diff --git a/pyproject.toml b/pyproject.toml index d2fcef5..2ee42e4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,11 +22,10 @@ dependencies = [ "bioio-tifffile>=1.3", "fire>=0.7.1", "jinja2>=3.1.6", - "mahotas>=1.4.18", + "matplotlib>=3.10.8", "pandas>=3.0.2", "pyarrow>=24", "scikit-image>=0.26", - "tqdm>=4.67.3", ] scripts.ZedProfiler = "ZedProfiler.cli:trigger" diff --git a/src/zedprofiler/featurization/neighbors.py b/src/zedprofiler/featurization/neighbors.py index 44dac09..d76ad3d 100644 --- a/src/zedprofiler/featurization/neighbors.py +++ b/src/zedprofiler/featurization/neighbors.py @@ -1,10 +1,612 @@ -"""Neighbors featurization module scaffold.""" +"""Neighbors featurization module.""" -from __future__ import annotations +from typing import Dict, Tuple, Union -from zedprofiler.exceptions import ZedProfilerError +import matplotlib.pyplot as plt +import numpy +import pandas +import skimage.measure +from zedprofiler.IO.loading_classes import ObjectLoader -def compute() -> dict[str, list[float]]: - """Placeholder for neighbors computation implementation.""" - raise ZedProfilerError("neighbors.compute is not implemented yet") +BBoxCoord = Union[int, float] +BBox3D = Tuple[BBoxCoord, BBoxCoord, BBoxCoord, BBoxCoord, BBoxCoord, BBoxCoord] +SMALL_SAMPLE_THRESHOLD = 20 + + +def neighbors_expand_box( + min_coor: Union[int, float], + max_coord: Union[int, float], + current_min: Union[int, float], + current_max: Union[int, float], + expand_by: int, +) -> Tuple[Union[int, float], Union[int, float]]: + """ + Expand the bounding box of the object by a specified distance in each direction. + + Parameters + ---------- + min_coor : Union[int, float] + The global minimum coordinate of the image. + max_coord : Union[int, float] + The global maximum coordinate of the image. + current_min : Union[int, float] + The current minimum coordinate of the object. + current_max : Union[int, float] + The current maximum coordinate of the object. + expand_by : int + The distance by which to expand the bounding box. + + Returns + ------- + Tuple[Union[int, float], Union[int, float]] + The new minimum and maximum coordinates of the bounding box. + """ + if current_min - expand_by < min_coor: + current_min = min_coor + else: + current_min -= expand_by + if current_max + expand_by > max_coord: + current_max = max_coord + else: + current_max += expand_by + return current_min, current_max + + +# crop the image to the bbox of the mask +def crop_3D_image( + image: numpy.ndarray, + bbox: BBox3D, +) -> numpy.ndarray: + """ + Crop the 3D image to the bounding box of the object. + + Parameters + ---------- + image : numpy.ndarray + The 3D image to be cropped. + bbox : BBox3D + The bounding box of the object in the format (z1, y1, x1, z2, y2, x2). + + Returns + ------- + numpy.ndarray + The cropped 3D image. + """ + z1, y1, x1, z2, y2, x2 = bbox + return image[z1:z2, y1:y2, x1:x2] + + +def compute_neighbors( + object_loader: ObjectLoader, + distance_threshold: int = 10, + anisotropy_factor: int = 10, +) -> Dict[str, list]: + """ + This function calculates the number of neighbors for each object in a 3D image. + + Parameters + ---------- + object_loader : ObjectLoader + The object loader object that contains the image and label image. + distance_threshold : int, optional + The distance threshold for counting neighbors, by default 10 + anisotropy_factor : int, optional + The anisotropy factor for the image where the anisotropy factor is the + ratio of the pixel size in the z direction to the pixel size in the x + and y directions, by default 10 + + Returns + ------- + Dict[str, list] + A dictionary containing the object ID and the number of neighbors for + each object. + """ + label_object = object_loader.label_image + labels = object_loader.object_ids + # set image global min and max coordinates + image_global_min_coord_z = 0 + image_global_min_coord_y = 0 + image_global_min_coord_x = 0 + image_global_max_coord_z = label_object.shape[0] + image_global_max_coord_y = label_object.shape[1] + image_global_max_coord_x = label_object.shape[2] + + neighbors_out_dict = { + "object_id": [], + "NeighborsCountAdjacent": [], + f"NeighborsCountByDistance-{distance_threshold}": [], + } + for index, label in enumerate(labels): + selected_label_object = label_object.copy() + selected_label_object[selected_label_object != label] = 0 + props_label = skimage.measure.regionprops_table( + selected_label_object, properties=["bbox"] + ) + # get the number of neighbors for each object + distance_x_y = distance_threshold + distance_z = numpy.ceil(distance_threshold / anisotropy_factor).astype(int) + # find how many other indexes are within a specified distance of the object + # first expand the mask image by a specified distance + z_min, y_min, x_min, z_max, y_max, x_max = ( + props_label["bbox-0"][0], + props_label["bbox-1"][0], + props_label["bbox-2"][0], + props_label["bbox-3"][0], + props_label["bbox-4"][0], + props_label["bbox-5"][0], + ) + original_bbox = (z_min, y_min, x_min, z_max, y_max, x_max) + + new_z_min, new_z_max = neighbors_expand_box( + min_coor=image_global_min_coord_z, + max_coord=image_global_max_coord_z, + current_min=z_min, + current_max=z_max, + expand_by=distance_z, + ) + new_y_min, new_y_max = neighbors_expand_box( + min_coor=image_global_min_coord_y, + max_coord=image_global_max_coord_y, + current_min=y_min, + current_max=y_max, + expand_by=distance_x_y, + ) + new_x_min, new_x_max = neighbors_expand_box( + min_coor=image_global_min_coord_x, + max_coord=image_global_max_coord_x, + current_min=x_min, + current_max=x_max, + expand_by=distance_x_y, + ) + bbox = (new_z_min, new_y_min, new_x_min, new_z_max, new_y_max, new_x_max) + croppped_neighbor_image = crop_3D_image(image=label_object, bbox=bbox) + self_cropped_neighbor_image = crop_3D_image( + image=label_object, bbox=original_bbox + ) + # find all the unique values in the cropped image of the object of interest + # this is the number of neighbors in the cropped image + n_neighbors_adjacent = ( + len( + numpy.unique( + self_cropped_neighbor_image[self_cropped_neighbor_image > 0] + ) + ) + - 1 + ) + + # find all the unique values in the expanded cropped image of the + # object of interest + # this gives the number of neighbors in a n distance of the object + n_neighbors_by_distance = ( + len(numpy.unique(croppped_neighbor_image[croppped_neighbor_image > 0])) - 1 + ) + neighbors_out_dict["object_id"].append(label) + neighbors_out_dict["NeighborsCountAdjacent"].append(n_neighbors_adjacent) + neighbors_out_dict[f"NeighborsCountByDistance-{distance_threshold}"].append( + n_neighbors_by_distance + ) + + return neighbors_out_dict + + +def get_coordinates( + nuclei_mask: numpy.ndarray, object_ids: list | None = None +) -> pandas.DataFrame: + """ + Extract coordinates from a labeled mask. + + Parameters + ---------- + nuclei_mask : ndarray + 3D labeled mask where each object has a unique ID + object_ids : list + List of object IDs to extract + + Returns + ------- + coords : pandas.DataFrame + DataFrame with columns: object_id, x, y, z + """ + if object_ids is None: + object_ids = [] + coords = {"object_id": [], "x": [], "y": [], "z": []} + + for obj_id in object_ids: + z, y, x = numpy.where(nuclei_mask == obj_id) + centroid = (numpy.mean(x), numpy.mean(y), numpy.mean(z)) + coords["object_id"].append(obj_id) + coords["x"].append(centroid[0]) + coords["y"].append(centroid[1]) + coords["z"].append(centroid[2]) + + return pandas.DataFrame(coords) + + +def calculate_centroid(coords: pandas.DataFrame) -> numpy.ndarray: + """Calculate the centroid of cell coordinates.""" + return numpy.mean(coords, axis=0) + + +def euclidean_distance_from_centroid( + coords: numpy.ndarray, centroid: numpy.ndarray +) -> numpy.ndarray: + """Calculate Euclidean distance from centroid for each cell.""" + coords = numpy.asarray(coords, dtype=float) + centroid = numpy.asarray(centroid, dtype=float) + return numpy.sqrt(numpy.sum((coords - centroid) ** 2, axis=1)) + + +def mahalanobis_distance_from_centroid( + coords: numpy.ndarray, centroid: numpy.ndarray, min_cells_threshold: int = 50 +) -> numpy.ndarray: + """ + Calculate Mahalanobis distance from centroid for each cell. + This accounts for the covariance structure (shape) of the organoid. + + For small sample sizes (<50 cells), uses regularization or falls back to Euclidean. + + Parameters + ---------- + coords : ndarray + Cell coordinates (n_cells, 3) + centroid : ndarray + Centroid coordinates (3,) + min_cells_threshold : int + Minimum cells needed for reliable Mahalanobis (default: 50) + + Returns + ------- + distances : ndarray + Mahalanobis distances for each cell + """ + coords = numpy.asarray(coords, dtype=float) + centroid = numpy.asarray(centroid, dtype=float) + + n_cells = len(coords) + + # For very small samples, use Euclidean distance instead + if n_cells < SMALL_SAMPLE_THRESHOLD: + print( + f" WARNING: Only {n_cells} cells. Using Euclidean distance " + f"instead of Mahalanobis." + ) + return euclidean_distance_from_centroid(coords, centroid) + + # Calculate covariance matrix + cov_matrix = numpy.cov(coords.T) + + # For small samples (20-50), use strong regularization + if n_cells < min_cells_threshold: + # Regularization strength inversely proportional to sample size + reg_strength = (min_cells_threshold - n_cells) / min_cells_threshold * 0.1 + cov_matrix += numpy.eye(3) * reg_strength * numpy.trace(cov_matrix) / 3 + print( + f" WARNING: Only {n_cells} cells. Using regularized covariance " + f"(λ={reg_strength:.3f})" + ) + else: + # Standard small regularization for numerical stability + cov_matrix += numpy.eye(3) * 1e-6 + + # Calculate inverse covariance matrix + try: + inv_cov = numpy.linalg.inv(cov_matrix) + except numpy.linalg.LinAlgError: + # Fallback to pseudo-inverse if singular + print(" WARNING: Singular covariance matrix. Using pseudo-inverse.") + inv_cov = numpy.linalg.pinv(cov_matrix) + + # Calculate Mahalanobis distance for each point + diff = coords - centroid + distances = numpy.sqrt(numpy.einsum("ij,jk,ik->i", diff, inv_cov, diff)) + + return distances + + +def classify_cells_into_shells( + coords: pandas.DataFrame | dict, + n_shells: int = 5, + method: str = "mahalanobis", + min_cells_per_shell: int = 3, + centroid: numpy.ndarray = None, +) -> dict: + """ + Classify cells into radial shells based on distance from centroid. + + Automatically adjusts n_shells for small organoids to ensure meaningful statistics. + + Parameters + ---------- + coords : pandas.DataFrame or dict + Cell coordinates with /keys: object_id, x, y, z + n_shells : int + Number of concentric shells to create (will be adjusted if needed) + method : str + 'euclidean' or 'mahalanobis' + min_cells_per_shell : int + Minimum average cells per shell (default: 3) + centroid : numpy.ndarray, optional + Pre-calculated centroid (if None, will be calculated from coords) + + Returns + ------- + results : dict + Dictionary containing: + - 'ShellAssignments': Shell number for each cell (0 = innermost) + - 'DistancesFromCenter': Distance from centroid for each cell + - 'DistancesFromExterior': Distance from exterior for each cell + - 'NormalizedDistancesFromCenter': Normalized distances (0-1) + """ + # Handle both DataFrame and dict input + if isinstance(coords, pandas.DataFrame): + object_ids = coords["object_id"].to_numpy() + coords_array = coords[["x", "y", "z"]].to_numpy() + else: + object_ids = numpy.array(coords["object_id"]) + coords_array = numpy.column_stack([coords["x"], coords["y"], coords["z"]]) + if len(coords_array) == 0: + results = { + "object_id": [], + "ShellAssignments": [], + "DistancesFromCenter": [], + "DistancesFromExterior": [], + "NormalizedDistancesFromCenter": [], + "MaxShellsUsed": [], + } + centroid = None + return results, centroid + n_cells = len(coords_array) + if centroid is None: + centroid = calculate_centroid(coords_array) + + # Adjust number of shells for small organoids + max_shells = max(2, n_cells // min_cells_per_shell) + if n_shells > max_shells: + print( + f" WARNING: {n_cells} cells with {n_shells} shells = " + f"{n_cells / n_shells:.1f} cells/shell" + ) + print(f" Reducing to {max_shells} shells for statistical reliability") + n_shells = max_shells + + # Calculate distances based on method + if method == "mahalanobis": + distances = mahalanobis_distance_from_centroid(coords_array, centroid) + else: # euclidean + distances = euclidean_distance_from_centroid(coords_array, centroid) + + # Normalize distances to 0-1 range + max_distance = numpy.percentile( + distances, 95 + ) # Use 95 percentile to avoid outliers + if max_distance == 0: + # All cells are at the same location; assign all to shell 0 + normalized_distances = numpy.zeros_like(distances) + else: + normalized_distances = distances / max_distance + + # Assign shells (0 = innermost, n_shells-1 = outermost) + shell_assignments = numpy.minimum( + numpy.floor(normalized_distances * n_shells).astype(int), + n_shells - 1, + ) + + # Calculate distance from exterior (inverse of distance from center) + distance_from_exterior = max_distance - distances + + results = { + "object_id": object_ids, + "ShellAssignments": shell_assignments, + "DistancesFromCenter": distances, + "DistancesFromExterior": distance_from_exterior, + "NormalizedDistancesFromCenter": normalized_distances, + "ShellsUsed": n_shells, + } + + return results, centroid + + +def create_results_dataframe(results: dict) -> pandas.DataFrame: + """ + Create a pandas DataFrame with all cell information. + + Parameters + ---------- + results : dict + Results from classify_cells_into_shells + + Returns + ------- + df : pandas.DataFrame + DataFrame with cell information + """ + # Handle both DataFrame and dict input + if isinstance(results, dict): + df = pandas.DataFrame.from_dict(results) + else: + raise ValueError( + "Input must be a results dictionary from classify_cells_into_shells." + ) + + return df + + +def visualize_organoid_shells( + coords: pandas.DataFrame, + classification_results: dict, + title: str = "Organoid Shell Classification", + centroid: numpy.ndarray = None, +) -> plt.figure: + """ + Create 3D visualization of organoid with shell coloring. + + Parameters + ---------- + coords : pandas.DataFrame or dict + Cell coordinates with columns/keys: object_id, x, y, z + classification_results : dict + Results from classify_cells_into_shells + title : str + Plot title + """ + # Handle both DataFrame and dict input + if isinstance(coords, pandas.DataFrame): + x_coords = coords["x"].to_numpy() + y_coords = coords["y"].to_numpy() + z_coords = coords["z"].to_numpy() + else: + x_coords = numpy.array(coords["x"]) + y_coords = numpy.array(coords["y"]) + z_coords = numpy.array(coords["z"]) + + fig = plt.figure(figsize=(14, 6)) + + # 3D scatter plot + ax1 = fig.add_subplot(121, projection="3d") + + shell_assignments = classification_results["ShellAssignments"] + n_shells = classification_results.get( + "ShellsUsed", len(numpy.unique(shell_assignments)) + ) + + # Red to blue color gradient + colors = plt.cm.RdYlBu_r(numpy.linspace(0, 1, n_shells)) + + for shell in range(n_shells): + mask = shell_assignments == shell + if numpy.sum(mask) > 0: # Only plot if shell has cells + ax1.scatter( + x_coords[mask], + y_coords[mask], + z_coords[mask], + c=[colors[shell]], + label=f"Shell {shell + 1} (n={numpy.sum(mask)})", + s=50, + alpha=0.7, + edgecolors="black", + linewidths=0.5, + ) + + if centroid is not None: + ax1.scatter( + *centroid, + c="black", + s=200, + marker="*", + label="Centroid", + edgecolors="white", + linewidths=2, + ) + + ax1.set_xlabel("X") + ax1.set_ylabel("Y") + ax1.set_zlabel("Z") + ax1.set_title(title) + ax1.legend(loc="upper right", fontsize=8) + + # Shell distribution histogram + ax2 = fig.add_subplot(122) + shell_counts = [numpy.sum(shell_assignments == i) for i in range(n_shells)] + bars = ax2.bar( + range(1, n_shells + 1), shell_counts, color=colors, alpha=0.7, edgecolor="black" + ) + ax2.set_xlabel("Shell Number") + ax2.set_ylabel("Number of Cells") + ax2.set_title("Cell Distribution Across Shells") + ax2.set_xticks(range(1, n_shells + 1)) + + # Add percentage labels on bars + total_cells = len(x_coords) + for i, (bar, count) in enumerate(zip(bars, shell_counts)): + height = bar.get_height() + percentage = (count / total_cells) * 100 + ax2.text( + bar.get_x() + bar.get_width() / 2.0, + height, + f"{count}\n({percentage:.1f}%)", + ha="center", + va="bottom", + fontsize=9, + ) + + # Add horizontal line for average + avg_per_shell = total_cells / n_shells + ax2.axhline( + y=avg_per_shell, + color="red", + linestyle="--", + alpha=0.5, + label=f"Average ({avg_per_shell:.1f})", + ) + ax2.legend() + + plt.tight_layout() + return fig + + +def plot_distance_distributions( + classification_results: dict, n_shells: int | None = None +) -> plt.figure: + """ + Plot distance distributions for each shell. + + Parameters + ---------- + classification_results : dict + Results from classify_cells_into_shells + n_shells : int, optional + Number of shells (will use ShellsUsed from results if not provided) + """ + if n_shells is None: + n_shells = classification_results.get( + "ShellsUsed", + len(numpy.unique(classification_results["ShellAssignments"])), + ) + + fig, axes = plt.subplots(1, 2, figsize=(14, 5)) + + shell_assignments = classification_results["ShellAssignments"] + distances_from_center = classification_results["DistancesFromCenter"] + distances_from_exterior = classification_results["DistancesFromExterior"] + + colors = plt.cm.RdYlBu_r(numpy.linspace(0, 1, n_shells)) + + # Distance from center + for shell in range(n_shells): + mask = shell_assignments == shell + if numpy.sum(mask) > 0: + axes[0].hist( + distances_from_center[mask], + bins=20, + alpha=0.5, + color=colors[shell], + label=f"Shell {shell + 1}", + edgecolor="black", + ) + + axes[0].set_xlabel("Distance from Center") + axes[0].set_ylabel("Number of Cells") + axes[0].set_title("Distance from Center Distribution") + axes[0].legend() + + # Distance from exterior + for shell in range(n_shells): + mask = shell_assignments == shell + if numpy.sum(mask) > 0: + axes[1].hist( + distances_from_exterior[mask], + bins=20, + alpha=0.5, + color=colors[shell], + label=f"Shell {shell + 1}", + edgecolor="black", + ) + + axes[1].set_xlabel("Distance from Exterior") + axes[1].set_ylabel("Number of Cells") + axes[1].set_title("Distance from Exterior Distribution") + axes[1].legend() + + plt.tight_layout() + return fig diff --git a/tests/featurization/test_neighbors.py b/tests/featurization/test_neighbors.py new file mode 100644 index 0000000..1dd1c66 --- /dev/null +++ b/tests/featurization/test_neighbors.py @@ -0,0 +1,257 @@ +import sys +import types + +import matplotlib +import numpy as np +import pandas as pd +import pytest + +from zedprofiler.featurization import neighbors as neighbors_module + +matplotlib.use("Agg", force=True) + + +EXPECTED_SHELLS_USED = 2 +EXPECTED_OBJECT_COUNT = 6 +EXPECTED_SECOND_OBJECT_ID = 2 + +if "image_analysis_3D" not in sys.modules: + image_analysis_3D = types.ModuleType("image_analysis_3D") + image_analysis_3D.__path__ = [] # type: ignore[attr-defined] + sys.modules["image_analysis_3D"] = image_analysis_3D + +if "image_analysis_3D.featurization_utils" not in sys.modules: + featurization_utils = types.ModuleType("image_analysis_3D.featurization_utils") + featurization_utils.__path__ = [] # type: ignore[attr-defined] + sys.modules["image_analysis_3D.featurization_utils"] = featurization_utils + +if "image_analysis_3D.featurization_utils.loading_classes" not in sys.modules: + loading_classes = types.ModuleType( + "image_analysis_3D.featurization_utils.loading_classes" + ) + + class ObjectLoader: + pass + + loading_classes.ObjectLoader = ObjectLoader + sys.modules["image_analysis_3D.featurization_utils.loading_classes"] = ( + loading_classes + ) + + +def test_neighbors_expand_box_clamps_to_global_bounds() -> None: + result = neighbors_module.neighbors_expand_box( + min_coor=0, + max_coord=10, + current_min=2, + current_max=8, + expand_by=3, + ) + assert result == (0, 10) + + +def test_neighbors_expand_box_expands_without_clamping() -> None: + result = neighbors_module.neighbors_expand_box( + min_coor=0, + max_coord=10, + current_min=2, + current_max=8, + expand_by=1, + ) + assert result == (1, 9) + + +def test_crop_3d_image_returns_expected_subvolume() -> None: + image = np.arange(3 * 4 * 5).reshape(3, 4, 5) + cropped = neighbors_module.crop_3D_image(image=image, bbox=(1, 1, 1, 3, 4, 5)) + assert cropped.shape == (2, 3, 4) + np.testing.assert_array_equal(cropped, image[1:3, 1:4, 1:5]) + + +def test_compute_neighbors_counts_adjacent_and_distance_neighbors() -> None: + label_image = np.zeros((1, 1, 4), dtype=int) + label_image[0, 0, 0] = 1 + label_image[0, 0, 1] = 2 + label_image[0, 0, 3] = 3 + + object_loader = types.SimpleNamespace( + label_image=label_image, + object_ids=[1, 2, 3], + ) + + result = neighbors_module.compute_neighbors( + object_loader=object_loader, + distance_threshold=1, + anisotropy_factor=1, + ) + + assert result["object_id"] == [1, 2, 3] + assert result["NeighborsCountAdjacent"] == [0, 0, 0] + assert result["NeighborsCountByDistance-1"] == [1, 1, 0] + + +def test_get_coordinates_returns_centroids_for_selected_objects() -> None: + nuclei_mask = np.zeros((2, 2, 2), dtype=int) + nuclei_mask[0, 0, 0] = 1 + nuclei_mask[1, 1, 1] = 2 + + coords = neighbors_module.get_coordinates(nuclei_mask, object_ids=[1, 2]) + + assert list(coords.columns) == ["object_id", "x", "y", "z"] + assert coords.shape == (2, 4) + assert coords.loc[coords["object_id"] == 1, ["x", "y", "z"]].iloc[0].tolist() == [ + 0.0, + 0.0, + 0.0, + ] + assert coords.loc[ + coords["object_id"] == EXPECTED_SECOND_OBJECT_ID, ["x", "y", "z"] + ].iloc[0].tolist() == [ + 1.0, + 1.0, + 1.0, + ] + + +def test_calculate_centroid_uses_column_mean() -> None: + coords = np.array([[0.0, 0.0, 0.0], [2.0, 4.0, 6.0]]) + centroid = neighbors_module.calculate_centroid(coords) + np.testing.assert_allclose(centroid, np.array([1.0, 2.0, 3.0])) + + +def test_euclidean_distance_from_centroid_matches_expected_values() -> None: + coords = np.array([[1.0, 1.0, 1.0], [4.0, 5.0, 6.0]]) + centroid = np.array([1.0, 1.0, 1.0]) + + distances = neighbors_module.euclidean_distance_from_centroid(coords, centroid) + + np.testing.assert_allclose(distances, np.array([0.0, np.sqrt(50.0)])) + + +def test_mahalanobis_distance_falls_back_to_euclidean_for_small_samples() -> None: + coords = np.array([[0.0, 0.0, 0.0], [3.0, 4.0, 0.0]]) + centroid = np.array([0.0, 0.0, 0.0]) + + mahalanobis = neighbors_module.mahalanobis_distance_from_centroid(coords, centroid) + euclidean = neighbors_module.euclidean_distance_from_centroid(coords, centroid) + + np.testing.assert_allclose(mahalanobis, euclidean) + + +def test_mahalanobis_distance_uses_pseudo_inverse_for_singular_covariance() -> None: + coords = np.zeros((20, 3)) + centroid = np.zeros(3) + + distances = neighbors_module.mahalanobis_distance_from_centroid(coords, centroid) + + np.testing.assert_allclose(distances, np.zeros(20)) + + +def test_classify_cells_into_shells_handles_empty_input() -> None: + results, centroid = neighbors_module.classify_cells_into_shells( + coords={"object_id": [], "x": [], "y": [], "z": []} + ) + + assert centroid is None + assert results == { + "object_id": [], + "ShellAssignments": [], + "DistancesFromCenter": [], + "DistancesFromExterior": [], + "NormalizedDistancesFromCenter": [], + "MaxShellsUsed": [], + } + + +def test_classify_cells_into_shells_adjusts_shell_count_and_returns_results() -> None: + coords = pd.DataFrame( + { + "object_id": [1, 2, 3, 4, 5, 6], + "x": [0.0, 1.0, 2.0, 3.0, 4.0, 5.0], + "y": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + "z": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + } + ) + + results, centroid = neighbors_module.classify_cells_into_shells( + coords=coords, + n_shells=5, + method="euclidean", + min_cells_per_shell=3, + ) + + assert centroid.shape == (3,) + assert results["ShellsUsed"] == EXPECTED_SHELLS_USED + assert len(results["object_id"]) == EXPECTED_OBJECT_COUNT + assert len(results["ShellAssignments"]) == EXPECTED_OBJECT_COUNT + assert len(results["DistancesFromCenter"]) == EXPECTED_OBJECT_COUNT + assert len(results["DistancesFromExterior"]) == EXPECTED_OBJECT_COUNT + assert len(results["NormalizedDistancesFromCenter"]) == EXPECTED_OBJECT_COUNT + + +def test_create_results_dataframe_builds_dataframe_from_results_dict() -> None: + results = { + "object_id": np.array([1, 2]), + "ShellAssignments": np.array([0, 1]), + "DistancesFromCenter": np.array([0.5, 1.5]), + "DistancesFromExterior": np.array([1.0, 0.0]), + "NormalizedDistancesFromCenter": np.array([0.25, 1.0]), + "ShellsUsed": 2, + } + + df = neighbors_module.create_results_dataframe(results) + + assert list(df.columns) == [ + "object_id", + "ShellAssignments", + "DistancesFromCenter", + "DistancesFromExterior", + "NormalizedDistancesFromCenter", + "ShellsUsed", + ] + assert df.shape == (2, 6) + + +def test_create_results_dataframe_rejects_non_dict_input() -> None: + with pytest.raises(ValueError, match="Input must be a results dictionary"): + neighbors_module.create_results_dataframe([1, 2, 3]) + + +def test_visualize_organoid_shells_returns_figure() -> None: + coords = pd.DataFrame( + { + "object_id": [1, 2, 3], + "x": [0.0, 1.0, 2.0], + "y": [0.0, 1.0, 2.0], + "z": [0.0, 1.0, 2.0], + } + ) + classification_results = { + "ShellAssignments": np.array([0, 1, 1]), + "ShellsUsed": 2, + } + + fig = neighbors_module.visualize_organoid_shells( + coords=coords, + classification_results=classification_results, + centroid=np.array([1.0, 1.0, 1.0]), + ) + + expected_axes = 2 + assert len(fig.axes) == expected_axes + fig.canvas.draw() + + +def test_plot_distance_distributions_returns_figure() 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