def get_demographics_profile(geo_code, geo_level, session):
try:
sex_dist_data, total_pop = get_stat_data('sex', geo_level, geo_code, session, table_fields=['sex'])
urban_dist_data, _ = get_stat_data('rural or urban', geo_level, geo_code, session,
table_fields=['rural or urban'])
except LocationNotFound:
sex_dist_data, total_pop = LOCATIONNOTFOUND, 0
urban_dist_data = LOCATIONNOTFOUND
total_urbanised = 0
for data, value in urban_dist_data.get('Urban', {}).iteritems():
if data == 'numerators':
total_urbanised += value['this']
final_data = {
'sex_ratio': sex_dist_data,
'urban_distribution': urban_dist_data,
'urbanised': {
'name': 'In urban areas',
'numerators': {'this': total_urbanised},
},
'total_population': {
"name": "People",
"values": {"this": total_pop}
}}
try:
final_data['urbanised']['values'] = {
'this': round(total_urbanised / total_pop * 100, 2)}
except ZeroDivisionError:
final_data['urbanised']['values'] = {'this': 0}
return final_data
def get_demographics_profile(geo, session):
year = current_context().get('year')
with dataset_context(year=year):
# gender
gender_dist_data, total_pop = get_stat_data(
'gender', geo, session,
table_fields=['gender', 'age group'])
# age group
age_group_dist_data, _ = get_stat_data(
'age group', geo, session,
table_fields=['gender', 'age group'])
total_under_15 = age_group_dist_data['0-14 Years']['numerators']['this']
# rural or urban
rural_dist_data, _ = get_stat_data(
['rural or urban','gender'], geo, session,
table_fields=['gender', 'rural or urban'])
final_data = {
'gender_ratio': gender_dist_data,
'age_group_distribution': age_group_dist_data,
'under_15': {
'name': 'Under 15 years',
'values': {'this': total_under_15}
},
'rural_distribution': rural_dist_data,
'total_population': {
"name": "People",
"values": {"this": total_pop}
}}
return final_data
def get_disabilities_profile(geocode, geo_level, session):
try:
disabled_or_not, total_ = get_stat_data('disabled or not', geo_level, geocode, session,
table_fields=['disabled or not'])
disability, _ = get_stat_data('disability', geo_level, geocode, session, table_fields=['disability'])
except LocationNotFound:
disabled_or_not, total_ = LOCATIONNOTFOUND, 0
disability = LOCATIONNOTFOUND
total_disabled = 0
for data, value in disabled_or_not.get('With disability', {}).iteritems():
if data == 'numerators':
total_disabled += value['this']
final_data = {
'disabled_or_not_distribution': disabled_or_not,
'disability': disability,
'total_disabled': {
'name': 'Disabled',
'numerators': {'this': total_disabled},
},
'total_': {
"name": "Population",
"values": {"this": total_}
}
}
try:
final_data['total_disabled']['values'] = {
'this': round(total_disabled / total_ * 100, 2)}
except ZeroDivisionError:
final_data['total_disabled']['values'] = {'this': 0}
return final_data
def get_teachers_profile(geo,session,request):
table = 'teachers_type_2005'
if request.GET.get('release') is not None:
tablereq = 'teachers_type_' + request.GET.get('release')
if tablereq in ('teachers_type_2005','teachers_type_2006','teachers_type_2007','teachers_type_2008',
'teachers_type_2009','teachers_type_2010','teachers_type_2011','teachers_type_2012','teachers_type_2013',
'teachers_type_2015','teachers_type_default'):
table = tablereq
else:
table = 'teachers_type_default'
teachers_by_type,t_lit = get_stat_data(
['type'],geo,session,
table_fields=['teachers','type','year'],
table_name = table
)
teachers_by_school_type,_ = get_stat_data(
['teachers','type'],geo,session,
table_fields=['teachers','type','year'],
percent_grouping=['type'],
table_name = table,
)
final_data = {
'teachers_by_type_distribution': teachers_by_type,
'teachers_by_schools_type_distribution':teachers_by_school_type,
'total_teachers': {
"name": "Total teachers",
"values": {"this":t_lit}
}
}
return final_data
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, session):
stats_dist, s_ = get_stat_data("crimereport", geo, session)
crimes_dist, c_ = get_stat_data("typesofcrime", geo, 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_demographics_profile(geo_code, geo_level, session):
pop_dist_data, pop_total = get_stat_data(
['population group'], geo_level, geo_code, session)
youth_pop_dist_data, youth_pop_total = get_stat_data(['age in completed years'], geo_level, geo_code, session, table_name='youth_gender_age_in_completed_years')
youth_gender_data, _ = get_stat_data(['gender'], geo_level, geo_code, session, table_name='youth_gender_population_group')
youth_pop_group_data, _ = get_stat_data(['population group'], geo_level, geo_code, session, table_name='youth_gender_population_group')
final_data = {
'total_population': {
"name": "People",
"values": {"this": pop_total}
},
'youth_population_total': {
"name": "Youth aged 15-24",
"values": {"this": youth_pop_total}
},
'youth_population_perc': {
"name": "Of population are youth aged 15-24",
"values": {"this": percent(youth_pop_total, pop_total)},
},
'youth_population_by_year': youth_pop_dist_data,
'youth_population_by_gender': youth_gender_data,
'youth_population_by_pop_group': youth_pop_group_data
}
geo = geo_data.get_geography(geo_code, geo_level)
if geo.square_kms:
final_data['population_density'] = {
'name': "people per square kilometre",
'values': {"this": pop_total / geo.square_kms}
}
return final_data
def get_itn_profile(geo, session):
# household possession and use of ITN
possession_dist, _ = get_stat_data(
['household possession of itn'], geo, 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, 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_contraceptive_use_profile(geo, session):
# contraceptive_use stats
contraceptive_use_dist_data, _ = get_stat_data(
'contraceptive_use', geo, 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, session)
contraceptive_traditional_method_dist_data, _ = get_stat_data(
'contraceptive_traditional_method', geo, 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_households_profile(geo, session, year):
with dataset_context(year=year):
try:
permanency, _ = get_stat_data('household percentage by permanency', geo,
session,
table_fields=[
'household percentage by permanency'])
except Exception:
permanency = LOCATIONNOTFOUND
try:
light_source, total_households = get_stat_data(
'household distribution by light source', geo,
session, table_fields=['household distribution by light source'])
energy_source, _ = get_stat_data(
'household distribution by energy source', geo, session,
table_fields=['household distribution by energy source'])
except Exception:
total_households = 0
light_source = LOCATIONNOTFOUND
energy_source, _ = LOCATIONNOTFOUND, 0
is_missing = permanency.get('is_missing') and light_source.get('is_missing') and energy_source.get('is_missing')
final_data = {
'is_missing': is_missing,
'percentage_by_permanency': permanency,
'light_source_distribution': light_source,
'energy_source_distribution': energy_source,
'total_households': {
"name": "Households",
"values": {"this": total_households}
}
}
return final_data
def get_health_centers_distribution_profile(geo, session):
if geo.geo_level == 'ward': return {}
hc_data, total = get_stat_data('health centers',
geo=geo,
session=session,
order_by='-total')
ho_data, _ = get_stat_data('health center ownership', geo, session)
hiv_centers_data, hiv_c = get_stat_data('hiv centers', geo, 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_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},
'metadata': possession_dist['metadata']
},
'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_child_households_profile(geo_code, geo_level, session):
# head of household
# gender
head_gender_dist, total_households = get_stat_data(
['gender of head of household'], geo_level, geo_code, session,
order_by='gender of head of household',
table_name='genderofheadofhouseholdunder18')
female_heads = head_gender_dist['Female']['numerators']['this']
# annual household income
income_dist_data, _ = get_stat_data(
['annual household income'], geo_level, geo_code, session,
exclude=['Unspecified'],
recode=HOUSEHOLD_INCOME_RECODE,
key_order=HOUSEHOLD_INCOME_RECODE.values(),
table_name='annualhouseholdincomeunder18')
# median income
median = calculate_median_stat(income_dist_data)
median_income = HOUSEHOLD_INCOME_ESTIMATE[median]
# type of dwelling
type_of_dwelling_dist, _ = get_stat_data(
['type of main dwelling'], geo_level, geo_code, session,
recode=TYPE_OF_DWELLING_RECODE,
order_by='-total')
informal = type_of_dwelling_dist['Shack']['numerators']['this']
# size of household
household_size_dist, _ = get_stat_data(
['household size', 'age of household head'],
geo_level, geo_code, session
)
return {
'total_households': {
'name': 'Households with heads under 18 years old',
'values': {'this': total_households},
},
'type_of_dwelling_distribution': type_of_dwelling_dist,
'informal': {
'name': 'Child-headed households that are informal dwellings (shacks)',
'values': {'this': percent(informal, total_households)},
'numerators': {'this': informal},
},
'annual_income_distribution': income_dist_data,
'median_annual_income': {
'name': 'Average annual child-headed household income',
'values': {'this': median_income},
},
'household_size_distribution': household_size_dist,
'head_of_household': {
'gender_distribution': head_gender_dist,
'female': {
'name': 'Child-headed households with women as their head',
'values': {'this': percent(female_heads, total_households)},
'numerators': {'this': female_heads},
},
},
}
def get_access_profile(geo, session):
view_ft_users = OrderedDict()
view_ft_users_population = OrderedDict()
REGION_RECODES = OrderedDict()
REGION_POPULATION = OrderedDict()
try:
if geo.geo_level == 'country':
REGION_RECODES = OrderedDict([('country', geo.name),
('continent', geo.parent.name)])
view_ft_users, _ = get_stat_data(
['type'],
geo,
session,
table_name='ft_users_country_continent',
recode=dict(REGION_RECODES),
key_order=REGION_RECODES.values())
elif geo.geo_level == 'continent':
REGION_RECODES = OrderedDict([('continent', geo.name),
('world', geo.parent.name)])
view_ft_users, _ = get_stat_data(
['type'],
geo,
session,
table_name='ft_users_continent_world',
recode=dict(REGION_RECODES),
key_order=REGION_RECODES.values())
elif geo.geo_level == 'world':
view_ft_users, _ = get_stat_data(
['type'], geo, session, table_name='ft_users_world_continent')
except Exception as e:
view_ft_users = None
try:
REGION_POPULATION = OrderedDict([('users', 'Users'),
('population', 'Non-users')])
view_ft_users_population, _ = get_stat_data(
['type'],
geo,
session,
table_name='ft_users_population',
recode=dict(REGION_POPULATION),
key_order=REGION_POPULATION.values())
except Exception as e:
view_ft_users_population = {'Non-users': {'numerators': {'this': 0}}}
return {
'view_ft_users_population_users': {
"name": "World bank data.",
"values": {
"this":
view_ft_users_population['Non-users']['numerators']['this']
}
},
'view_ft_users': view_ft_users,
'view_ft_users_population': view_ft_users_population,
}
def get_maternal_care_indicators_profile(geo, session):
# maternal care indicators stats
maternal_care_indicators_data, _ = get_stat_data(
'maternal care indicators', geo, 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, 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_households_profile(geo, session):
# head of household
# gender
head_gender_dist, total_households = get_stat_data(
['gender of household head'], geo, session,
order_by='gender of household head')
female_heads = head_gender_dist['Female']['numerators']['this']
# age
db_model_u18 = get_model_from_fields(
['gender of head of household'], geo.geo_level,
table_name='genderofheadofhouseholdunder18'
)
objects = get_objects_by_geo(db_model_u18, geo, session)
total_under_18 = float(sum(o[0] for o in objects))
# type of dwelling
type_of_dwelling_dist, _ = get_stat_data(
['type of dwelling'], geo, session,
recode=TYPE_OF_DWELLING_RECODE,
order_by='-total')
informal = type_of_dwelling_dist['Shack']['numerators']['this']
_, total_ecd_children = get_stat_data(
['age in completed years'], geo, session,
table_name='ageincompletedyears',
only=['0', '1', '2', '3', '4', '5'])
ecd_children_per_household = ratio(total_ecd_children, total_households)
return {
'total_households': {
'name': 'Households',
'values': {'this': total_households},
},
'type_of_dwelling_distribution': type_of_dwelling_dist,
'informal': {
'name': 'Households that are informal dwellings (shacks)',
'values': {'this': percent(informal, total_households)},
'numerators': {'this': informal},
},
'head_of_household': {
'gender_distribution': head_gender_dist,
'female': {
'name': 'Households with women as their head',
'values': {'this': percent(female_heads, total_households)},
'numerators': {'this': female_heads},
},
'under_18': {
'name': 'Households with heads under 18 years old',
'values': {'this': total_under_18},
}
},
'ecd_children_per_household': {
'name': 'Average number of children (aged 0-5) in each household',
'values': {'this': ecd_children_per_household},
},
}
def get_education_profile(geo_code, geo_level, session):
# 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_child_households_profile(geo, session):
# head of household
# gender
head_gender_dist, total_households = get_stat_data(
['gender of head of household'], geo, session,
order_by='gender of head of household',
table_name='genderofheadofhouseholdunder18')
female_heads = head_gender_dist['Female']['numerators']['this']
# annual household income
if geo.version == '2011':
HOUSEHOLD_INCOME_RECODE = HOUSEHOLD_INCOME_RECODE_2011
else:
HOUSEHOLD_INCOME_RECODE = COLLAPSED_ANNUAL_INCOME_CATEGORIES
income_dist_data, _ = get_stat_data(
['annual household income'], geo, session,
exclude=['Unspecified'],
recode=HOUSEHOLD_INCOME_RECODE,
key_order=HOUSEHOLD_INCOME_RECODE.values(),
table_name='annualhouseholdincomeunder18')
# median income
median = calculate_median_stat(income_dist_data)
median_income = HOUSEHOLD_INCOME_ESTIMATE[median]
# type of dwelling
type_of_dwelling_dist, _ = get_stat_data(
['type of main dwelling'], geo, session,
recode=TYPE_OF_DWELLING_RECODE,
order_by='-total')
informal = type_of_dwelling_dist['Shack']['numerators']['this']
return {
'total_households': {
'name': 'Households with heads under 18 years old',
'values': {'this': total_households},
},
'type_of_dwelling_distribution': type_of_dwelling_dist,
'informal': {
'name': 'Child-headed households that are informal dwellings (shacks)',
'values': {'this': percent(informal, total_households)},
'numerators': {'this': informal},
},
'annual_income_distribution': income_dist_data,
'median_annual_income': {
'name': 'Average annual child-headed household income',
'values': {'this': median_income},
},
'head_of_household': {
'gender_distribution': head_gender_dist,
'female': {
'name': 'Child-headed households with women as their head',
'values': {'this': percent(female_heads, total_households)},
'numerators': {'this': female_heads},
},
},
}
def get_education_profile(geo, session):
edu_dist_data, total_over_20 = get_stat_data(
["highest educational level"],
geo,
session,
recode=COLLAPSED_EDUCATION_CATEGORIES,
table_universe="Individuals 20 and older",
key_order=EDUCATION_KEY_ORDER,
)
GENERAL_EDU = (
EDUCATION_GET_OR_HIGHER
if str(current_context().get("year")) == "2011"
else EDUCATION_GET_OR_HIGHER_2016
)
general_edu, total_general_edu = get_stat_data(
["highest educational level"],
geo,
session,
table_universe="Individuals 20 and older",
only=GENERAL_EDU,
)
FURTHER_EDU = (
EDUCATION_FET_OR_HIGHER
if str(current_context().get("year")) == "2011"
else EDUCATION_FET_OR_HIGHER_2016
)
further_edu, total_further_edu = get_stat_data(
["highest educational level"],
geo,
session,
table_universe="Individuals 20 and older",
only=FURTHER_EDU,
)
edu_split_data = {
"percent_general_edu": {
"name": "Completed Grade 9 or higher",
"numerators": {"this": total_general_edu},
"values": {"this": round(total_general_edu / total_over_20 * 100, 2)},
},
"percent_further_edu": {
"name": "Completed Matric or higher",
"numerators": {"this": total_further_edu},
"values": {"this": round(total_further_edu / total_over_20 * 100, 2)},
},
"metadata": general_edu["metadata"],
}
profile = {
"educational_attainment_distribution": edu_dist_data,
"educational_attainment": edu_split_data,
}
return profile
def get_water_sources_profile(geo, session):
if geo.geo_level == 'ward' or geo.geo_level == 'district':
return {}
try:
_, total_pop = get_stat_data(
'sex',
geo=geo,
session=session,
table_fields=['age in completed years', 'sex', 'rural or urban'])
WATER_POINT_STATUS_RECODES = OrderedDict([
('functional', 'Functional'), ('nonfunctional', 'Non Functional'),
('needsrepair', 'Functional Needs Repair')
])
water_source_dist, n_sources = get_stat_data('water sources', geo=geo,\
session=session, recode=WATER_POINT_STATUS_RECODES)
except Exception as e:
#Location not found in dataset
return {}
#Functional + Needs repair
all_functional = water_source_dist['Functional']['numerators']['this'] + \
water_source_dist['Functional Needs Repair']['numerators']['this']
#Number of people per water point
n_people_per_point = round(total_pop / all_functional)
return {
'water_sources_dist': water_source_dist,
'number_of_water_sources': {
'name': 'Total number of water points',
'numerators': {
'this': n_sources
},
'values': {
'this': n_sources
}
},
'source_link':
'http://www.opendata.go.tz/dataset/hali-halisi-ya-vitoa-maji-kwa-mikoa-yote-tanzania',
'source_name': 'opendata.go.tz',
'n_people_per_point': {
'name':
'Number of people using a working water point(Functional/Needs repair water point)',
'numerators': {
'this': n_people_per_point
},
'values': {
'this': n_people_per_point
}
},
}
def get_type_treatment_profile(geo, session):
# Treatment for acute respiratory infection symptoms, fever, and diarrhoea
# stats
dist, _ = get_stat_data(['type', 'treatment'], geo, 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 BaseException:
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, 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_land_audit_profile(geo, session):
year = current_context().get('year')
with dataset_context(year=year):
land_use_dist = LOCATIONNOTFOUND
land_user_dist = LOCATIONNOTFOUND
land_distribution_gender = LOCATIONNOTFOUND
land_ownership = LOCATIONNOTFOUND
try:
land_use_dist, _ = get_stat_data('land_use', geo, session,
table_name='landuse',
table_fields=['land_use'])
except Exception as e:
pass
try:
land_user_dist, _ = get_stat_data('land_user', geo, session,
table_name='landuser',
table_fields=['land_user'])
except Exception:
pass
try:
land_distribution_gender, _ = get_stat_data(
'land_ownership_by_gender', geo, session,
table_name='privatelanddistributionbygender',
table_fields=['land_ownership_by_gender'])
except Exception:
pass
try:
land_ownership, _ = get_stat_data('private_vs_state_ownership', geo,
session,
table_name='landownership',
table_fields=[
'private_vs_state_ownership'])
except Exception:
pass
is_missing = land_user_dist.get('is_missing') and \
land_use_dist.get('is_missing') and \
land_distribution_gender.get('is_missing') and \
land_ownership.get('is_missing')
return {
'is_missing': is_missing,
'land_user_dist': land_user_dist,
'land_use_dist': land_use_dist,
'land_distribution_gender': land_distribution_gender,
'land_ownership': land_ownership,
}
def get_demographics_profile(geo, session):
geo.version = str(geo.version)
# sex
try:
sex_dist_data, total_pop = get_stat_data('sex',
geo,
session,
table_fields=['sex'])
except LocationNotFound:
sex_dist_data = LOCATIONNOTFOUND
total_pop = 0
# urban/rural by sex
total_urbanised = 0
try:
urban_dist_data, _ = get_stat_data(['rural or urban'],
geo,
session,
table_fields=['rural or urban'])
for data in urban_dist_data['Urban'].itervalues():
if 'numerators' in data:
total_urbanised += data['numerators']['this']
except LocationNotFound:
urban_dist_data = LOCATIONNOTFOUND
final_data = {
'sex_ratio': sex_dist_data,
'urban_distribution': urban_dist_data,
'urbanised': {
'name': 'In urban areas',
'numerators': {
'this': total_urbanised
},
},
'total_population': {
"name": "People",
"values": {
"this": total_pop
}
}
}
try:
final_data['urbanised']['values'] = {
'this': round(total_urbanised / total_pop * 100, 2)
}
except ZeroDivisionError:
final_data['urbanised']['values'] = {'this': 0}
return final_data
def get_households_profile(geocode, geo_level, session):
try:
light_source, total_households = get_stat_data('household distribution by light source', geo_level, geocode,
session, table_fields=['household distribution by light source'])
energy_source, _ = get_stat_data('household distribution by energy source', geo_level, geocode, session,
table_fields=['household distribution by energy source'])
except LocationNotFound:
household_dist, total_households = LOCATIONNOTFOUND, 0
light_source = LOCATIONNOTFOUND
energy_source, _ = LOCATIONNOTFOUND, 0
total_electrified_lighting = 0
for data, value in light_source.get('Electricity', {}).iteritems():
if data == 'numerators':
total_electrified_lighting += value['this']
total_charcoal_energy = 0
for data, value in energy_source.get('Charcoal', {}).iteritems():
if data == 'numerators':
total_charcoal_energy += value['this']
final_data = {
'light_source_distribution': light_source,
'electrified_lighting': {
'name': 'Lighting with Electricity',
'numerators': {'this': total_electrified_lighting},
},
'energy_source_distribution': energy_source,
'charcoal_energy': {
'name': 'Cooking with Charcoal',
'numerators': {'this': total_charcoal_energy},
},
'total_households': {
"name": "Households",
"values": {"this": total_households}
}
}
try:
final_data['electrified_lighting']['values'] = {
'this': round(total_electrified_lighting / total_households * 100, 2)}
except ZeroDivisionError:
final_data['electrified_lighting']['values'] = {'this': 0}
try:
final_data['charcoal_energy']['values'] = {
'this': round(total_electrified_lighting / _ * 100, 2)}
except ZeroDivisionError:
final_data['charcoal_energy']['values'] = {'this': 0}
return final_data
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},
'metadata': dist['metadata']
},
}
def get_demographics_profile(geo, session, year):
with dataset_context(year=year):
geo.version = str(geo.version)
# sex
try:
sex_dist_data, total_pop = get_stat_data(
'sex', geo, session,
table_fields=['sex'])
except Exception:
sex_dist_data = LOCATIONNOTFOUND
total_pop = 0
# urban/rural by sex
total_urbanised = 0
try:
urban_dist_data, _ = get_stat_data(
['rural or urban'], geo, session,
table_fields=['rural or urban'])
for data in urban_dist_data['Urban'].values():
if 'numerators' in data:
total_urbanised += data['numerators']['this']
except Exception:
urban_dist_data = LOCATIONNOTFOUND
is_missing = sex_dist_data.get('is_missing') and urban_dist_data.get('is_missing')
final_data = {
'is_missing': is_missing,
'sex_ratio': sex_dist_data,
'urban_distribution': urban_dist_data,
'urbanised': {
'name': 'In urban areas',
'numerators': {'this': total_urbanised},
},
'total_population': {
"name": "People",
"values": {"this": total_pop}
}}
try:
final_data['urbanised']['values'] = {
'this': round(total_urbanised / total_pop * 100, 2)}
except ZeroDivisionError:
final_data['urbanised']['values'] = {'this': 0}
return final_data
def get_population(geo, session):
with dataset_context(year='2017'):
sex_dist, total_population_sex = LOCATIONNOTFOUND, 0
residence_dist, total_population_residence = LOCATIONNOTFOUND, 0
religion_dist, total_population_religion = LOCATIONNOTFOUND, 0
try:
sex_dist, total_population_sex = get_stat_data(
'sex', geo, session, table_fields=['sex'])
except Exception:
pass
try:
residence_dist, total_population_residence = get_stat_data(
'residence', geo, session,
table_fields=['residence'])
except Exception:
pass
try:
religion_dist, total_population_religion = get_stat_data(
'religion', geo, session,
table_fields=['religion'])
except Exception:
pass
total_population = 0
is_missing = sex_dist.get('is_missing') and \
residence_dist.get('is_missing') and \
religion_dist.get('is_missing')
if not is_missing:
total_population = total_population_sex if total_population_sex > 0 else total_population_residence
total_population_dist = _create_single_value_dist(
'People', total_population)
demographics_data = {
'is_missing': is_missing,
'sex_dist': sex_dist,
'residence_dist': residence_dist,
'religion_dist': religion_dist,
'total_population': total_population_dist,
}
if geo.square_kms:
demographics_data['population_density'] = {
'name': "people per square kilometre",
'values': {"this": total_population / geo.square_kms},
}
return demographics_data
def get_hospitals_profile(geo, session):
# population group
_, total_pop = get_stat_data(['population group'],
geo,
session,
table_dataset='Census 2011')
# Hospitals
table = get_datatable('hospitals_2012')
keys = [
'regional_hospital', 'central_hospital', 'district_hospital', 'clinic',
'chc'
]
hospital_breakdown, total_hospitals = table.get_stat_data(
geo, keys, percent=False, recode={'chc': 'Community health centre'})
people_per_hospital = ratio(total_pop, total_hospitals)
_, ecd_children = get_stat_data(['age in completed years'],
geo,
session,
table_name='ageincompletedyears',
only=['0', '1', '2', '3', '4', '5'])
children_0_to_5_per_hospital = ratio(ecd_children, total_hospitals)
return {
"total_hospitals": {
"name": "Hospitals / Clinics",
"values": {
"this": total_hospitals
}
},
"hospital_breakdown": hospital_breakdown,
"people_per_hospital": {
"name": "People living in the area for each hospital / clinic",
"values": {
"this": people_per_hospital
}
},
"children_0_to_5_per_hospital": {
"name":
"Children (aged 0-5 years) living in the area for each hospital / clinic",
"values": {
"this": children_0_to_5_per_hospital
}
},
}
def get_fertility_profile(geo, session):
# fertility
dist, _ = get_stat_data(['fertility'], geo, 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_health_ratios_profile(geo, session):
ratios_dist, _ = get_stat_data("healthratios", geo, 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_pupil_teacher_ratios_profile(geo_code, geo_level, session):
# 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)
teachers_absent_dist = ratio_data['Teachers absent']['numerators']['this']
teachers_absent_dist = get_dictionary("Teachers absent", "Teachers present", teachers_absent_dist)
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_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},
'metadata': dist['metadata']
},
'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},
'metadata': dist['metadata']
},
}
def stat_data(self):
"""
Calculate the statistical data
"""
try:
self.distribution, self.total = get_stat_data(
[self.profile.field_name],
self.geo,
self.session,
table_name=self.profile.table_name.name,
exclude_zero=self.profile.exclude_zero,
percent=self.profile.percent,
recode=self.profile.recode,
key_order=self.profile.key_order,
exclude=self.profile.exclude,
order_by=self.profile.order_by,
)
self.compare_geos()
if self.profile.group_remainder:
group_remainder(self.distribution, self.profile.group_remainder)
self.distribution = enhance_api_data(self.distribution)
return {"stat_values": self.distribution}
except KeyError as error:
log.warn(
"Unable to calculate statistics for profile: %s", self.profile.title
)
log.warn("error: %s", error)
return {}
except DataNotFound as error:
log.warn(
"Unable to calculate statistics for profile: %s", self.profile.title
)
log.warn("Unable to find data for this indicator: %s", error)
return {}
def get_tetanus_vaccine_profile(geo, session):
if geo.geo_level != 'region' and geo.geo_level != 'country': return {}
tetanus_vaccine_data, _ = get_stat_data('tetanus vaccine',
geo=geo,
session=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_v6marketshare_profile(geo, session):
view_ft_marketshare_v6users = OrderedDict()
ispAmount = 0
try:
view_ft_marketshare_v6users, _ = get_stat_data(
'asname',
geo,
session,
order_by='-total',
table_fields='total',
table_name='ft_marketshare_v6users',
exclude_zero=True)
ispAmount = len(view_ft_marketshare_v6users) - 1
except Exception as e:
ispAmount = 0
view_ft_marketshare_users = None
return {
'isp_count': {
"name": "IPv6 ISP's",
"values": {
'this': ispAmount
}
},
'view_ft_marketshare_v6users': view_ft_marketshare_v6users
}
def dataset_context_stat_data(self):
"""
Calulate statistical data with a particular context
"""
with dataset_context(year=str(self.profile.dataset_context)):
try:
self.distribution, self.total = get_stat_data(
[self.profile.field_name],
self.geo,
self.session,
table_name=self.profile.table_name.name,
exclude_zero=self.profile.exclude_zero,
percent=self.profile.percent,
recode=self.profile.recode,
key_order=self.profile.key_order,
exclude=self.profile.exclude,
order_by=self.profile.order_by,
)
self.compare_geos()
if self.profile.group_remainder:
group_remainder(self.distribution, self.profile.group_remainder)
self.distribution = enhance_api_data(self.distribution)
return {"stat_values": self.distribution}
except DataNotFound:
return {}
def get_primary_school_teachers_profile(geo, session):
if geo.geo_level == 'ward':
return {}
try:
ps_teachers, n_teachers = get_stat_data('primary school teachers',\
geo=geo, session=session, order_by='-total')
except Exception as e:
ps_teachers = None
n_teachers = None
return {
'number_of_teachers': {
'name': 'Number of primary school teachers (2016)',
'numerators': {
'this': n_teachers
},
'values': {
'this': n_teachers
}
},
'source_name': 'opendata.go.tz',
'source_link':
'http://www.opendata.go.tz/dataset/number-of-primary-school-teachers',
'primary_school_teachers': ps_teachers
}
def get_election_data(geo, election, session):
party_data, total_valid_votes = get_stat_data(
['party'],
geo,
session,
table_dataset=election['dataset'],
recode=lambda f, v: make_party_acronym(v),
order_by='-total')
results = {
'name': election['name'],
'party_distribution': party_data,
'geo_version': election['geo_version']
}
# voter registration and turnout
table = get_datatable('voter_turnout_%s' % election['table_code'])
results.update(
table.get_stat_data(
geo,
'registered_voters',
percent=False,
recode={'registered_voters': 'Number of registered voters'})[0])
results.update(
table.get_stat_data(
geo,
'total_votes',
percent=True,
total='registered_voters',
recode={'total_votes': 'Of registered voters cast their vote'})[0])
return results
def get_gdpyearly_profile(geo,session,request):
yearSetsIni = []
ExcYearSet = []
for i in range(2001,2018):
yearSetsIni.append(i)
cyear = 2011
for y in yearSetsIni:
if y != cyear:
ExcYearSet.append(y)
ExcYearSet = map(str, ExcYearSet)
if request.GET.get('release') is not None:
ExcYearSet = []
ryear = int(request.GET.get('release'))
ExcYearSet = [e for e in yearSetsIni if e not in {ryear}]
#ExcYearSet = list(set(yearSetsIni)-set(ryear))
ExcYearSet = map(str, ExcYearSet)
gdp_single_year,t_lit = get_stat_data(
['gdpyear'],geo,session,
table_fields =['gdpyear'],
exclude = ExcYearSet,
)
final_data = {
'gdp_ratio': gdp_single_year,
'total_gdp':{
"name": "Total GDP in crore",
"values": {"this":t_lit}
}
}
return final_data
def get_elections2016_profile(geocode, geo_level, session):
try:
candidate, total_votes = get_stat_data('presidential candidate', geo_level, geocode, session,
table_fields=['presidential candidate'])
except LocationNotFound:
candidate, total_votes = LOCATIONNOTFOUND, 0
total_besigye = 0
for data, value in candidate.get('Kizza besigye', {}).iteritems():
if data == 'numerators':
total_besigye += value['this']
final_data = {
'candidate_distribution': candidate,
'besigye_votes': {
'name': 'Besigye Votes',
'numerators': {'this': total_besigye},
},
'total_votes': {
"name": "Votes",
"values": {"this": total_votes}
}
}
try:
final_data['besigye_votes']['values'] = {
'this': round(total_besigye / total_votes * 100, 2)}
except ZeroDivisionError:
final_data['besigye_votes']['values'] = {'this': 0}
return final_data
def get_drinkingsource_profile(geo,session,request):
table = 'drinkingsource_2011'
if request.GET.get('release') is not None:
tablereq = 'drinkingsource_' + request.GET.get('release')
if tablereq in ('drinkingsource_2011'):
table = tablereq
else:
table = 'drinkingsource_default'
drinking_source_dis_data,t_lit= get_stat_data(
'drinkingsource',geo,session,
table_fields=['drinkingsource'],
table_name=table
)
final_data = {
'drinkingsource_ratio':drinking_source_dis_data,
'total_population':{
"name": "Total Household",
"values": {"this":t_lit}
}
}
return final_data
def get_employment_profile(geo, session):
# employment status
employment_activity_dist, total_workers = get_stat_data(
['employment activity status', 'sex'],
geo,
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_ruralpopcovered_profile(geo,session,request):
table = 'ruralpopcovered_2017'
if request.GET.get('release') is not None:
tablereq = 'ruralpopcovered_' + request.GET.get('release')
if tablereq in ('ruralpopcovered_2017'):
table = tablereq
else:
table = 'ruralpopcovered_default'
if request.GET.get('table') is not None:
if request.GET.get('table') in ('ruralpopcovered_2017'):
table = request.GET.get('table')
ruralpopcovered_dis_data,t_lit = get_stat_data(
['ruralpopcovered'],geo,session,
table_fields=['ruralpopcovered','year'],
table_name=table
)
final_data = {
'ruralpopcovered_dis_data_distribution': ruralpopcovered_dis_data,
'total_population': {
"name": "Total rural population covered",
"values": {"this": t_lit}
}
}
return final_data
def get_economics_profile(geo_code, geo_level, session):
# income
income_dist_data, total_workers = get_stat_data(
['employed individual monthly income'], geo_level, geo_code, session,
exclude=['Not applicable'],
recode=COLLAPSED_INCOME_CATEGORIES,
key_order=COLLAPSED_INCOME_CATEGORIES.values())
# median income
median = calculate_median_stat(income_dist_data)
median_income = ESTIMATED_INCOME_CATEGORIES[median]
# employment status
employ_status, total_workers = get_stat_data(
['official employment status'], geo_level, geo_code, session,
exclude=['Age less than 15 years', 'Not applicable'],
order_by='official employment status',
table_name='officialemploymentstatus')
# sector
sector_dist_data, _ = get_stat_data(
['type of sector'], geo_level, geo_code, session,
exclude=['Not applicable'],
order_by='type of sector')
# access to internet
internet_access_dist, total_with_access = get_stat_data(
['access to internet'], geo_level, geo_code, session, exclude=['No access to internet'],
order_by='access to internet')
_, total_without_access = get_stat_data(
['access to internet'], geo_level, geo_code, session, only=['No access to internet'])
total_households = total_with_access + total_without_access
return {'individual_income_distribution': income_dist_data,
'median_individual_income': {
'name': 'Average monthly income',
'values': {'this': median_income},
},
'employment_status': employ_status,
'sector_type_distribution': sector_dist_data,
'internet_access_distribution': internet_access_dist,
'internet_access': {
'name': 'Households with internet access',
'values': {'this': percent(total_with_access, total_households)},
'numerators': {'this': total_with_access},
}
}
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},
'metadata': dist['metadata']
},
}
def get_voterregistration_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']
return {
'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)},
},
'metadata': stats_dist['metadata']
},
'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)},
},
'metadata': stats_dist['metadata']
},
}
def test_get_stat_data_nulls(self):
self.field_table(['gender'], """
lev,code,Male,10
lev,code,Female,
""")
data, total = get_stat_data(['gender'], self.geo, self.s)
self.assertEqual(total, 10)
self.assertEqual(data['Male']['numerators']['this'], 10)
self.assertEqual(data['Male']['values']['this'], 100)
self.assertIsNone(data['Female']['numerators']['this'])
self.assertIsNone(data['Female']['values']['this'])
data, total = get_stat_data(['gender'], self.geo, self.s, percent=False)
self.assertEqual(total, 10)
self.assertIsNone(data['Male']['numerators']['this'])
self.assertEqual(data['Male']['values']['this'], 10)
self.assertIsNone(data['Female']['numerators']['this'])
self.assertIsNone(data['Female']['values']['this'])
def get_schools_profile(geo, session):
# population group
_, total_pop = get_stat_data(
['population group'], geo, session, table_dataset='Census 2011')
# Schools
table = get_datatable('schools_2015')
keys = ['primary_schools', 'combined_schools', 'intermediate_schools', 'secondary_schools']
school_breakdown, total_schools = table.get_stat_data(
geo, keys, percent=False)
primary_school_ages = ['6', '7', '8', '9', '10', '11', '12', '13']
secondary_school_ages = ['14', '15', '16', '17', '18']
_, total_primary_children = get_stat_data(
['age in completed years'], geo, session,
table_name='ageincompletedyears',
only=primary_school_ages)
_, total_secondary_children = get_stat_data(
['age in completed years'], geo, session,
table_name='ageincompletedyears',
only=secondary_school_ages)
children_per_primary_school = ratio(total_primary_children, school_breakdown['primary_schools']['values']['this'])
children_per_secondary_school = ratio(total_secondary_children, school_breakdown['secondary_schools']['values']['this'])
final_data = {
'total_schools': {
"name": "Schools",
"values": {"this": total_schools}
},
"school_breakdown": school_breakdown,
"children_per_primary_school": {
"name": "Children (6-13 years) in the area for each primary school",
"values": {"this": children_per_primary_school}
},
"children_per_secondary_school": {
"name": "Children (14-18 years) for each secondary school",
"values": {"this": children_per_secondary_school}
}
}
return final_data
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_education_profile(geo_code, geo_level, session):
youth_completed_grade9, _ = get_stat_data(['completed grade9'], geo_level, geo_code, session, table_name='youth_age_16_to_17_gender_completed_grade9')
youth_gender_completed_grade9, _ = get_stat_data(['gender', 'completed grade9'], geo_level, geo_code, session, table_name='youth_age_16_to_17_gender_completed_grade9')
db_model_gender_completed_grade9 = get_model_from_fields(['gender'], geo_level, table_name='youth_age_16_to_17_gender_completed_grade9')
gender_completed_grade9_data = OrderedDict(( # census data refers to sex as gender
('Female', {
"name": "Female",
"values": {"this": youth_gender_completed_grade9['Female']['Yes']['values']['this']},
"numerators": {"this": youth_gender_completed_grade9['Female']['Yes']['numerators']['this']},
}),
('Male', {
"name": "Male",
"values": {"this": youth_gender_completed_grade9['Male']['Yes']['values']['this']},
"numerators": {"this": youth_gender_completed_grade9['Male']['Yes']['numerators']['this']},
}),
))
add_metadata(gender_completed_grade9_data, db_model_gender_completed_grade9)
youth_education_level, youth_pop_20_to_24 = get_stat_data(['education level'], geo_level, geo_code, session, table_name='youth_age_20_to_24_gender_education_level')
matric_or_equiv = (
youth_education_level['Matric']['numerators']['this'] +
youth_education_level['Tertiary']['numerators']['this'] +
youth_education_level['Some secondary']['numerators']['this'])
final_data = {
'youth_completed_grade9': youth_completed_grade9,
'youth_perc_completed_grade9': {
"name": "Of youth aged 16-17 have completed grade 9",
"values": {"this": youth_completed_grade9['Yes']['values']['this']},
},
'youth_gender_completed_grade9': gender_completed_grade9_data,
'youth_perc_matric': {
"name": "Of youth aged 20-24 have completed matric or matric equivalent",
"values": {"this": percent(matric_or_equiv, youth_pop_20_to_24)},
},
'youth_education_level': youth_education_level
}
return final_data
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},
'metadata': number_of_protests_dist['metadata']
},
}
def get_crimereport_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},
'metadata': crimes_dist['metadata']
},
'crimesindex': {
'name': 'Crime index per 100,000 people',
'numerators': {'this': crimeindex},
'values': {'this': crimeindex},
'metadata': crimes_dist['metadata']
},
'crimes_dist': crimes_dist,
}
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_schoolfires_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_disabilities_profile(geo, session, year):
with dataset_context(year=year):
try:
disabled_or_not, total_ = get_stat_data('disabled or not', geo, session,
table_fields=[
'disabled or not'])
disability, _ = get_stat_data('disability', geo, session,
table_fields=['disability'])
except Exception:
disabled_or_not, total_ = LOCATIONNOTFOUND, 0
disability = LOCATIONNOTFOUND
total_disabled = 0
for data, value in disabled_or_not.get('With disability', {}).items():
if data == 'numerators':
total_disabled += value['this']
is_missing = disability.get('is_missing') and disabled_or_not.get('is_missing')
final_data = {
'is_missing': is_missing,
'disabled_or_not_distribution': disabled_or_not,
'disability': disability,
'total_disabled': {
'name': 'Disabled',
'numerators': {'this': total_disabled},
},
'total_': {
"name": "Population",
"values": {"this": total_}
}
}
try:
final_data['total_disabled']['values'] = {
'this': round(total_disabled / total_ * 100, 2)}
except ZeroDivisionError:
final_data['total_disabled']['values'] = {'this': 0}
return final_data
def test_get_stat_data_nulls_with_denominator_key(self):
table = self.field_table(['household goods'], None, 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'], self.geo, 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'])