Update plot script in `connection_scoring` benchmark

benches/connection_scoring/bench.py

@@ -4,6 +4,9 @@ import glob
 import time
 import statistics
 import json
+import sys
+
+sys.path.append(os.path.realpath(os.path.join(__file__, "..", "..", "..")))
 
 import tqdm
 
@@ -45,7 +48,7 @@ for filename in tqdm.tqdm(glob.glob(os.path.join(args.data, "*.fna"))):
     # run connection scoring
     for backend in ["avx", "sse", "generic", None]:
         times = []
-        for run in tqdm.tqdm(range(args.runs), description=str(backend), leave=False):
+        for run in tqdm.tqdm(range(args.runs), desc=str(backend), leave=False):
             # initialize scorer
             scorer = ConnectionScorer(backend=backend)
             scorer_nodes = nodes.copy()

benches/connection_scoring/plot.py

@@ -8,7 +8,7 @@ import math
 import numpy
 import matplotlib.pyplot as plt
 import scipy.stats
-from palettable.cartocolors.qualitative import Bold_3
+from palettable.cartocolors.qualitative import Bold_4
 
 plt.rcParams["svg.fonttype"] = "none"
 
@@ -25,6 +25,8 @@ with open(args.input) as f:
 for result in data["results"]:
     if result["backend"] is None:
         result["backend"] = "None"
+    elif result["backend"] == "generic":
+        result["backend"] = "Generic"
     else:
         result["backend"] = result["backend"].upper()
 
@@ -33,15 +35,15 @@ plt.figure(1, figsize=(12, 6))
 plt.subplot(1, 2, 1)
 data["results"].sort(key=lambda r: (r["backend"], r["node_count"]))
 for color, (backend, group) in zip(
-    Bold_3.hex_colors, itertools.groupby(data["results"], key=lambda r: r["backend"])
+    Bold_4.hex_colors, itertools.groupby(data["results"], key=lambda r: r["backend"])
 ):
     group = list(group)
     X = numpy.array([r["node_count"] for r in group])
     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="")
-    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={reg.rvalue:.3f})")
+    # ci = [1.96 * r["stddev"] / math.sqrt(len(r["times"])) for r in group]
+    plt.scatter(X, Y, marker="+", color=color, label=f"{backend} (R={reg.rvalue:.3f})")
 plt.legend()
 plt.xlabel("Node count")
 plt.ylabel("Time (s)")
@@ -50,15 +52,15 @@ plt.ylabel("Time (s)")
 plt.subplot(1, 2, 2)
 data["results"].sort(key=lambda r: (r["backend"], r["nucleotide_count"]))
 for color, (backend, group) in zip(
-    Bold_3.hex_colors, itertools.groupby(data["results"], key=lambda r: r["backend"])
+    Bold_4.hex_colors, itertools.groupby(data["results"], key=lambda r: r["backend"])
 ):
     group = list(group)
     X = numpy.array([r["nucleotide_count"] / 1_000_000 for r in group])
     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="")
-    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={reg.rvalue:.3f})")
+    # ci = [1.96 * r["stddev"] / math.sqrt(len(r["times"])) for r in group]
+    plt.scatter(X, Y, marker="+", color=color, label=f"{backend} (R={reg.rvalue:.3f})")
 plt.legend()
 plt.xlabel("Nucleotide count (Mbp)")
 plt.ylabel("Time (s)")