+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional, Protocol
+
+
+
+[docs]
+@dataclass
+class FeatureSpec:
+ """Describes input features (shapes, dtypes, normalization)."""
+ input_dim: Optional[int] = None
+ channels: Optional[int] = None
+ description: Optional[str] = None
+ extra: Dict[str, Any] = field(default_factory=dict)
+
+
+
+
+[docs]
+@dataclass
+class LabelSpec:
+ """Describes labels/targets for downstream tasks."""
+ num_targets: Optional[int] = None
+ task_type: str = "regression" # classification|regression|segmentation
+ description: Optional[str] = None
+ extra: Dict[str, Any] = field(default_factory=dict)
+
+
+
+
+[docs]
+@dataclass
+class DataSplit:
+ """Container for a single split."""
+ inputs: Any
+ targets: Any
+ metadata: Dict[str, Any] = field(default_factory=dict)
+
+
+
+
+[docs]
+@dataclass
+class DataBundle:
+ """
+ Bundle of train/val/test splits plus metadata.
+ Keeps feature/label specs to make model construction easy.
+ """
+ splits: Dict[str, DataSplit]
+ feature_spec: FeatureSpec
+ label_spec: LabelSpec
+ metadata: Dict[str, Any] = field(default_factory=dict)
+
+
+[docs]
+ def get_split(self, name: str) -> DataSplit:
+ if name not in self.splits:
+ raise KeyError(f"Split '{name}' not found. Available: {list(self.splits.keys())}")
+ return self.splits[name]
+
+
+
+
+
+[docs]
+class Transform(Protocol):
+ """Callable data transform."""
+
+ def __call__(self, bundle: DataBundle) -> DataBundle:
+ ...
+
+
+
+
+[docs]
+class Dataset:
+ """
+ Base class for hazard datasets.
+ Subclasses should load data and return a DataBundle with splits ready for training.
+ """
+
+ name: str = "base"
+
+ def __init__(self, cache_dir: Optional[str] = None):
+ self.cache_dir = cache_dir
+
+
+[docs]
+ def load(self, split: Optional[str] = None, transforms: Optional[List[Transform]] = None) -> DataBundle:
+ """
+ Return a DataBundle. Optionally return a specific split if provided.
+ """
+ bundle = self._load()
+ if transforms:
+ for t in transforms:
+ bundle = t(bundle)
+ if split:
+ return DataBundle(
+ splits={split: bundle.get_split(split)},
+ feature_spec=bundle.feature_spec,
+ label_spec=bundle.label_spec,
+ metadata=bundle.metadata,
+ )
+ return bundle
+
+
+
+[docs]
+ def _load(self) -> DataBundle:
+ raise NotImplementedError("Subclasses must implement _load() to return a DataBundle.")
+
+
+
+from __future__ import annotations
+
+from typing import Any, Dict, List, Optional, Tuple
+
+import torch
+from torch.utils.data import Dataset
+
+
+
+[docs]
+class GraphTemporalDataset(Dataset):
+ """
+ Simple container for county/day style tensors with an optional adjacency.
+
+ Each sample is a window of shape (past_days, num_counties, num_features) and a label
+ of shape (num_counties,).
+ """
+
+ def __init__(
+ self,
+ x: torch.Tensor,
+ y: torch.Tensor,
+ adjacency: Optional[torch.Tensor] = None,
+ ):
+ """
+ Args:
+ x: Tensor (samples, past_days, num_counties, num_features)
+ y: Tensor (samples, num_counties) or (samples, num_counties, targets)
+ adjacency: Optional Tensor
+ - (num_counties, num_counties) global adjacency
+ - (samples, num_counties, num_counties) per-sample adjacency
+ """
+ if x.ndim != 4:
+ raise ValueError("x must be (samples, past_days, num_counties, num_features)")
+ if y.ndim not in (2, 3):
+ raise ValueError("y must be (samples, num_counties) or (samples, num_counties, targets)")
+ if adjacency is not None and adjacency.ndim not in (2, 3):
+ raise ValueError("adjacency must be None, (N,N), or (B,N,N)")
+ if adjacency is not None and adjacency.ndim == 2 and adjacency.size(0) != x.size(2):
+ raise ValueError("adjacency size mismatch with num_counties")
+ if adjacency is not None and adjacency.ndim == 3 and adjacency.size(1) != x.size(2):
+ raise ValueError("adjacency size mismatch with num_counties")
+
+ self.x = x
+ self.y = y
+ self.adj = adjacency
+
+ def __len__(self) -> int:
+ return self.x.size(0)
+
+ def __getitem__(self, idx: int) -> Tuple[Dict[str, Any], torch.Tensor]:
+ adj = None
+ if self.adj is not None:
+ adj = self.adj if self.adj.ndim == 2 else self.adj[idx]
+ return {"x": self.x[idx], "adj": adj}, self.y[idx]
+
+
+
+
+[docs]
+def graph_collate(batch: List[Tuple[Dict[str, Any], torch.Tensor]]):
+ """
+ Collate function that stacks x and adjacency if provided.
+ """
+ xs, ys = zip(*batch)
+ x_tensor = torch.stack([item["x"] for item in xs], dim=0)
+ adj_list = [item["adj"] for item in xs]
+ adj = None
+ if any(a is not None for a in adj_list):
+ # If some entries are None, replace with first non-None
+ first = next(a for a in adj_list if a is not None)
+ adj = torch.stack([a if a is not None else first for a in adj_list], dim=0)
+ y_tensor = torch.stack(ys, dim=0)
+ return {"x": x_tensor, "adj": adj}, y_tensor
+
+
+
+__all__ = ["GraphTemporalDataset", "graph_collate"]
+
+from typing import Any, Callable, Dict
+
+from .base import Dataset
+
+_DATASET_REGISTRY: Dict[str, Callable[..., Dataset]] = {}
+
+
+
+[docs]
+def register_dataset(name: str, builder: Callable[..., Dataset]) -> None:
+ if name in _DATASET_REGISTRY:
+ raise ValueError(f"Dataset '{name}' already registered.")
+ _DATASET_REGISTRY[name] = builder
+
+
+
+
+
+
+
+
+[docs]
+def load_dataset(name: str, **kwargs: Any) -> Dataset:
+ if name not in _DATASET_REGISTRY:
+ raise KeyError(f"Dataset '{name}' is not registered. Known: {available_datasets()}")
+ return _DATASET_REGISTRY[name](**kwargs)
+
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Literal
+
+import torch
+
+Strategy = Literal["auto", "ddp", "dp", "none"]
+
+
+
+[docs]
+@dataclass
+class DistributedConfig:
+ strategy: Strategy = "auto"
+ devices: int | None = None
+
+
+
+
+[docs]
+def select_strategy(prefer: Strategy = "auto") -> Strategy:
+ if prefer == "auto":
+ if torch.cuda.is_available() and torch.cuda.device_count() > 1:
+ return "ddp"
+ if torch.cuda.is_available():
+ return "none"
+ return "none"
+ return prefer
+
+
+from __future__ import annotations
+
+from typing import Any, Callable, Iterable, List
+
+import torch
+
+
+
+[docs]
+class SlidingWindowInference:
+ """
+ Placeholder for sliding-window inference over large rasters or grids.
+ Implement windowing logic and stitching as needed.
+ """
+
+ def __init__(self, model: torch.nn.Module, window_fn: Callable[..., Iterable[Any]] | None = None):
+ self.model = model
+ self.window_fn = window_fn
+
+ def __call__(self, inputs: Any) -> List[torch.Tensor]:
+ if self.window_fn is None:
+ raise NotImplementedError("Provide a window_fn to generate windows from inputs.")
+ outputs: List[torch.Tensor] = []
+ self.model.eval()
+ with torch.no_grad():
+ for window in self.window_fn(inputs):
+ outputs.append(self.model(window))
+ return outputs
+
+
+from __future__ import annotations
+
+from typing import Any, Callable, Dict, Iterable, List, Optional
+
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader, TensorDataset, Dataset
+
+from ..datasets.base import DataBundle
+from ..metrics import MetricBase
+from ..utils.hardware import auto_device
+from .distributed import select_strategy
+
+
+
+[docs]
+class Trainer:
+ """
+ Lightweight training abstraction with a familiar API:
+ fit -> evaluate -> predict.
+ """
+
+ def __init__(
+ self,
+ model: nn.Module,
+ device: Optional[torch.device | str] = None,
+ metrics: Optional[List[MetricBase]] = None,
+ strategy: str = "auto",
+ mixed_precision: bool = False,
+ ):
+ self.model = model
+ self.device = torch.device(device) if device else auto_device()
+ self.metrics = metrics or []
+ self.strategy = select_strategy(strategy)
+ self.mixed_precision = mixed_precision
+ self.model.to(self.device)
+
+
+[docs]
+ def fit(
+ self,
+ data: DataBundle,
+ train_split: str = "train",
+ val_split: Optional[str] = None,
+ max_epochs: int = 1,
+ optimizer: Optional[torch.optim.Optimizer] = None,
+ loss_fn: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
+ batch_size: int = 32,
+ num_workers: int = 0,
+ collate_fn: Optional[Callable[[List[Any]], Any]] = None,
+ ) -> None:
+ """
+ Minimal fit loop that works for tensor-based splits.
+ Extend/replace with custom DataLoaders for complex data.
+ """
+ if optimizer is None or loss_fn is None:
+ raise ValueError("optimizer and loss_fn must be provided.")
+
+ train_split_data = data.get_split(train_split)
+ train_loader = self._make_loader(train_split_data.inputs, train_split_data.targets, batch_size, num_workers, collate_fn)
+ scaler = torch.cuda.amp.GradScaler(enabled=self.mixed_precision and self.device.type == "cuda")
+
+ self.model.train()
+ for _ in range(max_epochs):
+ for x, y in train_loader:
+ x = self._to_device(x)
+ y = self._to_device(y)
+ optimizer.zero_grad()
+ with torch.cuda.amp.autocast(enabled=scaler.is_enabled()):
+ out = self.model(x)
+ loss = loss_fn(out, y)
+ scaler.scale(loss).backward()
+ scaler.step(optimizer)
+ scaler.update()
+
+ if val_split:
+ self.evaluate(data, split=val_split)
+
+
+
+[docs]
+ def evaluate(
+ self,
+ data: DataBundle,
+ split: str = "test",
+ batch_size: int = 64,
+ num_workers: int = 0,
+ collate_fn: Optional[Callable[[List[Any]], Any]] = None,
+ ) -> Dict[str, float]:
+ split_data = data.get_split(split)
+ loader = self._make_loader(split_data.inputs, split_data.targets, batch_size, num_workers, collate_fn, shuffle=False)
+ self.model.eval()
+ for metric in self.metrics:
+ metric.reset()
+ with torch.no_grad():
+ for x, y in loader:
+ x = self._to_device(x)
+ y = self._to_device(y)
+ preds = self.model(x)
+ for metric in self.metrics:
+ metric.update(preds, y)
+ results: Dict[str, float] = {}
+ for metric in self.metrics:
+ results.update(metric.compute())
+ return results
+
+
+
+[docs]
+ def predict(
+ self,
+ data: DataBundle,
+ split: str = "test",
+ batch_size: int = 64,
+ num_workers: int = 0,
+ collate_fn: Optional[Callable[[List[Any]], Any]] = None,
+ ) -> List[torch.Tensor]:
+ split_data = data.get_split(split)
+ loader = self._make_loader(split_data.inputs, split_data.targets, batch_size, num_workers, collate_fn, shuffle=False)
+ self.model.eval()
+ outputs: List[torch.Tensor] = []
+ with torch.no_grad():
+ for x, _ in loader:
+ x = self._to_device(x)
+ preds = self.model(x)
+ outputs.append(preds.cpu())
+ return outputs
+
+
+
+[docs]
+ def save_checkpoint(self, path: str) -> None:
+ torch.save({"model_state": self.model.state_dict()}, path)
+
+
+
+[docs]
+ def _make_loader(
+ self,
+ inputs: Any,
+ targets: Any,
+ batch_size: int,
+ num_workers: int,
+ collate_fn: Optional[Callable[[List[Any]], Any]],
+ shuffle: bool = True,
+ ) -> Iterable:
+ # Accept torch tensors
+ if isinstance(inputs, torch.Tensor) and isinstance(targets, torch.Tensor):
+ dataset = TensorDataset(inputs, targets)
+ return DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers, collate_fn=collate_fn)
+ # Accept torch.utils.data.Dataset directly (for complex dict/graph batches)
+ if isinstance(inputs, Dataset):
+ return DataLoader(inputs, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers, collate_fn=collate_fn)
+ raise TypeError("Trainer only supports tensor pairs or torch Dataset inputs. Wrap custom logic in a Dataset.")
+
+
+
+[docs]
+ def _to_device(self, obj: Any) -> Any:
+ if obj is None:
+ return None
+ if isinstance(obj, torch.Tensor):
+ return obj.to(self.device)
+ if isinstance(obj, (list, tuple)):
+ return type(obj)(self._to_device(o) for o in obj)
+ if isinstance(obj, dict):
+ return {k: self._to_device(v) for k, v in obj.items()}
+ return obj
+
+
+
+from abc import ABC, abstractmethod
+from typing import Dict, List, Optional
+
+import torch
+import torch.nn.functional as F
+
+
+
+[docs]
+class MetricBase(ABC):
+
+[docs]
+ @abstractmethod
+ def update(self, preds: torch.Tensor, targets: torch.Tensor) -> None:
+ ...
+
+
+
+
+
+
+
+
+
+
+
+[docs]
+class ClassificationMetrics(MetricBase):
+ def __init__(self):
+ self.reset()
+
+
+[docs]
+ def reset(self) -> None:
+ self._preds: List[torch.Tensor] = []
+ self._targets: List[torch.Tensor] = []
+
+
+
+[docs]
+ def update(self, preds: torch.Tensor, targets: torch.Tensor) -> None:
+ self._preds.append(preds.detach().cpu())
+ self._targets.append(targets.detach().cpu())
+
+
+
+[docs]
+ def compute(self) -> Dict[str, float]:
+ preds = torch.cat(self._preds)
+ targets = torch.cat(self._targets)
+ pred_labels = preds.argmax(dim=-1)
+ acc = (pred_labels == targets).float().mean().item()
+ return {"Acc": acc}
+
+
+
+
+
+[docs]
+class RegressionMetrics(MetricBase):
+ def __init__(self):
+ self.reset()
+
+
+[docs]
+ def reset(self) -> None:
+ self._preds: List[torch.Tensor] = []
+ self._targets: List[torch.Tensor] = []
+
+
+
+[docs]
+ def update(self, preds: torch.Tensor, targets: torch.Tensor) -> None:
+ self._preds.append(preds.detach().cpu())
+ self._targets.append(targets.detach().cpu())
+
+
+
+[docs]
+ def compute(self) -> Dict[str, float]:
+ preds = torch.cat(self._preds)
+ targets = torch.cat(self._targets)
+ mae = F.l1_loss(preds, targets).item()
+ rmse = torch.sqrt(F.mse_loss(preds, targets)).item()
+ return {"MAE": mae, "RMSE": rmse}
+
+
+
+
+
+[docs]
+class SegmentationMetrics(MetricBase):
+ def __init__(self, num_classes: Optional[int] = None):
+ self.num_classes = num_classes
+ self.reset()
+
+
+[docs]
+ def reset(self) -> None:
+ self._preds: List[torch.Tensor] = []
+ self._targets: List[torch.Tensor] = []
+
+
+
+[docs]
+ def update(self, preds: torch.Tensor, targets: torch.Tensor) -> None:
+ self._preds.append(preds.detach().cpu())
+ self._targets.append(targets.detach().cpu())
+
+
+
+[docs]
+ def compute(self) -> Dict[str, float]:
+ preds = torch.cat(self._preds)
+ targets = torch.cat(self._targets)
+ pred_labels = preds.argmax(dim=1)
+ # simple pixel accuracy; extend to IoU/Dice as needed
+ acc = (pred_labels == targets).float().mean().item()
+ return {"PixelAcc": acc}
+
+
+
+
+__all__ = ["MetricBase", "ClassificationMetrics", "RegressionMetrics", "SegmentationMetrics"]
+
+import torch
+import torch.nn as nn
+
+
+
+[docs]
+class MLPBackbone(nn.Module):
+ """Simple MLP for tabular features."""
+
+ def __init__(self, input_dim: int, hidden_dim: int = 256, depth: int = 2):
+ super().__init__()
+ layers = []
+ dim = input_dim
+ for _ in range(depth):
+ layers.extend([nn.Linear(dim, hidden_dim), nn.ReLU()])
+ dim = hidden_dim
+ self.net = nn.Sequential(*layers)
+
+
+
+
+
+
+
+[docs]
+class CNNPatchEncoder(nn.Module):
+ """Lightweight CNN encoder for raster patches."""
+
+ def __init__(self, in_channels: int = 3, hidden_dim: int = 64):
+ super().__init__()
+ self.features = nn.Sequential(
+ nn.Conv2d(in_channels, hidden_dim, kernel_size=3, padding=1),
+ nn.ReLU(),
+ nn.Conv2d(hidden_dim, hidden_dim, kernel_size=3, padding=1),
+ nn.ReLU(),
+ nn.AdaptiveAvgPool2d(1),
+ )
+
+
+
+
+
+
+
+[docs]
+class TemporalEncoder(nn.Module):
+ """GRU-based encoder for time-series signals."""
+
+ def __init__(self, input_dim: int, hidden_dim: int = 128, num_layers: int = 1):
+ super().__init__()
+ self.rnn = nn.GRU(input_dim, hidden_dim, num_layers=num_layers, batch_first=True)
+
+
+[docs]
+ def forward(self, x):
+ # x: (batch, seq, features)
+ out, _ = self.rnn(x)
+ return out[:, -1, :]
+
+
+
+
+__all__ = ["MLPBackbone", "CNNPatchEncoder", "TemporalEncoder"]
+
+from __future__ import annotations
+
+from typing import Any, Dict
+
+import torch.nn as nn
+
+from .backbones import CNNPatchEncoder, MLPBackbone, TemporalEncoder
+from .heads import ClassificationHead, RegressionHead, SegmentationHead
+from .registry import get_model_config
+
+
+
+[docs]
+def build_model(name: str, task: str, **kwargs: Any) -> nn.Module:
+ """
+ Build a model by name and task.
+ This delegates to registry metadata to keep a consistent interface.
+ """
+ cfg = get_model_config(name)
+ if cfg is None:
+ raise KeyError(f"Model '{name}' is not registered.")
+
+ task = task.lower()
+ builder = cfg["builder"]
+ defaults: Dict[str, Any] = cfg.get("defaults", {})
+ merged = {**defaults, **kwargs, "task": task}
+ return builder(**merged)
+
+
+
+
+[docs]
+def default_builder(name: str, task: str, **kwargs: Any) -> nn.Module:
+ """
+ Generic builder for standard backbones + heads.
+ """
+ task = task.lower()
+ if name == "mlp":
+ backbone = MLPBackbone(kwargs["in_dim"], hidden_dim=kwargs.get("hidden_dim", 256), depth=kwargs.get("depth", 2))
+ head = _make_head(task, kwargs)
+ return _combine(backbone, head)
+ if name == "cnn":
+ backbone = CNNPatchEncoder(kwargs.get("in_channels", 3), hidden_dim=kwargs.get("hidden_dim", 64))
+ head = _make_head(task, kwargs, backbone_out_dim=kwargs.get("hidden_dim", 64))
+ return _combine(backbone, head)
+ if name == "temporal":
+ backbone = TemporalEncoder(kwargs["in_dim"], hidden_dim=kwargs.get("hidden_dim", 128), num_layers=kwargs.get("num_layers", 1))
+ head = _make_head(task, kwargs)
+ return _combine(backbone, head)
+ raise ValueError(f"Unknown backbone '{name}'.")
+
+
+
+def _make_head(task: str, kwargs: Dict[str, Any], backbone_out_dim: int | None = None) -> nn.Module:
+ if task == "classification":
+ in_dim = backbone_out_dim or kwargs.get("hidden_dim") or kwargs["in_dim"]
+ return ClassificationHead(in_dim=in_dim, num_classes=kwargs["out_dim"])
+ if task == "regression":
+ in_dim = backbone_out_dim or kwargs.get("hidden_dim") or kwargs["in_dim"]
+ return RegressionHead(in_dim=in_dim, out_dim=kwargs.get("out_dim", 1))
+ if task == "segmentation":
+ in_channels = kwargs.get("hidden_dim") or backbone_out_dim or kwargs.get("in_channels", 1)
+ return SegmentationHead(in_channels=in_channels, num_classes=kwargs["out_dim"])
+ raise ValueError(f"Unsupported task '{task}'.")
+
+
+def _combine(backbone: nn.Module, head: nn.Module) -> nn.Module:
+ return nn.Sequential(backbone, head)
+
+
+__all__ = ["build_model", "default_builder"]
+
+from __future__ import annotations
+
+from typing import Sequence
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+# ---------------------------------------------------------------------
+# Basic blocks
+# ---------------------------------------------------------------------
+
+class ConvBNReLU(nn.Module):
+ def __init__(
+ self,
+ in_ch: int,
+ out_ch: int,
+ k: int = 3,
+ s: int = 1,
+ p: int = 1,
+ d: int = 1,
+ ):
+ super().__init__()
+ self.conv = nn.Conv2d(
+ in_ch,
+ out_ch,
+ kernel_size=k,
+ stride=s,
+ padding=p,
+ dilation=d,
+ bias=False,
+ )
+ self.bn = nn.BatchNorm2d(out_ch)
+ self.act = nn.ReLU(inplace=True)
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ return self.act(self.bn(self.conv(x)))
+
+
+# ---------------------------------------------------------------------
+# ASPP
+# ---------------------------------------------------------------------
+
+class ASPP(nn.Module):
+ """
+ Atrous Spatial Pyramid Pooling (ASPP).
+
+ Parallel atrous convolutions + image pooling branch,
+ followed by projection.
+ """
+
+ def __init__(
+ self,
+ in_ch: int,
+ out_ch: int,
+ dilations: Sequence[int] = (1, 3, 6, 12),
+ ):
+ super().__init__()
+
+ if len(dilations) != 4:
+ raise ValueError("ASPP expects exactly 4 dilation rates")
+
+ d1, d2, d3, d4 = dilations
+
+ self.b1 = ConvBNReLU(in_ch, out_ch, k=1, p=0, d=d1)
+ self.b2 = ConvBNReLU(in_ch, out_ch, k=3, p=d2, d=d2)
+ self.b3 = ConvBNReLU(in_ch, out_ch, k=3, p=d3, d=d3)
+ self.b4 = ConvBNReLU(in_ch, out_ch, k=3, p=d4, d=d4)
+
+ self.pool = nn.Sequential(
+ nn.AdaptiveAvgPool2d(1),
+ ConvBNReLU(in_ch, out_ch, k=1, p=0),
+ )
+
+ self.proj = ConvBNReLU(out_ch * 5, out_ch, k=1, p=0)
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ b, c, h, w = x.shape
+
+ p = self.pool(x)
+ p = F.interpolate(p, size=(h, w), mode="bilinear", align_corners=False)
+
+ y = torch.cat(
+ [self.b1(x), self.b2(x), self.b3(x), self.b4(x), p],
+ dim=1,
+ )
+ return self.proj(y)
+
+
+# ---------------------------------------------------------------------
+# CNN + ASPP model
+# ---------------------------------------------------------------------
+
+
+[docs]
+class WildfireCNNASPP(nn.Module):
+ """
+ CNN + ASPP wildfire segmentation model.
+
+ Input:
+ x : (B, C, H, W) float tensor
+
+ Output:
+ logits : (B, 1, H, W) float tensor
+ (sigmoid applied externally)
+ """
+
+ def __init__(
+ self,
+ in_channels: int = 12,
+ base_channels: int = 32,
+ aspp_channels: int = 32,
+ dilations: Sequence[int] = (1, 3, 6, 12),
+ dropout: float = 0.0,
+ ):
+ super().__init__()
+
+ self.stem = nn.Sequential(
+ ConvBNReLU(in_channels, base_channels, k=3, p=1),
+ ConvBNReLU(base_channels, base_channels, k=3, p=1),
+ )
+
+ self.aspp = ASPP(
+ in_ch=base_channels,
+ out_ch=aspp_channels,
+ dilations=dilations,
+ )
+
+ self.drop = nn.Dropout2d(dropout) if dropout > 0 else nn.Identity()
+ self.head = nn.Conv2d(aspp_channels, 1, kernel_size=1)
+
+
+[docs]
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ if x.ndim != 4:
+ raise ValueError(
+ f"Expected input of shape (B,C,H,W), got {tuple(x.shape)}"
+ )
+
+ f = self.stem(x)
+ y = self.aspp(f)
+ y = self.drop(y)
+ return self.head(y)
+
+
+
+
+# ---------------------------------------------------------------------
+# PyHazards model builder
+# ---------------------------------------------------------------------
+
+
+[docs]
+def cnn_aspp_builder(
+ task: str,
+ in_channels: int = 12,
+ base_channels: int = 32,
+ aspp_channels: int = 32,
+ dilations: Sequence[int] = (1, 3, 6, 12),
+ dropout: float = 0.0,
+ **kwargs,
+) -> nn.Module:
+ """
+ PyHazards-style model builder.
+ """
+ _ = kwargs # explicitly ignore unused builder args
+
+ if "segmentation" not in task:
+ raise ValueError(
+ f"WildfireCNNASPP is segmentation-only. Got task='{task}'"
+ )
+
+ return WildfireCNNASPP(
+ in_channels=in_channels,
+ base_channels=base_channels,
+ aspp_channels=aspp_channels,
+ dilations=dilations,
+ dropout=dropout,
+ )
+
+
+import torch.nn as nn
+
+
+
+[docs]
+class ClassificationHead(nn.Module):
+ """Simple classification head."""
+
+ def __init__(self, in_dim: int, num_classes: int):
+ super().__init__()
+ self.fc = nn.Linear(in_dim, num_classes)
+
+
+
+
+
+
+
+[docs]
+class RegressionHead(nn.Module):
+ """Regression head for scalar or multi-target outputs."""
+
+ def __init__(self, in_dim: int, out_dim: int = 1):
+ super().__init__()
+ self.fc = nn.Linear(in_dim, out_dim)
+
+
+
+
+
+
+
+[docs]
+class SegmentationHead(nn.Module):
+ """Segmentation head for raster masks."""
+
+ def __init__(self, in_channels: int, num_classes: int):
+ super().__init__()
+ self.conv = nn.Conv2d(in_channels, num_classes, kernel_size=1)
+
+
+
+
+
+
+__all__ = ["ClassificationHead", "RegressionHead", "SegmentationHead"]
+
+from typing import Any, Callable, Dict, Optional
+
+import torch.nn as nn
+
+_MODEL_REGISTRY: Dict[str, Dict[str, Any]] = {}
+
+
+
+[docs]
+def register_model(name: str, builder: Callable[..., nn.Module], defaults: Optional[Dict[str, Any]] = None) -> None:
+ if name in _MODEL_REGISTRY:
+ raise ValueError(f"Model '{name}' already registered.")
+ _MODEL_REGISTRY[name] = {"builder": builder, "defaults": defaults or {}}
+
+
+
+
+
+
+
+
+[docs]
+def get_model_config(name: str) -> Optional[Dict[str, Any]]:
+ return _MODEL_REGISTRY.get(name)
+
+
+
+__all__ = ["register_model", "available_models", "get_model_config"]
+
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+
+from .cnn_aspp import WildfireCNNASPP, cnn_aspp_builder
+
+
+
+[docs]
+class WildfireASPP(WildfireCNNASPP):
+ """
+ Backward-compatible name for the CNN + ASPP wildfire model.
+ """
+
+
+
+
+[docs]
+def wildfire_aspp_builder(*args, **kwargs) -> nn.Module:
+ return cnn_aspp_builder(*args, **kwargs)
+
+
+
+
+[docs]
+class TverskyLoss(nn.Module):
+ """
+ Tversky loss for binary segmentation.
+ """
+
+ def __init__(
+ self,
+ alpha: float = 0.5,
+ beta: float = 0.5,
+ smooth: float = 1e-6,
+ from_logits: bool = True,
+ ):
+ super().__init__()
+ self.alpha = float(alpha)
+ self.beta = float(beta)
+ self.smooth = float(smooth)
+ self.from_logits = bool(from_logits)
+
+
+[docs]
+ def forward(self, logits: torch.Tensor, targets: torch.Tensor) -> torch.Tensor:
+ if self.from_logits:
+ probs = torch.sigmoid(logits)
+ else:
+ probs = logits
+
+ targets = targets.float()
+
+ probs = probs.view(probs.size(0), -1)
+ targets = targets.view(targets.size(0), -1)
+
+ tp = (probs * targets).sum(dim=1)
+ fp = (probs * (1 - targets)).sum(dim=1)
+ fn = ((1 - probs) * targets).sum(dim=1)
+
+ tversky = (tp + self.smooth) / (
+ tp + self.alpha * fp + self.beta * fn + self.smooth
+ )
+ loss = 1.0 - tversky
+ return loss.mean()
+
+
+
+from __future__ import annotations
+
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def _normalize_adjacency(adj: torch.Tensor) -> torch.Tensor:
+ """
+ Row-normalize an adjacency matrix and ensure self-loops.
+ Accepts (N, N) or (B, N, N) and returns the same rank.
+ """
+ if adj.dim() == 2:
+ adj = adj.unsqueeze(0)
+ eye = torch.eye(adj.size(-1), device=adj.device, dtype=adj.dtype)
+ adj = adj.float() + eye.unsqueeze(0)
+ return adj / adj.sum(-1, keepdim=True).clamp(min=1e-6)
+
+
+class SelectiveSSMBlock(nn.Module):
+ """
+ Lightweight selective state-space block inspired by Mamba.
+
+ Operates over a single temporal stream: (batch, time, features) -> (batch, time, hidden_dim).
+ """
+
+ def __init__(self, in_dim: int, hidden_dim: int, state_dim: int = 64, conv_kernel: int = 5, dropout: float = 0.1):
+ super().__init__()
+ self.in_proj = nn.Linear(in_dim, hidden_dim)
+ self.dwconv = nn.Conv1d(hidden_dim, hidden_dim, kernel_size=conv_kernel, padding=conv_kernel // 2, groups=hidden_dim)
+ self.gate = nn.Linear(hidden_dim, hidden_dim)
+ self.A = nn.Parameter(torch.randn(hidden_dim, state_dim) * 0.02)
+ self.B = nn.Parameter(torch.randn(state_dim, hidden_dim) * 0.02)
+ self.out_proj = nn.Linear(hidden_dim, hidden_dim)
+ self.norm = nn.LayerNorm(hidden_dim)
+ self.drop = nn.Dropout(dropout)
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ # x: (B, T, F)
+ h = self.in_proj(x) # (B, T, H)
+ h_conv = self.dwconv(h.transpose(1, 2)).transpose(1, 2)
+ g = torch.sigmoid(self.gate(h_conv))
+ B, T, H = h_conv.shape
+ state = torch.zeros(B, H, device=h_conv.device, dtype=h_conv.dtype)
+ outputs = []
+ for t in range(T):
+ # selective update: gates decide how much new signal to mix into the running state
+ state = g[:, t, :] * (state @ self.A @ self.B + h_conv[:, t, :]) + (1 - g[:, t, :]) * state
+ outputs.append(state)
+ y = torch.stack(outputs, dim=1)
+ y = self.out_proj(self.drop(y)) + h_conv
+ return self.norm(y)
+
+
+class MambaTemporalEncoder(nn.Module):
+ """Stack of selective SSM blocks; returns the last hidden state."""
+
+ def __init__(self, in_dim: int, hidden_dim: int = 128, num_layers: int = 2, state_dim: int = 64, conv_kernel: int = 5, dropout: float = 0.1):
+ super().__init__()
+ self.blocks = nn.ModuleList(
+ [
+ SelectiveSSMBlock(
+ in_dim=in_dim if i == 0 else hidden_dim,
+ hidden_dim=hidden_dim,
+ state_dim=state_dim,
+ conv_kernel=conv_kernel,
+ dropout=dropout,
+ )
+ for i in range(num_layers)
+ ]
+ )
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ h = x
+ for block in self.blocks:
+ h = block(h)
+ return h[:, -1, :]
+
+
+class SimpleGCN(nn.Module):
+ """Two-layer GCN that mixes counties with a fixed adjacency."""
+
+ def __init__(self, in_dim: int, hidden_dim: int = 64, out_dim: int = 64, dropout: float = 0.1):
+ super().__init__()
+ self.lin1 = nn.Linear(in_dim, hidden_dim)
+ self.lin2 = nn.Linear(hidden_dim, out_dim)
+ self.drop = nn.Dropout(dropout)
+
+ def forward(self, H: torch.Tensor, adj: torch.Tensor) -> torch.Tensor:
+ # H: (B, N, D); adj: (B, N, N)
+ z = torch.matmul(adj, H)
+ z = F.relu(self.lin1(z))
+ z = self.drop(z)
+ z = torch.matmul(adj, z)
+ return F.relu(self.lin2(z))
+
+
+
+[docs]
+class WildfireMamba(nn.Module):
+ """
+ Mamba-based spatio-temporal wildfire model for county-day ERA5 features.
+
+ Input shape: (batch, past_days, num_counties, num_features)
+ Output: logits per county for the next day (use sigmoid for probabilities)
+ """
+
+ def __init__(
+ self,
+ in_dim: int,
+ num_counties: int,
+ past_days: int,
+ hidden_dim: int = 128,
+ gcn_hidden: int = 64,
+ mamba_layers: int = 2,
+ state_dim: int = 64,
+ conv_kernel: int = 5,
+ dropout: float = 0.1,
+ adjacency: Optional[torch.Tensor] = None,
+ with_count_head: bool = False,
+ ):
+ super().__init__()
+ self.num_counties = num_counties
+ self.past_days = past_days
+ self.with_count_head = with_count_head
+ self.temporal = MambaTemporalEncoder(
+ in_dim=in_dim,
+ hidden_dim=hidden_dim,
+ num_layers=mamba_layers,
+ state_dim=state_dim,
+ conv_kernel=conv_kernel,
+ dropout=dropout,
+ )
+ # differential branch is shallower and gates how much change to inject
+ self.delta_temporal = MambaTemporalEncoder(
+ in_dim=in_dim,
+ hidden_dim=hidden_dim,
+ num_layers=max(1, mamba_layers - 1),
+ state_dim=state_dim,
+ conv_kernel=conv_kernel,
+ dropout=dropout,
+ )
+ self.delta_gate = nn.Linear(hidden_dim, hidden_dim)
+ self.gcn = SimpleGCN(hidden_dim, hidden_dim=gcn_hidden, out_dim=gcn_hidden, dropout=dropout)
+ self.cls_head = nn.Linear(gcn_hidden, 1)
+ if self.with_count_head:
+ self.count_head = nn.Linear(gcn_hidden, 1)
+ self.dropout = nn.Dropout(dropout)
+ self.register_buffer("_adjacency", None)
+ if adjacency is not None:
+ self.set_adjacency(adjacency)
+
+
+[docs]
+ def set_adjacency(self, adj: torch.Tensor) -> None:
+ """Set/override the spatial adjacency."""
+ adj = _normalize_adjacency(adj.detach())
+ self._adjacency = adj
+
+
+
+[docs]
+ def _get_adjacency(self, batch_size: int) -> torch.Tensor:
+ if self._adjacency is None:
+ eye = torch.eye(self.num_counties, device=self.cls_head.weight.device)
+ adj = _normalize_adjacency(eye)
+ else:
+ adj = self._adjacency
+ if adj.dim() == 2:
+ adj = adj.unsqueeze(0)
+ if adj.size(0) == 1 and batch_size > 1:
+ adj = adj.expand(batch_size, -1, -1)
+ return adj
+
+
+
+[docs]
+ @staticmethod
+ def _temporal_delta(x: torch.Tensor) -> torch.Tensor:
+ # prepend zeros so delta has the same length as the input sequence
+ zeros = torch.zeros(x.size(0), 1, x.size(2), device=x.device, dtype=x.dtype)
+ return torch.cat([zeros, x[:, 1:] - x[:, :-1]], dim=1)
+
+
+
+[docs]
+ def forward(self, x: torch.Tensor, adjacency: Optional[torch.Tensor] = None):
+ """
+ Args:
+ x: Tensor shaped (batch, past_days, num_counties, in_dim)
+ adjacency: Optional (N, N) or (B, N, N) adjacency override.
+ Returns:
+ - logits: (batch, num_counties)
+ - optional counts: (batch, num_counties) if with_count_head is enabled.
+ """
+ B, T, N, F = x.shape
+ if T != self.past_days:
+ raise ValueError(f"Expected past_days={self.past_days}, got {T}.")
+ if N != self.num_counties:
+ raise ValueError(f"Expected num_counties={self.num_counties}, got {N}.")
+
+ # flatten counties into the batch for temporal encoding
+ x_flat = x.permute(0, 2, 1, 3).reshape(B * N, T, F)
+ base = self.temporal(x_flat)
+ delta = self.delta_temporal(self._temporal_delta(x_flat))
+ gate = torch.sigmoid(self.delta_gate(delta))
+ fused = base * gate + delta
+ fused = fused.view(B, N, -1)
+
+ adj = _normalize_adjacency(adjacency) if adjacency is not None else self._get_adjacency(B)
+ spatial = self.gcn(fused, adj)
+ spatial = self.dropout(spatial)
+ logits = self.cls_head(spatial).squeeze(-1)
+ if self.with_count_head:
+ counts = F.relu(self.count_head(spatial)).squeeze(-1)
+ return logits, counts
+ return logits
+
+
+
+
+
+[docs]
+def wildfire_mamba_builder(
+ task: str,
+ in_dim: int,
+ num_counties: int,
+ past_days: int,
+ **kwargs,
+) -> WildfireMamba:
+ """
+ Builder used by the model registry.
+ """
+ if task.lower() not in {"classification", "binary_classification"}:
+ raise ValueError("WildfireMamba is designed for binary per-county classification.")
+ return WildfireMamba(
+ in_dim=in_dim,
+ num_counties=num_counties,
+ past_days=past_days,
+ hidden_dim=kwargs.get("hidden_dim", 128),
+ gcn_hidden=kwargs.get("gcn_hidden", 64),
+ mamba_layers=kwargs.get("mamba_layers", 2),
+ state_dim=kwargs.get("state_dim", 64),
+ conv_kernel=kwargs.get("conv_kernel", 5),
+ dropout=kwargs.get("dropout", 0.1),
+ adjacency=kwargs.get("adjacency"),
+ with_count_head=kwargs.get("with_count_head", False),
+ )
+
+
+
+__all__ = ["WildfireMamba", "wildfire_mamba_builder"]
+
+import logging
+import os
+import random
+from typing import Optional
+
+import numpy as np
+import torch
+
+
+
+[docs]
+def seed_all(seed: int = 42, deterministic: bool = False) -> None:
+ random.seed(seed)
+ np.random.seed(seed)
+ torch.manual_seed(seed)
+ if torch.cuda.is_available():
+ torch.cuda.manual_seed_all(seed)
+ if deterministic:
+ torch.backends.cudnn.deterministic = True
+ torch.backends.cudnn.benchmark = False
+
+
+
+
+[docs]
+def get_logger(name: Optional[str] = None) -> logging.Logger:
+ logging.basicConfig(level=os.getenv("PYHAZARD_LOGLEVEL", "INFO"))
+ return logging.getLogger(name or "pyhazards")
+
+
+from __future__ import annotations
+
+import os
+from typing import Optional
+
+import torch
+
+_DEFAULT_DEVICE_STR = os.getenv("PYHAZARDS_DEVICE") or ("cuda:0" if torch.cuda.is_available() else "cpu")
+_default_device = torch.device(_DEFAULT_DEVICE_STR)
+
+
+
+[docs]
+def auto_device(prefer: str | None = None) -> torch.device:
+ """
+ Choose a device automatically. Respects PYHAZARDS_DEVICE and prefer flag.
+ """
+ if prefer:
+ return torch.device(prefer)
+ return _default_device
+
+
+
+
+[docs]
+def num_devices() -> int:
+ if torch.cuda.is_available():
+ return torch.cuda.device_count()
+ return 0
+
+
+
+
+
+
+
+
+[docs]
+def set_device(device_str: str | torch.device) -> None:
+ global _default_device
+ _default_device = torch.device(device_str)
+
+
+ ' + + '' + + _("Hide Search Matches") + + "
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Remove the inheritance of text transform in Firefox.\n */\n\nbutton,\nselect { /* 1 */\n text-transform: none;\n}\n\n/**\n * Correct the inability to style clickable types in iOS and Safari.\n */\n\nbutton,\n[type=\"button\"],\n[type=\"reset\"],\n[type=\"submit\"] {\n -webkit-appearance: button;\n}\n\n/**\n * Remove the inner border and padding in Firefox.\n */\n\nbutton::-moz-focus-inner,\n[type=\"button\"]::-moz-focus-inner,\n[type=\"reset\"]::-moz-focus-inner,\n[type=\"submit\"]::-moz-focus-inner {\n border-style: none;\n padding: 0;\n}\n\n/**\n * Restore the focus styles unset by the previous rule.\n */\n\nbutton:-moz-focusring,\n[type=\"button\"]:-moz-focusring,\n[type=\"reset\"]:-moz-focusring,\n[type=\"submit\"]:-moz-focusring {\n outline: 1px dotted ButtonText;\n}\n\n/**\n * Correct the padding in Firefox.\n */\n\nfieldset {\n padding: 0.35em 0.75em 0.625em;\n}\n\n/**\n * 1. Correct the text wrapping in Edge and IE.\n * 2. Correct the color inheritance from `fieldset` elements in IE.\n * 3. Remove the padding so developers are not caught out when they zero out\n * `fieldset` elements in all browsers.\n */\n\nlegend {\n box-sizing: border-box; /* 1 */\n color: inherit; /* 2 */\n display: table; /* 1 */\n max-width: 100%; /* 1 */\n padding: 0; /* 3 */\n white-space: normal; /* 1 */\n}\n\n/**\n * Add the correct vertical alignment in Chrome, Firefox, and Opera.\n */\n\nprogress {\n vertical-align: baseline;\n}\n\n/**\n * Remove the default vertical scrollbar in IE 10+.\n */\n\ntextarea {\n overflow: auto;\n}\n\n/**\n * 1. Add the correct box sizing in IE 10.\n * 2. Remove the padding in IE 10.\n */\n\n[type=\"checkbox\"],\n[type=\"radio\"] {\n box-sizing: border-box; /* 1 */\n padding: 0; /* 2 */\n}\n\n/**\n * Correct the cursor style of increment and decrement buttons in Chrome.\n */\n\n[type=\"number\"]::-webkit-inner-spin-button,\n[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\n\n/**\n * 1. Correct the odd appearance in Chrome and Safari.\n * 2. Correct the outline style in Safari.\n */\n\n[type=\"search\"] {\n -webkit-appearance: textfield; /* 1 */\n outline-offset: -2px; /* 2 */\n}\n\n/**\n * Remove the inner padding in Chrome and Safari on macOS.\n */\n\n[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\n\n/**\n * 1. Correct the inability to style clickable types in iOS and Safari.\n * 2. Change font properties to `inherit` in Safari.\n */\n\n::-webkit-file-upload-button {\n -webkit-appearance: button; /* 1 */\n font: inherit; /* 2 */\n}\n\n/* Interactive\n ========================================================================== */\n\n/*\n * Add the correct display in Edge, IE 10+, and Firefox.\n */\n\ndetails {\n display: block;\n}\n\n/*\n * Add the correct display in all browsers.\n */\n\nsummary {\n display: list-item;\n}\n\n/* Misc\n ========================================================================== */\n\n/**\n * Add the correct display in IE 10+.\n */\n\ntemplate {\n display: none;\n}\n\n/**\n * Add the correct display in IE 10.\n */\n\n[hidden] {\n display: none;\n}\n","// This file contains styles for managing print media.\n\n////////////////////////////////////////////////////////////////////////////////\n// Hide elements not relevant to print media.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Hide icon container.\n .content-icon-container\n display: none !important\n\n // Hide showing header links if hovering over when printing.\n .headerlink\n display: none !important\n\n // Hide mobile header.\n .mobile-header\n display: none !important\n\n // Hide navigation links.\n .related-pages\n display: none !important\n\n////////////////////////////////////////////////////////////////////////////////\n// Tweaks related to decolorization.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Apply a border around code which no longer have a color background.\n .highlight\n border: 0.1pt solid var(--color-foreground-border)\n\n////////////////////////////////////////////////////////////////////////////////\n// Avoid page break in some relevant cases.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n ul, ol, dl, a, table, pre, blockquote, p\n page-break-inside: avoid\n\n h1, h2, h3, h4, h5, h6, img, figure, caption\n page-break-inside: avoid\n page-break-after: avoid\n\n ul, ol, dl\n page-break-before: avoid\n",".visually-hidden\n position: absolute !important\n width: 1px !important\n height: 1px !important\n padding: 0 !important\n margin: -1px !important\n overflow: hidden !important\n clip: rect(0,0,0,0) !important\n white-space: nowrap !important\n border: 0 !important\n color: var(--color-foreground-primary)\n background: var(--color-background-primary)\n\n:-moz-focusring\n outline: auto\n","// This file serves as the \"skeleton\" of the theming logic.\n//\n// This contains the bulk of the logic for handling dark mode, color scheme\n// toggling and the handling of color-scheme-specific hiding of elements.\n\nbody\n @include fonts\n @include spacing\n @include icons\n @include admonitions\n @include default-admonition(#651fff, \"abstract\")\n @include default-topic(#14B8A6, \"pencil\")\n\n @include colors\n\n.only-light\n display: block !important\nhtml body .only-dark\n display: none !important\n\n// Ignore dark-mode hints if print media.\n@media not print\n // Enable dark-mode, if requested.\n body[data-theme=\"dark\"]\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n // Enable dark mode, unless explicitly told to avoid.\n @media (prefers-color-scheme: dark)\n body:not([data-theme=\"light\"])\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n//\n// Theme toggle presentation\n//\nbody[data-theme=\"auto\"]\n .theme-toggle svg.theme-icon-when-auto-light\n display: block\n\n @media (prefers-color-scheme: dark)\n .theme-toggle svg.theme-icon-when-auto-dark\n display: block\n .theme-toggle svg.theme-icon-when-auto-light\n display: none\n\nbody[data-theme=\"dark\"]\n .theme-toggle svg.theme-icon-when-dark\n display: block\n\nbody[data-theme=\"light\"]\n .theme-toggle svg.theme-icon-when-light\n display: block\n","// Fonts used by this theme.\n//\n// There are basically two things here -- using the system font stack and\n// defining sizes for various elements in %ages. We could have also used `em`\n// but %age is easier to reason about for me.\n\n@mixin fonts {\n // These are adapted from https://systemfontstack.com/\n --font-stack: -apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial,\n sans-serif, Apple Color Emoji, Segoe UI Emoji;\n --font-stack--monospace: \"SFMono-Regular\", Menlo, Consolas, Monaco,\n Liberation Mono, Lucida Console, monospace;\n --font-stack--headings: var(--font-stack);\n\n --font-size--normal: 100%;\n --font-size--small: 87.5%;\n --font-size--small--2: 81.25%;\n --font-size--small--3: 75%;\n --font-size--small--4: 62.5%;\n\n // Sidebar\n --sidebar-caption-font-size: var(--font-size--small--2);\n --sidebar-item-font-size: var(--font-size--small);\n --sidebar-search-input-font-size: var(--font-size--small);\n\n // Table of Contents\n --toc-font-size: var(--font-size--small--3);\n --toc-font-size--mobile: var(--font-size--normal);\n --toc-title-font-size: var(--font-size--small--4);\n\n // Admonitions\n //\n // These aren't defined in terms of %ages, since nesting these is permitted.\n --admonition-font-size: 0.8125rem;\n --admonition-title-font-size: 0.8125rem;\n\n // Code\n --code-font-size: var(--font-size--small--2);\n\n // API\n --api-font-size: var(--font-size--small);\n}\n","// Spacing for various elements on the page\n//\n// If the user wants to tweak things in a certain way, they are permitted to.\n// They also have to deal with the consequences though!\n\n@mixin spacing {\n // Header!\n --header-height: calc(\n var(--sidebar-item-line-height) + 4 * #{var(--sidebar-item-spacing-vertical)}\n );\n --header-padding: 0.5rem;\n\n // Sidebar\n --sidebar-tree-space-above: 1.5rem;\n --sidebar-caption-space-above: 1rem;\n\n --sidebar-item-line-height: 1rem;\n --sidebar-item-spacing-vertical: 0.5rem;\n --sidebar-item-spacing-horizontal: 1rem;\n --sidebar-item-height: calc(\n var(--sidebar-item-line-height) + 2 *#{var(--sidebar-item-spacing-vertical)}\n );\n\n --sidebar-expander-width: var(--sidebar-item-height); // be square\n\n --sidebar-search-space-above: 0.5rem;\n --sidebar-search-input-spacing-vertical: 0.5rem;\n --sidebar-search-input-spacing-horizontal: 0.5rem;\n --sidebar-search-input-height: 1rem;\n --sidebar-search-icon-size: var(--sidebar-search-input-height);\n\n // Table of Contents\n --toc-title-padding: 0.25rem 0;\n --toc-spacing-vertical: 1.5rem;\n --toc-spacing-horizontal: 1.5rem;\n --toc-item-spacing-vertical: 0.4rem;\n --toc-item-spacing-horizontal: 1rem;\n}\n","// Expose theme icons as CSS variables.\n\n$icons: (\n // Adapted from tabler-icons\n // url: https://tablericons.com/\n \"search\":\n url('data:image/svg+xml;charset=utf-8,'),\n // Factored out from mkdocs-material on 24-Aug-2020.\n // url: https://squidfunk.github.io/mkdocs-material/reference/admonitions/\n \"pencil\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"abstract\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"info\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"flame\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"question\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"warning\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"failure\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"spark\":\n url('data:image/svg+xml;charset=utf-8,')\n);\n\n@mixin icons {\n @each $name, $glyph in $icons {\n --icon-#{$name}: #{$glyph};\n }\n}\n","// Admonitions\n\n// Structure of these is:\n// admonition-class: color \"icon-name\";\n//\n// The colors are translated into CSS variables below. The icons are\n// used directly in the main declarations to set the `mask-image` in\n// the title.\n\n// prettier-ignore\n$admonitions: (\n // Each of these has an reST directives for it.\n \"caution\": #ff9100 \"spark\",\n \"warning\": #ff9100 \"warning\",\n \"danger\": #ff5252 \"spark\",\n \"attention\": #ff5252 \"warning\",\n \"error\": #ff5252 \"failure\",\n \"hint\": #00c852 \"question\",\n \"tip\": #00c852 \"info\",\n \"important\": #00bfa5 \"flame\",\n \"note\": #00b0ff \"pencil\",\n \"seealso\": #448aff \"info\",\n \"admonition-todo\": #808080 \"pencil\"\n);\n\n@mixin default-admonition($color, $icon-name) {\n --color-admonition-title: #{$color};\n --color-admonition-title-background: #{rgba($color, 0.2)};\n\n --icon-admonition-default: var(--icon-#{$icon-name});\n}\n\n@mixin default-topic($color, $icon-name) {\n --color-topic-title: #{$color};\n --color-topic-title-background: #{rgba($color, 0.2)};\n\n --icon-topic-default: var(--icon-#{$icon-name});\n}\n\n@mixin admonitions {\n @each $name, $values in $admonitions {\n --color-admonition-title--#{$name}: #{nth($values, 1)};\n --color-admonition-title-background--#{$name}: #{rgba(\n nth($values, 1),\n 0.2\n )};\n }\n}\n","// Colors used throughout this theme.\n//\n// The aim is to give the user more control. Thus, instead of hard-coding colors\n// in various parts of the stylesheet, the approach taken is to define all\n// colors as CSS variables and reusing them in all the places.\n//\n// `colors-dark` depends on `colors` being included at a lower specificity.\n\n@mixin colors {\n --color-problematic: #b30000;\n\n // Base Colors\n --color-foreground-primary: black; // for main text and headings\n --color-foreground-secondary: #5a5c63; // for secondary text\n --color-foreground-muted: #6b6f76; // for muted text\n --color-foreground-border: #878787; // for content borders\n\n --color-background-primary: white; // for content\n --color-background-secondary: #f8f9fb; // for navigation + ToC\n --color-background-hover: #efeff4ff; // for navigation-item hover\n --color-background-hover--transparent: #efeff400;\n --color-background-border: #eeebee; // for UI borders\n --color-background-item: #ccc; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #0a4bff;\n --color-brand-content: #2757dd;\n --color-brand-visited: #872ee0;\n\n // API documentation\n --color-api-background: var(--color-background-hover--transparent);\n --color-api-background-hover: var(--color-background-hover);\n --color-api-overall: var(--color-foreground-secondary);\n --color-api-name: var(--color-problematic);\n --color-api-pre-name: var(--color-problematic);\n --color-api-paren: var(--color-foreground-secondary);\n --color-api-keyword: var(--color-foreground-primary);\n\n --color-api-added: #21632c;\n --color-api-added-border: #38a84d;\n --color-api-changed: #046172;\n --color-api-changed-border: #06a1bc;\n --color-api-deprecated: #605706;\n --color-api-deprecated-border: #f0d90f;\n --color-api-removed: #b30000;\n --color-api-removed-border: #ff5c5c;\n\n --color-highlight-on-target: #ffffcc;\n\n // Inline code background\n --color-inline-code-background: var(--color-background-secondary);\n\n // Highlighted text (search)\n --color-highlighted-background: #ddeeff;\n --color-highlighted-text: var(--color-foreground-primary);\n\n // GUI Labels\n --color-guilabel-background: #ddeeff80;\n --color-guilabel-border: #bedaf580;\n --color-guilabel-text: var(--color-foreground-primary);\n\n // Admonitions!\n --color-admonition-background: transparent;\n\n //////////////////////////////////////////////////////////////////////////////\n // Everything below this should be one of:\n // - var(...)\n // - *-gradient(...)\n // - special literal values (eg: transparent, none)\n //////////////////////////////////////////////////////////////////////////////\n\n // Tables\n --color-table-header-background: var(--color-background-secondary);\n --color-table-border: var(--color-background-border);\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: transparent;\n --color-card-marginals-background: var(--color-background-secondary);\n\n // Header\n --color-header-background: var(--color-background-primary);\n --color-header-border: var(--color-background-border);\n --color-header-text: var(--color-foreground-primary);\n\n // Sidebar (left)\n --color-sidebar-background: var(--color-background-secondary);\n --color-sidebar-background-border: var(--color-background-border);\n\n --color-sidebar-brand-text: var(--color-foreground-primary);\n --color-sidebar-caption-text: var(--color-foreground-muted);\n --color-sidebar-link-text: var(--color-foreground-secondary);\n --color-sidebar-link-text--top-level: var(--color-brand-primary);\n\n --color-sidebar-item-background: var(--color-sidebar-background);\n --color-sidebar-item-background--current: var(\n --color-sidebar-item-background\n );\n --color-sidebar-item-background--hover: linear-gradient(\n 90deg,\n var(--color-background-hover--transparent) 0%,\n var(--color-background-hover) var(--sidebar-item-spacing-horizontal),\n var(--color-background-hover) 100%\n );\n\n --color-sidebar-item-expander-background: transparent;\n --color-sidebar-item-expander-background--hover: var(\n --color-background-hover\n );\n\n --color-sidebar-search-text: var(--color-foreground-primary);\n --color-sidebar-search-background: var(--color-background-secondary);\n --color-sidebar-search-background--focus: var(--color-background-primary);\n --color-sidebar-search-border: var(--color-background-border);\n --color-sidebar-search-icon: var(--color-foreground-muted);\n\n // Table of Contents (right)\n --color-toc-background: var(--color-background-primary);\n --color-toc-title-text: var(--color-foreground-muted);\n --color-toc-item-text: var(--color-foreground-secondary);\n --color-toc-item-text--hover: var(--color-foreground-primary);\n --color-toc-item-text--active: var(--color-brand-primary);\n\n // Actual page contents\n --color-content-foreground: var(--color-foreground-primary);\n --color-content-background: transparent;\n\n // Links\n --color-link: var(--color-brand-content);\n --color-link-underline: var(--color-background-border);\n --color-link--hover: var(--color-brand-content);\n --color-link-underline--hover: var(--color-foreground-border);\n\n --color-link--visited: var(--color-brand-visited);\n --color-link-underline--visited: var(--color-background-border);\n --color-link--visited--hover: var(--color-brand-visited);\n --color-link-underline--visited--hover: var(--color-foreground-border);\n}\n\n@mixin colors-dark {\n --color-problematic: #ee5151;\n\n // Base Colors\n --color-foreground-primary: #cfd0d0; // for main text and headings\n --color-foreground-secondary: #9ca0a5; // for secondary text\n --color-foreground-muted: #81868d; // for muted text\n --color-foreground-border: #666666; // for content borders\n\n --color-background-primary: #131416; // for content\n --color-background-secondary: #1a1c1e; // for navigation + ToC\n --color-background-hover: #1e2124ff; // for navigation-item hover\n --color-background-hover--transparent: #1e212400;\n --color-background-border: #303335; // for UI borders\n --color-background-item: #444; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #3d94ff;\n --color-brand-content: #5ca5ff;\n --color-brand-visited: #b27aeb;\n\n // Highlighted text (search)\n --color-highlighted-background: #083563;\n\n // GUI Labels\n --color-guilabel-background: #08356380;\n --color-guilabel-border: #13395f80;\n\n // API documentation\n --color-api-keyword: var(--color-foreground-secondary);\n --color-highlight-on-target: #333300;\n\n --color-api-added: #3db854;\n --color-api-added-border: #267334;\n --color-api-changed: #09b0ce;\n --color-api-changed-border: #056d80;\n --color-api-deprecated: #b1a10b;\n --color-api-deprecated-border: #6e6407;\n --color-api-removed: #ff7575;\n --color-api-removed-border: #b03b3b;\n\n // Admonitions\n --color-admonition-background: #18181a;\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: #18181a;\n --color-card-marginals-background: var(--color-background-hover);\n}\n","// This file contains the styling for making the content throughout the page,\n// including fonts, paragraphs, headings and spacing among these elements.\n\nbody\n font-family: var(--font-stack)\npre,\ncode,\nkbd,\nsamp\n font-family: var(--font-stack--monospace)\n\n// Make fonts look slightly nicer.\nbody\n -webkit-font-smoothing: antialiased\n -moz-osx-font-smoothing: grayscale\n\n// Line height from Bootstrap 4.1\narticle\n line-height: 1.5\n\n//\n// Headings\n//\nh1,\nh2,\nh3,\nh4,\nh5,\nh6\n line-height: 1.25\n font-family: var(--font-stack--headings)\n font-weight: bold\n\n border-radius: 0.5rem\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n margin-left: -0.5rem\n margin-right: -0.5rem\n padding-left: 0.5rem\n padding-right: 0.5rem\n\n + p\n margin-top: 0\n\nh1\n font-size: 2.5em\n margin-top: 1.75rem\n margin-bottom: 1rem\nh2\n font-size: 2em\n margin-top: 1.75rem\nh3\n font-size: 1.5em\nh4\n font-size: 1.25em\nh5\n font-size: 1.125em\nh6\n font-size: 1em\n\nsmall\n opacity: 75%\n font-size: 80%\n\n// Paragraph\np\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n\n// Horizontal rules\nhr.docutils\n height: 1px\n padding: 0\n margin: 2rem 0\n background-color: var(--color-background-border)\n border: 0\n\n.centered\n text-align: center\n\n// Links\na\n text-decoration: underline\n\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n &:visited\n color: var(--color-link--visited)\n text-decoration-color: var(--color-link-underline--visited)\n &:hover\n color: var(--color-link--visited--hover)\n text-decoration-color: var(--color-link-underline--visited--hover)\n\n &:hover\n color: var(--color-link--hover)\n text-decoration-color: var(--color-link-underline--hover)\n &.muted-link\n color: inherit\n &:hover\n color: var(--color-link--hover)\n text-decoration-color: var(--color-link-underline--hover)\n &:visited\n color: var(--color-link--visited--hover)\n text-decoration-color: var(--color-link-underline--visited--hover)\n","// This file contains the styles for the overall layouting of the documentation\n// skeleton, including the responsive changes as well as sidebar toggles.\n//\n// This is implemented as a mobile-last design, which isn't ideal, but it is\n// reasonably good-enough and I got pretty tired by the time I'd finished this\n// to move the rules around to fix this. Shouldn't take more than 3-4 hours,\n// if you know what you're doing tho.\n\n// HACK: Not all browsers account for the scrollbar width in media queries.\n// This results in horizontal scrollbars in the breakpoint where we go\n// from displaying everything to hiding the ToC. We accomodate for this by\n// adding a bit of padding to the TOC drawer, disabling the horizontal\n// scrollbar and allowing the scrollbars to cover the padding.\n// https://www.456bereastreet.com/archive/201301/media_query_width_and_vertical_scrollbars/\n\n// HACK: Always having the scrollbar visible, prevents certain browsers from\n// causing the content to stutter horizontally between taller-than-viewport and\n// not-taller-than-viewport pages.\n\nhtml\n overflow-x: hidden\n overflow-y: scroll\n scroll-behavior: smooth\n\n.sidebar-scroll, .toc-scroll, article[role=main] *\n // Override Firefox scrollbar style\n scrollbar-width: thin\n scrollbar-color: var(--color-foreground-border) transparent\n\n // Override Chrome scrollbar styles\n &::-webkit-scrollbar\n width: 0.25rem\n height: 0.25rem\n &::-webkit-scrollbar-thumb\n background-color: var(--color-foreground-border)\n border-radius: 0.125rem\n\n//\n// Overalls\n//\nhtml,\nbody\n height: 100%\n color: var(--color-foreground-primary)\n background: var(--color-background-primary)\n\n.skip-to-content\n position: fixed\n padding: 1rem\n border-radius: 1rem\n left: 0.25rem\n top: 0.25rem\n z-index: 40\n background: var(--color-background-primary)\n color: var(--color-foreground-primary)\n\n transform: translateY(-200%)\n transition: transform 300ms ease-in-out\n\n &:focus-within\n transform: translateY(0%)\n\narticle\n color: var(--color-content-foreground)\n background: var(--color-content-background)\n overflow-wrap: break-word\n\n.page\n display: flex\n // fill the viewport for pages with little content.\n min-height: 100%\n\n.mobile-header\n width: 100%\n height: var(--header-height)\n background-color: var(--color-header-background)\n color: var(--color-header-text)\n border-bottom: 1px solid var(--color-header-border)\n\n // Looks like sub-script/super-script have this, and we need this to\n // be \"on top\" of those.\n z-index: 10\n\n // We don't show the header on large screens.\n display: none\n\n // Add shadow when scrolled\n &.scrolled\n border-bottom: none\n box-shadow: 0 0 0.2rem rgba(0, 0, 0, 0.1), 0 0.2rem 0.4rem rgba(0, 0, 0, 0.2)\n\n .header-center\n a\n color: var(--color-header-text)\n text-decoration: none\n\n.main\n display: flex\n flex: 1\n\n// Sidebar (left) also covers the entire left portion of screen.\n.sidebar-drawer\n box-sizing: border-box\n\n border-right: 1px solid var(--color-sidebar-background-border)\n background: var(--color-sidebar-background)\n\n display: flex\n justify-content: flex-end\n // These next two lines took me two days to figure out.\n width: calc((100% - #{$full-width}) / 2 + #{$sidebar-width})\n min-width: $sidebar-width\n\n// Scroll-along sidebars\n.sidebar-container,\n.toc-drawer\n box-sizing: border-box\n width: $sidebar-width\n\n.toc-drawer\n background: var(--color-toc-background)\n // See HACK described on top of this document\n padding-right: 1rem\n\n.sidebar-sticky,\n.toc-sticky\n position: sticky\n top: 0\n height: min(100%, 100vh)\n height: 100vh\n\n display: flex\n flex-direction: column\n\n.sidebar-scroll,\n.toc-scroll\n flex-grow: 1\n flex-shrink: 1\n\n overflow: auto\n scroll-behavior: smooth\n\n// Central items.\n.content\n padding: 0 $content-padding\n width: $content-width\n\n display: flex\n flex-direction: column\n justify-content: space-between\n\n.icon\n display: inline-block\n height: 1rem\n width: 1rem\n svg\n width: 100%\n height: 100%\n\n//\n// Accommodate announcement banner\n//\n.announcement\n background-color: var(--color-announcement-background)\n color: var(--color-announcement-text)\n\n height: var(--header-height)\n display: flex\n align-items: center\n overflow-x: auto\n & + .page\n min-height: calc(100% - var(--header-height))\n\n.announcement-content\n box-sizing: border-box\n padding: 0.5rem\n min-width: 100%\n white-space: nowrap\n text-align: center\n\n a\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-announcement-text)\n\n &:hover\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-link--hover)\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for theme\n////////////////////////////////////////////////////////////////////////////////\n.no-js .theme-toggle-container // don't show theme toggle if there's no JS\n display: none\n\n.theme-toggle-container\n display: flex\n\n.theme-toggle\n display: flex\n cursor: pointer\n border: none\n padding: 0\n background: transparent\n\n.theme-toggle svg\n height: 1.25rem\n width: 1.25rem\n color: var(--color-foreground-primary)\n display: none\n\n.theme-toggle-header\n display: flex\n align-items: center\n justify-content: center\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for elements\n////////////////////////////////////////////////////////////////////////////////\n.toc-overlay-icon, .nav-overlay-icon\n display: none\n cursor: pointer\n\n .icon\n color: var(--color-foreground-secondary)\n height: 1.5rem\n width: 1.5rem\n\n.toc-header-icon, .nav-overlay-icon\n // for when we set display: flex\n justify-content: center\n align-items: center\n\n.toc-content-icon\n height: 1.5rem\n width: 1.5rem\n\n.content-icon-container\n float: right\n display: flex\n margin-top: 1.5rem\n margin-left: 1rem\n margin-bottom: 1rem\n gap: 0.5rem\n\n .edit-this-page, .view-this-page\n svg\n color: inherit\n height: 1.25rem\n width: 1.25rem\n\n.sidebar-toggle\n position: absolute\n display: none\n//