ENH: save net.spiketimes as list of list

examples/plot_simulate_evoked.py

@@ -35,14 +35,15 @@
 # Now let's simulate the dipole
 # You can simulate multiple trials in parallel by using n_jobs > 1
 net = Network(params)
-dpls = simulate_dipole(net, n_jobs=3, n_trials=3)
+dpls = simulate_dipole(net, n_jobs=1, n_trials=2)
 
 ###############################################################################
 # and then plot it
 import matplotlib.pyplot as plt
 fig, axes = plt.subplots(2, 1, sharex=True, figsize=(6, 6))
 for dpl in dpls:
     dpl.plot(ax=axes[0])
+net.plot_input(ax=axes[1])
 
 ###############################################################################
 # Finally, we can also plot the spikes.

mne_neuron/dipole.py

@@ -18,13 +18,15 @@ def _hammfilt(x, winsz):
 
 def _clone_and_simulate(params, trial_idx):
     from .network import Network
+
+    if trial_idx != 0:
+        params['prng_*'] = trial_idx
+
     net = Network(params, n_jobs=1)
 
     from neuron import h
-    # create sources and init
     net._create_all_src()
     net.state_init()
-    # parallel network connector
     net._parnet_connect()
     # set to record spikes
     net.spiketimes = h.Vector()
@@ -44,9 +46,6 @@ def _simulate_single_trial(net):
     if rank == 0:
         print("running on %d cores" % nhosts)
 
-    # if trial_idx != 0:
-    #     params['prng_*'] = trial_idx
-
     # global variables, should be node-independent
     h("dp_total_L2 = 0.")
     h("dp_total_L5 = 0.")
@@ -117,7 +116,7 @@ def prsimtime():
         dpl.convert_fAm_to_nAm()
         dpl.scale(net.params['dipole_scalefctr'])
         dpl.smooth(net.params['dipole_smooth_win'] / h.dt)
-    return dpl
+    return dpl, net.spiketimes.to_python(), net.spikegids.to_python()
 
 
 def simulate_dipole(net, n_trials=1, n_jobs=1):
@@ -139,8 +138,10 @@ def simulate_dipole(net, n_trials=1, n_jobs=1):
         The dipole object or list of dipole objects if n_trials > 1
     """
     parallel, myfunc = _parallel_func(_clone_and_simulate, n_jobs=n_jobs)
-    dpl = parallel(myfunc(net.params, idx) for idx in range(n_trials))
-
+    out = parallel(myfunc(net.params, idx) for idx in range(n_trials))
+    dpl, spiketimes, spikegids = zip(*out)
+    net.spiketimes = spiketimes
+    net.spikegids = spikegids
     return dpl
 
 # TODO: add crop method to dipole
@@ -173,7 +174,6 @@ def __init__(self, times, data):  # noqa: D102
         self.t = times
         self.dpl = {'agg': data[:, 0], 'L2': data[:, 1], 'L5': data[:, 2]}
 
-    # conversion from fAm to nAm
     def convert_fAm_to_nAm(self):
         """ must be run after baseline_renormalization()
         """

mne_neuron/network.py

@@ -28,11 +28,20 @@ class Network(object):
     ----------
     cells : list of Cell objects.
         The list of cells
+    gid_dict : dict
+        Dictionary with keys 'evprox1', 'evdist1' etc.
+        containing the range of Cell IDs of different cell types.
     ext_list : dictionary of list of ExtFeed.
         Keys are:
             'evprox1', 'evprox2', etc.
             'evdist1', etc.
             'extgauss', 'extpois'
+    spiketimes : tuple (n_trials, ) of list of float
+        Each element of the tuple is a trial.
+        The list contains the time stamps of spikes.
+    spikegids : tuple (n_trials, ) of list of float
+        Each element of the tuple is a trial.
+        The list contains the cell IDs of neurons that spiked.
     """
 
     def __init__(self, params, n_jobs=1):
@@ -422,8 +431,8 @@ def plot_input(self, ax=None, show=True):
             The matplotlib figure handle.
         """
         import matplotlib.pyplot as plt
-        spikes = np.array(self.spiketimes.to_python())
-        gids = np.array(self.spikegids.to_python())
+        spikes = np.array(sum(self.spiketimes, []))
+        gids = np.array(sum(self.spikegids, []))
         valid_gids = np.r_[[v for (k, v) in self.gid_dict.items()
                             if k.startswith('evprox')]]
         mask_evprox = np.in1d(gids, valid_gids)
@@ -458,8 +467,8 @@ def plot_spikes(self, ax=None, show=True):
             The matplotlib figure object
         """
         import matplotlib.pyplot as plt
-        spikes = np.array(self.spiketimes.to_python())
-        gids = np.array(self.spikegids.to_python())
+        spikes = np.array(sum(self.spiketimes, []))
+        gids = np.array(sum(self.spikegids, []))
         spike_times = np.zeros((4, spikes.shape[0]))
         cell_types = ['L5_pyramidal', 'L5_basket', 'L2_pyramidal', 'L2_basket']
         for idx, key in enumerate(cell_types):