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import gradio as gr |
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from PIL import Image |
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from matplotlib import pyplot as plt |
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import numpy as np |
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import pickle |
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import gzip |
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import seaborn as sns |
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sns.set_theme() |
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def flip(n, t, seed=13): |
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np.random.seed(seed) |
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flips = np.random.randint(0, 2, size=(n, t)) |
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heads = flips.cumsum(axis=1) |
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return heads.astype(np.uint8) |
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def load(filename): |
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file = gzip.GzipFile(filename, 'rb') |
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data = file.read() |
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object = pickle.loads(data) |
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file.close() |
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return object |
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def calc_outcome(p_win, win, loss, leverage): |
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n = 1000000 |
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t = 60 |
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base=100 |
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heads = load('flips.pkl.gz') |
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tails = np.arange(t)+1 - heads |
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p_loss = 1 - p_win |
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log_win = np.log(1+leverage*win) |
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log_loss = np.log(1-leverage*loss) |
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log_outcome = heads*log_win + tails*log_loss |
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outcome = base*(np.exp(log_outcome)) |
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return outcome |
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def process(p_win, win, loss, leverage): |
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outcome = calc_outcome(p_win/100., win/100., loss/100., leverage/100.) |
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ensemble = fig2img(plot_ensemble(outcome)[0]) |
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time = fig2img(plot_time(outcome)[0]) |
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richest = fig2img(plot_richest(outcome)[0]) |
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return ensemble, time, richest |
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def fig2img(fig): |
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"""Convert a Matplotlib figure to a PIL Image and return it""" |
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import io |
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buf = io.BytesIO() |
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fig.savefig(buf) |
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buf.seek(0) |
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img = Image.open(buf) |
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return img |
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def plot_ensemble(outcome): |
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fig, ax = plt.subplots(1, 1, figsize=(4, 4)) |
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ax.plot(outcome.mean(axis=0)) |
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ax.set_xlabel('Time') |
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ax.set_ylabel('Average Wealth ($)') |
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ax.set_title('Ensemble (Collective) Perspective') |
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fig.tight_layout() |
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return fig, ax |
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def plot_richest(outcome): |
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fig, ax = plt.subplots(1, 1, figsize=(4, 4)) |
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ax.plot(outcome.max(axis=0)/1e6) |
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ax.set_xlabel('Time') |
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ax.set_ylabel('Wealth ($ million)') |
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ax.set_title('Richest Individual') |
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fig.tight_layout() |
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return fig, ax |
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def plot_time(outcome): |
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fig, ax = plt.subplots(1, 1, figsize=(4, 4)) |
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ax.plot(np.percentile(outcome, 50, axis=0)) |
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ax.set_xlabel('Time') |
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ax.set_ylabel('Median Wealth ($)') |
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ax.set_title('Time (Individual) Perspective') |
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fig.tight_layout() |
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return fig, ax |
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css = "" |
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with gr.Blocks(css=css) as demo: |
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with gr.Column(): |
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with gr.Row(): |
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p_win = gr.Slider(0, 100, value=50, label='Win Prob.(%)', interactive=True) |
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with gr.Row(): |
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win = gr.Number(value=50, label='Gain (%)', interactive=True) |
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loss = gr.Number(value=40, label='Loss (%)', interactive=True) |
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with gr.Row(): |
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leverage = gr.Slider(0, 100, value=100, label='Leverage (%)', interactive=True) |
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with gr.Row(): |
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btn = gr.Button('Go!') |
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with gr.Column(scale=1, min_width=900): |
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with gr.Row(): |
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ensemble = gr.Image(label="Ensemble Perspective", shape=(300, 300), elem_id="plot_ensemble") |
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time = gr.Image(label="Time Perspective", shape=(300, 300), elem_id="plot_time") |
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richest = gr.Image(label="Richest Individual", shape=(300, 300), elem_id="plot_richest") |
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btn.click(process, [p_win, win, loss, leverage], [ensemble, time, richest]) |
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demo.launch() |
