very large refactoring
diff --git a/wally/assumptions_check.py b/wally/assumptions_check.py
new file mode 100644
index 0000000..41ae2e0
--- /dev/null
+++ b/wally/assumptions_check.py
@@ -0,0 +1,170 @@
+import sys
+
+import texttable as TT
+
+import numpy as np
+import matplotlib.pyplot as plt
+from numpy.polynomial.chebyshev import chebfit, chebval
+
+from .io_results_loader import load_data, filter_data
+from .statistic import approximate_line, difference
+
+
+def linearity_plot(data, types, vals=None):
+ fields = 'blocksize_b', 'iops_mediana', 'iops_stddev'
+
+ names = {}
+ for tp1 in ('rand', 'seq'):
+ for oper in ('read', 'write'):
+ for sync in ('sync', 'direct', 'async'):
+ sq = (tp1, oper, sync)
+ name = "{0} {1} {2}".format(*sq)
+ names["".join(word[0] for word in sq)] = name
+
+ colors = ['red', 'green', 'blue', 'cyan',
+ 'magenta', 'black', 'yellow', 'burlywood']
+ markers = ['*', '^', 'x', 'o', '+', '.']
+ color = 0
+ marker = 0
+
+ for tp in types:
+ filtered_data = filter_data('linearity_test_' + tp, fields)
+ x = []
+ y = []
+ e = []
+ # values to make line
+ ax = []
+ ay = []
+
+ for sz, med, dev in sorted(filtered_data(data)):
+ iotime_ms = 1000. // med
+ iotime_max = 1000. // (med - dev * 3)
+
+ x.append(sz / 1024.0)
+ y.append(iotime_ms)
+ e.append(iotime_max - iotime_ms)
+ if vals is None or sz in vals:
+ ax.append(sz / 1024.0)
+ ay.append(iotime_ms)
+
+ plt.errorbar(x, y, e, linestyle='None', label=names[tp],
+ color=colors[color], ecolor="black",
+ marker=markers[marker])
+ ynew = approximate_line(ax, ay, ax, True)
+ plt.plot(ax, ynew, color=colors[color])
+ color += 1
+ marker += 1
+ plt.legend(loc=2)
+ plt.title("Linearity test by %i dots" % (len(vals)))
+
+
+def linearity_table(data, types, vals):
+ """ create table by pyplot with diferences
+ between original and approximated
+ vals - values to make line"""
+ fields = 'blocksize_b', 'iops_mediana'
+ for tp in types:
+ filtered_data = filter_data('linearity_test_' + tp, fields)
+ # all values
+ x = []
+ y = []
+ # values to make line
+ ax = []
+ ay = []
+
+ for sz, med in sorted(filtered_data(data)):
+ iotime_ms = 1000. // med
+ x.append(sz / 1024.0)
+ y.append(iotime_ms)
+ if sz in vals:
+ ax.append(sz / 1024.0)
+ ay.append(iotime_ms)
+
+ ynew = approximate_line(ax, ay, x, True)
+
+ dif, _, _ = difference(y, ynew)
+ table_data = []
+ for i, d in zip(x, dif):
+ row = ["{0:.1f}".format(i), "{0:.1f}".format(d[0]), "{0:.0f}".format(d[1]*100)]
+ table_data.append(row)
+
+ tab = TT.Texttable()
+ tab.set_deco(tab.VLINES)
+
+ header = ["BlockSize, kB", "Absolute difference (ms)", "Relative difference (%)"]
+ tab.add_row(header)
+ tab.header = header
+
+ for row in table_data:
+ tab.add_row(row)
+
+ # uncomment to get table in pretty pictures :)
+ # colLabels = ("BlockSize, kB", "Absolute difference (ms)", "Relative difference (%)")
+ # fig = plt.figure()
+ # ax = fig.add_subplot(111)
+ # ax.axis('off')
+ # #do the table
+ # the_table = ax.table(cellText=table_data,
+ # colLabels=colLabels,
+ # loc='center')
+ # plt.savefig(tp+".png")
+
+
+def th_plot(data, tt):
+ fields = 'concurence', 'iops_mediana', 'lat_mediana'
+ conc_4k = filter_data('concurrence_test_' + tt, fields, blocksize='4k')
+ filtered_data = sorted(list(conc_4k(data)))
+
+ x, iops, lat = zip(*filtered_data)
+
+ _, ax1 = plt.subplots()
+
+ xnew = np.linspace(min(x), max(x), 50)
+ # plt.plot(xnew, power_smooth, 'b-', label='iops')
+ ax1.plot(x, iops, 'b*')
+
+ for degree in (3,):
+ c = chebfit(x, iops, degree)
+ vals = chebval(xnew, c)
+ ax1.plot(xnew, vals, 'g--')
+
+ # ax1.set_xlabel('thread count')
+ # ax1.set_ylabel('iops')
+
+ # ax2 = ax1.twinx()
+ # lat = [i / 1000 for i in lat]
+ # ax2.plot(x, lat, 'r*')
+
+ # tck = splrep(x, lat, s=0.0)
+ # power_smooth = splev(xnew, tck)
+ # ax2.plot(xnew, power_smooth, 'r-', label='lat')
+
+ # xp = xnew[0]
+ # yp = power_smooth[0]
+ # for _x, _y in zip(xnew[1:], power_smooth[1:]):
+ # if _y >= 100:
+ # xres = (_y - 100.) / (_y - yp) * (_x - xp) + xp
+ # ax2.plot([xres, xres], [min(power_smooth), max(power_smooth)], 'g--')
+ # break
+ # xp = _x
+ # yp = _y
+
+ # ax2.plot([min(x), max(x)], [20, 20], 'g--')
+ # ax2.plot([min(x), max(x)], [100, 100], 'g--')
+
+ # ax2.set_ylabel("lat ms")
+ # plt.legend(loc=2)
+
+
+def main(argv):
+ data = list(load_data(open(argv[1]).read()))
+ linearity_table(data, ["rwd", "rws", "rrd"], [4096, 4096*1024])
+ # linearity_plot(data, ["rwd", "rws", "rrd"])#, [4096, 4096*1024])
+ # linearity_plot(data, ["rws", "rwd"])
+ # th_plot(data, 'rws')
+ # th_plot(data, 'rrs')
+ plt.show()
+
+
+if __name__ == "__main__":
+ exit(main(sys.argv))