added a --tag

benchpress/visualizer/cli_series.py

@@ -10,16 +10,16 @@ def visualize(args):
 
     # First we create `means` which map a command label and date to a pair of mean and standard deviation
     # e.g. means['Bean']['2017-05-02 13:29:34.87'] = (0.1289, 0.0006)
-    (means, cmd_labels, creation_dates) = util.means_series_map(args)
+    (means, cmd_labels, meta_keys) = util.means_series_map(args)
 
     # Then we write the data
     ret = "Label%s " % args.csv_separator
-    for creation_date in creation_dates:
-        ret += "%s%s " % (creation_date, args.csv_separator)
+    for meta_key in meta_keys:
+        ret += "%s%s " % (meta_key, args.csv_separator)
     for cmd_label in cmd_labels:
         ret += "\n%s%s " % (cmd_label, args.csv_separator)
-        for creation_date in creation_dates:
-            (mean, std) = means[cmd_label].get(creation_date, (0, 0))
+        for meta_key in meta_keys:
+            (mean, std) = means[cmd_label].get(meta_key, (0, 0))
             ret += "%.4f (%.4f)%s " % (mean, std, args.csv_separator)
     return ret
 
@@ -33,6 +33,13 @@ def main():
         metavar="sep",
         help="Use the CSV format using 'sep' as the separator."
     )
+    parser.add_argument(
+        '--meta-key',
+        default='creation_date_utc',
+        type=str,
+        choices=['creation_date_utc', 'tag'],
+        help="The meta key, which value vary throughout the series"
+    )
     args = parser.parse_args()
     if args.output is not None:
         args.output.write(visualize(args))

benchpress/visualizer/series_per_cmd.py

@@ -18,15 +18,15 @@ def line_per_cmd(args):
 
     # First we create `means` which map a command label and date to a pair of mean and standard deviation
     # e.g. means['Bean']['2017-05-02 13:29:34.87'] = (0.1289, 0.0006)
-    (means, cmd_labels, creation_dates) = util.means_series_map(args)
+    (means, cmd_labels, meta_keys) = util.means_series_map(args)
 
     # Let's write a plot line for each command label
     fig, ax = plt.subplots()
     lines = []
     for cmd_label in cmd_labels:
         x, y, err = ([], [], [])
-        for i in range(len(creation_dates)):
-            (mean, std) = means[cmd_label].get(creation_dates[i], (0, 0))
+        for i in range(len(meta_keys)):
+            (mean, std) = means[cmd_label].get(meta_keys[i], (0, 0))
             x.append(i)
             y.append(mean)
             err.append(std)
@@ -40,8 +40,8 @@ def line_per_cmd(args):
         plt.title(util.texsafe(args.title))
 
     # Add some text for labels, title and axes ticks
-    ax.set_xticks(range(len(creation_dates)))
-    ax.set_xticklabels(creation_dates, rotation=args.xticklabel_rotation)
+    ax.set_xticks(range(len(meta_keys)))
+    ax.set_xticklabels(meta_keys, rotation=args.xticklabel_rotation)
 
     if not args.no_legend:
         ax.legend(lines, cmd_labels, loc='best', fancybox=True, shadow=True)
@@ -132,6 +132,13 @@ def main():
         choices=plt.style.available,
         help="The matplotlib.pyplot.style to use"
     )
+    parser.add_argument(
+        '--meta-key',
+        default='creation_date_utc',
+        type=str,
+        choices=['creation_date_utc', 'tag'],
+        help="The meta key, which value vary throughout the series"
+    )
     parser.add_argument(
         '--mpld3',
         action='store_true',

benchpress/visualizer/util.py

@@ -281,7 +281,7 @@ def texsafe(text):
 
 
 def means_series_map(args):
-    """Create a dict that map a command label and date to a pair of mean and standard deviation
+    """Create a dict that map a command label and meta-key to a pair of mean and standard deviation
 
     Such as means['Bean']['2017-05-02 13:29:34.87'] = (0.1289, 0.0006)
 
@@ -296,17 +296,17 @@ def means_series_map(args):
         The dict that maps command label and date to a pair of mean and standard deviation
     cmd_labels : list
         List of commands labels found in `args`
-    creation_dates : list
-        List of creation dates found in `args`
+    meta-keys : list
+        List of meta key values found in `args`
     """
     # First we create `means` which map a command label and date to a pair of mean and standard deviation
     # e.g. means['Bean']['2017-05-02 13:29:34.87'] = (0.1289, 0.0006)
     means = {}
     cmd_labels = set()
-    creation_dates = set()
+    meta_keys = set()
     for result in args.results:
         suite = json.load(result)
-        creation_date = suite['creation_date_utc']
+        meta_key = suite[args.meta_key]
         cmd_list = suite['cmd_list']
         cmd_list = filter_cmd_list(cmd_list, args.labels_to_include, args.labels_to_exclude)
         for cmd in cmd_list:
@@ -315,7 +315,7 @@ def means_series_map(args):
             std = standard_deviation(succeed_values)
             if cmd['label'] not in means:
                 means[cmd['label']] = {}
-            means[cmd['label']][creation_date] = (avg, std)
+            means[cmd['label']][meta_key] = (avg, std)
             cmd_labels.add(cmd['label'])
-            creation_dates.add(creation_date)
-    return (means, sorted(list(cmd_labels)), sorted(list(creation_dates)))
+            meta_keys.add(meta_key)
+    return (means, sorted(list(cmd_labels)), sorted(list(meta_keys)))