Refactor batch simulation parameters and backend

Signed-off-by: samadpls <abdulsamadsid1@gmail.com>
jonescompneurolab · Oct 19, 2024 · dd261d8 · dd261d8
1 parent 08ce63b
commit dd261d8
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Showing 2 changed files with 18 additions and 16 deletions.
diff --git a/examples/howto/plot_batch_simulate.py b/examples/howto/plot_batch_simulate.py
@@ -119,8 +119,8 @@ def summary_func(results):
                                  summary_func=summary_func)
 simulation_results = batch_simulation.run(param_grid,
                                           n_jobs=n_jobs,
-                                          combinations=False,
-                                          backend='multiprocessing')
+                                          combinations=True,
+                                          backend='loky')
 # backend='dask' if installed
 print("Simulation results:", simulation_results)
 ###############################################################################

diff --git a/hnn_core/batch_simulate.py b/hnn_core/batch_simulate.py
@@ -9,6 +9,7 @@
 import os
 from joblib import Parallel, delayed, parallel_config
 
+from .parallel_backends import JoblibBackend
 from .network import Network
 from .externals.mne import _validate_type, _check_option
 from .dipole import simulate_dipole
@@ -196,14 +197,14 @@ def run(self, param_grid, return_output=True,
         param_combinations = self._generate_param_combinations(
             param_grid, combinations)
         total_sims = len(param_combinations)
-        num_sims_per_batch = max(total_sims // self.batch_size, 1)
+        num_sims_per_batch = max(total_sims // n_jobs, 1)
         batch_size = min(self.batch_size, total_sims)
 
         results = []
         simulated_data = []
-        for i in range(batch_size):
-            start_idx = i * num_sims_per_batch
-            end_idx = start_idx + num_sims_per_batch
+        for i in range(0, total_sims, num_sims_per_batch):
+            start_idx = i
+            end_idx = min(i + num_sims_per_batch, total_sims)
             if i == batch_size - 1:
                 end_idx = len(param_combinations)
             batch_results = self.simulate_batch(
@@ -269,10 +270,10 @@ def simulate_batch(self, param_combinations, n_jobs=1,
         with parallel_config(backend=backend):
             res = Parallel(n_jobs=n_jobs, verbose=verbose)(
                 delayed(self._run_single_sim)(
-                    params) for params in param_combinations)
+                    params, n_jobs) for params in param_combinations)
         return res
 
-    def _run_single_sim(self, param_values):
+    def _run_single_sim(self, param_values, n_jobs=1):
         """Run a single simulation.
 
         Parameters
@@ -296,14 +297,15 @@ def _run_single_sim(self, param_values):
         results = {'net': net, 'param_values': param_values}
 
         if self.save_dpl:
-            dpl = simulate_dipole(net,
-                                  tstop=self.tstop,
-                                  dt=self.dt,
-                                  n_trials=self.n_trials,
-                                  record_vsec=self.record_vsec,
-                                  record_isec=self.record_isec,
-                                  postproc=self.postproc)
-            results['dpl'] = dpl
+            with JoblibBackend(n_jobs=n_jobs):
+                dpl = simulate_dipole(net,
+                                      tstop=self.tstop,
+                                      dt=self.dt,
+                                      n_trials=self.n_trials,
+                                      record_vsec=self.record_vsec,
+                                      record_isec=self.record_isec,
+                                      postproc=self.postproc)
+                results['dpl'] = dpl
 
         if self.save_spiking:
             results['spiking'] = {