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# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
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# software distributed under the License is distributed on an
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# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
from typing import Dict, Optional, Tuple

import pandas as pd
from sqlalchemy import BigInteger, Date, DateTime, String

from superset import db
from superset.models.slice import Slice
from superset.utils.core import get_example_database

from .helpers import (
    config,
    get_example_data,
    get_slice_json,
    merge_slice,
    misc_dash_slices,
    TBL,
)


def load_multiformat_time_series(
    only_metadata: bool = False, force: bool = False
) -> None:
    """Loading time series data from a zip file in the repo"""
    tbl_name = "multiformat_time_series"
    database = 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("multiformat_time_series.json.gz")
        pdf = pd.read_json(data)

        pdf.ds = pd.to_datetime(pdf.ds, unit="s")
        pdf.ds2 = pd.to_datetime(pdf.ds2, unit="s")
        pdf.to_sql(
            tbl_name,
            database.get_sqla_engine(),
            if_exists="replace",
            chunksize=500,
            dtype={
                "ds": Date,
                "ds2": DateTime,
                "epoch_s": BigInteger,
                "epoch_ms": BigInteger,
                "string0": String(100),
                "string1": String(100),
                "string2": String(100),
                "string3": String(100),
            },
            index=False,
        )
        print("Done loading table!")
        print("-" * 80)

    print(f"Creating table [{tbl_name}] reference")
    obj = db.session.query(TBL).filter_by(table_name=tbl_name).first()
    if not obj:
        obj = TBL(table_name=tbl_name)
    obj.main_dttm_col = "ds"
    obj.database = database
    dttm_and_expr_dict: Dict[str, Tuple[Optional[str], None]] = {
        "ds": (None, None),
        "ds2": (None, None),
        "epoch_s": ("epoch_s", None),
        "epoch_ms": ("epoch_ms", None),
        "string2": ("%Y%m%d-%H%M%S", None),
        "string1": ("%Y-%m-%d^%H:%M:%S", None),
        "string0": ("%Y-%m-%d %H:%M:%S.%f", None),
        "string3": ("%Y/%m/%d%H:%M:%S.%f", None),
    }
    for col in obj.columns:
        dttm_and_expr = dttm_and_expr_dict[col.column_name]
        col.python_date_format = dttm_and_expr[0]
        col.dbatabase_expr = dttm_and_expr[1]
        col.is_dttm = True
    db.session.merge(obj)
    db.session.commit()
    obj.fetch_metadata()
    tbl = obj

    print("Creating Heatmap charts")
    for i, col in enumerate(tbl.columns):
        slice_data = {
            "metrics": ["count"],
            "granularity_sqla": col.column_name,
            "row_limit": config["ROW_LIMIT"],
            "since": "2015",
            "until": "2016",
            "viz_type": "cal_heatmap",
            "domain_granularity": "month",
            "subdomain_granularity": "day",
        }

        slc = Slice(
            slice_name=f"Calendar Heatmap multiformat {i}",
            viz_type="cal_heatmap",
            datasource_type="table",
            datasource_id=tbl.id,
            params=get_slice_json(slice_data),
        )
        merge_slice(slc)
    misc_dash_slices.add("Calendar Heatmap multiformat 0")