De-duplicate dependency tree calculations and data structures, de-clutter notebook

message_tree/message_tree_plots.py

@@ -0,0 +1,49 @@
+from typing import List
+
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.axes import Axes
+from matplotlib.figure import Figure
+
+from message_tree.message_tree_structure import E2EBreakdownItem
+
+
+def e2e_breakdown_type_hist(items: List[E2EBreakdownItem]):
+    """
+    Given a list of e2e breakdown instances of the form `("<type>", <duration>)`, plots a histogram for each encountered
+    type.
+    """
+    plot_types = ("dds", "idle", "cpu")
+    assert all(item.type in plot_types for item in items)
+
+    fig: Figure
+    fig, axes = plt.subplots(1, 3, num="E2E type breakdown histograms")
+    fig.suptitle("E2E Latency Breakdown by Resource Type")
+
+    for type, ax in zip(plot_types, axes):
+        ax: Axes
+
+        durations = [item.duration for item in items if item.type == type]
+
+        ax.set_title(type)
+        ax.hist(durations)
+        ax.set_xlabel("Duration [s]")
+        ax.set_ylabel("Occurrences")
+
+    return fig
+
+
+def e2e_breakdown_inst_stack(*paths: List[E2EBreakdownItem]):
+    fig: Figure
+    ax: Axes
+    fig, ax = plt.subplots(num="E2E instance breakdown stackplot")
+    fig.suptitle("Detailed E2E Latency Path Breakdown")
+
+    bottom = 0
+    for i in range(len(paths)):
+        e2e_items = [path[i] for path in paths]
+        durations = np.array([item.duration for item in e2e_items])
+        ax.bar(range(len(paths)), durations, bottom=bottom)
+        bottom = durations + bottom
+
+    return fig