benchmark: add trend lines to latency plots (#54)

* benchmark: add trend lines to plots

* benchmark: close figure after use

* benchmark: sort sizes in-place

betree/haura-benchmarks/haura-plots/haura_plots/__init__.py

@@ -218,8 +218,20 @@ def plot_evaluation_latency(path, variant):
     fig, ax = plt.subplots(1,1,figsize=(6,4))
     reads = data[data['op'] == 'r']
     writes = data[data['op'] == 'w']
-    ax.scatter(reads['size'], reads['latency_ns'], marker='x', label="read")
-    ax.scatter(writes['size'], writes['latency_ns'], marker='.', label="write")
+    if len(reads) > 0:
+        ax.scatter(reads['size'], reads['latency_ns'], marker='x', label="read")
+        size = reads['size'].to_numpy()
+        latency = reads['latency_ns'].to_numpy()
+        p = np.polynomial.Polynomial.fit(size, latency, 1)
+        size.sort()
+        ax.plot(size, p(size), linestyle=':', label='read trend', color='black')
+    if len(writes) > 0:
+        ax.scatter(writes['size'], writes['latency_ns'], marker='.', label="write")
+        size = writes['size'].to_numpy()
+        latency = writes['latency_ns'].to_numpy()
+        p = np.polynomial.Polynomial.fit(size, latency, 1)
+        size.sort()
+        ax.plot(size, p(size), linestyle='-.', label='write trend', color='black')
     xticks = np.arange(0, 12 * 1024 * 1024 + 1, 2 * 1024 * 1024)
     ax.set_xticks(xticks, [int(x / 1024) for x in xticks])
     ax.set_xlabel("Size in KiB")

betree/haura-benchmarks/haura-plots/haura_plots/metrics_plots.py

@@ -146,3 +146,4 @@ def plot_system(path):
 
     fig.tight_layout()
     fig.savefig(f"{path}/proc.svg")
+    plt.close(fig)