Merge pull request #76 from converged-computing/fix-mixbench-stream-colors

analysis: fix colors for stream, mixbench, magma

Author: vsoch

analysis/magma/1-run-analysis.py

@@ -246,7 +246,8 @@ def plot_results(results, df, outdir):
         plt.figure(figsize=(10, 6))
 
         offsets = [-0.75, -0.5, -0.25, 0.25, 0.5, 0.75]
-        colors = ["#003f5c", "#58508d", "#bc5090", "#de5a79", "#ff6361", "#ffa600"]
+        palette = sns.color_palette("hls", 6)
+        colors = palette.as_hex()
         for experiment, values in ms_vectors.items():
             positions = np.array(np.arange(len(values))) * 2.0 + offsets.pop(0)
             plot = plt.boxplot(

analysis/magma/data/img/magma-milliseconds-size-16.png

@@ -6,7 +6,6 @@
 import re
 import sys
 import pandas
-import matplotlib.pylab as plt
 
 import seaborn as sns
 
@@ -196,7 +195,6 @@ def parse_data(indir, outdir, files):
 
     # It's important to just parse raw data once, and then use intermediate
     for filename in files:
-
         # Underscore means skip, also skip configs and runs without efa
         # runs with google and shared memory were actually slower...
         dirname = os.path.basename(filename)
@@ -229,7 +227,6 @@ def parse_data(indir, outdir, files):
         # Now we can read each result file to get metrics.
         results = list(ps.get_outfiles(filename))
         for result in results:
-
             # Skip over found erroneous results
             if errors and re.search(error_regex, result):
                 print(f"Skipping {result} due to known missing result or error.")
@@ -363,7 +360,6 @@ def plot_results(results, outdir):
             palette = collections.OrderedDict()
             for t in types:
                 palette[t] = hexcolors.pop(0)
-            title = " ".join([x.capitalize() for x in metric.split("_")])
 
             if len(metric_df.value.unique()) == 1:
                 print(

analysis/magma/data/img/magma-milliseconds-size-32.png

@@ -2,14 +2,19 @@
 
 import argparse
 import os
-import re
 import matplotlib.pylab as plt
 import csv
 import seaborn as sns
 import numpy as np
+import sys
 
 here = os.path.dirname(os.path.abspath(__file__))
 sns.set_theme(style="whitegrid", palette="pastel")
+analysis_root = os.path.dirname(here)
+root = os.path.dirname(analysis_root)
+sys.path.insert(0, analysis_root)
+
+import performance_study as ps
 
 
 def get_parser():
@@ -227,7 +232,8 @@ def plot_results(results, metric_type, outdir):
             # side
             plt.figure(figsize=(20, 6))
             offsets = [-0.75, -0.5, -0.25, 0.25, 0.5, 0.75]
-            colors = ["#003f5c", "#58508d", "#bc5090", "#de5a79", "#ff6361", "#ffa600"]
+            palette = sns.color_palette("hls", 6)
+            colors = palette.as_hex()
             for experiment, values in vectors.items():
                 # TODO figure this out...
                 positions = np.array(np.arange(len(values))) * 2.0 + offsets.pop(0)