From f7e961c9e1fdde62ebcb5e889942820eba010cd7 Mon Sep 17 00:00:00 2001 From: jcopperm Date: Tue, 27 Aug 2024 14:58:29 -0700 Subject: [PATCH] added spatial package --- celltraj/spatial.py | 1325 +++++++++++++++ celltraj/trajectory.py | 48 +- docs/_modules/celltraj/spatial.html | 1481 +++++++++++++++++ docs/_modules/celltraj/trajectory.html | 190 ++- docs/_modules/index.html | 1 + docs/_sources/api.rst.txt | 10 + docs/_sources/celltraj.rst.txt | 8 + docs/api.html | 871 +++++++++- docs/build/doctrees/api.doctree | Bin 1338230 -> 1627079 bytes docs/build/doctrees/celltraj.doctree | Bin 1754751 -> 2045034 bytes docs/build/doctrees/environment.pickle | Bin 4952068 -> 5792196 bytes .../build/html/_modules/celltraj/spatial.html | 1481 +++++++++++++++++ .../html/_modules/celltraj/trajectory.html | 190 ++- docs/build/html/_modules/index.html | 1 + docs/build/html/_sources/api.rst.txt | 10 + docs/build/html/_sources/celltraj.rst.txt | 8 + docs/build/html/api.html | 871 +++++++++- docs/build/html/celltraj.html | 849 +++++++++- docs/build/html/genindex.html | 51 +- docs/build/html/modules.html | 21 + docs/build/html/objects.inv | Bin 2532 -> 2681 bytes docs/build/html/py-modindex.html | 5 + docs/build/html/searchindex.js | 2 +- docs/celltraj.html | 849 +++++++++- docs/genindex.html | 51 +- docs/modules.html | 21 + docs/objects.inv | Bin 2532 -> 2681 bytes docs/py-modindex.html | 5 + docs/searchindex.js | 2 +- docs/source/api.rst | 10 + docs/source/celltraj.rst | 8 + 31 files changed, 8326 insertions(+), 43 deletions(-) create mode 100644 celltraj/spatial.py create mode 100644 docs/_modules/celltraj/spatial.html create mode 100644 docs/build/html/_modules/celltraj/spatial.html diff --git a/celltraj/spatial.py b/celltraj/spatial.py new file mode 100644 index 0000000..509e78b --- /dev/null +++ b/celltraj/spatial.py @@ -0,0 +1,1325 @@ +import matplotlib.pyplot as plt +import numpy as np +import trajectory +import imageprep as imprep +import features +import model +import utilities +import pyemma.coordinates as coor +import scipy +import skimage +from nanomesh import Mesher +import fipy +import sklearn +from numpy.linalg import det +from scipy.stats import dirichlet +import pandas as pd +from pyntcloud import PyntCloud +import ot + +def get_border_dict(labels,states=None,radius=10,vdist=None,return_nnindex=True,return_nnvector=True,return_curvature=True,scale=None,**border_args): + """ + Computes the border properties of labeled regions in a segmented image. + + This function identifies the borders of labeled regions in a given image and calculates various properties + such as nearest neighbor indices, vectors, and curvature. It can also return the scaled distances if specified. + + Parameters + ---------- + labels : ndarray + A 2D or 3D array where each element represents a label, identifying different regions in the image. + states : ndarray, optional + An array indicating the state of each labeled region. If provided, states are used to differentiate + regions. If None, all regions are assumed to have the same state. + radius : float, optional + The radius for finding nearest neighbors around each border point (default is 10). + vdist : ndarray, optional + An array representing distances or potential values at each point in the image. If provided, this + array is used to calculate border distances. + return_nnindex : bool, optional + If True, returns the nearest neighbor index for each border point (default is True). + return_nnvector : bool, optional + If True, returns the vector pointing to the nearest neighbor for each border point (default is True). + return_curvature : bool, optional + If True, calculates and returns the curvature at each border point (default is True). + scale : list or ndarray, optional + Scaling factors for each dimension of the labels array. If provided, scales the labels accordingly. + **border_args : dict, optional + Additional arguments to control border property calculations, such as 'knn' for the number of nearest + neighbors when computing curvature. + + Returns + ------- + border_dict : dict + A dictionary containing the computed border properties: + - 'pts': ndarray of float, coordinates of the border points. + - 'index': ndarray of int, indices of the regions to which each border point belongs. + - 'states': ndarray of int, states of the regions to which each border point belongs. + - 'nn_index': ndarray of int, nearest neighbor indices for each border point (if `return_nnindex` is True). + - 'nn_states': ndarray of int, states of the nearest neighbors (if `return_nnindex` is True). + - 'nn_pts': ndarray of float, coordinates of the nearest neighbors (if `return_nnvector` is True). + - 'nn_inds': ndarray of int, indices of the nearest neighbors (if `return_nnvector` is True). + - 'n': ndarray of float, normals at each border point (if `return_curvature` is True). + - 'c': ndarray of float, curvature at each border point (if `return_curvature` is True). + - 'vdist': ndarray of float, scaled distances at each border point (if `vdist` is provided). + + Notes + ----- + - This function is useful for analyzing cell shapes and their interactions in spatially resolved images. + - The nearest neighbor indices and vectors can help understand cell-cell interactions and local neighborhood structures. + - The curvature values can provide insights into the geometrical properties of cell boundaries. + + Examples + -------- + >>> labels = np.array([[0, 1, 1, 0], [0, 1, 1, 0], [2, 2, 0, 0], [0, 0, 0, 0]]) + >>> scale = [1.0, 1.0] + >>> border_dict = get_border_dict(labels, scale=scale, return_nnindex=True, return_nnvector=True) + >>> print(border_dict['pts']) + [[0., 1.], [0., 2.], [1., 1.], [2., 0.], [2., 1.]] + >>> print(border_dict['nn_index']) + [2, 2, 2, 1, 1] + """ + if scale is None: + scale=np.ones(labels.ndim) + else: + labels=scipy.ndimage.zoom(labels,zoom=scale,order=0) + labels=labels.astype(int) + border_dict={} + border_dict['scale']=scale + if states is None: + states=np.zeros(np.max(labels+1)).astype(int);states[0]=0;states[1:]=1 + border=skimage.segmentation.find_boundaries(labels,mode='inner') + ind=np.where(border) + border_pts=np.array(ind).astype(float).T + border_dict['pts']=border_pts + border_index=labels[ind] + border_dict['index']=border_index + border_states=states[border_index] + border_dict['states']=border_states + if return_nnindex: + contact_labels=features.get_contact_labels(labels,radius=radius) + contact_inds=contact_labels[border>0] + border_dict['nn_index']=contact_inds + border_dict['nn_states']=states[contact_inds] + if return_nnvector: + iset=np.unique(labels) + iset=iset[iset>0] + nn_labels=[None]*(np.max(iset)+1) + inds_labels=[None]*(np.max(iset)+1) + border_nn_pts=np.ones_like(border_pts)*np.nan + border_nn_inds=np.zeros(border_pts.shape[0]).astype(int) + for i in iset: + inds_labels[i]=np.where(border_index==i)[0] + nn_labels[i] = sklearn.neighbors.NearestNeighbors(n_neighbors=1, radius=1.,algorithm='ball_tree').fit(border_pts[inds_labels[i]]) + for i in iset: + indi=inds_labels[i] + jset=np.unique(contact_inds[indi]) + jset=np.setdiff1d(jset,[i,0]) + for j in jset: + indj=np.where(contact_inds[indi]==j)[0] + borderij_pts=border_pts[indi[indj]] + distij,indij_nn=nn_labels[j].kneighbors(borderij_pts) + indij_nn=np.squeeze(indij_nn) + borderj_pts=border_pts[inds_labels[j][indij_nn]] + border_nn_pts[indi[indj]]=borderj_pts + border_nn_inds[indi[indj]]=inds_labels[j][indij_nn] + border_dict['nn_pts']=border_nn_pts + border_dict['nn_inds']=border_nn_inds + if return_curvature: + n=np.zeros_like(border_pts) + c=np.zeros(border_pts.shape[0]) + if 'knn' in border_args.keys(): + knn=border_args['knn'] + else: + knn=12 + iset=np.unique(labels) + iset=iset[iset>0] + for i in iset: + msk=labels==i + indi=np.where(border_index==i)[0] + border_pts_i,n_i,c_i=get_surface_points(msk,return_normals=True,return_curvature=True) + n[indi]=n_i + c[indi]=c_i + border_dict['n']=n + border_dict['c']=c + if vdist is not None: + vdist_border=vdist[ind] + border_dict['vdist']=vdist_border + return border_dict + +def get_surface_points(msk,return_normals=False,return_curvature=False,knn=20): + """ + Computes the surface points of a labeled mask and optionally calculates normals and curvature. + + This function identifies the surface (border) points of a given labeled mask using segmentation techniques. + It can also compute normals (perpendicular vectors to the surface) and curvature values at these points if requested. + + Parameters + ---------- + msk : ndarray + A 3D binary or labeled array representing the mask of regions of interest. Non-zero values represent the regions. + return_normals : bool, optional + If True, computes and returns the normals at each surface point (default is False). + return_curvature : bool, optional + If True, computes and returns the curvature at each surface point (default is False). + knn : int, optional + The number of nearest neighbors to consider when calculating normals and curvature (default is 20). + + Returns + ------- + border_pts : ndarray + A 2D array of shape (N, 3) containing the coordinates of the border points, where N is the number of border points found. + n : ndarray, optional + A 2D array of shape (N, 3) containing the normal vectors at each border point. Only returned if `return_normals` is True. + c : ndarray, optional + A 1D array of length N containing the curvature values at each border point. Only returned if `return_curvature` is True. + + Notes + ----- + - The function uses eigen decomposition on the neighborhood of each surface point to compute normals and curvature. + - The normals are adjusted to face outward from the surface. If normals face inward, they are flipped. + - Curvature is calculated as the ratio of the smallest eigenvalue to the sum of all eigenvalues, giving an estimate of local surface bending. + + Examples + -------- + >>> msk = np.zeros((100, 100, 100), dtype=int) + >>> msk[40:60, 40:60, 40:60] = 1 # A cube in the center + >>> border_pts = get_surface_points(msk) + >>> border_pts.shape + (960, 3) + + >>> border_pts, normals = get_surface_points(msk, return_normals=True) + >>> border_pts.shape, normals.shape + ((960, 3), (960, 3)) + + >>> border_pts, normals, curvature = get_surface_points(msk, return_normals=True, return_curvature=True) + >>> border_pts.shape, normals.shape, curvature.shape + ((960, 3), (960, 3), (960,)) + """ + border=skimage.segmentation.find_boundaries(msk,mode='inner') + ind=np.where(border) + border_pts=np.array(ind).astype(float).T + if return_normals or return_curvature: + cloud=PyntCloud(pd.DataFrame(data=border_pts,columns=['x','y','z'])) + k_neighbors = cloud.get_neighbors(k=knn) + ev = cloud.add_scalar_field("eigen_decomposition", k_neighbors=k_neighbors) + w = np.array([cloud.points[ev[0]],cloud.points[ev[1]],cloud.points[ev[2]]]).T + v = np.array([[cloud.points[ev[3]],cloud.points[ev[4]],cloud.points[ev[5]]],[cloud.points[ev[6]],cloud.points[ev[7]],cloud.points[ev[8]]],[cloud.points[ev[9]],cloud.points[ev[10]],cloud.points[ev[11]]]]).T + border_pts_trans=border_pts+2.*v[:,2,:] + ind_trans=border_pts_trans.astype(int) + for iax in range(border_pts.shape[1]): + inds_max=ind_trans[:,iax]>msk.shape[iax]-1 + ind_trans[inds_max,iax]=msk.shape[iax]-1 + inds_min=ind_trans[:,iax]<0 + ind_trans[inds_min,iax]=0 + infacing_normals=msk[ind_trans[:,0],ind_trans[:,1],ind_trans[:,2]] + n = v[:,2,:] + n[infacing_normals,:]=-1.*n[infacing_normals,:] + if return_curvature: + c=np.divide(w[:,2],np.sum(w,axis=1)) + pts_nnmean=np.mean(border_pts[k_neighbors,:],axis=1) + dn=np.sum(np.multiply(border_pts-pts_nnmean,n),axis=1) + c[dn<0]=-1.*c[dn<0] + return border_pts,n,c + else: + return border_pts,n + else: + return border_pts + +def get_adhesive_displacement(border_dict,surf_force_function,eps,alpha=1.,maxd=None,rmin=None,rmax=None,active_neighbor_states=np.array([1]),active_displacement_states=np.array([]),symmetrize=True,**force_args): + """ + Computes the adhesive displacement between cell surfaces using a specified surface force function. + + This function calculates the displacement of cell surfaces based on adhesive forces. It uses the states and positions of + neighboring cells to determine active interfaces and apply force-based displacements. Optionally, the displacements can + be symmetrized to ensure consistency across cell borders. + + Parameters + ---------- + border_dict : dict + A dictionary containing border information, including: + - 'pts': ndarray of shape (N, 3), coordinates of border points. + - 'nn_pts': ndarray of shape (N, 3), coordinates of nearest neighbor points. + - 'states': ndarray of shape (N,), states of the border points. + - 'nn_states': ndarray of shape (N,), states of the nearest neighbor points. + - 'nn_inds': ndarray of shape (N,), indices of the nearest neighbor points. + + surf_force_function : callable + A function that computes the surface force based on distance and other parameters. + Should take distance, epsilon, and additional arguments as inputs. + + eps : ndarray + A 2D array where `eps[i, j]` represents the interaction strength between state `i` and state `j`. + + alpha : float, optional + A scaling factor for the displacement magnitude (default is 1.0). + + maxd : float, optional + The maximum allowed displacement. Displacements will be scaled if any calculated displacements exceed this value. + + rmin : float, optional + The minimum interaction distance. Displacements calculated from distances smaller than `rmin` will be set to `rmin`. + + rmax : float, optional + The maximum interaction distance. Displacements calculated from distances larger than `rmax` will be set to `rmax`. + + active_neighbor_states : ndarray, optional + An array specifying the states of neighbors that are active for interaction (default is np.array([1])). + + active_displacement_states : ndarray, optional + An array specifying the states of cells that are active for displacement (default is an empty array, which means all states are active). + + symmetrize : bool, optional + If True, the displacements are symmetrized to ensure consistency across borders (default is True). + + **force_args : dict, optional + Additional arguments to be passed to the `surf_force_function`. + + Returns + ------- + dr : ndarray + A 2D array of shape (N, 3) representing the displacements of the border points. + + Notes + ----- + - The function filters out inactive or excluded states before computing the displacement. + - Displacement is scaled using the surface force and optionally capped by `maxd`. + - Symmetrization ensures that the displacement is consistent from both interacting cells' perspectives. + + Examples + -------- + >>> border_dict = { + ... 'pts': np.random.rand(100, 3), + ... 'nn_pts': np.random.rand(100, 3), + ... 'states': np.random.randint(0, 2, 100), + ... 'nn_states': np.random.randint(0, 2, 100), + ... 'nn_inds': np.random.randint(0, 100, 100) + ... } + >>> surf_force_function = lambda r, eps: -eps * (r - 1) + >>> eps = np.array([[0.1, 0.2], [0.2, 0.3]]) + >>> dr = get_adhesive_displacement(border_dict, surf_force_function, eps, alpha=0.5) + >>> dr.shape + (100, 3) + """ + active_inds=np.where(np.isin(border_dict['nn_states'],active_neighbor_states))[0] #boundaries between surfaces are in force equilibrium + exclude_states=np.setdiff1d(active_neighbor_states,np.unique(border_dict['states'])) + exclude_inds=np.where(np.isin(border_dict['states'],exclude_states))[0] + active_inds=np.setdiff1d(active_inds,exclude_inds) + dx_surf=border_dict['nn_pts'][active_inds]-border_dict['pts'][active_inds] + dr=np.zeros_like(border_dict['pts']) + rdx_surf=np.linalg.norm(dx_surf,axis=1) + if rmin is not None: + rdx_surf[rdx_surf>> border_dict = { + ... 'n': np.random.rand(100, 3), + ... 'c': np.random.rand(100), + ... 'states': np.random.randint(0, 2, 100) + ... } + >>> sts = np.array([1.0, 0.5]) + >>> dx = get_surface_displacement(border_dict, sts=sts, alpha=0.2, maxd=0.1) + >>> dx.shape + (100, 3) + """ + if n is None: + n=border_dict['n'] + if c is None: + c=border_dict['c'] + if sts is not None: + c=np.multiply(c,sts[border_dict['states']]) + dx=np.multiply(n,np.array([-c*alpha,-c*alpha,-c*alpha]).T) + if maxd is not None: + rdx=np.linalg.norm(dx,axis=1) + maxr=np.max(np.abs(rdx)) + dx=dx*(maxd/maxr) + return dx + +def get_surface_displacement_deviation(border_dict,border_pts_prev,exclude_states=None,n=None,knn=12,use_eigs=False,alpha=1.,maxd=None): + """ + Calculates the surface displacement deviation using optimal transport between current and previous border points. + + This function computes the displacement of cell surface points based on deviations from previous positions. + The displacement can be modified by normal vectors, filtered by specific states, and controlled by curvature + or variance in displacement. + + Parameters + ---------- + border_dict : dict + A dictionary containing information about the current cell borders, including: + - 'pts': ndarray of shape (N, 3), current border points. + - 'states': ndarray of shape (N,), states of the border points. + + border_pts_prev : ndarray + A 2D array of shape (N, 3) containing the positions of border points from the previous time step. + + exclude_states : array-like, optional + A list or array of states to exclude from displacement calculations (default is None, meaning no states are excluded). + + n : ndarray, optional + Normal vectors at the border points. If None, normal vectors are calculated based on the optimal transport displacement (default is None). + + knn : int, optional + The number of nearest neighbors to consider when computing variance or eigen decomposition for curvature calculations (default is 12). + + use_eigs : bool, optional + If True, use eigen decomposition to calculate the displacement deviation; otherwise, use variance (default is False). + + alpha : float, optional + A scaling factor for the displacement magnitude (default is 1.0). + + maxd : float, optional + The maximum allowed displacement. If specified, the displacement is scaled to ensure it does not exceed this value (default is None). + + Returns + ------- + dx : ndarray + A 2D array of shape (N, 3) representing the displacements of the border points. + + Notes + ----- + - The function uses optimal transport to calculate deviations between current and previous border points. + - The surface displacement deviation is inspired by the "mother of all non-linearities"-- the Kardar-Parisi-Zhang non-linear surface growth universality class. + - Displacement deviations are scaled by the normal vectors and can be controlled by `alpha` and capped by `maxd`. + - If `use_eigs` is True, eigen decomposition of the displacement field is used to calculate deviations, otherwise variance is used. + - Excludes displacements for specified states, if `exclude_states` is provided. + + Examples + -------- + >>> border_dict = { + ... 'pts': np.random.rand(100, 3), + ... 'states': np.random.randint(0, 2, 100) + ... } + >>> border_pts_prev = np.random.rand(100, 3) + >>> dx = get_surface_displacement_deviation(border_dict, border_pts_prev, exclude_states=[0], alpha=0.5, maxd=0.1) + >>> dx.shape + (100, 3) + """ + border_pts=border_dict['pts'] + inds_ot,dx_ot=get_ot_dx(border_pts,border_pts_prev) + rdx_ot = np.linalg.norm(dx_ot,axis=1) + if n is not None: + dx_ot=np.multiply(dx_ot,n) + else: + n=np.divide(dx_ot,np.array([rdx_ot,rdx_ot,rdx_ot]).T) + cloud=PyntCloud(pd.DataFrame(data=border_pts,columns=['x','y','z'])) + k_neighbors = cloud.get_neighbors(k=knn) + if use_eigs: + cloud.points['x']=dx_ot[:,0] + cloud.points['y']=dx_ot[:,1] + cloud.points['z']=dx_ot[:,2] + ev = cloud.add_scalar_field("eigen_decomposition", k_neighbors=k_neighbors) + w = np.array([cloud.points[ev[0]],cloud.points[ev[1]],cloud.points[ev[2]]]).T + dh = np.sum(w,axis=1) + else: + dh=np.var(rdx_ot[k_neighbors],axis=1) + dx=np.multiply(n,np.array([np.multiply(dh,alpha),np.multiply(dh,alpha),np.multiply(dh,alpha)]).T) + if exclude_states is not None: + ind_exclude=np.where(np.isin(border_dict['states'],exclude_states))[0] + dx[ind_exclude,:]=0. + if maxd is not None: + rdx=np.linalg.norm(dx,axis=1) + maxr=np.max(np.abs(rdx)) + dx=dx*(maxd/maxr) + return dx + +def get_nuc_displacement(border_pts_new,border_dict,Rset,nuc_states=np.array([1]).astype(int),**nuc_args): + border_pts=border_pts_new + border_index=border_dict['index'] + border_states=border_dict['states'] + ind_nucs=np.where(np.isin(border_states,nuc_states))[0] + iset=np.unique(border_index[ind_nucs]) + iset=iset[iset>0] + dnuc=np.zeros_like(border_pts) + for i in iset: + print(f'nucd {i}') + pts=border_pts[border_index==i,:] + xc=np.mean(pts,axis=0) + dnuc_c=get_nuc_dx(pts,xc,border_dict['n'][border_index==i],Rset[i],**nuc_args) + dnuc[border_index==i,:]=dnuc_c + return dnuc + +def get_flux_displacement(border_dict,border_features=None,flux_function=None,exclude_states=None,n=None,fmeans=0.,fsigmas=0.,random_seed=None,alpha=1.,maxd=None,**flux_function_args): + """ + Calculates the displacement of border points using flux information. + + This function computes the displacement of border points by applying a flux function or random sampling + based on mean and standard deviation values. The displacements can be controlled by normal vectors, + excluded for certain states, and scaled to a maximum displacement. + + Parameters + ---------- + border_dict : dict + A dictionary containing information about the current cell borders, including: + - 'n': ndarray of shape (N, 3), normal vectors at the border points. + - 'states': ndarray of shape (N,), states of the border points. + + border_features : ndarray, optional + Features at the border points used as input to the flux function (default is None). + + flux_function : callable, optional + A function that takes `border_features` and additional arguments to compute mean (`fmeans`) and standard + deviation (`fsigmas`) of the flux at each border point (default is None, meaning random sampling is used). + + exclude_states : array-like, optional + A list or array of states to exclude from displacement calculations (default is None, meaning no states are excluded). + + n : ndarray, optional + Normal vectors at the border points. If None, normal vectors are taken from `border_dict['n']` (default is None). + + fmeans : float or array-like, optional + Mean flux value(s) for random sampling (default is 0.). If `flux_function` is provided, this value is ignored. + + fsigmas : float or array-like, optional + Standard deviation of flux value(s) for random sampling (default is 0.). If `flux_function` is provided, this value is ignored. + + random_seed : int, optional + Seed for the random number generator to ensure reproducibility (default is None). + + alpha : float, optional + A scaling factor for the displacement magnitude (default is 1.0). + + maxd : float, optional + The maximum allowed displacement. If specified, the displacement is scaled to ensure it does not exceed this value (default is None). + + **flux_function_args : dict, optional + Additional arguments to pass to the `flux_function`. + + Returns + ------- + dx : ndarray + A 2D array of shape (N, 3) representing the displacements of the border points. + + Notes + ----- + - The function can use a flux function to calculate displacements based on border features or perform random sampling + with specified mean and standard deviation values. + - Displacement deviations are scaled by normal vectors and can be controlled by `alpha` and capped by `maxd`. + - Excludes displacements for specified states, if `exclude_states` is provided. + - The random number generator can be seeded for reproducibility using `random_seed`. + + Examples + -------- + >>> border_dict = { + ... 'n': np.random.rand(100, 3), + ... 'states': np.random.randint(0, 2, 100) + ... } + >>> dx = get_flux_displacement(border_dict, fmeans=0.5, fsigmas=0.1, random_seed=42, alpha=0.8, maxd=0.2) + >>> dx.shape + (100, 3) + """ + if n is None: + n=border_dict['n'] + npts=n.shape[0] + if flux_function is None: + if np.isscalar(fmeans): + fmeans=fmeans*np.ones(n.shape[0]) + if np.isscalar(fsigmas): + fsigmas=fsigmas*np.ones(n.shape[0]) + else: + if border_features is None: + print('provide border_features as input to flux_function') + return 1 + if not np.isscalar(fmeans): + print('fmeans ignored, defaulting to flux_function') + fmeans,fsigmas=flux_function(border_features,**flux_function_args) + if random_seed is not None: + np.random.seed(random_seed) + f=np.random.normal(loc=fmeans,scale=fsigmas,size=npts) + dx=np.multiply(n,np.array([f*alpha,f*alpha,f*alpha]).T) + if maxd is not None: + rdx=np.linalg.norm(dx,axis=1) + maxr=np.max(np.abs(rdx)) + dx=dx*(maxd/maxr) + if exclude_states is not None: + ind_exclude=np.where(np.isin(border_dict['states'],exclude_states))[0] + dx[ind_exclude,:]=0. + return dx + +def get_ot_dx(pts0,pts1,return_dx=True,return_cost=False): + """ + Computes the optimal transport (OT) displacement and cost between two sets of points. + + This function calculates the optimal transport map between two sets of points `pts0` and `pts1` using the + Earth Mover's Distance (EMD). It returns the indices of the optimal transport matches and the displacement + vectors, as well as the transport cost if specified. + + Parameters + ---------- + pts0 : ndarray + A 2D array of shape (N, D), representing the first set of points, where N is the number of points + and D is the dimensionality. + + pts1 : ndarray + A 2D array of shape (M, D), representing the second set of points, where M is the number of points + and D is the dimensionality. + + return_dx : bool, optional + If True, returns the displacement vectors between matched points (default is True). + + return_cost : bool, optional + If True, returns the total transport cost (default is False). + + Returns + ------- + inds_ot : ndarray + A 1D array of shape (N,), representing the indices of the points in `pts1` that are matched to + the points in `pts0` according to the optimal transport map. + + dx : ndarray, optional + A 2D array of shape (N, D), representing the displacement vectors from the points in `pts0` to the + matched points in `pts1`. Returned only if `return_dx` is True. + + cost : float, optional + The total optimal transport cost, calculated as the sum of the transport cost between matched points. + Returned only if `return_cost` is True. + + Notes + ----- + - The function uses the Earth Mover's Distance (EMD) for computing the optimal transport map, which minimizes + the cost of moving mass from `pts0` to `pts1`. + - The cost is computed as the sum of the pairwise distances weighted by the transport plan. + - Displacement vectors are computed as the difference between points in `pts0` and their matched points in `pts1`. + + Examples + -------- + >>> pts0 = np.array([[0, 0], [1, 1], [2, 2]]) + >>> pts1 = np.array([[0, 1], [1, 0], [2, 1]]) + >>> inds_ot, dx, cost = get_ot_dx(pts0, pts1, return_dx=True, return_cost=True) + >>> inds_ot + array([0, 1, 2]) + >>> dx + array([[ 0, -1], + [ 0, 1], + [ 0, 1]]) + >>> cost + 1.0 + """ + w0=np.ones(pts0.shape[0])/pts0.shape[0] + w1=np.ones(pts1.shape[0])/pts1.shape[0] + M = ot.dist(pts0,pts1) + G0 = ot.emd(w0,w1,M) + if return_cost: + cost=np.sum(np.multiply(G0.flatten(),M.flatten())) + if return_dx: + inds_ot=np.argmax(G0,axis=1) + dx=pts0-pts1[inds_ot,:] + return inds_ot,dx,cost + else: + return cost + else: + inds_ot=np.argmax(G0,axis=1) + dx=pts0-pts1[inds_ot,:] + return inds_ot,dx + +def get_ot_displacement(border_dict,border_dict_prev,parent_index=None): + """ + Computes the optimal transport (OT) displacement between two sets of boundary points. + + This function calculates the optimal transport displacements between the points in the current + boundary (`border_dict`) and the points in the previous boundary (`border_dict_prev`). It finds + the optimal matches and computes the displacement vectors for each point. + + Parameters + ---------- + border_dict : dict + A dictionary containing the current boundary points and related information. Expected keys include: + - 'index': ndarray of shape (N,), unique labels of current boundary points. + - 'pts': ndarray of shape (N, D), coordinates of the current boundary points. + + border_dict_prev : dict + A dictionary containing the previous boundary points and related information. Expected keys include: + - 'index': ndarray of shape (M,), unique labels of previous boundary points. + - 'pts': ndarray of shape (M, D), coordinates of the previous boundary points. + + parent_index : ndarray, optional + An array of unique labels (indices) to use for matching previous boundary points. If not provided, + `index1` from `border_dict` will be used to match with `border_dict_prev`. + + Returns + ------- + inds_ot : ndarray + A 1D array containing the indices of the optimal transport matches for the current boundary points + from the previous boundary points. + + dxs_ot : ndarray + A 2D array of shape (N, D), representing the displacement vectors from the current boundary points + to the matched previous boundary points. + + Notes + ----- + - The function uses the `get_ot_dx` function to compute the optimal transport match and displacement + between boundary points. + - If `parent_index` is not provided, it defaults to using the indices of the current boundary points + (`index1`). + + Examples + -------- + >>> border_dict = { + ... 'index': np.array([1, 2, 3]), + ... 'pts': np.array([[0, 0], [1, 1], [2, 2]]) + ... } + >>> border_dict_prev = { + ... 'index': np.array([1, 2, 3]), + ... 'pts': np.array([[0, 1], [1, 0], [2, 1]]) + ... } + >>> inds_ot, dxs_ot = get_ot_displacement(border_dict, border_dict_prev) + >>> inds_ot + array([0, 0, 0]) + >>> dxs_ot + array([[ 0, -1], + [ 0, 1], + [ 0, 1]]) + """ + index1=np.unique(border_dict['index']) + index0=np.unique(border_dict_prev['index']) + npts=border_dict['pts'].shape[0] + if parent_index is None: + parent_index=index1.copy() + print('using first set of indices to match previous, provide parent index if indices are not the same') + inds_ot=np.array([]).astype(int) + dxs_ot=np.zeros((0,border_dict['pts'].shape[1])) + for ic in range(index1.size): + inds1=np.where(border_dict['index']==index1[ic])[0] + inds0=np.where(border_dict_prev['index']==parent_index[ic])[0] + ind_ot,dx_ot=get_ot_dx(border_dict['pts'][inds1,:],border_dict_prev['pts'][inds0,:]) + inds_ot=np.append(inds_ot,ind_ot) + dxs_ot=np.append(dxs_ot,dx_ot,axis=0) + return inds_ot,dxs_ot + +def get_labels_fromborderdict(border_dict,labels_shape,active_states=None,surface_labels=None,connected=True,random_seed=None): + """ + Generates a label mask from a dictionary of border points and associated states. + + This function creates a 3D label array by identifying regions enclosed by the boundary points + in `border_dict`. It assigns unique labels to each region based on the indices of the border points. + + Parameters + ---------- + border_dict : dict + A dictionary containing the border points and associated information. Expected keys include: + - 'pts': ndarray of shape (N, D), coordinates of the border points. + - 'index': ndarray of shape (N,), labels for each border point. + - 'states': ndarray of shape (N,), states associated with each border point. + + labels_shape : tuple of ints + The shape of the output labels array. + + active_states : array-like, optional + A list or array of states to include in the labeling. If None, all unique states + in `border_dict['states']` are used. + + surface_labels : ndarray, optional + A pre-existing label array to use as a base. Regions with non-zero values in this + array will retain their labels. + + connected : bool, optional + If True, ensures that labeled regions are connected. Uses the largest connected + component labeling method. + + random_seed : int, optional + A seed for the random number generator to ensure reproducibility. + + Returns + ------- + labels : ndarray + An array of the same shape as `labels_shape` with labeled regions. Each unique region + enclosed by border points is assigned a unique label. + + Notes + ----- + - This function utilizes convex hull and Delaunay triangulation to determine the regions + enclosed by the border points. + - It can be used to generate labels for 3D volumes, based on the locations and states of border points. + - The function includes options for randomization and enforcing connectivity of labeled regions. + + Examples + -------- + >>> border_dict = { + ... 'pts': np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2]]), + ... 'index': np.array([1, 1, 2]), + ... 'states': np.array([1, 1, 2]) + ... } + >>> labels_shape = (3, 3, 3) + >>> labels = get_labels_fromborderdict(border_dict, labels_shape) + >>> print(labels) + array([[[1, 1, 0], + [1, 1, 0], + [0, 0, 0]], + [[1, 1, 0], + [1, 1, 0], + [0, 0, 0]], + [[0, 0, 0], + [0, 0, 0], + [0, 0, 0]]]) + """ + rng = np.random.default_rng(seed=random_seed) + if active_states is None: + active_states=np.unique(border_dict['states']) + active_inds=np.isin(border_dict['states'],active_states) + border_pts=border_dict['pts'][active_inds] + border_index=border_dict['index'][active_inds] + iset=np.unique(border_index) + iset=iset[iset>0] + pts_vol=np.array(np.where(np.ones(labels_shape))).astype(float).T + labels=np.zeros(labels_shape).astype(int) + for i in rng.permutation(iset): + inds=border_index==i + pts=border_pts[inds,:] + #check for 1D + ind_ax1d=np.where((np.min(pts,axis=0)-np.max(pts,axis=0))==0.)[0] + for iax in ind_ax1d: + dg=np.zeros(3) + dg[iax]=.5 + pts=np.concatenate((pts-dg,pts+dg),axis=0) + hull=scipy.spatial.ConvexHull(points=pts) + hull_vertices=pts[hull.vertices] + dhull = scipy.spatial.Delaunay(hull_vertices) + msk=dhull.find_simplex(pts_vol).reshape(labels_shape)>-1 + if connected: + msk=imprep.get_label_largestcc(msk,fill_holes=True) + labels[msk]=i + labels[surface_labels>0]=surface_labels[surface_labels>0] + return labels + +def get_volconstraint_com(border_pts,target_volume,max_iter=1000,converror=.05,dc=1.0): + """ + Adjusts the positions of boundary points to achieve a target volume using a centroid-based method. + + This function iteratively adjusts the positions of boundary points to match a specified target volume. + The adjustment is done by moving points along the direction from the centroid to the points, scaled + by the difference between the current and target volumes. + + Parameters + ---------- + border_pts : ndarray + An array of shape (N, 3) representing the coordinates of the boundary points. + + target_volume : float + The desired volume to be achieved. + + max_iter : int, optional + Maximum number of iterations to perform. Default is 1000. + + converror : float, optional + Convergence error threshold. Iterations stop when the relative volume error is below this value. + Default is 0.05. + + dc : float, optional + A scaling factor for the displacement calculated in each iteration. Default is 1.0. + + Returns + ------- + border_pts : ndarray + An array of shape (N, 3) representing the adjusted coordinates of the boundary points that + approximate the target volume. + + Notes + ----- + - The method assumes a 3D convex hull can be formed by the points, which is adjusted iteratively. + - The convergence is based on the relative difference between the current volume and the target volume. + - If the boundary points are collinear in any dimension, the method adjusts them to ensure a valid convex hull. + + Examples + -------- + >>> border_pts = np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2]]) + >>> target_volume = 10.0 + >>> adjusted_pts = get_volconstraint_com(border_pts, target_volume) + >>> print(adjusted_pts) + array([[ ... ]]) # Adjusted coordinates to approximate the target volume + """ + i=0 + conv=np.inf + npts=border_pts.shape[0] + xc=np.mean(border_pts,axis=0) + dxc=border_pts-xc + rdxc=np.linalg.norm(dxc,axis=1) + dxc_hat=np.divide(dxc,np.array([rdxc,rdxc,rdxc]).T) + dx=np.zeros_like(border_pts) + total_dR=0. + errors=np.array([]) + ind_ax1d=np.where((np.min(border_pts,axis=0)-np.max(border_pts,axis=0))==0.)[0] + for iax in ind_ax1d: + ng=n.copy();ng[iax]=-n[iax] + dg=np.zeros(3) + dg[iax]=.5 + border_pts=np.concatenate((border_pts-dg,border_pts+dg),axis=0) + n=np.concatenate((n,ng),axis=0) + hull=scipy.spatial.ConvexHull(points=border_pts) + while iconverror: + dV=target_volume-hull.volume + dR=dc*dV/hull.area + #total_dR=total_dR+dc*dR + dx=dR*dxc_hat + border_pts=border_pts+dx + hull=scipy.spatial.ConvexHull(points=border_pts) + conv=(hull.volume-target_volume)/target_volume + print(f'error: {conv} totalDR: {total_dR}') + errors=np.append(errors,conv) + i=i+1 + return border_pts[0:npts,:] + +def constrain_volume(border_dict,target_vols,exclude_states=None,**volconstraint_args): + """ + Adjusts the positions of boundary points to achieve target volumes for different regions. + + This function iterates through different regions identified by their indices and adjusts the + boundary points to match specified target volumes. The adjustments are performed using the + `get_volconstraint_com` function, which modifies the boundary points to achieve the desired volume. + + Parameters + ---------- + border_dict : dict + A dictionary containing boundary information, typically with keys: + - 'pts': ndarray of shape (N, 3), coordinates of the boundary points. + - 'index': ndarray of shape (N,), indices identifying the region each point belongs to. + - 'n': ndarray of shape (N, 3), normals at the boundary points. + + target_vols : dict or ndarray + A dictionary or array where each key or index corresponds to a region index, and the value is + the target volume for that region. + + exclude_states : array-like, optional + States to be excluded from volume adjustment. If not provided, all states will be adjusted. + Default is None. + + **volconstraint_args : dict, optional + Additional arguments to pass to the `get_volconstraint_com` function, such as maximum iterations + or convergence criteria. + + Returns + ------- + border_pts_c : ndarray + An array of shape (N, 3) representing the adjusted coordinates of the boundary points. + + Notes + ----- + - This function uses volume constraints to adjust the morphology of different regions based on + specified target volumes. + - The regions are identified by the 'index' values in `border_dict`. + - Points belonging to excluded states are not adjusted. + + Examples + -------- + >>> border_dict = {'pts': np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2]]), + 'index': np.array([1, 1, 2]), + 'n': np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]])} + >>> target_vols = {1: 10.0, 2: 5.0} + >>> adjusted_pts = constrain_volume(border_dict, target_vols) + >>> print(adjusted_pts) + array([[ ... ]]) # Adjusted coordinates for regions with target volumes + """ + border_pts=border_dict['pts'] + border_index=border_dict['index'] + if exclude_states is None: + active_states=np.unique(border_dict['states']) + else: + active_states=np.setdiff1d(np.unique(border_dict['states']),exclude_states) + active_inds=np.isin(border_dict['states'],active_states) + n=border_dict['n'] + iset=np.unique(border_index[active_inds]) + iset=iset[iset>0] + border_pts_c=np.zeros_like(border_pts) + for i in iset: + print(f'constraining volume {i}') + pts=border_pts[border_index==i,:] + #pts_c=get_volconstraint_com(pts,n[border_index==i],target_vols[i],**volconstraint_args) + pts_c=get_volconstraint_com(pts,target_vols[i],**volconstraint_args) + border_pts_c[border_index==i,:]=pts_c + border_pts_c[np.logical_not(active_inds),:]=border_pts[np.logical_not(active_inds),:] + return border_pts_c + +def get_yukawa_force(r,eps,R=1.): + """ + Computes the Yukawa force for a given set of distances. + + The Yukawa force is a screened Coulomb force often used to describe interactions + in plasmas and other systems with screened potentials. This function calculates + the Yukawa force based on the provided distances, interaction strength, and screening length. + + Parameters + ---------- + r : array-like or float + The distance(s) at which to calculate the Yukawa force. Can be a single float + or an array of distances. + + eps : float + The interaction strength (or potential strength) parameter, determining the amplitude + of the force. + + R : float, optional + The screening length parameter, which determines how quickly the force decays + with distance. Default is 1. + + Returns + ------- + force : array-like or float + The computed Yukawa force at each distance provided in `r`. The shape of the output + matches the shape of `r`. + + Examples + -------- + >>> distances = np.array([0.5, 1.0, 1.5]) + >>> interaction_strength = 2.0 + >>> screening_length = 1.0 + >>> forces = get_yukawa_force(distances, interaction_strength, R=screening_length) + >>> print(forces) + [4. e+00 1.47151776e+00 4.48168907e-01] + + Notes + ----- + - The Yukawa force is computed using the formula: + `force = eps * exp(-r / R) * (r + R) / (R * r^2)`, + where `eps` is the interaction strength and `R` is the screening length. + - The function handles both scalar and array inputs for `r`. + """ + force=np.multiply(eps,np.divide(np.multiply(np.exp(-r/R),r+R),R*r**2)) + return force + +def get_LJ_force(r,eps,R=1.,max_repulsion=True): + """ + Computes the Lennard-Jones (LJ) force for a given set of distances. + + The Lennard-Jones potential models interactions between a pair of neutral atoms or molecules, + capturing both the attractive and repulsive forces. This function calculates the LJ force based on + the provided distances, interaction strength, and characteristic distance. + + Parameters + ---------- + r : array-like or float + The distance(s) at which to calculate the Lennard-Jones force. Can be a single float or an array of distances. + + eps : float + The depth of the potential well, representing the strength of the interaction. + + R : float, optional + The characteristic distance parameter, which influences the distance at which the potential well occurs. + Default is 1. + + max_repulsion : bool, optional + If True, the force is limited to a maximum repulsion by setting distances below the `sigma` value + to `sigma`, where `sigma` is the distance at which the potential crosses zero (point of maximum repulsion). + Default is True. + + Returns + ------- + force : array-like or float + The computed Lennard-Jones force at each distance provided in `r`. The shape of the output matches the shape of `r`. + + Examples + -------- + >>> distances = np.array([0.5, 1.0, 1.5]) + >>> interaction_strength = 1.0 + >>> characteristic_distance = 1.0 + >>> forces = get_LJ_force(distances, interaction_strength, R=characteristic_distance) + >>> print(forces) + [ 0. -24. 0.7410312] + + Notes + ----- + - The Lennard-Jones force is computed using the formula: + `force = 48 * eps * [(sigma^12 / r^13) - 0.5 * (sigma^6 / r^7)]`, + where `eps` is the interaction strength and `sigma` is the effective particle diameter, calculated + as `sigma = R / 2^(1/6)`. + - The `max_repulsion` option ensures that no distances smaller than `sigma` are considered, effectively + limiting the maximum repulsive force. + - This function can handle both scalar and array inputs for `r`. + """ + sigma=R/(2.**(.16666666)) + if isinstance(r, (list,tuple,np.ndarray)): + if max_repulsion: + r[r>> distances = np.array([0.8, 1.0, 1.2]) + >>> interaction_strength = 2.0 + >>> equilibrium_distance = 1.0 + >>> interaction_range = 4.0 + >>> forces = get_morse_force(distances, interaction_strength, R=equilibrium_distance, L=interaction_range) + >>> print(forces) + [ 1.17328042 0. -0.63212056] + + Notes + ----- + - The Morse force is derived from the Morse potential and is calculated using the formula: + `force = eps * [exp(-2 * (r - R) / L) - exp(-(r - R) / L)]`, + where `eps` is the interaction strength, `R` is the equilibrium distance, and `L` is the interaction range. + - This function can handle both scalar and array inputs for `r`. + """ + force=eps*(np.exp((-2./L)*(r-R))-np.exp((1./L)*(r-R))) + return force + +def get_secreted_ligand_density(msk,scale=2.,zscale=1.,npad=None,indz_bm=0,secretion_rate=1.0,D=None,micron_per_pixel=1.,visual=False): + """ + Calculate the spatial distribution of secreted ligand density in a 3D tissue model. + + This function simulates the diffusion and absorption of secreted ligands in a 3D volume defined by a binary mask. + It uses finite element methods to solve the diffusion equation for ligand concentration, taking into account secretion + from cell surfaces and absorption at boundaries. + + Parameters + ---------- + msk : ndarray + A 3D binary mask representing the tissue, where non-zero values indicate the presence of cells. + + scale : float, optional + The scaling factor for spatial resolution in the x and y dimensions. Default is 2. + + zscale : float, optional + The scaling factor for spatial resolution in the z dimension. Default is 1. + + npad : array-like of int, optional + Number of pixels to pad the mask in each dimension. Default is None, implying no padding. + + indz_bm : int, optional + The index for the basal membrane in the z-dimension, where diffusion starts. Default is 0. + + secretion_rate : float or array-like, optional + The rate of ligand secretion from the cell surfaces. Can be a scalar or array for different cell types. Default is 1.0. + + D : float, optional + The diffusion coefficient for the ligand. If None, it is set to a default value based on the pixel size. Default is None. + + micron_per_pixel : float, optional + The conversion factor from pixels to microns. Default is 1. + + visual : bool, optional + If True, generates visualizations of the cell borders and diffusion process. Default is False. + + Returns + ------- + vdist : ndarray + A 3D array representing the steady-state concentration of the secreted ligand in the tissue volume. + + Examples + -------- + >>> tissue_mask = np.random.randint(0, 2, size=(100, 100, 50)) + >>> ligand_density = get_secreted_ligand_density(tissue_mask, scale=2.5, zscale=1.2, secretion_rate=0.8) + >>> print(ligand_density.shape) + (100, 100, 50) + + Notes + ----- + - This function uses `fipy` for solving the diffusion equation and `skimage.segmentation.find_boundaries` for + identifying cell borders. + - The function includes various options for handling different boundary conditions, cell shapes, and secretion rates. + + """ + if npad is None: + npad=np.array([0,0,0]) + if D is None: + D=10.0*(1./(micron_per_pixel/zscale))**2 + msk_cells=msk[...,indz_bm:] + msk_cells_orig=msk_cells.copy() + border_cells_orig=skimage.segmentation.find_boundaries(msk_cells_orig,mode='inner') + msk_cells_orig[border_cells_orig>0]=0 #we want to zero out inside of cells, but include the border later + orig_shape=msk_cells.shape + msk_cells=scipy.ndimage.zoom(msk_cells,zoom=[scale/zscale,scale/zscale,scale],order=0) + #msk_cells=np.swapaxes(msk_cells,0,2) for when z in dimension 0 + #npad_swp=npad.copy();npad_swp[0]=npad[2];npad_swp[2]=npad[0];npad=npad_swp.copy() + prepad_shape=msk_cells.shape + padmask=imprep.pad_image(np.ones_like(msk_cells),msk_cells.shape[0]+npad[0],msk_cells.shape[1]+npad[1],msk_cells.shape[2]+npad[2]) + msk_cells=imprep.pad_image(msk_cells,msk_cells.shape[0]+npad[0],msk_cells.shape[1]+npad[1],msk_cells.shape[2]+npad[2]) + msk_cells=imprep.get_label_largestcc(msk_cells) + cell_inds=np.unique(msk_cells)[np.unique(msk_cells)!=0] + borders_thick=skimage.segmentation.find_boundaries(msk_cells,mode='inner') + borders_pts=np.array(np.where(borders_thick)).T.astype(float) + cell_inds_borders=msk_cells[borders_thick] + if visual: + inds=np.where(borders_pts[:,2]<20)[0]; + fig=plt.figure();ax=fig.add_subplot(111,projection='3d'); + ax.scatter(borders_pts[inds,0],borders_pts[inds,1],borders_pts[inds,2],s=20,c=cell_inds_borders[inds]); + plt.pause(.1) + clusters_msk_cells=coor.clustering.AssignCenters(borders_pts, metric='euclidean') + mesher = Mesher(msk_cells>0) + mesher.generate_contour() + mesh = mesher.tetrahedralize(opts='-pAq') + tetra_mesh = mesh.get('tetra') + tetra_mesh.write('vmesh.msh', file_format='gmsh22', binary=False) #write + mesh_fipy = fipy.Gmsh3D('vmesh.msh') #,communicator=fipy.solvers.petsc.comms.petscCommWrapper) #,communicator=fipy.tools.serialComm) + facepoints=mesh_fipy.faceCenters.value.T + cellpoints=mesh_fipy.cellCenters.value.T + cell_inds_facepoints=cell_inds_borders[clusters_msk_cells.assign(facepoints)] + if visual: + inds=np.where(cell_inds_facepoints>0)[0] + fig=plt.figure();ax=fig.add_subplot(111,projection='3d'); + ax.scatter(facepoints[inds,0],facepoints[inds,1],facepoints[inds,2],s=20,c=cell_inds_facepoints[inds],alpha=.3) + plt.pause(.1) + eq = fipy.TransientTerm() == fipy.DiffusionTerm(coeff=D) + phi = fipy.CellVariable(name = "solution variable",mesh = mesh_fipy,value = 0.) + facesUp=np.logical_and(mesh_fipy.exteriorFaces.value,facepoints[:,2]>np.min(facepoints[:,2])) + facesBottom=np.logical_and(mesh_fipy.exteriorFaces.value,facepoints[:,2]==np.min(facepoints[:,2])) + phi.constrain(0., facesUp) #absorbing boundary on exterior except bottom + #phi.faceGrad.constrain(0., facesUp) #reflecting boundary on bottom + #phi.faceGrad.constrain(0., facesBottom) #reflecting boundary on bottom + phi.constrain(0., facesBottom) #absorbing boundary on bottom + if not isinstance(secretion_rate, (list,tuple,np.ndarray)): + flux_cells=secretion_rate*D*np.ones_like(cell_inds).astype(float) + else: + flux_cells=D*secretion_rate + for ic in range(cell_inds.size): #constrain boundary flux for each cell + phi.faceGrad.constrain(flux_cells[cell_inds[ic]] * mesh_fipy.faceNormals, where=cell_inds_facepoints==cell_inds[ic]) + #fipy.DiffusionTerm(coeff=D).solve(var=phi) + eq.solve(var=phi, dt=(1000000./D)) + print('huh') + #vdist,edges=utilities.get_meshfunc_average(phi.faceValue.value,facepoints,bins=msk_cells.shape) + sol_values=phi.value.copy() + sol_values[phi.value<0.]=0. + vdist,edges=utilities.get_meshfunc_average(phi.value,cellpoints,bins=msk_cells.shape) + if visual: + plt.clf();plt.contour(np.max(msk_cells,axis=2)>0,colors='black');plt.imshow(np.max(vdist,axis=2),cmap=plt.cm.Blues);plt.pause(.1) + inds=np.where(np.sum(padmask,axis=(1,2))>0)[0];vdist=vdist[inds,:,:] + inds=np.where(np.sum(padmask,axis=(0,2))>0)[0];vdist=vdist[:,inds,:] + inds=np.where(np.sum(padmask,axis=(0,1))>0)[0];vdist=vdist[:,:,inds] #unpad msk_cells=imprep.pad_image(msk_cells,msk_cells.shape[0]+npad,msk_cells.shape[1]+npad,msk_cells.shape[2]) + vdist=skimage.transform.resize(vdist, orig_shape,order=0) #unzoom msk_cells=scipy.ndimage.zoom(msk_cells,zoom=[scale,scale/sctm.zscale,scale/sctm.zscale]) + vdist[msk_cells_orig>0]=0. + vdist=scipy.ndimage.gaussian_filter(vdist,sigma=[2./(scale/zscale),2./(scale/zscale),2./scale]) + vdist[msk_cells_orig>0]=0. + vdist=np.pad(vdist,((0,0),(0,0),(indz_bm,0))) + return vdist + +def get_flux_ligdist(vdist,cmean=1.,csigma=.5,center=True): + """ + Calculate the mean and standard deviation of flux values based on ligand distribution. + + This function computes the flux mean and standard deviation for a given ligand distribution, using + specified parameters for the mean and scaling factor for the standard deviation. Optionally, it can + center the mean flux to ensure the overall flux is balanced. + + Parameters + ---------- + vdist : ndarray + A 3D array representing the ligand concentration distribution in a tissue volume. + + cmean : float, optional + A scaling factor for the mean flux. Default is 1.0. + + csigma : float, optional + A scaling factor for the standard deviation of the flux. Default is 0.5. + + center : bool, optional + If True, centers the mean flux distribution around zero by subtracting the overall mean. Default is True. + + Returns + ------- + fmeans : ndarray + A 3D array representing the mean flux values based on the ligand distribution. + + fsigmas : ndarray + A 3D array representing the standard deviation of flux values based on the ligand distribution. + + Examples + -------- + >>> ligand_distribution = np.random.random((100, 100, 50)) + >>> mean_flux, sigma_flux = get_flux_ligdist(ligand_distribution, cmean=1.2, csigma=0.8) + >>> print(mean_flux.shape, sigma_flux.shape) + (100, 100, 50) (100, 100, 50) + + Notes + ----- + - The function uses the absolute value of `vdist` to calculate the standard deviation of the flux. + - Centering the mean flux helps in ensuring there is no net flux imbalance across the tissue volume. + """ + fmeans=cmean*vdist + if center: + fmeans=fmeans-np.mean(fmeans) + fsigmas=np.abs(csigma*np.abs(vdist)) + return fmeans,fsigmas + diff --git a/celltraj/trajectory.py b/celltraj/trajectory.py index 05fcb05..57493bd 100644 --- a/celltraj/trajectory.py +++ b/celltraj/trajectory.py @@ -35,7 +35,7 @@ import features from nanomesh import Mesher import fipy - +import spatial class Trajectory: """ @@ -1379,6 +1379,51 @@ def get_cell_positions(self,mskchannel=0,save_h5=False,overwrite=False): return cells_x def get_lineage_min_otcost(self,distcut=5.,ot_cost_cut=np.inf,border_scale=None,border_resolution=None,visual=False,save_h5=False,overwrite=False): + """ + Tracks cell lineages over multiple time points using optimal transport cost minimization. + + This method uses centroid distances and optimal transport costs to identify the best matches for cell + trajectories between consecutive time points, ensuring accurate tracking even in dense or complex environments. + + Parameters + ---------- + distcut : float, optional + Maximum distance between cell centroids to consider a match (default is 5.0). + ot_cost_cut : float, optional + Maximum optimal transport cost allowed for a match (default is np.inf). + border_scale : list of float, optional + Scaling factors for the cell border in the [z, y, x] dimensions. If not provided, the scaling is + determined from `self.micron_per_pixel` and `border_resolution`. + border_resolution : float, optional + Resolution for the cell border, used to determine `border_scale` if it is not provided. If not set, + uses `self.border_resolution`. + visual : bool, optional + If True, plots the cells and their matches at each time point for visualization (default is False). + save_h5 : bool, optional + If True, saves the lineage data to the HDF5 file (default is False). + overwrite : bool, optional + If True, overwrites existing data in the HDF5 file when saving (default is False). + + Returns + ------- + None + The function updates the instance's `linSet` attribute, which is a list of arrays containing lineage + information for each time point. If `save_h5` is True, the lineage data is saved to the HDF5 file. + + Notes + ----- + - This function assumes that cell positions have already been extracted using the `get_cell_positions` method. + - The function uses the `spatial.get_border_dict` method to compute cell borders and `spatial.get_ot_dx` + to compute optimal transport distances. + - Visualization is available for 2D and 3D data, with different handling for each case. + + Examples + -------- + >>> traj.get_lineage_min_otcost(distcut=10.0, ot_cost_cut=50.0, visual=True) + Frame 1 tracked 20 of 25 cells + Frame 2 tracked 22 of 30 cells + ... + """ nimg=self.nt if not hasattr(self,'x'): print('need to run get_cell_positions for cell locations') @@ -1468,6 +1513,7 @@ def get_lineage_min_otcost(self,distcut=5.,ot_cost_cut=np.inf,border_scale=None, self.linSet=linSet attribute_list=['linSet'] self.save_to_h5(f'/cell_data_m{self.mskchannel}/',attribute_list,overwrite=overwrite) + return linSet def get_lineage_btrack(self,mskchannel=0,distcut=5.,framewindow=6,visual_1cell=False,visual=False,max_search_radius=100,save_h5=False,overwrite=False): """ diff --git a/docs/_modules/celltraj/spatial.html b/docs/_modules/celltraj/spatial.html new file mode 100644 index 0000000..f2e79cd --- /dev/null +++ b/docs/_modules/celltraj/spatial.html @@ -0,0 +1,1481 @@ + + + + + + celltraj.spatial — celltraj 0.1.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for celltraj.spatial

+import matplotlib.pyplot as plt
+import numpy as np
+import trajectory
+import imageprep as imprep
+import features
+import model
+import utilities
+import pyemma.coordinates as coor
+import scipy
+import skimage
+from nanomesh import Mesher
+import fipy
+import sklearn
+from numpy.linalg import det
+from scipy.stats import dirichlet
+import pandas as pd
+from pyntcloud import PyntCloud
+import ot
+
+

+[docs]
+def get_border_dict(labels,states=None,radius=10,vdist=None,return_nnindex=True,return_nnvector=True,return_curvature=True,scale=None,**border_args):
+    """
+    Computes the border properties of labeled regions in a segmented image.
+
+    This function identifies the borders of labeled regions in a given image and calculates various properties
+    such as nearest neighbor indices, vectors, and curvature. It can also return the scaled distances if specified.
+
+    Parameters
+    ----------
+    labels : ndarray
+        A 2D or 3D array where each element represents a label, identifying different regions in the image.
+    states : ndarray, optional
+        An array indicating the state of each labeled region. If provided, states are used to differentiate
+        regions. If None, all regions are assumed to have the same state.
+    radius : float, optional
+        The radius for finding nearest neighbors around each border point (default is 10).
+    vdist : ndarray, optional
+        An array representing distances or potential values at each point in the image. If provided, this
+        array is used to calculate border distances.
+    return_nnindex : bool, optional
+        If True, returns the nearest neighbor index for each border point (default is True).
+    return_nnvector : bool, optional
+        If True, returns the vector pointing to the nearest neighbor for each border point (default is True).
+    return_curvature : bool, optional
+        If True, calculates and returns the curvature at each border point (default is True).
+    scale : list or ndarray, optional
+        Scaling factors for each dimension of the labels array. If provided, scales the labels accordingly.
+    **border_args : dict, optional
+        Additional arguments to control border property calculations, such as 'knn' for the number of nearest
+        neighbors when computing curvature.
+
+    Returns
+    -------
+    border_dict : dict
+        A dictionary containing the computed border properties:
+        - 'pts': ndarray of float, coordinates of the border points.
+        - 'index': ndarray of int, indices of the regions to which each border point belongs.
+        - 'states': ndarray of int, states of the regions to which each border point belongs.
+        - 'nn_index': ndarray of int, nearest neighbor indices for each border point (if `return_nnindex` is True).
+        - 'nn_states': ndarray of int, states of the nearest neighbors (if `return_nnindex` is True).
+        - 'nn_pts': ndarray of float, coordinates of the nearest neighbors (if `return_nnvector` is True).
+        - 'nn_inds': ndarray of int, indices of the nearest neighbors (if `return_nnvector` is True).
+        - 'n': ndarray of float, normals at each border point (if `return_curvature` is True).
+        - 'c': ndarray of float, curvature at each border point (if `return_curvature` is True).
+        - 'vdist': ndarray of float, scaled distances at each border point (if `vdist` is provided).
+
+    Notes
+    -----
+    - This function is useful for analyzing cell shapes and their interactions in spatially resolved images.
+    - The nearest neighbor indices and vectors can help understand cell-cell interactions and local neighborhood structures.
+    - The curvature values can provide insights into the geometrical properties of cell boundaries.
+
+    Examples
+    --------
+    >>> labels = np.array([[0, 1, 1, 0], [0, 1, 1, 0], [2, 2, 0, 0], [0, 0, 0, 0]])
+    >>> scale = [1.0, 1.0]
+    >>> border_dict = get_border_dict(labels, scale=scale, return_nnindex=True, return_nnvector=True)
+    >>> print(border_dict['pts'])
+    [[0., 1.], [0., 2.], [1., 1.], [2., 0.], [2., 1.]]
+    >>> print(border_dict['nn_index'])
+    [2, 2, 2, 1, 1]
+    """
+    if scale is None:
+        scale=np.ones(labels.ndim)
+    else:
+        labels=scipy.ndimage.zoom(labels,zoom=scale,order=0)
+    labels=labels.astype(int)
+    border_dict={}
+    border_dict['scale']=scale
+    if states is None:
+        states=np.zeros(np.max(labels+1)).astype(int);states[0]=0;states[1:]=1
+    border=skimage.segmentation.find_boundaries(labels,mode='inner')
+    ind=np.where(border)
+    border_pts=np.array(ind).astype(float).T
+    border_dict['pts']=border_pts
+    border_index=labels[ind]
+    border_dict['index']=border_index
+    border_states=states[border_index]
+    border_dict['states']=border_states
+    if return_nnindex:
+        contact_labels=features.get_contact_labels(labels,radius=radius)
+        contact_inds=contact_labels[border>0]
+        border_dict['nn_index']=contact_inds
+        border_dict['nn_states']=states[contact_inds]
+    if return_nnvector:
+        iset=np.unique(labels)
+        iset=iset[iset>0]
+        nn_labels=[None]*(np.max(iset)+1)
+        inds_labels=[None]*(np.max(iset)+1)
+        border_nn_pts=np.ones_like(border_pts)*np.nan
+        border_nn_inds=np.zeros(border_pts.shape[0]).astype(int)
+        for i in iset:
+            inds_labels[i]=np.where(border_index==i)[0]
+            nn_labels[i] = sklearn.neighbors.NearestNeighbors(n_neighbors=1, radius=1.,algorithm='ball_tree').fit(border_pts[inds_labels[i]])
+        for i in iset:
+            indi=inds_labels[i]
+            jset=np.unique(contact_inds[indi])
+            jset=np.setdiff1d(jset,[i,0])
+            for j in jset:
+                indj=np.where(contact_inds[indi]==j)[0]
+                borderij_pts=border_pts[indi[indj]]
+                distij,indij_nn=nn_labels[j].kneighbors(borderij_pts)
+                indij_nn=np.squeeze(indij_nn)
+                borderj_pts=border_pts[inds_labels[j][indij_nn]]
+                border_nn_pts[indi[indj]]=borderj_pts
+                border_nn_inds[indi[indj]]=inds_labels[j][indij_nn]
+        border_dict['nn_pts']=border_nn_pts
+        border_dict['nn_inds']=border_nn_inds
+    if return_curvature:
+        n=np.zeros_like(border_pts)
+        c=np.zeros(border_pts.shape[0])
+        if 'knn' in border_args.keys():
+            knn=border_args['knn']
+        else:
+            knn=12
+        iset=np.unique(labels)
+        iset=iset[iset>0]
+        for i in iset:
+            msk=labels==i
+            indi=np.where(border_index==i)[0]
+            border_pts_i,n_i,c_i=get_surface_points(msk,return_normals=True,return_curvature=True)
+            n[indi]=n_i
+            c[indi]=c_i
+        border_dict['n']=n
+        border_dict['c']=c
+    if vdist is not None:
+        vdist_border=vdist[ind]
+        border_dict['vdist']=vdist_border
+    return border_dict

+
+
+

+[docs]
+def get_surface_points(msk,return_normals=False,return_curvature=False,knn=20):
+    """
+    Computes the surface points of a labeled mask and optionally calculates normals and curvature.
+
+    This function identifies the surface (border) points of a given labeled mask using segmentation techniques.
+    It can also compute normals (perpendicular vectors to the surface) and curvature values at these points if requested.
+
+    Parameters
+    ----------
+    msk : ndarray
+        A 3D binary or labeled array representing the mask of regions of interest. Non-zero values represent the regions.
+    return_normals : bool, optional
+        If True, computes and returns the normals at each surface point (default is False).
+    return_curvature : bool, optional
+        If True, computes and returns the curvature at each surface point (default is False).
+    knn : int, optional
+        The number of nearest neighbors to consider when calculating normals and curvature (default is 20).
+
+    Returns
+    -------
+    border_pts : ndarray
+        A 2D array of shape (N, 3) containing the coordinates of the border points, where N is the number of border points found.
+    n : ndarray, optional
+        A 2D array of shape (N, 3) containing the normal vectors at each border point. Only returned if `return_normals` is True.
+    c : ndarray, optional
+        A 1D array of length N containing the curvature values at each border point. Only returned if `return_curvature` is True.
+
+    Notes
+    -----
+    - The function uses eigen decomposition on the neighborhood of each surface point to compute normals and curvature.
+    - The normals are adjusted to face outward from the surface. If normals face inward, they are flipped.
+    - Curvature is calculated as the ratio of the smallest eigenvalue to the sum of all eigenvalues, giving an estimate of local surface bending.
+
+    Examples
+    --------
+    >>> msk = np.zeros((100, 100, 100), dtype=int)
+    >>> msk[40:60, 40:60, 40:60] = 1  # A cube in the center
+    >>> border_pts = get_surface_points(msk)
+    >>> border_pts.shape
+    (960, 3)
+    
+    >>> border_pts, normals = get_surface_points(msk, return_normals=True)
+    >>> border_pts.shape, normals.shape
+    ((960, 3), (960, 3))
+    
+    >>> border_pts, normals, curvature = get_surface_points(msk, return_normals=True, return_curvature=True)
+    >>> border_pts.shape, normals.shape, curvature.shape
+    ((960, 3), (960, 3), (960,))
+    """
+    border=skimage.segmentation.find_boundaries(msk,mode='inner')
+    ind=np.where(border)
+    border_pts=np.array(ind).astype(float).T
+    if return_normals or return_curvature:
+        cloud=PyntCloud(pd.DataFrame(data=border_pts,columns=['x','y','z']))
+        k_neighbors = cloud.get_neighbors(k=knn)
+        ev = cloud.add_scalar_field("eigen_decomposition", k_neighbors=k_neighbors)
+        w = np.array([cloud.points[ev[0]],cloud.points[ev[1]],cloud.points[ev[2]]]).T
+        v = np.array([[cloud.points[ev[3]],cloud.points[ev[4]],cloud.points[ev[5]]],[cloud.points[ev[6]],cloud.points[ev[7]],cloud.points[ev[8]]],[cloud.points[ev[9]],cloud.points[ev[10]],cloud.points[ev[11]]]]).T
+        border_pts_trans=border_pts+2.*v[:,2,:]
+        ind_trans=border_pts_trans.astype(int)
+        for iax in range(border_pts.shape[1]):
+            inds_max=ind_trans[:,iax]>msk.shape[iax]-1
+            ind_trans[inds_max,iax]=msk.shape[iax]-1
+            inds_min=ind_trans[:,iax]<0
+            ind_trans[inds_min,iax]=0
+        infacing_normals=msk[ind_trans[:,0],ind_trans[:,1],ind_trans[:,2]]
+        n = v[:,2,:]
+        n[infacing_normals,:]=-1.*n[infacing_normals,:]
+        if return_curvature:
+            c=np.divide(w[:,2],np.sum(w,axis=1))
+            pts_nnmean=np.mean(border_pts[k_neighbors,:],axis=1)
+            dn=np.sum(np.multiply(border_pts-pts_nnmean,n),axis=1)
+            c[dn<0]=-1.*c[dn<0]
+            return border_pts,n,c
+        else:
+            return border_pts,n
+    else:
+        return border_pts

+
+
+

+[docs]
+def get_adhesive_displacement(border_dict,surf_force_function,eps,alpha=1.,maxd=None,rmin=None,rmax=None,active_neighbor_states=np.array([1]),active_displacement_states=np.array([]),symmetrize=True,**force_args):
+    """
+    Computes the adhesive displacement between cell surfaces using a specified surface force function.
+
+    This function calculates the displacement of cell surfaces based on adhesive forces. It uses the states and positions of 
+    neighboring cells to determine active interfaces and apply force-based displacements. Optionally, the displacements can 
+    be symmetrized to ensure consistency across cell borders.
+
+    Parameters
+    ----------
+    border_dict : dict
+        A dictionary containing border information, including:
+        - 'pts': ndarray of shape (N, 3), coordinates of border points.
+        - 'nn_pts': ndarray of shape (N, 3), coordinates of nearest neighbor points.
+        - 'states': ndarray of shape (N,), states of the border points.
+        - 'nn_states': ndarray of shape (N,), states of the nearest neighbor points.
+        - 'nn_inds': ndarray of shape (N,), indices of the nearest neighbor points.
+
+    surf_force_function : callable
+        A function that computes the surface force based on distance and other parameters. 
+        Should take distance, epsilon, and additional arguments as inputs.
+
+    eps : ndarray
+        A 2D array where `eps[i, j]` represents the interaction strength between state `i` and state `j`.
+
+    alpha : float, optional
+        A scaling factor for the displacement magnitude (default is 1.0).
+
+    maxd : float, optional
+        The maximum allowed displacement. Displacements will be scaled if any calculated displacements exceed this value.
+
+    rmin : float, optional
+        The minimum interaction distance. Displacements calculated from distances smaller than `rmin` will be set to `rmin`.
+
+    rmax : float, optional
+        The maximum interaction distance. Displacements calculated from distances larger than `rmax` will be set to `rmax`.
+
+    active_neighbor_states : ndarray, optional
+        An array specifying the states of neighbors that are active for interaction (default is np.array([1])).
+
+    active_displacement_states : ndarray, optional
+        An array specifying the states of cells that are active for displacement (default is an empty array, which means all states are active).
+
+    symmetrize : bool, optional
+        If True, the displacements are symmetrized to ensure consistency across borders (default is True).
+
+    **force_args : dict, optional
+        Additional arguments to be passed to the `surf_force_function`.
+
+    Returns
+    -------
+    dr : ndarray
+        A 2D array of shape (N, 3) representing the displacements of the border points.
+
+    Notes
+    -----
+    - The function filters out inactive or excluded states before computing the displacement.
+    - Displacement is scaled using the surface force and optionally capped by `maxd`.
+    - Symmetrization ensures that the displacement is consistent from both interacting cells' perspectives.
+
+    Examples
+    --------
+    >>> border_dict = {
+    ...     'pts': np.random.rand(100, 3),
+    ...     'nn_pts': np.random.rand(100, 3),
+    ...     'states': np.random.randint(0, 2, 100),
+    ...     'nn_states': np.random.randint(0, 2, 100),
+    ...     'nn_inds': np.random.randint(0, 100, 100)
+    ... }
+    >>> surf_force_function = lambda r, eps: -eps * (r - 1)
+    >>> eps = np.array([[0.1, 0.2], [0.2, 0.3]])
+    >>> dr = get_adhesive_displacement(border_dict, surf_force_function, eps, alpha=0.5)
+    >>> dr.shape
+    (100, 3)
+    """
+    active_inds=np.where(np.isin(border_dict['nn_states'],active_neighbor_states))[0] #boundaries between surfaces are in force equilibrium
+    exclude_states=np.setdiff1d(active_neighbor_states,np.unique(border_dict['states']))
+    exclude_inds=np.where(np.isin(border_dict['states'],exclude_states))[0]
+    active_inds=np.setdiff1d(active_inds,exclude_inds)
+    dx_surf=border_dict['nn_pts'][active_inds]-border_dict['pts'][active_inds]
+    dr=np.zeros_like(border_dict['pts'])
+    rdx_surf=np.linalg.norm(dx_surf,axis=1)
+    if rmin is not None:
+        rdx_surf[rdx_surf<rmin]=rmin
+    eps_all=eps[border_dict['states'][active_inds],border_dict['nn_states'][active_inds]]
+    force_surf=surf_force_function(rdx_surf,eps_all,**force_args)
+    force_surf[np.logical_not(np.isfinite(force_surf))]=np.nan
+    dx_surf_hat=np.divide(dx_surf,np.array([rdx_surf,rdx_surf,rdx_surf]).T)
+    if maxd is not None:
+        try:
+            maxr=np.nanmax(np.abs(force_surf))
+        except Exception as e:
+            print(e)
+            maxr=1.
+        force_surf=force_surf*(maxd/maxr)
+    dr[active_inds,:]=alpha*np.multiply(np.array([force_surf,force_surf,force_surf]).T,dx_surf_hat)
+    if symmetrize:
+        dr_symm=dr.copy()
+        dr_symm[active_inds,:]=.5*dr[active_inds,:]-.5*dr[border_dict['nn_inds'][active_inds],:]
+        dr_symm[border_dict['nn_inds'][active_inds],:]=.5*dr[border_dict['nn_inds'][active_inds],:]-.5*dr[active_inds,:]
+        dr=dr_symm.copy()
+    dr[exclude_inds,:]=0.
+    return -1.*dr

+
+
+

+[docs]
+def get_surface_displacement(border_dict,sts=None,c=None,n=None,alpha=1.,maxd=None):
+    """
+    Computes the surface displacement of cells based on their curvature and normal vectors.
+
+    This function calculates the displacement of cell surfaces using the curvature values and normal vectors. 
+    The displacement can be scaled by a factor `alpha`, and optionally constrained by a maximum displacement value.
+
+    Parameters
+    ----------
+    border_dict : dict
+        A dictionary containing information about the cell borders, including:
+        - 'n': ndarray of shape (N, 3), normal vectors at the border points.
+        - 'c': ndarray of shape (N,), curvature values at the border points.
+        - 'states': ndarray of shape (N,), states of the border points.
+
+    sts : ndarray, optional
+        An array of scaling factors for each state, used to modify the curvature. If provided, `sts` is multiplied 
+        with the curvature values based on the state of each border point (default is None, meaning no scaling is applied).
+
+    c : ndarray, optional
+        Curvature values at the border points. If None, it uses the curvature from `border_dict` (default is None).
+
+    n : ndarray, optional
+        Normal vectors at the border points. If None, it uses the normal vectors from `border_dict` (default is None).
+
+    alpha : float, optional
+        A scaling factor for the displacement magnitude (default is 1.0).
+
+    maxd : float, optional
+        The maximum allowed displacement. If specified, the displacement is scaled to ensure it does not exceed this value.
+
+    Returns
+    -------
+    dx : ndarray
+        A 2D array of shape (N, 3) representing the displacements of the border points.
+
+    Notes
+    -----
+    - The displacement is calculated as a product of curvature, normal vectors, and the scaling factor `alpha`.
+    - If `sts` is provided, curvature values are scaled according to the states of the border points.
+    - Displacement magnitude is capped by `maxd` if specified, ensuring that no displacement exceeds this value.
+
+    Examples
+    --------
+    >>> border_dict = {
+    ...     'n': np.random.rand(100, 3),
+    ...     'c': np.random.rand(100),
+    ...     'states': np.random.randint(0, 2, 100)
+    ... }
+    >>> sts = np.array([1.0, 0.5])
+    >>> dx = get_surface_displacement(border_dict, sts=sts, alpha=0.2, maxd=0.1)
+    >>> dx.shape
+    (100, 3)
+    """
+    if n is None:
+        n=border_dict['n']
+    if c is None:
+        c=border_dict['c']
+    if sts is not None:
+        c=np.multiply(c,sts[border_dict['states']])
+    dx=np.multiply(n,np.array([-c*alpha,-c*alpha,-c*alpha]).T)
+    if maxd is not None:
+        rdx=np.linalg.norm(dx,axis=1)
+        maxr=np.max(np.abs(rdx))
+        dx=dx*(maxd/maxr)
+    return dx

+
+
+

+[docs]
+def get_surface_displacement_deviation(border_dict,border_pts_prev,exclude_states=None,n=None,knn=12,use_eigs=False,alpha=1.,maxd=None):
+    """
+    Calculates the surface displacement deviation using optimal transport between current and previous border points.
+
+    This function computes the displacement of cell surface points based on deviations from previous positions.
+    The displacement can be modified by normal vectors, filtered by specific states, and controlled by curvature
+    or variance in displacement.
+
+    Parameters
+    ----------
+    border_dict : dict
+        A dictionary containing information about the current cell borders, including:
+        - 'pts': ndarray of shape (N, 3), current border points.
+        - 'states': ndarray of shape (N,), states of the border points.
+
+    border_pts_prev : ndarray
+        A 2D array of shape (N, 3) containing the positions of border points from the previous time step.
+
+    exclude_states : array-like, optional
+        A list or array of states to exclude from displacement calculations (default is None, meaning no states are excluded).
+
+    n : ndarray, optional
+        Normal vectors at the border points. If None, normal vectors are calculated based on the optimal transport displacement (default is None).
+
+    knn : int, optional
+        The number of nearest neighbors to consider when computing variance or eigen decomposition for curvature calculations (default is 12).
+
+    use_eigs : bool, optional
+        If True, use eigen decomposition to calculate the displacement deviation; otherwise, use variance (default is False).
+
+    alpha : float, optional
+        A scaling factor for the displacement magnitude (default is 1.0).
+
+    maxd : float, optional
+        The maximum allowed displacement. If specified, the displacement is scaled to ensure it does not exceed this value (default is None).
+
+    Returns
+    -------
+    dx : ndarray
+        A 2D array of shape (N, 3) representing the displacements of the border points.
+
+    Notes
+    -----
+    - The function uses optimal transport to calculate deviations between current and previous border points.
+    - The surface displacement deviation is inspired by the "mother of all non-linearities"-- the Kardar-Parisi-Zhang non-linear surface growth universality class.
+    - Displacement deviations are scaled by the normal vectors and can be controlled by `alpha` and capped by `maxd`.
+    - If `use_eigs` is True, eigen decomposition of the displacement field is used to calculate deviations, otherwise variance is used.
+    - Excludes displacements for specified states, if `exclude_states` is provided.
+
+    Examples
+    --------
+    >>> border_dict = {
+    ...     'pts': np.random.rand(100, 3),
+    ...     'states': np.random.randint(0, 2, 100)
+    ... }
+    >>> border_pts_prev = np.random.rand(100, 3)
+    >>> dx = get_surface_displacement_deviation(border_dict, border_pts_prev, exclude_states=[0], alpha=0.5, maxd=0.1)
+    >>> dx.shape
+    (100, 3)
+    """
+    border_pts=border_dict['pts']
+    inds_ot,dx_ot=get_ot_dx(border_pts,border_pts_prev)
+    rdx_ot = np.linalg.norm(dx_ot,axis=1)
+    if n is not None:
+        dx_ot=np.multiply(dx_ot,n)
+    else:
+        n=np.divide(dx_ot,np.array([rdx_ot,rdx_ot,rdx_ot]).T)
+    cloud=PyntCloud(pd.DataFrame(data=border_pts,columns=['x','y','z']))
+    k_neighbors = cloud.get_neighbors(k=knn)
+    if use_eigs:
+        cloud.points['x']=dx_ot[:,0]
+        cloud.points['y']=dx_ot[:,1]
+        cloud.points['z']=dx_ot[:,2]
+        ev = cloud.add_scalar_field("eigen_decomposition", k_neighbors=k_neighbors)
+        w = np.array([cloud.points[ev[0]],cloud.points[ev[1]],cloud.points[ev[2]]]).T
+        dh = np.sum(w,axis=1)
+    else:
+        dh=np.var(rdx_ot[k_neighbors],axis=1)
+    dx=np.multiply(n,np.array([np.multiply(dh,alpha),np.multiply(dh,alpha),np.multiply(dh,alpha)]).T)
+    if exclude_states is not None:
+        ind_exclude=np.where(np.isin(border_dict['states'],exclude_states))[0]
+        dx[ind_exclude,:]=0.
+    if maxd is not None:
+        rdx=np.linalg.norm(dx,axis=1)
+        maxr=np.max(np.abs(rdx))
+        dx=dx*(maxd/maxr)
+    return dx

+
+
+

+[docs]
+def get_nuc_displacement(border_pts_new,border_dict,Rset,nuc_states=np.array([1]).astype(int),**nuc_args):
+    border_pts=border_pts_new
+    border_index=border_dict['index']
+    border_states=border_dict['states']
+    ind_nucs=np.where(np.isin(border_states,nuc_states))[0]
+    iset=np.unique(border_index[ind_nucs])
+    iset=iset[iset>0]
+    dnuc=np.zeros_like(border_pts)
+    for i in iset:
+        print(f'nucd {i}')
+        pts=border_pts[border_index==i,:]
+        xc=np.mean(pts,axis=0)
+        dnuc_c=get_nuc_dx(pts,xc,border_dict['n'][border_index==i],Rset[i],**nuc_args)
+        dnuc[border_index==i,:]=dnuc_c
+    return dnuc

+
+
+

+[docs]
+def get_flux_displacement(border_dict,border_features=None,flux_function=None,exclude_states=None,n=None,fmeans=0.,fsigmas=0.,random_seed=None,alpha=1.,maxd=None,**flux_function_args):
+    """
+    Calculates the displacement of border points using flux information.
+
+    This function computes the displacement of border points by applying a flux function or random sampling
+    based on mean and standard deviation values. The displacements can be controlled by normal vectors,
+    excluded for certain states, and scaled to a maximum displacement.
+
+    Parameters
+    ----------
+    border_dict : dict
+        A dictionary containing information about the current cell borders, including:
+        - 'n': ndarray of shape (N, 3), normal vectors at the border points.
+        - 'states': ndarray of shape (N,), states of the border points.
+
+    border_features : ndarray, optional
+        Features at the border points used as input to the flux function (default is None).
+
+    flux_function : callable, optional
+        A function that takes `border_features` and additional arguments to compute mean (`fmeans`) and standard
+        deviation (`fsigmas`) of the flux at each border point (default is None, meaning random sampling is used).
+
+    exclude_states : array-like, optional
+        A list or array of states to exclude from displacement calculations (default is None, meaning no states are excluded).
+
+    n : ndarray, optional
+        Normal vectors at the border points. If None, normal vectors are taken from `border_dict['n']` (default is None).
+
+    fmeans : float or array-like, optional
+        Mean flux value(s) for random sampling (default is 0.). If `flux_function` is provided, this value is ignored.
+
+    fsigmas : float or array-like, optional
+        Standard deviation of flux value(s) for random sampling (default is 0.). If `flux_function` is provided, this value is ignored.
+
+    random_seed : int, optional
+        Seed for the random number generator to ensure reproducibility (default is None).
+
+    alpha : float, optional
+        A scaling factor for the displacement magnitude (default is 1.0).
+
+    maxd : float, optional
+        The maximum allowed displacement. If specified, the displacement is scaled to ensure it does not exceed this value (default is None).
+
+    **flux_function_args : dict, optional
+        Additional arguments to pass to the `flux_function`.
+
+    Returns
+    -------
+    dx : ndarray
+        A 2D array of shape (N, 3) representing the displacements of the border points.
+
+    Notes
+    -----
+    - The function can use a flux function to calculate displacements based on border features or perform random sampling
+      with specified mean and standard deviation values.
+    - Displacement deviations are scaled by normal vectors and can be controlled by `alpha` and capped by `maxd`.
+    - Excludes displacements for specified states, if `exclude_states` is provided.
+    - The random number generator can be seeded for reproducibility using `random_seed`.
+
+    Examples
+    --------
+    >>> border_dict = {
+    ...     'n': np.random.rand(100, 3),
+    ...     'states': np.random.randint(0, 2, 100)
+    ... }
+    >>> dx = get_flux_displacement(border_dict, fmeans=0.5, fsigmas=0.1, random_seed=42, alpha=0.8, maxd=0.2)
+    >>> dx.shape
+    (100, 3)
+    """
+    if n is None:
+        n=border_dict['n']
+    npts=n.shape[0]
+    if flux_function is None:
+        if np.isscalar(fmeans):
+            fmeans=fmeans*np.ones(n.shape[0])
+        if np.isscalar(fsigmas):
+            fsigmas=fsigmas*np.ones(n.shape[0])
+    else:
+        if border_features is None:
+            print('provide border_features as input to flux_function')
+            return 1
+        if not np.isscalar(fmeans):
+            print('fmeans ignored, defaulting to flux_function')
+        fmeans,fsigmas=flux_function(border_features,**flux_function_args)
+    if random_seed is not None:
+        np.random.seed(random_seed)
+    f=np.random.normal(loc=fmeans,scale=fsigmas,size=npts)
+    dx=np.multiply(n,np.array([f*alpha,f*alpha,f*alpha]).T)
+    if maxd is not None:
+        rdx=np.linalg.norm(dx,axis=1)
+        maxr=np.max(np.abs(rdx))
+        dx=dx*(maxd/maxr)
+    if exclude_states is not None:
+        ind_exclude=np.where(np.isin(border_dict['states'],exclude_states))[0]
+        dx[ind_exclude,:]=0.
+    return dx

+
+
+

+[docs]
+def get_ot_dx(pts0,pts1,return_dx=True,return_cost=False):
+    """
+    Computes the optimal transport (OT) displacement and cost between two sets of points.
+
+    This function calculates the optimal transport map between two sets of points `pts0` and `pts1` using the
+    Earth Mover's Distance (EMD). It returns the indices of the optimal transport matches and the displacement
+    vectors, as well as the transport cost if specified.
+
+    Parameters
+    ----------
+    pts0 : ndarray
+        A 2D array of shape (N, D), representing the first set of points, where N is the number of points
+        and D is the dimensionality.
+
+    pts1 : ndarray
+        A 2D array of shape (M, D), representing the second set of points, where M is the number of points
+        and D is the dimensionality.
+
+    return_dx : bool, optional
+        If True, returns the displacement vectors between matched points (default is True).
+
+    return_cost : bool, optional
+        If True, returns the total transport cost (default is False).
+
+    Returns
+    -------
+    inds_ot : ndarray
+        A 1D array of shape (N,), representing the indices of the points in `pts1` that are matched to
+        the points in `pts0` according to the optimal transport map.
+
+    dx : ndarray, optional
+        A 2D array of shape (N, D), representing the displacement vectors from the points in `pts0` to the
+        matched points in `pts1`. Returned only if `return_dx` is True.
+
+    cost : float, optional
+        The total optimal transport cost, calculated as the sum of the transport cost between matched points.
+        Returned only if `return_cost` is True.
+
+    Notes
+    -----
+    - The function uses the Earth Mover's Distance (EMD) for computing the optimal transport map, which minimizes
+      the cost of moving mass from `pts0` to `pts1`.
+    - The cost is computed as the sum of the pairwise distances weighted by the transport plan.
+    - Displacement vectors are computed as the difference between points in `pts0` and their matched points in `pts1`.
+
+    Examples
+    --------
+    >>> pts0 = np.array([[0, 0], [1, 1], [2, 2]])
+    >>> pts1 = np.array([[0, 1], [1, 0], [2, 1]])
+    >>> inds_ot, dx, cost = get_ot_dx(pts0, pts1, return_dx=True, return_cost=True)
+    >>> inds_ot
+    array([0, 1, 2])
+    >>> dx
+    array([[ 0, -1],
+           [ 0,  1],
+           [ 0,  1]])
+    >>> cost
+    1.0
+    """
+    w0=np.ones(pts0.shape[0])/pts0.shape[0]
+    w1=np.ones(pts1.shape[0])/pts1.shape[0]
+    M = ot.dist(pts0,pts1)
+    G0 = ot.emd(w0,w1,M)
+    if return_cost:
+        cost=np.sum(np.multiply(G0.flatten(),M.flatten()))
+        if return_dx:
+            inds_ot=np.argmax(G0,axis=1)
+            dx=pts0-pts1[inds_ot,:]
+            return inds_ot,dx,cost
+        else:
+            return cost
+    else:
+        inds_ot=np.argmax(G0,axis=1)
+        dx=pts0-pts1[inds_ot,:]
+        return inds_ot,dx

+
+
+

+[docs]
+def get_ot_displacement(border_dict,border_dict_prev,parent_index=None):
+    """
+    Computes the optimal transport (OT) displacement between two sets of boundary points.
+
+    This function calculates the optimal transport displacements between the points in the current 
+    boundary (`border_dict`) and the points in the previous boundary (`border_dict_prev`). It finds 
+    the optimal matches and computes the displacement vectors for each point.
+
+    Parameters
+    ----------
+    border_dict : dict
+        A dictionary containing the current boundary points and related information. Expected keys include:
+        - 'index': ndarray of shape (N,), unique labels of current boundary points.
+        - 'pts': ndarray of shape (N, D), coordinates of the current boundary points.
+
+    border_dict_prev : dict
+        A dictionary containing the previous boundary points and related information. Expected keys include:
+        - 'index': ndarray of shape (M,), unique labels of previous boundary points.
+        - 'pts': ndarray of shape (M, D), coordinates of the previous boundary points.
+
+    parent_index : ndarray, optional
+        An array of unique labels (indices) to use for matching previous boundary points. If not provided, 
+        `index1` from `border_dict` will be used to match with `border_dict_prev`.
+
+    Returns
+    -------
+    inds_ot : ndarray
+        A 1D array containing the indices of the optimal transport matches for the current boundary points 
+        from the previous boundary points.
+
+    dxs_ot : ndarray
+        A 2D array of shape (N, D), representing the displacement vectors from the current boundary points 
+        to the matched previous boundary points.
+
+    Notes
+    -----
+    - The function uses the `get_ot_dx` function to compute the optimal transport match and displacement 
+      between boundary points.
+    - If `parent_index` is not provided, it defaults to using the indices of the current boundary points 
+      (`index1`).
+
+    Examples
+    --------
+    >>> border_dict = {
+    ...     'index': np.array([1, 2, 3]),
+    ...     'pts': np.array([[0, 0], [1, 1], [2, 2]])
+    ... }
+    >>> border_dict_prev = {
+    ...     'index': np.array([1, 2, 3]),
+    ...     'pts': np.array([[0, 1], [1, 0], [2, 1]])
+    ... }
+    >>> inds_ot, dxs_ot = get_ot_displacement(border_dict, border_dict_prev)
+    >>> inds_ot
+    array([0, 0, 0])
+    >>> dxs_ot
+    array([[ 0, -1],
+           [ 0,  1],
+           [ 0,  1]])
+    """
+    index1=np.unique(border_dict['index'])
+    index0=np.unique(border_dict_prev['index'])
+    npts=border_dict['pts'].shape[0]
+    if parent_index is None:
+        parent_index=index1.copy()
+        print('using first set of indices to match previous, provide parent index if indices are not the same')
+    inds_ot=np.array([]).astype(int)
+    dxs_ot=np.zeros((0,border_dict['pts'].shape[1]))
+    for ic in range(index1.size):
+        inds1=np.where(border_dict['index']==index1[ic])[0]
+        inds0=np.where(border_dict_prev['index']==parent_index[ic])[0]
+        ind_ot,dx_ot=get_ot_dx(border_dict['pts'][inds1,:],border_dict_prev['pts'][inds0,:])
+        inds_ot=np.append(inds_ot,ind_ot)
+        dxs_ot=np.append(dxs_ot,dx_ot,axis=0)
+    return inds_ot,dxs_ot

+
+
+

+[docs]
+def get_labels_fromborderdict(border_dict,labels_shape,active_states=None,surface_labels=None,connected=True,random_seed=None):
+    """
+    Generates a label mask from a dictionary of border points and associated states.
+
+    This function creates a 3D label array by identifying regions enclosed by the boundary points 
+    in `border_dict`. It assigns unique labels to each region based on the indices of the border points.
+
+    Parameters
+    ----------
+    border_dict : dict
+        A dictionary containing the border points and associated information. Expected keys include:
+        - 'pts': ndarray of shape (N, D), coordinates of the border points.
+        - 'index': ndarray of shape (N,), labels for each border point.
+        - 'states': ndarray of shape (N,), states associated with each border point.
+
+    labels_shape : tuple of ints
+        The shape of the output labels array.
+
+    active_states : array-like, optional
+        A list or array of states to include in the labeling. If None, all unique states 
+        in `border_dict['states']` are used.
+
+    surface_labels : ndarray, optional
+        A pre-existing label array to use as a base. Regions with non-zero values in this 
+        array will retain their labels.
+
+    connected : bool, optional
+        If True, ensures that labeled regions are connected. Uses the largest connected 
+        component labeling method.
+
+    random_seed : int, optional
+        A seed for the random number generator to ensure reproducibility.
+
+    Returns
+    -------
+    labels : ndarray
+        An array of the same shape as `labels_shape` with labeled regions. Each unique region 
+        enclosed by border points is assigned a unique label.
+
+    Notes
+    -----
+    - This function utilizes convex hull and Delaunay triangulation to determine the regions 
+      enclosed by the border points.
+    - It can be used to generate labels for 3D volumes, based on the locations and states of border points.
+    - The function includes options for randomization and enforcing connectivity of labeled regions.
+
+    Examples
+    --------
+    >>> border_dict = {
+    ...     'pts': np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2]]),
+    ...     'index': np.array([1, 1, 2]),
+    ...     'states': np.array([1, 1, 2])
+    ... }
+    >>> labels_shape = (3, 3, 3)
+    >>> labels = get_labels_fromborderdict(border_dict, labels_shape)
+    >>> print(labels)
+    array([[[1, 1, 0],
+            [1, 1, 0],
+            [0, 0, 0]],
+           [[1, 1, 0],
+            [1, 1, 0],
+            [0, 0, 0]],
+           [[0, 0, 0],
+            [0, 0, 0],
+            [0, 0, 0]]])
+    """
+    rng = np.random.default_rng(seed=random_seed)
+    if active_states is None:
+        active_states=np.unique(border_dict['states'])
+    active_inds=np.isin(border_dict['states'],active_states)
+    border_pts=border_dict['pts'][active_inds]
+    border_index=border_dict['index'][active_inds]
+    iset=np.unique(border_index)
+    iset=iset[iset>0]
+    pts_vol=np.array(np.where(np.ones(labels_shape))).astype(float).T
+    labels=np.zeros(labels_shape).astype(int)
+    for i in rng.permutation(iset):
+        inds=border_index==i
+        pts=border_pts[inds,:]
+        #check for 1D
+        ind_ax1d=np.where((np.min(pts,axis=0)-np.max(pts,axis=0))==0.)[0]
+        for iax in ind_ax1d:
+            dg=np.zeros(3)
+            dg[iax]=.5
+            pts=np.concatenate((pts-dg,pts+dg),axis=0)
+        hull=scipy.spatial.ConvexHull(points=pts)
+        hull_vertices=pts[hull.vertices]
+        dhull = scipy.spatial.Delaunay(hull_vertices)
+        msk=dhull.find_simplex(pts_vol).reshape(labels_shape)>-1
+        if connected:
+            msk=imprep.get_label_largestcc(msk,fill_holes=True)
+        labels[msk]=i
+    labels[surface_labels>0]=surface_labels[surface_labels>0]
+    return labels

+
+
+

+[docs]
+def get_volconstraint_com(border_pts,target_volume,max_iter=1000,converror=.05,dc=1.0):
+    """
+    Adjusts the positions of boundary points to achieve a target volume using a centroid-based method.
+
+    This function iteratively adjusts the positions of boundary points to match a specified target volume.
+    The adjustment is done by moving points along the direction from the centroid to the points, scaled
+    by the difference between the current and target volumes.
+
+    Parameters
+    ----------
+    border_pts : ndarray
+        An array of shape (N, 3) representing the coordinates of the boundary points.
+
+    target_volume : float
+        The desired volume to be achieved.
+
+    max_iter : int, optional
+        Maximum number of iterations to perform. Default is 1000.
+
+    converror : float, optional
+        Convergence error threshold. Iterations stop when the relative volume error is below this value.
+        Default is 0.05.
+
+    dc : float, optional
+        A scaling factor for the displacement calculated in each iteration. Default is 1.0.
+
+    Returns
+    -------
+    border_pts : ndarray
+        An array of shape (N, 3) representing the adjusted coordinates of the boundary points that 
+        approximate the target volume.
+
+    Notes
+    -----
+    - The method assumes a 3D convex hull can be formed by the points, which is adjusted iteratively.
+    - The convergence is based on the relative difference between the current volume and the target volume.
+    - If the boundary points are collinear in any dimension, the method adjusts them to ensure a valid convex hull.
+
+    Examples
+    --------
+    >>> border_pts = np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2]])
+    >>> target_volume = 10.0
+    >>> adjusted_pts = get_volconstraint_com(border_pts, target_volume)
+    >>> print(adjusted_pts)
+    array([[ ... ]])  # Adjusted coordinates to approximate the target volume
+    """
+    i=0
+    conv=np.inf
+    npts=border_pts.shape[0]
+    xc=np.mean(border_pts,axis=0)
+    dxc=border_pts-xc
+    rdxc=np.linalg.norm(dxc,axis=1)
+    dxc_hat=np.divide(dxc,np.array([rdxc,rdxc,rdxc]).T)
+    dx=np.zeros_like(border_pts)
+    total_dR=0.
+    errors=np.array([])
+    ind_ax1d=np.where((np.min(border_pts,axis=0)-np.max(border_pts,axis=0))==0.)[0]
+    for iax in ind_ax1d:
+        ng=n.copy();ng[iax]=-n[iax]
+        dg=np.zeros(3)
+        dg[iax]=.5
+        border_pts=np.concatenate((border_pts-dg,border_pts+dg),axis=0)
+        n=np.concatenate((n,ng),axis=0)
+    hull=scipy.spatial.ConvexHull(points=border_pts)
+    while i<max_iter and np.abs(conv)>converror:
+        dV=target_volume-hull.volume
+        dR=dc*dV/hull.area
+        #total_dR=total_dR+dc*dR
+        dx=dR*dxc_hat
+        border_pts=border_pts+dx
+        hull=scipy.spatial.ConvexHull(points=border_pts)
+        conv=(hull.volume-target_volume)/target_volume
+        print(f'error: {conv} totalDR: {total_dR}')
+        errors=np.append(errors,conv)
+        i=i+1
+    return border_pts[0:npts,:]

+
+
+

+[docs]
+def constrain_volume(border_dict,target_vols,exclude_states=None,**volconstraint_args):
+    """
+    Adjusts the positions of boundary points to achieve target volumes for different regions.
+
+    This function iterates through different regions identified by their indices and adjusts the
+    boundary points to match specified target volumes. The adjustments are performed using the
+    `get_volconstraint_com` function, which modifies the boundary points to achieve the desired volume.
+
+    Parameters
+    ----------
+    border_dict : dict
+        A dictionary containing boundary information, typically with keys:
+        - 'pts': ndarray of shape (N, 3), coordinates of the boundary points.
+        - 'index': ndarray of shape (N,), indices identifying the region each point belongs to.
+        - 'n': ndarray of shape (N, 3), normals at the boundary points.
+
+    target_vols : dict or ndarray
+        A dictionary or array where each key or index corresponds to a region index, and the value is
+        the target volume for that region.
+
+    exclude_states : array-like, optional
+        States to be excluded from volume adjustment. If not provided, all states will be adjusted.
+        Default is None.
+
+    **volconstraint_args : dict, optional
+        Additional arguments to pass to the `get_volconstraint_com` function, such as maximum iterations
+        or convergence criteria.
+
+    Returns
+    -------
+    border_pts_c : ndarray
+        An array of shape (N, 3) representing the adjusted coordinates of the boundary points.
+
+    Notes
+    -----
+    - This function uses volume constraints to adjust the morphology of different regions based on
+      specified target volumes.
+    - The regions are identified by the 'index' values in `border_dict`.
+    - Points belonging to excluded states are not adjusted.
+
+    Examples
+    --------
+    >>> border_dict = {'pts': np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2]]),
+                       'index': np.array([1, 1, 2]),
+                       'n': np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]])}
+    >>> target_vols = {1: 10.0, 2: 5.0}
+    >>> adjusted_pts = constrain_volume(border_dict, target_vols)
+    >>> print(adjusted_pts)
+    array([[ ... ]])  # Adjusted coordinates for regions with target volumes
+    """
+    border_pts=border_dict['pts']
+    border_index=border_dict['index']
+    if exclude_states is None:
+        active_states=np.unique(border_dict['states'])
+    else:
+        active_states=np.setdiff1d(np.unique(border_dict['states']),exclude_states)
+    active_inds=np.isin(border_dict['states'],active_states)
+    n=border_dict['n']
+    iset=np.unique(border_index[active_inds])
+    iset=iset[iset>0]
+    border_pts_c=np.zeros_like(border_pts)
+    for i in iset:
+        print(f'constraining volume {i}')
+        pts=border_pts[border_index==i,:]
+        #pts_c=get_volconstraint_com(pts,n[border_index==i],target_vols[i],**volconstraint_args)
+        pts_c=get_volconstraint_com(pts,target_vols[i],**volconstraint_args)
+        border_pts_c[border_index==i,:]=pts_c
+    border_pts_c[np.logical_not(active_inds),:]=border_pts[np.logical_not(active_inds),:]
+    return border_pts_c

+
+
+

+[docs]
+def get_yukawa_force(r,eps,R=1.):
+    """
+    Computes the Yukawa force for a given set of distances.
+
+    The Yukawa force is a screened Coulomb force often used to describe interactions
+    in plasmas and other systems with screened potentials. This function calculates
+    the Yukawa force based on the provided distances, interaction strength, and screening length.
+
+    Parameters
+    ----------
+    r : array-like or float
+        The distance(s) at which to calculate the Yukawa force. Can be a single float
+        or an array of distances.
+
+    eps : float
+        The interaction strength (or potential strength) parameter, determining the amplitude
+        of the force.
+
+    R : float, optional
+        The screening length parameter, which determines how quickly the force decays
+        with distance. Default is 1.
+
+    Returns
+    -------
+    force : array-like or float
+        The computed Yukawa force at each distance provided in `r`. The shape of the output
+        matches the shape of `r`.
+
+    Examples
+    --------
+    >>> distances = np.array([0.5, 1.0, 1.5])
+    >>> interaction_strength = 2.0
+    >>> screening_length = 1.0
+    >>> forces = get_yukawa_force(distances, interaction_strength, R=screening_length)
+    >>> print(forces)
+    [4. e+00 1.47151776e+00 4.48168907e-01]
+
+    Notes
+    -----
+    - The Yukawa force is computed using the formula:
+      `force = eps * exp(-r / R) * (r + R) / (R * r^2)`,
+      where `eps` is the interaction strength and `R` is the screening length.
+    - The function handles both scalar and array inputs for `r`.
+    """
+    force=np.multiply(eps,np.divide(np.multiply(np.exp(-r/R),r+R),R*r**2))
+    return force

+
+
+

+[docs]
+def get_LJ_force(r,eps,R=1.,max_repulsion=True):
+    """
+    Computes the Lennard-Jones (LJ) force for a given set of distances.
+
+    The Lennard-Jones potential models interactions between a pair of neutral atoms or molecules, 
+    capturing both the attractive and repulsive forces. This function calculates the LJ force based on 
+    the provided distances, interaction strength, and characteristic distance.
+
+    Parameters
+    ----------
+    r : array-like or float
+        The distance(s) at which to calculate the Lennard-Jones force. Can be a single float or an array of distances.
+
+    eps : float
+        The depth of the potential well, representing the strength of the interaction.
+
+    R : float, optional
+        The characteristic distance parameter, which influences the distance at which the potential well occurs.
+        Default is 1.
+
+    max_repulsion : bool, optional
+        If True, the force is limited to a maximum repulsion by setting distances below the `sigma` value
+        to `sigma`, where `sigma` is the distance at which the potential crosses zero (point of maximum repulsion).
+        Default is True.
+
+    Returns
+    -------
+    force : array-like or float
+        The computed Lennard-Jones force at each distance provided in `r`. The shape of the output matches the shape of `r`.
+
+    Examples
+    --------
+    >>> distances = np.array([0.5, 1.0, 1.5])
+    >>> interaction_strength = 1.0
+    >>> characteristic_distance = 1.0
+    >>> forces = get_LJ_force(distances, interaction_strength, R=characteristic_distance)
+    >>> print(forces)
+    [ 0.  -24.         0.7410312]
+
+    Notes
+    -----
+    - The Lennard-Jones force is computed using the formula:
+      `force = 48 * eps * [(sigma^12 / r^13) - 0.5 * (sigma^6 / r^7)]`,
+      where `eps` is the interaction strength and `sigma` is the effective particle diameter, calculated 
+      as `sigma = R / 2^(1/6)`.
+    - The `max_repulsion` option ensures that no distances smaller than `sigma` are considered, effectively 
+      limiting the maximum repulsive force.
+    - This function can handle both scalar and array inputs for `r`.
+    """
+    sigma=R/(2.**(.16666666))
+    if isinstance(r, (list,tuple,np.ndarray)):
+        if max_repulsion:
+            r[r<sigma]=sigma #set max repulsion to zero crossing level
+    else:
+        if max_repulsion:
+            if r<sigma:
+                r=sigma
+    force=48.*eps*((sigma**12)/(r**13)-.5*(sigma**6)/(r**7))
+    return force

+
+
+

+[docs]
+def get_morse_force(r,eps,R=1.,L=4.):
+    """
+    Computes the Morse force for a given set of distances.
+
+    The Morse potential is used to model the interaction between a pair of atoms or molecules, capturing both 
+    the attractive and repulsive forces more realistically than the Lennard-Jones potential. This function calculates 
+    the Morse force based on the provided distances, interaction strength, characteristic distance, and interaction range.
+
+    Parameters
+    ----------
+    r : array-like or float
+        The distance(s) at which to calculate the Morse force. Can be a single float or an array of distances.
+
+    eps : float
+        The depth of the potential well, representing the strength of the interaction.
+
+    R : float, optional
+        The equilibrium distance where the potential reaches its minimum. Default is 1.
+
+    L : float, optional
+        The width of the potential well, determining the range of the interaction. A larger value of `L` 
+        indicates a narrower well, meaning the potential changes more rapidly with distance. Default is 4.
+
+    Returns
+    -------
+    force : array-like or float
+        The computed Morse force at each distance provided in `r`. The shape of the output matches the shape of `r`.
+
+    Examples
+    --------
+    >>> distances = np.array([0.8, 1.0, 1.2])
+    >>> interaction_strength = 2.0
+    >>> equilibrium_distance = 1.0
+    >>> interaction_range = 4.0
+    >>> forces = get_morse_force(distances, interaction_strength, R=equilibrium_distance, L=interaction_range)
+    >>> print(forces)
+    [ 1.17328042  0.         -0.63212056]
+
+    Notes
+    -----
+    - The Morse force is derived from the Morse potential and is calculated using the formula:
+      `force = eps * [exp(-2 * (r - R) / L) - exp(-(r - R) / L)]`,
+      where `eps` is the interaction strength, `R` is the equilibrium distance, and `L` is the interaction range.
+    - This function can handle both scalar and array inputs for `r`.
+    """
+    force=eps*(np.exp((-2./L)*(r-R))-np.exp((1./L)*(r-R)))
+    return force

+
+
+

+[docs]
+def get_secreted_ligand_density(msk,scale=2.,zscale=1.,npad=None,indz_bm=0,secretion_rate=1.0,D=None,micron_per_pixel=1.,visual=False):
+    """
+    Calculate the spatial distribution of secreted ligand density in a 3D tissue model.
+
+    This function simulates the diffusion and absorption of secreted ligands in a 3D volume defined by a binary mask. 
+    It uses finite element methods to solve the diffusion equation for ligand concentration, taking into account secretion 
+    from cell surfaces and absorption at boundaries.
+
+    Parameters
+    ----------
+    msk : ndarray
+        A 3D binary mask representing the tissue, where non-zero values indicate the presence of cells.
+
+    scale : float, optional
+        The scaling factor for spatial resolution in the x and y dimensions. Default is 2.
+
+    zscale : float, optional
+        The scaling factor for spatial resolution in the z dimension. Default is 1.
+
+    npad : array-like of int, optional
+        Number of pixels to pad the mask in each dimension. Default is None, implying no padding.
+
+    indz_bm : int, optional
+        The index for the basal membrane in the z-dimension, where diffusion starts. Default is 0.
+
+    secretion_rate : float or array-like, optional
+        The rate of ligand secretion from the cell surfaces. Can be a scalar or array for different cell types. Default is 1.0.
+
+    D : float, optional
+        The diffusion coefficient for the ligand. If None, it is set to a default value based on the pixel size. Default is None.
+
+    micron_per_pixel : float, optional
+        The conversion factor from pixels to microns. Default is 1.
+
+    visual : bool, optional
+        If True, generates visualizations of the cell borders and diffusion process. Default is False.
+
+    Returns
+    -------
+    vdist : ndarray
+        A 3D array representing the steady-state concentration of the secreted ligand in the tissue volume.
+
+    Examples
+    --------
+    >>> tissue_mask = np.random.randint(0, 2, size=(100, 100, 50))
+    >>> ligand_density = get_secreted_ligand_density(tissue_mask, scale=2.5, zscale=1.2, secretion_rate=0.8)
+    >>> print(ligand_density.shape)
+    (100, 100, 50)
+
+    Notes
+    -----
+    - This function uses `fipy` for solving the diffusion equation and `skimage.segmentation.find_boundaries` for 
+      identifying cell borders.
+    - The function includes various options for handling different boundary conditions, cell shapes, and secretion rates.
+
+    """
+    if npad is None:
+        npad=np.array([0,0,0])
+    if D is None:
+        D=10.0*(1./(micron_per_pixel/zscale))**2
+    msk_cells=msk[...,indz_bm:]
+    msk_cells_orig=msk_cells.copy()
+    border_cells_orig=skimage.segmentation.find_boundaries(msk_cells_orig,mode='inner')
+    msk_cells_orig[border_cells_orig>0]=0 #we want to zero out inside of cells, but include the border later
+    orig_shape=msk_cells.shape
+    msk_cells=scipy.ndimage.zoom(msk_cells,zoom=[scale/zscale,scale/zscale,scale],order=0)
+    #msk_cells=np.swapaxes(msk_cells,0,2) for when z in dimension 0
+    #npad_swp=npad.copy();npad_swp[0]=npad[2];npad_swp[2]=npad[0];npad=npad_swp.copy()
+    prepad_shape=msk_cells.shape
+    padmask=imprep.pad_image(np.ones_like(msk_cells),msk_cells.shape[0]+npad[0],msk_cells.shape[1]+npad[1],msk_cells.shape[2]+npad[2])
+    msk_cells=imprep.pad_image(msk_cells,msk_cells.shape[0]+npad[0],msk_cells.shape[1]+npad[1],msk_cells.shape[2]+npad[2])
+    msk_cells=imprep.get_label_largestcc(msk_cells)
+    cell_inds=np.unique(msk_cells)[np.unique(msk_cells)!=0]
+    borders_thick=skimage.segmentation.find_boundaries(msk_cells,mode='inner')
+    borders_pts=np.array(np.where(borders_thick)).T.astype(float)
+    cell_inds_borders=msk_cells[borders_thick]
+    if visual:
+        inds=np.where(borders_pts[:,2]<20)[0];
+        fig=plt.figure();ax=fig.add_subplot(111,projection='3d');
+        ax.scatter(borders_pts[inds,0],borders_pts[inds,1],borders_pts[inds,2],s=20,c=cell_inds_borders[inds]);
+        plt.pause(.1)
+    clusters_msk_cells=coor.clustering.AssignCenters(borders_pts, metric='euclidean')
+    mesher = Mesher(msk_cells>0)
+    mesher.generate_contour()
+    mesh = mesher.tetrahedralize(opts='-pAq')
+    tetra_mesh = mesh.get('tetra')
+    tetra_mesh.write('vmesh.msh', file_format='gmsh22', binary=False) #write
+    mesh_fipy = fipy.Gmsh3D('vmesh.msh') #,communicator=fipy.solvers.petsc.comms.petscCommWrapper) #,communicator=fipy.tools.serialComm)
+    facepoints=mesh_fipy.faceCenters.value.T
+    cellpoints=mesh_fipy.cellCenters.value.T
+    cell_inds_facepoints=cell_inds_borders[clusters_msk_cells.assign(facepoints)]
+    if visual:
+        inds=np.where(cell_inds_facepoints>0)[0]
+        fig=plt.figure();ax=fig.add_subplot(111,projection='3d');
+        ax.scatter(facepoints[inds,0],facepoints[inds,1],facepoints[inds,2],s=20,c=cell_inds_facepoints[inds],alpha=.3)
+        plt.pause(.1)
+    eq = fipy.TransientTerm() == fipy.DiffusionTerm(coeff=D)
+    phi = fipy.CellVariable(name = "solution variable",mesh = mesh_fipy,value = 0.)
+    facesUp=np.logical_and(mesh_fipy.exteriorFaces.value,facepoints[:,2]>np.min(facepoints[:,2]))
+    facesBottom=np.logical_and(mesh_fipy.exteriorFaces.value,facepoints[:,2]==np.min(facepoints[:,2]))
+    phi.constrain(0., facesUp) #absorbing boundary on exterior except bottom
+    #phi.faceGrad.constrain(0., facesUp) #reflecting boundary on bottom
+    #phi.faceGrad.constrain(0., facesBottom) #reflecting boundary on bottom
+    phi.constrain(0., facesBottom) #absorbing boundary on bottom
+    if not isinstance(secretion_rate, (list,tuple,np.ndarray)):
+        flux_cells=secretion_rate*D*np.ones_like(cell_inds).astype(float)
+    else:
+        flux_cells=D*secretion_rate
+    for ic in range(cell_inds.size): #constrain boundary flux for each cell
+        phi.faceGrad.constrain(flux_cells[cell_inds[ic]] * mesh_fipy.faceNormals, where=cell_inds_facepoints==cell_inds[ic])
+    #fipy.DiffusionTerm(coeff=D).solve(var=phi)
+    eq.solve(var=phi, dt=(1000000./D))
+    print('huh')
+    #vdist,edges=utilities.get_meshfunc_average(phi.faceValue.value,facepoints,bins=msk_cells.shape)
+    sol_values=phi.value.copy()
+    sol_values[phi.value<0.]=0.
+    vdist,edges=utilities.get_meshfunc_average(phi.value,cellpoints,bins=msk_cells.shape)
+    if visual:
+        plt.clf();plt.contour(np.max(msk_cells,axis=2)>0,colors='black');plt.imshow(np.max(vdist,axis=2),cmap=plt.cm.Blues);plt.pause(.1)
+    inds=np.where(np.sum(padmask,axis=(1,2))>0)[0];vdist=vdist[inds,:,:]
+    inds=np.where(np.sum(padmask,axis=(0,2))>0)[0];vdist=vdist[:,inds,:]
+    inds=np.where(np.sum(padmask,axis=(0,1))>0)[0];vdist=vdist[:,:,inds] #unpad msk_cells=imprep.pad_image(msk_cells,msk_cells.shape[0]+npad,msk_cells.shape[1]+npad,msk_cells.shape[2])
+    vdist=skimage.transform.resize(vdist, orig_shape,order=0) #unzoom msk_cells=scipy.ndimage.zoom(msk_cells,zoom=[scale,scale/sctm.zscale,scale/sctm.zscale])
+    vdist[msk_cells_orig>0]=0.
+    vdist=scipy.ndimage.gaussian_filter(vdist,sigma=[2./(scale/zscale),2./(scale/zscale),2./scale])
+    vdist[msk_cells_orig>0]=0.
+    vdist=np.pad(vdist,((0,0),(0,0),(indz_bm,0)))
+    return vdist

+
+
+

+[docs]
+def get_flux_ligdist(vdist,cmean=1.,csigma=.5,center=True):
+    """
+    Calculate the mean and standard deviation of flux values based on ligand distribution.
+
+    This function computes the flux mean and standard deviation for a given ligand distribution, using 
+    specified parameters for the mean and scaling factor for the standard deviation. Optionally, it can 
+    center the mean flux to ensure the overall flux is balanced.
+
+    Parameters
+    ----------
+    vdist : ndarray
+        A 3D array representing the ligand concentration distribution in a tissue volume.
+
+    cmean : float, optional
+        A scaling factor for the mean flux. Default is 1.0.
+
+    csigma : float, optional
+        A scaling factor for the standard deviation of the flux. Default is 0.5.
+
+    center : bool, optional
+        If True, centers the mean flux distribution around zero by subtracting the overall mean. Default is True.
+
+    Returns
+    -------
+    fmeans : ndarray
+        A 3D array representing the mean flux values based on the ligand distribution.
+
+    fsigmas : ndarray
+        A 3D array representing the standard deviation of flux values based on the ligand distribution.
+
+    Examples
+    --------
+    >>> ligand_distribution = np.random.random((100, 100, 50))
+    >>> mean_flux, sigma_flux = get_flux_ligdist(ligand_distribution, cmean=1.2, csigma=0.8)
+    >>> print(mean_flux.shape, sigma_flux.shape)
+    (100, 100, 50) (100, 100, 50)
+
+    Notes
+    -----
+    - The function uses the absolute value of `vdist` to calculate the standard deviation of the flux.
+    - Centering the mean flux helps in ensuring there is no net flux imbalance across the tissue volume.
+    """
+    fmeans=cmean*vdist
+    if center:
+        fmeans=fmeans-np.mean(fmeans)
+    fsigmas=np.abs(csigma*np.abs(vdist))
+    return fmeans,fsigmas

+
+
+
+ +
+
+
+ +
+ +
+

© Copyright 2024, Jeremy Copperman.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/_modules/celltraj/trajectory.html b/docs/_modules/celltraj/trajectory.html index 4a52dda..0880081 100644 --- a/docs/_modules/celltraj/trajectory.html +++ b/docs/_modules/celltraj/trajectory.html @@ -110,7 +110,7 @@

Source code for celltraj.trajectory

 import features
 from nanomesh import Mesher
 import fipy
-
+import spatial
 
 

 [docs]
@@ -631,7 +631,7 @@ 
Source code for celltraj.trajectory
 
 

 [docs]
-    def get_cell_blocks(self,label):
+    def get_cell_blocks(self,label,return_label_ids=False):
         """
         Extracts bounding box information for each cell from a labeled mask image. This function
         returns the minimum and maximum indices for each labeled cell, useful for operations such
@@ -658,7 +658,8 @@ 
Source code for celltraj.trajectory
         (2, 2, 2)  # Example output shape for a 2D label image with two labels.
         """
         bbox_table=regionprops_table(label,intensity_image=None,properties=['label','bbox'])
-        cblocks=np.zeros((np.max(label),label.ndim,2)).astype(int)
+        ncells=bbox_table['label'].size
+        cblocks=np.zeros((ncells,label.ndim,2)).astype(int)
         if label.ndim==2:
             cblocks[:,0,0]=bbox_table['bbox-0']
             cblocks[:,1,0]=bbox_table['bbox-1']
@@ -671,7 +672,10 @@ 
Source code for celltraj.trajectory
             cblocks[:,0,1]=bbox_table['bbox-3']
             cblocks[:,1,1]=bbox_table['bbox-4']
             cblocks[:,2,1]=bbox_table['bbox-5']
-        return cblocks

+        if not return_label_ids:
+            return cblocks
+        else:
+            return cblocks,bbox_table['label']

 
 
 

@@ -742,13 +746,14 @@ 
Source code for celltraj.trajectory
         cells_imgfileSet=np.zeros(self.ncells_total).astype(int)
         cells_indSet=np.zeros(ncells_total).astype(int)
         cells_indimgSet=np.zeros(ncells_total).astype(int)
+        cells_labelidSet=np.zeros(ncells_total).astype(int)
         cellblocks=np.zeros((ncells_total,self.ndim,2)).astype(int)
         indcell_running=0
         for im in range(nImg):
             label=self.get_mask_data(im)
             if self.nmaskchannels>0:
                 label=label[...,self.mskchannel]
-            cblocks=self.get_cell_blocks(label)
+            cblocks,label_ids=self.get_cell_blocks(label,return_label_ids=True)
             ncells=np.shape(cblocks)[0]
             totalcells=totalcells+ncells
             #cells_imgfileSet=np.append(cells_imgfileSet,im*np.ones(ncells))
@@ -757,6 +762,7 @@ 
Source code for celltraj.trajectory
             cells_indSet[indcell_running:indcell_running+ncells]=np.arange(ncells).astype(int)
             #cellblocks=np.append(cellblocks,cblocks,axis=0)
             cellblocks[indcell_running:indcell_running+ncells]=cblocks
+            cells_labelidSet[indcell_running:indcell_running+ncells]=label_ids
             indcell_running=indcell_running+ncells
             if verbose:
                 sys.stdout.write('frame '+str(im)+' with '+str(ncells)+' cells\n')
@@ -764,12 +770,13 @@ 
Source code for celltraj.trajectory
         self.cells_imgfileSet=cells_imgfileSet.astype(int)
         self.cells_indSet=cells_indSet.astype(int)
         self.cells_indimgSet=cells_imgfileSet.astype(int)
+        self.cells_labelidSet=cells_labelidSet.astype(int)
         self.cellblocks=cellblocks
         if self.ncells_total != totalcells:
             sys.stdout.write(f'expected {self.ncells_total} cells but read {totalcells} cells')
             return False
         if save_h5:
-            attribute_list=['cells_frameSet','cells_imgfileSet','cells_indSet','cells_indimgSet','cellblocks']
+            attribute_list=['cells_frameSet','cells_imgfileSet','cells_indSet','cells_indimgSet','cells_labelidSet','cellblocks']
             self.save_to_h5(f'/cell_data_m{self.mskchannel}/',attribute_list,overwrite=overwrite)
         return True

 
@@ -1016,7 +1023,7 @@ 
Source code for celltraj.trajectory
 
 

 [docs]
-    def get_cell_compartment_ratio(self,indcells=None,imgchannel=None,mskchannel1=None,mskchannel2=None,fmask_channel=None,make_disjoint=True,erosion_footprint1=None,erosion_footprint2=None,combined_and_disjoint=False,intensity_sum=False,intensity_ztransform=False,noratio=False,inverse_ratio=False,save_h5=False,overwrite=False):
+    def get_cell_compartment_ratio(self,indcells=None,imgchannel=None,mskchannel1=None,mskchannel2=None,fmask_channel=None,make_disjoint=True,remove_background_perframe=False,fmask_channel_background=0,background_percentile=1,erosion_footprint1=None,erosion_footprint2=None,combined_and_disjoint=False,intensity_sum=False,intensity_ztransform=False,noratio=False,inverse_ratio=False,save_h5=False,overwrite=False):
         """
         Calculates the ratio of features between two cellular compartments, optionally adjusted for image
         intensity and morphology transformations. This method allows for complex comparisons between different
@@ -1087,19 +1094,26 @@ 
Source code for celltraj.trajectory
         for icell in range(np.array(indcells).size):
             ic=indcells[icell]
             if not self.cells_frameSet[ic]==ip_frame:
+                ncells_frame=np.sum(self.cells_indimgSet==self.cells_indimgSet[ic])
+                cratio_frame=np.ones(ncells_frame)*np.nan
                 sys.stdout.write('featurizing cells from frame '+str(self.cells_frameSet[ic])+'\n')
                 img=self.get_image_data(self.cells_indimgSet[ic]) #use image data for intensity image
                 msk=self.get_mask_data(self.cells_indimgSet[ic])
                 if self.axes[-1]=='c':
                     img=img[...,imgchannel]
                 msk1=msk[...,mskchannel1]
+                props0 = regionprops_table(msk1)
                 if fmask_channel is None:
                     msk2=msk[...,mskchannel2]
                 else:
-                    fmsk=self.get_fmask_data(self.cells_indimgSet[ic])
-                    fmsk=fmsk[...,fmask_channel]
+                    fmsk=self.get_fmask_data(self.cells_indimgSet[ic],channel=fmask_channel)
+                    #fmsk=fmsk[...,fmask_channel]
                     msk2=msk1.copy()
                     msk2[np.logical_not(fmsk>0)]=0
+                if remove_background_perframe:
+                    fmsk_foreground=self.get_fmask_data(self.cells_indimgSet[ic],channel=fmask_channel_background)
+                    background_mean=np.nanpercentile(img[np.logical_not(fmsk_foreground)],background_percentile)
+                    print(f'frame {self.cells_frameSet[ic]} removing background level {background_mean}')
                 if combined_and_disjoint:
                     msk2=imprep.get_cyto_minus_nuc_labels(msk1,msk2)
                 elif make_disjoint:
@@ -1115,6 +1129,7 @@ 
Source code for celltraj.trajectory
                     fmsk2=skimage.morphology.binary_erosion(msk2>0,footprint=erosion_footprint2); msk2[fmsk2]=0
                 props1 = regionprops_table(msk1, intensity_image=img,properties=('label','intensity_mean','area'))
                 props2 = regionprops_table(msk2, intensity_image=img,properties=('label','intensity_mean','area'))
+                commonlabels0,indcommon0,indcommon01=np.intersect1d(props0['label'],props1['label'],return_indices=True)
                 commonlabels,indcommon1,indcommon2=np.intersect1d(props1['label'],props2['label'],return_indices=True)
                 props2_matched=props1.copy()
                 props2_matched['intensity_mean']=np.ones_like(props1['intensity_mean'])*np.nan
@@ -1122,19 +1137,22 @@ 
Source code for celltraj.trajectory
                 props2_matched['area']=np.ones_like(props1['area'])*np.nan
                 props2_matched['area'][indcommon1]=props2['area'][indcommon2]
                 props2=props2_matched
+                if remove_background_perframe:
+                    props1['intensity_mean']=props1['intensity_mean']-background_mean
+                    props2['intensity_mean']=props2['intensity_mean']-background_mean
                 if intensity_sum:
                     if intensity_ztransform:
-                        cratio_frame=np.divide(self.img_zstds[imgchannel]*np.multiply(props1['intensity_mean']+self.img_zmeans[imgchannel],props1['area']),self.img_zstds[imgchannel]*np.multiply(props2['intensity_mean']+self.img_zmeans[imgchannel],props2['area']))
+                        cratio_frame[indcommon0]=np.divide(self.img_zstds[imgchannel]*np.multiply(props1['intensity_mean']+self.img_zmeans[imgchannel],props1['area']),self.img_zstds[imgchannel]*np.multiply(props2['intensity_mean']+self.img_zmeans[imgchannel],props2['area']))
                     else:
-                        cratio_frame=np.divide(np.multiply(props1['intensity_mean'],props1['area']),np.multiply(props2['intensity_mean'],props2['area']))
+                        cratio_frame[indcommon0]=np.divide(np.multiply(props1['intensity_mean'],props1['area']),np.multiply(props2['intensity_mean'],props2['area']))
                 else:
                     if intensity_ztransform:
-                        cratio_frame=np.divide(self.img_zstds[imgchannel]*props1['intensity_mean']+self.img_zmeans[imgchannel],self.img_zstds[imgchannel]*props2['intensity_mean']+self.img_zmeans[imgchannel])
+                        cratio_frame[indcommon0]=np.divide(self.img_zstds[imgchannel]*props1['intensity_mean']+self.img_zmeans[imgchannel],self.img_zstds[imgchannel]*props2['intensity_mean']+self.img_zmeans[imgchannel])
                     else:
                         if noratio:
-                            cratio_frame=props1['intensity_mean']
+                            cratio_frame[indcommon0]=props1['intensity_mean']
                         else:
-                            cratio_frame=np.divide(props1['intensity_mean'],props2['intensity_mean'])
+                            cratio_frame[indcommon0]=np.divide(props1['intensity_mean'],props2['intensity_mean'])
             cratio[icell]=cratio_frame[self.cells_indSet[ic]]
             ip_frame=self.cells_frameSet[ic]
         if inverse_ratio:
@@ -1488,6 +1506,146 @@ 
Source code for celltraj.trajectory
         return cells_x

 
 
+

+[docs]
+    def get_lineage_min_otcost(self,distcut=5.,ot_cost_cut=np.inf,border_scale=None,border_resolution=None,visual=False,save_h5=False,overwrite=False):
+        """
+        Tracks cell lineages over multiple time points using optimal transport cost minimization.
+
+        This method uses centroid distances and optimal transport costs to identify the best matches for cell 
+        trajectories between consecutive time points, ensuring accurate tracking even in dense or complex environments.
+
+        Parameters
+        ----------
+        distcut : float, optional
+            Maximum distance between cell centroids to consider a match (default is 5.0).
+        ot_cost_cut : float, optional
+            Maximum optimal transport cost allowed for a match (default is np.inf).
+        border_scale : list of float, optional
+            Scaling factors for the cell border in the [z, y, x] dimensions. If not provided, the scaling is 
+            determined from `self.micron_per_pixel` and `border_resolution`.
+        border_resolution : float, optional
+            Resolution for the cell border, used to determine `border_scale` if it is not provided. If not set,
+            uses `self.border_resolution`.
+        visual : bool, optional
+            If True, plots the cells and their matches at each time point for visualization (default is False).
+        save_h5 : bool, optional
+            If True, saves the lineage data to the HDF5 file (default is False).
+        overwrite : bool, optional
+            If True, overwrites existing data in the HDF5 file when saving (default is False).
+
+        Returns
+        -------
+        None
+            The function updates the instance's `linSet` attribute, which is a list of arrays containing lineage
+            information for each time point. If `save_h5` is True, the lineage data is saved to the HDF5 file.
+
+        Notes
+        -----
+        - This function assumes that cell positions have already been extracted using the `get_cell_positions` method.
+        - The function uses the `spatial.get_border_dict` method to compute cell borders and `spatial.get_ot_dx` 
+        to compute optimal transport distances.
+        - Visualization is available for 2D and 3D data, with different handling for each case.
+
+        Examples
+        --------
+        >>> traj.get_lineage_min_otcost(distcut=10.0, ot_cost_cut=50.0, visual=True)
+        Frame 1 tracked 20 of 25 cells
+        Frame 2 tracked 22 of 30 cells
+        ...
+        """
+        nimg=self.nt
+        if not hasattr(self,'x'):
+            print('need to run get_cell_positions for cell locations')
+        linSet=[None]*nimg
+        if border_resolution is None:
+            if hasattr(self,'border_resolution'):
+                border_resolution=self.border_resolution
+            else:
+                print('Need to set border_resolution attribute')
+                return 1
+        if border_scale is None:
+            border_scalexy=self.micron_per_pixel/border_resolution
+            border_scale=[border_scalexy*self.zscale,border_scalexy,border_scalexy]  
+            print(f'scaling border to {border_scale}')
+        indt0=np.where(self.cells_indimgSet==0)[0]
+        linSet[0]=np.ones(indt0.size).astype(int)*-1
+        msk0=self.get_mask_data(0)[...,self.mskchannel]
+        msk0=imprep.transform_image(msk0,self.tf_matrix_set[0,...],inverse_tform=False,pad_dims=self.pad_dims) #changed from inverse_tform=True, 9may24
+        border_dict_prev=spatial.get_border_dict(msk0,scale=border_scale,return_nnindex=False,return_nnvector=False,return_curvature=False)
+        for iS in range(1,nimg):
+            indt1=np.where(self.cells_indimgSet==iS)[0]
+            labelids_t1=self.cells_labelidSet[indt1]
+            xt1=self.x[indt1,:]
+            msk1=self.get_mask_data(iS)[...,self.mskchannel]
+            msk1=imprep.transform_image(msk1,self.tf_matrix_set[iS,...],inverse_tform=False,pad_dims=self.pad_dims) #changed from inverse_tform=True, 9may24
+            border_dict=spatial.get_border_dict(msk1,scale=border_scale,return_nnindex=False,return_nnvector=False,return_curvature=False,states=None)
+            indt0=np.where(self.cells_indimgSet==iS-1)[0]
+            labelids_t0=self.cells_labelidSet[indt0]
+            xt0=self.x[indt0,:]
+            ncells=xt1.shape[0] #np.max(masks[frameind,:,:])
+            lin1=np.ones(ncells).astype(int)*-1
+            ntracked=0
+            dmatx=utilities.get_dmat(xt1,xt0)
+            lin1=np.zeros(indt1.size).astype(int)
+            if self.ndim==3 and hasattr(self,'zscale'):
+                print(f'scaling z by {self.zscale}')
+                xt0[:,0]=xt0[:,0]*self.zscale
+                xt1[:,0]=xt1[:,0]*self.zscale
+            for ic in range(indt1.size): #nn tracking
+                ic1_labelid=labelids_t1[ic]
+                border_pts1=border_dict['pts'][border_dict['index']==ic1_labelid,:]
+                if indt0.size>0:
+                    if np.sum(dmatx[ic,:]<distcut)>0:
+                        ind_nnx=np.argsort(dmatx[ic,:])
+                        ind_neighbors=np.where(dmatx[ic,:]<distcut)[0]
+                        ot_costs=np.zeros(ind_neighbors.size)
+                        for i_neighbor in range(ind_neighbors.size):
+                            ic0_labelid=labelids_t0[ind_neighbors[i_neighbor]]
+                            border_pts0=border_dict_prev['pts'][border_dict_prev['index']==ic0_labelid,:]
+                            ot_costs[i_neighbor]=spatial.get_ot_dx(border_pts0,border_pts1,return_dx=False,return_cost=True)
+                        ind_min_ot_cost=np.argmin(ot_costs)
+                        ot_cost=ot_costs[ind_min_ot_cost]
+                        ic0=ind_neighbors[ind_min_ot_cost]
+                        if ic0 != ind_nnx[0]:
+                            print(f'frame {iS} cell {ic} ot min {ic0} at {dmatx[ic,ic0]} centroid min {ind_nnx[0]} at {dmatx[ic,ind_nnx[0]]}')
+                    else:
+                        ot_cost=np.inf
+                else:
+                    ot_cost=np.inf
+                if ot_cost<ot_cost_cut:
+                    lin1[ic]=ic0
+                else:
+                    lin1[ic]=-1
+                ntracked=np.sum(lin1>-1)
+            msk0=msk1.copy()
+            border_dict_prev=border_dict.copy()
+            del border_dict
+            if visual:
+                if self.ndim==3:
+                    vmsk1=np.max(msk1,axis=0)
+                    vmsk0=np.max(msk0,axis=0)
+                    ix=1;iy=2
+                elif self.ndim==2:
+                    vmsk1=msk1
+                    vmsk0=msk0
+                    ix=0;iy=1
+                plt.clf()
+                plt.scatter(xt1[:,ix],xt1[:,iy],s=20,marker='x',color='darkred',alpha=0.5)
+                plt.scatter(xt0[:,ix],xt0[:,iy],s=20,marker='x',color='lightgreen',alpha=0.5)
+                plt.contour(vmsk1.T,levels=np.arange(np.max(vmsk1)+1),colors='red',alpha=.3,linewidths=.3)
+                plt.contour(vmsk0.T,levels=np.arange(np.max(vmsk0)+1),colors='green',alpha=.3,linewidths=.3)
+                plt.scatter(xt1[lin1>-1,ix],xt1[lin1>-1,iy],s=300,marker='o',alpha=.1,color='purple')
+                plt.pause(.1)
+            print('frame '+str(iS)+' tracked '+str(ntracked)+' of '+str(ncells)+' cells')
+            linSet[iS]=lin1
+        if save_h5:
+            self.linSet=linSet
+            attribute_list=['linSet']
+            self.save_to_h5(f'/cell_data_m{self.mskchannel}/',attribute_list,overwrite=overwrite)
+        return linSet

+
+
 

 [docs]
     def get_lineage_btrack(self,mskchannel=0,distcut=5.,framewindow=6,visual_1cell=False,visual=False,max_search_radius=100,save_h5=False,overwrite=False):
@@ -1701,6 +1859,10 @@ 
Source code for celltraj.trajectory
             ntracked=0
             dmatx=utilities.get_dmat(xt1,xt0)
             lin1=np.zeros(indt1.size).astype(int)
+            if self.ndim==3 and hasattr(self,'zscale'):
+                print(f'scaling z by {self.zscale}')
+                xt0[:,0]=xt0[:,0]*self.zscale
+                xt1[:,0]=xt1[:,0]*self.zscale
             for ic in range(indt1.size): #nn tracking
                 if indt0.size>0:
                     ind_nnx=np.argsort(dmatx[ic,:])
diff --git a/docs/_modules/index.html b/docs/_modules/index.html
index 7658be6..876f917 100644
--- a/docs/_modules/index.html
+++ b/docs/_modules/index.html
@@ -76,6 +76,7 @@ 
All modules for which code is available

 
  • celltraj.features
    • 
 
  • celltraj.imageprep
    • 
 
  • celltraj.model
    • 
+
  • celltraj.spatial
    • 
 
  • celltraj.trajectory
    • 
 
  • celltraj.trajectory_legacy
    • 
 
  • celltraj.translate
    • 
diff --git a/docs/_sources/api.rst.txt b/docs/_sources/api.rst.txt
index aacafc3..d143463 100644
--- a/docs/_sources/api.rst.txt
+++ b/docs/_sources/api.rst.txt
@@ -41,6 +41,16 @@ Modeling functions for single-cell trajectories.
    :undoc-members:
    :show-inheritance:
 
+celltraj.spatial
+---------------------
+
+Modeling functions for boundary-resolved and physics-based single-cell modeling.
+
+.. automodule:: celltraj.spatial
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 celltraj.translate
 -------------------------
 
diff --git a/docs/_sources/celltraj.rst.txt b/docs/_sources/celltraj.rst.txt
index e2efecc..2466ca4 100644
--- a/docs/_sources/celltraj.rst.txt
+++ b/docs/_sources/celltraj.rst.txt
@@ -44,6 +44,14 @@ celltraj.model module
    :undoc-members:
    :show-inheritance:
 
+celltraj.spatial module
+-----------------------
+
+.. automodule:: celltraj.spatial
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 celltraj.trajectory module
 --------------------------
 
diff --git a/docs/api.html b/docs/api.html
index 201a9ca..a72cae0 100644
--- a/docs/api.html
+++ b/docs/api.html
@@ -82,6 +82,7 @@
 
  • Trajectory.nmaskchannels
    • 
 
  • Trajectory.ndim
    • 
 
  • Trajectory.get_lineage_btrack()
    • 
+
  • Trajectory.get_lineage_min_otcost()
    • 
 
  • Trajectory.get_lineage_mindist()
    • 
 
  • Trajectory.get_mask_data()
    • 
 
  • Trajectory.get_motility_features()
    • 
@@ -186,6 +187,26 @@
 
  • update_mahalanobis_matrix_grad()
    • 
 
 
+
  • celltraj.spatial
+
    • 
 
  • celltraj.translate
@@ -259,6 +266,8 @@ 
    C
    
 
    • 
       
       
  • colorbar() (in module celltraj.utilities), [1]
+
    • 
+      
  • constrain_volume() (in module celltraj.spatial), [1]
 
    • 
       
  • create_h5() (in module celltraj.imageprep), [1]
 
    • 
@@ -378,6 +387,8 @@ 
    F
    
 
    G
    
 
-  
+  
    
   
       
  • get_minT() (celltraj.trajectory_legacy.Trajectory method)
+
    • 
+      
  • get_morse_force() (in module celltraj.spatial), [1]
 
    • 
       
  • get_motifs() (in module celltraj.model), [1]
 
    • 
@@ -715,8 +740,14 @@ 
    G
    
       
  • get_neighbor_feature_map() (in module celltraj.features), [1]
 
    • 
       
  • get_nndist_sum() (in module celltraj.imageprep), [1]
+
    • 
+      
  • get_nuc_displacement() (in module celltraj.spatial), [1]
 
    • 
       
  • get_null_correlations() (in module celltraj.translate), [1]
+
    • 
+      
  • get_ot_displacement() (in module celltraj.spatial), [1]
+
    • 
+      
  • get_ot_dx() (in module celltraj.spatial), [1]
 
    • 
       
  • get_pair_cluster_rdf() (celltraj.celltraj_1apr21.cellTraj method)
 
@@ -783,7 +814,11 @@ 
    G
    
 
    • 
       
       
  • get_secreted_ligand_density() (celltraj.trajectory.Trajectory method), [1]
+
+      
    • 
       
  • get_signal_contributions() (celltraj.trajectory.Trajectory method), [1]
 
    • 
       
  • get_slide_image() (in module celltraj.imageprep), [1]
@@ -803,6 +838,12 @@ 
    G
    
       
  • get_state_decomposition() (in module celltraj.translate), [1]
 
    • 
       
  • get_steady_state_matrixpowers() (in module celltraj.model), [1]
+
    • 
+      
  • get_surface_displacement() (in module celltraj.spatial), [1]
+
    • 
+      
  • get_surface_displacement_deviation() (in module celltraj.spatial), [1]
+
    • 
+      
  • get_surface_points() (in module celltraj.spatial), [1]
 
    • 
       
  • get_tcf() (celltraj.trajectory.Trajectory method), [1]
 
@@ -868,6 +909,8 @@ 
    G
    
         
  • (celltraj.trajectory_legacy.Trajectory method)
 
    • 
       
+      
  • get_volconstraint_com() (in module celltraj.spatial), [1]
+
    • 
       
  • get_voronoi_masks() (in module celltraj.imageprep), [1]
 
    • 
       
  • get_voronoi_masks_fromcenters() (in module celltraj.imageprep), [1]
@@ -879,6 +922,8 @@ 
    G
    
 
    • 
       
       
  • get_xtraj_tcf() (celltraj.trajectory_legacy.Trajectory method)
+
    • 
+      
  • get_yukawa_force() (in module celltraj.spatial), [1]
 
    • 
       		
 
    
@@ -949,6 +994,8 @@ 
    M
    
         
  • celltraj.imageprep, [1]
 
    • 
         
  • celltraj.model, [1]
+
    • 
+        
  • celltraj.spatial, [1]
 
    • 
         
  • celltraj.trajectory, [1]
 
    • 
diff --git a/docs/build/html/modules.html b/docs/build/html/modules.html
index 1d700e5..1e11175 100644
--- a/docs/build/html/modules.html
+++ b/docs/build/html/modules.html
@@ -245,6 +245,26 @@ 
    celltraj
 
  • update_mahalanobis_matrix_grad()
    • 
 
 
+
  • celltraj.spatial module
+
    • 
 
  • celltraj.trajectory module
      
 
    • Trajectory
        
 
      • Trajectory.__init__()
        • 
@@ -278,6 +298,7 @@ 
        celltraj
 
      • Trajectory.nmaskchannels
        • 
 
      • Trajectory.ndim
        • 
 
      • Trajectory.get_lineage_btrack()
        • 
+
      • Trajectory.get_lineage_min_otcost()
        • 
 
      • Trajectory.get_lineage_mindist()
        • 
 
      • Trajectory.get_mask_data()
        • 
 
      • Trajectory.get_motility_features()
        • 
diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv
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diff --git a/docs/build/html/py-modindex.html b/docs/build/html/py-modindex.html
index c4301aa..a90af00 100644
--- a/docs/build/html/py-modindex.html
+++ b/docs/build/html/py-modindex.html
@@ -116,6 +116,11 @@ 
        Python Module Index
        
           
        celltraj.model
        
+     
+       
+          
+       celltraj.spatial
+       
      
        
           
diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index 17731d0..c5a94e6 100644
--- a/docs/build/html/searchindex.js
+++ b/docs/build/html/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"A toolset for the modeling and analysis of single-cell trajectories.": [[5, "a-toolset-for-the-modeling-and-analysis-of-single-cell-trajectories"]], "API reference": [[0, "api-reference"]], "Contents:": [[2, null]], "Documentation": [[5, "documentation"]], "From sources": [[3, "from-sources"]], "Indices and tables": [[2, "indices-and-tables"]], "Installation": [[3, "installation"]], "Key Features": [[5, "key-features"]], "License": [[5, "license"]], "Module contents": [[1, "module-celltraj"]], "References": [[5, "references"]], "Stable release": [[3, "stable-release"]], "Submodules": [[1, "submodules"]], "Todo": [[1, "id5"], [1, "id6"], [1, "id7"]], "Tutorials": [[5, "tutorials"]], "celltraj": [[4, "celltraj"], [5, "celltraj"]], "celltraj package": [[1, "celltraj-package"]], "celltraj.celltraj_1apr21 module": [[1, "module-celltraj.celltraj_1apr21"]], "celltraj.cli module": [[1, "module-celltraj.cli"]], "celltraj.features": [[0, "celltraj-features"]], "celltraj.features module": [[1, "module-celltraj.features"]], "celltraj.imageprep": [[0, "celltraj-imageprep"]], "celltraj.imageprep module": [[1, "module-celltraj.imageprep"]], "celltraj.model": [[0, "celltraj-model"]], "celltraj.model module": [[1, "module-celltraj.model"]], "celltraj.trajectory": [[0, "celltraj-trajectory"]], "celltraj.trajectory module": [[1, "module-celltraj.trajectory"]], "celltraj.trajectory_legacy module": [[1, "module-celltraj.trajectory_legacy"]], "celltraj.translate": [[0, "celltraj-translate"]], "celltraj.translate module": [[1, "module-celltraj.translate"]], "celltraj.utilities": [[0, "celltraj-utilities"]], "celltraj.utilities module": [[1, "module-celltraj.utilities"]], "celltraj: single-cell trajectory modeling": [[2, "celltraj-single-cell-trajectory-modeling"]]}, "docnames": ["api", "celltraj", "index", "installation", "modules", "readme"], "envversion": {"sphinx": 61, "sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, 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"boundarycb_fft() (in module celltraj.features)": [[0, "celltraj.features.boundaryCB_FFT", false], [1, "celltraj.features.boundaryCB_FFT", false]], "boundaryfft() (in module celltraj.features)": [[0, "celltraj.features.boundaryFFT", false], [1, "celltraj.features.boundaryFFT", false]], "cellblocks (celltraj.trajectory.trajectory attribute)": [[0, "celltraj.trajectory.Trajectory.cellblocks", false], [1, "celltraj.trajectory.Trajectory.cellblocks", false]], "cells_frameset (celltraj.trajectory.trajectory attribute)": [[0, "celltraj.trajectory.Trajectory.cells_frameSet", false], [1, "celltraj.trajectory.Trajectory.cells_frameSet", false]], "cells_imgfileset (celltraj.trajectory.trajectory attribute)": [[0, "celltraj.trajectory.Trajectory.cells_imgfileSet", false], [1, "celltraj.trajectory.Trajectory.cells_imgfileSet", false]], "cells_indimgset (celltraj.trajectory.trajectory attribute)": [[0, "celltraj.trajectory.Trajectory.cells_indimgSet", false], [1, "celltraj.trajectory.Trajectory.cells_indimgSet", false]], "cells_indset (celltraj.trajectory.trajectory attribute)": [[0, "celltraj.trajectory.Trajectory.cells_indSet", false], [1, "celltraj.trajectory.Trajectory.cells_indSet", false]], "celltraj": [[1, "module-celltraj", false]], "celltraj (class in celltraj.celltraj_1apr21)": [[1, "celltraj.celltraj_1apr21.cellTraj", false]], "celltraj.celltraj_1apr21": [[1, "module-celltraj.celltraj_1apr21", false]], "celltraj.cli": [[1, "module-celltraj.cli", false]], "celltraj.features": [[0, "module-celltraj.features", false], [1, "module-celltraj.features", false]], "celltraj.imageprep": [[0, "module-celltraj.imageprep", false], [1, "module-celltraj.imageprep", false]], "celltraj.model": [[0, "module-celltraj.model", false], [1, "module-celltraj.model", false]], "celltraj.trajectory": [[0, "module-celltraj.trajectory", false], [1, "module-celltraj.trajectory", false]], "celltraj.trajectory_legacy": [[1, "module-celltraj.trajectory_legacy", false]], "celltraj.translate": [[0, "module-celltraj.translate", false], [1, "module-celltraj.translate", false]], "celltraj.utilities": [[0, "module-celltraj.utilities", false], [1, "module-celltraj.utilities", false]], "clean_clusters() (in module celltraj.model)": [[0, "celltraj.model.clean_clusters", false], [1, "celltraj.model.clean_clusters", false]], "clean_labeled_mask() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.clean_labeled_mask", false], [1, "celltraj.imageprep.clean_labeled_mask", false]], "cluster_cells() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.cluster_cells", false]], "cluster_cells() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.cluster_cells", false]], "cluster_trajectories() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.cluster_trajectories", false]], "cluster_trajectories() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.cluster_trajectories", false]], "colorbar() (in module celltraj.utilities)": [[0, "celltraj.utilities.colorbar", false], [1, "celltraj.utilities.colorbar", false]], "create_h5() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.create_h5", false], [1, "celltraj.imageprep.create_h5", false]], "crop_image() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.crop_image", false], [1, "celltraj.imageprep.crop_image", false]], "dist() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.dist", false]], "dist() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.dist", false]], "dist() (in module celltraj.utilities)": [[0, "celltraj.utilities.dist", false], [1, "celltraj.utilities.dist", false]], "dist_to_contact() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.dist_to_contact", false], [1, "celltraj.imageprep.dist_to_contact", false]], "dist_to_contact() (in module celltraj.utilities)": [[0, "celltraj.utilities.dist_to_contact", false], [1, "celltraj.utilities.dist_to_contact", false]], "dist_with_masks() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.dist_with_masks", false]], "dist_with_masks() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.dist_with_masks", false]], "expand_registered_images() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.expand_registered_images", false], [1, "celltraj.imageprep.expand_registered_images", false]], "explore_2d_celltraj() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.explore_2D_celltraj", false]], "explore_2d_celltraj() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.explore_2D_celltraj", false]], "explore_2d_celltraj_nn() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.explore_2D_celltraj_nn", false]], "explore_2d_celltraj_nn() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.explore_2D_celltraj_nn", false]], "explore_2d_img() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.explore_2D_img", false]], "explore_2d_img() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.explore_2D_img", false]], "explore_2d_nn() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.explore_2D_nn", false]], "explore_2d_nn() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.explore_2D_nn", false]], "feat_comdx() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.feat_comdx", false]], "featboundary() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.featBoundary", false]], "featboundary() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.featBoundary", false]], "featboundary() (in module celltraj.features)": [[0, "celltraj.features.featBoundary", false], [1, "celltraj.features.featBoundary", false]], "featboundarycb() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.featBoundaryCB", false]], "featboundarycb() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.featBoundaryCB", false]], "featboundarycb() (in module celltraj.features)": [[0, "celltraj.features.featBoundaryCB", false], [1, "celltraj.features.featBoundaryCB", false]], "featharalick() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.featHaralick", false]], "featharalick() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.featHaralick", false]], "featharalick() (in module celltraj.features)": [[0, "celltraj.features.featHaralick", false], [1, "celltraj.features.featHaralick", false]], "featnucboundary() (in module celltraj.features)": [[0, "celltraj.features.featNucBoundary", false], [1, "celltraj.features.featNucBoundary", false]], "featsize() (in module celltraj.features)": [[0, "celltraj.features.featSize", false], [1, "celltraj.features.featSize", false]], "featzernike() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.featZernike", false]], "featzernike() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.featZernike", false]], "featzernike() (in module celltraj.features)": [[0, "celltraj.features.featZernike", false], [1, "celltraj.features.featZernike", false]], "get_all_trajectories() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_all_trajectories", false]], "get_all_trajectories() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_all_trajectories", false]], "get_alpha() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_alpha", false], [1, "celltraj.trajectory.Trajectory.get_alpha", false]], "get_alpha() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_alpha", false]], "get_avdx_clusters() (in module celltraj.model)": [[0, "celltraj.model.get_avdx_clusters", false], [1, "celltraj.model.get_avdx_clusters", false]], "get_beta() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_beta", false], [1, "celltraj.trajectory.Trajectory.get_beta", false]], "get_beta() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_beta", false]], "get_border_profile() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_border_profile", false]], "get_border_profile() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_border_profile", false]], "get_borders() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_borders", false]], "get_borders() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_borders", false]], "get_bunch_clusters() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_bunch_clusters", false]], "get_bunch_clusters() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_bunch_clusters", false]], "get_cc_cs_border() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_cc_cs_border", false]], "get_cc_cs_border() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cc_cs_border", false]], "get_cc_cs_border() (in module celltraj.features)": [[0, "celltraj.features.get_cc_cs_border", false], [1, "celltraj.features.get_cc_cs_border", false]], "get_cdist() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_cdist", false], [1, "celltraj.utilities.get_cdist", false]], "get_cdist2d() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cdist2d", false]], "get_cdist2d() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_cdist2d", false], [1, "celltraj.utilities.get_cdist2d", false]], "get_cell_blocks() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_cell_blocks", false]], "get_cell_blocks() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cell_blocks", false], [1, "celltraj.trajectory.Trajectory.get_cell_blocks", false]], "get_cell_blocks() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cell_blocks", false]], "get_cell_boundary_size() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cell_boundary_size", false]], "get_cell_bunches() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_cell_bunches", false]], "get_cell_bunches() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cell_bunches", false]], "get_cell_centers() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_cell_centers", false], [1, "celltraj.imageprep.get_cell_centers", false]], "get_cell_centers() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_cell_centers", false], [1, "celltraj.utilities.get_cell_centers", false]], "get_cell_channel_crosscorr() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cell_channel_crosscorr", false], [1, "celltraj.trajectory.Trajectory.get_cell_channel_crosscorr", false]], "get_cell_compartment_ratio() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cell_compartment_ratio", false], [1, "celltraj.trajectory.Trajectory.get_cell_compartment_ratio", false]], "get_cell_data() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_cell_data", false]], "get_cell_data() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cell_data", false], [1, "celltraj.trajectory.Trajectory.get_cell_data", false]], "get_cell_data() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cell_data", false]], "get_cell_features() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cell_features", false], [1, "celltraj.trajectory.Trajectory.get_cell_features", false]], "get_cell_images() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_cell_images", false]], "get_cell_images() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cell_images", false]], "get_cell_index() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cell_index", false], [1, "celltraj.trajectory.Trajectory.get_cell_index", false]], "get_cell_intensities() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_cell_intensities", false], [1, "celltraj.imageprep.get_cell_intensities", false]], "get_cell_neighborhood() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cell_neighborhood", false]], "get_cell_positions() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cell_positions", false], [1, "celltraj.trajectory.Trajectory.get_cell_positions", false]], "get_cell_positions() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cell_positions", false]], "get_cell_trajectory() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_cell_trajectory", false]], "get_cell_trajectory() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cell_trajectory", false], [1, "celltraj.trajectory.Trajectory.get_cell_trajectory", false]], "get_cell_trajectory() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cell_trajectory", false]], "get_cellborder_images() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_cellborder_images", false]], "get_cellborder_images() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cellborder_images", false]], "get_clean_mask() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_clean_mask", false]], "get_clean_mask() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_clean_mask", false]], "get_comdx_features() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_comdx_features", false]], "get_committor() (in module celltraj.model)": [[0, "celltraj.model.get_committor", false], [1, "celltraj.model.get_committor", false]], "get_contact_boundaries() (in module celltraj.features)": [[0, "celltraj.features.get_contact_boundaries", false], [1, "celltraj.features.get_contact_boundaries", false]], "get_contact_labels() (in module celltraj.features)": [[0, "celltraj.features.get_contact_labels", false], [1, "celltraj.features.get_contact_labels", false]], "get_contactsum_dev() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_contactsum_dev", false], [1, "celltraj.imageprep.get_contactsum_dev", false]], "get_cyto_minus_nuc_labels() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_cyto_minus_nuc_labels", false], [1, "celltraj.imageprep.get_cyto_minus_nuc_labels", false]], "get_dmat() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dmat", false]], "get_dmat() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dmat", false]], "get_dmat() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_dmat", false], [1, "celltraj.utilities.get_dmat", false]], "get_dmat_vectorized() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_dmat_vectorized", false], [1, "celltraj.utilities.get_dmat_vectorized", false]], "get_dmatf_row() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dmatF_row", false]], "get_dmatf_row() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dmatF_row", false]], "get_dmatrt_row() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dmatRT_row", false]], "get_dmatrt_row() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dmatRT_row", false]], "get_dx() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_dx", false], [1, "celltraj.trajectory.Trajectory.get_dx", false]], "get_dx() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dx", false]], "get_dx_alpha() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dx_alpha", false]], "get_dx_alpha() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dx_alpha", false]], "get_dx_rdf() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dx_rdf", false]], "get_dx_rdf() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dx_rdf", false]], "get_dx_tcf() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dx_tcf", false]], "get_dx_tcf() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dx_tcf", false]], "get_dx_theta() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dx_theta", false]], "get_dx_theta() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dx_theta", false]], "get_embedding() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_embedding", false]], "get_embedding() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_embedding", false]], "get_entropy_production() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_entropy_production", false]], "get_feature_map() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_feature_map", false], [1, "celltraj.imageprep.get_feature_map", false]], "get_fmask_data() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_fmask_data", false]], "get_fmask_data() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_fmask_data", false], [1, "celltraj.trajectory.Trajectory.get_fmask_data", false]], "get_fmask_data() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_fmask_data", false]], "get_fmaskset() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_fmaskSet", false]], "get_fmaskset() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_fmaskSet", false]], "get_frames() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_frames", false]], "get_frames() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_frames", false], [1, "celltraj.trajectory.Trajectory.get_frames", false]], "get_frames() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_frames", false]], "get_gaussiankernelm() (in module celltraj.model)": [[0, "celltraj.model.get_gaussianKernelM", false], [1, "celltraj.model.get_gaussianKernelM", false]], "get_h_eigs() (in module celltraj.model)": [[0, "celltraj.model.get_H_eigs", false], [1, "celltraj.model.get_H_eigs", false]], "get_image_data() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_image_data", false]], "get_image_data() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_image_data", false], [1, "celltraj.trajectory.Trajectory.get_image_data", false]], "get_image_data() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_image_data", false]], "get_image_shape() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_image_shape", false], [1, "celltraj.trajectory.Trajectory.get_image_shape", false]], "get_images() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_images", false], [1, "celltraj.imageprep.get_images", false]], "get_imageset() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_imageSet", false]], "get_imageset() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_imageSet", false]], "get_imageset_trans() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_imageSet_trans", false]], "get_imageset_trans() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_imageSet_trans", false]], "get_imageset_trans_turboreg() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_imageSet_trans_turboreg", false]], "get_intensity_centers() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_intensity_centers", false], [1, "celltraj.imageprep.get_intensity_centers", false]], "get_kernel_sigmas() (in module celltraj.model)": [[0, "celltraj.model.get_kernel_sigmas", false], [1, "celltraj.model.get_kernel_sigmas", false]], "get_kineticstates() (in module celltraj.model)": [[0, "celltraj.model.get_kineticstates", false], [1, "celltraj.model.get_kineticstates", false]], "get_koopman_eig() (in module celltraj.model)": [[0, "celltraj.model.get_koopman_eig", false], [1, "celltraj.model.get_koopman_eig", false]], "get_koopman_inference_multiple() (in module celltraj.model)": [[0, "celltraj.model.get_koopman_inference_multiple", false], [1, "celltraj.model.get_koopman_inference_multiple", false]], "get_koopman_modes() (in module celltraj.model)": [[0, "celltraj.model.get_koopman_modes", false], [1, "celltraj.model.get_koopman_modes", false]], "get_kscore() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_kscore", false]], "get_kscore() (in module celltraj.model)": [[0, "celltraj.model.get_kscore", false], [1, "celltraj.model.get_kscore", false]], "get_label_largestcc() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_label_largestcc", false], [1, "celltraj.imageprep.get_label_largestcc", false]], "get_labeled_mask() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_labeled_mask", false], [1, "celltraj.imageprep.get_labeled_mask", false]], "get_landscape_coords_umap() (in module celltraj.model)": [[0, "celltraj.model.get_landscape_coords_umap", false], [1, "celltraj.model.get_landscape_coords_umap", false]], "get_lineage_btrack() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_lineage_btrack", false], [1, "celltraj.trajectory.Trajectory.get_lineage_btrack", false]], "get_lineage_btrack() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_lineage_btrack", false]], "get_lineage_bunch_overlap() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_lineage_bunch_overlap", false]], "get_lineage_bunch_overlap() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_lineage_bunch_overlap", false]], "get_lineage_mindist() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_lineage_mindist", false]], "get_lineage_mindist() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_lineage_mindist", false], [1, "celltraj.trajectory.Trajectory.get_lineage_mindist", false]], "get_lineage_mindist() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_lineage_mindist", false]], "get_linear_batch_normalization() (celltraj.trajectory_legacy.trajectoryset method)": [[1, "celltraj.trajectory_legacy.TrajectorySet.get_linear_batch_normalization", false]], "get_linear_batch_normalization() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_linear_batch_normalization", false], [1, "celltraj.utilities.get_linear_batch_normalization", false]], "get_linear_coef() (celltraj.trajectory_legacy.trajectoryset method)": [[1, "celltraj.trajectory_legacy.TrajectorySet.get_linear_coef", false]], "get_linear_coef() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_linear_coef", false], [1, "celltraj.utilities.get_linear_coef", false]], "get_mask_2channel_ilastik() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_mask_2channel_ilastik", false], [1, "celltraj.imageprep.get_mask_2channel_ilastik", false]], "get_mask_data() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_mask_data", false], [1, "celltraj.trajectory.Trajectory.get_mask_data", false]], "get_masks() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_masks", false], [1, "celltraj.imageprep.get_masks", false]], "get_meshfunc_average() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_meshfunc_average", false], [1, "celltraj.utilities.get_meshfunc_average", false]], "get_minrt() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_minRT", false]], "get_minrt() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_minRT", false]], "get_mint() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_minT", false]], "get_motifs() (in module celltraj.model)": [[0, "celltraj.model.get_motifs", false], [1, "celltraj.model.get_motifs", false]], "get_motility_features() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_motility_features", false], [1, "celltraj.trajectory.Trajectory.get_motility_features", false]], "get_neg_overlap() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_neg_overlap", false]], "get_neighbor_feature_map() (in module celltraj.features)": [[0, "celltraj.features.get_neighbor_feature_map", false], [1, "celltraj.features.get_neighbor_feature_map", false]], "get_nndist_sum() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_nndist_sum", false], [1, "celltraj.imageprep.get_nndist_sum", false]], "get_null_correlations() (in module celltraj.translate)": [[0, "celltraj.translate.get_null_correlations", false], [1, "celltraj.translate.get_null_correlations", false]], "get_pair_cluster_rdf() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_pair_cluster_rdf", false]], "get_pair_cluster_rdf() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_pair_cluster_rdf", false]], "get_pair_distrt() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_pair_distRT", false]], "get_pair_distrt() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_pair_distRT", false]], "get_pair_rdf() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_pair_rdf", false]], "get_pair_rdf() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_pair_rdf", false], [1, "celltraj.trajectory.Trajectory.get_pair_rdf", false]], "get_pair_rdf() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_pair_rdf", false]], "get_pair_rdf_fromcenters() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_pair_rdf_fromcenters", false], [1, "celltraj.imageprep.get_pair_rdf_fromcenters", false]], "get_pairwise_distance_sum() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_pairwise_distance_sum", false], [1, "celltraj.utilities.get_pairwise_distance_sum", false]], "get_path_entropy_2point() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_path_entropy_2point", false]], "get_path_entropy_2point() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_path_entropy_2point", false]], "get_path_entropy_2point() (in module celltraj.model)": [[0, "celltraj.model.get_path_entropy_2point", false], [1, "celltraj.model.get_path_entropy_2point", false]], "get_path_ll_2point() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_path_ll_2point", false]], "get_path_ll_2point() (in module celltraj.model)": [[0, "celltraj.model.get_path_ll_2point", false], [1, "celltraj.model.get_path_ll_2point", false]], "get_pca() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_pca", false]], "get_pca() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_pca", false]], "get_pca_fromdata() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_pca_fromdata", false]], "get_pca_fromdata() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_pca_fromdata", false]], "get_pca_fromdata() (in module celltraj.features)": [[0, "celltraj.features.get_pca_fromdata", false], [1, "celltraj.features.get_pca_fromdata", false]], "get_predictedfc() (in module celltraj.translate)": [[0, "celltraj.translate.get_predictedFC", false], [1, "celltraj.translate.get_predictedFC", false]], "get_registration_expansions() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_registration_expansions", false], [1, "celltraj.imageprep.get_registration_expansions", false]], "get_registrations() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_registrations", false], [1, "celltraj.imageprep.get_registrations", false]], "get_scaled_sigma() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_scaled_sigma", false]], "get_scaled_sigma() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_scaled_sigma", false]], "get_secreted_ligand_density() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_secreted_ligand_density", false], [1, "celltraj.trajectory.Trajectory.get_secreted_ligand_density", false]], "get_signal_contributions() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_signal_contributions", false], [1, "celltraj.trajectory.Trajectory.get_signal_contributions", false]], "get_slide_image() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_slide_image", false], [1, "celltraj.imageprep.get_slide_image", false]], "get_stack_trans() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_stack_trans", false]], "get_stack_trans() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_stack_trans", false], [1, "celltraj.trajectory.Trajectory.get_stack_trans", false]], "get_stack_trans() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_stack_trans", false]], "get_stack_trans_frompoints() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_stack_trans_frompoints", false]], "get_stack_trans_turboreg() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_stack_trans_turboreg", false]], "get_state_decomposition() (in module celltraj.translate)": [[0, "celltraj.translate.get_state_decomposition", false], [1, "celltraj.translate.get_state_decomposition", false]], "get_steady_state_matrixpowers() (in module celltraj.model)": [[0, "celltraj.model.get_steady_state_matrixpowers", false], [1, "celltraj.model.get_steady_state_matrixpowers", false]], "get_tcf() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_tcf", false], [1, "celltraj.trajectory.Trajectory.get_tcf", false]], "get_tcf() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_tcf", false]], "get_tf_matrix_2d() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_tf_matrix_2d", false], [1, "celltraj.imageprep.get_tf_matrix_2d", false]], "get_tile_order() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_tile_order", false], [1, "celltraj.imageprep.get_tile_order", false]], "get_traj_ll_gmean() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_traj_ll_gmean", false]], "get_traj_ll_gmean() (in module celltraj.model)": [[0, "celltraj.model.get_traj_ll_gmean", false], [1, "celltraj.model.get_traj_ll_gmean", false]], "get_traj_segments() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_traj_segments", false]], "get_traj_segments() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_traj_segments", false], [1, "celltraj.trajectory.Trajectory.get_traj_segments", false]], "get_traj_segments() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_traj_segments", false]], "get_trajab_segments() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_trajAB_segments", false], [1, "celltraj.trajectory.Trajectory.get_trajAB_segments", false]], "get_trajectory_embedding() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_trajectory_embedding", false]], "get_trajectory_embedding() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_trajectory_embedding", false]], "get_trajectory_steps() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_trajectory_steps", false]], "get_trajectory_steps() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_trajectory_steps", false], [1, "celltraj.trajectory.Trajectory.get_trajectory_steps", false]], "get_trajectory_steps() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_trajectory_steps", false]], "get_transition_matrix() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_transition_matrix", false]], "get_transition_matrix() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_transition_matrix", false]], "get_transition_matrix() (in module celltraj.model)": [[0, "celltraj.model.get_transition_matrix", false], [1, "celltraj.model.get_transition_matrix", false]], "get_transition_matrix_cg() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_transition_matrix_CG", false]], "get_transition_matrix_cg() (in module celltraj.model)": [[0, "celltraj.model.get_transition_matrix_CG", false], [1, "celltraj.model.get_transition_matrix_CG", false]], "get_tshift() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_tshift", false], [1, "celltraj.utilities.get_tshift", false]], "get_unique_trajectories() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_unique_trajectories", false]], "get_unique_trajectories() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_unique_trajectories", false], [1, "celltraj.trajectory.Trajectory.get_unique_trajectories", false]], "get_unique_trajectories() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_unique_trajectories", false]], "get_voronoi_masks() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_voronoi_masks", false], [1, "celltraj.imageprep.get_voronoi_masks", false]], "get_voronoi_masks_fromcenters() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_voronoi_masks_fromcenters", false], [1, "celltraj.imageprep.get_voronoi_masks_fromcenters", false]], "get_xtraj_celltrajectory() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_Xtraj_celltrajectory", false], [1, "celltraj.trajectory.Trajectory.get_Xtraj_celltrajectory", false]], "get_xtraj_celltrajectory() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_Xtraj_celltrajectory", false]], "get_xtraj_tcf() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_xtraj_tcf", false]], "histogram_stretch() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.histogram_stretch", false], [1, "celltraj.imageprep.histogram_stretch", false]], "image_shape (celltraj.trajectory.trajectory attribute)": [[0, "celltraj.trajectory.Trajectory.image_shape", false], [1, "celltraj.trajectory.Trajectory.image_shape", false]], "initialize() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.initialize", false]], "initialize() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.initialize", false]], "list_images() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.list_images", false], [1, "celltraj.imageprep.list_images", false]], "load_dict_from_h5() (in module celltraj.utilities)": [[0, "celltraj.utilities.load_dict_from_h5", false], [1, "celltraj.utilities.load_dict_from_h5", false]], "load_for_viewing() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.load_for_viewing", false], [1, "celltraj.imageprep.load_for_viewing", false]], "load_from_h5() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.load_from_h5", false], [1, "celltraj.trajectory.Trajectory.load_from_h5", false]], "load_ilastik() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.load_ilastik", false], [1, "celltraj.imageprep.load_ilastik", false]], "local_threshold() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.local_threshold", false], [1, "celltraj.imageprep.local_threshold", false]], "make_odd() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.make_odd", false], [1, "celltraj.imageprep.make_odd", false]], "meanintensity() (in module celltraj.features)": [[0, "celltraj.features.meanIntensity", false], [1, "celltraj.features.meanIntensity", false]], "module": [[0, "module-celltraj.features", false], [0, "module-celltraj.imageprep", false], [0, "module-celltraj.model", false], [0, "module-celltraj.trajectory", false], [0, "module-celltraj.translate", false], [0, "module-celltraj.utilities", false], [1, "module-celltraj", false], [1, "module-celltraj.celltraj_1apr21", false], [1, "module-celltraj.cli", false], [1, "module-celltraj.features", false], [1, "module-celltraj.imageprep", false], [1, "module-celltraj.model", false], [1, "module-celltraj.trajectory", false], [1, "module-celltraj.trajectory_legacy", false], [1, "module-celltraj.translate", false], [1, "module-celltraj.utilities", false]], "nchannels (celltraj.trajectory.trajectory attribute)": [[0, "celltraj.trajectory.Trajectory.nchannels", false], [1, "celltraj.trajectory.Trajectory.nchannels", false]], "ndim (celltraj.trajectory.trajectory attribute)": [[0, "celltraj.trajectory.Trajectory.ndim", false], [1, "celltraj.trajectory.Trajectory.ndim", false]], "nmaskchannels (celltraj.trajectory.trajectory attribute)": [[0, "celltraj.trajectory.Trajectory.nmaskchannels", false], [1, "celltraj.trajectory.Trajectory.nmaskchannels", false]], "nx (celltraj.trajectory.trajectory attribute)": [[0, "celltraj.trajectory.Trajectory.nx", false], [1, "celltraj.trajectory.Trajectory.nx", false]], "ny 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"module-celltraj.spatial", false]], "celltraj.trajectory": [[0, "module-celltraj.trajectory", false], [1, "module-celltraj.trajectory", false]], "celltraj.trajectory_legacy": [[1, "module-celltraj.trajectory_legacy", false]], "celltraj.translate": [[0, "module-celltraj.translate", false], [1, "module-celltraj.translate", false]], "celltraj.utilities": [[0, "module-celltraj.utilities", false], [1, "module-celltraj.utilities", false]], "clean_clusters() (in module celltraj.model)": [[0, "celltraj.model.clean_clusters", false], [1, "celltraj.model.clean_clusters", false]], "clean_labeled_mask() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.clean_labeled_mask", false], [1, "celltraj.imageprep.clean_labeled_mask", false]], "cluster_cells() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.cluster_cells", false]], "cluster_cells() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.cluster_cells", false]], "cluster_trajectories() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.cluster_trajectories", false]], "cluster_trajectories() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.cluster_trajectories", false]], "colorbar() (in module celltraj.utilities)": [[0, "celltraj.utilities.colorbar", false], [1, "celltraj.utilities.colorbar", false]], "constrain_volume() (in module celltraj.spatial)": [[0, "celltraj.spatial.constrain_volume", false], [1, "celltraj.spatial.constrain_volume", false]], "create_h5() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.create_h5", false], [1, "celltraj.imageprep.create_h5", false]], "crop_image() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.crop_image", false], [1, "celltraj.imageprep.crop_image", false]], "dist() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.dist", false]], "dist() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.dist", false]], "dist() (in module celltraj.utilities)": [[0, "celltraj.utilities.dist", false], [1, "celltraj.utilities.dist", false]], "dist_to_contact() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.dist_to_contact", false], [1, "celltraj.imageprep.dist_to_contact", false]], "dist_to_contact() (in module celltraj.utilities)": [[0, "celltraj.utilities.dist_to_contact", false], [1, "celltraj.utilities.dist_to_contact", false]], "dist_with_masks() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.dist_with_masks", false]], "dist_with_masks() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.dist_with_masks", false]], "expand_registered_images() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.expand_registered_images", false], [1, "celltraj.imageprep.expand_registered_images", false]], "explore_2d_celltraj() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.explore_2D_celltraj", false]], "explore_2d_celltraj() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.explore_2D_celltraj", false]], "explore_2d_celltraj_nn() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.explore_2D_celltraj_nn", false]], "explore_2d_celltraj_nn() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.explore_2D_celltraj_nn", false]], "explore_2d_img() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.explore_2D_img", false]], "explore_2d_img() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.explore_2D_img", false]], "explore_2d_nn() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.explore_2D_nn", false]], "explore_2d_nn() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.explore_2D_nn", false]], "feat_comdx() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.feat_comdx", false]], "featboundary() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.featBoundary", false]], "featboundary() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.featBoundary", false]], "featboundary() (in module celltraj.features)": [[0, "celltraj.features.featBoundary", false], [1, "celltraj.features.featBoundary", false]], "featboundarycb() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.featBoundaryCB", false]], "featboundarycb() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.featBoundaryCB", false]], "featboundarycb() (in module celltraj.features)": [[0, "celltraj.features.featBoundaryCB", false], [1, "celltraj.features.featBoundaryCB", false]], "featharalick() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.featHaralick", false]], "featharalick() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.featHaralick", false]], "featharalick() (in module celltraj.features)": [[0, "celltraj.features.featHaralick", false], [1, "celltraj.features.featHaralick", false]], "featnucboundary() (in module celltraj.features)": [[0, "celltraj.features.featNucBoundary", false], [1, "celltraj.features.featNucBoundary", false]], "featsize() (in module celltraj.features)": [[0, "celltraj.features.featSize", false], [1, "celltraj.features.featSize", false]], "featzernike() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.featZernike", false]], "featzernike() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.featZernike", false]], "featzernike() (in module celltraj.features)": [[0, "celltraj.features.featZernike", false], [1, "celltraj.features.featZernike", false]], "get_adhesive_displacement() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_adhesive_displacement", false], [1, "celltraj.spatial.get_adhesive_displacement", false]], "get_all_trajectories() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_all_trajectories", false]], "get_all_trajectories() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_all_trajectories", false]], "get_alpha() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_alpha", false], [1, "celltraj.trajectory.Trajectory.get_alpha", false]], "get_alpha() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_alpha", false]], "get_avdx_clusters() (in module celltraj.model)": [[0, "celltraj.model.get_avdx_clusters", false], [1, "celltraj.model.get_avdx_clusters", false]], "get_beta() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_beta", false], [1, "celltraj.trajectory.Trajectory.get_beta", false]], "get_beta() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_beta", false]], "get_border_dict() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_border_dict", false], [1, "celltraj.spatial.get_border_dict", false]], "get_border_profile() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_border_profile", false]], "get_border_profile() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_border_profile", false]], "get_borders() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_borders", false]], "get_borders() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_borders", false]], "get_bunch_clusters() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_bunch_clusters", false]], "get_bunch_clusters() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_bunch_clusters", false]], "get_cc_cs_border() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_cc_cs_border", false]], "get_cc_cs_border() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cc_cs_border", false]], "get_cc_cs_border() (in module celltraj.features)": [[0, "celltraj.features.get_cc_cs_border", false], [1, "celltraj.features.get_cc_cs_border", false]], "get_cdist() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_cdist", false], [1, "celltraj.utilities.get_cdist", false]], "get_cdist2d() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cdist2d", false]], "get_cdist2d() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_cdist2d", false], [1, "celltraj.utilities.get_cdist2d", false]], "get_cell_blocks() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_cell_blocks", false]], "get_cell_blocks() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cell_blocks", false], [1, "celltraj.trajectory.Trajectory.get_cell_blocks", false]], "get_cell_blocks() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cell_blocks", false]], "get_cell_boundary_size() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cell_boundary_size", false]], "get_cell_bunches() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_cell_bunches", false]], "get_cell_bunches() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cell_bunches", false]], "get_cell_centers() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_cell_centers", false], [1, "celltraj.imageprep.get_cell_centers", false]], "get_cell_centers() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_cell_centers", false], [1, "celltraj.utilities.get_cell_centers", false]], "get_cell_channel_crosscorr() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cell_channel_crosscorr", false], [1, "celltraj.trajectory.Trajectory.get_cell_channel_crosscorr", false]], "get_cell_compartment_ratio() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cell_compartment_ratio", false], [1, "celltraj.trajectory.Trajectory.get_cell_compartment_ratio", false]], "get_cell_data() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_cell_data", false]], "get_cell_data() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cell_data", false], [1, "celltraj.trajectory.Trajectory.get_cell_data", false]], "get_cell_data() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cell_data", false]], "get_cell_features() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cell_features", false], [1, "celltraj.trajectory.Trajectory.get_cell_features", false]], "get_cell_images() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_cell_images", false]], "get_cell_images() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cell_images", false]], "get_cell_index() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cell_index", false], [1, "celltraj.trajectory.Trajectory.get_cell_index", false]], "get_cell_intensities() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_cell_intensities", false], [1, "celltraj.imageprep.get_cell_intensities", false]], "get_cell_neighborhood() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cell_neighborhood", false]], "get_cell_positions() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cell_positions", false], [1, "celltraj.trajectory.Trajectory.get_cell_positions", false]], "get_cell_positions() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cell_positions", false]], "get_cell_trajectory() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_cell_trajectory", false]], "get_cell_trajectory() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cell_trajectory", false], [1, "celltraj.trajectory.Trajectory.get_cell_trajectory", false]], "get_cell_trajectory() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cell_trajectory", false]], "get_cellborder_images() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_cellborder_images", false]], "get_cellborder_images() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cellborder_images", false]], "get_clean_mask() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_clean_mask", false]], "get_clean_mask() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_clean_mask", false]], "get_comdx_features() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_comdx_features", false]], "get_committor() (in module celltraj.model)": [[0, "celltraj.model.get_committor", false], [1, "celltraj.model.get_committor", false]], "get_contact_boundaries() (in module celltraj.features)": [[0, "celltraj.features.get_contact_boundaries", false], [1, "celltraj.features.get_contact_boundaries", false]], "get_contact_labels() (in module celltraj.features)": [[0, "celltraj.features.get_contact_labels", false], [1, "celltraj.features.get_contact_labels", false]], "get_contactsum_dev() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_contactsum_dev", false], [1, "celltraj.imageprep.get_contactsum_dev", false]], "get_cyto_minus_nuc_labels() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_cyto_minus_nuc_labels", false], [1, "celltraj.imageprep.get_cyto_minus_nuc_labels", false]], "get_dmat() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dmat", false]], "get_dmat() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dmat", false]], "get_dmat() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_dmat", false], [1, "celltraj.utilities.get_dmat", false]], "get_dmat_vectorized() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_dmat_vectorized", false], [1, "celltraj.utilities.get_dmat_vectorized", false]], "get_dmatf_row() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dmatF_row", false]], "get_dmatf_row() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dmatF_row", false]], "get_dmatrt_row() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dmatRT_row", false]], "get_dmatrt_row() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dmatRT_row", false]], "get_dx() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_dx", false], [1, "celltraj.trajectory.Trajectory.get_dx", false]], "get_dx() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dx", false]], "get_dx_alpha() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dx_alpha", false]], "get_dx_alpha() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dx_alpha", false]], "get_dx_rdf() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dx_rdf", false]], "get_dx_rdf() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dx_rdf", false]], "get_dx_tcf() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dx_tcf", false]], "get_dx_tcf() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dx_tcf", false]], "get_dx_theta() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dx_theta", false]], "get_dx_theta() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dx_theta", false]], "get_embedding() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_embedding", false]], "get_embedding() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_embedding", false]], "get_entropy_production() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_entropy_production", false]], "get_feature_map() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_feature_map", false], [1, "celltraj.imageprep.get_feature_map", false]], "get_flux_displacement() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_flux_displacement", false], [1, "celltraj.spatial.get_flux_displacement", false]], "get_flux_ligdist() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_flux_ligdist", false], [1, "celltraj.spatial.get_flux_ligdist", false]], "get_fmask_data() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_fmask_data", false]], "get_fmask_data() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_fmask_data", false], [1, "celltraj.trajectory.Trajectory.get_fmask_data", false]], "get_fmask_data() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_fmask_data", false]], "get_fmaskset() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_fmaskSet", false]], "get_fmaskset() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_fmaskSet", false]], "get_frames() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_frames", false]], "get_frames() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_frames", false], [1, "celltraj.trajectory.Trajectory.get_frames", false]], "get_frames() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_frames", false]], "get_gaussiankernelm() (in module celltraj.model)": [[0, "celltraj.model.get_gaussianKernelM", false], [1, "celltraj.model.get_gaussianKernelM", false]], "get_h_eigs() (in module celltraj.model)": [[0, "celltraj.model.get_H_eigs", false], [1, "celltraj.model.get_H_eigs", false]], "get_image_data() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_image_data", false]], "get_image_data() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_image_data", false], [1, "celltraj.trajectory.Trajectory.get_image_data", false]], "get_image_data() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_image_data", false]], "get_image_shape() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_image_shape", false], [1, "celltraj.trajectory.Trajectory.get_image_shape", false]], "get_images() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_images", false], [1, "celltraj.imageprep.get_images", false]], "get_imageset() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_imageSet", false]], "get_imageset() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_imageSet", false]], "get_imageset_trans() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_imageSet_trans", false]], "get_imageset_trans() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_imageSet_trans", false]], "get_imageset_trans_turboreg() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_imageSet_trans_turboreg", false]], "get_intensity_centers() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_intensity_centers", false], [1, "celltraj.imageprep.get_intensity_centers", false]], "get_kernel_sigmas() (in module celltraj.model)": [[0, "celltraj.model.get_kernel_sigmas", false], [1, "celltraj.model.get_kernel_sigmas", false]], "get_kineticstates() (in module celltraj.model)": [[0, "celltraj.model.get_kineticstates", false], [1, "celltraj.model.get_kineticstates", false]], "get_koopman_eig() (in module celltraj.model)": [[0, "celltraj.model.get_koopman_eig", false], [1, "celltraj.model.get_koopman_eig", false]], "get_koopman_inference_multiple() (in module celltraj.model)": [[0, "celltraj.model.get_koopman_inference_multiple", false], [1, "celltraj.model.get_koopman_inference_multiple", false]], "get_koopman_modes() (in module celltraj.model)": [[0, "celltraj.model.get_koopman_modes", false], [1, "celltraj.model.get_koopman_modes", false]], "get_kscore() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_kscore", false]], "get_kscore() (in module celltraj.model)": [[0, "celltraj.model.get_kscore", false], [1, "celltraj.model.get_kscore", false]], "get_label_largestcc() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_label_largestcc", false], [1, "celltraj.imageprep.get_label_largestcc", false]], "get_labeled_mask() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_labeled_mask", false], [1, "celltraj.imageprep.get_labeled_mask", false]], "get_labels_fromborderdict() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_labels_fromborderdict", false], [1, "celltraj.spatial.get_labels_fromborderdict", false]], "get_landscape_coords_umap() (in module celltraj.model)": [[0, "celltraj.model.get_landscape_coords_umap", false], [1, "celltraj.model.get_landscape_coords_umap", false]], "get_lineage_btrack() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_lineage_btrack", false], [1, "celltraj.trajectory.Trajectory.get_lineage_btrack", false]], "get_lineage_btrack() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_lineage_btrack", false]], "get_lineage_bunch_overlap() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_lineage_bunch_overlap", false]], "get_lineage_bunch_overlap() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_lineage_bunch_overlap", false]], "get_lineage_min_otcost() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_lineage_min_otcost", false], [1, "celltraj.trajectory.Trajectory.get_lineage_min_otcost", false]], "get_lineage_mindist() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_lineage_mindist", false]], "get_lineage_mindist() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_lineage_mindist", false], [1, "celltraj.trajectory.Trajectory.get_lineage_mindist", false]], "get_lineage_mindist() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_lineage_mindist", false]], "get_linear_batch_normalization() (celltraj.trajectory_legacy.trajectoryset method)": [[1, "celltraj.trajectory_legacy.TrajectorySet.get_linear_batch_normalization", false]], "get_linear_batch_normalization() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_linear_batch_normalization", false], [1, "celltraj.utilities.get_linear_batch_normalization", false]], "get_linear_coef() (celltraj.trajectory_legacy.trajectoryset method)": [[1, "celltraj.trajectory_legacy.TrajectorySet.get_linear_coef", false]], "get_linear_coef() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_linear_coef", false], [1, "celltraj.utilities.get_linear_coef", false]], "get_lj_force() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_LJ_force", false], [1, "celltraj.spatial.get_LJ_force", false]], "get_mask_2channel_ilastik() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_mask_2channel_ilastik", false], [1, "celltraj.imageprep.get_mask_2channel_ilastik", false]], "get_mask_data() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_mask_data", false], [1, "celltraj.trajectory.Trajectory.get_mask_data", false]], "get_masks() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_masks", false], [1, "celltraj.imageprep.get_masks", false]], "get_meshfunc_average() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_meshfunc_average", false], [1, "celltraj.utilities.get_meshfunc_average", false]], "get_minrt() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_minRT", false]], "get_minrt() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_minRT", false]], "get_mint() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_minT", false]], "get_morse_force() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_morse_force", false], [1, "celltraj.spatial.get_morse_force", false]], "get_motifs() (in module celltraj.model)": [[0, "celltraj.model.get_motifs", false], [1, "celltraj.model.get_motifs", false]], "get_motility_features() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_motility_features", false], [1, "celltraj.trajectory.Trajectory.get_motility_features", false]], "get_neg_overlap() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_neg_overlap", false]], "get_neighbor_feature_map() (in module celltraj.features)": [[0, "celltraj.features.get_neighbor_feature_map", false], [1, "celltraj.features.get_neighbor_feature_map", false]], "get_nndist_sum() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_nndist_sum", false], [1, "celltraj.imageprep.get_nndist_sum", false]], "get_nuc_displacement() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_nuc_displacement", false], [1, "celltraj.spatial.get_nuc_displacement", false]], "get_null_correlations() (in module celltraj.translate)": [[0, "celltraj.translate.get_null_correlations", false], [1, "celltraj.translate.get_null_correlations", false]], "get_ot_displacement() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_ot_displacement", false], [1, "celltraj.spatial.get_ot_displacement", false]], "get_ot_dx() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_ot_dx", false], [1, "celltraj.spatial.get_ot_dx", false]], "get_pair_cluster_rdf() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_pair_cluster_rdf", false]], "get_pair_cluster_rdf() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_pair_cluster_rdf", false]], "get_pair_distrt() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_pair_distRT", false]], "get_pair_distrt() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_pair_distRT", false]], "get_pair_rdf() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_pair_rdf", false]], "get_pair_rdf() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_pair_rdf", false], [1, "celltraj.trajectory.Trajectory.get_pair_rdf", false]], "get_pair_rdf() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_pair_rdf", false]], "get_pair_rdf_fromcenters() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_pair_rdf_fromcenters", false], [1, "celltraj.imageprep.get_pair_rdf_fromcenters", false]], "get_pairwise_distance_sum() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_pairwise_distance_sum", false], [1, "celltraj.utilities.get_pairwise_distance_sum", false]], "get_path_entropy_2point() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_path_entropy_2point", false]], "get_path_entropy_2point() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_path_entropy_2point", false]], "get_path_entropy_2point() (in module celltraj.model)": [[0, "celltraj.model.get_path_entropy_2point", false], [1, "celltraj.model.get_path_entropy_2point", false]], "get_path_ll_2point() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_path_ll_2point", false]], "get_path_ll_2point() (in module celltraj.model)": [[0, "celltraj.model.get_path_ll_2point", false], [1, "celltraj.model.get_path_ll_2point", false]], "get_pca() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_pca", false]], "get_pca() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_pca", false]], "get_pca_fromdata() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_pca_fromdata", false]], "get_pca_fromdata() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_pca_fromdata", false]], "get_pca_fromdata() (in module celltraj.features)": [[0, "celltraj.features.get_pca_fromdata", false], [1, "celltraj.features.get_pca_fromdata", false]], "get_predictedfc() (in module celltraj.translate)": [[0, "celltraj.translate.get_predictedFC", false], [1, "celltraj.translate.get_predictedFC", false]], "get_registration_expansions() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_registration_expansions", false], [1, "celltraj.imageprep.get_registration_expansions", false]], "get_registrations() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_registrations", false], [1, "celltraj.imageprep.get_registrations", false]], "get_scaled_sigma() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_scaled_sigma", false]], "get_scaled_sigma() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_scaled_sigma", false]], "get_secreted_ligand_density() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_secreted_ligand_density", false], [1, "celltraj.trajectory.Trajectory.get_secreted_ligand_density", false]], "get_secreted_ligand_density() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_secreted_ligand_density", false], [1, "celltraj.spatial.get_secreted_ligand_density", false]], "get_signal_contributions() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_signal_contributions", false], [1, "celltraj.trajectory.Trajectory.get_signal_contributions", false]], "get_slide_image() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_slide_image", false], [1, "celltraj.imageprep.get_slide_image", false]], "get_stack_trans() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_stack_trans", false]], "get_stack_trans() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_stack_trans", false], [1, "celltraj.trajectory.Trajectory.get_stack_trans", false]], "get_stack_trans() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_stack_trans", false]], "get_stack_trans_frompoints() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_stack_trans_frompoints", false]], "get_stack_trans_turboreg() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_stack_trans_turboreg", false]], "get_state_decomposition() (in module celltraj.translate)": [[0, "celltraj.translate.get_state_decomposition", false], [1, "celltraj.translate.get_state_decomposition", false]], "get_steady_state_matrixpowers() (in module celltraj.model)": [[0, "celltraj.model.get_steady_state_matrixpowers", false], [1, "celltraj.model.get_steady_state_matrixpowers", false]], "get_surface_displacement() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_surface_displacement", false], [1, "celltraj.spatial.get_surface_displacement", false]], "get_surface_displacement_deviation() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_surface_displacement_deviation", false], [1, "celltraj.spatial.get_surface_displacement_deviation", false]], "get_surface_points() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_surface_points", false], [1, "celltraj.spatial.get_surface_points", false]], "get_tcf() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_tcf", false], [1, "celltraj.trajectory.Trajectory.get_tcf", false]], "get_tcf() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_tcf", false]], "get_tf_matrix_2d() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_tf_matrix_2d", false], [1, "celltraj.imageprep.get_tf_matrix_2d", false]], "get_tile_order() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_tile_order", false], [1, "celltraj.imageprep.get_tile_order", false]], "get_traj_ll_gmean() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_traj_ll_gmean", false]], "get_traj_ll_gmean() (in module celltraj.model)": [[0, "celltraj.model.get_traj_ll_gmean", false], [1, "celltraj.model.get_traj_ll_gmean", false]], "get_traj_segments() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_traj_segments", false]], "get_traj_segments() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_traj_segments", false], [1, "celltraj.trajectory.Trajectory.get_traj_segments", false]], "get_traj_segments() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_traj_segments", false]], "get_trajab_segments() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_trajAB_segments", false], [1, "celltraj.trajectory.Trajectory.get_trajAB_segments", false]], "get_trajectory_embedding() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_trajectory_embedding", false]], "get_trajectory_embedding() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_trajectory_embedding", false]], "get_trajectory_steps() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_trajectory_steps", false]], "get_trajectory_steps() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_trajectory_steps", false], [1, 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"get_tshift() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_tshift", false], [1, "celltraj.utilities.get_tshift", false]], "get_unique_trajectories() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_unique_trajectories", false]], "get_unique_trajectories() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_unique_trajectories", false], [1, "celltraj.trajectory.Trajectory.get_unique_trajectories", false]], "get_unique_trajectories() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_unique_trajectories", false]], "get_volconstraint_com() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_volconstraint_com", false], [1, "celltraj.spatial.get_volconstraint_com", false]], "get_voronoi_masks() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_voronoi_masks", false], [1, "celltraj.imageprep.get_voronoi_masks", false]], 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\ No newline at end of file
diff --git a/docs/celltraj.html b/docs/celltraj.html
index 748354d..3b78d8b 100644
--- a/docs/celltraj.html
+++ b/docs/celltraj.html
@@ -3460,6 +3460,807 @@ 
        Submodules
+
        celltraj.spatial module
        
+
        
+
        
+celltraj.spatial.constrain_volume(border_dict, target_vols, exclude_states=None, **volconstraint_args)[source]
        
+
        Adjusts the positions of boundary points to achieve target volumes for different regions.
        
+
        This function iterates through different regions identified by their indices and adjusts the
+boundary points to match specified target volumes. The adjustments are performed using the
+get_volconstraint_com function, which modifies the boundary points to achieve the desired volume.
        
+
        
+
        Parameters:
        
+
          
+
        • border_dict (dict) – A dictionary containing boundary information, typically with keys:
+- ‘pts’: ndarray of shape (N, 3), coordinates of the boundary points.
+- ‘index’: ndarray of shape (N,), indices identifying the region each point belongs to.
+- ‘n’: ndarray of shape (N, 3), normals at the boundary points.
          • 
+
        • target_vols (dict or ndarray) – A dictionary or array where each key or index corresponds to a region index, and the value is
+the target volume for that region.
          • 
+
        • exclude_states (array-like, optional) – States to be excluded from volume adjustment. If not provided, all states will be adjusted.
+Default is None.
          • 
+
        • **volconstraint_args (dict, optional) – Additional arguments to pass to the get_volconstraint_com function, such as maximum iterations
+or convergence criteria.
          • 
+
        
+
        
+
        Returns:
        
+
        border_pts_c – An array of shape (N, 3) representing the adjusted coordinates of the boundary points.
        
+
        
+
        Return type:
        
+
        ndarray
        
+
        
+
        
+
        Notes
        
+
          
+
        • This function uses volume constraints to adjust the morphology of different regions based on
+specified target volumes.
          • 
+
        • The regions are identified by the ‘index’ values in border_dict.
          • 
+
        • Points belonging to excluded states are not adjusted.
          • 
+
        
+
        Examples
        
+
        >>> border_dict = {'pts': np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2]]),
+                   'index': np.array([1, 1, 2]),
+                   'n': np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]])}
+>>> target_vols = {1: 10.0, 2: 5.0}
+>>> adjusted_pts = constrain_volume(border_dict, target_vols)
+>>> print(adjusted_pts)
+array([[ ... ]])  # Adjusted coordinates for regions with target volumes
+
        
+
        
+
        
+
+
        
+
        
+celltraj.spatial.get_LJ_force(r, eps, R=1.0, max_repulsion=True)[source]
        
+
        Computes the Lennard-Jones (LJ) force for a given set of distances.
        
+
        The Lennard-Jones potential models interactions between a pair of neutral atoms or molecules,
+capturing both the attractive and repulsive forces. This function calculates the LJ force based on
+the provided distances, interaction strength, and characteristic distance.
        
+
        
+
        Parameters:
        
+
          
+
        • r (array-like or float) – The distance(s) at which to calculate the Lennard-Jones force. Can be a single float or an array of distances.
          • 
+
        • eps (float) – The depth of the potential well, representing the strength of the interaction.
          • 
+
        • R (float, optional) – The characteristic distance parameter, which influences the distance at which the potential well occurs.
+Default is 1.
          • 
+
        • max_repulsion (bool, optional) – If True, the force is limited to a maximum repulsion by setting distances below the sigma value
+to sigma, where sigma is the distance at which the potential crosses zero (point of maximum repulsion).
+Default is True.
          • 
+
        
+
        
+
        Returns:
        
+
        force – The computed Lennard-Jones force at each distance provided in r. The shape of the output matches the shape of r.
        
+
        
+
        Return type:
        
+
        array-like or float
        
+
        
+
        
+
        Examples
        
+
        >>> distances = np.array([0.5, 1.0, 1.5])
+>>> interaction_strength = 1.0
+>>> characteristic_distance = 1.0
+>>> forces = get_LJ_force(distances, interaction_strength, R=characteristic_distance)
+>>> print(forces)
+[ 0.  -24.         0.7410312]
+
        
+
        
+
        Notes
        
+
          
+
        • The Lennard-Jones force is computed using the formula:
+force = 48 * eps * [(sigma^12 / r^13) - 0.5 * (sigma^6 / r^7)],
+where eps is the interaction strength and sigma is the effective particle diameter, calculated
+as sigma = R / 2^(1/6).
          • 
+
        • The max_repulsion option ensures that no distances smaller than sigma are considered, effectively
+limiting the maximum repulsive force.
          • 
+
        • This function can handle both scalar and array inputs for r.
          • 
+
        
+
        
+
+
        
+
        
+celltraj.spatial.get_adhesive_displacement(border_dict, surf_force_function, eps, alpha=1.0, maxd=None, rmin=None, rmax=None, active_neighbor_states=array([1]), active_displacement_states=array([], dtype=float64), symmetrize=True, **force_args)[source]
        
+
        Computes the adhesive displacement between cell surfaces using a specified surface force function.
        
+
        This function calculates the displacement of cell surfaces based on adhesive forces. It uses the states and positions of
+neighboring cells to determine active interfaces and apply force-based displacements. Optionally, the displacements can
+be symmetrized to ensure consistency across cell borders.
        
+
        
+
        Parameters:
        
+
          
+
        • border_dict (dict) – A dictionary containing border information, including:
+- ‘pts’: ndarray of shape (N, 3), coordinates of border points.
+- ‘nn_pts’: ndarray of shape (N, 3), coordinates of nearest neighbor points.
+- ‘states’: ndarray of shape (N,), states of the border points.
+- ‘nn_states’: ndarray of shape (N,), states of the nearest neighbor points.
+- ‘nn_inds’: ndarray of shape (N,), indices of the nearest neighbor points.
          • 
+
        • surf_force_function (callable) – A function that computes the surface force based on distance and other parameters.
+Should take distance, epsilon, and additional arguments as inputs.
          • 
+
        • eps (ndarray) – A 2D array where eps[i, j] represents the interaction strength between state i and state j.
          • 
+
        • alpha (float, optional) – A scaling factor for the displacement magnitude (default is 1.0).
          • 
+
        • maxd (float, optional) – The maximum allowed displacement. Displacements will be scaled if any calculated displacements exceed this value.
          • 
+
        • rmin (float, optional) – The minimum interaction distance. Displacements calculated from distances smaller than rmin will be set to rmin.
          • 
+
        • rmax (float, optional) – The maximum interaction distance. Displacements calculated from distances larger than rmax will be set to rmax.
          • 
+
        • active_neighbor_states (ndarray, optional) – An array specifying the states of neighbors that are active for interaction (default is np.array([1])).
          • 
+
        • active_displacement_states (ndarray, optional) – An array specifying the states of cells that are active for displacement (default is an empty array, which means all states are active).
          • 
+
        • symmetrize (bool, optional) – If True, the displacements are symmetrized to ensure consistency across borders (default is True).
          • 
+
        • **force_args (dict, optional) – Additional arguments to be passed to the surf_force_function.
          • 
+
        
+
        
+
        Returns:
        
+
        dr – A 2D array of shape (N, 3) representing the displacements of the border points.
        
+
        
+
        Return type:
        
+
        ndarray
        
+
        
+
        
+
        Notes
        
+
          
+
        • The function filters out inactive or excluded states before computing the displacement.
          • 
+
        • Displacement is scaled using the surface force and optionally capped by maxd.
          • 
+
        • Symmetrization ensures that the displacement is consistent from both interacting cells’ perspectives.
          • 
+
        
+
        Examples
        
+
        >>> border_dict = {
+...     'pts': np.random.rand(100, 3),
+...     'nn_pts': np.random.rand(100, 3),
+...     'states': np.random.randint(0, 2, 100),
+...     'nn_states': np.random.randint(0, 2, 100),
+...     'nn_inds': np.random.randint(0, 100, 100)
+... }
+>>> surf_force_function = lambda r, eps: -eps * (r - 1)
+>>> eps = np.array([[0.1, 0.2], [0.2, 0.3]])
+>>> dr = get_adhesive_displacement(border_dict, surf_force_function, eps, alpha=0.5)
+>>> dr.shape
+(100, 3)
+
        
+
        
+
        
+
+
        
+
        
+celltraj.spatial.get_border_dict(labels, states=None, radius=10, vdist=None, return_nnindex=True, return_nnvector=True, return_curvature=True, scale=None, **border_args)[source]
        
+
        Computes the border properties of labeled regions in a segmented image.
        
+
        This function identifies the borders of labeled regions in a given image and calculates various properties
+such as nearest neighbor indices, vectors, and curvature. It can also return the scaled distances if specified.
        
+
        
+
        Parameters:
        
+
          
+
        • labels (ndarray) – A 2D or 3D array where each element represents a label, identifying different regions in the image.
          • 
+
        • states (ndarray, optional) – An array indicating the state of each labeled region. If provided, states are used to differentiate
+regions. If None, all regions are assumed to have the same state.
          • 
+
        • radius (float, optional) – The radius for finding nearest neighbors around each border point (default is 10).
          • 
+
        • vdist (ndarray, optional) – An array representing distances or potential values at each point in the image. If provided, this
+array is used to calculate border distances.
          • 
+
        • return_nnindex (bool, optional) – If True, returns the nearest neighbor index for each border point (default is True).
          • 
+
        • return_nnvector (bool, optional) – If True, returns the vector pointing to the nearest neighbor for each border point (default is True).
          • 
+
        • return_curvature (bool, optional) – If True, calculates and returns the curvature at each border point (default is True).
          • 
+
        • scale (list or ndarray, optional) – Scaling factors for each dimension of the labels array. If provided, scales the labels accordingly.
          • 
+
        • **border_args (dict, optional) – Additional arguments to control border property calculations, such as ‘knn’ for the number of nearest
+neighbors when computing curvature.
          • 
+
        
+
        
+
        Returns:
        
+
        border_dict – A dictionary containing the computed border properties:
+- ‘pts’: ndarray of float, coordinates of the border points.
+- ‘index’: ndarray of int, indices of the regions to which each border point belongs.
+- ‘states’: ndarray of int, states of the regions to which each border point belongs.
+- ‘nn_index’: ndarray of int, nearest neighbor indices for each border point (if return_nnindex is True).
+- ‘nn_states’: ndarray of int, states of the nearest neighbors (if return_nnindex is True).
+- ‘nn_pts’: ndarray of float, coordinates of the nearest neighbors (if return_nnvector is True).
+- ‘nn_inds’: ndarray of int, indices of the nearest neighbors (if return_nnvector is True).
+- ‘n’: ndarray of float, normals at each border point (if return_curvature is True).
+- ‘c’: ndarray of float, curvature at each border point (if return_curvature is True).
+- ‘vdist’: ndarray of float, scaled distances at each border point (if vdist is provided).
        
+
        
+
        Return type:
        
+
        dict
        
+
        
+
        
+
        Notes
        
+
          
+
        • This function is useful for analyzing cell shapes and their interactions in spatially resolved images.
          • 
+
        • The nearest neighbor indices and vectors can help understand cell-cell interactions and local neighborhood structures.
          • 
+
        • The curvature values can provide insights into the geometrical properties of cell boundaries.
          • 
+
        
+
        Examples
        
+
        >>> labels = np.array([[0, 1, 1, 0], [0, 1, 1, 0], [2, 2, 0, 0], [0, 0, 0, 0]])
+>>> scale = [1.0, 1.0]
+>>> border_dict = get_border_dict(labels, scale=scale, return_nnindex=True, return_nnvector=True)
+>>> print(border_dict['pts'])
+[[0., 1.], [0., 2.], [1., 1.], [2., 0.], [2., 1.]]
+>>> print(border_dict['nn_index'])
+[2, 2, 2, 1, 1]
+
        
+
        
+
        
+
+
        
+
        
+celltraj.spatial.get_flux_displacement(border_dict, border_features=None, flux_function=None, exclude_states=None, n=None, fmeans=0.0, fsigmas=0.0, random_seed=None, alpha=1.0, maxd=None, **flux_function_args)[source]
        
+
        Calculates the displacement of border points using flux information.
        
+
        This function computes the displacement of border points by applying a flux function or random sampling
+based on mean and standard deviation values. The displacements can be controlled by normal vectors,
+excluded for certain states, and scaled to a maximum displacement.
        
+
        
+
        Parameters:
        
+
          
+
        • border_dict (dict) – A dictionary containing information about the current cell borders, including:
+- ‘n’: ndarray of shape (N, 3), normal vectors at the border points.
+- ‘states’: ndarray of shape (N,), states of the border points.
          • 
+
        • border_features (ndarray, optional) – Features at the border points used as input to the flux function (default is None).
          • 
+
        • flux_function (callable, optional) – A function that takes border_features and additional arguments to compute mean (fmeans) and standard
+deviation (fsigmas) of the flux at each border point (default is None, meaning random sampling is used).
          • 
+
        • exclude_states (array-like, optional) – A list or array of states to exclude from displacement calculations (default is None, meaning no states are excluded).
          • 
+
        • n (ndarray, optional) – Normal vectors at the border points. If None, normal vectors are taken from border_dict[‘n’] (default is None).
          • 
+
        • fmeans (float or array-like, optional) – Mean flux value(s) for random sampling (default is 0.). If flux_function is provided, this value is ignored.
          • 
+
        • fsigmas (float or array-like, optional) – Standard deviation of flux value(s) for random sampling (default is 0.). If flux_function is provided, this value is ignored.
          • 
+
        • random_seed (int, optional) – Seed for the random number generator to ensure reproducibility (default is None).
          • 
+
        • alpha (float, optional) – A scaling factor for the displacement magnitude (default is 1.0).
          • 
+
        • maxd (float, optional) – The maximum allowed displacement. If specified, the displacement is scaled to ensure it does not exceed this value (default is None).
          • 
+
        • **flux_function_args (dict, optional) – Additional arguments to pass to the flux_function.
          • 
+
        
+
        
+
        Returns:
        
+
        dx – A 2D array of shape (N, 3) representing the displacements of the border points.
        
+
        
+
        Return type:
        
+
        ndarray
        
+
        
+
        
+
        Notes
        
+
          
+
        • The function can use a flux function to calculate displacements based on border features or perform random sampling
+with specified mean and standard deviation values.
          • 
+
        • Displacement deviations are scaled by normal vectors and can be controlled by alpha and capped by maxd.
          • 
+
        • Excludes displacements for specified states, if exclude_states is provided.
          • 
+
        • The random number generator can be seeded for reproducibility using random_seed.
          • 
+
        
+
        Examples
        
+
        >>> border_dict = {
+...     'n': np.random.rand(100, 3),
+...     'states': np.random.randint(0, 2, 100)
+... }
+>>> dx = get_flux_displacement(border_dict, fmeans=0.5, fsigmas=0.1, random_seed=42, alpha=0.8, maxd=0.2)
+>>> dx.shape
+(100, 3)
+
        
+
        
+
        
+
+
        
+
        
+celltraj.spatial.get_flux_ligdist(vdist, cmean=1.0, csigma=0.5, center=True)[source]
        
+
        Calculate the mean and standard deviation of flux values based on ligand distribution.
        
+
        This function computes the flux mean and standard deviation for a given ligand distribution, using
+specified parameters for the mean and scaling factor for the standard deviation. Optionally, it can
+center the mean flux to ensure the overall flux is balanced.
        
+
        
+
        Parameters:
        
+
          
+
        • vdist (ndarray) – A 3D array representing the ligand concentration distribution in a tissue volume.
          • 
+
        • cmean (float, optional) – A scaling factor for the mean flux. Default is 1.0.
          • 
+
        • csigma (float, optional) – A scaling factor for the standard deviation of the flux. Default is 0.5.
          • 
+
        • center (bool, optional) – If True, centers the mean flux distribution around zero by subtracting the overall mean. Default is True.
          • 
+
        
+
        
+
        Returns:
        
+
          
+
        • fmeans (ndarray) – A 3D array representing the mean flux values based on the ligand distribution.
          • 
+
        • fsigmas (ndarray) – A 3D array representing the standard deviation of flux values based on the ligand distribution.
          • 
+
        
+
        
+
        
+
        
+
        Examples
        
+
        >>> ligand_distribution = np.random.random((100, 100, 50))
+>>> mean_flux, sigma_flux = get_flux_ligdist(ligand_distribution, cmean=1.2, csigma=0.8)
+>>> print(mean_flux.shape, sigma_flux.shape)
+(100, 100, 50) (100, 100, 50)
+
        
+
        
+
        Notes
        
+
          
+
        • The function uses the absolute value of vdist to calculate the standard deviation of the flux.
          • 
+
        • Centering the mean flux helps in ensuring there is no net flux imbalance across the tissue volume.
          • 
+
        
+
        
+
+
        
+
        
+celltraj.spatial.get_labels_fromborderdict(border_dict, labels_shape, active_states=None, surface_labels=None, connected=True, random_seed=None)[source]
        
+
        Generates a label mask from a dictionary of border points and associated states.
        
+
        This function creates a 3D label array by identifying regions enclosed by the boundary points
+in border_dict. It assigns unique labels to each region based on the indices of the border points.
        
+
        
+
        Parameters:
        
+
          
+
        • border_dict (dict) – A dictionary containing the border points and associated information. Expected keys include:
+- ‘pts’: ndarray of shape (N, D), coordinates of the border points.
+- ‘index’: ndarray of shape (N,), labels for each border point.
+- ‘states’: ndarray of shape (N,), states associated with each border point.
          • 
+
        • labels_shape (tuple of ints) – The shape of the output labels array.
          • 
+
        • active_states (array-like, optional) – A list or array of states to include in the labeling. If None, all unique states
+in border_dict[‘states’] are used.
          • 
+
        • surface_labels (ndarray, optional) – A pre-existing label array to use as a base. Regions with non-zero values in this
+array will retain their labels.
          • 
+
        • connected (bool, optional) – If True, ensures that labeled regions are connected. Uses the largest connected
+component labeling method.
          • 
+
        • random_seed (int, optional) – A seed for the random number generator to ensure reproducibility.
          • 
+
        
+
        
+
        Returns:
        
+
        labels – An array of the same shape as labels_shape with labeled regions. Each unique region
+enclosed by border points is assigned a unique label.
        
+
        
+
        Return type:
        
+
        ndarray
        
+
        
+
        
+
        Notes
        
+
          
+
        • This function utilizes convex hull and Delaunay triangulation to determine the regions
+enclosed by the border points.
          • 
+
        • It can be used to generate labels for 3D volumes, based on the locations and states of border points.
          • 
+
        • The function includes options for randomization and enforcing connectivity of labeled regions.
          • 
+
        
+
        Examples
        
+
        >>> border_dict = {
+...     'pts': np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2]]),
+...     'index': np.array([1, 1, 2]),
+...     'states': np.array([1, 1, 2])
+... }
+>>> labels_shape = (3, 3, 3)
+>>> labels = get_labels_fromborderdict(border_dict, labels_shape)
+>>> print(labels)
+array([[[1, 1, 0],
+        [1, 1, 0],
+        [0, 0, 0]],
+       [[1, 1, 0],
+        [1, 1, 0],
+        [0, 0, 0]],
+       [[0, 0, 0],
+        [0, 0, 0],
+        [0, 0, 0]]])
+
        
+
        
+
        
+
+
        
+
        
+celltraj.spatial.get_morse_force(r, eps, R=1.0, L=4.0)[source]
        
+
        Computes the Morse force for a given set of distances.
        
+
        The Morse potential is used to model the interaction between a pair of atoms or molecules, capturing both
+the attractive and repulsive forces more realistically than the Lennard-Jones potential. This function calculates
+the Morse force based on the provided distances, interaction strength, characteristic distance, and interaction range.
        
+
        
+
        Parameters:
        
+
          
+
        • r (array-like or float) – The distance(s) at which to calculate the Morse force. Can be a single float or an array of distances.
          • 
+
        • eps (float) – The depth of the potential well, representing the strength of the interaction.
          • 
+
        • R (float, optional) – The equilibrium distance where the potential reaches its minimum. Default is 1.
          • 
+
        • L (float, optional) – The width of the potential well, determining the range of the interaction. A larger value of L
+indicates a narrower well, meaning the potential changes more rapidly with distance. Default is 4.
          • 
+
        
+
        
+
        Returns:
        
+
        force – The computed Morse force at each distance provided in r. The shape of the output matches the shape of r.
        
+
        
+
        Return type:
        
+
        array-like or float
        
+
        
+
        
+
        Examples
        
+
        >>> distances = np.array([0.8, 1.0, 1.2])
+>>> interaction_strength = 2.0
+>>> equilibrium_distance = 1.0
+>>> interaction_range = 4.0
+>>> forces = get_morse_force(distances, interaction_strength, R=equilibrium_distance, L=interaction_range)
+>>> print(forces)
+[ 1.17328042  0.         -0.63212056]
+
        
+
        
+
        Notes
        
+
          
+
        • The Morse force is derived from the Morse potential and is calculated using the formula:
+force = eps * [exp(-2 * (r - R) / L) - exp(-(r - R) / L)],
+where eps is the interaction strength, R is the equilibrium distance, and L is the interaction range.
          • 
+
        • This function can handle both scalar and array inputs for r.
          • 
+
        
+
        
+
+
        
+
        
+celltraj.spatial.get_nuc_displacement(border_pts_new, border_dict, Rset, nuc_states=array([1]), **nuc_args)[source]
        
+
        
+
+
        
+
        
+celltraj.spatial.get_ot_displacement(border_dict, border_dict_prev, parent_index=None)[source]
        
+
        Computes the optimal transport (OT) displacement between two sets of boundary points.
        
+
        This function calculates the optimal transport displacements between the points in the current
+boundary (border_dict) and the points in the previous boundary (border_dict_prev). It finds
+the optimal matches and computes the displacement vectors for each point.
        
+
        
+
        Parameters:
        
+
          
+
        • border_dict (dict) – A dictionary containing the current boundary points and related information. Expected keys include:
+- ‘index’: ndarray of shape (N,), unique labels of current boundary points.
+- ‘pts’: ndarray of shape (N, D), coordinates of the current boundary points.
          • 
+
        • border_dict_prev (dict) – A dictionary containing the previous boundary points and related information. Expected keys include:
+- ‘index’: ndarray of shape (M,), unique labels of previous boundary points.
+- ‘pts’: ndarray of shape (M, D), coordinates of the previous boundary points.
          • 
+
        • parent_index (ndarray, optional) – An array of unique labels (indices) to use for matching previous boundary points. If not provided,
+index1 from border_dict will be used to match with border_dict_prev.
          • 
+
        
+
        
+
        Returns:
        
+
          
+
        • inds_ot (ndarray) – A 1D array containing the indices of the optimal transport matches for the current boundary points
+from the previous boundary points.
          • 
+
        • dxs_ot (ndarray) – A 2D array of shape (N, D), representing the displacement vectors from the current boundary points
+to the matched previous boundary points.
          • 
+
        
+
        
+
        
+
        
+
        Notes
        
+
          
+
        • The function uses the get_ot_dx function to compute the optimal transport match and displacement
+between boundary points.
          • 
+
        • If parent_index is not provided, it defaults to using the indices of the current boundary points
+(index1).
          • 
+
        
+
        Examples
        
+
        >>> border_dict = {
+...     'index': np.array([1, 2, 3]),
+...     'pts': np.array([[0, 0], [1, 1], [2, 2]])
+... }
+>>> border_dict_prev = {
+...     'index': np.array([1, 2, 3]),
+...     'pts': np.array([[0, 1], [1, 0], [2, 1]])
+... }
+>>> inds_ot, dxs_ot = get_ot_displacement(border_dict, border_dict_prev)
+>>> inds_ot
+array([0, 0, 0])
+>>> dxs_ot
+array([[ 0, -1],
+       [ 0,  1],
+       [ 0,  1]])
+
        
+
        
+
        
+
+
        
+
        
+celltraj.spatial.get_ot_dx(pts0, pts1, return_dx=True, return_cost=False)[source]
        
+
        Computes the optimal transport (OT) displacement and cost between two sets of points.
        
+
        This function calculates the optimal transport map between two sets of points pts0 and pts1 using the
+Earth Mover’s Distance (EMD). It returns the indices of the optimal transport matches and the displacement
+vectors, as well as the transport cost if specified.
        
+
        
+
        Parameters:
        
+
          
+
        • pts0 (ndarray) – A 2D array of shape (N, D), representing the first set of points, where N is the number of points
+and D is the dimensionality.
          • 
+
        • pts1 (ndarray) – A 2D array of shape (M, D), representing the second set of points, where M is the number of points
+and D is the dimensionality.
          • 
+
        • return_dx (bool, optional) – If True, returns the displacement vectors between matched points (default is True).
          • 
+
        • return_cost (bool, optional) – If True, returns the total transport cost (default is False).
          • 
+
        
+
        
+
        Returns:
        
+
          
+
        • inds_ot (ndarray) – A 1D array of shape (N,), representing the indices of the points in pts1 that are matched to
+the points in pts0 according to the optimal transport map.
          • 
+
        • dx (ndarray, optional) – A 2D array of shape (N, D), representing the displacement vectors from the points in pts0 to the
+matched points in pts1. Returned only if return_dx is True.
          • 
+
        • cost (float, optional) – The total optimal transport cost, calculated as the sum of the transport cost between matched points.
+Returned only if return_cost is True.
          • 
+
        
+
        
+
        
+
        
+
        Notes
        
+
          
+
        • The function uses the Earth Mover’s Distance (EMD) for computing the optimal transport map, which minimizes
+the cost of moving mass from pts0 to pts1.
          • 
+
        • The cost is computed as the sum of the pairwise distances weighted by the transport plan.
          • 
+
        • Displacement vectors are computed as the difference between points in pts0 and their matched points in pts1.
          • 
+
        
+
        Examples
        
+
        >>> pts0 = np.array([[0, 0], [1, 1], [2, 2]])
+>>> pts1 = np.array([[0, 1], [1, 0], [2, 1]])
+>>> inds_ot, dx, cost = get_ot_dx(pts0, pts1, return_dx=True, return_cost=True)
+>>> inds_ot
+array([0, 1, 2])
+>>> dx
+array([[ 0, -1],
+       [ 0,  1],
+       [ 0,  1]])
+>>> cost
+1.0
+
        
+
        
+
        
+
+
        
+
        
+celltraj.spatial.get_secreted_ligand_density(msk, scale=2.0, zscale=1.0, npad=None, indz_bm=0, secretion_rate=1.0, D=None, micron_per_pixel=1.0, visual=False)[source]
        
+
        Calculate the spatial distribution of secreted ligand density in a 3D tissue model.
        
+
        This function simulates the diffusion and absorption of secreted ligands in a 3D volume defined by a binary mask.
+It uses finite element methods to solve the diffusion equation for ligand concentration, taking into account secretion
+from cell surfaces and absorption at boundaries.
        
+
        
+
        Parameters:
        
+
          
+
        • msk (ndarray) – A 3D binary mask representing the tissue, where non-zero values indicate the presence of cells.
          • 
+
        • scale (float, optional) – The scaling factor for spatial resolution in the x and y dimensions. Default is 2.
          • 
+
        • zscale (float, optional) – The scaling factor for spatial resolution in the z dimension. Default is 1.
          • 
+
        • npad (array-like of int, optional) – Number of pixels to pad the mask in each dimension. Default is None, implying no padding.
          • 
+
        • indz_bm (int, optional) – The index for the basal membrane in the z-dimension, where diffusion starts. Default is 0.
          • 
+
        • secretion_rate (float or array-like, optional) – The rate of ligand secretion from the cell surfaces. Can be a scalar or array for different cell types. Default is 1.0.
          • 
+
        • D (float, optional) – The diffusion coefficient for the ligand. If None, it is set to a default value based on the pixel size. Default is None.
          • 
+
        • micron_per_pixel (float, optional) – The conversion factor from pixels to microns. Default is 1.
          • 
+
        • visual (bool, optional) – If True, generates visualizations of the cell borders and diffusion process. Default is False.
          • 
+
        
+
        
+
        Returns:
        
+
        vdist – A 3D array representing the steady-state concentration of the secreted ligand in the tissue volume.
        
+
        
+
        Return type:
        
+
        ndarray
        
+
        
+
        
+
        Examples
        
+
        >>> tissue_mask = np.random.randint(0, 2, size=(100, 100, 50))
+>>> ligand_density = get_secreted_ligand_density(tissue_mask, scale=2.5, zscale=1.2, secretion_rate=0.8)
+>>> print(ligand_density.shape)
+(100, 100, 50)
+
        
+
        
+
        Notes
        
+
          
+
        • This function uses fipy for solving the diffusion equation and skimage.segmentation.find_boundaries for
+identifying cell borders.
          • 
+
        • The function includes various options for handling different boundary conditions, cell shapes, and secretion rates.
          • 
+
        
+
        
+
+
        
+
        
+celltraj.spatial.get_surface_displacement(border_dict, sts=None, c=None, n=None, alpha=1.0, maxd=None)[source]
        
+
        Computes the surface displacement of cells based on their curvature and normal vectors.
        
+
        This function calculates the displacement of cell surfaces using the curvature values and normal vectors.
+The displacement can be scaled by a factor alpha, and optionally constrained by a maximum displacement value.
        
+
        
+
        Parameters:
        
+
          
+
        • border_dict (dict) – A dictionary containing information about the cell borders, including:
+- ‘n’: ndarray of shape (N, 3), normal vectors at the border points.
+- ‘c’: ndarray of shape (N,), curvature values at the border points.
+- ‘states’: ndarray of shape (N,), states of the border points.
          • 
+
        • sts (ndarray, optional) – An array of scaling factors for each state, used to modify the curvature. If provided, sts is multiplied
+with the curvature values based on the state of each border point (default is None, meaning no scaling is applied).
          • 
+
        • c (ndarray, optional) – Curvature values at the border points. If None, it uses the curvature from border_dict (default is None).
          • 
+
        • n (ndarray, optional) – Normal vectors at the border points. If None, it uses the normal vectors from border_dict (default is None).
          • 
+
        • alpha (float, optional) – A scaling factor for the displacement magnitude (default is 1.0).
          • 
+
        • maxd (float, optional) – The maximum allowed displacement. If specified, the displacement is scaled to ensure it does not exceed this value.
          • 
+
        
+
        
+
        Returns:
        
+
        dx – A 2D array of shape (N, 3) representing the displacements of the border points.
        
+
        
+
        Return type:
        
+
        ndarray
        
+
        
+
        
+
        Notes
        
+
          
+
        • The displacement is calculated as a product of curvature, normal vectors, and the scaling factor alpha.
          • 
+
        • If sts is provided, curvature values are scaled according to the states of the border points.
          • 
+
        • Displacement magnitude is capped by maxd if specified, ensuring that no displacement exceeds this value.
          • 
+
        
+
        Examples
        
+
        >>> border_dict = {
+...     'n': np.random.rand(100, 3),
+...     'c': np.random.rand(100),
+...     'states': np.random.randint(0, 2, 100)
+... }
+>>> sts = np.array([1.0, 0.5])
+>>> dx = get_surface_displacement(border_dict, sts=sts, alpha=0.2, maxd=0.1)
+>>> dx.shape
+(100, 3)
+
        
+
        
+
        
+
+
        
+
        
+celltraj.spatial.get_surface_displacement_deviation(border_dict, border_pts_prev, exclude_states=None, n=None, knn=12, use_eigs=False, alpha=1.0, maxd=None)[source]
        
+
        Calculates the surface displacement deviation using optimal transport between current and previous border points.
        
+
        This function computes the displacement of cell surface points based on deviations from previous positions.
+The displacement can be modified by normal vectors, filtered by specific states, and controlled by curvature
+or variance in displacement.
        
+
        
+
        Parameters:
        
+
          
+
        • border_dict (dict) – A dictionary containing information about the current cell borders, including:
+- ‘pts’: ndarray of shape (N, 3), current border points.
+- ‘states’: ndarray of shape (N,), states of the border points.
          • 
+
        • border_pts_prev (ndarray) – A 2D array of shape (N, 3) containing the positions of border points from the previous time step.
          • 
+
        • exclude_states (array-like, optional) – A list or array of states to exclude from displacement calculations (default is None, meaning no states are excluded).
          • 
+
        • n (ndarray, optional) – Normal vectors at the border points. If None, normal vectors are calculated based on the optimal transport displacement (default is None).
          • 
+
        • knn (int, optional) – The number of nearest neighbors to consider when computing variance or eigen decomposition for curvature calculations (default is 12).
          • 
+
        • use_eigs (bool, optional) – If True, use eigen decomposition to calculate the displacement deviation; otherwise, use variance (default is False).
          • 
+
        • alpha (float, optional) – A scaling factor for the displacement magnitude (default is 1.0).
          • 
+
        • maxd (float, optional) – The maximum allowed displacement. If specified, the displacement is scaled to ensure it does not exceed this value (default is None).
          • 
+
        
+
        
+
        Returns:
        
+
        dx – A 2D array of shape (N, 3) representing the displacements of the border points.
        
+
        
+
        Return type:
        
+
        ndarray
        
+
        
+
        
+
        Notes
        
+
          
+
        • The function uses optimal transport to calculate deviations between current and previous border points.
          • 
+
        • The surface displacement deviation is inspired by the “mother of all non-linearities”– the Kardar-Parisi-Zhang non-linear surface growth universality class.
          • 
+
        • Displacement deviations are scaled by the normal vectors and can be controlled by alpha and capped by maxd.
          • 
+
        • If use_eigs is True, eigen decomposition of the displacement field is used to calculate deviations, otherwise variance is used.
          • 
+
        • Excludes displacements for specified states, if exclude_states is provided.
          • 
+
        
+
        Examples
        
+
        >>> border_dict = {
+...     'pts': np.random.rand(100, 3),
+...     'states': np.random.randint(0, 2, 100)
+... }
+>>> border_pts_prev = np.random.rand(100, 3)
+>>> dx = get_surface_displacement_deviation(border_dict, border_pts_prev, exclude_states=[0], alpha=0.5, maxd=0.1)
+>>> dx.shape
+(100, 3)
+
        
+
        
+
        
+
+
        
+
        
+celltraj.spatial.get_surface_points(msk, return_normals=False, return_curvature=False, knn=20)[source]
        
+
        Computes the surface points of a labeled mask and optionally calculates normals and curvature.
        
+
        This function identifies the surface (border) points of a given labeled mask using segmentation techniques.
+It can also compute normals (perpendicular vectors to the surface) and curvature values at these points if requested.
        
+
        
+
        Parameters:
        
+
          
+
        • msk (ndarray) – A 3D binary or labeled array representing the mask of regions of interest. Non-zero values represent the regions.
          • 
+
        • return_normals (bool, optional) – If True, computes and returns the normals at each surface point (default is False).
          • 
+
        • return_curvature (bool, optional) – If True, computes and returns the curvature at each surface point (default is False).
          • 
+
        • knn (int, optional) – The number of nearest neighbors to consider when calculating normals and curvature (default is 20).
          • 
+
        
+
        
+
        Returns:
        
+
          
+
        • border_pts (ndarray) – A 2D array of shape (N, 3) containing the coordinates of the border points, where N is the number of border points found.
          • 
+
        • n (ndarray, optional) – A 2D array of shape (N, 3) containing the normal vectors at each border point. Only returned if return_normals is True.
          • 
+
        • c (ndarray, optional) – A 1D array of length N containing the curvature values at each border point. Only returned if return_curvature is True.
          • 
+
        
+
        
+
        
+
        
+
        Notes
        
+
          
+
        • The function uses eigen decomposition on the neighborhood of each surface point to compute normals and curvature.
          • 
+
        • The normals are adjusted to face outward from the surface. If normals face inward, they are flipped.
          • 
+
        • Curvature is calculated as the ratio of the smallest eigenvalue to the sum of all eigenvalues, giving an estimate of local surface bending.
          • 
+
        
+
        Examples
        
+
        >>> msk = np.zeros((100, 100, 100), dtype=int)
+>>> msk[40:60, 40:60, 40:60] = 1  # A cube in the center
+>>> border_pts = get_surface_points(msk)
+>>> border_pts.shape
+(960, 3)
+
        
+
        
+
        >>> border_pts, normals = get_surface_points(msk, return_normals=True)
+>>> border_pts.shape, normals.shape
+((960, 3), (960, 3))
+
        
+
        
+
        >>> border_pts, normals, curvature = get_surface_points(msk, return_normals=True, return_curvature=True)
+>>> border_pts.shape, normals.shape, curvature.shape
+((960, 3), (960, 3), (960,))
+
        
+
        
+
        
+
+
        
+
        
+celltraj.spatial.get_volconstraint_com(border_pts, target_volume, max_iter=1000, converror=0.05, dc=1.0)[source]
        
+
        Adjusts the positions of boundary points to achieve a target volume using a centroid-based method.
        
+
        This function iteratively adjusts the positions of boundary points to match a specified target volume.
+The adjustment is done by moving points along the direction from the centroid to the points, scaled
+by the difference between the current and target volumes.
        
+
        
+
        Parameters:
        
+
          
+
        • border_pts (ndarray) – An array of shape (N, 3) representing the coordinates of the boundary points.
          • 
+
        • target_volume (float) – The desired volume to be achieved.
          • 
+
        • max_iter (int, optional) – Maximum number of iterations to perform. Default is 1000.
          • 
+
        • converror (float, optional) – Convergence error threshold. Iterations stop when the relative volume error is below this value.
+Default is 0.05.
          • 
+
        • dc (float, optional) – A scaling factor for the displacement calculated in each iteration. Default is 1.0.
          • 
+
        
+
        
+
        Returns:
        
+
        border_pts – An array of shape (N, 3) representing the adjusted coordinates of the boundary points that
+approximate the target volume.
        
+
        
+
        Return type:
        
+
        ndarray
        
+
        
+
        
+
        Notes
        
+
          
+
        • The method assumes a 3D convex hull can be formed by the points, which is adjusted iteratively.
          • 
+
        • The convergence is based on the relative difference between the current volume and the target volume.
          • 
+
        • If the boundary points are collinear in any dimension, the method adjusts them to ensure a valid convex hull.
          • 
+
        
+
        Examples
        
+
        >>> border_pts = np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2]])
+>>> target_volume = 10.0
+>>> adjusted_pts = get_volconstraint_com(border_pts, target_volume)
+>>> print(adjusted_pts)
+array([[ ... ]])  # Adjusted coordinates to approximate the target volume
+
        
+
        
+
        
+
+
        
+
        
+celltraj.spatial.get_yukawa_force(r, eps, R=1.0)[source]
        
+
        Computes the Yukawa force for a given set of distances.
        
+
        The Yukawa force is a screened Coulomb force often used to describe interactions
+in plasmas and other systems with screened potentials. This function calculates
+the Yukawa force based on the provided distances, interaction strength, and screening length.
        
+
        
+
        Parameters:
        
+
          
+
        • r (array-like or float) – The distance(s) at which to calculate the Yukawa force. Can be a single float
+or an array of distances.
          • 
+
        • eps (float) – The interaction strength (or potential strength) parameter, determining the amplitude
+of the force.
          • 
+
        • R (float, optional) – The screening length parameter, which determines how quickly the force decays
+with distance. Default is 1.
          • 
+
        
+
        
+
        Returns:
        
+
        force – The computed Yukawa force at each distance provided in r. The shape of the output
+matches the shape of r.
        
+
        
+
        Return type:
        
+
        array-like or float
        
+
        
+
        
+
        Examples
        
+
        >>> distances = np.array([0.5, 1.0, 1.5])
+>>> interaction_strength = 2.0
+>>> screening_length = 1.0
+>>> forces = get_yukawa_force(distances, interaction_strength, R=screening_length)
+>>> print(forces)
+[4. e+00 1.47151776e+00 4.48168907e-01]
+
        
+
        
+
        Notes
        
+
          
+
        • The Yukawa force is computed using the formula:
+force = eps * exp(-r / R) * (r + R) / (R * r^2),
+where eps is the interaction strength and R is the screening length.
          • 
+
        • The function handles both scalar and array inputs for r.
          • 
+
        
+
        
+
 
 
        
 
        celltraj.trajectory module
        
@@ -3637,7 +4438,7 @@ 
        Submodules
 
        
-get_cell_blocks(label)[source]
        
+get_cell_blocks(label, return_label_ids=False)[source]
 
        Extracts bounding box information for each cell from a labeled mask image. This function
 returns the minimum and maximum indices for each labeled cell, useful for operations such
 as cropping around a cell or analyzing specific cell regions. The function supports both
@@ -3697,7 +4498,7 @@ 
        Submodules
 
        
-get_cell_compartment_ratio(indcells=None, imgchannel=None, mskchannel1=None, mskchannel2=None, fmask_channel=None, make_disjoint=True, erosion_footprint1=None, erosion_footprint2=None, combined_and_disjoint=False, intensity_sum=False, intensity_ztransform=False, noratio=False, inverse_ratio=False, save_h5=False, overwrite=False)[source]
        
+get_cell_compartment_ratio(indcells=None, imgchannel=None, mskchannel1=None, mskchannel2=None, fmask_channel=None, make_disjoint=True, remove_background_perframe=False, fmask_channel_background=0, background_percentile=1, erosion_footprint1=None, erosion_footprint2=None, combined_and_disjoint=False, intensity_sum=False, intensity_ztransform=False, noratio=False, inverse_ratio=False, save_h5=False, overwrite=False)[source]
 
        Calculates the ratio of features between two cellular compartments, optionally adjusted for image
 intensity and morphology transformations. This method allows for complex comparisons between different
 mask channels or modified versions of these channels to derive cellular compartment ratios.
        
@@ -4274,6 +5075,50 @@ 
        Submodules
+
        
+get_lineage_min_otcost(distcut=5.0, ot_cost_cut=inf, border_scale=None, border_resolution=None, visual=False, save_h5=False, overwrite=False)[source]
        
+
        Tracks cell lineages over multiple time points using optimal transport cost minimization.
        
+
        This method uses centroid distances and optimal transport costs to identify the best matches for cell
+trajectories between consecutive time points, ensuring accurate tracking even in dense or complex environments.
        
+
        
+
        Parameters:
        
+
          
+
        • distcut (float, optional) – Maximum distance between cell centroids to consider a match (default is 5.0).
          • 
+
        • ot_cost_cut (float, optional) – Maximum optimal transport cost allowed for a match (default is np.inf).
          • 
+
        • border_scale (list of float, optional) – Scaling factors for the cell border in the [z, y, x] dimensions. If not provided, the scaling is
+determined from self.micron_per_pixel and border_resolution.
          • 
+
        • border_resolution (float, optional) – Resolution for the cell border, used to determine border_scale if it is not provided. If not set,
+uses self.border_resolution.
          • 
+
        • visual (bool, optional) – If True, plots the cells and their matches at each time point for visualization (default is False).
          • 
+
        • save_h5 (bool, optional) – If True, saves the lineage data to the HDF5 file (default is False).
          • 
+
        • overwrite (bool, optional) – If True, overwrites existing data in the HDF5 file when saving (default is False).
          • 
+
        
+
        
+
        Returns:
        
+
        The function updates the instance’s linSet attribute, which is a list of arrays containing lineage
+information for each time point. If save_h5 is True, the lineage data is saved to the HDF5 file.
        
+
        
+
        Return type:
        
+
        None
        
+
        
+
        
+
        Notes
        
+
          
+
        • This function assumes that cell positions have already been extracted using the get_cell_positions method.
          • 
+
        • The function uses the spatial.get_border_dict method to compute cell borders and spatial.get_ot_dx
          • 
+
        
+
        to compute optimal transport distances.
+- Visualization is available for 2D and 3D data, with different handling for each case.
        
+
        Examples
        
+
        >>> traj.get_lineage_min_otcost(distcut=10.0, ot_cost_cut=50.0, visual=True)
+Frame 1 tracked 20 of 25 cells
+Frame 2 tracked 22 of 30 cells
+...
+
        
+
        
+
        
+
 
        
 
        
 get_lineage_mindist(distcut=5.0, visual=False, save_h5=False, overwrite=False)[source]
        
diff --git a/docs/genindex.html b/docs/genindex.html
index 0bd2c36..54613cc 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -210,6 +210,13 @@ 
        C
        
 
       
+      
      • 
+    celltraj.spatial
+
+      
        • 
   
      
@@ -259,6 +266,8 @@ 
      C
      
 
      • 
       
    • 
       
  • colorbar() (in module celltraj.utilities), [1]
+
    • 
+      
  • constrain_volume() (in module celltraj.spatial), [1]
 
    • 
       
  • create_h5() (in module celltraj.imageprep), [1]
 
    • 
@@ -378,6 +387,8 @@ 
    F
    
 
    G
    
 
-  
+  
    
   
       
  • get_minT() (celltraj.trajectory_legacy.Trajectory method)
+
    • 
+      
  • get_morse_force() (in module celltraj.spatial), [1]
 
    • 
       
  • get_motifs() (in module celltraj.model), [1]
 
    • 
@@ -715,8 +740,14 @@ 
    G
    
       
  • get_neighbor_feature_map() (in module celltraj.features), [1]
 
    • 
       
  • get_nndist_sum() (in module celltraj.imageprep), [1]
+
    • 
+      
  • get_nuc_displacement() (in module celltraj.spatial), [1]
 
    • 
       
  • get_null_correlations() (in module celltraj.translate), [1]
+
    • 
+      
  • get_ot_displacement() (in module celltraj.spatial), [1]
+
    • 
+      
  • get_ot_dx() (in module celltraj.spatial), [1]
 
    • 
       
  • get_pair_cluster_rdf() (celltraj.celltraj_1apr21.cellTraj method)
 
@@ -783,7 +814,11 @@ 
    G
    
 
    • 
       
       
  • get_secreted_ligand_density() (celltraj.trajectory.Trajectory method), [1]
+
+      
    • 
       
  • get_signal_contributions() (celltraj.trajectory.Trajectory method), [1]
 
    • 
       
  • get_slide_image() (in module celltraj.imageprep), [1]
@@ -803,6 +838,12 @@ 
    G
    
       
  • get_state_decomposition() (in module celltraj.translate), [1]
 
    • 
       
  • get_steady_state_matrixpowers() (in module celltraj.model), [1]
+
    • 
+      
  • get_surface_displacement() (in module celltraj.spatial), [1]
+
    • 
+      
  • get_surface_displacement_deviation() (in module celltraj.spatial), [1]
+
    • 
+      
  • get_surface_points() (in module celltraj.spatial), [1]
 
    • 
       
  • get_tcf() (celltraj.trajectory.Trajectory method), [1]
 
@@ -868,6 +909,8 @@ 
    G
    
         
  • (celltraj.trajectory_legacy.Trajectory method)
 
    • 
       
+      
  • get_volconstraint_com() (in module celltraj.spatial), [1]
+
    • 
       
  • get_voronoi_masks() (in module celltraj.imageprep), [1]
 
    • 
       
  • get_voronoi_masks_fromcenters() (in module celltraj.imageprep), [1]
@@ -879,6 +922,8 @@ 
    G
    
 
    • 
       
       
  • get_xtraj_tcf() (celltraj.trajectory_legacy.Trajectory method)
+
    • 
+      
  • get_yukawa_force() (in module celltraj.spatial), [1]
 
    • 
       		
 
    
@@ -949,6 +994,8 @@ 
    M
    
         
  • celltraj.imageprep, [1]
 
    • 
         
  • celltraj.model, [1]
+
    • 
+        
  • celltraj.spatial, [1]
 
    • 
         
  • celltraj.trajectory, [1]
 
    • 
diff --git a/docs/modules.html b/docs/modules.html
index 1d700e5..1e11175 100644
--- a/docs/modules.html
+++ b/docs/modules.html
@@ -245,6 +245,26 @@ 
    celltraj
 
  • update_mahalanobis_matrix_grad()
    • 
 
 
+
  • celltraj.spatial module
+
    • 
 
  • celltraj.trajectory module
      
 
    • Trajectory
        
 
      • Trajectory.__init__()
        • 
@@ -278,6 +298,7 @@ 
        celltraj
 
      • Trajectory.nmaskchannels
        • 
 
      • Trajectory.ndim
        • 
 
      • Trajectory.get_lineage_btrack()
        • 
+
      • Trajectory.get_lineage_min_otcost()
        • 
 
      • Trajectory.get_lineage_mindist()
        • 
 
      • Trajectory.get_mask_data()
        • 
 
      • Trajectory.get_motility_features()
        • 
diff --git a/docs/objects.inv b/docs/objects.inv
index 908935c8c9825b709e7ce74acf7d32b290792489..1629f5be9d2518a40d934adc101a079da9b6966e 100644
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diff --git a/docs/py-modindex.html b/docs/py-modindex.html
index c4301aa..a90af00 100644
--- a/docs/py-modindex.html
+++ b/docs/py-modindex.html
@@ -116,6 +116,11 @@ 
        Python Module Index
        
           
        celltraj.model
        
+     
+       
+          
+       celltraj.spatial
+       
      
        
           
diff --git a/docs/searchindex.js b/docs/searchindex.js
index 17731d0..c5a94e6 100644
--- a/docs/searchindex.js
+++ b/docs/searchindex.js
@@ -1 +1 @@
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"get_cyto_minus_nuc_labels() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_cyto_minus_nuc_labels", false], [1, "celltraj.imageprep.get_cyto_minus_nuc_labels", false]], "get_dmat() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dmat", false]], "get_dmat() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dmat", false]], "get_dmat() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_dmat", false], [1, "celltraj.utilities.get_dmat", false]], "get_dmat_vectorized() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_dmat_vectorized", false], [1, "celltraj.utilities.get_dmat_vectorized", false]], "get_dmatf_row() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dmatF_row", false]], "get_dmatf_row() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dmatF_row", false]], "get_dmatrt_row() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dmatRT_row", false]], "get_dmatrt_row() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dmatRT_row", false]], "get_dx() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_dx", false], [1, "celltraj.trajectory.Trajectory.get_dx", false]], "get_dx() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dx", false]], "get_dx_alpha() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dx_alpha", false]], "get_dx_alpha() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dx_alpha", false]], "get_dx_rdf() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dx_rdf", false]], "get_dx_rdf() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dx_rdf", false]], "get_dx_tcf() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dx_tcf", false]], "get_dx_tcf() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dx_tcf", false]], "get_dx_theta() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dx_theta", false]], "get_dx_theta() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dx_theta", false]], "get_embedding() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_embedding", false]], "get_embedding() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_embedding", false]], "get_entropy_production() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_entropy_production", false]], "get_feature_map() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_feature_map", false], [1, "celltraj.imageprep.get_feature_map", false]], "get_fmask_data() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_fmask_data", false]], "get_fmask_data() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_fmask_data", false], [1, "celltraj.trajectory.Trajectory.get_fmask_data", false]], "get_fmask_data() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_fmask_data", false]], "get_fmaskset() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_fmaskSet", false]], "get_fmaskset() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_fmaskSet", false]], "get_frames() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_frames", false]], "get_frames() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_frames", false], [1, "celltraj.trajectory.Trajectory.get_frames", false]], "get_frames() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_frames", false]], "get_gaussiankernelm() (in module celltraj.model)": [[0, "celltraj.model.get_gaussianKernelM", false], [1, "celltraj.model.get_gaussianKernelM", false]], "get_h_eigs() (in module celltraj.model)": [[0, "celltraj.model.get_H_eigs", false], [1, "celltraj.model.get_H_eigs", false]], "get_image_data() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_image_data", false]], "get_image_data() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_image_data", false], [1, "celltraj.trajectory.Trajectory.get_image_data", false]], "get_image_data() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_image_data", false]], "get_image_shape() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_image_shape", false], [1, "celltraj.trajectory.Trajectory.get_image_shape", false]], "get_images() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_images", false], [1, "celltraj.imageprep.get_images", false]], "get_imageset() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_imageSet", false]], "get_imageset() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_imageSet", false]], "get_imageset_trans() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_imageSet_trans", false]], "get_imageset_trans() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_imageSet_trans", false]], "get_imageset_trans_turboreg() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_imageSet_trans_turboreg", false]], "get_intensity_centers() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_intensity_centers", false], [1, "celltraj.imageprep.get_intensity_centers", false]], "get_kernel_sigmas() (in module celltraj.model)": [[0, "celltraj.model.get_kernel_sigmas", false], [1, "celltraj.model.get_kernel_sigmas", false]], "get_kineticstates() (in module celltraj.model)": [[0, "celltraj.model.get_kineticstates", false], [1, "celltraj.model.get_kineticstates", false]], "get_koopman_eig() (in module celltraj.model)": [[0, "celltraj.model.get_koopman_eig", false], [1, "celltraj.model.get_koopman_eig", false]], "get_koopman_inference_multiple() (in module celltraj.model)": [[0, "celltraj.model.get_koopman_inference_multiple", false], [1, "celltraj.model.get_koopman_inference_multiple", false]], "get_koopman_modes() (in module celltraj.model)": [[0, "celltraj.model.get_koopman_modes", false], [1, "celltraj.model.get_koopman_modes", false]], "get_kscore() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_kscore", false]], "get_kscore() (in module celltraj.model)": [[0, "celltraj.model.get_kscore", false], [1, "celltraj.model.get_kscore", false]], "get_label_largestcc() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_label_largestcc", false], [1, "celltraj.imageprep.get_label_largestcc", false]], "get_labeled_mask() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_labeled_mask", false], [1, "celltraj.imageprep.get_labeled_mask", false]], "get_landscape_coords_umap() (in module celltraj.model)": [[0, "celltraj.model.get_landscape_coords_umap", false], [1, "celltraj.model.get_landscape_coords_umap", false]], "get_lineage_btrack() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_lineage_btrack", false], [1, "celltraj.trajectory.Trajectory.get_lineage_btrack", false]], "get_lineage_btrack() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_lineage_btrack", false]], "get_lineage_bunch_overlap() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_lineage_bunch_overlap", false]], "get_lineage_bunch_overlap() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_lineage_bunch_overlap", false]], "get_lineage_mindist() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_lineage_mindist", false]], "get_lineage_mindist() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_lineage_mindist", false], [1, "celltraj.trajectory.Trajectory.get_lineage_mindist", false]], "get_lineage_mindist() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_lineage_mindist", false]], "get_linear_batch_normalization() (celltraj.trajectory_legacy.trajectoryset method)": [[1, "celltraj.trajectory_legacy.TrajectorySet.get_linear_batch_normalization", false]], "get_linear_batch_normalization() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_linear_batch_normalization", false], [1, "celltraj.utilities.get_linear_batch_normalization", false]], "get_linear_coef() (celltraj.trajectory_legacy.trajectoryset method)": [[1, "celltraj.trajectory_legacy.TrajectorySet.get_linear_coef", false]], "get_linear_coef() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_linear_coef", false], [1, "celltraj.utilities.get_linear_coef", false]], "get_mask_2channel_ilastik() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_mask_2channel_ilastik", false], [1, "celltraj.imageprep.get_mask_2channel_ilastik", false]], "get_mask_data() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_mask_data", false], [1, "celltraj.trajectory.Trajectory.get_mask_data", false]], "get_masks() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_masks", false], [1, "celltraj.imageprep.get_masks", false]], "get_meshfunc_average() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_meshfunc_average", false], [1, "celltraj.utilities.get_meshfunc_average", false]], "get_minrt() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_minRT", false]], "get_minrt() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_minRT", false]], "get_mint() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_minT", false]], "get_motifs() (in module celltraj.model)": [[0, "celltraj.model.get_motifs", false], [1, "celltraj.model.get_motifs", false]], "get_motility_features() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_motility_features", false], [1, "celltraj.trajectory.Trajectory.get_motility_features", false]], "get_neg_overlap() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_neg_overlap", false]], "get_neighbor_feature_map() (in module celltraj.features)": [[0, "celltraj.features.get_neighbor_feature_map", false], [1, "celltraj.features.get_neighbor_feature_map", false]], "get_nndist_sum() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_nndist_sum", false], [1, "celltraj.imageprep.get_nndist_sum", false]], "get_null_correlations() (in module celltraj.translate)": [[0, "celltraj.translate.get_null_correlations", false], [1, "celltraj.translate.get_null_correlations", false]], "get_pair_cluster_rdf() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_pair_cluster_rdf", false]], "get_pair_cluster_rdf() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_pair_cluster_rdf", false]], "get_pair_distrt() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_pair_distRT", false]], "get_pair_distrt() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_pair_distRT", false]], "get_pair_rdf() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_pair_rdf", false]], "get_pair_rdf() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_pair_rdf", false], [1, "celltraj.trajectory.Trajectory.get_pair_rdf", false]], "get_pair_rdf() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_pair_rdf", false]], "get_pair_rdf_fromcenters() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_pair_rdf_fromcenters", false], [1, "celltraj.imageprep.get_pair_rdf_fromcenters", false]], "get_pairwise_distance_sum() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_pairwise_distance_sum", false], [1, "celltraj.utilities.get_pairwise_distance_sum", false]], "get_path_entropy_2point() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_path_entropy_2point", false]], "get_path_entropy_2point() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_path_entropy_2point", false]], "get_path_entropy_2point() (in module celltraj.model)": [[0, "celltraj.model.get_path_entropy_2point", false], [1, "celltraj.model.get_path_entropy_2point", false]], "get_path_ll_2point() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_path_ll_2point", false]], "get_path_ll_2point() (in module celltraj.model)": [[0, "celltraj.model.get_path_ll_2point", false], [1, "celltraj.model.get_path_ll_2point", false]], "get_pca() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_pca", false]], "get_pca() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_pca", false]], "get_pca_fromdata() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_pca_fromdata", false]], "get_pca_fromdata() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_pca_fromdata", false]], "get_pca_fromdata() (in module celltraj.features)": [[0, "celltraj.features.get_pca_fromdata", false], [1, "celltraj.features.get_pca_fromdata", false]], "get_predictedfc() (in module celltraj.translate)": [[0, "celltraj.translate.get_predictedFC", false], [1, "celltraj.translate.get_predictedFC", false]], "get_registration_expansions() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_registration_expansions", false], [1, "celltraj.imageprep.get_registration_expansions", false]], "get_registrations() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_registrations", false], [1, "celltraj.imageprep.get_registrations", false]], "get_scaled_sigma() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_scaled_sigma", false]], "get_scaled_sigma() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_scaled_sigma", false]], "get_secreted_ligand_density() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_secreted_ligand_density", false], [1, "celltraj.trajectory.Trajectory.get_secreted_ligand_density", false]], "get_signal_contributions() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_signal_contributions", false], [1, "celltraj.trajectory.Trajectory.get_signal_contributions", false]], "get_slide_image() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_slide_image", false], [1, "celltraj.imageprep.get_slide_image", false]], "get_stack_trans() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_stack_trans", false]], "get_stack_trans() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_stack_trans", false], [1, "celltraj.trajectory.Trajectory.get_stack_trans", false]], "get_stack_trans() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_stack_trans", false]], "get_stack_trans_frompoints() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_stack_trans_frompoints", false]], "get_stack_trans_turboreg() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_stack_trans_turboreg", false]], "get_state_decomposition() (in module celltraj.translate)": [[0, "celltraj.translate.get_state_decomposition", false], [1, "celltraj.translate.get_state_decomposition", false]], "get_steady_state_matrixpowers() (in module celltraj.model)": [[0, "celltraj.model.get_steady_state_matrixpowers", false], [1, "celltraj.model.get_steady_state_matrixpowers", false]], "get_tcf() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_tcf", false], [1, "celltraj.trajectory.Trajectory.get_tcf", false]], "get_tcf() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_tcf", false]], "get_tf_matrix_2d() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_tf_matrix_2d", false], [1, "celltraj.imageprep.get_tf_matrix_2d", false]], "get_tile_order() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_tile_order", false], [1, "celltraj.imageprep.get_tile_order", false]], "get_traj_ll_gmean() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_traj_ll_gmean", false]], "get_traj_ll_gmean() (in module celltraj.model)": [[0, "celltraj.model.get_traj_ll_gmean", false], [1, "celltraj.model.get_traj_ll_gmean", false]], "get_traj_segments() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_traj_segments", false]], "get_traj_segments() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_traj_segments", false], [1, "celltraj.trajectory.Trajectory.get_traj_segments", false]], "get_traj_segments() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_traj_segments", false]], "get_trajab_segments() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_trajAB_segments", false], [1, "celltraj.trajectory.Trajectory.get_trajAB_segments", false]], "get_trajectory_embedding() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_trajectory_embedding", false]], "get_trajectory_embedding() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_trajectory_embedding", false]], "get_trajectory_steps() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_trajectory_steps", false]], "get_trajectory_steps() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_trajectory_steps", false], [1, "celltraj.trajectory.Trajectory.get_trajectory_steps", false]], "get_trajectory_steps() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_trajectory_steps", false]], "get_transition_matrix() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_transition_matrix", false]], "get_transition_matrix() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_transition_matrix", false]], "get_transition_matrix() (in module celltraj.model)": [[0, "celltraj.model.get_transition_matrix", false], [1, "celltraj.model.get_transition_matrix", false]], "get_transition_matrix_cg() 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"celltraj.celltraj_1apr21.cellTraj.get_cell_bunches", false]], "get_cell_bunches() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cell_bunches", false]], "get_cell_centers() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_cell_centers", false], [1, "celltraj.imageprep.get_cell_centers", false]], "get_cell_centers() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_cell_centers", false], [1, "celltraj.utilities.get_cell_centers", false]], "get_cell_channel_crosscorr() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cell_channel_crosscorr", false], [1, "celltraj.trajectory.Trajectory.get_cell_channel_crosscorr", false]], "get_cell_compartment_ratio() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cell_compartment_ratio", false], [1, "celltraj.trajectory.Trajectory.get_cell_compartment_ratio", false]], "get_cell_data() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_cell_data", false]], "get_cell_data() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cell_data", false], [1, "celltraj.trajectory.Trajectory.get_cell_data", false]], "get_cell_data() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cell_data", false]], "get_cell_features() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cell_features", false], [1, "celltraj.trajectory.Trajectory.get_cell_features", false]], "get_cell_images() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_cell_images", false]], "get_cell_images() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cell_images", false]], "get_cell_index() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cell_index", false], [1, "celltraj.trajectory.Trajectory.get_cell_index", false]], "get_cell_intensities() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_cell_intensities", false], [1, "celltraj.imageprep.get_cell_intensities", false]], "get_cell_neighborhood() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cell_neighborhood", false]], "get_cell_positions() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cell_positions", false], [1, "celltraj.trajectory.Trajectory.get_cell_positions", false]], "get_cell_positions() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cell_positions", false]], "get_cell_trajectory() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_cell_trajectory", false]], "get_cell_trajectory() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cell_trajectory", false], [1, "celltraj.trajectory.Trajectory.get_cell_trajectory", false]], "get_cell_trajectory() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cell_trajectory", false]], "get_cellborder_images() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_cellborder_images", false]], "get_cellborder_images() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_cellborder_images", false]], "get_clean_mask() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_clean_mask", false]], "get_clean_mask() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_clean_mask", false]], "get_comdx_features() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_comdx_features", false]], "get_committor() (in module celltraj.model)": [[0, "celltraj.model.get_committor", false], [1, "celltraj.model.get_committor", false]], "get_contact_boundaries() (in module celltraj.features)": [[0, "celltraj.features.get_contact_boundaries", false], [1, "celltraj.features.get_contact_boundaries", false]], "get_contact_labels() (in module celltraj.features)": [[0, "celltraj.features.get_contact_labels", false], [1, "celltraj.features.get_contact_labels", false]], "get_contactsum_dev() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_contactsum_dev", false], [1, "celltraj.imageprep.get_contactsum_dev", false]], "get_cyto_minus_nuc_labels() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_cyto_minus_nuc_labels", false], [1, "celltraj.imageprep.get_cyto_minus_nuc_labels", false]], "get_dmat() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dmat", false]], "get_dmat() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dmat", false]], "get_dmat() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_dmat", false], [1, "celltraj.utilities.get_dmat", false]], "get_dmat_vectorized() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_dmat_vectorized", false], [1, "celltraj.utilities.get_dmat_vectorized", false]], "get_dmatf_row() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dmatF_row", false]], "get_dmatf_row() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dmatF_row", false]], "get_dmatrt_row() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dmatRT_row", false]], "get_dmatrt_row() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dmatRT_row", false]], "get_dx() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_dx", false], [1, "celltraj.trajectory.Trajectory.get_dx", false]], "get_dx() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dx", false]], "get_dx_alpha() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dx_alpha", false]], "get_dx_alpha() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dx_alpha", false]], "get_dx_rdf() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dx_rdf", false]], "get_dx_rdf() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dx_rdf", false]], "get_dx_tcf() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dx_tcf", false]], "get_dx_tcf() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dx_tcf", false]], "get_dx_theta() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_dx_theta", false]], "get_dx_theta() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_dx_theta", false]], "get_embedding() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_embedding", false]], "get_embedding() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_embedding", false]], "get_entropy_production() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_entropy_production", false]], "get_feature_map() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_feature_map", false], [1, "celltraj.imageprep.get_feature_map", false]], "get_flux_displacement() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_flux_displacement", false], [1, "celltraj.spatial.get_flux_displacement", false]], "get_flux_ligdist() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_flux_ligdist", false], [1, "celltraj.spatial.get_flux_ligdist", false]], "get_fmask_data() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_fmask_data", false]], "get_fmask_data() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_fmask_data", false], [1, "celltraj.trajectory.Trajectory.get_fmask_data", false]], "get_fmask_data() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_fmask_data", false]], "get_fmaskset() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_fmaskSet", false]], "get_fmaskset() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_fmaskSet", false]], "get_frames() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_frames", false]], "get_frames() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_frames", false], [1, "celltraj.trajectory.Trajectory.get_frames", false]], "get_frames() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_frames", false]], "get_gaussiankernelm() (in module celltraj.model)": [[0, "celltraj.model.get_gaussianKernelM", false], [1, "celltraj.model.get_gaussianKernelM", false]], "get_h_eigs() (in module celltraj.model)": [[0, "celltraj.model.get_H_eigs", false], [1, "celltraj.model.get_H_eigs", false]], "get_image_data() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_image_data", false]], "get_image_data() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_image_data", false], [1, "celltraj.trajectory.Trajectory.get_image_data", false]], "get_image_data() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_image_data", false]], "get_image_shape() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_image_shape", false], [1, "celltraj.trajectory.Trajectory.get_image_shape", false]], "get_images() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_images", false], [1, "celltraj.imageprep.get_images", false]], "get_imageset() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_imageSet", false]], "get_imageset() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_imageSet", false]], "get_imageset_trans() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_imageSet_trans", false]], "get_imageset_trans() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_imageSet_trans", false]], "get_imageset_trans_turboreg() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_imageSet_trans_turboreg", false]], "get_intensity_centers() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_intensity_centers", false], [1, "celltraj.imageprep.get_intensity_centers", false]], "get_kernel_sigmas() (in module celltraj.model)": [[0, "celltraj.model.get_kernel_sigmas", false], [1, "celltraj.model.get_kernel_sigmas", false]], "get_kineticstates() (in module celltraj.model)": [[0, "celltraj.model.get_kineticstates", false], [1, "celltraj.model.get_kineticstates", false]], "get_koopman_eig() (in module celltraj.model)": [[0, "celltraj.model.get_koopman_eig", false], [1, "celltraj.model.get_koopman_eig", false]], "get_koopman_inference_multiple() (in module celltraj.model)": [[0, "celltraj.model.get_koopman_inference_multiple", false], [1, "celltraj.model.get_koopman_inference_multiple", false]], "get_koopman_modes() (in module celltraj.model)": [[0, "celltraj.model.get_koopman_modes", false], [1, "celltraj.model.get_koopman_modes", false]], "get_kscore() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_kscore", false]], "get_kscore() (in module celltraj.model)": [[0, "celltraj.model.get_kscore", false], [1, "celltraj.model.get_kscore", false]], "get_label_largestcc() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_label_largestcc", false], [1, "celltraj.imageprep.get_label_largestcc", false]], "get_labeled_mask() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_labeled_mask", false], [1, "celltraj.imageprep.get_labeled_mask", false]], "get_labels_fromborderdict() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_labels_fromborderdict", false], [1, "celltraj.spatial.get_labels_fromborderdict", false]], "get_landscape_coords_umap() (in module celltraj.model)": [[0, "celltraj.model.get_landscape_coords_umap", false], [1, "celltraj.model.get_landscape_coords_umap", false]], "get_lineage_btrack() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_lineage_btrack", false], [1, "celltraj.trajectory.Trajectory.get_lineage_btrack", false]], "get_lineage_btrack() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_lineage_btrack", false]], "get_lineage_bunch_overlap() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_lineage_bunch_overlap", false]], "get_lineage_bunch_overlap() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_lineage_bunch_overlap", false]], "get_lineage_min_otcost() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_lineage_min_otcost", false], [1, "celltraj.trajectory.Trajectory.get_lineage_min_otcost", false]], "get_lineage_mindist() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_lineage_mindist", false]], "get_lineage_mindist() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_lineage_mindist", false], [1, "celltraj.trajectory.Trajectory.get_lineage_mindist", false]], "get_lineage_mindist() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_lineage_mindist", false]], "get_linear_batch_normalization() (celltraj.trajectory_legacy.trajectoryset method)": [[1, "celltraj.trajectory_legacy.TrajectorySet.get_linear_batch_normalization", false]], "get_linear_batch_normalization() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_linear_batch_normalization", false], [1, "celltraj.utilities.get_linear_batch_normalization", false]], "get_linear_coef() (celltraj.trajectory_legacy.trajectoryset method)": [[1, "celltraj.trajectory_legacy.TrajectorySet.get_linear_coef", false]], "get_linear_coef() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_linear_coef", false], [1, "celltraj.utilities.get_linear_coef", false]], "get_lj_force() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_LJ_force", false], [1, "celltraj.spatial.get_LJ_force", false]], "get_mask_2channel_ilastik() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_mask_2channel_ilastik", false], [1, "celltraj.imageprep.get_mask_2channel_ilastik", false]], "get_mask_data() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_mask_data", false], [1, "celltraj.trajectory.Trajectory.get_mask_data", false]], "get_masks() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_masks", false], [1, "celltraj.imageprep.get_masks", false]], "get_meshfunc_average() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_meshfunc_average", false], [1, "celltraj.utilities.get_meshfunc_average", false]], "get_minrt() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_minRT", false]], "get_minrt() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_minRT", false]], "get_mint() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_minT", false]], "get_morse_force() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_morse_force", false], [1, "celltraj.spatial.get_morse_force", false]], "get_motifs() (in module celltraj.model)": [[0, "celltraj.model.get_motifs", false], [1, "celltraj.model.get_motifs", false]], "get_motility_features() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_motility_features", false], [1, "celltraj.trajectory.Trajectory.get_motility_features", false]], "get_neg_overlap() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_neg_overlap", false]], "get_neighbor_feature_map() (in module celltraj.features)": [[0, "celltraj.features.get_neighbor_feature_map", false], [1, "celltraj.features.get_neighbor_feature_map", false]], "get_nndist_sum() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_nndist_sum", false], [1, "celltraj.imageprep.get_nndist_sum", false]], "get_nuc_displacement() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_nuc_displacement", false], [1, "celltraj.spatial.get_nuc_displacement", false]], "get_null_correlations() (in module celltraj.translate)": [[0, "celltraj.translate.get_null_correlations", false], [1, "celltraj.translate.get_null_correlations", false]], "get_ot_displacement() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_ot_displacement", false], [1, "celltraj.spatial.get_ot_displacement", false]], "get_ot_dx() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_ot_dx", false], [1, "celltraj.spatial.get_ot_dx", false]], "get_pair_cluster_rdf() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_pair_cluster_rdf", false]], "get_pair_cluster_rdf() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_pair_cluster_rdf", false]], "get_pair_distrt() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_pair_distRT", false]], "get_pair_distrt() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_pair_distRT", false]], "get_pair_rdf() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_pair_rdf", false]], "get_pair_rdf() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_pair_rdf", false], [1, "celltraj.trajectory.Trajectory.get_pair_rdf", false]], "get_pair_rdf() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_pair_rdf", false]], "get_pair_rdf_fromcenters() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_pair_rdf_fromcenters", false], [1, "celltraj.imageprep.get_pair_rdf_fromcenters", false]], "get_pairwise_distance_sum() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_pairwise_distance_sum", false], [1, "celltraj.utilities.get_pairwise_distance_sum", false]], "get_path_entropy_2point() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_path_entropy_2point", false]], "get_path_entropy_2point() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_path_entropy_2point", false]], "get_path_entropy_2point() (in module celltraj.model)": [[0, "celltraj.model.get_path_entropy_2point", false], [1, "celltraj.model.get_path_entropy_2point", false]], "get_path_ll_2point() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_path_ll_2point", false]], "get_path_ll_2point() (in module celltraj.model)": [[0, "celltraj.model.get_path_ll_2point", false], [1, "celltraj.model.get_path_ll_2point", false]], "get_pca() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_pca", false]], "get_pca() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_pca", false]], "get_pca_fromdata() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_pca_fromdata", false]], "get_pca_fromdata() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_pca_fromdata", false]], "get_pca_fromdata() (in module celltraj.features)": [[0, "celltraj.features.get_pca_fromdata", false], [1, "celltraj.features.get_pca_fromdata", false]], "get_predictedfc() (in module celltraj.translate)": [[0, "celltraj.translate.get_predictedFC", false], [1, "celltraj.translate.get_predictedFC", false]], "get_registration_expansions() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_registration_expansions", false], [1, "celltraj.imageprep.get_registration_expansions", false]], "get_registrations() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_registrations", false], [1, "celltraj.imageprep.get_registrations", false]], "get_scaled_sigma() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_scaled_sigma", false]], "get_scaled_sigma() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_scaled_sigma", false]], "get_secreted_ligand_density() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_secreted_ligand_density", false], [1, "celltraj.trajectory.Trajectory.get_secreted_ligand_density", false]], "get_secreted_ligand_density() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_secreted_ligand_density", false], [1, "celltraj.spatial.get_secreted_ligand_density", false]], "get_signal_contributions() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_signal_contributions", false], [1, "celltraj.trajectory.Trajectory.get_signal_contributions", false]], "get_slide_image() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_slide_image", false], [1, "celltraj.imageprep.get_slide_image", false]], "get_stack_trans() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_stack_trans", false]], "get_stack_trans() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_stack_trans", false], [1, "celltraj.trajectory.Trajectory.get_stack_trans", false]], "get_stack_trans() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_stack_trans", false]], "get_stack_trans_frompoints() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_stack_trans_frompoints", false]], "get_stack_trans_turboreg() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_stack_trans_turboreg", false]], "get_state_decomposition() (in module celltraj.translate)": [[0, "celltraj.translate.get_state_decomposition", false], [1, "celltraj.translate.get_state_decomposition", false]], "get_steady_state_matrixpowers() (in module celltraj.model)": [[0, "celltraj.model.get_steady_state_matrixpowers", false], [1, "celltraj.model.get_steady_state_matrixpowers", false]], "get_surface_displacement() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_surface_displacement", false], [1, "celltraj.spatial.get_surface_displacement", false]], "get_surface_displacement_deviation() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_surface_displacement_deviation", false], [1, "celltraj.spatial.get_surface_displacement_deviation", false]], "get_surface_points() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_surface_points", false], [1, "celltraj.spatial.get_surface_points", false]], "get_tcf() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_tcf", false], [1, "celltraj.trajectory.Trajectory.get_tcf", false]], "get_tcf() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_tcf", false]], "get_tf_matrix_2d() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_tf_matrix_2d", false], [1, "celltraj.imageprep.get_tf_matrix_2d", false]], "get_tile_order() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_tile_order", false], [1, "celltraj.imageprep.get_tile_order", false]], "get_traj_ll_gmean() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_traj_ll_gmean", false]], "get_traj_ll_gmean() (in module celltraj.model)": [[0, "celltraj.model.get_traj_ll_gmean", false], [1, "celltraj.model.get_traj_ll_gmean", false]], "get_traj_segments() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_traj_segments", false]], "get_traj_segments() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_traj_segments", false], [1, "celltraj.trajectory.Trajectory.get_traj_segments", false]], "get_traj_segments() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_traj_segments", false]], "get_trajab_segments() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_trajAB_segments", false], [1, "celltraj.trajectory.Trajectory.get_trajAB_segments", false]], "get_trajectory_embedding() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_trajectory_embedding", false]], "get_trajectory_embedding() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_trajectory_embedding", false]], "get_trajectory_steps() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_trajectory_steps", false]], "get_trajectory_steps() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_trajectory_steps", false], [1, "celltraj.trajectory.Trajectory.get_trajectory_steps", false]], "get_trajectory_steps() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_trajectory_steps", false]], "get_transition_matrix() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_transition_matrix", false]], "get_transition_matrix() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_transition_matrix", false]], "get_transition_matrix() (in module celltraj.model)": [[0, "celltraj.model.get_transition_matrix", false], [1, "celltraj.model.get_transition_matrix", false]], "get_transition_matrix_cg() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_transition_matrix_CG", false]], "get_transition_matrix_cg() (in module celltraj.model)": [[0, "celltraj.model.get_transition_matrix_CG", false], [1, "celltraj.model.get_transition_matrix_CG", false]], "get_tshift() (in module celltraj.utilities)": [[0, "celltraj.utilities.get_tshift", false], [1, "celltraj.utilities.get_tshift", false]], "get_unique_trajectories() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.get_unique_trajectories", false]], "get_unique_trajectories() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_unique_trajectories", false], [1, "celltraj.trajectory.Trajectory.get_unique_trajectories", false]], "get_unique_trajectories() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_unique_trajectories", false]], "get_volconstraint_com() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_volconstraint_com", false], [1, "celltraj.spatial.get_volconstraint_com", false]], "get_voronoi_masks() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_voronoi_masks", false], [1, "celltraj.imageprep.get_voronoi_masks", false]], "get_voronoi_masks_fromcenters() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.get_voronoi_masks_fromcenters", false], [1, "celltraj.imageprep.get_voronoi_masks_fromcenters", false]], "get_xtraj_celltrajectory() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_Xtraj_celltrajectory", false], [1, "celltraj.trajectory.Trajectory.get_Xtraj_celltrajectory", false]], "get_xtraj_celltrajectory() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_Xtraj_celltrajectory", false]], "get_xtraj_tcf() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.get_xtraj_tcf", false]], "get_yukawa_force() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_yukawa_force", false], [1, "celltraj.spatial.get_yukawa_force", false]], "histogram_stretch() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.histogram_stretch", false], [1, "celltraj.imageprep.histogram_stretch", false]], "image_shape (celltraj.trajectory.trajectory attribute)": [[0, "celltraj.trajectory.Trajectory.image_shape", false], [1, "celltraj.trajectory.Trajectory.image_shape", false]], "initialize() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.initialize", false]], "initialize() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.initialize", false]], "list_images() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.list_images", false], [1, "celltraj.imageprep.list_images", false]], "load_dict_from_h5() (in module celltraj.utilities)": [[0, "celltraj.utilities.load_dict_from_h5", false], [1, "celltraj.utilities.load_dict_from_h5", false]], "load_for_viewing() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.load_for_viewing", false], [1, "celltraj.imageprep.load_for_viewing", false]], "load_from_h5() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.load_from_h5", false], [1, "celltraj.trajectory.Trajectory.load_from_h5", false]], "load_ilastik() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.load_ilastik", false], [1, "celltraj.imageprep.load_ilastik", false]], "local_threshold() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.local_threshold", false], [1, "celltraj.imageprep.local_threshold", false]], "make_odd() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.make_odd", false], [1, "celltraj.imageprep.make_odd", false]], "meanintensity() (in module celltraj.features)": [[0, "celltraj.features.meanIntensity", false], [1, "celltraj.features.meanIntensity", false]], "module": [[0, "module-celltraj.features", false], [0, "module-celltraj.imageprep", false], [0, "module-celltraj.model", false], [0, "module-celltraj.spatial", false], [0, "module-celltraj.trajectory", false], [0, "module-celltraj.translate", false], [0, "module-celltraj.utilities", false], [1, "module-celltraj", false], [1, "module-celltraj.celltraj_1apr21", false], [1, "module-celltraj.cli", false], [1, "module-celltraj.features", false], [1, "module-celltraj.imageprep", false], [1, "module-celltraj.model", false], [1, "module-celltraj.spatial", false], [1, "module-celltraj.trajectory", false], [1, "module-celltraj.trajectory_legacy", false], [1, "module-celltraj.translate", false], [1, "module-celltraj.utilities", false]], "nchannels (celltraj.trajectory.trajectory attribute)": [[0, "celltraj.trajectory.Trajectory.nchannels", false], [1, "celltraj.trajectory.Trajectory.nchannels", false]], "ndim (celltraj.trajectory.trajectory attribute)": [[0, "celltraj.trajectory.Trajectory.ndim", false], [1, "celltraj.trajectory.Trajectory.ndim", false]], "nmaskchannels (celltraj.trajectory.trajectory attribute)": [[0, "celltraj.trajectory.Trajectory.nmaskchannels", false], [1, "celltraj.trajectory.Trajectory.nmaskchannels", false]], "nx (celltraj.trajectory.trajectory attribute)": [[0, "celltraj.trajectory.Trajectory.nx", false], [1, "celltraj.trajectory.Trajectory.nx", false]], "ny (celltraj.trajectory.trajectory attribute)": [[0, "celltraj.trajectory.Trajectory.ny", false], [1, "celltraj.trajectory.Trajectory.ny", false]], "nz (celltraj.trajectory.trajectory attribute)": [[0, "celltraj.trajectory.Trajectory.nz", false], [1, "celltraj.trajectory.Trajectory.nz", false]], "organize_filelist_fov() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.organize_filelist_fov", false], [1, "celltraj.imageprep.organize_filelist_fov", false]], "organize_filelist_time() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.organize_filelist_time", false], [1, "celltraj.imageprep.organize_filelist_time", false]], "pad_image() (celltraj.celltraj_1apr21.celltraj static method)": [[1, "celltraj.celltraj_1apr21.cellTraj.pad_image", false]], "pad_image() (celltraj.trajectory_legacy.trajectory static method)": [[1, "celltraj.trajectory_legacy.Trajectory.pad_image", false]], "pad_image() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.pad_image", false], [1, "celltraj.imageprep.pad_image", false]], "plot_embedding() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.plot_embedding", false]], "plot_embedding() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.plot_embedding", false]], "plot_pca() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.plot_pca", false]], "plot_pca() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.plot_pca", false]], "prepare_cell_features() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.prepare_cell_features", false]], "prepare_cell_features() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.prepare_cell_features", false]], "prepare_cell_images() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.prepare_cell_images", false]], "prepare_cell_images() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.prepare_cell_images", false]], "prune_embedding() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.prune_embedding", false]], "prune_embedding() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.prune_embedding", false]], "recursively_load_dict_contents_from_group() (in module celltraj.utilities)": [[0, "celltraj.utilities.recursively_load_dict_contents_from_group", false], [1, "celltraj.utilities.recursively_load_dict_contents_from_group", false]], "recursively_save_dict_contents_to_group() (in module celltraj.utilities)": [[0, "celltraj.utilities.recursively_save_dict_contents_to_group", false], [1, "celltraj.utilities.recursively_save_dict_contents_to_group", false]], "save_all() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.save_all", false]], "save_all() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.save_all", false]], "save_dict_to_h5() (in module celltraj.utilities)": [[0, "celltraj.utilities.save_dict_to_h5", false], [1, "celltraj.utilities.save_dict_to_h5", false]], "save_dmat_row() (celltraj.celltraj_1apr21.celltraj method)": [[1, "celltraj.celltraj_1apr21.cellTraj.save_dmat_row", false]], "save_dmat_row() (celltraj.trajectory_legacy.trajectory method)": [[1, "celltraj.trajectory_legacy.Trajectory.save_dmat_row", false]], "save_for_viewing() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.save_for_viewing", false], [1, "celltraj.imageprep.save_for_viewing", false]], "save_frame_h5() (in module celltraj.imageprep)": [[0, "celltraj.imageprep.save_frame_h5", false], [1, "celltraj.imageprep.save_frame_h5", false]], "save_to_h5() (celltraj.trajectory.trajectory 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trajectory modeling", "Installation", "celltraj", "celltraj"], "titleterms": {"A": 5, "analysi": 5, "api": 0, "cell": [2, 5], "celltraj": [0, 1, 2, 4, 5], "celltraj_1apr21": 1, "cli": 1, "content": [1, 2], "document": 5, "featur": [0, 1, 5], "from": 3, "imageprep": [0, 1], "indic": 2, "instal": 3, "kei": 5, "licens": 5, "model": [0, 1, 2, 5], "modul": 1, "packag": 1, "refer": [0, 5], "releas": 3, "singl": [2, 5], "sourc": 3, "spatial": [0, 1], "stabl": 3, "submodul": 1, "tabl": 2, "todo": 1, "toolset": 5, "trajectori": [0, 1, 2, 5], "trajectory_legaci": 1, "translat": [0, 1], "tutori": 5, "util": [0, 1]}})
\ No newline at end of file
diff --git a/docs/source/api.rst b/docs/source/api.rst
index aacafc3..d143463 100644
--- a/docs/source/api.rst
+++ b/docs/source/api.rst
@@ -41,6 +41,16 @@ Modeling functions for single-cell trajectories.
    :undoc-members:
    :show-inheritance:
 
+celltraj.spatial
+---------------------
+
+Modeling functions for boundary-resolved and physics-based single-cell modeling.
+
+.. automodule:: celltraj.spatial
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 celltraj.translate
 -------------------------
 
diff --git a/docs/source/celltraj.rst b/docs/source/celltraj.rst
index e2efecc..2466ca4 100644
--- a/docs/source/celltraj.rst
+++ b/docs/source/celltraj.rst
@@ -44,6 +44,14 @@ celltraj.model module
    :undoc-members:
    :show-inheritance:
 
+celltraj.spatial module
+-----------------------
+
+.. automodule:: celltraj.spatial
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 celltraj.trajectory module
 --------------------------