diff --git a/scripts/disk_io_pp.py b/scripts/disk_io_pp.py
new file mode 100644
index 0000000..bf901d3
--- /dev/null
+++ b/scripts/disk_io_pp.py
@@ -0,0 +1,60 @@
+import sys
+import collections
+
+import scipy.stats as stats
+import matplotlib.mlab as mlab
+import matplotlib.pyplot as plt
+
+from data_stat import med_dev, round_deviation
+from data_stat import read_data_agent_result
+
+data = read_data_agent_result(sys.argv[1])
+
+# for run in data:
+#     for name, numbers in run['res'].items():
+#         # med, dev = round_deviation(med_dev(numbers['iops']))
+#         # print name, med, '~', dev
+#         distr = collections.defaultdict(lambda: 0.0)
+#         for i in numbers['iops']:
+#             distr[i] += 1
+
+#         print name
+#         for key, val in sorted(distr.items()):
+#             print "    ", key, val
+#         print
+
+
+
+# # example data
+# mu = 100 # mean of distribution
+# sigma = 15 # standard deviation of distribution
+# x = mu + sigma * np.random.randn(10000)
+
+x = data[0]['res'][sys.argv[2]]['iops']
+# mu, sigma = med_dev(x)
+# print mu, sigma
+
+# med_sz = 1
+# x2 = x[:len(x) // med_sz * med_sz]
+# x2 = [sum(vals) / len(vals) for vals in zip(*[x2[i::med_sz]
+#                                               for i in range(med_sz)])]
+
+mu, sigma = med_dev(x)
+print mu, sigma
+print stats.normaltest(x)
+
+num_bins = 20
+# the histogram of the data
+n, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='green', alpha=0.5)
+# add a 'best fit' line
+
+y = mlab.normpdf(bins, mu, sigma)
+plt.plot(bins, y, 'r--')
+
+plt.xlabel('Smarts')
+plt.ylabel('Probability')
+plt.title(r'Histogram of IQ: $\mu={}$, $\sigma={}$'.format(mu, sigma))
+
+# Tweak spacing to prevent clipping of ylabel
+plt.subplots_adjust(left=0.15)
+plt.show()