diff options
Diffstat (limited to 'tools/gdb-sd_dump_hashmaps.py')
| -rw-r--r-- | tools/gdb-sd_dump_hashmaps.py | 14 |
1 files changed, 7 insertions, 7 deletions
diff --git a/tools/gdb-sd_dump_hashmaps.py b/tools/gdb-sd_dump_hashmaps.py index b3c356b57..7f5d31eca 100644 --- a/tools/gdb-sd_dump_hashmaps.py +++ b/tools/gdb-sd_dump_hashmaps.py @@ -51,7 +51,7 @@ class sd_dump_hashmaps(gdb.Command): t = ["plain", "ordered", "set"][int(h["type"])] - print "%s, %s, %s, %d, %d, %d, %s (%s:%d)" % (t, h["hash_ops"], bool(h["has_indirect"]), n_entries, d["max_entries"], n_buckets, d["func"], d["file"], d["line"]) + print "{}, {}, {}, {}, {}, {}, {} ({}:{})".format(t, h["hash_ops"], bool(h["has_indirect"]), n_entries, d["max_entries"], n_buckets, d["func"], d["file"], d["line"]) if arg != "" and n_entries > 0: dib_raw_addr = storage_ptr + (all_entry_sizes[h["type"]] * n_buckets) @@ -63,10 +63,10 @@ class sd_dump_hashmaps(gdb.Command): for dib in sorted(iter(histogram)): if dib != 255: - print "%3d %8d %f%% of entries" % (dib, histogram[dib], 100.0*histogram[dib]/n_entries) + print "{:>3} {:>8} {} of entries".format(dib, histogram[dib], 100.0*histogram[dib]/n_entries) else: - print "%3d %8d %f%% of slots" % (dib, histogram[dib], 100.0*histogram[dib]/n_buckets) - print "mean DIB of entries: %f" % (sum([dib*histogram[dib] for dib in iter(histogram) if dib != 255])*1.0/n_entries) + print "{:>3} {:>8} {} of slots".format(dib, histogram[dib], 100.0*histogram[dib]/n_buckets) + print "mean DIB of entries: {}".format(sum([dib*histogram[dib] for dib in iter(histogram) if dib != 255])*1.0/n_entries) blocks = [] current_len = 1 @@ -87,9 +87,9 @@ class sd_dump_hashmaps(gdb.Command): if len(blocks) > 1 and blocks[0][0] == blocks[0][1] and blocks[-1][0] == n_buckets - 1: blocks[0][1] += blocks[-1][1] blocks = blocks[0:-1] - print "max block: %s" % max(blocks, key=lambda a: a[1]) - print "sum block lens: %d" % sum(b[1] for b in blocks) - print "mean block len: %f" % (1.0 * sum(b[1] for b in blocks) / len(blocks)) + print "max block: {}".format(max(blocks, key=lambda a: a[1])) + print "sum block lens: {}".format(sum(b[1] for b in blocks)) + print "mean block len: {}".format((1.0 * sum(b[1] for b in blocks) / len(blocks))) d = d["debug_list_next"] |