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import os

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import sys

sys.path.append("../")

from model import WindFM, WindFMTokenizer, WindFMPredictor

def plot_probabilistic_forecast(

ground_truth,

pred_df,

):

pred_samples = pred_df.values

pred_len = len(pred_df)

q5 = np.quantile(pred_samples, 0.05, axis=1)

q25 = np.quantile(pred_samples, 0.25, axis=1)

q50_median = np.quantile(pred_samples, 0.50, axis=1)

q75 = np.quantile(pred_samples, 0.75, axis=1)

q95 = np.quantile(pred_samples, 0.95, axis=1)

forecast_idx = np.arange(pred_len)

c_truth = 'darkblue'

c_forecast = '#1F77B4'

plt.figure(figsize=(8, 5))

plt.fill_between(forecast_idx, q5, q95, color=c_forecast, alpha=0.15, label='90% Confidence Interval', zorder=1)

plt.fill_between(forecast_idx, q25, q75, color=c_forecast, alpha=0.3, label='50% Confidence Interval', zorder=2)

plt.plot(forecast_idx, ground_truth, color=c_truth, linestyle='-', linewidth=1.0, label='Ground Truth', zorder=4)

plt.plot(forecast_idx, q50_median, color=c_forecast, linestyle='-', linewidth=1.0, label='Median Forecast', zorder=5)

plt.xlabel("Forecast Horizon (Time Steps)")

plt.ylabel("Power (MW)")

plt.grid(True, axis='both', linestyle='--', alpha=0.6)

plt.legend()

plt.tight_layout()

plt.show()

# 1. Load Model and Tokenizer

tokenizer = WindFMTokenizer.from_pretrained("NeoQuasar/WindFM-Tokenizer")

model = WindFM.from_pretrained("NeoQuasar/WindFM")

# 2. Instantiate Predictor

predictor = WindFMPredictor(model, tokenizer, device="cuda:0", max_context=512, clip=5)

# 3. Prepare Data

df = pd.read_csv("./data/121522.csv")

df['time'] = pd.to_datetime(df['time'], utc=True)

lookback = 240

pred_len = 80

x_df = df.loc[:lookback-1, ['wind_speed', 'wind_direction', 'power', 'density', 'temperature', 'pressure']]

x_timestamp = df.loc[:lookback-1, 'time']

y_timestamp = df.loc[lookback:lookback+pred_len-1, 'time']

# 4. Make Prediction

pred_df = predictor.predict(

df=x_df,

x_timestamp=x_timestamp,

y_timestamp=y_timestamp,

pred_len=pred_len,

T=1.0,

top_p=1.0,

sample_count=100,

verbose=True

)

# 5. Visualize Results

print("Forecasted Data Head:")

print(pred_df.head())

# Combine historical and forecasted data for plotting

y_power = df.loc[lookback:lookback+pred_len-1, 'power'].values

# visualize

plot_probabilistic_forecast(y_power, pred_df)