def run(self,
fig_num,
is_noise=True,
noise_level=10**(-2),
output_csv=OUTPUT_CSV):
"""
run simulation and save result
:return:
"""
tout, self.y, x = signal.lsim2(self.system, self.u, self.t)
noise = np.zeros(len(self.t))
if is_noise:
noise = np.random.rand(len(self.t)) * noise_level * max(self.y)
self.y += noise
mycsv.save(self.t,
self.u,
self.y,
save_name=self.path + output_csv,
header=("t", "u", "y"))
myplot.plot(fig_num, self.t, self.u)
myplot.plot(fig_num, self.t, self.y)
myplot.save(fig_num,
label=("time [s]", "u/y []"),
save_name=self.path + "Simulation_Result",
leg=("u", "y"))
def save_plots(self, outdir, tag=None):
# CDFs
for cdf in AwazzaLogAnalyzer.CDFS:
myplot.cdf(cdf['data'](self), numbins=cdf['numbins'],\
xlabel=cdf['xlabel'],\
labels=cdf['labels'],\
filename=os.path.join(outdir, '%s_%s.pdf' % (tag, cdf['filename'])),\
**cdf['kwargs'])
# Data usage bar chart
bar_labels = ('From Origin', 'To Client', 'Cached', 'Compressed', 'Closed Early')
bar_values = (self._request_ints['kb-from-origin'] / 1000000.0,\
self._request_ints['kb-to-client'] / 1000000.0,\
self._request_ints['kb-cached'] / 1000000.0,\
self._request_ints['kb-compressed'] / 1000000.0,\
self._request_ints['kb-lost-client-closed'] / 1000000.0)
myplot.plot([bar_labels], [bar_values], type='bar', label_bars=True,\
ylabel='Data (GB)', bar_padding=0.5, barwidth=0.5,\
filename=os.path.join(outdir, '%s_%s.pdf' % (tag, 'data_usage')))
def plot(msg):
helboard = telebot.types.ReplyKeyboardMarkup(True, True)
helpbutton = telebot.types.KeyboardButton('/plot 5:1 4:2 3:3 2:4')
helboard.add(helpbutton)
text = msg.text
if len(re.findall(r'\s*\d+\s*:\s*\d+\s*', text)) == 0:
send_mess = 'Чтобы воспользоваться коммандой /plot укажите через пробел пары чисел в формате Yi:Xi' \
' пример приложе в клавиатуре'
bot.send_message(msg.chat.id, send_mess, reply_markup=helboard)
else:
patY = r'\s*(\d+)\s*:'
patX = r':\s*(\d+)\s*'
try:
Y = list(map(float, re.findall(patY, text)))
X = list(map(float, re.findall(patX, text)))
myplot.plot(X, Y, user)
img = open('tmp.png', 'rb')
bot.send_photo(msg.chat.id, img)
img.close()
except:
bot.send_message(msg.chat.id,
"Что-то не так! Провертье формат записи Y:X",
reply_markup=helboard)
# coding the matrix
# task 2.5.4
from myplot import plot
def scalar_vector_mult(alpha, v):
return [alpha*v[i] for i in range(len(v))]
L = [[2, 2], [3, 2], [1.75, 1], [2, 1], [2.25, 1], [2.5, 1], [2,75, 1], [3, 1], [3.25, 1]]
L2 = [scalar_vector_mult(0.5, v) for v in L]
L3 = [scalar_vector_mult(-0.5, v) for v in L2]
L2.extend(L3)
plot(L2, size=4, file='task2.5.4.4.png')
def plot_results(filename_to_results, filenames=None):
# use the filenames list to make sure we process files in order
# (so we can control the order of the series on the plot)
if not filenames: filenames = filename_to_results.keys()
filename_to_data = defaultdict(lambda: defaultdict(list))
fraction_data = []
fraction_labels = []
absolute_data = []
absolute_labels = []
mean_percents_by_size = []
mean_absolutes_by_size = []
mean_by_size_xs = []
mean_by_size_ys = []
mean_by_size_yerrs = []
mean_by_size_labels = []
for filename in filenames:
results = filename_to_results[filename]
for r in results:
if r.status == SUCCESS:
filename_to_data[filename]['both_success'].append(r.url)
filename_to_data[filename]['mean_percent_inflations'].append(
r.https_mean / r.http_mean)
filename_to_data[filename]['mean_absolute_inflations'].append(
r.https_mean - r.http_mean)
filename_to_data[filename]['median_percent_inflations'].append(
r.https_median / r.http_median)
filename_to_data[filename][
'median_absolute_inflations'].append(r.https_median -
r.http_median)
if r.size:
filename_to_data[filename]['mean_percent_by_size'].append(
(r.size / 1000.0, r.https_mean / r.http_mean,
r.http_stddev))
filename_to_data[filename]['mean_absolute_by_size'].append(
(r.size / 1000.0, r.https_mean - r.http_mean,
r.http_stddev))
filename_to_data[filename]['mean_http_by_size'].append(
(r.size / 1000.0, r.http_mean, r.http_stddev))
filename_to_data[filename]['mean_https_by_size'].append(
(r.size / 1000.0, r.https_mean, r.http_stddev))
elif r.status == FAILURE_NO_HTTP:
filename_to_data[filename]['no_http'].append(r.url)
elif r.status == FAILURE_NO_HTTPS:
filename_to_data[filename]['no_https'].append(r.url)
else:
filename_to_data[filename]['other_error'].append(r.url)
print '%i sites were accessible over both protocols' %\
len(filename_to_data[filename]['both_success'])
print '%i sites were not accessible over HTTP' %\
len(filename_to_data[filename]['no_http'])
print '%i sites were not accessible over HTTPS' %\
len(filename_to_data[filename]['no_https'])
print '%i sites were not accessible for other reasons' %\
len(filename_to_data[filename]['other_error'])
if 'pit' in filename:
location = 'PIT'
elif '3g' in filename:
location = '3G'
else:
location = 'Fiber'
fraction_data.append(
filename_to_data[filename]['mean_percent_inflations'])
fraction_labels.append('Mean (%s)' % location)
fraction_data.append(
filename_to_data[filename]['median_percent_inflations'])
fraction_labels.append('Median (%s)' % location)
absolute_data.append(
numpy.array(filename_to_data[filename]
['mean_absolute_inflations'])) # * 1000) # s -> ms
absolute_labels.append('Mean (%s)' % location)
absolute_data.append(
numpy.array(filename_to_data[filename]
['median_absolute_inflations'])) # * 1000) # s -> ms
absolute_labels.append('Median (%s)' % location)
try:
mean_by_size_xs.append(
zip(*sorted(filename_to_data[filename]['mean_http_by_size']))
[0])
mean_by_size_ys.append(
zip(*sorted(filename_to_data[filename]['mean_http_by_size']))
[1])
mean_by_size_yerrs.append(
zip(*sorted(filename_to_data[filename]['mean_http_by_size']))
[2])
mean_by_size_labels.append('Mean HTTP (%s)' % location)
mean_by_size_xs.append(
zip(*sorted(filename_to_data[filename]['mean_https_by_size']))
[0])
mean_by_size_ys.append(
zip(*sorted(filename_to_data[filename]['mean_https_by_size']))
[1])
mean_by_size_yerrs.append(
zip(*sorted(filename_to_data[filename]['mean_https_by_size']))
[2])
mean_by_size_labels.append('Mean HTTPS (%s)' % location)
except Exception as e:
logging.warn('Error processing size data: %s' % e)
if location == 'BCN':
mean_percents_by_size.append(
filename_to_data[filename]['mean_percent_by_size'])
mean_absolutes_by_size.append(
filename_to_data[filename]['mean_absolute_by_size'])
myplot.cdf(fraction_data,
xlabel='Load Time Ratio (HTTPS/HTTP)',
labels=fraction_labels,
filename=os.path.join(args.outdir,
'%s_fraction_inflation.pdf' % args.tag),
height_scale=0.7,
numbins=10000,
xlim=(1, 3),
legend='lower right')
myplot.cdf(absolute_data,
xlabel='Load Time Difference (HTTPS-HTTP) [s]',
labels=absolute_labels,
filename=os.path.join(args.outdir,
'%s_absolute_inflation.pdf' % args.tag),
height_scale=0.7,
numbins=10000,
xlim=(0, 3),
legend='lower right')
myplot.cdf(absolute_data,
xlabel='Load Time Difference (HTTPS-HTTP) [s]',
labels=absolute_labels,
filename=os.path.join(
args.outdir, '%s_absolute_inflation_log.pdf' % args.tag),
height_scale=0.7,
numbins=10000,
xscale='log',
xlim=(0, 10),
legend='lower right')
# Plot fraction and absolute in same figure as subplots
fig, ax_array = myplot.subplots(1, 2, height_scale=0.75, width_scale=1.2)
myplot.cdf(fraction_data,
fig=fig,
ax=ax_array[0],
xlabel='Load Time Ratio\n(HTTPS/HTTP)',
labels=fraction_labels,
numbins=10000,
xlim=(1, 3),
show_legend=False)
lines, labels = myplot.cdf(absolute_data,
fig=fig,
ax=ax_array[1],
xlabel='Load Time Difference\n(HTTPS-HTTP) [s]',
labels=absolute_labels,
numbins=10000,
xlim=(0, 3),
legend='lower right',
labelspacing=0.1,
handletextpad=0.4)
# shrink plots to make room for legend underneath
#for ax in ax_array:
# box = ax.get_position()
# ax.set_position([box.x0, box.y0 + box.height * 0.25,
# box.width, box.height * 0.75])
# shrink plots to make room for title above
for ax in ax_array:
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width, box.height * 0.95])
#myplot.save_plot(os.path.join(args.outdir, '%s_combined_inflation_no_legend.pdf' % args.tag))
#fig.legend(lines, labels, loc='lower center', ncol=2, prop={'size':20}, frameon=False,
# bbox_to_anchor=(.5, -.03))
fig.suptitle('O-Proxy Top 2000 Objects')
myplot.save_plot(
os.path.join(args.outdir, '%s_combined_inflation.pdf' % args.tag))
try:
myplot.plot([zip(*mean_percents_by_size[0])[0]],
[zip(*mean_percents_by_size[0])[1]],
xlabel='Object Size (KB)',
ylabel='Fraction Inflation (HTTPS/HTTP)',
linestyles=[''],
xscale='log',
filename=os.path.join(
args.outdir, '%s_fraction_by_size.pdf' % args.tag))
myplot.plot([zip(*mean_absolutes_by_size[0])[0]],
[zip(*mean_absolutes_by_size[0])[1]],
xlabel='Object Size (KB)',
ylabel='Absolute Inflation (HTTPS-HTTP) [sec]',
linestyles=[''],
xscale='log',
filename=os.path.join(
args.outdir, '%s_absolute_by_size.pdf' % args.tag))
myplot.plot(
mean_by_size_xs,
mean_by_size_ys,
yerrs=mean_by_size_yerrs,
xlabel='Object Size (KB)',
ylabel='Load Time [sec]',
xscale='log',
marker=None,
labels=mean_by_size_labels, # legend='lower left',
legend_cols=2,
width_scale=2,
filename=os.path.join(args.outdir,
'%s_mean_lt_by_size.pdf' % args.tag))
except Exception as e:
logging.warn('Error processing size data: %s', e)
# coding the matrix
# task 2.4.3
from myplot import plot
def add2(v, w):
return [v[0] + w[0], v[1] + w[1]]
L = [[2, 2], [3, 2], [1.75, 1], [2, 1], [2.25, 1], [2.5, 1], [2, 75, 1],
[3, 1], [3.25, 1]]
plot([add2(v, [1, 2]) for v in L], size=4, file='task2.4.3.png')
# # sampleY.append(y[int(math.pow(10, i))]);
#
# sampleY = lengthOfLog;
#
# # 拟合
# params = ss.norm.fit(sampleY);
# norm = ss.norm.pdf(sampleX, loc=params[0], scale=params[1]);
# print (params);
# sum = 0;
# for i in range(0, len(norm)):
# norm[i] /= 100;
# sum += norm[i];
# print sum;
#使用累积值
ysum = y
for i in range(1, np.power(10, powerRank)):
ysum[i] = ysum[i] + ysum[i-1]
# 画图
# myplot.plot(x, y, label='count', xlabel='Length', ylabel='Views', xAxieIsLog=True, yAxieIsLog=False);
print("plotting...");
# fig2_1
# myplot.plot(x, y, xlabel='Length', ylabel='Count', label='Source data', linewidth=1, xAxieIsLog=True, deleteZero=False);
# fig2_2
myplot.plot(x, ysum, xlabel='Length', ylabel='Aggregation of videos', label='', linewidth=1, xAxieIsLog=True, deleteZero=False);
# plt.plot(sampleX, norm, label='Normal distribution', linewidth=1, color='red');
# plt.plot(sampleX, sampleY, color='green', label='count', linewidth=1);
# plt.ylabel('Count for length >= x');
import parse_function as pf
import numpy as np
import myplot as mp
#import matplotlib as plt
error = ["File is empty", "Error: File not found", "No data files found"]
selfname = sys.argv[0].split('/')[-1]
try:
filelist.clear()
x.clear()
except:
i = 0
for i in range(0, len(os.listdir())):
if ("." in os.listdir()[i] and ".txt" in os.listdir()[i]):
try:
filelist.append(os.listdir()[i])
except NameError:
filelist = ([os.listdir()[i]])
#for i in range(0,len(filelist)):
#x = np.append(x,[pf.parse(filelist[i])],2)
x = pf.parse(filelist[1])
if (type(x) == int):
print(error[x - 1])
mp.plot(x[0][:, 0], x[0][:, 1], x[0][:, 1], 0, x[0, 0, 3], x[0, 0, 4])
#emu = np.append(emu,np.zeros((1,5)),0)
x86 = np.delete(x86, -1, 0)
if (type(emu) == int):
print(error[emu - 1])
#mp.plot(np.array((emu[0][:,0]),dtype=int),emu[0][:,1],x86[0][:,1],0,int(x[0,0,3]),int(x[0,0,4]))
#mp.plot(np.array((emu[sp][:,0]),dtype=int), #thread count axis
# emu[sp][:,op+1], #EMU clock cycles
# x86[sp][:,op+1], #x86 clock cycles
# op, #operation
# int(emu[sp,0,3]), #sparsity
# int(emu[sp,0,4]), #matrix size
# ps)
plt = mp.plot(
np.array((x86[:, 0]), dtype=int), #X data
np.array(
[emu[:, 2], x86[:, 2], -2.8 * x86[:, 2], 1.6 * x86[:, 2],
x86[:, 2]]), #Y data
[0, 0, 1, 0, 0], #Plot Type
["blue", "red", "purple", "green", "orange"], #Colors
[
"Threads", "Clock Cycles", "EMU vs AMD", "EMU", "x86", "x88", "x89",
"x80"
],
[1, 0, 1, 1, 0]) #Labels
plt.savefig('test.svg', transparent=True)
def plotall(fig, fbc, ndata=NDATA, datapath=DATA):
"""
:param fig:
:return:
"""
fbc.split(ndata)
olist = fbc.olist
o = olist[0]
F = len(o)
Llist = fbc.Llist
gcf = fbc.calc_gcf()
print("Gain Crossover Frequencies (Hz):", gcf)
print("\tMin. (Hz):", min(gcf))
print("\tAve. (Hz):", np.mean(gcf))
print("\tMax. (Hz):", max(gcf))
f_gc = min(gcf)
##########Plot1##########
fig += 1
for L in Llist:
# Nyquist
myplot.plot(fig, np.real(L), np.imag(L), lw=6, line_style="-")
_theta = np.linspace(0, 2 * np.pi, 100)
myplot.plot(fig, (np.cos(_theta)), (np.sin(_theta)),
line_style="r--",
lw=1) # r=1
myplot.plot(fig, (0, ), (0, ), line_style="r+") # origin
myplot.plot(fig, (-1, ), (0, ), line_style="r+") # critical
# Stanility Constraint
myplot.plot(fig, (fbc.rm * np.cos(_theta)) + fbc.sigma,
fbc.rm * np.sin(_theta),
line_style="y:") # r=1
myplot.plot(
fig, (-1 / fbc.g_dgm, -np.cos(fbc.theta_dpm), -np.cos(fbc.theta_dpm2)),
(0, -np.sin(fbc.theta_dpm), np.sin(fbc.theta_dpm2)),
line_style="yo")
myplot.save(fig,
xl=[-1, 1],
yl=[-1, 1],
leg=(*["Nyquist" + str(k) for k in range(ndata)], "r=1",
"Origin", "(-1,j0)", "Stb. Cond.", "Margins"),
label=("Re", "Im"),
save_name=datapath + "/" + str(fig) + "_nyquist_enlarged",
title="Optimized Gain-Crossover Frequency (Hz): " + str(f_gc))
myplot.save(fig,
xl=[-3, 3],
yl=[-3, 3],
leg=(*["Nyquist" + str(k) for k in range(ndata)], "r=1",
"Origin", "(-1,j0)", "Stb. Cond.", "Margins"),
label=("Re", "Im"),
save_name=datapath + "/" + str(fig) + "_nyquist",
title="Optimized Gain-Crossover Frequency (Hz): " + str(f_gc))
##########Plot2##########
fig += 1
# L(s)
for L in Llist:
myplot.bodeplot(fig,
o[:F] / 2 / np.pi,
20 * np.log10(abs(L)),
np.angle(L, deg=True),
line_style='-')
myplot.save(fig,
save_name=datapath + "/" + str(fig) + "_bode",
title="Openloop L(s)",
leg=["L" + str(k) for k in range(ndata)])
##########Plot3##########
fig += 1
try:
c = fbc.freqresp()[:F]
except:
c = fbc.C[:F]
myplot.bodeplot(fig,
o[:F] / 2 / np.pi,
20 * np.log10(abs(c)),
np.angle(c, deg=True),
line_style='-')
g = fbc.g[:F]
myplot.bodeplot(fig,
o[:F] / 2 / np.pi,
20 * np.log10(abs(g)),
np.angle(g, deg=True),
line_style='-')
myplot.save(fig,
title='C(s)',
leg=("Controller", "Plant"),
save_name=datapath + "/Controller")
##########Plot4##########
fig += 1
m = fbc.nominal_sensitivity / (-20)
x = (fbc.o_dgc / fbc.o[:F])**m
myplot.plot(fig,
o[:F] / 2 / np.pi,
-20 * np.log10(abs(x)),
line_style='k--',
plotfunc=plt.semilogx)
for L in Llist:
T = 1 / (1 + L)
S = 1 - T
myplot.plot(fig,
o[:F] / 2 / np.pi,
20 * np.log10(abs(T)),
line_style='b-',
plotfunc=plt.semilogx)
myplot.plot(fig,
o[:F] / 2 / np.pi,
20 * np.log10(abs(S)),
line_style='r-',
plotfunc=plt.semilogx)
myplot.save(fig,
save_name=datapath + "/" + str(fig) + "_ST",
title="S(s) (Blue) and T(s) (Red).",
leg=("Nominal:" + str(fbc.nominal_sensitivity) + " dB/dec", ),
yl=(-80, 10))
##########Plot5##########
fig += 1
try:
c = fbc.freqresp(obj="pid")[:F]
myplot.bodeplot(fig,
o[:F] / 2 / np.pi,
20 * np.log10(abs(c)),
np.angle(c, deg=True),
line_style='-')
except:
pass
try:
c = fbc.freqresp(obj="fir")[:F]
myplot.bodeplot(fig,
o[:F] / 2 / np.pi,
20 * np.log10(abs(c)),
np.angle(c, deg=True),
line_style='-')
except:
pass
myplot.save(fig, title='Controllers', leg=("PID", "FIR"))
return fig
for i in x:
z[i] = y[i] * i
# 计算累积值
for i in range(1, np.power(10, powerRank)):
z[i] = z[i] + z[i - 1]
# 去掉 y = 0 的点,加快绘图速度
xx = []
yy = []
for i in x:
if y[i] != 0:
xx.append(x[i])
yy.append(y[i])
print("data size: " + len(xx).__str__())
# 画图
#fig1_1
myplot.plot(xx,
yy,
label='',
xlabel='Views',
ylabel='Number of videos',
xAxieIsLog=True,
yAxieIsLog=False)
#fig1_2
# myplot.plot(x, y, label='', xlabel='Views', ylabel='Aggregation of views where views <= x', xAxieIsLog=True, yAxieIsLog=False);
# myplot.scatter(x, z, label='count', xlabel='Views', ylabel='Number of video', xAxieIsLog=True, yAxieIsLog=False);
#(8,10) 512
#(7,9) 1024
emu = pf.parse(filelist[3])
x86 = pf.parse(filelist[1])
#if emu[sp,0,4] != x86[sp,0,4]:
# print("Matrix Size Mismatch")
#x86 = np.append(np.zeros((2,1,5)),x86,1)
#x86 = np.append(x86,np.zeros((2,6,5)),1)
if (type(emu) == int):
print(error[emu-1])
#mp.plot(np.array((emu[0][:,0]),dtype=int),emu[0][:,1],x86[0][:,1],0,int(x[0,0,3]),int(x[0,0,4]))
#mp.plot(np.array((emu[sp][:,0]),dtype=int), #thread count axis
# emu[sp][:,op+1], #EMU clock cycles
# x86[sp][:,op+1], #x86 clock cycles
# op, #operation
# int(emu[sp,0,3]), #sparsity
# int(emu[sp,0,4]), #matrix size
# ps)
mp.plot(np.array((emu[:,0]),dtype=int), #thread count axis
emu[:,2], #EMU clock cycles
x86[:,2], #x86 clock cycles
op, #operation
int(emu[0,3]), #sparsity
int(emu[0,4]), #matrix size
ps)
# coding the matrix
# task 2.6.9
from myplot import plot
period = 100
pt1 = [3.5, 3]
pt2 = [0.5, 1]
s = []
for a in range(period):
alpha = (1.0 / period) * a
beta = 1.0 - alpha
x = pt1[0] * alpha + pt2[0] * beta
y = pt1[1] * alpha + pt2[1] * beta
s.append((x, y))
plot(s, file='task2.6.9.png')
# coding the matrix
# task 2.4.2
from myplot import plot
L = [[2, 2], [3, 2], [1.75, 1], [2, 1], [2.25, 1], [2.5, 1], [2, 75, 1],
[3, 1], [3.25, 1]]
plot(L, size=4, file='task2.4.2.png')
# 使用累积平均值
avrg_sumY = list(avrgY)
myplot.normalize(avrg_sumY)
for i in range(1, np.power(10, powerRank)):
avrg_sumY[i] = avrg_sumY[i] + avrg_sumY[i - 1]
x = range(0, np.power(10, powerRank))
y = list(viewTimes)
print(y[0])
print(y[1])
#fig4_1
# myplot.plot(x, y, label='', xlabel='Ratings', ylabel='Views', xAxieIsLog=True, yAxieIsLog=False, powerRank=powerRank);
#fig4_2
# myplot.plot(x, sumy, label='', xlabel='Ratings', ylabel='Aggregation of views with <= x ratings', xAxieIsLog=True, yAxieIsLog=False, powerRank=powerRank);
#fig4_3
# myplot.plot(x, avrgY, label='', xlabel='Ratings', ylabel='Average views', xAxieIsLog=True, yAxieIsLog=False, powerRank=powerRank);
#fig4_4
myplot.plot(x,
avrg_sumY,
label='',
xlabel='Ratings',
ylabel='Aggregation of average views',
xAxieIsLog=True,
yAxieIsLog=False,
powerRank=powerRank)
countOfstars = np.zeros(11);
index = 0;
for line in dataset:
stars = line['stars'];
# 减少噪点
# if lengthOfInt >= np.power(10, powerRank) - 1:
# continue;
countOfstars[stars / 5] += 1;
viewTimes[stars / 5] += int(line['views']);
if index % (len(dataset) / 10) == 0:
print("running... " + str(index / (len(dataset) / 10)) + "0%");
index += 1;
#使用累积值
# for i in reversed(range(0,np.power(10, powerRank) - 1)):
# viewTimes[i] = viewTimes[i] + viewTimes[i+1];
# 取平均值
avrgY = list(viewTimes);
for i in range(0, 11):
if(not(countOfstars[i] == 0)):
avrgY[i] /= countOfstars[i];
x = range(0, 11);
y = list(viewTimes);
myplot.plot(x, y, label='count', xlabel='Stars', ylabel='Views', xAxieIsLog=False, yAxieIsLog=True);
myplot.plot(x, avrgY, label='count', xlabel='Stars', ylabel='Average of views', xAxieIsLog=False, yAxieIsLog=True);
def main():
# will replace file paths with Log object
http_bytes_to_log = {
1000:'1kb-http.csv',
10000:'10kb-http.csv',
100000:'100kb-http.csv',
1000000:'1mb-http.csv',
10000000:'10mb-http.csv',
}
http_cache_bytes_to_log = {
1000:'1kb-http-cache.csv',
10000:'10kb-http-cache.csv',
100000:'100kb-http-cache.csv',
1000000:'1mb-http-cache.csv',
10000000:'10mb-http-cache.csv',
}
https_bytes_to_log = {
1000:'1kb-https.csv',
10000:'10kb-https.csv',
100000:'100kb-https.csv',
1000000:'1mb-https.csv',
10000000:'10mb-https.csv',
}
# load files and make log objects
for size in http_bytes_to_log:
path = os.path.join(args.logdir, http_bytes_to_log[size])
log = PowerMonitorLog(path)
http_bytes_to_log[size] = log
for size in http_bytes_to_log:
path = os.path.join(args.logdir, http_cache_bytes_to_log[size])
# do we have stats from cache load?
have_cache = os.path.exists(path)
if not have_cache: break
log = PowerMonitorLog(path)
http_cache_bytes_to_log[size] = log
for size in https_bytes_to_log:
path = os.path.join(args.logdir, https_bytes_to_log[size])
log = PowerMonitorLog(path)
https_bytes_to_log[size] = log
# plot stuff
sizes = sorted(http_bytes_to_log.keys())
xsizes = numpy.array(sizes)/1000.0
# energy consumed
http_extra_energy = []
http_cache_extra_energy = []
https_extra_energy = []
http_duration = []
http_cache_duration = []
https_duration = []
for size in sizes:
http_log = http_bytes_to_log[size]
http_extra_energy.append(http_log.above_baseline_energy_uAh / 1000.0 / 100.0) # uAh -> mAh -> per object
http_duration.append(http_log.duration_seconds / 100.0) # per object time, not total
if have_cache:
http_cache_log = http_cache_bytes_to_log[size]
http_cache_extra_energy.append(http_cache_log.above_baseline_energy_uAh / 1000.0 / 100.0) # uAh -> mAh -> per object
http_cache_duration.append(http_cache_log.duration_seconds / 100.0) # per-object time, not total
https_log = https_bytes_to_log[size]
https_extra_energy.append(https_log.above_baseline_energy_uAh / 1000.0 / 100.0) # uAh -> mAh -> per object
https_duration.append(https_log.duration_seconds / 100.0) # per-object time, not total
if have_cache:
myplot.plot([xsizes, xsizes, xsizes, xsizes, xsizes, xsizes],
[https_extra_energy, https_duration, http_extra_energy, http_duration, http_cache_extra_energy, http_cache_duration],
labels=['HTTPS Energy', 'HTTPS Time', 'HTTP Energy', 'HTTP Time', 'HTTP Cache Energy', 'HTTP Cache Time'],
colors=['red', 'red', 'black', 'black', 'green', 'green'], linestyles=['-', '--', '-', '--', '-', '--'],
axis_assignments=[0, 1, 0, 1, 0, 1],
xlabel='File Size [kB]', ylabel='Energy per Object [mAh]',
num_series_on_addl_y_axis=2, additional_ylabels=['Time per Object [s]'],
xscale='log', height_scale=0.85, legend_text_size=16,
legend='upper left', labelspacing=0.1, handletextpad=0.4,
ylim=(0, 1.8), additional_ylims=[(0, 35)],
filename=os.path.join(args.logdir, 'energy_consumption.pdf'))
else:
myplot.plot([xsizes, xsizes, xsizes, xsizes],
[https_extra_energy, https_duration, http_extra_energy, http_duration],
labels=['HTTPS Energy', 'HTTPS Time', 'HTTP Energy', 'HTTP Time'],
colors=['red', 'red', 'black', 'black'], linestyles=['-', '--', '-', '--'],
axis_assignments=[0, 1, 0, 1],
xlabel='File Size [kB]', #ylabel='Energy per Object [mAh]',
title='Wi-Fi',
show_y_tick_labels = False,
num_series_on_addl_y_axis=2, additional_ylabels=['Time per Object [s]'],
legend='upper left', labelspacing=0.1, handletextpad=0.4,
xscale='log', height_scale=0.7, width_scale=0.6, ylim=(0, 1.8), additional_ylims=[(0, 35)],
filename=os.path.join(args.logdir, 'energy_consumption_small.pdf'))
myplot.plot([xsizes, xsizes, xsizes, xsizes],
[https_extra_energy, https_duration, http_extra_energy, http_duration],
labels=['HTTPS Energy', 'HTTPS Time', 'HTTP Energy', 'HTTP Time'],
colors=['red', 'red', 'black', 'black'], linestyles=['-', '--', '-', '--'],
axis_assignments=[0, 1, 0, 1],
xlabel='File Size [kB]', ylabel='Energy per Object [mAh]',
#title='Wi-Fi',
num_series_on_addl_y_axis=2, additional_ylabels=['Time per Object [s]'],
#legend='upper left',
labelspacing=0.1, handletextpad=0.4,
xscale='log', yscale='log', additional_yscales=['log'],
height_scale=0.7,
#ylim=(0, 1.8), additional_ylims=[(0, 35)],
filename=os.path.join(args.logdir, 'energy_consumption.pdf'))
# average current
http_mean_current = []
https_mean_current = []
http_stddev = []
https_stddev = []
http_mean_current_per_byte = []
https_mean_current_per_byte = []
http_stddev_per_byte = []
https_stddev_per_byte = []
for size in sizes:
http_log = http_bytes_to_log[size]
http_mean_current.append(http_log.mean_current - http_log.baseline)
http_stddev.append(http_log.stddev_current)
http_mean_current_per_byte.append((http_log.mean_current - http_log.baseline) / float(size))
http_stddev_per_byte.append(http_log.stddev_current / float(size))
https_log = https_bytes_to_log[size]
https_mean_current.append(https_log.mean_current - https_log.baseline)
https_stddev.append(https_log.stddev_current)
https_mean_current_per_byte.append((https_log.mean_current - https_log.baseline) / float(size))
https_stddev_per_byte.append(https_log.stddev_current / float(size))
# average current
myplot.plot([xsizes, xsizes], [https_mean_current, http_mean_current],
labels=['HTTPS', 'HTTP'], yerrs=[https_stddev, http_stddev],
linestyles=['-', '-'], colors=['red', 'black'],
xlabel='File Size (kB)', ylabel='Mean Current (mA)',
xscale='log', height_scale=0.7,
filename=os.path.join(args.logdir, 'mean_current.pdf'))
# average current per byte
myplot.plot([xsizes, xsizes], [https_mean_current_per_byte, http_mean_current_per_byte],
labels=['HTTPS', 'HTTP'], yerrs=[https_stddev_per_byte, http_stddev_per_byte],
linestyles=['-', '-'], colors=['red', 'black'],
xlabel='File Size (kB)', ylabel='Mean Current per Byte (mA/B)',
xscale='log', height_scale=0.7,
filename=os.path.join(args.logdir, 'mean_current_per_byte.pdf'))
"threads", "sparsity", "compression time taken", "solution time taken"
]
Case_sensitivity = [0, 0, 0, 0]
Method = [1, 0, 0, 0]
Splitting = [0, 2, 3, 3]
t_x86 = mpr.parse(x86, Keywords, Case_sensitivity, Method, Splitting)
t_x86TH = mpr.parse(x86TH, Keywords, Case_sensitivity, Method, Splitting)
t_emu = mpr.parse(emu, Keywords, Case_sensitivity, Method, Splitting)
t_emuTH = mpr.parse(emuTH, Keywords, Case_sensitivity, Method, Splitting)
if (type(t_emu) == int):
print(error[t_emu - 1])
#plt = mp.plot(np.array((x86[:,0]),dtype=int), #X data
# np.array([emuTH[:,2],x86TH[:,2]*np.ones((x86.shape[0],))]), #Y data
# [0,0], #Plot Type
# ["blue","orange"], #Colors
# ["Threads","Clock Cycles","EMU vs x86 1024","EMU","x86"], #Names
# [1,1]) #Labels
plt = mpl.plot(
["emu1", "emu2", "emu3", "emu4", "emu5", "emu6", "emu7", "emu8"], #X data
np.array([t_x86[0, :, 3], t_emu[0, :, 3]]), #Y data
[0, 0], #Plot Type
["blue", "orange"], #Colors
["Threads", "Clock Cycles", "EMU vs x86 1024", "EMU", "x86"], #Names
[1, 1]) #Labels
plt.savefig('1024emuVSx86.png', transparent=True)
plt.savefig('1024emuVSx86.svg', transparent=True)
xlabel='Mean # Conflicting Policies per Context across 1000 Apps',
filename='mean_conflicts_per_app_across_contexts.pdf')
myplot.cdf([num_contexts_per_app_with_any_conflict], height_scale=0.6,
xlabel='% of Contexts Producing a Conflict Across 1000 Apps',
filename='num_contexts_per_app_with_any_conflict.pdf')
##
## IRC plot
##
x_plain = [10, 20, 30, 50, 100]
y_plain = [437, 1872, 2857, 3392, 3389]
x_dummy = [10, 20, 30, 40, 50, 100]
y_dummy = [536, 1862, 2520, 2925, 3073, 3290]
x_enc = [10, 20, 30, 40, 50, 100]
y_enc = [51, 124, 128, 229, 303, 411]
myplot.plot([x_plain, x_dummy, x_enc], [y_plain, y_dummy, y_enc],
labels=['No Shim', 'Shim w/o Encryption', 'Shim w/ Encryption'],
xlabel='Number of Connected Clients', ylabel='Messages per Second',
height_scale=0.6, legend='right',
filename='irc-tput-all.pdf')
myplot.plot([x_plain, x_dummy, x_enc], [y_plain, y_dummy, y_enc],
labels=['No Shim', 'Shim w/o Encryption', 'Shim w/ Encryption'],
xlabel='Number of Connected Clients', ylabel='Messages per Second',
height_scale=0.6, yscale='log',
filename='irc-tput-all-log.pdf')
def plot_results(filename_to_results, filenames=None):
# use the filenames list to make sure we process files in order
# (so we can control the order of the series on the plot)
if not filenames: filenames = filename_to_results.keys()
filename_to_data = defaultdict(lambda: defaultdict(list))
fraction_data = []
fraction_labels = []
absolute_data = []
absolute_labels = []
mean_percents_by_size = []
mean_absolutes_by_size = []
mean_by_size_xs = []
mean_by_size_ys = []
mean_by_size_yerrs = []
mean_by_size_labels = []
for filename in filenames:
results = filename_to_results[filename]
for r in results:
if r.status == SUCCESS:
filename_to_data[filename]['both_success'].append(r.url)
filename_to_data[filename]['mean_percent_inflations'].append(r.https_mean / r.http_mean)
filename_to_data[filename]['mean_absolute_inflations'].append(r.https_mean - r.http_mean)
filename_to_data[filename]['median_percent_inflations'].append(r.https_median / r.http_median)
filename_to_data[filename]['median_absolute_inflations'].append(r.https_median - r.http_median)
if r.size:
filename_to_data[filename]['mean_percent_by_size'].append( (r.size/1000.0, r.https_mean / r.http_mean, r.http_stddev) )
filename_to_data[filename]['mean_absolute_by_size'].append( (r.size/1000.0, r.https_mean - r.http_mean, r.http_stddev) )
filename_to_data[filename]['mean_http_by_size'].append( (r.size/1000.0, r.http_mean, r.http_stddev) )
filename_to_data[filename]['mean_https_by_size'].append( (r.size/1000.0, r.https_mean, r.http_stddev) )
elif r.status == FAILURE_NO_HTTP:
filename_to_data[filename]['no_http'].append(r.url)
elif r.status == FAILURE_NO_HTTPS:
filename_to_data[filename]['no_https'].append(r.url)
else:
filename_to_data[filename]['other_error'].append(r.url)
print '%i sites were accessible over both protocols' %\
len(filename_to_data[filename]['both_success'])
print '%i sites were not accessible over HTTP' %\
len(filename_to_data[filename]['no_http'])
print '%i sites were not accessible over HTTPS' %\
len(filename_to_data[filename]['no_https'])
print '%i sites were not accessible for other reasons' %\
len(filename_to_data[filename]['other_error'])
if 'pit' in filename:
location = 'PIT'
elif '3g' in filename:
location = '3G'
else:
location = 'Fiber'
fraction_data.append(filename_to_data[filename]['mean_percent_inflations'])
fraction_labels.append('Mean (%s)' % location)
fraction_data.append(filename_to_data[filename]['median_percent_inflations'])
fraction_labels.append('Median (%s)' % location)
absolute_data.append(numpy.array(filename_to_data[filename]['mean_absolute_inflations']))# * 1000) # s -> ms
absolute_labels.append('Mean (%s)' % location)
absolute_data.append(numpy.array(filename_to_data[filename]['median_absolute_inflations']))# * 1000) # s -> ms
absolute_labels.append('Median (%s)' % location)
try:
mean_by_size_xs.append(zip(*sorted(filename_to_data[filename]['mean_http_by_size']))[0])
mean_by_size_ys.append(zip(*sorted(filename_to_data[filename]['mean_http_by_size']))[1])
mean_by_size_yerrs.append(zip(*sorted(filename_to_data[filename]['mean_http_by_size']))[2])
mean_by_size_labels.append('Mean HTTP (%s)' % location)
mean_by_size_xs.append(zip(*sorted(filename_to_data[filename]['mean_https_by_size']))[0])
mean_by_size_ys.append(zip(*sorted(filename_to_data[filename]['mean_https_by_size']))[1])
mean_by_size_yerrs.append(zip(*sorted(filename_to_data[filename]['mean_https_by_size']))[2])
mean_by_size_labels.append('Mean HTTPS (%s)' % location)
except Exception as e:
logging.warn('Error processing size data: %s' % e)
if location == 'BCN':
mean_percents_by_size.append(filename_to_data[filename]['mean_percent_by_size'])
mean_absolutes_by_size.append(filename_to_data[filename]['mean_absolute_by_size'])
myplot.cdf(fraction_data,
xlabel='Load Time Ratio (HTTPS/HTTP)', labels=fraction_labels,
filename=os.path.join(args.outdir, '%s_fraction_inflation.pdf' % args.tag),
height_scale=0.7, numbins=10000, xlim=(1, 3), legend='lower right')
myplot.cdf(absolute_data,
xlabel='Load Time Difference (HTTPS-HTTP) [s]', labels=absolute_labels,
filename=os.path.join(args.outdir, '%s_absolute_inflation.pdf' % args.tag),
height_scale=0.7, numbins=10000, xlim=(0,3), legend='lower right')
myplot.cdf(absolute_data,
xlabel='Load Time Difference (HTTPS-HTTP) [s]', labels=absolute_labels,
filename=os.path.join(args.outdir, '%s_absolute_inflation_log.pdf' % args.tag),
height_scale=0.7, numbins=10000, xscale='log', xlim=(0, 10), legend='lower right')
# Plot fraction and absolute in same figure as subplots
fig, ax_array = myplot.subplots(1, 2, height_scale=0.75, width_scale=1.2)
myplot.cdf(fraction_data, fig=fig, ax=ax_array[0],
xlabel='Load Time Ratio\n(HTTPS/HTTP)', labels=fraction_labels,
numbins=10000, xlim=(1, 3), show_legend=False)
lines, labels = myplot.cdf(absolute_data, fig=fig, ax=ax_array[1],
xlabel='Load Time Difference\n(HTTPS-HTTP) [s]', labels=absolute_labels,
numbins=10000, xlim=(0,3), legend='lower right', labelspacing=0.1, handletextpad=0.4)
# shrink plots to make room for legend underneath
#for ax in ax_array:
# box = ax.get_position()
# ax.set_position([box.x0, box.y0 + box.height * 0.25,
# box.width, box.height * 0.75])
# shrink plots to make room for title above
for ax in ax_array:
box = ax.get_position()
ax.set_position([box.x0, box.y0,
box.width, box.height * 0.95])
#myplot.save_plot(os.path.join(args.outdir, '%s_combined_inflation_no_legend.pdf' % args.tag))
#fig.legend(lines, labels, loc='lower center', ncol=2, prop={'size':20}, frameon=False,
# bbox_to_anchor=(.5, -.03))
fig.suptitle('O-Proxy Top 2000 Objects')
myplot.save_plot(os.path.join(args.outdir, '%s_combined_inflation.pdf' % args.tag))
try:
myplot.plot([zip(*mean_percents_by_size[0])[0]], [zip(*mean_percents_by_size[0])[1]],
xlabel='Object Size (KB)', ylabel='Fraction Inflation (HTTPS/HTTP)',
linestyles=[''], xscale='log',
filename=os.path.join(args.outdir, '%s_fraction_by_size.pdf' % args.tag))
myplot.plot([zip(*mean_absolutes_by_size[0])[0]], [zip(*mean_absolutes_by_size[0])[1]],
xlabel='Object Size (KB)', ylabel='Absolute Inflation (HTTPS-HTTP) [sec]',
linestyles=[''], xscale='log',
filename=os.path.join(args.outdir, '%s_absolute_by_size.pdf' % args.tag))
myplot.plot(mean_by_size_xs, mean_by_size_ys, yerrs=mean_by_size_yerrs,
xlabel='Object Size (KB)', ylabel='Load Time [sec]', xscale='log',
marker=None, labels=mean_by_size_labels,# legend='lower left',
legend_cols=2, width_scale=2,
filename=os.path.join(args.outdir, '%s_mean_lt_by_size.pdf' % args.tag))
except Exception as e:
logging.warn('Error processing size data: %s', e)
if (not (countOfLength[i] == 0)):
avrgY[i] /= countOfLength[i]
# 使用累积平均值
avrg_sumY = list(avrgY)
myplot.normalize(avrg_sumY)
for i in range(1, np.power(10, powerRank)):
avrg_sumY[i] = avrg_sumY[i] + avrg_sumY[i - 1]
x = range(0, np.power(10, powerRank))
#fig3_1
# myplot.bar(x, y, label='', xlabel='Length', ylabel='Views', xAxieIsLog=True, yAxieIsLog=False, powerRank=powerRank, deleteZero=True);
#fig3_2
# myplot.plot(x, sumy, label='', xlabel='Length', ylabel='Aggregation of views rate', xAxieIsLog=True, yAxieIsLog=False, powerRank=powerRank, deleteZero=False);
#fig3_3
# myplot.plot(x, avrgY, label='count', xlabel='Length', ylabel='Average views', xAxieIsLog=True, yAxieIsLog=False, powerRank=powerRank, deleteZero=True);
#fig3_4
myplot.plot(x,
avrg_sumY,
label='count',
xlabel='Length',
ylabel='Aggregation of average views rate',
xAxieIsLog=True,
yAxieIsLog=False,
powerRank=powerRank,
deleteZero=True)
def plot_series(machine, remote, result_files):
out_filename, out_filepath = outfile(args.opt, remote, machine)
xs = [] # holds arrays of x values, 1 per series
ys = [] # holds arrays of y values, 1 per series
yerrs = [] # holds arrays of std devs, 1 per series
labels = []
plot_title = ''
# maps X value (e.g., num slices or num mboxes) to a list of measured or
# calculated RTTs for that value (sometimes RTT depends on X, e.g., #mbox)
x_to_rtts = defaultdict(list)
for protocol in PROTOCOLS[args.opt]:
if protocol not in result_files: continue
filepath = result_files[protocol]
print '[IN]', protocol, filepath
data = numpy.loadtxt(filepath)
if len(data) == 0 or\
len(data.shape) != 2 or\
data.shape[1] not in (5, 7): # should be either 5 or 7 cols
print 'WARNING: malformed data: %s' % filepath
continue
transform = numpy.vectorize(DATA_TRANSFORMS[args.opt])
xs.append(data[:, 0])
ys.append(transform(data[:, 3]))
yerrs.append(transform(data[:, 4]))
labels.append(LEGEND_STRINGS[protocol])
plot_title = title(args.opt, remote, data)
# RTT measurements
rtts, _, _ = get_params(args.opt, remote, data)
for i in range(data.shape[0]):
x_to_rtts[data[i, 0]].append(rtts[i])
rtt_lines = []
if SHOW_RTTS[args.opt]:
# average RTT measurements
for x in x_to_rtts:
x_to_rtts[x] = numpy.mean(x_to_rtts[x])
# find RTT line endpoints
e1 = (xs[0][0], x_to_rtts[xs[0][0]])
e2 = (xs[0][-1], x_to_rtts[xs[0][-1]])
rtt_lines = make_rtt_lines((e1, e2), SHOW_RTTS[args.opt])
# TODO: avg RTT measurment
rtt = 75
if len(xs) != len(ys) or len(ys) != len(yerrs) or\
len(yerrs) != len(labels) or len(ys) == 0:
print 'ERROR: no well-formed data to plot ***'
return
print '[OUT]', out_filepath
myplot.plot(xs, ys, yerrs=yerrs, labels=labels, xlabel=X_AXIS[args.opt],\
ylabel=Y_AXIS[args.opt], guide_lines=rtt_lines[1:],\
#title=plot_title,\
#builds = [[], [3], [3, 1, 2], [3, 1, 2, 0], [3, 1, 2, 0, 4]],\
#builds = [[], [1, 2], [0, 1, 2], [0, 1, 2, 3, 4]],\
filename=out_filepath, **MANUAL_ARGS[out_filename])
dump_to_file_for_xkcd(xs, ys, yerrs, labels, out_filepath)
2013: "http://sdibc.nju.edu.cn/zg/2013s07.htm",
}
M0, M1, M2 = ReadResults(url[year])
return M0, M1, M2
def split(M_1, M):
x = []
y = []
c = len(M)
for i in range(c):
x.append(i + 1)
y.append((M[i] - M_1[i]) / M[i] * 100)
return x, y
if __name__ == "__main__":
M0, M1, M2 = ReadYear(2016)
M0_1, M1_1, M2_1 = ReadYear(2015)
m0x, m0y = split(M0_1, M0)
m1x, m1y = split(M1_1, M1)
m2x, m2y = split(M2_1, M2)
myplot.plot(m0x, m0y, label="M0")
myplot.plot(m1x, m1y, label="M1")
myplot.plot(m2x, m2y, label="M2")
myplot.Show(title=str(2016) + u'货币供应量同比增长率M0，M1与M2对比',
xlabel=u'月份',
ylabel=u'月同比增长率',
legend=True)
arr_x50.append(x)
c50 = 'pink'
if arr_new[-1] > 3:
print(" > 30")
else:
arr_y30.append(y)
arr_x30.append(x)
c30 = 'yellow'
if arr_new[-1] > 1.5:
print(" > 20")
else:
arr_y20.append(y)
arr_x20.append(x)
c20 = 'red'
if arr_new[-1] > 0.75:
print(" > 10")
else:
arr_y10.append(y)
arr_x10.append(x)
c10 = 'blue'
if arr_new[-1] > 0.325:
print(" > 5")
else:
arr_y5.append(y)
arr_x5.append(x)
c5 = 'black'
iIm += 0.01
iRe += 0.01
plot(arr_x50, arr_y50, c50, arr_x30, arr_y30, c30, arr_x20, arr_y20, c20,
arr_x10, arr_y10, c10, arr_x5, arr_y5, c5)
viewTimes = []
index = 0
for line in dataset:
views = line['views']
# 减少噪点
# if lengthOfInt >= np.power(10, powerRank) - 1:
# continue;
viewTimes.append(int(line['views']))
if index % (len(dataset) / 10) == 0:
print("running... " + str(index / (len(dataset) / 10)) + "0%")
index += 1
viewTimes.sort()
viewTimes.reverse()
#使用累积值
# for i in reversed(range(0,np.power(10, powerRank) - 1)):
# viewTimes[i] = viewTimes[i] + viewTimes[i+1];
x = range(0, len(viewTimes))
y = viewTimes
myplot.plot(x,
y,
label='count',
xlabel='Rank',
ylabel='Views',
xAxieIsLog=True,
yAxieIsLog=True)
# [1,0,1,1,0]) #Labels
#
#plt.savefig('test.svg',transparent = True)
#plt = mp.plot(np.array((x86[:,0]),dtype=int), #X data
# np.array([emu[:,2],emuTH[:,2],x86[1,2]*np.ones((x86.shape[0],)),x86TH[1,2]*np.ones((x86.shape[0],))]), #Y data
# [0,0,1,1], #Plot Type
# ["blue","red","purple","orange"], #Colors
# ["Threads","Clock Cycles","EMU vs AMD 1024","EMU","EMU TH","x86","x86 TH"],
# [1,1,1,1]) #Labels
#plt = mp.plot(np.array((x86[:,0]),dtype=int), #X data
# np.array([emu[:,2],emuTH[:,2],x86[:,2],x86TH[:,2]]), #Y data
# [0,0,0,0], #Plot Type
# ["blue","red","purple","orange"], #Colors
# ["Threads","Clock Cycles","EMU vs AMD 1024","EMU","EMU TH","x86","x86 TH"],
# [0,0,0,0]) #Labels
#plt.savefig('1024allThreads.png',transparent = True)
#plt.savefig('1024allThreads.svg',transparent = True)
plt = mp.plot(
np.array((x86[:, 0]), dtype=int), #X data
np.array([emuTH[:, 2], x86TH[1, 2] * np.ones((x86.shape[0], ))]), #Y data
[0, 1], #Plot Type
["blue", "orange"], #Colors
["Threads", "Clock Cycles", "EMU vs x86 1024", "EMU", "x86"], #Names
[1, 1]) #Labels
plt.savefig('1024emuVSx86.png', transparent=True)
plt.savefig('1024emuVSx86.svg', transparent=True)
def plot_series(machine, remote, result_files):
out_filename, out_filepath = outfile(args.opt, remote, machine)
xs = [] # holds arrays of x values, 1 per series
ys = [] # holds arrays of y values, 1 per series
yerrs = [] # holds arrays of std devs, 1 per series
labels = []
plot_title = ''
# maps X value (e.g., num slices or num mboxes) to a list of measured or
# calculated RTTs for that value (sometimes RTT depends on X, e.g., #mbox)
x_to_rtts = defaultdict(list)
for protocol in PROTOCOLS[args.opt]:
if protocol not in result_files: continue
filepath = result_files[protocol]
print '[IN]', protocol, filepath
data = numpy.loadtxt(filepath)
if len(data) == 0 or\
len(data.shape) != 2 or\
data.shape[1] not in (5, 7): # should be either 5 or 7 cols
print 'WARNING: malformed data: %s' % filepath
continue
transform = numpy.vectorize(DATA_TRANSFORMS[args.opt])
xs.append(data[:,0])
ys.append(transform(data[:,3]))
yerrs.append(transform(data[:,4]))
labels.append(LEGEND_STRINGS[protocol])
plot_title = title(args.opt, remote, data)
# RTT measurements
rtts, _, _ = get_params(args.opt, remote, data)
for i in range(data.shape[0]):
x_to_rtts[data[i,0]].append(rtts[i])
rtt_lines = []
if SHOW_RTTS[args.opt]:
# average RTT measurements
for x in x_to_rtts:
x_to_rtts[x] = numpy.mean(x_to_rtts[x])
# find RTT line endpoints
e1 = (xs[0][0], x_to_rtts[xs[0][0]])
e2 = (xs[0][-1], x_to_rtts[xs[0][-1]])
rtt_lines = make_rtt_lines((e1, e2), SHOW_RTTS[args.opt])
# TODO: avg RTT measurment
rtt = 75
if len(xs) != len(ys) or len(ys) != len(yerrs) or\
len(yerrs) != len(labels) or len(ys) == 0:
print 'ERROR: no well-formed data to plot ***'
return
print '[OUT]', out_filepath
myplot.plot(xs, ys, yerrs=yerrs, labels=labels, xlabel=X_AXIS[args.opt],\
ylabel=Y_AXIS[args.opt], guide_lines=rtt_lines[1:],\
#title=plot_title,\
#builds = [[], [3], [3, 1, 2], [3, 1, 2, 0], [3, 1, 2, 0, 4]],\
#builds = [[], [1, 2], [0, 1, 2], [0, 1, 2, 3, 4]],\
filename=out_filepath, **MANUAL_ARGS[out_filename])
dump_to_file_for_xkcd(xs, ys, yerrs, labels, out_filepath)
