Merge branch 'development'

cytopath/markov_chain_sampler.py

@@ -245,6 +245,7 @@ def sampling(data, auto_adjust=True, matrix_key = 'T_forward', cluster_key = 'lo
     ratio_obtained = 0
 
     count = 0 # Iterate sampling runs
+    old_step_increment=0
     # Resample samples until the number of trajectories for each end point has been reached.
     while (min(traj_num) < traj_number) or (ratio_obtained < 0.1):
 
@@ -321,7 +322,16 @@ def sampling(data, auto_adjust=True, matrix_key = 'T_forward', cluster_key = 'lo
                 raise ValueError('Sampling failed. Try lowering min_sim_ratio or increase rounds_limit.')
 
         if min(traj_num) < min_sim_ratio*traj_number:
-            max_steps = math.ceil(traj_number/(max(max_steps, min(traj_num))+1)) + max_steps
+
+            # Break out of klein increments
+            new_step_increment = math.ceil(traj_number/(max(max_steps, min(traj_num))+1))
+            if new_step_increment==old_step_increment:
+                new_step_increment=old_step_increment*3
+
+            # Adjust max steps
+            max_steps = new_step_increment + max_steps
+
+            old_step_increment = new_step_increment
 
             glob_all_seq = []
             glob_prob_all_seq = []

cytopath/trajectory_estimation/cyto_path.py

@@ -77,7 +77,13 @@ def cell_neighborhood_finder(adata, map_state, end_point, neighbors_basis='pca',
     # Cluster assignment
     if n_neighbors_cluster=='auto':
         min_cluster_size = adata.obs[adata.uns['run_info']['cluster_key']].value_counts().min()
-        n_neighbors_cluster=int(max(min_cluster_size - min_cluster_size*cluster_freq, adata.shape[0]/200))
+        n_neighbors_cluster = min_cluster_size - min_cluster_size*cluster_freq
+        if n_neighbors_cluster < 10:
+            n_neighbors_cluster = 10
+        if adata.shape[0]/200 > 10:
+            n_neighbors_cluster = adata.shape[0]/200
+
+        adata.uns['run_info']['n_neighbors_cluster'] = n_neighbors_cluster
 
     # Find clusters composing the trajectory
     compositional_clusters = []
@@ -116,6 +122,7 @@ def cell_neighborhood_finder(adata, map_state, end_point, neighbors_basis='pca',
     if n_neighbors == 'auto':
         n_neighbors = adata.obs.loc[adata.obs[adata.uns['run_info']['cluster_key']].astype(str).isin(adata.uns['trajectories']['compositional_clusters'][end_point]), 
                       adata.uns['run_info']['cluster_key']].value_counts().max()
+        adata.uns['run_info']['n_neighbors'] = n_neighbors
 
     # Create neighborhood
     neighborhood_sequence=[]