linting

autostreamtree/functions.py

@@ -249,7 +249,7 @@ def parse_subgraph_from_points(params, point_coords, pop_coords, G):
     else:
         print(
             "NOTE: Not over-writing existing network. To change this, use",
-                "--overwrite"
+            "--overwrite"
             )
 
     network_plot = str(params.out) + ".subGraph.pdf"
@@ -440,7 +440,7 @@ def parse_input_genmat(params, inmat, point_coords, popmap, seqs=None):
                 del inmat
         elif params.geopop or params.clusterpop:
             if (len(inmat.columns)) != len(point_coords):
-                print("Found", str(len(inmat.columns)), 
+                print("Found", str(len(inmat.columns)),
                       "columns in provided matrix. This doesn't match number",
                       "of individuals.")
                 print("When using --geopop or --clusterpop, the provided",
@@ -1233,7 +1233,7 @@ def extract_minimal_existing(subgraph, graph, nodelist, id_col, dist_col,
         graph (NetworkX Graph): The input graph.
         nodelist (list): The list of nodes.
         id_col (str): The column name for edge ID.
-        dist_col (str): Column name for dist attribute 
+        dist_col (str): Column name for dist attribute
         path (list): The path between nodes.
     """
     curr_edge = {id_col: list(), dist_col: 0}
@@ -1285,7 +1285,7 @@ def write_geodataframe(gdf, output_prefix, output_driver):
     extension = {
         "SHP": ".shp",
         "GPKG": ".gpkg",
-        "GDB": ".gdb"  
+        "GDB": ".gdb"
     }.get(output_driver.upper(), ".gpkg")  # Default to .gpkg
 
     output_path = f"{output_prefix}{extension}"

autostreamtree/genetic_distances.py

@@ -184,7 +184,7 @@ def get_genmat(dist, points, seqs, ploidy, het, loc_agg):
     genmat[:] = np.nan
 
     # NOT USED CURRENTLY
-    # for models which relax equal nuc frequencies, get global frequencies 
+    # for models which relax equal nuc frequencies, get global frequencies
     # for each locus
     # freqs will be a list of loci, with each locus as a dist of freqs
     # if dist in ["TN84", "TN93"]:
@@ -459,19 +459,15 @@ def hamming_distance(seq1, seq2):
 # e.g. ['A/A', 'A/B', 'B/B', ...].
 #         s2 (List[str]): A list of phased genotypes from population 2,
 # e.g. ['A/A', 'A/C', 'C/C', ...].
-
 #     Returns:
 #         float: The Nei's 1983 Da estimator.
-
 #     """
 #     # Clean the input lists by removing individuals with unknown or gap
 # alleles.
 #     s1 = clean_list(s1, ["n", "?", "-", "N"])
 #     s2 = clean_list(s2, ["n", "?", "-", "N"])
-
 #     # Get the list of unique alleles from both populations.
 #     uniques = uniq_alleles(s1+s2)
-
 #     # Compute the sum of squared roots of frequencies for each allele.
 #     sumSqRt = 0.0
 #     for allele in uniques:
@@ -499,11 +495,9 @@ def hamming_distance(seq1, seq2):
 #     """
 #     # Get unique alleles in the two populations
 #     uniques = seq.uniq_alleles(s1+s2)
-
 #     # Clean the sequences by removing any unknown or gap alleles
 #     s1 = clean_list(s1, ["n", "?", "-", "N"])
 #     s2 = clean_list(s2, ["n", "?", "-", "N"])
-
 #     # Calculate Euclidean distance
 #     sumSq = 0.0
 #     for allele in uniques:
@@ -789,4 +783,3 @@ def uniq_alleles(s):
         set: A set of unique alleles.
     """
     return set(sum([x.split("/") for x in s], []))
-

autostreamtree/params.py

@@ -17,7 +17,7 @@ def __init__(self):
                  "loc_agg=", "pop_agg=", "sdist_agg=", "clusterpop",
                  "epsilon=", "min_samples=", "sclusterpop", "network=",
                  "overwrite", "reachid_col=", "length_col=", "coercemat",
-                 "locmatdir=", "vcf=", "concat=", "edge_list=", "gdf_out=", 
+                 "locmatdir=", "vcf=", "concat=", "edge_list=", "gdf_out=",
                  "seed="]
             )
         except getopt.GetoptError as err: