Add utils

utils/eval_rewiring.py

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+# /usr/bin/env python
+
+import os
+
+import utils as utils
+from spaghetti import spaghetti_global as spaghetti
+from spaghetti.mc_lif_group import McLifGroup
+from spaghetti.poisson_pattern_group import PoissonPatternGroup
+from spaghetti.rewiring_connection import RewiringConnection
+from spaghetti.spike_monitor import SpikeMonitor
+from spaghetti.voltage_monitor import VoltageMonitor
+
+
+def main(simulation_time, experiment, sim_date, assemblies,
+         assembly_neurons_idc, config, output_directory):
+
+    input_params = config["input_parameters"]
+    connection_params = config["connection_parameters"]
+    neuron_params = config["neuron_parameters"]
+    simulation_time_per_pattern = (input_params["pattern_duration"] +
+                                   input_params["pattern_delay"])
+
+    # Initialize the simulation environment.
+    spaghetti.spaghetti_init(directory=output_directory)
+
+    # Set the random seed.
+    spaghetti.kernel.set_master_seed(config["master_seed"])
+
+    # Create input neurons.
+    inp = PoissonPatternGroup(input_params["num_inputs"], input_params["rate"],
+                              input_params["rate_bg"], params=input_params)
+
+    # Create the neuron.
+    neuron = McLifGroup(1, neuron_params["num_branches"], neuron_params)
+
+    # Connect input to neuron.
+    conn = RewiringConnection(inp, neuron, neuron.branch.syn_current,
+                              connection_params)
+    conn.learn = False
+
+    # Create some monitors which will record the simulation data.
+    SpikeMonitor(neuron, spaghetti.kernel.fn("test_output", "ras"))
+    SpikeMonitor(inp, spaghetti.kernel.fn("test_input", "ras"))
+    VoltageMonitor(neuron.soma, 0, spaghetti.kernel.fn("test_soma", "mem"),
+                   paste_spikes=True, paste_spikes_from=neuron)
+    for i in range(neuron_params["num_branches"]):
+        VoltageMonitor(neuron.branch, (i, 0),
+                       spaghetti.kernel.fn("test_branch", "mem", i))
+
+    # Now simulate the model.
+    conn.set_weights(weights_pre)
+    for assembly in assemblies:
+        inp.set_assemblies(assembly)
+        inp.set_assembly_neurons_idc(assembly_neurons_idc)
+        spaghetti.kernel.run_chunk(
+            simulation_time_per_pattern, 0, simulation_time)
+
+    conn.set_weights(weights_post)
+    for assembly in assemblies:
+        inp.set_assemblies(assembly)
+        inp.set_assembly_neurons_idc(assembly_neurons_idc)
+        spaghetti.kernel.run_chunk(
+            simulation_time_per_pattern, 0, simulation_time)
+
+    spaghetti.kernel.run_chunk(input_params["pattern_delay"], 0,
+                               simulation_time)
+
+
+if __name__ == '__main__':
+    import numpy as np
+
+    experiment = "rewiring_ex1"
+    sim_date = "181228_163308-19"
+    master_seed = 13
+    simulation_time = 3.2
+    assemblies = [0, 1, 2]
+    assembly_neurons_idc = [i * 40 + np.arange(40) for i in assemblies[0:2]]
+    assembly_neurons_idc += [4 * 40 + np.arange(40)]
+
+    input_directory = os.path.join("results", experiment, sim_date, "data")
+
+    # Load the configuration file.
+    config = utils.load_configuration(os.path.join(
+        input_directory, "..", "config_" + experiment + ".yaml"))
+    num_branches = config["neuron_parameters"]["num_branches"]
+    config["master_seed"] = master_seed
+    if experiment == "rewiring_ex3":
+        assembly_neurons_idc = []
+        assembly_neurons_idc = np.loadtxt(os.path.join(input_directory,
+                                          "assembly_neurons_idc"),
+                                          dtype=np.int)
+    else:
+        np.save(os.path.join(input_directory, "assembly_neurons_idc"),
+                assembly_neurons_idc)
+
+    # Load the weights.
+    header_lenght = 3
+    with open(os.path.join(input_directory, "weights.0.dat"), "rb") as f:
+        lines = f.readlines()
+    weights_pre = np.loadtxt(lines[header_lenght:num_branches + header_lenght])
+    weights_post = np.loadtxt(lines[-num_branches:])
+
+    main(simulation_time, experiment, sim_date, assemblies,
+         assembly_neurons_idc, config, input_directory)

utils/eval_rewiring_squential.py

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+# /usr/bin/env python
+
+import os
+
+import utils as utils
+from spaghetti import spaghetti_global as spaghetti
+from spaghetti.mc_lif_group import McLifGroup
+from spaghetti.poisson_pattern_group import PoissonPatternGroup
+from spaghetti.rewiring_connection import RewiringConnection
+from spaghetti.spike_monitor import SpikeMonitor
+from spaghetti.voltage_monitor import VoltageMonitor
+
+
+def main(experiment, sim_date, assemblies, weights,
+         assembly_neurons_idc, config, output_directory):
+
+    input_params = config["input_parameters"]
+    connection_params = config["connection_parameters"]
+    neuron_params = config["neuron_parameters"]
+    simulation_time_per_pattern = input_params["pattern_duration"] + input_params["pattern_delay"]
+
+    # Initialize the simulation environment.
+    spaghetti.spaghetti_init(directory=output_directory)
+
+    # Set the random seed.
+    spaghetti.kernel.set_master_seed(config["master_seed"])
+
+    # Create input neurons.
+    inp = PoissonPatternGroup(input_params["num_inputs"], input_params["rate"], input_params["rate_bg"],
+                              params=input_params)
+
+    # Create the neuron.
+    neuron = McLifGroup(1, neuron_params["num_branches"], neuron_params)
+
+    # Connect input to neuron.
+    conn = RewiringConnection(inp, neuron, neuron.branch.syn_current, connection_params)
+    conn.learn = False
+
+    # Create some monitors which will record the simulation data.
+    SpikeMonitor(neuron, spaghetti.kernel.fn("test_output", "ras"))
+    SpikeMonitor(inp, spaghetti.kernel.fn("test_input", "ras"))
+    VoltageMonitor(neuron.soma, 0, spaghetti.kernel.fn("test_soma", "mem"),
+                   paste_spikes=True, paste_spikes_from=neuron)
+    for i in range(neuron_params["num_branches"]):
+        VoltageMonitor(neuron.branch, (i, 0), spaghetti.kernel.fn("test_branch", "mem", i))
+
+    # Now simulate the model.
+    for w in weights:
+        conn.set_weights(w)
+        for assembly in assemblies:
+            inp.set_assemblies(assembly)
+            inp.set_assembly_neurons_idc(assembly_neurons_idc)
+            spaghetti.kernel.run_chunk(
+                simulation_time_per_pattern, 0,
+                len(assemblies) * len(weights) * simulation_time_per_pattern +
+                input_params["pattern_delay"])
+
+    spaghetti.kernel.run_chunk(input_params["pattern_delay"], 0,
+                               len(assemblies) * len(weights) *
+                               simulation_time_per_pattern +
+                               input_params["pattern_delay"])
+
+
+if __name__ == '__main__':
+    import numpy as np
+
+    experiment = "rewiring_ex3"
+    sim_date = "191204_132602/17"
+    master_seed = 4
+    np.random.seed(master_seed)
+    assemblies = [0, 3, 6]
+    assembly_neurons_idc = [i * 40 + np.arange(40) for i in range(8)]
+
+    # t = 1000s
+    # idc1 = 5424
+    # idc2 = 21674
+    # idc3 = 43345
+    # t = 2000s
+    idc1 = 10845
+    idc2 = 43345
+    idc3 = 75845
+    # t = 5000s
+    # idc1 = 27095
+    # idc2 = 108345
+    # idc3 = 189595
+
+    input_directory = os.path.join("results", experiment, sim_date, "data")
+
+    # Load the configuration file.
+    config = utils.load_configuration(os.path.join(
+        input_directory, "..", "config_" + experiment + ".yaml"))
+    num_branches = config["neuron_parameters"]["num_branches"]
+    config["master_seed"] = master_seed
+
+    # Load the weights.
+    with open(os.path.join(input_directory, "weights.0.dat"), "rb") as f:
+        lines = f.readlines()
+
+    weights1 = np.loadtxt(lines[idc1:idc1 + num_branches])
+    weights2 = np.loadtxt(lines[idc2:idc2 + num_branches])
+    weights3 = np.loadtxt(lines[idc3:idc3 + num_branches])
+    weights = [weights1, weights2, weights3]
+
+    main(experiment, sim_date, assemblies, weights, assembly_neurons_idc, config, input_directory)