Improve code style and correct copyright header

Author: FinnBurkhardt

models/ou_noise_generator.h

@@ -50,7 +50,8 @@ Description
 +++++++++++
 
 The `ou_noise_generator` can be used to inject a temporally correlated noise current into a node.
-The current I(t) follows an Ornstein-Uhlenbeck (OU) process, which is described by the following stochastic differential equation:
+The current I(t) follows an Ornstein-Uhlenbeck (OU) process, which is described by the following stochastic differential
+equation:
 
 .. math::
 
@@ -62,13 +63,18 @@ The current I(t) follows an Ornstein-Uhlenbeck (OU) process, which is described
  - :math:`\sigma_{stat}` is the stationary standard deviation of the process (`std` parameter).
  - :math:`dW` is a Wiener process (Gaussian white noise).
 
-The generator integrates this process at a user-defined interval `dt` and delivers the resulting current to its targets. A larger time constant :math:`\tau` results in a more slowly varying noise signal.
+The generator integrates this process at a user-defined interval `dt` and delivers the resulting current to its targets.
+A larger time constant :math:`\tau` results in a more slowly varying noise signal.
 
-All targets of a noise generator receive different, independent noise currents, but the currents for all targets are updated at the same points in time. The interval `dt` between updates must be a multiple of the simulation time step.
+All targets of a noise generator receive different, independent noise currents, but the currents for all targets are
+updated at the same points in time. The interval `dt` between updates must be a multiple of the simulation time step.
 
 .. admonition:: Recording the generated current
 
-   You can use a :doc:`multimeter <multimeter>` to record the average current sent to all targets for each time step if simulating on a single thread; multiple MPI processes with one thread each also work. In this case, the recording interval of the multimeter should be equal to the `dt` of the generator to avoid aliasing effects. In multi-threaded mode, recording of noise currents is prohibited for technical reasons.
+   You can use a :doc:`multimeter <multimeter>` to record the average current sent to all targets for each time step if
+simulating on a single thread; multiple MPI processes with one thread each also work. In this case, the recording
+interval of the multimeter should be equal to the `dt` of the generator to avoid aliasing effects. In multi-threaded
+mode, recording of noise currents is prohibited for technical reasons.
 
 
 .. include:: ../models/stimulation_device.rst

testsuite/pytests/sli2py_stimulating/test_ou_noise_generator.py

@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 #
-# test_noise_generator.py
+# test_ou_noise_generator.py
 #
 # This file is part of NEST.
 #
@@ -34,10 +34,12 @@ def prepare_kernel():
     nest.ResetKernel()
     nest.resolution = 0.1
 
+
 def burn_in_start(dt, tau, k=10):
     """Return number of steps to discard for a k*tau burn-in."""
     return int(round((k * tau) / dt))
 
+
 def test_ou_noise_generator_set_parameters(prepare_kernel):
     params = {"mean": 210.0, "std": 60.0, "dt": 0.1}
 
@@ -54,24 +56,25 @@ def test_ou_noise_generator_incorrect_noise_dt(prepare_kernel):
     with pytest.raises(nest.kernel.NESTError, match="StepMultipleRequired"):
         nest.Create("ou_noise_generator", {"dt": 0.25})
 
+
 def test_ou_noise_mean_and_variance(prepare_kernel):
     # run for resolution dt=0.1 project to iaf_psc_alpha.
     # create 100 repetitions of 1000ms simulations
 
-    oung = nest.Create('ou_noise_generator', {'mean':0.0, 'std': 60.0, 'tau':1., 'dt':0.1})
+    oung = nest.Create("ou_noise_generator", {"mean": 0.0, "std": 60.0, "tau": 1.0, "dt": 0.1})
     neuron = nest.Create("iaf_psc_alpha")
-    
+
     # we need to connect to a neuron otherwise the generator does not generate
     nest.Connect(oung, neuron)
-    mm = nest.Create('multimeter', 1, {'record_from':['I'], "interval": 0.1})
-    nest.Connect(mm, oung, syn_spec={'weight': 1})
+    mm = nest.Create("multimeter", 1, {"record_from": ["I"], "interval": 0.1})
+    nest.Connect(mm, oung, syn_spec={"weight": 1})
 
     # Simulate for 100 times
     n_sims = 100
     ou_current = np.empty(n_sims)
     for i in range(n_sims):
         nest.Simulate(1000.0)
-        ou_current[i] = mm.get('events')['I'][-1]
+        ou_current[i] = mm.get("events")["I"][-1]
 
     curr_mean = np.mean(ou_current)
     curr_var = np.var(ou_current)
@@ -88,51 +91,47 @@ def test_ou_noise_generator_autocorrelation(prepare_kernel):
     dt = 0.1
     tau = 1.0
 
-    oung = nest.Create('ou_noise_generator', {'mean':0.0, 'std': 60.0, 'tau':tau,'dt': nest.resolution})
+    oung = nest.Create("ou_noise_generator", {"mean": 0.0, "std": 60.0, "tau": tau, "dt": nest.resolution})
     neuron = nest.Create("iaf_psc_alpha")
-    
+
     # we need to connect to a neuron otherwise the generator does not generate
     nest.Connect(oung, neuron)
-    mm = nest.Create('multimeter', 1, {'record_from':['I'], "interval": dt})
-    nest.Connect(mm, oung, syn_spec={'weight': 1 })
+    mm = nest.Create("multimeter", 1, {"record_from": ["I"], "interval": dt})
+    nest.Connect(mm, oung, syn_spec={"weight": 1})
     nest.Simulate(10000.0)
-    ou_current = mm.get('events')['I']
+    ou_current = mm.get("events")["I"]
 
-    ## drop first k*tau
+    # drop first k*tau
     cutoff = burn_in_start(dt=dt, tau=tau)
     x = ou_current[cutoff:]
     x -= x.mean()
-    
+
     # empirical lag-1 autocorrelation
-    emp_ac1 = np.dot(x[:-1], x[1:]) / ((len(x)-1)*x.var())
+    emp_ac1 = np.dot(x[:-1], x[1:]) / ((len(x) - 1) * x.var())
     # theoretical lag-1: exp(-dt/tau)
     theor_ac1 = np.exp(-dt / tau)
 
-    assert abs(emp_ac1 - theor_ac1) < 0.05, \
-        f"autocorr {emp_ac1:.3f} vs theoretical {theor_ac1:.3f}"
-    
+    assert abs(emp_ac1 - theor_ac1) < 0.05, f"autocorr {emp_ac1:.3f} vs theoretical {theor_ac1:.3f}"
+
 
 def test_cross_correlation(prepare_kernel):
     # two neurons driven by the same OU noise should remain uncorrelated
     dt, tau = 0.1, 1.0
 
-    ou = nest.Create("ou_noise_generator", 
-                     {"mean": 0.0, "std": 50.0, "tau": tau, "dt": dt})
-    neurons = nest.Create("iaf_psc_alpha", 2,
-                          {"E_L": 0.0, "V_th": 1e9, "C_m": 1.0, "tau_m": tau})
+    ou = nest.Create("ou_noise_generator", {"mean": 0.0, "std": 50.0, "tau": tau, "dt": dt})
+    neurons = nest.Create("iaf_psc_alpha", 2, {"E_L": 0.0, "V_th": 1e9, "C_m": 1.0, "tau_m": tau})
     nest.Connect(ou, neurons)
 
-    mm = nest.Create("multimeter",
-                     params={"record_from": ["V_m"], "interval": dt})
+    mm = nest.Create("multimeter", params={"record_from": ["V_m"], "interval": dt})
     nest.Connect(mm, neurons)
 
     simtime = 50000.0
     nest.Simulate(simtime)
 
     ev = mm.get("events")
     senders = np.asarray(ev["senders"], int)
-    times   = np.asarray(ev["times"])
-    vms     = np.asarray(ev["V_m"])
+    times = np.asarray(ev["times"])
+    vms = np.asarray(ev["V_m"])
 
     # build time array
     n_steps = int(round(simtime / dt)) + 1
@@ -150,7 +149,7 @@ def test_cross_correlation(prepare_kernel):
     # cross-corr via FFT
     corr = fftconvolve(X1, X2[::-1], mode="full")
     corr /= np.sqrt(np.dot(X1, X1) * np.dot(X2, X2))
-    mid  = corr.size // 2
+    mid = corr.size // 2
 
     # maximum absolute cross-corr should be near zero
-    assert np.max(np.abs(corr[mid:])) < 0.05
+    assert np.max(np.abs(corr[mid:])) < 0.05