ENH: add new property to access spike times by cell type

examples/workflows/plot_simulate_evoked.py

@@ -93,8 +93,24 @@
 from hnn_core import JoblibBackend
 
 with JoblibBackend(n_jobs=2):
-    dpls = simulate_dipole(net, tstop=170., n_trials=2)
+    dpls = simulate_dipole(net, tstop=170., n_trials=1)
 
+import numpy as np
+
+spike_types_data = np.concatenate(np.array(net.cell_response.spike_types, dtype=object))
+unique_cell_types = np.unique(spike_types_data)  # net.cell_response.cell_types ?
+
+spike_times_by_type = dict()
+for cell_type in unique_cell_types:
+    spike_times_by_type[cell_type] = list()
+    for trial_spike_times, trial_spike_types in zip(net.cell_response.spike_times, net.cell_response.spike_types):
+        mask = np.isin(trial_spike_types, cell_type)
+        trial_cell_spike_times = np.array(trial_spike_times)[mask].tolist()
+        spike_times_by_type[cell_type].append(trial_cell_spike_times)
+
+# spike_types_mask = {s_type: np.isin(spike_types_data, s_type)
+#                     for s_type in unique_types}
+sdfdff
 ###############################################################################
 # Rather than reading smoothing and scaling parameters from file, we recommend
 # explicit use of the :meth:`~hnn_core.dipole.Dipole.smooth` and

hnn_core/cell_response.py

@@ -153,6 +153,24 @@ def __eq__(self, other):
     def spike_times(self):
         return self._spike_times
 
+    @property
+    def cell_types(self):
+        """Get unique cell types."""
+        spike_types_data = np.concatenate(np.array(self.spike_types, dtype=object))
+        return np.unique(spike_types_data).tolist()
+
+    @property
+    def spike_times_by_type(self):
+        """Get a dictionary of spike times by cell type"""
+        spike_times = dict()
+        for cell_type in self.cell_types:
+            spike_times[cell_type] = list()
+            for trial_spike_times, trial_spike_types in zip(self.spike_times, self.spike_types):
+                mask = np.isin(trial_spike_types, cell_type)
+                trial_cell_spike_times = np.array(trial_spike_times)[mask].tolist()
+                spike_times[cell_type].append(trial_cell_spike_times)
+        return spike_times
+
     @property
     def spike_gids(self):
         return self._spike_gids

hnn_core/tests/test_cell_response.py

@@ -24,6 +24,11 @@ def test_cell_response(tmp_path):
                                  spike_gids=spike_gids,
                                  spike_types=spike_types,
                                  times=sim_times)
+
+    assert set(cell_response.cell_types) == set(gid_ranges.keys())
+    assert cell_response.spike_times_by_type['L2_basket'] == [[7.89], []]
+    assert cell_response.spike_times_by_type['L5_pyramidal'] == [[], [4.2812]]
+
     kwargs_hist = dict(alpha=0.25)
     fig = cell_response.plot_spikes_hist(show=False, **kwargs_hist)
     assert all(patch.get_alpha() == kwargs_hist['alpha']