def DashboardData(request, blockid = None):
if blockid:
blocks = Block.objects.filter(id=blockid)
if blocks.count() == 0:
return HttpResponseBadRequest('wrong block id provided in the request')
blockname = blocks[0].name
else:
blocks = Block.objects.all()
blockname = 'All'
timezone = 'Asia/Kolkata'
tz = pytz.timezone(timezone)
today_utc = utcnow_aware()
today = today_utc.astimezone(tz)
this_month_date = today.replace(hour=6, minute=0, second=0, day=1).astimezone(pytz.utc)
#process GET params
since_months = int(request.GET.get('since_months')) if request.GET.get('since_months') else None
if since_months > 12:
since_months =12
if since_months:
months = since_months
since_months = this_month_date - relativedelta(months=since_months)
else:
months = 12
since_months = this_month_date - relativedelta(months=12)
data = []
summary = {"Good":0, "Poor":0, "Average":0, "Excellent":0}
summary_anc = {"Good":0, "Poor":0, "Average":0, "Excellent":0}
summary_pnc = {"Good":0, "Poor":0, "Average":0, "Excellent":0}
summary_imm = {"Good":0, "Poor":0, "Average":0, "Excellent":0}
# 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
from common.models import AnalyticsData
from mcts_transactions.tasks import analytics_aggregator_allblocks
for block in blocks:
analytics_data = AnalyticsData.objects.filter(block=block, since_months=months, month= this_month_date.month, year=this_month_date.year)
if not analytics_data:
continue
analytics_aggregator_allblocks.delay()
return HttpResponseNotAllowed('Check after 5 mins. Data generation in progress.')
data += json.loads(analytics_data[0].data)
summary["Good"] += json.loads(analytics_data[0].summary)["Good"]
summary["Poor"] += json.loads(analytics_data[0].summary)["Poor"]
summary["Average"] += json.loads(analytics_data[0].summary)["Average"]
summary["Excellent"] += json.loads(analytics_data[0].summary)["Excellent"]
summary_anc["Good"] += json.loads(analytics_data[0].summary_anc)["Good"]
summary_anc["Poor"] += json.loads(analytics_data[0].summary_anc)["Poor"]
summary_anc["Average"] += json.loads(analytics_data[0].summary_anc)["Average"]
summary_anc["Excellent"] += json.loads(analytics_data[0].summary_anc)["Excellent"]
summary_pnc["Good"] += json.loads(analytics_data[0].summary_pnc)["Good"]
summary_pnc["Poor"] += json.loads(analytics_data[0].summary_pnc)["Poor"]
summary_pnc["Average"] += json.loads(analytics_data[0].summary_pnc)["Average"]
summary_pnc["Excellent"] += json.loads(analytics_data[0].summary_pnc)["Excellent"]
summary_imm["Good"] += json.loads(analytics_data[0].summary_imm)["Good"]
summary_imm["Poor"] += json.loads(analytics_data[0].summary_imm)["Poor"]
summary_imm["Average"] += json.loads(analytics_data[0].summary_imm)["Average"]
summary_imm["Excellent"] += json.loads(analytics_data[0].summary_imm)["Excellent"]
block_data = {}
block_data["data"] = data
block_data["summary"] = summary
block_data["summary_anc"] = summary_anc
block_data["summary_pnc"] = summary_pnc
block_data["summary_imm"] = summary_imm
block_data["blockid"] = blockid
block_data["blockname"] = blockname
return HttpResponse(json.dumps(block_data), mimetype="application/json")
def DashboardData(request, blockid = None):
if blockid:
blocks = Block.objects.filter(id=blockid)
if blocks.count() == 0:
return HttpResponseBadRequest('wrong block id provided in the request')
blockname = blocks[0].name
else:
blocks = Block.objects.all()
blockname = 'All'
timezone = 'Asia/Kolkata'
tz = pytz.timezone(timezone)
today_utc = utcnow_aware()
today = today_utc.astimezone(tz)
this_month_date = today.replace(hour=6, minute=0, second=0, day=1).astimezone(pytz.utc)
#process GET params
since_months = int(request.GET.get('since_months')) if request.GET.get('since_months') else None
if since_months > 12:
since_months =12
if since_months:
months = since_months
since_months = this_month_date - relativedelta(months=since_months)
else:
months = 12
since_months = this_month_date - relativedelta(months=12)
data = []
summary = {"Good":0, "Poor":0, "Average":0}
summary_anc = {"Good":0, "Poor":0, "Average":0}
summary_pnc = {"Good":0, "Poor":0, "Average":0}
summary_imm = {"Good":0, "Poor":0, "Average":0}
# 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
from common.models import AnalyticsData
from mcts_transactions.tasks import analytics_aggregator_allblocks
for block in blocks:
analytics_data = AnalyticsData.objects.filter(block=block, since_months=months, month= this_month_date.month, year=this_month_date.year)
if not analytics_data:
continue
analytics_aggregator_allblocks.delay()
return HttpResponseNotAllowed('Check after 5 mins. Data generation in progress.')
data += json.loads(analytics_data[0].data)
summary["Good"] += json.loads(analytics_data[0].summary)["Good"]
summary["Poor"] += json.loads(analytics_data[0].summary)["Poor"]
summary["Average"] += json.loads(analytics_data[0].summary)["Average"]
summary_anc["Good"] += json.loads(analytics_data[0].summary_anc)["Good"]
summary_anc["Poor"] += json.loads(analytics_data[0].summary_anc)["Poor"]
summary_anc["Average"] += json.loads(analytics_data[0].summary_anc)["Average"]
summary_pnc["Good"] += json.loads(analytics_data[0].summary_pnc)["Good"]
summary_pnc["Poor"] += json.loads(analytics_data[0].summary_pnc)["Poor"]
summary_pnc["Average"] += json.loads(analytics_data[0].summary_pnc)["Average"]
summary_imm["Good"] += json.loads(analytics_data[0].summary_imm)["Good"]
summary_imm["Poor"] += json.loads(analytics_data[0].summary_imm)["Poor"]
summary_imm["Average"] += json.loads(analytics_data[0].summary_imm)["Average"]
# #find all the subcenters in the block
# subs = SubCenter.objects.filter(block=block)
# for sub in subs:
# 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()
# 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
# sub_data["lat"] = sub._lat if sub._lat else block._lat
# sub_data["long"] = sub._long if sub._long else block._long
# #TODO remove dummy lat, long later
# #_lat = 25.619626
# #_long = 79.180409
# #sign = randint(1,2)
# #if sign == 1:
# # sub_data["lat"] = _lat + randint(1,100)*0.01
# # sub_data["long"] = _long + randint(1,100)*0.01
# #else:
# # sub_data["lat"] = _lat - randint(1,100)*0.01
# # sub_data["long"] = _long - randint(1,100)*0.01
# data.append(sub_data)
block_data = {}
block_data["data"] = data
block_data["summary"] = summary
block_data["summary_anc"] = summary_anc
block_data["summary_pnc"] = summary_pnc
block_data["summary_imm"] = summary_imm
block_data["blockid"] = blockid
block_data["blockname"] = blockname
return HttpResponse(json.dumps(block_data), mimetype="application/json")
