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# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
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#
#   http://www.apache.org/licenses/LICENSE-2.0
#
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"""Loads datasets, dashboards and slices in a new superset instance"""
import textwrap

import pandas as pd
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 .helpers import (
    get_example_data,
    get_table_connector_registry,
    merge_slice,
    misc_dash_slices,
)


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()
    engine = database.get_sqla_engine()
    schema = inspect(engine).default_schema_name
    table_exists = database.has_table_by_name(tbl_name)

    if not only_metadata and (not table_exists or force):
        data = get_example_data("energy.json.gz")
        pdf = pd.read_json(data)
        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",
        )

    print("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)
    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})")
        )

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

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

    slc = Slice(
        slice_name="Energy Force Layout",
        viz_type="graph_chart",
        datasource_type="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",
        datasource_type="table",
        datasource_id=tbl.id,
        params=textwrap.dedent(
            """\
        {
            "all_columns_x": "source",
            "all_columns_y": "target",
            "canvas_image_rendering": "pixelated",
            "collapsed_fieldsets": "",
            "linear_color_scheme": "blue_white_yellow",
            "metric": "sum__value",
            "normalize_across": "heatmap",
            "slice_name": "Heatmap",
            "viz_type": "heatmap",
            "xscale_interval": "1",
            "yscale_interval": "1"
        }
        """
        ),
    )
    misc_dash_slices.add(slc.slice_name)
    merge_slice(slc)