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| import sys
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| import warnings
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| from pathlib import Path
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|
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| import matplotlib.pyplot as plt
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| import numpy as np
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| import pandas as pd
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| from IPython.display import display
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| repo_src = Path.cwd() / "src"
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| if (repo_src / "detime").exists():
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| sys.meta_path[:] = [
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| finder for finder in sys.meta_path
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| if finder.__class__.__module__ != "_de_time_editable"
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| ]
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| sys.path.insert(0, str(repo_src))
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|
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| from detime import DecompositionConfig, MethodRegistry, decompose
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|
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| warnings.filterwarnings("ignore", category=FutureWarning)
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| plt.rcParams.update(
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| {
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| "figure.figsize": (7.6, 3.0),
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| "figure.dpi": 130,
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| "savefig.dpi": 220,
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| "axes.grid": False,
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| "axes.spines.top": False,
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| "axes.spines.right": False,
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| "font.size": 10,
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| }
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| )
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|
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| rng = np.random.default_rng(42)
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| t = np.arange(96, dtype=float)
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| seasonal = np.sin(2.0 * np.pi * t / 12.0)
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| slow = 0.018 * t + 0.25 * np.sin(2.0 * np.pi * t / 48.0)
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| noise = 0.05 * rng.standard_normal(t.shape)
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| series = slow + seasonal + noise
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| panel = np.column_stack(
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| [
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| series,
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| 0.8 * slow + np.sin(2.0 * np.pi * (t + 2.0) / 12.0) + 0.04 * rng.standard_normal(t.shape),
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| 1.15 * slow + 0.7 * np.sin(2.0 * np.pi * (t + 4.0) / 12.0) + 0.04 * rng.standard_normal(t.shape),
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| ]
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| )
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|
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| CASES = {
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| "SSA": {"data": series, "config": {"method": "SSA", "params": {"window": 24, "rank": 6, "primary_period": 12}, "backend": "auto", "speed_mode": "exact"}},
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| "STD": {"data": series, "config": {"method": "STD", "params": {"period": 12}, "backend": "auto"}},
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| "STDR": {"data": series, "config": {"method": "STDR", "params": {"period": 12}, "backend": "auto"}},
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| "MSSA": {"data": panel, "config": {"method": "MSSA", "params": {"window": 24, "rank": 6, "primary_period": 12}, "backend": "python", "channel_names": ["a", "b", "c"]}},
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| "STL": {"data": series, "config": {"method": "STL", "params": {"period": 12}}},
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| "MSTL": {"data": series, "config": {"method": "MSTL", "params": {"periods": [12, 24]}}},
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| "ROBUST_STL": {"data": series, "config": {"method": "ROBUST_STL", "params": {"period": 12}}},
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| "EMD": {"data": series, "config": {"method": "EMD", "params": {"primary_period": 12, "n_imfs": 4}}},
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| "CEEMDAN": {"data": series, "config": {"method": "CEEMDAN", "params": {"primary_period": 12, "trials": 3, "noise_width": 0.03}}},
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| "VMD": {"data": series, "config": {"method": "VMD", "params": {"K": 4, "alpha": 300.0, "primary_period": 12}}},
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| "WAVELET": {"data": series, "config": {"method": "WAVELET", "params": {"wavelet": "db4", "level": 3}}},
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| "MA_BASELINE": {"data": series, "config": {"method": "MA_BASELINE", "params": {"trend_window": 7, "season_period": 12}}},
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| "MVMD": {"data": panel, "config": {"method": "MVMD", "params": {"K": 4, "alpha": 300.0, "primary_period": 12}, "channel_names": ["a", "b", "c"]}},
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| "MEMD": {"data": panel, "config": {"method": "MEMD", "params": {"primary_period": 12}, "channel_names": ["a", "b", "c"]}},
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| "GABOR_CLUSTER": {"data": series, "skip": "requires a trained GaborClusterModel or model_path plus the experimental clustering backend"},
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| }
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| GALLERY_RESULTS = []
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|
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| def _plot_vector(values):
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| arr = np.asarray(values, dtype=float)
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| if arr.ndim == 2:
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| return arr[:, 0]
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| return arr
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|
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|
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| def _style_gallery_axis(ax, title):
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| ax.set_facecolor("#ffffff")
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| ax.grid(True, axis="y", alpha=0.22, color="#94a3b8", linewidth=0.8)
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| ax.grid(False, axis="x")
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| ax.spines["left"].set_color("#cbd5e1")
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| ax.spines["bottom"].set_color("#cbd5e1")
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| ax.tick_params(colors="#334155")
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| ax.set_title(title, loc="left", fontsize=12, fontweight="bold", color="#0f172a")
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|
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|
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| def run_gallery_case(name):
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| case = CASES[name]
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| metadata = MethodRegistry.get_metadata(name)
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| print(f"{name}: {metadata['summary']}")
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| if "skip" in case:
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| row = {
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| "method": name,
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| "status": "skipped",
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| "reason": case["skip"],
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| "input_mode": metadata["input_mode"],
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| "output_shape": "",
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| "residual_rmse": np.nan,
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| }
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| GALLERY_RESULTS.append(row)
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| display(pd.DataFrame([row]))
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| return
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|
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| data = case["data"]
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| cfg = DecompositionConfig(**case["config"])
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| try:
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| result = decompose(data, cfg)
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| except Exception as exc:
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| row = {
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| "method": name,
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| "status": "skipped",
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| "reason": f"{type(exc).__name__}: {exc}",
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| "input_mode": metadata["input_mode"],
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| "output_shape": "",
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| "residual_rmse": np.nan,
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| }
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| GALLERY_RESULTS.append(row)
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| display(pd.DataFrame([row]))
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| return
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|
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| original = _plot_vector(data)
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| trend = _plot_vector(result.trend)
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| season = _plot_vector(result.season)
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| residual = _plot_vector(result.residual)
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| reconstruction = trend + season + residual
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| rmse = float(np.sqrt(np.mean((original - reconstruction) ** 2)))
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|
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| row = {
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| "method": name,
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| "status": "ran",
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| "reason": "",
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| "input_mode": metadata["input_mode"],
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| "output_shape": str(np.asarray(result.trend).shape),
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| "residual_rmse": round(rmse, 8),
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| }
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| GALLERY_RESULTS.append(row)
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| display(pd.DataFrame([row]))
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|
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| fig, ax = plt.subplots(facecolor="#f8fafc")
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| ax.plot(original, label="input", color="#0f172a", linewidth=1.6)
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| ax.plot(trend, label="trend", color="#2563eb", linewidth=1.4)
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| ax.plot(season, label="season", color="#0f766e", linewidth=1.2)
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| ax.plot(residual, label="residual", color="#f97316", linewidth=1.0, alpha=0.85)
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| _style_gallery_axis(ax, f"{name} decomposition")
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| ax.set_xlabel("time step")
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| ax.legend(loc="upper right", ncol=2, fontsize=8, frameon=True, framealpha=0.92)
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| fig.tight_layout()
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| plt.show()
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| run_gallery_case("SSA")
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| run_gallery_case("STD")
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| run_gallery_case("STDR")
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| run_gallery_case("MSSA")
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| run_gallery_case("STL")
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| run_gallery_case("MSTL")
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| run_gallery_case("ROBUST_STL")
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| run_gallery_case("EMD")
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| run_gallery_case("CEEMDAN")
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| run_gallery_case("VMD")
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| run_gallery_case("WAVELET")
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| run_gallery_case("MA_BASELINE")
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| run_gallery_case("MVMD")
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| run_gallery_case("MEMD")
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| run_gallery_case("GABOR_CLUSTER")
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|
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| summary = pd.DataFrame(GALLERY_RESULTS)
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| print(summary)
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|
