"""Immutable view dataclasses for time series data.
This module uses timesmith's SeriesLike and PanelLike types for time series data.
ScoreView and LabelView are kept for anomaly-specific outputs.
"""
from dataclasses import dataclass
from typing import TYPE_CHECKING
import numpy as np
import pandas as pd
# Use timesmith types directly (now a required dependency)
from timesmith.typing import PanelLike, SeriesLike
# Type aliases for backward compatibility and clarity
SeriesView = SeriesLike # Alias for timesmith SeriesLike
PanelView = PanelLike # Alias for timesmith PanelLike
[docs]
@dataclass(frozen=True)
class ScoreView:
"""Immutable view of anomaly scores aligned to an index.
Attributes:
index: Time index (must match input series index)
scores: Anomaly scores as 1D array (higher = more anomalous)
"""
index: pd.Index
scores: np.ndarray
def __post_init__(self) -> None:
"""Validate inputs after initialization."""
if len(self.index) != len(self.scores):
raise ValueError(
f"Index length ({len(self.index)}) must match scores length "
f"({len(self.scores)})"
)
if self.scores.ndim != 1:
raise ValueError(f"Scores must be 1D, got shape {self.scores.shape}")
[docs]
@dataclass(frozen=True)
class LabelView:
"""Immutable view of binary anomaly labels aligned to an index.
Attributes:
index: Time index (must match input series index)
labels: Binary flags as 1D array (1 = anomaly, 0 = normal)
"""
index: pd.Index
labels: np.ndarray
def __post_init__(self) -> None:
"""Validate inputs after initialization."""
if len(self.index) != len(self.labels):
raise ValueError(
f"Index length ({len(self.index)}) must match labels length "
f"({len(self.labels)})"
)
if self.labels.ndim != 1:
raise ValueError(f"Labels must be 1D, got shape {self.labels.shape}")
unique_vals = np.unique(self.labels)
if not np.all(np.isin(unique_vals, [0, 1])):
raise ValueError(
f"Labels must be binary (0 or 1), got unique values {unique_vals}"
)