File size: 5,538 Bytes
f115572 ec3a39e f115572 ec3a39e f115572 ec3a39e f115572 ec3a39e f115572 ec3a39e f115572 ec3a39e f115572 ec3a39e f115572 ec3a39e f115572 b934a0e f115572 c878be0 f115572 ec3a39e f115572 35908ef f115572 35908ef f115572 fcf43ac f115572 fcf43ac f115572 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 |
import matplotlib.pyplot as plt
import random
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):
if random_seed:
random.seed(random_seed)
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]
def distance_from_start(final_coord, start_coord, round_to=2):
return round(np.sqrt((final_coord[0] - start_coord[0])**2 + (final_coord[1] - start_coord[1])**2), round_to)
def step_addition(old_coord, step):
return [sum(x) for x in zip(old_coord, step)]
def step_determination():
direction = random.choice(directions)
if direction == 'east':
return [1*step_size, 0]
elif direction == 'west':
return [-1*step_size, 0]
elif direction == 'north':
return [0, 1*step_size]
elif direction == 'south':
return [0, -1*step_size]
coordinate_list = [start_point]
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)
x = [i[0] for i in coordinate_list]
y = [i[1] for i in coordinate_list]
df = pd.DataFrame({'x':x,'y':y})
#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() #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)
iters = int(iters)
def displacement_calc(df):
x1,y1 = df.iloc[0]
x2,y2 = df.iloc[-1]
return np.round(np.sqrt((x2-x1)**2 + (y2-y1)**2),1)
if random_seed is None:
random_seed = random.randint(0,1000000)
random_seed = int(random_seed)
assert type(random_seed) == int, "Random seed must be an integer"
#Generates a list of random seeds for each agent
random.seed(random_seed)
random_numbers = [random.randint(0,100000) for _ in range(agent_count)]
fig, ax = plt.subplots(figsize=(8,8))
color_list = []
for i in range(agent_count):
if i == 0:
ax, df, color = single_random_walk(iters=iters, ax=ax, step_size=step_size, agent_number=i, random_seed=random_numbers[i])
color_list.append(color)
else:
ax, df_new, color = single_random_walk(iters=iters, ax=ax, step_size=step_size, agent_number=i, random_seed=random_numbers[i])
df = pd.concat([df,df_new], axis=1)
x_columns = [f'x{i}' for i in range(1, i+2)]
y_columns = [f'y{i}' for i in range(1, i+2)]
new_column_names = [val for pair in zip(x_columns, y_columns) for val in pair]
df.columns = new_column_names
color_list.append(color)
ax.plot(0,0, marker='X', markersize=8, color='black')
ax.text(0, 0, 'Start (0,0)')
plt.grid()
plt.title('Random 2D Walk with {} agents\n #Steps = {}, Step size = {}, random seed = {}\nAll agents start from the origin'.format(agent_count, iters, step_size, random_seed))
displacement = [displacement_calc(df.iloc[:,[i,i+1]]) for i in range(0,agent_count*2,2)]
end_point = [(df.iloc[-1,i]) for i in range(0,agent_count*2,2)]
end_point = [(df.iloc[-1,i], df.iloc[-1,i+1]) for i in range(0,agent_count*2,2)]
agent_number = [i+1 for i in range(agent_count)]
legend_df = pd.DataFrame({'#':agent_number, 'dis.':displacement, 'End Point':end_point, })
info_box = legend_df.to_string(index=False)
ax.text(0.01, 0.99, info_box,
transform=ax.transAxes,
verticalalignment='top',
bbox=dict(boxstyle='round', facecolor='white', alpha=0.5)
)
lines = []
for i in range(len(color_list)):
lines.append(Line2D([0], [0], color=color_list[i], lw=9, linestyle=':'))
labels = [f'Agent {i+1}' for i in range(len(color_list))]
plt.legend(lines, labels,
loc='best',
handlelength=1.01,
handletextpad=0.21,
fancybox=True,
fontsize=10,
)
fig.canvas.draw()
image_array = np.array(fig.canvas.renderer.buffer_rgba())
csv_file = "2d_random_walk_coordinates.csv"
df.to_csv(csv_file, index=False)
try:
return image_array, csv_file
except:
return image_array, None
# _, df = multi_agent_walk(agent_count=9, iters=1e5, step_size=1, random_seed=123);
|