fix perf parsing and add new plot functions and use them

1 files changed, 6 insertions, 32 deletions

diff --git a/larson.py b/larson.py
index d7d1033..728401b 100644
--- a/larson.py
+++ b/larson.py
@@ -1,7 +1,4 @@
-import matplotlib.pyplot as plt
 import multiprocessing
-import numpy as np
-import os
 import re
 
 from benchmark import Benchmark
@@ -32,35 +29,12 @@ class Benchmark_Larson( Benchmark ):
             if res:
                 result["throughput"] = int(res.group("throughput"))
                 return
-        print(stdout)
-        print("no match")
-
-    def summary(self, sd=None):
-        # Speedup thrash
-        args = self.results["args"]
-        nthreads = args["threads"]
-        targets = self.results["targets"]
-
-        sd = sd or ""
-
-        for arg in args:
-            loose_arg = [a for a in args if a != arg][0]
-            for arg_value in args[arg]:
-                for target in targets:
-                    y_vals = []
-                    for perm in self.iterate_args_fixed({arg : arg_value}, args=args):
-                        d = [m["throughput"] for m in self.results[target][perm]]
-                        y_vals.append(np.mean(d))
-                    x_vals = list(range(1, len(y_vals) + 1))
-                    plt.plot(x_vals, y_vals, marker='.', linestyle='-',
-                        label=target, color=targets[target]["color"])
-                plt.legend()
-                plt.xticks(x_vals, args[loose_arg])
-                plt.xlabel(loose_arg)
-                plt.ylabel("OPS/s")
-                plt.title("Larson: " + arg + " " + str(arg_value))
-                plt.savefig(os.path.join(sd, ".".join([self.name, arg, str(arg_value), "png"])))
-                plt.clf()
 
+    def summary(self, sumdir):
+        # Plot threads->throughput and maxsize->throughput
+        self.plot_fixed_args("{throughput}",
+                    ylabel="'OPS/s'",
+                    title = "'Larson: ' + arg + ' ' + str(arg_value)",
+                    sumdir=sumdir)
 
 larson = Benchmark_Larson()