def deviation_generation(fname, groups_list):
""" Print deviation by groups for data from fname """
CONC_POS = key_pos['concurence']
int_list = [int(i) for i in groups_list]
data = list(io_test.load_io_py_file(fname))
item = io_test.Data("hdr")
for key, vals in io_test.groupby_globally(data, io_test.key_func).items():
item.series[key] = [val['iops'] * key[CONC_POS] for val in vals]
print deviation_on_deviation(int_list, item.series[key])
def deviation_generation(fname, groups_list):
""" Print deviation by groups for data from fname """
CONC_POS = key_pos['concurence']
int_list = [int(i) for i in groups_list]
data = list(io_test.load_io_py_file(fname))
item = io_test.Data("hdr")
for key, vals in io_test.groupby_globally(data, io_test.key_func).items():
item.series[key] = [val['iops'] * key[CONC_POS] for val in vals]
print deviation_on_deviation(int_list, item.series[key])
def plot_generation(fname, group_by):
""" plots for value group_by in imgs by actions"""
data = list(io_test.load_io_py_file(fname))
item = io_test.Data("hdr")
for key, vals in io_test.groupby_globally(data, io_test.key_func).items():
item.series[key] = [val['iops'] for val in vals]
io_test.process_inplace(item)
pr_data = generate_groups(item.processed_series, group_by)
print pr_data
#fig = plt.figure()
plot = plt.subplot(111)
for action in actions:
for tp in types:
color = 0
hasPlot = False
for key, val in pr_data.items():
if action in key and tp in key:
ok = gen_app_plot(key, val, plot, colors[color])
hasPlot = hasPlot or ok
color += 1
# use it for just connect dots
#save_plot(key, val)
if hasPlot:
# Shrink current axis by 10%
box = plot.get_position()
plot.set_position([
box.x0, box.y0 + box.height * 0.1, box.width,
box.height * 0.9
])
# Put a legend to the bottom
plot.legend(loc='lower center',
bbox_to_anchor=(0.5, -0.25),
fancybox=True,
shadow=True,
ncol=4,
fontsize='xx-small')
plt.title("Plot for %s on %s" % (group_by, action))
plt.ylabel("time")
plt.xlabel(group_by)
plt.grid()
# use it if want scale plot somehow
# plt.axis([0.0, 5000.0, 0.0, 64.0])
name = "%s__%s_%s.png" % (group_by, action, tp)
plt.savefig(name, format='png', dpi=100)
plt.clf()
plot = plt.subplot(111)
color = 0
def plot_generation(fname, group_by):
""" plots for value group_by in imgs by actions"""
data = list(io_test.load_io_py_file(fname))
item = io_test.Data("hdr")
for key, vals in io_test.groupby_globally(data, io_test.key_func).items():
item.series[key] = [val['iops'] for val in vals]
io_test.process_inplace(item)
pr_data = generate_groups(item.processed_series, group_by)
print pr_data
#fig = plt.figure()
plot = plt.subplot(111)
for action in actions:
for tp in types:
color = 0
hasPlot = False
for key, val in pr_data.items():
if action in key and tp in key:
ok = gen_app_plot(key, val, plot, colors[color])
hasPlot = hasPlot or ok
color += 1
# use it for just connect dots
#save_plot(key, val)
if hasPlot:
# Shrink current axis by 10%
box = plot.get_position()
plot.set_position([box.x0, box.y0 + box.height * 0.1,
box.width, box.height * 0.9])
# Put a legend to the bottom
plot.legend(loc='lower center', bbox_to_anchor=(0.5, -0.25),
fancybox=True, shadow=True, ncol=4,
fontsize='xx-small')
plt.title("Plot for %s on %s" % (group_by, action))
plt.ylabel("time")
plt.xlabel(group_by)
plt.grid()
# use it if want scale plot somehow
# plt.axis([0.0, 5000.0, 0.0, 64.0])
name = "%s__%s_%s.png" % (group_by, action, tp)
plt.savefig(name, format='png', dpi=100)
plt.clf()
plot = plt.subplot(111)
color = 0
