Import QEMU upstream snapshot d2e570c

Upstream: https://gitlab.com/qemu-project/qemu.git

Upstream-Commit: d2e570cc0f97b936902a5b1b86b73c0f5998b475

scripts/simplebench/results_to_text.py

@@ -0,0 +1,126 @@
+#!/usr/bin/env python3
+#
+# Simple benchmarking framework
+#
+# Copyright (c) 2019 Virtuozzo International GmbH.
+#
+# This program is free software; you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation; either version 2 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+#
+
+import math
+import tabulate
+
+# We want leading whitespace for difference row cells (see below)
+tabulate.PRESERVE_WHITESPACE = True
+
+
+def format_value(x, stdev):
+    stdev_pr = stdev / x * 100
+    if stdev_pr < 1.5:
+        # don't care too much
+        return f'{x:.2g}'
+    else:
+        return f'{x:.2g} ± {math.ceil(stdev_pr)}%'
+
+
+def result_to_text(result):
+    """Return text representation of bench_one() returned dict."""
+    if 'average' in result:
+        s = format_value(result['average'], result['stdev'])
+        if 'n-failed' in result:
+            s += '\n({} failed)'.format(result['n-failed'])
+        return s
+    else:
+        return 'FAILED'
+
+
+def results_dimension(results):
+    dim = None
+    for case in results['cases']:
+        for env in results['envs']:
+            res = results['tab'][case['id']][env['id']]
+            if dim is None:
+                dim = res['dimension']
+            else:
+                assert dim == res['dimension']
+
+    assert dim in ('iops', 'seconds')
+
+    return dim
+
+
+def results_to_text(results):
+    """Return text representation of bench() returned dict."""
+    n_columns = len(results['envs'])
+    named_columns = n_columns > 2
+    dim = results_dimension(results)
+    tab = []
+
+    if named_columns:
+        # Environment columns are named A, B, ...
+        tab.append([''] + [chr(ord('A') + i) for i in range(n_columns)])
+
+    tab.append([''] + [c['id'] for c in results['envs']])
+
+    for case in results['cases']:
+        row = [case['id']]
+        case_results = results['tab'][case['id']]
+        for env in results['envs']:
+            res = case_results[env['id']]
+            row.append(result_to_text(res))
+        tab.append(row)
+
+        # Add row of difference between columns. For each column starting from
+        # B we calculate difference with all previous columns.
+        row = ['', '']  # case name and first column
+        for i in range(1, n_columns):
+            cell = ''
+            env = results['envs'][i]
+            res = case_results[env['id']]
+
+            if 'average' not in res:
+                # Failed result
+                row.append(cell)
+                continue
+
+            for j in range(0, i):
+                env_j = results['envs'][j]
+                res_j = case_results[env_j['id']]
+                cell += ' '
+
+                if 'average' not in res_j:
+                    # Failed result
+                    cell += '--'
+                    continue
+
+                col_j = tab[0][j + 1] if named_columns else ''
+                diff_pr = round((res['average'] - res_j['average']) /
+                                res_j['average'] * 100)
+                cell += f' {col_j}{diff_pr:+}%'
+            row.append(cell)
+        tab.append(row)
+
+    return f'All results are in {dim}\n\n' + tabulate.tabulate(tab)
+
+
+if __name__ == '__main__':
+    import sys
+    import json
+
+    if len(sys.argv) < 2:
+        print(f'USAGE: {sys.argv[0]} results.json')
+        exit(1)
+
+    with open(sys.argv[1]) as f:
+        print(results_to_text(json.load(f)))