boundary_conditions_time' : self.cycle_bc_times,
@@ -435,7 +438,7 @@ def dashboard_string(self):
 
 
         # log of workers performance
-        worker_table_colnames = ('cycle_idx', 'n_steps', 'worker_idx', 'segment_time',)
+        worker_table_colnames = ('cycle_idx', 'n_steps', 'worker_idx', 'segment_time (s)',)
         worker_table_df = pd.DataFrame(self.worker_records, columns=worker_table_colnames)
         worker_table_str = worker_table_df.to_string()
 

wepy/work_mapper/mapper.py

@@ -46,7 +46,7 @@ def __init__(self, num_workers=None, worker_type=None,
                  debug_prints=False):
 
         self.num_workers = num_workers
-        self.worker_segment_times = {worker_idx : [] for i in range(num_workers)}
+        self.worker_segment_times = {i : [] for i in range(self.num_workers)}
 
         # choose the type of the worker
         if worker_type is None:
@@ -161,7 +161,7 @@ def map(self, *args, debug_prints=False):
 
         # save the task run times, so they can be accessed if desired,
         # after clearing the task times from the last mapping
-        self.worker_segment_times = {worker_idx : [] for i in range(num_workers)}
+        self.worker_segment_times = {i : [] for i in range(self.num_workers)}
         for task_idx, worker_idx, task_time, result in results:
             self.worker_segment_times[worker_idx].append(task_time)