Merge branch 'main' into update_release_job

.github/workflows/ci.yml

@@ -25,7 +25,9 @@ jobs:
         repository: 'Lava'    
 
     - name: Run flakeheaven (flake8)
-      run: poetry run flakeheaven lint src/lava tests/
+      run: |
+        poetry run flakeheaven lint src/lava tests/
+        poetry run find tutorials/ -name '*.py' -exec flakeheaven lint {} \+
 
   security-lint:
     name: Security Lint Code

src/lava/magma/core/callback_fx.py

@@ -73,14 +73,14 @@ def __init__(self,
 
     def pre_run_callback(self,
                          board: NxBoard = None,
-                         _var_id_to_var_model_map: dict = None
+                         var_id_to_var_model_map: dict = None
                          ) -> None:
         for fx in self.pre_run_fxs:
             fx(board)
 
     def post_run_callback(self,
                           board: NxBoard = None,
-                          _var_id_to_var_model_map: dict = None
+                          var_id_to_var_model_map: dict = None
                           ) -> None:
         for fx in self.post_run_fxs:
             fx(board)

src/lava/magma/core/process/process.py

@@ -347,7 +347,7 @@ def run(self,
                 raise ValueError("run_cfg must not be None when calling"
                                  " Process.run() unless the process has already"
                                  " been compiled.")
-            self.create_runtime(run_cfg, compile_config)
+            self.create_runtime(run_cfg=run_cfg, compile_config=compile_config)
         self._runtime.start(condition)
 
     def create_runtime(self, run_cfg: ty.Optional[RunConfig] = None,

tutorials/end_to_end/convert_params.py

@@ -38,6 +38,7 @@ def _mean_input(num_neurons_exc, gamma, g_factor, weight, rate, bias):
 
     return mean_inp
 
+
 def _std_input(num_neurons_exc, gamma, g_factor, weight, rate):
     '''
     Calculate mean input to single neuron given mean excitatory weight.
@@ -62,6 +63,7 @@ def _std_input(num_neurons_exc, gamma, g_factor, weight, rate):
     '''
     return num_neurons_exc * (1 + gamma * g_factor**2) * weight ** 2 * rate
 
+
 def _y_th(vth, mean, std, dv_exc, du_exc):
     '''
     Effective threshold, see Grytskyy et al. 2013.
@@ -89,6 +91,7 @@ def _y_th(vth, mean, std, dv_exc, du_exc):
 
     return y_th
 
+
 def _y_r(mean, std, dv_exc, du_exc):
     '''
     Effective reset, see Grytskyy et al. 2013.
@@ -116,12 +119,14 @@ def _y_r(mean, std, dv_exc, du_exc):
 
     return y_r
 
+
 def f(y):
     '''
     Derivative of transfer function of LIF neuron at given argument.
     '''
     return np.exp(y ** 2) * (1 + erf(y))
 
+
 def _alpha(vth, mean, std, dv_exc, du_exc):
     '''
     Auxiliary variable describing contribution of weights for weight
@@ -152,6 +157,7 @@ def _alpha(vth, mean, std, dv_exc, du_exc):
 
     return val
 
+
 def _beta(vth, mean, std, dv_exc, du_exc):
     '''
     Auxiliary variable describing contribution of square of weights for
@@ -176,14 +182,16 @@ def _beta(vth, mean, std, dv_exc, du_exc):
         Contribution of square of weights
     '''
     val = np.sqrt(np.pi) * (mean * dv_exc * 0.01) ** 2
-    val *= 1/(2 * std ** 2)
+    val *= 1 / (2 * std ** 2)
     val *= (f(_y_th(vth, mean, std, dv_exc, du_exc)) * (vth - mean) / std
             - f(_y_r(mean, std, dv_exc, du_exc)) * (-1 * mean) / std)
 
     return val
 
-def convert_rate_to_lif_params(shape_exc, dr_exc, bias_exc, shape_inh, dr_inh,
-                              bias_inh, g_factor, q_factor, weights, **kwargs):
+
+def convert_rate_to_lif_params(
+        shape_exc, dr_exc, bias_exc, shape_inh, dr_inh, bias_inh, g_factor,
+        q_factor, weights, **kwargs):
     '''Convert rate parameters to LIF parameters.
     The mapping is based on A unified view on weakly correlated recurrent
     network, Grytskyy et al. 2013.
@@ -224,7 +232,8 @@ def convert_rate_to_lif_params(shape_exc, dr_exc, bias_exc, shape_inh, dr_inh,
     gamma = float(num_neurons_exc) / float(num_neurons_inh)
 
     # Assert that network is balanced.
-    assert gamma * g_factor > 1, "Network not balanced, increase g_factor"
+    if gamma * g_factor <= 1:
+        raise AssertionError("Network not balanced, increase g_factor")
 
     # Set timescales of neurons.
     dv_exc = 1 * dr_exc  # Dynamics of membrane potential as fast as rate.
@@ -275,7 +284,7 @@ def func(weight):
         mean_inp = _mean_input(num_neurons_exc, gamma,
                                g_factor, weight, rate, bias)
         std_inp = _std_input(num_neurons_exc, gamma,
-                            g_factor, weight, rate)
+                             g_factor, weight, rate)
         alpha = _alpha(vth_exc, mean_inp, std_inp, dv_exc, du_inh)
         beta = _beta(vth_exc, mean_inp, std_inp, dv_exc, du_inh)
 

tutorials/in_depth/three_factor_learning/utils.py

@@ -185,29 +185,29 @@ def generate_post_spikes(pre_spike_times, num_steps, spike_prob_post):
     """generates specific post synaptic spikes to
     demonstrate potentiation and depression.
     """
-    pre_synaptic_spikes = np.where(pre_spike_times==1)[1]
+    pre_synaptic_spikes = np.where(pre_spike_times == 1)[1]
 
     spike_raster_post = np.zeros((len(spike_prob_post), num_steps))
 
     for ts in range(num_steps):
         for pre_ts in pre_synaptic_spikes:
-            if ts in range(pre_ts, pre_ts+20):
+            if ts in range(pre_ts, pre_ts + 20):
                 if np.random.rand(1) < spike_prob_post[0]:
                     spike_raster_post[0][ts] = 1
 
     for ts in range(num_steps):
         for pre_ts in pre_synaptic_spikes:
-            if ts in range(pre_ts-12, pre_ts-2):
+            if ts in range(pre_ts - 12, pre_ts - 2):
                 if np.random.rand(1) < spike_prob_post[1]:
                     spike_raster_post[1][ts] = 1
-    
+
     return spike_raster_post
 
 
 def plot_spikes(spikes, figsize, legend, colors, title, num_steps):
     offsets = list(range(1, len(spikes) + 1))
-    num_x_ticks = np.arange(0, num_steps+1, 25)
-    
+    num_x_ticks = np.arange(0, num_steps + 1, 25)
+
     plt.figure(figsize=figsize)
 
     plt.eventplot(positions=spikes,
@@ -223,7 +223,7 @@ def plot_spikes(spikes, figsize, legend, colors, title, num_steps):
     plt.grid(which='minor', color='lightgrey', linestyle=':', linewidth=0.5)
     plt.grid(which='major', color='lightgray', linewidth=0.8)
     plt.minorticks_on()
-    
+
     plt.yticks(ticks=offsets, labels=legend)
 
     plt.show()
@@ -232,26 +232,26 @@ def plot_spikes(spikes, figsize, legend, colors, title, num_steps):
 def plot_time_series(time, time_series, ylabel, title, figsize, color):
     plt.figure(figsize=figsize)
     plt.step(time, time_series, color=color)
-   
+
     plt.title(title)
     plt.xlabel("Time steps")
     plt.grid(which='minor', color='lightgrey', linestyle=':', linewidth=0.5)
     plt.grid(which='major', color='lightgray', linewidth=0.8)
     plt.minorticks_on()
 
     plt.ylabel(ylabel)
-    
+
     plt.show()
 
 
 def plot_time_series_subplots(time, time_series_y1, time_series_y2, ylabel,
                               title, figsize, color, legend,
                               leg_loc="upper left"):
     plt.figure(figsize=figsize)
-    
+
     plt.step(time, time_series_y1, label=legend[0], color=color[0])
     plt.step(time, time_series_y2, label=legend[1], color=color[1])
-        
+
     plt.title(title)
     plt.xlabel("Time steps")
     plt.ylabel(ylabel)
@@ -261,20 +261,20 @@ def plot_time_series_subplots(time, time_series_y1, time_series_y2, ylabel,
     plt.xlim(0, len(time_series_y1))
 
     plt.legend(loc=leg_loc)
-    
+
     plt.show()
 
 
 def plot_spikes_time_series(time, time_series, spikes, figsize, legend,
                             colors, title, num_steps):
 
     offsets = list(range(1, len(spikes) + 1))
-    num_x_ticks = np.arange(0, num_steps+1, 25)
-    
+    num_x_ticks = np.arange(0, num_steps + 1, 25)
+
     plt.figure(figsize=figsize)
-    
+
     plt.subplot(211)
-    plt.eventplot(positions=spikes, 
+    plt.eventplot(positions=spikes,
                   lineoffsets=offsets,
                   linelength=0.9,
                   colors=colors)
@@ -287,13 +287,13 @@ def plot_spikes_time_series(time, time_series, spikes, figsize, legend,
     plt.grid(which='minor', color='lightgrey', linestyle=':', linewidth=0.5)
     plt.grid(which='major', color='lightgray', linewidth=0.8)
     plt.minorticks_on()
-    
+
     plt.yticks(ticks=offsets, labels=legend)
     plt.tight_layout(pad=3.0)
 
     plt.subplot(212)
     plt.step(time, time_series, color=colors)
-   
+
     plt.title(title[0])
     plt.xlabel("Time steps")
     plt.grid(which='minor', color='lightgrey', linestyle=':', linewidth=0.5)
@@ -302,5 +302,5 @@ def plot_spikes_time_series(time, time_series, spikes, figsize, legend,
     plt.margins(x=0)
 
     plt.ylabel("Trace Value")
-    
+
     plt.show()