From 9178ef8104a8e2f75f8ef2e8f6f568e5686587e1 Mon Sep 17 00:00:00 2001 From: jcopperm Date: Fri, 8 Nov 2024 09:25:23 -0800 Subject: [PATCH] docs for boundary library --- docs/_modules/celltraj/features.html | 4 +- docs/_modules/celltraj/imageprep.html | 7 + docs/_modules/celltraj/spatial.html | 351 ++++++++++++++- docs/_modules/celltraj/trajectory.html | 420 +++++++++++++++++- docs/_modules/celltraj/utilities.html | 11 +- docs/api.html | 248 ++++++++++- docs/build/doctrees/api.doctree | Bin 1687570 -> 1803143 bytes docs/build/doctrees/celltraj.doctree | Bin 2106017 -> 2222847 bytes docs/build/doctrees/environment.pickle | Bin 5979895 -> 6330148 bytes .../html/_modules/celltraj/features.html | 4 +- .../html/_modules/celltraj/imageprep.html | 7 + .../build/html/_modules/celltraj/spatial.html | 351 ++++++++++++++- .../html/_modules/celltraj/trajectory.html | 420 +++++++++++++++++- .../html/_modules/celltraj/utilities.html | 11 +- docs/build/html/api.html | 248 ++++++++++- docs/build/html/celltraj.html | 242 +++++++++- docs/build/html/genindex.html | 20 +- docs/build/html/modules.html | 6 + docs/build/html/objects.inv | Bin 2719 -> 2773 bytes docs/build/html/searchindex.js | 2 +- docs/celltraj.html | 242 +++++++++- docs/genindex.html | 20 +- docs/modules.html | 6 + docs/objects.inv | Bin 2719 -> 2773 bytes docs/searchindex.js | 2 +- 25 files changed, 2540 insertions(+), 82 deletions(-) diff --git a/docs/_modules/celltraj/features.html b/docs/_modules/celltraj/features.html index cc639ca..504e1a1 100644 --- a/docs/_modules/celltraj/features.html +++ b/docs/_modules/celltraj/features.html @@ -963,13 +963,13 @@

Source code for celltraj.features

     labels_inv=-1*labels_inv
     contact_msk=get_contact_boundaries(labels0,boundary_only=True,radius=radius)
     contact_labels=np.zeros_like(labels0)
-    for i in np.unique(labels0[labels0>0]):
+    for i in np.flip(np.unique(labels0[labels0>0])):
         indi=np.where(np.logical_and(labels0==i,contact_msk))
         jset1=np.unique(labels[indi])
         jset2=np.unique(labels_inv[indi])
         jset=np.unique(np.concatenate((jset1,jset2)))
         jset=np.setdiff1d(jset,[i])
-        for j in jset:
+        for j in np.flip(jset):
             if labels0.ndim==2:
                 contact_labels[indi[0][labels[indi]==j],indi[1][labels[indi]==j]]=j
                 contact_labels[indi[0][labels_inv[indi]==j],indi[1][labels_inv[indi]==j]]=j
diff --git a/docs/_modules/celltraj/imageprep.html b/docs/_modules/celltraj/imageprep.html
index 1265415..ba61893 100644
--- a/docs/_modules/celltraj/imageprep.html
+++ b/docs/_modules/celltraj/imageprep.html
@@ -307,6 +307,13 @@ 
Source code for celltraj.imageprep
     return img

 
 
+

+[docs]
+def rescale_to_int(img,maxint=2**16-1,dtype=np.uint16):
+    img=maxint*((img-np.min(img))/np.max(img-np.min(img)))
+    return img.astype(dtype)

+
+
 

 [docs]
 def histogram_stretch(img,lp=1,hp=99):
diff --git a/docs/_modules/celltraj/spatial.html b/docs/_modules/celltraj/spatial.html
index f2e79cd..c7bc677 100644
--- a/docs/_modules/celltraj/spatial.html
+++ b/docs/_modules/celltraj/spatial.html
@@ -91,10 +91,11 @@ 
Source code for celltraj.spatial
 import pandas as pd
 from pyntcloud import PyntCloud
 import ot
+from multipoles import MultipoleExpansion
 
 

 [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):
+def get_border_dict(labels,states=None,radius=10,vdist=None,return_nnindex=True,return_nnvector=True,return_curvature=True,nn_states=None,scale=None,**border_args):
     """
     Computes the border properties of labeled regions in a segmented image.
 
@@ -111,8 +112,7 @@ 
Source code for celltraj.spatial
     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.
+        An array representing a scalar value in the image, such as estimated ligand concentration, to store in the border point dictionary.
     return_nnindex : bool, optional
         If True, returns the nearest neighbor index for each border point (default is True).
     return_nnvector : bool, optional
@@ -160,6 +160,8 @@ 
Source code for celltraj.spatial
         scale=np.ones(labels.ndim)
     else:
         labels=scipy.ndimage.zoom(labels,zoom=scale,order=0)
+        if vdist is not None:
+            vdist=scipy.ndimage.zoom(vdist,zoom=scale)
     labels=labels.astype(int)
     border_dict={}
     border_dict['scale']=scale
@@ -178,16 +180,16 @@ 
Source code for celltraj.spatial
         contact_inds=contact_labels[border>0]
         border_dict['nn_index']=contact_inds
         border_dict['nn_states']=states[contact_inds]
-    if return_nnvector:
+    if return_nnvector or nn_states is not None:
         iset=np.unique(labels)
         iset=iset[iset>0]
-        nn_labels=[None]*(np.max(iset)+1)
+        knn_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]])
+            knn_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])
@@ -195,13 +197,40 @@ 
Source code for celltraj.spatial
             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)
+                distij,indij_nn=knn_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 nn_states is not None:
+            border_nn_pts_state=np.ones((border_pts.shape[0],np.max(states)+1,border_pts.shape[1]))*np.nan
+            border_nn_inds_state=np.ones((border_pts.shape[0],np.max(states)+1)).astype(int)*-1
+            #knn_states=[None]*(np.max(states)+1)
+            inds_states=[None]*(np.max(states)+1)
+            iset=np.where(np.isin(states,nn_states))[0]
+            stateset=np.unique(states)
+            for istate in stateset:
+                inds_states[istate]=np.where(border_dict['states']==istate)[0]
+                #if inds_states[istate].size>0:
+                    #knn_states[istate] = sklearn.neighbors.NearestNeighbors(n_neighbors=1, radius=1.,algorithm='ball_tree').fit(border_pts[inds_states[istate]])
+            for i in iset:
+                print(f'getting nn points for cell {i}')
+                indi=inds_labels[i]
+                if indi is not None:
+                    for istate in stateset:
+                        indistate=np.setdiff1d(inds_states[istate],indi)
+                        if indistate.size>0:
+                            knn_states = sklearn.neighbors.NearestNeighbors(n_neighbors=1, radius=1.,algorithm='ball_tree').fit(border_pts[indistate])
+                            borderij_pts=border_pts[indi]
+                            distij,indij_nn=knn_states.kneighbors(borderij_pts)
+                            indij_nn=np.squeeze(indij_nn)
+                            borderj_pts=border_pts[indistate[indij_nn]]
+                            border_nn_pts_state[indi,istate,:]=borderj_pts
+                            border_nn_inds_state[indi,istate]=indistate[indij_nn]
+            border_dict['nn_pts_state']=border_nn_pts_state
+            border_dict['nn_inds_state']=border_nn_inds_state
     if return_curvature:
         n=np.zeros_like(border_pts)
         c=np.zeros(border_pts.shape[0])
@@ -213,6 +242,7 @@ 
Source code for celltraj.spatial
         iset=iset[iset>0]
         for i in iset:
             msk=labels==i
+            print(f'label {i}, pixels {np.sum(msk)}')
             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
@@ -227,7 +257,7 @@ 
Source code for celltraj.spatial
 
 

 [docs]
-def get_surface_points(msk,return_normals=False,return_curvature=False,knn=20):
+def get_surface_points(msk,return_normals=False,return_curvature=False,knn=20,rscale=.1):
     """
     Computes the surface points of a labeled mask and optionally calculates normals and curvature.
 
@@ -279,30 +309,55 @@ 
Source code for celltraj.spatial
     border=skimage.segmentation.find_boundaries(msk,mode='inner')
     ind=np.where(border)
     border_pts=np.array(ind).astype(float).T
+    npts=border_pts.shape[0]
+    if knn>int(npts/4):
+        knn=int(npts/4)
+        print(f'adjusted knn: {knn} npts: {npts}')
     if return_normals or return_curvature:
-        cloud=PyntCloud(pd.DataFrame(data=border_pts,columns=['x','y','z']))
+        if msk.ndim==3:
+            rand_dx=np.array([np.random.normal(loc=0.,scale=rscale,size=npts),np.random.normal(loc=0.,scale=rscale,size=npts),np.random.normal(loc=0.,scale=rscale,size=npts)]).T
+        elif msk.ndim==2:
+            rand_dx=np.array([np.random.normal(loc=0.,scale=rscale,size=npts),np.random.normal(loc=0.,scale=rscale,size=npts),np.zeros(npts)]).T
+            border_pts=np.concatenate((border_pts,np.zeros((border_pts.shape[0],1))),axis=1)
+        cloud=PyntCloud(pd.DataFrame(data=border_pts+rand_dx,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,:]
+        if msk.ndim==3:
+            border_pts_trans=border_pts+2.*v[:,2,:]
+        if msk.ndim==2:
+            border_pts_trans=border_pts+2.*v[:,1,:]
         ind_trans=border_pts_trans.astype(int)
-        for iax in range(border_pts.shape[1]):
+        for iax in range(msk.ndim): #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,:]
+        if msk.ndim==3:
+            infacing_normals=msk[ind_trans[:,0],ind_trans[:,1],ind_trans[:,2]]
+            n = v[:,2,:]
+        elif msk.ndim==2:
+            infacing_normals=msk[ind_trans[:,0],ind_trans[:,1]]
+            n = v[:,1,:]
         n[infacing_normals,:]=-1.*n[infacing_normals,:]
         if return_curvature:
-            c=np.divide(w[:,2],np.sum(w,axis=1))
+            if msk.ndim==3:
+                c=np.divide(w[:,2],np.sum(w,axis=1))
+            elif msk.ndim==2:
+                c=np.divide(w[:,1],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
+            if msk.ndim==3:
+                return border_pts,n,c
+            elif msk.ndim==2:
+                return border_pts[:,0:2],n[:,0:2],c
         else:
-            return border_pts,n
+            if msk.ndim==3:
+                return border_pts,n
+            elif msk.ndim==2:
+                return border_pts[:,0:2],n[:,0:2]
     else:
         return border_pts

 
@@ -484,6 +539,24 @@ 
Source code for celltraj.spatial
     return dx

 
 
+

+[docs]
+def get_surface_gradvariance(border_pts,dx_ot,knn=12,use_eigs=False):
+    rdx_ot = np.linalg.norm(dx_ot,axis=1)
+    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)
+    return dh

+
+
 

 [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):
@@ -1380,7 +1453,6 @@ 
Source code for celltraj.spatial
         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.
@@ -1449,6 +1521,251 @@ 
Source code for celltraj.spatial
     return fmeans,fsigmas

 
 
+

+[docs]
+def get_border_properties(cell_labels,surfaces=None,cell_states=None,surface_states=None,cell_labels_next=None,cell_labels_prev=None,lin_next=None,lin_prev=None,vdist=None,border_scale=1.0,radius=2.0,order=1,return_border_properties_list=False):
+    """
+    Calculate geometric and physical properties of cell borders, with optional tracking between frames 
+    and surface contact details.
+
+    This function computes properties of cell borders based on `cell_labels`, surface contacts, and 
+    multipole moments up to a specified order. It supports tracking cell border displacement across 
+    frames using optional next and previous frame labels (`cell_labels_next`, `cell_labels_prev`). 
+    Outputs include multipole moments and border displacements useful in characterizing cell interactions 
+    and tracking cell behaviors.
+
+    Parameters
+    ----------
+    cell_labels : ndarray
+        Array of labels identifying distinct cells within the domain.
+    
+    surfaces : list of ndarrays, optional
+        List of binary masks indicating surface layers adjacent to cells (e.g., membranes or other 
+        boundaries). Each is assigned a unique identifier.
+
+    cell_states : ndarray of int, optional
+        Array specifying a state for each cell in `cell_labels`. Defaults to 1 if not provided.
+    
+    surface_states : ndarray of int, optional
+        Array of states corresponding to each surface in `surfaces`. These states must not overlap 
+        with `cell_states`.
+
+    cell_labels_next : ndarray, optional
+        Label array for the next frame to track cell borders over time.
+    
+    cell_labels_prev : ndarray, optional
+        Label array for the previous frame, used for backward tracking of cells.
+    
+    lin_next : list of lists, optional
+        Mapping of cells in `cell_labels` to corresponding cells in `cell_labels_next`. Each entry 
+        contains IDs of linked cells in the next frame.
+    
+    lin_prev : list of lists, optional
+        Mapping of cells in `cell_labels` to corresponding cells in `cell_labels_prev`, used for 
+        backward tracking.
+
+    border_scale : float, optional
+        Resolution in microns applied to border point extraction (default is 1.0).
+    
+    radius : float, optional
+        Radius in pixels for calculating border properties such as contact neighbors (default is 2.0).
+    
+    order : int, optional
+        Order of multipole moments to calculate for each border contact (default is 1).
+    
+    return_border_properties_list : bool, optional
+        If True, returns a list of property names for multipole moments alongside the border 
+        dictionary.
+
+    Returns
+    -------
+    border_dict : dict
+        Dictionary containing calculated properties of each cell's border, including:
+        
+        - `pts` : Array of border points.
+        - `index` : Label of each cell at the border.
+        - `nn_states` : Neighboring cell states at each border point.
+        - `contact_properties` : Array of multipole moment properties calculated up to `order`.
+        - `border_dx_next`, `border_dx_prev` : Displacements of each border in the next and previous 
+          frames, if applicable.
+        - `dx_next_properties`, `dx_prev_properties` : Arrays of multipole moments based on border 
+          displacement for next and previous frames.
+    
+    border_properties_list : list of str, optional
+        List of property names for `contact_properties` array if `return_border_properties_list` 
+        is True.
+
+    Notes
+    -----
+    - **Multipole Moment Calculation**: Moments up to the specified `order` are computed based on 
+      border charges (representing contact fraction) for each border point.
+    - **Cell Tracking**: When provided with `cell_labels_next` or `cell_labels_prev`, the function 
+      calculates optimal transport (OT) vectors to track border movement between frames.
+
+    Examples
+    --------
+    Calculate properties of cell borders and multipole moments with next-frame tracking:
+
+    >>> border_dict, property_list = get_border_properties(
+    ...     cell_labels=my_cell_labels, surfaces=my_surfaces, cell_states=my_states,
+    ...     cell_labels_next=my_next_frame_labels, lin_next=my_next_frame_links, 
+    ...     border_scale=0.8, radius=1.5, order=2, return_border_properties_list=True
+    ... )
+
+    """
+    labels=cell_labels.copy()
+    max_cellid=np.max(labels)
+    cell_ids=np.unique(labels)
+    cell_ids=cell_ids[cell_ids>0]
+    n_moments=sum(2 * l + 1 for l in range(order + 1))
+    if cell_states is None:
+        cell_states=[1]*max_cellid
+        cell_states=np.array(cell_states).astype(int)
+        cell_states=np.insert(cell_states,0,0)
+    max_cell_state=np.max(cell_states)
+    if surfaces is None:
+        n_surf=0
+    else:
+        n_surf=len(surfaces)
+        surface_ids=(np.arange(n_surf)+max_cellid+1).astype(int)
+        if surface_states is None:
+            surface_states=(np.arange(n_surf)+max_cell_state+1).astype(int)
+        else:
+            if np.intersect1d(cell_states,surface_states).size>0:
+                print('Conflicting labels between cell states and surface states provided')
+        nn_states=cell_states.copy()
+        cell_states=np.append(cell_states,surface_states)
+        surface_overlay=surfaces[0].copy()
+        for i_surf in range(n_surf):
+            overlapping=np.sum(np.logical_and(labels>0,surfaces[i_surf]))/np.sum(labels>0)
+            if overlapping>0:
+                print(f'Warning: {overlapping:.2e} fraction overlap between surface {i_surf} and cell labels, cell labels will overlay surface labels')
+            if i_surf>0:
+                overlapping_surf=np.sum(np.logical_and(surfaces[i_surf],surface_overlay))/np.sum(surfaces[i_surf])
+                if overlapping_surf>0.:
+                    print(f'Warning: {overlapping_surf:.2e} fraction overlap between surface {i_surf} and previous surfaces, {i_surf} will overlay')
+            surface_overlay=np.logical_or(surface_overlay,surfaces[i_surf])
+            labels[surfaces[i_surf]]=surface_ids[i_surf]
+    border_dict=get_border_dict(labels,states=cell_states,nn_states=nn_states,vdist=vdist,radius=radius,scale=border_scale)
+    contact_properties=np.ones((max_cellid+1,np.max(cell_states)+1,1+n_moments))*np.nan
+    border_properties_list=[None]*(1+n_moments)
+    border_properties_list[0]='total'
+    i_prop=1
+    for l in range(order+1):
+        for m in range(-l,l+1):
+            border_properties_list[i_prop]=f'({l},{m})'
+            i_prop=i_prop+1
+    for ic in range(cell_ids.size):
+        cellid=cell_ids[ic]
+        indcell=np.where(border_dict['index']==cellid)[0]
+        border_pts=border_dict['pts'][indcell,:]
+        border_nn_states=border_dict['nn_states'][indcell]
+        if indcell.size>0:
+            for i_state in np.unique(cell_states[cell_states>0]):
+                indcell_surf=np.where(border_nn_states==i_state)[0]
+                if indcell_surf.size>0:
+                    border_charge=np.ones(indcell.size)*-1
+                    border_charge[indcell_surf]=1
+                    av_surf_fraction=indcell_surf.size/indcell.size
+                    print(f'cell {cellid} state {i_state} contact fraction {av_surf_fraction:.2f}')
+                    charges_list = [{'q': q, 'xyz': tuple(border_pts)} for q, border_pts in zip(border_charge, border_pts)]
+                    charge_dist = {'discrete': True, 'charges': charges_list}
+                    Phi = MultipoleExpansion(charge_dist, order)
+                    contact_properties[cellid,i_state,0]=indcell.size
+                    i_prop=1
+                    for l in range(order+1):
+                        for m in range(-l,l+1):
+                            contact_properties[cellid,i_state,i_prop]=Phi.multipole_moments[l,m]
+                            i_prop=i_prop+1
+                else:
+                    print(f'cell {cellid} state {i_state} contact fraction {0.0:.2f}')
+    border_dict['contact_properties']=contact_properties
+    border_dict['border_properties_list']=border_properties_list
+    if cell_labels_next is not None:
+        dx_next_properties=np.ones((max_cellid+1,1+n_moments))*np.nan
+        border_dict_next=get_border_dict(cell_labels_next,states=None,scale=border_scale,return_nnindex=False,return_nnvector=False,return_curvature=False)
+        cell_ids_next=np.unique(cell_labels_next)
+        cell_ids_next=cell_ids_next[cell_ids_next>0]
+        max_cellid_next=np.max(cell_ids_next)
+        if lin_next is None:
+            print('no tracking provided, matching cells by ID')
+            lin_next = [[] for _ in range(cell_ids.size + 1)]
+            for cellid in cell_ids:
+                if np.isin(cellid,cell_ids_next):
+                    lin_next[cellid].append(cellid)
+        border_dx_next=np.zeros_like(border_dict['pts'])
+        inds_ot_next=np.ones(border_dict['pts'].shape[0]).astype(int)*-1
+        for ic in range(cell_ids.size):
+            cellid=cell_ids[ic]
+            if len(lin_next[cellid])>0:
+                indcell=np.where(border_dict['index']==cellid)[0]
+                border_pts=border_dict['pts'][indcell,:]
+                indcell_next=np.where(np.isin(border_dict_next['index'],lin_next[cellid]))[0]
+                if indcell_next.size>0:
+                    border_pts_next=border_dict_next['pts'][indcell_next,:]
+                    inds_ot,dx_ot=get_ot_dx(border_pts,border_pts_next,return_dx=True,return_cost=False)
+                    border_dx_next[indcell,:]=-dx_ot
+                    inds_ot_next[indcell]=indcell_next[inds_ot]
+                    dxc=np.sum(np.multiply(-dx_ot,border_dict['n'][indcell,:]),axis=1)
+                    border_charge=dxc
+                    charges_list = [{'q': q, 'xyz': tuple(border_pts)} for q, border_pts in zip(border_charge, border_pts)]
+                    charge_dist = {'discrete': True, 'charges': charges_list}
+                    Phi = MultipoleExpansion(charge_dist, order)
+                    dx_next_properties[cellid,0]=indcell.size
+                    i_prop=1
+                    for l in range(order+1):
+                        for m in range(-l,l+1):
+                            dx_next_properties[cellid,i_prop]=Phi.multipole_moments[l,m]
+                            i_prop=i_prop+1
+        border_dict['dh_next']=get_surface_gradvariance(border_dict['pts'],border_dx_next)
+        border_dict['border_dx_next']=border_dx_next
+        border_dict['inds_ot_next']=inds_ot_next
+        border_dict['dx_next_properties']=dx_next_properties
+    if cell_labels_prev is not None:
+        border_dict_prev=get_border_dict(cell_labels_prev,states=None,scale=border_scale,return_nnindex=False,return_nnvector=False,return_curvature=False)
+        dx_prev_properties=np.ones((max_cellid+1,1+n_moments))*np.nan
+        cell_ids_prev=np.unique(cell_labels_prev)
+        cell_ids_prev=cell_ids_prev[cell_ids_prev>0]
+        max_cellid_prev=np.max(cell_ids_prev)
+        if lin_prev is None:
+            print('no tracking provided, matching cells by ID')
+            lin_prev = [[] for _ in range(cell_ids.size + 1)]
+            for cellid in cell_ids:
+                if np.isin(cellid,cell_ids_prev):
+                    lin_prev[cellid].append(cellid)
+        border_dx_prev=np.zeros_like(border_dict['pts'])
+        inds_ot_prev=np.ones(border_dict['pts'].shape[0]).astype(int)*-1
+        for ic in range(cell_ids.size):
+            cellid=cell_ids[ic]
+            if len(lin_prev[cellid])>0:
+                indcell=np.where(border_dict['index']==cellid)[0]
+                border_pts=border_dict['pts'][indcell,:]
+                indcell_prev=np.where(np.isin(border_dict_prev['index'],lin_prev[cellid]))[0]
+                if indcell_prev.size>0:
+                    border_pts_prev=border_dict_prev['pts'][indcell_prev,:]
+                    inds_ot,dx_ot=get_ot_dx(border_pts,border_pts_prev,return_dx=True,return_cost=False)
+                    border_dx_prev[indcell,:]=-dx_ot
+                    inds_ot_prev[indcell]=indcell_prev[inds_ot]
+                    dxc=np.sum(np.multiply(-dx_ot,border_dict['n'][indcell,:]),axis=1)
+                    border_charge=dxc
+                    charges_list = [{'q': q, 'xyz': tuple(border_pts)} for q, border_pts in zip(border_charge, border_pts)]
+                    charge_dist = {'discrete': True, 'charges': charges_list}
+                    Phi = MultipoleExpansion(charge_dist, order)
+                    dx_prev_properties[cellid,0]=indcell.size
+                    i_prop=1
+                    for l in range(order+1):
+                        for m in range(-l,l+1):
+                            dx_prev_properties[cellid,i_prop]=Phi.multipole_moments[l,m]
+                            i_prop=i_prop+1
+        border_dict['dh_prev']=get_surface_gradvariance(border_dict['pts'],border_dx_prev)
+        border_dict['border_dx_prev']=border_dx_prev
+        border_dict['inds_ot_prev']=inds_ot_prev
+        border_dict['dx_prev_properties']=dx_prev_properties
+    if return_border_properties_list:
+        return border_dict,border_properties_list
+    else:
+        return border_dict

+
 

 
            

diff --git a/docs/_modules/celltraj/trajectory.html b/docs/_modules/celltraj/trajectory.html
index 158e6cb..be30e2f 100644
--- a/docs/_modules/celltraj/trajectory.html
+++ b/docs/_modules/celltraj/trajectory.html
@@ -228,7 +228,7 @@ 
Source code for celltraj.trajectory
                     f.close()
                     return True
                 except Exception as error:
-                    print(f'error loading metadata from {self.h5filename}: {error}')
+                    print(f'error loading data from {self.h5filename}: {error}')
                     f.close()
                     return False
             else:
@@ -891,7 +891,7 @@ 
Source code for celltraj.trajectory
                 
 

 [docs]
-    def get_cell_features(self,function_tuple,indcells=None,imgchannel=0,mskchannel=0,use_fmask_for_intensity_image=False,fmskchannel=None,use_mask_for_intensity_image=False,bordersize=10,apply_contact_transform=False,return_feature_list=False,save_h5=False,overwrite=False,concatenate_features=False):
+    def get_cell_features(self,function_tuple,indcells=None,imgchannel=0,mskchannel=0,use_fmask_for_intensity_image=False,fmskchannel=None,use_mask_for_intensity_image=False,bordersize=10,apply_contact_transform=False,preprocess_functions=(None),return_feature_list=False,save_h5=False,overwrite=False,concatenate_features=False):
         """
         Extracts complex cell features based on the specified custom functions and imaging data.
         This method allows customization of the feature extraction process using region properties
@@ -987,6 +987,10 @@ 
Source code for celltraj.trajectory
                     if img.ndim==3:
                         for iz in range(img.shape[0]):
                             img[iz,...]=features.get_contact_boundaries(img[iz,...],radius=bordersize)
+                if preprocess_functions is not None:
+                    nfunc=len(preprocess_functions)
+                    for ifunc in range(nfunc):
+                        img=preprocess_functions[ifunc](img)
                 props = regionprops_table(msk, intensity_image=img,properties=('label',), extra_properties=function_tuple)
                 Xf_frame=np.zeros((props['label'].size,len(props.keys())))
                 for i,key in enumerate(props.keys()):
@@ -1419,7 +1423,8 @@ 
Source code for celltraj.trajectory
                     t_global=np.zeros(self.ndim)
                 t_local=utilities.get_tshift(centers0,centers1+t_global,dist_function,ntrans=ntrans,maxt=maxt,**dist_function_keys)
                 #t_local=self.get_minT(msk0,msk1,nt=self.ntrans,dt=self.maxtrans)
-                tSet[iS,:]=t_global+t_local
+                #tSet[iS,:]=t_global+t_local
+                tSet[iS,:]=t_global-t_local #changes 24sep24
                 if zscale is not None:
                     tSet[iS,0]=tSet[iS,0]/zscale
                 sys.stdout.write(f'frame {iS} translation {t_global+t_local}\n')
@@ -1996,7 +2001,7 @@ 
Source code for celltraj.trajectory
 
 

 [docs]
-    def get_unique_trajectories(self,cell_inds=None,verbose=False,extra_depth=None):
+    def get_unique_trajectories(self,cell_inds=None,verbose=False,extra_depth=None,save_h5=False,overwrite=False):
         """
         Computes unique trajectories for a set of cells over multiple frames, minimizing redundancy by
         ensuring that no two trajectories cover the same cell path beyond a specified overlap (extra_depth).
@@ -2061,7 +2066,10 @@ 
Source code for celltraj.trajectory
             else:
                 if n_untracked%100 == 0:
                     sys.stdout.write('tracked cell '+str(indc)+', '+str(cell_traj.size)+' tracks, '+str(n_untracked)+' left\n')
-        self.trajectories=trajectories

+        self.trajectories=trajectories
+        if save_h5:
+            attribute_list=['trajectories']
+            self.save_to_h5(f'/cell_data_m{self.mskchannel}/',attribute_list,overwrite=overwrite)

 
 
 

@@ -3307,6 +3315,408 @@ 
Source code for celltraj.trajectory
                 attribute_list=['linSet']
                 self.save_to_h5(f'/cell_data_m{self.mskchannel}/',attribute_list,overwrite=overwrite)
         return vals_fate,inds_fate

+
+
+

+[docs]
+    def get_border_properties_dict(self,iS,cell_states=None,secretion_rates=None,surfaces=None,surface_fmask_channels=None,surface_states_baseid=1000,add_label0_surf=True,make_cells_and_surfaces_disjoint=True,border_scale=None,border_resolution=None,vdist_scale=0.3,radius=2.0,order=1,visual=False,z_viz=None):
+        """
+        Calculate border properties for cells and surfaces at a given frame in a trajectory.
+
+        This function extracts data required to characterize the borders of cells within a specified 
+        frame (`iS`) in a 3D or 2D cell image sequence. The borders are characterized based on geometric 
+        and physical properties, with optional visualization and multipole moment expansion up to the 
+        specified `order`. Supports the inclusion of surface contacts, tracking cell borders over time, 
+        and calculating secreted ligand distributions.
+
+        Parameters
+        ----------
+        iS : int
+            Frame index within the trajectory for which to calculate border properties.
+
+        cell_states : ndarray, optional
+            Array of states for each cell in the current frame. If not provided, all cells are assigned 
+            state 1 by default.
+
+        secretion_rates : ndarray, optional
+            Array of secretion rates for each cell, used to calculate secreted ligand distribution 
+            along cell borders. If not provided, no secretion is considered.
+
+        surfaces : list of ndarrays, optional
+            List of binary masks for surfaces adjacent to cells (e.g., membranes or other boundaries). 
+            Surfaces are optionally assigned states via `surface_fmask_channels`.
+
+        surface_fmask_channels : list of int, optional
+            List of channels in the frame data specifying each surface's mask. Used if `surfaces` is 
+            None. Each channel mask is assigned a state from `surface_states_baseid` onward.
+
+        surface_states_baseid : int, optional
+            Base state ID for assigning states to surfaces. Defaults to 1000.
+
+        add_label0_surf : bool, optional
+            If True, includes cells labeled as 0 (background) as a surface layer. Default is True.
+
+        make_cells_and_surfaces_disjoint : bool, optional
+            If True, cells and surfaces are treated as mutually exclusive regions. Default is True.
+
+        border_scale : float or list of float, optional
+            Scale for border calculations. If None, it defaults to the `border_resolution` attribute 
+            if available.
+
+        border_resolution : float, optional
+            Resolution of the border calculations in units of pixels. If None, the value from the 
+            object's `border_resolution` attribute is used if present.
+
+        vdist_scale : float, optional
+            Scale factor for calculating the secreted ligand distribution. Default is 0.3.
+
+        radius : float, optional
+            Radius for identifying contact neighbors when calculating border properties. Default is 2.0.
+
+        order : int, optional
+            Order of multipole moments to compute for cell border properties. Default is 1.
+
+        visual : bool, optional
+            If True, generates a visualization of the borders, including displacements from the 
+            previous and next frames.
+
+        z_viz : int, optional
+            Specific z-plane to visualize in 3D data. If None, a central z-plane is chosen.
+
+        Returns
+        -------
+        border_dict : dict
+            Dictionary containing computed properties for cell borders, including:
+            
+            - `pts`: Coordinates of each border point.
+            - `index`: Cell IDs associated with each border point.
+            - `nn_states`: States of neighboring cells at each border point.
+            - `c`: Curvature at each border point.
+            - `global_index`: Global indices for each border point in the dataset.
+            - `border_dx_prev`, `border_dx_next`: Displacement vectors for borders to previous and 
+            next frames, if applicable.
+            - `contact_properties`: Multipole moment properties based on cell-state contacts.
+            - `vdist`: Secreted ligand density distribution, if `secretion_rates` is provided.
+
+        Notes
+        -----
+        - **Tracking**: If previous (`cell_labels_prev`) or next frame (`cell_labels_next`) is provided, 
+        the function calculates the optimal transport vector between consecutive frames for each cell.
+        - **Surface Assignment**: When `surface_fmask_channels` is provided, each channel is used to 
+        define a distinct surface mask, and `surface_states` are assigned based on `surface_states_baseid`.
+        - **Multipole Moments**: Higher-order moments are calculated based on border charges determined 
+        from neighboring cell states.
+
+        Examples
+        --------
+        Compute border properties for frame 10 with secreted ligand distribution and visualize:
+
+        >>> border_properties = self.get_border_properties_dict(
+        ...     iS=10, cell_states=my_cell_states, secretion_rates=my_secretion_rates,
+        ...     surface_fmask_channels=[0, 1], border_resolution=1.0, vdist_scale=0.5, order=2,
+        ...     visual=True, z_viz=15
+        ... )
+        """
+        if border_resolution is None and border_scale 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}')
+        if iS<np.max(self.cells_frameSet):
+            indt1=np.where(self.cells_indimgSet==iS+1)[0]
+            labelids_t1=self.cells_labelidSet[indt1]
+            cell_labels1=self.get_mask_data(iS+1)[...,self.mskchannel]
+            cell_labels1=imprep.transform_image(cell_labels1,self.tf_matrix_set[iS+1,...],inverse_tform=False,pad_dims=self.pad_dims) #changed from inverse_tform=True, 9may24
+        else:
+            cell_labels1=None
+        if iS>np.min(self.cells_frameSet):
+            indt0=np.where(self.cells_indimgSet==iS-1)[0]
+            labelids_t0=self.cells_labelidSet[indt0]
+            cell_labels0=self.get_mask_data(iS-1)[...,self.mskchannel]
+            cell_labels0=imprep.transform_image(cell_labels0,self.tf_matrix_set[iS-1,...],inverse_tform=False,pad_dims=self.pad_dims) #changed from inverse_tform=True, 9may24
+        else:
+            cell_labels0=None
+        indt_current=np.where(self.cells_indimgSet==iS)[0]
+        labelids_current=self.cells_labelidSet[indt_current]
+        cell_labels_current=self.get_mask_data(iS)[...,self.mskchannel]
+        cell_labels_current=imprep.transform_image(cell_labels_current,self.tf_matrix_set[iS,...],inverse_tform=False,pad_dims=self.pad_dims) #changed from inverse_tform=True, 9may24
+        max_cellid_current=np.max(cell_labels_current)
+        cell_ids_current=np.unique(cell_labels_current)
+        cell_ids_current=cell_ids_current[cell_ids_current>0]
+        if cell_states is None:
+            cell_states_current=np.ones_like(indt_current)
+        else:
+            cell_states_current=cell_states[indt_current]
+        cell_states_labelid=np.ones(max_cellid_current+1).astype(int)*-1
+        cell_states_labelid[0]=0
+        for ic in range(labelids_current.size):
+            ilabel=labelids_current[ic]
+            cell_states_labelid[ilabel]=cell_states_current[ic]
+        if surfaces is None:
+            if surface_fmask_channels is not None:
+                surfaces=[None]*len(surface_fmask_channels)
+                surface_label0=np.zeros_like(cell_labels_current).astype(bool)
+                for i_surf in range(len(surface_fmask_channels)):
+                    surfaces[i_surf]=self.get_fmask_data(iS,channel=surface_fmask_channels[surface_fmask_channels[i_surf]])
+                    if make_cells_and_surfaces_disjoint:
+                        surfaces[i_surf][cell_labels_current>0]=0
+                    surface_label0=np.logical_or(surface_label0,np.logical_not(surfaces[i_surf]))
+                if add_label0_surf:
+                    surfaces.append(np.logical_and(cell_labels_current==0,surface_label0))
+                n_surf=len(surfaces)
+                surface_states=np.arange(surface_states_baseid,surface_states_baseid+n_surf).astype(int)
+            else:
+                n_surf=0
+                surface_states=None
+        else:
+            n_surf=len(surfaces)
+            surface_states=np.ones(surface_states_baseid).astype(int)*-1
+            surface_states=np.append(surface_states,np.arange(surface_states_baseid,surface_states_baseid+n_surf).astype(int))
+        if iS<np.max(self.cells_frameSet):
+            lin1=self.linSet[iS+1]
+            lin_next = [[] for _ in range(max_cellid_current + 1)]
+            for ilabel in range(lin1.size):
+                if lin1[ilabel]>-1:
+                    i_next=labelids_t1[ilabel]
+                    i_current=labelids_current[lin1[ilabel]]
+                    lin_next[i_current].append(i_next)
+        else:
+            lin_next=None
+        if iS>np.min(self.cells_frameSet):
+            lin0=self.linSet[iS]
+            lin_prev = [[] for _ in range(max_cellid_current + 1)]
+            for ilabel in range(lin0.size):
+                if lin0[ilabel]>-1:
+                    i_current=labelids_current[ilabel]
+                    i_prev=labelids_t0[lin0[ilabel]]
+                    lin_prev[i_current].append(i_prev)
+        else:
+            lin_prev=None
+        if secretion_rates is not None:
+            secretion_rates_current=secretion_rates[indt_current]
+            secretion_rates_labelid=np.zeros(max_cellid_current+1).astype(int)
+            secretion_rates_labelid[0]=0
+            for ic in range(labelids_current.size):
+                ilabel=labelids_current[ic]
+                secretion_rates_labelid[ilabel]=secretion_rates_current[ic]
+            vdist=spatial.get_secreted_ligand_density(cell_labels_current,scale=vdist_scale,secretion_rate=secretion_rates_labelid,D=None,micron_per_pixel=border_resolution,visual=False)
+        else:
+            vdist=None
+        border_dict=spatial.get_border_properties(cell_labels_current,cell_states=cell_states_labelid,surfaces=surfaces,surface_states=surface_states,cell_labels_next=cell_labels1,lin_next=lin_next,cell_labels_prev=cell_labels0,lin_prev=lin_prev,vdist=vdist,border_scale=border_scale,order=order)
+        global_cellinds=np.ones(border_dict['pts'].shape[0]).astype(int)*-1
+        indcells=np.where(np.isin(border_dict['states'],np.unique(cell_states_current)))[0]
+        cellids_current_post=np.unique(border_dict['index'][indcells])
+        cellids_current_post=cellids_current_post[cellids_current_post>0]
+        for cellid in cellids_current_post:
+            indcell=np.where(border_dict['index']==cellid)[0]
+            ilabel=np.where(labelids_current==cellid)[0][0]
+            iglobal=indt_current[ilabel]
+            global_cellinds[indcell]=iglobal
+        border_dict['global_index']=global_cellinds
+        if visual:
+            if self.ndim==3:
+                ix=1;iy=2
+                if z_viz is None:
+                    z_viz=int((np.max(border_dict['pts'][:,0])-np.min(border_dict['pts'][:,0]))/2)
+                indz=np.where(np.logical_and(border_dict['pts'][:,0]>z_viz-1,border_dict['pts'][:,0]<z_viz+1))[0]
+            elif self.ndim==2:
+                ix=0;iy=1
+                indz=np.where(border_dict['pts'][:,0]<np.inf)[0]                
+            indcells=np.where(np.isin(border_dict['states'],np.unique(cell_states_current)))[0]
+            indpts=np.intersect1d(indz,indcells)
+            plt.close('all');plt.figure(figsize=(8,8))
+            plt.scatter(border_dict['pts'][indz,1],border_dict['pts'][indz,2],s=10,c=border_dict['states'][indz],cmap=plt.cm.Greens,alpha=.7)
+            plt.scatter(border_dict['pts'][indpts,1],border_dict['pts'][indpts,2],s=30,c=border_dict['c'][indpts],cmap=plt.cm.seismic,clim=(-3,3))
+            ax=plt.gca()
+            for ipt in range(indpts.size):
+                if iS>np.min(self.cells_frameSet):
+                    ax.arrow(border_dict['pts'][indpts[ipt],1],border_dict['pts'][indpts[ipt],2],border_dict['border_dx_prev'][indpts[ipt],1],border_dict['border_dx_prev'][indpts[ipt],2],color='gray',head_width=1,alpha=.8)
+                if iS<np.max(self.cells_frameSet):
+                    ax.arrow(border_dict['pts'][indpts[ipt],1],border_dict['pts'][indpts[ipt],2],border_dict['border_dx_next'][indpts[ipt],1],border_dict['border_dx_next'][indpts[ipt],2],color='black',head_width=1)
+            plt.axis('off')
+        return border_dict

+
+
+

+[docs]
+    def get_cellboundary_library(self,frames=None,indcells=None,secretion_rates=None,cell_states=None,surface_fmask_channels=[0],surface_states_baseid=None,border_resolution=1.0,vdist_scale=.3,visual=False,save_h5=False,overwrite=False,**border_property_args):
+        """
+        Collect and create a library of boundary properties for cells across specified frames in a trajectory.
+
+        This function gathers cell and surface boundary properties across frames or specific cell indices. 
+        It collects properties such as position, displacement, neighboring point states, curvatures, 
+        and secreted ligand distributions, if applicable. This data is returned in a library format 
+        and optionally saved to an HDF5 file for later use.
+
+        Parameters
+        ----------
+        frames : list of int, optional
+            Frame indices to include in the boundary library. If None, includes all frames in `cells_frameSet`.
+            
+        indcells : ndarray, optional
+            Array of specific cell indices to include in the library. If None, all cells are included.
+            
+        secretion_rates : ndarray, optional
+            Array of secretion rates for each cell, used to calculate ligand distribution along borders.
+            
+        cell_states : ndarray, optional
+            Array of cell states for each cell. If not provided, cells are assigned state 1.
+            
+        surface_fmask_channels : list of int, default=[0]
+            List of channels indicating each surface's mask in the frame data.
+            
+        surface_states_baseid : int, optional
+            Starting ID for surface states. Defaults to one more than the maximum `cell_states`.
+            
+        border_resolution : float, optional
+            Resolution for border properties in units of pixels. Default is 1.0.
+            
+        vdist_scale : float, optional
+            Scale for secreted ligand distribution. Default is 0.3.
+            
+        visual : bool, optional
+            If True, displays a visualization of the boundaries. Default is False.
+            
+        save_h5 : bool, optional
+            If True, saves the resulting boundary library to an HDF5 file. Default is False.
+            
+        overwrite : bool, optional
+            If True, overwrites the HDF5 data file if it exists. Default is False.
+            
+        **border_property_args
+            Additional arguments passed to the `get_border_properties_dict` function.
+
+        Returns
+        -------
+        boundary_library : dict
+            Dictionary containing collected boundary properties for the specified cells and frames:
+            
+            - `global_index`: Global cell indices for each boundary point.
+            - `states`: Cell or surface state at each boundary point.
+            - `pts`: Coordinates of each boundary point.
+            - `dx_prevs`, `dx_nexts`: Displacement vectors for boundaries to previous and next frames.
+            - `surface_normals`: Surface normal vectors at each boundary point.
+            - `curvatures`: Curvature at each boundary point.
+            - `nn_pts`: Coordinates of neighboring points.
+            - `nn_states`: States of neighboring points.
+            - `nn_pts_states`: States of neighboring points with finer resolution.
+            - `surf_vars`: Variance of surface displacement in the previous frame.
+            - `vdists`: Secreted ligand density distribution, if `secretion_rates` is provided.
+
+        Notes
+        -----
+        - **Frames and Cell Selection**: If both `frames` and `indcells` are provided, only frames derived from 
+        `indcells` are used.
+        - **Surfaces and States**: `surface_fmask_channels` and `surface_states_baseid` define and initialize 
+        surfaces in each frame, using additional states based on `surface_states_baseid`.
+        - **Ligand Distribution**: Secreted ligand density distribution (`vdists`) is included if `secretion_rates` 
+        is specified.
+
+        Examples
+        --------
+        Generate a cell boundary library for frames 10, 15, and 20, with visualization enabled:
+
+        >>> boundary_library = self.get_cellboundary_library(
+        ...     frames=[10, 15, 20], secretion_rates=my_secretion_rates, cell_states=my_cell_states,
+        ...     surface_fmask_channels=[0, 1], visual=True, border_resolution=2.0
+        ... )
+
+        Save the boundary library to HDF5 with overwrite enabled:
+
+        >>> boundary_library = self.get_cellboundary_library(
+        ...     indcells=my_indcells, save_h5=True, overwrite=True
+        ... )
+        """
+        if frames is None and indcells is None:
+            frames=np.unique(self.cells_frameSet)
+            indcells=np.arange(self.cells_indSet.size).astype(int)
+        elif frames is not None and indcells is None:
+            indcells=np.array([]).astype(int)
+            for iS in frames:
+                indcells=np.append(indcells,np.where(self.cells_frameSet==iS)[0])
+        elif indcells is not None:
+            if frames is not None:
+                print('both cell index and frames provided, using frames implied from cell index')
+            frames=np.unique(self.cells_frameSet[indcells])
+        if cell_states is None:
+            cell_states=np.ones(np.max(indcells)).astype(int)
+        if surface_states_baseid is None:
+            surface_states_baseid=np.max(cell_states)+1
+        global_index=[]
+        states=[]
+        pts=[]
+        dx_prevs=[]
+        dx_nexts=[]
+        surface_normals=[]
+        curvatures=[]
+        nn_pts=[]
+        nn_states=[]
+        nn_pts_states=[]
+        surf_vars=[]
+        if secretion_rates is not None:
+            vdists=[]
+        for iS in frames:
+            print(f'Extracting boundary library from frame {iS}')
+            indframe=np.where(self.cells_frameSet==iS)[0]
+            border_dict=self.get_border_properties_dict(iS,cell_states=cell_states,secretion_rates=secretion_rates,surface_fmask_channels=surface_fmask_channels,surface_states_baseid=surface_states_baseid,border_resolution=border_resolution,vdist_scale=vdist_scale,visual=visual)
+            if visual:
+                plt.show()
+            indcells_boundary=np.where(np.isin(border_dict['global_index'],indframe))[0]
+            global_index.append(border_dict['global_index'][indcells_boundary])
+            states.append(border_dict['states'][indcells_boundary])
+            pts.append(border_dict['pts'][indcells_boundary,:])
+            if iS>np.min(self.cells_frameSet):
+                dx_prevs.append(border_dict['border_dx_prev'][indcells_boundary,:])
+                surf_vars.append(border_dict['dh_prev'][indcells_boundary])
+            else:
+                dx_prevs.append(np.ones((indcells_boundary.size,self.ndim))*np.nan)
+                surf_vars.append(np.ones(indcells_boundary.size)*np.nan)
+            if iS<np.max(self.cells_frameSet):
+                dx_nexts.append(border_dict['border_dx_next'][indcells_boundary,:])
+            else:
+                dx_nexts.append(np.ones((indcells_boundary.size,self.ndim))*np.nan)
+            surface_normals.append(border_dict['n'][indcells_boundary,:])
+            curvatures.append(border_dict['c'][indcells_boundary])
+            nn_pts.append(border_dict['nn_pts'][indcells_boundary,:])
+            nn_states.append(border_dict['nn_states'][indcells_boundary])
+            nn_pts_states.append(border_dict['nn_pts_state'][indcells_boundary,:,:])
+            if secretion_rates is not None:
+                vdists.append(border_dict['vdist'][indcells_boundary])
+        boundary_library={'indcells':indcells,
+                        'cell_states':cell_states,
+                        'secretion_rates':secretion_rates,
+                        'surface_fmask_channels':surface_fmask_channels,
+                        'surface_states_baseid':surface_states_baseid,
+                        'border_resolution':border_resolution,
+                        'vdist_scale':vdist_scale}
+        boundary_library['global_index']=np.concatenate(global_index)
+        boundary_library['states']=np.concatenate(states)
+        boundary_library['pts']=np.vstack(pts)
+        boundary_library['dx_prevs']=np.vstack(dx_prevs)
+        boundary_library['dx_nexts']=np.vstack(dx_nexts)
+        boundary_library['surface_normals']=np.vstack(surface_normals)
+        boundary_library['curvatures']=np.concatenate(curvatures)
+        boundary_library['nn_pts']=np.vstack(nn_pts)
+        boundary_library['nn_pts_states']=np.vstack(nn_pts_states)
+        boundary_library['nn_states']=np.concatenate(nn_states)
+        boundary_library['surf_vars']=np.concatenate(surf_vars)
+        if secretion_rates is not None:
+            boundary_library['vdists']=np.concatenate(vdists)
+        if save_h5:
+            self.boundary_library=boundary_library
+            try:
+                attribute_list=['boundary_library']
+                data_written = self.save_to_h5(f'/cell_data_m{self.mskchannel}/',attribute_list,overwrite=overwrite)
+            except Exception as e:
+                print(f'error writing data, {e}')
+            return boundary_library
+        else:
+            return boundary_library

 

 
 

diff --git a/docs/_modules/celltraj/utilities.html b/docs/_modules/celltraj/utilities.html
index 8d6fe8e..d70b9fa 100644
--- a/docs/_modules/celltraj/utilities.html
+++ b/docs/_modules/celltraj/utilities.html
@@ -126,6 +126,13 @@ 
Source code for celltraj.utilities
     return probc1

 
 
+

+[docs]
+def rescale_to_int(img,maxint=2**16-1,dtype=np.uint16):
+    img=maxint*((img-np.min(img))/np.max(img-np.min(img)))
+    return img.astype(dtype)

+
+
 

 [docs]
 def colorbar(mappable):
@@ -242,7 +249,7 @@ 
Source code for celltraj.utilities
                 item = np.array(item).astype('|S32')
                 h5file[path + key] = item
             if not np.array_equal(h5file[path + key][()], item):
-                raise ValueError('The data representation in the HDF5 file does not match the original dict.')
+                print(f'{key}:{item} -- the data representation in the HDF5 file does not match the original dict.')
         # save dictionaries
         elif isinstance(item, dict):
             recursively_save_dict_contents_to_group(h5file, path + key + '/', item)
@@ -378,6 +385,8 @@ 
Source code for celltraj.utilities
 [docs]
 def get_tshift(centers1,centers2,dist_function,ntrans=100,maxt=10, **dist_function_keys):
     ndim=centers1.shape[1]
+    centers1=centers1[np.isfinite(np.sum(centers1,axis=1)),:]
+    centers2=centers2[np.isfinite(np.sum(centers2,axis=1)),:]
     if not isinstance(ntrans, (list,tuple,np.ndarray)):
         ntrans=[ntrans]*ndim
     if not isinstance(maxt, (list,tuple,np.ndarray)):
diff --git a/docs/api.html b/docs/api.html
index b7a6e83..88c3906 100644
--- a/docs/api.html
+++ b/docs/api.html
@@ -55,6 +55,7 @@
 
  • Trajectory.get_Xtraj_celltrajectory()
    • 
 
  • Trajectory.get_alpha()
    • 
 
  • Trajectory.get_beta()
    • 
+
  • Trajectory.get_border_properties_dict()
    • 
 
  • Trajectory.get_cell_blocks()
    • 
 
  • Trajectory.get_cell_channel_crosscorr()
    • 
 
  • Trajectory.get_cell_children()
    • 
@@ -70,6 +71,7 @@
 
  • Trajectory.get_cell_positions()
    • 
 
  • Trajectory.get_cell_sandwich()
    • 
 
  • Trajectory.get_cell_trajectory()
    • 
+
  • Trajectory.get_cellboundary_library()
    • 
 
  • Trajectory.get_cells_nchildren()
    • 
 
  • Trajectory.get_dx()
    • 
 
  • Trajectory.get_fmask_data()
    • 
@@ -159,6 +161,7 @@
 
  • organize_filelist_fov()
    • 
 
  • organize_filelist_time()
    • 
 
  • pad_image()
    • 
+
  • rescale_to_int()
    • 
 
  • save_for_viewing()
    • 
 
  • save_frame_h5()
    • 
 
  • transform_image()
    • 
@@ -196,6 +199,7 @@
 
  • get_LJ_force()
    • 
 
  • get_adhesive_displacement()
    • 
 
  • get_border_dict()
    • 
+
  • get_border_properties()
    • 
 
  • get_flux_displacement()
    • 
 
  • get_flux_ligdist()
    • 
 
  • get_labels_fromborderdict()
    • 
@@ -206,6 +210,7 @@
 
  • get_secreted_ligand_density()
    • 
 
  • get_surface_displacement()
    • 
 
  • get_surface_displacement_deviation()
    • 
+
  • get_surface_gradvariance()
    • 
 
  • get_surface_points()
    • 
 
  • get_volconstraint_com()
    • 
 
  • get_yukawa_force()
    • 
@@ -234,6 +239,7 @@
 
  • load_dict_from_h5()
    • 
 
  • recursively_load_dict_contents_from_group()
    • 
 
  • recursively_save_dict_contents_to_group()
    • 
+
  • rescale_to_int()
    • 
 
  • save_dict_to_h5()
    • 
 
 
@@ -442,6 +448,81 @@ 
    celltraj.trajectory
+
    
+get_border_properties_dict(iS, cell_states=None, secretion_rates=None, surfaces=None, surface_fmask_channels=None, surface_states_baseid=1000, add_label0_surf=True, make_cells_and_surfaces_disjoint=True, border_scale=None, border_resolution=None, vdist_scale=0.3, radius=2.0, order=1, visual=False, z_viz=None)[source]
    
+
    Calculate border properties for cells and surfaces at a given frame in a trajectory.
    
+
    This function extracts data required to characterize the borders of cells within a specified
+frame (iS) in a 3D or 2D cell image sequence. The borders are characterized based on geometric
+and physical properties, with optional visualization and multipole moment expansion up to the
+specified order. Supports the inclusion of surface contacts, tracking cell borders over time,
+and calculating secreted ligand distributions.
    
+
    
+
    Parameters:
    
+
      
+
    • iS (int) – Frame index within the trajectory for which to calculate border properties.
      • 
+
    • cell_states (ndarray, optional) – Array of states for each cell in the current frame. If not provided, all cells are assigned
+state 1 by default.
      • 
+
    • secretion_rates (ndarray, optional) – Array of secretion rates for each cell, used to calculate secreted ligand distribution
+along cell borders. If not provided, no secretion is considered.
      • 
+
    • surfaces (list of ndarrays, optional) – List of binary masks for surfaces adjacent to cells (e.g., membranes or other boundaries).
+Surfaces are optionally assigned states via surface_fmask_channels.
      • 
+
    • surface_fmask_channels (list of int, optional) – List of channels in the frame data specifying each surface’s mask. Used if surfaces is
+None. Each channel mask is assigned a state from surface_states_baseid onward.
      • 
+
    • surface_states_baseid (int, optional) – Base state ID for assigning states to surfaces. Defaults to 1000.
      • 
+
    • add_label0_surf (bool, optional) – If True, includes cells labeled as 0 (background) as a surface layer. Default is True.
      • 
+
    • make_cells_and_surfaces_disjoint (bool, optional) – If True, cells and surfaces are treated as mutually exclusive regions. Default is True.
      • 
+
    • border_scale (float or list of float, optional) – Scale for border calculations. If None, it defaults to the border_resolution attribute
+if available.
      • 
+
    • border_resolution (float, optional) – Resolution of the border calculations in units of pixels. If None, the value from the
+object’s border_resolution attribute is used if present.
      • 
+
    • vdist_scale (float, optional) – Scale factor for calculating the secreted ligand distribution. Default is 0.3.
      • 
+
    • radius (float, optional) – Radius for identifying contact neighbors when calculating border properties. Default is 2.0.
      • 
+
    • order (int, optional) – Order of multipole moments to compute for cell border properties. Default is 1.
      • 
+
    • visual (bool, optional) – If True, generates a visualization of the borders, including displacements from the
+previous and next frames.
      • 
+
    • z_viz (int, optional) – Specific z-plane to visualize in 3D data. If None, a central z-plane is chosen.
      • 
+
    
+
    
+
    Returns:
    
+
    border_dict – Dictionary containing computed properties for cell borders, including:
    
+
      
+
    • pts: Coordinates of each border point.
      • 
+
    • index: Cell IDs associated with each border point.
      • 
+
    • nn_states: States of neighboring cells at each border point.
      • 
+
    • c: Curvature at each border point.
      • 
+
    • global_index: Global indices for each border point in the dataset.
      • 
+
    • border_dx_prev, border_dx_next: Displacement vectors for borders to previous and
      • 
+
    
+
    next frames, if applicable.
+- contact_properties: Multipole moment properties based on cell-state contacts.
+- vdist: Secreted ligand density distribution, if secretion_rates is provided.
    
+
    
+
    
+
    Return type:
    
+
    dict
    
+
    
+
    
+
    Notes
    
+
      
+
    • Tracking: If previous (cell_labels_prev) or next frame (cell_labels_next) is provided,
      • 
+
    
+
    the function calculates the optimal transport vector between consecutive frames for each cell.
+- Surface Assignment: When surface_fmask_channels is provided, each channel is used to
+define a distinct surface mask, and surface_states are assigned based on surface_states_baseid.
+- Multipole Moments: Higher-order moments are calculated based on border charges determined
+from neighboring cell states.
    
+
    Examples
    
+
    Compute border properties for frame 10 with secreted ligand distribution and visualize:
    
+
    >>> border_properties = self.get_border_properties_dict(
+...     iS=10, cell_states=my_cell_states, secretion_rates=my_secretion_rates,
+...     surface_fmask_channels=[0, 1], border_resolution=1.0, vdist_scale=0.5, order=2,
+...     visual=True, z_viz=15
+... )
+
    
+
    
+
    
+
 
    
 
    
 get_cell_blocks(label, return_label_ids=False)[source]
    
@@ -614,7 +695,7 @@ 
    celltraj.trajectory
 
    
-get_cell_features(function_tuple, indcells=None, imgchannel=0, mskchannel=0, use_fmask_for_intensity_image=False, fmskchannel=None, use_mask_for_intensity_image=False, bordersize=10, apply_contact_transform=False, return_feature_list=False, save_h5=False, overwrite=False, concatenate_features=False)[source]
    
+get_cell_features(function_tuple, indcells=None, imgchannel=0, mskchannel=0, use_fmask_for_intensity_image=False, fmskchannel=None, use_mask_for_intensity_image=False, bordersize=10, apply_contact_transform=False, preprocess_functions=None, return_feature_list=False, save_h5=False, overwrite=False, concatenate_features=False)[source]
 
    Extracts complex cell features based on the specified custom functions and imaging data.
 This method allows customization of the feature extraction process using region properties
 from segmented cell data, with optional transformations like border erosion or contact
@@ -869,6 +950,77 @@ 
    celltraj.trajectory
+
    
+get_cellboundary_library(frames=None, indcells=None, secretion_rates=None, cell_states=None, surface_fmask_channels=[0], surface_states_baseid=None, border_resolution=1.0, vdist_scale=0.3, visual=False, save_h5=False, overwrite=False, **border_property_args)[source]
    
+
    Collect and create a library of boundary properties for cells across specified frames in a trajectory.
    
+
    This function gathers cell and surface boundary properties across frames or specific cell indices.
+It collects properties such as position, displacement, neighboring point states, curvatures,
+and secreted ligand distributions, if applicable. This data is returned in a library format
+and optionally saved to an HDF5 file for later use.
    
+
    
+
    Parameters:
    
+
      
+
    • frames (list of int, optional) – Frame indices to include in the boundary library. If None, includes all frames in cells_frameSet.
      • 
+
    • indcells (ndarray, optional) – Array of specific cell indices to include in the library. If None, all cells are included.
      • 
+
    • secretion_rates (ndarray, optional) – Array of secretion rates for each cell, used to calculate ligand distribution along borders.
      • 
+
    • cell_states (ndarray, optional) – Array of cell states for each cell. If not provided, cells are assigned state 1.
      • 
+
    • surface_fmask_channels (list of int, default [0]) – List of channels indicating each surface’s mask in the frame data.
      • 
+
    • surface_states_baseid (int, optional) – Starting ID for surface states. Defaults to one more than the maximum cell_states.
      • 
+
    • border_resolution (float, optional) – Resolution for border properties in units of pixels. Default is 1.0.
      • 
+
    • vdist_scale (float, optional) – Scale for secreted ligand distribution. Default is 0.3.
      • 
+
    • visual (bool, optional) – If True, displays a visualization of the boundaries. Default is False.
      • 
+
    • save_h5 (bool, optional) – If True, saves the resulting boundary library to an HDF5 file. Default is False.
      • 
+
    • overwrite (bool, optional) – If True, overwrites the HDF5 data file if it exists. Default is False.
      • 
+
    • **border_property_args – Additional arguments passed to the get_border_properties_dict function.
      • 
+
    
+
    
+
    Returns:
    
+
    boundary_library – Dictionary containing collected boundary properties for the specified cells and frames:
    
+
      
+
    • global_index: Global cell indices for each boundary point.
      • 
+
    • states: Cell or surface state at each boundary point.
      • 
+
    • pts: Coordinates of each boundary point.
      • 
+
    • dx_prevs, dx_nexts: Displacement vectors for boundaries to previous and next frames.
      • 
+
    • surface_normals: Surface normal vectors at each boundary point.
      • 
+
    • curvatures: Curvature at each boundary point.
      • 
+
    • nn_pts: Coordinates of neighboring points.
      • 
+
    • nn_states: States of neighboring points.
      • 
+
    • nn_pts_states: States of neighboring points with finer resolution.
      • 
+
    • surf_vars: Variance of surface displacement in the previous frame.
      • 
+
    • vdists: Secreted ligand density distribution, if secretion_rates is provided.
      • 
+
    
+
    
+
    
+
    Return type:
    
+
    dict
    
+
    
+
    
+
    Notes
    
+
      
+
    • Frames and Cell Selection: If both frames and indcells are provided, only frames derived from
      • 
+
    
+
    indcells are used.
+- Surfaces and States: surface_fmask_channels and surface_states_baseid define and initialize
+surfaces in each frame, using additional states based on surface_states_baseid.
+- Ligand Distribution: Secreted ligand density distribution (vdists) is included if secretion_rates
+is specified.
    
+
    Examples
    
+
    Generate a cell boundary library for frames 10, 15, and 20, with visualization enabled:
    
+
    >>> boundary_library = self.get_cellboundary_library(
+...     frames=[10, 15, 20], secretion_rates=my_secretion_rates, cell_states=my_cell_states,
+...     surface_fmask_channels=[0, 1], visual=True, border_resolution=2.0
+... )
+
    
+
    
+
    Save the boundary library to HDF5 with overwrite enabled:
    
+
    >>> boundary_library = self.get_cellboundary_library(
+...     indcells=my_indcells, save_h5=True, overwrite=True
+... )
+
    
+
    
+
    
+
 
    
 
    
 get_cells_nchildren()[source]
    
@@ -1631,7 +1783,7 @@ 
    celltraj.trajectory
 
    
-get_unique_trajectories(cell_inds=None, verbose=False, extra_depth=None)[source]
    
+get_unique_trajectories(cell_inds=None, verbose=False, extra_depth=None, save_h5=False, overwrite=False)[source]
 
    Computes unique trajectories for a set of cells over multiple frames, minimizing redundancy by
 ensuring that no two trajectories cover the same cell path beyond a specified overlap (extra_depth).
    
 
    
@@ -3800,6 +3952,11 @@ 
    celltraj.imageprep
+
    
+celltraj.imageprep.rescale_to_int(img, maxint=65535, dtype=<class 'numpy.uint16'>)[source]
    
+
    
+
 
    
 
    
 celltraj.imageprep.save_for_viewing(data, fname, metadata=None, overwrite=False)[source]
    
@@ -4938,7 +5095,7 @@ 
    celltraj.spatial
 
    
-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]
    
+celltraj.spatial.get_border_dict(labels, states=None, radius=10, vdist=None, return_nnindex=True, return_nnvector=True, return_curvature=True, nn_states=None, 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.
    
@@ -4949,8 +5106,7 @@ 
    celltraj.spatialndarray, 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.
    • 
+
  • vdist (ndarray, optional) – An array representing a scalar value in the image, such as estimated ligand concentration, to store in the border point dictionary.
    • 
 
  • 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).
    • 
@@ -4994,6 +5150,76 @@ 
    celltraj.spatial
+
    
+celltraj.spatial.get_border_properties(cell_labels, surfaces=None, cell_states=None, surface_states=None, cell_labels_next=None, cell_labels_prev=None, lin_next=None, lin_prev=None, vdist=None, border_scale=1.0, radius=2.0, order=1, return_border_properties_list=False)[source]
    
+
    Calculate geometric and physical properties of cell borders, with optional tracking between frames
+and surface contact details.
    
+
    This function computes properties of cell borders based on cell_labels, surface contacts, and
+multipole moments up to a specified order. It supports tracking cell border displacement across
+frames using optional next and previous frame labels (cell_labels_next, cell_labels_prev).
+Outputs include multipole moments and border displacements useful in characterizing cell interactions
+and tracking cell behaviors.
    
+
    
+
    Parameters:
    
+
      
+
    • cell_labels (ndarray) – Array of labels identifying distinct cells within the domain.
      • 
+
    • surfaces (list of ndarrays, optional) – List of binary masks indicating surface layers adjacent to cells (e.g., membranes or other
+boundaries). Each is assigned a unique identifier.
      • 
+
    • cell_states (ndarray of int, optional) – Array specifying a state for each cell in cell_labels. Defaults to 1 if not provided.
      • 
+
    • surface_states (ndarray of int, optional) – Array of states corresponding to each surface in surfaces. These states must not overlap
+with cell_states.
      • 
+
    • cell_labels_next (ndarray, optional) – Label array for the next frame to track cell borders over time.
      • 
+
    • cell_labels_prev (ndarray, optional) – Label array for the previous frame, used for backward tracking of cells.
      • 
+
    • lin_next (list of lists, optional) – Mapping of cells in cell_labels to corresponding cells in cell_labels_next. Each entry
+contains IDs of linked cells in the next frame.
      • 
+
    • lin_prev (list of lists, optional) – Mapping of cells in cell_labels to corresponding cells in cell_labels_prev, used for
+backward tracking.
      • 
+
    • border_scale (float, optional) – Resolution in microns applied to border point extraction (default is 1.0).
      • 
+
    • radius (float, optional) – Radius in pixels for calculating border properties such as contact neighbors (default is 2.0).
      • 
+
    • order (int, optional) – Order of multipole moments to calculate for each border contact (default is 1).
      • 
+
    • return_border_properties_list (bool, optional) – If True, returns a list of property names for multipole moments alongside the border
+dictionary.
      • 
+
    
+
    
+
    Returns:
    
+
      
+
    • border_dict (dict) – Dictionary containing calculated properties of each cell’s border, including:
      
+
        
+
      • pts : Array of border points.
        • 
+
      • index : Label of each cell at the border.
        • 
+
      • nn_states : Neighboring cell states at each border point.
        • 
+
      • contact_properties : Array of multipole moment properties calculated up to order.
        • 
+
      • border_dx_next, border_dx_prev : Displacements of each border in the next and previous
+frames, if applicable.
        • 
+
      • dx_next_properties, dx_prev_properties : Arrays of multipole moments based on border
+displacement for next and previous frames.
        • 
+
      
+
      • 
+
    • border_properties_list (list of str, optional) – List of property names for contact_properties array if return_border_properties_list
+is True.
      • 
+
    
+
    
+
    
+
    
+
    Notes
    
+
      
+
    • Multipole Moment Calculation: Moments up to the specified order are computed based on
+border charges (representing contact fraction) for each border point.
      • 
+
    • Cell Tracking: When provided with cell_labels_next or cell_labels_prev, the function
+calculates optimal transport (OT) vectors to track border movement between frames.
      • 
+
    
+
    Examples
    
+
    Calculate properties of cell borders and multipole moments with next-frame tracking:
    
+
    >>> border_dict, property_list = get_border_properties(
+...     cell_labels=my_cell_labels, surfaces=my_surfaces, cell_states=my_states,
+...     cell_labels_next=my_next_frame_labels, lin_next=my_next_frame_links,
+...     border_scale=0.8, radius=1.5, order=2, return_border_properties_list=True
+... )
+
    
+
    
+
    
+
 
    
 
    
 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]
    
@@ -5444,9 +5670,14 @@ 
    celltraj.spatial
+
    
+celltraj.spatial.get_surface_gradvariance(border_pts, dx_ot, knn=12, use_eigs=False)[source]
    
+
    
+
 
    
 
    
-celltraj.spatial.get_surface_points(msk, return_normals=False, return_curvature=False, knn=20)[source]
    
+celltraj.spatial.get_surface_points(msk, return_normals=False, return_curvature=False, knn=20, rscale=0.1)[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.
    
@@ -5776,6 +6007,11 @@ 
    celltraj.utilitiescelltraj.utilities.recursively_save_dict_contents_to_group(h5file, path, dic)[source]
 
    
 
+
    
+
    
+celltraj.utilities.rescale_to_int(img, maxint=65535, dtype=<class 'numpy.uint16'>)[source]
    
+
    
+
 
    
 
    
 celltraj.utilities.save_dict_to_h5(dic, h5file, path)[source]
    
diff --git a/docs/build/doctrees/api.doctree b/docs/build/doctrees/api.doctree
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         labels_inv=-1*labels_inv
     contact_msk=get_contact_boundaries(labels0,boundary_only=True,radius=radius)
     contact_labels=np.zeros_like(labels0)
-    for i in np.unique(labels0[labels0>0]):
+    for i in np.flip(np.unique(labels0[labels0>0])):
         indi=np.where(np.logical_and(labels0==i,contact_msk))
         jset1=np.unique(labels[indi])
         jset2=np.unique(labels_inv[indi])
         jset=np.unique(np.concatenate((jset1,jset2)))
         jset=np.setdiff1d(jset,[i])
-        for j in jset:
+        for j in np.flip(jset):
             if labels0.ndim==2:
                 contact_labels[indi[0][labels[indi]==j],indi[1][labels[indi]==j]]=j
                 contact_labels[indi[0][labels_inv[indi]==j],indi[1][labels_inv[indi]==j]]=j
diff --git a/docs/build/html/_modules/celltraj/imageprep.html b/docs/build/html/_modules/celltraj/imageprep.html
index 1265415..ba61893 100644
--- a/docs/build/html/_modules/celltraj/imageprep.html
+++ b/docs/build/html/_modules/celltraj/imageprep.html
@@ -307,6 +307,13 @@ 
    Source code for celltraj.imageprep
         return img
    
 
 
+
    
+[docs]
+def rescale_to_int(img,maxint=2**16-1,dtype=np.uint16):
+    img=maxint*((img-np.min(img))/np.max(img-np.min(img)))
+    return img.astype(dtype)
    
+
+
 
    
 [docs]
 def histogram_stretch(img,lp=1,hp=99):
diff --git a/docs/build/html/_modules/celltraj/spatial.html b/docs/build/html/_modules/celltraj/spatial.html
index f2e79cd..c7bc677 100644
--- a/docs/build/html/_modules/celltraj/spatial.html
+++ b/docs/build/html/_modules/celltraj/spatial.html
@@ -91,10 +91,11 @@ 
    Source code for celltraj.spatial
     import pandas as pd
 from pyntcloud import PyntCloud
 import ot
+from multipoles import MultipoleExpansion
 
 
    
 [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):
+def get_border_dict(labels,states=None,radius=10,vdist=None,return_nnindex=True,return_nnvector=True,return_curvature=True,nn_states=None,scale=None,**border_args):
     """
     Computes the border properties of labeled regions in a segmented image.
 
@@ -111,8 +112,7 @@ 
    Source code for celltraj.spatial
         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.
+        An array representing a scalar value in the image, such as estimated ligand concentration, to store in the border point dictionary.
     return_nnindex : bool, optional
         If True, returns the nearest neighbor index for each border point (default is True).
     return_nnvector : bool, optional
@@ -160,6 +160,8 @@ 
    Source code for celltraj.spatial
             scale=np.ones(labels.ndim)
     else:
         labels=scipy.ndimage.zoom(labels,zoom=scale,order=0)
+        if vdist is not None:
+            vdist=scipy.ndimage.zoom(vdist,zoom=scale)
     labels=labels.astype(int)
     border_dict={}
     border_dict['scale']=scale
@@ -178,16 +180,16 @@ 
    Source code for celltraj.spatial
             contact_inds=contact_labels[border>0]
         border_dict['nn_index']=contact_inds
         border_dict['nn_states']=states[contact_inds]
-    if return_nnvector:
+    if return_nnvector or nn_states is not None:
         iset=np.unique(labels)
         iset=iset[iset>0]
-        nn_labels=[None]*(np.max(iset)+1)
+        knn_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]])
+            knn_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])
@@ -195,13 +197,40 @@ 
    Source code for celltraj.spatial
                 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)
+                distij,indij_nn=knn_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 nn_states is not None:
+            border_nn_pts_state=np.ones((border_pts.shape[0],np.max(states)+1,border_pts.shape[1]))*np.nan
+            border_nn_inds_state=np.ones((border_pts.shape[0],np.max(states)+1)).astype(int)*-1
+            #knn_states=[None]*(np.max(states)+1)
+            inds_states=[None]*(np.max(states)+1)
+            iset=np.where(np.isin(states,nn_states))[0]
+            stateset=np.unique(states)
+            for istate in stateset:
+                inds_states[istate]=np.where(border_dict['states']==istate)[0]
+                #if inds_states[istate].size>0:
+                    #knn_states[istate] = sklearn.neighbors.NearestNeighbors(n_neighbors=1, radius=1.,algorithm='ball_tree').fit(border_pts[inds_states[istate]])
+            for i in iset:
+                print(f'getting nn points for cell {i}')
+                indi=inds_labels[i]
+                if indi is not None:
+                    for istate in stateset:
+                        indistate=np.setdiff1d(inds_states[istate],indi)
+                        if indistate.size>0:
+                            knn_states = sklearn.neighbors.NearestNeighbors(n_neighbors=1, radius=1.,algorithm='ball_tree').fit(border_pts[indistate])
+                            borderij_pts=border_pts[indi]
+                            distij,indij_nn=knn_states.kneighbors(borderij_pts)
+                            indij_nn=np.squeeze(indij_nn)
+                            borderj_pts=border_pts[indistate[indij_nn]]
+                            border_nn_pts_state[indi,istate,:]=borderj_pts
+                            border_nn_inds_state[indi,istate]=indistate[indij_nn]
+            border_dict['nn_pts_state']=border_nn_pts_state
+            border_dict['nn_inds_state']=border_nn_inds_state
     if return_curvature:
         n=np.zeros_like(border_pts)
         c=np.zeros(border_pts.shape[0])
@@ -213,6 +242,7 @@ 
    Source code for celltraj.spatial
             iset=iset[iset>0]
         for i in iset:
             msk=labels==i
+            print(f'label {i}, pixels {np.sum(msk)}')
             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
@@ -227,7 +257,7 @@ 
    Source code for celltraj.spatial
     
 
    
 [docs]
-def get_surface_points(msk,return_normals=False,return_curvature=False,knn=20):
+def get_surface_points(msk,return_normals=False,return_curvature=False,knn=20,rscale=.1):
     """
     Computes the surface points of a labeled mask and optionally calculates normals and curvature.
 
@@ -279,30 +309,55 @@ 
    Source code for celltraj.spatial
         border=skimage.segmentation.find_boundaries(msk,mode='inner')
     ind=np.where(border)
     border_pts=np.array(ind).astype(float).T
+    npts=border_pts.shape[0]
+    if knn>int(npts/4):
+        knn=int(npts/4)
+        print(f'adjusted knn: {knn} npts: {npts}')
     if return_normals or return_curvature:
-        cloud=PyntCloud(pd.DataFrame(data=border_pts,columns=['x','y','z']))
+        if msk.ndim==3:
+            rand_dx=np.array([np.random.normal(loc=0.,scale=rscale,size=npts),np.random.normal(loc=0.,scale=rscale,size=npts),np.random.normal(loc=0.,scale=rscale,size=npts)]).T
+        elif msk.ndim==2:
+            rand_dx=np.array([np.random.normal(loc=0.,scale=rscale,size=npts),np.random.normal(loc=0.,scale=rscale,size=npts),np.zeros(npts)]).T
+            border_pts=np.concatenate((border_pts,np.zeros((border_pts.shape[0],1))),axis=1)
+        cloud=PyntCloud(pd.DataFrame(data=border_pts+rand_dx,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,:]
+        if msk.ndim==3:
+            border_pts_trans=border_pts+2.*v[:,2,:]
+        if msk.ndim==2:
+            border_pts_trans=border_pts+2.*v[:,1,:]
         ind_trans=border_pts_trans.astype(int)
-        for iax in range(border_pts.shape[1]):
+        for iax in range(msk.ndim): #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,:]
+        if msk.ndim==3:
+            infacing_normals=msk[ind_trans[:,0],ind_trans[:,1],ind_trans[:,2]]
+            n = v[:,2,:]
+        elif msk.ndim==2:
+            infacing_normals=msk[ind_trans[:,0],ind_trans[:,1]]
+            n = v[:,1,:]
         n[infacing_normals,:]=-1.*n[infacing_normals,:]
         if return_curvature:
-            c=np.divide(w[:,2],np.sum(w,axis=1))
+            if msk.ndim==3:
+                c=np.divide(w[:,2],np.sum(w,axis=1))
+            elif msk.ndim==2:
+                c=np.divide(w[:,1],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
+            if msk.ndim==3:
+                return border_pts,n,c
+            elif msk.ndim==2:
+                return border_pts[:,0:2],n[:,0:2],c
         else:
-            return border_pts,n
+            if msk.ndim==3:
+                return border_pts,n
+            elif msk.ndim==2:
+                return border_pts[:,0:2],n[:,0:2]
     else:
         return border_pts
    
 
@@ -484,6 +539,24 @@ 
    Source code for celltraj.spatial
         return dx
    
 
 
+
    
+[docs]
+def get_surface_gradvariance(border_pts,dx_ot,knn=12,use_eigs=False):
+    rdx_ot = np.linalg.norm(dx_ot,axis=1)
+    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)
+    return dh
    
+
+
 
    
 [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):
@@ -1380,7 +1453,6 @@ 
    Source code for celltraj.spatial
             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.
@@ -1449,6 +1521,251 @@ 
    Source code for celltraj.spatial
         return fmeans,fsigmas
    
 
 
+
    
+[docs]
+def get_border_properties(cell_labels,surfaces=None,cell_states=None,surface_states=None,cell_labels_next=None,cell_labels_prev=None,lin_next=None,lin_prev=None,vdist=None,border_scale=1.0,radius=2.0,order=1,return_border_properties_list=False):
+    """
+    Calculate geometric and physical properties of cell borders, with optional tracking between frames 
+    and surface contact details.
+
+    This function computes properties of cell borders based on `cell_labels`, surface contacts, and 
+    multipole moments up to a specified order. It supports tracking cell border displacement across 
+    frames using optional next and previous frame labels (`cell_labels_next`, `cell_labels_prev`). 
+    Outputs include multipole moments and border displacements useful in characterizing cell interactions 
+    and tracking cell behaviors.
+
+    Parameters
+    ----------
+    cell_labels : ndarray
+        Array of labels identifying distinct cells within the domain.
+    
+    surfaces : list of ndarrays, optional
+        List of binary masks indicating surface layers adjacent to cells (e.g., membranes or other 
+        boundaries). Each is assigned a unique identifier.
+
+    cell_states : ndarray of int, optional
+        Array specifying a state for each cell in `cell_labels`. Defaults to 1 if not provided.
+    
+    surface_states : ndarray of int, optional
+        Array of states corresponding to each surface in `surfaces`. These states must not overlap 
+        with `cell_states`.
+
+    cell_labels_next : ndarray, optional
+        Label array for the next frame to track cell borders over time.
+    
+    cell_labels_prev : ndarray, optional
+        Label array for the previous frame, used for backward tracking of cells.
+    
+    lin_next : list of lists, optional
+        Mapping of cells in `cell_labels` to corresponding cells in `cell_labels_next`. Each entry 
+        contains IDs of linked cells in the next frame.
+    
+    lin_prev : list of lists, optional
+        Mapping of cells in `cell_labels` to corresponding cells in `cell_labels_prev`, used for 
+        backward tracking.
+
+    border_scale : float, optional
+        Resolution in microns applied to border point extraction (default is 1.0).
+    
+    radius : float, optional
+        Radius in pixels for calculating border properties such as contact neighbors (default is 2.0).
+    
+    order : int, optional
+        Order of multipole moments to calculate for each border contact (default is 1).
+    
+    return_border_properties_list : bool, optional
+        If True, returns a list of property names for multipole moments alongside the border 
+        dictionary.
+
+    Returns
+    -------
+    border_dict : dict
+        Dictionary containing calculated properties of each cell's border, including:
+        
+        - `pts` : Array of border points.
+        - `index` : Label of each cell at the border.
+        - `nn_states` : Neighboring cell states at each border point.
+        - `contact_properties` : Array of multipole moment properties calculated up to `order`.
+        - `border_dx_next`, `border_dx_prev` : Displacements of each border in the next and previous 
+          frames, if applicable.
+        - `dx_next_properties`, `dx_prev_properties` : Arrays of multipole moments based on border 
+          displacement for next and previous frames.
+    
+    border_properties_list : list of str, optional
+        List of property names for `contact_properties` array if `return_border_properties_list` 
+        is True.
+
+    Notes
+    -----
+    - **Multipole Moment Calculation**: Moments up to the specified `order` are computed based on 
+      border charges (representing contact fraction) for each border point.
+    - **Cell Tracking**: When provided with `cell_labels_next` or `cell_labels_prev`, the function 
+      calculates optimal transport (OT) vectors to track border movement between frames.
+
+    Examples
+    --------
+    Calculate properties of cell borders and multipole moments with next-frame tracking:
+
+    >>> border_dict, property_list = get_border_properties(
+    ...     cell_labels=my_cell_labels, surfaces=my_surfaces, cell_states=my_states,
+    ...     cell_labels_next=my_next_frame_labels, lin_next=my_next_frame_links, 
+    ...     border_scale=0.8, radius=1.5, order=2, return_border_properties_list=True
+    ... )
+
+    """
+    labels=cell_labels.copy()
+    max_cellid=np.max(labels)
+    cell_ids=np.unique(labels)
+    cell_ids=cell_ids[cell_ids>0]
+    n_moments=sum(2 * l + 1 for l in range(order + 1))
+    if cell_states is None:
+        cell_states=[1]*max_cellid
+        cell_states=np.array(cell_states).astype(int)
+        cell_states=np.insert(cell_states,0,0)
+    max_cell_state=np.max(cell_states)
+    if surfaces is None:
+        n_surf=0
+    else:
+        n_surf=len(surfaces)
+        surface_ids=(np.arange(n_surf)+max_cellid+1).astype(int)
+        if surface_states is None:
+            surface_states=(np.arange(n_surf)+max_cell_state+1).astype(int)
+        else:
+            if np.intersect1d(cell_states,surface_states).size>0:
+                print('Conflicting labels between cell states and surface states provided')
+        nn_states=cell_states.copy()
+        cell_states=np.append(cell_states,surface_states)
+        surface_overlay=surfaces[0].copy()
+        for i_surf in range(n_surf):
+            overlapping=np.sum(np.logical_and(labels>0,surfaces[i_surf]))/np.sum(labels>0)
+            if overlapping>0:
+                print(f'Warning: {overlapping:.2e} fraction overlap between surface {i_surf} and cell labels, cell labels will overlay surface labels')
+            if i_surf>0:
+                overlapping_surf=np.sum(np.logical_and(surfaces[i_surf],surface_overlay))/np.sum(surfaces[i_surf])
+                if overlapping_surf>0.:
+                    print(f'Warning: {overlapping_surf:.2e} fraction overlap between surface {i_surf} and previous surfaces, {i_surf} will overlay')
+            surface_overlay=np.logical_or(surface_overlay,surfaces[i_surf])
+            labels[surfaces[i_surf]]=surface_ids[i_surf]
+    border_dict=get_border_dict(labels,states=cell_states,nn_states=nn_states,vdist=vdist,radius=radius,scale=border_scale)
+    contact_properties=np.ones((max_cellid+1,np.max(cell_states)+1,1+n_moments))*np.nan
+    border_properties_list=[None]*(1+n_moments)
+    border_properties_list[0]='total'
+    i_prop=1
+    for l in range(order+1):
+        for m in range(-l,l+1):
+            border_properties_list[i_prop]=f'({l},{m})'
+            i_prop=i_prop+1
+    for ic in range(cell_ids.size):
+        cellid=cell_ids[ic]
+        indcell=np.where(border_dict['index']==cellid)[0]
+        border_pts=border_dict['pts'][indcell,:]
+        border_nn_states=border_dict['nn_states'][indcell]
+        if indcell.size>0:
+            for i_state in np.unique(cell_states[cell_states>0]):
+                indcell_surf=np.where(border_nn_states==i_state)[0]
+                if indcell_surf.size>0:
+                    border_charge=np.ones(indcell.size)*-1
+                    border_charge[indcell_surf]=1
+                    av_surf_fraction=indcell_surf.size/indcell.size
+                    print(f'cell {cellid} state {i_state} contact fraction {av_surf_fraction:.2f}')
+                    charges_list = [{'q': q, 'xyz': tuple(border_pts)} for q, border_pts in zip(border_charge, border_pts)]
+                    charge_dist = {'discrete': True, 'charges': charges_list}
+                    Phi = MultipoleExpansion(charge_dist, order)
+                    contact_properties[cellid,i_state,0]=indcell.size
+                    i_prop=1
+                    for l in range(order+1):
+                        for m in range(-l,l+1):
+                            contact_properties[cellid,i_state,i_prop]=Phi.multipole_moments[l,m]
+                            i_prop=i_prop+1
+                else:
+                    print(f'cell {cellid} state {i_state} contact fraction {0.0:.2f}')
+    border_dict['contact_properties']=contact_properties
+    border_dict['border_properties_list']=border_properties_list
+    if cell_labels_next is not None:
+        dx_next_properties=np.ones((max_cellid+1,1+n_moments))*np.nan
+        border_dict_next=get_border_dict(cell_labels_next,states=None,scale=border_scale,return_nnindex=False,return_nnvector=False,return_curvature=False)
+        cell_ids_next=np.unique(cell_labels_next)
+        cell_ids_next=cell_ids_next[cell_ids_next>0]
+        max_cellid_next=np.max(cell_ids_next)
+        if lin_next is None:
+            print('no tracking provided, matching cells by ID')
+            lin_next = [[] for _ in range(cell_ids.size + 1)]
+            for cellid in cell_ids:
+                if np.isin(cellid,cell_ids_next):
+                    lin_next[cellid].append(cellid)
+        border_dx_next=np.zeros_like(border_dict['pts'])
+        inds_ot_next=np.ones(border_dict['pts'].shape[0]).astype(int)*-1
+        for ic in range(cell_ids.size):
+            cellid=cell_ids[ic]
+            if len(lin_next[cellid])>0:
+                indcell=np.where(border_dict['index']==cellid)[0]
+                border_pts=border_dict['pts'][indcell,:]
+                indcell_next=np.where(np.isin(border_dict_next['index'],lin_next[cellid]))[0]
+                if indcell_next.size>0:
+                    border_pts_next=border_dict_next['pts'][indcell_next,:]
+                    inds_ot,dx_ot=get_ot_dx(border_pts,border_pts_next,return_dx=True,return_cost=False)
+                    border_dx_next[indcell,:]=-dx_ot
+                    inds_ot_next[indcell]=indcell_next[inds_ot]
+                    dxc=np.sum(np.multiply(-dx_ot,border_dict['n'][indcell,:]),axis=1)
+                    border_charge=dxc
+                    charges_list = [{'q': q, 'xyz': tuple(border_pts)} for q, border_pts in zip(border_charge, border_pts)]
+                    charge_dist = {'discrete': True, 'charges': charges_list}
+                    Phi = MultipoleExpansion(charge_dist, order)
+                    dx_next_properties[cellid,0]=indcell.size
+                    i_prop=1
+                    for l in range(order+1):
+                        for m in range(-l,l+1):
+                            dx_next_properties[cellid,i_prop]=Phi.multipole_moments[l,m]
+                            i_prop=i_prop+1
+        border_dict['dh_next']=get_surface_gradvariance(border_dict['pts'],border_dx_next)
+        border_dict['border_dx_next']=border_dx_next
+        border_dict['inds_ot_next']=inds_ot_next
+        border_dict['dx_next_properties']=dx_next_properties
+    if cell_labels_prev is not None:
+        border_dict_prev=get_border_dict(cell_labels_prev,states=None,scale=border_scale,return_nnindex=False,return_nnvector=False,return_curvature=False)
+        dx_prev_properties=np.ones((max_cellid+1,1+n_moments))*np.nan
+        cell_ids_prev=np.unique(cell_labels_prev)
+        cell_ids_prev=cell_ids_prev[cell_ids_prev>0]
+        max_cellid_prev=np.max(cell_ids_prev)
+        if lin_prev is None:
+            print('no tracking provided, matching cells by ID')
+            lin_prev = [[] for _ in range(cell_ids.size + 1)]
+            for cellid in cell_ids:
+                if np.isin(cellid,cell_ids_prev):
+                    lin_prev[cellid].append(cellid)
+        border_dx_prev=np.zeros_like(border_dict['pts'])
+        inds_ot_prev=np.ones(border_dict['pts'].shape[0]).astype(int)*-1
+        for ic in range(cell_ids.size):
+            cellid=cell_ids[ic]
+            if len(lin_prev[cellid])>0:
+                indcell=np.where(border_dict['index']==cellid)[0]
+                border_pts=border_dict['pts'][indcell,:]
+                indcell_prev=np.where(np.isin(border_dict_prev['index'],lin_prev[cellid]))[0]
+                if indcell_prev.size>0:
+                    border_pts_prev=border_dict_prev['pts'][indcell_prev,:]
+                    inds_ot,dx_ot=get_ot_dx(border_pts,border_pts_prev,return_dx=True,return_cost=False)
+                    border_dx_prev[indcell,:]=-dx_ot
+                    inds_ot_prev[indcell]=indcell_prev[inds_ot]
+                    dxc=np.sum(np.multiply(-dx_ot,border_dict['n'][indcell,:]),axis=1)
+                    border_charge=dxc
+                    charges_list = [{'q': q, 'xyz': tuple(border_pts)} for q, border_pts in zip(border_charge, border_pts)]
+                    charge_dist = {'discrete': True, 'charges': charges_list}
+                    Phi = MultipoleExpansion(charge_dist, order)
+                    dx_prev_properties[cellid,0]=indcell.size
+                    i_prop=1
+                    for l in range(order+1):
+                        for m in range(-l,l+1):
+                            dx_prev_properties[cellid,i_prop]=Phi.multipole_moments[l,m]
+                            i_prop=i_prop+1
+        border_dict['dh_prev']=get_surface_gradvariance(border_dict['pts'],border_dx_prev)
+        border_dict['border_dx_prev']=border_dx_prev
+        border_dict['inds_ot_prev']=inds_ot_prev
+        border_dict['dx_prev_properties']=dx_prev_properties
+    if return_border_properties_list:
+        return border_dict,border_properties_list
+    else:
+        return border_dict
    
+
 
    
 
            
    
diff --git a/docs/build/html/_modules/celltraj/trajectory.html b/docs/build/html/_modules/celltraj/trajectory.html
index 158e6cb..be30e2f 100644
--- a/docs/build/html/_modules/celltraj/trajectory.html
+++ b/docs/build/html/_modules/celltraj/trajectory.html
@@ -228,7 +228,7 @@ 
    Source code for celltraj.trajectory
                         f.close()
                     return True
                 except Exception as error:
-                    print(f'error loading metadata from {self.h5filename}: {error}')
+                    print(f'error loading data from {self.h5filename}: {error}')
                     f.close()
                     return False
             else:
@@ -891,7 +891,7 @@ 
    Source code for celltraj.trajectory
                     
 
    
 [docs]
-    def get_cell_features(self,function_tuple,indcells=None,imgchannel=0,mskchannel=0,use_fmask_for_intensity_image=False,fmskchannel=None,use_mask_for_intensity_image=False,bordersize=10,apply_contact_transform=False,return_feature_list=False,save_h5=False,overwrite=False,concatenate_features=False):
+    def get_cell_features(self,function_tuple,indcells=None,imgchannel=0,mskchannel=0,use_fmask_for_intensity_image=False,fmskchannel=None,use_mask_for_intensity_image=False,bordersize=10,apply_contact_transform=False,preprocess_functions=(None),return_feature_list=False,save_h5=False,overwrite=False,concatenate_features=False):
         """
         Extracts complex cell features based on the specified custom functions and imaging data.
         This method allows customization of the feature extraction process using region properties
@@ -987,6 +987,10 @@ 
    Source code for celltraj.trajectory
                         if img.ndim==3:
                         for iz in range(img.shape[0]):
                             img[iz,...]=features.get_contact_boundaries(img[iz,...],radius=bordersize)
+                if preprocess_functions is not None:
+                    nfunc=len(preprocess_functions)
+                    for ifunc in range(nfunc):
+                        img=preprocess_functions[ifunc](img)
                 props = regionprops_table(msk, intensity_image=img,properties=('label',), extra_properties=function_tuple)
                 Xf_frame=np.zeros((props['label'].size,len(props.keys())))
                 for i,key in enumerate(props.keys()):
@@ -1419,7 +1423,8 @@ 
    Source code for celltraj.trajectory
                         t_global=np.zeros(self.ndim)
                 t_local=utilities.get_tshift(centers0,centers1+t_global,dist_function,ntrans=ntrans,maxt=maxt,**dist_function_keys)
                 #t_local=self.get_minT(msk0,msk1,nt=self.ntrans,dt=self.maxtrans)
-                tSet[iS,:]=t_global+t_local
+                #tSet[iS,:]=t_global+t_local
+                tSet[iS,:]=t_global-t_local #changes 24sep24
                 if zscale is not None:
                     tSet[iS,0]=tSet[iS,0]/zscale
                 sys.stdout.write(f'frame {iS} translation {t_global+t_local}\n')
@@ -1996,7 +2001,7 @@ 
    Source code for celltraj.trajectory
     
 
    
 [docs]
-    def get_unique_trajectories(self,cell_inds=None,verbose=False,extra_depth=None):
+    def get_unique_trajectories(self,cell_inds=None,verbose=False,extra_depth=None,save_h5=False,overwrite=False):
         """
         Computes unique trajectories for a set of cells over multiple frames, minimizing redundancy by
         ensuring that no two trajectories cover the same cell path beyond a specified overlap (extra_depth).
@@ -2061,7 +2066,10 @@ 
    Source code for celltraj.trajectory
                 else:
                 if n_untracked%100 == 0:
                     sys.stdout.write('tracked cell '+str(indc)+', '+str(cell_traj.size)+' tracks, '+str(n_untracked)+' left\n')
-        self.trajectories=trajectories
    
+        self.trajectories=trajectories
+        if save_h5:
+            attribute_list=['trajectories']
+            self.save_to_h5(f'/cell_data_m{self.mskchannel}/',attribute_list,overwrite=overwrite)
    
 
 
 
    
@@ -3307,6 +3315,408 @@ 
    Source code for celltraj.trajectory
                     attribute_list=['linSet']
                 self.save_to_h5(f'/cell_data_m{self.mskchannel}/',attribute_list,overwrite=overwrite)
         return vals_fate,inds_fate
    
+
+
+
    
+[docs]
+    def get_border_properties_dict(self,iS,cell_states=None,secretion_rates=None,surfaces=None,surface_fmask_channels=None,surface_states_baseid=1000,add_label0_surf=True,make_cells_and_surfaces_disjoint=True,border_scale=None,border_resolution=None,vdist_scale=0.3,radius=2.0,order=1,visual=False,z_viz=None):
+        """
+        Calculate border properties for cells and surfaces at a given frame in a trajectory.
+
+        This function extracts data required to characterize the borders of cells within a specified 
+        frame (`iS`) in a 3D or 2D cell image sequence. The borders are characterized based on geometric 
+        and physical properties, with optional visualization and multipole moment expansion up to the 
+        specified `order`. Supports the inclusion of surface contacts, tracking cell borders over time, 
+        and calculating secreted ligand distributions.
+
+        Parameters
+        ----------
+        iS : int
+            Frame index within the trajectory for which to calculate border properties.
+
+        cell_states : ndarray, optional
+            Array of states for each cell in the current frame. If not provided, all cells are assigned 
+            state 1 by default.
+
+        secretion_rates : ndarray, optional
+            Array of secretion rates for each cell, used to calculate secreted ligand distribution 
+            along cell borders. If not provided, no secretion is considered.
+
+        surfaces : list of ndarrays, optional
+            List of binary masks for surfaces adjacent to cells (e.g., membranes or other boundaries). 
+            Surfaces are optionally assigned states via `surface_fmask_channels`.
+
+        surface_fmask_channels : list of int, optional
+            List of channels in the frame data specifying each surface's mask. Used if `surfaces` is 
+            None. Each channel mask is assigned a state from `surface_states_baseid` onward.
+
+        surface_states_baseid : int, optional
+            Base state ID for assigning states to surfaces. Defaults to 1000.
+
+        add_label0_surf : bool, optional
+            If True, includes cells labeled as 0 (background) as a surface layer. Default is True.
+
+        make_cells_and_surfaces_disjoint : bool, optional
+            If True, cells and surfaces are treated as mutually exclusive regions. Default is True.
+
+        border_scale : float or list of float, optional
+            Scale for border calculations. If None, it defaults to the `border_resolution` attribute 
+            if available.
+
+        border_resolution : float, optional
+            Resolution of the border calculations in units of pixels. If None, the value from the 
+            object's `border_resolution` attribute is used if present.
+
+        vdist_scale : float, optional
+            Scale factor for calculating the secreted ligand distribution. Default is 0.3.
+
+        radius : float, optional
+            Radius for identifying contact neighbors when calculating border properties. Default is 2.0.
+
+        order : int, optional
+            Order of multipole moments to compute for cell border properties. Default is 1.
+
+        visual : bool, optional
+            If True, generates a visualization of the borders, including displacements from the 
+            previous and next frames.
+
+        z_viz : int, optional
+            Specific z-plane to visualize in 3D data. If None, a central z-plane is chosen.
+
+        Returns
+        -------
+        border_dict : dict
+            Dictionary containing computed properties for cell borders, including:
+            
+            - `pts`: Coordinates of each border point.
+            - `index`: Cell IDs associated with each border point.
+            - `nn_states`: States of neighboring cells at each border point.
+            - `c`: Curvature at each border point.
+            - `global_index`: Global indices for each border point in the dataset.
+            - `border_dx_prev`, `border_dx_next`: Displacement vectors for borders to previous and 
+            next frames, if applicable.
+            - `contact_properties`: Multipole moment properties based on cell-state contacts.
+            - `vdist`: Secreted ligand density distribution, if `secretion_rates` is provided.
+
+        Notes
+        -----
+        - **Tracking**: If previous (`cell_labels_prev`) or next frame (`cell_labels_next`) is provided, 
+        the function calculates the optimal transport vector between consecutive frames for each cell.
+        - **Surface Assignment**: When `surface_fmask_channels` is provided, each channel is used to 
+        define a distinct surface mask, and `surface_states` are assigned based on `surface_states_baseid`.
+        - **Multipole Moments**: Higher-order moments are calculated based on border charges determined 
+        from neighboring cell states.
+
+        Examples
+        --------
+        Compute border properties for frame 10 with secreted ligand distribution and visualize:
+
+        >>> border_properties = self.get_border_properties_dict(
+        ...     iS=10, cell_states=my_cell_states, secretion_rates=my_secretion_rates,
+        ...     surface_fmask_channels=[0, 1], border_resolution=1.0, vdist_scale=0.5, order=2,
+        ...     visual=True, z_viz=15
+        ... )
+        """
+        if border_resolution is None and border_scale 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}')
+        if iS<np.max(self.cells_frameSet):
+            indt1=np.where(self.cells_indimgSet==iS+1)[0]
+            labelids_t1=self.cells_labelidSet[indt1]
+            cell_labels1=self.get_mask_data(iS+1)[...,self.mskchannel]
+            cell_labels1=imprep.transform_image(cell_labels1,self.tf_matrix_set[iS+1,...],inverse_tform=False,pad_dims=self.pad_dims) #changed from inverse_tform=True, 9may24
+        else:
+            cell_labels1=None
+        if iS>np.min(self.cells_frameSet):
+            indt0=np.where(self.cells_indimgSet==iS-1)[0]
+            labelids_t0=self.cells_labelidSet[indt0]
+            cell_labels0=self.get_mask_data(iS-1)[...,self.mskchannel]
+            cell_labels0=imprep.transform_image(cell_labels0,self.tf_matrix_set[iS-1,...],inverse_tform=False,pad_dims=self.pad_dims) #changed from inverse_tform=True, 9may24
+        else:
+            cell_labels0=None
+        indt_current=np.where(self.cells_indimgSet==iS)[0]
+        labelids_current=self.cells_labelidSet[indt_current]
+        cell_labels_current=self.get_mask_data(iS)[...,self.mskchannel]
+        cell_labels_current=imprep.transform_image(cell_labels_current,self.tf_matrix_set[iS,...],inverse_tform=False,pad_dims=self.pad_dims) #changed from inverse_tform=True, 9may24
+        max_cellid_current=np.max(cell_labels_current)
+        cell_ids_current=np.unique(cell_labels_current)
+        cell_ids_current=cell_ids_current[cell_ids_current>0]
+        if cell_states is None:
+            cell_states_current=np.ones_like(indt_current)
+        else:
+            cell_states_current=cell_states[indt_current]
+        cell_states_labelid=np.ones(max_cellid_current+1).astype(int)*-1
+        cell_states_labelid[0]=0
+        for ic in range(labelids_current.size):
+            ilabel=labelids_current[ic]
+            cell_states_labelid[ilabel]=cell_states_current[ic]
+        if surfaces is None:
+            if surface_fmask_channels is not None:
+                surfaces=[None]*len(surface_fmask_channels)
+                surface_label0=np.zeros_like(cell_labels_current).astype(bool)
+                for i_surf in range(len(surface_fmask_channels)):
+                    surfaces[i_surf]=self.get_fmask_data(iS,channel=surface_fmask_channels[surface_fmask_channels[i_surf]])
+                    if make_cells_and_surfaces_disjoint:
+                        surfaces[i_surf][cell_labels_current>0]=0
+                    surface_label0=np.logical_or(surface_label0,np.logical_not(surfaces[i_surf]))
+                if add_label0_surf:
+                    surfaces.append(np.logical_and(cell_labels_current==0,surface_label0))
+                n_surf=len(surfaces)
+                surface_states=np.arange(surface_states_baseid,surface_states_baseid+n_surf).astype(int)
+            else:
+                n_surf=0
+                surface_states=None
+        else:
+            n_surf=len(surfaces)
+            surface_states=np.ones(surface_states_baseid).astype(int)*-1
+            surface_states=np.append(surface_states,np.arange(surface_states_baseid,surface_states_baseid+n_surf).astype(int))
+        if iS<np.max(self.cells_frameSet):
+            lin1=self.linSet[iS+1]
+            lin_next = [[] for _ in range(max_cellid_current + 1)]
+            for ilabel in range(lin1.size):
+                if lin1[ilabel]>-1:
+                    i_next=labelids_t1[ilabel]
+                    i_current=labelids_current[lin1[ilabel]]
+                    lin_next[i_current].append(i_next)
+        else:
+            lin_next=None
+        if iS>np.min(self.cells_frameSet):
+            lin0=self.linSet[iS]
+            lin_prev = [[] for _ in range(max_cellid_current + 1)]
+            for ilabel in range(lin0.size):
+                if lin0[ilabel]>-1:
+                    i_current=labelids_current[ilabel]
+                    i_prev=labelids_t0[lin0[ilabel]]
+                    lin_prev[i_current].append(i_prev)
+        else:
+            lin_prev=None
+        if secretion_rates is not None:
+            secretion_rates_current=secretion_rates[indt_current]
+            secretion_rates_labelid=np.zeros(max_cellid_current+1).astype(int)
+            secretion_rates_labelid[0]=0
+            for ic in range(labelids_current.size):
+                ilabel=labelids_current[ic]
+                secretion_rates_labelid[ilabel]=secretion_rates_current[ic]
+            vdist=spatial.get_secreted_ligand_density(cell_labels_current,scale=vdist_scale,secretion_rate=secretion_rates_labelid,D=None,micron_per_pixel=border_resolution,visual=False)
+        else:
+            vdist=None
+        border_dict=spatial.get_border_properties(cell_labels_current,cell_states=cell_states_labelid,surfaces=surfaces,surface_states=surface_states,cell_labels_next=cell_labels1,lin_next=lin_next,cell_labels_prev=cell_labels0,lin_prev=lin_prev,vdist=vdist,border_scale=border_scale,order=order)
+        global_cellinds=np.ones(border_dict['pts'].shape[0]).astype(int)*-1
+        indcells=np.where(np.isin(border_dict['states'],np.unique(cell_states_current)))[0]
+        cellids_current_post=np.unique(border_dict['index'][indcells])
+        cellids_current_post=cellids_current_post[cellids_current_post>0]
+        for cellid in cellids_current_post:
+            indcell=np.where(border_dict['index']==cellid)[0]
+            ilabel=np.where(labelids_current==cellid)[0][0]
+            iglobal=indt_current[ilabel]
+            global_cellinds[indcell]=iglobal
+        border_dict['global_index']=global_cellinds
+        if visual:
+            if self.ndim==3:
+                ix=1;iy=2
+                if z_viz is None:
+                    z_viz=int((np.max(border_dict['pts'][:,0])-np.min(border_dict['pts'][:,0]))/2)
+                indz=np.where(np.logical_and(border_dict['pts'][:,0]>z_viz-1,border_dict['pts'][:,0]<z_viz+1))[0]
+            elif self.ndim==2:
+                ix=0;iy=1
+                indz=np.where(border_dict['pts'][:,0]<np.inf)[0]                
+            indcells=np.where(np.isin(border_dict['states'],np.unique(cell_states_current)))[0]
+            indpts=np.intersect1d(indz,indcells)
+            plt.close('all');plt.figure(figsize=(8,8))
+            plt.scatter(border_dict['pts'][indz,1],border_dict['pts'][indz,2],s=10,c=border_dict['states'][indz],cmap=plt.cm.Greens,alpha=.7)
+            plt.scatter(border_dict['pts'][indpts,1],border_dict['pts'][indpts,2],s=30,c=border_dict['c'][indpts],cmap=plt.cm.seismic,clim=(-3,3))
+            ax=plt.gca()
+            for ipt in range(indpts.size):
+                if iS>np.min(self.cells_frameSet):
+                    ax.arrow(border_dict['pts'][indpts[ipt],1],border_dict['pts'][indpts[ipt],2],border_dict['border_dx_prev'][indpts[ipt],1],border_dict['border_dx_prev'][indpts[ipt],2],color='gray',head_width=1,alpha=.8)
+                if iS<np.max(self.cells_frameSet):
+                    ax.arrow(border_dict['pts'][indpts[ipt],1],border_dict['pts'][indpts[ipt],2],border_dict['border_dx_next'][indpts[ipt],1],border_dict['border_dx_next'][indpts[ipt],2],color='black',head_width=1)
+            plt.axis('off')
+        return border_dict
    
+
+
+
    
+[docs]
+    def get_cellboundary_library(self,frames=None,indcells=None,secretion_rates=None,cell_states=None,surface_fmask_channels=[0],surface_states_baseid=None,border_resolution=1.0,vdist_scale=.3,visual=False,save_h5=False,overwrite=False,**border_property_args):
+        """
+        Collect and create a library of boundary properties for cells across specified frames in a trajectory.
+
+        This function gathers cell and surface boundary properties across frames or specific cell indices. 
+        It collects properties such as position, displacement, neighboring point states, curvatures, 
+        and secreted ligand distributions, if applicable. This data is returned in a library format 
+        and optionally saved to an HDF5 file for later use.
+
+        Parameters
+        ----------
+        frames : list of int, optional
+            Frame indices to include in the boundary library. If None, includes all frames in `cells_frameSet`.
+            
+        indcells : ndarray, optional
+            Array of specific cell indices to include in the library. If None, all cells are included.
+            
+        secretion_rates : ndarray, optional
+            Array of secretion rates for each cell, used to calculate ligand distribution along borders.
+            
+        cell_states : ndarray, optional
+            Array of cell states for each cell. If not provided, cells are assigned state 1.
+            
+        surface_fmask_channels : list of int, default=[0]
+            List of channels indicating each surface's mask in the frame data.
+            
+        surface_states_baseid : int, optional
+            Starting ID for surface states. Defaults to one more than the maximum `cell_states`.
+            
+        border_resolution : float, optional
+            Resolution for border properties in units of pixels. Default is 1.0.
+            
+        vdist_scale : float, optional
+            Scale for secreted ligand distribution. Default is 0.3.
+            
+        visual : bool, optional
+            If True, displays a visualization of the boundaries. Default is False.
+            
+        save_h5 : bool, optional
+            If True, saves the resulting boundary library to an HDF5 file. Default is False.
+            
+        overwrite : bool, optional
+            If True, overwrites the HDF5 data file if it exists. Default is False.
+            
+        **border_property_args
+            Additional arguments passed to the `get_border_properties_dict` function.
+
+        Returns
+        -------
+        boundary_library : dict
+            Dictionary containing collected boundary properties for the specified cells and frames:
+            
+            - `global_index`: Global cell indices for each boundary point.
+            - `states`: Cell or surface state at each boundary point.
+            - `pts`: Coordinates of each boundary point.
+            - `dx_prevs`, `dx_nexts`: Displacement vectors for boundaries to previous and next frames.
+            - `surface_normals`: Surface normal vectors at each boundary point.
+            - `curvatures`: Curvature at each boundary point.
+            - `nn_pts`: Coordinates of neighboring points.
+            - `nn_states`: States of neighboring points.
+            - `nn_pts_states`: States of neighboring points with finer resolution.
+            - `surf_vars`: Variance of surface displacement in the previous frame.
+            - `vdists`: Secreted ligand density distribution, if `secretion_rates` is provided.
+
+        Notes
+        -----
+        - **Frames and Cell Selection**: If both `frames` and `indcells` are provided, only frames derived from 
+        `indcells` are used.
+        - **Surfaces and States**: `surface_fmask_channels` and `surface_states_baseid` define and initialize 
+        surfaces in each frame, using additional states based on `surface_states_baseid`.
+        - **Ligand Distribution**: Secreted ligand density distribution (`vdists`) is included if `secretion_rates` 
+        is specified.
+
+        Examples
+        --------
+        Generate a cell boundary library for frames 10, 15, and 20, with visualization enabled:
+
+        >>> boundary_library = self.get_cellboundary_library(
+        ...     frames=[10, 15, 20], secretion_rates=my_secretion_rates, cell_states=my_cell_states,
+        ...     surface_fmask_channels=[0, 1], visual=True, border_resolution=2.0
+        ... )
+
+        Save the boundary library to HDF5 with overwrite enabled:
+
+        >>> boundary_library = self.get_cellboundary_library(
+        ...     indcells=my_indcells, save_h5=True, overwrite=True
+        ... )
+        """
+        if frames is None and indcells is None:
+            frames=np.unique(self.cells_frameSet)
+            indcells=np.arange(self.cells_indSet.size).astype(int)
+        elif frames is not None and indcells is None:
+            indcells=np.array([]).astype(int)
+            for iS in frames:
+                indcells=np.append(indcells,np.where(self.cells_frameSet==iS)[0])
+        elif indcells is not None:
+            if frames is not None:
+                print('both cell index and frames provided, using frames implied from cell index')
+            frames=np.unique(self.cells_frameSet[indcells])
+        if cell_states is None:
+            cell_states=np.ones(np.max(indcells)).astype(int)
+        if surface_states_baseid is None:
+            surface_states_baseid=np.max(cell_states)+1
+        global_index=[]
+        states=[]
+        pts=[]
+        dx_prevs=[]
+        dx_nexts=[]
+        surface_normals=[]
+        curvatures=[]
+        nn_pts=[]
+        nn_states=[]
+        nn_pts_states=[]
+        surf_vars=[]
+        if secretion_rates is not None:
+            vdists=[]
+        for iS in frames:
+            print(f'Extracting boundary library from frame {iS}')
+            indframe=np.where(self.cells_frameSet==iS)[0]
+            border_dict=self.get_border_properties_dict(iS,cell_states=cell_states,secretion_rates=secretion_rates,surface_fmask_channels=surface_fmask_channels,surface_states_baseid=surface_states_baseid,border_resolution=border_resolution,vdist_scale=vdist_scale,visual=visual)
+            if visual:
+                plt.show()
+            indcells_boundary=np.where(np.isin(border_dict['global_index'],indframe))[0]
+            global_index.append(border_dict['global_index'][indcells_boundary])
+            states.append(border_dict['states'][indcells_boundary])
+            pts.append(border_dict['pts'][indcells_boundary,:])
+            if iS>np.min(self.cells_frameSet):
+                dx_prevs.append(border_dict['border_dx_prev'][indcells_boundary,:])
+                surf_vars.append(border_dict['dh_prev'][indcells_boundary])
+            else:
+                dx_prevs.append(np.ones((indcells_boundary.size,self.ndim))*np.nan)
+                surf_vars.append(np.ones(indcells_boundary.size)*np.nan)
+            if iS<np.max(self.cells_frameSet):
+                dx_nexts.append(border_dict['border_dx_next'][indcells_boundary,:])
+            else:
+                dx_nexts.append(np.ones((indcells_boundary.size,self.ndim))*np.nan)
+            surface_normals.append(border_dict['n'][indcells_boundary,:])
+            curvatures.append(border_dict['c'][indcells_boundary])
+            nn_pts.append(border_dict['nn_pts'][indcells_boundary,:])
+            nn_states.append(border_dict['nn_states'][indcells_boundary])
+            nn_pts_states.append(border_dict['nn_pts_state'][indcells_boundary,:,:])
+            if secretion_rates is not None:
+                vdists.append(border_dict['vdist'][indcells_boundary])
+        boundary_library={'indcells':indcells,
+                        'cell_states':cell_states,
+                        'secretion_rates':secretion_rates,
+                        'surface_fmask_channels':surface_fmask_channels,
+                        'surface_states_baseid':surface_states_baseid,
+                        'border_resolution':border_resolution,
+                        'vdist_scale':vdist_scale}
+        boundary_library['global_index']=np.concatenate(global_index)
+        boundary_library['states']=np.concatenate(states)
+        boundary_library['pts']=np.vstack(pts)
+        boundary_library['dx_prevs']=np.vstack(dx_prevs)
+        boundary_library['dx_nexts']=np.vstack(dx_nexts)
+        boundary_library['surface_normals']=np.vstack(surface_normals)
+        boundary_library['curvatures']=np.concatenate(curvatures)
+        boundary_library['nn_pts']=np.vstack(nn_pts)
+        boundary_library['nn_pts_states']=np.vstack(nn_pts_states)
+        boundary_library['nn_states']=np.concatenate(nn_states)
+        boundary_library['surf_vars']=np.concatenate(surf_vars)
+        if secretion_rates is not None:
+            boundary_library['vdists']=np.concatenate(vdists)
+        if save_h5:
+            self.boundary_library=boundary_library
+            try:
+                attribute_list=['boundary_library']
+                data_written = self.save_to_h5(f'/cell_data_m{self.mskchannel}/',attribute_list,overwrite=overwrite)
+            except Exception as e:
+                print(f'error writing data, {e}')
+            return boundary_library
+        else:
+            return boundary_library
    
 
    
 
 
    
diff --git a/docs/build/html/_modules/celltraj/utilities.html b/docs/build/html/_modules/celltraj/utilities.html
index 8d6fe8e..d70b9fa 100644
--- a/docs/build/html/_modules/celltraj/utilities.html
+++ b/docs/build/html/_modules/celltraj/utilities.html
@@ -126,6 +126,13 @@ 
    Source code for celltraj.utilities
         return probc1
    
 
 
+
    
+[docs]
+def rescale_to_int(img,maxint=2**16-1,dtype=np.uint16):
+    img=maxint*((img-np.min(img))/np.max(img-np.min(img)))
+    return img.astype(dtype)
    
+
+
 
    
 [docs]
 def colorbar(mappable):
@@ -242,7 +249,7 @@ 
    Source code for celltraj.utilities
                     item = np.array(item).astype('|S32')
                 h5file[path + key] = item
             if not np.array_equal(h5file[path + key][()], item):
-                raise ValueError('The data representation in the HDF5 file does not match the original dict.')
+                print(f'{key}:{item} -- the data representation in the HDF5 file does not match the original dict.')
         # save dictionaries
         elif isinstance(item, dict):
             recursively_save_dict_contents_to_group(h5file, path + key + '/', item)
@@ -378,6 +385,8 @@ 
    Source code for celltraj.utilities
     [docs]
 def get_tshift(centers1,centers2,dist_function,ntrans=100,maxt=10, **dist_function_keys):
     ndim=centers1.shape[1]
+    centers1=centers1[np.isfinite(np.sum(centers1,axis=1)),:]
+    centers2=centers2[np.isfinite(np.sum(centers2,axis=1)),:]
     if not isinstance(ntrans, (list,tuple,np.ndarray)):
         ntrans=[ntrans]*ndim
     if not isinstance(maxt, (list,tuple,np.ndarray)):
diff --git a/docs/build/html/api.html b/docs/build/html/api.html
index b7a6e83..88c3906 100644
--- a/docs/build/html/api.html
+++ b/docs/build/html/api.html
@@ -55,6 +55,7 @@
 
  • Trajectory.get_Xtraj_celltrajectory()
    • 
 
  • Trajectory.get_alpha()
    • 
 
  • Trajectory.get_beta()
    • 
+
  • Trajectory.get_border_properties_dict()
    • 
 
  • Trajectory.get_cell_blocks()
    • 
 
  • Trajectory.get_cell_channel_crosscorr()
    • 
 
  • Trajectory.get_cell_children()
    • 
@@ -70,6 +71,7 @@
 
  • Trajectory.get_cell_positions()
    • 
 
  • Trajectory.get_cell_sandwich()
    • 
 
  • Trajectory.get_cell_trajectory()
    • 
+
  • Trajectory.get_cellboundary_library()
    • 
 
  • Trajectory.get_cells_nchildren()
    • 
 
  • Trajectory.get_dx()
    • 
 
  • Trajectory.get_fmask_data()
    • 
@@ -159,6 +161,7 @@
 
  • organize_filelist_fov()
    • 
 
  • organize_filelist_time()
    • 
 
  • pad_image()
    • 
+
  • rescale_to_int()
    • 
 
  • save_for_viewing()
    • 
 
  • save_frame_h5()
    • 
 
  • transform_image()
    • 
@@ -196,6 +199,7 @@
 
  • get_LJ_force()
    • 
 
  • get_adhesive_displacement()
    • 
 
  • get_border_dict()
    • 
+
  • get_border_properties()
    • 
 
  • get_flux_displacement()
    • 
 
  • get_flux_ligdist()
    • 
 
  • get_labels_fromborderdict()
    • 
@@ -206,6 +210,7 @@
 
  • get_secreted_ligand_density()
    • 
 
  • get_surface_displacement()
    • 
 
  • get_surface_displacement_deviation()
    • 
+
  • get_surface_gradvariance()
    • 
 
  • get_surface_points()
    • 
 
  • get_volconstraint_com()
    • 
 
  • get_yukawa_force()
    • 
@@ -234,6 +239,7 @@
 
  • load_dict_from_h5()
    • 
 
  • recursively_load_dict_contents_from_group()
    • 
 
  • recursively_save_dict_contents_to_group()
    • 
+
  • rescale_to_int()
    • 
 
  • save_dict_to_h5()
    • 
 
 
@@ -442,6 +448,81 @@ 
    celltraj.trajectory
+
    
+get_border_properties_dict(iS, cell_states=None, secretion_rates=None, surfaces=None, surface_fmask_channels=None, surface_states_baseid=1000, add_label0_surf=True, make_cells_and_surfaces_disjoint=True, border_scale=None, border_resolution=None, vdist_scale=0.3, radius=2.0, order=1, visual=False, z_viz=None)[source]
    
+
    Calculate border properties for cells and surfaces at a given frame in a trajectory.
    
+
    This function extracts data required to characterize the borders of cells within a specified
+frame (iS) in a 3D or 2D cell image sequence. The borders are characterized based on geometric
+and physical properties, with optional visualization and multipole moment expansion up to the
+specified order. Supports the inclusion of surface contacts, tracking cell borders over time,
+and calculating secreted ligand distributions.
    
+
    
+
    Parameters:
    
+
      
+
    • iS (int) – Frame index within the trajectory for which to calculate border properties.
      • 
+
    • cell_states (ndarray, optional) – Array of states for each cell in the current frame. If not provided, all cells are assigned
+state 1 by default.
      • 
+
    • secretion_rates (ndarray, optional) – Array of secretion rates for each cell, used to calculate secreted ligand distribution
+along cell borders. If not provided, no secretion is considered.
      • 
+
    • surfaces (list of ndarrays, optional) – List of binary masks for surfaces adjacent to cells (e.g., membranes or other boundaries).
+Surfaces are optionally assigned states via surface_fmask_channels.
      • 
+
    • surface_fmask_channels (list of int, optional) – List of channels in the frame data specifying each surface’s mask. Used if surfaces is
+None. Each channel mask is assigned a state from surface_states_baseid onward.
      • 
+
    • surface_states_baseid (int, optional) – Base state ID for assigning states to surfaces. Defaults to 1000.
      • 
+
    • add_label0_surf (bool, optional) – If True, includes cells labeled as 0 (background) as a surface layer. Default is True.
      • 
+
    • make_cells_and_surfaces_disjoint (bool, optional) – If True, cells and surfaces are treated as mutually exclusive regions. Default is True.
      • 
+
    • border_scale (float or list of float, optional) – Scale for border calculations. If None, it defaults to the border_resolution attribute
+if available.
      • 
+
    • border_resolution (float, optional) – Resolution of the border calculations in units of pixels. If None, the value from the
+object’s border_resolution attribute is used if present.
      • 
+
    • vdist_scale (float, optional) – Scale factor for calculating the secreted ligand distribution. Default is 0.3.
      • 
+
    • radius (float, optional) – Radius for identifying contact neighbors when calculating border properties. Default is 2.0.
      • 
+
    • order (int, optional) – Order of multipole moments to compute for cell border properties. Default is 1.
      • 
+
    • visual (bool, optional) – If True, generates a visualization of the borders, including displacements from the
+previous and next frames.
      • 
+
    • z_viz (int, optional) – Specific z-plane to visualize in 3D data. If None, a central z-plane is chosen.
      • 
+
    
+
    
+
    Returns:
    
+
    border_dict – Dictionary containing computed properties for cell borders, including:
    
+
      
+
    • pts: Coordinates of each border point.
      • 
+
    • index: Cell IDs associated with each border point.
      • 
+
    • nn_states: States of neighboring cells at each border point.
      • 
+
    • c: Curvature at each border point.
      • 
+
    • global_index: Global indices for each border point in the dataset.
      • 
+
    • border_dx_prev, border_dx_next: Displacement vectors for borders to previous and
      • 
+
    
+
    next frames, if applicable.
+- contact_properties: Multipole moment properties based on cell-state contacts.
+- vdist: Secreted ligand density distribution, if secretion_rates is provided.
    
+
    
+
    
+
    Return type:
    
+
    dict
    
+
    
+
    
+
    Notes
    
+
      
+
    • Tracking: If previous (cell_labels_prev) or next frame (cell_labels_next) is provided,
      • 
+
    
+
    the function calculates the optimal transport vector between consecutive frames for each cell.
+- Surface Assignment: When surface_fmask_channels is provided, each channel is used to
+define a distinct surface mask, and surface_states are assigned based on surface_states_baseid.
+- Multipole Moments: Higher-order moments are calculated based on border charges determined
+from neighboring cell states.
    
+
    Examples
    
+
    Compute border properties for frame 10 with secreted ligand distribution and visualize:
    
+
    >>> border_properties = self.get_border_properties_dict(
+...     iS=10, cell_states=my_cell_states, secretion_rates=my_secretion_rates,
+...     surface_fmask_channels=[0, 1], border_resolution=1.0, vdist_scale=0.5, order=2,
+...     visual=True, z_viz=15
+... )
+
    
+
    
+
    
+
 
    
 
    
 get_cell_blocks(label, return_label_ids=False)[source]
    
@@ -614,7 +695,7 @@ 
    celltraj.trajectory
 
    
-get_cell_features(function_tuple, indcells=None, imgchannel=0, mskchannel=0, use_fmask_for_intensity_image=False, fmskchannel=None, use_mask_for_intensity_image=False, bordersize=10, apply_contact_transform=False, return_feature_list=False, save_h5=False, overwrite=False, concatenate_features=False)[source]
    
+get_cell_features(function_tuple, indcells=None, imgchannel=0, mskchannel=0, use_fmask_for_intensity_image=False, fmskchannel=None, use_mask_for_intensity_image=False, bordersize=10, apply_contact_transform=False, preprocess_functions=None, return_feature_list=False, save_h5=False, overwrite=False, concatenate_features=False)[source]
 
    Extracts complex cell features based on the specified custom functions and imaging data.
 This method allows customization of the feature extraction process using region properties
 from segmented cell data, with optional transformations like border erosion or contact
@@ -869,6 +950,77 @@ 
    celltraj.trajectory
+
    
+get_cellboundary_library(frames=None, indcells=None, secretion_rates=None, cell_states=None, surface_fmask_channels=[0], surface_states_baseid=None, border_resolution=1.0, vdist_scale=0.3, visual=False, save_h5=False, overwrite=False, **border_property_args)[source]
    
+
    Collect and create a library of boundary properties for cells across specified frames in a trajectory.
    
+
    This function gathers cell and surface boundary properties across frames or specific cell indices.
+It collects properties such as position, displacement, neighboring point states, curvatures,
+and secreted ligand distributions, if applicable. This data is returned in a library format
+and optionally saved to an HDF5 file for later use.
    
+
    
+
    Parameters:
    
+
      
+
    • frames (list of int, optional) – Frame indices to include in the boundary library. If None, includes all frames in cells_frameSet.
      • 
+
    • indcells (ndarray, optional) – Array of specific cell indices to include in the library. If None, all cells are included.
      • 
+
    • secretion_rates (ndarray, optional) – Array of secretion rates for each cell, used to calculate ligand distribution along borders.
      • 
+
    • cell_states (ndarray, optional) – Array of cell states for each cell. If not provided, cells are assigned state 1.
      • 
+
    • surface_fmask_channels (list of int, default [0]) – List of channels indicating each surface’s mask in the frame data.
      • 
+
    • surface_states_baseid (int, optional) – Starting ID for surface states. Defaults to one more than the maximum cell_states.
      • 
+
    • border_resolution (float, optional) – Resolution for border properties in units of pixels. Default is 1.0.
      • 
+
    • vdist_scale (float, optional) – Scale for secreted ligand distribution. Default is 0.3.
      • 
+
    • visual (bool, optional) – If True, displays a visualization of the boundaries. Default is False.
      • 
+
    • save_h5 (bool, optional) – If True, saves the resulting boundary library to an HDF5 file. Default is False.
      • 
+
    • overwrite (bool, optional) – If True, overwrites the HDF5 data file if it exists. Default is False.
      • 
+
    • **border_property_args – Additional arguments passed to the get_border_properties_dict function.
      • 
+
    
+
    
+
    Returns:
    
+
    boundary_library – Dictionary containing collected boundary properties for the specified cells and frames:
    
+
      
+
    • global_index: Global cell indices for each boundary point.
      • 
+
    • states: Cell or surface state at each boundary point.
      • 
+
    • pts: Coordinates of each boundary point.
      • 
+
    • dx_prevs, dx_nexts: Displacement vectors for boundaries to previous and next frames.
      • 
+
    • surface_normals: Surface normal vectors at each boundary point.
      • 
+
    • curvatures: Curvature at each boundary point.
      • 
+
    • nn_pts: Coordinates of neighboring points.
      • 
+
    • nn_states: States of neighboring points.
      • 
+
    • nn_pts_states: States of neighboring points with finer resolution.
      • 
+
    • surf_vars: Variance of surface displacement in the previous frame.
      • 
+
    • vdists: Secreted ligand density distribution, if secretion_rates is provided.
      • 
+
    
+
    
+
    
+
    Return type:
    
+
    dict
    
+
    
+
    
+
    Notes
    
+
      
+
    • Frames and Cell Selection: If both frames and indcells are provided, only frames derived from
      • 
+
    
+
    indcells are used.
+- Surfaces and States: surface_fmask_channels and surface_states_baseid define and initialize
+surfaces in each frame, using additional states based on surface_states_baseid.
+- Ligand Distribution: Secreted ligand density distribution (vdists) is included if secretion_rates
+is specified.
    
+
    Examples
    
+
    Generate a cell boundary library for frames 10, 15, and 20, with visualization enabled:
    
+
    >>> boundary_library = self.get_cellboundary_library(
+...     frames=[10, 15, 20], secretion_rates=my_secretion_rates, cell_states=my_cell_states,
+...     surface_fmask_channels=[0, 1], visual=True, border_resolution=2.0
+... )
+
    
+
    
+
    Save the boundary library to HDF5 with overwrite enabled:
    
+
    >>> boundary_library = self.get_cellboundary_library(
+...     indcells=my_indcells, save_h5=True, overwrite=True
+... )
+
    
+
    
+
    
+
 
    
 
    
 get_cells_nchildren()[source]
    
@@ -1631,7 +1783,7 @@ 
    celltraj.trajectory
 
    
-get_unique_trajectories(cell_inds=None, verbose=False, extra_depth=None)[source]
    
+get_unique_trajectories(cell_inds=None, verbose=False, extra_depth=None, save_h5=False, overwrite=False)[source]
 
    Computes unique trajectories for a set of cells over multiple frames, minimizing redundancy by
 ensuring that no two trajectories cover the same cell path beyond a specified overlap (extra_depth).
    
 
    
@@ -3800,6 +3952,11 @@ 
    celltraj.imageprep
+
    
+celltraj.imageprep.rescale_to_int(img, maxint=65535, dtype=<class 'numpy.uint16'>)[source]
    
+
    
+
 
    
 
    
 celltraj.imageprep.save_for_viewing(data, fname, metadata=None, overwrite=False)[source]
    
@@ -4938,7 +5095,7 @@ 
    celltraj.spatial
 
    
-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]
    
+celltraj.spatial.get_border_dict(labels, states=None, radius=10, vdist=None, return_nnindex=True, return_nnvector=True, return_curvature=True, nn_states=None, 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.
    
@@ -4949,8 +5106,7 @@ 
    celltraj.spatialndarray, 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.
    • 
+
  • vdist (ndarray, optional) – An array representing a scalar value in the image, such as estimated ligand concentration, to store in the border point dictionary.
    • 
 
  • 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).
    • 
@@ -4994,6 +5150,76 @@ 
    celltraj.spatial
+
    
+celltraj.spatial.get_border_properties(cell_labels, surfaces=None, cell_states=None, surface_states=None, cell_labels_next=None, cell_labels_prev=None, lin_next=None, lin_prev=None, vdist=None, border_scale=1.0, radius=2.0, order=1, return_border_properties_list=False)[source]
    
+
    Calculate geometric and physical properties of cell borders, with optional tracking between frames
+and surface contact details.
    
+
    This function computes properties of cell borders based on cell_labels, surface contacts, and
+multipole moments up to a specified order. It supports tracking cell border displacement across
+frames using optional next and previous frame labels (cell_labels_next, cell_labels_prev).
+Outputs include multipole moments and border displacements useful in characterizing cell interactions
+and tracking cell behaviors.
    
+
    
+
    Parameters:
    
+
      
+
    • cell_labels (ndarray) – Array of labels identifying distinct cells within the domain.
      • 
+
    • surfaces (list of ndarrays, optional) – List of binary masks indicating surface layers adjacent to cells (e.g., membranes or other
+boundaries). Each is assigned a unique identifier.
      • 
+
    • cell_states (ndarray of int, optional) – Array specifying a state for each cell in cell_labels. Defaults to 1 if not provided.
      • 
+
    • surface_states (ndarray of int, optional) – Array of states corresponding to each surface in surfaces. These states must not overlap
+with cell_states.
      • 
+
    • cell_labels_next (ndarray, optional) – Label array for the next frame to track cell borders over time.
      • 
+
    • cell_labels_prev (ndarray, optional) – Label array for the previous frame, used for backward tracking of cells.
      • 
+
    • lin_next (list of lists, optional) – Mapping of cells in cell_labels to corresponding cells in cell_labels_next. Each entry
+contains IDs of linked cells in the next frame.
      • 
+
    • lin_prev (list of lists, optional) – Mapping of cells in cell_labels to corresponding cells in cell_labels_prev, used for
+backward tracking.
      • 
+
    • border_scale (float, optional) – Resolution in microns applied to border point extraction (default is 1.0).
      • 
+
    • radius (float, optional) – Radius in pixels for calculating border properties such as contact neighbors (default is 2.0).
      • 
+
    • order (int, optional) – Order of multipole moments to calculate for each border contact (default is 1).
      • 
+
    • return_border_properties_list (bool, optional) – If True, returns a list of property names for multipole moments alongside the border
+dictionary.
      • 
+
    
+
    
+
    Returns:
    
+
      
+
    • border_dict (dict) – Dictionary containing calculated properties of each cell’s border, including:
      
+
        
+
      • pts : Array of border points.
        • 
+
      • index : Label of each cell at the border.
        • 
+
      • nn_states : Neighboring cell states at each border point.
        • 
+
      • contact_properties : Array of multipole moment properties calculated up to order.
        • 
+
      • border_dx_next, border_dx_prev : Displacements of each border in the next and previous
+frames, if applicable.
        • 
+
      • dx_next_properties, dx_prev_properties : Arrays of multipole moments based on border
+displacement for next and previous frames.
        • 
+
      
+
      • 
+
    • border_properties_list (list of str, optional) – List of property names for contact_properties array if return_border_properties_list
+is True.
      • 
+
    
+
    
+
    
+
    
+
    Notes
    
+
      
+
    • Multipole Moment Calculation: Moments up to the specified order are computed based on
+border charges (representing contact fraction) for each border point.
      • 
+
    • Cell Tracking: When provided with cell_labels_next or cell_labels_prev, the function
+calculates optimal transport (OT) vectors to track border movement between frames.
      • 
+
    
+
    Examples
    
+
    Calculate properties of cell borders and multipole moments with next-frame tracking:
    
+
    >>> border_dict, property_list = get_border_properties(
+...     cell_labels=my_cell_labels, surfaces=my_surfaces, cell_states=my_states,
+...     cell_labels_next=my_next_frame_labels, lin_next=my_next_frame_links,
+...     border_scale=0.8, radius=1.5, order=2, return_border_properties_list=True
+... )
+
    
+
    
+
    
+
 
    
 
    
 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]
    
@@ -5444,9 +5670,14 @@ 
    celltraj.spatial
+
    
+celltraj.spatial.get_surface_gradvariance(border_pts, dx_ot, knn=12, use_eigs=False)[source]
    
+
    
+
 
    
 
    
-celltraj.spatial.get_surface_points(msk, return_normals=False, return_curvature=False, knn=20)[source]
    
+celltraj.spatial.get_surface_points(msk, return_normals=False, return_curvature=False, knn=20, rscale=0.1)[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.
    
@@ -5776,6 +6007,11 @@ 
    celltraj.utilitiescelltraj.utilities.recursively_save_dict_contents_to_group(h5file, path, dic)[source]
 
    
 
+
    
+
    
+celltraj.utilities.rescale_to_int(img, maxint=65535, dtype=<class 'numpy.uint16'>)[source]
    
+
    
+
 
    
 
    
 celltraj.utilities.save_dict_to_h5(dic, h5file, path)[source]
    
diff --git a/docs/build/html/celltraj.html b/docs/build/html/celltraj.html
index 493a7cd..66d764f 100644
--- a/docs/build/html/celltraj.html
+++ b/docs/build/html/celltraj.html
@@ -2483,6 +2483,11 @@ 
    Submodules
+
    
+celltraj.imageprep.rescale_to_int(img, maxint=65535, dtype=<class 'numpy.uint16'>)[source]
    
+
    
+
 
    
 
    
 celltraj.imageprep.save_for_viewing(data, fname, metadata=None, overwrite=False)[source]
    
@@ -3619,7 +3624,7 @@ 
    Submodules
 
    
-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]
    
+celltraj.spatial.get_border_dict(labels, states=None, radius=10, vdist=None, return_nnindex=True, return_nnvector=True, return_curvature=True, nn_states=None, 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.
    
@@ -3630,8 +3635,7 @@ 
    Submodulesndarray, 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.
    • 
+
  • vdist (ndarray, optional) – An array representing a scalar value in the image, such as estimated ligand concentration, to store in the border point dictionary.
    • 
 
  • 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).
    • 
@@ -3675,6 +3679,76 @@ 
    Submodules
+
    
+celltraj.spatial.get_border_properties(cell_labels, surfaces=None, cell_states=None, surface_states=None, cell_labels_next=None, cell_labels_prev=None, lin_next=None, lin_prev=None, vdist=None, border_scale=1.0, radius=2.0, order=1, return_border_properties_list=False)[source]
    
+
    Calculate geometric and physical properties of cell borders, with optional tracking between frames
+and surface contact details.
    
+
    This function computes properties of cell borders based on cell_labels, surface contacts, and
+multipole moments up to a specified order. It supports tracking cell border displacement across
+frames using optional next and previous frame labels (cell_labels_next, cell_labels_prev).
+Outputs include multipole moments and border displacements useful in characterizing cell interactions
+and tracking cell behaviors.
    
+
    
+
    Parameters:
    
+
      
+
    • cell_labels (ndarray) – Array of labels identifying distinct cells within the domain.
      • 
+
    • surfaces (list of ndarrays, optional) – List of binary masks indicating surface layers adjacent to cells (e.g., membranes or other
+boundaries). Each is assigned a unique identifier.
      • 
+
    • cell_states (ndarray of int, optional) – Array specifying a state for each cell in cell_labels. Defaults to 1 if not provided.
      • 
+
    • surface_states (ndarray of int, optional) – Array of states corresponding to each surface in surfaces. These states must not overlap
+with cell_states.
      • 
+
    • cell_labels_next (ndarray, optional) – Label array for the next frame to track cell borders over time.
      • 
+
    • cell_labels_prev (ndarray, optional) – Label array for the previous frame, used for backward tracking of cells.
      • 
+
    • lin_next (list of lists, optional) – Mapping of cells in cell_labels to corresponding cells in cell_labels_next. Each entry
+contains IDs of linked cells in the next frame.
      • 
+
    • lin_prev (list of lists, optional) – Mapping of cells in cell_labels to corresponding cells in cell_labels_prev, used for
+backward tracking.
      • 
+
    • border_scale (float, optional) – Resolution in microns applied to border point extraction (default is 1.0).
      • 
+
    • radius (float, optional) – Radius in pixels for calculating border properties such as contact neighbors (default is 2.0).
      • 
+
    • order (int, optional) – Order of multipole moments to calculate for each border contact (default is 1).
      • 
+
    • return_border_properties_list (bool, optional) – If True, returns a list of property names for multipole moments alongside the border
+dictionary.
      • 
+
    
+
    
+
    Returns:
    
+
      
+
    • border_dict (dict) – Dictionary containing calculated properties of each cell’s border, including:
      
+
        
+
      • pts : Array of border points.
        • 
+
      • index : Label of each cell at the border.
        • 
+
      • nn_states : Neighboring cell states at each border point.
        • 
+
      • contact_properties : Array of multipole moment properties calculated up to order.
        • 
+
      • border_dx_next, border_dx_prev : Displacements of each border in the next and previous
+frames, if applicable.
        • 
+
      • dx_next_properties, dx_prev_properties : Arrays of multipole moments based on border
+displacement for next and previous frames.
        • 
+
      
+
      • 
+
    • border_properties_list (list of str, optional) – List of property names for contact_properties array if return_border_properties_list
+is True.
      • 
+
    
+
    
+
    
+
    
+
    Notes
    
+
      
+
    • Multipole Moment Calculation: Moments up to the specified order are computed based on
+border charges (representing contact fraction) for each border point.
      • 
+
    • Cell Tracking: When provided with cell_labels_next or cell_labels_prev, the function
+calculates optimal transport (OT) vectors to track border movement between frames.
      • 
+
    
+
    Examples
    
+
    Calculate properties of cell borders and multipole moments with next-frame tracking:
    
+
    >>> border_dict, property_list = get_border_properties(
+...     cell_labels=my_cell_labels, surfaces=my_surfaces, cell_states=my_states,
+...     cell_labels_next=my_next_frame_labels, lin_next=my_next_frame_links,
+...     border_scale=0.8, radius=1.5, order=2, return_border_properties_list=True
+... )
+
    
+
    
+
    
+
 
    
 
    
 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]
    
@@ -4125,9 +4199,14 @@ 
    Submodules
+
    
+celltraj.spatial.get_surface_gradvariance(border_pts, dx_ot, knn=12, use_eigs=False)[source]
    
+
    
+
 
    
 
    
-celltraj.spatial.get_surface_points(msk, return_normals=False, return_curvature=False, knn=20)[source]
    
+celltraj.spatial.get_surface_points(msk, return_normals=False, return_curvature=False, knn=20, rscale=0.1)[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.
    
@@ -4436,6 +4515,81 @@ 
    Submodules
+
    
+get_border_properties_dict(iS, cell_states=None, secretion_rates=None, surfaces=None, surface_fmask_channels=None, surface_states_baseid=1000, add_label0_surf=True, make_cells_and_surfaces_disjoint=True, border_scale=None, border_resolution=None, vdist_scale=0.3, radius=2.0, order=1, visual=False, z_viz=None)[source]
    
+
    Calculate border properties for cells and surfaces at a given frame in a trajectory.
    
+
    This function extracts data required to characterize the borders of cells within a specified
+frame (iS) in a 3D or 2D cell image sequence. The borders are characterized based on geometric
+and physical properties, with optional visualization and multipole moment expansion up to the
+specified order. Supports the inclusion of surface contacts, tracking cell borders over time,
+and calculating secreted ligand distributions.
    
+
    
+
    Parameters:
    
+
      
+
    • iS (int) – Frame index within the trajectory for which to calculate border properties.
      • 
+
    • cell_states (ndarray, optional) – Array of states for each cell in the current frame. If not provided, all cells are assigned
+state 1 by default.
      • 
+
    • secretion_rates (ndarray, optional) – Array of secretion rates for each cell, used to calculate secreted ligand distribution
+along cell borders. If not provided, no secretion is considered.
      • 
+
    • surfaces (list of ndarrays, optional) – List of binary masks for surfaces adjacent to cells (e.g., membranes or other boundaries).
+Surfaces are optionally assigned states via surface_fmask_channels.
      • 
+
    • surface_fmask_channels (list of int, optional) – List of channels in the frame data specifying each surface’s mask. Used if surfaces is
+None. Each channel mask is assigned a state from surface_states_baseid onward.
      • 
+
    • surface_states_baseid (int, optional) – Base state ID for assigning states to surfaces. Defaults to 1000.
      • 
+
    • add_label0_surf (bool, optional) – If True, includes cells labeled as 0 (background) as a surface layer. Default is True.
      • 
+
    • make_cells_and_surfaces_disjoint (bool, optional) – If True, cells and surfaces are treated as mutually exclusive regions. Default is True.
      • 
+
    • border_scale (float or list of float, optional) – Scale for border calculations. If None, it defaults to the border_resolution attribute
+if available.
      • 
+
    • border_resolution (float, optional) – Resolution of the border calculations in units of pixels. If None, the value from the
+object’s border_resolution attribute is used if present.
      • 
+
    • vdist_scale (float, optional) – Scale factor for calculating the secreted ligand distribution. Default is 0.3.
      • 
+
    • radius (float, optional) – Radius for identifying contact neighbors when calculating border properties. Default is 2.0.
      • 
+
    • order (int, optional) – Order of multipole moments to compute for cell border properties. Default is 1.
      • 
+
    • visual (bool, optional) – If True, generates a visualization of the borders, including displacements from the
+previous and next frames.
      • 
+
    • z_viz (int, optional) – Specific z-plane to visualize in 3D data. If None, a central z-plane is chosen.
      • 
+
    
+
    
+
    Returns:
    
+
    border_dict – Dictionary containing computed properties for cell borders, including:
    
+
      
+
    • pts: Coordinates of each border point.
      • 
+
    • index: Cell IDs associated with each border point.
      • 
+
    • nn_states: States of neighboring cells at each border point.
      • 
+
    • c: Curvature at each border point.
      • 
+
    • global_index: Global indices for each border point in the dataset.
      • 
+
    • border_dx_prev, border_dx_next: Displacement vectors for borders to previous and
      • 
+
    
+
    next frames, if applicable.
+- contact_properties: Multipole moment properties based on cell-state contacts.
+- vdist: Secreted ligand density distribution, if secretion_rates is provided.
    
+
    
+
    
+
    Return type:
    
+
    dict
    
+
    
+
    
+
    Notes
    
+
      
+
    • Tracking: If previous (cell_labels_prev) or next frame (cell_labels_next) is provided,
      • 
+
    
+
    the function calculates the optimal transport vector between consecutive frames for each cell.
+- Surface Assignment: When surface_fmask_channels is provided, each channel is used to
+define a distinct surface mask, and surface_states are assigned based on surface_states_baseid.
+- Multipole Moments: Higher-order moments are calculated based on border charges determined
+from neighboring cell states.
    
+
    Examples
    
+
    Compute border properties for frame 10 with secreted ligand distribution and visualize:
    
+
    >>> border_properties = self.get_border_properties_dict(
+...     iS=10, cell_states=my_cell_states, secretion_rates=my_secretion_rates,
+...     surface_fmask_channels=[0, 1], border_resolution=1.0, vdist_scale=0.5, order=2,
+...     visual=True, z_viz=15
+... )
+
    
+
    
+
    
+
 
    
 
    
 get_cell_blocks(label, return_label_ids=False)[source]
    
@@ -4608,7 +4762,7 @@ 
    Submodules
 
    
-get_cell_features(function_tuple, indcells=None, imgchannel=0, mskchannel=0, use_fmask_for_intensity_image=False, fmskchannel=None, use_mask_for_intensity_image=False, bordersize=10, apply_contact_transform=False, return_feature_list=False, save_h5=False, overwrite=False, concatenate_features=False)[source]
    
+get_cell_features(function_tuple, indcells=None, imgchannel=0, mskchannel=0, use_fmask_for_intensity_image=False, fmskchannel=None, use_mask_for_intensity_image=False, bordersize=10, apply_contact_transform=False, preprocess_functions=None, return_feature_list=False, save_h5=False, overwrite=False, concatenate_features=False)[source]
 
    Extracts complex cell features based on the specified custom functions and imaging data.
 This method allows customization of the feature extraction process using region properties
 from segmented cell data, with optional transformations like border erosion or contact
@@ -4863,6 +5017,77 @@ 
    Submodules
+
    
+get_cellboundary_library(frames=None, indcells=None, secretion_rates=None, cell_states=None, surface_fmask_channels=[0], surface_states_baseid=None, border_resolution=1.0, vdist_scale=0.3, visual=False, save_h5=False, overwrite=False, **border_property_args)[source]
    
+
    Collect and create a library of boundary properties for cells across specified frames in a trajectory.
    
+
    This function gathers cell and surface boundary properties across frames or specific cell indices.
+It collects properties such as position, displacement, neighboring point states, curvatures,
+and secreted ligand distributions, if applicable. This data is returned in a library format
+and optionally saved to an HDF5 file for later use.
    
+
    
+
    Parameters:
    
+
      
+
    • frames (list of int, optional) – Frame indices to include in the boundary library. If None, includes all frames in cells_frameSet.
      • 
+
    • indcells (ndarray, optional) – Array of specific cell indices to include in the library. If None, all cells are included.
      • 
+
    • secretion_rates (ndarray, optional) – Array of secretion rates for each cell, used to calculate ligand distribution along borders.
      • 
+
    • cell_states (ndarray, optional) – Array of cell states for each cell. If not provided, cells are assigned state 1.
      • 
+
    • surface_fmask_channels (list of int, default [0]) – List of channels indicating each surface’s mask in the frame data.
      • 
+
    • surface_states_baseid (int, optional) – Starting ID for surface states. Defaults to one more than the maximum cell_states.
      • 
+
    • border_resolution (float, optional) – Resolution for border properties in units of pixels. Default is 1.0.
      • 
+
    • vdist_scale (float, optional) – Scale for secreted ligand distribution. Default is 0.3.
      • 
+
    • visual (bool, optional) – If True, displays a visualization of the boundaries. Default is False.
      • 
+
    • save_h5 (bool, optional) – If True, saves the resulting boundary library to an HDF5 file. Default is False.
      • 
+
    • overwrite (bool, optional) – If True, overwrites the HDF5 data file if it exists. Default is False.
      • 
+
    • **border_property_args – Additional arguments passed to the get_border_properties_dict function.
      • 
+
    
+
    
+
    Returns:
    
+
    boundary_library – Dictionary containing collected boundary properties for the specified cells and frames:
    
+
      
+
    • global_index: Global cell indices for each boundary point.
      • 
+
    • states: Cell or surface state at each boundary point.
      • 
+
    • pts: Coordinates of each boundary point.
      • 
+
    • dx_prevs, dx_nexts: Displacement vectors for boundaries to previous and next frames.
      • 
+
    • surface_normals: Surface normal vectors at each boundary point.
      • 
+
    • curvatures: Curvature at each boundary point.
      • 
+
    • nn_pts: Coordinates of neighboring points.
      • 
+
    • nn_states: States of neighboring points.
      • 
+
    • nn_pts_states: States of neighboring points with finer resolution.
      • 
+
    • surf_vars: Variance of surface displacement in the previous frame.
      • 
+
    • vdists: Secreted ligand density distribution, if secretion_rates is provided.
      • 
+
    
+
    
+
    
+
    Return type:
    
+
    dict
    
+
    
+
    
+
    Notes
    
+
      
+
    • Frames and Cell Selection: If both frames and indcells are provided, only frames derived from
      • 
+
    
+
    indcells are used.
+- Surfaces and States: surface_fmask_channels and surface_states_baseid define and initialize
+surfaces in each frame, using additional states based on surface_states_baseid.
+- Ligand Distribution: Secreted ligand density distribution (vdists) is included if secretion_rates
+is specified.
    
+
    Examples
    
+
    Generate a cell boundary library for frames 10, 15, and 20, with visualization enabled:
    
+
    >>> boundary_library = self.get_cellboundary_library(
+...     frames=[10, 15, 20], secretion_rates=my_secretion_rates, cell_states=my_cell_states,
+...     surface_fmask_channels=[0, 1], visual=True, border_resolution=2.0
+... )
+
    
+
    
+
    Save the boundary library to HDF5 with overwrite enabled:
    
+
    >>> boundary_library = self.get_cellboundary_library(
+...     indcells=my_indcells, save_h5=True, overwrite=True
+... )
+
    
+
    
+
    
+
 
    
 
    
 get_cells_nchildren()[source]
    
@@ -5625,7 +5850,7 @@ 
    Submodules
 
    
-get_unique_trajectories(cell_inds=None, verbose=False, extra_depth=None)[source]
    
+get_unique_trajectories(cell_inds=None, verbose=False, extra_depth=None, save_h5=False, overwrite=False)[source]
 
    Computes unique trajectories for a set of cells over multiple frames, minimizing redundancy by
 ensuring that no two trajectories cover the same cell path beyond a specified overlap (extra_depth).
    
 
    
@@ -6561,6 +6786,11 @@ 
    Submodulescelltraj.utilities.recursively_save_dict_contents_to_group(h5file, path, dic)[source]
 
    
 
+
    
+
    
+celltraj.utilities.rescale_to_int(img, maxint=65535, dtype=<class 'numpy.uint16'>)[source]
    
+
    
+
 
    
 
    
 celltraj.utilities.save_dict_to_h5(dic, h5file, path)[source]
    
diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html
index ad132a5..25ae7c1 100644
--- a/docs/build/html/genindex.html
+++ b/docs/build/html/genindex.html
@@ -417,6 +417,10 @@ 
    G
    
         
  • (celltraj.trajectory_legacy.Trajectory static method)
 
    • 
       
+      
  • get_border_properties() (in module celltraj.spatial), [1]
+
    • 
+      
  • get_border_properties_dict() (celltraj.trajectory.Trajectory method), [1]
+
    • 
       
  • get_borders() (celltraj.celltraj_1apr21.cellTraj static method)
 
       
    • 
+      
  • get_cellboundary_library() (celltraj.trajectory.Trajectory method), [1]
+
    • 
       
  • get_cells_nchildren() (celltraj.trajectory.Trajectory method), [1]
 
    • 
       
  • get_clean_mask() (celltraj.celltraj_1apr21.cellTraj static method)
@@ -660,11 +666,11 @@ 
    G
    
       
  • get_kernel_sigmas() (in module celltraj.model), [1]
 
    • 
       
  • get_kineticstates() (in module celltraj.model), [1]
-
    • 
-      
  • get_koopman_eig() (in module celltraj.model), [1]
 
    • 
   
   
 
diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv
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diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index 0abb9e2..dc5bfbe 100644
--- a/docs/build/html/searchindex.js
+++ b/docs/build/html/searchindex.js
@@ -1 +1 @@
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"celltraj.trajectory.Trajectory.get_cell_sandwich", false], [1, "celltraj.trajectory.Trajectory.get_cell_sandwich", 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_cells_nchildren() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cells_nchildren", false], [1, "celltraj.trajectory.Trajectory.get_cells_nchildren", 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, 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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)": 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(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.spatial": [[0, "module-celltraj.spatial", false], [1, "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_border_properties() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_border_properties", false], [1, "celltraj.spatial.get_border_properties", false]], "get_border_properties_dict() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_border_properties_dict", false], [1, "celltraj.trajectory.Trajectory.get_border_properties_dict", 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_children() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cell_children", false], [1, "celltraj.trajectory.Trajectory.get_cell_children", 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_sandwich() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cell_sandwich", false], [1, "celltraj.trajectory.Trajectory.get_cell_sandwich", 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_cellboundary_library() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cellboundary_library", false], [1, "celltraj.trajectory.Trajectory.get_cellboundary_library", false]], "get_cells_nchildren() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cells_nchildren", false], [1, "celltraj.trajectory.Trajectory.get_cells_nchildren", 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_gradvariance() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_surface_gradvariance", false], [1, "celltraj.spatial.get_surface_gradvariance", 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, 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\ No newline at end of file
diff --git a/docs/celltraj.html b/docs/celltraj.html
index 493a7cd..66d764f 100644
--- a/docs/celltraj.html
+++ b/docs/celltraj.html
@@ -2483,6 +2483,11 @@ 
    Submodules
+
    
+celltraj.imageprep.rescale_to_int(img, maxint=65535, dtype=<class 'numpy.uint16'>)[source]
    
+
    
+
 
    
 
    
 celltraj.imageprep.save_for_viewing(data, fname, metadata=None, overwrite=False)[source]
    
@@ -3619,7 +3624,7 @@ 
    Submodules
 
    
-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]
    
+celltraj.spatial.get_border_dict(labels, states=None, radius=10, vdist=None, return_nnindex=True, return_nnvector=True, return_curvature=True, nn_states=None, 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.
    
@@ -3630,8 +3635,7 @@ 
    Submodulesndarray, 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.
    • 
+
  • vdist (ndarray, optional) – An array representing a scalar value in the image, such as estimated ligand concentration, to store in the border point dictionary.
    • 
 
  • 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).
    • 
@@ -3675,6 +3679,76 @@ 
    Submodules
+
    
+celltraj.spatial.get_border_properties(cell_labels, surfaces=None, cell_states=None, surface_states=None, cell_labels_next=None, cell_labels_prev=None, lin_next=None, lin_prev=None, vdist=None, border_scale=1.0, radius=2.0, order=1, return_border_properties_list=False)[source]
    
+
    Calculate geometric and physical properties of cell borders, with optional tracking between frames
+and surface contact details.
    
+
    This function computes properties of cell borders based on cell_labels, surface contacts, and
+multipole moments up to a specified order. It supports tracking cell border displacement across
+frames using optional next and previous frame labels (cell_labels_next, cell_labels_prev).
+Outputs include multipole moments and border displacements useful in characterizing cell interactions
+and tracking cell behaviors.
    
+
    
+
    Parameters:
    
+
      
+
    • cell_labels (ndarray) – Array of labels identifying distinct cells within the domain.
      • 
+
    • surfaces (list of ndarrays, optional) – List of binary masks indicating surface layers adjacent to cells (e.g., membranes or other
+boundaries). Each is assigned a unique identifier.
      • 
+
    • cell_states (ndarray of int, optional) – Array specifying a state for each cell in cell_labels. Defaults to 1 if not provided.
      • 
+
    • surface_states (ndarray of int, optional) – Array of states corresponding to each surface in surfaces. These states must not overlap
+with cell_states.
      • 
+
    • cell_labels_next (ndarray, optional) – Label array for the next frame to track cell borders over time.
      • 
+
    • cell_labels_prev (ndarray, optional) – Label array for the previous frame, used for backward tracking of cells.
      • 
+
    • lin_next (list of lists, optional) – Mapping of cells in cell_labels to corresponding cells in cell_labels_next. Each entry
+contains IDs of linked cells in the next frame.
      • 
+
    • lin_prev (list of lists, optional) – Mapping of cells in cell_labels to corresponding cells in cell_labels_prev, used for
+backward tracking.
      • 
+
    • border_scale (float, optional) – Resolution in microns applied to border point extraction (default is 1.0).
      • 
+
    • radius (float, optional) – Radius in pixels for calculating border properties such as contact neighbors (default is 2.0).
      • 
+
    • order (int, optional) – Order of multipole moments to calculate for each border contact (default is 1).
      • 
+
    • return_border_properties_list (bool, optional) – If True, returns a list of property names for multipole moments alongside the border
+dictionary.
      • 
+
    
+
    
+
    Returns:
    
+
      
+
    • border_dict (dict) – Dictionary containing calculated properties of each cell’s border, including:
      
+
        
+
      • pts : Array of border points.
        • 
+
      • index : Label of each cell at the border.
        • 
+
      • nn_states : Neighboring cell states at each border point.
        • 
+
      • contact_properties : Array of multipole moment properties calculated up to order.
        • 
+
      • border_dx_next, border_dx_prev : Displacements of each border in the next and previous
+frames, if applicable.
        • 
+
      • dx_next_properties, dx_prev_properties : Arrays of multipole moments based on border
+displacement for next and previous frames.
        • 
+
      
+
      • 
+
    • border_properties_list (list of str, optional) – List of property names for contact_properties array if return_border_properties_list
+is True.
      • 
+
    
+
    
+
    
+
    
+
    Notes
    
+
      
+
    • Multipole Moment Calculation: Moments up to the specified order are computed based on
+border charges (representing contact fraction) for each border point.
      • 
+
    • Cell Tracking: When provided with cell_labels_next or cell_labels_prev, the function
+calculates optimal transport (OT) vectors to track border movement between frames.
      • 
+
    
+
    Examples
    
+
    Calculate properties of cell borders and multipole moments with next-frame tracking:
    
+
    >>> border_dict, property_list = get_border_properties(
+...     cell_labels=my_cell_labels, surfaces=my_surfaces, cell_states=my_states,
+...     cell_labels_next=my_next_frame_labels, lin_next=my_next_frame_links,
+...     border_scale=0.8, radius=1.5, order=2, return_border_properties_list=True
+... )
+
    
+
    
+
    
+
 
    
 
    
 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]
    
@@ -4125,9 +4199,14 @@ 
    Submodules
+
    
+celltraj.spatial.get_surface_gradvariance(border_pts, dx_ot, knn=12, use_eigs=False)[source]
    
+
    
+
 
    
 
    
-celltraj.spatial.get_surface_points(msk, return_normals=False, return_curvature=False, knn=20)[source]
    
+celltraj.spatial.get_surface_points(msk, return_normals=False, return_curvature=False, knn=20, rscale=0.1)[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.
    
@@ -4436,6 +4515,81 @@ 
    Submodules
+
    
+get_border_properties_dict(iS, cell_states=None, secretion_rates=None, surfaces=None, surface_fmask_channels=None, surface_states_baseid=1000, add_label0_surf=True, make_cells_and_surfaces_disjoint=True, border_scale=None, border_resolution=None, vdist_scale=0.3, radius=2.0, order=1, visual=False, z_viz=None)[source]
    
+
    Calculate border properties for cells and surfaces at a given frame in a trajectory.
    
+
    This function extracts data required to characterize the borders of cells within a specified
+frame (iS) in a 3D or 2D cell image sequence. The borders are characterized based on geometric
+and physical properties, with optional visualization and multipole moment expansion up to the
+specified order. Supports the inclusion of surface contacts, tracking cell borders over time,
+and calculating secreted ligand distributions.
    
+
    
+
    Parameters:
    
+
      
+
    • iS (int) – Frame index within the trajectory for which to calculate border properties.
      • 
+
    • cell_states (ndarray, optional) – Array of states for each cell in the current frame. If not provided, all cells are assigned
+state 1 by default.
      • 
+
    • secretion_rates (ndarray, optional) – Array of secretion rates for each cell, used to calculate secreted ligand distribution
+along cell borders. If not provided, no secretion is considered.
      • 
+
    • surfaces (list of ndarrays, optional) – List of binary masks for surfaces adjacent to cells (e.g., membranes or other boundaries).
+Surfaces are optionally assigned states via surface_fmask_channels.
      • 
+
    • surface_fmask_channels (list of int, optional) – List of channels in the frame data specifying each surface’s mask. Used if surfaces is
+None. Each channel mask is assigned a state from surface_states_baseid onward.
      • 
+
    • surface_states_baseid (int, optional) – Base state ID for assigning states to surfaces. Defaults to 1000.
      • 
+
    • add_label0_surf (bool, optional) – If True, includes cells labeled as 0 (background) as a surface layer. Default is True.
      • 
+
    • make_cells_and_surfaces_disjoint (bool, optional) – If True, cells and surfaces are treated as mutually exclusive regions. Default is True.
      • 
+
    • border_scale (float or list of float, optional) – Scale for border calculations. If None, it defaults to the border_resolution attribute
+if available.
      • 
+
    • border_resolution (float, optional) – Resolution of the border calculations in units of pixels. If None, the value from the
+object’s border_resolution attribute is used if present.
      • 
+
    • vdist_scale (float, optional) – Scale factor for calculating the secreted ligand distribution. Default is 0.3.
      • 
+
    • radius (float, optional) – Radius for identifying contact neighbors when calculating border properties. Default is 2.0.
      • 
+
    • order (int, optional) – Order of multipole moments to compute for cell border properties. Default is 1.
      • 
+
    • visual (bool, optional) – If True, generates a visualization of the borders, including displacements from the
+previous and next frames.
      • 
+
    • z_viz (int, optional) – Specific z-plane to visualize in 3D data. If None, a central z-plane is chosen.
      • 
+
    
+
    
+
    Returns:
    
+
    border_dict – Dictionary containing computed properties for cell borders, including:
    
+
      
+
    • pts: Coordinates of each border point.
      • 
+
    • index: Cell IDs associated with each border point.
      • 
+
    • nn_states: States of neighboring cells at each border point.
      • 
+
    • c: Curvature at each border point.
      • 
+
    • global_index: Global indices for each border point in the dataset.
      • 
+
    • border_dx_prev, border_dx_next: Displacement vectors for borders to previous and
      • 
+
    
+
    next frames, if applicable.
+- contact_properties: Multipole moment properties based on cell-state contacts.
+- vdist: Secreted ligand density distribution, if secretion_rates is provided.
    
+
    
+
    
+
    Return type:
    
+
    dict
    
+
    
+
    
+
    Notes
    
+
      
+
    • Tracking: If previous (cell_labels_prev) or next frame (cell_labels_next) is provided,
      • 
+
    
+
    the function calculates the optimal transport vector between consecutive frames for each cell.
+- Surface Assignment: When surface_fmask_channels is provided, each channel is used to
+define a distinct surface mask, and surface_states are assigned based on surface_states_baseid.
+- Multipole Moments: Higher-order moments are calculated based on border charges determined
+from neighboring cell states.
    
+
    Examples
    
+
    Compute border properties for frame 10 with secreted ligand distribution and visualize:
    
+
    >>> border_properties = self.get_border_properties_dict(
+...     iS=10, cell_states=my_cell_states, secretion_rates=my_secretion_rates,
+...     surface_fmask_channels=[0, 1], border_resolution=1.0, vdist_scale=0.5, order=2,
+...     visual=True, z_viz=15
+... )
+
    
+
    
+
    
+
 
    
 
    
 get_cell_blocks(label, return_label_ids=False)[source]
    
@@ -4608,7 +4762,7 @@ 
    Submodules
 
    
-get_cell_features(function_tuple, indcells=None, imgchannel=0, mskchannel=0, use_fmask_for_intensity_image=False, fmskchannel=None, use_mask_for_intensity_image=False, bordersize=10, apply_contact_transform=False, return_feature_list=False, save_h5=False, overwrite=False, concatenate_features=False)[source]
    
+get_cell_features(function_tuple, indcells=None, imgchannel=0, mskchannel=0, use_fmask_for_intensity_image=False, fmskchannel=None, use_mask_for_intensity_image=False, bordersize=10, apply_contact_transform=False, preprocess_functions=None, return_feature_list=False, save_h5=False, overwrite=False, concatenate_features=False)[source]
 
    Extracts complex cell features based on the specified custom functions and imaging data.
 This method allows customization of the feature extraction process using region properties
 from segmented cell data, with optional transformations like border erosion or contact
@@ -4863,6 +5017,77 @@ 
    Submodules
+
    
+get_cellboundary_library(frames=None, indcells=None, secretion_rates=None, cell_states=None, surface_fmask_channels=[0], surface_states_baseid=None, border_resolution=1.0, vdist_scale=0.3, visual=False, save_h5=False, overwrite=False, **border_property_args)[source]
    
+
    Collect and create a library of boundary properties for cells across specified frames in a trajectory.
    
+
    This function gathers cell and surface boundary properties across frames or specific cell indices.
+It collects properties such as position, displacement, neighboring point states, curvatures,
+and secreted ligand distributions, if applicable. This data is returned in a library format
+and optionally saved to an HDF5 file for later use.
    
+
    
+
    Parameters:
    
+
      
+
    • frames (list of int, optional) – Frame indices to include in the boundary library. If None, includes all frames in cells_frameSet.
      • 
+
    • indcells (ndarray, optional) – Array of specific cell indices to include in the library. If None, all cells are included.
      • 
+
    • secretion_rates (ndarray, optional) – Array of secretion rates for each cell, used to calculate ligand distribution along borders.
      • 
+
    • cell_states (ndarray, optional) – Array of cell states for each cell. If not provided, cells are assigned state 1.
      • 
+
    • surface_fmask_channels (list of int, default [0]) – List of channels indicating each surface’s mask in the frame data.
      • 
+
    • surface_states_baseid (int, optional) – Starting ID for surface states. Defaults to one more than the maximum cell_states.
      • 
+
    • border_resolution (float, optional) – Resolution for border properties in units of pixels. Default is 1.0.
      • 
+
    • vdist_scale (float, optional) – Scale for secreted ligand distribution. Default is 0.3.
      • 
+
    • visual (bool, optional) – If True, displays a visualization of the boundaries. Default is False.
      • 
+
    • save_h5 (bool, optional) – If True, saves the resulting boundary library to an HDF5 file. Default is False.
      • 
+
    • overwrite (bool, optional) – If True, overwrites the HDF5 data file if it exists. Default is False.
      • 
+
    • **border_property_args – Additional arguments passed to the get_border_properties_dict function.
      • 
+
    
+
    
+
    Returns:
    
+
    boundary_library – Dictionary containing collected boundary properties for the specified cells and frames:
    
+
      
+
    • global_index: Global cell indices for each boundary point.
      • 
+
    • states: Cell or surface state at each boundary point.
      • 
+
    • pts: Coordinates of each boundary point.
      • 
+
    • dx_prevs, dx_nexts: Displacement vectors for boundaries to previous and next frames.
      • 
+
    • surface_normals: Surface normal vectors at each boundary point.
      • 
+
    • curvatures: Curvature at each boundary point.
      • 
+
    • nn_pts: Coordinates of neighboring points.
      • 
+
    • nn_states: States of neighboring points.
      • 
+
    • nn_pts_states: States of neighboring points with finer resolution.
      • 
+
    • surf_vars: Variance of surface displacement in the previous frame.
      • 
+
    • vdists: Secreted ligand density distribution, if secretion_rates is provided.
      • 
+
    
+
    
+
    
+
    Return type:
    
+
    dict
    
+
    
+
    
+
    Notes
    
+
      
+
    • Frames and Cell Selection: If both frames and indcells are provided, only frames derived from
      • 
+
    
+
    indcells are used.
+- Surfaces and States: surface_fmask_channels and surface_states_baseid define and initialize
+surfaces in each frame, using additional states based on surface_states_baseid.
+- Ligand Distribution: Secreted ligand density distribution (vdists) is included if secretion_rates
+is specified.
    
+
    Examples
    
+
    Generate a cell boundary library for frames 10, 15, and 20, with visualization enabled:
    
+
    >>> boundary_library = self.get_cellboundary_library(
+...     frames=[10, 15, 20], secretion_rates=my_secretion_rates, cell_states=my_cell_states,
+...     surface_fmask_channels=[0, 1], visual=True, border_resolution=2.0
+... )
+
    
+
    
+
    Save the boundary library to HDF5 with overwrite enabled:
    
+
    >>> boundary_library = self.get_cellboundary_library(
+...     indcells=my_indcells, save_h5=True, overwrite=True
+... )
+
    
+
    
+
    
+
 
    
 
    
 get_cells_nchildren()[source]
    
@@ -5625,7 +5850,7 @@ 
    Submodules
 
    
-get_unique_trajectories(cell_inds=None, verbose=False, extra_depth=None)[source]
    
+get_unique_trajectories(cell_inds=None, verbose=False, extra_depth=None, save_h5=False, overwrite=False)[source]
 
    Computes unique trajectories for a set of cells over multiple frames, minimizing redundancy by
 ensuring that no two trajectories cover the same cell path beyond a specified overlap (extra_depth).
    
 
    
@@ -6561,6 +6786,11 @@ 
    Submodulescelltraj.utilities.recursively_save_dict_contents_to_group(h5file, path, dic)[source]
 
    
 
+
    
+
    
+celltraj.utilities.rescale_to_int(img, maxint=65535, dtype=<class 'numpy.uint16'>)[source]
    
+
    
+
 
    
 
    
 celltraj.utilities.save_dict_to_h5(dic, h5file, path)[source]
    
diff --git a/docs/genindex.html b/docs/genindex.html
index ad132a5..25ae7c1 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -417,6 +417,10 @@ 
    G
    
         
  • (celltraj.trajectory_legacy.Trajectory static method)
 
    • 
       
+      
  • get_border_properties() (in module celltraj.spatial), [1]
+
    • 
+      
  • get_border_properties_dict() (celltraj.trajectory.Trajectory method), [1]
+
    • 
       
  • get_borders() (celltraj.celltraj_1apr21.cellTraj static method)
 
       
    • 
+      
  • get_cellboundary_library() (celltraj.trajectory.Trajectory method), [1]
+
    • 
       
  • get_cells_nchildren() (celltraj.trajectory.Trajectory method), [1]
 
    • 
       
  • get_clean_mask() (celltraj.celltraj_1apr21.cellTraj static method)
@@ -660,11 +666,11 @@ 
    G
    
       
  • get_kernel_sigmas() (in module celltraj.model), [1]
 
    • 
       
  • get_kineticstates() (in module celltraj.model), [1]
-
    • 
-      
  • get_koopman_eig() (in module celltraj.model), [1]
 
    • 
   
   
 
diff --git a/docs/objects.inv b/docs/objects.inv
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diff --git a/docs/searchindex.js b/docs/searchindex.js
index 0abb9e2..dc5bfbe 100644
--- a/docs/searchindex.js
+++ b/docs/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"]], 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"celltraj.trajectory.Trajectory.get_cells_nchildren", false], [1, "celltraj.trajectory.Trajectory.get_cells_nchildren", 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 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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)": 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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_border_properties() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_border_properties", false], [1, "celltraj.spatial.get_border_properties", false]], "get_border_properties_dict() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_border_properties_dict", false], [1, "celltraj.trajectory.Trajectory.get_border_properties_dict", 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_children() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cell_children", false], [1, "celltraj.trajectory.Trajectory.get_cell_children", 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_sandwich() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cell_sandwich", false], [1, "celltraj.trajectory.Trajectory.get_cell_sandwich", 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_cellboundary_library() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cellboundary_library", false], [1, "celltraj.trajectory.Trajectory.get_cellboundary_library", false]], "get_cells_nchildren() (celltraj.trajectory.trajectory method)": [[0, "celltraj.trajectory.Trajectory.get_cells_nchildren", false], [1, "celltraj.trajectory.Trajectory.get_cells_nchildren", 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_gradvariance() (in module celltraj.spatial)": [[0, "celltraj.spatial.get_surface_gradvariance", false], [1, "celltraj.spatial.get_surface_gradvariance", 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, 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"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, 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\ No newline at end of file