Remove debugging prints

PopPUNK/network.py

@@ -16,7 +16,6 @@
 from multiprocessing import Pool
 import pickle
 import graph_tool.all as gt
-import pp_sketchlib
 
 # Load GPU libraries
 try:
@@ -1196,8 +1195,6 @@ def construct_network_from_assignments(rlist, qlist, assignments, within_label =
         weights = process_weights(distMat, weights_type)
 
     # Convert edge indices to tuples
-    # TODO remove this print
-    print(f"{len(assignments)} assignments {len(rlist)} refs {len(qlist)} queries\n")
     connections = poppunk_refine.generateTuples(assignments,
                                                 within_label,
                                                 self = (rlist == qlist),
@@ -2092,81 +2089,3 @@ def remove_non_query_components(G, rlist, qlist, use_gpu = False):
         query_subgraph = gt.GraphView(G, vfilt=query_filter)
 
     return query_subgraph, pruned_names
-
-def generate_network_from_distances(mode,
-                                    model,
-                                    core_distMat = None,
-                                    acc_distMat = None,
-                                    sparse_mat = None,
-                                    previous_mst = None,
-                                    combined_seq = None,
-                                    rlist = None,
-                                    old_rlist = None,
-                                    distance_type = 'core',
-                                    threads = 1,
-                                    gpu_graph = False):
-    """
-    Generates a network from a distance matrix.
-    
-    Args:
-        mode (str)
-            Whether a core or sparse distance matrix is being analysed
-        model (ClusterFit or LineageFit)
-            A fitted model object
-        coreMat (numpy.array)
-            NxN array of core distances for N sequences
-        accMat (numpy.array)
-            NxN array of accessory distances for N sequences
-        sparse_mat (scipy or cupyx sparse matrix)
-            Sparse matrix of kNN from lineage fit
-        previous_mst (str or graph object)
-            Path of file containing existing network, or already-loaded
-            graph object
-        combined_seq (list)
-            Ordered list of isolate names
-        rlist (list)
-            List of reference sequence labels
-        old_rlist (list)
-            List of reference sequence labels for previous MST
-        distance_type (str)
-            Whether to use core or accessory distances for MST calculation
-            or dense network weighting
-        threads (int)
-            Number of threads to use in calculations
-        use_gpu (bool)
-            Whether to use GPUs for network construction
-
-    Returns:
-        G (graph)
-            The resulting network
-        pruned_names (list)
-            The labels of the sequences in the pruned network
-    """
-    if mode == 'sparse':
-        G = generate_mst_from_sparse_input(sparse_mat,
-                                            rlist,
-                                            old_rlist = old_rlist,
-                                            previous_mst = previous_mst,
-                                            gpu_graph = gpu_graph)
-    elif mode == 'dense':
-        # Get distance matrix
-        complete_distMat = \
-            np.hstack((pp_sketchlib.squareToLong(core_distMat, threads).reshape(-1, 1),
-                    pp_sketchlib.squareToLong(acc_distMat, threads).reshape(-1, 1)))
-        # Identify short distances and use these to extend the model
-        indivAssignments = model.assign(complete_distMat)
-        G = construct_network_from_assignments(combined_seq,
-                                                combined_seq,
-                                                indivAssignments,
-                                                model.within_label,
-                                                distMat = complete_distMat,
-                                                weights_type = distance_type,
-                                                use_gpu = gpu_graph,
-                                                summarise = False)
-        if gpu_graph:
-            G = cugraph.minimum_spanning_tree(G, weight='weights')
-
-    else:
-        sys.stderr.write('Unknown network mode - expect dense or sparse\n')
-
-    return G

PopPUNK/sketchlib.py

@@ -581,8 +581,6 @@ def queryDatabase(rNames, qNames, dbPrefix, queryPrefix, klist, self = True, num
 
         # Calls to library
         query_db = queryPrefix + "/" + os.path.basename(queryPrefix)
-        # TODO remove this print
-        print(f"{len(rNames)} refs {len(qNames)} queries\n")
         distMat = pp_sketchlib.queryDatabase(ref_db_name=ref_db,
                                              query_db_name=query_db,
                                              rList=rNames,