def std_dev(start, stop):
start = make_start_date(date=start)
stop = make_end_date(date=stop)
rg = ReportGroup('dev', Kard.objects.filter(done_date__gte=start, done_date__lte=stop))
cards = [c for c in rg.queryset if c.is_card]
cycle_times = [c.cycle_time for c in cards]
over_21 = [c for c in cycle_times if c > 21]
over_21_pct = float(len(over_21)) / len(cycle_times)
print "%s -- %s" % (start, stop)
print "\t Sample: %s" % len(cards)
print "\t Over 21: %s / %s" % (len(over_21), over_21_pct * 100)
print "\t Ave: %s" % average(cycle_times)
print "\t Stdev: %s" % standard_deviation(cycle_times)
ct, pct = percentile(.8, cycle_times)
print "\t 80pct: %s / %s" % (ct, pct * 100)
cards_by_class = {}
for c in cards:
cards_by_class.setdefault(c.service_class['name'], [])
cards_by_class[c.service_class['name']].append(c)
for sclass, cards in cards_by_class.items():
cycle_times = [c.cycle_time for c in cards]
print "\t ## %s" % (sclass)
print "\t\t Sample: %s" % len(cards)
print "\t\t Ave: %s" % average(cycle_times)
print "\t\t Stdev: %s" % standard_deviation(cycle_times)
def report_on_cards(rg):
data = {}
for k in rg.queryset():
class_cards = data.get(k.service_class.get('name'), [])
class_cards.append(k)
data[k.service_class.get('name')] = class_cards
total = sum([len(v) for k, v in data.items()])
report = {}
for classname, cards in data.items():
sclass = cards[0].service_class
cycle_time_average = int(round(average(
[c.current_cycle_time() for c in cards])))
cards_hit_goal = len([c.key for c in cards
if c.current_cycle_time() <= sclass.get('upper')])
report[classname] = {
'service_class': sclass.get('name'),
'wip': len(cards),
'wip_percent': len(cards) / float(total),
'cycle_time_average': cycle_time_average,
'cards_hit_goal': cards_hit_goal,
'cards_hit_goal_percent': cards_hit_goal / float(len(cards)),
}
return report
def report_on_cards(cards):
data = {}
for k in cards:
class_cards = data.get(k.service_class.get('name'), [])
class_cards.append(k)
data[k.service_class.get('name')] = class_cards
total = sum([len(v) for k, v in data.items()])
report = {}
for classname, cards in data.items():
sclass = cards[0].service_class
cycle_time_average = int(round(average(
[c.current_cycle_time() for c in cards])))
cards_hit_goal = len([c.key for c in cards
if c.current_cycle_time() <= sclass.get('upper')])
report[classname] = {
'service_class': sclass.get('name'),
'wip': len(cards),
'wip_percent': len(cards) / float(total),
'cycle_time_average': cycle_time_average,
'cards_hit_goal': cards_hit_goal,
'cards_hit_goal_percent': cards_hit_goal / float(len(cards)),
}
return report
def report_on_cards(cards):
data = {}
for k in cards:
class_cards = data.get(k.service_class.get("name"), [])
class_cards.append(k)
data[k.service_class.get("name")] = class_cards
total = sum([len(v) for k, v in data.items()])
report = {}
for classname, cards in data.items():
sclass = cards[0].service_class
cycle_time_average = int(round(average([c.current_cycle_time() for c in cards])))
cards_hit_goal = len([c.key for c in cards if c.current_cycle_time() <= sclass.get("upper")])
report[classname] = {
"service_class": sclass.get("name"),
"wip": len(cards),
"wip_percent": len(cards) / float(total),
"cycle_time_average": cycle_time_average,
"cards_hit_goal": cards_hit_goal,
"cards_hit_goal_percent": cards_hit_goal / float(len(cards)),
}
return report
def state_transition_counts(state, months, count_type="exit", raw=False):
end = make_end_date(date=datetime.datetime.now())
start = end - relativedelta(months=months)
start = make_start_date(date=start)
counts = []
data = []
current_date = start
while current_date <= end:
if current_date.weekday() == 5: # Saturday
current_date += relativedelta(days=2)
elif current_date.weekday() == 6: # Sunday
current_date += relativedelta(days=1)
range_start = make_start_date(date=current_date)
range_end = make_end_date(date=current_date)
if count_type == "exit":
kwargs = dict(
exited__gte=range_start,
exited__lte=range_end,
)
elif count_type == "enter":
kwargs = dict(
entered__gte=range_start,
entered__lte=range_end,
)
count = StateLog.objects.filter(
state=state,
**kwargs
).count()
data.append((current_date, count))
counts.append(count)
current_date = current_date + relativedelta(days=1)
counts.sort()
print "%s\t%s" % (start, end)
print "Median\t%s" % median(counts)
print "Average\t%s" % average(counts)
print "Min\t%s" % counts[0]
print "Max\t%s" % counts[-1]
hist = histogram(counts)
keys = hist.keys()
keys.sort()
for k in keys:
print "%s\t%s" % (k, hist[k])
if raw is True:
for date, count in data:
print "%s\t%s" % (date, count)
def state_transition_counts(state, months, count_type="exit", raw=False):
end = make_end_date(date=datetime.datetime.now())
start = end - relativedelta(months=months)
start = make_start_date(date=start)
counts = []
data = []
current_date = start
while current_date <= end:
if current_date.weekday() == 5: # Saturday
current_date += relativedelta(days=2)
elif current_date.weekday() == 6: # Sunday
current_date += relativedelta(days=1)
range_start = make_start_date(date=current_date)
range_end = make_end_date(date=current_date)
if count_type == "exit":
kwargs = dict(
exited__gte=range_start,
exited__lte=range_end,
)
elif count_type == "enter":
kwargs = dict(
entered__gte=range_start,
entered__lte=range_end,
)
count = StateLog.objects.filter(state=state, **kwargs).count()
data.append((current_date, count))
counts.append(count)
current_date = current_date + relativedelta(days=1)
counts.sort()
print "%s\t%s" % (start, end)
print "Median\t%s" % median(counts)
print "Average\t%s" % average(counts)
print "Min\t%s" % counts[0]
print "Max\t%s" % counts[-1]
hist = histogram(counts)
keys = hist.keys()
keys.sort()
for k in keys:
print "%s\t%s" % (k, hist[k])
if raw is True:
for date, count in data:
print "%s\t%s" % (date, count)
def year_std_dev(year):
year_start = make_start_date(date=datetime(year, 1, 1))
year_end = make_end_date(date=datetime(year, 12, 31))
rg = ReportGroup('dev', Kard.objects.filter(start_date__gte=year_start, done_date__lte=year_end))
cards = [c for c in rg.queryset if c.is_card]
cycle_times = [c.cycle_time for c in cards]
data = {}
data['n'] = len(cards)
data['ave'] = average(cycle_times)
data['std'] = standard_deviation(cycle_times)
return data
def moving_data(report_group_slug, start, stop):
query = Kard.objects.filter(
done_date__gte=start,
done_date__lte=stop,
)
kards = list(ReportGroup(report_group_slug, query).queryset)
bad_kards = [k for k in kards if k.cycle_time is None]
print "Bad cards"
print "*" * 10
print[k.key for k in bad_kards]
features = [k for k in kards if k.is_card]
defects = [k for k in kards if not k.is_card]
over_sla = [k for k in kards if k.cycle_time > k.service_class['upper']]
card_cycle_ave = average([k.cycle_time for k in kards]) or 0
card_stddev = standard_deviation([k.cycle_time for k in kards]) or 0
wip = find_wip(report_group_slug, stop)
tpa = daily_throughput_average(report_group_slug, stop)
try:
little_law = int(round(wip / float(tpa)))
except:
little_law = ""
cycle_time_ave = find_cycle_time_ave(report_group_slug, stop)
data = {
'start': start,
'stop': stop,
'features': len(features),
'bugfixes': len(defects),
'little_law': little_law,
'cycle_time_ave': int(round(cycle_time_ave)),
'wip': wip,
'cycle_average': int(round(card_cycle_ave)),
'stddev': int(round(card_stddev)),
'over_sla': len(over_sla),
}
return data
def report_suite(name, start, end):
start, end = _get_time_range(None, start, end)
try:
team = _get_team(name)
done, wip = _get_cards(team, start, end)
except ValueError:
rg_slug = _verify_rg(name)
done, wip = _get_cards_by_report_group(rg_slug, start, end)
done = [k for k in done if k.is_card]
wip = [k for k in done if k.is_card]
for k in done:
print "%s - %s - %s" % (k.key, k.cycle_time, k._service_class)
cycle_times = [c.cycle_time for c in done]
hist = histogram(cycle_times)
print "Sample size: %s" % len(cycle_times)
print "Average: %s" % average(cycle_times)
for k, v in hist.items():
print "%s\t%s" % (k, v)
def card_state_averages(card_state_time):
averages = {}
for state, day_data in card_state_time.items():
averages[state] = average(day_data)
return averages
def card_state_averages(card_state_time):
averages = {}
for state, day_data in card_state_time.items():
averages[state] = average(day_data)
return averages
def average(self, weeks=4):
ave = average(self.cycle_times(weeks))
if ave is not None:
ave = int(round(ave))
return ave
def average(self, field_str):
values = [getattr(k, field_str) for k in self.filter().only(field_str)]
if len(values) == 0:
return 0
return average(values)
def average(self, field_str):
values = [getattr(k, field_str) for k in self.filter().only(field_str)]
values = [value for value in values if value is not None]
if len(values) == 0:
return 0
return average(values)
def average(self, weeks=4):
ave = average(self.cycle_times(weeks))
if ave is not None:
ave = int(round(ave))
return ave
def moving_data(report_group_slug, start, stop):
query = Kard.objects.filter(
done_date__gte=start,
done_date__lte=stop,)
kards = list(ReportGroup(report_group_slug, query).queryset)
bad_kards = [k for k in kards if k.cycle_time is None]
print "Bad cards"
print "*"*10
print [k.key for k in bad_kards]
features = [k for k in kards if k.is_card]
defects = [k for k in kards if not k.is_card]
over_sla = [k for k in kards if k.cycle_time > k.service_class['upper']]
card_cycle_ave = average([k.cycle_time for k in kards]) or 0
card_stddev = standard_deviation([k.cycle_time for k in kards]) or 0
wip = find_wip(report_group_slug, stop)
tpa = daily_throughput_average(report_group_slug, stop)
try:
little_law = wip / float(tpa)
except:
little_law = 0
cycle_time_ave = find_cycle_time_ave(report_group_slug, stop)
data = {
'start': start,
'stop': stop,
def average(self, field_str):
values = [getattr(k, field_str) for k in self.filter().only(field_str)]
values = [value for value in values if value is not None]
if len(values) == 0:
return 0
return average(values)
