diff --git a/celltraj/features.py b/celltraj/features.py
index 6808bdf..953ce03 100644
--- a/celltraj/features.py
+++ b/celltraj/features.py
@@ -436,7 +436,7 @@ def featNucBoundary(regionmask, intensity):
         xf = np.ones(15)*np.nan
     return xf
 
-def get_cc_cs_border(mskcell,fmskcell,bordersize=10):
+def get_cc_cs_border(mskcell,fmskcell,bordersize=0):
     """
     Identifies core-cell (cc) and cell-surrounding (cs) borders within a given cell mask by applying
     morphological operations and boundary detection.
@@ -513,7 +513,7 @@ def get_cc_cs_border(mskcell,fmskcell,bordersize=10):
     csborder=csborder.astype(int)
     return ccborder,csborder
 
-def boundaryCB_FFT(msk,fmsk,ncomp=15,center=None,nth=256,bordersize=1):
+def boundaryCB_FFT(msk,fmsk,ncomp=15,center=None,nth=256,bordersize=0):
     """
     Computes the Fourier Transform of boundary data for a mask distinguishing between core-cell
     and cell-surrounding regions, encoding the shape information in frequency space.
@@ -649,7 +649,7 @@ def featBoundaryCB(regionmask, intensity):
     - Visualization commands within the function (commented out) can be enabled for debugging or understanding
       the process by visual inspection.
     """
-    regionmask=skimage.morphology.binary_erosion(regionmask)
+    #regionmask=skimage.morphology.binary_erosion(regionmask)
     #plt.clf();plt.imshow(np.max(intensity,axis=0));plt.contour(np.max(regionmask,axis=0),colors='red');plt.pause(2)
     intensity=intensity>0
     if regionmask.ndim==3:
diff --git a/celltraj/translate.py b/celltraj/translate.py
index bc41966..e818913 100644
--- a/celltraj/translate.py
+++ b/celltraj/translate.py
@@ -6,6 +6,7 @@
 import scipy
 import pyemma.coordinates as coor
 from adjustText import adjust_text
+import itertools
 
 #class Translate():
 """
@@ -51,7 +52,7 @@ def get_predictedFC(state_probs,statesFC):
         x_FC_predicted[itr,:]=(np.tile(statep,(nG,1))*statesFC.T).sum(-1)
     return x_FC_predicted
 
-def get_state_decomposition(self,x_fc,state_probs,npermutations=500,inds_tm_training=None,save_file=None,visual=False,verbose=True,nchunk=100,gene_names=None,lb=None,ub=None):
+def get_state_decomposition(x_fc,state_probs,npermutations=500,inds_tm_training=None,save_file=None,visual=False,verbose=True,nchunk=100,gene_names=None,lb=None,ub=None):
     n=state_probs.shape[1]
     ntr=state_probs.shape[0]
     nG=x_fc.shape[1]
@@ -103,18 +104,21 @@ def get_state_decomposition(self,x_fc,state_probs,npermutations=500,inds_tm_trai
         np.save(save_file,x_fc_states)
     return x_fc_states
 
-def get_null_correlations(self,x_fc,x_fc_states,x_fc_predicted,nrandom=500,nchunk=20,save_file=None):
+def get_null_correlations(x_fc,x_fc_states,x_fc_predicted,nrandom=500,seed=None,tmfSet=None):
     n=x_fc_states.shape[0]
     ntr=x_fc.shape[0]
-    nrandom=500
-    xr=6
+    if tmfSet is None:
+        tmfSet=np.arange(ntr).astype(str)
+    if seed is None:
+        seed=0
+    rng = np.random.default_rng(seed=seed)
     corrSet_pred=np.zeros(ntr)
     corrSet_predrand=np.zeros((nrandom,ntr))
     corrSet_rand=np.zeros((nrandom,ntr))
     for ir in range(nrandom):
         state_probs_r=np.zeros((ntr,n))
         for itr in range(ntr):
-            rp=np.random.rand(n)
+            rp=rng.random(n)
             rp=rp/np.sum(rp)
             state_probs_r[itr,:]=rp.copy()
         x_fc_null=get_predictedFC(state_probs_r,x_fc_states)
@@ -127,8 +131,5 @@ def get_null_correlations(self,x_fc,x_fc_states,x_fc_predicted,nrandom=500,nchun
             corrSet_pred[itr]=rhoSet[0,2]
             corrSet_rand[ir,itr]=rhoSet[1,2]
             corrSet_predrand[ir,itr]=rhoSet[0,1]
-            print(tmfSet[itr]+f' correlation: prediction {rhoSet[0,2]:.2f}, null {rhoSet[1,2]:.2f} prednull {rhoSet[0,1]:.2f}, ir: {ir} of {nrandom}')
-            if ir%nchunk==0 or ir==nrandom-1:
-                if save_file is not None:
-                    np.save(save_file,corrSet_rand)
-        return corrSet_pred, corrSet_rand, corrSet_predrand
+            #print(tmfSet[itr]+f' correlation: prediction {rhoSet[0,2]:.2f}, null {rhoSet[1,2]:.2f} prednull {rhoSet[0,1]:.2f}, ir: {ir} of {nrandom}')
+    return corrSet_pred, corrSet_rand, corrSet_predrand
diff --git a/environment.yml b/environment.yml
index be7a3b8..6560b74 100644
--- a/environment.yml
+++ b/environment.yml
@@ -33,3 +33,4 @@ dependencies:
    - adjustText
    - cmapfile
    - nanomesh
+   - seaborn
diff --git a/pyproject.toml b/pyproject.toml
index 7889b65..3fbcc91 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "celltraj"
-version = "0.1.51"
+version = "0.1.52"
 authors = [
   { name="Jeremy Copperman", email="copperma@ohsu.edu" },
 ]