Loads the world bank health dataset, slices and a dashboard

def load_world_bank_health_n_pop():
    """Loads the world bank health dataset, slices and a dashboard"""
    tbl_name = 'wb_health_population'
    data = get_example_data('countries.json.gz')
    pdf = pd.read_json(data)
    pdf.columns = [col.replace('.', '_') for col in pdf.columns]
    pdf.year = pd.to_datetime(pdf.year)
    pdf.to_sql(
        tbl_name,
        db.engine,
        if_exists='replace',
        chunksize=50,
        dtype={
            'year': DateTime(),
            'country_code': String(3),
            'country_name': String(255),
            'region': String(255),
        },
        index=False)

    print('Creating table [wb_health_population] reference')
    tbl = db.session.query(TBL).filter_by(table_name=tbl_name).first()
    if not tbl:
        tbl = TBL(table_name=tbl_name)
    tbl.description = utils.readfile(os.path.join(DATA_FOLDER, 'countries.md'))
    tbl.main_dttm_col = 'year'
    tbl.database = utils.get_or_create_main_db()
    tbl.filter_select_enabled = True

    metrics = [
        'sum__SP_POP_TOTL', 'sum__SH_DYN_AIDS', 'sum__SH_DYN_AIDS',
        'sum__SP_RUR_TOTL_ZS', 'sum__SP_DYN_LE00_IN',
    ]
    for m in metrics:
        if not any(col.metric_name == m for col in tbl.metrics):
            tbl.metrics.append(SqlMetric(
                metric_name=m,
                expression=f'{m[:3]}({m[5:]})',
            ))

    db.session.merge(tbl)
    db.session.commit()
    tbl.fetch_metadata()

    defaults = {
        'compare_lag': '10',
        'compare_suffix': 'o10Y',
        'limit': '25',
        'granularity_sqla': 'year',
        'groupby': [],
        'metric': 'sum__SP_POP_TOTL',
        'metrics': ['sum__SP_POP_TOTL'],
        'row_limit': config.get('ROW_LIMIT'),
        'since': '2014-01-01',
        'until': '2014-01-02',
        'time_range': '2014-01-01 : 2014-01-02',
        'where': '',
        'markup_type': 'markdown',
        'country_fieldtype': 'cca3',
        'secondary_metric': 'sum__SP_POP_TOTL',
        'entity': 'country_code',
        'show_bubbles': True,
    }

    print('Creating slices')
    slices = [
        Slice(
            slice_name='Region Filter',
            viz_type='filter_box',
            datasource_type='table',
            datasource_id=tbl.id,
            params=get_slice_json(
                defaults,
                viz_type='filter_box',
                date_filter=False,
                filter_configs=[
                    {
                        'asc': False,
                        'clearable': True,
                        'column': 'region',
                        'key': '2s98dfu',
                        'metric': 'sum__SP_POP_TOTL',
                        'multiple': True,
                    }, {
                        'asc': False,
                        'clearable': True,
                        'key': 'li3j2lk',
                        'column': 'country_name',
                        'metric': 'sum__SP_POP_TOTL',
                        'multiple': True,
                    },
                ])),
        Slice(
            slice_name="World's Population",
            viz_type='big_number',
            datasource_type='table',
            datasource_id=tbl.id,
            params=get_slice_json(
                defaults,
                since='2000',
                viz_type='big_number',
                compare_lag='10',
                metric='sum__SP_POP_TOTL',
                compare_suffix='over 10Y')),
        Slice(
            slice_name='Most Populated Countries',
            viz_type='table',
            datasource_type='table',
            datasource_id=tbl.id,
            params=get_slice_json(
                defaults,
                viz_type='table',
                metrics=['sum__SP_POP_TOTL'],
                groupby=['country_name'])),
        Slice(
            slice_name='Growth Rate',
            viz_type='line',
            datasource_type='table',
            datasource_id=tbl.id,
            params=get_slice_json(
                defaults,
                viz_type='line',
                since='1960-01-01',
                metrics=['sum__SP_POP_TOTL'],
                num_period_compare='10',
                groupby=['country_name'])),
        Slice(
            slice_name='% Rural',
            viz_type='world_map',
            datasource_type='table',
            datasource_id=tbl.id,
            params=get_slice_json(
                defaults,
                viz_type='world_map',
                metric='sum__SP_RUR_TOTL_ZS',
                num_period_compare='10')),
        Slice(
            slice_name='Life Expectancy VS Rural %',
            viz_type='bubble',
            datasource_type='table',
            datasource_id=tbl.id,
            params=get_slice_json(
                defaults,
                viz_type='bubble',
                since='2011-01-01',
                until='2011-01-02',
                series='region',
                limit=0,
                entity='country_name',
                x='sum__SP_RUR_TOTL_ZS',
                y='sum__SP_DYN_LE00_IN',
                size='sum__SP_POP_TOTL',
                max_bubble_size='50',
                filters=[{
                    'col': 'country_code',
                    'val': [
                        'TCA', 'MNP', 'DMA', 'MHL', 'MCO', 'SXM', 'CYM',
                        'TUV', 'IMY', 'KNA', 'ASM', 'ADO', 'AMA', 'PLW',
                    ],
                    'op': 'not in'}],
            )),
        Slice(
            slice_name='Rural Breakdown',
            viz_type='sunburst',
            datasource_type='table',
            datasource_id=tbl.id,
            params=get_slice_json(
                defaults,
                viz_type='sunburst',
                groupby=['region', 'country_name'],
                secondary_metric='sum__SP_RUR_TOTL',
                since='2011-01-01',
                until='2011-01-01')),
        Slice(
            slice_name="World's Pop Growth",
            viz_type='area',
            datasource_type='table',
            datasource_id=tbl.id,
            params=get_slice_json(
                defaults,
                since='1960-01-01',
                until='now',
                viz_type='area',
                groupby=['region'])),
        Slice(
            slice_name='Box plot',
            viz_type='box_plot',
            datasource_type='table',
            datasource_id=tbl.id,
            params=get_slice_json(
                defaults,
                since='1960-01-01',
                until='now',
                whisker_options='Min/max (no outliers)',
                x_ticks_layout='staggered',
                viz_type='box_plot',
                groupby=['region'])),
        Slice(
            slice_name='Treemap',
            viz_type='treemap',
            datasource_type='table',
            datasource_id=tbl.id,
            params=get_slice_json(
                defaults,
                since='1960-01-01',
                until='now',
                viz_type='treemap',
                metrics=['sum__SP_POP_TOTL'],
                groupby=['region', 'country_code'])),
        Slice(
            slice_name='Parallel Coordinates',
            viz_type='para',
            datasource_type='table',
            datasource_id=tbl.id,
            params=get_slice_json(
                defaults,
                since='2011-01-01',
                until='2011-01-01',
                viz_type='para',
                limit=100,
                metrics=[
                    'sum__SP_POP_TOTL',
                    'sum__SP_RUR_TOTL_ZS',
                    'sum__SH_DYN_AIDS'],
                secondary_metric='sum__SP_POP_TOTL',
                series='country_name')),
    ]
    misc_dash_slices.add(slices[-1].slice_name)
    for slc in slices:
        merge_slice(slc)

    print("Creating a World's Health Bank dashboard")
    dash_name = "World's Bank Data"
    slug = 'world_health'
    dash = db.session.query(Dash).filter_by(slug=slug).first()

    if not dash:
        dash = Dash()
    js = textwrap.dedent("""\
{
    "CHART-36bfc934": {
        "children": [],
        "id": "CHART-36bfc934",
        "meta": {
            "chartId": 40,
            "height": 25,
            "sliceName": "Region Filter",
            "width": 2
        },
        "type": "CHART"
    },
    "CHART-37982887": {
        "children": [],
        "id": "CHART-37982887",
        "meta": {
            "chartId": 41,
            "height": 25,
            "sliceName": "World's Population",
            "width": 2
        },
        "type": "CHART"
    },
    "CHART-17e0f8d8": {
        "children": [],
        "id": "CHART-17e0f8d8",
        "meta": {
            "chartId": 42,
            "height": 92,
            "sliceName": "Most Populated Countries",
            "width": 3
        },
        "type": "CHART"
    },
    "CHART-2ee52f30": {
        "children": [],
        "id": "CHART-2ee52f30",
        "meta": {
            "chartId": 43,
            "height": 38,
            "sliceName": "Growth Rate",
            "width": 6
        },
        "type": "CHART"
    },
    "CHART-2d5b6871": {
        "children": [],
        "id": "CHART-2d5b6871",
        "meta": {
            "chartId": 44,
            "height": 52,
            "sliceName": "% Rural",
            "width": 7
        },
        "type": "CHART"
    },
    "CHART-0fd0d252": {
        "children": [],
        "id": "CHART-0fd0d252",
        "meta": {
            "chartId": 45,
            "height": 50,
            "sliceName": "Life Expectancy VS Rural %",
            "width": 8
        },
        "type": "CHART"
    },
    "CHART-97f4cb48": {
        "children": [],
        "id": "CHART-97f4cb48",
        "meta": {
            "chartId": 46,
            "height": 38,
            "sliceName": "Rural Breakdown",
            "width": 3
        },
        "type": "CHART"
    },
    "CHART-b5e05d6f": {
        "children": [],
        "id": "CHART-b5e05d6f",
        "meta": {
            "chartId": 47,
            "height": 50,
            "sliceName": "World's Pop Growth",
            "width": 4
        },
        "type": "CHART"
    },
    "CHART-e76e9f5f": {
        "children": [],
        "id": "CHART-e76e9f5f",
        "meta": {
            "chartId": 48,
            "height": 50,
            "sliceName": "Box plot",
            "width": 4
        },
        "type": "CHART"
    },
    "CHART-a4808bba": {
        "children": [],
        "id": "CHART-a4808bba",
        "meta": {
            "chartId": 49,
            "height": 50,
            "sliceName": "Treemap",
            "width": 8
        },
        "type": "CHART"
    },
    "COLUMN-071bbbad": {
        "children": [
            "ROW-1e064e3c",
            "ROW-afdefba9"
        ],
        "id": "COLUMN-071bbbad",
        "meta": {
            "background": "BACKGROUND_TRANSPARENT",
            "width": 9
        },
        "type": "COLUMN"
    },
    "COLUMN-fe3914b8": {
        "children": [
            "CHART-36bfc934",
            "CHART-37982887"
        ],
        "id": "COLUMN-fe3914b8",
        "meta": {
            "background": "BACKGROUND_TRANSPARENT",
            "width": 2
        },
        "type": "COLUMN"
    },
    "GRID_ID": {
        "children": [
            "ROW-46632bc2",
            "ROW-3fa26c5d",
            "ROW-812b3f13"
        ],
        "id": "GRID_ID",
        "type": "GRID"
    },
    "HEADER_ID": {
        "id": "HEADER_ID",
        "meta": {
            "text": "World's Bank Data"
        },
        "type": "HEADER"
    },
    "ROOT_ID": {
        "children": [
            "GRID_ID"
        ],
        "id": "ROOT_ID",
        "type": "ROOT"
    },
    "ROW-1e064e3c": {
        "children": [
            "COLUMN-fe3914b8",
            "CHART-2d5b6871"
        ],
        "id": "ROW-1e064e3c",
        "meta": {
            "background": "BACKGROUND_TRANSPARENT"
        },
        "type": "ROW"
    },
    "ROW-3fa26c5d": {
        "children": [
            "CHART-b5e05d6f",
            "CHART-0fd0d252"
        ],
        "id": "ROW-3fa26c5d",
        "meta": {
            "background": "BACKGROUND_TRANSPARENT"
        },
        "type": "ROW"
    },
    "ROW-46632bc2": {
        "children": [
            "COLUMN-071bbbad",
            "CHART-17e0f8d8"
        ],
        "id": "ROW-46632bc2",
        "meta": {
            "background": "BACKGROUND_TRANSPARENT"
        },
        "type": "ROW"
    },
    "ROW-812b3f13": {
        "children": [
            "CHART-a4808bba",
            "CHART-e76e9f5f"
        ],
        "id": "ROW-812b3f13",
        "meta": {
            "background": "BACKGROUND_TRANSPARENT"
        },
        "type": "ROW"
    },
    "ROW-afdefba9": {
        "children": [
            "CHART-2ee52f30",
            "CHART-97f4cb48"
        ],
        "id": "ROW-afdefba9",
        "meta": {
            "background": "BACKGROUND_TRANSPARENT"
        },
        "type": "ROW"
    },
    "DASHBOARD_VERSION_KEY": "v2"
}
    """)
    pos = json.loads(js)
    update_slice_ids(pos, slices)

    dash.dashboard_title = dash_name
    dash.position_json = json.dumps(pos, indent=4)
    dash.slug = slug

    dash.slices = slices[:-1]
    db.session.merge(dash)
    db.session.commit()