def _generate_graphical_summary_report(self, metrics, page, avg_png_file,
labels):
"""
Generate the graph of average time taken for each instance type.
"""
avg_png_fpath = path.join(self.reports_dir, avg_png_file)
avg_graph_data = {}
for instance_type_id, graph_data in metrics.iteritems():
instance_type_name = instance_type_id_name_map[instance_type_id]
key = "%s" % instance_type_name
avg_graph_data[key] = graph_data['avg']
cairoplot.dot_line_plot(
avg_png_fpath,
avg_graph_data,
self.IMG_WIDTH,
self.IMG_HEIGHT,
axis=True,
series_legend=True,
y_title="Time in ms",
x_title="Average API Response Time Summary - Per "\
"instance type",
x_labels=labels)
page.img(src=avg_png_file, alt="Instance Type Summary report")
page.br()
def MakeGraph(self, Data, FileName): """ Funzione che produce un grafico temporale. :param Data: Serie di dati da dover graficare. :param FileName: Nome file da assegnare al grafico prodotto. :returns: *nulla* """ global Interval,TimeStep Markers=[] FileName = 'extra/MonitorGraph/'+FileName for x in range((TimeStep-1)*Interval,-1,-Interval): Markers.append(str(x)) #ogni volta cerca di acquisire il lock per creare una nuova immagine, se non riesce, rompe il lock TempLock = FileLock(FileName) try: TempLock.acquire(timeout=Interval) except LockTimeout: TempLock.break_lock() else: cairoplot.dot_line_plot(FileName, dict(zip(self.ProbeList,Data[:])), 600, 200, axis=True, grid=True, series_legend=False, x_labels=Markers, series_colors=self.Colors) TempLock.release()
def _generate_graphical_summary_report(self, metrics, page, avg_png_file,
labels):
"""
Generate the graph of average time taken for each instance type.
"""
avg_png_fpath = path.join(self.reports_dir, avg_png_file)
avg_graph_data = {}
for instance_type_id, graph_data in metrics.iteritems():
instance_type_name = instance_type_id_name_map[instance_type_id]
key = "%s" % instance_type_name
avg_graph_data[key] = graph_data['avg']
cairoplot.dot_line_plot(
avg_png_fpath,
avg_graph_data,
self.IMG_WIDTH,
self.IMG_HEIGHT,
axis=True,
series_legend=True,
y_title="Time in ms",
x_title="Average API Response Time Summary - Per "\
"instance type",
x_labels=labels)
page.img(src=avg_png_file, alt="Instance Type Summary report")
page.br()
def createGraph(self, filename):
if self.getNumSamples() < 5:
self.logging.debug("Not enough samples to create %s graph" % filename)
return
else:
self.logging.debug("Updating %s graph" % filename)
if self.calibrated:
y_bounds = (-4, 4)
y_title = "Acceleration (g)"
else:
y_bounds = None
y_title = "ADC Counts"
data = {}
for axis in ["x", "y", "z"]:
index = min(len(getattr(self, axis)), SensorList.max_graph_data)
data[axis] = getattr(self, axis)[-index:]
cairoplot.dot_line_plot(
name=filename,
data=data,
width=900,
height=900,
border=3,
axis=True,
grid=True,
series_legend=True,
x_labels=self.time,
x_title="Time (minutes:seconds)",
y_bounds=y_bounds,
y_title=y_title,
)
def websiteVisit(confId):
'''
confId : A conference Id
A graph of last week website visit
'''
listOfVisits = [1,2,3,4,2,3,2,14,2,2,422,4,2,2,14,1,41,41,24,42,41,41]
# Get list of visits from an API
cairoplot.dot_line_plot('weekVisit.png', listOfVisits[-7:], 1000, 700, axis=True, dots=True, border = 50, x_title="Number of days", y_title="Number of Visits",series_colors =[(1.0,0.0,0.0)])
def _generate_graph_from_metrics(self, csv_fname, page):
"""
Generate a line graph (png file) from the available metrics.
"""
fname_name_ext = list(path.splitext(path.basename(csv_fname)))
avg_png_file = fname_name_ext[0] + '_average.png'
labels, metrics = self._fetch_metrics(csv_fname)
if 'request_count' in metrics:
#generate ungrouped report.
png_file = fname_name_ext[0] + '.png'
png_fpath = path.join(self.reports_dir, png_file)
instance_count = metrics.pop('request_count')
alt_text = "Service Level Summary Report"
cairoplot.dot_line_plot(
png_fpath,
metrics,
self.IMG_WIDTH,
self.IMG_HEIGHT,
axis=True,
series_legend=True,
y_title="Time in ms",
x_title="Response Time Trend - Across services",
x_labels=labels)
page.img(src=png_file, alt=alt_text)
page.br()
else:
png_file = fname_name_ext[0] + '_%s.png'
png_fpath = path.join(self.reports_dir, png_file)
#generate reports grouped by instance_type.
#generate graph for average time taken for each instance type.
self._generate_graphical_summary_report(metrics, page,
avg_png_file, labels)
#generate summary csvs.
self._generate_tabular_summary_report(metrics, page, labels)
#generate the min-avg-max graph for each instance type.
for instance_type, graph_data in metrics.iteritems():
instance_count = graph_data.pop('request_count')
instance_type_name = instance_type_id_name_map[instance_type]
alt_text = "Instance type '%s' summary report" % \
instance_type_name
cairoplot.dot_line_plot(
png_fpath % instance_type,
graph_data,
self.IMG_WIDTH,
self.IMG_HEIGHT,
axis=True,
series_legend=True,
y_title="Time in ms",
x_title="Response Time Trend (For Instance Type: "\
"%(instance_type_name)s, Instance Count: "\
"%(instance_count)s)" % locals(),
x_labels=labels)
page.img(src=png_file % instance_type, alt=alt_text)
page.br()
def _generate_graph_from_metrics(self, csv_fname, page):
"""
Generate a line graph (png file) from the available metrics.
"""
fname_name_ext = list(path.splitext(path.basename(csv_fname)))
avg_png_file = fname_name_ext[0] + '_average.png'
labels, metrics = self._fetch_metrics(csv_fname)
if 'request_count' in metrics:
#generate ungrouped report.
png_file = fname_name_ext[0] + '.png'
png_fpath = path.join(self.reports_dir, png_file)
instance_count = metrics.pop('request_count')
alt_text = "Service Level Summary Report"
cairoplot.dot_line_plot(
png_fpath,
metrics,
self.IMG_WIDTH,
self.IMG_HEIGHT,
axis=True,
series_legend=True,
y_title="Time in ms",
x_title="Response Time Trend - Across services",
x_labels=labels)
page.img(src=png_file, alt=alt_text)
page.br()
else:
png_file = fname_name_ext[0] + '_%s.png'
png_fpath = path.join(self.reports_dir, png_file)
#generate reports grouped by instance_type.
#generate graph for average time taken for each instance type.
self._generate_graphical_summary_report(metrics, page,
avg_png_file, labels)
#generate summary csvs.
self._generate_tabular_summary_report(metrics, page, labels)
#generate the min-avg-max graph for each instance type.
for instance_type, graph_data in metrics.iteritems():
instance_count = graph_data.pop('request_count')
instance_type_name = instance_type_id_name_map[instance_type]
alt_text = "Instance type '%s' summary report" % \
instance_type_name
cairoplot.dot_line_plot(
png_fpath % instance_type,
graph_data,
self.IMG_WIDTH,
self.IMG_HEIGHT,
axis=True,
series_legend=True,
y_title="Time in ms",
x_title="Response Time Trend (For Instance Type: "\
"%(instance_type_name)s, Instance Count: "\
"%(instance_count)s)" % locals(),
x_labels=labels)
page.img(src=png_file % instance_type, alt=alt_text)
page.br()
def createGraph(self, filename):
if self.getNumDataPoints() < 5:
return
else:
pass
data = {}
for axis in ["x", "y", "z"]:
index = min(len(self.avg[axis]), Activity.Properties.max_graph_data)
data[axis] = self.avg[axis][-index:]
file = filename + "_avg"
cairoplot.dot_line_plot(name=file,
data=data,
width=900,
height=900,
border=3,
axis=True,
grid=True,
y_bounds=(-2,2),
series_legend=True,
x_title = "Time (minutes:seconds)",
y_title = "Average")
data = {}
for axis in ["x", "y", "z"]:
index = min(len(self.std_deviation[axis]), Activity.Properties.max_graph_data)
data[axis] = self.std_deviation[axis][-index:]
file = filename + "_std_dev"
cairoplot.dot_line_plot(name=file,
data=data,
width=900,
height=900,
border=3,
axis=True,
grid=True,
y_bounds=(0,1),
series_legend=True,
x_title = "Time (minutes:seconds)",
y_title = "Standard Deviation")
def MakeLegend(self, FileName): """ Funzione che produce un'immagine con la legenda. :param FileName: Nome file da assegnare al risultato. :returns: *nulla* """ global Interval FileName = 'extra/MonitorGraph/'+FileName FakeValues = range(len(self.ProbeList)) #ogni volta cerca di acquisire il lock per creare una nuova immagine, se non riesce, rompe il lock TempLock = FileLock(FileName) try: TempLock.acquire(timeout=Interval) except LockTimeout: TempLock.break_lock() else: cairoplot.dot_line_plot(FileName, dict(zip(self.ProbeList,FakeValues)), 200, 10*len(self.ProbeList)+50, axis=False, grid=False, series_legend=True,series_colors=self.Colors) TempLock.release()
import cairoplot
data = [3141, 1729, 3248626 - 3007797 - 240008 - 563, 2507, 992, 861, 221, 1597, 6 + 581]
datax = [
"11 Dev 2011",
"18 Dev 2011",
" 1 Jan 2012",
" 8 Jan 2012",
"15 Jan 2012",
"22 Jan 2012",
"29 Jan 2012",
"19 Feb 2012",
"25 Mar 2012",
]
cairoplot.dot_line_plot(
"images/git_work_time.png",
data,
1000,
300,
x_labels=datax,
border=0,
axis=True,
grid=True,
x_title="Date",
y_title="Line of Code changed",
)
data[-1][6] += [min(5,(tc.target-tc.progress-tc.d)*10.) ]
data[-1][1] += [tc.progress]
data[-1][2] += [tc.currentvel]
#print tc.currentvel
data[-1][3] += [tc.current_accel]
data[-1][4] += [tc.d]
data[-1][5] += [0]#,tc.maxvel]
data[-1][0] += [tc.current_jerk]#[(tc.d-tc.target)*10 if tc.d-tc.target>0 else 0]
#if pr : print tc.target-tc.progress-tc.d
pr = i%1000==0
#if pr : print "\n",i,"\n%4.5f %4.5f v%4.5f a%4.5f j%4.5f %4.5f %4.5f "% (tc.target, tc.progress, tc.currentvel, tc.current_accel, tc.current_jerk, tc.maxvel, tc.maxaccel)
if pr : print "\n", i
print tc.d," - p",
print tc.target - tc.progress, tc.target <=tc.progress
print tc.currentvel, tc.current_accel
n+=1
cairoplot.dot_line_plot ("test%d.png"%n,
data[-1],
800,
600,
background = (.95,.95,.95),
border = 0,
axis = False,
grid = True,
dots = False,
series_colors = [(.8,0,0),(0,0.8,0),(0,0,0.8),(.4,0.4,1),(.4,0,0.4),(0,0.4,0.4),(.4,.4,0.4),]
)
f = [math.exp(x) for x in t]
g = [10*math.cos(x) for x in t]
h = [10*math.sin(x) for x in t]
erx = [0.1*random() for x in t]
ery = [5*random() for x in t]
data = Series({"exp" : [t,f], "cos" : [t,g], "sin" : [t,h]})
series_colors = [ (1,0,0), (0,0,0), (0,0,1) ]
cairoplot.scatter_plot ( 'cross_r_exponential_series.png', data = data, errorx = [erx,erx], errory = [ery,ery], width = 800, height = 600, border = 20,
axis = True, discrete = False, dots = 5, grid = True,
x_title = "t", y_title = "f(t) g(t)", series_legend=True, series_colors = series_colors )
if test_dot_line_plot:
#Default plot
data = [ 0, 1, 3.5, 8.5, 9, 0, 10, 10, 2, 1 ]
cairoplot.dot_line_plot( "dot_line_1_default_series.png", data, 400, 300, border = 50, axis = True, grid = True,
x_title = "x axis", y_title = "y axis" )
#Labels
data = { "john" : [-5, -2, 0, 1, 3], "mary" : [0, 0, 3, 5, 2], "philip" : [-2, -3, -4, 2, 1] }
x_labels = [ "jan/2008", "feb/2008", "mar/2008", "apr/2008", "may/2008" ]
y_labels = [ "very low", "low", "medium", "high", "very high" ]
cairoplot.dot_line_plot( "dot_line_2_dictionary_labels_series.png", data, 400, 300, x_labels = x_labels,
y_labels = y_labels, axis = True, grid = True,
x_title = "x axis", y_title = "y axis", series_legend=True )
#Series legend
data = { "john" : [10, 10, 10, 10, 30], "mary" : [0, 0, 3, 5, 15], "philip" : [13, 32, 11, 25, 2] }
x_labels = [ "jan/2008", "feb/2008", "mar/2008", "apr/2008", "may/2008" ]
cairoplot.dot_line_plot( 'dot_line_3_series_legend_series.png', data, 400, 300, x_labels = x_labels,
axis = True, grid = True, series_legend = True )
data = [
3141,
1729,
3248626-3007797-240008-563,
2507,
992,
861,
221,
1597,
6+581
]
datax = [
"11 Dev 2011",
"18 Dev 2011",
" 1 Jan 2012",
" 8 Jan 2012",
"15 Jan 2012",
"22 Jan 2012",
"29 Jan 2012",
"19 Feb 2012",
"25 Mar 2012"
]
cairoplot.dot_line_plot(
"images/git_work_time.png",
data,
1000, 300,
x_labels = datax,
border = 0, axis = True, grid = True,
x_title = "Date", y_title = "Line of Code changed"
)
latency += 1
## Sleep efficiency determination and annotation of the classified dataset
efficiency = N.sum(count)/N.size(count)
labels = 'Sleep', 'Wake'
fracs = [efficiency, 1-efficiency]
## Report string for sleep efficiency (SE) and sleep onset latency (SOL)
report = 'SE: ' + str(100*efficiency) + '; SOL: ' + str(latency) + ' minutes'
## Use cairoplot to generate some quick plots of the sleep data
background=cairo.LinearGradient(300,0,300,400)
background.add_color_stop_rgb(0.0, .54, .54, .54)
background.add_color_stop_rgb(1.0, .24, .24, .24)
data = {"SLEEP": float(efficiency) , "WAKE": float(1-efficiency)}
print("Efficiency: " + str(efficiency) + " and ~Eff: " + str(1-efficiency))
cairoplot.pie_plot("SleepEfficiency.png", data, 400, 400, gradient=True, shadow=True, background=background)
users={ "none": user, "none2":[0],"sleep": [0]}
cairoplot.dot_line_plot("SleepWake.png", users, 400, 400, axis=True, grid=True, y_title='Sleep=1; Wake=0', x_title=report, y_bounds=(0,1.11), background=background)
#py.figure(1, figsize=(6,6))
#ax = py.axes([0.1, 0.1, 0.8, 0.8])
#py.pie(fracs, labels=labels, autopct='%1.1f%%', shadow=True)
#py.title('Sleep Efficiency with ' + report)
#py.axis([0,N.size(user), 0, 1.2])
#py.grid(True)
#py.show()
#classifier(e)
start = time.time()
count = 0
while time.time() < start + 60:
if geiger.read(1) != "":
print("Count")
count = count + 1
return count
# our read attempts will time out after 1 second
geiger = serial.Serial(port = geigerPort, baudrate = 9600, timeout = 1)
# plot the past plotLength minutes, initially all zero
times = [""] * 30
counts = [0] * 30
# once a minute, add a new count, drop the oldest, and replot
while 1 == 1:
# drop the oldest data point
counts = counts[1:len(counts)]
times = times[1:len(times)]
# append the newest data point
counts.append(countsPerMinute(geiger))
times.append(time.strftime("%I:%M %p"))
# plot the resulting graph
cairoplot.dot_line_plot(chartPath, counts, 400, 300,
background = "white white",
border = 1,
axis = True,
x_labels = times,
x_title = "CPM as of %s" % time.asctime())
