mockups.py

from __future__ import division
from math import ceil
from importd import d
d(INSTALLED_APPS=["django.contrib.humanize"])

AGE_GENDER_GROUPS = [
    {'0-10': {
        'M': 244,
        'F': 256,
    }},
    {'11-19': {
        'M': 326,
        'F': 410,
    }},
    {'20-29': {
        'M': 550,
        'F': 513,
    }},
    {'30-39': {
        'M': 602,
        'F': 547,
    }},
    {'40-49': {
        'M': 814,
        'F': 843,
    }},
    {'50-59': {
        'M': 754,
        'F': 901,
    }},
    {'60-69': {
        'M': 399,
        'F': 423,
    }},
    {'70+': {
        'M': 277,
        'F': 281,
    }},
]

EDUCATIONAL_ATTAINMENT_GROUPS = [
    {
        'name': 'No school',
        'value': 344,
    },
    {
        'name': '< 8th grade',
        'value': 105,
    },
    {
        'name': 'No HS diploma',
        'value': 264,
    },
    {
        'name': 'HS grad',
        'value': 756,
    },
    {
        'name': 'Some college',
        'value': 1012,
    },
    {
        'name': 'College degree',
        'value': 966,
    },
    {
        'name': 'Advanced degree',
        'value': 415,
    },
]

MEDIAN_PER_CAPITA_INCOME = {
    'geography': {
        'name': 'Example County',
        'value': 44885,
    },
    'state': {
        'name': 'Washington',
        'value': 43878,
    },
    'nation': {
        'name': 'national',
        'value': 41560,
    },
    'time_series': {
        'labels': ['2007','2008','2009','2010','2011'],
        'values': [45133,44638,42110,42244,44885],
    }
}

EMPLOYMENT_COMMUTE_TYPES = {
    'drove_alone': {
        'name': 'Drove alone',
        'commute_time': {
            'value': 1154,
            'label': '19:14'
        },
    },
    'carpooled': {
        'name': 'Carpooled',
        'commute_time': {
            'value': 1251,
            'label': '20:51'
        },
    },
    'public_transit': {
        'name': 'Public transit',
        'commute_time': {
            'value': 1712,
            'label': '28:32'
        },
    },
    'walked': {
        'name': 'Walked',
        'commute_time': {
            'value': 887,
            'label': '14:47'
        },
    },
    'taxi': {
        'name': 'Taxi, motorcycle, bike',
        'commute_time': {
            'value': 1348,
            'label': '22:28'
        },
    },
    'home': {
        'name': 'Worked at home',
        'commute_time': {
            'value': 0,
            'label': '0:00'
        },
    },
}

DIVORCES_LAST_YEAR = {
    'male': 387,
    'female': 325,
}

MARITAL_STATUS_GROUPS = [
    {'Never married': {
        'M': 724,
        'F': 536,
    }},
    {'Spouse present': {
        'M': 550,
        'F': 513,
    }},
    {'Spouse absent': {
        'M': 326,
        'F': 410,
    }},
    {'Separated': {
        'M': 602,
        'F': 547,
    }},
    {'Other': {
        'M': 184,
        'F': 113,
    }},
    {'Widowed': {
        'M': 754,
        'F': 901,
    }},
    {'Divorced': {
        'M': 637,
        'F': 541,
    }},
]

HOUSING_PRICE_GROUPS = [
    {
        'name': '< $100K',
        'value': 344,
    },
    {
        'name': '$100K to $200K',
        'value': 564,
    },
    {
        'name': '$200K to $300K',
        'value': 1756,
    },
    {
        'name': '$300K to $400K',
        'value': 812,
    },
    {
        'name': '$400K to $500K',
        'value': 846,
    },
    {
        'name': '> $500K',
        'value': 142,
    },
]

MEDIAN_HOUSING_VALUES = {
    'geography': {
        'name': 'Example County',
        'value': 224845,
    },
    'state': {
        'name': 'Washington',
        'value': 243878,
    },
    'nation': {
        'name': 'national',
        'value': 238160,
    },
}

VETERAN_SERVICE_GROUPS = [
    {
        'name': 'Gulf War (2000s)',
        'value': 484,
    },
    {
        'name': 'Gulf War (1990s)',
        'value': 605,
    },
    {
        'name': 'Vietnam Era',
        'value': 634,
    },
    {
        'name': 'Korean War',
        'value': 246,
    },
    {
        'name': 'World War II',
        'value': 102,
    },
]

HEALTH_INSURANCE_VALUES = {
    'geography': {
        'name': 'Example County',
        'value': 22,
    },
    'state': {
        'name': 'Washington',
        'value': 15,
    },
    'nation': {
        'name': 'national',
        'value': 18,
    },
}

HEALTH_INSURANCE_GROUPS = [
    {
        'name': 'Employer- based',
        'value': 1144,
    },
    {
        'name': 'Direct purchase',
        'value': 234,
    },
    {
        'name': 'Medicare',
        'value': 644,
    },
    {
        'name': 'Medicaid',
        'value': 722,
    },
    {
        'name': 'TRICARE / military',
        'value': 136,
    },
    {
        'name': 'VA Health Care',
        'value': 242,
    },
]

LANGUAGE_GROUPS = [
    {'English only': {
        'N': 244,
        'F': 256,
    }},
    {'Spanish': {
        'N': 326,
        'F': 410,
    }},
    {'Other Indo-European': {
        'N': 550,
        'F': 513,
    }},
    {'Asian / Pacific Island': {
        'N': 602,
        'F': 547,
    }},
    {'Other': {
        'N': 277,
        'F': 281,
    }},
]



def get_ratio(num1, num2):
    return round(num1 / num2, 2)*100

MEDIAN_HOUSING_INDEXES = {
    'median_against_state': get_ratio(MEDIAN_HOUSING_VALUES['geography']['value'], MEDIAN_HOUSING_VALUES['state']['value']),
    'median_against_nation': get_ratio(MEDIAN_HOUSING_VALUES['geography']['value'], MEDIAN_HOUSING_VALUES['nation']['value']),
    'geography_against_nation_index': get_ratio(MEDIAN_HOUSING_VALUES['geography']['value'], MEDIAN_HOUSING_VALUES['nation']['value'])/2,
    'state_against_nation_index': get_ratio(MEDIAN_HOUSING_VALUES['state']['value'], MEDIAN_HOUSING_VALUES['nation']['value'])/2,
}

HEALTH_INSURANCE_INDEXES = {
    'median_against_state': get_ratio(HEALTH_INSURANCE_VALUES['geography']['value'], HEALTH_INSURANCE_VALUES['state']['value']),
    'median_against_nation': get_ratio(HEALTH_INSURANCE_VALUES['geography']['value'], HEALTH_INSURANCE_VALUES['nation']['value']),
    'geography_against_nation_index': get_ratio(HEALTH_INSURANCE_VALUES['geography']['value'], HEALTH_INSURANCE_VALUES['nation']['value'])/2,
    'state_against_nation_index': get_ratio(HEALTH_INSURANCE_VALUES['state']['value'], HEALTH_INSURANCE_VALUES['nation']['value'])/2,
}

def drill(item):
    if isinstance(item, int):
        yield item
    elif isinstance(item, list):
        for i in item:
            for result in drill(i):
                yield result
    elif isinstance(item, dict):
        for k,v in item.items():
            for result in drill(v):
                yield result

def get_max_value(nested_dicts):
    max_value = max([item for item in drill(nested_dicts)])
    return max_value

page_context = {
    'age_gender_groups': AGE_GENDER_GROUPS,
    'age_gender_max': get_max_value(AGE_GENDER_GROUPS),
    'educational_attainment_groups': EDUCATIONAL_ATTAINMENT_GROUPS,
    'educational_attainment_max': get_max_value(EDUCATIONAL_ATTAINMENT_GROUPS),
    'median_per_capita_income': MEDIAN_PER_CAPITA_INCOME,
    'employment_commute_types': EMPLOYMENT_COMMUTE_TYPES,
    'marital_status_groups': MARITAL_STATUS_GROUPS,
    'marital_status_max': get_max_value(MARITAL_STATUS_GROUPS),
    'divorces_last_year': DIVORCES_LAST_YEAR,
    'housing_price_groups': HOUSING_PRICE_GROUPS,
    'housing_price_group_max': get_max_value(HOUSING_PRICE_GROUPS),
    'median_housing_values': MEDIAN_HOUSING_VALUES,
    'median_housing_indexes': MEDIAN_HOUSING_INDEXES,
    'veteran_service_groups': VETERAN_SERVICE_GROUPS,
    'veteran_service_groups_max': get_max_value(VETERAN_SERVICE_GROUPS),
    'health_insurance_values': HEALTH_INSURANCE_VALUES,
    'health_insurance_indexes': HEALTH_INSURANCE_INDEXES,
    'health_insurance_groups': HEALTH_INSURANCE_GROUPS,
    'health_insurance_groups_max': get_max_value(HEALTH_INSURANCE_GROUPS),
    'language_groups': LANGUAGE_GROUPS,
    'language_groups_max': get_max_value(LANGUAGE_GROUPS),
}

@d("/")
def index(request):
    return d.HttpResponse("hello world")

@d
def geo_profile(request):
    return "geo_profile.html", page_context

@d
def test(request):
    return "test.html", page_context