def analytics_aggregator_allblocks(district_mcts_id='36', rw=False):
try:
district = District.objects.get(MCTS_ID=district_mcts_id)
except ObjectDoesNotExist:
print "specified district does not exist"
return
timezone = 'Asia/Kolkata'
tz = pytz.timezone(timezone)
today_utc = utcnow_aware()
today = today_utc.astimezone(tz)
today_date = today.date()
anc_cutoff = today_date - timedelta(days=280)
imm_cutoff = today_date - timedelta(days=365)
yesterday_utc = today_utc - timedelta(days=1)
dt_month = today.date().month
dt_year = today.date().year
fin_marker = today_date.replace(month = 4, day=1)
if today_date.month < 4:
fin_marker = fin_marker.replace(year=today_date.year-1)
mreg_district_target = NHMTargets.objects.get(target_type='MREG', district=district, target_year=fin_marker.year).target_value
creg_district_target = NHMTargets.objects.get(target_type='CREG', district=district, target_year=fin_marker.year).target_value
district_population = PopulationData.objects.get(unit_type=District.__name__, MCTS_ID= district.MCTS_ID, year=fin_marker.year).population
month_span = [1, 2, 3, 4, 5, 6, 12]
for months in month_span:
this_month_date = today.replace(hour=6, minute=0, second=0, day=1).astimezone(pytz.utc)
since_months = this_month_date - relativedelta(months=(months-1))
blocks = SubCenter.objects.filter(district=district).values('block_id').distinct()
for block_entry in blocks:
block = Block.objects.get(id=block_entry['block_id'])
if not rw and AnalyticsData.objects.filter(block=block, since_months=months, month= this_month_date.month, year=this_month_date.year):
continue
subcenters = SubCenter.objects.filter(block=block)
data = []
num_good_anc = 0
num_poor_anc = 0
num_avg_anc = 0
num_exc_anc = 0
num_good_pnc = 0
num_poor_pnc = 0
num_avg_pnc = 0
num_exc_pnc = 0
num_good_imm = 0
num_poor_imm = 0
num_avg_imm = 0
num_exc_imm = 0
for sub in subcenters:
sub_data = {}
#total beneficiaries.
anc_benefs = ANCBenef.objects.filter(subcenter = sub)
pnc_benefs = PNCBenef.objects.filter(subcenter = sub)
imm_benefs = IMMBenef.objects.filter(subcenter = sub)
sub_data["Beneficiaries_anc"] = anc_benefs.filter(LMP__gte=anc_cutoff).count()
sub_data["Beneficiaries_pnc"] = pnc_benefs.count()
sub_data["Beneficiaries_imm"] = imm_benefs.filter(dob__gte=imm_cutoff).count()
from mcts_transactions.views import ProcessSubcenterData, get_status, increment_count_on_status, get_reg_status
data_anc = ProcessSubcenterData(anc_benefs, sub, since_months, Events.ANC_REG_VAL, months)
data_pnc = ProcessSubcenterData(pnc_benefs, sub, since_months, Events.PNC_REG_VAL, months)
data_imm = ProcessSubcenterData(imm_benefs, sub, since_months, Events.IMM_REG_VAL, months)
mreg_status = 0
creg_status = 0
try:
subc_population = PopulationData.objects.get(unit_type=SubCenter.__name__, MCTS_ID=sub.MCTS_ID, year=fin_marker.year).population
mreg_target = ceil((mreg_district_target * subc_population) / (district_population) )
creg_target = ceil((creg_district_target * subc_population) / (district_population) )
sub_data["mreg_target"] = mreg_target
sub_data["creg_target"] = creg_target
mreg_status = sub_data["Beneficiaries_anc"]/mreg_target if mreg_target else 0
creg_target = sub_data["Beneficiaries_imm"]/creg_target if creg_target else 0
except:
sub_data["mreg_target"] = 'NA'
sub_data["creg_target"] = 'NA'
sub_data["Adherence_anc"] = data_anc["Adherence"]
sub_data["Adherence_pnc"] = data_pnc["Adherence"]
sub_data["Adherence_imm"] = data_imm["Adherence"]
sub_data["new_reg_anc"] = data_anc["new_reg"]
sub_data["new_reg_pnc"] = data_pnc["new_reg"]
sub_data["new_reg_imm"] = data_imm["new_reg"]
sub_data["Overdue_anc"] = data_anc["Overdue"]
sub_data["Overdue_pnc"] = data_pnc["Overdue"]
sub_data["Overdue_imm"] = data_imm["Overdue"]
sub_data["overdue_sg_anc"] = data_anc["overdue_sg"]
sub_data["overdue_sg_pnc"] = data_pnc["overdue_sg"]
sub_data["overdue_sg_imm"] = data_imm["overdue_sg"]
sub_data["due_sg_anc"] = data_anc["due_sg"]
sub_data["due_sg_pnc"] = data_pnc["due_sg"]
sub_data["due_sg_imm"] = data_imm["due_sg"]
sub_data["given_sg_anc"] = data_anc["given_sg"]
sub_data["given_sg_pnc"] = data_pnc["given_sg"]
sub_data["given_sg_imm"] = data_imm["given_sg"]
sub_data["GivenServices_anc"] = data_anc["GivenServices"]
sub_data["GivenServices_pnc"] = data_pnc["GivenServices"]
sub_data["GivenServices_imm"] = data_imm["GivenServices"]
sub_data["DueServices_anc"] = data_anc["DueServices"]
sub_data["DueServices_pnc"] = data_pnc["DueServices"]
sub_data["DueServices_imm"] = data_imm["DueServices"]
sub_data["OverDueRate_anc"] = data_anc["OverDueRate"]
sub_data["OverDueRate_pnc"] = data_pnc["OverDueRate"]
sub_data["OverDueRate_imm"] = data_imm["OverDueRate"]
sub_data["ProgressData_anc"] = data_anc["ProgressData"]
sub_data["ProgressData_pnc"] = data_pnc["ProgressData"]
sub_data["ProgressData_imm"] = data_imm["ProgressData"]
status_anc, reason_anc = get_status(data_anc["OverDueRate"], 3, 5)
status_pnc, reason_pnc = get_status(data_pnc["OverDueRate"], 3, 5)
status_imm, reason_imm = get_status(data_imm["OverDueRate"], 3, 5)
if sub_data["Beneficiaries_anc"] == 0 or sub_data["DueServices_anc"] < 3:
if status_anc == 0:
reason_anc += "& Low beneficiary registration or updation"
else:
status_anc = 0
reason_anc += "But Low beneficiary registration or updation"
if sub_data["Beneficiaries_pnc"] == 0 or sub_data["DueServices_pnc"] < 3:
if status_pnc == 0:
reason_pnc += "& Low beneficiary registration or updation"
else:
status_pnc = 0
reason_pnc += "But Low beneficiary registration or updation"
if sub_data["Beneficiaries_imm"] == 0 or sub_data["DueServices_imm"] < 3:
if status_imm == 0:
reason_imm += "& Low beneficiary registration or updation"
else:
status_imm = 0
reason_imm += "But Low beneficiary registration or updation"
status_anc, reason_anc = get_reg_status(mreg_status, status_anc, reason_anc, .4, .7)
status_imm, reason_imm = get_reg_status(creg_status, status_imm, reason_imm, .4, .7)
num_good_anc, num_avg_anc, num_poor_anc, num_exc_anc = increment_count_on_status(status_anc, num_good_anc, num_avg_anc, num_poor_anc, num_exc_anc)
num_good_pnc, num_avg_pnc, num_poor_pnc, num_exc_pnc = increment_count_on_status(status_pnc, num_good_pnc, num_avg_pnc, num_poor_pnc, num_exc_pnc)
num_good_imm, num_avg_imm, num_poor_imm, num_exc_imm = increment_count_on_status(status_imm, num_good_imm, num_avg_imm, num_poor_imm, num_exc_imm)
sub_data["status_anc"] = status_anc
sub_data["status_pnc"] = status_pnc
sub_data["status_imm"] = status_imm
sub_data["reason_anc"] = reason_anc
sub_data["reason_pnc"] = reason_pnc
sub_data["reason_imm"] = reason_imm
sub_data["status"] = ceil((status_anc + status_pnc + status_imm)/3 - 0.5)
sub_data["Subcenter"] = sub.name
sub_data["SubcenterId"] = sub.id
AshaDetails = []
cgs = Beneficiary.objects.all().filter(subcenter = sub).values("caregiver").distinct()
for cg in cgs:
if cg["caregiver"]:
_cg = CareGiver.objects.get(id=cg["caregiver"])
AshaDetails.append(_cg.first_name+':'+_cg.phone)
sub_data["AshaDetails"] = AshaDetails
ANMDetails = []
cps = Beneficiary.objects.all().filter(subcenter = sub).values("careprovider").distinct()
for cp in cps:
if cp["careprovider"]:
_cp = CareProvider.objects.get(id=cp["careprovider"])
ANMDetails.append(_cp.first_name+':'+_cp.phone)
sub_data["ANMDetails"] = ANMDetails
# TODO remove dummy lat, long later
_lat = block._lat if block._lat else 25.619626
_long = block._long if block._long else 79.180409
sign = randint(1,2)
if sign == 1:
_lat = _lat + randint(1,100)*0.005
_long = _long + randint(1,100)*0.005
else:
_lat = _lat - randint(1,100)*0.005
_long = _long - randint(1,100)*0.005
sub_data["lat"] = sub._lat if sub._lat else _lat
sub_data["long"] = sub._long if sub._long else _long
data.append(sub_data)
block_data = {}
block_data["data"] = data
block_data["summary"] = {"Excellent":num_exc_anc, "Good":num_good_anc , "Poor":num_poor_anc ,"Average":num_avg_anc }
block_data["summary_anc"] = {"Excellent":num_exc_anc, "Good":num_good_anc , "Poor":num_poor_anc ,"Average":num_avg_anc }
block_data["summary_pnc"] = {"Excellent":num_exc_pnc, "Good":num_good_pnc , "Poor":num_poor_pnc ,"Average":num_avg_pnc }
block_data["summary_imm"] = {"Excellent":num_exc_imm, "Good":num_good_imm , "Poor":num_poor_imm ,"Average":num_avg_imm }
block_data["blockid"] = block.id
block_data["blockname"] = block.name
existing_analytics = AnalyticsData.objects.filter(block=block, since_months=months, month= this_month_date.month, year=this_month_date.year)
if existing_analytics:
existing_analytics.delete()
print "adding: "+str(block.id)+" , "+"months "
ad = AnalyticsData.objects.create(block = block, data = json.dumps(block_data["data"]), summary=json.dumps(block_data["summary"]),\
summary_anc=json.dumps(block_data["summary_anc"]), summary_imm=json.dumps(block_data["summary_imm"]),\
summary_pnc=json.dumps(block_data["summary_pnc"]), since_months = months, month = this_month_date.month, year=this_month_date.year)
def analytics_aggregator_allblocks(district_mcts_id='36'):
try:
district = District.objects.get(MCTS_ID=district_mcts_id)
except ObjectDoesNotExist:
print "specified district does not exist"
return
timezone = 'Asia/Kolkata'
tz = pytz.timezone(timezone)
today_utc = utcnow_aware()
today = today_utc.astimezone(tz)
dt_month = today.date().month
dt_year = today.date().year
month_span = [1, 2, 3, 4, 5, 6, 12]
for months in month_span:
this_month_date = today.replace(hour=6, minute=0, second=0, day=1).astimezone(pytz.utc)
since_months = this_month_date - relativedelta(months=(months-1))
blocks = SubCenter.objects.filter(district=district).values('block_id')
for block_entry in blocks:
block = Block.objects.get(id=block_entry['block_id'])
if AnalyticsData.objects.filter(block=block, since_months=months, month= this_month_date.month, year=this_month_date.year):
continue
subcenters = SubCenter.objects.filter(block=block)
data = []
num_good_anc = 0
num_poor_anc = 0
num_avg_anc = 0
num_good_pnc = 0
num_poor_pnc = 0
num_avg_pnc = 0
num_good_imm = 0
num_poor_imm = 0
num_avg_imm = 0
for sub in subcenters:
sub_data = {}
#total beneficiaries.
anc_benefs = ANCBenef.objects.filter(subcenter = sub)
pnc_benefs = PNCBenef.objects.filter(subcenter = sub)
imm_benefs = IMMBenef.objects.filter(subcenter = sub)
sub_data["Beneficiaries_anc"] = anc_benefs.count()
sub_data["Beneficiaries_pnc"] = pnc_benefs.count()
sub_data["Beneficiaries_imm"] = imm_benefs.count()
from mcts_transactions.views import ProcessSubcenterData, get_status, increment_count_on_status
data_anc = ProcessSubcenterData(anc_benefs, sub, since_months, Events.ANC_REG_VAL, months)
data_pnc = ProcessSubcenterData(pnc_benefs, sub, since_months, Events.PNC_REG_VAL, months)
data_imm = ProcessSubcenterData(imm_benefs, sub, since_months, Events.IMM_REG_VAL, months)
sub_data["Beneficiaries_anc"] = anc_benefs.count()
sub_data["Beneficiaries_pnc"] = pnc_benefs.count()
sub_data["Beneficiaries_imm"] = imm_benefs.count()
sub_data["Adherence_anc"] = data_anc["Adherence"]
sub_data["Adherence_pnc"] = data_pnc["Adherence"]
sub_data["Adherence_imm"] = data_imm["Adherence"]
sub_data["new_reg_anc"] = data_anc["new_reg"]
sub_data["new_reg_pnc"] = data_pnc["new_reg"]
sub_data["new_reg_imm"] = data_imm["new_reg"]
sub_data["Overdue_anc"] = data_anc["Overdue"]
sub_data["Overdue_pnc"] = data_pnc["Overdue"]
sub_data["Overdue_imm"] = data_imm["Overdue"]
sub_data["overdue_sg_anc"] = data_anc["overdue_sg"]
sub_data["overdue_sg_pnc"] = data_pnc["overdue_sg"]
sub_data["overdue_sg_imm"] = data_imm["overdue_sg"]
sub_data["GivenServices_anc"] = data_anc["GivenServices"]
sub_data["GivenServices_pnc"] = data_pnc["GivenServices"]
sub_data["GivenServices_imm"] = data_imm["GivenServices"]
sub_data["OverDueRate_anc"] = data_anc["OverDueRate"]
sub_data["OverDueRate_pnc"] = data_pnc["OverDueRate"]
sub_data["OverDueRate_imm"] = data_imm["OverDueRate"]
sub_data["ProgressData_anc"] = data_anc["ProgressData"]
sub_data["ProgressData_pnc"] = data_pnc["ProgressData"]
sub_data["ProgressData_imm"] = data_imm["ProgressData"]
status_anc = get_status(data_anc["OverDueRate"], 5, 7)
status_pnc = get_status(data_pnc["OverDueRate"], 5, 7)
status_imm = get_status(data_imm["OverDueRate"], 5, 7)
num_good_anc, num_avg_anc, num_poor_anc = increment_count_on_status(status_anc, num_good_anc, num_avg_anc, num_poor_anc)
num_good_pnc, num_avg_pnc, num_poor_pnc = increment_count_on_status(status_pnc, num_good_pnc, num_avg_pnc, num_poor_pnc)
num_good_imm, num_avg_imm, num_poor_imm = increment_count_on_status(status_imm, num_good_imm, num_avg_imm, num_poor_imm)
sub_data["status_anc"] = status_anc
sub_data["status_pnc"] = status_pnc
sub_data["status_imm"] = status_imm
from math import ceil
sub_data["status"] = ceil((status_anc + status_pnc + status_imm)/3 - 0.5)
sub_data["Subcenter"] = sub.name
sub_data["SubcenterId"] = sub.id
AshaDetails = []
cgs = Beneficiary.objects.all().filter(subcenter = sub).values("caregiver").distinct()
for cg in cgs:
if cg["caregiver"]:
_cg = CareGiver.objects.get(id=cg["caregiver"])
AshaDetails.append(_cg.first_name+':'+_cg.phone)
sub_data["AshaDetails"] = AshaDetails
ANMDetails = []
cps = Beneficiary.objects.all().filter(subcenter = sub).values("careprovider").distinct()
for cp in cps:
if cp["careprovider"]:
_cp = CareProvider.objects.get(id=cp["careprovider"])
ANMDetails.append(_cp.first_name+':'+_cp.phone)
sub_data["ANMDetails"] = ANMDetails
# TODO remove dummy lat, long later
_lat = block._lat if block._lat else 25.619626
_long = block._long if block._long else 79.180409
sign = randint(1,2)
if sign == 1:
_lat = _lat + randint(1,100)*0.005
_long = _long + randint(1,100)*0.005
else:
_lat = _lat - randint(1,100)*0.005
_long = _long - randint(1,100)*0.005
sub_data["lat"] = sub._lat if sub._lat else _lat
sub_data["long"] = sub._long if sub._long else _long
data.append(sub_data)
block_data = {}
block_data["data"] = data
block_data["summary"] = { "Good":num_good_anc , "Poor":num_poor_anc ,"Average":num_avg_anc }
block_data["summary_anc"] = { "Good":num_good_anc , "Poor":num_poor_anc ,"Average":num_avg_anc }
block_data["summary_pnc"] = { "Good":num_good_pnc , "Poor":num_poor_pnc ,"Average":num_avg_pnc }
block_data["summary_imm"] = { "Good":num_good_imm , "Poor":num_poor_imm ,"Average":num_avg_imm }
block_data["blockid"] = block.id
block_data["blockname"] = block.name
if not AnalyticsData.objects.filter(block=block, since_months=months, month= this_month_date.month, year=this_month_date.year):
print "adding: "+str(block.id)+" , "+"months "
ad = AnalyticsData.objects.create(block = block, data = json.dumps(block_data["data"]), summary=json.dumps(block_data["summary"]),\
summary_anc=json.dumps(block_data["summary_anc"]), summary_imm=json.dumps(block_data["summary_imm"]),\
summary_pnc=json.dumps(block_data["summary_pnc"]), since_months = months, month = this_month_date.month, year=this_month_date.year)