Change tab-spacing to four spaces spacing.

Author: Dan Saunders

bindsnet/analysis/plotting.py

@@ -9,109 +9,109 @@
 
 
 def plot_weights_movie(ws, sample_every=1):
-	"""
-	Create and plot movie of weights (:code:`ws`).
-	
-	Inputs:
-	
-		| :code:`ws` (:code:`numpy.array`): Numpy array of shape :code:`[N_examples, source, target, time]`
-		| :code:`sample_every` (:code:`int`): Sub-sample using this parameter. For example if :code:`time` is
-																  too large (500), set this parameter to 20 to sample weights 
-																  every 20 iterations. 
-	"""
-	weights = []
-	
-	# Obtain samples from the weights for every example
-	for i in range(ws.shape[0]):
-		sub_sampled_weight = ws[i, :, :, range(0, ws[i].shape[2], sample_every)]
-		weights.append(sub_sampled_weight)
-	else:
-		weights = np.concatenate(weights, axis=0)
-	
-	# Initialize plot
-	fig = plt.figure()
-	im = plt.imshow(weights[0, :, :], cmap='hot_r', animated=True, vmin=0, vmax=1)
-	plt.axis('off'); plt.colorbar(im)
-		
-	# Update function for the animation
-	def update(j):
-		im.set_data(weights[j, :, :])
-		return [im]
-	
-	# Initialize animatino
-	global ani; ani=0
-	ani = animation.FuncAnimation(fig, update, frames=weights.shape[-1], interval=1000, blit=True)
-	plt.show()
-	
+    """
+    Create and plot movie of weights (:code:`ws`).
+    
+    Inputs:
+    
+        | :code:`ws` (:code:`numpy.array`): Numpy array of shape :code:`[N_examples, source, target, time]`
+        | :code:`sample_every` (:code:`int`): Sub-sample using this parameter. For example if :code:`time` is
+                                                                  too large (500), set this parameter to 20 to sample weights 
+                                                                  every 20 iterations. 
+    """
+    weights = []
+    
+    # Obtain samples from the weights for every example
+    for i in range(ws.shape[0]):
+        sub_sampled_weight = ws[i, :, :, range(0, ws[i].shape[2], sample_every)]
+        weights.append(sub_sampled_weight)
+    else:
+        weights = np.concatenate(weights, axis=0)
+    
+    # Initialize plot
+    fig = plt.figure()
+    im = plt.imshow(weights[0, :, :], cmap='hot_r', animated=True, vmin=0, vmax=1)
+    plt.axis('off'); plt.colorbar(im)
+        
+    # Update function for the animation
+    def update(j):
+        im.set_data(weights[j, :, :])
+        return [im]
+    
+    # Initialize animatino
+    global ani; ani=0
+    ani = animation.FuncAnimation(fig, update, frames=weights.shape[-1], interval=1000, blit=True)
+    plt.show()
+    
 def plot_spike_trains_for_example(spikes, n_ex=None, top_k=None, indices=None):
-	'''
-	Plot spike trains for top-k neurons or for specific indices.
-	
-	Inputs:
-		
-		| :code:`spikes` (:code:`torch.Tensor (n_examples, n_neurons, time)`): Spiking train data for a population of neurons for one example.
-		| :code:`n_ex` (:code:`int`): Allows user to pick which example to plot spikes for. Must be >= 0.
-		| :code:`top_k` (:code:`int`): Plot k neurons that spiked the most for n_ex example.
-		| :code:`indices` (:code:`list(int)`): Plot specific neurons' spiking activity instead of top_k. Meant to replace top_k. 
-	'''
+    '''
+    Plot spike trains for top-k neurons or for specific indices.
+    
+    Inputs:
+        
+        | :code:`spikes` (:code:`torch.Tensor (n_examples, n_neurons, time)`): Spiking train data for a population of neurons for one example.
+        | :code:`n_ex` (:code:`int`): Allows user to pick which example to plot spikes for. Must be >= 0.
+        | :code:`top_k` (:code:`int`): Plot k neurons that spiked the most for n_ex example.
+        | :code:`indices` (:code:`list(int)`): Plot specific neurons' spiking activity instead of top_k. Meant to replace top_k. 
+    '''
 
-	assert (n_ex is not None and n_ex >= 0 and n_ex < spikes.shape[0])
-	
-	plt.figure()
-	
-	if top_k is None and indices is None: # Plot all neurons' spiking activity
-		spike_per_neuron = [np.argwhere(i==1).flatten() for i in spikes[n_ex, :, :]]
-		plt.title('Spiking activity for all %d neurons'%spikes.shape[1])
-		
-	elif top_k is None: # Plot based on indices parameter
-		assert (indices is not None)
-		spike_per_neuron = [np.argwhere(i==1).flatten() for i in spikes[n_ex, indices, :]]
-		
-	elif indices is None: # Plot based on top_k parameter
-		assert (top_k is not None)
-		# Obtain the top k neurons that fired the most
-		top_k_loc = np.argsort(np.sum(spikes[n_ex,:,:], axis=1), axis=0)[::-1]
-		spike_per_neuron = [np.argwhere(i==1).flatten() for i in spikes[n_ex, top_k_loc[0:top_k], :]]
-		plt.title('Spiking activity for top %d neurons'%top_k)
-		
-	plt.eventplot(spike_per_neuron, linelengths= [0.5]*len(spike_per_neuron))
-	plt.xlabel('Simulation Time'); plt.ylabel('Neuron index')
-	plt.show()
+    assert (n_ex is not None and n_ex >= 0 and n_ex < spikes.shape[0])
+    
+    plt.figure()
+    
+    if top_k is None and indices is None: # Plot all neurons' spiking activity
+        spike_per_neuron = [np.argwhere(i==1).flatten() for i in spikes[n_ex, :, :]]
+        plt.title('Spiking activity for all %d neurons'%spikes.shape[1])
+        
+    elif top_k is None: # Plot based on indices parameter
+        assert (indices is not None)
+        spike_per_neuron = [np.argwhere(i==1).flatten() for i in spikes[n_ex, indices, :]]
+        
+    elif indices is None: # Plot based on top_k parameter
+        assert (top_k is not None)
+        # Obtain the top k neurons that fired the most
+        top_k_loc = np.argsort(np.sum(spikes[n_ex,:,:], axis=1), axis=0)[::-1]
+        spike_per_neuron = [np.argwhere(i==1).flatten() for i in spikes[n_ex, top_k_loc[0:top_k], :]]
+        plt.title('Spiking activity for top %d neurons'%top_k)
+        
+    plt.eventplot(spike_per_neuron, linelengths= [0.5]*len(spike_per_neuron))
+    plt.xlabel('Simulation Time'); plt.ylabel('Neuron index')
+    plt.show()
 
 def plot_voltage(voltage, n_ex=0, n_neuron=0, time=None, threshold=None):
-	'''
-	Plot voltage for a single neuron on a specific example.
-	
-	Inputs:
-		
-		| :code:`voltage` (:code:`torch.Tensor` or :code:`numpy.array`): Tensor or array of shape :code:`[n_examples, n_neurons, time]`.
-		| :code:`n_ex` (:code:`int`): Allows user to pick which example to plot voltage for.
-		| :code:`n_neuron` (:code:`int`): Neuron index for which to plot voltages for.
-		| :code:`time` (:code:`tuple(int)`): Plot spiking activity of neurons between the given range of time. 
-		| :code:`threshold` (:code:`float`): Neuron spiking threshold. Will be shown on the plot.
-	'''
-	
-	assert (n_ex >= 0 and n_neuron >= 0)
-	assert (n_ex < voltage.shape[0] and n_neuron < voltage.shape[1])
+    '''
+    Plot voltage for a single neuron on a specific example.
+    
+    Inputs:
+        
+        | :code:`voltage` (:code:`torch.Tensor` or :code:`numpy.array`): Tensor or array of shape :code:`[n_examples, n_neurons, time]`.
+        | :code:`n_ex` (:code:`int`): Allows user to pick which example to plot voltage for.
+        | :code:`n_neuron` (:code:`int`): Neuron index for which to plot voltages for.
+        | :code:`time` (:code:`tuple(int)`): Plot spiking activity of neurons between the given range of time. 
+        | :code:`threshold` (:code:`float`): Neuron spiking threshold. Will be shown on the plot.
+    '''
+    
+    assert (n_ex >= 0 and n_neuron >= 0)
+    assert (n_ex < voltage.shape[0] and n_neuron < voltage.shape[1])
 
-	if time is None:
-		time = (0, voltage.shape[-1])
-	else:
-		assert (time[0] < time[1])
-		assert (time[1] <= voltage.shape[-1])
-	
-	timer = np.arange(time[0], time[1])
-	time_ticks = np.arange(time[0], time[1]+1, 10)
-	
-	plt.figure()
-	plt.plot(voltage[n_ex, n_neuron, timer])
-	plt.xlabel('Simulation Time'); plt.ylabel('Voltage'); plt.title('Membrane voltage of neuron %d for example %d'%(n_neuron, n_ex+1))
-	locs, labels = plt.xticks()
-	locs = range(int(locs[1]), int(locs[-1]), 10)
-	plt.xticks(locs, time_ticks)
-	
-	# Draw threshold line only if given
-	if threshold is not None:
-		plt.axhline(threshold, linestyle='--', color='black', zorder=0)
-		
-	plt.show()
+    if time is None:
+        time = (0, voltage.shape[-1])
+    else:
+        assert (time[0] < time[1])
+        assert (time[1] <= voltage.shape[-1])
+    
+    timer = np.arange(time[0], time[1])
+    time_ticks = np.arange(time[0], time[1]+1, 10)
+    
+    plt.figure()
+    plt.plot(voltage[n_ex, n_neuron, timer])
+    plt.xlabel('Simulation Time'); plt.ylabel('Voltage'); plt.title('Membrane voltage of neuron %d for example %d'%(n_neuron, n_ex+1))
+    locs, labels = plt.xticks()
+    locs = range(int(locs[1]), int(locs[-1]), 10)
+    plt.xticks(locs, time_ticks)
+    
+    # Draw threshold line only if given
+    if threshold is not None:
+        plt.axhline(threshold, linestyle='--', color='black', zorder=0)
+        
+    plt.show()