def plotGraph(list_of_list, diagramName='', mvaLength=21):
''' [[xValues],[yValues]]
This Plots all given values and his filtered line.
'''
if not len(list_of_list) % 2 == 0:
raise ArgMismatch('Please provide [[xValues],[yValues], ...] as arguments')
time_start = time.time()
#------------------------------------------------------------------------------
oneHouer = 60 * 60
quater_day = oneHouer * 6
half_a_day = oneHouer * 12
oneDay = oneHouer * 24
oneWeek = oneDay * 7
oneMonth = oneWeek * 4
half_a_Year = oneMonth * 6
one_Year = half_a_Year * 2
tmin = 0
now = time.mktime(time.localtime())
for i in range(0, len(list_of_list), 2):
# ---- Calc filtered values ------
list_length = len(list_of_list[i])
if list_length < mvaLength:
mva = MovingAverage(list_length)
else:
mva = MovingAverage(mvaLength)
for j in range(0, len(list_of_list[i])):
mva.addValue(list_of_list[i][j], list_of_list[i+1][j])
filterd_values = mva.getAverage()
fvTimes = []
fvValues = []
for item in filterd_values:
fvTimes.append(item[0])
fvValues.append(item[1])
# ---- Append org values
dates = [dt.datetime.fromtimestamp(ts) for ts in list_of_list[i]]
datenums = md.date2num(dates)
ax = plt.gca()
xfmt = md.DateFormatter('%Y-%m-%d %H:%M')
ax.xaxis.set_major_formatter(xfmt)
plt.plot(datenums, list_of_list[i + 1], lw=1, color='gray')
# ---- Append filtered values ------
dates = [dt.datetime.fromtimestamp(ts) for ts in fvTimes]
datenums = md.date2num(dates)
tmin = min(datenums)
ax = plt.gca()
xfmt = md.DateFormatter('%Y-%m-%d %H:%M')
ax.xaxis.set_major_formatter(xfmt)
plt.plot(datenums, fvValues, lw=2)
plt.subplots_adjust(bottom=0.2)
plt.xticks(rotation=90)
ax.grid(True)
plt.title(diagramName)
#plt.text(tmin, -300, r'Read')
#plt.text(tmin, +200, r'Write')
plt.xlabel('Time')
plt.ylabel('Speed [MB/s]')
plt.savefig('plt/' + str(diagramName) + '.png')
plt.close('all')
#------------------------------------------------------------------------------
time_end = time.time()
def plotJob(timestamps, rbs, rio, wbs, wio, title, verbose):
# convert timestamps
dates1 = [dt.datetime.fromtimestamp(ts) for ts in timestamps]
# calculate filter size
fsize = int(math.sqrt(len(dates1)))
if fsize < 3:
fsize = 3
# claculate filterd values
mvaRB = MovingAverage(fsize)
mvaWB = MovingAverage(fsize)
for i in range(len(timestamps)):
mvaWB.addValue(timestamps[i], wbs[i])
mvaRB.addValue(timestamps[i], rbs[i])
filterd_WB = mvaWB.getAverage()
filterd_RB = mvaRB.getAverage()
WB_Values = []
for item in filterd_WB:
WB_Values.append(item[1])
RB_Values = []
for item in filterd_RB:
RB_Values.append(item[1])
# Write
fig = plt.figure(figsize=(16, 10))
ax1 = fig.add_subplot(2, 3, 1)
plt.xticks(rotation=45)
plt.xlabel('Time')
plt.ylabel('Speed [MB/s]')
ax2 = fig.add_subplot(2, 3, 2)
plt.xlabel('IO Size [KB]')
plt.ylabel('IOs')
#plt.gca().yaxis.set_major_formatter(formatter)
ax3 = fig.add_subplot(2, 3, 3)
plt.ylabel('Speed [MB/s]')
plt.xlabel('IO Size [KB]')
# Read
ax4 = fig.add_subplot(2, 3, 4)
plt.xticks(rotation=45)
plt.xlabel('Time')
plt.ylabel('Speed [MB/s]')
ax5 = fig.add_subplot(2, 3, 5)
plt.xlabel('IO Size [KB]')
plt.ylabel('IOs')
#plt.gca().yaxis.set_major_formatter(formatter)
ax6 = fig.add_subplot(2, 3, 6)
plt.ylabel('Speed [MB/s]')
plt.xlabel('IO Size [KB]')
# Speed
ax1.plot(dates1, wbs, label='Exact Data', lw=1, color='gray')
ax1.plot(dates1, WB_Values, label='Filterd Data', lw=2, color='green')
ax1.set_title('Write MB')
ax1.legend(loc='best')
ax4.plot(dates1, rbs, label='Exact Data', lw=1, color='gray')
ax4.plot(dates1, RB_Values, label='Filterd Data', lw=2, color='blue')
ax4.set_title('Read MB')
ax4.legend(loc='best')
# Histograms
bins1 = 30
# avoid arrays with only one elemet. important!
plot_wio = np.append(wio[wio > 0], 1)
plot_wbs = np.append(wbs[wbs > 0], 1)
plot_rio = np.append(rio[rio > 0], 1)
plot_rbs = np.append(rbs[rbs > 0], 1)
ax2.hist(plot_wio, bins=bins1, color='green')
ax2.set_title('Histogram of Write IO Size')
ax5.hist(plot_rio, bins=bins1, color='blue')
ax5.set_title('Histogram of Read IO Size')
# ------ scatter plots --------
if len(plot_wio) > 1 and len(plot_wbs) > 1:
ax3.hexbin(plot_wio, plot_wbs, bins='log', mincnt=1)
# ax3.scatter(wio, wbs, color='green', s=1)
ax3.set_title('Scatter Plots Write')
if len(plot_rio) > 1 and len(plot_rbs) > 1:
ax6.hexbin(plot_rio, plot_rbs, bins='log', mincnt=1)
#ax6.scatter(rio[rio > 0], rbs[rbs > 0], color='blue', s=1)
ax6.set_title('Scatter Plots Read')
# show data plot
plt.tight_layout()
plt.savefig(str(title) + '.png', dpi=120)
print 'plot to', str(title) + '.png'
#plt.show()
plt.close('all')
