Merge pull request #10 from vsbogd/move-make_benchmark_graphs.py-into…

…-benchmarks-repo

Move make_benchmark_graphs.py into benchmarks repo

atomspace/atomspace/README.md

@@ -64,11 +64,11 @@ records (you can always output to file with -f and do stats calculations later)
 
 ## Graphs ##
 
-There is a script scripts/make_benchmark_graphs.py which will create graphs
-from the csv files. You must have matplotlib (Python graphing library)
-installed for this to work. If you run the script from a directory with
-files ending in "_benchmark.csv" in it, it will create a .png file for
-each.
+There is a script atomspace/atomspace/make_benchmark_graphs.py which will
+create graphs from the csv files. You must have matplotlib (Python graphing
+library) version 2.0 or greater installed for this to work. If you run the
+script from a directory with files ending in "_benchmark.csv" in it, it will
+create a .png file for each.
 
 ## TODO ##
 

atomspace/atomspace/make_benchmark_graphs.py

@@ -0,0 +1,112 @@
+#!/usr/bin/env python
+
+# Requires matplotlib for graphing
+# reads *_benchmark.csv files as output by atomspace_bm and turns them into
+# graphs.
+
+
+import csv
+import numpy as np
+import matplotlib.colors as colors
+#import matplotlib.finance as finance
+import matplotlib.dates as mdates
+import matplotlib.ticker as mticker
+import matplotlib.mlab as mlab
+import matplotlib.pyplot as plt
+#import matplotlib.font_manager as font_manager
+
+import glob
+import pdb
+
+
+def moving_average(x, n, type='simple'):
+    """
+    compute an n period moving average.
+
+    type is 'simple' | 'exponential'
+
+    """
+    x = np.asarray(x)
+    if type=='simple':
+        weights = np.ones(n)
+    else:
+        weights = np.exp(np.linspace(-1., 0., n))
+
+    weights /= weights.sum()
+
+    a =  np.convolve(x, weights, mode='full')[:len(x)]
+    a[:n] = a[n]
+    return a
+
+def graph_file(fn,delta_rss=True):
+    print "Graphing " + fn
+    records = csv.reader(open(fn,'rb'),delimiter=",")
+    sizes=[]; times=[]; times_seconds=[]; memories=[]
+    for row in records:
+        sizes.append(int(row[0]))
+        times.append(int(row[1]))
+        memories.append(int(row[2]))
+        times_seconds.append(float(row[3]))
+
+    left, width = 0.1, 0.8
+    rect1 = [left, 0.5, width, 0.4]  #left, bottom, width, height
+    rect2 = [left, 0.1, width, 0.4]
+
+    fig = plt.figure(facecolor='white')
+    axescolor  = '#f6f6f6'  # the axies background color
+
+    ax1 = fig.add_axes(rect1, facecolor=axescolor)
+    ax2 = fig.add_axes(rect2, facecolor=axescolor, sharex=ax1)
+
+    ax1.plot(sizes,times_seconds,color='black')
+    if len(times_seconds) > 1000:
+        ax1.plot(sizes,moving_average(times_seconds,len(times_second) / 100),color='blue')
+    if delta_rss:
+        oldmemories = list(memories)
+        for i in range(1,len(memories)): memories[i] = oldmemories[i] - oldmemories[i-1]
+    ax2.plot(sizes,memories,color='black')
+
+    for label in ax1.get_xticklabels():
+        label.set_visible(False)
+
+    class MyLocator(mticker.MaxNLocator):
+        def __init__(self, *args, **kwargs):
+            mticker.MaxNLocator.__init__(self, *args, **kwargs)
+
+        def __call__(self, *args, **kwargs):
+            return mticker.MaxNLocator.__call__(self, *args, **kwargs)
+
+    # at most 7 ticks, pruning the upper and lower so they don't overlap
+    # with other ticks
+    fmt = mticker.ScalarFormatter()
+    fmt.set_powerlimits((-3, 4))
+    ax1.yaxis.set_major_formatter(fmt)
+
+    ax2.yaxis.set_major_locator(MyLocator(7, prune='upper'))
+    fmt = mticker.ScalarFormatter()
+    fmt.set_powerlimits((-3, 4))
+    ax2.yaxis.set_major_formatter(fmt)
+    ax2.yaxis.offsetText.set_visible(False)
+    fig.show()
+    size = int(fmt.orderOfMagnitude) / 3
+    labels = ["B","KB","MB","GB"]
+    label = labels[size]
+    labels = ["","(10s)","(100s)"]
+    label += " " + labels[int(fmt.orderOfMagnitude) % 3]
+
+    ax2.set_xlabel("AtomSpace Size")
+    ax2.set_ylabel("RSS " + label)
+    ax1.set_ylabel("Time (seconds)")
+    ax1.set_title(fn)
+    fig.show()
+
+    fig.savefig(fn+".png",format="png")
+
+files_to_graph = glob.glob("*_benchmark.csv")
+
+for fn in files_to_graph:
+    graph_file(fn);
+
+
+
+