diff --git a/README.md b/README.md index 86bea46..3d8974f 100644 --- a/README.md +++ b/README.md @@ -5,9 +5,9 @@ [![Test Build](https://github.com/stanfish06/scLoop/actions/workflows/test-build.yml/badge.svg)](https://github.com/stanfish06/scLoop/actions/workflows/test-build.yml) [![PyPI](https://img.shields.io/pypi/v/scloop)](https://pypi.org/project/scloop) -**Note: this package is in early stages of development. (manuscirpt in prep)** +**Note: this package is in early stages of development. (manuscript in prep)** -scLoop is a library to identify statistically significant loops in single-cell RNA-seq data. +scLoop is a library to identify statistically significant loops in single-cell RNA-seq data. In brief, it bootstraps the data points and matches loops across the bootstrapped samples through both image homology and topological equivalence, which assigns confidence to individual loops in the dataset. The core computation routine is compiled and parallelized. diff --git a/mise.toml b/mise.toml index f79e8dd..601fb36 100644 --- a/mise.toml +++ b/mise.toml @@ -34,7 +34,7 @@ run = [ [tasks.sync] run = [ - "uv sync", + "uv sync --dev", ] [tasks.format] diff --git a/pyproject.toml b/pyproject.toml index 56df49f..1e55478 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,12 +17,14 @@ classifiers = [ "Programming Language :: Python :: 3.12", ] dependencies = [ + "adjusttext>=1.4", "cython>=3.0.12", "glasbey==0.3", "huggingface-hub", "igraph>=0.11", "kmapper>=2.1", "loguru==0.7.3", + "mellon>=1.7.1", "numba>=0.62.1", "numpy<2", "pandas<2", @@ -49,7 +51,9 @@ urls.Repository = "https://github.com/stanfish06/scloop.git" [dependency-groups] dev = [ "fa2-modified>=0.4", + "jupyterlab>=4.6.1", "magic-impute>=3", + "notebook>=7.6.0", "phate>=2", "pyright==1.1.406", "ruff>=0.15.5", diff --git a/src/scloop/analyzing/bootstrap.py b/src/scloop/analyzing/bootstrap.py index fe05aaa..7388662 100644 --- a/src/scloop/analyzing/bootstrap.py +++ b/src/scloop/analyzing/bootstrap.py @@ -5,15 +5,18 @@ ThreadPoolExecutor, as_completed, ) +from typing import Literal import numpy as np from anndata import AnnData from loguru import logger -from pydantic import BaseModel, ConfigDict +from pydantic import BaseModel, ConfigDict, Field from rich.progress import Progress from scipy.sparse import csr_matrix from ..computing.homology import ( + _persistence_pairs_from_ripser_result, + compute_image_bootstrap_homology, compute_persistence_diagram_and_cocycles, compute_sparse_pairwise_distance, ) @@ -27,7 +30,7 @@ compute_geometric_distance, ) from ..data.analysis_containers import LoopMatch -from ..data.base_components import LoopClass +from ..data.base_components import ImagePairRecord, LoopClass, PersistencePair from ..data.boundary import BoundaryMatrixD1 from ..data.constants import ( DEFAULT_EXTRA_DIAM_EQUIVALENCE, @@ -55,10 +58,108 @@ class BootstrapResult(BaseModel): idx_bootstrap: int persistence_diagram: list | None + persistence_pair_simplices: list | None cocycles: list | None indices_resample: list[int] loop_classes: list[LoopClass | None] matches: dict[int, list[LoopMatch]] + reference_image_pairs: list[ImagePairRecord] = Field(default_factory=list) + bootstrap_image_pairs: list[ImagePairRecord] = Field(default_factory=list) + + +class _MatchCandidate(BaseModel): + target_class_idx: int + geometric_distance: float | None = None + neighbor_rank: int | None = None + image_death_simplex: list[int] | None = None + + +def _select_geometric_candidates( + pairwise_distances: np.ndarray, + k: int, + require_homological_equivalence: bool, +) -> dict[int, list[_MatchCandidate]]: + if k <= 0: + raise ValueError("k must be positive") + n_candidates = k if require_homological_equivalence else 1 + neighbor_indices, neighbor_distances = nearest_neighbor_per_row( + pairwise_distances, n_candidates + ) + candidates: dict[int, list[_MatchCandidate]] = {} + for source_idx in range(pairwise_distances.shape[0]): + for rank in range(n_candidates): + target_idx = int(neighbor_indices[source_idx, rank]) + if target_idx < 0: + continue + candidates.setdefault(source_idx, []).append( + _MatchCandidate( + target_class_idx=target_idx, + geometric_distance=float(neighbor_distances[source_idx, rank]), + neighbor_rank=rank, + ) + ) + return candidates + + +def _select_image_candidates( + reference_records: list[ImagePairRecord], + bootstrap_records: list[ImagePairRecord], +) -> dict[int, list[_MatchCandidate]]: + targets_by_death_simplex: dict[tuple[int, ...], list[int]] = {} + for record in bootstrap_records: + if record.image_pair is None or not record.image_pair.death_simplex: + continue + death_simplex = tuple(sorted(record.image_pair.death_simplex)) + targets_by_death_simplex.setdefault(death_simplex, []).append( + record.source_class_idx + ) + + candidates: dict[int, list[_MatchCandidate]] = {} + for record in reference_records: + if record.image_pair is None or not record.image_pair.death_simplex: + continue + death_simplex = tuple(sorted(record.image_pair.death_simplex)) + for target_idx in sorted(targets_by_death_simplex.get(death_simplex, [])): + candidates.setdefault(record.source_class_idx, []).append( + _MatchCandidate( + target_class_idx=target_idx, + image_death_simplex=list(death_simplex), + ) + ) + return candidates + + +def _build_image_pair_records( + loop_classes: list[LoopClass | None], + image_pairs: list[PersistencePair], + vertex_offset: int, +) -> list[ImagePairRecord]: + image_pairs_by_birth = { + tuple(sorted(pair.birth_simplex)): pair + for pair in image_pairs + if pair.birth_simplex + } + records: list[ImagePairRecord] = [] + for class_idx, loop_class in enumerate(loop_classes): + if loop_class is None: + continue + source_pair = PersistencePair( + birth=loop_class.birth, + death=loop_class.death, + birth_simplex=[v + vertex_offset for v in loop_class.birth_simplex], + death_simplex=[v + vertex_offset for v in loop_class.death_simplex], + ) + image_pair = image_pairs_by_birth.get( + tuple(sorted(source_pair.birth_simplex)) + ) + records.append( + ImagePairRecord( + source_class_idx=class_idx, + source_pair=source_pair, + image_pair=image_pair, + ) + ) + return records def run_single_bootstrap( @@ -94,21 +195,53 @@ def run_single_bootstrap( full_pairwise_distance_matrix: csr_matrix | None = None, full_vertex_ids: list[int] | None = None, reconstruct_on_full_data: bool = False, + candidate_method: Literal["geometric", "image"] = "geometric", + require_homological_equivalence: bool = True, **kwargs, ) -> BootstrapResult: - ( - persistence_diagram, - cocycles, - indices_resample, - sparse_pairwise_distance_matrix, - ) = compute_persistence_diagram_and_cocycles( - adata=adata, - meta=meta, - thresh=thresh, - bootstrap=True, - noise_scale=noise_scale, - **kwargs, - ) + if candidate_method not in {"geometric", "image"}: + raise ValueError(f"unknown candidate method: {candidate_method}") + reference_image_pairs: list[ImagePairRecord] = [] + bootstrap_image_pairs: list[ImagePairRecord] = [] + image_homology_result = None + if candidate_method == "image": + if thresh is None: + raise ValueError("image matching requires a finite homology threshold") + image_homology_result = compute_image_bootstrap_homology( + adata=adata, + meta=meta, + thresh=thresh, + noise_scale=noise_scale, + **kwargs, + ) + ( + persistence_diagram, + persistence_pair_simplices, + cocycles, + indices_resample, + sparse_pairwise_distance_matrix, + ) = ( + image_homology_result.persistence_diagram, + image_homology_result.persistence_pair_simplices, + image_homology_result.cocycles, + image_homology_result.indices_resample, + image_homology_result.bootstrap_distance_matrix, + ) + else: + ( + persistence_diagram, + persistence_pair_simplices, + cocycles, + indices_resample, + sparse_pairwise_distance_matrix, + ) = compute_persistence_diagram_and_cocycles( + adata=adata, + meta=meta, + thresh=thresh, + bootstrap=True, + noise_scale=noise_scale, + **kwargs, + ) assert meta.preprocess is not None assert meta.preprocess.embedding_method is not None @@ -130,6 +263,7 @@ def run_single_bootstrap( embedding=embedding, pairwise_distance_matrix=full_pairwise_distance_matrix, persistence_diagram=persistence_diagram[1], + persistence_pair_simplices=persistence_pair_simplices[1], cocycles=remapped_cocycles, boundary_matrix_d1=original_boundary_matrix_d1, vertex_ids=full_vertex_ids, @@ -155,6 +289,7 @@ def run_single_bootstrap( embedding=embedding, pairwise_distance_matrix=sparse_pairwise_distance_matrix, persistence_diagram=persistence_diagram[1], + persistence_pair_simplices=persistence_pair_simplices[1], cocycles=cocycles[1], boundary_matrix_d1=original_boundary_matrix_d1, vertex_ids=indices_resample, @@ -175,6 +310,22 @@ def run_single_bootstrap( bootstrap=True, ) + if image_homology_result is not None: + reference_image_pairs = _build_image_pair_records( + original_loop_classes, + _persistence_pairs_from_ripser_result( + image_homology_result.reference_image_result.image + ), + vertex_offset=0, + ) + bootstrap_image_pairs = _build_image_pair_records( + bootstrap_loop_classes, + _persistence_pairs_from_ripser_result( + image_homology_result.bootstrap_image_result.image + ), + vertex_offset=image_homology_result.n_reference_vertices, + ) + n_original = len(original_loop_classes) n_bootstrap = len(bootstrap_loop_classes) @@ -183,118 +334,126 @@ def run_single_bootstrap( return BootstrapResult( idx_bootstrap=idx_bootstrap, persistence_diagram=persistence_diagram, + persistence_pair_simplices=persistence_pair_simplices, cocycles=cocycles, indices_resample=indices_resample, loop_classes=bootstrap_loop_classes, matches=matches, + reference_image_pairs=reference_image_pairs, + bootstrap_image_pairs=bootstrap_image_pairs, ) - assert meta.preprocess is not None - assert meta.preprocess.num_vertices is not None - if meta.preprocess.indices_downsample is not None: - original_vertex_ids = meta.preprocess.indices_downsample + if candidate_method == "geometric": + pairwise_geo_dist = np.full((n_original, n_bootstrap), np.nan) + for i, src_loop in enumerate(original_loop_classes): + if src_loop is None: + continue + src_coords = src_loop.coordinates_vertices_representatives + if src_coords is None: + continue + for j, tgt_loop in enumerate(bootstrap_loop_classes): + if tgt_loop is None: + continue + tgt_coords = tgt_loop.coordinates_vertices_representatives + if tgt_coords is None: + continue + pairwise_geo_dist[i, j] = compute_geometric_distance( + source_coords_list=src_coords, + target_coords_list=tgt_coords, + method=method_geometric_equivalence, + n_workers=1, + ) + candidates = _select_geometric_candidates( + pairwise_geo_dist, + k=k_neighbors_check_equivalence, + require_homological_equivalence=require_homological_equivalence, + ) else: - original_vertex_ids = list(range(meta.preprocess.num_vertices)) - cocycle_edge_masks: list[np.ndarray | None] = [] - for loop_class in original_loop_classes: - if loop_class is None or loop_class.cocycles is None: - cocycle_edge_masks.append(None) - continue - cocycle_edge_masks.append( - cocycle_to_edge_mask( - cocycle=loop_class.cocycles, - boundary_matrix_d1=original_boundary_matrix_d1, - vertex_ids=original_vertex_ids, - ) + candidates = _select_image_candidates( + reference_image_pairs, bootstrap_image_pairs ) - pairwise_geo_dist = np.full((n_original, n_bootstrap), np.nan) - - for i, src_loop in enumerate(original_loop_classes): - if src_loop is None or src_loop.representatives is None: - continue - src_coords = src_loop.coordinates_vertices_representatives - if src_coords is None: - continue - cocycle_edge_mask = cocycle_edge_masks[i] - - for j, tgt_loop in enumerate(bootstrap_loop_classes): - if tgt_loop is None or tgt_loop.representatives is None: + cocycle_edge_masks: list[np.ndarray | None] = [None] * n_original + if require_homological_equivalence: + assert meta.preprocess is not None + assert meta.preprocess.num_vertices is not None + original_vertex_ids = ( + meta.preprocess.indices_downsample + if meta.preprocess.indices_downsample is not None + else list(range(meta.preprocess.num_vertices)) + ) + for i, loop_class in enumerate(original_loop_classes): + if loop_class is None or loop_class.cocycles is None: continue - tgt_coords = tgt_loop.coordinates_vertices_representatives - assert tgt_coords is not None - - dist = compute_geometric_distance( - source_coords_list=src_coords, - target_coords_list=tgt_coords, - method=method_geometric_equivalence, - n_workers=1, + cocycle_edge_masks[i] = cocycle_to_edge_mask( + cocycle=loop_class.cocycles, + boundary_matrix_d1=original_boundary_matrix_d1, + vertex_ids=original_vertex_ids, ) - pairwise_geo_dist[i, j] = dist - - neighbor_indices, neighbor_distances = nearest_neighbor_per_row( - pairwise_geo_dist, k_neighbors_check_equivalence - ) - for si in range(n_original): - src_loop = original_loop_classes[si] - if src_loop is None or src_loop.representatives is None: + for source_idx, source_candidates in candidates.items(): + source_loop = original_loop_classes[source_idx] + if source_loop is None: continue - - source_lifetime = src_loop.death - src_loop.birth - source_loop_death = src_loop.death - - for k in range(k_neighbors_check_equivalence): - tj = neighbor_indices[si, k] - if tj < 0: + for candidate in source_candidates: + target_loop = bootstrap_loop_classes[candidate.target_class_idx] + if target_loop is None: continue - tgt_loop = bootstrap_loop_classes[tj] - if tgt_loop is None or tgt_loop.representatives is None: - continue - - target_lifetime = tgt_loop.death - tgt_loop.birth - target_loop_death = tgt_loop.death - max_lifetime = max(source_lifetime, target_lifetime) - - max_column_diameter = None - if filter_column_homology_equivalence: - max_column_diameter = ( - max(source_loop_death, target_loop_death) - + float(extra_diameter_homology_equivalence) * max_lifetime + is_equivalent = not require_homological_equivalence + if require_homological_equivalence: + if ( + source_loop.representatives is None + or target_loop.representatives is None + ): + continue + max_column_diameter = None + if filter_column_homology_equivalence: + if extra_diameter_homology_equivalence < 0: + raise ValueError( + "extra_diameter_homology_equivalence must be nonnegative" + ) + max_lifetime = max( + source_loop.lifetime, target_loop.lifetime + ) + max_column_diameter = ( + max(source_loop.death, target_loop.death) + + float(extra_diameter_homology_equivalence) * max_lifetime + ) + is_equivalent = check_homological_equivalence( + source_loops=source_loop.representatives, + target_loops=target_loop.representatives, + boundary_matrix_d1=original_boundary_matrix_d1, + n_pairs_check=n_pairs_check_equivalence, + with_relaxation=with_relaxation_equivalence, + n_hubs_relaxation=n_hubs_relaxation_equivalence, + max_n_edges_relaxation=max_n_edges_relaxation_equivalence, + max_column_diameter=max_column_diameter, + cocycle_edge_mask=cocycle_edge_masks[source_idx], ) - is_equivalent = check_homological_equivalence( - source_loops=src_loop.representatives, - target_loops=tgt_loop.representatives, - boundary_matrix_d1=original_boundary_matrix_d1, - n_pairs_check=n_pairs_check_equivalence, - with_relaxation=with_relaxation_equivalence, - n_hubs_relaxation=n_hubs_relaxation_equivalence, - max_n_edges_relaxation=max_n_edges_relaxation_equivalence, - max_column_diameter=max_column_diameter, - cocycle_edge_mask=cocycle_edge_mask, - ) - if is_equivalent: - if si not in matches: - matches[si] = [] - matches[si].append( + matches.setdefault(source_idx, []).append( LoopMatch( idx_bootstrap=idx_bootstrap, - target_class_idx=int(tj), - geometric_distance=float(neighbor_distances[si, k]), - neighbor_rank=int(k), + target_class_idx=candidate.target_class_idx, + candidate_method=candidate_method, + geometric_distance=candidate.geometric_distance, + neighbor_rank=candidate.neighbor_rank, + image_death_simplex=candidate.image_death_simplex, ) ) return BootstrapResult( idx_bootstrap=idx_bootstrap, persistence_diagram=persistence_diagram, + persistence_pair_simplices=persistence_pair_simplices, cocycles=cocycles, indices_resample=indices_resample, loop_classes=bootstrap_loop_classes, matches=matches, + reference_image_pairs=reference_image_pairs, + bootstrap_image_pairs=bootstrap_image_pairs, ) diff --git a/src/scloop/computing/homology.py b/src/scloop/computing/homology.py index 75dd970..295a739 100644 --- a/src/scloop/computing/homology.py +++ b/src/scloop/computing/homology.py @@ -1,7 +1,8 @@ # Copyright 2025 Zhiyuan Yu (Heemskerk's lab, University of Michigan) from __future__ import annotations -from typing import TYPE_CHECKING +from dataclasses import dataclass +from typing import TYPE_CHECKING, Literal import numpy as np from anndata import AnnData @@ -10,11 +11,13 @@ from scipy.spatial.distance import directed_hausdorff from sklearn.neighbors import radius_neighbors_graph +from ..data.base_components import PersistencePair from ..data.constants import DEFAULT_LOOP_DIST_METHOD, DEFAULT_N_MAX_WORKERS from ..data.metadata import ScloopMeta from ..data.ripser_lib import ( # type: ignore[import-not-found] get_boundary_matrix, ripser, + ripser_image, ) from ..data.types import ( Count_t, @@ -39,27 +42,80 @@ from ..data.containers import BoundaryMatrixD1 +@dataclass +class ImageBootstrapHomologyResult: + persistence_diagram: list + persistence_pair_simplices: list + cocycles: list + indices_resample: list[int] + bootstrap_distance_matrix: csr_matrix + reference_image_result: object + bootstrap_image_result: object + n_reference_vertices: int + + +def _cap_infinite_deaths(diagrams: list, cap: float | None) -> list: + if cap is None or not np.isfinite(cap): + return diagrams + capped = [] + for dim_pd in diagrams: + if len(dim_pd) < 2: + capped.append(dim_pd) + continue + births = np.asarray(dim_pd[0]) + deaths = np.asarray(dim_pd[1]) + deaths = np.where(np.isinf(deaths), cap, deaths) + capped.append([births, deaths]) + return capped + + +def _persistence_pairs_from_ripser_result( + result: object, dim: int = 1 +) -> list[PersistencePair]: + births, deaths = result.births_and_deaths_by_dim[dim] + birth_simplices, death_simplices = result.births_and_deaths_simplex_by_dim[ + dim + ] + if not ( + len(births) + == len(deaths) + == len(birth_simplices) + == len(death_simplices) + ): + raise ValueError("Ripser persistence pairs and critical simplices are misaligned") + return [ + PersistencePair( + birth=float(birth), + death=float(death), + birth_simplex=list(birth_simplex), + death_simplex=list(death_simplex), + ) + for birth, death, birth_simplex, death_simplex in zip( + births, deaths, birth_simplices, death_simplices + ) + ] + + def _sample_bootstrap_embedding( adata: AnnData, meta: ScloopMeta, - selected_indices: list[int], - sample_idx: np.ndarray, + source_indices: list[int], + bootstrap_indices: list[int], bootstrap_noise_model: str, noise_scale: float, sanity_n_posterior: int = 1000, -) -> tuple[np.ndarray, list[int]]: +) -> np.ndarray: assert meta.preprocess is not None assert meta.preprocess.embedding_method is not None - boot_idx = [selected_indices[int(i)] for i in sample_idx.tolist()] emb = np.asarray(adata.obsm[f"X_{meta.preprocess.embedding_method}"]) - X = emb[selected_indices] + X = emb[source_indices] if bootstrap_noise_model == "sanity": if meta.preprocess.embedding_method == "pca": X = sample_posterior_predictive_counts( adata=adata, - cell_idx=np.asarray(boot_idx, dtype=np.int64), + cell_idx=np.asarray(bootstrap_indices, dtype=np.int64), scale_before_pca=meta.preprocess.scale_before_pca, n_pca_comps=meta.preprocess.n_pca_comps, n_posterior=sanity_n_posterior, @@ -69,7 +125,7 @@ def _sample_bootstrap_embedding( if meta.preprocess.embedding_neighbors == "pca": X = sample_posterior_predictive_counts( adata=adata, - cell_idx=np.asarray(boot_idx, dtype=np.int64), + cell_idx=np.asarray(bootstrap_indices, dtype=np.int64), scale_before_pca=meta.preprocess.scale_before_pca, n_pca_comps=meta.preprocess.n_pca_comps, n_posterior=sanity_n_posterior, @@ -84,12 +140,71 @@ def _sample_bootstrap_embedding( emb_reference=emb_reference, emb_query=X ) else: - X_ref = emb[selected_indices] - X = emb[boot_idx] + X_ref = emb[source_indices] + X = emb[bootstrap_indices] std_X = np.std(X_ref, axis=0) X = X + np.random.normal(scale=std_X * noise_scale, size=X.shape) - return X, boot_idx + return X + + +def select_bootstrap_sample( + source_indices: list[int], + source_embedding: np.ndarray, + bootstrap_sampling: Literal[ + "resample", "downsample", "fps", "fps_random", "herding" + ] = "resample", + bootstrap_downsample_fraction: Percent_t = 2 / 3, + bootstrap_fps_top_k: int = 5, + bootstrap_fps_alpha: float = 1.0, + bootstrap_herding_n_features: int = 1000, + bootstrap_herding_seed: int | None = None, +): + match bootstrap_sampling: + case "resample" | "downsample": + sample_idx = np.random.choice( + len(source_indices), + size=len(source_indices) + if bootstrap_sampling == "resample" + else int(len(source_indices) * bootstrap_downsample_fraction), + replace=True if bootstrap_sampling == "resample" else False, + ) + case "fps": + n_keep = max( + 2, int(round(len(source_indices) * bootstrap_downsample_fraction)) + ) + n_keep = min(n_keep, len(source_indices)) + sample_idx = sample_farthest_points(source_embedding, n_keep) + case "fps_random": + if bootstrap_fps_top_k <= 0: + raise ValueError("bootstrap_fps_top_k must be > 0.") + if bootstrap_fps_alpha < 0: + raise ValueError("bootstrap_fps_alpha must be >= 0.") + n_keep = max( + 2, int(round(len(source_indices) * bootstrap_downsample_fraction)) + ) + n_keep = min(n_keep, len(source_indices)) + sample_idx = sample_farthest_points_randomized( + source_embedding, + n_keep, + top_k=bootstrap_fps_top_k, + alpha=bootstrap_fps_alpha, + ) + case "herding": + n_keep = max( + 2, int(round(len(source_indices) * bootstrap_downsample_fraction)) + ) + n_keep = min(n_keep, len(source_indices)) + if bootstrap_herding_seed is None: + bootstrap_herding_seed = int(np.random.randint(0, 1_000_000)) + sample_idx = kernel_herding_main( + sample_set_ind=np.arange(len(source_indices)), + X=source_embedding, + num_subsamples=n_keep, + frequency_seed=bootstrap_herding_seed, + n_features=int(bootstrap_herding_n_features), + ) + return [source_indices[int(i)] for i in sample_idx.tolist()] def compute_sparse_pairwise_distance( @@ -100,7 +215,9 @@ def compute_sparse_pairwise_distance( sanity_n_posterior: int = 1000, bootstrap_noise_model: str = "gaussian", thresh: Diameter_t | None = None, - bootstrap_sampling: str = "resample", + bootstrap_sampling: Literal[ + "resample", "downsample", "fps", "fps_random", "herding" + ] = "resample", bootstrap_downsample_fraction: Percent_t = 2 / 3, bootstrap_fps_top_k: int = 5, bootstrap_fps_alpha: float = 1.0, @@ -121,80 +238,25 @@ def compute_sparse_pairwise_distance( X = emb[selected_indices] boot_idx = None if bootstrap: - if bootstrap_sampling == "resample": - sample_idx = np.random.choice( - len(selected_indices), size=len(selected_indices), replace=True - ) - X, boot_idx = _sample_bootstrap_embedding( - adata=adata, - meta=meta, - selected_indices=selected_indices, - sample_idx=np.asarray(sample_idx, dtype=np.int64), - bootstrap_noise_model=bootstrap_noise_model, - noise_scale=noise_scale, - sanity_n_posterior=sanity_n_posterior, - ) - elif bootstrap_sampling == "fps": - n_keep = max( - 2, int(round(len(selected_indices) * bootstrap_downsample_fraction)) - ) - n_keep = min(n_keep, len(selected_indices)) - sample_idx = sample_farthest_points(X, n_keep) - X, boot_idx = _sample_bootstrap_embedding( - adata=adata, - meta=meta, - selected_indices=selected_indices, - sample_idx=np.asarray(sample_idx, dtype=np.int64), - bootstrap_noise_model=bootstrap_noise_model, - noise_scale=noise_scale, - sanity_n_posterior=sanity_n_posterior, - ) - elif bootstrap_sampling == "fps_random": - if bootstrap_fps_top_k <= 0: - raise ValueError("bootstrap_fps_top_k must be > 0.") - if bootstrap_fps_alpha < 0: - raise ValueError("bootstrap_fps_alpha must be >= 0.") - n_keep = max( - 2, int(round(len(selected_indices) * bootstrap_downsample_fraction)) - ) - n_keep = min(n_keep, len(selected_indices)) - sample_idx = sample_farthest_points_randomized( - X, n_keep, top_k=bootstrap_fps_top_k, alpha=bootstrap_fps_alpha - ) - X, boot_idx = _sample_bootstrap_embedding( - adata=adata, - meta=meta, - selected_indices=selected_indices, - sample_idx=np.asarray(sample_idx, dtype=np.int64), - bootstrap_noise_model=bootstrap_noise_model, - noise_scale=noise_scale, - sanity_n_posterior=sanity_n_posterior, - ) - elif ( - bootstrap_sampling == "herding" - ): # TODO: increase randomness of thsi approach - n_keep = max( - 2, int(round(len(selected_indices) * bootstrap_downsample_fraction)) - ) - n_keep = min(n_keep, len(selected_indices)) - if bootstrap_herding_seed is None: - bootstrap_herding_seed = int(np.random.randint(0, 1_000_000)) - sample_idx = kernel_herding_main( - sample_set_ind=np.arange(len(selected_indices)), - X=X, - num_subsamples=n_keep, - frequency_seed=bootstrap_herding_seed, - n_features=int(bootstrap_herding_n_features), - ) - X, boot_idx = _sample_bootstrap_embedding( - adata=adata, - meta=meta, - selected_indices=selected_indices, - sample_idx=np.asarray(sample_idx, dtype=np.int64), - bootstrap_noise_model=bootstrap_noise_model, - noise_scale=noise_scale, - sanity_n_posterior=sanity_n_posterior, - ) + boot_idx = select_bootstrap_sample( + source_indices=selected_indices, + source_embedding=X, + bootstrap_sampling=bootstrap_sampling, + bootstrap_downsample_fraction=bootstrap_downsample_fraction, + bootstrap_fps_top_k=bootstrap_fps_top_k, + bootstrap_fps_alpha=bootstrap_fps_alpha, + bootstrap_herding_n_features=bootstrap_herding_n_features, + bootstrap_herding_seed=bootstrap_herding_seed, + ) + X = _sample_bootstrap_embedding( + adata=adata, + meta=meta, + source_indices=selected_indices, + bootstrap_indices=boot_idx, + bootstrap_noise_model=bootstrap_noise_model, + noise_scale=noise_scale, + sanity_n_posterior=sanity_n_posterior, + ) else: boot_idx = selected_indices return ( @@ -207,6 +269,146 @@ def compute_sparse_pairwise_distance( ) +def _remap_subfiltration_simplices_to_local( + simplices_by_dim: list, vertex_offset: int +) -> list: + remapped = [] + for births, deaths in simplices_by_dim: + remapped.append( + [ + [[int(v) - vertex_offset for v in simplex] for simplex in births], + [[int(v) - vertex_offset for v in simplex] for simplex in deaths], + ] + ) + return remapped + + +def _remap_subfiltration_cocycles_to_local( + cocycles_by_dim: list, vertex_offset: int +) -> list: + remapped = [] + for dim_cocycles in cocycles_by_dim: + remapped_dim = [] + for cocycle in dim_cocycles: + remapped_dim.append( + [ + [ + [int(v) - vertex_offset for v in simplex_vertices], + int(coefficient), + ] + for simplex_vertices, coefficient in cocycle + ] + ) + remapped.append(remapped_dim) + return remapped + + +def compute_image_bootstrap_homology( + adata: AnnData, + meta: ScloopMeta, + thresh: Diameter_t, + noise_scale: float = 1e-3, + sanity_n_posterior: int = 1000, + bootstrap_noise_model: str = "gaussian", + bootstrap_sampling: Literal[ + "resample", "downsample", "fps", "fps_random", "herding" + ] = "resample", + bootstrap_downsample_fraction: Percent_t = 2 / 3, + bootstrap_fps_top_k: int = 5, + bootstrap_fps_alpha: float = 1.0, + bootstrap_herding_n_features: int = 1000, + bootstrap_herding_seed: int | None = None, + **nei_kwargs, +) -> ImageBootstrapHomologyResult: + assert meta.preprocess is not None + assert meta.preprocess.embedding_method is not None + + embedding = np.asarray( + adata.obsm[f"X_{meta.preprocess.embedding_method}"] + ) + source_indices = ( + meta.preprocess.indices_downsample + if meta.preprocess.indices_downsample is not None + else list(range(embedding.shape[0])) + ) + source_embedding = embedding[source_indices] + bootstrap_indices = select_bootstrap_sample( + source_indices=source_indices, + source_embedding=source_embedding, + bootstrap_sampling=bootstrap_sampling, + bootstrap_downsample_fraction=bootstrap_downsample_fraction, + bootstrap_fps_top_k=bootstrap_fps_top_k, + bootstrap_fps_alpha=bootstrap_fps_alpha, + bootstrap_herding_n_features=bootstrap_herding_n_features, + bootstrap_herding_seed=bootstrap_herding_seed, + ) + bootstrap_embedding = _sample_bootstrap_embedding( + adata=adata, + meta=meta, + source_indices=source_indices, + bootstrap_indices=bootstrap_indices, + bootstrap_noise_model=bootstrap_noise_model, + noise_scale=noise_scale, + sanity_n_posterior=sanity_n_posterior, + ) + + n_reference_vertices = len(source_indices) + union_embedding = np.vstack([source_embedding, bootstrap_embedding]) + nei_kwargs.setdefault("mode", "distance") + union_distance_matrix = radius_neighbors_graph( + X=union_embedding, + radius=thresh, + **nei_kwargs, + ).tocsr() + bootstrap_offset = n_reference_vertices + bootstrap_union_indices = np.arange( + bootstrap_offset, + bootstrap_offset + len(bootstrap_indices), + dtype=np.intc, + ) + + reference_image_result = ripser_image( + distance_matrix=union_distance_matrix.tocoo(copy=False), + sub_indices=np.arange(n_reference_vertices, dtype=np.intc), + modulus=2, + dim_max=1, + threshold=thresh, + do_subfiltration_cocycles=False, + ) + bootstrap_image_result = ripser_image( + distance_matrix=union_distance_matrix.tocoo(copy=False), + sub_indices=bootstrap_union_indices, + modulus=2, + dim_max=1, + threshold=thresh, + do_subfiltration_cocycles=True, + ) + bootstrap_subfiltration = bootstrap_image_result.subfiltration + persistence_pair_simplices = _remap_subfiltration_simplices_to_local( + bootstrap_subfiltration.births_and_deaths_simplex_by_dim, + bootstrap_offset, + ) + cocycles = _remap_subfiltration_cocycles_to_local( + bootstrap_subfiltration.cocycles_by_dim, + bootstrap_offset, + ) + + return ImageBootstrapHomologyResult( + persistence_diagram=_cap_infinite_deaths( + bootstrap_subfiltration.births_and_deaths_by_dim, thresh + ), + persistence_pair_simplices=persistence_pair_simplices, + cocycles=cocycles, + indices_resample=bootstrap_indices, + bootstrap_distance_matrix=union_distance_matrix[ + bootstrap_offset:, bootstrap_offset: + ].tocsr(), + reference_image_result=reference_image_result, + bootstrap_image_result=bootstrap_image_result, + n_reference_vertices=n_reference_vertices, + ) + + def compute_persistence_diagram_and_cocycles( adata: AnnData, meta: ScloopMeta, @@ -214,21 +416,7 @@ def compute_persistence_diagram_and_cocycles( bootstrap: bool = False, noise_scale: float = 1e-3, **nei_kwargs, -) -> tuple[list, list, IndexListDistMatrix | None, csr_matrix]: - def _cap_infinite_deaths(diagrams: list, cap: float | None) -> list: - if cap is None or not np.isfinite(cap): - return diagrams - capped = [] - for dim_pd in diagrams: - if len(dim_pd) < 2: - capped.append(dim_pd) - continue - births = np.asarray(dim_pd[0]) - deaths = np.asarray(dim_pd[1]) - deaths = np.where(np.isinf(deaths), cap, deaths) - capped.append([births, deaths]) - return capped - +) -> tuple[list, list, list, IndexListDistMatrix | None, csr_matrix]: sparse_pairwise_distance_matrix, boot_idx = compute_sparse_pairwise_distance( adata=adata, meta=meta, @@ -246,6 +434,7 @@ def _cap_infinite_deaths(diagrams: list, cap: float | None) -> list: ) return ( _cap_infinite_deaths(result.births_and_deaths_by_dim, thresh), + result.births_and_deaths_simplex_by_dim, result.cocycles_by_dim, boot_idx, sparse_pairwise_distance_matrix, diff --git a/src/scloop/computing/landscape.py b/src/scloop/computing/landscape.py new file mode 100644 index 0000000..a465fed --- /dev/null +++ b/src/scloop/computing/landscape.py @@ -0,0 +1,110 @@ +# Copyright 2026 Zhiyuan Yu (Heemskerk's lab, University of Michigan) +from typing import Literal + +import numpy as np +from pynndescent import NNDescent + + +# compute density for energy landscape calculation +def compute_density( + adata, + basis: str = "X_diffmap", + n_neighbors: int = 10, + flavor: Literal["custom", "mellon"] = "mellon", + density_key="scloop_log_density", + score_key="scloop_score", +) -> None: + density_embedding = adata.obsm[basis] + match flavor: + case "custom": + index = NNDescent(density_embedding, n_neighbors=n_neighbors) + _, distances = index.query(density_embedding, k=n_neighbors) + distances = distances / np.median(distances) + d_sq = distances**2 + bandwidth_inv = d_sq.mean(axis=1, keepdims=True) + similarities = np.exp(-d_sq * bandwidth_inv / 2) + log_density = np.log(similarities.sum(axis=1)) + case "mellon": + import mellon + + model = mellon.DensityEstimator() + model.fit(density_embedding) + predictor = model.predict + log_density = np.asarray(predictor(density_embedding)) + adata.obsm[score_key] = np.asarray(predictor.gradient(density_embedding)) + adata.obs[density_key] = log_density + + +# compute drift and diffusion tensors by using knn graph and pseudotime +def compute_drift_and_diffusion_tensors( + adata, + basis: str = "X_diffmap", + n_neighbors: int = 30, + pseudotime_key: str = "dpt_pseudotime", + threshold_scheme: Literal["hard", "soft"] = "hard", + frac_to_keep: float = 0.3, + b: float = 10.0, + nu: float = 0.5, + drift_key="scloop_drift", + diffusion_key="scloop_diffusion", +) -> tuple[np.ndarray, np.ndarray]: + X = adata.obsm[basis] + pseudotime = np.asarray(adata.obs[pseudotime_key]) + + index = NNDescent(X, n_neighbors=n_neighbors + 1) + neighbors, distances = index.query(X, k=n_neighbors + 1) + neighbors, distances = neighbors[:, 1:], distances[:, 1:] + + bandwidth = distances[:, -1][:, None] + affinity = np.exp(-(distances**2) / (bandwidth**2)) + + cell_pt = pseudotime[:, None] + neigh_pt = pseudotime[neighbors] + + if threshold_scheme == "hard": + k_thresh = min(30, int(np.floor(n_neighbors * frac_to_keep))) + is_close = np.arange(n_neighbors)[None, :] < k_thresh + keep = is_close | (neigh_pt >= cell_pt) + biased = affinity * keep + else: + dt = cell_pt - neigh_pt + weights = np.where(dt > 0, 2.0 / (1.0 + np.exp(b * dt)) ** (1.0 / nu), 1.0) + biased = affinity * weights + + T = biased / biased.sum(axis=1, keepdims=True) + + displacements = X[neighbors] - X[:, None, :] + drift = np.einsum("nk,nkd->nd", T, displacements) + + residuals = displacements - drift[:, None, :] + D = 0.5 * np.einsum("nk,nkd,nke->nde", T, residuals, residuals).mean(axis=0) + D = 0.5 * (D + D.T) + + adata.obsm[drift_key] = drift + adata.uns[diffusion_key] = D + return drift, D + + +# decompose drift into gradient and rotational part +def compute_drift_decomposition( + adata, + drift_key="scloop_drift", + diffusion_key="scloop_diffusion", + score_key="scloop_score", + density_key="scloop_log_density", + gradient_drift_key="scloop_gradient_drift", + flux_key="scloop_flux_velocity", + current_key="scloop_probability_current", +) -> None: + drift = adata.obsm[drift_key] + D = adata.uns[diffusion_key] + score = adata.obsm[score_key] + log_density = np.asarray(adata.obs[density_key]) + + gradient_drift = score @ D + flux_velocity = drift - gradient_drift + current = np.exp(log_density)[:, None] * flux_velocity + + adata.obsm[gradient_drift_key] = gradient_drift + adata.obsm[flux_key] = flux_velocity + adata.obsm[current_key] = current diff --git a/src/scloop/computing/loops.py b/src/scloop/computing/loops.py index 4fa1efd..ecb571b 100644 --- a/src/scloop/computing/loops.py +++ b/src/scloop/computing/loops.py @@ -115,6 +115,7 @@ def compute_loop_representatives( cocycles: list, boundary_matrix_d1: BoundaryMatrixD1, vertex_ids: list[int], + persistence_pair_simplices: tuple[list, list] | list | None = None, top_k: Count_t | None = None, n_reps_per_loop: int = DEFAULT_N_REPS_PER_LOOP, life_pct: Percent_t = DEFAULT_LIFE_PCT, @@ -164,6 +165,14 @@ def compute_loop_representatives( for i, loop_idx in enumerate(indices_top_k): loop_birth = loop_births[loop_idx].item() loop_death = loop_deaths[loop_idx].item() + birth_simplex: list[int] = [] + death_simplex: list[int] = [] + if persistence_pair_simplices is not None: + births, deaths = persistence_pair_simplices + if loop_idx < len(births): + birth_simplex = list(births[loop_idx]) + if loop_idx < len(deaths): + death_simplex = list(deaths[loop_idx]) valid_cocycles = [] n_cocycles_original = len(cocycles[loop_idx]) @@ -241,8 +250,11 @@ def compute_loop_representatives( results[i] = LoopClass( rank=i + rank_offset, + persistence_index=int(loop_idx), birth=loop_birth, death=loop_death, + birth_simplex=birth_simplex, + death_simplex=death_simplex, cocycles=cocycles[loop_idx], representatives=loops, coordinates_vertices_representatives=loops_coords, diff --git a/src/scloop/data/analysis_containers.py b/src/scloop/data/analysis_containers.py index 3752f97..043b6f9 100644 --- a/src/scloop/data/analysis_containers.py +++ b/src/scloop/data/analysis_containers.py @@ -1,7 +1,7 @@ # Copyright 2025 Zhiyuan Yu (Heemskerk's lab, University of Michigan) from __future__ import annotations -from typing import TYPE_CHECKING, Optional +from typing import TYPE_CHECKING, Literal, Optional import numpy as np from pydantic import BaseModel, ConfigDict, Field @@ -12,7 +12,12 @@ from ..computing import compute_weighted_hodge_embedding from ..utils.pvalues import correct_pvalues -from .base_components import LoopClass, PersistenceTestResult, PresenceTestResult +from .base_components import ( + ImagePairRecord, + LoopClass, + PersistenceTestResult, + PresenceTestResult, +) from .constants import ( DEFAULT_HALF_WINDOW, DEFAULT_N_NEIGHBORS_EDGE_EMBEDDING, @@ -42,8 +47,10 @@ class LoopMatch: idx_bootstrap: int target_class_idx: int + candidate_method: Literal["geometric", "image"] = "geometric" geometric_distance: Optional[float] = None neighbor_rank: Optional[int] = None + image_death_simplex: list[int] | None = None def _serialize_loop_matches( @@ -78,6 +85,23 @@ def _serialize_loop_matches( group.create_dataset( "neighbor_rank", data=np.array(neighbor_ranks, dtype=np.int64), **kw ) + group.create_dataset( + "candidate_method", + data=np.array( + [0 if m.candidate_method == "geometric" else 1 for m in matches], + dtype=np.int8, + ), + **kw, + ) + image_death_simplices = np.full((len(matches), 3), -1, dtype=np.int64) + for i, match in enumerate(matches): + if match.image_death_simplex is not None: + image_death_simplices[i, : len(match.image_death_simplex)] = ( + match.image_death_simplex + ) + group.create_dataset( + "image_death_simplex", data=image_death_simplices, **kw + ) def _deserialize_loop_matches(group: h5py.Group) -> list[LoopMatch]: @@ -89,6 +113,16 @@ def _deserialize_loop_matches(group: h5py.Group) -> list[LoopMatch]: target_class_idxs = np.asarray(group["target_class_idx"]) geo_dists = np.asarray(group["geometric_distance"]) neighbor_ranks = np.asarray(group["neighbor_rank"]) + candidate_methods = ( + np.asarray(group["candidate_method"]) + if "candidate_method" in group + else np.zeros(count, dtype=np.int8) + ) + image_death_simplices = ( + np.asarray(group["image_death_simplex"]) + if "image_death_simplex" in group + else np.full((count, 3), -1, dtype=np.int64) + ) matches = [] for i in range(count): @@ -98,8 +132,15 @@ def _deserialize_loop_matches(group: h5py.Group) -> list[LoopMatch]: LoopMatch( idx_bootstrap=int(idx_bootstraps[i]), target_class_idx=int(target_class_idxs[i]), + candidate_method=( + "geometric" if candidate_methods[i] == 0 else "image" + ), geometric_distance=geo_dist, neighbor_rank=rank, + image_death_simplex=[ + int(v) for v in image_death_simplices[i] if v >= 0 + ] + or None, ) ) return matches @@ -143,7 +184,10 @@ def from_hdf5_group(cls, group: h5py.Group) -> LoopTrack: class BootstrapAnalysis: num_bootstraps: Size_t = 0 persistence_diagrams: list[list] = Field(default_factory=list) + persistence_pair_simplices: list[list] = Field(default_factory=list) cocycles: list[list] = Field(default_factory=list) + reference_image_pairs: list[list[ImagePairRecord]] = Field(default_factory=list) + bootstrap_image_pairs: list[list[ImagePairRecord]] = Field(default_factory=list) selected_loop_classes: list[list[LoopClass | None]] = Field(default_factory=list) loop_tracks: dict[Index_t, LoopTrack] = Field(default_factory=dict) presence_test_result: PresenceTestResult | None = None @@ -421,6 +465,20 @@ def to_hdf5_group(self, group: h5py.Group, compress: bool = True) -> None: lc_grp.attrs["_is_none"] = False lc.to_hdf5_group(lc_grp, compress=compress) + for field_name, records_by_bootstrap in ( + ("reference_image_pairs", self.reference_image_pairs), + ("bootstrap_image_pairs", self.bootstrap_image_pairs), + ): + records_grp = group.create_group(field_name) + records_grp.attrs["_count"] = len(records_by_bootstrap) + for boot_idx, records in enumerate(records_by_bootstrap): + boot_grp = records_grp.create_group(str(boot_idx)) + boot_grp.attrs["_count"] = len(records) + for record_idx, record in enumerate(records): + record.to_hdf5_group( + boot_grp.create_group(str(record_idx)), compress=compress + ) + # loop_tracks: dict[int, LoopTrack] tracks_grp = group.create_group("loop_tracks") for track_id, track in self.loop_tracks.items(): @@ -458,6 +516,22 @@ def from_hdf5_group(cls, group: h5py.Group) -> BootstrapAnalysis: loop_classes.append(LoopClass.from_hdf5_group(lc_grp)) selected_loop_classes.append(loop_classes) + image_records: dict[str, list[list[ImagePairRecord]]] = {} + for field_name in ("reference_image_pairs", "bootstrap_image_pairs"): + records_by_bootstrap: list[list[ImagePairRecord]] = [] + if field_name in group: + records_grp: h5py.Group = group[field_name] # type: ignore[assignment] + n_record_bootstraps = int(records_grp.attrs["_count"]) + for boot_idx in range(n_record_bootstraps): + boot_grp: h5py.Group = records_grp[str(boot_idx)] # type: ignore[assignment] + records_by_bootstrap.append( + [ + ImagePairRecord.from_hdf5_group(boot_grp[str(i)]) + for i in range(int(boot_grp.attrs["_count"])) + ] + ) + image_records[field_name] = records_by_bootstrap + # loop_tracks loop_tracks: dict[int, LoopTrack] = {} tracks_grp: h5py.Group = group["loop_tracks"] # type: ignore[assignment] @@ -481,7 +555,10 @@ def from_hdf5_group(cls, group: h5py.Group) -> BootstrapAnalysis: return cls( num_bootstraps=num_bootstraps, persistence_diagrams=[], + persistence_pair_simplices=[], cocycles=[], + reference_image_pairs=image_records["reference_image_pairs"], + bootstrap_image_pairs=image_records["bootstrap_image_pairs"], selected_loop_classes=selected_loop_classes, loop_tracks=loop_tracks, presence_test_result=presence_test_result, @@ -602,8 +679,11 @@ def from_super( return cls( rank=super_obj.rank, + persistence_index=super_obj.persistence_index, birth=super_obj.birth, death=super_obj.death, + birth_simplex=super_obj.birth_simplex, + death_simplex=super_obj.death_simplex, cocycles=super_obj.cocycles, representatives=representatives, coordinates_vertices_representatives=[ diff --git a/src/scloop/data/base_components.py b/src/scloop/data/base_components.py index e5fcc0f..e9389b1 100644 --- a/src/scloop/data/base_components.py +++ b/src/scloop/data/base_components.py @@ -4,7 +4,7 @@ from typing import TYPE_CHECKING import numpy as np -from pydantic import BaseModel, model_validator +from pydantic import BaseModel, Field, model_validator from pydantic.dataclasses import dataclass from typing_extensions import Self @@ -14,10 +14,75 @@ import h5py +class PersistencePair(BaseModel): + birth: Diameter_t + death: Diameter_t + birth_simplex: list[Index_t] + death_simplex: list[Index_t] + + @model_validator(mode="after") + def check_birth_death(self) -> Self: + if self.birth > self.death: + raise ValueError("loop dies before its birth") + return self + + def to_hdf5_group(self, group: h5py.Group, compress: bool = True) -> None: + group.attrs["birth"] = self.birth + group.attrs["death"] = self.death + kw = {"compression": "gzip"} if compress else {} + group.create_dataset( + "birth_simplex", data=np.asarray(self.birth_simplex, dtype=np.int64), **kw + ) + group.create_dataset( + "death_simplex", data=np.asarray(self.death_simplex, dtype=np.int64), **kw + ) + + @classmethod + def from_hdf5_group(cls, group: h5py.Group) -> PersistencePair: + return cls( + birth=float(group.attrs["birth"]), + death=float(group.attrs["death"]), + birth_simplex=np.asarray(group["birth_simplex"]).tolist(), + death_simplex=np.asarray(group["death_simplex"]).tolist(), + ) + + +class ImagePairRecord(BaseModel): + source_class_idx: Index_t + source_pair: PersistencePair + image_pair: PersistencePair | None = None + + def to_hdf5_group(self, group: h5py.Group, compress: bool = True) -> None: + group.attrs["source_class_idx"] = self.source_class_idx + self.source_pair.to_hdf5_group( + group.create_group("source_pair"), compress=compress + ) + if self.image_pair is not None: + self.image_pair.to_hdf5_group( + group.create_group("image_pair"), compress=compress + ) + + @classmethod + def from_hdf5_group(cls, group: h5py.Group) -> ImagePairRecord: + image_pair = ( + PersistencePair.from_hdf5_group(group["image_pair"]) + if "image_pair" in group + else None + ) + return cls( + source_class_idx=int(group.attrs["source_class_idx"]), + source_pair=PersistencePair.from_hdf5_group(group["source_pair"]), + image_pair=image_pair, + ) + + class LoopClass(BaseModel): rank: Index_t + persistence_index: Index_t | None = None birth: Diameter_t = 0.0 death: Diameter_t = 0.0 + birth_simplex: list[Index_t] = Field(default_factory=list) + death_simplex: list[Index_t] = Field(default_factory=list) cocycles: list | None = None representatives: list[list[Index_t]] | None = None coordinates_vertices_representatives: list[list[list[float]]] | None = None @@ -34,13 +99,31 @@ def check_birth_death(self) -> Self: def lifetime(self): return self.death - self.birth + @property + def persistence_pair(self) -> PersistencePair: + return PersistencePair( + birth=self.birth, + death=self.death, + birth_simplex=self.birth_simplex, + death_simplex=self.death_simplex, + ) + def to_hdf5_group(self, group: h5py.Group, compress: bool = True) -> None: group.attrs["_type"] = "LoopClass" group.attrs["rank"] = self.rank + group.attrs["persistence_index"] = ( + self.rank if self.persistence_index is None else self.persistence_index + ) group.attrs["birth"] = self.birth group.attrs["death"] = self.death kw = {"compression": "gzip"} if compress else {} + group.create_dataset( + "birth_simplex", data=np.asarray(self.birth_simplex, dtype=np.int64), **kw + ) + group.create_dataset( + "death_simplex", data=np.asarray(self.death_simplex, dtype=np.int64), **kw + ) if self.cocycles is not None and len(self.cocycles) > 0: verts_list = [] @@ -80,8 +163,19 @@ def to_hdf5_group(self, group: h5py.Group, compress: bool = True) -> None: @classmethod def from_hdf5_group(cls, group: h5py.Group) -> LoopClass: rank = int(group.attrs["rank"]) # type: ignore[arg-type] + persistence_index = int(group.attrs.get("persistence_index", rank)) birth = float(group.attrs["birth"]) # type: ignore[arg-type] death = float(group.attrs["death"]) # type: ignore[arg-type] + birth_simplex = ( + np.asarray(group["birth_simplex"], dtype=np.int64).tolist() + if "birth_simplex" in group + else [] + ) + death_simplex = ( + np.asarray(group["death_simplex"], dtype=np.int64).tolist() + if "death_simplex" in group + else [] + ) cocycles = None if "cocycles" in group: @@ -112,8 +206,11 @@ def from_hdf5_group(cls, group: h5py.Group) -> LoopClass: return cls( rank=rank, + persistence_index=persistence_index, birth=birth, death=death, + birth_simplex=birth_simplex, + death_simplex=death_simplex, cocycles=cocycles, representatives=representatives, coordinates_vertices_representatives=coordinates_vertices_representatives, diff --git a/src/scloop/data/containers.py b/src/scloop/data/containers.py index 5096bdc..b8ae5ae 100644 --- a/src/scloop/data/containers.py +++ b/src/scloop/data/containers.py @@ -3,7 +3,7 @@ import time from concurrent.futures import ThreadPoolExecutor, as_completed -from typing import Any +from typing import Any, Literal import numpy as np from anndata import AnnData @@ -94,6 +94,7 @@ class HomologyData: meta: ScloopMeta persistence_diagram: list | None = None + persistence_pair_simplices: list | None = None cocycles: list | None = None selected_loop_classes: list[LoopClass | None] = Field(default_factory=list) boundary_matrix_d1: BoundaryMatrixD1 | None = None @@ -290,6 +291,7 @@ def _compute_homology( ) -> csr_matrix: ( persistence_diagram, + persistence_pair_simplices, cocycles, indices_resample, sparse_pairwise_distance_matrix, @@ -302,6 +304,7 @@ def _compute_homology( ) if not bootstrap: self.persistence_diagram = persistence_diagram + self.persistence_pair_simplices = persistence_pair_simplices self.cocycles = cocycles else: assert self.bootstrap_data is not None @@ -310,6 +313,9 @@ def _compute_homology( assert self.meta.bootstrap.indices_resample is not None self.bootstrap_data.persistence_diagrams.append(persistence_diagram) + self.bootstrap_data.persistence_pair_simplices.append( + persistence_pair_simplices + ) self.bootstrap_data.cocycles.append(cocycles) self.meta.bootstrap.indices_resample.append(indices_resample) return sparse_pairwise_distance_matrix @@ -486,20 +492,28 @@ def _compute_loop_representatives( # Extract data from self based on bootstrap flag if not bootstrap: assert self.persistence_diagram is not None + assert self.persistence_pair_simplices is not None assert self.cocycles is not None persistence_diagram = self.persistence_diagram[1] + persistence_pair_simplices = self.persistence_pair_simplices[1] cocycles = self.cocycles[1] vertex_ids = self._original_vertex_ids else: assert self.bootstrap_data is not None assert len(self.bootstrap_data.persistence_diagrams) > idx_bootstrap assert len(self.bootstrap_data.cocycles) > idx_bootstrap + assert ( + len(self.bootstrap_data.persistence_pair_simplices) > idx_bootstrap + ) assert self.meta.bootstrap is not None assert self.meta.bootstrap.indices_resample is not None assert len(self.meta.bootstrap.indices_resample) > idx_bootstrap persistence_diagram = self.bootstrap_data.persistence_diagrams[ idx_bootstrap ][1] + persistence_pair_simplices = ( + self.bootstrap_data.persistence_pair_simplices[idx_bootstrap][1] + ) cocycles = self.bootstrap_data.cocycles[idx_bootstrap][1] vertex_ids = self.meta.bootstrap.indices_resample[idx_bootstrap] @@ -507,6 +521,7 @@ def _compute_loop_representatives( embedding=embedding, pairwise_distance_matrix=pairwise_distance_matrix, persistence_diagram=persistence_diagram, + persistence_pair_simplices=persistence_pair_simplices, cocycles=cocycles, boundary_matrix_d1=self.boundary_matrix_d1, vertex_ids=vertex_ids, @@ -814,6 +829,8 @@ def _bootstrap( n_max_workers: Count_t = DEFAULT_N_MAX_WORKERS, k_neighbors_check_equivalence: Count_t = DEFAULT_K_NEIGHBORS_CHECK_EQUIVALENCE, method_geometric_equivalence: LoopDistMethod = DEFAULT_LOOP_DIST_METHOD, + candidate_method: Literal["geometric", "image"] = "geometric", + require_homological_equivalence: bool = True, reconstruct_on_full_data: bool = False, verbose: bool = False, progress_main: Progress | None = None, @@ -830,7 +847,11 @@ def _bootstrap( else: self.meta.bootstrap.indices_resample.clear() - if use_parallel: + if ( + use_parallel + or candidate_method == "image" + or not require_homological_equivalence + ): self._bootstrap_parallel( adata=adata, n_bootstrap=n_bootstrap, @@ -856,9 +877,11 @@ def _bootstrap( max_n_edges_relaxation_equivalence=max_n_edges_relaxation_equivalence, extra_diameter_homology_equivalence=extra_diameter_homology_equivalence, filter_column_homology_equivalence=filter_column_homology_equivalence, - n_max_workers=n_max_workers, + n_max_workers=n_max_workers if use_parallel else 1, k_neighbors_check_equivalence=k_neighbors_check_equivalence, method_geometric_equivalence=method_geometric_equivalence, + candidate_method=candidate_method, + require_homological_equivalence=require_homological_equivalence, reconstruct_on_full_data=reconstruct_on_full_data, verbose=verbose, progress_main=progress_main, @@ -1063,6 +1086,8 @@ def _bootstrap_parallel( n_max_workers: int = DEFAULT_N_MAX_WORKERS, k_neighbors_check_equivalence: int = DEFAULT_K_NEIGHBORS_CHECK_EQUIVALENCE, method_geometric_equivalence: LoopDistMethod = DEFAULT_LOOP_DIST_METHOD, + candidate_method: Literal["geometric", "image"] = "geometric", + require_homological_equivalence: bool = True, reconstruct_on_full_data: bool = False, verbose: bool = False, progress_main: Progress | None = None, @@ -1098,6 +1123,8 @@ def _bootstrap_parallel( do_force_deviate_random_walk=do_force_deviate_random_walk, k_neighbors_check_equivalence=k_neighbors_check_equivalence, method_geometric_equivalence=method_geometric_equivalence, + candidate_method=candidate_method, + require_homological_equivalence=require_homological_equivalence, n_pairs_check_equivalence=n_pairs_check_equivalence, with_relaxation_equivalence=with_relaxation_equivalence, n_hubs_relaxation_equivalence=n_hubs_relaxation_equivalence, @@ -1117,8 +1144,18 @@ def _bootstrap_parallel( self.bootstrap_data.persistence_diagrams.append( result.persistence_diagram # type: ignore[arg-type] ) + if result.persistence_pair_simplices is not None: + self.bootstrap_data.persistence_pair_simplices.append( + result.persistence_pair_simplices + ) if result.cocycles is not None: self.bootstrap_data.cocycles.append(result.cocycles) # type: ignore[arg-type] + self.bootstrap_data.reference_image_pairs.append( + result.reference_image_pairs + ) + self.bootstrap_data.bootstrap_image_pairs.append( + result.bootstrap_image_pairs + ) self.bootstrap_data.selected_loop_classes.append(result.loop_classes) for source_idx, matches in result.matches.items(): diff --git a/src/scloop/data/ripser.cpp b/src/scloop/data/ripser.cpp index def53ef..9e2dbc7 100644 --- a/src/scloop/data/ripser.cpp +++ b/src/scloop/data/ripser.cpp @@ -204,31 +204,79 @@ void set_coefficient(entry_t& e, const coefficient_t c) {} const entry_t& get_entry(const entry_t& e) { return e; } -typedef std::pair diameter_index_t; -value_t get_diameter(const diameter_index_t& i) { return i.first; } -index_t get_index(const diameter_index_t& i) { return i.second; } +struct diameter_index_t { + value_t diameter; + value_t diameter_sub; + index_t index; + + diameter_index_t() + : diameter(0), diameter_sub(0), index(-1) + { + } + + diameter_index_t(value_t _diameter, index_t _index) + : diameter(_diameter), diameter_sub(_diameter), index(_index) + { + } + + diameter_index_t(value_t _diameter, value_t _diameter_sub, index_t _index) + : diameter(_diameter), diameter_sub(_diameter_sub), index(_index) + { + } +}; + +value_t get_diameter(const diameter_index_t& i) { return i.diameter; } +value_t get_diameter_sub(const diameter_index_t& i) { return i.diameter_sub; } +index_t get_index(const diameter_index_t& i) { return i.index; } typedef std::pair index_diameter_t; index_t get_index(const index_diameter_t& i) { return i.first; } value_t get_diameter(const index_diameter_t& i) { return i.second; } struct diameter_entry_t : std::pair { - using std::pair::pair; - diameter_entry_t() {} + using Base = std::pair; + value_t diameter_sub; + + diameter_entry_t() + : Base(), diameter_sub(0) + { + } + + diameter_entry_t(value_t _diameter, entry_t _entry) + : Base(_diameter, _entry), diameter_sub(_diameter) + { + } + + diameter_entry_t(value_t _diameter, value_t _diameter_sub, index_t _index, + coefficient_t _coefficient) + : Base(_diameter, make_entry(_index, _coefficient)), + diameter_sub(_diameter_sub) + { + } + diameter_entry_t(value_t _diameter, index_t _index, coefficient_t _coefficient) - : diameter_entry_t(_diameter, make_entry(_index, _coefficient)) + : Base(_diameter, make_entry(_index, _coefficient)), + diameter_sub(_diameter) { } + diameter_entry_t(const diameter_index_t& _diameter_index, coefficient_t _coefficient) - : diameter_entry_t(get_diameter(_diameter_index), - make_entry(get_index(_diameter_index), _coefficient)) + : Base(get_diameter(_diameter_index), + make_entry(get_index(_diameter_index), _coefficient)), + diameter_sub(get_diameter_sub(_diameter_index)) + { + } + + diameter_entry_t(const index_t& _index) + : Base(0, make_entry(_index, 0)), diameter_sub(0) { } - diameter_entry_t(const index_t& _index) : diameter_entry_t(0, _index, 0) {} }; +value_t get_diameter_sub(const diameter_entry_t& e) { return e.diameter_sub; } + const entry_t& get_entry(const diameter_entry_t& p) { return p.second; } entry_t& get_entry(diameter_entry_t& p) { return p.second; } const index_t get_index(const diameter_entry_t& p) @@ -245,13 +293,45 @@ void set_coefficient(diameter_entry_t& p, const coefficient_t c) set_coefficient(get_entry(p), c); } +enum class reduction_mode { ambient, subfiltration, image }; + +template +value_t get_birth_diameter(const Entry& entry, reduction_mode mode) +{ + return mode == reduction_mode::ambient ? get_diameter(entry) + : get_diameter_sub(entry); +} + +template +value_t get_coboundary_diameter(const Entry& entry, reduction_mode mode) +{ + return mode == reduction_mode::subfiltration ? get_diameter_sub(entry) + : get_diameter(entry); +} + +bool pivots_use_sub_diameter(reduction_mode mode) +{ + return mode == reduction_mode::subfiltration; +} + template struct greater_diameter_or_smaller_index { + bool use_diameter_sub; + + greater_diameter_or_smaller_index(bool _use_diameter_sub = false) + : use_diameter_sub(_use_diameter_sub) + { + } + bool operator()(const Entry& a, const Entry& b) { - return (get_diameter(a) > get_diameter(b)) || - ((get_diameter(a) == get_diameter(b)) && - (get_index(a) < get_index(b))); + return use_diameter_sub + ? (get_diameter_sub(a) > get_diameter_sub(b)) || + ((get_diameter_sub(a) == get_diameter_sub(b)) && + (get_index(a) < get_index(b))) + : (get_diameter(a) > get_diameter(b)) || + ((get_diameter(a) == get_diameter(b)) && + (get_index(a) < get_index(b))); } }; @@ -492,6 +572,7 @@ typedef struct { and likewise for d-dimensional persistence in births_and_deaths_by_dim[d] */ std::vector> births_and_deaths_by_dim; + std::vector>> births_and_deaths_simplex_by_dim; /* The second variable is a vector of representative cocycles for each dimension. For now, only cocycles above dimension 0 are added, so @@ -512,6 +593,11 @@ typedef struct { int num_edges; } ripserResults; +typedef struct { + ripserResults subfiltration; + ripserResults image; +} imageRipserResults; + /* This is the data structure for returning dimension 2 boundary matrix */ typedef struct { /* Vector of triangles, where each triangle is represented by its 3 vertex indices */ @@ -534,6 +620,8 @@ class ripser // If this flag is off, don't extract the representative cocycles to save // time const int do_cocycles; + const int do_image_cocycles; + std::vector sub_vertex_mask; struct entry_hash { std::size_t operator()(const entry_t& e) const @@ -557,21 +645,32 @@ class ripser public: mutable std::vector> births_and_deaths_by_dim; + mutable std::vector>> births_and_deaths_simplex_by_dim; mutable std::vector>> cocycles_by_dim; ripser(DistanceMatrix&& _dist, index_t _dim_max, value_t _threshold, - float _ratio, coefficient_t _modulus, int _do_cocycles) + float _ratio, coefficient_t _modulus, int _do_cocycles, + int _do_image_cocycles = 0, + std::vector _sub_vertex_mask = {}) : dist(std::move(_dist)), n(dist.size()), dim_max(_dim_max), threshold(_threshold), ratio(_ratio), modulus(_modulus), binomial_coeff(n, dim_max + 2), multiplicative_inverse(multiplicative_inverse_vector(_modulus)), - do_cocycles(_do_cocycles) + do_cocycles(_do_cocycles), + do_image_cocycles(_do_image_cocycles), + sub_vertex_mask(std::move(_sub_vertex_mask)) { + if (sub_vertex_mask.empty()) + sub_vertex_mask.assign(n, 1); + assert(sub_vertex_mask.size() == static_cast(n)); } + bool is_sub_vertex(index_t i) const { return sub_vertex_mask[i] != 0; } + void copy_results(ripserResults& res) { res.births_and_deaths_by_dim = births_and_deaths_by_dim; + res.births_and_deaths_simplex_by_dim = births_and_deaths_simplex_by_dim; res.cocycles_by_dim = cocycles_by_dim; } @@ -638,7 +737,7 @@ class ripser } simplex_coboundary_enumerator trigs(diameter_entry_t(edge, 1), 1, *this); while (trigs.has_next(false)) { - auto trig = trigs.next(); + diameter_entry_t trig = trigs.next(); index_t trig_idx = get_index(trig); // skip if already seen this triangle if (!seen_trigs.insert(trig_idx).second) { @@ -650,7 +749,7 @@ class ripser continue; } // Check if this triangle should be included - simplex_coboundary_enumerator tetrads(diameter_entry_t(trig, 2), 2, *this); + simplex_coboundary_enumerator tetrads(diameter_entry_t(diameter_index_t(trig.first, trig.second), 2), 2, *this); bool should_add = false; bool is_redundant = false; // Check if triangle is part of any tetrahedron @@ -698,7 +797,8 @@ class ripser void assemble_columns_to_reduce(std::vector& simplices, std::vector& columns_to_reduce, - entry_hash_map& pivot_column_index, index_t dim) + entry_hash_map& pivot_column_index, index_t dim, + reduction_mode mode = reduction_mode::ambient) { #ifdef INDICATE_PROGRESS std::cerr << clear_line << "assembling columns" << std::flush; @@ -729,12 +829,14 @@ class ripser if (get_diameter(cofacet) <= threshold) { if (dim != dim_max) next_simplices.push_back( - {get_diameter(cofacet), get_index(cofacet)}); + {get_diameter(cofacet), + get_diameter_sub(cofacet), get_index(cofacet)}); if (pivot_column_index.find(get_entry(cofacet)) == pivot_column_index.end()) columns_to_reduce.push_back( - {get_diameter(cofacet), get_index(cofacet)}); + {get_diameter(cofacet), + get_diameter_sub(cofacet), get_index(cofacet)}); } } } @@ -747,7 +849,8 @@ class ripser #endif std::sort(columns_to_reduce.begin(), columns_to_reduce.end(), - greater_diameter_or_smaller_index()); + greater_diameter_or_smaller_index( + mode != reduction_mode::ambient)); #ifdef INDICATE_PROGRESS std::cerr << clear_line << std::flush; @@ -798,6 +901,118 @@ class ripser } } + void compute_image_dim_0_pairs( + std::vector& edges, + std::vector& columns_to_reduce, + imageRipserResults& results) + { + edges = get_edges(); + + std::vector sub_edges = edges; + std::sort( + sub_edges.rbegin(), sub_edges.rend(), + greater_diameter_or_smaller_index(true)); + + union_find sub_components(n); + for (index_t i = 0; i < n; ++i) + sub_components.set_birth(i, get_vertex_birth(i)); + + std::vector vertices_of_edge(2); + for (const auto& edge : sub_edges) { + if (get_diameter_sub(edge) > threshold) + continue; + + get_simplex_vertices(get_index(edge), 1, n, + vertices_of_edge.rbegin()); + index_t u = sub_components.find(vertices_of_edge[0]); + index_t v = sub_components.find(vertices_of_edge[1]); + if (u != v) { + value_t birth = std::max(sub_components.get_birth(u), + sub_components.get_birth(v)); + value_t death = get_diameter_sub(edge); + if (death > birth) { + results.subfiltration.births_and_deaths_by_dim[0] + .push_back(birth); + results.subfiltration.births_and_deaths_by_dim[0] + .push_back(death); + } + sub_components.link(u, v); + } + } + + for (index_t i = 0; i < n; ++i) { + if (is_sub_vertex(i) && sub_components.find(i) == i) { + results.subfiltration.births_and_deaths_by_dim[0].push_back( + sub_components.get_birth(i)); + results.subfiltration.births_and_deaths_by_dim[0].push_back( + std::numeric_limits::infinity()); + } + } + + union_find ordering_components(n); + for (const auto& edge : sub_edges) { + get_simplex_vertices(get_index(edge), 1, n, + vertices_of_edge.rbegin()); + index_t u = ordering_components.find(vertices_of_edge[0]); + index_t v = ordering_components.find(vertices_of_edge[1]); + if (u != v) + ordering_components.link(u, v); + else + columns_to_reduce.push_back(edge); + } + std::reverse(columns_to_reduce.begin(), columns_to_reduce.end()); + + std::vector ambient_edges = edges; + std::sort( + ambient_edges.rbegin(), ambient_edges.rend(), + greater_diameter_or_smaller_index(false)); + + union_find ambient_components(n); + std::vector contains_sub_vertex(n, 0); + for (index_t i = 0; i < n; ++i) { + ambient_components.set_birth( + i, is_sub_vertex(i) + ? get_vertex_birth(i) + : std::numeric_limits::infinity()); + contains_sub_vertex[i] = is_sub_vertex(i); + } + + for (const auto& edge : ambient_edges) { + get_simplex_vertices(get_index(edge), 1, n, + vertices_of_edge.rbegin()); + index_t u = ambient_components.find(vertices_of_edge[0]); + index_t v = ambient_components.find(vertices_of_edge[1]); + if (u == v) + continue; + + bool u_has_sub_vertex = contains_sub_vertex[u] != 0; + bool v_has_sub_vertex = contains_sub_vertex[v] != 0; + if (u_has_sub_vertex && v_has_sub_vertex) { + value_t birth = std::max(ambient_components.get_birth(u), + ambient_components.get_birth(v)); + value_t death = get_diameter(edge); + if (death > birth) { + results.image.births_and_deaths_by_dim[0].push_back(birth); + results.image.births_and_deaths_by_dim[0].push_back(death); + } + } + + ambient_components.link(u, v); + index_t root = ambient_components.find(u); + contains_sub_vertex[root] = + u_has_sub_vertex || v_has_sub_vertex; + } + + for (index_t i = 0; i < n; ++i) { + if (ambient_components.find(i) == i && contains_sub_vertex[i]) { + results.image.births_and_deaths_by_dim[0].push_back( + ambient_components.get_birth(i)); + results.image.births_and_deaths_by_dim[0].push_back( + std::numeric_limits::infinity()); + } + } + } + template diameter_entry_t pop_pivot(Column& column) { @@ -840,17 +1055,19 @@ class ripser template diameter_entry_t init_coboundary_and_get_pivot( const diameter_entry_t simplex, Column& working_coboundary, - const index_t& dim, entry_hash_map& pivot_column_index) + const index_t& dim, entry_hash_map& pivot_column_index, + reduction_mode mode) { bool check_for_emergent_pair = true; cofacet_entries.clear(); simplex_coboundary_enumerator cofacets(simplex, dim, *this); while (cofacets.has_next()) { diameter_entry_t cofacet = cofacets.next(); - if (get_diameter(cofacet) <= threshold) { + if (get_coboundary_diameter(cofacet, mode) <= threshold) { cofacet_entries.push_back(cofacet); if (check_for_emergent_pair && - (get_diameter(simplex) == get_diameter(cofacet))) { + (get_coboundary_diameter(simplex, mode) == + get_coboundary_diameter(cofacet, mode))) { if (pivot_column_index.find(get_entry(cofacet)) == pivot_column_index.end()) return cofacet; @@ -867,13 +1084,14 @@ class ripser void add_simplex_coboundary(const diameter_entry_t simplex, const index_t& dim, Column& working_reduction_column, - Column& working_coboundary) + Column& working_coboundary, + reduction_mode mode) { working_reduction_column.push(simplex); simplex_coboundary_enumerator cofacets(simplex, dim, *this); while (cofacets.has_next()) { diameter_entry_t cofacet = cofacets.next(); - if (get_diameter(cofacet) <= threshold) + if (get_coboundary_diameter(cofacet, mode) <= threshold) working_coboundary.push(cofacet); } } @@ -884,19 +1102,19 @@ class ripser const std::vector& columns_to_reduce, const size_t index_column_to_add, const coefficient_t factor, const size_t& dim, Column& working_reduction_column, - Column& working_coboundary) + Column& working_coboundary, reduction_mode mode) { diameter_entry_t column_to_add(columns_to_reduce[index_column_to_add], factor); add_simplex_coboundary(column_to_add, dim, working_reduction_column, - working_coboundary); + working_coboundary, mode); for (diameter_entry_t simplex : reduction_matrix.subrange(index_column_to_add)) { set_coefficient(simplex, get_coefficient(simplex) * factor % modulus); add_simplex_coboundary(simplex, dim, working_reduction_column, - working_coboundary); + working_coboundary, mode); } } @@ -907,7 +1125,9 @@ class ripser diameter_entry_t cocycle_e; std::vector cocycle_simplex; std::vector thiscocycle; - inline void compute_cocycles(working_t cocycle, index_t dim) + inline void compute_cocycles( + working_t cocycle, index_t dim, + std::vector>>& result_cocycles) { thiscocycle.clear(); while (get_index(cocycle_e = get_pivot(cocycle)) != -1) { @@ -921,14 +1141,20 @@ class ripser normalize(get_coefficient(cocycle_e), modulus)); cocycle.pop(); } - cocycles_by_dim[dim].push_back(thiscocycle); + result_cocycles[dim].push_back(thiscocycle); } - void compute_pairs(std::vector& columns_to_reduce, - entry_hash_map& pivot_column_index, index_t dim) + void compute_pairs( + std::vector& columns_to_reduce, + entry_hash_map& pivot_column_index, index_t dim, reduction_mode mode, + std::vector>& result_intervals, + std::vector>>& result_simplices, + std::vector>>& result_cocycles) { compressed_sparse_matrix reduction_matrix; size_t index_column_to_add; + const bool extract_cocycles = + mode == reduction_mode::image ? do_image_cocycles : do_cocycles; #ifdef INDICATE_PROGRESS std::chrono::steady_clock::time_point next = @@ -940,17 +1166,20 @@ class ripser ++index_column_to_reduce) { diameter_entry_t column_to_reduce( columns_to_reduce[index_column_to_reduce], 1); - value_t diameter = get_diameter(column_to_reduce); + value_t diameter = get_birth_diameter(column_to_reduce, mode); reduction_matrix.append_column(); - working_t working_reduction_column; - working_t working_coboundary; + greater_diameter_or_smaller_index comparator( + pivots_use_sub_diameter(mode)); + working_t working_reduction_column(comparator); + working_t working_coboundary(comparator); working_reduction_column.push(column_to_reduce); diameter_entry_t pivot = init_coboundary_and_get_pivot( - column_to_reduce, working_coboundary, dim, pivot_column_index); + column_to_reduce, working_coboundary, dim, pivot_column_index, + mode); while (true) { #ifdef INDICATE_PROGRESS @@ -978,24 +1207,39 @@ class ripser add_coboundary(reduction_matrix, columns_to_reduce, index_column_to_add, factor, dim, working_reduction_column, - working_coboundary); + working_coboundary, mode); pivot = get_pivot(working_coboundary); } else { - value_t death = get_diameter(pivot); + value_t death = get_coboundary_diameter(pivot, mode); if (death > diameter * ratio) { - births_and_deaths_by_dim[dim].push_back(diameter); - births_and_deaths_by_dim[dim].push_back(death); - if (do_cocycles) { + result_intervals[dim].push_back(diameter); + result_intervals[dim].push_back(death); + if (extract_cocycles) { // Representative cocycle - compute_cocycles(working_reduction_column, dim); + compute_cocycles(working_reduction_column, dim, + result_cocycles); } + // TODO: add a flag here, skip if no need for cross match + index_t birth_simplex_i = get_index(column_to_reduce); + index_t death_simplex_i = get_index(pivot); + std::vector birth_simplex_vertices; + std::vector death_simplex_vertices; + get_simplex_vertices(birth_simplex_i, dim, n, + std::back_inserter(birth_simplex_vertices)); + get_simplex_vertices(death_simplex_i, dim + 1, n, + std::back_inserter(death_simplex_vertices)); + result_simplices[dim].push_back(birth_simplex_vertices); + result_simplices[dim].push_back(death_simplex_vertices); } pivot_column_index.insert( {get_entry(pivot), index_column_to_reduce}); - pop_pivot(working_reduction_column); + // Here need small modification to accommodate the image PH + // Old: pop_pivot(working_reduction_column) assume the pivot is diagonal, which is not in the case of image PH + // New: get diagonal column and avoid adding the diagonal entry (10 lines below) + index_t diagonal_index = get_index(column_to_reduce); while (true) { diameter_entry_t e = pop_pivot(working_reduction_column); @@ -1003,18 +1247,31 @@ class ripser if (get_index(e) == -1) break; assert(get_coefficient(e) > 0); + if (get_index(e) == diagonal_index) + continue; reduction_matrix.push_back(e); } break; } } else { - births_and_deaths_by_dim[dim].push_back(diameter); - births_and_deaths_by_dim[dim].push_back( - std::numeric_limits::infinity()); - - if (do_cocycles) { - // Representative cocycle - compute_cocycles(working_reduction_column, dim); + if (diameter <= threshold) { + result_intervals[dim].push_back(diameter); + result_intervals[dim].push_back( + std::numeric_limits::infinity()); + + std::vector birth_simplex_vertices; + get_simplex_vertices( + get_index(column_to_reduce), dim, n, + std::back_inserter(birth_simplex_vertices)); + result_simplices[dim].push_back( + birth_simplex_vertices); + result_simplices[dim].push_back({}); + + if (extract_cocycles) { + // Representative cocycle + compute_cocycles(working_reduction_column, dim, + result_cocycles); + } } break; } @@ -1025,6 +1282,15 @@ class ripser #endif } + void compute_pairs(std::vector& columns_to_reduce, + entry_hash_map& pivot_column_index, index_t dim, + reduction_mode mode) + { + compute_pairs(columns_to_reduce, pivot_column_index, dim, mode, + births_and_deaths_by_dim, + births_and_deaths_simplex_by_dim, cocycles_by_dim); + } + std::vector get_edges(); void compute_barcodes() { @@ -1035,6 +1301,7 @@ class ripser dim_max = 0; births_and_deaths_by_dim.resize(dim_max + 1); + births_and_deaths_simplex_by_dim.resize(dim_max + 1); cocycles_by_dim.resize(dim_max + 1); compute_dim_0_pairs(simplices, columns_to_reduce); @@ -1043,13 +1310,60 @@ class ripser entry_hash_map pivot_column_index; pivot_column_index.reserve(columns_to_reduce.size()); - compute_pairs(columns_to_reduce, pivot_column_index, dim); + compute_pairs(columns_to_reduce, pivot_column_index, dim, + reduction_mode::ambient); if (dim < dim_max) assemble_columns_to_reduce(simplices, columns_to_reduce, pivot_column_index, dim + 1); } } + + imageRipserResults compute_image_barcodes() + { + if (dim_max < 0) + dim_max = 0; + + imageRipserResults results; + results.subfiltration.births_and_deaths_by_dim.resize(dim_max + 1); + results.subfiltration.births_and_deaths_simplex_by_dim.resize( + dim_max + 1); + results.subfiltration.cocycles_by_dim.resize(dim_max + 1); + results.image.births_and_deaths_by_dim.resize(dim_max + 1); + results.image.births_and_deaths_simplex_by_dim.resize(dim_max + 1); + results.image.cocycles_by_dim.resize(dim_max + 1); + + std::vector simplices, columns_to_reduce; + compute_image_dim_0_pairs(simplices, columns_to_reduce, results); + + for (index_t dim = 1; dim <= dim_max; ++dim) { + entry_hash_map image_pivot_column_index; + image_pivot_column_index.reserve(columns_to_reduce.size()); + compute_pairs( + columns_to_reduce, image_pivot_column_index, dim, + reduction_mode::image, + results.image.births_and_deaths_by_dim, + results.image.births_and_deaths_simplex_by_dim, + results.image.cocycles_by_dim); + + entry_hash_map sub_pivot_column_index; + sub_pivot_column_index.reserve(columns_to_reduce.size()); + compute_pairs( + columns_to_reduce, sub_pivot_column_index, dim, + reduction_mode::subfiltration, + results.subfiltration.births_and_deaths_by_dim, + results.subfiltration.births_and_deaths_simplex_by_dim, + results.subfiltration.cocycles_by_dim); + + if (dim < dim_max) { + assemble_columns_to_reduce( + simplices, columns_to_reduce, sub_pivot_column_index, + dim + 1, reduction_mode::subfiltration); + } + } + + return results; + } }; template <> @@ -1075,6 +1389,7 @@ class ripser::simplex_coboundary_enumerator const diameter_entry_t simplex; const coefficient_t modulus; const compressed_lower_distance_matrix& dist; + const std::vector& sub_vertex_mask; const binomial_coeff_table& binomial_coeff; public: @@ -1084,6 +1399,7 @@ class ripser::simplex_coboundary_enumerator : idx_below(get_index(_simplex)), idx_above(0), v(parent.n - 1), k(_dim + 1), vertices(_dim + 1), simplex(_simplex), modulus(parent.modulus), dist(parent.dist), + sub_vertex_mask(parent.sub_vertex_mask), binomial_coeff(parent.binomial_coeff) { parent.get_simplex_vertices(get_index(_simplex), _dim, parent.n, @@ -1107,12 +1423,21 @@ class ripser::simplex_coboundary_enumerator value_t cofacet_diameter = get_diameter(simplex); for (index_t w : vertices) cofacet_diameter = std::max(cofacet_diameter, dist(v, w)); + value_t cofacet_diameter_sub = get_diameter_sub(simplex); + if (!sub_vertex_mask[v]) { + cofacet_diameter_sub = + std::numeric_limits::infinity(); + } else { + for (index_t w : vertices) + cofacet_diameter_sub = + std::max(cofacet_diameter_sub, dist(v, w)); + } index_t cofacet_index = idx_above + binomial_coeff(v--, k + 1) + idx_below; coefficient_t cofacet_coefficient = (k & 1 ? modulus - 1 : 1) * get_coefficient(simplex) % modulus; - return diameter_entry_t(cofacet_diameter, cofacet_index, - cofacet_coefficient); + return diameter_entry_t(cofacet_diameter, cofacet_diameter_sub, + cofacet_index, cofacet_coefficient); } }; @@ -1124,6 +1449,7 @@ class ripser::simplex_coboundary_enumerator const diameter_entry_t simplex; const coefficient_t modulus; const sparse_distance_matrix& dist; + const std::vector& sub_vertex_mask; const binomial_coeff_table& binomial_coeff; std::vector::const_reverse_iterator>& neighbor_it; @@ -1137,7 +1463,8 @@ class ripser::simplex_coboundary_enumerator const ripser& parent) : idx_below(get_index(_simplex)), idx_above(0), k(_dim + 1), vertices(_dim + 1), simplex(_simplex), modulus(parent.modulus), - dist(parent.dist), binomial_coeff(parent.binomial_coeff), + dist(parent.dist), sub_vertex_mask(parent.sub_vertex_mask), + binomial_coeff(parent.binomial_coeff), neighbor_it(dist.neighbor_it), neighbor_end(dist.neighbor_end) { neighbor_it.clear(); @@ -1185,12 +1512,20 @@ class ripser::simplex_coboundary_enumerator ++neighbor_it[0]; value_t cofacet_diameter = std::max(get_diameter(simplex), get_diameter(neighbor)); + value_t cofacet_diameter_sub = get_diameter_sub(simplex); + if (!sub_vertex_mask[get_index(neighbor)]) { + cofacet_diameter_sub = + std::numeric_limits::infinity(); + } else { + cofacet_diameter_sub = + std::max(cofacet_diameter_sub, get_diameter(neighbor)); + } index_t cofacet_index = idx_above + binomial_coeff(get_index(neighbor), k + 1) + idx_below; coefficient_t cofacet_coefficient = (k & 1 ? modulus - 1 : 1) * get_coefficient(simplex) % modulus; - return diameter_entry_t(cofacet_diameter, cofacet_index, - cofacet_coefficient); + return diameter_entry_t(cofacet_diameter, cofacet_diameter_sub, + cofacet_index, cofacet_coefficient); } }; @@ -1203,8 +1538,13 @@ ripser::get_edges() for (index_t index = binomial_coeff(n, 2); index-- > 0;) { get_simplex_vertices(index, 1, dist.size(), vertices.rbegin()); value_t length = dist(vertices[0], vertices[1]); - if (length <= threshold) - edges.push_back({length, index}); + if (length <= threshold) { + value_t sub_length = + is_sub_vertex(vertices[0]) && is_sub_vertex(vertices[1]) + ? length + : std::numeric_limits::infinity(); + edges.push_back({length, sub_length, index}); + } } return edges; } @@ -1216,8 +1556,15 @@ std::vector ripser::get_edges() for (index_t i = 0; i < n; ++i) for (auto n : dist.neighbors[i]) { index_t j = get_index(n); - if (i > j) - edges.push_back({get_diameter(n), get_edge_index(i, j)}); + if (i > j) { + value_t length = get_diameter(n); + value_t sub_length = + is_sub_vertex(i) && is_sub_vertex(j) + ? length + : std::numeric_limits::infinity(); + edges.push_back( + {length, sub_length, get_edge_index(i, j)}); + } } return edges; } @@ -1303,6 +1650,38 @@ ripserResults rips_dm_sparse(int* I, int* J, float* V, int NEdges, int N, return res; } +imageRipserResults rips_image_sparse( + int* I, int* J, float* V, int NEdges, int N, int* sub_indices, + int n_sub_indices, int modulus, int dim_max, float threshold, + int do_subfiltration_cocycles, int do_image_cocycles) +{ + std::vector sub_vertex_mask(N, 0); + for (int i = 0; i < n_sub_indices; ++i) { + if (sub_indices[i] >= 0 && sub_indices[i] < N) + sub_vertex_mask[sub_indices[i]] = 1; + } + + float ratio = 1.0; + ripser r( + sparse_distance_matrix(I, J, V, NEdges, N, threshold), dim_max, + threshold, ratio, modulus, do_subfiltration_cocycles, + do_image_cocycles, sub_vertex_mask); + imageRipserResults results = r.compute_image_barcodes(); + + int ambient_edges = 0; + int subfiltration_edges = 0; + for (int i = 0; i < NEdges; ++i) { + if (I[i] < J[i] && V[i] <= threshold) { + ++ambient_edges; + if (sub_vertex_mask[I[i]] && sub_vertex_mask[J[i]]) + ++subfiltration_edges; + } + } + results.image.num_edges = ambient_edges; + results.subfiltration.num_edges = subfiltration_edges; + return results; +} + boundaryMatrixResults get_boundary_matrix_sparse(int* I, int* J, float* V, int NEdges, int N, float threshold) diff --git a/src/scloop/data/ripser.hpp b/src/scloop/data/ripser.hpp index 7d84e4c..e36edb4 100644 --- a/src/scloop/data/ripser.hpp +++ b/src/scloop/data/ripser.hpp @@ -4,6 +4,7 @@ #include #include #include +#include #include typedef float value_t; @@ -16,13 +17,80 @@ template class hash_map : public hash_map_imp {}; typedef index_t entry_t; -typedef std::pair diameter_index_t; +struct diameter_index_t { + value_t diameter; + value_t diameter_sub; + index_t index; + + diameter_index_t() + : diameter(0), diameter_sub(0), index(-1) + { + } + + diameter_index_t(value_t _diameter, index_t _index) + : diameter(_diameter), diameter_sub(_diameter), index(_index) + { + } + + diameter_index_t(value_t _diameter, value_t _diameter_sub, index_t _index) + : diameter(_diameter), diameter_sub(_diameter_sub), index(_index) + { + } +}; + struct diameter_entry_t : std::pair { - using std::pair::pair; + using Base = std::pair; + value_t diameter_sub; + + diameter_entry_t() + : Base(), diameter_sub(0) + { + } + + diameter_entry_t(value_t _diameter, entry_t _entry) + : Base(_diameter, _entry), diameter_sub(_diameter) + { + } + + diameter_entry_t(value_t _diameter, value_t _diameter_sub, index_t _index, + coefficient_t _coefficient) + : Base(_diameter, _index), diameter_sub(_diameter_sub) + { + (void) _coefficient; + } + + diameter_entry_t(value_t _diameter, index_t _index, + coefficient_t _coefficient) + : Base(_diameter, _index), diameter_sub(_diameter) + { + (void) _coefficient; + } + + diameter_entry_t(const diameter_index_t& _diameter_index, + coefficient_t _coefficient) + : Base(_diameter_index.diameter, _diameter_index.index), + diameter_sub(_diameter_index.diameter_sub) + { + (void) _coefficient; + } + + diameter_entry_t(const index_t& _index) + : Base(0, _index), diameter_sub(0) + { + } }; +enum class reduction_mode { ambient, subfiltration, image }; + template struct greater_diameter_or_smaller_index { + bool use_diameter_sub; + + greater_diameter_or_smaller_index(bool _use_diameter_sub = false) + : use_diameter_sub(_use_diameter_sub) + { + } + bool operator()(const Entry& a, const Entry& b); }; enum compressed_matrix_layout { LOWER_TRIANGULAR, UPPER_TRIANGULAR }; @@ -52,10 +120,16 @@ class compressed_sparse_matrix; */ typedef struct { std::vector> births_and_deaths_by_dim; + std::vector>> births_and_deaths_simplex_by_dim; std::vector>> cocycles_by_dim; int num_edges; } ripserResults; +typedef struct { + ripserResults subfiltration; + ripserResults image; +} imageRipserResults; + /* Data structure for returning dimension 2 boundary matrix */ @@ -76,6 +150,8 @@ class ripser { const coefficient_t modulus; mutable std::vector cofacet_entries; const int do_cocycles; + const int do_image_cocycles; + std::vector sub_vertex_mask; public: mutable std::vector> births_and_deaths_by_dim; mutable std::vector>> cocycles_by_dim; @@ -91,7 +167,11 @@ class ripser { typedef hash_map entry_hash_map; ripser(DistanceMatrix&& _dist, index_t _dim_max, value_t _threshold, - float _ratio, coefficient_t _modulus, int _do_cocycles); + float _ratio, coefficient_t _modulus, int _do_cocycles, + int _do_image_cocycles = 0, + std::vector _sub_vertex_mask = {}); + + bool is_sub_vertex(index_t i) const; index_t get_edge_index(const index_t i, const index_t j) const; @@ -103,30 +183,38 @@ class ripser { void assemble_columns_to_reduce(std::vector& simplices, std::vector& columns_to_reduce, - entry_hash_map& pivot_column_index, index_t dim); + entry_hash_map& pivot_column_index, index_t dim, + reduction_mode mode = reduction_mode::ambient); value_t get_vertex_birth(index_t i); void compute_dim_0_pairs(std::vector& edges, std::vector& columns_to_reduce); + void compute_image_dim_0_pairs( + std::vector& edges, + std::vector& columns_to_reduce, + imageRipserResults& results); + template diameter_entry_t init_coboundary_and_get_pivot( const diameter_entry_t simplex, Column& working_coboundary, - const index_t& dim, entry_hash_map& pivot_column_index); + const index_t& dim, entry_hash_map& pivot_column_index, + reduction_mode mode); template void add_simplex_coboundary(const diameter_entry_t simplex, const index_t& dim, Column& working_reduction_column, - Column& working_coboundary); + Column& working_coboundary, + reduction_mode mode); template void add_coboundary(compressed_sparse_matrix& reduction_matrix, const std::vector& columns_to_reduce, const size_t index_column_to_add, const coefficient_t factor, const size_t& dim, Column& working_reduction_column, - Column& working_coboundary); + Column& working_coboundary, reduction_mode mode); typedef std::priority_queue< diameter_entry_t, std::vector, @@ -139,12 +227,22 @@ class ripser { void compute_cocycles(working_t cocycle, index_t dim); void compute_pairs(std::vector& columns_to_reduce, - entry_hash_map& pivot_column_index, index_t dim); + entry_hash_map& pivot_column_index, index_t dim, + reduction_mode mode); + + void compute_pairs( + std::vector& columns_to_reduce, + entry_hash_map& pivot_column_index, index_t dim, reduction_mode mode, + std::vector>& result_intervals, + std::vector>>& result_simplices, + std::vector>>& result_cocycles); std::vector get_edges(); void compute_barcodes(); + imageRipserResults compute_image_barcodes(); + void copy_results(ripserResults& res); std::vector, value_t>> @@ -171,6 +269,11 @@ ripserResults rips_dm_sparse(int* I, int* J, float* V, int NEdges, int N, int modulus, int dim_max, float threshold, int do_cocycles); +imageRipserResults rips_image_sparse( + int* I, int* J, float* V, int NEdges, int N, int* sub_indices, + int n_sub_indices, int modulus, int dim_max, float threshold, + int do_subfiltration_cocycles, int do_image_cocycles = 0); + /** * @brief Compute dimension 2 boundary matrix without redundant columns * @details For each tetrahedron, only 3 of the 4 triangular faces are included, diff --git a/src/scloop/data/ripser_lib.cpp b/src/scloop/data/ripser_lib.cpp index a9fc161..db6808e 100644 --- a/src/scloop/data/ripser_lib.cpp +++ b/src/scloop/data/ripser_lib.cpp @@ -1,4 +1,4 @@ -/* Generated by Cython 3.2.5 */ +/* Generated by Cython 3.2.8 */ /* BEGIN: Cython Metadata { @@ -42,8 +42,8 @@ END: Cython Metadata */ #elif PY_VERSION_HEX < 0x03080000 #error Cython requires Python 3.8+. #else -#define __PYX_ABI_VERSION "3_2_5" -#define CYTHON_HEX_VERSION 0x030205F0 +#define __PYX_ABI_VERSION "3_2_8" +#define CYTHON_HEX_VERSION 0x030208F0 #define CYTHON_FUTURE_DIVISION 1 /* CModulePreamble */ #include @@ -2527,9 +2527,29 @@ static int __Pyx_VectorcallBuilder_AddArgStr(const char *key, PyObject *value, P /* BufferIndexError.proto */ static void __Pyx_RaiseBufferIndexError(int axis); +/* PyLongCompare.proto */ +static CYTHON_INLINE int __Pyx_PyLong_BoolNeObjC(PyObject *op1, PyObject *op2, long intval, long inplace); + +/* PyLongCompare.proto */ +static CYTHON_INLINE int __Pyx_PyLong_BoolEqObjC(PyObject *op1, PyObject *op2, long intval, long inplace); + /* AllocateExtensionType.proto */ static PyObject *__Pyx_AllocateExtensionType(PyTypeObject *t, int is_final); +/* DeallocKeepAlive.proto */ +#if CYTHON_COMPILING_IN_CPYTHON_FREETHREADING +#define __Pyx_DeallocKeepAliveBegin(o) do {\ + _Py_atomic_store_uintptr_relaxed(&(o)->ob_tid, _Py_ThreadId());\ + _Py_atomic_store_uint32_relaxed(&(o)->ob_ref_local, 1);\ + _Py_atomic_store_ssize_relaxed(&(o)->ob_ref_shared, 0);\ + } while (0) +#define __Pyx_DeallocKeepAliveEnd(o)\ + _Py_atomic_store_uint32_relaxed(&(o)->ob_ref_local, 0) +#else +#define __Pyx_DeallocKeepAliveBegin(o) __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1) +#define __Pyx_DeallocKeepAliveEnd(o) __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1) +#endif + /* CallTypeTraverse.proto */ #if !CYTHON_USE_TYPE_SPECS || (!CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x03090000) #define __Pyx_call_type_traverse(o, always_call, visit, arg) 0 @@ -3009,6 +3029,8 @@ static int __pyx_memoryview_thread_locks_used; static PyThread_type_lock __pyx_memoryview_thread_locks[8]; static PyObject *__pyx_f_6scloop_4data_10ripser_lib_converting_cocycles_to_list(std::vector > > , int); /*proto*/ static PyObject *__pyx_f_6scloop_4data_10ripser_lib_converting_birth_death_to_list(std::vector > , int); /*proto*/ +static PyObject *__pyx_f_6scloop_4data_10ripser_lib_converting_birth_death_simplex_to_list(std::vector > > , int); /*proto*/ +static PyObject *__pyx_f_6scloop_4data_10ripser_lib__convert_ripser_results(ripserResults, int); /*proto*/ static int __pyx_array_allocate_buffer(struct __pyx_array_obj *); /*proto*/ static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char const *, char *); /*proto*/ static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo const *); /*proto*/ @@ -3103,7 +3125,8 @@ static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject *__pyx_v___pyx_state); /* proto */ static PyObject *__pyx_pf_6scloop_4data_10ripser_lib_ripser(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_distance_matrix, int __pyx_v_modulus, int __pyx_v_dim_max, float __pyx_v_threshold, PyObject *__pyx_v_do_cocycles); /* proto */ -static PyObject *__pyx_pf_6scloop_4data_10ripser_lib_2get_boundary_matrix(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_distance_matrix, float __pyx_v_threshold); /* proto */ +static PyObject *__pyx_pf_6scloop_4data_10ripser_lib_2ripser_image(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_distance_matrix, PyObject *__pyx_v_sub_indices, int __pyx_v_modulus, int __pyx_v_dim_max, float __pyx_v_threshold, PyObject *__pyx_v_do_subfiltration_cocycles, PyObject *__pyx_v_do_image_cocycles); /* proto */ +static PyObject *__pyx_pf_6scloop_4data_10ripser_lib_4get_boundary_matrix(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_distance_matrix, float __pyx_v_threshold); /* proto */ static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ @@ -3140,9 +3163,9 @@ typedef struct { __Pyx_CachedCFunction __pyx_umethod_PyDict_Type_pop; __Pyx_CachedCFunction __pyx_umethod_PyDict_Type_values; PyObject *__pyx_slice[1]; - PyObject *__pyx_tuple[1]; - PyObject *__pyx_codeobj_tab[2]; - PyObject *__pyx_string_tab[164]; + PyObject *__pyx_tuple[2]; + PyObject *__pyx_codeobj_tab[3]; + PyObject *__pyx_string_tab[184]; PyObject *__pyx_number_tab[5]; /* #### Code section: module_state_contents ### */ /* CommonTypesMetaclass.module_state_decls */ @@ -3225,129 +3248,149 @@ static __pyx_mstatetype * const __pyx_mstate_global = &__pyx_mstate_global_stati #define __pyx_kp_u_strided_and_direct __pyx_string_tab[38] #define __pyx_kp_u_strided_and_direct_or_indirect __pyx_string_tab[39] #define __pyx_kp_u_strided_and_indirect __pyx_string_tab[40] -#define __pyx_kp_u_unable_to_allocate_array_data __pyx_string_tab[41] -#define __pyx_kp_u_unable_to_allocate_shape_and_str __pyx_string_tab[42] -#define __pyx_n_u_ASCII __pyx_string_tab[43] -#define __pyx_n_u_BoundaryMatrixResults __pyx_string_tab[44] -#define __pyx_n_u_Ellipsis __pyx_string_tab[45] -#define __pyx_n_u_I __pyx_string_tab[46] -#define __pyx_n_u_I_2 __pyx_string_tab[47] -#define __pyx_n_u_J __pyx_string_tab[48] -#define __pyx_n_u_J_2 __pyx_string_tab[49] -#define __pyx_n_u_N __pyx_string_tab[50] -#define __pyx_n_u_NEdges __pyx_string_tab[51] -#define __pyx_n_u_Pyx_PyDict_NextRef __pyx_string_tab[52] -#define __pyx_n_u_RipserResults __pyx_string_tab[53] -#define __pyx_n_u_Sequence __pyx_string_tab[54] -#define __pyx_n_u_V __pyx_string_tab[55] -#define __pyx_n_u_V_2 __pyx_string_tab[56] -#define __pyx_n_u_View_MemoryView __pyx_string_tab[57] -#define __pyx_n_u_abc __pyx_string_tab[58] -#define __pyx_n_u_allocate_buffer __pyx_string_tab[59] -#define __pyx_n_u_annotations __pyx_string_tab[60] -#define __pyx_n_u_ascontiguousarray __pyx_string_tab[61] -#define __pyx_n_u_asyncio_coroutines __pyx_string_tab[62] -#define __pyx_n_u_base __pyx_string_tab[63] -#define __pyx_n_u_births_and_deaths_by_dim __pyx_string_tab[64] -#define __pyx_n_u_c __pyx_string_tab[65] -#define __pyx_n_u_class __pyx_string_tab[66] -#define __pyx_n_u_class_getitem __pyx_string_tab[67] -#define __pyx_n_u_cline_in_traceback __pyx_string_tab[68] -#define __pyx_n_u_cocycle_representatives __pyx_string_tab[69] -#define __pyx_n_u_cocycles_by_dim __pyx_string_tab[70] -#define __pyx_n_u_col __pyx_string_tab[71] -#define __pyx_n_u_coo_matrix __pyx_string_tab[72] -#define __pyx_n_u_count __pyx_string_tab[73] -#define __pyx_n_u_data __pyx_string_tab[74] -#define __pyx_n_u_dataclass __pyx_string_tab[75] -#define __pyx_n_u_dataclasses __pyx_string_tab[76] -#define __pyx_n_u_dict __pyx_string_tab[77] -#define __pyx_n_u_dim_max __pyx_string_tab[78] -#define __pyx_n_u_distance_matrix __pyx_string_tab[79] -#define __pyx_n_u_do_cocycles __pyx_string_tab[80] -#define __pyx_n_u_do_cocycles_int __pyx_string_tab[81] -#define __pyx_n_u_doc __pyx_string_tab[82] -#define __pyx_n_u_dtype __pyx_string_tab[83] -#define __pyx_n_u_dtype_is_object __pyx_string_tab[84] -#define __pyx_n_u_encode __pyx_string_tab[85] -#define __pyx_n_u_enumerate __pyx_string_tab[86] -#define __pyx_n_u_error __pyx_string_tab[87] -#define __pyx_n_u_flags __pyx_string_tab[88] -#define __pyx_n_u_float32 __pyx_string_tab[89] -#define __pyx_n_u_format __pyx_string_tab[90] -#define __pyx_n_u_fortran __pyx_string_tab[91] -#define __pyx_n_u_func __pyx_string_tab[92] -#define __pyx_n_u_get_boundary_matrix __pyx_string_tab[93] -#define __pyx_n_u_getstate __pyx_string_tab[94] -#define __pyx_n_u_i __pyx_string_tab[95] -#define __pyx_n_u_id __pyx_string_tab[96] -#define __pyx_n_u_import __pyx_string_tab[97] -#define __pyx_n_u_index __pyx_string_tab[98] -#define __pyx_n_u_int __pyx_string_tab[99] -#define __pyx_n_u_intc __pyx_string_tab[100] -#define __pyx_n_u_is_coroutine __pyx_string_tab[101] -#define __pyx_n_u_items __pyx_string_tab[102] -#define __pyx_n_u_itemsize __pyx_string_tab[103] -#define __pyx_n_u_list __pyx_string_tab[104] -#define __pyx_n_u_main __pyx_string_tab[105] -#define __pyx_n_u_memview __pyx_string_tab[106] -#define __pyx_n_u_metaclass __pyx_string_tab[107] -#define __pyx_n_u_mode __pyx_string_tab[108] -#define __pyx_n_u_module __pyx_string_tab[109] -#define __pyx_n_u_modulus __pyx_string_tab[110] -#define __pyx_n_u_n_triangles __pyx_string_tab[111] -#define __pyx_n_u_name __pyx_string_tab[112] -#define __pyx_n_u_name_2 __pyx_string_tab[113] -#define __pyx_n_u_ndim __pyx_string_tab[114] -#define __pyx_n_u_new __pyx_string_tab[115] -#define __pyx_n_u_nnz __pyx_string_tab[116] -#define __pyx_n_u_np __pyx_string_tab[117] -#define __pyx_n_u_num_edges __pyx_string_tab[118] -#define __pyx_n_u_numpy __pyx_string_tab[119] -#define __pyx_n_u_obj __pyx_string_tab[120] -#define __pyx_n_u_pack __pyx_string_tab[121] -#define __pyx_n_u_persistence_diagrams __pyx_string_tab[122] -#define __pyx_n_u_pop __pyx_string_tab[123] -#define __pyx_n_u_prepare __pyx_string_tab[124] -#define __pyx_n_u_pyx_checksum __pyx_string_tab[125] -#define __pyx_n_u_pyx_state __pyx_string_tab[126] -#define __pyx_n_u_pyx_type __pyx_string_tab[127] -#define __pyx_n_u_pyx_unpickle_Enum __pyx_string_tab[128] -#define __pyx_n_u_pyx_vtable __pyx_string_tab[129] -#define __pyx_n_u_qualname __pyx_string_tab[130] -#define __pyx_n_u_reduce __pyx_string_tab[131] -#define __pyx_n_u_reduce_cython __pyx_string_tab[132] -#define __pyx_n_u_reduce_ex __pyx_string_tab[133] -#define __pyx_n_u_register __pyx_string_tab[134] -#define __pyx_n_u_res __pyx_string_tab[135] -#define __pyx_n_u_return __pyx_string_tab[136] -#define __pyx_n_u_ripser __pyx_string_tab[137] -#define __pyx_n_u_row __pyx_string_tab[138] -#define __pyx_n_u_scipy_sparse __pyx_string_tab[139] -#define __pyx_n_u_scloop_data_ripser_lib __pyx_string_tab[140] -#define __pyx_n_u_set_name __pyx_string_tab[141] -#define __pyx_n_u_setdefault __pyx_string_tab[142] -#define __pyx_n_u_setstate __pyx_string_tab[143] -#define __pyx_n_u_setstate_cython __pyx_string_tab[144] -#define __pyx_n_u_shape __pyx_string_tab[145] -#define __pyx_n_u_size __pyx_string_tab[146] -#define __pyx_n_u_start __pyx_string_tab[147] -#define __pyx_n_u_step __pyx_string_tab[148] -#define __pyx_n_u_stop __pyx_string_tab[149] -#define __pyx_n_u_struct __pyx_string_tab[150] -#define __pyx_n_u_test __pyx_string_tab[151] -#define __pyx_n_u_threshold __pyx_string_tab[152] -#define __pyx_n_u_triangle __pyx_string_tab[153] -#define __pyx_n_u_triangle_diameters __pyx_string_tab[154] -#define __pyx_n_u_triangle_vertices __pyx_string_tab[155] -#define __pyx_n_u_typing __pyx_string_tab[156] -#define __pyx_n_u_unpack __pyx_string_tab[157] -#define __pyx_n_u_update __pyx_string_tab[158] -#define __pyx_n_u_values __pyx_string_tab[159] -#define __pyx_n_u_x __pyx_string_tab[160] -#define __pyx_kp_b_iso88591_r_1O6_1_r_1O6_1_QoWHBa_2Qa_2Qa __pyx_string_tab[161] -#define __pyx_kp_b_iso88591_r_1O6_1_r_1O6_1_QoWHBa_2Qa_2Qa_2 __pyx_string_tab[162] -#define __pyx_n_b_O __pyx_string_tab[163] +#define __pyx_kp_u_sub_indices_contains_a_vertex_ou __pyx_string_tab[41] +#define __pyx_kp_u_sub_indices_must_be_a_non_empty __pyx_string_tab[42] +#define __pyx_kp_u_unable_to_allocate_array_data __pyx_string_tab[43] +#define __pyx_kp_u_unable_to_allocate_shape_and_str __pyx_string_tab[44] +#define __pyx_n_u_ASCII __pyx_string_tab[45] +#define __pyx_n_u_BoundaryMatrixResults __pyx_string_tab[46] +#define __pyx_n_u_Ellipsis __pyx_string_tab[47] +#define __pyx_n_u_I __pyx_string_tab[48] +#define __pyx_n_u_I_2 __pyx_string_tab[49] +#define __pyx_n_u_ImageRipserResults __pyx_string_tab[50] +#define __pyx_n_u_J __pyx_string_tab[51] +#define __pyx_n_u_J_2 __pyx_string_tab[52] +#define __pyx_n_u_N __pyx_string_tab[53] +#define __pyx_n_u_NEdges __pyx_string_tab[54] +#define __pyx_n_u_Pyx_PyDict_NextRef __pyx_string_tab[55] +#define __pyx_n_u_RipserResults __pyx_string_tab[56] +#define __pyx_n_u_Sequence __pyx_string_tab[57] +#define __pyx_n_u_V __pyx_string_tab[58] +#define __pyx_n_u_V_2 __pyx_string_tab[59] +#define __pyx_n_u_View_MemoryView __pyx_string_tab[60] +#define __pyx_n_u_abc __pyx_string_tab[61] +#define __pyx_n_u_allocate_buffer __pyx_string_tab[62] +#define __pyx_n_u_annotate __pyx_string_tab[63] +#define __pyx_n_u_annotations __pyx_string_tab[64] +#define __pyx_n_u_any __pyx_string_tab[65] +#define __pyx_n_u_ascontiguousarray __pyx_string_tab[66] +#define __pyx_n_u_asyncio_coroutines __pyx_string_tab[67] +#define __pyx_n_u_base __pyx_string_tab[68] +#define __pyx_n_u_births_and_deaths_by_dim __pyx_string_tab[69] +#define __pyx_n_u_births_and_deaths_simplex_by_dim __pyx_string_tab[70] +#define __pyx_n_u_c __pyx_string_tab[71] +#define __pyx_n_u_class __pyx_string_tab[72] +#define __pyx_n_u_class_getitem __pyx_string_tab[73] +#define __pyx_n_u_cline_in_traceback __pyx_string_tab[74] +#define __pyx_n_u_cocycle_representatives __pyx_string_tab[75] +#define __pyx_n_u_cocycles_by_dim __pyx_string_tab[76] +#define __pyx_n_u_col __pyx_string_tab[77] +#define __pyx_n_u_coo_matrix __pyx_string_tab[78] +#define __pyx_n_u_count __pyx_string_tab[79] +#define __pyx_n_u_data __pyx_string_tab[80] +#define __pyx_n_u_dataclass __pyx_string_tab[81] +#define __pyx_n_u_dataclasses __pyx_string_tab[82] +#define __pyx_n_u_dict __pyx_string_tab[83] +#define __pyx_n_u_dim_max __pyx_string_tab[84] +#define __pyx_n_u_distance_matrix __pyx_string_tab[85] +#define __pyx_n_u_do_cocycles __pyx_string_tab[86] +#define __pyx_n_u_do_cocycles_int __pyx_string_tab[87] +#define __pyx_n_u_do_image_cocycles __pyx_string_tab[88] +#define __pyx_n_u_do_image_cocycles_int __pyx_string_tab[89] +#define __pyx_n_u_do_subfiltration_cocycles __pyx_string_tab[90] +#define __pyx_n_u_do_subfiltration_cocycles_int __pyx_string_tab[91] +#define __pyx_n_u_doc __pyx_string_tab[92] +#define __pyx_n_u_dtype __pyx_string_tab[93] +#define __pyx_n_u_dtype_is_object __pyx_string_tab[94] +#define __pyx_n_u_encode __pyx_string_tab[95] +#define __pyx_n_u_enumerate __pyx_string_tab[96] +#define __pyx_n_u_error __pyx_string_tab[97] +#define __pyx_n_u_flags __pyx_string_tab[98] +#define __pyx_n_u_float32 __pyx_string_tab[99] +#define __pyx_n_u_format __pyx_string_tab[100] +#define __pyx_n_u_fortran __pyx_string_tab[101] +#define __pyx_n_u_func __pyx_string_tab[102] +#define __pyx_n_u_get_boundary_matrix __pyx_string_tab[103] +#define __pyx_n_u_getstate __pyx_string_tab[104] +#define __pyx_n_u_i __pyx_string_tab[105] +#define __pyx_n_u_id __pyx_string_tab[106] +#define __pyx_n_u_image __pyx_string_tab[107] +#define __pyx_n_u_import __pyx_string_tab[108] +#define __pyx_n_u_index __pyx_string_tab[109] +#define __pyx_n_u_int __pyx_string_tab[110] +#define __pyx_n_u_intc __pyx_string_tab[111] +#define __pyx_n_u_is_coroutine __pyx_string_tab[112] +#define __pyx_n_u_items __pyx_string_tab[113] +#define __pyx_n_u_itemsize __pyx_string_tab[114] +#define __pyx_n_u_list __pyx_string_tab[115] +#define __pyx_n_u_main __pyx_string_tab[116] +#define __pyx_n_u_memview __pyx_string_tab[117] +#define __pyx_n_u_metaclass __pyx_string_tab[118] +#define __pyx_n_u_mode __pyx_string_tab[119] +#define __pyx_n_u_module __pyx_string_tab[120] +#define __pyx_n_u_modulus __pyx_string_tab[121] +#define __pyx_n_u_n_sub_indices __pyx_string_tab[122] +#define __pyx_n_u_n_triangles __pyx_string_tab[123] +#define __pyx_n_u_name __pyx_string_tab[124] +#define __pyx_n_u_name_2 __pyx_string_tab[125] +#define __pyx_n_u_ndim __pyx_string_tab[126] +#define __pyx_n_u_new __pyx_string_tab[127] +#define __pyx_n_u_nnz __pyx_string_tab[128] +#define __pyx_n_u_np __pyx_string_tab[129] +#define __pyx_n_u_num_edges __pyx_string_tab[130] +#define __pyx_n_u_numpy __pyx_string_tab[131] +#define __pyx_n_u_obj __pyx_string_tab[132] +#define __pyx_n_u_pack __pyx_string_tab[133] +#define __pyx_n_u_persistence_diagrams __pyx_string_tab[134] +#define __pyx_n_u_persistence_pair_simplices __pyx_string_tab[135] +#define __pyx_n_u_pop __pyx_string_tab[136] +#define __pyx_n_u_prepare __pyx_string_tab[137] +#define __pyx_n_u_pyx_checksum __pyx_string_tab[138] +#define __pyx_n_u_pyx_state __pyx_string_tab[139] +#define __pyx_n_u_pyx_type __pyx_string_tab[140] +#define __pyx_n_u_pyx_unpickle_Enum __pyx_string_tab[141] +#define __pyx_n_u_pyx_vtable __pyx_string_tab[142] +#define __pyx_n_u_qualname __pyx_string_tab[143] +#define __pyx_n_u_reduce __pyx_string_tab[144] +#define __pyx_n_u_reduce_cython __pyx_string_tab[145] +#define __pyx_n_u_reduce_ex __pyx_string_tab[146] +#define __pyx_n_u_register __pyx_string_tab[147] +#define __pyx_n_u_res __pyx_string_tab[148] +#define __pyx_n_u_return __pyx_string_tab[149] +#define __pyx_n_u_ripser __pyx_string_tab[150] +#define __pyx_n_u_ripser_image __pyx_string_tab[151] +#define __pyx_n_u_row __pyx_string_tab[152] +#define __pyx_n_u_scipy_sparse __pyx_string_tab[153] +#define __pyx_n_u_scloop_data_ripser_lib __pyx_string_tab[154] +#define __pyx_n_u_set_name __pyx_string_tab[155] +#define __pyx_n_u_setdefault __pyx_string_tab[156] +#define __pyx_n_u_setstate __pyx_string_tab[157] +#define __pyx_n_u_setstate_cython __pyx_string_tab[158] +#define __pyx_n_u_shape __pyx_string_tab[159] +#define __pyx_n_u_size __pyx_string_tab[160] +#define __pyx_n_u_start __pyx_string_tab[161] +#define __pyx_n_u_step __pyx_string_tab[162] +#define __pyx_n_u_stop __pyx_string_tab[163] +#define __pyx_n_u_struct __pyx_string_tab[164] +#define __pyx_n_u_sub_indices __pyx_string_tab[165] +#define __pyx_n_u_sub_indices_2 __pyx_string_tab[166] +#define __pyx_n_u_sub_indices_array __pyx_string_tab[167] +#define __pyx_n_u_sub_indices_ptr __pyx_string_tab[168] +#define __pyx_n_u_subfiltration __pyx_string_tab[169] +#define __pyx_n_u_test __pyx_string_tab[170] +#define __pyx_n_u_threshold __pyx_string_tab[171] +#define __pyx_n_u_triangle __pyx_string_tab[172] +#define __pyx_n_u_triangle_diameters __pyx_string_tab[173] +#define __pyx_n_u_triangle_vertices __pyx_string_tab[174] +#define __pyx_n_u_typing __pyx_string_tab[175] +#define __pyx_n_u_unpack __pyx_string_tab[176] +#define __pyx_n_u_update __pyx_string_tab[177] +#define __pyx_n_u_values __pyx_string_tab[178] +#define __pyx_n_u_x __pyx_string_tab[179] +#define __pyx_kp_b_iso88591_A_c_3_7vS_j_r_Q_S_a__F_1_j_r_1O __pyx_string_tab[180] +#define __pyx_kp_b_iso88591_r_1O6_1_r_1O6_1_QoWHBa_2Qa_2Qa __pyx_string_tab[181] +#define __pyx_kp_b_iso88591_r_1O6_1_r_1O6_1_QoWHBa_2Qa_2Qa_2 __pyx_string_tab[182] +#define __pyx_n_b_O __pyx_string_tab[183] #define __pyx_int_0 __pyx_number_tab[0] #define __pyx_int_neg_1 __pyx_number_tab[1] #define __pyx_int_1 __pyx_number_tab[2] @@ -3376,9 +3419,9 @@ static CYTHON_SMALL_CODE int __pyx_m_clear(PyObject *m) { Py_CLEAR(clear_module_state->__pyx_memoryviewslice_type); Py_CLEAR(clear_module_state->__pyx_type___pyx_memoryviewslice); for (int i=0; i<1; ++i) { Py_CLEAR(clear_module_state->__pyx_slice[i]); } - for (int i=0; i<1; ++i) { Py_CLEAR(clear_module_state->__pyx_tuple[i]); } - for (int i=0; i<2; ++i) { Py_CLEAR(clear_module_state->__pyx_codeobj_tab[i]); } - for (int i=0; i<164; ++i) { Py_CLEAR(clear_module_state->__pyx_string_tab[i]); } + for (int i=0; i<2; ++i) { Py_CLEAR(clear_module_state->__pyx_tuple[i]); } + for (int i=0; i<3; ++i) { Py_CLEAR(clear_module_state->__pyx_codeobj_tab[i]); } + for (int i=0; i<184; ++i) { Py_CLEAR(clear_module_state->__pyx_string_tab[i]); } for (int i=0; i<5; ++i) { Py_CLEAR(clear_module_state->__pyx_number_tab[i]); } /* #### Code section: module_state_clear_contents ### */ /* CommonTypesMetaclass.module_state_clear */ @@ -3411,9 +3454,9 @@ static CYTHON_SMALL_CODE int __pyx_m_traverse(PyObject *m, visitproc visit, void Py_VISIT(traverse_module_state->__pyx_memoryviewslice_type); Py_VISIT(traverse_module_state->__pyx_type___pyx_memoryviewslice); for (int i=0; i<1; ++i) { __Pyx_VISIT_CONST(traverse_module_state->__pyx_slice[i]); } - for (int i=0; i<1; ++i) { __Pyx_VISIT_CONST(traverse_module_state->__pyx_tuple[i]); } - for (int i=0; i<2; ++i) { __Pyx_VISIT_CONST(traverse_module_state->__pyx_codeobj_tab[i]); } - for (int i=0; i<164; ++i) { __Pyx_VISIT_CONST(traverse_module_state->__pyx_string_tab[i]); } + for (int i=0; i<2; ++i) { __Pyx_VISIT_CONST(traverse_module_state->__pyx_tuple[i]); } + for (int i=0; i<3; ++i) { __Pyx_VISIT_CONST(traverse_module_state->__pyx_codeobj_tab[i]); } + for (int i=0; i<184; ++i) { __Pyx_VISIT_CONST(traverse_module_state->__pyx_string_tab[i]); } for (int i=0; i<5; ++i) { __Pyx_VISIT_CONST(traverse_module_state->__pyx_number_tab[i]); } /* #### Code section: module_state_traverse_contents ### */ /* CommonTypesMetaclass.module_state_traverse */ @@ -15915,7 +15958,7 @@ static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *__ return __pyx_r; } -/* "scloop/data/ripser_lib.pyx":39 +/* "scloop/data/ripser_lib.pyx":50 * triangle_diameters: list * * cdef list converting_cocycles_to_list(vector[vector[vector[int]]] cocycles_by_dim, int dim): # <<<<<<<<<<<<<< @@ -15958,19 +16001,19 @@ static PyObject *__pyx_f_6scloop_4data_10ripser_lib_converting_cocycles_to_list( int __pyx_clineno = 0; __Pyx_RefNannySetupContext("converting_cocycles_to_list", 0); - /* "scloop/data/ripser_lib.pyx":54 + /* "scloop/data/ripser_lib.pyx":65 * representative cocycle * ''' * cdef list cocycle_representatives = [] # <<<<<<<<<<<<<< * cdef vector[vector[int]]* cocycles = &cocycles_by_dim[dim] * cdef int nc = cocycles.size() */ - __pyx_t_1 = PyList_New(0); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 54, __pyx_L1_error) + __pyx_t_1 = PyList_New(0); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 65, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_v_cocycle_representatives = ((PyObject*)__pyx_t_1); __pyx_t_1 = 0; - /* "scloop/data/ripser_lib.pyx":55 + /* "scloop/data/ripser_lib.pyx":66 * ''' * cdef list cocycle_representatives = [] * cdef vector[vector[int]]* cocycles = &cocycles_by_dim[dim] # <<<<<<<<<<<<<< @@ -15979,7 +16022,7 @@ static PyObject *__pyx_f_6scloop_4data_10ripser_lib_converting_cocycles_to_list( */ __pyx_v_cocycles = (&(__pyx_v_cocycles_by_dim[__pyx_v_dim])); - /* "scloop/data/ripser_lib.pyx":56 + /* "scloop/data/ripser_lib.pyx":67 * cdef list cocycle_representatives = [] * cdef vector[vector[int]]* cocycles = &cocycles_by_dim[dim] * cdef int nc = cocycles.size() # <<<<<<<<<<<<<< @@ -15988,7 +16031,7 @@ static PyObject *__pyx_f_6scloop_4data_10ripser_lib_converting_cocycles_to_list( */ __pyx_v_nc = __pyx_v_cocycles->size(); - /* "scloop/data/ripser_lib.pyx":65 + /* "scloop/data/ripser_lib.pyx":76 * cdef int end_idx * * for i in range(nc): # <<<<<<<<<<<<<< @@ -16000,7 +16043,7 @@ static PyObject *__pyx_f_6scloop_4data_10ripser_lib_converting_cocycles_to_list( for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_i = __pyx_t_4; - /* "scloop/data/ripser_lib.pyx":66 + /* "scloop/data/ripser_lib.pyx":77 * * for i in range(nc): * rep_i = &cocycles[0][i] # <<<<<<<<<<<<<< @@ -16009,7 +16052,7 @@ static PyObject *__pyx_f_6scloop_4data_10ripser_lib_converting_cocycles_to_list( */ __pyx_v_rep_i = (&((__pyx_v_cocycles[0])[__pyx_v_i])); - /* "scloop/data/ripser_lib.pyx":67 + /* "scloop/data/ripser_lib.pyx":78 * for i in range(nc): * rep_i = &cocycles[0][i] * chunk_size = dim + 2 # <<<<<<<<<<<<<< @@ -16018,7 +16061,7 @@ static PyObject *__pyx_f_6scloop_4data_10ripser_lib_converting_cocycles_to_list( */ __pyx_v_chunk_size = (__pyx_v_dim + 2); - /* "scloop/data/ripser_lib.pyx":68 + /* "scloop/data/ripser_lib.pyx":79 * rep_i = &cocycles[0][i] * chunk_size = dim + 2 * n_simplices = rep_i.size() // chunk_size # <<<<<<<<<<<<<< @@ -16028,23 +16071,23 @@ static PyObject *__pyx_f_6scloop_4data_10ripser_lib_converting_cocycles_to_list( __pyx_t_5 = __pyx_v_rep_i->size(); if (unlikely(__pyx_v_chunk_size == 0)) { PyErr_SetString(PyExc_ZeroDivisionError, "integer division or modulo by zero"); - __PYX_ERR(0, 68, __pyx_L1_error) + __PYX_ERR(0, 79, __pyx_L1_error) } __pyx_v_n_simplices = (__pyx_t_5 / __pyx_v_chunk_size); - /* "scloop/data/ripser_lib.pyx":69 + /* "scloop/data/ripser_lib.pyx":80 * chunk_size = dim + 2 * n_simplices = rep_i.size() // chunk_size * cocycle_rep_members = [] # <<<<<<<<<<<<<< * for j in range(n_simplices): * simplex = [] */ - __pyx_t_1 = PyList_New(0); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 69, __pyx_L1_error) + __pyx_t_1 = PyList_New(0); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 80, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_XDECREF_SET(__pyx_v_cocycle_rep_members, ((PyObject*)__pyx_t_1)); __pyx_t_1 = 0; - /* "scloop/data/ripser_lib.pyx":70 + /* "scloop/data/ripser_lib.pyx":81 * n_simplices = rep_i.size() // chunk_size * cocycle_rep_members = [] * for j in range(n_simplices): # <<<<<<<<<<<<<< @@ -16052,7 +16095,7 @@ static PyObject *__pyx_f_6scloop_4data_10ripser_lib_converting_cocycles_to_list( * start_idx = j * chunk_size */ __pyx_t_6 = NULL; - __pyx_t_7 = __Pyx_PyLong_From_int(__pyx_v_n_simplices); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 70, __pyx_L1_error) + __pyx_t_7 = __Pyx_PyLong_From_int(__pyx_v_n_simplices); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 81, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_8 = 1; { @@ -16060,12 +16103,12 @@ static PyObject 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PyIter_Next : __Pyx_PyObject_GetIterNextFunc(__pyx_t_7); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 70, __pyx_L1_error) + __pyx_t_9 = (CYTHON_COMPILING_IN_LIMITED_API) ? 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__Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - __pyx_t_10 = __Pyx_PyLong_As_int(__pyx_t_6); if (unlikely((__pyx_t_10 == (int)-1) && PyErr_Occurred())) __PYX_ERR(0, 72, __pyx_L1_error) + __pyx_t_10 = __Pyx_PyLong_As_int(__pyx_t_6); if (unlikely((__pyx_t_10 == (int)-1) && PyErr_Occurred())) __PYX_ERR(0, 83, __pyx_L1_error) __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_v_start_idx = __pyx_t_10; - /* "scloop/data/ripser_lib.pyx":73 + /* "scloop/data/ripser_lib.pyx":84 * simplex = [] * start_idx = j * chunk_size * end_idx = start_idx + chunk_size - 1 # <<<<<<<<<<<<<< @@ -16120,7 +16163,7 @@ static PyObject *__pyx_f_6scloop_4data_10ripser_lib_converting_cocycles_to_list( */ __pyx_v_end_idx = ((__pyx_v_start_idx + __pyx_v_chunk_size) - 1); - /* "scloop/data/ripser_lib.pyx":74 + /* "scloop/data/ripser_lib.pyx":85 * start_idx = j * chunk_size * end_idx = start_idx + chunk_size - 1 * for k in range(start_idx, end_idx): # <<<<<<<<<<<<<< @@ -16132,7 +16175,7 @@ static PyObject *__pyx_f_6scloop_4data_10ripser_lib_converting_cocycles_to_list( for (__pyx_t_12 = __pyx_v_start_idx; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) { __pyx_v_k = __pyx_t_12; - /* "scloop/data/ripser_lib.pyx":75 + /* "scloop/data/ripser_lib.pyx":86 * end_idx = start_idx + chunk_size - 1 * for k in range(start_idx, end_idx): * simplex.append(int(rep_i[0][k])) # <<<<<<<<<<<<<< @@ -16140,7 +16183,7 @@ static PyObject *__pyx_f_6scloop_4data_10ripser_lib_converting_cocycles_to_list( * cocycle_representatives.append(cocycle_rep_members) */ __pyx_t_1 = NULL; - __pyx_t_13 = __Pyx_PyLong_From_int(((__pyx_v_rep_i[0])[__pyx_v_k])); if (unlikely(!__pyx_t_13)) __PYX_ERR(0, 75, __pyx_L1_error) + __pyx_t_13 = __Pyx_PyLong_From_int(((__pyx_v_rep_i[0])[__pyx_v_k])); if (unlikely(!__pyx_t_13)) __PYX_ERR(0, 86, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_13); __pyx_t_8 = 1; { @@ -16148,14 +16191,14 @@ static PyObject *__pyx_f_6scloop_4data_10ripser_lib_converting_cocycles_to_list( __pyx_t_6 = __Pyx_PyObject_FastCall((PyObject*)(&PyLong_Type), __pyx_callargs+__pyx_t_8, (2-__pyx_t_8) | (__pyx_t_8*__Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET)); 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- /* "scloop/data/ripser_lib.pyx":77 + /* "scloop/data/ripser_lib.pyx":88 * simplex.append(int(rep_i[0][k])) * cocycle_rep_members.append([simplex, int(rep_i[0][end_idx])]) * cocycle_representatives.append(cocycle_rep_members) # <<<<<<<<<<<<<< * return cocycle_representatives * */ - __pyx_t_14 = __Pyx_PyList_Append(__pyx_v_cocycle_representatives, __pyx_v_cocycle_rep_members); if (unlikely(__pyx_t_14 == ((int)-1))) __PYX_ERR(0, 77, __pyx_L1_error) + __pyx_t_14 = __Pyx_PyList_Append(__pyx_v_cocycle_representatives, __pyx_v_cocycle_rep_members); if (unlikely(__pyx_t_14 == ((int)-1))) __PYX_ERR(0, 88, __pyx_L1_error) } - /* "scloop/data/ripser_lib.pyx":78 + /* "scloop/data/ripser_lib.pyx":89 * cocycle_rep_members.append([simplex, int(rep_i[0][end_idx])]) * cocycle_representatives.append(cocycle_rep_members) * return cocycle_representatives # <<<<<<<<<<<<<< @@ -16217,7 +16260,7 @@ static PyObject *__pyx_f_6scloop_4data_10ripser_lib_converting_cocycles_to_list( __pyx_r = __pyx_v_cocycle_representatives; 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if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 129, __pyx_L1_error) + __pyx_t_4 = __Pyx_PyLong_From_int(__pyx_v_NEdges); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 142, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_7 = 1; #if CYTHON_UNPACK_METHODS @@ -16988,19 +17056,19 @@ static PyObject *__pyx_pf_6scloop_4data_10ripser_lib_ripser(CYTHON_UNUSED PyObje } #endif { - PyObject *__pyx_callargs[4] = {__pyx_t_1, __pyx_v_persistence_diagrams, __pyx_v_cocycle_representatives, __pyx_t_4}; - __pyx_t_2 = __Pyx_PyObject_FastCall((PyObject*)__pyx_t_5, __pyx_callargs+__pyx_t_7, (4-__pyx_t_7) | (__pyx_t_7*__Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET)); + PyObject *__pyx_callargs[5] = {__pyx_t_1, __pyx_v_persistence_diagrams, __pyx_v_persistence_pair_simplices, __pyx_v_cocycle_representatives, __pyx_t_4}; + __pyx_t_2 = __Pyx_PyObject_FastCall((PyObject*)__pyx_t_5, __pyx_callargs+__pyx_t_7, (5-__pyx_t_7) | (__pyx_t_7*__Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET)); __Pyx_XDECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_4); 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/*proto*/ -PyDoc_STRVAR(__pyx_doc_6scloop_4data_10ripser_lib_2get_boundary_matrix, "Compute dimension 2 boundary matrix without redundant columns.\n\n For each tetrahedron, only 3 of the 4 triangular faces are included,\n as the 4th can be derived from the other 3 (in Z/2 homology).\n\n Args:\n distance_matrix: Sparse COO format distance matrix\n threshold: Maximum diameter for simplices to include\n "); -static PyMethodDef __pyx_mdef_6scloop_4data_10ripser_lib_3get_boundary_matrix = {"get_boundary_matrix", (PyCFunction)(void(*)(void))(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw_6scloop_4data_10ripser_lib_3get_boundary_matrix, __Pyx_METH_FASTCALL|METH_KEYWORDS, __pyx_doc_6scloop_4data_10ripser_lib_2get_boundary_matrix}; -static PyObject *__pyx_pw_6scloop_4data_10ripser_lib_3get_boundary_matrix(PyObject *__pyx_self, +PyDoc_STRVAR(__pyx_doc_6scloop_4data_10ripser_lib_2ripser_image, "Compute PH of an induced subfiltration and its image in the ambient complex."); +static PyMethodDef __pyx_mdef_6scloop_4data_10ripser_lib_3ripser_image = {"ripser_image", (PyCFunction)(void(*)(void))(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw_6scloop_4data_10ripser_lib_3ripser_image, __Pyx_METH_FASTCALL|METH_KEYWORDS, __pyx_doc_6scloop_4data_10ripser_lib_2ripser_image}; 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": Buffer view does not expose stridesCan only create } } Py_XDECREF(data); - for (Py_ssize_t i = 0; i < 164; i++) { + for (Py_ssize_t i = 0; i < 184; i++) { if (unlikely(PyObject_Hash(stringtab[i]) == -1)) { __PYX_ERR(0, 1, __pyx_L1_error) } } #if CYTHON_IMMORTAL_CONSTANTS { - PyObject **table = stringtab + 161; - for (Py_ssize_t i=0; i<3; ++i) { - #if CYTHON_COMPILING_IN_CPYTHON_FREETHREADING + PyObject **table = stringtab + 180; + for (Py_ssize_t i=0; i<4; ++i) { + #if PY_VERSION_HEX >= 0x030F0000 + PyUnstable_SetImmortal(table[i]); + #elif CYTHON_COMPILING_IN_CPYTHON_FREETHREADING + if ((PY_SSIZE_T_MAX <= _Py_IMMORTAL_REFCNT_LOCAL)) break; #if PY_VERSION_HEX < 0x030E0000 if (_Py_IsOwnedByCurrentThread(table[i]) && Py_REFCNT(table[i]) == 1) #else if (PyUnstable_Object_IsUniquelyReferenced(table[i])) #endif { - Py_SET_REFCNT(table[i], _Py_IMMORTAL_REFCNT_LOCAL); + Py_SET_REFCNT(table[i], ((Py_ssize_t)_Py_IMMORTAL_REFCNT_LOCAL + 1)); } #else + if ((PY_SSIZE_T_MAX < _Py_IMMORTAL_INITIAL_REFCNT)) break; Py_SET_REFCNT(table[i], _Py_IMMORTAL_INITIAL_REFCNT); #endif } @@ -19964,16 +21558,20 @@ const char* const bytes = ": Buffer view does not expose stridesCan only create { PyObject **table = __pyx_mstate->__pyx_number_tab; for (Py_ssize_t i=0; i<5; ++i) { - #if CYTHON_COMPILING_IN_CPYTHON_FREETHREADING + #if PY_VERSION_HEX >= 0x030F0000 + PyUnstable_SetImmortal(table[i]); + #elif CYTHON_COMPILING_IN_CPYTHON_FREETHREADING + if ((PY_SSIZE_T_MAX <= _Py_IMMORTAL_REFCNT_LOCAL)) break; #if PY_VERSION_HEX < 0x030E0000 if (_Py_IsOwnedByCurrentThread(table[i]) && Py_REFCNT(table[i]) == 1) #else if (PyUnstable_Object_IsUniquelyReferenced(table[i])) #endif { - Py_SET_REFCNT(table[i], _Py_IMMORTAL_REFCNT_LOCAL); + Py_SET_REFCNT(table[i], ((Py_ssize_t)_Py_IMMORTAL_REFCNT_LOCAL + 1)); } #else + if ((PY_SSIZE_T_MAX < _Py_IMMORTAL_INITIAL_REFCNT)) break; Py_SET_REFCNT(table[i], _Py_IMMORTAL_INITIAL_REFCNT); #endif } @@ -20007,14 +21605,19 @@ static int __Pyx_CreateCodeObjects(__pyx_mstatetype *__pyx_mstate) { PyObject* tuple_dedup_map = PyDict_New(); if (unlikely(!tuple_dedup_map)) return -1; { - const __Pyx_PyCode_New_function_description descr = {5, 0, 0, 19, (unsigned int)(CO_OPTIMIZED|CO_NEWLOCALS), 97}; - PyObject* const varnames[] = {__pyx_mstate->__pyx_n_u_distance_matrix, __pyx_mstate->__pyx_n_u_modulus, __pyx_mstate->__pyx_n_u_dim_max, __pyx_mstate->__pyx_n_u_threshold, __pyx_mstate->__pyx_n_u_do_cocycles, __pyx_mstate->__pyx_n_u_I, __pyx_mstate->__pyx_n_u_J, __pyx_mstate->__pyx_n_u_V, __pyx_mstate->__pyx_n_u_I_2, __pyx_mstate->__pyx_n_u_J_2, __pyx_mstate->__pyx_n_u_V_2, __pyx_mstate->__pyx_n_u_NEdges, __pyx_mstate->__pyx_n_u_N, __pyx_mstate->__pyx_n_u_do_cocycles_int, __pyx_mstate->__pyx_n_u_res, __pyx_mstate->__pyx_n_u_persistence_diagrams, __pyx_mstate->__pyx_n_u_cocycle_representatives, __pyx_mstate->__pyx_n_u_i, __pyx_mstate->__pyx_n_u_i}; + const __Pyx_PyCode_New_function_description descr = {5, 0, 0, 21, (unsigned int)(CO_OPTIMIZED|CO_NEWLOCALS), 108}; + PyObject* const varnames[] = {__pyx_mstate->__pyx_n_u_distance_matrix, __pyx_mstate->__pyx_n_u_modulus, __pyx_mstate->__pyx_n_u_dim_max, __pyx_mstate->__pyx_n_u_threshold, __pyx_mstate->__pyx_n_u_do_cocycles, __pyx_mstate->__pyx_n_u_I, __pyx_mstate->__pyx_n_u_J, __pyx_mstate->__pyx_n_u_V, __pyx_mstate->__pyx_n_u_I_2, __pyx_mstate->__pyx_n_u_J_2, __pyx_mstate->__pyx_n_u_V_2, __pyx_mstate->__pyx_n_u_NEdges, __pyx_mstate->__pyx_n_u_N, __pyx_mstate->__pyx_n_u_do_cocycles_int, __pyx_mstate->__pyx_n_u_res, __pyx_mstate->__pyx_n_u_persistence_diagrams, __pyx_mstate->__pyx_n_u_persistence_pair_simplices, __pyx_mstate->__pyx_n_u_cocycle_representatives, __pyx_mstate->__pyx_n_u_i, __pyx_mstate->__pyx_n_u_i, __pyx_mstate->__pyx_n_u_i}; __pyx_mstate_global->__pyx_codeobj_tab[0] = __Pyx_PyCode_New(descr, varnames, __pyx_mstate->__pyx_kp_u_src_scloop_data_ripser_lib_pyx, __pyx_mstate->__pyx_n_u_ripser, __pyx_mstate->__pyx_kp_b_iso88591_r_1O6_1_r_1O6_1_QoWHBa_2Qa_2Qa, tuple_dedup_map); if (unlikely(!__pyx_mstate_global->__pyx_codeobj_tab[0])) goto bad; } { - const __Pyx_PyCode_New_function_description descr = {2, 0, 0, 16, (unsigned int)(CO_OPTIMIZED|CO_NEWLOCALS), 132}; + const __Pyx_PyCode_New_function_description descr = {7, 0, 0, 22, (unsigned int)(CO_OPTIMIZED|CO_NEWLOCALS), 145}; + PyObject* const varnames[] = {__pyx_mstate->__pyx_n_u_distance_matrix, __pyx_mstate->__pyx_n_u_sub_indices, __pyx_mstate->__pyx_n_u_modulus, __pyx_mstate->__pyx_n_u_dim_max, __pyx_mstate->__pyx_n_u_threshold, __pyx_mstate->__pyx_n_u_do_subfiltration_cocycles, __pyx_mstate->__pyx_n_u_do_image_cocycles, __pyx_mstate->__pyx_n_u_sub_indices_array, __pyx_mstate->__pyx_n_u_I, __pyx_mstate->__pyx_n_u_J, __pyx_mstate->__pyx_n_u_V, __pyx_mstate->__pyx_n_u_sub_indices_2, __pyx_mstate->__pyx_n_u_I_2, __pyx_mstate->__pyx_n_u_J_2, __pyx_mstate->__pyx_n_u_V_2, __pyx_mstate->__pyx_n_u_sub_indices_ptr, __pyx_mstate->__pyx_n_u_NEdges, __pyx_mstate->__pyx_n_u_N, __pyx_mstate->__pyx_n_u_n_sub_indices, __pyx_mstate->__pyx_n_u_do_subfiltration_cocycles_int, __pyx_mstate->__pyx_n_u_do_image_cocycles_int, __pyx_mstate->__pyx_n_u_res}; + __pyx_mstate_global->__pyx_codeobj_tab[1] = __Pyx_PyCode_New(descr, varnames, __pyx_mstate->__pyx_kp_u_src_scloop_data_ripser_lib_pyx, __pyx_mstate->__pyx_n_u_ripser_image, __pyx_mstate->__pyx_kp_b_iso88591_A_c_3_7vS_j_r_Q_S_a__F_1_j_r_1O, tuple_dedup_map); if (unlikely(!__pyx_mstate_global->__pyx_codeobj_tab[1])) goto bad; + } + { + const __Pyx_PyCode_New_function_description descr = {2, 0, 0, 16, (unsigned int)(CO_OPTIMIZED|CO_NEWLOCALS), 193}; PyObject* const varnames[] = {__pyx_mstate->__pyx_n_u_distance_matrix, __pyx_mstate->__pyx_n_u_threshold, __pyx_mstate->__pyx_n_u_I, __pyx_mstate->__pyx_n_u_J, __pyx_mstate->__pyx_n_u_V, __pyx_mstate->__pyx_n_u_I_2, __pyx_mstate->__pyx_n_u_J_2, __pyx_mstate->__pyx_n_u_V_2, __pyx_mstate->__pyx_n_u_NEdges, __pyx_mstate->__pyx_n_u_N, __pyx_mstate->__pyx_n_u_res, __pyx_mstate->__pyx_n_u_triangle_vertices, __pyx_mstate->__pyx_n_u_triangle_diameters, __pyx_mstate->__pyx_n_u_n_triangles, __pyx_mstate->__pyx_n_u_i, __pyx_mstate->__pyx_n_u_triangle}; - __pyx_mstate_global->__pyx_codeobj_tab[1] = __Pyx_PyCode_New(descr, varnames, __pyx_mstate->__pyx_kp_u_src_scloop_data_ripser_lib_pyx, __pyx_mstate->__pyx_n_u_get_boundary_matrix, __pyx_mstate->__pyx_kp_b_iso88591_r_1O6_1_r_1O6_1_QoWHBa_2Qa_2Qa_2, tuple_dedup_map); if (unlikely(!__pyx_mstate_global->__pyx_codeobj_tab[1])) goto bad; + __pyx_mstate_global->__pyx_codeobj_tab[2] = __Pyx_PyCode_New(descr, varnames, __pyx_mstate->__pyx_kp_u_src_scloop_data_ripser_lib_pyx, __pyx_mstate->__pyx_n_u_get_boundary_matrix, __pyx_mstate->__pyx_kp_b_iso88591_r_1O6_1_r_1O6_1_QoWHBa_2Qa_2Qa_2, tuple_dedup_map); if (unlikely(!__pyx_mstate_global->__pyx_codeobj_tab[2])) goto bad; } Py_DECREF(tuple_dedup_map); return 0; @@ -23043,6 +24646,128 @@ static void __Pyx_RaiseBufferIndexError(int axis) { "Out of bounds on buffer access (axis %d)", axis); } +/* PyLongCompare */ +static CYTHON_INLINE int __Pyx_PyLong_BoolNeObjC(PyObject *op1, PyObject *op2, long intval, long inplace) { + CYTHON_MAYBE_UNUSED_VAR(intval); + CYTHON_UNUSED_VAR(inplace); + if (op1 == op2) { + return 0; + } + #if CYTHON_USE_PYLONG_INTERNALS + if (likely(PyLong_CheckExact(op1))) { + int unequal; + unsigned long uintval; + Py_ssize_t size = __Pyx_PyLong_DigitCount(op1); + const digit* digits = __Pyx_PyLong_Digits(op1); + if (intval == 0) { + return (__Pyx_PyLong_IsZero(op1) != 1); + } else if (intval < 0) { + if (__Pyx_PyLong_IsNonNeg(op1)) + return 1; + intval = -intval; + } else { + if (__Pyx_PyLong_IsNeg(op1)) + return 1; + } + uintval = (unsigned long) intval; +#if PyLong_SHIFT * 4 < SIZEOF_LONG*8 + if (uintval >> (PyLong_SHIFT * 4)) { + unequal = (size != 5) || (digits[0] != (uintval & (unsigned long) PyLong_MASK)) + | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[3] != ((uintval >> (3 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[4] != ((uintval >> (4 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)); + } else +#endif +#if PyLong_SHIFT * 3 < SIZEOF_LONG*8 + if (uintval >> (PyLong_SHIFT * 3)) { + unequal = (size != 4) || (digits[0] != (uintval & (unsigned long) PyLong_MASK)) + | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[3] != ((uintval >> (3 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)); + } else +#endif +#if PyLong_SHIFT * 2 < SIZEOF_LONG*8 + if (uintval >> (PyLong_SHIFT * 2)) { + unequal = (size != 3) || (digits[0] != (uintval & (unsigned long) PyLong_MASK)) + | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)); + } else +#endif +#if PyLong_SHIFT * 1 < SIZEOF_LONG*8 + if (uintval >> (PyLong_SHIFT * 1)) { + unequal = (size != 2) || (digits[0] != (uintval & (unsigned long) PyLong_MASK)) + | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)); + } else +#endif + unequal = (size != 1) || (((unsigned long) digits[0]) != (uintval & (unsigned long) PyLong_MASK)); + return (unequal != 0); + } + #endif + if (PyFloat_CheckExact(op1)) { + const long b = intval; + double a = __Pyx_PyFloat_AS_DOUBLE(op1); + return ((double)a != (double)b); + } + return __Pyx_PyObject_IsTrueAndDecref( + PyObject_RichCompare(op1, op2, Py_NE)); +} + +/* PyLongCompare */ +static CYTHON_INLINE int __Pyx_PyLong_BoolEqObjC(PyObject *op1, PyObject *op2, long intval, long inplace) { + CYTHON_MAYBE_UNUSED_VAR(intval); + CYTHON_UNUSED_VAR(inplace); + if (op1 == op2) { + return 1; + } + #if CYTHON_USE_PYLONG_INTERNALS + if (likely(PyLong_CheckExact(op1))) { + int unequal; + unsigned long uintval; + Py_ssize_t size = __Pyx_PyLong_DigitCount(op1); + const digit* digits = __Pyx_PyLong_Digits(op1); + if (intval == 0) { + return (__Pyx_PyLong_IsZero(op1) == 1); + } else if (intval < 0) { + if (__Pyx_PyLong_IsNonNeg(op1)) + return 0; + intval = -intval; + } else { + if (__Pyx_PyLong_IsNeg(op1)) + return 0; + } + uintval = (unsigned long) intval; +#if PyLong_SHIFT * 4 < SIZEOF_LONG*8 + if (uintval >> (PyLong_SHIFT * 4)) { + unequal = (size != 5) || (digits[0] != (uintval & (unsigned long) PyLong_MASK)) + | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[3] != ((uintval >> (3 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[4] != ((uintval >> (4 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)); + } else +#endif +#if PyLong_SHIFT * 3 < SIZEOF_LONG*8 + if (uintval >> (PyLong_SHIFT * 3)) { + unequal = (size != 4) || (digits[0] != (uintval & (unsigned long) PyLong_MASK)) + | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[3] != ((uintval >> (3 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)); + } else +#endif +#if PyLong_SHIFT * 2 < SIZEOF_LONG*8 + if (uintval >> (PyLong_SHIFT * 2)) { + unequal = (size != 3) || (digits[0] != (uintval & (unsigned long) PyLong_MASK)) + | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)); + } else +#endif +#if PyLong_SHIFT * 1 < SIZEOF_LONG*8 + if (uintval >> (PyLong_SHIFT * 1)) { + unequal = (size != 2) || (digits[0] != (uintval & (unsigned long) PyLong_MASK)) + | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)); + } else +#endif + unequal = (size != 1) || (((unsigned long) digits[0]) != (uintval & (unsigned long) PyLong_MASK)); + return (unequal == 0); + } + #endif + if (PyFloat_CheckExact(op1)) { + const long b = intval; + double a = __Pyx_PyFloat_AS_DOUBLE(op1); + return ((double)a == (double)b); + } + return __Pyx_PyObject_IsTrueAndDecref( + PyObject_RichCompare(op1, op2, Py_EQ)); +} + /* AllocateExtensionType */ static PyObject *__Pyx_AllocateExtensionType(PyTypeObject *t, int is_final) { if (is_final || likely(!__Pyx_PyType_HasFeature(t, Py_TPFLAGS_IS_ABSTRACT))) { @@ -24585,6 +26310,7 @@ __Pyx_CyFunction_get_kwdefaults(__pyx_CyFunctionObject *op, void *context) { __Pyx_END_CRITICAL_SECTION(); return result; } +static int __Pyx_CyFunction_set_annotate_in_dict_if_exists(PyObject *op_in, PyObject *value); static int __Pyx_CyFunction_set_annotations(__pyx_CyFunctionObject *op, PyObject* value, void *context) { CYTHON_UNUSED_VAR(context); @@ -24599,8 +26325,54 @@ __Pyx_CyFunction_set_annotations(__pyx_CyFunctionObject *op, PyObject* value, vo __Pyx_BEGIN_CRITICAL_SECTION(op); __Pyx_Py_XDECREF_SET(op->func_annotations, value); __Pyx_END_CRITICAL_SECTION(); + if (unlikely(__Pyx_CyFunction_set_annotate_in_dict_if_exists((PyObject*) op, Py_None) < 0)) return -1; return 0; } +static int +__Pyx_CyFunction_get_dict_if_exists(PyObject *op_in, PyObject **dict) { + /* Return 1 if the function dict exists, 0 otherwise. This cannot fail: + * _PyObject_GetDictPtr() may clear errors internally, but never reports them. */ +#if CYTHON_COMPILING_IN_PYPY + *dict = PyObject_GenericGetDict(op_in, NULL); +#elif CYTHON_COMPILING_IN_LIMITED_API || PY_VERSION_HEX < 0x030C0000 + *dict = ((__pyx_CyFunctionObject*) op_in)->func_dict; +#else + PyObject **dictptr = _PyObject_GetDictPtr(op_in); + *dict = likely(dictptr) ? *dictptr : NULL; +#endif + return *dict ? 1 : 0; +} +static int +__Pyx_CyFunction_get_annotate_from_dict_if_exists(PyObject *op_in, PyObject **annotate) { + PyObject *dict; + int dict_found; + *annotate = NULL; + dict_found = __Pyx_CyFunction_get_dict_if_exists(op_in, &dict); + if (!dict_found) return 0; + return __Pyx_PyDict_GetItemRef(dict, __pyx_mstate_global->__pyx_n_u_annotate, annotate); +} +static int +__Pyx_CyFunction_set_annotate_in_dict_if_exists(PyObject *op_in, PyObject *value) { + PyObject *dict; + int dict_found; + dict_found = __Pyx_CyFunction_get_dict_if_exists(op_in, &dict); + if (!dict_found) return 0; + return PyDict_SetItem(dict, __pyx_mstate_global->__pyx_n_u_annotate, value); +} +static int +__Pyx_CyFunction_set_annotate_in_dict(PyObject *op_in, PyObject *value) { + PyObject *dict; + int result; +#if CYTHON_COMPILING_IN_LIMITED_API && __PYX_LIMITED_VERSION_HEX < 0x030A0000 + dict = __Pyx_CyFunction_get_dict((__pyx_CyFunctionObject*) op_in, NULL); +#else + dict = PyObject_GenericGetDict(op_in, NULL); +#endif + if (unlikely(!dict)) return -1; + result = PyDict_SetItem(dict, __pyx_mstate_global->__pyx_n_u_annotate, value); + Py_DECREF(dict); + return result; +} static PyObject * __Pyx_CyFunction_get_annotations_locked(__pyx_CyFunctionObject *op) { PyObject* result = op->func_annotations; @@ -24613,14 +26385,88 @@ __Pyx_CyFunction_get_annotations_locked(__pyx_CyFunctionObject *op) { return result; } static PyObject * -__Pyx_CyFunction_get_annotations(__pyx_CyFunctionObject *op, void *context) { - PyObject *result; +__Pyx_CyFunction_get_annotations(PyObject *op_in, void *context) { + PyObject *annotate = NULL; + PyObject *result = NULL; + __pyx_CyFunctionObject *op = (__pyx_CyFunctionObject*) op_in; CYTHON_UNUSED_VAR(context); - __Pyx_BEGIN_CRITICAL_SECTION(op); - result = __Pyx_CyFunction_get_annotations_locked(op); + __Pyx_BEGIN_CRITICAL_SECTION(op_in); + result = __Pyx_XNewRef(op->func_annotations); + __Pyx_END_CRITICAL_SECTION(); + if (result) return result; + if (unlikely(__Pyx_CyFunction_get_annotate_from_dict_if_exists(op_in, &annotate) < 0)) return NULL; + if (!annotate || annotate == Py_None) { + Py_XDECREF(annotate); + __Pyx_BEGIN_CRITICAL_SECTION(op_in); + result = __Pyx_CyFunction_get_annotations_locked(op); + __Pyx_END_CRITICAL_SECTION(); + return result; + } + PyObject *format = PyLong_FromLong(1L); // annotationlib.Format.VALUE + if (likely(format)) { + result = __Pyx_PyObject_CallOneArg(annotate, format); + Py_DECREF(format); + } + Py_DECREF(annotate); + if (unlikely(!result)) return NULL; + if (unlikely(!PyDict_Check(result))) { + PyErr_SetString(PyExc_TypeError, "__annotate__ must return a dict"); + Py_DECREF(result); + return NULL; + } + __Pyx_BEGIN_CRITICAL_SECTION(op_in); + __Pyx_Py_XDECREF_SET(op->func_annotations, __Pyx_NewRef(result)); __Pyx_END_CRITICAL_SECTION(); return result; } +static PyObject *__Pyx_CyFunction_annotate_impl(PyObject *self, PyObject *args) { + CYTHON_UNUSED_VAR(args); + if (unlikely(!self)) { + PyErr_SetString(PyExc_SystemError, "cython __annotate__ called without 'self' argument"); + } + Py_XINCREF(self); + return self; +} +static PyMethodDef __Pyx_CyFunction_annotate_method = { + "__annotate__", + (PyCFunction)(void (*)(void))__Pyx_CyFunction_annotate_impl, + METH_VARARGS, + "Placeholder __annotate__ function to allow 'functools.wraps' to work " + "on Cython functions." +}; +static PyObject * +__Pyx_CyFunction_get_annotate(PyObject *op_in, void *context) { + PyObject *annotate = NULL; + CYTHON_UNUSED_VAR(context); + if (unlikely(__Pyx_CyFunction_get_annotate_from_dict_if_exists(op_in, &annotate) < 0)) return NULL; + if (annotate) return annotate; + PyObject *annotations = __Pyx_CyFunction_get_annotations(op_in, NULL); + if (unlikely(!annotations)) return NULL; + PyObject *method = PyCFunction_New( + &__Pyx_CyFunction_annotate_method, + annotations); + Py_DECREF(annotations); + return method; +} +static int +__Pyx_CyFunction_set_annotate(PyObject *op_in, PyObject* value, void *context) { + CYTHON_UNUSED_VAR(context); + if (value == NULL) { + PyErr_SetString(PyExc_TypeError, "__annotate__ cannot be deleted"); + return -1; + } + if (unlikely(value != Py_None && !PyCallable_Check(value))) { + PyErr_SetString(PyExc_TypeError, "__annotate__ must be callable or None"); + return -1; + } + if (value != Py_None) { + __pyx_CyFunctionObject *op = (__pyx_CyFunctionObject*) op_in; + __Pyx_BEGIN_CRITICAL_SECTION(op_in); + Py_CLEAR(op->func_annotations); + __Pyx_END_CRITICAL_SECTION(); + } + return __Pyx_CyFunction_set_annotate_in_dict(op_in, value); +} static PyObject * __Pyx_CyFunction_get_is_coroutine_value(__pyx_CyFunctionObject *op) { int is_coroutine = op->flags & __Pyx_CYFUNCTION_COROUTINE; @@ -24735,6 +26581,7 @@ static PyGetSetDef __pyx_CyFunction_getsets[] = { {"__defaults__", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0}, {"__kwdefaults__", (getter)__Pyx_CyFunction_get_kwdefaults, (setter)__Pyx_CyFunction_set_kwdefaults, 0, 0}, {"__annotations__", (getter)__Pyx_CyFunction_get_annotations, (setter)__Pyx_CyFunction_set_annotations, 0, 0}, + {"__annotate__", (getter)__Pyx_CyFunction_get_annotate, (setter)__Pyx_CyFunction_set_annotate, 0, 0}, {"_is_coroutine", (getter)__Pyx_CyFunction_get_is_coroutine, 0, 0, 0}, #if CYTHON_COMPILING_IN_LIMITED_API {"__module__", (getter)__Pyx_CyFunction_get_module, (setter)__Pyx_CyFunction_set_module, 0, 0}, diff --git a/src/scloop/data/ripser_lib.pyx b/src/scloop/data/ripser_lib.pyx index 8eeb545..189e8e9 100644 --- a/src/scloop/data/ripser_lib.pyx +++ b/src/scloop/data/ripser_lib.pyx @@ -11,20 +11,31 @@ ctypedef long index_t cdef extern from "ripser.hpp": cdef cppclass ripserResults: vector[vector[value_t]] births_and_deaths_by_dim + vector[vector[vector[int]]] births_and_deaths_simplex_by_dim vector[vector[vector[int]]] cocycles_by_dim int num_edges + cdef cppclass imageRipserResults: + ripserResults subfiltration + ripserResults image cdef cppclass boundaryMatrixResults: vector[vector[index_t]] triangle_vertices vector[value_t] triangle_diameters cdef ripserResults rips_dm_sparse(int* I, int* J, float* V, int NEdges, int N, int modulus, int dim_max, float threshold, int do_cocycles) nogil + cdef imageRipserResults rips_image_sparse(int* I, int* J, float* V, int NEdges, int N, int* sub_indices, int n_sub_indices, int modulus, int dim_max, float threshold, int do_subfiltration_cocycles, int do_image_cocycles) nogil cdef boundaryMatrixResults get_boundary_matrix_sparse(int* I, int* J, float* V, int NEdges, int N, float threshold) nogil @dataclasses.dataclass class RipserResults: births_and_deaths_by_dim: list + births_and_deaths_simplex_by_dim: list cocycles_by_dim: list num_edges: int +@dataclasses.dataclass +class ImageRipserResults: + subfiltration: RipserResults + image: RipserResults + @dataclasses.dataclass class BoundaryMatrixResults: """Results from dimension 2 boundary matrix assembly. @@ -122,13 +133,63 @@ def ripser( with nogil: res = rips_dm_sparse(I, J, V, NEdges, N, modulus, dim_max, threshold, do_cocycles_int) cdef list persistence_diagrams = [converting_birth_death_to_list(res.births_and_deaths_by_dim, i) for i in range(dim_max + 1)] + cdef list persistence_pair_simplices = [converting_birth_death_simplex_to_list(res.births_and_deaths_simplex_by_dim, i) for i in range(dim_max + 1)] cdef list cocycle_representatives = [converting_cocycles_to_list(res.cocycles_by_dim, i) for i in range(dim_max + 1)] return RipserResults( persistence_diagrams, + persistence_pair_simplices, cocycle_representatives, NEdges ) +def ripser_image( + distance_matrix: coo_matrix, + sub_indices, + int modulus, + int dim_max, + float threshold, + bool do_subfiltration_cocycles, + bool do_image_cocycles=False, +) -> ImageRipserResults: + """Compute PH of an induced subfiltration and its image in the ambient complex.""" + sub_indices_array = np.ascontiguousarray(sub_indices, dtype=np.intc) + if sub_indices_array.ndim != 1 or sub_indices_array.size == 0: + raise ValueError("sub_indices must be a non-empty one-dimensional array") + if np.any(sub_indices_array < 0) or np.any( + sub_indices_array >= distance_matrix.shape[0] + ): + raise ValueError("sub_indices contains a vertex outside the distance matrix") + + cdef int[::1] _I = np.ascontiguousarray(distance_matrix.row, dtype=np.intc) + cdef int[::1] _J = np.ascontiguousarray(distance_matrix.col, dtype=np.intc) + cdef float[::1] _V = np.ascontiguousarray( + distance_matrix.data, dtype=np.float32 + ) + cdef int[::1] _sub_indices = sub_indices_array + + cdef int* I = &_I[0] + cdef int* J = &_J[0] + cdef float* V = &_V[0] + cdef int* sub_indices_ptr = &_sub_indices[0] + cdef int NEdges = distance_matrix.nnz + cdef int N = distance_matrix.shape[0] + cdef int n_sub_indices = _sub_indices.shape[0] + cdef int do_subfiltration_cocycles_int = int(do_subfiltration_cocycles) + cdef int do_image_cocycles_int = int(do_image_cocycles) + cdef imageRipserResults res + + with nogil: + res = rips_image_sparse( + I, J, V, NEdges, N, sub_indices_ptr, n_sub_indices, + modulus, dim_max, threshold, do_subfiltration_cocycles_int, + do_image_cocycles_int + ) + + return ImageRipserResults( + subfiltration=_convert_ripser_results(res.subfiltration, dim_max), + image=_convert_ripser_results(res.image, dim_max), + ) + def get_boundary_matrix( distance_matrix: coo_matrix, float threshold, @@ -173,3 +234,53 @@ def get_boundary_matrix( triangle_vertices=triangle_vertices, triangle_diameters=triangle_diameters ) + +cdef list converting_birth_death_simplex_to_list( + vector[vector[vector[int]]] births_and_deaths_simplex_by_dim, + int dim, +): + ''' + Convert the flattened simplex pairs + [birth0, death0, birth1, death1, ...] into + [[birth0, birth1, ...], [death0, death1, ...]]. + ''' + cdef list birth = [] + cdef list death = [] + cdef list simplex_vertices + cdef vector[vector[int]]* birth_death = &births_and_deaths_simplex_by_dim[dim] + cdef vector[int]* simplex + cdef int n_pairs = birth_death.size() // 2 + + for i in range(n_pairs): + simplex = &birth_death[0][i * 2] + simplex_vertices = [] + for j in range(simplex[0].size()): + simplex_vertices.append(int(simplex[0][j])) + birth.append(simplex_vertices) + + simplex = &birth_death[0][i * 2 + 1] + simplex_vertices = [] + for j in range(simplex[0].size()): + simplex_vertices.append(int(simplex[0][j])) + death.append(simplex_vertices) + + return [birth, death] + +cdef object _convert_ripser_results(ripserResults res, int dim_max): + return RipserResults( + [ + converting_birth_death_to_list(res.births_and_deaths_by_dim, i) + for i in range(dim_max + 1) + ], + [ + converting_birth_death_simplex_to_list( + res.births_and_deaths_simplex_by_dim, i + ) + for i in range(dim_max + 1) + ], + [ + converting_cocycles_to_list(res.cocycles_by_dim, i) + for i in range(dim_max + 1) + ], + res.num_edges, + ) diff --git a/src/scloop/matching/utils.py b/src/scloop/matching/utils.py new file mode 100644 index 0000000..3fefe6c --- /dev/null +++ b/src/scloop/matching/utils.py @@ -0,0 +1,860 @@ +import os +import re + +import numpy as np + +import subprocess + +def send_cmd_windows(cmd) : # cmd is a string + 'dont forget stdout = good output, stderr = error like outputs e.g. help output for windows' + out = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate()[0].decode('UTF-8').split('\n') + # choosing first good output + return out + +def send_cmd_linux(cmd) : + out = os.popen(cmd).read().split('\n') + return out + +#### define your system +send_cmd = send_cmd_windows + +############ + +from match.utils_plot import * + + +################ + +##### PERSISTENCE + +def compute_bars_tightreps(inp = None, filename = 'data') : + '''This function computes the barcode and representatives of a: + - inp = point cloud in the form of an array + - filename = file containing the lower diagonal matrix containing the pairwise distances for a finite metric space''' + if inp is None : # a filename input which gives lower distance matrix + if filename.endswith('.lower_distance_matrix') : + ldm_file = filename + else : + ldm_file = filename + '.lower_distance_matrix' + else : + data = inp # a point cloud in 2D or 3D + ldm_file = '{}.lower_distance_matrix'.format(filename) + pairwise = np.sqrt( np.sum( (data[:, None, :] - data[None, :, :])**2, axis=-1) ) + + f = open(ldm_file, "w") # erase possibly pre-existing + for i in range(len(pairwise)) : + f.write(', '.join([ str(x) for x in pairwise[i,:i]])) + f.write('\n') + f.close() + + software = "./ripser-representatives" + options = "" + + command = "{} {} {}".format(software, options, ldm_file) + + out = send_cmd(command) + return out + +def extract_bars_reps(out, only_dim_1 = False, verbose = False) : + '''This function converts the output of compute_bars_tight_reps into list of bars and reps, organised by dimension''' + # find after which line it starts enumerating intervals in dim 0,1,2 + line_PH = {0:len(out), 1:len(out), 2:len(out)} + for i in range(len(out)) : + if out[i].startswith('persistent homology intervals in dim ') : + dim = out[i].rstrip().split(' ')[-1][:-1] # after split '0:' + dim = int(dim) + line_PH[dim] = i + + bars = {0:[],1:[],2:[]} + reps = {0:[],1:[],2:[]} + tight_reps = {0:[],1:[],2:[]} + + # reps 0 [ [[0],[1]], [[1],[2]], etc ] + # reps 1 [ [ [0,1], [1,2], [2,3], [3,0] ], same] + + if not only_dim_1 : + + # 0-dim PH bars and reps + dim = 0 + i = line_PH[dim]+1 + while i < line_PH[dim + 1] : + x = re.search(r"\[(\d*.\d*),(\d*.\d*)\)", out[i]) + if not x : raise ValueError("no match found") + if x.group(2) != ' ' : # finite bars first + bars[dim] += [ [float(x.group(1)), float(x.group(2))] ] + y = re.search(r"\{\[(\d+)\], \[(\d+)\]\}", out[i]) + if not y : + raise ValueError("finite interval detected but not represented by two vertices") + reps[dim] += [ [ [int(y.group(1))], [int(y.group(2))] ] ] + else : + bars[dim] += [ [float(x.group(1)), np.inf] ] + y = re.search(r"\{\[(\d+)\].*\}", out[i]) + reps[dim] += [ [ [int(y.group(1))] ] ] + i += 1 + + # 1-dim PH bars and reps + dim = 1 + + i = line_PH[dim]+1 + while i < line_PH[dim + 1] : + x = re.search(r"\[(\d*.\d*),(\d*.\d*)\)", out[i]) + if i == len(out) - 1 : # trivial string '' + break + + # all "finite" bars (no missing second endpoint) + bars[dim] += [ [float(x.group(1)), float(x.group(2))] ] + + i += 1 # next line for tight reps + y = re.findall(r"\[(\d+),(\d+)\] \(\d*.\d*\)", out[i]) + y = [list(elem) for elem in y] + y = [[int(e[0]), int(e[1])] for e in y] + tight_reps[dim] += [ y ] + + i += 1 # again, next line for reps + y = re.findall(r"\[(\d+),(\d+)\] \(\d*.\d*\)", out[i]) + y = [list(elem) for elem in y] + y = [[int(e[0]), int(e[1])] for e in y] + reps[dim] += [ y ] + + i += 1 + + if verbose : + print(bars) + print(reps) + print(tight_reps) + + return bars, reps, tight_reps + +def extract_bars_reps_indices(out, only_dim_1 = False, verbose = False) : + '''This function converts the output of compute_bars_tight_reps into list of bars, representatives and indices of the persistence pairs, + organised by dimension. REMARK: you need to use the modified version or ripser_tight_representative_cycles''' + # find after which line it starts enumerating intervals in dim 0,1,2 + line_PH = {0:len(out), 1:len(out), 2:len(out)} + for i in range(len(out)) : + if out[i].startswith('persistent homology intervals in dim ') : + dim = out[i].rstrip().split(' ')[-1][:-1] # after split '0:' + dim = int(dim) + line_PH[dim] = i + # print(line_PH) + # allows for repeated line: then takes last one + + bars = {0:[],1:[],2:[]} + reps = {0:[],1:[],2:[]} + tight_reps = {0:[],1:[],2:[]} + indices = {1:[],2:[]} + + # reps 0 [ [[0],[1]], [[1],[2]], etc ] + # reps 1 [ [ [0,1], [1,2], [2,3], [3,0] ], same] + + if not only_dim_1 : + + # 0-dim PH bars and reps + dim = 0 + i = line_PH[dim]+1 + while i < line_PH[dim + 1] : + x = re.search(r"\[(\d*.\d*),(\d*.\d*)\)", out[i]) + if not x : raise ValueError("no intervals found") + if x.group(2) != ' ' : # finite bars first + bars[dim] += [ [float(x.group(1)), float(x.group(2))] ] + y = re.search(r"\{\[(\d+)\], \[(\d+)\]\}", out[i]) + if not y : + raise ValueError("finite interval detected but not represented by two vertices") + reps[dim] += [ [ [int(y.group(1))], [int(y.group(2))] ] ] + else : + bars[dim] += [ [float(x.group(1)), np.inf] ] + y = re.search(r"\{\[(\d+)\].*\}", out[i]) + reps[dim] += [ [ [int(y.group(1))] ] ] + i += 1 + + + # 1-dim PH bars and reps + dim = 1 + + i = line_PH[dim]+1 + while i < line_PH[dim + 1] : + x = re.search(r"\[(\d*.\d*),(\d*.\d*)\)", out[i]) + if i == len(out) - 1 : # trivial string '' + break + + # all "finite" bars (no missing second endpoint) + bars[dim] += [ [float(x.group(1)), float(x.group(2))] ] + + + # indices + z = re.search(r"indices: (\d*)-(\d*)", out[i]) + if not z : raise ValueError("no iindices found --- are you using the modified version of ripser-tight-representative-cycles?") + indices[dim] += [ [int(z.group(1)), int(z.group(2))] ] + + i += 1 # next line for tight reps + y = re.findall(r"\[(\d+),(\d+)\] \(\d*.\d*\)", out[i]) + y = [list(elem) for elem in y] + y = [[int(e[0]), int(e[1])] for e in y] + tight_reps[dim] += [ y ] + + i += 1 # again, next line for reps + y = re.findall(r"\[(\d+),(\d+)\] \(\d*.\d*\)", out[i]) + y = [list(elem) for elem in y] + y = [[int(e[0]), int(e[1])] for e in y] + reps[dim] += [ y ] + + i += 1 + + if verbose : + print(bars) + print(reps) + print(tight_reps) + + return bars, reps, tight_reps, indices + +##### IMAGE-PERSISTENCE + +def compute_image_bars(filename_X = 'X', filename_Z = 'Z', threshold = None) : + '''This function computes the barcode of the image-persistence of X inside of Z. + The input consists on the two lower distance matrices, using the extension explained in the reference paper, and the treshold up + to which their VR complexes coincide.''' + + if filename_X.endswith('.lower_distance_matrix') : + ldm_file_X = filename_X + ldm_file_Z = filename_Z + else : + ldm_file_X = filename_X + '.lower_distance_matrix' + ldm_file_Z = filename_Z + '.lower_distance_matrix' + + software = "./ripser-image" + + if threshold is None : + options = "--dim 1 --subfiltration {}".format(ldm_file_X) + else : + options = "--dim 1 --threshold {} --subfiltration {}".format(threshold, ldm_file_X) + + command = "{} {} {}".format(software, options, ldm_file_Z) + + out = send_cmd(command) + + return out + +def extract_bars(out, only_dim_1 = False, verbose = False) : + ''' This function converts the output of compute_image_bars into list of bars organised by dimension + (simpler version than extract_bars_reps, no reps for image-persistence)''' + + line_PH = {0:len(out), 1:len(out), 2:len(out)} + for i in range(len(out)) : + if out[i].startswith('persisten') : # not the same output message depending on ripser-feature version! + dim = out[i].rstrip().split(' ')[-1][:-1] # after split '0:' + dim = int(dim) + line_PH[dim] = i + + bars = {0:[],1:[],2:[]} + + if not only_dim_1 : + + # 0-dim PH bars + dim = 0 + i = line_PH[dim]+1 + while i < line_PH[dim + 1] : + x = re.search(r"\[(\d*.\d*),(\d*.\d*)\)", out[i]) + if not x : raise ValueError("no match found") + if x.group(2) != ' ' : # finite bars first + bars[dim] += [ [float(x.group(1)), float(x.group(2))] ] + else : + bars[dim] += [ [float(x.group(1)), np.inf] ] + i += 1 + + # 1-dim PH bars + dim = 1 + i = line_PH[dim]+1 + while i < line_PH[dim + 1] : + x = re.search(r"\[(\d*.\d*),(\d*.*\d*)\)", out[i]) + if i == len(out) - 1 : # trivial string '' + break + if x : + if x.group(2) != ' ' : + # "finite" bar (no missing second endpoint) + bars[dim] += [ [float(x.group(1)), float(x.group(2))] ] + else : + bars[dim] += [ [float(x.group(1)), np.inf] ] + + i += 1 + + if verbose : + print(bars) + + return bars + + +def extract_bars_indices(out, only_dim_1 = False, verbose = False) : + ''' This function converts the output of compute_image_bars into list of bars and indices of the persistence pairs organised by dimension + (simpler version than extract_bars_reps_indices, no reps for image-persistence). REMARK: need to use the modified version of ripser-image!''' + + line_PH = {0:len(out), 1:len(out), 2:len(out)} + for i in range(len(out)) : + if out[i].startswith('persisten') : # not the same output message depending on ripser-feature version! + dim = out[i].rstrip().split(' ')[-1][:-1] # after split '0:' + dim = int(dim) + line_PH[dim] = i + + + bars = {0:[],1:[],2:[]} + indices = {1: [], 2: []} + + if not only_dim_1 : + + # 0-dim PH bars + dim = 0 + i = line_PH[dim]+1 + while i < line_PH[dim + 1] : + x = re.search(r"\[(\d*.\d*),(\d*.\d*)\)", out[i]) + if not x : raise ValueError("no match found") + if x.group(2) != ' ' : # finite bars first + bars[dim] += [ [float(x.group(1)), float(x.group(2))] ] + else : + bars[dim] += [ [float(x.group(1)), np.inf] ] + i += 1 + + # 1-dim PH bars + dim = 1 + i = line_PH[dim]+1 + while i < line_PH[dim + 1] : + + #bars + x = re.search(r"\[(\d*.\d*),(\d*.*\d*)\)", out[i]) + if i == len(out) - 1 : # trivial string '' + break + if x : + if x.group(2) != ' ' : + # "finite" bar (no missing second endpoint) + bars[dim] += [ [float(x.group(1)), float(x.group(2))] ] + else : + bars[dim] += [ [float(x.group(1)), np.inf] ] + + #indices + z = re.search(r"indices: (\d*)-(\d*)", out[i]) + indices[dim] += [ [int(z.group(1)), int(z.group(2))] ] + if not z : raise ValueError("no iindices found --- are you using the modified version of ripser-image?") + + i += 1 + + if verbose : + print(bars) + + return bars, indices + +##### MATCHING + +def Jaccard(a,b,c,d) : + # Jaccard index = intersection over union of two intervals [a,b] and [c,d] + # used to measure affinity of two intervals + M1 = max(a,c) + m1 = min(b,d) + if M1 < m1 : + Jac = (m1 - M1) / (max(b,d) - min(a,c)) + else : + Jac = 0 + return Jac + +def compute_affinity(birth_X, death_X, death, birth_Y, death_Y, affinity_method = 'A') : + if affinity_method == 'A' : # Yohai's and Omer's + a_X_Y = Jaccard( birth_X, death_X, birth_Y, death_Y ) + a_X_Z = Jaccard( birth_X, death_X, birth_X, death ) + a_Y_Z = Jaccard( birth_Y, death_Y, birth_Y, death ) + affinity = a_X_Y * a_X_Z * a_Y_Z + + if affinity_method == 'B' : + a_XZ_YZ = Jaccard( birth_X, death, birth_Y, death ) + a_X_Z = Jaccard( birth_X, death_X, birth_X, death ) + a_Y_Z = Jaccard( birth_Y, death_Y, birth_Y, death ) + affinity = a_XZ_YZ * a_X_Z * a_Y_Z + + if affinity_method == 'C' : + a_X_Y = Jaccard( birth_X, death_X, birth_Y, death_Y ) + a_XZ_YZ = Jaccard( birth_X, death, birth_Y, death ) + a_X_Z = Jaccard( birth_X, death_X, birth_X, death ) + a_Y_Z = Jaccard( birth_Y, death_Y, birth_Y, death ) + affinity = a_X_Y * a_XZ_YZ * a_X_Z * a_Y_Z + + if affinity_method == 'D' : + a_X_Y = Jaccard( birth_X, death_X, birth_Y, death_Y ) + a_XZ_YZ = Jaccard( birth_X, death, birth_Y, death ) + affinity = a_X_Y * a_XZ_YZ + + return affinity + + +def argsort(seq, option = 'desc'): + # what permutation to apply to indices in order to sort seq in ascending / descending order + if option == 'asc' : + return sorted(range(len(seq)), reverse = False, key= seq.__getitem__) + elif option == 'desc' : + return sorted(range(len(seq)), reverse = True, key= seq.__getitem__) + +def show_matches(X, Y, matched_X_Y, affinity_X_Y, tight_reps_X, tight_reps_Y, dim = 1, + zoom_factor = 3, show_together = False) : + ''' This function displays the matches between two point-clouds X and Y after performing the matching and obtaining + the lists: matched_X_Y and affinity_X_Y. Also needed the corresponding lists of tight_reps.''' + + Z = np.vstack((X,Y)) + arg = argsort(affinity_X_Y) + affinity_X_Y = np.array(affinity_X_Y)[arg] + matched_X_Y = np.array(matched_X_Y)[arg] + + if len(matched_X_Y) == 1 or not show_together : + for match, aff in zip(matched_X_Y, affinity_X_Y) : + print('new match') + a,b = match + + if X.shape[1] == 2 : + fig, axes = plt.subplots(1,2, figsize = (8,5), sharex = True, sharey = True) + axes[0].scatter(Y[:,0], Y[:,1], alpha = .2) + plot_cycreps(Z, [tight_reps_X[dim][a]], pts_to_show = X, ax = axes[0]) + axes[1].scatter(X[:,0], X[:,1], alpha = .2) + plot_cycreps(Z, [tight_reps_Y[dim][b]], pts_to_show = Y, ax = axes[1]) + for ax in axes: + ax.set_aspect('equal') + axes[0].set_xlabel('X') + axes[1].set_xlabel('Y') + #plt.text(2, 5, 'a match with affinity {}'.format(aff)) + fig.suptitle('a match with affinity {}'.format(aff), y=0.78) + plt.show() + + if X.shape[1] == 3 : + fig = plt.figure(figsize=plt.figaspect(1.)*zoom_factor) # figaspect(0.5)*1.5 + fig.suptitle('a match with affinity {}'.format(aff), y=0.7) + ax = fig.add_subplot(1,2,1, projection='3d') + ax.scatter(Y[:,0],Y[:,1],Y[:,2], alpha = .1) + plot_cycreps(Z, [tight_reps_X[dim][a]], pts_to_show = X, ax = ax) + ax.set_title('X') + + ax = fig.add_subplot(1,2,2, projection='3d') + ax.scatter(X[:,0],X[:,1],X[:,2], alpha = .1) #, c = '#729dcf', s = 50, edgecolors='black') + plot_cycreps(Z, [tight_reps_Y[dim][b]], pts_to_show = Y, ax = ax) + ax.set_title('Y') + plt.show() + + else : # show_together : + + if X.shape[1] == 2 : + fig, axes = plt.subplots(len(matched_X_Y), 2, figsize = (10, 6 * len(matched_X_Y)), + sharex = True, sharey = True) + # will be buggy if len(matched_X_Y) == 1 + i = 0 + for match, aff in zip(matched_X_Y, affinity_X_Y) : + a,b = match + + axes[i,0].scatter(Y[:,0], Y[:,1], alpha = .2) + plot_cycreps(Z, [tight_reps_X[dim][a]], pts_to_show = X, ax = axes[i,0]) + axes[i,1].scatter(X[:,0], X[:,1], alpha = .2) + plot_cycreps(Z, [tight_reps_Y[dim][b]], pts_to_show = Y, ax = axes[i,1]) + + axes[i,0].set_xlabel('X') + axes[i,0].set_title('aff =') + axes[i,1].set_title(aff) + axes[i,1].set_xlabel('Y') + #plt.text(2, 5, 'a match with affinity {}'.format(aff)) + #fig.suptitle('a match with affinity {}'.format(aff), y=0.78) + for ax in axes.ravel(): + ax.set_aspect('equal') + plt.show() + + + if X.shape[1] == 3 : + fig, axes = plt.subplots(len(matched_X_Y), 2, subplot_kw={'projection': '3d'}, + figsize = (10, 4.5 * len(matched_X_Y))) + #fig = plt.figure(figsize=plt.figaspect(1.)*zoom_factor) # figaspect(0.5)*1.5 + #fig.suptitle('all matches', y=0.7) + + i = 0 + for match, aff in zip(matched_X_Y, affinity_X_Y) : + a,b = match + #print('a,b=',a,b) + ax = axes[i,0] + #ax = fig.add_subplot(i,2,1, projection='3d') + ax.scatter(Y[:,0],Y[:,1],Y[:,2], alpha = .1) + plot_cycreps(Z, [tight_reps_X[dim][a]], pts_to_show = X, ax = ax) + ax.set_title('X aff = {}'.format(aff)) + + ax = axes[i,1] + #ax = fig.add_subplot(i,2,2, projection='3d') + ax.scatter(X[:,0],X[:,1],X[:,2], alpha = .1) #, c = '#729dcf', s = 50, edgecolors='black') + plot_cycreps(Z, [tight_reps_Y[dim][b]], pts_to_show = Y, ax = ax) + ax.set_title('Y a = {} b = {}'.format(a,b)) + + i += 1 + + plt.tight_layout() + plt.show() + + +def duplicates_list(a) : # duplicates_list([1,2,3,1,2,2]) = [1, 2, 2] + seen = set() + dupes = [x for x in a if x in seen or seen.add(x)] + return dupes + +def find_occurences_list(a, val) : + indices = [i for i, x in enumerate(a) if x == val] + return indices + + +def find_match(bars_X, bars_X_Z, indices_X, indices_X_Z, bars_Y, bars_Y_Z, indices_Y, indices_Y_Z, dim = 1, affinity_method = 'A', + check_Morse = False, check_ambiguous_deaths = False) : + ''' This funtion find the matches between the barcodes of X and Y providing the barcodes of their image-persistence modules in the union. + Affinity score is automatically set to A but can be changed. Optiona outputs to check if the filtrations provided are Morse and if + there are image-bars sharing death times in the barcodes.''' + + matched_X_Y = [] + affinity_X_Y = [] + + # consider all image-bars + births_X_Z = [a[0] for a in bars_X_Z[dim]] + births_Y_Z = [a[0] for a in bars_Y_Z[dim]] + deaths_X_Z = [a[1] for a in bars_X_Z[dim]] + deaths_Y_Z = [a[1] for a in bars_Y_Z[dim]] + + # consider normal bars + births_X = [a[0] for a in bars_X[dim]] + births_Y = [a[0] for a in bars_Y[dim]] + deaths_X = [a[1] for a in bars_X[dim]] + deaths_Y = [a[1] for a in bars_Y[dim]] + + if check_Morse : + # adding noise to your point clouds does not solve the following exceptions. + # It will create a distance matrix with unique values, so that only adding 1 edge at a time + # but possibly many triangles that kill cycles simultaneously in Rips complexes + if len(duplicates_list(deaths_X_Z)) > 0 : + print('Found duplicate deaths in X_Z') + if len(duplicates_list(deaths_Y_Z)) > 0 : + print('Found duplicate deaths in Y_Z') + if len(duplicates_list(births_X)) > 0 : + print('Found duplicate births in X') # should never happen for unique distance values + if len(duplicates_list(births_Y)) > 0 : + print('Found duplicate births in Y') # should never happen for unique distance values + + + considered_deaths_X_Z = set(deaths_X_Z) + considered_deaths_Y_Z = set(deaths_Y_Z) + + # find common (considered) deaths in image + common_deaths = considered_deaths_X_Z.intersection(considered_deaths_Y_Z) + + + if check_ambiguous_deaths : + if set(duplicates_list(deaths_X_Z)).intersection(set(duplicates_list(deaths_Y_Z))) != set() : + print('Found common duplicate deaths in X_Z and Y_Z!!!') + if set(duplicates_list(deaths_X_Z)).intersection(common_deaths) != set() : + print('Found duplicate death in X_Z common with Y_Z') + if set(duplicates_list(deaths_Y_Z)).intersection(common_deaths) != set() : + print('Found duplicate death in Y_Z common with X_Z') + + # determine ambiguous deaths + ambiguous_deaths_X_Z = set(duplicates_list(deaths_X_Z)).intersection(common_deaths) + ambiguous_deaths_Y_Z = set(duplicates_list(deaths_Y_Z)).intersection(common_deaths) + ambiguous_deaths = ambiguous_deaths_X_Z.union(ambiguous_deaths_Y_Z) + if ambiguous_deaths != set() : + print('We will solve ambiguous deaths matching.') + + # now, find common births with the normal bars + + # First case: non-ambiguous matching + # in this case, deaths in X_Z and Y_Z are unique so matching can be made without ambiguity (even if duplicate deaths in X or in Y) + + for death in common_deaths.difference(ambiguous_deaths) : + oXZ = deaths_X_Z.index(death) + oYZ = deaths_Y_Z.index(death) + birth_X = births_X_Z[oXZ] + birth_Y = births_Y_Z[oYZ] + + # Now we match with the persistence bars of X and Y + Occ_X = find_occurences_list(births_X, birth_X) + Occ_Y = find_occurences_list(births_Y, birth_Y) + + if len(Occ_X) == 1 and len(Occ_Y) == 1: # if there are no ambiguous births + a = births_X.index(birth_X) # unique + b = births_Y.index(birth_Y) # unique + + matched_X_Y += [[a, b]] + affinity = compute_affinity(birth_X, deaths_X[a], death, birth_Y, deaths_Y[b], affinity_method = affinity_method) + affinity_X_Y += [affinity] + else: + # the way we are computing persistent homology, the indices of the + # persistent homology of Y can be compared with the indices in the + # image - persistent homology of Y inside Z + + for k, oX in enumerate(Occ_X): + pos_index_X = indices_X[dim][oX][0] + pos_index_XZ = indices_X_Z[dim][oXZ][0] + if pos_index_X == pos_index_XZ: + a = oX + for l, oY in enumerate(Occ_Y): + pos_index_Y = indices_Y[dim][oY][0] + pos_index_YZ = indices_Y_Z[dim][oYZ][0] # the bars are presented in the same order, independently on how we arrange X an Y + if pos_index_Y == pos_index_YZ: + b = oY + matched_X_Y += [[a, b]] + affinity = compute_affinity(birth_X, deaths_X[a], death, birth_Y, deaths_Y[b], affinity_method = affinity_method) + affinity_X_Y += [affinity] + + # Second case: ambiguous matching + + for death in ambiguous_deaths : + # detect the indices of the bars with ambiguous death times + Occ_XZ = find_occurences_list(deaths_X_Z, death) + Occ_YZ = find_occurences_list(deaths_Y_Z, death) + + for i, oXZ in enumerate(Occ_XZ) : + # extract negative index of the image persistence bar of X + neg_index_XZ = indices_X_Z[dim][oXZ][1] + for j, oYZ in enumerate(Occ_YZ) : + # extract negative index of the image persistence bar of Y + neg_index_YZ = indices_Y_Z[dim][oYZ][1] + if neg_index_XZ == neg_index_YZ: + # match the image bars when the indices coincide + birth_X = births_X_Z[oXZ] # ! not i + birth_Y = births_Y_Z[oYZ] # ! not j + + # Now we match with the persistence bars of X and Y + Occ_X = find_occurences_list(births_X, birth_X) + Occ_Y = find_occurences_list(births_Y, birth_Y) + + if len(Occ_X) == 1 and len(Occ_Y) == 1: # if there are no ambiguous births + a = births_X.index(birth_X) # unique + b = births_Y.index(birth_Y) # unique + matched_X_Y += [[a, b]] + affinity = compute_affinity(birth_X, deaths_X[a], death, birth_Y, deaths_Y[b], affinity_method = affinity_method) + affinity_X_Y += [affinity] + else: + # the way we are computing persistent homology, the indices of the + # persistent homology of Y can be compared with the indices in the + # image - persistent homology of Y inside Z + for k, oX in enumerate(Occ_X): + pos_index_X = indices_X[dim][oX][0] + pos_index_XZ = indices_X_Z[dim][oXZ][0] + if pos_index_X == pos_index_XZ: + a = oX + for l, oY in enumerate(Occ_Y): + pos_index_Y = indices_Y[dim][oY][0] + pos_index_YZ = indices_Y_Z[dim][oYZ][0] # the bars are presented in the same order, independently on how we arrange X an Y + if pos_index_Y == pos_index_YZ: + b = oY + matched_X_Y += [[a, b]] + affinity = compute_affinity(birth_X, deaths_X[a], death, birth_Y, deaths_Y[b], affinity_method = affinity_method) + affinity_X_Y += [affinity] + + return matched_X_Y, affinity_X_Y + + +def create_matrices_image(X, Y, filename_X = 'X', filename_Y = 'Y', filename_Z = 'Z', return_thr = False): + '''Function to create the matrices for the computation of image-persistence so that we can compare the indices of the persistence pairs''' + Z = np.vstack((X,Y)) + nb_X = len(X) + ldm_file_X = '{}.lower_distance_matrix'.format(filename_X) + ldm_file_Y = '{}.lower_distance_matrix'.format(filename_Y) + ldm_file_Z = '{}.lower_distance_matrix'.format(filename_Z) + + pairwise_Z = np.sqrt( np.sum( (Z[:, None, :] - Z[None, :, :])**2, axis=-1) ) + maxi = np.max(pairwise_Z) + pairwise_X = pairwise_Z.copy() + pairwise_X[nb_X:] = 2 * maxi + 1 # add min offset 1 because maxi can be small + pairwise_Y = pairwise_Z.copy() + pairwise_Y[:,:nb_X] = 2 * maxi + 1 #observe here the change wrt the previous line + + # so that later we can apply thresholding in Ripser-image (bug fixed) + # threshold = 2 * maxi # not 2 * maxi - 1 as maxi could be very small + + f = open(ldm_file_X, "w") # erase possibly pre-existing + for i in range(len(pairwise_X)) : + f.write(', '.join([ str(x) for x in pairwise_X[i,:i]])) + f.write('\n') + f.close() + + f = open(ldm_file_Y, "w") # erase possibly pre-existing + for i in range(len(pairwise_Y)) : + f.write(', '.join([ str(x) for x in pairwise_Y[i,:i]])) + f.write('\n') + f.close() + + f = open(ldm_file_Z, "w") # erase possibly pre-existing + for i in range(len(pairwise_Z)) : + f.write(', '.join([ str(x) for x in pairwise_Z[i,:i]])) + f.write('\n') + f.close() + + if return_thr : + threshold = 2 * maxi + 0.5 # to make sure we still include people <= maxi (in case maxi = 0... paranoia lol) + return ldm_file_X, ldm_file_Y, ldm_file_Z, threshold + + return ldm_file_X, ldm_file_Y, ldm_file_Z + + +def matching(X,Y, dim = 1, verbose_figs = False, affinity_method = 'A', check_Morse = False) : + + '''Function that takes as input two pointclouds X and Y and computes the relevant barcodes and the matching.''' + + # Compute the lower distance matrices + ldm_file_X, ldm_file_Y, ldm_file_Z, threshold = \ + create_matrices_image(X, Y, filename_X = 'X', filename_Y = 'Y', return_thr = True) + + # Image persistence - apply thresholding (bug fixed) + out_X_Z = compute_image_bars(filename_X = ldm_file_X, filename_Z = ldm_file_Z, threshold = threshold) + bars_X_Z, indices_X_Z = extract_bars_indices(out_X_Z, only_dim_1 = True) + + out_Y_Z = compute_image_bars(filename_X = ldm_file_Y, filename_Z = ldm_file_Z, threshold = threshold) + bars_Y_Z, indices_Y_Z = extract_bars_indices(out_Y_Z, only_dim_1 = True) + + # Persistent homology + out_X = compute_bars_tightreps(inp = None, filename = ldm_file_X) + # this way we obtain the same bars but the vertices are indexes in the same way as in the image persistence + bars_X, reps_X, tight_reps_X, indices_X = extract_bars_reps_indices(out_X, only_dim_1 = True) + + out_Y = compute_bars_tightreps(inp = None, filename = ldm_file_Y) + bars_Y, reps_Y, tight_reps_Y, indices_Y = extract_bars_reps_indices(out_Y, only_dim_1 = True) + + matched_X_Y, affinity_X_Y = find_match(bars_X, bars_X_Z, indices_X, indices_X_Z, + bars_Y, bars_Y_Z, indices_Y, indices_Y_Z, dim = 1, + affinity_method = affinity_method, check_Morse = check_Morse, + check_ambiguous_deaths = False) + + if verbose_figs : + show_matches(X,Y,matched_X_Y, affinity_X_Y, tight_reps_X, tight_reps_Y, dim = dim, show_together = True) + + return matched_X_Y, affinity_X_Y, (bars_X, reps_X, tight_reps_X), (bars_Y, reps_Y, tight_reps_Y) + + +###### PREVALENCE, CROSS-PREVALENCE + +def multiple_matching(X, list_Y, dim = 1, verbose_figs = False, affinity_method = 'A') : + + out_X = compute_bars_tightreps(X) + bars_X, reps_X, tight_reps_X, indices_X = extract_bars_reps_indices(out_X, only_dim_1 = True) + + list_matched_X_Y = {} + list_affinity_X_Y = {} + + list_bars_reps_Y = [] + + for y, Y in enumerate(list_Y) : + print('Matching X to Y_{} ...'.format(y)) + + # Compute the lower distance matrices + ldm_file_X, ldm_file_Y, ldm_file_Z, threshold = \ + create_matrices_image(X, Y, filename_X = 'X', filename_Y = 'Y', return_thr = True) + + # Image persistence - apply thresholding + out_X_Z = compute_image_bars(filename_X = ldm_file_X, filename_Z = ldm_file_Z, threshold = threshold) + bars_X_Z, indices_X_Z = extract_bars_indices(out_X_Z, only_dim_1 = True) + + out_Y_Z = compute_image_bars(filename_X = ldm_file_Y, filename_Z = ldm_file_Z, threshold = threshold) + bars_Y_Z, indices_Y_Z = extract_bars_indices(out_Y_Z, only_dim_1 = True) + + out_Y = compute_bars_tightreps(Y) + bars_Y, reps_Y, tight_reps_Y, indices_Y = extract_bars_reps_indices(out_Y, only_dim_1 = True) + + list_bars_reps_Y += [ [bars_Y, reps_Y, tight_reps_Y] ] + + matched_X_Y, affinity_X_Y = find_match(bars_X, bars_X_Z, indices_X, indices_X_Z, + bars_Y, bars_Y_Z, indices_Y, indices_Y_Z, dim = 1, + affinity_method = affinity_method, check_Morse = False, + check_ambiguous_deaths = False) + + list_matched_X_Y[y] = matched_X_Y + list_affinity_X_Y[y] = affinity_X_Y + + if verbose_figs : + + show_matches(X, list_Y[y], matched_X_Y[y], affinity_X_Y[y], tight_reps_X, tight_reps_Y, dim = dim, show_together = True) + + list_matched_X_Y = list(list_matched_X_Y.values()) + list_affinity_X_Y = list(list_affinity_X_Y.values()) + bars_reps_X = [bars_X, reps_X, tight_reps_X] + + return list_matched_X_Y, list_affinity_X_Y, bars_reps_X, list_bars_reps_Y + +def cross_matching(list_X, dim = 1, verbose_figs = False, affinity_method = 'A') : + + list_matched_X_Y = {} + list_affinity_X_Y = {} + list_bars_reps_indices_X = {} + list_bars_reps_X = {} + + # compute PH and reps and indices of individual spaces + for i,X in enumerate(list_X) : + out_X = compute_bars_tightreps(X) + bars_X, reps_X, tight_reps_X, indices_X = extract_bars_reps_indices(out_X, only_dim_1 = True) + list_bars_reps_indices_X[i] = [bars_X, reps_X, tight_reps_X, indices_X] + list_bars_reps_X[i] = [bars_X, reps_X, tight_reps_X] + + # match any X_i to any X_j (j > i) + for i,X in enumerate(list_X) : + for j in range(i+1, len(list_X)) : + print('Matching X_{} to X_{} ...'.format(i,j)) + + X = list_X[i] + Y = list_X[j] + + # Compute the lower distance matrices + ldm_file_X, ldm_file_Y, ldm_file_Z, threshold = \ + create_matrices_image(X, Y, filename_X = 'X', filename_Y = 'Y', return_thr = True) + + # Image persistence - apply thresholding (bug fixed) + out_X_Z = compute_image_bars(filename_X = ldm_file_X, filename_Z = ldm_file_Z, threshold = threshold) + bars_X_Z, indices_X_Z = extract_bars_indices(out_X_Z, only_dim_1 = True) + + out_Y_Z = compute_image_bars(filename_X = ldm_file_Y, filename_Z = ldm_file_Z, threshold = threshold) + bars_Y_Z, indices_Y_Z = extract_bars_indices(out_Y_Z, only_dim_1 = True) + + bars_X, reps_X, tight_reps_X, indices_X = list_bars_reps_indices_X[i] + bars_Y, reps_Y, tight_reps_Y, indices_Y = list_bars_reps_indices_X[j] + + + matched_X_Y, affinity_X_Y = find_match(bars_X, bars_X_Z, indices_X, indices_X_Z, + bars_Y, bars_Y_Z, indices_Y, indices_Y_Z, dim = 1, + affinity_method = affinity_method, check_Morse = False, + check_ambiguous_deaths = False) + + list_matched_X_Y[i,j] = matched_X_Y + list_affinity_X_Y[i,j] = affinity_X_Y + + for i in range(len(list_X)) : + for j in range(i) : + aa = list_matched_X_Y[j,i] + if len(aa) > 0 : + list_matched_X_Y[i,j] = np.array(aa)[:,::-1].tolist() # reverse column order + else : + list_matched_X_Y[i,j] = [] + list_affinity_X_Y[i,j] = list_affinity_X_Y[j,i] + return list_matched_X_Y, list_affinity_X_Y, list_bars_reps_X + +##### SOME FUNCTIONS TO ENABLE TRACKING CYCLES + +def track_cycles_from_slice(list_matched_X_Y, list_affinity_X_Y, cycle, list_indices, initial_slice = 0): + ''' From a list of matched cycles between consecutive slices, obtains a list in which we store the matches + that track a particular cycle from a some chosen slice + output = [[cycle, a],[a, b], [b, c] ...] + rmk: set of indices does not include the initial slice, counts from the second slice studied''' + + tracked_cycle = [] + tracked_affinity = [] + current_match = [] + + # initialise + for i, match in enumerate(list_matched_X_Y[initial_slice]): + if match[0] == cycle: + tracked_cycle += [match] + current_match = match + tracked_affinity.append(list_affinity_X_Y[initial_slice][i]) + + + # track the cycle + for i in list_indices: + next_cycle = current_match[1] + tracked_copy = tracked_cycle.copy() + for j, match in enumerate(list_matched_X_Y[i]): + if match[0] == next_cycle: + #print(match) + tracked_cycle += [match] + current_match = match + tracked_affinity.append(list_affinity_X_Y[i][j]) + continue + if len(tracked_copy) == len(tracked_cycle): # in case we don't find a next cycle we stop tracking + + break + + return tracked_cycle, tracked_affinity diff --git a/src/scloop/tools/_loops.py b/src/scloop/tools/_loops.py index 5cb1de8..cdc8bed 100644 --- a/src/scloop/tools/_loops.py +++ b/src/scloop/tools/_loops.py @@ -2,7 +2,7 @@ from __future__ import annotations from contextlib import nullcontext -from typing import Annotated, Any +from typing import Annotated, Any, Literal import numpy as np from anndata import AnnData @@ -66,6 +66,8 @@ def find_loops( bootstrap_fps_alpha: float = 1.0, bootstrap_herding_n_features: int = 1000, bootstrap_herding_seed: int | None = None, + bootstrap_candidate_method: Literal["geometric", "image"] = "geometric", + require_bootstrap_homological_equivalence: bool = True, n_check_per_candidate: NonZeroCount_t = 1, max_columns_boundary_matrix: NonZeroCount_t = DEFAULT_MAX_COLUMNS_BOUNDARY_MATRIX, auto_shrink_boundary_matrix: bool = True, @@ -206,6 +208,14 @@ def find_loops( - map loops to original loops ============================= """ + bootstrap_kwargs = dict(kwargs_bootstrap or {}) + bootstrap_candidate_method = bootstrap_kwargs.pop( + "candidate_method", bootstrap_candidate_method + ) + require_bootstrap_homological_equivalence = bootstrap_kwargs.pop( + "require_homological_equivalence", + require_bootstrap_homological_equivalence, + ) hd._bootstrap( adata=adata, n_bootstrap=n_bootstrap, @@ 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