Source code for spateo.tdr.morphometrics.morphology

from typing import Any, Dict, Optional, Tuple, Union

import numpy as np
from pyvista import DataSet, PolyData, UnstructuredGrid
from sklearn.neighbors import KernelDensity

from spateo.logging import logger_manager as lm
from spateo.tdr.models import add_model_labels

[docs]def model_morphology( model: Union[PolyData, UnstructuredGrid], pc: Optional[PolyData or UnstructuredGrid] = None, ) -> Dict[str, Union[float, Any]]: """ Return the basic morphological characteristics of model, including model volume, model surface area, volume / surface area ratio,etc. Args: model: A reconstructed surface model or volume model. pc: A point cloud representing the number of cells. Returns: morphology: A dictionary containing the following model morphological features: morphology['Length(x)']: Length (x) of model. morphology['Width(y)']: Width (y) of model. morphology['Height(z)']: Height (z) of model. morphology['Surface_area']: Surface area of model. morphology['Volume']: Volume of model. morphology['V/SA_ratio']: Volume / surface area ratio of model; morphology['cell_density']: Cell density of model. """ model_surf = model.extract_surface() morphology = {} # Length, width and height of model model_bounds = np.asarray(model.bounds) model_x = round(abs(model_bounds[1] - model_bounds[0]), 5) model_y = round(abs(model_bounds[3] - model_bounds[2]), 5) model_z = round(abs(model_bounds[5] - model_bounds[4]), 5) morphology["Length(x)"], morphology["Width(y)"], morphology["Height(z)"] = ( model_x, model_y, model_z, ) lm.main_info(f"Length (x) of model: {morphology['Length(x)']};", indent_level=1) lm.main_info(f"Width (y) of model: {morphology['Width(y)']};", indent_level=1) lm.main_info(f"Height (z) of model: {morphology['Height(z)']};", indent_level=1) # Surface area of model model_sa = round(model_surf.area, 5) morphology["Surface_area"] = model_sa lm.main_info(f"Surface area of model: {morphology['Surface_area']};", indent_level=1) # Volume of model model_v = round(model_surf.volume, 5) morphology["Volume"] = model_v lm.main_info(f"Volume of model: {morphology['Volume']};", indent_level=1) # Volume / surface area ratio of model model_vsa = round(model_v / model_sa, 5) morphology["V/SA_ratio"] = model_vsa lm.main_info(f"Volume / surface area ratio of model: {morphology['V/SA_ratio']}.", indent_level=1) # cell density if not (pc is None): model_cd = round(pc.n_points / model_v, 5) morphology["cell_density"] = model_cd lm.main_info(f"Cell density of model: {morphology['cell_density']}.", indent_level=1) return morphology
[docs]def pc_KDE( pc: PolyData, key_added: str = "kde", kernel: str = "gaussian", bandwidth: float = 1.0, colormap: Union[str, list, dict] = "hot_r", alphamap: Union[float, list, dict] = 1.0, inplace: bool = False, ) -> Tuple[Union[DataSet, PolyData, None], Optional[str]]: """ Calculate the kernel density of a 3D point cloud model. Args: pc: A point cloud model. key_added: The key under which to add the labels. kernel: The kernel to use. Available `kernel` are: * `'gaussian'` * `'tophat'` * `'epanechnikov'` * `'exponential'` * `'linear'` * `'cosine'` bandwidth: The bandwidth of the kernel. colormap: Colors to use for plotting pcd. The default colormap is `'hot_r'`. alphamap: The opacity of the colors to use for plotting pcd. The default alphamap is `1.0`. inplace: Updates model in-place. Returns: pc: Reconstructed 3D point cloud, which contains the following properties: `pc[key_added]`, the kernel density. plot_cmap: Recommended colormap parameter values for plotting. """ pc = pc.copy() if not inplace else pc coords = pc.points pc_kde = KernelDensity(kernel=kernel, bandwidth=bandwidth).fit(coords).score_samples(coords) _, plot_cmap = add_model_labels( model=pc, labels=pc_kde, key_added=key_added, where="point_data", colormap=colormap, alphamap=alphamap, inplace=True, ) return pc if not inplace else None, plot_cmap