def get_context_data(self, **kwargs):
api = ExpaApi()
context = super(GetLCScoreboard, self).get_context_data(**kwargs)
try:
office_name = self.kwargs['office_name']
except KeyError:
office_name = 'ANDES'
context['office_name'] = office_name
office = Office.objects.select_related(
'superoffice',
'superoffice__superoffice').get(name=office_name.replace('_', ' '))
officeID = office.expaID
context['officeID'] = officeID
products = []
for product in Program.objects.all():
products.append(product.name)
context['products'] = products
#Prepara la información respecto a los logros del LC a partir de la información dentro de la base de datos
achieved = {}
total = 0
for logro in LogrosPrograma.objects.filter(office=officeID):
program = logro.program_id
achieved[program] = logro
total += achieved[program].realized
achieved['total'] = {'realized': total}
context['achieved'] = achieved
#Carga de la base de datos los logros planeados
planned = {}
total_meta = 0
for meta in MonthlyGoal.objects.filter(
office_id=officeID).values('program').annotate(
realized=Sum('realized'), approved=Sum('approved')):
program = meta['program']
planned[program] = meta
meta = planned[program]['realized']
if meta is None:
meta = 0
total_meta += meta
planned['total'] = {'realized': total_meta}
context['planned'] = planned
#Agrega los rankings
rankings = {
'rankings': {
'approved': tools.getLCRankings('Total', 'approved',
lc=office),
'realized': tools.getLCRankings('Total', 'realized', lc=office)
}
}
context.update(rankings)
return context
def get_context_data(self, **kwargs):
context = super(GetAreaScoreboard, self).get_context_data(**kwargs)
api = ExpaApi()
office_name = self.kwargs['office_name']
context['office_name'] = office_name
office = Office.objects.select_related('superoffice', 'superoffice__superoffice').get(name=office_name.replace('_', ' '))
officeID = office.expaID
program = self.kwargs['programa']
context['officeID'] = officeID
context['program'] = program
#Prepara la información respecto a los logros del LC a partir de la información dentro de la base de datos
achieved = {}
for logro in LogrosPrograma.objects.filter(office=officeID, program_id=program):
achieved = logro
context['achieved'] = achieved
#Carga de la base de datos los logros planeados
planned = {}
for meta in MonthlyGoal.objects.filter(office_id=officeID, program_id=program).values('program').annotate(realized=Sum('realized'), approved=Sum('approved')):
planned = meta
context['planned'] = planned
#context['performance'] = api.getProgramWeeklyPerformance(programa)
#columns = zip(context['performance']['weekly']['MA'], context['performance']['weekly']['RE'])
#Creates the JSON data to be used by Google Charts
#chartList = [['Semana', 'Matches', 'Realizaciones']]
#for index, week in enumerate(columns):
# chartList.append(['Semana %i' % index, columns[index][0], columns[index][1]])
#context['weeklyJSON'] = json.dumps(chartList)
#Finds the top for LATAM and inside own country
rankings = {
'rankings': {
'approved':tools.getLCRankings(program, 'approved', lc=office),
'realized':tools.getLCRankings(program, 'realized', lc=office),
}
}
context.update(rankings)
#These analytics are related to the fulfillment of the current monthly goal
context['planned_monthly'] = MonthlyGoal.objects.filter(office=officeID, program=program.upper()).order_by('year', 'month')
context['achieved_monthly'] = MonthlyAchievement.objects.filter(office=officeID, program=program.upper()).order_by('month')
#context['monthly_achievements'] = api.getProgramMonthlyPerformance(programa, 1395)
if program[0].lower() == 'o': #Specific analytics for OGX
#Context data for total uncontacted EPs
try:
context['uncontacted'] = api.getUncontactedEPs(officeID)['total']
except ValueError as e:
print e #TODO: Logging?
context['uncontacted'] = "EXPA Error"
#Context data for Weekly registered/contacted and contacted/interviewed analytics
try:
weekRegisteredEPs = api.getWeekRegistered(officeID)
weekRegisteredContacted = 0.0
for ep in weekRegisteredEPs['eps']:
if ep['contacted_at']:
weekRegisteredContacted += 1
try:
contacted_rate = weekRegisteredContacted/weekRegisteredEPs['total']
except ZeroDivisionError:
contacted_rate = 0
context['weekRegisteredAnalytics'] = {
'total':weekRegisteredEPs['total'],
'nContacted':weekRegisteredContacted,
'rate':contacted_rate*100,
'gap':weekRegisteredContacted - weekRegisteredEPs['total']
}
except ValueError as e:
context['weekRegisteredAnalytics'] = {
'total':'EXPA Error',
'nContacted':'EXPA Error',
'rate':'EXPA Error',
'gap':'EXPA Error',
}
try:
weekContactedEPs = api.getWeekContacted(officeID)
weekContactedInterviewed = 0.0
for ep in weekContactedEPs['eps']:
if ep['interviewed']:
weekContactedInterviewed += 1
try:
interviewed_rate = weekContactedInterviewed/weekContactedEPs['total']
except ZeroDivisionError:
interviewed_rate = 0
context['weekContactedAnalytics'] = {
'total':weekContactedEPs['total'],
'nContacted':weekContactedInterviewed,
'rate':interviewed_rate*100,
'gap':weekContactedInterviewed - weekContactedEPs['total']
}
except ValueError as e:#TODO: Mover a la EXPA API
context['weekContactedAnalytics'] = {
'total':'EXPA Error',
'nContacted':'EXPA Error',
'rate':'EXPA Error',
'gap':'EXPA Error',
}
else:
print program[0]
return context
def get_context_data(self, **kwargs):
api = ExpaApi()
context = super(GetColombianEBs, self).get_context_data(**kwargs)
context['lcs'] = api.getColombiaContactList()
return context
def get_context_data(self, **kwargs):
api = ExpaApi()
context = super(GetLCWeeklyGoals, self).get_context_data(**kwargs)
context['performance'] = api.getLCWeeklyPerformance()
return context
def get_context_data(self, **kwargs):
api = ExpaApi()
context = super(GetAndesYearlyPerformance, self).get_context_data(**kwargs)
context['programs'] = api.getLCYearlyPerformance(2015)
return context
def get_context_data(self, **kwargs):
context = super(GetBangladeshScoreboard, self).get_context_data(**kwargs)
api = ExpaApi('*****@*****.**')
office_id = 2010
context['office_id'] = office_id
office = Office.objects.select_related('superoffice').get(expaID=office_id)
office_name = office.name
program = 'ogv'
context['office_name'] = office_name
context['program'] = program
#Prepara la información respecto a los logros del LC a partir de la información dentro de la base de datos
achieved = {}
#TODO: En esta parte voy a crear el diccionario para el scoreboard del MC de una manera super folclórica, pero con el objetivo de mejorar más tade obviamente
year_metrics = [] #Here I will save all metrics in a format understandable by the scoreboard
measurements = ['applicants', 'accepted', 'realized', 'completed']
logros = api.getCurrentMCYearStats('ogv', office_id)
planned = {
'applicants': 785,
'accepted': 314,
'approved': 157,
'realized': 125,
'completed': 100,
}
for measurement in measurements:
measurement_data = {
'planned': planned[measurement],
'executed': logros[measurement],
'gap': planned[measurement] - logros[measurement],
'name': measurement,
}
year_metrics.append(measurement_data)
context['year_metrics'] = year_metrics
wig_measure = 'approved'
wig = {
'planned': planned[wig_measure],
'executed': logros[wig_measure],
'gap': planned[wig_measure] - logros[wig_measure],
'name': wig_measure,
}
context['wig'] = wig
context['finance'] = {
'planned':500000,
'executed':300000,
}
quarter_achievements = api.get_stats(office_id, program, '2018-04-01', datetime.now().strftime('%Y-%m-%d'))
context['quarter_execution'] = quarter_achievements
context['month_execution'] = quarter_achievements
#context['achieved_last_week'] = api.get_past_stats(7, program, office_id)
context['countdown'] = (datetime.strptime('2018-08-01', '%Y-%m-%d') - datetime.now()).days
#TM scoreboard: See how many members are on each stage of performance
vd_api = PodioApi(19600457)
individual_performance = {}
approvals = vd_api.get_filtered_items({
157141796:{'from':'2018-04-01'}
})
for customer in approvals:
ep_manager = customer['values'][157144216]['value']['name']
if ep_manager in individual_performance:
individual_performance[ep_manager] += 1
else:
individual_performance[ep_manager] = 1
aggregation = {
'1':0,
'2':0,
'3':0,
'4':0,
}
for manager, approvals in individual_performance.items():
if approvals == 1:
aggregation['1'] += 1
elif approvals == 2:
aggregation['2'] += 2
elif approvals == 3:
aggregation['3'] += 3
elif approvals >= 4:
aggregation['4'] += 4
context['tm_performance'] = aggregation
return context
def get_context_data(self, **kwargs):
api = ExpaApi()
context = super(GetIndex, self).get_context_data(**kwargs)
context['lcs'] = Office.objects.filter(superoffice_id=1551).order_by('name')
return context
def get_context_data(self, **kwargs):
api = ExpaApi()
context = super(GetLCWeeklyGoals, self).get_context_data(**kwargs)
context['performance'] = api.getLCWeeklyPerformance()
return context
def get_context_data(self, **kwargs):
context = super(GetTeamsScoreboard, self).get_context_data(**kwargs)
api = ExpaApi('*****@*****.**')
office_id = 2010
context['office_id'] = office_id
office = Office.objects.select_related('superoffice').get(expaID=office_id)
office_name = office.name
program = 'ogv'
context['office_name'] = office_name
context['program'] = program
#Prepara la información respecto a los logros del LC a partir de la información dentro de la base de datos
achieved = {}
#TODO: En esta parte voy a crear el diccionario para el scoreboard del MC de una manera super folclórica, pero con el objetivo de mejorar más tade obviamente
year_metrics = [] #Here I will save all metrics in a format understandable by the scoreboard
year_metrics_dict={}
measurements = ['applicants', 'accepted', 'realized', 'completed']
logros = api.get_stats(office_id, 'ogv', '2018-04-01')
planned = {
'applicants': 500,
'accepted': 200,
'approved': 100,
'realized': 90,
'completed': 82,
}
team_planned = {
'applicants': 125,
'accepted': 50,
'approved': 25,
'realized': 23,
'completed': 21,
}
for measurement in measurements:
measurement_data = {
'planned': planned[measurement],
'executed': logros[measurement],
'gap': planned[measurement] - logros[measurement],
'name': measurement,
}
year_metrics.append(measurement_data)
year_metrics_dict[measurement] = measurement_data
context['year_metrics'] = year_metrics
wig_measure = 'approved'
wig = {
'planned': planned[wig_measure],
'executed': logros[wig_measure],
'gap': planned[wig_measure] - logros[wig_measure],
'name': wig_measure,
}
context['wig'] = wig
all_metrics = {'all': year_metrics_dict}
all_metrics['all'][wig_measure] = wig
quarter_achievements = api.get_stats(office_id, program, '2018-04-01', datetime.now().strftime('%Y-%m-%d'))
context['quarter_execution'] = quarter_achievements
context['month_execution'] = quarter_achievements
#context['achieved_last_week'] = api.get_past_stats(7, program, office_id)
context['countdown'] = (datetime.strptime('2018-08-01', '%Y-%m-%d') - datetime.now()).days
cons_api = PodioApi(19156174)
vd_api = PodioApi(19156174)
team_leaders = Member.objects.filter(is_active=True).order_by('-extra_points')
context['teams'] = team_leaders
performance = {}
for team_leader in team_leaders:
performance[team_leader.podio_id] = {
'applicants': 0,
'accepted': 0,
'approved': 0,
'realized': 0,
'completed': 0,
}
recent_items = cons_api.get_items_by_view(37897730)
start_date = '2018-04-01'
for item in recent_items:
if 151795765 not in item['values']:
continue
tl_id = item['values'][151795765]['value']['user_id']
if tl_id in performance:
if 151950042 in item['values']:
registration_date = item['values'][151950042]['value']['start_date']
status = item['values'][151795769]['value']
status_number = status.split(' - ')[0]
if status_number != 0: #if uncontacted, ignores everything
if 159724899 in item['values']:
contacted_date = item['values'][159724899]['value']['start_date']
if contacted_date >= start_date:
pass
# performance[tl_id]['contacted'] += 1
if 158519539 in item['values']:
application_date = item['values'][158519539]['value']['start_date']
if application_date >= start_date:
performance[tl_id]['applicants'] += 1
if 169759824 in item['values']:
acceptance_date = item['values'][169759824]['value']['start_date']
if acceptance_date >= start_date:
performance[tl_id]['accepted'] += 1
if 159728809 in item['values']:
approval_date = item['values'][159728809]['value']['start_date']
if approval_date >= start_date:
performance[tl_id]['approved'] += 1
team_performance = {}
measurements.append(wig_measure)
for tl in team_leaders:
data = {}
for measurement in measurements:
measurement_data = {
'planned': team_planned[measurement],
'executed': performance[tl.podio_id][measurement],
'gap': team_planned[measurement] - performance[tl.podio_id][measurement]
}
data[measurement] = measurement_data
team_performance[tl.podio_id] = data
all_metrics.update(team_performance)
context['metrics_json'] = json.dumps(all_metrics)
return context
def get_context_data(self, **kwargs):
context = super(GetAreaScoreboard, self).get_context_data(**kwargs)
api = ExpaApi(account='*****@*****.**')
office_name = self.kwargs['office_name']
context['office_name'] = office_name
office = Office.objects.select_related(
'superoffice',
'superoffice__superoffice').get(name=office_name.replace('_', ' '))
officeID = office.expaID
program = self.kwargs['programa']
context['officeID'] = officeID
context['program'] = program
#Prepara la información respecto a los logros del LC a partir de la información dentro de la base de datos
achieved = {}
for logro in LogrosPrograma.objects.filter(office=officeID,
program_id=program):
achieved = logro
context['achieved'] = achieved
#Carga de la base de datos los logros planeados
planned = {}
for meta in MonthlyGoal.objects.filter(
office_id=officeID,
program_id=program).values('program').annotate(
realized=Sum('realized'), approved=Sum('approved')):
planned = meta
context['planned'] = planned
#context['performance'] = api.getProgramWeeklyPerformance(programa)
#columns = zip(context['performance']['weekly']['MA'], context['performance']['weekly']['RE'])
#Creates the JSON data to be used by Google Charts
#chartList = [['Semana', 'Matches', 'Realizaciones']]
#for index, week in enumerate(columns):
# chartList.append(['Semana %i' % index, columns[index][0], columns[index][1]])
#context['weeklyJSON'] = json.dumps(chartList)
#Finds the top for LATAM and inside own country
rankings = {
'rankings': {
'approved': tools.getLCRankings(program, 'approved',
lc=office),
'realized': tools.getLCRankings(program, 'realized',
lc=office),
}
}
context.update(rankings)
#These analytics are related to the fulfillment of the current monthly goal
context['planned_monthly'] = MonthlyGoal.objects.filter(
office=officeID,
program=program.upper()).order_by('year', 'month')
context['achieved_monthly'] = MonthlyAchievement.objects.filter(
office=officeID, program=program.upper()).order_by('month')
#This data is related to the planned and achieved goals during the current month
current_month = {}
current_month_number, current_year_number = map(
int,
datetime.now().strftime("%m,%Y").split(','))
try:
current_month['planned'] = MonthlyGoal.objects.get(
office=officeID,
program=program.upper(),
month=current_month_number)
except MonthlyGoal.DoesNotExist:
current_month['planned'] = "Falta llenar metas en PODIO"
current_month['achieved'] = api.getMonthStats(current_month_number,
current_year_number,
program.upper(),
officeID)
context['current_month'] = current_month
#context['monthly_achievements'] = api.getProgramMonthlyPerformance(programa, 1395)
#These analytics are related to the achievements durint a past period of time
context['achieved_past_week'] = api.get_past_stats(
7, program, officeID)
if program[0].lower() == 'o': #Specific analytics for OGX
#Context data for total uncontacted EPs
try:
context['uncontacted'] = api.getUncontactedEPs(
officeID)['total']
except APIUnavailableException as e:
print e #TODO: Logging?
context['uncontacted'] = "EXPA Error"
#Context data for Weekly registered/contacted and contacted/interviewed analytics
try:
weekRegisteredEPs = api.getWeekRegistered(officeID)
weekRegisteredContacted = 0.0
for ep in weekRegisteredEPs['eps']:
if ep['contacted_at']:
weekRegisteredContacted += 1
try:
contacted_rate = weekRegisteredContacted / weekRegisteredEPs[
'total']
except ZeroDivisionError:
contacted_rate = 0
context['weekRegisteredAnalytics'] = {
'total': weekRegisteredEPs['total'],
'nContacted': weekRegisteredContacted,
'rate': contacted_rate * 100,
'gap': weekRegisteredContacted - weekRegisteredEPs['total']
}
except APIUnavailableException as e:
context['weekRegisteredAnalytics'] = {
'total': 'EXPA Error',
'nContacted': 'EXPA Error',
'rate': 'EXPA Error',
'gap': 'EXPA Error',
}
try:
weekContactedEPs = api.getWeekContacted(officeID)
weekContactedInterviewed = 0.0
for ep in weekContactedEPs['eps']:
if ep['interviewed']:
weekContactedInterviewed += 1
try:
interviewed_rate = weekContactedInterviewed / weekContactedEPs[
'total']
except ZeroDivisionError:
interviewed_rate = 0
context['weekContactedAnalytics'] = {
'total': weekContactedEPs['total'],
'nContacted': weekContactedInterviewed,
'rate': interviewed_rate * 100,
'gap': weekContactedInterviewed - weekContactedEPs['total']
}
except APIUnavailableException as e: #TODO: Mover a la EXPA API
context['weekContactedAnalytics'] = {
'total': 'EXPA Error',
'nContacted': 'EXPA Error',
'rate': 'EXPA Error',
'gap': 'EXPA Error',
}
else:
print program[0]
return context
def get_context_data(self, **kwargs):
api = ExpaApi()
context = super(GetColombianEBs, self).get_context_data(**kwargs)
context['lcs'] = api.getColombiaContactList()
return context
def get_context_data(self, **kwargs):
api = ExpaApi()
context = super(GetAndesYearlyPerformance,
self).get_context_data(**kwargs)
context['programs'] = api.getLCYearlyPerformance(2015)
return context
