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#!/usr/bin python3

# Imports
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Python:
import logging

# 3rd party:
from pandas import DataFrame, to_datetime
from numpy import NaN

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#!/usr/bin python3

# Imports
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Python:
from typing import Dict, Callable, Union
from operator import itemgetter

# 3rd party:
from plotly import graph_objects as go

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WITH
     location AS (
        SELECT id, ref.area_type, area_code, area_name, postcode, priority
        FROM covid19.postcode_lookup
            JOIN covid19.area_reference  AS ref ON ref.id = area_id
            JOIN covid19.area_priorities AS ap  ON ref.area_type = ap.area_type
        WHERE UPPER(REPLACE(postcode, ' ', '')) = $2
    ),
     metrics AS (
        SELECT id, metric

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library(dplyr)
library(tidyr)

data <- read.csv("https://api.coronavirus.data.gov.uk/v2/data?areaType=ltla&metric=newCasesBySpecimenDateAgeDemographics&format=csv")

# Add the age bands you want to exclude
excluded_age_bands <- c("unassigned", "60+", "00_59")

# Add area codes you want to include - leave blank for everything.
included_area_codes <- c()

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SELECT
    'area-' || release_date::TEXT || '-' || area_id::TEXT AS key,
    JSONB_AGG(
        JSONB_BUILD_OBJECT(
            'area_code', area_code,
            'area_type', area_type,
            'area_name', area_name,
            'date', to_char(date::DATE, 'YYYY-MM-DD'),
            'metric', metric,
            'value', value,

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[
  {
    "age": "0_to_5",
    "rate": 175.2,
    "value": 868
  },
  {
    "age": "65_to_84",
    "rate": 2802.1,
    "value": 32250