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import matplotlib.pyplot as plt
import random
import gradio as gr
import numpy as np
import pandas as pd
def generate_random_walk(iters, step_size = 1, random_seed = None):
# random.seed(random_seed)
iters = int(iters)
directions = ['east', 'north', 'west', 'south']
start_point = [0, 0]
if random_seed is None:
random_seed = random.randint(1, 100000)
else:
random_seed = random_seed
random.seed(random_seed)
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 i in range(iters):
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})
csv_file = "2d_random_walk_coordinates.csv"
df.to_csv(csv_file, index=False)
fig, ax = plt.subplots(1)
base_marker_size = 10
markersize = base_marker_size / np.sqrt(iters)
ax.plot(x, y, marker='o', markersize=markersize, linestyle='None')
ax.plot(x[0], y[0], marker='o', markersize=5, color="red")
ax.plot(x[-1], y[-1], marker='o', markersize=5, color="orange")
ax.text(start_point[0], start_point[1], 'Start', color='red')
ax.text(x[-1], y[-1], 'End', color='orange')
x_max_index = x.index(max(x))
x_min_index = x.index(min(x))
y_max_index = y.index(max(y))
y_min_index = y.index(min(y))
info_text = 'Start point=' + str(start_point) + '\n' +'End point=' + str([x[-1],y[-1]]) + '\n' +'Displacement =' + str(distance_from_start([x[-1], y[-1]], start_point)) + '\n' +'Max x = ' + str(max(x)) + '\n' + 'Min x = ' + str(min(x)) + '\n' + 'Max y = ' + str(max(y)) + '\n' + 'Min y = ' + str(min(y))
ax.legend([info_text], loc='best', handlelength=0, handletextpad=0, fancybox=True, fontsize=8)
plt.title( '2D Random Walk\nsteps=' + str(iters)+', step size='+ str(step_size)+ ', seed = '+str((random_seed)) )
plt.grid()
fig.canvas.draw()
image_array = np.array(fig.canvas.renderer.buffer_rgba())
return image_array, csv_file
iters = gr.Number(value=1e5,label="How many random steps?")
step_size = gr.Number(value=1,label="Step size")
random_seed = gr.Number(value=42,label="Random seed (Delete it to go full random mode)")
iface = gr.Interface(fn=generate_random_walk, inputs=[iters, step_size, random_seed], outputs=["image","file"], title="2-D Random Walk", description="Uniform steps along NEWS directions only")
iface.launch()
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