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"""Loads datasets, dashboards and slices in a new superset instance""" import json import os import textwrap import pandas as pd from sqlalchemy import DateTime, String from sqlalchemy.sql import column from superset import db from superset.connectors.sqla.models import SqlMetric from superset.models.dashboard import Dashboard from superset.models.slice import Slice from superset.utils import core as utils from .helpers import ( config, EXAMPLES_FOLDER, get_example_data, get_slice_json, merge_slice, misc_dash_slices, TBL, update_slice_ids, ) def load_world_bank_health_n_pop( # pylint: disable=too-many-locals only_metadata: bool = False, force: bool = False ) -> None: """Loads the world bank health dataset, slices and a dashboard""" tbl_name = "wb_health_population" database = utils.get_example_database() table_exists = database.has_table_by_name(tbl_name) if not only_metadata and (not table_exists or force): 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, database.get_sqla_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(EXAMPLES_FOLDER, "countries.md")) tbl.main_dttm_col = "year" tbl.database = database 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", "sum__SP_RUR_TOTL", ] for metric in metrics: if not any(col.metric_name == metric for col in tbl.metrics): aggr_func = metric[:3] col = str(column(metric[5:]).compile(db.engine)) tbl.metrics.append( SqlMetric(metric_name=metric, expression=f"{aggr_func}({col})") ) db.session.merge(tbl) db.session.commit() tbl.fetch_metadata() metric = "sum__SP_POP_TOTL" metrics = ["sum__SP_POP_TOTL"] secondary_metric = { "aggregate": "SUM", "column": { "column_name": "SP_RUR_TOTL", "optionName": "_col_SP_RUR_TOTL", "type": "DOUBLE", }, "expressionType": "SIMPLE", "hasCustomLabel": True, "label": "Rural Population", } defaults = { "compare_lag": "10", "compare_suffix": "o10Y", "limit": "25", "granularity_sqla": "year", "groupby": [], "row_limit": config["ROW_LIMIT"], "since": "2014-01-01", "until": "2014-01-02", "time_range": "2014-01-01 : 2014-01-02", "markup_type": "markdown", "country_fieldtype": "cca3", "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", secondary_metric=secondary_metric, ), ), 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", adhoc_filters=[ { "clause": "WHERE", "expressionType": "SIMPLE", "filterOptionName": "2745eae5", "comparator": [ "TCA", "MNP", "DMA", "MHL", "MCO", "SXM", "CYM", "TUV", "IMY", "KNA", "ASM", "ADO", "AMA", "PLW", ], "operator": "NOT IN", "subject": "country_code", } ], ), ), 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"], since="2011-01-01", until="2011-01-01", metric=metric, secondary_metric=secondary_metric, ), ), 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"], metrics=metrics, ), ), 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"], metrics=metrics, ), ), 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 Bank's Data" slug = "world_health" dash = db.session.query(Dashboard).filter_by(slug=slug).first() if not dash: dash = Dashboard() dash.published = True 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()