code: # Licensed to the Apache Software Foundation (ASF) under one
  # or more contributor license agreements.  See the NOTICE file
  # 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
  #
  # Unless required by applicable law or agreed to in writing,
  # software distributed under the License is distributed on an
  # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  # 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)
          # TODO(bkyryliuk): move load examples data into the pytest fixture
          if database.backend == "presto":
              pdf.ds = pd.to_datetime(pdf.ds, unit="s")
              pdf.ds = pdf.ds.dt.strftime("%Y-%m-%d")
              pdf.ds2 = pd.to_datetime(pdf.ds2, unit="s")
              pdf.ds2 = pdf.ds2.dt.strftime("%Y-%m-%d %H:%M%:%S")
          else:
              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": String(255) if database.backend == "presto" else Date,
                  "ds2": String(255) if database.backend == "presto" else 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")
  
apis: pandas.to_datetime pandas.read_json