Added initial implementation of plotGraph.py .

More charts should be added

Author: jmfernandez

README.md

@@ -84,6 +84,12 @@ You have a sample directory obtained from measuring a workflow execution using W
 
 The command line is something like:
 
+```bash
+python execution-metrics-collector.py {base_metrics_directory} {command line} {and} {parameters}
+```
+
+The equivalent old wrapper version would be:
+
 ```bash
 ./execution-metrics-collector.sh {base_metrics_directory} {command line} {and} {parameters}
 ```
@@ -100,6 +106,15 @@ For instance, the sample directory was obtained just running next command line:
 ~/projects/execution-process-metrics-collector/execution-metrics-collector.sh ~/projects/execution-process-metrics-collector/Wetlab2Variations_metrics python WfExS-backend.py -L workflow_examples/local_config.yaml staged-workdir offline-exec 01a1db90-1508-4bad-beb7-7f7989838542
 ```
 
+## Time series charts
+The program `plotGraph.py` is a replacement for the original `plotGraph.sh`. It generates
+several line charts for each monitored process, comparing the time series of
+interesting metrics.
+
+```bash
+python plotGraph.py sample-series/Wetlab2Variations_metrics/2025_05_20-02_19-14001/ dest_directory
+```
+
 ## Digestion
 
 The program `tdp-finder.py` helps to obtain the TDP of an Intel processor, using the gathered metadata stored at `cpu_details.json` within the series directory.

execution_process_metrics_collector/aggregator.py

@@ -46,6 +46,7 @@
 
 from .parser import (
     metrics_parser,
+    timedelta_noday_formatter,
 )
 
 logger = logging.getLogger(__name__)
@@ -128,6 +129,8 @@ def draw_tree(
         matplotlib.use("agg")
         fig.savefig(outputs_dir / "graph.png")
 
+    plt.close(fig)
+
 
 def draw_spiral(
     pids: "pd.DataFrame",
@@ -195,26 +198,7 @@ def draw_spiral(
         matplotlib.use("agg")
         fig.savefig(outputs_dir / "spiral-graph.png")
 
-
-def timedelta_full_formatter(td: "pd.Timedelta") -> "str":
-    return str(td)
-
-
-def timedelta_noday_formatter(td: "pd.Timedelta") -> "str":
-    if td.components.days > 0:
-        return timedelta_full_formatter(td)
-
-    if td.components.milliseconds > 0:
-        return "{0:02d}:{1:02d}:{2:02d}.{3:03d}".format(
-            td.components.hours,
-            td.components.minutes,
-            td.components.seconds,
-            td.components.milliseconds,
-        )
-    else:
-        return "{0:02d}:{1:02d}:{2:02d}".format(
-            td.components.hours, td.components.minutes, td.components.seconds
-        )
+    plt.close(fig)
 
 
 def draw_consumptions_chart(
@@ -322,6 +306,8 @@ def draw_consumptions_chart(
         matplotlib.use("agg")
         fig.savefig(outputs_dir / "consumptions.png")
 
+    plt.close(fig)
+
 
 def draw_lollipop_chart(
     node_consumptions: "pd.DataFrame",
@@ -460,6 +446,8 @@ def draw_lollipop_chart(
         matplotlib.use("agg")
         fig.savefig(outputs_dir / "timeline.png")
 
+    plt.close(fig)
+
 
 def metrics_aggregator(
     series_dir: "pathlib.Path",

execution_process_metrics_collector/parser.py

@@ -181,3 +181,24 @@ def metrics_parser(
     pids["subtree_root"] = subtree_root
 
     return pids, num_cpu_cores, sampling_period_seconds
+
+
+def timedelta_full_formatter(td: "pd.Timedelta") -> "str":
+    return str(td)
+
+
+def timedelta_noday_formatter(td: "pd.Timedelta") -> "str":
+    if td.components.days > 0:
+        return timedelta_full_formatter(td)
+
+    if td.components.milliseconds > 0:
+        return "{0:02d}:{1:02d}:{2:02d}.{3:03d}".format(
+            td.components.hours,
+            td.components.minutes,
+            td.components.seconds,
+            td.components.milliseconds,
+        )
+    else:
+        return "{0:02d}:{1:02d}:{2:02d}".format(
+            td.components.hours, td.components.minutes, td.components.seconds
+        )