code: def make_equity_minute_bar_data(cls):
      equities_cal = cls.trading_calendars[Equity]
  
      data = {}
      sids = {2, 5, cls.SHORT_ASSET_SID, cls.HALF_DAY_TEST_ASSET_SID}
      for sid in sids:
          asset = cls.asset_finder.retrieve_asset(sid)
          data[sid] = create_minute_df_for_asset(
              equities_cal,
              asset.start_date,
              asset.end_date,
              start_val=2,
          )
  
      data[1] = create_minute_df_for_asset(
          equities_cal,
          pd.Timestamp("2014-01-03", tz="utc"),
          pd.Timestamp("2016-01-29", tz="utc"),
          start_val=2,
      )
  
      asset2 = cls.asset_finder.retrieve_asset(2)
      data[asset2.sid] = create_minute_df_for_asset(
          equities_cal,
          asset2.start_date,
          equities_cal.previous_session_label(asset2.end_date),
          start_val=2,
          minute_blacklist=[
              pd.Timestamp("2015-01-08 14:31", tz="UTC"),
              pd.Timestamp("2015-01-08 21:00", tz="UTC"),
          ],
      )
  
      # Start values are crafted so that the thousands place are equal when
      # adjustments are applied correctly.
      # The splits and mergers are defined as 4:1 then 2:1 ratios, so the
      # prices approximate that adjustment by quartering and then halving
      # the thousands place.
      data[cls.MERGER_ASSET_SID] = data[
          cls.SPLIT_ASSET_SID
      ] = pd.concat(
          (
              create_minute_df_for_asset(
                  equities_cal,
                  pd.Timestamp("2015-01-05", tz="UTC"),
                  pd.Timestamp("2015-01-05", tz="UTC"),
                  start_val=8000,
              ),
              create_minute_df_for_asset(
                  equities_cal,
                  pd.Timestamp("2015-01-06", tz="UTC"),
                  pd.Timestamp("2015-01-06", tz="UTC"),
                  start_val=2000,
              ),
              create_minute_df_for_asset(
                  equities_cal,
                  pd.Timestamp("2015-01-07", tz="UTC"),
                  pd.Timestamp("2015-01-07", tz="UTC"),
                  start_val=1000,
              ),
              create_minute_df_for_asset(
                  equities_cal,
                  pd.Timestamp("2015-01-08", tz="UTC"),
                  pd.Timestamp("2015-01-08", tz="UTC"),
                  start_val=1000,
              ),
          )
      )
      asset3 = cls.asset_finder.retrieve_asset(3)
      data[3] = create_minute_df_for_asset(
          equities_cal,
          asset3.start_date,
          asset3.end_date,
          start_val=2,
          interval=10,
      )
      return iteritems(data)
  
imports: from textwrap import dedent
  from nose_parameterized import parameterized
  import numpy
  from numpy import nan
  import pandas
  from six import iteritems
  from catalyst import TradingAlgorithm
  from catalyst._protocol import handle_non_market_minutes, BarData
  from catalyst.assets import Asset, Equity
  from catalyst.errors import HistoryInInitialize, HistoryWindowStartsBeforeData
  from catalyst.finance.trading import SimulationParameters
  from catalyst.finance.asset_restrictions import NoRestrictions
  from catalyst.testing import create_minute_df_for_asset, str_to_seconds, MockDailyBarReader
  from catalyst.testing.fixtures import WithCreateBarData, WithDataPortal, CatalystTestCase, alias
label_desc: Use image node_id 1 for calling the MinuteEquityHistoryTestCase obj's underlying member method code with example usage: obj.make_equity_minute_bar_data(cls) and returns: iteritems