Update `figure2.svg` to use latest benchmark results

benches/connection_scoring/plot.py

@@ -58,13 +58,9 @@ for color, (backend, group) in zip(
     Y = numpy.array([r["mean"] for r in group])
     reg = scipy.stats.linregress(X, Y)
     plt.plot([ 0, max(X) ], [ reg.intercept, reg.slope*max(X) + reg.intercept ], color=color, linestyle="--", marker="")
-<<<<<<< HEAD
     # ci = [1.96 * r["stddev"] / math.sqrt(len(r["times"])) for r in group]
     plt.scatter(X, Y, marker="+", color=color, label=f"{backend} (R²={reg.rvalue**2:.3f})")
-=======
-    ci = [1.96 * r["stddev"] / math.sqrt(len(r["times"])) for r in group]
-    plt.errorbar(X, Y, ci, linestyle='', marker="+", color=color, elinewidth=0.3, ecolor='black', label=f"{backend} ($R^2$={reg.rvalue**2:.3f})")
->>>>>>> d7a9c03 (Update connection scoring benchmark plot to show R² instead of R)
+
 plt.legend()
 plt.xlabel("Nucleotide count (Mbp)")
 plt.ylabel("Time (s)")

paper/figure2.svg

-../benches/connection_scoring/v0.6.4.svg
\ No newline at end of file
+../benches/connection_scoring/v0.7.2.svg
\ No newline at end of file

paper/paper.md

@@ -129,8 +129,9 @@ the heuristic filter in Pyrodigal saves about half of the time needed to score
 connections between all the nodes of a sequence.
 
 ![Evaluation of the connection scoring performance with different heuristic
-filter SIMD backends (SSE2 or AVX2) or without enabling the filter (None).
-*Each sequence was processed 10 times on a quiet i7-8550U CPU @ 1.80GHz*. \label{fig:benchmark}](figure2.svg){width=100%}
+filter SIMD backends (SSE2 or AVX2), with a generic backend (Generic) or without enabling 
+the filter (None).
+*Each sequence was processed 10 times on a quiet i7-10710U CPU @ 1.10GHz*. \label{fig:benchmark}](figure2.svg){width=100%}
 
 
 # Availability