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feat(example): add interactive forecast animation with slider
Create an all-out demonstration showing how TimesFM forecasts evolve as more historical data is added: - generate_animation_data.py: Runs 25 incremental forecasts (12→36 points) - interactive_forecast.html: Single-file HTML with Chart.js slider - Play/Pause animation control - Shows historical data, forecast, 80%/90% CIs, and actual future data - Live stats: forecast mean, max, min, CI width - generate_gif.py: Creates animated GIF for embedding in markdown - forecast_animation.gif: 25-frame animation (896 KB) Interactive features: - Slider to manually step through forecast evolution - Auto-play with 500ms per frame - Shows how each additional data point changes the forecast - Confidence intervals narrow as more data is added
This commit is contained in:
Clayton Young
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#!/usr/bin/env python3
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"""
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Generate animation data for interactive forecast visualization.
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This script runs TimesFM forecasts incrementally, starting with minimal data
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and adding one point at a time, saving all forecasts for an interactive slider.
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Output: animation_data.json with all forecast steps
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"""
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from __future__ import annotations
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import json
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from pathlib import Path
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import numpy as np
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import pandas as pd
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import timesfm
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# Configuration
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MIN_CONTEXT = 12 # Minimum points to start forecasting
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HORIZON = 12 # Always forecast 12 months ahead
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INPUT_FILE = Path(__file__).parent / "temperature_anomaly.csv"
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OUTPUT_FILE = Path(__file__).parent / "animation_data.json"
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def main() -> None:
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print("=" * 60)
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print(" TIMESFM ANIMATION DATA GENERATOR")
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print("=" * 60)
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# Load data
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df = pd.read_csv(INPUT_FILE, parse_dates=["date"])
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df = df.sort_values("date").reset_index(drop=True)
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all_dates = df["date"].tolist()
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all_values = df["anomaly_c"].values.astype(np.float32)
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print(f"\n📊 Total data: {len(all_values)} months")
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print(
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f" Date range: {all_dates[0].strftime('%Y-%m')} to {all_dates[-1].strftime('%Y-%m')}"
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)
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print(f" Animation steps: {len(all_values) - MIN_CONTEXT + 1}")
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# Load TimesFM
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print("\n🤖 Loading TimesFM 1.0 (200M) PyTorch...")
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hparams = timesfm.TimesFmHparams(horizon_len=HORIZON)
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checkpoint = timesfm.TimesFmCheckpoint(
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huggingface_repo_id="google/timesfm-1.0-200m-pytorch"
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)
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model = timesfm.TimesFm(hparams=hparams, checkpoint=checkpoint)
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# Generate forecasts for each step
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animation_steps = []
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for n_points in range(MIN_CONTEXT, len(all_values) + 1):
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step_num = n_points - MIN_CONTEXT + 1
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total_steps = len(all_values) - MIN_CONTEXT + 1
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print(f"\n📈 Step {step_num}/{total_steps}: Using {n_points} points...")
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# Get historical data up to this point
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historical_values = all_values[:n_points]
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historical_dates = all_dates[:n_points]
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# Run forecast
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point, quantiles = model.forecast(
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[historical_values],
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freq=[0],
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)
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# Determine forecast dates
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last_date = historical_dates[-1]
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forecast_dates = pd.date_range(
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start=last_date + pd.DateOffset(months=1),
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periods=HORIZON,
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freq="MS",
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)
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# Store step data
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step_data = {
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"step": step_num,
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"n_points": n_points,
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"last_historical_date": historical_dates[-1].strftime("%Y-%m"),
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"historical_dates": [d.strftime("%Y-%m") for d in historical_dates],
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"historical_values": historical_values.tolist(),
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"forecast_dates": [d.strftime("%Y-%m") for d in forecast_dates],
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"point_forecast": point[0].tolist(),
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"q10": quantiles[0, :, 0].tolist(),
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"q20": quantiles[0, :, 1].tolist(),
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"q80": quantiles[0, :, 7].tolist(),
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"q90": quantiles[0, :, 8].tolist(),
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}
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animation_steps.append(step_data)
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# Show summary
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print(f" Last date: {historical_dates[-1].strftime('%Y-%m')}")
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print(f" Forecast mean: {point[0].mean():.3f}°C")
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# Create output
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output = {
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"metadata": {
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"model": "TimesFM 1.0 (200M) PyTorch",
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"total_steps": len(animation_steps),
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"min_context": MIN_CONTEXT,
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"horizon": HORIZON,
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"data_source": "NOAA GISTEMP Global Temperature Anomaly",
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"full_date_range": f"{all_dates[0].strftime('%Y-%m')} to {all_dates[-1].strftime('%Y-%m')}",
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},
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"actual_data": {
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"dates": [d.strftime("%Y-%m") for d in all_dates],
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"values": all_values.tolist(),
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},
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"animation_steps": animation_steps,
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}
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# Save
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with open(OUTPUT_FILE, "w") as f:
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json.dump(output, f, indent=2)
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print(f"\n" + "=" * 60)
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print(" ✅ ANIMATION DATA COMPLETE")
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print("=" * 60)
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print(f"\n📁 Output: {OUTPUT_FILE}")
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print(f" Total steps: {len(animation_steps)}")
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print(f" Each step shows forecast as one more data point is added")
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if __name__ == "__main__":
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main()
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#!/usr/bin/env python3
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"""
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Generate animated GIF showing forecast evolution.
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Creates a GIF animation showing how the TimesFM forecast changes
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as more historical data points are added.
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"""
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from __future__ import annotations
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import json
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from pathlib import Path
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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import numpy as np
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import pandas as pd
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from PIL import Image
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# Configuration
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EXAMPLE_DIR = Path(__file__).parent
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DATA_FILE = EXAMPLE_DIR / "animation_data.json"
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OUTPUT_FILE = EXAMPLE_DIR / "forecast_animation.gif"
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DURATION_MS = 500 # Time per frame in milliseconds
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def create_frame(ax, step_data: dict, actual_data: dict, total_steps: int) -> None:
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"""Create a single frame of the animation."""
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ax.clear()
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# Parse dates
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historical_dates = pd.to_datetime(step_data["historical_dates"])
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forecast_dates = pd.to_datetime(step_data["forecast_dates"])
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# Plot historical data
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ax.plot(
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historical_dates,
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step_data["historical_values"],
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color="#3b82f6",
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linewidth=2,
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marker="o",
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markersize=4,
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label="Historical",
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)
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# Plot 90% CI (outer)
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ax.fill_between(
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forecast_dates,
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step_data["q10"],
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step_data["q90"],
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alpha=0.1,
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color="#ef4444",
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label="90% CI",
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)
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# Plot 80% CI (inner)
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ax.fill_between(
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forecast_dates,
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step_data["q20"],
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step_data["q80"],
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alpha=0.2,
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color="#ef4444",
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label="80% CI",
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)
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# Plot forecast
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ax.plot(
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forecast_dates,
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step_data["point_forecast"],
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color="#ef4444",
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linewidth=2,
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marker="s",
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markersize=4,
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label="Forecast",
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)
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# Plot actual future data if available
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actual_dates = pd.to_datetime(actual_data["dates"])
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actual_values = actual_data["values"]
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# Find which actual points fall in forecast period
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forecast_start = forecast_dates[0]
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forecast_end = forecast_dates[-1]
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future_mask = (actual_dates >= forecast_start) & (actual_dates <= forecast_end)
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future_dates = actual_dates[future_mask]
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future_values = np.array(actual_values)[future_mask]
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if len(future_dates) > 0:
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ax.plot(
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future_dates,
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future_values,
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color="#10b981",
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linewidth=1,
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linestyle="--",
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marker="o",
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markersize=3,
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alpha=0.7,
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label="Actual (future)",
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)
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# Add vertical line at forecast boundary
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ax.axvline(
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x=historical_dates[-1],
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color="#6b7280",
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linestyle="--",
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linewidth=1,
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alpha=0.5,
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)
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# Formatting
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ax.set_xlabel("Date", fontsize=11)
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ax.set_ylabel("Temperature Anomaly (°C)", fontsize=11)
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ax.set_title(
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f"TimesFM Forecast Evolution\n"
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f"Step {step_data['step']}/{total_steps}: {step_data['n_points']} points → {step_data['last_historical_date']}",
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fontsize=13,
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fontweight="bold",
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)
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ax.grid(True, alpha=0.3)
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ax.legend(loc="upper left", fontsize=9)
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ax.set_ylim(0.5, 1.6)
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# Format x-axis
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ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m"))
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ax.xaxis.set_major_locator(mdates.MonthLocator(interval=6))
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plt.setp(ax.xaxis.get_majorticklabels(), rotation=45, ha="right")
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def main() -> None:
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print("=" * 60)
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print(" GENERATING ANIMATED GIF")
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print("=" * 60)
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# Load data
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with open(DATA_FILE) as f:
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data = json.load(f)
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total_steps = len(data["animation_steps"])
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print(f"\n📊 Total frames: {total_steps}")
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# Create figure
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fig, ax = plt.subplots(figsize=(12, 6))
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# Generate frames
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frames = []
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for i, step in enumerate(data["animation_steps"]):
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print(f" Frame {i + 1}/{total_steps}...")
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create_frame(ax, step, data["actual_data"], total_steps)
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# Save frame to buffer
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fig.canvas.draw()
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# Convert to PIL Image
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buf = fig.canvas.buffer_rgba()
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width, height = fig.canvas.get_width_height()
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img = Image.frombytes("RGBA", (width, height), buf)
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frames.append(img.convert("RGB"))
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plt.close()
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# Save as GIF
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print(f"\n💾 Saving GIF: {OUTPUT_FILE}")
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frames[0].save(
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OUTPUT_FILE,
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save_all=True,
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append_images=frames[1:],
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duration=DURATION_MS,
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loop=0, # Loop forever
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)
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# Get file size
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size_kb = OUTPUT_FILE.stat().st_size / 1024
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print(f" File size: {size_kb:.1f} KB")
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print(f"\n✅ Done!")
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,569 @@
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>TimesFM Interactive Forecast Animation</title>
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<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
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<script src="https://cdn.jsdelivr.net/npm/chartjs-plugin-annotation"></script>
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<style>
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* {
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margin: 0;
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padding: 0;
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box-sizing: border-box;
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}
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body {
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font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, sans-serif;
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background: linear-gradient(135deg, #1a1a2e 0%, #16213e 100%);
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min-height: 100vh;
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color: #e0e0e0;
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padding: 20px;
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}
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.container {
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max-width: 1200px;
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margin: 0 auto;
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}
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header {
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text-align: center;
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margin-bottom: 30px;
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}
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h1 {
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font-size: 2rem;
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margin-bottom: 10px;
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background: linear-gradient(90deg, #60a5fa, #a78bfa);
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-webkit-background-clip: text;
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-webkit-text-fill-color: transparent;
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background-clip: text;
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}
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.subtitle {
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color: #9ca3af;
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font-size: 1.1rem;
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}
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.chart-container {
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background: rgba(255, 255, 255, 0.05);
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border-radius: 16px;
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padding: 20px;
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margin-bottom: 20px;
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box-shadow: 0 4px 20px rgba(0, 0, 0, 0.3);
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}
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#chart {
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width: 100% !important;
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height: 400px !important;
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}
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.controls {
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display: flex;
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flex-direction: column;
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gap: 20px;
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background: rgba(255, 255, 255, 0.05);
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border-radius: 16px;
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padding: 20px;
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}
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.slider-container {
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display: flex;
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flex-direction: column;
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gap: 10px;
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}
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.slider-label {
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display: flex;
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justify-content: space-between;
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align-items: center;
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}
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.slider-label span {
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font-size: 0.9rem;
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color: #9ca3af;
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}
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.slider-label .value {
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font-weight: 600;
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color: #60a5fa;
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font-size: 1.1rem;
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}
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input[type="range"] {
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width: 100%;
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height: 8px;
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border-radius: 4px;
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background: #374151;
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outline: none;
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-webkit-appearance: none;
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}
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input[type="range"]::-webkit-slider-thumb {
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-webkit-appearance: none;
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width: 24px;
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height: 24px;
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border-radius: 50%;
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background: linear-gradient(135deg, #60a5fa, #a78bfa);
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cursor: pointer;
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box-shadow: 0 2px 10px rgba(96, 165, 250, 0.5);
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}
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input[type="range"]::-moz-range-thumb {
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width: 24px;
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height: 24px;
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border-radius: 50%;
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background: linear-gradient(135deg, #60a5fa, #a78bfa);
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cursor: pointer;
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border: none;
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box-shadow: 0 2px 10px rgba(96, 165, 250, 0.5);
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}
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.buttons {
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display: flex;
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gap: 10px;
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flex-wrap: wrap;
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}
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button {
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flex: 1;
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min-width: 100px;
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padding: 12px 20px;
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border: none;
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border-radius: 8px;
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font-size: 1rem;
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font-weight: 600;
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cursor: pointer;
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transition: all 0.2s ease;
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}
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.btn-primary {
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background: linear-gradient(135deg, #60a5fa, #a78bfa);
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color: white;
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}
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.btn-primary:hover {
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transform: translateY(-2px);
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box-shadow: 0 4px 15px rgba(96, 165, 250, 0.4);
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}
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.btn-secondary {
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background: #374151;
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color: #e0e0e0;
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}
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.btn-secondary:hover {
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background: #4b5563;
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}
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.stats {
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display: grid;
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grid-template-columns: repeat(auto-fit, minmax(150px, 1fr));
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gap: 15px;
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margin-top: 20px;
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}
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.stat-card {
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background: rgba(255, 255, 255, 0.05);
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border-radius: 12px;
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padding: 15px;
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||||
text-align: center;
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||||
}
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.stat-card .label {
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font-size: 0.8rem;
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color: #9ca3af;
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margin-bottom: 5px;
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}
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.stat-card .value {
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font-size: 1.3rem;
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||||
font-weight: 600;
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||||
color: #60a5fa;
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||||
}
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||||
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.legend {
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||||
display: flex;
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justify-content: center;
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||||
gap: 20px;
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||||
flex-wrap: wrap;
|
||||
margin-top: 15px;
|
||||
padding-top: 15px;
|
||||
border-top: 1px solid rgba(255, 255, 255, 0.1);
|
||||
}
|
||||
|
||||
.legend-item {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
font-size: 0.9rem;
|
||||
}
|
||||
|
||||
.legend-color {
|
||||
width: 16px;
|
||||
height: 16px;
|
||||
border-radius: 4px;
|
||||
}
|
||||
|
||||
footer {
|
||||
text-align: center;
|
||||
margin-top: 30px;
|
||||
color: #6b7280;
|
||||
font-size: 0.9rem;
|
||||
}
|
||||
|
||||
footer a {
|
||||
color: #60a5fa;
|
||||
text-decoration: none;
|
||||
}
|
||||
|
||||
@media (max-width: 768px) {
|
||||
h1 {
|
||||
font-size: 1.5rem;
|
||||
}
|
||||
|
||||
.buttons {
|
||||
flex-direction: column;
|
||||
}
|
||||
|
||||
button {
|
||||
width: 100%;
|
||||
}
|
||||
}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<div class="container">
|
||||
<header>
|
||||
<h1>TimesFM Zero-Shot Forecast Animation</h1>
|
||||
<p class="subtitle">Watch the forecast evolve as more data is added</p>
|
||||
</header>
|
||||
|
||||
<div class="chart-container">
|
||||
<canvas id="chart"></canvas>
|
||||
</div>
|
||||
|
||||
<div class="controls">
|
||||
<div class="slider-container">
|
||||
<div class="slider-label">
|
||||
<span>Historical Data Points</span>
|
||||
<span class="value" id="points-value">12 / 36</span>
|
||||
</div>
|
||||
<input type="range" id="slider" min="0" max="24" value="0" step="1">
|
||||
<div class="slider-label">
|
||||
<span id="date-start">2022-01</span>
|
||||
<span id="date-end">2022-12 → Forecast to 2023-12</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="buttons">
|
||||
<button class="btn-primary" id="play-btn">▶ Play</button>
|
||||
<button class="btn-secondary" id="reset-btn">↺ Reset</button>
|
||||
</div>
|
||||
|
||||
<div class="stats">
|
||||
<div class="stat-card">
|
||||
<div class="label">Forecast Mean</div>
|
||||
<div class="value" id="stat-mean">0.86°C</div>
|
||||
</div>
|
||||
<div class="stat-card">
|
||||
<div class="label">Forecast Max</div>
|
||||
<div class="value" id="stat-max">--</div>
|
||||
</div>
|
||||
<div class="stat-card">
|
||||
<div class="label">Forecast Min</div>
|
||||
<div class="value" id="stat-min">--</div>
|
||||
</div>
|
||||
<div class="stat-card">
|
||||
<div class="label">80% CI Width</div>
|
||||
<div class="value" id="stat-ci">--</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="legend">
|
||||
<div class="legend-item">
|
||||
<div class="legend-color" style="background: #3b82f6;"></div>
|
||||
<span>Historical Data</span>
|
||||
</div>
|
||||
<div class="legend-item">
|
||||
<div class="legend-color" style="background: #ef4444;"></div>
|
||||
<span>Forecast</span>
|
||||
</div>
|
||||
<div class="legend-item">
|
||||
<div class="legend-color" style="background: rgba(239, 68, 68, 0.2);"></div>
|
||||
<span>80% Confidence Interval</span>
|
||||
</div>
|
||||
<div class="legend-item">
|
||||
<div class="legend-color" style="background: rgba(239, 68, 68, 0.1);"></div>
|
||||
<span>90% Confidence Interval</span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<footer>
|
||||
<p>TimesFM 1.0 (200M) PyTorch • <a href="https://github.com/google-research/timesfm">Google Research</a></p>
|
||||
<p style="margin-top: 5px;">Generated by <a href="https://github.com/K-Dense-AI/claude-scientific-skills">claude-scientific-skills</a></p>
|
||||
</footer>
|
||||
</div>
|
||||
|
||||
<script>
|
||||
// Load animation data
|
||||
let animationData = null;
|
||||
let chart = null;
|
||||
let isPlaying = false;
|
||||
let playInterval = null;
|
||||
let currentStep = 0;
|
||||
|
||||
// Fetch the JSON data
|
||||
fetch('animation_data.json')
|
||||
.then(response => response.json())
|
||||
.then(data => {
|
||||
animationData = data;
|
||||
initChart();
|
||||
updateChart(0);
|
||||
})
|
||||
.catch(err => {
|
||||
console.error('Error loading animation data:', err);
|
||||
document.querySelector('.chart-container').innerHTML =
|
||||
'<p style="text-align: center; padding: 50px; color: #ef4444;">Error loading data. Make sure animation_data.json is in the same directory.</p>';
|
||||
});
|
||||
|
||||
function initChart() {
|
||||
const ctx = document.getElementById('chart').getContext('2d');
|
||||
|
||||
chart = new Chart(ctx, {
|
||||
type: 'line',
|
||||
data: {
|
||||
labels: [],
|
||||
datasets: [
|
||||
{
|
||||
label: 'Historical',
|
||||
data: [],
|
||||
borderColor: '#3b82f6',
|
||||
backgroundColor: 'rgba(59, 130, 246, 0.1)',
|
||||
borderWidth: 2,
|
||||
pointRadius: 3,
|
||||
pointBackgroundColor: '#3b82f6',
|
||||
fill: false,
|
||||
tension: 0.1,
|
||||
},
|
||||
{
|
||||
label: 'Forecast',
|
||||
data: [],
|
||||
borderColor: '#ef4444',
|
||||
backgroundColor: 'rgba(239, 68, 68, 0.1)',
|
||||
borderWidth: 2,
|
||||
pointRadius: 3,
|
||||
pointBackgroundColor: '#ef4444',
|
||||
fill: false,
|
||||
tension: 0.1,
|
||||
},
|
||||
{
|
||||
label: '90% CI Lower',
|
||||
data: [],
|
||||
borderColor: 'transparent',
|
||||
backgroundColor: 'rgba(239, 68, 68, 0.08)',
|
||||
fill: '+1',
|
||||
pointRadius: 0,
|
||||
tension: 0.1,
|
||||
},
|
||||
{
|
||||
label: '90% CI Upper',
|
||||
data: [],
|
||||
borderColor: 'transparent',
|
||||
backgroundColor: 'rgba(239, 68, 68, 0.08)',
|
||||
fill: false,
|
||||
pointRadius: 0,
|
||||
tension: 0.1,
|
||||
},
|
||||
{
|
||||
label: '80% CI Lower',
|
||||
data: [],
|
||||
borderColor: 'transparent',
|
||||
backgroundColor: 'rgba(239, 68, 68, 0.15)',
|
||||
fill: '+1',
|
||||
pointRadius: 0,
|
||||
tension: 0.1,
|
||||
},
|
||||
{
|
||||
label: '80% CI Upper',
|
||||
data: [],
|
||||
borderColor: 'transparent',
|
||||
backgroundColor: 'rgba(239, 68, 68, 0.15)',
|
||||
fill: false,
|
||||
pointRadius: 0,
|
||||
tension: 0.1,
|
||||
},
|
||||
{
|
||||
label: 'Actual (Future)',
|
||||
data: [],
|
||||
borderColor: '#10b981',
|
||||
backgroundColor: 'transparent',
|
||||
borderWidth: 1,
|
||||
borderDash: [5, 5],
|
||||
pointRadius: 2,
|
||||
pointBackgroundColor: '#10b981',
|
||||
fill: false,
|
||||
tension: 0.1,
|
||||
},
|
||||
]
|
||||
},
|
||||
options: {
|
||||
responsive: true,
|
||||
maintainAspectRatio: false,
|
||||
interaction: {
|
||||
intersect: false,
|
||||
mode: 'index',
|
||||
},
|
||||
plugins: {
|
||||
legend: {
|
||||
display: false,
|
||||
},
|
||||
tooltip: {
|
||||
backgroundColor: 'rgba(0, 0, 0, 0.8)',
|
||||
titleColor: '#fff',
|
||||
bodyColor: '#fff',
|
||||
padding: 12,
|
||||
displayColors: true,
|
||||
},
|
||||
},
|
||||
scales: {
|
||||
x: {
|
||||
grid: {
|
||||
color: 'rgba(255, 255, 255, 0.05)',
|
||||
},
|
||||
ticks: {
|
||||
color: '#9ca3af',
|
||||
maxRotation: 45,
|
||||
minRotation: 45,
|
||||
},
|
||||
},
|
||||
y: {
|
||||
grid: {
|
||||
color: 'rgba(255, 255, 255, 0.05)',
|
||||
},
|
||||
ticks: {
|
||||
color: '#9ca3af',
|
||||
callback: function(value) {
|
||||
return value.toFixed(2) + '°C';
|
||||
}
|
||||
},
|
||||
min: 0.5,
|
||||
max: 1.6,
|
||||
},
|
||||
},
|
||||
animation: {
|
||||
duration: 200,
|
||||
},
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
function updateChart(stepIndex) {
|
||||
if (!animationData || !chart) return;
|
||||
|
||||
const step = animationData.animation_steps[stepIndex];
|
||||
const actual = animationData.actual_data;
|
||||
|
||||
// Build all dates
|
||||
const allDates = [...step.historical_dates, ...step.forecast_dates];
|
||||
|
||||
// Historical data (with nulls for forecast period)
|
||||
const historicalData = [...step.historical_values, ...Array(step.forecast_dates.length).fill(null)];
|
||||
|
||||
// Forecast data (with nulls for historical period)
|
||||
const forecastData = [...Array(step.historical_dates.length).fill(null), ...step.point_forecast];
|
||||
|
||||
// Confidence intervals
|
||||
const q90Lower = [...Array(step.historical_dates.length).fill(null), ...step.q10];
|
||||
const q90Upper = [...Array(step.historical_dates.length).fill(null), ...step.q90];
|
||||
const q80Lower = [...Array(step.historical_dates.length).fill(null), ...step.q20];
|
||||
const q80Upper = [...Array(step.historical_dates.length).fill(null), ...step.q80];
|
||||
|
||||
// Actual future data (if available)
|
||||
const actualFuture = [];
|
||||
for (let i = 0; i < allDates.length; i++) {
|
||||
const dateIdx = actual.dates.indexOf(allDates[i]);
|
||||
if (dateIdx >= step.n_points) {
|
||||
actualFuture.push(actual.values[dateIdx]);
|
||||
} else {
|
||||
actualFuture.push(null);
|
||||
}
|
||||
}
|
||||
|
||||
// Update chart data
|
||||
chart.data.labels = allDates;
|
||||
chart.data.datasets[0].data = historicalData;
|
||||
chart.data.datasets[1].data = forecastData;
|
||||
chart.data.datasets[2].data = q90Lower;
|
||||
chart.data.datasets[3].data = q90Upper;
|
||||
chart.data.datasets[4].data = q80Lower;
|
||||
chart.data.datasets[5].data = q80Upper;
|
||||
chart.data.datasets[6].data = actualFuture;
|
||||
chart.update('none');
|
||||
|
||||
// Update UI
|
||||
document.getElementById('slider').value = stepIndex;
|
||||
document.getElementById('points-value').textContent = `${step.n_points} / 36`;
|
||||
document.getElementById('date-end').textContent = `${step.last_historical_date} → Forecast to ${step.forecast_dates[step.forecast_dates.length - 1]}`;
|
||||
|
||||
// Update stats
|
||||
const mean = (step.point_forecast.reduce((a, b) => a + b, 0) / step.point_forecast.length).toFixed(3);
|
||||
const max = Math.max(...step.point_forecast).toFixed(3);
|
||||
const min = Math.min(...step.point_forecast).toFixed(3);
|
||||
const ciWidth = ((step.q80[0] - step.q20[0]).toFixed(3));
|
||||
|
||||
document.getElementById('stat-mean').textContent = mean + '°C';
|
||||
document.getElementById('stat-max').textContent = max + '°C';
|
||||
document.getElementById('stat-min').textContent = min + '°C';
|
||||
document.getElementById('stat-ci').textContent = '±' + (ciWidth / 2).toFixed(2) + '°C';
|
||||
|
||||
currentStep = stepIndex;
|
||||
}
|
||||
|
||||
// Slider control
|
||||
document.getElementById('slider').addEventListener('input', (e) => {
|
||||
updateChart(parseInt(e.target.value));
|
||||
});
|
||||
|
||||
// Play button
|
||||
document.getElementById('play-btn').addEventListener('click', () => {
|
||||
const btn = document.getElementById('play-btn');
|
||||
|
||||
if (isPlaying) {
|
||||
clearInterval(playInterval);
|
||||
btn.textContent = '▶ Play';
|
||||
isPlaying = false;
|
||||
} else {
|
||||
btn.textContent = '⏸ Pause';
|
||||
isPlaying = true;
|
||||
|
||||
if (currentStep >= animationData.animation_steps.length - 1) {
|
||||
currentStep = 0;
|
||||
}
|
||||
|
||||
playInterval = setInterval(() => {
|
||||
if (currentStep >= animationData.animation_steps.length - 1) {
|
||||
clearInterval(playInterval);
|
||||
document.getElementById('play-btn').textContent = '▶ Play';
|
||||
isPlaying = false;
|
||||
} else {
|
||||
currentStep++;
|
||||
updateChart(currentStep);
|
||||
}
|
||||
}, 500);
|
||||
}
|
||||
});
|
||||
|
||||
// Reset button
|
||||
document.getElementById('reset-btn').addEventListener('click', () => {
|
||||
if (isPlaying) {
|
||||
clearInterval(playInterval);
|
||||
document.getElementById('play-btn').textContent = '▶ Play';
|
||||
isPlaying = false;
|
||||
}
|
||||
updateChart(0);
|
||||
});
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
Reference in New Issue
Block a user