run pre-commit

Author: ClaudiaMer

pynest/nest/versionchecker.py

@@ -0,0 +1,32 @@
+# -*- coding: utf-8 -*-
+#
+# versionchecker.py
+#
+# This file is part of NEST.
+#
+# Copyright (C) 2004 The NEST Initiative
+#
+# NEST is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+#
+# NEST is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with NEST.  If not, see <http://www.gnu.org/licenses/>.
+
+
+"""
+Check that the Python compiletime and runtime versions match.
+
+"""
+
+v_major_mismatch = sys.version_info.major != 3
+v_minor_mismatch = sys.version_info.minor != 13
+if v_major_mismatch or v_minor_mismatch:
+    msg = "Python runtime version does not match 'nest' compiletime version. " + "Please use Python 3.13."
+    raise Exception(msg)

testsuite/pytests/test_sir_neuron.py

@@ -31,26 +31,27 @@ def test_single_neuron_dynamics(self, tau_m=1, simtime=5):
 
         nest.ResetKernel()
 
-        print('Testing single neuron dynamics of sir_neuron model')
+        print("Testing single neuron dynamics of sir_neuron model")
 
-        h_expected = [1.] * int((simtime - 1) * 10)
+        h_expected = [1.0] * int((simtime - 1) * 10)
         # expected output S: 1 time step in initial 0 state, then it takes tau_m to update
         # to the infected state which happens with prob. 1, then it takes tau_m to updae
         # to recovered state which happens with prob 1.
-        S_expected = ([0.] * int(1 + tau_m * 10) + [1.] * int(tau_m * 10)
-                      + [2.] * int((simtime - 1) * 10 - 1 - 2 * 10 * tau_m))
+        S_expected = (
+            [0.0] * int(1 + tau_m * 10) + [1.0] * int(tau_m * 10) + [2.0] * int((simtime - 1) * 10 - 1 - 2 * 10 * tau_m)
+        )
         # expected spike output: one spike one time step after neuron gets infected,
         # two spikes one time step after neuron leaves infectious state
         spikes_expected = [0.1 + tau_m + 0.1, 0.1 + 2 * tau_m + 0.1, 0.1 + 2 * tau_m + 0.1]
 
-        nrn = nest.Create('sir_neuron')
+        nrn = nest.Create("sir_neuron")
         nrn.h = 1
         nrn.mu_sir = 1
         nrn.beta_sir = 1
         nrn.tau_m = 1
 
         multi = nest.Create("multimeter", {"record_from": ["S", "h"], "interval": 0.1})
-        spike_recorder = nest.Create('spike_recorder')
+        spike_recorder = nest.Create("spike_recorder")
 
         nest.Connect(multi, nrn)
         nest.Connect(nrn, spike_recorder)
@@ -59,7 +60,7 @@ def test_single_neuron_dynamics(self, tau_m=1, simtime=5):
 
         S_recorded = multi.events["S"]
         h_recorded = multi.events["h"]
-        spikes_recorded = spike_recorder.events['times']
+        spikes_recorded = spike_recorder.events["times"]
 
         assert np.allclose(h_expected, h_recorded)
         assert np.allclose(S_expected, S_recorded)
@@ -70,16 +71,17 @@ def test_propagation_of_infection(self, tau_m=1, simtime=5):
 
         nest.ResetKernel()
 
-        print('Testing propagation of infection in sir_neuron model')
+        print("Testing propagation of infection in sir_neuron model")
 
-        S_expected = ([0.] * int(1 + tau_m * 10) + [0.] * int(tau_m * 10)
-                      + [1.] * int((simtime - 1) * 10 - 1 - 2 * 10 * tau_m))
+        S_expected = (
+            [0.0] * int(1 + tau_m * 10) + [0.0] * int(tau_m * 10) + [1.0] * int((simtime - 1) * 10 - 1 - 2 * 10 * tau_m)
+        )
         # expected output S: 1 time step in initial 0 state, then it takes tau_m to update
         # neuron 1 to the infected state during which neuron 2 stays in the susceptible 0 state,
         # then it takes tau_m to update neuron 2 to the infected state
 
-        nrn_1 = nest.Create('sir_neuron')
-        nrn_2 = nest.Create('sir_neuron')
+        nrn_1 = nest.Create("sir_neuron")
+        nrn_2 = nest.Create("sir_neuron")
 
         # infect nrn_1 at time 0.1ms + tau_m
         nrn_1.h = 1
@@ -100,7 +102,7 @@ def test_propagation_of_infection(self, tau_m=1, simtime=5):
         assert np.allclose(S_expected, S_recorded)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     test = TestSirNeuron()
     test.test_single_neuron_dynamics()
     test.test_propagation_of_infection()

testsuite/pytests/test_sirs_neuron.py

@@ -35,28 +35,31 @@ def test_single_neuron_dynamics(self):
 
         nest.ResetKernel()
 
-        print('Testing single neuron dynamics of sirs_neuron model')
+        print("Testing single neuron dynamics of sirs_neuron model")
 
-        h_expected = [1.] * int((simtime - 1) * 10)
+        h_expected = [1.0] * int((simtime - 1) * 10)
         # expected output S: 1 time step in initial 0 state, then it takes tau_m to update
         # to the infected state which happens with prob. 1, then it takes tau_m to updae
         # to recovered state which happens with prob 1.
-        S_expected = ([0.] * int(1 + tau_m * 10) + [1.] * int(tau_m * 10)
-                      + [2.] * int(tau_m * 10)
-                      + [0.] * int((simtime - 1) * 10 - 1 - 3 * 10 * tau_m))
+        S_expected = (
+            [0.0] * int(1 + tau_m * 10)
+            + [1.0] * int(tau_m * 10)
+            + [2.0] * int(tau_m * 10)
+            + [0.0] * int((simtime - 1) * 10 - 1 - 3 * 10 * tau_m)
+        )
         # expected spike output: one spike one time step after neuron gets infected,
         # two spikes one time step after neuron leaves infectious state
         spikes_expected = [0.1 + tau_m + 0.1, 0.1 + 2 * tau_m + 0.1, 0.1 + 2 * tau_m + 0.1]
 
-        nrn = nest.Create('sirs_neuron')
+        nrn = nest.Create("sirs_neuron")
         nrn.h = 1
         nrn.mu_sirs = 1
         nrn.beta_sirs = 1
         nrn.eta_sirs = 1
         nrn.tau_m = 1
 
         multi = nest.Create("multimeter", {"record_from": ["S", "h"], "interval": 0.1})
-        spike_recorder = nest.Create('spike_recorder')
+        spike_recorder = nest.Create("spike_recorder")
 
         nest.Connect(multi, nrn)
         nest.Connect(nrn, spike_recorder)
@@ -67,7 +70,7 @@ def test_single_neuron_dynamics(self):
         h_recorded = multi.events["h"]
         # since we only record the first (simtime - 1)ms for S and h,
         # also only record the first (simtime - 1)ms of spikes
-        spikes_recorded = spike_recorder.events['times'][spike_recorder.events['times'] <= (simtime - 1)]
+        spikes_recorded = spike_recorder.events["times"][spike_recorder.events["times"] <= (simtime - 1)]
 
         assert np.allclose(h_expected, h_recorded)
         assert np.allclose(S_expected, S_recorded)
@@ -78,16 +81,17 @@ def test_propagation_of_infection(self, tau_m=1, simtime=5):
 
         nest.ResetKernel()
 
-        print('Testing propagation of infection in sirs_neuron model')
+        print("Testing propagation of infection in sirs_neuron model")
 
-        S_expected = ([0.] * int(1 + tau_m * 10) + [0.] * int(tau_m * 10)
-                      + [1.] * int((simtime - 1) * 10 - 1 - 2 * 10 * tau_m))
+        S_expected = (
+            [0.0] * int(1 + tau_m * 10) + [0.0] * int(tau_m * 10) + [1.0] * int((simtime - 1) * 10 - 1 - 2 * 10 * tau_m)
+        )
         # expected output S: 1 time step in initial 0 state, then it takes tau_m to update
         # neuron 1 to the infected state during which neuron 2 stays in the susceptible 0 state,
         # then it takes tau_m to update neuron 2 to the infected state
 
-        nrn_1 = nest.Create('sirs_neuron')
-        nrn_2 = nest.Create('sirs_neuron')
+        nrn_1 = nest.Create("sirs_neuron")
+        nrn_2 = nest.Create("sirs_neuron")
 
         # infect nrn_1 at time 0.1ms + tau_m
         nrn_1.h = 1
@@ -108,7 +112,7 @@ def test_propagation_of_infection(self, tau_m=1, simtime=5):
         assert np.allclose(S_expected, S_recorded)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     test = TestSirNeuron()
     test.test_single_neuron_dynamics()
     test.test_propagation_of_infection()

testsuite/pytests/test_sis_neuron.py

@@ -35,26 +35,27 @@ def test_single_neuron_dynamics(self):
 
         nest.ResetKernel()
 
-        print('Testing single neuron dynamics of sis_neuron model')
+        print("Testing single neuron dynamics of sis_neuron model")
 
-        h_expected = [1.] * int((simtime - 1) * 10)
+        h_expected = [1.0] * int((simtime - 1) * 10)
         # expected output S: 1 time step in initial 0 state, then it takes tau_m to update
         # to the infected state which happens with prob. 1, then it takes tau_m to updae
         # to recovered state which happens with prob 1.
-        S_expected = ([0.] * int(1 + tau_m * 10) + [1.] * int(tau_m * 10)
-                      + [0.] * int((simtime - 1) * 10 - 1 - 2 * 10 * tau_m))
+        S_expected = (
+            [0.0] * int(1 + tau_m * 10) + [1.0] * int(tau_m * 10) + [0.0] * int((simtime - 1) * 10 - 1 - 2 * 10 * tau_m)
+        )
         # expected spike output: one spike one time step after neuron gets infected,
         # two spikes one time step after neuron leaves infectious state
         spikes_expected = [0.1 + tau_m + 0.1, 0.1 + 2 * tau_m + 0.1, 0.1 + 2 * tau_m + 0.1]
 
-        nrn = nest.Create('sis_neuron')
+        nrn = nest.Create("sis_neuron")
         nrn.h = 1
         nrn.mu_sis = 1
         nrn.beta_sis = 1
         nrn.tau_m = 1
 
         multi = nest.Create("multimeter", {"record_from": ["S", "h"], "interval": 0.1})
-        spike_recorder = nest.Create('spike_recorder')
+        spike_recorder = nest.Create("spike_recorder")
 
         nest.Connect(multi, nrn)
         nest.Connect(nrn, spike_recorder)
@@ -65,7 +66,7 @@ def test_single_neuron_dynamics(self):
         h_recorded = multi.events["h"]
         # since we only record the first (simtime - 1)ms for S and h,
         # also only record the first (simtime - 1)ms of spikes
-        spikes_recorded = spike_recorder.events['times'][spike_recorder.events['times'] <= (simtime - 1)]
+        spikes_recorded = spike_recorder.events["times"][spike_recorder.events["times"] <= (simtime - 1)]
 
         assert np.allclose(h_expected, h_recorded)
         assert np.allclose(S_expected, S_recorded)
@@ -76,16 +77,17 @@ def test_propagation_of_infection(self, tau_m=1, simtime=5):
 
         nest.ResetKernel()
 
-        print('Testing propagation of infection in sis_neuron model')
+        print("Testing propagation of infection in sis_neuron model")
 
-        S_expected = ([0.] * int(1 + tau_m * 10) + [0.] * int(tau_m * 10)
-                      + [1.] * int((simtime - 1) * 10 - 1 - 2 * 10 * tau_m))
+        S_expected = (
+            [0.0] * int(1 + tau_m * 10) + [0.0] * int(tau_m * 10) + [1.0] * int((simtime - 1) * 10 - 1 - 2 * 10 * tau_m)
+        )
         # expected output S: 1 time step in initial 0 state, then it takes tau_m to update
         # neuron 1 to the infected state during which neuron 2 stays in the susceptible 0 state,
         # then it takes tau_m to update neuron 2 to the infected state
 
-        nrn_1 = nest.Create('sis_neuron')
-        nrn_2 = nest.Create('sis_neuron')
+        nrn_1 = nest.Create("sis_neuron")
+        nrn_2 = nest.Create("sis_neuron")
 
         # infect nrn_1 at time 0.1ms + tau_m
         nrn_1.h = 1
@@ -106,7 +108,7 @@ def test_propagation_of_infection(self, tau_m=1, simtime=5):
         assert np.allclose(S_expected, S_recorded)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     test = TestSirNeuron()
     test.test_single_neuron_dynamics()
     test.test_propagation_of_infection()