def generate_control_chart(tickets: List[Issue],
chart_class: Type[IChart] = Chart) -> None:
if not tickets:
print('No data. Check that the JSON file was correctly populated ' +
'when you ran the `fetch` command.')
return
completed_cycle_times: List[CycleTime] = list(
sorted(
(get_cycle_time(ticket) for ticket in tickets
if ticket.completed is not None and ticket.started is not None),
key=attrgetter("completed"),
))
if not completed_cycle_times:
print('Could not process cycle time, check that the statuses ' +
'in config.yaml are correct.')
return
cycle_time_plot = CycleTimeScatterPlot(cycle_times=completed_cycle_times,
data_source=ColumnDataSource)
average_cycle_time_plot = AverageCycleTimePlot(
cycle_times=completed_cycle_times, data_source=ColumnDataSource)
rolling_cycle_time_plot = RollingAverageCycleTimePlot(
cycle_times=completed_cycle_times, data_source=ColumnDataSource)
cycle_time_deviation_plot = CycleTimeDeviationPlot(
cycle_times=completed_cycle_times, data_source=ColumnDataSource)
start_date, end_date = (
completed_cycle_times[0].completed,
completed_cycle_times[-1].completed,
)
chart = chart_class(
x=Axis(
label="Closed (date)",
values=[
d[0].date().isoformat() for d in Arrow.span_range(
"day",
Arrow(start_date.year, start_date.month, start_date.day),
Arrow(end_date.year, end_date.month, end_date.day),
)
],
size=1800,
),
y=Axis(label="Cycle time (days)", values=None, size=900),
label="Ticket",
tooltips=[
("Ticket", "@label"),
("Closed (date)", "@x"),
("Cycle time (days)", "@y"),
],
)
cycle_time_plot.draw(chart)
average_cycle_time_plot.draw(chart)
rolling_cycle_time_plot.draw(chart)
cycle_time_deviation_plot.draw(chart)
chart.render()
def generate_control_chart(tickets: List[Issue],
chart_class: Type[IChart] = Chart) -> None:
if not tickets:
return
completed_cycle_times: List[CycleTime] = list(
sorted(
(get_cycle_time(ticket) for ticket in tickets
if ticket.completed is not None and ticket.started is not None),
key=attrgetter("completed"),
))
cycle_time_plot = CycleTimeScatterPlot(cycle_times=completed_cycle_times,
data_source=ColumnDataSource)
average_cycle_time_plot = AverageCycleTimePlot(
cycle_times=completed_cycle_times, data_source=ColumnDataSource)
rolling_cycle_time_plot = RollingAverageCycleTimePlot(
cycle_times=completed_cycle_times, data_source=ColumnDataSource)
cycle_time_deviation_plot = CycleTimeDeviationPlot(
cycle_times=completed_cycle_times, data_source=ColumnDataSource)
start_date, end_date = (
completed_cycle_times[0].completed,
completed_cycle_times[-1].completed,
)
chart = chart_class(
x=Axis(
label="Closed (date)",
values=[
d[0].date().isoformat() for d in Arrow.span_range(
"day",
Arrow(start_date.year, start_date.month, start_date.day),
Arrow(end_date.year, end_date.month, end_date.day),
)
],
size=1800,
),
y=Axis(label="Cycle time (days)", values=None, size=900),
label="Ticket",
tooltips=[
("Ticket", "@label"),
("Closed (date)", "@x"),
("Cycle time (days)", "@y"),
],
)
cycle_time_plot.draw(chart)
average_cycle_time_plot.draw(chart)
rolling_cycle_time_plot.draw(chart)
cycle_time_deviation_plot.draw(chart)
chart.render()
def index(self, request, extra_context=None):
"""
Add some extra index context such as a dataset to graph.
"""
today = Arrow.fromdatetime(now())
last_year = today.replace(months=-11)
site_dataset = OrderedDict()
sites = []
if request.user.is_superuser:
sites = Site.objects.annotate(video_count=Count('camera__video'))
else:
sites = Site.objects.annotate(
video_count=Count('camera__video')).filter(id__lte=2).exclude(
pk=17)
gf = GraphForm(request.GET)
startd = last_year
endd = today
if gf.is_valid():
startd = Arrow.fromdate(gf.cleaned_data['start_date'])
endd = Arrow.fromdate(gf.cleaned_data['end_date'])
difference = (endd - startd).days
period = 'month'
if difference < 31:
period = 'day'
elif difference < (30 * 6):
period = 'week'
else:
period = 'month'
# For every site, aggregate the average number of returning penguins
# across the entire month. These calculations are the average over
# the median number of penguins observed each day.
site_dataset['Total Penguins'] = []
for site in sites:
site_dataset[site.name] = []
for start, end in Arrow.span_range(period, startd, endd):
average = site.penguincount_set.filter(
date__gte=start.date(), date__lte=end.date()).aggregate(
penguins=Avg('total_penguins'))
site_dataset[site.name].append({
'date':
start.date(),
'value':
"%0.2f" % average['penguins'] if
(average['penguins'] > 0) else 0.0
})
for item in site_dataset['Total Penguins']:
if item['date'] == start.date():
item['value'] = "%0.2f" % (
float(item['value']) +
(average['penguins'] if
(average['penguins'] > 0) else 0.0))
break
else:
site_dataset['Total Penguins'].append({
'date':
start.date(),
'value':
"%0.2f" % average['penguins'] if
(average['penguins'] > 0) else 0.0
})
context = {
'sites': sites,
'site_dataset': site_dataset,
'title': _("Penguin island sites"),
'gform': gf,
}
context.update(extra_context or {})
return super(PenguinSite, self).index(request, context)
def total_on_each(self, starts_on: Arrow, ends_on: Arrow):
for i, v in enumerate(
list(Arrow.span_range('year', starts_on, ends_on)), 1):
yield (self.loan * self.interest) * i, v[-1]
def total_on(self, end_date: Arrow):
total_years = len(
list(Arrow.span_range('year', self.starts_on, end_date))) - 1
self._total_loan = (self.loan * self.interest) * total_years
return self._total_loan
def generate_control_chart(
tickets: List[Issue], chart_class: Type[IChart] = Chart
) -> None:
"""Generate the ticket control chart with the provided list of tickets.
:param tickets: The list of tickets to generate your control chart from.
:param chart_class: The chart class to output into, defaults to Chart.
:raises NoTicketsProvided: If len(tickets) == 0.
:raises ChartError: On any error attempting to generate a chart.
"""
if not tickets:
raise NoTicketsProvided
completed_cycle_times: List[CycleTime] = list(
sorted(
(
get_cycle_time(ticket)
for ticket in tickets
if ticket.completed is not None and ticket.started is not None
),
key=attrgetter("completed"),
)
)
if not completed_cycle_times:
raise ChartError(
"Could not process cycle time. Check your config.yaml statuses."
)
cycle_time_plot = CycleTimeScatterPlot(
cycle_times=completed_cycle_times, data_source=ColumnDataSource
)
average_cycle_time_plot = AverageCycleTimePlot(
cycle_times=completed_cycle_times, data_source=ColumnDataSource
)
rolling_cycle_time_plot = RollingAverageCycleTimePlot(
cycle_times=completed_cycle_times, data_source=ColumnDataSource
)
cycle_time_deviation_plot = CycleTimeDeviationPlot(
cycle_times=completed_cycle_times, data_source=ColumnDataSource
)
start_date, end_date = (
completed_cycle_times[0].completed,
completed_cycle_times[-1].completed,
)
chart = chart_class(
x=Axis(
label="Closed (date)",
values=[
d[0].date().isoformat()
for d in Arrow.span_range(
"day",
Arrow(start_date.year, start_date.month, start_date.day),
Arrow(end_date.year, end_date.month, end_date.day),
)
],
size=1800,
),
y=Axis(label="Cycle time (days)", values=None, size=900),
label="Ticket",
tooltips=[
("Ticket", "@label"),
("Closed (date)", "@x"),
("Cycle time (days)", "@y"),
],
)
cycle_time_plot.draw(chart)
average_cycle_time_plot.draw(chart)
rolling_cycle_time_plot.draw(chart)
cycle_time_deviation_plot.draw(chart)
chart.render()