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# to you under the Apache License, Version 2.0 (the
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#
#   http://www.apache.org/licenses/LICENSE-2.0
#
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import logging
import textwrap

from sqlalchemy import Float, inspect, String
from sqlalchemy.sql import column

import superset.utils.database as database_utils
from superset import db
from superset.connectors.sqla.models import SqlMetric
from superset.models.slice import Slice
from superset.sql.parse import Table
from superset.utils.core import DatasourceType

from .helpers import (
    get_slice_json,
    get_table_connector_registry,
    merge_slice,
    misc_dash_slices,
    read_example_data,
)

logger = logging.getLogger(__name__)


def load_energy(
    only_metadata: bool = False, force: bool = False, sample: bool = False
) -> None:
    """Loads an energy related dataset to use with sankey and graphs"""
    tbl_name = "energy_usage"
    database = database_utils.get_example_database()

    with database.get_sqla_engine() as engine:
        schema = inspect(engine).default_schema_name
        table_exists = database.has_table(Table(tbl_name, schema))

        if not only_metadata and (not table_exists or force):
            pdf = read_example_data("examples://energy.json.gz", compression="gzip")
            pdf = pdf.head(100) if sample else pdf
            pdf.to_sql(
                tbl_name,
                engine,
                schema=schema,
                if_exists="replace",
                chunksize=500,
                dtype={"source": String(255), "target": String(255), "value": Float()},
                index=False,
                method="multi",
            )

    logger.debug("Creating table [wb_health_population] reference")
    table = get_table_connector_registry()
    tbl = db.session.query(table).filter_by(table_name=tbl_name).first()
    if not tbl:
        tbl = table(table_name=tbl_name, schema=schema)
        db.session.add(tbl)
    tbl.description = "Energy consumption"
    tbl.database = database
    tbl.filter_select_enabled = True

    if not any(col.metric_name == "sum__value" for col in tbl.metrics):
        col = str(column("value").compile(db.engine))
        tbl.metrics.append(
            SqlMetric(metric_name="sum__value", expression=f"SUM({col})")
        )

    tbl.fetch_metadata()

    slc = Slice(
        slice_name="Energy Sankey",
        viz_type="sankey_v2",
        datasource_type=DatasourceType.TABLE,
        datasource_id=tbl.id,
        params=textwrap.dedent(
            """\
        {
            "collapsed_fieldsets": "",
            "source": "source",
            "target": "target",
            "metric": "sum__value",
            "row_limit": "5000",
            "slice_name": "Energy Sankey",
            "viz_type": "sankey_v2"
        }
        """
        ),
    )
    misc_dash_slices.add(slc.slice_name)
    merge_slice(slc)

    slc = Slice(
        slice_name="Energy Force Layout",
        viz_type="graph_chart",
        datasource_type=DatasourceType.TABLE,
        datasource_id=tbl.id,
        params=textwrap.dedent(
            """\
        {
            "source": "source",
            "target": "target",
            "edgeLength": 400,
            "repulsion": 1000,
            "layout": "force",
            "metric": "sum__value",
            "row_limit": "5000",
            "slice_name": "Force",
            "viz_type": "graph_chart"
        }
        """
        ),
    )
    misc_dash_slices.add(slc.slice_name)
    merge_slice(slc)

    slc = Slice(
        slice_name="Heatmap",
        viz_type="heatmap_v2",
        datasource_type=DatasourceType.TABLE,
        datasource_id=tbl.id,
        params=get_slice_json(
            defaults={},
            viz_type="heatmap_v2",
            x_axis="source",
            groupby="target",
            legend_type="continuous",
            metric="sum__value",
            sort_x_axis="value_asc",
            sort_y_axis="value_asc",
        ),
    )
    misc_dash_slices.add(slc.slice_name)
    merge_slice(slc)