remove unnecessary int

balanced_random/balanced_random.py

@@ -22,8 +22,8 @@
 p.set_number_of_neurons_per_core(p.IF_curr_exp, 64)
 p.set_number_of_neurons_per_core(p.SpikeSourcePoisson, 64)
 n_neurons = 500
-n_exc = int(round(n_neurons * 0.8))
-n_inh = int(round(n_neurons * 0.2))
+n_exc = round(n_neurons * 0.8)
+n_inh = round(n_neurons * 0.2)
 weight_exc = 0.1
 weight_inh = -5.0 * weight_exc
 weight_input = 0.001

balanced_random/split/balanced_random_split.py

@@ -22,8 +22,8 @@
 p.set_number_of_neurons_per_core(p.IF_curr_exp, 64)
 p.set_number_of_neurons_per_core(p.SpikeSourcePoisson, 64)
 n_neurons = 500
-n_exc = int(round(n_neurons * 0.8))
-n_inh = int(round(n_neurons * 0.2))
+n_exc = round(n_neurons * 0.8)
+n_inh = round(n_neurons * 0.2)
 weight_exc = 0.1
 weight_inh = -5.0 * weight_exc
 weight_input = 0.001

examples/external_devices_examples/live_examples/balanced_random_live_rate.py

@@ -25,8 +25,8 @@
 p.setup(timestep=0.1, time_scale_factor=10.0)
 p.set_number_of_neurons_per_core(p.SpikeSourcePoisson, 50)
 n_neurons = 500
-n_exc = int(round(n_neurons * 0.8))
-n_inh = int(round(n_neurons * 0.2))
+n_exc = round(n_neurons * 0.8)
+n_inh = round(n_neurons * 0.2)
 weight_exc = 0.1
 weight_inh = -5.0 * weight_exc
 weight_input = 0.001

examples/extra_models_examples/LGN_Izhikevich.py

@@ -132,16 +132,16 @@ def print_synchrony():
 """ Initialising Time and Frequency parameters """
 
 # total duration of simulation
-TotalDuration = int(1000)
+TotalDuration = 1000
 
 # this is in ms.
-Duration_Inp = int(1000)
+Duration_Inp = 1000
 
 # 50 ms at both start and end are disregarded to avoid transients
-Start_Inp = int(0)
+Start_Inp = 0
 End_Inp = int(Start_Inp + Duration_Inp)
 
-Rate_Inp = int(22)
+Rate_Inp = 22
 Inp_isi = int(1000 / Rate_Inp)
 
 """ Initialising Model connectivity parameters """

examples/pynnBrunnel.py

@@ -34,8 +34,8 @@
 v_rest = 0.0
 tauSyn = 1.0
 
-N_E = int(round(Neurons * 0.8))
-N_I = int(round(Neurons * 0.2))
+N_E = round(Neurons * 0.8)
+N_I = round(Neurons * 0.2)
 
 C_E = N_E * 0.1
 C_I = N_I * 0.1

examples/split_examples/pynnBrunnelSplit.py

@@ -36,8 +36,8 @@
 v_rest = 0.0
 tauSyn = 1.0
 
-N_E = int(round(Neurons * 0.8))
-N_I = int(round(Neurons * 0.2))
+N_E = round(Neurons * 0.8)
+N_I = round(Neurons * 0.2)
 
 C_E = N_E * 0.1
 C_I = N_I * 0.1

examples/split_examples/va_benchmark_split.py

@@ -92,7 +92,7 @@
 Rm = 1e-6 / (g_leak * area)            # membrane resistance in MΩ
 assert tau_m == cm * Rm                # just to check
 
-n_exc = int(round((n * r_ei / (1 + r_ei))))  # number of excitatory cells
+n_exc = round((n * r_ei / (1 + r_ei)))  # number of excitatory cells
 n_inh = n - n_exc                            # number of inhibitory cells
 
 print(n_exc, n_inh)

examples/va_benchmark.py

@@ -95,8 +95,8 @@
 Rm = 1e-6 / (g_leak * area)            # membrane resistance in MΩ
 assert tau_m == cm * Rm                # just to check
 
-n_exc = int(round((n * r_ei / (1 + r_ei))))  # number of excitatory cells
-n_inh = n - n_exc                            # number of inhibitory cells
+n_exc = round((n * r_ei / (1 + r_ei)))  # number of excitatory cells
+n_inh = n - n_exc                       # number of inhibitory cells
 
 print(n_exc, n_inh)
 

learning/random_dist.py

@@ -18,8 +18,8 @@
 from pyNN.random import RandomDistribution
 
 n_neurons = 1000
-n_exc = int(round(n_neurons * 0.8))
-n_inh = int(round(n_neurons * 0.2))
+n_exc = round(n_neurons * 0.8)
+n_inh = round(n_neurons * 0.2)
 simtime = 1000
 
 sim.setup(timestep=0.1)

spiNNaker_start/spinnaker_start.py

@@ -497,14 +497,14 @@ def set_ethernet_netinit_phase(self, x, y, phase):
     def set_ethernet_n_routes(self, x, y, n_routes):
         colour = self._get_max_value(x, y)
         max_n_routes = self._width * self._height
-        value = int(round((float(colour) / max_n_routes) * n_routes))
+        value = round((float(colour) / max_n_routes) * n_routes)
         self._image_data[y, x] = [255 - value, 0, 255 - value]
 
     def add_cores(self, eth_x, eth_y, x, y, n_cores):
         colour = self._get_max_value(eth_x, eth_y)
         with self._update_lock:
             self._total_cores += n_cores
-            value = int(round((float(colour) / 18.0) * n_cores))
+            value = round((float(colour) / 18.0) * n_cores)
             self._image_data[y, x] = [0, value, 0]