Update multi_agent_2D.py

multi_agent_2D.py

@@ -4,12 +4,12 @@ import numpy as np
 import pandas as pd
 from matplotlib.lines import Line2D
 
+#single_random_walk takes a single particle and plots its trajectory on the axis passed to it
 def single_random_walk(iters, agent_number, ax, step_size = 1, random_seed = None):
-    # random.seed(random_seed)
-    if random_seed:
+    if random_seed: 
         random.seed(random_seed)
 
-    iters = int(iters)
+    iters = int(iters) #Because for some reason, the input from Gradio input components is in float
     directions = ['east', 'north', 'west', 'south']
     start_point = [0, 0]
     
@@ -32,7 +32,7 @@ def single_random_walk(iters, agent_number, ax, step_size = 1, random_seed = Non
     
     coordinate_list = [start_point]
     
-    for _ in range(iters):
+    for _ in range(iters): #Key part that decides the trajectory of the agent
         new_step = step_determination()
         new_coordinate = step_addition(coordinate_list[-1], new_step)
         coordinate_list.append(new_coordinate)
@@ -41,19 +41,20 @@ def single_random_walk(iters, agent_number, ax, step_size = 1, random_seed = Non
     y = [i[1] for i in coordinate_list]
     df = pd.DataFrame({'x':x,'y':y})
     
-
-    #Add the axis from the argument to the figure
+    #This to determine the markersize. This is is only a makeshift solution.
     base_marker_size = 10
     markersize = base_marker_size / np.sqrt(iters)
 
+    #Plot on the axis passed, do not make a new figure.
     plot = ax.plot(x, y, marker='o', markersize=markersize, linestyle='None', alpha=0.5, label = 'Agent {i}'.format(i=agent_number+1))
-    color = plot[0].get_color()
+    color = plot[0].get_color() #Get the color so that we can add label with proper colors later
     ax.plot(x[-1], y[-1], marker='o', markersize=5, color = 'black')
     ax.text(x[-1], y[-1], 'End {i}'.format(i=agent_number+1), color = 'black', alpha=1.0)
     
     return ax, df, color
 
 
+#multi_agent_walk iteratively calls the single_random_walk function to plot different trajectories on the same axis.
 def multi_agent_walk(agent_count, iters, step_size = 1, random_seed = None):
     assert agent_count >= 1, "Number of agents must be >= than 1"
     agent_count = int(agent_count)
@@ -69,7 +70,7 @@ def multi_agent_walk(agent_count, iters, step_size = 1, random_seed = None):
 
     random_seed = int(random_seed)
     assert type(random_seed) == int, "Random seed must be an integer"
-    #Generate a list of random seeds for each agent
+    #Generates a list of random seeds for each agent
     random.seed(random_seed)
     random_numbers = [random.randint(0,100000) for _ in range(agent_count)]