Merge branch 'master' into push_bot_viewer

.github/workflows/python_actions.yml

@@ -22,13 +22,13 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.7, 3.8, 3.9, "3.10", "3.11"]
+        python-version: [3.8, 3.9, "3.10", "3.11", "3.12"]
 
     steps:
     - name: Checkout
-      uses: actions/checkout@v3
+      uses: actions/checkout@v4
     - name: Checkout SupportScripts
-      uses: actions/checkout@v3
+      uses: actions/checkout@v4
       with:
         repository: SpiNNakerManchester/SupportScripts
         path: support
@@ -40,10 +40,10 @@ jobs:
     - name: Install pip, etc
       uses: ./support/actions/python-tools
     - name: Install Spinnaker Dependencies
-      uses: ./support/actions/checkout-spinn-deps
+      uses: ./support/actions/install-spinn-deps
       with:
         repositories: >
-          SpiNNUtils SpiNNMachine SpiNNMan PACMAN DataSpecification spalloc
+          SpiNNUtils SpiNNMachine SpiNNMan PACMAN spalloc
           SpiNNFrontEndCommon sPyNNaker
         install: true
 
@@ -65,9 +65,9 @@ jobs:
     runs-on: ubuntu-latest
     steps:
     - name: Checkout
-      uses: actions/checkout@v3
+      uses: actions/checkout@v4
     - name: Checkout SupportScripts
-      uses: actions/checkout@v3
+      uses: actions/checkout@v4
       with:
         repository: SpiNNakerManchester/SupportScripts
         path: support

.ratexcludes

@@ -4,7 +4,6 @@
 **/SpiNNMachine/**
 **/SpiNNMan/**
 **/PACMAN/**
-**/DataSpecification/**
 **/spalloc/**
 **/SpiNNFrontEndCommon/**
 **/sPyNNaker/**

examples/extra_models_examples/LGN_Izhikevich.py

@@ -121,14 +121,10 @@ def print_synchrony():
 
 """ Initialising Model connectivity parameters """
 
-intra_pop_delay = RandomDistribution('uniform', (1, 3),
-                                     rng=NumpyRNG(seed=85520))
-intra_nucleus_delay = RandomDistribution('uniform', (1, 3),
-                                         rng=NumpyRNG(seed=85521))
-inter_nucleus_delay = RandomDistribution('uniform', (1, 3),
-                                         rng=NumpyRNG(seed=85522))
-inter_pop_delay = RandomDistribution('uniform', (1, 3),
-                                     rng=NumpyRNG(seed=85523))
+intra_pop_delay = RandomDistribution('uniform', (1, 3))
+intra_nucleus_delay = RandomDistribution('uniform', (1, 3))
+inter_nucleus_delay = RandomDistribution('uniform', (1, 3))
+inter_pop_delay = RandomDistribution('uniform', (1, 3))
 input_delay = inter_pop_delay
 
 # # input_delay is the delay of the spike source hitting the neuronal pops
@@ -262,25 +258,25 @@ def print_synchrony():
 NumCellsTRN = 4*scale_fact
 TCR_pop = p.Population(
     NumCellsTCR, p.extra_models.Izhikevich_cond, TCR_cell_params,
-    label='TCR_pop', initial_values=TCR_initial_values)
+    label='TCR_pop', initial_values=TCR_initial_values, seed=85520)
 IN_pop = p.Population(
     NumCellsIN, p.extra_models.Izhikevich_cond, IN_cell_params,
-    label='IN_pop', initial_values=IN_initial_values)
+    label='IN_pop', initial_values=IN_initial_values, seed=85521)
 TRN_pop = p.Population(
     NumCellsTRN, p.extra_models.Izhikevich_cond, TRN_cell_params,
-    label='TRN_pop', initial_values=TRN_initial_values)
+    label='TRN_pop', initial_values=TRN_initial_values, seed=85522)
 
 """ Poisson input for TCR """
 spike_source_TCR = p.Population(
     NumCellsTCR, p.SpikeSourcePoisson(rate=10, start=Start_Inp,
                                       duration=Duration_Inp),
-    label='spike_source_TCR')
+    label='spike_source_TCR', seed=85523)
 
 """ Poisson input for IN """
 spike_source_IN = p.Population(
     NumCellsIN, p.SpikeSourcePoisson(rate=10, start=Start_Inp,
                                      duration=Duration_Inp),
-    label='spike_source_IN')
+    label='spike_source_IN', seed=85524)
 
 """ Poisson Source to TCR population projections """
 Proj0 = p.Projection(

examples/extra_models_examples/vogel_2011/spynnaker.cfg

@@ -32,6 +32,3 @@ time_between_cores = 2.7
 
 # performance controller for how much of the time step to use for sending
 fraction_of_time_spike_sending = 0.99
-
-[Mapping]
-compressor = SpynnakerMachineBitFieldPairRouterCompressor

examples/extra_models_examples/vogel_2011/vogels_2011_live.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 
 import pyNN.spiNNaker as sim
-from pyNN.random import NumpyRNG
 
 
 # -------------------------------------------------------------------
@@ -45,8 +44,6 @@
 # Reduce number of neurons to simulate on each core
 sim.set_number_of_neurons_per_core(sim.IF_curr_exp, 100)
 
-rng = NumpyRNG(seed=59)
-
 # Create excitatory and inhibitory populations of neurons
 ex_pop = sim.Population(NUM_EXCITATORY, model(**cell_params),
                         label="Excitatory", additional_parameters={"seed": 2})
@@ -57,15 +54,15 @@
 ex_pop.record('spikes')
 
 # Make excitatory->inhibitory projections
-sim.Projection(ex_pop, in_pop, sim.FixedProbabilityConnector(0.02, rng=rng),
+sim.Projection(ex_pop, in_pop, sim.FixedProbabilityConnector(0.02),
                receptor_type='excitatory',
                synapse_type=sim.StaticSynapse(weight=0.029))
-sim.Projection(ex_pop, ex_pop, sim.FixedProbabilityConnector(0.02, rng=rng),
+sim.Projection(ex_pop, ex_pop, sim.FixedProbabilityConnector(0.02),
                receptor_type='excitatory',
                synapse_type=sim.StaticSynapse(weight=0.029))
 
 # Make inhibitory->inhibitory projections
-sim.Projection(in_pop, in_pop, sim.FixedProbabilityConnector(0.02, rng=rng),
+sim.Projection(in_pop, in_pop, sim.FixedProbabilityConnector(0.02),
                receptor_type='inhibitory',
                synapse_type=sim.StaticSynapse(weight=-0.29))
 
@@ -76,7 +73,7 @@
     weight_dependence=sim.AdditiveWeightDependence(w_min=0.0, w_max=1.0))
 
 # Make inhibitory->excitatory projections
-sim.Projection(in_pop, ex_pop, sim.FixedProbabilityConnector(0.02, rng=rng),
+sim.Projection(in_pop, ex_pop, sim.FixedProbabilityConnector(0.02),
                receptor_type='inhibitory',
                synapse_type=stdp_model)
 

examples/pynnBrunnel.py

@@ -16,7 +16,7 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
-from pyNN.random import NumpyRNG, RandomDistribution
+from pyNN.random import RandomDistribution
 from pyNN.utility.plotting import Figure, Panel
 
 simulator_Name = 'spiNNaker'
@@ -136,8 +136,6 @@ def poisson_generator(_rate, _rng, _t_start=0.0, _t_stop=1000.0, _debug=False):
     pynn.set_number_of_neurons_per_core(pynn.IF_curr_exp, 64)
     pynn.set_number_of_neurons_per_core(pynn.SpikeSourcePoisson, 64)
 
-rng = NumpyRNG(seed=1)
-
 exc_cell_params = {
     'cm': 1.0,  # pf
     'tau_m': tau_m,
@@ -164,29 +162,29 @@ def poisson_generator(_rate, _rng, _t_start=0.0, _t_stop=1000.0, _debug=False):
 
 # Set-up pynn Populations
 E_pop = pynn.Population(
-    N_E, pynn.IF_curr_exp(**exc_cell_params), label="E_pop")
+    N_E, pynn.IF_curr_exp(**exc_cell_params), label="E_pop", seed=1)
 
 I_pop = pynn.Population(
-    N_I, pynn.IF_curr_exp(**inh_cell_params), label="I_pop")
+    N_I, pynn.IF_curr_exp(**inh_cell_params), label="I_pop", seed=2)
 
 Poiss_ext_E = pynn.Population(
     N_E, pynn.SpikeSourcePoisson(rate=10.0), label="Poisson_pop_E",
-    additional_parameters={"seed": int(rng.next() * 0xFFFFFFFF)})
+    additional_parameters={"seed": 3})
 Poiss_ext_I = pynn.Population(
     N_I, pynn.SpikeSourcePoisson(rate=10.0), label="Poisson_pop_I",
-    additional_parameters={"seed": int(rng.next() * 0xFFFFFFFF)})
+    additional_parameters={"seed": 4})
 
 # Connectors
-E_conn = pynn.FixedProbabilityConnector(epsilon, rng=rng)
-I_conn = pynn.FixedProbabilityConnector(epsilon, rng=rng)
+E_conn = pynn.FixedProbabilityConnector(epsilon)
+I_conn = pynn.FixedProbabilityConnector(epsilon)
 
 # Use random delays for the external noise and
 # set the inital membrance voltage below the resting potential
 # to avoid the overshoot of activity in the beginning of the simulation
-delay_distr = RandomDistribution('uniform', low=0.1, high=12.8, rng=rng)
+delay_distr = RandomDistribution('uniform', low=0.1, high=12.8)
 Ext_conn = pynn.OneToOneConnector()
 
-uniformDistr = RandomDistribution('uniform', low=-10, high=0, rng=rng)
+uniformDistr = RandomDistribution('uniform', low=-10, high=0)
 E_pop.initialize(v=uniformDistr)
 I_pop.initialize(v=uniformDistr)
 

examples/split_examples/pynnBrunnelSplit.py

@@ -16,7 +16,7 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
-from pyNN.random import NumpyRNG, RandomDistribution
+from pyNN.random import RandomDistribution
 from pyNN.utility.plotting import Figure, Panel
 from spynnaker.pyNN.extra_algorithms.splitter_components import (
     SplitterAbstractPopulationVertexNeuronsSynapses, SplitterPoissonDelegate)
@@ -139,8 +139,6 @@ def poisson_generator(rate, rng, t_start=0.0, t_stop=1000.0, array=True,
     pynn.set_number_of_neurons_per_core(pynn.IF_curr_exp, 64)
     pynn.set_number_of_neurons_per_core(pynn.SpikeSourcePoisson, 64)
 
-rng = NumpyRNG(seed=1)
-
 exc_cell_params = {
     'cm': 1.0,  # pf
     'tau_m': tau_m,
@@ -169,35 +167,35 @@ def poisson_generator(rate, rng, t_start=0.0, t_stop=1000.0, array=True,
 E_pop_splitter = SplitterAbstractPopulationVertexNeuronsSynapses(3, 128, False)
 E_pop = pynn.Population(
     N_E, pynn.IF_curr_exp(**exc_cell_params), label="E_pop",
-    additional_parameters={"splitter": E_pop_splitter})
+    additional_parameters={"splitter": E_pop_splitter}, seed=1)
 
 I_pop_splitter = SplitterAbstractPopulationVertexNeuronsSynapses(3, 128, False)
 I_pop = pynn.Population(
     N_I, pynn.IF_curr_exp(**inh_cell_params), label="I_pop",
-    additional_parameters={"splitter": I_pop_splitter})
+    additional_parameters={"splitter": I_pop_splitter}, seed=2)
 
 Poiss_ext_E_splitter = SplitterPoissonDelegate()
 Poiss_ext_E = pynn.Population(
     N_E, pynn.SpikeSourcePoisson(rate=10.0), label="Poisson_pop_E",
-    additional_parameters={"seed": int(rng.next() * 0xFFFFFFFF),
+    additional_parameters={"seed": 3,
                            "splitter": Poiss_ext_E_splitter})
 Poiss_ext_I_splitter = SplitterPoissonDelegate()
 Poiss_ext_I = pynn.Population(
     N_I, pynn.SpikeSourcePoisson(rate=10.0), label="Poisson_pop_I",
-    additional_parameters={"seed": int(rng.next() * 0xFFFFFFFF),
+    additional_parameters={"seed": 4,
                            "splitter": Poiss_ext_I_splitter})
 
 # Connectors
-E_conn = pynn.FixedProbabilityConnector(epsilon, rng=rng)
-I_conn = pynn.FixedProbabilityConnector(epsilon, rng=rng)
+E_conn = pynn.FixedProbabilityConnector(epsilon)
+I_conn = pynn.FixedProbabilityConnector(epsilon)
 
 # Use random delays for the external noise and
 # set the inital membrance voltage below the resting potential
 # to avoid the overshoot of activity in the beginning of the simulation
-delay_distr = RandomDistribution('uniform', low=0.1, high=12.8, rng=rng)
+delay_distr = RandomDistribution('uniform', low=0.1, high=12.8)
 Ext_conn = pynn.OneToOneConnector()
 
-uniformDistr = RandomDistribution('uniform', low=-10, high=0, rng=rng)
+uniformDistr = RandomDistribution('uniform', low=-10, high=0)
 E_pop.initialize(v=uniformDistr)
 I_pop.initialize(v=uniformDistr)
 

examples/split_examples/va_benchmark_split.py

@@ -28,7 +28,7 @@
 """
 import socket
 import pyNN.spiNNaker as p
-from pyNN.random import NumpyRNG, RandomDistribution
+from pyNN.random import RandomDistribution
 from pyNN.utility import Timer
 from pyNN.utility.plotting import Figure, Panel
 import matplotlib.pyplot as plt
@@ -161,30 +161,29 @@
 exc_cells_splitter = SplitterAbstractPopulationVertexNeuronsSynapses(2)
 exc_cells = p.Population(
     n_exc, celltype(**cell_params), label="Excitatory_Cells",
-    additional_parameters={"splitter": exc_cells_splitter})
+    additional_parameters={"splitter": exc_cells_splitter}, seed=1)
 inh_cells_splitter = SplitterAbstractPopulationVertexNeuronsSynapses(3)
 inh_cells = p.Population(
     n_inh, celltype(**cell_params), label="Inhibitory_Cells",
-    additional_parameters={"splitter": inh_cells_splitter})
+    additional_parameters={"splitter": inh_cells_splitter}, seed=2)
 exc_conn = None
 ext_stim = None
 if benchmark == "COBA":
     ext_stim = p.Population(
         20, p.SpikeSourcePoisson(rate=rate, duration=stim_dur),
-        label="expoisson")
+        label="expoisson", seed=3)
     rconn = 0.01
     ext_conn = p.FixedProbabilityConnector(rconn)
     ext_stim.record("spikes")
 
 print("%s Initialising membrane potential to random values..." % node_id)
-rng = NumpyRNG(seed=rngseed, parallel_safe=parallel_safe)
-uniformDistr = RandomDistribution('uniform', [v_reset, v_thresh], rng=rng)
+uniformDistr = RandomDistribution('uniform', [v_reset, v_thresh])
 exc_cells.initialize(v=uniformDistr)
 inh_cells.initialize(v=uniformDistr)
 
 print("%s Connecting populations..." % node_id)
-exc_conn = p.FixedProbabilityConnector(pconn, rng=rng)
-inh_conn = p.FixedProbabilityConnector(pconn, rng=rng)
+exc_conn = p.FixedProbabilityConnector(pconn)
+inh_conn = p.FixedProbabilityConnector(pconn)
 
 connections = {
     'e2e': p.Projection(

examples/va_benchmark.py

@@ -29,7 +29,7 @@
 """
 import socket
 import pyNN.spiNNaker as p
-from pyNN.random import NumpyRNG, RandomDistribution
+from pyNN.random import RandomDistribution
 from pyNN.utility import Timer
 from pyNN.utility.plotting import Figure, Panel
 import matplotlib.pyplot as plt
@@ -157,28 +157,27 @@
 
 print("%s Creating cell populations..." % node_id)
 exc_cells = p.Population(
-    n_exc, celltype(**cell_params), label="Excitatory_Cells")
+    n_exc, celltype(**cell_params), label="Excitatory_Cells", seed=1)
 inh_cells = p.Population(
-    n_inh, celltype(**cell_params), label="Inhibitory_Cells")
+    n_inh, celltype(**cell_params), label="Inhibitory_Cells", seed=2)
 exc_conn = None
 ext_stim = None
 if benchmark == "COBA":
     ext_stim = p.Population(
         20, p.SpikeSourcePoisson(rate=rate, duration=stim_dur),
-        label="expoisson")
+        label="expoisson", seed=3)
     rconn = 0.01
     ext_conn = p.FixedProbabilityConnector(rconn)
     ext_stim.record("spikes")
 
 print("%s Initialising membrane potential to random values..." % node_id)
-rng = NumpyRNG(seed=rngseed, parallel_safe=parallel_safe)
-uniformDistr = RandomDistribution('uniform', [v_reset, v_thresh], rng=rng)
+uniformDistr = RandomDistribution('uniform', [v_reset, v_thresh])
 exc_cells.initialize(v=uniformDistr)
 inh_cells.initialize(v=uniformDistr)
 
 print("%s Connecting populations..." % node_id)
-exc_conn = p.FixedProbabilityConnector(pconn, rng=rng)
-inh_conn = p.FixedProbabilityConnector(pconn, rng=rng)
+exc_conn = p.FixedProbabilityConnector(pconn)
+inh_conn = p.FixedProbabilityConnector(pconn)
 
 connections = {
     'e2e': p.Projection(

requirements-test.txt

@@ -12,10 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-coverage >= 4.4, < 5.0
-flake8
 # pytest will be brought in by pytest-cov
 pytest-cov
-sphinx >= 2
-pylint
 testfixtures