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import matplotlib.pyplot as plt |
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import random |
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import gradio as gr |
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import numpy as np |
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import pandas as pd |
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def generate_random_walk(iters, step_size = 1, random_seed = None): |
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iters = int(iters) |
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directions = ['east', 'north', 'west', 'south'] |
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start_point = [0, 0] |
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if random_seed is None: |
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random_seed = random.randint(1, 100000) |
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else: |
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random_seed = random_seed |
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random.seed(random_seed) |
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def distance_from_start(final_coord, start_coord, round_to=2): |
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return round(np.sqrt((final_coord[0] - start_coord[0])**2 + (final_coord[1] - start_coord[1])**2), round_to) |
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def step_addition(old_coord, step): |
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return [sum(x) for x in zip(old_coord, step)] |
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def step_determination(): |
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direction = random.choice(directions) |
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if direction == 'east': |
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return [1*step_size, 0] |
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elif direction == 'west': |
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return [-1*step_size, 0] |
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elif direction == 'north': |
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return [0, 1*step_size] |
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elif direction == 'south': |
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return [0, -1*step_size] |
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coordinate_list = [start_point] |
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for i in range(iters): |
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new_step = step_determination() |
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new_coordinate = step_addition(coordinate_list[-1], new_step) |
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coordinate_list.append(new_coordinate) |
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x = [i[0] for i in coordinate_list] |
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y = [i[1] for i in coordinate_list] |
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df = pd.DataFrame({'x':x,'y':y}) |
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csv_file = "2d_random_walk_coordinates.csv" |
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df.to_csv(csv_file, index=False) |
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fig, ax = plt.subplots(1) |
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base_marker_size = 10 |
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markersize = base_marker_size / np.sqrt(iters) |
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ax.plot(x, y, marker='o', markersize=markersize, linestyle='None') |
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ax.plot(x[0], y[0], marker='o', markersize=5, color="red") |
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ax.plot(x[-1], y[-1], marker='o', markersize=5, color="orange") |
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ax.text(start_point[0], start_point[1], 'Start', color='red') |
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ax.text(x[-1], y[-1], 'End', color='orange') |
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x_max_index = x.index(max(x)) |
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x_min_index = x.index(min(x)) |
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y_max_index = y.index(max(y)) |
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y_min_index = y.index(min(y)) |
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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)) |
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ax.legend([info_text], loc='best', handlelength=0, handletextpad=0, fancybox=True, fontsize=8) |
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plt.title( '2D Random Walk\nsteps=' + str(iters)+', step size='+ str(step_size)+ ', seed = '+str((random_seed)) ) |
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plt.grid() |
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fig.canvas.draw() |
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image_array = np.array(fig.canvas.renderer.buffer_rgba()) |
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return image_array, csv_file |
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iters = gr.Number(value=1e6,label="How many random steps?") |
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step_size = gr.Number(value=1,label="Step size") |
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random_seed = gr.Number(value=42,label="Random seed. Delete it to go full random mode, keep it for reproducibility") |
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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") |
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iface.launch() |
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