From 044fa01aaa2e6ec559d5596141c074fe7ed9e12e Mon Sep 17 00:00:00 2001 From: Adam Bajger <469113@mail.muni.cz> Date: Fri, 9 Jan 2026 15:29:03 +0100 Subject: [PATCH 01/50] feat: try to migrate changes to RatioPath from RationAI masks --- pyproject.toml | 2 + ratiopath/masks/mask_builders/__init__.py | 12 + ratiopath/masks/mask_builders/mask_builder.py | 898 ++++++++++++++++++ ratiopath/masks/write_big_tiff.py | 58 ++ tests/conftest.py | 8 + tests/test_mask_builders.py | 413 ++++++++ 6 files changed, 1391 insertions(+) create mode 100644 ratiopath/masks/mask_builders/__init__.py create mode 100644 ratiopath/masks/mask_builders/mask_builder.py create mode 100644 ratiopath/masks/write_big_tiff.py create mode 100644 tests/conftest.py create mode 100644 tests/test_mask_builders.py diff --git a/pyproject.toml b/pyproject.toml index cd6cdc4..e739df9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,6 +27,8 @@ dependencies = [ "zarr>=3.1.1", "geopandas>=1.1.1", "rasterio>=1.4.3", + "jaxtyping", + ] [dependency-groups] diff --git a/ratiopath/masks/mask_builders/__init__.py b/ratiopath/masks/mask_builders/__init__.py new file mode 100644 index 0000000..864ce38 --- /dev/null +++ b/ratiopath/masks/mask_builders/__init__.py @@ -0,0 +1,12 @@ +from ratiopath.masks.mask_builders.mask_builder import ( + AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D, + AveragingScalarUniformTiledNumpyMaskBuilder, + MaxScalarUniformTiledNumpyMaskBuilder, +) + + +__all__ = [ + "AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D", + "AveragingScalarUniformTiledNumpyMaskBuilder", + "MaxScalarUniformTiledNumpyMaskBuilder", +] diff --git a/ratiopath/masks/mask_builders/mask_builder.py b/ratiopath/masks/mask_builders/mask_builder.py new file mode 100644 index 0000000..c8ce4e1 --- /dev/null +++ b/ratiopath/masks/mask_builders/mask_builder.py @@ -0,0 +1,898 @@ +import logging +import math +import tempfile +from abc import ABC, abstractmethod +from pathlib import Path +from typing import Any, TypeVar + +import numpy as np +import numpy.typing as npt +from jaxtyping import Int64, Shaped + + +logger = logging.getLogger(__name__) + + +AccumulatorType = np.ndarray + +SpatialDims = TypeVar("SpatialDims", bound=tuple[int, ...]) + + +def compute_acc_slices( + coords_batch: Int64[AccumulatorType, "N B"], + mask_tile_extents: Int64[AccumulatorType, " N"], +) -> list[list[slice]]: + """Compute slice objects for accumulator indexing. + + Args: + coords_batch: Array of shape (N, B) with top-left coordinates for B tiles in N dimensions. + mask_tile_extents: Array of shape (N,) with tile size in mask space for each dimension. + + Returns: + List of N lists, each containing B slice objects for indexing into accumulator. + """ + acc_ends = coords_batch + mask_tile_extents[:, np.newaxis] # shape (N, B) + + acc_slices_batch_per_dim = [] + for dimension in range(coords_batch.shape[0]): + acc_slices_batch_per_dim.append([ + slice(start, end) + for start, end + in zip(coords_batch[dimension], acc_ends[dimension], strict=True) + ]) + return acc_slices_batch_per_dim + + +class MaskBuilder(ABC): + """Abstract base class for building masks from tiled data. + + This base class establishes the interface for mask builders that assemble large masks + from batches of tiles. It uses a cooperative multiple inheritance pattern where: + - `update_batch()` is concrete and can be wrapped by mixins + - `update_batch_impl()` is abstract and must be implemented by concrete builders + - `allocate_accumulator()` is abstract and defines storage strategy (numpy array, memmap, etc.) + - `finalize()` is abstract and defines how to produce the final mask + + Subclasses can be composed using mixins to add features like edge clipping, + averaging, max aggregation, etc. Mixins should override `update_batch()` and call + `super().update_batch()` to maintain the cooperative chain. + """ + + accumulator: AccumulatorType + + def __init__( + self, mask_extents: Int64[AccumulatorType, " N"], channels: int, **kwargs: Any + ) -> None: + """Initialize the mask builder and allocate the accumulator. + + Args: + mask_extents: Array of shape (N,) specifying the spatial dimensions of the mask to build. + channels: Number of channels in the mask (e.g., 1 for grayscale, 3 for RGB). + **kwargs: Additional keyword arguments passed to `allocate_accumulator()`. + """ + super().__init__() + self.setup_memory(mask_extents, channels, **kwargs) + + @abstractmethod + def allocate_accumulator( + self, + mask_extents: Int64[AccumulatorType, " N"], + channels: int, + dtype: np.dtype, + **kwargs: Any, + ) -> AccumulatorType: + """Allocates the necessary accumulators for assembling the mask.""" + ... + + def setup_memory(self, mask_extents, channels, **kwargs) -> None: + """This method sets up memory structures needed for mask building. + + This methods can be overridden by mixins or concrete builders to set up any necessary memory structures. + + Some builders may require additional accumulators or data structures beyond the main accumulator. + Some mixins may require temporary files for memory-mapped storage. + All such setup should be defined in an overridden version of this method, which will be called by the base constructor + after the initialisation parameters are set by all classes/mixins in the MRO chain. + """ + self.accumulator = self.allocate_accumulator( + mask_extents, channels, **kwargs + ) + + def update_batch( + self, + data_batch: Shaped[AccumulatorType, "B C *SpatialDims"], + coords_batch: Shaped[AccumulatorType, " N B"], + ) -> None: + """Update the accumulator with a batch of tiles. + + This concrete method provides a stable entry point for mixins to wrap and extend + functionality. It delegates to `update_batch_impl()` which must be implemented by + concrete builders. Mixins can override this method and call `super().update_batch(...)` + to form a cooperative chain of processing steps (e.g., edge clipping, coordinate adjustment) + before the tiles reach the final implementation. + + This design allows unlimited stacking of mixins while avoiding issues with abstract methods + in the MRO chain. + + Args: + data_batch: Array of shape (B, C, *SpatialDims) containing B tiles with C channels. + coords_batch: Array of shape (N, B) containing the top-left corner coordinates + for each of the B tiles in N spatial dimensions. + """ + return self.update_batch_impl(data_batch=data_batch, coords_batch=coords_batch) + + @abstractmethod + def update_batch_impl( + self, + data_batch: Shaped[AccumulatorType, "B C *SpatialDims"], + coords_batch: Shaped[AccumulatorType, " N B"], + ) -> None: + """Core implementation for updating the accumulator with a batch of tiles. + + Concrete builders must implement this method with the actual logic for accumulating + tiles into the mask. Common strategies include: + - Addition (for averaging later) + - Maximum (for max pooling) + - Other aggregation operations + + This method is called by `update_batch()` after any mixin preprocessing has occurred. + + Args: + data_batch: Array of shape (B, C, *SpatialDims) containing B tiles with C channels. + coords_batch: Array of shape (N, B) containing the top-left corner coordinates + for each of the B tiles in N spatial dimensions. + """ + ... + + @abstractmethod + def finalize(self) -> tuple[AccumulatorType, ...] | AccumulatorType: + """Finalize the mask assembly and return the result. + + This method performs any necessary post-processing on the accumulator(s) and returns + the final mask. Common operations include: + - Averaging by overlap counts (for averaging builders) + - No-op (for max builders where the accumulator is already final) + - Other normalization or scaling operations + + Returns: + Tuple of arrays where the first element is always the finalized mask. + Additional elements may include auxiliary data like overlap counters. + """ + ... + + +class NumpyMemMapMaskBuilderAllocatorMixin(MaskBuilder): + """Mixin class to allocate accumulators as numpy memory-mapped files (memmaps). + + This mixin provides disk-backed storage for large masks that exceed available RAM. + Memory mapping allows the OS to manage paging between disk and memory transparently, + enabling processing of masks that would otherwise cause out-of-memory errors. + + **Temporary Files (default behavior when `filepath=None`):** + A temporary file is created and used as backing storage. The file is deleted when + the memmap is closed or garbage collected. Disk space is consumed during processing + but automatically reclaimed afterward. + + **Explicit Files (when `filepath` is provided):** + The memmap is backed by the specified file path, which persists after processing. + This is useful for caching results or processing masks too large for temporary storage. + If the file already exists, a FileExistsError is raised to prevent accidental data loss. + + This mixin uses NumPy's NPY format version 3.0 for compatibility with large arrays (>4GB). + """ + + def allocate_accumulator( + self, + mask_extents: Int64[AccumulatorType, " N"], + channels: int, + dtype: npt.DTypeLike = np.float32, + filepath: Path | None = None, + **kwargs: Any, + ) -> np.memmap: + if filepath is None: + with tempfile.NamedTemporaryFile() as temp_file: + return np.lib.format.open_memmap( + temp_file.name, + mode="w+", + shape=(channels, *mask_extents), + dtype=dtype, + version=(3, 0), + ) + else: + if filepath.exists(): + raise FileExistsError(f"Memmap filepath {filepath} already exists.") + return np.lib.format.open_memmap( + filepath, + mode="w+", + shape=(channels, *mask_extents), + dtype=dtype, + version=(3, 0), + ) + + +class NumpyArrayMaskBuilderAllocatorMixin(MaskBuilder): + """Mixin class to allocate accumulators as numpy arrays. + + This mixin implements the `allocate_accumulator` method to create + numpy arrays for the accumulator. + """ + + def allocate_accumulator( + self, + mask_extents: Int64[AccumulatorType, " N"], + channels: int, + dtype: npt.DTypeLike = np.float32, + **kwargs: Any, + ) -> np.ndarray: + return np.zeros((channels, *mask_extents), dtype=dtype) + + +class EdgeClippingMaskBuilderMixin(MaskBuilder): + """Mixin that clips edge pixels from tiles before accumulation. + + Edge clipping is useful for removing boundary artifacts from tiles, such as: + - Zero-padding introduced by neural networks + - Edge effects from convolution operations + - Border artifacts from image processing + + This mixin intercepts `update_batch()`, clips the specified number of pixels from tile edges, + adjusts the coordinates accordingly, and passes the clipped tiles to the next handler in the chain. + + **Important:** This mixin must appear BEFORE other mixins that implement `update_batch()` in the inheritance list (leftmost position) to ensure + its `update_batch()` method is called before others in the MRO chain. Keep this in mind when composing new classes. + + **Usage:** Subclasses or derived classes must provide `clip_start_indices` and `clip_end_indices` + during initialization to specify how many pixels to clip from the start (top/left) and + end (bottom/right) edges in each spatial dimension. + """ + + clip_start_indices: Int64[AccumulatorType, " N"] + clip_end_indices: Int64[AccumulatorType, " N"] + + def __init__( + self, + mask_extents: Int64[AccumulatorType, " N"], + channels: int, + clip_start_indices: Int64[AccumulatorType, " N"], + clip_end_indices: Int64[AccumulatorType, " N"], + **kwargs: Any, + ) -> None: + super().__init__(mask_extents, channels, **kwargs) + self.clip_start_indices = np.asarray(clip_start_indices, dtype=np.int64) + self.clip_end_indices = np.asarray(clip_end_indices, dtype=np.int64) + + def update_batch( + self, + data_batch: Shaped[AccumulatorType, "B C *SpatialDims"], + coords_batch: Shaped[AccumulatorType, "N B"], + ) -> None: + # Clip the edges of the input tiles + extents = np.asarray(data_batch.shape[2:], dtype=np.int64) # H, W, ... + slices = tuple( + slice(start, end) + for start, end in zip( + self.clip_start_indices, + extents - self.clip_end_indices, + strict=True, + ) + ) + adjusterd_coords_batch = coords_batch + self.clip_start_indices[:, np.newaxis] + super().update_batch( + data_batch=data_batch[..., *slices], # type: ignore[arg-type] + coords_batch=adjusterd_coords_batch, + ) + + +class EdgeClippingMaskBuilder2DMixin(EdgeClippingMaskBuilderMixin): + """2D-specific edge clipping mixin with convenient parameter formats. + + This specialized version of EdgeClippingMaskBuilderMixin provides intuitive parameter formats + for 2D masks. The `clip` parameter accepts three formats: + + 1. **Single integer:** `clip=5` → clips 5 pixels from all edges (top, bottom, left, right) + 2. **Tuple of 2 ints:** `clip=(5, 10)` → clips 5 pixels from top/bottom, 10 from left/right + 3. **Tuple of 4 ints:** `clip=(2, 3, 4, 5)` → clips 2 (top), 3 (bottom), 4 (left), 5 (right) + + **Important:** This mixin must appear FIRST in the inheritance list to ensure proper MRO. + + Example: + ```python + class MyBuilder( + EdgeClippingMaskBuilder2DMixin, # Must be first! + NumpyArrayMaskBuilderAllocatorMixin, + AveragingMaskBuilderMixin, + ): + pass + + + builder = MyBuilder(mask_extents=(1024, 1024), channels=3, clip=8) + ``` + """ + + def __init__( + self, + mask_extents: Int64[AccumulatorType, " N"], + channels: int, + clip: int | tuple[int, int] | tuple[int, int, int, int] = 0, + **kwargs: Any, + ) -> None: + if isinstance(clip, int): + clip_start_indices = clip_end_indices = (clip,) * len(mask_extents) + elif isinstance(clip, tuple) and len(clip) == 2: + clip_y, clip_x = clip + clip_start_indices = (clip_y, clip_x) + clip_end_indices = (clip_y, clip_x) + elif isinstance(clip, tuple) and len(clip) == 4: + clip_top, clip_bottom, clip_left, clip_right = clip + clip_start_indices = (clip_top, clip_left) + clip_end_indices = (clip_bottom, clip_right) + else: + raise ValueError( + "clip must be an int, a tuple of two ints, or a tuple of four ints." + ) + super().__init__( + mask_extents=mask_extents, + channels=channels, + clip_start_indices=np.asarray(clip_start_indices, dtype=np.int64), + clip_end_indices=np.asarray(clip_end_indices, dtype=np.int64), + **kwargs, + ) + + +class AveragingMaskBuilderMixin(MaskBuilder): + """Mixin that implements averaging aggregation for overlapping tiles. + + This mixin accumulates tiles by addition and tracks the overlap count at each pixel. + During finalization, the accumulated values are divided by the overlap count to compute + the average value at each position. This is useful for: + - Smoothly blending overlapping tile predictions + - Reducing edge artifacts in sliding window processing + - Computing ensemble averages from multiple passes + + The mixin allocates an additional `overlap_counter` accumulator with shape (1, *SpatialDims) + to track how many tiles contributed to each pixel position. + """ + + overlap_counter: AccumulatorType + + def __init__( + self, mask_extents: Int64[AccumulatorType, " N"], channels: int, **kwargs: Any + ) -> None: + super().__init__(mask_extents, channels, **kwargs) + + def setup_memory(self, mask_extents, channels, **kwargs) -> None: + # Perform base allocation then allocate the overlap counter. + super().setup_memory(mask_extents, channels, **kwargs) + self.overlap_counter = self.allocate_accumulator( + mask_extents=mask_extents, channels=1, **kwargs + ) + + def update_batch_impl( + self, + data_batch: Shaped[AccumulatorType, "B C *SpatialDims"], + coords_batch: Shaped[AccumulatorType, "N B"], + ) -> None: + mask_tile_extents = np.asarray(data_batch.shape[2:]) # H, W, ... + acc_slices_all_dims = compute_acc_slices( + coords_batch=coords_batch, + mask_tile_extents=mask_tile_extents, + ) + for acc_slices, data in zip( + zip(*acc_slices_all_dims, strict=True), + data_batch, + strict=True, + ): + self.accumulator[ + :, + *acc_slices, + ] += data + self.overlap_counter[ + :, + *acc_slices, + ] += 1 + + def finalize(self) -> tuple[AccumulatorType, ...]: + # Average the accumulated mask by the overlap counts + self.accumulator /= self.overlap_counter.clip(min=1) + return self.accumulator, self.overlap_counter + + +class MaxMaskBuilderMixin(MaskBuilder): + """Mixin that implements maximum aggregation for overlapping tiles. + + This mixin keeps only the maximum value at each pixel position when tiles overlap. + No additional storage is required, and finalization is a no-op since the accumulator + already contains the final max values. This is useful for: + - Maximum intensity projection + - Keeping the highest confidence prediction across overlapping tiles + - Peak detection across multiple scales + """ + + def update_batch_impl( + self, + data_batch: Shaped[AccumulatorType, "B C *SpatialDims"], + coords_batch: Shaped[AccumulatorType, "N B"], + ) -> None: + mask_tile_extents = np.asarray(data_batch.shape[2:], dtype=np.int64) # H, W, ... + acc_slices_all_dims = compute_acc_slices( + coords_batch=coords_batch, + mask_tile_extents=mask_tile_extents, # H, W, ... + ) + for acc_slices, data in zip( + zip(*acc_slices_all_dims, strict=True), + data_batch, + strict=True, + ): + self.accumulator[ + :, + *acc_slices, + ] = np.maximum( + self.accumulator[ + :, + *acc_slices, + ], + data, + ) + + + def finalize(self) -> tuple[AccumulatorType]: + return (self.accumulator,) + + +class AutoScalingConstantStrideMixin(MaskBuilder): + """Mixin that automatically scales coordinates to match input/output resolution differences. + + Generally, neural networks such as CNNs and ViTs produce outputs at a different spatial resolution + than their inputs due to pooling, striding, or patching operations. Assembling tile outputs of such models + should happen on the output resolution grid, but input coordinates are often provided in the input resolution space. + + Since we want to retain the exact spatial correspondence between input tiles and output mask positions to be able to + align predictions with the original input, we need to solve the resolution mismatch. + + Naively, one could rescale each mask tile using some kind of interpolation to match the input tile size and then assemble + on the input resolution grid. However, this approach is computationally expensive and can introduce interpolation artifacts. + Instead, this mixin assumes that the scaling between input and output resolutions is constant and can be expressed + as a ratio of the input tile extent to the output tile extent in each dimension. It must also hold that the input tile stride + multiplied by the output tile extent is divisible by the input tile extent to ensure integer strides in the mask coordinate space. + + This mixin addresses this by calculating the scale factors from the input tile extents and output mask tile extents, + adjusting the input coordinates accordingly before passing them to the next handler in the chain. + This mixin also automatically computes the built mask spatial dimensions based on the tiled input total + extent and the computed scale factors. + + Furthermore, this mixin readjusts the mask extent to cover for potential partial tiles at the edges. + The safe extent is computed also for the source, so that the resulting mask can be properly rescaled back to the input resolution + after assembly and finalization and then cropped to the original source extents to match exactly. + + This mixin works cooperatively with other mixins (like ScalarUniformTiledMaskBuilder) by computing + the output space dimensions and passing them via kwargs, but keep in mind that those mixins must appear + after this one in the inheritance list to ensure proper MRO. + + Args: + source_extents: Spatial dimensions of the entire input/source from which tiles are drawn. + channels: Number of channels in the output. + source_tile_extents: Spatial dimensions of the input/source tiles. + mask_tile_extents: Spatial dimensions of the output tiles/mask. + source_tile_strides: Stride between input/source tiles (optional, defaults to source_tile_extents). + """ + + # source_extents: Int64[AccumulatorType, " N"] + overflow_buffered_source_extents: Int64[AccumulatorType, " N"] + mask_extents: Int64[AccumulatorType, " N"] + mask_tile_extents: Int64[AccumulatorType, " N"] + source_tile_extents: Int64[AccumulatorType, " N"] + + def __init__( + self, + source_extents: Int64[AccumulatorType, " N"], + source_tile_extents: Int64[AccumulatorType, " N"], + source_tile_strides: Int64[AccumulatorType, " N"], + mask_tile_extents: Int64[AccumulatorType, " N"], + channels: int, + **kwargs: Any, + ) -> None: + # self.source_extents = source_extents + self.source_tile_extents = source_tile_extents + self.mask_tile_extents = mask_tile_extents + + multiplied_ = (source_tile_strides * self.mask_tile_extents) + # Ensure source_tile_strides * self.mask_tile_extents is divisible by self.source_tile_extents to avoid fractional strides + if not np.all(multiplied_ % self.source_tile_extents == 0): + raise ValueError( + f"source_tile_strides * mask_tile_extents must be divisible by source_tile_extents in all dimensions," + f" but {source_tile_strides}*{self.mask_tile_extents}={multiplied_}, which is not divisible by {self.source_tile_extents}." + ) + adjusted_mask_tile_strides = multiplied_ // self.source_tile_extents + + # adjusted_mask_extents = (source_extents // self.source_tile_extents) * self.mask_tile_extents + total_strides = (source_extents - source_tile_extents) / source_tile_strides + total_strides = np.ceil(total_strides).astype(np.int64) + # without the initial tile step, including partial tile at the edge + self.overflow_buffered_source_extents = (total_strides * source_tile_strides) + source_tile_extents + overflow_buffered_mask_extents = (total_strides * adjusted_mask_tile_strides) + self.mask_tile_extents + + + # Call next in MRO with computed parameters + super().__init__( + mask_extents=overflow_buffered_mask_extents, + channels=channels, + mask_tile_extents=self.mask_tile_extents, + mask_tile_strides=adjusted_mask_tile_strides, + **kwargs, + ) + + def update_batch( + self, + data_batch: Shaped[AccumulatorType, "B C *SpatialDims"], + coords_batch: Shaped[AccumulatorType, "N B"], + ) -> None: + adjusted_coords_batch = ((coords_batch * self.mask_tile_extents[:, np.newaxis]) // self.source_tile_extents[:, np.newaxis]) + super().update_batch( + data_batch=data_batch, coords_batch=adjusted_coords_batch + ) + +class ScalarUniformTiledMaskBuilder(MaskBuilder): + """Mask builder that expands scalar/vector values into uniform tiles. + + This builder is designed for scenarios where each tile's content is uniform (constant value). + Instead of storing full tiles, it compresses the representation by: + 1. Computing the GCD of tile extent and stride in each dimension + 2. Dividing the mask into a coarser grid with GCD granularity + 3. Expanding scalar values into the compressed grid + + This reduces memory usage and computation when tiles have uniform content, such as: + - Per-tile classification scores + - Per-tile feature vectors + + The builder automatically handles the coordinate transformation between the original + and compressed grids. + + This class can work cooperatively with other mixins (like AutoScalingConstantStrideMixin) + that compute `mask_extents`. If `mask_extents` is already provided via kwargs (from a parent + mixin), it will be used; otherwise it must be provided as a direct parameter. + + Args: + mask_extents: Spatial dimensions of the mask to build (may be precomputed by a mixin). + channels: Number of channels (features) in the scalar values. + mask_tile_extents: Size of tiles in each dimension in mask space at the original resolution. + mask_tile_strides: Stride between tile positions in mask space for each dimension. + """ + + compression_factors: Int64[AccumulatorType, " N"] + adjusted_tile_extents: Int64[AccumulatorType, " N"] + + def __init__( + self, + mask_extents: Int64[AccumulatorType, " N"], + channels: int, + mask_tile_extents: Int64[AccumulatorType, " N"], + mask_tile_strides: Int64[AccumulatorType, " N"], + **kwargs: Any, + ) -> None: + self.compression_factors = np.gcd(mask_tile_strides, mask_tile_extents) + adjusted_mask_extents = mask_extents // self.compression_factors + self.adjusted_tile_extents = mask_tile_extents // self.compression_factors + super().__init__( + mask_extents=adjusted_mask_extents, channels=channels, **kwargs + ) + + def update_batch( + self, + data_batch: Shaped[AccumulatorType, "B C"], + coords_batch: Shaped[AccumulatorType, "N B"], + ) -> None: + """For each scalar/vector in the batch, repeat it in each dimension to form a tile, then update the mask with the tile.""" + adjusted_tiles = np.zeros((*data_batch.shape, *self.adjusted_tile_extents)) + adjusted_tiles += data_batch[ + ..., *[np.newaxis] * len(self.adjusted_tile_extents) + ] + adjusted_coordinates = coords_batch // self.compression_factors[:, np.newaxis] + super().update_batch(adjusted_tiles, coords_batch=adjusted_coordinates) + + +class AutoScalingScalarUniformValueConstantStrideMaskBuilder( + NumpyArrayMaskBuilderAllocatorMixin, + AutoScalingConstantStrideMixin, + ScalarUniformTiledMaskBuilder, + AveragingMaskBuilderMixin +): + """Mask builder combining auto-scaling with scalar uniform tiling. + + This builder automatically handles the complete pipeline for building masks from scalar/vector + values when the network output resolution differs from input resolution: + 1. Computes output mask dimensions from source image and tile sizes + 2. Scales coordinates from input space to output space + 3. Compresses representation using GCD of tile extent/stride + 4. Expands scalar values into uniform tiles + + Typical use case: Neural network produces scalar predictions per input tile, and you want + to create a coverage mask at a different resolution. + + Args: + source_extents: Spatial dimensions of the source image (e.g., (10000, 10000)). + channels: Number of channels in scalar predictions. + source_tile_extents: Size of input tiles extracted from source (e.g., (512, 512)). + source_tile_strides: Stride between input tiles in source space (e.g., (256, 256)). + mask_tile_extents: Size you want each scalar to represent in output mask (e.g., (64, 64)). + + Example: + ```python + # Source image: 10000x10000 + # Input tiles: 512x512 at stride 256x256 + # Want each scalar to cover 64x64 in output + builder = AutoScalingScalarUniformTiledMaskBuilder( + source_extents=(10000, 10000), + channels=3, + source_tile_extents=(512, 512), + source_tile_strides=(256, 256), + mask_tile_extents=(64, 64), + ) + # Output mask will be: (10000/512)*64 x (10000/512)*64 = 1250x1250 + # Coordinates should be provided in input space (0, 256, 512, ...) + ``` + """ + + def __init__( + self, + source_extents: Int64[AccumulatorType, " N"], + source_tile_extents: Int64[AccumulatorType, " N"], + source_tile_strides: Int64[AccumulatorType, " N"], + mask_tile_extents: Int64[AccumulatorType, " N"], + channels: int, + **kwargs: Any, + ) -> None: + super().__init__( + source_extents=source_extents, + source_tile_extents=source_tile_extents, + source_tile_strides=source_tile_strides, + mask_tile_extents=mask_tile_extents, + channels=channels, + **kwargs, + ) + + +class AveragingScalarUniformTiledNumpyMaskBuilder( + NumpyArrayMaskBuilderAllocatorMixin, + ScalarUniformTiledMaskBuilder, + AveragingMaskBuilderMixin, +): + """Averaging scalar uniform tiled mask builder using numpy arrays as accumulators. + + The tiles are uniformly expanded from scalar/vector values before being assembled into the mask. + The overlaps are averaged during finalization. + The tiles are expected to have constant extents and strides. + These can vary across dimensions (e.g. different width and height), but must not change across tiles. + See `ScalarUniformTiledMaskBuilder` for details. + + Args: + mask_extents (tuple[int, int]): Spatial dimensions of the mask to build. + channels (int): Number of channels in the scalar values to be assembled into the mask. + mask_tile_extents (tuple[int, int]): Extents of the tiles in mask space for each spatial dimension (height, width). + mask_tile_strides (tuple[int, int]): Strides of the tiles in mask space for each spatial dimension (height, width). + + Example: + ```python + import openslide + from rationai.masks.mask_builders import ( + AveragingScalarUniformTiledNumpyMaskBuilder, + ) + import matplotlib.pyplot as plt + + LEVEL = 3 + tile_extents = (512, 512) + tile_strides = (256, 256) + slide = openslide.OpenSlide("path/to/slide.mrxs") + slide_extent_x, slide_extent_y = slide.dimensions[LEVEL] + vgg16_model = load_rationai_vgg16_model(...) # load your pretrained model here + mask_builder = AveragingScalarUniformTiledNumpyMaskBuilder( + mask_extents=(slide_extent_y, slide_extent_x), + channels=1, # for binary classification + mask_tile_extents=tile_extents, + mask_tile_strides=tile_strides, + ) + for tiles, xs, ys in generate_tiles_from_slide( + slide, LEVEL, tile_extents, tile_strides, batch_size=32 + ): + # tiles has shape (B, C, H, W) + features = vgg16_model.predict(tiles) # features has shape (B, channels) + mask_builder.update_batch(features, xs, ys) + assembled_mask, overlap = mask_builder.finalize() + plt.imshow(assembled_mask[0], cmap="gray", interpolation="nearest") + plt.axis("off") + plt.show() + ``` + """ + + def __init__( + self, + mask_extents: Int64[AccumulatorType, " N"], + channels: int, + mask_tile_extents: Int64[AccumulatorType, " N"], + mask_tile_strides: Int64[AccumulatorType, " N"], + **kwargs: Any, + ) -> None: + super().__init__( + mask_extents=mask_extents, + channels=channels, + mask_tile_extents=mask_tile_extents, + mask_tile_strides=mask_tile_strides, + **kwargs, + ) + + +class MaxScalarUniformTiledNumpyMaskBuilder( + NumpyArrayMaskBuilderAllocatorMixin, + ScalarUniformTiledMaskBuilder, + MaxMaskBuilderMixin, +): + """Max scalar uniform tiled mask builder using numpy arrays as accumulators. + + The tiles are uniformly expanded from scalar/vector values before being assembled into the mask. + The maximum value is taken at each pixel position during updates, no finalisation is required. + The tiles are expected to have constant extents and strides. + These can vary across dimensions (e.g. different width and height), but must not change across tiles. + See `ScalarUniformTiledMaskBuilder` for details. + + Args: + mask_extents (tuple[int, int]): Spatial dimensions of the mask to build. + channels (int): Number of channels in the scalar values to be assembled into the mask. + mask_tile_extents (tuple[int, int]): Extents of the tiles in mask space for each spatial dimension (height, width). + mask_tile_strides (tuple[int, int]): Strides of the tiles in mask space for each spatial dimension (height, width). + + Example: + ```python + import openslide + from rationai.masks.mask_builders import MaxScalarUniformTiledNumpyMaskBuilder + import matplotlib.pyplot as plt + + LEVEL = 3 + tile_extents = (512, 512) + tile_strides = (256, 256) + slide = openslide.OpenSlide("path/to/slide.mrxs") + slide_extent_x, slide_extent_y = slide.dimensions[LEVEL] + vgg16_model = load_rationai_vgg16_model(...) # load your pretrained model here + mask_builder = MaxScalarUniformTiledNumpyMaskBuilder( + mask_extents=(slide_extent_y, slide_extent_x), + channels=1, # for binary classification + mask_tile_extents=tile_extents, + mask_tile_strides=tile_strides, + ) + for tiles, xs, ys in generate_tiles_from_slide( + slide, LEVEL, tile_extents, tile_strides, batch_size=32 + ): + # tiles has shape (B, C, H, W) + features = vgg16_model.predict(tiles) # features has shape (B, channels) + mask_builder.update_batch(features, xs, ys) + (assembled_mask,) = mask_builder.finalize() + plt.imshow(assembled_mask[0], cmap="gray", interpolation="nearest") + plt.axis("off") + plt.show() + ``` + + """ + + def __init__( + self, + mask_extents: Int64[AccumulatorType, " N"], + channels: int, + mask_tile_extents: Int64[AccumulatorType, " N"], + mask_tile_strides: Int64[AccumulatorType, " N"], + **kwargs: Any, + ) -> None: + super().__init__( + mask_extents=mask_extents, + channels=channels, + mask_tile_extents=mask_tile_extents, + mask_tile_strides=mask_tile_strides, + **kwargs, + ) + + +class AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D( + NumpyMemMapMaskBuilderAllocatorMixin, + AutoScalingConstantStrideMixin, + EdgeClippingMaskBuilder2DMixin, + AveragingMaskBuilderMixin, +): + """Averaging mask builder with edge clipping using numpy memmaps as accumulators. + + This concrete builder combines three features: + 1. **Memory-mapped storage:** Handles large masks that exceed RAM capacity + 2. **Edge clipping:** Removes boundary artifacts from tiles + 3. **Averaging:** Smoothly blends overlapping tiles by computing pixel-wise averages + + The builder allocates two memmaps: + - Main accumulator for tile data + - Overlap counter (auto-suffixed with `.overlaps` if filepath is provided) + + Args: + mask_extents: Spatial dimensions of the full-resolution mask (height, width). + channels: Number of channels in the tiles/mask. + clip: Edge clipping specification. Accepts: + - int: same clipping on all edges + - (clip_y, clip_x): same for top/bottom and left/right + - (clip_top, clip_bottom, clip_left, clip_right): individual edge control + filepath: Optional path for the main accumulator memmap. If None, uses temporary file. + The overlap counter will use the same path with `.overlaps` suffix inserted before extension. + + Example: + ```python + import openslide + from rationai.masks.mask_builders import AveragingClippingNumpyMemMapMaskBuilder + import matplotlib.pyplot as plt + + LEVEL = 3 + tile_extents = (512, 512) + tile_strides = (256, 256) + slide = openslide.OpenSlide("path/to/slide.mrxs") + slide_extent_x, slide_extent_y = slide.dimensions[LEVEL] + vgg16_model = load_rationai_vgg16_model(...) # load your pretrained model here + mask_builder = AveragingClippingNumpyMemMapMaskBuilder( + mask_extents=(slide_extent_y, slide_extent_x), + channels=3, # for RGB masks + clip_top=4, + clip_bottom=4, + clip_left=4, + clip_right=4, + ) + for tiles, xs, ys in generate_tiles_from_slide( + slide, LEVEL, tile_extents, tile_strides, batch_size=32 + ): + # tiles has shape (B, C, H, W) + features = vgg16_model.predict(tiles) # features has shape (B, channels) + mask_builder.update_batch(features, xs, ys) + (assembled_mask,) = mask_builder.finalize() + plt.imshow(assembled_mask[0], cmap="gray", interpolation="nearest") + plt.axis("off") + plt.show() + ``` + + """ + + def __init__( + self, + source_extents: Int64[AccumulatorType, " N"], + source_tile_extents: Int64[AccumulatorType, " N"], + source_tile_strides: Int64[AccumulatorType, " N"], + mask_tile_extents: Int64[AccumulatorType, " N"], + channels: int, + clip: int | tuple[int, int] | tuple[int, int, int, int], + accumulator_filepath: Path | None = None, + overlap_counter_filepath: Path | None = None, + ) -> None: + super().__init__( + source_extents=source_extents, + source_tile_extents=source_tile_extents, + source_tile_strides=source_tile_strides, + mask_tile_extents=mask_tile_extents, + channels=channels, + clip=clip, + accumulator_filepath=accumulator_filepath, + overlap_counter_filepath=overlap_counter_filepath, + ) + + def setup_memory(self, mask_extents, channels, accumulator_filepath=None, overlap_counter_filepath=None, **kwargs) -> None: + self.accumulator = self.allocate_accumulator( + mask_extents=mask_extents, channels=channels, filepath=accumulator_filepath + ) + if overlap_counter_filepath is not None: + counter_filepath = overlap_counter_filepath + elif accumulator_filepath is not None: + suffix = accumulator_filepath.suffix + counter_filepath = accumulator_filepath.with_suffix(f".overlaps{suffix}") + else: + counter_filepath = None + self.overlap_counter = self.allocate_accumulator( + mask_extents=mask_extents, channels=1, filepath=counter_filepath + ) + + def get_vips_scale_factors(self) -> tuple[float, float]: + """Get the scaling factors to convert the built mask back to the original source resolution. + + The ideas is to obtain coefficients for the pyvips.affine() function to rescale the assembled mask + back to the original source resolution after assembly and finalization. + Returns: + tuple[float, float]: Scaling factors for height and width dimensions. + """ + scale_factors = self.overflow_buffered_source_extents / self.accumulator.shape[1:] # H, W + return tuple(scale_factors) # TODO: add tests for this method \ No newline at end of file diff --git a/ratiopath/masks/write_big_tiff.py b/ratiopath/masks/write_big_tiff.py new file mode 100644 index 0000000..c68ef3d --- /dev/null +++ b/ratiopath/masks/write_big_tiff.py @@ -0,0 +1,58 @@ +import warnings +from pathlib import Path + +import pyvips + + +def write_big_tiff( + image: pyvips.Image, + path: Path, + mpp_x: float, + mpp_y: float, + tile_width: int = 512, + tile_height: int = 512, +) -> None: + """Saves the image as a BigTIFF file using pyvips with predefined settings. + + This function exports an image in BigTIFF format, which is ideal for applications + that require multi-resolution pyramid TIFF files, such as xOpat. It leverages + [`pyvips.Image.tiffsave`](https://libvips.github.io/pyvips/vimage.html#pyvips.Image.tiffsave) + to apply specific parameters for efficient storage and optimal image tiling. + + The image is saved with the following fixed settings: + - BigTIFF format enabled (`bigtiff=True`). + - DEFLATE compression applied for efficient storage. + - Image is tiled with a tile size of tile_width x tile_height. + - Multi-level pyramid structure is used (`pyramid=True`), making the image suitable + for fast zooming in viewers. + + Args: + image: A pyvips Image object representing the input image. + path: The file path where the BigTIFF mask will be saved. + mpp_x: The horizontal resolution of the image in µm/pixel. + mpp_y: The vertical resolution of the image in µm/pixel. + tile_width: The width of each tile in pixels. Default is 512. + Must match with other masks used in xOpath. + tile_height: The height of each tile in pixels. Default is 512. + Must match with other masks used in xOpath. + """ + if mpp_x > 100 or mpp_y > 100: + # This warning is necessary because we change the units of mpp_x and mpp_y + warnings.warn( + "Make sure the resolution is in µm/pixel not in pixels/mm.", stacklevel=1 + ) + + xres = 1000 / mpp_x + yres = 1000 / mpp_y + + image.tiffsave( + path, + bigtiff=True, + compression=pyvips.enums.ForeignTiffCompression.DEFLATE, + tile=True, + tile_width=tile_width, + tile_height=tile_height, + xres=xres, + yres=yres, + pyramid=True, + ) diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..d76f034 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,8 @@ +import numpy as np +import pytest + + +@pytest.fixture(autouse=True) +def reset_random_seed(): + """Reset random seed before each test for deterministic behavior.""" + np.random.seed(42) diff --git a/tests/test_mask_builders.py b/tests/test_mask_builders.py new file mode 100644 index 0000000..15083bd --- /dev/null +++ b/tests/test_mask_builders.py @@ -0,0 +1,413 @@ +import tempfile +from pathlib import Path + +import numpy as np +import pytest + +from ratiopath.masks.mask_builders.mask_builder import ( + AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D, + AveragingScalarUniformTiledNumpyMaskBuilder, + AutoScalingScalarUniformValueConstantStrideMaskBuilder, + MaxScalarUniformTiledNumpyMaskBuilder, +) + + +@pytest.mark.parametrize("mask_extents", [(16, 16), (32, 64)]) +@pytest.mark.parametrize("channels", [1, 3, 8]) +@pytest.mark.parametrize("mask_tile_extents", [(4, 4), (4, 8), (8, 4), (3, 6), (9, 6)]) +@pytest.mark.parametrize("mask_tile_strides", [(2, 2), (3, 3), (3, 2), (2, 3)]) +def test_scalar_uniform_averaging_2d( + mask_extents, channels, mask_tile_extents, mask_tile_strides +): + mask_extents = np.asarray(mask_extents) + mask_tile_extents = np.asarray(mask_tile_extents) + mask_tile_strides = np.asarray(mask_tile_strides) + + batch_size = 4 + num_batches = 8 + + gcds = np.gcd(mask_tile_extents, mask_tile_strides) + adjusted_tile_extents = mask_tile_extents // gcds + + test_mask_builder = AveragingScalarUniformTiledNumpyMaskBuilder( + mask_extents=mask_extents, + channels=channels, + mask_tile_extents=mask_tile_extents, + mask_tile_strides=mask_tile_strides, + ) + + # create scalar batch (B, C) + example = np.ones((batch_size, channels), dtype=np.float32) + + for i in range(num_batches): + coords_batch = np.random.rand(len(mask_extents), batch_size) + coords_batch *= (np.array(mask_extents) - np.array(mask_tile_extents))[:, np.newaxis] + # force alignment to gcds + coords_batch = (coords_batch.astype(np.int64) // gcds[:, np.newaxis]) * gcds[ + :, np.newaxis + ] + + test_mask_builder.update_batch(example, coords_batch) + + assert np.isclose( + test_mask_builder.accumulator.sum(), + (i + 1) * batch_size * channels * np.prod(adjusted_tile_extents), + atol=1e-6, + ), ( + f"Accumulator sum after batch {i + 1} should be {(i + 1) * batch_size * channels * np.prod(adjusted_tile_extents)}, is {test_mask_builder.accumulator.sum()}" + ) + + assembled, overlap = test_mask_builder.finalize() + # assembled has shape (channels, H_adj, W_adj) + assert assembled.shape[0] == channels, ( + "Assembled mask should have correct number of channels" + ) + assert overlap.shape[0] == 1, "Overlap accumulator should have single channel" + assert np.isclose( + overlap.sum(), + num_batches * batch_size * np.prod(adjusted_tile_extents), + atol=1e-6, + ), ( + f"Overlap sum should be {num_batches * batch_size * np.prod(adjusted_tile_extents)}, is {overlap.sum()}" + ) + assert np.isclose(assembled.max(), 1.0, atol=1e-6), ( + f"Assembled max should be close to 1.0, is {assembled.max()}" + ) + + +@pytest.mark.parametrize("mask_extents", [(16, 16), (32, 64), (400, 35)]) +@pytest.mark.parametrize("channels", [1, 3, 8]) +@pytest.mark.parametrize("mask_tile_extents", [(4, 4), (4, 8), (8, 4), (3, 6), (9, 6)]) +@pytest.mark.parametrize("mask_tile_strides", [(2, 2), (3, 3), (3, 2), (2, 3)]) +def test_scalar_uniform_max_2d(mask_extents, channels, mask_tile_extents, mask_tile_strides): + + mask_extents = np.asarray(mask_extents) + mask_tile_extents = np.asarray(mask_tile_extents) + mask_tile_strides = np.asarray(mask_tile_strides) + + batch_size = 4 + num_batches = 10 + + gcds = np.gcd(mask_tile_extents, mask_tile_strides) + adjusted_tile_extents = mask_tile_extents // gcds + min_batch_increment = np.prod(adjusted_tile_extents) * channels + + test_mask_builder = MaxScalarUniformTiledNumpyMaskBuilder( + mask_extents=mask_extents, + channels=channels, + mask_tile_extents=mask_tile_extents, + mask_tile_strides=mask_tile_strides, + ) + + for i in range(num_batches): + coordinates_batch = np.random.rand(len(mask_extents), batch_size) + coordinates_batch *= (np.array(mask_extents) - np.array(mask_tile_extents))[ + :, np.newaxis + ] + # force alignment to gcds + coordinates_batch = ( + coordinates_batch.astype(np.int64) // gcds[:, np.newaxis] + ) * gcds[:, np.newaxis] + + for coordinate in zip(*coordinates_batch, strict=True): + for dim, coord in enumerate(coordinate): + assert 0 <= coord <= mask_extents[dim] - mask_tile_extents[dim], ( + f"Coordinate {coord} in dimension {dim} is out of bounds for mask dim {mask_extents[dim]} and tile extent {mask_tile_extents[dim]}" + ) + value = float(i + 1) + data = np.full((batch_size, channels), value, dtype=np.float32) + test_mask_builder.update_batch(data, coordinates_batch) + assert test_mask_builder.accumulator.max() == value, ( + f"Max value in accumulator after batch {i + 1} should be {value}, is {test_mask_builder.accumulator.max()}" + ) + assert ( + test_mask_builder.accumulator == float(i + 1) + ).sum() >= min_batch_increment, ( + f"Number of maxed pixels with value {float(i + 1)} after batch {i + 1} should be greater than {min_batch_increment}, is {(test_mask_builder.accumulator == float(i + 1)).sum()}" + ) + nonfinal_acc = test_mask_builder.accumulator.copy() + (final_acc,) = test_mask_builder.finalize() + # final_acc should contain the maximum value (num_batches) + assert (final_acc == nonfinal_acc).all(), ( + "Finalized accumulator should be equal to non-finalized accumulator for max aggregation" + ) + + +@pytest.mark.parametrize("clip", [0, 1, 3]) +@pytest.mark.parametrize("tile_extents", [(8, 8), (12, 9)]) +@pytest.mark.parametrize("channels", [1, 5]) +@pytest.mark.parametrize("mask_extents", [(32, 64), (64, 32), (100, 101)]) +@pytest.mark.parametrize("acc_filepath", [None, "test_heatamp.npy"]) +@pytest.mark.parametrize("overlap_counter_filepath", [None, "test_overlap.counter.npy"]) +def test_edge_clipping_heatmap_assembler( + clip, tile_extents, channels, mask_extents, acc_filepath, overlap_counter_filepath, tmp_path +): + # no mismatch in source and mask extents here + mask_extents = np.asarray(mask_extents) + tile_extents = np.asarray(tile_extents) + tile_strides = np.asarray((4, 4), dtype=np.int64) + + total_strides = np.ceil((mask_extents - tile_extents) / tile_strides).astype(np.int64) + expected_mask_extents = total_strides * tile_strides + tile_extents + + num_batches = 4 + batch_size = 8 + + np.random.seed(0) + if acc_filepath is not None: + tmp_path.mkdir(parents=True, exist_ok=True) + acc_filepath = Path((tmp_path / acc_filepath).as_posix()) + if overlap_counter_filepath is not None: + tmp_path.mkdir(parents=True, exist_ok=True) + overlap_counter_filepath = Path( + (tmp_path / overlap_counter_filepath).as_posix() + ) + + assembler = AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D( + source_extents=mask_extents, + source_tile_extents=tile_extents, + source_tile_strides=tile_strides, + mask_tile_extents=tile_extents, + channels=channels, + clip=clip, + accumulator_filepath=acc_filepath, + overlap_counter_filepath=overlap_counter_filepath, + ) + # create dummy data + example_tile_batch = np.ones( + (batch_size, channels, *tile_extents), dtype=np.float32 + ) + + increment = example_tile_batch[ + ..., clip : tile_extents[0] - clip, clip : tile_extents[1] - clip + ].sum() + assert increment == batch_size * channels * (tile_extents[0] - 2 * clip) * ( + tile_extents[1] - 2 * clip + ), ( + f"Checksum mismatch in example tile batch: Should be {batch_size * channels * (tile_extents[0] - 2 * clip) * (tile_extents[1] - 2 * clip)}, is {example_tile_batch[..., clip : tile_extents[0] - clip, clip : tile_extents[1] - clip].sum()}" + ) + + # add the tiles randomly to cover the heatmap + for i in range(num_batches): + coords_batch = np.random.rand(len(mask_extents), batch_size) + coords_batch *= ( + np.asarray(mask_extents) - (np.asarray(tile_extents) - clip) + )[:, np.newaxis] + coords_batch = coords_batch.astype(np.int64) + assembler.update_batch(example_tile_batch, coords_batch) + # plt.imshow((assembler.accumulator.transpose(1, 2, 0)*32).astype(np.uint8)) + # plt.show() + assert assembler.accumulator.sum() == (i + 1) * increment, ( + f"Checksum mismatch in heatmap accumulator after update {i + 1}. Is {assembler.accumulator.sum()}, but expected {(i + 1) * increment}" + ) + + assert ( + assembler.accumulator.sum() + == num_batches + * batch_size + * (tile_extents[0] - 2 * clip) + * (tile_extents[1] - 2 * clip) + * channels + ), ( + f"Checksum mismatch in heatmap accumulator after updates. Is {assembler.accumulator.sum()}, but expected {num_batches * batch_size * (tile_extents[0] - 2 * clip) * (tile_extents[1] - 2 * clip) * channels}" + ) + + assembled_heatmap, overlap_counter = assembler.finalize() + assert assembled_heatmap.shape == ( + channels, + *expected_mask_extents, + ), ( + f"Assembled heatmap has incorrect shape: {assembled_heatmap.shape}, shall be {(channels, *expected_mask_extents)}" + ) + assert overlap_counter.shape == (1, *expected_mask_extents), ( + f"Overlap counter has incorrect shape: {overlap_counter.shape}, shall be {(1, *expected_mask_extents)}" + ) + assert overlap_counter.sum() == num_batches * batch_size * ( + tile_extents[0] - 2 * clip + ) * (tile_extents[1] - 2 * clip), ( + f"Checksum mismatch in overlap counter after finalization. Is {overlap_counter.sum()}, but expected {num_batches * batch_size * (tile_extents[0] - 2 * clip) * (tile_extents[1] - 2 * clip)}" + ) + assert assembled_heatmap.max() == 1.0, ( + f"Assembled heatmap values should be normalized to 1.0 after finalization: {assembled_heatmap.max()}" + ) + + +def test_edge_clipping_clips_edges(): + """SImple test that ensures, that a tile put at [0,0], if clipped, does not write to the [0,0] corner of the heatmap.""" + clip = 1 + mask_extents = np.asarray((16, 16)) + mask_tile_extents = np.asarray((8, 8)) + channels = 1 + assembler = AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D( + source_extents=mask_extents, + source_tile_extents=mask_tile_extents, + source_tile_strides=np.asarray((4, 4)), + mask_tile_extents=mask_tile_extents, + clip=clip, + channels=channels, + ) + tile = np.ones((1, channels, *mask_tile_extents), dtype=np.float32) + assembler.update_batch(tile, coords_batch=np.asarray([[0], [0]])) + assembled_heatmap, overlap_counter = assembler.finalize() + assert (assembled_heatmap[0, 0] == 0.0).all(), ( + "Top-left corner of assembled heatmap should be zero due to clipping" + ) + assert (overlap_counter[0, 0] == 0.0).all(), ( + "Top-left corner of overlap counter should be zero due to clipping" + ) + + +def test_numpy_memmap_tempfile_management(monkeypatch): + """Test that temporary files created by NamedTemporaryFile are properly deleted.""" + captured_files = [] + original_namedtempfile = ( + tempfile.NamedTemporaryFile + ) # needs to be declared as a local variable to avoid recursion by monkeypatch + + def intercepting_namedtempfile(*args, **kwargs): + temp_file = original_namedtempfile(*args, **kwargs) + captured_files.append(Path(temp_file.name)) + return temp_file + + monkeypatch.setattr(tempfile, "NamedTemporaryFile", intercepting_namedtempfile) + + mask_tile_extents = np.asarray([8, 8], dtype=np.int64) + mask_extents = np.asarray([16, 16], dtype=np.int64) + tile_strides = np.asarray([4, 4], dtype=np.int64) + + assembler = AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D( + source_extents=mask_extents, + source_tile_extents=mask_tile_extents, + source_tile_strides=tile_strides, + mask_tile_extents=mask_tile_extents, + channels=1, + clip=1, + ) + + assert len(captured_files) >= 1, ( + "Expected at least one temporary file to be created" + ) + temp_filepath = captured_files[0] + + tile = np.ones((1, 1, 8, 8), dtype=np.float32) + assembler.update_batch(tile, coords_batch=np.asarray([[0], [0]])) + + del assembler + + assert not temp_filepath.exists(), ( + f"Temporary file {temp_filepath} should be deleted" + ) + + +def test_numpy_memmap_persistent_file(tmp_path): + """Test that a persistent file created by AveragingClippingNumpyMemMapMaskBuilder2D is not deleted upon finalization.""" + filepath = tmp_path / "persistent_heatmap.npy" + + mask_tile_extents = np.asarray([8, 8], dtype=np.int64) + mask_extents = np.asarray([16, 16], dtype=np.int64) + tile_strides = np.asarray([4, 4], dtype=np.int64) + + assembler = AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D( + source_extents=mask_extents, + source_tile_extents=mask_tile_extents, + source_tile_strides=tile_strides, + mask_tile_extents=mask_tile_extents, + clip=1, + channels=1, + accumulator_filepath=filepath, + overlap_counter_filepath=filepath.with_suffix(".overlap"+filepath.suffix), + ) + + tile_batch = np.ones((1, 1, *mask_tile_extents), dtype=np.float32) + assembler.update_batch(tile_batch, coords_batch=np.asarray([[0], [0]])) + + del assembler + + assert filepath.exists(), ( + f"Persistent file {filepath} should exist after finalization" + ) + + assert filepath.with_suffix(".overlap"+filepath.suffix).exists(), ( + f"Persistent overlap file {filepath.with_suffix('.overlap'+filepath.suffix)} should exist after finalization" + ) + + # Clean up + filepath.unlink() + + +@pytest.mark.parametrize("source_extents", [(32, 32), (64, 96)]) +@pytest.mark.parametrize("channels", [1, 3]) +@pytest.mark.parametrize("source_tile_extents", [(8, 8), (6, 12)]) +@pytest.mark.parametrize("mask_tile_extents", [(4, 4), (8, 8)]) +def test_autoscaling_scalar_uniform_value_constant_stride( + source_extents, channels, source_tile_extents, mask_tile_extents +): + """Test AutoScalingScalarUniformValueConstantStrideMaskBuilder with autoscaling and scalar tiling.""" + batch_size = 4 + num_batches = 8 + + source_extents = np.asarray(source_extents) + source_tile_extents = np.asarray(source_tile_extents) + source_tile_strides = source_tile_extents // 2 # 50% overlap + mask_tile_extents = np.asarray(mask_tile_extents) + + + + builder = AutoScalingScalarUniformValueConstantStrideMaskBuilder( + source_extents=source_extents, + channels=channels, + source_tile_extents=source_tile_extents, + source_tile_strides=source_tile_strides, + mask_tile_extents=mask_tile_extents, + ) + + mask_tile_strides = (source_tile_strides * mask_tile_extents) // source_tile_extents + total_strides = np.ceil((source_extents - source_tile_extents) / source_tile_strides).astype(np.int64) + expected_mask_extents = total_strides * mask_tile_strides + mask_tile_extents + + gcds = np.gcd(mask_tile_extents, mask_tile_strides) + adjusted_mask_tile_extents = mask_tile_extents // gcds + compressed_mask_extents = expected_mask_extents // gcds + + # Verify accumulator shape matches adjusted dimensions + assert builder.accumulator.shape == (channels, *compressed_mask_extents), ( + f"Accumulator shape mismatch: {builder.accumulator.shape} vs expected {(channels, *compressed_mask_extents)}" + ) + + # Create scalar batch data (B, C) + scalar_data = np.ones((batch_size, channels), dtype=np.float32) + + for i in range(num_batches): + # Generate coordinates in SOURCE space + coords_batch = np.random.rand(len(source_extents), batch_size) + coords_batch *= (source_extents - source_tile_extents)[:, np.newaxis] + + # Align to source tile strides + coords_batch = (coords_batch // source_tile_strides[:, np.newaxis]) * source_tile_strides[:, np.newaxis] + coords_batch = coords_batch.astype(np.int64) + + builder.update_batch(scalar_data, coords_batch) + + # Verify accumulator sum increases + expected_increment = (i + 1) * batch_size * channels * np.prod(adjusted_mask_tile_extents) + assert np.isclose(builder.accumulator.sum(), expected_increment, atol=1e-5), ( + f"Accumulator sum after batch {i + 1}: {builder.accumulator.sum()} vs expected {expected_increment}" + ) + + assembled, overlap = builder.finalize() + + # Verify output shapes + assert assembled.shape == (channels, *compressed_mask_extents), ( + f"Assembled mask shape mismatch: {assembled.shape} vs {(channels, *compressed_mask_extents)}" + ) + assert overlap.shape == (1, *compressed_mask_extents), ( + f"Overlap counter shape mismatch: {overlap.shape} vs {(1, *compressed_mask_extents)}" + ) + + # Verify values are averaged correctly + assert np.isclose(assembled.max(), 1.0, atol=1e-5), ( + f"Max value should be ~1.0 after averaging: {assembled.max()}" + ) + assert overlap.sum() == num_batches * batch_size * np.prod(adjusted_mask_tile_extents), ( + f"Overlap sum mismatch: {overlap.sum()} vs expected {num_batches * batch_size * np.prod(adjusted_mask_tile_extents)}" + ) From 12aaf8f32ac7dbf49c6874240bbbe0ba9ae1ad63 Mon Sep 17 00:00:00 2001 From: Adam Bajger <469113@mail.muni.cz> Date: Fri, 9 Jan 2026 15:39:19 +0100 Subject: [PATCH 02/50] refactor: split into files --- ratiopath/masks/mask_builders/__init__.py | 319 +++++++- ratiopath/masks/mask_builders/aggregation.py | 104 +++ ratiopath/masks/mask_builders/mask_builder.py | 738 ------------------ .../receptive_field_manipulation.py | 268 +++++++ ratiopath/masks/mask_builders/storage.py | 72 ++ tests/test_mask_builders.py | 7 +- 6 files changed, 763 insertions(+), 745 deletions(-) create mode 100644 ratiopath/masks/mask_builders/aggregation.py create mode 100644 ratiopath/masks/mask_builders/receptive_field_manipulation.py create mode 100644 ratiopath/masks/mask_builders/storage.py diff --git a/ratiopath/masks/mask_builders/__init__.py b/ratiopath/masks/mask_builders/__init__.py index 864ce38..53c1a9e 100644 --- a/ratiopath/masks/mask_builders/__init__.py +++ b/ratiopath/masks/mask_builders/__init__.py @@ -1,8 +1,258 @@ +from pathlib import Path +from ratiopath.masks.mask_builders.aggregation import AveragingMaskBuilderMixin, MaxMaskBuilderMixin +from typing import Any from ratiopath.masks.mask_builders.mask_builder import ( - AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D, - AveragingScalarUniformTiledNumpyMaskBuilder, - MaxScalarUniformTiledNumpyMaskBuilder, + AccumulatorType, ) +from ratiopath.masks.mask_builders.receptive_field_manipulation import AutoScalingConstantStrideMixin, EdgeClippingMaskBuilder2DMixin, ScalarUniformTiledMaskBuilder +from ratiopath.masks.mask_builders.storage import NumpyArrayMaskBuilderAllocatorMixin, NumpyMemMapMaskBuilderAllocatorMixin + + +class AveragingScalarUniformTiledNumpyMaskBuilder( + NumpyArrayMaskBuilderAllocatorMixin, + ScalarUniformTiledMaskBuilder, + AveragingMaskBuilderMixin, +): + """Averaging scalar uniform tiled mask builder using numpy arrays as accumulators. + + The tiles are uniformly expanded from scalar/vector values before being assembled into the mask. + The overlaps are averaged during finalization. + The tiles are expected to have constant extents and strides. + These can vary across dimensions (e.g. different width and height), but must not change across tiles. + See `ScalarUniformTiledMaskBuilder` for details. + + Args: + mask_extents (tuple[int, int]): Spatial dimensions of the mask to build. + channels (int): Number of channels in the scalar values to be assembled into the mask. + mask_tile_extents (tuple[int, int]): Extents of the tiles in mask space for each spatial dimension (height, width). + mask_tile_strides (tuple[int, int]): Strides of the tiles in mask space for each spatial dimension (height, width). + + Example: + ```python + import openslide + from rationai.masks.mask_builders import ( + AveragingScalarUniformTiledNumpyMaskBuilder, + ) + import matplotlib.pyplot as plt + + LEVEL = 3 + tile_extents = (512, 512) + tile_strides = (256, 256) + slide = openslide.OpenSlide("path/to/slide.mrxs") + slide_extent_x, slide_extent_y = slide.dimensions[LEVEL] + vgg16_model = load_rationai_vgg16_model(...) # load your pretrained model here + mask_builder = AveragingScalarUniformTiledNumpyMaskBuilder( + mask_extents=(slide_extent_y, slide_extent_x), + channels=1, # for binary classification + mask_tile_extents=tile_extents, + mask_tile_strides=tile_strides, + ) + for tiles, xs, ys in generate_tiles_from_slide( + slide, LEVEL, tile_extents, tile_strides, batch_size=32 + ): + # tiles has shape (B, C, H, W) + features = vgg16_model.predict(tiles) # features has shape (B, channels) + mask_builder.update_batch(features, xs, ys) + assembled_mask, overlap = mask_builder.finalize() + plt.imshow(assembled_mask[0], cmap="gray", interpolation="nearest") + plt.axis("off") + plt.show() + ``` + """ + + def __init__( + self, + mask_extents: Int64[AccumulatorType, " N"], + channels: int, + mask_tile_extents: Int64[AccumulatorType, " N"], + mask_tile_strides: Int64[AccumulatorType, " N"], + **kwargs: Any, + ) -> None: + super().__init__( + mask_extents=mask_extents, + channels=channels, + mask_tile_extents=mask_tile_extents, + mask_tile_strides=mask_tile_strides, + **kwargs, + ) + + +class MaxScalarUniformTiledNumpyMaskBuilder( + NumpyArrayMaskBuilderAllocatorMixin, + ScalarUniformTiledMaskBuilder, + MaxMaskBuilderMixin, +): + """Max scalar uniform tiled mask builder using numpy arrays as accumulators. + + The tiles are uniformly expanded from scalar/vector values before being assembled into the mask. + The maximum value is taken at each pixel position during updates, no finalisation is required. + The tiles are expected to have constant extents and strides. + These can vary across dimensions (e.g. different width and height), but must not change across tiles. + See `ScalarUniformTiledMaskBuilder` for details. + + Args: + mask_extents (tuple[int, int]): Spatial dimensions of the mask to build. + channels (int): Number of channels in the scalar values to be assembled into the mask. + mask_tile_extents (tuple[int, int]): Extents of the tiles in mask space for each spatial dimension (height, width). + mask_tile_strides (tuple[int, int]): Strides of the tiles in mask space for each spatial dimension (height, width). + + Example: + ```python + import openslide + from rationai.masks.mask_builders import MaxScalarUniformTiledNumpyMaskBuilder + import matplotlib.pyplot as plt + + LEVEL = 3 + tile_extents = (512, 512) + tile_strides = (256, 256) + slide = openslide.OpenSlide("path/to/slide.mrxs") + slide_extent_x, slide_extent_y = slide.dimensions[LEVEL] + vgg16_model = load_rationai_vgg16_model(...) # load your pretrained model here + mask_builder = MaxScalarUniformTiledNumpyMaskBuilder( + mask_extents=(slide_extent_y, slide_extent_x), + channels=1, # for binary classification + mask_tile_extents=tile_extents, + mask_tile_strides=tile_strides, + ) + for tiles, xs, ys in generate_tiles_from_slide( + slide, LEVEL, tile_extents, tile_strides, batch_size=32 + ): + # tiles has shape (B, C, H, W) + features = vgg16_model.predict(tiles) # features has shape (B, channels) + mask_builder.update_batch(features, xs, ys) + (assembled_mask,) = mask_builder.finalize() + plt.imshow(assembled_mask[0], cmap="gray", interpolation="nearest") + plt.axis("off") + plt.show() + ``` + + """ + + def __init__( + self, + mask_extents: Int64[AccumulatorType, " N"], + channels: int, + mask_tile_extents: Int64[AccumulatorType, " N"], + mask_tile_strides: Int64[AccumulatorType, " N"], + **kwargs: Any, + ) -> None: + super().__init__( + mask_extents=mask_extents, + channels=channels, + mask_tile_extents=mask_tile_extents, + mask_tile_strides=mask_tile_strides, + **kwargs, + ) + + +class AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D( + NumpyMemMapMaskBuilderAllocatorMixin, + AutoScalingConstantStrideMixin, + EdgeClippingMaskBuilder2DMixin, + AveragingMaskBuilderMixin, +): + """Averaging mask builder with edge clipping using numpy memmaps as accumulators. + + This concrete builder combines three features: + 1. **Memory-mapped storage:** Handles large masks that exceed RAM capacity + 2. **Edge clipping:** Removes boundary artifacts from tiles + 3. **Averaging:** Smoothly blends overlapping tiles by computing pixel-wise averages + + The builder allocates two memmaps: + - Main accumulator for tile data + - Overlap counter (auto-suffixed with `.overlaps` if filepath is provided) + + Args: + mask_extents: Spatial dimensions of the full-resolution mask (height, width). + channels: Number of channels in the tiles/mask. + clip: Edge clipping specification. Accepts: + - int: same clipping on all edges + - (clip_y, clip_x): same for top/bottom and left/right + - (clip_top, clip_bottom, clip_left, clip_right): individual edge control + filepath: Optional path for the main accumulator memmap. If None, uses temporary file. + The overlap counter will use the same path with `.overlaps` suffix inserted before extension. + + Example: + ```python + import openslide + from rationai.masks.mask_builders import AveragingClippingNumpyMemMapMaskBuilder + import matplotlib.pyplot as plt + + LEVEL = 3 + tile_extents = (512, 512) + tile_strides = (256, 256) + slide = openslide.OpenSlide("path/to/slide.mrxs") + slide_extent_x, slide_extent_y = slide.dimensions[LEVEL] + vgg16_model = load_rationai_vgg16_model(...) # load your pretrained model here + mask_builder = AveragingClippingNumpyMemMapMaskBuilder( + mask_extents=(slide_extent_y, slide_extent_x), + channels=3, # for RGB masks + clip_top=4, + clip_bottom=4, + clip_left=4, + clip_right=4, + ) + for tiles, xs, ys in generate_tiles_from_slide( + slide, LEVEL, tile_extents, tile_strides, batch_size=32 + ): + # tiles has shape (B, C, H, W) + features = vgg16_model.predict(tiles) # features has shape (B, channels) + mask_builder.update_batch(features, xs, ys) + (assembled_mask,) = mask_builder.finalize() + plt.imshow(assembled_mask[0], cmap="gray", interpolation="nearest") + plt.axis("off") + plt.show() + ``` + + """ + + def __init__( + self, + source_extents: Int64[AccumulatorType, " N"], + source_tile_extents: Int64[AccumulatorType, " N"], + source_tile_strides: Int64[AccumulatorType, " N"], + mask_tile_extents: Int64[AccumulatorType, " N"], + channels: int, + clip: int | tuple[int, int] | tuple[int, int, int, int], + accumulator_filepath: Path | None = None, + overlap_counter_filepath: Path | None = None, + ) -> None: + super().__init__( + source_extents=source_extents, + source_tile_extents=source_tile_extents, + source_tile_strides=source_tile_strides, + mask_tile_extents=mask_tile_extents, + channels=channels, + clip=clip, + accumulator_filepath=accumulator_filepath, + overlap_counter_filepath=overlap_counter_filepath, + ) + + def setup_memory(self, mask_extents, channels, accumulator_filepath=None, overlap_counter_filepath=None, **kwargs) -> None: + self.accumulator = self.allocate_accumulator( + mask_extents=mask_extents, channels=channels, filepath=accumulator_filepath + ) + if overlap_counter_filepath is not None: + counter_filepath = overlap_counter_filepath + elif accumulator_filepath is not None: + suffix = accumulator_filepath.suffix + counter_filepath = accumulator_filepath.with_suffix(f".overlaps{suffix}") + else: + counter_filepath = None + self.overlap_counter = self.allocate_accumulator( + mask_extents=mask_extents, channels=1, filepath=counter_filepath + ) + + def get_vips_scale_factors(self) -> tuple[float, float]: + """Get the scaling factors to convert the built mask back to the original source resolution. + + The ideas is to obtain coefficients for the pyvips.affine() function to rescale the assembled mask + back to the original source resolution after assembly and finalization. + Returns: + tuple[float, float]: Scaling factors for height and width dimensions. + """ + scale_factors = self.overflow_buffered_source_extents / self.accumulator.shape[1:] # H, W + return tuple(scale_factors) # TODO: add tests for this method __all__ = [ @@ -10,3 +260,66 @@ "AveragingScalarUniformTiledNumpyMaskBuilder", "MaxScalarUniformTiledNumpyMaskBuilder", ] + + +class AutoScalingScalarUniformValueConstantStrideMaskBuilder( + NumpyArrayMaskBuilderAllocatorMixin, + AutoScalingConstantStrideMixin, + ScalarUniformTiledMaskBuilder, + AveragingMaskBuilderMixin +): + """Mask builder combining auto-scaling with scalar uniform tiling. + + This builder automatically handles the complete pipeline for building masks from scalar/vector + values when the network output resolution differs from input resolution: + 1. Computes output mask dimensions from source image and tile sizes + 2. Scales coordinates from input space to output space + 3. Compresses representation using GCD of tile extent/stride + 4. Expands scalar values into uniform tiles + + Typical use case: Neural network produces scalar predictions per input tile, and you want + to create a coverage mask at a different resolution. + + Args: + source_extents: Spatial dimensions of the source image (e.g., (10000, 10000)). + channels: Number of channels in scalar predictions. + source_tile_extents: Size of input tiles extracted from source (e.g., (512, 512)). + source_tile_strides: Stride between input tiles in source space (e.g., (256, 256)). + mask_tile_extents: Size you want each scalar to represent in output mask (e.g., (64, 64)). + + Example: + ```python + # Source image: 10000x10000 + # Input tiles: 512x512 at stride 256x256 + # Want each scalar to cover 64x64 in output + builder = AutoScalingScalarUniformTiledMaskBuilder( + source_extents=(10000, 10000), + channels=3, + source_tile_extents=(512, 512), + source_tile_strides=(256, 256), + mask_tile_extents=(64, 64), + ) + # Output mask will be: (10000/512)*64 x (10000/512)*64 = 1250x1250 + # Coordinates should be provided in input space (0, 256, 512, ...) + ``` + """ + + def __init__( + self, + source_extents: Int64[AccumulatorType, " N"], + source_tile_extents: Int64[AccumulatorType, " N"], + source_tile_strides: Int64[AccumulatorType, " N"], + mask_tile_extents: Int64[AccumulatorType, " N"], + channels: int, + **kwargs: Any, + ) -> None: + super().__init__( + source_extents=source_extents, + source_tile_extents=source_tile_extents, + source_tile_strides=source_tile_strides, + mask_tile_extents=mask_tile_extents, + channels=channels, + **kwargs, + ) + + diff --git a/ratiopath/masks/mask_builders/aggregation.py b/ratiopath/masks/mask_builders/aggregation.py new file mode 100644 index 0000000..06d1b88 --- /dev/null +++ b/ratiopath/masks/mask_builders/aggregation.py @@ -0,0 +1,104 @@ +from ratiopath.masks.mask_builders.mask_builder import AccumulatorType, MaskBuilder, compute_acc_slices + + +from typing import Any + + +class AveragingMaskBuilderMixin(MaskBuilder): + """Mixin that implements averaging aggregation for overlapping tiles. + + This mixin accumulates tiles by addition and tracks the overlap count at each pixel. + During finalization, the accumulated values are divided by the overlap count to compute + the average value at each position. This is useful for: + - Smoothly blending overlapping tile predictions + - Reducing edge artifacts in sliding window processing + - Computing ensemble averages from multiple passes + + The mixin allocates an additional `overlap_counter` accumulator with shape (1, *SpatialDims) + to track how many tiles contributed to each pixel position. + """ + + overlap_counter: AccumulatorType + + def __init__( + self, mask_extents: Int64[AccumulatorType, " N"], channels: int, **kwargs: Any + ) -> None: + super().__init__(mask_extents, channels, **kwargs) + + def setup_memory(self, mask_extents, channels, **kwargs) -> None: + # Perform base allocation then allocate the overlap counter. + super().setup_memory(mask_extents, channels, **kwargs) + self.overlap_counter = self.allocate_accumulator( + mask_extents=mask_extents, channels=1, **kwargs + ) + + def update_batch_impl( + self, + data_batch: Shaped[AccumulatorType, "B C *SpatialDims"], + coords_batch: Shaped[AccumulatorType, "N B"], + ) -> None: + mask_tile_extents = np.asarray(data_batch.shape[2:]) # H, W, ... + acc_slices_all_dims = compute_acc_slices( + coords_batch=coords_batch, + mask_tile_extents=mask_tile_extents, + ) + for acc_slices, data in zip( + zip(*acc_slices_all_dims, strict=True), + data_batch, + strict=True, + ): + self.accumulator[ + :, + *acc_slices, + ] += data + self.overlap_counter[ + :, + *acc_slices, + ] += 1 + + def finalize(self) -> tuple[AccumulatorType, ...]: + # Average the accumulated mask by the overlap counts + self.accumulator /= self.overlap_counter.clip(min=1) + return self.accumulator, self.overlap_counter + + +class MaxMaskBuilderMixin(MaskBuilder): + """Mixin that implements maximum aggregation for overlapping tiles. + + This mixin keeps only the maximum value at each pixel position when tiles overlap. + No additional storage is required, and finalization is a no-op since the accumulator + already contains the final max values. This is useful for: + - Maximum intensity projection + - Keeping the highest confidence prediction across overlapping tiles + - Peak detection across multiple scales + """ + + def update_batch_impl( + self, + data_batch: Shaped[AccumulatorType, "B C *SpatialDims"], + coords_batch: Shaped[AccumulatorType, "N B"], + ) -> None: + mask_tile_extents = np.asarray(data_batch.shape[2:], dtype=np.int64) # H, W, ... + acc_slices_all_dims = compute_acc_slices( + coords_batch=coords_batch, + mask_tile_extents=mask_tile_extents, # H, W, ... + ) + for acc_slices, data in zip( + zip(*acc_slices_all_dims, strict=True), + data_batch, + strict=True, + ): + self.accumulator[ + :, + *acc_slices, + ] = np.maximum( + self.accumulator[ + :, + *acc_slices, + ], + data, + ) + + + def finalize(self) -> tuple[AccumulatorType]: + return (self.accumulator,) \ No newline at end of file diff --git a/ratiopath/masks/mask_builders/mask_builder.py b/ratiopath/masks/mask_builders/mask_builder.py index c8ce4e1..2208e24 100644 --- a/ratiopath/masks/mask_builders/mask_builder.py +++ b/ratiopath/masks/mask_builders/mask_builder.py @@ -1,12 +1,9 @@ import logging import math -import tempfile from abc import ABC, abstractmethod -from pathlib import Path from typing import Any, TypeVar import numpy as np -import numpy.typing as npt from jaxtyping import Int64, Shaped @@ -161,738 +158,3 @@ def finalize(self) -> tuple[AccumulatorType, ...] | AccumulatorType: ... -class NumpyMemMapMaskBuilderAllocatorMixin(MaskBuilder): - """Mixin class to allocate accumulators as numpy memory-mapped files (memmaps). - - This mixin provides disk-backed storage for large masks that exceed available RAM. - Memory mapping allows the OS to manage paging between disk and memory transparently, - enabling processing of masks that would otherwise cause out-of-memory errors. - - **Temporary Files (default behavior when `filepath=None`):** - A temporary file is created and used as backing storage. The file is deleted when - the memmap is closed or garbage collected. Disk space is consumed during processing - but automatically reclaimed afterward. - - **Explicit Files (when `filepath` is provided):** - The memmap is backed by the specified file path, which persists after processing. - This is useful for caching results or processing masks too large for temporary storage. - If the file already exists, a FileExistsError is raised to prevent accidental data loss. - - This mixin uses NumPy's NPY format version 3.0 for compatibility with large arrays (>4GB). - """ - - def allocate_accumulator( - self, - mask_extents: Int64[AccumulatorType, " N"], - channels: int, - dtype: npt.DTypeLike = np.float32, - filepath: Path | None = None, - **kwargs: Any, - ) -> np.memmap: - if filepath is None: - with tempfile.NamedTemporaryFile() as temp_file: - return np.lib.format.open_memmap( - temp_file.name, - mode="w+", - shape=(channels, *mask_extents), - dtype=dtype, - version=(3, 0), - ) - else: - if filepath.exists(): - raise FileExistsError(f"Memmap filepath {filepath} already exists.") - return np.lib.format.open_memmap( - filepath, - mode="w+", - shape=(channels, *mask_extents), - dtype=dtype, - version=(3, 0), - ) - - -class NumpyArrayMaskBuilderAllocatorMixin(MaskBuilder): - """Mixin class to allocate accumulators as numpy arrays. - - This mixin implements the `allocate_accumulator` method to create - numpy arrays for the accumulator. - """ - - def allocate_accumulator( - self, - mask_extents: Int64[AccumulatorType, " N"], - channels: int, - dtype: npt.DTypeLike = np.float32, - **kwargs: Any, - ) -> np.ndarray: - return np.zeros((channels, *mask_extents), dtype=dtype) - - -class EdgeClippingMaskBuilderMixin(MaskBuilder): - """Mixin that clips edge pixels from tiles before accumulation. - - Edge clipping is useful for removing boundary artifacts from tiles, such as: - - Zero-padding introduced by neural networks - - Edge effects from convolution operations - - Border artifacts from image processing - - This mixin intercepts `update_batch()`, clips the specified number of pixels from tile edges, - adjusts the coordinates accordingly, and passes the clipped tiles to the next handler in the chain. - - **Important:** This mixin must appear BEFORE other mixins that implement `update_batch()` in the inheritance list (leftmost position) to ensure - its `update_batch()` method is called before others in the MRO chain. Keep this in mind when composing new classes. - - **Usage:** Subclasses or derived classes must provide `clip_start_indices` and `clip_end_indices` - during initialization to specify how many pixels to clip from the start (top/left) and - end (bottom/right) edges in each spatial dimension. - """ - - clip_start_indices: Int64[AccumulatorType, " N"] - clip_end_indices: Int64[AccumulatorType, " N"] - - def __init__( - self, - mask_extents: Int64[AccumulatorType, " N"], - channels: int, - clip_start_indices: Int64[AccumulatorType, " N"], - clip_end_indices: Int64[AccumulatorType, " N"], - **kwargs: Any, - ) -> None: - super().__init__(mask_extents, channels, **kwargs) - self.clip_start_indices = np.asarray(clip_start_indices, dtype=np.int64) - self.clip_end_indices = np.asarray(clip_end_indices, dtype=np.int64) - - def update_batch( - self, - data_batch: Shaped[AccumulatorType, "B C *SpatialDims"], - coords_batch: Shaped[AccumulatorType, "N B"], - ) -> None: - # Clip the edges of the input tiles - extents = np.asarray(data_batch.shape[2:], dtype=np.int64) # H, W, ... - slices = tuple( - slice(start, end) - for start, end in zip( - self.clip_start_indices, - extents - self.clip_end_indices, - strict=True, - ) - ) - adjusterd_coords_batch = coords_batch + self.clip_start_indices[:, np.newaxis] - super().update_batch( - data_batch=data_batch[..., *slices], # type: ignore[arg-type] - coords_batch=adjusterd_coords_batch, - ) - - -class EdgeClippingMaskBuilder2DMixin(EdgeClippingMaskBuilderMixin): - """2D-specific edge clipping mixin with convenient parameter formats. - - This specialized version of EdgeClippingMaskBuilderMixin provides intuitive parameter formats - for 2D masks. The `clip` parameter accepts three formats: - - 1. **Single integer:** `clip=5` → clips 5 pixels from all edges (top, bottom, left, right) - 2. **Tuple of 2 ints:** `clip=(5, 10)` → clips 5 pixels from top/bottom, 10 from left/right - 3. **Tuple of 4 ints:** `clip=(2, 3, 4, 5)` → clips 2 (top), 3 (bottom), 4 (left), 5 (right) - - **Important:** This mixin must appear FIRST in the inheritance list to ensure proper MRO. - - Example: - ```python - class MyBuilder( - EdgeClippingMaskBuilder2DMixin, # Must be first! - NumpyArrayMaskBuilderAllocatorMixin, - AveragingMaskBuilderMixin, - ): - pass - - - builder = MyBuilder(mask_extents=(1024, 1024), channels=3, clip=8) - ``` - """ - - def __init__( - self, - mask_extents: Int64[AccumulatorType, " N"], - channels: int, - clip: int | tuple[int, int] | tuple[int, int, int, int] = 0, - **kwargs: Any, - ) -> None: - if isinstance(clip, int): - clip_start_indices = clip_end_indices = (clip,) * len(mask_extents) - elif isinstance(clip, tuple) and len(clip) == 2: - clip_y, clip_x = clip - clip_start_indices = (clip_y, clip_x) - clip_end_indices = (clip_y, clip_x) - elif isinstance(clip, tuple) and len(clip) == 4: - clip_top, clip_bottom, clip_left, clip_right = clip - clip_start_indices = (clip_top, clip_left) - clip_end_indices = (clip_bottom, clip_right) - else: - raise ValueError( - "clip must be an int, a tuple of two ints, or a tuple of four ints." - ) - super().__init__( - mask_extents=mask_extents, - channels=channels, - clip_start_indices=np.asarray(clip_start_indices, dtype=np.int64), - clip_end_indices=np.asarray(clip_end_indices, dtype=np.int64), - **kwargs, - ) - - -class AveragingMaskBuilderMixin(MaskBuilder): - """Mixin that implements averaging aggregation for overlapping tiles. - - This mixin accumulates tiles by addition and tracks the overlap count at each pixel. - During finalization, the accumulated values are divided by the overlap count to compute - the average value at each position. This is useful for: - - Smoothly blending overlapping tile predictions - - Reducing edge artifacts in sliding window processing - - Computing ensemble averages from multiple passes - - The mixin allocates an additional `overlap_counter` accumulator with shape (1, *SpatialDims) - to track how many tiles contributed to each pixel position. - """ - - overlap_counter: AccumulatorType - - def __init__( - self, mask_extents: Int64[AccumulatorType, " N"], channels: int, **kwargs: Any - ) -> None: - super().__init__(mask_extents, channels, **kwargs) - - def setup_memory(self, mask_extents, channels, **kwargs) -> None: - # Perform base allocation then allocate the overlap counter. - super().setup_memory(mask_extents, channels, **kwargs) - self.overlap_counter = self.allocate_accumulator( - mask_extents=mask_extents, channels=1, **kwargs - ) - - def update_batch_impl( - self, - data_batch: Shaped[AccumulatorType, "B C *SpatialDims"], - coords_batch: Shaped[AccumulatorType, "N B"], - ) -> None: - mask_tile_extents = np.asarray(data_batch.shape[2:]) # H, W, ... - acc_slices_all_dims = compute_acc_slices( - coords_batch=coords_batch, - mask_tile_extents=mask_tile_extents, - ) - for acc_slices, data in zip( - zip(*acc_slices_all_dims, strict=True), - data_batch, - strict=True, - ): - self.accumulator[ - :, - *acc_slices, - ] += data - self.overlap_counter[ - :, - *acc_slices, - ] += 1 - - def finalize(self) -> tuple[AccumulatorType, ...]: - # Average the accumulated mask by the overlap counts - self.accumulator /= self.overlap_counter.clip(min=1) - return self.accumulator, self.overlap_counter - - -class MaxMaskBuilderMixin(MaskBuilder): - """Mixin that implements maximum aggregation for overlapping tiles. - - This mixin keeps only the maximum value at each pixel position when tiles overlap. - No additional storage is required, and finalization is a no-op since the accumulator - already contains the final max values. This is useful for: - - Maximum intensity projection - - Keeping the highest confidence prediction across overlapping tiles - - Peak detection across multiple scales - """ - - def update_batch_impl( - self, - data_batch: Shaped[AccumulatorType, "B C *SpatialDims"], - coords_batch: Shaped[AccumulatorType, "N B"], - ) -> None: - mask_tile_extents = np.asarray(data_batch.shape[2:], dtype=np.int64) # H, W, ... - acc_slices_all_dims = compute_acc_slices( - coords_batch=coords_batch, - mask_tile_extents=mask_tile_extents, # H, W, ... - ) - for acc_slices, data in zip( - zip(*acc_slices_all_dims, strict=True), - data_batch, - strict=True, - ): - self.accumulator[ - :, - *acc_slices, - ] = np.maximum( - self.accumulator[ - :, - *acc_slices, - ], - data, - ) - - - def finalize(self) -> tuple[AccumulatorType]: - return (self.accumulator,) - - -class AutoScalingConstantStrideMixin(MaskBuilder): - """Mixin that automatically scales coordinates to match input/output resolution differences. - - Generally, neural networks such as CNNs and ViTs produce outputs at a different spatial resolution - than their inputs due to pooling, striding, or patching operations. Assembling tile outputs of such models - should happen on the output resolution grid, but input coordinates are often provided in the input resolution space. - - Since we want to retain the exact spatial correspondence between input tiles and output mask positions to be able to - align predictions with the original input, we need to solve the resolution mismatch. - - Naively, one could rescale each mask tile using some kind of interpolation to match the input tile size and then assemble - on the input resolution grid. However, this approach is computationally expensive and can introduce interpolation artifacts. - Instead, this mixin assumes that the scaling between input and output resolutions is constant and can be expressed - as a ratio of the input tile extent to the output tile extent in each dimension. It must also hold that the input tile stride - multiplied by the output tile extent is divisible by the input tile extent to ensure integer strides in the mask coordinate space. - - This mixin addresses this by calculating the scale factors from the input tile extents and output mask tile extents, - adjusting the input coordinates accordingly before passing them to the next handler in the chain. - This mixin also automatically computes the built mask spatial dimensions based on the tiled input total - extent and the computed scale factors. - - Furthermore, this mixin readjusts the mask extent to cover for potential partial tiles at the edges. - The safe extent is computed also for the source, so that the resulting mask can be properly rescaled back to the input resolution - after assembly and finalization and then cropped to the original source extents to match exactly. - - This mixin works cooperatively with other mixins (like ScalarUniformTiledMaskBuilder) by computing - the output space dimensions and passing them via kwargs, but keep in mind that those mixins must appear - after this one in the inheritance list to ensure proper MRO. - - Args: - source_extents: Spatial dimensions of the entire input/source from which tiles are drawn. - channels: Number of channels in the output. - source_tile_extents: Spatial dimensions of the input/source tiles. - mask_tile_extents: Spatial dimensions of the output tiles/mask. - source_tile_strides: Stride between input/source tiles (optional, defaults to source_tile_extents). - """ - - # source_extents: Int64[AccumulatorType, " N"] - overflow_buffered_source_extents: Int64[AccumulatorType, " N"] - mask_extents: Int64[AccumulatorType, " N"] - mask_tile_extents: Int64[AccumulatorType, " N"] - source_tile_extents: Int64[AccumulatorType, " N"] - - def __init__( - self, - source_extents: Int64[AccumulatorType, " N"], - source_tile_extents: Int64[AccumulatorType, " N"], - source_tile_strides: Int64[AccumulatorType, " N"], - mask_tile_extents: Int64[AccumulatorType, " N"], - channels: int, - **kwargs: Any, - ) -> None: - # self.source_extents = source_extents - self.source_tile_extents = source_tile_extents - self.mask_tile_extents = mask_tile_extents - - multiplied_ = (source_tile_strides * self.mask_tile_extents) - # Ensure source_tile_strides * self.mask_tile_extents is divisible by self.source_tile_extents to avoid fractional strides - if not np.all(multiplied_ % self.source_tile_extents == 0): - raise ValueError( - f"source_tile_strides * mask_tile_extents must be divisible by source_tile_extents in all dimensions," - f" but {source_tile_strides}*{self.mask_tile_extents}={multiplied_}, which is not divisible by {self.source_tile_extents}." - ) - adjusted_mask_tile_strides = multiplied_ // self.source_tile_extents - - # adjusted_mask_extents = (source_extents // self.source_tile_extents) * self.mask_tile_extents - total_strides = (source_extents - source_tile_extents) / source_tile_strides - total_strides = np.ceil(total_strides).astype(np.int64) - # without the initial tile step, including partial tile at the edge - self.overflow_buffered_source_extents = (total_strides * source_tile_strides) + source_tile_extents - overflow_buffered_mask_extents = (total_strides * adjusted_mask_tile_strides) + self.mask_tile_extents - - - # Call next in MRO with computed parameters - super().__init__( - mask_extents=overflow_buffered_mask_extents, - channels=channels, - mask_tile_extents=self.mask_tile_extents, - mask_tile_strides=adjusted_mask_tile_strides, - **kwargs, - ) - - def update_batch( - self, - data_batch: Shaped[AccumulatorType, "B C *SpatialDims"], - coords_batch: Shaped[AccumulatorType, "N B"], - ) -> None: - adjusted_coords_batch = ((coords_batch * self.mask_tile_extents[:, np.newaxis]) // self.source_tile_extents[:, np.newaxis]) - super().update_batch( - data_batch=data_batch, coords_batch=adjusted_coords_batch - ) - -class ScalarUniformTiledMaskBuilder(MaskBuilder): - """Mask builder that expands scalar/vector values into uniform tiles. - - This builder is designed for scenarios where each tile's content is uniform (constant value). - Instead of storing full tiles, it compresses the representation by: - 1. Computing the GCD of tile extent and stride in each dimension - 2. Dividing the mask into a coarser grid with GCD granularity - 3. Expanding scalar values into the compressed grid - - This reduces memory usage and computation when tiles have uniform content, such as: - - Per-tile classification scores - - Per-tile feature vectors - - The builder automatically handles the coordinate transformation between the original - and compressed grids. - - This class can work cooperatively with other mixins (like AutoScalingConstantStrideMixin) - that compute `mask_extents`. If `mask_extents` is already provided via kwargs (from a parent - mixin), it will be used; otherwise it must be provided as a direct parameter. - - Args: - mask_extents: Spatial dimensions of the mask to build (may be precomputed by a mixin). - channels: Number of channels (features) in the scalar values. - mask_tile_extents: Size of tiles in each dimension in mask space at the original resolution. - mask_tile_strides: Stride between tile positions in mask space for each dimension. - """ - - compression_factors: Int64[AccumulatorType, " N"] - adjusted_tile_extents: Int64[AccumulatorType, " N"] - - def __init__( - self, - mask_extents: Int64[AccumulatorType, " N"], - channels: int, - mask_tile_extents: Int64[AccumulatorType, " N"], - mask_tile_strides: Int64[AccumulatorType, " N"], - **kwargs: Any, - ) -> None: - self.compression_factors = np.gcd(mask_tile_strides, mask_tile_extents) - adjusted_mask_extents = mask_extents // self.compression_factors - self.adjusted_tile_extents = mask_tile_extents // self.compression_factors - super().__init__( - mask_extents=adjusted_mask_extents, channels=channels, **kwargs - ) - - def update_batch( - self, - data_batch: Shaped[AccumulatorType, "B C"], - coords_batch: Shaped[AccumulatorType, "N B"], - ) -> None: - """For each scalar/vector in the batch, repeat it in each dimension to form a tile, then update the mask with the tile.""" - adjusted_tiles = np.zeros((*data_batch.shape, *self.adjusted_tile_extents)) - adjusted_tiles += data_batch[ - ..., *[np.newaxis] * len(self.adjusted_tile_extents) - ] - adjusted_coordinates = coords_batch // self.compression_factors[:, np.newaxis] - super().update_batch(adjusted_tiles, coords_batch=adjusted_coordinates) - - -class AutoScalingScalarUniformValueConstantStrideMaskBuilder( - NumpyArrayMaskBuilderAllocatorMixin, - AutoScalingConstantStrideMixin, - ScalarUniformTiledMaskBuilder, - AveragingMaskBuilderMixin -): - """Mask builder combining auto-scaling with scalar uniform tiling. - - This builder automatically handles the complete pipeline for building masks from scalar/vector - values when the network output resolution differs from input resolution: - 1. Computes output mask dimensions from source image and tile sizes - 2. Scales coordinates from input space to output space - 3. Compresses representation using GCD of tile extent/stride - 4. Expands scalar values into uniform tiles - - Typical use case: Neural network produces scalar predictions per input tile, and you want - to create a coverage mask at a different resolution. - - Args: - source_extents: Spatial dimensions of the source image (e.g., (10000, 10000)). - channels: Number of channels in scalar predictions. - source_tile_extents: Size of input tiles extracted from source (e.g., (512, 512)). - source_tile_strides: Stride between input tiles in source space (e.g., (256, 256)). - mask_tile_extents: Size you want each scalar to represent in output mask (e.g., (64, 64)). - - Example: - ```python - # Source image: 10000x10000 - # Input tiles: 512x512 at stride 256x256 - # Want each scalar to cover 64x64 in output - builder = AutoScalingScalarUniformTiledMaskBuilder( - source_extents=(10000, 10000), - channels=3, - source_tile_extents=(512, 512), - source_tile_strides=(256, 256), - mask_tile_extents=(64, 64), - ) - # Output mask will be: (10000/512)*64 x (10000/512)*64 = 1250x1250 - # Coordinates should be provided in input space (0, 256, 512, ...) - ``` - """ - - def __init__( - self, - source_extents: Int64[AccumulatorType, " N"], - source_tile_extents: Int64[AccumulatorType, " N"], - source_tile_strides: Int64[AccumulatorType, " N"], - mask_tile_extents: Int64[AccumulatorType, " N"], - channels: int, - **kwargs: Any, - ) -> None: - super().__init__( - source_extents=source_extents, - source_tile_extents=source_tile_extents, - source_tile_strides=source_tile_strides, - mask_tile_extents=mask_tile_extents, - channels=channels, - **kwargs, - ) - - -class AveragingScalarUniformTiledNumpyMaskBuilder( - NumpyArrayMaskBuilderAllocatorMixin, - ScalarUniformTiledMaskBuilder, - AveragingMaskBuilderMixin, -): - """Averaging scalar uniform tiled mask builder using numpy arrays as accumulators. - - The tiles are uniformly expanded from scalar/vector values before being assembled into the mask. - The overlaps are averaged during finalization. - The tiles are expected to have constant extents and strides. - These can vary across dimensions (e.g. different width and height), but must not change across tiles. - See `ScalarUniformTiledMaskBuilder` for details. - - Args: - mask_extents (tuple[int, int]): Spatial dimensions of the mask to build. - channels (int): Number of channels in the scalar values to be assembled into the mask. - mask_tile_extents (tuple[int, int]): Extents of the tiles in mask space for each spatial dimension (height, width). - mask_tile_strides (tuple[int, int]): Strides of the tiles in mask space for each spatial dimension (height, width). - - Example: - ```python - import openslide - from rationai.masks.mask_builders import ( - AveragingScalarUniformTiledNumpyMaskBuilder, - ) - import matplotlib.pyplot as plt - - LEVEL = 3 - tile_extents = (512, 512) - tile_strides = (256, 256) - slide = openslide.OpenSlide("path/to/slide.mrxs") - slide_extent_x, slide_extent_y = slide.dimensions[LEVEL] - vgg16_model = load_rationai_vgg16_model(...) # load your pretrained model here - mask_builder = AveragingScalarUniformTiledNumpyMaskBuilder( - mask_extents=(slide_extent_y, slide_extent_x), - channels=1, # for binary classification - mask_tile_extents=tile_extents, - mask_tile_strides=tile_strides, - ) - for tiles, xs, ys in generate_tiles_from_slide( - slide, LEVEL, tile_extents, tile_strides, batch_size=32 - ): - # tiles has shape (B, C, H, W) - features = vgg16_model.predict(tiles) # features has shape (B, channels) - mask_builder.update_batch(features, xs, ys) - assembled_mask, overlap = mask_builder.finalize() - plt.imshow(assembled_mask[0], cmap="gray", interpolation="nearest") - plt.axis("off") - plt.show() - ``` - """ - - def __init__( - self, - mask_extents: Int64[AccumulatorType, " N"], - channels: int, - mask_tile_extents: Int64[AccumulatorType, " N"], - mask_tile_strides: Int64[AccumulatorType, " N"], - **kwargs: Any, - ) -> None: - super().__init__( - mask_extents=mask_extents, - channels=channels, - mask_tile_extents=mask_tile_extents, - mask_tile_strides=mask_tile_strides, - **kwargs, - ) - - -class MaxScalarUniformTiledNumpyMaskBuilder( - NumpyArrayMaskBuilderAllocatorMixin, - ScalarUniformTiledMaskBuilder, - MaxMaskBuilderMixin, -): - """Max scalar uniform tiled mask builder using numpy arrays as accumulators. - - The tiles are uniformly expanded from scalar/vector values before being assembled into the mask. - The maximum value is taken at each pixel position during updates, no finalisation is required. - The tiles are expected to have constant extents and strides. - These can vary across dimensions (e.g. different width and height), but must not change across tiles. - See `ScalarUniformTiledMaskBuilder` for details. - - Args: - mask_extents (tuple[int, int]): Spatial dimensions of the mask to build. - channels (int): Number of channels in the scalar values to be assembled into the mask. - mask_tile_extents (tuple[int, int]): Extents of the tiles in mask space for each spatial dimension (height, width). - mask_tile_strides (tuple[int, int]): Strides of the tiles in mask space for each spatial dimension (height, width). - - Example: - ```python - import openslide - from rationai.masks.mask_builders import MaxScalarUniformTiledNumpyMaskBuilder - import matplotlib.pyplot as plt - - LEVEL = 3 - tile_extents = (512, 512) - tile_strides = (256, 256) - slide = openslide.OpenSlide("path/to/slide.mrxs") - slide_extent_x, slide_extent_y = slide.dimensions[LEVEL] - vgg16_model = load_rationai_vgg16_model(...) # load your pretrained model here - mask_builder = MaxScalarUniformTiledNumpyMaskBuilder( - mask_extents=(slide_extent_y, slide_extent_x), - channels=1, # for binary classification - mask_tile_extents=tile_extents, - mask_tile_strides=tile_strides, - ) - for tiles, xs, ys in generate_tiles_from_slide( - slide, LEVEL, tile_extents, tile_strides, batch_size=32 - ): - # tiles has shape (B, C, H, W) - features = vgg16_model.predict(tiles) # features has shape (B, channels) - mask_builder.update_batch(features, xs, ys) - (assembled_mask,) = mask_builder.finalize() - plt.imshow(assembled_mask[0], cmap="gray", interpolation="nearest") - plt.axis("off") - plt.show() - ``` - - """ - - def __init__( - self, - mask_extents: Int64[AccumulatorType, " N"], - channels: int, - mask_tile_extents: Int64[AccumulatorType, " N"], - mask_tile_strides: Int64[AccumulatorType, " N"], - **kwargs: Any, - ) -> None: - super().__init__( - mask_extents=mask_extents, - channels=channels, - mask_tile_extents=mask_tile_extents, - mask_tile_strides=mask_tile_strides, - **kwargs, - ) - - -class AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D( - NumpyMemMapMaskBuilderAllocatorMixin, - AutoScalingConstantStrideMixin, - EdgeClippingMaskBuilder2DMixin, - AveragingMaskBuilderMixin, -): - """Averaging mask builder with edge clipping using numpy memmaps as accumulators. - - This concrete builder combines three features: - 1. **Memory-mapped storage:** Handles large masks that exceed RAM capacity - 2. **Edge clipping:** Removes boundary artifacts from tiles - 3. **Averaging:** Smoothly blends overlapping tiles by computing pixel-wise averages - - The builder allocates two memmaps: - - Main accumulator for tile data - - Overlap counter (auto-suffixed with `.overlaps` if filepath is provided) - - Args: - mask_extents: Spatial dimensions of the full-resolution mask (height, width). - channels: Number of channels in the tiles/mask. - clip: Edge clipping specification. Accepts: - - int: same clipping on all edges - - (clip_y, clip_x): same for top/bottom and left/right - - (clip_top, clip_bottom, clip_left, clip_right): individual edge control - filepath: Optional path for the main accumulator memmap. If None, uses temporary file. - The overlap counter will use the same path with `.overlaps` suffix inserted before extension. - - Example: - ```python - import openslide - from rationai.masks.mask_builders import AveragingClippingNumpyMemMapMaskBuilder - import matplotlib.pyplot as plt - - LEVEL = 3 - tile_extents = (512, 512) - tile_strides = (256, 256) - slide = openslide.OpenSlide("path/to/slide.mrxs") - slide_extent_x, slide_extent_y = slide.dimensions[LEVEL] - vgg16_model = load_rationai_vgg16_model(...) # load your pretrained model here - mask_builder = AveragingClippingNumpyMemMapMaskBuilder( - mask_extents=(slide_extent_y, slide_extent_x), - channels=3, # for RGB masks - clip_top=4, - clip_bottom=4, - clip_left=4, - clip_right=4, - ) - for tiles, xs, ys in generate_tiles_from_slide( - slide, LEVEL, tile_extents, tile_strides, batch_size=32 - ): - # tiles has shape (B, C, H, W) - features = vgg16_model.predict(tiles) # features has shape (B, channels) - mask_builder.update_batch(features, xs, ys) - (assembled_mask,) = mask_builder.finalize() - plt.imshow(assembled_mask[0], cmap="gray", interpolation="nearest") - plt.axis("off") - plt.show() - ``` - - """ - - def __init__( - self, - source_extents: Int64[AccumulatorType, " N"], - source_tile_extents: Int64[AccumulatorType, " N"], - source_tile_strides: Int64[AccumulatorType, " N"], - mask_tile_extents: Int64[AccumulatorType, " N"], - channels: int, - clip: int | tuple[int, int] | tuple[int, int, int, int], - accumulator_filepath: Path | None = None, - overlap_counter_filepath: Path | None = None, - ) -> None: - super().__init__( - source_extents=source_extents, - source_tile_extents=source_tile_extents, - source_tile_strides=source_tile_strides, - mask_tile_extents=mask_tile_extents, - channels=channels, - clip=clip, - accumulator_filepath=accumulator_filepath, - overlap_counter_filepath=overlap_counter_filepath, - ) - - def setup_memory(self, mask_extents, channels, accumulator_filepath=None, overlap_counter_filepath=None, **kwargs) -> None: - self.accumulator = self.allocate_accumulator( - mask_extents=mask_extents, channels=channels, filepath=accumulator_filepath - ) - if overlap_counter_filepath is not None: - counter_filepath = overlap_counter_filepath - elif accumulator_filepath is not None: - suffix = accumulator_filepath.suffix - counter_filepath = accumulator_filepath.with_suffix(f".overlaps{suffix}") - else: - counter_filepath = None - self.overlap_counter = self.allocate_accumulator( - mask_extents=mask_extents, channels=1, filepath=counter_filepath - ) - - def get_vips_scale_factors(self) -> tuple[float, float]: - """Get the scaling factors to convert the built mask back to the original source resolution. - - The ideas is to obtain coefficients for the pyvips.affine() function to rescale the assembled mask - back to the original source resolution after assembly and finalization. - Returns: - tuple[float, float]: Scaling factors for height and width dimensions. - """ - scale_factors = self.overflow_buffered_source_extents / self.accumulator.shape[1:] # H, W - return tuple(scale_factors) # TODO: add tests for this method \ No newline at end of file diff --git a/ratiopath/masks/mask_builders/receptive_field_manipulation.py b/ratiopath/masks/mask_builders/receptive_field_manipulation.py new file mode 100644 index 0000000..d801f01 --- /dev/null +++ b/ratiopath/masks/mask_builders/receptive_field_manipulation.py @@ -0,0 +1,268 @@ +from ratiopath.masks.mask_builders.mask_builder import AccumulatorType, MaskBuilder + + +from typing import Any + + +class EdgeClippingMaskBuilderMixin(MaskBuilder): + """Mixin that clips edge pixels from tiles before accumulation. + + Edge clipping is useful for removing boundary artifacts from tiles, such as: + - Zero-padding introduced by neural networks + - Edge effects from convolution operations + - Border artifacts from image processing + + This mixin intercepts `update_batch()`, clips the specified number of pixels from tile edges, + adjusts the coordinates accordingly, and passes the clipped tiles to the next handler in the chain. + + **Important:** This mixin must appear BEFORE other mixins that implement `update_batch()` in the inheritance list (leftmost position) to ensure + its `update_batch()` method is called before others in the MRO chain. Keep this in mind when composing new classes. + + **Usage:** Subclasses or derived classes must provide `clip_start_indices` and `clip_end_indices` + during initialization to specify how many pixels to clip from the start (top/left) and + end (bottom/right) edges in each spatial dimension. + """ + + clip_start_indices: Int64[AccumulatorType, " N"] + clip_end_indices: Int64[AccumulatorType, " N"] + + def __init__( + self, + mask_extents: Int64[AccumulatorType, " N"], + channels: int, + clip_start_indices: Int64[AccumulatorType, " N"], + clip_end_indices: Int64[AccumulatorType, " N"], + **kwargs: Any, + ) -> None: + super().__init__(mask_extents, channels, **kwargs) + self.clip_start_indices = np.asarray(clip_start_indices, dtype=np.int64) + self.clip_end_indices = np.asarray(clip_end_indices, dtype=np.int64) + + def update_batch( + self, + data_batch: Shaped[AccumulatorType, "B C *SpatialDims"], + coords_batch: Shaped[AccumulatorType, "N B"], + ) -> None: + # Clip the edges of the input tiles + extents = np.asarray(data_batch.shape[2:], dtype=np.int64) # H, W, ... + slices = tuple( + slice(start, end) + for start, end in zip( + self.clip_start_indices, + extents - self.clip_end_indices, + strict=True, + ) + ) + adjusterd_coords_batch = coords_batch + self.clip_start_indices[:, np.newaxis] + super().update_batch( + data_batch=data_batch[..., *slices], # type: ignore[arg-type] + coords_batch=adjusterd_coords_batch, + ) + + +class EdgeClippingMaskBuilder2DMixin(EdgeClippingMaskBuilderMixin): + """2D-specific edge clipping mixin with convenient parameter formats. + + This specialized version of EdgeClippingMaskBuilderMixin provides intuitive parameter formats + for 2D masks. The `clip` parameter accepts three formats: + + 1. **Single integer:** `clip=5` → clips 5 pixels from all edges (top, bottom, left, right) + 2. **Tuple of 2 ints:** `clip=(5, 10)` → clips 5 pixels from top/bottom, 10 from left/right + 3. **Tuple of 4 ints:** `clip=(2, 3, 4, 5)` → clips 2 (top), 3 (bottom), 4 (left), 5 (right) + + **Important:** This mixin must appear FIRST in the inheritance list to ensure proper MRO. + + Example: + ```python + class MyBuilder( + EdgeClippingMaskBuilder2DMixin, # Must be first! + NumpyArrayMaskBuilderAllocatorMixin, + AveragingMaskBuilderMixin, + ): + pass + + + builder = MyBuilder(mask_extents=(1024, 1024), channels=3, clip=8) + ``` + """ + + def __init__( + self, + mask_extents: Int64[AccumulatorType, " N"], + channels: int, + clip: int | tuple[int, int] | tuple[int, int, int, int] = 0, + **kwargs: Any, + ) -> None: + if isinstance(clip, int): + clip_start_indices = clip_end_indices = (clip,) * len(mask_extents) + elif isinstance(clip, tuple) and len(clip) == 2: + clip_y, clip_x = clip + clip_start_indices = (clip_y, clip_x) + clip_end_indices = (clip_y, clip_x) + elif isinstance(clip, tuple) and len(clip) == 4: + clip_top, clip_bottom, clip_left, clip_right = clip + clip_start_indices = (clip_top, clip_left) + clip_end_indices = (clip_bottom, clip_right) + else: + raise ValueError( + "clip must be an int, a tuple of two ints, or a tuple of four ints." + ) + super().__init__( + mask_extents=mask_extents, + channels=channels, + clip_start_indices=np.asarray(clip_start_indices, dtype=np.int64), + clip_end_indices=np.asarray(clip_end_indices, dtype=np.int64), + **kwargs, + ) + + +class AutoScalingConstantStrideMixin(MaskBuilder): + """Mixin that automatically scales coordinates to match input/output resolution differences. + + Generally, neural networks such as CNNs and ViTs produce outputs at a different spatial resolution + than their inputs due to pooling, striding, or patching operations. Assembling tile outputs of such models + should happen on the output resolution grid, but input coordinates are often provided in the input resolution space. + + Since we want to retain the exact spatial correspondence between input tiles and output mask positions to be able to + align predictions with the original input, we need to solve the resolution mismatch. + + Naively, one could rescale each mask tile using some kind of interpolation to match the input tile size and then assemble + on the input resolution grid. However, this approach is computationally expensive and can introduce interpolation artifacts. + Instead, this mixin assumes that the scaling between input and output resolutions is constant and can be expressed + as a ratio of the input tile extent to the output tile extent in each dimension. It must also hold that the input tile stride + multiplied by the output tile extent is divisible by the input tile extent to ensure integer strides in the mask coordinate space. + + This mixin addresses this by calculating the scale factors from the input tile extents and output mask tile extents, + adjusting the input coordinates accordingly before passing them to the next handler in the chain. + This mixin also automatically computes the built mask spatial dimensions based on the tiled input total + extent and the computed scale factors. + + Furthermore, this mixin readjusts the mask extent to cover for potential partial tiles at the edges. + The safe extent is computed also for the source, so that the resulting mask can be properly rescaled back to the input resolution + after assembly and finalization and then cropped to the original source extents to match exactly. + + This mixin works cooperatively with other mixins (like ScalarUniformTiledMaskBuilder) by computing + the output space dimensions and passing them via kwargs, but keep in mind that those mixins must appear + after this one in the inheritance list to ensure proper MRO. + + Args: + source_extents: Spatial dimensions of the entire input/source from which tiles are drawn. + channels: Number of channels in the output. + source_tile_extents: Spatial dimensions of the input/source tiles. + mask_tile_extents: Spatial dimensions of the output tiles/mask. + source_tile_strides: Stride between input/source tiles (optional, defaults to source_tile_extents). + """ + + # source_extents: Int64[AccumulatorType, " N"] + overflow_buffered_source_extents: Int64[AccumulatorType, " N"] + mask_extents: Int64[AccumulatorType, " N"] + mask_tile_extents: Int64[AccumulatorType, " N"] + source_tile_extents: Int64[AccumulatorType, " N"] + + def __init__( + self, + source_extents: Int64[AccumulatorType, " N"], + source_tile_extents: Int64[AccumulatorType, " N"], + source_tile_strides: Int64[AccumulatorType, " N"], + mask_tile_extents: Int64[AccumulatorType, " N"], + channels: int, + **kwargs: Any, + ) -> None: + # self.source_extents = source_extents + self.source_tile_extents = source_tile_extents + self.mask_tile_extents = mask_tile_extents + + multiplied_ = (source_tile_strides * self.mask_tile_extents) + # Ensure source_tile_strides * self.mask_tile_extents is divisible by self.source_tile_extents to avoid fractional strides + if not np.all(multiplied_ % self.source_tile_extents == 0): + raise ValueError( + f"source_tile_strides * mask_tile_extents must be divisible by source_tile_extents in all dimensions," + f" but {source_tile_strides}*{self.mask_tile_extents}={multiplied_}, which is not divisible by {self.source_tile_extents}." + ) + adjusted_mask_tile_strides = multiplied_ // self.source_tile_extents + + # adjusted_mask_extents = (source_extents // self.source_tile_extents) * self.mask_tile_extents + total_strides = (source_extents - source_tile_extents) / source_tile_strides + total_strides = np.ceil(total_strides).astype(np.int64) + # without the initial tile step, including partial tile at the edge + self.overflow_buffered_source_extents = (total_strides * source_tile_strides) + source_tile_extents + overflow_buffered_mask_extents = (total_strides * adjusted_mask_tile_strides) + self.mask_tile_extents + + + # Call next in MRO with computed parameters + super().__init__( + mask_extents=overflow_buffered_mask_extents, + channels=channels, + mask_tile_extents=self.mask_tile_extents, + mask_tile_strides=adjusted_mask_tile_strides, + **kwargs, + ) + + def update_batch( + self, + data_batch: Shaped[AccumulatorType, "B C *SpatialDims"], + coords_batch: Shaped[AccumulatorType, "N B"], + ) -> None: + adjusted_coords_batch = ((coords_batch * self.mask_tile_extents[:, np.newaxis]) // self.source_tile_extents[:, np.newaxis]) + super().update_batch( + data_batch=data_batch, coords_batch=adjusted_coords_batch + ) + + +class ScalarUniformTiledMaskBuilder(MaskBuilder): + """Mask builder that expands scalar/vector values into uniform tiles. + + This builder is designed for scenarios where each tile's content is uniform (constant value). + Instead of storing full tiles, it compresses the representation by: + 1. Computing the GCD of tile extent and stride in each dimension + 2. Dividing the mask into a coarser grid with GCD granularity + 3. Expanding scalar values into the compressed grid + + This reduces memory usage and computation when tiles have uniform content, such as: + - Per-tile classification scores + - Per-tile feature vectors + + The builder automatically handles the coordinate transformation between the original + and compressed grids. + + This class can work cooperatively with other mixins (like AutoScalingConstantStrideMixin) + that compute `mask_extents`. If `mask_extents` is already provided via kwargs (from a parent + mixin), it will be used; otherwise it must be provided as a direct parameter. + + Args: + mask_extents: Spatial dimensions of the mask to build (may be precomputed by a mixin). + channels: Number of channels (features) in the scalar values. + mask_tile_extents: Size of tiles in each dimension in mask space at the original resolution. + mask_tile_strides: Stride between tile positions in mask space for each dimension. + """ + + compression_factors: Int64[AccumulatorType, " N"] + adjusted_tile_extents: Int64[AccumulatorType, " N"] + + def __init__( + self, + mask_extents: Int64[AccumulatorType, " N"], + channels: int, + mask_tile_extents: Int64[AccumulatorType, " N"], + mask_tile_strides: Int64[AccumulatorType, " N"], + **kwargs: Any, + ) -> None: + self.compression_factors = np.gcd(mask_tile_strides, mask_tile_extents) + adjusted_mask_extents = mask_extents // self.compression_factors + self.adjusted_tile_extents = mask_tile_extents // self.compression_factors + super().__init__( + mask_extents=adjusted_mask_extents, channels=channels, **kwargs + ) + + def update_batch( + self, + data_batch: Shaped[AccumulatorType, "B C"], + coords_batch: Shaped[AccumulatorType, "N B"], + ) -> None: + """For each scalar/vector in the batch, repeat it in each dimension to form a tile, then update the mask with the tile.""" + adjusted_tiles = np.zeros((*data_batch.shape, *self.adjusted_tile_extents)) + adjusted_tiles += data_batch[ + ..., *[np.newaxis] * len(self.adjusted_tile_extents) + ] + adjusted_coordinates = coords_batch // self.compression_factors[:, np.newaxis] + super().update_batch(adjusted_tiles, coords_batch=adjusted_coordinates) \ No newline at end of file diff --git a/ratiopath/masks/mask_builders/storage.py b/ratiopath/masks/mask_builders/storage.py new file mode 100644 index 0000000..d7940ea --- /dev/null +++ b/ratiopath/masks/mask_builders/storage.py @@ -0,0 +1,72 @@ +from ratiopath.masks.mask_builders.mask_builder import AccumulatorType, MaskBuilder + + +import tempfile +from pathlib import Path +from typing import Any + + +class NumpyMemMapMaskBuilderAllocatorMixin(MaskBuilder): + """Mixin class to allocate accumulators as numpy memory-mapped files (memmaps). + + This mixin provides disk-backed storage for large masks that exceed available RAM. + Memory mapping allows the OS to manage paging between disk and memory transparently, + enabling processing of masks that would otherwise cause out-of-memory errors. + + **Temporary Files (default behavior when `filepath=None`):** + A temporary file is created and used as backing storage. The file is deleted when + the memmap is closed or garbage collected. Disk space is consumed during processing + but automatically reclaimed afterward. + + **Explicit Files (when `filepath` is provided):** + The memmap is backed by the specified file path, which persists after processing. + This is useful for caching results or processing masks too large for temporary storage. + If the file already exists, a FileExistsError is raised to prevent accidental data loss. + + This mixin uses NumPy's NPY format version 3.0 for compatibility with large arrays (>4GB). + """ + + def allocate_accumulator( + self, + mask_extents: Int64[AccumulatorType, " N"], + channels: int, + dtype: npt.DTypeLike = np.float32, + filepath: Path | None = None, + **kwargs: Any, + ) -> np.memmap: + if filepath is None: + with tempfile.NamedTemporaryFile() as temp_file: + return np.lib.format.open_memmap( + temp_file.name, + mode="w+", + shape=(channels, *mask_extents), + dtype=dtype, + version=(3, 0), + ) + else: + if filepath.exists(): + raise FileExistsError(f"Memmap filepath {filepath} already exists.") + return np.lib.format.open_memmap( + filepath, + mode="w+", + shape=(channels, *mask_extents), + dtype=dtype, + version=(3, 0), + ) + + +class NumpyArrayMaskBuilderAllocatorMixin(MaskBuilder): + """Mixin class to allocate accumulators as numpy arrays. + + This mixin implements the `allocate_accumulator` method to create + numpy arrays for the accumulator. + """ + + def allocate_accumulator( + self, + mask_extents: Int64[AccumulatorType, " N"], + channels: int, + dtype: npt.DTypeLike = np.float32, + **kwargs: Any, + ) -> np.ndarray: + return np.zeros((channels, *mask_extents), dtype=dtype) \ No newline at end of file diff --git a/tests/test_mask_builders.py b/tests/test_mask_builders.py index 15083bd..6337f3e 100644 --- a/tests/test_mask_builders.py +++ b/tests/test_mask_builders.py @@ -4,11 +4,10 @@ import numpy as np import pytest -from ratiopath.masks.mask_builders.mask_builder import ( +from ratiopath.masks.mask_builders import AveragingScalarUniformTiledNumpyMaskBuilder, MaxScalarUniformTiledNumpyMaskBuilder +from ratiopath.masks.mask_builders import AutoScalingScalarUniformValueConstantStrideMaskBuilder +from ratiopath.masks.mask_builders import ( AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D, - AveragingScalarUniformTiledNumpyMaskBuilder, - AutoScalingScalarUniformValueConstantStrideMaskBuilder, - MaxScalarUniformTiledNumpyMaskBuilder, ) From d29feb7bf739a6a0dcd6ca8cc8f167be6f17a521 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?V=C3=ADt=20Musil?= <23387713+vejtek@users.noreply.github.com> Date: Fri, 9 Jan 2026 15:45:43 +0100 Subject: [PATCH 03/50] Update ratiopath/masks/write_big_tiff.py --- ratiopath/masks/write_big_tiff.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/ratiopath/masks/write_big_tiff.py b/ratiopath/masks/write_big_tiff.py index c68ef3d..ca2db19 100644 --- a/ratiopath/masks/write_big_tiff.py +++ b/ratiopath/masks/write_big_tiff.py @@ -36,12 +36,6 @@ def write_big_tiff( tile_height: The height of each tile in pixels. Default is 512. Must match with other masks used in xOpath. """ - if mpp_x > 100 or mpp_y > 100: - # This warning is necessary because we change the units of mpp_x and mpp_y - warnings.warn( - "Make sure the resolution is in µm/pixel not in pixels/mm.", stacklevel=1 - ) - xres = 1000 / mpp_x yres = 1000 / mpp_y From f8002d46a0ef4cb5ac69b2b13c2e763816c166fb Mon Sep 17 00:00:00 2001 From: Adam Bajger <469113@mail.muni.cz> Date: Fri, 9 Jan 2026 15:50:22 +0100 Subject: [PATCH 04/50] fix: add numpy and jaxtyping imports to mask builder modules --- ratiopath/masks/mask_builders/__init__.py | 2 +- ratiopath/masks/mask_builders/aggregation.py | 4 +++- .../masks/mask_builders/receptive_field_manipulation.py | 3 ++- ratiopath/masks/mask_builders/storage.py | 5 +++-- ratiopath/masks/write_big_tiff.py | 1 - 5 files changed, 9 insertions(+), 6 deletions(-) diff --git a/ratiopath/masks/mask_builders/__init__.py b/ratiopath/masks/mask_builders/__init__.py index 53c1a9e..743072e 100644 --- a/ratiopath/masks/mask_builders/__init__.py +++ b/ratiopath/masks/mask_builders/__init__.py @@ -6,7 +6,7 @@ ) from ratiopath.masks.mask_builders.receptive_field_manipulation import AutoScalingConstantStrideMixin, EdgeClippingMaskBuilder2DMixin, ScalarUniformTiledMaskBuilder from ratiopath.masks.mask_builders.storage import NumpyArrayMaskBuilderAllocatorMixin, NumpyMemMapMaskBuilderAllocatorMixin - +from jaxtyping import Int64 class AveragingScalarUniformTiledNumpyMaskBuilder( NumpyArrayMaskBuilderAllocatorMixin, diff --git a/ratiopath/masks/mask_builders/aggregation.py b/ratiopath/masks/mask_builders/aggregation.py index 06d1b88..c7534b0 100644 --- a/ratiopath/masks/mask_builders/aggregation.py +++ b/ratiopath/masks/mask_builders/aggregation.py @@ -1,7 +1,9 @@ from ratiopath.masks.mask_builders.mask_builder import AccumulatorType, MaskBuilder, compute_acc_slices - +import numpy as np +import numpy.typing as npt from typing import Any +from jaxtyping import Shaped, Int64 class AveragingMaskBuilderMixin(MaskBuilder): diff --git a/ratiopath/masks/mask_builders/receptive_field_manipulation.py b/ratiopath/masks/mask_builders/receptive_field_manipulation.py index d801f01..fd4f1d7 100644 --- a/ratiopath/masks/mask_builders/receptive_field_manipulation.py +++ b/ratiopath/masks/mask_builders/receptive_field_manipulation.py @@ -2,7 +2,8 @@ from typing import Any - +from jaxtyping import Shaped, Int64 +import numpy as np class EdgeClippingMaskBuilderMixin(MaskBuilder): """Mixin that clips edge pixels from tiles before accumulation. diff --git a/ratiopath/masks/mask_builders/storage.py b/ratiopath/masks/mask_builders/storage.py index d7940ea..5b2f606 100644 --- a/ratiopath/masks/mask_builders/storage.py +++ b/ratiopath/masks/mask_builders/storage.py @@ -1,9 +1,10 @@ from ratiopath.masks.mask_builders.mask_builder import AccumulatorType, MaskBuilder - - +import numpy as np +import numpy.typing as npt import tempfile from pathlib import Path from typing import Any +from jaxtyping import Int64 class NumpyMemMapMaskBuilderAllocatorMixin(MaskBuilder): diff --git a/ratiopath/masks/write_big_tiff.py b/ratiopath/masks/write_big_tiff.py index ca2db19..1b79e09 100644 --- a/ratiopath/masks/write_big_tiff.py +++ b/ratiopath/masks/write_big_tiff.py @@ -1,4 +1,3 @@ -import warnings from pathlib import Path import pyvips From dc4b7be6f5d9e353200435010960348f650298e6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adam=20V=C3=A1clav=20Bajger?= <33228382+AdamBajger@users.noreply.github.com> Date: Fri, 9 Jan 2026 15:57:18 +0100 Subject: [PATCH 05/50] Update ratiopath/masks/mask_builders/receptive_field_manipulation.py Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> --- .../masks/mask_builders/receptive_field_manipulation.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/ratiopath/masks/mask_builders/receptive_field_manipulation.py b/ratiopath/masks/mask_builders/receptive_field_manipulation.py index fd4f1d7..81b4a90 100644 --- a/ratiopath/masks/mask_builders/receptive_field_manipulation.py +++ b/ratiopath/masks/mask_builders/receptive_field_manipulation.py @@ -54,10 +54,11 @@ def update_batch( strict=True, ) ) - adjusterd_coords_batch = coords_batch + self.clip_start_indices[:, np.newaxis] + adjusted_coords_batch = coords_batch + self.clip_start_indices[:, np.newaxis] super().update_batch( data_batch=data_batch[..., *slices], # type: ignore[arg-type] - coords_batch=adjusterd_coords_batch, + coords_batch=adjusted_coords_batch, + ) ) From f4ec8c76bf1327fc90a6b9e9389860441ef4174f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adam=20V=C3=A1clav=20Bajger?= <33228382+AdamBajger@users.noreply.github.com> Date: Fri, 9 Jan 2026 15:58:18 +0100 Subject: [PATCH 06/50] Update pyproject.toml Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- pyproject.toml | 1 - 1 file changed, 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index e739df9..65d0f26 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,7 +28,6 @@ dependencies = [ "geopandas>=1.1.1", "rasterio>=1.4.3", "jaxtyping", - ] [dependency-groups] From 3ded4966f348a724068cba09d50671706a1a21ca Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adam=20V=C3=A1clav=20Bajger?= <33228382+AdamBajger@users.noreply.github.com> Date: Fri, 9 Jan 2026 16:01:13 +0100 Subject: [PATCH 07/50] fix: overlap naming convention Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> --- tests/test_mask_builders.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_mask_builders.py b/tests/test_mask_builders.py index 6337f3e..57a7da2 100644 --- a/tests/test_mask_builders.py +++ b/tests/test_mask_builders.py @@ -314,7 +314,7 @@ def test_numpy_memmap_persistent_file(tmp_path): clip=1, channels=1, accumulator_filepath=filepath, - overlap_counter_filepath=filepath.with_suffix(".overlap"+filepath.suffix), + overlap_counter_filepath=filepath.with_suffix(".overlaps"+filepath.suffix), ) tile_batch = np.ones((1, 1, *mask_tile_extents), dtype=np.float32) From 215b9625bed5204d44d6b18edd874d6aea9eeff5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adam=20V=C3=A1clav=20Bajger?= <33228382+AdamBajger@users.noreply.github.com> Date: Fri, 9 Jan 2026 16:07:27 +0100 Subject: [PATCH 08/50] fix: all imports correction Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> --- ratiopath/masks/mask_builders/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/ratiopath/masks/mask_builders/__init__.py b/ratiopath/masks/mask_builders/__init__.py index 743072e..245aaae 100644 --- a/ratiopath/masks/mask_builders/__init__.py +++ b/ratiopath/masks/mask_builders/__init__.py @@ -259,6 +259,7 @@ def get_vips_scale_factors(self) -> tuple[float, float]: "AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D", "AveragingScalarUniformTiledNumpyMaskBuilder", "MaxScalarUniformTiledNumpyMaskBuilder", + "AutoScalingScalarUniformValueConstantStrideMaskBuilder", ] From a4e82027b6cf8b40565d96205de8b29360c3436d Mon Sep 17 00:00:00 2001 From: Adam Bajger <469113@mail.muni.cz> Date: Fri, 9 Jan 2026 16:10:07 +0100 Subject: [PATCH 09/50] refactor: move __all__ exports to top in mask builder module __init__ --- ratiopath/masks/mask_builders/__init__.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/ratiopath/masks/mask_builders/__init__.py b/ratiopath/masks/mask_builders/__init__.py index 245aaae..8ffb713 100644 --- a/ratiopath/masks/mask_builders/__init__.py +++ b/ratiopath/masks/mask_builders/__init__.py @@ -8,6 +8,14 @@ from ratiopath.masks.mask_builders.storage import NumpyArrayMaskBuilderAllocatorMixin, NumpyMemMapMaskBuilderAllocatorMixin from jaxtyping import Int64 + +__all__ = [ + "AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D", + "AveragingScalarUniformTiledNumpyMaskBuilder", + "MaxScalarUniformTiledNumpyMaskBuilder", + "AutoScalingScalarUniformValueConstantStrideMaskBuilder", +] + class AveragingScalarUniformTiledNumpyMaskBuilder( NumpyArrayMaskBuilderAllocatorMixin, ScalarUniformTiledMaskBuilder, @@ -255,12 +263,6 @@ def get_vips_scale_factors(self) -> tuple[float, float]: return tuple(scale_factors) # TODO: add tests for this method -__all__ = [ - "AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D", - "AveragingScalarUniformTiledNumpyMaskBuilder", - "MaxScalarUniformTiledNumpyMaskBuilder", - "AutoScalingScalarUniformValueConstantStrideMaskBuilder", -] class AutoScalingScalarUniformValueConstantStrideMaskBuilder( From c5405cfe6166c4725e261d9faa81b78c489e53b7 Mon Sep 17 00:00:00 2001 From: Adam Bajger <469113@mail.muni.cz> Date: Fri, 9 Jan 2026 16:15:01 +0100 Subject: [PATCH 10/50] chore: sync lockfile --- uv.lock | 2211 ++++++++++++++++++++++++++++++++----------------------- 1 file changed, 1270 insertions(+), 941 deletions(-) diff --git a/uv.lock 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+0100 Subject: [PATCH 11/50] fix: typo bracket --- ratiopath/masks/mask_builders/receptive_field_manipulation.py | 1 - 1 file changed, 1 deletion(-) diff --git a/ratiopath/masks/mask_builders/receptive_field_manipulation.py b/ratiopath/masks/mask_builders/receptive_field_manipulation.py index 81b4a90..94f4dd4 100644 --- a/ratiopath/masks/mask_builders/receptive_field_manipulation.py +++ b/ratiopath/masks/mask_builders/receptive_field_manipulation.py @@ -59,7 +59,6 @@ def update_batch( data_batch=data_batch[..., *slices], # type: ignore[arg-type] coords_batch=adjusted_coords_batch, ) - ) class EdgeClippingMaskBuilder2DMixin(EdgeClippingMaskBuilderMixin): From 39c34c6d5aa5e33d11d9681a88d0c5c3a04a37f1 Mon Sep 17 00:00:00 2001 From: Adam Bajger <469113@mail.muni.cz> Date: Fri, 9 Jan 2026 16:30:22 +0100 Subject: [PATCH 12/50] chore: ruff check and format --- ratiopath/masks/mask_builders/__init__.py | 47 +++++++---- ratiopath/masks/mask_builders/aggregation.py | 19 +++-- ratiopath/masks/mask_builders/mask_builder.py | 26 +++--- .../receptive_field_manipulation.py | 34 ++++---- ratiopath/masks/mask_builders/storage.py | 10 ++- tests/test_mask_builders.py | 82 +++++++++++-------- 6 files changed, 132 insertions(+), 86 deletions(-) diff --git a/ratiopath/masks/mask_builders/__init__.py b/ratiopath/masks/mask_builders/__init__.py index 8ffb713..a9f9045 100644 --- a/ratiopath/masks/mask_builders/__init__.py +++ b/ratiopath/masks/mask_builders/__init__.py @@ -1,21 +1,34 @@ from pathlib import Path -from ratiopath.masks.mask_builders.aggregation import AveragingMaskBuilderMixin, MaxMaskBuilderMixin from typing import Any + +from jaxtyping import Int64 + +from ratiopath.masks.mask_builders.aggregation import ( + AveragingMaskBuilderMixin, + MaxMaskBuilderMixin, +) from ratiopath.masks.mask_builders.mask_builder import ( AccumulatorType, ) -from ratiopath.masks.mask_builders.receptive_field_manipulation import AutoScalingConstantStrideMixin, EdgeClippingMaskBuilder2DMixin, ScalarUniformTiledMaskBuilder -from ratiopath.masks.mask_builders.storage import NumpyArrayMaskBuilderAllocatorMixin, NumpyMemMapMaskBuilderAllocatorMixin -from jaxtyping import Int64 +from ratiopath.masks.mask_builders.receptive_field_manipulation import ( + AutoScalingConstantStrideMixin, + EdgeClippingMaskBuilder2DMixin, + ScalarUniformTiledMaskBuilder, +) +from ratiopath.masks.mask_builders.storage import ( + NumpyArrayMaskBuilderAllocatorMixin, + NumpyMemMapMaskBuilderAllocatorMixin, +) __all__ = [ "AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D", + "AutoScalingScalarUniformValueConstantStrideMaskBuilder", "AveragingScalarUniformTiledNumpyMaskBuilder", "MaxScalarUniformTiledNumpyMaskBuilder", - "AutoScalingScalarUniformValueConstantStrideMaskBuilder", ] + class AveragingScalarUniformTiledNumpyMaskBuilder( NumpyArrayMaskBuilderAllocatorMixin, ScalarUniformTiledMaskBuilder, @@ -236,7 +249,14 @@ def __init__( overlap_counter_filepath=overlap_counter_filepath, ) - def setup_memory(self, mask_extents, channels, accumulator_filepath=None, overlap_counter_filepath=None, **kwargs) -> None: + def setup_memory( + self, + mask_extents, + channels, + accumulator_filepath=None, + overlap_counter_filepath=None, + **kwargs, + ) -> None: self.accumulator = self.allocate_accumulator( mask_extents=mask_extents, channels=channels, filepath=accumulator_filepath ) @@ -254,22 +274,23 @@ def setup_memory(self, mask_extents, channels, accumulator_filepath=None, overla def get_vips_scale_factors(self) -> tuple[float, float]: """Get the scaling factors to convert the built mask back to the original source resolution. - The ideas is to obtain coefficients for the pyvips.affine() function to rescale the assembled mask - back to the original source resolution after assembly and finalization. + The ideas is to obtain coefficients for the pyvips.affine() function to rescale the assembled mask + back to the original source resolution after assembly and finalization. + Returns: tuple[float, float]: Scaling factors for height and width dimensions. """ - scale_factors = self.overflow_buffered_source_extents / self.accumulator.shape[1:] # H, W + scale_factors = ( + self.overflow_buffered_source_extents / self.accumulator.shape[1:] + ) # H, W return tuple(scale_factors) # TODO: add tests for this method - - class AutoScalingScalarUniformValueConstantStrideMaskBuilder( NumpyArrayMaskBuilderAllocatorMixin, AutoScalingConstantStrideMixin, ScalarUniformTiledMaskBuilder, - AveragingMaskBuilderMixin + AveragingMaskBuilderMixin, ): """Mask builder combining auto-scaling with scalar uniform tiling. @@ -324,5 +345,3 @@ def __init__( channels=channels, **kwargs, ) - - diff --git a/ratiopath/masks/mask_builders/aggregation.py b/ratiopath/masks/mask_builders/aggregation.py index c7534b0..72d1794 100644 --- a/ratiopath/masks/mask_builders/aggregation.py +++ b/ratiopath/masks/mask_builders/aggregation.py @@ -1,9 +1,13 @@ -from ratiopath.masks.mask_builders.mask_builder import AccumulatorType, MaskBuilder, compute_acc_slices +from typing import Any import numpy as np -import numpy.typing as npt -from typing import Any -from jaxtyping import Shaped, Int64 +from jaxtyping import Int64, Shaped + +from ratiopath.masks.mask_builders.mask_builder import ( + AccumulatorType, + MaskBuilder, + compute_acc_slices, +) class AveragingMaskBuilderMixin(MaskBuilder): @@ -80,7 +84,9 @@ def update_batch_impl( data_batch: Shaped[AccumulatorType, "B C *SpatialDims"], coords_batch: Shaped[AccumulatorType, "N B"], ) -> None: - mask_tile_extents = np.asarray(data_batch.shape[2:], dtype=np.int64) # H, W, ... + mask_tile_extents = np.asarray( + data_batch.shape[2:], dtype=np.int64 + ) # H, W, ... acc_slices_all_dims = compute_acc_slices( coords_batch=coords_batch, mask_tile_extents=mask_tile_extents, # H, W, ... @@ -101,6 +107,5 @@ def update_batch_impl( data, ) - def finalize(self) -> tuple[AccumulatorType]: - return (self.accumulator,) \ No newline at end of file + return (self.accumulator,) diff --git a/ratiopath/masks/mask_builders/mask_builder.py b/ratiopath/masks/mask_builders/mask_builder.py index 2208e24..e108f07 100644 --- a/ratiopath/masks/mask_builders/mask_builder.py +++ b/ratiopath/masks/mask_builders/mask_builder.py @@ -1,5 +1,4 @@ import logging -import math from abc import ABC, abstractmethod from typing import Any, TypeVar @@ -29,14 +28,17 @@ def compute_acc_slices( List of N lists, each containing B slice objects for indexing into accumulator. """ acc_ends = coords_batch + mask_tile_extents[:, np.newaxis] # shape (N, B) - + acc_slices_batch_per_dim = [] for dimension in range(coords_batch.shape[0]): - acc_slices_batch_per_dim.append([ - slice(start, end) - for start, end - in zip(coords_batch[dimension], acc_ends[dimension], strict=True) - ]) + acc_slices_batch_per_dim.append( + [ + slice(start, end) + for start, end in zip( + coords_batch[dimension], acc_ends[dimension], strict=True + ) + ] + ) return acc_slices_batch_per_dim @@ -83,17 +85,15 @@ def allocate_accumulator( def setup_memory(self, mask_extents, channels, **kwargs) -> None: """This method sets up memory structures needed for mask building. - + This methods can be overridden by mixins or concrete builders to set up any necessary memory structures. - + Some builders may require additional accumulators or data structures beyond the main accumulator. Some mixins may require temporary files for memory-mapped storage. All such setup should be defined in an overridden version of this method, which will be called by the base constructor after the initialisation parameters are set by all classes/mixins in the MRO chain. """ - self.accumulator = self.allocate_accumulator( - mask_extents, channels, **kwargs - ) + self.accumulator = self.allocate_accumulator(mask_extents, channels, **kwargs) def update_batch( self, @@ -156,5 +156,3 @@ def finalize(self) -> tuple[AccumulatorType, ...] | AccumulatorType: Additional elements may include auxiliary data like overlap counters. """ ... - - diff --git a/ratiopath/masks/mask_builders/receptive_field_manipulation.py b/ratiopath/masks/mask_builders/receptive_field_manipulation.py index 94f4dd4..34790db 100644 --- a/ratiopath/masks/mask_builders/receptive_field_manipulation.py +++ b/ratiopath/masks/mask_builders/receptive_field_manipulation.py @@ -1,9 +1,10 @@ -from ratiopath.masks.mask_builders.mask_builder import AccumulatorType, MaskBuilder - - from typing import Any -from jaxtyping import Shaped, Int64 + import numpy as np +from jaxtyping import Int64, Shaped + +from ratiopath.masks.mask_builders.mask_builder import AccumulatorType, MaskBuilder + class EdgeClippingMaskBuilderMixin(MaskBuilder): """Mixin that clips edge pixels from tiles before accumulation. @@ -135,7 +136,7 @@ class AutoScalingConstantStrideMixin(MaskBuilder): This mixin addresses this by calculating the scale factors from the input tile extents and output mask tile extents, adjusting the input coordinates accordingly before passing them to the next handler in the chain. - This mixin also automatically computes the built mask spatial dimensions based on the tiled input total + This mixin also automatically computes the built mask spatial dimensions based on the tiled input total extent and the computed scale factors. Furthermore, this mixin readjusts the mask extent to cover for potential partial tiles at the edges. @@ -173,7 +174,7 @@ def __init__( self.source_tile_extents = source_tile_extents self.mask_tile_extents = mask_tile_extents - multiplied_ = (source_tile_strides * self.mask_tile_extents) + multiplied_ = source_tile_strides * self.mask_tile_extents # Ensure source_tile_strides * self.mask_tile_extents is divisible by self.source_tile_extents to avoid fractional strides if not np.all(multiplied_ % self.source_tile_extents == 0): raise ValueError( @@ -185,10 +186,13 @@ def __init__( # adjusted_mask_extents = (source_extents // self.source_tile_extents) * self.mask_tile_extents total_strides = (source_extents - source_tile_extents) / source_tile_strides total_strides = np.ceil(total_strides).astype(np.int64) - # without the initial tile step, including partial tile at the edge - self.overflow_buffered_source_extents = (total_strides * source_tile_strides) + source_tile_extents - overflow_buffered_mask_extents = (total_strides * adjusted_mask_tile_strides) + self.mask_tile_extents - + # without the initial tile step, including partial tile at the edge + self.overflow_buffered_source_extents = ( + total_strides * source_tile_strides + ) + source_tile_extents + overflow_buffered_mask_extents = ( + total_strides * adjusted_mask_tile_strides + ) + self.mask_tile_extents # Call next in MRO with computed parameters super().__init__( @@ -204,10 +208,10 @@ def update_batch( data_batch: Shaped[AccumulatorType, "B C *SpatialDims"], coords_batch: Shaped[AccumulatorType, "N B"], ) -> None: - adjusted_coords_batch = ((coords_batch * self.mask_tile_extents[:, np.newaxis]) // self.source_tile_extents[:, np.newaxis]) - super().update_batch( - data_batch=data_batch, coords_batch=adjusted_coords_batch - ) + adjusted_coords_batch = ( + coords_batch * self.mask_tile_extents[:, np.newaxis] + ) // self.source_tile_extents[:, np.newaxis] + super().update_batch(data_batch=data_batch, coords_batch=adjusted_coords_batch) class ScalarUniformTiledMaskBuilder(MaskBuilder): @@ -266,4 +270,4 @@ def update_batch( ..., *[np.newaxis] * len(self.adjusted_tile_extents) ] adjusted_coordinates = coords_batch // self.compression_factors[:, np.newaxis] - super().update_batch(adjusted_tiles, coords_batch=adjusted_coordinates) \ No newline at end of file + super().update_batch(adjusted_tiles, coords_batch=adjusted_coordinates) diff --git a/ratiopath/masks/mask_builders/storage.py b/ratiopath/masks/mask_builders/storage.py index 5b2f606..7302cbc 100644 --- a/ratiopath/masks/mask_builders/storage.py +++ b/ratiopath/masks/mask_builders/storage.py @@ -1,11 +1,13 @@ -from ratiopath.masks.mask_builders.mask_builder import AccumulatorType, MaskBuilder -import numpy as np -import numpy.typing as npt import tempfile from pathlib import Path from typing import Any + +import numpy as np +import numpy.typing as npt from jaxtyping import Int64 +from ratiopath.masks.mask_builders.mask_builder import AccumulatorType, MaskBuilder + class NumpyMemMapMaskBuilderAllocatorMixin(MaskBuilder): """Mixin class to allocate accumulators as numpy memory-mapped files (memmaps). @@ -70,4 +72,4 @@ def allocate_accumulator( dtype: npt.DTypeLike = np.float32, **kwargs: Any, ) -> np.ndarray: - return np.zeros((channels, *mask_extents), dtype=dtype) \ No newline at end of file + return np.zeros((channels, *mask_extents), dtype=dtype) diff --git a/tests/test_mask_builders.py b/tests/test_mask_builders.py index 57a7da2..495c212 100644 --- a/tests/test_mask_builders.py +++ b/tests/test_mask_builders.py @@ -4,10 +4,11 @@ import numpy as np import pytest -from ratiopath.masks.mask_builders import AveragingScalarUniformTiledNumpyMaskBuilder, MaxScalarUniformTiledNumpyMaskBuilder -from ratiopath.masks.mask_builders import AutoScalingScalarUniformValueConstantStrideMaskBuilder from ratiopath.masks.mask_builders import ( AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D, + AutoScalingScalarUniformValueConstantStrideMaskBuilder, + AveragingScalarUniformTiledNumpyMaskBuilder, + MaxScalarUniformTiledNumpyMaskBuilder, ) @@ -40,7 +41,9 @@ def test_scalar_uniform_averaging_2d( for i in range(num_batches): coords_batch = np.random.rand(len(mask_extents), batch_size) - coords_batch *= (np.array(mask_extents) - np.array(mask_tile_extents))[:, np.newaxis] + coords_batch *= (np.array(mask_extents) - np.array(mask_tile_extents))[ + :, np.newaxis + ] # force alignment to gcds coords_batch = (coords_batch.astype(np.int64) // gcds[:, np.newaxis]) * gcds[ :, np.newaxis @@ -78,8 +81,9 @@ def test_scalar_uniform_averaging_2d( @pytest.mark.parametrize("channels", [1, 3, 8]) @pytest.mark.parametrize("mask_tile_extents", [(4, 4), (4, 8), (8, 4), (3, 6), (9, 6)]) @pytest.mark.parametrize("mask_tile_strides", [(2, 2), (3, 3), (3, 2), (2, 3)]) -def test_scalar_uniform_max_2d(mask_extents, channels, mask_tile_extents, mask_tile_strides): - +def test_scalar_uniform_max_2d( + mask_extents, channels, mask_tile_extents, mask_tile_strides +): mask_extents = np.asarray(mask_extents) mask_tile_extents = np.asarray(mask_tile_extents) mask_tile_strides = np.asarray(mask_tile_strides) @@ -139,14 +143,22 @@ def test_scalar_uniform_max_2d(mask_extents, channels, mask_tile_extents, mask_t @pytest.mark.parametrize("acc_filepath", [None, "test_heatamp.npy"]) @pytest.mark.parametrize("overlap_counter_filepath", [None, "test_overlap.counter.npy"]) def test_edge_clipping_heatmap_assembler( - clip, tile_extents, channels, mask_extents, acc_filepath, overlap_counter_filepath, tmp_path + clip, + tile_extents, + channels, + mask_extents, + acc_filepath, + overlap_counter_filepath, + tmp_path, ): # no mismatch in source and mask extents here mask_extents = np.asarray(mask_extents) tile_extents = np.asarray(tile_extents) tile_strides = np.asarray((4, 4), dtype=np.int64) - total_strides = np.ceil((mask_extents - tile_extents) / tile_strides).astype(np.int64) + total_strides = np.ceil((mask_extents - tile_extents) / tile_strides).astype( + np.int64 + ) expected_mask_extents = total_strides * tile_strides + tile_extents num_batches = 4 @@ -189,9 +201,9 @@ def test_edge_clipping_heatmap_assembler( # add the tiles randomly to cover the heatmap for i in range(num_batches): coords_batch = np.random.rand(len(mask_extents), batch_size) - coords_batch *= ( - np.asarray(mask_extents) - (np.asarray(tile_extents) - clip) - )[:, np.newaxis] + coords_batch *= (np.asarray(mask_extents) - (np.asarray(tile_extents) - clip))[ + :, np.newaxis + ] coords_batch = coords_batch.astype(np.int64) assembler.update_batch(example_tile_batch, coords_batch) # plt.imshow((assembler.accumulator.transpose(1, 2, 0)*32).astype(np.uint8)) @@ -314,20 +326,20 @@ def test_numpy_memmap_persistent_file(tmp_path): clip=1, channels=1, accumulator_filepath=filepath, - overlap_counter_filepath=filepath.with_suffix(".overlaps"+filepath.suffix), + overlap_counter_filepath=filepath.with_suffix(".overlaps" + filepath.suffix), ) tile_batch = np.ones((1, 1, *mask_tile_extents), dtype=np.float32) assembler.update_batch(tile_batch, coords_batch=np.asarray([[0], [0]])) - + del assembler assert filepath.exists(), ( f"Persistent file {filepath} should exist after finalization" ) - assert filepath.with_suffix(".overlap"+filepath.suffix).exists(), ( - f"Persistent overlap file {filepath.with_suffix('.overlap'+filepath.suffix)} should exist after finalization" + assert filepath.with_suffix(".overlap" + filepath.suffix).exists(), ( + f"Persistent overlap file {filepath.with_suffix('.overlap' + filepath.suffix)} should exist after finalization" ) # Clean up @@ -344,14 +356,12 @@ def test_autoscaling_scalar_uniform_value_constant_stride( """Test AutoScalingScalarUniformValueConstantStrideMaskBuilder with autoscaling and scalar tiling.""" batch_size = 4 num_batches = 8 - + source_extents = np.asarray(source_extents) source_tile_extents = np.asarray(source_tile_extents) source_tile_strides = source_tile_extents // 2 # 50% overlap mask_tile_extents = np.asarray(mask_tile_extents) - - builder = AutoScalingScalarUniformValueConstantStrideMaskBuilder( source_extents=source_extents, channels=channels, @@ -359,42 +369,48 @@ def test_autoscaling_scalar_uniform_value_constant_stride( source_tile_strides=source_tile_strides, mask_tile_extents=mask_tile_extents, ) - + mask_tile_strides = (source_tile_strides * mask_tile_extents) // source_tile_extents - total_strides = np.ceil((source_extents - source_tile_extents) / source_tile_strides).astype(np.int64) + total_strides = np.ceil( + (source_extents - source_tile_extents) / source_tile_strides + ).astype(np.int64) expected_mask_extents = total_strides * mask_tile_strides + mask_tile_extents - + gcds = np.gcd(mask_tile_extents, mask_tile_strides) adjusted_mask_tile_extents = mask_tile_extents // gcds compressed_mask_extents = expected_mask_extents // gcds - + # Verify accumulator shape matches adjusted dimensions assert builder.accumulator.shape == (channels, *compressed_mask_extents), ( f"Accumulator shape mismatch: {builder.accumulator.shape} vs expected {(channels, *compressed_mask_extents)}" ) - + # Create scalar batch data (B, C) scalar_data = np.ones((batch_size, channels), dtype=np.float32) - + for i in range(num_batches): # Generate coordinates in SOURCE space coords_batch = np.random.rand(len(source_extents), batch_size) coords_batch *= (source_extents - source_tile_extents)[:, np.newaxis] - + # Align to source tile strides - coords_batch = (coords_batch // source_tile_strides[:, np.newaxis]) * source_tile_strides[:, np.newaxis] + coords_batch = ( + coords_batch // source_tile_strides[:, np.newaxis] + ) * source_tile_strides[:, np.newaxis] coords_batch = coords_batch.astype(np.int64) - + builder.update_batch(scalar_data, coords_batch) - + # Verify accumulator sum increases - expected_increment = (i + 1) * batch_size * channels * np.prod(adjusted_mask_tile_extents) + expected_increment = ( + (i + 1) * batch_size * channels * np.prod(adjusted_mask_tile_extents) + ) assert np.isclose(builder.accumulator.sum(), expected_increment, atol=1e-5), ( f"Accumulator sum after batch {i + 1}: {builder.accumulator.sum()} vs expected {expected_increment}" ) - + assembled, overlap = builder.finalize() - + # Verify output shapes assert assembled.shape == (channels, *compressed_mask_extents), ( f"Assembled mask shape mismatch: {assembled.shape} vs {(channels, *compressed_mask_extents)}" @@ -402,11 +418,13 @@ def test_autoscaling_scalar_uniform_value_constant_stride( assert overlap.shape == (1, *compressed_mask_extents), ( f"Overlap counter shape mismatch: {overlap.shape} vs {(1, *compressed_mask_extents)}" ) - + # Verify values are averaged correctly assert np.isclose(assembled.max(), 1.0, atol=1e-5), ( f"Max value should be ~1.0 after averaging: {assembled.max()}" ) - assert overlap.sum() == num_batches * batch_size * np.prod(adjusted_mask_tile_extents), ( + assert overlap.sum() == num_batches * batch_size * np.prod( + adjusted_mask_tile_extents + ), ( f"Overlap sum mismatch: {overlap.sum()} vs expected {num_batches * batch_size * np.prod(adjusted_mask_tile_extents)}" ) From 40807ee790a096954cb88a5da5a3c60ee8c60f11 Mon Sep 17 00:00:00 2001 From: Adam Bajger <469113@mail.muni.cz> Date: Fri, 9 Jan 2026 16:43:48 +0100 Subject: [PATCH 13/50] debug: install libvips and openslide --- .github/workflows/tests.yml | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index d747be4..f26938c 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -14,6 +14,14 @@ jobs: steps: - uses: actions/checkout@v4 + # Install Openslide dependencies + - name: Install Openslide dependencies + run: | + sudo apt update + sudo apt install python3-openslide + + - name: Setup LibVips + run: sudo apt install -y libvips-dev - name: Install uv uses: astral-sh/setup-uv@v5 From e285b6a7c05e75d04ad58bc56947a2218aea8201 Mon Sep 17 00:00:00 2001 From: Adam Bajger <469113@mail.muni.cz> Date: Fri, 9 Jan 2026 17:01:42 +0100 Subject: [PATCH 14/50] fix: test filenames --- .github/workflows/tests.yml | 1 + tests/test_mask_builders.py | 4 ++-- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index f26938c..c2da84d 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -22,6 +22,7 @@ jobs: - name: Setup LibVips run: sudo apt install -y libvips-dev + - name: Install uv uses: astral-sh/setup-uv@v5 diff --git a/tests/test_mask_builders.py b/tests/test_mask_builders.py index 495c212..e6818ce 100644 --- a/tests/test_mask_builders.py +++ b/tests/test_mask_builders.py @@ -338,8 +338,8 @@ def test_numpy_memmap_persistent_file(tmp_path): f"Persistent file {filepath} should exist after finalization" ) - assert filepath.with_suffix(".overlap" + filepath.suffix).exists(), ( - f"Persistent overlap file {filepath.with_suffix('.overlap' + filepath.suffix)} should exist after finalization" + assert filepath.with_suffix(".overlaps" + filepath.suffix).exists(), ( + f"Persistent overlap file {filepath.with_suffix('.overlaps' + filepath.suffix)} should exist after finalization" ) # Clean up From 561cb6a009b843e60a3d56c56b71277382ab41c7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adam=20Kuku=C4=8Dka?= Date: Fri, 9 Jan 2026 18:02:47 +0000 Subject: [PATCH 15/50] fix: update geopandas dependency version and refactor GeoJSONParser logic --- pyproject.toml | 2 +- ratiopath/parsers/geojson_parser.py | 6 +----- 2 files changed, 2 insertions(+), 6 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 65d0f26..0590c3a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,7 +25,7 @@ dependencies = [ "shapely>=2.0.0", "torch>=2.6.0", "zarr>=3.1.1", - "geopandas>=1.1.1", + "geopandas>=1.1.2", "rasterio>=1.4.3", "jaxtyping", ] diff --git a/ratiopath/parsers/geojson_parser.py b/ratiopath/parsers/geojson_parser.py index 5252956..cb1d5aa 100644 --- a/ratiopath/parsers/geojson_parser.py +++ b/ratiopath/parsers/geojson_parser.py @@ -43,11 +43,7 @@ def get_filtered_geodataframe( # If the first part of the key doesn't exist, return an empty frame return self.gdf.iloc[0:0] - series = filtered_gdf[subkeys[0]].astype(str) - if len(subkeys) > 1: - mask = series.apply(is_json_dict) - series = series[mask].apply(lambda x: json.loads(x)) - filtered_gdf = filtered_gdf[mask] + series = filtered_gdf[subkeys[0]] for subkey in subkeys[1:]: mask = series.apply( From 6531f875b11c94f2d7e0236a6e9cb03b8147d5f7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mat=C4=9Bj=20Pek=C3=A1r?= Date: Sat, 10 Jan 2026 17:58:40 +0100 Subject: [PATCH 16/50] fix: update lock + freeze sync --- .github/workflows/build-docs.yml | 4 ++-- .github/workflows/publish.yml | 4 ++-- .github/workflows/tests.yml | 4 ++-- uv.lock | 16 ++++++++-------- 4 files changed, 14 insertions(+), 14 deletions(-) diff --git a/.github/workflows/build-docs.yml b/.github/workflows/build-docs.yml index 53e27b5..79fe88b 100644 --- a/.github/workflows/build-docs.yml +++ b/.github/workflows/build-docs.yml @@ -18,12 +18,12 @@ jobs: run: | git config user.name github-actions[bot] git config user.email 41898282+github-actions[bot]@users.noreply.github.com - + - name: Install uv uses: astral-sh/setup-uv@v6 - name: Install dependencies - run: uv sync --locked --extra docs + run: uv sync --frozen --extra docs - name: Cache MkDocs / Material run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 5d3d936..5e0adb7 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -20,8 +20,8 @@ jobs: uses: astral-sh/setup-uv@v6 - name: Install dependencies - run: uv sync --locked - + run: uv sync --frozen + - name: Build package run: uv build diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index c2da84d..0ca7da2 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -19,7 +19,7 @@ jobs: run: | sudo apt update sudo apt install python3-openslide - + - name: Setup LibVips run: sudo apt install -y libvips-dev @@ -27,7 +27,7 @@ jobs: uses: astral-sh/setup-uv@v5 - name: Install dependencies - run: uv sync --dev --extra tests + run: uv sync --frozen --dev --extra tests - name: Run Pytest run: uv run pytest diff --git a/uv.lock b/uv.lock index 1deccaf..fceb0d9 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 2 +revision = 3 requires-python = ">=3.12" resolution-markers = [ "sys_platform == 'darwin'", @@ -939,7 +939,7 @@ name = "nvidia-cudnn-cu12" version = "9.10.2.21" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "nvidia-cublas-cu12" }, + { name = "nvidia-cublas-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/ba/51/e123d997aa098c61d029f76663dedbfb9bc8dcf8c60cbd6adbe42f76d049/nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:949452be657fa16687d0930933f032835951ef0892b37d2d53824d1a84dc97a8", size = 706758467, upload-time = "2025-06-06T21:54:08.597Z" }, @@ -950,7 +950,7 @@ name = "nvidia-cufft-cu12" version = "11.3.3.83" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "nvidia-nvjitlink-cu12" }, + { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/1f/13/ee4e00f30e676b66ae65b4f08cb5bcbb8392c03f54f2d5413ea99a5d1c80/nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4d2dd21ec0b88cf61b62e6b43564355e5222e4a3fb394cac0db101f2dd0d4f74", size = 193118695, upload-time = "2025-03-07T01:45:27.821Z" }, @@ -977,9 +977,9 @@ name = "nvidia-cusolver-cu12" version = "11.7.3.90" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "nvidia-cublas-cu12" }, - { name = "nvidia-cusparse-cu12" }, - { name = "nvidia-nvjitlink-cu12" }, + { name = "nvidia-cublas-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" }, + { name = "nvidia-cusparse-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" }, + { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/85/48/9a13d2975803e8cf2777d5ed57b87a0b6ca2cc795f9a4f59796a910bfb80/nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:4376c11ad263152bd50ea295c05370360776f8c3427b30991df774f9fb26c450", size = 267506905, upload-time = "2025-03-07T01:47:16.273Z" }, @@ -990,7 +990,7 @@ name = "nvidia-cusparse-cu12" version = "12.5.8.93" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "nvidia-nvjitlink-cu12" }, + { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/c2/f5/e1854cb2f2bcd4280c44736c93550cc300ff4b8c95ebe370d0aa7d2b473d/nvidia_cusparse_cu12-12.5.8.93-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1ec05d76bbbd8b61b06a80e1eaf8cf4959c3d4ce8e711b65ebd0443bb0ebb13b", size = 288216466, upload-time = "2025-03-07T01:48:13.779Z" }, @@ -1752,7 +1752,7 @@ dev = [ [package.metadata] requires-dist = [ { name = "albumentations", specifier = ">=2.0.8" }, - { name = "geopandas", specifier = ">=1.1.1" }, + { name = "geopandas", specifier = ">=1.1.2" }, { name = "imagecodecs", specifier = ">=2025.8.2" }, { name = "jaxtyping" }, { name = "mkdocs-material", marker = "extra == 'docs'", specifier = ">=9.6.18" }, From c2042d0127a8958445b1cb7eb6a5aaf001bf1421 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mat=C4=9Bj=20Pek=C3=A1r?= Date: Sat, 10 Jan 2026 18:05:54 +0100 Subject: [PATCH 17/50] fix: mypy --- ratiopath/tiling/overlays.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ratiopath/tiling/overlays.py b/ratiopath/tiling/overlays.py index 59a0b56..1b45670 100644 --- a/ratiopath/tiling/overlays.py +++ b/ratiopath/tiling/overlays.py @@ -273,4 +273,4 @@ def overlap_fraction(overlay: np.ma.MaskedArray) -> dict[str, float]: overlap_vectorized = np.vectorize(overlap_fraction, otypes=[object]) - return pa.array(overlap_vectorized(overlay)) + return pa.array(overlap_vectorized(overlay)) # type: ignore [return-value] From fd1effb9ee316f45e63f7d4c22ed42514aba49f6 Mon Sep 17 00:00:00 2001 From: Adam Bajger <469113@mail.muni.cz> Date: Tue, 13 Jan 2026 19:12:15 +0100 Subject: [PATCH 18/50] fix: unlink overlaps file in tests which was previously left linked --- tests/test_mask_builders.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/test_mask_builders.py b/tests/test_mask_builders.py index e6818ce..4478053 100644 --- a/tests/test_mask_builders.py +++ b/tests/test_mask_builders.py @@ -344,6 +344,7 @@ def test_numpy_memmap_persistent_file(tmp_path): # Clean up filepath.unlink() + filepath.with_suffix(".overlaps" + filepath.suffix).unlink() @pytest.mark.parametrize("source_extents", [(32, 32), (64, 96)]) From 74d2180cc4bb9d00a6551aab37b4a4bca244e211 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Thu, 15 Jan 2026 14:52:30 +0000 Subject: [PATCH 19/50] Initial plan From f52a84e6d55bd92c8636205e23974078d8bd4ef1 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Thu, 15 Jan 2026 14:56:59 +0000 Subject: [PATCH 20/50] fix: correct module and class names in example code Co-authored-by: AdamBajger <33228382+AdamBajger@users.noreply.github.com> --- ratiopath/masks/mask_builders/__init__.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/ratiopath/masks/mask_builders/__init__.py b/ratiopath/masks/mask_builders/__init__.py index a9f9045..e98f85f 100644 --- a/ratiopath/masks/mask_builders/__init__.py +++ b/ratiopath/masks/mask_builders/__init__.py @@ -196,7 +196,7 @@ class AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D( Example: ```python import openslide - from rationai.masks.mask_builders import AveragingClippingNumpyMemMapMaskBuilder + from ratiopath.masks.mask_builders import AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D import matplotlib.pyplot as plt LEVEL = 3 @@ -205,13 +205,13 @@ class AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D( slide = openslide.OpenSlide("path/to/slide.mrxs") slide_extent_x, slide_extent_y = slide.dimensions[LEVEL] vgg16_model = load_rationai_vgg16_model(...) # load your pretrained model here - mask_builder = AveragingClippingNumpyMemMapMaskBuilder( - mask_extents=(slide_extent_y, slide_extent_x), + mask_builder = AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D( + source_extents=(slide_extent_y, slide_extent_x), + source_tile_extents=tile_extents, + source_tile_strides=tile_strides, + mask_tile_extents=tile_extents, channels=3, # for RGB masks - clip_top=4, - clip_bottom=4, - clip_left=4, - clip_right=4, + clip=(4, 4, 4, 4), # (clip_top, clip_bottom, clip_left, clip_right) ) for tiles, xs, ys in generate_tiles_from_slide( slide, LEVEL, tile_extents, tile_strides, batch_size=32 From d5747e3fce489056dc825696322a93c101506237 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Thu, 15 Jan 2026 14:58:33 +0000 Subject: [PATCH 21/50] fix: update docstring Args to match constructor signature Co-authored-by: AdamBajger <33228382+AdamBajger@users.noreply.github.com> --- ratiopath/masks/mask_builders/__init__.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/ratiopath/masks/mask_builders/__init__.py b/ratiopath/masks/mask_builders/__init__.py index e98f85f..59d484e 100644 --- a/ratiopath/masks/mask_builders/__init__.py +++ b/ratiopath/masks/mask_builders/__init__.py @@ -181,17 +181,20 @@ class AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D( The builder allocates two memmaps: - Main accumulator for tile data - - Overlap counter (auto-suffixed with `.overlaps` if filepath is provided) + - Overlap counter (can be specified via overlap_counter_filepath, or auto-derived with `.overlaps` suffix if accumulator_filepath is provided) Args: - mask_extents: Spatial dimensions of the full-resolution mask (height, width). + source_extents: Spatial dimensions of the source space (height, width). + source_tile_extents: Extents of tiles in the source space (height, width). + source_tile_strides: Strides between tiles in the source space (height, width). + mask_tile_extents: Extents of tiles in the mask space (height, width). channels: Number of channels in the tiles/mask. clip: Edge clipping specification. Accepts: - int: same clipping on all edges - (clip_y, clip_x): same for top/bottom and left/right - (clip_top, clip_bottom, clip_left, clip_right): individual edge control - filepath: Optional path for the main accumulator memmap. If None, uses temporary file. - The overlap counter will use the same path with `.overlaps` suffix inserted before extension. + accumulator_filepath: Optional path for the main accumulator memmap. If None, uses temporary file. + overlap_counter_filepath: Optional path for the overlap counter memmap. If None, derives from accumulator_filepath with `.overlaps` suffix. Example: ```python From ee62864ebd3276931321be61d5e234368008755b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adam=20V=C3=A1clav=20Bajger?= <33228382+AdamBajger@users.noreply.github.com> Date: Thu, 15 Jan 2026 16:04:00 +0100 Subject: [PATCH 22/50] Update tests/test_mask_builders.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- tests/test_mask_builders.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/test_mask_builders.py b/tests/test_mask_builders.py index 4478053..3ad640d 100644 --- a/tests/test_mask_builders.py +++ b/tests/test_mask_builders.py @@ -164,7 +164,6 @@ def test_edge_clipping_heatmap_assembler( num_batches = 4 batch_size = 8 - np.random.seed(0) if acc_filepath is not None: tmp_path.mkdir(parents=True, exist_ok=True) acc_filepath = Path((tmp_path / acc_filepath).as_posix()) From a62c3c8179ada510dbdabb2a003b93bb7ec973a1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adam=20V=C3=A1clav=20Bajger?= <33228382+AdamBajger@users.noreply.github.com> Date: Thu, 15 Jan 2026 16:32:57 +0100 Subject: [PATCH 23/50] Update ratiopath/masks/mask_builders/__init__.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- ratiopath/masks/mask_builders/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ratiopath/masks/mask_builders/__init__.py b/ratiopath/masks/mask_builders/__init__.py index a9f9045..3873ff7 100644 --- a/ratiopath/masks/mask_builders/__init__.py +++ b/ratiopath/masks/mask_builders/__init__.py @@ -120,7 +120,7 @@ class MaxScalarUniformTiledNumpyMaskBuilder( Example: ```python import openslide - from rationai.masks.mask_builders import MaxScalarUniformTiledNumpyMaskBuilder + from ratiopath.masks.mask_builders import MaxScalarUniformTiledNumpyMaskBuilder import matplotlib.pyplot as plt LEVEL = 3 From 3d67b058af5a7f7493ac292bf76ce2c595d257bd Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Thu, 15 Jan 2026 15:33:56 +0000 Subject: [PATCH 24/50] Initial plan From 75f443a0593eb98d3e6f80dbefa9df0aa362687d Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Thu, 15 Jan 2026 15:37:42 +0000 Subject: [PATCH 25/50] =?UTF-8?q?fix:=20correct=20typo=20in=20test=20docst?= =?UTF-8?q?ring=20(SImple=20=E2=86=92=20Simple)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: AdamBajger <33228382+AdamBajger@users.noreply.github.com> --- tests/test_mask_builders.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_mask_builders.py b/tests/test_mask_builders.py index 3ad640d..d00df9e 100644 --- a/tests/test_mask_builders.py +++ b/tests/test_mask_builders.py @@ -243,7 +243,7 @@ def test_edge_clipping_heatmap_assembler( def test_edge_clipping_clips_edges(): - """SImple test that ensures, that a tile put at [0,0], if clipped, does not write to the [0,0] corner of the heatmap.""" + """Simple test that ensures, that a tile put at [0,0], if clipped, does not write to the [0,0] corner of the heatmap.""" clip = 1 mask_extents = np.asarray((16, 16)) mask_tile_extents = np.asarray((8, 8)) From b5a60df6d9113f2a704e657d97ebf30ab27753fa Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adam=20V=C3=A1clav=20Bajger?= <33228382+AdamBajger@users.noreply.github.com> Date: Thu, 15 Jan 2026 22:05:14 +0100 Subject: [PATCH 26/50] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- ratiopath/masks/mask_builders/__init__.py | 4 ++-- ratiopath/masks/write_big_tiff.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/ratiopath/masks/mask_builders/__init__.py b/ratiopath/masks/mask_builders/__init__.py index def3e70..b88c2ff 100644 --- a/ratiopath/masks/mask_builders/__init__.py +++ b/ratiopath/masks/mask_builders/__init__.py @@ -51,7 +51,7 @@ class AveragingScalarUniformTiledNumpyMaskBuilder( Example: ```python import openslide - from rationai.masks.mask_builders import ( + from ratiopath.masks.mask_builders import ( AveragingScalarUniformTiledNumpyMaskBuilder, ) import matplotlib.pyplot as plt @@ -277,7 +277,7 @@ def setup_memory( def get_vips_scale_factors(self) -> tuple[float, float]: """Get the scaling factors to convert the built mask back to the original source resolution. - The ideas is to obtain coefficients for the pyvips.affine() function to rescale the assembled mask + The idea is to obtain coefficients for the pyvips.affine() function to rescale the assembled mask back to the original source resolution after assembly and finalization. Returns: diff --git a/ratiopath/masks/write_big_tiff.py b/ratiopath/masks/write_big_tiff.py index 1b79e09..57471f0 100644 --- a/ratiopath/masks/write_big_tiff.py +++ b/ratiopath/masks/write_big_tiff.py @@ -31,9 +31,9 @@ def write_big_tiff( mpp_x: The horizontal resolution of the image in µm/pixel. mpp_y: The vertical resolution of the image in µm/pixel. tile_width: The width of each tile in pixels. Default is 512. - Must match with other masks used in xOpath. + Must match with other masks used in xOpat. tile_height: The height of each tile in pixels. Default is 512. - Must match with other masks used in xOpath. + Must match with other masks used in xOpat. """ xres = 1000 / mpp_x yres = 1000 / mpp_y From 35565d51e3d66da4192e7719deb2016b27e66bc6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adam=20V=C3=A1clav=20Bajger?= <33228382+AdamBajger@users.noreply.github.com> Date: Thu, 15 Jan 2026 22:11:08 +0100 Subject: [PATCH 27/50] Update tests/test_mask_builders.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- tests/test_mask_builders.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_mask_builders.py b/tests/test_mask_builders.py index d00df9e..f0ece7d 100644 --- a/tests/test_mask_builders.py +++ b/tests/test_mask_builders.py @@ -140,7 +140,7 @@ def test_scalar_uniform_max_2d( @pytest.mark.parametrize("tile_extents", [(8, 8), (12, 9)]) @pytest.mark.parametrize("channels", [1, 5]) @pytest.mark.parametrize("mask_extents", [(32, 64), (64, 32), (100, 101)]) -@pytest.mark.parametrize("acc_filepath", [None, "test_heatamp.npy"]) +@pytest.mark.parametrize("acc_filepath", [None, "test_heatmap.npy"]) @pytest.mark.parametrize("overlap_counter_filepath", [None, "test_overlap.counter.npy"]) def test_edge_clipping_heatmap_assembler( clip, From bc94f4e7e84826036b3efca0c0fad1c1a76a0f39 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Thu, 15 Jan 2026 21:19:29 +0000 Subject: [PATCH 28/50] Initial plan From b9874c2e963b86fcd43344a1bd4e94c97b303957 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Thu, 15 Jan 2026 21:21:10 +0000 Subject: [PATCH 29/50] chore: run ruff format to fix linting issues Co-authored-by: AdamBajger <33228382+AdamBajger@users.noreply.github.com> --- ratiopath/masks/mask_builders/__init__.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/ratiopath/masks/mask_builders/__init__.py b/ratiopath/masks/mask_builders/__init__.py index b88c2ff..b0271d8 100644 --- a/ratiopath/masks/mask_builders/__init__.py +++ b/ratiopath/masks/mask_builders/__init__.py @@ -199,7 +199,9 @@ class AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D( Example: ```python import openslide - from ratiopath.masks.mask_builders import AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D + from ratiopath.masks.mask_builders import ( + AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D, + ) import matplotlib.pyplot as plt LEVEL = 3 From d7664428d1394ee4f37483bae04d873c0460cb01 Mon Sep 17 00:00:00 2001 From: Adam Bajger <469113@mail.muni.cz> Date: Mon, 19 Jan 2026 21:54:24 +0100 Subject: [PATCH 30/50] docs: fix example code --- ratiopath/masks/mask_builders/__init__.py | 69 +++++++++++++------ ratiopath/masks/mask_builders/mask_builder.py | 2 + 2 files changed, 51 insertions(+), 20 deletions(-) diff --git a/ratiopath/masks/mask_builders/__init__.py b/ratiopath/masks/mask_builders/__init__.py index b0271d8..fb3b26e 100644 --- a/ratiopath/masks/mask_builders/__init__.py +++ b/ratiopath/masks/mask_builders/__init__.py @@ -51,6 +51,7 @@ class AveragingScalarUniformTiledNumpyMaskBuilder( Example: ```python import openslide + from ratiopath.masks.mask_builders import ( from ratiopath.masks.mask_builders import ( AveragingScalarUniformTiledNumpyMaskBuilder, ) @@ -61,7 +62,7 @@ class AveragingScalarUniformTiledNumpyMaskBuilder( tile_strides = (256, 256) slide = openslide.OpenSlide("path/to/slide.mrxs") slide_extent_x, slide_extent_y = slide.dimensions[LEVEL] - vgg16_model = load_rationai_vgg16_model(...) # load your pretrained model here + vgg16_model = load_vgg16_model(...) # load your pretrained model here mask_builder = AveragingScalarUniformTiledNumpyMaskBuilder( mask_extents=(slide_extent_y, slide_extent_x), channels=1, # for binary classification @@ -121,14 +122,17 @@ class MaxScalarUniformTiledNumpyMaskBuilder( ```python import openslide from ratiopath.masks.mask_builders import MaxScalarUniformTiledNumpyMaskBuilder + from ratiopath.masks.mask_builders import MaxScalarUniformTiledNumpyMaskBuilder import matplotlib.pyplot as plt + from rationai.explainability.model_probing import HookedModule LEVEL = 3 tile_extents = (512, 512) tile_strides = (256, 256) slide = openslide.OpenSlide("path/to/slide.mrxs") slide_extent_x, slide_extent_y = slide.dimensions[LEVEL] - vgg16_model = load_rationai_vgg16_model(...) # load your pretrained model here + vgg16_model = load_vgg16_model(...) # load your pretrained model here + hooked_model = HookedModule(vgg16_model, layer_name="backbone.9") # example layer mask_builder = MaxScalarUniformTiledNumpyMaskBuilder( mask_extents=(slide_extent_y, slide_extent_x), channels=1, # for binary classification @@ -139,7 +143,8 @@ class MaxScalarUniformTiledNumpyMaskBuilder( slide, LEVEL, tile_extents, tile_strides, batch_size=32 ): # tiles has shape (B, C, H, W) - features = vgg16_model.predict(tiles) # features has shape (B, channels) + outputs = hooked_model.predict(tiles) # outputs are not used directly + features = hooked_model.get_activations("backbone.9") # shape (B, C, H, W) mask_builder.update_batch(features, xs, ys) (assembled_mask,) = mask_builder.finalize() plt.imshow(assembled_mask[0], cmap="gray", interpolation="nearest") @@ -202,29 +207,32 @@ class AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D( from ratiopath.masks.mask_builders import ( AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D, ) + from rationai.explainability.model_probing import HookedModule import matplotlib.pyplot as plt LEVEL = 3 tile_extents = (512, 512) tile_strides = (256, 256) slide = openslide.OpenSlide("path/to/slide.mrxs") - slide_extent_x, slide_extent_y = slide.dimensions[LEVEL] - vgg16_model = load_rationai_vgg16_model(...) # load your pretrained model here + slide_extent_x, slide_extent_y = slide.level_dimensions[LEVEL] + vgg16_model = load_vgg16_model(...) # load your pretrained model here + hooked_model = HookedModule(vgg16_model, layer_name="backbone.9") # example layer mask_builder = AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D( source_extents=(slide_extent_y, slide_extent_x), source_tile_extents=tile_extents, source_tile_strides=tile_strides, - mask_tile_extents=tile_extents, + mask_tile_extents=(64, 64), # output resolution per tile channels=3, # for RGB masks - clip=(4, 4, 4, 4), # (clip_top, clip_bottom, clip_left, clip_right) + clip=(4, 4, 4, 4), # clip 4 pixels from each edge ) for tiles, xs, ys in generate_tiles_from_slide( slide, LEVEL, tile_extents, tile_strides, batch_size=32 ): # tiles has shape (B, C, H, W) - features = vgg16_model.predict(tiles) # features has shape (B, channels) + output = vgg16_model.predict(tiles) # outputs are not used directly + features = hooked_model.get_activations("backbone.9") # shape (B, C, H, W) mask_builder.update_batch(features, xs, ys) - (assembled_mask,) = mask_builder.finalize() + assembled_mask, overlap = mask_builder.finalize() plt.imshow(assembled_mask[0], cmap="gray", interpolation="nearest") plt.axis("off") plt.show() @@ -318,18 +326,39 @@ class AutoScalingScalarUniformValueConstantStrideMaskBuilder( Example: ```python - # Source image: 10000x10000 - # Input tiles: 512x512 at stride 256x256 - # Want each scalar to cover 64x64 in output - builder = AutoScalingScalarUniformTiledMaskBuilder( - source_extents=(10000, 10000), - channels=3, - source_tile_extents=(512, 512), - source_tile_strides=(256, 256), - mask_tile_extents=(64, 64), + import openslide + from ratiopath.masks.mask_builders import ( + AutoScalingScalarUniformValueConstantStrideMaskBuilder, ) - # Output mask will be: (10000/512)*64 x (10000/512)*64 = 1250x1250 - # Coordinates should be provided in input space (0, 256, 512, ...) + import matplotlib.pyplot as plt + + LEVEL = 3 + tile_extents = (512, 512) + tile_strides = (256, 256) + slide = openslide.OpenSlide("path/to/slide.mrxs") + slide_extent_x, slide_extent_y = slide.level_dimensions[LEVEL] + classifier_model = load_classifier_model(...) # load your pretrained model here + + # Build a mask where each scalar prediction covers 64x64 pixels in output + mask_builder = AutoScalingScalarUniformValueConstantStrideMaskBuilder( + source_extents=(slide_extent_y, slide_extent_x), + source_tile_extents=tile_extents, + source_tile_strides=tile_strides, + mask_tile_extents=(64, 64), # each scalar value expands to 64x64 + channels=3, # for multi-class predictions + ) + + for tiles, xs, ys in generate_tiles_from_slide( + slide, LEVEL, tile_extents, tile_strides, batch_size=32 + ): + # tiles has shape (B, C, H, W) + predictions = hooked_model.predict(tiles) # predictions has shape (B, channels) + mask_builder.update_batch(predictions, xs, ys) + + assembled_mask, overlap = mask_builder.finalize() + plt.imshow(assembled_mask[0], cmap="viridis", interpolation="nearest") + plt.axis("off") + plt.show() ``` """ diff --git a/ratiopath/masks/mask_builders/mask_builder.py b/ratiopath/masks/mask_builders/mask_builder.py index e108f07..78e663a 100644 --- a/ratiopath/masks/mask_builders/mask_builder.py +++ b/ratiopath/masks/mask_builders/mask_builder.py @@ -133,6 +133,8 @@ def update_batch_impl( - Other aggregation operations This method is called by `update_batch()` after any mixin preprocessing has occurred. + For example, one mixin may clip tile edges and another may adjust coordinates before + passing the data here. Args: data_batch: Array of shape (B, C, *SpatialDims) containing B tiles with C channels. From 1645cd071a0c0153f0bf19c4e1987f4ee501acad Mon Sep 17 00:00:00 2001 From: Adam Bajger <469113@mail.muni.cz> Date: Mon, 19 Jan 2026 22:03:09 +0100 Subject: [PATCH 31/50] refactor: remove obsolete field --- ratiopath/masks/mask_builders/receptive_field_manipulation.py | 1 - 1 file changed, 1 deletion(-) diff --git a/ratiopath/masks/mask_builders/receptive_field_manipulation.py b/ratiopath/masks/mask_builders/receptive_field_manipulation.py index 34790db..b250620 100644 --- a/ratiopath/masks/mask_builders/receptive_field_manipulation.py +++ b/ratiopath/masks/mask_builders/receptive_field_manipulation.py @@ -157,7 +157,6 @@ class AutoScalingConstantStrideMixin(MaskBuilder): # source_extents: Int64[AccumulatorType, " N"] overflow_buffered_source_extents: Int64[AccumulatorType, " N"] - mask_extents: Int64[AccumulatorType, " N"] mask_tile_extents: Int64[AccumulatorType, " N"] source_tile_extents: Int64[AccumulatorType, " N"] From 51921b1be284b7aa15cd700d9cb9d1f061456425 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adam=20V=C3=A1clav=20Bajger?= <33228382+AdamBajger@users.noreply.github.com> Date: Wed, 21 Jan 2026 20:31:33 +0100 Subject: [PATCH 32/50] chore: replace ellipsis by pass Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com> --- ratiopath/masks/mask_builders/mask_builder.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ratiopath/masks/mask_builders/mask_builder.py b/ratiopath/masks/mask_builders/mask_builder.py index 78e663a..509bcd2 100644 --- a/ratiopath/masks/mask_builders/mask_builder.py +++ b/ratiopath/masks/mask_builders/mask_builder.py @@ -141,7 +141,7 @@ def update_batch_impl( coords_batch: Array of shape (N, B) containing the top-left corner coordinates for each of the B tiles in N spatial dimensions. """ - ... + pass @abstractmethod def finalize(self) -> tuple[AccumulatorType, ...] | AccumulatorType: @@ -157,4 +157,4 @@ def finalize(self) -> tuple[AccumulatorType, ...] | AccumulatorType: Tuple of arrays where the first element is always the finalized mask. Additional elements may include auxiliary data like overlap counters. """ - ... + pass From 2c15c1c9ff513b85c80ad28fde2bfd4ca182fec5 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Wed, 21 Jan 2026 19:35:48 +0000 Subject: [PATCH 33/50] Initial plan From cb02638344e5b590c80895b613e2baacbaa057bf Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Wed, 21 Jan 2026 19:38:24 +0000 Subject: [PATCH 34/50] fix: correct docstring example - remove duplicate import, add numpy, fix update_batch signature Co-authored-by: AdamBajger <33228382+AdamBajger@users.noreply.github.com> --- ratiopath/masks/mask_builders/__init__.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/ratiopath/masks/mask_builders/__init__.py b/ratiopath/masks/mask_builders/__init__.py index fb3b26e..37f0d5b 100644 --- a/ratiopath/masks/mask_builders/__init__.py +++ b/ratiopath/masks/mask_builders/__init__.py @@ -50,8 +50,8 @@ class AveragingScalarUniformTiledNumpyMaskBuilder( Example: ```python + import numpy as np import openslide - from ratiopath.masks.mask_builders import ( from ratiopath.masks.mask_builders import ( AveragingScalarUniformTiledNumpyMaskBuilder, ) @@ -74,7 +74,9 @@ class AveragingScalarUniformTiledNumpyMaskBuilder( ): # tiles has shape (B, C, H, W) features = vgg16_model.predict(tiles) # features has shape (B, channels) - mask_builder.update_batch(features, xs, ys) + # Stack ys and xs into coords_batch with shape (N, B) where N=2 (y, x dimensions) + coords_batch = np.stack([ys, xs], axis=0) + mask_builder.update_batch(features, coords_batch) assembled_mask, overlap = mask_builder.finalize() plt.imshow(assembled_mask[0], cmap="gray", interpolation="nearest") plt.axis("off") From 736853ad2da6404b0a076aa35ef352889fe45b98 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Wed, 21 Jan 2026 19:39:46 +0000 Subject: [PATCH 35/50] fix: update all mask builder docstring examples with correct API signatures Co-authored-by: AdamBajger <33228382+AdamBajger@users.noreply.github.com> --- ratiopath/masks/mask_builders/__init__.py | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/ratiopath/masks/mask_builders/__init__.py b/ratiopath/masks/mask_builders/__init__.py index 37f0d5b..4fbd87b 100644 --- a/ratiopath/masks/mask_builders/__init__.py +++ b/ratiopath/masks/mask_builders/__init__.py @@ -122,9 +122,9 @@ class MaxScalarUniformTiledNumpyMaskBuilder( Example: ```python + import numpy as np import openslide from ratiopath.masks.mask_builders import MaxScalarUniformTiledNumpyMaskBuilder - from ratiopath.masks.mask_builders import MaxScalarUniformTiledNumpyMaskBuilder import matplotlib.pyplot as plt from rationai.explainability.model_probing import HookedModule @@ -147,7 +147,9 @@ class MaxScalarUniformTiledNumpyMaskBuilder( # tiles has shape (B, C, H, W) outputs = hooked_model.predict(tiles) # outputs are not used directly features = hooked_model.get_activations("backbone.9") # shape (B, C, H, W) - mask_builder.update_batch(features, xs, ys) + # Stack ys and xs into coords_batch with shape (N, B) where N=2 (y, x dimensions) + coords_batch = np.stack([ys, xs], axis=0) + mask_builder.update_batch(features, coords_batch) (assembled_mask,) = mask_builder.finalize() plt.imshow(assembled_mask[0], cmap="gray", interpolation="nearest") plt.axis("off") @@ -205,6 +207,7 @@ class AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D( Example: ```python + import numpy as np import openslide from ratiopath.masks.mask_builders import ( AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D, @@ -233,7 +236,9 @@ class AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D( # tiles has shape (B, C, H, W) output = vgg16_model.predict(tiles) # outputs are not used directly features = hooked_model.get_activations("backbone.9") # shape (B, C, H, W) - mask_builder.update_batch(features, xs, ys) + # Stack ys and xs into coords_batch with shape (N, B) where N=2 (y, x dimensions) + coords_batch = np.stack([ys, xs], axis=0) + mask_builder.update_batch(features, coords_batch) assembled_mask, overlap = mask_builder.finalize() plt.imshow(assembled_mask[0], cmap="gray", interpolation="nearest") plt.axis("off") @@ -328,6 +333,7 @@ class AutoScalingScalarUniformValueConstantStrideMaskBuilder( Example: ```python + import numpy as np import openslide from ratiopath.masks.mask_builders import ( AutoScalingScalarUniformValueConstantStrideMaskBuilder, @@ -354,8 +360,10 @@ class AutoScalingScalarUniformValueConstantStrideMaskBuilder( slide, LEVEL, tile_extents, tile_strides, batch_size=32 ): # tiles has shape (B, C, H, W) - predictions = hooked_model.predict(tiles) # predictions has shape (B, channels) - mask_builder.update_batch(predictions, xs, ys) + predictions = classifier_model.predict(tiles) # predictions has shape (B, channels) + # Stack ys and xs into coords_batch with shape (N, B) where N=2 (y, x dimensions) + coords_batch = np.stack([ys, xs], axis=0) + mask_builder.update_batch(predictions, coords_batch) assembled_mask, overlap = mask_builder.finalize() plt.imshow(assembled_mask[0], cmap="viridis", interpolation="nearest") From bb29f92ff3651da06bb889cd92e3b6e5ccb3d15a Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Wed, 21 Jan 2026 19:44:59 +0000 Subject: [PATCH 36/50] docs: add mask builders documentation and run ruff format Co-authored-by: AdamBajger <33228382+AdamBajger@users.noreply.github.com> --- docs/reference/masks/mask_builders.md | 250 ++++++++++++++++++++++ mkdocs.yml | 2 + ratiopath/masks/mask_builders/__init__.py | 22 +- 3 files changed, 266 insertions(+), 8 deletions(-) create mode 100644 docs/reference/masks/mask_builders.md diff --git a/docs/reference/masks/mask_builders.md b/docs/reference/masks/mask_builders.md new file mode 100644 index 0000000..ef4b062 --- /dev/null +++ b/docs/reference/masks/mask_builders.md @@ -0,0 +1,250 @@ +# Mask Builders + +Mask builders are tools for assembling feature masks from neural network predictions or other tile-level data. They handle the complexity of combining overlapping tiles, scaling between coordinate spaces, and managing memory for large output masks. + +## Overview + +When processing whole-slide images with neural networks, you often need to: + +1. Extract tiles from a slide +2. Run inference to get predictions or features for each tile +3. Assemble these predictions back into a full-resolution mask + +Mask builders automate step 3, handling: + +- **Coordinate transformation**: Converting from tile coordinates to mask coordinates +- **Overlap handling**: Averaging or taking the maximum when tiles overlap +- **Memory management**: Using in-memory arrays or memory-mapped files for large masks +- **Edge clipping**: Removing boundary artifacts from tiles + +## Available Builders + +### AveragingScalarUniformTiledNumpyMaskBuilder + +::: ratiopath.masks.mask_builders.AveragingScalarUniformTiledNumpyMaskBuilder + +**Use case**: You have scalar predictions (e.g., class probabilities) for each tile and want to create a mask where each prediction is uniformly expanded to fill the tile's footprint. Overlapping regions are averaged. + +**Example**: Creating a heatmap of tumor probability predictions. + +```python +import numpy as np +import openslide +from ratiopath.masks.mask_builders import ( + AveragingScalarUniformTiledNumpyMaskBuilder, +) +import matplotlib.pyplot as plt + +# Open slide and set up tiling parameters +LEVEL = 3 +tile_extents = (512, 512) +tile_strides = (256, 256) +slide = openslide.OpenSlide("path/to/slide.mrxs") +slide_extent_x, slide_extent_y = slide.level_dimensions[LEVEL] + +# Load your model +vgg16_model = load_vgg16_model(...) # load your pretrained model here + +# Initialize mask builder +mask_builder = AveragingScalarUniformTiledNumpyMaskBuilder( + mask_extents=(slide_extent_y, slide_extent_x), + channels=1, # for binary classification + mask_tile_extents=tile_extents, + mask_tile_strides=tile_strides, +) + +# Process tiles +for tiles, xs, ys in generate_tiles_from_slide( + slide, LEVEL, tile_extents, tile_strides, batch_size=32 +): + # tiles has shape (B, C, H, W) + features = vgg16_model.predict(tiles) # features has shape (B, channels) + # Stack ys and xs into coords_batch with shape (N, B) where N=2 (y, x dimensions) + coords_batch = np.stack([ys, xs], axis=0) + mask_builder.update_batch(features, coords_batch) + +# Finalize and visualize +assembled_mask, overlap = mask_builder.finalize() +plt.imshow(assembled_mask[0], cmap="gray", interpolation="nearest") +plt.axis("off") +plt.show() +``` + +--- + +### MaxScalarUniformTiledNumpyMaskBuilder + +::: ratiopath.masks.mask_builders.MaxScalarUniformTiledNumpyMaskBuilder + +**Use case**: Similar to the averaging builder, but takes the maximum value at each pixel instead of averaging. Useful when you want to preserve the strongest signal. + +**Example**: Creating activation maps from intermediate network layers. + +```python +import numpy as np +import openslide +from ratiopath.masks.mask_builders import MaxScalarUniformTiledNumpyMaskBuilder +import matplotlib.pyplot as plt +from rationai.explainability.model_probing import HookedModule + +LEVEL = 3 +tile_extents = (512, 512) +tile_strides = (256, 256) +slide = openslide.OpenSlide("path/to/slide.mrxs") +slide_extent_x, slide_extent_y = slide.level_dimensions[LEVEL] + +# Set up model with hooks to extract intermediate activations +vgg16_model = load_vgg16_model(...) +hooked_model = HookedModule(vgg16_model, layer_name="backbone.9") + +mask_builder = MaxScalarUniformTiledNumpyMaskBuilder( + mask_extents=(slide_extent_y, slide_extent_x), + channels=1, + mask_tile_extents=tile_extents, + mask_tile_strides=tile_strides, +) + +for tiles, xs, ys in generate_tiles_from_slide( + slide, LEVEL, tile_extents, tile_strides, batch_size=32 +): + # tiles has shape (B, C, H, W) + outputs = hooked_model.predict(tiles) # outputs are not used directly + features = hooked_model.get_activations("backbone.9") # shape (B, C, H, W) + # Stack ys and xs into coords_batch with shape (N, B) where N=2 (y, x dimensions) + coords_batch = np.stack([ys, xs], axis=0) + mask_builder.update_batch(features, coords_batch) + +(assembled_mask,) = mask_builder.finalize() +plt.imshow(assembled_mask[0], cmap="gray", interpolation="nearest") +plt.axis("off") +plt.show() +``` + +--- + +### AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D + +::: ratiopath.masks.mask_builders.AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D + +**Use case**: You have high-resolution feature maps from a network and need to: +- Handle masks too large for RAM (using memory-mapped files) +- Automatically scale coordinates from input to output space +- Remove edge artifacts from tiles + +**Example**: Building attention maps with edge clipping to remove boundary artifacts. + +```python +import numpy as np +import openslide +from ratiopath.masks.mask_builders import ( + AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D, +) +from rationai.explainability.model_probing import HookedModule +import matplotlib.pyplot as plt + +LEVEL = 3 +tile_extents = (512, 512) +tile_strides = (256, 256) +slide = openslide.OpenSlide("path/to/slide.mrxs") +slide_extent_x, slide_extent_y = slide.level_dimensions[LEVEL] + +vgg16_model = load_vgg16_model(...) +hooked_model = HookedModule(vgg16_model, layer_name="backbone.9") + +# This builder handles coordinate scaling and uses memory-mapped storage +mask_builder = AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D( + source_extents=(slide_extent_y, slide_extent_x), + source_tile_extents=tile_extents, + source_tile_strides=tile_strides, + mask_tile_extents=(64, 64), # output resolution per tile + channels=3, # for RGB masks + clip=(4, 4, 4, 4), # clip 4 pixels from each edge +) + +for tiles, xs, ys in generate_tiles_from_slide( + slide, LEVEL, tile_extents, tile_strides, batch_size=32 +): + # tiles has shape (B, C, H, W) + output = vgg16_model.predict(tiles) # outputs are not used directly + features = hooked_model.get_activations("backbone.9") # shape (B, C, H, W) + # Stack ys and xs into coords_batch with shape (N, B) where N=2 (y, x dimensions) + coords_batch = np.stack([ys, xs], axis=0) + mask_builder.update_batch(features, coords_batch) + +assembled_mask, overlap = mask_builder.finalize() +plt.imshow(assembled_mask[0], cmap="gray", interpolation="nearest") +plt.axis("off") +plt.show() +``` + +--- + +### AutoScalingScalarUniformValueConstantStrideMaskBuilder + +::: ratiopath.masks.mask_builders.AutoScalingScalarUniformValueConstantStrideMaskBuilder + +**Use case**: Your network outputs scalar predictions per tile, and you want each prediction to represent a fixed-size region in the output mask, automatically handling coordinate scaling. + +**Example**: Creating a low-resolution classification map where each tile's prediction covers a 64×64 region. + +```python +import numpy as np +import openslide +from ratiopath.masks.mask_builders import ( + AutoScalingScalarUniformValueConstantStrideMaskBuilder, +) +import matplotlib.pyplot as plt + +LEVEL = 3 +tile_extents = (512, 512) +tile_strides = (256, 256) +slide = openslide.OpenSlide("path/to/slide.mrxs") +slide_extent_x, slide_extent_y = slide.level_dimensions[LEVEL] +classifier_model = load_classifier_model(...) + +# Build a mask where each scalar prediction covers 64x64 pixels in output +mask_builder = AutoScalingScalarUniformValueConstantStrideMaskBuilder( + source_extents=(slide_extent_y, slide_extent_x), + source_tile_extents=tile_extents, + source_tile_strides=tile_strides, + mask_tile_extents=(64, 64), # each scalar value expands to 64x64 + channels=3, # for multi-class predictions +) + +for tiles, xs, ys in generate_tiles_from_slide( + slide, LEVEL, tile_extents, tile_strides, batch_size=32 +): + # tiles has shape (B, C, H, W) + predictions = classifier_model.predict(tiles) # predictions has shape (B, channels) + # Stack ys and xs into coords_batch with shape (N, B) where N=2 (y, x dimensions) + coords_batch = np.stack([ys, xs], axis=0) + mask_builder.update_batch(predictions, coords_batch) + +assembled_mask, overlap = mask_builder.finalize() +plt.imshow(assembled_mask[0], cmap="viridis", interpolation="nearest") +plt.axis("off") +plt.show() +``` + +## Choosing a Mask Builder + +| Builder | Scalar/Feature Map | Aggregation | Memory | Auto-scaling | Edge Clipping | +|---------|-------------------|-------------|---------|--------------|---------------| +| `AveragingScalarUniformTiledNumpyMaskBuilder` | Scalar | Average | RAM | No | No | +| `MaxScalarUniformTiledNumpyMaskBuilder` | Feature Map | Max | RAM | No | No | +| `AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D` | Feature Map | Average | Disk (memmap) | Yes | Yes | +| `AutoScalingScalarUniformValueConstantStrideMaskBuilder` | Scalar | Average | RAM | Yes | No | + +## Coordinate System Notes + +All mask builders expect coordinates in the format `(N, B)` where: +- `N` is the number of spatial dimensions (typically 2 for height and width) +- `B` is the batch size + +When using `generate_tiles_from_slide()`, you'll receive `xs` and `ys` arrays. Stack them as: + +```python +coords_batch = np.stack([ys, xs], axis=0) # Shape: (2, B) +``` + +Note the order: `[ys, xs]` not `[xs, ys]`, as the first dimension represents height (y) and the second represents width (x). diff --git a/mkdocs.yml b/mkdocs.yml index 7df23bc..d96dcbe 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -18,6 +18,8 @@ nav: - read_slide_tile: reference/tiling/read_slide_tile.md - tilers: reference/tiling/tilers.md - utils: reference/tiling/utils.md + - Masks: + - mask_builders: reference/masks/mask_builders.md - Parsers: - ASAPParser: reference/parsers/asap.md - GeoJSONParser: reference/parsers/geojson.md diff --git a/ratiopath/masks/mask_builders/__init__.py b/ratiopath/masks/mask_builders/__init__.py index 4fbd87b..156a2a2 100644 --- a/ratiopath/masks/mask_builders/__init__.py +++ b/ratiopath/masks/mask_builders/__init__.py @@ -134,7 +134,9 @@ class MaxScalarUniformTiledNumpyMaskBuilder( slide = openslide.OpenSlide("path/to/slide.mrxs") slide_extent_x, slide_extent_y = slide.dimensions[LEVEL] vgg16_model = load_vgg16_model(...) # load your pretrained model here - hooked_model = HookedModule(vgg16_model, layer_name="backbone.9") # example layer + hooked_model = HookedModule( + vgg16_model, layer_name="backbone.9" + ) # example layer mask_builder = MaxScalarUniformTiledNumpyMaskBuilder( mask_extents=(slide_extent_y, slide_extent_x), channels=1, # for binary classification @@ -146,7 +148,7 @@ class MaxScalarUniformTiledNumpyMaskBuilder( ): # tiles has shape (B, C, H, W) outputs = hooked_model.predict(tiles) # outputs are not used directly - features = hooked_model.get_activations("backbone.9") # shape (B, C, H, W) + features = hooked_model.get_activations("backbone.9") # shape (B, C, H, W) # Stack ys and xs into coords_batch with shape (N, B) where N=2 (y, x dimensions) coords_batch = np.stack([ys, xs], axis=0) mask_builder.update_batch(features, coords_batch) @@ -221,7 +223,9 @@ class AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D( slide = openslide.OpenSlide("path/to/slide.mrxs") slide_extent_x, slide_extent_y = slide.level_dimensions[LEVEL] vgg16_model = load_vgg16_model(...) # load your pretrained model here - hooked_model = HookedModule(vgg16_model, layer_name="backbone.9") # example layer + hooked_model = HookedModule( + vgg16_model, layer_name="backbone.9" + ) # example layer mask_builder = AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D( source_extents=(slide_extent_y, slide_extent_x), source_tile_extents=tile_extents, @@ -234,7 +238,7 @@ class AutoScalingAveragingClippingNumpyMemMapMaskBuilder2D( slide, LEVEL, tile_extents, tile_strides, batch_size=32 ): # tiles has shape (B, C, H, W) - output = vgg16_model.predict(tiles) # outputs are not used directly + output = vgg16_model.predict(tiles) # outputs are not used directly features = hooked_model.get_activations("backbone.9") # shape (B, C, H, W) # Stack ys and xs into coords_batch with shape (N, B) where N=2 (y, x dimensions) coords_batch = np.stack([ys, xs], axis=0) @@ -346,7 +350,7 @@ class AutoScalingScalarUniformValueConstantStrideMaskBuilder( slide = openslide.OpenSlide("path/to/slide.mrxs") slide_extent_x, slide_extent_y = slide.level_dimensions[LEVEL] classifier_model = load_classifier_model(...) # load your pretrained model here - + # Build a mask where each scalar prediction covers 64x64 pixels in output mask_builder = AutoScalingScalarUniformValueConstantStrideMaskBuilder( source_extents=(slide_extent_y, slide_extent_x), @@ -355,16 +359,18 @@ class AutoScalingScalarUniformValueConstantStrideMaskBuilder( mask_tile_extents=(64, 64), # each scalar value expands to 64x64 channels=3, # for multi-class predictions ) - + for tiles, xs, ys in generate_tiles_from_slide( slide, LEVEL, tile_extents, tile_strides, batch_size=32 ): # tiles has shape (B, C, H, W) - predictions = classifier_model.predict(tiles) # predictions has shape (B, channels) + predictions = classifier_model.predict( + tiles + ) # predictions has shape (B, channels) # Stack ys and xs into coords_batch with shape (N, B) where N=2 (y, x dimensions) coords_batch = np.stack([ys, xs], axis=0) mask_builder.update_batch(predictions, coords_batch) - + assembled_mask, overlap = mask_builder.finalize() plt.imshow(assembled_mask[0], cmap="viridis", interpolation="nearest") plt.axis("off") From 1da68db9a7f6fe6d4959db53b1678c85e166b708 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Wed, 21 Jan 2026 19:45:26 +0000 Subject: [PATCH 37/50] docs: validate mkdocs builds successfully Co-authored-by: AdamBajger <33228382+AdamBajger@users.noreply.github.com> --- site/404.html | 1566 ++++ site/assets/_mkdocstrings.css | 237 + site/assets/images/favicon.png | Bin 0 -> 1870 bytes .../assets/javascripts/bundle.79ae519e.min.js | 16 + .../javascripts/bundle.79ae519e.min.js.map | 7 + .../javascripts/lunr/min/lunr.ar.min.js | 1 + 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foreignObject{line-height:normal;overflow:visible}.label div .edgeLabel{color:var(--md-mermaid-label-fg-color)}.edgeLabel,.edgeLabel p,.label div .edgeLabel{background-color:var(--md-mermaid-label-bg-color)}.edgeLabel,.edgeLabel p{fill:var(--md-mermaid-label-bg-color);color:var(--md-mermaid-edge-color)}.edgePath .path,.flowchart-link{stroke:var(--md-mermaid-edge-color)}.edgePath .arrowheadPath{fill:var(--md-mermaid-edge-color);stroke:none}.cluster rect{fill:var(--md-default-fg-color--lightest);stroke:var(--md-default-fg-color--lighter)}.cluster span{color:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}g #flowchart-circleEnd,g #flowchart-circleStart,g #flowchart-crossEnd,g #flowchart-crossStart,g #flowchart-pointEnd,g #flowchart-pointStart{stroke:none}.classDiagramTitleText{fill:var(--md-mermaid-label-fg-color)}g.classGroup line,g.classGroup rect{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}g.classGroup text{fill:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.classLabel .box{fill:var(--md-mermaid-label-bg-color);background-color:var(--md-mermaid-label-bg-color);opacity:1}.classLabel .label{fill:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.node .divider{stroke:var(--md-mermaid-node-fg-color)}.relation{stroke:var(--md-mermaid-edge-color)}.cardinality{fill:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.cardinality text{fill:inherit!important}defs marker.marker.composition.class path,defs marker.marker.dependency.class path,defs marker.marker.extension.class path{fill:var(--md-mermaid-edge-color)!important;stroke:var(--md-mermaid-edge-color)!important}defs marker.marker.aggregation.class path{fill:var(--md-mermaid-label-bg-color)!important;stroke:var(--md-mermaid-edge-color)!important}.statediagramTitleText{fill:var(--md-mermaid-label-fg-color)}g.stateGroup rect{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}g.stateGroup .state-title{fill:var(--md-mermaid-label-fg-color)!important;font-family:var(--md-mermaid-font-family)}g.stateGroup .composit{fill:var(--md-mermaid-label-bg-color)}.nodeLabel,.nodeLabel p{color:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}a .nodeLabel{text-decoration:underline}.node circle.state-end,.node circle.state-start,.start-state{fill:var(--md-mermaid-edge-color);stroke:none}.end-state-inner,.end-state-outer{fill:var(--md-mermaid-edge-color)}.end-state-inner,.node circle.state-end{stroke:var(--md-mermaid-label-bg-color)}.transition{stroke:var(--md-mermaid-edge-color)}[id^=state-fork] rect,[id^=state-join] rect{fill:var(--md-mermaid-edge-color)!important;stroke:none!important}.statediagram-cluster.statediagram-cluster .inner{fill:var(--md-default-bg-color)}.statediagram-cluster rect{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}.statediagram-state rect.divider{fill:var(--md-default-fg-color--lightest);stroke:var(--md-default-fg-color--lighter)}defs #statediagram-barbEnd{stroke:var(--md-mermaid-edge-color)}[id^=entity] path,[id^=entity] rect{fill:var(--md-default-bg-color)}.relationshipLine{stroke:var(--md-mermaid-edge-color)}defs .marker.oneOrMore.er *,defs .marker.onlyOne.er *,defs .marker.zeroOrMore.er *,defs .marker.zeroOrOne.er *{stroke:var(--md-mermaid-edge-color)!important}text:not([class]):last-child{fill:var(--md-mermaid-label-fg-color)}.actor{fill:var(--md-mermaid-sequence-actor-bg-color);stroke:var(--md-mermaid-sequence-actor-border-color)}text.actor>tspan{fill:var(--md-mermaid-sequence-actor-fg-color);font-family:var(--md-mermaid-font-family)}line{stroke:var(--md-mermaid-sequence-actor-line-color)}.actor-man circle,.actor-man line{fill:var(--md-mermaid-sequence-actorman-bg-color);stroke:var(--md-mermaid-sequence-actorman-line-color)}.messageLine0,.messageLine1{stroke:var(--md-mermaid-sequence-message-line-color)}.note{fill:var(--md-mermaid-sequence-note-bg-color);stroke:var(--md-mermaid-sequence-note-border-color)}.loopText,.loopText>tspan,.messageText,.noteText>tspan{stroke:none;font-family:var(--md-mermaid-font-family)!important}.messageText{fill:var(--md-mermaid-sequence-message-fg-color)}.loopText,.loopText>tspan{fill:var(--md-mermaid-sequence-loop-fg-color)}.noteText>tspan{fill:var(--md-mermaid-sequence-note-fg-color)}#arrowhead path{fill:var(--md-mermaid-sequence-message-line-color);stroke:none}.loopLine{fill:var(--md-mermaid-sequence-loop-bg-color);stroke:var(--md-mermaid-sequence-loop-border-color)}.labelBox{fill:var(--md-mermaid-sequence-label-bg-color);stroke:none}.labelText,.labelText>span{fill:var(--md-mermaid-sequence-label-fg-color);font-family:var(--md-mermaid-font-family)}.sequenceNumber{fill:var(--md-mermaid-sequence-number-fg-color)}rect.rect{fill:var(--md-mermaid-sequence-box-bg-color);stroke:none}rect.rect+text.text{fill:var(--md-mermaid-sequence-box-fg-color)}defs #sequencenumber{fill:var(--md-mermaid-sequence-number-bg-color)!important}";var so,is=0;function as(){return typeof mermaid=="undefined"||mermaid instanceof Element?_t("https://unpkg.com/mermaid@11/dist/mermaid.min.js"):$(void 0)}function Zn(e){return 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T,c=a.pipe(oe(),ae(!0));z([a,Le(e),mt(e)]).pipe(W(c),$e(1,ye)).subscribe({next([{active:p},l]){let f=Be(p),{width:u}=de(p);e.style.setProperty("--md-indicator-x",`${f.x}px`),e.style.setProperty("--md-indicator-width",`${u}px`);let d=gr(o);(f.xd.x+l.width)&&o.scrollTo({left:Math.max(0,f.x-16),behavior:"smooth"})},complete(){e.style.removeProperty("--md-indicator-x"),e.style.removeProperty("--md-indicator-width")}}),z([Ge(o),Le(o)]).pipe(W(c)).subscribe(([p,l])=>{let f=At(o);i.hidden=p.x<16,s.hidden=p.x>f.width-l.width-16}),L(h(i,"click").pipe(m(()=>-1)),h(s,"click").pipe(m(()=>1))).pipe(W(c)).subscribe(p=>{let{width:l}=de(o);o.scrollBy({left:l*p,behavior:"smooth"})}),r.pipe(W(c),g(p=>n.includes(p))).subscribe(p=>p.click()),o.classList.add("tabbed-labels--linked");for(let p of n){let 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t.pipe(b(r=>L($(!0),$(!1).pipe(nt(2e3))).pipe(m(o=>({message:r,active:o})))))}function ni(e,t){let r=j(".md-typeset",e);return H(()=>{let o=new T;return o.subscribe(({message:n,active:i})=>{e.classList.toggle("md-dialog--active",i),r.textContent=n}),cs(e,t).pipe(O(n=>o.next(n)),A(()=>o.complete()),m(n=>P({ref:e},n)))})}var ps=0;function ls(e,t){document.body.append(e);let{width:r}=de(e);e.style.setProperty("--md-tooltip-width",`${r}px`),e.remove();let o=vr(t),n=typeof o!="undefined"?Ge(o):$({x:0,y:0}),i=L(Ye(t),it(t)).pipe(Y());return z([i,n]).pipe(m(([s,a])=>{let{x:c,y:p}=Be(t),l=de(t),f=t.closest("table");return f&&t.parentElement&&(c+=f.offsetLeft+t.parentElement.offsetLeft,p+=f.offsetTop+t.parentElement.offsetTop),{active:s,offset:{x:c-a.x+l.width/2-r/2,y:p-a.y+l.height+8}}}))}function ii(e){let t=e.title;if(!t.length)return y;let r=`__tooltip_${ps++}`,o=Dt(r,"inline"),n=j(".md-typeset",o);return n.innerHTML=t,H(()=>{let i=new T;return 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ms({viewport$:e}){if(!V("header.autohide"))return $(!1);let t=e.pipe(m(({offset:{y:n}})=>n),ot(2,1),m(([n,i])=>[nMath.abs(i-n.y)>100),m(([,[n]])=>n),Y()),o=Je("search");return z([e,o]).pipe(m(([{offset:n},i])=>n.y>400&&!i),Y(),b(n=>n?r:$(!1)),Q(!1))}function ai(e,t){return H(()=>z([Le(e),ms(t)])).pipe(m(([{height:r},o])=>({height:r,hidden:o})),Y((r,o)=>r.height===o.height&&r.hidden===o.hidden),Z(1))}function si(e,{header$:t,main$:r}){return H(()=>{let o=new T,n=o.pipe(oe(),ae(!0));o.pipe(ne("active"),Pe(t)).subscribe(([{active:s},{hidden:a}])=>{e.classList.toggle("md-header--shadow",s&&!a),e.hidden=a});let i=fe(M("[title]",e)).pipe(g(()=>V("content.tooltips")),J(s=>ii(s)));return r.subscribe(o),t.pipe(W(n),m(s=>P({ref:e},s)),Ve(i.pipe(W(n))))})}function fs(e,{viewport$:t,header$:r}){return Er(e,{viewport$:t,header$:r}).pipe(m(({offset:{y:o}})=>{let{height:n}=de(e);return{active:n>0&&o>=n}}),ne("active"))}function ci(e,t){return H(()=>{let r=new T;r.subscribe({next({active:n}){e.classList.toggle("md-header__title--active",n)},complete(){e.classList.remove("md-header__title--active")}});let o=ue(".md-content h1");return typeof o=="undefined"?y:fs(o,t).pipe(O(n=>r.next(n)),A(()=>r.complete()),m(n=>P({ref:e},n)))})}function pi(e,{viewport$:t,header$:r}){let o=r.pipe(m(({height:i})=>i),Y()),n=o.pipe(b(()=>Le(e).pipe(m(({height:i})=>({top:e.offsetTop,bottom:e.offsetTop+i})),ne("bottom"))));return z([o,n,t]).pipe(m(([i,{top:s,bottom:a},{offset:{y:c},size:{height:p}}])=>(p=Math.max(0,p-Math.max(0,s-c,i)-Math.max(0,p+c-a)),{offset:s-i,height:p,active:s-i<=c})),Y((i,s)=>i.offset===s.offset&&i.height===s.height&&i.active===s.active))}function us(e){let t=__md_get("__palette")||{index:e.findIndex(o=>matchMedia(o.getAttribute("data-md-color-media")).matches)},r=Math.max(0,Math.min(t.index,e.length-1));return $(...e).pipe(J(o=>h(o,"change").pipe(m(()=>o))),Q(e[r]),m(o=>({index:e.indexOf(o),color:{media:o.getAttribute("data-md-color-media"),scheme:o.getAttribute("data-md-color-scheme"),primary:o.getAttribute("data-md-color-primary"),accent:o.getAttribute("data-md-color-accent")}})),Z(1))}function li(e){let t=M("input",e),r=x("meta",{name:"theme-color"});document.head.appendChild(r);let o=x("meta",{name:"color-scheme"});document.head.appendChild(o);let n=Wt("(prefers-color-scheme: light)");return H(()=>{let i=new T;return i.subscribe(s=>{if(document.body.setAttribute("data-md-color-switching",""),s.color.media==="(prefers-color-scheme)"){let a=matchMedia("(prefers-color-scheme: light)"),c=document.querySelector(a.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: 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URL("search/search_index.js",Or.base)}`).pipe(m(()=>__index),Z(1)):ze(new URL("search/search_index.json",Or.base))}document.documentElement.classList.remove("no-js");document.documentElement.classList.add("js");var ct=an(),Kt=bn(),Ht=yn(Kt),mo=hn(),ke=Ln(),Lr=Wt("(min-width: 60em)"),Vi=Wt("(min-width: 76.25em)"),Ni=xn(),Or=Te(),zi=document.forms.namedItem("search")?ks():tt,fo=new T;di({alert$:fo});ui({document$:ct});var uo=new T,qi=kt(Or.base);V("navigation.instant")&&gi({sitemap$:qi,location$:Kt,viewport$:ke,progress$:uo}).subscribe(ct);var Di;((Di=Or.version)==null?void 0:Di.provider)==="mike"&&Ti({document$:ct});L(Kt,Ht).pipe(nt(125)).subscribe(()=>{at("drawer",!1),at("search",!1)});mo.pipe(g(({mode:e})=>e==="global")).subscribe(e=>{switch(e.type){case"p":case",":let t=ue("link[rel=prev]");typeof t!="undefined"&&st(t);break;case"n":case".":let r=ue("link[rel=next]");typeof r!="undefined"&&st(r);break;case"Enter":let o=Ne();o instanceof 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ft=ai(Ce("header"),{viewport$:ke}),qt=ct.pipe(m(()=>Ce("main")),b(e=>pi(e,{viewport$:ke,header$:ft})),Z(1)),Hs=L(...me("consent").map(e=>An(e,{target$:Ht})),...me("dialog").map(e=>ni(e,{alert$:fo})),...me("palette").map(e=>li(e)),...me("progress").map(e=>mi(e,{progress$:uo})),...me("search").map(e=>_i(e,{index$:zi,keyboard$:mo})),...me("source").map(e=>$i(e))),$s=H(()=>L(...me("announce").map(e=>_n(e)),...me("content").map(e=>oi(e,{sitemap$:qi,viewport$:ke,target$:Ht,print$:Ni})),...me("content").map(e=>V("search.highlight")?Ai(e,{index$:zi,location$:Kt}):y),...me("header").map(e=>si(e,{viewport$:ke,header$:ft,main$:qt})),...me("header-title").map(e=>ci(e,{viewport$:ke,header$:ft})),...me("sidebar").map(e=>e.getAttribute("data-md-type")==="navigation"?eo(Vi,()=>lo(e,{viewport$:ke,header$:ft,main$:qt})):eo(Lr,()=>lo(e,{viewport$:ke,header$:ft,main$:qt}))),...me("tabs").map(e=>Pi(e,{viewport$:ke,header$:ft})),...me("toc").map(e=>Ri(e,{viewport$:ke,header$:ft,main$:qt,target$:Ht})),...me("top").map(e=>Ii(e,{viewport$:ke,header$:ft,main$:qt,target$:Ht})))),Ki=ct.pipe(b(()=>$s),Ve(Hs),Z(1));Ki.subscribe();window.document$=ct;window.location$=Kt;window.target$=Ht;window.keyboard$=mo;window.viewport$=ke;window.tablet$=Lr;window.screen$=Vi;window.print$=Ni;window.alert$=fo;window.progress$=uo;window.component$=Ki;})(); 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"src/templates/assets/javascripts/components/dialog/index.ts", "src/templates/assets/javascripts/components/tooltip/index.ts", "src/templates/assets/javascripts/components/header/_/index.ts", "src/templates/assets/javascripts/components/header/title/index.ts", "src/templates/assets/javascripts/components/main/index.ts", "src/templates/assets/javascripts/components/palette/index.ts", "src/templates/assets/javascripts/components/progress/index.ts", "src/templates/assets/javascripts/integrations/sitemap/index.ts", "src/templates/assets/javascripts/integrations/alternate/index.ts", "src/templates/assets/javascripts/integrations/clipboard/index.ts", "src/templates/assets/javascripts/integrations/instant/index.ts", "src/templates/assets/javascripts/integrations/search/highlighter/index.ts", "src/templates/assets/javascripts/integrations/search/worker/message/index.ts", "src/templates/assets/javascripts/integrations/search/worker/_/index.ts", "src/templates/assets/javascripts/integrations/version/findurl/index.ts", "src/templates/assets/javascripts/integrations/version/index.ts", "src/templates/assets/javascripts/components/search/query/index.ts", "src/templates/assets/javascripts/components/search/result/index.ts", "src/templates/assets/javascripts/components/search/share/index.ts", "src/templates/assets/javascripts/components/search/suggest/index.ts", "src/templates/assets/javascripts/components/search/_/index.ts", "src/templates/assets/javascripts/components/search/highlight/index.ts", "src/templates/assets/javascripts/components/sidebar/index.ts", "src/templates/assets/javascripts/components/source/facts/github/index.ts", "src/templates/assets/javascripts/components/source/facts/gitlab/index.ts", "src/templates/assets/javascripts/components/source/facts/_/index.ts", "src/templates/assets/javascripts/components/source/_/index.ts", "src/templates/assets/javascripts/components/tabs/index.ts", "src/templates/assets/javascripts/components/toc/index.ts", "src/templates/assets/javascripts/components/top/index.ts", "src/templates/assets/javascripts/patches/ellipsis/index.ts", "src/templates/assets/javascripts/patches/indeterminate/index.ts", "src/templates/assets/javascripts/patches/scrollfix/index.ts", "src/templates/assets/javascripts/patches/scrolllock/index.ts", "src/templates/assets/javascripts/polyfills/index.ts"], + "sourcesContent": ["(function (global, factory) {\n typeof exports === 'object' && typeof module !== 'undefined' ? factory() :\n typeof define === 'function' && define.amd ? define(factory) :\n (factory());\n}(this, (function () { 'use strict';\n\n /**\n * Applies the :focus-visible polyfill at the given scope.\n * A scope in this case is either the top-level Document or a Shadow Root.\n *\n * @param {(Document|ShadowRoot)} scope\n * @see https://github.com/WICG/focus-visible\n */\n function applyFocusVisiblePolyfill(scope) {\n var hadKeyboardEvent = true;\n var hadFocusVisibleRecently = false;\n var hadFocusVisibleRecentlyTimeout = null;\n\n var inputTypesAllowlist = {\n text: true,\n search: true,\n url: true,\n tel: true,\n email: true,\n password: true,\n number: true,\n date: true,\n month: true,\n week: true,\n time: true,\n datetime: true,\n 'datetime-local': true\n };\n\n /**\n * Helper function for legacy browsers and iframes which sometimes focus\n * elements like document, body, and non-interactive SVG.\n * @param {Element} el\n */\n function isValidFocusTarget(el) {\n if (\n el &&\n el !== document &&\n el.nodeName !== 'HTML' &&\n el.nodeName !== 'BODY' &&\n 'classList' in el &&\n 'contains' in el.classList\n ) {\n return true;\n }\n return false;\n }\n\n /**\n * Computes whether the given element should automatically trigger the\n * `focus-visible` class being added, i.e. whether it should always match\n * `:focus-visible` when focused.\n * @param {Element} el\n * @return {boolean}\n */\n function focusTriggersKeyboardModality(el) {\n var type = el.type;\n var tagName = el.tagName;\n\n if (tagName === 'INPUT' && inputTypesAllowlist[type] && !el.readOnly) {\n return true;\n }\n\n if (tagName === 'TEXTAREA' && !el.readOnly) {\n return true;\n }\n\n if (el.isContentEditable) {\n return true;\n }\n\n return false;\n }\n\n /**\n * Add the `focus-visible` class to the given element if it was not added by\n * the author.\n * @param {Element} el\n */\n function addFocusVisibleClass(el) {\n if (el.classList.contains('focus-visible')) {\n return;\n }\n el.classList.add('focus-visible');\n el.setAttribute('data-focus-visible-added', '');\n }\n\n /**\n * Remove the `focus-visible` class from the given element if it was not\n * originally added by the author.\n * @param {Element} el\n */\n function removeFocusVisibleClass(el) {\n if (!el.hasAttribute('data-focus-visible-added')) {\n return;\n }\n el.classList.remove('focus-visible');\n el.removeAttribute('data-focus-visible-added');\n }\n\n /**\n * If the most recent user interaction was via the keyboard;\n * and the key press did not include a meta, alt/option, or control key;\n * then the modality is keyboard. Otherwise, the modality is not keyboard.\n * Apply `focus-visible` to any current active element and keep track\n * of our keyboard modality state with `hadKeyboardEvent`.\n * @param {KeyboardEvent} e\n */\n function onKeyDown(e) {\n if (e.metaKey || e.altKey || e.ctrlKey) {\n return;\n }\n\n if (isValidFocusTarget(scope.activeElement)) {\n addFocusVisibleClass(scope.activeElement);\n }\n\n hadKeyboardEvent = true;\n }\n\n /**\n * If at any point a user clicks with a pointing device, ensure that we change\n * the modality away from keyboard.\n * This avoids the situation where a user presses a key on an already focused\n * element, and then clicks on a different element, focusing it with a\n * pointing device, while we still think we're in keyboard modality.\n * @param {Event} e\n */\n function onPointerDown(e) {\n hadKeyboardEvent = false;\n }\n\n /**\n * On `focus`, add the `focus-visible` class to the target if:\n * - the target received focus as a result of keyboard navigation, or\n * - the event target is an element that will likely require interaction\n * via the keyboard (e.g. a text box)\n * @param {Event} e\n */\n function onFocus(e) {\n // Prevent IE from focusing the document or HTML element.\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (hadKeyboardEvent || focusTriggersKeyboardModality(e.target)) {\n addFocusVisibleClass(e.target);\n }\n }\n\n /**\n * On `blur`, remove the `focus-visible` class from the target.\n * @param {Event} e\n */\n function onBlur(e) {\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (\n e.target.classList.contains('focus-visible') ||\n e.target.hasAttribute('data-focus-visible-added')\n ) {\n // To detect a tab/window switch, we look for a blur event followed\n // rapidly by a visibility change.\n // If we don't see a visibility change within 100ms, it's probably a\n // regular focus change.\n hadFocusVisibleRecently = true;\n window.clearTimeout(hadFocusVisibleRecentlyTimeout);\n hadFocusVisibleRecentlyTimeout = window.setTimeout(function() {\n hadFocusVisibleRecently = false;\n }, 100);\n removeFocusVisibleClass(e.target);\n }\n }\n\n /**\n * If the user changes tabs, keep track of whether or not the previously\n * focused element had .focus-visible.\n * @param {Event} e\n */\n function onVisibilityChange(e) {\n if (document.visibilityState === 'hidden') {\n // If the tab becomes active again, the browser will handle calling focus\n // on the element (Safari actually calls it twice).\n // If this tab change caused a blur on an element with focus-visible,\n // re-apply the class when the user switches back to the tab.\n if (hadFocusVisibleRecently) {\n hadKeyboardEvent = true;\n }\n addInitialPointerMoveListeners();\n }\n }\n\n /**\n * Add a group of listeners to detect usage of any pointing devices.\n * These listeners will be added when the polyfill first loads, and anytime\n * the window is blurred, so that they are active when the window regains\n * focus.\n */\n function addInitialPointerMoveListeners() {\n document.addEventListener('mousemove', onInitialPointerMove);\n document.addEventListener('mousedown', onInitialPointerMove);\n document.addEventListener('mouseup', onInitialPointerMove);\n document.addEventListener('pointermove', onInitialPointerMove);\n document.addEventListener('pointerdown', onInitialPointerMove);\n document.addEventListener('pointerup', onInitialPointerMove);\n document.addEventListener('touchmove', onInitialPointerMove);\n document.addEventListener('touchstart', onInitialPointerMove);\n document.addEventListener('touchend', onInitialPointerMove);\n }\n\n function removeInitialPointerMoveListeners() {\n document.removeEventListener('mousemove', onInitialPointerMove);\n document.removeEventListener('mousedown', onInitialPointerMove);\n document.removeEventListener('mouseup', onInitialPointerMove);\n document.removeEventListener('pointermove', onInitialPointerMove);\n document.removeEventListener('pointerdown', onInitialPointerMove);\n document.removeEventListener('pointerup', onInitialPointerMove);\n document.removeEventListener('touchmove', onInitialPointerMove);\n document.removeEventListener('touchstart', onInitialPointerMove);\n document.removeEventListener('touchend', onInitialPointerMove);\n }\n\n /**\n * When the polfyill first loads, assume the user is in keyboard modality.\n * If any event is received from a pointing device (e.g. mouse, pointer,\n * touch), turn off keyboard modality.\n * This accounts for situations where focus enters the page from the URL bar.\n * @param {Event} e\n */\n function onInitialPointerMove(e) {\n // Work around a Safari quirk that fires a mousemove on whenever the\n // window blurs, even if you're tabbing out of the page. \u00AF\\_(\u30C4)_/\u00AF\n if (e.target.nodeName && e.target.nodeName.toLowerCase() === 'html') {\n return;\n }\n\n hadKeyboardEvent = false;\n removeInitialPointerMoveListeners();\n }\n\n // For some kinds of state, we are interested in changes at the global scope\n // only. For example, global pointer input, global key presses and global\n // visibility change should affect the state at every scope:\n document.addEventListener('keydown', onKeyDown, true);\n document.addEventListener('mousedown', onPointerDown, true);\n document.addEventListener('pointerdown', onPointerDown, true);\n document.addEventListener('touchstart', onPointerDown, true);\n document.addEventListener('visibilitychange', onVisibilityChange, true);\n\n addInitialPointerMoveListeners();\n\n // For focus and blur, we specifically care about state changes in the local\n // scope. This is because focus / blur events that originate from within a\n // shadow root are not re-dispatched from the host element if it was already\n // the active element in its own scope:\n scope.addEventListener('focus', onFocus, true);\n scope.addEventListener('blur', onBlur, true);\n\n // We detect that a node is a ShadowRoot by ensuring that it is a\n // DocumentFragment and also has a host property. This check covers native\n // implementation and polyfill implementation transparently. If we only cared\n // about the native implementation, we could just check if the scope was\n // an instance of a ShadowRoot.\n if (scope.nodeType === Node.DOCUMENT_FRAGMENT_NODE && scope.host) {\n // Since a ShadowRoot is a special kind of DocumentFragment, it does not\n // have a root element to add a class to. So, we add this attribute to the\n // host element instead:\n scope.host.setAttribute('data-js-focus-visible', '');\n } else if (scope.nodeType === Node.DOCUMENT_NODE) {\n document.documentElement.classList.add('js-focus-visible');\n document.documentElement.setAttribute('data-js-focus-visible', '');\n }\n }\n\n // It is important to wrap all references to global window and document in\n // these checks to support server-side rendering use cases\n // @see https://github.com/WICG/focus-visible/issues/199\n if (typeof window !== 'undefined' && typeof document !== 'undefined') {\n // Make the polyfill helper globally available. This can be used as a signal\n // to interested libraries that wish to coordinate with the polyfill for e.g.,\n // applying the polyfill to a shadow root:\n window.applyFocusVisiblePolyfill = applyFocusVisiblePolyfill;\n\n // Notify interested libraries of the polyfill's presence, in case the\n // polyfill was loaded lazily:\n var event;\n\n try {\n event = new CustomEvent('focus-visible-polyfill-ready');\n } catch (error) {\n // IE11 does not support using CustomEvent as a constructor directly:\n event = document.createEvent('CustomEvent');\n event.initCustomEvent('focus-visible-polyfill-ready', false, false, {});\n }\n\n window.dispatchEvent(event);\n }\n\n if (typeof document !== 'undefined') {\n // Apply the polyfill to the global document, so that no JavaScript\n // coordination is required to use the polyfill in the top-level document:\n applyFocusVisiblePolyfill(document);\n }\n\n})));\n", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n var str = '' + string;\n var match = matchHtmlRegExp.exec(str);\n\n if (!match) {\n return str;\n }\n\n var escape;\n var html = '';\n var index = 0;\n var lastIndex = 0;\n\n for (index = match.index; index < str.length; index++) {\n switch (str.charCodeAt(index)) {\n case 34: // \"\n escape = '"';\n break;\n case 38: // &\n escape = '&';\n break;\n case 39: // '\n escape = ''';\n break;\n case 60: // <\n escape = '<';\n break;\n case 62: // >\n escape = '>';\n break;\n default:\n continue;\n }\n\n if (lastIndex !== index) {\n html += str.substring(lastIndex, index);\n }\n\n lastIndex = index + 1;\n html += escape;\n }\n\n return lastIndex !== index\n ? html + str.substring(lastIndex, index)\n : html;\n}\n", "/*!\n * clipboard.js v2.0.11\n * https://clipboardjs.com/\n *\n * Licensed MIT \u00A9 Zeno Rocha\n */\n(function webpackUniversalModuleDefinition(root, factory) {\n\tif(typeof exports === 'object' && typeof module === 'object')\n\t\tmodule.exports = factory();\n\telse if(typeof define === 'function' && define.amd)\n\t\tdefine([], factory);\n\telse if(typeof exports === 'object')\n\t\texports[\"ClipboardJS\"] = factory();\n\telse\n\t\troot[\"ClipboardJS\"] = factory();\n})(this, function() {\nreturn /******/ (function() { // webpackBootstrap\n/******/ \tvar __webpack_modules__ = ({\n\n/***/ 686:\n/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {\n\n\"use strict\";\n\n// EXPORTS\n__webpack_require__.d(__webpack_exports__, {\n \"default\": function() { return /* binding */ clipboard; }\n});\n\n// EXTERNAL MODULE: ./node_modules/tiny-emitter/index.js\nvar tiny_emitter = __webpack_require__(279);\nvar tiny_emitter_default = /*#__PURE__*/__webpack_require__.n(tiny_emitter);\n// EXTERNAL MODULE: ./node_modules/good-listener/src/listen.js\nvar listen = __webpack_require__(370);\nvar listen_default = /*#__PURE__*/__webpack_require__.n(listen);\n// EXTERNAL MODULE: ./node_modules/select/src/select.js\nvar src_select = __webpack_require__(817);\nvar select_default = /*#__PURE__*/__webpack_require__.n(src_select);\n;// CONCATENATED MODULE: ./src/common/command.js\n/**\n * Executes a given operation type.\n * @param {String} type\n * @return {Boolean}\n */\nfunction command(type) {\n try {\n return document.execCommand(type);\n } catch (err) {\n return false;\n }\n}\n;// CONCATENATED MODULE: ./src/actions/cut.js\n\n\n/**\n * Cut action wrapper.\n * @param {String|HTMLElement} target\n * @return {String}\n */\n\nvar ClipboardActionCut = function ClipboardActionCut(target) {\n var selectedText = select_default()(target);\n command('cut');\n return selectedText;\n};\n\n/* harmony default export */ var actions_cut = (ClipboardActionCut);\n;// CONCATENATED MODULE: ./src/common/create-fake-element.js\n/**\n * Creates a fake textarea element with a value.\n * @param {String} value\n * @return {HTMLElement}\n */\nfunction createFakeElement(value) {\n var isRTL = document.documentElement.getAttribute('dir') === 'rtl';\n var fakeElement = document.createElement('textarea'); // Prevent zooming on iOS\n\n fakeElement.style.fontSize = '12pt'; // Reset box model\n\n fakeElement.style.border = '0';\n fakeElement.style.padding = '0';\n fakeElement.style.margin = '0'; // Move element out of screen horizontally\n\n fakeElement.style.position = 'absolute';\n fakeElement.style[isRTL ? 'right' : 'left'] = '-9999px'; // Move element to the same position vertically\n\n var yPosition = window.pageYOffset || document.documentElement.scrollTop;\n fakeElement.style.top = \"\".concat(yPosition, \"px\");\n fakeElement.setAttribute('readonly', '');\n fakeElement.value = value;\n return fakeElement;\n}\n;// CONCATENATED MODULE: ./src/actions/copy.js\n\n\n\n/**\n * Create fake copy action wrapper using a fake element.\n * @param {String} target\n * @param {Object} options\n * @return {String}\n */\n\nvar fakeCopyAction = function fakeCopyAction(value, options) {\n var fakeElement = createFakeElement(value);\n options.container.appendChild(fakeElement);\n var selectedText = select_default()(fakeElement);\n command('copy');\n fakeElement.remove();\n return selectedText;\n};\n/**\n * Copy action wrapper.\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @return {String}\n */\n\n\nvar ClipboardActionCopy = function ClipboardActionCopy(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n container: document.body\n };\n var selectedText = '';\n\n if (typeof target === 'string') {\n selectedText = fakeCopyAction(target, options);\n } else if (target instanceof HTMLInputElement && !['text', 'search', 'url', 'tel', 'password'].includes(target === null || target === void 0 ? void 0 : target.type)) {\n // If input type doesn't support `setSelectionRange`. Simulate it. https://developer.mozilla.org/en-US/docs/Web/API/HTMLInputElement/setSelectionRange\n selectedText = fakeCopyAction(target.value, options);\n } else {\n selectedText = select_default()(target);\n command('copy');\n }\n\n return selectedText;\n};\n\n/* harmony default export */ var actions_copy = (ClipboardActionCopy);\n;// CONCATENATED MODULE: ./src/actions/default.js\nfunction _typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return _typeof(obj); }\n\n\n\n/**\n * Inner function which performs selection from either `text` or `target`\n * properties and then executes copy or cut operations.\n * @param {Object} options\n */\n\nvar ClipboardActionDefault = function ClipboardActionDefault() {\n var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n // Defines base properties passed from constructor.\n var _options$action = options.action,\n action = _options$action === void 0 ? 'copy' : _options$action,\n container = options.container,\n target = options.target,\n text = options.text; // Sets the `action` to be performed which can be either 'copy' or 'cut'.\n\n if (action !== 'copy' && action !== 'cut') {\n throw new Error('Invalid \"action\" value, use either \"copy\" or \"cut\"');\n } // Sets the `target` property using an element that will be have its content copied.\n\n\n if (target !== undefined) {\n if (target && _typeof(target) === 'object' && target.nodeType === 1) {\n if (action === 'copy' && target.hasAttribute('disabled')) {\n throw new Error('Invalid \"target\" attribute. Please use \"readonly\" instead of \"disabled\" attribute');\n }\n\n if (action === 'cut' && (target.hasAttribute('readonly') || target.hasAttribute('disabled'))) {\n throw new Error('Invalid \"target\" attribute. You can\\'t cut text from elements with \"readonly\" or \"disabled\" attributes');\n }\n } else {\n throw new Error('Invalid \"target\" value, use a valid Element');\n }\n } // Define selection strategy based on `text` property.\n\n\n if (text) {\n return actions_copy(text, {\n container: container\n });\n } // Defines which selection strategy based on `target` property.\n\n\n if (target) {\n return action === 'cut' ? actions_cut(target) : actions_copy(target, {\n container: container\n });\n }\n};\n\n/* harmony default export */ var actions_default = (ClipboardActionDefault);\n;// CONCATENATED MODULE: ./src/clipboard.js\nfunction clipboard_typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { clipboard_typeof = function _typeof(obj) { return typeof obj; }; } else { clipboard_typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return clipboard_typeof(obj); }\n\nfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n\nfunction _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if (\"value\" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }\n\nfunction _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }\n\nfunction _inherits(subClass, superClass) { if (typeof superClass !== \"function\" && superClass !== null) { throw new TypeError(\"Super expression must either be null or a function\"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }\n\nfunction _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }\n\nfunction _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }\n\nfunction _possibleConstructorReturn(self, call) { if (call && (clipboard_typeof(call) === \"object\" || typeof call === \"function\")) { return call; } return _assertThisInitialized(self); }\n\nfunction _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError(\"this hasn't been initialised - super() hasn't been called\"); } return self; }\n\nfunction _isNativeReflectConstruct() { if (typeof Reflect === \"undefined\" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === \"function\") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }\n\nfunction _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }\n\n\n\n\n\n\n/**\n * Helper function to retrieve attribute value.\n * @param {String} suffix\n * @param {Element} element\n */\n\nfunction getAttributeValue(suffix, element) {\n var attribute = \"data-clipboard-\".concat(suffix);\n\n if (!element.hasAttribute(attribute)) {\n return;\n }\n\n return element.getAttribute(attribute);\n}\n/**\n * Base class which takes one or more elements, adds event listeners to them,\n * and instantiates a new `ClipboardAction` on each click.\n */\n\n\nvar Clipboard = /*#__PURE__*/function (_Emitter) {\n _inherits(Clipboard, _Emitter);\n\n var _super = _createSuper(Clipboard);\n\n /**\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n * @param {Object} options\n */\n function Clipboard(trigger, options) {\n var _this;\n\n _classCallCheck(this, Clipboard);\n\n _this = _super.call(this);\n\n _this.resolveOptions(options);\n\n _this.listenClick(trigger);\n\n return _this;\n }\n /**\n * Defines if attributes would be resolved using internal setter functions\n * or custom functions that were passed in the constructor.\n * @param {Object} options\n */\n\n\n _createClass(Clipboard, [{\n key: \"resolveOptions\",\n value: function resolveOptions() {\n var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n this.action = typeof options.action === 'function' ? options.action : this.defaultAction;\n this.target = typeof options.target === 'function' ? options.target : this.defaultTarget;\n this.text = typeof options.text === 'function' ? options.text : this.defaultText;\n this.container = clipboard_typeof(options.container) === 'object' ? options.container : document.body;\n }\n /**\n * Adds a click event listener to the passed trigger.\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n */\n\n }, {\n key: \"listenClick\",\n value: function listenClick(trigger) {\n var _this2 = this;\n\n this.listener = listen_default()(trigger, 'click', function (e) {\n return _this2.onClick(e);\n });\n }\n /**\n * Defines a new `ClipboardAction` on each click event.\n * @param {Event} e\n */\n\n }, {\n key: \"onClick\",\n value: function onClick(e) {\n var trigger = e.delegateTarget || e.currentTarget;\n var action = this.action(trigger) || 'copy';\n var text = actions_default({\n action: action,\n container: this.container,\n target: this.target(trigger),\n text: this.text(trigger)\n }); // Fires an event based on the copy operation result.\n\n this.emit(text ? 'success' : 'error', {\n action: action,\n text: text,\n trigger: trigger,\n clearSelection: function clearSelection() {\n if (trigger) {\n trigger.focus();\n }\n\n window.getSelection().removeAllRanges();\n }\n });\n }\n /**\n * Default `action` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultAction\",\n value: function defaultAction(trigger) {\n return getAttributeValue('action', trigger);\n }\n /**\n * Default `target` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultTarget\",\n value: function defaultTarget(trigger) {\n var selector = getAttributeValue('target', trigger);\n\n if (selector) {\n return document.querySelector(selector);\n }\n }\n /**\n * Allow fire programmatically a copy action\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @returns Text copied.\n */\n\n }, {\n key: \"defaultText\",\n\n /**\n * Default `text` lookup function.\n * @param {Element} trigger\n */\n value: function defaultText(trigger) {\n return getAttributeValue('text', trigger);\n }\n /**\n * Destroy lifecycle.\n */\n\n }, {\n key: \"destroy\",\n value: function destroy() {\n this.listener.destroy();\n }\n }], [{\n key: \"copy\",\n value: function copy(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n container: document.body\n };\n return actions_copy(target, options);\n }\n /**\n * Allow fire programmatically a cut action\n * @param {String|HTMLElement} target\n * @returns Text cutted.\n */\n\n }, {\n key: \"cut\",\n value: function cut(target) {\n return actions_cut(target);\n }\n /**\n * Returns the support of the given action, or all actions if no action is\n * given.\n * @param {String} [action]\n */\n\n }, {\n key: \"isSupported\",\n value: function isSupported() {\n var action = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ['copy', 'cut'];\n var actions = typeof action === 'string' ? [action] : action;\n var support = !!document.queryCommandSupported;\n actions.forEach(function (action) {\n support = support && !!document.queryCommandSupported(action);\n });\n return support;\n }\n }]);\n\n return Clipboard;\n}((tiny_emitter_default()));\n\n/* harmony default export */ var clipboard = (Clipboard);\n\n/***/ }),\n\n/***/ 828:\n/***/ (function(module) {\n\nvar DOCUMENT_NODE_TYPE = 9;\n\n/**\n * A polyfill for Element.matches()\n */\nif (typeof Element !== 'undefined' && !Element.prototype.matches) {\n var proto = Element.prototype;\n\n proto.matches = proto.matchesSelector ||\n proto.mozMatchesSelector ||\n proto.msMatchesSelector ||\n proto.oMatchesSelector ||\n proto.webkitMatchesSelector;\n}\n\n/**\n * Finds the closest parent that matches a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @return {Function}\n */\nfunction closest (element, selector) {\n while (element && element.nodeType !== DOCUMENT_NODE_TYPE) {\n if (typeof element.matches === 'function' &&\n element.matches(selector)) {\n return element;\n }\n element = element.parentNode;\n }\n}\n\nmodule.exports = closest;\n\n\n/***/ }),\n\n/***/ 438:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar closest = __webpack_require__(828);\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction _delegate(element, selector, type, callback, useCapture) {\n var listenerFn = listener.apply(this, arguments);\n\n element.addEventListener(type, listenerFn, useCapture);\n\n return {\n destroy: function() {\n element.removeEventListener(type, listenerFn, useCapture);\n }\n }\n}\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element|String|Array} [elements]\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction delegate(elements, selector, type, callback, useCapture) {\n // Handle the regular Element usage\n if (typeof elements.addEventListener === 'function') {\n return _delegate.apply(null, arguments);\n }\n\n // Handle Element-less usage, it defaults to global delegation\n if (typeof type === 'function') {\n // Use `document` as the first parameter, then apply arguments\n // This is a short way to .unshift `arguments` without running into deoptimizations\n return _delegate.bind(null, document).apply(null, arguments);\n }\n\n // Handle Selector-based usage\n if (typeof elements === 'string') {\n elements = document.querySelectorAll(elements);\n }\n\n // Handle Array-like based usage\n return Array.prototype.map.call(elements, function (element) {\n return _delegate(element, selector, type, callback, useCapture);\n });\n}\n\n/**\n * Finds closest match and invokes callback.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Function}\n */\nfunction listener(element, selector, type, callback) {\n return function(e) {\n e.delegateTarget = closest(e.target, selector);\n\n if (e.delegateTarget) {\n callback.call(element, e);\n }\n }\n}\n\nmodule.exports = delegate;\n\n\n/***/ }),\n\n/***/ 879:\n/***/ (function(__unused_webpack_module, exports) {\n\n/**\n * Check if argument is a HTML element.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.node = function(value) {\n return value !== undefined\n && value instanceof HTMLElement\n && value.nodeType === 1;\n};\n\n/**\n * Check if argument is a list of HTML elements.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.nodeList = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return value !== undefined\n && (type === '[object NodeList]' || type === '[object HTMLCollection]')\n && ('length' in value)\n && (value.length === 0 || exports.node(value[0]));\n};\n\n/**\n * Check if argument is a string.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.string = function(value) {\n return typeof value === 'string'\n || value instanceof String;\n};\n\n/**\n * Check if argument is a function.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.fn = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return type === '[object Function]';\n};\n\n\n/***/ }),\n\n/***/ 370:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar is = __webpack_require__(879);\nvar delegate = __webpack_require__(438);\n\n/**\n * Validates all params and calls the right\n * listener function based on its target type.\n *\n * @param {String|HTMLElement|HTMLCollection|NodeList} target\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listen(target, type, callback) {\n if (!target && !type && !callback) {\n throw new Error('Missing required arguments');\n }\n\n if (!is.string(type)) {\n throw new TypeError('Second argument must be a String');\n }\n\n if (!is.fn(callback)) {\n throw new TypeError('Third argument must be a Function');\n }\n\n if (is.node(target)) {\n return listenNode(target, type, callback);\n }\n else if (is.nodeList(target)) {\n return listenNodeList(target, type, callback);\n }\n else if (is.string(target)) {\n return listenSelector(target, type, callback);\n }\n else {\n throw new TypeError('First argument must be a String, HTMLElement, HTMLCollection, or NodeList');\n }\n}\n\n/**\n * Adds an event listener to a HTML element\n * and returns a remove listener function.\n *\n * @param {HTMLElement} node\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNode(node, type, callback) {\n node.addEventListener(type, callback);\n\n return {\n destroy: function() {\n node.removeEventListener(type, callback);\n }\n }\n}\n\n/**\n * Add an event listener to a list of HTML elements\n * and returns a remove listener function.\n *\n * @param {NodeList|HTMLCollection} nodeList\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNodeList(nodeList, type, callback) {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.addEventListener(type, callback);\n });\n\n return {\n destroy: function() {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.removeEventListener(type, callback);\n });\n }\n }\n}\n\n/**\n * Add an event listener to a selector\n * and returns a remove listener function.\n *\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenSelector(selector, type, callback) {\n return delegate(document.body, selector, type, callback);\n}\n\nmodule.exports = listen;\n\n\n/***/ }),\n\n/***/ 817:\n/***/ (function(module) {\n\nfunction select(element) {\n var selectedText;\n\n if (element.nodeName === 'SELECT') {\n element.focus();\n\n selectedText = element.value;\n }\n else if (element.nodeName === 'INPUT' || element.nodeName === 'TEXTAREA') {\n var isReadOnly = element.hasAttribute('readonly');\n\n if (!isReadOnly) {\n element.setAttribute('readonly', '');\n }\n\n element.select();\n element.setSelectionRange(0, element.value.length);\n\n if (!isReadOnly) {\n element.removeAttribute('readonly');\n }\n\n selectedText = element.value;\n }\n else {\n if (element.hasAttribute('contenteditable')) {\n element.focus();\n }\n\n var selection = window.getSelection();\n var range = document.createRange();\n\n range.selectNodeContents(element);\n selection.removeAllRanges();\n selection.addRange(range);\n\n selectedText = selection.toString();\n }\n\n return selectedText;\n}\n\nmodule.exports = select;\n\n\n/***/ }),\n\n/***/ 279:\n/***/ (function(module) {\n\nfunction E () {\n // Keep this empty so it's easier to inherit from\n // (via https://github.com/lipsmack from https://github.com/scottcorgan/tiny-emitter/issues/3)\n}\n\nE.prototype = {\n on: function (name, callback, ctx) {\n var e = this.e || (this.e = {});\n\n (e[name] || (e[name] = [])).push({\n fn: callback,\n ctx: ctx\n });\n\n return this;\n },\n\n once: function (name, callback, ctx) {\n var self = this;\n function listener () {\n self.off(name, listener);\n callback.apply(ctx, arguments);\n };\n\n listener._ = callback\n return this.on(name, listener, ctx);\n },\n\n emit: function (name) {\n var data = [].slice.call(arguments, 1);\n var evtArr = ((this.e || (this.e = {}))[name] || []).slice();\n var i = 0;\n var len = evtArr.length;\n\n for (i; i < len; i++) {\n evtArr[i].fn.apply(evtArr[i].ctx, data);\n }\n\n return this;\n },\n\n off: function (name, callback) {\n var e = this.e || (this.e = {});\n var evts = e[name];\n var liveEvents = [];\n\n if (evts && callback) {\n for (var i = 0, len = evts.length; i < len; i++) {\n if (evts[i].fn !== callback && evts[i].fn._ !== callback)\n liveEvents.push(evts[i]);\n }\n }\n\n // Remove event from queue to prevent memory leak\n // Suggested by https://github.com/lazd\n // Ref: https://github.com/scottcorgan/tiny-emitter/commit/c6ebfaa9bc973b33d110a84a307742b7cf94c953#commitcomment-5024910\n\n (liveEvents.length)\n ? e[name] = liveEvents\n : delete e[name];\n\n return this;\n }\n};\n\nmodule.exports = E;\nmodule.exports.TinyEmitter = E;\n\n\n/***/ })\n\n/******/ \t});\n/************************************************************************/\n/******/ \t// The module cache\n/******/ \tvar __webpack_module_cache__ = {};\n/******/ \t\n/******/ \t// The require function\n/******/ \tfunction __webpack_require__(moduleId) {\n/******/ \t\t// Check if module is in cache\n/******/ \t\tif(__webpack_module_cache__[moduleId]) {\n/******/ \t\t\treturn __webpack_module_cache__[moduleId].exports;\n/******/ \t\t}\n/******/ \t\t// Create a new module (and put it into the cache)\n/******/ \t\tvar module = __webpack_module_cache__[moduleId] = {\n/******/ \t\t\t// no module.id needed\n/******/ \t\t\t// no module.loaded needed\n/******/ \t\t\texports: {}\n/******/ \t\t};\n/******/ \t\n/******/ \t\t// Execute the module function\n/******/ \t\t__webpack_modules__[moduleId](module, module.exports, __webpack_require__);\n/******/ \t\n/******/ \t\t// Return the exports of the module\n/******/ \t\treturn module.exports;\n/******/ \t}\n/******/ \t\n/************************************************************************/\n/******/ \t/* webpack/runtime/compat get default export */\n/******/ \t!function() {\n/******/ \t\t// getDefaultExport function for compatibility with non-harmony modules\n/******/ \t\t__webpack_require__.n = function(module) {\n/******/ \t\t\tvar getter = module && module.__esModule ?\n/******/ \t\t\t\tfunction() { return module['default']; } :\n/******/ \t\t\t\tfunction() { return module; };\n/******/ \t\t\t__webpack_require__.d(getter, { a: getter });\n/******/ \t\t\treturn getter;\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/define property getters */\n/******/ \t!function() {\n/******/ \t\t// define getter functions for harmony exports\n/******/ \t\t__webpack_require__.d = function(exports, definition) {\n/******/ \t\t\tfor(var key in definition) {\n/******/ \t\t\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n/******/ \t\t\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n/******/ \t\t\t\t}\n/******/ \t\t\t}\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/hasOwnProperty shorthand */\n/******/ \t!function() {\n/******/ \t\t__webpack_require__.o = function(obj, prop) { return Object.prototype.hasOwnProperty.call(obj, prop); }\n/******/ \t}();\n/******/ \t\n/************************************************************************/\n/******/ \t// module exports must be returned from runtime so entry inlining is disabled\n/******/ \t// startup\n/******/ \t// Load entry module and return exports\n/******/ \treturn __webpack_require__(686);\n/******/ })()\n.default;\n});", "/*\n * Copyright (c) 2016-2025 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport \"focus-visible\"\n\nimport {\n EMPTY,\n NEVER,\n Observable,\n Subject,\n defer,\n delay,\n filter,\n map,\n merge,\n mergeWith,\n shareReplay,\n switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"./_\"\nimport {\n at,\n getActiveElement,\n getOptionalElement,\n requestJSON,\n setLocation,\n setToggle,\n watchDocument,\n watchKeyboard,\n watchLocation,\n watchLocationTarget,\n watchMedia,\n watchPrint,\n watchScript,\n watchViewport\n} from \"./browser\"\nimport {\n getComponentElement,\n getComponentElements,\n mountAnnounce,\n mountBackToTop,\n mountConsent,\n mountContent,\n mountDialog,\n mountHeader,\n mountHeaderTitle,\n mountPalette,\n mountProgress,\n mountSearch,\n mountSearchHiglight,\n mountSidebar,\n mountSource,\n mountTableOfContents,\n mountTabs,\n watchHeader,\n watchMain\n} from \"./components\"\nimport {\n SearchIndex,\n fetchSitemap,\n setupAlternate,\n setupClipboardJS,\n setupInstantNavigation,\n setupVersionSelector\n} from \"./integrations\"\nimport {\n patchEllipsis,\n patchIndeterminate,\n patchScrollfix,\n patchScrolllock\n} from \"./patches\"\nimport \"./polyfills\"\n\n/* ----------------------------------------------------------------------------\n * Functions - @todo refactor\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch search index\n *\n * @returns Search index observable\n */\nfunction fetchSearchIndex(): Observable {\n if (location.protocol === \"file:\") {\n return watchScript(\n `${new URL(\"search/search_index.js\", config.base)}`\n )\n .pipe(\n // @ts-ignore - @todo fix typings\n map(() => __index),\n shareReplay(1)\n )\n } else {\n return requestJSON(\n new URL(\"search/search_index.json\", config.base)\n )\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Application\n * ------------------------------------------------------------------------- */\n\n/* Yay, JavaScript is available */\ndocument.documentElement.classList.remove(\"no-js\")\ndocument.documentElement.classList.add(\"js\")\n\n/* Set up navigation observables and subjects */\nconst document$ = watchDocument()\nconst location$ = watchLocation()\nconst target$ = watchLocationTarget(location$)\nconst keyboard$ = watchKeyboard()\n\n/* Set up media observables */\nconst viewport$ = watchViewport()\nconst tablet$ = watchMedia(\"(min-width: 60em)\")\nconst screen$ = watchMedia(\"(min-width: 76.25em)\")\nconst print$ = watchPrint()\n\n/* Retrieve search index, if search is enabled */\nconst config = configuration()\nconst index$ = document.forms.namedItem(\"search\")\n ? fetchSearchIndex()\n : NEVER\n\n/* Set up Clipboard.js integration */\nconst alert$ = new Subject()\nsetupClipboardJS({ alert$ })\n\n/* Set up language selector */\nsetupAlternate({ document$ })\n\n/* Set up progress indicator */\nconst progress$ = new Subject()\n\n/* Set up sitemap for instant navigation and previews */\nconst sitemap$ = fetchSitemap(config.base)\n\n/* Set up instant navigation, if enabled */\nif (feature(\"navigation.instant\"))\n setupInstantNavigation({ sitemap$, location$, viewport$, progress$ })\n .subscribe(document$)\n\n/* Set up version selector */\nif (config.version?.provider === \"mike\")\n setupVersionSelector({ document$ })\n\n/* Always close drawer and search on navigation */\nmerge(location$, target$)\n .pipe(\n delay(125)\n )\n .subscribe(() => {\n setToggle(\"drawer\", false)\n setToggle(\"search\", false)\n })\n\n/* Set up global keyboard handlers */\nkeyboard$\n .pipe(\n filter(({ mode }) => mode === \"global\")\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Go to previous page */\n case \"p\":\n case \",\":\n const prev = getOptionalElement(\"link[rel=prev]\")\n if (typeof prev !== \"undefined\")\n setLocation(prev)\n break\n\n /* Go to next page */\n case \"n\":\n case \".\":\n const next = getOptionalElement(\"link[rel=next]\")\n if (typeof next !== \"undefined\")\n setLocation(next)\n break\n\n /* Expand navigation, see https://bit.ly/3ZjG5io */\n case \"Enter\":\n const active = getActiveElement()\n if (active instanceof HTMLLabelElement)\n active.click()\n }\n })\n\n/* Set up patches */\npatchEllipsis({ viewport$, document$ })\npatchIndeterminate({ document$, tablet$ })\npatchScrollfix({ document$ })\npatchScrolllock({ viewport$, tablet$ })\n\n/* Set up header and main area observable */\nconst header$ = watchHeader(getComponentElement(\"header\"), { viewport$ })\nconst main$ = document$\n .pipe(\n map(() => getComponentElement(\"main\")),\n switchMap(el => watchMain(el, { viewport$, header$ })),\n shareReplay(1)\n )\n\n/* Set up control component observables */\nconst control$ = merge(\n\n /* Consent */\n ...getComponentElements(\"consent\")\n .map(el => mountConsent(el, { target$ })),\n\n /* Dialog */\n ...getComponentElements(\"dialog\")\n .map(el => mountDialog(el, { alert$ })),\n\n /* Color palette */\n ...getComponentElements(\"palette\")\n .map(el => mountPalette(el)),\n\n /* Progress bar */\n ...getComponentElements(\"progress\")\n .map(el => mountProgress(el, { progress$ })),\n\n /* Search */\n ...getComponentElements(\"search\")\n .map(el => mountSearch(el, { index$, keyboard$ })),\n\n /* Repository information */\n ...getComponentElements(\"source\")\n .map(el => mountSource(el))\n)\n\n/* Set up content component observables */\nconst content$ = defer(() => merge(\n\n /* Announcement bar */\n ...getComponentElements(\"announce\")\n .map(el => mountAnnounce(el)),\n\n /* Content */\n ...getComponentElements(\"content\")\n .map(el => mountContent(el, { sitemap$, viewport$, target$, print$ })),\n\n /* Search highlighting */\n ...getComponentElements(\"content\")\n .map(el => feature(\"search.highlight\")\n ? mountSearchHiglight(el, { index$, location$ })\n : EMPTY\n ),\n\n /* Header */\n ...getComponentElements(\"header\")\n .map(el => mountHeader(el, { viewport$, header$, main$ })),\n\n /* Header title */\n ...getComponentElements(\"header-title\")\n .map(el => mountHeaderTitle(el, { viewport$, header$ })),\n\n /* Sidebar */\n ...getComponentElements(\"sidebar\")\n .map(el => el.getAttribute(\"data-md-type\") === \"navigation\"\n ? at(screen$, () => mountSidebar(el, { viewport$, header$, main$ }))\n : at(tablet$, () => mountSidebar(el, { viewport$, header$, main$ }))\n ),\n\n /* Navigation tabs */\n ...getComponentElements(\"tabs\")\n .map(el => mountTabs(el, { viewport$, header$ })),\n\n /* Table of contents */\n ...getComponentElements(\"toc\")\n .map(el => mountTableOfContents(el, {\n viewport$, header$, main$, target$\n })),\n\n /* Back-to-top button */\n ...getComponentElements(\"top\")\n .map(el => mountBackToTop(el, { viewport$, header$, main$, target$ }))\n))\n\n/* Set up component observables */\nconst component$ = document$\n .pipe(\n switchMap(() => content$),\n mergeWith(control$),\n shareReplay(1)\n )\n\n/* Subscribe to all components */\ncomponent$.subscribe()\n\n/* ----------------------------------------------------------------------------\n * Exports\n * ------------------------------------------------------------------------- */\n\nwindow.document$ = document$ /* Document observable */\nwindow.location$ = location$ /* Location subject */\nwindow.target$ = target$ /* Location target observable */\nwindow.keyboard$ = keyboard$ /* Keyboard observable */\nwindow.viewport$ = viewport$ /* Viewport observable */\nwindow.tablet$ = tablet$ /* Media tablet observable */\nwindow.screen$ = screen$ /* Media screen observable */\nwindow.print$ = print$ /* Media print observable */\nwindow.alert$ = alert$ /* Alert subject */\nwindow.progress$ = progress$ /* Progress indicator subject */\nwindow.component$ = component$ /* Component observable */\n", "/******************************************************************************\nCopyright (c) Microsoft Corporation.\n\nPermission to use, copy, modify, and/or distribute this software for any\npurpose with or without fee is hereby granted.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH\nREGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY\nAND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,\nINDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM\nLOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR\nOTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR\nPERFORMANCE OF THIS SOFTWARE.\n***************************************************************************** */\n/* global Reflect, Promise, SuppressedError, Symbol, Iterator */\n\nvar extendStatics = function(d, b) {\n extendStatics = Object.setPrototypeOf ||\n ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||\n function (d, b) { for (var p in b) if (Object.prototype.hasOwnProperty.call(b, p)) d[p] = b[p]; };\n return extendStatics(d, b);\n};\n\nexport function __extends(d, b) {\n if (typeof b !== \"function\" && b !== null)\n throw new TypeError(\"Class extends value \" + String(b) + \" is not a constructor or null\");\n extendStatics(d, b);\n function __() { this.constructor = d; }\n d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());\n}\n\nexport var __assign = function() {\n __assign = Object.assign || function __assign(t) {\n for (var s, i = 1, n = arguments.length; i < n; i++) {\n s = arguments[i];\n for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];\n }\n return t;\n }\n return __assign.apply(this, arguments);\n}\n\nexport function __rest(s, e) {\n var t = {};\n for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0)\n t[p] = s[p];\n if (s != null && typeof Object.getOwnPropertySymbols === \"function\")\n for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) {\n if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i]))\n t[p[i]] = s[p[i]];\n }\n return t;\n}\n\nexport function __decorate(decorators, target, key, desc) {\n var c = arguments.length, r = c < 3 ? target : desc === null ? desc = Object.getOwnPropertyDescriptor(target, key) : desc, d;\n if (typeof Reflect === \"object\" && typeof Reflect.decorate === \"function\") r = Reflect.decorate(decorators, target, key, desc);\n else for (var i = decorators.length - 1; i >= 0; i--) if (d = decorators[i]) r = (c < 3 ? d(r) : c > 3 ? d(target, key, r) : d(target, key)) || r;\n return c > 3 && r && Object.defineProperty(target, key, r), r;\n}\n\nexport function __param(paramIndex, decorator) {\n return function (target, key) { decorator(target, key, paramIndex); }\n}\n\nexport function __esDecorate(ctor, descriptorIn, decorators, contextIn, initializers, extraInitializers) {\n function accept(f) { if (f !== void 0 && typeof f !== \"function\") throw new TypeError(\"Function expected\"); return f; }\n var kind = contextIn.kind, key = kind === \"getter\" ? \"get\" : kind === \"setter\" ? \"set\" : \"value\";\n var target = !descriptorIn && ctor ? contextIn[\"static\"] ? ctor : ctor.prototype : null;\n var descriptor = descriptorIn || (target ? Object.getOwnPropertyDescriptor(target, contextIn.name) : {});\n var _, done = false;\n for (var i = decorators.length - 1; i >= 0; i--) {\n var context = {};\n for (var p in contextIn) context[p] = p === \"access\" ? {} : contextIn[p];\n for (var p in contextIn.access) context.access[p] = contextIn.access[p];\n context.addInitializer = function (f) { if (done) throw new TypeError(\"Cannot add initializers after decoration has completed\"); extraInitializers.push(accept(f || null)); };\n var result = (0, decorators[i])(kind === \"accessor\" ? { get: descriptor.get, set: descriptor.set } : descriptor[key], context);\n if (kind === \"accessor\") {\n if (result === void 0) continue;\n if (result === null || typeof result !== \"object\") throw new TypeError(\"Object expected\");\n if (_ = accept(result.get)) descriptor.get = _;\n if (_ = accept(result.set)) descriptor.set = _;\n if (_ = accept(result.init)) initializers.unshift(_);\n }\n else if (_ = accept(result)) {\n if (kind === \"field\") initializers.unshift(_);\n else descriptor[key] = _;\n }\n }\n if (target) Object.defineProperty(target, contextIn.name, descriptor);\n done = true;\n};\n\nexport function __runInitializers(thisArg, initializers, value) {\n var useValue = arguments.length > 2;\n for (var i = 0; i < initializers.length; i++) {\n value = useValue ? initializers[i].call(thisArg, value) : initializers[i].call(thisArg);\n }\n return useValue ? value : void 0;\n};\n\nexport function __propKey(x) {\n return typeof x === \"symbol\" ? x : \"\".concat(x);\n};\n\nexport function __setFunctionName(f, name, prefix) {\n if (typeof name === \"symbol\") name = name.description ? \"[\".concat(name.description, \"]\") : \"\";\n return Object.defineProperty(f, \"name\", { configurable: true, value: prefix ? \"\".concat(prefix, \" \", name) : name });\n};\n\nexport function __metadata(metadataKey, metadataValue) {\n if (typeof Reflect === \"object\" && typeof Reflect.metadata === \"function\") return Reflect.metadata(metadataKey, metadataValue);\n}\n\nexport function __awaiter(thisArg, _arguments, P, generator) {\n function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }\n return new (P || (P = Promise))(function (resolve, reject) {\n function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }\n function rejected(value) { try { step(generator[\"throw\"](value)); } catch (e) { reject(e); } }\n function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }\n step((generator = generator.apply(thisArg, _arguments || [])).next());\n });\n}\n\nexport function __generator(thisArg, body) {\n var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g = Object.create((typeof Iterator === \"function\" ? Iterator : Object).prototype);\n return g.next = verb(0), g[\"throw\"] = verb(1), g[\"return\"] = verb(2), typeof Symbol === \"function\" && (g[Symbol.iterator] = function() { return this; }), g;\n function verb(n) { return function (v) { return step([n, v]); }; }\n function step(op) {\n if (f) throw new TypeError(\"Generator is already executing.\");\n while (g && (g = 0, op[0] && (_ = 0)), _) try {\n if (f = 1, y && (t = op[0] & 2 ? y[\"return\"] : op[0] ? y[\"throw\"] || ((t = y[\"return\"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;\n if (y = 0, t) op = [op[0] & 2, t.value];\n switch (op[0]) {\n case 0: case 1: t = op; break;\n case 4: _.label++; return { value: op[1], done: false };\n case 5: _.label++; y = op[1]; op = [0]; continue;\n case 7: op = _.ops.pop(); _.trys.pop(); continue;\n default:\n if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }\n if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }\n if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }\n if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }\n if (t[2]) _.ops.pop();\n _.trys.pop(); continue;\n }\n op = body.call(thisArg, _);\n } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }\n if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };\n }\n}\n\nexport var __createBinding = Object.create ? (function(o, m, k, k2) {\n if (k2 === undefined) k2 = k;\n var desc = Object.getOwnPropertyDescriptor(m, k);\n if (!desc || (\"get\" in desc ? !m.__esModule : desc.writable || desc.configurable)) {\n desc = { enumerable: true, get: function() { return m[k]; } };\n }\n Object.defineProperty(o, k2, desc);\n}) : (function(o, m, k, k2) {\n if (k2 === undefined) k2 = k;\n o[k2] = m[k];\n});\n\nexport function __exportStar(m, o) {\n for (var p in m) if (p !== \"default\" && !Object.prototype.hasOwnProperty.call(o, p)) __createBinding(o, m, p);\n}\n\nexport function __values(o) {\n var s = typeof Symbol === \"function\" && Symbol.iterator, m = s && o[s], i = 0;\n if (m) return m.call(o);\n if (o && typeof o.length === \"number\") return {\n next: function () {\n if (o && i >= o.length) o = void 0;\n return { value: o && o[i++], done: !o };\n }\n };\n throw new TypeError(s ? \"Object is not iterable.\" : \"Symbol.iterator is not defined.\");\n}\n\nexport function __read(o, n) {\n var m = typeof Symbol === \"function\" && o[Symbol.iterator];\n if (!m) return o;\n var i = m.call(o), r, ar = [], e;\n try {\n while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value);\n }\n catch (error) { e = { error: error }; }\n finally {\n try {\n if (r && !r.done && (m = i[\"return\"])) m.call(i);\n }\n finally { if (e) throw e.error; }\n }\n return ar;\n}\n\n/** @deprecated */\nexport function __spread() {\n for (var ar = [], i = 0; i < arguments.length; i++)\n ar = ar.concat(__read(arguments[i]));\n return ar;\n}\n\n/** @deprecated */\nexport function __spreadArrays() {\n for (var s = 0, i = 0, il = arguments.length; i < il; i++) s += arguments[i].length;\n for (var r = Array(s), k = 0, i = 0; i < il; i++)\n for (var a = arguments[i], j = 0, jl = a.length; j < jl; j++, k++)\n r[k] = a[j];\n return r;\n}\n\nexport function __spreadArray(to, from, pack) {\n if (pack || arguments.length === 2) for (var i = 0, l = from.length, ar; i < l; i++) {\n if (ar || !(i in from)) {\n if (!ar) ar = Array.prototype.slice.call(from, 0, i);\n ar[i] = from[i];\n }\n }\n return to.concat(ar || Array.prototype.slice.call(from));\n}\n\nexport function __await(v) {\n return this instanceof __await ? (this.v = v, this) : new __await(v);\n}\n\nexport function __asyncGenerator(thisArg, _arguments, generator) {\n if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\n var g = generator.apply(thisArg, _arguments || []), i, q = [];\n return i = Object.create((typeof AsyncIterator === \"function\" ? AsyncIterator : Object).prototype), verb(\"next\"), verb(\"throw\"), verb(\"return\", awaitReturn), i[Symbol.asyncIterator] = function () { return this; }, i;\n function awaitReturn(f) { return function (v) { return Promise.resolve(v).then(f, reject); }; }\n function verb(n, f) { if (g[n]) { i[n] = function (v) { return new Promise(function (a, b) { q.push([n, v, a, b]) > 1 || resume(n, v); }); }; if (f) i[n] = f(i[n]); } }\n function resume(n, v) { try { step(g[n](v)); } catch (e) { settle(q[0][3], e); } }\n function step(r) { r.value instanceof __await ? Promise.resolve(r.value.v).then(fulfill, reject) : settle(q[0][2], r); }\n function fulfill(value) { resume(\"next\", value); }\n function reject(value) { resume(\"throw\", value); }\n function settle(f, v) { if (f(v), q.shift(), q.length) resume(q[0][0], q[0][1]); }\n}\n\nexport function __asyncDelegator(o) {\n var i, p;\n return i = {}, verb(\"next\"), verb(\"throw\", function (e) { throw e; }), verb(\"return\"), i[Symbol.iterator] = function () { return this; }, i;\n function verb(n, f) { i[n] = o[n] ? function (v) { return (p = !p) ? { value: __await(o[n](v)), done: false } : f ? f(v) : v; } : f; }\n}\n\nexport function __asyncValues(o) {\n if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\n var m = o[Symbol.asyncIterator], i;\n return m ? m.call(o) : (o = typeof __values === \"function\" ? __values(o) : o[Symbol.iterator](), i = {}, verb(\"next\"), verb(\"throw\"), verb(\"return\"), i[Symbol.asyncIterator] = function () { return this; }, i);\n function verb(n) { i[n] = o[n] && function (v) { return new Promise(function (resolve, reject) { v = o[n](v), settle(resolve, reject, v.done, v.value); }); }; }\n function settle(resolve, reject, d, v) { Promise.resolve(v).then(function(v) { resolve({ value: v, done: d }); }, reject); }\n}\n\nexport function __makeTemplateObject(cooked, raw) {\n if (Object.defineProperty) { Object.defineProperty(cooked, \"raw\", { value: raw }); } else { cooked.raw = raw; }\n return cooked;\n};\n\nvar __setModuleDefault = Object.create ? (function(o, v) {\n Object.defineProperty(o, \"default\", { enumerable: true, value: v });\n}) : function(o, v) {\n o[\"default\"] = v;\n};\n\nexport function __importStar(mod) {\n if (mod && mod.__esModule) return mod;\n var result = {};\n if (mod != null) for (var k in mod) if (k !== \"default\" && Object.prototype.hasOwnProperty.call(mod, k)) __createBinding(result, mod, k);\n __setModuleDefault(result, mod);\n return result;\n}\n\nexport function __importDefault(mod) {\n return (mod && mod.__esModule) ? mod : { default: mod };\n}\n\nexport function __classPrivateFieldGet(receiver, state, kind, f) {\n if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a getter\");\n if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot read private member from an object whose class did not declare it\");\n return kind === \"m\" ? f : kind === \"a\" ? f.call(receiver) : f ? f.value : state.get(receiver);\n}\n\nexport function __classPrivateFieldSet(receiver, state, value, kind, f) {\n if (kind === \"m\") throw new TypeError(\"Private method is not writable\");\n if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a setter\");\n if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot write private member to an object whose class did not declare it\");\n return (kind === \"a\" ? f.call(receiver, value) : f ? f.value = value : state.set(receiver, value)), value;\n}\n\nexport function __classPrivateFieldIn(state, receiver) {\n if (receiver === null || (typeof receiver !== \"object\" && typeof receiver !== \"function\")) throw new TypeError(\"Cannot use 'in' operator on non-object\");\n return typeof state === \"function\" ? receiver === state : state.has(receiver);\n}\n\nexport function __addDisposableResource(env, value, async) {\n if (value !== null && value !== void 0) {\n if (typeof value !== \"object\" && typeof value !== \"function\") throw new TypeError(\"Object expected.\");\n var dispose, inner;\n if (async) {\n if (!Symbol.asyncDispose) throw new TypeError(\"Symbol.asyncDispose is not defined.\");\n dispose = value[Symbol.asyncDispose];\n }\n if (dispose === void 0) {\n if (!Symbol.dispose) throw new TypeError(\"Symbol.dispose is not defined.\");\n dispose = value[Symbol.dispose];\n if (async) inner = dispose;\n }\n if (typeof dispose !== \"function\") throw new TypeError(\"Object not disposable.\");\n if (inner) dispose = function() { try { inner.call(this); } catch (e) { return Promise.reject(e); } };\n env.stack.push({ value: value, dispose: dispose, async: async });\n }\n else if (async) {\n env.stack.push({ async: true });\n }\n return value;\n}\n\nvar _SuppressedError = typeof SuppressedError === \"function\" ? SuppressedError : function (error, suppressed, message) {\n var e = new Error(message);\n return e.name = \"SuppressedError\", e.error = error, e.suppressed = suppressed, e;\n};\n\nexport function __disposeResources(env) {\n function fail(e) {\n env.error = env.hasError ? new _SuppressedError(e, env.error, \"An error was suppressed during disposal.\") : e;\n env.hasError = true;\n }\n var r, s = 0;\n function next() {\n while (r = env.stack.pop()) {\n try {\n if (!r.async && s === 1) return s = 0, env.stack.push(r), Promise.resolve().then(next);\n if (r.dispose) {\n var result = r.dispose.call(r.value);\n if (r.async) return s |= 2, Promise.resolve(result).then(next, function(e) { fail(e); return next(); });\n }\n else s |= 1;\n }\n catch (e) {\n fail(e);\n }\n }\n if (s === 1) return env.hasError ? Promise.reject(env.error) : Promise.resolve();\n if (env.hasError) throw env.error;\n }\n return next();\n}\n\nexport default {\n __extends,\n __assign,\n __rest,\n __decorate,\n __param,\n __metadata,\n __awaiter,\n __generator,\n __createBinding,\n __exportStar,\n __values,\n __read,\n __spread,\n __spreadArrays,\n __spreadArray,\n __await,\n __asyncGenerator,\n __asyncDelegator,\n __asyncValues,\n __makeTemplateObject,\n __importStar,\n __importDefault,\n __classPrivateFieldGet,\n __classPrivateFieldSet,\n __classPrivateFieldIn,\n __addDisposableResource,\n __disposeResources,\n};\n", "/**\n * Returns true if the object is a function.\n * @param value The value to check\n */\nexport function isFunction(value: any): value is (...args: any[]) => any {\n return typeof value === 'function';\n}\n", "/**\n * Used to create Error subclasses until the community moves away from ES5.\n *\n * This is because compiling from TypeScript down to ES5 has issues with subclassing Errors\n * as well as other built-in types: https://github.com/Microsoft/TypeScript/issues/12123\n *\n * @param createImpl A factory function to create the actual constructor implementation. The returned\n * function should be a named function that calls `_super` internally.\n */\nexport function createErrorClass(createImpl: (_super: any) => any): T {\n const _super = (instance: any) => {\n Error.call(instance);\n instance.stack = new Error().stack;\n };\n\n const ctorFunc = createImpl(_super);\n ctorFunc.prototype = Object.create(Error.prototype);\n ctorFunc.prototype.constructor = ctorFunc;\n return ctorFunc;\n}\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface UnsubscriptionError extends Error {\n readonly errors: any[];\n}\n\nexport interface UnsubscriptionErrorCtor {\n /**\n * @deprecated Internal implementation detail. Do not construct error instances.\n * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n */\n new (errors: any[]): UnsubscriptionError;\n}\n\n/**\n * An error thrown when one or more errors have occurred during the\n * `unsubscribe` of a {@link Subscription}.\n */\nexport const UnsubscriptionError: UnsubscriptionErrorCtor = createErrorClass(\n (_super) =>\n function UnsubscriptionErrorImpl(this: any, errors: (Error | string)[]) {\n _super(this);\n this.message = errors\n ? `${errors.length} errors occurred during unsubscription:\n${errors.map((err, i) => `${i + 1}) ${err.toString()}`).join('\\n ')}`\n : '';\n this.name = 'UnsubscriptionError';\n this.errors = errors;\n }\n);\n", "/**\n * Removes an item from an array, mutating it.\n * @param arr The array to remove the item from\n * @param item The item to remove\n */\nexport function arrRemove(arr: T[] | undefined | null, item: T) {\n if (arr) {\n const index = arr.indexOf(item);\n 0 <= index && arr.splice(index, 1);\n }\n}\n", "import { isFunction } from './util/isFunction';\nimport { UnsubscriptionError } from './util/UnsubscriptionError';\nimport { SubscriptionLike, TeardownLogic, Unsubscribable } from './types';\nimport { arrRemove } from './util/arrRemove';\n\n/**\n * Represents a disposable resource, such as the execution of an Observable. A\n * Subscription has one important method, `unsubscribe`, that takes no argument\n * and just disposes the resource held by the subscription.\n *\n * Additionally, subscriptions may be grouped together through the `add()`\n * method, which will attach a child Subscription to the current Subscription.\n * When a Subscription is unsubscribed, all its children (and its grandchildren)\n * will be unsubscribed as well.\n */\nexport class Subscription implements SubscriptionLike {\n public static EMPTY = (() => {\n const empty = new Subscription();\n empty.closed = true;\n return empty;\n })();\n\n /**\n * A flag to indicate whether this Subscription has already been unsubscribed.\n */\n public closed = false;\n\n private _parentage: Subscription[] | Subscription | null = null;\n\n /**\n * The list of registered finalizers to execute upon unsubscription. Adding and removing from this\n * list occurs in the {@link #add} and {@link #remove} methods.\n */\n private _finalizers: Exclude[] | null = null;\n\n /**\n * @param initialTeardown A function executed first as part of the finalization\n * process that is kicked off when {@link #unsubscribe} is called.\n */\n constructor(private initialTeardown?: () => void) {}\n\n /**\n * Disposes the resources held by the subscription. May, for instance, cancel\n * an ongoing Observable execution or cancel any other type of work that\n * started when the Subscription was created.\n */\n unsubscribe(): void {\n let errors: any[] | undefined;\n\n if (!this.closed) {\n this.closed = true;\n\n // Remove this from it's parents.\n const { _parentage } = this;\n if (_parentage) {\n this._parentage = null;\n if (Array.isArray(_parentage)) {\n for (const parent of _parentage) {\n parent.remove(this);\n }\n } else {\n _parentage.remove(this);\n }\n }\n\n const { initialTeardown: initialFinalizer } = this;\n if (isFunction(initialFinalizer)) {\n try {\n initialFinalizer();\n } catch (e) {\n errors = e instanceof UnsubscriptionError ? e.errors : [e];\n }\n }\n\n const { _finalizers } = this;\n if (_finalizers) {\n this._finalizers = null;\n for (const finalizer of _finalizers) {\n try {\n execFinalizer(finalizer);\n } catch (err) {\n errors = errors ?? [];\n if (err instanceof UnsubscriptionError) {\n errors = [...errors, ...err.errors];\n } else {\n errors.push(err);\n }\n }\n }\n }\n\n if (errors) {\n throw new UnsubscriptionError(errors);\n }\n }\n }\n\n /**\n * Adds a finalizer to this subscription, so that finalization will be unsubscribed/called\n * when this subscription is unsubscribed. If this subscription is already {@link #closed},\n * because it has already been unsubscribed, then whatever finalizer is passed to it\n * will automatically be executed (unless the finalizer itself is also a closed subscription).\n *\n * Closed Subscriptions cannot be added as finalizers to any subscription. Adding a closed\n * subscription to a any subscription will result in no operation. (A noop).\n *\n * Adding a subscription to itself, or adding `null` or `undefined` will not perform any\n * operation at all. (A noop).\n *\n * `Subscription` instances that are added to this instance will automatically remove themselves\n * if they are unsubscribed. Functions and {@link Unsubscribable} objects that you wish to remove\n * will need to be removed manually with {@link #remove}\n *\n * @param teardown The finalization logic to add to this subscription.\n */\n add(teardown: TeardownLogic): void {\n // Only add the finalizer if it's not undefined\n // and don't add a subscription to itself.\n if (teardown && teardown !== this) {\n if (this.closed) {\n // If this subscription is already closed,\n // execute whatever finalizer is handed to it automatically.\n execFinalizer(teardown);\n } else {\n if (teardown instanceof Subscription) {\n // We don't add closed subscriptions, and we don't add the same subscription\n // twice. Subscription unsubscribe is idempotent.\n if (teardown.closed || teardown._hasParent(this)) {\n return;\n }\n teardown._addParent(this);\n }\n (this._finalizers = this._finalizers ?? []).push(teardown);\n }\n }\n }\n\n /**\n * Checks to see if a this subscription already has a particular parent.\n * This will signal that this subscription has already been added to the parent in question.\n * @param parent the parent to check for\n */\n private _hasParent(parent: Subscription) {\n const { _parentage } = this;\n return _parentage === parent || (Array.isArray(_parentage) && _parentage.includes(parent));\n }\n\n /**\n * Adds a parent to this subscription so it can be removed from the parent if it\n * unsubscribes on it's own.\n *\n * NOTE: THIS ASSUMES THAT {@link _hasParent} HAS ALREADY BEEN CHECKED.\n * @param parent The parent subscription to add\n */\n private _addParent(parent: Subscription) {\n const { _parentage } = this;\n this._parentage = Array.isArray(_parentage) ? (_parentage.push(parent), _parentage) : _parentage ? [_parentage, parent] : parent;\n }\n\n /**\n * Called on a child when it is removed via {@link #remove}.\n * @param parent The parent to remove\n */\n private _removeParent(parent: Subscription) {\n const { _parentage } = this;\n if (_parentage === parent) {\n this._parentage = null;\n } else if (Array.isArray(_parentage)) {\n arrRemove(_parentage, parent);\n }\n }\n\n /**\n * Removes a finalizer from this subscription that was previously added with the {@link #add} method.\n *\n * Note that `Subscription` instances, when unsubscribed, will automatically remove themselves\n * from every other `Subscription` they have been added to. This means that using the `remove` method\n * is not a common thing and should be used thoughtfully.\n *\n * If you add the same finalizer instance of a function or an unsubscribable object to a `Subscription` instance\n * more than once, you will need to call `remove` the same number of times to remove all instances.\n *\n * All finalizer instances are removed to free up memory upon unsubscription.\n *\n * @param teardown The finalizer to remove from this subscription\n */\n remove(teardown: Exclude): void {\n const { _finalizers } = this;\n _finalizers && arrRemove(_finalizers, teardown);\n\n if (teardown instanceof Subscription) {\n teardown._removeParent(this);\n }\n }\n}\n\nexport const EMPTY_SUBSCRIPTION = Subscription.EMPTY;\n\nexport function isSubscription(value: any): value is Subscription {\n return (\n value instanceof Subscription ||\n (value && 'closed' in value && isFunction(value.remove) && isFunction(value.add) && isFunction(value.unsubscribe))\n );\n}\n\nfunction execFinalizer(finalizer: Unsubscribable | (() => void)) {\n if (isFunction(finalizer)) {\n finalizer();\n } else {\n finalizer.unsubscribe();\n }\n}\n", "import { Subscriber } from './Subscriber';\nimport { ObservableNotification } from './types';\n\n/**\n * The {@link GlobalConfig} object for RxJS. It is used to configure things\n * like how to react on unhandled errors.\n */\nexport const config: GlobalConfig = {\n onUnhandledError: null,\n onStoppedNotification: null,\n Promise: undefined,\n useDeprecatedSynchronousErrorHandling: false,\n useDeprecatedNextContext: false,\n};\n\n/**\n * The global configuration object for RxJS, used to configure things\n * like how to react on unhandled errors. Accessible via {@link config}\n * object.\n */\nexport interface GlobalConfig {\n /**\n * A registration point for unhandled errors from RxJS. These are errors that\n * cannot were not handled by consuming code in the usual subscription path. For\n * example, if you have this configured, and you subscribe to an observable without\n * providing an error handler, errors from that subscription will end up here. This\n * will _always_ be called asynchronously on another job in the runtime. This is because\n * we do not want errors thrown in this user-configured handler to interfere with the\n * behavior of the library.\n */\n onUnhandledError: ((err: any) => void) | null;\n\n /**\n * A registration point for notifications that cannot be sent to subscribers because they\n * have completed, errored or have been explicitly unsubscribed. By default, next, complete\n * and error notifications sent to stopped subscribers are noops. However, sometimes callers\n * might want a different behavior. For example, with sources that attempt to report errors\n * to stopped subscribers, a caller can configure RxJS to throw an unhandled error instead.\n * This will _always_ be called asynchronously on another job in the runtime. This is because\n * we do not want errors thrown in this user-configured handler to interfere with the\n * behavior of the library.\n */\n onStoppedNotification: ((notification: ObservableNotification, subscriber: Subscriber) => void) | null;\n\n /**\n * The promise constructor used by default for {@link Observable#toPromise toPromise} and {@link Observable#forEach forEach}\n * methods.\n *\n * @deprecated As of version 8, RxJS will no longer support this sort of injection of a\n * Promise constructor. If you need a Promise implementation other than native promises,\n * please polyfill/patch Promise as you see appropriate. Will be removed in v8.\n */\n Promise?: PromiseConstructorLike;\n\n /**\n * If true, turns on synchronous error rethrowing, which is a deprecated behavior\n * in v6 and higher. This behavior enables bad patterns like wrapping a subscribe\n * call in a try/catch block. It also enables producer interference, a nasty bug\n * where a multicast can be broken for all observers by a downstream consumer with\n * an unhandled error. DO NOT USE THIS FLAG UNLESS IT'S NEEDED TO BUY TIME\n * FOR MIGRATION REASONS.\n *\n * @deprecated As of version 8, RxJS will no longer support synchronous throwing\n * of unhandled errors. All errors will be thrown on a separate call stack to prevent bad\n * behaviors described above. Will be removed in v8.\n */\n useDeprecatedSynchronousErrorHandling: boolean;\n\n /**\n * If true, enables an as-of-yet undocumented feature from v5: The ability to access\n * `unsubscribe()` via `this` context in `next` functions created in observers passed\n * to `subscribe`.\n *\n * This is being removed because the performance was severely problematic, and it could also cause\n * issues when types other than POJOs are passed to subscribe as subscribers, as they will likely have\n * their `this` context overwritten.\n *\n * @deprecated As of version 8, RxJS will no longer support altering the\n * context of next functions provided as part of an observer to Subscribe. Instead,\n * you will have access to a subscription or a signal or token that will allow you to do things like\n * unsubscribe and test closed status. Will be removed in v8.\n */\n useDeprecatedNextContext: boolean;\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetTimeoutFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearTimeoutFunction = (handle: TimerHandle) => void;\n\ninterface TimeoutProvider {\n setTimeout: SetTimeoutFunction;\n clearTimeout: ClearTimeoutFunction;\n delegate:\n | {\n setTimeout: SetTimeoutFunction;\n clearTimeout: ClearTimeoutFunction;\n }\n | undefined;\n}\n\nexport const timeoutProvider: TimeoutProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n setTimeout(handler: () => void, timeout?: number, ...args) {\n const { delegate } = timeoutProvider;\n if (delegate?.setTimeout) {\n return delegate.setTimeout(handler, timeout, ...args);\n }\n return setTimeout(handler, timeout, ...args);\n },\n clearTimeout(handle) {\n const { delegate } = timeoutProvider;\n return (delegate?.clearTimeout || clearTimeout)(handle as any);\n },\n delegate: undefined,\n};\n", "import { config } from '../config';\nimport { timeoutProvider } from '../scheduler/timeoutProvider';\n\n/**\n * Handles an error on another job either with the user-configured {@link onUnhandledError},\n * or by throwing it on that new job so it can be picked up by `window.onerror`, `process.on('error')`, etc.\n *\n * This should be called whenever there is an error that is out-of-band with the subscription\n * or when an error hits a terminal boundary of the subscription and no error handler was provided.\n *\n * @param err the error to report\n */\nexport function reportUnhandledError(err: any) {\n timeoutProvider.setTimeout(() => {\n const { onUnhandledError } = config;\n if (onUnhandledError) {\n // Execute the user-configured error handler.\n onUnhandledError(err);\n } else {\n // Throw so it is picked up by the runtime's uncaught error mechanism.\n throw err;\n }\n });\n}\n", "/* tslint:disable:no-empty */\nexport function noop() { }\n", "import { CompleteNotification, NextNotification, ErrorNotification } from './types';\n\n/**\n * A completion object optimized for memory use and created to be the\n * same \"shape\" as other notifications in v8.\n * @internal\n */\nexport const COMPLETE_NOTIFICATION = (() => createNotification('C', undefined, undefined) as CompleteNotification)();\n\n/**\n * Internal use only. Creates an optimized error notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function errorNotification(error: any): ErrorNotification {\n return createNotification('E', undefined, error) as any;\n}\n\n/**\n * Internal use only. Creates an optimized next notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function nextNotification(value: T) {\n return createNotification('N', value, undefined) as NextNotification;\n}\n\n/**\n * Ensures that all notifications created internally have the same \"shape\" in v8.\n *\n * TODO: This is only exported to support a crazy legacy test in `groupBy`.\n * @internal\n */\nexport function createNotification(kind: 'N' | 'E' | 'C', value: any, error: any) {\n return {\n kind,\n value,\n error,\n };\n}\n", "import { config } from '../config';\n\nlet context: { errorThrown: boolean; error: any } | null = null;\n\n/**\n * Handles dealing with errors for super-gross mode. Creates a context, in which\n * any synchronously thrown errors will be passed to {@link captureError}. Which\n * will record the error such that it will be rethrown after the call back is complete.\n * TODO: Remove in v8\n * @param cb An immediately executed function.\n */\nexport function errorContext(cb: () => void) {\n if (config.useDeprecatedSynchronousErrorHandling) {\n const isRoot = !context;\n if (isRoot) {\n context = { errorThrown: false, error: null };\n }\n cb();\n if (isRoot) {\n const { errorThrown, error } = context!;\n context = null;\n if (errorThrown) {\n throw error;\n }\n }\n } else {\n // This is the general non-deprecated path for everyone that\n // isn't crazy enough to use super-gross mode (useDeprecatedSynchronousErrorHandling)\n cb();\n }\n}\n\n/**\n * Captures errors only in super-gross mode.\n * @param err the error to capture\n */\nexport function captureError(err: any) {\n if (config.useDeprecatedSynchronousErrorHandling && context) {\n context.errorThrown = true;\n context.error = err;\n }\n}\n", "import { isFunction } from './util/isFunction';\nimport { Observer, ObservableNotification } from './types';\nimport { isSubscription, Subscription } from './Subscription';\nimport { config } from './config';\nimport { reportUnhandledError } from './util/reportUnhandledError';\nimport { noop } from './util/noop';\nimport { nextNotification, errorNotification, COMPLETE_NOTIFICATION } from './NotificationFactories';\nimport { timeoutProvider } from './scheduler/timeoutProvider';\nimport { captureError } from './util/errorContext';\n\n/**\n * Implements the {@link Observer} interface and extends the\n * {@link Subscription} class. While the {@link Observer} is the public API for\n * consuming the values of an {@link Observable}, all Observers get converted to\n * a Subscriber, in order to provide Subscription-like capabilities such as\n * `unsubscribe`. Subscriber is a common type in RxJS, and crucial for\n * implementing operators, but it is rarely used as a public API.\n */\nexport class Subscriber extends Subscription implements Observer {\n /**\n * A static factory for a Subscriber, given a (potentially partial) definition\n * of an Observer.\n * @param next The `next` callback of an Observer.\n * @param error The `error` callback of an\n * Observer.\n * @param complete The `complete` callback of an\n * Observer.\n * @return A Subscriber wrapping the (partially defined)\n * Observer represented by the given arguments.\n * @deprecated Do not use. Will be removed in v8. There is no replacement for this\n * method, and there is no reason to be creating instances of `Subscriber` directly.\n * If you have a specific use case, please file an issue.\n */\n static create(next?: (x?: T) => void, error?: (e?: any) => void, complete?: () => void): Subscriber {\n return new SafeSubscriber(next, error, complete);\n }\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n protected isStopped: boolean = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n protected destination: Subscriber | Observer; // this `any` is the escape hatch to erase extra type param (e.g. R)\n\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n * There is no reason to directly create an instance of Subscriber. This type is exported for typings reasons.\n */\n constructor(destination?: Subscriber | Observer) {\n super();\n if (destination) {\n this.destination = destination;\n // Automatically chain subscriptions together here.\n // if destination is a Subscription, then it is a Subscriber.\n if (isSubscription(destination)) {\n destination.add(this);\n }\n } else {\n this.destination = EMPTY_OBSERVER;\n }\n }\n\n /**\n * The {@link Observer} callback to receive notifications of type `next` from\n * the Observable, with a value. The Observable may call this method 0 or more\n * times.\n * @param value The `next` value.\n */\n next(value: T): void {\n if (this.isStopped) {\n handleStoppedNotification(nextNotification(value), this);\n } else {\n this._next(value!);\n }\n }\n\n /**\n * The {@link Observer} callback to receive notifications of type `error` from\n * the Observable, with an attached `Error`. Notifies the Observer that\n * the Observable has experienced an error condition.\n * @param err The `error` exception.\n */\n error(err?: any): void {\n if (this.isStopped) {\n handleStoppedNotification(errorNotification(err), this);\n } else {\n this.isStopped = true;\n this._error(err);\n }\n }\n\n /**\n * The {@link Observer} callback to receive a valueless notification of type\n * `complete` from the Observable. Notifies the Observer that the Observable\n * has finished sending push-based notifications.\n */\n complete(): void {\n if (this.isStopped) {\n handleStoppedNotification(COMPLETE_NOTIFICATION, this);\n } else {\n this.isStopped = true;\n this._complete();\n }\n }\n\n unsubscribe(): void {\n if (!this.closed) {\n this.isStopped = true;\n super.unsubscribe();\n this.destination = null!;\n }\n }\n\n protected _next(value: T): void {\n this.destination.next(value);\n }\n\n protected _error(err: any): void {\n try {\n this.destination.error(err);\n } finally {\n this.unsubscribe();\n }\n }\n\n protected _complete(): void {\n try {\n this.destination.complete();\n } finally {\n this.unsubscribe();\n }\n }\n}\n\n/**\n * This bind is captured here because we want to be able to have\n * compatibility with monoid libraries that tend to use a method named\n * `bind`. In particular, a library called Monio requires this.\n */\nconst _bind = Function.prototype.bind;\n\nfunction bind any>(fn: Fn, thisArg: any): Fn {\n return _bind.call(fn, thisArg);\n}\n\n/**\n * Internal optimization only, DO NOT EXPOSE.\n * @internal\n */\nclass ConsumerObserver implements Observer {\n constructor(private partialObserver: Partial>) {}\n\n next(value: T): void {\n const { partialObserver } = this;\n if (partialObserver.next) {\n try {\n partialObserver.next(value);\n } catch (error) {\n handleUnhandledError(error);\n }\n }\n }\n\n error(err: any): void {\n const { partialObserver } = this;\n if (partialObserver.error) {\n try {\n partialObserver.error(err);\n } catch (error) {\n handleUnhandledError(error);\n }\n } else {\n handleUnhandledError(err);\n }\n }\n\n complete(): void {\n const { partialObserver } = this;\n if (partialObserver.complete) {\n try {\n partialObserver.complete();\n } catch (error) {\n handleUnhandledError(error);\n }\n }\n }\n}\n\nexport class SafeSubscriber extends Subscriber {\n constructor(\n observerOrNext?: Partial> | ((value: T) => void) | null,\n error?: ((e?: any) => void) | null,\n complete?: (() => void) | null\n ) {\n super();\n\n let partialObserver: Partial>;\n if (isFunction(observerOrNext) || !observerOrNext) {\n // The first argument is a function, not an observer. The next\n // two arguments *could* be observers, or they could be empty.\n partialObserver = {\n next: (observerOrNext ?? undefined) as ((value: T) => void) | undefined,\n error: error ?? undefined,\n complete: complete ?? undefined,\n };\n } else {\n // The first argument is a partial observer.\n let context: any;\n if (this && config.useDeprecatedNextContext) {\n // This is a deprecated path that made `this.unsubscribe()` available in\n // next handler functions passed to subscribe. This only exists behind a flag\n // now, as it is *very* slow.\n context = Object.create(observerOrNext);\n context.unsubscribe = () => this.unsubscribe();\n partialObserver = {\n next: observerOrNext.next && bind(observerOrNext.next, context),\n error: observerOrNext.error && bind(observerOrNext.error, context),\n complete: observerOrNext.complete && bind(observerOrNext.complete, context),\n };\n } else {\n // The \"normal\" path. Just use the partial observer directly.\n partialObserver = observerOrNext;\n }\n }\n\n // Wrap the partial observer to ensure it's a full observer, and\n // make sure proper error handling is accounted for.\n this.destination = new ConsumerObserver(partialObserver);\n }\n}\n\nfunction handleUnhandledError(error: any) {\n if (config.useDeprecatedSynchronousErrorHandling) {\n captureError(error);\n } else {\n // Ideal path, we report this as an unhandled error,\n // which is thrown on a new call stack.\n reportUnhandledError(error);\n }\n}\n\n/**\n * An error handler used when no error handler was supplied\n * to the SafeSubscriber -- meaning no error handler was supplied\n * do the `subscribe` call on our observable.\n * @param err The error to handle\n */\nfunction defaultErrorHandler(err: any) {\n throw err;\n}\n\n/**\n * A handler for notifications that cannot be sent to a stopped subscriber.\n * @param notification The notification being sent.\n * @param subscriber The stopped subscriber.\n */\nfunction handleStoppedNotification(notification: ObservableNotification, subscriber: Subscriber) {\n const { onStoppedNotification } = config;\n onStoppedNotification && timeoutProvider.setTimeout(() => onStoppedNotification(notification, subscriber));\n}\n\n/**\n * The observer used as a stub for subscriptions where the user did not\n * pass any arguments to `subscribe`. Comes with the default error handling\n * behavior.\n */\nexport const EMPTY_OBSERVER: Readonly> & { closed: true } = {\n closed: true,\n next: noop,\n error: defaultErrorHandler,\n complete: noop,\n};\n", "/**\n * Symbol.observable or a string \"@@observable\". Used for interop\n *\n * @deprecated We will no longer be exporting this symbol in upcoming versions of RxJS.\n * Instead polyfill and use Symbol.observable directly *or* use https://www.npmjs.com/package/symbol-observable\n */\nexport const observable: string | symbol = (() => (typeof Symbol === 'function' && Symbol.observable) || '@@observable')();\n", "/**\n * This function takes one parameter and just returns it. Simply put,\n * this is like `(x: T): T => x`.\n *\n * ## Examples\n *\n * This is useful in some cases when using things like `mergeMap`\n *\n * ```ts\n * import { interval, take, map, range, mergeMap, identity } from 'rxjs';\n *\n * const source$ = interval(1000).pipe(take(5));\n *\n * const result$ = source$.pipe(\n * map(i => range(i)),\n * mergeMap(identity) // same as mergeMap(x => x)\n * );\n *\n * result$.subscribe({\n * next: console.log\n * });\n * ```\n *\n * Or when you want to selectively apply an operator\n *\n * ```ts\n * import { interval, take, identity } from 'rxjs';\n *\n * const shouldLimit = () => Math.random() < 0.5;\n *\n * const source$ = interval(1000);\n *\n * const result$ = source$.pipe(shouldLimit() ? take(5) : identity);\n *\n * result$.subscribe({\n * next: console.log\n * });\n * ```\n *\n * @param x Any value that is returned by this function\n * @returns The value passed as the first parameter to this function\n */\nexport function identity(x: T): T {\n return x;\n}\n", "import { identity } from './identity';\nimport { UnaryFunction } from '../types';\n\nexport function pipe(): typeof identity;\nexport function pipe(fn1: UnaryFunction): UnaryFunction;\nexport function pipe(fn1: UnaryFunction, fn2: UnaryFunction): UnaryFunction;\nexport function pipe(fn1: UnaryFunction, fn2: UnaryFunction, fn3: UnaryFunction): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction,\n fn9: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction,\n fn9: UnaryFunction,\n ...fns: UnaryFunction[]\n): UnaryFunction;\n\n/**\n * pipe() can be called on one or more functions, each of which can take one argument (\"UnaryFunction\")\n * and uses it to return a value.\n * It returns a function that takes one argument, passes it to the first UnaryFunction, and then\n * passes the result to the next one, passes that result to the next one, and so on. \n */\nexport function pipe(...fns: Array>): UnaryFunction {\n return pipeFromArray(fns);\n}\n\n/** @internal */\nexport function pipeFromArray(fns: Array>): UnaryFunction {\n if (fns.length === 0) {\n return identity as UnaryFunction;\n }\n\n if (fns.length === 1) {\n return fns[0];\n }\n\n return function piped(input: T): R {\n return fns.reduce((prev: any, fn: UnaryFunction) => fn(prev), input as any);\n };\n}\n", "import { Operator } from './Operator';\nimport { SafeSubscriber, Subscriber } from './Subscriber';\nimport { isSubscription, Subscription } from './Subscription';\nimport { TeardownLogic, OperatorFunction, Subscribable, Observer } from './types';\nimport { observable as Symbol_observable } from './symbol/observable';\nimport { pipeFromArray } from './util/pipe';\nimport { config } from './config';\nimport { isFunction } from './util/isFunction';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A representation of any set of values over any amount of time. This is the most basic building block\n * of RxJS.\n */\nexport class Observable implements Subscribable {\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n */\n source: Observable | undefined;\n\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n */\n operator: Operator | undefined;\n\n /**\n * @param subscribe The function that is called when the Observable is\n * initially subscribed to. This function is given a Subscriber, to which new values\n * can be `next`ed, or an `error` method can be called to raise an error, or\n * `complete` can be called to notify of a successful completion.\n */\n constructor(subscribe?: (this: Observable, subscriber: Subscriber) => TeardownLogic) {\n if (subscribe) {\n this._subscribe = subscribe;\n }\n }\n\n // HACK: Since TypeScript inherits static properties too, we have to\n // fight against TypeScript here so Subject can have a different static create signature\n /**\n * Creates a new Observable by calling the Observable constructor\n * @param subscribe the subscriber function to be passed to the Observable constructor\n * @return A new observable.\n * @deprecated Use `new Observable()` instead. Will be removed in v8.\n */\n static create: (...args: any[]) => any = (subscribe?: (subscriber: Subscriber) => TeardownLogic) => {\n return new Observable(subscribe);\n };\n\n /**\n * Creates a new Observable, with this Observable instance as the source, and the passed\n * operator defined as the new observable's operator.\n * @param operator the operator defining the operation to take on the observable\n * @return A new observable with the Operator applied.\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n * If you have implemented an operator using `lift`, it is recommended that you create an\n * operator by simply returning `new Observable()` directly. See \"Creating new operators from\n * scratch\" section here: https://rxjs.dev/guide/operators\n */\n lift(operator?: Operator): Observable {\n const observable = new Observable();\n observable.source = this;\n observable.operator = operator;\n return observable;\n }\n\n subscribe(observerOrNext?: Partial> | ((value: T) => void)): Subscription;\n /** @deprecated Instead of passing separate callback arguments, use an observer argument. Signatures taking separate callback arguments will be removed in v8. Details: https://rxjs.dev/deprecations/subscribe-arguments */\n subscribe(next?: ((value: T) => void) | null, error?: ((error: any) => void) | null, complete?: (() => void) | null): Subscription;\n /**\n * Invokes an execution of an Observable and registers Observer handlers for notifications it will emit.\n *\n * Use it when you have all these Observables, but still nothing is happening.\n *\n * `subscribe` is not a regular operator, but a method that calls Observable's internal `subscribe` function. It\n * might be for example a function that you passed to Observable's constructor, but most of the time it is\n * a library implementation, which defines what will be emitted by an Observable, and when it be will emitted. This means\n * that calling `subscribe` is actually the moment when Observable starts its work, not when it is created, as it is often\n * the thought.\n *\n * Apart from starting the execution of an Observable, this method allows you to listen for values\n * that an Observable emits, as well as for when it completes or errors. You can achieve this in two\n * of the following ways.\n *\n * The first way is creating an object that implements {@link Observer} interface. It should have methods\n * defined by that interface, but note that it should be just a regular JavaScript object, which you can create\n * yourself in any way you want (ES6 class, classic function constructor, object literal etc.). In particular, do\n * not attempt to use any RxJS implementation details to create Observers - you don't need them. Remember also\n * that your object does not have to implement all methods. If you find yourself creating a method that doesn't\n * do anything, you can simply omit it. Note however, if the `error` method is not provided and an error happens,\n * it will be thrown asynchronously. Errors thrown asynchronously cannot be caught using `try`/`catch`. Instead,\n * use the {@link onUnhandledError} configuration option or use a runtime handler (like `window.onerror` or\n * `process.on('error)`) to be notified of unhandled errors. Because of this, it's recommended that you provide\n * an `error` method to avoid missing thrown errors.\n *\n * The second way is to give up on Observer object altogether and simply provide callback functions in place of its methods.\n * This means you can provide three functions as arguments to `subscribe`, where the first function is equivalent\n * of a `next` method, the second of an `error` method and the third of a `complete` method. Just as in case of an Observer,\n * if you do not need to listen for something, you can omit a function by passing `undefined` or `null`,\n * since `subscribe` recognizes these functions by where they were placed in function call. When it comes\n * to the `error` function, as with an Observer, if not provided, errors emitted by an Observable will be thrown asynchronously.\n *\n * You can, however, subscribe with no parameters at all. This may be the case where you're not interested in terminal events\n * and you also handled emissions internally by using operators (e.g. using `tap`).\n *\n * Whichever style of calling `subscribe` you use, in both cases it returns a Subscription object.\n * This object allows you to call `unsubscribe` on it, which in turn will stop the work that an Observable does and will clean\n * up all resources that an Observable used. Note that cancelling a subscription will not call `complete` callback\n * provided to `subscribe` function, which is reserved for a regular completion signal that comes from an Observable.\n *\n * Remember that callbacks provided to `subscribe` are not guaranteed to be called asynchronously.\n * It is an Observable itself that decides when these functions will be called. For example {@link of}\n * by default emits all its values synchronously. Always check documentation for how given Observable\n * will behave when subscribed and if its default behavior can be modified with a `scheduler`.\n *\n * #### Examples\n *\n * Subscribe with an {@link guide/observer Observer}\n *\n * ```ts\n * import { of } from 'rxjs';\n *\n * const sumObserver = {\n * sum: 0,\n * next(value) {\n * console.log('Adding: ' + value);\n * this.sum = this.sum + value;\n * },\n * error() {\n * // We actually could just remove this method,\n * // since we do not really care about errors right now.\n * },\n * complete() {\n * console.log('Sum equals: ' + this.sum);\n * }\n * };\n *\n * of(1, 2, 3) // Synchronously emits 1, 2, 3 and then completes.\n * .subscribe(sumObserver);\n *\n * // Logs:\n * // 'Adding: 1'\n * // 'Adding: 2'\n * // 'Adding: 3'\n * // 'Sum equals: 6'\n * ```\n *\n * Subscribe with functions ({@link deprecations/subscribe-arguments deprecated})\n *\n * ```ts\n * import { of } from 'rxjs'\n *\n * let sum = 0;\n *\n * of(1, 2, 3).subscribe(\n * value => {\n * console.log('Adding: ' + value);\n * sum = sum + value;\n * },\n * undefined,\n * () => console.log('Sum equals: ' + sum)\n * );\n *\n * // Logs:\n * // 'Adding: 1'\n * // 'Adding: 2'\n * // 'Adding: 3'\n * // 'Sum equals: 6'\n * ```\n *\n * Cancel a subscription\n *\n * ```ts\n * import { interval } from 'rxjs';\n *\n * const subscription = interval(1000).subscribe({\n * next(num) {\n * console.log(num)\n * },\n * complete() {\n * // Will not be called, even when cancelling subscription.\n * console.log('completed!');\n * }\n * });\n *\n * setTimeout(() => {\n * subscription.unsubscribe();\n * console.log('unsubscribed!');\n * }, 2500);\n *\n * // Logs:\n * // 0 after 1s\n * // 1 after 2s\n * // 'unsubscribed!' after 2.5s\n * ```\n *\n * @param observerOrNext Either an {@link Observer} with some or all callback methods,\n * or the `next` handler that is called for each value emitted from the subscribed Observable.\n * @param error A handler for a terminal event resulting from an error. If no error handler is provided,\n * the error will be thrown asynchronously as unhandled.\n * @param complete A handler for a terminal event resulting from successful completion.\n * @return A subscription reference to the registered handlers.\n */\n subscribe(\n observerOrNext?: Partial> | ((value: T) => void) | null,\n error?: ((error: any) => void) | null,\n complete?: (() => void) | null\n ): Subscription {\n const subscriber = isSubscriber(observerOrNext) ? observerOrNext : new SafeSubscriber(observerOrNext, error, complete);\n\n errorContext(() => {\n const { operator, source } = this;\n subscriber.add(\n operator\n ? // We're dealing with a subscription in the\n // operator chain to one of our lifted operators.\n operator.call(subscriber, source)\n : source\n ? // If `source` has a value, but `operator` does not, something that\n // had intimate knowledge of our API, like our `Subject`, must have\n // set it. We're going to just call `_subscribe` directly.\n this._subscribe(subscriber)\n : // In all other cases, we're likely wrapping a user-provided initializer\n // function, so we need to catch errors and handle them appropriately.\n this._trySubscribe(subscriber)\n );\n });\n\n return subscriber;\n }\n\n /** @internal */\n protected _trySubscribe(sink: Subscriber): TeardownLogic {\n try {\n return this._subscribe(sink);\n } catch (err) {\n // We don't need to return anything in this case,\n // because it's just going to try to `add()` to a subscription\n // above.\n sink.error(err);\n }\n }\n\n /**\n * Used as a NON-CANCELLABLE means of subscribing to an observable, for use with\n * APIs that expect promises, like `async/await`. You cannot unsubscribe from this.\n *\n * **WARNING**: Only use this with observables you *know* will complete. If the source\n * observable does not complete, you will end up with a promise that is hung up, and\n * potentially all of the state of an async function hanging out in memory. To avoid\n * this situation, look into adding something like {@link timeout}, {@link take},\n * {@link takeWhile}, or {@link takeUntil} amongst others.\n *\n * #### Example\n *\n * ```ts\n * import { interval, take } from 'rxjs';\n *\n * const source$ = interval(1000).pipe(take(4));\n *\n * async function getTotal() {\n * let total = 0;\n *\n * await source$.forEach(value => {\n * total += value;\n * console.log('observable -> ' + value);\n * });\n *\n * return total;\n * }\n *\n * getTotal().then(\n * total => console.log('Total: ' + total)\n * );\n *\n * // Expected:\n * // 'observable -> 0'\n * // 'observable -> 1'\n * // 'observable -> 2'\n * // 'observable -> 3'\n * // 'Total: 6'\n * ```\n *\n * @param next A handler for each value emitted by the observable.\n * @return A promise that either resolves on observable completion or\n * rejects with the handled error.\n */\n forEach(next: (value: T) => void): Promise;\n\n /**\n * @param next a handler for each value emitted by the observable\n * @param promiseCtor a constructor function used to instantiate the Promise\n * @return a promise that either resolves on observable completion or\n * rejects with the handled error\n * @deprecated Passing a Promise constructor will no longer be available\n * in upcoming versions of RxJS. This is because it adds weight to the library, for very\n * little benefit. If you need this functionality, it is recommended that you either\n * polyfill Promise, or you create an adapter to convert the returned native promise\n * to whatever promise implementation you wanted. Will be removed in v8.\n */\n forEach(next: (value: T) => void, promiseCtor: PromiseConstructorLike): Promise;\n\n forEach(next: (value: T) => void, promiseCtor?: PromiseConstructorLike): Promise {\n promiseCtor = getPromiseCtor(promiseCtor);\n\n return new promiseCtor((resolve, reject) => {\n const subscriber = new SafeSubscriber({\n next: (value) => {\n try {\n next(value);\n } catch (err) {\n reject(err);\n subscriber.unsubscribe();\n }\n },\n error: reject,\n complete: resolve,\n });\n this.subscribe(subscriber);\n }) as Promise;\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): TeardownLogic {\n return this.source?.subscribe(subscriber);\n }\n\n /**\n * An interop point defined by the es7-observable spec https://github.com/zenparsing/es-observable\n * @return This instance of the observable.\n */\n [Symbol_observable]() {\n return this;\n }\n\n /* tslint:disable:max-line-length */\n pipe(): Observable;\n pipe(op1: OperatorFunction): Observable;\n pipe(op1: OperatorFunction, op2: OperatorFunction): Observable;\n pipe(op1: OperatorFunction, op2: OperatorFunction, op3: OperatorFunction): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction,\n op9: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction,\n op9: OperatorFunction,\n ...operations: OperatorFunction[]\n ): Observable;\n /* tslint:enable:max-line-length */\n\n /**\n * Used to stitch together functional operators into a chain.\n *\n * ## Example\n *\n * ```ts\n * import { interval, filter, map, scan } from 'rxjs';\n *\n * interval(1000)\n * .pipe(\n * filter(x => x % 2 === 0),\n * map(x => x + x),\n * scan((acc, x) => acc + x)\n * )\n * .subscribe(x => console.log(x));\n * ```\n *\n * @return The Observable result of all the operators having been called\n * in the order they were passed in.\n */\n pipe(...operations: OperatorFunction[]): Observable {\n return pipeFromArray(operations)(this);\n }\n\n /* tslint:disable:max-line-length */\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(): Promise;\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(PromiseCtor: typeof Promise): Promise;\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(PromiseCtor: PromiseConstructorLike): Promise;\n /* tslint:enable:max-line-length */\n\n /**\n * Subscribe to this Observable and get a Promise resolving on\n * `complete` with the last emission (if any).\n *\n * **WARNING**: Only use this with observables you *know* will complete. If the source\n * observable does not complete, you will end up with a promise that is hung up, and\n * potentially all of the state of an async function hanging out in memory. To avoid\n * this situation, look into adding something like {@link timeout}, {@link take},\n * {@link takeWhile}, or {@link takeUntil} amongst others.\n *\n * @param [promiseCtor] a constructor function used to instantiate\n * the Promise\n * @return A Promise that resolves with the last value emit, or\n * rejects on an error. If there were no emissions, Promise\n * resolves with undefined.\n * @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise\n */\n toPromise(promiseCtor?: PromiseConstructorLike): Promise {\n promiseCtor = getPromiseCtor(promiseCtor);\n\n return new promiseCtor((resolve, reject) => {\n let value: T | undefined;\n this.subscribe(\n (x: T) => (value = x),\n (err: any) => reject(err),\n () => resolve(value)\n );\n }) as Promise;\n }\n}\n\n/**\n * Decides between a passed promise constructor from consuming code,\n * A default configured promise constructor, and the native promise\n * constructor and returns it. If nothing can be found, it will throw\n * an error.\n * @param promiseCtor The optional promise constructor to passed by consuming code\n */\nfunction getPromiseCtor(promiseCtor: PromiseConstructorLike | undefined) {\n return promiseCtor ?? config.Promise ?? Promise;\n}\n\nfunction isObserver(value: any): value is Observer {\n return value && isFunction(value.next) && isFunction(value.error) && isFunction(value.complete);\n}\n\nfunction isSubscriber(value: any): value is Subscriber {\n return (value && value instanceof Subscriber) || (isObserver(value) && isSubscription(value));\n}\n", "import { Observable } from '../Observable';\nimport { Subscriber } from '../Subscriber';\nimport { OperatorFunction } from '../types';\nimport { isFunction } from './isFunction';\n\n/**\n * Used to determine if an object is an Observable with a lift function.\n */\nexport function hasLift(source: any): source is { lift: InstanceType['lift'] } {\n return isFunction(source?.lift);\n}\n\n/**\n * Creates an `OperatorFunction`. Used to define operators throughout the library in a concise way.\n * @param init The logic to connect the liftedSource to the subscriber at the moment of subscription.\n */\nexport function operate(\n init: (liftedSource: Observable, subscriber: Subscriber) => (() => void) | void\n): OperatorFunction {\n return (source: Observable) => {\n if (hasLift(source)) {\n return source.lift(function (this: Subscriber, liftedSource: Observable) {\n try {\n return init(liftedSource, this);\n } catch (err) {\n this.error(err);\n }\n });\n }\n throw new TypeError('Unable to lift unknown Observable type');\n };\n}\n", "import { Subscriber } from '../Subscriber';\n\n/**\n * Creates an instance of an `OperatorSubscriber`.\n * @param destination The downstream subscriber.\n * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n * and send to the `destination` error handler.\n * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n * this handler are sent to the `destination` error handler.\n * @param onFinalize Additional teardown logic here. This will only be called on teardown if the\n * subscriber itself is not already closed. This is called after all other teardown logic is executed.\n */\nexport function createOperatorSubscriber(\n destination: Subscriber,\n onNext?: (value: T) => void,\n onComplete?: () => void,\n onError?: (err: any) => void,\n onFinalize?: () => void\n): Subscriber {\n return new OperatorSubscriber(destination, onNext, onComplete, onError, onFinalize);\n}\n\n/**\n * A generic helper for allowing operators to be created with a Subscriber and\n * use closures to capture necessary state from the operator function itself.\n */\nexport class OperatorSubscriber extends Subscriber {\n /**\n * Creates an instance of an `OperatorSubscriber`.\n * @param destination The downstream subscriber.\n * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n * and send to the `destination` error handler.\n * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n * this handler are sent to the `destination` error handler.\n * @param onFinalize Additional finalization logic here. This will only be called on finalization if the\n * subscriber itself is not already closed. This is called after all other finalization logic is executed.\n * @param shouldUnsubscribe An optional check to see if an unsubscribe call should truly unsubscribe.\n * NOTE: This currently **ONLY** exists to support the strange behavior of {@link groupBy}, where unsubscription\n * to the resulting observable does not actually disconnect from the source if there are active subscriptions\n * to any grouped observable. (DO NOT EXPOSE OR USE EXTERNALLY!!!)\n */\n constructor(\n destination: Subscriber,\n onNext?: (value: T) => void,\n onComplete?: () => void,\n onError?: (err: any) => void,\n private onFinalize?: () => void,\n private shouldUnsubscribe?: () => boolean\n ) {\n // It's important - for performance reasons - that all of this class's\n // members are initialized and that they are always initialized in the same\n // order. This will ensure that all OperatorSubscriber instances have the\n // same hidden class in V8. This, in turn, will help keep the number of\n // hidden classes involved in property accesses within the base class as\n // low as possible. If the number of hidden classes involved exceeds four,\n // the property accesses will become megamorphic and performance penalties\n // will be incurred - i.e. inline caches won't be used.\n //\n // The reasons for ensuring all instances have the same hidden class are\n // further discussed in this blog post from Benedikt Meurer:\n // https://benediktmeurer.de/2018/03/23/impact-of-polymorphism-on-component-based-frameworks-like-react/\n super(destination);\n this._next = onNext\n ? function (this: OperatorSubscriber, value: T) {\n try {\n onNext(value);\n } catch (err) {\n destination.error(err);\n }\n }\n : super._next;\n this._error = onError\n ? function (this: OperatorSubscriber, err: any) {\n try {\n onError(err);\n } catch (err) {\n // Send any errors that occur down stream.\n destination.error(err);\n } finally {\n // Ensure finalization.\n this.unsubscribe();\n }\n }\n : super._error;\n this._complete = onComplete\n ? function (this: OperatorSubscriber) {\n try {\n onComplete();\n } catch (err) {\n // Send any errors that occur down stream.\n destination.error(err);\n } finally {\n // Ensure finalization.\n this.unsubscribe();\n }\n }\n : super._complete;\n }\n\n unsubscribe() {\n if (!this.shouldUnsubscribe || this.shouldUnsubscribe()) {\n const { closed } = this;\n super.unsubscribe();\n // Execute additional teardown if we have any and we didn't already do so.\n !closed && this.onFinalize?.();\n }\n }\n}\n", "import { Subscription } from '../Subscription';\n\ninterface AnimationFrameProvider {\n schedule(callback: FrameRequestCallback): Subscription;\n requestAnimationFrame: typeof requestAnimationFrame;\n cancelAnimationFrame: typeof cancelAnimationFrame;\n delegate:\n | {\n requestAnimationFrame: typeof requestAnimationFrame;\n cancelAnimationFrame: typeof cancelAnimationFrame;\n }\n | undefined;\n}\n\nexport const animationFrameProvider: AnimationFrameProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n schedule(callback) {\n let request = requestAnimationFrame;\n let cancel: typeof cancelAnimationFrame | undefined = cancelAnimationFrame;\n const { delegate } = animationFrameProvider;\n if (delegate) {\n request = delegate.requestAnimationFrame;\n cancel = delegate.cancelAnimationFrame;\n }\n const handle = request((timestamp) => {\n // Clear the cancel function. The request has been fulfilled, so\n // attempting to cancel the request upon unsubscription would be\n // pointless.\n cancel = undefined;\n callback(timestamp);\n });\n return new Subscription(() => cancel?.(handle));\n },\n requestAnimationFrame(...args) {\n const { delegate } = animationFrameProvider;\n return (delegate?.requestAnimationFrame || requestAnimationFrame)(...args);\n },\n cancelAnimationFrame(...args) {\n const { delegate } = animationFrameProvider;\n return (delegate?.cancelAnimationFrame || cancelAnimationFrame)(...args);\n },\n delegate: undefined,\n};\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface ObjectUnsubscribedError extends Error {}\n\nexport interface ObjectUnsubscribedErrorCtor {\n /**\n * @deprecated Internal implementation detail. Do not construct error instances.\n * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n */\n new (): ObjectUnsubscribedError;\n}\n\n/**\n * An error thrown when an action is invalid because the object has been\n * unsubscribed.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n *\n * @class ObjectUnsubscribedError\n */\nexport const ObjectUnsubscribedError: ObjectUnsubscribedErrorCtor = createErrorClass(\n (_super) =>\n function ObjectUnsubscribedErrorImpl(this: any) {\n _super(this);\n this.name = 'ObjectUnsubscribedError';\n this.message = 'object unsubscribed';\n }\n);\n", "import { Operator } from './Operator';\nimport { Observable } from './Observable';\nimport { Subscriber } from './Subscriber';\nimport { Subscription, EMPTY_SUBSCRIPTION } from './Subscription';\nimport { Observer, SubscriptionLike, TeardownLogic } from './types';\nimport { ObjectUnsubscribedError } from './util/ObjectUnsubscribedError';\nimport { arrRemove } from './util/arrRemove';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A Subject is a special type of Observable that allows values to be\n * multicasted to many Observers. Subjects are like EventEmitters.\n *\n * Every Subject is an Observable and an Observer. You can subscribe to a\n * Subject, and you can call next to feed values as well as error and complete.\n */\nexport class Subject extends Observable implements SubscriptionLike {\n closed = false;\n\n private currentObservers: Observer[] | null = null;\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n observers: Observer[] = [];\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n isStopped = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n hasError = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n thrownError: any = null;\n\n /**\n * Creates a \"subject\" by basically gluing an observer to an observable.\n *\n * @deprecated Recommended you do not use. Will be removed at some point in the future. Plans for replacement still under discussion.\n */\n static create: (...args: any[]) => any = (destination: Observer, source: Observable): AnonymousSubject => {\n return new AnonymousSubject(destination, source);\n };\n\n constructor() {\n // NOTE: This must be here to obscure Observable's constructor.\n super();\n }\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n lift(operator: Operator): Observable {\n const subject = new AnonymousSubject(this, this);\n subject.operator = operator as any;\n return subject as any;\n }\n\n /** @internal */\n protected _throwIfClosed() {\n if (this.closed) {\n throw new ObjectUnsubscribedError();\n }\n }\n\n next(value: T) {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n if (!this.currentObservers) {\n this.currentObservers = Array.from(this.observers);\n }\n for (const observer of this.currentObservers) {\n observer.next(value);\n }\n }\n });\n }\n\n error(err: any) {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n this.hasError = this.isStopped = true;\n this.thrownError = err;\n const { observers } = this;\n while (observers.length) {\n observers.shift()!.error(err);\n }\n }\n });\n }\n\n complete() {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n this.isStopped = true;\n const { observers } = this;\n while (observers.length) {\n observers.shift()!.complete();\n }\n }\n });\n }\n\n unsubscribe() {\n this.isStopped = this.closed = true;\n this.observers = this.currentObservers = null!;\n }\n\n get observed() {\n return this.observers?.length > 0;\n }\n\n /** @internal */\n protected _trySubscribe(subscriber: Subscriber): TeardownLogic {\n this._throwIfClosed();\n return super._trySubscribe(subscriber);\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n this._throwIfClosed();\n this._checkFinalizedStatuses(subscriber);\n return this._innerSubscribe(subscriber);\n }\n\n /** @internal */\n protected _innerSubscribe(subscriber: Subscriber) {\n const { hasError, isStopped, observers } = this;\n if (hasError || isStopped) {\n return EMPTY_SUBSCRIPTION;\n }\n this.currentObservers = null;\n observers.push(subscriber);\n return new Subscription(() => {\n this.currentObservers = null;\n arrRemove(observers, subscriber);\n });\n }\n\n /** @internal */\n protected _checkFinalizedStatuses(subscriber: Subscriber) {\n const { hasError, thrownError, isStopped } = this;\n if (hasError) {\n subscriber.error(thrownError);\n } else if (isStopped) {\n subscriber.complete();\n }\n }\n\n /**\n * Creates a new Observable with this Subject as the source. You can do this\n * to create custom Observer-side logic of the Subject and conceal it from\n * code that uses the Observable.\n * @return Observable that this Subject casts to.\n */\n asObservable(): Observable {\n const observable: any = new Observable();\n observable.source = this;\n return observable;\n }\n}\n\nexport class AnonymousSubject extends Subject {\n constructor(\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n public destination?: Observer,\n source?: Observable\n ) {\n super();\n this.source = source;\n }\n\n next(value: T) {\n this.destination?.next?.(value);\n }\n\n error(err: any) {\n this.destination?.error?.(err);\n }\n\n complete() {\n this.destination?.complete?.();\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n return this.source?.subscribe(subscriber) ?? EMPTY_SUBSCRIPTION;\n }\n}\n", "import { Subject } from './Subject';\nimport { Subscriber } from './Subscriber';\nimport { Subscription } from './Subscription';\n\n/**\n * A variant of Subject that requires an initial value and emits its current\n * value whenever it is subscribed to.\n */\nexport class BehaviorSubject extends Subject {\n constructor(private _value: T) {\n super();\n }\n\n get value(): T {\n return this.getValue();\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n const subscription = super._subscribe(subscriber);\n !subscription.closed && subscriber.next(this._value);\n return subscription;\n }\n\n getValue(): T {\n const { hasError, thrownError, _value } = this;\n if (hasError) {\n throw thrownError;\n }\n this._throwIfClosed();\n return _value;\n }\n\n next(value: T): void {\n super.next((this._value = value));\n }\n}\n", "import { TimestampProvider } from '../types';\n\ninterface DateTimestampProvider extends TimestampProvider {\n delegate: TimestampProvider | undefined;\n}\n\nexport const dateTimestampProvider: DateTimestampProvider = {\n now() {\n // Use the variable rather than `this` so that the function can be called\n // without being bound to the provider.\n return (dateTimestampProvider.delegate || Date).now();\n },\n delegate: undefined,\n};\n", "import { Subject } from './Subject';\nimport { TimestampProvider } from './types';\nimport { Subscriber } from './Subscriber';\nimport { Subscription } from './Subscription';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * A variant of {@link Subject} that \"replays\" old values to new subscribers by emitting them when they first subscribe.\n *\n * `ReplaySubject` has an internal buffer that will store a specified number of values that it has observed. Like `Subject`,\n * `ReplaySubject` \"observes\" values by having them passed to its `next` method. When it observes a value, it will store that\n * value for a time determined by the configuration of the `ReplaySubject`, as passed to its constructor.\n *\n * When a new subscriber subscribes to the `ReplaySubject` instance, it will synchronously emit all values in its buffer in\n * a First-In-First-Out (FIFO) manner. The `ReplaySubject` will also complete, if it has observed completion; and it will\n * error if it has observed an error.\n *\n * There are two main configuration items to be concerned with:\n *\n * 1. `bufferSize` - This will determine how many items are stored in the buffer, defaults to infinite.\n * 2. `windowTime` - The amount of time to hold a value in the buffer before removing it from the buffer.\n *\n * Both configurations may exist simultaneously. So if you would like to buffer a maximum of 3 values, as long as the values\n * are less than 2 seconds old, you could do so with a `new ReplaySubject(3, 2000)`.\n *\n * ### Differences with BehaviorSubject\n *\n * `BehaviorSubject` is similar to `new ReplaySubject(1)`, with a couple of exceptions:\n *\n * 1. `BehaviorSubject` comes \"primed\" with a single value upon construction.\n * 2. `ReplaySubject` will replay values, even after observing an error, where `BehaviorSubject` will not.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n * @see {@link shareReplay}\n */\nexport class ReplaySubject extends Subject {\n private _buffer: (T | number)[] = [];\n private _infiniteTimeWindow = true;\n\n /**\n * @param _bufferSize The size of the buffer to replay on subscription\n * @param _windowTime The amount of time the buffered items will stay buffered\n * @param _timestampProvider An object with a `now()` method that provides the current timestamp. This is used to\n * calculate the amount of time something has been buffered.\n */\n constructor(\n private _bufferSize = Infinity,\n private _windowTime = Infinity,\n private _timestampProvider: TimestampProvider = dateTimestampProvider\n ) {\n super();\n this._infiniteTimeWindow = _windowTime === Infinity;\n this._bufferSize = Math.max(1, _bufferSize);\n this._windowTime = Math.max(1, _windowTime);\n }\n\n next(value: T): void {\n const { isStopped, _buffer, _infiniteTimeWindow, _timestampProvider, _windowTime } = this;\n if (!isStopped) {\n _buffer.push(value);\n !_infiniteTimeWindow && _buffer.push(_timestampProvider.now() + _windowTime);\n }\n this._trimBuffer();\n super.next(value);\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n this._throwIfClosed();\n this._trimBuffer();\n\n const subscription = this._innerSubscribe(subscriber);\n\n const { _infiniteTimeWindow, _buffer } = this;\n // We use a copy here, so reentrant code does not mutate our array while we're\n // emitting it to a new subscriber.\n const copy = _buffer.slice();\n for (let i = 0; i < copy.length && !subscriber.closed; i += _infiniteTimeWindow ? 1 : 2) {\n subscriber.next(copy[i] as T);\n }\n\n this._checkFinalizedStatuses(subscriber);\n\n return subscription;\n }\n\n private _trimBuffer() {\n const { _bufferSize, _timestampProvider, _buffer, _infiniteTimeWindow } = this;\n // If we don't have an infinite buffer size, and we're over the length,\n // use splice to truncate the old buffer values off. Note that we have to\n // double the size for instances where we're not using an infinite time window\n // because we're storing the values and the timestamps in the same array.\n const adjustedBufferSize = (_infiniteTimeWindow ? 1 : 2) * _bufferSize;\n _bufferSize < Infinity && adjustedBufferSize < _buffer.length && _buffer.splice(0, _buffer.length - adjustedBufferSize);\n\n // Now, if we're not in an infinite time window, remove all values where the time is\n // older than what is allowed.\n if (!_infiniteTimeWindow) {\n const now = _timestampProvider.now();\n let last = 0;\n // Search the array for the first timestamp that isn't expired and\n // truncate the buffer up to that point.\n for (let i = 1; i < _buffer.length && (_buffer[i] as number) <= now; i += 2) {\n last = i;\n }\n last && _buffer.splice(0, last + 1);\n }\n }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Subscription } from '../Subscription';\nimport { SchedulerAction } from '../types';\n\n/**\n * A unit of work to be executed in a `scheduler`. An action is typically\n * created from within a {@link SchedulerLike} and an RxJS user does not need to concern\n * themselves about creating and manipulating an Action.\n *\n * ```ts\n * class Action extends Subscription {\n * new (scheduler: Scheduler, work: (state?: T) => void);\n * schedule(state?: T, delay: number = 0): Subscription;\n * }\n * ```\n */\nexport class Action extends Subscription {\n constructor(scheduler: Scheduler, work: (this: SchedulerAction, state?: T) => void) {\n super();\n }\n /**\n * Schedules this action on its parent {@link SchedulerLike} for execution. May be passed\n * some context object, `state`. May happen at some point in the future,\n * according to the `delay` parameter, if specified.\n * @param state Some contextual data that the `work` function uses when called by the\n * Scheduler.\n * @param delay Time to wait before executing the work, where the time unit is implicit\n * and defined by the Scheduler.\n * @return A subscription in order to be able to unsubscribe the scheduled work.\n */\n public schedule(state?: T, delay: number = 0): Subscription {\n return this;\n }\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetIntervalFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearIntervalFunction = (handle: TimerHandle) => void;\n\ninterface IntervalProvider {\n setInterval: SetIntervalFunction;\n clearInterval: ClearIntervalFunction;\n delegate:\n | {\n setInterval: SetIntervalFunction;\n clearInterval: ClearIntervalFunction;\n }\n | undefined;\n}\n\nexport const intervalProvider: IntervalProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n setInterval(handler: () => void, timeout?: number, ...args) {\n const { delegate } = intervalProvider;\n if (delegate?.setInterval) {\n return delegate.setInterval(handler, timeout, ...args);\n }\n return setInterval(handler, timeout, ...args);\n },\n clearInterval(handle) {\n const { delegate } = intervalProvider;\n return (delegate?.clearInterval || clearInterval)(handle as any);\n },\n delegate: undefined,\n};\n", "import { Action } from './Action';\nimport { SchedulerAction } from '../types';\nimport { Subscription } from '../Subscription';\nimport { AsyncScheduler } from './AsyncScheduler';\nimport { intervalProvider } from './intervalProvider';\nimport { arrRemove } from '../util/arrRemove';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncAction extends Action {\n public id: TimerHandle | undefined;\n public state?: T;\n // @ts-ignore: Property has no initializer and is not definitely assigned\n public delay: number;\n protected pending: boolean = false;\n\n constructor(protected scheduler: AsyncScheduler, protected work: (this: SchedulerAction, state?: T) => void) {\n super(scheduler, work);\n }\n\n public schedule(state?: T, delay: number = 0): Subscription {\n if (this.closed) {\n return this;\n }\n\n // Always replace the current state with the new state.\n this.state = state;\n\n const id = this.id;\n const scheduler = this.scheduler;\n\n //\n // Important implementation note:\n //\n // Actions only execute once by default, unless rescheduled from within the\n // scheduled callback. This allows us to implement single and repeat\n // actions via the same code path, without adding API surface area, as well\n // as mimic traditional recursion but across asynchronous boundaries.\n //\n // However, JS runtimes and timers distinguish between intervals achieved by\n // serial `setTimeout` calls vs. a single `setInterval` call. An interval of\n // serial `setTimeout` calls can be individually delayed, which delays\n // scheduling the next `setTimeout`, and so on. `setInterval` attempts to\n // guarantee the interval callback will be invoked more precisely to the\n // interval period, regardless of load.\n //\n // Therefore, we use `setInterval` to schedule single and repeat actions.\n // If the action reschedules itself with the same delay, the interval is not\n // canceled. If the action doesn't reschedule, or reschedules with a\n // different delay, the interval will be canceled after scheduled callback\n // execution.\n //\n if (id != null) {\n this.id = this.recycleAsyncId(scheduler, id, delay);\n }\n\n // Set the pending flag indicating that this action has been scheduled, or\n // has recursively rescheduled itself.\n this.pending = true;\n\n this.delay = delay;\n // If this action has already an async Id, don't request a new one.\n this.id = this.id ?? this.requestAsyncId(scheduler, this.id, delay);\n\n return this;\n }\n\n protected requestAsyncId(scheduler: AsyncScheduler, _id?: TimerHandle, delay: number = 0): TimerHandle {\n return intervalProvider.setInterval(scheduler.flush.bind(scheduler, this), delay);\n }\n\n protected recycleAsyncId(_scheduler: AsyncScheduler, id?: TimerHandle, delay: number | null = 0): TimerHandle | undefined {\n // If this action is rescheduled with the same delay time, don't clear the interval id.\n if (delay != null && this.delay === delay && this.pending === false) {\n return id;\n }\n // Otherwise, if the action's delay time is different from the current delay,\n // or the action has been rescheduled before it's executed, clear the interval id\n if (id != null) {\n intervalProvider.clearInterval(id);\n }\n\n return undefined;\n }\n\n /**\n * Immediately executes this action and the `work` it contains.\n */\n public execute(state: T, delay: number): any {\n if (this.closed) {\n return new Error('executing a cancelled action');\n }\n\n this.pending = false;\n const error = this._execute(state, delay);\n if (error) {\n return error;\n } else if (this.pending === false && this.id != null) {\n // Dequeue if the action didn't reschedule itself. Don't call\n // unsubscribe(), because the action could reschedule later.\n // For example:\n // ```\n // scheduler.schedule(function doWork(counter) {\n // /* ... I'm a busy worker bee ... */\n // var originalAction = this;\n // /* wait 100ms before rescheduling the action */\n // setTimeout(function () {\n // originalAction.schedule(counter + 1);\n // }, 100);\n // }, 1000);\n // ```\n this.id = this.recycleAsyncId(this.scheduler, this.id, null);\n }\n }\n\n protected _execute(state: T, _delay: number): any {\n let errored: boolean = false;\n let errorValue: any;\n try {\n this.work(state);\n } catch (e) {\n errored = true;\n // HACK: Since code elsewhere is relying on the \"truthiness\" of the\n // return here, we can't have it return \"\" or 0 or false.\n // TODO: Clean this up when we refactor schedulers mid-version-8 or so.\n errorValue = e ? e : new Error('Scheduled action threw falsy error');\n }\n if (errored) {\n this.unsubscribe();\n return errorValue;\n }\n }\n\n unsubscribe() {\n if (!this.closed) {\n const { id, scheduler } = this;\n const { actions } = scheduler;\n\n this.work = this.state = this.scheduler = null!;\n this.pending = false;\n\n arrRemove(actions, this);\n if (id != null) {\n this.id = this.recycleAsyncId(scheduler, id, null);\n }\n\n this.delay = null!;\n super.unsubscribe();\n }\n }\n}\n", "import { Action } from './scheduler/Action';\nimport { Subscription } from './Subscription';\nimport { SchedulerLike, SchedulerAction } from './types';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * An execution context and a data structure to order tasks and schedule their\n * execution. Provides a notion of (potentially virtual) time, through the\n * `now()` getter method.\n *\n * Each unit of work in a Scheduler is called an `Action`.\n *\n * ```ts\n * class Scheduler {\n * now(): number;\n * schedule(work, delay?, state?): Subscription;\n * }\n * ```\n *\n * @deprecated Scheduler is an internal implementation detail of RxJS, and\n * should not be used directly. Rather, create your own class and implement\n * {@link SchedulerLike}. Will be made internal in v8.\n */\nexport class Scheduler implements SchedulerLike {\n public static now: () => number = dateTimestampProvider.now;\n\n constructor(private schedulerActionCtor: typeof Action, now: () => number = Scheduler.now) {\n this.now = now;\n }\n\n /**\n * A getter method that returns a number representing the current time\n * (at the time this function was called) according to the scheduler's own\n * internal clock.\n * @return A number that represents the current time. May or may not\n * have a relation to wall-clock time. May or may not refer to a time unit\n * (e.g. milliseconds).\n */\n public now: () => number;\n\n /**\n * Schedules a function, `work`, for execution. May happen at some point in\n * the future, according to the `delay` parameter, if specified. May be passed\n * some context object, `state`, which will be passed to the `work` function.\n *\n * The given arguments will be processed an stored as an Action object in a\n * queue of actions.\n *\n * @param work A function representing a task, or some unit of work to be\n * executed by the Scheduler.\n * @param delay Time to wait before executing the work, where the time unit is\n * implicit and defined by the Scheduler itself.\n * @param state Some contextual data that the `work` function uses when called\n * by the Scheduler.\n * @return A subscription in order to be able to unsubscribe the scheduled work.\n */\n public schedule(work: (this: SchedulerAction, state?: T) => void, delay: number = 0, state?: T): Subscription {\n return new this.schedulerActionCtor(this, work).schedule(state, delay);\n }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Action } from './Action';\nimport { AsyncAction } from './AsyncAction';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncScheduler extends Scheduler {\n public actions: Array> = [];\n /**\n * A flag to indicate whether the Scheduler is currently executing a batch of\n * queued actions.\n * @internal\n */\n public _active: boolean = false;\n /**\n * An internal ID used to track the latest asynchronous task such as those\n * coming from `setTimeout`, `setInterval`, `requestAnimationFrame`, and\n * others.\n * @internal\n */\n public _scheduled: TimerHandle | undefined;\n\n constructor(SchedulerAction: typeof Action, now: () => number = Scheduler.now) {\n super(SchedulerAction, now);\n }\n\n public flush(action: AsyncAction): void {\n const { actions } = this;\n\n if (this._active) {\n actions.push(action);\n return;\n }\n\n let error: any;\n this._active = true;\n\n do {\n if ((error = action.execute(action.state, action.delay))) {\n break;\n }\n } while ((action = actions.shift()!)); // exhaust the scheduler queue\n\n this._active = false;\n\n if (error) {\n while ((action = actions.shift()!)) {\n action.unsubscribe();\n }\n throw error;\n }\n }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\n/**\n *\n * Async Scheduler\n *\n * Schedule task as if you used setTimeout(task, duration)\n *\n * `async` scheduler schedules tasks asynchronously, by putting them on the JavaScript\n * event loop queue. It is best used to delay tasks in time or to schedule tasks repeating\n * in intervals.\n *\n * If you just want to \"defer\" task, that is to perform it right after currently\n * executing synchronous code ends (commonly achieved by `setTimeout(deferredTask, 0)`),\n * better choice will be the {@link asapScheduler} scheduler.\n *\n * ## Examples\n * Use async scheduler to delay task\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * const task = () => console.log('it works!');\n *\n * asyncScheduler.schedule(task, 2000);\n *\n * // After 2 seconds logs:\n * // \"it works!\"\n * ```\n *\n * Use async scheduler to repeat task in intervals\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * function task(state) {\n * console.log(state);\n * this.schedule(state + 1, 1000); // `this` references currently executing Action,\n * // which we reschedule with new state and delay\n * }\n *\n * asyncScheduler.schedule(task, 3000, 0);\n *\n * // Logs:\n * // 0 after 3s\n * // 1 after 4s\n * // 2 after 5s\n * // 3 after 6s\n * ```\n */\n\nexport const asyncScheduler = new AsyncScheduler(AsyncAction);\n\n/**\n * @deprecated Renamed to {@link asyncScheduler}. Will be removed in v8.\n */\nexport const async = asyncScheduler;\n", "import { AsyncAction } from './AsyncAction';\nimport { Subscription } from '../Subscription';\nimport { QueueScheduler } from './QueueScheduler';\nimport { SchedulerAction } from '../types';\nimport { TimerHandle } from './timerHandle';\n\nexport class QueueAction extends AsyncAction {\n constructor(protected scheduler: QueueScheduler, protected work: (this: SchedulerAction, state?: T) => void) {\n super(scheduler, work);\n }\n\n public schedule(state?: T, delay: number = 0): Subscription {\n if (delay > 0) {\n return super.schedule(state, delay);\n }\n this.delay = delay;\n this.state = state;\n this.scheduler.flush(this);\n return this;\n }\n\n public execute(state: T, delay: number): any {\n return delay > 0 || this.closed ? super.execute(state, delay) : this._execute(state, delay);\n }\n\n protected requestAsyncId(scheduler: QueueScheduler, id?: TimerHandle, delay: number = 0): TimerHandle {\n // If delay exists and is greater than 0, or if the delay is null (the\n // action wasn't rescheduled) but was originally scheduled as an async\n // action, then recycle as an async action.\n\n if ((delay != null && delay > 0) || (delay == null && this.delay > 0)) {\n return super.requestAsyncId(scheduler, id, delay);\n }\n\n // Otherwise flush the scheduler starting with this action.\n scheduler.flush(this);\n\n // HACK: In the past, this was returning `void`. However, `void` isn't a valid\n // `TimerHandle`, and generally the return value here isn't really used. So the\n // compromise is to return `0` which is both \"falsy\" and a valid `TimerHandle`,\n // as opposed to refactoring every other instanceo of `requestAsyncId`.\n return 0;\n }\n}\n", "import { AsyncScheduler } from './AsyncScheduler';\n\nexport class QueueScheduler extends AsyncScheduler {\n}\n", "import { QueueAction } from './QueueAction';\nimport { QueueScheduler } from './QueueScheduler';\n\n/**\n *\n * Queue Scheduler\n *\n * Put every next task on a queue, instead of executing it immediately\n *\n * `queue` scheduler, when used with delay, behaves the same as {@link asyncScheduler} scheduler.\n *\n * When used without delay, it schedules given task synchronously - executes it right when\n * it is scheduled. However when called recursively, that is when inside the scheduled task,\n * another task is scheduled with queue scheduler, instead of executing immediately as well,\n * that task will be put on a queue and wait for current one to finish.\n *\n * This means that when you execute task with `queue` scheduler, you are sure it will end\n * before any other task scheduled with that scheduler will start.\n *\n * ## Examples\n * Schedule recursively first, then do something\n * ```ts\n * import { queueScheduler } from 'rxjs';\n *\n * queueScheduler.schedule(() => {\n * queueScheduler.schedule(() => console.log('second')); // will not happen now, but will be put on a queue\n *\n * console.log('first');\n * });\n *\n * // Logs:\n * // \"first\"\n * // \"second\"\n * ```\n *\n * Reschedule itself recursively\n * ```ts\n * import { queueScheduler } from 'rxjs';\n *\n * queueScheduler.schedule(function(state) {\n * if (state !== 0) {\n * console.log('before', state);\n * this.schedule(state - 1); // `this` references currently executing Action,\n * // which we reschedule with new state\n * console.log('after', state);\n * }\n * }, 0, 3);\n *\n * // In scheduler that runs recursively, you would expect:\n * // \"before\", 3\n * // \"before\", 2\n * // \"before\", 1\n * // \"after\", 1\n * // \"after\", 2\n * // \"after\", 3\n *\n * // But with queue it logs:\n * // \"before\", 3\n * // \"after\", 3\n * // \"before\", 2\n * // \"after\", 2\n * // \"before\", 1\n * // \"after\", 1\n * ```\n */\n\nexport const queueScheduler = new QueueScheduler(QueueAction);\n\n/**\n * @deprecated Renamed to {@link queueScheduler}. Will be removed in v8.\n */\nexport const queue = queueScheduler;\n", "import { AsyncAction } from './AsyncAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\nimport { SchedulerAction } from '../types';\nimport { animationFrameProvider } from './animationFrameProvider';\nimport { TimerHandle } from './timerHandle';\n\nexport class AnimationFrameAction extends AsyncAction {\n constructor(protected scheduler: AnimationFrameScheduler, protected work: (this: SchedulerAction, state?: T) => void) {\n super(scheduler, work);\n }\n\n protected requestAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle {\n // If delay is greater than 0, request as an async action.\n if (delay !== null && delay > 0) {\n return super.requestAsyncId(scheduler, id, delay);\n }\n // Push the action to the end of the scheduler queue.\n scheduler.actions.push(this);\n // If an animation frame has already been requested, don't request another\n // one. If an animation frame hasn't been requested yet, request one. Return\n // the current animation frame request id.\n return scheduler._scheduled || (scheduler._scheduled = animationFrameProvider.requestAnimationFrame(() => scheduler.flush(undefined)));\n }\n\n protected recycleAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle | undefined {\n // If delay exists and is greater than 0, or if the delay is null (the\n // action wasn't rescheduled) but was originally scheduled as an async\n // action, then recycle as an async action.\n if (delay != null ? delay > 0 : this.delay > 0) {\n return super.recycleAsyncId(scheduler, id, delay);\n }\n // If the scheduler queue has no remaining actions with the same async id,\n // cancel the requested animation frame and set the scheduled flag to\n // undefined so the next AnimationFrameAction will request its own.\n const { actions } = scheduler;\n if (id != null && id === scheduler._scheduled && actions[actions.length - 1]?.id !== id) {\n animationFrameProvider.cancelAnimationFrame(id as number);\n scheduler._scheduled = undefined;\n }\n // Return undefined so the action knows to request a new async id if it's rescheduled.\n return undefined;\n }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\nexport class AnimationFrameScheduler extends AsyncScheduler {\n public flush(action?: AsyncAction): void {\n this._active = true;\n // The async id that effects a call to flush is stored in _scheduled.\n // Before executing an action, it's necessary to check the action's async\n // id to determine whether it's supposed to be executed in the current\n // flush.\n // Previous implementations of this method used a count to determine this,\n // but that was unsound, as actions that are unsubscribed - i.e. cancelled -\n // are removed from the actions array and that can shift actions that are\n // scheduled to be executed in a subsequent flush into positions at which\n // they are executed within the current flush.\n let flushId;\n if (action) {\n flushId = action.id;\n } else {\n flushId = this._scheduled;\n this._scheduled = undefined;\n }\n\n const { actions } = this;\n let error: any;\n action = action || actions.shift()!;\n\n do {\n if ((error = action.execute(action.state, action.delay))) {\n break;\n }\n } while ((action = actions[0]) && action.id === flushId && actions.shift());\n\n this._active = false;\n\n if (error) {\n while ((action = actions[0]) && action.id === flushId && actions.shift()) {\n action.unsubscribe();\n }\n throw error;\n }\n }\n}\n", "import { AnimationFrameAction } from './AnimationFrameAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\n\n/**\n *\n * Animation Frame Scheduler\n *\n * Perform task when `window.requestAnimationFrame` would fire\n *\n * When `animationFrame` scheduler is used with delay, it will fall back to {@link asyncScheduler} scheduler\n * behaviour.\n *\n * Without delay, `animationFrame` scheduler can be used to create smooth browser animations.\n * It makes sure scheduled task will happen just before next browser content repaint,\n * thus performing animations as efficiently as possible.\n *\n * ## Example\n * Schedule div height animation\n * ```ts\n * // html:
\n * import { animationFrameScheduler } from 'rxjs';\n *\n * const div = document.querySelector('div');\n *\n * animationFrameScheduler.schedule(function(height) {\n * div.style.height = height + \"px\";\n *\n * this.schedule(height + 1); // `this` references currently executing Action,\n * // which we reschedule with new state\n * }, 0, 0);\n *\n * // You will see a div element growing in height\n * ```\n */\n\nexport const animationFrameScheduler = new AnimationFrameScheduler(AnimationFrameAction);\n\n/**\n * @deprecated Renamed to {@link animationFrameScheduler}. Will be removed in v8.\n */\nexport const animationFrame = animationFrameScheduler;\n", "import { Observable } from '../Observable';\nimport { SchedulerLike } from '../types';\n\n/**\n * A simple Observable that emits no items to the Observer and immediately\n * emits a complete notification.\n *\n * Just emits 'complete', and nothing else.\n *\n * ![](empty.png)\n *\n * A simple Observable that only emits the complete notification. It can be used\n * for composing with other Observables, such as in a {@link mergeMap}.\n *\n * ## Examples\n *\n * Log complete notification\n *\n * ```ts\n * import { EMPTY } from 'rxjs';\n *\n * EMPTY.subscribe({\n * next: () => console.log('Next'),\n * complete: () => console.log('Complete!')\n * });\n *\n * // Outputs\n * // Complete!\n * ```\n *\n * Emit the number 7, then complete\n *\n * ```ts\n * import { EMPTY, startWith } from 'rxjs';\n *\n * const result = EMPTY.pipe(startWith(7));\n * result.subscribe(x => console.log(x));\n *\n * // Outputs\n * // 7\n * ```\n *\n * Map and flatten only odd numbers to the sequence `'a'`, `'b'`, `'c'`\n *\n * ```ts\n * import { interval, mergeMap, of, EMPTY } from 'rxjs';\n *\n * const interval$ = interval(1000);\n * const result = interval$.pipe(\n * mergeMap(x => x % 2 === 1 ? of('a', 'b', 'c') : EMPTY),\n * );\n * result.subscribe(x => console.log(x));\n *\n * // Results in the following to the console:\n * // x is equal to the count on the interval, e.g. (0, 1, 2, 3, ...)\n * // x will occur every 1000ms\n * // if x % 2 is equal to 1, print a, b, c (each on its own)\n * // if x % 2 is not equal to 1, nothing will be output\n * ```\n *\n * @see {@link Observable}\n * @see {@link NEVER}\n * @see {@link of}\n * @see {@link throwError}\n */\nexport const EMPTY = new Observable((subscriber) => subscriber.complete());\n\n/**\n * @param scheduler A {@link SchedulerLike} to use for scheduling\n * the emission of the complete notification.\n * @deprecated Replaced with the {@link EMPTY} constant or {@link scheduled} (e.g. `scheduled([], scheduler)`). Will be removed in v8.\n */\nexport function empty(scheduler?: SchedulerLike) {\n return scheduler ? emptyScheduled(scheduler) : EMPTY;\n}\n\nfunction emptyScheduled(scheduler: SchedulerLike) {\n return new Observable((subscriber) => scheduler.schedule(() => subscriber.complete()));\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport function isScheduler(value: any): value is SchedulerLike {\n return value && isFunction(value.schedule);\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\nimport { isScheduler } from './isScheduler';\n\nfunction last(arr: T[]): T | undefined {\n return arr[arr.length - 1];\n}\n\nexport function popResultSelector(args: any[]): ((...args: unknown[]) => unknown) | undefined {\n return isFunction(last(args)) ? args.pop() : undefined;\n}\n\nexport function popScheduler(args: any[]): SchedulerLike | undefined {\n return isScheduler(last(args)) ? args.pop() : undefined;\n}\n\nexport function popNumber(args: any[], defaultValue: number): number {\n return typeof last(args) === 'number' ? args.pop()! : defaultValue;\n}\n", "export const isArrayLike = ((x: any): x is ArrayLike => x && typeof x.length === 'number' && typeof x !== 'function');", "import { isFunction } from \"./isFunction\";\n\n/**\n * Tests to see if the object is \"thennable\".\n * @param value the object to test\n */\nexport function isPromise(value: any): value is PromiseLike {\n return isFunction(value?.then);\n}\n", "import { InteropObservable } from '../types';\nimport { observable as Symbol_observable } from '../symbol/observable';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being Observable (but not necessary an Rx Observable) */\nexport function isInteropObservable(input: any): input is InteropObservable {\n return isFunction(input[Symbol_observable]);\n}\n", "import { isFunction } from './isFunction';\n\nexport function isAsyncIterable(obj: any): obj is AsyncIterable {\n return Symbol.asyncIterator && isFunction(obj?.[Symbol.asyncIterator]);\n}\n", "/**\n * Creates the TypeError to throw if an invalid object is passed to `from` or `scheduled`.\n * @param input The object that was passed.\n */\nexport function createInvalidObservableTypeError(input: any) {\n // TODO: We should create error codes that can be looked up, so this can be less verbose.\n return new TypeError(\n `You provided ${\n input !== null && typeof input === 'object' ? 'an invalid object' : `'${input}'`\n } where a stream was expected. You can provide an Observable, Promise, ReadableStream, Array, AsyncIterable, or Iterable.`\n );\n}\n", "export function getSymbolIterator(): symbol {\n if (typeof Symbol !== 'function' || !Symbol.iterator) {\n return '@@iterator' as any;\n }\n\n return Symbol.iterator;\n}\n\nexport const iterator = getSymbolIterator();\n", "import { iterator as Symbol_iterator } from '../symbol/iterator';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being an Iterable */\nexport function isIterable(input: any): input is Iterable {\n return isFunction(input?.[Symbol_iterator]);\n}\n", "import { ReadableStreamLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport async function* readableStreamLikeToAsyncGenerator(readableStream: ReadableStreamLike): AsyncGenerator {\n const reader = readableStream.getReader();\n try {\n while (true) {\n const { value, done } = await reader.read();\n if (done) {\n return;\n }\n yield value!;\n }\n } finally {\n reader.releaseLock();\n }\n}\n\nexport function isReadableStreamLike(obj: any): obj is ReadableStreamLike {\n // We don't want to use instanceof checks because they would return\n // false for instances from another Realm, like an