test script combined and split

examples/extra_models_examples/IF_cond_exp_stoc.py

@@ -33,88 +33,107 @@
 from pyNN.utility.plotting import Figure, Panel
 import matplotlib.pyplot as plt
 
-sim.setup(timestep=1.0, min_delay=1.0)
-
-stoc_cell = sim.Population(1, sim.extra_models.IFCondExpStoc(**{
-    'i_offset': 0.1,
-    'tau_refrac': 3.0,
-    'v_thresh': -51.0,
-    'v_reset': -70.0,
-    'tau_syn_E': 5.0,
-    'tau_syn_I': 5.0}))
-
-exp_cell = sim.Population(1, sim.IF_cond_exp(**{
-    'i_offset': 0.1,
-    'tau_refrac': 3.0,
-    'v_thresh': -51.0,
-    'v_reset': -70.0,
-    'tau_syn_E': 5.0,
-    'tau_syn_I': 5.0}))
-
-
-spike_sourceE = sim.Population(1, sim.SpikeSourceArray(**{
-    'spike_times': [float(i) for i in range(5, 105, 10)]}))
-spike_sourceI = sim.Population(1, sim.SpikeSourceArray(**{
-    'spike_times': [float(i) for i in range(155, 255, 10)]}))
-
-sim.Projection(spike_sourceE, exp_cell,
-               sim.OneToOneConnector(),
-               synapse_type=sim.StaticSynapse(weight=0.15, delay=2.0),
-               receptor_type='excitatory')
-sim.Projection(spike_sourceI, exp_cell,
-               sim.OneToOneConnector(),
-               synapse_type=sim.StaticSynapse(weight=-0.15, delay=4.0),
-               receptor_type='inhibitory')
-sim.Projection(spike_sourceE, stoc_cell,
-               sim.OneToOneConnector(),
-               synapse_type=sim.StaticSynapse(weight=0.15, delay=2.0),
-               receptor_type='excitatory')
-sim.Projection(spike_sourceI, stoc_cell,
-               sim.OneToOneConnector(),
-               synapse_type=sim.StaticSynapse(weight=-0.15, delay=4.0),
-               receptor_type='inhibitory')
-
-stoc_cell.record('all')
-exp_cell.record('all')
-
-runtime = 200.0
-
-sim.run(runtime)
-
-stoc_data = stoc_cell.get_data()
-exp_data = exp_cell.get_data()
-
-# Plot
-Figure(
-    # raster plot of the presynaptic neuron spike times
-    Panel(stoc_data.segments[0].spiketrains,
-          yticks=True, markersize=0.2, xlim=(0, runtime)),
-    Panel(exp_data.segments[0].spiketrains,
-          yticks=True, markersize=0.2, xlim=(0, runtime)),
-    # membrane potential of the postsynaptic neuron
-    Panel(stoc_data.segments[0].filter(name='v')[0],
-          ylabel="Membrane potential (mV)",
-          data_labels=[stoc_cell.label], yticks=True, xlim=(0, runtime)),
-    Panel(stoc_data.segments[0].filter(name='gsyn_exc')[0],
-          ylabel="gsyn excitatory (mV)",
-          data_labels=[stoc_cell.label], yticks=True, xlim=(0, runtime)),
-    Panel(stoc_data.segments[0].filter(name='gsyn_inh')[0],
-          ylabel="gsyn inhibitory (mV)",
-          data_labels=[stoc_cell.label], yticks=True, xlim=(0, runtime)),
-    # membrane potential of the postsynaptic neuron
-    Panel(exp_data.segments[0].filter(name='v')[0],
-          ylabel="Membrane potential (mV)",
-          data_labels=[exp_cell.label], yticks=True, xlim=(0, runtime)),
-    Panel(exp_data.segments[0].filter(name='gsyn_exc')[0],
-          ylabel="gsyn excitatory (mV)",
-          data_labels=[exp_cell.label], yticks=True, xlim=(0, runtime)),
-    Panel(exp_data.segments[0].filter(name='gsyn_inh')[0],
-          ylabel="gsyn inhibitory (mV)",
-          data_labels=[exp_cell.label], yticks=True, xlim=(0, runtime)),
-    title="IF_cond_exp_stoc example",
-    annotations=f"Simulated with {sim.name()}"
-)
-plt.show()
-
-sim.end()
-pylab.show()
+
+def run_script(*, split: bool = False) -> None:
+    """
+    Runs the example script
+
+    :param split: If True will split the Populations that receive data
+        into synapse and neuron cores.
+        This requires more cores but allows more spikes to be received.
+    """
+    sim.setup(timestep=1.0, min_delay=1.0)
+
+    if split:
+        sim.extra_models.IFCondExpStoc.set_model_n_synapse_cores(1)
+        sim.IF_cond_exp.set_model_n_synapse_cores(1)
+
+    stoc_cell = sim.Population(1, sim.extra_models.IFCondExpStoc(**{
+        'i_offset': 0.1,
+        'tau_refrac': 3.0,
+        'v_thresh': -51.0,
+        'v_reset': -70.0,
+        'tau_syn_E': 5.0,
+        'tau_syn_I': 5.0}))
+
+    exp_cell = sim.Population(1, sim.IF_cond_exp(**{
+        'i_offset': 0.1,
+        'tau_refrac': 3.0,
+        'v_thresh': -51.0,
+        'v_reset': -70.0,
+        'tau_syn_E': 5.0,
+        'tau_syn_I': 5.0}))
+
+    spike_sourceE = sim.Population(1, sim.SpikeSourceArray(**{
+        'spike_times': [float(i) for i in range(5, 105, 10)]}))
+    spike_sourceI = sim.Population(1, sim.SpikeSourceArray(**{
+        'spike_times': [float(i) for i in range(155, 255, 10)]}))
+
+    sim.Projection(spike_sourceE, exp_cell,
+                   sim.OneToOneConnector(),
+                   synapse_type=sim.StaticSynapse(weight=0.15, delay=2.0),
+                   receptor_type='excitatory')
+    sim.Projection(spike_sourceI, exp_cell,
+                   sim.OneToOneConnector(),
+                   synapse_type=sim.StaticSynapse(weight=-0.15, delay=4.0),
+                   receptor_type='inhibitory')
+    sim.Projection(spike_sourceE, stoc_cell,
+                   sim.OneToOneConnector(),
+                   synapse_type=sim.StaticSynapse(weight=0.15, delay=2.0),
+                   receptor_type='excitatory')
+    sim.Projection(spike_sourceI, stoc_cell,
+                   sim.OneToOneConnector(),
+                   synapse_type=sim.StaticSynapse(weight=-0.15, delay=4.0),
+                   receptor_type='inhibitory')
+
+    stoc_cell.record('all')
+    exp_cell.record('all')
+
+    runtime = 200.0
+
+    sim.run(runtime)
+
+    stoc_data = stoc_cell.get_data()
+    exp_data = exp_cell.get_data()
+
+    # Plot
+    Figure(
+        # raster plot of the presynaptic neuron spike times
+        Panel(stoc_data.segments[0].spiketrains,
+              yticks=True, markersize=0.2, xlim=(0, runtime)),
+        Panel(exp_data.segments[0].spiketrains,
+              yticks=True, markersize=0.2, xlim=(0, runtime)),
+        # membrane potential of the postsynaptic neuron
+        Panel(stoc_data.segments[0].filter(name='v')[0],
+              ylabel="Membrane potential (mV)",
+              data_labels=[stoc_cell.label], yticks=True, xlim=(0, runtime)),
+        Panel(stoc_data.segments[0].filter(name='gsyn_exc')[0],
+              ylabel="gsyn excitatory (mV)",
+              data_labels=[stoc_cell.label], yticks=True, xlim=(0, runtime)),
+        Panel(stoc_data.segments[0].filter(name='gsyn_inh')[0],
+              ylabel="gsyn inhibitory (mV)",
+              data_labels=[stoc_cell.label], yticks=True, xlim=(0, runtime)),
+        # membrane potential of the postsynaptic neuron
+        Panel(exp_data.segments[0].filter(name='v')[0],
+              ylabel="Membrane potential (mV)",
+              data_labels=[exp_cell.label], yticks=True, xlim=(0, runtime)),
+        Panel(exp_data.segments[0].filter(name='gsyn_exc')[0],
+              ylabel="gsyn excitatory (mV)",
+              data_labels=[exp_cell.label], yticks=True, xlim=(0, runtime)),
+        Panel(exp_data.segments[0].filter(name='gsyn_inh')[0],
+              ylabel="gsyn inhibitory (mV)",
+              data_labels=[exp_cell.label], yticks=True, xlim=(0, runtime)),
+        title="IF_cond_exp_stoc example",
+        annotations=f"Simulated with {sim.name()}"
+    )
+    plt.show()
+
+    sim.end()
+    pylab.show()
+
+# combined binaries [IF_cond_exp_stoc.aplx, IF_cond_exp.aplx]
+# split binaries [IF_cond_exp_stoc_neuron.aplx, IF_cond_exp_neuron.aplx]
+
+
+if __name__ == "__main__":
+    run_script()

examples/extra_models_examples/IF_curr_exp_ca2_adaptive.py

@@ -25,91 +25,111 @@
 import matplotlib.pyplot as pylab
 import pyNN.spiNNaker as sim
 
-# Timestep (ms)
-# **NOTE** the 2.5Khz input frequency is not going to work particularly well
-# at 1ms
-dt = 0.1
-
-# Number of neurons - used to gather enough ISIs to get a smooth estimate
-N = 300
-
-# Time (ms) to simulate for
-T = 250
-
-# Setup simulator
-sim.setup(timestep=dt, min_delay=1.0)
-
-# Create population of neurons
-cell = sim.Population(N, sim.extra_models.IFCurrExpCa2Adaptive(**{
-    "tau_m": 20.0, "cm": 0.5,
-    "v_rest": -70.0,
-    "v_reset": -60.0,
-    "v_thresh": -54.0,
-    "i_alpha": 0.1,
-    "tau_ca2": 50.0}))
-
-# Create poisson spike source
-spike_source = sim.Population(N, sim.SpikeSourcePoisson(rate=2500.0))
-
-sim.Projection(spike_source, cell,
-               sim.OneToOneConnector(),
-               sim.StaticSynapse(weight=0.1, delay=0.1),
-               receptor_type="excitatory")
-
-cell.record('spikes')
-# cell.record_gsyn()
-
-sim.run(T)
-
-spike_times = cell.spinnaker_get_data('spikes')
-# ca2 = cell.get_gsyn(compatible_output=True)
-
-# Split into list of spike times for each neuron
-neuron_spikes = [spike_times[spike_times[:, 0] == n, 1] for n in range(N)]
-
-# Calculate interspike intervals
-# and pair these with bin index of last spike time in pair
-# **NOTE** using first spike time in pair leads to a dip at the
-# end as the end of long pairs goes off the end of the simulation
-binned_isis = numpy.hstack([
-    numpy.vstack(
-        (t[1:] - t[:-1],
-         numpy.digitize(t[1:], numpy.arange(T)) - 1))
-    for t in neuron_spikes])
-
-# Split interspike intervals into separate array for each time bin
-time_binned_isis = [binned_isis[0, binned_isis[1] == t] for t in range(T)]
-
-# Create a dictionary of non-empty time bins to mean interspike interval
-mean_isis = {t: numpy.average(i)
-             for (t, i) in enumerate(time_binned_isis) if len(i) > 0}
-
-# Calculate the coefficient of variance for each time bin
-isi_cv = {t: math.sqrt(
-    numpy.sum(numpy.power(time_binned_isis[t] - mean_isi, 2)) /
-    float(len(time_binned_isis[t]))) / mean_isi
-    for (t, mean_isi) in mean_isis.items()}
-
-# Take average CA2 level across all neurons
-# average_ca2 = numpy.average(numpy.reshape(ca2[:,2], (N, int(T / dt))),
-#                             axis=0)
-
-# Plot
-fig, axes = pylab.subplots(2, sharex=True)
-
-axes[0].scatter(mean_isis.keys(),
-                [1000.0 / i for i in mean_isis.values()], s=2)
-axes[0].set_ylabel("Firing rate/Hz")
-
-# axes[1].scatter(numpy.arange(0.0, T, dt), average_ca2, s=2)
-# axes[1].set_ylabel("CA2/mA")
-# axes[1].set_ylim((0.0, numpy.amax(average_ca2) * 1.25))
-
-axes[1].scatter(isi_cv.keys(), isi_cv.values(), s=2)
-axes[1].set_ylabel("Coefficient of ISI variance")
-
-axes[1].set_xlim((0.0, T))
-axes[1].set_xlabel("Time/ms")
-
-sim.end()
-pylab.show()
+
+def run_script(*, split: bool = False) -> None:
+    """
+    Runs the example script
+
+    :param split: If True will split the Populations that receive data
+        into synapse and neuron cores.
+        This requires more cores but allows more spikes to be received.
+    """
+    # Timestep (ms)
+    # **NOTE**
+    # the 2.5Khz input frequency is not going to work particularly well at 1ms
+    dt = 0.1
+
+    # Number of neurons - used to gather enough ISIs to get a smooth estimate
+    N = 300
+
+    # Time (ms) to simulate for
+    T = 250
+
+    # Setup simulator
+    sim.setup(timestep=dt, min_delay=1.0)
+
+    if split:
+        sim.extra_models.IFCurrExpCa2Adaptive.set_model_n_synapse_cores(1)
+
+    # Create population of neurons
+    cell = sim.Population(N, sim.extra_models.IFCurrExpCa2Adaptive(**{
+        "tau_m": 20.0, "cm": 0.5,
+        "v_rest": -70.0,
+        "v_reset": -60.0,
+        "v_thresh": -54.0,
+        "i_alpha": 0.1,
+        "tau_ca2": 50.0}))
+
+    # Create poisson spike source
+    spike_source = sim.Population(N, sim.SpikeSourcePoisson(rate=2500.0))
+
+    sim.Projection(spike_source, cell,
+                   sim.OneToOneConnector(),
+                   sim.StaticSynapse(weight=0.1, delay=0.1),
+                   receptor_type="excitatory")
+
+    cell.record('spikes')
+    # cell.record_gsyn()
+
+    sim.run(T)
+
+    spike_times = cell.spinnaker_get_data('spikes')
+    # ca2 = cell.get_gsyn(compatible_output=True)
+
+    # Split into list of spike times for each neuron
+    neuron_spikes = [spike_times[spike_times[:, 0] == n, 1] for n in range(N)]
+
+    # Calculate interspike intervals
+    # and pair these with bin index of last spike time in pair
+    # **NOTE** using first spike time in pair leads to a dip at the
+    # end as the end of long pairs goes off the end of the simulation
+    binned_isis = numpy.hstack([
+        numpy.vstack(
+            (t[1:] - t[:-1],
+             numpy.digitize(t[1:], numpy.arange(T)) - 1))
+        for t in neuron_spikes])
+
+    # Split interspike intervals into separate array for each time bin
+    time_binned_isis = [binned_isis[0, binned_isis[1] == t] for t in range(T)]
+
+    # Create a dictionary of non-empty time bins to mean interspike interval
+    mean_isis = {t: numpy.average(i)
+                 for (t, i) in enumerate(time_binned_isis) if len(i) > 0}
+
+    # Calculate the coefficient of variance for each time bin
+    isi_cv = {t: math.sqrt(
+        numpy.sum(numpy.power(time_binned_isis[t] - mean_isi, 2)) /
+        float(len(time_binned_isis[t]))) / mean_isi
+        for (t, mean_isi) in mean_isis.items()}
+
+    # Take average CA2 level across all neurons
+    # average_ca2 = numpy.average(numpy.reshape(ca2[:,2], (N, int(T / dt))),
+    #                             axis=0)
+
+    # Plot
+    _, axes = pylab.subplots(2, sharex=True)
+
+    axes[0].scatter(mean_isis.keys(),
+                    [1000.0 / i for i in mean_isis.values()], s=2)
+    axes[0].set_ylabel("Firing rate/Hz")
+
+    # axes[1].scatter(numpy.arange(0.0, T, dt), average_ca2, s=2)
+    # axes[1].set_ylabel("CA2/mA")
+    # axes[1].set_ylim((0.0, numpy.amax(average_ca2) * 1.25))
+
+    axes[1].scatter(isi_cv.keys(), isi_cv.values(), s=2)
+    axes[1].set_ylabel("Coefficient of ISI variance")
+
+    axes[1].set_xlim((0.0, T))
+    axes[1].set_xlabel("Time/ms")
+
+    sim.end()
+    pylab.show()
+
+
+# combined binaries [IF_curr_exp_ca2_adaptive.aplx]
+# split binaries [IF_curr_exp_ca2_adaptive_neuron.aplx]
+
+
+if __name__ == "__main__":
+    run_script()