[MRG] add --distance-matrix option to sourmash compare

doc/command-line.md

@@ -210,7 +210,8 @@ sourmash compare file1.sig [ file2.sig ... ]
 
 Options:
 
-* `--output` -- save the distance matrix to this file (as a numpy binary matrix)
+* `--output` -- save the output matrix to this file (as a numpy binary matrix).
+* `--distance-matrix` -- create and output a distance matrix, instead of a similarity matrix.
 * `--ksize` -- do the comparisons at this k-mer size.
 * `--containment` -- calculate containment instead of similarity; `C(i, j) = size(i intersection j) / size(i)`
 * `--ani` -- output estimates of Average Nucleotide Identity (ANI) instead of Jaccard similarity or containment.
@@ -238,7 +239,7 @@ ANI output matrix will be asymmetric as discussed above.
 ### `sourmash plot` - cluster and visualize comparisons of many signatures
 
 The `plot` subcommand produces two plots -- a dendrogram and a
-dendrogram+matrix -- from a distance matrix created by `sourmash compare
+dendrogram+matrix -- from a matrix created by `sourmash compare
 --output <matrix>`.  The default output is two PNG files.
 
 Usage:

src/sourmash/cli/compare.py

@@ -1,4 +1,4 @@
-"""compare sequence signatures made by compute"""
+"""create a similarity matrix comparing many samples"""
 
 usage="""
 
@@ -39,8 +39,6 @@ def subparser(subparsers):
     subparser.add_argument(
         '-q', '--quiet', action='store_true', help='suppress non-error output'
     )
-    add_ksize_arg(subparser)
-    add_moltype_args(subparser)
     subparser.add_argument(
         '-o', '--output', metavar='F',
         help='file to which output will be written; default is terminal '
@@ -82,6 +80,18 @@ def subparser(subparsers):
     subparser.add_argument(
         '-p', '--processes', metavar='N', type=int, default=None,
         help='Number of processes to use to calculate similarity')
+    subparser.add_argument(
+        '--distance-matrix', action='store_true',
+        help='output a distance matrix, instead of a similarity matrix'
+    )
+    subparser.add_argument(
+        '--similarity-matrix', action='store_false',
+        dest='distance_matrix',
+        help='output a similiarty matrix; this is the default',
+    )
+
+    add_ksize_arg(subparser)
+    add_moltype_args(subparser)
     add_picklist_args(subparser)
     add_pattern_args(subparser)
 

src/sourmash/commands.py

@@ -169,16 +169,26 @@ def compare(args):
         similarity = compare_all_pairs(siglist, args.ignore_abundance,
                                        n_jobs=args.processes, return_ani=return_ani)
 
+    # if distance matrix desired, switch to 1-similarity
+    if args.distance_matrix:
+        matrix = 1 - similarity
+    else:
+        matrix = similarity
+
     if len(siglist) < 30:
-        for i, E in enumerate(siglist):
+        for i, ss in enumerate(siglist):
             # for small matrices, pretty-print some output
-            name_num = '{}-{}'.format(i, str(E))
+            name_num = '{}-{}'.format(i, str(ss))
             if len(name_num) > 20:
                 name_num = name_num[:17] + '...'
-            print_results('{:20s}\t{}'.format(name_num, similarity[i, :, ],))
+            print_results('{:20s}\t{}'.format(name_num, matrix[i, :, ],))
 
-    print_results('min similarity in matrix: {:.3f}', numpy.min(similarity))
-    # shall we output a matrix?
+    if args.distance_matrix:
+        print_results('max distance in matrix: {:.3f}', numpy.max(matrix))
+    else:
+        print_results('min similarity in matrix: {:.3f}', numpy.min(matrix))
+
+    # shall we output a matrix to stdout?
     if args.output:
         labeloutname = args.output + '.labels.txt'
         notify(f'saving labels to: {labeloutname}')
@@ -187,7 +197,7 @@ def compare(args):
 
         notify(f'saving comparison matrix to: {args.output}')
         with open(args.output, 'wb') as fp:
-            numpy.save(fp, similarity)
+            numpy.save(fp, matrix)
 
     # output CSV?
     if args.csv:
@@ -198,11 +208,14 @@ def compare(args):
             for i in range(len(labeltext)):
                 y = []
                 for j in range(len(labeltext)):
-                    y.append('{}'.format(similarity[i][j]))
+                    y.append(str(matrix[i][j]))
                 w.writerow(y)
 
     if size_may_be_inaccurate:
-        notify("WARNING: size estimation for at least one of these sketches may be inaccurate. ANI values will be set to 0 for these comparisons.")
+        if args.distance_matrix:
+            notify("WARNING: size estimation for at least one of these sketches may be inaccurate. ANI distances will be set to 1 for these comparisons.")
+        else:
+            notify("WARNING: size estimation for at least one of these sketches may be inaccurate. ANI values will be set to 1 for these comparisons.")
 
 
 def plot(args):