def get_contraceptive_use_profile(geo_code, geo_level, session):
# contraceptive_use stats
contraceptive_use_dist_data, _ = get_stat_data(
'contraceptive_use',
geo_level,
geo_code,
session,
key_order=['Modern', 'Traditional', 'Not using'])
modern = contraceptive_use_dist_data['Modern']['numerators']['this']
traditional = contraceptive_use_dist_data['Traditional']['numerators'][
'this']
cpr = modern + traditional
contraceptive_modern_method_dist_data, _ = get_stat_data(
'contraceptive_modern_method', geo_level, geo_code, session)
contraceptive_traditional_method_dist_data, _ = get_stat_data(
'contraceptive_traditional_method', geo_level, geo_code, session)
return {
'contraceptive_use_distribution': contraceptive_use_dist_data,
'modern_methods_distribution': contraceptive_modern_method_dist_data,
'traditional_methods_distribution':
contraceptive_traditional_method_dist_data,
'cpr': {
'name': 'Contraceptive prevalence rate',
'numerators': {
'this': cpr
},
'values': {
'this': cpr
},
}
}
def get_crime_report_profile(geo_code, geo_level, session):
stats_dist, s_ = get_stat_data("crimereport", geo_level, geo_code, session)
crimes_dist, c_ = get_stat_data("typesofcrime", geo_level, geo_code,
session)
crimes = stats_dist['Crimes']['numerators']['this']
crimeindex = stats_dist['Crimesindex']['numerators']['this']
return {
'crimes': {
'name': 'Number of crimes',
'numerators': {
'this': crimes
},
'values': {
'this': crimes
},
},
'crimesindex': {
'name': 'Crime index per 100,000 people',
'numerators': {
'this': crimeindex
},
'values': {
'this': crimeindex
},
},
'crimes_dist': crimes_dist,
'metadata': crimes_dist['metadata']
}
def get_education_profile(geo_code, geo_level, session):
if geo_level == "ward":
return {}
# highest level reached
edu_dist_data, total_pop = get_stat_data('highest education level reached',
geo_level,
geo_code,
session,
key_order=[
'None', 'Pre-primary',
'Primary', 'Secondary',
'Tertiary', 'University',
'Youth polytechnic',
'Basic literacy', 'Madrassa'
])
secondary_or_higher = 0
for key, data in edu_dist_data.iteritems():
if key in ['Secondary', 'Tertiary', 'University', 'Youth polytechnic']:
secondary_or_higher += data['numerators']['this']
# school attendance by sex
school_attendance_dist, _ = get_stat_data(
['school attendance', 'sex'],
geo_level,
geo_code,
session,
key_order={
'school attendance':
['Never attended', 'At school', 'Left school', 'Unspecified'],
'sex': ['Female', 'Male']
})
total_never = 0.0
for data in school_attendance_dist['Never attended'].itervalues():
if 'numerators' in data:
total_never += data['numerators']['this']
return {
'education_reached_distribution': edu_dist_data,
'education_reached_secondary_or_higher': {
'name': 'Reached Secondary school or higher',
'numerators': {
'this': secondary_or_higher
},
'values': {
'this': round(secondary_or_higher / total_pop * 100, 2)
}
},
'school_attendance_distribution': school_attendance_dist,
'school_attendance_never': {
'name': 'Never attended school',
'numerators': {
'this': total_never
},
'values': {
'this': round(total_never / total_pop * 100, 2)
}
},
}
def get_maternal_care_indicators_profile(geo_code, geo_level, session):
# maternal care indicators stats
maternal_care_indicators_data, _ = get_stat_data(
'maternal care indicators', geo_level, geo_code, session)
delivery_in_health_facility = \
maternal_care_indicators_data['Percentage delivered in a health facility']['numerators']['this']
antenatal_care_by_skilled_provider = \
maternal_care_indicators_data['Percentage with antenatal care from a skilled provider']['numerators']['this']
antenatal_dist = get_dictionary('From a skilled provider',
'From a non-skilled provider',
antenatal_care_by_skilled_provider,
maternal_care_indicators_data)
anc_visits = maternal_care_indicators_data[
'Percentage with 4+ ANC visits']['numerators']['this']
anc_visits_dist = get_dictionary('4+', 'Less than 4', anc_visits,
maternal_care_indicators_data)
delivery_by_skilled_provider = \
maternal_care_indicators_data['Percentage delivered by a skilled provider']['numerators']['this']
delivery_by_skilled_provider_dist = get_dictionary(
'Skilled provider', 'Non-skilled provider',
delivery_by_skilled_provider, maternal_care_indicators_data)
#Use of IPT
use_of_ipt_dist, _ = get_stat_data('use of ipt', geo_level, geo_code,
session)
one_or_more_dist = use_of_ipt_dist['1 or more']['numerators']['this']
one_or_more_dist = get_dictionary('1 or more', 'Less', one_or_more_dist,
maternal_care_indicators_data)
two_or_more_dist = use_of_ipt_dist['2 or more']['numerators']['this']
two_or_more_dist = get_dictionary('2 or more', 'Less', two_or_more_dist,
maternal_care_indicators_data)
three_or_more_dist = use_of_ipt_dist['3 or more']['numerators']['this']
three_or_more_dist = get_dictionary('3 or more', 'Less',
three_or_more_dist,
maternal_care_indicators_data)
return {
'maternal_care_indicators': maternal_care_indicators_data,
'antenatal_dist': antenatal_dist,
'anc_visits_dist': anc_visits_dist,
'delivery_by_skilled_provider_dist': delivery_by_skilled_provider_dist,
'delivery_in_health_facility': {
'name': 'babies delivered in a health facility',
'numerators': {
'this': delivery_in_health_facility
},
'values': {
'this': round(delivery_in_health_facility, 2)
}
},
'one_or_more_dist': one_or_more_dist,
'two_or_more_dist': two_or_more_dist,
'three_or_more_dist': three_or_more_dist,
}
def get_vaccinations_profile(geo_code, geo_level, session):
# vaccinations
dist, _ = get_stat_data(['vaccinations'], geo_level, geo_code, session,
key_order=['BCG','Pentavalent 1','Pentavalent 2','Pentavalent 3','Polio 0','Polio 1', \
'Polio 2','Polio 3','Measles','Pneumococcal 1', \
'Pneumococcal 2','Pneumococcal 3',],
only=['BCG','Pentavalent 1','Pentavalent 2','Pentavalent 3','Polio 0','Polio 1', \
'Polio 2','Polio 3','Measles','Pneumococcal 1', \
'Pneumococcal 2','Pneumococcal 3',],
)
# need to use numerators instead of values
for key in dist:
if key == 'metadata':
continue
dist[key]['values']['this'] = dist[key]['numerators']['this']
vacc_dist, _ = get_stat_data(
['vaccinations'],
geo_level,
geo_code,
session,
# only=['All basic vaccinations', 'Percentage with vaccination card',\
# 'Fully vaccinated', 'Not vaccinated'],
)
fully_vaccinated = vacc_dist['Fully vaccinated']['numerators']['this']
fully_vaccinated = get_dictionary('Fully vaccinated',
'Not fully vaccinated', fully_vaccinated,
dist)
all_basic_vaccinations = vacc_dist['All basic vaccinations']['numerators'][
'this']
all_basic_vaccinations = get_dictionary('All basic vaccinations', 'Basic vaccinations not administered', \
all_basic_vaccinations, dist)
percentage_with_vaccination_cards = vacc_dist[
'Percentage with vaccination card']['numerators']['this']
percentage_with_vaccination_cards = get_dictionary('Have vaccination cards', 'No vaccination cards',\
percentage_with_vaccination_cards, dist)
not_vaccinated = vacc_dist['Not vaccinated']['numerators']['this']
return {
'distribution': dist,
'fully_vaccinated': fully_vaccinated,
'all_basic_vaccinations': all_basic_vaccinations,
'percentage_with_vaccination_cards': percentage_with_vaccination_cards,
'not_vaccinated': {
'name': 'Not vaccinated',
'numerators': {
'this': not_vaccinated
},
'values': {
'this': not_vaccinated
},
}
}
def get_school_attendance_profile(geo_code, geo_level, session):
if geo_level == "ward":
return {}
# attendance stats
attendance_data, _ = get_stat_data('school attendance', geo_level,
geo_code, session)
dropped_out_dist = \
attendance_data['Pupils in school']['numerators']['this']
dropped_out_dist = {
'Pupils in school': {
'name': 'Pupils in school',
'numerators': {
'this': dropped_out_dist
},
'values': {
'this': round(dropped_out_dist, 2)
},
},
'Dropped out': {
'name': 'Dropped out',
'numerators': {
'this': 100 - dropped_out_dist
},
'values': {
'this': 100 - round(dropped_out_dist, 2)
},
},
'metadata': attendance_data['metadata']
}
out_of_school_dist = attendance_data['Drop outs']['numerators']['this']
out_of_school_dist = {
'Out of school': {
'name': 'Dropped out',
'numerators': {
'this': out_of_school_dist
},
'values': {
'this': round(out_of_school_dist, 2)
},
},
'In school': {
'name': 'In school',
'numerators': {
'this': 100 - out_of_school_dist
},
'values': {
'this': 100 - round(out_of_school_dist, 2)
},
},
'metadata': attendance_data['metadata']
}
return {
'attendance_data': attendance_data,
'dropped_out_dist': dropped_out_dist,
'out_of_school_dist': out_of_school_dist,
'drop_out_sort': '-value' if dropped_out_dist <= 50 else 'value',
'out_of_school_sort': '-value' if out_of_school_dist <= 50 else 'value'
}
def get_health_ratios_profile(geo_code, geo_level, session):
ratios_dist, _ = get_stat_data("healthratios", geo_level, geo_code,
session)
dr = ratios_dist['Doctor ratio']['numerators']['this']
nr = ratios_dist['Nurse ratio']['numerators']['this']
return {
'doctorratio': {
'name': 'Doctor to population ratio',
'numerators': {
'this': dr
},
'values': {
'this': dr
},
},
'nurseratio': {
'name': 'Nurse to population ratio',
'numerators': {
'this': nr
},
'values': {
'this': nr
},
},
'metdata': ratios_dist['metadata']
}
def get_employment_profile(geo_code, geo_level, session):
# employment status
employment_activity_dist, total_workers = get_stat_data(
['employment activity status', 'sex'],
geo_level,
geo_code,
session,
recode={'employment activity status': dict(EMPLOYMENT_RECODES)},
key_order={
'employment activity status': EMPLOYMENT_RECODES.values(),
'sex': ['Female', 'Male']
})
total_employed = 0.0
for data in employment_activity_dist['Employed'].itervalues():
if 'numerators' in data:
total_employed += data['numerators']['this']
return {
'activity_status_distribution': employment_activity_dist,
'employed': {
'name': 'Employment',
'numerators': {
'this': total_employed
},
'values': {
'this': round(total_employed / total_workers * 100, 2)
},
},
}
def get_itn_profile(geo_code, geo_level, session):
# household possession and use of ITN
possession_dist, _ = get_stat_data(['household possession of itn'],
geo_level, geo_code, session)
households_with_at_least_one_itn = \
possession_dist['Households with at least one ITN']['numerators']['this']
households_with_at_least_one_itn = get_dictionary(
'Possess at least one ITN', 'No ITN', households_with_at_least_one_itn,
possession_dist)
percentage_households_with_ITN_for_every_two_people = \
possession_dist['Percentage households with ITN for every 2 people in household']['numerators']['this']
percentage_households_with_ITN_for_every_two_people = get_dictionary('1:2',\
'less than 1:2',\
percentage_households_with_ITN_for_every_two_people, possession_dist)
average_itn_per_household = possession_dist['Average ITN per household'][
'numerators']['this']
use_dist, _ = get_stat_data(['household', 'users', 'itn_use'], geo_level,
geo_code, session)
households_with_at_least_one_itn_use_dist = use_dist[
'With at least one ITN']
all_households_use_dist = use_dist['All']
return {
'households_with_at_least_one_itn': households_with_at_least_one_itn,
'percentage_households_with_ITN_for_every_two_people':
percentage_households_with_ITN_for_every_two_people,
'average_itn_per_household': {
'name':
'Average number of insecticide-treated nets (ITNs) per household',
'numerators': {
'this': average_itn_per_household
},
'values': {
'this': average_itn_per_household
}
},
'households_with_at_least_one_itn_use_dist':
households_with_at_least_one_itn_use_dist,
'all_households_use_dist': all_households_use_dist
}
def get_causes_of_death_profile(geo_code, geo_level, session):
if geo_level != 'region' and geo_level != 'country':
return {}
causes_of_death_under_five_data, _ = get_stat_data(
'causes of death under five',
geo_level,
geo_code,
session,
order_by='-total')
causes_of_death_over_five_data, _ = get_stat_data(
'causes of death over five',
geo_level,
geo_code,
session,
order_by='-total')
inpatient_diagnosis_over_five_data, _ = get_stat_data(
'inpatient diagnosis over five',
geo_level,
geo_code,
session,
order_by='-total')
outpatient_diagnosis_over_five_data, _ = get_stat_data(
'outpatient diagnosis over five',
geo_level,
geo_code,
session,
order_by='-total')
inpatient_diagnosis_under_five_data, _ = get_stat_data(
'inpatient diagnosis under five',
geo_level,
geo_code,
session,
order_by='-total')
outpatient_diagnosis_under_five_data, _ = get_stat_data(
'outpatient diagnosis under five',
geo_level,
geo_code,
session,
order_by='-total')
return {
'causes_of_death_under_five_data': causes_of_death_under_five_data,
'causes_of_death_over_five_data': causes_of_death_over_five_data,
'inpatient_diagnosis_under_five_data':
inpatient_diagnosis_under_five_data,
'inpatient_diagnosis_over_five_data':
inpatient_diagnosis_over_five_data,
'outpatient_diagnosis_over_five_data':
outpatient_diagnosis_over_five_data,
'outpatient_diagnosis_under_five_data':
outpatient_diagnosis_under_five_data,
'source_link':
'http://www.opendata.go.tz/dataset/number-and-causes-of-death-occured-by-region',
'source_link_2':
'http://www.opendata.go.tz/dataset/idadi-ya-magonjwa-kutoka-idara-ya-wagonjwa-waliolazwa-kwa-mikoa',
'source_link_3':
'http://www.opendata.go.tz/dataset/idadi-ya-magonjwa-kutoka-idara-ya-wagonjwa-wa-nje-kwa-mikoa',
'source_name': 'opendata.go.tz',
}
def test_get_stat_data_percentage(self):
self.field_table(['gender'], """
lev,code,Male,10
lev,code,Female,20
""")
data, total = get_stat_data(['gender'], 'lev', 'code', self.s)
self.assertEqual(total, 30)
self.assertEqual(data['Male']['numerators']['this'], 10)
self.assertEqual(data['Male']['values']['this'], 33.33)
self.assertEqual(data['Female']['numerators']['this'], 20)
self.assertEqual(data['Female']['values']['this'], 66.67)
data, total = get_stat_data(['gender'],
'lev',
'code',
self.s,
percent=False)
self.assertEqual(total, 30)
self.assertIsNone(data['Male']['numerators']['this'])
self.assertEqual(data['Male']['values']['this'], 10)
self.assertIsNone(data['Female']['numerators']['this'])
self.assertEqual(data['Female']['values']['this'], 20)
def get_health_centers_distribution_profile(geo_code, geo_level, session):
if geo_level == 'ward':
return {}
hc_data, total = get_stat_data('health centers',
geo_level,
geo_code,
session,
order_by='-total')
ho_data, _ = get_stat_data('health center ownership', geo_level, geo_code,
session)
hiv_centers_data, hiv_c = get_stat_data('hiv centers', geo_level, geo_code,
session)
return {
'total': {
'name': 'Total health centers in operation (2014)',
'numerators': {
'this': total
},
'values': {
'this': total
}
},
'hiv_centers': {
'name': 'HIV care and treatment centers (2014)',
'numerators': {
'this': hiv_c
},
'values': {
'this': hiv_c
}
},
'health_center_data': hc_data,
'health_center_ownership': ho_data,
'source_link':
'http://www.opendata.go.tz/dataset/list-of-health-facilities-with-geographical-location',
'source_name': 'opendata.go.tz',
}
def get_protests_profile(geo_code, geo_level, session):
number_of_protests_dist, _ = get_stat_data("protests", geo_level, geo_code,
session)
number_of_protests = number_of_protests_dist['Number of protests'][
'numerators']['this']
return {
'number_of_protests': {
'name': 'Number of protests',
'numerators': {
'this': number_of_protests
},
'values': {
'this': number_of_protests
},
}
}
def test_get_stat_data_nulls_with_denominator_key(self):
table = FieldTable(['household goods'],
universe='Households',
denominator_key='total households')
self.load_data(
table, """
lev,code,fridge,10
lev,code,computer,5
lev,code,total households,
""")
data, total = get_stat_data(['household goods'], 'lev', 'code', self.s)
self.assertEqual(total, None)
self.assertEqual(data['Fridge']['numerators']['this'], 10)
self.assertIsNone(data['Fridge']['values']['this'])
self.assertEqual(data['Computer']['numerators']['this'], 5)
self.assertIsNone(data['Computer']['values']['this'])
def get_pupil_teacher_ratios_profile(geo_code, geo_level, session):
if geo_level == "ward":
return {}
# pupil teacher ratios
ratio_data, _ = get_stat_data('pupil teacher ratios', geo_level, geo_code,
session)
pupil_teacher_ratio = ratio_data['Pupil teacher ratio']['numerators'][
'this']
pupils_per_textbook = ratio_data['Pupils per textbook']['numerators'][
'this']
pupil_attendance_rate_dist = \
ratio_data['Government school attendance rate']['numerators']['this']
pupil_attendance_rate_dist = get_dictionary("Attending school", "Absent",
pupil_attendance_rate_dist,
ratio_data)
teachers_absent_dist = ratio_data['Teachers absent']['numerators']['this']
teachers_absent_dist = get_dictionary("Teachers absent",
"Teachers present",
teachers_absent_dist, ratio_data)
return {
'pupil_attendance_rate_dist': pupil_attendance_rate_dist,
'teachers_absent_dist': teachers_absent_dist,
'pupil_teacher_ratio': {
'name':
'For every one teacher there are ' + str(pupil_teacher_ratio) +
" pupils",
'numerators': {
'this': pupil_teacher_ratio
},
'values': {
'this': pupil_teacher_ratio
}
},
'pupils_per_textbook': {
'name': 'Pupils per textbook',
'numerators': {
'this': pupils_per_textbook
},
'values': {
'this': pupils_per_textbook
}
}
}
def get_school_fires_profile(geo_code, geo_level, session):
school_fires_dist, number_of_school_fires = get_stat_data(
"schoolfires", geo_level, geo_code, session)
schools = school_fires_dist[school_fires_dist.keys()[0]]['name'].replace(
',', '<br>').replace('"', '')
return {
'schoolfires': {
'name': 'Number of school fires',
'numerators': {
'this': number_of_school_fires
},
'values': {
'this': number_of_school_fires
},
},
'metadata': school_fires_dist['metadata'],
'schools': schools
}
def get_knowledge_of_hiv_prevention_methods_profile(geo_code, geo_level,
session):
dist, _ = get_stat_data(
['knowledge of hiv prevention methods', 'sex'],
geo_level,
geo_code,
session,
key_order={
'knowledge of hiv prevention methods':
{'Using condoms', 'Being faithful', 'Both'},
'sex': ['Female', 'Male']
})
# need to use numerators instead of values
for key in dist:
if key == 'metadata': continue
dist[key]['Female']['values'][
'this'] = 100 - dist[key]['Female']['numerators']['this']
dist[key]['Male']['values'][
'this'] = 100 - dist[key]['Male']['numerators']['this']
return {'distribution': dist}
def get_health_workers_distribution_profile(geo_code, geo_level, session):
if geo_level != 'region' and geo_level != 'country':
return {}
hw_data, total = get_stat_data('health workers',
geo_level,
geo_code,
session,
order_by='-total')
hrh_patient_ratio = hw_data['HRH patient ratio']['numerators']['this']
del hw_data['HRH patient ratio']
del hw_data['MO and AMO per 10000']
del hw_data['Nurses and midwives per 10000']
del hw_data['Pharmacists per 10000']
del hw_data['Clinicians per 10000']
return {
'total': {
'name': 'Total health worker population (2014)',
'numerators': {
'this': total
},
'values': {
'this': total
}
},
'hrh_patient_ratio': {
'name': 'Skilled health worker to patient ratio (2014)',
'numerators': {
'this': hrh_patient_ratio
},
'values': {
'this': hrh_patient_ratio
}
},
'health_worker_data': hw_data,
'source_link':
'http://www.opendata.go.tz/dataset/idadi-ya-wafanyakazi-wa-afya-kwa-mikoa',
'source_name': 'opendata.go.tz',
}
def get_place_of_delivery_profile(geo_code, geo_level, session):
if geo_level != 'region' and geo_level != 'country':
return {}
delivery_data, _ = get_stat_data('place of delivery',
geo_level,
geo_code,
session,
order_by='-total')
anc_projection = delivery_data['ANC projection']['numerators']['this']
facility_birth_rate = delivery_data['Facility birth rate']['numerators'][
'this']
del delivery_data['ANC projection']
del delivery_data['Facility birth rate']
return {
'anc_projection': {
'name': 'Antenatal care projection (2014)',
'numerators': {
'this': anc_projection
},
'values': {
'this': anc_projection
}
},
'facility_birth_rate': {
'name': 'Facility Birth Rate (2014)',
'numerators': {
'this': facility_birth_rate
},
'values': {
'this': facility_birth_rate
}
},
'delivery_data': delivery_data,
'source_link':
'http://www.opendata.go.tz/dataset/idadi-ya-wanaojifungulia-kwenye-vituo-vya-kutolea-huduma-za-afya-na-sehemu-zingine',
'source_name': 'opendata.go.tz',
}
def get_school_amenities_profile(geo_code, geo_level, session):
if geo_level == "ward":
return {}
# school amenities
data, _ = get_stat_data('school amenity', geo_level, geo_code, session)
library_data = data['Library']['numerators']['this']
library_data = get_dictionary("Have a library", "Don't", library_data,
data)
drinking_water_data = data['Drinking water']['numerators']['this']
drinking_water_data = get_dictionary("Have clean drinking water", "Don't",
drinking_water_data, data)
feeding_program_data = data['Feeding program']['numerators']['this']
feeding_program_data = get_dictionary("Have a feeding program", "Don't",
feeding_program_data, data)
return {
'library_data': library_data,
'drinking_water_data': drinking_water_data,
'feeding_program_data': feeding_program_data,
}
def get_tetanus_vaccine_profile(geo_code, geo_level, session):
if geo_level != 'region' and geo_level != 'country':
return {}
tetanus_vaccine_data, _ = get_stat_data('tetanus vaccine',
geo_level,
geo_code,
session,
order_by='-total')
number_vaccinated = tetanus_vaccine_data['Vaccinated']['numerators'][
'this']
coverage = tetanus_vaccine_data['Coverage']['numerators']['this']
return {
'number_vaccinated': {
'name':
'Number of pregnant women received two or more Tetanus Toxoid vaccine (TT2+) (2014)',
'numerators': {
'this': number_vaccinated
},
'values': {
'this': number_vaccinated
}
},
'coverage': {
'name':
'Coverage of pregnant women who received two or more Tetanus Toxoid vaccine (TT2+) (2014)',
'numerators': {
'this': coverage
},
'values': {
'this': coverage
}
},
'source_link':
'http://www.opendata.go.tz/dataset/number-and-percentage-of-pregnant-women-received-two-or-more-tetanus-toxoid-vaccine-tt2-by-region',
'source_name': 'opendata.go.tz',
}
def get_fertility_profile(geo_code, geo_level, session):
# fertility
dist, _ = get_stat_data(['fertility'], geo_level, geo_code, session)
percentage_women_age_15_49_currently_pregnant = dist['Pregnant'][
'numerators']['this']
percentage_women_age_15_49_currently_pregnant = get_dictionary('Pregnant', 'Not pregnant',\
percentage_women_age_15_49_currently_pregnant, dist)
fertility_rate = dist['Rate']['numerators']['this']
mean_number_of_children_ever_born_to_women_aged_40_49 = dist['Mean'][
'numerators']['this']
return {
'percentage_women_age_15_49_currently_pregnant':
percentage_women_age_15_49_currently_pregnant,
'fertility_rate': {
'name': 'Fertility',
'numerators': {
'this': fertility_rate
},
'values': {
'this': fertility_rate
}
},
'mean_number_of_children_ever_born_to_women_aged_40_49': {
'name': 'Mean number of children ever born to women aged 40-49',
'numerators': {
'this': mean_number_of_children_ever_born_to_women_aged_40_49
},
'values': {
'this': mean_number_of_children_ever_born_to_women_aged_40_49
}
},
}
def get_voter_registration_profile(geo_code, geo_level, session):
stats_dist, _ = get_stat_data("voterregistration", geo_level, geo_code,
session)
ids_issued = stats_dist['IDs issued']['numerators']['this']
dead_with_ids = stats_dist['Dead with IDs']['numerators']['this']
reg_centers = stats_dist['Registration centers']['numerators']['this']
reg = stats_dist['Registered voters']['numerators']['this']
aft = stats_dist['Additional voters registered']['numerators']['this']
total = stats_dist['Total registered']['numerators']['this']
not_registered = stats_dist['Potential voting population not registered'][
'numerators']['this']
r = {
'ids_issued': {
'name': 'Number of IDs issued as at Dec 2015',
'numerators': {
'this': ids_issued
},
'values': {
'this': ids_issued
},
},
'dead_with_ids': {
'name': 'Projected dead with IDs 10.57%',
'numerators': {
'this': dead_with_ids
},
'values': {
'this': dead_with_ids
},
},
'reg_centers': {
'name': 'Number of registration centers',
'numerators': {
'this': reg_centers
},
'values': {
'this': reg_centers
},
},
'total': {
'name': 'Total population registered to vote',
'numerators': {
'this': total
},
'values': {
'this': total
},
},
'registration': {
'march': {
'name': 'As at Mar 2013',
'numerators': {
'this': reg
},
'values': {
'this': round((reg / total) * 100)
},
},
'oct': {
'name': 'As at Oct 2015',
'numerators': {
'this': aft
},
'values': {
'this': round((aft / total) * 100)
},
}
},
'registration_ratio': {
'march': {
'name': 'Registered',
'numerators': {
'this': total
},
'values': {
'this': round((total / (total + not_registered)) * 100)
},
},
'oct': {
'name': 'Not registered',
'numerators': {
'this': not_registered
},
'values': {
'this':
round((not_registered / (total + not_registered)) * 100)
},
}
},
'metdata': stats_dist['metadata']
}
return r
def get_households_profile(geo_code, geo_level, session):
# main source of water
water_source_dist, total_households = get_stat_data(
'main source of water',
geo_level,
geo_code,
session,
recode=dict(WATER_SOURCE_RECODES),
key_order=WATER_SOURCE_RECODES.values())
total_piped = water_source_dist['Piped']['numerators']['this']
# main mode of waste disposal
waste_disposal_dist, _ = get_stat_data('main mode of human waste disposal',
geo_level,
geo_code,
session,
key_order=[
'Main sewer', 'Septic tank',
'Cess pool', 'Bucket', 'Bush',
'Other'
])
total_sewer_or_septic = 0.0
for key in ['Main sewer', 'Septic tank']:
if key in waste_disposal_dist:
total_sewer_or_septic += waste_disposal_dist[key]['numerators'][
'this']
# lighting
lighting_dist, _ = get_stat_data('main type of lighting fuel',
geo_level,
geo_code,
session,
key_order=[
'Electricity', 'Solar', 'Gas lamps',
'Pressure lamps', 'Tin lamps',
'Lanterns', 'Wood', 'Other'
])
total_electricity = lighting_dist['Electricity']['numerators']['this']
# construction materials
roofing_dist, _ = get_stat_data('main type of roofing material',
geo_level,
geo_code,
session,
order_by='-total')
wall_dist, _ = get_stat_data('main type of wall material',
geo_level,
geo_code,
session,
order_by='-total')
floor_dist, _ = get_stat_data('main type of floor material',
geo_level,
geo_code,
session,
order_by='-total')
return {
'total_households': {
'name': 'Households',
'values': {
'this': total_households
},
},
'water_source_distribution': water_source_dist,
'piped_water': {
'name': 'Have piped water',
'numerators': {
'this': total_piped
},
'values': {
'this': round(total_piped / total_households * 100, 2)
},
},
'waste_disposal_distribution': waste_disposal_dist,
'sewer_or_septic': {
'name': 'Have a sewer or septic tank',
'numerators': {
'this': total_sewer_or_septic
},
'values': {
'this': round(total_sewer_or_septic / total_households * 100,
2)
},
},
'lighting_distribution': lighting_dist,
'lighting_electricity': {
'name': 'Use electricity for lighting',
'numerators': {
'this': total_electricity
},
'values': {
'this': round(total_electricity / total_households * 100, 2)
},
},
'roofing_material_distribution': roofing_dist,
'floor_material_distribution': floor_dist,
'wall_material_distribution': wall_dist,
}
def get_family_planning_clients_profile(geo_code, geo_level, session):
if geo_level != 'region' and geo_level != 'country':
return {}
family_planning_clients_data, _ = get_stat_data('family planning clients',
geo_level,
geo_code,
session,
order_by='-total')
total = family_planning_clients_data['Total']['numerators']['this']
rate = family_planning_clients_data['New client rate']['numerators'][
'this']
# age in 10 year groups
def age_recode(f, x):
age = int(x.replace('+', ''))
if age > 49:
return '80+'
if age < 15:
return 'under 15'
return "15-49"
age_dist_data, _ = get_stat_data(
'age in completed years',
geo_level,
geo_code,
session,
table_fields=['age in completed years', 'sex', 'rural or urban'],
recode=age_recode,
exclude=['male'])
all_women_aged_15_49 = age_dist_data['15-49']['numerators']['this']
percentage_of_population = round((total / all_women_aged_15_49) * 100)
return {
'total': {
'name':
'Total number of women aged 15-49 using family planning methods (2013)',
'numerators': {
'this': total
},
'values': {
'this': total
}
},
'rate': {
'name': 'New client rate (2013)',
'numerators': {
'this': rate
},
'values': {
'this': rate
}
},
'percentage_of_population': {
'name': 'Percentage of the population of women aged 15-49',
'numerators': {
'this': percentage_of_population
},
'values': {
'this': percentage_of_population
}
},
'source_link':
'http://www.opendata.go.tz/dataset/idadi-na-asilimia-ya-wateja-wa-huduma-ya-uzazi-wa-mpango-kwa-mikoa',
'source_name': 'opendata.go.tz',
}
def get_demographics_profile(geo_code, geo_level, session):
# sex
sex_dist_data, total_pop = get_stat_data(
'sex',
geo_level,
geo_code,
session,
table_fields=['age in completed years', 'sex', 'rural or urban'])
# urban/rural by sex
urban_dist_data, _ = get_stat_data(
['rural or urban', 'sex'],
geo_level,
geo_code,
session,
table_fields=['age in completed years', 'sex', 'rural or urban'])
total_urbanised = 0
for data in urban_dist_data['Urban'].itervalues():
if 'numerators' in data:
total_urbanised += data['numerators']['this']
# median age
db_model_age = get_model_from_fields(
['age in completed years', 'sex', 'rural or urban'], geo_level)
objects = get_objects_by_geo(db_model_age, geo_code, geo_level, session,
['age in completed years'])
objects = sorted(
(o for o in objects
if getattr(o, 'age in completed years') != 'unspecified'),
key=lambda x: int(
getattr(x, 'age in completed years').replace('+', '')))
median = calculate_median(objects, 'age in completed years')
# age in 10 year groups
def age_recode(f, x):
age = int(x.replace('+', ''))
if age >= 80:
return '80+'
bucket = 10 * (age / 10)
return '%d-%d' % (bucket, bucket + 9)
age_dist_data, _ = get_stat_data(
'age in completed years',
geo_level,
geo_code,
session,
table_fields=['age in completed years', 'sex', 'rural or urban'],
recode=age_recode,
exclude=['unspecified'])
# age category
def age_cat_recode(f, x):
age = int(x.replace('+', ''))
if age < 18:
return 'Under 18'
elif age >= 65:
return '65 and over'
else:
return '18 to 64'
age_cats, _ = get_stat_data(
'age in completed years',
geo_level,
geo_code,
session,
table_fields=['age in completed years', 'sex', 'rural or urban'],
recode=age_cat_recode,
exclude=['unspecified'])
final_data = {
'sex_ratio': sex_dist_data,
'urban_distribution': urban_dist_data,
'urbanised': {
'name': 'In urban areas',
'numerators': {
'this': total_urbanised
},
'values': {
'this': round(total_urbanised / total_pop * 100, 2)
}
},
'age_group_distribution': age_dist_data,
'age_category_distribution': age_cats,
'median_age': {
"name": "Median age",
"values": {
"this": median
},
},
'total_population': {
"name": "People",
"values": {
"this": total_pop
}
}
}
return final_data
def get_nutrition_profile(geo_code, geo_level, session):
# nutritional stats among children
height_for_age_dist, _ = get_stat_data(['height for age'], geo_level,
geo_code, session)
for key in height_for_age_dist:
if key == 'metadata': continue
height_for_age_dist[key]['values']['this'] = height_for_age_dist[key][
'numerators']['this']
height_for_age_below_minus_three = height_for_age_dist['Below -3'][
'numerators']['this']
height_for_age_below_minus_three_dist = get_dictionary(
'Below -3 SD', 'Above -3 SD', height_for_age_below_minus_three,
height_for_age_dist)
height_for_age_below_minus_two = height_for_age_dist['Below -2'][
'numerators']['this']
height_for_age_below_minus_two_dist = get_dictionary(
'Below -2 SD', 'Above -2 SD', height_for_age_below_minus_two,
height_for_age_dist)
height_for_age_mean_z_score = height_for_age_dist['Mean Z-score'][
'numerators']['this']
weight_for_height_dist, _ = get_stat_data(['weight for height'], geo_level,
geo_code, session)
for key in weight_for_height_dist:
if key == 'metadata': continue
weight_for_height_dist[key]['values']['this'] = weight_for_height_dist[
key]['numerators']['this']
weight_for_height_below_minus_three = weight_for_height_dist['Below -3'][
'numerators']['this']
weight_for_height_below_minus_three_dist = get_dictionary(
'Below -3 SD', 'Above -3 SD', weight_for_height_below_minus_three,
weight_for_height_dist)
weight_for_height_below_minus_two = weight_for_height_dist['Below -2'][
'numerators']['this']
weight_for_height_below_minus_two_dist = get_dictionary(
'Below -2 SD', 'Above -2 SD', weight_for_height_below_minus_two,
weight_for_height_dist)
weight_for_height_above_plus_two = weight_for_height_dist['Above +2'][
'numerators']['this']
weight_for_height_above_plus_two_dist = get_dictionary(
'Above +2 SD', 'Below +2 SD', weight_for_height_above_plus_two,
weight_for_height_dist)
weight_for_height_mean_z_score = weight_for_height_dist['Mean Z-score'][
'numerators']['this']
weight_for_age_dist, _ = get_stat_data(['weight for height'], geo_level,
geo_code, session)
for key in weight_for_age_dist:
if key == 'metadata': continue
weight_for_age_dist[key]['values']['this'] = weight_for_age_dist[key][
'numerators']['this']
weight_for_age_below_minus_three = weight_for_age_dist['Below -3'][
'numerators']['this']
weight_for_age_below_minus_three_dist = get_dictionary(
'Below -3 SD', 'Above -3 SD', weight_for_age_below_minus_three,
weight_for_age_dist)
weight_for_age_below_minus_two = weight_for_age_dist['Below -2'][
'numerators']['this']
weight_for_age_below_minus_two_dist = get_dictionary(
'Below -2 SD', 'Above -2 SD', weight_for_age_below_minus_two,
weight_for_age_dist)
weight_for_age_above_plus_two = weight_for_age_dist['Above +2'][
'numerators']['this']
weight_for_age_above_plus_two_dist = get_dictionary(
'Above +2 SD', 'Below +2 SD', weight_for_age_above_plus_two,
weight_for_age_dist)
weight_for_age_mean_z_score = weight_for_age_dist['Mean Z-score'][
'numerators']['this']
return {
'height_for_age_below_minus_three_dist':
height_for_age_below_minus_three_dist,
'height_for_age_below_minus_two_dist':
height_for_age_below_minus_two_dist,
'height_for_age_mean_z_score': {
'name': 'Mean Z score(height-for-age)',
'numerators': {
'this': height_for_age_mean_z_score
},
'values': {
'this': height_for_age_mean_z_score
},
},
'weight_for_height_below_minus_three_dist':
weight_for_height_below_minus_three_dist,
'weight_for_height_below_minus_two_dist':
weight_for_height_below_minus_two_dist,
'weight_for_height_above_plus_two_dist':
weight_for_height_above_plus_two_dist,
'weight_for_height_mean_z_score': {
'name': 'Mean Z score (weight-for-height)',
'numerators': {
'this': weight_for_height_mean_z_score
},
'values': {
'this': weight_for_height_mean_z_score
},
},
'weight_for_age_below_minus_three_dist':
weight_for_age_below_minus_three_dist,
'weight_for_age_below_minus_two_dist':
weight_for_age_below_minus_two_dist,
'weight_for_age_above_plus_two_dist':
weight_for_age_above_plus_two_dist,
'weight_for_age_mean_z_score': {
'name': 'Mean Z score (weight-for-age)',
'numerators': {
'this': weight_for_age_mean_z_score
},
'values': {
'this': weight_for_age_mean_z_score
},
}
}
def get_crop_production_profile(geo_code, geo_level, session):
dist, _ = get_stat_data("crop_production",
geo_level,
geo_code,
session,
percent=False)
area_dist = OrderedDict()
area_dist['maize'] = dist['Maize']
area_dist['beans'] = dist['Beans']
area_dist['wheat'] = dist['Wheat']
area_dist['barley'] = dist['Barley']
area_dist['rice'] = dist['Rice']
area_dist['sorghum'] = dist['Sorghum']
area_dist['millet'] = dist['Millet']
area_dist['cowpeas'] = dist['Cowpeas']
area_dist['greengrams'] = dist['Greengrams']
area_dist['sweetpotatoes'] = dist['Sweetpotatoes']
area_dist['cassava'] = dist['Cassava']
area_dist['cocoyam'] = dist['Cocoyam']
area_dist['irishpotatoes'] = dist['Irishpotatoes']
area_dist['metadata'] = dist['metadata']
prod_dist_k = OrderedDict()
prod_dist_k['maize'] = replace_name(dist['Maize (90kg bags)'], 'Maize')
prod_dist_k['beans'] = replace_name(dist['Beans (90kg bags)'], 'Beans')
prod_dist_k['wheat'] = replace_name(dist['Wheat (90kg bags)'], 'Wheat')
prod_dist_k['sorghum'] = replace_name(dist['Sorghum (90kg bags)'],
'Sorghum')
prod_dist_k['millet'] = replace_name(dist['Millet (90kg bags)'], 'Millet')
prod_dist_k['cowpeas'] = replace_name(dist['Cowpeas (90kg bags)'],
'Cowpeas')
prod_dist_k['greengrams'] = replace_name(dist['Greengrams (90kg bags)'],
'Green grams')
prod_dist_t = OrderedDict()
prod_dist_t['barley'] = replace_name(dist['Barley (Ton)'], 'Barley')
prod_dist_t['rice'] = replace_name(dist['Rice (Ton)'], 'Rice')
prod_dist_t['sweetpotatoes'] = replace_name(dist['Sweetpotatoes (Ton)'],
'Sweet Potatoes')
prod_dist_t['cassava'] = replace_name(dist['Cassava (Ton)'], 'Cassava')
prod_dist_t['cocoyam'] = replace_name(dist['Cocoyam (Ton)'], 'Cocoyam')
prod_dist_t['irishpotatoes'] = replace_name(dist['Irishpotatoes (Ton)'],
'Irish potatoes')
prod_dist_t['metadata'] = dist['metadata']
yield_dist = OrderedDict()
yield_dist['maize'] = replace_name(dist['Maize yield'], 'Maize')
yield_dist['beans'] = replace_name(dist['Beans yield'], 'Beans')
yield_dist['wheat'] = replace_name(dist['Wheat yield'], 'Wheat')
yield_dist['barley'] = replace_name(dist['Barley yield'], 'Barley')
yield_dist['rice'] = replace_name(dist['Rice yield'], 'Rice')
yield_dist['sorghum'] = replace_name(dist['Sorghum yield'], 'Sorghum')
yield_dist['millet'] = replace_name(dist['Millet yield'], 'Millet')
yield_dist['cowpeas'] = replace_name(dist['Cowpeas yield'], 'Cowpeas')
yield_dist['greengrams'] = replace_name(dist['Greengrams yield'],
'Green grams')
yield_dist['sweetpotatoes'] = replace_name(dist['Sweetpotatoes yield'],
'Sweet potatoes')
yield_dist['cassava'] = replace_name(dist['Cassava yield'], 'Cassava')
yield_dist['cocoyam'] = replace_name(dist['Cocoyam yield'], 'Cocoyam')
yield_dist['irishpotatoes'] = replace_name(dist['Irishpotatoes yield'],
'Irish potatoes')
yield_dist['metadata'] = dist['metadata']
return {
'area_dist': area_dist,
'prod_dist_t': prod_dist_t,
'prod_dist_k': prod_dist_k,
'yield_dist': yield_dist,
}
def get_type_treatment_profile(geo_code, geo_level, session):
# Treatment for acute respiratory infection symptoms, fever, and diarrhoea stats
dist, _ = get_stat_data(['type', 'treatment'], geo_level, geo_code, session,
key_order={
'type': ['ARI', 'Fever','Diarrhoea'],
'treatment': ['Sought treatment from health facility or provider', \
'ORT', 'Zinc', 'ORS and zinc','Fluid from ORS packet'
]
})
# need to use numerators instead of values
for key in dist:
if key == 'metadata': continue
for other_key in dist[key]:
if other_key == 'metadata': continue
try:
dist[key][other_key]['values']['this'] = dist[key][other_key][
'numerators']['this']
except:
dist[key][other_key] = {
'values': {
'this': 0
},
'numerators': {
'this': 0
}
}
ari = dist['ARI']['Sought treatment from health facility or provider'][
'numerators']['this']
fever = dist['Fever']['Sought treatment from health facility or provider'][
'numerators']['this']
dist.pop('ARI')
dist.pop('Fever')
ari_dist = get_dictionary('Sought', 'Did not seek', ari, dist)
fever_dist = get_dictionary('Sought', 'Did not seek', fever, dist)
treatment_of_chidren_with_fever_dist, _ = get_stat_data(
['treatment of children with fever'], geo_level, geo_code, session)
children_with_fever = treatment_of_chidren_with_fever_dist['Had fever'][
'numerators']['this']
treatment_of_chidren_with_fever_dist.pop('Had fever')
# need to use numerators instead of values
for v in treatment_of_chidren_with_fever_dist:
if v == 'metadata': continue
treatment_of_chidren_with_fever_dist[v]['values'][
'this'] = treatment_of_chidren_with_fever_dist[v]['numerators'][
'this']
return {
'treatment_distribution': dist,
'ari_dist': ari_dist,
'fever_dist': fever_dist,
'treatment_of_chidren_with_fever_dist':
treatment_of_chidren_with_fever_dist,
'children_with_fever': {
'name':
'Percentage of children with fever in the two weeks preceding the survey',
'numerators': {
'this': children_with_fever
},
'values': {
'this': children_with_fever
}
},
}
def get_livestock_profile(geo_code, geo_level, session):
dist, _ = get_stat_data("livestock_population",
geo_level,
geo_code,
session,
percent=False)
pop_dist = OrderedDict()
pop_dist['cattle'] = sum_up([dist['Cattle meat'], dist['Cattle dairy']],
'Cattle')
pop_dist['sheep'] = sum_up([dist['Sheep wool'], dist['Sheep hair']],
'Sheep')
pop_dist['goats dairy'] = sum_up([dist['Goat dairy'], dist['Goat meat']],
'Goat')
pop_dist['poultry'] = sum_up([
dist['Broilers'], dist['Layers'], dist['Kienyeji'], dist['Others'],
dist['Turkey'], dist['Ducks'], dist['Quails'], dist['Guinea fowl'],
dist['Geese']
], 'Poultry')
pop_dist['pigs'] = dist['Pigs']
pop_dist['rabbits'] = dist['Rabbits']
pop_dist['donkeys'] = dist['Donkeys']
pop_dist['camels'] = dist['Camels']
pop_dist['crocodiles'] = dist['Crocodiles']
pop_dist['metadata'] = dist['metadata']
cattle_pop = OrderedDict()
cattle_pop['meat'] = divide_by_one_thousand(dist['Cattle meat'])
cattle_pop['dairy'] = divide_by_one_thousand(dist['Cattle dairy'])
cattle_pop['metadata'] = dist['metadata']
goat_pop = OrderedDict()
goat_pop['meat'] = divide_by_one_thousand(dist['Goat meat'])
goat_pop['dairy'] = divide_by_one_thousand(dist['Goat dairy'])
goat_pop['metadata'] = dist['metadata']
hives_pop = OrderedDict()
hives_pop['log'] = dist['Hives log']
hives_pop['KTBH'] = dist['Hives KTBH']
hives_pop['lang'] = dist['Hives lang']
hives_pop['box'] = dist['Hives box']
sheep_pop = OrderedDict()
sheep_pop['meat'] = divide_by_one_thousand(dist['Sheep wool'])
sheep_pop['dairy'] = divide_by_one_thousand(dist['Sheep hair'])
sheep_pop['metadata'] = dist['metadata']
poultry_pop = OrderedDict()
poultry_pop['broilers'] = divide_by_one_thousand(dist['Broilers'])
poultry_pop['layers'] = divide_by_one_thousand(dist['Layers'])
poultry_pop['kienyeji'] = divide_by_one_thousand(dist['Kienyeji'])
poultry_pop['others'] = divide_by_one_thousand(dist['Others'])
poultry_pop['turkey'] = divide_by_one_thousand(dist['Turkey'])
poultry_pop['ducks'] = divide_by_one_thousand(dist['Ducks'])
poultry_pop['quails'] = divide_by_one_thousand(dist['Quails'])
poultry_pop['guineafowl'] = divide_by_one_thousand(dist['Guinea fowl'])
poultry_pop['geese'] = divide_by_one_thousand(dist['Geese'])
poultry_pop['metadata'] = dist['metadata']
prod_dist, _ = get_stat_data("livestock_products",
geo_level,
geo_code,
session,
percent=False)
total_hives = sum_up([
dist['Hives log'], dist['Hives KTBH'], dist['Hives lang'],
dist['Hives box']
], 'Hives')['values']['this']
return {
'pop_dist': pop_dist,
'cattle_pop': cattle_pop,
'goat_pop': goat_pop,
'sheep_pop': sheep_pop,
'poultry_pop': poultry_pop,
'prod_dist': prod_dist,
'hives_pop': hives_pop,
'hives': {
'name': 'Total number of hives',
'numerators': {
'this': total_hives
},
'values': {
'this': total_hives
},
}
}
